diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..877fcab --- /dev/null +++ b/.gitignore @@ -0,0 +1,22 @@ +# VS Code +.vscode + +# Compiled python modules +*.pyc + +# Python egg metadata +validation.egg-info + +# emacs and vim backups +*~ +*.swp + +# macOS +*.DS_Store + +# tex outputs +*.tex + +# OpenMC Output Files +*.h5 +*.ppm \ No newline at end of file diff --git a/AP1000/PWR 2.1/problem/geometry.xml b/AP1000/PWR 2.1/problem/geometry.xml new file mode 100644 index 0000000..cb4a857 --- /dev/null +++ b/AP1000/PWR 2.1/problem/geometry.xml @@ -0,0 +1,70 @@ + + + + + + + + + + + + + + + + + 17 17 + -10.659 -10.659 + 1.26 1.26 + + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 2 1 1 2 1 1 2 1 1 1 1 1 + 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 + 1 1 1 1 1 2 1 1 2 1 1 2 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/AP1000/PWR 2.1/problem/materials.xml b/AP1000/PWR 2.1/problem/materials.xml new file mode 100644 index 0000000..3fd32de --- /dev/null +++ b/AP1000/PWR 2.1/problem/materials.xml @@ -0,0 +1,84 @@ + + + + + + /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml + + 71c + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/AP1000/PWR 2.1/problem/plots.xml b/AP1000/PWR 2.1/problem/plots.xml new file mode 100644 index 0000000..2d7c0c1 --- /dev/null +++ b/AP1000/PWR 2.1/problem/plots.xml @@ -0,0 +1,24 @@ + + + + + + + + + 0. 0. 0. + 25.50 25.50 + 1000 1000 + + + + + + diff --git a/AP1000/PWR 2.1/problem/settings.xml b/AP1000/PWR 2.1/problem/settings.xml new file mode 100644 index 0000000..f5a528c --- /dev/null +++ b/AP1000/PWR 2.1/problem/settings.xml @@ -0,0 +1,29 @@ + + + + + + + 600 + 50 + 10000 + + + + + box + + -10.659 -10.659 -182.9 + 10.659 10.659 182.9 + + + + + diff --git a/AP1000/PWR 2.1/problem/tallies.xml b/AP1000/PWR 2.1/problem/tallies.xml new file mode 100644 index 0000000..5711d9e --- /dev/null +++ b/AP1000/PWR 2.1/problem/tallies.xml @@ -0,0 +1,77 @@ + + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 2 3 4 5 6 + + + + 1 + flux + + + + 2 + nu-fission + + + + + 3 + delayed-nu-fission + + + + 4 + fission + + + + 5 + absorption + + + + 6 + scatter + + + + 7 + total + + + + 8 + delayed-nu-fission + + + diff --git a/AP1000/PWR 2.6/problem/geometry.xml b/AP1000/PWR 2.6/problem/geometry.xml new file mode 100644 index 0000000..cb4a857 --- /dev/null +++ b/AP1000/PWR 2.6/problem/geometry.xml @@ -0,0 +1,70 @@ + + + + + + + + + + + + + + + + + 17 17 + -10.659 -10.659 + 1.26 1.26 + + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 2 1 1 2 1 1 2 1 1 1 1 1 + 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 + 1 1 1 1 1 2 1 1 2 1 1 2 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/AP1000/PWR 2.6/problem/materials.xml b/AP1000/PWR 2.6/problem/materials.xml new file mode 100644 index 0000000..0ea721a --- /dev/null +++ b/AP1000/PWR 2.6/problem/materials.xml @@ -0,0 +1,84 @@ + + + + + + /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml + + 71c + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/AP1000/PWR 2.6/problem/plots.xml b/AP1000/PWR 2.6/problem/plots.xml new file mode 100644 index 0000000..2d7c0c1 --- /dev/null +++ b/AP1000/PWR 2.6/problem/plots.xml @@ -0,0 +1,24 @@ + + + + + + + + + 0. 0. 0. + 25.50 25.50 + 1000 1000 + + + + + + diff --git a/AP1000/PWR 2.6/problem/settings.xml b/AP1000/PWR 2.6/problem/settings.xml new file mode 100644 index 0000000..f5a528c --- /dev/null +++ b/AP1000/PWR 2.6/problem/settings.xml @@ -0,0 +1,29 @@ + + + + + + + 600 + 50 + 10000 + + + + + box + + -10.659 -10.659 -182.9 + 10.659 10.659 182.9 + + + + + diff --git a/AP1000/PWR 2.6/problem/tallies.xml b/AP1000/PWR 2.6/problem/tallies.xml new file mode 100644 index 0000000..91bc7b1 --- /dev/null +++ b/AP1000/PWR 2.6/problem/tallies.xml @@ -0,0 +1,77 @@ + + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 2 3 4 5 6 + + + + 1 + flux + + + + 2 + nu-fission + + + + + 3 + delayed-nu-fission + + + + 4 + fission + + + + 5 + absorption + + + + 6 + scatter + + + + 7 + total + + + + 8 + delayed-nu-fission + + + diff --git a/AP1000/PWR 3.10/problem/geometry.xml b/AP1000/PWR 3.10/problem/geometry.xml new file mode 100644 index 0000000..cb4a857 --- /dev/null +++ b/AP1000/PWR 3.10/problem/geometry.xml @@ -0,0 +1,70 @@ + + + + + + + + + + + + + + + + + 17 17 + -10.659 -10.659 + 1.26 1.26 + + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 2 1 1 2 1 1 2 1 1 1 1 1 + 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 + 1 1 1 1 1 2 1 1 2 1 1 2 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/AP1000/PWR 3.10/problem/materials.xml b/AP1000/PWR 3.10/problem/materials.xml new file mode 100644 index 0000000..322b1a4 --- /dev/null +++ b/AP1000/PWR 3.10/problem/materials.xml @@ -0,0 +1,84 @@ + + + + + + /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml + + 71c + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/AP1000/PWR 3.10/problem/plots.xml b/AP1000/PWR 3.10/problem/plots.xml new file mode 100644 index 0000000..2d7c0c1 --- /dev/null +++ b/AP1000/PWR 3.10/problem/plots.xml @@ -0,0 +1,24 @@ + + + + + + + + + 0. 0. 0. + 25.50 25.50 + 1000 1000 + + + + + + diff --git a/AP1000/PWR 3.10/problem/settings.xml b/AP1000/PWR 3.10/problem/settings.xml new file mode 100644 index 0000000..f5a528c --- /dev/null +++ b/AP1000/PWR 3.10/problem/settings.xml @@ -0,0 +1,29 @@ + + + + + + + 600 + 50 + 10000 + + + + + box + + -10.659 -10.659 -182.9 + 10.659 10.659 182.9 + + + + + diff --git a/AP1000/PWR 3.10/problem/tallies.xml b/AP1000/PWR 3.10/problem/tallies.xml new file mode 100644 index 0000000..5711d9e --- /dev/null +++ b/AP1000/PWR 3.10/problem/tallies.xml @@ -0,0 +1,77 @@ + + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 2 3 4 5 6 + + + + 1 + flux + + + + 2 + nu-fission + + + + + 3 + delayed-nu-fission + + + + 4 + fission + + + + 5 + absorption + + + + 6 + scatter + + + + 7 + total + + + + 8 + delayed-nu-fission + + + diff --git a/ARC700/CANDU 2.1/problem/geometry.xml b/ARC700/CANDU 2.1/problem/geometry.xml new file mode 100644 index 0000000..46b98ed --- /dev/null +++ b/ARC700/CANDU 2.1/problem/geometry.xml @@ -0,0 +1,217 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + 0. 0. 0. + 25.0 25.0 + 1000 1000 + + + + diff --git a/ARC700/CANDU 2.1/problem/settings.xml b/ARC700/CANDU 2.1/problem/settings.xml new file mode 100644 index 0000000..92c7e8b --- /dev/null +++ b/ARC700/CANDU 2.1/problem/settings.xml @@ -0,0 +1,31 @@ + + + + + + + + 600 + 50 + 10000 + + + + + + box + + -11 -11 -24.765 + 11 11 24.765 + + + + + diff --git a/ARC700/CANDU 2.1/problem/tallies.xml b/ARC700/CANDU 2.1/problem/tallies.xml new file mode 100644 index 0000000..87686fe --- /dev/null +++ b/ARC700/CANDU 2.1/problem/tallies.xml @@ -0,0 +1,77 @@ + + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 1 2 3 4 5 6 + + + + 1 + flux + + + + 2 + nu-fission + + + + + 3 + delayed-nu-fission + + + + 4 + fission + + + + 5 + absorption + + + + 6 + scatter + + + + 7 + total + + + + 8 + delayed-nu-fission + + + diff --git a/ARC700/CANDU 2.6/problem/geometry.xml b/ARC700/CANDU 2.6/problem/geometry.xml new file mode 100644 index 0000000..46b98ed --- /dev/null +++ b/ARC700/CANDU 2.6/problem/geometry.xml @@ -0,0 +1,217 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/ARC700/CANDU 2.6/problem/materials.xml b/ARC700/CANDU 2.6/problem/materials.xml new file mode 100644 index 0000000..2063dcb --- /dev/null +++ b/ARC700/CANDU 2.6/problem/materials.xml @@ -0,0 +1,152 @@ + + + + + + /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml + + 71c + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/ARC700/CANDU 2.6/problem/plots.xml b/ARC700/CANDU 2.6/problem/plots.xml new file mode 100644 index 0000000..ff0aa53 --- /dev/null +++ b/ARC700/CANDU 2.6/problem/plots.xml @@ -0,0 +1,20 @@ + + + + + + + 0. 0. 0. + 25.0 25.0 + 1000 1000 + + + + diff --git a/ARC700/CANDU 2.6/problem/settings.xml b/ARC700/CANDU 2.6/problem/settings.xml new file mode 100644 index 0000000..92c7e8b --- /dev/null +++ b/ARC700/CANDU 2.6/problem/settings.xml @@ -0,0 +1,31 @@ + + + + + + + + 600 + 50 + 10000 + + + + + + box + + -11 -11 -24.765 + 11 11 24.765 + + + + + diff --git a/ARC700/CANDU 2.6/problem/tallies.xml b/ARC700/CANDU 2.6/problem/tallies.xml new file mode 100644 index 0000000..6d61397 --- /dev/null +++ b/ARC700/CANDU 2.6/problem/tallies.xml @@ -0,0 +1,77 @@ + + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 1 2 3 4 5 6 + + + + 1 + flux + + + + 2 + nu-fission + + + + + 3 + delayed-nu-fission + + + + 4 + fission + + + + 5 + absorption + + + + 6 + scatter + + + + 7 + total + + + + 8 + delayed-nu-fission + + + diff --git a/ARC700/CANDU 3.1/problem/geometry.xml b/ARC700/CANDU 3.1/problem/geometry.xml new file mode 100644 index 0000000..46b98ed --- /dev/null +++ b/ARC700/CANDU 3.1/problem/geometry.xml @@ -0,0 +1,217 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/ARC700/CANDU 3.1/problem/materials.xml b/ARC700/CANDU 3.1/problem/materials.xml new file mode 100644 index 0000000..d13cddb --- /dev/null +++ b/ARC700/CANDU 3.1/problem/materials.xml @@ -0,0 +1,152 @@ + + + + + + /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml + + 71c + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/ARC700/CANDU 3.1/problem/plots.xml b/ARC700/CANDU 3.1/problem/plots.xml new file mode 100644 index 0000000..ff0aa53 --- /dev/null +++ b/ARC700/CANDU 3.1/problem/plots.xml @@ -0,0 +1,20 @@ + + + + + + + 0. 0. 0. + 25.0 25.0 + 1000 1000 + + + + diff --git a/ARC700/CANDU 3.1/problem/settings.xml b/ARC700/CANDU 3.1/problem/settings.xml new file mode 100644 index 0000000..92c7e8b --- /dev/null +++ b/ARC700/CANDU 3.1/problem/settings.xml @@ -0,0 +1,31 @@ + + + + + + + + 600 + 50 + 10000 + + + + + + box + + -11 -11 -24.765 + 11 11 24.765 + + + + + diff --git a/ARC700/CANDU 3.1/problem/tallies.xml b/ARC700/CANDU 3.1/problem/tallies.xml new file mode 100644 index 0000000..6d61397 --- /dev/null +++ b/ARC700/CANDU 3.1/problem/tallies.xml @@ -0,0 +1,77 @@ + + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 93 87 + + + + 1 2 3 4 5 6 + + + + 1 + flux + + + + 2 + nu-fission + + + + + 3 + delayed-nu-fission + + + + 4 + fission + + + + 5 + absorption + + + + 6 + scatter + + + + 7 + total + + + + 8 + delayed-nu-fission + + + diff --git a/BWR/BWR.ipynb b/BWR/BWR.ipynb new file mode 100644 index 0000000..8860762 --- /dev/null +++ b/BWR/BWR.ipynb @@ -0,0 +1,1014 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A BWR geometry \n", + "This notebook can be used as a template for modeling BWR reactors." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "EChOeJ7qVUB7" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import openmc" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "-0xO897aWbft" + }, + "outputs": [], + "source": [ + "# Materials definitions\n", + "\n", + "uo2 = openmc.Material(name='UO2')\n", + "uo2.add_element('U', 1.0, enrichment=1.5)\n", + "uo2.add_element('O', 2.0)\n", + "uo2.set_density('g/cc', 10.0)\n", + "\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide('Zr90', 7.2758e-3)\n", + "\n", + "steel = openmc.Material(name='Stainless Steel')\n", + "steel.set_density('g/cm3', 8.00)\n", + "steel.add_element('C', 0.08, percent_type='wo')\n", + "steel.add_element('Si', 1.00, percent_type='wo')\n", + "steel.add_element('Mn', 2.00, percent_type='wo')\n", + "steel.add_element('P', 0.045, percent_type='wo')\n", + "steel.add_element('S', 0.030, percent_type='wo')\n", + "steel.add_element('Cr', 20.0, percent_type='wo')\n", + "steel.add_element('Ni', 11.0, percent_type='wo')\n", + "steel.add_element('Fe', 65.845, percent_type='wo')\n", + "\n", + "water = openmc.Material(name='Water')\n", + "water.set_density('g/cm3', 0.76)\n", + "water.add_element('H', 2)\n", + "water.add_element('O', 1)\n", + "water.add_s_alpha_beta('c_H_in_H2O')\n", + "\n", + "# Instantiate a Materials collection and export to xml\n", + "materials_file = openmc.Materials([uo2, water, zircaloy, steel])\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Geometry definitions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "J90KA6zdol04" + }, + "outputs": [], + "source": [ + "bottom = openmc.ZPlane(z0=-1.0, boundary_type = 'reflective')\n", + "top = openmc.ZPlane(z0=1.0, boundary_type = 'reflective')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "7UFi6yivaxDS" + }, + "outputs": [], + "source": [ + "pitch = 1.6256\n", + "pin_cell_box = openmc.rectangular_prism(width=pitch, height=pitch)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Geometry definitions for the fuel rod\n", + "\n", + "fuel_or = openmc.ZCylinder(r=1.0414/2, name='Fuel OR')\n", + "fclad_ir = openmc.ZCylinder(r=1.06426/2, name='Clad IR')\n", + "fclad_or = openmc.ZCylinder(r=1.22682/2, name='Clad OR')\n", + "\n", + "fuel_region = -fuel_or \n", + "gap_region = +fuel_or & -fclad_ir\n", + "fclad_region = +fclad_ir & -fclad_or\n", + "fwater_region = pin_cell_box & +fclad_or\n", + "\n", + "fuel_cell = openmc.Cell(name='fuel')\n", + "fuel_cell.fill = uo2\n", + "fuel_cell.region = fuel_region \n", + "\n", + "gap_cell = openmc.Cell(name='air gap')\n", + "gap_cell.region = gap_region\n", + "\n", + "clad_cell = openmc.Cell(name='clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = fclad_region\n", + "\n", + "fwater_cell = openmc.Cell(name='fwater')\n", + "fwater_cell.fill = water\n", + "fwater_cell.region = fwater_region\n", + "\n", + "fuel_pin_universe = openmc.Universe(cells=[fuel_cell, gap_cell, clad_cell, fwater_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAD4CAYAAADhGCPfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPhElEQVR4nO3dXYxc5X2A8edfXMhFpfBhKxCDY1BRGu5KVpSmUhSFVAIuMDShAi4CFZFDW27ai8YSUo2RqpDkomkU1NQiKM4FX0VK47SuUClF3BTCIqUBQ0kWVNd23bABihRFJSX8e7FnYbx+5/vMOWd2np9keT6O57x7dufxe86ZmY3MRJI2+pW2ByCpm4yDpCLjIKnIOEgqMg6Sira0PYB+tm7dmjt37mx7GNKm9uyzz/40M7eV7utsHHbu3Mny8nLbw5A2tYg40u8+dyskFRkHSUXGQVKRcZBUZBwkFRkHSUXGQVKRcZBUZBwkFRkHSUXGQVKRcZBUZBwkFRkHSUXGQVKRcZBUZBwkFdUSh4i4LyJejYjn+9wfEfG1iFiJiB9GxKV1rFfS7NQ1c/gWcOWA+68CLq7+7Ab+uqb1SpqRWuKQmU8Crw9YZBfw7VzzFHBmRJxXx7olzUZTxxy2A0d7rh+rbjtJROyOiOWIWF5dXW1oaJJKOnVAMjP3Z+ZSZi5t21b8tGxJDWkqDseBC3qun1/dJqmjmorDQeCz1VmLy4E3M/NEQ+uWNIFafqlNRDwAfALYGhHHgL3ArwJk5jeAQ8DVwArwc+AP6livpNmpJQ6ZeeOQ+xP44zrWJakZnTogKak7jIOkIuMgqcg4SCoyDpKKjIOkIuMgqcg4SCoyDpKKjIOkolpePr1IItoegeZRZtsjGJ8zB0lFxkFSkXGQVGQcJBUZB0lFxkFSkXGQVGQcJBUZB0lFxkFSkXGQVGQcJBUZB0lFxkFSkXGQVGQcJBUZB0lFfhKURnbnnfum/Pd7axqJmmAcVDRtCEZ9TIPRXcZBwGxiMMl6jUV3GIcF1lYQBukdk6Fol3FYMF0MQj+Gol3GYUHMUxRK1sdvJJpTSxwi4krgr4DTgHsz8+4N998CfAU4Xt309cy8t451a7AmorB378lP2H37ZrdOI9GcyCl/20ZEnAb8CPhd4BjwDHBjZr7Qs8wtwFJm3j7q4y4tLeXy8vJUY5uFefqlNnWFYeOT/133D9kYN5V/tuqKxzwFoqu/1CYins3MpdJ9dcwcLgNWMvOVamUPAruAFwb+K81EHUE4KQb3B9x/52QP1Cceey/mpHBMGguPScxWHXHYDhztuX4M+K3Ccp+OiI+zNsv4k8w8WlhGU5gmDKcEYdZ61rF3bz2hMBD1auqA5PeABzLzrYj4PHAA+OTGhSJiN7AbYMeOHQ0NbXOYJAyNB6GfmkJhIOpVx3srjgMX9Fw/n/cOPAKQma9l5lvV1XuBj5YeKDP3Z+ZSZi5t27athqFJmlQdByS3sLarcAVrUXgGuCkzD/csc15mnqguXwd8ITMvH/S4HpAczVQzhjZnC6OojktMsqvRtRnEPB6QnDoO1QquBr7K2qnM+zLzLyLiLmA5Mw9GxBeBa4C3gdeBP8zMfx/0mMZhuHHDMDdR2GjCSHQpEAsbh1kwDoNNFIZ5i8JGN+XcBqKjTzPjUKcuxGGcMMztbKGfCWYRXQhER59mA+Pgh73MmbHDcH9snjDAu19P3xdmFcz7S8fbYhzmyERh2KwMxMy5WzGmtnYrRvnh7szrFpo05ist29rF6OjTzN2KRXDSsYVFCQOc9PWOM5PQcM4cxtT0zGGsGcMiRaFkjIOVTc8gOvo0c+YwrwzDmMaYQXgMYjjj0FFj/fAahveMsS0MxGDGQVKRcZhjm/505aTGPM2pMuPQQSMfazAM/Y0YCHct+jMOHWMYamQgpmIcJBUZhw5x1jADzh4mZhzmiGGYkAcoJ2IcOsL/udrn9+BkxmFOOGuYkrOHsRkHSUW+8WpMs3jj1aDprO+dqNkIb86axZuyOvo0841XksZnHCQVGYeWDd2lWLQPb5m1ET6D0rMWa4yDpCLjIKnIOHSUr2uYMV/3MJRxaJH7tt3l98Y4SOrDOHSVuxSz5zYeyDh0kPvCzXFb92ccJBUZh5Z4wKv7Fv17ZBwkFRkHSUXGQVKRcegiT7E1x23dVy1xiIgrI+KliFiJiD2F+8+IiIeq+5+OiJ11rFfS7Ewdh4g4DbgHuAq4BLgxIi7ZsNitwBuZ+evAXwJfmna9m5Xn3ZvnNi+rY+ZwGbCSma9k5i+AB4FdG5bZBRyoLj8CXBExiw9ck1SXOuKwHTjac/1YdVtxmcx8G3gTOGfjA0XE7ohYjojl1dXVGoYmaVKdOiCZmfszcykzl7Zt29b2cKSFVkccjgMX9Fw/v7qtuExEbAHeD7xWw7o3nUGfiqzZcJuX1RGHZ4CLI+LCiDgduAE4uGGZg8DN1eXPAI9nVz8TXxJQQxyqYwi3A48CLwIPZ+bhiLgrIq6pFvsmcE5ErAB/CpxyulM9brKbjXFb97WljgfJzEPAoQ23/XnP5f8Frq9jXZKa0akDkpK6wzhIKjIOLZnF72NUvRb9e2QcJBUZhw7yvHtz3Nb9GYeu8hTb7LmNBzIOkoqMQ4sW/YBXl/m9MQ6dtW/fPqe9s3RTerxhCOMgqcg4SCoyDi0btG/77q6Fuxf1qbbnoF0KjzesMQ6SioyDpKLo6meuLC0t5fLyctvDOMWsPhZ32O9l3Lt3r79jYVojnKGY1S5FR59mRMSzmblUus+Zg6Qi4zAnfN3DlHxdw9iMQ0d4hLx9fg9OZhzmiLOHCTlrmIhx6JBR/ucyEGMaMQzOGk5lHCQVGYeOcfZQI2cNUzEOHWQgamAYpmYc5piB6MMDkLUwDpKKjENHjTXddfbwnjG2hbsUgxmHDhv52AMYCHh3G3isoR6+8WpMs3rj1TAjvTELFvfNWSOGoa0odPRp5huvFsFJM4hFmkX0fL0ehKyXM4cxtTVzgOGzh14L8RbvMc9KtLkr0dGnmTOHzWKcH+5Nf5pzjsIwr4zDnJkoEJspEiN8BuRGhmEy7laMqc3dil7j7GLAJtnNmODFTV0JQ0efZu5WbEbj/tDP9SxigtkCdCcM82qqmUNEnA08BOwE/gP4/cx8o7DcL4Hnqqv/mZnXDHtsZw6jGXcGAXN02nOKsxBdC8M8zhymjcOXgdcz8+6I2AOclZlfKCz3s8z8tXEe2ziMZ6pIQHdC0TOz2QxRWDePcZh2t2IXcKC6fAC4dsrHk9QR084c/iczz6wuB/DG+vUNy70N/AB4G7g7M/9u2GM7cxjfJLOHda3OIqacLazr6qwB5nPmMDQOEfEYcG7hrjuAA70xiIg3MvOswmNsz8zjEXER8DhwRWa+XFhuN7AbYMeOHR89cuTIwLG1octxgOkCsa6RUNQUhHVdDgNs0jgMeeCXgE9k5omIOA94IjM/POTffAv4+8x8ZNByzhymV0coYEMseg0LR58zI3W9zLnrQei1iHH4CvBazwHJszPzzzYscxbw88x8KyK2Av8K7MrMFwY9tnGoR12BGGRjPJp4j8M8hQEWMw7nAA8DO4AjrJ3KfD0iloDbMvNzEfEx4G+Ad1g7APrVzPzmsMc2DvVqIhJNmLcorFu4OMyScZiNeY3EvEZhXUefZgPjsKXpwahdvU+yrodi3oMw74zDAutiKAxCdxgHAac+KZuKhTHoLuOgotKTdtpgGIL5Yhw0Mp/ci8W3bEsqMg6SioyDpCLjIKnIOEgqMg6SioyDpCLjIKnIOEgqMg6SioyDpCLjIKnIOEgqMg6SioyDpCLjIKnIOEgq8pOgxtTVjxiX6ubMQVKRcZBUZBwkFRkHSUXGQVKRcZBUZBwkFRkHSUXGQVKRcZBUZBwkFRkHSUVTxSEiro+IwxHxTkQsDVjuyoh4KSJWImLPNOuU1IxpZw7PA78HPNlvgYg4DbgHuAq4BLgxIi6Zcr2SZmyqt2xn5osAETFoscuAlcx8pVr2QWAX8MI065Y0W00cc9gOHO25fqy67RQRsTsiliNieXV1tYGhSepn6MwhIh4Dzi3cdUdmfrfOwWTmfmA/wNLSkh+rIrVoaBwy81NTruM4cEHP9fOr2yR1WBO7Fc8AF0fEhRFxOnADcLCB9UqawrSnMq+LiGPAbwP/EBGPVrd/MCIOAWTm28DtwKPAi8DDmXl4umFLmrVpz1Z8B/hO4fb/Aq7uuX4IODTNuiQ1y1dISioyDpKKjIOkIuMgqcg4SCoyDpKKjIOkIuMgqcg4SCoyDpKKjIOkIuMgqcg4SCoyDpKKjIOkIuMgqSgyu/k5rhGxChyZwUNvBX46g8edlXka7zyNFeZrvLMa64cyc1vpjs7GYVYiYjkz+/52rq6Zp/HO01hhvsbbxljdrZBUZBwkFS1iHPa3PYAxzdN452msMF/jbXysC3fMQdJoFnHmIGkExkFS0aaPQ0RcHxGHI+KdiOh7KigiroyIlyJiJSL2NDnGDeM4OyL+KSJ+XP19Vp/lfhkRP6j+NPrrBYdtq4g4IyIequ5/OiJ2Njm+DWMZNtZbImK1Z1t+ro1xVmO5LyJejYjn+9wfEfG16mv5YURcOtMBZeam/gN8BPgw8ASw1GeZ04CXgYuA04F/Ay5pabxfBvZUl/cAX+qz3M9aGt/QbQX8EfCN6vINwEMdHustwNfbGF9hvB8HLgWe73P/1cA/AgFcDjw9y/Fs+plDZr6YmS8NWewyYCUzX8nMXwAPArtmP7qiXcCB6vIB4NqWxtHPKNuq92t4BLgiIqLBMa7r0vd1qMx8Enh9wCK7gG/nmqeAMyPivFmNZ9PHYUTbgaM9149Vt7XhA5l5orr838AH+iz3vohYjoinIuLaZoYGjLat3l0m136R8pvAOY2Mrs84Kv2+r5+upumPRMQFzQxtIo3+nE71i3S7IiIeA84t3HVHZn636fEMM2i8vVcyMyOi37nmD2Xm8Yi4CHg8Ip7LzJfrHusC+B7wQGa+FRGfZ23G88mWx9QJmyIOmfmpKR/iOND7P8b51W0zMWi8EfGTiDgvM09UU8ZX+zzG8ervVyLiCeA3Wdu/nrVRttX6MsciYgvwfuC1Bsa20dCxZmbvuO5l7ZhPVzX6c+puxZpngIsj4sKIOJ21g2iNngHocRC4ubp8M3DKzCcizoqIM6rLW4HfAV5oaHyjbKver+EzwONZHVFr2NCxbthnvwZ4scHxjesg8NnqrMXlwJs9u6D1a/sIbQNHgK9jbd/sLeAnwKPV7R8EDm04Evwj1v73vaPF8Z4D/DPwY+Ax4Ozq9iXg3uryx4DnWDv6/hxwa8NjPGVbAXcB11SX3wf8LbACfB+4qMXtOWysXwQOV9vyX4DfaHGsDwAngP+rfmZvBW4DbqvuD+Ce6mt5jj5n3+r648unJRW5WyGpyDhIKjIOkoqMg6Qi4yCpyDhIKjIOkor+Hy26s80pblM5AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fuel_pin_universe.plot(width=(2.5, 2.5), colors = {fuel_cell: 'orange', gap_cell: 'white', clad_cell: 'grey', fwater_cell: 'blue'})" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Geometry definitions for the water rod\n", + "\n", + "water_or = openmc.ZCylinder(r=1.34874/2)\n", + "wclad_or = openmc.ZCylinder(r=1.50114/2)\n", + "\n", + "wwater_inner_region = -water_or\n", + "wclad_region = -wclad_or & +water_or \n", + "wwater_outer_region = pin_cell_box & +wclad_or\n", + "\n", + "wwater_inner_cell = openmc.Cell(name='wwater_inner')\n", + "wwater_inner_cell.region = wwater_inner_region\n", + "wwater_inner_cell.fill = water\n", + "\n", + "wclad_cell = openmc.Cell(name='wclad')\n", + "wclad_cell.fill = zircaloy\n", + "wclad_cell.region = wclad_region \n", + "\n", + "wwater_outer_cell = openmc.Cell(name='wwater_outer')\n", + "wwater_outer_cell.fill = water\n", + "wwater_outer_cell.region = wwater_outer_region\n", + "\n", + "water_pin_universe = openmc.Universe(cells=[wwater_inner_cell, wclad_cell, wwater_outer_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "FklxPQ-1xvNs", + "outputId": "9e4cb490-0ba5-4838-9f47-0442493b9f6d" + }, + "outputs": [], + "source": [ + "# Geometry definitions for the corner rod\n", + "\n", + "corner_fuel_or = openmc.ZCylinder(r=1.0414/2, name = 'Corner Cell: Fuel OR')\n", + "corner_clad_ir = openmc.ZCylinder(r=1.06426/2, name = 'Corner Cell: Clad IR')\n", + "corner_clad_or = openmc.ZCylinder(r=1.22682/2, name = 'Corner Cell: Clad OR')\n", + "\n", + "corner_fuel_cell = openmc.Cell(name='fuel')\n", + "corner_fuel_cell.fill = uo2\n", + "corner_fuel_cell.region = -corner_fuel_or \n", + "\n", + "corner_gap_cell = openmc.Cell(name='air gap')\n", + "corner_gap_cell.region = -corner_clad_ir & +corner_fuel_or\n", + "\n", + "corner_clad_cell = openmc.Cell(name='clad')\n", + "corner_clad_cell.fill = zircaloy\n", + "corner_clad_cell.region = +corner_clad_ir & -corner_clad_or" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Defining an Assembly" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "margin = 1e-8\n", + "sleave_thickness = 0.2032\n", + "sleave_inner_radius = 0.9652 + margin" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "FklxPQ-1xvNs", + "outputId": "9e4cb490-0ba5-4838-9f47-0442493b9f6d" + }, + "outputs": [], + "source": [ + "ur_corner_fuel_or = openmc.ZCylinder(r=1.0414/2, name='ur Corner Cell: Fuel OR')\n", + "ur_corner_clad_ir = openmc.ZCylinder(r=1.06426/2, name='ur Corner Cell: Clad IR')\n", + "ur_corner_clad_or = openmc.ZCylinder(r=1.22682/2, name='ur Corner Cell: Clad OR')\n", + "\n", + "ur_corner_fuel_cell = openmc.Cell(name='ur fuel')\n", + "ur_corner_fuel_cell.fill = uo2\n", + "ur_corner_fuel_cell.region = -ur_corner_fuel_or \n", + "\n", + "ur_corner_gap_cell = openmc.Cell(name='ul air gap')\n", + "ur_corner_gap_cell.region = -ur_corner_clad_ir & +ur_corner_fuel_or\n", + "\n", + "ur_corner_clad_cell = openmc.Cell(name='ul clad')\n", + "ur_corner_clad_cell.fill = zircaloy\n", + "ur_corner_clad_cell.region = +ur_corner_clad_ir & -ur_corner_clad_or\n", + "\n", + "ur_sleave_ir = openmc.ZCylinder(x0=pitch/2-sleave_inner_radius-margin, y0=pitch/2-sleave_inner_radius+margin, r=sleave_inner_radius)\n", + "ur_sleave_or = openmc.ZCylinder(x0=pitch/2-sleave_inner_radius-margin, y0=pitch/2-sleave_inner_radius+margin, r=sleave_inner_radius+sleave_thickness)\n", + "\n", + "ur_sleave_hor_bound = openmc.XPlane(x0=pitch/2 - sleave_inner_radius - margin)\n", + "ur_sleave_ver_bound = openmc.YPlane(y0=pitch/2 - sleave_inner_radius + margin)\n", + "\n", + "ur_sleave_cell = openmc.Cell(name='ur sleave cell')\n", + "ur_sleave_cell.region = -ur_sleave_or & +ur_sleave_ir & +ur_sleave_hor_bound & +ur_sleave_ver_bound & pin_cell_box\n", + "ur_sleave_cell.fill = zircaloy\n", + "\n", + "ur_sleave_water_cell = openmc.Cell(name = 'ur sleave water')\n", + "ur_sleave_water_cell.region = pin_cell_box & +ur_sleave_hor_bound & +ur_sleave_ver_bound & +ur_sleave_or\n", + "ur_sleave_water_cell.fill = water\n", + "\n", + "ur_sleave_ver_water_cell = openmc.Cell(name='ur vertically-equivalent sleave water')\n", + "ur_sleave_ver_water_cell.region = pin_cell_box & -ur_sleave_hor_bound & +ur_sleave_ver_bound & +ur_sleave_ir\n", + "ur_sleave_ver_water_cell.fill = water \n", + "\n", + "ur_opposite_water_cell = openmc.Cell(name='ur opposite water')\n", + "ur_opposite_water_cell.region = pin_cell_box & -ur_sleave_hor_bound & -ur_sleave_ver_bound & +ur_sleave_ir\n", + "ur_opposite_water_cell.fill = water \n", + "\n", + "ur_sleave_hor_water_cell = openmc.Cell(name='ur horizontally-equivalent sleave water')\n", + "ur_sleave_hor_water_cell.region = pin_cell_box & +ur_sleave_hor_bound & -ur_sleave_ver_bound & +ur_sleave_ir\n", + "ur_sleave_hor_water_cell.fill = water \n", + "\n", + "ur_inner_water_cell = openmc.Cell(name='ur inner water cell')\n", + "ur_inner_water_cell.region = pin_cell_box & -ur_sleave_ir & +ur_corner_clad_or\n", + "ur_inner_water_cell.fill = water\n", + "\n", + "ur_corner_pin_universe = openmc.Universe(cells=[ur_corner_fuel_cell, ur_corner_gap_cell, ur_corner_clad_cell, ur_sleave_cell, ur_inner_water_cell, ur_sleave_water_cell, ur_sleave_ver_water_cell, ur_opposite_water_cell, ur_sleave_hor_water_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ur_corner_pin_universe.plot(width=(2.5, 2.5), basis = 'xy', colors = {ur_corner_fuel_cell: 'orange', ur_corner_gap_cell: 'white', ur_corner_clad_cell: 'grey', ur_sleave_cell:'brown', ur_inner_water_cell: 'blue', ur_sleave_water_cell: 'blue', ur_sleave_ver_water_cell: 'blue', ur_opposite_water_cell: 'blue', ur_sleave_hor_water_cell: 'blue'})" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "ul_corner_fuel_or = openmc.ZCylinder(r=(1.0414/2), name='ul Corner Cell: Fuel OR')\n", + "ul_corner_clad_ir = openmc.ZCylinder(r=(1.06426/2), name='ul Corner Cell: Clad IR')\n", + "ul_corner_clad_or = openmc.ZCylinder(r=(1.22682/2), name='ul Corner Cell: Clad OR')\n", + "\n", + "ul_corner_fuel_cell = openmc.Cell(name='ul fuel')\n", + "ul_corner_fuel_cell.fill = uo2\n", + "ul_corner_fuel_cell.region = -ul_corner_fuel_or \n", + "\n", + "ul_corner_gap_cell = openmc.Cell(name='ul air gap')\n", + "ul_corner_gap_cell.region = -ul_corner_clad_ir & +ul_corner_fuel_or\n", + "\n", + "ul_corner_clad_cell = openmc.Cell(name='ul clad')\n", + "ul_corner_clad_cell.fill = zircaloy\n", + "ul_corner_clad_cell.region = +ul_corner_clad_ir & -ul_corner_clad_or\n", + "\n", + "ul_sleave_ir = openmc.ZCylinder(x0=-pitch/2+sleave_inner_radius+margin, y0=pitch/2-sleave_inner_radius+margin, r=sleave_inner_radius) #Shouldn't have margin\n", + "ul_sleave_or = openmc.ZCylinder(x0=-pitch/2+sleave_inner_radius+margin, y0=pitch/2-sleave_inner_radius+margin, r=sleave_inner_radius + sleave_thickness)\n", + "\n", + "ul_sleave_hor_bound = openmc.XPlane(x0=-pitch/2+sleave_inner_radius+margin)\n", + "ul_sleave_ver_bound = openmc.YPlane(y0=pitch/2-sleave_inner_radius+margin)\n", + "\n", + "ul_sleave_cell = openmc.Cell(name='ul sleave cell')\n", + "ul_sleave_cell.region = -ul_sleave_or & +ul_sleave_ir & -ul_sleave_hor_bound & +ul_sleave_ver_bound & pin_cell_box\n", + "ul_sleave_cell.fill = zircaloy\n", + "\n", + "ul_sleave_water_cell = openmc.Cell(name='ul sleave water')\n", + "ul_sleave_water_cell.region = pin_cell_box & -ul_sleave_hor_bound & +ul_sleave_ver_bound & +ul_sleave_or\n", + "ul_sleave_water_cell.fill = water\n", + "\n", + "ul_sleave_ver_water_cell = openmc.Cell(name='ul vertically-equivalent sleave water')\n", + "ul_sleave_ver_water_cell.region = pin_cell_box & +ul_sleave_hor_bound & +ul_sleave_ver_bound & +ul_sleave_ir\n", + "ul_sleave_ver_water_cell.fill = water \n", + "\n", + "ul_opposite_water_cell = openmc.Cell(name='ul opposite water')\n", + "ul_opposite_water_cell.region = pin_cell_box & +ul_sleave_hor_bound & -ul_sleave_ver_bound & +ul_sleave_ir\n", + "ul_opposite_water_cell.fill = water \n", + "\n", + "ul_sleave_hor_water_cell = openmc.Cell(name='ul horizontally-equivalent sleave water')\n", + "ul_sleave_hor_water_cell.region = pin_cell_box & -ul_sleave_hor_bound & -ul_sleave_ver_bound & +ul_sleave_ir\n", + "ul_sleave_hor_water_cell.fill = water \n", + "\n", + "ul_inner_water_cell = openmc.Cell(name='ul inner water cell')\n", + "ul_inner_water_cell.region = pin_cell_box & -ul_sleave_ir & +ul_corner_clad_or\n", + "ul_inner_water_cell.fill = water\n", + "\n", + "ul_corner_pin_universe = openmc.Universe(cells=[ul_corner_fuel_cell, ul_corner_gap_cell, ul_corner_clad_cell, ul_sleave_cell, ul_inner_water_cell, ul_sleave_water_cell, ul_sleave_ver_water_cell, ul_opposite_water_cell, ul_sleave_hor_water_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ul_corner_pin_universe.plot(width=(2.5, 2.5), basis = 'xy', colors = {ul_corner_fuel_cell: 'orange', ul_corner_gap_cell: 'white', ul_corner_clad_cell: 'grey', ul_sleave_cell:'brown', ul_inner_water_cell: 'blue', ul_sleave_water_cell: 'blue', ul_sleave_ver_water_cell: 'blue', ul_opposite_water_cell: 'blue', ul_sleave_hor_water_cell: 'blue'})" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "ll_corner_fuel_or = openmc.ZCylinder(r=1.0414/2, name='ll Corner Cell: Fuel OR')\n", + "ll_corner_clad_ir = openmc.ZCylinder(r=1.06426/2, name='ll Corner Cell: Clad IR')\n", + "ll_corner_clad_or = openmc.ZCylinder(r=1.22682/2, name='ll Corner Cell: Clad OR')\n", + "\n", + "ll_corner_fuel_cell = openmc.Cell(name='ll fuel')\n", + "ll_corner_fuel_cell.fill = uo2\n", + "ll_corner_fuel_cell.region = -ll_corner_fuel_or \n", + "\n", + "ll_corner_gap_cell = openmc.Cell(name='ll air gap')\n", + "ll_corner_gap_cell.region = -ll_corner_clad_ir & +ll_corner_fuel_or\n", + "\n", + "ll_corner_clad_cell = openmc.Cell(name='ll clad')\n", + "ll_corner_clad_cell.fill = zircaloy\n", + "ll_corner_clad_cell.region = +ll_corner_clad_ir & -ll_corner_clad_or\n", + "\n", + "ll_sleave_ir = openmc.ZCylinder(x0=-pitch/2+sleave_inner_radius+margin, y0=-pitch/2+sleave_inner_radius-margin, r=sleave_inner_radius)\n", + "ll_sleave_or = openmc.ZCylinder(x0=-pitch/2+sleave_inner_radius+margin, y0=-pitch/2+sleave_inner_radius-margin, r=sleave_inner_radius+sleave_thickness)\n", + "\n", + "ll_sleave_hor_bound = openmc.XPlane(x0=-pitch/2+sleave_inner_radius+margin)\n", + "ll_sleave_ver_bound = openmc.YPlane(y0=-pitch/2+sleave_inner_radius-margin)\n", + "\n", + "ll_sleave_cell = openmc.Cell(name='ul sleave cell')\n", + "ll_sleave_cell.region = -ll_sleave_or & +ll_sleave_ir & -ll_sleave_hor_bound & -ll_sleave_ver_bound & pin_cell_box\n", + "ll_sleave_cell.fill = zircaloy\n", + "\n", + "ll_sleave_water_cell = openmc.Cell(name='ul sleave water')\n", + "ll_sleave_water_cell.region = pin_cell_box & -ll_sleave_hor_bound & -ll_sleave_ver_bound & +ll_sleave_or\n", + "ll_sleave_water_cell.fill = water\n", + "\n", + "ll_sleave_ver_water_cell = openmc.Cell(name='ul vertically-equivalent sleave water')\n", + "ll_sleave_ver_water_cell.region = pin_cell_box & +ll_sleave_hor_bound & -ll_sleave_ver_bound & +ll_sleave_ir\n", + "ll_sleave_ver_water_cell.fill = water \n", + "\n", + "ll_opposite_water_cell = openmc.Cell(name='ul opposite water')\n", + "ll_opposite_water_cell.region = pin_cell_box & +ll_sleave_hor_bound & +ll_sleave_ver_bound & +ll_sleave_ir\n", + "ll_opposite_water_cell.fill = water \n", + "\n", + "ll_sleave_hor_water_cell = openmc.Cell(name='ul horizontally-equivalent sleave water')\n", + "ll_sleave_hor_water_cell.region = pin_cell_box & -ll_sleave_hor_bound & +ll_sleave_ver_bound & +ll_sleave_ir\n", + "ll_sleave_hor_water_cell.fill = water \n", + "\n", + "ll_inner_water_cell = openmc.Cell(name='ul inner water cell')\n", + "ll_inner_water_cell.region = pin_cell_box & -ll_sleave_ir & +ll_corner_clad_or\n", + "ll_inner_water_cell.fill = water\n", + "\n", + "ll_corner_pin_universe = openmc.Universe(cells=[ll_corner_fuel_cell, ll_corner_gap_cell, ll_corner_clad_cell, ll_sleave_cell, ll_inner_water_cell, ll_sleave_water_cell, ll_sleave_ver_water_cell, ll_opposite_water_cell, ll_sleave_hor_water_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ll_corner_pin_universe.plot(width=(2.5, 2.5), basis = 'xy', colors = {ll_corner_fuel_cell: 'orange', ll_corner_gap_cell: 'white', ll_corner_clad_cell: 'grey', ll_sleave_cell:'brown', ll_inner_water_cell: 'blue', ll_sleave_water_cell: 'blue', ll_sleave_ver_water_cell: 'blue', ll_opposite_water_cell: 'blue', ll_sleave_hor_water_cell: 'blue'})" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "lr_corner_fuel_or = openmc.ZCylinder(r=1.0414/2, name='lr Corner Cell: Fuel OR')\n", + "lr_corner_clad_ir = openmc.ZCylinder(r=1.06426/2, name='lr Corner Cell: Clad IR')\n", + "lr_corner_clad_or = openmc.ZCylinder(r=1.22682/2, name='lr Corner Cell: Clad OR')\n", + "\n", + "lr_corner_fuel_cell = openmc.Cell(name='lr fuel')\n", + "lr_corner_fuel_cell.fill = uo2\n", + "lr_corner_fuel_cell.region = -lr_corner_fuel_or \n", + "\n", + "lr_corner_gap_cell = openmc.Cell(name='lr air gap')\n", + "lr_corner_gap_cell.region = -lr_corner_clad_ir & +lr_corner_fuel_or\n", + "\n", + "lr_corner_clad_cell = openmc.Cell(name='lr clad')\n", + "lr_corner_clad_cell.fill = zircaloy\n", + "lr_corner_clad_cell.region = +lr_corner_clad_ir & -lr_corner_clad_or\n", + "\n", + "lr_sleave_ir = openmc.ZCylinder(x0=pitch/2-sleave_inner_radius-margin, y0=-pitch/2+sleave_inner_radius-margin, r=sleave_inner_radius)\n", + "lr_sleave_or = openmc.ZCylinder(x0=pitch/2-sleave_inner_radius-margin, y0=-pitch/2+sleave_inner_radius-margin, r=sleave_inner_radius+sleave_thickness)\n", + "\n", + "lr_sleave_hor_bound = openmc.XPlane(x0=pitch/2 - sleave_inner_radius - margin)\n", + "lr_sleave_ver_bound = openmc.YPlane(y0=-pitch/2 + sleave_inner_radius - margin)\n", + "\n", + "lr_sleave_cell = openmc.Cell(name='ur sleave cell')\n", + "lr_sleave_cell.region = -lr_sleave_or & +lr_sleave_ir & +lr_sleave_hor_bound & -lr_sleave_ver_bound & pin_cell_box\n", + "lr_sleave_cell.fill = zircaloy\n", + "\n", + "lr_sleave_water_cell = openmc.Cell(name='ur sleave water')\n", + "lr_sleave_water_cell.region = pin_cell_box & +lr_sleave_hor_bound & -lr_sleave_ver_bound & +lr_sleave_or\n", + "lr_sleave_water_cell.fill = water\n", + "\n", + "lr_sleave_ver_water_cell = openmc.Cell(name='ur vertically-equivalent sleave water')\n", + "lr_sleave_ver_water_cell.region = pin_cell_box & -lr_sleave_hor_bound & -lr_sleave_ver_bound & +lr_sleave_ir\n", + "lr_sleave_ver_water_cell.fill = water \n", + "\n", + "lr_opposite_water_cell = openmc.Cell(name='ur opposite water')\n", + "lr_opposite_water_cell.region = pin_cell_box & -lr_sleave_hor_bound & +lr_sleave_ver_bound & +lr_sleave_ir\n", + "lr_opposite_water_cell.fill = water \n", + "\n", + "lr_sleave_hor_water_cell = openmc.Cell(name='ur horizontally-equivalent sleave water')\n", + "lr_sleave_hor_water_cell.region = pin_cell_box & +lr_sleave_hor_bound & +lr_sleave_ver_bound & +lr_sleave_ir\n", + "lr_sleave_hor_water_cell.fill = water \n", + "\n", + "lr_inner_water_cell = openmc.Cell(name='ur inner water cell')\n", + "lr_inner_water_cell.region = pin_cell_box & -lr_sleave_ir & +lr_corner_clad_or\n", + "lr_inner_water_cell.fill = water\n", + "\n", + "lr_corner_pin_universe = openmc.Universe(cells=[lr_corner_fuel_cell, lr_corner_gap_cell, lr_corner_clad_cell, lr_sleave_cell, lr_inner_water_cell, lr_sleave_water_cell, lr_sleave_ver_water_cell, lr_opposite_water_cell, lr_sleave_hor_water_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lr_corner_pin_universe.plot(width=(2.5, 2.5), basis = 'xy', colors = {lr_corner_fuel_cell: 'orange', lr_corner_gap_cell: 'white', lr_corner_clad_cell: 'grey', lr_sleave_cell:'brown', lr_inner_water_cell: 'blue', lr_sleave_water_cell: 'blue', lr_sleave_ver_water_cell: 'blue', lr_opposite_water_cell: 'blue', lr_sleave_hor_water_cell: 'blue'})" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/miriamrathbun/codes/openmc-mak/openmc/surface.py:1510: FutureWarning: \"ZCylinder(...) accepts an argument named 'r', not 'R'. Future versions of OpenMC will not accept the capitalized version.\n", + " warn(_WARNING_UPPER.format(type(self).__name__, 'r', 'R'),\n" + ] + } + ], + "source": [ + "quarter_pitch = pitch * 8\n", + "\n", + "assembly = openmc.RectLattice(name='Quarter Assembly')\n", + "assembly.pitch = (pitch, pitch)\n", + "assembly.lower_left = [-quarter_pitch/2, -quarter_pitch/2]\n", + "\n", + "assembly.universes = [\n", + " [ul_corner_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, ur_corner_pin_universe],\n", + " [fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe],\n", + " [fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe],\n", + " [fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, water_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe],\n", + " [fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, water_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe],\n", + " [fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe],\n", + " [fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe],\n", + " [ll_corner_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, fuel_pin_universe, lr_corner_pin_universe]\n", + "]\n", + "\n", + "assembly_region = openmc.rectangular_prism(width=quarter_pitch, height=quarter_pitch, origin=(0,0))\n", + "assembly_cell = openmc.Cell(name='quarter assembly cell', fill=assembly, region=assembly_region)\n", + "\n", + "assembly_sleave = openmc.Cell(name='quarter assembly sleave')\n", + "assembly_sleave.region = openmc.rectangular_prism(width=quarter_pitch+2*sleave_thickness, height=quarter_pitch+2*sleave_thickness, corner_radius=sleave_inner_radius+sleave_thickness) & ~assembly_cell.region\n", + "assembly_sleave.fill = zircaloy\n", + "\n", + "assembly_outer_water = openmc.Cell(name='assembly outer water')\n", + "assembly_outer_water.region = ~assembly_sleave.region & ~assembly_cell.region & openmc.rectangular_prism(width=quarter_pitch+2*sleave_thickness+1, height=quarter_pitch+2*sleave_thickness+1, boundary_type='reflective')\n", + "assembly_outer_water.fill = water\n", + "\n", + "quarter_assembly_universe = openmc.Universe(cells=[assembly_cell, assembly_sleave, assembly_outer_water])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "quarter_assembly_universe.plot(width=(15,15), colors={assembly_outer_water: 'blue'})" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "geom = openmc.Geometry(quarter_assembly_universe)\n", + "geom.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JPOk4874OR3w", + "outputId": "b15d56c2-f9a1-42c4-b06f-3e779a1953ec" + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "\n", + "point = openmc.stats.Point((0, 0, 0))\n", + "src = openmc.Source(space=point)\n", + "settings = openmc.Settings()\n", + "settings.source = src\n", + "settings.batches = 100\n", + "settings.inactive = 10\n", + "settings.particles = 1000\n", + "settings.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lkX_1LrAzw71", + "outputId": "6331f2a0-42fc-4919-91d3-7bca6b5d735f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.1-dev\n", + " Git SHA1 | 0228ddba1d6399e654a295e51539590f076b2a3c\n", + " Date/Time | 2021-04-12 15:15:21\n", + " OpenMP Threads | 8\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U234.h5\n", + " Reading U235 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U235.h5\n", + " Reading U238 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U238.h5\n", + " Reading U236 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U236.h5\n", + " Reading O16 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/O16.h5\n", + " Reading O17 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/O17.h5\n", + " Reading Zr90 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr90.h5\n", + " Reading C0 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/C0.h5\n", + " Reading Si28 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Si28.h5\n", + " Reading Si29 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Si29.h5\n", + " Reading Si30 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Si30.h5\n", + " Reading Mn55 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Mn55.h5\n", + " Reading P31 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/P31.h5\n", + " Reading S32 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/S32.h5\n", + " Reading S33 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/S33.h5\n", + " Reading S34 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/S34.h5\n", + " Reading S36 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/S36.h5\n", + " Reading Cr50 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Cr50.h5\n", + " Reading Cr52 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Cr52.h5\n", + " Reading Cr53 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Cr53.h5\n", + " Reading Cr54 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Cr54.h5\n", + " Reading Ni58 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ni58.h5\n", + " Reading Ni60 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ni60.h5\n", + " Reading Ni61 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ni61.h5\n", + " Reading Ni62 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ni62.h5\n", + " Reading Ni64 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ni64.h5\n", + " Reading Fe54 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Fe54.h5\n", + " Reading Fe56 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Fe56.h5\n", + " Reading Fe57 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Fe57.h5\n", + " Reading Fe58 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Fe58.h5\n", + " Reading H1 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/H1.h5\n", + " Reading H2 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/H2.h5\n", + " Reading c_H_in_H2O from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/c_H_in_H2O.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.20596\n", + " 2/1 1.17725\n", + " 3/1 1.22903\n", + " 4/1 1.25595\n", + " 5/1 1.25464\n", + " 6/1 1.19484\n", + " 7/1 1.20845\n", + " 8/1 1.11385\n", + " 9/1 1.20241\n", + " 10/1 1.17714\n", + " 11/1 1.21900\n", + " 12/1 1.24344 1.23122 +/- 0.01222\n", + " 13/1 1.18577 1.21607 +/- 0.01671\n", + " 14/1 1.25341 1.22540 +/- 0.01506\n", + " 15/1 1.16432 1.21319 +/- 0.01689\n", + " 16/1 1.19189 1.20964 +/- 0.01424\n", + " 17/1 1.18475 1.20608 +/- 0.01255\n", + " 18/1 1.23268 1.20941 +/- 0.01137\n", + " 19/1 1.09926 1.19717 +/- 0.01582\n", + " 20/1 1.24832 1.20228 +/- 0.01505\n", + " 21/1 1.13900 1.19653 +/- 0.01478\n", + " 22/1 1.27620 1.20317 +/- 0.01503\n", + " 23/1 1.29588 1.21030 +/- 0.01556\n", + " 24/1 1.14165 1.20540 +/- 0.01522\n", + " 25/1 1.19325 1.20459 +/- 0.01419\n", + " 26/1 1.23371 1.20641 +/- 0.01340\n", + " 27/1 1.23268 1.20795 +/- 0.01268\n", + " 28/1 1.10873 1.20244 +/- 0.01316\n", + " 29/1 1.14639 1.19949 +/- 0.01280\n", + " 30/1 1.24465 1.20175 +/- 0.01235\n", + " 31/1 1.21818 1.20253 +/- 0.01177\n", + " 32/1 1.17997 1.20151 +/- 0.01127\n", + " 33/1 1.19293 1.20113 +/- 0.01078\n", + " 34/1 1.20978 1.20149 +/- 0.01032\n", + " 35/1 1.24639 1.20329 +/- 0.01006\n", + " 36/1 1.19880 1.20312 +/- 0.00967\n", + " 37/1 1.21696 1.20363 +/- 0.00932\n", + " 38/1 1.18314 1.20290 +/- 0.00901\n", + " 39/1 1.16487 1.20159 +/- 0.00879\n", + " 40/1 1.25905 1.20350 +/- 0.00871\n", + " 41/1 1.18639 1.20295 +/- 0.00844\n", + " 42/1 1.25987 1.20473 +/- 0.00836\n", + " 43/1 1.21967 1.20518 +/- 0.00812\n", + " 44/1 1.18532 1.20460 +/- 0.00790\n", + " 45/1 1.10094 1.20164 +/- 0.00822\n", + " 46/1 1.17034 1.20077 +/- 0.00804\n", + " 47/1 1.19107 1.20050 +/- 0.00782\n", + " 48/1 1.17497 1.19983 +/- 0.00764\n", + " 49/1 1.24290 1.20094 +/- 0.00752\n", + " 50/1 1.18399 1.20051 +/- 0.00735\n", + " 51/1 1.25434 1.20183 +/- 0.00728\n", + " 52/1 1.10167 1.19944 +/- 0.00750\n", + " 53/1 1.24434 1.20049 +/- 0.00740\n", + " 54/1 1.23006 1.20116 +/- 0.00726\n", + " 55/1 1.19075 1.20093 +/- 0.00710\n", + " 56/1 1.15255 1.19987 +/- 0.00702\n", + " 57/1 1.22948 1.20050 +/- 0.00690\n", + " 58/1 1.16780 1.19982 +/- 0.00679\n", + " 59/1 1.13035 1.19841 +/- 0.00680\n", + " 60/1 1.24983 1.19943 +/- 0.00674\n", + " 61/1 1.11795 1.19784 +/- 0.00680\n", + " 62/1 1.23461 1.19854 +/- 0.00670\n", + " 63/1 1.17725 1.19814 +/- 0.00659\n", + " 64/1 1.18257 1.19785 +/- 0.00647\n", + " 65/1 1.16513 1.19726 +/- 0.00638\n", + " 66/1 1.23539 1.19794 +/- 0.00630\n", + " 67/1 1.14146 1.19695 +/- 0.00627\n", + " 68/1 1.24386 1.19776 +/- 0.00621\n", + " 69/1 1.15268 1.19699 +/- 0.00615\n", + " 70/1 1.21613 1.19731 +/- 0.00606\n", + " 71/1 1.17274 1.19691 +/- 0.00597\n", + " 72/1 1.18517 1.19672 +/- 0.00588\n", + " 73/1 1.14883 1.19596 +/- 0.00583\n", + " 74/1 1.16359 1.19545 +/- 0.00576\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 75/1 1.19540 1.19545 +/- 0.00567\n", + " 76/1 1.15401 1.19483 +/- 0.00562\n", + " 77/1 1.33939 1.19698 +/- 0.00594\n", + " 78/1 1.24558 1.19770 +/- 0.00590\n", + " 79/1 1.20401 1.19779 +/- 0.00581\n", + " 80/1 1.15683 1.19720 +/- 0.00576\n", + " 81/1 1.19522 1.19718 +/- 0.00568\n", + " 82/1 1.24057 1.19778 +/- 0.00563\n", + " 83/1 1.25570 1.19857 +/- 0.00561\n", + " 84/1 1.19560 1.19853 +/- 0.00553\n", + " 85/1 1.22024 1.19882 +/- 0.00547\n", + " 86/1 1.20019 1.19884 +/- 0.00539\n", + " 87/1 1.18864 1.19871 +/- 0.00533\n", + " 88/1 1.16494 1.19827 +/- 0.00527\n", + " 89/1 1.23639 1.19876 +/- 0.00523\n", + " 90/1 1.19507 1.19871 +/- 0.00516\n", + " 91/1 1.19785 1.19870 +/- 0.00510\n", + " 92/1 1.24397 1.19925 +/- 0.00507\n", + " 93/1 1.17376 1.19894 +/- 0.00502\n", + " 94/1 1.29989 1.20015 +/- 0.00510\n", + " 95/1 1.23815 1.20059 +/- 0.00506\n", + " 96/1 1.22451 1.20087 +/- 0.00501\n", + " 97/1 1.15918 1.20039 +/- 0.00497\n", + " 98/1 1.13020 1.19959 +/- 0.00498\n", + " 99/1 1.16169 1.19917 +/- 0.00494\n", + " 100/1 1.23024 1.19951 +/- 0.00490\n", + " Creating state point statepoint.100.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 8.1559e+00 seconds\n", + " Reading cross sections = 8.0639e+00 seconds\n", + " Total time in simulation = 3.0575e+00 seconds\n", + " Time in transport only = 3.0383e+00 seconds\n", + " Time in inactive batches = 2.5988e-01 seconds\n", + " Time in active batches = 2.7976e+00 seconds\n", + " Time synchronizing fission bank = 3.6140e-03 seconds\n", + " Sampling source sites = 2.9180e-03 seconds\n", + " SEND/RECV source sites = 5.9700e-04 seconds\n", + " Time accumulating tallies = 4.0000e-05 seconds\n", + " Time writing statepoints = 8.9120e-03 seconds\n", + " Total time for finalization = 7.0000e-06 seconds\n", + " Total time elapsed = 1.1256e+01 seconds\n", + " Calculation Rate (inactive) = 38479.0 particles/second\n", + " Calculation Rate (active) = 32170.1 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.20249 +/- 0.00447\n", + " k-effective (Track-length) = 1.19951 +/- 0.00490\n", + " k-effective (Absorption) = 1.20115 +/- 0.00350\n", + " Combined k-effective = 1.20089 +/- 0.00309\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "thick_thin.IPYNB", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Depletion/chain_simple.xml b/Depletion/chain_simple.xml new file mode 100644 index 0000000..3b94ad5 --- /dev/null +++ b/Depletion/chain_simple.xml @@ -0,0 +1,81 @@ + + + + + + + + + + + + + + + + + + + + 283500.0 4.235177914969822e-15 + + + 513971.0 892130.0 1175630.0 6.915023250821416e-10 4.235177914969822e-15 3.870076370575872e-11 + + + + + + + + + + + + + + + + + + + 4470.0 31817.0 32194.0 36304.0 36378.0 37255.0 661657.0 4.372683136122041e-05 9.525884763557368e-05 0.00017377297596020668 1.6633087848867745e-05 3.2112650589648466e-05 1.0158463048637348e-05 0.004069614128287558 + + + 3670.0 26400.0 624216.4 655668.2 660364.2 661404.0 661636.9 0.0003537897992698539 3.7210487357645964e-05 0.00037210482830822686 6.708752100247119e-05 1.4350025269251869e-05 3.08729347663194e-06 4.6173597451093537e-07 + + + + + + + + + 2.53000e-02 + + Gd157 Gd156 I135 Xe135 Xe136 Cs135 + 1.093250e-04 2.087260e-04 2.780820e-02 6.759540e-03 2.392300e-02 4.356330e-05 + + + + + + + 2.53000e-02 + + Gd157 Gd156 I135 Xe135 Xe136 Cs135 + 6.142710e-5 1.483250e-04 0.0292737 0.002566345 0.0219242 4.9097e-6 + + + + + + + 2.53000e-02 + + Gd157 Gd156 I135 Xe135 Xe136 Cs135 + 4.141120e-04 7.605360e-04 0.0135457 0.00026864 0.0024432 3.7100E-07 + + + + diff --git a/Depletion/depletion.ipynb b/Depletion/depletion.ipynb new file mode 100644 index 0000000..51bd4a5 --- /dev/null +++ b/Depletion/depletion.ipynb @@ -0,0 +1,1556 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Depletion\n", + "This notebook is intended to introduce the reader to the depletion interface contained in OpenMC. It is recommended that you are moderately familiar with building models using the OpenMC Python API. The earlier examples are excellent starting points, as this notebook will not focus heavily on model building.\n", + "\n", + "If you have a real power reactor, the fuel composition is constantly changing as fission events produce energy, remove some fissile isotopes, and produce fission products. Other reactions, like $(n, \\alpha)$ and $(n, \\gamma)$ will alter the composition as well. Furthermore, some nuclides undergo spontaneous decay with widely ranging frequencies. Depletion is the process of modeling this behavior.\n", + "\n", + "In this notebook, we will model a simple fuel pin in an infinite lattice using the Python API. We will then build and examine some of the necessary components for performing depletion analysis. Then, we will use the depletion interface in OpenMC to simulate the fuel pin producing power over several months. Lastly, we will wrap up with some helpful tips to improve the fidelity of depletion simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import math\n", + "import openmc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build the Geometry\n", + "\n", + "Much of this section is borrowed from the \"Modeling a Pin-Cell\" example. If you find yourself not understanding some aspects of this section, feel free to refer to that example, as some details may be glossed over for brevity.\n", + "\n", + "First, we will create our fuel, cladding, and water materials to represent a typical PWR." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "fuel = openmc.Material(name=\"uo2\")\n", + "fuel.add_element(\"U\", 1, percent_type=\"ao\", enrichment=4.25)\n", + "fuel.add_element(\"O\", 2)\n", + "fuel.set_density(\"g/cc\", 10.4)\n", + "\n", + "clad = openmc.Material(name=\"clad\")\n", + "clad.add_element(\"Zr\", 1)\n", + "clad.set_density(\"g/cc\", 6)\n", + "\n", + "water = openmc.Material(name=\"water\")\n", + "water.add_element(\"O\", 1)\n", + "water.add_element(\"H\", 2)\n", + "water.set_density(\"g/cc\", 1.0)\n", + "water.add_s_alpha_beta(\"c_H_in_H2O\")\n", + "materials = openmc.Materials([fuel, clad, water])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we are going to use the `openmc.model.pin` function to build our pin cell. The `pin` function anticipates concentric cylinders and materials to fill the inner regions. One additional material is needed than the number of cylinders to cover the domain outside the final ring. \n", + "\n", + "To do this, we define two radii for the outer radius of our fuel pin, and the outer radius of the cladding." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "radii = [0.42, 0.45]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using these radii, we define concentric `ZCylinder` objects. So long as the cylinders are concentric and increasing in radius, any orientation can be used. We also take advantage of the fact that the `openmc.Materials` object is a subclass of the `list` object to assign materials to the regions defined by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "pin_surfaces = [openmc.ZCylinder(r=r) for r in radii]\n", + "pin_univ = openmc.model.pin(pin_surfaces, materials)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first material, in our case `fuel`, is placed inside the first cylinder in the inner-most region. The second material, `clad`, fills the space between our cylinders, while `water` is placed outside the last ring. The `pin` function returns an `openmc.Universe` object, and has some additional features we will mention later. Finally, we need to place the fuel pin universe in a bounding cell." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "bound_box = openmc.rectangular_prism(1.24, 1.24, boundary_type=\"reflective\")\n", + "root_cell = openmc.Cell(fill=pin_univ, region=bound_box)\n", + "geometry = openmc.Geometry([root_cell])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To ensure our geometry looks right, let's plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "geometry.root_universe.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly we construct our settings. For the sake of time, a relatively low number of particles will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "settings = openmc.Settings()\n", + "settings.particles = 1000\n", + "settings.inactive = 10\n", + "settings.batches = 50" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The depletion interface relies on `OpenMC` to perform the transport simulation and obtain reaction rates and other important information. Normally, we would need to export XML files before running OpenMC, but the depletion interface takes care of this for us." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, we must first add one bit of information: the volume of our fuel. In order to translate the reaction rates obtained by `openmc` to meaningful units for depletion, we have to normalize them to a correct power. This requires us to know, or be able to calculate, how much fuel is in our problem. Correctly setting the volumes is a critical step, and can lead to incorrect answers, as the fuel is over- or under-depleted due to poor normalization.\n", + "\n", + "For our problem, we can assign the \"volume\" to be the cross-sectional area of our fuel. This is identical to modeling our fuel pin inside a box with height of 1 cm." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "fuel.volume = math.pi * radii[0] ** 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up for depletion\n", + "\n", + "The OpenMC depletion interface can be accessed from the `openmc.deplete` module, and has a variety of classes that will help us." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import openmc.deplete" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to run the depletion calculation we need the following information:\n", + "\n", + "1. Nuclide decay, fission yield, and reaction data\n", + "2. Operational power or power density\n", + "3. Desired depletion schedule\n", + "4. Desired time integration scheme\n", + "\n", + "The first item is necessary to determine the paths by which nuclides transmute over the depletion simulation. This includes spontaneous decay, fission product yield distributions, and nuclides produced through neutron-reactions. For example,\n", + "* Te129 decays to I129 with a half life of ~70 minutes\n", + "* A fission event for U-235 produces fission products like Xe135 according to a distribution\n", + "* For thermal problems, Am241 will produce metastable Am242 about 8% of the time during an $(n,\\gamma)$ reaction. The other 92% of capture reactions will produce ground state Am242\n", + "\n", + "These data are often distributed with other nuclear data, like incident neutron cross sections with ENDF/B-VII.\n", + "OpenMC uses the [`openmc.deplete.Chain`](https://docs.openmc.org/en/latest/pythonapi/generated/openmc.deplete.Chain.html#openmc.deplete.Chain) to collect represent the various decay and transmutation pathways in a single object.\n", + "While a complete `Chain` can be created using nuclear data files, users may prefer to download pre-generated XML-representations instead.\n", + "Such files can be found at https://openmc.org/depletion-chains/ and include full and compressed chains, with capture branching ratios derived using PWR- or SFR-spectra.\n", + "\n", + "For this problem, we will be using a much smaller depletion chain that contains very few nuclides. In a realistic problem, over 1000 isotopes may be included in the depletion chain." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([('I135', 0),\n", + " ('Xe135', 1),\n", + " ('Xe136', 2),\n", + " ('Cs135', 3),\n", + " ('Gd157', 4),\n", + " ('Gd156', 5),\n", + " ('U234', 6),\n", + " ('U235', 7),\n", + " ('U238', 8)])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain = openmc.deplete.Chain.from_xml(\"./chain_simple.xml\")\n", + "chain.nuclide_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The primary entry point for depletion is the `openmc.deplete.Operator`. It relies on the `openmc.deplete.Chain` and helper classes to run `openmc`, retrieve and normalize reaction rates, and other perform other tasks. For a thorough description, please see the full API documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will create our Operator using the geometry and settings from above, and our simple chain file. The materials are read in automatically using the `materials.xml` file." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "model = openmc.Model(geometry=geometry, settings=settings)\n", + "operator = openmc.deplete.CoupledOperator(model, \"./chain_simple.xml\")" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "We will then simulate our fuel pin operating at linear power of 174 W/cm, or 174 W given a unit height for our problem." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "power = 174" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this problem, we will take depletion step sizes of 30 days, and instruct OpenMC to re-run a transport simulation every 30 days until we have modeled the problem over a six month cycle. The depletion interface expects the time to be given in seconds, so we will have to convert. Note that these values are not cumulative." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "time_steps = [30] * 6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And lastly, we will use the basic predictor, or forward Euler, time integration scheme. Other, more advanced methods are provided to the user through `openmc.deplete`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "integrator = openmc.deplete.PredictorIntegrator(operator, time_steps, power, timestep_units='d')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To perform the simulation, we use the `integrate` method, and let `openmc` take care of the rest." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-05 19:02:05\n", + " OpenMP Threads | 2\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U234.h5\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n", + " Reading O17 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O17.h5\n", + " Reading U236 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U236.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr90.h5\n", + " Reading Zr91 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr91.h5\n", + " Reading Zr92 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr92.h5\n", + " Reading Zr94 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr94.h5\n", + " Reading Zr96 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr96.h5\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n", + " Reading H2 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H2.h5\n", + " Reading c_H_in_H2O from /home/pshriwise/data/xs/openmc/nndc_hdf5/c_H_in_H2O.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + "[openmc.deplete] t=0.0 s, dt=2592000 s, source=174\n", + " Reading I135 from /home/pshriwise/data/xs/openmc/nndc_hdf5/I135.h5\n", + " Reading Xe135 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Xe135.h5\n", + " Reading Xe136 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Xe136.h5\n", + " Reading Cs135 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Cs135.h5\n", + " Reading Gd157 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Gd157.h5\n", + " Reading Gd156 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Gd156.h5\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.49721\n", + " 2/1 1.49226\n", + " 3/1 1.44838\n", + " 4/1 1.47163\n", + " 5/1 1.41429\n", + " 6/1 1.42100\n", + " 7/1 1.43656\n", + " 8/1 1.41211\n", + " 9/1 1.45570\n", + " 10/1 1.33919\n", + " 11/1 1.47822\n", + " 12/1 1.47425 1.47624 +/- 0.00198\n", + " 13/1 1.39919 1.45056 +/- 0.02571\n", + " 14/1 1.35785 1.42738 +/- 0.02946\n", + " 15/1 1.38972 1.41985 +/- 0.02403\n", + " 16/1 1.47812 1.42956 +/- 0.02189\n", + " 17/1 1.50905 1.44092 +/- 0.02171\n", + " 18/1 1.48299 1.44618 +/- 0.01952\n", + " 19/1 1.49089 1.45114 +/- 0.01792\n", + " 20/1 1.50543 1.45657 +/- 0.01692\n", + " 21/1 1.41183 1.45251 +/- 0.01584\n", + " 22/1 1.49487 1.45604 +/- 0.01488\n", + " 23/1 1.45853 1.45623 +/- 0.01369\n", + " 24/1 1.45628 1.45623 +/- 0.01268\n", + " 25/1 1.37824 1.45103 +/- 0.01290\n", + " 26/1 1.40298 1.44803 +/- 0.01243\n", + " 27/1 1.48608 1.45027 +/- 0.01189\n", + " 28/1 1.46773 1.45124 +/- 0.01125\n", + " 29/1 1.46127 1.45177 +/- 0.01066\n", + " 30/1 1.38587 1.44847 +/- 0.01063\n", + " 31/1 1.42115 1.44717 +/- 0.01020\n", + " 32/1 1.45934 1.44772 +/- 0.00974\n", + " 33/1 1.40481 1.44586 +/- 0.00949\n", + " 34/1 1.47602 1.44711 +/- 0.00917\n", + " 35/1 1.47601 1.44827 +/- 0.00887\n", + " 36/1 1.44354 1.44809 +/- 0.00853\n", + " 37/1 1.54954 1.45185 +/- 0.00902\n", + " 38/1 1.46876 1.45245 +/- 0.00872\n", + " 39/1 1.51863 1.45473 +/- 0.00872\n", + " 40/1 1.51529 1.45675 +/- 0.00866\n", + " 41/1 1.39841 1.45487 +/- 0.00858\n", + " 42/1 1.58314 1.45888 +/- 0.00923\n", + " 43/1 1.51678 1.46063 +/- 0.00911\n", + " 44/1 1.44328 1.46012 +/- 0.00886\n", + " 45/1 1.42208 1.45903 +/- 0.00867\n", + " 46/1 1.50118 1.46020 +/- 0.00850\n", + " 47/1 1.50796 1.46150 +/- 0.00837\n", + " 48/1 1.44889 1.46116 +/- 0.00816\n", + " 49/1 1.48715 1.46183 +/- 0.00797\n", + " 50/1 1.49559 1.46267 +/- 0.00782\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 1.1968e+00 seconds\n", + " Reading cross sections = 1.1906e+00 seconds\n", + " Total time in simulation = 1.3040e+01 seconds\n", + " Time in transport only = 1.3031e+01 seconds\n", + " Time in inactive batches = 2.2501e+00 seconds\n", + " Time in active batches = 1.0790e+01 seconds\n", + " Time synchronizing fission bank = 4.3167e-03 seconds\n", + " Sampling source sites = 3.9236e-03 seconds\n", + " SEND/RECV source sites = 3.6606e-04 seconds\n", + " Time accumulating tallies = 2.4592e-04 seconds\n", + " Time writing statepoints = 1.4237e-03 seconds\n", + " Total time for finalization = 5.2430e-05 seconds\n", + " Total time elapsed = 1.4251e+01 seconds\n", + " Calculation Rate (inactive) = 4444.21 particles/second\n", + " Calculation Rate (active) = 3707.27 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.46857 +/- 0.00652\n", + " k-effective (Track-length) = 1.46267 +/- 0.00782\n", + " k-effective (Absorption) = 1.46527 +/- 0.00455\n", + " Combined k-effective = 1.46478 +/- 0.00392\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n0.h5...\n", + "[openmc.deplete] t=2592000.0 s, dt=2592000 s, source=174\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.53241\n", + " 2/1 1.40513\n", + " 3/1 1.45199\n", + " 4/1 1.48296\n", + " 5/1 1.45516\n", + " 6/1 1.49568\n", + " 7/1 1.41651\n", + " 8/1 1.41637\n", + " 9/1 1.41213\n", + " 10/1 1.43048\n", + " 11/1 1.43207\n", + " 12/1 1.51388 1.47297 +/- 0.04091\n", + " 13/1 1.44591 1.46395 +/- 0.02528\n", + " 14/1 1.50325 1.47378 +/- 0.02040\n", + " 15/1 1.44463 1.46795 +/- 0.01684\n", + " 16/1 1.45528 1.46583 +/- 0.01391\n", + " 17/1 1.39473 1.45568 +/- 0.01554\n", + " 18/1 1.41153 1.45016 +/- 0.01454\n", + " 19/1 1.36173 1.44033 +/- 0.01616\n", + " 20/1 1.48965 1.44527 +/- 0.01527\n", + " 21/1 1.42710 1.44361 +/- 0.01391\n", + " 22/1 1.41558 1.44128 +/- 0.01291\n", + " 23/1 1.39316 1.43758 +/- 0.01244\n", + " 24/1 1.46698 1.43968 +/- 0.01171\n", + " 25/1 1.42137 1.43846 +/- 0.01097\n", + " 26/1 1.44859 1.43909 +/- 0.01028\n", + " 27/1 1.39159 1.43630 +/- 0.01005\n", + " 28/1 1.51030 1.44041 +/- 0.01033\n", + " 29/1 1.58514 1.44802 +/- 0.01239\n", + " 30/1 1.33291 1.44227 +/- 0.01309\n", + " 31/1 1.38694 1.43963 +/- 0.01272\n", + " 32/1 1.46008 1.44056 +/- 0.01217\n", + " 33/1 1.39234 1.43847 +/- 0.01181\n", + " 34/1 1.40957 1.43726 +/- 0.01138\n", + " 35/1 1.47587 1.43881 +/- 0.01102\n", + " 36/1 1.43015 1.43847 +/- 0.01059\n", + " 37/1 1.38802 1.43661 +/- 0.01036\n", + " 38/1 1.33953 1.43314 +/- 0.01057\n", + " 39/1 1.48062 1.43478 +/- 0.01033\n", + " 40/1 1.44053 1.43497 +/- 0.00998\n", + " 41/1 1.46435 1.43592 +/- 0.00970\n", + " 42/1 1.49916 1.43789 +/- 0.00960\n", + " 43/1 1.46589 1.43874 +/- 0.00934\n", + " 44/1 1.40238 1.43767 +/- 0.00913\n", + " 45/1 1.44733 1.43795 +/- 0.00887\n", + " 46/1 1.35951 1.43577 +/- 0.00889\n", + " 47/1 1.45303 1.43623 +/- 0.00866\n", + " 48/1 1.43982 1.43633 +/- 0.00843\n", + " 49/1 1.53025 1.43874 +/- 0.00855\n", + " 50/1 1.45253 1.43908 +/- 0.00834\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.2879e+01 seconds\n", + " Time in transport only = 1.2870e+01 seconds\n", + " Time in inactive batches = 2.1932e+00 seconds\n", + " Time in active batches = 1.0686e+01 seconds\n", + " Time synchronizing fission bank = 4.6210e-03 seconds\n", + " Sampling source sites = 4.2406e-03 seconds\n", + " SEND/RECV source sites = 3.5471e-04 seconds\n", + " Time accumulating tallies = 2.2210e-04 seconds\n", + " Time writing statepoints = 2.5594e-03 seconds\n", + " Total time for finalization = 5.4901e-05 seconds\n", + " Total time elapsed = 1.2893e+01 seconds\n", + " Calculation Rate (inactive) = 4559.54 particles/second\n", + " Calculation Rate (active) = 3743.15 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.43941 +/- 0.00730\n", + " k-effective (Track-length) = 1.43908 +/- 0.00834\n", + " k-effective (Absorption) = 1.43889 +/- 0.00475\n", + " Combined k-effective = 1.43891 +/- 0.00466\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n1.h5...\n", + "[openmc.deplete] t=5184000.0 s, dt=2592000 s, source=174\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.35648\n", + " 2/1 1.41942\n", + " 3/1 1.36608\n", + " 4/1 1.46674\n", + " 5/1 1.47455\n", + " 6/1 1.40320\n", + " 7/1 1.36282\n", + " 8/1 1.41123\n", + " 9/1 1.45872\n", + " 10/1 1.43235\n", + " 11/1 1.43551\n", + " 12/1 1.42639 1.43095 +/- 0.00456\n", + " 13/1 1.38410 1.41533 +/- 0.01584\n", + " 14/1 1.40010 1.41152 +/- 0.01183\n", + " 15/1 1.45122 1.41946 +/- 0.01212\n", + " 16/1 1.46395 1.42688 +/- 0.01237\n", + " 17/1 1.50374 1.43786 +/- 0.01516\n", + " 18/1 1.41657 1.43520 +/- 0.01340\n", + " 19/1 1.39988 1.43127 +/- 0.01245\n", + " 20/1 1.48773 1.43692 +/- 0.01248\n", + " 21/1 1.50428 1.44304 +/- 0.01285\n", + " 22/1 1.40603 1.43996 +/- 0.01213\n", + " 23/1 1.44173 1.44009 +/- 0.01115\n", + " 24/1 1.47605 1.44266 +/- 0.01064\n", + " 25/1 1.41864 1.44106 +/- 0.01004\n", + " 26/1 1.42455 1.44003 +/- 0.00944\n", + " 27/1 1.43444 1.43970 +/- 0.00888\n", + " 28/1 1.39999 1.43749 +/- 0.00866\n", + " 29/1 1.45318 1.43832 +/- 0.00823\n", + " 30/1 1.46514 1.43966 +/- 0.00792\n", + " 31/1 1.49160 1.44213 +/- 0.00793\n", + " 32/1 1.34013 1.43750 +/- 0.00887\n", + " 33/1 1.48829 1.43971 +/- 0.00876\n", + " 34/1 1.47141 1.44103 +/- 0.00849\n", + " 35/1 1.39193 1.43906 +/- 0.00838\n", + " 36/1 1.39036 1.43719 +/- 0.00826\n", + " 37/1 1.44249 1.43739 +/- 0.00795\n", + " 38/1 1.48357 1.43904 +/- 0.00784\n", + " 39/1 1.40641 1.43791 +/- 0.00765\n", + " 40/1 1.42094 1.43734 +/- 0.00741\n", + " 41/1 1.40773 1.43639 +/- 0.00723\n", + " 42/1 1.45978 1.43712 +/- 0.00704\n", + " 43/1 1.42308 1.43669 +/- 0.00683\n", + " 44/1 1.47645 1.43786 +/- 0.00673\n", + " 45/1 1.51314 1.44001 +/- 0.00688\n", + " 46/1 1.48159 1.44117 +/- 0.00679\n", + " 47/1 1.48586 1.44238 +/- 0.00671\n", + " 48/1 1.38670 1.44091 +/- 0.00669\n", + " 49/1 1.53726 1.44338 +/- 0.00697\n", + " 50/1 1.44556 1.44344 +/- 0.00680\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.2995e+01 seconds\n", + " Time in transport only = 1.2986e+01 seconds\n", + " Time in inactive batches = 2.2152e+00 seconds\n", + " Time in active batches = 1.0780e+01 seconds\n", + " Time synchronizing fission bank = 4.3730e-03 seconds\n", + " Sampling source sites = 3.9823e-03 seconds\n", + " SEND/RECV source sites = 3.6477e-04 seconds\n", + " Time accumulating tallies = 2.3376e-04 seconds\n", + " Time writing statepoints = 2.6625e-03 seconds\n", + " Total time for finalization = 6.8176e-03 seconds\n", + " Total time elapsed = 1.3016e+01 seconds\n", + " Calculation Rate (inactive) = 4514.3 particles/second\n", + " Calculation Rate (active) = 3710.67 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.43781 +/- 0.00627\n", + " k-effective (Track-length) = 1.44344 +/- 0.00680\n", + " k-effective (Absorption) = 1.43243 +/- 0.00527\n", + " Combined k-effective = 1.43604 +/- 0.00493\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n2.h5...\n", + "[openmc.deplete] t=7776000.0 s, dt=2592000 s, source=174\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.52145\n", + " 2/1 1.45763\n", + " 3/1 1.38715\n", + " 4/1 1.49491\n", + " 5/1 1.45451\n", + " 6/1 1.41857\n", + " 7/1 1.36661\n", + " 8/1 1.35323\n", + " 9/1 1.32219\n", + " 10/1 1.45559\n", + " 11/1 1.56651\n", + " 12/1 1.37746 1.47198 +/- 0.09452\n", + " 13/1 1.41360 1.45252 +/- 0.05794\n", + " 14/1 1.46543 1.45575 +/- 0.04110\n", + " 15/1 1.36700 1.43800 +/- 0.03645\n", + " 16/1 1.41995 1.43499 +/- 0.02991\n", + " 17/1 1.48070 1.44152 +/- 0.02611\n", + " 18/1 1.46150 1.44402 +/- 0.02275\n", + " 19/1 1.34499 1.43302 +/- 0.02288\n", + " 20/1 1.47485 1.43720 +/- 0.02089\n", + " 21/1 1.50424 1.44329 +/- 0.01985\n", + " 22/1 1.41394 1.44085 +/- 0.01829\n", + " 23/1 1.43248 1.44020 +/- 0.01683\n", + " 24/1 1.38295 1.43611 +/- 0.01611\n", + " 25/1 1.42331 1.43526 +/- 0.01503\n", + " 26/1 1.44462 1.43585 +/- 0.01407\n", + " 27/1 1.43415 1.43575 +/- 0.01321\n", + " 28/1 1.48582 1.43853 +/- 0.01276\n", + " 29/1 1.44884 1.43907 +/- 0.01209\n", + " 30/1 1.34656 1.43444 +/- 0.01236\n", + " 31/1 1.44433 1.43492 +/- 0.01177\n", + " 32/1 1.48062 1.43699 +/- 0.01141\n", + " 33/1 1.37226 1.43418 +/- 0.01126\n", + " 34/1 1.31991 1.42942 +/- 0.01179\n", + " 35/1 1.48850 1.43178 +/- 0.01155\n", + " 36/1 1.47446 1.43342 +/- 0.01122\n", + " 37/1 1.49478 1.43569 +/- 0.01103\n", + " 38/1 1.35729 1.43289 +/- 0.01099\n", + " 39/1 1.37560 1.43092 +/- 0.01079\n", + " 40/1 1.41980 1.43055 +/- 0.01043\n", + " 41/1 1.36832 1.42854 +/- 0.01029\n", + " 42/1 1.41065 1.42798 +/- 0.00997\n", + " 43/1 1.36825 1.42617 +/- 0.00984\n", + " 44/1 1.37304 1.42461 +/- 0.00967\n", + " 45/1 1.40813 1.42414 +/- 0.00940\n", + " 46/1 1.39351 1.42329 +/- 0.00918\n", + " 47/1 1.46699 1.42447 +/- 0.00900\n", + " 48/1 1.41202 1.42414 +/- 0.00877\n", + " 49/1 1.45476 1.42493 +/- 0.00858\n", + " 50/1 1.38327 1.42388 +/- 0.00842\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.3031e+01 seconds\n", + " Time in transport only = 1.3022e+01 seconds\n", + " Time in inactive batches = 2.2077e+00 seconds\n", + " Time in active batches = 1.0824e+01 seconds\n", + " Time synchronizing fission bank = 4.5578e-03 seconds\n", + " Sampling source sites = 4.1598e-03 seconds\n", + " SEND/RECV source sites = 3.7168e-04 seconds\n", + " Time accumulating tallies = 2.3355e-04 seconds\n", + " Time writing statepoints = 2.7490e-03 seconds\n", + " Total time for finalization = 4.9850e-05 seconds\n", + " Total time elapsed = 1.3045e+01 seconds\n", + " Calculation Rate (inactive) = 4529.68 particles/second\n", + " Calculation Rate (active) = 3695.64 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.42700 +/- 0.00561\n", + " k-effective (Track-length) = 1.42388 +/- 0.00842\n", + " k-effective (Absorption) = 1.43128 +/- 0.00502\n", + " Combined k-effective = 1.42959 +/- 0.00429\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n3.h5...\n", + "[openmc.deplete] t=10368000.0 s, dt=2592000 s, source=174\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.38925\n", + " 2/1 1.42976\n", + " 3/1 1.41226\n", + " 4/1 1.39211\n", + " 5/1 1.50564\n", + " 6/1 1.47182\n", + " 7/1 1.40604\n", + " 8/1 1.47053\n", + " 9/1 1.38703\n", + " 10/1 1.40210\n", + " 11/1 1.49465\n", + " 12/1 1.38331 1.43898 +/- 0.05567\n", + " 13/1 1.36733 1.41510 +/- 0.04004\n", + " 14/1 1.43836 1.42091 +/- 0.02891\n", + " 15/1 1.28111 1.39295 +/- 0.03582\n", + " 16/1 1.46121 1.40433 +/- 0.03138\n", + " 17/1 1.33978 1.39511 +/- 0.02808\n", + " 18/1 1.41161 1.39717 +/- 0.02440\n", + " 19/1 1.39461 1.39689 +/- 0.02152\n", + " 20/1 1.47066 1.40426 +/- 0.02062\n", + " 21/1 1.42437 1.40609 +/- 0.01874\n", + " 22/1 1.46622 1.41110 +/- 0.01782\n", + " 23/1 1.37032 1.40797 +/- 0.01669\n", + " 24/1 1.41432 1.40842 +/- 0.01546\n", + " 25/1 1.38921 1.40714 +/- 0.01445\n", + " 26/1 1.40164 1.40679 +/- 0.01352\n", + " 27/1 1.42757 1.40802 +/- 0.01276\n", + " 28/1 1.32544 1.40343 +/- 0.01288\n", + " 29/1 1.45468 1.40613 +/- 0.01247\n", + " 30/1 1.49292 1.41047 +/- 0.01261\n", + " 31/1 1.38744 1.40937 +/- 0.01204\n", + " 32/1 1.46078 1.41171 +/- 0.01171\n", + " 33/1 1.42757 1.41240 +/- 0.01122\n", + " 34/1 1.44109 1.41359 +/- 0.01080\n", + " 35/1 1.39698 1.41293 +/- 0.01038\n", + " 36/1 1.47438 1.41529 +/- 0.01025\n", + " 37/1 1.45001 1.41658 +/- 0.00995\n", + " 38/1 1.42864 1.41701 +/- 0.00960\n", + " 39/1 1.44516 1.41798 +/- 0.00931\n", + " 40/1 1.46250 1.41946 +/- 0.00912\n", + " 41/1 1.36719 1.41778 +/- 0.00898\n", + " 42/1 1.32118 1.41476 +/- 0.00920\n", + " 43/1 1.48217 1.41680 +/- 0.00915\n", + " 44/1 1.46043 1.41808 +/- 0.00897\n", + " 45/1 1.47425 1.41969 +/- 0.00886\n", + " 46/1 1.46925 1.42107 +/- 0.00872\n", + " 47/1 1.46076 1.42214 +/- 0.00854\n", + " 48/1 1.34478 1.42010 +/- 0.00856\n", + " 49/1 1.38493 1.41920 +/- 0.00839\n", + " 50/1 1.34114 1.41725 +/- 0.00841\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.3126e+01 seconds\n", + " Time in transport only = 1.3116e+01 seconds\n", + " Time in inactive batches = 2.2380e+00 seconds\n", + " Time in active batches = 1.0888e+01 seconds\n", + " Time synchronizing fission bank = 4.1247e-03 seconds\n", + " Sampling source sites = 3.7224e-03 seconds\n", + " SEND/RECV source sites = 3.7812e-04 seconds\n", + " Time accumulating tallies = 2.2789e-04 seconds\n", + " Time writing statepoints = 3.0614e-03 seconds\n", + " Total time for finalization = 5.4060e-05 seconds\n", + " Total time elapsed = 1.3140e+01 seconds\n", + " Calculation Rate (inactive) = 4468.2 particles/second\n", + " Calculation Rate (active) = 3673.84 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.41574 +/- 0.00666\n", + " k-effective (Track-length) = 1.41725 +/- 0.00841\n", + " k-effective (Absorption) = 1.42868 +/- 0.00429\n", + " Combined k-effective = 1.42755 +/- 0.00464\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n4.h5...\n", + "[openmc.deplete] t=12960000.0 s, dt=2592000 s, source=174\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.41046\n", + " 2/1 1.38415\n", + " 3/1 1.41886\n", + " 4/1 1.49276\n", + " 5/1 1.51088\n", + " 6/1 1.47652\n", + " 7/1 1.35478\n", + " 8/1 1.35145\n", + " 9/1 1.49048\n", + " 10/1 1.44388\n", + " 11/1 1.46152\n", + " 12/1 1.46663 1.46408 +/- 0.00255\n", + " 13/1 1.47189 1.46668 +/- 0.00299\n", + " 14/1 1.39703 1.44927 +/- 0.01754\n", + " 15/1 1.41472 1.44236 +/- 0.01524\n", + " 16/1 1.42886 1.44011 +/- 0.01265\n", + " 17/1 1.45836 1.44272 +/- 0.01100\n", + " 18/1 1.44504 1.44301 +/- 0.00953\n", + " 19/1 1.35868 1.43364 +/- 0.01259\n", + " 20/1 1.36823 1.42710 +/- 0.01302\n", + " 21/1 1.44284 1.42853 +/- 0.01186\n", + " 22/1 1.52754 1.43678 +/- 0.01362\n", + " 23/1 1.44989 1.43779 +/- 0.01257\n", + " 24/1 1.48022 1.44082 +/- 0.01202\n", + " 25/1 1.40460 1.43840 +/- 0.01145\n", + " 26/1 1.36586 1.43387 +/- 0.01163\n", + " 27/1 1.35671 1.42933 +/- 0.01183\n", + " 28/1 1.54670 1.43585 +/- 0.01292\n", + " 29/1 1.41323 1.43466 +/- 0.01228\n", + " 30/1 1.41355 1.43361 +/- 0.01170\n", + " 31/1 1.56992 1.44010 +/- 0.01288\n", + " 32/1 1.35745 1.43634 +/- 0.01284\n", + " 33/1 1.42131 1.43569 +/- 0.01229\n", + " 34/1 1.45210 1.43637 +/- 0.01179\n", + " 35/1 1.36098 1.43335 +/- 0.01170\n", + " 36/1 1.45621 1.43423 +/- 0.01128\n", + " 37/1 1.47355 1.43569 +/- 0.01095\n", + " 38/1 1.44393 1.43598 +/- 0.01055\n", + " 39/1 1.52203 1.43895 +/- 0.01061\n", + " 40/1 1.43143 1.43870 +/- 0.01025\n", + " 41/1 1.44187 1.43880 +/- 0.00991\n", + " 42/1 1.27834 1.43379 +/- 0.01083\n", + " 43/1 1.38906 1.43243 +/- 0.01058\n", + " 44/1 1.41543 1.43193 +/- 0.01028\n", + " 45/1 1.39747 1.43095 +/- 0.01003\n", + " 46/1 1.38755 1.42974 +/- 0.00982\n", + " 47/1 1.40362 1.42904 +/- 0.00958\n", + " 48/1 1.32535 1.42631 +/- 0.00971\n", + " 49/1 1.38622 1.42528 +/- 0.00952\n", + " 50/1 1.40968 1.42489 +/- 0.00928\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.3098e+01 seconds\n", + " Time in transport only = 1.3089e+01 seconds\n", + " Time in inactive batches = 2.2179e+00 seconds\n", + " Time in active batches = 1.0881e+01 seconds\n", + " Time synchronizing fission bank = 4.5930e-03 seconds\n", + " Sampling source sites = 4.1842e-03 seconds\n", + " SEND/RECV source sites = 3.7955e-04 seconds\n", + " Time accumulating tallies = 2.4737e-04 seconds\n", + " Time writing statepoints = 2.7292e-03 seconds\n", + " Total time for finalization = 5.2371e-05 seconds\n", + " Total time elapsed = 1.3112e+01 seconds\n", + " Calculation Rate (inactive) = 4508.68 particles/second\n", + " Calculation Rate (active) = 3676.29 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.42490 +/- 0.00784\n", + " k-effective (Track-length) = 1.42489 +/- 0.00928\n", + " k-effective (Absorption) = 1.42584 +/- 0.00494\n", + " Combined k-effective = 1.42575 +/- 0.00483\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n5.h5...\n", + "[openmc.deplete] t=15552000.0 (final operator evaluation)\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.46692\n", + " 2/1 1.42016\n", + " 3/1 1.35815\n", + " 4/1 1.38064\n", + " 5/1 1.44504\n", + " 6/1 1.36182\n", + " 7/1 1.31279\n", + " 8/1 1.47632\n", + " 9/1 1.48287\n", + " 10/1 1.40492\n", + " 11/1 1.34730\n", + " 12/1 1.46660 1.40695 +/- 0.05965\n", + " 13/1 1.47009 1.42800 +/- 0.04036\n", + " 14/1 1.41037 1.42359 +/- 0.02888\n", + " 15/1 1.35040 1.40895 +/- 0.02673\n", + " 16/1 1.41945 1.41070 +/- 0.02190\n", + " 17/1 1.54521 1.42992 +/- 0.02668\n", + " 18/1 1.45638 1.43322 +/- 0.02334\n", + " 19/1 1.38313 1.42766 +/- 0.02132\n", + " 20/1 1.48971 1.43386 +/- 0.02006\n", + " 21/1 1.43673 1.43412 +/- 0.01814\n", + " 22/1 1.38255 1.42983 +/- 0.01711\n", + " 23/1 1.47338 1.43318 +/- 0.01609\n", + " 24/1 1.43212 1.43310 +/- 0.01490\n", + " 25/1 1.47334 1.43578 +/- 0.01413\n", + " 26/1 1.44139 1.43613 +/- 0.01322\n", + " 27/1 1.47399 1.43836 +/- 0.01262\n", + " 28/1 1.45271 1.43916 +/- 0.01192\n", + " 29/1 1.41352 1.43781 +/- 0.01136\n", + " 30/1 1.40013 1.43593 +/- 0.01094\n", + " 31/1 1.44858 1.43653 +/- 0.01042\n", + " 32/1 1.38137 1.43402 +/- 0.01025\n", + " 33/1 1.37217 1.43133 +/- 0.01015\n", + " 34/1 1.36865 1.42872 +/- 0.01007\n", + " 35/1 1.35863 1.42592 +/- 0.01005\n", + " 36/1 1.41942 1.42567 +/- 0.00966\n", + " 37/1 1.47196 1.42738 +/- 0.00945\n", + " 38/1 1.50806 1.43026 +/- 0.00956\n", + " 39/1 1.38029 1.42854 +/- 0.00938\n", + " 40/1 1.32528 1.42510 +/- 0.00969\n", + " 41/1 1.43717 1.42549 +/- 0.00938\n", + " 42/1 1.40786 1.42494 +/- 0.00910\n", + " 43/1 1.45468 1.42584 +/- 0.00887\n", + " 44/1 1.32809 1.42296 +/- 0.00907\n", + " 45/1 1.37289 1.42153 +/- 0.00892\n", + " 46/1 1.39638 1.42083 +/- 0.00870\n", + " 47/1 1.45351 1.42172 +/- 0.00851\n", + " 48/1 1.37909 1.42059 +/- 0.00836\n", + " 49/1 1.41817 1.42053 +/- 0.00814\n", + " 50/1 1.36996 1.41927 +/- 0.00803\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.3234e+01 seconds\n", + " Time in transport only = 1.3225e+01 seconds\n", + " Time in inactive batches = 2.2456e+00 seconds\n", + " Time in active batches = 1.0989e+01 seconds\n", + " Time synchronizing fission bank = 4.2442e-03 seconds\n", + " Sampling source sites = 3.8368e-03 seconds\n", + " SEND/RECV source sites = 3.8075e-04 seconds\n", + " Time accumulating tallies = 2.4804e-04 seconds\n", + " Time writing statepoints = 2.7128e-03 seconds\n", + " Total time for finalization = 5.0970e-05 seconds\n", + " Total time elapsed = 1.3248e+01 seconds\n", + " Calculation Rate (inactive) = 4453.13 particles/second\n", + " Calculation Rate (active) = 3640.13 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.42429 +/- 0.00588\n", + " k-effective (Track-length) = 1.41927 +/- 0.00803\n", + " k-effective (Absorption) = 1.42275 +/- 0.00472\n", + " Combined k-effective = 1.42368 +/- 0.00460\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n6.h5...\n" + ] + } + ], + "source": [ + "integrator.integrate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Processing the outputs\n", + "\n", + "The depletion simulation produces a few output files. First, the statepoint files from each individual transport simulation are written to `openmc_simulation_n.h5`, where `` indicates the current depletion step. Any tallies that we defined in `tallies.xml` will be included in these files across our simulations. We have 7 such files, one for each our of 6 depletion steps and the initial state." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c5g7.h5\t\t\t openmc_simulation_n2.h5 openmc_simulation_n6.h5\n", + "depletion_results.h5\t openmc_simulation_n3.h5 statepoint.50.h5\n", + "openmc_simulation_n0.h5 openmc_simulation_n4.h5 summary.h5\n", + "openmc_simulation_n1.h5 openmc_simulation_n5.h5\n" + ] + } + ], + "source": [ + "!ls *.h5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `depletion_results.h5` file contains information that is aggregated over all time steps through depletion. This includes the multiplication factor, as well as concentrations. We can process this file using the `openmc.deplete.Results` object" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "results = openmc.deplete.Results(\"./depletion_results.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "time, k = results.get_keff()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "time /= (24 * 60 * 60) # convert back to days from seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.46477526, 0.00392422],\n", + " [1.43890826, 0.00465543],\n", + " [1.43604211, 0.00493173],\n", + " [1.42958815, 0.00429288],\n", + " [1.42754967, 0.00463556],\n", + " [1.42575323, 0.00483288],\n", + " [1.42368305, 0.00459654]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "k" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first column of `k` is the value of `k-combined` at each point in our simulation, while the second column contains the associated uncertainty. We can plot this using `matplotlib`" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.errorbar(time, k[:, 0], yerr=k[:, 1])\n", + "pyplot.xlabel(\"Time [d]\")\n", + "pyplot.ylabel(\"$k_{eff}\\pm \\sigma$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Due to the low number of particles selected, the uncertainty on each value is rather high. However, we can still see the decline in `k` over time due to fuel consumption." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then examine concentrations of atoms in each of our materials. This requires knowing the material ID, which can be obtained from the `materials.xml` file." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "_, u235 = results.get_atoms(\"1\", \"U235\")\n", + "_, xe135 = results.get_atoms(\"1\", \"Xe135\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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S1cTurVvaUKJYEtc9P4ffqomdSMSi2obazNYDOwEHRrl7rv/iN7PfAg3c/dc5rOsH9AOoWbNmy40bN0YpscS7A4eP8thHaxiV8QWVyxRnSK9GdGtcLehYIoHLqw31cQuBmdUG7gDSyHZHM3fvGcE3ru7um82sKjAFuMPdM3LY7kJgJNDe3b/L6zN1PwKJxNLNu7j7ncUs37Kb7o1P48+9GlG1XMmgY4kEJq9CkOutKrN5F3gOeB84oat43H1z+Os2MxsLnAf8qBCEp6M+C3Q/XhEQiVRWE7unM75gxEdr+HTddzxwSUN6t6iOmVpmiWQXyTmCA+7+mLt/7O7Tsh7He5OZlTGzclnPgS7A0mO2qQmMAa5199UnkV8kV9mb2NULN7G7/oW5bNq5L+hoIjElkkNDvwTqAZOBH5rEu3ueLanDJ5XHhl+mAK+7+1/NrH/4/U+Z2bNAHyDroP+R3IYuWXRoSE5GZqbzymcbGTpxJQbc070B17SuRVKSRgeSGPJ7juAh4FpgHf87NOTuflGBpoyQCoHkx6ad+/jD2KVkrN7OuWmV+HufptRNLRt0LJGoy28hWAs0dPdD0Qh3olQIJL/cndELNjPkg+XsP3yUuzrV4+YOdSiWrNZbUnTl9+b1S4GKBZpIJEDZm9h1OjvcxO6JmSzdrCZ2kpgiKQQVgZVmNsnMxmU9opxLJOqqlivJyKtb8tQ1Ldi6+yC9nlATO0lMkUwf/VPUU4gEqFvjarSpU4UhHy7niY/XMWHpNzzcpymt0ioHHU2kUBx3RBCeKroSKBd+rIhk+qhIPKlQuhj/uLwZL//qPA4ezuTyUWpiJ4kjkjbUVwBzgMsJNZ6bbWZ9ox1MJAjpZ6UyeWA617dJUxM7SRiRzBpaBHR2923h16nAVHcPpGW0Zg1JYZm3YQd3j17MF9v30rdlDe6/+Gwqli4edCyRk5LfWUNJWUUg7LsI3ycS11qlVWb8nR24/cK6jP18M52GZTBhyZagY4kUuEj+oE8Mzxi6wcxuAD4EJkQ3lkhsKFksmd91bcC437Tj1PIluPW1Bdz66ny2fX8g6GgiBSaiNtRm1htoH3453d3H5rV9NOnQkATl8NFMnpn+BY9OXUOpYsncf/HZ9G1ZQ03sJC7k69CQmQ119zHuPij8GGtmQws+pkhsK5acxG0XnMmEAR0469Sy/O6dxVz3/By+2qEmdhLfIjk01DmHZd0LOohIvKibWpZ/92vDg70asWDjTro+msFLn24gMzN6N3kSiaZcC4GZ3WpmS4D64RvMZz3WA4sLL6JI7ElKMq5rk8akgem0SqvMn8Yt44pRs1i7bU/Q0UROWK7nCMysAlAJeAi4N9uq7919RyFky5HOEUiscXfGLNjMgx8sZ/+howzoVI9+6WpiJ7HlpM4RuPsud9/g7le5+0ZgP6F7D5cN31BGRAg1sevTsgZTB3WkU8OqPDJpFb0eVxM7iR+RnCy+1MzWAOuBacAGNH1U5CdSy5UIN7FryfY9oSZ2QyeqiZ3EvkjGrn8BzgdWu3tt4GfAZ1FNJRLHujU+jakDO9KnRXWe/GQdPUZMZ+6GwI6mihxXJIXgcPim8klmluTuHwN53k5SJNFVKF2Mh/s249WbWnPoaCaXPzWLB95byh41sZMYFEkh+K+ZlQUygNfMbASwN7qxRIqG9vWqMOmudG5sl8Yrn22k6/AMPlm17fhvFClEkRSCXsA+YCAwkdC9iy+NZiiRoqRMiRT+dGkj3unfllLFk7nhhbkMemshO/fGxN1fRSJrMRFLNH1U4tnBI0d54j9rGfnJOiqWLsafezamR5PT1KZCoi6/3UdFpICUSElmUJf6jPtNe6pVKMXtry/gllfms223mthJcFQIRALQ8PTyjL2tLb/v3oBpq7fzs2HTeGvuV8TbCF2KhrxaTHwU/qoGcyJRkJKcxC0d6zLxrnTOrlaeu0cv5prnZvPld2piJ4UrrxFBNTNrC/Q0s3PMrEX2R2EFFCnqalcpw5s3n89fLmvMoq920fXRDJ6bsZ6jamInhSSvXkN9gZsI3Yfg2LOz7u4XRTlbjnSyWIqyr/+7n/vfXcp/Vm7jnJoVebhPU+qdWi7oWFIE5HWyOJJ7Fv/R3YdEJdlJUCGQos7dGbfoawaPW8aeg0e446J69O9Yl+IpOqUnJy9fhSD8AT2B9PDLT9z9gwLMd0JUCCRRfLfnIIPfX877i76mwWnlGNqnKc3OqBh0LIlT+b1D2UPAAGB5+DHAzP5WsBFF5FinlC3Bv646h2eua8XOfYf4+ciZPDR+BfsPqYmdFKxIDg0tBpq7e2b4dTLwubs3LYR8P6ERgSSi3QcO89D4lbwx50vSTinNQ72b0qbuKUHHkjhSEBeUVcz2vEK+E4nICSlfshgP9W7C6ze3xoGrnvmMP4xdwu4Dh4OOJkVAJIXgIeBzM3vRzF4C5gN/jW4sEclJ27pVmDggnZs71ObNOV/SZVgG/1m5NehYEuciPVlcDTg3/HKOu38T1VR50KEhkZCFX/2Xe95ZzKqt39Or+ek8cElDTilbIuhYEqPyPWsolqgQiPzPoSOZPPnJOh7/eA3lShbjT5c2pGez09XETn5CTedEiqjiKUkM6FSPD+7owBmVSzPgzYX8+qV5bNm1P+hoEkdUCESKgPqnlWPMrW25/+KzmbnuW7oMy+D12V+SqTYVEoE8C4GZJZvZysIKIyInLznJ+HWHOky+qyNNalTgD2OX8MtnP2PDt7qhoOQtz0Lg7keBVWZWs5DyiEg+1TylNK/9ujV/792EZZt30/XRDJ7OWMeRo5lBR5MYlRLBNpWAZWY2h2z3Knb3nlFLJSL5YmZceV5NLqhflfvfXcrfxq/kw8VbGNq3KQ1OKx90PIkxkVxZ3DGn5e4+7bgfbrYB+B44Chw59oy1haY2jAB6ELov8g3uviCvz9SsIZET4+58uGQLf3pvGbv2H+a2C8/k9gvrUiIlOehoUojymjV03BGBu08zs1pAPXefamalgRP5DbrQ3b/NZV13oF740Rp4MvxVRAqImXFJ09NpV7cKD36wnMc+WsOEJaHRQYualYKOJzEgkqZzNwPvAKPCi6oD7xbQ9+8FvOwhnwEVwxeviUgBq1SmOMN/0ZwXbjiXvQeP0OfJTxnywXL2HToSdDQJWCTTR28H2gG7Adx9DVA1ws93YLKZzTezfjmsrw58le31pvCyHzGzfmY2z8zmbd++PcJvLSI5ubBBVSYNTOea1rV4bsZ6uj6awcy1uQ3aJRFEUggOuvuhrBdmlkLoD3wk2rt7C0KHgG43s/TjvSEn7v60u7dy91apqakn8xEikk25ksUYcllj/t3vfFKSkrj62dnc885idu1XE7tEFEkhmGZmfwBKmVln4G3g/Ug+3N03h79uA8YC5x2zyWbgjGyva4SXiUghaF3nFCYM6ED/jnV5Z8EmOg+bxuRlgbUSk4BEUgjuBbYDS4BbgPHA/cd7k5mVMbNyWc+BLsDSYzYbB1xnIecDu9x9ywnkF5F8KlksmXu7N+Dd29pxStkS9HtlPre/voDt3x8MOpoUkkhmDWWG20/PJnRIaJVH1qnuVGBsuPlVCvC6u080s/7hz32KUFHpAawlNH30xpP6rxCRfGtSowLjftOOUdPW8dhHa5m59lseuKQhPz+nuprYFXGRXEdwMfAUsA4woDZwi7tPiH68n9J1BCLRt3bb99wzegnzN+6k41mp/K13E6pXLBV0LMmHfLWhDvcausTd14Zf1wU+dPcGBZ40AioEIoUjM9N55bONDJ24EgPu6d6Aa1rXIilJo4N4lN821N9nFYGwLwhdLSwiRVhSknF92zQm3ZVOi1qVeOC9Zfzi6Vms274n6GhSwHItBGbW28x6A/PMbLyZ3WBm1xOaMTS30BKKSKDOqFyal391Hv+4vBmrt+6h+4jpPPHxWg6riV2RkdfJ4kuzPd8KZPUc2g7oYKFIAjEz+rasQfpZVRg8bhmPTFrFh4u38HDfpjSuXiHoeJJPulWliJywiUu38Mf3lrFj7yH6pddhwM/qUbKYmtjFsnw1nTOz2sAdQFr27dWGWiRxdWtcjTZ1qvDX8ct58pN1TFr6DUP7NuXctMpBR5OTEMmsoUXAc4QuKPvhoGAkbaijQSMCkdgyY8233DtmMZt27ue6NrW4u1sDypaI5FYnUpjyO310trvHTGtoFQKR2LPv0BH+MWk1L3y6nmrlS/LX3k24sH6kvSmlMOR3+ugIM/uTmbUxsxZZjwLOKCJxrHTxFB64tCHv9G9LmRIp3PjCXAb9eyE79x46/pslcJGM35oA1wIX8b9DQx5+LSLyg5a1KvHBne154uN1jPx4LdNWb+fPvRpxcZNqalMRwyI5NLQWaJi9FXWQdGhIJD6s2LKbe0YvZvGmXXRueCp/uawxp5YvGXSshJXfQ0NLgYoFmkhEiryzq5VnzK1t+UOPBmSs3k6nYdN4c86XxNuU9UQQSSGoCKw0s0lmNi7rEeVcIlIEpCQn0S+9LpPuSqfR6eW5d8wSrn52Nhu/2xt0NMkmkkNDHXNarumjInIiMjOdN+d+xUPjV3A4M5PfdqnPje1qk6wmdoUiX9NHY40KgUh827JrP/ePXcpHK7fR7IyKPNynKfVPKxd0rCIvX+cIzOx7M9sdfhwws6NmtrvgY4pIIqhWoRTPXt+Kx646h6927OOSf01n+JTVHDqiJnZBOW4hcPdy7l7e3csTajbXBxgZ9WQiUmSZGT2bnc7UQR25uEk1Rny0hkv+NZ3Pv9wZdLSEFMnJ4h94yLtA1+jEEZFEUrlMcR698hyev6EV3x84Qu8nP2XIB8vZd+hI0NESSiRN53pne5kEtAIORC2RiCScixqcyuSBlRk6cSXPzVjP5OXf8PfeTWl3ZpWgoyWESEYEl2Z7dCV0d7Je0QwlIomnXMli/OWyJvy73/mkJCVx9bOzuXf0YnbtPxx0tCJPs4ZEJOYcOHyUR6eu4ZnpX3BKmeIMuawxXRudFnSsuHZS00fN7IE8PtPdfUhBhDtRKgQiiWPJpl3cPXoxK7bs5uIm1RjcsxGp5UoEHSsunez00b05PABuAu4p0IQiIjloUqMC437Tjt91rc+U5VvpPHwaYxZsUpuKAhbRoSEzKwcMIFQE3gL+6e7bopwtRxoRiCSmtdv2cM/oxczfuJOOZ6Xy1583pkal0kHHihsnfUGZmVU2s78AiwnNMGrh7vcEVQREJHGdWbUsb9/Shj/3bMTcDTvoOjyDl2dtIDNTo4P8yrUQmNkjwFxCs4SauPtgd9fVHiISmKQk4/q2aUwemE7LtMo88N4yfvH0LNZt3xN0tLiW18niTOAgcITQjWh+WEXoZHH56Mf7KR0aEhEAd2f0gs0M+WA5+w8fZcDP6tEvvQ7Fkk/oOtmEcVKHhtw9yd1LZW8xEX6UC6oIiIhkMTP6tqzBlEHpdDq7Ko9MWkWvx2eydPOuoKPFHZVOEYlrVcuVZOTVLXnqmpZs33OQXk/MZOjElRw4fDToaHFDhUBEioRujU9j6sCO9GlRnSc/WUePEdOZs35H0LHiggqBiBQZFUoX4+G+zXj1ptYcOprJFaNm8cd3l/L9AbWpyIsKgYgUOe3rVWHywHRubJfGq7M30nV4Bh+v0qz33KgQiEiRVLp4Cn+6tBGjb21LmRIp3PjCXAb+eyE79h4KOlrMUSEQkSKtRc1KfHBne+78WT3eX/Q1nYdN4/1FX6tNRTYqBCJS5JVISWZQ57N4/472VK9Uijve+JybX57PN7t0axVQIRCRBHJ2tfKMubUt9/U4mxlrt9N52DTemPNlwo8OVAhEJKGkJCdxc3odJg5Ip1H18vx+zBJ++cxsNny79/hvLqJUCEQkIaVVKcMbN5/PQ72bsHTzLrqNyOCZjC84moBN7FQIRCRhmRlXnVeTKYM60v7MVP46fgW9R85k5Te7g45WqFQIRCThnVahJM9c15J/XXUOm3bu55LHZjBsymoOHkmMNhVRLwRmlmxmn5vZBzmsq2lmH4fXLzazHtHOIyKSEzPj0manM2VQRy5tdjqPfbSGSx6bwYIvi373/cIYEQwAVuSy7n7gLXc/B7gSGFkIeUREclW5THGG/6I5L9xwLnsPHqHPk5/y4PvL2XfoSNDRoiaqhcDMagAXA8/msokDWS2tKwBfRzOPiEikLmxQlUkD07mmdS2en7meLsMzmLHm26BjRUW0RwSPAncDmbmsHwxcY2abgPHAHTltZGb9zGyemc3bvn17NHKKiPxEuZLFGHJZY966pQ3Fk5O45rnZ3P3OInbtK1pN7KJWCMzsEmCbu8/PY7OrgBfdvQbQA3jFzH6Syd2fdvdW7t4qNTU1SolFRHJ2Xu3KjB/QgVsvqMvoBZvpNHwaE5d+E3SsAhPNEUE7oKeZbQDeBC4ys1eP2eYm4C0Ad58FlASqRDGTiMhJKVksmXu6NeC929uRWrYE/V+dz22vzWfb9/HfpiJqhcDdf+/uNdw9jdCJ4P+4+zXHbPYl8DMAMzubUCHQsR8RiVmNq1fgvd+043dd6zN1xTY6D8vgnfmb4rpNRaFfR2BmD5pZz/DL/wNuNrNFwBvADR7Pe1NEEkKx5CRuv/BMxt/ZgXpVy/Lbtxdx3fNz+GrHvqCjnRSLt7+7rVq18nnz5gUdQ0QEgMxM57XZG/n7hJU4cHfX+lzXJo2kJAs62o+Y2Xx3b5XTOl1ZLCKSD0lJxrVt0pg8qCPnplVm8PvLuXzULNZu+z7oaBFTIRARKQDVK5bixRvPZdgVzVi3fQ89Rszg8f+s4fDR3GbPxw4VAhGRAmJm9G5RgykDO9K50an8Y/JqLv3XDJZs2hV0tDypEIiIFLDUciV44pctGHVtS3bsPUSvJ2bw0IQVHDgcm03sVAhERKKka6PTmDKoI7849wxGTfuCbo9m8NkX3wUd6ydUCEREoqhCqWI81Lspr/+6NZkOVz79GfeNXcL3B2KnTYUKgYhIIWh7ZhUm3ZXOzR1q88acL+kyPIP/rNwadCxAhUBEpNCUKp7MfRc3ZMxt7Shfshi/enEeA978nO/2HAw0lwqBiEgha35GRd6/oz13darH+CVb6Dw8g/cWbg6sTYUKgYhIAIqnJHFXp7P48M4O1KxcmgFvLuTXL81jy679hZ5FhUBEJEBnnVqO0be25Y+XNOTTdd/ReVgGr83eSGZm4Y0OVAhERAKWnGTc1L42k+5Kp9kZFbhv7FKueuYz1n+7t1C+vwqBiEiMqHlKaV69qTUP92nK8i276fZoBqOmreNIlNtUqBCIiMQQM+OKc89g6qCOdDwrlYcmrOTnIz9l+de7o/Y9VQhERGLQqeVLMuraloy8ugVbdu2n5+MzeG7G+qh8r5SofKqIiOSbmdGjSTXa1DmFv3y4glqVS0fl+6gQiIjEuEplivPPK5pF7fN1aEhEJMGpEIiIJDgVAhGRBKdCICKS4FQIREQSnAqBiEiCUyEQEUlwKgQiIgnOgroRwskys+3AxpN8exXg2wKME03xklU5C168ZFXOghXtnLXcPTWnFXFXCPLDzOa5e6ugc0QiXrIqZ8GLl6zKWbCCzKlDQyIiCU6FQEQkwSVaIXg66AAnIF6yKmfBi5esylmwAsuZUOcIRETkpxJtRCAiIsdQIRARSXAJUwjMrJuZrTKztWZ2b9B5spjZGWb2sZktN7NlZjYgvHywmW02s4XhR48YyLrBzJaE88wLL6tsZlPMbE34a6UYyFk/235baGa7zeyuWNinZva8mW0zs6XZluW4Dy3ksfDv7GIzaxFwzkfMbGU4y1gzqxhenmZm+7Pt16cKK2ceWXP9WZvZ78P7dJWZdQ0457+zZdxgZgvDywt3n7p7kX8AycA6oA5QHFgENAw6VzhbNaBF+Hk5YDXQEBgM/DbofMdk3QBUOWbZw8C94ef3AkODzpnDz/4boFYs7FMgHWgBLD3ePgR6ABMAA84HZgecswuQEn4+NFvOtOzbxcg+zfFnHf5/axFQAqgd/ruQHFTOY9b/E3ggiH2aKCOC84C17v6Fux8C3gR6BZwJAHff4u4Lws+/B1YA1YNNdUJ6AS+Fn78EXBZclBz9DFjn7id7NXqBcvcMYMcxi3Pbh72Alz3kM6CimVULKqe7T3b3I+GXnwE1CiPL8eSyT3PTC3jT3Q+6+3pgLaG/D1GXV04zM+AK4I3CyHKsRCkE1YGvsr3eRAz+sTWzNOAcYHZ40W/Cw/DnY+GQC+DAZDObb2b9wstOdfct4effAKcGEy1XV/Lj/7libZ9C7vswln9vf0VotJKltpl9bmbTzKxDUKGOkdPPOlb3aQdgq7uvybas0PZpohSCmGdmZYHRwF3uvht4EqgLNAe2EBo2Bq29u7cAugO3m1l69pUeGtPGzHxkMysO9ATeDi+KxX36I7G2D3NiZvcBR4DXwou2ADXd/RxgEPC6mZUPKl9YzP+sj3EVP/4HS6Hu00QpBJuBM7K9rhFeFhPMrBihIvCau48BcPet7n7U3TOBZyik4Wte3H1z+Os2YCyhTFuzDleEv24LLuFPdAcWuPtWiM19GpbbPoy531szuwG4BLg6XLQIH2b5Lvx8PqHj7mcFFpI8f9axuE9TgN7Av7OWFfY+TZRCMBeoZ2a1w/9KvBIYF3Am4Idjg88BK9x9WLbl2Y8F/xxYeux7C5OZlTGzclnPCZ04XEpoP14f3ux64L1gEuboR//KirV9mk1u+3AccF149tD5wK5sh5AKnZl1A+4Gerr7vmzLU80sOfy8DlAP+CKYlD9kyu1nPQ640sxKmFltQlnnFHa+Y3QCVrr7pqwFhb5PC+usdNAPQjMwVhOqrPcFnSdbrvaEDgUsBhaGHz2AV4Al4eXjgGoB56xDaLbFImBZ1j4ETgE+AtYAU4HKQe/TcK4ywHdAhWzLAt+nhArTFuAwoePTN+W2DwnNFnoi/Du7BGgVcM61hI6vZ/2ePhXetk/4d2IhsAC4NAb2aa4/a+C+8D5dBXQPMmd4+YtA/2O2LdR9qhYTIiIJLlEODYmISC5UCEREEpwKgYhIglMhEBFJcCoEIiIJToVARCTBqRBIwjKzU7K1+f0mW9viPWY2Mgrf70UzW29m/XNZvyf8tW5WjoLOIJITXUcgQqh/PbDH3f8Rxe/xIvCBu7+Ty/o97l42t9ci0aIRgcgxzOwCM/sg/Hywmb1kZtPNbKOZ9Tazhy10g56J4T5RmFnLcJfI+WY2KZJ20eGWJ7PCn/WXaP93ieRGhUDk+OoCFxHqZPoq8LG7NwH2AxeHi8G/gL7u3hJ4HvhrBJ87Angy/FmB9RASSQk6gEgcmODuh81sCaE7nk0ML19C6E5S9YHGwJRQD0GSiewPeztCPWUg1BtnaAFmFomYCoHI8R0EcPdMMzvs/zuxlkno/yEDlrl7m5P4bJ2kk8Dp0JBI/q0CUs2sDYTuL2FmjSJ430xCLdEBro5WOJHjUSEQyScP3Qe7LzDUzBYRah3cNoK3DiB0p7clxMbtEiVBafqoSCE53vTRHLbX9FEpFBoRiBSeXcCQ3C4oy5J1QRmwtVBSScLTiEBEJMFpRCAikuBUCEREEpwKgYhIglMhEBFJcP8PpwfWdmqPvHMAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.plot(time, xe135, label=\"Xe135\")\n", + "pyplot.xlabel(\"Time [d]\")\n", + "pyplot.ylabel(\"Number of atoms - Xe135\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also examine reaction rates over time using the `Results` object." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "_, u235_fission = results.get_reaction_rate(\"1\", \"U235\", \"fission\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.plot(time, u235_fission)\n", + "pyplot.xlabel(\"Time [d]\")\n", + "pyplot.ylabel(\"Fission reactions / s\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Helpful tips\n", + "\n", + "Depletion is a tricky task to get correct. Use too short of a time step, and you will incur a steep cost in computational time due to excessive transport simulations. Use too long of a time step, and you may miss physics happening on shorter timescales, leading to incorrect answers. Consider the xenon plot from above. Xenon-135 is a fission product with a thermal absorption cross section on the order of millions of barns, but it has a half life of ~9 hours. Taking smaller time steps at the beginning of your simulation to build up some equilibrium in your fission products is highly recommended.\n", + "\n", + "When possible, differentiate materials that reappear in multiple places. If we had built an entire core with the single `fuel` material, every pin would be depleted using the same averaged spectrum and reaction rates, which is incorrect. Pins experiencing different flux will deplete at different rates. The `Operator` can differentiate these materials using the `diff_burnable_mats` argument, but note that the volumes will be copied from the original material.\n", + "\n", + "Using higher-order integrators, like the `CECMIntegrator`, `EPCRK4Integrator` with a fourth order Runge-Kutta, or the `LEQIIntegrator`, can improve the accuracy of a simulation, or at least allow you to take longer depletion steps between transport simulations with similar accuracy.\n", + "\n", + "Fuel pins with integrated burnable absorbers, like gadolinia, experience strong flux gradients until the absorbers are mostly burned away. This means that the spectrum and magnitude of the flux at the edge of the fuel pin can be vastly different than that in the interior. The helper `pin` function can be used to subdivide regions into equal volume segments, as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "div_surfs_1 = [openmc.ZCylinder(r=1)]\n", + "div_1 = openmc.model.pin(div_surfs_1, [fuel, water], subdivisions={0: 10})" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "div_1.plot(width=(2.0, 2.0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The innermost region has been divided into 10 equal volume regions. We can pass additional arguments to divide multiple regions, except for the region outside the last cylinder." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register depletion chain\n", + "\n", + "The depletion chain we created can be registered into the OpenMC `cross_sections.xml` file, so we don't have to always pass the `chain_file` argument to the `Operator`. To do this, we create a `DataLibrary` using `openmc.data`. Without any arguments, the `from_xml` method will look for the file located at `OPENMC_CROSS_SECTIONS`. For this example, we will just create a bare library." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "data_lib = openmc.data.DataLibrary()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "data_lib.register_file(\"./chain_simple.xml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "data_lib.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + " \n", + "\n" + ] + } + ], + "source": [ + "!cat cross_sections.xml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This allows us to make an `Operator` simply with the geometry and settings arguments, provided we exported our library to `OPENMC_CROSS_SECTIONS`. For a problem where we built and registered a `Chain` using all the available nuclear data, we might see something like the following." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "model = openmc.Model(geometry=geometry, settings=settings)\n", + "new_op = openmc.deplete.CoupledOperator(model, \"./chain_simple.xml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(new_op.chain.nuclide_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['I135', 'Xe135', 'Xe136', 'Cs135', 'Gd157', 'Gd156', 'U234', 'U235', 'U238']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[nuc.name for nuc in new_op.chain.nuclides[:10]]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['I135', 'Xe135', 'Xe136', 'Cs135', 'Gd157', 'Gd156', 'U234', 'U235', 'U238']" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[nuc.name for nuc in new_op.chain.nuclides[-10:]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Choice of depletion step size\n", + "\n", + "A general rule of thumb is to use depletion step sizes around 2 MWd/kgHM, where kgHM is really the initial heavy metal mass in kg. If your problem includes integral burnable absorbers, these typically require shorter time steps at or below 1 MWd/kgHM. These are typically valid for the predictor scheme, as the point of recent schemes is to extend this step size. A good convergence study, where the step size is decreased until some convergence metric is satisfied, is a beneficial exercise.\n", + "\n", + "We can use the `Operator` to determine our maximum step size using this recommendation. The `heavy_metal` attribute returns the mass of initial heavy metal in g, which, using our power, can be used to compute this step size. $$\\frac{2\\,MWd}{kgHM} = \\frac{P\\times\\Delta}{hm_{op}}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.080339195584719" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "operator.heavy_metal" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "max_step = 2 * operator.heavy_metal / power * 1E3" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"Maximum\" depletion step: 58.4 [d]\n" + ] + } + ], + "source": [ + "print(\"\\\"Maximum\\\" depletion step: {:5.3} [d]\".format(max_step))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, if we were provided the power density of our problem, we can provide this directly with `openmc.deplete.PredictorIntegrator(operator, time_steps, power_density=pdens)`. The values of `power` and `power_density` do not have to be scalars. For problems with variable power, we can provide an iterable with the same number of elements as `time_steps`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..50f1162 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Adam Parler + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/RBMK/RBMK.ipynb b/RBMK/RBMK.ipynb new file mode 100644 index 0000000..09185c4 --- /dev/null +++ b/RBMK/RBMK.ipynb @@ -0,0 +1,559 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A RBMK geometry \n", + "This notebook can be used as a template for modeling RBMK reactors.\n", + "\n", + "SOURCES\n", + "\"Fuel Assembly\": https://web.archive.org/web/20090908013425/http:/www.insc.anl.gov:80/rbmk/reactor/assembly.html\n", + "\"Fuel with burnable absorber for RBMK-1500\", pg250-252: https://www.osti.gov/etdeweb/servlets/purl/20269236\n", + "\"DESIGN AND FABRICATION OF NUCLEAR FUEL FOR WWER AND RBMK REACTORS\"\n", + "Slides 8-11: indico.ictp.it/event/a04215/session/26/contribution/16/material/0/1.pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from math import pi, sin, cos, sqrt\n", + "import numpy as np\n", + "import openmc" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Materials definitions\n", + "\n", + "fuel = openmc.Material(name='2.0% Fuel')\n", + "fuel.add_element('U', 0.995, enrichment=2.0)\n", + "fuel.add_element('Er', 0.005)\n", + "fuel.add_nuclide('O16', 2.0)\n", + "fuel.set_density('g/cm3', 10.400)\n", + "\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.add_element('Zr', 0.99)\n", + "zircaloy.add_element('Nb', 0.01)\n", + "zircaloy.set_density('g/cm3', 8.59)\n", + "\n", + "wall = openmc.Material(name='Carrier Rod Material')\n", + "wall.add_element('Zr', 0.975)\n", + "wall.add_element('Nb', 0.025)\n", + "wall.set_density('g/cm3', 8.59)\n", + "\n", + "helium = openmc.Material(name='Helium')\n", + "helium.add_element('He', 1)\n", + "helium.set_density('g/cm3', 0.178)\n", + "\n", + "# Instantiate a Materials collection and export to xml\n", + "materials_file = openmc.Materials([fuel, zircaloy, helium, wall])\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Geometry definitions for the fuel rod\n", + "\n", + "#Making the boundary planes for the sleeves\n", + "sleeve_min_z = openmc.ZPlane(z0=-12)\n", + "sleeve_max_z = openmc.ZPlane(z0=+12)\n", + "\n", + "# Create cylinders for the fuel and clad\n", + "fuel_inner_radius = openmc.ZCylinder(r=0.55)\n", + "fuel_outer_radius = openmc.ZCylinder(r=0.566)\n", + "clad_inner_radius = openmc.ZCylinder(r=0.596)\n", + "clad_outer_radius = openmc.ZCylinder(r=0.68)\n", + "\n", + "# Create a universe to encapsulate a fuel rod\n", + "pin_cell_universe = openmc.Universe(name='2.0% Fuel Pin') \n", + "\n", + "# Create fuel cell\n", + "fuel_cell_top = openmc.Cell(name='2.0% Fuel')\n", + "fuel_cell_top.fill = fuel\n", + "fuel_cell_top.region = -fuel_inner_radius & +sleeve_max_z\n", + "pin_cell_universe.add_cell(fuel_cell_top)\n", + "\n", + "# Create void space\n", + "void_space_top = openmc.Cell(name='empty_space')\n", + "void_space_top.fill = helium\n", + "void_space_top.region = +fuel_inner_radius & -clad_inner_radius & +sleeve_max_z\n", + "pin_cell_universe.add_cell(void_space_top)\n", + "\n", + "# Create a clad cell\n", + "clad_cell_top = openmc.Cell(name='2.0% Clad')\n", + "clad_cell_top.fill = zircaloy\n", + "clad_cell_top.region = +clad_inner_radius & -clad_outer_radius & +sleeve_max_z\n", + "pin_cell_universe.add_cell(clad_cell_top)\n", + "\n", + "# Create fuel cell\n", + "fuel_cell_bot = openmc.Cell(name='2.0% Fuel')\n", + "fuel_cell_bot.fill = fuel\n", + "fuel_cell_bot.region = -fuel_inner_radius & -sleeve_min_z\n", + "pin_cell_universe.add_cell(fuel_cell_bot)\n", + "\n", + "# Create void space\n", + "void_space_bot = openmc.Cell(name='empty_space')\n", + "void_space_bot.fill = helium\n", + "void_space_bot.region = +fuel_inner_radius & -clad_inner_radius & -sleeve_min_z\n", + "pin_cell_universe.add_cell(void_space_bot)\n", + "\n", + "# Create a clad cell\n", + "clad_cell_bot = openmc.Cell(name='2.0% Clad')\n", + "clad_cell_bot.fill = zircaloy\n", + "clad_cell_bot.region = +clad_inner_radius & -clad_outer_radius & -sleeve_min_z\n", + "pin_cell_universe.add_cell(clad_cell_bot)\n", + "\n", + "# Create an outside of pin cell\n", + "moderator = openmc.Cell(name='Moderator')\n", + "moderator.fill = helium\n", + "moderator.region = +clad_outer_radius\n", + "pin_cell_universe.add_cell(moderator)\n", + "\n", + "# Create a 'sleeve' cell that slices through the middle of the elongated element\n", + "sleeve_cell = openmc.Cell(name='Sleeve')\n", + "sleeve_cell.fill = helium\n", + "sleeve_cell.region = -clad_outer_radius & +sleeve_min_z & -sleeve_max_z\n", + "pin_cell_universe.add_cell(sleeve_cell)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Geometry definitions for the fuel rod\n", + "\n", + "rod_inner_radius = openmc.ZCylinder(r=0.75) \n", + "rod_outer_radius = openmc.ZCylinder(r=0.7625)\n", + "\n", + "rod_cell_universe = openmc.Universe(name='Carrier Rod')\n", + "\n", + "inner_rod = openmc.Cell(name='Inner Rod')\n", + "inner_rod.fill = helium\n", + "inner_rod.region = -rod_inner_radius\n", + "rod_cell_universe.add_cell(inner_rod)\n", + "\n", + "outer_rod = openmc.Cell(name='Outer Rod')\n", + "outer_rod.fill = wall\n", + "outer_rod.region = +rod_inner_radius & -rod_outer_radius\n", + "rod_cell_universe.add_cell(outer_rod)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a circular Lattice for the bundle\n", + "\n", + "element_min_z = openmc.ZPlane(z0=-364, boundary_type='reflective')\n", + "element_max_z = openmc.ZPlane(z0=+364, boundary_type='reflective')\n", + "carrier_min_z = openmc.ZPlane(z0=-400, boundary_type='reflective')\n", + "carrier_max_z = openmc.ZPlane(z0=+600, boundary_type='reflective')\n", + "\n", + "circlat = openmc.Universe(name='Circular Lattice')\n", + "\n", + "channel = openmc.ZCylinder(r=4.5, boundary_type='reflective')\n", + "\n", + "channel_cell = openmc.Cell(name='Channel')\n", + "channel_cell.fill = helium\n", + "channel_cell.region = -channel & +rod_outer_radius & +element_min_z & -element_max_z\n", + "\n", + "#Calculations before finding pin placement\n", + "r_channel = 4.0\n", + "r_element = 0.68\n", + "r_outer = r_channel-r_element\n", + "\n", + "padding = pi*r_outer/6-2*r_element\n", + "r_inner = r_outer*cos(pi/12)-sqrt((r_element*2+padding)**2-(r_element+padding)**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Add the fuel pins into the channel\n", + "\n", + "num_in_rings = [6, 12]\n", + "radii = [r_inner, r_outer]\n", + "angles = [0, pi/12]\n", + "\n", + "for index in range(len(num_in_rings)):\n", + " elem_num = num_in_rings[index]\n", + " ring_radius = radii[index]\n", + " theta_0 = angles[index]\n", + " theta = 2*pi/elem_num\n", + " for element in range(elem_num):\n", + " x = ring_radius*cos(element*theta + theta_0)\n", + " y = ring_radius*sin(element*theta + theta_0)\n", + "\n", + " pin_boundary = openmc.ZCylinder(x0=x, y0=y, r=r_element)\n", + " pin = openmc.Cell(fill=pin_cell_universe, region=-pin_boundary & +element_min_z & -element_max_z)\n", + " pin.translation = (x,y,0)\n", + " pin.id = (index+1)*100 + element\n", + " channel_cell.region &= ~pin.region\n", + " circlat.add_cell(pin)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Add the 3 Components of the Carrier Rod\n", + "rod_bound = openmc.ZCylinder(r=0.7625, boundary_type='reflective')\n", + "rod_channel_min = openmc.ZPlane(z0=-364)\n", + "rod_channel_max = openmc.ZPlane(z0=+364)\n", + "\n", + "rod_above = openmc.Cell(fill=rod_cell_universe, region=-rod_bound & -carrier_max_z & +rod_channel_max)\n", + "rod_mid = openmc.Cell(fill=rod_cell_universe, region=-rod_outer_radius & +rod_channel_min & -rod_channel_max)\n", + "rod_below = openmc.Cell(fill=rod_cell_universe, region=-rod_bound & -rod_channel_min & +carrier_min_z)\n", + "\n", + "circlat.add_cell(rod_above)\n", + "circlat.add_cell(rod_mid)\n", + "circlat.add_cell(rod_below)\n", + "circlat.add_cell(channel_cell)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "geometry = openmc.Geometry(circlat)\n", + "geometry.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "circlat.plot(width=(12, 12), basis='xy')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "\n", + "batches = 100\n", + "inactive = 10\n", + "particles = 5000\n", + "\n", + "settings_file = openmc.Settings()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "\n", + "bounds = [-3, -3, -2.4, 3, 3, 2.4]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=False) #only_fissionable != True due to lots of helium\n", + "settings_file.source = openmc.Source(space=uniform_dist)\n", + "\n", + "settings_file.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.1-dev\n", + " Git SHA1 | 0228ddba1d6399e654a295e51539590f076b2a3c\n", + " Date/Time | 2021-04-12 12:40:32\n", + " OpenMP Threads | 8\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U234.h5\n", + " Reading U235 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U235.h5\n", + " Reading U238 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U238.h5\n", + " Reading U236 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U236.h5\n", + " Reading Er162 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Er162.h5\n", + " Reading Er164 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Er164.h5\n", + " Reading Er166 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Er166.h5\n", + " Reading Er167 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Er167.h5\n", + " Reading Er168 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Er168.h5\n", + " Reading Er170 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Er170.h5\n", + " Reading O16 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/O16.h5\n", + " Reading Zr90 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr90.h5\n", + " Reading Zr91 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr91.h5\n", + " Reading Zr92 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr92.h5\n", + " Reading Zr94 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr94.h5\n", + " Reading Zr96 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr96.h5\n", + " Reading Nb93 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Nb93.h5\n", + " Reading He3 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/He3.h5\n", + " Reading He4 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/He4.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.36726\n", + " 2/1 0.41917\n", + " 3/1 0.41996\n", + " 4/1 0.42104\n", + " 5/1 0.42013\n", + " 6/1 0.41539\n", + " 7/1 0.41936\n", + " 8/1 0.41825\n", + " 9/1 0.43324\n", + " 10/1 0.43023\n", + " 11/1 0.43863\n", + " 12/1 0.41949 0.42906 +/- 0.00957\n", + " 13/1 0.41553 0.42455 +/- 0.00713\n", + " 14/1 0.43122 0.42622 +/- 0.00531\n", + " 15/1 0.43523 0.42802 +/- 0.00449\n", + " 16/1 0.42006 0.42670 +/- 0.00390\n", + " 17/1 0.42561 0.42654 +/- 0.00330\n", + " 18/1 0.41654 0.42529 +/- 0.00312\n", + " 19/1 0.43361 0.42621 +/- 0.00290\n", + " 20/1 0.41500 0.42509 +/- 0.00283\n", + " 21/1 0.42439 0.42503 +/- 0.00256\n", + " 22/1 0.41977 0.42459 +/- 0.00238\n", + " 23/1 0.41752 0.42405 +/- 0.00225\n", + " 24/1 0.43619 0.42491 +/- 0.00226\n", + " 25/1 0.41760 0.42443 +/- 0.00216\n", + " 26/1 0.42986 0.42477 +/- 0.00205\n", + " 27/1 0.41637 0.42427 +/- 0.00199\n", + " 28/1 0.41170 0.42357 +/- 0.00200\n", + " 29/1 0.41593 0.42317 +/- 0.00193\n", + " 30/1 0.41912 0.42297 +/- 0.00184\n", + " 31/1 0.41365 0.42253 +/- 0.00181\n", + " 32/1 0.41400 0.42214 +/- 0.00177\n", + " 33/1 0.41753 0.42194 +/- 0.00170\n", + " 34/1 0.41432 0.42162 +/- 0.00166\n", + " 35/1 0.41323 0.42128 +/- 0.00163\n", + " 36/1 0.43010 0.42162 +/- 0.00160\n", + " 37/1 0.41552 0.42140 +/- 0.00156\n", + " 38/1 0.41385 0.42113 +/- 0.00152\n", + " 39/1 0.42110 0.42113 +/- 0.00147\n", + " 40/1 0.41254 0.42084 +/- 0.00145\n", + " 41/1 0.41489 0.42065 +/- 0.00141\n", + " 42/1 0.42521 0.42079 +/- 0.00138\n", + " 43/1 0.42345 0.42087 +/- 0.00134\n", + " 44/1 0.42805 0.42108 +/- 0.00131\n", + " 45/1 0.42292 0.42114 +/- 0.00128\n", + " 46/1 0.42032 0.42111 +/- 0.00124\n", + " 47/1 0.42226 0.42114 +/- 0.00121\n", + " 48/1 0.42795 0.42132 +/- 0.00119\n", + " 49/1 0.41490 0.42116 +/- 0.00117\n", + " 50/1 0.42862 0.42134 +/- 0.00115\n", + " 51/1 0.42480 0.42143 +/- 0.00113\n", + " 52/1 0.41355 0.42124 +/- 0.00112\n", + " 53/1 0.42440 0.42131 +/- 0.00109\n", + " 54/1 0.41865 0.42125 +/- 0.00107\n", + " 55/1 0.42069 0.42124 +/- 0.00105\n", + " 56/1 0.41623 0.42113 +/- 0.00103\n", + " 57/1 0.41685 0.42104 +/- 0.00101\n", + " 58/1 0.41834 0.42099 +/- 0.00099\n", + " 59/1 0.43448 0.42126 +/- 0.00101\n", + " 60/1 0.40917 0.42102 +/- 0.00102\n", + " 61/1 0.42187 0.42104 +/- 0.00100\n", + " 62/1 0.42270 0.42107 +/- 0.00098\n", + " 63/1 0.42030 0.42105 +/- 0.00096\n", + " 64/1 0.43457 0.42130 +/- 0.00098\n", + " 65/1 0.41390 0.42117 +/- 0.00097\n", + " 66/1 0.42198 0.42118 +/- 0.00095\n", + " 67/1 0.42026 0.42117 +/- 0.00093\n", + " 68/1 0.41652 0.42109 +/- 0.00092\n", + " 69/1 0.41120 0.42092 +/- 0.00092\n", + " 70/1 0.42104 0.42092 +/- 0.00090\n", + " 71/1 0.40883 0.42072 +/- 0.00091\n", + " 72/1 0.42478 0.42079 +/- 0.00090\n", + " 73/1 0.42060 0.42079 +/- 0.00088\n", + " 74/1 0.41950 0.42077 +/- 0.00087\n", + " 75/1 0.41800 0.42072 +/- 0.00086\n", + " 76/1 0.42313 0.42076 +/- 0.00085\n", + " 77/1 0.42090 0.42076 +/- 0.00083\n", + " 78/1 0.41947 0.42074 +/- 0.00082\n", + " 79/1 0.42478 0.42080 +/- 0.00081\n", + " 80/1 0.43185 0.42096 +/- 0.00082\n", + " 81/1 0.41999 0.42094 +/- 0.00080\n", + " 82/1 0.41669 0.42089 +/- 0.00079\n", + " 83/1 0.42688 0.42097 +/- 0.00079\n", + " 84/1 0.42583 0.42103 +/- 0.00078\n", + " 85/1 0.41937 0.42101 +/- 0.00077\n", + " 86/1 0.42302 0.42104 +/- 0.00076\n", + " 87/1 0.41306 0.42093 +/- 0.00076\n", + " 88/1 0.42048 0.42093 +/- 0.00075\n", + " 89/1 0.41601 0.42087 +/- 0.00074\n", + " 90/1 0.42380 0.42090 +/- 0.00073\n", + " 91/1 0.42252 0.42092 +/- 0.00072\n", + " 92/1 0.42163 0.42093 +/- 0.00071\n", + " 93/1 0.42060 0.42093 +/- 0.00071\n", + " 94/1 0.41860 0.42090 +/- 0.00070\n", + " 95/1 0.41718 0.42086 +/- 0.00069\n", + " 96/1 0.42078 0.42085 +/- 0.00068\n", + " 97/1 0.41894 0.42083 +/- 0.00068\n", + " 98/1 0.40850 0.42069 +/- 0.00068\n", + " 99/1 0.41439 0.42062 +/- 0.00068\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 100/1 0.41280 0.42054 +/- 0.00068\n", + " Creating state point statepoint.100.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 3.5405e+00 seconds\n", + " Reading cross sections = 3.4979e+00 seconds\n", + " Total time in simulation = 7.6338e+01 seconds\n", + " Time in transport only = 7.6297e+01 seconds\n", + " Time in inactive batches = 7.5955e+00 seconds\n", + " Time in active batches = 6.8742e+01 seconds\n", + " Time synchronizing fission bank = 1.7017e-02 seconds\n", + " Sampling source sites = 1.3410e-02 seconds\n", + " SEND/RECV source sites = 3.5210e-03 seconds\n", + " Time accumulating tallies = 3.9000e-05 seconds\n", + " Time writing statepoints = 5.7760e-03 seconds\n", + " Total time for finalization = 4.0000e-06 seconds\n", + " Total time elapsed = 7.9891e+01 seconds\n", + " Calculation Rate (inactive) = 6582.89 particles/second\n", + " Calculation Rate (active) = 6546.18 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 0.42078 +/- 0.00067\n", + " k-effective (Track-length) = 0.42054 +/- 0.00068\n", + " k-effective (Absorption) = 0.42137 +/- 0.00110\n", + " Combined k-effective = 0.42081 +/- 0.00058\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/README.md b/README.md new file mode 100644 index 0000000..f98d4d7 --- /dev/null +++ b/README.md @@ -0,0 +1,4 @@ +# OpenMCProject +This repository is amalgamation of various simulations I found interesting. They are joined together to make one giant simulation repo. + +Files have been updated to run with `v0.13.3` of [OpenMC](https://github.com/openmc-dev/openmc/tree/v0.13.3) diff --git a/SFR/SFR.ipynb b/SFR/SFR.ipynb new file mode 100644 index 0000000..ecfe1e3 --- /dev/null +++ b/SFR/SFR.ipynb @@ -0,0 +1,755 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q9Vo6HOQUQbS", + "outputId": "020190f0-fec4-4a85-a716-2a98b03b3340" + }, + "source": [ + "# A Sodium Fast Reactor geometry \n", + "This notebook can be used as a template for modeling Sodium fast reactors.\n", + "\n", + "SOURCES:\n", + "A. Facchini, V. Giusti, R. Ciolini, K. Tucek, D. Thomas, E. D'Agata, \"Detailed Neutornics Study of the Power Evolution for the European Sodium Fast Reactor During a Positive Insertion of Reactivity,\" Nuc. Eng. Design 313 1-9 (2017)\n", + "A. Ponomarev, A. Bednarova, K. Mikityuk, \"New Sodium Fast Reactor Neutronics Benchmark,\" PHYSOR 2018" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "EChOeJ7qVUB7" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import openmc" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "jlu98MGXV9MP" + }, + "outputs": [], + "source": [ + "# Materials definitions\n", + "\n", + "u235 = openmc.Material(name='U235')\n", + "u235.add_nuclide('U235', 1.0)\n", + "u235.set_density('g/cm3', 10.0)\n", + "\n", + "u238 = openmc.Material(name='U238')\n", + "u238.add_nuclide('U238', 1.0)\n", + "u238.set_density('g/cm3', 10.0)\n", + "\n", + "pu238 = openmc.Material(name='Pu238')\n", + "pu238.add_nuclide('Pu238', 1.0)\n", + "pu238.set_density('g/cm3', 10.0)\n", + "\n", + "pu239 = openmc.Material(name='U235')\n", + "pu239.add_nuclide('Pu239', 1.0)\n", + "pu239.set_density('g/cm3', 10.0)\n", + "\n", + "pu240 = openmc.Material(name='Pu240')\n", + "pu240.add_nuclide('Pu240', 1.0)\n", + "pu240.set_density('g/cm3', 10.0)\n", + "\n", + "pu241 = openmc.Material(name='Pu241')\n", + "pu241.add_nuclide('Pu241', 1.0)\n", + "pu241.set_density('g/cm3', 10.0)\n", + "\n", + "pu242 = openmc.Material(name='Pu242')\n", + "pu242.add_nuclide('Pu242', 1.0)\n", + "pu242.set_density('g/cm3', 10.0)\n", + "\n", + "am241 = openmc.Material(name='Am241')\n", + "am241.add_nuclide('Am241', 1.0)\n", + "am241.set_density('g/cm3', 10.0)\n", + "\n", + "o16 = openmc.Material(name='O16')\n", + "o16.add_nuclide('O16', 1.0)\n", + "o16.set_density('g/cm3', 10.0)\n", + "\n", + "sodium = openmc.Material(name='Na')\n", + "sodium.add_nuclide('Na23', 1.0)\n", + "sodium.set_density('g/cm3', 0.96)\n", + "\n", + "cu63 = openmc.Material(name='Cu63')\n", + "cu63.set_density('g/cm3', 10.0)\n", + "cu63.add_nuclide('Cu63', 1.0)\n", + "\n", + "Al2O3 = openmc.Material(name='Al2O3')\n", + "Al2O3.set_density('g/cm3', 10.0)\n", + "Al2O3.add_element('O', 3.0)\n", + "Al2O3.add_element('Al', 2.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "_ltK5VzGZ8kk" + }, + "outputs": [], + "source": [ + "# Material mixtures\n", + "inner = openmc.Material.mix_materials(\n", + " [u235, u238, pu238, pu239, pu240, pu241, pu242, am241, o16],\n", + " [0.0019, 0.7509, 0.0046, 0.0612, 0.0383, 0.0106, 0.0134, 0.001, 0.1181],\n", + " 'wo')\n", + "outer = openmc.Material.mix_materials(\n", + " [u235, u238, pu238, pu239, pu240, pu241, pu242, am241, o16],\n", + " [0.0018, 0.73, 0.0053, 0.0711, 0.0445, 0.0124, 0.0156, 0.0017, 0.1176],\n", + " 'wo')\n", + "clad = openmc.Material.mix_materials(\n", + " [cu63,Al2O3], [0.997,0.003], 'wo')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "A2kNKaErZ7JR" + }, + "outputs": [], + "source": [ + "# Instantiate a Materials collection and export to xml\n", + "materials_file = openmc.Materials([inner, outer, sodium, clad])\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "OrML6oju4spK" + }, + "outputs": [], + "source": [ + "# Geometry definitions\n", + "\n", + "fuel_or = openmc.ZCylinder(surface_id=1, r=0.943/2) \n", + "clad_ir = openmc.ZCylinder(surface_id=2, r=0.973/2) \n", + "clad_or = openmc.ZCylinder(surface_id=3, r=1.073/2) \n", + "\n", + "top = openmc.ZPlane(surface_id=4, z0=+50, boundary_type='vacuum')\n", + "bottom = openmc.ZPlane(surface_id=5, z0=-50, boundary_type='vacuum') \n", + "\n", + "fuel_region = -fuel_or & -top & +bottom\n", + "gap_region = +fuel_or & -clad_ir & -top & +bottom\n", + "clad_region = +clad_ir & -clad_or & -top & +bottom\n", + "moderator_region = +clad_or & -top & +bottom\n", + " \n", + "gap_cell = openmc.Cell(cell_id=1, fill=inner, region=gap_region)\n", + "clad_cell = openmc.Cell(cell_id=2, fill=clad, region=clad_region)\n", + "sodium_cell = openmc.Cell(cell_id=3, fill=sodium, region=moderator_region)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "8L0zqqhZfxKh" + }, + "outputs": [], + "source": [ + "inner_fuel_cell = openmc.Cell(cell_id=4, fill=inner, region=fuel_region)\n", + "inner_u = openmc.Universe(universe_id=1, cells=(inner_fuel_cell, gap_cell, clad_cell, sodium_cell))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "iAHLhKZYh6y5" + }, + "outputs": [], + "source": [ + "outer_fuel_cell = openmc.Cell(cell_id=5, fill=outer, region=fuel_region)\n", + "outer_u = openmc.Universe(universe_id=2, cells=(outer_fuel_cell, gap_cell, clad_cell, sodium_cell))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating filling for emtpy space in the core\n", + "\n", + "sodium_mod_cell = openmc.Cell(cell_id=6, fill=sodium)\n", + "sodium_mod_u = openmc.Universe(universe_id=3, cells=(sodium_mod_cell,))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "cWTxCkwKRwYj" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Lattice instance already exists with id=1.\n", + " warn(msg, IDWarning)\n" + ] + } + ], + "source": [ + "# Define a lattice for inner assemblies\n", + "in_lat = openmc.HexLattice(lattice_id=1, name='inner assembly')\n", + "in_lat.center = (0., 0.)\n", + "in_lat.pitch = (21.08/17,)\n", + "in_lat.orientation = 'x'\n", + "in_lat.outer = sodium_mod_u\n", + "\n", + "# Create rings of fuel universes that will fill the lattice\n", + "inone = [inner_u]*48\n", + "intwo = [inner_u]*42\n", + "inthree = [inner_u]*36\n", + "infour = [inner_u]*30\n", + "infive = [inner_u]*24\n", + "insix = [inner_u]*18\n", + "inseven = [inner_u]*12\n", + "ineight = [inner_u]*6\n", + "innine = [inner_u]*1\n", + "in_lat.universes = [inone,intwo,inthree,infour,infive,insix,inseven,ineight,innine]\n", + "\n", + "# Create the prism that will contain the lattice\n", + "outer_in_surface = openmc.model.hexagonal_prism(edge_length=12.1705, orientation='x')\n", + "\n", + "# Fill a cell with the lattice. This cell is filled with the lattice and contained within the prism.\n", + "main_in_assembly = openmc.Cell(cell_id=7, fill=in_lat, region=outer_in_surface & -top & +bottom)\n", + "\n", + "# Fill a cell with a material that will surround the lattice\n", + "out_in_assembly = openmc.Cell(cell_id=8, fill=sodium, region=~outer_in_surface & -top & +bottom)\n", + "\n", + "# Create a universe that contains both \n", + "main_in_u = openmc.Universe(universe_id=4, cells=[main_in_assembly, out_in_assembly])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "main_in_u.plot(origin = (0,0,0), pixels=(500, 500), width = (30.,30.), color_by = 'material')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "1SIgRmjLTuPJ" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Lattice instance already exists with id=2.\n", + " warn(msg, IDWarning)\n" + ] + } + ], + "source": [ + "# Define a lattice for outer assemblies\n", + "out_lat = openmc.HexLattice(lattice_id=2, name='outer assembly')\n", + "out_lat.center = (0., 0.)\n", + "out_lat.pitch = (21.08/17,)\n", + "out_lat.orientation = 'x'\n", + "out_lat.outer = sodium_mod_u\n", + "\n", + "# Create rings of fuel universes that will fill the lattice\n", + "outone = [outer_u]*48\n", + "outtwo = [outer_u]*42\n", + "outthree = [outer_u]*36\n", + "outfour = [outer_u]*30\n", + "outfive = [outer_u]*24\n", + "outsix = [outer_u]*18\n", + "outseven = [outer_u]*12\n", + "outeight = [outer_u]*6\n", + "outnine = [outer_u]*1\n", + "out_lat.universes = [outone,outtwo,outthree,outfour,outfive,outsix,outseven,outeight,outnine]\n", + "\n", + "# Create the prism that will contain the lattice\n", + "outer_out_surface = openmc.model.hexagonal_prism(edge_length=12.1705)\n", + "\n", + "# Fill a cell with the lattice. This cell is filled with the lattice and contained within the prism.\n", + "main_out_assembly = openmc.Cell(cell_id=9, fill=out_lat, region=outer_out_surface & -top & +bottom)\n", + "\n", + "# Fill a cell with a material that will surround the lattice\n", + "out_out_assembly = openmc.Cell(cell_id=10, fill=sodium, region=~outer_out_surface & -top & +bottom)\n", + "\n", + "# Create a universe that contains both \n", + "main_out_u = openmc.Universe(universe_id=5, cells=[main_out_assembly, out_out_assembly])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "pDf5K0t-eYRM", + "outputId": "97396467-3a79-41bb-e9bd-8d4ff43369cf" + }, + "outputs": [], + "source": [ + "# Create a hexagonal water cell\n", + "\n", + "reflector_assembly = openmc.model.hexagonal_prism(edge_length=12.1705, orientation='x')\n", + "ref_cell = openmc.Cell(cell_id=11, fill=sodium, region=reflector_assembly & -top & +bottom)\n", + "out_ref_cell = openmc.Cell(cell_id=12, fill=sodium, region=~reflector_assembly & -top & +bottom)\n", + "ref_u = openmc.Universe(universe_id=6, cells=[ref_cell, out_ref_cell])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have 3 types of assemblies created. We can now make the entire reactor core by creating a lattice that is filled with the assemblies. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "6lDqg4lLWD7v" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Lattice instance already exists with id=3.\n", + " warn(msg, IDWarning)\n" + ] + } + ], + "source": [ + "# Define the core lattice\n", + "\n", + "core_lat = openmc.HexLattice(lattice_id=3, name='core')\n", + "core_lat.center = (0., 0.)\n", + "core_lat.pitch = (21.08,)\n", + "core_lat.outer = sodium_mod_u" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "M1FcPGAbjH3J" + }, + "outputs": [], + "source": [ + "# Create rings of fuel universes that will fill the lattice\n", + "ref_one = [ref_u] * 96\n", + "ref_two = [ref_u] * 90\n", + "ref_three = [ref_u] * 84\n", + "ref_four = ([ref_u] * 5 + [main_out_u] * 4 + [ref_u] * 4) * 6\n", + "ref_five = ([ref_u] + [main_out_u] * 11) * 6\n", + "out_one = [main_out_u]*66\n", + "out_two = [main_out_u]*60\n", + "out_three = ([main_out_u]*2 + [main_in_u]*6 + [main_out_u] * 1)*6\n", + "in_one = [main_in_u]*48\n", + "in_two = [main_in_u]*42\n", + "in_three = [main_in_u]*36\n", + "in_four = [main_in_u]*30\n", + "in_five = [main_in_u]*24\n", + "in_six = [main_in_u]*18\n", + "in_seven = [main_in_u]*12\n", + "in_eight = [main_in_u]*6\n", + "in_nine = [main_in_u]*1\n", + "core_lat.universes = [ref_one,ref_two,ref_three,ref_four,ref_five,out_one,out_two,out_three,in_one,in_two,in_three,in_four,in_five,in_six,in_seven,in_eight,in_nine]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "nzVEvJgpjyY2", + "outputId": "3f678f02-4a6c-4768-dc46-419ad37019a0", + "scrolled": true + }, + "outputs": [], + "source": [ + "# Create the prism that will contain the lattice\n", + "outer_core_surface = openmc.model.hexagonal_prism(edge_length=347.82, boundary_type='vacuum')\n", + "\n", + "# Fill a cell with the lattice. This cell is filled with the lattice and contained within the prism.\n", + "core = openmc.Cell(cell_id=13, fill=core_lat, region=outer_core_surface & -top & +bottom)\n", + "\n", + "# Fill a cell with a material that will surround the lattice\n", + "out_core = openmc.Cell(cell_id=14, fill=outer, region=~outer_core_surface & -top & +bottom)\n", + "\n", + "# Create a universe that contains both \n", + "main_u = openmc.Universe(universe_id=7, cells=[core, out_core]) " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "main_u.plot(origin = (0,0,0), pixels=(1000, 1000), width = (660.,660.), color_by = 'material')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have an entire reactor core defined! We can export the geometry and run the code. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "geom = openmc.Geometry(main_u)\n", + "geom.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5mPhUg7QWVw4", + "outputId": "f110f4ab-94b5-477b-8c55-cab8ed7c3116" + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "\n", + "lower_left = [-300, -300, -50]\n", + "upper_right = [300, 300, 50]\n", + "uniform_dist = openmc.stats.Box(lower_left, upper_right, only_fissionable=True)\n", + "src = openmc.Source(space=uniform_dist)\n", + "\n", + "settings = openmc.Settings()\n", + "settings.source = src\n", + "settings.batches = 100\n", + "settings.inactive = 10\n", + "settings.particles = 1000\n", + "\n", + "settings.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "A_f292ebWZFM", + "outputId": "d6710d6c-aea6-43c9-e63c-b2e62ea7ea08" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.1-dev\n", + " Git SHA1 | 0228ddba1d6399e654a295e51539590f076b2a3c\n", + " Date/Time | 2021-02-22 22:22:08\n", + " OpenMP Threads | 8\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading Na23 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Na23.h5\n", + " Reading U235 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U235.h5\n", + " Reading U238 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U238.h5\n", + " Reading Pu239 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Pu239.h5\n", + " Reading Pu240 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Pu240.h5\n", + " Reading Pu241 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Pu241.h5\n", + " Reading Pu242 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Pu242.h5\n", + " Reading Am241 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Am241.h5\n", + " Reading O16 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/O16.h5\n", + " Reading Cu63 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Cu63.h5\n", + " Reading O17 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/O17.h5\n", + " Reading Al27 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Al27.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for Na23\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.02481\n", + " 2/1 1.12641\n", + " 3/1 1.19114\n", + " 4/1 1.14944\n", + " 5/1 1.24417\n", + " 6/1 1.18112\n", + " 7/1 1.22681\n", + " 8/1 1.25324\n", + " 9/1 1.26160\n", + " 10/1 1.24391\n", + " 11/1 1.26299\n", + " 12/1 1.22630 1.24465 +/- 0.01834\n", + " 13/1 1.22617 1.23849 +/- 0.01225\n", + " 14/1 1.17487 1.22258 +/- 0.01811\n", + " 15/1 1.23711 1.22549 +/- 0.01433\n", + " 16/1 1.25744 1.23081 +/- 0.01285\n", + " 17/1 1.19600 1.22584 +/- 0.01195\n", + " 18/1 1.22848 1.22617 +/- 0.01035\n", + " 19/1 1.24147 1.22787 +/- 0.00929\n", + " 20/1 1.23635 1.22872 +/- 0.00835\n", + " 21/1 1.22312 1.22821 +/- 0.00757\n", + " 22/1 1.22163 1.22766 +/- 0.00693\n", + " 23/1 1.21767 1.22689 +/- 0.00642\n", + " 24/1 1.16644 1.22257 +/- 0.00735\n", + " 25/1 1.19885 1.22099 +/- 0.00702\n", + " 26/1 1.21883 1.22086 +/- 0.00657\n", + " 27/1 1.24819 1.22246 +/- 0.00638\n", + " 28/1 1.26476 1.22481 +/- 0.00645\n", + " 29/1 1.21935 1.22453 +/- 0.00611\n", + " 30/1 1.23808 1.22520 +/- 0.00584\n", + " 31/1 1.16740 1.22245 +/- 0.00620\n", + " 32/1 1.21038 1.22190 +/- 0.00593\n", + " 33/1 1.20477 1.22116 +/- 0.00572\n", + " 34/1 1.26182 1.22285 +/- 0.00573\n", + " 35/1 1.22385 1.22289 +/- 0.00550\n", + " 36/1 1.30378 1.22600 +/- 0.00613\n", + " 37/1 1.26827 1.22757 +/- 0.00610\n", + " 38/1 1.27147 1.22914 +/- 0.00609\n", + " 39/1 1.23282 1.22926 +/- 0.00587\n", + " 40/1 1.25327 1.23006 +/- 0.00573\n", + " 41/1 1.19087 1.22880 +/- 0.00569\n", + " 42/1 1.28910 1.23068 +/- 0.00582\n", + " 43/1 1.23440 1.23080 +/- 0.00564\n", + " 44/1 1.22528 1.23063 +/- 0.00547\n", + " 45/1 1.25779 1.23141 +/- 0.00537\n", + " 46/1 1.26413 1.23232 +/- 0.00530\n", + " 47/1 1.27685 1.23352 +/- 0.00529\n", + " 48/1 1.27599 1.23464 +/- 0.00527\n", + " 49/1 1.28307 1.23588 +/- 0.00528\n", + " 50/1 1.26215 1.23654 +/- 0.00519\n", + " 51/1 1.22880 1.23635 +/- 0.00507\n", + " 52/1 1.22389 1.23605 +/- 0.00495\n", + " 53/1 1.23361 1.23600 +/- 0.00484\n", + " 54/1 1.22966 1.23585 +/- 0.00473\n", + " 55/1 1.22266 1.23556 +/- 0.00463\n", + " 56/1 1.25986 1.23609 +/- 0.00456\n", + " 57/1 1.31764 1.23782 +/- 0.00479\n", + " 58/1 1.29656 1.23905 +/- 0.00484\n", + " 59/1 1.25418 1.23936 +/- 0.00475\n", + " 60/1 1.21933 1.23896 +/- 0.00467\n", + " 61/1 1.23886 1.23895 +/- 0.00458\n", + " 62/1 1.22153 1.23862 +/- 0.00451\n", + " 63/1 1.25440 1.23892 +/- 0.00443\n", + " 64/1 1.23758 1.23889 +/- 0.00435\n", + " 65/1 1.29599 1.23993 +/- 0.00439\n", + " 66/1 1.25912 1.24027 +/- 0.00433\n", + " 67/1 1.25699 1.24057 +/- 0.00426\n", + " 68/1 1.21444 1.24011 +/- 0.00421\n", + " 69/1 1.25080 1.24030 +/- 0.00414\n", + " 70/1 1.30284 1.24134 +/- 0.00420\n", + " 71/1 1.26208 1.24168 +/- 0.00415\n", + " 72/1 1.25869 1.24195 +/- 0.00409\n", + " 73/1 1.24739 1.24204 +/- 0.00403\n", + " 74/1 1.25263 1.24220 +/- 0.00397\n", + " 75/1 1.22410 1.24193 +/- 0.00391\n", + " 76/1 1.29268 1.24269 +/- 0.00393\n", + " 77/1 1.22569 1.24244 +/- 0.00388\n", + " 78/1 1.28431 1.24306 +/- 0.00387\n", + " 79/1 1.22645 1.24282 +/- 0.00382\n", + " 80/1 1.24248 1.24281 +/- 0.00377\n", + " 81/1 1.22740 1.24259 +/- 0.00372\n", + " 82/1 1.29676 1.24335 +/- 0.00374\n", + " 83/1 1.26406 1.24363 +/- 0.00370\n", + " 84/1 1.18714 1.24287 +/- 0.00373\n", + " 85/1 1.24634 1.24291 +/- 0.00368\n", + " 86/1 1.25176 1.24303 +/- 0.00364\n", + " 87/1 1.30837 1.24388 +/- 0.00369\n", + " 88/1 1.24285 1.24386 +/- 0.00364\n", + " 89/1 1.22941 1.24368 +/- 0.00360\n", + " 90/1 1.18066 1.24289 +/- 0.00364\n", + " 91/1 1.28586 1.24342 +/- 0.00363\n", + " 92/1 1.26556 1.24369 +/- 0.00360\n", + " 93/1 1.22569 1.24348 +/- 0.00356\n", + " 94/1 1.20319 1.24300 +/- 0.00355\n", + " 95/1 1.27998 1.24343 +/- 0.00354\n", + " 96/1 1.20130 1.24294 +/- 0.00353\n", + " 97/1 1.24200 1.24293 +/- 0.00349\n", + " 98/1 1.27352 1.24328 +/- 0.00347\n", + " 99/1 1.30545 1.24398 +/- 0.00350\n", + " 100/1 1.31981 1.24482 +/- 0.00356\n", + " Creating state point statepoint.100.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 2.3018e+00 seconds\n", + " Reading cross sections = 2.2651e+00 seconds\n", + " Total time in simulation = 1.0711e+00 seconds\n", + " Time in transport only = 1.0512e+00 seconds\n", + " Time in inactive batches = 9.2840e-02 seconds\n", + " Time in active batches = 9.7826e-01 seconds\n", + " Time synchronizing fission bank = 4.0280e-03 seconds\n", + " Sampling source sites = 3.3290e-03 seconds\n", + " SEND/RECV source sites = 5.3600e-04 seconds\n", + " Time accumulating tallies = 2.5000e-05 seconds\n", + " Time writing statepoints = 8.3760e-03 seconds\n", + " Total time for finalization = 7.0000e-06 seconds\n", + " Total time elapsed = 3.3798e+00 seconds\n", + " Calculation Rate (inactive) = 107712.0 particles/second\n", + " Calculation Rate (active) = 92000.4 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.24514 +/- 0.00362\n", + " k-effective (Track-length) = 1.24482 +/- 0.00356\n", + " k-effective (Absorption) = 1.24692 +/- 0.00415\n", + " Combined k-effective = 1.24588 +/- 0.00254\n", + " Leakage Fraction = 0.11453 +/- 0.00147\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "FastReactor_3layers.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/TRIGA/TRIGA.ipynb b/TRIGA/TRIGA.ipynb new file mode 100644 index 0000000..20ad616 --- /dev/null +++ b/TRIGA/TRIGA.ipynb @@ -0,0 +1,1144 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q9Vo6HOQUQbS", + "outputId": "a8c1843c-272d-461a-faf3-4b78fdbced3b" + }, + "source": [ + "# A TRIGA geometry \n", + "This notebook can be used as a template for modeling TRIGA reactors." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "EChOeJ7qVUB7" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import openmc" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "jlu98MGXV9MP" + }, + "outputs": [], + "source": [ + "# Materials definitions\n", + "\n", + "# Borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", + "water.add_nuclide('B10', 8.0042e-6)\n", + "\n", + "# 20% enriched uranium zirconium hydride fuel\n", + "uzrh = openmc.Material(name='UZrH')\n", + "uzrh.set_density('g/cm3', 6.128)\n", + "uzrh.add_nuclide('U235', .02376, 'wo')\n", + "uzrh.add_nuclide('U238', .09619, 'wo')\n", + "uzrh.add_element('H', .03, 'wo')\n", + "uzrh.add_element('Zr', .85, 'wo')\n", + "\n", + "molybdenum = openmc.Material(name='Molybdenum')\n", + "molybdenum.add_element('Mo', 1.0)\n", + "molybdenum.set_density('g/cm3', 10.22)\n", + "\n", + "graphite = openmc.Material(name='Graphite')\n", + "graphite.set_density('g/cm3', 1.70)\n", + "graphite.add_element('C', 1.0)\n", + "graphite.add_s_alpha_beta('c_Graphite')\n", + "\n", + "# Stainless steel\n", + "ss304 = openmc.Material(name='Stainless Steel 304')\n", + "ss304.set_density('g/cm3', 8.0)\n", + "ss304.add_element('C',.002,'wo')\n", + "ss304.add_element('Si',.004,'wo')\n", + "ss304.add_element('P',.0003,'wo')\n", + "ss304.add_element('S',.0002,'wo')\n", + "ss304.add_element('V',.003,'wo')\n", + "ss304.add_element('Cr',.115,'wo')\n", + "ss304.add_element('Mn',.006,'wo')\n", + "ss304.add_element('Fe',.8495,'wo')\n", + "ss304.add_element('Ni',.005,'wo')\n", + "ss304.add_element('Mo',.01,'wo')\n", + "ss304.add_element('W',.005,'wo')\n", + "\n", + "# Boron carbide\n", + "b4c = openmc.Material(name='Boron Carbide')\n", + "b4c.set_density('g/cm3', 2.52)\n", + "b4c.add_element('B', 4)\n", + "b4c.add_element('C', 1)\n", + "\n", + "zirconium = openmc.Material(name='Zirconium')\n", + "zirconium.add_element('Zr', 1.0)\n", + "zirconium.set_density('g/cm3', 6.506)\n", + "\n", + "void = openmc.Material(name='Void')\n", + "void.set_density('g/cm3', 0.001205)\n", + "void.add_element('Ni', 0.755268, 'wo')\n", + "void.add_element('C', 0.000124, 'wo')\n", + "void.add_element('O', 0.231781, 'wo')\n", + "void.add_element('Ar', 0.012827, 'wo')\n", + "\n", + "aluminum = openmc.Material(name='Aluminum')\n", + "aluminum.add_element('Al', 1.0)\n", + "aluminum.set_density('g/cm3', 2.6)\n", + "\n", + "# Instantiate a Materials collection and export to xml\n", + "materials_file = openmc.Materials([aluminum, zirconium, b4c, ss304, graphite, molybdenum, uzrh, water, void])\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "6lDqg4lLWD7v" + }, + "outputs": [], + "source": [ + "# Geometry definitions for the fuel rod\n", + "\n", + "rod_outer_radius = openmc.ZCylinder(r=0.635)\n", + "uzrh_outer_radius = openmc.ZCylinder(r=3.6449)\n", + "molybdenum_outer_radius = openmc.ZCylinder(r=3.6449)\n", + "graphite_outer_radius = openmc.ZCylinder(r=3.6449)\n", + "ss304_outer_radius = openmc.ZCylinder(r=3.6449)\n", + "clad_outer_radius = openmc.ZCylinder(r=3.75412)\n", + "\n", + "empty_space_min = openmc.ZPlane(z0=-114.3)\n", + "empty_space_max = openmc.ZPlane(z0=-55.079265)\n", + "rod_min = openmc.ZPlane(z0=-55.079265)\n", + "rod_max = openmc.ZPlane(z0=-16.979265)\n", + "uzrh_min = openmc.ZPlane(z0=-55.079265)\n", + "uzrh_max = openmc.ZPlane(z0=-16.979265)\n", + "molybdenum_min = openmc.ZPlane(z0=-55.15864)\n", + "molybdenum_max = openmc.ZPlane(z0=-55.079265)\n", + "graphite_upper_min = openmc.ZPlane(z0=-16.979265)\n", + "graphite_upper_max = openmc.ZPlane(z0=-10.375265)\n", + "graphite_lower_min = openmc.ZPlane(z0=-64.621664)\n", + "graphite_lower_max = openmc.ZPlane(z0=-55.15864)\n", + "ss304_upper_min = openmc.ZPlane(z0=-10.375265)\n", + "ss304_upper_max = openmc.ZPlane(z0=+0)\n", + "ss304_lower_min = openmc.ZPlane(z0=-72.0598)\n", + "ss304_lower_max = openmc.ZPlane(z0=-64.621664)\n", + "clad_min = openmc.ZPlane(z0=-72.0598)\n", + "clad_max = openmc.ZPlane(z0=0)\n", + "\n", + "# Create a Universe to encapsulate the fuel rod\n", + "fuel_universe = openmc.Universe(name='UZrH Fuel Universe')\n", + "\n", + "# Create rod cell\n", + "rod_cell = openmc.Cell(name='Zr Rod')\n", + "rod_cell.fill = zirconium\n", + "rod_cell.region = -rod_outer_radius & +rod_min & -rod_max\n", + "fuel_universe.add_cell(rod_cell)\n", + "\n", + "# Create uzrh cell\n", + "uzrh_cell = openmc.Cell(name='UZrH')\n", + "uzrh_cell.fill = uzrh\n", + "uzrh_cell.region = +rod_outer_radius & -uzrh_outer_radius & +uzrh_min & -uzrh_max\n", + "fuel_universe.add_cell(uzrh_cell)\n", + "\n", + "# Create molybdenum disk\n", + "molybdenum_cell = openmc.Cell(name='Molybdenum')\n", + "molybdenum_cell.fill = molybdenum\n", + "molybdenum_cell.region = -molybdenum_outer_radius & +molybdenum_min & -molybdenum_max\n", + "fuel_universe.add_cell(molybdenum_cell)\n", + "\n", + "# Create upper graphite cell\n", + "graphite_upper_cell = openmc.Cell(name='Upper Graphite')\n", + "graphite_upper_cell.fill = graphite\n", + "graphite_upper_cell.region = -graphite_outer_radius & +graphite_upper_min & -graphite_upper_max\n", + "fuel_universe.add_cell(graphite_upper_cell)\n", + "\n", + "# Create lower graphite cell\n", + "graphite_lower_cell = openmc.Cell(name='Lower Graphite')\n", + "graphite_lower_cell.fill = graphite\n", + "graphite_lower_cell.region = -graphite_outer_radius & +graphite_lower_min & -graphite_lower_max\n", + "fuel_universe.add_cell(graphite_lower_cell)\n", + "\n", + "# Create upper ss304 cell\n", + "ss304_upper_cell = openmc.Cell(name='Upper Stainless Steel 304')\n", + "ss304_upper_cell.fill = ss304\n", + "ss304_upper_cell.region = -ss304_outer_radius & +ss304_upper_min & -ss304_upper_max \n", + "fuel_universe.add_cell(ss304_upper_cell)\n", + "\n", + "# Create lower ss304 cell\n", + "ss304_lower_cell = openmc.Cell(name='Lower Stainless Steel 304')\n", + "ss304_lower_cell.fill = ss304\n", + "ss304_lower_cell.region = -ss304_outer_radius & +ss304_lower_min & -ss304_lower_max\n", + "fuel_universe.add_cell(ss304_lower_cell)\n", + "\n", + "# Create clad cell\n", + "clad_cell = openmc.Cell(name='Stainless Steel 304 Cladding')\n", + "clad_cell.fill = ss304\n", + "clad_cell.region = -clad_outer_radius & +ss304_outer_radius & +clad_min & -clad_max\n", + "fuel_universe.add_cell(clad_cell)\n", + "\n", + "# Create empty space cell\n", + "empty_space_cell = openmc.Cell(name='Empty space before fuel rod')\n", + "empty_space_cell.fill = water\n", + "empty_space_cell.region = -clad_outer_radius & +empty_space_min & -empty_space_max\n", + "fuel_universe.add_cell(empty_space_cell)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "osv05TS3yNUT" + }, + "outputs": [], + "source": [ + "# Geometry definitions for the transient rod\n", + "\n", + "void_outer_radius = openmc.ZCylinder(r=3.03276)\n", + "b4c_outer_radius = openmc.ZCylinder(r=3.03276)\n", + "clad_outer_radius = openmc.ZCylinder(r=3.38455)\n", + "aluminum_outer_radius = openmc.ZCylinder(r=3.03276)\n", + "\n", + "aluminum_1_min = openmc.ZPlane(z0=-114.3)\n", + "aluminum_1_max = openmc.ZPlane(z0=-113.03)\n", + "void_1_min = openmc.ZPlane(z0=-113.03)\n", + "void_1_max = openmc.ZPlane(z0=-57.785)\n", + "aluminum_2_min = openmc.ZPlane(z0=-57.785)\n", + "aluminum_2_max = openmc.ZPlane(z0=-56.515)\n", + "b4c_min = openmc.ZPlane(z0=-56.515)\n", + "b4c_max = openmc.ZPlane(z0=-18.415)\n", + "void_2_min = openmc.ZPlane(z0=-18.415)\n", + "void_2_max = openmc.ZPlane(z0=-18.0975)\n", + "aluminum_3_min = openmc.ZPlane(z0=-18.0975)\n", + "aluminum_3_max = openmc.ZPlane(z0=-16.8275)\n", + "void_3_min = openmc.ZPlane(z0=-16.8275)\n", + "void_3_max = openmc.ZPlane(z0=-7.3025)\n", + "aluminum_4_min = openmc.ZPlane(z0=-7.3025)\n", + "aluminum_4_max = openmc.ZPlane(z0=0)\n", + "clad_min = openmc.ZPlane(z0=-114.3)\n", + "clad_max = openmc.ZPlane(z0=0)\n", + "\n", + "# Create a Universe to encapsulate the transient rod\n", + "transient_universe = openmc.Universe(name='Transient Universe')\n", + "\n", + "# Create void 1 cell\n", + "void_1_cell = openmc.Cell(name= 'Void 1')\n", + "void_1_cell.fill = void\n", + "void_1_cell.region = -void_outer_radius & +void_1_min & -void_1_max\n", + "transient_universe.add_cell(void_1_cell)\n", + "\n", + "# Create void 2 cell\n", + "void_2_cell = openmc.Cell(name= 'Void 2')\n", + "void_2_cell.fill = void\n", + "void_2_cell.region = -void_outer_radius & +void_2_min & -void_2_max \n", + "transient_universe.add_cell(void_2_cell)\n", + "\n", + "# Create void 3 cell\n", + "void_3_cell = openmc.Cell(name= 'Void 3')\n", + "void_3_cell.fill = void\n", + "void_3_cell.region = -void_outer_radius & +void_3_min & -void_3_max \n", + "transient_universe.add_cell(void_3_cell)\n", + "\n", + "# Create b4c cell\n", + "b4c_cell = openmc.Cell(name='Boron Carbide')\n", + "b4c_cell.fill = b4c\n", + "b4c_cell.region = -b4c_outer_radius & -b4c_max & +b4c_min\n", + "transient_universe.add_cell(b4c_cell)\n", + "\n", + "# Create aluminum 1 cell\n", + "aluminum_1_cell = openmc.Cell(name='Aluminum')\n", + "aluminum_1_cell.fill = aluminum\n", + "aluminum_1_cell.region = -aluminum_outer_radius & +aluminum_1_min & -aluminum_1_max \n", + "transient_universe.add_cell(aluminum_1_cell)\n", + "\n", + "# Create aluminum 2 cell\n", + "aluminum_2_cell = openmc.Cell(name='Aluminum')\n", + "aluminum_2_cell.fill = aluminum\n", + "aluminum_2_cell.region = -aluminum_outer_radius & +aluminum_2_min & -aluminum_2_max\n", + "transient_universe.add_cell(aluminum_2_cell)\n", + "\n", + "# Create aluminum 3 cell\n", + "aluminum_3_cell = openmc.Cell(name='Aluminum')\n", + "aluminum_3_cell.fill = aluminum\n", + "aluminum_3_cell.region = -aluminum_outer_radius & +aluminum_3_min & -aluminum_3_max \n", + "transient_universe.add_cell(aluminum_3_cell)\n", + "\n", + "# Create aluminum 4 cell\n", + "aluminum_4_cell = openmc.Cell(name='Aluminum')\n", + "aluminum_4_cell.fill = aluminum\n", + "aluminum_4_cell.region = -aluminum_outer_radius & +aluminum_4_min & -aluminum_4_max\n", + "transient_universe.add_cell(aluminum_4_cell)\n", + "\n", + "# Create a clad cell\n", + "clad_cell = openmc.Cell(name='Aluminum Cladding')\n", + "clad_cell.fill = aluminum\n", + "clad_cell.region = -clad_outer_radius & +b4c_outer_radius & +clad_min & -clad_max \n", + "transient_universe.add_cell(clad_cell)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "-0xO897aWbft" + }, + "outputs": [], + "source": [ + "# Geometry definitions for the control rod\n", + "\n", + "rod_outer_radius = openmc.ZCylinder(r=0.635)\n", + "uzrh_outer_radius = openmc.ZCylinder(r=3.33375)\n", + "void_outer_radius = openmc.ZCylinder(r=3.33375)\n", + "b4c_outer_radius = openmc.ZCylinder(r=3.33375)\n", + "ss304_outer_radius = openmc.ZCylinder(r=3.33375)\n", + "clad_outer_radius = openmc.ZCylinder(r=3.38455)\n", + "\n", + "ss304_5_min = openmc.ZPlane(z0=-114.3)\n", + "ss304_5_max = openmc.ZPlane(z0=-113.03)\n", + "void_4_min = openmc.ZPlane(z0=-113.03)\n", + "void_4_max = openmc.ZPlane(z0=-99.06)\n", + "ss304_4_min = openmc.ZPlane(z0=-99.06)\n", + "ss304_4_max = openmc.ZPlane(z0=-96.52)\n", + "rod_min = openmc.ZPlane(z0=-96.52)\n", + "rod_max = openmc.ZPlane(z0=-58.42)\n", + "uzrh_min = openmc.ZPlane(z0=-96.52)\n", + "uzrh_max = openmc.ZPlane(z0=-58.42)\n", + "void_1_min = openmc.ZPlane(z0=-58.42)\n", + "void_1_max = openmc.ZPlane(z0=-57.785)\n", + "ss304_1_min = openmc.ZPlane(z0=-57.785)\n", + "ss304_1_max = openmc.ZPlane(z0=-56.515)\n", + "b4c_1_min = openmc.ZPlane(z0=-56.515)\n", + "b4c_1_max = openmc.ZPlane(z0=-18.415)\n", + "void_2_min = openmc.ZPlane(z0=-18.415)\n", + "void_2_max = openmc.ZPlane(z0=-18.0975)\n", + "ss304_2_min = openmc.ZPlane(z0=-18.0975)\n", + "ss304_2_max = openmc.ZPlane(z0=-16.8275)\n", + "void_3_min = openmc.ZPlane(z0=-16.8275)\n", + "void_3_max = openmc.ZPlane(z0=-7.3025)\n", + "ss304_3_min = openmc.ZPlane(z0=-7.3025)\n", + "ss304_3_max = openmc.ZPlane(z0=0.0)\n", + "clad_min = openmc.ZPlane(z0=-114.3)\n", + "clad_max = openmc.ZPlane(z0=0.0)\n", + "\n", + "# Create a Universe to encapsulate the control rod\n", + "control_universe = openmc.Universe(name='Control Universe')\n", + "\n", + "# Create rod cell\n", + "rod_cell = openmc.Cell(name='Zr Rod')\n", + "rod_cell.fill = zirconium\n", + "rod_cell.region = -rod_outer_radius & +rod_min & -rod_max\n", + "control_universe.add_cell(rod_cell)\n", + "\n", + "# Create uzrh cell\n", + "uzrh_cell = openmc.Cell(name='UZrH')\n", + "uzrh_cell.fill = uzrh\n", + "uzrh_cell.region = +rod_outer_radius & -uzrh_outer_radius & +uzrh_min & -uzrh_max\n", + "control_universe.add_cell(uzrh_cell)\n", + "\n", + "# Create void 1 cell\n", + "void_1_cell = openmc.Cell(name= 'Void 1')\n", + "void_1_cell.fill = void\n", + "void_1_cell.region = -void_outer_radius & +void_1_min & -void_1_max\n", + "control_universe.add_cell(void_1_cell)\n", + "\n", + "# Create void 2 cell\n", + "void_2_cell = openmc.Cell(name= 'Void 2')\n", + "void_2_cell.fill = void\n", + "void_2_cell.region = -void_outer_radius & +void_2_min & -void_2_max \n", + "control_universe.add_cell(void_2_cell)\n", + "\n", + "# Create void 3 cell\n", + "void_3_cell = openmc.Cell(name= 'Void 3')\n", + "void_3_cell.fill = void\n", + "void_3_cell.region = -void_outer_radius & +void_3_min & -void_3_max \n", + "control_universe.add_cell(void_3_cell)\n", + "\n", + "# Create void 4 cell\n", + "void_4_cell = openmc.Cell(name= 'Void 3')\n", + "void_4_cell.fill = void\n", + "void_4_cell.region = -void_outer_radius & +void_4_min & -void_4_max \n", + "control_universe.add_cell(void_4_cell)\n", + "\n", + "# Create ss304 1 cell\n", + "ss304_1_cell = openmc.Cell(name='Stainless Steel 304 Cell 1')\n", + "ss304_1_cell.fill = ss304\n", + "ss304_1_cell.region = -ss304_outer_radius & +ss304_1_min & -ss304_1_max \n", + "control_universe.add_cell(ss304_1_cell)\n", + "\n", + "# Create ss304 2 cell\n", + "ss304_2_cell = openmc.Cell(name='Stainless Steel 304 Cell 2')\n", + "ss304_2_cell.fill = ss304\n", + "ss304_2_cell.region = -ss304_outer_radius & +ss304_2_min & -ss304_2_max \n", + "control_universe.add_cell(ss304_2_cell)\n", + "\n", + "# Create ss304 3 cell\n", + "ss304_3_cell = openmc.Cell(name='Stainless Steel 304 Cell 3')\n", + "ss304_3_cell.fill = ss304\n", + "ss304_3_cell.region = -ss304_outer_radius & +ss304_3_min & -ss304_3_max \n", + "control_universe.add_cell(ss304_3_cell)\n", + "\n", + "# Create ss304 4 cell\n", + "ss304_4_cell = openmc.Cell(name='Stainless Steel 304 Cell 4')\n", + "ss304_4_cell.fill = ss304\n", + "ss304_4_cell.region = -ss304_outer_radius & +ss304_4_min & -ss304_4_max \n", + "control_universe.add_cell(ss304_4_cell)\n", + "\n", + "# Create ss304 5 cell\n", + "ss304_5_cell = openmc.Cell(name='Stainless Steel 304 Cell 5')\n", + "ss304_5_cell.fill = ss304\n", + "ss304_5_cell.region = -ss304_outer_radius & +ss304_5_min & -ss304_5_max \n", + "control_universe.add_cell(ss304_5_cell)\n", + "\n", + "# Create b4c 1 cell\n", + "b4c_1_cell = openmc.Cell(name='B4C cell')\n", + "b4c_1_cell.fill = b4c\n", + "b4c_1_cell.region = -b4c_outer_radius & +b4c_1_min & -b4c_1_max\n", + "control_universe.add_cell(b4c_1_cell) \n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='Stainless Steel 304 Cladding')\n", + "clad_cell.fill = ss304\n", + "clad_cell.region = -clad_outer_radius & +ss304_outer_radius & +clad_min & -clad_max #Miriam: cladding is only the exterior coat.\n", + "control_universe.add_cell(clad_cell)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "8h9JMENTw9P3" + }, + "outputs": [], + "source": [ + "# Create water universe to surround the lattice\n", + "\n", + "all_water_cell = openmc.Cell(fill=water)\n", + "outer_universe = openmc.Universe(cells=(all_water_cell,))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "q-VaKsxT8v3b" + }, + "outputs": [], + "source": [ + "# Create surfaces that will divide rings in the circular lattice\n", + "\n", + "ring_radii = np.array([0.0, 8.0, 16.0, 24.0, 32.0, 40.0])\n", + "radial_surf = [openmc.ZCylinder(r=r) for r in\n", + " (ring_radii[:-1] + ring_radii[1:])/2]\n", + "\n", + "water_cells = []\n", + "for i in range(ring_radii.size):\n", + " # Create annular region\n", + " if i == 0:\n", + " water_region = -radial_surf[i]\n", + " elif i == ring_radii.size - 1:\n", + " water_region = +radial_surf[i-1]\n", + " else:\n", + " water_region = +radial_surf[i-1] & -radial_surf[i]\n", + " water_cells.append(openmc.Cell(fill=water, region=water_region))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "Ggrtg9BT8w9D", + "outputId": "9ba193b2-edc8-4540-d234-1f0cb00d5a5d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the rings to visualize the circular lattice, without rods\n", + "\n", + "plot_args = {'width': (2*24.1, 2*24.1)}\n", + "bundle_universe = openmc.Universe(cells=water_cells)\n", + "bundle_universe.plot(**plot_args)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DGqaeEPN8z2K", + "outputId": "e68c720d-cb6d-45c2-92b2-a96b9e1d6dd2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding in a control rod...\n", + "Adding in a transient rod...\n", + "Adding in a water rod...\n", + "Adding in a transient rod...\n", + "Adding in a control rod...\n", + "Adding in a control rod...\n", + "Adding in a water rod...\n" + ] + } + ], + "source": [ + "# Arrange the pins in the circular lattice \n", + "\n", + "num_pins = [1, 6, 12, 18, 24, 30]\n", + "angles = [0, 0, 0, 0, 0, 0]\n", + "\n", + "controlRods = {'numPins' :[num_pins[1], num_pins[3], num_pins[5]],\n", + " 'howLeftFrom3oclock':[4 , 2 , 0]}\n", + "\n", + "transientRods = {'numPins' :[num_pins[3], num_pins[2]],\n", + " 'howLeftFrom3oclock':[1 , 0]}\n", + "\n", + "waterRods = {'numPins' :[num_pins[5], num_pins[2]],\n", + " 'howLeftFrom3oclock':[1 , 8]}\n", + "\n", + "def ControlRod(controlRods,n,j):\n", + " for irod in range(len(controlRods['numPins'])): \n", + " if n == controlRods['numPins'][irod] and \\\n", + " j-1 == controlRods['howLeftFrom3oclock'][irod]:\n", + " return True\n", + " return False\n", + "\n", + "def TransientRod(transientRods,n,j):\n", + " for irod in range(len(transientRods['numPins'])): \n", + " if n == transientRods['numPins'][irod] and \\\n", + " j-1 == transientRods['howLeftFrom3oclock'][irod]:\n", + " return True\n", + " return False\n", + "\n", + "def WaterRod(waterRods,n,j):\n", + " for irod in range(len(waterRods['numPins'])): \n", + " if n == waterRods['numPins'][irod] and \\\n", + " j-1 == waterRods['howLeftFrom3oclock'][irod]:\n", + " return True\n", + " return False\n", + "\n", + "\n", + "for i, (r, n, a) in enumerate(zip(ring_radii, num_pins, angles)):\n", + " \n", + " for j in range(n):\n", + " \n", + " # Determine location of center of pin\n", + " theta = (a + j/n*360.) * np.pi/180.\n", + " x = r*np.cos(theta)\n", + " y = r*np.sin(theta)\n", + " \n", + " pin_boundary = openmc.ZCylinder(x0=x, y0=y, r=clad_outer_radius.r)\n", + " water_cells[i].region &= +pin_boundary\n", + " \n", + " # Create each fuel pin -- note that we explicitly assign an ID so \n", + " # that we can identify the pin later when looking at tallies\n", + " if ControlRod(controlRods,n,j):\n", + " print('Adding in a control rod...')\n", + " pin = openmc.Cell(fill=control_universe, region=-pin_boundary)\n", + " elif TransientRod(transientRods,n,j):\n", + " print('Adding in a transient rod...')\n", + " pin = openmc.Cell(fill=transient_universe, region=-pin_boundary)\n", + " elif WaterRod(waterRods,n,j):\n", + " print('Adding in a water rod...')\n", + " pin = openmc.Cell(fill=outer_universe, region=-pin_boundary)\n", + " else:\n", + " pin = openmc.Cell(fill=fuel_universe, region=-pin_boundary)\n", + " pin.translation = (x, y, 0)\n", + " pin.id = (i + 1)*100 + j\n", + " bundle_universe.add_cell(pin)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "HyyFkCzS843z", + "outputId": "cbaa5f36-cfe2-4e1b-9178-00cb8272ea21" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the rings to visualize the filled circular lattice\n", + "\n", + "bundle_universe.plot(width=(100, 100), origin=[0,0,-40], \n", + " basis='xy', color_by='material',\n", + " colors={water:'blue',uzrh:'orange',\n", + " zirconium:'green',graphite:'gray',\n", + " b4c:'yellow'})" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "XTzZHTnr0fO7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting fuel rod\n", + "\n", + "fuel_universe.plot(width=(20, 150), origin=[0,0,-40], basis='yz', color_by='material', colors={ss304:'fuchsia'})" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting transient rod\n", + "\n", + "transient_universe.plot(width=(20, 150), origin=[0,0,-40], basis='yz', color_by='material', colors={water:'fuchsia'})" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting control rod\n", + "\n", + "control_universe.plot(width=(20, 150), origin=[0,0,-40], basis='yz', color_by='material', colors={ss304:'fuchsia'})" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Geometry definitions for the reactor\n", + "\n", + "reactor_wall = openmc.ZCylinder(r=50.0, boundary_type='vacuum')\n", + "reactor_top = openmc.ZPlane(z0=0.0, boundary_type='vacuum')\n", + "reactor_bottom = openmc.ZPlane(z0=-114.3, boundary_type='vacuum')\n", + "reactor = openmc.Cell()\n", + "reactor.region = -reactor_wall & -reactor_top & +reactor_bottom\n", + "reactor.fill = bundle_universe\n", + "reactor_universe = openmc.Universe(cells=[reactor])\n", + "\n", + "reactor_universe.plot(width=(100, 100), origin=[0,0,-40], \n", + " basis='yz', color_by='material',\n", + " colors={water:'blue',uzrh:'orange',\n", + " zirconium:'green',graphite:'gray',\n", + " b4c:'yellow'})\n", + "\n", + "geometry = openmc.Geometry(reactor_universe)\n", + "geometry.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "cellView": "code", + "id": "5mPhUg7QWVw4" + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "\n", + "batches = 100\n", + "inactive = 10\n", + "particles = 5000\n", + "\n", + "settings_file = openmc.Settings()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "\n", + "bounds = [-28.527375, -28.527375, -28.527375, 28.527375, 28.527375, 28.527375]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings_file.source = openmc.Source(space=uniform_dist)\n", + "\n", + "settings_file.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "A_f292ebWZFM" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.1-dev\n", + " Git SHA1 | 0228ddba1d6399e654a295e51539590f076b2a3c\n", + " Date/Time | 2021-04-12 10:00:55\n", + " OpenMP Threads | 8\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading H1 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/H1.h5\n", + " Reading O16 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/O16.h5\n", + " Reading B10 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/B10.h5\n", + " Reading U235 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U235.h5\n", + " Reading U238 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U238.h5\n", + " Reading H2 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/H2.h5\n", + " Reading Zr90 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr90.h5\n", + " Reading Zr91 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr91.h5\n", + " Reading Zr92 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr92.h5\n", + " Reading Zr94 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr94.h5\n", + " Reading Zr96 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr96.h5\n", + " Reading Mo100 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Mo100.h5\n", + " Reading Mo92 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Mo92.h5\n", + " Reading Mo94 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Mo94.h5\n", + " Reading Mo95 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Mo95.h5\n", + " Reading Mo96 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Mo96.h5\n", + " Reading Mo97 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Mo97.h5\n", + " Reading Mo98 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Mo98.h5\n", + " Reading C0 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/C0.h5\n", + " Reading Si28 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Si28.h5\n", + " Reading Si29 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Si29.h5\n", + " Reading Si30 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Si30.h5\n", + " Reading P31 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/P31.h5\n", + " Reading S32 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/S32.h5\n", + " Reading S33 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/S33.h5\n", + " Reading S34 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/S34.h5\n", + " Reading S36 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/S36.h5\n", + " Reading V50 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/V50.h5\n", + " Reading V51 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/V51.h5\n", + " Reading Cr50 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Cr50.h5\n", + " Reading Cr52 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Cr52.h5\n", + " Reading Cr53 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Cr53.h5\n", + " Reading Cr54 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Cr54.h5\n", + " Reading Mn55 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Mn55.h5\n", + " Reading Fe54 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Fe54.h5\n", + " Reading Fe56 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Fe56.h5\n", + " Reading Fe57 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Fe57.h5\n", + " Reading Fe58 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Fe58.h5\n", + " Reading Ni58 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ni58.h5\n", + " Reading Ni60 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ni60.h5\n", + " Reading Ni61 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ni61.h5\n", + " Reading Ni62 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ni62.h5\n", + " Reading Ni64 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ni64.h5\n", + " Reading W180 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/W180.h5\n", + " Reading W182 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/W182.h5\n", + " Reading W183 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/W183.h5\n", + " Reading W184 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/W184.h5\n", + " Reading W186 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/W186.h5\n", + " Reading B11 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/B11.h5\n", + " Reading O17 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/O17.h5\n", + " Reading Ar36 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ar36.h5\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", + " Reading Ar38 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ar38.h5\n", + " Reading Ar40 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Ar40.h5\n", + " Reading Al27 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Al27.h5\n", + " Reading c_Graphite from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/c_Graphite.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for H1\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.10145\n", + " 2/1 1.11882\n", + " 3/1 1.14717\n", + " 4/1 1.17966\n", + " 5/1 1.15913\n", + " 6/1 1.20627\n", + " 7/1 1.18170\n", + " 8/1 1.18638\n", + " 9/1 1.17219\n", + " 10/1 1.18670\n", + " 11/1 1.16349\n", + " 12/1 1.20555 1.18452 +/- 0.02103\n", + " 13/1 1.13633 1.16845 +/- 0.02013\n", + " 14/1 1.17646 1.17046 +/- 0.01438\n", + " 15/1 1.16547 1.16946 +/- 0.01118\n", + " 16/1 1.19266 1.17333 +/- 0.00991\n", + " 17/1 1.16401 1.17199 +/- 0.00848\n", + " 18/1 1.18430 1.17353 +/- 0.00751\n", + " 19/1 1.19096 1.17547 +/- 0.00690\n", + " 20/1 1.16996 1.17492 +/- 0.00619\n", + " 21/1 1.21025 1.17813 +/- 0.00646\n", + " 22/1 1.16321 1.17689 +/- 0.00603\n", + " 23/1 1.17115 1.17644 +/- 0.00556\n", + " 24/1 1.22113 1.17964 +/- 0.00606\n", + " 25/1 1.22173 1.18244 +/- 0.00630\n", + " 26/1 1.20065 1.18358 +/- 0.00600\n", + " 27/1 1.16530 1.18251 +/- 0.00574\n", + " 28/1 1.19524 1.18321 +/- 0.00546\n", + " 29/1 1.21012 1.18463 +/- 0.00535\n", + " 30/1 1.17722 1.18426 +/- 0.00509\n", + " 31/1 1.22580 1.18624 +/- 0.00523\n", + " 32/1 1.22713 1.18810 +/- 0.00532\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 33/1 1.18152 1.18781 +/- 0.00509\n", + " 34/1 1.19174 1.18797 +/- 0.00488\n", + " 35/1 1.16542 1.18707 +/- 0.00477\n", + " 36/1 1.17726 1.18669 +/- 0.00459\n", + " 37/1 1.18762 1.18673 +/- 0.00442\n", + " 38/1 1.22714 1.18817 +/- 0.00450\n", + " 39/1 1.20317 1.18869 +/- 0.00437\n", + " 40/1 1.19103 1.18877 +/- 0.00422\n", + " 41/1 1.18732 1.18872 +/- 0.00409\n", + " 42/1 1.17655 1.18834 +/- 0.00397\n", + " 43/1 1.19444 1.18852 +/- 0.00386\n", + " 44/1 1.20193 1.18892 +/- 0.00376\n", + " 45/1 1.18119 1.18870 +/- 0.00366\n", + " 46/1 1.20957 1.18928 +/- 0.00360\n", + " 47/1 1.22192 1.19016 +/- 0.00361\n", + " 48/1 1.21260 1.19075 +/- 0.00357\n", + " 49/1 1.21932 1.19148 +/- 0.00355\n", + " 50/1 1.19490 1.19157 +/- 0.00346\n", + " 51/1 1.18269 1.19135 +/- 0.00338\n", + " 52/1 1.22270 1.19210 +/- 0.00338\n", + " 53/1 1.18729 1.19199 +/- 0.00331\n", + " 54/1 1.19737 1.19211 +/- 0.00323\n", + " 55/1 1.20254 1.19234 +/- 0.00317\n", + " 56/1 1.15489 1.19153 +/- 0.00320\n", + " 57/1 1.16295 1.19092 +/- 0.00319\n", + " 58/1 1.21431 1.19141 +/- 0.00316\n", + " 59/1 1.19051 1.19139 +/- 0.00310\n", + " 60/1 1.17480 1.19106 +/- 0.00305\n", + " 61/1 1.18701 1.19098 +/- 0.00300\n", + " 62/1 1.18980 1.19095 +/- 0.00294\n", + " 63/1 1.19358 1.19100 +/- 0.00288\n", + " 64/1 1.24258 1.19196 +/- 0.00298\n", + " 65/1 1.26457 1.19328 +/- 0.00321\n", + " 66/1 1.18476 1.19313 +/- 0.00316\n", + " 67/1 1.19036 1.19308 +/- 0.00310\n", + " 68/1 1.20020 1.19320 +/- 0.00305\n", + " 69/1 1.18108 1.19300 +/- 0.00301\n", + " 70/1 1.16760 1.19257 +/- 0.00299\n", + " 71/1 1.16753 1.19216 +/- 0.00297\n", + " 72/1 1.17831 1.19194 +/- 0.00293\n", + " 73/1 1.21037 1.19223 +/- 0.00289\n", + " 74/1 1.18342 1.19209 +/- 0.00285\n", + " 75/1 1.17585 1.19184 +/- 0.00282\n", + " 76/1 1.20600 1.19206 +/- 0.00278\n", + " 77/1 1.20947 1.19232 +/- 0.00275\n", + " 78/1 1.19107 1.19230 +/- 0.00271\n", + " 79/1 1.24353 1.19304 +/- 0.00278\n", + " 80/1 1.19198 1.19303 +/- 0.00274\n", + " 81/1 1.18376 1.19290 +/- 0.00270\n", + " 82/1 1.20239 1.19303 +/- 0.00267\n", + " 83/1 1.17231 1.19274 +/- 0.00264\n", + " 84/1 1.23129 1.19326 +/- 0.00266\n", + " 85/1 1.19862 1.19334 +/- 0.00262\n", + " 86/1 1.18867 1.19327 +/- 0.00259\n", + " 87/1 1.17876 1.19309 +/- 0.00256\n", + " 88/1 1.20506 1.19324 +/- 0.00254\n", + " 89/1 1.16997 1.19295 +/- 0.00252\n", + " 90/1 1.18842 1.19289 +/- 0.00249\n", + " 91/1 1.20587 1.19305 +/- 0.00246\n", + " 92/1 1.18877 1.19300 +/- 0.00243\n", + " 93/1 1.20260 1.19311 +/- 0.00241\n", + " 94/1 1.16801 1.19281 +/- 0.00240\n", + " 95/1 1.22211 1.19316 +/- 0.00239\n", + " 96/1 1.20895 1.19334 +/- 0.00237\n", + " 97/1 1.17232 1.19310 +/- 0.00236\n", + " 98/1 1.17738 1.19292 +/- 0.00234\n", + " 99/1 1.18179 1.19280 +/- 0.00231\n", + " 100/1 1.21148 1.19300 +/- 0.00230\n", + " Creating state point statepoint.100.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 9.2068e+00 seconds\n", + " Reading cross sections = 9.0835e+00 seconds\n", + " Total time in simulation = 6.2805e+00 seconds\n", + " Time in transport only = 6.2389e+00 seconds\n", + " Time in inactive batches = 6.3362e-01 seconds\n", + " Time in active batches = 5.6468e+00 seconds\n", + " Time synchronizing fission bank = 1.6112e-02 seconds\n", + " Sampling source sites = 1.2967e-02 seconds\n", + " SEND/RECV source sites = 3.0640e-03 seconds\n", + " Time accumulating tallies = 3.5000e-05 seconds\n", + " Time writing statepoints = 8.1710e-03 seconds\n", + " Total time for finalization = 4.0000e-06 seconds\n", + " Total time elapsed = 1.5534e+01 seconds\n", + " Calculation Rate (inactive) = 78911.5 particles/second\n", + " Calculation Rate (active) = 79690.4 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.19261 +/- 0.00231\n", + " k-effective (Track-length) = 1.19300 +/- 0.00230\n", + " k-effective (Absorption) = 1.19197 +/- 0.00160\n", + " Combined k-effective = 1.19231 +/- 0.00151\n", + " Leakage Fraction = 0.04087 +/- 0.00031\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "2 TRIGA_IPYNB.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/VHTR/VHTR.ipynb b/VHTR/VHTR.ipynb new file mode 100644 index 0000000..4688bdc --- /dev/null +++ b/VHTR/VHTR.ipynb @@ -0,0 +1,883 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "s8q2_PI6vJDd", + "outputId": "b4a99b1c-90b1-4484-bb73-ed358a0d9b2b" + }, + "source": [ + "# A VHTR geometry \n", + "This notebook can be used as a template for modeling VHTR reactors." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "ztdDaVCavN_I" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import openmc" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ran-jogzvQvC", + "outputId": "422e9494-2575-4625-c4c1-20a55a442142" + }, + "outputs": [], + "source": [ + "# Materials definitions\n", + "\n", + "fuel = openmc.Material(name='Fuel')\n", + "fuel.add_element('U', 1.0, enrichment=4.0)\n", + "fuel.add_nuclide('O16', 2.0)\n", + "fuel.set_density('g/cm3', 10.41)\n", + "\n", + "buffer = openmc.Material(name='Buffer')\n", + "buffer.add_element('C', 1.0)\n", + "buffer.set_density('g/cm3', 0.95)\n", + "buffer.add_s_alpha_beta('c_Graphite')\n", + "\n", + "IPyC = openmc.Material(name='Inner PyC')\n", + "IPyC.add_element('C', 1.0)\n", + "IPyC.set_density('g/cm3', 1.9)\n", + "IPyC.add_s_alpha_beta('c_Graphite')\n", + "\n", + "SiC = openmc.Material(name = \"SiC\")\n", + "SiC.add_element(\"Si\", 0.5)\n", + "SiC.add_element(\"C\", 0.5)\n", + "SiC.set_density(\"g/cm3\", 3.18)\n", + "\n", + "OPyC = openmc.Material(name='Outer PyC')\n", + "OPyC.add_element('C', 1.0)\n", + "OPyC.set_density('g/cm3', 1.9)\n", + "OPyC.add_s_alpha_beta('c_Graphite')\n", + "\n", + "graphite = openmc.Material(name='Graphite')\n", + "graphite.add_element('C', 1.0)\n", + "graphite.set_density('g/cm3', 1.7)\n", + "graphite.add_s_alpha_beta('c_Graphite')\n", + "\n", + "helium = openmc.Material(name='Helium')\n", + "helium.add_element('He', 1.0)\n", + "helium.set_density('g/cm3', 0.000166)\n", + "\n", + "b4c = openmc.Material(name='B4C Poison')\n", + "b4c.add_element('B', 4.0, enrichment=18.7, enrichment_target='B10', enrichment_type='wo')\n", + "b4c.add_element('C', 1.0)\n", + "b4c.set_density('g/cm3', 1.82)\n", + "\n", + "# Instantiate a Materials collection and export to xml\n", + "materials_list = [fuel, buffer, IPyC, SiC, OPyC, graphite, helium, b4c]\n", + "materials_file = openmc.Materials(materials_list)\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "OU_alkFfwVGd", + "outputId": "7c06ff69-2077-456e-b860-9b385465151f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Geometry definitions for TRISO particles\n", + "\n", + "kernelsph = openmc.Sphere(r=250e-4)\n", + "buffsph = openmc.Sphere(r=350e-4)\n", + "IPyCsph = openmc.Sphere(r=390e-4)\n", + "SiCsph = openmc.Sphere(r=425e-4)\n", + "OPyCsph = openmc.Sphere(r=470e-4)\n", + "\n", + "layers = [kernelsph, buffsph, IPyCsph, SiCsph, OPyCsph]\n", + "triso_mats = [fuel, buffer, IPyC, SiC, OPyC]\n", + "triso_cells = []\n", + "for i in range(5):\n", + " if (i == 0):\n", + " triso_cells.append(openmc.Cell(fill=triso_mats[0], region=-layers[0]))\n", + " else:\n", + " triso_cells.append(openmc.Cell(fill=triso_mats[i], region=+layers[i-1] & -layers[i]))\n", + "\n", + "triso_universe = openmc.Universe(cells=triso_cells)\n", + "triso_colors = {triso_cells[0]: (0.78, 0.2, 0.2), triso_cells[1]: (0.69, 0.89, 0.29), \n", + " triso_cells[2]: (0.29, 0.73, 0.47), triso_cells[3]: (0.12, 0.29, 0.42), \n", + " triso_cells[4]: (0.29, 0.73, 0.47)}\n", + "triso_universe.plot(width = (0.1, 0.1), colors = triso_colors)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iRZUbB1uqld6", + "outputId": "371a1ccc-3603-447a-8721-2c0090b2448c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Cell\n", + "\tID =\t1\n", + "\tName =\t\n", + "\tFill =\tMaterial 1\n", + "\tRegion =\t-1\n", + "\tRotation =\tNone\n", + "\tTemperature =\tNone\n", + "\tTranslation =\tNone\n", + "\tVolume =\tNone\n", + ", Cell\n", + "\tID =\t2\n", + "\tName =\t\n", + "\tFill =\tMaterial 2\n", + "\tRegion =\t(1 -2)\n", + "\tRotation =\tNone\n", + "\tTemperature =\tNone\n", + "\tTranslation =\tNone\n", + "\tVolume =\tNone\n", + ", Cell\n", + "\tID =\t3\n", + "\tName =\t\n", + "\tFill =\tMaterial 3\n", + "\tRegion =\t(2 -3)\n", + "\tRotation =\tNone\n", + "\tTemperature =\tNone\n", + "\tTranslation =\tNone\n", + "\tVolume =\tNone\n", + ", Cell\n", + "\tID =\t4\n", + "\tName =\t\n", + "\tFill =\tMaterial 4\n", + "\tRegion =\t(3 -4)\n", + "\tRotation =\tNone\n", + "\tTemperature =\tNone\n", + "\tTranslation =\tNone\n", + "\tVolume =\tNone\n", + ", Cell\n", + "\tID =\t5\n", + "\tName =\t\n", + "\tFill =\tMaterial 5\n", + "\tRegion =\t(4 -5)\n", + "\tRotation =\tNone\n", + "\tTemperature =\tNone\n", + "\tTranslation =\tNone\n", + "\tVolume =\tNone\n", + "]\n" + ] + } + ], + "source": [ + "print(triso_cells)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "9ZjOD3umK-RO" + }, + "outputs": [], + "source": [ + "# Generating TRISO particle lattice in cylindrical pin cell\n", + "\n", + "cylsurf = openmc.ZCylinder(r=0.6225)\n", + "maxz = openmc.ZPlane(z0=1.95, boundary_type='reflective')\n", + "minz = openmc.ZPlane(z0=-1.95, boundary_type='reflective')\n", + "\n", + "lattice_region = -cylsurf & -maxz & +minz\n", + "triso_outer_radius = 465e-4\n", + "spheres = openmc.model.pack_spheres(radius=triso_outer_radius, region=lattice_region, pf=0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "u7tWPGkaQaPP" + }, + "outputs": [], + "source": [ + "triso_particles = [openmc.model.TRISO(triso_outer_radius, fill=triso_universe, center=c) for c in spheres]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OIyhbji9Rvzr", + "outputId": "4f4e84a2-0b07-4318-c1b6-1b1f55a1a232" + }, + "outputs": [], + "source": [ + "vol_triso = 4/3 * 3.14 * triso_outer_radius**3 * len(triso_particles)\n", + "actual_pf = vol_triso/(3.14 * (0.6225) ** 2 * 3.9)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "2Z8EXBSgR_yq" + }, + "outputs": [], + "source": [ + "lattice_cell = openmc.Cell(region=lattice_region)\n", + "lower_left, upp_right = lattice_cell.region.bounding_box\n", + "shape = (4, 4, 4)\n", + "pitch = (upp_right - lower_left)/shape\n", + "triso_latt = openmc.model.create_triso_lattice(triso_particles, lower_left, pitch, shape, graphite)\n", + "lattice_cell.fill = triso_latt\n", + "\n", + "lattice_universe = openmc.Universe(cells=[lattice_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "8S3JIn-x9doF", + "outputId": "da1312f4-92c6-4100-97a2-66af845295f7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lattice_universe.plot(width=(1.4,1.4), color_by='material', colors = {graphite: (0.08, 0.09, 0.26)})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j3_6A6g-L_d4" + }, + "source": [ + "**Testing Pin Cell Embedded In Graphite Block**" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "t80buCTqL-84" + }, + "outputs": [], + "source": [ + "hexaregion = openmc.model.hexagonal_prism(edge_length = 0.85, orientation = 'x', boundary_type='reflective')\n", + "\n", + "outer_region = hexaregion & +cylsurf & -maxz & +minz\n", + "outer_pin_cell = openmc.Cell(fill=graphite, region = outer_region)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "ho_e-y7Cgi_q" + }, + "outputs": [], + "source": [ + "pin_cell_universe = openmc.Universe(cells=[lattice_cell, outer_pin_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "76wi7ezZgoHj", + "outputId": "55590ede-ff67-4a78-96d2-87d902b2c366" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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u4IlGufRy/fqrFYgwcdDoZQNQcoG3n9fGvQdf8v8Omwa+YGhDkahAfP9DFgLh5lakQCWrwYzp14uD110MP32CvsnCxT4Z7WKH+czClXvL+yYOXncxK/futJZAh/kPwgj+z4Kiqk147U9IiqSWA0Ea3f/QshmSzRi6DOPgdRf7P1HQa2zbePuw/b/zp08w/vplHF20kPHXL6v62334wPaiWpPgN7Q5jDdrzAYxQaJ0kKqVen9GW1IcsvinBwfEmr/BPuD2qRuf8QfjBtFmepJRC0dlZk7O0tE5WzQNPDjwt6NzNjXro54C0ZLi4HA4KuN8DjVQyd8AnhVw5PRsSRs0s7nKU+96puR5faO7PCdiSEbgUzfGP/9yHLzuYlYaFk1SUY1K6DBnWJKUpqNzlqOvZ5NsptGOxjHLHFD9vsyEPLeR/Q4tJw5ZmmmXn7GZvskri8bLm+PebBe6t2x4hr7JK4vvf9czqQuDJitBCOLnTUBROz1TWIO5EFnS/cBOL7Lh13/sZOaJ2VBhSIp65T1kMrdCRP4I+BTwC+A4sFYpdTSJY2dNFKuhNCPSu9hNf0GlCz1J34ItpNk3uit1ERg2Up51FKNcroK2HgCrxVUugzIravEtNKr1EFscjLkVH8HrPD0hIo8opQ4bm/0LsEwpdUpE/jMwBPx+3GNXS9pWQ1hGZFKZjbXwwtkfZHBpW8nIuRE2sDJFgeg6/yiXGeXQ7cwXfnWcfzQ0/0HfPzy53Q+PAjWFRpuJelgPSVgO/twKABHRcyt8cVBKHTC2/zbQl8Bxc4VOYLJNshqcGmDF7tJsyLTxEpTaSgqMTk68wdymexhhAyRQgBVk+MB2LgskRpmVmtP9AwxPbi+7PDDrI/TfjmxJIlphm1tRbvLCOuDrtgdE5HYROSQih44fP57Aqc2TXOWlHXOiVHCSVfvwVmvOwIrdV7Ji95UcOb2NI6e3lRRbxUWfE9hHzk1PtbFy78tFGZFpHDfY2CXs/9HMTJ+7PPY+sg5rZhrKFJE+YBnYO2kopXYopZYppZYtWrQoseNmsZwIS84xL8Tgxe8tQdYW+h16ef5JC4R5DlHRgpG0aDjik6VAZDG3AgAR+TCwGehSSv00geM2NN4ypLgoarrQpgyeSfRYUadcacel6TN54ezqnZdmObSNuF2cksLszQCFxLWUkpkakSTEYQLoFJF34InCTUDR/CAReR/wRaBbKfVK6S7SIUmVjRKlqEQ1vRyS8E/YplxBcf8Es3Q72DkKandednR6jV3CeiXUWmGZFLra0qzlGJwaYKxztqSAKimSilpk1XMytjgopX4hIp8BHmV+bsVzgbkVdwMLgF2FqVfHlFIfi3vsvKATnvTFENbIxIZtLHxSjktdFGWbchUMZ9qcl2bbtJGeNZGdl/2rCt2Y2FCy3PKLsuokDLoC06wk9d+vQu/LMUorLFuRTOZWKKU+nMRxqiFr5025zkI6ecfMbfAzKKfaSkbSJZnsFKyaBFjQOcs7f/pEqCVgOhB1hME2I6IcOtowPLm9+P5FG+tqMcw3bintzaBf7/RUG+3ntVnLt/O0DEk7vOlqKxwOh5WWS59Oi6dufIYVu3cWpUrDvBltWyr46dVGw1JzO1vkwrYf7UgMopcNuq9DVKI6MKOQt/wE/d60l9km2CLfXIpMT813pxpcuraof2Sz0ZTikPSSIqoz0lYXUakmQj/Hu136ePCiX7EbDhpd4rQw2NrRDU4NsHJv9M5R2okIpeXl6xNsLddImEsJ03mp/RUjQxugCv9E0qnUaS4tmk4csvI16FRpE/3Nn2UmpCkMts5Jg/3RBEJXYwZDkNp5GbUhre4BGWzfXqsFYe4vyr7Meg6N6T/QjuFy1Z9mi3wob22E+SeyJC2BaDpxyIIjp7f59QoaPUptxe5ki6ZMgs1h/AgD9s5JlVquB7E5L9dP7qxKGHQ4VP9vdJVluXqKcvvTPSXnNm0tjNmbpW/Unnqttw+25xucmm8fr/+HlVrkm92nW5WmEocsrAZb8hLg1wzE2a+JtkBW7Kbk/tOnopv3/tSpiD6HoH+imqWELU9C345ST2GiL3TbuLrBqQG2HNhe0tHaFp40LSjTP6Db0pst8m39GXQLe/0aKrWxrxdpWA9NJQ55oJYMR71EMR2ZR057bejjWiFZ+Qs830fpFCn9bVxtc1jb0F1zf1G3LwrHGk5G3ZshaCXZ+jNoayPYhKdSo5dGp2nEIS2rIYnMyHKYwhBMpQ72gAhy8LqLeeHs8slXWaIH5SaJLWpSTmjKjeYb6VlT5DycOTlb/HfIOfj+BN3sRd9fY6OXtPo7JG09NEWeQ9YJT7YmsKZJWi22ITYQrftz3+guFhS+/YKNT5MeYFOOg9ddXJQFap6HPpdaqIfIlWPm5Kz/k0eSvBaaxnLICu0HCDMxk3JGRs1K1FGGEUq3zTr8WK7gqpaJVcHU8nLt4vpGd3HvJnuOhm4fv35yZ9lvem0VVLrwg5mS+vh5FYxaaXhxqEfrbmufx8naoxRmfoGmmsrFsCSnrPMS+lcVpmGH9MysRhh0o91g8VZYu7j+VRtZMHSUuU33hBaZhTkO9cU+WDjnewvb2+orbAVbUBwRqTdJLS8aXhzqSS1iYItKrNg9H/LTpNVt2symNJdASQlJsKZCX8TV5jnYircqTdLqK1hQ01NtfhSiUodo82IPdvCyFWDZHJ9hEZFGpyl8Dg6HI3kaepBuFkuKJKMVR05v82+b3Zg96+HKkm/0MMsk6pRtE7MDdTBWDyTiuNz57zdX3qhGhg9UlyNh/i/Dvsm11RA2PVs7dPXzK20PWAfq2siiG3WUpYUbpJswemlQzbLCu/iLZz3Ot4arXGNhHvdg9drgp1mb2ZR+/B8qplnbiruyaG+vqWZZYjan7V+1EcpM2Q7rVnXe8nNgYmvk2olaMlLzTiLLChHpFpHvicjzIvJZy+Nni8hXC48/KSIdcY9ZzxmCtdZPmMIQ9qGslii9Hlfufdk/tq3RrD6fsNCp195+LXOb7in6GVy6lhfO/mACryJd2s9r839M9OtNogI1j8IQ9xqJLQ7G3IqPAkuAT4jIksBm64DXlFK/BtwDfC7OMes9fbhWzDyAWuL+QVEyRaGSQJT7hix3cWhhsXXWbh/eytyme6oSiOED2+k6/yhd5x/1i6qiPs/8qeZ5MC8Epkjo5VvUC9tcntiek9a07TjEuVYymVtR+PvPCrd3A/9bRETl1eERk9KJV8X5ESb1HHgTBe2dL5d5OD3VxjsXl9+P9gOYg24qDbgJPrd4Yld0H4T+n5uVnRpbmz4Iz43YMlnadk/vR4dLtfjkqWtULSQhDra5Fe8P26bQc/IkcAGBjFURuR24HWDx4gqftox46Qt/HHnb3kX7rROv3rzjBAB7gN7Ah3PP8Wt46QvVqXv7u3RWRLG18PMnLy3duLBd2EUQxNzHVa8+6Vd9VmLl3pcZPzwe+njX5nmfh8YccDN+OHyuQ9fmNVZfzaXbwo+nGQ7asAVuPvg1VgNbuj1/i1mApenonGX19vuLuiXr59ia+nzp093AmJ8vYWte620DXPGOiudeb3LlkFRK7QB2gBetqPPpVEXvov3WePl0/wAdtzMvEMeTWxI9fkFQg8O3O9ZZWoNha+gSdZ9R6VrieeUHp97r/1+CDtGRoQ1weDr0+SN0APNLI70k61oyUVZUAMYPL6eLeUFePXZ/0eOrx+6vcLGXsnrsfr7E/ezrXuf//Tiwr3sdV1kmjAF+89pbh8ZC95s3sppbobd5SUTOBM4DXq31gMd/vD93foewqkAmvG/HLYt2JSoM1aIThHQGocYMZQaFwRSVSjx+wftR/EfF7ap1/o0fXs6LPcXnaj4WhaAg2B63XezV7jfqEmxf9zoW8M1I5x6XOJmSSUQr/LkVInIW3tyKRwLbPAJ8snD7RuCbcf0N9RhJXg5bNEBTLhLQu2g/vYvSfy2PX/B+Fj9+jPUrL2HB0Ab/Z8vkTqswaPpGdzHTP2B1wFVqu2+jFq9+3+iuomPUWuBWidVj91cUknKETT2rF3GvkazmVtwPPCgizwMn8ASkaehdtJ9B1lb9HC+f33veYbwPvV5+pMXjF7wfHj9W/HeF7Y9ZGqPAvBNusbG/IPrb/cWe8v0pxwm3AoJLAyajWw1R0VYDVLY0WoWs5la8ASQ+ObUeywvbt/ye49dwyrKm1+gLYA/X+M83k5I0Qf9EXlj8+DH6Xn2yqDEKeFWfj/8wmo/CrHswfQcdnbOMf63yhR5HDMwLX2MKwL7udUUZlfu619UkEMEpX2FLqNVj9/N/U3ZIJmFZu9oKh8NhJVfRiryjlwK6LsJnx4myE686Omdh0rutv6F016Sg5z4PzksbweWIf19EzKWBtkDWT+6MZDXY6FoyUZL7AHDptumy22k6vrjGGjWw5ULYMJ2XmmA/C21FBpvvrI74GutNw4tDVksLUxiAIoHoXbTfX1qYTUjNcuHgxR7MOTB7HY4MbYCcLS2SwF8aFMKW5fwM5XhxcweDU+8tChtrf8aLbKBvdJcnRoYwBP/f0/0DNS0f9BLk1kIEhS96YqKdmX7eBFjnk67enr4/IylnfcOLQ554844TbFm0C1ZewkjPGs/PcOAa9mAXr2rCetpXkTeLImte3NxRUkCmOTnxBnOb7vG6YhUEKKwFH3hWwpfwLlbdXKaS1WB+QejJV+Z+tEAA/ufA2+fOTIQhSZpCHLKwHvYcv4bewgeoZFkR2A6AHSdCRUGT5Ni5ViG4HDMxcwn6llQuie4b3cXqT88vDfq6y0cq9nWv49aeYqtPLwVNK8Tcx5e4n9Wfjvrq4uMazFpIK+/h1p9vLvpbx9z7Rnf5P9V8m+vn6IQeW1PYvBTvXPXqk1z16pMcu2oxV736ZE376FoyUfITZdsXN3eUbFvuuWHYmgHr+4NEWWIkkcuQVqTCza2oI9VYBZX20Vso4IF581S3QbOJTdbLiatefdK3kqanPHP6qtFdVTkhtW+g5P6eWd8voNH+AZORnjV0savmMKZ2gprNgM2elrWGK4PDbUyHczPRVOKQx7RqjS0/YstkoYnIykuA8v6JeqOLnYIRizBe3NwRut6fgyK/QPA4muC3u5dKHS2dWwuKLYEKakt0Wj12P33d3rRt0xpZ0Dlb98QpNyuzAdEFWYMUj3bTlkLeEp6SoGvJhFdoZWmnBhT5BYJWQaVUbB0u1NmawSIyHS40IyHBhCeAW784VrLfKBe4X6hlJE01mqMxKk0nDllaD6Y1YDP7tTCYVXrmeLi8ZkSamBdrX5XLinIEqzFLvuELBMXD2658T4Ww3AkzE1JPJteMsKGiQ1KjC7WATJ2NYaTlb2voBrPlSFogvvTL24r+1nkPJkHn5KnbFzK36Z7QiITZxLQan8J8P4d0CTohKwmD+pZXldm1ZIJ7D74E2KMKZjPWYNJSVLRzMtiU1+af2HZsPeBZC0FRMFkwtCH1cuqknZFxhcE1mE2YYEKUZqRnDb2GQKQxOzJLolgJpoCYpnyl5jJRCq7KUUtCVfD9CC5JdDl1Nf6DYN1GvX0PSdI0oUyHw5EsTWs55DlyYRIcDd8o6FDn4NR7/Pu+tdSLwJhRBdMnYPpcolZjBjFzHaoJcXpdmqo+XNn9jfSs4aqlxdbjloh+iyRIu6dJ04pDHrDNwAzihcQqz0XIC3oZYSs5B6+uoIudMDpRNMpOOw/NepNqlhQ6D0JHQWb6B3wxgspCsXrsfjq+uKbo/bAVyEWJPGhhCHv9On260ZcYscRBRBYCXwU6gGng40qp1wLbXAl8AXgLcBrYppT6apzjRkUraxIWxK0/31zilNSefHN6lelYNMNuYROSOjpn2XMg/xaOJujtL3E4TngzIz/0te/A4Wk/jTlYiVmtMAT7c5rDeAC62FlRIPz3g9LCN+0DiRp9sKVxFxXOgR/R0CTljMyqC1pcy+GzwH6l1F2FYTafBf4ksM0p4Fal1JSIXAw8LSKPKqVej3nsuqHrLIqYLA1nmpWatu7GC4a8CsK8Jj5VolxdiG7+mkQlZmh/TvD/r4P9A3D4O2X3YxZFBSeBr5/cGfmb3jwfE7O2oxmIKw7XA1cXbv8d8BgBcVBKHTFuvywirwCLgNdjHjsyafgfooYedaWm/jAG8wZavcoyCuWKrUyidKPWAlDUUHZ7tsVRcciyd2pccbhIKfXDwu0fAReV21hEVgBnAS+EPJ7a3IokBMK2tIjCnuPXwI4TLOEEHDDub1CLoVmo1ScQbOqiCTbcNTtYJ7GkyLqpckVxEJF/Bt5meaioXFEppUQkNKNKRH4FeBD4pFLK2sO8kedWJEEwsSqPloVONrL5UuJGIsKI4tjNEtO5GXRqat8F5CN7Mg4VxUEp9eGwx0TkxyLyK0qpHxYu/ldCtnsL8E/AZqXUt2s+25jkObxpDsXRDE4NcKrwLZSXFGudGBXWkVr7UWpNbrIR5tg1L8ykBSnKOZnRGH0O1fguqqEeoxhipU+LyN3Aq4ZDcqFSalNgm7OArwP/qJT666j7jps+HUYS4lDL0qIcwRoME3PojLYiskqfroTOddAEy7BtRPEL2Air8jT/P2H71enTaRClpX2elxRppk/fBXxNRNYBR4GPFw64DFivlPpU4b6rgAtE5LbC825TSj0T89g1kWfrAeyhMSCXfSWDTWejXPS19ma4dJsXFrVFGeLsNy5Z5DLUa4BTLHFQSr0KpV41pdQh4FOF2yPASJzj5I1aHZOOeIwfXg6Hp4tmLya5fEmDtOdTpImrrXA4HFZaMn06z0sL26wDwJ+Y5QinFn9G3qsq6zkTtiXFAbKdd6EJC0sGZ14EvfBAQ6VYZ8mLmzsAL1FqkPfyraXzg3YrCYUWhmBPiLxQ72HRLSsOcYnidzh1+0J/UC7AKWM6dFAo9NyEaaM3oTlN2lkNpegZFkBRAdQcXhg4Sr2FGW0Z6VkTuRtUFBrZ3wAtLg5pWg+6C5TtQwte92lTIHQW5ZZF899eeW44W2+6lkxw76ZR/+/QAihLE9tasY3AS4t6Ww3Q4uIQFz3TwtZCbnBqbWjVHoSHJtPMiDS7NiXVC7JejPSsYQ57C77gcJuw2ZS6m7Q56Sp44Zt9J3Xvhg5jBJ6NRrcYNC0vDmlYD7pqL/jBNR2N01NtmQ7MDSYsVTuDIm9EacEXpZGObhZrS3Xe172uKGvVnMs5ODWQWmOXPFgN4EKZiRCcipVH5gfUtPkzKGqdYpUHKrWwB6yTrqphpGdNUUbmecvP8QW/fXgrg0vXlkQ7msVqAGc5AMlVbP7hn34OgHfPePdFmYX59P+o/i1YfuQubhhfVZK6/FDXgTLP+rz13hNL77Lef8P4KoAK+yzmnMv/LfK28fHy6rQlFvZ/Hlv5MG869faq997V281VS+2l4qYFCLC5sP/f/cBZVR8nSF6sBnCWg0+Sb0pwFqYmGKLc3H9tTfu/YXxVwdy9x/8ZXLqWuYV2AdDnFOXbVu/fdjtP9I3uYkGh4xIUzxw1W/43EnkSBnCWQ6L8zZ//CX/4p59jc/+1bBm2D17RH+i+0V0l4rBt+NGSmLttm+CgHAAmtnplzUOeQCw4cYf1HM1ZkdVYBXljbOXDdI8WxupROkZv/eROxlY+XPP+x/eM8eKDxWXZwVb2JklYDXmjaYfaxCHuEkMvL8yLHeyCoNEXvYmZ56Cf9+6Zt4T2b9TfmOBVKdou/ijLhaC1EFVEzvlZlsuKYroPXu/fDorCm+48u6Z9dvV2l62W7eic5dJb1sUWhnpaDOWqMt2ywuFwWHHLihTZ3H8t3+Xfiv62EVwqaKb7B3zLw9wPVO6nGEYUK6ARlxtxlhBhjO8Zo6t3vnN2cOly6S3rwp7aFDhxsBA3eqF9D3FpH94KhtMtS2pdWqSFXjakIQLlGN8zBngTuft65+dojm/17mvkJUUlUp9bYWz7FuAw8JBS6jNxjpsFeRGIMCqFSeNczDpMan5Tzi1cE+rkTJOZt414RVVL9d9edmLWIgFaKOZpZmGA+D4HPbeiE9hf+DuMv6C4Ia+jArYkHjOEFxa+SyKEZ06q0mQd1tTC0D681f/RYVvTAelIh7g9JL8HXG00mH1MKfXrlu1+C7gTzz5bFsVyqGe0wiSpyEUl3j3zlqIL0hwbB8X+Ch0FCQ5PCYtQmNwwvqriNnMLP1/Sr1ELT7nnJhmt6D54vTVkq8OIC4Y20P6jvkj7qjVaUY5msRrS7CFZcW6FiPwS8FdAHxDaybqwbWpzK+pF1OXFd9v/jW2Fsm2GNni9ESftTsyHug7AiQNsmVxVdN9DXfZ9m8uEwaXeEqFcnkPf6K6isGrcNORaCJtyVVRUNXl91cuLLsNvYOaUBJcM5WjGnAYbWcytuAPYq5R6SUTKHiuPcyuSSK2OKhBmdCNK9mQUv8J8NuXWksKhcn6EDt163vj7oQsaL4ph0tXb7QvC9FSbLz6DUwO8+OCaSCLRbCnS5chibsVK4EMicgewADhLROaUUuX8E01HNQ7KbcOPAtEEohxaGNqHSytEmdjKdP+Ab32YQvNQ1wFuKLRK8MuZJwm1TNIgylSpsbbqrAY9GRuKm8O0D3vZpV62Zbg4tIrFoInrkHwE+GTh9ieBkndLKfWflFKLlVIdwEbgS40mDEkp/d/8eXDGcClaGIK3g9wwvoq5hZ/3f8KchbbsPvDM8/bhrYz0rCnK4tR4y5QDLDhxh387S8ZWPuyneptOV/CWOdW2dOvq7S7y0egKS7PScnqqrWjZofndD5yVmDA0itUA2cytaAqS6vugBSJOmNNcKmgGpwa4YbzYAijXECVtkshLsE2VAs/xWu1+wyZjVyJJa6GRhAEymFsRuP8B4IE4x2wWoiwzbMsKW+GV7jA12O8JBBSLRJTS8TwytvJh+NHD9E1eX3q/hWB4M24uRKstI4K4wqsqSbprVLUWRFjhlRniA68qs5zPIVjWnMSyoZ6FVzonwkRbGG+68+ySIirb/26mf4APbf04kLww5NVqcIVXDoejalxtRZ2pNs1a904M606kvz2XXuBZA3ML11inYUN14claOkNlwczbvI5QtiG7g/0DdB+Eccb8/gx+eDbw/9BW1DhuOaFx4lAlaTSkTbMOY8GJO+jo/HxRzgJ4whC1VkILw0jPGj/EmQeR8LIoi9O8zUxKvz19ITx56S3r4EGvIWwwh2P95E7G94ylIgx5XVJUwolDTogqEB2ds353Itu62W8FZ3S9X3DijqJsSqDqhKZguDNKGnbWhGZS9nb7yU26zDpYYTl+i7MYgjhxqIG0huFEEQgzOSjYrmymf8AfSR9MWEr6Qs6bMFRL0hWWYTSq1QAuWhGLNGdtlhMJs/AqWKSVxkVrS7CyHace0QrtczAjOMGeneWasqRpLTSCMJSLVjhxiEHag3jDBGL5ES/B1Lxos/4mN/0QOluxHj0WdG6DrZOW72QMqZdIexnhxCElGkEcNFlM6zaFQotDPTBFIdg2rV5NWMATCfOczPMxS7az8Cs0giho0izZdmRE2p2lomAO0rENCR5hA/yoWBzMrMU0rQwzm9K7DWMri7dxDsfqcOKQAGlO6zbRdRkjnyhf+p4mfkOakMfMHgu6YQvoysd7uHeTZ+53H0xnGWLbZ5ai0EhWQyVchmQD0veV/CwFw2o2zE5OwRwEPWcyi1ZvN/5DW+WNHFac5ZAQWVkPGi0Q9bQiwD79CcpPGgdgopCg9KN0fBT1EIVmshrAWQ4OhyMEJw4JUo9vjr6vqMyWGQ91HaCjc5aOztmSvpI612Js5cN0H7y+pELSRpRtquXGf2hzVkNCZDK3QkQWA/cBlwIKuE4pNR3n2I5iggKR1nJDRxtGetaU1Cd4ocPq9td9sPomsSbOp5AecVvTDwEnlFJ3ichngbcqpUp6oYnIY8A2pdQ3RGQB8B9KqVPl9t1IeQ5BsvQ9VCJNn0QwCcvMkJx52whzm8K7UJ2ceKOq9vJB8iQKjWw1pJnncD1wdeH23wGPAUXiICJLgDOVUt8AUErNxTymowpMiyJpoSiXlalrQMzSaDO1WdeBVGNp5EkQWoG4lsPrSqnzC7cFeE3/bWxzA17LuJ8B7wD+GfisUup0uX03suUA+bIebKRhUQRrK4J5Dhqd1hxlOZF3QWhkqwFipk9XmFvxd6YYiMhrSqm3Bp5/I3A/8D7gGJ6PYq9S6n7LscyhNr919OjRsueWd/IuEEHiCoat8KqaDMm8C0GQRhcGSLG2Iso4PBH5APA5pVRX4e9bgA8opf5LuX03uuUAjScONqoRjGqqMhtNCGw0uzjE9TnouRV3ETK3ApgAzheRRUqp48DvAI191Uck68SoNKguTNr4F3xUmkEYKhE3z+Eu4CMiMoU3B/Mu8OZWiMh9AAXfwkZgv4h8BxBgOOZxHQ5HyriS7QxodOvBUUwzWQ2uNb3D4agaJw4Oh8OKE4cMaCYztNVppffSiUNGtNKHqllptffQiYPD4bDixCFDWu2bp1k4/uP9LfneOXFwOBxWnDhkTKt+CzUqrfxeOXFwOBxWnDjUiVb+RmoUWv09cuJQR1r9w5dn3HvjWtPXHfchdOQVZzk4HA4rThwcDocVJw4Oh8OKEweHw2ElljiIyEIR+YaITBV+vzVkuyEReU5Evisi/6vQqdrhcOSYuJbDZ4H9SqlOYH/h7yJE5LeBDwJXAEuB5UBXzOM6HI6UiSsO1+MNs6Hw+wbLNgo4BzgLOBv4ZeDHMY/rcDhSJq44XKSU+mHh9o+Ai4IbKKUOAgeAHxZ+HlVKfde2MxG5XUQOicih48ePxzw1h8MRh4pJUBWG2vgopZSIlHSrFZFfA94NXFK46xsi8iGl1LeC2yqldgA7wGswW/n0HQ5HWlQUB6XUh8MeE5Efi8ivGENtXrFs1gN8W8/IFJGvAyuBEnFwOBz5Ie6yQg+1gfChNseALhE5U0R+Gc8ZaV1WOByO/JD6UBtgN/AC8B3gX4F/VUr9Y8zjOhyOlIlVeKWUehUomdiilDqEN1lbT7z6dJzjOByO7HEZkg6Hw4oTB4fDYcWJg8PhsOLEweFwWHHi4HA4rDhxcDgcVkSpfGYpi8hx4Gi9zyMm7cBMvU8iQ1rt9ULjv+bLlFKLbA/kVhyaARE5pJRaVu/zyIpWe73Q3K/ZLSscDocVJw4Oh8OKE4d02VHvE8iYVnu90MSv2fkcHA6HFWc5OBwOK04cHA6HFScOCVJFq/7TIvJM4eeRrM8zLiLSLSLfE5HnRcTWcfxsEflq4fEnRaSjDqeZGBFe720ictx4Tz9Vj/NMGudzSBARGQJOKKXuKnyI3qqU+hPLdnNKqQXZn2F8ROQM4AjwEeAlYAL4hFLqsLHNHcAVSqn1InIT0KOU+v26nHBMIr7e24BlSqnP1OUkU8JZDskSpVV/o7MCeF4p9X2l1M+Av8d73Sbm/2E3cE0DDzKK8nqbEicOyVKxVX+Bcwot+L8tIjdkc2qJ8XbgRePvlwr3WbdRSv0COAlckMnZJU+U1wvweyLyrIjsFpFLszm1dInVJq4Viduqv8BlSqkfiMivAt8Uke8opV5I+lwdmfGPwFeUUj8VkU/jWU2/U+dzio0ThypJoFU/SqkfFH5/X0QeA96H14S3EfgBYH4zXlK4z7bNSyJyJnAe8Go2p5c4FV9voZeq5j5gKIPzSh23rEiWiq36ReStInJ24XY73hzRw8HtcswE0Cki7xCRs4Cb8F63ifl/uBH4pmpcz3fF11v4ItB8jGYZvaCUcj8J/eCtq/cDU8A/AwsL9y8D7ivc/m3m2/R/B1hX7/Ou4XVeh+fBfwHYXLhvK/Cxwu1zgF3A88BTwK/W+5xTfr1/CTxXeE8PAO+q9zkn8eNCmQ6Hw4pbVjgcDitOHBwOhxUnDg6Hw4oTB4fDYcWJg8PhsOLEweFwWHHi4HA4rPx/DidQIxkXzooAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pin_cell_universe.plot(width=(1.8, 1.8), color_by='cell', colors = {outer_pin_cell: (0.08, 0.09, 0.26)})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jJasUqJAhn_e" + }, + "source": [ + "As expected, the outside hexagonal block and the non-triso area of the pin cell are both filled with graphite:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "SnF1LEnihncs", + "outputId": "f9671980-db4b-4ce0-99a4-867aa59fe5d9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pin_cell_universe.plot(width=(1.9, 1.9), color_by='material', colors = {graphite: (0.08, 0.09, 0.26), fuel: (1, 0.86, 0.57)})" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Mp8W0NWEhzhZ", + "outputId": "2204e776-fed4-4007-a05d-cba6acb11789" + }, + "outputs": [], + "source": [ + "geom = openmc.Geometry(pin_cell_universe)\n", + "geom.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OKwVl93-GboP" + }, + "source": [ + "**Creating Coolant Channels/Cells (Helium fill)**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "lLnEBbE3aJqa" + }, + "outputs": [], + "source": [ + "small_coolant_surf = openmc.ZCylinder(r=0.6375)\n", + "big_coolant_surf = openmc.ZCylinder(r=0.79375)\n", + "\n", + "small_coolant_cell = openmc.Cell(region=-small_coolant_surf & -maxz & +minz, fill=helium)\n", + "big_coolant_cell = openmc.Cell(region=-big_coolant_surf & -maxz & +minz, fill=helium)\n", + "\n", + "outside_small_cell = openmc.Cell(region=+small_coolant_surf & -maxz & +minz, fill=graphite)\n", + "outside_big_cell = openmc.Cell(region=+big_coolant_surf & -maxz & +minz, fill=graphite)\n", + "\n", + "small_coolant_universe = openmc.Universe(cells=[small_coolant_cell, outside_small_cell])\n", + "big_coolant_universe = openmc.Universe(cells=[big_coolant_cell, outside_big_cell])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2wVWLYBZAJdL" + }, + "source": [ + "**Burnable Poison Rod Geometry**" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "id": "aJnsdDgr7KeF", + "outputId": "f73b7161-dd64-49db-cd48-cde093cb4af9" + }, + "outputs": [], + "source": [ + "poison_surf = openmc.ZCylinder(r=0.6375)\n", + "poison_cell = openmc.Cell(region=-poison_surf & -maxz & +minz, fill=b4c)\n", + "outside_poison_cell = openmc.Cell(region=+poison_surf & -maxz & +minz, fill=graphite)\n", + "\n", + "poison_universe = openmc.Universe(cells=[poison_cell, outside_poison_cell])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FSEppy7-wB1-" + }, + "source": [ + "**Defining Material Outside Pin Cell For Use in Assemblies**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "ijZDqnyY7Y2f" + }, + "outputs": [], + "source": [ + "outer_pin_region = +cylsurf & -maxz & +minz\n", + "outer_pin_cell = openmc.Cell(fill=graphite, region=outer_pin_region)\n", + "\n", + "total_pin_cell_universe = openmc.Universe(cells=[lattice_cell, outer_pin_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "s-DGB0Nf8guO", + "outputId": "355536e5-e765-4900-9c97-e2ed00503f8f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "total_pin_cell_universe.plot(width=(1.9, 1.9), color_by='material', colors = {graphite: (0.08, 0.09, 0.26)})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LfIatUoiIeSa" + }, + "source": [ + "**Constructing Geometry of Hexagonal Fuel Assembly**" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "gxixGDvIIXnH" + }, + "outputs": [], + "source": [ + "outer_graphite_cell = openmc.Cell(fill=graphite)\n", + "outer_graphite_universe = openmc.Universe(cells=[outer_graphite_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "0lt1V5rQJFlY" + }, + "outputs": [], + "source": [ + "assemblylat = openmc.HexLattice(name=\"Assembly\")\n", + "assemblylat.outer = outer_graphite_universe\n", + "assemblylat.pitch = (1.9,)\n", + "assemblylat.center = (0.0, 0.0)\n", + "assemblylat.orientation = 'x'" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "6NyajaT_Dhdf" + }, + "outputs": [], + "source": [ + "def repeat_lat_element(n, pad_left, pad_right):\n", + " ring = []\n", + " for i in range(6):\n", + " ring += pad_left\n", + " for i in range(n):\n", + " ring.extend([total_pin_cell_universe]*2 + [big_coolant_universe])\n", + " ring += pad_right\n", + "\n", + " return ring" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "eMfy_09gPIjE" + }, + "outputs": [], + "source": [ + "def build_outer_ring(n, insert_poison_rod):\n", + " REPEAT_SECTION = 2\n", + " ring = []\n", + "\n", + " # burnable poison rods sides\n", + " poisonring = []\n", + " poisonring += [insert_poison_rod] + [total_pin_cell_universe] + [big_coolant_universe]\n", + " for i in range(REPEAT_SECTION):\n", + " poisonring.extend([total_pin_cell_universe]*2 + [big_coolant_universe])\n", + " poisonring += [total_pin_cell_universe]\n", + "\n", + " # normal sides\n", + " base = []\n", + " base += [outer_graphite_universe] + [total_pin_cell_universe] + [big_coolant_universe]\n", + " for i in range(REPEAT_SECTION):\n", + " base.extend([total_pin_cell_universe]*2 + [big_coolant_universe])\n", + " base += [total_pin_cell_universe]\n", + "\n", + " ring.extend(poisonring * n + base * (6 - n))\n", + "\n", + " return ring" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "vygU8F5vLG2x" + }, + "outputs": [], + "source": [ + "def build_all_rings(insert_poison_rod):\n", + " rings = []\n", + "\n", + " outer_ring = build_outer_ring(2, insert_poison_rod)\n", + " rings.append(outer_ring)\n", + "\n", + " for i in range(1, 8): # rings 1 through 7\n", + " ring_left = []\n", + " ring_right = []\n", + " if (i % 3 == 1):\n", + " ring_left = [big_coolant_universe]\n", + " ring_right = [total_pin_cell_universe]*2\n", + " elif (i % 3 == 2):\n", + " ring_left = [total_pin_cell_universe] + [big_coolant_universe]\n", + " else:\n", + " ring_right = [total_pin_cell_universe]\n", + "\n", + " n = 0\n", + " if (i <= 3):\n", + " n = 2\n", + " elif (i <= 6):\n", + " n = 1\n", + " else:\n", + " n = 0\n", + " \n", + " cur_ring = repeat_lat_element(n, ring_left, ring_right)\n", + " rings.append(cur_ring)\n", + "\n", + " inner_ring = []\n", + " for i in range(6):\n", + " inner_ring.extend([total_pin_cell_universe] + [small_coolant_universe])\n", + " rings.append(inner_ring)\n", + " rings.append([outer_graphite_universe]*6)\n", + " rings.append([outer_graphite_universe])\n", + "\n", + " return rings" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "KAqeuKY2j19E" + }, + "outputs": [], + "source": [ + "rings = build_all_rings(poison_universe)\n", + "assemblylat.universes = rings" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "LZWflaDwla6d" + }, + "outputs": [], + "source": [ + "assembly_surf = openmc.model.hexagonal_prism(edge_length=21, orientation='x', boundary_type='reflective')\n", + "assembly_cell = openmc.Cell(fill=assemblylat, region=assembly_surf & +minz & -maxz)\n", + "assembly_universe = openmc.Universe(cells=[assembly_cell])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "iMr7NMMTs6kT", + "outputId": "a1ece088-efa8-4cc1-da2a-258fbe0ac6d6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "assembly_universe.plot(width=(42, 42), color_by='material', colors={helium: (0.99, 0, 0.27), graphite: (0.08, 0.09, 0.26), b4c: (1, 0.85, 0.1)})" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "pIFgdKfmyY7b", + "outputId": "b3e9bf24-0450-47ad-9430-023413ec58f6" + }, + "outputs": [], + "source": [ + "geom = openmc.Geometry(assembly_universe)\n", + "geom.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "id": "RUFinDv9-4AH" + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "\n", + "batches = 100\n", + "inactive = 10\n", + "particles = 5000\n", + "\n", + "settings_file = openmc.Settings()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "settings_file.output = {'tallies': True}\n", + "\n", + "bounds = [-5, -5, -0.63, 5, 5, 0.63]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings_file.source = openmc.Source(space=uniform_dist)\n", + "\n", + "settings_file.export_to_xml()" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "VHTR.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/VVER/VVER 2.1/problem/geometry.xml b/VVER/VVER 2.1/problem/geometry.xml new file mode 100644 index 0000000..d9aef56 --- /dev/null +++ b/VVER/VVER 2.1/problem/geometry.xml @@ -0,0 +1,111 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1.275 + 900 +
0.0 0.0
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0.0 0.0
+ 909 + 909 909 + 909 909 909 + 909 909 909 909 + 909 909 909 909 909 + 909 909 909 909 909 909 + 909 909 909 909 909 909 909 + 909 909 909 909 909 909 909 909 + 909 909 709 909 909 909 709 909 909 + 909 909 909 909 909 909 909 909 909 909 + 909 909 909 909 909 808 909 909 909 909 909 + 909 909 909 808 909 909 808 909 909 909 + 909 909 909 909 909 909 909 909 909 909 909 + 909 909 909 909 709 909 909 909 909 909 + 909 909 909 909 909 909 909 909 909 909 909 + 909 909 808 909 909 808 709 808 909 909 + 909 909 909 909 808 909 909 909 909 909 909 + 909 909 909 909 909 909 909 909 909 909 + 909 909 909 709 909 909 909 909 909 909 909 + 909 909 909 909 909 909 808 909 909 909 + 909 709 808 909 909 505 909 909 808 709 909 + 909 909 909 808 909 909 909 909 909 909 + 909 909 909 909 909 909 909 709 909 909 909 + 909 909 909 909 909 909 909 909 909 909 + 909 909 909 909 909 909 808 909 909 909 909 + 909 909 808 709 808 909 909 808 909 909 + 909 909 909 909 909 909 909 909 909 909 909 + 909 909 909 909 909 709 909 909 909 909 + 909 909 909 909 909 909 909 909 909 909 909 + 909 909 909 808 909 909 808 909 909 909 + 909 909 909 909 909 808 909 909 909 909 909 + 909 909 909 909 909 909 909 909 909 909 + 909 909 709 909 909 909 709 909 909 + 909 909 909 909 909 909 909 909 + 909 909 909 909 909 909 909 + 909 909 909 909 909 909 + 909 909 909 909 909 + 909 909 909 909 + 909 909 909 + 909 909 + 909 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/VVER/VVER 2.6/problem/materials.xml b/VVER/VVER 2.6/problem/materials.xml new file mode 100644 index 0000000..92bce5c --- /dev/null +++ b/VVER/VVER 2.6/problem/materials.xml @@ -0,0 +1,85 @@ + + + + + + /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml + + 71c + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/VVER/VVER 2.6/problem/plots.xml b/VVER/VVER 2.6/problem/plots.xml new file mode 100644 index 0000000..b038c55 --- /dev/null +++ b/VVER/VVER 2.6/problem/plots.xml @@ -0,0 +1,28 @@ + + + + + + + + + 0. 0. 0. + 30.50 30.50 + 1000 1000 + + + + + 0. 0. 0. + 50.50 50.50 + 1000 1000 + + + diff --git a/VVER/VVER 2.6/problem/settings.xml b/VVER/VVER 2.6/problem/settings.xml new file mode 100644 index 0000000..72d4b3f --- /dev/null +++ b/VVER/VVER 2.6/problem/settings.xml @@ -0,0 +1,28 @@ + + + + + + + 600 + 50 + 10000 + + + + + point + + 0.0 0.0 0.0 + + + + + diff --git a/VVER/VVER 2.6/problem/tallies.xml b/VVER/VVER 2.6/problem/tallies.xml new file mode 100644 index 0000000..91bc7b1 --- /dev/null +++ b/VVER/VVER 2.6/problem/tallies.xml @@ -0,0 +1,77 @@ + + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 2 3 4 5 6 + + + + 1 + flux + + + + 2 + nu-fission + + + + + 3 + delayed-nu-fission + + + + 4 + fission + + + + 5 + absorption + + + + 6 + scatter + + + + 7 + total + + + + 8 + delayed-nu-fission + + + diff --git a/VVER/VVER 3.6/problem/geometry.xml b/VVER/VVER 3.6/problem/geometry.xml new file mode 100644 index 0000000..d9aef56 --- /dev/null +++ b/VVER/VVER 3.6/problem/geometry.xml @@ -0,0 +1,111 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1.275 + 900 +
0.0 0.0
+ 909 + 909 909 + 909 909 909 + 909 909 909 909 + 909 909 909 909 909 + 909 909 909 909 909 909 + 909 909 909 909 909 909 909 + 909 909 909 909 909 909 909 909 + 909 909 709 909 909 909 709 909 909 + 909 909 909 909 909 909 909 909 909 909 + 909 909 909 909 909 808 909 909 909 909 909 + 909 909 909 808 909 909 808 909 909 909 + 909 909 909 909 909 909 909 909 909 909 909 + 909 909 909 909 709 909 909 909 909 909 + 909 909 909 909 909 909 909 909 909 909 909 + 909 909 808 909 909 808 709 808 909 909 + 909 909 909 909 808 909 909 909 909 909 909 + 909 909 909 909 909 909 909 909 909 909 + 909 909 909 709 909 909 909 909 909 909 909 + 909 909 909 909 909 909 808 909 909 909 + 909 709 808 909 909 505 909 909 808 709 909 + 909 909 909 808 909 909 909 909 909 909 + 909 909 909 909 909 909 909 709 909 909 909 + 909 909 909 909 909 909 909 909 909 909 + 909 909 909 909 909 909 808 909 909 909 909 + 909 909 808 709 808 909 909 808 909 909 + 909 909 909 909 909 909 909 909 909 909 909 + 909 909 909 909 909 709 909 909 909 909 + 909 909 909 909 909 909 909 909 909 909 909 + 909 909 909 808 909 909 808 909 909 909 + 909 909 909 909 909 808 909 909 909 909 909 + 909 909 909 909 909 909 909 909 909 909 + 909 909 709 909 909 909 709 909 909 + 909 909 909 909 909 909 909 909 + 909 909 909 909 909 909 909 + 909 909 909 909 909 909 + 909 909 909 909 909 + 909 909 909 909 + 909 909 909 + 909 909 + 909 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/VVER/VVER 3.6/problem/materials.xml b/VVER/VVER 3.6/problem/materials.xml new file mode 100644 index 0000000..c282828 --- /dev/null +++ b/VVER/VVER 3.6/problem/materials.xml @@ -0,0 +1,85 @@ + + + + + + /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml + + 71c + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/VVER/VVER 3.6/problem/plots.xml b/VVER/VVER 3.6/problem/plots.xml new file mode 100644 index 0000000..b038c55 --- /dev/null +++ b/VVER/VVER 3.6/problem/plots.xml @@ -0,0 +1,28 @@ + + + + + + + + + 0. 0. 0. + 30.50 30.50 + 1000 1000 + + + + + 0. 0. 0. + 50.50 50.50 + 1000 1000 + + + diff --git a/VVER/VVER 3.6/problem/settings.xml b/VVER/VVER 3.6/problem/settings.xml new file mode 100644 index 0000000..72d4b3f --- /dev/null +++ b/VVER/VVER 3.6/problem/settings.xml @@ -0,0 +1,28 @@ + + + + + + + 600 + 50 + 10000 + + + + + point + + 0.0 0.0 0.0 + + + + + diff --git a/VVER/VVER 3.6/problem/tallies.xml b/VVER/VVER 3.6/problem/tallies.xml new file mode 100644 index 0000000..91bc7b1 --- /dev/null +++ b/VVER/VVER 3.6/problem/tallies.xml @@ -0,0 +1,77 @@ + + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 + + + + 1 2 3 4 5 6 + + + + 1 + flux + + + + 2 + nu-fission + + + + + 3 + delayed-nu-fission + + + + 4 + fission + + + + 5 + absorption + + + + 6 + scatter + + + + 7 + total + + + + 8 + delayed-nu-fission + + + diff --git a/VVER/VVER.ipynb b/VVER/VVER.ipynb new file mode 100644 index 0000000..c312733 --- /dev/null +++ b/VVER/VVER.ipynb @@ -0,0 +1,842 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7SVc4tVhUN8l", + "outputId": "91d742b3-4226-4a1c-e1a6-51bbde0949e3" + }, + "source": [ + "# A VVER geometry \n", + "This notebook can be used as a template for modeling VVER reactors." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "BpmPdBBHTz2E" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import openmc" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TBEO1_TrW7up", + "outputId": "f6c529e7-05aa-4e22-de7a-2d2ffc9c9421" + }, + "outputs": [], + "source": [ + "# Materials definitions\n", + "\n", + "au13=openmc.Material(name='au13')\n", + "au13.add_element('U', 1.0, enrichment=1.3)\n", + "au13.add_element('O', 2.0)\n", + "au13.set_density('g/cc', 10.4)\n", + "\n", + "au22=openmc.Material(name='au22')\n", + "au22.add_element('U', 1.0, enrichment=2.2)\n", + "au22.add_element('O', 2.0)\n", + "au22.set_density('g/cc', 10.4)\n", + "\n", + "av5=openmc.Material(name='av5')\n", + "av5.add_element('U', 1.0, enrichment=3.0)\n", + "av5.add_element('O', 2.0)\n", + "av5.set_density('g/cc', 10.4)\n", + "\n", + "awu=openmc.Material(name='awu')\n", + "awu.add_element('U', 1.0, enrichment=3.3)\n", + "awu.add_element('O', 2.0)\n", + "awu.set_density('g/cc', 10.4)\n", + "\n", + "go=openmc.Material(name='go')\n", + "go.add_element('U', 1.0, enrichment=4.0)\n", + "go.add_element('O', 2.0)\n", + "go.set_density('g/cc', 10.4)\n", + "\n", + "Gd2O3=openmc.Material(name='Gd2O3')\n", + "Gd2O3.add_element('Gd', 2.0)\n", + "Gd2O3.add_element('O', 3.0)\n", + "Gd2O3.set_density('g/cm3', 7.41)\n", + "\n", + "water = openmc.Material(name='water')\n", + "water.add_nuclide('H1', 2.0)\n", + "water.add_nuclide('O16', 1.0)\n", + "water.set_density('g/cm3', 1.0)\n", + "water.add_s_alpha_beta('c_H_in_H2O')\n", + "\n", + "zirconium = openmc.Material(name=\"zirconium\")\n", + "zirconium.add_element('Zr', 1.0)\n", + "zirconium.set_density('g/cm3', 6.6)\n", + "\n", + "niobium=openmc.Material(name='niobium')\n", + "niobium.add_element('Nb',1.0)\n", + "niobium.set_density('g/cm3',8.57)\n", + "\n", + "helium=openmc.Material(name='Helium')\n", + "helium.add_element('He', 1.0)\n", + "helium.set_density('g/cm3', 0.178e-3)\n", + "\n", + "reflector_mat = openmc.Material(name='Reflector')\n", + "reflector_mat.add_nuclide('Be9', 1.0)\n", + "reflector_mat.add_nuclide('O16', 1.0)\n", + "reflector_mat.set_density('g/cm3', 2.9)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "-VU_fMs2W751" + }, + "outputs": [], + "source": [ + "# Material mixtures\n", + "\n", + "alloy = openmc.Material.mix_materials([niobium, zirconium], [0.01, 0.99], 'wo')\n", + "fuel_awu = openmc.Material.mix_materials([Gd2O3, awu], [0.05, 0.95], 'wo')\n", + "fuel_go = openmc.Material.mix_materials([Gd2O3, go], [0.05, 0.95], 'wo')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "116XndltahRq" + }, + "outputs": [], + "source": [ + "# Instantiate a Materials collection and export to xml\n", + "materials_file = openmc.Materials([au13, au22, av5, fuel_awu, fuel_go, water, alloy, helium, reflector_mat])\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "htyVmeh8ahca", + "outputId": "20ecf082-9e37-4dc4-f987-cd42a1948e1c" + }, + "outputs": [], + "source": [ + "# Geometry definitions\n", + "\n", + "# Boudries and outer universe\n", + "all_water_out=openmc.Cell(cell_id=200, fill=water)\n", + "\n", + "# Top & bottom of the assembly \n", + "assembly_z0 = openmc.ZPlane(surface_id=300, z0=-75)\n", + "assembly_z1 = openmc.ZPlane(surface_id=301, z0=75)\n", + "assembly = openmc.model.hexagonal_prism(edge_length=18, orientation='y')\n", + "\n", + "# Top & bottom of the reflector\n", + "reflector_z0 = openmc.ZPlane(surface_id=303, z0=-95, boundary_type='vacuum')\n", + "reflector_z1 = openmc.ZPlane(surface_id=304, z0= 95, boundary_type='vacuum')\n", + "\n", + "# Reflector hexagon\n", + "reflector = openmc.model.hexagonal_prism(edge_length=19, orientation='y',\n", + " boundary_type='vacuum')\n", + "\n", + "assembly_cell = openmc.Cell()\n", + "reflect_cell = openmc.Cell()\n", + "top_reflect_cell = openmc.Cell()\n", + "bot_reflect_cell = openmc.Cell()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "ku5Yp2wzyXTg" + }, + "outputs": [], + "source": [ + "assembly_cell.region = assembly & -assembly_z1 & +assembly_z0\n", + "\n", + "reflect_cell.region = ~assembly & reflector \\\n", + "& -assembly_z1 & +assembly_z0\n", + "\n", + "top_reflect_cell.region = reflector & +assembly_z1 & -reflector_z1\n", + "\n", + "bot_reflect_cell.region = reflector & -assembly_z0 & +reflector_z0\n", + "\n", + "reflect_cell.fill = reflector_mat\n", + "top_reflect_cell.fill = reflector_mat\n", + "bot_reflect_cell.fill = reflector_mat\n", + "\n", + "# Create universes\n", + "all_water_out_u=openmc.Universe(cells=[all_water_out])\n", + "u_reflect = openmc.Universe(cells=(reflect_cell,top_reflect_cell,bot_reflect_cell))\n", + "u_root = openmc.Universe()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "id": "Sl9zZQjpahiO", + "outputId": "dbbad02d-0f40-4689-fdb7-af90e0b60b53" + }, + "outputs": [], + "source": [ + "fuel_or1 = openmc.ZCylinder(surface_id=400, r=0.3765)\n", + "clad_ir1 = openmc.ZCylinder(surface_id=401, r=0.4)\n", + "clad_or1 = openmc.ZCylinder(surface_id=402, r=0.465)\n", + "\n", + "fuel_region1 = -fuel_or1 & -assembly_z1 & +assembly_z0\n", + "clad_region1 = +fuel_or1 & -clad_or1 & -assembly_z1 & +assembly_z0\n", + "\n", + "moderator_region1 = +clad_or1 & -assembly_z1 & +assembly_z0 \n", + "\n", + "fuel_cell1 = openmc.Cell(cell_id=400, fill=water, region=fuel_region1)\n", + "clad_cell1 = openmc.Cell(cell_id=401, fill=alloy, region=clad_region1)\n", + "water_cell1 = openmc.Cell(cell_id=402, fill=water, region=moderator_region1)\n", + "\n", + "central_tube_u = openmc.Universe(cells=[fuel_cell1, clad_cell1, water_cell1])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "id": "s9WlvAAxahlm", + "outputId": "172bc8df-6006-44b8-e8e1-41142ef675ea" + }, + "outputs": [], + "source": [ + "# Geometry of au13 rod\n", + "\n", + "fuel_or2 = openmc.ZCylinder(surface_id=410, r=0.3765)\n", + "clad_ir2 = openmc.ZCylinder(surface_id=411, r=0.4)\n", + "clad_or2 = openmc.ZCylinder(surface_id=412, r=0.465)\n", + "\n", + "fuel_region2 = -fuel_or2 & -assembly_z1 & +assembly_z0\n", + "gap_region2 = +fuel_or2 & -clad_ir2 & -assembly_z1 & +assembly_z0\n", + "clad_region2 = +clad_ir2 & -clad_or2 & -assembly_z1 & +assembly_z0\n", + "\n", + "moderator_region2 = +clad_or2 & -assembly_z1 & +assembly_z0 \n", + "\n", + "fuel_cell2 = openmc.Cell(cell_id=410, fill=au13, region=fuel_region2)\n", + "gap_cell2 = openmc.Cell(cell_id=411, fill=helium, region=gap_region2)\n", + "clad_cell2 = openmc.Cell(cell_id=412, fill=alloy, region=clad_region2)\n", + "water_cell2 = openmc.Cell(cell_id=413, fill=water, region=moderator_region2)\n", + "\n", + "au13_u = openmc.Universe(cells=[fuel_cell2, gap_cell2, clad_cell2, water_cell2])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "zVCVJ9IHahot" + }, + "outputs": [], + "source": [ + "# Geometry of au22 rod\n", + "\n", + "fuel_or3 = openmc.ZCylinder(surface_id=420, r=0.3765)\n", + "clad_ir3 = openmc.ZCylinder(surface_id=421, r=0.4)\n", + "clad_or3 = openmc.ZCylinder(surface_id=422, r=0.465)\n", + "\n", + "fuel_region3 = -fuel_or3 & -assembly_z1 & +assembly_z0\n", + "gap_region3 = +fuel_or3 & -clad_ir3 & -assembly_z1 & +assembly_z0\n", + "clad_region3 = +clad_ir3 & -clad_or3 & -assembly_z1 & +assembly_z0\n", + "\n", + "moderator_region3 = +clad_or3 & -assembly_z1 & +assembly_z0 \n", + "\n", + "fuel_cell3 = openmc.Cell(cell_id=420, fill=au22, region=fuel_region3)\n", + "gap_cell3 = openmc.Cell(cell_id=421, fill=helium, region=gap_region3)\n", + "clad_cell3 = openmc.Cell(cell_id=422, fill=alloy, region=clad_region3)\n", + "water_cell3 = openmc.Cell(cell_id=423, fill=water, region=moderator_region3)\n", + "\n", + "au22_u = openmc.Universe(cells=[fuel_cell3, gap_cell3, clad_cell3, water_cell3])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "3MlM4EUuahu9" + }, + "outputs": [], + "source": [ + "# Geometry of av5 rod\n", + "\n", + "fuel_or4 = openmc.ZCylinder(surface_id=430, r=0.3765)\n", + "clad_ir4 = openmc.ZCylinder(surface_id=431, r=0.4)\n", + "clad_or4 = openmc.ZCylinder(surface_id=432, r=0.465)\n", + "\n", + "fuel_region4 = -fuel_or4 & -assembly_z1 & +assembly_z0\n", + "gap_region4 = +fuel_or4 & -clad_ir4 & -assembly_z1 & +assembly_z0\n", + "clad_region4 = +clad_ir4 & -clad_or4 & -assembly_z1 & +assembly_z0\n", + "\n", + "moderator_region4 = +clad_or4 & -assembly_z1 & +assembly_z0 \n", + "\n", + "fuel_cell4 = openmc.Cell(cell_id=430, fill=av5, region=fuel_region4)\n", + "gap_cell4 = openmc.Cell(cell_id=431, fill=helium, region=gap_region4)\n", + "clad_cell4 = openmc.Cell(cell_id=432, fill=alloy, region=clad_region4)\n", + "water_cell4 = openmc.Cell(cell_id=433, fill=water, region=moderator_region4)\n", + "\n", + "av5_u = openmc.Universe(cells=[fuel_cell4, gap_cell4, clad_cell4, water_cell4])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "Ks9nRXR4ah45" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Surface instance already exists with id=440.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Surface instance already exists with id=441.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Surface instance already exists with id=442.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=440.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=441.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=442.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=443.\n", + " warn(msg, IDWarning)\n" + ] + } + ], + "source": [ + "# Geometry of awu rod\n", + "\n", + "fuel_or5 = openmc.ZCylinder(surface_id=440, r=0.3765)\n", + "clad_ir5 = openmc.ZCylinder(surface_id=441, r=0.4)\n", + "clad_or5 = openmc.ZCylinder(surface_id=442, r=0.465)\n", + "\n", + "fuel_region5 = -fuel_or5 & -assembly_z1 & +assembly_z0\n", + "gap_region5 = +fuel_or5 & -clad_ir5 & -assembly_z1 & +assembly_z0\n", + "clad_region5 = +clad_ir5 & -clad_or5 & -assembly_z1 & +assembly_z0\n", + "\n", + "moderator_region5 = +clad_or5 & -assembly_z1 & +assembly_z0 \n", + "\n", + "fuel_cell5 = openmc.Cell(cell_id=440, fill=fuel_awu, region=fuel_region5)\n", + "gap_cell5 = openmc.Cell(cell_id=441, fill=helium, region=gap_region5)\n", + "clad_cell5 = openmc.Cell(cell_id=442, fill=alloy, region=clad_region5)\n", + "water_cell5 = openmc.Cell(cell_id=443, fill=water, region=moderator_region5)\n", + "\n", + "awu_u = openmc.Universe(cells=[fuel_cell5, gap_cell5, clad_cell5, water_cell5])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "NV8O7-cZaiuX" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Surface instance already exists with id=450.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Surface instance already exists with id=451.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Surface instance already exists with id=452.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=450.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=451.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=452.\n", + " warn(msg, IDWarning)\n", + "/Users/miriamrathbun/codes/openmc-mak/openmc/mixin.py:68: IDWarning: Another Cell instance already exists with id=453.\n", + " warn(msg, IDWarning)\n" + ] + } + ], + "source": [ + "# Geometry of go rod\n", + "\n", + "fuel_or6 = openmc.ZCylinder(surface_id=450, r=0.3765)\n", + "clad_ir6 = openmc.ZCylinder(surface_id=451, r=0.4)\n", + "clad_or6 = openmc.ZCylinder(surface_id=452, r=0.465)\n", + "\n", + "fuel_region6 = -fuel_or6 & -assembly_z1 & +assembly_z0\n", + "gap_region6 = +fuel_or6 & -clad_ir6 & -assembly_z1 & +assembly_z0\n", + "clad_region6 = +clad_ir6 & -clad_or6 & -assembly_z1 & +assembly_z0\n", + "\n", + "moderator_region6 = +clad_or6 & -assembly_z1 & +assembly_z0 \n", + "\n", + "fuel_cell6 = openmc.Cell(cell_id=450, fill=fuel_go, region=fuel_region6)\n", + "gap_cell6 = openmc.Cell(cell_id=451, fill=helium, region=gap_region6)\n", + "clad_cell6 = openmc.Cell(cell_id=452, fill=alloy, region=clad_region6)\n", + "water_cell6 = openmc.Cell(cell_id=453, fill=water, region=moderator_region6)\n", + "\n", + "go_u = openmc.Universe(cells=[fuel_cell6, gap_cell6, clad_cell6, water_cell6])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Lnnkq16Oaisk", + "outputId": "676d78e5-ddf5-4fd9-b6b0-8e432a33a1da" + }, + "outputs": [], + "source": [ + "# Creating the hexagonal lattice\n", + "\n", + "lat=openmc.HexLattice(name='assembly')\n", + "lat.center = (0., 0.)\n", + "lat.pitch = (1.275,)\n", + "lat.outer=all_water_out_u" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "0wD6nzv-aihX" + }, + "outputs": [], + "source": [ + "ring7=[awu_u, awu_u, go_u,awu_u, go_u, awu_u, awu_u]*6\n", + "ring6= [au22_u, av5_u, au22_u,\n", + " av5_u, au22_u, av5_u,\n", + " au13_u,av5_u, au22_u,\n", + " av5_u,au22_u,av5_u,\n", + " au22_u,av5_u,au22_u,\n", + " av5_u,au22_u,av5_u,\n", + " au22_u,av5_u,au22_u,\n", + " av5_u,au22_u,av5_u,\n", + " au13_u,av5_u,au22_u,\n", + " av5_u,au22_u,av5_u,\n", + " au13_u,av5_u,au22_u,\n", + " av5_u,au22_u,av5_u]\n", + "ring5= [au13_u, au13_u, au22_u, au13_u, au13_u]*6\n", + "ring4=[au22_u, av5_u, au13_u, av5_u]*6\n", + "ring3=[au13_u, au22_u, au13_u]*6\n", + "ring2=[au22_u, av5_u]*6\n", + "ring1=[au13_u]*6\n", + "ring0=[central_tube_u]\n", + "lat.universes = [ring7, ring6, ring5, ring4, ring3, ring2, ring1, ring0]\n", + "lat.orientation='y'" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "3qjNGajDaifZ", + "outputId": "d5c8cad5-7430-4fb7-8bb1-7930f82841c0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "u_root.plot(basis='yz',origin=(0,0,0),width=(50,50),color_by='material',colors={water:'blue'},pixels=[200,200])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "EzaW9J5l8vRP" + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "\n", + "batches = 100\n", + "inactive = 10\n", + "particles = 5000\n", + "\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "\n", + "uniform_dist = openmc.stats.Box([-24,-24,-75],[24,24,75],only_fissionable=True)\n", + "settings.source = openmc.source.Source(space=uniform_dist)\n", + "\n", + "settings.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "mzhyBvEOaiR8", + "outputId": "bb783e58-6ac3-4193-e64a-818bf9c0fa6d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.1-dev\n", + " Git SHA1 | 0228ddba1d6399e654a295e51539590f076b2a3c\n", + " Date/Time | 2021-04-12 10:35:09\n", + " OpenMP Threads | 8\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U234.h5\n", + " Reading U235 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U235.h5\n", + " Reading U238 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U238.h5\n", + " Reading U236 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/U236.h5\n", + " Reading O16 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/O16.h5\n", + " Reading O17 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/O17.h5\n", + " Reading H1 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/H1.h5\n", + " Reading He3 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/He3.h5\n", + " Reading He4 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/He4.h5\n", + " Reading Be9 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Be9.h5\n", + " Reading Nb93 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Nb93.h5\n", + " Reading Zr90 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr90.h5\n", + " Reading Zr91 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr91.h5\n", + " Reading Zr92 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr92.h5\n", + " Reading Zr94 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr94.h5\n", + " Reading Zr96 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Zr96.h5\n", + " Reading Gd152 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Gd152.h5\n", + " Reading Gd154 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Gd154.h5\n", + " Reading Gd155 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Gd155.h5\n", + " Reading Gd156 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Gd156.h5\n", + " Reading Gd157 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Gd157.h5\n", + " Reading Gd158 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Gd158.h5\n", + " Reading Gd160 from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/Gd160.h5\n", + " Reading c_H_in_H2O from\n", + " /Users/miriamrathbun/codes/openmc/nucleardata/endfb71_hdf5/c_H_in_H2O.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.32731\n", + " 2/1 0.36782\n", + " 3/1 0.36363\n", + " 4/1 0.38721\n", + " 5/1 0.36266\n", + " 6/1 0.35733\n", + " 7/1 0.34599\n", + " 8/1 0.35970\n", + " 9/1 0.34754\n", + " 10/1 0.36502\n", + " 11/1 0.36282\n", + " 12/1 0.37708 0.36995 +/- 0.00713\n", + " 13/1 0.35447 0.36479 +/- 0.00660\n", + " 14/1 0.35739 0.36294 +/- 0.00502\n", + " 15/1 0.36162 0.36268 +/- 0.00390\n", + " 16/1 0.37096 0.36406 +/- 0.00347\n", + " 17/1 0.36046 0.36354 +/- 0.00298\n", + " 18/1 0.35110 0.36199 +/- 0.00301\n", + " 19/1 0.36965 0.36284 +/- 0.00279\n", + " 20/1 0.36503 0.36306 +/- 0.00250\n", + " 21/1 0.36692 0.36341 +/- 0.00229\n", + " 22/1 0.37157 0.36409 +/- 0.00220\n", + " 23/1 0.35954 0.36374 +/- 0.00205\n", + " 24/1 0.36843 0.36407 +/- 0.00193\n", + " 25/1 0.37429 0.36476 +/- 0.00192\n", + " 26/1 0.35738 0.36429 +/- 0.00186\n", + " 27/1 0.35847 0.36395 +/- 0.00178\n", + " 28/1 0.36363 0.36393 +/- 0.00168\n", + " 29/1 0.34334 0.36285 +/- 0.00192\n", + " 30/1 0.35107 0.36226 +/- 0.00191\n", + " 31/1 0.37232 0.36274 +/- 0.00188\n", + " 32/1 0.35593 0.36243 +/- 0.00182\n", + " 33/1 0.33948 0.36143 +/- 0.00201\n", + " 34/1 0.35854 0.36131 +/- 0.00192\n", + " 35/1 0.36516 0.36147 +/- 0.00185\n", + " 36/1 0.34700 0.36091 +/- 0.00186\n", + " 37/1 0.36810 0.36118 +/- 0.00181\n", + " 38/1 0.35695 0.36102 +/- 0.00175\n", + " 39/1 0.36776 0.36126 +/- 0.00171\n", + " 40/1 0.35303 0.36098 +/- 0.00167\n", + " 41/1 0.35764 0.36088 +/- 0.00162\n", + " 42/1 0.36321 0.36095 +/- 0.00157\n", + " 43/1 0.35907 0.36089 +/- 0.00153\n", + " 44/1 0.37837 0.36141 +/- 0.00157\n", + " 45/1 0.34815 0.36103 +/- 0.00157\n", + " 46/1 0.36897 0.36125 +/- 0.00154\n", + " 47/1 0.37862 0.36172 +/- 0.00157\n", + " 48/1 0.37571 0.36208 +/- 0.00157\n", + " 49/1 0.35559 0.36192 +/- 0.00154\n", + " 50/1 0.35851 0.36183 +/- 0.00150\n", + " 51/1 0.36326 0.36187 +/- 0.00147\n", + " 52/1 0.36563 0.36196 +/- 0.00143\n", + " 53/1 0.34442 0.36155 +/- 0.00146\n", + " 54/1 0.34858 0.36125 +/- 0.00145\n", + " 55/1 0.37772 0.36162 +/- 0.00147\n", + " 56/1 0.36296 0.36165 +/- 0.00144\n", + " 57/1 0.36482 0.36172 +/- 0.00141\n", + " 58/1 0.34370 0.36134 +/- 0.00143\n", + " 59/1 0.36959 0.36151 +/- 0.00141\n", + " 60/1 0.35648 0.36141 +/- 0.00138\n", + " 61/1 0.36666 0.36151 +/- 0.00136\n", + " 62/1 0.37184 0.36171 +/- 0.00135\n", + " 63/1 0.38048 0.36207 +/- 0.00137\n", + " 64/1 0.36500 0.36212 +/- 0.00134\n", + " 65/1 0.37043 0.36227 +/- 0.00133\n", + " 66/1 0.37493 0.36250 +/- 0.00132\n", + " 67/1 0.35683 0.36240 +/- 0.00130\n", + " 68/1 0.35266 0.36223 +/- 0.00129\n", + " 69/1 0.39498 0.36278 +/- 0.00139\n", + " 70/1 0.35308 0.36262 +/- 0.00137\n", + " 71/1 0.37292 0.36279 +/- 0.00136\n", + " 72/1 0.34912 0.36257 +/- 0.00136\n", + " 73/1 0.35564 0.36246 +/- 0.00134\n", + " 74/1 0.37878 0.36272 +/- 0.00134\n", + " 75/1 0.35663 0.36262 +/- 0.00133\n", + " 76/1 0.38357 0.36294 +/- 0.00134\n", + " 77/1 0.36621 0.36299 +/- 0.00132\n", + " 78/1 0.38089 0.36325 +/- 0.00133\n", + " 79/1 0.36460 0.36327 +/- 0.00131\n", + " 80/1 0.39157 0.36368 +/- 0.00135\n", + " 81/1 0.37191 0.36379 +/- 0.00134\n", + " 82/1 0.35763 0.36371 +/- 0.00132\n", + " 83/1 0.35565 0.36360 +/- 0.00131\n", + " 84/1 0.36902 0.36367 +/- 0.00129\n", + " 85/1 0.36607 0.36370 +/- 0.00128\n", + " 86/1 0.35523 0.36359 +/- 0.00127\n", + " 87/1 0.38251 0.36384 +/- 0.00127\n", + " 88/1 0.36575 0.36386 +/- 0.00126\n", + " 89/1 0.35643 0.36377 +/- 0.00124\n", + " 90/1 0.36464 0.36378 +/- 0.00123\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 91/1 0.36379 0.36378 +/- 0.00121\n", + " 92/1 0.37030 0.36386 +/- 0.00120\n", + " 93/1 0.36556 0.36388 +/- 0.00119\n", + " 94/1 0.39963 0.36430 +/- 0.00125\n", + " 95/1 0.37369 0.36441 +/- 0.00124\n", + " 96/1 0.37141 0.36449 +/- 0.00123\n", + " 97/1 0.36872 0.36454 +/- 0.00121\n", + " 98/1 0.37599 0.36467 +/- 0.00121\n", + " 99/1 0.38600 0.36491 +/- 0.00122\n", + " 100/1 0.36745 0.36494 +/- 0.00120\n", + " Creating state point statepoint.100.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 4.6179e+00 seconds\n", + " Reading cross sections = 4.5650e+00 seconds\n", + " Total time in simulation = 1.2380e+01 seconds\n", + " Time in transport only = 1.2340e+01 seconds\n", + " Time in inactive batches = 1.1459e+00 seconds\n", + " Time in active batches = 1.1234e+01 seconds\n", + " Time synchronizing fission bank = 1.6836e-02 seconds\n", + " Sampling source sites = 1.3449e-02 seconds\n", + " SEND/RECV source sites = 3.2980e-03 seconds\n", + " Time accumulating tallies = 1.4600e-04 seconds\n", + " Time writing statepoints = 4.2660e-03 seconds\n", + " Total time for finalization = 5.0000e-06 seconds\n", + " Total time elapsed = 1.7078e+01 seconds\n", + " Calculation Rate (inactive) = 43632.5 particles/second\n", + " Calculation Rate (active) = 40055.6 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 0.36479 +/- 0.00104\n", + " k-effective (Track-length) = 0.36494 +/- 0.00120\n", + " k-effective (Absorption) = 0.36468 +/- 0.00113\n", + " Combined k-effective = 0.36475 +/- 0.00094\n", + " Leakage Fraction = 0.27198 +/- 0.00064\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + } + ], + "metadata": { + "colab": { + "name": "VVER.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/WBN1/CASL-U-2012-0131-004.pdf b/WBN1/CASL-U-2012-0131-004.pdf new file mode 100644 index 0000000..9bdb33a Binary files /dev/null and b/WBN1/CASL-U-2012-0131-004.pdf differ diff --git a/WBN1/Problem-1.ipynb b/WBN1/Problem-1.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/WBN1/Problem-10.ipynb b/WBN1/Problem-10.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/WBN1/Problem-2.ipynb b/WBN1/Problem-2.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/WBN1/Problem-3.ipynb b/WBN1/Problem-3.ipynb new file mode 100644 index 0000000..e69de29 diff --git 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b/examples/hexagon/hexagonal-lattice.ipynb new file mode 100644 index 0000000..87f2a2a --- /dev/null +++ b/examples/hexagon/hexagonal-lattice.ipynb @@ -0,0 +1,411 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling Hexagonal Lattices\n", + "In this example, we will create a hexagonal lattice and show how the orientation can be changed via the cell rotation property. Let's first just set up some materials and universes that we will use to fill the lattice." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import openmc" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "fuel = openmc.Material(name='fuel')\n", + "fuel.add_nuclide('U235', 1.0)\n", + "fuel.set_density('g/cm3', 10.0)\n", + "\n", + "fuel2 = openmc.Material(name='fuel2')\n", + "fuel2.add_nuclide('U238', 1.0)\n", + "fuel2.set_density('g/cm3', 10.0)\n", + "\n", + "water = openmc.Material(name='water')\n", + "water.add_nuclide('H1', 2.0)\n", + "water.add_nuclide('O16', 1.0)\n", + "water.set_density('g/cm3', 1.0)\n", + "\n", + "materials = openmc.Materials((fuel, fuel2, water))\n", + "materials.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our three materials, we will set up two universes that represent pin-cells: one with a small pin and one with a big pin. Since we will be using these universes in a lattice, it's always a good idea to have an \"outer\" universe as well that is applied outside the defined lattice." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "r_pin = openmc.ZCylinder(r=0.25)\n", + "fuel_cell = openmc.Cell(fill=fuel, region=-r_pin)\n", + "water_cell = openmc.Cell(fill=water, region=+r_pin)\n", + "pin_universe = openmc.Universe(cells=(fuel_cell, water_cell))\n", + "\n", + "r_big_pin = openmc.ZCylinder(r=0.5)\n", + "fuel2_cell = openmc.Cell(fill=fuel2, region=-r_big_pin)\n", + "water2_cell = openmc.Cell(fill=water, region=+r_big_pin)\n", + "big_pin_universe = openmc.Universe(cells=(fuel2_cell, water2_cell))\n", + "\n", + "all_water_cell = openmc.Cell(fill=water)\n", + "outer_universe = openmc.Universe(cells=(all_water_cell,))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create a hexagonal lattice using the `HexLattice` class:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "lattice = openmc.HexLattice()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to set the `center` of the lattice, the `pitch`, an `outer` universe (which is applied to all lattice elements outside of those that are defined), and a list of `universes`. Let's start with the easy ones first. Note that for a 2D lattice, we only need to specify a single number for the pitch." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "lattice.center = (0., 0.)\n", + "lattice.pitch = (1.25,)\n", + "lattice.outer = outer_universe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to set the `universes` property on our lattice. It needs to be set to a list of lists of Universes, where each list of Universes corresponds to a ring of the lattice. The rings are ordered from outermost to innermost, and within each ring the indexing starts at the \"top\". To help visualize the proper indices, we can use the `show_indices()` helper method." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (0, 0)\n", + " (0,17) (0, 1)\n", + " (0,16) (1, 0) (0, 2)\n", + "(0,15) (1,11) (1, 1) (0, 3)\n", + " (1,10) (2, 0) (1, 2)\n", + "(0,14) (2, 5) (2, 1) (0, 4)\n", + " (1, 9) (3, 0) (1, 3)\n", + "(0,13) (2, 4) (2, 2) (0, 5)\n", + " (1, 8) (2, 3) (1, 4)\n", + "(0,12) (1, 7) (1, 5) (0, 6)\n", + " (0,11) (1, 6) (0, 7)\n", + " (0,10) (0, 8)\n", + " (0, 9)\n" + ] + } + ], + "source": [ + "print(lattice.show_indices(num_rings=4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's set up a lattice where the first element in each ring is the big pin universe and all other elements are regular pin universes. \n", + "\n", + "From the diagram above, we see that the outer ring has 18 elements, the first ring has 12, and the second ring has 6 elements. The innermost ring of any hexagonal lattice will have only a single element. \n", + "\n", + "We build these rings through 'list concatenation' as follows: " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "outer_ring = [big_pin_universe] + [pin_universe]*17 # Adds up to 18\n", + "\n", + "ring_1 = [big_pin_universe] + [pin_universe]*11 # Adds up to 12\n", + "\n", + "ring_2 = [big_pin_universe] + [pin_universe]*5 # Adds up to 6\n", + "\n", + "inner_ring = [big_pin_universe]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now assign the rings (and the universes they contain) to our lattice. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HexLattice\n", + "\tID =\t4\n", + "\tName =\t\n", + "\tOrientation =\ty\n", + "\t# Rings =\t4\n", + "\t# Axial =\tNone\n", + "\tCenter =\t(0.0, 0.0)\n", + "\tPitch =\t(1.25,)\n", + "\tOuter =\t3\n", + "\tUniverses \n", + " 2\n", + " 1 1\n", + " 1 2 1\n", + "1 1 1 1\n", + " 1 2 1\n", + "1 1 1 1\n", + " 1 2 1\n", + "1 1 1 1\n", + " 1 1 1\n", + "1 1 1 1\n", + " 1 1 1\n", + " 1 1\n", + " 1\n" + ] + } + ], + "source": [ + "lattice.universes = [outer_ring, \n", + " ring_1, \n", + " ring_2,\n", + " inner_ring]\n", + "print(lattice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's put our lattice inside a circular cell that will serve as the top-level cell for our geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "outer_surface = openmc.ZCylinder(r=5.0, boundary_type='vacuum')\n", + "main_cell = openmc.Cell(fill=lattice, region=-outer_surface)\n", + "geometry = openmc.Geometry([main_cell])\n", + "geometry.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create a plot to see what our geometry looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot = openmc.Plot.from_geometry(geometry)\n", + "plot.color_by = 'material'\n", + "plot.colors = colors = {\n", + " water: 'blue',\n", + " fuel: 'olive',\n", + " fuel2: 'yellow'\n", + "}\n", + "plot.to_ipython_image()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, if we wanted to simulate the model, we would need to create an instance of `openmc.Settings`, export it to XML, and run." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lattice orientation\n", + "\n", + "Now let's say we want our hexagonal lattice orientated such that two sides of the lattice are parallel to the x-axis. This can be achieved by two means: either we can rotate the cell that contains the lattice, or we can can change the `HexLattice.orientation` attribute. By default, the `orientation` is set to \"y\", indicating that two sides of the lattice are parallel to the y-axis, but we can also change it to \"x\" to make them parallel to the x-axis." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Change the orientation of the lattice and re-export the geometry\n", + "lattice.orientation = 'x'\n", + "geometry.export_to_xml()\n", + "\n", + "# Run OpenMC in plotting mode\n", + "plot.to_ipython_image()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we change the orientation to 'x', you can see that the first universe in each ring starts to the right along the x-axis. As before, the universes are defined in a clockwise fashion around each ring. To see the proper indices for a hexagonal lattice in this orientation, we can again call `show_indices` but pass an extra orientation argument:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (0,12) (0,13) (0,14) (0,15)\n", + "\n", + " (0,11) (1, 8) (1, 9) (1,10) (0,16)\n", + "\n", + " (0,10) (1, 7) (2, 4) (2, 5) (1,11) (0,17)\n", + "\n", + "(0, 9) (1, 6) (2, 3) (3, 0) (2, 0) (1, 0) (0, 0)\n", + "\n", + " (0, 8) (1, 5) (2, 2) (2, 1) (1, 1) (0, 1)\n", + "\n", + " (0, 7) (1, 4) (1, 3) (1, 2) (0, 2)\n", + "\n", + " (0, 6) (0, 5) (0, 4) (0, 3)\n" + ] + } + ], + "source": [ + "print(lattice.show_indices(4, orientation='x'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hexagonal prisms\n", + "\n", + "OpenMC also contains a convenience function that can create a hexagonal prism representing the interior region of six surfaces defining a hexagon. This can be useful as a bounding surface of a hexagonal lattice. For example, if we wanted the outer boundary of our geometry to be hexagonal, we could change the `region` of the main cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "main_cell.region = openmc.model.hexagonal_prism(\n", + " edge_length=4*lattice.pitch[0],\n", + " orientation='x',\n", + " boundary_type='vacuum'\n", + ")\n", + "geometry.export_to_xml()\n", + "\n", + "# Run OpenMC in plotting mode\n", + "plot.color_by = 'cell'\n", + "plot.to_ipython_image()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/post_process/post-processing.ipynb b/examples/post_process/post-processing.ipynb new file mode 100644 index 0000000..65d83bc --- /dev/null +++ b/examples/post_process/post-processing.ipynb @@ -0,0 +1,992 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post Processing\n", + "This notebook demonstrates some basic post-processing tasks that can be performed with the Python API, such as plotting a 2D mesh tally and plotting neutron source sites from an eigenvalue calculation. The problem we will use is a simple reflected pin-cell." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from IPython.display import Image\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import openmc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pin." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# 1.6 enriched fuel\n", + "fuel = openmc.Material(name='1.6% Fuel')\n", + "fuel.set_density('g/cm3', 10.31341)\n", + "fuel.add_nuclide('U235', 3.7503e-4)\n", + "fuel.add_nuclide('U238', 2.2625e-2)\n", + "fuel.add_nuclide('O16', 4.6007e-2)\n", + "\n", + "# borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", + "water.add_nuclide('B10', 8.0042e-6)\n", + "\n", + "# zircaloy\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide('Zr90', 7.2758e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our three materials, we can now create a materials file object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a Materials collection\n", + "materials = openmc.Materials([fuel, water, zircaloy])\n", + "\n", + "# Export to \"materials.xml\"\n", + "materials.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Create cylinders for the fuel and clad\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n", + "\n", + "# Create boundary planes to surround the geometry\n", + "min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n", + "max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n", + "min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n", + "max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n", + "min_z = openmc.ZPlane(z0=-0.63, boundary_type='reflective')\n", + "max_z = openmc.ZPlane(z0=+0.63, boundary_type='reflective')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a fuel pin\n", + "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", + "\n", + "# Create fuel Cell\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", + "fuel_cell.fill = fuel\n", + "fuel_cell.region = -fuel_outer_radius\n", + "pin_cell_universe.add_cell(fuel_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='1.6% Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "pin_cell_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius\n", + "pin_cell_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Create root Cell\n", + "root_cell = openmc.Cell(name='root cell')\n", + "root_cell.fill = pin_cell_universe\n", + "\n", + "# Add boundary planes\n", + "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n", + "\n", + "# Create root Universe\n", + "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", + "root_universe.add_cell(root_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "geometry = openmc.Geometry(root_universe)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Export to \"geometry.xml\"\n", + "geometry.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 90 active batches each with 5000 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "settings = openmc.Settings()\n", + "settings.batches = 100\n", + "settings.inactive = 10\n", + "settings.particles = 5000\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings.source = openmc.Source(space=uniform_dist)\n", + "\n", + "# Export to \"settings.xml\"\n", + "settings.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot = openmc.Plot.from_geometry(geometry)\n", + "plot.pixels = (250, 250)\n", + "plot.to_ipython_image()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the plot, we have a nice pin cell with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a 2D mesh tally." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate an empty Tallies object\n", + "tallies = openmc.Tallies()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Create mesh which will be used for tally\n", + "mesh = openmc.RegularMesh()\n", + "mesh.dimension = [100, 100]\n", + "mesh.lower_left = [-0.63, -0.63]\n", + "mesh.upper_right = [0.63, 0.63]\n", + "\n", + "# Create mesh filter for tally\n", + "mesh_filter = openmc.MeshFilter(mesh)\n", + "\n", + "# Create mesh tally to score flux and fission rate\n", + "tally = openmc.Tally(name='flux')\n", + "tally.filters = [mesh_filter]\n", + "tally.scores = ['flux', 'fission']\n", + "tallies.append(tally)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Export to \"tallies.xml\"\n", + "tallies.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we a have a complete set of inputs, so we can go ahead and run our simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:28:06\n", + " OpenMP Threads | 2\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.05252\n", + " 2/1 1.03787\n", + " 3/1 1.01943\n", + " 4/1 1.03989\n", + " 5/1 1.06679\n", + " 6/1 1.03713\n", + " 7/1 1.02400\n", + " 8/1 1.04289\n", + " 9/1 1.05130\n", + " 10/1 1.00878\n", + " 11/1 1.06773\n", + " 12/1 1.03922 1.05347 +/- 0.01426\n", + " 13/1 1.05156 1.05283 +/- 0.00826\n", + " 14/1 1.06049 1.05475 +/- 0.00614\n", + " 15/1 1.01018 1.04583 +/- 0.01010\n", + " 16/1 1.04020 1.04490 +/- 0.00830\n", + " 17/1 1.05579 1.04645 +/- 0.00719\n", + " 18/1 1.01592 1.04264 +/- 0.00730\n", + " 19/1 1.06881 1.04554 +/- 0.00707\n", + " 20/1 1.02985 1.04397 +/- 0.00651\n", + " 21/1 1.01496 1.04134 +/- 0.00645\n", + " 22/1 1.05330 1.04233 +/- 0.00598\n", + " 23/1 1.05170 1.04305 +/- 0.00554\n", + " 24/1 1.02888 1.04204 +/- 0.00523\n", + " 25/1 1.04083 1.04196 +/- 0.00487\n", + " 26/1 1.01235 1.04011 +/- 0.00492\n", + " 27/1 1.02785 1.03939 +/- 0.00468\n", + " 28/1 1.04556 1.03973 +/- 0.00442\n", + " 29/1 1.05400 1.04048 +/- 0.00425\n", + " 30/1 1.06213 1.04157 +/- 0.00417\n", + " 31/1 0.99934 1.03955 +/- 0.00445\n", + " 32/1 1.04433 1.03977 +/- 0.00425\n", + " 33/1 1.05184 1.04030 +/- 0.00409\n", + " 34/1 1.03971 1.04027 +/- 0.00392\n", + " 35/1 1.05272 1.04077 +/- 0.00379\n", + " 36/1 1.06881 1.04185 +/- 0.00380\n", + " 37/1 1.03344 1.04154 +/- 0.00367\n", + " 38/1 1.04726 1.04174 +/- 0.00354\n", + " 39/1 1.01440 1.04080 +/- 0.00354\n", + " 40/1 1.03534 1.04062 +/- 0.00343\n", + " 41/1 1.04429 1.04073 +/- 0.00332\n", + " 42/1 1.02142 1.04013 +/- 0.00327\n", + " 43/1 1.03895 1.04010 +/- 0.00317\n", + " 44/1 1.05985 1.04068 +/- 0.00313\n", + " 45/1 1.04737 1.04087 +/- 0.00304\n", + " 46/1 1.04796 1.04106 +/- 0.00297\n", + " 47/1 1.06708 1.04177 +/- 0.00297\n", + " 48/1 1.06523 1.04238 +/- 0.00295\n", + " 49/1 0.99626 1.04120 +/- 0.00311\n", + " 50/1 1.04077 1.04119 +/- 0.00303\n", + " 51/1 1.06327 1.04173 +/- 0.00301\n", + " 52/1 1.06508 1.04229 +/- 0.00299\n", + " 53/1 1.03689 1.04216 +/- 0.00292\n", + " 54/1 1.02899 1.04186 +/- 0.00287\n", + " 55/1 1.03267 1.04166 +/- 0.00281\n", + " 56/1 1.05790 1.04201 +/- 0.00277\n", + " 57/1 1.04353 1.04204 +/- 0.00271\n", + " 58/1 1.04657 1.04214 +/- 0.00266\n", + " 59/1 1.02914 1.04187 +/- 0.00261\n", + " 60/1 1.04882 1.04201 +/- 0.00257\n", + " 61/1 1.01905 1.04156 +/- 0.00255\n", + " 62/1 1.03995 1.04153 +/- 0.00251\n", + " 63/1 1.05377 1.04176 +/- 0.00247\n", + " 64/1 1.02909 1.04153 +/- 0.00243\n", + " 65/1 1.06892 1.04202 +/- 0.00244\n", + " 66/1 1.04216 1.04203 +/- 0.00240\n", + " 67/1 1.03473 1.04190 +/- 0.00236\n", + " 68/1 1.04114 1.04188 +/- 0.00232\n", + " 69/1 1.04955 1.04201 +/- 0.00228\n", + " 70/1 1.05464 1.04222 +/- 0.00225\n", + " 71/1 1.02859 1.04200 +/- 0.00223\n", + " 72/1 1.05387 1.04219 +/- 0.00220\n", + " 73/1 1.05039 1.04232 +/- 0.00217\n", + " 74/1 1.04338 1.04234 +/- 0.00213\n", + " 75/1 1.05838 1.04259 +/- 0.00211\n", + " 76/1 1.03831 1.04252 +/- 0.00208\n", + " 77/1 1.03555 1.04242 +/- 0.00205\n", + " 78/1 1.05684 1.04263 +/- 0.00204\n", + " 79/1 1.04267 1.04263 +/- 0.00201\n", + " 80/1 1.05813 1.04285 +/- 0.00199\n", + " 81/1 1.03512 1.04274 +/- 0.00196\n", + " 82/1 1.07081 1.04313 +/- 0.00198\n", + " 83/1 1.04476 1.04315 +/- 0.00195\n", + " 84/1 1.05153 1.04327 +/- 0.00192\n", + " 85/1 1.03939 1.04322 +/- 0.00190\n", + " 86/1 1.04218 1.04320 +/- 0.00187\n", + " 87/1 1.03688 1.04312 +/- 0.00185\n", + " 88/1 1.03480 1.04301 +/- 0.00183\n", + " 89/1 1.05089 1.04311 +/- 0.00181\n", + " 90/1 1.06251 1.04336 +/- 0.00180\n", + " 91/1 1.04054 1.04332 +/- 0.00178\n", + " 92/1 1.05340 1.04344 +/- 0.00176\n", + " 93/1 1.05938 1.04364 +/- 0.00175\n", + " 94/1 1.02741 1.04344 +/- 0.00174\n", + " 95/1 1.08249 1.04390 +/- 0.00178\n", + " 96/1 1.02858 1.04372 +/- 0.00177\n", + " 97/1 1.03983 1.04368 +/- 0.00175\n", + " 98/1 1.04715 1.04372 +/- 0.00173\n", + " 99/1 1.07443 1.04406 +/- 0.00175\n", + " 100/1 1.04461 1.04407 +/- 0.00173\n", + " Creating state point statepoint.100.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 2.4568e-01 seconds\n", + " Reading cross sections = 2.3233e-01 seconds\n", + " Total time in simulation = 1.1761e+02 seconds\n", + " Time in transport only = 1.1757e+02 seconds\n", + " Time in inactive batches = 2.0641e+00 seconds\n", + " Time in active batches = 1.1554e+02 seconds\n", + " Time synchronizing fission bank = 2.1808e-02 seconds\n", + " Sampling source sites = 1.8421e-02 seconds\n", + " SEND/RECV source sites = 3.3183e-03 seconds\n", + " Time accumulating tallies = 2.6283e-03 seconds\n", + " Time writing statepoints = 4.2804e-03 seconds\n", + " Total time for finalization = 2.2731e-02 seconds\n", + " Total time elapsed = 1.1789e+02 seconds\n", + " Calculation Rate (inactive) = 24223.6 particles/second\n", + " Calculation Rate (active) = 3894.66 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.04491 +/- 0.00149\n", + " k-effective (Track-length) = 1.04407 +/- 0.00173\n", + " k-effective (Absorption) = 1.04203 +/- 0.00169\n", + " Combined k-effective = 1.04355 +/- 0.00131\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "# Run OpenMC!\n", + "openmc.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and 'reading' the results. By default, data from the statepoint file is only read into memory when it is requested. This helps keep the memory use to a minimum even when a statepoint file may be huge." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Load the statepoint file\n", + "sp = openmc.StatePoint('statepoint.100.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need to get the tally, which can be done with the ``StatePoint.get_tally(...)`` method." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t1\n", + "\tName =\tflux\n", + "\tFilters =\tMeshFilter\n", + "\tNuclides =\ttotal\n", + "\tScores =\t['flux', 'fission']\n", + "\tEstimator =\ttracklength\n" + ] + } + ], + "source": [ + "tally = sp.get_tally(scores=['flux'])\n", + "print(tally)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint file actually stores the sum and sum-of-squares for each tally bin from which the mean and variance can be calculated as described [here](../methods/tallies.rst#variance). The sum and sum-of-squares can be accessed using the ``sum`` and ``sum_sq`` properties:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[0.41279586, 0. ]],\n", + "\n", + " [[0.41176924, 0. ]],\n", + "\n", + " [[0.41096843, 0. ]],\n", + "\n", + " ...,\n", + "\n", + " [[0.4095409 , 0. ]],\n", + "\n", + " [[0.40836217, 0. ]],\n", + "\n", + " [[0.40852022, 0. ]]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tally.sum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, the mean and standard deviation of the mean are usually what you are more interested in. The Tally class also has properties ``mean`` and ``std_dev`` which automatically calculate these statistics on-the-fly." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 1, 2)\n" + ] + }, + { + "data": { + "text/plain": [ + "(array([[[0.00458662, 0. ]],\n", + " \n", + " [[0.00457521, 0. ]],\n", + " \n", + " [[0.00456632, 0. ]],\n", + " \n", + " ...,\n", + " \n", + " [[0.00455045, 0. ]],\n", + " \n", + " [[0.00453736, 0. ]],\n", + " \n", + " [[0.00453911, 0. ]]]),\n", + " array([[[1.74741992e-05, 0.00000000e+00]],\n", + " \n", + " [[1.68457472e-05, 0.00000000e+00]],\n", + " \n", + " [[1.75888801e-05, 0.00000000e+00]],\n", + " \n", + " ...,\n", + " \n", + " [[1.79971274e-05, 0.00000000e+00]],\n", + " \n", + " [[1.89308740e-05, 0.00000000e+00]],\n", + " \n", + " [[1.75231302e-05, 0.00000000e+00]]]))" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(tally.mean.shape)\n", + "(tally.mean, tally.std_dev)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tally data has three dimensions: one for filter combinations, one for nuclides, and one for scores. We see that there are 10000 filter combinations (corresponding to the 100 x 100 mesh bins), a single nuclide (since none was specified), and two scores. If we only want to look at a single score, we can use the ``get_slice(...)`` method as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t2\n", + "\tName =\tflux\n", + "\tFilters =\tMeshFilter\n", + "\tNuclides =\ttotal\n", + "\tScores =\t['flux']\n", + "\tEstimator =\ttracklength\n" + ] + } + ], + "source": [ + "flux = tally.get_slice(scores=['flux'])\n", + "fission = tally.get_slice(scores=['fission'])\n", + "print(flux)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the bins into a form that we can plot, we can simply change the shape of the array since it is a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "flux.std_dev.shape = (100, 100)\n", + "flux.mean.shape = (100, 100)\n", + "fission.std_dev.shape = (100, 100)\n", + "fission.mean.shape = (100, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.subplot(121)\n", + "fig.imshow(flux.mean)\n", + "fig2 = plt.subplot(122)\n", + "fig2.imshow(fission.mean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's say we want to look at the distribution of relative errors of our tally bins for flux. First we create a new variable called ``relative_error`` and set it to the ratio of the standard deviation and the mean, being careful not to divide by zero in case some bins were never scored to." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/28/+zIL+Le1a/gSdecO51r/FwMJW523AfwCXt/V/4diLch8t1Lel7W/Q+ei/Za6du3H7XsyxFxyHdu5G1L+hnr+B/DcbwElYS+eOj+8Cn2plfwlc0ZZPaf8wxoBvAWd37fuptt/TwGWt7DfpTLc8BjzaXmvbtkXAA8AzwL8DZwz8P9hw+/ePwONt2w66wn4O9e9XgEdaH54A/ryr/tntGGPtmAsK9e3Bdu6eAL5Mu6tmLvVv3LEv5tjwG+q5G0H/hn7++v3y8QOSVNCcuaAqSeqd4S5JBRnuklSQ4S5JBRnuklSQ4S5JBRnuklTQ/wMXWF17qjWDXQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Determine relative error\n", + "relative_error = np.zeros_like(flux.std_dev)\n", + "nonzero = flux.mean > 0\n", + "relative_error[nonzero] = flux.std_dev[nonzero] / flux.mean[nonzero]\n", + "\n", + "# distribution of relative errors\n", + "ret = plt.hist(relative_error[nonzero], bins=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source Sites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Source sites can be accessed from the ``source`` property. As shown below, the source sites are represented as a numpy array with a structured datatype." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([((0.20665803, 0.15081559, -0.57355059), ( 0.49473673, 0.67921184, -0.54213177), 2077978.15846043, 1., 0, 0, 0),\n", + " ((0.02302023, -0.02944101, -0.45025678), ( 0.53648981, 0.51827967, 0.66600666), 206149.19886773, 1., 0, 0, 0),\n", + " ((0.19282602, 0.25572118, -0.11262284), ( 0.75853515, 0.55187444, 0.34649535), 1153689.72115824, 1., 0, 0, 0),\n", + " ...,\n", + " ((0.14718062, -0.23794414, -0.17253588), (-0.27354594, 0.15713747, 0.94893648), 350211.6847914 , 1., 0, 0, 0),\n", + " ((0.14718062, -0.23794414, -0.17253588), ( 0.16444666, -0.98360966, 0.0739549 ), 3259134.69914602, 1., 0, 0, 0),\n", + " ((0.14718062, -0.23794414, -0.17253588), ( 0.16444666, -0.98360966, 0.0739549 ), 3259134.69914602, 1., 0, 0, 0)],\n", + " dtype={'names':['r','u','E','wgt','delayed_group','surf_id','particle'], 'formats':[[('x', '" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create log-spaced energy bins from 1 keV to 10 MeV\n", + "energy_bins = np.logspace(3,7)\n", + "\n", + "# Calculate pdf for source energies\n", + "probability, bin_edges = np.histogram(sp.source['E'], energy_bins, density=True)\n", + "\n", + "# Make sure integrating the PDF gives us unity\n", + "print(sum(probability*np.diff(energy_bins)))\n", + "\n", + "# Plot source energy PDF\n", + "plt.semilogx(energy_bins[:-1], probability*np.diff(energy_bins), drawstyle='steps')\n", + "plt.xlabel('Energy (eV)')\n", + "plt.ylabel('Probability/eV')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also look at the spatial distribution of the sites. To make the plot a little more interesting, we can also include the direction of the particle emitted from the source and color each source by the logarithm of its energy." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5, 0.5)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.quiver(sp.source['r']['x'], sp.source['r']['y'],\n", + " sp.source['u']['x'], sp.source['u']['y'],\n", + " np.log(sp.source['E']), cmap='jet', scale=20.0)\n", + "plt.colorbar()\n", + "plt.xlim((-0.5,0.5))\n", + "plt.ylim((-0.5,0.5))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Close the statepoint file as a matter of best practice\n", + "sp.close()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/mc-performance/mcnp/input b/mc-performance/mcnp/input new file mode 100644 index 0000000..bd983e3 --- /dev/null +++ b/mc-performance/mcnp/input @@ -0,0 +1,389 @@ +Monte Carlo Performance Benchmark, version 1.2, July 2011 +c +c Core Lattice (Lower Half) +c +100 0 -6 34 -35 fill=10 imp:n=1 +1 0 25 -26 27 -28 imp:n=1 lat=1 u=10 fill=-10:10 -10:10 0:0 + 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 + 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 + 5 5 5 5 5 5 5 6 6 6 6 6 6 6 5 5 5 5 5 5 5 + 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 + 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 + 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 + 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 + 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 + 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 + 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 + 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 + 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 + 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 + 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 + 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 + 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 + 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 + 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 + 5 5 5 5 5 5 5 6 6 6 6 6 6 6 5 5 5 5 5 5 5 + 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 + 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 +c +c Core Lattice (Lower Half) +c +c +200 0 -6 35 -36 fill=20 imp:n=1 +2 0 25 -26 27 -28 imp:n=1 lat=1 u=20 fill=-10:10 -10:10 0:0 + 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 + 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 + 7 7 7 7 7 7 7 8 8 8 8 8 8 8 7 7 7 7 7 7 7 + 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 7 + 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 + 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 + 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 + 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 + 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 + 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 + 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 + 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 + 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 + 7 7 8 8 8 8 8 8 8 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-0.43313403903 + 40091.70c -0.09549277374 + 40092.70c -0.14759527104 + 40094.70c -0.15280552077 + 40096.70c -0.02511169542 +mt11 lwtr.10t +m12 1001.70c -0.0292856 + 8016.70c -0.2323919 + 5010.70c -9.44159e-5 + 5011.70c -4.30120e-4 + 40090.70c -0.3741373658 + 40091.70c -0.0824858164 + 40092.70c -0.1274914944 + 40094.70c -0.1319920622 + 40096.70c -0.0216912612 +mt12 lwtr.10t +kcode 10000 0.0 150 1150 +sdef x=d1 y=d2 z=d3 erg=2 +si1 -140 140 +sp1 0 1 +si2 -140 140 +sp2 0 1 +si3 -183 183 +sp3 0 1 diff --git a/setup.sh b/setup.sh new file mode 100644 index 0000000..5a13602 --- /dev/null +++ b/setup.sh @@ -0,0 +1,44 @@ +#!/bin/bash + +BASE_DIR=/opt/ + +##### DOWNLOAD DATA ##### +mkdir -p $BASE_DIR/xdata && cd $BASE_DIR/xdata + +wget -O endfb71.tar.xz https://anl.box.com/shared/static/9igk353zpy8fn9ttvtrqgzvw1vtejoz6.xz +tar -xf endfb71.tar.xz + +wget -O endfb80.tar.xz https://anl.box.com/shared/static/uhbxlrx7hvxqw27psymfbhi7bx7s6u6a.xz +tar -xf endfb80.tar.xz + +wget -O jeff33.tar.xz https://anl.box.com/shared/static/4jwkvrr9pxlruuihcrgti75zde6g7bum.xz +tar -xf jeff33.tar.xz + +wget -O lib80x.tar.xz https://anl.box.com/shared/static/nd7p4jherolkx4b1rfaw5uqp58nxtstr.xz +tar -xf lib80x.tar.xz + +wget -O fendl32.tar.xz https://anl.box.com/shared/static/3cb7jetw7tmxaw6nvn77x6c578jnm2ey.xz +tar -xf fendl32.tar.xz + +git clone https://github.com/ahmedkmadani/OpenMCProject.git openmc-test + +######################### + +module load gcc/10.2.0 +module load openmpi/4.1.4 +module load python/miniconda23.5.2 +source /share/apps/python/miniconda23.5.2/etc/profile.d/conda.sh +conda info --envs +conda create -n openmc-env +conda activate openmc-env +conda install -c conda-forge openmc (nodagmc_nompi_py311h86d942f_0) +conda list +conda search openmc +conda search openmc --channel conda-forge +echo $OPENMC_CROSS_SECTIONS +python --version + +## ADD %env OPENMC_CROSS_SECTIONS /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml +## to the top of any Notebook files you use + +openmc -g -n 1000000 -s 32 -t -e problem diff --git a/stress-test/benchmark-1/geometry.xml b/stress-test/benchmark-1/geometry.xml new file mode 100644 index 0000000..23c7233 --- /dev/null +++ b/stress-test/benchmark-1/geometry.xml @@ -0,0 +1,104 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 + + + + + + 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 + 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 + 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 + + + + diff --git a/stress-test/benchmark-1/materials.xml b/stress-test/benchmark-1/materials.xml new file mode 100644 index 0000000..9611bad --- /dev/null +++ b/stress-test/benchmark-1/materials.xml @@ -0,0 +1,66 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-1/plots.xml b/stress-test/benchmark-1/plots.xml new file mode 100644 index 0000000..076aa47 --- /dev/null +++ b/stress-test/benchmark-1/plots.xml @@ -0,0 +1,31 @@ + + + + + + + 324. 45. 100. + 728. 151. + 3000 600 + + + + 324. 45. -20. + 728. 151. + 3000 600 + + + + 324. 45. 380. + 728. 151. + 3000 600 + + + \ No newline at end of file diff --git a/stress-test/benchmark-1/settings.xml b/stress-test/benchmark-1/settings.xml new file mode 100644 index 0000000..566d641 --- /dev/null +++ b/stress-test/benchmark-1/settings.xml @@ -0,0 +1,35 @@ + + + + + + eigenvalue + 520 + 20 + 10000 + + + + box + + 0. 0. 0. + 648. 81. 360. + + + + + + 0. 0. 0. + 648. 81. 360. + 24 3 10 + + 1 + + diff --git a/stress-test/benchmark-2/case1_1/geometry.xml b/stress-test/benchmark-2/case1_1/geometry.xml new file mode 100644 index 0000000..bc6956b --- /dev/null +++ b/stress-test/benchmark-2/case1_1/geometry.xml @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case1_1/materials.xml b/stress-test/benchmark-2/case1_1/materials.xml new file mode 100644 index 0000000..ce08d43 --- /dev/null +++ b/stress-test/benchmark-2/case1_1/materials.xml @@ -0,0 +1,238 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case1_1/settings.xml b/stress-test/benchmark-2/case1_1/settings.xml new file mode 100644 index 0000000..3a82903 --- /dev/null +++ b/stress-test/benchmark-2/case1_1/settings.xml @@ -0,0 +1,35 @@ + + + + + + eigenvalue + 1200 + 200 + 10000 + + + + box + + -0.665 -0.665 0. + 0.665 0.665 429.2 + + + + + + -0.665 -0.665 0. + 0.665 0.665 429.2 + 1 1 400 + + 1 + + diff --git a/stress-test/benchmark-2/case1_2/geometry.xml b/stress-test/benchmark-2/case1_2/geometry.xml new file mode 100644 index 0000000..f998951 --- /dev/null +++ b/stress-test/benchmark-2/case1_2/geometry.xml @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case1_2/materials.xml b/stress-test/benchmark-2/case1_2/materials.xml new file mode 100644 index 0000000..ce08d43 --- /dev/null +++ b/stress-test/benchmark-2/case1_2/materials.xml @@ -0,0 +1,238 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case1_2/settings.xml b/stress-test/benchmark-2/case1_2/settings.xml new file mode 100644 index 0000000..3a82903 --- /dev/null +++ b/stress-test/benchmark-2/case1_2/settings.xml @@ -0,0 +1,35 @@ + + + + + + eigenvalue + 1200 + 200 + 10000 + + + + box + + -0.665 -0.665 0. + 0.665 0.665 429.2 + + + + + + -0.665 -0.665 0. + 0.665 0.665 429.2 + 1 1 400 + + 1 + + diff --git a/stress-test/benchmark-2/case1_3/geometry.xml b/stress-test/benchmark-2/case1_3/geometry.xml new file mode 100644 index 0000000..38e2d17 --- /dev/null +++ b/stress-test/benchmark-2/case1_3/geometry.xml @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case1_3/materials.xml b/stress-test/benchmark-2/case1_3/materials.xml new file mode 100644 index 0000000..ce08d43 --- /dev/null +++ b/stress-test/benchmark-2/case1_3/materials.xml @@ -0,0 +1,238 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case1_3/settings.xml b/stress-test/benchmark-2/case1_3/settings.xml new file mode 100644 index 0000000..3a82903 --- /dev/null +++ b/stress-test/benchmark-2/case1_3/settings.xml @@ -0,0 +1,35 @@ + + + + + + eigenvalue + 1200 + 200 + 10000 + + + + box + + -0.665 -0.665 0. + 0.665 0.665 429.2 + + + + + + -0.665 -0.665 0. + 0.665 0.665 429.2 + 1 1 400 + + 1 + + diff --git a/stress-test/benchmark-2/case2_1/geometry.xml b/stress-test/benchmark-2/case2_1/geometry.xml new file mode 100644 index 0000000..a451f57 --- /dev/null +++ b/stress-test/benchmark-2/case2_1/geometry.xml @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case2_1/materials.xml b/stress-test/benchmark-2/case2_1/materials.xml new file mode 100644 index 0000000..ce08d43 --- /dev/null +++ b/stress-test/benchmark-2/case2_1/materials.xml @@ -0,0 +1,238 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case2_1/settings.xml b/stress-test/benchmark-2/case2_1/settings.xml new file mode 100644 index 0000000..3a82903 --- /dev/null +++ b/stress-test/benchmark-2/case2_1/settings.xml @@ -0,0 +1,35 @@ + + + + + + eigenvalue + 1200 + 200 + 10000 + + + + box + + -0.665 -0.665 0. + 0.665 0.665 429.2 + + + + + + -0.665 -0.665 0. + 0.665 0.665 429.2 + 1 1 400 + + 1 + + diff --git a/stress-test/benchmark-2/case2_2/geometry.xml b/stress-test/benchmark-2/case2_2/geometry.xml new file mode 100644 index 0000000..d4f9d75 --- /dev/null +++ b/stress-test/benchmark-2/case2_2/geometry.xml @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case2_2/materials.xml b/stress-test/benchmark-2/case2_2/materials.xml new file mode 100644 index 0000000..ce08d43 --- /dev/null +++ b/stress-test/benchmark-2/case2_2/materials.xml @@ -0,0 +1,238 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case2_2/settings.xml b/stress-test/benchmark-2/case2_2/settings.xml new file mode 100644 index 0000000..3a82903 --- /dev/null +++ b/stress-test/benchmark-2/case2_2/settings.xml @@ -0,0 +1,35 @@ + + + + + + eigenvalue + 1200 + 200 + 10000 + + + + box + + -0.665 -0.665 0. + 0.665 0.665 429.2 + + + + + + -0.665 -0.665 0. + 0.665 0.665 429.2 + 1 1 400 + + 1 + + diff --git a/stress-test/benchmark-2/case2_3/geometry.xml b/stress-test/benchmark-2/case2_3/geometry.xml new file mode 100644 index 0000000..aa1629a --- /dev/null +++ b/stress-test/benchmark-2/case2_3/geometry.xml @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case2_3/materials.xml b/stress-test/benchmark-2/case2_3/materials.xml new file mode 100644 index 0000000..ce08d43 --- /dev/null +++ b/stress-test/benchmark-2/case2_3/materials.xml @@ -0,0 +1,238 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/stress-test/benchmark-2/case2_3/settings.xml b/stress-test/benchmark-2/case2_3/settings.xml new file mode 100644 index 0000000..3a82903 --- /dev/null +++ b/stress-test/benchmark-2/case2_3/settings.xml @@ -0,0 +1,35 @@ + + + + + + eigenvalue + 1200 + 200 + 10000 + + + + box + + -0.665 -0.665 0. + 0.665 0.665 429.2 + + + + + + -0.665 -0.665 0. + 0.665 0.665 429.2 + 1 1 400 + + 1 + + diff --git a/stress-test/lattice-computations/BEAVRS.ipynb b/stress-test/lattice-computations/BEAVRS.ipynb new file mode 100644 index 0000000..4248bde --- /dev/null +++ b/stress-test/lattice-computations/BEAVRS.ipynb @@ -0,0 +1,1283 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lattice computations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n" + ] + } + ], + "source": [ + "%env OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n", + "import openmc\n", + "from IPython.display import Image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we will build one of the assemblies from the BEAVRS benchmark. This is a PWR assembly with fuel pins, guide tubes, and borosilicate glass burnable poisons. A diagram of the assembly is plotted below. To make it a little easier (and improve our statistics!) we will only build one quarter of it." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image('assembly_diagram.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Materials\n", + "\n", + "Again we have UO2, Zr, and H2O. We also have borosilicate glass (pyrex). Note the shortcut for defining enriched uranium. Also note that we can use `add_nuclide` and `add_element` directly with a string. We do not have to create a `Nuclide` or `Element` object first." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uo2 = openmc.Material(name='uo2')\n", + "uo2.add_element('U', 1.0, enrichment=3.0)\n", + "uo2.add_nuclide('O16', 2.0)\n", + "uo2.set_density('g/cm3', 10.0)\n", + "\n", + "zirconium = openmc.Material(name='zirconium')\n", + "zirconium.add_element('Zr', 1.0)\n", + "zirconium.set_density('g/cm3', 6.55)\n", + "\n", + "water = openmc.Material(name='water')\n", + "water.add_nuclide('H1', 2)\n", + "water.add_nuclide('O16', 1)\n", + "water.set_density('g/cm3', 0.701)\n", + "water.add_s_alpha_beta('c_H_in_H2O')\n", + "\n", + "pyrex = openmc.Material(name='pyrex')\n", + "pyrex.add_element('B', 0.49)\n", + "pyrex.add_element('O', 4.7)\n", + "pyrex.add_element('Al', 0.17)\n", + "pyrex.add_element('Si', 1.8)\n", + "pyrex.set_density('g/cm3', 2.26)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mf = openmc.Materials((uo2, zirconium, water, pyrex))\n", + "mf.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Later in this example, we will make a bunch of geometry plots. By default, every region is colored randomly and the results are not always pretty. Since we know our materials, let's define a dictonary of colors to use when plotting our geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "colors = {}\n", + "colors[water] = (100, 200, 200)\n", + "colors[zirconium] = (150, 150, 150)\n", + "colors[pyrex] = (100, 255, 100)\n", + "colors[uo2] = (255, 50, 50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fuel pin\n", + "\n", + "This is similar to the pincell example, but we don't have boundary conditions. This `fuel_pin` universe extends to infinity" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pitch = 1.26\n", + "\n", + "fuel_or = openmc.ZCylinder(r=0.39)\n", + "clad_ir = openmc.ZCylinder(r=0.40)\n", + "clad_or = openmc.ZCylinder(r=0.46)\n", + "\n", + "fuel = openmc.Cell(1, 'fuel')\n", + "fuel.fill = uo2\n", + "fuel.region = -fuel_or\n", + "\n", + "gap = openmc.Cell(2, 'air gap')\n", + "gap.fill = None\n", + "gap.region = +fuel_or & -clad_ir\n", + "\n", + "clad = openmc.Cell(3, 'clad')\n", + "clad.fill = zirconium\n", + "clad.region = +clad_ir & -clad_or\n", + "\n", + "moderator = openmc.Cell(4, 'moderator')\n", + "moderator.fill = water\n", + "moderator.region = +clad_or\n", + "\n", + "fuel_pin = openmc.Universe()\n", + "fuel_pin.add_cells((fuel, gap, clad, moderator))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When building a complex geometry, it is helpful to plot each universe as you go along. Let's plot this pincell now" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# We need a cell to contain the fuel_pin universe.\n", + "main = openmc.Cell()\n", + "main.fill = fuel_pin\n", + "\n", + "root = openmc.Universe()\n", + "root.add_cell(main)\n", + "\n", + "g = openmc.Geometry()\n", + "g.root_universe = root\n", + "g.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p = openmc.Plot()\n", + "p.width = [pitch, pitch]\n", + "p.pixels = [400, 400]\n", + "p.color_by = 'material'\n", + "p.colors = colors\n", + "\n", + "openmc.plot_inline(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Guide tube" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "clad_ir = openmc.ZCylinder(r=0.56)\n", + "clad_or = openmc.ZCylinder(r=0.60)\n", + "\n", + "inner = openmc.Cell()\n", + "inner.fill = water\n", + "inner.region = -clad_ir\n", + "\n", + "clad = openmc.Cell()\n", + "clad.fill = zirconium\n", + "clad.region = +clad_ir & -clad_or\n", + "\n", + "outer = openmc.Cell()\n", + "outer.fill = water\n", + "outer.region = +clad_or\n", + "\n", + "guide_tube = openmc.Universe()\n", + "guide_tube.add_cells((inner, clad, outer))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "main = openmc.Cell()\n", + "main.fill = guide_tube\n", + "\n", + "root = openmc.Universe()\n", + "root.add_cell(main)\n", + "\n", + "g = openmc.Geometry()\n", + "g.root_universe = root\n", + "g.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p = openmc.Plot()\n", + "p.width = [pitch, pitch]\n", + "p.pixels = [400, 400]\n", + "p.color_by = 'material'\n", + "p.colors = colors\n", + "\n", + "openmc.plot_inline(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pyrex burnable poison" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"pyrex_diagram.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define the cylinders which bound each radial zone.\n", + "radii = [0.21, 0.23, 0.24, 0.43, 0.44, 0.48, 0.56, 0.60]\n", + "cyls = [openmc.ZCylinder(r=R) for R in radii]\n", + "\n", + "# Initialize a list of cells.\n", + "bp_cells = []\n", + "\n", + "# Define the inner void zone first.\n", + "c = openmc.Cell()\n", + "c.region = -cyls[0]\n", + "c.fill = None\n", + "bp_cells.append(c)\n", + "\n", + "# Now all the sandwiched layers.\n", + "mats = [zirconium, None, pyrex, None, zirconium, water, zirconium]\n", + "for i in range(len(mats)):\n", + " c = openmc.Cell()\n", + " c.region = +cyls[i] & -cyls[i+1]\n", + " c.fill = mats[i]\n", + " bp_cells.append(c)\n", + "\n", + "# And the outer moderator region.\n", + "c = openmc.Cell()\n", + "c.region = +cyls[-1]\n", + "c.fill = water\n", + "bp_cells.append(c)\n", + "\n", + "# Make a universe containing these cells\n", + "burn = openmc.Universe()\n", + "burn.add_cells(bp_cells)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "main = openmc.Cell()\n", + "main.fill = burn\n", + "\n", + "root = openmc.Universe()\n", + "root.add_cell(main)\n", + "\n", + "g = openmc.Geometry()\n", + "g.root_universe = root\n", + "g.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p = openmc.Plot()\n", + "p.width = [pitch, pitch]\n", + "p.pixels = [400, 400]\n", + "p.color_by = 'material'\n", + "p.colors = colors\n", + "\n", + "openmc.plot_inline(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lattices in OpenMC\n", + "\n", + "OpenMC has `RectLattice` and `HexLattice` objects. This demo will use a `RectLattice`. Let's look at a simple one before we do the quarter assembly.\n", + "\n", + "First, we also need to define a universe that is all water." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "moderator = openmc.Cell()\n", + "moderator.fill = water\n", + "\n", + "all_water = openmc.Universe()\n", + "all_water.add_cell(moderator)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "lattice = openmc.RectLattice()\n", + "\n", + "# First we specify the dimension---the number of lattice tiles in x and y.\n", + "lattice.dimension = [2, 2]\n", + "\n", + "# Next are the xy-coordiantes of the lower-left corner of the lattice.\n", + "lattice.lower_left = [0.0, 0.0]\n", + "\n", + "# Next is the pitch---the size of the lattice tiles in each direction.\n", + "lattice.pitch = [pitch]*2\n", + "\n", + "# Now we specify what is actually inside the lattice. This 2D lattice will be a\n", + "# list of lists like\n", + "# lattice.universes = [ [a1, a2, ...], [b1, b2, ...], ...]\n", + "# The inner lists specify columns from left to right. The outer lists specify\n", + "# rows from top to bottom.\n", + "lattice.universes = [\n", + " [fuel_pin, fuel_pin],\n", + " [guide_tube, fuel_pin]\n", + "#\n", + "]\n", + "\n", + "# We also need to specify what is outside of the lattice. In this case, it is\n", + "# the infinite water universe.\n", + "lattice.outer = all_water" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "main = openmc.Cell()\n", + "main.fill = lattice\n", + "\n", + "root = openmc.Universe()\n", + "root.add_cell(main)\n", + "\n", + "g = openmc.Geometry()\n", + "g.root_universe = root\n", + "g.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p = openmc.Plot()\n", + "p.origin = (pitch, pitch, 0)\n", + "p.width = (3*pitch, 3*pitch)\n", + "p.pixels = (400, 400)\n", + "p.color_by = 'material'\n", + "p.colors = colors\n", + "\n", + "openmc.plot_inline(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What exactly does `outer` mean?\n", + "\n", + "We are taking that one universe and tiling it infinitely outside the lattice." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Try a different outer universe to see what I mean\n", + "lattice.outer = guide_tube\n", + "\n", + "g.export_to_xml()\n", + "\n", + "openmc.plot_inline(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The BEAVRS assembly" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image('assembly_diagram.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "lattice = openmc.RectLattice()\n", + "\n", + "lattice.dimension = [9, 9]\n", + "lattice.pitch = [pitch]*2\n", + "lattice.outer = all_water\n", + "\n", + "# I want (x0, y0) = (0, 0) to be the center of the instrument tube so that means\n", + "# the lower-left will be -half a pin pitch in x and y.\n", + "lattice.lower_left = [-pitch/2.0]*2\n", + "\n", + "# Most of the lattice positions are fuel pins so rather than type all of those\n", + "# out, I will use a Python list comprehension to start with a 9x9 array of fuel.\n", + "lattice.universes = [[fuel_pin for i in range(9)] for j in range(9)]\n", + "\n", + "# Then I will replace some fuel pins with guide tubes. First index is the row,\n", + "# starting from the top, and the second is the column (like a matrix).\n", + "lattice.universes[2][0] = guide_tube\n", + "lattice.universes[2][3] = guide_tube\n", + "lattice.universes[5][0] = guide_tube\n", + "lattice.universes[5][3] = guide_tube\n", + "lattice.universes[5][6] = guide_tube\n", + "lattice.universes[8][0] = guide_tube\n", + "lattice.universes[8][3] = guide_tube\n", + "lattice.universes[8][6] = guide_tube\n", + "\n", + "# And the burnable poison rod.\n", + "#lattice.universes[3][5] = burn\n", + "lattice.universes[2][6] = burn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we just have to add the boundary conditions and root universe to finish the geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "height = 100 # Finite height is not strictly necessary but may avoid floating-point errors\n", + "assembly_pitch = 21.5\n", + "x0 = openmc.XPlane(x0=0.0, boundary_type='reflective')\n", + "x1 = openmc.XPlane(x0=assembly_pitch/2.0, boundary_type='reflective')\n", + "y0 = openmc.YPlane(y0=0.0, boundary_type='reflective')\n", + "y1 = openmc.YPlane(y0=assembly_pitch/2.0, boundary_type='reflective')\n", + "z0 = openmc.ZPlane(z0=-height/2.0, boundary_type='reflective')\n", + "z1 = openmc.ZPlane(z0=height/2.0, boundary_type='reflective')\n", + "\n", + "main = openmc.Cell()\n", + "main.region = +x0 & -x1 & +y0 & -y1 & +z0 & -z1\n", + "main.fill = lattice" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "root = openmc.Universe()\n", + "root.add_cell(main)\n", + "\n", + "g = openmc.Geometry()\n", + "g.root_universe = root\n", + "g.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p = openmc.Plot()\n", + "p.origin = (0.25*assembly_pitch, 0.25*assembly_pitch, 0)\n", + "p.width = (0.55*assembly_pitch, 0.55*assembly_pitch)\n", + "p.pixels = (700, 700)\n", + "p.color_by = 'material'\n", + "p.colors = colors\n", + "\n", + "openmc.plot_inline(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "settings = openmc.Settings()\n", + "settings.source = openmc.Source(space=openmc.stats.Box((0.1, 0.1, 0), (0.49*assembly_pitch, 0.49*assembly_pitch, 0)))\n", + "settings.batches = 50\n", + "settings.inactive = 10\n", + "settings.particles = 1000\n", + "settings.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.3\n", + " Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n", + " Date/Time | 2023-10-19 22:47:40\n", + " OpenMP Threads | 4\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from /opt/xdata/endfb-vii.1-hdf5/neutron/U234.h5\n", + " Reading U235 from /opt/xdata/endfb-vii.1-hdf5/neutron/U235.h5\n", + " Reading U238 from /opt/xdata/endfb-vii.1-hdf5/neutron/U238.h5\n", + " Reading U236 from /opt/xdata/endfb-vii.1-hdf5/neutron/U236.h5\n", + " Reading O16 from /opt/xdata/endfb-vii.1-hdf5/neutron/O16.h5\n", + " Reading Zr90 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr90.h5\n", + " Reading Zr91 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr91.h5\n", + " Reading Zr92 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr92.h5\n", + " Reading Zr94 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr94.h5\n", + " Reading Zr96 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr96.h5\n", + " Reading H1 from /opt/xdata/endfb-vii.1-hdf5/neutron/H1.h5\n", + " Reading B10 from /opt/xdata/endfb-vii.1-hdf5/neutron/B10.h5\n", + " Reading B11 from /opt/xdata/endfb-vii.1-hdf5/neutron/B11.h5\n", + " Reading O17 from /opt/xdata/endfb-vii.1-hdf5/neutron/O17.h5\n", + " Reading Al27 from /opt/xdata/endfb-vii.1-hdf5/neutron/Al27.h5\n", + " Reading Si28 from /opt/xdata/endfb-vii.1-hdf5/neutron/Si28.h5\n", + " Reading Si29 from /opt/xdata/endfb-vii.1-hdf5/neutron/Si29.h5\n", + " Reading Si30 from /opt/xdata/endfb-vii.1-hdf5/neutron/Si30.h5\n", + " Reading c_H_in_H2O from /opt/xdata/endfb-vii.1-hdf5/neutron/c_H_in_H2O.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.35290\n", + " 2/1 1.29703\n", + " 3/1 1.34523\n", + " 4/1 1.32638\n", + " 5/1 1.34864\n", + " 6/1 1.46246\n", + " 7/1 1.27655\n", + " 8/1 1.35373\n", + " 9/1 1.36660\n", + " 10/1 1.29825\n", + " 11/1 1.40694\n", + " 12/1 1.30211 1.35453 +/- 0.05241\n", + " 13/1 1.27009 1.32638 +/- 0.04133\n", + " 14/1 1.34940 1.33214 +/- 0.02978\n", + " 15/1 1.27348 1.32040 +/- 0.02588\n", + " 16/1 1.33208 1.32235 +/- 0.02122\n", + " 17/1 1.33995 1.32486 +/- 0.01811\n", + " 18/1 1.31032 1.32305 +/- 0.01579\n", + " 19/1 1.31485 1.32214 +/- 0.01395\n", + " 20/1 1.40171 1.33009 +/- 0.01480\n", + " 21/1 1.38497 1.33508 +/- 0.01429\n", + " 22/1 1.30876 1.33289 +/- 0.01323\n", + " 23/1 1.29851 1.33024 +/- 0.01245\n", + " 24/1 1.39786 1.33507 +/- 0.01250\n", + " 25/1 1.26751 1.33057 +/- 0.01248\n", + " 26/1 1.35265 1.33195 +/- 0.01175\n", + " 27/1 1.31182 1.33077 +/- 0.01110\n", + " 28/1 1.34023 1.33129 +/- 0.01048\n", + " 29/1 1.42655 1.33631 +/- 0.01111\n", + " 30/1 1.26505 1.33274 +/- 0.01113\n", + " 31/1 1.41047 1.33644 +/- 0.01121\n", + " 32/1 1.39542 1.33912 +/- 0.01102\n", + " 33/1 1.33338 1.33887 +/- 0.01053\n", + " 34/1 1.26051 1.33561 +/- 0.01060\n", + " 35/1 1.22201 1.33107 +/- 0.01114\n", + " 36/1 1.27395 1.32887 +/- 0.01092\n", + " 37/1 1.47329 1.33422 +/- 0.01179\n", + " 38/1 1.37005 1.33550 +/- 0.01144\n", + " 39/1 1.31772 1.33488 +/- 0.01105\n", + " 40/1 1.26834 1.33267 +/- 0.01090\n", + " 41/1 1.34448 1.33305 +/- 0.01055\n", + " 42/1 1.29664 1.33191 +/- 0.01028\n", + " 43/1 1.33513 1.33201 +/- 0.00997\n", + " 44/1 1.28174 1.33053 +/- 0.00978\n", + " 45/1 1.34897 1.33106 +/- 0.00951\n", + " 46/1 1.39485 1.33283 +/- 0.00941\n", + " 47/1 1.31665 1.33239 +/- 0.00916\n", + " 48/1 1.24309 1.33004 +/- 0.00922\n", + " 49/1 1.26441 1.32836 +/- 0.00914\n", + " 50/1 1.30363 1.32774 +/- 0.00893\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 1.7103e+00 seconds\n", + " Reading cross sections = 1.6833e+00 seconds\n", + " Total time in simulation = 2.7935e+00 seconds\n", + " Time in transport only = 2.7625e+00 seconds\n", + " Time in inactive batches = 4.1086e-01 seconds\n", + " Time in active batches = 2.3827e+00 seconds\n", + " Time synchronizing fission bank = 3.8710e-03 seconds\n", + " Sampling source sites = 3.4089e-03 seconds\n", + " SEND/RECV source sites = 4.4113e-04 seconds\n", + " Time accumulating tallies = 1.2799e-02 seconds\n", + " Time writing statepoints = 7.3130e-03 seconds\n", + " Total time for finalization = 7.2897e-04 seconds\n", + " Total time elapsed = 4.5103e+00 seconds\n", + " Calculation Rate (inactive) = 24339.4 particles/second\n", + " Calculation Rate (active) = 16787.9 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.33237 +/- 0.00615\n", + " k-effective (Track-length) = 1.32774 +/- 0.00893\n", + " k-effective (Absorption) = 1.33247 +/- 0.00479\n", + " Combined k-effective = 1.33354 +/- 0.00432\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tallies\n", + "\n", + "Okay, that was cool, but $k_\\text{eff}$ isn't everyting. We also want to know reaction rates so we can compute the power distribution, depletion rate, etc. If we want pin-by-pin reaction rates, we have two options. First, we can use a tally mesh. This will lay a rectangular grid over the geometry and tally the reaction rates in each mesh bin." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tallies = openmc.Tallies()\n", + "\n", + "mesh = openmc.RegularMesh()\n", + "#mesh.dimension = lattice.dimension # Could set dimension from the lattice\n", + "mesh.dimension = [3, 3] # But we will use 3x3 for this example\n", + "mesh.lower_left = lattice.lower_left\n", + "mesh.width = lattice.pitch\n", + "\n", + "mesh_filt = openmc.MeshFilter(mesh)\n", + "\n", + "t = openmc.Tally(1)\n", + "t.filters = [mesh_filt]\n", + "t.scores = ['total', 'fission']\n", + "t.nuclides = ['total', 'U235']\n", + "tallies.append(t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can use something called a \"distribcell\" filter. Note that the bin specifies a cell id." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dist_filt = openmc.DistribcellFilter(fuel.id)\n", + "t = openmc.Tally(2)\n", + "t.filters = [dist_filt]\n", + "t.scores = ['total', 'fission']\n", + "t.nuclides = ['total', 'U235']\n", + "tallies.append(t)\n", + "\n", + "tallies.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.3\n", + " Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n", + " Date/Time | 2023-10-19 22:47:45\n", + " OpenMP Threads | 4\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from /opt/xdata/endfb-vii.1-hdf5/neutron/U234.h5\n", + " Reading U235 from /opt/xdata/endfb-vii.1-hdf5/neutron/U235.h5\n", + " Reading U238 from /opt/xdata/endfb-vii.1-hdf5/neutron/U238.h5\n", + " Reading U236 from /opt/xdata/endfb-vii.1-hdf5/neutron/U236.h5\n", + " Reading O16 from /opt/xdata/endfb-vii.1-hdf5/neutron/O16.h5\n", + " Reading Zr90 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr90.h5\n", + " Reading Zr91 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr91.h5\n", + " Reading Zr92 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr92.h5\n", + " Reading Zr94 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr94.h5\n", + " Reading Zr96 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr96.h5\n", + " Reading H1 from /opt/xdata/endfb-vii.1-hdf5/neutron/H1.h5\n", + " Reading B10 from /opt/xdata/endfb-vii.1-hdf5/neutron/B10.h5\n", + " Reading B11 from /opt/xdata/endfb-vii.1-hdf5/neutron/B11.h5\n", + " Reading O17 from /opt/xdata/endfb-vii.1-hdf5/neutron/O17.h5\n", + " Reading Al27 from /opt/xdata/endfb-vii.1-hdf5/neutron/Al27.h5\n", + " Reading Si28 from /opt/xdata/endfb-vii.1-hdf5/neutron/Si28.h5\n", + " Reading Si29 from /opt/xdata/endfb-vii.1-hdf5/neutron/Si29.h5\n", + " Reading Si30 from /opt/xdata/endfb-vii.1-hdf5/neutron/Si30.h5\n", + " Reading c_H_in_H2O from /opt/xdata/endfb-vii.1-hdf5/neutron/c_H_in_H2O.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.35290\n", + " 2/1 1.29703\n", + " 3/1 1.34523\n", + " 4/1 1.32638\n", + " 5/1 1.34864\n", + " 6/1 1.46246\n", + " 7/1 1.27655\n", + " 8/1 1.35373\n", + " 9/1 1.36660\n", + " 10/1 1.29825\n", + " 11/1 1.40694\n", + " 12/1 1.30211 1.35453 +/- 0.05241\n", + " 13/1 1.27009 1.32638 +/- 0.04133\n", + " 14/1 1.34940 1.33214 +/- 0.02978\n", + " 15/1 1.27348 1.32040 +/- 0.02588\n", + " 16/1 1.33208 1.32235 +/- 0.02122\n", + " 17/1 1.33995 1.32486 +/- 0.01811\n", + " 18/1 1.31032 1.32305 +/- 0.01579\n", + " 19/1 1.31485 1.32214 +/- 0.01395\n", + " 20/1 1.40171 1.33009 +/- 0.01480\n", + " 21/1 1.38497 1.33508 +/- 0.01429\n", + " 22/1 1.30876 1.33289 +/- 0.01323\n", + " 23/1 1.29851 1.33024 +/- 0.01245\n", + " 24/1 1.39786 1.33507 +/- 0.01250\n", + " 25/1 1.26751 1.33057 +/- 0.01248\n", + " 26/1 1.35265 1.33195 +/- 0.01175\n", + " 27/1 1.31182 1.33077 +/- 0.01110\n", + " 28/1 1.34023 1.33129 +/- 0.01048\n", + " 29/1 1.42655 1.33631 +/- 0.01111\n", + " 30/1 1.26505 1.33274 +/- 0.01113\n", + " 31/1 1.41047 1.33644 +/- 0.01121\n", + " 32/1 1.39542 1.33912 +/- 0.01102\n", + " 33/1 1.33338 1.33887 +/- 0.01053\n", + " 34/1 1.26051 1.33561 +/- 0.01060\n", + " 35/1 1.22201 1.33107 +/- 0.01114\n", + " 36/1 1.27395 1.32887 +/- 0.01092\n", + " 37/1 1.47329 1.33422 +/- 0.01179\n", + " 38/1 1.37005 1.33550 +/- 0.01144\n", + " 39/1 1.31772 1.33488 +/- 0.01105\n", + " 40/1 1.26834 1.33267 +/- 0.01090\n", + " 41/1 1.34448 1.33305 +/- 0.01055\n", + " 42/1 1.29664 1.33191 +/- 0.01028\n", + " 43/1 1.33513 1.33201 +/- 0.00997\n", + " 44/1 1.28174 1.33053 +/- 0.00978\n", + " 45/1 1.34897 1.33106 +/- 0.00951\n", + " 46/1 1.39485 1.33283 +/- 0.00941\n", + " 47/1 1.31665 1.33239 +/- 0.00916\n", + " 48/1 1.24309 1.33004 +/- 0.00922\n", + " 49/1 1.26441 1.32836 +/- 0.00914\n", + " 50/1 1.30363 1.32774 +/- 0.00893\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 1.5860e+00 seconds\n", + " Reading cross sections = 1.5596e+00 seconds\n", + " Total time in simulation = 2.7953e+00 seconds\n", + " Time in transport only = 2.7587e+00 seconds\n", + " Time in inactive batches = 4.0746e-01 seconds\n", + " Time in active batches = 2.3879e+00 seconds\n", + " Time synchronizing fission bank = 3.9311e-03 seconds\n", + " Sampling source sites = 3.4452e-03 seconds\n", + " SEND/RECV source sites = 4.6194e-04 seconds\n", + " Time accumulating tallies = 1.8771e-02 seconds\n", + " Time writing statepoints = 7.6215e-03 seconds\n", + " Total time for finalization = 7.3739e-04 seconds\n", + " Total time elapsed = 4.3878e+00 seconds\n", + " Calculation Rate (inactive) = 24542.4 particles/second\n", + " Calculation Rate (active) = 16751.2 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.33237 +/- 0.00615\n", + " k-effective (Track-length) = 1.32774 +/- 0.00893\n", + " k-effective (Absorption) = 1.33247 +/- 0.00479\n", + " Combined k-effective = 1.33354 +/- 0.00432\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ============================> TALLY 1 <============================\n", + "\n", + " Mesh Index (1, 1)\n", + " Total Material\n", + " Total Reaction Rate 0.131468 +/- 0.0036769\n", + " Fission Rate 0 +/- 0\n", + " U235\n", + " Total Reaction Rate 0 +/- 0\n", + " Fission Rate 0 +/- 0\n", + " Mesh Index (2, 1)\n", + " Total Material\n", + " Total Reaction Rate 0.212182 +/- 0.00352323\n", + " Fission Rate 0.00412647 +/- 0.000185732\n", + " U235\n", + " Total Reaction Rate 0.00536945 +/- 0.000226616\n", + "...\n", + " Fission Rate 0.00680248 +/- 0.000316935\n", + " Distributed Cell u11->c22->l10(7,8)->u1->c1\n", + " Total Material\n", + " Total Reaction Rate 0.0827467 +/- 0.0014009\n", + " Fission Rate 0.00781763 +/- 0.000310541\n", + " U235\n", + " Total Reaction Rate 0.010213 +/- 0.000379642\n", + " Fission Rate 0.00743933 +/- 0.000311658\n", + " Distributed Cell u11->c22->l10(8,8)->u1->c1\n", + " Total Material\n", + " Total Reaction Rate 0.0825296 +/- 0.00125014\n", + " Fission Rate 0.00740099 +/- 0.000264317\n", + " U235\n", + " Total Reaction Rate 0.00969887 +/- 0.000320549\n", + " Fission Rate 0.00701317 +/- 0.000264028\n" + ] + } + ], + "source": [ + "!cat tallies.out | head -n 15\n", + "print('...')\n", + "!cat tallies.out | tail -n 15" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the Distributed Cell description showing the path from the root universe all the way down to the fuel cell. This path can become a lot more complicated as we build more complex geometries: for instance, we could take our BEAVRS assembly and place it into a lattice as well." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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PDisB6Zvf+a6YGWcc64pbbhPUHJMvXPnT/xRyOcDhX7vh+9Guw+NtjVffeFP9u29bIn/u7/9Qs2unFfJefuvPW7du6ZvZfs0N32vZtDHZWn/xr+5ueWOj6fWc/ecHm7dtTVVUXHnrLe2vrD22YPY1P/5J+9o1PdNnXvKrX7avf9mQvQseXTx5/aujtbWX33bz9JfXjs9svujmX0xbuaJv5qyL7ru3/ZW1piwtXPRQ+8uvjjQ1X/bLX8xd8VzXwnlX3vTTmSueH29tOOuxJTOefzZXWzX9xRcu/N3vxlob5i5f/oX77uua1zFvxfMX3HN3qSLRvOX18359bzEcrn9725W33zbW1jjruecu/O29qZa65vUbL77rl0owUL9vz0V3/NL2CtW73r309ttTDVXT1qy56K67co1V9a+/eekv79BFvmH/u+f//G6Xp6v2vX/Frbfmaiua16+76M478zVltTvfu/Tmn7leMdxz+LKbfgp5OtF5+KqbbkpVVsZ6jl59442kbTK28uXv/cA7OmwHpW9dd71YyJKWcfmNNzGWKqbTV9x8s6CUMNT+2g3fD/b1wCB/9Xf/zT86SAD3ip/dIik5qlC44pZbPJk0xhPXfO8H0e7uXEX0m9f/S2B4ABDoZbfd5h8dICz78ptvDo2OZMLh66//dtn+D9NNNd/67g2BYz2G4Lnq1p+Fe7sBjl9+662Rrs6eadOv+871U19e0zO345I7b5/yyhojFOh4dHH7uleTNTVfvPOO6ate7O2Y+dWf3TLt+eeOzZ594W/unvHSXyzZM+/xpe0vrRmvq7v4nrs6Vr5wtGPm5TffMvu5Z3tmz77gT3+cseI5IxLoeHzx1BdezNdUfOH3v+tYvqz77I7Lf/6LeU89mWyrn7XsyXnLl+nhwNwXnpu7/Ml8bfk5D/x5wWOLexbMvug39y54dFHfjBlTN71y0a9+bYt8xd73Lrvt5+naiimvvfbFX92Vqaus37z10jt+oXrFqq4DX7z5NoSjEh/tu/zmm62Ev+71Ny+6485CVaJux87LbrvVFoVwb+dlN/4UYYjYsa4v3XiTUh6t3brlkttvV8rC5e/vu/LmnyIU6R8buOrH/0GiZnjg2GU3/UwPeqv37L309tvtqF8+1Pmln94EcFzKJ7/67/+BAweFztU/+Y9wsp9QtEt/frs0Porw5Ne+c0PiWE+mIvatf/lOsK8HcPQVt/5cHhvEDfPS2271jQxDif7q939U8eGe8ZaGb113fbjnqOrzX/Gz/4z2dmEkecltt0Z7jqYqKr/17Wur9+4ea2u89vs/iOzfVwoGrrjtlkTXIRxzLrrzrrJ9+0cbm6677tqmPbsGJzVd84Mfle3dW4rFLr/zF+X7941FwsSHH2DFwn9Nh3/6JcJ/DAiuq9fUFL5wIVbIUQODx4UYJnwwC1imsHUzOTSoNbe6PK9MnW7U1lFHu//K3iYwLQDAcZiDH9Hd3Xp9g8vzp8NxWAjVSZNths2uW2tZZlmiEv2sZYA+A5mG0bHhNetf7I9FMj/8dzNRDkzjNGBXeCbtfWmVb+UKl6YnKA0HIYJhdiCY/Oa38p87DysWqb5e7+q/iJs2QBRMPB8GIYKittdXOOfczBVXuRx3MtNZAJ1sIm4wtIuCXDBo0jSkyEI8atKUi5HZeAwSGAZAvixuegQXA7lgwOQZiOOFaMRiGIhhuUTcIjCAoflEwmJpBEPzoaDOsQ6BF6NRh+MBANloxOZ4BMBsIm7wHAKQXEDWOdZBkVw4aDE0gmO5eMymSODY+WhEEzwIhAXZ73o9KIKU/D6d4xHHzkfCLoYD6JZCIQtHAXRVUYQEQFBQCgYwgGCOVQjIGHRRxy76vC6EGOKqkmjZFACI6vdZCokDqASDlmlijqX5JJOlccfSPLxFkwBFVK+E4zqAdikYQDAUc2xF9tsUgTmWznPFUAhAVxf4fCiEoIjOc9loBHMdk2OyibiLQIuhC5EwANDk2Hwo5OKYzjK5eAxxXRtHc4k4QuA2juVjUYciTZbJhQIQQ21RyMVjDgocls3EowiGISSVj8ccmjKgnQ8GIYHbDJNPxBEUdUkyW5ZwMRQhqEIs6hI4xEAhFHRIwnbsTCzsMgxAYD4RN3geoEguEtEFDhJoIRZ1GApgWC4aNSgCoCAXizqiCBGkGAwYoujieD4SNjkWIDAXjRgkAR07G4uaPIugoBgO6wKHAKQoBwyBgxiaj0UNjsMcOxcJaz4JQLcQDKiSCFBYCAQMgQMoyEfChsACBCoB2YUQtU3dI7gcDaFb8nkdhsKgowRku0hirlPyeQGOAeiaXm8RRzAEql6vAwgUcUt+H0bgKHQUSXQwlLBNTfIAMwhdV/NJ0HBQ1yn5vKhuAIAoHgGFEWBbBksXomEEBapHIEJBiKGKJJFBGYOuIfty0QiErklTuVDQRYFFk4VwCJKERRHZeAzBgEUSmXgMwVCXoPPxqEUSkMAK4YiDEy7LFBJxhMBNAs3EYwhBQBTkEzGbIgFN5KMRB0NtlsknEgBBIIFl4jGHJFySzMeikGFtkihEwi5NW5SZjUddHIOok4nHbAxxcTQbDZsUBTGsEA5bHgEAJBcJW4IAEDeXiBuSgCIwF/BrAu8isBAKGDwHMDQfi1o0CWy7GIuqogQQWAyFVJ+EII4S8Buix3XtfCRsCDzm2IVoRPV40OMm80ooAEowaAochsBCOGSIAura+VBQkyQUOgWfV/GKwDaLAb8qCidt9gXYtkuSYz/6Sf6c8wOPL6aPHQUT7nYCABKksG0rc/Bg8l++W5o6rTh7jl5ZKT/5BL9zB6qqE+NYEMP4Xe8QoyPJb1+vtk89Ha6IhjD3xUsdjn1n8aOmoZ/VcR5BkJ/htdCfaokQANDX3/PKxtVDtTXpG35kRaOnPLtCUcRx6O6u4OJFzIH9LsNM1BUtR5TU9qnJb11nRqJ4JsN+sFd++kmqp/uENN9d1xEEtaUtc+XVWnMLahgng5jvPy8R2lbm6q8YV16aOHJ4vCzB2BqRLloSb1E0N5pyJKbA+8JdnSO19RFlCKasdDhEIhaZKloe1mRofjzjeuicxx/u7ByrqmFdlR9KZkMhHHXJdMFlSF3gubEMS5mjgbJAZ/d4eSWNmkL/aC4SwTCXSJcQElUliR9NIRye8YfCh4+kyytJzOb6x4qhoEvjZLbEmEomGhOGR02adSRWGBxRRS/C4VhawW1TC/vRgsEr+XQiLgyP2ySVC4cDvT0mx7s8gRUMwtCT8TJPKimU8qmyBDeWhhiWDYXlgT6bZjhSL5o0rZTGK6o8qSSlKLlEVB4a1AgmFwr7hwchQG0fBxWby2VLiTBStLhCLlMe96SSiOmaPh4xIZdOj1VWE5oWGugbrq2V8ikip46Xl7PFAl0oWj7ecTF+LKnF/BpgQke7hhsbpXyKyKr5WIgpFFDNsvy8DXFxZESJBVWUCR/tGm1oiGb6dQUrhvyUpmJF3fLxFiCk0TE16i8RfKTzyEhDo6eUITOlQkgmTYPMlTLhqEOR8YMHRmtqGagLA2PZcJhAHSJTchjS5Fg2mYUcmfMGwocPj1dUHTdHPhIRnbxeRBECaJKHG88gDJ6RQ+EjnalEOYnb/MB4MRBASYRIFRECaD4vO5YBFMgGwqGuo5lITPMI4e5OxS8DGsWziovjeTngHxrESJAKREI9PblAsOj1y0MDuG0lE2WedFooZFNlcW48CxGQiUQCA30WxbgiheZ1SlXHKyr5dFosZItRmU1mDYzKhiP+4UEHxR0vCxSLyedGq2qEdJrP59Llcf/IsIHRpo9nU1kHQW0fB3XIp1OlsrCrunxqPFNZ7h8dMQBp+gVmPAMBavkF14Ce8bHBhqZAephIl/KJCFMsoIph+QQHwfixpBb2aRgb6u4cbmoSi2kqXcpHgpSmoCWT4pESwfPjGTXsVQhP5MjBoaZmUcmSqUIhFCAtAytojsSaGCmNjGkRf4HyxA59NNzQJOh5ajynh7yMoVol1/EwBsVIw6NqyFtgpfihg6N19axZpMYLpZDsYhibzPCYNh5KeLv7MrE4SThC33AuHMUISKSLCAY0v5cZz2IETAcjoc7ubCyOky7fP/ZXk2VKDknk5aB/eAjDYToYCfb05gJBVRTDPd2qJEEWxzMqApBsOCwPD2MY1LyCZ2A0I4dUSQod69F53hVoLK+hjp2JxuWBAZulsyVDvv9+YuRkKhH+bysecBzvmtXixvXEyChwbYhOVJ/dcSAAhfM+n7n8SjMWRxBEem2d9OrLZF/v8RA7kTkty+W4zNVfzn/uPEeUTnnBdwAQCPktbwaXL5uRqJo371yW4T6r1sJPr0SIomjX0cMvb1w1PKkt/b0f2eEQME9tKVFIEFix6NmyOfzAH8nBwQlKfR4X262oylz9peS117uCQHce8a5dHVi+FM9lJ34juusiEOqNTdnLrkx941o7FEIN42TZnfzTEqHrqi1tIiDPve/3DoJbJPW53/xB53hCMToefKQky/Rwct4jjym85NGK0/+wCLqo4fF87te/d0gKmO7cBx41BJ7OKB0PLbJIFqJg/m//hOtmKRg671e/dQHqIOjchxYTOOLqcM6DjzoOanmEhb/+PV3S07HYub/6LWbaFk7OfWQxggDbQjoeeAR1EM3rPeeXv6FzxeGWlo4/Lwoe6crLocnPrJQ7j+bjsbkPPRY5cLh71uxz7vlt4sMDA22TZix+MrJ/fy4UbXvhpeiBg6M1DR0PPlz23t7Ojo7P3Xt/fNfe7qkz5y5dlnhvTz6SaF71cnzvB4M1jXMfW1q19e2+uTNm3/9Yxds7u6bMnPXs8+Xv7MqHY22r1sTf2T3Q1DZ3yfK6t7YeOmvhvD89UvfmW0Pt7S2rXql6a9tofUPtlu3Tnn6+b8qUxlc3Tn1mxeHZC5o2vTHt6efGK6rj+z6c8vTKdKw8fODQnCXLetun1Ly+ZfoTTw+2TynbuXf60ieSifJQd/f0Zc/kg+Hgke7Zi5cNtrRWb985bfnTQ62t4fc+nL10eS4SD/d0tS15thgI+Xv75y16fKSxKf7e+9MefyJZXxfY3zXvsaXFQFgYH5/18BJFDojDYx0PPjpU18iPjJ/14MMmzQMUzP/N/YRuFYPB8+/+HXBcCyPmPvCoSxCY7nQ88piD4CbLLLz3fqakFcOBs+75I6brOifMe2gxcF3bBvMeehQ1XU0Sz7nnPjaTH62pOe/O39CKqnKeuQ8vxnXdxOi5Dz9KKXoyVn7hHb/0jIwPNzZ+/q57yWyxIMkdDz/K5bIqLcx9ZDGbzHVNm3XB3XdXvLu7c8bcjiWPx3e+l4/Em15aV/7enqGaxjlLHq/e9k7ngvkL73+w4u13u6bPnbn8yart24uRcMPLGxLbdw01tM568pmG1988fPbC+X96tOaNt7pmzpv+/AvVW7dnovHmV9ZXbXlnvLZu6nMra1974+A558xetKxp3WsDU6Y2vrKxdv3GTKys4Y3NtRveHK2vn7xiVdO6DUcWnDVz+bONL6/rnjGnateuqcueLgZCck/vrEWPDzU2Vbyze8bjTw63tET37J+1ZFkmVu4fGJj56PKCHBT7B+c9tDhdXRF+/+C0x58aq6sLHDw677ElhVCcHx+f9cBjmuQTxpMLHnw0WVkZOnBk+mPLMjVV3qOD8x95tOCVqVKx44+PWIKHSafn/vmxdCLu6+mf/diyfDzODCYX/PlBTfBilnnWfX/SPZJB0fPvf4gxVAuSsxYtAaqp+3wLf/MHPp0bqa09785fM7lCSfLNfXgxqWkGIOc8sphSdEWWz77nPmEkOdjaev6dv2Ez+awcWfDQI2wmq9P87Mce51LZkYqaC++6x9vT19/eft7dv+WGk+lIouPhR8WxUZNmpi95ih8YHWpo/cLdvw4d6uqZMePcu38n9g6kYpXzliz1DI6OBsPkh++fbCXCf9joIgBobZO01jZU1/BMBlOV492CE9nSIwh9+BB78COX561AUJk6Ta+uxYtFYnxsgq2LGAYsi39vF1YoWMGgLQf3OU9GAAAgAElEQVQAPOU5lllZbQYCma2b7XQyHI5RJPWZPAj+qfxZgGHYvgN7NmxaO7bwnMz1/+pw/KnOrlyKpvqOeV9eI65/FYFwYiU8YFkuzZRmzExf9WW9oQFVVeHtbdLLa9h9H7oMcwKa74YZiZbmzst9/gKjugZV1VPlbZOuWYwEMAKSwNYiAZTCCGhoIRnFEdo1SokIgVikA0uxEIE6lGMYEg9whEJMPeRDcYS0tUIsjCEWaWlG0IcTCOXqql9EaIx2DC0gISRKWZoWlnHcpSy1FA8DAtKOYYb8rkCT0FRlCVAYZ6lqPIQQCKWXSokIIAFhG5aHcXiGtTRLFl2GYUxVDcsuwBizZMuSTVGUqVoC7RIMZyqOhwUowugFPejDbIs1SoaXdxDA2hrCkhbFM6ZieXlE0zizZPkEoOOsrtgiBxiCM0suR+iYyNiq5eVwAucczfDyOGYzhgIFRsFlWi+5HGHIIqMrLkPoQR9t6wiFKdEAaymABMVogHJMDAdqyEdBE1K47hdJaOGYq4b9pKWhGCxVxFjHwHCkFPLhwAYUaspeEpoo5pbiYcrWSGCXyiKUrWOYa8oS7eoARxS/SEITx1w9FiIdg0DsfCJM2BpKInokQAIbwaAqSxhwKVMtxkIEtChLVRMRlICkYyiy6NI45RhG0AdojDaVYjSIA5sxVSUcAqhLOboW8kGepm1Nk0WExnlLM8IyRgDG0ouxECABY2lmyGfzNGlrmldEKYyxNTUSQEiENUpKLOTSOG2pmixaAs04uuX3uAzB2poS8gEapS3VETkTAayt2RyFkCJtqabEARJwZsmQRaSksmrBlniHJhizhDK46fXQpmpJHAIAayumxJnAYrWiwxGGLHFmyeFJVRYZS7NF1nQc2tVNnqZFlrY0wGClaJDSFZcljEiAtVSHo3Sfh7J12yeYuo8xFUhjpXiYNhQCc9WQjLsGJFE97CeBjaJOKRo8brtSeYx2DByHSshHICaOQd0vEoiNo64SC1GOTlpqPhGhbA3FHTPoJRHLxglFljDg4JijJcKEa2COUQrLlK3ZLlAjAQKaNkapkQCOIxhilcJ+AjiI65Yq4jjmENAuRYMo6hKuZfhFlyEoU1WiIYwEtKko8QhCAtrSzJDsiizt6LrfAxmCM1Uz6EUIQBlKKRYGJKBt3fR7TJ5mXd2SeITGaEtTgj6XRjmjpIT8Dk+zetGReFegWEs1/R6Ho2hTU8MyYHHWVLSA1/byrF40fILlFVhb1f0eh6cweCpc/wIhMAyjvGLs+z8U3tkhbniN2f8hcJyJnCcBAFIUdbQ79NCf6XPPz33hQr2+YfTf/0N69WVxw3qq75hL/b9vy1HUpWnxtXXk4EDm8iuL8xYA6J7CqSwIEQQpdSyABPHOY49Ym9ctXHiBwHk+fRnSTzyDBQAAAH1v7zubt6wfueiS7DeudVgWOKdyzyCKQgzj9+4OPLlM2Lb1uALvRPzNNM1YPHv5lemvfM2oqqKP9fhfeM7/wvPk0BCk6Qkme20bOE5x/oLMVV/OXXyJI4qoYZyEL/CfZrD05pbs2QtxU++e12FIQsEXHK+tzkRjmuhN11aP1DcCxzky/yzHyyg43zd3blH29bZNTdVWFQIBTZBSddUDDU2E4x6e22H6PIONLQNtkyyWLgSjqdrqdFW1zgq5qlh/Q+t4ZXV/e7vhFx2c7p42Tff78r5AurIiWVOt01ymtmqwuQ2zrK65c9VwwMKI/qnTdJ9YkkNFv7d3yhQLp0YaGtI1VQ6C9U+ZqoTkoj+UTsRTdTV5f9CQff2T28fLKoaamsYrqwEChyZNUsJyXg7n4vH+5pZcPDZeVztWU22QdLqudqihEcWw/uYWxMd2ts0YaWnJxuO6z1f0+nqnTkEAGKmpG6xvQAhipLG5FAvmQtGS3zc0qS3nC+o+38CUdkgQybLKTGVZNhBRvb7eSe2FSBgAcGR+h8OzRdF/dPo0g+PyZWWFWHi4rmGwpVUNB0YqqlGIdJ51ls3RiuAdntSWi0aOtU9VwoFMMGxK0lhL42hlHYDOkQULIE8emTInWV9n8VwmlkhVlacS5RbDDk1qGamoIVz30IIFtodTPd7+SW2qJBWi8eGGxnRlBWnbh+bOVUOyC/GeObNVv1SU5GRdTbqyUuc9qerKvsYW3HG7585TZK9V7jk2ZarNs+PBskx1Zbq62mD4VHXFUENTsrJyoKVFCckOSvROn6HJ3rw/nK4oT9VW6pVyurpysLYFs+3uWbPS8QQGYf/UqWpQzvtD6fKK4fp6hyTT1ZW9zW24bffNnl3w+U2vN52IDzQ26ZJoeL2906eZJJ2qrh5sagYAGWpuLsXCGX9QicV6W1uzifh4XW2musKg2fGq2qHGJkAQg/X1alAeaGwdbmrOxGKKLGsesXfGDATDRqpqMxWJ8fLKnpmzHIHORssUn3d4clvWHzI9nt5Zs1wMHa+oTtXX6JwwWl2TL4slg1Hb5zs2dSrCUkVB6psxzeS4TKwsU1WejcZ1URppaRyrqEZd9/D8+QbP6qxnaOqUXDTS0z7dDHnHIwnD4x1paxmtqsNd96P58y2J72ubMlZfZ3JcNhJN11RlyipNihluaxmurEnV1Ay0TbIlQeOlgbYW3SumouWZ6op0eaWNE0PtrSNV9bhlHp6/wJAEjfMMtbZosl8VvYXySG/bFOjAntmz1ZDs4FT31Gl6wJf3yunKivGaao3zZCvKBptbk5VVQ83NpWjIBnj/jBmaXyqEYsny8uG6+lRlRbq6arSmFrjw2PSZqbIyFHH72qeoIf+xlkljDQ25WNRimHRF2Whzk+uC3mnTk2VlOI71trUq4WDeF8zFYn2tbQ5JJmtrsizH79iBlYonbQbrf0lloaheW6c3NDg8TwwPY8XiBLuXMAw1DObQQbr3GCRJo7pGbW2zYjFg21TP0YkJykOCIMbGmI8OoJapV9dAmjmlhbIAgliJMjsWz727TRnsi0XLKIr+lJ/hkyVYAAAUxd7ZteXtbRuGr/5K9ktfgTR9StsMYjiwLWnDevmpZXR3N5zYbXquCyBUpk1PXXtdccFCR/BIr2/wP/uUZ+sWxLYneIsWhKiqmhUV6a9+I3fpZXp9A3Cck/RV//clwpDttD+/kjRMhyBnLHnCYhmmUJq0cpUmid6jffUbXkdM21dM17y0gSwp/VPax4MJi6Zrtr/d+pc1Jsd6h0brX9tEGuZoQ8NIpMIk6bIjh6c//hREAa7qLS+uYWxtqLxuJFFtkkzVB3vaVqzm05l8NDz3ocUIgmCm07pqLYYCKp1vWLeByhZchmx/+gVPMjnU0jxj8XJxfBzB8epNm/09fZbANa1dFztwsH9q+7yHHvP1HEtXVk56fpXc358qqxhKVBd9sjQwOP3Z5xIf7OudMb3j0cflzu7BSVPHwzFd8MgDg43rNoYPHUlHE22r15Tt3pNtqj5W1lyQfFwqPW3li3JXD6So6i3bwh9+lK6qa1u9pvrdXT2zZsxevDxw6Eg+Ea/f9FZ4/0dKIBDf+37Luo2jDXX1G9+ofmvbYHNby1tbK7ZuNwQxeuhg1aYtqscnH+trf3FVtiw+UNOYlSOsVqp/ZWPN1m02xQYG+upe3WSxbH9Ta1qOoABpX72m5o0t2fJE4t291W9ttSgqXVfdH6vFXKfpjTdbVq3N1tUkduysfX1zKRyM7T9c+/qbEMWEdKr5xbUWQ3tS6fbnXhivqAr09Da/8ippObiptb6wmsnk1GBg9qKlAADUttue/wskME8q07B+I1VQEBk7h2N4ZayQ1KYufQa1bQQiLStfIm1rtKxqNF6pMXz1h3vaVqwWR8dTlRUdDzwCNB2tCnagFuUY7pH+xpc38PlS3uOdvWy5d3hkvK52/sNLUMO0aLZ95V/YYh6zkcZ1G4WR8d62yef8+QG5s2ukuW3aU88Eeo85FF2+bWfscGcuEG55+ZXyXe/1T5syd/mzgQMHB9rak5G4JniCfcca1m0M7j+YLqtoeeXV6vf2DLU0Dceqil6Zzeamr1oTPHDIoanKd3bGdu3NJeJD1Q05b4DRlDlLloc/PJAvi1Vv2Bz7YJ/DMIm975fv2FWIRes2vFGzZXvfjGnTn1sZfX/fSHVt9ft7ql9702YYaXBo0gurk7XVle+8V7fpzXx5Iv7u3urNb9kE6R8frV+z3qao/pa2lByjXaP55deq39hSiIajHxysfeNNl2aGGhvHA3EEBY3btrWtWJ0vT4Tf31/32ibdJ47GqlKxcsR2G97e2vTSeoABppBvfebFYjQSONRV/9rrlk/ydvY2rN+A2i5l6pOffdFmaIskpz7xLO0YRKZQu+FNNpN3aHLycyul4dFUVcW8Bx/FNd0hqUkrVzOaOpqoHE1UqQxffuCDSc+vkgeHRhoaOh54hM4VBtrak6GYSxLhI92ta17xpNK5UGzW0mX+Y719M6aPhisUTgh1dk95cZWQTpO6Xr1xs2dwJFlRM3fJ0lDf4FBj/ZyHF9PZvO7xtq9dy48l014fsf/Dk7lE+L9vgx1R1Osbjbp6VDfoY0eRCfGh45ekkUODdOdhPJsxqmv06lq9rt71eqmj3ahSmkgowTBM15lDHxEjI1aizAoGT3UFJTNRZlVUFLa9me09WlleQ1H0p3nm/ZO8ERMAAMDb77y5c/f2wW9+K/+FiyBBnNIH6CBBYIW8//lnPa9vxBRlYt2CwHEgQWQuvSJ3wYVmNIYX8vJTT3jefB1Pp+BEhRiAbSMA5C68OP+FC/WaWgRFT1Hli6IgGh5PUZYNjlV8PtUjapJU8vp0QVBJ3qGoYigCPI7h9RYDQcR1IUARBDFEUQ0EdF4oevw6w+T8MqFrjpckLNOgaUX2ax6xJPlU2a/7fDZBWjTDZdIlv6xyfDEcNmmm5JcNQVAkSfV6VYHPBYMWx6mybHg8piTlAkEHw4oBGTBEwR/QPZLB86rkNQRBZzmLpvOBAIqhBs+rwRBpawZNH1da0nnB8PsUnLAoOu/1Asc2aBpA6GC4SdOqKGG2pXk8Oi9YPK9zPIJABycgAEWvjyqVioGA7vVBytRYVhdFBEKLojPRqJhJ64Kger2Yrpe8Xp3jCj6fwXHFSEQcGtV5Pu+VZIbJ+2VGL1miR/X7CMssyX6d54HrOhTuAlQJBC2Wy4ZCaMpRRVH1iAiCQIBCAJRgWBE8ukfMhcI2y5QCQcyxXRRHINQ4riTLOssq4bDh8aiiaNm4ztB5OUjijub3qXIAsy1F9us8bzKswbLFQFAXJUOSCqGwQTMlr08VBFX0an6/4RGLgbDBcflgyKA9uWJviuAsllP9su7xKKKk+ryqV3Jw3KJoplBQ/LIuCHlZNhmmEAhYHk+JZNK5gQIlFP0BXeCLXknzeCyvlA8GTYoueL0axykeUfX5NL9cDARMQSj6fTZJFvwy6jo6y6rhEFSKeTkgCYJjEIogGDzvcpzJsgVZxvM5k2FRx7FIymQ53edDGEvzeFSeRwFqsizqujZBOihWkCRe1/JyQBNFx8EcgnBwHHVdF8ezwZAXIpooqn6ZKxZygYCP41CfT/V4dI+nIAcsmi5E4mhJ00SpKEqmKKo+P5IDJVnWOb7g86kej+bxIIGAzXL5UASaRd3nUz0i4kKIoi6GK+GwJkqa5LUt3GDokhzEDdNlcMSFGsMWfT6d4WAgqPt8utcHXGiRFJvLleSAKklqMAhRqPplnRcsBNNFjyJ5dR63Oa4QDDkeXpVERfCYDFsKBA1JLIXCNsflA0FNlFSOL0Sif/MgVRQVn08RRRdFbYIkVUX1+ixJygaDFkUV/bImiiZBHndblec1jiuKksayuiSV4nGbIBEIXQwzKUr1ejWfrxiOODxfCARMjlM8gur1mixbjMV0waOIHkUUFclrUdQJSTp/RhwLwTB10mQrGlWmTZOffpIYH3UnlF9xKYpIpbwvr6GPHE5/41tK+5TsJZfplVX+557mPvzAYdn/1xQARFHEcYVtW8mRkcxVXyqcfQ5aKp2yMRsC21Zb2uB/3nr49791Xl3xxQuv9vDSp3Yt9CfVRQgAQCCydcfru/fuGLz+O4WF50CKOqVbQCGOk0NDwUcfYvfvQ+wJ3rsJbNsOBMe+90O1tc0RJeaj/fJTy5kDB4BtTViGBDV0M5ZIfe3ryrQZLsed/C/5/9RFeNVl1e/vGamuphCLGUlrftFmGE/voB72aQTDpzNFvyw7Waw/m6qqwoCNFg2Xwl2K9AyOGH5P1heseG/3wKTJNNS5oWQuFqNMhR7L2iKniaKnf4RmnYF4TaCzJ1lZybqa96OubGUlirtYVkMozOA5cWDE8nKpYKzigw+Ga2oJEvo6e3PxuMWSzHiW1UpjtTW+o30GyxpBKXDkaMEfcLws1TeIQ7NYUUOmi4KSH66pFkaSNoHnwpH4oYO6KNkiTaUKuKYN1zfwmYyYT6cSCal30CXJZFlZtLvLIimad3OIQKtaKhr1jo0xuWyyuip09ICG0unqhlBfrwtQI+zFiiafHMvUVuAZRchmRxpqfcNDmGpqYR8wHGFsfLCxiVJK8a6uY21t/vSoq7v5YJBRSlw2qwW9Jk7zY+N6yGsgVOLgRz1Tp/oyY8xoOlORICwL6jbkScQB3p7eXH2FSvAVe3YfmzE9muxTdaIUCXC5LJkraSGv6wCipKAckeX9FXv39M6c6SmkuaFktjJBqCqTyScT5RbLVu96d6C5lQam/0hPsqoKQ21mKGWLvMGzRK6EEyDnC8Y/+GCopZVELbm/Lx2JecyimXMQhlQk0dM/7HqYZDQR6uxOx+MUZsuHjmbicYTB6NEsQmLFoF8aG3cZIinHyj7cN1ZebnK8d3TEIknIU8x41iTJTDwR6e5GaGw8mkjs25+qqMh75XBfD6HrQ3X1vtFRMZcerq2RBkcgAJlwxJNKAgSxJZZMFylFGWxs4vN5MTVWiIekgRGDYJLl5aGj3RBFjaDk6pBSSql4mTg+KqTTow314f5eE5BqUCRzRRcloECCkiWOjaTrqtCcRhiaKvu8yXHHAaWIzI+noe3qER9QbGmgv2f6zND4ADuSSlVVsMU8XtC0kBc6wNs/mKtJqCZa0bX7aMs8r57jBsfTlWWkY7u6w5CGgTKevuF8dVwhPRW7d/VMnyEqWTyjFGJhLp8hM0U96DVJRu45VqiI5VkpevjQSFOzp5ji+8dK5RGmlHFTBTMS1mkhcLSnUBnPc97q3e/1TW7nzKLQN5JNJByS8AyPMoQ5EquK7D84UltHEK7vQGe2ogIlETSrAgI1BY4fTkKWSMbKgkd70pEoiTv+I8fy0ZjLoMxY1iGIZKLMPzKMo246EE4cPJSKRIt+f9mhg4rPZwsUWjAhCgoBOdRzDBCI4fWQqYLOsLlINH7wgCZ4LL9AJQvAtseqqiLd3RbPpXRXvv8PJ3MX4f8lC2Xb5Miw96VV0qsvuxQ1QR0HCBEIbVnOX3Bx6qvXANMkhwa9a1+S1r0CEWSCVUjXtWU5d/ElmSu/DGzr1O4uxDBicCB61y/ipnPFpdd4Jd+nI0P6iZQIAQAQwh3vvvXu7u3D1/1r4ZxzT0jX/+RwA/bgwci9v6KPHkVcd4LsyrKUadOHb73dqK6BBCFtei308APUsWPAdSbIrlwXQZDiWeeM/vDHWlPzKUNh/7sSYWtbtKDMe/gRqqhYFHXOPffZJMnlC3MWLUUw0NfQNharsAA+ec/bbYue4tIZw+O54NY7AYaRJXX2g4/aNOk9OjDluZV0voRCd8F9fxZHxoqx2Pm3/wpxEULRZi1aymAuO5SasfQJJpl1SaLjT494R8cPdXSMhSoMlqt+e8eMxU8Qlskkc1OfflZIZW0PP//3D0g9vYOTJp993/3+gQEXJ5tXrAp/dEj3eacte6py+7vHpk69Gh2oYomBvDXnkccDhw45BNW68qWqDw9kw9HZSx6v3rylu2Pe+Xf8OvbBvsGmtoX3/zHx/gc2w9Wve71yx7upeMXspY83bn27b9aUhff8sXLLtuHG1jnLnojs3edGhDkiXo8XB4Aw68ElTetf61x41sJf/77ynV2Z6qr2l9bHtu8oRaI1296e8cSzg5Pa2latbVu5urdl0pSX1zWufaXoD4zW1I5GKlAHmbbqpdlLlvVOaW9b+dL0Z1akmhpqN21tfmmNRVDhrq7JT6/UOSH00aEFf3xwtKGhcfPWthUvZmqqKje/3bp6DYIT8f6jkxY96dC0r7d/3kOPKrH40eb2tBwFAE5eu65txV+AA/nk+MxFj9sMK42Nz//jQyOVteEjXdOffJo2HNzUZz30mDg0XAxHzr/jHqZQ6J4xZyRaaTFU7Y5d0554mk3lEAKfc99D8vBYISSf/cvfYaoGETB70VIcQmY0NXPpcn4kaQv8vD88KB89NlpX/4Xb7qQUFcGIWQ8vpTI5oqi1P/28f2T8aMvkZLRMEaRI15HP/eq3ZK5k4dTcRY8J6RRwkPanng8c7e2cOeeyW29LvL+vf+rMBQ88FPngQ5ui617dWL1j51Bt43BVbU4KcqX8+b/5Q9mOnf2Tpi546OHKd3ehHFuz4c3KzduSFdXTn3ymZePrR+bP/9w999a98VZ/Y8us519I7NhpU3TdpjdqXn8rX1017ekVk15Y9dF555597x+qt+9INtQfq2rKewOeZHLa8ytqNmxOV5RPemFVy+qXj3Z0zF+8rOr1zcNNrTVvb5v8zIsOy/p6euc++OhoQ0P9xs2Tn38hXVnV7FHOYnhVH/Pt6Zyy9CmbZry9/efc98d8eaxy5962Z1bmE/Gyd3ZPWrkKELRnZGjunx5BAMJncvPvfzBbXl72znvtz6xQQsHEgc7pS5dhlsOUSnMeWoy7jljlu5Au5VkxsPbNKc+8YAT8/n2Hpz67AtVN0jLn3f8QxDCLpOb//s+8pZPpQtuzK4WxlMMxc//0SLBv4FDH/NFwhcYJFXv2zHp0GV0qErnijMef8g2OmF6x4/6HAwcPDU2adOGtv6SLisGJC+//Ez82hup2+zMr5KO9yWjivHvvCx08PDhp8hdvvUMYHFJE/9xFjwWGhsarq3srG/JiQO4fOPfX9yY+Otzb3n7enfd4BoYVQZr95FPeY/3jgSB+YP8pVCL8R2IEgCOKWkubXt/AHvwIKxaQCZ18RwBAVZU5cpg9sE9tm2xFolpzqx0MMEcOo5o2kZh1fMJDh4jUuNraCkkSnLopEghdj0eZOQt9642+/e9XV9WzLPcp1Ao/fp1TAIDrOjt3b9+2Z/vot64rnHc+JE5xdgWAsPmN2O23EGOjf/2UJ1QZTF173dDP77TlAKqpocWLQn++H0+nJzwh4rquKI798N9HfnKTFQ4fF/84td8zRJRAEEcQJRiyOB7yvOkRixzv0rQq+RwEuCiGWlZOEHAEFn2yxXKOKBk0W2I5l2ZMUcqFwwyO5wMBRZZRgih4vQZO2JLX4QVV8kKS1Gg6EwjiCFDCYVWUMIou8TzqQBdBXQSYggBZ1hI8uXicgKAgikWPhBCk5vPbFGlKEgQgGwgiDGt6RMXrBRStyrLKsHld0YqpEkYrXh8gyEI4inCczjB5yefihOb16wyn+/2ApBRR0uSgSxCZcNQlSVsQij4/JMgSy5oMVxQESFAFn98QJQQnMp6Aaikl1ymxPKRpyy/rLK/5/AiOFySfwrI4RRUDQYdmDMGjM5zq8SAErgbDJb9MQaQUCAHHgRBYGKYxrOX1OjSjywFI04rky3slynHzZRWG6AUEqcoBm+MdUTR5ocBwGMUovKcQCFKOm5GDJYZFCLLk9Vu84Hg8Ok0D23EBShhmzidzGJYNhQ3Ji5D/g7z3DrekqtKHd6ocT7z3nptj39s5AJ2I5jiigDIjpplv1NHRmW9mFIVRQYQZFFQQybHppqFBcoMgGcm5aTr37b45nHxO5aq99++P1pn5ftmrPo/tV3+vZ5+qvers9dZ611qv1NSNUJSpacWa0cjlJITrlt1MpwUAGm2FUFGZbUemlWACGEcJbeiGBLHTkm/aNsakqhmxokS6yXTDt1NcUWJJrLd3EITdfItn2UhS3FxLpKixnUpEsWGlYkVNDKNRaJcQatophjBFGFAWKWqcznBFaebyTFFCTavkWwUAvUw2wSTM5xNRchTVtWwoSrW2dqaoia5TIjCIIGUJEVw7BSXFNUzXshnGxWwuFoVQ05qWDWTFNe1QVrxUmguS29IWmhYmQq3QwVQ11IyGZsSiFObzlAh+vgUQsZFKoyThACaCRDWD6npgp7hhUtsONa2uaERWHNMK02kgiPV0LhalI9vopjNQUQPLnPOSJJwtAcWVFSQIXiYbq2poGJGi1jUDYuKaZj3fqkBYTae9VApIUjPfEskKN4xQ1Zr5PMbIse1yKi0jXG9t9TUdiFJd0QLG6/ViI4ndTA5j5BlGo6NTorTZ1hYYJlDUwEpFskxlJZTESjZHAHCzuaZhEVl2dBNRxgDiEMWqluhaomm1zm6MSdMyG2YKCIKXyUSyEmWzsSA1TCtStdgwa23tBGE/k/VyLRATz7JCWfFsmytq07aprPiqlkAIOOQcOKYNZcWx7EDVQ9NksuK0FrisJIYZy8pRRxH+T8K/ojjrN4z/6MfNk0+BcbTAwxxCGIXqG691feuf9ZdepIZZe/+Hpr5zfjAwtMD8E4QwCu2HHiz86GJxbm7hCiV/GpucZHMT//ajWUXa9otbqrUKQviPjh3+4BQhpfTNt1955MUnyp/9Qv3DfwEYP9rf/tyN16fu/cXvM0udmtbM2ef4i5cAxsTZmbZLLpb27/u9xAoR8pcun/37f4jzeUjpUfae/68pwtJZnxN9L5EkPWw0iSHzEAAQAlGnro9ltVJ1UukOd3JaLVBR1MNmUzBkGgAIIi7o1HVkA0VJLEl2UHWBzARBS9wmMSQaQAQDILY5U2WjhSYwkUQ7qLpQZTvjqhIAACAASURBVAQbYSNxKSE8MM2IET1xXNnAYRzJ8n/YaInbEEwtbDJBiDhhCOvUiZgQiZIV1RvEwJQqIPivNhxBRgQhCBJBsKJ6QzAhY1QQEU3UwKGS+B82OIoYwl2NwxNmN2SMCgJiFCUUYy4GvivqTBBwHHGEzLhRFy0himQYNgUTJzHGHCexj1WdOgGWAeWxJAEAxMBPJDFdnY9i5KVTFBPIuEZdnygg4Tp3a3JKDPxEkoyg7mCdYEaS2MOqTp3/aiOEIRdwa31yWu8QEMU08bCiU5fFQGw0kSFU9JwQhlQUfrMOopjRAMscIQ6hEIZH3OEBmQqiHjcbgimxSIhD4MUiTupWFsTsP9zBCWptTk+aXTINIOQhEPXY8SQdxkksynZYdaFKCTGOrJMECIPgv9gkogQ4t+bnEklEKmliHQJOBYGEoZa4nmwcseEQIkqPbDVkTAldIOAQiACARBSt2VlGMFHRb112xMaTYBRx4osaIwTHMYfQjBsNwcRJEksSZAwnMcJAigJHNDTqRYDEWLB+6zIF+GFMxMCPbIMkkSsaGvMi/p82JI5/u40axkBIQg+rOnUDInMKdObWpJQYeLGsWEGtecSGRg7RW93phmRHQDCYc8RliShaQc1BGsacsNjFqp64gaCAmOrMqym2EEaJJFp+rYl1hftK7M8reSNpBoJ6xKau2EIQxLJ8xAYjRjj1kdziTNfVdMxJIoi/cYdAzKCeeJQgFlpmyEU9bvqSBmL6ny4TiBE1G4IBOWeCgKNIix1f1kFMj7hDCENGsB47/2GD4lhNPJHFoBH4uunrBo5jgKAeO01iIEZjSSJRxAVBPHig9dIfHq0U4f8IaJLEfOqJzJZbcbWyQLoQAMA5l+XqqZ+onP4pJgjYdVquuFx7+cUFBwuYJGFv39xXvx4sGj7aJ5Fix+n47jnt9eYRrvCPOoP0D0wRUpq8veuNh154onrmWbWPnw4ZO6rRFXbd9gvPN59+coFgiHMAobdy9dT5F4ZdXSiK9JdebP/+d4Xi/MI/BSBkul769GfnvvaPXJKOypbM/3UXYWuSfO7LX5SiQED8M3//NYSAUSyefvY3maGmD49//AcXJKKQcYqnfv0bltNINPmzf/slIQm1ZvNT3z4HSsiaK57xjX8hEEg8Ouvr/2jPz/lp+7N/+0Xdd6QoPP2b31QTV6w5Z5x7jtqoc13+yy99JTs90ewofPqf/kVrNjBLPv6tb6uhK7r+J8/5tl6pRJZ+1he/3D5xeGrZktPOPnvoheeCbOb9P/np4Esvuu0tHzv33JGnnho9fv1nv/YPyx57dGr50g9dcOHSpx73C23vuuzy4aeeLPX3nfGv56z55cP7Tjr+zG98c8X9946v33DqRReseHi725I78aqrV/7q0bmRkTPO+86ae++ePOG4j55z3rp77x5du+7Dl/xw1f33+oWWk6+9ZtUDD84uW/7RC85bd/dde9998ifP/de1W2+bWb38+KuvXbftjtLIomPuuftDl14ytWLZxs23fvBHPzywYeO6e+/+8A8uqA30D7/8/Lt+enm9o33w2Wc+ccH50yuXr73rFx+66AfTq5YvffSxUy+8wGlt6dn5xke+d36YTQ288vInvn/+7OLhlfff/9GLLppbPLTomV9/4vzveWm7b/dbH/reRXHa7Nrx5mnfPqcy1D/y1NMfvOTSynB/z6tvnPatb8bpVOv4odPP+ddEV1v27fnUN79ZGhjIHxo981/+CVEq0fCsr/+/6WrJzaY/88UvGY2qgOCp559PeCxX62d8+2zVc5GEz/rK32fGD4WFzJlf+cf0/AxA8LSzv617TckPzjjnXLNSjm39r7745dbRA5WBvi988YuZ6UkqCqd++9xMrQQROu3ss+3Z6Vp7x+e+9pW2Q6NzI4s+8/d/nzt8KEmnzzj32y3jh2JR/sS3v9U2Pn7wmGP/5utfXXnvLw6u3/DxCy9Yvf0Bt6P9+GuuW/HIL4sD/R+55IcrH35o/8knfPJb56y9a9vo+o0f/emPj7nrDi+f3XjTzau3PzQ/PPyRi36w4c479p1y4mn/+p31WzaPrt/w3qt+vnbrbW5X+4k333TsnXeWh/ree+mlJ27ZvOc97/rYhRduvOH6qTWr1t6+bd22251C63Hbbl+36dbK0MB7r/j5KTfduPeUEz/07xefcsP1o8cdt/LZJz/03fPCtNX71lunfe9788ODyx765Ucu+H5x+eLhJ5/+xPfP8/LZ9v17PvatcxLL6Niz64xzznG7WweeeuZjP7iwOtTb+8JLp517TmCaLZOHz/j2uVyVc4dHz/jGN5rd7cMvvvTx886rdRfa3tp55re+QTUtVZr95D9/A2GWm5k8/VvnevlM95tvfvzcc92O1pa9B848+5uAENVrfOprX0cEIQw/9U//nKrN4TD5+He/Z8/PUUs78ytfbTuwvzQ88Ol//KfMzHSsKad++5xUaRZRftp3v5uenvKzqU9/+e969+yeWr7k81/+u/z+vV4286lzvtU6cZgqyqnnnNMyMV7u7v7bv/ti72uvTa5aftbffbVr926nte307/5r+553uEQ+cPGPWg6PzQ2PfP5vPr/olZfHj1l11te+3v366/WOjlMvOL/rnZ2zhYL45htHK0X4Pzseg5HF3uo14uyMUCwuPJWVJOqOt8SpqbCvj6YzzZNOAUSQD+yHcbSwuyKVsv7i83FbIW5tWzjy+1P4wpckZ90G/tLzszvf6O7oVRTtj4dS/pDbRGmyc/dbDzz3WP2MT1U/eSZMkqOVtIIQcC6NHuz85j+pb73JFzo3gcty9bRPTv3r95JUitRr2Vtvbv/BeTCK+EJLDrko+ouXjF98afXjp8Mg+DORQP8vF0miZmsLAJwB7poGQ5BAHpsmJwgDVs+kCeACo83W1ggjzGlgmUAUEALNlM0QFMKg2ZJHNCFx6FgmkASCgJ9KJZIIWNLIpKEkSjRxLJMjhJPYy2WAICCWeJoaSwKO49A0AMYkieuZNIBQjEM3lwlFATEaGnpkmWIYepoayhIJg9AwmCSQJPY0NTANQmOqyb5l4iAIdY1pqhSGfiYdaCqmSSxLgWkKoReJgmebYhJHlhloshD4jqGHqkriMFYkR1OlwI00xU+lhCgIDC3QVRIHsapQUSBxFKiKl8uIgcc0xU3ZJA5jWfQMA7EkEUW3pYVEIaSxm80IYcAwctO2wBhX5MAyAeQJBr5tiUkEIa9lM5glAAA3m4E0SQjyFQUxCghxsmkMAeC8ns2INEEIOrbFk4QKxE3ZAAGOYSObQTRGUeDlMjiJOATNdAoRBEXBTac4hISzZj5HCCKc1bMZyhlmcWiaTBRgEjmGwSRR4rSZzwGMAE0c20KShBjzTSOSJASBm0kzUSBh0EjZHHASBm4uyyQRscQ1zViWME1iU08IxlHUzGWRJIpR4OZysSRiGkWqGssyDjzX0GNVlmjk5nOUIMxooCqhbYuMJors2hYJg1hVEomInutbVqRrJIkiRXYsE8dhDEFgGlIcRoYWSCKOQ980Ak3FSRTLkp9JC4HHCPbTaTEMA0MPNU3klOpaqKqI05gQL5sW44AK2LFMEgVUlmJVQUnka4qnKoQmTJab6RTmlDHq5bKYJlTAnmUBwAECgW2hOAKA17IZHIUQAqclDxBkCAaaCiFABDcyaU4pBKDR2iICCjFspmzAKUAwsG0AAeW0nkljBCTAnUwGccYgdzJpTAhHyDUMRjAEwMukCEsgjWvZDGQJYomXzTCCGQDNlM0VWUriZtpORAGxJLAtKGBME09VQlnCSRzYFpMlIQ5dQ+cQSlHo5TKBJCKW+LqeGLrIaKhriSgIge/msgABwfecVCpRZELjxNBDWUJJ6Bp6oipiHDmZNAWcxGFomUwSMI0C04wsUwi80NASRUZHWwr//xx/wjDs6p767vnlT56ZZDKAL4gFgpBjbD7xWOHffqC++Try/fKZfzVz9jlRZ9fC6kk4IchxChd9397+AHKdo1gcmnOaSs2c851RU9v+yN3lyjz8oz3LH4YihBAmSfL2rtcfeO6x5mmfLJ312T8hYZbfdfMxRmGov/RC/uorcbOxEDDEOQAgLrSXPv3Z5oknwTiWRg/mr7lSfWcnW5icDueQsTibq3/ww+VPngkAgEfzmfK/7yLs3rljvr9fij1xvh7bmm9ZQsPDAncVs/2dXZMji9u8GTjtlLq7BRDRCEICOILqTJGm9Foq3/XOzsmhEZW5sOY7uZzu18Wyk6hiYJnGdFGRosnCgD0+WS0UtMQzD0/VOjsRTKT5GpeI05IXaw4mrGFm2t9+e25gUEKxeXim1tbGZCJWm5rfLPb02IcmQk1LbE2u1BvprMICUm6SOPIKOVL3tWZtvq9XrjQSQXBTqcKe3b5pM0vGFUeKo9meXtspG/XKfEunOT1PCa4W2vOHD8WirCnRvJCRw6CZShulktJsVHq7c4dGfUmrtRW0Rj1BSCQMuLFRLtb6u2Aj1KvVUl+PXZwDAY1yJvBivVyeGRyCjGWmp4rd3ZnStFhszA0Mao263GjEad1VTb1YprbiQ7l9z+7xFStStaJYatQ72kgUxgwTCXCG7ImpZleLT7SOPbsnlixpqU/VuR6lTL1Sxk4QZcwIktT4pNPT1pBTreOHZrt7Uo2SOFtpdBakMBCrTrnQnkhS7zs7pgYWySCwDoyXerpFlEiBlxAhEFR9Zp4ZUjXT1rlr13Rfv4wiUvOauVxrfdJvIiATL2UZ00WuCeV8oWNs31xrp8ipeWiqVigggYnzDSjAZmuLUHcJTBpWurBnb6mzyzfN9nd2NlpboUoE32NEqKrp3PgYElGp0FnYs7dcKDRTmezEmBhHcz195vycWSvNDw6INZdD2EynW0YPJoKUZHRS80Tfm+vtF11Hd+px2jSm5wMiVdrbsxMTDKEkrflAkptNJ5fXqxW9XC4O9ObGx0IkhTkT+3GEBZFQHgJrbrbW18ndxJqfnR8asMpFX9SAQrSZeUBB2GqzCNoz02OLl+bLU1KxUe3uUBp13AzijO5pplaqMEP2iNqx8+2xVavtZkmZrdY6CySJYkpM6PhQ1meKbmfeIXr37ncOL11uBhW56dRzLVLDEepulDZCIqfHxtzOlpqWbRsbne3usZ2yMlv12jKK7yQOo7bStLPG7Dy1lKZsdr/5xuSy5XrYUGYq9UIbI1ifLWrYn23rye05UOzpEQklFaeRz+t+g5SbQMBeLq3NlJAIioUue3yy3tKqct86NFVrbUMiEEoNLgjltoJRKgowaaRzrXv3VdsKnp1q27vbs2xmyUkIIIS+bmQmJwjmXjZtj46X29o9y9IrlVgURUxx1cU0KXb35scOJ4pS8aI/cS3ChZMVqqq+/lp2y63y3t1wYTI4AMA4Yppe+sznmyecFGezpFptueIy7c3XkectLLoh36/+xcerp58RtRWO3ilZHGNxajL74x8t8aP3nvKRbDb/x6h5/wNQhEeq2t/Y8cpDLzzROONT5U8fzeiKCLjZSD14f+7G67G3kElXkFKAsbvmmPkvfcVZv4FUq8azT7f99FJxanJhrZQwSbgk+UuXz//dVxvves+f7vjQPwRFaGPpxCuuTCCJJemkn1zJMT60ZOV8rgNx1vnqW2tv2uSqluXXV167GQXRxLJlM/meRJB6X3hx7S23BZYl1Jzjrr8pltR6a8tY72LOQNuBAxsvv5pzwDk67oZNEk+m2vvH+0ZAELdMjG386ZWi61fbCu+65HKx6Y4tXTnd0gMgzO05sOG6m0DMQ9s64bKrlfnS9JIlG35+ffbweNPOLr3rvvTBsVqhcLhvSYBl2XdPufyqlh3vTC1desym29t27prpG5jqGPBUU6o0T7z2+vY3dxzYuOGky65sf2PH6PoNJxV39LjVss9H7nus/bU3pwdG1t14S+8bO2aWj0y0DtbNtDZbOnbrto433260tC556JGOV3fM9AzMdPV5iiX7zvrrbul54eXpxUuWPPBI969fmB8Y6nv2+VV3PzCxYuXgo08uv+f+sSUr63ZurtCjlSqLnv31sru3lwtd+d371t1y63xf/0x7XzHfoTqNvsd+veaOuyrtnflDh1duudO1UpNDi8upNgDAivsfWnnbtpmRkdY3967esrXa1t5oazncPkyCcOTRx9ZsvmNuZHHhpdfX3H5Xqa+vbOYmexcplVrPW28de9MWJ5U25kvrrrlxZmCRMTO3/trrY8UAPNl45fXYDZr97e+fex1AEB0sr795CxUI9pPjbriFUVDr7Djcs5gx2HFwz9orb8FBGIrK2utvgQCBlPju+iSgnuvCE35ypVYszw0MvvuSy5RydWz5qumWHoBgZv+hDdfdJHrhfEvne39ymTEzP7Ni5MPTLysYV+bDjbdsVhr1QDHX3rJFmSsdWHXsB390aftrbx44dsPaWzZ3vfnWTN/QdMeAo5pSzTnhuhu6Xn39wMYNJ157c9czz+1bd0LdzDRS2ez0xLJ7t7e/+tbUwMgxm28bfOHl0WOOLWXa66mcUqwcc9fdXS+8XG1pH378ie7nXil3dU30DDuaLUbh+mtu6n/6uallywYfenzwqWdKXT1znb0lu01t1NfefNvgE08f2Hj8sVvvGvrlr0ZXH9vz2msrt/6imcmmRsfW3rq12t4x1dE/39KpNuvdz7507NY7KrmWzNz86hs3B6o2PrK8mClISTi8/Vcrb/9FcXAo+/beY27d0mwt2Fp8UnnUl0TxjUPrrrup1NvfsmvvsVvuqLa3F7MdUz2DUq3R9faO467dFKmKXG8cd/2WwDAne4dnCr1S6Le9smPjTbe4qikkycafXRupWiRKG6+8Xo6DmOJjNm0lIa10tB/uXUI5Tk9OnHT51VK96djpdddvkh13tqN3sncR5Tg/NbHx8mvMiampJUvfc/FPtLnS6OpjS7l2hknq0OS6G29RyrX57oH3XvJje3z6wLp1xXS7q5j5PfvXb9psVCqxJK267S5zen5s0dL51s5A1qzi3PE/v96YnK20dqzbvMWYmpvL5v/EtQgXHlvjOC60u+vWwTgRSiVcry2kwRBjGMfGi88jz6XZXFxodzaeAAAQZmdw7XdfEEIuCOrOt8WpyaizK8m3HKWthZBzmkpHi4Ybb77q7t/bkm/TNP0Pz8n8/uiKc/7ya88//uqztTM/Xf3kmUczuiLC7EzmztvNJx6DcbwwdEV1vf7e91dPPS3q7JL370s9cK/1y4cgTRbCM3IOkzjOtzbe/d7qX5xKbXuB9PlR9NIz6mRzkHMIgGeZCREgZ0IYHgGazVweJjGmwMnmGMYQACGOEGccY880GUZCFLq5PKY0lmUhDAAHEMLQNKmqIEY9y2IEAwjEwKeShMPAzbcwDhClvmHGsgQ5I3GEGMVx1MxmOWc4ipx0mhICGY80leq6EAahrieSxDEW4ohCSOIwVLVIIDiJI1mmSMSMkjhihJA49A0DUUqiKNH0mDFCKZXNRBZEJwpVlWJ4ZNYoj2IShZAxklBOcCxLHGMhCkNFIQxxBEkUMkwwTWJB8G2bxFEiyX4qRZI4VuTAMFASc4xcOyUkMYdQ8r0jnduuaSKaUFH0TQsCADknUQQggoA3cjkSRQACz04BCCDnmCaIMw6A09KCOIc0aebzKI4AgkIUQgiooviWBQEHEDiZNGYJJ0QMPCoIkFHXthHnAKPAsgDGJIqcfAukFCVJM51hEGJCaKYTCCbkZc80KSEkCp1cDmKcSJIQBpAzwLmnabEkIcY8O8UIEXgStQ1CGOJq7GazVBQRo4FuxLIEaULiCDKKOGhms4BgkkSubTOMcZzQdHuiZokXBZqeCIIQho5tM1HCjEaGwSglNElEwYc6ZhTHEceY0MQ3DNHzSBjGshRbNqYUIChEAUySUJYRQ5gmgWFKnAtRABklNOEEJxj7liUlUSwrUAkBhCSJGJYgZ1QSvXSaRAElxDMtyDjkDCcxYjTS1UDTcRJThJ1c/kgdt5dOQ8aYIPiGCSCAgAtRyAhGjDdyORLHEHE3k2GEQMhxEkPOOEZOJgsZRRA0W1pJFCKoxfluRDnAMDAtBAHgvJlKIc44hmLgUVFAlLqZDDxSKnqEIuRMDH2GEUniRi4PGYOMeZkMw5gD4JkmlUQSBG46zThnCIm+BzmDEAS6HikySZLAtBJZgoCLoc8IJkHgZbOUCJixwLYjVYWcH3l2AqibsjkmKI5dO5UIhMQRShJME45hoGmxKOIodC0rwZgjKIY+QAjHcaQooaaROAoUNVZV9GdXKfH/ORKjkMnK/Be/HAwPWw89qL3+GkcQ/K7tbwgxWbYfuF8cH69+4nRnw/Hlv/x02Nef2bpF3v0Ox/h3zY0dSa21lUqlz33BWb8RcH40Vr7DOA77+mtf+fqOKy8HTz/8npM+9AfPY/1eGSwIIeD8+Zeeevalp0qf++vqJ06HcXxUoisIOSbywQP5G68xfv0sYGwBHwowSaJCe/kzn6+e+glq2/rLL+VuvvE3eZoFtIMyBjn3lq8sf/YL9fe9nynK0S5Z8H/MYAVLls68530Ewv3HHx+kzFq+MLd4hIkke+gwRnx6cBGEeM9JJ9O04iqZfevWIYJSY5ME0EpXl5PJlYb6xxcvRwDtOuFECcTZ/QeBLLtpu9rWMT84MN/T61np2mB3JVto27U7VlWnozWS9YPrN/gZu9pSmBte5Nl27sCoSMOpoRHE4f4TTnRas5FijK45NkyblbYutzU/umpVqGjTi5c021uzB0eFMAozdqWlvdzXWxwaqOUKTktuengkPTapOM1iTy8UxfE1a7yWTKmlo9rTPT440qz5Vd8b7VsRqvrs0NDk8AggZGLFyiRrkHIzNTdb7Op2slknnx87Zg0XxKmRpbMDgy1jh5VaNUrbpa4+pyU/tXxpNdfmZLNjq1dRUZzrH6r0dlXbu51cft+xa/VatXX/vmpnZ5hLN1PZA8etDXS92t1TGuwTGm527HCYsWb6hgHBu08+JTa1RqZlYvVKtV63p2a4Iha7er10ZmbZ4umefkTI3hOOz5Wm1cliaKhOLlfu6iv1dc/3DUSqMblmZQzFzl276i35IJdupnLjx6z2Una5o3tq0aK5vj7CwZ6Nx3uFFoqlfcef4Ova/JwzLVnl3kHXTpcXDYwtXoYA2nXCCQJk2X0HICE0pc+1D5QGeucHBn07UxrsPdy2KJg+tEvvdlrzkaQfWLcuSNuVlkJx0aCTy+b2j8qxPz2wCEJ0YO26Ylc3EoRD69Z6mdR00ZtBymTPcKJrlcH+w0tXIA73r13rZLJuKl3t7RkbXhxks14uM7VsqTUxpddqxe4eLkmTK1c5hVy5paPa3jG2dGlufCwzM1XvbPcMe3poeGJkMZPlqSVLGu0t+nw5PTNV7uxutrS4mczourVMEmcGh8sDvfbUjFKpJrY239Hr5fNTK5ZW2rq8VGpi5Yrs5IRaqXn5jGulZ4cX17rb5zv6gnTq4DHHMl1x7Ozh444NTKPU3VccGsBBlBsdjVLGdN8ihNCuU94dmZqTbhlbs0p2ndTkDBHYbM+ga6Vnli2eGBgmCO088eQQ4Eq5Pprt9TKZSmdPcaB3vrc/NsypVStCQenc+XYzm/PzaddMT65eGaRTc539s0uGKcSt+w8AiUwOLUGM7XnXu8KU6aRyEyuXeynLSeednvbRFWs4Fvdt2MgVMbvvIMA4yNiVlo7SQF9xYMC1M6WBvkpbe9uevRzjRmdbLGoHjj0uyKXK+fb5wcFKW1th/z4l9Gb6hxjEo8cdN9/djQRh7NjjQlu3xqdl15nrH4gMo9zbM7VkMUPi/nXrG7l8Yc8eIQr9fLZS6K52dY4tXxFr6syi4bqiGM/9+qjQIlxwzdCRJj5/yTIuCNLEOHIWIoPDRVGYnVV37kRRFPT2BQODweAi5HvS1ASMf/fR2RiTel3d8RbHOBgY4oJwNFIrkNIkl0/6hxqvPO8c2FNo61RV7U8CYEEIMcLPPP/YC688O/f5v6599GOQsaMVXUGovfFq/tqr1Z1vc4QWQHXDMPRWrZ7/0leaG0+ASWw/vD17603ygX0LXC1JgChWP/qx8lmf9UcWQ86Pelrw/44iLMTJmtu3yq6DCFx3w81MFsx6bf3NmxJTzR8aW/zwQySKc425wXseUpt1oCun/PRnGDDFdVbdeRcXcWZyasnDDyuBLyTB+htuNsulMGNvvOo6jLjkeSvuuluFoTpXXnPnnWqjiQWwevPWVLno5FInXnkt5okUBGs3bxF5olVqS7dv12tVKKJjNm2xi/PzI0Prr78pNTuFMRx65LGWgwepqR6z9Y6et3ZMrV520s+vzh880OjuWLV1W27iMJClJQ//svOdnV4qtfqeu7tfe3V61bITr7sxv39faWDguFtubdm5ByhS3zPPdrzzjtPWtuqeX/S9/HJ5xdD6627pev31+UUjx9zzi9Z9+5CAB59+qvD667XevuPu2Lr4mWfG1q7ZeN0NhZ07vfaW4Ucea3/7rSib7nn9taXbHyoP9Y88+ujQk0/ODQ4te+rJ5du3R7bV+c5bg48+Hut6/tDBY+7cNrdk0eJfPb7y/vtrvV19z70w9NSTVBJaDx8cfvBhpkot+/evu3VzZaB34PmXFj36qNPZ2vvSq0OPP4Yxbps4sOTO+7gsZaenVt++rd7b2fviS8O/eizIpTrf2rn67l8gguzZqWX33A9FYs3PrdmytdLd0zZ6cPn990phqAbNFXfebZaKUcraeNl1JI6lyF+17S6MmDVXXLL9Ic11IObHX3dDen42yhrHXbdJiAKB0RV3/UJisV6pLN5yj16rQxGu3nJ7Znam1lU46YqrxNDHjB136xY9dKUgXPLgg2al7Jvm8TffmB47XOvpOumy64SGCwWyZtsdZr0iRPGS7dvTc/MTS5a8/2eX5/fuJHhGJAAAIABJREFUKS4aXrd5U8vB/UAShx9/sufNN51MesX99/e/9OLkmuUbr7+5bdc7peHhdVu39rz+KpJI7/Mvtr/1drNQWP7gA4Mvvjhx7MqTrrq296WXZ4eG1jz8UGHHDiCRged+3fXSK15H64r77lvy2OOHNq7dcOMt7W++0exoG3n08c43XueyOPjiC0NPPOW25UYe+dWip56eWrN87e3bOl55pdTfP/TaS0PbHwESyUxOrLrjzspAz+DzL6y6595md3vXCy8teuoJSHB+fnrk3vu5ImYnxtffdEvYnu14/a2hXz3mtWa63nx7+FePIgztudmlt9wFCTIr5WNuu91tb+18/Y3FjzwS2Vrrrr3H3LkNY6TXyyu23SNAalRKK7fe6eWzhV27V995J81a2T0Hlj78kBBFcuiu3rIVCJgJ5LhNW/TEUSq14YcfNSplroobr7uhZXKi0tN+0hVXy14TYrRi211K5Emut2bbNmu+CBSyevPW3MREaVH/yVdcJbouleXjb77RKs+TOFny8C9T83NOyt540035QweKw4Pv+cnlRrEYW9aaO7elSrNq4Aw++lh2cqrc3fOen/20a9++mcWLTrzyaq1USnR99X332bOzVdsSdrz1Z0kR/vf0iKb7S5clbQVSrYhTk5yQ37nSHGMYBMqud8TZ2aSlzV+8OBxaxGRVOjSKXPd3TisgBINA3fUOdpphXz8zjKOxOPi3GKvffem56v7dvd0DkvQHm6xGFopJIELouReffOX152f+5ouN933gCNA+OnNX2Hrs0cyWW4W52YWMY+AcBX71tE9W/+LUsKubVKvZzbcYTz2Bm82FtR/COI4K7eXPfcFdtYYaxlGqKriwy9N1SBPPtmNZpoqayIovq5EgRooSCCqEyE2loE4BAEEqHYpSKEmhogSaTkUxUVTXTjFKXcsODZMTwbVSkSBFshQJYmhaTBQjQXTSGU4EN2WHusEBcDU9keRE16is+IaRCGIkSa6dQgB4phWqOkc4sG2KcahpAEDXtJgkxroWmBYlxNf0RBAjVU9EEihapGkCSNxUOhFEhlFgmBRjqhuxpISKwuMoUrXINKHbaJpWjAkQWCArTJR8FUSy4mm6HPiBooSKisLIseyYEKTpoabFCAealkhSaFo4jn3dCGVZQcQzrYQIkShGsuyrGhfFUNV83eCcuqm0XZ+nRPBNS3ScWJYTUfRsKxHFSNU8y4YQenZaZhGH0DctEkWxpseS7KkqgzDSdNeyEYTNdFqre1SUQjsF6rVIkiJR9g2LYxzqBg0R5cy1MyIPmCB4xm/mQkWSTAWBc+AaZmCYHMAglY5EOZbFWJIDw0wkOZYVx7I5AK5uRIZBBdExrUiSY1VNZNk3jIQIERHcVAaKQmDbkW5yADxNS0QxVtVEFH3DiCUpUlTHtiEHvqqFms4gCg0zkpVIkROMQ8NgohRKctOyOeeBoiSCGCkKoEkoK4Fuqk7NS6UiQYCU+prBRdFX1ViSA0UWaRJKcmiaotdoptIZInCJh7pBMQ4kKZZkVzcMykJVCyRJBMBNZagoRqoaKmosiKGmUUHwbEtu1EPd8HXDJMS17JiQSFECTU9kOVaVWJI9RVMgCgwz0HWOoGdasAFiVUsk2dP0BONI0/xUCgHo2ikYNhiEvmHJnhNpeiQryLQ5IbGmuwYDjDqmLaOISYJvpwCnsShFohTYNkUoMEwGE4aQa1pQExghvqYDRUgUNZZlZiOKkK+okSQhCD3bjhWVIRzKCpXlRFEiQXRSGYCQZ1qhblCEHd2IRTlRVaoovm5QQUiI4FopgIlnWYGmc4T8VCoWxFCSEkICTYtkJVYUN5XmjAaKGioaEIinarEk+6oaExKoKhXFUJKaqQznINSNWFESQvzf2nBR8nUjwTiSpFgUj/pBo//XFRQAo+bxJ4RdXakH7jN/9QhMfvfiE4QAY8YzTwkz05XTP1V/93uqp34i6ujI3nyjODX5O0dAhGAY2tsfIOVy6bOfD7u6URAcjTxsMLio/C/f2n/xRfc9fOcnPvKXmqb/QeZjLaSLEEIIIXrh5adffPmZ8b/+fxrveg8XjtpXHJPMlk32g/dhx1lY0RXHeP4rX2tu2EhNW5ieavn55eo7by9YrBAFQfPkU4pnfT5uazvyT/jzOyb+d12Ep3+87+03ZwYGZBrIpUZgabEgmDPFKGtW7Wzfrt2Hhha1ejPCRGOut0fkkVxqJKbqy7I9PR+l9Uoq17dn7+H+AYUHmYMTxfZ2QrhSbkaK6JuGNTknKcl0a0/Xnv3j/QMyDNOHpku5HJaxXGowWXBty56YSQy5lC/07Nw5PTAowig9PlvOZKkuy5WG5jXmenrSkzOBokZpPXd4sqab3FbEUkNktNGaEWq+0ajO9venxiZjWSoXOjpHD7iSRFOaWPXEwJ/qH0gdEb8bGEhNzTKEih0dbYdGY0lR1NgLRMVpTg4MpubmtGZzrre79fCoL2nljo7WscMUkzCjo7pvOc1SVwE3QqNSnh3sT89Ow5AGOQt4kVWpTg4OSb7fcXD/6NLlmcqsXKxPDw1p9Zpab/hZk0Y8Uy7XOvI+Urr27D60bFmqOq+U6uXuTtVpYicIM2YS88zcXK2n4BK1b8+e0cUj7eUxWqWVrnbZc6RmEKSNCODc5FStp+AIev/ePQdGRmynos5WK+2tchxJteZ8W3ssS4Pv7BzrG1BQlN13eK67W8BMKTVjVfR1zZqaSyylnGnp2b1noq9fRnF6dLLckreglzRhImPPsqypOaYK5Xyhc8+eme4egdDsoalSNotUQSo1kIAambQ5NQcUMt/S3v3OO7M9Pb5hdu3ZU0/bXJeUYjMRSbm1rfXQKJDwfKGze/ee+bZCI53NT4xJSTzd05uanzcrxdnBAWt6niM4397Zdng0EsQkrZG6LzebU4NDdqmYKhdLPZ32zHxIpGJHR/7wIYZJlDVgzdMdZ3JwkV0qmpXKzEBfy/jhCEtu3rbmyzFHUdYAzShdr5W6C8CnqdmZmcH+/OREDAUvn9JLVU55kDOZG2dKpYNLl7fOTyjztVJft96oYycIMkaSgMx8sd7Z4mK1e++eQ0uW2PWyNlcp9nQqvis2Q6KDJlat2VKjM98Ujf7duw4sXmJ7VX2qVO7qkKJAaviBrUZYzI2N13oKR2wOLl5i+TVtuuy0pNQkYE0ephRf0vKjhxudLU3V7tm18/DwiBE5xnSpnM8xWbJm5hUSzrT1FPYfmuruFgnNjk6WMlmkCVKxDkTipC1zuggkVGzr7Ny1e767GwssMz5TTmWgRqRig4tiqaWl9fAhJKK5Qlfn3r2ltkLTtjv37XUsm5mSVGpywEsdna1jY5CAZi6dPzhebC000+n2vXsC04psRSg1CITzHZ2tY4djVS26ce7o1SJcaF4A+b7x3LOZzZtIqbhAJTrOk3Sm9rGPVz5+GuBcPnggf/WVyp5dC2ych9AfWVL8m78NhkdgGB6VgQljaXKi7YLzujn65KlnqYrG+O8bfxdCERJCXn3jxeeefXTyrz5Tf9/7j1adQQgBQq0/vcR65GEUhgsh8uIoyeanv3Oeu+ZYpunqzrfbLzhPGju0QLFCxiBlc1/9h8qn/pKm00drRvD3owjbYnr85VdInh+J4nsu+lEsilrD2XjNDb5lZfccXLllKwiStFdZfs1mtVwJbPs9P/ghEwSp6ay78tpEle2xmZWbbhO9kPBk3eVXG3NzTqH9veddBBkTHH/tNTfIkIrTlZWbb1fKdaYox//kCmuuVOnoeP/3LkRRjBK29rqbCOBKsbp60xZlrhybxvGXXJY6NDa1cuUpP/5Z5tBhJitL7rin9Z3dfto+9sZbe155Y3TtcR84/99a39gxOzy8/uobWvbsSSRl+N7tHTt2VvKFddfd0P/0s/tPPOEDF13S/sZb4yNLT7zqmta336a6sejhx3pefKXY3r3++psGn352cu2q9Zf+vOuFl6dGlq3fvKXwxg6maSMPbu955oW5wUVrb960+Je/2nPyiadc/JPu514s9/ctv/vBrl8/32hr63/+hbW33Tm1ZOnSu+9fcec9h1euWbn94ZF7H/DsdMfrry+74x7XSuf37D/+51eNr1y1bPsjy++4a354qPfRp5bdc1+kaC2HD63YdHtoGNm9+zdcee3MyMjI079ecftdpf7+3iefW3rX3QkW2scPLrlpW6Iq6cPjG664prh4cd8TT6/Yemejq73j5beWb70DYNGcmz3uqhsSRTFniyf89OdzfYMt+0ePufFmMeY49NddcZ0xN19vbXvfeReiOIEJXX/ldZwzs1Rftek2tVpnGG687KrU1IzTljvxB5eKrscgWn/1DQKjUqWxetMWrViJdfWEn/w8MzE919f3ge9eQJoORWTdtTfK9Rpxw9W3brWLlUoq+76Lf5jZd3B6yeIPfv8ise6GRNp4zfV6uQQBWb1pS+bwxP5j133sO99pf+W1sWPWnXjZ5W073o5VbeiXj/e88HK10HnsLZuGHnvywPEb3/fTq7qff/HwslXHX3tdx+tvMFnsf+Txnqeem+/uO27rHcsef3Lfhg0nX3p53zPPTy5dsXbrHR0vvBSr2qJHHu177Ol6X9/yrXctvW/77ve8+90/uaLnyWdKQ4OL77q//+lfJ7rR/+vnhx5+rNzTs+LOu5fcff+BE0448fqb+x97cmpk6dBLLyy/9Y5IUdIHD238+TVzixYNPvP8qq3biv19Pc+8uHzbXbGkZOdnV11/a6Ko9tj4xp9dXetu737+1RW33VntKHS9/Nby27dxSTWmJ4+79hYqCtp86cQf/6zS19f10qsrN9/h5DNt7xxYufk2TLncbKy74loEoVIub7jsmlpbW+Ht3atvvS3MZVK7R9ds2owSJoTB8ZddzUQ5wfjEH/9Mi3xcaS7fss2YLSa6tvGnV2anpmf7+j943g/kaj2W1XXX3CAHvtjwVm7eqk/NRCn7+EuvaNm1Z2r5ig9+53yp1giN1MYrrtRKJUjhyi23Z8cmy60d7734h/nde6dWLP/gRZeoc0VHszZefa09P0/iaOntv8jt3jczOPKBH13SuWvP4eXL33vBxeb4lGtn1928yRqbnM/lyds7/uwpwv8eDQhC2NPrLV8hTUwIxfkFSg36nvLOTlKrBiOL49Y2Z90GoTgvjh3+nZlHCAHnQnFe3bkj6uiIC+1HZT0W59Sy/OUrwK8enjq4d/Hwcox/Xy0d9LsHR/TWzteeefaRqU/9Vf3DH+WSfJTGeBRFHeeebTzzFKQL0VqGYegvXTH5g3/3hxczWU7de3fHd88hlQVqC8Ikidvaxi/9af1972eyDPj/LzLe/+PlK4rs+4GsUEEEEFIiBJICozhG2M3mpEYzSKUCRZXj2LfsRJQgABSiQFZwTCNRdtIZqdHwVL1p2qLrh4YdIgQBZETwTQtDRAnxcjmp2QzSGV9VBNeLJDkmAsOEIxwoKk4SCmEjl5ejODH0UNOkhMaWlRCcQIQpa2g6powKoqebJIoSgYSywmQFMx5pOpdkzHgjlcYAAoA80yKU0iMcCsYwin3DCnWDRHHDTsGEMkL8dBYiBEQpESQkqxhAL52JBRGHUd20EURMkl1VhwlNiBCLcqIbJEl8y2aIEMbcVBogzBiLRClRNcK5b1ieoiqu55pWZFpiQn3T5hxAIjJRDBVVSFikqIlhKK7XzLfEmAhRFCgqwxhgnIiiL2vE9wNZ8jNZuVYP7FQsiAIAvmExIkAOQgBDVZeCwDUsz7LlWt3VDV/RSByHmp5ABAGIieDZKanpeIoaaLro+65pJZLEOaCY+LoBOGdEbJqW2GgElu3bKcEPIk2PJRlgwogYKCqmlELUNC3ZD3zN8FVdDIJIkiJFAaKIIIxMUwCQyqqTzclRFAmiZ9okimPTjCWVI8Ioc1MZABHF2LFSkh+EupFgwhWVUB6IUqxohPFGJgcpYxA17RSCiAlCJCkxhChOQisVm7YQxUEmSwDisuKlUpCyGOJIUqiqYc5d3YgwFqKomc5wIkJBDBQVMc5lhRIhVFQpSQJd57JMElpPZwBjgINI1TjEgJBYFENZFVzP1/VAkuWERqbFMYGERIoayTIJw1BWPMPUHNcxrRBhMUl8WaGSjCBiRIg0XQjC0LR8OyXX6o6mxZKCo9g1LEoEzhiFyDVMIQxjzXANU67VnVQ6UlQhiiPNoIIEEKKy4suyHEW+JDumpTqurxuhJJM4TgSSSDIiAsfIy+ZExw3tlK+qguuFRIxFkSHCEQoVBTHGOWjkclIUU1ULNV1KktiyY0mCsgIod1UVQkxltZlKS0EYSpKbSiPKqCBGksIwBgwEmRwURIBQkM1JYRSmMr5uwCgOEQ5llUsKhMjRDUApQCg5qgXyFo6wOIAw7B+YOu+C+ns/sMCxnxDCJLYfvK/t3y8Sp6epac7889nlz3xuIcwJhABCYXq68MN/M55/7mjdVcaizq7JCy+e9Jt33LMpTuLfcwbp70YRQggPjO59YPu2mQ9/pPLpz3JRPCqhAEJCsVi48DxxbGyBuxbHjfd/sPjZL1DLgnGcv+oK84lfAYgWvtrJpxS/9BX6R5jDcXRRhKWzPgcphZDrkdMQTJkFJI5jhlXmN7QUpCwWxJ7qwXGrB0Jg+rUgIYTwRJR8LJtRwxM1wADFJNOc86kACMYCaIimkvgQApeohcZkQ7J5RBnGCopqoo0g16NmQzBlGpAkihlRWOBoFqAgEYgdVOuCCRHUo2ZdsuXYQ5CHUGYImUEtoTBBgorCumhhRlXq1SVbiT0IQUAhwJgLIkoSinEqqNbkFKKUIyS5jpiEVJFdUYMAHNHFo5j85rnimMkSZBxwLrEIMeoJGgBc8j3AuELimpzCSaJRtyFaiFEBxJhSV9TNqO4JGmeAYSwGPkqSRJJU5jcEEyAIGeMQ6YnDAsYgVFBUVTMkjhnBetSsi5bMAsS5K+hmWAuIkkBsh7WanCJxDDAsVMbm5BYRxIkkeUQ1o4YvqBQgK6q7UMVxxARBBmFDMCUeYZp4RAUQAAgxpRRjO6geuQ0zqAcJIYhRSfSwcmQdzuERmyaTgCK11ScnrG4l8RHgrqAZUSPhBEbxEZfVRQtAaIb1umjJNECQu1w0YieQdcYRwwgAcGSfraDmU4FjFCkq4FyPnFCQE445/s3/FFHKMAYcSElAWBIxjDiLZAVyTjFJBZX/dJnnEhqLhPqC6hMF/NZldlCNEgwT6lnWkS9gkceYJq6oG3GTRYwxKJOkpqQQo0bcrIs2ppSAWPY9H0kyin1BjZFgR7WanIKMQQiMsFEXLZFHAo1doumJQylkFGggqOhZEseUEDOs1yRbYiFJIodobe5MXU0HSD6yDqaUIWREjZqckpOA0MgVdCNuBkROAPmNje8xRTGDel20tcRRIq+o5Y24+R+/VdUzOE6O6AzWJFtiEWGxI+itzemmbEVQTAhJu2WfixwjGSV1wZRoiAF1BV2PmpQjkDCKsQaChmByjI7cM2IMciY5rswD10jFkHD0W5dhZPk1j4sAwEhVAedq7Mk0aApGTETIueQ6AEKJJDXJxowxhCBjXBDkfXv/fLQIF3aiSpL10Pb8TddB319gDEqSqKNj9h/+ORhaxDHW/ht77x1vV3GeC8+smVl9l7P36WWf3rsK6oBxj2OKMbhhJ7HjxIlzHWNsMDa92IANtmMMMRgZIdHVQVQhegdR1HV0dI5O372tvmbN3D+UfEnu9yWx9Lu/+/107fl7t/W+s9Y8+33e93l2v1N3008E76SYPs6BICx852Jj1RqO0CkaUnFuNvH977bXNl5wzkXHtaj+T1CEUzMTjz29eX7N6fm/+Do/RQstEEoTRxuuuYLMzZ0M6ueci1L2i1/JfPUvmaqiXK7h+qu1t988SW8mzpksZ77xzexFf8FkGfyRrP+cIqzh7Bvf+hvkuQTyr/yP78i2OTU8Ymlh1TU7Xn31wssu5ZIYs9Kf+8dLVdtc6O0pVFRjToefe/bcq6+GiEXSmQt/eBnmrJCoz8frgAA7dr/1xYu/pzsW9r3PX/6jCiMz095TqqgUgiBx5OBF3/r7+MJ8ubbqom//Q9XMsamR0XK4Qgzchv37v3TJxXqx6FfoX/2bb9XMz8739Zz/4yu6X3nRqK765K23db7+ailRn61tDBAOG7kvXvz94Wefmh8Z/MxPbxrY9Vy5reYc4+BQeXKeqxf88PKRp54aO2PVly65dNHj2w+dcaaj6kBE8Zljn7r1F4O7nlvo6//cdVcv3b516rSRC2ZfWl0+eshTP337b5Zs2VxuqP3o7+4afuyxVHtHsaaWilK0kDn7hhtXP/TA3PDA6rt+t/yRh9MDfUu2b/30LbfMDQ+ufOihT9/6s6mhYSMW9xQ1lM8s3bHtM7f8vFhf3/nSi5/7yQ3F5oaFlnZH1SvyqUVbtp19/bX5luaO998555rrSjXVOvUHX3q50Fi7/P77z77+utRQf9vLr51/1RVmXY1dG09X1IvUW/now+dcf32mo7XvhRfPufaafEtTvrLWCUeUcmnghec+d+VVNKLXHjx4wZVXZNvaqmamv/S970IWSIB++X/8YzQ5PzsyXIxWCpD3v7jrvKuukQNXLhQv+OFlou8Bnf2jdyA8vdcp0Au/e2kkkwQi/vxlPwzlMwvtHUY0DgGomzh80d98q35mKt3e8rW/+3bV1LGgo+qryddjVsY7lr3w0ksr5ucKjY3f/NY3G/d+OLliWSkUYxi37H7n/KuvqluY8bH4+csvq56dnRhd9PWLvzOyZePRFSvPveWmxY9tne/vLVZUBaKkFgtfuPyy05584vCaVRdc9sNlmzcdPvNMW9WYiOtmJz9++x2DTzyZ7O//7M03rXjogdnFw5mqel9RoqnkJ++4ffkDD5RaEqs33Lfs0Y2lprp0Q8JR9XA5e/ZNN59+912zi4ZX3rdh9b1rM51txZoaR9J1o3juDTesWr/uyBmrP/mzW8/67T+Pr1o1+NKuc669zolHW95/75xrry831CSb2xwtFClmh7c99rmrryzV1zYc3n/+j65wNaUt6l40/1JJIB0bnzr7hhsyfV0tb+++4IeXmvF4IKLVO58vibjvjdfO//EV5UR9++tvnnfNNaVEQxOd/bY7Xpw/Fhmb/cL3LkGYV87PnPujq7gqLbR3WXpYdUodr7z+pUsuZiJRrdKX/8c/CjLmCF146aWVmVmB8nN/fIVeLBiNtdmqesh549jBL178vdpjRx1dP//HV1QvzMx3dJYjMQCF+qmxi/7u203jYwu9PV/7+2837Pnw6LIVVjgi8qD2yNgXLvth1fRUJtH89b/9684PP5g4bXExXMkxSuzfd94N1ycO7Yeq/Nlrrq85MrbQ2WnG4gHC9ZNjF/z4irbd7+YbGs674drmvR8u1NX9X+VFeBJnWhA43d3G8hXqng+RUQaMnfDRJgi4WAy9/ILX2ESra7ymhLlshfb+bmQYJ1PKAiD80gtBOOS2toNTE2MFobA1NBI8+VhuYa6jvRsJJ3kVfzgsgAup+Sd2bl8YGsr/1TcCVT31+q8hBABob7/VcN1VJJU8OXRF4/H0X/9N9stf5RgrBw80/fD7ysEDJ4OuOAcAeM0t8z+6Kv/Z8zhCf7S04L9fimOZEV2mLqYelQjiAQIBZlSAXDVKdjQsGiXFtu1ISLZNBJgYuAJgQuBREWPAFKtsRUKSbUAESeBhTmVGmUwgp6ptUokgyCXfFaknQK6UC240hANPpB7HCAoABx7mAWZUK2SdSAg7lmSW3bBOqEd8DwiQaUrYKAUSgQgiHmDmCwLQSgXIKSWIODbjAZWJbhVYgCEQNLPoEwQCT3ItAQFfIopjIshwQOWAMkUUBK4XMkzEHALRsaAoBiSk2yVMPV9XdLPsIwgAI75z/HIIdbBnuyJRykUkcKpKWiEHAAOqIvuO4NqeLOnlAuKUBD7iTLQMRxEVsyRggWEkujZmPgk8xAPsWHZFJJxJYh44uiIGngpYLPBk6hHOvEhIcizVKFkVEcUoChBgFgiACcynIpYAFx3L0VXVMREPCPUQCGTP8hRJdCwi8AALiHrHU6YyKtumo6uEepj5hHqYURz4gYQgo7pjOdGwbFsaDCwmyrKIPSfAUIBMNsu+ImGBS9QjgY8Z1c2yFw0Lviu6NoecQa5wjyEFMaDbZScSkiDQzJIrERT4OPAwo4S6smszEXHq6o7hRsOy54jUAyBgqqL6LvQdn2DZMgTIEaeaUeYiDqirOKYgkUCRlHJRgBwzH3sOFYAAmVrMMREJsog4JYySwMcBRYz6uhoqZCEIAh5g1yaBRwJPAAwHvhvWFMuAPPA1WTNKJPAFyCTPZhgCBCXPIZB7kZBsGZLv2qoiWiYALMBAMUqEURJ4gsAlz7EqInq5KPPACWty4CEBUajigBLf9sKaYpZUs2zFo5rnyL4TF5FmlQUQBBJGgS96lqvJkmuJ1DZcSRJ8yba8kCZSXwhoIGLi2oj5hPkQC6pRsmIR2bUl6nohDVFf9BxGEMSCWso7Yf14xEjgCZBj7gMMOYayZ1GFQMCO50sMXKVcckMaph7xHI4EKGI5cAnzBU5l13ZCKg483SxRVQ58F4GAMIoDX3YsgAAXgFTI2ZosWwbxXUI9zKkAGGM0QFA3y0wWgQCQ7/3pyXlc8/3YL28vnfERLssncTpzhKDn119/TfTx7ahUdJubp3/6c2t4BMCTmbViklR9x+1V990rWOYp6gztdHXPXvqj/bOTu55/klJ6clzhH0QRQgjTmeSWHQ9PJpoyF3+fxitPPe0AQYCUhl58vuretaiQP+HRVs4BAG5be+YrXzNWr0GFQui1V6rW3o0KhZPRYmCMi6J52vLMV//Sa2o6RWcu/rdThNkLv4TP+XjHu28t9PWJviUn805FmCMkZwosohSjlY3790+zT1iEAAAgAElEQVR391S6GeloKtvZiQNXzJaoJru6Hppd8OOhQrSq5dDBqbaOsJUnWcMN6TSsqAu5IKRY4XBkcgaFUFGr0FJZs6KCh6TKI1PpunokC9JCnmmSp6lyOg8VlKuqb9y3f6GtDRMeHZ8uVVc50UgomZbtcqq9I3b0mK2HeEhUF7KeJPlVETlVwL5baqwj2XK4lE+2tkWnJnwk5prb6sfHHEX1K8NStiz53lyiOT47K/p2qaZGTecAxqnGpvqj4y4mUhTZGUfzzJnu4VhqXikUk13ttePjjqTm6+tjM9MAQLc6igqmXijk2hOoYIfyuYWujsrkAjQdqzYGLT+SSs309oeyaTWfLzQ2alZRTubnurtDxYJaLFnVFYLlSEWDxvSyEmk8dHByaDhaTKvz2Vx7i+DYgQ+QirkXVM7O5dsaLUFJ7N8/3dfbmDxqUNmNRTGnYt6wKyM+JPFjx4rtTdSDkeRCsa5O4p4ymy4kGkXXVvOlhcYWKpKOvR/MtHeKmMYOHMm1dWDuiZlCoMpuOKzPLtCImo/XNB/YP9PWIQo0dmh/Ltagh4Qg5wJdNiORyNScH1EsNRRaSFmRKI/IsfHpQmUl0CU5meeiUI5Eo1OTPKyn65qbDxxINiWscKRp/95SZSXXZSmVp5JUqqmpmpjgMk41JJr37MkmEplYbcPUEeJ5c61t0WQyks+k21rldF5gQa6hqWZq0iPEq4yQTFF37Kn2ztj8nGwZTnVMyeR9LKUSzfXHJjwgeNVRKZkXgiDf2KgYRjibme/sqJk86iLJjYfUdI4BwauuEEq2ns/l2puB6ceSC+m2lmhyngbIrY6quQLzmVsb42UvnkoeHl7ckJqU5zPpzna9VMAl266JQceT8yUaDxXVaPPevROjo5FSRp9N5dpapXIep3OwKmwTPTK3UGptMLDWemDfRN+A7pY9k/JoSLEMuWjasZCPpNjRo6X2JoPJzUd2T3WOhpilTqeM+mrZt0HZ9yojAQNyNs9Dci5W1/LBe8eGhnW3rM0ki7W1vqpGp6ZFMUjVN9ceHFtoaVO4LScLrqrSmCbP54CEjarK8PQ8kFE5UhFaSLmqRuN6dHy6HI+zsKwkC4EkFmPxirk5LASZusb6I+PZ+kYjGmk6eKAUjbKwIqXyjIiF2tqqY5MQAaOupvLAWDbRbOrh6qljniT6sbCYKQiMpVra6iYnfFWZ8sXoP/1anJv9o6UI//1RzYkYffLx+EMP4HSKC8JJ1BEE0yh98tPpi/7Sr68XLKvq3rXh53eenHGh4DilMz6S/ua3/KqqU1QoW3v9tdo7fr2qa3D16o8jhE6UK/zvKUJBQNlc+rEnNx2tjuf+4bt+Xd0ph664gATXjT7xeOWGdahUPBl0BaE1uij9N39nLl0mLszHNm+Mb1iHDOMk0BVkLKioKJx9bvovvk7jcej98f39+k8oQntgSNTiS353X1kOW3JoyV0bSuGYT9HofY/m6hph1h54ZFtOr1Q8p2v9Yy7RChVVy+64t6xFXSAOrt9YjlYyk/U/tKUsRywtvOSOe22k5moalv7mXlvUbaT237+FibgI9dH1G0tKzAxFh+95kDks2di67I7fe4CU1Irh9RsdWTeBMvjgZoeJpVjVors2CKY/1Tc4fN8mdXohW93YvmOXMp1MtXQM3r9Jn5g/vGzV0jvWRQ9NTgwv6Xnk8ejEdLqxreWZ18KTyamOgeEHNsX2HD5w+keW/u7ByJ6xg0vXDDy6vXLvofmW7sTO12IHJya7hocf2V75/sHJZUt673+8at/kgdHlfY89Hd07lq5vadv5YsWe8fHBpUOPbGt4b/+eMz4yvG5jzbt7jw2OJp59tea9/bNd/fVvf9CxY9f46NLGF97oeOr5fcvOqHt3b/+mx471jcb3j7XveH4+0Rk6Mj248bEjo0vr3/igZ+tTR5Ysq/jwSPe2p+Yb20Kzyc7Nz6TqmyuOTC9a/+hk/3D1uwe6HntmemAksneid+sTqdpEOJXsfPjJVFObPJvtf3DrdN9Q7MPD3VuenOvqlaazow9uWqhvE/Plvge2ZqvqSaY8uGHzVPcQyRrD9z9aVio8LI7c8xClQrqh+bQ71jlENcXQwIaNlhryfdT30FYL6Y4eHlm7iXug0FA/9NsHKSMlraL//i0eksokMrruYRNrpXj1yF0boOFPd/Qu+/U9gcvzsdr+h3ZwQMpytO/BLYEL55o6V95+F0qXJocWLbtzXWCzdFXT8IZHoe0VQlX9D26FBj24aPmaO++p+PDg/mVnDj28NbZ/LJnoaN71evzAxGTn4MCj26t27z+w5szRdRur3z+497TT+7c/Xfv2++nm9sYX3oztPTLRM9K35cnad/bsO+OsgfWbGt56f8/ys7qf2Fm1e2+6qS3x4uuV7+yf6R3s3L6z8Y3393zkY4MPba9/fffE8KLG599ueO3dhURH0+vv1r7+wXTvQPtTLza9+Nb+NWf2bHqy6ZW3xpasrNt3qH3zU5maJmU23f/Q9mN9Q7G9R/o37Tg6uiSyZ7x325PJuhY1ne95+PFcTZOYLvU/8mS6vT1yYLJz+8657j5tbL730e2ZmhYxV1x614ZCrFYo2oMbNs61dYfGZno2Pr7Q1iXN5Qcf2pGtbRJsf2D9JiMUhYbTt2FbMtEmLRSG7nt0oatXWCgNPbgpH66iAI+sfbgUqTK18PC6RyBjBlR7N+7wAlyorD3tznsDi8509S2/fS23aS5W0/fgVsZgXo2PbNjoMFKorl909/1izpwYHF12++9Z2VtoaB+97yFoeflIbd8j24HhTye6V9z5O2k+d3TR0mV33kdy5mxT5/BDW3DBMNVw1+Yncc4e7110xu13ybOZw0uWLbljnZgqzTV19m/cjrJmsrJKeu9dVC7/0VKE/ytd2NXj9PSIszM4l4VBcKIFJC6K0tiYcviQ39jkNTRZI6NMVqTJo8g84VoUx1ianJDHx9zOLhqvguxUkyGFkNY3UFUtvfw8obSurkk4wQj8N69GCOXymaee2XIkouX/4btuInHKAQIuCMgoxx95MP7A+pMR/2QMCELx459Kfudiu7dPPnK4au3vKjY/KrjuiaMrDil12jvSf/23mYv+ksnyH5WI6B+yZMukiiKZJqYeAABRKvkuw1gIqGiZvijKti25NlVk5FjEc5kAEQsk1+EYI99Ty0VKiGRbomn6iiJ6DqEeAFwIKHEsRjDiTDPKviQRxxZdJ0BI8j3iuwBwgTHRMgOMUUBly/AJkVxXcuwAI0SpEFDAKMBYMQ0uQBRQySgHGIu+j333uBmc5Ng4oAwj2SwDCAROJctghCDIieuAgHIBio4tUJ9LolYuQgECwEXf9QWOIZAcBwHuC1z0HIEzTrBqlAFCAmeibXEBAhaIriMwxkRRciwcUIag7NoccgCA6DoiCwJCsGOLjuWpqlIqYuYzkUiODTkDEKCAIkopIaJjyabhS6JsmcTzGMGiaUBGGQSY+pLvUkKw6xDb8iVJtS0SUF+SiG3hwAeQE+pLrkNlSbQtxSh7sqSYZeQ5TCSS5+DABxBg6iumQUUi2pZkmVRViG2JngsAR9QXHYtjhBiVjRIlRHJs4jpUJCL1iOdCCAGjomtzLCDOFaPoK5JoW6JtBhiJnoupf9ykT7EtgAWBB7JRDgjBnitaRiBLmFHRcwBnAHDRMjmCGHC1XApkCVHvuOMkE6DkujigXCSyUQKAQ8Ak22BIwICLroNY4CMoeh6kPpMl3TKgABnnoucGAkScEdfGgAeiKNoGpj5DSC6XAIQQAsl1uAAh4CjwEaUBRpJlEtcJMFKMEoAQciZaFmQUAkY8V6K+TwhxbOS7TCSybWJGj4dRcl2KMbEtyTJ8SZTMskj9QCTYsYWAHk+HSP2AYOx5smX6kqRYBvFcqsiiYxHqAQ4w9UTHDBRZdG3ZLPuypFim4HsBxqLnkoACCATqE8uiqiJ5rmyWfUWWTIM4NkMCDnyB+kwQBMBUwzi+PWTX9iRJ8j3iuYBzyJloWwBCxJhiGb5IRM+VbCvACFGfeC7kjAMuOTbACEKuGMWAYOI7xDEpRoQHxLEhAFSAouMwCBHgmlUOJAl6rkg9FwuIB6LnIAFyJEi2CQWAGEWUAgD/9PD8N1RAfbunb+4HPyx+4lOBpp3EKcMlST58sPbnN4df2AUgzJ9zXuqb3/ISCRgEJ9rKwglR9u2tvfVmdc8HnIgAnlKZ4pxjXP7Ep+bOPvfV/bv37HmHMXZCXOF/VcFCCBdLhSef2nwAg9x3L3HbO4VTjswSBFwqVd33++iOxwTfP1FIBAPKZTl34ZezX74oqKhQ93xYtfbuf5lBPdF6KecwYOXTz8x+9S+MFSsFx4F/tE1X/7kX4dwnPilgMrZ6tV0ZzzUkFvp6s4nmUmVNqqdzqn+IC8KBsz7G4moxUndo1Sq7siJb25Ts6Uq3tZfi1am+rmMDIxzhA2d+xKqrsmX9yOlnWBWRbH1Tqrc72d5uxKtLnU1HhpYGCB848yyrJu4o4bGVq+yqWLaxNdPdmezsNCsqUz2dkwPDEOHDa9YYtZWuXnFkxUo3omXqm42GuqOLFzt6dHZoONPV5kvqkWUr7epYpqE519ae7O4o1CXK9bXjixa7aniup3emrx/I8tElp9k18Wx9ItvSeqx/wKypMepqx5ed5qjhhZ6e6Z5epqhTo6NeVXiutS+XaJ4YHrFisVJd/cSypVRRZwaGZ3p6qapODw4ZDTWp5o5yfd3MYH+uuqFcWzc5PEQVdb6rN9fWnG5uL9bVT4wuKtTVB5q+f9VqL1ZRrGkYW7rMDoUzbW35lkSmuc2oqZkf6p9v7WKqsu/MM/2IXqyuO7Z0sR2LJdu6sh2tqdZ2oyI+P9I/29HLMD5w1lkgLKXq244tXmTFKlIt7emO1mRHlxWOzi4enuno5QTv+8hHvVikUFk3vXSJUVGRSbTO9vYl2zuAAA+ecaZVV+0ooYNrzrArK3J1iWRvd6q9rVRZk+nuONY/zBA+ePpHjLoqR9LGV6+hUXW2qSPT05Vsbzdi1ZmutsmhRQCTQ6d/xKqrtNXw2MpVTjyaaWjJdrQtdHdZsapUV+dk/xDAZGz5inRLK5fk8RUrrapYtr451dEx09fvRaLp7s7JoRGOyNiKlUYsblRVZVrbpvv7zeoao7Z6fOlSW48udHVN9Q8xRTm25DSjtirT0JxvbpkcGDSqq62qqqnTllh6ZKZ3YLq3j6rq9NCw0VCTbukq1DdMLFpsxuNGdc340qWBrs71DKS62u1QZGpwxKyrTrZ0mDU1syOD2fpms6rqyPLlgSLPd/emervMivhM32CxuXGhpcOMx8eXLvXDWrmybmLZUicSSbZ1ZTta0ok2o6pqYahvrmuAEbz/ox/3InqpsnZiySIrHku3tJdaG+c7ekuxqtmRgdnOPo6EfWd91I1Hi/GaidOWWvFYprkt3dm20NZpR2PzI4PTHb1cQPs++nG3IlyKVc8sWWTFonOt3Znu9mRrhxuJzQz1T/UOCgLa/7FPuLFQMV4zPTpixmOF6oZyW8P4yFKKpUNrTjfqq11ZH1u1xq6KZRtajt9BRqw609k6MTgKsHhk5cpyQ62nR8ZWrrbj0Ux9ItXRMdvT60ajmc6OyaERLkpHli5LtbYzWZ5YsdKsimcbmtNtbdN9fW4knOtomxsa8kR1bMWqdGOCqerkosVGXXUm0ZFtaZ0cHPJ0fb63vyjLodf+r/YiPAmMxRjTQ/bAENND4swUKpVOwgYHGYb64QcAQq+11e7t8xqbxPk5kkodV2Q4sVM4l1MO7A/ilW5TAgJ4KsmSc84J8ds7PNvMv/pCWFarKmv+NwAshJBhlp9+ZssBz0pfcqnT1XPqkVmCgHPZmrvuDL+wC3B+ojsMep5fXZ35xt8WPvPnXJZDr7xctfZuZe8eTsiJVkohpUAk2S9/NXfBF7zmllPRTOD/AMCyBobqfDq6eaNkmxCBpffdz4kQKhaGtm7zQ3LVxFTPs88gSqtLybYdO9VSEajSst+vgyJWS4WhTVu4IsZm5nqeeVp2HJHaQxu36rkMjUWX33MvErhsmQPbHtOAK6dy3c/s1ExTEILRRzZFclmrMrri7rWIUdFxBrc/JnFPy+X7nnlWKxSgJIw8vCmSnE93dixbt74ikxQ463xuV+XEUaaK/Tueatq7Z364f80991YeOVJqbhzZtCU+PQUwan/51frxI2ZFxfD2bYkP3p8fGVzz+/tiY4dzHR1LNj5aPT4GMWp97Y2GA/vM6prhx7a1vvdeobdl+IGNNYcP51taR7dvqx4/IiDY8fJLdR98WKyvH3pse8fbb80uGlq+bkPt/n1WfW33rhfq9u93K2PN7+8eeOqZXEdL97M72196OdXZ1f/Cc22vvcY0teHw/o5dL/q6Xj0xsXjrllRvR/fzL3a89FK5qa7ljbc6Xn2VS2Lt5Hjn088xmcSPTQ5t3VZoSXS8+VbH888bDdUt77zf+eLzAgL1s0dbnnkJSDg2NzO8aUu5NdH8xhtdu563amONH+zrem4nEWAomxzY9hggKJJOj27clEsk6iaPDuzYoVBftosDmx/T81kaCy+/ey3mAfHd4U1bBMiimVzP08/olkEAHdq4uSKT8uLa4nsfFKmHqT+0dbsIqJbK9D3zjJrPQlEYfWRzbG62lGhYc/da0bEQ4IPbtiuOIdtO79PPRAo5KxRavn5dbG620Nq0+u57sG0Dgke3bNYLGZEGvU8/HUstzPT2ffy3d1aPHU53dy998IHqyXEokbbXXm/Yv9esjA8+8UTr228uDPet2PBA9f79me7uJZs31x0+iDFIvP5m/Z595bq6wR2Pd7z7ztxw32n33le3b1+2uXlkx+N1+w9ACbe9+Wbju7ud2qruZ5/rfP2NY8tGl6/bUL9vr1lf07nrhcY9H0KCWna/m3jzLau2uuu5XZ0vvTw/0r9045b693dn29o63n+n89nngEgqZmZGtm4rtCXaX3+ja9fzRmNN05vvdL30EgSgKrPQveMpQFBFcmFk0xa3Ltb4/p6OXS84NZWJD/Z07noOIyGSmu3b/gQkQjibGX34EbOxvuHDD7uf3enFQnWHjnbvfIawQDcKQ5u3iwLTCrmhjdvsmsra/Qd6du4Mwmr80JG+nTux5yiOOfLoZkgQw3jJQw/r1JTzpa6dz4WKRQGDkUc2x1PJYn31yrvukS0TCGBoy3bFNZWy0btzZyiTgQoefnRzfGoy19F6+t1rRcsMFGXRI4/oZoH4fu/Tz0STC2ZlfMX6+6omxnOdravXrpOLhUDTFm3eHM2nZddq3/l8fHa22Ni44vdra49NpjtbV9x1j5bNUFkeefyxaDKZD4f/GLwITwJjcSK6HR1eooUszJ8cMIKeqxzYj4pFt7nF7u1z29pRoSBOTx2nz07sLC4W5UMHWEh3W9uOC5OeMpHknEuS39nl5zK5116KhaPxePUf+F78n0RDcF33ueceP5RLpX58tdPVfeoxgwiRTLr2tlvUvXs4PMFePwgFy3K6e9Jf/6Y1PMIxjm3eWLFlE06nuCSdMJxwHK+hMf3Nb5mji5goQe9PMy//6fI1XbEdqqgBJiQIOCYeEUXPB1h0iKK5nifLjgQU06SEUIQJ5wBhj0gipQBhR9N0n3qK6suqZlsUYUpEAiCAgi+KousGULA1TXccXxSprMqe7yNMRREDAAH0FVXyfY6Jq+myafqi6Cua5HkBgAwjiBD2fEcPYc49QfBUXaG+qYYoESELMIOeJDGEReq5qkpcl8mSq6hSQCngvihxCGXOPEIAgMT37VAYcy5w7ksS8TwGgScrHAqSY/qyDBESfd/WdIExCIAvKxJjEAq+JDHOZEpdRWEIE8d1JVngADNORek43RNIEld1zTJtPcyyC6LjerKiCVAIAgYRRYLsur6q+pIsG4araYFvSb7vq5psmoQGASG+gGTPC2TFkyXd9WxVo0FJtixfknBACYABIT4WZdcNJMVR/bDrWnpIAp7keR7BWEAEAC4gV1Y0z3NlxVV11bYCIlIiEQgBxr4oi74PBcHWNM33fVHyJEm17SQSGBEJ55ADX1YIpQAKbjgkuR6VZF9RVd/LSDLFRAgCgXNPUTANAAeurMiWSUXJkxXFp3kIqSgJUCAAekQiQQAQsvWw7Ho5KAQIc8Yw574kAYSIbdmajhjDAFJFI74PAPQkmSMiUcsjBEAgep6p6TUcIMY8IhLGBQ58SeZQkFzHVzVIRJFSWw8JQSAw7kkyZgEIKBNwgATJ911NYwiLjuOGQggAAqCvKIhzxBmVJAqh4lMqywxh0fNdRSGujYOAIeRLkur7VFY8RdFs29Z1kVqS4/iKKrg2YSwgoouw7LqBKNqqqruereoicBTH9UUZYYFAGGDii5LiOL4o+ZoUtmxXVQEhoud6CAOMCWNcEKimK5bly4qHoWxZnqIySZJpEBCRCQL2fc6Bpaqq51OCfVVVXTcjCFSUEACQA19RCWdQwK6uy6ZBCfG0f7mDAoxBQAXOPVkmQQAZd1Rd9jwgSlSSRc81MPElWQAQ+76LCQooEKClarLnFQTBI0TxKRUET5IBACgIfE0nAAAkBAT/kXgRnjjGCgCE5qLFfk1t/KH7wy/sOuG2d0GAnhd59mmcz6W/9nWnqyf1rb+nlZUV27acKB3EESKpVOXv10LXK37iU6eWERykNND0wte+Lhjm0y8+KclKc1PbH2JW+P8xRXhcVuuZndvfP3pw5oc/soZGTjkFAU6IODtb99PrpcmJk5gRFQzDWLU68xdfd1vbOELVv70j/NyzyDRPZozCMMwVK9N/+Q23te14vfFPt/1/5UV44eea33s32d0tU1vMlr2IBjgXCwbQxXy0qnFsbLqju9aeFWbKqZYWJXBw3mQKcfWQvpCmUbUQrWrYt2+uqzvsFEm27KpqEFGldIkp2AqHw7MpWaV5JabmCuWKOFKE8MRsvq4eEy5my0whviRJ2RKSYbayrn5sbKGlTUJ+eHKuUFnFQoqYK2lmMd3aFp2Zd1RVkKCcLXpY9KvCUrok8MCqjqGioxez+USTlCkAAPL19bXHJh1JZhUqzhmi6y60tEVmp8PlbLKzLzy3EGCcb2iomjjqi5KuUqcEIGfZxoSeyyrFYq69pWpywpa1cmVVdGGBQUhjOiw7ulEuNNVBww9lM+mO1mhyATqBWxUBLg1lsnNd3aFsRimWirU1mlUSs6VkZ6daLCrFolsVEQxHMu2gQi2qFY2HD03190cLaSmZL7Q0KeUStLwgqnlIjM7OGYlaE6lNhw7O9PYk5sfKVHNiYRRQUna8uO4JUnxqqtRcFzhcLxaK8UqFu3IyX2iqlxxbLBjZ2noqi8379s51dCnAiY5PZ5pbCHfF6fkgoruVVfpcimlirrK28cCB+bb2sFPEOdNXJFnlbgkAGVmRiD6zAHQpE61qHNubrG+TVCEyMVuorRUkKGbKXMKupkrZEsZBtqah7tDhVHOrE9IbDx8qRaNAI+J0MlClQmNr1eQElIRMfaJu3/5cY2O5IlY5fYwENNXcGk6lwvlMLpEQ8yUUBPmGhsqpY54oBRUqKtiyaS60t1fMz0mOTSt0XDJdJOUaGqunJgOE/coQSZcw9TNNzWqpGEon052dVccmXKIGUVXMlxjjNB5mNo3kcoWWBm4HkXQql2iMphZogLzKsFgygM+9qhBzQXRh/lj/YHV6Rkrmc+0tWrmIDNeNh6Dty2WTRaR8qLLp0KHJvv5oOaskc/lEo2waguEoclBSotp82mqqNojedPDAsf6BSCmtTM/mEq0KpyRvejHdE9XYsSmjqcb3QSifL8UrJcGX53N2bVzxTGpyGtUCBpV8EWo4Ha9PHNg/3dMTckrKfLZYW8tErM+nNezM17XUHJlMNjXpvoHzJiXEj4eldImLghWLajMpqKBSRTw8n3Q0nUfkyORcMV4JVUQyZSZL5WhFdH4eY56tqa85fDjf0GRGK+oPHzSiFVDFUrroS2KxuiY2PY0wsKrj0fFj+doGKxSunD7GMXYrIyhnIM7SiZbqyaNUUbI2rbrttj9modE/BNwgoxx9ckfl+nX8ROtY4N9U41N/+/dW/wAqFqNPP1n9u98yjE/4oxjjipL94pfzf342wOTUEnviCCOjXH3LTxonJi8896LKePV/i7H+V4rweAPXS6/ufH/fu9OX/MAaWXTqbSZRlA/ub7z+mv9wy/3hWNV1S5/+TOavvuk2JSCljTdeq7/6ysmYFXIuOE7u/Asyf/UNv77hT9DqD6EIq116+h13EtOmovTxW34BqX942aqF2uaA4K4XXlm0bgMIQNwuDv/zer1Ymli8dLa+zVPVrhdfXHbPOipL4dnkovUPEMeb6++baei09Ujb+++dfssvIAPED5bdtVbzrInu4dmmDkfWmvftWfWrf44kU/mGhk/dcLNaKBwZXTLX1BGIpPHdD05bu07JFX1NOf2226PJ9Gx/3xm/uL1qYjKQlP5Ht9Tt259tbT3W3peP10QzyY/efFvD+x/O9/Qs/+d76g8dWujqnU50lqOV4WTqjH/6Tfsbb42tWvHxm39Z//a7sytXfNw80CYJVirTtfnp5rfeSSdaV963vuOFlxdG+460jxQrqrRUZvW96+rf+5Dqeu+OJ9peeWOuo3umvacYq1Ys46xf/qbl5VezHe2Dmx5LvP5mqSnR9sqrS+9/eHZwYGDL9uHtOxaaWuZau3LV9dh2Fz35eP+j26xovHr/oTV3/y6faJroHclUN8q2ObD1icFNW2w9XHN0fGTDI56qTg4tStYkGEHL739o+OFN6a7O1lfeHnpkoxeKZDrbJxJ9AMKRHTuW/u7edHdX20uvjj680ayummrpSdclAON9Lzy/5Pf3+4oanptf/avfzHf0VI5PLPv9OuL6mNHlv74rlE4bQx0XglmkEBlXzMIAACAASURBVPr++Krf/h4IUE/nFq1/QC6Ukz3d083dZijS/sHbq277Z2LagYCW3/V74npqBfi4jlVesFL26tt+E52eTXZ0fOq6n4aSyYnRJbNNHYyQmn0Hl629L5JMFSprPnbzz6qOHptfPPh5Nq7qIXNsbtVda7VcljNh8YYHY5NTY0tXnnPd9Y1vvH1s0dLTf3Nn0/sfpDs7p9p6ixWV2nzqjLt+17HrhfE1q8667deJV9+YWLoiWd9sVMRic7Mjj2xqff3tVFvXkvX39z/3/LFFo9Mt3YVYtZIrrr5vfdMbb3uRaM/OXe3PPl9INE12D2Yr60XP/sg/3dm+64V0Z3vf1ic7dr1YqKuf6e5NVTeppeKq397T//gTY2tOX/3btR3PPT8zNNL51uvD6x/xFCU2MbXyt78r1dQcHRhNVTeK1Ovb9uTwo5t8Waucmx1du4FK8uTwooXaZiywkY3bRh7eXGxsqHvnw9GHHmGSFK8UPqF6HnOUtw6dfsdduZbmxDu7F214yK6qnGnuTjU0g4B3vfHa8rvWCZArufzKX9/t6urRgdFkXTPAsPP5V5esWy8wQFx39S/vpBgHkrLmn+5QXZOU7OEND2up7Hxvz0yiuxyJ1R4d+/hNtymFoivJK+5eqxWLsx29c4kOWw01HDqw+ld3Vh+bmh4Y+LOrbwzNzo+ftjJZl/AVuebw+Glr74vNzGVq6j/5i19W79k3dsaa2fp2IxSt33dg1drfR5ILOAgGH9xcMz45MTCcbGotRivjc9Nn/exX0dkFM1yx/L4Nkem5VGXVnyjCP4Tkcjq77d7+0FtvQM87UUoHcI5zOX33O15jk5dIuO0dTmen/sZr8ERFTSGEvq9++AEEwOnuPbVkSCFnXJKspcuE11+d/vDdno4+UZT/6+qp8P++/Ld3v/bu7tdm/u7b1uIlp1jPPwBcFNX3djf85HqUzZwEToeU5s/9XPrr3/Tq6nAhn7js++p7uyGlJ4PTMU797d9nv/ZXNBb/E7r6A5enKFq+EGDCBUQsG0LEgMABFCijhOj5QiDLjoD0UtmDAiWECYhzCAUsWzbg0FM0vWxQSRF8ygTEAPQwEX0KAPQQlkyLc8CgwATEOXcVVS6XAcYUYeJ6MAg4xhxABqCnh7RiiWHsyqpcKjPGAowx46Lt2KoqOi6ijHPAIOIc+KIsMC46LhVlGDBsmh4RORQ4AK6sSqYFA0axKARMdD1XknngSYJnCRK2bMS5o4eIaSHf5wAyIAQCYgAyIEiW7Wgh0fMFjwYIMwEFAgIcwIDJZdNVNcC4ZFqOqgkBR7ZNEREQUYolRw8BABkUOAC+KCmlsifJAmPYo0zADAqBgABnARH1fMHRdCYgxTAYwscjAwBkApbLJhWl4zH3JRkELIACg4gzLroehQLHklIoeorKAWQQgYBRUZLLRiAgDqDouBweD2PRJaInKyHD4FBgAsZ+CXKfIkl2XACgJatqoUgVlXMQCAgEPEBE9HyBBlRSZNeFAFgCkUDJ9T1XFJVymWMcYIIpPW4KCThkUPBVTS2VqIBtLaQaJoeQSpJAbQF4rqQTx8UAOoqmFoqBpAQIA8okz3exKABATMsnIggYB9DVdGJahAOfyNCnom15sgo44AAyARHXQ5S5iop9H/p+ICDOBSYgDiGHglw2HV0XfA8f337HdxoAPGBy2XAUDQIom6arahwIgIGAEOR5hDKKMYNIKpY8WaEClktlKikC58TxKCb/kpeAB4ToubyraT7CqmkxhBmAgYA5B5xDuVT2RNHXND2XtyXF5wJ2c67vA4hE1+UCCpColQwfY845E1AAkYcIKZcDTKAgiI4LoHB85wDGPEnWc3lKJB9h2bQAESkAomVDylxVU4slTjCiQSAgwHggEOL5yPMDURJtBzJOEWYCYkBwFFUplVnAAkwQ5yhgLhE5hIADX5TVUpkh7GghuVgSEQYBYFAIEPaIhB0Pub6rqGqpFGCRCYhBzIEAGSA0wLZtazp2PEQD9sfpRXiiizGOkDUyeuwXt3tNiRNWpYIQQIiy2fqfXBd6+SWOkLF02cwNN/OTkBwXBMB5/MH74w9uOPXm6BljmjZ33Y0ZBB/ZusF1nf96qPA/UISCgPYd/OCZZ7dNf/HLxbPP5YScWsiAE6K981btL35+MpVhzjkh+fPOz134JSZJ8vhY7S03i/OzJ5eDIBZPf+Ob5dVngFNO+eP/V4owc9FfEMcJRPL/+AMS3/MpVLlTDMUEn3mS1JIfn9WaGEFhu+B6AkbcVxWPE90vm3JI8AJfEmNGxuYigJAQXiIhOXAgBDaU64zZkhhhPgtEUQWOATUmYt0tl8T/8F2lUAxQRkUx6uRLKAQFoFGzJEZU1+AYuYAwAYXdIvN4ICCJBCUSFjhTA+v4a6AAqQ8gYK6qCQELBCHqFYtiBHLOEIKUymYJ6IrLRQABwxj5fiCg5uJEklRBxuxQGHIuBAwLFPu+JWpcEGTTgIxJJCiKERRQldklMYwoxQJD1LewFqJlGyucgYAQ0bGR7weiKHPHRHogEhgEgAM9MAOfAy+QMc2FqojrBiLR3VKZhEXgi47lB0jCgaGEoc90ZhTkCuK6nKCG7OSCVE0QZyKxkBLyy5aoAZ+FmGEBGfk+R4Ik0DLSsRDggNpIAQLkgkBc1xfFqJM3oBZIJOQUi0iXmScg6EAp5JUtWQM+o6IYL6csIEMAYqwwHUoogQ0hd6Ac8sqWrDPTZpoedfNlpHMkhLxySQyLgSf6jhdglduGFuUBp4QACI//npBXLgoaBJxLkuBTzTdsSQOUBYQc938EADCEIOOyZyHI/ABBzlxFhYyxf02ZEAQMY8kyMfMlgTpIsYjKMUK+HwhC1CvZjAhBYOshCIAQMAwp8V1DDOnUAA6lEMuEHk+ZxqyiGEXUxzBQzLIDZREHtqhSiCN+8XioqSSG7WIZ61hgOPAsrOnU4B5jAVcE/3jKqCSGnUIZhTBismV4AYoFxVy42mco9K8po5IYtvIlLokSwoyaWNN9wyEKp/x4yrDrBpIkA7eMdIU7qmem1BqdGsALWAAUwcuFKonrUUkMO8USDhFAMaeWoNYac0W1wueEiiRmpJ2AcIRFEpRwSGQeAoEtKLpveEgUbC8gWIXuv6WMhAEEAmPEsWXumWqIcYEhxP81ZWG35PoCF5Cj60IQqL5FgG8D2ZEUCIBSKnJBEEVWJGGBMUYI8n1GiHRk7I/ci/CEKQTHrvnlbaHXX+UnhU0F6qf++lv5z3wWCAJJzjdecyWZnz+J/hnoeYVzzkv/1V+fepaFEOJMpvnif2iJVn7hc19Dwn8qQPpvFKEgCMemJ5597rGZj5xZ+PyFXFZOMXSFcfiFXbW/+SdUKp1E7YppWv7zX8h++SIuCNr779Xdeos4f5JmhV5La/I7F5eXr4L0TzJXJ0gRQvC1Sy7GvocE8KWLL0EIKMXCuTfc6EX1itn5L1z5Y08UK+zceZdfpRglpikX/PBHhFOpXDz/qquBJuqp7AU/vhwiAUnwi5dcKpaLfjR80cXfUy2DeO7nrr1W9w1u089fd53AOZPxV753STQ5b9RVX3Tx90LZFJDEs396k0Qd5AcXXH65ZBpBVPvK975fOXl0ob/33Guu7X7njXJl5Sd+fXv7e7vLddXn3PKz1rffmVi+9Es/vLz/mafmRoY+9ctfDby4q1Bfe8bd93S/+cZCe8f5N1w38NzO8dUrv3DlVcPbtk6ctuwzt/9q5NmnivX1Z96zduC5Zxd6us+9/trRZ5+eXbn4Y7f8avSx7ZMrVn7yn36x6MkdpaaGM++5e3Dnrrmens/cctPiZ54+fMaaz974kxUbH50dGTj93nVLH3001du9eNvWT95++/zQwGkPP/KpX/1i7PQzF23b+ulbf5bp6+168/VP3npbvqGh59VXzrvlpvnBvuEnn/zkHXfOjQ527nrx3J/cUGhpbtv7wWd+erNVGWv98INP33pbsrd76Omn/vznP1vo6+p4/c3zr7+2VFvVcmT/x37+a6uyIrFvz3nX35DtaOnatevcm29K93bWf7D3/GuvKdXXV89NnXfFVY6uNowfOf+aa9ItbfG5mS9ffhngHAP/K9//gZbPubHIRf94ccgsQgDOv+pqMXClUvnCK68k1Oea+KVLLtUySb+24nPfv7wivcAIOe+a63S7JHj+hVddqVgmDasXXfKDyqnJXFvzRd/9XmzmmB8Offamm0OFTEDEz1/x43gmnamv/8Z3vl07djjV2/XVH/ygcuKoFYuff/21lXNTvqJ+/uqr4tPHJkcXfe1Hly3auvnoihWf/sWti556It/SvGrdur6dO5N9/WffdOPiJx4fX73y/OtvWPLow0dWrf7oP9+5ZMdjxUTjyvXrh598ar6n589uvnHVjsfHVq/89M9uPW3TxvFVaz72m18v2/hIsbV51f0bRrdtz3a1n3XHncu2bzt8xuo/+9mtpz/4wOzw4GkPPLjm/g3FlsRp27cu2rwl3dt11l13rV5/3/jqFR/71a8/sn7dkcVLBl55/rO3/NyORxPv7z7npzdluju6X371z3/1q/m+ro633jn/2qsLtTVN44fPvv5GL6zXjY995qabjda6lldeO/vmm7Pd7U3vfXDh1VcasYqquanzr7vR1dWaiaPnX3llKdHQ8u675914Y76lofrgkQuuu8aoiseyyQt+dAXEIL4wd/bV19nV8dpDh8+++WajsSY2MfXlyy/zdS1klr54+Y+AAACCn7/iylgxxSj/3HXXaqWSF9G+cNnlsbnZbHvzRd/9XtXUpBcNn3v9DdHMAsPknOuvUwsFrzL6pe//oH7/3tRA30Xf/37t4UPF2tpzf3Jj5cKsr4fOu+KKqmOTma6uv/r7byX27pkbHrjgiitrDx4sNjScd8N19ZNjnqZ89saf1B49Ot/d/ZXvfLvj8KFjw4MX/ujHre/tLjQ0nP2T65sOHkjW1orv7/6T0OiJnJXEXLkKMq4c3P/vH8J/6NsFpL/xOgTc7hsIIlFj+Up57DBOp0/4uERI2b9PnF8wT1t2ytnpME2zFi/h27fkUwu93YP/DUUIIVxIzT/3whPH+noLF3450EKnUvcZhADCyHPPVv3uLlQsnkSzVBCLZS/6y8xXvgppEH5hV+2tN5P5eX7in8MRshYtnvvRVdbQiOA6f7qTT3TpxaJDsGKWsWOzgCLLUl2HCQD7rp7P2iLRykXdMmxClHKJ2BagHmaBatsBYNg0QoW8rSpqqagW8jYhqlEWWcCphzxXMcqUMxzQSD5nSZJSKGiW4SFB8RzRMoFjC9SXi8WAM+I64VTKVSTFKCnlkg+BaJnY9wXPDRAKF/MBo9CxtXLJg0AyypJjQ8viAEimgS2Tch5JpzhnAuB6sUA5w5YpupZglDnBqmngYoEKMJLPcgEKnqfl8wxC0fNE2xRdmyGoZdPYcShGkUyas0Aol9RiAYgElcuybRKjTCHXigXs+wFGeiEHWSBwJrk2sU0qCEo2rdqWo2taKimVigHgoUIOAc49V/BcUir6gMtGSSkWbE0NpZLEMnwBKmYZug73PWKbqu95CMmmqebzpqqEcjnJc3wIlHIJuQ4PfJExzSh7AlTyuVC5ZGlqKJ+VzLIvQM02MaOc+sRzQpmMJRLdcbRSwSZYKebFwOcs+J/svWeYHcd1JtxV1dXx5jA3TM6DMIMMAoxipqIlWw6y5bRrr8PacljvOsjKEsUcRFIUMwmCCQwgCYIJTCBBMCDnOHnm5ns7567u7wdk2aa836Oh/X1rPMt+6kc/955b3bdO16nT5z31Huj7gqGFxKNtM6apFs8KcktQZAvTom3Ttg19H/g+pykkJAhQsUbd5jhWknhNdyDgNZWxLMpxYBgKuhaEAe04sXrNYVnWtUVF9oOA1VXGNoHjAAgjshT6HnLshCrbLMtqGk08WtcIjQTHZnTVh1S0Wg6DgIYg0qyHhCDTYB0Ley5BSFBV1tB8GiVbjTAIgGOJUouCCHoe41hYV30IxHqVIb6P6WilRPkudGxR00IIgWEg38OK4gGKV2VsGgSCaL0WEh8Qn9W10HUp12EcWyC+AyhB1xjTcCDgVQUSP/RcbFmCIrtUyNkW26gbPB9t1NmAuABwqoJchwoC2nFE23JomtO0SL2u83zC0FlDcwElaApN/CAIaNfhVcWmEacpUUPXeS7WqLO67kLI2xZybMr3kevwquIgyNl2pFE3BF5o1BnT9IKAcWzadQJCUBDE5ZbNMJyqiIbmIsj7HmMaoW0Bz+VUJSA+7bmxes1lMGfovKb4ADCmgR0bmiaFYETXQt+DjhWpVlye5VxHaNR9BGnLYBwLE5+iwojUComHiJ9sNT0acVJLUNWQYZBlsraJDD0MiSi1KBoh18G2/QnR6IJ9LEQ3vvo71T/7i0AUP0YkJeC41BOP5m+8Dhm6l8uX//YfjDVrKSpcMA0pw0S3v1G46fqPQRP/f3oEQ6e7p/zXf3NiZvzNt1+mzvAL/+8gQllpvfLGC/t5RvnaXzvd3WcTlQCEVBDEX9uW2fAAUuSPQdTuFYqNr/6uesWVSJbj215JP7rx45XTCXheP/eC+h/+ERHET2JXHw8itH/1S4N7d88PD7PEFiotOx13OS4xPW+3JaRMPnd6vNzb36HO0jNStbeXAT5fajjJqBOJJGZKbibWSue7d++eHV3GU3bmxGS1p4dGhC83/QhvJhKJmTIrBvOF/q79B2YXLWGQlz16ut7TDTiaMgkExOO5xEzJjwv1XHvvvn2loWEaB6kTk1JHpy+yfE0SbK3aP5CemLIjUScbzxw/rabbSILjVRUFoRpPsJIRNeTSwEB6YsZj2UZXV8fJE3Y05sU53NQY0ywNj6QrlZjUKA0NJqfmCE3Xu7uLp0+5LM9FiG1iTlPmRhYlSyVRUapDA8XytMUK9Wwxd/p0QCOnLYE0N9psNPu7kGTEms3youF0aQ6YnpVPQS+MVSqzI4sYw+g8eWJ8xcqk2kC6V+vsjFfLoqKYuVRAQGquJPd3WJDvPnhgfNXqlFTlq1Kjr1tUZKzZVi7hByA7Od0a7jWR0Lt/3+SqVe31Sb/ltXo6eV3Dsu5k44YQS5ardltcx5HevXsm16xJqE2x1Gh2dzC2ydflWlePJ/ADH74/M7qMg2722HitpxfRAVdpEpE3UkmuqUIeyolM9+49c4uXsrSfPT7R6OiIQcOXAsIiM52KT5dIlGsUOrr37y8PDdM0SR2fkDo6AQ/5ihQyUMtm4zOlIMLWCx09+w9UenrNRDI7P2cmE3TosnWFYLrV0Vk4fTrk6Wqxq+vgwUZnl5LO5qfGsePMD42katV4s1YaHk7MzAcANLq6UtWqKwgAB0xD4wx9btHiRK2arFeb/V3x+apDc/Xu7tz4aQKRk0tgzRVbzZlFi+PVSrzZLI0MF6YmXJrTc6n4XIkAZOeSQPeS1Wp9pNf3YKxRb3W256cn3RCbxXSkXAchZeZSlOGm5+ZOrT4nX5sRy81af4+oKli1rLYECWBydlbp6zC5SN/4sVODS5NSPVJq1Hu7eVPHiomStAGFxGxZ6u8wmGjv7l3ja89J6K3IbLXZ08U4FtNS3WzCRUx2clru79DYWN+uDyfWrI2ZcnSmonW0CZQT2qHNczbi2yYmle6CGkn179k9uXJlxFQiMxWpqyPAdGy+ImCv1NFXPHS0PDBIM2H2yKl6dxfgMWURGPqewMfmqxRH19o7uw4cqvX1QQ6kjk9I7R0Uj/hKy2dws6OrMDEOWFhp7+46dLje0aGn091HDuvZrC8ybFWiIKh19+THT0Ma6PlMtNxsteUtUew8ctiMxb10lGvpwPeq3T2F8dOeKDbsIHPzJ7sIP05gIoRQ3L0r+8C9zNzsT0MVC+rA981ly6t/9pdOZyduNjIP3h/b/ibl+wvnifSMlWsqf/U3fip11pUsjL38YsfDD120+oJVK9efYWD4CET4Gcs233znlT2Wpv3pX1gjI2cTXTuAwPfir72a3riBlqUQ44X92vOc/oH6f/sT9aKLcaOeenZz+tGNyDQWTPju+ySdlj//S/U//OMQQUA+ybv6mBChyEXOeeAhHzEew6695yFLjCLbXf7Y0040Wk8XZ/oXQdPtGT86svFZH9JOJHbOvRs8hqNIuPyxp20xwsjmiic3O4wIqHDlg48BP9DT2fV3PehjhgB67LGnEaYCmxp94hkvgH5EXHX/Rmw4U8tWVNNFh+GKh44ufer5AEHiw2WPPxn6lBuLr7lnA21Y1aGh5Y88lZye0VPZwa2vxmfm9ba2ZZs2J8dnZpYsuVA63EH5sz635OmtqfEJtS3f/+qbqfGpemffqsc3ZY8cn1y75pz7NmaPHJ8aW7Xiyafbjh7T2go9r73dduJUuW9o+ZNPF/bsr6wcW7Lx6fyBI5OjK8ZeeLHt0DGtIzfm1fN2c0bMLXv0qc49B06vX7d846auXXsqI4v6XnunsP9gvX+gc9eeRVtfLS1d2vP2zpGXXju9Yk3/zg+HX3y51jugpjMz7QPQ8YfefHvJlpfmRkc739uzeMuL5cVL8nuPLHp+a6vQmZ6dHtn8ktaWS0zOjD71bHl4pGPPoUVbX6mODGcOnFj63Ba1rZidm+p/dpuaaYuXKsue2KwWi6XuwVKxV9D14u6Dyzc/p6VyYrMxuuk5I5URa80Vjz9dGhiO1Fqrn3jSZkVIhSsfeBT6gZbJnnvXg8jxppeMlXPdBNNtx06v2PSMj1iPZVY98Ai2XDOTWnn3RuATW4gue/xpClDEh2NPPA1c4sSia+7biDWj3tO77o57ke3aQmzZps3I9x2aH3viaeT4871D1UKXjZhEtbL2/o3QsPVEZsUjj3OmYXLRsU3PMJo5MbriwrvvzR0+NrFi7apNT7UdOabmiz1v7sgdPlbt7Ct19ul8jHGdNQ8+mj94eGLF2tHNzxf3HTAybT1v72zbf7Q8MDL63JbuD/dOrFu34pEnO3btnRhdteSlVwt7DiiFYt/2d4q7DjZ7e0e2vtr1wZ7T5507tunZnnffmx8drSWL9VxntNFY9Orr7e/vrff3D720reftnRPr140+s7V75wdTy1Z2Hjo48uyLWjqbmJ0f3fRsZWiocODIkue2VgeHckHrXE+rEBI/PrP4mRe0bC5ari5/9Gm5qz19fGpky0uNvr7EyZllm5/X0zmx1Rx9bLOZTPEtedUjTza7e1Knp5Y+/bzc3RWZqix/+lktkWF0ffmjT7ssF2kT1trzTTYa2Xtq6TPPacUCW5GWP/aELcSQ76/c8LgVjTksv2LDEyyxvYBZ8tRzoRO4idjK+x/hJG16+YpqqsPmxPyJE0ufeh56ngvY0U3PIMuzUsk19z7MSkplZHj9T+6nNUtJ51ZtfIzRdZuPjT65mVONWrH7grvvic6V55YsPueuh1hJlXLtK554Klqry4Xi+MCoQzGcZp57973J6fnZZWPn3LNBLNeb+Y7lT23mm0ojlf6kFuHHXD/D0O3ptQcHmUoF12vUQrcEQshUysLRw25vv9PdYy9eCl2HnZyAC90WBhFTmmenp+2hYZJMgrMKPXN7+zzPVd95IyZEstn8R+JYkBB/5wfb91bnjD/4Y3N0DFrWWfTfAPETL23NPngfLbUW6l1BxzHHllW/9tf6mnOYcin92CPpRx+G7oIrDALHcXp667//B43f/j3ge+CsIvb4z3bwhgE9n1MUxrYZx2FtizMM2nU4VQkwxp5DAYrVNOj7UVlmbItRlTPl8LBtYdsSVDkIAkGVWUPHritILezatKEzliWckbEsodWAQRDRdE7XgOcLmooCHxEfUiF2bNq2GNuOyhIKQ07TGEODvh9RZEh8TpVp4ouagj2PNS1e14HvC4pMW2Y0nmRZgaU8RlWw50YUhbEszjBYQweeJ8gSdhzWMJDrsrbFqyrtOIIsYc9lDIMzTey4vGFgx+ZVlXEc1rI4w0CeGzFVjhMFXuR8HzkuaxjYdaKKBDyPM3TW0GnHERUFOw5rO9ixeUVGtsWaJm+agBDe0CkAsOfQvocti9N17DgRRaYdhzF1TlUABFFFYiwLuQ6naYxtc4bBODavq9D3GU0TNTUEQGg1GNNgLDOiKtiysGVi34NBwLi2zzKipgRhKCgStkzatjhNoW2bNgzGtiOyFAaEV2RWU2FABEliXIe2TNq1IUUh4lMU4HQ1DAJRbvG6Cl2Pl1rYsbFtM67LqQptmYzriLJEE8KrGmuawPcFWcauw9sW7bqsriHDYE0zKkuIEF5VESG051IUYGyLMU3kupyuM46NTUOUZUB8UdNoz+N1Hdk2Y1uMqtKuK0oS7TiMbaPAR4EPwoCxDN6ykOuyphHRzwy1RNs267msZdKuy9kWdm2x1aRNi7NMzrKh50UUmXUcbBisadKWJSoq8ryoLAHf51QlQDTtOogQ7NjYMjlDx67LmRZ2HEFTIfE5TRUsEzoOp6lnnhbGdXhVgZ7HmmYMA1rIRonJOjZ0HF6RGdtmTIOxbU6RzvxTsdUIqFBUJcbQGcfmVfnMmGPHFlQVEh9rqiC3gO9HW03ONJDj8IrCAopjoyxCnK5A12VtK9JqAgh5RWFsE7numQeDtQzatoVWgwoDUdc4TQX+T2cQJD4MA+w42DKxbUUUGQeEVRVG1yniRyUJuy5n6Mi2OUNnLJMzdFGRoO+zhsGZBnJcVtMY2xZ0Dds2bxisbTOWeaaIYcBgxjRox+Fti7EtTpaRbQuGgT2XsUzkeZ9AhB9/FXVse2RR9b//ufqpSyiaXmhqUIgQOz6ev+m6yIfvk1i88dXfbf3qr5OFby0MERL37s7fciM3MR4yzFkEFIYYq1/68tz69Ts+eGtmZvwjDhZ45tNfemPfztaffE258qqzy7uiwjD5/LOpxx9Fhr5QrwjatvapS+q/8/tOVzeuVrIP3R97fVuIg5dLwAAAIABJREFU0EJjm9C2jVVrmr/5VWP5iv/bC+D8R0CEra98dXTbq5PnrGWAlz46oXQVLFHMHzut9hZayVxmZrZRbM+7lcSuU3OrVgUMxZWaXiJCWDZ37JTWU6hliovffvvkmrURyo5MlZV8juLp9PEpK5dSs9nC/sNhGz9fHMicnmx2dtFs2PnB/tJAX5CM0A09FLDL87nDx+1CqtI9uPj1NyZWrABR3PnunkZ/n52NxybLvKlMr1jZfuCIFY0qvcXuXfvkVNbszSdKszj0y4W+2HQ5rrZmViyLTpcIQtX+gZGd79rxuNpbiJ2a5Szr+DnrChOnBUOt9fenT05CGk2Ojg1/+IHJMLggmApgPHdueKQ4Mc43mzOrV/TOHLEgO929uH//vgAieagrMlOLV8vT61YJc41UrTJxzuri6dNI0lvDPWxDyU5NH/7UxVGp1b9r16HLLku3ykBzG+0diXo9XSm3BrpCyxfqDbMnb4fs0AfvH770skx1JnFqdn7FKGfoWDHsfCo0nPbxyfkViyzAj7y749hFn+qsnLA0pPW0C6oSnatJ/e02YCOS7GajGhKXvLvj8EUXpfRW5tjE/NIRxrZSM+WZxUtsgR/b/taJlatZNujZsWtubIyIOH3ktJVNKvk8X20hHjSTucU7d44vX0kzYfepg6VMF89TzJTkJqNSvi1/4KiVTzUKnbmTp6ViMYyx3Tt3V3t7/UwsfWLKi/CNjkJh6pQfj80XBxdt3z69eImWTOdmJo14gmbD+KlZNyLODwwN7PrQTwgzw0sWvbm9NjJSK7b3Hj1C6/qJdes7jh9LNasTa9dmjpykIJgbWZQuzRMAwpQgTFViunZk/Xm56SlRbmq97cnT0z5kpsaWDRzY51JQG2hnKjJr6HNLRrOzM8lSaWLd2p7DB5wQywOdYrkehsAppriKnJqbnTpvNWxZvGHInYXOY0d8HzQXDWTHJ6HlNZb0MaVWfn7+0IUXFivTieOT06tXxKVGpNxsDXaHpps/NV5atcSmmJHZA4d616TUevbI6dLKZci1cNPACWgT3HZyorx6qQH4JTvfPXjBhWm1ztRUtbPAWEZ8uiQNdAWI7di7v7R6VENC4dSp6tBg1NWzh0/XBroE2mUUTcm02SHXvXtvec2oyiWXvvXmsfPPE4jVduB4fXjAF/ns0VMoCqYHlvbv+GB2bIwSYNcH+8q9vX4mRtc1iqPdiJA5Nk4SfK2rL3N6QktnSEbs3rGr0dNj5xLpkzMWx5UHBwsnTtAsqHT1Dbz7XnVgoNbZteytN5q5vNWRZUqtkMFSe0fXgf2BgLXOAleRtHhSyhdG33lbSqXMnnzi2CSAcGLFyv69e+x4vBUEhRuu/QQi/HfZZIZBkpTc8lxy81PAdT9GLTgvX6j/7n/RLrwIBEHixa3pRx6Cur6wfsKQCkN78ZLaf/sTe2gYnD1IWkjTuFFP3vGjxTNzV13xpVy2EIQ/9S9Rp9Ro/MEfKVd8GrpnETIIKIpKPb0p9eQTC/auwhC6jvT5L9Z/7/e9YjszP5e7847oO2+FNF7o0wAdR7niqvrv/1dz+QqKokKG/aT9Ii3geaZSjr35BhUGH4EI8yG1/PlnWcemaLD6sScCjhEVZWzLVi8mpianVj/1FPD9rNbofeUtTpWrS0Zqxe6QZfo/eH/Jiy+HPJOcK428+SZrWq2+rnJnH2GY7lPHVj62CYKANfTFW1+KEqva3l3t6vUYpvfI/tHnt8ZaTTObvOS2O1nHRAFZsvUlBhC+JY289hovS5Cllz3zbKxWawz2r3r08WSjCiHo3/5OemYmFNlFr77WfuxYZenw+p9sSB85pfa0jz67NTs3pRbyla4+I5VKzc2u3vxM8fDhytiS9Rsezo6PVxctauULdjyeLc0tev3NwskTaiYztvWFrkMH9cGO6e4RLZ2ONRqrn302Oz0FWbr/te2F3UfUzo6xl17s27NnfsXo6o2P5U6dNPNtw9vfyR87ZqcSnQf2L37jTamve+Dtt/vfe682NLzkne29H37gi2LXkYNLn9vqRqPZ2ellW57X89ly74BUbI+aytCrrw+89x7hmNzs1MAb20MWl4eHm21FmvJXbH2x/913tfZcx659g+++GzC02tsx2z2MA2/x29sXv/iS2tvV8+GHy599zipk2g8fH3rnbRgGcbmx6IWXQoZONBtjm59tdXS0TYwv3baN9TzOM5c8v5VXZC8eOWfDI4gKWMc+Z+MjiCKxljTyxhusquA0PC+CIlbVqevLH30K+w4MyNIXXuI9q1lsr3X1uhzff2T/0ue2JqoVtSO/7v4N2NRxT3Id4wrEBuOzI6++HlUUPR6/9M472qYmpf6e8x7cQNt2iPGyLVtEQ2b8YNFrr8cq5flFiy++5+7s+OnG0NCqTZuyM1MhRj0f7Go/eVxva1v91KaBnTvLy5ees+np3LFj1eGRVr5gJ+K5uanhN97MHT6q5fNLXn21f/+++nB/rb1bzbZFm83VW7fkTp6kGNSze1f73v1mLlMeGFJTmYihrn3kscLxY2ahbWjb68Pv7ggFrvvg/s7de818duitt/vff788tnjV5ueKhw83err79+/uf2tHyNLJudnRLVvlno6e3XsG39lhFNs69h4afG5byDHZamno1ddDjMrDI81cu0iMRdte79ux08wmi0eOD73zNoVxo7+3WuxCVDCyc8folq1GIZs/fHTozbfcuNDKdzY7u5DvD3347sgrr9PAjzSbSzc+Z2XSuVOnht7aHsSE1OTMyOuv0UHA2/qyzVtCjEIElz/5jEgsVlKH3nyLl2XAwLFnX0hWympn8dIf3RGVW4RjR7dsFS2tWWyvdve6PN99/NDocy+kZmekvp719z0kKEp10aJWoRiwdOHU6SUvv5Ko1YxsZu1jj6bmZquji+vt3XYkmj9+bPmW5+NKC/vu2ObnUxOTamfnukcezs3NNQZ6znnoEVGRfVEce/nFeL0uR6P4wP5/RTQahn4q3fzN36bCT8zyL9YQIomkPTRCEknh2BFo2wtbDSFEqsofPxbyvNPXb40s8rNZ4fAhYNsLwAoBoCiKrtfYmWmns8tvy50tcBAIAhKJ+n391qH95omjXd39HMefScai67/+G8oVVwFyNmWWhQjl7r4ztu0VaFkL866CAASk9eVfb/3Kr3nZLHf6ZP62W7ljx0K8sJgkICSEsPmV35S+8Mshx3b8w99+kne1kNkIkCT9S+/qnzMnAIjV65RPqDDkJRl4foiQ0GxAzyN8TGzUA4wDBkbqDRhQgIQBQCEFUBAIkgw8zxeZSKkUrlyFXM9jOURISAKhJSHHDRAtKIpDSAiQy3KiLPksGymXG/19AUDY0JHjhgCIktRyXBfjaKOOgpGAYaKNhtLREQLAGkYAiMtxrKZSYegzrFivW5EYoTFj6DSmA4w5x6ZNh0BEEA2DgHC8oCiEYXyMGU0HQegjFEDkMwyBkFEUOiABw/Kahj3PZ9gA4QAC5Hm0YzOK4mGGtW1gOx6iWVVlVIUgmvb9SEsiDIsdh5ckHzO0ZfHNFoGIJoHQavmIDj0SrVQ9hEIABE0NaYxtV1CUkKJCAHxEAxJAAKL1mocx8Emk2aKCIICQ0DjwEAhDsVwOIaJoHCuXCMOAIPARTQUhsmxekoOAAgAJrVYAEaHp6Nw8wRiGQaTZpBANiC/IMhUEAcuJ5VJAgYDGkXoDEBIAyEsybdsEIUbXESEEM2KpDJaMBRAJ1dOE5QM6yysysp0AIlGSFM8PIe0xLGOarsBHqxUlnyeI5lWFtWwCAK7PAi7m04lIvd7q6iIsK8iqUmwnELGSzOi6DxGvqbab9WhaqNfVXI4gmtF1GIYEYcb3eFNzGZbTVMZ2PBozusF4rs+yrKpiXQtoGgShxzAhojnDBLZLGJZTVFZWfMwGiCYIUWEIbUdoNl2GwZbFWyYKggCgkIIhALTnRet1wnE0IaymuZjBhsmpGkGI0XSsqoTGkBCxXCIME4ZhVGpRCNGezytKQAGKBJFKJaAxFYbRRs3DDO+6YqMBfUKFIUE0QTSEMFKvhzRNIToyN0cQDQjxMRO4ELouL8kgCEMAxGaTgjTwfQ8zotwiCIv1GqBpiqJ4TaXCMCBhpFYLEPIwilUqAQUoCMVaDZCAApBTNcfzCM9HajVqaDjAOFIuKcUCgTRtGLRtBwAIimzabWFI+TTGjuMLfLRRN9MpghCnKpymEgAIjQmNCUSRZkPLtvk0FlotJxbzMEMAChEFKIo3DGI7hOUFRVGzOYIw32wRzHgMyxomq6oejTlFcQUx+PklHEJcrXT/1Z+fdRnT/ye9BNdRPvsF7ZJL/bZs7vYf4Up5QVk3IU3TzUbmofuhaUqf/yX1U5cEkWju1hvpVmsBNFcAUBTFHT2Su/P22h//qTWy+GxhIgW+73R1t/7wj4/eepPw1ktXXvFFhmHDMAS/9PLrAcedRZRXIcPmb7s5+vo24HkL87KDgKKo1q99Rf7CF71MVti3J3/7rczc7EJZzoDvBxzX/O3fUy6/kggCrch9v/tbn8zkhcYgPzLsP61F+OUvtp883mpvZ0MXyaYf4X2O5SvNIM5psUzu5Iny4FDRKPkNVykUaMrzfIRgQEHIN+UgwiixTO7EiVpfP0/ZlOHZibhgqkgyAoF1oxGuLkewXcr1iLWGlkqLviHMVLViAUGCZBOwyBZFodaiRCyl2nKnx1vtHQzyhVJTz6QDlsayzjtmq70YKdc9jg/iXHSuYkTjIIKhZNIhsdNxoLsRQ212tGPVJAha8UR2ZsYVBEpAUDJp4jc6OrHjRA1FT8TFciOkaTmXT8/P+QwbxWadyWLPNaLRiCSxuq50FNKVsoUFpS2bKpfCkCIpMbQCQVWMQpoyfF5V5M72aKNOOYGfioSEElpyrbsbW1bbzHR5cDChNmnVrnd28qrKaRqJCyYfYTUD8NACfG5qojQ0lFAatGTo+SwKiBfSGJEgAJFSxW7PmHQ0f/pEeWQ4p5RkEPejvChLQHf8lOghLl6pmPmUgSP5kycqwyNRU8YtXc9nGcukVVPKFwOWKR47Uuvt5yk7MleTCwVMeXRTO6MOvi5RAi3HM20nTzV6+zhgRzVVjiUTRsu0MGCgHRGFmkQJdCtbiNVqaiod8U1xrqqmM4gFSDIghlYyJhgGhGErkm4bn5DyBSsay01NmskEwBTd0gnLKJlsulRCmGpmcm3jk0o+r8WT6dIc7XuNjq6I1IqpUrO9KNSlEAA5X0jPz/ksF0Qxki1sW/XuHsayo7rsJCKRWtOhOSmXT83PhRCSBG8jgbFMPZ6ISC1BUaSu9lS16iDOj7HA9l2aZaEbuiDSbGid+cAKoq1Gq7szVSm7FO2mopyskYAKkgJxQaRaLY8syrTKjGQqxTbe0CnT9eMCCWC00bTzKYMWcxOnS8PDCa2JG5qWz6Iw8EI6HigWFIRay84nDRzNnThWGVkkujpxKS8qCooENZskRRdzsXLVziUUPpmslORCMa63mKbmZOOcY7gWDKKsQ7OJUsUqpFQ+UTxxvDIwGHU0pq5o2TSFaa4pR4BZa+tMjU83OzpZ5AnTFa1YQCg4ow47FuHqMmRAq60QrdW1VEYITHG+YaRTAFO0ZASYlnJ5UVHY0NWiiezElJLNmvF429SUlYgDFroEUWHoimKyUkEwsFKx+Ey11ZYzY7Hs1KQriEGMg6oDAyLl8+n5OZ/nZc3O3HzzRwujheHHqcDxf/Nrr2nW/uwvpM9+PmQYZnoqf+vN3PGjIcsuNPQQcJz8hS82fuO3Qszwx48Wrr8GVysLRZlAGDg9fZW//B92X/9ZFLwIaVr84L3cbbecP7D0oguuoCgKDf3BH51N3hXL5W+9Mfrm62Che0GDgIKw+Ztflb70K346E925I3/bLbha+Rhgs59M1f/wj5XLrwwxBmEIXTfxwnNnXps+aQtoPxdlNZeOZb3gotvvoC3HY9lLrrnJZxhRNc79yb1WIp6YKa/e8ChFqJStrPjJRqybbkT87De/ByGkbWf9nfd4PBebq6ze+BhyvBDAy66+Ptpo6vn85VffQAUhdLz19zzIAR/X1XPvvIfRLcKzF95yR6zekIuFK75/HXJcKgjX3fMgTXykO2sffJhrqX5UuOhHd8Zm50tLl1x4651tk5MeJ44++Wzu6DErnV5z/8Ndu/aMr193+TU3tu87UBkZWX/fhtyRI04ktuyJp3v3HqgXO8974MGed98fX7/u0utu7tizb3Z0xcW33tb94S47kRp5cVv3h7tq/UPr7r1/aMfO0qrRC2/88cAbb80sX33Owxs79h1w44nFL7zY8/bOytDidQ9uGH7tjVMXXHDhj+4c2Pl+ra937PmXu3e8p3R39+14d/WjT84vXbLk+ZfGnnpmanTF2KuvLd38nJZu6zhyeOyxp/RUNnf81Pn33jezfPnoc1vXbXi0NjTQs/29sac3m4l04dTJ5RufsOLx/OFjn7r19srIyPA776147MnG0EDnh/uWP/Ek4YWO6dOrfrLBESPJ6Znz77q/PjLS887OFY9uUtsLxYMnVjz2uM9wYrO5/q4HvIgYL1cvuOPuct9AemL6nAcfwiFExD339nv5RtNoy176g+uR74cBtf4n9wEARMVYteERRjMDjj3/hz9KVepaLnPRNbdgyw5ovO6u+zHxsaSdd+fdQq3pxSMX3nR7cna+Ojh45fevYxWV0Oy6O+5lFZVyw9UPbYw1pWYmf9UNNyUnpypDw1d8/zpGUm0xdt7d90WlZgCZVQ9tTMzMja8653M/+EH37n2TK9ZcePc97fv2W/HE8NZXe3fvbRQ7127YOPjW2xPrz7n4tru6duycXrX2grvvHXj7nSAeG3zlje4dH9T6h9Y8vHHRm2+fOm/9pdfdPPzytpmly9c8/Uz3O+/Z8eTwttf6X98u9fWtffjxZc8+f/ySSy649ceDO3bW+/sWP/9y3/YdVjwx+Nb2oVfeaPb2jD25eemWF8fPP++8BzYOvvra3NjygfffW75xkxeJJscnz73ngcrI8MD2HSsffaIxNNC544MVTz5t82KmUll1/0YnEk1Pz1x8w61aZ6Frz8Hlj2xq9fYU9x5c9fgmEonFy/MX3nR7QNMRST7vtjul7q783kNrNj6uFfOFY+Pn3vmTADKcYZx3x90QY77RXH/bPUpHe9uR46sfftTOZRPHJ9Y+/AhFQuy55//oJwGNPZa78Ec/iTgGUq3lj2ziZNWLihfc+uPU7Hytv/+K713DtyRHENfd/QBv6MAh5951b3y+4iRiF952d/rU6fnRpVd+9xq+pVjx5CU33xKr1YIQrtz4WHJ2vt7dd8U11+dOnZ5buuTz37o6PjWjtRXPvee+ZKVCU+HYo09mJmYqQyNXXH1t8dTpmbGll/7wxvj0nJbMrnt4Y2J67t+sRUgBQJ3Jqf2k/WINEGKsXWcPDlIAkkTCWLOWqVXZ06cWtnsMQuD7/InjtKKYK1b6may5arW4dzdSFGpBcSwAkSQJhw9ZY8tIPHG2uCggDN2OzpDnW9teFDFbLHTAsyx2dcsN0bfeWPCrSRCEHNf87d9r/cqvk0Qivu3l3O230AsvVngmla/6tb9SLr3sTJb9J+89/+EqjtTqENJUCHhJgj7xSSDKCiRhQIJoteoD4FBQrFQxIQGked0Anh/4gdBoQY8Qhok1JQrSIQRiSwodzw9CXtWw5wOIxGYrtF0X0kKjBXwSQDoqyZTrEYA4WaFtJ0SYlxVEwgDhSLUeBsRmOLFWZ1w3QDRvWoysuJjhVA1bto8ZQdUY1w8g4lWNUVSfxtC0BM0IWI4zbGg7HsOyjRan6YTGnGWLsupjFrkeq+o+jRlN5yzHB0iQFSQrHmahbnKy4iKadTxOVj0as5rGGJZPM7xm8Ibl05jTDbZWdxkO2K4gKz7NYMcTdD0AkLadSK3hczwFYLTeCBkWuZ7QlHwKAsfldROEFA5CviW5LBeEYaxSowCEFBSbEoA0JAGrmyFFhbYdleSAQpTrRSs1B9K+H/DNFqAoREJB1aggBK4frTV8mgkAlWi0AkRDQsRGM3BJ6PmCqkEKhJiJt6TQIx6kI5UKcv2AxhFVwx6hABQbTeC4LkCxlgQBCBCKShKx7AAATlaxaVEUEGQFuJ7PsEJLAiFFaCxKMk2CAEBekmjL8hHNKhrtuiFmoo0WCCgfs3y1ypp2QGPBtlnbCRDDt2Rk2h7CkUaLCUEAIO+4rKx6iEa6KchKQDOsYbGW5fEipyisqhGIsWlFNN2DGFk2o2o2omlNx4rqASQYFmuYBNK0aXGS4vECdjxBVghmON1gNN2DNDJN3nJCCnAkEOpNn+GA40YazZBhseNyuhHSDGvZnKr5AAHdiMmqDzGigNiSfIiA5wmSBEgIAyoiqyGFKABjlWqA6NAnYqOBKAAcj1W1kAK+64m1OkUBCuF4o0kCKggBL8kBgMAPIopKURCRQKzWCAUciorKKkVjEIairPieB0jAyzL0CB2CSL0ZUpDQTKxcDSEMAYhIMk0BEFJCS6Is26Gx2Ggg1yOQjkoycBwf0byi0pYV0phTVNr1A4SFlhQGxGZ4oVZnbDcASLBs2rI8gBjVwJZDWF5sSXQIfJoRGg1aMwjEjG2zjusDxCoqNEwbYb7RAqbpQRzVNGzZPmJ4VWU03RdEzjCxZYdnGwn42WCIQz+ZrHztL6Uv/cqCN2+dYaZ8aWv+RzcDErjF9tnvXWOPLAKeu9B+8Nxs+/e/jSvls2jcACHyZz8vX/npbe++Oj07AT7/5o6zBVHK3/Gj6BuvLRiMC8OQ46Rf/nLjN34rpOnEyy9lHroPKcrCy+kEXkdX5Wt/bY2M/PM9AIA0rfe//s4nEOG/G8P2Wr/2FfdXv8hpKuFZ3jVNWsSBCyjKCxEfWCYXRbbr8lyHMlNl20KG5l3TRAITuCEEXkgLxDS5KG3bLi9EbcUK2YDFgmv8TMYNcZtVkyKZwAt8jovaih0yAca8bxm0yBI7hMAPEE8si49Ax/M4LmorFuBChHDouQBzjklYxg9RAJFATJ9AD7Oirxt0BAaEDR2DFgVbJxzj+zBEMMCYdpwAop/KEN/nOOD7vG0QlvEDGMKfyhBIF435slg8IwN9H3o+xIC2LZsVP9IPIh5LeTqOIM+DGCDXdRHHB6YDOSoIfJalKIoxDF8URFOxIO/xHPI8KqT4wHYgGwahEFoaF8eW5XNcxFYtwEIaIOLbiBOI5UCWIoSnnDMyAce0qeUqn6NhgIjvQJYnloM5yvupDGManiBELNWEPEIhIr4LGQ8zAADW1M+oww4ZwjJn1IFDDwDKDWmBWBYnQtvxOCHqKHbIBAxuM6plscgSGwDK+yd1AMfzWe6nMhjzvmnQEYY4AAE3QIJvWnwEWo7P8yEAtOOECPG+aSIRUIHPMLTj8L71MxkPYcZ1/kkdhLP1kGW8AFIAEIZBjhN+VGWEsw2MiB9AGwsBgz8qw7KAEOR5gEGMZZpshA9tP0QexBFfN+gI7TkM9HUmSrsuwJCxTAsLHOX61D/LIM+1xUjCaJqAhwykPddGHE8sDzIhCX6mDp/nIpZiURzAkCaehfiMXdfZmEegENoa/y/VwUEEaOI5kOUC26VZygv4j8rwLHA5z26xKYGYLs1Sjs8DV+N/di3VAhxFQ0gFXogyVl0Tkr4PfJb9qDoCB4LQCWiBWDYnUq5/RmVnZtC/VofL+abDicDxfJYNIfy3VOayvkXDwCXI4cUzMgGiBd/4l2qlMO3O1dI/DxF+cnwMiPBP/1z+9GdC+p9DVsBxkluezTy8IURwwfEIQvR155b/9h9CAHGzXrj2au7EsX/Z+S8YHPHbcrM/vNZPZc4mBxUzxR98J3NgPxr+vf/yn9+7oigqd+9dsW2vLBxWD4NItPmVrza/8lVA/OTWLdkH70equuCpCIDTO1D6+687ff3/ypcCALpu8vnNn9Bf/XuVTAJzdEzE4i//r793WYEw7Be++X2XF6BqXP7DG4xkmq20Pvv9H6iZQsSUL/nGtSCEVjT2ub/7BqEgJODKa250IyLU7E9/+3sWG/VZ/Evf+C6yPTWZ/tzffQP5AYH4squvpynfDbnPfO+HfoDceOwzf/cNVlJb3d1f/MfvMrpps+KlP7wBhYGL+M9867tUgLRU6gv/8x8ijdbc0qUXX3dLx559je6+8+5+oHD0RLO795Jrbyju3n/6/As++90fDux8f3L5ynNvv6f/vfcbvYNr73+488Pds4uWXXnN9X3vfXj8wk9d8YNrBl9768R5F13847sGtr9d6x1Y/dCjA+9+MLV0+eU33DzwxpvTF6w/96Y7l7746tF1F1543wOD216v9Q2ueeTx/u3vTY6tuvimWxe/+tqxSy675Obbx57bMrVu/djGTWMvvDg3tmLJK9vW33P/1IpVS1945YIf33X0U1cMv/zaxbfcOrt0efe+feffcW+tZ7D9w72X3XbH9IqVw6++ccFtP55as6Zr18HLr7+xNLQof/ToBTf/WOrsLh46euntP5lZOtb7+vaL77hrdnQsv/vQldddX+3pbz917Nyb7lILxcyJ05dcf2t5aLhz78FLb7qtvHRJ6sj4Vd+/ulXoitZqV1x3i5ppi09OX3719aWhxUK1+blvfddkIyGLP/8P30G2q2Syn//6d5DleJi78tqbQgiJE3z6e1f7FHZE4Qtf/w7bkvT2/FXfuo6TZFOIXvrDG5DruVj87PevBj5lpNOf/dt/jFSblaGhL/7jd8VGS4smLrn2Zt7QTTH16e9dTctatW/kV//qfyTnK7OLl372698SSzU5137ltTdEmk05nrnqez+MNpUTq9d96dvfGXz5taPnXXzJXfcMvf5mZWBo5cYn+t7ZObNkxWU33Tr01jvHP3Xx5dfcsOSlbUfP+9SF9z2w6MWXGr19yzY9O/gSJm6sAAAgAElEQVTWu9NLll18x52LX3r56CWXXXzzHcs2P3f0osvXPvjw6PNbK0MjK598ZuSVN8uLFp9z9wPLt7x05JJLL7z9ruWbnppcd+7o8y+v3PRUpX9w6XNbFj//cmnJ0nUbHl31zJajF198zk/uW/vE08fXnT+we/cFN94mFTryJ09fessd84sWd7/93kW33j63cmVx39Err72uOjCSGp+85NqbtVwhNT556fW3Sn3duV0HL771x6VFI9nDpz599TXNYles0bjihzcZqXRkvnz5NTc2unpyx05ddsOttaHB6KmZz33vB3Imz6vKVd+9zhaEaEu+9NpbpXwhMT5z2TXXN7t7hLr6hW98W0vnkOte9Y3vemLUZbgrvnetYCgWG7viB9dB0zMz6c/8/TfFcq2yaPGX/vHb0UpdTrddcu1NnKKaQvyqq69jDEfO5T73v76emp2fGVv+S1//dmR6rlnsueq6GyK1upLMXvGDa/mGXBpa8qt//Tfp8emZZcs/84/fyUzM1Dv7Lr/h5uTMjJFMferG2+Oz1ZlFY1/+n3+bP3J04px1n/n21bmjJ8v9I5fedHNyaracL7J7P0o0+smxYAvsecaac+zBQQr+CywPY3t4hMRiwqGDYOE0pOzsjHD4oH7+BX4iaa5aw0xPM6X5hS2+AEBdj763Uz/v/EAQz5rBDANj/bncznfo//R3CqgwTD77TPS1hWe1hyGJxqQv/1rr134D6nr89W2ZjQ8hTV1w7W4IreGR0te/SaKxX3BTQ4hQiDEFf4bAAor62QlFUeG/Pvnfyfzsw48nQ/3ch//WV/+Mdf6bV/n/SQZgHGIcb9VDGsbLJaM9ixWZa7VCTLOuw9kGVnWC6GRpji1wFARircLqPaxl8a7jKjIydE5R0rYX0HSyXgEpLnC9aLXC+y5vGYxpCM0G8hzBdVKlOQKoaK1iygWaBElV4VQFNxvY0CKNKvYcztATM9MBgrFqOSZ1QEKEZhO7rqApbiaZKZdoQ2coKlItI8+Lt5qcYXCNOolFI5oSlSUi4Nj8LO3YLISJRo32XVpXOUuPt1oujSPNhtCsIximKyXs+4yuRasV5NiCobOWGW01ASGxRo1XFBgGmco8Y5tY1mLNBuM60VaLNXWhXkO+F2vUYoYOfC9WrdCWyes6b+qRVgMRX6hXU7VKgHF6blaUW8h3E406RzxWVWnbFJoNmvhRRU6U5j0IktNToq0i4kerFWzoTKvFKXJCU4DniroWq1Z8ms5UyoJjIt+NVKs08Thd410nWqmEvhdpNMT5SoBxujIfCDSwzKjUChHkLIs1dEFVQwhSigSafEj8eLkkmIagKbyhRZt1rKuMrmUa9RCheL1qqdmQ+ElVaZgGJ7ewaYiyxLiO4Dmp6ckgCCLleaFZRITEmnXWNJlmgzH0mNRkHYsx9fjcTACohKZFG1VEfKFR43U1KsucY8frVWwavK5lqxWKRkKtil2bb9QDjKOmHmnWqcDPzM0wts3perw8jzxXlCTW1KOqGjqOKEtCvQqpMF0ts47NyK1Iq4kcJ6JqnKlHmw1E/EilFJMlEIaZ2WlsG5ymRFWZ9VxOatKeIzTqiJBYsxFp1EPPS83OsJ7LaZrQatKGzjbrnKYlNCX03Ygq860GCki8XmU9h5ElVlNjrSbt+0KzGS3N+RCkpiZZHMCQiK0GIIQzVNbUY4oEfE+UpGilQjCdbtQJQ0HbitaqnsBxmsZbZqxSBp4TkVuMZBGaTlQqdi6BfCeuq07oMqrMmIbYaEGKSmiKo9Z8BBPzcwqdx67LKTK2DGwZnOemSnMhoMRmXZQldGYGaQpuNLChRZo1xrY420rNTAdBEG3UYlILBQHfbDK2yUktlvgxpYVNndfVzOwMBUGyUY9Uy5D40Vadta2IYSi2FWnUsaFzrpOulEEYRGtlUVeR58Y07QxDqZGIx8vznOsImsradsDzgSD80/L/80bvIyb3/8VG/SIy/3GG8Wdf/fzJ/8dGGBDyCxXEC8OQpqUvfDHkucyGB+ECvdgQIe7okfbvfrP099/wcrnqf/9a7se3CQf2UhRYQD8Q4lq1/fvfnv/m9/xU6uyokhyGIU3Pf+O7/7khQgAAIYkXnk8//gg0zYU5RkFAEsnmV74q/fKvIFlOvPxiZsMDwHUX1kkYhjRtrFlb+Zu/CxANAvLzd/jzECEgvj04rF56OaZCgugAI8YwfYYNaUg7bgiAz2DkeIgKPJalbZfQKMA0Y9oE0wGNkONRVEg4FjkeCAOX47BlB2dkLJsgFGAauR4IQkcQsONA4ruCgC0rBNDjONY0A4QCjJDnA0IcQcSODQnxeA7bTgCgx7HY9ZDn2qKIfJ+xbUcUAAlox3EEkXZdGAQUhmFAIdezIxHkeYxt29EI7brQ811BQJ6LCCEsQ/kB7bm2GEG+z1iWHYnQvos83+U45PvAJ4TFIAix67k8G4aANQ07EqF9D3m+x7KQEOATikFBCLBla4sXge6Ogf17S8NDjGvylaadSXiCmJgtWZmYEkv37t07uXxl1qoJU43S4ABLHL4ueVHREoX49LyXjbeyxf5duyaXLecDO3X0VL2/D4NAqMuuyJmJRGKmhIWwVOjt3rtvbtFimg7aTk7Ue3ohIGytFbLYyGaT0/NeQmi0FXs+3FVavJimg+zJiUZXd8BhWrZEU2v0dkYnSp7A+ZlocnxWa8tRHAWbBu26dkeGlixBVxv93dGpcsCxUnu+7ehJOx6j4ixUbOy4je7OaLUekVvNod7IbJ1gWi7mM1OzPoMFwTMszOlmdag/Wm/ysiL3d6QmZ2yGV9oL6dkSoRFJcJTui/W6OtQFVVdoSo2h7lSpBG3PziYox4+XK3NLljKO23Hk0MTq1elGmS8350cWRRSJV1QrmwhClJmZafR3OYDt2r9/Yv26VKsmVFuN3m5BaiLdttuSAWQyU1PN/i4bsD0H94+vXlOsT1Mtt9Hbxesqp5pWJu4COjM+KQ/1GFysf/eHp9euSyh1Yb7W7O7kHYuTtHqxwxX4rgOHy8ODPOVET8y0ersY6LNNw+cZOyII83UqxsltueLB45WhPhZ6sVPzamcxQlRHDgBHW+mEON+kRLqVy+f2H2v1d2McxMfn5c4OCD26rgMMrGxKnKtTIpbyhdyhE1JPpxWNFA4d1QptgEd0ywwwVtsyqYlpyMFWe3vu6Hiro2DG4pn5Gc5x5vv6k5VKvNUoLR5JTM+HNF3r6i4eP+ZynJeJYdngNH1+eCRWr6VrlfpQb2Km5CCm3t2Tm5kKIHLSUajakWZjdnQsUa/HqtXS4pHCxGkHMHoxmyxXPYCcTAw4YWp+tjoyABU7WZovLRvNT096IW3k05FSJQwpO5+iHJCenT69YlW2UWIrstLbwaoK1BwvEw0QG58t6V1ZC/G5QyfmV48llAZbUeSeAqdpULVxDFlsVJyr6d1Zk410HDg6u2JpTGsxcy21p8g5NlZtN8E7NJuYnLN6czoX6zhwZG7Zkoip8CXJKqZY2/BVQlKCw0dSk3NmZ1oRk+17DlbGFomuLpRaSkchhFSsVONo9/8h7z1jJMsPO7GXU73KOeeqzpPTzs7ucoNo+U4QfPAHwzAOJ4mUTjpAwNkGzvYdfDb8xQYEyyeffTIpieRqSS6DyOVyJ/X0hO6ZnpnOuau7uqtD5fxe1auXkz9QsGmfJGsEw9iDv//xB96Hh/8Pv1iPJCNbu410BiIg/0Gpm0gCsEG0+wAMjQI+e7VlknA7HIutrbdTGZCC/QfH3WgMQAGiOzRRpBsMBY6PAAJpx1PRrZ1uPD5yOKLbWyOPR7dReHcAgGAnFgucnEAIMAz4fIWjTjjGud2Ro6JgpTU7hXY5WFPamWygVNJwvO70WVfXQVkEUcjUAUhVJQuNKjKsaoqFwiTJNAGFJDBRAkBAx1BQ1RFNVSgS0gxYURSKxBTZNACNwBBZAUxTxzFQN1BFVUgC0A1UlmULhSoKYJgajiGKApgmgMKGAaKKolhIQDdRSfrLM7qhExikapBuaARmmCAh8JKFxnQFVDQVx0HAhBVNxxATAFFJ1khcB2FiNJJoGjZ1RJRV4hdnVB1DTQBEZdnEYAXBCW4kWSwwYCCSouEYAACwougoakIQKskajuoQQoxGMm2BTBORZQ1FAQiEZVVDUQOGcUHQCExHUGLIKRSJnJ46f/bTX+5M/yslwv/TooNhtidzno+/jXTab8wUGoaczjT+2X8lx+J4pez95v9mWV3+5Z20v6XgKKcz9f/yXyj+wL8zuUII+hJLhBAE6pr9/j33978L82+2Dwjquu5yd//RbzG//h8gDGOffeD982+/KboCdd0kiOEHH7X+6X8GABBoGn81gflvSYSgpgnTM/pXPpgsFlGnS5/ITm1sY6SFv3NtanXTDsOjG1cyheMwrCrj2VipQlhodTo3uVugMZy9c31yfcdp6MO3rmeOzjyQwU9Ppo5PKYJULkxM7h9YDYD54PbU5r5LUasffjh+cODXpNHli4mzih0AWrfvzGxvu3Sdef/2+N6Re8RXP/qV3NFxSBaVmXyqXLXpZvnDXxk7O0uel09+7dfj7Xbu9GR0/ZLdQFKnZ51bt/zcKFWr69fHA30ucV4++/d/Ldzv5U9K/bffDvNsotrs3rjpEcTceZl552aw08+cnZ999d+L8KPcYbF362ZQl5JnNf7StEeRUyfnwzvXA4NR7vR8cPuKVYcn9vb7V6/5UTN1fMZfmHSbevr41Lycs0tarnQmjiV0Xss/eyHDqIbhqScvZNICKVp8cU202aChFH+9NiKtdon1vdrRdECzWFJzzzWc0HUg/mJZpq0gr8ZerkiYBQCB5LOXug4qdnt6dl5DUN0Eoy9WMcSUVCj6el1GCBNDk/NLgKTyLldm9rkBwRqMxV6tmRCkalD85bIKoTpJpOaXIU5oJjJMCRM0CwLJta5PUCgclRttn8yCiBdqlaysZMMcZq9CD0UaQ5RmzyvxmGbFunW7MkRgL8yeUB3OZvjI0TkyFK0EprT7Ho4jNDvRK5MSh9vsQqPibPMu002MzsC+5KJQqd+hWd6uO/DeOSEOCMQHssdoR/bhNn3QpFojF2bRQwf7oc1CLx7z7JeC2/vl/GRwpxDY2GZDUUe57N8sMN4gXe8kl1Y7iYR7rxjcOWSiEctZM7q+ybr89nYrsLbH2+1Uh4ksbbDRiPP4PLSxx4SDVKUdXdse2p2ebtO1diBYrRTDRV+tDsJh+rQWWd0aREJErRtbWudsTmLERV6sCjYHMRRiz5f6wQivWWpNHwgYqCGf1qOmpkMWtHZsV2EMBIB61WVisCkZ5W4IME0UUM9rQUBSCEI9PQ9KMAFCQLPqhGBQV41yO2yAMAqrlVrQEA3AjjRKdhEiIRRo1pyGCZkAUGkGQB0QLVTnzKYpEORAesc0C1gNEu1XrboBgxBQbfoUFVZ89PTsY/q8Vs1NJl8u22uNkdvj2S1ay7VuMBpfWnVUGu1sOv1izVY6q+YmEi+XnJW64HR49o8tlXY7Ek8sLnnOa81sJvF82XFaqaXGYstrjrPqyOUO7BXo8yYXCoZWttzF0/rEeGJh2XVW6YdDrv0T99HJyOX2FUuOo/NBMBDY2vMcnrRyucjrdc/xWS2R0ftGtROACYOXyHbNDjthuQM2e17UqvEsXu6FUFSBBb3cDoKIISpEq+akLArfh6rdIEGr4gCpdEMYohiSWWkGYVSXNbxZtkF2RGKAesdPWBRpgJR7IRRWTQ2oVr04IGgGel7zgVZEHoKNto/AZYFHyp0whJqgbtZqXgMGaaUfm18mAFU20PDSpgYiOoElF5YhXhr6vZnZBVDTJdoWf7kKGboCoLFXq7oBahYqubCM9IdsLJp7OG9K6tDpTb94jSiKSNqii8uwqPR8oYnZxygnMtFw9vEiyAkDly/14hUmSSqGhVa2wZHUCccnvrhPDYVONJp+sogMxYHTm1xcgkdSP5Ec39sLiJJ2ayJy2gx3uue/+vdS5Uqkz3A3riYbVS8ntN5+J3N6GmUG3Q/vpI7PYu0O++6tADOKNZqD61cTvXaA4UbXLsXq9Ui71/vonWS5kWw0mTvXPEMpVakMrl6OjwbBdn90/VKs2Yo2O/rbU572IFOpsu9cdwpq9uxseO1KiB9G6i3uxuVotx+v1pj3b9MGdGFnh714IYYrsYOynM/QDuvY3oF8adICo+P7B+K1GYyyz2xsDi9ctHjo8fUdOZ+lvM6x7X1lepzEiXzhyDIWkFyRi+sbo6lJIuie2tzTUgkk6B3f2lMns4TNMbF/qE+kDU/g4soKPzGBR3yTm7t6NAwlolObu6OpKdjtmdneNvJJLZq6+Pq1nM33AkH740e/nBD8qyXCX3oNpfyY5vXh5XOYZd8MY4Eg0u8RxaKcy8mJlJTNop02Vqu+GcaCILjfJw8K4tS07nT/FWTHl5LH+rJKhCAIGIbt8WP397/7pl3toKZqHl/nN742+JWvIkzf+flPXZ9+H1TfEF1pmm61sr/+D3r/0X8MGMabBQZBwABAZ6WSnn/WSyRBAkwvzHudrt7lyfH79zivt5dLjs090gJ2EUVTLxYEn1+3E6knjxWns3xlcvL+3ZHT2ZrMZ+ceKR67QFtSLxdFmtbcltTTpxoEnb1zY+L+XZ62lq7fyD6eMy340OlMvHwJQlAzk0svPINU7eTO9YnZhzIAlG7czD17ikCmZidir19Bsr5/5056/qn/8HDjV381s7AQ2t9lYwG62k0/X+gkk6Ht7bHFRfZCIvPiuWd7d/fDr6ZfLsY2N9q5bHhvI7K81YrFfEfFyQcPTm9cTL1cjGxu7r59J7G0lHi+0E7EvI1y+tHCwOtwViqTD+aq1y8kXr+Kv1qqXsj71/bS80/7Qb9N4XMPHww9DnunPfnFfW48HFjaji++bk1lSADPzs2ahla/MjNz9y5AIrzPNzF7X3YRponmFp6JFgvpx/KP5zBJOKOQmS8+30dBMhiYfDQL2Ak7acvOPzNwvD+RHHvyhByyRa9j6t7dQ+0DXBInHj9qm1eNvphbmIdNs4ZO5x/N0pcu8GHPzL0vjt5/FzKNscePyupNhBNyL54jslSjbuYfPbTnc4e331MPBJQ2MUgCjkwTB2FcEYuQxRSRFGgeaCoKoX5ROTQwWIEx1SgBOmiiblApabDIIxlE2gdUFCPTsngsgaqIYYp5DmkyQPoh4UTBRRENyeYep8I0nYCEUxnQZBQdmaeQqhhUABDPVXOgIDnTOFRlw0S8onGiGyMId2mRvZ3Mwuva9ZnU8qvY6vr+Bx8k11dTL192s1l/7ST1dKEfi9varam7X5RvXEysriZfL7UujXlXtnPPF5hk3MYx+bnZkc9pYZkLd3/evjCW3NxIPZ1vz2Q8+6fZZ0+4gMcj9SJzT2WPHROF6S9+3pvO+9c3ck8XmOkMddbMzj8Z+b0ACU/MzSpuG4gi03e/aE7kh1aLWgBQTYNdI/UQBUISaoWEXYPI64iqagc6oAuYBVIPDIBX4JgqH8A2N4+SolKQyaQG6ap8YAApCeUAs6gDggGnVbEAk04e8SL6vm7EdBjUlEPDDAiIqmhF1QiZiBURDmXcJkABVNoFwSQIk4ZYlAAnh5mYfqxDLgDOqhOPHuoAUPzgg9z8U8xQh0FfYmWZHI2a4xPp1y8tfebk/VsTT+dMbnT4/vv5xReUwGl+R3RtBWNGjanJ1MqSq9crfvDW2JNH+Ig/uv12ZvmVpdsbhf2xzQ2q2ubGEsnl166zcuHvfzj29AnV6zYujqdfLXoqFT4Wim5t2IunvbFkannZWzwufXhn7MVz2/nZ3u23xRYiHyIArgAqoO2hcEDTK7pwYro9CtAyjSKMYCIMa1IBtSEyAELyngFZRailymco5JagrqYUcQyVIUhWCzAESwCO8ruG06VAXV0tmhDNY5KhFHAIlCGrrhQAWBNAC6jukbBd1jhQLBgQKSOKaRziAKCCHkPaBQgIcJKticeP+jcnNUHLPp8nRKFMXMnNzbomxvvZ6Mz9u+Url1UbnXsy17oyDah6dvEFyTLnNiw/N9uLRNqXJqbu3z2/dGnk8+efPu5NZFUUz75c7LWbnVR67OnjQTDQvDw5/ehhNZMZhsP5hflRKoKAWnrxhScUaU5Mji08k/z+8tWpyQf3u4nEIBTMvnw+CATb2Wzy1UtXp9O9lhufm8X7bOG99zOLL6zdzjDsjy4vW+qdaj6fWH7tPy4dfHg7//gx3WnXLk+mXzx3npwMwv7Q2pqreNpLhBKvXgWKpaOP7mSfzDnL5erFseSrxcDe3iDoDR0dele3eqlwfGkpsLvfe2s8/+yZ5/i4fnEssrgS2d4ehv3e4kFkab2bT0aXlqKra2d3bkTX1lNPHjMetxPiE7MPVApVaGryZ58NkmH6vJJ/NMtmIlhnmHr6mHM5Va9t7NFDjUD4UHDy859xQQ/BDvKPHp66IVCtp589Ea30MB7I3b+nQ0Y/Hp3++eeC1w4pWnb2gewirTCZmX8mEcRgIj129wtAEVrKxOTnn/V9PlDTM4/nNCuKu5ns/LyBItXLV9+Y8RBF7s47hsXi+fafkgeFN9OCIJA8LPj/5/+p87XfFmYudn7j6wAA0K9fvinGwo+K/v/lj1q//58q4TD478KS95eVwQJB+9wj9yffQQbsG/VwgIqi+gKdr/0O9/4HkCi4f/QD96ffgwztzdCVomhuzy9Ks34xkPQ3WfD+bQbL0NVItP/hh6rffXb1CpuOSjZ78b13Ra+jH43VL0wz6VgvnuRSkcqlCyOvr3LlMpuKSk5n8b13ZY+9F483pyf72WQ/nhikYuVLM6LDUbl6hUnHJJfr6N13Za+9E423pica+dwoFGRT0fPLlySHs3rlYiubVu306VtvCSFPN5Zoj+fq42OjUIBNxWoXpwWX6/zqlV48NvJ7e+Nj9fEcG4tKfs/ZretsIDiMhU+vXJbdjsbMlBD3N5PZdjZTn55kwyEx6Du6fUtx2zvJ1NnVy4rH0ZieYJPRbirFJGKVSxfZSEjyuEt33lJctl46U7t6UXQ5a1PTg3S0nckO49H2zFgzk1WdtoP331Ndtl4yVb1yQfS461NTo1Sonh0bxqKNS1PdcFRzWI/fe0dx2jqpdOvCRD8ZZ+Px3ni2Nj6u260H73/F9Fh6gejxu29LblsnlWlNj3eTMTYa7U7lKxNTupUqvPee5Hfxbk/x3TuSx9FNpltT+W4mNYhE2bHE+cxlxWopfuU92eca+fzH770ju+2dZLo9ke/m0sNQuDORq0xNaRbq6P2viAEX7/Mfv3vHsOO0TbW6FTVlp1GRjIJoALUiAp7FQAdK06LVr8BBnLQbVqekp20UJlEh3YhYXBBDpBHIiRJWxeFX9DBpo3i7XVazDhJXLQEVCJFOZEDFAcwJQh7U6paNGGWjeItT1dNWGyIQYdAMUS6MoyI65EExF+BwjOA4jlpNq000klbFbq3OXOin4/14opdK1mam2HhM8jqLb7+teOz9eOL0xjXJZW9NjLHJaDedZpPx1vRYM5tXnLbD995VXPZ+IlG5fllyOuqTk0wu2c5kBrFIa3q8mc1pLtvBV94DHHgjNVa5eknwOJuTk71sspVOj8LBxsXJRm5Mt9P7H3yguay9WLx87bLgc7fy+erklOEnfWDPGLOhLtiGcNA4jdCm3SqQIcD0EzQtkiFIjdrcIAOMWTAXaEN4aJwiaZ1waGTQgIKEjZaIIKjE7U6zj4xRuB2w4QIyRsBWyGoVqIABhwirRSKDgBa1OqABmkEBL+EBu2iOgB0wZZOpgAEEMRc2JEOgnHA6oQGWgjSHhQ/6mtMTjVyOi4S4ePjs6hXB7W7OTDbG86qdPrt5Uwj7urFEZzxXHx/nwkEuHqlcuSC4PdWZmUYup3gc51evjCK+biLVy6QrF2dGoQAbi5xfu6w56MqFi71sUvB4zq9dFcPeZiY7SCYaMxPdWIILB89uXVPt1tqFC71cind7Klcuc/FQK5sbxiLlyxdtuGClZTRDEKRq9UhIEAW9qIPm8SgK+DAP1NOnXDhtWC0SkiYISrO7JdIH6AHKTotIHDN8hA9i1GknbjGslAhmLLhFczhFLIgAftxBcEiKVP0WH9gzJm0ErdO0DKdJktYpl4oFYdiPOogRlsKUkNVrdOApK2nRaauMJhDZbWdjsUEufn7hsmy3Hb1zRwx5BLen+JX3JI+9m0i1Jsd6udQgEunl05XpaZW2lN59hw95Ba/36L13ZY+tk0i2psZb+awQ8HYmspWpadVKl95+i4mGFaft5O23xaC7G0u2xnPNsbzo93bGc83JccnpPHr7NhMOKl536cZ1PuTtJtOdfLY2My15XY2JsWYuq9rp8vVrasBWT09085na9CQXCnCx8Nm1K6rDWpuerk+OKx7n+ZXLfMTfSaX7qURrZqIbjXPh4NmNq5rDVp+e6Y5nRl5v5fKlUTTYyuaHsWjz4kQnmhCCvpO3b6kOa2N8sjOeGfm8tcuXpbCrkpsaxiP1i1OdWEIM+o5v39LsdH18sj2RGwX8tZkZJh1vp9Kaw3rw/vuAA2/FM2c3r4luR2tsvDuWbqdSXDjUnJmo5ccNK7X/4QeK28aGwqe3roteZzuX746l25kMFwoxU+nTiYs6TR58+IHqtjKR2PlbNwSvq53JdvOZTi7HBwONC5O1/JhBUwcffah47INw5OzmNcHn6qQzrYmxdi4teVy1KzP1XN6gqcJ77xh9xrow/7dnsP6PM0o0riQSaKeNVcpvUG0FgAAAoO0Wfn6mhKNSfkxOpdFOFy+fv2ngDO120EZDTmV0l+vLrxV+GRksE0Fs8089H/8ZzL4ZuoIkSYnHO//oa9xbt0FV9XzysfNnPwVM04TgN7skEu3+J/+Qe+e9/wd09Td8AgTb2y1/sYizAwZIeI+PSZY99S4fYDYAACAASURBVDuiG5uS0y6EfcHtbYwEFYvFUzyinF0QBzxHR3SrU/S+F11bVy3UKOwLbW4CJMrbbO7jktXShEjIWziwWmqFqC+6va1jWDObj+xsg5AhuN2ekxMThgYut794ZILgMBGIrq3rENjIjQd3d3Fd1mjcfXysm1AtnQ3vFxzVyumFi9GdbfdJiYuHkPbAe3jYDUWc5bL/sFj13HZuF221Rnn6QrB46C4eeRNJT+XMfnjKhMKOWi2yszOMBkP7e97SSWVqOlgsuk9O3Jm0s1Xz7B4KTpu12w3sFEYxv3f/yH9wyKbCZKXjOi4FYnEbz3h39wSP09LrhtZ2qu7bnnLXXTgcJIIwzPkODhUMH0X8sfX1KmzKDntoc8sgYazLuo5LQZfHRsruQkFFYC4Vi25uNgEN87hDu3stDMD6nKd0wtscqs/mOzwEIbAznomtr7emJyBd9+/squNhVYbcxyWJtPDJoH9vD5GlxoWJ2OpqdzwLmkZgZ4fR0rpiuEslFcW4TNS/s4vwPBMIiU0YhxELOer3UYgDcFQc9jFoaDodcq+BQjDg9Kh8A0VAjOpJgwGOgAbsUPk+DgKA0yVxDVgHITysiw1I1VBrh+/3cdOAUJfO9TFQNby0MKySholgI03oIIZiOi0jgYElHca82rCHQpLuCsh8HTEkwuWV5S6pCoRtKLtqVXfheBTyBAoF78lJeWo6WNh3H5d8qbSnXXXvFQbBIMkwsfWNYSzoLR4H9gqDVJiqdlzHJX8yZR12/bt7gsNKclxkdUMIeZzn1cDu3iAZwhqM67gUDIUcQsuyW5LtFkyRI6trkt9lqbf8hYNRLOAcSJ6Tk1AojIJqaG9fc9pAXYutbzChUIeKsAOKbCsYyTcYCwVLUMhkGjhlGpgpsi3UCcmYJgxYEumYoE3uM5QN1GCPxNZp0qcTsMK2MYcuISo/YCmsrgEeqd8hCF1HUaPfwEm3QSAy28JsTgmC1cGQIjAQwhWuR8AagJPSoI4AHgQjNbaNOWwiivIsQyAADAaM8PY2ZBjVyeno9jaqCKOA33NygsoS6/X5jkuoJA+Twcj6Oqwolcnp0M6OhR8oTou7dAwIaj8U9h4dk8MhmwrFNjdxni9PTAb390mGEQI+9+kZMuBVn8N7XCLYYW88Hd3cpLtdJhN1F45sjcYo4HOdneEdRvTYvcfHdKvD5BKx9XW61bJcvqr3daZH2a2KoYFck3S4dJ3Rhx3c65Y0HuIHFropArrM9Cw2m67qwLCGWwgREBWmR/lcvKGgzNBC1QUYkHpdyk5LOgQMG7jDrgO8wfbJoEtAdJgZWvC2bsAa2yV9OG+AOtOwOGnd0LRhnwzYeciE+wML1VAMQmVaOGEBHWg/sL1tJL2KgbqPjzUQGuZivsIBxgvnt67EVleZREwnsODW9igRUk3YfVwyDZMdT/p39zB2cOq6Fd/Y7Meigt0V3NkVQx4VJTylEipJAmUJFg5srfaR1x5dW2WDwZHH69/bV91WGNDcR0cIMxx6vP7dHafdwQfd8fV13ukcerz+QkFwOJlQxHN8TA6GVf+dwPoqxvPlyelgoWDpdod+n/v0FGFH/UjUc3Rs63TZTCy2vW3pdgeZmLtYstXrg2jYWT4nm33RY/OdnFgarX42HltftzUbvWzMWTh1n50Ow0HX+RlZbQshj7dUslUa5+GvBLeLtmqVSUfpk4a7VBqE/I5a1VEqSyGPt1RynZaH6Sh93nAfHwcCQafOWPcOJNoCYnB0bU110hjHB/YLUtBlE2uek9NQ8ADCgeB+QbdaVAsRW1/XbaQpq/79fc5uGm3JXTrx+/y60xLa3jFQWHTaY6trBonSzbZvb191UEhn6D4uBZ0u1WsPbm3rMDgK+mIbG6LNbu10fIUCYMWQgeg+PhKs1iOL7e/o3BEFKT/W/p3fc1to+9ysQZJ/e9LEhGHi4MD3zX/T/u3fE6ZmOr/1dRPDbI9n32CqDgQB07SsLpsI0vna76iBwJecx/rSFYeYKGpdmPf+6TfgweDN0JUsSZlM+7d/b3TzFgCCvm/+sfPzz35R4P5G6EpKZ9q//bvcnXd/oaH+3b7CgCCc41PzL4I7+ygvjT1ZSLxaVU04/3Au+eIVKOv5pwvh9S2sP0guvg6vb6EjKf1kPrH4SgXQsScLqZdLoKSnni9GVjdwlku9Xomub4GCknqykFtYVCEs/3Qh8ey5KWnJV8vxlXV8MIq9Wo6tbICynnn+MrOwqAFI+tlCbv4FKOvJlfX4q2W8P4gvraaW1gwdjK5vjT2YNQ04vLOfn3uKM0NPsZSbe0owXKBwNDE7B0pqZGN76sGsqQGBvcP87GOCGbpLZ/nHz6jewHN4PD47B0lqZGt3/P4jUzY8+4fjDx5RXdZVruYfPCI7fU/pbOrhHMzLge29sbsPYUH2FUsTc08tra6tXBu795Cut92l88n7s8iQ9+4Wpu49xAcjnBmO373vKR7jQ37q/qzrrELV22P3HlirTVulMf5wznFaxpjhxN2Hof1DZCROfXHXeXRCtXpTs48d1Qbd7uXvz7pPyygnTD2cC2zvg7w8+cV93+ER3WEmHsw6SmdUszP++Jnv4AjhpfG7D4Mb26CkTc3O+Q+OyD6bm33sKp1RXWbs8TN/4QiQlPF7D+NrmzJISEV1WIEhTuXLqFwGTRlgS4h6KKoQqhzofAnSVHBUMkdlGOa1YRnlzkxNAQYnqHEgqhAqH2mDI8gwEOkcGFYwgFOG57B4AmgaJJzCyqGiQqh8rPIniGYg0qk+OEOhkcpXYeEU0BWIO4HloqbAuHkoDo5RXQWFc31wigGS4SsUJx7MQaIS2t6bvDcLSpq3eDJ2b5bsD9yn5fFHT8lWz3F0Mnn/IcJLwb3D8bsPoJHkOTqdePSEbrTt1cbY/Vm62XGflqcezqGc4N07GLv7EGU5z8nZ+MM5S71lrTYn7s/a6y37WWXy3j2MGfqKpcn7j3CWc5Zr+bsPbY0W3epOPXhkK9etlfrUvXt0u2cySveIVJqqzuvsIQpWFU2FhwVQbEMgZ4iHoNQB9b7SOSb1umJK5uAAlc4VTYOFA0BqQPpQHxyAcgcAhwpzSukN3VAh5gBWS5JqoPKeLjZglQfYIqx2AIhT+keEXFE1DWYPUKAkKiYqHuijOgKIoHACyw3THCq9Y0yv6AqE5RcW04+fGYoRX1pNLS7hg1FkZT3xegUS1dTiUvbZggqiqYWXubmngGImNraTz19h/UFkdTPzahkRtfiL17knCyqIphZf5x49AVQzvLGTml/EB1xkcyf9/CXMS7FXS2OPn6ogkny9kpt9DIhaaHsvu7BI9geBnf3M0wVkJMXXNscfP9NMOPZqeWz2McxLBgv0DxCVMeQ+JO4ZigjrNYXdgzQZBppKv2QBGFVjzf4+rHdVmYW5AqSKkFZXhwVIF0CzKbWKpNGVFQ4aHqFmX1OG8LAAqjwMtVXuAFE5U+2qrSML0JW1Ecjtg0pLVYYgvw8oQ1Brq/0Cog8NoK/0SxazraoiNNgH1LZBsez4w8euw2Oy3cs/WfAVioig5O/PhlfWTQ2YnH0S3t7DBqPs3FPv4THZY/PPFgK7BUBU8/dnU0urhgGNP3ocWNtChnx2/kVg/5BgR5nHz4K7BUA1Jx49Trxe0UwoP/sktrqJjqTc85eB7X2sP8g8WYhs7QIamH8wl3y1rJvQ2NzT6PIawsup56+Cm7umDqSWVvNP5jUATi0uTcw9NTUwuraZfvaCYLnQ9m5mYRERlMjyWu7JMx2AU6+W84+eAIoe2NjOPlkgBqPg3mH6yTw8kiLrW5NzzwwTir1eyT+YgyU1tFPILSySvYG3UMzNPUWGQmRjZ2L2samZkbXNiYdzCC/59wrZR0+pDuMplsYfz2NDIbi1N37/IagaztL5+IM5a7VJNTuT9x66z6vWenvq3j2y1XUdn009nCN7rKPaGLv7wF5tUD128uGc8+ScaHan7t6zNDues8rEF/fJZoeuNiYePXGXzgh2OHb3vuvohOyxUw9mbfWWvVLP339oaXboZnvi4Zz7+AwZ8hP37vuLJZzhJu89pMs1a6058XDOUW2SPXb87kN36UyH/+7cCijLSijc+c2v9//BfwgLwhsJU3+Jsf7NvyYLe0ow1PnNr7F//9chWXqjSwDApJdfez7+Ftpuv3Gx1v/HUtyXKkVoYphlZSnwh3+AsOybinpqONz+nX/CX7lqoqj/j/7Q9mQOVNU3yykoipxKd77+j4Xpmb8tuvorU4Sqwr39Tv/rv0tJkkIQOKrDfV4mKdgCmYwEwqBpIw1OsUus6LBrAqBiKIabroOSYLVLMR/ASCBgGg5S51RSEyWbzXZe1whc8TsgRlAJErQiACubpsl7PIHjI1wVO+k0OJJMGBk5nc5WU4cR0I4arAKZ5tDvtzB9QhoBVgLkZAmjhh4P3e+josCGI9RwYOn3NK9d1FGCGw79AYIfIaJkxWVeJ0BZHfiDzmbD2mr1MklCFTXJHPr9qCDggmBaMJIZkr1+M583DZNm+lzAbx/1FdHUbZS110U4SQi4JQTHOB5wU4JJ0Exv5PVZBUYTDNVlRRXFEA0rLnKgBRlJgJNgcYezXhXcbgJQzIFi0pgBwxArIBaYx2mqx3Bud2RUGUiE7HSgoGa0ecCKmxSOsDxMQhxlp9pd3uOxqUN78XzgDyheO8AIIInoBA4zggMYduxBvNEVPB4KVYCeoFhpBAeAgQxhkEoSMMujmMlZnZZ2l3c4KVgBu7xqtRgWnJMJUhUhCyTKmAlBNoTneVRHEQfODyQLZBoWQmJVmtIEDDPkvglAoOnGNQE0EciO8wPZYhoATENoa4SJouEjRqAFBADTioFDxQTAuHTWUjymARgBSlVhUNUJSgc5RUQp04YBQwUwATvBc4oFVHTaIrM6Dak6bjGoIWsopmknTEnHRkI/HMYkke52h8Ggg+tqkjn0+hBJIkYj00EaooH2WCBkE0CK7nU5n9cmDjReV102lOdNTgW8Ft0A0cEIdBI8bKF7fd7jDPQrjEjqfgesq6aggjZcMyCCHQJOkkOsdLvN+320NDBGumqnYEMHR/LI41FhDByoBo04NXYoUqAFoQGeUWgSVhDUEESMBkciadFGAGBB3FybH2CQBXIibAWLkaAEoyYvYRZAACDI6KiAHcNwlZMpkABpRBxKFhyUcUAVWdAK8iOfSxVAk0QAAob6MoiDFkQcShYABUAKAYYqbY4ki0XjTIBEDQviaDZAXR8EgnS/T0ojye00RzJoAiO329ZumyAIOnBjoEC6NggELe0eOWIBnxXgFQUmOLeb7nZB0wQduNEVEVFk0kmKZVFJVFx2K9MTIQKw4VBnYJow6LfoIx0TeN1DKwqM87zsslkGjARgoA2HOck0AMCBa7yBCwIbCloFjlEtOGUgmiqoJE2IsoGqIuighAFsAzkVtCO0NGRVmsA1fMQLEuFDuwztkVTcTvADxGZyGmSFHUxH4HHEAekYwhukDRUUCFMFwAkNBJMyeyrkQXFEYRUrjQqELrVBrxUZKTCh8qYD54e4HRiogBW1asOBZEFJgwRlkBUdxqDv8KMsL9hsv/g7ZNqCkBDAyiACaDQJsQKG6COrk6y1RaeLIDSoyyu0BaJgkBFVHFMpKlAsQiTcjiWJLiNZraLV6qrVVIIAacRsjgwcE/1ea6eDAgpAYSAr8laH4HDYazUTQwErBrKSCUGcx23tdHQE5d1ua6cDGKYD5weqBVI1NhiiWAYTBdntsDJdCcA5n8/S70G6AVII2WZwbijEgxxEEaOR5HFaB33JREEKtTZaoGrwIY9iIDjPG15aVmCCHyl22lOraCoohr2GZhoa4MCFvmnH+ZHhtogaSnCc7HZYOFbVINNBgoJiKjrkwAUDt/Z7I487NKgwGq06rYiumiMFtGK6CWODEWjHONRGt9sjv8856lrOmoNISKVJcCiBNKbCKNofOjGhjfsCxWIrncZw02AkzW6BIRMYyiCNAaruOC3LYU/XGbC3W5zbSxkCwEqa3QLCAMSKgsNhILCt3YFphLW4bI36yONDdrdD/91/88v809+UIvxrHFEQx7l++mP3975rkMSbwQ5dl5PJxn/+z+R4EuaGvj/5hv3+3Tcgw4C/3L4bfvTVzj/8Dd1q/dJqhV8iD5aJYdTWZvAP/oc32y0CAFBTVV+g87V/zF+/YaJo4F/9j7Y3n9MBVVVOpVv/5PfFick34K7+Gg+WnEih4fC7n/2EYvqS2/72dz6mm81+JvHen30rUDnvJ2JXP/1RqFoauVwXP/u5s9MRvc5KboJze2ievfPtj0PHxW42ffUnP4uUjlqZTDsaF2x2Whrd+c6f+05KjemJO598L7q/e3rl6tDrFdwua7939WefB09KnUz2zne+HTws1Gemb333+8mdrcrE5LW/+HFyf2cY8E09eOA/LFampm988fnF2dmTa9cm5ucvf/F5N5cO7h9cuX+vF4+nl5dmHtwfjCWTTxcuPnhQunYd7NZMD6Gwo/zB3tT9B/1EIr25fu3e3UYqowgsYIU4iBhbX7v52U+66VTkqHDx5/dYu5OnEFDlRpR98vmL6w/utccy/kLxrR/8oJ+I+0+Prnz2OR8MBIuHl3762SgR8m/uXvvsZ518ChOUj77xxzpJQDD4zp992yBwRODv/OCHssOKj/h3v/Nt3ma3acMb3/qeCQBGyPqhsAvjkHnSuvmDHxg2C8yLH37vuxKGSV5nOTupkUT4pHj7k+9jgAEA5s3vfQ+DNJ1GPjIrsikZvPbhtz5GhJHg937lm3+Kq5JOErc+/SEKGSqKv/etPzNNXXFYv/LNPyW5QTM3Dr9m4LZIUIq6raJdgbLIxoaC11gkggAvObMqkD5D25axGo/jAwJgML5tmIixJuP1ARLDkOddsCrDUcDSLZEgpwu6fgwSVQ70IMDSADvr4z4DbR6RFk0yCGJ3YJQVi1UC9nmjpqIeEF4bUicslMDA133jRCYCgFkQoFMJ9xiTLxemH852cpnM4svLX/z87PqN/OtXN3/6k046FT4+vPizL/qhUOTo8MbnP+umEknl/G2o0UBp39r27R/9sJfJBM9KV37y2cjlCtnEW/JRl3QEF17fvHu3nU15iidvf/8TJhiIQb0bODMAAe/u8c2/+AkTCwUKhzf+4ie9XMpxcv7+Dz/tu13OQf/GJ98XnDZXu3XrRz9mI2FFRcDXPA6IOChLCxKhSziumAsjVJMRQFWXeAxSEEUGliXSEECQt8MdoN+AAYu4JBOKgsGqviJgioJIbZocIN2WCdPmkkRxQ8gJG/MDWFAJiqHEBqBLkAIZqwou8ZoDhx53iCGPeBH4aRcSDYQ0gNcDXOARzNCWRIofqUH6oz//dnxj/fzChWs//Ul6e7MfiUw9epzY221nsjd/+Gl0e7N+Yfr6D36YX1k5u37lJr83AzHdkTI2txDZ3GpnMm999heJ9bXq9NRtbn8KG5ZQ9+UHD/KvXzGx6OT8s9jqOhsK3EJqGen0jA5d+eznE88XmpNjmacLky+es8n4+IvnmdfLTDo+8+Dh2IsXlcsXpj7/+cXnz09nLhCVobSlExYVakvAOo95AORsCOwopFsFG7K2KlJWBetzyqqKITIBM7bROQRocgXUdlTKpYBdGViRCFzA5a4F7gOyYPYwaGWIW1WwI6srohMfaIOG0yHqrQ6sEfIrkYJHqKDIKwpl1aCeqK9KVloweEB7yVO4hOma8YrHUNUltW588j1aHckIfvuT70EQqFLEh9/5GGeZQSLylW/8iXXIii7nzR/+2CKMZC/9kVmGxK4IEO9988+svQ6Tir/37Y+djXr5wkUmENQowtLpvfPJJxTH9RPJr/7rf2Vr1nvT+V8Zbjoxtc+Zb336fUe/I9no65/+gGKYXiL5q3/8v7rqtXYu89Z3PvbUamwsdvuHnzpbrXYydeuHnyZXl3oXxiZ+/LOJxRenV65cnn2Qf/mil4xPPnuaXF3vZDLXvvj52NKrs4kZoF8zHIQpSGPLa9Pzz5hkLP/qRfrVUtftVTAdUEY85bhy//7FhWfNiVxiafXi7MNuNKIQhqJLOo5fmp3LP3nSn0onHi9cejTbnMjFNnauPrjbj4SS25vjs3P9RHRs8eWFhw/rFyb9xdI7H3+HDQR8vdql7/9EttscbP+tT38w8nmc5+WbP/rRIBmxVhsffv+7rNMpBDyV9JiJIOmN1Rt/8RPJ67JX67c+/VTz0s1AvJXOgIAZOju688n3FRzFNfXtjz8BCLSWHWvHkiiguqqN97/xDZGmYAR450+/ZcAQAoN3/vwTwWEnWfbO976ruGygan70J99UcaxPkrZnT9/Ug/V/7zDCcSmbNex2+vVrE0HeLBLIMJatLeHSZc3lFienkAFDHh68gWYFgoBp4qVjSNPF8QkA+ZLG9b4sEqGJomRhL/gH/z3McW8k6oG6rnl8nd/6be6t2yYM+//oD63Pnvwt60B/GQ5L2Xzjv/gX0tj439l39X+5D4ZdjbpvYyO8sek+OfXt7GSWlkhVSywuhtfXrd1ufHnZVzoJn5T8e7vRtVVIVQ0Y1hGUYpn44ovgxibdasWWlrz7eyTL6DCi4oSj3QptbyUXX6Cykpp/Ft3eto04BSN0BLF2O/6dncjmprN8HtzZTr9+jYli9vWrwMams92Or6/5Dw8jJ8f+w2JiZZkccZGVlfDqCt1nki9feA8OQocHwcNicGsrcHTkPTrKLi1ZmH5qZzeytuZsNhBI43IxF8ME9/dD6xv+YtF1epZcmKdrdRM1h9kIJQjp/YJndzewt+cuFiMb66HjY81lE2IeQ1NiKyuB5WVbqx1dW/MW9kM72/56Pby27jk+dp+eZhZf0P1eYn8vtLnhrtU8pWP/1lZkY8PSasVfvHAfHflr1ejysqtcjuzsevf2ors7/nrDv70T3dywG9oF1PTKQ0/pKLay4iodxfb3XNvb8Y11HUYMGNIw3M4wyecL7uKh+/Q0srrmPzsP87U8TOeNoaPR8K+sJLe3LZ1u/Plzd/HIXToJr6x4jorhnR3P/n5iZ9fdaobX1+MrK7oKDsoQz6B4ix81TL5mQH2x08SG5yAuCL1TiGth+ECQygbbwZAWI7qtoM9BccKwgw/PIJzn+2Ws3yYQQdEpSIh4iBYjdhGmgYGMwrYxuYbCIx5w00IyCNY7bAPjeijW4IU2yJUBkNUGbax7huI8PzwHBx2S7HF6C+q3UKQjeEvH6RcvrL1eZHs7srZmL5/HNjc9e7vB/T3f4UF4YyN8euqtVNPz89R5JaMPczBk53uJnV3P/n5oc91zfBRaW/eXihEIuADrTl1OHB6GVldczUZsa9O7vx8rFrwGl5aUpMw6j0rJhQUbw0Z3dkLr6+7ScXx/z7W1Hd/edp6eRtZWgycn7no9tTDvqJXBhtJpYVBTI9rDXpdSTiWA19vnqNgBoI7I1lCzo+AtqdvBpYqBtJrMeBpBAXggDc9hsQMYXaVXQ5U+hDA9LhM1EB1ipWYd589NZCQy56jUBSlNlpNBE1LNyqjTxo0WiDM826H4solyQqeMsz1M7yhMHdFaOtYU2S7O1WBI1xPz89GtbcdgkFxZ8e7vBw8K/r290Oam+/QksLOTfvUaE4XM0lJgfc0xYPISG5GNMFMJHB7G1tbcjbpvZzf78hU6GMxAQgY07O1KdGPTu7cXLOx7C/uJlRVXtxbR1YuIhrNs8vnz0NaWq9mI7u76dncCuzvew8PIyoqtVvfv7MRfPCeGw+z2lm99zdbuCj2AbaBIU9BYo3uOAaws141mDQdZRa1I/S4BlTlkYPZahFbhNQyQMhHQ1JC63G8ScF80y0qng6Mt2TDkYSYKmYrKmJ1zzBjoekXstQm0ydlkoZ9PoKoI9DSmSaBtBWCU/jmiMrpa17otwmQM8HTQ6xJqRTIZpVUlxC5o77ZiKyv+cjlaPPTt70fW112thndlNbW5SfX78efPPYWC8/w8srzkOTiIds8zOpZFZHenFd7cSLx+jY/41LNn4YMCIQoqhuso6qmUvbs7sfV1W6Uc2thIra7h3DAnMxkE9lZPI+sboaOjSOnYd3CYXF629Xuh1ZXU8jLBj1IvF337e57j4+Dmln9nx1Wr+vf3ciur+GiUW1sPbW46G43Y2pqvcBAuFn0HhejKsqta9RUKyfl5rNc3CIjLhm3cMLm7597d9RUK7oPDxNqqp1yWQx4xaDcFIfVy0b+25mi0Imtrvr1dz0FBtVF6zIdpSnB3N7O4SPKjzMZ6YGvLW63ENte9u3uRoyP38XH89WtXoxHY30+8eEH3+pHNLW9hP7q/66/XQhubwYOCvVJJLsw7Op3IwUF4bc11chIv7Du3t2M7OyqGGzBiYKi3XkstLNjL5cDhQXBzK1ip/MJ2AoCgt3QcWl6OHBZtlUr8xQvvQQFWFAOGdQQJ7e569/fje3vuRiO6uhra37c1m6mFBVetHj48DG1ueA8Pg4U9z95udGPDgOD/N55t0yAp5u/9Wuv3/ymoa2/2bkIQWq9F/uU/x9ptg6bbX//dwa98FRLFN9QKAedPf+z66Y/f+MX//5VEaMIw1qiH/9v/+o1nHQ1Dd7o6v/n14Qcfgqrq++Yf22Yf/B1mAZVItP7P/6USDL4x0/jXSISj2+9wX//NaPmEc3l0J+UpnrD+gEljZLVtYpjoc5GNnl3ot7JZy3mTdzpR1BBgCtR1BAfwZh8GzFHAQ3SGpCb1ggFDA0wAQjHDUTrnXR7dTuBtFtb0ViqFjUa4oWoYam92FRzrhcLRg4JMkpLPgXcGiKI0MllXo0EPGTYeslebAmVrh6PeZo1kmHp+jGJZf/msnU+DI8Xe7VQzY5ZmF+VFzAnpBkwNBrVsDuoz9FmJm7ngYdoIwzczGbrfo0Yjzu3Wh0OsXh1eu0lwXPDk1DZzWAAAIABJREFUpDw95ey3sT7Xj0SgXgcYDPRwGIdgR7XaT8Y4jYgUCuVLM75hE+8O2FiYEniQk1A7xMMWe6Xez8RHuD25t92KxTBQw9us7KA1grRW6orXPnT6IltblcmpAN8Aa1w/FkNhley1JYtVJe3WakNxWwcOb2Rzsz45bZVZDqVBACAMiWgyms0iWWm62iIsRsMdCVf3G74sioGOg1M2FgMxgGgxBoUJdru92tTsVN8TiO7tNZMpFDXsx+VhMKRYSaCvYJqiBqxGWzEIBLOaYE0ArLiGQyprgpoJe2FQMDBZlv00dHJmYLjh9yM9GaRQ2GKYQ8NQTSHoIE9KOM8JuTFwoIIwKHttRHMAQKYVHo0YAR6NuMtXie4AEFTdS0ENQcIIw2+h2wwMGaAd1gUIGCmQH1UECOFVNWSxt9qwIIt+JyRplm6vns/jIz50dno+MeFkOkR30EhnqCFLMazodwGaau1WuUhKNrBw8fB8ZsbJdokO24+EKGWIDDnR7TF0xHFeHmRiIkJFd3fKF2ZCvVNdUAb+OCYIaJ+T/Q4NRJ3nlWEqzCOW2NZm5fJlesRQje4gGkZlEe9z3UjMIFDiqKNE7BQg6U1V95IopKl9DSIgjSS0joYShuEmkXNWizgQVUQPC2o4ZsFBTrZguKmTsNlVIAKU7BS9scancwhJIG1Bd5IIbuqsAaKAQQLwSRmyWoRIjKgOFQ/Jk1bitAc6cIzUwIGq4ZjiponmEINV0WWFK5zhoXi7LXB+gihKM5N1NBo2jm0lE7ZGxwSBTiQaODvTEFTx0Hh3iIpiPZtzdmq2UZvxxa2dvoIQ3WjUd3pqgqDkcyAMYxGYSvaKvd20sYNmKuGrVRQQFXxOa79hArDocEGcYu20mWwcHCr2bruVzfjqVVUHBb+L6jKApksBF8hr9lrt9PIVd6+lsYDuwxFJ1UUAtkOAAQJdCfDhLEAT5YGctDvlgcboupeCB32g2yU8NoV2GX0d9iESSqEnPSXlwlgGOT9XkikAwiBeg+2wBiNgR7ZQYo922taWuQtXKMhQOyrmhABJ4yQKtZkgCmEdAfKiI4LGzxglaic1weiohocCEMBab1Go2gzGfYWjdiqFoIazUBpEYwAJ4Y0+gCOC20nX2gCF9nzhcHm3445CBOo4OucCAZNCiVZfw7B+MIQLAqFLIkUHSqd9n59zuyOHB4LNrtlxvNsBMKzrT/pPTyDEHPncruNyPxThXK7Q4YFssSguK94ZAoDZjcX8Z6cajjejidDpsWmCuB3438l7719L8sS6r3Kuujnne1/O3f26e3pC9+QdcnZJiqa4KxoQTYkwYVmEbMNBhgzIkkxZFmSDECBZssCkQEqUZqdneqZzfv06ve6Xc7o5p7q3cvYPgg0ZMA02uSsSVv0Bhfr+9P3gnFPnKApCCkJlbNzV6bCtZn1kzFOoGADcyyRDtSIMOILXC3Q6aKdpz00DQ93dbjbGR/3VimUCQigIVyu2rjuJJGGYXL3WG0vbsuWrVBpTE1Q5r6smmIgyvGDrBsIivMl4KpXuRA6xTF/+tDE95W3WHc2Won6i20cUXY76VZRKbGyUFhaivZLV0fhkHNMVrDPUApwF44Gj4/5oSiBcybXV8tmzrNQXUAYGbUKV8Z6gezkNxT3lKuzHG2wEMi0HAkhbZ0sNPh6FHJPo8KabERkO1G3GFPusP7W1WZmcpEzZc1zqZdIAZJPN/jAY0EkqenwsRv0Dxpva3qqMjOoHp6Ff+5/+WBbh/7PVyH3jW/9v/hMABN+sIstxLL+/9Hf+nunzw4IQ/Cf/O3vvroPjb6YSGUbjP//VwSef/Skco/xT8EEQhHa7kb/7P6P12pvis+X2dH7xLww+/Q6oKv7f+U3XzetvTLIgqKUzlV/7X/4odPX/GXKHhkr4xYZr79QWjejT1eDmoYQyqeVXkZVNEaFOcrMn43NwTwytbHm2jiSEmfvdf5N4viaiTGxpJfxyU3Ow0It1z9quqYGj1+8nnq6YOhh/tRV6uSkQrtjy6+jLDcXGcw+f5+4uGRroXdnyv962h8bu9IVCclyGqejLzcjTNc3GAqs7gc0DULEDmwfB19sKRHjW9tL3lhUHd+2dxpdfg4pNldrR5ddImx9A1tAY9FHKt3uSvPtEAYhAvjrzbB2UbarSjj155fAKc1JN3nliOWCg1Jx/vadrkKtQjz1+CQ40stJOLq2gghystWeeb4IQyu2eJO8uaTJo9do1L2b2eKQmJJdfwX2ZKLez957Yiu0qNJKPXoCKBfBy4vYjplC3ZDtz+zFR6cB9KfHoBdoWsEE3x/KU1IdlI/X4BZuvGQqYufGSKXahoZp8soLXukiDTyy9pE6rqoVe+M1/EdjL6yaSffCUydeAoZF4+pqud5GBFruxShY7tgYkHr3w7J1oGJYz825UATpSbOklVayDAzW69Mp1WLIMKP3klXf7UES4boXstBhFRbtVYlBBdB1plqhmER/CjNM5MbSapGJiBes0aE3HOvLocBAdIu5+ne6UMIFwtU+w9ikug2RPSnT0CRPA+02aL6OyjXdK+LCEayQ9FJINaVKxMb6CdmqMKAKG3gYHRcO2u2W8dYQJuKtTxgdFXDMQsY62KpRiU9xxMfnwuWEirpNq+v5TVYe540r07hLAK5QlpJAG7BhUsZ15+MwwQPawlr29bqsAe1qNPXoJ9mSs1onfX4b7ElHoJm+9NGWAzVcTj547qk0VW4nHL7C2QLTF+N0NuCPhtV72wbItmCwo5fAmYJlksRl//AKtduGelHz8EmsLWGuYu7sE85IoEZ0CpfC4ZmCNAic0ENGm+/twv0UqumGrJVUdqEO4W6SEDmZBREU4qygBHcT4fajfwiWDbBVpSSRUDa8qZwTB44Bw84TuNoghwrWPUKGJ6DbdFicGZkTV8UaREpuoppq2UbH71R7o6RxhvTomq0gnjwk8qRhEt0wP67CAuyLP1uJPX8sOEdzYD7zetiXTvb4XeLmpD/U40o0bZYHgwq+240srKkh6N07DT49AA/TtnoRWNnUD9r/ajC6/EnE2tJGPPNpRTdi3deh/+toRDffuafT5uqmDoZdHsbuvRZwLrO6knq5aFuw1u0mchySF2zmOPH1tGFBgbS/xeEWG6eD2cXT5lWVAgkC0C5QuouIA6x4gA4vpdMnaMSWqBNBp2EjN7PN9gWkWGEnGJSfY646qkEcZEM08oxq42Mc6eVoTMMNhq9KCZLhkhejswUOFGPJEM88MYK8u0VVpUdE4RcU7BU4aUqqhgP1DTVOGfbx8ykkqLg6w9iml8IiqYc1jujegYFmPP11jyk24r8SWXzGHJVsH40sr3p1jGSVzRt6PS1ZPjT15xRyXAUEL391075ctA0o9fe3b2JcQKvnohXf31FLB6a9vh9Z2LckKP3zh2T5STSTx4Flg51hGmNSjTe/rU0t1wi/WXUcVSNbieC+oNBSIjN9fDq7uSBgTe7Hm29g3FSf4Yp3bPjYcNPBqO/ZiXUKo8OpO/OFzFSC8mwehlS1rqHUBc2iJqgMG1/diD55pBBU7LM2uHxo66D0ohJ9vAILhOizGX2wAhp0+LEyu7OgU7X21lXjw1DQg114h/Hwd4NVgqT3xatfSIc/2cfbBc8XEFbFfY2GjL7NHleizNWSgMcV6fHnFlizPfiF9d0m3UKLcTT56jjV4jJeSSyt4k0c6Yu7BMtHgu7R/9e1PJIgiyu344xdYuWOL5oXf/j220oEHau7uE7grEs1BcuklOpCRrnTpH/8WWh8AohG/t0yVW8BAS99dRjoCWe2c+53fg7oi1JMSD58zhbqIMGsXPmh6I0hbyNxfxhoDpCMmHr3Amj2wJyduL7EnFQPBf5QSCYr2f+pn2v/pf+Zg2Bt2RoJwp5P8q/812m6bLlfrl39F+OCjN73EbRQN//1fZ16t/PGtp//f1TRAEDwchP+3v4ufHAFvKFpaLNv7/p/rf/49pN/zfvH7nq++BN50jRIAtFS6+jf+tunifrQpORtBIoV89vUrb6NRQK3kq1eh3Z3+7Ojk7dtCMHD4yfsdV9iASG+5lHn+LOTzwRScWltL7e7Uzk3P3bopBPztmfHJB/eVsN/i2MzKS40gTQ8Vf/48Y9ulyxdmb94QvL6Tty6N37ppuRnd7UptrGMAUDpzTvT7dZSim4W5O7clDD95993xhw8hyHFIOL6yAqvG/rvvTS4v+44ON37u52fv3PGWCsN0lKx1Uy9fNOPxg88vYwEMr/Hjd25588XNz35iculxaGenNzkeOtoJv9zsZXOeUmH2zu3Slbdm7t8N7OwFrrw/8fhxZH1tbDTr6dSTT54I0QBbr8/cvF15++z08lJobbO4eKGc9GpnZtx3V1J7J9nHj8SQn+51p65/K84mo0uvw2tbzfkJL0gk19YADC1fPDd157bFEorfN/nooeZlE67UOXxcEk+B7Xrq2VNElU8/vDx56yZAwFy7NXrvrslgIeQ0vrVlM0xrMpt+/oIQBgc//d2pmzchx6AG/Oj9e23rHJpvxtbXQdupvH02t/KC63ftqfiH/uAGgqNHOyNLSwRsMq1eYm0VVtUS+FZ6acldr+xfuiIcgTTnuKBus+KFYcBFSoMWx5YljGoAUynL7caWStXTBIVrLNZvVjwogOB+s1clWGHoz8i9PCVjeIi3uycoASgxpNWpBwyDoOLAoE7hvJgJd062IgrBeNq2XMZB0/EDeX3CZeMoplmNBg3xmn+6P9yCuwibjgtCAdNVyFvoxvd3R+8v1y/Oj9+9Hdna2fno46lnTyObm921V4lxdhoL9oQ6ur0zc+N66dLi5NKj6Ou16sW58LO1+PrqVDpJaUL2+XM5FsJFcfbmjebZmdzT5/EXK83zs6G1/djGhhAOcY4QffRE9bGIZU7fuNGeGhuJgWe5dG/YoJ/vJra2pEjY4MiRhw80N+3g+OTNm7XpiQFHtzps9FCAfcNeNeqXBi7cLpR9Hkhz+0VpLodWu85hp94OuR0FSMh81Uf028SsUCuMBgDZZYt8kWOcPtZVGg0PZ+iOo3cbrqAwwL1g+YRjTcMNqu08E9dkROL5JsdZNoM09XcWsK19stJsFlwOQHAcwJcJfNhhVLXZ8KAKSI1Is7du6hi29+EnEzdvQhikBnzRtTVakjsLY3Ocj7Frp7Xq3N27pqHvfvLpxIP7hCKZbir8ah3vDesTk6m19WCjnv/43dlrX0GGcXj5yuSz52i3q8VCqbXXZKk+GEslVlYCheLB9z45c+NbWJTq7y7MBeGoHh42dyNb257j/GA8m3q1Ejo4Ovnk8tz1a3int3t0rHSpdtkH06INwO08m0kOnVOrXXPRURlhVO3iPHv/tSUnKxVPlBIMGK7kOb+36NTVfjPsCfWBrt3pcqkD3sa0fsHPIn2AJVplbzbBm3W5V/b43A1I0Dsdd2hPsNwGX3SzYpFc0PTLZ5HTmrQlDJremLepqGatzQUOZTNg9cu032X6sPz4ndu9t2aQRi++torJctG+mF156a9V5enk+4HACUIZR4ejjx831XlClJOrr4h+P4/ZqSdPgsdH3anR2evX8xcuSF5f7v49fnoMtpzE9pZ70OfjiezT5cj+bnsiM3fnVnFichCJjCwvC5Eg5VPPuNINp19v1EeeP7d8vtri3NzXX9VHx4RYfPTlysDv7+RGkqurvlKxf2F0+vq3sKLufPzJxJMlstdrJeKVuYzpYVhRTK2uRk6Oj3/yozPXv0EGw/Li3PiTJaZakZKR2NaGZ++kn43HX72O7h+efPb+/N07ZKdTe3sx82jJUyrI0UCwcBp4vcWPJNMrLyLrW80zk2KAU2fn3A82ph49DBwdKokIVy4ml1cG45ns6uvY85Xip5dT95ajGxvjwQBLmonHjwwaV72eqevX1ai/MLNgQ6DCuRau3UxsbauRsBRwZ58sWSQqJOJTN28Mk2Gq3c0+foz4UU7Zi25uTrvc/Fhy7OkySKLt0ezMzRty1I+LcvrFCy0ZCplQfH1Noem19A9sBLEcOHx4OHXzRi+VQhUl9eyp4ya8nlp8Z8tycbV33v2RmlAOAIL8598DDd37r34PUpQ3uIhBEO504v/Df1f5G7+mxxPtX/qLkKbRL587EPSHf4lNENFf+5uVv/lr8sKZP1WY9ScZcncgGBaF0D/6B8zKS+ANJwJthu3/2e/3vv/nkH7f8/WXvn/5u2/ayAA4jjI9U/tr/6Ppdv3R6eoPLhptfPaTWihwdOlSc2bCIOntTz8bJCKdZKa2uNhLxsge7xl0BrFoLxg7ffvt9syEwXJb738gJmPtRLq6sFCfnWonMv1s6vDyFYlzn156qzk/ozDswUcfC/FQOzdenZ7KnzkjRKP9bOrg3fcUzpW/cKGTSjH9LiHLNkNUs2PdyYnjxUUxHOqlk8fvvydz7tMLbzVHR4fRaGd87PjMQjeTVr3uvU8/bsZSYjx2+MEHVLNH1HuO31sbneyNjh5fvNjP5RQXt/bZT6h+bzuZ3rtyRfF6arOzrcnRRna0l0wevfdeK5czXNzm599V/Z5WMnN85T0p4C/Pzrenxhq5sUE8fvrWeRMmPcUqnx4fhgKDZPL0yntCJFyZme+NZwpTc0I0VnznYj03bqHw1uefS+FAK5OrnD3TGBnpxxONxfnjwEhXPV73n1Vj/j4X3P/8cyEa6mRHi2cWGuPjg0i8fnbu6Ox5hyTXPvuJYTqusu7tzz4bxsKt7Fj97Hx9dmYQjLbmxw4uvWfg5Nbn3xWSUcXt3frko2EocCqIpxhdTk0MQ+H6mbmT85dMDN36/PNBNiFy3sOPP7a8FMOZiMe2J1wgCRFpCEvAJKThY6iVClHVDlzraRM50mMybhOaZCESxOMAkKEZQCRHYCdE46zJBAwtzbKUTHhsc9rtEJA7ajhJwg0JaBbGAogZJD0+zc7RbkqBPKA17iH6CiBYUijgQmU2adlRGvZClFvHRnHAg1Ae255waZy7ODvfnBpvZkZ6qdTR2++0cxmdZTc++2wIw0Vb2g1Mif5AY2a2PTHSHJvoxuKltxZLU3MGQ21876fkgLcbiR99+L4YDFSnZpqzE9WRMTEWL11cLM2dNWhq7ae+Z/q5RmLk9IMrQjhUn5iuzU2V3KGK3DgNjp+Ozzg0tfrd76khXz+aPLz83iAaaU5OnS5esJMsAyrWLIsFEBQ28Rna9uEcJaBJRGdIutwFEFIZCZGIic2RuB+gEMOZYTAvjHoAMg4icZIkdDQOm6MeFDKwaQIPAASsY1Ok7ScYViNiAJQiCcpkYoAz6kIhHZtA1bDfs3lkY5wRCzGcikdBO0HQiEwkIHnMhwO6exyQfZyYTNamp07PnBkm4sNEfP/KFdHjqyzM5yfPDuX6FuHlw4laMtudGD9eXBRiUT4WOfrwiuj2ls6cy587Z7m4o4sX+7lUPT3SzWYP33mHj8cG0ejeBx/oLJM/s1hdmBF8/vyld/hUtDE+3UslTy+cL1tIFRjsxRd1jinPnamenROCofzi+e5EtpIdFVOp43ffZigNoW10ikQZgPFpUJKEAijD6kgGNTCaLrflZBZwQTRtkBME7IJYn4bFES3uYhiDyCFWgqVg1Tzjwl0gSjvQFAtyIMeJYJIEohRJ6NgYoaY8DKxhZ2iSs3HaBmZcCAxBp23T5SOSDIGqyAhmpD04oGLzJM7ZBGXiOUSNBHvxZGc6t//OZY1kdr7z2SCb1DjP5icfC9FwXhCPEaKSGB9EYs3ZyaO33jEIYuc7n/ZHMjLr3v/0UyEabI2Ml2dnCvMLcjDQmJk8vPiWReB7H31UGx2zSGLvww8H6XgzlW1OT+fPnlW8nvrcdHFktmO3NshEPZa1Xdzuu+8NUrFmKtecGD966y3F46nMzxbnF2yGOr54SU6HCiOzvbGx44tvCYl4PxI5eu8y2x8ifbUfiWteT/78hX422ciMdTKp4jsXa+kRMRLe++RjnWVKswuVcwuiP5BfPN8bzdRHxgfJRP6tC7WxCTEQ2Pn0U5WhGzPzlcUzQiSaP3uOH02rou2vtvqxVHNmRvL7Nz/9RHdztanZ8rkFPhiqzs03picqswsmjq/9mZ8xglw7nD7++CMhEmyMT5Xnpm0Iovt90pRPp2Ydinj9+XflaHAYCO9/+OEwGmmOT9bmZ+qz84LPV784v3/uHRvDNn7mp+VIYBCMHFx5T4jFGiNjjbnpyuyc5PaU3n0rP3/ORpGNn/4p3NawoYg4lhL0NyaminOzlekZze3KXzp/Mn8OQJDNn/hJu9dlHz/8Y4Xc/98eZXoGQGD85ATStDdiLEiS6PVVef6snkxpmQxWraLNxv8dtPpDvoRdfqzMzZvB0J8exkL+JOlKlvz/7HfYJ0vOG4GRbTsk2fv5H3S//wswz7tu3/T+/r8EbfsNah0cB3Qc6dxi/b/5qxZJ/Tj+8HRA0N1vB8unmC7zmOFvVlHQqoao2OGOwTJyzJO9v4zgdvnM2VC9IKmDLu1462VcEYpxd6J4YuKokg5Giocw5MjVUKhR5OQ+6kKDtaI26KpJb+xw24Sg7tRorHiEWJpYD/vrJbOLipHAwvWvTZw4+vRK4mQPsmx/ZyxSPMJU2ax6/M2qBbXa4yOh0pGnXPYOzyUKR752LVgvYu1hoHzSbaaDJye+fL7x/iJdKnkqNW+vGT3d9zVK0VrB3yx76qVBo+RqVkInB1I2FCif+Cun/m49lD8K1Iuh8omvWfGUTpRUkGu1A/lTNRt0Fwq+yklwmCWa/eyLFzuffcb1W4FKXq6H6F7Pf3jYCmNInQ9UC4NWEkWFQKMsF490vydROIJZRJXpcPnU8ZPEcBi8tzq4wrhRyd8oW6cuMRaMHGzDqCkbUriahzwoqii+WiFaCFo+OtAoW16ah2Kxg60uMILaWqh4pGFhAyZCzbJW8Kphr79WcgioQzjxWyu9bBJOJUKVvEDaAAyFGmW96JWsYLhyiiG2GPabHRkHTbKvA30IlEAct42hBUuOyyNJLcYGAbfcHbZBxNLxrq51YQhyALfjDGR4YFE+WGzbjg3Rogh1ZcgwyI7q9FHItkkfBPAK7DgMOxy0AdAAyKEIdWRcczi3ozdBxyGZgGL1FdAw6aCqtixdBQles1qQLTlkV/W2Ku58SU37vZVCoJqv9BrBwrGvUQzWSv560bd5yH/E0d1u/GBLSfnd+VKwfCJ0MnhnGGyWw5VTV68ZqBXkapgZDkKFQy0dYKqNQK0w6CSQvhSoF6PFE9+wylSKWsWHy3L4eFdLeMjOILS6IX7CeBTLWytEi8eEo4bqRa3hh3QterDNJyM84bN6CsqoFCnrAoa3FdQBhl2HgA0MgsQaw4QNCOSVno0QMMaqKo/QoET7Jb4D4baFQqDOAwRsYLAFDyy4g+AeU+VtDFJxHJK6DmwBMKqaXQC1VBC1zb6FIAqKs0qDQ92gh+kIbRs2LJy2bV4hTBUnbJG3IASmfFLscBswjOb8dLxwSErCsFEK1EuwaQwTYf/DLci2lc+oxOkeJiu+biOaP6T4rl4L+psVULMGmbi/VkBVVc6F4oUjasD7O/Xw6T7T7gj1hL9VAUXNSnhDlTwhysOxSPRwm+60+VbOc5D3lkrSB5yvVUV4UU35Q9UiyQ/FyUQ8f8i1Wly3ifUEmMeJjgYalt4FaK+m9y21C9BhCJYx9YSkMJXUxB6PIT0dsGy9DVGUAKiqxKMYbxi6CfRNsqURlmLwGNWRLACUew7pEWwNgIYgxeuObWo9A2soKKRBPdiFawhit1teltYtU9eHDjk0HAlABhrW0TDU0Po2ylksxofzh4bp0Ukm1ChZBVqygr56EUTshguN314ZxkLOpB0qnyhOyKLIYKNku8ghFAtV84Sj214ifrDVD0eMgDtaOlECbs3N+holEIMMjgrViqSlOmEmfrInen1i2Bcun+oeBmFh/8NNJNZTL3GB0ikt9MyYK144VBlGaJYjlWOZdfGphL9eQmW5lSA9+QN6OPB2a5H8Ad1sCp1ycncNHSo7bs5fLVDDgTQaiRUP6WZj0B7z5E+8tfKwfOprVJDuQE94g40S3RtIE/HI8a6r2ej1R13FUqBVEWpFf6dO1Ttyyu8vnVCNDpJmEqclf7F48BMfUcVGoF7kK3l/p85WGlo64KuVXOXqcDJJNnvBZjlaOvHJLVe1oJW8IAhED7fNIB1oVFMvXp18+I7bhH21Yqx4jKB2qF7Ua14LQ6IH27aXcCwnWMkTURwQrGCzPCgcAhQaqOQNH2242ET+EPSSBt8N1ktWI4APhsFmeVg8sjjy/PU7tTPzYigQO9zWPIw9wELVghOiYd30NYrDQrgE/hiufscBLav78z9wYMT7+78HD4dvhEdotRr+9b/X/Cv/lToy2v7lXwn+439Ibm++wZYOCIKmFf3bf6v21/66PD3zpyT2/icEWBAE6arn6heuO7ecN6JU23YIov+zP9f72Z+DJNF1+2bgd37zzVacHQe0LPHtd5p/+b+wCerfVZ5+lBYhDLPNXurxCzqd1Tku8fCpOxAsnVscu3l/GA2XZucSD59aEffAHYw9fikE/MNAMHV/SfcFjt5+e+TbO5LXU5iZy9xb1n2unjcSXXqpuT2DUCT14KlJULuffJK7cV91ufYufZh+sGwRaDcYj71YA0CoOL4Qe7YKWfb2p5+M3XmswfD2W+/HH7/AHFP2+aIvVgHFfHXlO6nllfDu3sp3/2z02Wp0Z6s9MsYdV9JPn5dyM+6d49GHD+vn5xJPXwV3DlY+/m5kZSu18rI2seA/OE48elHJTHkPT6du3D659G7k2evk67X1j78Xfb2TWHpWGp1xVQvZu0vdRNpbLo9/df148Xz09XZ6+Wl58Xzo9U7s4bNqZsI17OTuLXVjSVe9PvH1zd7sWHh1O730tDo95QBS9v4TDSTqi3NjV69LGImoukiQAAAgAElEQVQGA2Pf3hW8XsIAUo+f9j0BIkRk7jyyNFuh35745o6FEqBsZW/cU0kSJQfpB09VjOtMZtK3H4JDRfAHJ765vWeDpglm7j5uARdVAUrdX1Z1qP7uYvbGPfz8YjueGvv21tGHV1QIy91dKlq2Ktrxh89MDSxRb+VuPqDm59be/Wx47FCUAVF6K48jOABxRrNAEY6GjIO1PRAmUSShdk4IHEG8nNmu0BhgUVGsW+RIRYSn0P6hraCkexyVCxRq6yFW5SuEaYJ0HOfLECWIgQzS34clmHGNYnqZsjWHZMR+BZM1gkuhfIVDeRmbBjv7uuxQRErjS7giApGw7ts5HL23dHr+QmRlPf1iZfXD78ZWttJLLypjs+7iSebRcn1kkq3Xx6/dOrz0dmhtJ/doqXj+fHh1N3lvuZIYcYn93J3H/XCM7vfHv7qRn5uPbOzl7t4vzc97N/ZT95+0ommX3EreesD7g5iuTV67XZtbCGzspu89qs9MMcVG9sHTfihlE2D227sDtx8EgYlrt4pT81VXeHAK+WETC0LlQzogKiwDtQ5xl2PRttY4xIMQgMpg+5SkHACOW9VjJhiWWVZp7BJszmJhu3mCA5YM6UDtlOYMC0Sd+jHtCWpsGKrsQFwGYFCrdUoAUQcC4UaBdGk24kc7J4zbrYJxqLMPOVmaYYHBiRMMmBjsdIo0IkPIFJm99dCBoI2PPk89eg4bajuajK5skqJQHF+Iv1yleH73Ox+P3H0Cquralc9iSy8YcSCGw5FXm1iHP5k5H3226m00tj/9NHtvCR8MNi5/J/VslWq1u/FU4PUWW+82p6YSL9ddhdLq976XeviMazaK59+Kv1z3nBw3k+ng5r77qFCfnI4+Xw0cn25+8nHmyQpXyK9d/s6gjTWOmRCrOwbS3oGjUdOowe1TAk2qYg3q5cmIR3ccpHHCBBgdcID2LuryyHIbrp0wSFAS+yh/ikc4w0aQ6hEdwRQHh1v7KBww9I7VPCAxr63JUCvPuGnLYpz6IYEhFkRYjV0ccOumAndOMdozlA2neUwHcBP3YY0D1M3AQayQuf2w9/acouOJh8+soV6mL2VvPmCnp6sTk2PXb5cXzw7c/tzdpfr5BcnCEg+eQryqslz21qPuSC5/bnHsmzvFc4udcCp9/0l3ckTEXKmlFSrXr2Smsnce8fF44dy58Rv3K+MT9Wg2+eCpmImbKJ54tkKFaydz5zN3HqnBwOGlSyPX73bSmXpqPPX4ZT8cyU/MR5699lSr1XcXc/eWcJ5/ffk7sWdrbKPRSY0E1vfYUv1w/mJsZSN8fLzz6XeSd5dcrVbx7XfjLze8pyf19Khva9+3d9KYmIqtbAa2d7c+/ji99NJbLBTPX4g+Xw/t7rTimeDOUWBtpzY5FX61FV3fqr93LvHkZeDoKH/uXHhtN7W22klkPUfHyScva+OT0dXtxMrq0ftX/Ot7qXtPWqGEC5Iytx8IrNvkmMlrtzsjY0y1lri3VB8fw1uDzP1l3hdT/Uz2+n2JYKVwcOLarV4iifFC9s7jfNhtWULq7lKf8oqjibHrdw2U7KST41dv8L4AqJu5O4/FcBgC2sn7T4aEqz81kr3zWMUZDcQmv7rZCqchx87cfayxjO0zMg+eqQh7cv78j0c4cSBV6/3szwEQ6PvdfwGJwhsxFnmwH/oHf7/5l35VmZjo/OIvBX7j/yD2997MUBKE0K//r/X/9r9XxydATfsP0iIEQcCy3Ddv+P71v4J07Q3MQdsGIKj3g1/ofv8XANt2370d+O3fgDTtzejKNIUrH7R+5S9ZLhfg2H/8s/wBFmGy//GHRshXPH+OzyZUr/fo8mUl4BZDwfrcTD+X6qbTYjZROTMv+gOlxXNSMqq43afnz0uxYDeeaM5MdccyvURqMJKuzUwqHm9jdpofS8sez9H7V3Q/102mmnPTjdERJeDt59K1+Tkp4K+cmW+OjRoeNv/O21LUP/T5mrPTzfExIRYe5JLVM7OKz1dcXOylk0rQ354ar4+PD1IxORoqXTzX59yCz1N8523V727MzcjpcCs90hofq87OiCG/jCLHH72vB9ztbK6weFbzupozU71cqh+O8B535Z1LfDyqBb3H775teNhuLldZPKPhWD2T6U2OdkdyfCrZnJ1oZnOaz334/hWDxnseX+3SeTnor09PS9lobXRikEzUz852Y3HLwx1deU/3cu10pjE33Y/FukF/Z3q8Pj1juJijjz6wfXQnHD+58p7pZvrJZGN6op3L8slkd3q8MjVputiDD95XQ16BYY4vX1aD3k4m25yZ6OSyg1iMn8zUxyYkr6d4YVFKhCS//+jKe5rf1U5l2lPj7Wx64PO2Z8ars3MmSx+/f0WJ+gSf//j9y7abYFiN8RlmhmEwhYwBSASjpSY2QiB+lGY0LqQjUQx32S6Pamc5UusSIQuKkx5kQI/AoAclOYMLGXaUoKEBQwzN6RBJaFzEsKOkBx1SKQBzAw5hs4wAjXtcpES7DTvLcLiCxwAnRnJqkwqbcJQkPI7Lo6ApHGYBl1u1s6zpYiuzM72RdD+V6o1kK/OzfCAgu8mTy1eMkLubzhbOn1N97tb0JJ9LdaLxftDXPjffyuVUr+vog/cNL9tLZ8vnz6gMVctk+5Mj7ezIIBlrzU02c6O613X04fsOi9aiycpbF9SgrzE52RnLtLJZIRlvLkzVUlkdhw4++sQIuHrpTOncghLwtcbHKjOzdpgKOG172oW5QQ6TkAkKZRwa6JERB0zQLKMSUdBIsB6gD09QiBthxCo0xVKsQ3psKmJDYYylVSwMGCm3q5tHpjjUC3O4jI3jiAtiOZUJW3AQJa0eEQWdjMsD8cgIDvgxP9QjxjDIhRDwkPTpUJp24wIdBZSk2wsLRA62XIQUCbbmputjo2IsPMgmy2cWRIauj4/VZ2YML1e8dFGKhTrxRHt6oj41OfR7h5FA7cKiHPCXF840J8cMn6tw7qyYiLRDkW4qXj6/KIYCw3SidP6M4XFVFhY6o2mJpgvzC0oq0s6N8JlkY2G6F08M4pHSW+d1Gq+OT3YnR8RgoHT2jJiKNrMjw1S8dO4sg8kMq2M5nCR11q+jERR0w6zdwnMkGMB8CG/PeDDK5BgNyeEYrHJ4n4rCVozlGAlN4WAI94Fdc9aD4Sbh9JAJDmNszqtiUQgKYm5aIhKwHua87UPwfIhiTJrR0CxO0iblNYkICHlRTm8gY7QZYQJAB5pkcMZiWA1PI5qP4xOJwXi6ND+vctzx5ffUSECgiKPLl7Wwr51Kt6bGO6PZQTzemRxpjY0qHndl8ZyQjkoe39GH7+s+tpPONGenmrms5GLaU2PV+XnDxZ28famXihte7uS9d+WQdxAINman/6+9wrHW9Ljq8R6/9y4fj6kkdnLurJKJd1OZ1uRodWpK9XsaM5ONsVHTzZbPL+pRTz0z1pkcq87Nyh5u4OYKl982vVxldrY+NaH7XJWzZ4aJaD8c7vu9jUvnOrGkEI+ULp4zXExtdq49OaZgaGVqcjCS6YyM9NPJxtxUJ5kSY6HC2xdNBGpksu25KSEUrJ5ZUBP+ytjMMBWrnZ3rhWMyjpx+/L7u5urTM+2JESEUrM/M9HLJdiZneNjDjz903HgzNVJ667zuYTvZXHtipJXLDZOJ1txkdWzcdDH7H33ocGQvHi+fW5DCgfboWGdypDMyIsQivZlcLTum+jyFty4oUT8fjRYuXZQD3nYu15kc7SUTw1i0MzVSnZoxWfroo4/0gLsfiRXevqh7uX4qVZ+abI/mlJC/tjhfHx21Xcz++1csnv8jbBH+4buTlOkZmyTJ3R1Q194oGI02m2ijrmWyytSMEY2Shwdwv/9miS5RwPOn6viE4Q+ClvkfHmABAPf4of+f/TYsCs4fvh/s38qP//Gf7/+Z/8gmCM/N6/7f+U1YEh0YeTO6+vCj9i/9RcvrA34k2tUfWDSawVOpxO4ObFo2gcY2trChMEjHT6cX+rG4r1GJrW5RumiiqOfwlFA0OeA9PnO+F415eq3U05e4JEqRYHx9i5KGfDRWnJ7nI1F3u5HY3Ka63V4uM/LkGTkcNsfG6tmRQSzC9LrhnT1KGArBYOr1a7rTHqbixYm5VibDdjqpjQ160LcpwntSoHv9VjaXWX3tKxQak5OJzQ1vuSh7Xd1QoJOLgIYTPT4OHh8rMb/3qOjN59vj44qj1ReniO4wUjj1FMqSP+BqNGJ7u91ghPfQrZkRQDaSR0e+01PZ43W3G76jvIqTrVSklwoZAJra2vGXykIsxJXrgeNj2cWJHqo+mYMcJ5wvBnf3lWjAXar6jk/FWBiR9fSr1zaB2yicfP5Kp0gsQmeDiEZiWK0b3t3TCNKtDzzbxwAIdrLp0+kzqscVPjqMbm7rLIVpRnh7xyIIlAVGUixIOppoZp6+tAgMBMHoxjaBWPVY+vTseY2hg7VSbHUTBgDVxaaXn1soDMZdaT8M0ijQEaOb2zYIASiYWN+Cda2VysqnFmRDBKwPO7hhojTAAxkccoYoBIsVUlMh0mVJVdjUIBIeOmEIIXRYMsUmbsoQ7TOGRUxTESwIYkAXDEBIV5EFVpNAxAWrFceWQRYdWIwJJwhrYGpNxNQQGlXVDqjKGEyYSEBHaA1xALGMqipK0rrSQRQZJWgzcrIfPM4PEjH/wZH/5LSdHYngg2TMy0O256TsPTqVvF6m14tvbvYj4YjPSUTZHgCyh8Xg6anq9bp6bf/BscYx7iidDsB9gvYenAYOj4VokKu1goeHKsf4WT0c9xoEjOVr0c1tMRF1lSqhg0Mx5GcoIxv3iohFNnqRjR2do3FZiq2u8/GYapBCA8URmzCEXpMDLIOgJTsAQKRt9WyhReKwBpsO3yZgCMC1FjBOQ0of06BWnQNhAIUsoYGhoAOrLXCMgjUBUmGhRSGGgbK2UMBsCCIoEQiCqC3bEix0SBgAHAKUS4BjAqRLcVgNcCMWD6pNBLZ0FHaGLdyxQCeAzzy8T/Z6nWwu9fIl0+9pIVc0RLk8yADG4ytrXKPRzybTr9bpRrM9MTpODD0BWlNkz2GRbXcG4XBsfd3bbndjkTGX6o97myoY39tjmk3dxQXyp3S1qQY8yRAaCKJNmEk9X3F1ukLQ5z08cTebRoCLBFCPHxUwIri64ymVe2OZ5MYWW640M1mQNwddEsdMUwXFFk6wDgx1gSxFKLI0oKQeimIWpendNg3ZKsqpUBBEDNVoQfyAYVlNHYBCjyAxA7K7QJZAdMUQSLGBYJxjDW2hR/qstmkMnRkXJsqQDPENkoUkwHI6TRZjQQQe2BmCEPuqSIkdDEQB0tb4BgligMfoxl+vY4htmUBw7wCEYICDR5MsTFiCDmefPAcgwKTJ+PoWAlj9cDR/ZlFyefz1UnxjB5UkIR4ZWXrmOICeDo1xBsqi2lANb+4gtiX6fLnlZVSW+rlMcXK+F4l6q9XkxiZmqgAC+/cOMVXjY8E5v0244Z6KZJ6/hHVDdbsTW5uwpomhcGJt1V1vyIlgcH2PbTSb42MSarenR9h2L3p8xDRaw3AkenDgK5Wa8eTQRbSmR+hOL3xS4Go1zcX5i0VXpS5RTDMXG8T8lgGOrK25Gg0xFvKclLyViswyvYS/E48gIJjY2vHki3I26t8+dJcrQiLax9Hm3AjRHYRLRfdpUQoHAqdF//FJfyTjLtVC+/sKw/iEjnuvYOFoY2ysMDXnEGh0azt8cCgFvEx/GNzd1V3sMBo+mV+0KCJ2fBBZ25SDPqrbD+8fIgxajo0Uzi7aCBIunkY2d22SAB0r/mrNprD66HhhdgGGHU+5FtvcMnAcdoz42hYIAo3xicLUrMJy4f392O6OxZCgCcTW102C6MEY9+jhjwmwAAAAbVsdn7A8Hnpj/Q0mVUAQAEG0UUNbLS2bU8fHjWiM3NmCBeGNKA3p9dBWUxsbs9yeP9mS93/vNQ0gSK++8v/Wb8A8/6Z01fvBL/R/+mcslnXfvOH/p78FD4d/FLr6879k+vzAj8cZ/HdqGmCSH07cupd4tY4OpYmvr2cfL+sQqqC0glGIoIzfvBN/toJ3+NF7j1PPXgCKocKEglK2Bcx8e3P0wRKsWpP3HqWXXzomqMG4glKAbk1cvz1++54OolPf3hq/fd8EMA3CNYhAZGPkwZPs46egbIzdvDtx655tAhLGqDDpqFb2wVLm0VO8O0gvPcs9WLJMIPv05fyX12zdia+sT399g2h2QEFAMMgSpPDrrdmr34KykXr+avaLrx3ZgDsdxEVDlXpw93Dq2k2y2Q1t7c9fvQbzMtrvw4hjSFpka2/66jdMo+0/OJm5doMp1EzbxjVRs6HE6tbCF1fRgRzaPZz96lumWAVkGUUcR5IDOwdzP7yKDcTQ2tbsl1/i3QHVHc798Kv49gHGCwtXv/bvn7g04YJYcuui67Q4c/Ubz1Ee7/KzX3wVXd9SUUrGaAWj2XJt5odfu0+LrnJ95uo3ocNTXJEvWd30oAoPlIUffh3ePaQb7emvvvHtHZg2pEK4glLoUJ67+k3q1RooawtfXots7FKqeE6phpQuVW1MfX09vLUHy/rsD79OP32uQqS05wxOIXCg8yeonAfBvgJiKNzvGRA23LaGR7BpgPKBPThFoM4QwFDAMGzF5A9QdVvXQUzftYZ7kGEAgCzbKArUe0Ielo4AXYV6h7CybWoWjAx4C0MM0ZRPQf4YgXh9WICFI9gYmA4M44KgO7C6q/d3EUsHlYLTO0QB2Q5v7c5/eQ0RlcTK6twPrwKyER/Wzzs83W/7D0+nvvqWrrf9+yfzX3yF9ISkUL9g1BFDC+0dzXx5ja00vKXa7NVvXIVKyFEvqWUSsCOvN+d/+BXOC4Hdg9mr33DFqlcVLphNt9T3HRfPXP2a6A5C23uzP7xKdPgAXz8P8O5+jS7VZ7646i5U3MXqwldfM40W2De7+4Rd1W0JaG6h2qlhGibiWIBl6i2jtwVJbRjoaN19wqnqpqgDDAX2eU1F+E1AqsK2AAx2ILUOQs2u4fNAvb6tgIMt2CyYmokMNhyxgjiyCkKAZVpAW2ntEXbR0Aykt4WoB7rhgIQsOwis983+PixXQYjXu/uoVrAMEJ24eXfqxl3HALOPlnP3HxO9/qjenVSasGHm7j2avHHbANDJOw8mv70FWsCY0pxzBkS/k3j6cuTuQ0jSc4+WZ67fNixgTigtGm1AN9JPV8buPKC6g8SL16O378NDadzonpUKugONPliavnYdUfTEq/Wxm3eJRjut9eeEEmoYqSfPZ776xrSg7KPlma+vo6JqtKz+Dmr3bLmLDDYcRUSg4RBCIFBS7ZrR2qXttmZ2re4WbDRMw3ZAx7QtSy3p3U1YEwCorrf3SKCumILiYAgwGKhdSNgADBHWa1ZrEzF5G2h1zXAAbLTVAdTfBPW6ofJQfwNQ+jAoiABNOrxgd4zWHu00DUMEhxuA0QLIfn/qq+uB7T263pr65lZkfQsShfN6I9fNO4o199U38dcbVF+cvn47urVnOqgGYgpKIaI6c/Va7vETywRmv/om+fw1buhn1FraFJj+YOL67eTKmqPZ01e/GXnwRAdRCadVlER5cfzmnejqFtHpj9+8m1p+6ejmWaU6I5QNxZr+9lZ6+QUmquP3HiVfrgGqkXnwZOr6LROAR+4/mv76umUA8JCHSQSuNuIvV8dvP0AENfP42ew3Nw0dRNtNCAMBXkq83hi/eYdq9WKrG1O37qL1jgPYhDxUbTj39OXcV98gghpb3Zi4foeo1EHLQC3V0szE05dzX33jGE7yyfOZH16DhjLY70MsCTdboc29qa+/xXgpubK6cPUaqFn+g+PZL69xhQpda858cdV3cKJgtIwxMk4F9g5nvviSbHV9p8W5L7/yF6oahCkIqSKku1BZuHqNqzZ9R/mZf3OVKVVNAFFBTEVIptGe//Ja8OCEanQXvvzaf5TXIUyFCQ0h2VJ95suv/Ycn+ECa++JqZGNbxmkZY2SMchcq0z+85s6X2Xpn5ouvo7uH1o97ws9xQMMcfvBR8y//FQfH34ByQBAAQHrlhf+f/hZar0tnzjZ/9b98M0Hk3w5Cv37l+91/jnQ7zp9oyfu/36JRCMIL+djf+utIq/VGxwY1tf+zP9/7/g+MQJB7cC/4j/4hzPff4J9BxwEtS7j8fvsv/PKPmK7+4C1C5T/5Rc+wp5MURIB4o6dRjOMhh4QbtgxWHgBDjTEExeuxh7qJ4xAJC5TbBkGXwiNdEQABw8vBvIxZmuj3SzgNAACn8HibNzDC9lJIRwQAh4/FHMfBHBPVNbQ3tBFE9Afc1YoFI46HHFAeyLYh06J6XUJXLDcN85KGkf1I1N1s4pLYTaXofp/i+1rAzRMsANoAhLJ9HlVUggFUHYFkpZdI2pYBwABiWN5hz1JsIRzCZJkQRdXFyQRuwSDowLQs053OMBpmhb6jWIqLU3HURFFCNwhJxYWhEeA0B2NbzUEkAgOagDOkadCiCEg6yTiyhSOCZARcIun2FQuK3wdBFtKTTIawWRJ3LNs2FRtnWu1BIBRR6tIQVD1uh8ZFnMNsjRAErDcEWFymOabeFINBAtYxGFJBxNEMtCfaNG7iGNIVOERu+uIyxSG67lL7SFvUWAYgYaQrOgRiuGkccEBLFwGKqbcknw9HTKQ10Bla87g1AcQtFWJgRYYBFGFg0QQtE0BICBjKFOyYFGWIGonpCswABgwAjgPYqCWBDgyzlCIqFGDbjheDNQG1dNvGDItwAMDhMJDXARAKWvUe7QJB20BYQHZAw8RpxxF0DSEBBkEtAbQdDHQklQYNk2RN0aRBzcQ4gOa7oGqbXhpQDGwo9FIpEtAYXeUxiu11HckchsOoppKDoeGhQRLDLUO3bdXEuGZjEIuxytAZaprPheEgDEO65ThDHRMEy88qAM62W2IoGDI7IuXXHAAWFVjUTA9tGw7e560ApzCsWxZ4iqH4Acgrus8NORYykPlw2IIxsK8CLMpYgiDgEAVTiKhBMOLYtoWpCkqjikGS5tAGGIw0hiYOOBboVuQGGCQQHUEdRUYIUDMpGAR1G8Rw3VIVFCIgHNUEmcQhHcUNHQJR3dBR1hJskEZtEobaKohDFC7rDmDDiAHS4NCgQMVgSHNgOxRqckSgXAIts59IsM0mocpyyAfDEOTYA4zlKjUAhCwfBfMqZOj9RJyzZFJXdduCeVWHUCEY4lpN0AFMD4lgKGqZHZSlu31EkbWAl+52dBCzPRQOWg6K6IYDDVRMUfSgy1QdXBCUoJcBdRNCTABEO4Jj2WaAgwYaqsidZIoVeEGnMMaBTEvRMYrULchyAIsyrQHqBfsq4MEZbSioFEI5MKSZKOSSBMnhVANlKUVGWGdogByCa7xBoJAFAiaqGQhNarqNGhLgh3p9zo1asolQpKYLKslgCuboHcvL4KqN2DZkk5om4H6gpwIegjGGokLCNIiBGt7mGUjm3SGsyytuD4qaOARqEGKbNtoVHAw2WQbtCTDqDDx+DSNB23bLPaQ9NCjKdhFoWzBxTA75XYYIm4YAUVS9pXGc5Hb7CgWDoiw3KRBuyLZg3WA6bQS2bYpAeoLMuiSvx68PTAg2dRvhZRsCxWCIazYsBOGDYXe9BjkATZuigiK61k0kHVMFQJAwDFevawDoMBRiOm3YslUXI2OYA4OcItoGjAtDJeBj+K5pIxpDyRTuQBCh6oSsYqKoB12GDpEDXgr4HNhWEJK2dIIfArpNsbagE5goGiF3H2NAwMIs28X3Lc02/S5EUiDV0AMuw4K4VnMYCoXEuiChhpczKVLCacqU0YGE9weWn1FQmq3XhUgUBXWe9uGGwg558P/k7j2DJMnT8770pqoys7K866rqququ9mZ6vNnd29u7vcP5I0SAEEADQJBCQgSlkEIhOkAUJYoSDwJJAIQhcAd7u3vrx+zM7MyO99M9PT3tbXV5X1mV3qc+UCETIZI3Ebg7Bv7fMzIi88P/ifd53t8jaDZDmgCMdweUSy/70wZOYppKSz2or+peCoQchJMcDyYwPg0lWKmnAhhdrwvhMOmocEc0aLdOuxXE5UAQIQqeVgukCd7DMpWKFArLe4eR//mf/GWBRv/9x/PkUeQ3/veXDfMAAMB/4c3WL/2K7Xa7l5di//SfgKr6EnMsxwEgiPvq17s/9/MOQf6oRyr/EViEIAjzg/j/9Gtoo/FS6gpS1f5Xv9H72Z8zQiHP40eh3/0t5KXUFQCAjiOeOt36z//LvzRn8D9sEWaA4dzChYuQrItB/8Jb7+Ga0cxkXvut3wvv7NbHJybOX/Y3alw8OfbJNaLb5xOJs7/9e/5ipTIzffSt932Fw+b4+NjFT73lUncofez77/iLFS6dnn/nfarRKs/Ozr/zfuDgsDQ+c/r7byeWl1vp7NinN+hqvToyduytt/3V2uHCkdO//930w8eFY6emLl+NbG1xqeTI9dveYq04PTfx6bXcnXs7p85mHj2ZuHqtOT0VfL599MPzzZHxxPKLkdt3u+O52KNnozdv75w8O/Lo6fE/+351aja2u5u7dqsxMhbe2Mx/drM2M5W7/eD42+/tn341tvJi9vyFyvhM6OBg9JPr3WwmtbZx5L3zrYl85MX65OVrtdkZeruwcP5CbXwqtLd/7K0P+okh/35h/NpNLpdi98rjl642xsdMzTnzR9+VWb+Foke+/67q9TqqNfNH35f8AVAxj33/bS6RJi15+q2PFTdlkeQrv/W7gANYODn3wXmFoUwbOf7WO31fyELho//i35gwIXmZhb/4gQ1BGuGaef9jyIWoKPW5f/lbBoIrPvbod//UhlA+Ejn2/XdB21ZIeuYPv28BsEz7Tvz5X2huRvaxJ77356BpVUcn9eey0zEhL6Rs2HbfgAOYcN802iCUJPVHgtNx4Bgir5lWU0eCqLAMmjC5wewAACAASURBVA3HGmL1RQlqSM4wpT8R9IbjJN3GomKWACyECFs22DHtiMtYEoGa4krBwiIg74NOhnHWBLXqYH5A39aMpuPE3doT3SwYYJYylwStaONJXNk1jKIBx4nM0yfZG7drU1PJ5dWJq9d2jp+KLq7M/uk75YmZ0GFh7JNr7eGcb/9w6srV+vh45MnK+Fsf1ubmfYXK/HsfVMam2Ep54tKnXDLp3T6YeOdCc2TUv3sweeFiY2aGPqgsvPd+c3TCXzrM/vkFLhKjm62Ji1faw8O+cn3644vN/AhR7h39N3/cSuRcPDfz4SU+EiG73MyH59vpjAQx2gMRcMEoCiiPTRCwQA8q3HUcCwZRR102YBw0IVh5rME4ABMgd91EHZigLGHRdgwTdMHSsokCpoPjwm0LAGDYA4oPNEczwQBhPhQBCwRcKP/ARkwb8ODyYw1wbDPIGLc4SNHtiEe+rWoKCTKIviihlmW6EGnRgC1LTvhOvPX96Ob2/uzRmaufRtdWW7nRzKXPAitblZHx+Y8+iuwXDhcWpj44H19+cXD89PhHl+N3HnQz2eF7j4LbB9XxyenzFyLrG4cnjk++9XHy+t3dE6dHbt1KLS43R0az9+5H1rZbIyP5859Gbz8pHjs2duV65uHj8tzc0OKLzP2HjZGx9I27sXvPWrmRkRu3Ms/XCkcXsjfvZm/f2Tt5Fq5LgxUHYyGnb+srChjCjKojPbbgIdJomdozHQujzkAXXziIF3J6lvzEdvscYwBKLyw0BBs8pDxRID+CaIB0z4bckK056ooJM6DVM9UXJhJBVAkXbxqQFwMMW1rUcY8Dqba4bEI0oAuo9MDGvKBpoOojFfY4jgNKj1SHhGmrP/3+BRw0RSZw5O33dBhTAoGF3/x9UDJ7w6ljf/4DVJL5YHjqvY8g05S9gdd+518jpsNHIye+9+eIorayI0e+/wNckLuh+Nz33sJ6fS4QWXjnPce0m8nMuT/8Q1KQ6/nRV37rd9lSpZ0Znf/gI7LfF4OBmffOI6pZHRk7/Z3fdpUb9emZ+Xc+oJqtRiY3+/EFstevjY7Pf/RxdG2jNTuevXon9eTp3vHTU1euTXx6ozk2nnv4KLyyURmfHL92I7H8vHxsYfb9C7OXrlZOHI89Wsrdu9/Ijw8/fhJdXGmP5+c+uZa/frt8fCF74+7InXuV+fnw4srYvXuN/Hju/sP89budXG74zoPhJ0ut6Xzs6droZzer0zOJ55tH33mvNToW39zMPHjSymaST5ez9x5WpqeYg8qxt99pjYyzvebYh1cHkQjV6Zz6oz8WQiEPN5j58EInk0Z70om33m4l0qTAv/rbf6DRNKibc+9+yEciGMfPfXRBHgo5EvCF3/gN3h8EEGT+z96WWD8AAPPvfsQHg2R3cO5f/x4fDtkOevp7f8xHEzYCH/2zt1SKcRzwld//Q8lNI6az8PYPxHBQA/Ez3/uTgS/AoTh9++aPziL8fx89GtMyGc/jR5Bh/LAXNwgCAIAfHMCqIk/PGJGoks/TN2+8BLgBBAHbJg72bZpRs7mfFOQd+bGpK9AyE7/+D9BazXkZ5BWkacK5V3s/8zeMUMi1/Cz8O/8SeanIGwAAjiMtHG3+6n9tUdSPTcbaMOyrVGLPX+DcwGJdkbV1pto4fOVU+t79fiJ+cOZk5v4DPRWSotHwizWPzyel45HNrVCltvnlN0bu3OEDgcIrZ4YfPpRD/n4sEVzf9NJ0LzsUXVm1IfjFt7+eu3FL8fmYN78y9HTRcOPxzEZ4fQN2nMPpudjqOqprz7/9tZHHTxQUo6q19MNHAAoJyVhoYxOVtadf/+bwk8Xw5gbV5XL37wd2dmJr665iI/ZiLbS5HdzZzd29V3rzTGpxKbK+RVXrqcdPwzt7Qzu70fW1xPLz0tFjgd39sZu3N7/6pdTycnx5hanVsovPghvb0dXVQKOceLbcmJ30VauZO3fXvvql9JOnicWlna+8EV9+HlzfTD175lGE+LPl2swk02jmbtyqvrowtLSUWHp2+LlTLoCMra7LgUDp1LHc3Xt8IuIOhjKLi73pPKtZoY2toaUlNOGOLq/oJLlNuYbv3ROjYdQ0kw8f8YkwXWmF1jeGQ+HO1Gh8ddUmCTEezt28pVJuG4CGniziAcIRrMDWbpwNAAwxtLyCAWAvkxy5ecsiMBPDks9XrAANykZofUt10xaFx5eeIZq69OY3BocIScN0ja82vAhq0z65wzFkU/bOKOWSCySR4b7AldwYDmEVhet6Sdug2mqnQ3kkOyZKzSKhYER44IglGIIRuizI3YClOnTS4NoUKkiJqe6gyMoI5e/ofJWwbTBU7fVbuCKj/jGz3XYjHEzLSreICIDH3xnIFbeoEK66GNrdzd97sP3VLw09eTq0uMRWatkni6HtncTqeriyn1h+Xj5y1N1q5m7cXPvqlxLLz4eWn2+XyoknK8HN7cSzZ4wySCwutcZHycEgd/fu9hdfG15+nlx6vv/laujZWmhjO7m87DW45NJSN5+FTXPk5q3C515JLC7Fl54VXzlJH1RD2zvp5WeWG00+ftIbydgwnLt7Z/uVcw0+0O7RsbIEq1KtFY7YPdwHdEskS5ooIHcrHpRScEGqcF7mUCEBtdPzE8gAotRO0edBTAbXu1UPgVu4JnQ5NlBRPYTWbLEBnXcltfqhh4FtllC5FoViAgRqnR7lh1QspFRb3oDc92eUaskDYiTbNrgagVqCC1YqPQY3LXzWzt26bSMo/e2/PvzwEeCYsfX16OaWi+cPjyzE1japTufZT38j/+CRo2qeenP40SNMkwcbm+GNTbLd2zl5Orq2EWo0Vn72r+UePEBFyVcqZ589x9qd2PpGZGubKjfqJxdiL1Z9ewePf+kXMnfvuTnOWy4PPV3yFg/jYyvhvT3/5n7l9NH42kZ4e+fJX//WyMOHnnKFrjbEFtppUURNsG24UaSSeVWvWK2Ol+nyZgVucszwAQcjQKvhSQQEG0JaZSKYbpoNsNUM+FptvQe2+ky6NLAxs9n0DvkHlovoFHAirVl1o9lgvZ0OLIBtgQ0XRdRrtRsM7e2YpNUus7GUag7MVpvy97qOJDQ5NlEW7YDVbDBs2M6SrfTjxx3CNG00tL6hI5jj8wytrnl0oz4/NXL7NqIdFyPh1OISCjsmgAS2dkwYV8JsbPk52e0dnDs9+tmNwsmTneFsemmprYyobia0sYVJSi07OrS8wtQau6+dGXn0pDgx6RvdTz55OhgZBjxkeHOL7HB7R44lnz/X6/XdN9/I3b3bzGbDYxPJ5ZVeNHI4Mx9d22DL5cI3X8/evUsIAl2ppp8uunu9oc2t8PoGXaztv/JabG09trW1/LM/nX34yN1ueyvVzOISXSqF19cDu3uBte3qyYXw+kZodW3p538md+ceXa/5KpXh5ZXgzlZyei18WAw9fV48dSy6sRl/trL/zdcz9x/49vYCxcPE4mJ4Z3dobSNwsJu697B4fCH2Yi31+Onat7469Px5cHM7sbhI41p8cYlLxhSWzdy7Xzp9gi2Wh54uVk/MkfVeaH1z+NmSEvLGlp9z6dQgEc3duV2bnSZ6XHzpmTadJLSGf2cv/mJ1MJpOLS4psUgzkx65fac1mYd5KbK22ZqfovYqwc3toaeL7enRoSdPxaDfAYHhO3eK03NoqRRfeSGmo7i/H9rYyoQihVdf+3E6V9KRo43/9r+P/B//HNS0l9BYjsNc+Ngm8M7P/U1lcrr2D34t/j/+moOhP/x7QdMM/Ml3Tb9PPHnmr7RFCEKJX/t75Nrqyz1kGsrkdPNX/646nCH2dmPf+WdoufyyrHY1P1799X9sE66/hJ3BH9oiFM+8Iv7S3xne2+yHI6afij5f76XSBkMwpYZtW3w64aq2GY1v5rLMdlHyetWIL7yyxkcTut/DlBoACHDJmLvSJpVBfXwytLmteFklzIY3tgWG1SI+d6kOA2B1NB86OHCJ/drkZHBjW/NQ7Uw2vfJM8dBKzO+udlBJLM3OhQoFj8B1R4Z9e4eyi67nRkKH+55utzh3hO50ors71SPTEK/6K+Xi7BxbrWCSDEY8hmS7G63iwlF3rxfb3tw/cTJYLxHtQWVqmmo1XFxPTMVhXmbr9YOFY6QoJp8/2z1zNtAoE41ee3yU4npoXxATYUA2/KVSe2pUN+H02ou9Eyd9nYar3m6NZN1cD+dlIERKEMXu7XWm8jzuHb1/uzo3jzk6XWnKAVYjXd6DQzkW5Gn/8OJiYeFoWGniu83GSA6BLarYUEN+lfL49g6VEMsFIulHj8qzczhsBVe32vk8iDhUuaF5SMnv9+2XUAauxTPJJ0vliUmEAEMrG91M1iFgutIyKJIPBAJbu7qfacdT2cePqpPTEOaE1rZ70ZgS9AMdFVcVPclCVckmUMgHI4W+SrkxFgJaGgBCTggzOQsTZSPNghUJwBE55vPsNywMRgKo1dJsC9SGg2SDQ0XZSDJOQwMgQEn4XQctAEdYj9SX3YCoS6MRvDGABM1JefBK30BxJeEjyz3AMuEIYfZMVNTsNGUIAMwpRpb1FYuoqAmpKCwqbLV6cOQoIQjJ58/2zp4LNCtkrVOZmfX0OU+7JQxFQFn3VaqdiZzhYOnnz3aOn/T1mp5SrTk14eZ6BCeIiZBtAv69/e7UqGaj6efLBydPxloFp6V0cmlS4EmOF+NhC0SCWzvd8ayMuocfPzo4eYoROapU74xmMVV211qNTM4gcM9WQ80GSUcBD0U9RiOIbbQM2A0CFA7VZYiCVB+NbXfUIZZEdLsgOnGacfr8gARwwGJJsCqjbkANMPhOTxtiMNQAD3gtSGMux2rrMAHbXtyuyRhpyyEfudXShliNdtNbVcNLIjRkNjTTRdghD3HYRXFLDvuw3Z4RcktBf3x7DTOt8vhkoFSkOs36zJR3r+jAUGM0n1xb1QlCjgddtS4+6BePLPhLJbbTbI/nvIcVHcYaI6PRrU0HBKVkhGj1Pb3uwZGjvmqFaTSqs9OR3W0DxIV01FtpWKYpJaNYe+BtNRszE0hf9lUrlZnp8P6OaUHCcIKpNCDT4lMRhNe8xcLuybP+dk1vW0CKgmUd4E3AjxoWhDYEMOlRQNy135XHQi5RsJq6maQQWXV4m2J0GfHYNRlKEApMEnsddTzskgSnqZsxD2iads+A/YgNoUhNAGOETHiIzaY6GiA12WrqaBTHNUUSMdAL6xiOFftQgpQJj3urJY8GCEN2qooepWEUYAtlgrRrQ9n4s5V6bgT0oOHn6710xvZgdLlhkjgfDnl3C44Hb6ay6cXFVm7UccPhtW0uHDZ9lKdYN93uTmIourEBkHBzeCT15Ek7kxuEw7nFJ4NgSA8wnlLTgsDWyEhscxPEIT4WCa9ttxPJfiyWerYkM4wa9XvKLRCwq6PjiY01gyQro+PJ1ecOgiIBXB9Yrm73cOEoW6uy9Xpldia6v2PZcC2fDx8cgIYmpWJok2O6ndb0GMRr/uJheX4usr9tG+Agm2RqDVA1hFQE7km+ark5PwUO1FCpWJqfCxULgGL0hxNUrQEbNhgmJcvl299rzUwCohY6LJQWjgRLh5Bi9FNxst3BeXGQSeoAklla2j91KtotQlW+NZIlFNHV5qRYyETwwPZufyQlkHT24cODEyc9Ku/dK3XyI4ipuWstOeRTCVdo98BOMHVfMvvw4cHCUdKS2e1CL5eFHdNT78ghVnZR0Rer/XymTwdGH94vHD1GmDK7c8gNJQAUoqrNfiyuudyJlWVhONHzRXL375bmF5TdYvh/+TFZhP/3oe7dCf+r3wRNAwBfxoAyjNZ/8V/1v/I10DSpu7ej//yf2QTxMtMO23a5a//o15Wxib+aIXcHw0K//zvk+tpLToFsbTjb+sVfUTNZrF4L/97vYKXiy6krENTSw7W//48s8kejrv59IXcIVC32sOGpdk0TDuyWqbU9CfX4l7e8pZbq4L6tQ0+xZvMac1DzlNqmDgYLdWarIOC0b2WH3TxUHZzdPqTqnKPa9EHdvV81NIDdKnq3igLBBDYOfesHkkMw20W61LJVgKp0vYWaZmPe3ap380AgGHbzgN2tqBbK7JWpQt1SLOqwweyWZJD0lDvRtX0ZdJHlln/70FJstC2w20VbsvHGILiyrdqop1ALr+4qAOGq93xbRVux0Y7o3yyYkkk0+cj6gQqSZLEZfLErWxhx2GR3yo5goJwc2CzYAxWt98LPNlQHczW44GbBMiCiPvBvHTqcgnBSYG0fFA2cU8LLW7oO4YVqeKPgaACg2qGdMlHpGDoUfroGdURH0MIbB7BoIF05uF2Em7wj6aHVbbLeMywssrqLNPt2Xwm+2IEFDR6oob0y3hFNEwqvHxB7ZdVGQ8tbeE+2JSuwtou1B05P9u8UyVrfMqDwdsldqKkQGX62gVd7tmgGtor4QAF5jd0q4cWGbkCB1T3PYV1CKbHjGvCUrGM9ju5zblVHuD4r9VwCwfSaHq5OyADZb5G8wMg6zgnefocULaLXo3ttj0AwPEdLHbcIuLiuuz9gJJPs8zTfdSk23uvTvSYpop5ek+z1aMUhJY7s84xskgOB7nfcokXyXTffYwSc6bZdvS6tWPigjXMDRrJJsjUIru5oDkqWWsHVPdUh3NWOf7fiKA7SkwIb+xavwU0+vLyp26ir1gut7po6iJVa7EYBFHRUUINbRaivoF0pvLSumQhR6US2i5YO4m0hsH0IdEW4L4VWd0BBh0Uj+mzT1By01Aqv79kGiHXE0F4ZGaiAbAZXtsDWAOSUyIsdQLEUzcVxLD/AZQPrtFmu75Jgj9JwSTLFm+5OixEUUpCwHsfKIqnYeI/z95uwCHq6NQ8vUopJDjqUpLgkjej16MGA1Eyk1/PLA5eAMv2ae9AnRQPvtylB8sga3un7hB4mAq5um+Y4j4AxQoeSebdgkFyXFkS3ZJI9ziv2cAGn/FtF/4tdEXJRO0VvoW6pgLvOsfs1w0Togzq7vi/gNLt9GNguyQ5BH1Tp/aqlWO5yy7tdUg3EW2gENg9FnKG3Dv0bBRUg6MMGfdiwNcBd6Xq3CpqJMFuHodV9Eae9+1Xf5qFugK6DGrNbcWTL3eKDu2XdQqjDeuD5lohR9FYhuFMyTVhQXP0uw0uYIJPdiosHaElyc12fZqKiQHY7zEAhFQ3vdWhFJSSd6tc9gunuc2i3yyoWqUgkx/lEHhUNV6fFDFSXrLulhluyqb7k6rS8koHzPM71fYpMyibZa3sHfWSgu3tVt2B6VAnvdryqjguqq9th+jyh6BjX8UmK21HNwPoe0eg5fcW3W3ZVe6YBhXfKnv2yhLhDy1vuYtNQncB2keyJoKizOyXisK4bUGB1n9qvSagn9GLXXWioGujfKpCNvq063oMGVWqqAOHfKDDbRRHz+Df2PZW2oQLs9iFR61mK5d0tUYWaBJChzYJ3vyqiFLu25zlsmTro3S2Tdc4AMO9Bzb+6K6Eu72YhsHGgAgRdbNL7VUt1yFrXu1PUDJg5qAU3iyJG0zulwPqBZiHu/ap3r2LLpqvFs9tFXQM8e+Xgi20Ro7zFRmivYhqQq9xld0s2r5ONnn/70DARqtYLrmzLIOHePAhuF00LJtqCb6fkcAre5gOru7oB0tVudG1fg3CsIwW3D8G2CPFq6MUO0pNA2Y4ubzmiAdd74bVdW3WwjhTaLaGc4qhOeHUXrnUd0Yg83wZkG+lIoRfb0EAG20Jw+xBr8Y4KRDYLSIOzVCC8uAr0VaQjBLdLsGRAnOLfKKCVjqUCoRc7WKtv6mDk2SbQV52eFNo4gAYKINuBrSJZ7xk/6pD7/991LJx9pfOLv+xg+EuR1h0UDf3ub1N3bzsoKpw91/7F/+zlAFcQBEti5De/83+h4f9KWYQg6MCw/+2/oK9/+nL0escxItH2L/6yMjODNhqhP/hdcm31ZdcBtFTqR9Ez+EMJLASJ7Gzlrl0LDiUOtRPJ2zcjLpc4MzL/0Qf9aGQwFJn+8EM9GQAdO3Hrvub1IrCevn59mCDa06NzH30gBAPd7NDU+Y+VWNABnOyd2zpBoiSYvX0L0o3qqfkjH30geL2lI0emLl6wWMoi8dS9O7Dj9BJDmds3CVGsnZg9dv68iGGHR46MX70CwwCK2rGHj1HZ2Dt+YubyJ77d3Rdf+OL8J5fYwoEcYshaL/n0SS8eD21sTN68pYzHxj696ts9WPvc61NXr4TWNwaJWHh3I7q01o9G2EJh7tLF2kx+9rNrwbWNzZOnZm59Fn32rB8Osd16+rN7OuPyNJtTl67W5ydmPrkUerHWzqejS6vJJ09EhiItLXv5ikYRbo6b/PCimvHH7z6NPFvtZOMBGxu+8Rkk8uXTx6c/+gCALCXgn/z4YxMHIc1K3r6tAQAaIzI3b+Fcr/DGqzPvvI3pXxtEw+OXLgKoHUWI1O3boK43J7KZ69dcjeq252vz776zJX6RSqfGP/mkrRwhi43U7dsoz1eOz2avXGZLh0sUMfveuztvvEYOuPwnlyrSSfqgNHzvDtHrFT93cvTap6H9nd1jZ7gXjoeFw067deCDUMcFqfUiTcl8LCrubrM2BmSi/GDTrRJ2COw2S34chHBWbZZIShD8w0ppza2gRDhr9Hdg1AYTYKtcD+gG4Y4CgxJJ8rI70pXWoiLm8WUsYQ8FTCsFtdpFgtddbNRoFkmor+dS3cEq2kWo4Ygo7ZOaCiWZbur5Uu763fZUbvLTK5HV9e0zZ6euX48uLQ2iYX+rFr95nw8GXYPB/Hvv1ucmJ659Gn/2vDORiTx5kVx8Kgb8bpUfvnpddeO4LM2+/2F7PJN78Dj+8HE7EwuubCcfP5I9LsbmY1fumS4UtsyZd3/A5VOxR4vJB496w1H2sJ68dVtzkaoHH7tyxSJRB8dm33mnNRQTqfla0RWzBMzH7xVjoUHfNe+sbPjDusLyvYNtJmpK7qZaKPl8igQOqfUDf7TZImCpuhUIqIpXE+rbVEof4K1WseSnZM3O6NUDb7jOudxgectLpQyvrbT2qIjYxwSuXKa9ig0Qar3g8Td4wi8X1tzQMM7gVusAD9MipbYPyiwmgK6sPP/B+zqG7509O3PhIoRDhpeK3X/sEoTu0NDw3TtMo149Obdw8YKh6ztnzkxevUKoEuSGI0+eY91+O5sZvnM7WKuVTs8vfPwhqOm7x45PXPsU5/qWh0guPnWXG2o6mnlwP7B/sPfG2SPvv4vISnc8M/zpDabdMmgyvrbKbO0LiWD2zq3wxnbhleMLF89j7c7WwnGe89T3/XFUVECkte0ZjvLWnlGrs+kgb9SURtUzhLVhzKjth+Iw76BIeZNmKQ6pyuVGNOPlrI7ZKHvSaB9E9fpeMAVxths/2KBGfTxUNyolNu9pkLJRLHnDAG95rdYONaK2HS9W3vJlGF7j7EaBHnfXIdWsVJkALBsBu75NBGA9RBxMnj/fPT2FNXvp27dcrWbp7LHMlSvBTOpeNDD73rvFE0cB0B67dLF1bIZstIfv3qGq1QL4yshn12KhYD+fXvjgvYOjxzSKGrt8mZsaxRQtfe+uv1gQAsGRa58mWG9vInvsw/cPxyck1pe/dk1Kxxihl75/L7i1000Pj165bLJsezK78MF7jZFRwcfmb94QgkEuHEk/uB8qVW4eSc9f+BiWle3TZyavXiE4zmCp5JNHVKXVzmRS9+/Ft7cOT88fvXQBHfDN2Xz+xg1PtaKwVGLthX9jT475M/fvxzZ3Cq+eXHj/Xazba+TTwzce+Pb3VTcZKh+GnjwXo/7MrZuxlTV+Pj118QJVb7RGU6l7i8GdHY12MZXS0P2nQjqavXM7/mSxenQqdfvh0J07ooukcC1x7YYDO6rfO/uDd6R4kDko5K7dHMT9VIMbun1bx3Eh7B25ehkADCE5NPvO26rf42p2ctc+IyiL1baSt28bIMhlEqMXL0Ka3M5lZj94X2M9GC/mPr1qMWjURFP37lqO3ZkeHf/kE1wRKvOzcx+8JwQDqCRnPvsMwgE/W0rfuwtaZv2V137cWuPf9hX+1NdAw/C/9RcvlVh3MCz6nf/Nohlp/kj/K1+D+xx74eMfvqzQgWG0Xov85ndqf//XLJr+qxJyB0EHgug7twJ/9ieQ9nL5f4tlO3/rF/nX38BazeCffJe69f+J4/0w0y99KFn9x//U9Hp/tOrq3wkaTTS/8jXdz+6eOdeYHjcwYv2Lbw6GIu1UtjQ/1xob6Qyl+Vx65+xZgfHtnz7TnhyVKGbzC2/yQ9F2Ml2bnanPTHLxVDeT3j53buALHJw62Zwc0z3M5utvCMlIO56qT03tz88LiXhnJLd78pRMMaWFhcPZOYskdj73+iCdqKdz9bF8cX5eiIS54fTBK2clitk/dqKRz/PhUC+X2z12vJdOKYHAxhffaEZTXCS8c+ac6mNr09P9/HA9O9bOZHZPnuyGo0IwuP7ml+RIsJMc3nr1VcXL1CcmWpNj9exoJ5PZP3WqMzys0/TKl7+kBnzd1PDeubO831cam2xOjjdyeT4WK545URqfNj3uZ1//uuJjm7Fk4fRJIR6tjU/2JrKVsdl+KHx47lQtnbNxfOvLX+ZjoU4yWz56pJ0f7SSS9dnJ/aMnLAxd/trXjZif84Y3vvAFPh7ppIfLR4/UR/P9cLS2MLt//KQNwctf/4YwFOXZwOYbb/CxSCeVqc7P1MbHB+Fod2Zs8/hZiyafv/lTQjqhkNTGm2/yQ9FWOlufna7NzPTjifr0xO6xkw6Gr335y4NUXGV8G5//vOH34JTtDtj2OAMRAJlC8CGEsCUoizlxmnDpdEgHh904ZeM+x8lTMGoSMRtMkQwooKO4E3bhbp0KmnqWdnlU3G85E14Ah9wRA0gSNCQRwxASQAwWpWnZHqU9hIz5IHDcg2K2KwGYaYp2ODKiOUkv5EdoWkVzKETDhNe2m3D3FgAAIABJREFU8ozq9pSnZ5vT481cnkultk+d7iWHtBDz4gs/JUYCvXhy87XX5IC/NTbWmhhtZnJ8Jn14bKHyb3/HV7+qhnxcdGj/1XN8LFKemWtNjTVG8nwgWDx9ojgzbxH4i69/w/JT9eGR3dOnhEikkZ+oTU9WR8fEYOjw7KnDqTnAgz37yjfkcIALx/ZeOcvHo63c6MHRY3rS63JEaIZGAzAG6eiUy/QTLNbHk7CZpElSJ4dgI8eigIbMUIgPwmwRn3SDPgxlLHIIAuIkSRrkEKzmAygoozOUy++QiAGOk0CIdLk1MuZACYLAVCwGGlkvCepEHrEjJAsOoHGX4ycIQsbDgJN20aiMJ2F9hEVB3Z1x1JBXjISbk5P7R44I8Vg/ldw694rk91enJw+OHDU9rr1XXuWyqdbQcCuX3T92TIiGB8PJg3PnBK+vODt/eGTBcLsKp073culabrydH907fkKIhLlEbPNzn9MYujwzX5mZlIOBwvHjXCbZyOT76WTh9MlmKsPHohuvv66zdGV2vjI7LQRCh8eOdcZyjZHxQTi089qrblzFKQDLY4gH8LAamHI7ftRNSEQOt4fcblBxjvpwCsBIG86TCOW4KRFLIE6cxl0GnsP1BEUCCrTgRSkHpR1o1A17YR8jg0O4HSFchE5mYCXpI+wBMsdgXgcjLWzcDTEQyVpIEgP8iBuT0BGXNsx6HAmd95CMibtsOINpIW8vluQmRzZPndMJcu3NLw/SCdHr23zzTSERaacytdnp2sw0H4rWJ8d3Tpy0affG65/nMkmNZjfeeEOIh9tD6erM9OHMrBSN1Gcm9o6dMDF8+9XXqvm87SK23vjiIBVvpdL18bHC/ILi8zZnJkvHjymEZ/PzrzcyWdXL7Lz62iCdaKZHmmP5/RMnNIYuzcwczh2xXOThiZPicLSSnWzncrsnT/X9gU4yuf3653WWKU/P7h89ZlKew/n5zuhILZntpJLFk8ebyYwQiWy8/rrB0NXpucrCHB8KHxw/weXSjZFxPhYrnDtdz4zK4dDam28qDF0aHa8emRMi0dLc3GA0VRqbG4RChVfPllM5IRTcePNNjaGq41OlhXk+GqvPztUm86WJWRtDlr/5LStAN6PpnVfP8dFwbXyqPjNZGx3jg6Hy6ROFuaMOBD771rflSKAXSey++sogFmlnR6uzU7X8uBgMN4/PbB45rXvcL77yVSka5H3B3c+/PohHWtnR2uxUfXpmEImUj87vzy2AKLryta+J0ZAQiu2+co6PhRvZ0dLsXG1yUme9B2dOFKbmHBha/fJP2Rz3o+gi/GGOMjEF6Tqxtwua5kuoAgiiHtxXJ6f1ZFLLZJF+Dz8svETmHYaxWhUe9OW5OQfD/ipMsBwYdr14HvjT78Gi8FL7mTbp6n37Pxm8+VNou+V77wf0tasvx3QwDDU3Uv8f/qHJ/sQgYw4I0f1OoFOHEQDHDW+/AzUwMOQuL8wihu7vNoLtKg7pwVY10G3gjo54kdKZE44D0oNuuFxwMETsRwPNMgLZbaEHD0UgFKHEPtuuYoasZoLBRhkAHEbut8eyhK6wXDPQbRgyMejG/J2aKfb1IW9rLGvDkL9TDzQrbkXUW1V/t+FG+k0pH2xW2W6d5TuR8oGvW/c3q03YVcslCKHn6zV99TKYotyNGtWps1zbsLXD2VGyUQ22a3SzEmyWvf12sFnR2sFOOLafiLo1MVQvMr1msF1juZa/Ve2XIxzl5uM+W5QDjbKvW+9yLazBsb16uFZSQIsfSSCa5Os0fI0SlKKgjuTjWly/jUCyn2tJjZLGUuFqAaLgcmD68PQxjyGG6kUf14zWi15Y9HUbSjugMbHUOA2jOt6sBroNsOtCLNM3aEcqB3IicPD6ORAEw/VSsFaE3RCMAf5OzXTrIdaTPDqkOJw0gNl+26oXe6QVKR8ItiTGw6XjRyhlEGjXvN16qFpQoDDbqlgULiUisCCjgIVymjUAQBmEUYv09kHZQAdBsQ9CgOMSdGcAQLaNdHgYkUAIgVqyIdiworj8kMQDtg26Zd4PtGhMqQkBkgdBEEBkBZBUzDZhTKLMFugBpS6Oibqj2C7OsQVNdzCwCxLuLihJaN+Ae6Aj2YSAWjxsiRbOGyzf9dar0iDG1kuBdtXfq4ewQXJhqCFW/a2qr10NduueXidYLwlchCW12JSvp/eRapfp1sP1opfv+rt1uVHupFJcLO7WxXCt6ONa3KAL8oqv3wo1SywqMROM6vSRXi/UKKmDCNYX/P2W0K6ZFJ4+l6+LJVeH9/cacreBmnqwUe4MMpABOIKEcDaBioaMoB0ZcizbPYAsDGhTugAShGXBNijoMGcDVp/y86AEYwaJ824YATEUAEQARQ2ccsapVsXQYcOGeYvEYMwFCAMAsx2UFFA3Dyg23LVN0UYHCOgCEEEBQRXDbRPpWjhjtG24L7tt3SBtSDRBAnVUMdgow7rO8p1QveQSBgOuGh0iIMvkhKa/UwcBUMmGw7VDVJFZsRtlNNbHKL2yr1u3DKfHtfyDDqpqSibIZZO9XMrXbwZbVQ/X5ttVf6cOyIbKBSIpEjP1gciFmyWqWWW4lrde9LZrwVYpFIIxEFY0LtAo44LEi6lwtUD3O/SAI0TZEDC8DwC6rQwcsq87as9mTUT2EpJhCTrWNXFdwSQM6akgyBMuGRcQWKVMEcFF0LZsS5RxzsHduj3ixkwJ7BqyABGSgSuWLMF0s2uQkifIm60BQXos0Xb3dQi01IGL8OqYLTisjAiEW7ENQUXbBoJZyMBCWctj8oF2xUbocLPs6zWV2qGERfc/dw6ybLbbDFcORCNoeV2BVkWCgwZPx6eDJiQOeIttVRwCsiKeYL0k6WI3n6kcmfKoor9V9XNNp+VSg4y/VcUh20xQ8TiouVxCpxpo1yzDA3oJf69h1TAlyB6ePQGbpq/TiFQLuoscdGqBbkPRPFy/zXItTFGgtJupHRIDnul3JAIUs3GqXg50aiCv9rpplmuSPN/qZAXEGoylPb1OqFn29prBbp3lWmiXVxve9ljOAhFaHYSqBabX8PaaVKnk69Z99VKfIgw0iCuCr11zd3qA6CNrdXbQZnotzrRK42myVfNzLXeno3Ixf7tK1ZqcmEK7Asu1I/WSV2p5OjWtHeykU4enjrkNKbK75+dacqfucTo+vhuqHSph78HnziKAFd7dCjZKetxrO6CvW8d7ZJOJ7n7x87Ch+9pNf6+pNcu6iwhXChCNlMcmD1457ZV6oVaV6TbC1UObIX3NkuF3Nybyh6eOARAYOij4W1WzxUC64e+3+81KBfgJ1SE7Dmjb3Z/9T0HD8F48D+r6D6+xQF2Lfud/rf29fyhPz3b/xi/APO96tvTDayybIJhrVyy/v/0LfxuAoB9PIfSPKoPlIAheOAj8yffQRuPl6BcwzH3zW72f/hm4z3kvXfB+9MEPPwkEAADUdW040/hv/js9Gv0JIlxtGHZXmulrdxJ3HrtqnezVm5nLn0kw2aVCHW8EGKj581djj58z+5XkjfvDNx+YGtBmY21v1DDAiY8vp2/ch/tK5vLN2L2ndq032VdjtR7ESelrd8Y+uqJA5OSHFzNXb9mK0/ZG22wM5LWh2w+Hr91FO2L62p38x59oANZmY106bCr2yNWbibuP6f1y/O6TkSs3bM0ZvnU///5FQDSTtx+mr9z0FKsAZFkhD6BrkacrU++eBwQtdevhxAeXEE4CAd1MsFi3G1pez35ynSrU/c+3pn7wEdIVO95o2xuVAWLo0XL26k12pxhY3cld/NS7uWe5MTDoUhFk6Ob9kY+vEE0uuriauXqL3dyzINuMe23ICa1sTr/zEdITEw+X8uevuMsN6qCSvXg1/HgF7fCT710IrmwNXL6WL9HzBAPre9nLN0NL61S5NXLh6tC9p4hpHdV6EV1ybRXGPvzEu1MMvNhOX70dfbgkYHTLl+i7WKLVn/rBR8HlTU+hPvrxFf/6XqBfn+xKabNPNLnRC1eTtx86ojn13oXY42UFxJu+RIeNUIVa9tPb8UfLWE8YufBp9rO7PETVN8l2kdDbQOeA7BZIqycbqbCJO4YNd9fx2j6tyXBjx909IOyOZLAkQMKWBtT26e4qrDpYZw2t7Hh0GYgVm2MDXeponSLZ2CFkAW3seTqrmG6CkAtWh2NmT27ukq0D0mg7nQLe2HGrXV2PeC0G1w2rtw6X97y6hPQO8Na+y+w5oWerU++dB3ktcfdJ/oNLcFdOKM3pgcYM6sGV7ZFL1+m9Crt5MP2DD6HGIC11jsg8biqxpy9yV2/5N/bZzf3c+avM9uHA7evQYR5n4o+W8+evkM1e4vHzzJVbgfWdgMLP8QO/KTHr+1Nvf4h1hMjjldGPLrvK7YhQm+yKIbFB7VXGPrrMbhfog+r0Wx9Q5ZpcB6p7tFwElQFS2qR6+4hlaWbcZ3pwqWpUNtz9Fq7Vgdo+NTgAAVnSR4YAR9cVuLLq6ZdxmUOqmy6+gStN8dTAJOucIiLlbaazCakG3loh2lUPaFtGyGtjgN406nvufgFXFLy4Sfc3IRNwbBY3/R6ladZ3Pf0SYrTB2q6ru4fJEDn+8eXc5RuAYA5fuz106wFdr2T6/bzM4ao29Nn9sQ8vqQ6W/+R67sKnoGqNm3y+zzOdeuze4siVG1hrMHTj/sT5y4YOdKlQh4kYGpi+fjd58z5zUI09fpa9chPr9LJif0GqayY0cv5q9uotrMMPPXg2/Nk9plhKKuKk2MYMPXHv6eT7Fw0Tzl67m/vkM6zDi3WksukS2tigQzZWPQoH25CpZ6K2rgpluLJHKxVH7aLlTfeg5Jg4bMd9Nmj1D8HqFqVwkFhwKvuMWgN4gqn7U7zLJ9bA6opb6mODClrd9Mg9FBr01OyQLYtSF61ueIQ6InBoZZUWuxiA2GomBuiyUrZre7RYgUUeraxT3RruanXHPr4SXN1htwrD1+4k7z2VIbLNxrtMGBooU+9djD59QdQ62YvXA4urbL810VeG5RbWF7OXruWu3DRMZPrdj4fuPNFtrM3Gu94w3uTS1+6kbj6AOWX04rXM5c90B14QasOG5mq0Ri7fiD55Tu+XU5/dzV2/ozp4l450vFFLtcbfuxC795SsdTOf3o4+WMI6g/SN+xMfXNJNaPTStfyFq6CkAZhjRRi81Yo+fJa9cgNrDuK3H0++f8HWbQs17VQA6fVTd5+kP7vL7FXCT1dyl67jrUHfHejQERGnMp98lv3kM7zVTzx8lr52h94vWi7EDpAWCKbuPpl897yj2sPX745euoY3u7aj2TEW6XaDy5sjH3yCN7jog6XJH3wMCVps8UX2ys3gyiZzUB396HLg+VaPDra90b7bF1haH/vosqfUDC+uDl+5GV5e77v9LTbGE17vXmn6rQ88h/Xgyvbox1fY/bIKYG1vVMBo5qCc//hyaHmDqHen3j0fWNsRMarli/fooG9jP3f1ZmRpjWhx+Q8/iT153qNDbTbGuQPsViF36Zpvfde7Uxz+5LPEg2cGigM/sdvRdkCw8wt/a/DFL70EKvzfrpR1u+F/9S/IzQ0tk+38/N9WR/MvpZNs0uX94H3v1csvuyr3H5dF6CAI2moGv/uH7pXllxs+2Rb3rb/W+Zt/B9J15vq1wPf/DDKMH76sEFJVfTjT/NW/q+VGwB8PkeHf3UUovXYGDNG1I7PicMxkPIfnzgAMhpqmRxNw2BoMJaRUuD49rvrY+pEZM+DBDY00FBeo8kOJ7tTYIJsQohFxONadGuuZWtvrcXwuk3KXTx01QgwfiXQn893hIUoZeGXOokiT9rRmJ3r5LEjhlRMLeoRFTIPSRBh1lEhASoYbs+Mm5a4dmeVSSd1PDUaG2+M5JRHRQ97asVndgj09TmP9kI/qjGWV4QgXjQ5GM7WpcVAzqW5PjkShgLufTtYW5izG1RkfGYykIMimVIGwVCnkM4Pew5PHAC/RTyaqR2YRVUNEFXG7lERESMd706NcKmWynsK505iuIZwEuN2An2rnR+XhcCeZkpKx1txkPxKBPNjhq2dsv2eQTnZmxiyKcKmiV+mJ4ZDtdRdOnwR8uBAIFc+d0t1Iy7ZbpHcQDEtDUW4y28qPAIzr4NxpHDExXXUZMoJY/XS6OzUySCfkcFDIJ2vxjGT2t6moSbkVv7d48rgZYgbpZGcqr4ZYt8zT6kAO+kEPXjh9Qg8zqs9bPnXM8rm9pOCjeXDY7UMFOmahfsRTrzuWg/holuTZoIqFIZdbC1GcnaJdfR5WNZD1BOA+nbIRP0yTYsAvAXFSwRwB1Iyo3wsN2LACRdEo2PbEVNIHWqJCDXgjEg6QfZYSoIzLD3N01AJibrZRQSQFDvppj+L38tgQQpBagOKdtAv0EM2x3CCXEOMRPpNszk3JKCaa/IE/Y/upwVCscmzOol290YwwNszheBewOy4/F09Yfvrg3GnbSwqJeHN+EgMtXFcZbSD6WSEV702NdFMpm3UXTh0HYLuOoCUqZHnp7miun43300k5HurMjXFMmJfqh+G8xXiEoVh9flrzUVxuuDY1CQaxuFODcrjL6/ihLj5G4oTjqrdQAITDtN8jUBETiOBhp4WNYjAJ+2oVEyVcFEIzChvR4ADE4jwV1q0YMxg0O4mwx2X5IM6dhQjaZl08HTYAF0g0miiEOnE2CrSxYRDzQxG77slCMAVjrQ6uW0DYG8V7dMQw466w1XRnQJvBTb+nOzHaHhnWI345GalNTsmg1UDxNhsFPUT16JwW9w9C/sH4SHM0JznmADBrgSGDoZozk+18DvC6qjPjaiKE2CalCghoKtGAlIxWj846FNGYmuBSCQ0y9yFCprx8LDrIDnWmRsV4VEqEqnOzlq1WMDfn8eo+tjE/Iw2F+pGwmI5X52Z8MOclRTSLMZjM+kQ8jgCWTrc7kNuNB7EI2IQnGQKVfASP5UjSVIh2B8NxKOgKeThyCIKCSBRsgjnCaww86oC0FApVfaxIRmwsCIewLjkEyR46VC0pjJ+hbC8xwNKIx6UzrOKJWDboMLUa6KHhGBWz6+i4y0MqXnLgSoI265YiQSEXr05MWIy7eOYkTEKYoZGmQiDmYDjdnRzpZ5NyONAfyzSSw6rBFTxhhaY0L106fdwI0nw81p7KC7EQI3GMwql+L+jGyiePCak4QBOl08eNiI9zgCqItIIJy09z+XRnPGt5XIdnTml+xmNILl0kQGOQGurls62JvOV1tydHWyM52IPXZsfNhK8bjQ/GcvWxPCpIpCAp0QjEkq3Jifb4qEO76vNTYiruaKa7L9gMJSRiajxcXZgBPHhjYkzIxjHQJnWJ1vl+IiZkEr3pPJ+IK9Fg9dgRXBRB2YDcpBnwNqfGtSF/KzkspWLt6XHDQqgBrwZCkM/VHs/3xjJq0NeeHOvnkoNEwvG6Ds6eBGmUSwzVj8/DiE3qCq311ZBfGop0Z/KdzDDode2dPeV2FExXXaYMEEh3JMuNZ/qphBoOcBMZhaJphUdBA6BwMRKqLMwZYXaQSXenRgzGTYkDyhCEaBik8P2zp0y/WwqH6sdmHRdKaopbF/UAY/qZ1sJkO5eFXcj+K2c0QfL8yLoI/8PXpuM4MKxMTSODAbmz/RKQKhCE+328XNZyOWVi0vQHyJ0tZDB4mbJCwPXihREOa6NjP4amwr98i9CBYVgU2Q/fdy8+eTnklapy3/x29+d+HrBtz8P7/r/4U0iWf3h9Bmmqlh5u/fKvqPmxnxS29f/5CBDoGgyoesM2bMCNelptx7CVuP+1P/g9zcvsfPF1tlKBcYdpNqlmC9H1/5O694yxJMvu/ML7ePH8e/lcem/Km67uru7qnh7XQ47jCJSw5CyNSGEpriRiIQuCxPqlpIUgYBe7WqyZJTmmTVV3ee+rsjKzqjIrvXv5vDfxwvsIfSAgEBC47AKHnNnz+d5A3LgB3B/OOff/dwLUsR9/onCBrQ+/FMrnLQLHh9KBQgEmkFi1klhaNkiqcXiSbbYoRe1ODofKZQdGKFk+dO0K4tnZN87Q7baHIOTAINNoUhDUGR98+49/AILg4re/G6hUMUvzNZt0t4t3BWRGD1Sq/moF1fVgoUB3ea7VCtblvt3t7XPvkjwfKJX0kbivWuMaLVxR0tlsbHNr6ysfMK22r9ag222K74ULhc7k8KEbNyK7u49/5VcDtSrV6XJ8lxYEX73B9vO+eiu2tbf19S8F6zWuVm8LAtEQqE4nUK/TfCe6vFl46xQpC8F83syEsGqbbTbJnuDBFtNqBUpFLREJ5vMGxyS2tkbvPSy+eUIlWLrd4aoVhjSZVitQq4pw3H/lmXloygsH/aWyFWA8zaabzUC5Ykd9p8//oDIz3ZqZCOTzLoG4KOKv1w3K4Yo14slyYNbWUyGu3rBxohmgQ7lcBxoI53Jjt+52ZyfqyQGq0w2UyzKW4Gp1EMPqQ6NWD/ZgnOxpPZWALZAmjWY5DiMAF7EEkYAgkDEcQ0QggMJ1oNuMA6CLBVDN9BDD9RmmKOAuAEK6J1RZxcJoQpUMAnABWLNVnXQtJxDtNeoxwIORMGAqqGsijKBrOq67KOgAnUYfZLr+uCUJqGthnGKZMm6aNKVaNM8HSuXO2JC/WuMqVVyWuc0surHh+8DPtlq+eoNtNElRCBeLncnhwF6J2tzBPzyHVztUp+Nv1Fm556vV+YF0LHeQer6y8QtfZdptrlbvCALalqlOl6vVKaVJrRb8Z04ghhbM5YWhFFnv+Gr1rtAj8wa5uMqeoXHE8ZUrUiwKem6wUKiNjhsWLpo+VPQQU+sYPpK3gRDSKKaomEuAtijhHGFAni5YLNZzacqrFzNYwAogQkMM0aiNKrasECiuUR1jr9NPwhDoV2SdJkUX4BxJJHHIgwiQb/QHOB0jbdFiIRGEfbao0bgIkBzQq2eAAIKQnqSQDKAigi7aPkSCIccNlSug4+C6HigUCVP1tdrkxh5iGOTZINNsQabRmRwKlSuYYeGSzD3aJETedxKmux1Qd8gRgW22MEVuzU2e/vgnBN979rd+xV+tUoLINZp0uwNJutyfIJ9t47JS+3YyXKkSnQ4p9HyFItPp+DtdfCeHtATi3Bm20aC6QmNmNFwuM+0WIYmeAUkGRYq2aUGqSLKG44pcvRYKEbpjuqrlI3smbDi8zjCKZVtMpxJI9POwYUo6E1J0wEQEy0cKtudY+o5DjcImgkgC6g+7nmELGo1ZKqDhpUI/PQQCqKroNCdqLuxJIuEL2o5O1kr98QHFs62u5SN7jofaikrhKkjYkr9StcBAoFalW22/r6IgsVP//k/aAwO5t08HczkxGdc4n79c1sC4Vm8TD1Z9/RlgfMBXb8AgLKdjoWKx15cI1GuHrl5V+sKlqTmq3fWjeC8W99XqkGFK6Zj/2gs5GqWPcVy9bgQYiyGpLh+Aiu1U+p1/929lP7f19Q+ChaLq89E8z9YbqE+jBIFptzFBMMYTgXKZVDVSkkY2tqhWO//2G3S7g4gaMTrGtFs0361PT0y+WCHbnd1vvO+vValOx8fzjCAgTV4cSh2++DnbbL/8L78bLpXpVosQer5qlWm3/O1WeD9LVZr5d99gm0261tTGEmyxyLbalCBEsrXo/n7u7Bmyy7PlmjCQYptNrlBqzE4w1SbV6XLVGu3wbKMpdru+VmP43qO9L58DDdtXqQpDKUxzqWYrUKkiuHf6+t3c6VOG3xfKF9REhLJdttEEEmxc3J2+c3v3nXdtP+mv1rRQUA9wwVxejQTSr16NPHxcfO8tgeCodtdfKdthH1etGuEA5DmTV2/tvPuuRZBss6m3g7YFUZ1uoFpp4ezP9ogEHcfF8favfB+yTO7alddQXgBBYmsj9Cf/sfWbv6WcONVWlei/+hevYQgNgqBlhv/jf3D8AfnkaUhT/7MqEYIgZBjc9avczeug533xNUOKIp57r/PL/5WLk9TaauTf/Guk1/3idAWapplKt/72b6iHjgA/B+FCMNaVB68/iC+uArw+cvFW//1nCkz333kSW3hlqe7Q1XuJhRW8q/TffhyfX3FFK3F/IflsRYHo8QvXB288snRw6MaDvqcvoLaSvL/Q92wF0LzRq3eHbjwUEd/YxZsDNx6YFpJ8uhx+uQW35fT9hfSDRUvxBq7eHb3xQELY9IPFvkfPTR1MP1xMPVjAO0rq4VLmzrzhYplHi5OfXNY9Iv705cjnN7C2FNzeG7l9H+0qwVc7U5/dcDQ3tbAy8cllw0Ziq1ujN+7grV5gOz/6+XW0LgTW96c/vmSrXnB1d+j2E1uHIqu7Y59dRytddqc4/vkNqs4H9w6mPr/iGU78+frUJxc90Qy+2hk7f52otLlybeLKDbLW5nYKM59cBkQ9troz+dEliFfQhjBx/mro1S7AGzMff+bbzqMtafDmQ7TSpfL1sfNX/dkK2pUnP70aernlStbMxSv+nTxW702cv0Lvl+lCc/TC9eDmgcdrmVuPQ+tZR/VmPrkUWNtHqvzYhevcfomsdceu3Apu7LuiNfnx5cTSqqkBUz/+PLy8DbeV9L0FOlvFar2Rz65HXu14mjd1/lry6UsB5RrbRKPoMyWoeuBrHhCGidVzrLwLKCjd3CRrO5TmEN1dopr36SLQLFO9Iq7aZG2f62whEsK0tvDyrk9x6fYuWi/4LBmuFHzNLCU6bCnr47cwAyaULFw/oFWAah+Q5VzAFKFGkW3skLpNNgsUv4MoKNPdRqo7nGbifIEoZf2qjobWdiY/vmyrbnxpbfLTi6biRdf2xq/cxqtdJlsZ/+w6Uev5tgvTH11yJCeysjl98SqgOOH17NinV/FKl8lWxi9co/J1MlcfvnbXkezI6s7kJ5cAXvfvFMY/vcKU23S5OXHxBlFsstnqzKefg7weXt+b+OQS3JQD+6Xxi9d9+Rpa6Ux+coUqNuhiY+bTS0S9q/JIdjso1xDcwX9gAAAgAElEQVRdRYqbfjnrqQ7Z2CZ7LUoVkeoGzbdJk4dzOyGhgmgWVt5lzKKnOUR9lWxWaVnGy5tMr0XqPFDcC/TKiGqShXWul4UlgK2vEq0qZeloZ58UOrjWA/O7QbGEyTZV3eT4XUiGqM4W2m4wqoqVt+hejTREOL8X7BQwGWFGr9wZvnxbt7HB+/OZu/NwR0k+fTH4aMGR3fStR+PX7kkIM3zt/vDlW4aDp54t9z94hneUxPxK/+0ntgam782PXbwlQ1Tyycv0/WemCqTmlwduPkRaUmxxdeTGA091U0+WJi7dlGF64M6T8c+vu4rXt7g2cO0+3BSjy5ujN+66kpV8sDDx6WUFINMPFsbOX3MVp9fGy1uc1kX5Dl17RQomo9Sg2halmIRUgbPbEa0Nyj28uMFJHVSUyc4WruuYVgOLaz5BI9UGsr8V1tqwJBG5fFRuo4JAVVYZQaGlKlTc9CsKZnfBYjagt4GeTJbWWLWJiCJRWaF6Mi234eoua6qI2gCyWxG5BkkqXXrFCi0C4eWxz2+GNrNEvTdy8Vbo1bYj2Zmbj2LL24YFT390KbawCnbV0Uu3g2t7aEMYvn4vtrLtGsDUhevphwuqR05+ciX2bMWTnNSDRW67gDSFoav3+hZXDQMa+/TK4IMFFSAnLt+ML6y4gjlw42HsxSbeUYau3e+bX9YdPH3nSWJ+RQWI8Ys3k4+WAMEcuv04/mzFNsH0/fnxK3d0Dx++83T04k3DRZNLK4MPnmAdKb60NnT1rifZyYdLkxeuaxDZ/3B+8sZtwAD6ltaHrt1HGkL45cbo1bugYISWtwZuPVZAsv/+s4lPrziyE11YHbpyF2v2wlu7o9fvgJIRX3g1df6a6cCZ+Zdjn1zxZDO2vjN86x7R5IPr++Pnr4GCEV9cnf7kiqMD7G5h/NPLbKlJVTuTn15ldotoXRi6dR/qqv6dwuQnl5Faz79XHjt/1bdfhppS/83HdKGB1Xszn17Eyx1mpzj56RWq1qFKrdTtJ1S2AraUqU8uBXYKcEOY/vgCWWhQhWbmzjxe6RKl1vinl0M7BUg0Zj69ElzPeoI5fOMeWu0x+frE+WtUtkKU2uPnr4XW9i0E/5mfkqDjOCzb+v6vCx98BdK0L84YgOsyz54GP/4J0mlLb77d+v6veyj6GrVCEEQ67fAf/wdif9fF/3q/w09baNQDmMX5+P/1zyFN/eLpK0jTlOMnG3/nd81UCi8Wkn//D9Ba9TXoyrbtQLD1678pnX33z+Rf/wb/kb/Yi/D7vxrpNHSaAUmYqrU1lnMDJF5peyhihDmkLTOurAaDYEcxSQpgMbLWNijGCVJ4rQsCnh4LIm0ZA0w5HMabvItjHkcQ1bZJUHaExeo86LrdTH+gWkFsQ4lFsVbPQ2AhFgsVii6GmREf2uzBtt3pH/DVa4Sh2mEO6YgGgncyA4FalRSF5sgo3W4x3Y6ejJomQPM8n06TgoApKuaHTR1CJKU5PEz1ekyr1evPcELblUwhnSIkiRAlPR4AZZMUhNbQECmKTLPZy6RZmQckU48EMFWBFNOIB0DFIvie2RcwHDRQLXcGBxmlBwq6HgmghgGLGhaANJdAeVGPB2XcFynklWAQRj2s0bOCrINieIt3/aRKMIFSmU+lY3pd4wE14IdwCG/wNkfbBEE0uy6LqzTnL5V7iQQOGni1q4VCAAnj9a7NUhZJYm2BxY0OF2fKVSHeh8E2Xu3qAb9Ho3it61KYyTJ4qwdSiOQLcqWyEo4guEdUuxrn00IBowcSng6ysCmCLgrThKkLoINhLK2JMolaBuH3ZIXAHB3ywYYMARDo+XGorbkwwjKaJJOQbblRGujquKlDAUxTIBAA3CAJtxQPhhNIo+rEQNN2YjTUMwDLwTgQFAwdIoAgAXVUF4RZRpdUEjZNknMUgwQMGwnCXLsJqqYRC0CKQfSE1tAQKUlss8n3Z1ipC4iGmEigmkrxPT0eBLU/246gacNsvd7rz7BKD/iz7dB1RFSNeAAwXKLLm7GADuKBcknoz0TEqqygRtiP2ibCy2bM71oA1eqYMb8Ok/5yqZfJkIYMdRUjEoQdC+uK3VTaQEi0ITpByueKioABNEJihiTiOG7DGKBLMI3pOk17bd32Uz5AkkUMIaGw16oaUQy3YRzUZIRBVOP/GwPKcg8FKYTEDVnGCdCAKFiTEQrRDJryOqbH4h6NwDXZpTCa1GWJABHPZXGwozGwrvsYt226DGYHqFguC1tWe3DIX6uipq5GwwgvQ57bi/f5K2UAgs0oh7YExDBaQ8O+Rp1SJCvqR3jJBFEhmfSXKwDgmTE/0lFQVWkPDflaLURV1XiE6bRtD7HDPqLZdQHQiAeRroIpkp4Ie7KNy5LSF2W6HduGrIgPb/dAy9GTYbinYbLUHB5hREHWSYz1YNNUTZxiLctGbcVlWEOGfUhbdqM0rUuiRmKMB7u2pqFRrCtDrKbCDGMoiA9uy26Epg1RUXCEBWDPUVWMpk0TQG0Z8NGajPmgluJEaNqUZJWgSRN3tI7pp2jTAjFb8ny0Jv3ZmDDN2rIk47APJgCNaHYZxOADUarSlCIRFPPwasfgOJfF8VrXwxHD78NaPZiAJC7IlmtaMACTEFHt6Azr+Em81rUJXA5H/OUyhINSIMxW6jrnk4OhyEHWYhgrQON13kFRMRb1VyoI4jk+Gq3zis8vxaLR/X2LIq0QizUEDwLFeNxfrdooyieSoVIR9ACCAzQZRDW9OTTkazYxRVESMV+raTlQL53iajXIcYx4AO4qhKJoibCn2LgoKokYw7cdC7QiHN7peQ5g9AVgXsMlUU+EHM1lul0xnWR6XcdwzYgfEyRIt/AgLJsELghGImQbINNpi6k+pse7mmPGAqgow6qhJ0KWDQXLJT6Tick1WYb1kB/xbLQjmWHOBSCy2bEjPg2lA6ViN52hLAVpiVo0BAEu1hasEGsDCNHpMazTYPoCxWI3lSZdHWkKRjgAQB7W6tkB1oYxst11g5REcMFCQUwkcMhCGz096PdQiGjwciTiIbCvWnP9pMgEg4WCGIsrhfLfmBfhX3o9EOa78X/xfzOPH/359/lLG7kABOl+75e73/klF8OC5z+N/ODfeq/ph6McO978nb9rhcJ//vj+uc5g4ZVi9F//y9eiK9A0jZHR1q/9hplMIr1e3x/9E7TxOmaFruswTPe/+GXpnXN/03T1nyoRQrBqjN28G1vdsl148urN5PMVHcIn7z4YfLpg2+Dww6eZhZeAbg/fe5xY23JtYOrqzf7nyzpCjt2+P/R00bHB4SfP+uefA7o78mg+ufTKdeCJ2/cHF19oCDX+4Mno/Semh2Tmnw8uvHANZ+jRfHrxpQVgo/cfDz58ouL0yMOno/ceWQDa//zV0OMF0HAHni72L62YIJZaejlz5YbuIaHd3Oyla55qBrKlqRt3YNWKbu5O3rzrWkDf6ubMpWs2iId3c3MXr3qazWULUzfvALIR2s5OXb3hGm5sc3fms8u2AwcOSocvXYVkw5evzFy/jUl6aPdg+uoNQHcjmzuHP7vk2SBXqMxeuAzzsq/anLl6E+9KgVxp6tpNSDaCe7m5Tz6DDQcR1MOffh7Il2FJn7l6w5cr47w4e+kaXW2SPWX204tUvQuJ2qELl4LZAqQYM1euB7IFpCtOX75G11pIT5799DO2UHVN4NCFS6HtPc9wpq/dCu9kkZ4yfe2WP1+CZX3mwuVAseaZ7qELF2Pbe67pTV27Gd3eQ0Rt+vrt4H4BUq25S9cCeznXgWYvXkm82tAhwt5R7QPLNkB9z7bytuki1qbhbYsaQoEbsr5t2R7i5ix733ZNwNq33axhOoi2ZQN7ioZQ3rpgbZm6h9pFx9h3bMMzD2xn33A8WNt13G3NQAhvV9O2bAdEnZxh7lq2ARg5xzywdRcx9yxvVdBQCthX9HXbBlCnaFk7lu0g0e29qeu3PNONrW/NXr5uOrB/v3DowiVQUNlKY/bqDbinBLLFmeu3QM2KbO/PXbwK2ACXKx2+cAkSNaZUn718He+I/nx56soNSNJD2cKh85cg3eaqrblPPkd5ma40Zy9fI1q8r1CZuXIdFlT/QWHuwiVAt8lm9/BH57F6B++Ic59fIettptqcvXKdaHVd1VNeGh7vmhasLhtG0zUt2F5RzKpt6LC2buptz9MAZdn0mrbp4dpz3auauofb67pddS0NsFdVq+XZqqutOW7VNF3MWta8vGqAhPdK1suOY0HWpmHVXU93zXXLqxi6h2mrFlDSNZD0lntm0bEtyNxxjKrrGoCxYXllU0Oo0Ufz47fvGwCWWloZffjENZ3Ms+f9TxYtDx5+sjB875GGUUOPn03cumeBaGplY+TeI9BwU89fDT5dsh1o8OnC+J2HGkZl5hcnb94zPDS+sj5++4Gn26mV9eEHTx0L6F94PnH3oQET6aWXM9duuR4aW90cv3Uf0OzE8trY/Uee4WZerIzfvGNCWHJ1Y/bzK44D2V3XXjNcBTA6gLMsmwbq1HT7hWLZmN225WXHEh1LBJwV1RFdmwesV7qpwG7T0p9rpo25vC0/N13ZM2RQfWUaEmQKoLMsm5JndT19WTc0yJM8+bnp9RxTR62Xqss7pgRbq5otg1bbtpdVS4VM0dOXba9jGQbivJSdloUo6sSNu+GdfUgxpy5dC+XKruXNXbjUt7ZlefDk9VuJ1Q1IMSdv349s7oKaPXPtZnhz3/WQmUvXUq/WLQCdvnUv9WLV1Z2xuw9j2/ueZk9dvxVd3zJgfPrardTCCwPGx2/fTz1f8Sxg9MGT+Oo2aHgTN+/0be0aLjp17dbA0yUdIUfvP0ovLtuGN3z/ceLlqgViA08WR+8/MiF84NmLyWu3bIhIvNocv/3A1d2+5bXhx89sE8gsvBy/eceAiPTzV5OXr9seHNvYmbp9D9CsvtWtsYdPPQtILb2cvHnHBPHk0su5S9ccD4lsZyeu3wYUI7a1N37nAaDbyZW1iZt3bRfue7Uxd+Gi60Ch3YPJy9chxQxv743fuufpdt/q5tTla67hsE1+7uMLeFfEm925z69QtTZVa89cuY63e2yxOvfZJUjW8SZ/6KPzVK0DS9rcZ5eZUo1o92au3iAbHbrWPHTpKtHpYbw89/EFtsnDonLos0tcqYZ1pZkrN5lqk2p0Dl28SjS6mKgdOX+RLdYg1Zr7/GogW8IEde7qDbbcQFvC3KWrZKcH9dQj5y9yuaIDo8DPSbiu4w80fvvvqEePQYb+xbEMsO3gxz9mHz0AXZf/5rf4D7/xepzkefTLF4GPPwIt66+v5/2n2YMFK0r8//zfkW73NejKcexQuPkbv2UMj4COnfgn/wArFV+DQz3Pw7DeL3yz941fBDzv54SuAABwYSRUKKSWX1GdHsDiiZVXXK5YO3t69OZtMZkoHj40eueO0R9TAoHky+VgoahHuL6VV/GD/P7bb0zevSeFw8VTx4YePNJiASkaTb14qZOk3N+XfP4SAMCtL58bu3JNDYfDX/7ayL17Dsf0orG+1TUEhiszh9OLS4hhbP7CV6fvP1QRhDv3paG79yAMVhOR+OoaKuvLH3x16Ol8fHuH+5W/PXX3bnhvN7M+xxYbqaXn8dnD0Y2tiTt3qmePDj16HNnY4r77veHHT+Jr60Mbm/GNjfT8YmXmUGh/f/z6ze0vvzfy8FFyZcX/rcLos4Xo8mp65kWkWc08ftocHvCXilM3b+29/87w/Hx68cXeV95LzL+MbWwOLS/7dKn/6XxzdMjXaExeu9E6PZ2ef556/qLwzhkGwJPPX6o+X+n08fFbd9RwUOF7w4+f8MOZ8PZB3/rGYPIxlmZSS88tCMwS58Zv3TH9HKGoA4+eKH2RYL4S39geiS60ZkeTz1+6AGjHIxPXbzoIDBvG4KPHdcrxWmp8bcNguOLZE+ml55jj9BKxqVt3QBBwIaT/yVOXgFFRjb9aNQEox2CZ+Wckz79696t8gaB8XoIRazUOQQGWU+stjrS01JRUPyBNHGG7YvuAxVAgyiqdup8APTJhNpssrUipSaWWYwwCj4uOuA8BIJ4uyc1myNABf9pq1xlKUvrGBH4blTEu1jS6JQp0gYES36hjmoYGBp1W0we09eFjSnsHkkCGaQhKiZYUPFURY9tbE3cf77//zuCzxfTS8/A3vzv6bDG2up55tRavHKSfPqtMTDOdzvjVq1vvvTP4dL5/fuHg/bf7Hi/FV9eGFxcpTczML7RHBklBmLp9++CdM0OLS5mFxewHZ2NLa/GNzYH5ZyFXTD9+2k0nUMuauHm79PaZxMJSenGp9OZxrlCPbW4PP3/hMtjAk6e9/hSAYeM3b2ePHm1zgSYfTB9oYEitN2MxrwcxcL3ABQmTA7ROgYZJlVHkNh8IZDXWVhutMOzyAUKpZoMh0OYgo1Fhk4SMK1K5FQjCBoMalUYwZMjhuFI6YEjXDRFGo8wmbB60tFrLF4FsL2DU6v6QLIb6tEqeg1HS13C6RQJQdR+ilNs+3HLxaXvs2nUHwwLf/qWRO3dhGOik032ra7Qslw8dST5/wbRaG9/82vTDx46msR9+c/jOXdLUpMFU3+oq2ewezB1OvlyJNhqr3/nG5O27qKJEvv6NkWeLeLudGR5MLr+iitXW0ZnU8kpwN/v8e9+evHkL7/VC594efDofqFW7o0Oxnd3g1n7l2FzqxXJsc3v1e9+eunuPLJX8lZrcQKtlNhWQLAdu5amBEUUvAvV6cLQtOmWv0fUP5ngQASq1QCokeSBUO2BCcclqAI1GmK11zTbR6IWGDyQHsVrVYNov2hRWy3OpQd2t6Y0a56s1QRVo9ILJPQkJurUqR9Mtl/aqB8GBhGIJXq0eGGt0UFUqdAKJnIJGnEolEIx4g3hl+MGDtnvE1ry+1XUPQnPYW6nnLxhFbUyMTN2+kzt1Qg6FBh49Qa3DgA30rawCqu6EmPSzBV+lWjoyO3blav7Mm3xmcPDR405v3ATQ+OoaKcqt4fH+p/OB7EHhzVMz9+7nJicbmYGBR0/E0QEQBeKra0y1kR+bGphfMGPR/Xffmrx+szEy0hoe7V96wcdixem55MvlSKVc/PIbY7duY5Lk/8XC0JMnNM8PTk8m1tZ9hWr++MnEq9W+jY3V73xz6uZNstPZ/+CdkYUFNpdLjY7ENzZDm3vV6bHkympyc3v129+YuH+frdZCX3lveP5ZeGdnYPpVfGcntrRcOTKTer6cfLlS+tqZiXv3uYODyP5+an4hub7RXnkV2NsdevisduJwauVV/9OFzW99OPjwcXx9q//pvJ8wM0+fCZGwEfJP3LzVODLnOyhkni3Wjs3RbSm2sTW0uKDHAplnC3IsIvenJ27cbI0OUZ1u+um8NRwh1GJ8fWv40ePueP/A02daKNgcGZq8dbs70o8oeurZQnc0HbDzsdW1wVC4eWR64OEjncQrRw6P3rhRG5uCNTW5sKTGQ1SwL/ZqbdgfzL97Dvj5Cc+zw5H67/4PfX/0j4m9HQ/FvjhjRf/Nv7IiUW12rvXrv4W22syzp180OwOCgOtyd25aiT7+m98Grb+WJNZPs0SY+KN/xDx9+lqiDB6KNn/rvxG/9BUPQZL/8A/p50uv158OQeK59xu/+98Drvuzoau/yIvwrbPyr31/IrfXDYa0iD/zcqXZlzLjgchu3sHQTn+Sy9dYS6qOjkZ2C2IorEa4/rWtjo9TMzH/fhEGgfZwP1eoU5ZanJhIrm8rHCclI+mNHZHl5FSEy1dR287NzPbt7LCKUJid69vZM0myOjw6uPJSIwmlP+Er1nFZzh491re/71OE2vhwfPdAJpnS2GSykPM36rvHT7DNZmZ78+DkMa9jxEulgyOHo/UiJUl2knMlm603d46fphrdzPbW5ptvZJoHZK17cPhIuFFjer12fxrsKMFiae+NNwJyd2BtdevNN6ONElPvlsfGqHoHExRhOEkqcrRSqUyP2RY4uLa6c/KUr9Fiy43m9JhP4cmOaPcxqgXHipXK7ISEMjML84XJSRh2/bmyFI+YOB7NlcR0vOcPTywt7cwdjtgd30axMjEBYmDooKREgjLLRneySirWDUQml5f3pqZR1Eu+WK2OjIIMFsyV1SAn+/3RnQMwhJf7BseXXmTnDkGIm97YrcdjdoANZEumnxHD4fjOgR72NZL94/PzhekZgIRS6zvtWJ8Uj6BNFdV1PeUDapZLIAAHojlVJzEsDkNtC7RdN4G7XZdUVTnFOUUbQAGj38/lGi7swXECalmgbneG+5CiQEmyNerHmroHeko6yGRbAAbTtNYVaEh1lImov9mBVNvM0FSR11FcTEaY/TYIg2AfCvEGolj2IGP3QETQzGEuUiyiksYPJFBeDNYa+ydOMl1+YHN988yZcLPClpu5uTmO73LNZncwhYhapFKtTY1YLjK8/HLrrbdCraq/1KhMjNKiQDb53lASUM2+cqU6OWKA+PjLF1snjic7RaQs1iZHKVVhGp1ef59teYndvfrcpAbgk69WNo8f98ndwH6xPj5CWIav2iqNjlkE4d9tyWkfAZvgvqz3sRAGelULZGHPByE1FfbDmp+ms5Ka9kGeBR3oehiLkJIiUgAJej4EqmgI7fJcENuTtCTDUTpWUIwAAXIw0LBBDNA5EigbGGEbCR+51zP7GD3IcFtVi0UBP+GWDYdAjaTPV2hiuCP3hag9Xo+QcjQ0sLWO6/r+ocPRg4Ngp1k4fChyUAA8oDQx0b+2ZiGQNJBkqm2q19s/diKaOwh1mpWZ8UiuaAJIeWKyf2vTgWEh00eV6qwg7p88GS0UuE6rMDuT3Nu1AIQfSkX3Cw4A8kNJqtYN8O3y1ATWlcLVav7IoeT+rmuDneFUsFgBTLs3nMGa3Wi7s3bidKRZdTuAkaZR1QBE1w2jpgKgTcMbwC0YJ/M9bTRIqyJQt4wU66mu2/NYv2ZAKNKxvRSuISSzz0sjfkQ2nLLhZmgKMOGe44YQw4Owsgz2k7xHs1lRHfVzruTVLCAMEa6pSjgQRAwARfIylEJ0hqH2utqwn7I1uKrrMQrEoejuAcKC5dTw0MpacWwCIKHM6nYjGrGifv9+yaGJXl88up+3WbyWGRpdWKyOjtkckV7d6kRiepQLHFRskqgODGZW1wAarQyNjrx82UplOonE6OKCEArqfRF/ruzCcHlsfGBj3SPRbrIvs7pZS6Y7qfT40pIc8EuJiC9XAmGkMj7Rv7FuUlRhfHLw1bIHwUCcdjsG3eV3jp0K5suBRj1/bG4wu2nbYGl6Or27A3teK5NCy91As1E9Ns30xHC5dHD0SPJgH7C8+kDGf1ABbE8cSTLtTrjVLM1OwIIWKxayhw5FsgVQs3vjmWCjjmiGnWQ1CYjXaoW5KafnpLLZ/aOzyUYe7qnd0QFfq0PKWnMobYLY5NLi5okTfUKFyHeqk2OEqfsqjV46bgJI385ua2pUQenp5ZcbR44yphzZ3KtNjKGAy5UbYjJqIHhia0cfitR8iamFZ1un3qBMObabr6cTIIZwxarSF9EIOrO51Rrt57no1JPHe8dP4IAR2z5opZIugYRy5XY6IzPMyIsX3dH+biA6/Ww+OzMn5Ss/LyXCP3eY4gfZxD/+B2ir+RoU4bqOjyv/439mJlOgYWT+p9/DCoXXuJbouo7f3/ydvyu98SZoWT+vJUIQDP3wT5jHj19P8goE+e9+T3z/yy6Oh3/w76nll69LV8qx443f+d2fGV39JxYHQp7pwdkqWBcsFyELLaIhaiC5MXl8Z/ywq3lksYnUOo4Nw8UWXOk4urt46t3C0LQG4L6DKlnuqCCJ5WtwqWnYyMrxs/n0qA7g+HaeLLckgmP2yli2LMPMwfDM2rG3TAuEC02k2FQ9jM7X6WJbhajs4Mzasbc82SErbaTUdgwAKXfIUltDKKTYILdyEsyQDQEv1F3N6RHYQV9AdTykIzN7JQ3A0VrXt1fUIEIytK2xlKvpIK9R+ZprgG5boTaysoV1ULjUH9MACG5L+F7JMUBAMvHtvC2YTR/b8BMdkgNaErGRNTwUEU18r+QagIhDB6mwCEFgV2W2c7qLwoJB7uRtC7RMCN868Lqq4WL+3RLQM1yW4lKwR8CeYCDbBUB1Hd0l90pIS+Lp4KvDZ0rxQVCy6HzDU2xXdZHtPNDTTRf0j1IEC6kOSm4eAG3JtmDyoAJ1ZcOGH733C206bLoIvr6PtCQNIrndItRSbAzzJWGIgS3Dw/bLcFsxXJRc28dagowyjQ7dlFgVpFsSKyi0ZQAiq2qAoFBsp010eVKGmVaXakqsYUM6p9mwCMtqu0UJMicRXL1NVXlWg3EX7GmcpalQXfJ1eFL1yK7ItruMCPtcr6dTomZDbYGqij7FIVsqJ4qs7mAqLqpgT6UYXmTrbcbwcF4kmj1GASioKdAbWc3D0bbi2y2qLg7DJj2IOR4EiQa1W3A0z+F1diuvWyjCAoE0bIAI3NXw3aKtuo5s4Tt5S3IAFvFHLIOioK6Kr2dNGwK7KrJT8EwQJuDAIOHgsMNr1EZWt2BYNOmDmmVDEOzhacDzHEfzqIOKp7mWZNEb+7bmSCZZajFdg9Vdot4LqCar4Yju1jVY1UC2I3CSSUkOU+owPZmQFUcJKpAl8kCg3SJ7Cil4TK3DNBTOMhyzzwQMoefQ5ZavrTASynWaBK8ytusahChBhmJgdZ7pypQGkvUmI+l+lSR0oKNhqm5hHcHXNXyyTZZ5VhRJCfdR2Qq+X1Igmio20ErbcRE0jBJ+R4cwstSgC02J4Mj9MrlXUmGSoCw6RbgWAFe6ZLGlASSar1PbBQljGdb2pWDJgJFKG8nWLBtGGj3yoKppIBkEuKAlkxxeaRMbOQPE0aaE5mqW4aERjIl4BkaiVd63V5ZxH1blsc0D00Fkhym3aNGkBJNuN3AJ9GmQIXKyaZu6SdXarO1t6RAAACAASURBVOLSokmWuxyvUQpk23BLgSDe9FXbrOZgooKX2qxgMrplOWHNcMym4a80SN5lVJtpCH5PsGxD02KqLguqTdTarGCzgsu0m6ToMLqlKUFNd0zFpWsdVjMp2SXLLZY3WdcGyFwVakueCSL7FagpmjbI9sMkB8kQze2VkKZoOAi2VwKbou6g8+c+bHBRw8WI9Sze6Ckow2zngUrHxCiuD4b9sGkC6F4ZqnUVgGT3y2SVFwluZ/zozuRRSDCJQgNqCK7hobkaUe+pNkbHbNYPygjD5OpYuW3YCHZQBWq85hFkuUVnKwpKE4UGe1BWEbIL2NmBmKsZcFOkSy3DI+Bym9jKCyDbxaFif8yxXLjeQw9qtgVDLQHbLxmK1wiwTR/WJfxItYNvHuggjrZk7KDm6V4rQJdCjITiaKXD7BYVhMLbErGTt1yEd82dobijm1BXpUtNAySgGk9sZDUIB3kN2Sm4BugpFrlfgrtqLZR8deiNZiCB9DQmV3cs0JMseDPnSVaPDiy8+2E70OfxKrWxbyk2IFvkfhkQdRUgHnzluwLK2rpLbGQBybJNiNspAoLRI7mnZ7/Wo4KO5uE7BahnmCZIrWcRXm0FEq+OvVWJZtC2TOYqoOZaOohv5sCO/HNUIvxzSGAMjzT+2//O8flegwQgCBF6iX/6D2FZ8nC88gd/3w34X296pxP86MdEdh+AfvqyoD+NEiEEMc+ehH78J180s/dnSOZ54nvvd7/9Sx6GBT+/4L966bVEXQEQ1MYnan/vfwZA6GcuyvD/DwdBUltbs7du9BKJvHp67M6tfvLJtek/1CNxxLW5eu3YT36k90dxy0jffmIyzHzi19xoWsPwSKFy8ic/FsIhOR09/snHRjzUmJoUfGElGBp5envy7h0YBGvHZk599BPZxx68fbaeGaAMOZbPTd+/ByCwEouN3b5FaXrz8EQ7EwM9L1gpH/78cw/xCEBLP1mCdSd35MiR69fCe3u7X/rgyPlPg8W8kQivz05bk2lfWRx++nT04YPHw6HJy1eCuwe7b74tcYg5kIrcfTG5uBBeWFZD4UAhf/jK5W5fojoUUg+Pslutw1evJFZWtEQs0Kr1335ke8DGL3/DTtNQvnnsswvx7W1xYqBvaS3z4rkR9G+/OetMpplyZ3j+6cTl6/MpX+LhYt/yujzQZwDk5L17tCQW3zp97Id/iqq/qPsPf8l0HkCCeffRxIN7iOfASXr41i223bz7P/6eTjGQDfe/WDr86Udb9tdTIDpx/z6lqvw3jp3DAtHm3pLgO/3DH+58+JVOszF38XPhzdn80LQLQwbNDD66PnPrZrJcWmS+c+pHf7p99u3gAPWeg246Iru0Pf7wIdvpFN89PXPrZjK3tz93QlwDaD8SB9vNgwAIuf6xtnl6ANnNMa3WznoQJMDhqFjdYCwCTXp5aSoKISRUNbolHy7L/XG5te7TcSw2pUMh3EER8nmW5ycsm+KiTjNPooKahApW3GeNDJLP6539CGhbUajTKmCCjgbRhn18iGo2kUpJeM5JKMslJGWHtnQ84+8Mvlwau/2oNzMyd+1qfOXV/tHjp5LSITMuVpYC91cHniwaoRDB8yc/+qidjh9NOIfwUFOt9l15lFlY0EIB1lKGr94EKST45ROnYECS+YFrV5LPX8pj6dja7sCTp56PiJ+ITcEhzzxwn28d+ckn2nAqsfhy4Mm8Evf7x/CvolNW9bmz3D30+WcI5DoEfuJHP5QyyW3maKNIRZ0eEhabuUS41Q6f4I1Tk2iH15/k2oV03BCJit4qBgK6TiWa0luT2KststzaXhsODah+Vaju+hIJmRYK0tlpfL9sl5Rmnos3OxiLFtdYNmOnA4o81gdli8B2q1mKMqqF42Yrz/irQtynmCNRi+HAxW5zh0kFO7RstoocJsJ0v3rqk48NBCmePH38swsu6Hlpei6eJFFQbBRGb98NdNrVk3Onz3/qOE7hg3ffsnpB2wdWnsYfLOKC0k2lxh4+jFarlbmxD1AeB8IHQu7IzVso3wF8ZP/Soq9Qc4aCJ8Ns2O7sV8qnf/IjRFZ608Nj1+4xlYoXwWZGkxHP0rq1iZs3+rK50pmjZ85/grU6uROnJJ5t54JJQrUAsLrBDrAiERSVQyPEdq63xXQrVJroEIiRy0YiagM97gGHBom1TWVFbYsZn58H2l6nRCfRNpqRlKl+dq8s5PHqOjvm61k1q1kJxa0KFzGEtw/TCxuGmujs0kGjCrJIZT3Zj4tMWhfnBpmVdXWf7JS4CKQgYauzS4dRK0ruHT5/nj8zy1SaI/fuBiqV/Pfeeo8O7tW37qDuqR/9af7kccwxjlz8vHN0pjE8JvkCcig6c+P89O3b6vrq7YHfO/2THx0cPubNpt823Q5kkM8fjT96lMjnDNY3dfWKEQ7Xjk53IxHIdYKl4tyVy2KmL8jXhx8+SKxvybMDX0ICvNaotN0zP/5RZWTE8Afm7t4VImG1LzHy4EG4ULg/nTj58U9gTd9695wWox2/L/nw5cTjR2SxxqdT448fJ3d3ymNjit/TZyeSj1aOXL3O1GpOyJdaexXa3FNhpPidc6ChILniG598RHQ6wuTg4J0noUJe89PZs0cAhmK74uSd2/H1zfvTyZkf/cRXb3QmB41k3B5ORp6uDj2dTz17YYa5ofsPU4svusen++/PDz+475E4RRj9V+5ittH4r39Tx3yoKU7cuT16574Rotlqe/jhfYjAnv76rzoYZgNEZmP9+A9/CARostUdu36rjqvZ9LQLgQ6Gj84/mb12lbWNxsjgyR/+6ab2jdrwmI1hBk3NXrg89vgR4rrt2dFDVy77hU55dtrAachzRhYXZq7fID097I+NPX6EO/btd84BP4fhuurRY63f/O3o//MvQUX9gsTjwTBWKPT9H/+s8r/8vh0MV/63P0j9/v8KatoXnY6i5Ppa8KMft37jt+xg8KebrPmrCo16MELs7yb+6T8C3dfzctYOHW7+xm/boRC7MB/+wb9Dej3gdVqvjIGh6u//oUPT4M+Wrv5CodF0/esfWpxv9823GodmXBjZ/PBDLehDZB03dMtHtVID0lBm9+y7Cu3LnjljhAOA4+GaCpBIJ56qzc6UDs12E+nuyGBlchIxLUoSlWTcQvGtd94VB5LteLI5M5M7dJjtdghTV4JBxe8vHjmaO3oMxpDds2d7A0lU1lHLMGlGiEWFwf69s28bNJs/cqw6MSlHwt3Bwf2Tp4T+jOFjdj9433BQKlcWwgmL42rT092pkfrAGJ9M7px5C2rwdLXRG5nW4mG+L7V97pzK+erj4+Xjxy3DYQ/KfGaoNzxskfjyN76hBzm+L7V/7l1QVJBC3e5Lt0dG5Egk9/aZ6tiUg2Erv/gLhKohpbbmC+qRcGN8oj07Xp48JAeCubNnqgOjIAKvfe1rciLK9yVLJ4414smmKRWp8MHYIQCCXn37O0YiKNLBzQ8+cGkClTXc1OVknxhP1o4e2jv1Buh5L7/1LZ4L9tTKi9CQFIp1U5nSkcO1yUk5GG4eme5EE4huYoYq9qcMjNr44AMpHe/Gk9XDc8X0YEvt5Ej/3tQJ0HU3vv5hb7jfQImtc+9ZUY5gHCpgWRMcBltkGgBjBJatui5uJeMMqviitjvIEJRN+3RzLOx1NUAwnAjHQQoxjttJlqAtNmiaGRYutRDJMIYyGA3QMcsboPxmhxxG4RTr8AZYbKijIyylEpzjTrEkpNNx1xyJMFtZt6c7Q/2YH/TRCjxKIixEUIY1EzIpqjIxVZ+bag8MSonE9tmzTcXp2eWV/jeNeLgXiW+8/yUtGGyODNePHapiZMMSDgLD1dFpB4VXvvMdNejvxZM7b58VADuvG+VIupYZlSOR/FunS2NTHgqvfPu7JuDkXX0rNiqGY43RsdrcVH1qWgv48++8WWb7eXV7JXFSjUfFaN/u++ekaLQ9OLx/4qTeH/DrXeCwn4ijpK1ghzknxEKr+w6IAWNpBteoDGKOBGlTBI74PAJF94ogwXkRH8PoRBJw+xkaN5AkrGTiZKmhwRQSY1hA9mY5KISxuEwlQduPe/kWAOLmcB/lqcQYCiTJsNXBpmknygJ7VUi27cE+PyISKVAfDVGOio9CRpSTw8HW5OT+yVNqPCak+nbefK9rSTnb3UtNgTiWffNMd2Sg3ZdqDQ/vHTvBy1ITULb6j2r+YGlq5uDESYckcsePd2cnS5ZTs+XN2CGpL9rr69v+4H2XJouzhwrjU6LBb9lwNz1SywxJffHc2bfayX4pFt1878uqJe6BSCE2oHHB4qFDzenxemZYise2z73LIAqBmegUhVKuz6dCI4zjgPhB3fFxUIplHNE7EYQolyRsaDbgGQacrYLBsDMQZ1AFH8WMDMeZvHssDBgWVm5rVAAOkUFOBvtJMEGxsEyPod1AnMoWdS5KhlECNcAZDvUBNKPDQ4TheWS+ATCcORz1aT30MI1xAIEZ0BhlhHxCNN6ZHt15610Hgje+8pVeNCGo1dXgiBCJ87FkdW62NDsnRmLVQzPtdAbTDVISxcGMhWAb778nZRLtRLo2O50fmZSUZg6jtsaOAxi2/e671akpGIG33ntPSsQxUUEdS/VzWiBQn53Onzxho8TOO++U4oNKL7uOR/jUYDeZaY+N7Z0+ZTB0ae5Q7shREARyp9+Qh1PVwfFuKr138hRerOE9mR+bsvxsdXJ6/+QpkyYLR4/W52a9nsIUG1KmvzU0JoUDG1/+skWTtcnp4qmTSKkBNXrW4HB7cEiOxbLvvN3MDKnB4M7771OdLtCQtXBECwQqc4f5qcHS+CE5GMydfdMwYKrWEgbH7JC/Nj5VOHFMjEarU9PVuenyyAQIQcu/9D0nxLYjyb2zb3sYgkkK7hr8wKASDhXePH1w+BjoeS++8x1KlzzLQx3T8HOt4dHy4dnqxITuD1RPH+mxYVzXYNuUMknZF9w+966YiHdS/YWTxwyKQg2DVYTS1BxkW6vf+o6UiskMt/vO2xZDY4oKu7aYShk0lT/3dnb2CAKBr776NUcQf1ZehH9p6EPDHopSW5uvYVYIw1i5CCuKeviIHY5Y8TizuAA4zhd1g0ZRYnfH8fv1kREA/mk2pv+VnuUhCNps9v3zP4JU9bW8nM3+/vbf+r6ZyZAb6+E//gHaaHxxL2fQccxkqv57f8/2+//6blf+lcELYBTBZ0iu2EZ4jDIUrlG2+7hDt28YJFE6ccQvdUjYDrQqrCpAItKNMWeunBeD4dKJOU7mXQTi5G5A4RHIDXYbA/OLij/QnhmjbRUU2qoqBhXec5yQ0Bp7/JAw9eypkz5FsG09ILRIXUJ6lmikpp8sQpq6+uWv+WWeNDW/0GJ0GbNMWhMDco9Vun6F9/eatK35uo1AXY3vbG37w4wh+TTBVkVS5mlT8cl8uNPJrK1tfD3ECm3GkDmh5VN4v8xzBs+VyqHswcvEQLBbZ2wt3KpyYtenS5zSZarNYK5U4Fi/0GQd1Sd2EUGlHTXYrnFdPrSxXziNMqrAyryl9P5f5t4ryLIsO8873p/rvb9509vKzPKmu9rNTM9Mz4yAMQAGBEAyCIgASEoQAwqEGBIDIigXVIQeFAKB4QBqX22mq6uqTXV3eZ9ZNrOyKr29/tx77vHe6IHSg0QgohsAqT6xX3fE2ftlffGvtf8f4DXWUlhVAA2bcfWIyNkAFVAFUxPwmk3dXacnIZ+yaFcLt6tBUKItNSxyJOaUr97uDvdL0SgjdS0l7HUR2jNifNNjMPzScngM1AvpoCr4Kg0JUEAXHRWOtavDFy5vHjhoxYOsZ0T4JhSEA6oA6LjbblF3Nt1iOpZGaFsN9ZomZjKOHpY6slVADZ3wLV/2AcsHIRjzfL0TBnGIlkXD9DzApzQHMgDUtnwNVFoUiIJeEoN8DVdUXNUNA3Qsj9AtQA1DusGEDNsEPM9HNA3yPNTQgqbSkIKOARKWhWk6aPmEbPmmCYIAYZu6GEEAkJUlW7ZdD0Q13dUh1/EwUWA1IagJoioGBY40lJDcDTW6xP3N0LcqrNBhLDUscqzCh2Re0CV2t4GtbgReiEOSQdtatF0NKr2ApURUnuJ5dnWLORaiRZF19aDMA7JFO3q4tUcJHLlUDR2gcdsKqYKqSzAv0brMSrxnAfjlldALGcpSaLUX6jRQzw1oQkAVQBeEARdUZdgyUNeDRBmBYEnO47hPwoJlOIjhk5LvAo4vSjjhKY0oFPeCSM+wAojpoKpjWR7j2LJGm1s0XCQDtmp4LiwJKAmploeYLqghWitIBm1UVRzbRAzP0GDIdTBFgWVElVOOjxBhA7VtTDchBXEd01E90FAiSg/StbDSZUSOMdUQ16Af78KuEw7kKVP2LUVUhLDaQw0xJHaCqw2i1w0fijC65Nt+WOJYXUQ9T1TF0OIuwffC3xkNiR3WkMPdJqMInuWFbZl8tAPrJhXOxrpNxjOCAsdKHdaUop0GvVqHZSN4MMRoAq5qrNKLCi3GMwJSDzNMwoVRVQYM37R9UpUsCbVaEToMuJYJ+A4kyIylAQ4M6a6jo3ovHWIk0OkaDoQZEOPJHuAiigy5sFoNEiiAIYphAYShuYbvOI7tQ4gOmnthZBDHfdW1PUZQEMhXbYrQVNPDrXqYKTuEqPiAA2omAemA40GqTKFqUO35qh1u7dGWEhLaJhnFrq+HCpYzDAfUHkwChtwNGZKuw7FOY/iLC/XRMbWQoh09InYcLRaRuzLihXtd6u4WGg1Eh4OMIYcETpXipKkExbahRPuv3FQYZnt2P6tLLurYAkfbWlDshEyRvL0NJFRlJhiQujZBhAQuoAgY5AXlLm2rsOkDepSRecpWQ3IvW2+Gm83Nkwyj9iDNCckdVu2RhtkzeqmtXabVWo+Gw0IrYKmxTj2giaiqBU0xsbFJivJmMhYTWoynB6UuLXUYSw4JXGq3hnJijSVZTSIV0dZEpMMxthqQetF6L/v06frzFK0IlNQJSlxQERhN5FUR7amUq4dbe6zWow05oPARpZN58Lh2YJ9nObQmsRJPgArtGdFOA/fNmSundw7O+iQeUHqaKniuzxgKIPNJe2v8whdPTj4Pk3DA0SK9ls2QAbWnGzJeMyt37u7tn8QphXb0EF8HKZSxtYAqkA2vdOPO9sysQdOMrbK9tgNCtKWGem0d8L6mpdP3Qd8Xvv0KLIjhMx98ecbyMTx07owbi3V+/KvKoaP8D2uRd98GXPdLCjceScZe/UsnFpdOPv+1ACwfhmFVTfzsT9FG4yvRlRsKdX/lp+rMfnxzPfbGq/j66pfvLYKOYyeSrd//p2ap/B9jJO3vrEUII4Fqc+js+Xalf+PFZ4bOfCrEE1cmJsff+oWYTDT6+sffO2unQzpM9H9yQUmlLYbpO/e5FYttzkxPvXZKjUUbg8Mj758zY0EFY0qXbmoMq8ViQx9+asPI5qFDk3/5ph4Kb0wfGzhzHqDxbihR/uwKiMCNwuDgmfOQ46wfPDTxwUemDzydPjZ07jME9CySLF24iuj24qFn+89+mnv69MHJ7wx8eimz8KiXTAW3m/0XvugUS5mHj0YvXfpi4L8e/PRi8v6jx8dfHLpypXR3vjM8lH70sHj1TiuWS60sT535cHd0bPDKlcLc/NNjx8tXb1cuXmwlcvG9rcFPL4qBUGxrc/Lsp419o6WL1/uu36wPDafvPBj+7LNuKBk25eEPPtCjgUC9MfXe6SuFP8hev9d3+Vq7vwL7xOjbHwDfUqsHpmd+/vq9X/2xmslMnXp/nviVgIcMf/CJCpB4Eht750PsZG/j2aP7Xju1+JMfwrnc5FvvL/7KL1NsZ+gXHzkW1B3tG/vgHNnsrvzSK7P/7rWll78BDQ+Nnzrd+MYRO6X3fXrRtaDqkemxN96rHjp470f/2exfvLH80vPeNDD2/pnt54+BPX347HmEk3ZefGbs7V80xkafTD8rP3JJFosgRmuZQgggiVutTZr0dbwCdxYwE0MKKa25RKAwEkFNfpsgYABNgK0VljaUeBmp3wNcjAhWEGsVBlwsxVrcRtDRXTaB8KsBVtWCr8DSoi+DdHwQ5J7ivofkMKm7gesmQadgeZcGejoxhHQfAbJHFRNabwXTZCgTMbNzD4Yu3awNj5Q/v1K+Pbd08Jm+q7f7L19q5MupvfXKxxc7kXSo0Zh57dW9kdHi5Zv912+0JkZTN+8NX/iim8yzcnfsnTMKxTLdzswbp5r9ldLcvf5PP2+Wy/GF1aGzH/eCsZjOld8/b1AUZuj7X3ubL5ZSc/dGP/2sWyywe+3hs59IwShIwRNv/sJEKQiB9//5q614phUIdh8zadeGot7OUigqqsFpqLOAhUZc0PNqi0xsxCAkb3eJZSUjVvEba3RCFC0aqt6lQoMe69m1BTozYIABp71FsY5LD8Jbi2w0qgTCSOs+jJWAMGBxa2Qs40Cu03jKRiTPo5HG42A4qAXjML8A+mWSYQFuiQrHHAp095ZZLOQg/cTEa6c8CF4++tLw6U9w1+oFY8XLN2hVqZeGKp9cpHl+/dDhqffOArK8NHt86NMLtCwYkVDx8k2iK+4Wh/rPX452u6uHj4y9+yEhy08Pnhj87DLVagnhWOH6HWqv1csXKhevRbd2ll94fuT9M2ynWxsa6fv0Unx3R4wmM3fuRVc2O6Vi5fNr8eWVtSOHR859HtzcfLzvqFRH6k/YNKZbFiTcRzJRx9jzuDW6mNPVmt9dCaYwFcHQ7UdMCtI9EOksopEQqDbB6nqwHNPUltNeCRYJHYD81iqdInWTwJrzSIp1vLbdehossRJkONU1Oo5qcBjZXSCKDg9TUPUenaUtW/G5FboSlkzdaT4JRgALTKA7D7AI6CfIrfG3P+CO7zNFd/DMebwtbT93ZOy909zk1NVCYeYv39w5dEAn2NF3TtcPTruKW/n8KtrsbXz3pbF3TndKxfrg8L6fvbZz+LAQyox+cK4zMayB5MC5z0KDQ+10aezUaSGbqQ2PTr3z4W5/fzuWG/ngI6m/BLhe/0efx7P53eLQxAfn9Gx2Z2Ji6rVTXF8fF8sOn/usm85US0N95y9HW61Pp/7F+Dsfknz36ZHnRi9cCHCcXMgWrt5md1u7peHKxZuJ1ZXVw4cmzn3ENBuNmfHKhWvR1VU+kszeno8ub3TSmYGrVzIraxvPHhk682lka7s6Mlq+cD2zuCBEkvknj5NzD8Rytnz5Zubh4hfT/3Lk4y8SKyuN0ZH81Zulhw/VXDqxtFi8fqdbLBWv3CremtuemsrdfjD8wUcCEw1D8sDbH7oeaLL09KunxHSW3d6deP8DMZ0iOtLw++dkjLXjweFffGTCuJrL7P/Zq1d//3dIQRp77/Qa+X3Ablc+viBTEbEvO/HaO8AvfZ8rF/f//PUbv/MPIMcf/OBjjaEpmBo+/bHpwPzk0ORr78DffbkxOT75f77dCyaDrjf6zoe+/z0ooQ6/f87VvN1Dh7621RPwPB/Fej/8EdLrBi5fAr60Y7mP4+FTb1vJlPjiN3qvfB/huOAX5/0vuR0EAQiK/7s/s5MpfWz872ru6G/aIgRB0POjp95kL10APe/LA5aPot2f/obw7e9irVbsjVfZq1d8BPmS20HHcUJh7h//nrr/4NeFrv76LELhuZN2PLIzOyNU8hbLrp54RkuGpUy2tm9CKBeUSFTqL9Qmx+VYsjo7LfTljHB47egRLR0V09nG5Hh3oE/M5IRKsTo1oYfDtekpoVLUQ6GNY0f1TLyXyTXHhhsjQ1oyLlSKu7PTRjhUmxxvDQ74BFqbnRb7cnwy0xoZbIyP64mY0FesT43p4cjO7EynVDISUW6wvzY2qmTTWjqxc3i/kEiJhcz2gQMmijVLfdJAsVMocf2V6r4pOZVUU4mNI4esSKDb17e9f9YMs62xUX6w3Mvl+HJ5b3qfnIxpidjm8aN2mO2U+2oz+/RIqDE62h3s40slsVhojI90MiUTBFe+8U0rSPPlvur0lB4LNUdG5P58o9gnFfKN6Uk+lXHCgY3jx8xYsFsoNvdNCIWckM9zY4PN/n4LgVeffx6Ikp1UYeP4UTMR7haKjcnRbqEgZTLtieH68LDNUKsnnzFSUS0cWX/muB4L9vLF5sQIXymLmaw4XNwdm7RZdv3ZE3oyosbi68eP6okwH0u1Rga7QwNyOt0eH6yNjjksvXHihJ6OqZHY5vEjVjxIMQ4bNpwiTeEWmQfRJMIgGl5B/DCOggYeNpA8SQa8QNgAyjSJW3jK83N0GBLoIgjGMZJ1mLgF5EiGMgIhw+ljcdyhUy6Yo4KQRBUBMuwBEYyJmkCZYQidCdtOicEtGU3CQJ4KIjJT8OAEhgf9YFiHCzjC+EzIBEqMzVK10TF+qNzL5vi+0t7MPikVN5LxjWeOWwG6F09tHTlshFlueLA32Mfn80K51J4c4YplMxpaPfmsHWb4Yrk6u0+PhprDI/xIfyefFwv59uRIu1y2wsG1Z58BA1ijb6i6f1qLRVrDw9xQH5fOytFY/cC+Vl/FDjCrLz5vR1ghm989MK3FI1x//97kpJdmIgAPDDFoGGJhBRqmUdanWYtIA24Ex0AVT8NukQ0CAjJM42GIQVRoAKcDLhICqIwPpTCGtoika+YDIUCEh0g86LOYjg9gcBihaItMuUgaoyiTTnteng1AMtyHuXGaVVp4BYdiGM2aeNIH03gIV6i0Z+UDAUgm+mA3QpmJaGN0qD46rCYTUim3e2BGj0aaI0ONsVEHgXdmZuRKlk9nWsOD9YkJLRmTivnavgktGq1NTDZGh60QuzszLRdTvWy+O9i/N7NPi4bFcnH34KwdYKoTk52hshYO7+7frxTS3UKxVyk3p0Z78ZSUjO0cO+IEmfr4ZGeookbC1elpsVLoZPNiubgzM8ViGh2wkT4Cp2wm6qAZBI7CQVZHsogToVi54c2mChFKwAAAIABJREFUCMJkAjZSxnHKoSI2nfLcKBFgDbSE+3EiDIveWBCnXZoywX4Kp10mYuFpEIzhIUpBy7gdp8IAD40FCNalKAMp4wjhw6SDZyA4gYcpFclDdiYQBbrQKEsyLk1beBmxwoyYyYpDxb3xSTMY3DxxVEvHtGh87dgRPRnpRRLN0SF+ZFBKZzoj/dXxMYtlto4dVbMJLRzZOHHMSMX4XL4xNtIaHjDi0c7oYHV83GaZzcMH+L6Sy5Cbx49p2UQ3k22ODrdGR4x4hBsZaA0PGQi2fvQY31e0Q+zm4YNKNiFkc+2Rofr4mBEONCfHWkNDLk3uzs6Y2Wir2NceHKjum1TjMbGY356dtSCkPjBU2zdphZnq7IxUzvZy+W6lrzUxwqezUi6ze2i/HWTro2Od0QEtGqlOTwt9eb5QFvsKzalRPptXMqmtwwetAN0YHeNGBpRopL5vSs6n6vG8FI7sHTncy+fVVGLzyEE7yDSGR9qjg0oi3hgf6w6Vu/mCHQysnnwWCGCt0sDOof16PNIeGGiN9ovJpJzJtKdGG/1DNkutvPi8FQ1IqfTW4QNaLMyV+7ixoU65T8pme6Pl3ZEJl6VXnz9pRVkxk90+uF9PRDvlMjc62OkrK+l0Y99oY2DYZcj1507aYUaJxWozk3Ih2y2VamMjnf6yGYvU9k81KwMuS6+cOOrICvv/XxbhlxjR9jySMvoHsHoNq9e+PGCAnkc+WbIzWWNszE6n8b1drFH/stNHEARpGrG1qU3PeGzg72QY62+oYPkoGvzsfPD8J5Btf3nbKtBx+F/6Ue97P4BlOfTJucClCz4Efdm78zwfxzu/8fflw0e/ztrV//OKECQUhez1bBTzAzgmSSGiozlputWyaMpH4IcvfztoirFGjRBFHwDMGIv1egEAFCo5tt22CRxTFbrVBHGYEkRckkDHMZQYIQiA7fDuANvpeCiK60ag04F9i+12cUnCEARX5PvffQUAgESnyraaPgDghk5zHOaaTI8nJBEGVNQymU6H4nnMsdhWC5MlVhRBQSZFiWy3qyOVZikV8OxYmyN5HtM0psPhikLzPC1JBM/TkoirGttuoYbBcB261yN1jRUEVFVJUcBVheR5ShQITWfaHGKaDMcR3Q4sqF0cu/sbv4xyCiooBM+ToogpCttuG0aS7MmYKOKSaGMeJghMq2UkIyzX0ZIxEAAojpOTESaIhl4+EIANoq3hkkjzXZ1I0FzHiIY8BKV6PUOOBHwYV5QAx3khkpCkQI8XgyTNcWaIsVWV7PFeEAyIIqqqdLttwhGc5wPttp0Nx47lQYQxOzzJcQ6LUxSPyArDdzUSInmeYamWN2QokA+jpGYKDgaqPqGZqoXBCojKJphRcIpwe7quMY7vUbqjuwTi+4jpmDYG2l7AthUN8XwQsT1DgV0Ho1VLtXDfAxDTNSwU8gHEEg014No+YriGiQAOQHYlIOfCoAI6pGFjkO0Stq2rkGviYcOWTcpxYMawcUVhW62O1s/yXVoQUFUN8D1UURi+w0aR6POjNGbiqs50urBl0b0eyXcxTcV6MqKodI8nJInodkm+i6kK2253DIPudnGeR1WV5BVUUQICj2saxvdIoYdaJtNsonKRisPRb+/rOgbZEjBVDXBtzDVIQaAVGXZsmuMwQ1ehgOrTmOphjim4FCE5EOYqGobjPk4qwKgH6hooIqpPIwYIQqbskLThQ7hjGoRP2JjuKgaGkC6mGLJHYooLII5i4qgKMEFH1VEC92HDUw0CwTRQcxWfxk0IlUV0GvSknidHdA0DUAQxPNXAEMSFFUtxSdCEQdelOQ7yPMw0gx0O02S2xxOiiNg2IfGhg2XANTjbYptN2DQxXWPabUyVmB5PiAJguoSuEqKIaxpsDQU5DpdlXJZZQcAlieW7hCxBiiVqGikriKxCnsNyHCEIuKGFyky4MMYYPC6IiKDgSo4URbInwrYdbLcxQcB0wzNB2aZYzQVtWNNh1nZdBdJNjFFcCGgjzzFet+VYqGwRtOb5LqjrWNjSQBNQbCquaq6LGh5Fyjbk2KpNsortgYCq4SHLBg1HtUlKN0DQVQCG0H0PcDWbDMgGxOjYCO45ji1Aqk0kdAW0TMljKMVxUVcxMdyAWEQn+S5ABFhZxBSFbnMaDuLdLssweiEeO1qQYFLr8WS7BaAe3ethikzzPBDAiV6PJQi5nGE7HR9BSVWlu7wJOWy3iysyy/M9WaZE0UcR0cyxzRYQi+KqSvI9xyEZjQ49O2oDIOfaBM9Dnsf3F4JtTmcZUhQpSQI9D1dkXBRxQzfNJNtqY5KEaSrNd3FJwrjO5siAgyGQ75KiRIoibNmBdpsWRVLXmF4PU1VKFHFFwboSoWqkrJBcF7ZtttnEeB6XZLrDYbLCCAIuyzjXw1SFFEWmw4tqjsun6uNlqi3S3S4mywzfxVWF6XZJXaOEHsO1MdMkuC6iKAGhi+sa2evRPA+gMNNqqZn4ztjk9uRMjK8HdpuoqgY5DoYdUhRYSXJxlOl0JCOLOhohCIAeZAEBUVSG43wKITodpscbDMm02kouhegm3u0Scoq2QVRRGZ4XSpkbv/brlCbnV5aYTgfXddyxCUGgBcHyUEyRWZ4X/6NZa/6dMZZtO4kk9w9/GxYlYnX5y8fuwZIYe/UvnHBEm5rq/PpvJkUR39r8kuZYPoria6vxn/1Z/Y/+GwCC//aM9TcBLB8nqAf3Iu+dgiXxqzi2m/LJF/gf/QR0HOb2zfAv3gMd58tu933Q8zq/+lP55HOg6wJf+8+DYabFjZ8+2xwcXmVe2veLM71cvjoztf/1t6VUYu3kSR2hCdsgud74mY962dxCLDz5/od6PLFx6MCBU+/LkXBzbGzi/TNmMqwGIyMfndcj0QepxMT7HzpsYO25Z2fffk+n6bX9R8fPfuwSqBxNDn/8OYDA20PjRpLwINiz/el3fuHC8NqBY2Offo445iOWHjx/AXTAxydeGDp/If/wwf2Xvzd4+Vphfk4qFEJL66MXvuBCcSE9Sauy6xGDn13MP3y08NJ3ytduDV67xhX7MosLlc8udwqVzKNHM6c/2JmdGbh6o3z12tIzLxZv3Bn59BO+UM5srw2/86ESjye3tqbe/WB3Zl/l8rXBSxd3RyYp3LPG9pEPniZXN6dPva2Gw+Fabf+pdz8f+qPc9fmhz77oDvVDvnzg9Tcfffd71WMHjrz++p1f/3UxmTzw6hu3/v5vuAHjGOorNgjUugdee+vJy99af/6Zw6+99uCHP+xWKgffPHXv7/2EZELTb78H2AA3NbT/rXdXnzu5GPr24VdfXXjllcbExP433tn93rNuwdv39nvIy9/eO3Fg/+tv7Rw9Jk5VToirC4GyudmdefOd1e++6A6606feX33pG+svHJt9463aoYNLh5+XHqh6EKFRhV+mCcRhKLezRAU9CYjBlK8DOGXsGuICheIoQ6q9FQb17VDM55bwgClT/ah83zUgPFqBjKe+CxA0IQorNGi5gRQgPMVQVXfykPzQNXwy0g+qS57pkKXynjNIo7Zt2Q6/hGKyRQ0hxgO9Z7OhjK089VQVY2N6ce7eyMdfVPdN9l++MXD1yuKJF4o374x+8kmnWMrlvYO4V/fAwN2FI+++UZ2eqly/PfT5+ca+ifzdxcn33u2l8xG+MfX6O3ogGOA7h15/szE+Wr55Z+Tjj7jRwcTi2vSpd9V4Mio3hl8/fZOhcUU5/PrrnWKpgvrHMadhR6gHyzNvvqMFIiABHXzj7Rs0DXje4dffaPX188FQY4HKWIqftFsPQ/l8z6aJ7hzEDgI0pQFxEtRst6o2FpIpRfL7vPZDIpvuOTNgbx6k+2HWd7r3UGIYwKNWYymSLijOgNd6gMVjAhpnhDmPHoAZ0OYeIVgfRLhmayEYy+rkuAHlSbjR0J2IOOf5FSSM+8ICgqQBwjNai+FwTPcHsZl3P/Bdd+nEC2PnPkVNXU5lBz6/TMrybl/fVKDDGJ3HWHrfL84girL0zAsjn19iuu174WD/xaskL++MT4+c+zTSai2/8Nzk6XMkxy0de67/84vBak1OpStXbrK79c5g/9Ann0V39p5+66Wpsx8HdnZqR2dH0E4BAJsaWrp+O/ZktTM4MHzhSmp59elLL4x99Gl0ff3pseecFtR6gOKI6TmocMfHkrC7rnMrLBr0SLtpTe3HF+dNp9R+iOZB2YUwfh6KBiCrZnCrdCToeV29thTqQ3o2DnQe4AwgGjjRuQMSQQhum9xSMMKqtmrWFoJlSHBYoHMPISoaOWbiDGLagL2pN9eiMVwEXKu+EOwDJDMKdObhKOCydHv/W+82n9vvmMjUux+snXhm86UTs2+caszMcNPDx6XVDTorVq3pt9/be+aw7aGT757eOXxkOfTi7Btvt8cnqvsmD7365saBg0Iste/9063pMR1hJt/7sD4+XisN7n/73U6ptDM9deD1t/bGJ/h0Yer02V4pvx09cSCObmvyOpqbfeNtNZffOjA7+9Y7rVK5nSuPnz7H53K7w5Nj578I1hufHJ6Y+PAcy3GPn/vm0GcXI9VqM5IwhjMo7FqmOXT+i8TGxsoLJ8fOfRra2d47ONt/8UpieVnM5su35mIPl7r9lYGLV9OLS8svvTB+7uPo6lp9el//jbnc3Xk5myvM3c/cutcdqPRfulZ48Kg2OU67glschpe6fddv9d26JSfT2aWF/ovX+YH+vss3Sjdu7Rw9nHm0NPPWO1okEfJ746+9Pfdbv24x9OHXX79B/6OVqUMmgBkIObjwZPatdww6YMWDB9545y4IS8n44ddeuxgJE5Jy4NU3Hv/eTyFInHnrXQfGhb7cwdffePCTX+H6y0dff/1K8HcBw9r/+tt3Av8Aw9mZN0/5AMqViwZEAJgX2dg+/OprMhuBYGj2zXce4b8GRPTZ194Gf9nePnzk619GQcuy8oX2f/67mT/5Y6TDfVkhCgSx6l7szdda4bA2Mcn/+Ffif/aniND7krDh4zg9dyf+85+1f++fgobxtz3CVzUa9REEazYT//Z/p+fnvgJd2bY+Mtr4o39hR2PExnr2j/9bpNv9CmmDpsn/6Cf8j3/Vo6ivl+XVX5tF+Kzx936aaNUNhgECBLPb1AMBOxYgdxoADCmFTI+JBi2RlkSoJdkU6YbpzL1HaigiDJbonSYIAWo2gTV43DOlVDq+tGyyrFJIMbtNi6aNdITaaQIg2CmVc4uPCMesjY1RDc6DESGd0UJB1DCCGk9WOdD32/39kb0qbqpmMoK3exZKtvoqse0tUhIbo2PBdovlWnIx6xo+2+HahbIKgqBtx1wVUwxcUZsDQ/Hd7dDeXmNygjAVSNS7pTIhClSvp6XjbKvDNls7M7OwaYRqtc7gQFDsgLyippNBrk12evxAybN9qssL2WwPpQDHQBAiLnUwQVRyaUzXEUGB44ThoCTHq4WUjLHp5adiNgtjAFXjjFjIwTCqzjkRRotFkrLQYsOJbtVum3IqBWIgsdmwEyGHpak654ZplWLDWztCLofBTvr+406pqCci1E7TCTEWy1L1NkXaXDgbWN0W8zmUBKjthh6NuFGadU0bgDQbputtP4BL4Xh0fVPMZBAcoLcbejQiJxJW1yUdzY9RNu/5KEzSttXxXByFccf2HNi1QYRybIy0VTiAeg3dg2EnwwIdw8MwOmiqPQR0fT9B4lUBM3Q7GzB0GABAL0HDLcUF4RzS6IhByLLNUthTPNiy/QACKryNEX4oirRVwAfoiKtKGKybeBxUdBzRLTBJhJs1WNK0fBKRNLInNIeHyV4vVKtxQ4MRT8J8sMlE0LbAdjpqIQmLOtngtIGs4yHhnR1uoJ9VeKQjK7kMpqooJ2qlJGQ4VLujFxKWh4Z39/hKKcFXDd5Ti2nUNjFOMDIxiMaDtiFBsG7Akd1dvlwiLRVri2o6Djs20eY75T4TIdG6aCfYoCdpXcgPESRqKD0UwXyAcB3LQD3AocMQp7kRKmzyRtMHg2iAUptWAsNskIIt3qMwy8MwaEdy4wzCuDoP+QxK0Y7aRXDMgTHPbdgUZsqZuN/SnTDtEjCqdDwPwlDCUUCAgLwQCbdUEjGMEOu3dI8hrTiTW14CPa89MBDZ3SUMRcrlMK4Huh5fyCcdEQJBEUTJPQ5x7ObgUHhnj5QFvZjEu4IDYN1CIbqzDbmuWkgRuxyiae2JsWC9jqmqXMyGGnXbg41MjNxtgo6nDuTwVg+XJKUvCyMIYxldjAjUmq4DGpk42eEhw1aKSYyTCEmqD4+ykqDIKBxFUdvSFZgMu7aNeJKDBAAdhBBLcvEQbVmGBiEhmFBUQ4IjrCxSEVMGmbAjQwzcVr0kFRS6Bg9BKdyDIU1F6KBjA5jXs1jW1F0Srkl+lkUxWxExjHJRV5YRHAZhD6IhwWBCrkSEkKrgpgOUI2sCAoZRErboapPGrG40Q+825HgcpiB6q26Ew06cYV3TAUDVRak6B1GwEE2GlzeUdApiEWarrofDVoxltpsmQ6vRaP7hAy9ANfsGQpvbWjgiJZPplWWLps1EiNyouwQhFPPR7W0IBex4IOg4PYISsGDqyROHJPVMlN5tuwjcyxei21sOQXQKxcTmJui6aByzeAczjMbQcKhWIxS5ky9Ymm7jOMQEUrtbsGVpmVhou0YKPX580DIAWugJpUKo3XAswEyEI9t7iG52RvogUSckSS1lfNWh+K6SyyS2NwDN7g2WEVmDdMsLU0004II+5fqUY9KdrlTMMUIX0BwtG8e7AqzqWjHtWkBkZ6fb15eSa0bPUzNJ2LXxFm8lQmIsqTCheK/h6U5ke4cvFWlLiS6tc4MVhySIBmclIw6EMvUWEYY4Mlmav7M3uQ8iQLLKack4gEBko2Mmwp7rp548FQbLvVA8sbrGl0oQAXGRNGmoYa6Jt3tiJuPBSHRn206ERCaSWFnh8wWl2kz9qz/+ehmN/vXIQS08yv6rfwkaxpfVsXwfAEHxG9/i/uFv+zgeeeftyHunQNP8KrNMGPePfqf3yg8gTf3b/PxXdNaCIFhRQuc+pO/O+1/JLDUYbP2T/9KJRBGhl/pf/5evRFeQrsnPvdD7wS+7gaAPwz6CfI0Wiv6VB/FBELDcyt2Hke2aDaD9N25Fnq4bMD5w535+ec2z/SOvvbXvF2dc0yvefRBf2/Fs/+FzLy/PHrUApHj3Qe7BYxvEcgtPso+XDRhfOnJyc2RKx5m+O3czD5cMhCo8WCzN3dcwemXmyNMDx2wXyj9YTD1ZtTDqmX/7s9m331XJQG7hSXn+gQFgycWnuQeLtgtmHz5OP1oyQSy+ujF49aYJ4YG9xsDFa64HsbVW5fa87wBDc/dPnHoXBbzo2sbApWsGiHEAWMsnuiDK1tt912/7phfZrlVuzlkO2kbQRjLMgyhb54YvXgU0m25wAzfmgJ7ewskGjXN4MLBVHblyA4bA7M7ey3/+l7RpUp3u4PU7iGqytWb/tVuA4bDbtaFLVwHTBU139NI1ptUFDbf/2m26xcOCOnjtFimpSEut/Pw9dIvzLH/k88vsXtOH/eFgJ8S4AK9Wrt0kOwKg2WNXbxKcYMH43De/Xy0PE7w0dGs+XGuBqtl/9SZd6wC+M0J3aEx3fWT06s3gxo5jQaXXPwndXQFtr//WfLDBARYwePUWs1M3QWzwyo3Y6qaJEEDVsaue40D2nuc2XAdAvB3X37M8loaeAMYTDMQRt+66u45p2QDIQ07LsWxrywW2NQ1ngC3d27QsBAFczod6mg24dd+v2pYPO1sOsK4ISBDWdhGko/so0LCdHc+HEWAL8VYRB0C9bRvY0DWcBnZ1a9MxQdxruvaWY/twZKdauX3X9aDQ5s7ApWs2hAdq7dEr1z3DIdbqhVfP+C0lWGtXbtz2VTeMqSPhHkBi5F5r+NI10PKpjjh47TbSlVnCGg72IAKht/ZGr97wXYjkxLGLl0FJIxFrNCzCmEu3e8O35iHdxp/slf/0LV92UEGduHwdlg1M1geuXEd4meJ6QzfnMElzLRBc9wDe1gHCfmLZPGh5KLyqA4LnuoR9H3FFwrN8cN0FRE/yYApp2ianurS7orsc4Fmgv2FrHdjQDQRvuUpT9wh/zQFajgESwLputkDHt2GgYQK6ZQDeugu0bROjrTsguAe7FAGsaW7DtR3Y2bAszrdd2FtzfM42ECL3aKnv9l0TwGNP1gr3FmwXTC+t5BafODCRPn05/ouLOkZnHz+t3L5roESSUSoh1XHd1OPV7P0Fy4Pzj54U7i/qKJUlxaGIYiJEfG2rPH/fsYHEykbmwaKle8WQ3kdxBkqkHi8P3Lhj+2jwxkLu3fOeCSXWtor3Hrk+lF5cLt+5ZyJUdGll+MYdB8RcyXPXbMcEzR7gr5m2gzld11+2QBAGFMy6hfoe4RqAv2pbCmJ6Om5v6YCndwB32bIc1Fd9cNV1Fc9wLBRtmI5mygiyptsGbMmQ/dR2FcAwZRJv6UrXsnBn2XRUwNUw+y4MGTioA96yZRqwp/nWiu/Jnuug/qrlij5kmH035gI7dU+3B6/eDNQ5F0BHr90KrW5YHl5883z0+qLvgn1z98PbdR/0h0NSCBIthBi4divxdM0G8fL8/cziU5GJLB5/YXt40rGBgZtzka09CyX7r9yILz41KLoSENKM6uhOYe5+dH3bMfzcm2cjNxZ1EB+8OZd6umogZG7+fnpp1QGQwoPFxOqWCeLZ+wv5O3d1jE4tPK5cu2WAWGxzpzx33wegybk7h89+Ajl+ZvFpcf6+jAQ4BG6morINRrf2+m7M+aYXW9sq3XtkKU6TIhsU1kUDsZWN4Wu3HBAN7dT6b845tt8KBetBRkCJ5NP1vht3fAwenL//zb94FUMAptkdujXnmV5wt166OeeaXnxjd/DmXceHCV4eu3AFFlVE0oYuXsW6EiEow7fmEcWMLa18+0/+B6rNw6I2eekaImoSE57/5iutTClQbQ7fnCMEleiIQxeuIKImE8yd7/xyN5ZGdGfkyk2izsGK2X/9dqDWbGeK89/8fieZgzR35OJVotGBZf3b/9O/yf/7SMfrd+gmD4nawJUbWE/yDXfs0lW2wbl/pRkBBPkI6iPo16rIAgCoTe1r/sEffoU0GxAEPC9w6ULw0499EOR/+GPl8NGv5CMK2lbk1FvUg3s+hv1tAOsrtgg9j56/HfrorP/vz/Alu3sA0Pjnf2Rls4DvZ/7HP8H2dr+SdqVO7+/82q/DmkrcXgW/Zp6iPgjCmv4fimoegiTW1/NXr9E7exDk5m7dCT9e7h6aHj53TshmqoOVoc8/N/Nxk2Hzt+4kQuHWSMXECQ+ECVEe+/CskohVp0YHP/vcCjFr07Od4bSDoOWFlcKNmwAIrb74zPjpM0Yo+OCVHzSyed9U0yvL+dt3QBSt9w8Wbt1GdX352y9NfnJeRZDYsWeGL14CUNCOBrP37qOqufD8i0MXLibX1m796FdHz30U39riRgeZaqdw7Ua1MpR++mT04sXu8X3DV67Flpbvv/J9HQf4yYnI5UeFufnMzXv1/sH49vbEmbNr41OdXKA7M0GsiyM3bqbv3S+Mjsaae+UrVzuh6NIPXrDzQbtnDZ3/PL+0tHHiUObKneyjh5Ub1wOuWbx0uVPIBjhu/MzZ3r6B/PU72Vt3qwf3sQaUvXXbwLDdgzOjZ84aJKal04NfXJTTsZjpZe89ECJxvMDmbt32fHdr4McnGOyaIWEPtgY++8IMUBF8O3V7bhiE7vz2b1kY4cNwhOuMnjnrOyYiipWLl1uAjiThWTpP+MLSRrtw+SouSlo6Pn7m7LL1AuC6fZcuQ66BdqTs/LxrO7sEWLpylWk2F469KKxidABNbkn13QCM+hlar9ZDuKbk+6XNddojkEpPqq8yCBrIEdvyaJx0fYzTtxoRUlfzw9LOKmURVEq1zQhtBFnyYavWqliGH8659UaQUdRQqmlko3IxB9xtt7bioO8XNntcG1d1Ilhxm60w0NEqE3JtGVbAYH9bVndoRSWKVSW9sDD0xbWtYwf7L18r3L//4JsvD127npq72zc0kmzsFG/cbA0OM93uxIdnN8bHRnPWBBnYUrj0tWuZ+buVvj5GFYpXrvKZRPLF6WMOUrW04p254rUb2wenEw+fpubuDhQLyf2RCYDUtI71YGHkg9PVmcnMrbnSzTuN2cngbjt5+85gLOYEqcoXF+V4DCSw0Q/PbA8Pd1l6qx7KEDoSkdZbqZgjkjPQ9looAloBQ27vsUlKZRRtsxkK+no0xbWenaEePoG6emMtHfLtgG9Wt9mkb1PadvfZCeTxBsjZO9VIuCfGY9reKksWgUJGlacGkc09cIPfbSXjoAsG9HorGlHlWE7ZXmNhn2Rom9tlE2qPAuWddhgzPXzYHT9z1sGwhZdfGT3/GQz5QiqVm5unFbXaP1SYn2fb7dXvfGPi/Oeurkd+8L0Dnkp6pFx9mp2/R/HC7vhk9tbtRLu9+OzRo5BGYuH77a2BK9dwvtNLJ7P37tO7zd5ocSgKpSnwEc+PnPuIlOXt44cGrt8I1WtCLp1+uBBZ3WqN9Bdu3kourz5++YWpy1fJajX84stSm6xWg7mwarhwe5UqFTR9zW1VIwMV2dj1Or1YbpmHSWi7GklAHWgf6lXG6c1db0WptlMDhZ7Z8vdakeK25CRtZXaMXq2KTai1GiokNael1OuREL3JBtXWyVny+qKtRGq7oX687dFAbStezKqWYDXr0eFMz5XEOheJryhwyt/ZCoYZr0ztDn7+eefEDKBY2bl50LS3gMO5q9eYZqs32Dd29uzOkYNGgKlcuFSfGkOyxH4iTcHi493d0tVrkY3N+r7x8Q9O16emHr/0rU40jmhe9sGjzJ15mut2svnylWuxeKx6YvaAJbTDSX5zuXLlqtxfwnwn83AhWGvWRsZKFy9nkomtQ7Ok66qYAAAgAElEQVTjZ861hoa4XL7v+k0+Hq/1D+Tn78ZrtfZzsxPnv0AkOfat7wx8fpGSxEpfOf/oYWCrtjs9k7v/IL24uHD0GZtFlfHB9O0nw9duMFub7b5S6uFCfGWzEU3sfvMgrEseL0+c+4jhOusnDvV/cSGxudnIZrafnbbzCUowi7dupx89bj6/f+zCF+G9vd0Th/KXb2dXlvuLhfD6aunKzfbEcHFuLn/zzupzR0tX76Tn5gcy2QBhlq5eVyMhIxQY+eA0N9gX2NguX7vBjQ5QnJS4PTcQjc3/5k9NjEIAP7m1NXr6bCebort86doNOMs+7Zs2MRK27MyDB+VLl02abJUKYx+e8RB/dXK/SdO2rQzduZiZvyuxwe70SN/FSzaK1PZNjn14hiuUkrpWvHbDirB0uJGauzeAEjsvvvgf2gJgO1vM7ZsAjADA16lN5AMeyygzs+1/8s+S/9u/8QjyS4pBoGlGT73pRGPysyfbv/OPseoevrnxFdiowyV+/ud7f/yvfZL8Gz8q/CotQgjCN9az//1/h/R6X16+gnS99fv/TPzGtzySTPzZ/xE6c/rLgyToulYu3/6d35WPHk/8/M9CH37wlVS+/3R9wv/3WBhoW/LxZ/Tf+s3Bxfu9VFrLJUs35/hcXs3F449XPQzlBsrhtZ2Ao9RGRqKLa3I8bkcZE8QBz0dQP/p0HYRAbmQgsr6LO0ZtcND1YQ8EUdgr3HsoRWNSKRN9ugk79sbBQ4QoYZ7loUhiec0kqdro6MD8nEFRQiUfXduBNX3j0KHsyjIj8u2JkcTSqkqzO6MT+bXlYKOxfPxEqNUsPF7cOnoA7arxna31Q4ejOzu0KJqlGCiYgXrz6bHjGNfCO0292J/i6lSjs3HgQGSvGupw9f5+XxShXkeenAlxXHFx4emzz8RatcBOozo+7skC1OPtfDGgqMnVteqhaceBKrdvLZ98NtptBbfrtbEhRpKoZtcsRW0HjT55Wj16UEaZyauXdybGQRSMPlmXChmbYRJPVoRyXowkBq9cWTtyLG62Aos71ZFhMIjHulWJCRs+lXyyIpazQiQ5ePny5qHDOGyrEA0CPuVo4dUtPR6RYtHU0hoYJ2rpUr+4vk3kAQIv3LrbHho0A1TsyZoRDUrpdObxspKKtjPF4WvXdsbGgQCRn3/IFYtiJg3VVMyz7Rxt7zkeARNxENpQfBYDEqhTcyDHgYqE03IRy3bTGNho+hDsxRJw3QJoBErAXs3ybU8fiDJrK4jn6tkC0PVByDeKYXy9C2EQTelap4e4tjgyRTYlQHXgAg5VNQtGzWKI2OxCIABlUK/rA5INlXGL90HZdfvZxNYG1ZM7Q304L0aqteWjx1iO63u88Pj4iXinEd6qrc/Osh0uXKt3hiq4p4flZiPe72pu5f69JydPRrvN8MZedXyEATRW5rloFhbN5PJK9cA+20cHblxfOflsRtzCPKfJ5nBBDuw1ukNlwPLTT57WDkyZPjp4/erKyecYVYwtr7fGRxDDCG9Xd8YnbIpGFtr+YIjyVXPTBrMkTPj2rgmFECiAulULC3pWlPWWRL8Sgm0Fre+4FEsGAoqAIjToBxCwZoA0ZDAIvbdhRJJgIIhsyUCCAAKwU7VhEvQCHlKrAyStp7LIhuonMTPGko/bYByHAqC3Zzo0ZmcC2KZI4JaZDvrLMpAklFS0cn8eM/S1w0eyKytBobO1b198ZQMA/N2Jyb6HDxwE6fUXIptVTBTXjh5LN9dDrlxns4mtPQtCd8cnSo8eASDQGSgFe03GUZ5mp9PbW6FOe3NmurCy7LoQN1CKSVXEA9uRDLPXCjebOwemiWY3VqttHpzNra0ChsMN90W3q5Cmd8YG6CYf3txYfOnlaKtutUGwgIOa4/ZcOIOBlgfUDbCP1DwcXpf80QCtKk7TszIUovUgro3GIy4atBo22ocbEAU+6QJjYVjkkXbdjqc8JAB2DCSN+QDs18xASOlSAWpvw0gXcRy3dwwqC4Guo/RwNAG7AAhXdSSPKDgNPZX8AYbwLH/X8HI0iHrJJ6toAK6Whoo379TGJz0WL9yc7/RX9FggtrRms3SvkEs9WbNCdLNYGuBXa2jCpej83INuPqelovGlDZOmaiOjuKIQrmEQZN/c3Xap1CmWRm5cl2IxuZCMdKoeSVYDxdLCQ5/EhL5Cbu5hs9zHFYrDN29o4bBQzERWtzwIqo6OlRcemRS1NT5ZuTsPwrCTD0Itje52l48eS25thuv1rYP7c8tPPBfanprMLy6irluvVOBmE9RkcKAfb/di1b2NQwey66uQbjcGBsFWDTQMq9gX5bjo9vbOsYMoryRXVrYOH8DbdUez/Hw2vleDFcOqxBzZj66u7h4/jAha+snjrePH4tubqKRxIwOBvRrZE1uTI46PDl69svLcc5nuNr7ZqY0N45YR2qr2+gsugKSXnjT3jWkINXTl0uqzJwnP0CAKgnxG6gW3a2I5Z6J49vFTsz9RjRQADwBBgDaV1KMnrdFhH/Qja1tyIS1Gk77jMZYiU8HRK1dWDx7CYTf16Gl7oM/DkNjKZrvSp1PM4L277dFKLxAbvXJlY2ZWqrb+P1mE/7eC9TUsr55nDA3v/ev/GbTM2OuvRt57xyOIr4AQhWLjv/pDY2AQrdYKf/gHsCx+pZad/Myzjf/in4N/0zi+Ly2agSDc4+Ov/gXCcV/h2aBlCd/+rvzsSY8kQx+d/Up0Bfi+Ew7zP/yJcuAg6NiA54Ou+/Vcf+V1OR4E9HRPcU0f9XkVFE2JCKJtGejpGkyDHdXTXNvHAF7zJFsiw+XProSWdyQ6DHZUsKOoCA10FFc0VYhK33oQXdowQQLqqGBXl4kg1FWhjqqAVOrBk/i9xwpM+YIBCboGkmBDgHq6TASBjgJ3ZAWmAV7zRdP2MU80AF5TEdrvGWidl2EG6KheV7UB3NJckJMNF3U0D2mJGkwCogk3BRlhSdEqPdgwYdqxQKAlmQbkqi7c6Fk4SSp2ZbWmQ5SneWBHNhzUMQCoq7kAGhD07Bbno6Qv21BL0CHClWygq1oebtsQ0BRMB7EMAG0KOkh4mgd1VNOFLRsG2pKnODpIoI2eZUG6i0JN0bJA04JATrYNwAFxoKN6sqM5OHK36XKuCeJQR7FNwLJhqKM4uq+g7OC5z+h6V8YDWFO0Dd90UZBTXAeyfBK4tGPqiA5gYEtyJVtBGbgtu5pvuCjQlh3dN/4v6t4zWJLzuhJM711VljevnqnnfXvfDTQAwtJI9KBIiXY5HFKxmo0JxUqjkagdjbSrmZFEoyFhCTo4wgONRjcaAGEbbdHudT9v65VN7+3+wASD0milxoxiV5u/MjJvZlVm5Bf3xL3nnhMicceKjdCGSbihxppnYEzTEZpewgTpti/IPudARN1JNJyETvCSw8kWY8KM5nJtnzdRrhEOdsI+ic9JrtCweZ3g264g2byO8g1ooB4PehTX9nnJYXWIkVxesliPoSRoqB6MGBgnO0zDFTSIlUKuY7M6xCgO17Z4nRCaDi9brA3ims+3Pd6EacAMoJpkQGRkRch62wQpwPCBhuoGiOfEcVP1fMS3AbShWCjltwLs5KYDEJEVxS3dCRDfBcCW7oeI1/TAs3UrIAIrghqqG6O+HYMtPQiQyEbid+q2BXk+hDY1GyIjJwZbuhfCgQtCLd3zYTeEoabu+bAfwGhD8UJYA5iOK+ghY8NUy0vJDq2jnGQLasRpANOyBR1gTYDueAk1YHyaW7cnDKLPJQjZYOSQ1SC2ZiV1kLHJ5LI7JWGVEMNqltjyExrOSzanerSDiY1gTEWKJsy0HF7xOQNmJFdouryG821bkD1ej+mWzakxb4BMy+EVl9ZxFpItpKnrCAt1zEC2nQiNVBeSLRsggYYGS5ZO8HHbfD8mXreBc80QoUPNAzqmCVJAxwDqik7y4JoNnapbGAfoXtQ23RD1dQ9s6w5MITMycKmp4zwgO3BDcWAC0LxQstwQDQ0faOkWRAFtA24ZOsHHsgO1LQfEtJhtuQkrZrSIaZucivDtMLFhJW2Q0EO64Yl6zOgAu2ElDZDR8GIzmnCIlA6wDU80QUqLmLaXNCDWx5M1f1rHCxbMSJagQZwcsg0nIXE5k8it6xMaXTJisuUk1ZjXY0YyGBVklYjbcFJmTOsR23CTesxZILlhiVIk+CEMtszQA70QAduGb4U2iEFNNVJdA+OgphbrvgPgYEsPrNAO8fitWtzybZhEmhqgeAbGwS0t1jwnwspvnmYWNyyAADsmqHkmREM1CdA9A+WQS+3oqmLBNCBZkRX6ERp1TEBxdIRFNuWobegEB7aNWLJtkAIlK9A8CyRB2UaamoEyUMcEG6qOsIDiRG3TibDQCoGmZoU4qLhQQ3UYlpXt3rmahZCAGQGS6YRoaIZg2/IQPLOp5lYln2YAyYaamg1goeoCiu1CFNex84sNDyBC3ceaqokygOZDHcsB8cAMwY7lRFjogmBDtyMsNgKkodkw5TkA1NQ8DwpiDGjpvgO4IYo2VCfGAxcEW4YXQIELwi3d82AvgKsvvIzIlgMSWF1xQ9S3AbCpBTEauPDoo08HVuxEKFRXfQewARypq26EQYo9cOSV0InsAAHbRmxFLoAhdSWwYgck0LrienAQIHFL8zzQ8RG4pcVmEELoP9ih+heZYYP3C0gxinU+9Vnt0A3XLyMQwzC2tCD+7Mdove4XC81vfuuDGQ6GIf3uu4nnn/0fbhR+gFZd4vln6Xfeiij6+kUZ7LEJ6ROfDoQEMT+XfvC+D/RsMY6rt9yq3nob5Dh/H2j/i99CBC1fujj1/HNqobB4aM/EkSMuSb7e9W93PXi/Vix4KX73z37illOUInUde9MWhDnrhrGXjoIY9nz1Dw48+ICeSTnFzPaf/8wpZAjXqR592SfIq6A7euR5NAyfmPiL/fffp4tic3Bw26MP+wJDhkH3iddgELQEYfzoEcIyO0PdB378kEmSjeGRbY8/BsEA6+nl109ClrsxPLLtqSdSs7PzBw7tfOxRcXUZZlBqvV059a6TSGSvXBk58fLrhd8d/sVz4uzi8patW59+Knv5SpBOZudn8u+eDzk2ubw8+dxzykBlyxNPZC5dWZ2YnDx2NH/2rM9zyXat69jrEBJxm5vjz71oVMsTzzyfPX/B7C0WT77XdeY0iKNk6FSffhFFYlqWx5545q30vy6+9m7+/EWvkPRjfOKZp/l6bW3vzr0P/eiC9VE7JW575OFzoNtrBUMvvYRbFlymq08+l15ZXLxx/977773ysbu0fG7Lz382Y98WAejg8WOcptYnBqefeTq3MDfzsTv33nvP1dtv6fR0Tz/ySPPGrQI7O3jsGN9sbOzeMv3s010L10596uMH7rt39uYbG436lsce3bhhT2p+cezFF8TVlZXDe6eefKJS7X10+M+MMwEthLyrNBZFGI5j31IWWNbSmVRYO83HBNGb0jfO0RQRsb7cXBagOKYpt7mAsZpGp/zWacbFiXSXJ18ACcBLuFKrlvJ9hEn40hxGqibDtJZOoibGJIuudgUGgjAbNNoruOpiouh15nFI9lOFjvYWIiFsT9K0ZhDHBitUu/+dt/qPvWb2FseeejZ/4dLa9Japo0cKZ854Ap9qrJdPvBlRFCnLOx5+WKlWxp4/Ujh91ujNF94+Vzl9CqApxtN7nj6KAD5uWVOPPmEXU/1vvF165127K5M9N9N98h0IAXlALz77BhXZcBhMPfRzL58svnOq8tbJMM0Iy/W+V06gEOgI9NSjj5GBFWHY9vt+ZIr8DDPdWiCztgKntM5MIb2pUJPRwtlUtmolk2rtvED22+iyqSwmBVWFumzlqkisNukJY/PsQKrHEhRVvcjy3RZWs4w5gZWsqNeWZ/nMmowfQNZOsVyPL3hm+yJTzNtY22vOc4zk44jbvIKJhEYRUf0MC/UiNOQrl2A6oZCy1VgQkDZEF6x9Dz7gYdjq9PT2R34OoBAGA6U3TpKmaQnJseMv8Y1Ge7jnwE9/HITh2pYt2x57jHBNGnRz757DO6qZyYwceymzsdGZHt7zwH2QF9QmJrY+/RQmKxgGVd49Sa1uxil2+NhL6cXl2o6p/Q89AJum1lsaeO4Y12ziKFi8fEG4Mh+K7NhLR7NXZ1tTQ/t++mOs01mfnNRlrj1HopEZxVD7PNXDa8C816glhIQebsTaBsGFbRTz5CsZKtRDBK6d57JQE6jpSiOfpHSo6cnrbDnuxIQjXUx32VLMErWz3ACpRPVAWkuIcBvSXHlVyISdKBnLlxjRqAMcsnku0YupsRxLi0wR6UCWry1yqGcEaV+7RImxnSdmd/3sJ+0949zccv9Lx9MrKysHd04+91zvpQuvf+2LB++7b2XXdty1tj7ycGPnVGKjNvrikey1a4u3Hpp4+ikrlz1e+N0D992zsGNnjCDbHntUGh3g2+2Rl46Wr1z2CHLLU086qdTx3O8efOjB5dGxgCK3PfmkXsm3Gmt9x47rZ86YYmrqySeCROJItXTgvns3BwYjhpl+9lk1nXISydGXjqZX1070/W+7HnoQtp31yamtTz1JyDJCod0n32ZW61YuN/TysdLsbGN6+MCPf4RoutrfNfj8cba2AVJ46cL5xJX5UCCHXj5WvHKttXVk708fIjqS3lfqO/Z6cmUZQcHM8mLm1HtRkhk49nLx/MVXqr8/9uDP+UbT7sp0vXk6MzuL4DC/sdb1xrtAmu17+dXSyVPqeLVy/I3q67+EYJAm/a7HX2ZN1RGFnffeC3CEsLTcf+wEwGHsZqf68stYHOvF1LZfPM4asl4u7rn3nrexL5NtefDFY6vQ4ZSNjbx8HPc8ZaBr2xOPC4bc6Ovd/8D9J5HfRlV9+IWjBBp0hfDoi0cITWtPDm179JFEp74+ObH3gftCHMNcZ/LppzlX0/j0yIsvUqp6/J9Vr/z/vfF8hml/4UvoZp1YnL/eqUAMZ199xe3plT/2m/ruvfhn7k7+9McfoK6kqcJzz7hdXdbE1P+Ac8z1Co3SZ07lvved621/AgAYhn4m0/rK1+zhUaxeL/7pv4fVD1aaM/bub3316+8/Uoyi9Plz5NUZ8Lrdhf4/LWmGfqncuvmWmKMXdu5sToyCKHr18GGpUjSS6fr05MbkmJHJ612Fub17XYZf2bW7OTEcI9jMnr1qtdvI5lrj42sTo1oqo/WUZw8dCkhqdXpyc3IkQtH5AweV7qKWLbQG+ue3bXMTgl4pXz2wPyDIjbGxlW1bYQha2LlTHuyTkml1sH9++3afZ7VyYe7gvoAgVqe3bIyMOgler5Tntm/Xi/mQoS5/6GYpm/dSyauHDoUM3aj2S6P9zXyXUanM7t1jFPIBSZy7/XY/werZ/JVDh3yWaVf7NidGpGLZyOeuHtgv57MATb53112+wCqZ3NzB/Z7ANwcG6hMjrXLFTKWW9+2qD46ABHb2wx/2OFrP5hcO7nfSqWa12h6p1noH3Exm6cCeRk8VjfyLt95qFXNGobi6bbrZXTHF1Oa2qdWprTAMvXfbbV5BNAnu6h236YWckcmvbplsDA44qXRt69Ti1BYEBM7fdpvR2xXh5JWbD2vFnJYrbG6Z3BwfdXihPTYwu20PjMCXbr1V7ymFGHHlwAG9u2wUSo3JsfXRYYfj6tMTC9t3YgB4+fCNSl9XgOFzh25wswJH2BTnRKNJCvHpMoiWYdZXsX48KrI8ZvKiC/SQOOkLrA2M8ATsk5kg6mHEQMK74bjMUqiZTFh+HyugOsu63piIgZ6QcYAKngpkrBRhBTyioZRgxVVKgHWC8cKxBA3adDYKemjR77AFL6pwCB0lWBPuxyEy4lnXH0nEBL5Z7a9NjUn5olUozO7fr+ayMYmfv/MuN8EamdylQ4dcnpP6euvjw1Klx0inl/fs2Ozphyji/IfvchOcnkzNHTrgZlLtan9terxVLNnpzOrenbWhMQhDzt9xe5RgpFx57tABOy12evo2Jkaa1X5fTC7t371WHURg8OxdH/byKVNMz+7fa2Szalf3wo5dYRefdDvhOIdnUdbXkQkmTqIiKJFdYFjhOMwiy5Dbm+B9FR1niDTABXo8wmAiRJI+W4yBMsnBOlYAgkFR8HV8lMTFmPU1dJQCMoSA6nQuAsoEC5tMMQ4GEryroFUELBIpt40MEHGOZBGdyYZgGU/GMlEE7H6RdxSuClg5wU8K7f7qwvbttpi0irmZgwddhm0O9C/t3Amh8NLWrdJIf0dM69Xq3I4dXkKwculrh/b7NFMbGVvcuhUh8MXJic7IgJQt6JXy7P59Fs+b+eyVwzcABLYxMr42PRbQ9OrWrdJAj1Tu1vLZpX275VzJzmdnbj4c4Wh9aHh925SfTG6MjzfGhlq5op3Pzx46yOIWhVjwMEWSocCZUA8FiSiPaEQVhQpUMlCD7WmM8FjYwkZpgokFxsTKsJdleNwm+pGoyAi+7G9JUDzAIDY4xiNUKOIK1EOCBUqANGSQ8oqc6EnIFp5iPBp1gX4K5wCeddEuDMmhQqxAVTzsSYqOhE4zBOPTkIX2E15WMMSMPNQ7u3MPiGJXbrpJ7atEKH5l/36tt8vIFRqTY2tjo5YgtMdH5nbtQSDo2sED0kBPiGJzN9ygVopqtlAfGV7ZMh1ydGNkYHbXHhiI5/buq42NoGB8dd9epb9HSaalkeGlLVtiimiMDC7v2AZA8OyBQ5uDAziGzOzcoQz0KqlsZ7B/fsf2EEdrExNLW7agMLQ8OaEPVBq5it7bPbtntysmzXTq0k03xhRRHxqe37EDxNG1yYn2yEAnXzRKxcU9OzrZvJNNz9xyc0zitf7BtR1bfIapTU42RwY6xS4rl108sKdT6XNF4dLNNwUE3hocWt2+xRNTGyMjynD3Rs+gl8ssHNrf6OrxBO7SLTdHPNPsH1zdvsUUhNbA4MbUWGNgBEagc3fcGaVYJZGbvekGM5OSuyprU2PN/n4vmVzdt3NlcAQFgbN33eUUsw4vXDt4QM9n1VJXbctkY3jYpdn69on5ie0YDF6468NWIW0z/LV9e4yuolaubE6ObwwO+TS1um/X8vg0Hkfv3XqLUco5LL94YJ9aKmj54urk5ObwMEjiy3t2LE9uIXzv0i0f8gzj73kR/svNp3EcpNLaTbe839eKWNbr6qLOn4dN43rtnDGMeu+c293jFYrWxBS+soyvLF+/+ijaaUOWZY+Oxx9cxAC5nh/AVldy3/mr6LqZ6UAURSQlfeLT5pZtiK5lfvB9tFH/IKS22BkabnztX8Ug+C+N1X6dnwQAAoLZEVwJNBCyA3GuHBkUrNEJuxVAVE5aE/VNHPKzygbvtDEtRiSIDRTIQWJlI2m2IgTOKhtJt4OGfq6xlDAbBOpBSo3zFEyLXC0l6psxCBaVddFq4r6T76wKTjuIiVxnlfckCrY9pZZ22nAQ5OQ10WoRrp6T1nlHpiNbNJops0n7ak6rZbQ6E+l5aQ3VDdaVs9Ja0mxwbhs0mrjdYj0pK62Lao0NtZK0ltIbrK8U5XXWaiWcjqlt8lqd9aS8vJ7UGqyv5usLolJjPTmnrLNOm3c6abVGq0021DLaJqw6bKAUWyu81eY8Ka1sMIbM2W3AEEDDYV1JlDcJVGNDPWvUI4Dg7Xags25ICYEa6A0ytFlPzsobCdjkfCXbWbVjnnM7kUExmC+4UqTVMcClIz0vrwMRyXpyRqvbpCfYbcgksE4k2G3fBnx1g/XkrLIeQAwbalmrRaqwYLUQEwA0SPAVRKvHTZT1Ohmz6SoO66uhsmHaWchR0dCDFN833ThEMR30Ajs2TVKBPSuIQADTDduOcNcGJMc3AxCBAj2EHAkJIkzBHMePQpDQEcyWEdcBFcd1whgESS3G3U4MgqLuRA4BuyGuQYijg66PKQZo6T6EB1oEhBocRIRieKYfuhGuE6QNQpYLSDZndzhPsrRNzmqxvpKVVhNGkw31fHslpdU5TyqoG7TZTphNQ6szcoPz5IxSgw2P8eR8Y5FT23ygZrVNWlcEp5NWa4SpcqGaUjZBy2d8JSevJ6w26Uo5aQ1zbMFuO3oDNR3Wk9PyRuADdGzkpVXSt3lPzukN2HN5p5M0mqZvo74MaC7lep7vAaqFALEd+ohjIYoe2i5qgjRsh74ZqQ7te67rgIYpQFbsUagVAzrqeS7pAoHkx64eaRYeB07oArpNkoDrhqADgjrqOy5lBETHi10jtqhAdXGvA5ooosKO50cuDhge4sm4FbCKF3gGYJKggYr6JuT7BWUjZTZoR853VhNWC4qinLTGOx0Agl2llrHbmGfl5LWU2aQ9JSdtCHYnDoG8vM64HcT3XaWWtZqkJ+c6aym7zbpyvr0iWK3Iiz11U3Ak2HYNrS7K64yvZJUN1m7xdivfWBKsNuh6GbUmmE3MtdNKLWU12VBJqnVebSIeCOsO7PiBF+Gq5ptgEHikhRGeBXkqILm461h+AOse4nq2CwimQwSE73qkhqGRBQYGpAa4b/leCClmHIVeFOCmDQSQ77qkjpCAAQRGrPgU4GBuQBoQCgaah+EGGIBoGJiUhZKuA3papPo46Nq+B5gBHdhJsxGxnKPUWE/KqhsuZrKhmrHbmEImrBZu+oFKJjzF1QG/ucS5nbTVopWQ8xVQWY81WjQaOOa78ppgtX3ccdsrnKcERiPorHK+Ato4qNREp+2gbl5aFexWgFKARLG+Ghh1R16nPRmyo1ippey2H+F5aT3hKaTpp+VNzulAvofqTdpu0a6Sl9aSRoPz5FJnLWG1ITsoymucKxO2Zaq1jF6nPCmr1lizydud3OaCYDQhx/HkdcGVccvIKDVR26QDJavWKLXFeXJBXhfdDmJrGWUjYTRQWwH0Fqp3WF/JdNYwWeIDpSCvJcw25UiOvM47HSZQRL2FKcb7KyhptxhPyklrYBzxTsc1GlAYc56ckmtcBCz6IKEAACAASURBVFOxkZdWMTDkPDmrNwAY4J2OpzdCAOI9CbbaMQAzvpKVViEU4EI1azTDCBfstq/zDIbzgebINdwxGV/J6HUoQDhX8uR1F+AFq5U2GkGksZ6cVjdhKKIjLaVt6jEcA///3OLYHhlr//YXM//1e7BhXG8dC0ayf/vdIJ2xR0Yb3/gm2mwQV2euE19GOM6887ZfLLU/c3eMEx+I8I78k+gK0rXs978Da9r1jv7FMQDD2uGbtBsOg76ffORn1Lkz128nBESR11VpfP2bEcP8k61WMAxB3wdAIAZAAABAIAYA8NeGD/7bkfd3fhXz63/lnzfm/VOQ50VRTG6qled+2eztW7iZ7XrqFT6XW/x3//ZDP3lWKRWWekZ7Hz/mFxMykSq98JaeSUu/ma48+bKXTM1M7fzQQ08ayeRidazv8eNOim9SudyL77gC3xKy3U+9EpDUxb2Hb/rZcw7HX5i+oefpVyMCafDl/MunAQhcqEx0PfsaHEUXdt9w02NHXQg+N3Gg9PwbaBwoXDb3ymnQCd7ZfVv5xTcLFy+dvPFjxRffKl68sFkc4OfXul85sZgfZs8tjLx8/OWhkerRd/IXL7+9986xE2e6T55c7ZnkLy10n3hjNTvIX10ae+bp2bHtgy+/W3nn5Nk9d+beeK/rxIml4jCzvlZ95mhLKLDLy6NPPDM/ONn76tm+199cGZnOvXW+cuz4RqqXUTvVR462uAJV3xx75InXvv1viq+f633l9dXqCBI7fY+/5BrQ+o7p0XseOXP3p+JsbvjHT5/84ufgsNX9xPEOmMALRN/jx0I5WDi0f/z+xy984mN+Jar+5NmLn/poTOu9Txy3XEIaq/Y+fATYr8185I6J+x+/+JE77EGo79Gjm7ftNSS48tQJR4nXD+7s/+lzxK6dm5/6jbF7Hpm55bAxjfc/enT+pgOmAnU99Yrf9JZuOTDws+dqW7ac2n6bdpkg2ThDW6uLIoLEGdpbnc9QsZ2pImvvRQCJlYrO+hUcw6IU566vsAQQUDmivcjStpYZwOoXYxsjk4O4dRWFgKjAu+trfOBFTJ5QlzlSM5BufekibcFsoooY8xQQQF2c1l5jbAtjy7S0wGGKlR8EpYuuFjG9XXZjgTANqJh3xk7PDB97bX5468CJ0z1vvX1m54dyvzxXeeXEUmmEW17qe+HljXQ/vbEx+tPHr41t73n9Yv+JE4ujW3LvnKscOb4m9lCGXH30xQ6TpaTO2M8eXfqzP668dan/xZeW+8dT52e6nz3eSHTRVqP34SMykcQcZ+LBxzd7RsTTF6vPH9ko9SVXGr2PH2/TBQAH+3/yrIImQAgYv+/R5eLgslDqzPI50GUz1rVrpYyuM9PI2nsoPxyxkbt+KZ0edTAbXJ9N8WEgdtlzs8WsqseT8JVzabYaMlC4MUPmqxbqRCtzYsL2E4PBwhVKSFuJDLJ2FmD6IA7x16+R2bIBQWhtMZ20fThJN2foVMLk8mjtPBD30RwTK1fpVM4gUWR1KU0YETSaqj58BATBM3tu7372NdS162J37rVzpKYtViaKL75NK8p7e2684YnjoGmd2nFL8YU3aE2Wk4XM6+8RLXmmb2vhxbdTzeZ7+27a+9hLuKqe3X5L6ejbTKNRT1XSb1yg15ublaHiS+8Iy6tnb7ht5zOvchsb82M78sfeFedma4my+O5McmZhozKYf+nt7Oz8+d2Hdjz3Ore48O6OW2ud1MpVLk/ZUQB1ToOFVOCtRI15qqdgG+twa04scxYO4vPXuBxlxwDYPIcOch2rjq7OC70ZTZeJ9myqzLoQjK5cZguIGRFo8zyaT/phw928musTNcckNq6lMqRHstTqZbIHVGAyWjiTyDNeoAONa7mqoFgusjGbyiEOnqFWL+ECDo6QM32PvNg6MK0ZeNfTr4Z1e/nw/r6fv8COj6/+L18cue/R5Z3bO7hYfezo+s5p1SFKz7wGTmuzd9zU//CR1sDg7NiOW3/05PL2HTWxt/eJ4+3xAQlMll94kxhoLRVH+h59USmXZ0e3feinz62OjKykql3PvKZVuyyAKh15kyqsXundUn34iJ3PzUzuPvyTp1u9fav5oe7nXpMKxWu9W/LH302urx/dtm3yF8cIVT2589bCi28LjUY9P5B+8yK3vD7btzX/8qn83NyFPTfuevIEu1lbnNpdePl0cm52PV9NnLqWunh1ozyQf/lU9uLlC7sPbXnmNXF5aWlse+74u4Url+vpnsT5uey759fLg+lXz5bOnD869ScTL/wyc/Xa/NBU+vUL3adPNcUedma257W310vV7C/Pd79z6ur0nvS7M92/OFZnixRs9D16VIM5j2Um7nvsld//N+zqRvWx5+rpstDS+n5xTMIzrshUf/qcGZBGITtx76Ov/q//mpa16qNHZ7nfYAOj+xfHJIDXBiqDDz3tfRxu9VbGfvDzN77xFdwNqo8eVehkBCrdvzimBlR7fHDoJ89e+uiHaxOjY/c/3vpmAY/j3sdecgDCTkc9T56wHOLqzp2w64QQBPydpPb3pvd+PaXG/3iS/XUFqr93q7+XNwEA+O9zaPyrUyAUo+g/hhbiGABBY88+bLOWeOwR0PevqxYFQZBpZr7/ndof/nsvX2h841vFb/8R0m5fL6oBQe7oi05Pn77/AACC11/HQv4pGQJI/PnPyIsXPhDJyx6fkD728ZDnhSPP88eOQp53vZKkURQKQuvLX3N6eiDX/SdfmVvp1rdui0AQ8TwAAAIMgz0PAMEAwxDPg6PQxUnE96Ao8nAC9V0gBnwMwzw3AqEAxRD/VzE+FAUeTmKeAwCAhxGY58QA5GO/FhP4cBi4OIH4PhjHAYahnhcDgI/hcOAjQeASJBL4SOC5KB5Uur3+3rN3f1IVM2YpfeYLn1XTGR9H3/hXX/Vo0sqm3vnS50nQbQ4NORBucZzeXT77W58xEsmYRF//2pcBBDIz4qkvfBYBw8bIyHsg4NO0Vimf/dynbD4BwcBbX/8KCIBWij/9uU9hvrs5OhiDQICiail79u5PhxgKEMjrX/gcDMR2IX32M5+gHKM9OujDqIMREU1c+Ngdq7unHY668NHbmxPVzfHhVjarl5Lt8aGIx5WBsp1KXPrYHcs7p52cePWuW7RqYX2kP0gS7VKhPtrvJGmjLNqZ5JW7PtQYqxqF1Mwth9SCUJ8e8wuCmkw0J0bsvKgW0lY+PXPHLe3BbrWUNw7TTo5Z3T6ltTcNmmxOjepK3siKejF/9dbD7d5yu9prwdTZL352bXQi4Ki3vvGl5uCAyzDvfPXzrZEhi2Bw0FvZsZePFe+Ln62NjLpJ/q1vfbXTXTEE4dTXPi/1dWucSET20uTWmEbPfvlzm9V+M51461tflSplPZU5/aXPBUWhke/DXHNtdCJIcae//LlWb6+bTrz5jS9phZxSLJ35nc9q5Xwr33Xe+Xh9aMzJi2e+9LlOTy8JO8hOAI6DIE/xcATCMZhGU1MujMMAjiT3ekDswTyS2B4jYQiX8CQSQGAMiHB60oRBGMBhfrfPxC7IINxWAPajoEzSWIAAEZACk5MuEoExjvC7YDJ0YREXp8LADYMiycIeFscgH6UmXdiPQQJhdiGE40AiSk/EhOVDeWr54N5Ob7eZTV6985b2+IBRSF/90CGrnNqcGvWLgp5Jb04MacV0mKLdbHL2tsNKX0nvKs6zvJtm1ndtM+SGy1Cb02OUpjhJ3ijnr950QCln1e6Sks7GCWJt1zZXWpWY1MbUFOGYb3Jfk8o5g99vFlOdgd5GpQ+iwOU920nXOPXVL6xPjKNh8Obvfq09WMVINOvpYJkEkCi7wyVSOMJG4v6QEACYwcQ9IZGAQo5IxS5axAEKz+xwsCQGMl56X4BwMZZA0zsDjAMigUrFHpqGQR7J7HDwJAIxcGq/h7IRmkRSOwKCheIklgldREQiAcrudDAGBmk4uc+POBdKY4ntAcnAQYpM+h6WBB0wOP2V30I9zxGY9z71G5Sh1EcHIxSGgkCp5C9//C4QiAESPfnZj+Ou42aTFz75UV5uNcaGAwwJQ9CoFGZ+807YsWMSPfP5T1GaauTFyx+9lZGk+vgQgoSgGyil3JWP3Q67HoRB5+7+BNeoGeX81Ttv5jZH6lPjEIMu79imlXIzH7510TAjmjj7yY/ymxt2IU1hcpryiBICuCFEhGgGgeg4k3LhFIQxcIZ1gAEKsMwM4pElJA6iDB6gGQRNYpmUA+YJIotlCCfqIzAvTO/2sQIGRHHqQIBnAD9BZHgPKqAgRGRRG+sl0DhO7/bgLIbFfnq/h+dBJI9kaBcswCBGZSAb7UJRNMru8bACqqDlU1/+HJCm610DSOBsVgfdnHj2i5/tlCs+z7z19S9ZYkLqrZz97c/Y6US9Ur3g/Ea7t2qWc2d+5zNyuRKT2Dvf/IohJKTu4tnPf8pLsPXegYuf/qhULJm55Nkv3q2l0iFDvPnl33IEQa12nb/74yFHKd2VS5/8aCdf9NLCmS/dbbFsyBAnv/IFn6akntL5T37EYTmjmLn00VtRz4tJ7PTnP0WoilNMX/7NO9hGvTZchWAPNGylnJ256+ZFc09E4Wfv/k2m1dCLuct33cxvDDXHhyEiXpme0CrFmdsPL+zbETHk+c98jK+tq8XszG03Nif6NyZGAhZdHxnSekozt9+4snurxzHvfeIjybUVuafLphitv7g2MWyJZLO/T66Urn3o0Mb0mJPk5m88GLHk6t7dll5rfy1RHx9F4vDV3/tWs7+32d1lppLN4QFvCI1JeGnHTixy3/76l+vDQwAGv/p732qNDIQg7HCsM1xoMwUYDJe3bkNw8M2vf6U5MhiQ+Cu/+43OUNVHcZ8mW6MDOpM8GbkbW7YANPbmN77aGhzwOPqXv/fNzeFhH8ch8AudkarCiae++Pm18QkAp+q/9TsgDMGB72M4HIZQGPo4DoUhFAQBjoNRhLmuSxBgHMO+H2DYf4vBcCiKoMB/P4ZwbJuk3o8JMQyIYiTwAgwHohgJfB/DwTjCXMclSDCOUc/zMRwAYsT3AgwH4hj1XA8ngBjAXdvFSQACiVaTOncOVpV/DDZFUYRhyu13IY0G9+oJ4PrckGMEwZcW0/f8YPP3/8Dt7ml94YvZv/0u6DjXg89iGIY1VXzsYa/c5fb0/PNwsCKSFI48Lz768w82+pfNtb78NXtkjD57OvWjB9Bm8/r9cAAIan316/r+g5D3d9DVP8jBimHY2LvP+/TdvYpq7N8LDgyULad5y61gb7W/I7Vu/hAyVO2WNWP/fmBsuGzYzp7d8chol+NZ+/cEI+NVqdO+8WZ4bKhb0c09u8OpibJuxdun4uGhouU6+3a741N9nY58w+F4aqy3o1g7dnjbt3YZjr912p+cLJuOu2u7vWVbtdVS9+6Ltm/pbUne1CS4fTJnR3Ah6yZ5ttmyec4XWKbRsljOTfJ0Ww5QxEonCVlDYEBPJjDF8BjG4xm6IzsMY4sJuiOHKGrmsoRqQDCkp1O0onoE4SU4qiO7BGFnRKrZDiFIKZUYWYYhQMtmSVULMEzP5YRG3ScIK5OiZDWOY7lQZGUJBmJTTOK6GWC4mkl7RI+S2uJyQUAUNXFrSDsm06MJ4x4HumRFF4Yh2taYfim1zedDH04r4i6fD0M0KWV2OjzskmUjMRZwoU12yemdlhBHSFIRd/lMEKKJTnpXwMQOWVRTW0M2tsmimtkZMp7JlJXELpeNYzzRSe3yOdAlsrK4FaIclepSUrtDLrBwZg3pt1N8jKMLxLCXYD2CWiEHQ550KGYJG3JEhoycy8xWOyGAGLSIDTgJ3iPIZawaJFmXphexQTfNQTg8jwwaYhIioCWk3+T4kMSWiX6QRg2aXcSH3HQCRuJ5fNQSkyCJrKD9WjIVUvgqPhBwlE+RS+iAlRRQNJrDRwwhgdMQFbI0igVUSMUJnCIh3GNjEaOwiPS4UCBwKiIdAhAYGA3IgIoFHMNcBkgELEFiIR3QEUdgjMtFfEQxMBZQAQUIOIL5TCx4NIlhMGEgUIZCaI8PaR+nMCaiPCakcZJxGYD3aYogQtonI47CuJCycYClYToiHRkUN9gqlEAkOLXMDMRp0gTYVWowTmEeRC0yw0GS0hGhhneBAqrAwgo7jAmAjCaXqSFAxAOYXGZGwgSho8IK3Q8JiIYml5khhAd1NLFEDEZJDACgq+xkLOAmwq3QAyCPKmhileiHRMwghCV8KEpTPkouEoN+krIxZhmvukkWh2I+EmM6QpCQj8SYQSAsZIEUTIExDrJxAqWACIO4SASoCEYCPkhBFICiDgrkMRqJsYgBkjgR+QSciJMgh6KIxwEZhIpjPOYiASXQmIiYWMDxKMBBPkyAFOhRUNpjURqOqIgJBYQkQwpIRiyOxQEJC1ECJCCXhjq+uM71wEm0BeU6TAkUkE200uYKgIDXoWKbK2AcUIfLa2wvnIBlMNWkywgL1LFyg+sCOKQBFtp8GeOAOlRY46ugiMmAuMn2QgIswZl1oRdj4hpcajBdGA80gey6MIDzcR0rbDK9iACpSHpDqKIcWEPKNa4H56MmlNvg+gERZd2AhkWADjAAZeBEzAQoRPBwAmQ8AGUSgOCxIQUANCTGdIiBMA0mUNKOIZIFRZD1ABDlgVTE+jgIMYAYMTGKIAyYAOgAQggeEAAmiGA0EYkhE+AgyMRJiPRRGMThLESFIIoLgADRboCRYpSMeICAQyZOwXRk4ug62hcypE1Ri9iwLSYRLFrAR3UxBVHQCtKniDmQQVfxfpflQhpfQQeMZBLFwgVsRBdEhAZXgK52ohBz+CbSZfOJgCVW0X49IQIctgp0a8kMzIDrcHdLKAIM0oBLJpsASWgJH1TFLEDDG0C3IhYQBqjBXc1EV8zjdagoC3mQhV1i2Ej2IaQuMxNyasrnAhctaampgPFdslvOTHgsYJGDujgQM45BDsjZbRFpGmSfKk6GlOcSFTk9GZOuzo5o6dGIdi2qqma2x4xrMH1qcipgfI8sy6nJiPJMakARx1FC7VBDamp7zLoWXpTF7SEXeUSpI06FTGjTVTk9HbIe5Ptcpy0VSrSng2ZgppIgGOOaaaaTUQxSqmoWsz6Kc82WVC7DEEB2ZD2TBhEYV1RLFCIAIjUtSNAawfCtllIqgVBMKrqZSkUoSqq6LSZCnKBU1UknXYIU6nUlnwdQkOwoVjIREjjRkfWUGOI4J0l2JmlTrNBoKNlslM+U/BjtKuk3HCp4EdpXkQ8c7NaMcGS0ectt/Z02Uu2Xbru5bLlYqdy89fYuzycqJenGw12mFfcPNm+7o7/Vwrp7rJv3ZwKIyOWbt99Zcj24p9c4uL9iWcDA0OZtd/RLElaptO+8reCGTDol33JTJgaxYkk/dKDLdsDuXu2WG8u6zRTyzQ/fkY9ANpXZ3Lmbeu8c2mn/CnX8HQ7Wr2k3RAzjlbvwhXm01bre/hgM46srIBCb23b4uTzke9TFC8B1GiLDMNpqwrpmTm+Jcfx/FmDFGEZdvfJ+m/P6NUVjDGv/zpf1/QfwtZX0jx8kr1z+AHY6rtf5rS8od9z13wsf/MMkdwgKuiol173tT79tpBLZubmD3/luo6enev7cnnt+aHBsdm1h3/d+AEUB6lq3/tlfOAKbXFk+8J3vmVkxe+ny3nvv9VBUNJX9/+m/IL4PgcGtf/ofYhIWNjf2fv+HrsilZq7tvfceCMdJvXPjX/4X3DIjGrv9j74dkSjTaR/4m++GFJrc2Nz7X/8Wcl0M8g//+f/FaHJEIzd9+z+CMBCQ1O4f3s82O67A7v3uD1KzC+v79tz2h3+SXFisDw8d/Ju/TdVWfYoef/gXydV1I5fZ+70fFOYW5vbsuuMP/jg9N785Ob7vOz8QV1cclp/++WPi3LxeKuz9/j3FM+ev3XT49j/8k+y1uZXtuw/+9d/k5mZNPjn++JPFy1frA8MHv/PdyjvvXr358B3/8T/lzp1fnN62/4f3lC5f9hLCyFPPdr17dm7XduZqL7Wa1Uta4jKPr2UxaBWWssRSPqBttJFKXBWA5Bo1n4dXu6y8xs4J5GoOwZrkOoKs9gakhbaT3JW0l2qy1xLYapeRUcUlBl0qAmiHbGPYYg+ISkhHYK+WgmSdWkhiyxVA2MRWBWKpBMVN1sTh2R4IlRCZpWa6EWaRWE0gy30IW1NUQD7ho4GHocHmUQDHfNzx2q8DOO1HraB1EiRiK2W159/mccdGiLDxEkiiAeK4zddimnBBJWy+A5KRR0RW7VUENwwqAW6+EMFAgPhe6w1IjBVfD5vvopjnEahXO4GQuoUIcfOFCIwjPPbbb4BEZEGW13wHhm2XpILacRiWTKLK0WdKmEkhgAwt9qAyCeIqOjeE13Ev0+DODsaKGHM1YqaKKCQIKPBqLykzDuuQMz30OmYXm8yZAVDJ2GlNuJhF2jQKdeCNHqiZcHmbutrNbpBxZgU/NxDLRVfUmJk8KgkY0EBXclAzYydteraCr/FuocafKQNSMWbrxFIZaqYjqq3O+u7pkC6H1hlLugDj3UhwyVXOxwTtIat64yyGc7HbgIxf2mQ36J53pPcgIe8ZVwPlPMThZtgJW+/CFOm6zbjzRsQWvPia3zwL8VnHWYilMwCL6UTbWD1F07jtS7H0RiwUQm/G7pyFk0nTXQPapwAScRHNrb8Ospgd62Dr1QjKgLxGEHP9RKDDvoHMTGGmFbEOeXYMBEI4sPHLAyBgElaIzA+CtovCJnJ5ktAsmGzDF3ZAEQDGFnZ1AI9dyPLQ+UHY8UHMRC+N47LnCzp7ZiQKISg2sWuDqOvEfoDODxIeYNIhf64fl0MvqXJnBv2IiRGHuVxGTQcGPGR+ENBRo+iaT8rmBsz0oeorZrDuU7jdvoz4qxEmQvLbYXRFZ4ZA67ivrsBIBXXesL0VX8RV+TJkLEFgBtVPhtCsQw7E6hFLXUGJMmSf8oyliMEt57KvzEF8xpdOg84ljxqD9aOmuoQKOVc5F9lzEUdYxjyozcRs2pXOgubFINHvu294nVmELgNCDYeXhkBYgXSavNoX8m18nUeW+mK+idR4dKkXilqMgwCzwxCswiZJzAwi1Bq2yUArwyDbgGssutxPAB3YjNFrwzCkQC5BXh4IWQXfxJHFfhjfJCUKmh+AQhUNY+zyMB42IRdCZiZjWkE6FLrQj+ItsI1hC4NgYCJAhF4chiBHiw3leMD5amz7jdMYankE4dVOILRqoZm4+XwcuxGJ+q3XAdRzUM9tnYRA3aHooPYyDDUMogK3nvaCAMNpQH7VxQ0LAaLWSQjQIiQJd1704E2D6oa0F3zdQBAG0F/3cM1mQn3zDA60gihPaEc9rOngXXHnOdfWMYyN9bd8XwrwNMLNdvNLRJxdIc/3QM2CndW4mTTeSGBQg1hOILWsy9v0YoFZ5JzCZuJsHm4UIr5GLeXRWhpE2kQtga1lAaqJL5eZxaSb22AvFJDNQszXsMU0vpZGwQbVSsJLOZBp4SsFcimHirP4TAnZ7ALZdWxZJNcLONyEGywxnwfYJr6WIxfyXrY9/NKRHT/6iZlI5uqr0z/8icuwlCQf+qvvdoaG8ucu7nzgIamvJ3tlbtf9DzoMR5na/r/6W49mcNe78f/8z+1KV2Z2fucDPw7ySWKpvue+B3yCgeLwhr/865AggTC8+S/+Ui4Wkyvru+570E4n2fXmnh/cEyI4gMI3/vl/DmE4RLGb/+wv5EqfsLq+94f3+hyDaPaB734fgFC9kPnI//6HbL3m8OyB73xPaNXiINh9//2pRqOTzdz5p9/OXb26Pj32kT/4d1ytpmcyN/31XyXXlgAU3/XAA6m5a62enju//cflmSut8b6b/uTPxcVFPZM58P3vJWs1GAp23X9/7vJMbXj4w9/+4/KZMyu7tt75R3+SuTZjZcRdDz6UuXoVwKAdDz1UfO9iu7/7lj/7897Tpxd3bbvt//gP+cuXVvr7kYsXUKnzjwOs9wlCYSLpF4r0hfOQaV4/xiLm5yKeM6e2BOks2moSiwvXawWNIPjKcsTQ9vDo/xTAiiEIsqzc9/4GX176AFN7IKjeerv08U/Cup588nHulRMACF7n5ZBparff0fn03f+gEdL/E8Dyenv9nVt9jmqPD1vppJVNb06MWlkxoonVHVtDkfVYdm3HVk9kzXSqPTpoZtN2RmwP9yulIkDhyzu3+yLjc/zatik3yRpiShmtGrm0Iabbo/1KV1dM4iu7tnsi59P0+rYpN5OwUmJrdEDPZZwE3x4bkMolgMRWd+/wUnxAketbJsIMp6ZzzeF+tVKKCLQxNqKXczGOrm2Zcoopl2baIwNqX5fDcWZPoTVYdRmmOTKkVwoxTW1MjFkF0eO49lBVqXZ5PK/3lJqD1ZDAW6NDencxIonNqQmznPU5rjPQ2xwZiCncqBTqo4MxRbYG+9rVHhiD6lNjZjnrslynr9IcHYppwuzKN4erAU01hwZb1V4SNmG85WZ9FNJItONnjYiMUVR2sg4GahDVxjgpICKQkJycj8AGDnecvI3BBkiabtZGIQMlO4Cgg5gDE7Kb81FYRyHJLdg4bICYEaZ1GHMAWgMSGoTbCC5Fou7TIQHITsHCER3CDD9lQIgNkgrGdzwyRHE1yBgBBadAFa+AOB0ShM8WIoyNaNKj8hGYQBjIwnqQBGGABER2AQQXk0RAFyKUjynco3MRmEIZyMZ7EIIJSMglKyDBRwQeMPkISwAU7iXSVpClKdjDuhGSCxjEI4sRLsQ4EdDZABcBGneZbBCncBq0iG6EZEMKcugyiAgAChoo3XFEF4adKKEBvInHWiRKIGsDkAOynShpQYiNkpKfcWDYBXjFE30qlGJBAjgLRlyAbnsZHwdUlJTcjAvBLsDKfsqnAglIdDBa9ogYoiQn62OAhpCKn7Yw0Ix4w0v5hC9BCQnkdBBxIOGTtQAAIABJREFUYVoKUxaAujCu+BkHhxxaCMhMhFExw3lwF07iLo9ZWC9KoQ7BhGgFJVCXpW0qF+NkyLIuWQBAHuNRA+nFGNyh6AAvgTjh03xA52KUDDnWJwpxzCE8YuA9iIAZIAOTJYCgQooLqFyE0DFDulQJAHiURSy8F6VwjyR8sgTgdETSAdyFo0xARIqXNyHSwgDdT6sQ7UCIEQtyTNswrIOC6gkhCph+VodpC4v1INUhaNPHgliQIs7BYB3kO04iwmPdyxkwaWKxFmZkgHEA2AR5OeJtBNJBXvKSEQ4YfloL+ZD2W6Eog4wNw3bIK0HCJWIZ5GVXjPBID9JqwEccYLKiC5UIBrZ4zkFLCI5HtOhDeZQFDLYQEskIwyMq4cFlnIYtnrXxAoDiMZ6KkSLKAAabDfFkiJMxm/SgLpxGbIZx8TJMww6RBogsQEA2lY6oTIRjIZtw8QIE0yBLO2gZYRGHFGM8E+Ooz2RCMh1heMQJflQhmVgBESPI6DDmgKQWJzQQt1FMjkQ9YCIylpyihSE6jOp+WodQG8RVlG8HhA/hRpTSAjoiINXJmwhmQogepDSYdEBcjRNqTDgYokQpzWNiAtK8vI5iJgxpsSghhBuQNpBQI8olEDlKyi4LkLHilEwUNVBYC9IaSPo06iRTdpwlCcTHKwjJhQzqksUIE2IC/9UK8ti0D2RwCrTJCkzy768gAE/EJB5SmQDJIhxssSkPzOE05BKlGEohQqzSZQAXIhwPyXSI5hAONrmUh2RAHAnxEghlMD78v0l7z2DNkvrMM93JzOPP6+57vS9vbtk21U11Y7pprGZCO1ppTczGbMRG7O7EaEfs7EgzK4SEFTASEDhpByNaQggNEkgCgQQCQQNNG6rLdLlbdev695rXHe8y8+yHmQntxApUvXs+5/n+i+f/GN+crJgnGZdmS5IxYqPYHFHVGDfKAfB6zOwXrEJGPxstNeBrfFiMJBTFlRMVrYLKAXL70AmhlmGjL5sxoAXRBmU7YTCqrEA1EowT4PjADZGWanpfNiLFJdUGxWjMUFhZoarHCCXAGjC7m+lAYwPRiqReUW1QtGMNJcDwq3qMtRRag6oWZp5dWWztgXPY0Yat0c6pY2XTC8fH+ofnw5FG3qzvnzjkj49XBlt9+AHhmbnrbJ85UTTdsD3SO7oYjY7kjVpwZHZvag4Y9N5DD8i6VdRq2yeP5i0vGWn1ji6G4+2yXts7frA/Pg4ZXr3wYFm3Cs/tnD6ej9TTRmPn2OFwYrRyjL3jhwbTU5jh1QfOipqZ23b3yIHhwkzh2MMDs/uHDlacdk6dGMxMEoq2ziwlE63c9fYPzHUPLiiTDRdndw8dAEzbXjruz01jDW6fWSrHan6j3T8wt3v4QGWw/sH5/uIM4Gzr9InB3DTCaufs6WhqJHPd4eJs79BCYZn9+dn+oXnA6fapE9H0mDCN3RNHg9mx3HEHB+Y7o6P8xRdJv/8PAtZ/KisYHRX1uvXC81CI+4QNqBRbXc3nFrIDB6RXY3eXSbcL7lMJglC/fSs9dFiMjv7/8GBpWvNTv8tfvvZKo3/df/rPQFWZLzzvffUvgFL3eVuEeZ6cOdv7+f/mlfZdSYR8qzb0WkOnDiBy7XroNCIAR3W725qwi6Cm24NGG2EQuC3frudct3QvsLzI9ELN7LWndBnVuN2ttyHHgVOP7HriuNTZD02v1xwbZ9Z+fZSxKjKcfmtCmHzgNn2rljQartOI3Ga3PTWhmXsjkxoHvuH121PEhMRtRXatX283da9Xa8X1kVh3BuMzge4M3Wahc99p6N6IVaX99iSzdn23Oay1Amr066ORVRu6TYBhYNd1u8lU1mtNOPZa4jWH9ZGIWYPGaGw63UZbE3JgNwKvxdOg3xq3rK3SNAbeSGw4Sa0em17faWgkHrot325IRPr1tml0EmZGuqsxwTUZMUb0DPswtY0s4RZVkY5AKBTFpW7nSmeRjJle6SUmxDcMkoMqA4muiRwCKlPT0gThcZVyDZqowjiwdCQqmODU1JkEEovUsBRgZljlulZgTBCMHB0GEGokNnUmBcVC6lbCHB6CQkOZbkiSANNQVEKaFJwnFlcsqjjKDL1Cpap7aRaWRBO2LiwoeJYyAh2m9FjoIHXtConcsQkGUiuqmh1yJXkqDQocInieG2bWqJWrAbANouMcZdKzmVEBLZSMliYvaF7ppKi5igywY5ZmVpC8snWqUclJKavIgswnkqrKIACB1DSkhQytQgDGhqkxImAVWroWaQjLRIcmEpLT1PRKDlEBIsYYLZTCoW1oMQVAxBwbME8403Un1TkVRcw55pJAkloGGlYlqGJOKZUVgKXpMVPTApnpGsg0XKLY0gmTJa+EoQNbQD/JdEOzKkBV5diklGKolGUoURKzTA2zlAAMI82kghoKFaDmVhVINKiZHKsc0jw1rAJi2A+hwXKNQlzIRi1TcUk1ZXOYK8XziPMKo6obFYaWYhvCovRsHCnJVFazYF4qPct0U3BEkfRN3SQ5QCC2ecWJjpWgpLBMQ0OQk8DhcBsErm7AVGKYeY5ipdSU0FBmGlLDkLLI1jBCCUOarikIY8eGhqZrqmI4NQ1BCeI0sCnQSMZVyomAZWrxwiSCgUJDMcecSKRrvsWgRmK9SphVmYFWyNiwbD3NZYU8J/MlrkRpGlxLSk2Tli70TFay1HXOY1WyytHKQAoJc0PHLJdVVZh6ZWVQFLnOiVHKQlauQXpKCC0zWKXDQgNAN3NP4CImli4zLEMgPYv4YQ41YVHIYqBkbLm5q3AS5aYexxkMcWpwmlUUi0Q3FGGGD0qu5RJpEEaODqIKayQydJaVFAtpmKlh0wiUGo5Mg+xWvm04mcQExraBZUWxyjlXFMMBKE0aUpNsV75rWJnAFEvX4yKTocg5KxEBBJc6j7lOIUw5UkArMU5MQ2FY6jDX9bRRKzYSaOnEIjlKE9dmJqhoIKmWmHrB8oqTsuZIzUe2WZpFgdPKMUpDK40EMKoMXaNpbhi560gaIVMrDMMkfmHx0qSCp5ITZXCqJYluEAfnWgkMlhkGpaHQCbFYZaYKwsLkmOfSoIKzAmY504Rup4bOiiJmOtKFFCSyTBwIUVYx0zBVBQKF6VKTsKLKuFaVVEtQZBswlhKSwjD0AEIpEtNFOTVSleqakBiEKLINmCpRkdw0ua8IUdJwImgZscgYyRAHfhVahpPkiuHUslgOcVElXE/qo2Oa2R+b0btSGcN+Y4yKnDlN33CVTWvWZuA2I2qPa8Z+e9LJg9CuDUcmsSp0r+U7jRJrjrkZus1+YybSzH5zjMFiyKx+YxRoaOg0Qque6KbH7KHXHHjtmFmD1gTWpM+dQb0tLKNfG/FNr3C9kFt+s9312qHu9Jtj0jSG3khpm0O3ZTsNYend1ljLdHv1Ud9rxYY7aI6HptfzWiVjA7fl2/XKZL16u2U4/fro0G6G3PZrI5nhALcFlBx4I0O3mZnmoDEa6rbvtYdWPXBasVuLTG9QH2VpNKy3dXe30EivNd4wvWFtJLDrodMsOA+tWrcxxuIwMSz3lVQhQCGiC4/2Nzcbn3/6/v8i+3ut3/v01r/51eT4icE/+tnWZz6FwuB+zfJp2v7ER9c/8CFlGv9fFCyl6863/6b+lT95BbIbAMq0tt7+G6Je5yt3x377gzgK7xMJoZRla2T3X/zLYnrmJ8LbT1Cw5PTMVCFe86Hfzpr15u3b5z73B73Z+QPP/uDYV7+KMB7Z2Tz5hS/xKMIYXvztj2SNWnNj/YGnfz9t1qZefOnY179By6IeB6d+7/NWMAScPv5bH6lM6m1unf78FwvPmb5y7ejXvmbECU+Cc7/3B/b+nnSt17zvg8rkbmfn7NOfFwYdvbl8/C++6vb7EKqHPvV79b3d0jMf+62PKw3zPD/zhS+21tcQUkt/9KXJly7tnDv95Dve3VxfC2amH/nYJ+u9HRZHh772V+07d5WunfnCHy9cuXrv/Jk3vft99ZUVf2b6kU/8Tm1/j4ryyFe/Pnr7trD101/44uJzzy8/fvEt73h3feVu98DBxz75ycbaPSLE4b/65uTVa2F79IHPPX3whz9cvnjhje95f3P5TvfgoUc/85mR5Vtc5Qe+9Z2pH1/eXDpq3TsJe3PliG/fGoP+pJuvoX4bdQ8AWpLhqLkyZvDrdGVO+sdEM7DuTeDeuFV2jD2j7J1AWk78lrkyi6xV/Xa7Gh6RztBZbYL+nCl6Rp/KvRMMDElYR+snkH3Pu+Wo4XFCO2yjDQeLVrpnBkh0lnS5T6Ia3Dxradf4XVf4pzjZyvbSrZdMGqRMEys/8LhWmoNg+5JJWak2i85d2wyGzaBz7eY09WOLFsvPeDoujOFg/ZKloxRtptt3XcMP9Ty8e7XB9/qWXd7+bo3BksfJxmW7KXpgN9+4W8d+qoNs7ZJb2x9QR6x8t4ag4nG69ZJhywj2ss6yA4YFI8Xaiw7vxvyAa15aoAW3ky7YPUSGNq0GoLPkbUDZXOc3HgJJi/FNdPc4TQ0jH8K9RTqsVSwjG4f0DR15d8zr51QyrrzQujWrRZaV7lf785o/XpkZ3ThgbDmGdYncOCuKOeVG9t0ZHLhu2sF7U9VgBjoxW1+0Oq2qftu+NC/zeUZ38foCCVoEdvdWQXCZ1Fth/JLcWrH5GMquyt49WpNDuZFu3a0xTcRd0n+BuOO5+HHauefatSy+Ue2tGVbh0262ft21qigb4M2XnHojAteizbuua0f5rWpnzarFe6zrL98acyu/HOLVF+12I8xfTjZXvAYb5Muys2o70bAKy43Lllf2yxDfe96x6kW7y9HOUTOKeDyQOxfMbkzZACw/pqnCDPtw7RRRodGToHvc8EsCYrhx3t0PONosV9+AlOJZiNaXbOHrA1l1T7AYaiqEG+e9vVg5fXrjAqiwViZo/YRRpCxWcOcoizXFBL1zTN+XyOgYNx4UyMZVye4dMOOUJRnYOUpTr6zvDr5eDbvcaav+D1TWg27e794z4o5GTbB3jYrbRX0m3v827nVNPk6C56p8t2pnne4q73csViO9q0Qsy+aU3/s23OvaegtmL6lgG9WKvn+PDNZ0z0461/VwmTbmk/Cvi72u69rJ4BoZdrRa3os30P4yq5vh7k2jd8ccm+gn38k7+zXbK0Z3uNg7RVVIEhOvnULWlnuXV93jhO2yzRYcHDSTrh2KsnNWL/okM+DGAyZa1teoGJxhpKNv1UD/sBV3aSyrzTNmsodzDtYe4GzLWKeqt6Srdb5jVf1jZhxiUZG1JTfa0HIstx7DdI/vmGDvWE2t4V0T9E/wLMel0lZPchn3VbT3A80tfdyNN+7W0DDXQbJ2qeZ1+rxRrHy3XglgymTrRd0oU22Ybi/baCAYydcvuWw7NCfU5jf1XFKO5N4LRAtjFiadW5YcQmSCzrMUdTKvnXT+hkXS1Bjq/hhrw9gLuxt3aklPoy1t9xmEd6U3Eux8SxsWJtcq/zJS/cJ0Ce8cMzdc3bxMbi7JbEHWQvPuNBnWnWxb22urwSIwU7o9a22OQu+mcWVBpQuM75CNeeyPWfk+6zar7gLXdtDOAtlZRN6N+uXxMj1EyRZdn0L+pJ3u0EGt2lnU8SbeOwD3jjj0R/T6jEyPcbTO1sfwcMZOdpDfwJvzBlpFe4tg5yi375z68h8f/qu/1vKsvbN65It/rlXSHg4e/dgn/PmZ2WefO/6nX0lb3vwPnzvyjW+YSWIMu2c/93kiS54kFz/80XhyfPKlKye+/BXNoc0rN4997S/1MGJF8tCnPqdLgcri8Q9/NBlvj127dvJPv1JZfOTm8sk/+3M9CDRVPvx/fZqXeYXx6377I/7YxPitm6e+9CekErXNnVNf+pIVBsPpiTf8+rv1wJeW8cCnPmsloRHFJ77y5621tajVvvjxj49fubx95tQb3/lePhjmjnvhU5/y+vs0L4/92V+M3b07mJ55/KMfnbpyZe/UkVf/5ofM/iCvNx5++nPe3p6Vp0f+4mujV691j5949W//1uzly/cePv+Gd77H2elIy1z64z9prG9wUB776l9OvnhpeOTAIx/5+MwLL6w++tAb3/M+b2Nrd3IS3r6l9Xr3o2D9Xa7u2HG2uUHX1+6fWMigj8MwfvChYmqa9Hv8zvLfl6T8+xkLhwHb3vJf+7q/f8flpwDWf7wytp7+LFtbewWjgWXR+Tdvzw4ewr7f/uTH+J3lV9DpANH+//y/xqdO/7RXPwGw0gMHho9clBCunj8/nJ7Jqb78yCPdyWmSpVfe9JZkfERm4sbrXheNjmS6vX7uTHdyKids/aHzO0dPoDS9+ua3BO0GyMrrr3siGB/LdatzZml3YTHV7a3zp9cOHiN5fuWpN0TT4zATN596qj/Wzri1cXpp9+hRobGts6dXj57UkvTqE09GM5OgVLcvXoymRgOrsb10Yu3kSSDUytlzu0vHSaHuPPqqvcW5HNOdw4c2zp3JuR1OtO8++qoM841Tp3dOHkNpuXzu/P7RQyVme4cPrZ05k3ArmBi9dfGxSlZbJ5a2T51ASXHv4Qv7hxZzzPbn5pYfulBhHE5O3Hr81UKCzqFDd889wIt8bWlp9/iRBPPh1NTtRx6tqBaMjt599BEBte2jx9eWTgMREdjtz1QUDHEV+bNFrBNN9sKJhFRpxQMxViQ6wWjQn5ZYDZCMe4sK4hygJJhMK5ACtJ9MAckVlAN/QQqcsnw4PCgwTmWVRNMZwhlE3WSySnWIxCCbiUsH4TwcHJIVzisVRzNFpRUQ9cVYFtV0JIN8Kg7rjhEMyXGT1LAOMjpL8xGTqNyYRmrSokkCzzQMq1AC0iNG1dC4TMgMVeMWBcKYRtm0S5OkOmbzEURygY46qsmYSvg4AJMWq3JrtIwPjuIkp0cN2gA4zdVBC47phkrYOFKThiZzfQKUC3UahOyoSZuQJJl20IR1qGQCWDCYR5USZT0uRjOUJFE7zcYJzANo9sMJpVSO2DCcq1QpcjuIJgQPh6IVxhOaKiLAB8NpxcsBoFF/DqhKVbwbjAsj8fNGpEZETDWs9QdTghQDRdPhjCKqkHY0HFcsHhZWL50kFRYA9tPpuEAKkDCZEayMsCnhoq0MamtJdcTGTOhFXJ2p61wCKMFhC2vK1iI8pwOHMZCyg1TVdZ6G6EwNM4WhwEdMpgOT52DBLGzOQcoPUtEwaR6jsw2dFooScsRCXHItg/NGVTcMkPFDNGm5PI3AKVe3JIYVPOJUFjJRrBbdogZ4HPTnVeUVOMnjhTz1ACqGeTNKRjFSqWhFg3GqJXEwGUE3RWkRTheqLqRKy3oQtwEqoqIRD6cojtNkKpX1BEVpOJnlLYyLgawFcUuhIlGN0J9ANIzCsTgdkfqwn0+n8QgGeVB4YdIu9GxQNJL+pKaladHo+qOmkw2oK+RBj1eJbhTwuCcrwF0BFg07D0kbVbN2VQnTSMFB24AZ1QU84gAEWA2oRVNPhrShqgVHEWwbuTpiYZUxJtAJl6kc1hCZY1SVel2AOQswrJOUHuZAJwzn6KSLVYE9hBdNDgqjVqpFp2DUIAk6ahOcSZFE0xmkOQK9ZFykFkJymE/HuYtwFgwPS0lzUKbRbK5YicBAjCVh3YQqLCbCwIE0i3oHFdQzKPJ4QRS6gKqfjpepB0kZZtNpv6GzOPDnIqynlcjjGaEsIUGYjWWpq7TMj6cyf1RnoR/Op5WRVHkaT2TQrRgorVaRHxyBWakd1NkIxEkmD1hwwtRFyseAmjKIKvR2VS7WtTilB3XaRjhKtUMmaDNWJriNqhlTF7E5DvMDNRIl2gyGU7ob+WiGqSkTqdJoKDjDzTLS2kgt2KgQxiwuJ03b7+IJXM06ACheE3DeoFlMxrRyRjdiv/BCNVrGlBDcH0wLrexXKBnMV7gqlBENJwTNAqH3kilSYVGBQToT50QhEIQLAsuk4Ek8URCVKLafTONUA7jqZ3NJySoIY39RVFVR0jiZkrBKAO+V42XkGEQN0pkk1iEE8XBRYpRLI49mKgUyQLrxlOqOTZA8v/KP/rF09Ii5y695ddioRU5949yZ/vSs0I3VRy9sLBzSsuzSm96cjrUF5suPXfRbrdhyN86d3p8/ILC2c/b4naUHaRBdefNb4tFWWeHbjz4SToxnlrdx5lRn/kBVgdULD64fX+K+f+1NbwnGW1Lhu49d9MdHU27eO3N2b2ERSbl28dGVwyf04fDa656Ix9s5NfYOH1o/ebLAtH/owJ0HH8Zxdvf8A5snl/hweO/iY7uHFjNEdw8cXDn/AIJwf3Hu9gMP4SxfO3N27cSSmSYrDzzgH5wNjEZ/fv72+QehVJ3Fg2tnT2MhVs8/uHL8BI+i1VOn904eS3V7MD628siFHNPdxQPrD5zHWbZy5vzOsaOwQhsnl7aXjkfMHkxO3TtyxH72B2TQf0WABSBMzp63n/kejsL7L4SiOzuK83TpdDE3x+8s007nfpkHQrq1WVl2euToT6/q/C8BC0IkROOLf2h//5lXYE4Xov9zP+8/9UZYFPU/+Q/e1796/x57VBa9X/hv/SefAj8VyH4SYJXzc+jMcb0q87ZXUYwYicealUG9Iu4tzDCV6RAM5iYpKAGjZcPJG+7u0cNYR0Ijbpn2picYkVypcHocU9A5fARaRKsKyHjRdDPPaYisOz1uqHTj5JIwmQ5LxFnZcATTtErmI17uWPUi6S3OaljqlfLHmjopS90sm05h6Y4s03ZDWdRII398FOuIIli2asLWMQTExGG7oYsiHW1VhmbUZTzeRgRCCKRniZqpYYhtljmGPz2ZtBsaUSaU0UgTGhgRDFwrbtW4Kci4ERquqYqiVcsarpXH8UgNmhqiGnCMsN1gMqcGjtsNvczjZi2te54YWlW/qGEqIy/pynqpAeHIYVEHRhlyONRpiGDBK7+oYavcnmNXBrrtlL6moqIO7TIwsY94YYLtNr4WWDUdxG4+yOvCKgKOYmULr7xnk3uAIyIzrxpWtpBIePmwcEtLhHoVAbvgKqUo1jVfgdKuQunKiqDRrK81AIe5XmXMEhwXNZBwWwEDNzMfNHCtCrgoSAN58U6r+7LNM2RQU8TMVsiAo3kAmlgPBhN7z2MYEdP0cMptRZh0yojrAhfhTO8SYhJy1qpizRFcK3mV6YagXHgyMcwSWriWBKhFOMnrIia20FhlyFBHvrCkWcSIJZjmXu4DHmlazlTKSAT1glWJBYa5LsbALZ3uZ4bpZT1EhxotuAp1FJQOpLCLzKEGC0NlGo2EDd2kh1lk4oDjrRq9Fzp1pxwaKFRO6cSDimbSAbWky/QY04yI0ISBdJUGclP5wpF2mdisZKZkMrJholq6XSVNEcARzSwDE+WgpTkycUlKNOl074wlN6nLlE5rIkIjxClDG+WaKZ3uSt2/hTnWGGihBHsQMdjMfFCDrc6yl9wFDHJOPZoxQxBcukVAagjTaqwMqhY2VGaqBDuVDTKb5KBGqVa00qGwU4YyM/WFlesoNVWo8RjgQs99wlPFRDMLhFdSmE6r5yXPOSgBKDWeYi23Sl9jkdDEvHou0RVFlVv4yowZzplKNJogFo3C64wGmc7dZFg5WcVBLd6tWMRorstQY6mwKjvpMy0obdiIB8IpKgbrpe+yXNU0W4Y1GAEP2yK19Bx41E2HXM84lxbMXS2rbNTqXGsnq5VjWLikXAKXNHLfcgRRRX3v5Waxqpp1C2UeTFENOlnALEWIGNm5Ui83ga7ruKhrGXIhhtKBKa5DrwgNXVBT6iLnPOe61FXa1NJsxHLTrlElyim4iDWUYD2jKnKVDywhsahlw9wrTBmZMqzs0q46Ll6mWi6BsqqwsktAq3Y+zJzMVMO8nmgoNlRmwBDrKYaZVQyAUyKSHgI/HFqWDgpDRoBHBogQLgjPNJzb5QCaqSKqlg5zrzBVaqpAGSnWpFNGDk+FTb08Ik3ESNksI80uuVZylepGSXXhyYRypcloau8FTlJlGk0Qa3bJqDBBbuqCwmiy86KhfFF3GmXEahUwST3uUqtwVdDce9mFfdlwHRHZPNNM5WQRqiNsoHba5/WKaYUhItOQwCONfGgYQrjMTXqUhiYJqqpgMCjr2BaBWQXCK51kALVUuNDL+pxFiOZ2td4iN1Ldwai0hS/c0sl9TUuAIRrFDYNvV1SjInKBL20JYWkLv7Tytrxna+uSIl4VlMS65otKOiAQjtSwrIth4RRmHmIQQSM3VaRrEeCFNJiXR93ZSadKKkyjiRFKADJ1UTMF01hVpmON0tCbMt+bneKk1EZwaLtUg8jgomFLg/FKyDEn01jn1HHIMcOSERyN1DCFgBJRt4TDzUmaWFZu2bUk6C3OMiwoQdFIHTGEGc1btcIxrDxJR2u5bdWLaDA/rUGBNaw8s6hZFCPl8LhZd/I4mhorbd0SadTysEkwJcozk6YXjbbKllNxzRZ5NNEuHcPKomRsRCfF5sGjwdQYqwqOlWg4RcOxk3AwMQY57h1ciKfGzCKqNA3aPGnXKaiUY+QNyy6zaHwMmJhUKh1tAkNDCKCa43POn3v+FQMWABUh8fkH3G/+1f2asSBEWabt7xWTU/niAdFoGi9fQ3F0/xqYcfVK9PAjynF/Si3WfwlYGDvf/lbtS198BX4xKdPDR3Z/8ZcAQsaVy6Mf/fD90xUUInr4kf4/+XnpeT8dA39iinBmbqysnvi1Xw9a7fqd1Uc/8vGtuYMHnn/x5NN/mBHe6HTOfPzTJC0VAE+8472KkOsPPtqrjyOlTn75a6d/7/crQOwkPv+hT2hxduNVj+02JgGGS3/+1bOf/GxU81rXbi995mmm0PLZc7uNyYwZh7//zGve9YGc6UZ3cOGjv5tZZmt95+SnPqf5UUXQo+/7kNMbRPXaa//tuyXGFdLO/c6nvHsbQufnP/mZsUtXNl71qjf80q80bi7vHDj42Pv3NtIuAAAgAElEQVQ/VNvaBAoc+cMvt16+lUxYTxlkonvruj79M//6V1sv39o5cuTRf/exxp07a8dPbU4fDOzGxPLtRz7w4dlnnn35iSff8i9/uXnt5vZrH/+ZZGVa4nh17fDn/nTq0pX96YVXfeSjC99+5vqTT/7M29/VeuHy2tGTj33kY+2XbwCNHPzy1+a+++ydB89pt8/J3ePpmO9cGy8GJ0yxXg3ny87JUstVb1a/vQBq63j5UN59IK0PXhs8czphXZAVawfLvQcATaU/S24dDa39t6gbFwi+hihdPpzvL0HR04ZevvkQhL0HjK2nquEKZNmtpWLvPLI72vp4uXua57s0cdP1hyEKgN+Gy+exdY+uT6U7DyNtqzdAd37gwERgDSx/y8NE4Vgt/8CDJiw3yuWrDRpHXjJ87tI87sVgPBucPqIICS4XK5dajJRqX914qY4ToYHd3Tc+WvmhDL1b3zQFwCgp7vyophcJBv2t11+otnbzyL3+/YY2FLgGVr5hlxVBhVz+kQcrgHrZ7StN1S84V1f/tll1C3qgpl06CbImz/vl5hIaNAgZ5msXtE6zGNliL10owhlgbsHb52U4WpOXXiWGszBaqVpi+SLfbGSjHXbpQhnOS29ze2Im57o5qNC947I/mxsxWjmjbzaFc+ut6Z1DlF8uXbR8vvLHjXJXdQ6B/fnCTeHKKbQ+nY9uGJeWsvg4ZB26erTozyrc3b6r7T9vGOOifwmv3GjoEyi6DFZueTrK5Hpx61qbsWrYNTe/i3FNVV6+9+g5c2ew+xJfvVV3RJD0teVLDZz65QEzOjyTpzJ9Ubt1vWk3i+Dl6s6NupX3US3bftWDOEj3lp3VH5rumApflssvN2t2lNzDN6/WdZGpQNx6tmGhJIvYnb+1QQPUBrZYP29kGcqSfP0JNhSlWaIrjwmg4aIAdx6qqtIMYLr1IApBC7/0FijteH01n1Y3Xq8UAyKXKxeARDPw6hsldfBwM25nd15HB6xwhvTyxUw4k9aVN8jYSIPt3mi+eVFPeUxL7eYF1mdZzaeXXpXgkUqVdPkkSgArynzrYRh54Vjc+ZLo7Lj2DN7+FhjucUsl927Xhutcc+Dai1Z8o7IPVrvf5GtbNbKAgSkGRxb1K5vrd0b2VkzQ1DZ/bOY3EJxUpVMNHj6T3ez2r9S2NkxTRf1lurXsmnAQPjgXN+wsqnrfofc2m7V60nlR21sz3SrcXbO3rpt2Ld+8YnevMnehGH6X3L7XsMYqr+OmmxewDFTcwMvnhbWnbY4X2w8Ca1fbbBc753i+R1MjXb8IVHLCvPeGvNsVRbR2LN95BGg7ZLuZdc6b0u9OkN3GNKwqb9mSdx8VVsC2nLLzMIK7B7IXXpvxtOx0w6PqzsN60Vc5k8uvUUYA92vl5kOU9MieXmxfoFkKKihvXYQEDES09m2DibzqFzeujoCBYqy88r0W7Qk8Ala+bqcFpai880M3TwnLN/dfexZmRT7k159pqK2STpPbf2YEpWNNlcPF0QLCckUtXxst+gQ7cPlv7GJLsoMgHqv35meyl/ytlxoqkGbs37g+mm4jMWasfctQW4BNqbW/5L3YY0xtPa8PexpzIF0/z1fbVWsFXz2XBSdzt0/vHJaDWUPsgp1ZtXdYWAncPIbuzUX1zV8obp3U2M0cwHvn894iKbugOyM7xyu+/Xpt7yKKLisLvXwmHS5hYxOvzov9w3a1chpunQ73dxGK1h4Ha4e12nV881g6OIu0DbgxL/aOGGJfDsbB+jFkdKvtg9XayaK9d+KP/+jk578oIWnvbJz45NMSIN4fvuY9H+gePDT17ItnPv20PzEx+eNrJz77+wggb9Z+g8gkTMCLq699zwd6E5Njl66e+cwfIJvdOHK+15oogXb0b7/90G99DEFcCfXkO39z0BgZGwevyUWU97y/vXb2M7+Pg0hh/Pi7P4gKWWD6xK/9xmBybuT67fP//jNA0/RO/4Hf/TQL0u7c7M+87Vf4zn7sNS58+JNGrwtSsfT0F0bWt3dHJ55813snrt6489CFt77tV5y17dsXLnbGZ0vGJm7eeeAT/75548723IHXv/M905cu33zd46tjhxPDHrm5/NiHP+ps7xOlTvzBF6cuXbnx4KPd9mRoeM2Ntafe/m7vzkrcbJ377Oe9m8sAkxNf+A+TP3px+9ix177r/fM/ePbG46954zveU7t+e+3gYXzj5Vd6IvzPS4V2OTZmf/+Z/8hP9zWD0+3iJEkPHU6PHEF5zm/dvP/BQSilfvNG8Nonforu9f8ALITYyt3m55+m21vg/g58UEpRq23/6m9I19P2dibf+WuwLO/byS/z2fnuP/1n+eKBf7Cx/ScBVrZ4ID992gj91QfPx6OjJC+WL16Mmo1ab//qU0/lLc/Z7y2/5jVJq46zfO3B84PJqaoCVhrkhunt7Fx9/euTkZqz319+8MFktFkQbuYRUbLK5dZD5/bmD9Q3N6489ZS0WE51WuS0SGAutk+d6M3M8CjePnNy+9CR+trajde/Ppwat7c69y4+Gk2OgAJ0Th7bOnrM3t3dOn5i+9QJb7uzfuZs58gBc+D3FuY2zpyicZ616ysPPaQF8e7xY50Tx+10957mdFozrDfwZ2fWzp7WsjJptzaOHxcaJbJEXHN3drdOnOwsHdeTLBgdvfnQQ1p/K0DV9ca8EcS92ek7Dz7U2Ol0F+fXHzirDcNopHX71a/W86xwrNVXPcKCeO/AwXtnz+qRz9WgNytxlelZFM0EBcJ6NohmMlIWWjVUY5GCAMvQPyAxkijduuwsIIRhJYZTKS1Tveyks0Cke1mwd2XiFBSpmcTBXExApokiHU+g3E+S8HZtSkig52Eym2QEGEEyWEgxKmiWphMRQgWRgWr7BQU4jdP5rNCJ2xuyRUJcqEWpNo1RQ2NxwqYQbOukM6AnHdNIVAToAap5iN1dFVLBeoNlGZ2lVYvSvYAdothE2uZ6JZDwGmYS8fEKtTWa5k67SOoG21oHUlONutEP9HmsRrg2iPgoIKMaTzKjLeW0w3oRW9D4KNF6sT6D4BilQUxY4E+WJK2kFeajuTFMld2LpzD3Y0CDfCojhSTYD6cgB+GuhPea0+YgA24/mQDcH2Ia9GYBkSUVJYQCpxWivWhamcMImAM5nhfhMIHyWuuoGQ8hjMPJlCWiNDN/onT8AGk78QzUiozAMJlNqqLUQJzNpFqWMJjCgyZSwqwycaLGVWzFIT6maxSQLEdHTFoVtcqHh1yWDOnmZmm5lWmaUQRPOTpMaVFoh3RSBHR1XdRGEKN6HrPDHDqM9n2+ZPF8UO32K2JgjZlVAmc1wBAPI3LMkA7j+wPtGEeWxuKYHOSYQ6NMwKKBXGB1Q38mq5ycDcp4MlFOxoJY1oZpQ+EoVw0/agO9l6aTUWFLN+ncoo3AaaKsVDU/r+c8ylTd77nAiVavENd362Y3KVv9rI14EConiL0Ehfsdam/VRqygTBr9cBS7u345MsxGgT4YSCeK27kRp5UzHI5Bq5+VzV4wyuqDfUMv5CFbzxKTFeiQjtPCsAp50NX7vmEX1SGHBJHBc3zQYNsdMhhKzyOQUBeQA4Y+DHQzrQ64dGtT2+8lY9O6yAyYakc5FYJZAExrrLOu7faK8WkmCxPl9KhRyYqpDB8zWZowXWpzGskFZRk+ZOGy0FVWnfR0EZGizKYjBHNS+Ol0AWCqp0E6k2a0MgbJYDFFWs6SLB4NCR4W6XDNaIaaQ9Ikn0tLQ5n9aLCQEZiUmLIy1fIclXHRDgoDsiTOp6KYa162dtVbyLhFg7icDBUXoMjL0bBkhRnH2YQfOsQdJMFUCM1SH0Z52y8dxeLMbWZyzkP9lB+kuAHZfsTmkBoztH7ImwpPUJbmRkOIMcPc3izyCjVrfBDrUxBO6lYQ0CaQLWSsrsEKl6264ad8TIh5z97t62MSThn47j0ikWiPGmms14U2y1W34HMETnJ7t8daolqwtTBlNVDNcSvw9bpSC6YZJIgP1GhSCYSQHyxKPRkSFfszKc1LSQt/qrDCEKOdZB7L4XacDpbbh6UQTCbRTMiLHGlp0i5outkt8uXGLM5yLoNoLhOgYkUynMpYuR9peLU5U2UQk4EaDXNISBqmB3IFBU2jYC7DsgQojyczrcxgNczGwrRe97a3rr71rVBHVQ7uPPaqwrWhgmvnTkeex+PkzuOP9trj9a2Ny69/feyYJNm/bo7GmoWrauPcqcHouJ6k2w+e3G1PVxizLEUa4H549+KjUauBsmL7zFKv2cDp7oo32xmda26sX3/iiWyibXQHqw8/GEyNs6xcOXWqOz3l+sOVi490ZhdGNjdvvPrxYHqShXFvfmb77BkWp/2F2dVz52rd/fWTSxsnl0ZW722dPbt9/Ajzw/783MqZM0QKXqQZ12s7O1unljbOnm1srG8vLQ0PzYXU1kRJRMGztDc9vXb2lLu/v3rmzN7CoqKaVhYMFFjBuN28d+EhFsT+1NT6+TNOv985fmLn5DFjv7t36NDG+TMkjJORkbsnThrP/eg+U4T/76+YmoaiNF6+BhC8H0NVhRBbX6tMI19YTJZO8du36NbmK+gRDQJUlvHps/AniFjwLd/+T7iH4rj1uc94X/kTZZj3Z59SAOGdX3xb+KqLKM/H3vdu88cv3K/1SinpON3/4X8cvuFNKMv+4ee63vrsp2tf/hLM878rGiUkeu3ryv/uF+ord/OGqwDUe/5wbBQp5W1tDSanOCr2a2N6FHh+Dw2TvOlqWVa7uxrMT4VWzd3aHkxMmirBe0FWr1FQuHfWZdOK641qkFYOT7nlbW4OxiesMq5dX+5PT4u6re35lcVirxaanpsMqrLyNjaGY+MMFtquX3q2CdPMr6TFctc1O7u5aSEds71BbtqVy+jOoMIob3m+UbNkbIQBGKSFYUCDsPXdwnZV06C7PoBV0fRwP9ZAkdZqtTurJefpRIvtDwtuyLpB9gKk5GByyt3YIjJPxtq051cER41mrutYCCcb4l6EhehPT9u7uzxPRMMh/TDXeH98giVIS8q0iUkMaFJiK02AoyUy80gj39NkipQaoAmQo6RBUIpxWFUNYcVhKu3SgSRXqMSEB5nwUIrKhsB5RSNReMhM40zaWM9ABjPl6npPKA5TRI2g0kDL3+vUxrVY5cJGeo6EEsKowa2QNFSuET0KqI27CbA0HaZxTBmXkMA8wpQUmYZR6pdmfTLa3QUtaGAd5VHAuV5CCrIIWzRJdRt2U1E3LRlFQ4odwnESx5RSCSnMYjKi9gO3KfaEaJm29ONIgzpBVVoqgSGESJcFNkmSmWbVzSuH2yiKAgoMjRpKJTqVqTIqlTJJECORirmgGiMDW4QQAF9zy8LVZCoNUCQmREA6ig+FxISxYVF4qBRZjZg7qZFG8bhR5hxCkDuED8oK41a1vgMXQSFFU9FQQVEhM2vFOzmmXXtU81GFMKXDvPS0okRGkksHlVLZCoZpLqhhFblgVVSqERPmEg1T1dDxsCcIlKZHSggyyc0yL7kKpVPPwsqEwwy0dCMJosLQzAoVKs2paWVlpclYWVYeEod0I9jkdX+vo0aZLbEss5xQJnMEYBFrCgu7hruRaplGHkYpZ1aFpMxSLOs6Rtjo5bmrmWJYZI5iFcVxkTqYFBoNrTyWEPb1UW0AMocalZ+GDayXDbnRq6YwzoGmRGboOEi4AQekdIgNumVsKw41EhaZS0GiOMwTV0d+bhLiA2EQqYup4XpM9QyZZeFWSJYmpkOpwzixdOwDYZDUJqSb4KKUIyYa5rjMiYOzBEMASpPiuK8g4pgUkqFCyLYFhiWNEtMTeQIzrKs6x71EQWwZWZRZMCnUuI6DAuSCuAiFWQZ1wyyLISwxs+wkyTjKCmKoBEBcJJDbWipyxE2jKBIgKmJbWZQbIC1l26qFfi4cqBekLAtpER5JxUCGKQ8VA+3BznZ9giYyKx2oF1BUeeE28UZCLJFzyoNEc/mgyGqaEw1gj+QtDDRY5qbGQgEZSAljfsTqpFuVDWgVg6JwqRY7ag8gnGEWggbMCKfDkLpsKAuPGKVfZE5lKIXKIoQjqhvbbjGsSld3QBQHGtA1DJJCCgQhQlyWRIdpaZtyr6xcZqEoDYnSNZNlUcQQrpTDQbfgKBMOV4OiMpkyCd6Noa7pPI8CHSBV1RnqpwbIFJS5KgXRgeuRvbjixORZlOiokqrGcT+rMCzrpjEsAUTNar2HpmCh0gahsUKZqpxyJO4ISPadNg4IAJiyYVK2cKaY1c8qh6RSOYrFZQZNjUUy5gUyDX2vEA7OJDHjFLhaIpStSKJS5XB9CAqsFK3DjZRxJ/F93YtQi0aFdABL81xZhEdVSZWgxAiFALXNjeHUVDvqhDEp6i6pBB5E0jMjp5bodj3YExJ7mxvDqSk9j1DHz0ebGCvSj0TNlJCwvm9Zoqc1Ji5f3jy5pGFBumFe9wAGtBtIzxBAYzvdasSKDK+2sRGOjSIKem7bSEIrHGr9MGo0K0Ls3Z3K4aHp1dY3wnYbckR3hwiDvOZq/RATFXl1s7OX23Zcq5eckaJwkwHuJ5LgqNEcv/EyomhvbsHY2S9MM2y1mvfuCZ2busCrPaFpnSNHva0toVFgM323mxlWMDo6/6NnC8MYHJojewGVRTLSGFoNWCkv7On7/ZwZlaezTk8wKhoO6UUAVIMKuZ/4HX7372zcUMn08NGN933wvgZqIERpOvaB95rPP3efU4NQSmnbu//8F8MLj5IgmPy3//oVmOWrSplm523/R3z+wb9XJ/rPClZV2c98r/kHn6sovV8JSojhm986fOObK8Yaf/R59xt/eb/T3FVVaZr/xjf3/6ufQ/+QdvXTFax8fmFUgJ/9V/9qMDpZW1l747veu3b01OSlK0++7/398en92dm+NaIUPPqd77zhHe8MGyPmXu/xD35kb2GxeXPlife+P2yM6Gnypl9+u1IwM603vv1daa1m9AZPvPP9/ti4s7r1xLt/Mze9CoEnf/03KwlSr/azv/S/AwFuXLgYGDVQVfM/eO6pd7xLElboxj/+pV/mcRI3am992/9ZQZTY3uve80FvvROOtJ56+7va12/duXjxn/wv/9vk5atXn3yq647hSo7eWr74wY82764NJiae+o33Tl+99vJrXvNf//NfmnrxpdVz5554529OXL8xbI2d+/Tvt5bv9RYWnvqN984/+/xLT73h5/7F26Z/9MLthx970/veN/3S5eHoxEO/+5nZ5164u3S215qIddvp7b/1l9++8MwPbz7y+JP/7kPz3/9hPDZ27jNPz33vR7ceftC7doDfmR/OBrVLLtlcouSO3JvRVw4L3nuU3H1ieGsF8erqcXrv2HAqaNx0tbXjiKwbOy66c0roPu5O1i5NJKMD50bduHs4GBu6t21y7yzSNvV9W7t1CtAe7rW9q7Pp6MC94ZLlJVHrnMmuvwEkm0FY7C9q109BrYsHLevKYeBt8bs1fOc8tNejoVr7Kmd5DDjpfAlABlFcbn8dA46NYLk8u6C/9DIbwJe/3eZJDCy8/WWANUCyYvPrhNES9Yrl73lWETFc3PlL0wh8VGdbX4IAQCzExl/TehWoSNx6pq6nMWVi5S9MveeDCcFgXGGY3hSdZ0yucpiJu99xWZRQu1r9M532YnKg5j4zR4IGBjvwzhm2Z0De066dc9exP9777/0XDpbBpWrKvnyY9EcQ2tVWT+i7VubE1tUT9ioLp4eN7x+C+9Nx27euzsJ4BqMOWDupd+pxPbWvHnFWTDG2YT1/mOzPRCN+7eok2J014bUn0caRbPcmHdVeOuPe9YKZbv0HM2TngKht67fnUWceWJ3udTD4tuJzKH6u2PkBJQe5/HG68SPb0wda3gGzTbjT6640w7/O6IJWXko6P6TOVBm/rFaftWtmpHbKe98xDTNPdnHvm4rPwPJytvGM7ozl+bJa+b7TpEO6n9z8Xs3W07SHd74BaZuY8T0530J3t8s1fuf7Xg35Iqo2/orZLE19bffrEIzRmk/ojYdZMcAy1K68mvf9wlX2989ISSbZSz+brUkp+rsNePvVNAkgjo1LD1v9gbIj9vwjQBKgUuPyaU3mKEm15Qs8CgGL2QuPmP00aona905UJYcosa6eoEUByky7dYEGpbA6/1N2oxntv1Qttn54rBR1gRL78lGWKShjfOsCG4LhlIj+b9LeNNi29Kzve+c1D3ve55x95vGeO089amg1EghDLDuVxC5ScWGcVMopVznlShynTGFbxGAJgRAgJguEJDQhIWyQggUCoRHUGrr79u3uO517zj3DPmfPe695eId8oJI4pAxXeH1fH1etXz3P//n9PxmMHzC6pUefT6YH1Dei7otWugeJPqPzRD89zPXK9LNicl+nW1r0x9lsD8/po5Nb1uQuJQt0+CWYv5QZF8jss2n/jqat0/gr+eCuUTWD0S06uY3dOXH2pyR9qdQua/Hn4tPbprOQsbQvlxrs1v7obm38ErDmxPDrOPiWMs+j+PNJ9xXbXpHVU4vcuQnxEIQ158XtrBbYDy1253LhD65kt38Qxt0gzIfL5PZNDMcocbyXdqB9ph+a5N4T0j3VuhZ68AYmT1CGwYM3YzgEuWt96wL3Qu2YaXdvIP2IDnR6/1nGTyEn5os3aDFYM++9I97vEyPYX9Hu3NTZkRrp9O6bWNEHCpnfvElQPJFJ//egVcQoLe5+pcaiTNPLvc9aei9QHaGDEDCc3St7XzVoXhJePPyKQ6eJ4Yv93zPoWQzWjP4nyjjRqQ2GnxcwLjWVH3zFRaNctYzub0t0mpNVMvztMgh17KHhFwCcCC+4l11a0g/2E3u+92kpDwu2QUafyqdjU/PB6Isg7QPWQu7r5937Lm8fmd/ewaebwfys8kobnG7p6LXn1f6VYnCPVOFrN/y71XBpWHthXutu8OqJfn8JdXcwPtJOFtnDFeEOtb117347XBrUXmihowuycqQ9mMdH5xl6xM7mtAfL0h1o+6vmvVVef/T88JvPW+r2DMBHO/jRRQYOSb+tv74mvQE9WDPvrGcLw50/+Pxb/817wtZc4/TwuXf+TOpXrMHoB//Fvwpac/evPBFpDuHl7ue+8LaffHfiVVkS/8CPvTN3PRYl7/jnPzZcXm28fv+tP/nT2VKbHQzf9LPvTxwfcfGO/+1HuW6qgv+t//2fT+YXK3sHb3v3T0cLC/rp+Af+5Y+XzDzePT9yWxzRc3/8x3/jX7xzNLfkn5y9/cf/der7ZJL+4I/9KwXp2fr63/mH/9g77k4Wl7/v/3iXMxwmhvvWd7+3cnx6/+rNmd9INau1v/e3/tcfrewfnq1tfc9PvdcdjSOv9tx7fra+d7B/6frf/Z//ydKLtx4+++Tb/uVPeQfH3Y1zb3/XT1X2HuW+/9x7f7596/X7Tzz7jn/+Ywu3Xr391rf+l//kn6186zt33vjcoLZQIlo7OXn7T7y7862XD24+8Tf/6Y9ufvlrL3//9//tf/qjS3/2zbtXrtPbjyUa/UtoIV/fNF99hYwfz26FEIoi2jvLzu0WCwu83ba/+Q1YFI8Z5IJFwbrd5MpV4Xj//6QT3v7hHwEQaoePWr/0C9+FW6HIk0tXRn/v75fttvulLzY+9OvflY80uX5j+MP/QDIGH6+Y+j+ZwVpdRZfPYaQGFzaFZxWO1buwIyymbL135SI0ETcMN55YZVTa9uj8ZunbpWtNd9byqi8do3fpPLBJqeuDi+fKmiM0Ntleyape5nnTc6tZxVOW1rt4TvmGYHRweTdvehKT0YXNZK4JMfSTMS0yYLLBxV1RtZTGBjvrvOnlujk+vxU3a4jC8eZqtlAHlAy3NtLlNhRivL0abC5Jgr1iZiYzrmnT9ZWsU4cUDzfXorU5AsB4ey1Y6yiMkpX52UoHaHS62ok7DYXgeGcjXFsgUkw2VoZba1SW8XJ7urYkNRqsdKYri8AgZh756QRBOFtfGW6vMVUmneZsZU7obLrcGWxtarKg2rhsRBooGB1Cf6yYYmTGayGT0xTgKWIpMrAZ8cpMhznDI1WdaTCGesZrEQMhpVNUGWIkoDbjtZDBjOExqAc6iiTNVGXCcIq0FPkDjEqkzYg3AFSILD+xqlAByDLojylJlJbpdpdTjI0I+SOuE0cG5rxynIJR5bRL3Sg1xt1GjhxMeYoEsjEHOvHmhekUBAinXeq2IFT4rQLa0CKZMSdtKydYmm1hOQVC0mvlhis0zGvVkHu6SQqrLV07I4ibLcFsTpQgEhCIDJ1XGxn0sIVSrQ0sOyegdOeEVlFMZMQcADchuETODJsTAnLgB9iZEp5OABsyV0GlGQNQSTAsoDmVXqKXIXRn2BkhJbE1FtVYlynV+sqNMCqwNZNOZItIWjPDOsshI/aM+4EhU6KPkB9oMhkTo6dXWJFDe4rdEVIKmmPkjwGSVB+DSqipzLS528wRVpbDrQ6gKnf12OwACgoAJYZUw8A2MqddMCI1s3DbpWTY1xJrAVg0o5pw2sI0St1SbisnRJpWWWnnimFXS1lbuSQCFnXnSk0rmc7dRkl0iQXXOACW5tLEmJO2kRMm7RbXdK5pXOsgXROGmohKyLQAgVLWZ9QIkMqRNyJWKIUcIzZhrgZjVQ00NsWolP7U0MYlIcTtITPGKCN2T7pSA4mqBFSbYFDAyhTZMyw59kbKjBDKNbsnXUFlJP0AWrHF4x6zp9ggsITuBDipoUJq9rnDmYqgPyvc0slDq8b1ltJk6ruJ3lCUCMsrWANqKiNSUUx0JJ1qqbWBKRLXSYy6IFRqvtSawJGJVS3deoYAsCrcnAeazFwnM5vSQBn1lF0vNViaFe42C6Kk6RVWnUMksSgZpAYRuifcRqkRYVSk18wQULZXsA72ZKhIKqtTShLEUuwPMC6QFhKvrzQps/TYqkKlIMuhP6Y0ATTTnK7QACslviQAACAASURBVNJT6vUEgzqaitpMIxEgBahOCUkQjlFliBgnNCL+gDOko6mqzTQaQFxCd6izWaxwn9o51ggJidvjNtHBTNZmjAQI56A2g3pqgLxaiaTPdFr++RdEETebXHNKAiSRgABo6LJSz5CPTJRpTWU5BQGlMy8tv6BAWA2hVaUFUq+akioyYG42hV5VFk+MJnerGVbSbQq9Km0ZO9WUuUpBgRRChumVsdnkTjVHCtgNrteVLhO7xnEd2zwG5sxwznLIiDXjlUBXGWUjVAk0EE+xfqpXSFFgc4K8EYYKGBPsjxVWjI1hNdBAJIwUehMKC2iFxB0gILExwf4IMkjZBPiBBhNgJMgbU5hLMzGtE8BgyNWZ5nKsMToBlRnFiTJz5E8xypE2hdUJAqVwzbPdbWahyKsML2xzzywse3J+I6t4CKlKMkaCK0c/u3AOeEZpmaPdzbJic9McXdgofa90zWhzYVapAVvvX9hRFVPoen93q2z40rRG5zdy3+GOOT2/kVQ8qJOz89uyaglNc9KZXYSlYw8u7BS+pUxturMaNxuQwd7Fc0XTx0pOd9aCxZakJNpcCpYWAEXDrbV4vgl0YqeBWwSA4vHW2nRtkWIVrsxPlxYgw5OttaDTNlQ52FzJV5oFQLPN1elqh2E5WVtOFhpQI6OtjcnKosHT4fZ6uN7BUkzXl4K1RakxTWR+HhAg+utr8UqLQDDeXg/WOwio6eZqrz1HX375PwewAADCdXmtbr5yCyXJYx0GEsJOTxHn6e6FfH0DCGG+/OLjIw2eTWFZJpcuA4z/wpgNb//wj8Asa/7bXzFfefkx8+mQc15vDv/+jyTnL+r7e61f+Fk8mz3+SK2cX+j9o39czs0/fprsP21yX5dXLmtxNFleLC0blXywsZGbljubHp0754XT7S/8kdC1vOoBAWaducx2IOfB0kJUbTiz6cn580InWhCdbW9JUwMCxHON1PdAKaLO3LTecqaT7va2dAwWxIPNzdLSkQDTpQValNtf/jLGYNJsO5PJyc6OsjQ6C4drq9LRS0XidnPWbplRPFlYSFt1PQhGK2tp1SU5jxv1tOqv/+mfNY/3J4tLqBBBux0361oYjhc6WaOGuMw9d9qZBxIInfbX1rUkCRuNeL6lhdG01UpadZiVueMMVlYYLxSjvY0NLYnjWm3a6Zz/4hcah4eTtWWYFdwyz9bWWZEpgkbrqyxJo0ZrsLhkZAHlUdLAVOVWOErbSAClZ9O4QcOZczpuiAqWUjARR02CZG6F43CeaTxBZZI0EC0zKoKyAqHIaR7GbQZFZoeToIW1MoFFmtch4xkSIfckh4BlAW+CEW+Ojp3JXJXKBBVpXgNEZognyMkySvV0ljdgquv+sC8XHY1yVuTUh9xkLE9JBXLTU1/rZatLrpagqFDzJtEULTLsAeloepaSCuKeYc+mct7CJjRmoZqzkA60NKUukC5jSWw4PKn71nQK5gxkABbGsqkTk6k7GVQEz9t6GhFHlVXLnkxEy6CWYkEMK1h5GsozqsKkSbUklVrOPcXCuDBL5cP+WfO0aMmqUpxrfBLXCU1SRdOsRswggHpSVjBOYwTTpIH1PNZ4EDcpTTJIkqRK7DCQJEKe4KVkII4aWMsiKqKooQd9p1u044pmpBHEQVnBuEiJiMuGUiVnPEprSMtSCnLQ0CiUZhnzjktV4UTTYqVCIlneycRik6ncEDGqUaiEXia4rQFG7GBcLjtUFrTIwbypyUJXOapSjrGRR7ClKZ3aswlfdEyVgFKCtk4gZzLHNZSXlnppilZqwMTObCKWHIo5y3I1Z2IkdJ7yhiUN6E0mUQ1AQxnTIGsgwAqWJcrKAt077bVmupO61J7N4ppCutCmYVoFVM9lwaGR5jakaarMPPU1ZzpNqhIaggZx7nJgSJLGSi9KF9A4hloSesyZTjNfZL412PN6pAYqiiSx1Mrch1Y0g3oaVTRrPC38MnYNJxwyInjLNMrMEpFYcHBWaLTgC1V1a6pSRuZNmqc6Lsu2rZepyUM5b9I0o5oSTVOPI8YK0DBIWRog4wsuK1JDpGLBNOMI6gjXMM1yykpUZZCXuszgnFlOGbgXqo2mkYVYA6hOWVFotAR1TQhg8jhbqpnZDKVJ1oBMZoRH3BMcKZZFvC7HojE8dEbtOoE5SeKsDqnKoEihneaMsSQoayrTsTObhC1MsKBpnNUhAjnlCXe50LERTYs6TDXsTsdBG1OU4zguayoR5uFoKTFYaSIjnOZ1kJvUGw+jJsKkxHGS+0LpgCWxaRZp0zOCADY1ZCEWxLKpE1tTd3JQYtJxWJYwU/C6ZU1nqqFTG7Igwj6CHqFJik0ga7oZh7rJ84ZjTaeoSmTVcCZjWoHA11iSEBOIumFGM83gZbsivjUVZkXWDCeYEEeBqsayjGmCN00zDokFsobthAFAAfSFKAQDUdggNI9ZEYRNPRrZ3aQdVQ0jjSAOyypGeUp4XDaU5ELPg7gO9SQSuOS+1IoMwKCsICkFKwLeBAIoPQuiFmFZLFFZ+pKWOVAxdtNH2fqw5+dtjSNlprOoiRnPACjKiiJFSkBSVEBumM5scnThgoa4KMFwY0NqVEE8We7Yo9HOl7/CfWtWrTvTyfGFi0AnJCsGG+tCo0rBcKGdOC5L0mSxNWh2vMno5PwFqROcFsPVFWGZEMBorhlVa1ocjVcWg1rTGw2653Y1nu184Y+YElGzAUsxnZ+Pa1UrmE7XliaNtj/odc/tckvHJc8q/mxhDuVl6TmDzqI1nY06naRRv/T7n60MBuO1JZTx1PdGy8t6muS+M1hetiaTaaczmZv3JqNpq1W2KqXAhef2V1b0OEoq/mxpwZxOx0vL01bLCmazdiuea6JSFI4VtZsr33jB751Ff/5P7Cyl7RpNi7DRCOdaksvCMof11l/vivAvDHHKVgsoZbx2G6jHrYLWH9znjUa+tp5t72hHR9reg8cKo0OIOKf9/p+/+xdmRnj7R/57/z98rvI7v610/XHNVYRM3/G3Z9/zNshF65d+Qb975/FZT2la73/6R8nlK6govgtL/H8KsFZWq5C96WfeN1pctg/Pbn7gw492r8y/evfSx39r0uyY08nVX/toankcszf+zPvH7XmjN775gQ8PV9a8eweXPvap0Pb1orj2y7/BsZYb1pt+6heSakWbRtc++IlJe9456F7+2KcKu6KEePL9vwZKGfm1N/+b98VeBcbF9Q9+dNZsmcPgykc+wZFWGubTP/crpBBhvfaG9/xSrpupbl/90Cf03jjx/eu//lH34dHBs898z796l3PUPTp/8clf/ZAzHKZOZfu3/0/n+GzWbl//wG827+3fecMb3/bOd3sPHx1euHjzVz7knpzGTuXcZz7n759Ml5au/9uPtG/duf293/e9P/5u/8GjhzeeeeaXP1B5eBD59a3f/YP6q/eOt84/+YHfaN2+e/utb3vre36+8urdvWtPPfnrH6rf20srtY3P/VHzxVfvPfmEvn9B9LdnS6lxd06MtzDpl+GqPNtObQmnHeOwo2pddLBeDC/N5lPt0YIYbAMjIH2n6F9NbcFni/r+Wtqasb35cnApmMut4wbv70otRjOvPL3C9USGbfRoN2tM6EFbDC6XlQk8a4HRRYCmNHGL42vCSGTUhAeXoXcCuvNl/5q0B8MZOfqGJQECDD38sisNpDJw9E0H+rSYwO7DBsPcL8KXXuyAUkmL7H/JVToFpXr0gg0NUEzg/u0KFBJDcf+FmsoEqbIHf2xxxqSEB990HZrnCXn4SlVyhAz44OsVlEjcxEdfd7mhK6UOvuVCBEUGD255ghNqgQdf80WkyGaV3t5WRRWCiJ/siqgqzFQ+uob6brIwNV67KorF0huivYsqqSmS8bPzMGzEnsIPz9MzL1qYGLcvp+lq2MyMO6syakAS8sGumC4kFYEf7rJ+jbeOwetX8mQzauRsb01ECxhN4NmqDJajOkf751ivHS0MjZc382Rb+BN4tMWni9wOTvfp6JbOlsn0FXh41wNrVvyaOr7v6o5UveLo0Ryq0NnIHH6T4DUteBUe3fGNDpzt0aM7rq4V+Rjt3a5SCwRj7fhbFu3A7AF89Lqvz8HgITm845uswNPitVfaVBdhqB+9YJtLJD5Qpwd1p5EHh/TgTpURXsZg/9s+M0Sa6I/+zJbzmh064OgKAlyJgh+8AaYytzl67amCUCA5PrgiqcAZ4ifXpUAA5+LhMyQFypyIe28pkSagVPtXIYIqVfz4miwI10t1/2kQkbSes1du5MiWqAT7V4AEQmFxdA1yI3KhfucKjklST9mt6zFp5BSQBzuwgAIqcXxNZN60wye/z/tDj6wZw6/DoM90jR8/8CZdQ9XZ4HU7P4L6Lhl+mR2f+mDNmvyZmvZ0jyXdh07/xJFzVvclPd9DdIcOvohOzipwSZ++CAddy9SL8QN2euQZLXhy257eZ/SCPvwT0O36zoIc3dMnA8/SstGhcfrQNpqy+6o9vKebW2r2DXx0WNGWmNO3i9MbkmUyraKHl7LKDJ/WxemVohqgXh0MLys4ZbmRH94UWi4zHz68BuweGtT42Q1uj9WgIc6uIByrjMqDGwomJffgg+uFH+OeI7pXpTlGU593ryuYK0nB3k0CAyCYOnqm9FI1rYjjK8iYibEpujeE4hxRcO+GYGACs+5XdA2UsoB7r1RFjpEFHvxZBc4k7aDDr7o5MZGmDl5whESgBAe3vDInzAH3v+7ziYJr5vHn2Qx40Kanf8ZEqhSAB7f9LGK8bhx9SStHAK+w7h9pQ+5LR+u9QEUCLZAcPmznMSs77tGfMDmCaJV2/xAP8gp06eA7NAwZqWvs0QXSbcjWMbxzKQ+3g3bKHq7I2TImE9RbELONpMrB8bbWnY8WhsYr63mwK+oTeLTBJ2uKRXDY4cMt7gTydAt1V5O5PnttrZydL70ROF4Sow1EpnDWKc+2hTtTp5uwu4VqD+H+lgjOC7uvuktytI1oICdtcLQpnInsrYPubtoer3/hCxc/+Zmw2qwPe+d+49Ox49NZ/Mwv/Nuzc+drrz+8+PHf7m1sVm/fv/TJz6SmR5P0xq98KLE9XIpn3/tL/fUN//6jyx/9rXyxTR8Nr/7mJzPdAQo89f4P5LrNFXrDz7x/0FlyDk4uf/S3wmZL746vf/hjObNKw3z6fb+SmU6mmW9+z8/3Vjad4961j3wsdRw0y2588DeFIv219be98100iCfthZsf+AiJk4LZlz72aWscHK/vPvfT73OOTu8/8+zbfvzdeBQMO6tP/eqvGeNJZlcvfuJ3nOOzw+2Lb3nPe+sPDx89ef2Zd/+ifjbsL67f/I2PGMOwNIwLn/yM9+Bw/9qTb3nXe1qv33/9zc89/5M/6xyejFfWz3/q9+zjflKpXvj0v6veeXh0+cqz7/3FuZdffe0tzz/3rp/z9o/2t3fJa7f/eleEf4EZyoUO7fe0g4PHGmJBCAHQ799Lz1/kzWa+tm6/+B0cPK7eHScxmU6z7R1erf3HjIUvvuX59vt+5v8DLn9VIix+4snRD/13vF6vf/w33T/6w+/i3bIc/90fmr79bzxm9OqvBqz1NXnjKtTwYHcrq3ul55xe3C3qHlXlo6duioopGeteu1RWncI0hhe2ikZF6drwwnbYqCMKj5+4ISomoLR743JRdYTGZjsrcbuRuu5odyPoLGAkj5+4zn0TA9W9fjlrVbltjc5txO0Gt83xzlqwsKCV6eHTTxQNT2Hcu3pB1K3E9kY767PlDoFqsLsdL7YAQmeXziedlgJwvLMx21jkhpF2GqOdDUHZYGcjXF1gRda/dD5cakuMJuvL061VpbF4eX6wvQkgHG+shmsdBNXZpfPxUktRNllbHpzbooon8/WzC+cgIZOttdHGqgbFYGcjXJ1XiMxWFnsXz0ECs/nmaHdTETLYXB9ubWgqY/o4bxW6mDAayua01DDBcdkKKYqJNtbckWAKGFHeyimIGEp5a6ajGLC0bEQaTgkbA3+mqCAkLOYShkIGg6IdMpxAmoHaBKFcsgRWppjlhAaqNlOk0GBYtEOKM0W4qk8wyRALNHcgGEcsls1QmNiTsd2RusU1mFltqXtKw9yaE6BKLBmby8jXQoGJtaR0R2hY2C2heUIjhdMsUZ3ZKmWr1LZyIoW9KHVPMMydttB8pZOyVo9AXaei0JeR5XJNZM4iYK6goLCbhVaBOhJuq0R1qsnc7kjTFbpIjSXEPEllRMwpr8UIlMCeQS8ivFSVKfJCqApsDFU1QiAndshrEVElsmdlNTPLmfInyI0g4NieFPVcEyHRI9EIERTImZa1XBcBdMa6PeIIQSvM27kuA2yEshYymCo7LOuFXibAniFvCnFJtImshQoXWItEM9ZBZtrcbAnGpG3lZJGaWmmhTFtFJsmJoYxFqNHSMTO7JYgGDJY7HYl0YODMWMWGVui6tOaFzkpN59ac0AxpsNxakMomDoisVeSQWOrMWlS6wU2ztNpC04WplfYcB77uyFRbpyZLGeP2gmSGNIySLDJicauYFq2IsoCoQlYGyCkg4MCfIivAMEXeuPSEzqOiFWEzoUUKqkPNDASS0JsAJ6EwRe648DnhnNemxI4YT0BjgqwEqQy5I+gkVOXQmxSVUpOprE25U9r5WFXH0EkhEMoPpRvpfEa8SV7hmsxFbco94fLQrhSkwywZWW6hz0OMoV0RdA45IrLr3KhLgqRTKUmH6iBzrdztSIwBqQE6T1wZWTVu1gXFynZLskQsmFlWacwDDZW6L622ZKrQqspucUalaWZWByCsDKMwFqGOue4qsy01kBmV0mxJRpVh5mhFt1UASSFrY0ISQCNQnUFaIpLC+ljSQgdBMRcxEkOcq/oE0QKjkHl9qZeQJqo+47Y0ZZDPRQRHAAlQG2E9RywClSmgMSEprI64Dc0iyBciQmMEYlgdEpZIkoPKVOk5gQWqDUpb6TwsmjOqJxTEqj5DRm6ivFqNYVNjoKRL2PYEKwtnCWiuIKBw6oVeAzrmbqMgTUJ5aXeA6XNdpOYiMjyuYWE2OGsgUyROvcBtaoDCXAC4hrx8Zs4royopVGZDshYyVeLVCrMOEBC0g2iDeEVotpVR5RriVl3QNrJBatQknNMMESF7pNtjTiG0g6JVaCrGLBWNmQZTZURlIzNFCK0J9AOFFNYCVZ9BUiISilbIYKr0CFYDLAthRagyxaQgbCbrAUAFoyFvhhQWUkthdYpQDs2J5g45lUgLZSOQGqAo4a2ZBiNkhKoSYlIic6aqQeYYmMLjJ29gC0Vu5ezaRe7ZmeuNz62nVZ979vD8RthuUwoePXVTuJoi9OzKhaLq5L432t1ImjVum8G55dH8Epb88IlrvOZKjfWuns8rbmlbo92NtFHDlPQvn5stzBPAj29e41VbMnp29UJR80rD6l3YSdo1RODw4s50YV6D8ujJ62XVkoRMNldm60tKY7P1peHGGuN5/9LuZGlBl/nZ5QvxYksyNtlaH22sMFlGq53+9iaBqndpd7K6qKmyf2GnXKgWVJ+uLfXP71AKJ6uLs/UljED/3PZ4Y9Uu4sHO5my9IxiLlueHOxuC0elKZ7K1wgQfbG+EawsAqMG5rWCtI3U9WO6cdjqP30X4l8GGUtKyyva8fv8uHfQfN4yVxNrBfvim50SlUrZa7p/8CUDwMcNYZDgAhGRbO4qx/2dRiN9ydsq6J48bveK86CwOfvgfZNs7zte/Wv2tj+NgBuDjFQ4KHj317OBH/ofHF8T/1Sb3ja34xlP6eDJYWUkcD0X5yda50Ks+unB5VmsASrRx2N29UBgmTPhkodNfXHm0fTHznMBvOL3h3pUbwjLYYHa6uh42m/s7l+NmXWIqIz5eWRo1Fpzu2f7l69zQHly4PmnNAYRgriYLC5Nak0zjyfJSr7346PyVSb3FTU0/HfU3twvHzHMUtNvD5VV9MBksrkw780Z30FteC1pNVANRpzn05mBcSlPrbmzDaTKeX0yqlYOdy73OitSIylTieoO1dZjwgrHu9jlaV9N2a1JrG6Ng1O7MFuZAVGSmc7hzflpvDdfWYqeCwySs1k7Wt+3+cNxamC0uKAeUS7UDf03Lco7JYHsbzZJprdVbXsOZAFKN5nQoJBAsanKAZ3V6v1+ty1JTEApPpMQWAk3mqAmGLfzyodeGiBRKD5pYCQyEiOu6pg2q8JXDyiJASOVa0FJYqVLqSUWZ6MQl+7N6LYemKkjRyGPDhhmbzEFD9H19b1bxSmCUAghfJMziBcsbZWw6csLhvCFMJnIk6pYwAZUj5dHc8/MZKJccG6eTzBUtAzgIFT1R0XK7UiQQ1rSy7uZjoFacoiywGZYmTuwGzITwtNI1ihRTS01dk2lB7jCumUWI4JxZuobIoPR1YSsCp8hBcaXGAyDnLGyoIqK8YQGH8JwqBsM64qVV2CgzZat8STTUzK2LFAuqkgaWJRUExxUZ+KQwssTWaAgym2RVKjMoMJy2cK28Zxpnfa9RCqfEPKxTEoHCRMhIBxUjrqkSI8VBSfS0IUHBMp1MPK1VvoyNwaQ2VwpdKZQ2ylwaArG0LlUmS0hFy8qxpgqQrVRhPGJOklUqCuGcMzFvcUCgkFnNJazAaMh9JzddHgO+6hEl84KJpilBTNQgaTQFs4oE4HktM+08AGLJJulU2Ly09IzYQADesnLKihSijpFiQMggr7pA07IEyzlLMKYKkLb90sQoYlETKk3w2IzqSNq8Lm+XDh9bLZlT7ojY11RsBHWAQTxu0cyWBKpI2aUNUhuLTBOuiDzSEd8MXK00mErN0EOFS2WGSgMmFRi6Spp5bBgg0dOKjFzKAhBXaOmrirwL7LhnNmmBCktFVU3FRmYXYcXFYa4oThYqOC05ZqKtAZJDEqeNGogVdzTRtMoccwWKjqMnfezJzDBzaJYaK9u2CkppsrTmYj7GLAibc5CDAhDVMXmOuMYK30B5nzllVG2WkkIF0TzLoF5KKts6A4HScl5zS84UxXzO4YoVOeArHuYlF2ZW4xxRIVBS06gd1cVLI6eSWA5Mtck8QlAWuZF5InfUrKKQwTNslaVe1MtQN2CshXOKoqBO705rbkYcWIi0ilJNkxktqkVga0ETFpoAGPJML6qKU5JJLatiaCYt9PLYNKZulcQsnAMQcVGYsQ9LDRUZ1i2RNP0opGrOhpQzOeAuzWs1kQLpaKpu8BSVJk1dXdcCziS3LJGQsm4KXysTJRyjcJGuhsIhaaVSRAhUWdZw0aTgDUtWdZgPoKOSal0lEloENPQo1lSVpRXbivalA/JmsyiI0Egx58pIlraWN00aAa4h6fEQelLB6Rzx5LFL9069JpRaRumsQXEKOBZJwzBp14Ov951WgTQpWdSQsMScsNhDFXXf0Lvj+lwpmeI4bxYpdaRgQUO54pFu9Wa+z7nGgRIVEWFHlDRvlYolDfXyabUNBM2gFjcJEEgomdZwZPlO92zv+hMa4GWkTte3yrqlGnTYXoioS2ZRf2d7Up93Dk8e3nxKUvzg0s1ZswWVkgUYLS1Gls9mSbja6c4tu7X40colTikdhWdbO6ntqEJN5+f788uPzl0qPGfsNyvds4NLV0tTp7O0t7qRtCughvqthcBsmNNZf3N92Fp0j46Pzl/KXQdFRexXhisrOMyTit9bWj3e2p002tN6yzvtDRdXg7k2jIrY8Y83d0bzncniYlBpaIPxcK7TX1r1ur3J/GLa8PY2Lp+tb+VYw1ESVBvD1RVjMO0vrU1qjbOVjZPNbUWpzEHJtNOtbRQVketP5+e769tnS2uF47BJPGu0JsvLIFcl087a886ffv0/d0X4f5c683odUKrfu4Oy7LE4CWM6HuJZEL7xzcKvAELsb72gKHscwAJKaY8O8rX1orP4/wLWG9P0u7lItKd/828F3/O92vFh/cO/oT+4/7i0pJTw/O6P/ktp2+C7qXL8K7oIl5c7JX/+ve/NGrXmgwdPfPgjuWEcXroS+xWWJJe/+AeXP/ZpMwwQVM/97M8Dje5dvznzG0wW1z/9Oxd/93e1NPHDydUPfdSczR4+88yk2qJQXPm93736sU/lvr146/aFz37OjeO9J29MvYakdPcbX3vjT/+sNHWv37v54d9EWHXXd2aNloLo0hf/4MaHP1Y57ZZV+7l3vw9SrOXF1Y99snF0iJC69vHfWnz5pf7Vc3+HTzoYnh2cvuGXfq066OppsvPZzy+89nrvwk5/fjm1nPrZyfe/81/X9/aCpcWnf/FXq90TrU2eJ+4Sy9Nu78oHPrL29a/vvfmNP/jjP9G4c+fk5hO9hUVJSefu69c/8anOq6+G8wtPf/CDm1/98oOrl35InS0p/TRPnnn/B+Ye3NVEsfmHf7zyze8cX9qxHl2G/WXZmLj35tFwrpa9dh2fPhPzHlLh2XXvYcPUXiUPV8F4O6/Fbwu+8oa0Mil7Yn8FjHcxSfG0YR2sCbz3A8WrbyTufVhqe9twsGQUA2vE1Ol5HQ+fMbvfB9OjNEevrsjJOaydmUcNOFj14sOr+N5zmYyi3mR4jR1dcsht/WFFTi5p5CTtZacv6jjMdVzsf9U1ZOY5/WitZSSz/GXZu2ebUdiYnb76+gIdTF1vIC6taWlQfmN49nrFxCnuZt17thEkFuwmT2wavTMyQA+/5jJVGFF09JLlxyMr74ZPbOonXdGn3Zdsuz/Rrfzhl1zERa0eZmtNOjpVd8vTux6cFCbND15waC+0NmzzxXWWaU7Uh6ebZOz7+NY7KFjtv/5A2sa9N4Koxtgxu79LAwPR3rTuJr7ndBPj4a57rMvavvPqdR7PmbXjv53snS/JeBIWp9fotMWtQj9Yc04cS3vpcGm71JgWSu/BJp3abtylp/NgtEwao3ekJ9f54HYIvFcviHSZagPt0Qqe1CgaDA/A7CVcq82Cb/Penq3PIVMLAoXF+wAAIABJREFU0u156zuvyyPt6K6jUZ4N8egF5NgRxoP0ykbl6Hj4ijna1yv5FA7yR695WtBjq4ivNvFxV3yjOD3wPSdKXufDh4aXDG1zOLu4YQ0H4X395EW7XQ2K2/HpA7eqTWnWTZ/YMV+9x0/17stmjY9FiPZfsE0/a4903N1xw1BPpqr7pDVKGsZr/xWfarI86/ra0WUmAmckQO+8Pc3TuXxaqQlG5w/6/OB7qRB6GqFHF/U82RDf+i+4Z+Pe6awFD677g1TZQ/3OszThwh8OW/MYFO4RocfnzIQWlFv3du0+N/VXfkgMLKydFB12f8ucRVaSgNNdGntlrT/6fT4d6v6cHHxF8oGql0fZggFrJghQ75ta8Vra6szGX2bDvuksQ6KFomY7394bPaqMjwytgke3sLgnPPtManF6ZYvePpm+5sQnsFZOoodgeGD5RVden1MWhN1x9CU56DsVOxq8jOMTUi1OZIeULd+Zhd1vWcO7+sLcMP5Scdb3HL9onWni9JLGA5zY7OCyYmdvmHz1eWL15JTvn0PDdSc8s0PFu1eZCOJ2MfWbbjb0b3tyeJHSM+vYg8MtP+6dU7f+BtBBfNob76CDG7p+ah5roH/eEoezqhZ7LuaydbcE3atueERKqg7fgMj0SvS175VmVvaSoxXYP6/HKeaSPrhgyHgqwsFXsVdMwSg7ueeRcV5Fj6ZP7LCzEy2AD7/kqkIaPDn+tsGSwg0fZFcXtTSRZ+DoOw45ipy58uQLRl5qlVbMWxoKJ+hB1rvnyykgLup9FYHDuNYYyLqZzdfBy5PBbVubhk44Ob7r5mPm4G5xY9lMAnwy63+ZhbnFsJi+CNSoNF1mHG87R55hvkRfvyzjFVgd/MDsxUu5MYsn8tEOmixDI6Yny1Z3QbJX/pv0wTXm7+cRfHQDjetm0dcHNXC2qpsnbyejZ3B8J+DWK5s83iTsVD9cQMP5SnrvDeTsejgdFml6/BTtbbv0G+zumoy2TfLwyfjlZwsvjI6T/kV6smThR/BsHXbPMefh1c98cvf3/wMti/bp/u4n/h3heW2j9QPFbIro4qc/e/nffzZtVNe+8a3dz31OL4u9J5+aunWE4JU//Pwz7/+VeGGu8+JLlz7z77AGW170N8mWKB+Qb+89/cEPs6LAZfGW9/4coOjgyrVZtYmRuvLvP3f5M58xZwFV/Nlf/FU2GZNzS/81n4Z+o/EfvnD1k59iGPjds6u/9Sk7CGYLcz/4zp8wJxOp60/92m+4w15/dX00txh7/uo3X3jzL//y0qu3j65f/cF3/oTb7R7duDmc6wAEVm7duvHRj7f29saLS8//3PuWbt06efra4cJWybSFO6+/8YO/7p+e2Ulw/nd/r/PSrb0nnpo224Vhtg/3vu9dP+WdnAjbuvqp327efzBeX+0trkRudfnuK2/8ufevfec7+08/8f0/+W7v4NFpp4O++y7Cv2SOla9vkH5PO9iHSj5W1SBE7PBA1GrZzrmy0WBHh+zk8SZQEKI8p92T5PJV4Xl/bsbCTxvGd3H9d/Hy4H/8h0DK6u982v3iHz2uLxUAIFX3x95ZLC2Bv9bzl4hGo6efxQjtP3lj2mxJpr/2/Fu5pnGmkTwXjkkEevXNb4rbTUH042tXZ42GANjIo6DasvL8pe99e9xu0Fzce+65pOqVkJpFLBDO7Er35rWTrV0rz1967nmKRGT5WpZAioTlHV+9fLa2hhA9unF13JzPDdMIw7zus1Lde+JmsjSXat7ppQuHFy4yLg6vXDu9uGumxd61G2e75/j48BGmh81taVjpQuv+zScV0k4uXo5a9cR0kOAEKwnoeGPj4OZ1SfVoefHe1aeS4mxP4uPmqpbxR1ev9y7sSEWmyyt3nniS5jlWghsGBLi/ubl344aVJCe7589uXkuD4RkWr3nbmJG41bz/7FNQodPN7f0r15hMDTgaLgqiAkPFkxUxRSAshnf9JawAINNyLs8YxigMlvMkSVN1+krtAtQgBul0IaUqpXgULeMEl8NicNvZEbiweDxeLwnOEUynjaSUo7MselhbyUzDgGE2N4stZOdhbwNwEE9leL+2JjHDaFy2ktDWGErTTpTVXTeckU2Kq9QuErKmF7oiJ31AjaJedcqM77qOnSlOyK4NfKpefcCxVs7PG4AbK6Scd6wsRecsRSTt9QqpFbWGLyNjkZRtU5el3RKzuofGYwEMUrNtXopVHcxbdhkb87g0pTo6w1TP5uetJIXrTK9DLc3xqo6bmMgU6sF4GRIhuBfGNSTi/X2r3m2tkSyC+iRaKJXMqRmFTZ5rBs3ywsJ2HGWVOFnAqEyJNhkuqDyenKnofnMbKqj0UTRXmElUuIGqZyF1AIBYFFSFUEuny5KJUtjBqApRfHDI5cniOSElJZO4E3HFMUuipULnmWZwteEAnTgkBbsOPD2jk2lW7xCT6JDLbYti7tJYbvigyHBvKE2n9ByPZ+KSr1lShwLvOLBI6PFZUV8QjuXRUt9AomI6ZcLPOywLilkmNA+Zuk8itaoXruHAUtvWIgGNwSA3q6xqUinkpols7KNEbHq8At0kGi7mwudmymedPPVkOjk+0L1+bVEXmWhEoya2sjRcTAmexZqtpYkFUg6lqARpUzKe8FrYq7pp+NqrxkJs61pWRp0srSOazrgfJjXBIbPSODeImWVRM4iayptNo3aWzpN4cvqI6EdW2ykCWY2GHWYmWVYbTtuWl80cO0+3fEsktinKDU/GGexPynrVKnLWgmrdQ6Jw7JKv6qQ/BnFW1hqUEc0Hcs3w80iv8mKrSYMQD6dJq2OTUjeE2nU1UVJPiRUHn3XhMMg7K5QiR8vIpiYZsTWuthwczkRvxv2KLqXllXLbExg5OgfnLAILLNNwIUKkRGCaLspEl7Okt+euhY7hpEF/CyCWEZnnc2GpAwGIU0Q5JhQX2XyUeNDO4v5SVmhylp3era0mzDTkMJ4rEgeZPI4X01TXSk3ToxBYiggezafKElAleSsJTJjlZ/fsxVG97ofBaDnFRkQkT+ZT4ShDcqeeJys1o+RoXUcupMdneQFEZ8EuIqMF1KJuyEJvo6JTgYM+zwCoumae4xUNLJh6mWkNVTQYOz6FipadBTPL9UUoO4YTBnSRyBVX7p+olBetlssTvQnksslyQTs47/j2vbtlWPD1NSS4VZVwTbeyiM4zvqxbcVRaMzFXphhjEkwXizSaTlB0t7ZNoJJ6NJkv7TxU5jhaYalIz/h4z1zNqWI4ni2XGsi5lgS1ghfdo1I8aq5xDes4SObDnEEdRMNVKPmoh9VBfQVLBdiYz2eBbhgwjBaTMSBx0X29sYsIATQNOpLCjJJRsCimlYYp+Etvezvw9Jw699785sDRwrD/sLIwcttYM/bf+HRvecMq8ltv+z4ERWI4LEsABJlfP7p+tb+0jBA+u3lpr7mZh6/e8raTahMhcu/pp8KFOUGNwxvXJq15jmll0h/OLdlRdPv556NOG0n44A1vGM83wmn3vtk88+cNJR8+dfNo56IzC15//vlgoa0k7m9tPrpySSI22t46W9vIDMuIwll73pvN9q5d75/flogNN7cOds/jstR4ntiulheHly8fXb5sz4KDq1dnG0sRsaFSihCEcW9z69H1q0ZW7l+7PlhZVYSQokAU5sQIlpb2nrgpAe1vbY3WlgvNQAUXlk5yfnrh0vGVi5yZ0fLy/saW9cKf/uevCP9jbkkvXDRfuUWHg8cCLACgUsad18I3P8/rdVFv2N/408c1qCNE+z2lsXxr5889Xo8NWErxeuPsf/lnvFZzvv612sc/irL0McdXMM/H/+3fC9/05r/GcvCv7CKUT1wGWOY1R5pMmCyab1IMLn3lC1FnjlKppJhurTIsc9dOWr5eZquvvowNnFY8JvPx5hrBAgA52li2s3Dh3h1bpUW9kutaUXNSz2FFMtrZ0ECx9a0XlK0DHaWmUVRt7tmSkbzpc9u49KUvZHWfUSWBDLfWDJRHXiWv2kXFxWUezjeVrSGeT7c3CJXlFKQFEjW71DTosHi+CZSK2nVec9a+/Q1DcmhiYRpJ3Re+yTUmDRzOtcnhKNY85egYiOniPNFhqdO0XS98p/Pw3tzRXjTfJGUWt+t5zdN5Ei7OIQOXU5XPymSxjZECFktbNSxFMNfKqr6ZDQmZcR8bKtCLgfJ5Kt0kraQ12ygmhEwsGipSYjUuq0QAI51WsjqzijGCsagoJiMCx1RP48IMo2reMHWcmEm/aACznEEQoioXpTkp6kADAOWs7EtfKVLa2bBsSFTgadrIXJ2JAJHAZIEyAMmHwBecUT84JQu6AVKd5KZdUJ3RADGbIgc5yZjMaR4IKShwAxuA01zTLV0zpa1CzeTKpVY0BHOaY0jWE7TlWyQxcEqNgpnKArFnJbJZMY9y0PYMrTSyKWtRA6UGzjWHE5uxKdB0CnzNTUZ0jumksMqQViFhEslQg1PhA70IgBFji6czf6w1NJIiGVAjQibHMKFqIn1Y60ZWliob20kfmCGjOeIBYAl3MU+NLHe5B1gRIi0ULrLSPjT/L9reM0izrLzzPP5c+3qTb3pbmVVZ3nVXVdMG192ANBI7s9LEbMzsxJrY2Ri0gEBCApkWVgiBhBMgQEgggQTSzjICgUb47obuLrq7unxVVqXPfDNf/15/7zH7YWNnP2xIKpk53++9EffTP57f/zw/z6YB74vS/iCpMi76XPdVTlhxF5iRzoMscIewTHmKtMdhT+c1RglXPVnQZhaYRmrYwiCpg3xVMRg33O1QT+TdrM9wTKrMRIlNA8vJMGTWQJGKQS3oxh00wiwRMB2wEWIITQU3bWBYyhJ9XMKQg1zcJWNGScYgMHCJWyAwecIdxU3tih4tQp3LO1shmCja0rdAyCvEhBEnkc4zQlPX38uqyoARTzqymGCoO9l4zHLUiJjsYSNULrT9VloQHGfVuz3OpQF9iWLEfWAKKgbYCFKHpf2qV8gzkvK4LXIZRwHWQ2xHmOnS9tASUZZDznBflAXkwA12tRsQLPtJ3Sdl7AoraRPmSQfYfksWEs1RPu2YOQVzxNWeBYa4xvlAUWqCopWPOlZeGCwzYGLQEFQsM9a2nxk1wkVEbYkK1EoHlpNaVkYE4b4CjZwJfEd7qIzteMAcZZWAEWMMuW1JilOX+LhMOZWO9lCNm4MUY4vlsAEi20kZF4ahLNnPGrlc3IbKA0XBQYBhn/Ag0q4XlGUeayqcuJVVlCl9KvtpSbt+WNz3HOhrB+Csi9xUUWWHe+kIwgIMwnpoG0wHGnqYR5glRtaGbgoxmLzeFAVk6hDJPi0mhvIUDogRxsz2+yWRZ4pLN2yJquIwImlXuoISYQM/Z8a6aFpRn1SQYUPaFKRWNlFk4pjbgjnAkIFlxrpq29sJrhUsQ9jZkJaASRIOI54HyGVmX5gEwhKzgh6qEWSiUrhvlIFBJEsZ0QxXmJMNLDNhDjCygJQRzjOznRHHMVlqodgwEl3iuaxvGKkomHawj8yhiz1BBIXdtMhEZodhQZSgHXcBj1QO8KyPSJ/x2IsdPyzrMsQ4NURHlJQZ9QEPSS6Lk0JfFpEhAYi52FdFAFFiyp4saR3yrq4rC1E5QGxoUU+akKT7IK8jlI+CSlLhdtKD1EdWinUA8BCbWWqbTMTdxTkHRKHtehMjJE7iAcxsS5TymqC4VojyORZ57QOzburPXHoe5E1gktSxsoKV5hxAiaq7w1zVuLbVGpukREiM/MlRTHSad5NakUC1+MyTsuhExbyZBp2lAwxlklF/vIalyroyslxZdLFK49FSnM8Z8bC1OM91mtlGViuKoqMIFkUrqpWXnn0aGlQ5hiGjwUSDGChjJKwWRNGduXG5OGz59QrP4sHEqMhZTMbDqTGLieLGVrW56U80iEqTopuVcyyL+5PjSTm38NKPHa+jC2ZmcpW3opEyoDgt2EmtOPf8s1xnyELaNPx6GdokZSzLWQPLMp959p8FEf5/ezc5TyemnOeeQUlyr5bkNDXu3B6++jFRKGhC7ecv3mONCmBsXb8WHD8lqlUA4b0GLGWY7X/374Oz9xl37lT+5I/43Tv3qtMRIjh9pvU//S+aMfCPPX9rwJqaaUTiNb/yxLA2Urqz9uCHPr67cGj+yR+e/MM/jrhT3N0697HPkCDSiDz6jnf6pUp+fftlv/Ox4djo6AvXTn3uC1qhXODf/6HfY0GUUuM1v/quzLHs3f3zH/5UWC5Wr62c/twXSKppGj/wgQ9bnV6YL73ul34tNW1rr/PA7348cZ3Syubpz36eDENFySPv+1C+0/WLhcfe/m4JsYTk3Cc/W1rdFKZx/8c+PX7xxbUHHnjdG95SvbnSXFx6xXs/WN7egogs//GfV6/fisqVcx/51OxzL9y48MBPveWX61euNxcPPvxbHy6tb0iAj/3JV0Yv3/Aq5fMf+eTM93945dHHfuqNv9h48crm0dOv/MBv127cEtRY/ou/nHr2+b3x6Yc+8vGFb3/vyqOP/tQv/frI85fWD594+Yc+PHL5qmb84P/5tZnvPXX77Clr5bTeWQ5HvMJLI1n3qJWty96c3j0qSQQ6M9bteVDYwCtLqnUmKHWdlUnZPmqAFtvNieZZRWI5mLZvLqWVlnl7UuyfTUsd93YtbZ0Cqs+6ebl5FsEB7I+i2yez0p6xMi727sP2DlurZ/tnzLjJgnyycR7DPhjW8a0z2F0na2Pp/gWK17sdcPdHReSlmILb/yWPCEBDsfp0Hts625J3LlfMMChE/WefnSH9CNh45Zs5gCAOsttPFThJVVPeuVxlXmII/8qPRkknpAV8/Ws5hQiIxcrTxQIMZUfceKmOhplBsms/qLF2jIpg5euu1ATG4u4zRawlaCcrL1VRX3CeXf1eTXSEuZA3f7wMkpoZt8TWcdwtEdwTGy8ztvNxvclffEAPppS9A2+chkEdiqHYPc47VWEE6O4Zc6sUjzStS+eEN5cUPPulWe01eNYW+8dhezIxA7Z6km/UdPUuful+ERyMnZ51a1EPJ+20CXYXwd6sKIR45ZixNRGNbtvPH0n9w9TYwncPid4cIJ3dFdL6EbcmdPciWL9eNkaB/7xev1V0UCg2s5XLdcPQvaa5/V3KZ/DgBb16pZyrxt4VtHqjbEkv7cCVFyomSvwW3XjSsUZEdFmtXKnky/HwGly9Vs5nQ9xJXro4asHQHxir37edhg4ui9tXamW7798md65VrDRUXnbrR1UbRuGQrXzXBTWS7+bA5n1GENI0TNZebbSTzMn4pZdpTXAcw1v3QSB4T4mdC9AHGATq7qvsntS8Ba4+JrWBsgTeOUeUot0027nABhrhUKy8indQmvfYiw9paQMRgjv3owyCCKjtc8aQJmZGrz9gtEhaGpovPJCAilYpvXmERgAnqdi5AAd5bzxu/oXa23LdSdT6NujtGTntr98q9dc4deT28zn/qrQXYfMbdGu7xCZo93uqt2OVVG/7dq51x0YVun3RjK9Be17ufRNvbZfNmhw8S3a23Bzw27eNvdtOoZiuPe+2XmLOEdz8BtjaKOeLQet5vrPquGrQXnea15xcId5+0Wm9ZOTmsva30epaJTciczt5sXUBqSEMyujW2SzfNTZqYud+aO+xjUq6f78R7fHIStYeJHKoowK5eT+2dslWJdt5GWG7aLcq9u43og4Oibz7Mpr2VVZg1++T7tDYsLOdC4TssmY+3b2A4gBlGNx50Ej6IGHg1iPC9lgzL7fPGWgfN41s90EeBkpAcPthCFVHehvfsi2ZoH5y46W67gmDZdeerJHdmNXBna+5cUwZTO88nQcCoH5056Wy7ijDTK5+vyZ2MjZN7/wny09sTtXWD0zgZTiRdy+XsxZEjl77bj7dzIwptPWXRjvJEw53nmTaU27s3bxcj3egbljr3zTAjuaTeutrrB0UDCqaz1pelxouMDZPG2sNXb2LL5+RwyNhvmvfmlf9OVs00e60ai7JXIg2lo21mXh0x3rxYDo4Tq1NcvdA2l1CsodbU2rnEOQttHUYrS+m9U37paW0f4KYW2R1WrQPWWkT9qfV5jIw9tH2Iby+jIs3ybVDae8Mxatwc1a3Dxvpnu6Nw9VDwGyhnUWyfjittY78xVdOfuFLivDa7uaJj/6BRsjoDl/57t9szS1M/fDimd//3HB8dPyZF0594YtIQaPff+CDHwcAkSB+1RPv7kxMj7x45ezvf05X8/mrq6c/+0colUTKR977QayATsVjv/7eweho7dL1s5/9fJLPF67fve8znyNhqhF6+Tvfj6QSiD3+jl/vjkzWb925/1OfAZSYu91zn/yM2fdbs7P/4o2/6Oy1/GL5wQ99zOp2dSxPfe5P6iurrfGZR59499ilKyvnzv/0z7/d3tkfVEce/uDvFnaaCtETf/Sl+rVbO9MLr33Xeyeee37j3JlHf+ld+bWtXnnkwY9/qrC1RyA4/vkvjT3/0ubxs4+/41dnn3n+2iOP/MTbfrV8605QGznz6c8Xb9yWhJ744pcnnn5u59jRV/36+6a///TNRx557dufqFy7vbGwhK9f++dChP+1jJWNjkEAzKtXoJT3+BTpdQEm4cmTKl9ge022sXGPoBAqZayv+ecvaM7vNWAFp0+3/tf/gPv9wje+lv/GX93rxiwpRam896a3iEoV/BPO344I57Pjx+1eZ+3+s1GhwH3/9oMP+8V8qblz9dHHskre2WquvPzlUbVkeMHGmZNetcL9cPO+073aSGmvefXxx4OCk99rrVy4EI5UjL63d/LooNEgQbJ16nh7era0vXXt5Y+EY7VCc//O+fODsRHD87ePHemOT1hDb+fUseb0XHlr8/bDD3uj9dxOc/2+s+FYHXnJ3qHF3eXl3P7+7qFDzeXFwtbO9smTu0vzbrvbnZ7aOnuKDby4Xlo9fcboea252eaxw7nt7Z2DyzsnDjutbn+0sXH/GT70w3LxzvnzTrfXmZ/bPXksv727t7S4c+Ko3ekNRxu3zp1zB72wlF954AGr1+9OTtw5d391c6O7sLB+5qTZ6viV8u1HXm4P+mnOXjt/1uh73anp1dNnjCAiyhvOJCwLeZLE08MYETP1g8mQpCkVPTXmSwGICIbzGU89FsX9KY/DDGQymAiQzFjWCSdSnKUs9QcLCmWB6Ufh9IDpGKQqGRsSnRExjMYTpQRPgmgqyKCyw9Sfj5ROSCKSUQ+phIhI13sZJSRJwkk/tpnR7FsLjOQBHQTGJAFFioehMUNRHtFuQJcd24qyAeKzhJUgGcR8AsIS5UHCJ6GsmkbHwwuclDFpRcYcBWVMh7HZAKRKiJ/kR5J0rIg6kTkN8QhDe74xCeGoyXs+q2syQtEgsUYknLBJKzTnCK5Tsuc74wCOGsTPMBl4ozGNkTS9ZCQx+llm9+IJZfRDZPnxaIBjjfEwHg9IqJUx9CaA0wkAbwdTkA0iwAJvMjaDmODQn4hAjDDv++PC7kbA6OnRUEUAocCbTY3ABzAOxnwWK2VEg/HMHCSQtsJJxbwEQz+eDlWqiQqSqRBHMdUxXrSYkkYayqMFriIWRGzZoEigRJADBgIylw3YLCNaGGnIDxqIIToI6DGboRTE0lqgGAlbJWTBUAjQKDQOckg0HUb8qG2iMMuIMU8xVSxL6QzGJuFRSBeZsLnV9/CySQxA/YTNM2QAHoV43qKucrrxcCxShczoqqg+EIWEBUoUB2kuYaGUNd8vArOfJY2hyqe8J5PqABYiEELh9tNyRkMp892gqq2+jmqDrCjMTpKU+lldW/1AFfy0nBBfonw3qAmrJ6Jy3x+l+d2BKPbCBmT9MMuHcTWyhrHOe4OaMgZKF9vDBsvt75mOkgu2GYYcJ/SAoWNtWqlecM3OMJfL1AEX9QLGBV22DT9gOOXzRCeA2RovmKznm2YEDuaZF3OcqKNFGvgcZvwAw1HKTEBmKA5S2wzJvEn9yIAJP2QiqalK2EGTxAk1AJ8lKEhcS4BFG8SCqxgeLdiZByOdjA8xykjix2ORhILHfjwZZljafjqcizSIaKzi0SFCCU0iXe+nDJEkjce8zAJWPxpMR8gQ3M+ihqe5oGma1AeCSiNO07GhbxGnF4WTQ2ikxMtkYwCNDMQirQ4zU5lhnNQ7YYE6vdQb6wNH8kGc1AbSlcyLc5VYTOZhNzEnABnlaM//fzyDvOvzqiZjDA4TqyzQtI3bkTmF8Qgle741qvG4yXs+rUI4zvnANysSTlmom5ijWk7nne222QBg2iYdjxUBn+G87/OipBNUD6Q5CuGUZez2jEoG5xzaC1kB6Hmb94asqPWcZfcSSHt6NJAxQiDw5lIeeEgn3qTPUqVIPBxLuZ8g1A4nBfVTovxoJsykZmkYTw5YkmqcpmM+SwRCvXBSwDhl0g+nQyWUIVN/OkRpClCajfo4SjEKVKOfAk5FGE15EiMWhd50zJRAIIkaPpYSwzBuDFNKi629q4+/FloQDdOVlz2Q5nM4FRunT4Tliun7dx64v1cbLe/tXnnlq5Nyzu707144FxYLLIw3z570KlVr6O/cd3R3crGyuX7t4Yejkaq117p77lzQqDMv2DpxtD86arc7a+fva8/N19ZXb77yVX6j6jZb6/fdNxxtmEG0euLEYHw8t7+3ceG+5sx8dW3t+iMPexMNpzvozc1sHTtqdAeDmcn106dLO9ubR45sHT8xcvf29uHlneNHrXa3Mz29duZMrtMezEzePXEqv7Oze+TIxskTtbU7zYMHO0cXUT8ejDfunL/gdtudsbHtU8dyO7u7h4+sHj9W2VzfX5jbOn3SavfCem31wjk+GA5GR7fPnMzv7DSXDu2eOFJottrTU+v3n+EDP6pWVg4fsZ579p8TEf6/g57o8FHj9q17Vw1CKfnmerS0HC8uKsM0r13BQXCvoLC5qymNDh+Bby6V/n44WK9v/NYXTBZHAAAgAElEQVTvyFzOfu6Z0d98D8iye4J9WgGI9t7wxuFDj/wD9rz/A12Eyb/9N8WNjSxvaw3YwBvWaggAd3d3ODLiJEPYj+NSgQCBBpHMmRph2veBy0LuuM3moD7iyAC2/aRUxEjBQcQMLTFRvgAOjQ3X3t/3KlVTRbjtJYU8YhD3Qm1zQQntDKHLQivn7O37laqhY9zxhW3aOAlCDEyaOg5v9zLTxByy/V5iOTDHcDcAGImcCQexkw2jQkH5QnCODUQ7w4ybOsdwN4AQiKKDBjGVSVQqkp4nGMcWou1hxgxdMHA3BEAPR0ac/X0ukrBcIn1fERKUy/mdHYmJLhhokEApBiMjdqdjpJHKmWgYJdQY1Ed4CHGq4jwksWahwEYY4RyOdeoinGqYobLeHqCqkiTJQ5wAGkrpKjOMYu1mDiAZQBmidJhJE2YozkMiIPGVdJUVBpHOYSOGGci0zelAKBOkmLOBTwrMF5kL7NiPZB4bMZQwVXZZbw5RSWWcGZ7HXdQOkUu4jvzYNFgGEYgjYrM45pYaCuXyCX99S44gB1sg8hLLICnEIA6xTePEtMVQQJc5YugHHJrYIokXmwZJIQFhSEfUXt+tZD5QDsspzw8NzCEnsR+bHGeIgTgkDo4i01YDoVyeU0M/5JAhZmkZm1yEytQiNRVGBh6q2JSEMDKIsyJWgvAgETmeRcrUIjMBAqkF+VBpBDkdpKIAlEpcRAPN01hZSmQmgDC1IfO1Bqim1vfRJFQ6zSEaaSQU4gkJdUxM4QDmaw0wp70kyyMhiRHGIIdSBa1UhyKVzDbiWDAVK10xYSLBMEElZgaen5mgwGCmdKQsM0kFlQlwrXiIc3AQwyKzI89LTe5omMkoY7YRp5LKBLpmNCQ5OExRDle95g6oG7ZCUsYps404UUyE2rVjn+b0IMUFasaeH5vMVkirOGEqzwgAzNOZBWwxSOOcMgBHfpIVCIoBliI2OfISw0ARESa05CBNcpqIktprwSmKYkiESEwDeLFpIB9lNrFlL0vyimmK/CQrEhhBKkViGsCPTQ4DokyYMWAMtKKAES9LCworYUDqa0P7scVRgIWBYxvTbgSlVCUDDFOWxsTFUUIBADrHYC/WELtGGCSWlkqVOPAFjyPTzJKERMQEOYYGCdDQMUIvsWAmdMWEgdCJ4A5AfhIiyzaTNEICUNcMg9gEmbTtdKgdFKXUhThIImhaRprGKNMkZ4ZBaupUqbJV8PuxzEMzxUKm0mFsKBUDKeVs4NM882TmQjv2IplDRoaVTKRb0jsBcoSwOBuENEcCIGxtJV6S5aCRYS0z6TAyzAADEeF8GLAcDYC0tZn6cZbneMh12od1jocCMxBikw08XmBDmbnYSgdJlgeGUEikPqqKlu/m45Boi+a0F4QcMmSy2I9MigTlOoqoBcPUsrKh1DbLAc8POGTIYokfmZBobVPQTy0YCcfKhgqYRFsEtiLAscMiP7YA1spmYJhaOmJMBiHLLBPYBHUiSJHFIj82IdQ6x+EgARiKvMk9BTWsqvU2moAKJDlIYoATBayUBTLBVupAFiqgMaf9JHOR0NTwI1jAsQRWxsIsQQ5lvkqZAIZBu6nMoUxT7gWoQCKpLWFEaQQdxgIpuNS8ptZaeBRliBnDEOVxrIGZsljE2mY8UJJLxSgbSgGc1r5Xq414O8OYZ6UcUQJ6icqbUkLeG6iSHTHbaTb9kYaZeqgXJsU8hhp5sXK4RJh2hzkjabqj7v6+X62ZMkL9KM07EGk8CLXDBWGsM4B57vFcrtn0azVDxagXZK4NKKK9ICwVFULOfgu6zLOLuZ2doFbDWJJOAAnIXAsPQgplUCgarW7qumEuV9raygxT5zjuhYpgv1Ry9/cpEEGpbLR6mWUGxWJhe1sx7vDEiyhA0K9U3P19SSk0idHuRZYTVCrF7W1JqCoYqBtiLdJiDnmxhhA4zGj3Y9MGNiVdXyMkSzYaJADoQaryv/eJf7yL8O8uoYfh5FvfRLe37v1yXnj85O7b3i5tu/yFPyp95c+gFPfcO5db7/3APUywMN57w5vihUXW3Kl94uO02bxXOCjl4NHX9F/zOm0Y/8Rf83e4CBsZ+O/f/PPdsfHSyt3XPvHutYNHpy9dffSd7+6NTub2dl/7jncKxrUGr3/L27xyJddsve433tOdmizeWH3sne8d5st2Gr/2re8AGmaG8fo3vTWzHavde/SJ9w1GGoX13cefeHdq54AS/+ItvwxTGZfKP/OGN6WWQ7zw8V99Z1gs2q3B47/+Lo1Zyvnr3/hW7gdhpfyT/8fbgAZRqfrqd76vuN0cVquveccTtas37j744M/+bz83+tLVzWPHH/3Vd43euukVyuc//Kn6zZXexMRr3/HE5KUr1x566Gf+45snLj6/efL04+/5QOPK1WF15IEP/17j8vXO9PRrnnjvzJNPv/T4a37mDW+e+eFzN8899NhvvHv64sX+2OS5T3x25tkfrx0+8RO/8msHvvO9y48//q9+8VfmvvXdm+cfevX7PzD/9A+HjcbZz3x+7ntP3Th/f+nKvHVrqjfpFV8q8NXDFK/g/Qnj1oHUGpKd8dKlmqrsmDcm2K2lwaRfuuLy1aOYrhvbeXLnWGYNWXOk8sKo3xi4V8rG7YP9xrBy0yIrJxDe4vsOv3kUsjZu1XMvzoT1XuFm3rx1RBS36XqRr5yGesPum+TqCUg7uF1xXliAxR2+kiO3ToPcVtRXm39JcmmQMbLz5wBTRYO0+VeIOFrvZ+vftg3tl8LuS/+lbgQBtnHzK4ogBeN06+uEGpL10zvfcvIqQDBd+UvHHA5xkTa/rKDSWIitv6YF2ReBXv1O3ogCbqj1r3K776sy3f2ylgIaOtv5JmU6Q3F291s290PDFuv/l4FaAVss5p+apv0iBnv49nG+byPesq6ccjfJcHxQfvIg2a+lpf3cC3OkXUVon9w54jSdIBe6Lx5y71JvZlB9agHvjnkjXvH5cbZXJ2ibrh02NgtBKXZfWMzfNbPRbevZJbI74zeGxRcadG+cwQ12Z55uj4SVsHhpPnenMJjqlp8aJ7tzorhrXZ8k2zPA3m1fUeF3Mj4N/WeSvacIWbLAc976D61SLszWko0nLZ5TwTb1vhGyWRpfDPeeYrnxzL8mN5+yC1YA9uXmd8y8HQ93yf43tTkB1eVg6wdmrpFGN+XmD+w87bN9/+Z3iw6PkhZqf11a4zC+Gm1+38zXIrUu737HrnA/HaiNv6IOD9M+aX1NgjopD7Bx5T4ed5D02YsPsWEgc1nhyWNIQSAT8/mjSEfMj+n180bQ1zgyL16wOz3thsYP74MZRjqyXzzGRAyDmN24QP0QEc/98XmzmwTltPT9ZZByAHz70lEcC5jF7Pp9ziAa5GH5qcNmS4b1uPqDJZkUMhblXlhivkTKY9fvN7qqN6HCL3a9W5gsWoO/Cry7uGiHO8/byU1JRujet4C45PNlGnw1bl2j9AD3/ybybuGK2dt90RxeQ3iMdb6l4IshXSa9r0bta9yYZ+n3w/1rRsn2Oi+g3mXkjMj2kyi4mPKjhvdVr3PNzI0mnedg6zIvOf7gGhn8GDoNufcUGj6rnEMw/mawc8l2plVxy6LXTyLUhoNS/scLSXngrjnWjaOysEs28vzWWaQ3LQ+Ty2cx6kI/71xcQu4e2zDZjdM6t2dsGfTm/VxswJDyS2cR7OLYyT23JPM+2zb4tZPI2OT7Jrt+nmc7IKPWj0/wrAlS0/jxcen45j7n109xtqU6pnH9AklbWAH34gmEkr70974K7NgHSXb3e3nmRYYl1r5qWvt9MMp3/0xlATQN0fwmRokkIl37jk36semI9f9soC0PzZmdLyaJz5CL9r8u0DAzsFj9tkU7sRyx976cgs2QzdLBl5NBz8B52PmmQt3Uhd7d7+f1bqqn7NafJmwzQnO0++V40LHMou79jYz3ARvBuZeW8rdd0di2Lh4gG3P+uFd8scp2Zija4GtTZGMyKvq56zOFm6XBVLf0o1G6uSDKu/atMbxxgOAtvjlu3J0Ubtu4Pe/eaPhTrerFEbZxSBQ22coYXT9I4Lq5O8puzUq3zVdmnOsTur5qvDhO1o/pwrp5p8pWlxlYx3ujxvU5WWhbdyeda1PRWG/pr77+6Hve362N1Hc3HnniA1Gu4LQ6P/n2X9s/sDh68YVXve+3mkuLI9fuPPrEO8PqCBsMXve2X41txwiin/rFd3SnpmpXb77qPb8VjdfY6v7rnnhXbLlIyde/9e3CtECc/fQv/HKvMVa5u/nqd71vODpqbbVf9yu/lnJbU/zTb36b0kAQ/i9//he6jcncxs5rnnhXXCqSIPvJX/gliPne9PTP/oefK2xtd8cmH/uN9+WazcjMvfJ9HyjttjbmFn/2597YuHL15oMP/es3/Hzp9uru7NJrf+Pd5bW1oFh9xfs/WL11Z/XIyZ/93//j5OUrq+fPvvYtv1K/fH33wPLj73lf5e5GnM8/8oHfGblyc+XMhX/9xjfN/ujZy6981evf+AvjL1zanzvw0O/+XuPSlaBae+h3Pzp58dLqqVM/9dZfXvjek5cfe+z173hi+qkf3Thxkl7+p6py/o5JTTo+7v7oaSjuNSeRXldjFB09lo2N8/U1trtzr1s/leZrq39PwEJJ0n/tT3b/u3+Jkrj05T/Nf/dbyrgnpAizLFo62Pm3/2M2MnLv1PMfXnKfVcePAC33Di/FhXziODtHjmhOiBYbp08plyeG3Tx8MC7nM8L2ji0L2wQANA8v+fmCsIydI8u66GSM7ywfTCsFEiWd5YVwpBbZbmf5gJ8vZCZvHjsqXQOnYvfIcjheFxDvHToQVMvCtjpLc2GpQpNw+9iRpFaUiLSOHEpqhZiYreUlr1HTQLcX5oPRqsaktTA/nJ80eoPe9MTe8qIGKJgZ60+Ox9zozs/54zUldOvQUn9uAiZZe362uzCtIB5MjvamJ7EQvcmx4dSYJnR/bq63OENS0VqYbS4u0CQeTo51ZqcFo72pyfbcDJFZZ262tzjL/aDfqG+fPM6k8MZHBjMTGTd6k1N7S4tYIW1FSS0gSiHu6eIgMS1JU1kJMU6BkTBnN2WmNFNRC4DWiqWq4mMYC1Nn5RASAWiki12oAUJ+3IgxVIiFSTUiOBVcq+JAYSUMAEpdgEBqSZhvpdxExBflkDCRMKIKfcQywRXP7QrGhKVgqRMxykFGRyG1pULIHtHEgQIzsyZgjmIieAOVmReaOWNEExckkpoNwBwJGXRGpHYZRoKOYsPKaJIZI5pXkNDYHFE0BwAjlXIQ5VwJIB2jtpNIiPko4AUlAbHqEuWwhNQsZ7DIGJZ0BFlOqjDlDchKAEiNc77IxRJBkQuh6yOhlOmJsg80k64H8gMFKbQ9UYgFptKOskJMVaoKEcz3pGYyF2blkEcxod2smkmCpR3KUkJlqnKRYe/4pCKcWFU8JCWwQ5UPABSZm6WlxIgiwD1VGSjIlR3AQk9gqq1QFAOoM+QSuyEVQjQH6RhRADIq6Sg2WQZMzMYw4YpwZTW0gBTZxKkngjHTEHwcM5Jpi/AGoDAjUDjjChkUmNBpZMIwKc3MUWijJLDydkMhQwETu2NSE0hs6DZEwm0EJB1DlikAQ3ZDYQtqCskkJQZBOhLVEFNfESKKQ+TEqWayMABWpAlAuU7mIABFUoqp4afAAGWPGt2QuLDYl3akMYD5fmpDAGVWjIjlCUxFfghyAYBU5wfKSQSiMNdL8xoDIUpR5qZMpqIcAsfLtJUVA5WLiU6h080KAECVFcOkKFic0ApgoxiLzCpKXMUCEpKDpEaYTlkJODUhASNFTUcpFMCwhF0VmhNcxKyOscpYBVk1oWNluIJMcawldyWrQ4olLBCrIpXCME+tUSE1ZkVkV9OMMMuVvA6hBsql1oiEENAichsZwAzmIJ0ghk4Fh6o4ADRTTOlyDykBUKLK3dQwEA7SSsB4kjEkigPIZUogze1qjlIToEI75gbCoayGmMSSEVXsQyMTROpiT1MFuID5VmabAmSiGhIWSIJRsQNYmnAKyj0AM6xjVe7HBsUoSioR5UFGaFYcKjPBCJbLoSgYmkA+irktUkmtUcxLUmhi1SQtAk2QXcpUgUMASA3bbqIJZXVoVZTSmDUQKQAjDp1yJqsGQYrWMS4hKlKjDo2ySpWBK5DXMNWZW8pYAShCWB2jMoGRoCPULKdSY1aDuIIpELykcY0xKaQbG/ZOTPPKCbOKD6WWZqJLHoKZsEVWjrCW2gxBsSugJawEFdsSU2CEWSlEWGSm1IW+BlA5GSh2tCapk8F8O2UGor4qB4jo2ASgOIBIp3ZmutsJdaSdwGInorYisS77GGfCkrrQ1xhIM9GlnoY6tXjz6DKz0CBX2Tu8JGwDSL134nDsOEk+314+EOfymcl2jyzLvJ1htn/4YFIppJTvH1mK3VyazwWLk/18hWbJ1umTacnNKNs7fDAq51PK9w8fiAr5lBudg/N+paI53Tl8KCvYgpt7hw8mlQLJxPaxI2EpryzeOTg/rNQ1xTvLB6N6GUnVWpgbTI8rTHvzk/2JCZbG7QNznalJItP9g0vDmXGYif3FA73ZKSP0vKmx9vycIqQ9P9ubm2FZ1Fk8EE5WpcSd+ZnOwhzSsjM/643VFCH7szOthbl8p9WZmdxfXoRCdWen+rOTktLe9NRwapRFUXt6urcwDTPRXlzoLM2hNGvNze6NNvgLL5L/NgELKpWNT6Assy69eI/r3VGSkHYrnZiMlg9ry7auXEahf6+gsN36uwIWFFmysND8uTcDSp0fP1f99Cc1pffyaiilKBY7/+bfhseO37tw8B8zwZqbi0+fJlL2J8ajfF4D2Jmd2Z+a3jh1qjcyihgicbqzvKxNnhHmNerNhYVb953P8laYK5q+t3HsmDQpiUVzacmrVa4/8HDUqGiEUsKHE6Pd0Qln0Fs/ejwtujcuPNSan+VJmFHDG6171SqQ2psc252aWT95qj05BTnGUbK3tKhtGlHHa9R6o2M8DNsTE+FIlUVxc2FR2Ma18w/uHjqEoUypoWzWWlgAqeqOjoajNXu2tLMwFRuOBDgqFntT4wJSaZs7BxY3Dh/dOXhQuRYdeq35hbhSEABHpdLe/CydcNVYcbcySlM5rNfbk1NWFPQmp+Jy/tap+7aOH5OEaASVZbQXZnEmeyOjnfFxI/UwCPwywiAyk2FUV5zsT5HLrZILM4mgD3NpiiECoV/FBboxB57fLJU4EFBFQQUQmRAwlK7yqzKq+8LAEAorGPgNwHQCVBIVVZmsl9hLgWOmlNHUy8oypdoO+t4YzsHdBrviFblUAGkPuXFocZr4SVVHtpUfdOW4xahgIiElrF1tsyZ1dJZ3nGEfzOQMlqpIgjGT0MQiO7gApOtyldACTAum0+/LKdcwBJ41xahNs5SpjOQBKHCextRRYckq4E2VR8C2jNDXDQtbiGURcwEoAIs2WV4l5YI1GOBRg+YQ8kI8wqCNsIwRCv0KIVGkzBiasVdLwlpCINBZjGmQFbXSisIgqupRfZU5zV4xb3m+tEOZQygLIYn8kk6LoSwOY5vjJIM8DIrIjHxphCgnXXK7Yt7eyY8Y0kcgCqrQTKLMSFNHJ0UvqoYEaa0yqoO0oqVWRPlJBXCRECJAlWMKLR2lozmquyW+FddKJhZMCzVmYyAdFMm8QfkwZ+3rnAFd0/L62XSOwYzKTFcZrSA0ZxITCEzMLCANnlncHfb0bI6LHssNVYFRjC2cgDJTFrOzAI3whIoy2UhLNuOIqkyNmIRoQyeibmkL5Hrd4QiCpiJBmJQl5uEEfwna/pC7JAu1m/pF5vT7YU0hM54Dz/iFSFNTylSbUZYDNI2VlXarrBBd6dUJ5pD6YVoSylQoCaARq2I4hi8To9/N5W3fj0oizWFn0MvyqXazUfgCc/ttM2fGQ2THg5Jhe15SSCLXcKMBM1Vaty0ZmSgRI4aBOoYxyCoFOw54DuiyAWVm8kzUTFNuO3ZfFHJMK2hDWTXtJMCmQAUGpi02gSWjTCUmTOSoZaQRtZGwiU1b3O4KJ085NFSIxkyMIBOhHLMs2GK5EBYMJjLDzEDFVARZIkinC4YIcBZHFUlBDHSonCwoS78eEJwkDNte1xsjCAqShXFR5MhO3bomTTU0XZr4SUVnFnT7vf4EhTwk2S2/QgjQSAYyl6UGYLGXVXVqh0fQM/tFQxOM4jCpaIwSqRNoRkFJRTUfsCR0DLvbjeoSUoWTKCsJZQCaJZaTpfUC93zQMAgXDtsGJQQcm2chc5Quc5Jl2AZRjhTRBixA4NgsCHCDIxsxkWIbyBHLHIOsDFPHtD0PVogsGq7XJ1WictjQOzyXJPm8nQXMVqDCaZjqKk1tWgF3cS5WhQLVGTWVrFtW7BMHxhXTjHzNQuRmCYAIBUEV1fDKCL7RLBZ5lkqahCXEkxASLy3iPLjaMG508hUBAcuGUR3wNFI0TcsIgE1hNVXOkkhTMUwqSkLJ06E/gmrgTtFa9fOGTiXCHsqlvmHydBjXtMX3Ztj13YJNlNY4EUWFZIKgnxWU5+Ztb7h56hQlMgasPT/XGx+/fuGhuOAKbgCI+rOT3VLVHfTXj50ARY7mqzvT0wJSQY3BeMOrVHEmgsmR9QOH10+e6o5PEKxBpvYOLKS2JQj1GyPe/KQxVeiONtrFutvrbR8/Ll1TJ1lnfq4/Nnbj/Mv69brinGVZb26qU6m7w8H28mFhcwlwWK30x8eUhnG5tDu3sHHsxO78fOq49nDQnp+PywUFcFAu7c4vbB891p2b8fIF0/fbs3O9Wt0Mw+7ERFrLr8wt96envWqVZ5lXKvemxnkU7c/OB8XC6ukzG8eOMZkk3BKO3Zmf1RIEpVJvemLryNG14ycIBjBT/UYjaNRSYqSO1SpXrGd+9M95i/D/F06S+QW2sWGs3tGE3ktOIoM+iuP4wGK8dIj0+8atm/dqr0Hobw9YWgOMm296azo5iYbD0fe/9169PFoDQoavfmzwmp8ACP1Tuenfd4uwrvHLP/Dbg9FGYW3z3O//Qb8xtjsz3y9WSRQffPoHpz7zea2hxuiR3/5wapp3Tp7puxWexUt/851Tf/ylxHKd0D/7ic8AqVfPnO3m6wSI5b/+mxN/+CW/Uind3Tz1x18E1Ng+dLBVamgF5y8++9DvfjxxXDYY3vf7n5O2uTsx12pMwkzOvPjj8x/7fSsIo1L+ofd/RFAimHH2c3/sNluZa5/59B9Wb9+5e/5cqzyWcmPk9s1zH/90ubkjKD/0lf9cXN0IJ4uPg7jWu3uLjz76zt+srtxtHjx4/hOfKa+vbS8t7zcmY26NrK488NFPjr145ebLH3n8ifdUb97efNn5x/evjkfpcL+z/Gd/2bh6fX92/oFPfmrq2R/fftmFdmk0Muzi5vYDn/5M48Z16diLX/vm6MUXVk+fdO4cQM2ZaNTL3azB/Wkb31mUO/eH4TY1vJ1j7koVFzbpnQndWgpq3oP+pTNDuQUF2JxUzUPKSlCvbq9MyNx+q1b23AqOktLtst5dAHTIe5bePATZ4ARaPR/sr2Fbrcyh3UWVb7GdCtw9wODeErhzwfc6Kuu1jvHVA9TeoJtV3TyMjd1hTzV/yJCQlOrN75qYAivfjwoGT6NwBe1edqlKSmHv6vM1HMRmsacqDtFZcjNrvuhylumu3HnJoqlAY2y3MqkgcdY6a9+3AUYok9vPGraIeNYM52u42RKRu/1Dk3sJLoLtb3GlsVvxo6LBvF66Abcv5UAsmSE3vm/CYWbM5cyX5kjoUNWDWwfIIK/tzt7IaGjk8509fuUk8qvS3ae3lpBXcND1C0FrXIdruIZuHTN2zGS04146qIZjaWmvWx1JGc+1Yr46C/v1xI353QV7x1b5O68c3JpV8ApvGLcXca/EdYtsT8DOeJzzetVSaBYcr2u/OKeH09Bt0fVp0BmBZre9CofPQ2tc+5fk3nWLTTEz3YvGimy1mW7y7asOt4Hfop1nACkroveziYrT83o3zd3rpkVj1ZFbl2yDp9lkvpNvGCLMfhjtXLOsmvRXVOu6ZSPfgs3BZJ0MgsF6rnWRuXWZ3E63rzj5fISDZrxQN9abScfees5wWJz5ePtpDuu44Nl07SATEU19ffc+5ieus/bTwS7QarPdYHcPYxgzX6PNwzADJXz9dYlP+zs7egzfuF8DDFRK7hxmSgTpzr+Ksp1hoEGB3DjBfJjmPePSCaXNqnnjFX6bx14zaMCNEzRGsSXtq8u0D3F+9aeHawyztWTKvHmARSkRKdo4DCLHb0T9r8X9PcOawIMnhdckebKfFjHkSAlj7xkjvZ3ac2rwbbC/Y/M5TKOmKtj2yn5rtdhZ56TG2i8QeSuzp7K9ykRg5kgv9J/VvS3q0HBwC3bXTBv39ISpVKwSNHgadzYMp5YNrqDOGs+hNihmmWvyVDYv2v2bJDcvgh+I5oZtjKHyDldbRyHyUeDSlUOQtzojuX5hxPF6xlod7RzEoGv6WK0f1zhcYquPBPtdKYP1Obh7BJr7ZL+Itw9S3ePx5is2e3tRqLIDfGVZOgO6Z8HNw4S1JsPLr8loJ2v3vHm6cthUXZhgePcMht3eiNUtNqhMnFUTbxzBKkYa0ptHIBV9Gex/D9ky1KHYfMlVoSqQpjdbY52ulvbWt7gQhFKx+yxXCTTTzWS6wDwv9c3tp03QTtkk3f4GTkNiTOJWuQEhQOvh7kVD+BAW2d53kN5LrMkY6Siu5LPVrPOiiYeJI7z1y7moQ7gzBGMG9odK8Pa30DCymY36F2E8gLxEzPUlayOHKmv0ypIazGWV9isGVw+EaldovbEI2pNZIWbrM9ZWNSg1X9O7eljCW5DD9WXQHkekT/drcHfar4vTG7dnNlursxO558dAZxbkWmxjFLamTLx5Sm0d8bu7kMYbp+jWNCvcIpRzS00AACAASURBVLdmVOcAsTZORHfui8F27MWtw2RjCtotujtFt+ez6v6Bb3z9xJf/PLbd2t7WkT/4U8H52onTPbeKgTr+F//pxBe/PJicHLl88+SX/jQzbafOHh20IxGQq9sPfeRj3cmp2o3bJ7/wJVhxV6cOdqsNlIrFH/3w/k98RlMGpHz5hz7ar9RrDfIqfzCIh9bF22f/8AsKIgDhgx/8KJbq1v3n+05ZYHb4r//6zB99Qdgm7/jnPvMHUIHu5MRjv/ZuezDwSuVzn/wDp9fZXji4PzKZYVZbXXvFBz5Yu31n9b6zj//Ge3Jbu3fOnuuWRwAEjZsr9332DwvrW7tzi6/6wG83rt9YffjC4YvXxzTcydQrPvFJp9kGjBz/0leqt+7ePXGqW22k1Cjt7Tzyvg+V19a9xuiJP/ly6c7d1tz87vhMyO361trDH/ro2PMv3n7koVe+77fLt++uLyySq5f/GyHC/9reTian7cuX8GBwj3MsttcUxWIyNx/PL1hXLtN2+x6/9bcGLByGnf/h33kPPaINY+Qjv2NdelHfY/VKqfjgoda//59lPv9Ph4N/n4twTp08ikDWXj6Q5l1p8t3jh5XJuUh4Flko0wDunjyalVzNje6h+biUZ2nsJgPNKMF6+9QJUbKBVHsnjqQlF0tRCLtMpomb6x2c9SplgtTWiWPQoUhIO/YMnWaMdg7ORSNVyGnn0HxYKjGRchmbOoEQ7i8fyGr5lFndpfnB2AiRWXthLphsUC33l5fCsQpS0kxDW0aakHis2j4wpzDuHZgZNurE0jvQGBg5ClR/brK7MK0RCsdqvflpCLSZhoZOqEg7B+a82TEiZG9qfP/QIgdJaBnbRhFoPZga6yzOmWnYWZgNpkagBmYSMC0okmG93Fma0xAOpyf3Fw8wmRDSSxuJIYYE+7rU1wYUJFtnRawUYT2W7wgCKRkm9ZgkA2Dru6yIgQI0EVWfgJDgHiz2iY7taEC1pDrhIJDVvgF9jVNdHnAeRBA0jbzUEFEfVgeaCg6jtDZkyM8Y3MSOxgzSkOX3MgbJ/83bewfrlp3lnSvvvPeXv+/kdO+5p29Offt2t1pZIJGMLRceFzWeqZmaqvFQZGNMMEIyICMhJCQjkgwSGCMkEElgmSBLQkKhc/ftvt03nnzOd84Xd94rzh/MuOwpED1VMuv/t2rXW7V2PfU+z3p/JAfNsfKIw1Nv3ji+spDwehqiEqclQQS5tg0EWyQNKxaQRnMS+xBNM6wh9WybqqAjYA1Txe0F4NulJQq/SJgRCMqgI51IUSBqjZy71FJSG2ox4iDh9JRd0xaQXkchS+CkINKQ0LeNcOaBE0gqq6AnWR1SniNnqps5BgoECfanVBY2z32RUaiNNzH1BBlBrYmqZcQ1I4gO7TqrShRMQC1FSiBnzLvCKeMwG9uqBNBgZyIbpVdOTJDY/sDYNKZ42+s4IoNWYpox0dx4qWoUjojdKvV4RoACVmxaMQCSslQ1E6Yr2xFuV1GiHbsiSxZOYkcJBVnoAkSNtYAYESHNrA4EPKN5QYgFPerAyl9BDi4QMcGMshm3eBmUMdLCtlUwI7ANbcjZIrZ4aoQGGlsU23bl9RSztU1VMCsqA3yghcK+hxjW3qwmtrIwp4uUWdySuWilxI6pVqY+hn5hTNmnbsJchioUjYQviRayMUEsZRbfRx5ntsIIhyMY5ERXKByKBuTZeNisE6axqEBthPycmcpEE+OmmKgBc4aWz5SS9bGqCS+fmtoEhTmG/Ig5AytwZIr9sagJLDiKhlVdR8XEDgVdpLbI/UDQlgHK4Epp1/V47jWl29ZIKz+SaIbQ6ZQKCZlFbeSEis1iT2RuTXhNwVTlVSkx0jKV4whvXttA0NDQFmZFjAoBHc/F0vGFNwcQAa4jnB7AogKFIJRaULuR9HoKGuX4iiyQUMUaCdOcEFISkqHaCBDuFIkvMkOgBQrRTShJgZGwOSEsKbUYMSejLqEFrA8V0wyUopMgVsbIxO06xgDCDDQmiFUUl6A+EMRYdnGb1A1GBFSgNsR2aYiCjQmgpcOzKJ9qihxdiE6MrRyrXDem2C5tXdUbOWhb2GhvDlBH0axUObdD1wLCa0vWNNQIv8FhgG0ltIDEYbaugq60G9pS3Gob1kZ+EYf5CGFABXe7krSwX0y9nmaRMcMYIYodJzCF1+B2ywAAgq4yLcudjGCpmO8yKN2GYj1oy9xtKtiz/DIG3tQOBoIQao3LTsnElHt4l0TEaOBkol1YIkHOBNamFlaFR3ZIaBCgJDXNiaULZeeoNlE6Gweu9BnmJbIS0I4hUpRkspHYOI8JOmQhMtA4mR32OSHMSk19KClQtNomEcYQs8w0YggFoLFpxtrGBJvtq5eoh0rbPz73AK/5TFQ+Twg02nOOz66njTpGaufKRRM6wIF7JCwRkY41PHOybNQMxfGZlSRsMCNsnjum0pbVP3OKtyLj2MMzJzPfQR7e0yyOWpbiO5cvimYAETw+c6pq1xivHJ5ToDCBwzMnJnNzTBR7ly7wVkQQGK0tT1YWAILp6vx0YQ4D5RcxYMjl+fHGero8A7UerS4N19fsKo+yEcIAy2q0cWK8uuiX2dHGyWqpPbb92GE8cm1ZjlcWpqvzRKvh+onxypIlS4fnvkghwcn8zODUCYBAsjiXLXQxMk6RWZBjpSYnVycnloDW0+X5w4VF62vBIvzqR0WRCqPgi58HEP3dsygIoRBsb7c8uc6XV/j8QvCFz8NXxlP+mwUW5Lw4d37w7f9MNRrhp/+8+fHffqWpe61VvTH4X//38tTG18Qc/LtYhCfThx6heTlYWc39yHC1dfqCRvjkF74wWD4hfI+M0s0Ll4TjKAGOV1cNQLNP3yhr0aA77xwN7168IjyHDuL98xcNhDM3XjYYTFpdzeFoZfl4btkbTm9fvIIwXPzCV4qonjUahuvRwsKkPQPTcriyPIlaJ7/4pWl3tqyF1qTYWd+QoZtrazozc7SwzOL8aHFlOj9jH0/3VteTbqdz45afpIPlJaMJt9nexmmQyuHiynRunjx7d5c0k9kZlauk3hysrioOuev21051n3sBVWI6P0tH6XB5bTrT1ZomYbS9tgHu9YthtrP2AEnLaW9uf23dHYyGvfnJ0kL99mZj/3D71GnMpaDs4IEHcFKOegv9pVUsFARm2LONVkDStKf7cH6Y9Y6bPtBIYKgjlRAPAjicsXIZHSSLR90AG1MBa9pFQGMARd6mILfp0Jl2HIkMlFY6qxEwlbbzjpmKxl4xN20EnFCocdHimW3rypnOgEJ6+9nSpBFqjAQAqqYS2weSlG1ehDU9quCCq1wmOFBtRzg+P2Sw7slmwDMkl0KXlZPSZ4se93zRpzyqi2YoSgRbtKz7Osdy3sUE5HeBsC1Zc4GEoGHphicqbNVV2ptJtizda1gBFDkSHUfVXMmBqTEZefk+w6FfdetlgsychwJUpQh3HRVQoyAnMOkgIf3KNyJQ9CgAkhRNR3FUWaBoIa4pxCbtsKNi6QB1x02PZKDyQdWgWiBF4ajr2gNIUztpEa59bctpk9LCZC6ENbXF1455d9j2iFIlsdKOQZwWDpp0HK9PaIHLpsU1VQgWHZ1DW2OadwwSRlAme16FqFaoWq5BzYpdqtbaAJpKM74QCEQINGDWVSAojxhYDAvXFzkyKwFGoBQUzruiQPxAqZpTMQtKhGcpD3yeI73iW0qPh3U8Wzc2NRDLnldajq4AnLMzp1FtY7HYRAyXgoq5QFkMGVjO1qRPSMFGXQhsLXM364Dc845GnWOnldccySkP1bRuo8rJOlrY5Gi0tBP2oKMzbQvfVBHUFeG+LOreeDqXNzxgS1NYeQNUIVMVrDwwjbzDeH7CwqThgtLJWjoNLZroog6LmttP5g5Jd1pjqITS1+OWjXKL1/m0FtgVlzbL5+uIS4CQWgzzia+Uw2frJlPaZ7rrV4YQDPOFOkpwmVh4OSwl1R6rZkNaSOEzOROUfSOOTbVQhxpISPScqyvAHRssuOWQaBOY+cggKgxCC06GHAEIWvLlBFelb3qBVEhYRPX8EjKsoVgOkZFcOVlXK0SkgXmbAs6sY1dEOnUdUzqTGQOhVsKq2jIGtf18Pg3c1PGMpFWLZ74DuT2eMRCxUblUtn2FoZayaJHSYbqiVVv0vdnBZPGoGSKilHLSJtAMloYVTQQAoQe+9mQc+iC3p10BiFLCKhtGu0RKSnxT9Bp5RsGsQ0JabBIeNXTLl9zokKmmI0pEIyxmmsmOxRt11iQ8RbjniJoLJCprbtnwzWZpJKg6kUgR6tpFM4ATLluObgXF2NHAE4t1WEgdUNO2i4ygrp3PtPB9XngN0/U5R9JhYjYApZa+U3VcmuvCQSCUMfIhMsOenZX1/Wph1HKwRDnDkzYj3EimyrZ1VPb28l7aIjm2AGR5V0KJc0ZFXaWineCOqGFuCAK4aJY5c6Wx4g5I8vqBnE+aPtCopMDU1MSKkIJ5Vx2juX6+MGi50OCSsLyNtQIKyqJDk6jtDsd3rl4jRPESHZw/T8uqc/NOGQZx2MBp2d84NWjOBIPx7cvXJIDqua2d7koVhBqw4fJi0uzhuBidWBy5rROf/cvx3KIIPJTyvZPrZRAJCUeL82O7YZ6+3Z9fOe4s+keDe5eu8sBHSdXf2NCY9F54OXeDcbtnT+Kjk2v9meXo8Gjz0lXuuaoCcbM5WF41pSparcHC6uJXnuSWczS35PePj5bXkpmeFihpdfrzSwtPP2Nn2f7KSWc0PV5b788t+cNRf35JNEN1mBoujlbXraIc19vHqyvO8Xh/ea2/tLr6lcetvJrMz0mBSs/b33hAVSZptsfLywtPPKMUjGd6KCnHndnx0rxUhHt+v939WrEIv7p5J9sdVFXus8+8og2dCOHpBFVV+cAD1dIyzjLnxvOvxCX8mwSWMYDSo//zO6r1U3g6nXvnT6A8f4XJecPY5M3fMPmmb/kaqquvyiJcXlT69e/5mbLV7Ny5/dBH/sNgfuHUl7988Xd/FxjY3du68Fsf96ZTbPTr3vv+qhY0N7ce/tUPl+364uNPnf3kH7OyasSjS7/xUWcyRgS94Z3vhjau7+9d/o3f5pE3//wLZ//gD8I8t9LJ9Q99ODw+UqH/xne9Vzt2eHj44G/8prbp7K27Fz/+O+HxgGh+7Vd+rdE/kDXvtT/9PoiRJeWV3/rtzvYOQubSb31s/pmnj69cfMvb3tG6dWuytPjo+z/YONq3s2zjjz7Ve/ll7bJLv/7Rtaef3rp66Rve8ZPtO3fixYVHf/FX6ocHlPNzv/eHszdeUL5z9Tc/euJLX7zzqke/8W1vb9+5Mzq5/ur3f6Bz/z6FYONTf7Z044W43bn+kQ+f/KvP33/k+jf/23d3nn/+eP3Uq37lQzO3XrZFuf5nn158/Ind8w9498/Bo2XQGfkvzeLRTK24T8YzoL9uqEKjbnCn51k3rXsrenSKN5Pg/hzsL0R8zz1geniWkIJM296dJRhuB7e68PiUiJLGZgMdLzpi5AyZOTxj6TGaNNnWeeRtRnfrevAAsw6srR4eLPnpgR9TsXeZ6RGOa2j7YkhecO7V1OgsIwdpv+o/Y9OkshHf/HzooNKfxvtPB9RRcpv3X3bdyag9Obp5cxaPU4eJu5+ve5h7o/Huk46DC3JQ7b/k+UlmV+nmk5F7NIrc8vbnakRzNk13n/bafKCPyv3bNTYtLcS3ngi844kT8M3PhEgruyz3n/U9XcJBeXjThaPShcX24wE7Sq210Hn2pFUwPxvAgxPWKGBwjHbPRbuWrt+3XnwIpi1Kd9mdM1bmOOVY75+0xw3NKmvrRLRr6+Zm8MJFlc2iaOLfXLbGTlj00fEqHvekx63NVX/H97zn0I3LMl9RYebfWWKTIMr2yOEsHC2iMLXun/D2uqZxO3rmpMqXKD2yt5bJqMngcX8TxM/gVnOaPiWP7vlOD6oX9fFdVocJ2im2b4U2ltUADb+Mar1SPFsd3IvqUZ68YMb3WMQnYMB3Xwx8kFcDePC406zH5ka+dycMwzx9UQ3u2rVs4A+mt1+aiUDMx3D7Cb9Xi8Xz6f6doEVHehvs3/LrPDbTav9pt8EHOkGbXwmcsGoNXby/HsWJk43N3jV3lDM6QbdfR5Ry0jHZPMtkHA4EPHzAngimU33/obAf22RP3f06ohQtMrx9zuOJPeS0f86ZGgskcPNKOBAqGNgvXkcKsSrD9887RUlTDfc33JQoJtyXHwgGygR998Y1AUIKhHN3zYtTLyvg4QMorfHG8eBP1PSY1Tpq+JdKDkxTTgf33OqAUR8MnsP6ZtGei8efY0eHjjODx1+B8kB3i8PRfXu467EaPn4Wm1uqNTsZfhoM+57bBuXTOt5HDTnJ7oLj+04zyI5uuskt1lrNp39aDfpBzc9Gz8B8F9XFONvBg3tuw0/7Lzjjl9nszKj4HN8/jLya7O1b8vCcJaY49azNC9jZCTYDeHiG2n1rp42PV/ykHyRaHlymYoozF95/yIe3vS1bDS5Q0nf26vDoZC09tmJJdi752SEVDt68xti+s2PBozOu2gr6njo87yQx4QZtnqulu6TEeutVlPbpUYD3T4dmj/RdfPSAnZaWUuTeecsUI5Ec/SWuVRN0lO3fDvGwdEi5+VTN7cd+o7r/6dBU2lP5/pOOzUsyLfo3fTgWHi62nwjYXuL15Naf2UJQG4rjL0EnLZyiPLjp6DGEPj7+AiQ7WaOTHn3WmwqXUDB6ErFRGk0Ge7dr+TGxWuTw84T2Va0V9/8Mp9y1iZ4+DeVIOBF1djf8zdDznqMvXdDpiqyl3t0lPGzVi1162AbDVexldHcp2J0x9dvBs2tyeoJafXd7GQ07XjVgx03UX2W4Tw9OePurMHqp9vy8TE9Y7IBuLpJhN8r2rXFLHWwwvI8O1/D+qRr7inVzWaanLLBt7S6go6VacYhHNbq95KJNcrwGD87Q8O7Fj3/09H/6FCvymf2t0x/9fayVPxq+9r0/ly4tLH/pSxc+8ftls7b21DMP/OEnXV5648GDH/oNKrmbZa9+3weyXmfuyafOf+L3qUeaz7186ROfcJOUVfnDv/yrluCEV69/7/uzTmvuxovnf/sT0LfbN29d+MQn3MmEKf7oL/yyxUsD4Zve+a50ZnbupZcufexjDIHafv/Sxz7mx/F0tvuNb/833mikbOv6v/9wEE+sOLnw8d/pbW4mtcZrP/jziy/c2Lly8Zve8VPOYMij2qt/8Rfrh3u2EGf/4JO9W7dGi0uv+7n3LT337OD8xht+8t21g8Miqj386x+pHx76ZXrmj/547rkbw9Nn3/iun1574sn7Dz/4Df/mp6K9Xe05lz/2u627dy0tzv7+Hy5/+fHx2vKrP/ALa08+df/hB9/8znfXt7YOFxbgyy99bReN/s2iwrJkq2VtbtLjo1e0QRQh6/49Mb/IFxaKc+f9Jx4n49HfqYv+BoGFimL0j78tfv0bdRDMvfPfWJv3X/lH85XV/nd/H4Bfm+jVK2ERZpev0CTZfOjB8cwsAvD2w49MO11vOn3+zW/JOg1nmt16w+vTdgNKs/ng1fHsLCzFzvUHD5ZW/fH0ua97czrbsY9Ht173+nh2BnJ5cPHcaO2E1mj3wcsHa6fCo6MbX/+WbK5rxdmdV71qPDeL8nL34oWjtRNYqv0rF3dPnY36hzfe+OZ4ZcEeTTdf/Zp0pi0UOTh/dvv0WXs02T577vDsA8443n7o4f0H1q04Pz55cvvKFWBQMde5d/1hWIi902cOLp7zjgZbF68cnnsAZtVkaWnz2lXAddpt3Xr0VbSojtfXDy5d8KbJ5rkLh+fPoILHc/MvP/yopWTWab/06KMkzo4XF+8+eC0Yjw/PX9y9eBYWMmm1XnrNayjnVat59zWP4Uocrqzdv3KVlBVV49GSwDK2tEjXqoJSi0+S+YyoCpNYzJaSEmCyeLnCKraq4viEJlQZVcVzCRIl0pN8UQuKbJ1MFgsIclaW4zUOkcBKVHNTBLgxSb6oKoKwyMX8JLeAXZSTExIwDYUoFnKABFVj0cvKyIFSVgvTJPKtQeKsE1QnLM/oElVtH5TCW4Sy59mTFJ8Nw5rIK9tZp7BOvTRlc0jP+khoZx7wnsdGMdjwaAuBVNunbNW2vSS25qiZcwhX0awol5pkUsANz21DPa7QSQ/2LJplXg/AGYZywWYhX6qxSe6sYLsLaSLMikPbCJZc22W8KFGlRZDITooTnTWLYh6hrKRskM9VVAptldNFAQVQzjjtVUFWFI2imIUgKwGeThYqVmbYrqbLGnAArHE6UzlxDqKEz1QSUoCTZLFkRaxwFa9IqrSw0+lM5U1j6YyzRaQMsNG0mJloow2u8qWKljkl0pz0kc0sU4ANH4OC5YW5EGHLUKXouoWh9mAG11xpk0AV9joBNmZlpS/WCFFQSHudYaItUOCTLncdIirvFBGRa8cJvRh5lqiQzdYtTE0gU7PmgMjBUjoncRF59mhizofYR1gYsuFDG/giU6ciVUf+cBovVaomSIGKXlzVAZnGvJHkbYilVq00aSN7kscLpa6XOIV8oVRhBQou60nR1riQopVPe8iOeT6b6ihDU5POFlUb4jSD4bhqc1pJ3sjiHrLjsmiP05YJ4zzrZGUXojiXfpK1CyvPdasczRIn5rw5nM64XjLxnUquhz7PiGvUKV9V2nIrs+raaWZ3iT4RoFJYVoU2PIdnLlP0jGc0oqGBa7Y9TUiozHoEDbJZBU+5VpVipMBpHyttB4YuE7uoWCjhmicB9lFpnyIAQkwUOB0goZBtyAmLlaXjS3AyMIQSw9H5AAGOhazmJhBxqJJsXnELIl6IuUnuGCcrR2uVsQ0sVbGQGiaZmMheWtUcoFQ1P8187E3iyRoHjoQSZHO5ZIoU42KmKkNMpajmkqxGnWk2WS2hW6EK8PlMWQKIvJwpuadwLsrFLI6oGxfpUgKc3OQmn81NqIgwUVeKlRqKK3jScbtIjyq05sE5m6aZ1zFwwUK5pB0oViISF+4itGcQm3Kz4sB5h2WF01Bm2XXKkrVNtRziSWHNGrPguKPYWrH1ok+yktU0WbbsLHU6CK/5INN0DsJFxz4a4R4CqwGWmkUKLltWkuA2Mcu2ExfAT/hMpSAxKI2XS1bGEJbjFU2NljSbzpZOmko6yZegNMCG02JmrKBGpoyXK6yUIUU5k7GqlDTOlwAHCKu0WIw5MlRX0zWJjDakzOc5ESXBIz5bVp4PVVkuJyVBrEriVY6QVkSk8xKICulBvqgn9UY4Hj37jd8C6w7X7M6rX53XagaRrQcvT2fnkdT3H3nocGYxHBw9+3VvyWfapOB3XvOatNUAAG1fvTJcWCJC9q9f3DxxNhocP/v1byln2mSS3n3ssWmvYzTau3xxsLDEsnzz4Ws7JzbqR0cvveGN6cIMy6o716/HszNIqK2Ll47X1tw0vf/Igzur67Xd/Zuvec10cR7lfHxidffSRSL0YHX57tWH/NF46+Kl7XMXGqPh/YuX+2c20DQfLC3effBBqywnJ9duX7vuD0c7589vXbxUPzjcvXx5cHpNAGc0N3fn+nU7jo8WV7auXfWmyeb5C5vnz9cPD/ZPn9m7fEErmM/07j/0EOJ6sLS8dfWKO433z57fP3/GmSa766f2rl7WpUzb7bunz3pf+dL/aIvw/1nwWW8ai3lPPgG1fiVGIYDQefFGdu1hVa+Xpzai//wnfyfc8P8rsKAUxZnzo2/7p2J+vvafPxX9pz9+5bMobdsHP/hDotODWn2NW/G3hdxXVsSjD8pmWHXqKvTydiPvtkTkQd8eLS9SZsp6NF6axwzEsz3ejlTkl50Wb4dZuwlcOjqxzIiumo3J0jxhIJmdFS1fhm7SaIhWmLebxibDE8uUmqIWTdaWCDXxTK/q1kUtKBu1qh1lrQZgeLhxgmBZ+sFkdd4mYji/XPWaInS1a02X5kFkS9cZnlihWKWNerI4q32a12u67uadRuX5yfI88Jn0veHJVUpV3mqm8zM6sIpmQ3QbWbthXHu6OGsCxj1nfGKVEpW3mulsp2pEKnBEK8o6TeM78dIcrwcgcCaLc4TpvFlP53tlq6YCR9T9slOTjjNZWeJh4IERccYqxIzlNhzqoARYMDKRNWPBFDpTD8fCkthJZAAZySwzVHXp6BjauaoZC6bUHRNWEqtAdCpriKLMQSNRU65JAEu0LygugJdQVhCWMzI0gZKOccBQNpSjY0inwC8ZyZWXuySWliR4bAJlHNrFY9REDuFWoB1fOrb0ncIKNA5JgDLUY5GeYtvQJnBIZfnSChSzlWeV9l+j5WBKZphDhU8K2gIW4jTQTiiprR2rCt1C1BwP56xHbFQFtLBbxsLC8pQbSuKYwC7dUMEA12ACe8wm3Cal3QYESYxjm41lAChMTVBiW9hmAuqZBUtEU+il2JaaFhabKF9ZMIVeqn1kqRGKEgvmiCTIi1VILBxbZCx9SU0K/Ux7wDEjHaQhmJaOJm6ifGihmNEp8IWtYxVWxoMWGOEgYbjEVg7pFPrK0MpiU+VLF1dOJB1bMFt6AQc15ti8aeWwRQOUO54ETebgMggq25G2LV2npBEAPqnTBLVpgHLf4yQCNpNWpG1HOo70rJxFAPsowCnq0lBNkQ9YqB0qaChtT1JHBVZB6xB5KEQJnbNdWLg2t2qaEW6FEtYosSpHDHhDWqgkcKwbnMECWjH2cmwLgqfYzoQPLDVSLWmhkuiRrmUeSEtfMTeDrKJ4TJ1UesA2Y1nnDFcMjmEtZ7BAVorcHNgS4YQ6qXSNa8ayxqENbHUE6wUDOWIxDArjAwdOsDVVrrHVSDW5IaBGEycSIKQuqyInRxH2GHdrGoQkIrldlw6uLEs4kQIR9VjpWTkNjYWF1YDQxwHJ/ZqwmWCOcgIJ69SjZeRVqIZCPUbWiAAAIABJREFUmNM6oK6xaWU3tE257SjPLUkNYgeGdoHrKEK5U1PMUa6jaE07TDBbuk6u2m4gR5DGMhQUl8BNqJUTlltkBHwpXeCaoWhp1ySITEzALVwoN3dYrCyO0AT5XDvaVoOqpW2TIzIFEWcwB05M3ApaFYUj5FXcBY44lh1tmRyiMfYzigrpc2bnhlYOHkC/VC5w1VA0pQULiicgKgmWrlWETiZrtoMrq0tswgOaO21jIWF52qkpYmvf4X4gQIgjlOEOtaj46xvkoMr2lBUZ5KOIZF4gQYhrOKUdjBxUJ6nV0A6qbEdYNYBCHODM8SrsGQcL2iPQgk2aOg1pU2G5ygoMjlBEcydUKmAuGBs//usbRN1Y+pChmOKJCaWjp9qvdAAsMCF+THGJrQLSKQyUocIiExVKx0y1myFHYJIBP2Hkr/9aIxBq4CiGJiqSjp5oO8V2SWkuvNxH09KWDI9AIAETFIxkXds6AW6KnYqgFPoZtnnZqAGXDU8se6AYt2fSuQ6y8XSmKxpeVQ+relT1GlmzARw62FhjRBWNerrQgwxNZ2ZkK6iioKyHpuGMOz3gsOGpEwzJot1KFmeQjdJeV7SCqhbKdqOcaRa1CHrs+OQqJSqv19PFWWShtNfJe00eeSbyiplWUQtR4AxOrlqA5912PtMWoVfUIt6p5a26ca3x8oIKHOPQyfoqwTLrdrL5btWq69DlzTBv1bTNxieWlctM6EwW51zCh725cqZVtmom9IpeS9Y94LDh2rL2bBV58eoiZiDvdMpmWLUiHvhFr8UbgXGtwcoSdmDRqE9XFrAFim437zUzRr/GLMKvIpkAELNzJE3dF18wrwyDg/IcFWV26bJstSBE7tNPffXp138vsIwxjI3/8bel1x+hR/3eB977SoZg/1XujN76bfHr34QE/5o34m8NuS+vzJXqm37oR+Jmu37r3ut/5n37a6fWn3ruwV/6ldL267s7j77vFxmXxphv/KEfT2qN2v3t177rfdO5ud4Tz13/0Ie1BAEvX/WT72FZwZn1zT/4r6Vthf3BY+/9hbxR77xw66Ff+TWiESny1/3Ue9zBuGi0vuVf/LBgtnM0fM173i88t7V98NAvfsgap4CgN/zEu2vDcRaFb/mBH9cGKEhf9e9+uXl/h7vOq977wYXHn77/2Ku/9Tv/Re+Fm4cbp9/wE+/qbm8BiM9/5Le7L7xcNBqv+tl/t/LU07cefuRbv+cHZ555bv+BM6971/s6L9+SiF769Y/OPXsj7fVe/d4Prv7FZ174+je/9bt+YO65F7cuXn3TT72r8/wNwZwL//F3Vr785MHiide/+z3rf/GZF9/0pm/9wbfNf/Er2+evvP5nfnbh2eeNY5/57d9b+/PP3n7wivPyJbJ3Jp2ZBk93zNEZV2yryRraOaNoDo6WgpeWQbRl3T6p9i4VzZF3a0H3z1JwbO15aueKIgUcL7svrPPWMXtpAe1fLmqj5maH711EZkwHIdi6QvDYjGbYSxd549i5Na/2H0T+LrnbAQdXWXlo55G6ex2hGE461osXsb/F7s+J/Ycp2xodg7ufDXAhIAF3/9hDWKNU3f/LiNha7Mp7T9XtNG6W08f/apHEJXLR3T/2EUEo5Xc/G1AiYF/eebqJp6Wtixc/16bDjDXY/T+ypcGwFPf+MqybVB6LW0+10ZQzzG9+pkVGHEfm7h95SiMo1b0vhFgIOOF3nqjjceXa8qVPN/khd09EzpNnQdm2qoHePI+PaxiP9Oaj9natnOnbTzwMJ3PA37deuqinHSKnau+CO2pVVkHvXLY2o2rmyH3iQTM9UdYm3jOraNJhYqAOL+CjudIr2K3z4XZDt+7Tp67I8Skejt2X1s14wRZ9tLMG95eqqLBvn2f3Z8vZfefxM2a8ge1de+u0OF5FbHjwMjz6ouXNm8njZuvpkM3B4llz/7nAw7nY5nefaji2To6cw08jtoQnT+idZ2uNTjm9Ae8/V/N0wo/M7cfrDuXpIdn5jO13ZfkCuPdMI2qU8Ytg69lajY/osHr+Kz0Pl+mQ7vwX15+F2Q1+9+lG0xkX9+m9Zxt2mepY3/l85MGiSq3tv3B0izTGHtp8yClKVsbi7pvYUKhAWk89ahSFVYVvXiOqsqdG7z6KY8V0rO+80R8pYA3Qs2/Uihkp0K2HqDFkxM32w3iiMcnMy2+wBriqp87jj2jlYFXgW9dQoWFp9OZDbkozi3s3rpMjyhux+/gjwrSU5uzFc1aqsRR66zqZeNO58vjjcrzne0v46M/UaI/5Jt15IUzuUxKC3S/axXPCWwf9T7H9zcBepINPy3jH6pjh7k3v6GWLtOnel6z8BvDWzcEnzcH90O6ZyZfxwT0nBMngJj266dZbYu9J//gp4p7Fh59U/a16s5n3n6SHt70AJIM79sENN2jw3Sec46do7QQffwZt3WkEXRXu18zWQ8zEJq6zF6+KaGRtdvT2NegfkPsNc3CN5n2ndOTdV2GTmSyyblzB9h7bbavdRwnbI3sts/+Qy0ckgfrOY7QaG1Fjzz+k/ITuBGb7IQZ3neNQ7D1Ks4QIiG495vARLBh88THppqQfma3rjIzgnmV2H8VZgg2CL74aAj1W6c6fWp4s4bi69VTLjLhF+Eufa6G+YG119w/cKscUq3t/GZhS46S692QdDrjriJc+0+Q73F4im5+wCu5RZnY/TXQsUCHuPVnXx8CEZPNTltqu7CW080fWKPWJBfY/i9FURcX05aebxY5Bs879TzK1Z6xFs/17aBz7lOmDv7LSI2RH0L57Mbjf0a171nOXxPHpsj7yXlrTw1UmD8nOPNg7yf2CbZ227iyWMwf2UxtgeBZ5u+7WOu+vEz3Ch4t49xR0j9H2aXJ3vZrZc5/ekMMLyN3Bt5dAf8MSB3S8ZLbOAGeIt9fZ3XXcuEVf2BCDKxa+i3dWwNFZR/ThYAbd3gDeEO+t47tnRHdw7nd/58Ff/YjGdOZo7+rP/pLGxB2M3vz2dw5Onlr8q8cf/oVfnszOLDx546EPfQRybcfx6/7tewHAVsnf8qPvGM7Nzzxz45EPfgh0a+Fzd67/8q/CgmMu3vgT70IagoJ/w4++Y9Kdab94++Gf/2UeBLXb24/8/C+yQkCMv+5tP4XyShH2zT/89tHsYvulO4++/4OKEvdg+Nj7f94eJ8cn1v7Rd3x/eNDPmu3XvucD3sEh4Obar3y4t3PYn138ph97+8zjT9195NG3fuf3B3uHk9bM63/6Zxo7uxpZD/77X599/ub+2sY3/fC/Xnnm+c1rl9/8L9/euHVv0p597c99INraR0Zf+dVfn//y05vnL/+DH/mxlc/91c3Xv/6bv+9fdW/ezrq967/2m83nbmpKr374N5e/8tTe+fNv/rGfXP30Z2++6U3f/P0/1Lnx8uap0/jFF/4eLML/ahSqet3a3KTHx68oaI6xc/PF8tQGn5svT20EX/4SmYy/SiH57x8AquSR1yaPvRpA0PrNj5D+4SveCq+rtRPDf/JPUVWCv8ejMZrMzu5cvz5cWQHa7Fy5Op6dE647e/Jk//RpyxRHZ89unT3Ha97W9euT1VWJ8MG1a/HsTDm7tHDz5v7FS8gBx+fO7p8/n7fbOw9eGy0sprVa/cLF0cJCbvnLZ89unzol2sFgY2Pn0qWkXt9+5JHhysp4Zmb/0qXB0nIcNJZPru9eOJfMdvvnzt/feCBvNO4//MjkxMnh4uLg/PmD5eXB8vLx6dOH8wsFJdvXrhWtZtJqbV+5Sny0t7rqb2xM5+aHs7OTs2eO640iDHcevs5dJ+12964+CC3UP32mfet+EvjDubn+mdNpEAnL2nzs1cqYca2+c+ECxfrw1Ebr3rayrMnS0tGZs5XFKse9e+0a5XzSbm9fvOTxfHd1zTu5JZCVRc2gdkRAKixV9XKvf5C2cZXndrZfRdxSsQjHxrdMNCVwpwiwHw0sbsYNQ4CApl/WpQvGqD7mPkWNmOntIqJxknhFv2iUDBtQ9bPIUJCT+p7wSVWfeGAn9pHuVp7sxz0M0hL7B2VN0CLXjQPukapR2mAvjzCBrNPJ7TkCQ9Oar1ibCBs1uiWtY0VRo1/KhSDHcbOZ4zmKfNicq2gN8tCOZgVtW4qBzrGA80S2g2YrN3O2sGBtXjgtBBtW1OO6hopmWOtX9gJVNTDbK2HXyJrfWihYE6O23egJpwl516vvcziDi7rXaOd2V0sGdGOAkUibmJWxIZxHyPf60k8q1/VaB8qgPKBWc2rLMmtwqxhwzbOIuO6hspPS8/z6LkZe5QHZnuJSpi1E5QRJUQU69Po5yaDngfAIo6oMSRQeQuUmLRQViQaq8jV0DgGqKs+zm8dQlEndZtmY5lUR6aCu7DkpA4fOFE2Zm3bPHR23YoVmbWKrWlqJlotj0FqssGu7PQOrTNRdWOhGUqLlwBonrU5J2hSkoDVfmZqt2mWvLHSNEYAbMa/mQ5lW9VZpOhYToLUotAvBrN1ICzlTMy7sjDiYsyDR9R6XbQsB1FgUJnJKJLxhf1KvcGC7R7vFDFeWcqI95Se8Dt3aUVWThQPq8VHaTEVEfGc3iRLmW7pxYIJEhppFfemkaYMEyYC3C+mjMNirgjH3LLe+p7yiDKEXHMEg5Q0RjPt5NClqNnR2ZDipPNuPtoGDeU3CqM/9YlqHteGRDMcSk9ZSlWtQeK41l1tcmlkvyDQVXLWbUTuhgeRu4M5ljBvlN50ZQ1KRdn07ByZUKiLNVkq8iju+N5/QotTtdtjuM5eCWc8tuPZ1GVhOQzpMaurUlwo7KaqGRzu6Zim04HtVhbAAdStoa48pHviwm854Ja8Fukxh3k8amhUlre/KAJZF6vGd1AcaVH7Vn8wgWBY0OMhqFVEIRLvCgxxxpvaKCCmrrJdHg4ZikFijg6wFKSyt+q5wuaDQLfeyFpWlaEwPJs0cM20Fe2kkCdawsad9YZh2kgPuVXmd1fPDtMOVA4Jolwcx9HE4I2ANFN0gOhb2PFENu9cuYEvzWtBaKEgLw5bVnJVu05iFIDrgsIeKhtdo5XZDS8dtL2VVHVU1x+8mdguZWatxyJ2WLFuNZvuIhEC4TjiT6DrWdTfs5jgEec8P+xVoMeXRTntMa0C4TrSQipoCXbfemcCQiIYF+selyYHnmdoASVEEOAqPUB6kTRRVmXGGVaAC5wjVeeW7rDFEhcgixrMJA7qoVxYfC4xyHznuBIGce45oHFuVmgYYtTNrPMra2D1OQHTEA4PTiTKCu1bRypx0L21aEOaeGUybyKUlkMMigH4+RkJIx+ydPz97+9bOA6c5P3bOnTs8tYEJ3Hr4kWmrlVGrfu78eGVl0BJLZ85snztHfHp49tz+2qqKgq1HHkkWFqatbvvc2f7s7BFpLZ14dv/SZRVY/atX906eLLqt7UceiRfmJaaDs2eHq6tjvzk6dWrr9ANVr7t3+fLBAxtxt7v50PVxp1s5dv/8ucH6+qA5N97Y2LtwoQyCrcceG/c6w9nZ7UuXs/nO0cbG5NSpvZMnRt3u/rmzg8XlMgg2H3102uuN5ucPLlwoOrX9EycWTp0aLi1Nut3D8+cn3U7p+5uXLlaN5mB5+fDChUm7e3BqY+7kycHCStps9c+dK+r10g82rz8CGD6aX9g9tVH53vH6+nj95LjeTqPa7rnzKgwqz7v3yKOY0jwIbK3/3iQElLLYOD19w5vo3u4rzJpr1+1+8P3lxgOyXj/87u9d+JffD4z52wr/Gxah1mJ+vv9/fVf64EPRX/xp55d+Af83Y7qv7mVqx91513v4wiL4H9Oav41FGL/x60b/xz/393ZNYAMAcFxk9bpkTGMEDWhmAz0pi3rNNlynAjqk9P2cuvVsVAHqjUZJsx3I2EyqshYhC8Y0bBRDWla8QtQBqRU4g1HabPqgGLlNomWUjkWukY01o3iaE8skXq1iNtKmng/NlEvPCk06FR5mgLsumWTCdiwq8TgvHZc6AFJmjDZcqsJE5agMQ14iyaiNxdSta4hq1UQnEhltQktmyhFF0YwIgIoyzAs8SLjtYg+ZWBij03YHAECNIFLgaa4Jzmv1cHCsIcI+1owhrafY8yZTu0qBx0AmKstN63VcIiQADyGpDCskY4kgyBP5yGuBkgEJu3JrRLpasyqAno6DfDp2mk4uchhKD0JhkIA2nmLIbc2PnY4RhBVGejoo0hTUMCsDPTEIKIMzXQOCeHQckzortHR1veobACtqK+VWxu+IzQltKWm7ZDy2IjSpgE98lU2F51CBMK+M8bXISE1nRvt0aXpvG80jF3kmzjFlWhnNSm4FOM1tT6YG+tQrhtwmBlDCdaZdB1eIgKJineIwDiODFEe2V5aZcJANPVTE3LdhhZkoDfBlmbG6zox2aU1Pp4WLbOiRKleRI3JtayFdjZALRq7OK8QMJLmsI6MtmqS67opM26pECGmtiEMzpDH08DhTdWCA8k3Ix26Zxla9lAGEQDiQ5UYDOF/e3g1mINCSeLhCSBnMSpbrnHrChc2yDzBUCme6TqRmVpKCOpYGWSUoRKHt0MpLZUkBTcSwKrEsNLK9soi1bwIGhAKVsUkpkQZQ+UpOaROlHATEL+OpCghTFJaSUspFZRxVwZqdTkkNZhK5oD3dHkRtZJARuNRWyLIKWKqEIUtj5hJVaGR7vIylb1kSGp0LC3oYIURTIBwQiVEpasoBvh4ioAyABQyldF0zzSwHlVTYoMaPE2ohgDrZ4MA6wUAOiBTcc01cOSjk09QOLS5yXtPUuCjJZZ2BAuE8J8yTZUlCWBBpQc2AFRtNjQtHGGoF8ZS1aGZcFeeuDQsqLSRtgKbcaAMiBjNpVQVxdIkRAEAyDyYKGBBaacw9owGIKBEZ5QWThhu7JI7xKUg4ANBFGScIQcOJDyoDubJcjdMqo74Nc4OUQogJXUAfVjJwyykIUSmpI6lMC2K7QJfCEYZELImlDyoFazQq0lRH2OJUilL7FkkJKi1ZSkgmpMMKI1wTVEmma8jixGQFou1ilOKWUK5LxjGtkQJKVzX5EYCmxLZSTmV8B00FskBJHDzJCDNQAUR9zjNVs1Fsq2JEZm04hZh7ItcQDVmXZUC40BfTXNYQ4wjxrLR71UES1IrSMg6uq1GKkAHUA3rKfQYrwkxWWa7KpEMQlAJbTlkV3AYW8kkeVx7AAHqI8IyVBbciWQBjU2BjNK0gBj7JUoAQxgJ5MFeuziwiksqRrmMYYCLRBngAxNyFEGifwZQDBIzPaA6MgbP83iFbhhryAARiYlVFaoUsMwXxhQdxYaCGLh5DKG1VVsiZwhYVGtrcKkSGQ4skrsk0gkqhzNSRAA6LU1THHECrqFUDgYnElMtIAGuW3zm0lpAADp1wSvwqTayAFLBAgUUTqSypLR+PCkO90Thr1uemOyMditCjVANCgZKyNDQpUIATGvqD47Tddkw5dhu2KL1sqnKNPCIwo5OkjpOD7kpQFTGz/TRWsZChi6EGqcA2zL0gt7x6PsyhEx4fp82mpzI9FSpwkIMQwgWyFNf+cEBcPPEa4eFh2mo5sNJTgbBRng2SyoYiqbdKYiGtAIC1gwPuOMRFKhEGoazV1BD5ZawNssZT7rhFFEX9Q0VIHSdbtVUINJLKH48UodhG1miSe2EeRgBCAEEtH8tcU8Vl5OlUGISQiwvLUwbVqomJucYYBlSlChlT5Vn3/e+17t752rMIv0q4yoDe+382+Ox/eaUVQiRveOPBd38/lLL5H/9D4+Mf/dueIv6/FqExxrKmb/6GyTd8E9vba//6r1n3772iaD0AUKnB//K/pdcf/lptvfr/YRGurC6X1bd/5z8vOu2Zl1/+Rz/6o3GvN1pc5I5rZdm5L3/+W7/vBzAAlqy+/bu+B2Fw//LVirluGV/4k0/9wx9/m/C8MJ289Xu+zxLVzuVLmRvZsrj6h3/wje/4Sd6MWnc3/+GP/rBhdHRiOfEbRMozX/zct33v9yGCwvH4rT/yo8DCh0urhR8RXp147sn/6bu+NxwPVM37J9/xfRYvhO9/y9ve1t28J+vBW3/4R1aeeWbrwYvfefjp9WRva79669vfsXLjGR74b/zZn1t6/rnhydVxu6cRbh7t/c/f8V3rX/zCwbkz3/y2t68+9USx1Hxref9ksj1M1D/4/n91+gtfePkNr/tn3/GdD/zFn7/0hjdVroe16m3ee8v/Tdx7RsuW3uWdb955V85VJ+d7br631eogqRWGZDMCBrCZYJIHmPEEDDOeYbANxkZCBFkgIY0YqREooCwZYYFy6qDu27e7bw7nnnxO1alctfPe77vf+TDBXgyw+os03/eqverbs/7Ps3+/t/7WyrPPnCyv/viv/q9nvviF2699/B/vf/5y0L07xf/pb/32xle+GBVzj7/nvae+9o3ty+eqV2u5m5W41W9eYea9xQx86WJ07+9PtzuxhFvrjed1ktvPXCvmb7f82vD7Dr/05qh7MHa1nZp9e5EqXf0gX38+G9r9vzf42g9Fh9dSI3fDtm+va2Qn06bGS8sUHb8G3vjh7ot7UtFeLGduLeHsXva+krm9YfA755LrPzK5P/Gn8f569fkqs3az9zT75irTt+OB3/0kz/gDBSXTj/paGtnkgEUT3Z3C22H3SzgX9Uted+svzNzoRLeGltdX/Gl6N3L+SljIUftO5/MgF400b18tYuPOXR5kph+dqHGkh+7wz+NMMDEH98hcVn35Ogq17ieF3e0pOel82MV+nGOHWHp6r432494XqTkaGGrY/pjInPTVBbP2laLeN3Wxb99azhxhBd35WXF/9eClW3qz+dUZ69gG9kH+uWambQHzcJrJSkzN4aT67FL1XhI0+42vN/SDAqnu/tj+Vy9Fg/ZIsjubuT1F2OPac83CXahk7h5XFgRRMt1J7WpBPyha6QP7XtPeySTZ3k+Nn320f/0qLs98o2QcNLG5k71esfeqRDtwX/SSL0ytFk+fmrhfi7UFVHpwFTUzxgu30B7ufRXkdC/cEem/HyklaXWuMRPpnZ68lva/Bsuki478ky9AO+nq+tiedqQfy6e80dfSYtmFN53eV1AJtG1vF1mEHRyH9xXvc9NsJcbXRqOvyHJ2rB3exbMZ+8p1OFBHf8HzeCR6iftpl5VAecjzL2wYXseIB9krl/O9jm1v/3znGT12up1S9cqikky08TTz8jlr0gXWaJSvo1TMPDjOPHdO9WLGR/nnlrXQr0dXfjrYV8eHgzCXe/pspjMSOa/55TljlMT53iRT1SIv045zL5yxBiNhBM2vL+ePp7iw8/PH3yin0cGkUn1u1hq5NB7aL53J9jy3EckPdYIbsbUg08/1ozu8CrehGpvjLgyA/1ecXukZazD8+Mi/zq0lkdm9RnViXtsZv6zzG6Feke5fufT5oTKT2nvXaIaCA0c+Ezsv87ra95+Poxcjiww13TUOtjhQwV9Ox1fSct1NnvKdq2lF7jFlojgjPYycr0jx1DSzwvnneuMXUHYmqh4g8+qGmh6ZQ7X67UZsjF87/tpPxEc7IVe2Spmbpw3+wBwK+4VNIz7wKnJqVXPeSeUlxb6xRtV9cx9lrp02ovur8tZ/Mb0bjXtOf63yTAvrvcKuyF7bUPG2Z2Dfzit+WNmOcs+vm9Gh7sris8uSOhfcb/znwf7Ic9KjQu7aedM7oJwXnlrTxDhIRu4nPdsfa57b+SK1hsOsPIAWsO7eBTjrfmiKR5GJvcHnYjYJM+NbSkPX79wFIet+SmQOemyWeB+c8H6az/aMoEf7PXrkd7/MjJMxLcDgz6bKg6ExF9r7d7GKwbbvf4XT9jSX9NtfYeqxy8xJBo3NB/cEtsOPjqMOtMzQ/7wLjwOjJGpXZ0o3CKrs5p5uZXcqvHr0Q4dffTwctIehdn/Z3irIzCB/rVa9qfilzk8cfflNYnjLp9bNpr3T0tCuvV0wb5WRdvwj0dU39K7dhNnSMyVze57Y2/lbGXtr1hQ3nkhuPTG4fZICdn29fD1PCvfyV4vW/QVFv/NI/8qbeb/dG+CDtfK1EtEP7Xvl4kslUeuc+YvP/tC/+rUgm2kePvj+f/4bKYaVpvLT7W8OkDL3ic+/+S2/OV6YaV678eb/7VeSXLa3OO9rNuXxxc999kd+5VfcRm3m+vUf/Bf/gleyBTr9ufjQP9lRHpz82P/wixgBLfB+4p/+EmToYON0qBlG6Cx/8+kf/1/+GQIAA/4T/+S/Y2HIlgs/d/Q1l+nFLz/9w//8n8OMro6cH/un/yOFcNKs/dQ//q+rOw/cUvEHf/Vf1g92jtY33EwOpWnxYP8f/Te/sHjtxe2HL/9Xv/Dftl5+8f5rXhcpKuNRdWvrzb/6q817d9tr6z/9Cz+3+O1nDh57qJtrQABat268+V//Rv3ObWlq3/fWt85fvbp3/ryfyUqEK0c7/+B/+p+Xnn1mPDfzpt/5vYUXrvRXF6e5Eke00t7/h7/4S6e//KW7r3/dj/7SL69+4+sPzp6j117+joJG/8aAwatV7eYNPBm/otYOY/X+/WhxMVpeSWo1/eWXyHDwN+al/ydgQRiub/R/8mclIfnPfMr65jcAkK/kTSiKvIce7v/Uz3xH73h/x8gdbK6TODzZXI9MQzDWPn3KK+QBwSwJLWfECeturAWFrExB99TqpFGTCJmRQ4IwxbC9eUpmdAFwZ3Pdq5dioljR1HCngWb015b8bA4QfHJqw68VE6qoSWh6Ywlwd2PVL2QFRIP1pXG1JhTGksjyx1LC/qm1qJLjAnXXV9xaBXM+mJ/z6yXARW9labw4oyA4UHNtpMIU+K3qZLaVQDKcm5nON0LDJIJbwZg43mBhbrCygBI+namPl+YQUXtEHyOGg6S/OD9YXWCOO5qfbZ/ZFJRoia8FLuJi0qz3FuaV0B+1Wr31JRXBkZrfJxbzPK9ency1BMLDWq29sY6FAtQgLnu/Pu/wAAAgAElEQVSIS0B8WBilKhJc7hvFWKiSxczuxNhMtCQueSZ0khjtZopAkJiBuOgjJCBJ0txQxUFMtPtKHgINYD8pBhhEggKZG6vYjSQ+UrIBzAIlkbleQlUIfF7wNMVPUnRsFHxgpUTQTDuieqJKme37ikpFrNSQYgkuoFpJiQ0iQokECTExFLSG84rrEIvWILNFTBUIMQA0pcQsJcJQKBSkRjALI4ZkiinTAYBaVSIbCojzeT/JqyEGElBCFQChUkNaVsQJ0iopKeIEYZKmgtkISFrFhpXwlNAqVktQcoIMJ7VDIYEwApR1dMSPidbHlpCWMD2ZnUjJoOHwjC8YpCKmXFAuYzsB2ZHkmjADXvTMxHUBO1KzHOmpESbZkHAuzFix2o5uMRHShGOApeqlGRfJNDZFUvBt4YwQa2Nb4KzUPJAdp1hNFZ/nPJAmUIVGXaQSYQOSBgFEpilPkUYYTSlSGoQoEtNUKQmkSKkoTMKYmBjHrMUo4SkjSklCTSSYQEAAYkjHdkNERFNwpNaxgj1XNyFkEClIgVZdSIIkg1aVRxoTWAKoEEI4xnodEFUCjMgMpSplIuB5n6oelyTOedhyFIjazO7jjIQIZkbCQEjGcSEh2iShWOFxzh25NCcyE2nFEmJoTYAV6QAeaPYUalIyng+g5aSpktoTkfEFwWY4BZJBIdJ8wK2ExjzKhcgaqkCesFybZJFIkTlOMgCBNMlHUS7BbqTkJWtSHCbM4rRGYoIkxCkzUCxoFhhVzmNM8og0KOQhAJBKLFUFWIjWKBac2lIpCklhgrGUDCPCDKFUEIQptLCWT6UqBSIIUQEVZkm7msSQEU2wKoJEJExHhIkEIQNYDcFTik1AZ5jGY0FhmptAknCKZWZAqMuJekAtF+eh9JKCT0mcUJLmp0ALBULZYAiEGqsIZPqxplHg8WKgsAmE7FC1HZgBWMr8iFOYYo5yg1CDCAuWxFoUcspQbgRZFBI1zY+pGfJYtPXMBOdhGsVFHzFfIpIUPa7FMgX5fCCzqgCAVrFqxIHKoCSUsjhBalnSHEgB0vIpyKIES4kYQQQATKpEL4okxmoFkhISPAaxSBUbY0hLCOURDhNWAkpJCkQ5YQKqCEgjlygZkCBCiggVEIAcEhURKlLMioAUIU45ywNcpJSnQo9V6zjAOa6HvOia0vNSdmTmgSCJLuJCQISQWiJzQx3GHtH3sMGJDZjPcz4CXGgpyIw06DtIOaKZBGaAFslsP8YqQF5acFXiu0hp64WI60LjSubIx1muxyDbhypIIdtTTAFUrqQyN5UApipPC2OYRCnFndOnqA6nzOycWhO1YoqUQ2pHCYg1tXdqJdIMyWj71EZUysZUUXloudOYqd3T64FhJKrqrc+NCmWGjW3VSFIsAeqeWg/ymVSC3trSqNmQhFjhBMUcgrR9aoMXLJmCzqm1oFFGkOxrxTCWEoLBxpKTyyMgOxtrQaVAvaC/vDCebaE0HS+0+rNziapRESlRoAded2lhtDTHXL+/tNBdXZEYqSJUopDwZLC4MJydUbzpcGHOWZ5x9AwTkRb4VCTDuVmvUZGpGMzP9ZYWhUoZj63QRXEynmmMluYlRONmYzLXiHUDAmn5IxLG/ZlWf3OVecF4brbTaNIXX/ouByyYpkmtDtNUv37tFXqgJcba3TvOI4+JfEFkMsaV5/9Gfw5+taYBKVPD7P/Uz/qbZ4wrz+c/9hEyHr6S8xUUgufznV/8ZZHL//8SsKLFpeDhhwFGg9lWkMtzyo7WN3TXWfvmN/x8Ps7ZKAVHZ88KjUWqOVheUHx/8coVoNJ+c0b1/d0LF3lGR1webW4ocbTw/BVVxuNqNWb6ZL7VrzZ039u5cFGLvKWnngEIupVSpGjjVmNSrUmIRvMzHNO1b349UdSoUkA8PdpYk6biGrlpozpotnCcdBcWnUaVhtHRympUyAXXDvqTxFlcSJgmMkZnZUWmaDg/n+SshWefM6djt15NqOJUyuP5mVgzkqzdaS2Kmw+6QnfqNZLKztJyVMjEWHEr5UGzNffyS+XDvX5rBgA0bjX7c3MsDPsL83697N/tT45HnbVNwAi3ze7aCpRw0JodtGZo4jA8dYoUId+IRtMa7Sflk1G2V8gy4GEyBRnOiUDQc8tk4Of6Q7tXLyjARcB3yoimIUITXoIdv3A4KoxrWQR9NRg7dURlgKQfleTYt3enFS+nCQ0ofBIX01AFhjcctZQw1vdH1WEuS6APkQPt2NcJi6dRBUSWkfVGcEbHLFVkREskJga/5eOMCSqm5Y75fNZgIeQpbOoAkfimB1QNVG2VB6SEk6JhONN0zsSmIZ/pxeUqyyBdBjgDYE5R08jIxpNyHT51LGZaii3VOEItDepIkSHNwlg30xsO1o2kmTfdMWpp2IJaGKAqAQaGMiDEc8qERAG3Qp4lnZP8Lm7IPCHJVDIvyUsIYwzdKIOMDtKmiV9U1GDCrYBnEBA+VrxxTh1Mi0PfHNdswkPA3KCAtHAqdRdmhNIl2gAEJUKEi3HgVZAauVyP/Dwb9TJ7cUWU1RREWE6jkkxhQqQblQDjkaJJUFIQkyoMk4adBgQ+14nOLukoYDDhLZPIRCc+KiuJr8ibUzRfkBY1gylfzCqEqyBGs6YcCbGXoLKNs4qe+qTKUpPa/jRezKl+4t1OZL2kKolOYlBiQqdG4oGm7rE8+tZRvDarKoKKSNR1QlMNRaJiCD21psNpDUpNMs/xqiBiyl630qNZnkM4cYWVODlqOBO3LBji9h6lcQiyqUgjaYSxDQj3gR127WL/yDywKtjgNPTCQiI1ibkjzEhq2D6QLOFeFuvuJCjEoU0NZxAX4sjS9rvVE5QP8liLRtIInZKqe9MwE4S2ZkdjpvOkYqog1EkYtgrJgxA4kDcLRuywrJQFjQKuqDwpGeAkxXsOWK8pMiYWFGVNT3zVTmXFSDoS7ARic0YHvoIS3jK12McWgnWd33PlGOGZLGHSQAGoq5JCDQR8oQD3/biPUdViMtJMLosqVIkB/KiVUYSLhReVUwAiCBxRSHuydtQvenkt1IHhDsczlJCEJF5Q4swD9nGq4DAwMOFuUpKBBkxnNGnRUBp73VInW6QshHKaZNJYh0o4CcsAcJTdQ4DF0pA49qICxyQSIEgzYRdXut38pGC7NrMnfbcBKE0w96McBypQ09gwY7+W1TwXNTWQ0fmzI5Et4DxVZUBtAIuKIhPVFmGtip7tJMUKyxM99lGFIBMzERIbRhkb3poQqCQLZcOf4hqVec30xqRMUkuL7ofChXChYCVT1ZZpRVfCEFVZXMzCK/1UqqhhURkTQ4qaaSQetlBU0TV/CjQX2oIjAbE3LbPBJNf1suOqrSaOUEK3QNTETZWpKODDSenYKQQVliJOueNXgJJ4qRImOdmd5Pe8UlxWU5YyMYlKgONU5ZNpXXEdczdouFmNpi5gDrATT6VKMgmquB1XTkaZYSWnpD4nYVhERAYAO3EOTHMFPfB3Ll+mNA2YdXJqI3bC6e3OOFeclsoAwsHK4qDa1D1359KlzHS08O3nBCV+sRCpxqRZc0plCLE3XzvUG+hb395unQK2JjDrbKzFph6rxmBxnkbR0refAyrpNWcN19m7dEmYukS0u7YSpsi7dtAzC5N6g/Gkt7LQr7cM19k7e04YSsI0t1oez7QEVb1Kyc3llp59Romiweys4nsnq2tBMRczzanXp8XC0gtXsqNef3aOJLy7MD9uNNQ47C+vpBm1dv1OrnPcXVxCEDjV6mBuhoVhe3V9WiquPfN0ttuZzDQC1Yiydm9pKYXEKZf8UmHxyhVjPHbrVSHReHbGrZV9MxPnst180Xj2me/OV4R/bYwVLq+q2w/Y4cErhZV6HvY899HHRTZHxmPt7u3/r+0Gv1rTAMLOa183/NEfJ91u/pMfN669JCl9peXgT/60d+HSK93CfwdchGWA3/D2t08r1eze4aue/JP2/FLr9p2LH/6IU6pmO0eX/vgjIJVAgte9411esWh0+6968k9GzUbx9tb5j30iJswM/cvvfRLFnKva697+ztg2jd7g4pMf9kuFwoO98x/5GKAKjoNXv+dJKNLYsp74rd+NTVObOA89+aeRqZv9yeU/+aBEBGL4yDvfq02nYc5+/Hf/MGVMUOXyn3zYPulx07j85AdLd+8eX778xt/63cLeYX9l5ZE//KNC5yjFZP3Tf5HfP/SKhYfe/yeVBzs7ly78J//mbYUHO93V1Yff8/784RHX9c1/9/nC9k5QLl7+4w81rlzdeu3j3/Obv128u3V0+vxj73lvaWcnsDOn/t3n6zdvd+cXH3nf+2afu3L/NY99zzv+sHT73tHps4+878navXvC0Fb//ReaL1zbuXBGf7BKTmbdqmNvVXCvpchDPK3j3pLQORpUre0Ksg+UvVnRXfUrgbVfwoNFBXfVjiq7p7kao1HZ2G7Fhb65W0G9NT/v5PYzoLOE8YQNNXC0AckETwrawRov9PWdAjhZk1afHhbRySrhfd3XwPFpRCbIydG9TWLssf1CenIK611nLE+epSAWlKZ73zAwS5Uw7t2ymS1FTxzfMBgP897g1ks17EeqKY+eyyoGop5//LzK1BSMxME1k/FEB97BrYrq+1oG7H5VgwiSODl6XsuBqZjyzl6VilQh8d6zJh37JCsPv6qnEFLAu7ezGuHQjY9fNqQnFEXsPW3KYagu2NpL8yS0CB/BoxU8tRGdov0zxkCPSl3jxpnUqUizQ7fWsWMhOEXHi8o0G+qxsrOid7So1jevbUi3HuZd+34LTzNK2ocnS3hUCu1EfbBkdCxQ2KO3T6XOTFj0za0GHhcVcUKOm2DYjOxQ21/ST0pRtWO/PJu6c8DoKfuzYFgH2miwI52XkFHn7svi5K7BZpX4bjJq500tkEfh0S2bGDDo0/G3gT4Loju8f5jPVKPpXdm7pxs0TAfi4GVTVaPEob2bVqaW8DvR0W3TKEb+Nmzf0S3o6VN/d6dpqmE0hsfPa3ZFhHfD9h3TzgbxoezvFw0cATc5el4zsM89evisiksoP1HQwSmWhDRy4d5DzEuAHrA7lxCGJAno1iYGoeKm4OgMDWOEI3z/nOrFWOnLe49DgIGIyINTCoiIG6PjMziSiAZ464I65knW06+fE6kCQUh2NjBPUJKigw0WEV9LjTunlDGIc45983SCsoBwfWuR+jHhMTw8hQPdrQTuX4bDE92YIYOnRNhFBg2GB5lwqJAC6b9IkvuxuZhOvg67x7o6y7yXZDzVi2jY31JGhzoq0v5LON0S5mIyeQqMnCKrI/cFPjliJgncLdDf1TKFaLBleQeqtS6dr8TdY8suRsPraHKg2tT3O9r4yLDy0eCmMrzLsnOR9xRv75tqFeQ7mjw8DZELPUvZ3uDZod7OoPaGtAbkOI9P1nEy0H2c7p2F0MOhoTw4hfVj2rZl+zTS+6xtgc4pJR1RV8LDCxRMYaSRB2eANVU6GjreIKRDJjbZW0PAJwnEW6fVpA9jCncvS8MlQx23TzPSQ30FHZ+BPMIS4LtnMIrHqdf7JjaEL11+cDMDA6nS8PBGSXMjVkoPv6wlHFPMj59TKec0iro7RRgIXef7z5jyJFSb6OQLxBeaosvhNYWlKUl5+5qeOgjmWOfrCJxERiudPKO6KEN0OPg2xG5sxs7BLTsaEVxhg+dU5gClzrtfgtPAIBoYXYHxFCp5pu2umscZmN+lt9bEZCEs+dZOHU/qquyw47IcLcRWqBzMaoeVsNrJ3J6T0yVgdpSDFhi0MByxbgmczEO1T9oLWnc2rhybN1vpcBGYXXpYh71ZlnaVcQV2FqHeJ515crzIsvfovXk5XEF6WzmqoMGiBrpoUMBHC0jr4c4sOlpOy721L/zl2U9+KtKM8snh5p9+QqiqPp4+9t73DZeXGy++fP6jnxjPzVav3T73sY8Lpiqh//B73sc1TQmjx97xrslMq3L73rmPfFwULH376OLHPgWYgnj86Lv+SCKEuHjtO941qVay+0cXP/zRoFy0DtqXPvhhKSTC8LHff7dM05QpT7zzPaPmTG5n//KHPsx1TR26D/3xBzCXo1bj+/7N29T+0MvlX/W+D+jjkcDswkc+Zo3GvUrjDb//B6WtB7uXL33vb7xF6w8m1eaj73/S6naEYpz92CcLewcn80tv+L231+89OD6/8ejvvcc67ozrrYf/+APWSR8icPYTnynf3To4dfZNb3977fbdrccefdNbfie3v++Vyuc/+snc9i43zTOf/EzxztbJ2sprfv/drWs37j326Bvf9vb81s7+wtJ32kX4txd/KJpfNJ9+CoXBKxxv0ZNO3GiGG6ekqqp37pDJCCD81wMWLxTav/zPUkWxnn268ImP/R2T+L/GI3UffWz4wz8mdf07/cf/VtDo4lJ48TJK0/bZM14umyja7oWLUTYjEdp56KEkY6AE7F265JULCWLtzQ2vWBSK3tlcG5VrCIDtS5fjQiaVdP/cWa9STAAZbCxPy5VIMU5Orw1qTQzhzoULfjkvBTw4c8arVwQgJ5vrk0pFpuDkzMag2kAA7V646FcLUMCjzVNBtehj/WRteTg3C3naWVkdzbdQLA7XN8bL8zASveWl7tqyjKXfrLTX1iKkdhcXhwszSKL28mp/eR4kYLAw191Yk4BMW43jlRUhUX9hcbA8j1LYWVrqry0hDgatmb2zZ7EQbq16vLmZQjJstQ43NnXX6S8s9tZXIAfDemPv/HkkhFuttNdXU0S7M3NHGxsUYk5jp8ER4IDBpDQKNFMQ6ZcDgbBQOMoOfKYnGpjWOJJxymBYdgFJI031K0GKJKQxL3oCYa5Kp84JiLlC/bKLCU8UmhSmguGEgaToCgylCpPCOFA1TtSwOsU0iRUlLjqCoYRCnOv7Cks0RRRHvqHiFCoNhLKYQ6o0MLJgSphShzyjJEAlMySneSNaYHVA8kQArNSxtHGCFbUORU4RiLEWobYMU5U1IClQLolSRzBLEsyyxTAs2hHScRMpGSmAqlQBKtIYqLSCaR6liKplmRS0RDLaJCwLRUpoFZESESkBVhjkwpiacSZJbQciFts+z4dRaiZmKEq+gEyacZgPE2qkVuCWUiwgt3xeCCFQQi3yqhzLVGpxWAxiaiR67JcE4mlih8QaejQTm8KtJEik3JRJwRFICyzolxOcCGmFScHjKeGWTIpOjJVEB1HRT4FEOmENJCiBOgULBgYcMKLMU0xkrJh4jgKKGJO0iQXGxCCsiWJNE5jQOUqZFIqqNFCq4lQlahNJioGhqg0ZaZokjM1AncYTNas0IDRYyojahEKhnGnmTBrpeoopm8FYBwlRWBMBgwKKwayGDIQQ84seMCMOzSA/TTMilYznvcjkMdTTvBtYqSR6VPClGXJoJNkxtAOPZJKsz+04xUxmJl4GplgPiz7QfQDVKO8lmSQBCs/6PBMLyNKsG+RAivUk53oFjCMc5/00GwJA/Uwc5hMoQWp7fk5KqIVZP8gDFnOcw+mMjtKUWIg2cEI0bAHQ1KCAtIBYDQlJZYaAOQ0JQUzEaiCmeppVQEtDiWB5RGooAQxmKJjTiODQYkoDSQjTnMrqOE0RyhGlgVKASYYoTRgxQ+qK0gIIQ2lRVkccEpilagMJQGBWhbNMgQlXlLjocBUlFPGiKwiQCkzy41BTONKDqkNYFDMtLjpChTFBKDsIVZwoGi+OQ4OlRA3L01QTCVajwlTqUBDEC27EpGCUF8e+TgFVg5IDVM6xInLDVEcusXjJ4aoEFIvcwLVUgJWw4gI1kphFBTfRhQA0U4ySoh5gE9exkpUCMKUMUIlGUmVlxEpYIqaUgCipsWSkRlgeipSQCqIVkkqCygRUFJgCpQBgReOA0grkVQ2HnNUwLeNUkrTAYI2hVKoFQEoogBasUlHTcBCzKsJllAAKSwqqMZRKVGJpXcMccDPE9tBlmUQXbo0jwYUG4pKbIiUwcVCJIE+lEfGix1PMDclLDsc00XBUdBGBiQ6SoscRTYyUF10BSGrIuDAOFU0oOCo5iIBQR//XM1wXONN3qJloqigPQlVLFRQVp5LiWIFJ0ZWECk3wohswChHauXwZmdRh9uHZM2EhH6lG5/S6Y2clJkcXz07yZSTBzkOXw5zNATk4dzYs5GOmtc+cmubyXDXGp+Y71TkEwPbFi2ExKyE7PL3plosJUTunN9xSOWVae3O9X65jLnYuXQ5KeZikR6c33XpdIHpw+sy0UkYQdk6vD5qzJIr3Lpz3K8U0lv3lhf7KcgroaH62s7wCUniytna8tGxOJp319cHyAhLwZG7+eH0dCzGem2mvraFUtpdX2qsrmud1Tm04s1UfGf3ZuaONDcyTwfxCf2keCXm8un68uqY6Tnd9vbeykCZgPDvT2ViTKRo2Wr3VJQRRZ3FlsLIAYtFbXOquLwOJxzOzR42G/sKV7wZo9G8qCkU+nxqG9dQ3X+kAPUlo98S7/Kqk0UCBr924/n+v5v9DwFKU3s//gn/mnHJ0UP7f30X6ffAKpM5QiKRS6f/Uz8Qzs9+pbf8rI7k7jzxK4ng40wgzWSThyeKSm8sbo9HOuQuYod2NM5NyGTIiIBm3Gl6uAJJ0NNccl2v6YLh/4SI3FMXxDzc3ua6kAnrNqlMsigRMWo1evWUNR7tnz6W2rkzc49ObiaFLQJxaZVivdxZXwkJ2XKxao/H22bOpodKJd7KxDgwaIW1aqwxas4ob9GbnvFpJHU66GxtBPgM4cErFUbPRbc559bKfy8lEjpqtaaumjJ2TpeWwmAUJ8Av50VxLSJzoSnt9nbr+pN5wmjU2cbozc2E5H0scZrPtpSVFcK4qxxsb1Asn9dq4XB62Wu2VdchgDGlo28crqyRJUkZ6K4vEC4e1xrDZgjwiIJyWiSJcmEBe9n1ikDSeZnkO7JrqPjdogDQpIreKoAyowE4lUdKESxgUEiQlgEGSlYIAImK3DACKcAzdSszSWEgcF0MEgIQRz6YJQyjhSSHgNJgxbrRNC2OZShgVYpgKKENkR4GuwliInOtbNpmGpEkgAzSOSR6nGRVyATKYM27ijixZDCURV0gNA4MSnqAcBlkFJ4LlAM/ryOWgpSoKTz3JmkzohPIE5giwCYpTw47DckZ6kjQo1WXqp7CuCgXp0T6ypMhlYJKSDBAlA0w5rRNqyNRLYYliE6YgBZgH+QQkJNXC1A5RAMMMFzayxS3d7o6MLJApQDwo8jRVhBr6OWl4UWIlwgaCC0hjp4RZHCAogpJIhSKUwM8B1Qu4nhAj9KEGUeSUIeGhxNDPxiW0jbTeMGurYRKrkciAFAKUxkkh5BABKKNiTBMuKYQlhggiUiR1nREuQwCaDPsjxgaiYEJCKYhhgUqKoRC0hoTKkC/gvEZAIlKEawQDyUAMSkqqKzBOWBVzlaFA4FlVk0EIVFzDkCAGeZLTIPINeZTYZpo3iZuAWUVBXHJIGgogkEoeV61EB6onvBLHSoR9FBWjVIMkCRMzEWZQQLdTM5nYJguAU0owDYAPo5IgSswFFHoUZwHiUhiRb0PiozjnQz0BAQoKXOoAJnGqJ7EtZAyAHro5REISZn1fwwvgSmx6gZFNeSx0EWZSFiZQC90cISEOM25oaTQKmCKjmmmIACCJmmqcYKpLUdJMd5coblwoAQAITXndYEmAECBVlHCMdCQqGg0iZABYUBAAFHLe0KnkEiLcpDgWUEOkiFGSAh2gPBUYI8lliQDgaeoksTUpENIRLhGUcEa5rGgpJUgkomUwHnBB4mKAoAQgSjJprCCYcF4IEhbNGdfahomJlCkKCxEAKRQBtKNQZzJJ06zrGkzzpVONKYpBAuNSIjHAIoizqdAADbko+J6pKH46rcQERWmCkoJQ2EhnW7EJQ4XhRCY5PzQodWVU9CDhMoFRjkMqIAe6kUQVO/UhqRJqgtRLYV1NNGIE+9gUvJADPCWWFGVduoJWCTNl6qaoTGCGplxiC6cllYUhsSEv68BLaQnyvKZNXVIgMqOkHEADpSVGgwhZMNURAX2ohEmloLo+zBKYY4SnSAWyopI4kRblRaq4gVATbIY+UoEMnQrEIoQIubm4AHeZ3hnYlpKIhIUiC1IEiYzjQhgjDFMQlkKSyIRInkswT1MaJFmQEIh4khRCDiFKZVgKUQoETZOcgHECcQTsODWCMr0+0O1QZZQLvxxRLgRO44JEqZAwiPPAsbPmaLR34ZKCkoTj9voq11SOFadW6TdnjlfXU10Zluv2cLR96RJUMArFyan1RFU4Vsat+jRfZEHoztc6tQVr0N+9eDHVGQySk7VVrqkCkGmrNi1VqOf3VuYHtVam2z04f16qZG/t9LjZgFDGRBm2Wm6loo1Hg/WVXqmeHQz2zp2TGkslCkrFYasFIx4UMseLK/p4crK46BSLg9mZk6UljGRMFK9Q6C4sKmEQ5TLtxWV9PD1ZXBw2muZwMGjNiILpQyPM2MfLK0oUuYXCeH5WHY56i0v9RtPwvEGt7leLAilcU06Wl/rVZn9+Pihk1YnTm533KkUc8XGt5tYrEVZiTesWS5mnv/Xdrwj/32ATzc7R9rG6u/MK/ct4MoFCOI++RuTyyv6ecnDwHzNL8dlXP9r/yZ9Brpv7889Y3/rmKxIfAgAFH/3oP3Affc0rlUB/p1yEszNx8qbf+e04Z1du3374/U9O6vXVZ58++9lPM4xOVhYHxTqC8vxffe6xd783sY3S7u6r3/9kmLdnnnv+7J//uer7BXd04X0fsCcjjOEbfvv3oIqKBweX/vQjiaXOXb955tOfNpyp7oxe9b4PZNvH0tTf9Na3sTTZvXhpnC8DjZ773OcufOyjudGIiPCR976/0D4Slvq6t70Dw1SLo0sf/FB1Z5uk8cWPfHT2yvPdc6e/99f+Vf3+vcOHL5/UZqVG51584eIH/6x+9w7U6NL9SQgAACAASURBVKU//dDyi1cPzm3+wK//RvnePbdVf/Rd7y4eHypRePqzf964eSM1lEt/+qGVZ5/ZfvyRN//6vy7duT1aXHzinb9ffnBP4cnG5/9y4eoL7bX1YaMRGUbp5OD73vK26s2b46Wl1/7Rext3bmpxsPqFLy08d+VwcyW7vUY6M6AwsG5VWb+c9XfIoEQ7cwodvA7vv9E76k2HYGsTDVbS3Di7XaOdkh0emR0qT9YIDtgon91qYHPXvFNAvSVgDHLbWdxtmsnAHBB4tKrxAR1n2c4aNndzdyzYW1Fo+3F5/fVjGvpborMAD9Z00WMTm+2etvEN7YEtBxsKPk46fucKZWNXh9HuNw0dBuZocvK8QiE3+EFycc68dt06jO7fLitDx2Lx7tcNVQbGcHh8RTGkS4+Dk1uqNZnq3njvRVvvDGw9evBVS0tj1fGOr6rlqAs7wfEdkw09AwYH39bMo4FiduVyAVEpnu72bltWPMF9v3tTIaNAh/7ht3W15xjzhnV1XvWZ5fXw0YIyNDQxwAcb2X2U4pv/CB4vhOGhtNDNDc1lZjgk7Tmtb0Lqq7sL1i5F2a3MzTPSqSBzaN1saWNmeh3SmVVHZaj62s68va+b+svqrTPSbUBzmt1qsoFZT156U9zdiAcdaMKtM9mjrMxt2S/MIbepkiN1b5YNcirsDraF+4IsFB3vhah7X9VKKb/OJ/dhQfRYfMDPL2iD6eSeOnlOZmoBfzHo3tNyluPfFMMtagVDpecfXdctMYl6sn1FK+bG4JrbuaPnLDe8Iwb3SNHt6ifjB7cKGTFJh+DoOS2vTlWlF56bM+7viLv45K6S94Zw6Levqvmox6fg6Nu6kYmqQ5UeLFmTieGN5OElq+cqdETuPkJivi6e+cFgqnDHO8zA41PWNGbCIbvncicTDe3LB2+kCdf9Cd3eMKOJNoxxe1WfSCUd4+1z2RNPGn3txkOESzV06O66Gbqax8nhsjZBGfPef+kOZoLju2M9e+9imupURPr9OWs6VV0PH69onpXkus7n40mbZMvJ+OsRP+H5YDjaYuEhNpJjebbEwhEb+dNv4lFHtarQfSZJjuKa1548oOMDRbPB6CUpb4fF1nT4BT440fUcD6/y4DAtRAN3Kx1v0bzhda8z7zYqLnjuF8PBsZYxporp86phXr3n7WXG91FeG/dusPEdWqsPgq+G3SMjm4urx0Qeb2jxmDkqe7BJtMPcNkOdNYpPHgXX3zRmPHyQdGfA/oYeD5mn0J1zNryn7ymgu6ngtnWcRZ15e9pV3QTtnTbdDosY2b6gkkNzl6DOui729GMNdVZs36GhYA82rMnxKXb7B1IvEJG/N4OPVnN8n3ZU2lnR3IAlnG1taMKd8Gn/mygXDmnPPb5jkL5vQv/gOc08GCpaRy7loUnSp497tywjdNnI695UcT/UkXf4nK4euWY16XyRxiHSQDR4Fqijie763VsKGKRYFb1vAbLv5EreyRdRECsakeNvp/rQzfVv+RdnSeLB2Ox+VbLjKFuanvwVcENVw9x5QeB+aJtIP1jJ7hqG9rJ6exM4s8CeZLeatJ+pxtefiA/ORNMuUtOdM7n9HMht2Vdn4HRWpYfq3gwdlsy4r3WzuD2j4jY9nmMHsyRzK/9SXU7mNXKo7NRpv2IHbW2QRYezGj6i7Tl6tGiRq28cvPCYVjgedeD+JutVbK9NRhllr6XDPXoyR49XFHPr4qc+duovPqeGfv1o+9RHPqXwODvsv/b3/yApZA82z7iZHCLg1X/20dOf/bQZeFav89CTH1DiwHCdJ97+jrBWnrl69dzHP6kYuPTyrTOf+aw5HuuR9+i7/8iIfBr5b/i9fxsW841r189//ONIpdVbd85/6pPmeHx87nS/VIcYnvnql5545x86xVLz5o1LH/2owv38Uefin30kMx5N65W//y9/3ez3pUIf+T/eZ0+Hluud++Sn6lv39s+cnZbKsao2t+9+/1veZnZPuGk+/p5350+O1DA88+nPNO/enTYab/y3b59/+cXB5vITb/3dTLvNs5nH/vjJ/NGx6U9Pf/azzasvDldX3/g7b1t+6cXdyxf/3q/9enlra7CydDS/khK8cOvaqz74wZnnnx8vzr32HX+w8MwzBw9d/IHffGthe7vXqH93XIR/BzI9XFnNfOkLr3DtDjknk0ncbIZr61BK9d7d/5inhWfe8tupaaq7O+V3v/OvXbf+1l+MY+/ipdEP/Wc8m4Xf+fPV33XBWl4Oz1/U+v3dhx9yyxXm+fde89ppsZhrt1/+nu/HFEysvBr4RAoUi4OLF0blCpu6h69+1WB2IXtycuNN3+MWc9Zh+87rX+9Wyqrrt8+d7s+0sB+3L104nl3IHB3eeMOb/GbNPmxvPfH6Ub2qjN3jzY3u/EJMlOy45+p27uj45uuecOea9lFn+1Wvcmfq0Oft0xsHm6fNTvfw1ObJ2c3sYXvv0uX2qXX1ZDBstfYvnI+xYvhTCCDx487a6vGFs5mD4/219ePzZ8z+eDw3s/PQJeJFQT5z/7HXGMNRd2np6PxZ+7hzdObs8dlNtTecVKt3H3tMnzhR1r73xBNKfziYmTk6tRlpOgtDhCEZO24+f+fx1+i+Hxr6g9c8zsbT4ezC9kMPKa5H49FoQejJlPj+dCEKMFW94agaUd5x/elBccYThiqcwVzCohFzg8GKIGmCk9Cf8WEc0qjvzQIkBQvd8WIsuW9Mp8MVQUEMvcCf9bGIlGjkt5IYY+q74dzUFZJE2y9YqwKrKIy9mQCDiIajuO75uqkEQdSaTnKWcdij6zrNQs3xWIvERQNOQ2WGcFVqB/uRUcCmKidCWTNBDinDKW2RtGZRN9ZnUFI1aXeC1gxWwrgfolUbFIgymipVIJsWcQKzEvszRTb06TJjZSw7PpzTQEXV7t4HCUyKVeYFSh2Jpq13x3RRYWWEux5Z0FCZQC+G2Jm0UuqL1JhGtZAOkyAXBrNUnR4Pobir1UksMHYm85JMY6GMnSa3Bj6wps4soSMPosl0jhvuFEB/PJ/CIAWoP60n9tTn6iRpxnFIGHaGrUh1JwCEkxnIvIMuJg9Ks0ovAHTgzmHmJzh1ghlHRIJIz53l1HEIiOCqKSSwRAA3TEUGbDyR54s4CvBRJ7ILSCIzmeAlPVWo6nvqGk1Vak4m6IyFiERuRNd1haQG99CSHjKF+Z6+zoRGtJEDz2Z1FvEQszUdYqGEjlw0EffV7d3YKIh8VumP0Rmb6QB7CVi1kSI1bypWbWEBszedtBKRT5WBCGbCMJcqw0mSd508h177yCgeZqraIJjWPJCLlJF0a6HMxciJRNbxypJOIp5zp1VsjEKv6oh8yPrcq4ZxMdVHjrCnQYnTaZTmvXEN6yeuX5pMSnphfPMuNNq1JWU4SUwvrIT62Eky/qhBtH4YZ3rjhmF3u6qWyFVb9XwNhWgzK3yuqwFfyJiHu2Tk+jOLxPFVGoI1w/RdCkK0YYow1XQBl3U2mKhGIlcz0uc2jcG6qfgOSWN82mauTzRAFyh2Q00L4YqVRkBPPbyswNGYjCdhuYFiyVRBl1UWRCbz5Vom4ljlPjhjKyBB09Cf8RCMFX8UNKKIEep50ZzjSqBE968YKzHTkBt7Mz7CMfPHSc31TZ2GcdycuirUh+5oMUGaIK7w5mJBEuYM/XqUWERxvGA2dCxqDpzxrI/0GLnCb0RQnQbecFsvjdWcOnb9lu9lqD1wprMuNEI8ToJ6IGzJnMjMR+F8gQ4DOk+VGgEdH8yqsm6o9+6jQMSlGgsipSTFrK0NHDpLWQ2RrocXNNhQ2GBKiwDO6urY1UsyWczhk6nWAKCpZbpjUCVgwcIjj2VSPKdo4wnNy7RlK8fHiSd4vaof9mlZykUbTEI9k6J5RR2OUQEn83pm7HHqJK04jogCJ8PZWPXHkPujOcDC9hDKrdIsGUYQ9d15REKOuRvMTniSKrEzWeQsiFIYBM2A+jGVfWcOCgFp5ATzrpCpkgSTuQTFQsLIb4YsDIkcRK0oIIiHJzcKSyEkNPAmCwlJBYCJOytQGGA+cGdT1zBy3e7L3/8DwKRoGj94/es9ywRhfHj+zKRUiamSnQwcO589PLj+vT8QlvL6cLL1+GvcfA750f7FC5NmSxuNjx86s7+4WTw6vP493+tVS1qnt/Xqh91qlQbRwcVzg+aM2e3vPPZIe36pvL9/441v5Jbm6RnKExqHMIh2L1wYzsxa3ZODxx45WFovb927/bo3OM2qNnIGC/P7588pY2e0MPfg0quyh0e7Z88NWq1Y00nCEQZk7A6azZ2HH7ZGo1GzsfXQw9Zx+2h9Y/fsucrx0eHySu/cOpjEbrNx96FXG4PBoNU8uHDeap90zl/YPn+hsLPdWVk9vHyeOYFXzO+86uEEYIkxVHDm4PBoY/P43GmrOxjML+y96hKa+n6h8ODMue+Si/DvRImKbM761jfkK2jzAIR4OkVJ7F24GM0tKHu7yu7OfwhYS//kv0dBUPnDd7KD/VfUO6ZpatvDH/uH/rnzr1xT+J3DNMSPPxy0qkE5F9v6ZH7WqxbCUp6X88O5li6C5WsvRnkbmmy4uOiXc1Ehe3jxAjCpm8/F1dJgfoYy4M60RnNNQuX2pUuioKeaOlyYjypZp1yU/ydzbxZsW1aX+Y5mjjnH7Odc/Vq7788+fZ7MA5kkfVOWJYpAqei1rtdr3CoLFZVSkb7EHltERIqiLAQRREsQUBRIBCEzyeZkf/qz9z67X303+9HdByJu3BcxnyjH84iYMR5mxBff9/9/v2atu7Joi+TGvffmZV+jaLS2Fs/VCc8Xrz5NdDCtV1Wz2lld0oiM5uYGS7MmEcfrm3GzUrgmb9VHS/PK0pJapb2+amA+XVoerS0gHdT2dlv9/fFsI63XhysLwMTJbNA7saEjMZifnyzNSUefzsxkM7Xcszrra6PFWc0A2WyrvbJoYD6cX4hWFjLPyppuvtyI/BKvlkeri1norl59kmaRZsLx4mK0OJOV/KISFLP1tOrzaqW7upy7NtWGutNnrgaN2EEHrISQwUyzx8o4FU5HqxmICZcDZ8Q9rOFRyb4Wua6NRsAaZaFm4gl2R9gogD40zRtZ4BGaWFo3C6GlRsgdC0+ZqK2cQ0QloBl1+soWY8MfZOW47Fpogs2BcrlGEuBHljbNTU6svnCVMknTHMEGobgwS9LwhU5FGDDL55kXuFMtrZarZGwHUtZ1S04srUNDoLma62SGJ4SvzzgxrxtY8Qq7JUuQ6tivSSOQmErfzW2ryH2zGl8Vc4EGZS3IURXqCCjkY2rqASx5ueUJ4RtlPYItSnEeBhyXFcEC0zGlA+ZCQibCT5HBLH3Igpji/AjWhqgELQmtxDD6hQlssoP8iNnUxm3uRwbKgZ1o7phZGBkDEI40jes0An4sbGyiDghjV024Oyb2fuG5VBsa5qjwUZpbR7ReWIaDuyqMdZRxKzHsgXQAsHPdGHEP+Ebm1KFBhWFzN2QyNEyb152EVU0qARYWK/k+zd0qQ0TqcBxqB1rVRgEpmxGv2x6OvRIHoWbwoWUNsaXpHqyFBXaV8vWGHbGq7mZ90+jJQDct4DWkaYtCd4zcwCULB3jWifMatXDqeIUWAocyp6aUr0PKAq2TVZSBEl3rZiVmwhR5EXbigtCurCc6LVzkk25akQbKbPQUD6UNkiJgwEkQZTodanacWTDET8YlzcCM0gELUgPn0omIE0nKTbqjmZPc1m3UYTUBdDTIvL5VojiFdoTdhDmQog6xpoVPPNQuKgJowLcTvyKEQxwrDZxEhIZnFnZFct/SEyRMzyaFXQZOyKWnW6oboo6q+oFVkCoSjlYxY6sBkeSm1nfNvmiUXTMLvUIExNdjvYagiRx2aPoJNojp83KFQwczzTdyAqpWlUZmFRJLWhY3asrQue7KaqnIS5bD+9gZiUBRFIkgwmYBjcS0+8BmE8MbpJVpxbPQhNCB8ARFPegcUiMrTKlZA2Uz7oCAdJIycFgvaqaaFul6Bv0Imxk3CqodK48zE9T0xyehZaIE0xF0IwW0PX2xMAxgFabRgU7BbGDhTlZRJkyIPeRORojynMx3Ux6YJSsHTcPARdktcA0Y+Jt/kKUHIPQL0xHcI5XkOmiaiMByyHBFmqigJeWUgTRByG4bDuNlt27GqKFBC4d0atSVCXNDGzhuqqpeSGM3YMI1jETPyzVkoIYzoQ1ooML0hVmSwCclK6Gh4q5BURcEkQumhSeQPWSeppOxSQdZQPKCHuv11DJtrQeCKcEFMPqmdVM4tnIKQ+8zH9hwIIMYmpJqe8LtaDpUNDGdvrSlMLlh9JkPXHyA7CNlAEQzEEQOnhySRk9UhYuVLTyzmwXAAkPpTKBZECOGQYT0PKpWZKPSW5y1dN5dXhvP1qCtDzfW82aZFOnqM09In06rFTDbaC/NaVQNlxuTegV51nB9lVXcJPST2Zas2xM/3L14J/MdTFG0vDieqStHH6wss4qflPxko5mVS1GpzGthd33FFunijWsQcRE6o9WVtFlNyn7RKMetShIEslXrrC0boBguLqTzjcK3ornZvFGKSyGrBP2NVe5Z609f0lmKbTxeXIwXZzLX7K8uJvMN7pq8WeuvLknbyGulwea6DZKtcxcGK4vQQMVMLZ5tFqHNy8Hx+irUYOfMyc7mms3SwfxctjRbuLRxuFsadorQ4dVS58SGRtR0ptVbX9Z0MJ6ZSVcXYgC+bSzCb3GK+QVj55a+v/8sMzrS70kvSM+cFUFgPf0Unoy/KVTwxo/8qPPA/eWP/5mi9FkOgk1e9orR974GfFu8q3+paHSplcvvedObJ5Va+dqtl/3G7xwurq889Ojd7/tAZjrl46O7f+99OC2EBK/8+bcBAK8+9/m9UhNLefrTf/u8P3y/BJqTJy/45XcbSX713he0S7MQo3Of+cy9v/NHceDXr2w99w/erym0fe5cpzyXGdbGP3313/7iOwvdMIfjF/7Oe3OTlnY7d//+H9JpohB6+dt/xev249D/zje+HUrFNXrv7763dHOHOfYL3v2euYcv7Tz/hd/7Ez/TeOLp482TL/vl36zs3ZYKnfvQnzWeuBLVrFe7bK57+QpdeO0bfq516YmDk6de9hu/23jm8u1zdx7MLEd20NjZeukv/fraP91/+eUvf81P/pfWw5f2X/HSVw+eXFYy3j0688E/X3rw4fbi6kt+63fXvvSPV17xilf/4jvm7394+9QdL3v3b88+eklSevrPP7nypa/euHiXc/0OsLsZN8f+E03eucPk22C4om6fUXqm2kv21SXo38bX1sTBXXF5/G9G/3RvoY5EwrfX5P5zlJaowZx1eXPiDL6bPfliFD8lqHFjhR/eRWRXH/p8624Ees/Rb72SHe9AQ1w/I/cvInNX22nJ/YtGcWRMPX7reRiO4KCOL1/A9q6x3SgO7tXIzmSMr9/n6nGuNLT1WQsigGOx9Y8utiQ4YDcfDexsUon7DzywRDpjvZ4U81Uoefxksf1AqGOmOuzyN0p6kuvRzvTF58j+YR6Htz5rSAm1pLjxFc/MEn16u/eyu/Rrt3jkXPtyqA1yrQJ3P0OVQoSzm191NaXgILv2UBn0U0r50/8QqnZBV33z4VMgrhlFV+xcQP2Shnpy6/nmYSmtt+2H7hH9WekekqfPg8lsoB5/CWuvysGOCOXVF9LdcjrT9h65KAYreXjYacwUumENBbp1DndmMysm1+6wbpe5f/O16ZWzmnw88+mNC3IwbxWHan8THS7nfkKun6E7c8nMofPQGT44CemefuuE6K1C0jm+jnv3G2ZL9B+GO4/5dBZnj7GbTwa+kYrdfOvxsmnyUds8/iLUQqXhwfjcinv7uPu4cfPJkqcmvI+vPxgYyVDNIFG3iyjPHsK3Hi955Wz6tLp+KfTynq73e+c3jU6/fyPY+7LhtGT+VL71RKkWjJOb4PIjJU8kYsyvfTW0QZJMjO1/oLCCSyNL3rzbTFMtnbJb32kOcuZy/eF7pSS4yPGVu6EozLEsdp5PJqIKn/5+beJPb+9kc+DJlwOuQ17Aq3dDBufkE98LmSO6x3FFXHmFOSS5PzUfujfP3Vn78Vfmxx6LuqM623qxOUGJwcwn7yU9IytP7YfuyVVVycK4fF6Llc4yvv18Y2yPWln3E0V7x7IWtfbf88Gh6cHo9uVgfAvrATz4Bk2f4d6Gav+ddrTt6ksAsX6yOmc/fGPver193cQV7ehBmj0F0AzTwDQ+uVI8czx+zN3fdjw07T6jH122bNErTpUBUXmiRl/Udm745WrUu4TbW05Zm3ZvWUdPGE6ZHT5Cjx/R/TU+uQ9sXQvdhgiOvWLrXk2M5LSsP30X94d0vy53ngvNQ22vKvbv1vO2GdP81guxiM6Yt74n2R4okd48wfaej8kBOSyz3edRPhg0cac8r4AKr9no8t3cjeieLfZegNXhavbId0sQTw9G4w149V4r76mMgmdeJMxIb3ti53kG7sC2KXdfQJIpFgBefjFCapSPd//eMPMUjPJnHqmBPjON4vKXynonJy24+2nKMmRobPsrTpHpzujG8Pmn9KOOmBhX7gvhQaHNazt/ScaJ7dXivGHLNFFb+bVLDdFVwNe2/o4We9xa5sIAcbNaPD7aeyhQQx5kg2tPVPkRADN0/3NE7koyB25/mgzGrkHl4df0SY8YPjRuXrC2a6CypT1+gfXOZqWhe21Z9NZNeQT3FsHhCebGeGvT3Fqclg9/KHnqTo1djRHcuiA6m7rs4PaivH0CGEffAW69RHSfVC595g7WPofN23h7WR6dNsWt58Kd50b7baRlt+7FWxtaeM145kTeuaiTbbS7JA7OmOxY9Vp46zSwemh3Bd84VbQGZz/5yYt//EGG9ebh7Yu//X4JsdUffsfb3tVfXZt74OHn/vF/H83Pzz305HP+8P0AkbBJXw3GEmToG9vf+fZfGrVmZy49ec97/xh69Nr6nf1Ki0Nt8ytffsmv/DZUEBb8lb/4jlFQbtXlK3kUxT3v/ivP//0/xFEGMH75234Z54XQ9Ff+wluHzbnalRsv+P33CqKZ3ckL3/27ZpR1Fxf+/et/xt47iErVl/zG7/rHbcDUc9//32vbu+3G7He9/ZdmHnvyxr33/vuf+rnytVuX731Rtz7HCGnc2HnBb7+nefXG/tLaq970loWHL9180b07rc3YdOtXb778t34nuH2AILj43/5k7qFL1y/e063PJaZX3t/9rje9o/bkM9N64zkf/NPy5avKoHd96MPzX//G/pkz//Yt/3Xty1+9/PJXvOrtv1x/+NLOiW8ri/BbBIX50or3lS9BLv7lWO+bEOg8y5ZX05OnSbdDb938Zu+6hqfT2gffL///yOdvaV9lKyuTV3yHNAzIGPjffSRC42p1//TpUaMBITw6e3bSailKRysrnZUVivlwbb174kRaDg7P39FdW5MYYc6RYJ3l5dHS0vHKKvTIYH39eGEBCYGkBEoOZ+ePNzfHs7MZ9QYL84eLS0gIKAUSYlKv7509O2y1xjMzx5ubk5m5Yak+nJ87OLEZN5vd9bXjzZNpEOxfuDCcnx82m92lpc7Kam9mtr+03F9aTol2ePZsXArjUungjjuIg/uLi+HS0mR2bjKzdMj2ulatMOn+2bO566ZBcLi5iQyUU4q5ABBEYThYX5+WygWlB3feyTEea8auZukEH4eVytISo8aw2exubHCMc9PaO3UKchE1Gkfnz1vRaLi0NFpYzHUrcwNmDzSeMwrTSmL2x6kPizw17UHuccIS5UXcJDCIAWozT9tH837S6WOP+EyCSeZzXSbCzZSrbU8x4cXUDi1nahTjOBR4IpHXz1zQw/QW1MaGzeyhxtqZQyTPaD6JQqhyhtxB5nCNZNjvMBOpMFeim4e64sALC1TBwIZ2U6AQS0uZ5QI6EJeIechRy+bQ8Mocz1FJATnoCo2gauBWuCxTqAPT57CKlRGirX1RAO5rboNhH4mS4TSVFuLMcoydIwUId4kV5KSqhEnsJic+Uh5xKkJzhazZ1r406li6KKgyqw65Brg70HQZeYjEiSSZ8IhpDpga5JZlhj0AILN1HIwUgOMw7IGiyPOpY/vWUJoTRs3C7XJIU8fQWIGE4EQCO8JSMB8Be6hILCxra2T6GCd+YDoDxWjmAztNuQSZqSxzyGHBKRWlPuQq93TdGUMGmQcNT8FqwWzDqGYBK4rAMcqJO+EyxDqi7kTCsk5G0m9xEFKYALp9lBpUb+pOJETL1seRU+agasF0gntd7tdhDVtprjzdmNHMAYdNW3FCtg8E0EgInQbnFOMadqcsr7hQR2aHqwrimuVVOKzoECC3wYFrMcyxNU68XLcxMvtJjQkDam6HWQn3keGPoCNyS5DhRITpULPaWrLPA0F16Hekk+S2NO2+sNM2cY7hYFfS1NZN2uXuoLANM+xAJx1o9h4ad5AZG9C0x7kzyXwdWl3hTZhJuXfMKcp9YQUTYbHUU4Y1KrwRx5o7o5hgzLSsGYyYEjVqjCUqBCu5djkxTF6YjlFLZcGg5cC+wsf9InSpQpIDLSBmkGDCuGOL3R399mFSqmplYUcIVHR9Ahwbsoqrt4cwikR1gTZ5EHPpUVrXpA5YzdRjZkkkHWKUAQCcUUoamdXLuWfnSY7dQeYLVDDldwoHw2RC4uPMxkLldjqehgBwju1eZBdDTG9rxkizijBXvJsFBOiZNh3HHsNCIs4gUIUjoNvhBmNloKd9FqIebPTQsKNqDADD6RSBUASqoCNtzpCwrW5mssSh3mSUBBm2kGkeCzoGjmY3FQ4Qr9v0WNKWJj1iBQWqSm5Qq8mNECmXWBWhuzwLA/2wxwoJPD2ocFqC3DS8mVz6IoeIHvWAALxiO6HQK5KXbL8yMB3JdKrtdTCxi/mK0+fEQbBhm21l1LCysVViyBLM4zJs6gAAIABJREFUtr1WkgeyCHRaipELRaAre6RAygwDhWPAYeFh7oxAKhNXOUnO6SRzlGGNOePCMa8nWlOI2A2hM8KYJL50igSCYWGRtjAKniemZTodmOHEJSBNCJhGLukBAt0wckrSnWDBGNVFOVKTXhrqkEeKTTNfGbAA+ahwoG7HKu8KLI5Pn5p54vHe+jrMOp0Tm921NYThwblz4yDIl3F3fWPQaiG/Pjc3d7y8hDA9RPGhstIqODh3flKrTsJS4+TJYaslsQaVgkoOm83j9fXu4lJUKu+dvyNqNkaad5tNh8HsYAONl5Z66+txtdI5c6Zz4kRcqRzecWFSLhcGHaxv9NbWB15tuLx8sLqaO87+nXcNZ5tRo9E5dWq8MNNeXhqurByvrIzrje7Gen9lpbDsvbNnJ9VqbtsaYxoX0zAcLC31lpejZqu9uZk0m1LTkOBIysR3j05sTsqV9tzCaHm525rjuo6UBEoWtr1/111Ax6PWTHd1NXPdzvzcaHFxUq5lYdDd3MxtO7etvc0TCuPEdr6dLMJvcVirNXjtD1Q+/D+ezWC60jTrySfc+7/GWq3BD7zOvvSIsXULAAB/7Id+OPjMp59V8ZVSitL+9/9g/4d+GCXJt1VI/fMswv5/er19fAwsogCESZH6PpDSGo+TMPTzUZFBbpuGzHkGgKUZSWIe9YpGMHbK5ngchSWPTYpECceyioju97BHojDMCs3QRaw75niauG4p7Rv7vahaFYHNY451KDWiEmYQEVHXHE8Tz3NExBIFDC3g44HyNE1xk6IoZ7puokLFLKcWJZxFAmEITVzkKMiHzDQzTrhGqMbA4TD3A+pAFksMJLBJkUFTZoxS+6AjNcxrPk8F1w3dUCxVUIgkLJmdPs2jvFFVCVMIZa5rDwdSI8RQLFVIyrhUotMpLVJMochVptHUczHDiAFmK1QAwhRF44kWahxwQyEOgUT1/PZQrwupFzYAOYZTiQLu5lGEAmFIxAEUyEKjCIRgKmQZISYxQ8oo7DyJcMlAU5XCRHkuHRTIFFx3YW+ihSQHgnI7T6aoZKAISpABp5HvDI0qU5YD+yPdV4nERDoyHnOHagVEKuGmD6cJsWQGlYkWxls7xpKmS5vH49iiRoEoSpjhqWlqWCJDgCKnmEwnOgqpKaeRdCjOEVYRt5rpwdir5h2mqo7HhhPlYU06IBlzx4QZ1EAkLF9NMsPkGQI6CuR4xF2kAQcnkQoNnkKNFdJRENhglElPYOSAYaRKmuAUT6ewRHkGCEsyByAIbEkyoCCw4TBWJSglp8ruZ7TIsxJOkI+U4lSRFEiEZtNbu9oaYFKVgJYppaCOYsxgii1hSC0DACIbDmIQIqFMNJrCEElItATkPAamp8UZoLyAyiWICZUpbAE7i8bAhSYCTCoObJxkyhITFnjZRHNBIqCDnGw6BL6pFShlGTMsK8sxZRyX8GREfJhKjcrq8GgPzVBXYMESaXo4ypFZFCjE4zEJQKaQCex8OlYeJQWSIhUUmBhBqOWIERmwYaTKQGOWjCIV6jBFiGfSs+U403XAKCfSE+NRFGq2amQ7h3TZAClCPFWeI0apYYo+lIEeyG6kyhALE0xiVTJkArCMc99Vo9wmqjCkJiUBegoVEiacxjKESHICcI4cOUl1XTKqNMkNCGMOhAQOAZnQWU4MEQkLAgVMDcQMIOyjyUg4SCrlEhVJPYoch2XKSLAFTQQTLgF0YRRzB6RM1i2UMcmBqRcw5wm2bZTkU8gJdY0oBo5i0ifRGHqQSd3gKBcRtn00zRkpAAnIdCxdJQC2kZdOp6hMUKwJnkLPRGMGKBeGC/tTzdcyyE3p5tMJKus4RoWKuddQuxH1MuC6oD8lPs6x0Flp2i8SW7pSGjiTrgXHBTKEMFzQH5Ey6AtYwV4xnMCyLYcmT3t6ywajAhtcmK7qTXSPpIhT6bBpBEoEpwiwqbCa6VFsuzEzlYFCMR5yF2nAhdGYOQYsNE1Ohe2IKTMtNmDQo64cT6UDv3lHuBAqQIkaFZaMuWcWOYYEKgPDKUMEOjAeJTaGQoWGSoQtEhNlY+EyXYcUwQlTGvRRNBYugApQDFIBEAQm1jKoAJzJbh2ZS0AAZkFcKCgQxonOZIIdaUicAwCwDftjVcNR7jiTKQqQgFhL9ULG2LXgqEhogS3P6CbABwI6cDjBgcYA1Asciwj5rjESUmdAn8luHVkLQGg2HEy0EDOItFRnPEGuiSZcGhwYDhzEwLDGkzTwWuO9Dq4AkxDJeCYxhRzpKM51KiPimeNxGvhOOma9RNTLGuQiUxqFDBGYFFXZ7+mV8u3d/tw8MRRLlLJ0rLhMpUZhDonWHuolfWyXrOEo8zxbxiwFytQxlDLhuW0prJnjia7zsRlaw1HmuSbMWQII4NLUeaZMkCe2q00Sbhi5Y7u9Ptd1YkiWAoBgYZqVnR2iqV5rFscZ143Mdd1eVxJSEYOixyRCnZVVazwSCBOitCjNDCv1vNmnnuSWmczVWAp0UUhT57lSCBFd4SjLdJMSIWIuEdIsxHIEABDDYf29314W4T9vYuHRcOZX30WvXH42AglyXszOHb/hZ5M7Lnhf+kL9fX+A0hS/5Ojg2W4CQpiePtP9v/8fCMC3+anfgkU4n+U//JM/kVbLjevXv+9tb2uvrq0+8o3Xvu2tcbNR6Rx+/8/+HIJAL7IffMNPF54Tttv/7ld/dTw/03r6yqvf8bbC89zJ8HVv+BmiBMDoNW9+i3KMsH38qre9I68E1e3d17z5TdLQDcle9dZ36iwXNv3h//wTCCN7OHjNW96qXOoftl/7i2/CCCEd/h8//npv0JW+9f0/+UadZ9x2vuft72hsbRWh85pfePPiU0/tXbzwf/7Yf1x57NLxqc1Xv/0dK088Unjuy37r92YvP522aq99xztWH3146967/8OPv37j/q8dnjn1Pe9819qlh6Nq5YXv/+PZK1cmCzOvedObNx+4//qLX/gfXv9TG/d9affic1/7a+/aeOTBqFZ9+Xv+YOOB+9tr69/35jed++KXrr70xT/0hp8+c98Xty8+53ve/Ztnv/gPeb38ovd/4MR9X96+63zrUr38VDWd6zceJuWrCya6buxVKk83kDlwb5fmHzJwsFt5IvCvziXN4ezTuHR92UA3y7vEfWoJGl13L5h90GXVXv0JvXp9Ia9260+B8JlVQ9sNDkn42KIO990jt/FYTVS69ac0/4lF5O8F10nw9DqVW24Hh5dWDHToHNv1bzR1Z6d0lfhPrWvu7aKXjj6ZOsnAgHzy8YjCzBxNJ59KbDMnx1Hvc7zEB7XoeOdvqBcNiCEmn0wMkTpxNP5UZsOp2Zu0PyfKed8R0+PPoPKwbdhi/GdTynIzi0efzmpFRx8nx18mXjx09LT7icLvdEgIph+d6GlmsGT0N7mXT/Q07X1WBMOeRfPBJ3K3MzQW7eZXKu6xaag9/+kVf19peid8eK2+VaStQfO+uWDPAv5B9aGmd2Qb8MC/tlTaBdIZNR9eqF3N07nB3H11ezcsav3qpZrTL+lgz72yWL6tC39Sf3hm5jJT9b3G/U3nsFFUerOPeM5OyVRbpatN75bDw1H94WbzCkln2zNfKfk7DeDulp4oe9s14uwnjyfF50fOrFBfH02/nNElrD/UHd7HKs4Ib0XDL3DPTotbhfzcwJmX2sOD8T/ycitWj0/696myNdL3ov7nRaCPwUERfz52W0x7tD/+Ais1E/BM1P973tC6zlH/6MukrI/AEYs+NfFqBXlqMPgHUQ2G8FbS/byowT4aZqO/yct4JDtF8ulIr6raUJYfWneiY4sNwgfPB902tNLmfcs0y2k2qj6wZLGJNRoHj55yor4BhtWHztTaB9gclP7pNE2YwceVB5etdEKjUfnpM96op5FJ5asny91RESYz/zBvTiWV/fqjS24ak2JaeuSE1x8JJ5//x6XgYMIq0dwXZkhsYDhqPjgTjGKtGIWPbvqDNGrm8KNHyZOpuwzl53rsSlamo/H9Ej49pWUZfz6mj3atDZT/RT9/mtvLCHyxz67kc/rx6CGVP51bdZV9IdYe6LonIf/rXvyMtOYV+FI/fbyoWsP0oby4lJYr8fgrBXxk4p4E4FO9yUOs3IyLr8fRJV41h8WlLHsw9ctp+o8Juz/y17n4bHf6kPIWeeM2DC6tU3lg9WjtwZb0B9UbMHx0EfoH3i0cPLVJ5bY7EOEjJ0x1bI70xkMz1NwNt4D/+IZm7/t7MLi0abMdMwHOU2epPKJT0vj6HLZ6/o4oP7ZmkNvuoShfPuvwXSMR1a+vuukuTUDpwTVMB+6hKj+yZuLbRluWLp220wOdido/rdpqHLNR+pfTcNo30rj9eWCPxq6Rdj7BvYM2rYPJRydkmFlaMv5M5kSRyaLeF4Db67lO1v+LwtsfkFkYf2Qo+tyx8vRzU3M0sWXc+zvhdEa4hKKPjdztvjEH048P+AhZTpF/dmIejSp80P4iwgex0dKSjw2NG31zCeYf6+dHwHby/G8n6CCxqqrx+FzrSQlqe7X7m+52M60PZi/Z7s26CbdKN8re9bL0BpUn6q2njGy2Pfs1t7Q7A7yd8EnfvzlDtdvBrZL/TFkz90tXG9XLXjF7PHu/7VydR8FO8LTn35ij4FawF5avVrG9H16pVR8PtfLNyiO+e30Fu7fC667/zJwpt7x9v/xYGdmHwbVK7fEKb7RP/+1nXvP2t6al8tz+je9667uEY5cP9r7/539htDC78sijr/qv7xwvzs4+dfk1b/lF5nn+oPN9b3s7MInX677u534+atVnn3nme9/+DlnznL32d/3qr+SeZ2fT1/3UT2MEjXj6gz/zs2m1VL9+4zW/+it5s+rtH77uv/wXhCCB/Id/6meI4kjwH/qpn0pKpfrt29/3trcI27CGkx98489oAIzmZn7kx/5j89b1uF579dvfUTvcY9R81TvfWd/e6i0t/+jrf3zx0qM79zzn//pP/7l55XJ/YfGVv/5r9cO93HG/+13vmr1x/Xjz5I++/sc3HvrGwXPOvvKdv1a/dnWwtPSaX/nlmWeeEZ71yl//9YXHHts7d/4HfuHnVy5duvGie3/wp9+48eDXRwtz/+a3fnvp0qWiUvp3v/Ebqw88cHzu9Ot+9o2nv/CF6y978et+/k0nvvTFW+f/N7AI/3lHyeTlivvgA0A8i41ChEinoywrW9/ITmyaVy7rhwf4HoM+ux52Jfyg9yM/li+vfPvDwX9OYPGlRWNjxYmGvc01qSMkWPvMpqYhgkTnxBoxICzy3sYyCx2axIPNFWnqmuTDk6vC0DXFextr2DNwng3XV1joGEk8Wl/gjgWBGq0tcGrogA82N5RDSBIP15ezesmMosGJZRY4pMhG64vcdXTJ+qvL0qc0TUeri0XN15JsuL6cVgIrjSdzzbxZ0uN4PNuMNhbdXm8y3xyvztujYdaqTFs1JGTcqmetqpnE07nWeG3B7XQm863R2qIZR0mjHM3U9SKLmrVkpkrTJG7WBxtLfqcznZ/pb667/eO0Gk7nW0TwtF4eLc1540FUK402l/3RKGpWu+urwbCbVvzpXB2zIisHxydPmAxjkrLqxMyEhqbI7UGCCUhFEOkiQzAzvAMmKDI4D4YmVwSMYWlCWAo0pcJIB4UOUxS0UYEgKUQ4oFIQmKhwQlUigQTBSIMMQakFR0ggpDMSHEtAdJBAf2SiTAEFg6EGcggVdfcF1CER2D0uiEaTyG4Ay85lAbxaQS2OpPKqOSLKUIyWRUimiaJumZseV5n0G9ygXHAZ1HJEkaEYrSrbzBQDZpm7IRMZcKqcmhxyWQkjbmqaEmZZOHYOM2VVpeMzkSmnykxXIs79coGo0jnTy9JxMp4Du8RpFeqJ0swRcmIkALASYo80gZSZErejpQTaCfR6gBFdH0E3RgJgM1buVM+5oikO2iTToZ2I0tRKOCZD6EaaUoim0pnaOZM0NZ39nLmalQpvZBYS62PkTjRWKFrIMDYLjvUE+m2c6IDGmttFQNO0SHljVOQGUX49U0xounJnlMa5iXKrASiLAAa0rqgmdFm4rQIxoRHh11OusIUKq8JNrQBAuQ1BMIdQBs0cK0U06TcyKaGJCqvKXZjkyPCqTNMEAjJsFUBKhGXYyCXWKCismrS0TErg1phhSCilPoNMDZgsg8FU1yaKY+BPsT0lOYLeENIUCYSdLqSCSC79yNAGSGjKmVDa49LFbh/RGAtA3B4wuMa58mODjjDXgDPF9gBnBPkjYEaAQ93qSkvonEEvAlZspEI6keb2SUGUF0EroXmBzZ5yuc4Y9BIWZHp/agbKaQE0SWwrM8sKAUAtYVYUyTJisbCeoQxSV5CGMqaJRXMrZEAqzVR6FdIss13p1jMeSd2RdlOSnJm0sEpcZxkwgVdhmEudSq+Vq0jqjghquRDIMgqnzIlimAK/VkAuiSnDZgYLqJvSmEFBkUigVDAiuEBAYb+NpUSa0IK2hJquEhWMLZwoBZE/1FAKAdCdfYUwIIq4hxzphkpgaUJgBCSCwUgjOZIKBj0EJQJc84+VphGZS29s4AlSSPN6mGQSEuR3ERKaYiToSA3popDhlJIpklgGE0UiLSnCMAYWQkqZJeE4OcylWZFOWIgY2FVu+QIy4ZVybALCmVGSjpPJHFglYZcLFSu7qsxAaEnq+hnxMeHMLCnDlyTK9EAElRxlgJakEQgjSWwvNxymuKKhJGVoThO7LO1yxiJghtIqSSMvDI9rJeTkQmmx6e0XzMFWLvyhWUhNm0B/TFihdC7DmDKGSIL8NsoIMFLNayOga1oM/bEhckk48geahIjkmn+ICh3RXPPbChCCYuyNdMCUxrE70IRUhFvu7UK6iOaacyygYaAYBWNN5UpTKBhoCiCcq/LAmvQ1DXTXlqmhuELDjWXhUI0V/XObACoI1fDUmiAaQbK7sQJdgvNisL4kQ4dkaf/UGiQYQhlvzCeGbQDeX1uWHjXyvH9imYeukaW9k2vSoliy8YllRghVRWdtWYSWHsfDtaW87NEk6Z47pQys8WyyOp8HgVkk/Y3VrBb43fZ4eX4619CTOJlvxvWqwdLp0vxkpu73O+O51nh9we/3x8vz44WWMxnGzUo819SLNJ5tjmca4ag3nqnHKy3SH0bzM+P5GTceTedaWT3Us2TSqg/XlsP+cdysDU4s+cfH09nGZHFGZ0XSqGQzFT1LJs36ZGXWGQ2jVm1wctXu9aKFmePZWe2JJ/9VCKxvVkF5Hp5OzKtXns0KoMLYuL2TrW8UzVaxtOx99cv4nmfZww7R9MUvHX7va1Cef/uf+c86WCsrxT3PUQQPlubTMGSm1Vlbm1QrlOfbd1wQrgm53L3zgrKMzPNHi/NRpcxMc7Q035+dM/J05667mEOhkAd3nOcOzV1/OtuY1qoFtcfL8925eVrk2+fP89AFXB6cP89dK3f90fzMpNGUGhktz3fmFsw83blwQfgWVPBg84TyzYlfnszPDubmNAA6q6vTmbpWFAdnzuaBUxjWdG5mNNsqTIf51tGJDYVIb2VpMlPThNpf3ygqQW4601ZjPD+bW04ROEcnT2IF+ksL0VwTCtVZX82qYWY5Ua3aXl5Uhl74zuGpU1CB0fx8b3GB8KK7upo0Kgm18kr5aHVVUYM5ZntzA0rVX17pzS2YrEdgP6poGp5aaSeeIxwJkx3HdWiIKUJ9EEiGcwMMJ3WNwLGZdodz1FARlqO4CYmINNxjIQZaqvN+3CIARk7aG89gQ06RHGcNoIMEoh4PITeEzvqsJgtT2nFnPK9rKtHEOK8rDcQIDoDPElszi27WQIVrlcZHYNnVdW7DVKsg6RmWikkZyjINkgFf8kxakCLDSzamygURLiERUkvGegXzmlVKhmzR0RzkpBOx4GgUODDSSwCUDFtENBBpq+QnI7BgaZZyikjOUOwgB0ZGAFTFsHiklwCvO2HUV/MWcaCVR7ipKVfDcoLhKGroej6RTixCqSejPEilhzU+AMaUlaFSsQEGUZOQYqroJKloVtwT7lQEBMmRRqZxBRliQMBo2jK0bIqMYVwhbtpl5hj5gINM10bTGqZ8gMAkahEzG0orTsvYyvqADliJQBUROCxqSoKUgHFSx45IDCNTNarp0kcRn3OpXvj5sFgJLZSbKhELngFyj0SoTLChbBjhGQM6WpANitWQ4sJUKZizKCosI4MVXVo4UGPYosolYTpgy76FUg0JOGfpmNkkAWUiPcPnIzhnCc8oZcNiyTV0YapUzDpEY54Ws6YnHRlMjsYNABxlTAdpU0HKCR8INys8TNhI+XkUau60GzUktIQZ9aO61Awm1RTacR4gUgylm0UV3Z10kjqHFteTYVbOgaV03ld2modQz4fQSaZl3Z1203Ke+9iZHBelTLoI86Ey0ywEVtZVdjqpUWfUZqU48t1y0TOtomg4NkwcFPNFn4rMchlv2H42MkKlKiZRiWdkRcvxUGKpqVjwDJ5ZnhR1y2cT4jFQoQQzl8R8znNgYqNULLhOMdU9hWqE8sTyClgyCOa2FqNZU9OVA2K26FksIq6CNcOSKXUKUDMlAQGcpotlU4wwG6VNQGCKQZ+Fihlc5wNW4bml3Kg9WSAYZqQYpk2gwRipAfRY6mBa9IuaSh3gT9vDGYx1phWjrAERzjTQZ4FkDqBpt6jD2Ib+9Hg8hwjOSTFKqwprGYRj6RfMBWbSyWsq9TVvdBTNAkwyrRhnpQJZ0JaJ6RbZTMnNJnCOag508kjOUOwiG8TUl6BimDKlnuBN10+GYJYSD1n5lNQx9IktY81XomZ6bEw9kc/4fjzQWkSEtBT3tIYGQoOK2HSFqFt+MdJdIZuOHY/1GcIDWsoHqARAiVKYWTYTTdstxsSHed324o6kI+irAuYGGkzrGhUDTYwmMzotRsqIkgo28yGkfRZqEMZEDlhdCZhROYyaiOZjoUdFWeliCkm/KGlCKwzRy+tKaIyywXSW6MUUkIhVFBEToA2hLxIT20UnbSKmc7PojWc0XcZKi4sKxHKK8SAP0aRaoyzfvvNOzYCp4bTPnOaWkfrhaK41qdWVhvvrK/3mnMXyrTvvkjaVWD86tclsM/OD8cLMpNEEAIxXZo7mV80i2754l7SpIMbRiXXmuannT+aa45kZqFRvY6U/u2Bmye07LwjP4hrpnFjLfS933P7c7HimRQTrnVjrzi44SbR9153K1AvHnbbqw4U5Toy0Xj5eXTNYcbi+PpppGUXWPnEiq4WZ7U6b9e7iEsQwblSO1jb0NO2c2Bi0mlSy46VlUXEnTjlt1o9XVhAGo3qjv7JI8uxo89So2SBK9NZWk1o5td20UuqtrwpMokb9m3cOT2xmtZLS9P7SUtyoxI6XVcrtcu1fwxbh/2diSdOUrmtevaINnwWgGUKcpngyTi7cVczN4zjC9zy78XZWq7Xf8EZJKfw256DfWmAtLoeYPu+97x+25pzdozs+8vH99VONp66e/Ku/mZYbXr9/8iOf5JBwrN/93g+Oq3WzOzr/px8fzM4FV7dPfeqzqW7TPD/7J58QAOe6ec97PpC5njGYnPnzvx5Xqu7O8cm//DQnFmDFHf/jz2HG46D0vN/7o9gJyDg595FPxEFgHQ9O/cWnFNCEpl/44J+SKI3L4XPe86FCp7lunf7zvzI7w8yyz37sr4IbO7cvPueFv/n77t7R0frmnR/8iNvpMEKXP/NFd+84LlfO/tlfVq5v3Xzu3S9+93u87f3D9Y07/ufHneNOanmrn/l7f2t/Um+c++gnG09fv/Lil7z03e91b2zvnrnruR/6sL+zl7jhyufvq1y7dTS/cteHP9a69OTVF734Re/5QHD11s6ZOy9+9OOlG7eY7Sz+w1eqT127eeE83T1VdNbGs8y4NSMGKxj12XRedjdSV2OjWX13DvhHYH+1GJweNpW5P8N6q0CPcM/N++dTB/LJjLGznFQnZKdV9E9Pa5zuV1hvU9IMj72sfU7ohZg2wN7prDwmuzXWO8P8iTpqit5JCWMSO/nxBWHkPKrJvfPYPQCHjaJ/h7SHwwnefdRhAiuCtu73JdVAqvYe85RL0j7eveJDIb1s/MTT84pBZZFbX/OlQUAmbz/mQQqKEdq+EgAOkBI3LpVVzJGv3finQBBDCrT9uGfDNI20rSslzjDU4c1HSiCWMMRbX/VzZHABdh/3IIYsw7ef8fOCaCa8+Y1AxhAth9rlDVmEACT8+KSIytLI+d45bRBGjbF25a4ineHeEGyfUWlF4Zx3NtW0mngI7Z7UOn7SGtMr55JkIa5y/caKisoAxby3IaK5KETo9gbpVlVpT964kKWrk6oydpbZtIngBBwvFJOVuAzgzjrpNaPmwLi8kSerwp2ow3UxmRN2cnxb6zxt6jNoeBnv3/TlghNdQwc3XeqA/JBvXa8o1xgNzO5jBl7UJ1fR7o1Ar8Hhtr5/w9Oo4kOwdbmETDAdGLuPe3oTpjfB7o2SUQWjHbJ/w6eYoVF+5WoLGyqa0p1HfdpA01tq93rJ8ovpgb57zSdYFQncftzXdZ4k5s6jnqxTO/bV/mklFOI5238eTACzmbp+D8O6EkLunBNYgoTwo/NKEAALtns3jhSgw3zrZQxSAYG8fR5CIFMsj8+JwhC6ENsXUWIkYaFduZhBVyIpd89DCbkk/OCsZHbkEHL9LJwaaSUll++IcbUgGG9vwAJKCfnROcEqoybrfwm0ux5aMLvfQOO+QXVxuOUNjy1eMo+eNKNtQFdx++vGUTeUC97gUTRu6x5Ojnaco+NANNzjp2i0jfU17fgr6LhbUjPW6CncO7ZNg/e29ON936jAgyvucNvSTpD+19DBcUiroH3V7B7Yhs5G+/RoyzFK8uiq27nl2Ety+A20f1TSW7rXdbLjC0LjIgvB7XNZGGvHJX58hgWxatdk97QAiZ7q2eFFSZiuYXsKAAAgAElEQVTIAnX7TmC1UadcdC8wewr6NX58GsIMpBrfu0uBRHBf7tzFvRh2fN4+D4yBGoS8fV5BqYQmbl/AciK5me/dW7i5Gpf40VlEJ3zoyfZ5IZWEutq6IA00UPnBg6ahGEvR1pUyyzREwc1HS3IocF3b+oqXCKoIvH3JVQpzhnYve1lCNAvcfDgQIwjmzb0vmyPpKcs4ekTnOVAK7V72k4QWobX/AC16UJ/DB181eyKUrtm+pPMEGCy9da08njq85e1/TS/6SJvX9r9E+qwkHaP7hB5FBqrY2t6m1q6B0r66dS6PNsZ1SW8vFOM5iCaw3SrGG0kI1MGKfjwbNQf08koeneDBGBys8PES0FPQbxa9dWFHsr2h2qtpvUuvLebjE8IbqYNFMVwGKEKjButucnsq22uivYn9W3JrPZ+eEs4QHC+w/ipEkRrV5OG6tEeiuyLbJ4vaYOlL921+6nOJ5ZV6x2sf/1xmOmSSXPzAhzur69Wnrpz6y8/25+bLl2+d+OvPFIatRfH5//nnuWHDXDz3/R/qLSyHN25vfvJvWDXUdzqn//LTBTagBBc+9FFGqBDwnvd9aNCacbcPTn3y03GtRvd7Zz/xVwwQgcnFP/4wx0aum89773/rzCx5e8dnP/FXhWXhcXH2z/5CKq2zMP/SX/99PI4m5fodH/44STOu0c1PfsbqDtqzy/e+7wPBzZ2tu+958W/+Ae5P+o35i3/yEbM/zKmz+b8+5x50DlY3n/8H76/eun1wx5mL7/mQ0R72WgsXPvZJozeWGjnxqc8Ft3Z3z9557+/9Yf369tV77n3h77zP3T8a11ub/+tz9n67cLyNT/9d6crNw5OnnvNHH2o+eeXqC174/Pd8wN3ev728Rp75V+NgffPrpTIscuvyM0DKfzHuU5pmHOwXs3PF3Fy+svqsBBYUov0Tb0hPnf62FV89S4GVL68U586bk8nh6ZOp7yMBdi5cKByHJvHWxYvMM0lcHJw/m5UDnORH/y9zbxqsW3bW96157Xm/85nPuUP3nbvVaqlbDSqDqMKOS5SJCZCYwhmqXEWKSiJG20QWYAIRENtJIGDAYJsIMZkUBBEJCgkj1KDuVqun2/d2953vPfN55z3vNefDBUVgJKvyRfr+q7X2Wv96qp5a//08z5VLdb+HhT5+7GLRHfCqvv/U06ob06Lef9vb6lEf12J28ZFyODAanFy5mI3WvLLcffJJMUhp2R4+dqVcG7GiGV86n62sYKUnl84tRmteWew++Y56pcfmxfGVy2KlZxs3vnBuvrMTLLPJmbPLU5t8mU8uXJyeO+1NF4udU8eXzuNKiFH35Px50OjJqdOLR0/7s8X0kUcn58/yeVZsrB09domWbb0yOLx4iZT1cnNrdu6sP19OzpwdXzzHF3m+uvLgySfDMm+GvYNLl1nT5utre5eupIvZ4tTp8aXzbFnmK6N7T76Tl7nopJPLF6DU0/XNgyuXofIgtcWGpEoDSl1/UrMUUFyNagARwAZ3JoIkkuFiQ2KrHfbawZIiK1jYDGuIMIRGDXIDiKOkXGuRNQ557ShH2Egcmv4CIKgR0qPCQmIws4OZJAxAT6xkDxk1WFqCLYS4O1GUCxqY0bz2uW2Qvw5IiozAdB2TGElD/XVnEq4k8bZw6lUz1/M2IEmRlpitQtrB0tBgZF3KlSRsi3ihqpUfbUDYI7LF3hqCHSQM7fQa3Q8a5ZFN6ndMXTN/HeE+li32RhD3sNIsHFrd501LgjXIO7ZtCF0jbICcpC7QsltqmOhQ2jR3OtJxq/u1FYELhB6U1no2tLJXapCYUNZ9ySQxUaMHlRWhDU25IqgEMLCqm0vcNaGqBpIIbAJFOuMW9mUAqjVJlNMeNt0Fgr4MYTWUTGLjSz0srPaAB9RgqYBvOJbDwmhrKPU2EUAEUALOBBhY4zDbwQTaloTeNjEYIei8LeQwURDH21YTJizxTmFCbAt9fxMiAi3E/hZ0GEtEo23b8sBoHG5DD4sl6fqbAHJsAPK3IGJYIppuaBGERiG+QwjVwnnhBrQ+cQ6hHQ8FEGlfDErkVQZ0dbowHQOkb7qVDoVzPkhzETkHYjEoQVhr3bHpEsdl67q62+hEWBuAtGhS42wiuyWIKqcS3S11RxoVuKQ0SWtd6JKySS0wseqWTccFLRadynaFk4Hq6CZtsSY4KevUONARnbLpWlQoFEF0OsCthiH21mHjAhwhsMlha3kHsg3UCoYiDE/7Thjko3RV1zCQqY+3OGw0ipC3AYxmKMTgtIeUtZzwdWAstpEXrAMlIIyJtwW1oobiaMdJRzUjwSZwDpuA+RvIKggD7G9BranmhJxhnlUKJ6Y3dwRqQPSwcBhZxM1gLikF0G9HGSFKoUT1F5ZBCzDqTiUlkkZ2OBecAeCLYYapMCjWvaX1IXRYDQqDjcGB601EwAEI21EGuNIudJ0Z8JwCvh6UhlkHA9ed1BGDNhSDDHrS2kj3cx0oJUnaae3Aq7WPN2jQNXXN/FVERlg2xBsAMsRKU6/v3JA1gvkr0OtZUROyStgIyoagASErFAjg9R0YMiE5GyG74qNCeSuYrKC2ZahP4SozAngdF/Zt7mIwomDdw5lEA+KtQqM46lG0RoFwsIvdukcUsZ4inUkD+9IH5bok2hlOdX+BEJM+qYeCKGy50sPCGO48qPsLCX3LiBwsIcCSYdPPgSGKOzPKneWGEzOYK+Q5SnR/ARCRjOhBbh0z1JLuWKBQcW6GM0l8gKnqLyFGhhM9yC3wHLVqlEuEvLq8//QzxAdSk/0n3qaSCGp79PhlEadQm+MnrhTpICzyu08/rZIAN+rw7W9r0wRJc/z45SrtYKmWl89MBltemd97x1Oyn9KsPHribVWvQ1p5cuVS1e1RKY8fvzwfroWz2e5TT7WDDsvKw7c9Xg77tG73rjxWDfpeXZ88fnE2Wk+Wswdvf7JaGbJlsTy1PT13ltQi2948PvuolxfjR88dP3K2O5udPHpudu4MXxSLnZ2DCxeCsiy3N44fOceravzII0fnL6Qnx7Mzp/Ozm64B8431g8ceC6pyub4+f/QMr5vJmUcOLl7qTk5mW9vjy+dp3lQro6PLF1CjsrW16bmzvKwnm9uzi4/482y2uXXy+CVeNsVgsH/m9JdrFuEXMf70ygp/cP9L7RUKAL93t/wbX2vSFH5vr/cfbc1QPf2uw/f/MHBftuLJL1RFmP3N/2T+Hd/Ze3C/6acOk2gym62uaUJOXbt2//Jjka66d/eOz5zxVcXmleyEmvHocCwGSZH0t16/+uDK45HIu/cOTk7teEbQRQVDLBn3T5ZimC6Twc7VVx9cfiy0TffO/mRni1lJZ4WOPRGEncMT2Y/n6fDUa6/uXbriubZ3Z2++sRabStXAMVL00nh/2aQ+iHD3/iQfpMAjbFYAAMQgofMqltVite+dNG3k2YQm96dVNwQh54sSWFAPY7pofClm64P0YCFDTycs3p+3MbeRT+YFsnC6udadTv2mmW6MksNMcZwN+6O9Ix0QHXokr3GrJ9sb6WQS1k25mkYnee37eb/v55VXq/lqx2/rZCrKVaKkF+XtYsUPSk2F4LwVluEGzlY7vK7iuZtv8E5RwgYtRqFXatpKEyOoHS/tbDUlbdOZqPkGT8oaVKjpE95oKJxJsDWAZUAPnIQsPRKzLT8ua1gi0UOktUg47rUV5H7m2hEuadTdnVcrAXMaLZWLiKaYzCRMUBP60W6ZbcQrzbhZeM2QU+jAUrmQWI7pTMIYlmEQ7LXVuh+4lh7LZuBh5OBSAQ5NQPFcRbSad3vBflutBRxKdKRVj2ACUaYcQyqiZCqx78o0inbLZtVnSKFjpToYM+wqwJSuRz6dKOdhEDo8Vi7Azoe4MMA43aWucn4rypWQTpSlsOpHnePcech6AFQASZOvpnFW8UYUo5AuDICm7MfxcQ4ZDEjZmAi0drmWhssStUCMWDIpFWV5N4rGOcAQRBAJgEsth0TXiJVKrHneojIKgpRCbUmulut90tbRici2o6QoQemKtYSUileVS5k1Di+0G1GBWLhf5TtxVFWugHJAWaNga21CtQV8KuwqaxCP98tiOxxm06oJ5ICRVrvGgoQaCL1Ja1Zohf1ory53/Khp4MKIASfawlzW/RRwFN/Py43IBy07knWfY+xAbgGH1kN0oWEAyiQMH5TNWsCgZEdSdGnk6lb4jgAVETo3iNuyE8a7ZbMaEijQibEdAqjDuXUYqJjSucLcFmkU75XV0BeB1z9c6phYCmBhDcFNJ0gnOaI268TJYd10SBOE0XQKNSrWemGWe6WoVkO0sBCYahDH4xxS4AJsKksblW32g6zwc6GGmCyNJLTsJ/FJZjBEMYatxZVZbnS9Zckq26zyeFpKRF2K0UwA6EDKbGtoafUK1QKzpWzXvHBeS0BcikkmoXW2w3Rr+UJlp3udfAky0A491krYOJtg6xBeaDeAknjhbpGdSsOqRBkQA0qkQY3lXDaYkYXVI9JiL9wti9NJ2FR4Yds+w9qC2oIIGQi9qTRDUrEgvp9XO5EvGzwzpgO50bKlNkSKEm8ibQ9Wfti5n5U7CRMNXDjXQQYBtNQRqpe9Dj+S9dDnWKEjpboEM4Qy6TBUCcNzRaktumG4X7cDjxGNjqVKMeYYLKWhvOkFySTH2OS9ODxo2g4RcdjZn5uYOI5Arh1A1SCKxzlBxiaYnKiqF7RR1NmfyJDhAMHKWIvKQRiNC0tI1Q87kwpAHbC61gEWcLaahHnJa1SMcGdea0iyfhjNGuAMiBEUjlZW9JESOMx0scbSZSMt0SliuQbOmZhY6XgB5BAYTYOFyldpvGyNQTpFpDTQAo83hfOD3DarRCnqz3S5QZKstRKqDka1g8q5BLaYrd45OnpkfSU/NkuYrXaYUiRvVCfUGKdHi3otKVkwun0yfmQlEnVwkmerXWoMWVY68SWl6fESd9xJsrp2+/D47KqvhH9cVKMYA8eWtYq9lvPuwawZRXkQb9zcH59Z4VYFx1k1iCGEfF4W3Z4K6MqDo2YYZWGyfutwvDPgwNBFhaArB73weIYJmKxvbrz51mx9vUo7m3duF/0+YIBkDbR2trE5PDzA0IzXNtdv3V4Mh2Wnt377Zt3thqRtBMNSjLe2R3sPDKbV6mD1zp3FcC1P07W7d9o4thFDlfKrYrGxFhxPAYTF2mjt7r15dwAiGh7PFOcqDVCleFUuINr8Zz/B79758lcR/qUnHpZ88o+G//oX8ed5l1982s3im7558g++4z/+guUoOfqBD5gk+bIe7wv85L5zalObb/jgj6nQW73x1tf9Hz+zXFm98PJL7/rQL0OMVx/c+Opf+NdRmTPR/u0PftB6bOXe3ff8y59T3WTj5Vee+ZUPsboeNvm7//efjrMFBua9P/ZBiszwcP+rf/4XQUA3Xr/+rg99KFYqWhx/7U//TDo+BqH3Df/0fyLIdU6Ov+Zn/yVCZu3e7jO//G+TxdzX9Xv+xU8Nx0fQx3/rR3/Sa/NycMm7dynIaFrvgb1noj3Luyfhq++gWZeTA//Ny2HFO8WhOXzCm3uROkS7T/fHDAQP/KvvJFmf04PgxkVWJVF5gg8u8akfmUOy+85gP4S9g86rb8PFKvEn8evbLOsEzQQdXYpmKcTL4Pal6IGPenv9q5dsvgO9WefNbS/v9qsH4OgCPx6Kft5/fTU82NLdaff1lB1tderb3nzEH2wTUPCTQe/2ms9vRXe38f6jJh53b/X44XaqD6JdhPcvYpD7s3765iaMD+IbA75/1vona/civH/GV7NoTNn9c6EZ80kS3XgERwfJ3YG3/4hPH/C7HXb4SFzud3MK71z0zDSYh8HNcyG9Gd/voYMrIbhLCi+6eTkqa2qq8PUnmC7Tog1uPcbdIjgBfO9KlM/XsyO19554WTFYJVff5rs2yCr/xmVPL6O58x5ciqsqLsfkzjPJJPd4Gb/yODc2KCv/9pVhM+YzTfffFmRtpGb85tPdZQvJPH71CSZN0FTB7cuRqIJZ4+1dCZdtoGb+rafDqRPJbPmH0k7NQMxPXoZyT3ZxdvIZam+UvY3m+GOu2rPDJJ8/69pD1ZXLyTUM96TPxeQ5q66Xva12/PumOYBh32R/XMpdMdTz+ZukuqWCUM+es+5Gsz6aHv8Rmt/DSSqK50S7b1fVdPmazt8yfmqKz9jmtaa/U0//QCzu4ZVgUb0Klndcl1fJdLN7axsHB9GNnrd/3kaT4d2UH57leto9sGTvMrdLP0s710+DZBLf6gYHFwJ637sb+8cXwvo4zhi/ezHQM74IoxvnCT8Y7MZk/7KHd8PdmB9cSMuD4UzY3adCOfUyP3nrPA9PkjuU7T8WuPvRYeTtX4jqeZwJdvvxqB37FY/fvAii2t05OnkR9eoyKiZ3Px35k2UU6qPfd1TKoCmPnoWByOJJuf8y47O8o5f3/jSIx8sun9/9eIgb4bfV0Z/BRNXeeHnwEiWTvAPLvU8xdphHHXH4UQBqFcvi+NOIZwXP66OXSbhoCFVHn7Bgv0wG7fhjTtYwsO30WcWny1Q0B5/F7kjQTbT54mk+H+Fk3Hl11Z93g/aYHZ2NT1KEl9Gds8ndAHV3V66eRbNtF03711aDea9f36eH297hKmR5dPfMaDd2nd30pW06OwXY0eD2Kpuuhe1RtDdgB2sBOQwfnInur9vRfvflHW92yod3/Ttr/mQjbI+C8Yq/t8HJYXDvVPhgE8e3Vt7cgpNHAT9wb84PX2Ydk6mxmjwLkrTWb7Tjl8kgypo3xMlV0mumYdbee45GugAzdfIpOPSn9pbYe9Ub8bm6KY6vklW5xONs93meqBzmZvzvbRwLcLM8fJkN0Zjsyv1XSFrlvCj2/owP2ylcNnuf4lHQuHvN0WfxCs3MverwFRrmC1+0h3+CmWpSP4pff7TXZP6iInuPB0sV6jG/9fRgrgA9iV99O2+Ir5bhzYtBo4Jl4e1eDhc6MGP/1lPhMcTxpPvy40T53ObRG6fDXHl17T24GC+Jw3nn+uXoEOD4ePjqJdd2qcvTt06npe3kx3j/8fCE2bDsvX6pO6Yg3E8/ew43PWaWndunvCXHeBneOd3b7XrBm/6183i8A8Jx560VbzLsiD3/QY8ebEKWJw82krsj0LufvrJNxjseuZvcW8cnm6Ec+4c9f2/DQwfeg63w7gbq3kuvbbOTHZ/c43dW+NFGWu9Hsx6+v8PRcbC7HtzfTv3r4Vs7cPxo5G579wb+8Xa32WcnMb+3w+lxuLcW3tnC6UF8N0DHb4+Lw9VF0Rx8bdTMaU3Dtx7n3jzaQ2j/HYHdi058dPREVC3iooT3vioqp55C/htPUJpHxwjtv2NV3AInHJ08HRZZ0Fbk9ldFTY6F8d58O0F5NEV478nYjL0Th46eDos8UDW99YxXNdSo8I0nkG/DY00fPB7Xx7RkZPftYa0DfeMb/8k/7e3vgsj/mp/9ucHBA7+un/rwhzfv3ys6nff+xAd3Xvrs8eOX/u77f3Cwuyfi+Ot++qdW7t/ynHvqwx/evHo1X197749/8NFXX55f2PmbP/bPVm68pZLkPf/qF4a7D8K2eOpXPrzzmc9Mz579Oz/5E+c+9ScH73ziG37oRzbeuGbT8Ks+/Gtr1695Vr7z13/97IufWZ7e+vqf/Ofnn/3UwdNPvvfHfnzr9dePt7bItde/cizCv/gGK3dO8du3+O6DL6koECHv1s3y3X8DfQmJ2LfI9Q3wldH7668mXggZhKhSzvON53NrdeAL7kVZJpJIhlHcNCqKHEbUWsC59H1ujSVIpmmSZU2nIxCKhVCe7yglWkOMBKNcSstY3eumZVkzJsMoqCoZRQZCZg1ESAYB19oyXoVBUhRtmsgwjJpaY2wpQc5BhCxznpDENMrHft0i2CoAfJFzXTnimK6paiXRXCkGWkNtLFvYZtrzAlUy2TjimK64bIxnuFHEtdpDYVtyudCUeHrBm0phR13raQk86cmGtJn1gCcarjNNGWunvKkc1cSWTEjBNFOSusYQFNgFL3ODDKVt2CjNLbLCb2tAJbGK1QvLCNalV+XOw8RkYSMMkQiZQLWQSSSasJ0Cipkto6bUPgWm8BuFSEldFYrGEY2sDlTuKGY2i5rCYEdp4zVG+cCZNpI1xBJb5YscYmRtE9aV5dhiGTY1BA1EzhclRlYDE7QlghYSETbKEqWQDpsaQAGR8WWOtDDIcFFDAi1u/EZDUGufRE0NkDTOBrIgtnVYe6KGTilufKExajSFUVsCVVhKfFkQpwHRvqywaYyv/VZBWxsPBW1Bde4w8pzgSlgPUOs8oAGDoWwD0GjGUlBzKQBDAZCeEigA3ChP1tYngRKxqzXjPVfSqgLURcRwpZ0HmBSBaaCPuGh9XWrOA1d7bQMjFtiatRL4kDrjI4M4JHXVBZVmPHZ1LBsTUGZbT0rCDdat1ywshsTVQVNanyCdeZXArEbEBKLV1ECtQjFD0DCbx3VmGIJEeLWyvia28kUNsKJA+7K0jFhdx23tiEW4CVqtmbFQPWQIkIEonDUOKr9unIcdqoPGQNI4hoK2NkhDo4I2g05Tz/qtdEA6j/p1g5kDyAWyoVYBBgMtMIKWOq8RHtPAp2HTYCAAJYGuqVGIgUAJpAUIMGs1Zw4ymIiGI2EZSWxFtIIMhlpSqGkIeCO5qXXAE1FHUFjKOiqnokUeDK0i0Dpm/FoEsHEI+XLu1ZVEjgDhKwl8xVVDm8J6jivhqUwzxsScN5WjBruSt1IyQ7WmtnVcM1F77UJTSm3pt7WJCBE5b1vIBbOK2VYRwNomUguAoG+mYVNoD2HQMqmcp7iquaoAsVy3nioNY0BksWgsdRzJRDSEOWRtrGvIsIdlIiqHAWeWV8qGFFgZy5ZSQ4AJZY0IBFCHdQM48Tzj19IiDRkO2hZTh6wOVYOhQQyEbQM84pD2a8k8BSkJ6wpCjTEIVE2gJdRGbYuBRAHilWI+QBgkovagBMB4skZaKqo9oQlqLAFRWzlZGM58VRAjH0YQsa31jScUBI3mOBQl00tDsa8WVDQKW0+3FGjIhde2WC5tgP2mojbXlPJmSmXjqOKqxkpKqrxWItw4alldMrnUjPkmZ1paZllTEiuMD6hsaLswnDNT8Ka0HmI694XUTBNnPNs6qnBTh3JqKON6GbWV9gg2lS8kpKVnal/XlliiZaByw5in51FbGeI4brgwihusRKAqQDTVrS8KTaA1VdQ02kMENZ6QimoMLFetRYaoxhclQBbhJqylplojHTa1Q5JA5YvcWe2gCuraceRgFVYaksZyHDalRQpY7YscAemQCpvKOWm4DWuJcGMxjJoc2hoSGIgCQQuRiNoKgsb6NqgEAI0lIKwzbCtIXNgugRKW6qBtITOI1n7ZYpsZ7hGCsTHS86lzCMGy3/Ob2kDUxrGnlPW4YRxTgmXbxjElBEFYxnFQ15bzNkm4aDXBmnsAQmJM3eliY7C1Va8fiFYnsfR9WpXA44p7kFHmbBPF2GgMQNPtBEoCxjTnVEnMqGIcMOZpLRmDX4mphnMYL7/xm9Tq2peUC0EIrF35+Z/9ohahtWp1dfd/+xn7pU3R+bI0Gj153/egsvJs6wCUkDqMHYRUCMV5p12UKHQUB7IqaBzo2mAsAEtUXvIES/X5TCjLnCbddm4RzkmSqLzw/hJjKY5kmdPE143DSDoaq6L0YiT15xhI4LA4Poi3Al07hJQjBuNE58Iy4fmddpHRFDvj2yanSb+cWEpykliEEpU30FOM/xUmkJXDSAFqEE500QCuCU1lltMEAGAxhtYGsjYES8gchA4hrJTFOJVZxlJkjKEUWuvLhgMhAGtY4BCC1kLnLMYAAN42HKmcxMRozRjS2kK0XuxPw5FxyFAKAKBCWEbCtihpZCmFxjgAY1XUJADWfT4TtUVBYwYVNLYhfqLyikXOuEQXS69LhTCMxm1e0JhChYytSbBR7E+jkTUgstXS62KlHMGRLP78qhFsIU9UUfEISS25d2px5yDcsvQvMRKwSBWVFyNpFGeddlHC0DISiSJniWdaCEEDvfVifxaPjIGasU67yHEMCPpLDPJimdc8gso8ZAocAQRDXWUsDWQFMBKAOoQjXbSAS8a77SKnCXI2MHXO0kCUjmABKIDQEoKVsgg9lOPhnUPrfFk5ggTgAIKHjEF4J7u3m576nGTIWII0UapmoUMIGWMh6shlxlJsdGCbnCVYa4Is1qomQazLhvjOgodyEKUswXGblyQyjEJjgAORLhviWwf/khwiz2nCgMJG1ySIVVGzECob2fIh4yhezQ8Ooi0GvyBjOI3bvMARQYYY3WAPIOQQom37MDoqGGpOkzbLacKtgNC1wItVUXshUPbhVVcwsIysZft7yY5vGghdg/xYFg0LgLafk8NS8nAdZgWGroFeLIuah0BbQ6mDECvlMIpVkdEUAucwRkqHqmw+j0HG/EUEOV/WgMAGeRAAizFWyiDU+fwIcs4TtQeEBKymgSN/PYOMJVBTJUoWR7oUiGtIUpU9lCy0dcY6WCuMLNWyZHGsSomogjRV2cNr1JwlzbIgMUGWGFmTMFZFQwNrYaLz/5ChRpYkWi0Ps6CrLIn/Qg7NWdIuCxwTbKmWFQkjVbbUd9r9h4zv2kBW42AlVkVLPWNR+vl7tVlOYwoMsapGwZ/v5chfJ4fEwNQ4iGQhmO+0+xzjMIplkdMEQOAwRlqHsmx5YC20GP9/kskiYwmy1lKKtA5UTYFqgCe4/+eSQfBXGAChxRgpZRHezu7vpTufkwNrjbBjsq1paAlBxlgAU5nlLEHGRLbKWIcohYhjoq1YHNhKAarQn8uBlfocA4nzRFuyKLC1AlRitp3d3+ueIkIGoPkLBniiKVkU2EYBohBNVJ57HSKEYXRj+eAg2sbYMiUqGkaqFNSzBsSmzLwOEVJzlrbLAhjKVsgAACAASURBVMcIO67akkYPGWPQZrm739nBUj2ULCcJQfYhE6pKUq4tSvTDvf58nYwkBBhuREVCgKBDCEv5cC+stKYkbZc5TalVFKgaBZEsW+45DQ0lD+WwGCcyz1iKgLMYI60DWUnu2c9jHEab+e6D5DS22lKKlXIQ+ra1BrQ8eCiZgzCV2ZJ1uBYU6goFELiHTMPDVC5LEjkAE1UsvA52ju4+WP2pf8HvfGVZhJ/LQ0b/6uc6H/29L71T1RezCJGUx9/7D+XW9pfWKOvLUUV45uy2Nt/+3f+DTOO1mze+5QMfmJ46dfqVl7/1/T9QrKwMxwff8l3fiyEIm/Lbvvf7VOQPd3e/5f3vr0e99bdufvP7/0cVR3G5+Hvf833EaqrVt7/vuxmx3aOjv/ODPyzTYLB38K0/8I8to6Gqv+193xNVhfPYt3/Xd1On4/n8Wz/wg87D0fH4v/iBf4wQIk79vf/+fb35DET0P/+efxQV8+Xm5dGLW8lEBvao89Ll0YNM9Rabn9yOjxgKT0Z/ttqdIF/uR9cudI4EZbPBC+dW7hViuNz54/V4j+PoePj8anrCuDtMXz/T2zeETQYvPDq407Yb+dYfryZ7kerMtp/vxvu+Dw+S66f7u1DF5eZza2s3RL2Zb/5RJ9wbmMFy/YUk3Q0jeSd+63Rnz69H1dbz3uhav93Itl506RunY/QW2+8Pr60APkvvdzc/w0l62Luadq9tNOvZ6lXUu3aKoHujBzi+eoqwSbKfbr0QqMFi5TXWf21LDqer10332qOU7HX2YfrqGQ8epgfh6MWB6c5H12D/1R2cHnZugO61C4G5E09w/+XTDB1HB97aCyMe7XVusOTqIyTYpQVY+9NzUXHCrdz45CY3VVDUq5/eoqSIT6reK5ej5rBXHIcvPRMtTgiRa5/c8WQVlsXohR3PZPGy6r18MazGoVz2nn+sN58AVm9+coe3ktfZ6IXTnWbM86bzymPJ8oTiavTs+f50av1m64/W/EpzkQ2fPxW2S68p+p+9lCynGBcrz15IJ3k9qMzvzN3dtkeW2Sc1u50lQbn4qAyun3hncfvvFvaNurPalh8r7c227+fLZy1+c+l3TfN7hffa2D8L1W/N9XURbkP70bG53gz4Yvlpi98s+RA0v7egn530T4vqd8v2deNvQ/eJqbymRmzWfLo1r5bhKpS/n5EXZ/557H77pH5J91eq9tmyeUX1emV3d7T6Qqx7i7XX6fC19Wa4XH/LdV876+F73UOUvHKW4HF4HG0+n+recnAdDV/ZBulh7zbuXD3PwIPOie2+9IjnDsNjtvH8yCWz4U3Yf+UUSo66t13n6sVI3emNRfTqFW4Owzlfe24FhNPhbdN/+QzzdtMD2H/5QqB3w6UevnA21Ifekq5+ekMnLdk/XHxEpu081vn0t21nOvYiW/xGzusmEkX1kSrQtbfMph+1ab0MQXHyW7p3Mk6ievFbkizrCNTFR+qwysMqn3zUdOcznzez35Tp4ZgOYfNrSzpvA1Atfq8JizywzfSjOprMSBfLX5sHuzO+AsSvze3MRExWH8ni6dK31fRjNpxkYJuf+UTavZ+2/enOc2H0oBOgB/GbW/17zITLtRdXN66rZjPf+OM0eTCQK9n6C37nbi/St+Jbm+n91CTZ6KXR2jXYbCxO/UmY3lkR/dnGq35yexSg+52bo86NFAdHg9fW+tcDsTnb+FTQubUBk73+tSS9vebBe53bvd4bXewf9F8brl6LxerJxnN+enNb9U/49ZPJH9geycC+aD+SBUNNX5sVf9B2VlpybT79uFshUzquFh/TXbJ0B6L9SNFPc/RGPvtD3e2W+G61+H3Th1M+r7KPiJ6dw7lufjePE8FuzIs/1N1giR5U04/ZPpzTZb34nXaop2Qp8t9twqClu3n2MTH0FvSwGv8/dqgmWMjit6rQ5hHzV59d75ZLXuXJq4+lsxmlxejZC4OjmU6qrT9aDzLA3GLw/E5U1lwsey9f7MyWiGYrz57vHhRqWG//+zVvSTFarj+3Fi8E1/PeS+c7s1JG8tQnN4e7ZTusNj8+ZHmM2XL1+ZVkoqJmP379sc5EqV6z9Ym1/q5So+z0J7r+LIVsvvbiMBpDE2ejz66sXIV4dDB8bpTeXmlXFusve53bK567O7jRS250QbIcXR2sXaViY775p1H3xgbsPOheS9Lbmww96N5KkzcHzD/svzEavB7Ltcnmp1n3rS2U7naveZ2bW769k+52etcHODjuXOutXu2Q3t3eS5305mkc3E1ux/3rm569l+7Hw1e6xDvs3+oNXx24wUHvluu+eilS9/rzInz5CV8eeiVZ+7N1zBe9PTl48QyjB8mx7b98MZD7YSH6z5+P2yNag7U/3cQsjw/k8KVHU3uLznDv5StxtceMWfnUmbg5IcJt/skaxWV4IgaffcQHe/7SDl68FFeHxMjVT50N6zkGbvNT6xar7qwavnAm1ges0MMXLkX1lKKDv/++7956841q2P/GH/zh0d5dHUZ/94d/aOXevfnOzj/4zv/29Kuv7L/9bf/ld33X1vXXs63tb/qhD+zcvN5GyTf+yI9s3L41fuTR/+o7v+PCc58+efLyN3//+8++/NLs9On/9Mf/562rr4M4eO+P/ujpq68dXHnsv3nff3fp2T+5++5nvu37/9GFT/1xdmr7b/3z//XMZ19sB933fvCDZ59/fnLpwrd93/df+sTH7737mW/5Jx+48vGP37t8hb/2KvkKswg/N4i5PX8+/vSf4SL/UhtpfcG1lMy/9j3140982bOrL3pgg5TUGGGjsVaSM6Kk1zZ1mnht4wnRpglta2CNJhhpjYVQjBJrmGjqNGF15Yu2DnzSCmyMJgQYQ4w2gU+N8fKsTmJPNLStm8AjSmAlNYLAWSrbllFkTFiXdRKzsmRKqjjCSiCjNUYOAiytxtBBjKW1DGjIudGSMAsAttBRajHAGhlsLaZEOcWQQT7VShDPMQK0M5QaDJAkFgGNCJZWcehYiLUymGuMmXHaYYcdUthiYBEhjTKMNCSksnGUOwKxAghhhy0yyBDnAMLWOswkJlhbB6jxrFOEIKApIhpBCBTzsVYGMeNTKhREXHPHLdYOWQaRQRYARThREkKifEqkhoAZZoihEBNLHTDQQmioh42xgClOsDYAcsMM0oQ4ICiGBkGMNOPIOgCw9TFTGhqsuXOQAes09yxAyADNKHQWOKqZcQYyCwQDBGBonUIYQIQs1IxCa4CmijpoCZLacGQNodZKTJ0D2BHNCHIOamiosYATbRTHBnBstOY+wAhYrBkGAEFBDDXWUaK08jCgPtRGQQIIhMI6DA3CSFnDiYSUKCkhMxQDZS2EiCOnIIAOQIiEVoxoRImWGlOFMZQGOOAIsNJBAhSAsJGQU8MYUUIhCgjErbIQA+KAdAZDhzHWRmGsMCVSSMw0x1goBzGkAGoIIHScU6ME5MpnRCjnmGEGGoq1NQxgAyEEFjPknMOe9ChW2jpqmMYKA4c1g8BhgJCgPjQOOvxQMgeZYRoZgh1QFBGHHUCCMOKcdVQFFCvtDJPMQk2QNpphYBECQGOMHbACAA6hdkga6RFrgTNWYWoBBMZKip0xSFjoASMh0hYGVAMEtZGEWIyMAooSByGQAHpIO4Kk1R6RAGOtFaYWQ6esIwhibFoAGHAA47ptKDMAY60UIQ5DKDUkUAEIpUUe0AoiYwBmCmNurIEUUIslthhahLGwhqGGRkS2gDGDEdIAQmqIxZZYDByEREOLkKOMyrbBofYoEdpBYqhFmgDkDEZUA42QIxxZY1mgOSLSGEgc1lRAh6AmGFloKZHUI1oBxBRHWBngIODIKOeA04wha7VFyqNIaW0x9IARgFirGDYaQggUxtBa5yAIGJXaCGA9AJUD2ihKnAXGAoGptc4ZJxhG2iDlELdGAOSs85kzzlmrKLMAaI00J8ACJx30gdQYayM5UxYjiA0lAEKkoaXaQk6UURwZ6D2MIEcwMNAw7CBCkhhiDaBEasUxYAHWWiPmECIaaIQgcEgTQ60FjLZCc9zigKrWMQ4gxAoBgiAGyGBNDLAYKKAJgpRjKVoaWgqJcpZghxARFlBsuUeU0MSznFChHWKGWmqwRsARhDTQEGnMsZSWesrDVBiIqCWGGAYItgRCDQzGljAqhUae4ogo6yAzxBJNEIKKEmQAIERRjq1xiBoOmTQQEE0t1cgBoBhFFmqINSXIAAeZZgZoRKxTDBKLoQMKE+ycs0T5FOu/iCBDkNaaYWAQBkBhAi3AkEmOiAFAI82sBRxrozziHEHOKe45AICjikNnCVLIMGMdxVorj0LMkDYKMQSc00QzgLWDlmpmoKNYKQuAJoi0jaEYEuIXeRv4xGq/KlrPQ9YSLS0EBgBeFQ5DDYBfFSIKsWi8qrBR6DAiUhgILCW8zA1wjmJe5CLwkTaelBIhCByRwkGnGPOq0hJsMQ6rQoUBMprIVlKKnKVKAWOUx4gS7is24bBWJ+nsP/tWy/mXmqL89RahtY57ez/+v4jTZ74izvUFLMLi679e/td/f3DndjPoWIiiyXy2sakp3b7++t6lK5HI0zv74/PnfFHSRaWSoOUeXWYoZGXY2bxxc+/chUgVyZ39ydkzxEjQSoasI4RNc9WL8qS3+eab++cuerLii6wYjjzRePNMx34bxcnBse4Gi87K5hvXDx8959s6fnCcr68mOhcVsj4tO53opGojD/kmOBZFN6JE4Nw64HRKXQW7zTIfRGQJhE+hZ5LJpExSwBheaoCgSjEooS/aYhSms5nwPO1zfyyq0CeeAblBEOajtLM48YSYDVb9SWsoLAdJ92gpGUGhA6XDyi5XO8G8CJpWDBifKcHZcthLZgtey8V6nzdNPC7rdV8pFi6K5UbHzyUWKvSq2vikdouNLllMkt2jxZULaV66GuZriVcIVremy3RW+LP5/MJFqlTvYDk5NUjzDFSwHXp0PgVFDVYGCvn+vNVD1KCgd5xPNztxWYASiiFntQQCBKwsaErnUo1o4aejO8f5RsK1QLlzAZCc0blCoS3DsH+QTdd7q/WBXATZauzrkleF9oOGx3yuYGCKKOzuZYu1NABNMpkuh0NoAM6NY1CEjM9VjPNJrz86PpwMV5izZKyrfsihRLlxHMqQ8alEvs3SpL+3XKzEPmjZRJVpAD3oCu01VbPepePKcIojRI6KNo5o4MBCAghcl5kSeGVRbw28ydIyUvQGyf5EMUJiZDOJtCvXB1G2ZFVZrY7QpIII1KNucDizBPdYuYQRr5vF6pq3yHEl5UYaHM4k85phJzyeAQhgh6nSerXQK4FuIMuqZqsbzkuroUuwVdDL5Xyjy9o2PcynZ4ZptgQlyNZTXktWNjYmAOg4W1bdboXD/mE23eomVQEy14z8oC2IbEUQtSAI5rUZ4Br7g8NsupluZHtC0bzXIbVFrbMxktiLJpUc4JoEvb3lfLsTtRXKQNPnTEpUmazfcRR27x5lW6PANfiwalY7zAmwkMDHOvLwtMW+q9JOfH9crveYadkiE50kllUtfciAjjw4aTAHdSdJj46KQZ8Bi48r0Y8pNWCpIIMmpnheQgLL3jC5d1yt9WQYpA9OROxRH7iFtIy2vSQ4mhNqy2E/uDcWg7hKO92TMbZgudoNF6VfNsVazOYKApOPOulxZgiCEQAVIK1erHeSYhoXRdHv0YVWmObDJBnnAAKXINzUvhRHKzvBIuelKtfjZJIJzG0Mo3JpnZVRqgQJi6YdcS1JmLX5WphMcwmZTRHNNTDOpFgr6i+b6alhbz62C6XWU1q3rjYgoQoQNi3hiNc0jB+MszNrUbm0c61WY0/WTrkAtQWNwUygIa9pkNw/yc6sJfUClLrtp7RuXGVAQiX1vOPMDXkVJIN79+bbW0FbmblGPerLphYMhkhynx0s4YpXenHn3nG+MwpEaWdKDSJAEVvIxGXT/iA+rrNBxJFgJ6rshQxLnBnHoIoonWtMdd6JVk8O5t0+RpCPZZn4lBmYG8to3fX602MI4by3ku4u657fJGFvfy5ChnzLysJCPO+upOOMIKMTwo9F2Q2bJOwezLVPXAhB7hCE+TBOjxeG0OVKp3O0ANAlrMh1TFu72Oj4WenlqliP0mmmIC2GSTwpgDWuQ/Vs6ee5PL2pNI3mVbaRptNMAWY6GB+PgVRuY6QVC5aNXKHSsHhRL1ej5OhASmvX+rSGQIPIK+aoF2ZtO6JS02RWLTeSZFFoQ3UH00JACXSXtNgbHizHm921/LCu4mrkc9GiytkIScrDaaN7qKJBb28x3+7GMveXeT7oIwFQaWwIBffCcR2E5Umysn54/2hty5cSLVzT85iVqLQuQIrDzmTadOLM763cn062uqGu8dw2HU6AxoVp0lAznB5mtoOyOB3eGS83OwwoPs0BhXWvGxzPCVSTzZ3VG7cW6+tVmm7eulmkKYgYnRUAwunGZjwZUyPzlbWVm7eylZWq21u9dbONY4/pzHgE2KI/HOzvaULbUW9w6/ZytFZ2OvH42FBKAooWta/EcmPVHy8AANVqf3Tz9mJlHQbYHy8MJqoXoaxlUpwQr/dTP+19Jcwi/ELZCGPbP/AP/euv//+3CKGUi2/+1vKZrwKEfiUc6YvMIkz95Gt/4Zey0WpwcPLMr/zGwelHh3fuP/3hX58P1pLF/J0f+s2W+1Dbd//8v2niZNxNjSicQ6tXbz31a7+Zpz1ftO/6pQ9JR/ZGq6RdaO1WX33rHf/ut6t+n4+Xz/zqb1RRZw6w9p2d5uGifc8v/pIIQliJp3/5w00ck6z56g//eku4w/jdP/9vUd2qNH7m5/5PB+x4/bHo7iknfEcbdueCW1I9aKJrj8KqI+Oa3zzNK25gg/bPgyqC7OQ9+Y1efnIvPRW8fgaVfZmU3u1TqO5gevSu/EFf5xPUxXceo1OvWhPR1VOgXFGjxbvH1zZlfmj7aP88zeMydcGtHTr2qw2RvrEF8rWsb5KbKyxLAMnI4Y5brixWQfdm1z/aXKzr4G7HO1g3fg6yNX9/vYkdngzD+wPbWeC9NXx0Jlsz3m/8Bv3d3222z0fyFN4/1cSOznvR3ZVqDeFf/WX6f//e8sl3JfMNsn9Geo2/9NDBOckF/MTv0F/9v+qL57z8gn94RnRyfJyyg0cELf2S4wePKk/iPPXunnLxHBz16NEFk2RaxfFbZywAFrngzUcVhVCi4PYjJrC2YsH98wLKuMnN/ae0hpG3/3XzNzXGJ/m2f+eMZdY2LH5wTgKYooOvW96BujmCp6M3zhpEjIX89qPMNh48+ZpsV1m9tCvBrcugsSJ2wbWzFnILnHfnEYiA0YjffcQq6P5f6t7s1/bkOg+rueo37PHss8987rnz1ANvd9/uZrdIUaRlIRAQwE4QIPkXggTIY4C8B3mwY5mQbEuKJVGgpJAyJWqwI1GyI0qyRbElkj3e6dx77pn3Pnv8TTVX5eHKgRGbNt/SXs+FAgqrFrCwvm99H7fi4UugweWKKf5NtHOERZx+SNUU0TzOvsfNWUR7bP4noT6nbB1Ov0fNBcQ9ONrcrdJOrGzxHagOHbqeVn8aqnMGt+m8O6y7PTatFx9xdY78arJ8D9ij0F5Tz9dvzrtDNJPN+7E5YzT11SdhecrDWlp/L+oDAK8nxZ+Z4iSha6D8BC0OKe3DdLaeHG41A00Pu/TkSrHmWqc5O76iMiWmFB9dkxnARTd/vFH2/MvVx6/LsyPR9acb7OiKFZJXgjy/bpi9Bg7eLJ6d8nY43EpOr5luCS86yeE1R5bX/ON7zaQAoCpupvu7uqfRWcYOr7l2BWbt/OiqoQYryh5fc1ghkyVPrtRdaCfV4q9JRBBFO/rX3BpA2mT2bewh8QBNv4MgAkHD2fdZCCCaBUyDmS6Bzc6/I6ynAIPZe8R7HPXECu9npXGt+Xe5rwNYZbM/gUZTkjfGSt8Ys+TT7yfRQt1Kim9HO3dwKyn+79C41Aha/WUAygVKF39NTI3Mbr76vTW0WC/WbP5gjS26lmt6tkvn/aod0qe79DyrN1X7k20436rXm3emH17Ti7ET4PQamG+U3ZgfbGdnrcW6/vzk+zdC/SHfbj1dw7NNx+vkdAVMtm3evN08vFGffpJuZg+28Oyy6S7J4RBfbAZR8/EqHm/ZVs0Ot8nJWrNRJY/W2GSvXKnB82r0QQYzaKZw8R6CG0w/D4sPOV2D9WGcfiBwEmARRt8XmAS12Tld3ctsVX/oxh9lfADUOZi/L1ASQRsf7twk1sQnevo9jga0OY2z7xPWDjLLTvdu4EqhM3323YQiHRQcfTeBXWImcPZ9ytpBztHkrxggEQY4+w4LDJK0Kz6+xL3RHvLnt73HgJrk0Uu4Aqrrsg+uRp954sT+VRC5ACc/XjzHQU3ggD96GRZErbrWDy7b0Oft889fPMidHNk1/uwWNLxqo9aHV1BB3eryS+MP28A99ZdaT3apJgDU7PlNrzplD/Y/usSWohza7Afb3q6ZxLafbETdli2X7W+L844fTMnjy2i2Uwxt69kKnax7XomTPrjYrrshO9xITlfKLcu+/HP4X31bf+YNPrmKR9ue18l4BUx2tajoV38Z/4s/qt96Mz+8zCZ7urskxwM63jNhTP7gm/D3/tBd2ePV6+xoLfZH4OkWubjqOgtyvsrPdi2v6LRHjrZ1WyajdXa8LVdrPG4lB9cNrluV8c/vOWKRFcnjK7ZtwSThB9dcq4nLvPX8uiZ2GI8+t3iqQJjLa9mjyyZ1cJnwZ9cYmWZh/BPLwyVEpdlKH9/w0QbA0gdXVII33aN3i6MihLLZ5U9veOghAuLhbR+CxzT7+IpssViz/MkuIMrYNN+/HhCKdPbj//CfZOPpYrj2xle/li6WkmWv/cbXxbIeb+z+rZ/9R62Do6N7r33hyz/fOr/Yv3EblRNrXDJVb/3mN1rnk9NL17745Z8dHJ48vfOSnY9MkrDT5dvf+O18NFVpdu9r32gfnT3fuRp9gRcLR7N3fv6Xh88OJlu7r3zj9zoHh6rTefW3frf39Oj82tV3/9EvrX/48f5bb7/zi7+6/ujJs72r4nt//anSwfr3Cex2a7v1Z9/+UWRB0X8IHLTm0qXiC18MSfop6Rn/IyqryDnnPdGKGWMhJFonVaFFwqVkqnGYJFWJnQsxEm0AjGpjLWQpryrDeLJY8qbRXNCqhDDq7Y3ICG8ajxByNivLBpOkKgEI1dZGTCjVyhmLtOaq8QgRa7KyaAgWVc2VMhgLY7DW3nvoPVe+plmIjKjQ8Bw7yKyRtGdoTl1wKHU0EdpJmjqUMqNZe40SQpVSfEXTHFlgUGawSJxOWgOe5MQBxbKIBLHGia6mOfVR0JSLjtDSsEzjHHimaNvDjCrpYSJZCwPqaG5pkkhvaWJpTpxzMKtYG0QSo2iyLpcGBiSTDgzCsdzzLjUWAq55DiKD3oZuh5YliFgmPeipxqlkPawMdjYmAksJApdZB3sUAdGshUwEBMVEIKOQgTXLocMRpHXWZxrAiKRoQQsCpA3rYRdYgJKnyEakQ5N2oqUwokb0Q2TUOk1THzB2dMF72GKuncw62EOOkegMGUJMa0OSEBHVYMHb2CKmqmT1knAOO9+wXsCC2qh5DiNKbJP2LqUEIRsrlpOAkHUNHzqUQIskb8WIiAYy7YCYYAulaCHIoA86EEMFrrXCXEEedChp4hykxkiSNziBLppAFEmQ89QqBALSrsHcIU61llAUOIERAhSZkch6jWiDE++wYomFnGiNvaVOR4Qk4A0VRGqDuYRJcKDCSQ0TqpWDrKIJMNFEqniKPLAk1SQn2oBAFc9BINanddamOoJAGrGCHfaIG9GD1nMQebZKnQJB1FkXGwgcaHgbecgZS1orIgYaQcMzaAHyvEjaTEcWPE26WQgB0Jr1kQfUxlp0oI3YooXoEA2iA5LnzPkYccN6ABBgwILl0ERayzJtARuQc3Oc6kiwsQ1NfYBE2SXNvCMkuGpjDXKKjCtIbiGJ2tck8RGhpjabQ8+JN3FJs2AAdq4mmYIMI+SHK5EiZGyT5s6T6GJJ0wA5NqZCSY048LCmmfOINarimUUCO+d4R7MWdtCjRPMW086SRJEXFdTyKKdKepRI1iIAC8KY6CVGOiwMbeFIJU01bZO6ypMW4V1ipYYtxTOqgyOJITlFgPJW0hlQ2UQgJM+xhQHmknexAh4yhTJkoyaZ4n2iNQ2o5hmy0HvYsAyY6CApaQv4CEwoaeItsAZXPAONQz7ULPM+ohiotwACHKBkAhgfDVzwDOrgGWdaAggtwHOURR+R9ZVoIW0BBFw3gZConWKCOu89WOI8RGR1qEQGjQ/SV2nLaegcLEgabUQBWJYBiLmOpWiBwJEFNc9wxESbhg8tzpCFiuY+kjRo0d4SnGELpcgh5MQ5J7qWpNQHnvQEYVybJsktSIGniucxctbUIslE3qcIad4OkCTS1aLjUYYAqVnL4JxpGUhmaRodl6ztIccOG5YFknOlAEo1z2FgHuVS5Ex5T1IjetATSXNNu6SRKPqYCCRlhIlOWsRCh7nFGbI+MhI5w6oBjtQsRxYFkEqe0cZAGADF2DqLhWJ9agyFVKYdbAIIvOI5lyEAoknCjNEwqWmPWoccW6YdaiC1QaZtYgGIpGF9GBCzoeZtaAE1cC46xEBuVdLfFtaCgF5UELJApm1iY+plunIlhQE42LCc+AgCbMRKAJRCgLJBAj22sMk70DHocMM7KBIUoRHdABl2uEl70ENqYpV1XBDUGhBjDIE3DfAegpg0leWcacmb2gJInaNaQWu89yR4M+i5VCTlwiKEjeZaO0KI1kTWsd/Vgz7VMngfQ0iLhSeUKEVB1FsbeqVH6xqH4CJI6xJ5DyMQdeUIIVIyrTyIyHlqNNHSRECVip9iVhIAADrXvPJq8YUv/igN1r83wYoRRNMu5QAAIABJREFUQjj9b/7b+rXX4afmnT9sgiWv3bj4yZ+qVlfPb9+a7uwWm9uHL78yunwZcP7x53+86PUvNjefvX5fr/YmW5dOXr7rWdZ98Nx2u6d3PxMp+egnf7IZdFWr9+hzn0OCtz58CpNkdufObG1rdOfG8zuvRkw++sIXYSJW3n/U9Nfl5tp8Z+/s5s2z3b3zjc3xjZuHd16KGH/8pZ+s1lZk1tl/913by4/3bo5v3VxsdyLyzVC6ngrAqe1KdpOAFq5byiFC2TlvjWfDRHMqVyvVY2c6+6R9SfdbgRaxtWjWkKcStNV4mJzEzhPcrdsUIlNv1K5DLKt9upwMs7FJTnjnbNCLDNjuoujjLnmoB3Pd7yihY1YshgDSZcyM2tSOkaZf1n0OWIXEbL5OYLJAxNaXTJVxyMpiw4DquYX7cY3VXRJEudiA/vJ1sPFy8c7LSBiX6GJDA2JiNterQF67Cy69vHztJc9LRPx8RyNunNBqw4TBjrp93965obsgssauNUUq8fSDxa02FM4mTq0rz51PZqHvyj72TOsNtexyHmW9vvBtDPBcD63sY8eU7zdl13bzw4s1xNO64azaquqs9axJj7NVOUw9k67flEMAYSh36kYkBzZ70t3WLR7xzKyoegUEpnBnfriyeWL5x+2t2AoBuWpT+g6i7IlfqaoujUy7fjNfJyS6ZnPpcwuQaTZqkALcIbSHwuUcCMq3CFvHCEVyhccOJWso3QhxRdA2oj0YNrifR1ZLvd7JsCWXSOwL2EJpPzY7vXS0oJULQ+EykawGu5VmSLEdTLvQVBiVxq3mSeZwl8E9zlqAbSOznmdIsU0EVhPYp1k7oB0aW5S0cdilCEjdqtUqMp0QWbHYwoEvMfHzbUvEgrVOp6soMA/ExK7B02R4oPi4O5DdgJCfX9JQGCdcs9bMQPJYtcYra00HBibtUBZ9iIEpLxkV8AO8cdjfDIkPfCZXoerJLHtW9lG1QhD0y90GCuu4kdvOiBjZtFyncZVR4MCNlPUQpJhepaGTZJlD69T1acoXZEjUbg+jiG8ksMXj48JnbTIQqIPZOorrCU0hXUdmaw08q0OnT1cpoZBcY6EjeO6TITRtHo41TFpqb8BQoJeQWc177ojsYLvS461AV3HYFIJasUnUpS6CkV8CqtfGaBqyxWKdE7qImS02neXEt5bLFdgh+3Z1qvptLTRIi8mQnrvWCcom/bZPsezUxWpksdArlVvlT0PvOUjPBhuYL1wayk2NqDeduun6M8UeoNVmdaBaBvJaDqXJS9EdTVcJZN60GzUMENeuvVBD3rRsZKrYikkSSYbJZYpTyLoBrAsw4DSNbIfENSFWQLiS4RSinNBLBHiApooj69dylCJ6iZr1nECPbqVJU4WZRwzADs06HmwIMGAsAXyPaszRuYI5Ql2KEsguMdgleAuTAYYrjCWA7hC90xfRstuCZgClmO5Sn3onNGovZ1ssQFhu1bFlAnTVlrRdQvmT2F+WPR6p8r3mdLBy5sQjNnBtCKBptmrbwpaWpt1MutnIiOO0f7Haxig2q7N6hXA3U1tK9vN93T3kvUkvJaQIXVWt+DQfVf1F0UuTMGvWpO1RmxufN+Waw6iWfV8OAQ2l6Zeg7csOCkmx2ICILkISim0H5LEix+VORmFlugu7Spsb9+DuXf3yJSlMFK7YUoha02rMOgyDPfnqZ/2NS6plAZd6TTZtAJipruI42Gte+qy+fTlS5fJZ7McZr+LyA3W9XXUQYKba0ZB53VZyDUSmfDqXQ1z2Ag6m3FOM1VVC683GpzGwmRwA1VNpdlD3QTn4mwqqSPrUtw/6myaFkU3NwDc9D4hB/emT7tUzjT7pbqPMOuqaDW1bKNK5X9VnvHMS2+e99elAcG/qraVPTdo6qtaczqmni6ofTU9SpPVavRxwCky1PnetdLy1N71+9fzmzXJ1fXL18tNX7wXKn7xx/2Lv8rLT2n/9fjMcTrd2L67fGO9cWnl6zCM8u/uyF8nT+2+e7V2OWfb0/puhn6Pzmi2b81fv6W53dO36yWdeCTx58s6PXeztDd97H0SmruyOd64ud3ee3b4zXt84vXXr7MatIJL9+28WO1tNb/Ds3mvl1vpo58pid+d8faPzZ98m8/mnd4IFAHROX7vR+vNvo6b5j5PU/78NFjK6uff6/O/+177TgZ8a7asfKjS6d3kgxNu/+hXdb/ePj1755u8uNjZ2Pv7o9rf+MGRpLevFvavpYrH55Olrv/F1287649H9b3zTDLqrDx7d/uM/ggB06+Wd3/od7jRF4d2v/BpJcWs6uf17/5dvZ4Nnz29/61sshNws73/ttzJVw5Te/5WvAoRnw57cbgdprnzv/Ze+9S1mDIvm1a99I18uYEruffXrGNiqd10cb/EqJnqCR9eTqWf5iOzfpSrtioc/Ue9vm/liQcL4OqtoAmfm8F42JTA7ZU9ukzoldEwP97hEwhb+5CYpWhwt8cleNiWxO04fXwNNG2Rlsn8ZLlqJm+GLTVbmmJ3+tD24pk+eW5Y9vQbqXmjJ9sFALHFbnoHJFiwGtrPsPevRyZrrl62nHTbv5+YELlfYdAP4Gf/j38h+/xtip8cWL4HZluk2vfEqNa8k/iwfwTjdg0SxZTs/HsDsLD9bJ/4Nly/bJxmZDDlYpHMERpdonDOzzpf3UGucH6VoskOTKf79f5b9y9/GEbXSW+Bsh8M5LTJ2upeTp+w0B/PLjIxwLfjRLtGahYY+u4lBk1YNO74MsbpkH/xUKJwa0wlWF/eEkgSouP8mBj6tSnq8x8KSFYiOdrlqElvqg7fSUlJa0md3aDRMKXqy19UjVEN9+joxMQklPryeVrLDnvwX/jBDdrpYIcd7iatIE+jpNpOewYocXBONV62yeC+C0rZcNX+C/MSmRE4fUHSm2kNdrPRjStPJcvY+CgufQzl/SN040DRWH0dwJDvravFd1MywWInVX5kw8m1QLZ4TcwF4CxYfenBs1vuz8XdZcUbpEJuPrBm5tqh9n8l2W5R18b3gT2NvvSn+0jcT0s5l+Rips9hJTVIM+dEQJ2fZSQdPNl27ap+26GSYxLNXmg/ux7IIpplfbR0NYWsijrr2/CWWXLBRh16scz/NlwCMrlCwwGU3HL+Cxah1wtFkl/ExHXX4xXqmxp3CL8/fEXHOGkaOr2A2uyff/xwoymbpLrbpxVZiFqJ25PSycBOsGD++CrImnM3KBzBVTaqK0ScZX5ZZak7fE9i6PJnEPsBGgUNX7DOmVAs1Jx+tZLXq88Xz73exc8Lr6Uck9xVp7ORhC+mYYzl6n4mF5B07/guKnBPAXnzSTp0mxs4fINT4hC7gNiGzESjQ8n3qHCbQV+/7rCmpNrNHJFaADuPKgy1QdmNr2Xq6ygsm7ARPN+iyjen5T/mD2+rouaXps6ugWgldmT7bRNPVnjmCkzVQDEGmk+NhNkpB+1g8umIWV0O7bB326bKVugkfd9B8lfNJPL1BLnZi/6T1aA0uVxNx+prdvyfnZdPEi0t4OmBkzM432XSDpPutJ0NQboRkRp7Nx/sig9IvwvRj2u5K8ExN91mfzutBL7YplVo8W54/SjKs/CJM38OrYu6OzOQg6SS1OQ7FPm75Cldq9APR8kWUYPwB67aacCTnT1mXLNx5mH1AU6CJVNMPadss8CZd9gepr+0jO3tMB6ww52bxGFOriLXTHxCBdZrkYn+rreakcvhijzYhASU6up4tVMb3f9o970E9Lvvk+HJiJdXWHb5EJeawxM+vpYUH6Sx5fD0GjpGmz27QKlCjyGiXKeKZae1vpYsA0wv2+BVtVjBV+cFaWtqhf/C5MO/r01O2kz3aSQsM0rPswU50LYRVfjRkDQJci+P17DxJxWN8cBWUQ99p2od9uuiI2UfJP/8q+Ys/jTtXkvJuNmqB9lHr6BqGrxJ6xs7X6Lwr4lxMWmiyRvGYVbd5cwu3nrcer4Jig7IRO1mhs25qR4naxM1dRi7oeI1dbLTSJ+ArvyL+8l+SwZpQd9ikl9gLPk/p2RrH53SyTi+2afqcnvf4aCNRF/2yUhcvCbekGtOja5gsXtbvfx7MpVzai016sZ3oJVPWHrye6DlxgB7dpGwhFhSf7/bts1i2zcnrzCthNT28ktgFdoEdXMd4wQrhTz6ThAtccna6xazawI//tj0n8mJSbiSHV4JwSYH56WauzpGk7OwS9SHxB+/+/C+lxcKn4uXf+mZeLYl1t//wD7oX49nKSnV1AJfTZKk+92tfTeYz3eu9/bWvrU5HCMBb3/qj3ulptbHx9q/+ysazZ/XVjTe/+pvds3PV79/7vd9pjS+41zf+6I8H+0/ne5e/8Mv/dHd//+zuzbd/6Svd4+PZ1mazllscBo8OXvqjPx4+elTtbLz261/b+OST0St3PvsrX+0cH02GQ/rpExr9DzCx0jSkWfvP/vT/5Yr9CBBhCK7TLb7wE2Z9HXoPPvURIQyYEOcDpg5iImXA1DHBGmkpj5jy0Tho4wjF2kSILKbIuUCYTRJWN5YLQymv60CYRxj64AFyCBOtI0KaJ0JKw7jhCakbR7klDAcfIAoRJrMFtt4mKZPSMOYJ40p5hCyhyBgIoCOIGgWAc4IxqTzEBlNqamKUxsRqGa2ylBJjiFOBwExVMXpHOdUVdiZgiIPF2jiCma6h1w5Hog30xlJGdU2NDgggp4hRjhNqDXHSIRpN6YyyIsWmFlZH4KlpkFKKUaQVN5UnhDhFtQooIGBp1RhKAAiiLiOG0QdYS8dE8DZRDQAWW8mXM8tIAJHKJqKAvGW69oQE71BZRhxiNLxpPAYwBiKlIxA6zZpFgMBDyLQKOEaI0WTmkjREwKQMCKJgqaw8QhYR3kgAo6OYVyWgMMBIjYTBWUKZUigGh2mYn+qIDCG0aiCMHkGhlzA4QxnWFiDkKBRKBug9o0LWFmOHMNMVDC4QRKUKEViKE1niqDwCQikPgEMkNkuvpIOIagmi9xhw2cRoPY5Ea+hdRIh6h7WxnBJrSXCQYm4MCMEj7BC1hCHrQPBUqcgp9YZ6AwlESkPnLBPCKmYNJJAFh7Q2nBNjudEQhsRopJVhHDqTWoVgYE5TrSPGlgqHKUCIeceMtJRjZ6FUkSDsLTM6YkCdYaZxlHjvqdQRehCNqGvPicU0NgtPCfKOqSYACKJjuvY4BOB5U3sMA4RUNpEgGB3XRUDQIiSaJiIAgRFNbSjyAHJVRQxBDFzVHkIbYSgnljCPo5DS4xgwJsoYwkAMTNcRxEAQb5qIkeMcV00k1COSGImjDwDxRYmMi5QwKQECEaGkqR2AjlCmGuZtRJApCWL0jDDVYOgBxULpGEPEWKgaOu8JIlqi4CMjXCkMvAMoWRTIOJ8kiZEkWEQg0yr44ARjTc2D9Zj8TQVhAL3FSjtOiTPUSIdp0LXTyiYJMk1idACO24rISnIKreG6isAJq4lpLOPAaqpkRB57xbTyDGNroNEORmoV06VHBHlLlHY4emt9PdOUI+ewM55A7C1RpWXMgyikjAQhBGjdeIJAjFzWAEIPIdc6cgqdk2kLOO8R4k0DEcTRcysDghYR1iiIISAwaZpAccCEKO0ZgxCkpokQBspwrTxjkRKua0AAgoApFQAEMRqWBIghAKxpHEKREtY0CAOIIdUaRBARIFrFEC3BTEoUtUcgkY2HwGIWm8Kr2iFMjQLBewyEKkG0HkeqNQzOUUp1jZ0JGBBdQ+8cx1w2yGuAPFM6eGcpY7oUTkXgmaqjs4oloZ57AEIMiVHINpZyYAwyJiJPjUTeeeiFlkg3hnFgVaJljI5axWTj0iS6AKTxBBFnma4d5cgoVFURB+gNU9ITiJ1FRjuCkFVMLh2mAQKuZaAAAEelthQDa6isAwLUaaIqR2gMACwrl2YROKakZThGgKyzlKFguC4dJhBY0TSWIIsQbWREMILAVR0gNBCFcmoxdQQkUvoX+1WqtoRFCLiqIojxxWdA2BKYygICEyFgWjmEAwRUNwDEgKNolgDDAINomhCdgzjWM+tDIJjJGgYfUKR17RFyDCd1BYN2hCLnoPOaC2gdDMEkKdEmIOIgzhYLrI1NMhwiCtFxAX0IPsg0o0pBgCym3FjvvGE8WkcDsIQR40CMSqSkkQFAyxj2McZoKYfOIW0cImw2x1XjmSBSBUwME8h5HIGlzENAnHecQxA//b0H9L5658eal1+Fzv6oW4TQmPLzXxj99/9jyPNPlXT7D9sirL/0JfLf/VcbTx6Wg14AoH06utjb85jsffTBwUsvo6pJxxeL7Z2eLZOLuRz0LKWd07Fa6y6z7qUf/OD5K59JXdV/8HRy/QaNRoznIaMmSdKzqVrrLVsrex9+8PzWnQTo3oP96bVrBLj0YiHzrEjbYjzxeWK7nb2PPji8dTsBuvf4+XxrMwUqzrXv5MterzuWTUZBEvLjuuy3sbBkYjBBuodFUeS6mayssWnUKQV5zE6aspPhNOKZRQiaHkEFFLpZrrfzkTIcxRYQI6MFAW1M5gZGPF9vtyZlovRis51d6EDhot/aGT9ThKpWF80s9/Fis9eZLLk0zbpIR8pwOl5f708naeMvNjpJ07TPq2I3Dwq3Z8V0ZyU5O8XFkm90lUuTxo93VpJGts+q8dVBd7FEVZxvdZLKskbZLgkKJEsz3e0zKXvn9cVet7MsQQmadSFqA2VwPeI8yeZGD5FUtvfo2fTVO21p8MLLdcGlBU2gqalxnk1kvZUWvL31+HSx02Ne4kXwGbKcJhcqtmGd8e3T58frO31V+DGpN3MCHJ1bm2KbcnGhQAuUWTo8bS6GqUAmOVHFsE2RpjMbU6wzxkda8Gba6/eP69l6zpFJzl3ZF5SYtJx7xsukl1wolIRFrz14vlwOU0ocH1nZZrbF2aTGRqntFXJaekFxB7OzWiactiBSFlln88RVIK2rZmeFnFeAoHqtlx9eBIZhn6G5QR4sN/vZZMHKur60Sk9LRGK1ttI+mVgEWy1XVoRZv9hdyydzWBm1229fXGgmqm6vPVoEEGKfx4UltfZbuS8Dq1S902vP6yi9XWFRgazQFzt9rnT/vD7f662UF6KszjZ2aBWSsrE9Gi3gS2uGRAExOCnGl3utqsKL2GwkrFawib6LXSSti1qtMwnF4LiY7LYHy4muhVpPmDKo8rZLIwork/PF6rBG+dpZPdpMMy3JLNTDlBnFCjddW40k9J+cFtuDBBt4UNitHkEOz40XRDHKposgmOl3O8/H5daKwJY8XzbDThdUdYFJSkxLkFENU1j3u9mTsdzqc2LxSa26KU0AmqjIkO2l+LymIlaDfv50rIbtUuT9kyOdJjBPedF4xmUvy48niINiuNJ+dGbX29P+cO3kFAU02+hksyqv5GKrLeYOgTAftDcnzy1Est2Dc5vYMN7qt+dlspT1ViIutMdksdbpjwoXg+8zUAbeuOlur7WoeeWWm2l3tNSQux4ScxtjdD0aqyhqqzYo1LZTLEfrm+1JbQF3PSQWOjjgVqiXqDWvj69tr01Ow9SonR6vG1jZ0BcmYjFp4rqQVHSOzxa7W2lVgrF0W12qdSisyH2NMzauwlbW4KT7bLy4upaqOo6U32oTa1FpQ5taTMnJAm5lDUk7T8+XV9YyXYOx8oMk9Y0JFAiiseDH87CZ1rzVenRaX1tLbANGygzyKFgyrhPczFd67RO5HKaU2OxEFYOcUJsVs0BpkfXF1GDql/12/6go+gnhTpxblVOQQTqzjrGqn3bOloT4+Wpn5biqe6zO0+HRtMkYyBGZGkjwYpD3zhYQR9Ong7OzRb8/z1fXjqcNx7FD0dxBgIq1vDcqHCUXw/766cQHn+a6bnhi/Hh7pbUo+dLOd1rdg2fWxeLqle7CQm9sl4IyiNqqdeolzgu92Mo7F4WJ1K4QvrTQBdejTuN8qeU6tpq2Z3K+02pPimCxHRBe2GCiaJllbbPTkbx7wznWmdTz7TxfNMEgu0J5ZZEJsi8MQKtHy4vd7mp5YZZcbqTUWlw426aRhv5kVK4MStJaO61Hm2lmFb1w1TBjTpPC+TaxlCZnDevqi3x19XBxsdPJnKKTUA84CY4UPuRICdY+qe0Al2lreDCbbrXSKFvzWdFqB5Swham7ueW4f7SwA7zMOxvPprPNFkIhP7sAGBTrw9bRiJA43tnb/OTBfGNjlvdXjg+bLKc5a09nEeOLza3hwTMM/ejS5a0PP15ubCxXBrsPH1R5xtvYziwB8Xxvb/3pU0tptTnc+OTBbLhRrA53nu43QpiVFhsvuFajnV0yLQBEbqW1/fjhfLgeuknraOQYV6sdPKuFNQei2/rZfyL2H39qtwj/3Ug/eH/7f/mfI0EAwP8URBiCXx3O/87fVTdv/SjsrU8DRGgvX+mR5N1/8OXl6np2ePraL//66eUb6x89vPubv110V1fm0zd+4VeRj5awd37mH5edlWQ8f+2Xf32+sdV9cHD3n/2uJkmi9Cu/9BvARUnEOz/zj3Wailnx8le/UfdWWs/P73zttyNJkDH3fvHXcK2qbv/dv/ezKsl4Ub79i7/i2nk2Xt79P78BLfSY3P/5r/BlXfd7b/3vv+AAHG++7o9e9bKNw1Kdfx5PuVutwMN3jFw3eRMe3Xd6FZuynrwZq24kjTl7l03zerUgD96yctNkRXz2irPr0UszesVVq4FW7vRtMlspNiv68Rta7qq+oY+uO7UGQmMmr4ZyTWXOHrzjxltqOBeP7zXqWtV1Yv+Ka7aomajZq355abHu08eX7OjmcsuKR1t6+RKJI1tfcZOXFQ9e3ibje7F74Y9v2sVnFitanO6q+cuILuG4o6b3dRJssUMPrjcrBTzcs7PP1L2aHw/1/F4kEpc9ef6aJ42vtuLzV2SvoMdb5uKez6dxcsWqL8FY4aalRm95oly15g/voXwETjfU4m3IRsoOwuGb0URLMHz8tqEYaByP7pskxHlWjr7kleuqxWzy01hGlVDw6C3PGFDAHd4P2IE6UedvQhOQU/Lki7hyJgfx0TuaJNFCd3xf+No0XE7eAQoFbN3R51GFZCuGj78oUSeE6I7eiIj4BtvRW1azQLR7/uNIJvOeOvs3oqhSAc3Bw14x4Szx+x+s1CeEXcJP/yC5GLXYBjr6blaUOaXhYL9fjnls0+fvt4vnmF0lJ3+ano7bcCsdfwfNp2lCzNHhYHqexR4//SAvDlh3Wz35i+54shKGbPKBuJi2U2JOP0nHB624nh1/P1nsM3YVn/0ZOxmvpD17/qB1dt4RbQDLG/j4tu6W4HjXTO5VK4qdrKvpZyIu4aRXjD9vqXdyizy7Xa0qerplLl5z+dSPtszstegLLNvy7E2PpW+G8OCe6lXsdFVP7rt8Ac6GevYGdsukRMXFFyKURq/Fg9dcrwHHq+X4i4BN4nxNjV+HXgLFzclnYaycXYnPXq8HWJ43Tz/sUg+x1R//YDOUPvbYkz9pa8gAAE//aoAIBDV8/MmqN5hH/cn3N+HCso7/6M/XdGQBoKff60EIfRX3H6zaGmIRH3x31RYIDNmTf9VqYhohePa9LvTIerL/8Yqrie/wZ3/eVQtKt/Dzb+dz3w6UHv115pqIIX78YCBLbvZa7Q9u6/Jyvar4wytG7sJY6NlLfrElM2eP3nTnl9TqLHnyqqxvFQObPLlk6j3uLvTsjl1crToAnNxlF1vl2ow8uKvr27Lb0MPLqryK4zJcXNaz6yEv3dlLcHy12FqKhzdVedelE3dyp5m/imMRlpftxXWXFuHsLji/LlfH4vFVWb+q2gtzqB9+MsQ0FEVy8F6brOHqGX76YEUM/HyfPvtohWIHKvjgwzVMQVnyg/e6aVdXh3j/yUbW0ouz5OnDlRR7U4ZH7w8x8pVODv4iRwNSH8KnD1bzVNVj9vjBKgYg6Pjk+0MOVCPpo3+9AjqkPCf7nwzy1FZjvP9wCF30ET15bxAZoVkfPH6FBucaJCfvxIYGos3xj+OCya4JH31RhVWPrD98PYAkqGhG971MA2ns4edxlVUrmn78pgSrjkb47KVgRPDRjO4H2WryiJ68GZct3a/4o/sV2NYc4Od3gU6xLJYXf8uVq8s+oY9fw1W/6hf449dk3LHMosM7Rq/q1KCTO+h8K/ZP/P7rpr5T9BQ/uqaraxgtw+KOa95oeiAeXyNnl6q1GXt4S1UvhXQcz2+o4hbEVZzvmvFNl5bx/FY8uyWHE/b4mipeCtk0nF42i1skzEGxYyZ3XVKF8Q1/fgd3DuLxPRO+BNmZu7hq53cJWPrlhj+747PCX1yLJ7f06hwdD/TkLWCKvHGLi5+EUZvQi0/fcG0Nxr3y7IuRLuJioEZvQKeBhvr4x5CXFrbj/psqD3DRMqO383hSlQNz8Q4w1gPsD98F3hqYwif3dRrAMjejNxFXbpma0We9Dg5i9exv+0gVEfDxG3U7QUvhzl6DvraubU7eCp4EfP6F//Uf0FlR9If3funXRC0t5Le//s3W+fRi69KX/v7PrD94dPDmG1/8375MRrP5cPv+P/1KejE1vHXrt/559/np6aVrP/ZzvzB48OTk1btv/swvZmfT6WDztV//urhYekxufuP3+w/2D19+492//w833v/k4Wff+fzf+9n2wXE1XPvM17852H/u2+2bv/MvBu8/PLl19+2f+z823nv/0Ts/9u6Xf7776Nnzq9fJB/8ZQIQvwnfaZDZJHj36YUDhvwsRwuall6u33/3RZeD//59sQeQQYbWyiDoqhDKGpYqwdDLTImtE2lqWXiQREaqsJdwywYrKAqxZkk1mMs1ryrPp3FBhmaCNBgBZKtiyNkw0WTubzhtEtcjSZWUxc5jRRgGALEu51JHQmol0MjdJJrOWmC0tRB4Tql0EyHICDQEhGoKQwhiEBjOoAw7AEBQDAA45CqClDhDDCVTEOa8pjzYiHQzBwALkkOFh8nu7AAAgAElEQVQxBIF8cJwghYB3hgrkfFRQYowAgoFa6qOlwEXJOdQxcVqKzEsHDdKcOEigJYAF5KgDxBACfCQWWoI99MRwL7DxBBliObSAEmUcYcR7IKHO0hAM0kkDISAEWWwxiBFjEwxLcTBMAyUSFzxUwpLoACSGRASBh1g7zVMQPZZIUxajhZIaBkzESGGPoYeMau8wAdGjBkmReuiIZi4GhwhSwQFsIQISeEgNQ0gxQKLBCEtsMXQIEgusRw4joqED2EOPFQ8wKEqxQgBFhShSHkRoCIIGOYA9BUgLS5DjDNUYxGAIQ8YBDwyC2JAQgxEIGIGDc5xiCWLwltJog9PIMhIshg44zlwTqHMqySBA2MZICQnBGeQ5DhY5E00isAXcGc1TYBzQ8YX2I/AkcGIUAg7YLDUaEWs1E8wHo4DNMhSiVdhRFAGJETlKo4HYBy1SBADR0SbCuegN8oKACKP0kggCI1NR8iRGh7RwOAYCqCWWIOswMsFjGmLAEhjKUbRQCSOwB4AY6hAMAUHtLWY2BNQgS6mhCEnhcHAIQUMsRQAAbIICGKBAFbAIB+iwFh57gwnRMGBkIyYmeogiAb6BBkDHGZQARh8whdqDCAFB2HqAiSHQ1RBCoPMs1BH44AnFNrhIAsNAB+uRF8RJBAGwjIXS0+A1S7AN0EVIMTLAQhIY8g10Ptg0ZcbTYA1PkbTBIUdJ1AAhYjByDQEAWEyR80BDSQiECDliWYiOQAsUo8DExGqVZE56aIDlRCMKLYvUI499pJYT6KCXTrOMeEstajiP3iPDDQMRMhKRITgaDG2wREDvsIKWMeQddMywCDzGBlqMoY3QRc0T7zyUxDDqEAESQAxBhMQ4zQSEENTBEg4hcIoETjTAsYmBIIcpMj4gCiGIlddpCjFwNdQIesKgigDFCDDUIUACEEAqOMYMgq6GiCLNk1D7CGBEBDjgCAMUxQY4TDwnvoIQRYNZrCyKwWIMLQoBexKQYhYjyxiSJDinCUfGARcNRshg4KMWCNgERWc5wxKA4A0V0HhvsSIYexIhNiRAw0HwkgskA/dO8sxLHz22DANPQECQBWyoRdhxDA10yhueMm9RwIoSYBDw0CTUO0KMfSHZGjRSWRKDA0YoDBDA0AGLAQwY2aB5BoNlBqpEeBehYZbEGDCyyGMELYDaKp4gb7EkihIQAjRcMWg9gQY6ikMg1DhDGfQW1rBJUggc0ExjAAAlJngEoYfYQo+woQgpHmh0CGGNLIYBImKCjiSgSBVwLypICY+8wZQoFBA0EWMdIsQOQSyRQTgSgJQwFHnKUAMDABYTpEMAyMJINHPRaUGBEhA5RymRIYLoMMU6mIAtgcQwj4AFHmsRojU8gS4SEyxPmXbRxTpvi2VlXWjyVqo0wNTwFBpLtK1FhrQjEdUiF4vSQVJ3B6SooAeGJcRGKE3dXUE2wEbXeSepZcS84QmtJYhQiyxCwo2TWQsrA32o8rYom+iiZQltFHBBixT7yIraEA7BfzYRRDr/L/+Oz36o3sK/hQhDsOvro//hf2pe/cynsMH6T3gR1rUIOgBoECfWOMZoUZhO54WvH4IxtVVJ24mXESLjYCs0ZdJBzjtKu2q+YF0EQtKUCou2LT2lS9Ht6GXNc+iCozSvFwqxwLlQdZO0EicjRBKLfjUpsx5eFrrb7erFknYIcKvV6KS9k9omIqiRiAi29bLBqSO0q+YFyHFwgtgl6/abCYBgyboRobYtGktsmvX0bMG6JLjUyyXvJLYBCBoDA6VtXzU4eXHPknVhDAET4EOiK8CIQgJA8ML+KWDcVfMF6WBrfSpAiMLJ1DUNSRVJXswyXxwDABBnUy8L2sLWeiGgdx7h7eLwIlv3AAWMAYBENjHhmS4L2gYIwhAiRC1TNLwFlXWCvTC/iwRlpipYRwSFjW1o1vJVzTIfUdcsFqL3wiAvV0VBWgI56F1D883yaJoNLSQds1yIHgwBwpjr6oU/IIBQetL2VZO0oAuWsr35/mHnMoSx3SyshpgCkybqhYcgzUAEHuOumhcgBZzlpnjhVwgQrHG6WR6XtIMao7IsA82CdRGMLzwNX3gRNoG1XdkkLeiio+TFF4II5KZc8q6wDYJRQxEQapull8EjKphdsg4OPvXNkncT26AYovQwBtluwxg9xj01X4ge8j4ixKtK6Ma2spplEPxNyjwme/OnY7yCrK1XVmCMIAIeNAxe0iwimM7ngZCE2IXoYecyXxesg7yn0GLva5a3zbKhWQzgRcpwXcc0yXVRsH+bMgBzVwUDkDI0ifNs8DfpMOWSdUTQ5P+h7k1jf9vO+r41rz3v3/yfhzPfe+7g6dpgYlASiJK2UakoFEhKg1SpldrQONRQRSVNKgUitUkTFAmIBQWBMTYGTDB27QTwbF/feb7n3Hum/zn/+Tfuec2rLw5xk9RUfWn2y62lrWetZ3+XHj3PWs9HyobEqW86GhmIe/JPXQYw2Dm/e8YmMMSe4BaHDwF5FuBcrVbBgJSVzeJMFAXrcS+xNR0OAYIeQqL1Q5U9/HNSWZYg4kBDAlscZarsaOQtsJQkzarz1EfhWnF43NsNTYeAb2gcdaXiEWyESeKkXTVhBiDMZFGwPLACQd86GslGxglwwGIMHhLQEMpkUeAUO2sDDpxLVC1pYDz2GD2kXvwpjtMDbgQGrvMUQOgpRdZYTPpi8f+4rGkC0eIQtizuSAQgeOiyvlgsgwHpOhuF0DngPfMaW9OwJNE1KVuDKA38KuxjaxJTF6yHrcXQEikalqauUZAqzB6qAzoHIUhlWbCceUWtbkicmMYaD5QLqH7oMktIKouC97hXYVMpjfugXGRjgYJvqswSPCjOhaU+YoCghsSpqgQNDCC5KloYBlUl0zQEXUF7salD1U6jSWpqZzwQJiBmmY2wNpaQTBYr3uNOEadrmqxXx1WQK8gsIT2xKl3goiAXq4Ll3EoMbEOTRFUGElq2hrOA6JJmHqNMFN8E2wFlEl3LIPqmO5C1HqO8WymJPARdngMPIt0EVlQ01YQBAHArHGeZKVe8j511CEHnAIAeQWSdwWR/eft+/8LDnQ16j6TCDFKjGhoDCJF1D3eGIhyQrothV7AetgbDP0VDJqayBkka5KZcBT3oXKbKIuhhYzByTIqapbFrNSCS8L3i7oN8HzqX6qrgPdJ2MKJMiYff0ZAqxHJVlEGOtXEEbxX37+f7zGsuuwaEMewkCzUgD8cg6xxGWbcqUUIoZEY8xEdKGihId4t7h/keWZW6n+diteI95jUz8t+OCbXyua/LsIe1sZQ8XGoKDNeiAQHAyFMCrctUUfIcOvdNdzArKTA1SRJVa8p9K00YEmstxg/HrIIetvYh7TGQrQpCi4iH8Jsq21wcHOc7WCsTRdBaD2FsGg2IIgFAkNaN5Sw19Srocy2Y1w2JAACR7ZzQXZTlpqxJ4iDKVbEMBhACfu/uty2L8FvnqIQYfOLjw9/6iPtWLUX/bYkQ4+Z937H4oR9GUn47Hmb/sxuNbhnzN3/y76hetnb3zg/8g/+lHg1NMQsjwe4eX7hz60f+p78HKQqb6kd++qdBFKw4TOYHWMvLTz/3w3//f+7iJK+XP/JTP42b5nxtNDi5AZvq2jde/E//0T9S/XR0dPKf/9SHXBLXznIzTe4djM8Wf+MnP0i8DavyB37m7wembdoO5ja9fTAulz/6t38iLwsfkR/40N+Lu3K1eW3y9c182hGyGH7t0vBg5fKzHz/63HV1cktMNr8xHp067k+TV6/0znVADn58/rX9g+cPku2tL272jphPztafHucnNIGv/VD95qXF23PDx89cm9ypu8167/PD9H6Ehwc/dPffvKs9PF7h3uuXB/eM7Iutr042brbdZPlj9z7zfnf+OtzceSYd3GUpOMjf2O49YM2k3fs6Hr3a7zaLzVfQ+NVN4l7hn/jo5Jf+hd3eGhaP730D4Pxk/GoyeG2t226Gv/jPJj//f/j1jVG5PXhlCwbz7CDZeZqZddn73/7h2j//p+17ntq+F49evYjJg/4RnLy0TeB59Il/Of65nzOP7K8drK29uOXz0+wuHr10gepb6R/99uhnfw7nYc+9c/3rPRYdDt7G/Rf3cHZCC7/zpb2omRGvNr+8jY28JJ/+G0dfscja03TwzLVInYzLk+z5d8fLqe2Xi9EW8WrtZj1+eovCNllWw29cTbrTvnjjv29fiY7eOrO7e19cD6RkXbX+zO6wOq0yVwxHVInhaTn5ypX+fOpCsfWFTd74Nf/cDx99OdNVXaRrz16NV3OCq82vXOqdr+pBaz85xXe7lJTVFxS9U/bDVbm2ZghNmqX95Mq+VuebWn+2gDebdNDVvZ4NOK8b9clV9MoZu8rdx070q11yEYg4Bgyn03n3BUHfKNka1p9eJi+eDdeK08mejsL4bA7+ZGFfFYNwqb/a2te6OBPduKc575VT+KmpeF72NpX70kq9pPJhOzoYbD4T2f5y8ioev7TWrTffffal/2L56lGro8P+6MUdROfZUbD7dIqDs6rPm7yfV9Phm3Ty8kVCHvRPzOi5XezO3mtf+aHjr0whid/Mxs9vw/S49wD1n7sU+bs+U2ejnahbrb+Bxs9tgHjev22Hz+7AZPrI4rn/qnhlVSzNamvtq+uRPQmXZOvrE5MKfnyy+lddX69iWcx/10SrRRwvkvtv8q7wx777dBuJKm6q+afkQBRhdRigVXLjDWST6vcaXonANd2nKl6LcPpGEHbhjTeBTZrfbfLjU7JG7MfmaCZSdhwvD/lyRqa6/pzLZjPfo+oj5+nhjG3q3o3nIVLwzKnP1uHZIkNd+YcimRZuN7j6x1HvdiLHy+2n4/5BFtI3/9rq5fesbh6x/vDpnc03RbdZ7n8p7t0eqLVzEUY2oKPpdPzyOD9IbF6uPdvfesM2k9WPvv2pvygOXrP99ReSwe0BQ/dHb/X6b2WEHi8nfRElvfJs+6vR4NYGyB4MX097t9Y5vvV9q2e+6/ylOabJyxcmrwZ6/XznGdp7Y1MNp+yN0+Zzqscqf1+4z6ziTKhR0ib5cHnqX6mLz6o+XYTnZfkZles52AhXg0lPLtjXpsXnZD6W/HYx+0M1okvcx8vBeqg7/tx58wdF0tPoTll/uh1FK50FTX8QNFVwe1l9sh67OVzJ6lNtDxRmPa7zQdot/c2q/APZ80siZPs7ZWqLiMebXx4PygWRbe+5R7JqFZN7/83Zl4dHrx/SCztf3IgLR8By/enNfNUYviqHI4Dg6Hy+/rWrw6OVGnYX/njElrzHX/+xoy9vVCeLMhw/fzU7r9TA7f+byeCgVGvLVW/NUdQ/bjafmwzOu9ic9l+4ks2NHVZ/++hzF09euUV2L3whDxapZ6vtp/PkHNi82nh+vP2aAaOTjacHvbdH3ehs8r//7OCjv4E2t8dHF3tv5TpdTl7qbb4M5XY1+dAHR7/xq/bdT03eHg/eXif4/vBWOrrZA+ys90/+1+GHP6y+66md55PRm5s+P+zfCPtvblL5Zv6RX+p/+MPowsbo8PrkpYT0745fTnqvb6PkbvirvzL5pX+Bev1Bc334Yobis+Gb2eiFHIxOewdw+MyVyN8fz4rshcepPH+U3PjRky/V1oC388mzOyia5Udq+MzVUN/fla/91+VLdn7cLPd3vjrGrIxP9MZze7m9s17d+G/1reb8gWzXNr60nbanRMPNr2xAJK5UX/7R829oo9Sit/n0lbA94VZufWUvqJcbwds/fvh5Q1F3P9/8xnbkT1mtN752KWynlJ7/zf/xp7bfeK1ZX/vP/tHPbty7PY9iSpvw3p2o0z/2wf9h943Xj9755H/5wb+78/LL968/0j++QZbT3snsB//xz27eunV+9drf+on/7tpzz9558vHo5guUg+zg5Pv/6T/ZfeN1lcff/49/bvv11492duPibn73bRREP/gP/uGjX/rCan/3r/7zn7/4/HOrfspJF915G/PgR3/yQ4994fO3P/D+H/yZn3nij//o9vXHgpdeJH9OSoQAAMe5y7LoxRdwU3/T5n+/ROi9GQ6X3/8DUCnw5+qBzjElEYRUCVYW3lpe15STbnuT6C6tyo4SvlxgKb3WbLnEDIu9TUxJUqy6IEjKVSw7hVAoOo+AvHIBZSkrVh4C1tTxbKqSJKkqqKS5uAcizkSHnCVGR2UBgGddRwjQmxuO+HC5MIzyqiBSIK2J7EgHPCUeBGGjHGfOx8RrAjkwjnSddYFFmDTeQ+As58US0iQkiCrpYegQJ9p7FBgSpA5gRwJKAuskptpnTAoFY0PCEABOKfY889Bi6kkctMYSLlFGpQhYYBXgxcra0GICO2AAtDbAxjuUOp5Q46HkmmHaOmQURIjNl9ZTiVOsjXZc0oQ0hs6nPk1oVSOLDaZYYIioZT0sDdbKJTEpCwBiSREVBHkiCIfCIWAhQaStAMxUGDOlkWaWIdp5LFrAKC1L76kmMbbWG6Z5xDpNQCA5w5Zh6y0KkUWB0yRIedfABjqOUAeJBoIwqBlAADkDvQWeWhoxpbFkjiNgwh5y2kZJSJgHisTeE6yghgw6hJ1FzjhCuDQmCKCPiLEORt7jGIIgHFIlWaklRk5TqpwggbEJNtYAZhFinfEAGAO9R8hZT1BcLGvNJGawU8JSDRAwAEIAICBNpxBpDGei0Sh0lOFWAAQgcNBDj7AEFNZKQNo6jqyFwGNnPcTaM4cRba3xQBsAjMPOIuCpUpVikjBUdxoyDSFonYPU04Qp7VygWEKFDq2EYJiIFRRIEUoUAogqFCPlAATYW+yc00xQRGsIUaAppw5zTGmYc+ChC0wQEgP8w/WUGEIPgQMIe08MiagHUFMbhGErg67wvp/algtlaIgUxgArEmMHXOMcJ1AhJKxmFLQWGaUv7/tegsvWIAKsQ50HAdUtsE7ZvS07yGgnBOTGQai8Qgw6SIFXGxOYRFRKzZjyIe1EZbn2iGFkttYdJ642EiPrOW2EZGGrCGsrub3u+zmsOuORdwAKoBkREmOrNYg0CZkFzjEDUdB0zGEGaNS2DjMNUi46QDJLAywdQAB56y3UAFkfIIk9jQBOieiiLMEsi6xCKNaUEkmtRcYzZCF2BgHnEXIwlYRR6a2lFoKw7qjHAY+5MR4wg1OqJPCxCTiVGhjYEWaFdw5UICBFg7xF3gGErACWY9BYbKHE1AOKoEfOYme1hpIHuBWqgyAkREOAIQIOAg8s0DRCHsDOaUphp6D3yBmPEa0awUIrHVRGQO6lgc5hbz0huDOGY9t5qrSmoQYcW2gABwCT2jkKvea8XsKgFyFAtXYw9IBSDS3mHgWecgQcAJ52WhCubEqVlDBxmMcIIBxwiEODNCUQxkEtNIssSonREHrkLTBYe+Yhga1XBDsdstUc4jChDLedQqmDJJDe0dBjTjqvEZcooVpKH0ka01qSYuXDgBY1ANgByhUChFvaI0IgY1wck7L0OFYYUUk8JBIGqNOQQIQAaUqPByoIAmW9DRxBpDEPDSN14yBXOKZGOxsqHrJakqb0UUirCnjoEaPKA8Q8jaFDQDAbQNQAbKEkHCuIZIcAZcADRw0Pw05AE9gAUhuk0FsQJhwijxSJoYNEYoEZMjACQuloGAXYABNEwIfYeosiYGFKEA0mvFnSGnQMeUWx8YKEVtKgrTDmxHtikKYUdQArqjjBPmZdg+raO8eq0muFuzaslnI8hL0srEpHMF8tqWyxlBgCqqWcDH0/T7vGEMLrKpxPvbNEdNhIu7etR32+XGJnsXe9+VwjFJQrJlq9s2m31vhyTouVBy4qlsA7pGU0O28HPTcZMimcd7RteNcxYx1GvGv9n7cgRG1tF//xXwff6uQ6/MnBwCNU/LX/6PTvfAjX1bdpkPhnswj13/qxyZ1bzbBnAcjny9VoogBYf/obxx/4QOJF/+7hyeXLkaqDRd31UqNNcvO2urpXDCZ7N24eXHskltXg3tHphQukbYK799G4pwb9cFaoQbLMhhfffPPu1WthV/dffGV27RqOw2RVqjgQYdg7OnUpm/Un61/+2vlT7wkC2Lt3vJyMM9CaCuqIVIN+b9o1EYOBSU9UkYUktGghgTdmlMHK98SqGMdsiUVEfAii+2dN2icZIqWFHqgcoxoESi6GaTJbKB6BBKVz04QERgCWjngwH2fx+SIx3XJtks6FoaAcpIPTUlMIEuhWmhq53N7KFkXUyXZIo4XpOF2OBvl8GTVquj3kbdc/K8utRNU+OD6rr1+Kq5ZIEwZNZyPa2unWgJZdePNO/b4n+ospbOFiM49KyVph+lQ1jh1Pqyev0q4dnNTne71eWcIGtpOQzFau0mgzsw6lhe7GuAN8fNaeb2fJaqVPK3thEmiDBAh4XaAsLmw3RiXPN++dL9cT7hSuvI2gRpjNCtAL6jRdO6rPNrL19sQsw9UkDnUrVYCZdQQGK+cTVyTxxnF9upYEToaHs3I45tzR0hkOVUSDhU5IOY/7pAKGAxza8MzVOSXM4cICDjuKw3kJE1b0+6P75Wo9ZkBEc1cl1IcYljoUbbPeozNhOSbcmspbjGkG0UISD9QwcLUP26Ybpa40EHox7vVOFpoAmFFYaqRtsTmOzpfIGZcHsLIA+Hqtnx0vHMU9Uk5tTrSVg4TVHWt1u5lmJwvBgm6Q0WUNvKcpdLUNhFGTwHSQlaLdzpNlDSQ0PeQVCGsz2+zjSoxnJyfbe8N2hSqw2OwFreKN0DkBnQOGMNZWUTY8rM738qyqcA2accCaxlXaDyPnSbzScoRaEq4d19PNaGN2NCWbKDDYe9I6k0LlabYQYowrEG8c3j3ZuZibiqxAPaDcGNy45ajnCBjfO1tuDiIg2GFdr/Wol/bWKRombn1EzxsUwyrPegeLYrNPrQlefqveXe9HTsrAMahjTmcdZL7u99NnX22uXqAhDs+bphdQDmGhMYFdRPHbJ7gXNjtb2b3zdj1XcTQ4OBMJRQFyt89NEulLO+mDc0xdMRn27s6aYVL3e4OzGbV+vtZLV3Vct4u1LFhUwINibTSYVhZ5nyJYeyrtfLOfzkrkPQoNaYDCtBinvbPKA+dybJYq1HK+u52uirC2q42oPy0kClziYAOAhTB1RuGkMc0YO0WjlSjW43w+lxrZQcRbC43TPWIFTCp7sj8cLc7BynQbWdB2oLG2x7RBoHNhZBqepIfLYn+U1CVcGTmKqJRSkx6pGhLjlfFj2tB4cH+22B3lxby2EQ0ARg7VxqVEIhadlnYj7Bwjq86nlGEHZtr3UWSl6JhLsQbEti6hoswG+b1puTuIdIdmqhuEgNNgqVJQTgf9/pmej0IGdfBgWg9GNPJkZQGFMiF8aRExbchoDXSASGCjma0TgrhHhQOMrNKod3ZOKFgNR6PjsuqxNk3GR0sRYhBC10CPkUxptmgxNDIjyZku+mGTpcODB4pQOIpQYQFExSgenJWGksW4NzxdeggSVtYmY8LMNvt8WtDTqXjiUv9spiFdTfL+tALOmh7RK8umc//IuhYoXYrlVtyflRowPaDoaOEMQFupFTCtdTMmRpO8UMuNODqaKunBVs5aiywIebUA/axU7QR3kka3D9p3XevNl1Zj2cO8tUh7mWOB+fpxfbqVbJSnokmrcchEgyrteqFGNJ1LNYQVjzePyuOtLBYNPS+a0TBAFtXexlBQlk1FEFdHyfbG4d2j9Z0eaOnCNz1KgMWV8wlqPc7nczXMqjhbO1jOt9PQdGzp25xCb/CyFcO+4XR4tDJ9tIrzjYPFbDOhwISzAmFYjgbJ2QJjN1vbHH/9mXJ/T4xGW4cPVlnm04DPa4DAbG1j+NprCMPZ9evbt28vh8NqONq69VaX5gxJeVIgCOZPPjk5uGcJqyf9jbdvzScb9Wg0evppF8X6yg6Z17GWs631+GzuIWrXR8NnnivG62BrmJ0tJSFqmOJFEzp7iPnw25tF+C0qbIQEd26v//w/47du+n+fLojfH4Z2ND794IcAxt++E/izSoQXL6VJ7zt+8cPl2kZ8Nn/nRz5+ePn64P7hez/16dnOhaRcvuNXPiKjxDvw1C//RtPvs6p99+//YbGzEx9N3/mxT5T5KFDinb/yERklgNPv/Ojv6l5C6+7xj32yHQz4efHkb36sTnoU2qc+9kkfxioKv+MXfkWGEVL2nb/58XowQKV86l99SsS5J+SpD/8aMk5m8bt/+aMO+tnG4/jtbSdDRBp855oXse43/I2roBvqnqC39mnDPJbuwUXQhiBo0d0nUJeKURu+dgG0A5m16PY+rGMXavzgMuwyF3bo1kVU95p1Gb2ybetxM7LJ7S1U9X0g/YMdVKVNBuNbW2ieNZsyfn3bVTvNyMW3RmCZQVq5411YjFbrKHs7h0ebxbZL76T+ZBeEK1dvBotLXebQbMTujXx/5R9swLO91bpLTlK+ekxFNZ8G7uSiShxYDMO7o3aig5MxW10vxzI4jOmDbZl5tuT+/gUTStxOwtMr3ViQ4wF4sGcGNTzLyYNdHSjWBGT2pI4VWqXkzo7PVnA29kcXfFpIk/E39w3GHvngxmVNoXOU3n3URg42DN/Z19hlohT33uMh1gGKX9l1BDtA6Z2LnmovGLh1wSFIvMB3nwKQGG7o65cMIg4RfPsi860AUfjWvqIUEg1vXPOO6tjEb1w0kBuC8d1HIALGYXbngnHIU4VvPAJdWPXV8hnQLhCJ0Px1LBcI9/H0aSinkOyx5VdAMWN0DU1fQN0MwQFfvgrlFLpROHvO62OALgbLr9jFCYd7SfuC7o487JHZTdqeIjeOixd9c98lO/7sWdocM72fdy+r1TFliV/dBPUxc1tJ8azR9wy6EpXfcLP7NFyDizdR+QCTMeXTMT5Y78aaHI7o0fZqYpPTBN6/Yvo6mBN7dEUmzhW96NZ6OzF4OuR3trpxB6d98mBPRppV1B9c0ZHGdbfS+kQAACAASURBVB7cvywHisx68GDfDGo4zfC9XRuoqAbu3qM6cbCOyK1d2VN40cMPLrikAFUGDx5zkSQdQveuWm58F9K3dpshUfN29qx3hFBg7j8TQQtBSmcvppZHAMLz54nHwCp4/jyyHnNmH7wxAoBFsXrw9VBb7Bk+e44g5L3Ds1t9jxiL3P2vUicQHNH5F4GwFHI8fTPDlBlMZs8BrbHNw/MvWVdDsB4unw9a3IMhWT5rrfSO0NMXkBFY7mbDl0Z2NakmJn57DJcDEHb+5CIuBm0Owrsb6LzXbIrw9Q23XK83QXxnRE9zHzbgZMOv1tsBZLfX6Fmv2ZLhW7totVuOVHQwdNOJiwU5Hrn5hslacrBNz0blrg7f3gBnG6pfoaM1PF23QUvO1vxi3/Ra+mCDnKw3G01wZ8OdbbVj6R60Ry9xlGCxQvMXMZrQ9gFYvgDYNm0OwewVDBMMKnP0UgAzqiq0ehYEA9ec05NXeTAE7Rk+fwnCBFuJ5t9AKCWiRtNnEBxSfQ5OXmFBzzYFK562NguB9MfPUkqd7fzx8wHqka6gy284OsSypNPnIWAYOHD4NPEBoUmf3txlRilP4N1LzhDAJXr7XdBwlav49UvWRpo6fOsidEhSGr21azz3TOIb16COu4GJXt6VNjcxCG9dwIpYCtHtfaDDOoXZG7uuibuJTl7aNLonMhK+tQ4FhUDY+1egTOoBjF+7SGTejmXw+q5TAxGD4NaalVmXufDuJj7N/XABbl2Ay61iYtPDEWku2aCkxyMw3+j6nhyss5Nes6XiO5ukuaazBTxcw+frOjX0tOfONnXSsPnFYL7TrDf89ro73bbDCh2N4emG4R0vxqi4onsdPpzgo3U/mMKjPX+64/ICLdb46pplNVr00PGOyTp0MkYPNtSkwecpvrdvmEorKR680zLrdcRuXdKpgVVCDi7auPJlCm/tW2ao8vjgXZY7pym9ua9iB9sU373A8UzIHrn3uGcWegBuXnPEe0/Cty6q0AGVoLuP+FA4EfI7FxTyCDj01jVLiUE0vHm5TQLUMXLnAiGN8im5uWcJAXT5/l/4Vb5YFaO1d3zk40HdNGn/XX/wGdbKs/0rf+EXfjE7Pjt84onv+oVfDk6m871L7/2930/Kos4Gj3/8d6Pp8uTC5e/+l7+c3743f/LaO37rD3rz5dn2/rs+/jvB2VylyWO/8/vZyezw2mPf/Wu/MTo5u/vu93zHL/9G7/ad5dbOo7//mezwtBkOn/j057LT+fnVq+/5Pz+y9tqNO+9973t+7aODG28/uHQleOH5b2sW4bdKYtleH3dt+MorD2GF/06J0PvVf/LX9drat1Xjq//fE/NMCC4lb+qgLGjb8bpKVivvXbKYx8sl8i6ez5noeFXRpomamkpJmzqdzYCxyWIetTX2LlnMWdfRquRNExUr2ja0qdPlAjmfLRdBUSKto/NzLjreNrxro9WCdSKoq2wxh9amqyUvV0ipuFixtuVlyZqGtdaRDLmAtlaSjHSAKWHxwOGMCu1M4EHAa21QAmyEBbQkCRSiQlg40CClygMfexKx1loUexcRiTTNkSRESY+HluRYI+9iB0NaW0dSj3rIUoVybAImOghSQ3PkqfOZR3FYKQACC3tYGQ8SwwfIYqdjy9KgNcQQQ3vIhg5GHqRMSGKZRjl2xNtMszRsLdXQ4Bxq5nFs8ZAoAyXRMMEKYxuKcMQ6jw3SpI8VBo4p2EfGUYUMzbGCSAWS91lriYEGJkwA4LjGY6osbZ3FGZKAd1aHAyIJskiiIbQsFBrCEFlCBJZ8QFvAWq1ZD3aIGKD5GgScddKCCGpEO2iDPlGEdlaRlDaWGmvoGKKIdNbClGjIKyN4D0uGFTQsDyTAykg0cj6kEjicYodY5yXrYR1gjTXNkUDAOq2Ig5TVnXRMaOobLUnoFaRK1i7qXICkkZopwHDRSRgqH/jOKRBIiVnXCRdKHHkFWk01ZLzqjKcCxsCg1nGtGO9ap0gDQqhAZ0ODOK+lc6QxzEugUdi5iEqpOiRg6IXTlncwxJ31OPW4T5WhGmvUQ4ZAl6iozxoLNTE4x5ICFBo8QsYTgRTtYY2Q5IL3aQuwQYbmVEDvmEYDrC2XwJEUK0Qkk3zAG4el1yQnnYeeazIh1rPWaJQQ4ZjANshph5ACGiasVcgTTSfYIiRcQzPUOtxIiQJbaaJ142NrMW6EtBQoRyrVksQr5FstPMe1wlLWLlGWoNYowKGBqBQNCFQLXGskiaBwSJvKxdpS0NnOcys9qXVLU6uJk17DUAtEhOhcqADXAjY+BAbQWkgcSxcSpSwcWNLDBnqbWBTT2ngUOtLDmmqUYRsw0QGQGNpHhjqfWppEtYIgcDCHmlqSI5RTIYihGmVYEwhSy3JWWw8C51OsscOZgQOiFZZUs5woBG2kcE5bBxDzMCbSGZAaNKRSUIUtzZD0oLMtjn1rrUGVjb12SFpBIi+saUFNUlwprJ2AIWyN07D0CVIW10qAwHfGC9jQDNcadVqAANQCWNiAFBrnaqVggBqJa1Pz3LfeCSMBJ63yylcucRqA1gqaUKF9qWqSuhY46RQMoXLQAOsTZHFQaUVzpDmR0NEkkIBIJeHI+IhK4FAKPeWtkyxHOsQKadpDHSLKODryOEUKO5gCx2htDe95nyJDFc6pplRKh3JLcmSZwT3keFgrRxLnUmiIpT3kY9Z1yEYapkgTR3vAB1QhjVLgEi467EKNMuyI85kjcdhY6KkBOdLEkcyhAZESSKZRgjVGIFa8zxoLALcoJwo4H0rQJ0oRRSzLsQTAhJrkvDXQE+cjKp2DicFjJiTpoCEZEgAprkkStBZa7F1AOwVgbOiYKk0klXzAGkuE1STHnceeGDrGFj5UEBaeCWTCPhUYSa9RzBuNHTJ0gj3Frdckp8qxxknew4Iigz1JA+GghhINoaNEQMtyojwTQPABVhwYanBOOk8McHQIAEeaGppDAWnrZDSGNqZSBGXB2jasSta2tG2z+QwaE1VVWBZEqWi5ZF0X1jU1JiwKWlVcdNlyQawNmzrsOty2cVWwto2KFRIiqmsmOi5ltlwQY8LFnLctVTKsSiZFtFoQKeJixYWgbZPOZsiYaLngbcOahlclE11SVcRo1jbf5izCb50Dwrh673d2j17/D1pc4acuXjr7ib8Lv72hzn82i/DK+V/5q+Xa2vHj1xd7+6vt3YN3vvP80hXH+Wt/+XvbyVCkgxt/6S91g9507+LZY49O9/aLzZ2zxx65/9iTjrHXvu+vNOOByIY3vud72mF/tnNx9uiV84uXFuu75489cueJdwLKXvvL39tuTkTav/E939OMB9Odi6fXr51evlpPNk6fePTuY++AjL/yvd8n1oZdNnjre77bDNPDC9fPHn1kvtuHSDYTqUcaAVVvt6IfQrxQ/bodY8M7ElerLaBCKsaNHigMRLnVqUHgycrkZTeBNux80q22gKVejms9VBh21Vpje1TzyiXlaoMSunKpKHeAIUD1FtUEBbYQa7XqMxU0JqrLdY/w0qeq3jaW4W5Q1MMAsBIFi8UGxeECUN3syjphiBbFuoBE2Gzl+rbKoQvK1SZybEmoWuxZGCodynJTASJdMpcjpDINebncxi6sIFarPYGYsKHsNoTlxiYLNYJtbhGvxEQ0fY+QXu0ryJWNVL2hHNcumtuhXY0IZJ1ca1fDKHBVs12ZGHi6VBPdDLBjjR525dgToJb7lgaV5KTarEyGPZmroWxGBNBWj8VqHROvl7vCZwZAVe1JlRJA5mIo24EDtEP94nw7RshWO41LFECy2pY6o54u9EB0Iwdoq4fdcoNir6rtxqcaINGsNzABtId4D+iLqQ8J2yJkk3Li0KXQ94MgccEY+O2IZZDn3l1KAMfhOtC7SYIlu8DsMCQZCAdO7aZppGiO3JXMcxpMHrIIO75PSA/rcRL2rNxL41CSPvGXIkI93yRqK4mw4FvQj0M8IFFu4X4AM0Iy5PcZAI1KSzlCKtUgKFZbyIcrSHSxr2GoDJfFtnbMwPBcDb1INOaVWJPNwGMkVxcU5NIGqt0UjhsbLeQENblHrBFrouoDgtRyXzHWdDGoN6SNrA9m3Ri1fY9I022oYkQIkMt9gUJjAlluAxdaEMxWa9Su0RBqfy2mQ8ywR49ENudhoskmMRtxEEO6ReRuzpADj4S8BwgB6FpEUox6hK0jtxnxyNM1LPdzAgG7xukAMwrg5RD0eJAatobBVkhDz9aR3M8ocMFFrNaSPmrQ5dANApY6PEZgO4y45htY7PUo8mwfyH5G8NTHxXKDY7YEsSj3vGLQZMty4hmo5LhSA6bD1oZlsQ4QWfpY1dvacCzyqpw46ks5WKkh1bHwYbncgoAUJtL1tkZEdf1WjA3EreotdJ+IWKCg6jZUl3oXqmpXIaxV3og1D0hj8oWYBG2iMK9X2zAMLE0RvRzACEUDCzcCMOZh4ugOcesxw84/npLIswTDSwGMUdwzdB3L9ZRFgO4RtZkG2PjrCU0hi6G/HMOMRD3tNwK/FkahRxcDsZEF2MDrEY88iRG+GKCMsAFAmxys8SBweI+I7ZwDS68HJIUshngvcKF2QYfyYrYdAOyrrdplCgJZ7gidM08Xut92E+BZZ/rtahMjYJqt2mYaYlGvtybDhpWq19YTQHBhh2K1hQG07XjRjGhoF+1mq3rU8FplbTUyGJZ6KJoN5zFs1lbVgHAzaye1GnITNibryjWLYN0NTTmB1BZqXIHcljkCYbHcgJAsfaTLHQOplr2mXAMEVKq/0gMiUwWDsttQIjaeq2pLYix13oo164kw+UKOaJcZzKt2XYoMeK6qHQWJVlnbrnmPG5st3NAvBwSzptvo6hxjJlY7ClMts65ZR553Ll60E1L3HIFyedEwVosI1ZudiTzgczH27QAiUssNuRoT6tViX8DYOKbKHWNj7/m8G9su15h2YLQ83e4xL4q9GoTKUVlvaxMDz+dyZMRAQyLkWruYcOpEtVuBWDvaNutKp8CxZTP2qt9R2MgtsRwz4rp6Y2myaLp76fzaldNHHy0mm/NLF26/6ynLgjvf+f6TS5dBFL/1ge9u1obnOxfPLl168PgTYjicX750+91PWR7eet93nF64aMPg9nd9QI/Tw0uPzy5evPfEk6KXn129dviOJy0L3v4LHzjZ2zdJevup9zabk7P9q8v9vfvvemc5nJxeuXb4jscsD2+//wPF7mY1nNx773vL7c3T/SvL/Qsna+v5V778bc4i/NZJrOGQlMV/EKXgyz/xQXH12rc5GOf/g0U4jKL3/tZH1SDPDw8f++xnV+sbuzdvXPnCF2wUDc8eXPujLxAtiVPv+sQnTZ5kZ2ePffazspcO79y7+sUvAuB7TXH1M/+ayQ5j+K6P/w7gKFnMr/3rPzF5PDx4cPWLXyTOhW1x/dOfY6KBAXvPRz/mAxatVo/+X59zAemfnF37/OeZUszpx//g03G18hF94rd/nyDb9i6z4y3egtAs0fklNrM8PiP3rhMRMHxG7+9FwgRi5eYXWEu4L9D5xXRmQXzKDq4jEVN2zh7sUIG5qsh0k1SUgZKe7QczDPLz+M5F0GUwLqN7E95gbko83+BV6AMVHG2EM4zS4/TOjhdDl3TJwSisSSxO4XwdVwOV14O7fTwfm36VPujTZT/Sh7ga0PkEUE2LPDnKI36Pnq6D1ZbptdlRRhbD0E/DcwSLC4BKUiTJ0Qgm5+GDFBebKqn7JxFeTJhbRgVGs10CVqSK2ckOik/jwxgvtzGbslmfzNeYXqQN8Of7xK9oE5OzCxG+y04TWOwzcg67MDjaIlJTIMiDawQ2YdXQk31EGlpSOtsmXTFuZt3iHbwTHLXk/jUKuqBpyMk+RQ0vIZnuctUFukYnV3lZc9rgg0coUFwIcrqfq3NfIzy7QIVmviVHl6OqgayiB9eRt0wJerIb2JY2lpzvEKE5aMjRZV4ZmdXF8w61LrH16i61KxehdnmLw2ORTcT8BSpWME/q4g3sCxv5trpP/dziEFQ3vH/QZeuqfAHVCxwMYfua8QudurI+YnIKSALrmx4cq7XBYvoKrRcs6EPxprZzn7uiOfDNOaUZqG84f2yz9a56zjRLlsZdcwd15yALVViNo+MRDk/5cYYWO6bfJocxnY+5X8ZzDxZ7CAvWRNHh2Kfz+Cgmyy0SzMhZThdr3CyjCsLpHgUVbUNycgHz0/SYwdUeoed0ltP5BpfTfiXl/Dp3S9ZxcnyBBWf8iOLlHqNnbJaQ+RbXJe8sPt4L9JQqTo4vgrgBJ4vyFopkG4jq9O2E1k0S6tMXAgpMqNr5myTwHS3k6i7jQoS2nd6MwmWR8fLgtT6xhhmxuEES26JKru4yJHQEu9kbASu6MFOzF5g3NnBidZMGsiNSr25T2mnPUfGyI4s27uniBaQsodg1bzjW1IFWq9vU156MweitddD0QFbEd0es4YFe4sU6WyU+UMHJWnhOUXKS3t3y7djmbXJvEFRBIk/QYoLKkQ9lcDzJZ4FPjoI7W7CduKRO7/dolYZ2HkwzUIw5W5CzDb4YuP5J9tYYdhNCZ+RkTIs8NHO+zNBiTOiMTjfxfB1Hd7PbQ99u2WCB7q+W98IAtrZ0yxssy4W7J1cHLI47cQSqAxzZKiy789th4Ftf+9kNPgqX5lAv7sdZ1JhjVx7QzNa4aqdvh5GvQeunr7M0Ee5ILu7xnK78mVsdsMi0VKjFDZarJRTm+GaWREKfmeIu6eFST+3qLmNWMi0Xb1AGZBgmwb3NVC1h69B8nwrHfI1PriRFB4IlO7iOLGSuose73EjWaTLdJo3jsCbHl4OVgckyunPJO46RCO+vBUJQpcnZFu+IC3V8d5sXFkfT5Pa+8QkiOn4wiToZNQswv0CawCY6ub2ZVhCEZ8HtPW8zBEV8OKYCASbC4/VkGgThHXKwD5qJy7vsQZ+WaWTO+TSD1ZoPBT8ZxtMM5CfxrTFqJpjP+OkEFz3uFuEiRYsJJTM8XeezCcwOk9sjVK8TOqXTMVkMQj0NyxjM1imesfk6nm+m7AZ5sA7rLUbO+HmfLEehmtIiQdN1hs/JfI3MNml8xM5yNt+kYjasazF/nJuSKUQOLzE252cQL/YZnrJ5iOfbXFdcKnx8MRALZiE+vEzpii8Jmu4N9T1dpnS+R3XLjKFHF3i3ws6Tw6uYVqzAZLod+TkuKJtuU9lxq8nRBSZrAiy/f8EFhpWYn26Eakokoydb3PrAHXzHr/9WUK5sGDz2mc/F1YoYc/XzX+jN502ev+d3PzE4uLe4eum7fv0jwWql8t47/vBT6fwcA3jli1/qnxxXk8l7P/Hbaw/uVxc2n/jY7yWzuczzJz/zmWS14kZc/uKX+geHy52d9/3Wb27cu3v66LX3/fpH8um5SaJrf/KF/OSEYn/5q18b3b5dba2943d+b+PWrbPHr73vNz/WOzmZjkbstVf+HN0i/HejLDMchm/dpGdn3zQe1e//89RZ9P/FIgTImGS5fHitK5nPgfPA+/75GXLWe5CeniLroHXJYo60Rs5lsxl2DgCYn5167x3C2ekpVhpal83nxBjoQTqbYW2999nhAwAhgDCfnmNtHIDhYoGk8hCl8xnW2hKSn50iYzxC2dkZUgYAmK5WtBMWI9JYqJ3DiJcGa28wYY3ClfIQ0M4Q4TxGrNFUaggdKyyy0GIaVC1rlIOACEdbC5DntWKdhNCQ2mFpHCas6lhnAHBUGNo5gxBpHRHGe8MbQzttKCfCscZCp1inWGsARryzvFMOYyhM0FrgLVImrJTDCFnAagutotIGrbKYUGWC2kJvsFS88Q4CZD2vJXCWakcbbRFGFrDSQGCxVmGhgHfQeN5o4D2RNqoFAAg4RGsPEYDahKX00AFPWO2gdVjZsOoAhNBCXmiAIAAgKAzyHjgXlAJr4xFhtUfGYW+ClYLQA4h4oZHx3jlWCmS8A4hVBhroIaClAc55hIKVhhY4iIJKIGGAB6zS0ACAACs0sQYCx1YGeeghCsuWCA2AZ41D2gPoglJRpSGwrLRYWw8BaQRstceIVB0REmEIC0k6ZQkJ6pbUAgKAWo1q4SHElcCthNCjWhKpLaGglqxRADjSSVILRwhsNW4EBpbUkrfSUMo7RSqJocWNgLV0GOFWkVYA6GmjYNlZTLDUqJQQetJJUnbAG9Jp2hiLKdaO1xo4g63hlTMQQguCSkNnsbK8ksADaDytAQAOGx0WGkAHPGK1BcAjZcOyBR5CgNjKAO+JNUGhAALQQVYb6D2UhlfCewQd5KX2AAHg6coC6KGHvDIeIGg9LwU0DlgDZi0E3iNEli00wAFEqg4q6wEilQTaAQTQooNCQQxJIYD1AOOgarDU0Dv88A0CbF4RqSCCYCWxUB5hXjZEKAQArSUy1ntHVg1SGkKPC4G0sYTAWmFpkHe07pAyHmNcCiyEQ5hVHWsN8I5IwxtjEETCU2EgMOyhghgjwrLaQKeZNKwxHiMmLO8U9Jq1BrfaMEaF4rUB3hJtaOMM/L+pe9OgXZOzPKz3Z3+ed//W853vzBnNjGbTLjYnFmFxAcYyhhhciXEqv5MqKkkVFsYOGEIFi8RgkzK4bMc2QQaDWCwhCBhJoJE0oxlpNMs5c/bt29/12Xvvzo/BdqpS8W9P/+27qrvqqrv7qvvquy9EpGPcAG8pd6yRHiKkLG09AJZIFbbaI4A0osID6ElvwqZ3CGMHWakBBMA6UnHoPJI6qDvgATAebiSECFlL1x0E3gMENxxYi7QN6w4A6C3AZQ8ghFqDRQ8R8A7iDfceAeNI1XsHgAek7L1HAAK8aJGzwANUcuchtD6oO2QtshavOcAIAMA2LbYaeghLiYwBHtDOIA0AAkGtqdEQOlYa5JCDKKx7yrUHgHaWSAegC2pDjYHQstpiZRzEQc2ptMDboNNYeYNQ0DqkDHA2aA2VxlBGuKW9RVYFnSTSAYzCVlGlgdNhZ5FwhlLWK8oNAIZxQ7gBXjNuQq4soYHUQWehV0Royr1FkEjLOuWdZdKRThtCiPGs98BbInXQaA8clo5x44FnvY5a7hBGFpLWAQSw1GGrPALIINY54B0VOmq5xRhpENTaIwilZo0DGCDlaWctwlga1kgHEXYmePvU8jBoNHQeaBs0EjgIPAxq7QDy0NPSAOAAgKwxwCPgQNAIqC20/s+yDPpwI7Gz0DtaOwgR8D6qOVIGehDUDjoAgY1Kha2B3ga1RdYD64NaE+Ogs2GtgAcGwKi2wBiPUFKWYdN6hJLNhva9gyhfLYkUltB0sQyEtJiE5SZuG4dgUteh4JbSdL1iXecxSZfLkHNDadK0cVV6hOOqYk1rMEk2G8q5QzBv6qiuHMFh10VlaTGOyipsaotwul4HQjiMk00ZN7XFhHV9vN44St+hbARao3f32m/8Jpuk//45O/zez38R+P/U+yL/I12E6m/89cndO/248Jikpxeb7V3p8NZn//j827+zIF1x/2Rx9WogO7ao5GSglQ/fvGWePOy3ZvtvvHH03HOpbot7x8vDQwK0FT7C2iNEF7UepWU2vvzmG0dPP4t7nr/wUvW+99KMJau1zmKRpvnRqcvDRbo1+7efXXzLt6SpL+4fl3u7ualV5Vwa1ONxdtbzLESRCc91V4Q0MGBuPPJgykADc1m34witoI4JSEB40jdxHBQWVh45b0YYtihQsp3E0uYQudiX4VzzPESBBjVEHlSzLD5vYl7XB9NwpS0F3SguzhpHEMi9nTtsdXM4TdZtzKUaI7Z2grFyOsjWTdirzU7Oepkver7LlKDB2bp9cjeuOiJtHLa9jUnv13sF6V1y66h5/2P5aul7XO+kYasp17aAsAdQ4W7CsDbFRb8+SPOmhT0WY4JL4TsPZ9RCFq2FmcAeRMPjanV1nFWVmSt7OAy7znMQR11Nh8FGqylqwmL6cN1uhcxIUAOfIgUxW0k0QH0aDx+Wy0vj3f6E13k3CahTbmlhhm1C6drCxLZpMn5YrfYHBd8YnunQkcDgjfMhUDGlpctxeZ5vDU/L5XSYkY7OQT8JMVSo8pA6kUTsvMMpaMbF4EHVbKcB5Gzh+2EEmAO1plLKnQKdty5kYWBUCSD0aELdRkNj4SQ0LQhEr0exqRyCXsyK5Hhp4pBmwJQWW9ftjNiiZlq5UehqC6CXs0F0tvEYjVi95plzQG/ltO5wr81uGp1uJAv1ICZrjrQmY6IlpmXrdxPdQ9IKtZvFJXca+xx4BWljyr0Ct2Z4Mp8/sT9qVqhB5XbKuGGd9AUSnU9qZbcZx8HwuFldGeZtA1skR4hw5xSFiTaIRSthxoDjeHhUrg+HW+uzuo3FVhJrCXtgc6gQi5bKTkEN0q3T1XxvXJgGlEiOCVMat66aFo7AwZ3T5vIs9twfd3JniLUUt1dkHOO9AVp0NAZ9kaf3F+3eGFrnX75jnrg0jfqeMxRAmwVgqWjkumLQ95iGIHcNOBV6khBi3MYgCmQcmetnNA/04zvpo4Wc5SpN0nunepRD7PjtNYwD/+RucrKg1HXTYfhwrcZJPxgMztfY+mo7izdd1Kt6O6FzAb1vdwb5ovEIgByABmLtyq0sXPZZ18jtmFVGYdqMk2zZewBA4UEFoUPtJAx7ETS23Q6KZSshdTmgS62Mx9vUKhpXQk6xVjSuZLOXZmdr6UM/wazSwDg3IsYE0YYvLo9Gm4Utnd1OSMct92jIfG8Nh0mm66hI7l00T+6mXW3Xxs0izJUSeMiaLsjASqEZ7UiS37uoH99Om43oKMowhhbUBhZEkoCe1Wg7UBr52qEMMers0qAxCWXf9xQNiLXINj6NxXowze6c8itbkerd2phJDDBkpc1AuRqN0pO+nqUMa3os20EUJhaWHmBvCkJKQLAq86J4eFFvTWPWs7nrhyEhGlTAMdLnYbxRoevqYZJeqH5ARRoNTyodYZh4d2EAu7OrzgAAIABJREFUhf3OIJ83BGqToWBh2yLq8yR/sDCM4QlyDUQetJM4n7eW4GqaDi5aAFzOqtrkVNr17iBcc3qxEU/v5OcbBWg7TbI199aBATANpsvSXy00R3Gtmu0w33TaM5AY0FPvEUy1sUG84XKGtGbJqq8vF+npShoGtoOgFl6DJG43eBqvudwmQoXho4V891a6qrRldghZY6DyeoglCsaPNsvD8U592vUDPqFESFA6MMKGsnCp3Ai0QTJ9uFlcHiaiQ2e82y5ioFDjXQoUY9HSJHG5YBPWYR1bhhVeQzFmxGpYe5A4zpLkeO22wjbNxvc35X4WGY43UI4ZVBKutJrmJkSDs84OfZ0Uk4fVZjcOvQwuSohBvzWJz1cYu/l0f/TZLzTvekLtb+3evdnMtnxK2bwGGC2mu8NXXkUMV+97fufOzWo6a8fjnTu3RZImgZKl8wiu9g/GJ0eW0H5rNLt1ZzPbLkez2Z9+UWeZe/qQlU3YVuXhQXy28gi20+ngC19udi+jw3F6cmHCUE1zVKuw785ZNHqndRH+v769Qqjr9n76J8O3rr1NVPCTf+O/fQdUqv7/vAgPr0wQ/daf/4VuPBk8Ov7GX/lEOxyLAK6/9f341r2r9++//59/AisNIPzIL/6ypuzicKd/1zaE/uqffuV9v/FJi2ja1h/4J/8SOlddffwDX35FpNH4xq0P/p+/2g8Hwwcn7/v13wCYLDGq/vPnwXw+O1l95B/+oglYVFYf/hefINA3GM+/44PB8elkvvjmf/RPsqqWWfznfv6XgdPl1nvwo6dIFwZ64U8+FC+hKy6+37x1hXY32mFw75mwD0K51hfvAW2RmBvfmy33u3v36VZ47XnUjW20IXeewP1QwrMPP7gZrpcCMnj0ITZP5KxMrj0Dmi08Ov2u9vWrmM9rpM/fz8pChiJ+8GR0kfDh/KPytadDfV0PoztXSTOM5bmbPwFX024qils76Oyy2GqLWyO/elckb6o/+lT86U/qYoLBt4R3d0hyDI+ugIt3iXGX/NNfZJ//fZOOcvuUPXs3YBxsZvG9A8TO57ujepBBZUb3hm7+FMZ1sI7c6bPQlfizvxb9i38mHz+I1s+C86dBdEbOJmDxLOXH5EufYv/qV0BIYPzn6d2rNDkjx1M3fw6zM8cTev95xJUnPrzxHgPAFfrGd+EzpbtmvuPP3x+IetitmrNvw70aJQ9+ANyHkC8WO8GjZ4lvSYX92XuZUGIoL7ZnOmTjkx7d/oBHGGoL7z+X6mZibnxPJLA8LeUU3PtQWFsb8fD6e6wLd8Ib3wlOAr0uy4k/+SDuHYUNuPcNtEXtsF29gESJM9edvhWLOYoHZnNppx2NiuXi5IWwXwTxWF98hfQLEkx8tbct04Ru+uXLTD5y0YFf/inenLBozzfTiUkDuuoXb7D2lKABWX4ViXt+OimPLj8piwSv+upVWJ0HA9Cs77DqISNj1O2Nu9Ew6evln7jqPMlzvroRrI7DLDdsfSm8d2CKdfRohudPdUX3YfnqR1C7lDWYH7qzZyDlsJnEd6/wpP1m9bVvjapja/3RoV88Q+wqqAN7/Dxydb2Nl5Mp9nzw1sBfPIviBTofwvlzsT1/t3vjz7HauH61eTe79xTIyugkhhfPE3R6Rd34S7HeNKeivESOnme+AjKjd56WA6/PqtPXkkjLWLf3Xp2wdcMmPd4JceDsXTF/PaVAk9o8vJYj6ePukfimg2R5jlr88MXCWIShP3klotpYzP/ynesrKbXC914qwFrSCTr7HNMSF4PaXs4Qr9wFPLk+wK11IVx+ObBLE29zlFs/S81dsXw9Jr0k1h6/kdoK+Evh+LVDWO7IcZvf2APNTo5vfyM8eRItj0GO7z8fn+VqukiuP+42B3a2/gvVK0+jru6kPHsPWO7oXLIHj0VnQ5efne/udllEW5Pd2fPVpcAvycUOmh/AaPlt8O4H/KMbPo9uPuE3j+PwlBxd8uvHYvfgG9Dd9+DlAlL94Dl2dklPLrLrO7Z6UqdLd695dK0IkZQVuXglSMK+vTyrp5NIds01d3IzT32LSvXgtUEEhL2UrLb2UtvwV9Wjt4os6/sjdHIjy2Bv9tL1bAcjZ693J1+Ng6G3D9Xp9XwUtO1k2G5PLCL41vrRa4Pctq53D1/OIyL6g3E9m2DszA1x/GYRKA6MffRSiqCJkoLceDJRPe60Of8A4rgw1/9Kvh50Dx6Ag/j6c9BEwHN07xmoycRd/25aZfp8LQp//5vYBstRl7z5HBAJn7Tr8dBjE64IevQsa5kIdXrrmWBD/fD4+9S1rQjcb6fs/lOsw5FY6fP3oyp2+eK/And29dENt1O89YTVM4v66P4VIDIT8eDelfB0Sor7+M6zfnUok/Pkl36evPJFlG+F/fNwccmknDy6HJ9sy+k6/vjP0C9+1j32RLh53q0vU1SSxRY6uwxZudrJyuEgFHX65r7fPAHDE3KyD9dXaXOX/OGv09/5pN/fxu03k+NDWtwjd6+49VMY3AW/+Ynws78HWBzgD5KjAxKu0PkBPrnqR3NyMgQXz4dqmdeiO/vPoKoej+9/D3hQQSAe7eLz5yk6I6vCn7+H6ebAXf+L4cbxs2X/7uDO0y6WZBX5k+fH5tHZbFhOCgfQ8EiBRx+iqneOsNvPauqeQNe+g6ykWDXrS+DkfVgK4hS89yHA5U5856P+fh8H7dkWPnomshsvQnj0PFUOw5Nv/3u/kK1WXZ5/6J/9Sr5eLxlbf9OzVItsWf75f/xPt27dPn7uue/4+N9PT84evvspMQ1sEo5vP/jAb/726MHD+eWr3/rzv7Bz7a2TDz5/97Hn2mI0vX3nG3/1E9npOcHwuU/+zvaN248uX1Xv3vLE0VZ85Bf/8fT+g240fv6Tvzu697CjcPXhpxDy1LiP/OIv77/ytfvf8OGP/P1/OLl5+/jgCn3zHeNF+P9hKt6lKdAmvvYm1ApAiMA7eXiEIYDxag2dx8am5xfeA9f1keiR4JDQ/OTUOQ+tS1drDKDjnC0XSCjgwfBi7hE2CBcnp1gqA6DohbYeaJMtltRYD+Dg9MxJDTHJmirQxkKUrFZYKoBIvlojqawQqeC+aSBCg4u577kHMNmUAZcOOiQCaIAjiHGKJdAQZbojogIeBdx7SQ30SEVEWA1xLKtAcU1oIACRGEKABUQCGYJUL5UyilLUk7ATBlMqvOdEIxRgEwDuAUCSIAU8QbRHhDuHcCSaQJUGEWYQEMwCDyUjhjiMsbS4x5YQaDzhofUedD1arCzw2FHWWYsoEjbskWYUSInP5946YBAR1EMALCG9cxBbiA1hwHmjLeEJcgpbgDpqEfZCouXKOY+VJh3yhBBjmYgBAl5IPF96Y7BFtEcWEGAcazGACDiD2wA5hzxiPUIOQ+BTUXrggDWEJ9ZDDSFpWKCMcy4SG+qMc5B1GAAKkEV9DLz1HlrEvAXaI9wjpCF2lnXYGOQxSUHjpPLe4xYDZSwmAQfIEGh17nrirDeGiQhppwklLQ64dBDBRpoeAAyARFg6aJ1BxEBsKaWddp2zCDvufO8x9AZii4ijxLQ+aLjFBPfadQ5QYhBxACPgDYeYe0Ax4d630gNgIdaYOUwgt6YDHgHLARDOQWQw1ZgB64NemcobSgw3rgMAemww5cgiCpUnNXaEQtGPAu20x9pgwazzRmPWeYcpEjx3nbMAOctEgpwB1pGOQuctwgYzDyBQlvXEekCMwjw1FiAHMtcp0SODaAcdwNBC1lGHCZQqBTWwDgIctNgZby2iHfIeQqdNA7XxBmBfOWS99yDcbDAXECLUOquR9l7VCGtjMY6qyjWNQRT2FikAjPG1dhpYQtSqVB55Sl1lsdQOEdpqoKDRCpSl1wYAZxqohfGMmtKGXFqE2HJFpISE+M5Z6R3wqoZUWQsRlR5wrAFEGmKBvVYxMqHpLQREYNJbQxgWgAlsIQSap1hpi4Ai1GCHEJGEcAict5AYzLwHyAAoKPSKSIwUNh7FUEW6NYQEQuMea0KwAVQG3uoQ+US3wGqiSCCgIRRIR3vqAcAAmsp7ALyBuNHAQYOIxtQ7T631LYIIWgdtZYFxhjCNqQfQSYtqCyDGVusKOQ+htRpThxCwALQGOECdB7XTHnoPNKbeAwgxrIzW3nmEege19x4aTL0DCHjfIOyBR8SXGmmLvA866Aw2EBCZEKk0IoncMKUsogEHyGAAAeEYKqi1SVGHEFCE4g6HXDpMKPdeYAuAB9giCqBFPIDGOYpph7BwHqKY11S1DiImMNDYAo95QDzSJIz4JnXSYko6hwTxBEFJkIIWA6Yw7bQhDPUmEFhTArseLVbOOagJFsghBBUm3FpEYFnhi4WhzEpPeIi8wgoBTrzzFhGLqIMYC4U77Akh2hMVeeRh06HVxjtLDSIcWESgsqzDHiPQ9Xi+dM4DxyjHFiBoEOmAhxAZhXlmPbAeko4S64HVsawgcMAC1hGPCAQG9Ql0ygEUm4ogZiwmHYIOUWtoQ7WD3nmDqUXIIYxb5I0FAAU9hJ5gJQeg98YDawhPobUGUdpAqiwAIJUVctp5SHgArQdeoy6GzjiM07IK6tYiEq43AefA2lBxXG4Moen5edj2htCwboOm9YTFPcdSGoDS83kgtGHhYLVide0hsgAbTB3C0XoTNK0mLFssKRfOg6Cu6HJpWZBUVbhaS0yDTRlXjfWA9B1YryyhyXqTtp1FhDVdvN5Yxt7RnAQqVf8X36Z3d9+uBMHv/dwL/+lv+j/mRfjf/whumtArD6H2BDmnwhA1jcuygdh0IHIBTWTb4DTywgHILSy8aKOCSKnC6N/HxLovw+G4XwDna5pnpm3DDCmjgyDtyt5hEEeh7HmQvm2Qpzwp+LrKJr7rbZYNxaaFMUJ+0i9Os/3IcI+Q9sRinOtaOqqCsFBVA0IEXIhsQ/JxN7eU1iR3COWmEcqaIM5tX9MCWx0C2ZAskY2lVJDQA1jISvpAMVaoqqYFdM5SgiUPZeeSVDnsEXKEUCEtwbmuaxwjrU2SIWtD2QdQKU85SxxGyDrorKUUeBD2LaOuBiGW3OYFMtYBtNser+KpAdhSCpwlVekHg5TXHU50GGBjvAep7bRDUBuZpA4TJoQJWCbqhqQMWcT7HgcZVJymwLjUd2U4fDsm7datoyykEICexHvN8SqeWIdT3/6HGFk3JAu9At5KA1Igu2iAlFVBcLi5exrvWUZTUbUwDIAGmCpPUtu1YQaVNQEbtcseMAghJa4meWgFREAAttOerrKZ67jOioEsW5h4ghLdNTQLjYDIS+0TL7t4gKTWYTgQmxYmnuDEtDXNE9k6SpQnDuNcVNYAg2mIdU1zZG3seU3zWLYQI2MAck7ECTbGIZybuqY5cM4THPCeWWUp5SQCEDpCsNIOoYP6wQWbAgBEnELvsTEYeypFzxJHSNRUAMKAmIoNqFIhlDXNidEYe2w0J3FqWoEjYJ0OAgg8rUpTFFlfdTjWYYiNAR4kthOIealSbMpo9O8gq1qSEugC2SmHA2y7IAMGvA0HlcpTtFM+OglmLMDIuR7HmWk4iaBxieuraPBna/GqxQlBDjv7H7wIpTIBezvLLKWZrmsYBd4gjASgqen6IAXamYClXck9goxNu/lZcRBaDqGXgKW67YPkbQe0Ad+0MHaEZLqpaR4YgZAXBoZayDRH2mgWAAiplA7j1LQ1iqF3NoyIVIlp+zCD2hgWvO3/CJ2zlELrYtH6gEgDAEI2iIiUDuNc1zXN345BVgddHTEoPeUsdoQgrT1Eha6EwdA7keQAgD+DTImOZYnrjXKKBbntK1ZQrSPAK1YQrREBQVd3NImRUYBqzApdvT1lGc1E1eIUY0+N7HGSuM4Z762PvVjlW0xwHYS5KFucYAJC0SuHR3qzSSYK0My9DZk0jA3bZQ8DhCHAqMdxalpJAm986nvuGTbGBGHkeIPS2PeR5vN4KzOt19YClDi+TqdUCh2GOS9bnGLkiLcchVvtWRUPtaeG0oHcdAa6IEotr2keGImQ5zDIdMvDGLS9SbNCVh2MLSWZamqaQ+8h8FT0gdd9nAHjDWP+bcgISXXdoBg6a6MEaR3rnkIjfCDC2ENI+8ZRllrekAw5pwNGtAIAWkqR1g7ig/rBSX4A3oZVK9T3OIsD0fc0tYz+O8jqCoZI9GmIKjYgSiIKA8lbliW298oYiCOkq2BAtI4Br9iAKgkpYm3Z4TghTiGmcHBQ3T/OD7AxsecVSXHXoCwJJe9IEnmhIdWI5rqqwwGRyjK6Xz48SS4h4gPZ94jFXikSOQtS11XR4O00Kfi6cYwwQrztcJKaVtLQGbDXHV9EW9AYy4LE9Q1KMfLMyg4nie00ppbLFJsqHlEhdBgVfNPgFCEfWME98ZhYFjDOE8+raECFePviq0jOrCLIchgluhFBYrlycUSVBt47QlLTNCSHwBtKoZSR6HSSQmMNZR4hrBRAaL95dBZsAwRVGGGjPYCRl0DZPko9QohzR+nANBUtQs0R9hyGAIDISyO0iNKBaTsUe4Qy01S0wN7hk+PtX/jfg3eoRPg2M2Gs+Ld/OPul/wNKiZ/8b97BEqG68ti+B3/tx35UjAaT+/c/+r/89MXVq5evXfvBn/mpZndnMj/9vr/9E57RuK1+4O/8HZMlw+Ojv/Kzf09sjSb3Hnz/T/6EGI7SavkDP/bjBHpi1F/90b9JgcmW8+/5mZ/Vg6Q4Pf+rf/cnFWOJFz/4sR9jViMKf+hvfow4HbbNR3/6p0wWJavNX/vpnwIIIGh/6GM/HpVLGNOP/q2fzESznl3df3maLTih5dYrl5MT4QfrSy/sDebUJ8utF8eDucN+NXzjoJgrRDb7rx7mD3u5zQ8/PyjOApAvt18ZF2eQktX0te38SIOk3nl5Z3yvE7v9Y3+SZCeZGNV7L+ejixDBi8m17cmRVgO59+JocpuL3e7gT5PR6ZCPqt2XwtERpfB4eH0re0DabfXYV8D29UG7Xe28iWZvTkjwKDkd7F0f+agq7qd7LwM8XhWvh7M3p91Oc/Cmmd48gOHp5CEYXNsFYTk6Cg9foWrSjN+ik+s77a7YvaG2X98h7Hh04kdv7CO6zE/jvdfGZlxPr7mtN7d9Ph88QrNX9wk+LeZ++uZlSlb5Gd1+eUiTs+Qunb2xB9Jz1oK9F3ZjXVGnLn1hRn3PWnHp5R1CebQWO69cJno56ub5V56Ouw1DYv+Le0wL1vc7L+9Q1EXrZv+rh6y/YEjOXnqcth0h3aUvbCeSM9FuvbSdyxXozfbXn4j4ioD+0pf243XpU334uXHIDbHd1lf3UtlQxbdfPgzrNQrF/hf28mXVjSX89Arca9OIiy8Idr9KCt1/VrPbG/oYBb+1cm+16bZWf9zS202RdOJFAx9wOCX2M+vo+pw+HuBPnoLrgh1g9/kK3GizXMgvi/CtDZ4i+5k1e/W8uAqaz9TqLcsOcPjZM31dDuLGfrlxb/FwBu3nu+CNDX5XiD99qr4ukl0LvlzD1/t83E/vFzuvBHrUTK7B2esTvid2r4mt65cROx2e+dHre55WxXl0+BVmhvX0hp9d2zOTKr/ttl8/IMH54FyPv7Yb0GW8jLdendlBPbxl99/Yttkif+i3Xz1A4GS8kvnrT1C0yld478WhT9bFA7D12i7MFukjs/fGu5A5SXs4e2kXu2WygZdeGtuU07Mz85kullXg++a3eNI2YajNJ6tQCGaF+HQTGB70nf4DnvZlRHrxKR0sN4OUV7/dRw0PfS9/r4n6JjBK/Jsmbaow1P1vtdn5Ek+J+9cLvBEBUfKPRNY2sefi90Vc1W5A/a+dx2cbsk3ob5z5EgSBtp9Zx2UdIMV/v88WG30pfvpzOH9YiGm995VwfBQjfDa8uT17gHTBd746mr6lxG576Qvh9H7Rzeqdl9n4YUbx0eDmOL8X67w5+Fo+e8uI3Xb7pbR4uCV2uoOv+uG9ISGnk1tFdjeFyXJ6bWv3Bur36stfQsO7UztcDq7F0zsjxk5Hd7Lh3QFKl7Pro603mdjZbL+It+5udZN1euO8/WOdhBw+6v0fluHU4+uV/IKOty2804o/7POoDc6r/g9kTjt/ofo/6ouC+1ut+CxPR4I+bNT/zTNS0kY1vy/jQNB57z69jnKFH7T8D7p0wMGFkn+kE1LTVujfa2NT0lLwz/RpKsGZ6P5QFHkPV5p/qstBzZRSv1PFvkuCaOeFYdHVUPLZ167EbUlQe+mlx+KL2o7U4edGceMJqra/Msuanph26+tXo00Do37vhf3hWaXH4rHPFUET2KDffWWWNZq4cvurl4plJYfg8LOjwXGrpuLy57O0Ti1r9r6c52vJzHz86uVkqfVQPP4n0+JEyi1+8Nk4KjMQNQdfydIFUHmz+8pw9qYEO6vxl9LxnXG/VV3+Oh4+2sL0ZHozye8WNqt3X8/333Tdfrv3lWB8b8eMl9O3wumtISVH4zvJ4PYQxIvhjcnsds53y70X8eT2zOdn4zvh+K0pwcejh+nk9gQki+mNfPJ6gsdH+evZ9OY2yE6G9+Ptm3sYnRbH8fjNIUgW0xvR9muZnS6z+27na5eRPxtvmvyr7wrcKu7h3otbMKjSE7v96i5KVsmJ3P/aFaROY+W2v3JI9Drq3f6LU0Kr5MLMXtlN0CPbxDtfuxqAFZP60pd2qFxTAw5eGGHSRRu78+rlEF2w1u+8dEjFnEK7/4XtiK+p95e/OLYBiNdi/+XtwC9oJ/devJzyOaLrH/zY39q9+VY7m373z/1v20f3dZJ838c/Pjk5ujg8/Ov/0/+w/9b10/c+90Mf+9jB9Wvr/Usf/dn/9dL9m/1w+L0/+7O7d++eX736w//jjxy8+sryfc9850/8zGOvv7Y8PPzoz31899ZNNUi/6+c+vnfj5vlTT/3wj/3oUy+/eP/DH/zL//NPPvnilzYHl77jH/3SlddfE5PBd/+Df3D5tTcWTz3+/T/+t5/58gv3vvFDf+nv/vQzX3rh3pNPhq++St6hEuGfvXa3/Iknsy+9QDbrd7ZEiJxjXYvrOio38WZN+i5u6/FmrbzLTk/ii3Nn9HB+zgSnVcXKTbLZ4KoKmiY/PdHAZ8ePCt5Za9KLc9Z3QV0HXZu1DRUibJrR+akyerhZp8uFMzY/PQmFIOWGtW22XKC+j+p6vJhLa/Kzs6wsneDDpma8D8sNqcqgBwKmzqRBpTjKmKChswoMlM+I9M6nAEVZZw1gzsSB8A0qCBxGbcPBWPmUcKttZlCGa6RcAF2ctkqR3LgJVVK4LYOySDroUuVD1mINYwXGpDES59YPA9E7OOnwkErsQe5wnLXewgj4nGmj3KBHI2og7AMe5FHjkQ16MsSCeR96MmFCUp8JNgq5NW7EaRr2GCmiUEY4tiDuwYwI7XUq2TjsZGDjlqaQx8bGGiUh99BTAUZEaGiyDo+Icq4JOI1p7SkMBBogSb1jBgyoMpENBRkEvYUiEGwA+hAp18OxlziSUJAxMJj0jNMk7HyiPA+GRAZQQwFGAIShAgpmqHeEhy1JsIhZozguQoGZA8oPjI2wwAoVzJGktYJEnseBdA0ZE5exjvdwYh0jHCo08DYmFeIkpipKWinoyNoBVKaVVOOQ1lJoxCVGrahs0Igg4P1GBNyHjEsjUeuZKn1tqLEsaAWHUSPDsG9LldQwhtz0LRSe4VJJxyoTw9Z2MDIgDXhnTNSCGLeq1rEAjFRKOtILArhrXbAWSdi3jQh6lERN5wytLHMtMDBReMaUATYRbExrAWzR0Yx0FAqsUBYLiAAVYAYMNKbgdBy1gqmQ0xiXAOhIkgHkxGvMwRgLE7i4wSMmAZIxp3Ha+dAgSQrKkddUwBHxiAnMcRGUnKqoJXHAA8StQnkkIfFE+gEw1LWwBAEUKKz6hiSm0lDZtYm5wrDVnQugtKwxFQx4i2mrShAThbCQnQl7jX3vWxc5T9GKd5CJFuBOViDpOSNCbFSsHaGd4pZJhVTpKxgYQVjbtzjrBKWcL3Te+sC2trWBkYDUukNhIyJiNbczg/JIWWRTCSPWIgNihUa0cQplFgwDwT0Yt2REFQK+sCTNewBg6EDGJO7xwMIpE9y5QpIBrZUBI4ES2iLjGHBpIp1DMXdbyBhpJpIM4k4SHQgY4gpakChfYO6kiTkYs76ncNDgAeOu57gFAW6Us3CjI8CV4mhtYyiMqUyDQ7AR2KIaRrrz1sDehlBqIn0NE9wrW4ESBbgHsBUtCGGtnMUbEzsFXG1bHNNWBL0rUaBqCIXd+JhJB5SpTail1y1ocewkBMu+JZGuHeBm4xIhMLJYw5x4mrRW4siLJJCuISMKi6DrejAxLsQcKFBYn5AaCRQSHSatFHRo7JAozf2WA3EkoQWZM5S2lJNUuyltlKQj74pACINmAuVUUoMLDMK0A4qmUCVUopaMARgGfW/ASIOYNk7Cwrgo6LxGGSLTgPcYDQUdhtxpP1Y4iTrkXWBcQgRWKONgFvS9NQNFh0kriUt7nME+Vj61PgmF8zAWbsS48HbY4wEVzvShRGHYAogjCQoikfGxBQMmFdMhJ0XUaigjjuOkB1ATY+OAOwsyAUdEOiwjTpO4dbGBghZUYKCIgCPsSSCRwAWtJJVxS5JAhKQ3AhWRRNhh6YdeUyyZoINYw7h1nCauC7HwLZkwE2BhezgGBmFOOSmACmkFOE1CwaJeczr2JkEacrgFHWUCc5Lj3pMu7FluzDjo22C5ZFJkVRk2NWma4uTYKJmt13m5Rkoly3nYteFiAfs+Wy1pXbGuHZ+dOmPi1TIrN1DKQdOxvkvrBnNeLBe4bWjfjU5PgDHp2WlUV7DrwouLoG/j1RILMVyvAs5xVebHx06KbLWIu4bynl3MA96nizno+6DnHoJ3+oDWrv/LH/KEvLMlwubbv1398H+9detGPxtZyvKT8/nBZYfQ4Zuv33/P+zLZjO9sAs7xAAAgAElEQVQ+Or36WGR4MK/EuDCUFcenfHtcJ4PLr712/73vS1U7uv3g4upjgTfhvAQJUmkanqzEbFANppdffvnRe9+X2H5w59HiyhXqVXS+UYNEZNng0Yka55vh7PIrrxy99/2h56Ob9zeHB6mpdeVcGjXjQXyqZEZRrMNzL3MEQ4dXFhBsCghbnMq2HxOwCnWCYKyDU6cyCBPoS0CB0wMIKxQ420xpuAQmgCDW4RzolPjIwAoi58vtPFt3obL1hIQr4IhvJnFxzh3xIHWossCSajfJ1jzkWo5tsMYyIJtJka+qoFPr/WHQiWJl+i1rVBhXstxNotYQruOw4TbCAq93s7DjyVyXB2Fed14E5VYQNYr1yhYIWEwru9nPWNfnS7vZD/Kmhz3tRy7gzkvocugsitZGzXwP48G52lxiedODBskxZNq7HsZh05OEbpDcAg3LZw+rdowosrCCNoGAerICIHd9GhUPxfpSstue9M2YjyEBFpfORcBGhG68T1yXhMVDUe9HARDsAsoRhMjhjfUxNhEiG5jhzWI0So9ds8NCz8k5UEMIGYQlQIFTEQrWAMa2LuLhsawnNECSLqAaEMMALBVVSu3m/kJ6hmkB0Eln0ghn0JUGGgsnga09k0ruJHCuAAZ8q4iONjAmKAGuNFC77tI4WLVBJ/Ru7OYaESBmRXRaAgwGYVfKBCrfXRpFy8p3xu/F4XkrCROzPD7bAAjQkLgewFrCHaZbiDuj9+JoI6H0dgC9RrQF5W7K+r5Y2NWlIG971NHNLAh6E7bSDCBwmG2cnlgBw8G5Xl0K87aDDRYjQJX3HPoceOfZypstwHE0OOLl5XC7nDdqpIeeKIdaZwtoAQ7XXk9dj8LBEa8Oo1gIXCI5glhb0rpyNoTUhzfP1cEoBsKeKrsdE2jASoMIu5SCpSaRF4OE3Nnoy8MQSncswTTIXF12CQ2BTQiYKxIDPsrp/VJtZxGR/pjbcYID7zYGEe+KAC40CV0/yoMHlZpGKk+T+ws/DFHg4FobxsQkDU8rRm0/zuiDyk3jZjAYna+xduvdIi37hJt6immJoLf1NM7n0iMHMocqCw0u99K05FGr5cSyEilMm0mUzQX0yg0w6BCWoNyNk03HethuoWKtFKQms8HGOIhdAZzAYWPllreSRY1rtnC+FtoSO3C0gdYBUAAvPKvA8rHhaL2wG2+3QsyV6x0aUmshXvRoJ+hRFDwqxePjpKntyrrtiGhjG5fEQtLILizapoLG7PZCPTGJ+s6snd8KkVSgNnBALaHoguMp6YM4uL1UT05D3tmFYROEleQixDnSCOOzHm/TLsrCOyt1OIh07+baTGPIEC19BqrVKI/OQDulARL0DKghhAEEJcDE6RSRDcTM1IOoONXdkFAq2RyqggBmUAksxd0oKuYcE1MNguLU8AL1RTw86UwEQGRx6QwJuklQzDmG2uQumON2wPo8Gh43jlmXY1hDD3A7CYp5Zygpp8lo3gOns6BudEYUWu+mSdmzxnU7OF9LCYJmGuSLDjrvCggkpo3VU2cEjVrfbON8LbQjtvC0cc4hXwAnUFRpuQ2ljpJStzs4WwtrsBkC1nmjQBbXNRyEpRPbQOsgWch2n6aVtIaYASCNQQroEZY4GD7k68NopzpvxViNANEWNc5m0BIcrLwdu55Ggwd9eRjHmuMVkmOIjcWNdxkyFLMVSKPVWbE9fCTLS0GserREegwggKj2IPUKo3gF7NA2cTJ+xKttGnqJ11gPEQCOVLYvYsPg4ELZQjdxMn3EN9uUQhNdrCEC7dY0O10gCi72D/auvbXZ3q6ns4M332i2tlxMwvONJ3hx6WD24AFF9uLS4c61t6rdnXoyvXTjrT7NosTKGiDvLg6vbN27awjpdqY7N2+vp9vVbLZ784aKIzUp6IaHXbW+fCk9mXuCm93Z9q071WBsh3F2dKGCQE0Lsu4Czk+SfPzzvxC+kyXCPyMtQXD4I//dO10ivJoMph/8l5+oprNwWT77u585O3hs/Ojk6d/99GZrLy03z/zmp2SUeeff+69+qxmOWdk8+8lP1Ts74enq6U995m0vwmd//XcVDQwL3vd//WsdR4Trp37399vxOFg0z/zOp0QxQVY//2u/bQFRSfq+f/4JFSdeuWd/+/dEnpFWPfPb/0ayxDHy3l/9TQiQisPnfuW3LEKrrafJw32nIkg4PHoS9UyNRHDzqhWFygR+eMgEdVDY86tWpiDg8OgJ3MdizNmtx3xf6Fzgh5eBzAw14Owy6GLPenLyuKtSvqWCW5ddP2lHNrq/BfrMMOXPLsM27TIY3t8jddbPeHjnwLSzfuLCB1Pc5hg3bn5g23E1g8m9AVnu1tsmfJij1T6IStNs44sdkXmwmYQnE1+U4GLPzQ/qLRue5Gh+Saci2IT24oqMDarHwaMtPlX4dIKXB/VIxecpuDhQiaZV4C8ec6EA9RAdH4iJxOdDeHFoB5VbDPH5gWGC9aE/e1zHGjQFOboEsxJupu78qstKqfPg/hUHvCOI3nnMMAQcwY+umsjaPiTHVzS2hSz52Yec85YBevuqY8h5jB9edcQ4yfDRFQMBBBo+fDewwIQ+uH1VY+oAQsdXI98KE+HjxxQgCCv44GlosYotu3XVQmYhQEdXAXQSMPzoijMAUk0ePmUU64Z68zUkW4JDsLzFZIWDxF1ci/gS4QO2eAnzDaUztLlGuw3BCVreDeQa6mHUXMP9BcCX2fpFUK8CsBe3r2mxJiwFq3thN8d2GpevA3UOk10zfz3aXAR+L26v2XYZBKmv76HqgrpZ3FwD3REkjwWbr4FmHgZjX91j5TllY0zXE3K2K0cSX0zQ+X41sfF5DM4vq1gFFXMXj8vYwm4QPtzuJxZdDMnFgSkavxzB80MVa9ZQd/YuGyrQZfjokhxKthr486u2aNxmSE4PHBNpa8XF8yaQvs/oowM9VGiVgfOrPtqAZoBPLmtqsCLo6HHHlJcJfXjYDrHYiPXrCDNCvDp+PXPao5Scv0QMIg6j+RsME+A1nl+jFhJK3cmrMTaARubklUQB5imevxEgBLRBq2vUWMRCd/K1RAsIpsHiy8haAhlevBk4Bwyii2vEGyyzqHoFyBbCKVt8GbU28jEtX4PIeEfp4hqTAqv9bHB9BttpPbXxgxmoh5ZxN7+E67xLATvaJZucz/rw3iVTbXczFz4c43KEaQWWe7ba6geAPtoO1oNui7N7e77caUYmORraatsFnMwnbrPtsp6c7cPlVrcr2N1tXO6YrAbn236zY0NBFmO33jE5x6c7cLElZm34aMeuD/oRdyf84lZCEyBqvLzG4Ix1J3B5MwhnoDnD61sMRhB27ux6AmKkWrx6jYQDI5bs7FYcjn27oKvrmKbY9/b8WoyZU4LOv07wmPIlXNyMosLIDV1epyDEQPmLN6KQOS3A6esxHBC+QYu3wqiwXcVW16inEHlw/vXQBJgmBbl3OXRCOQKPr2rPIJXw/ruhpjLXwc3HnAsNc/jhFeCxghgfXfEaASbJw6csD8TIBDeuGF/IEAYP96FGGkF49JhXrC1IcmsfyUgMVXj7UJr/h7336rU1u870Zv7m/NJKO+9z9ol1KleRVYxKZJEyDF/4JxgwYMC/xt1utSC1ITWU1WIrUKJaaoFUq9ESm1LLCi0xVLHCSTuttfZKX555Tl+QUkuGfW8KntcP5s2DAQyMFwNjYjKQPD3BhkAgw/yRN+kwRvy920Rlw8ywD8+smyoR+PmR04UaBfb8mK1HYbKB5/d9ddIeOHE+AdVREJKsZmF7JEvPro/xzVQeK/LkGNWntqjwzXHcHnuhyWYKN8cuG/DyBN4cy6OBPj0E27MwquLyAK2PQzKwahrWt2yh0PIIL47AeAUXZ2FzxxdVXB/i1THgElVjsDh1+YBXB2B+YvZ6sB2TqzuO6XJQcvFxT2ywnD67a0uH2gxeP4hJHdoSX92z1CML0cWjiG0ICXl632Qe9CmaP0zJTadn5PK+JgGFCJ+/GElwgNHHd10a4yDg9UOQDNoW9Pk9Dz2EAD9/ZBF0mLLH92TOgGb04g7EvfIZevbAQQCT+u2f+zVRNc3+4Su/8wes6yQvXv7dP2CdXB3d+sy/+VJ6fTN/+ZVP/NyvsE21Prnz5m//blbVXTl96fe+yjfV4u4Ln/r5XxotblaP7r/6pX+Xzm/Wp3de/51/l+w6XRQv/fs/5PPV9QuvfvoXf3ny+Pnzt976+K/+xvhyXh0dP/rD/5Rer9Ro9OIf/Ify6ubmwYOP/9qXpx88ef7222/+ym+On19end39gbtF+P/2/HT2gx0RwhhF15U3y6xpy/WquL4SVV2uVmlVj5fL0XaTbLfF/Dppu3I+z6qq3G7L9Tqtq/FiwetmvLgudlux3YyWy7SpR9fXWb3LbpbZ8ibd7cbLuej70fwq3W3S9Xo8vxZyGK1u0rYp16t8seDVbrS4zpp2srhOt5u0qsqL82QYisUi2214b0EsqUlY76PPQMO4lDYcATdiykKZRc1Y44MviOJUAuAL2hKqFHCz4MdUR2hyZFnaOmCyYApkiHUl7wjVCppx8CWxKMgC6kS0HnlBQkksCb6EHWfDgPQIhDF0DJgymoTVFpgkmjE2jtgsuAkJBOkiuJTXjmoawRQbAVwaVEEHDfsk2hG0FLsywInoPOlhdCWxAoQMuQnVLumJt2NoGTJ5iCPeA6qRtyXULPoUhgOqHe8RCCVWiGrh44i3kUoQwphJCC114YBoj2oQfUEGIGQIPidaIIN8OMCG0c4hnSFDWI8dnNAWkc57l5OeUhOD34eW09YFnWGNkh5ENGYSs8F7N0I1Ztq6eIQcp0OMOocS0Ca6OEl6ghWEPmcNJNpFvx9dSjUOtiQGp40PsQQmhxZbV/IOAx9sh5wEtJZGET2QMEStKWwCU9IOrJMJkL7vcFCA1soOyCgWZewHHHaBKuUkVTqBBqqOmi6g7eAUMop5hbSmsoZMymEHpGRBASuZ1ZjsBtsF1VNogFTUDIxqFXe+H4hXwEhqJcGdgzFHdkyVJR0OboQdIW7kw1j0kQ3Q2wLpJPo0uCkxQXTA2RG0FCrh4yitA9UwupwqDBwP7pCqSNuIQo41ZD1xcELbiAYQTEEHhB114ZCaSDoUXEEUYAN2aI/1kEgQTEGagC1x4YBqiGqjfBoaBzotJQmVJ8ZKnQaDcKtVi1zvQa2VF6EJoDNaM7sxRJtapVZB1Fk9ENA7UuvBcNehOAStKW48UVoOidTUDaEfCBo8qZXWzEkaVex7AitHtXaKOUuDgkoS11hSSW2oHSjRGtqR82PiEJAZtEnaeWQE8GPiWPQF6gUbBmwKGEvoGNSjaARrHNAs2hLaBIYCmRGTEnXc2xFxBPkS+kx0AWsSTIENdn6MzJgYwztiwxgbHHUGXJa2DlkKTEkVCKEEbo8qRRsEQ05UxJ0ZFDU9MAoOQ4KUJY0xioUhhg70luNK405LRVHnvYy1TIFyfqu0JKC3sHEyZqDSobOyx6C10IRBC2g8qK0cSOxMqOzgUlBZ0DsjSahUkKGVAigPu6AUxZ2BtekdDy3wMipJ8eCRw0BlQGLaBAfGdKBEQuBL2iCqTfT70edUQWALYnDauOiLqHNkiHUj3mKiLXSTEHJsSFAFliTtPIwZ9Tm1OLgRahiVCpkxDDmyPLoRVIRVNtgUqBI6BsMYmYJKhfssupxYBn1JTEI0jj4PMmdSxiGNrkSGIj8BseStQ5JEWxLHYiiwm1Ctkj7xbowsg7aMIRddIJp4UyKNvR8hv0eVEgOJscAKYZ2GUPDGI02AGzEZgE19OEiUhjUOvqBDoJpHNGVdhIYAnSfSQZ/FeMCMpR22cEobgProbU4HRCxx/ogYRJoYbU405AMMaMJ6SGX0ZoQbQAzy4YBYTAcUbI76QBtg4zTpCTYYuYy1gRgY/AG0lGjmbZkoKJrgwBjoFFjiTcm7SAwMdgaDIDLxtmADSHsQYcFclvTdZD7nu53Y7bLlPK9245ubbLcbrddp3Yjlzezygio5Wa2zpimaplzMs9VyvJyndTVerXhTZ5tNeXHBZT+6OM82m2y3K7bbbLsdLRdiu5lcXws55OvVdLViUs7Oz/l6nVVVttkUm814vUqrXXl9yds2vVlOlksmh+linlZV0nc/iLcI/x9Twv7tT/xgT7DUw4fVFz7f709vXnmxvnXaHB8vXn15cXwqx6OPPvNZe7KnRPrhO5+zk2J3dmf1youro6PV4fH6xYfz115FFL//hXeGg4lNxJN3PteUo+XxyfrRw939e+3RyfrVFy5feQ0R+J133ukms7oon37yU8P+tLp1unrp4fbhA7m3t33pwbP7L8pR+f5nfsiezmxRPv6hz4Rptrj7aPPyo+q0pKGSB12YauYbddrZMeXwAzxZyD2OyBaJqr8dA7Z+bxdKOaLPu73GTQTCS5CvzUEApEK86o5dKlZusvUTS93OHlVuhDBdx3TTHcKcPk+Km90hxnDw5Uruh9Qs7H7lZwSxK56f1zMqwDymnTq1EOkwWsk9RtANpfP6CBN2w0A9nBmJeybfHc5AAmuQreDUqtxQuuyPIo4XuHmvfjGntI+0Ho4VgS0Sl3YPGrRC6t3hYQ74jse6PxsI6QKtzamCdhHCe/6IuVxRvDIHgx6ZJNTNfYviFsgP7BlAiYJ8Dqe6myKGNu5o6MckVUt1uwapx2jh9wZT2pQ/D6O+n3nmmvae5ry20PnTnc88EHM/7uw4YlLFadseBG7r9nYHM1PSZ92Jczkm8CpOWjvzGFZwsmsPMOC1PmpJOmC/7U4lKCHCczBuzFhmyUWYdN0BYrZRpxUsFHFbc9SiFBSZTgobH+SM+vQkij2f5ZbfRWDEilQWewafJjx3ea7xPUGLkB04cCrGocruQDBNRKaKiba3s1Ha5jMLbwvEwGRfgRM2jVVyG6ZlAPusOHD4kIxFT3IPH6YCq+wwxCM2Fl1xy6GSJJNY5JLeYbgAWWbwXcr8zmdrswdcJilZ9icRovk0P18fIZL0gNT9qUJ4YMmFH1tbSJCt4KiTYyNC3d2XhHQA1+pYUlHR9KnZgyp3HK/9fqcmlvm6u6dSuOlHyh1IxCSmV2ovmollcGOOZZvbffF4cxioUIjWwy0XE83IRXuEyD4a+Sa+mCZjmELFX0xChkt/xY4gvFUWXIljaG9neRz4iwwKUIYbcMbYmKYjlx0GckgyJukBtCd5GTb4jNApKmCfPKBwREfZIPYD3MMp3LFZBPdGWeiT+wjuJzO3TR7QWNI03vA9B06yMW74ETB3i9T14/teTcoMXACxao9wghYkqbsTl6Q7mF52M5C6lZ1t/YxYWiV82R5Giucg37k95ZmLxVruu9zf6NHazQhKN2n+fHfACFoh1sjbmoLelbswHmJSh8ky5tFnPaJrc6y9UIQ1w6lOyRoU12oGCKpisbD7RIuB0013GstEM26TB4xzNxpLckjxDJczLfY8PEry2KPXSipcxnR6n+IiFoc2n3h3kJZC8zMUD5Mi9PHlLEtdMg30jOE0jvMOHzN6QHMqk7vUH/BRLsnDJM1cyg26l/A8ZCNLjzCfgWykxWG0t8rSt+zlhGcuY4qdUSQ0IhUa7epbhPhBn9ZQDCV72u4PfpQgPI9lZfYchDs4qvvDCFlt9ndgZIjb6uMmFACTG1fUcs+P6FMwrqpDwlxt9tdyHHN9pY7aWACbbsBoa0uVxJUfd+bYoyDd/moYw/34HTnbuQmPdAezrTy0LGzcpOv3Y2Fu7HRHRm7IDWOL7ihQ8ySap91DwWDn811/6IVfhWLuZ8TEa2i/6+4nNukpqeXtgYLO5rU79nB4GvCH4SQzoqN4pY+kzTUlbXfHInkd0XN7igmpY76AE9dNAEcrfSwV73D33fYho3iIea2OIkA15lfqkJhSJa5uHxhOa0u0Pe1g2nP2vp4FNYEMru3h0E5j6nbtnZ5wmfLz7gR44Sm5cjPtxwqDCu5XmyOxxz6sDjTlPYTVcGpBajGZ+5myeTMS52qq+gPMzW44q7FQCGzMsYS5FslHcqJNqURY28O+3YNCbdXxxpdpdfts/ej+6uUXur395uGdZ6++0efFk9der+/ehil7+sOfVQfj7cmtzYN7Fy88aory5v6D8499DFJ0/plPbe7cRmny+NOf1JPRs5P7qwf3Fq+/7kfZ8uGD+RuvAEKefuYzq1u3h1Q8efNj+tbB7tZZfef06mNv9JPp9oUH81debnn60WtvDvdO5Gzv8u2P9ycHm1tn9d2zxfFJ8ad/Snb/FCZYIMYf7AbL3b27l6Vv/eav68lotJi/9B/+yOSFZWjz5gPy9Pz++eMHf/TH2DsK/Md+53c9I+u9sr69h2G8/9d/+8LXvx4pLfvq0Vf/CGo9PzrSZ1Ng7Z2//JtH3/iGzcXk+cXDr3+dRFATUr1xD3Xt3tXqra/8dkhY2tQv/tF/ZMH0CK0//gguFic3i5d//6v50IWMvP7l34PI95MH7PqIKcztFq/upbXDyeV/558covaqE8nlmdCBmwZsb5OeZeHJO2x12D9boFI8f4h1iuiGXt0iik3g08+G+ZFf10bA+X2+RWG644/vAFWK8eLHhvfuhnrX47i+S/o0ciOuDsSORHH14+b9M9Sfx1F6fpj0JDMrtDvE7UiP5ej5CO/2w2won+e0mXD1NP7p18TXfz+O9ih6PbvORHJJbg5gfezGQ/rlX07+/GsoHRfhNtwdo8SQthDXM5hX/Ld/kf3Z18yde6P6lGxnGLaixmBzG8c6+fOv8H//5XgyS9v7sDrGaZWsSrrdZ2aZ/PVXyde+ghNMxcfw/DRjF3SZw/oWxauoOb8+xd4yoJLLBxCYO+A7PwIW1g/DZp+ubzHbzbq13r7OlHKjdjcbYWRG14EuTgnsWY/xzQmz9hA8/gK4Eep6a/fJ+QMMHdUaL26PzW7IYjejnuLZoovLR9mgEWvF+UMQwRF9/GPwhsl1W5VkdUatp2Agl/cS6WU5NH8boAx5lO1zHKuQZVoelqZIJ7vl+pvMNjgtZPUujrXnudX7ecgTtuu690Bc6OLQVn8DhooUe87McpICvu37x0huIcnB8H6IC3M42m6OT+OIodYM73m3i2PQyoso14SVwe8LW4qi3bb/1Q8VKwrdP4F6FdPU8faAz6coXaWLAu+O3Ei9ob71w6DbdDu02YPVLUwU6dP0cp+Sm2YvGUa06Gu+nNHtPvU73iKwuUNg9wp59plwVXsHr/ZgdYqSiqxytj2ksYKFXO9PqFf5FUere4yv0yVB21OK1nf1+59HXTesbXtIb0552BBN8dXdkGs/37UfQe5UZrr1hyltWi524FgQpMGTrnnGOVSkNfVTgpSl+ia8ULDdMunA8v0ce0+83n1IqHOsvUIPM7pbg4HuPqCk1aIw2/9KgAd53oAZgn0d1qB9zrB0WMD2mx7VOiubkDkwFfFSy8eI6yFxtnpC/ADwIZp+dw/IMSzb4tmE9GkOnr4d5nfjeg1LfH073VBYzItnJ7HfC9lmmPCYmHLbsdUBbKYhtcliP9twx6+/aN5/AbfPYyqujkmbJ2GXrAvU7FGyaffoUPCir9LHB2DYI8k2WU5JPRLu/BPg/EW8q3yMywfJdgbzy/LpNAxHNuvwxW79TAisQRvqD0iRtOaw0NM80516EvsLlMGBNWr1hDNo8BGVs/HYVvZ9vb3kOe/VPLbnOEca7RG5V1JgwZNh85hnmY4LXZ+zEW1cnriDDITAnm7Xj8XI10jZ5Qd5jjp7kJr9ggIbn+r2CUqixc7u3iOUeM7T5Nlh7losPVrfJiqO4oc/Tjej/uImzrLn91FEOAx0fjsxxmRmmGKXkOmixfMHvPWx7PjjuyGKWfb8HfV06nZ1n6PlGTYscpc9P+JdBPlitzdxSUy3SFwfCmmF2sHtHSw5ydf/g/rwFrh5pvLi2WmMOSImu5xhhQAzYr4n1lykz8jlGez3PV9nv/Hz7Dt/RvJZqu+hegZTxZYzvi5jvhS/9DP0W3+Gbt/lzX1clzQ2yS4H1QEGq+QPfiX5+tfCo3vZ/Az0B0Ts2HLC6gkbztk3vkL++A/J/oya1/HmMOMfkcsD2B1RcIm++hX+Z18lScbJi3Q5ZWRFdgd4c4yzOVvmdHPEXD1tO7N9mej6Dnv2o/FKBmPnE7q5TdGa1hlZHSe+vx0//LF4g/SqGU7J1V3MWrrDaH17HOZ79tkXYe183fX75PpMuBaHkFzfA0Tfgx/+GNnp9kZWB2x1i3pDgmVXd4jsj9iTz4dzRaHe7LHlcWrXwDC2PEEBiHDxyX/z67zvnOCv/MHX8r6pEdy8fIc6M7tZv/n7vze5uto+vPeJX/sSr5qb+3fVhFmKD55dvPSf/2S0XLQHh299+Tf3Li+Wd24PI96eHU8/fPrmf/zDrKqS6B58409nV9eLw6Ph3p4HLtvVb/3O7xbrlSuyF/74T0aLhYRh/eJtgGFmzVtf/srB44+Wrzx669/+ZrG6WR0c4W99k/4gbxH+oz28H+gpXAAwmFjM19EC71BxPo8B2RDgam4R1ICWF9fAQe9RerkM2jvKaFs7H5yDxZML67ENpLxcAhNdiHC39sEHSLLrG++Awcn4+ZUHxGEC66313gNcLDZIeRtJerEA2luI4GYVrdOYF+fXwEQLaHZ1g1qpiUA9i5oYKkLLrUkHlORGp84MMPUmjZprzGFLvSKapvkwQGcGloEWxZ5qzKISUXOJE1xXUPaWENhx63PJsthTqFIJKVaSdK0CFEgOFFWIxiEFkg68oG0jtBxw7oyAkhucxIE7mUjCgaKoFwoQ65LQphYntpHx6VxF7KyIHZM0DYrFLlU4MZ0FHzxTiPrAQy90JN4muGMqKeyuBx+eS8SDZ6DlGiUmCNgLB7jbdeHZtYrExQw1wkBqHYlVolFipItPLlVAznNcQw0TCziQfiAAACAASURBVDJQC0WFRgK3iXeJiRTUxLrEIJwOHUTYAIYq4gI1JMVNokGmcdKJyUBT61lohQ3CAkZr5gNThKVdbaxROGMt8Y4pLGAvfOCKiCrfN4ApluGGu5hLnIaGeEUdobxroPcSi9gkwTKLGWyEDZkhzAzE9Fhhrmuge+g8bPJZn+SSpqoKpgYOJ27AqkOK8DaddCw3TJgKmB4NSeZbYFtkEO14WWfTgaS6R2ZACvO+p7aFiohGjGsx0YBahU2LFEp0j1WPTCCNGHesVFToAZsdcJRZiVSLDKLBUtgyRXNnOehSiRPgY7ZdG5bZwFGTWEC946BPFE4HltfFvoE0eBp3iUTcRoE6bgIPxvJ2qxExoIA1N4TbQPGOOEANYlW+rwgPhpCWGJjoIEKbGixcxFM5eEQN4KERGuY+cNIRG5nGid8EG5nGQq68dIkjFG22wRhFUrkDylKDhd8EE6mHKNQVjECTJGyMkdBgYRusDDOU+aryRmucmA3QLhloqnbeSRQRAm0brbGI6h20hhiSmE3UCishYF3HftCQqhpYhSXitoZaE0lEkAxJISEDisWea5pSpdgwKEzikIKB9aKMPQ1DajDtWdFkM0XTKBMvE41oUAK0ZEjHoK2Saq1IFjUFA9cw8ZpDyVykHR832VSRxPoMNcIQFjXzbaJoioxmcrCE+oGBBkuauZCDOjEkkYHZCljArMF6G1RgA02rYmYAMQ6pVdCQyZjYCupIJU1rMVGEG8/0BkiSapj4lbeAKsKrYl/S1BsYq2ABUyGRq2CwMIDsyj0biMKp3gENU+OJ31ingSK8ymYGMgWoWgXnkAbMbKEOzGARBxE8tzjBNXGWWCqKvoveSZKFGgVJDUzgILwXCvE621M4NTSBjbAhlzSNHY1GKIhp3wApNaCwF9EwhVnsUjCQIck6Pm7FWAMWpAgmsYiDllvDJEkz1aOuHZIiDolXqYIUSBY01wg7ncKeDUkWBxp6ITGzuz48vdYo8YZ7yRUgUQnUMyVKt6rA4wtFeLBJ7LkiiTUCSmFg4pe78Hwuaep9hhqhIfWGhoYpzN2285c3CmGvOamBIsL6DDRcY25rGZ9da0CdT92QSiyAorSjFifm+xXELBSw5RJmIMLSygCRDsK3wqDURUIr5gAzmIiuUjFYkJKW2JgomKGWW5jqiMu+Md5bLFArDMw14LEmztHImKi2AWKJBNgl3lOPKGyEQoUFKOtbEIAOFNSJgdwChlrhQ6JRksxXrOos5mK1g63UNAG7tZODYqI8vyaNlCxNlztetQZR0Oyikgon2WKDWj3QVMw3ybZVaYHaCtSVhSTZtKTqNRbp9Qq2ykACq63vapXlbF2ly42habJucNU7wmBbQ6M0E+liRZdbyTJWD+l8bTH9pxAQ/n1Q+IO9RfjFH9f/8/+09+yJnI4iRNlqU+8d1ISP1jfV/tFhqIuLm9WdM2F6smnNuOiTLPSKUeBFdvzBd69feiUzTXG52hyfOBec9SkJBAFetW6S1fn05N3vXL/0CpQKSq3yvHRS1K3LuREiX65BRq/Ko+xm2U0PZngoL5bN0UHuW9PEwEm3vydujM4ZYTpZ+aFICNW5aUEETTIGEo9M2094rJDlBHO712xqIRzNYAtAiGEEg2Spke2UTqqVToRLBN0ClVKcWNAhGFG7x2ftItVyOT5OauAx7MdidNN5BqEIXDUs+KvJnbQaUq3sGNIqSsaaWZnXQzLY+iBj0hTbQe1j3chseV2/8JLQCBmXs6aPORlidVSS9by8vNi99sZ4kEGSZj9NBke1j0X0mxVbrarXPs5sLFdDdZIWXR8V1RNEb65D08JbJx6XrPFh7CQU5bKrb5X5+irebM3ZKY0cGJixpiFjXjszQw0fHVzu6j0unIwdiimEzJZ9YxK+Y9PRvKtORkfDtexG/ZgmYHDaQ0oioqQFSPguz4qroTnKstBMm806GwGYwhYA5p3AtIEFqm4ms3S96caTNDqyBnLCCDKghRCHUIJRV3uE1/lBedV1+zmHA9khVRDAYmw9tcbsZ+hG+oSyNCBlPGMIRddE6D2aMiuRkIM8KFinAoZDMRLX25hQnEXXQ2i9Ohxx2aeqa0cTtDEQRTkr+bICFM9QtRMTrExbTnjdI+XtgchuKkW4nuZZV0eEEI5WYdxrdJAoSVgvzWGWNso7EosYHWKtq44KoZvj+ub54d2i7sCAm4OMKp/0OmbRoYiNxRQNuCjmbXW7LPsODESOcW52zOlBlDpmfGvCLPYwHS+a5lZx2FzuYOFFwmSIhsQ8OEBE5fwkSkrOdouL2YkYNOyQGhPiPOpjOy0iAfnT5XB7Lws9WGg9yxEwrlUoIZALXGsqYj8aJc826niClaR9Y/Ny5PveCkhizBnYOchim+X5zbwfTQUBaKvDmBPife0RiS5jsO1RjGo6y+aVnJUuT7LzlS8FZNE1MhJqynG+qRhxzWyaXlZmkvajcnKzRQHV+1lWD2JQ/YEohgYEvxodFisVCIBZCD2hxtWHGRvaTPauyGgVNGb9NC02Q0AQ5oHpOtX6Yv+BaGQyuGGPFtveYA6EA8Z4hCiB1nHWeT8L1iRJo+Vhst8uHSZDksEaAgBjEZ1LeKU3Z5NptfItsHuCDtJLiAsUCUTaIgZ7lKeXm+7eQTY0oY5+ylgwPqIidC0qYGXhjAwkF89Xw/39UjfABJ1yPFgnEcmBQ4RsBrRH+yQbLZftwb5QMlQeTgjXQ6cSkgLPKRtk5KRNRvnTG3l7xt3gdjFOEkAgbUIJ6u14zBe6m2YC99N2t83ygARsIsTR5gS3iCLdjLNsvR5GIwEs2SKVE0wtbJGnRI3wYXUNALgZHZc3eigSlbHJvLUpgolzLsIQZD4ptj2B1meQbUJbClWIs83TgXKVFLCHIMJ2JsrVEAisZ8Vo1QIESrxrwpioUB1m/PqCV1X34oujShrAupnIdhoCAHLvFoukrv0LD61L01Z1B6yopAFJyAO6PI/WwrPbLoikdX7ijeOiVv0Rzy+fW2Xi6THWFARYsGqD9vJG6xlUzZBeXw8vv1gM3joSioAkABaB0ksoJvOmujU6auadHssRzkLHnZQ0kyDltYuj0Cb5+KqrT/OR2eV9vcnH0HPYRZAGS6moQsZ3y2J2tr64mJ5wG2CFzJSQYEEHIQ8+g3u7dSeyrdibXtT1cZH5HtTYlCSBfabaKp1JnI5u+liGOhvNrtrqIOVRslWNEOj3Z+mqIthfT4/5ZjukBRX06PxpP53FjNBtDwBYHpyy3ZYFr6fj4/Nn7WTaTWdHjz+SZUmxXYGCeGfH44ObK4up3iv3np5Xs/0qnyS7rcckyxCtB6765vSI31QAwXYyItumy8oyCdn8xrHEzgrUaKrVCrPpv/zJfwJbhN97P9gRob1/f8bE53/iJ/vpbHR++clf+JXFnfv3vvvdH/6FX7TT6Xh59dYvfgm5EGP40Z/6GTmZTK7n7/zsz6n96fjJ+cd//bcsYaVRb//rX4LOkYT+9//bv8AJyqvdW7/0JTkdj55ffezf/iYiSWL7d37ip4Ucwrj43L/4KZ8ktO0/+Yu/alIxXu9+5Od+IYkBYvSZf/Wv06Y1ZfaZn/xZBGN9+Bp5/oj2lIQduHyLrQDYa+A3P23qI5hv6YePkp4SvbHLN0mXEbTTT39IrHMz2dJ334TdJOaV+PA+GkriOzN/O+ymBG7J5RtkmZqDOvv2i6DbN+XA3n3Bd8ckNmDxIq1Kk+nkowdskdm9NX/3zWH70I67/KNTXI+FW4SbR3i73+/p8sPDZH6rOxyKDydw9SCJV0g/RPUng7B4c5g+OyLFHF+cwflDdTjkVydQ/jDDa7YUYflqYJLU++mTUzPa8eV93H5WTvrRRRZuXoKoZjsBLl/CqILyjK8/bWddej7Fi0eIz+HNAVy+RMIm06OwewdShbsJefKIpIvkcuRv3iDJAkhBn77OtPQE0vdfj8gzBcJHn/LYkIaB+RtQt3vtTT//PJUG0JB+800MAdIOP30FIsU6CK7eoEZRO6jHX6CNBall770GIUbW4Wev5WYdWoSefZKaSECLnn4y7YwVmr33JgAYWR+efAK5iHSAV2+g3lHQoKdv4xb1k776RjQ1ycBw/V6mNijnavnnwj1x9A5afZ22C5zuuc3/ibs1TpFafDs1lwAWaPXXxDx34gysv06aOaUnrP7PpnuOSjrcPM67C0imdPNXyDwL05P+6o/Z5kmSnNL6r3y7TMao2XxA6guK92n153B41/MHaPsNsLvk+VjV75LNFc9Ky7e3xPMzP9rSZ/v4+qHck/n5ZLj+BEY7vmH++rWIB9xNxUd39KxPns7485ejWML5DK1epXDHKwquXsOo8e1h+OgtlzfFdQ4Xr0axpKspWLxK7M248ub5ZzHsgRqxx49AWbOrFC5eQ2zJNtP+/NPMt0RC+Ox1GnZI5+yjl/TI2+vd/JtZZg2z3ZO/nqJWiRFe/CeBAabAXf8lp1HTzl58p6TKZsw8/S97vBqyQj/5xih4RIFf/BVPgYWDu/zmBMpYMnn5lyXeaLSHln9MjYYMuMVf5MgG7MPVN3PaO5+Sm2/gsPHJPlz/CZcmIwlY/wWCveHRXX4r9zsfb4vZ357B6lBPu+y9Y9QdErDVi9fg+tSmhj27m1yVdn9Vvv/Qb2+p2TD67ildHQk/D6v7aHNicsWe3c2XEz27Id9+WW9etuOmeHoQd7dYXJObE7A8BdkuefqQn58Op0367VNc3YX0Gl3extUdBrbx5q6/fhDTGl/eI/MzP5uPPzyyu0c2X/mn/dW3C4GVrPD6r1hyAO0H9uavRTYx7QWafzdP4cBb++xvy4QYs8OLP8F5qc1zf/HuaDRSw0W4/m45gkOo9PNv5Axar8jqv2A2BebSXn07HyWNW8Dzv8wFsFC7i7/IRr61Epz/RcHG0F35q79Ix6kcVuDqWwX2mlk3/8sUQ5ekI/b+C5luw+D84m2iAQat+ejzvCEm69l7H4s2QXBgT18kNmJtydOP4RYRWKNnb+EddtM++9bL3hcBWfj+61Bz5C24fI0NRGUuffcR2TE/qUd/+7KT+4Eb/uR+0gGud3bxcdoIWTry7Y/RemJGW/6tR8HuRSLFsztwyH2mkqf3+HxKyufoo5fA9kzvy+zyPrQf4/GGzA/g+rbNFb+8Ja4O9N5Ndv46lJ8A2ZJeHMfdPQR2ZHWA57cA3+LtG7x+0xysi/dP4fY+4nO0uA1Xd1lcCXkP1G+DrKXz2/jyjI6f08e3wvZlzK/R9gGRn+JggzYTfHUPiIouTtjlmZ9u2HwMFq9ju5k03TD/ERa6YPL43bdDJukNRVevI7pOdmm8fiPxLVJAP/5RYnoYGfvglSgU3SXw6vWxe6rqqbr4EewscYY8/ThznQuMffBqSAzZJfb80wTUqOPw6k2iB+w0fvJx5KwDFL37tkoZqRE+fzkJO6gT+PwN6hxC8y/885/OljfDZPyJn//VbLsBhH32l371cLHYnNz63E/9q/2PHl+9/voX//lP5vNle3jyzs/+7MHVsyiyN37jt6fPzm/uvfD5n/iJow8+3L3y4PP/+/9x+PhJc3L6yV/+1fJmBRF6/bd+e//9J/NXXv0f/9k/e/A3f/PsE2997l/+9Oyjx/3e/uu/9ZXJk+chE5/60q/f+ea3V4/u//BP/eytb33n6ac+8bmf+pn99z+8PLtL//+I8P8rjReAWJnp5TVRmkg1vbiCPlDrpufnRGtk/fTikhqLbBhdzUnXE++n19dEG2Ld7Pk5sj5aN312LroBWju6vkrkAI0pzy9xr4gLs/MLNEgAwOTiPOl74MPk4pINEls3Oj9HxkLvp5dXTOrow+T8gjQNDHF0PWd1HQGEMoMaBwBgK6AiDmDWQaZJCBB2CRhoCBHqDCocIyQ9JwNwECc9JpKhCKikUCUgBDRwZEjAGA2ctcEjTDtEehogRpqggeHgkOJkwAEgLAUZsMc06SKVzAEEBgKVCCEiJaBKHCRUYtxyDzCyGA8FCBEqSDoWPQiGkY54QLBCuEsspFhG3ArgPLSEDGmMKGrGWhIgxX0kLXOIIBNRXxBrsQZkSGAA0FHc4BAQ0ZG0PEBCTMDDKLoYVcQ1wS5GTWhHI8DBINSkPmIIAOoEMQgExLoEORwdIh0FngIb0FACC12EqEmBwjEiojhwJAaMWhEdgS6AfkQc9ADhlgNLYsSspUAT4ABqk6hxjADYCdAgQII7gTUIgCQdRYbhAGhPocPARjQUyKOAKW457oGH2NfBtgCG4HoUhwgA1B0GOgaEXe1tjyPCcYiuA8gHM6A4gICQayGorMeUNC7U0UMUFDISgxBdF0EXHYCmjrH2DjPfRdMiCynog20hDNH2yA8oQGJ7FHvoMQW1dTUIGEcZTA2gD9BS1uAQCdaY9MJDgjXAQ4Z8gAahQUSAgaVJg6KHTEfcJRFR5CHsy2gjDAh1AjmIHGEdCZFAGXHLYcBQRzyMkIfOYzDk0SJkcdLhGDCwGDUiBAStR30JXQyeoFYET6DFtKXAYea9rXFULgCsNyAOIQAY6hA0CB7aXXQWAxtMhYn2LkC7C2GwASFXRzeA4IGpgVcAB+8aTGwAENlNiCp4gNzOe40ARL6NUUXsvKoQGrzDxO0A7GzABNcuDCFE4BrgFQQ+2BpCGQKApEO0+14FUTQkyHusGBlwAAApQQfsEaVtpH3iAQqKAJOHCJDmUHIPEFZJ0iKHCB0QkdwhgiWGKoMxQEmRSgLESDMykAAQ7wJpWcQEWQKGDHiPNSY9AwFgxWhHHCSxDbQTIMKovKsA9MA77KrgPAQOmgZ6gLEJqqbRRaeD3UZogrXINjAE5HTU22g8YtabigDtvCe6QdFGb6GrYgwwKGA2wAMCjFcdh8Zbh9wuOB29R7YC3kLgoWoxdADaoCuCfQwRhW2IOsSAUZtETWIASBVQgQgx6Tgeooc46Qg2DAVI+gRYGl2MpgSeBExwx1kfPSS0xVjSADFSCdQYB4dkhiXwEOOeE4kdorTH0HIfIOoTYLmPAA8ZVMhhQrqE9cAhkgwIag4gIEMCTeIhgDIhHXKYkQHjXjhIyABxlwDvoeZYiQAwlAntsMOMNAH3zEOMDUJDSpzBChNJI4BIU9wgDzCVkXQiQow1QKoAPsQB4gZBH6OkrGce4DhA2KYeIKY86tNoXbQMdUmMEGlGeh4ABiagYQRc9B6jRkSLoMdJh4HDwGHUihAwtAH2Y+xiCBi3PDoCHaYdBQ5BC1DDg0MxACxLqH2MlLQcGQg8Yh2DHjMHSMeRB9EG2JfAw4gYbhMoAYg4aRCwBASE+hRYCJ2HfUk0iBGWV9dp1YCIypsV39XQ+/H1tWg7R+j06jpbbTwm4+UqW29DBMVqldZNDGB0vRB1awEcXVxnm53HNF+tsvUqQFSsNtmmAhCP5kvRdB6R0fV1vt16TMr5Ml9tPCL5ZpuuNzCE0XJZVFWEuFyt8/kyQFJsd6PFTYT4n1BC+AMeEXZf/GL/v/4vSVUR5COAwUPHaMSYdZ3JMlr3ziOf8SJ2CnEGbIDIB8SjGnhBu17neaFqHajnTBgpES9cGyDukRBRDbwgw2DSrFC19jQkLPFSIYG9dQEHEymwbpRRKU2aFrpRgUECJ2q74vssGE9pdNETmvreWWQZz3w34AzFwKLpUTYaNpHTIXJPaeoH67BjLHPdgDPkHYNWopSb3vEk2OgJTYO0HjmSZL7rSYG8cwlD1nEzeJ44ByPGgRI86EBo7pvvMzyB1nE9UOytxzpJDUuotdhZlzDoA1OS0DjgFFtrBcfOR4APhvkumYQIXcJABGwYXMpT2SrEHU+QcyBCEQaNBHTOCv4PGYkEIQE5r1EigtSYAxcEVK2YJP1g0yQbmgFxQiL0QSN+oJZVMvEeJsi0fMS7zvNE2F4iQaMDENhIRBgGXuJemSy91Ty/IfuBM2H7AWcsaACh9VhEOYgCS22FKHStPA2MCT8MOGPBRARsIPvyZlfsA2VNmhW6VjEJhAgve5SxoCEG1mMRpBQFHuR/Yyil0WhAuRlCwryDnpA0SOuQo0nmO98H7G0YpwMU3PQhYc6jiJBnlEoZMM181+McBf89ZYkZ/iFDlA6YnPRX19kJ8t4nCXQOO4cISHc7jZiajJFzAeLcdz3OsdUM+55kxFpIEbbGYC7CoBGHzlvBAQCsH1wqUtkOQATBkLcgQBGlRglwQQDVilHSdSbLctlIyBEF2FmN+H9TBlQrRmwYPE8OmvkNP8AkYu804sIPGotofIq+z1ghctXIyBEFODgDmWUJhID1nRXpP9aRYu+8h8YRHqQvU2KU5WmhaxkTgNG+ulmI48RrgKEJOPVSihwP6ns6ZEwiIcIPA0ppMPDvGZ4jZZzgxmPc6cBIFnvLBATBU4qV4V4qkSFlHeeWUGY08n+vQ8aE2oABhJ5RLHXA36ugHHnvkgR6z9VAsXMBKyo8o0SbCHHm257k2FqbCug8NhYmiEkpacqBdgE5nHyPIdYwZDtSEq0h/78xNPN9j3PsrEqzcb8ZIMcUYmc1SnhUFiXRRQHVgNJyfl2f3spsN4AEUUi8k4jvq1XLShtw+n1lvRVprmoZOaIQB6dQIoKyiEXjBdKtGP8d0wxQcGAyWXeWYwFlWgDtBNStGCdda/I8l80AOcQIweg8mqlVm02CCeZ7WmMSKBWu71FOg8E46EBTPyieIWlMKgrd/D0z4Oz7OrThXmqeQeUcTyKCVCpPafp3jGMMayOcxCgqRy3nkSCiTcT4e/+g4C1PiDYAAM0F0ypCdNxfL7Lj7ysLHmsDE8y0ViT1FGPzD5WZBNmOFlRrkGAmpaQiASYEZBHNQv+PGCUjZ4nsJU0TYDzAFtLj4XqZ30LOcSBbWlCjIyOJ6hVKE2gcxA4SEWSfFGwYnOBH9dUyOUAUUm8UEjxIi5JgYopkJ0Z0GGya5rKWMYEU0fB3DE6CDYd6OR/dZn1v8jyXtYQCYUCCUZDzIC1Jog0C6E6MWNfpPC9k3YMUE0CD0TCJGAaEiVQC6k6UtJcmSzPdKpiQ4BCKJlLhBi0yoKzniY6UNb1jLAWDYwLG4BgjWidOGpEB5XzCAsZUqYDJ4bBYpEfIOycE0ToCyKAF2ukkDZR8j0l936OMWuUR0Z6CGBPqqdOap6kbNGDx/2LvzX50Te77vtrr2d+933577z7bzHBmOENyJEuRLIWU5NAJEhtwAkfIHxIEQeCEiiXFjOnYVhYgjp1cODbEiCKpocR9SM5whuQMZz0z55w+3af37nd99qWWpyoXigPFjuNbSghQl7+buijgg9+n8P0i5Jq6Qj6EVs1Wnd/77/9/RfizEdNw606PeZ/93G+lo3F4fv1rn//C1b1nh4+OPvvbv3N9cLepS9AucV6OZ8vf+O3PF52es4g/83f+Xrq15Z9PP/vbv7vY3GdV+dnP/a50AmvhX/nc7xjfp3n5l//u7xX9AYvLv/q5v50OxgiCf/e//G1tkXT9z/7W77YW3wx7PD41LeidT/+93/qvy2igMfnsf/E5YoAM/M/87S/gpp7uvdh7a4IKYngdvbXv5rgYNv3Xd+jCh8OrX796866Oz+sOe/AUW1oVquitXXcG8k2z9uqIzkIxqIN3J2zpt07p399xVkxFMnpr170C2Q4YvNpni2E2kmtv9+g00G7hPtjhUzcftv/R9NW92cMPB/dGP+yQ61Ex0YN3In7dAXzlHO7ya2+1iYZv8+jR2mq37X5IgwfbIFrYm1H34biMFP2n/0vnH/5e+/G77sVt/6NJvNNGj0j40a6IkvDa4R9uN5HkN/3Bu34x0eEDN7i/kezazhEOP9huOrk/Zc79vdYvnVmn826/HCv3iRd+sCEmwnz5Dzuf/3y1fcsFt/z3d7Rb8anbfX/NdlN61fHv76peJoUzemPf2sYi2H99wxJrJer9ZCI9MpCHfzP5cF5VsPbpux+3UhgOe69vW2ChAb2fbrVE2wb139rVUEGgop/cY1o3jh28vg0MbI2J3t51bKktDX96G8qy9VDnh7uk0qi7/BvXP3Ta+rLa7ry7g1rVAtN98wC3Sru288M9mutszTbfqeS5oZFNX7f6UuIeir+tzFlDtsjNeLfo9L1ZnL5uxLFifRi/YdoTYTaC8hsleJTbpzrVy0l5BMBBIF9JxFHLenb1E9A+kWbDL79VmAele5tk3xL5R8beCtvX0uqhJWsgH45WvXUsVf3Nsr1fgKc7zdfj8gPr7rPiTVW+r9km9C9G0btRMTHRoRN9sL7cBt0nJPhgTwaLe9mHf6U+PkdM3twZve2Xa613zKMPNur12r10gnf3ZJjxFfU/OGidkiy8/tvDak27F27w7qZYF+wcdd49MEEWxIbcf7rlFUn97tuTsmNurX781+vjJ5aq+frg7R3pVKTC0Tt7ltWwcLtvTqoebOMifblBxBJi46+1oBYgpOnLsm0gdRMenwKlVcqSb2gMLKR28VXJhGahXX2t1WWLOU6/raHSAMDVN1okJPDx6isCZtKO3forWVsh6MD4Wwo3CnC8+hMFK912bLB8DG8ua29LvBzXBQc+Kr9R4kpbjpbfNLhQ1V5v6/uucz2MJ3Lt3Q677Gk/dR5vOZdB3m//g8Ubd6cffji4PXyjy87H2bYevOM550PrLJzjTX7RLYdt9O5a5zGOD+zoR4F7Ml5utYP7vnO6pv3YPxo650PZrcKHk+AhT/bb3puBezwR6+mn5u/9Ynl2rCk72fdOBrJbeB+O+o+jZEd13o+cR5N8W6HH8fwVyDtQzWz5nQqsc/VYZK9IPkZ5p7uY7OKyASdV/D3AAqumuvxO4w6suDCr7xk6huraxl+XtAttYeJvSsJblcL82xJ3obpS6SvGGQExB/EfS+JZ09j4T6QHG7kRnm/cwaA1HzXp9wyPWp3B1dc0Ybo1KP2jBjJL3E7nJxNPCQ2h+SGuOQAAIABJREFU//ZtXDfaB53X91im9SD9D29e66j8ROx0391CArRIdd4+oI1Rgen8cI/FuhijtVcHtu4KTw9/OsYFVERF7x6gEmQ9PPn+2FvZdAONvt+zZU+GevDWEOeMwdO/ln/kVbMn4f7o1U3vxuabdvxqF+aDKmr7bw9w6tYD1X97HB4xtZn774zc43GyrXofuM7phsAX/v/6j+gffrV45tnu2b3oIc12Tf8ngXc8kRule9j1Ho9VmPqnPf5krLtl8HgcPPSzHRW9F/qHk3pcOYd+cLSpw8y96DqP12Wv9B72O48G7UYB/v7vBf/4nzSffMlZ7AT311VQuNdd/8FQRIV/OgwfrFWjzLkgnbcPjF8GmaTvP2tIjWqn++ZG49ud/Kd/vXh0aUmzHA/e2pWswhJ23roFUGMl6/54Q3kaxqTz3j7nc9WEnZ8cWJBZzLuvbwEojWHDN8bKNTAn0Tv7hme2od0f30K4Dvjsb87fEnU2VXdGPxpUHmcV6L61g0gqFev95Lax0vL0s5/7bwZnF8ut3V/5wj+MprNktPFrf+cL3jy+2Nlz4xN4fW680V/9nd/pPz653r/76S/8d8OLq+Vo49/+u38/upmd3Xv2r/2n/9n4waOrTzz/K7/7e+v3Pzy/+7Ff/b3/oXd0WozHv/L3/mHv5PLJcy/++3/rv9p4573Hf+kXf/13Pr/z5jtHH3uezI5Bk5Ncffof/I+jh0dXzz337/z257ffePPwl375V7/wD/Zf/8njjz3v/vTNvxhBo3/uFSFu2zCJo9mss1x05lM3if3Vcu3iHLTt4OSEpLF44WOux9zVypvN/D8NFF2t/Hg5uji3rVk7fjxcLlFV9c5PnTwLF3N3MQ/nMy9Jong5enwIdLt2ft65ucZFPrw484osvLkOFguKQfOxu5aR0fkZbM3g8eMoWfKqGlyc8zILpzdeljqpUqiHhR/EUtKhzT1PCN0O27brN3INIdeSsKhaEIK25+atJCMqu26eazsxeMgqbXXPgpDHUNvQqohmWpIh0CMm6tZMNOi7jW11FxjPj4EBkTWDIE8jt7PpM56n2Aw1HqKKWNWzwHcSa6xndJ9KjdqBhGPaIFZ1JOs6seHakWhIFpJWOa5Llq6gDBUdk8IiMZSsG2UQV9jggVNya8LWbjilQCIQfMJzzapA8AFNOZCuRn1SYqtdBbZYrWntSzrhZetcXQDOvKMjR3MF+6zCwPrSbhDRkpIJMnQzySsqaI9UPhFW4g0omFsajcdIkDEoPb4+kolbGkGGvHRxA5RZw9phhRVgSBvkpahmQ15wP5WCjFDGXNkqs2aVyxqs4YAo5qxawfpIRE6hBF2nTRBpOXbDPqFeDTToY+2yJRSsjyqf5lrSdah6QOkyp9IwvmoaiWVDaSoKG9Q5hcBCYLFpgTJlQYV1zEJWmkvpkKTJbVBXzC2yqvZy5aOibXLaaEYXTdvSonFtLCoTVBVnVVkWrLIhSERRuaLlrG4hNABat5GFdIva51XRlCyDIU5qWZLSOjZprQ0BnDiVhE0g+IZbKFT3JO36FV0nbUi6I9tA7Sq7iZSFMhR0QosWFZ5w+s4KEOlI1KclwcZVaJNW2impYmNeKtKEjTPgMaQNUqDPcoQU02ZEJZwgGbL+yNRuwSo2dDPCSyPQkFWQKKLtGGmiF7LAAUpauipXNjCJQaLNa0fViCAg7u5bj9FlVeFArAwpVA47KoG4qlPhS81NpjLpWInxvC6g1+aY5iIFHZkhWtXLOmokAakslasEVnNdIL8tiVcU8vmnYD8gaZE3Qa04yEylHF1aupINcrOUUSnadl3BoddYKzsW+P7KWhtaO/CLPHSCDZ87RYragcZDXCMr+y0MvQTY1jOmjyvaoj4CYy/LrBxItu7lGutuizpeioFxW9PzKqtNT9ktoqSVfUnXvEIMiQ1xZ10Ka4K27fHKAtSTduIUORORYmssk6oAJfB1onUDYhGhSrWZTUHEVgVACBkNAWCVybSnk1YrnIgQVMokbW4DmtZ6oQsakqVAqcjaAGTaCJBXDhAWxCozHswEWcqChnrVwkoXJjSFhdAiY4g1KrNp65HagGmV0wCkFpU6MZEoETHEgD7Sjhu3knbhv3hBRIShKMdu0GfcE1bbPmo9vkKC9EDt00wJOoaqT4WUZsvayKmQMn0iqJdiRbpY9txMST6xouuWJQRrLeyikmnbR9rri5SDYNTWtIEajSgYOkVhzLCFHTcHAPZhG7DMatyz7ZBVJZB9RddoboEaGhyFsxxnCS5zJ8016AEz4VUJVE+wMc8UFh1JeyRjxvja9GhpW9NRYNMpClx3BR27hSIqVKgTrDQxrrE9XhoAOsqOmRB8egMx4ucXqPY164WpAYoa03NqYFSo2yGTFtdB4wzYErAaSNhnOYaSKrNGJF6HImK9kW28nFZ86ObMybXAQ1phqpC2Y6QoaxxBhrxGzso2zpDUPquMJBNWUVJbhbewZqziEvdJ49AlbJwBLngk8o7bW0MS5kaALdxyWnBJBjwDbsEE65EmcossvLx0kjia3vjLhZtnw6PHuGkG19d+mornn4KDDk+T/tWVmya96U2wXHir5fj8lCrROTuNbm7cqupeX/GyiC7P3bzoXV4E83mQp+OzEyqa/sVZJ8vc5XJ4eelURff6muVZWGRmf0cNO8HNJRVN9/LcT1fOatm5unDzrH9+xorcyxKL/nxjyZ9HReiN/sk/+n/rIvx1/Z/8x5P772Wb6wai/tnF9d17mvG7r7/28Bf+LTKLg3feyV54IfRAcDUvRz3BneD8QkyGZTi4/ZMfH770854sxu99dP7cs0xLZx7DiGnuemfX9cYo6Qxv//hHj1/6eVcW3Sfni909alR0eVP1ehlx/ffvy73dZn/77huvnXz8RQ7l8P6j1a0D35ZgIWTgpuuj8LypI4p8HZyJcugBt+ULbQAQA8KWWbctFmsTtkDCRTY0nYtV1Qtaj/GFAhDWQ4JX1pUy2XQ7N4V0qQpJcC6qgIMOIAuNIFpudvqzOW9kPOm5123LQTrubpweNoDrcQfHGgu73OtF09ytRTOGzhzWjMaTXnca87Jd7PX8rIymTb6NtXCCZR0fdNnxOU6LYDdsbEgTvbg15MtleJasntvsJQUsyGo7cDLF81qPqElLb17MPnbgNKJ/Ws7uRZ04QykqNzmPM5jWeqPbGu7P6maLVNrp/Pgn6a/8YpSnOAblhLFK4QrwoCpJxKey2SIZ704ezOIdn1vFVkaHUHLizJTp2twNdq8en67fXm+u27mfbLgMSHdVqNBpXN+7EW1ki8jrHdfxjufaOrpMsknPAshWqnWQCKk3VR7L5sPB8CxerHcpbp1zXax5hEp3HrcuKzs971qAwGQ9f3hcxxsuw8I7l0XfaUOMVoLXdb3bJxelcSnsIXKciq7PAqBLACBgvJUVYnkp9gdwXgOGqlEvPLzWDsEDbuc1kLa8NfaWCa0auR6Ca4GQrbYG3vEUMtwJ6thEsBDV9po7jUGp7ZbP0kog2gw67vkCgRauuW2seN6ovY7JDEmq5lY/mBWgAWpIoADuUs5uD2gt+ifF/G6nu5y5VXWxsefXrZuWckChaoNVVkyiBriDs2p2y4/yEq9AscG9uqKVaLq+MsyfNWKCauQOT8vFbX8SX8nGyUYBrQwujOoTKfRaPE3W12vqDZ6Uy1t+UFV0aasJJ0LRVK/W+4Da8KPr6taaY2tykjebXYxbMK2NzwQh+METsNZvDybBw2m922NA4oukHUeBqauCAo5014WXBfZRPYz841kz6WFiyXEq1kPqAjRrrEN0l6HrDHNYjUf+g2mz3cuDbuf7b9RrQ7o9ZItcc0euR97pgjBTTvrug4UaB9loMDqfYgMX271onjm5zLcdNrcAmGSjt35+LFus1zsoMbRuF/v9zmIZlE0xcvgKCkTSSdi7yCyy7YCQvHYLebm/7a8KL1HZDu9cFxJz3YdBXBhjRD+0OfAzUWxzW5hetbwebw3mubRUDTGPFdJWDKmtobdsbu6uDRc3eqb0fpeWAmTKDrgyiF3lds8XmrK8lh3P1Y29afR2hKQEjQ14UyPPXtdgy62p6z+clx9b98vMpK0eeriWNpFgwFtC+UVqNr3aDZwnC7HTc5vK3Ag6IlQ1mQ2JazWh+CRF227lRdGHN8W9kSNLeFmLSQQYducyQNlirR+d6XSNEybdM1WMXMK1M48tw0Vv4E4l4m069MZnq3gYImb9c1l0HeRbstQtZ/nQGVzNEbGrUb97XJdDWvWDtcNl06EgAHyVt4it1ga9y4wQ1XTo6OZqFo2L4WB4spIusl2Mly0EKN4Mu+eZYWi12R2cxADbwCsKEdBcL24NgkXGM5vvueGHj0WLimfvdmYV0lKPKJjnTlLXd0amIcGiife93k2uDJVrhN8kQCi11bc19pZNvUt1Q6OlWu060cW8FUZtdXhqoAZOWC4L5j4+an/xeVXDcC5Wu25vVRmFmwEmWYsbI9eIBHR4Us5vB5PkRqRhNuFcKpK1ukca3Q5Xs2K0Vjr+4LhcHviBKPxFma13obI01rqDBSPRteCd/KYznjyZ3ewMnVaxm7YcO9RqFrcqwtqBncukHrmZF42PysWu55gmnM7TqAc5Y0tVDgLNYfe0aIcmjTprD9LVns+QCs+nlpF0Y71zfI6ovb59b+8nb80PDla98fg73y2HQ3Cw1bu5tBDf3L6zdnyEkZntHuy+885qsplsbOy99WY1GJAQ1YUlWs0Pbm88fKAoy3cm2+++v9zYWq1Pxg8/VJ6nR13ncuGI5nJ7333w2HBH3N3fu//OamNb9YP+yaWkpNoY8WnC6/KsO/qL0UX4r1eEP3vXsIT67/70X1WEYv+WN5y88KWvpOMJW8RP/cl3r/Zud04unvr295drGz2VfeJ7P2yiEED4sT94uer2Zt1OQYBBpH94+dS3vxd3h65onnr5WwI7l4ORQLqxuP/w9M73Xi+GQ5rWT3/ru7nXiS2OR6HNKqdoX/zKyyIMMAQvfv/Vdq0DM/HMN79buZHB6NmvfqMVUkXhvS993VC6XH+aXW0Z4wFcw+k9XXDVF+T4Fmg7IpT26inUBBpLs7xtVADw4iV9EsrkPNhiTw5a0RWhxJdbUHeQEz8rL31TxjCyV/dsFdZrij3Zbat+M6pfjD8a4+oGjOziFqzDKgL4/K6pR81azU93TbOW9qF7sYbqyJAaLLfaepSOkH85oMtxvG75VRevJsqtTDWmq/UyAFjt8fK27aRmtgGS3XQd4K99DX3nj+ude67ZNsudMoA46/LZerUG7ctftt/9dvr0CzzfwMl242leOGZ10DBl3/iW+drX67sHvL6N4l0ZZDZfc9qfqx3hlNgsbmlXw7oLZ3vWT/Syj5JbOshVGzgXexICQzE5PZCcmBaxm/2Wt0Z55ur5hsOwTpr5JxTEnjt7KT+UBM+qLXKz1zqmVQ6d7kuCfTj9eXPZajnF2/Rkv2VMAQyvD6ipMY4/KS9qDEvdJTdPGwVEhPHpswoHGlpyvQcJUIDj673GUkglvLzXGl71dPE+EhWBLopPnKqkxDHxkacSbHec5MeoniGwyVcfUVkyEKHlZKNyg7YB+X1ULTHe5dkHrEocu8HyoFt3I5KI+NStEqqHfvYQVQsSDerztYOi17e5qo9wGTMUouoQZDNHr/n1A1svHLTD4vdgsXTxCCXHtFi5sIedbIhmm3VfwtkQrrbzNeNOA7TaFmHLck/On69CCMqIX63nXXCnPXnKzq+RX8fbZLElPMNrYhe3Gqa30cUz9cXUjdR0QtM9GZamGJL5jqBiTz2+A5McsaLap1dbsitB1jeL502U27JH5zuSGySRvbmjiLLaJ1e7ZY/KVGSPmCKEWDX9KJCG4AjH73LDGHBIejmkIVEKJUdui2mrctlDIi2t9m8eRBpSQ3HykGtLWpnWPWLSSkovPgxahc2Ar35Km5aiUFRQKaFF48VHntREdZ3qcaSRT9dp+h6rrSddJ/8IWQ0U5/EhF4qKzTA6XDP5IFmDzumQVF3lahBvobJbRRZd3QblejOq2dk2rMb5SD2fHm7DdGGCdrlnyvW8i72bMU56xaB+sT7eBslDtuVcjWA1Vq5ki4HKN2QgnpWPt/XyyFtn55u22JRh2S62TXpg3IYl/TabyECQ+YaNJ9VaxU6HqN7NuzW4EfPTyLhI5Hj1kIN1LqcwOfVI3+Yrt/gAya5DCjU7CiCHYhzOB5tOW2VHdnkRsT6sEp4ec+MgGKLLzQPUtuZSx499OGDlEq8eOzRsSy9c7O+iUoKlnh0GkGjVsvn7LuhQmeH4yOGhySsnecQ0I0ibxcPAOAT6HXy2zdqmAg6a39KAAdzQm6etQE0H4JNnFexq0pKrXYAYwatPylNjVQr68PJeq9xm0PLjvcZ2cbj6VP7YxXJuJnh6YI1bhtg92WkbT/ezl/LHLm1PwK5ztQk1BahRsxeV7eddFB1vWBHWfUmPdxUcNK51L9aM7dSh5ZcTFPdMd9VeHthyMxsZ97oPizXFG17tAvNU2QH8ZkiyUbWmwD/9Z+bNH6unnwPpPizWlSNZ0tPppnAb+NUvtq+9Ub/4cXe2A/ItFRRmtY6ySQMz9IM/0d9/Ve3uUfmcXW6AaGZWtxh+SfqxTcZ4NVGswXnXLDZlJMhyRJabzaCyaZ/OdmpmgqKqVh/X3ALj4st9GWhQdczs+TasTBWx6a7itg+vX2yvSgBiuUcv91Rg28qH0wMGlxTmPyfPU8rrZkBu7ijUauzRs93GIwNz9oK+ygAo5C653qsxRMiasxck5y2l7GSnCN224c71tqFCgZBd7SlKLMle+OdfJlmRDkf3vv5dJmTJgzvf+QHO6+XG5i+9/HJYFvNnn3rhiy/ztDw9uCPaqoSYJfLp736fZNV0Z/+Tv/8H7nR5cetOIZu806HL6plXfkCzqvH9u9/5AVtml5OdGqtWtYb6H/vSH4ezRba9+dSP3uwUheiHd37wBr+cLQ/2PvbFr3bPr88+/sJzf/SN6Hp+ub7pvv0vdRECPRxmn/mNn1HA+v8s9iF/dg4q5T74iD85tuRn6ye/pdQ5PAR/lq7+FLGs9eNV//BR+Innkdb9kyf+fDGa3QTXV4OzU+pY7/x8dHq6ZGhwfDx/+g66tanGfVzK/uW5f3k5fHJsJ73g4mJw+Ojo6dt2a4ince/ivH98dDO96+upd34xvjibR067sUuf3ASzVe/hg+D2LpQyfPLEv7fHs9q7vBg+OVqxvejJk1En0NuD4eFhsTlwcqWbDgYZJq0WXV5KWtdWrBlQOeWFyUPISj9dZHUIW+XyfBMHXrVgZWnqkTGC19ew6rW06hTZlmU5MRfCNGrAy0yJBpYD48BAXm2alkIWpmnWhAS0RCyt7LKmNFVJi6iiDpUSNF2gyyBRdRMAGBJxjoqeaiKqK1oGSodeclQbquQA19o0Hs45KwXKHdUMoBT88pykqXN2Tjfu1nLAGmFUaMqQFjN8fkpmUxovHPyMVAOezlFNpBjwYgbjJbu80KsFgVjVAyxOYc5lE/mrc2pxVQ9JvgAtb6uxW7+LU1jXPVJBWlshB6xoLK+1GMO69ERhiq5tYlYRqXpumrqtStWQJmXYqbeZm7byPBW27KBy7jZAyj5LytCsdni3KFcnoG2bdVBLx1Sm7iFE+028S0exrVYNFtXABZmppK2GwJesrHQzcMqEtUqpHitb4sJWDlmmjDL5HPohDpZllXYQhdGgzXLuyiqqVJkGltpOWYvYaYlxG4WMMZSQeb7MqKta1tSLhSMRHje69n2qFBZaZky11K1AWTBeNayuLEIQWKhMnmCgUW+ZxgUpFQlrkxUcZapfV2ZFSss7RS1zJioQ5MrICFd9Vp3jnBoxwKLElS/VwInnrIKV7DllCbRv6j4uZiOU7Rj+vsrrwlF64CYLrHHTDJ16NeTgwHEfy9rmUFZ9XJ+BjAg58JPzHq+2cHiVr1YNU80Ga65QYrTokMKiBgk5cNMYImSqrptdGofpZoLrE70Scc7HcUPaOinC/rwgI7CMg35HRqjOV74Xaa7ttAh7C+kNK3F7kxVnuGySeAACESYijT3maA4Sub9jihmoTJI6vbbwtnWW+J6rI63bYQdfzc2izMpeJzewaNOMdk2NK53POXSJX9kiZw4svGU5K0JOARcClpOWEC5OcdUxQHnJTFQ+aAlpWqs6JG9YVdEiaqDPdbxlZdfwk3y1qp4zoEeFkqIXVIamD/ehYdDzVAXrXS2pF19B4QDZc/Tl2OIBp69nCU0OFB6y+kxXnhG+m1zYumPKHq+ujIhg3WHlQy9fK02fVAudqCzreGmtDMxSHpW1mqskDqNStiud1M5kViCs0jTkccn2rEUQIoASlSVhL69tjFaFs5XWattFxkAITG6WKx6UJVq0aR4M0xSMLda6JcTGIk+8YSRtgOPUCapSpTbNvVGWQ1UnpdtfqbZjsxj3YuH1sS17CDIvbYXskcJAhkTVd3VpqtrWQ+tYVqVtPYBE9L14B3Yk0TMBWjnketo2ClRD4KpIPtwCZKXVcVIJ0UMVwSJrRZ9KS/NkF1oHcVdbI4akrRyoKxnhqqVKSzHiOmXVKSlCyTCphRV9aEtciVZ1ednyuoJ5IGEXiQpU3VYyP72EJTE6IqI0qgcKwooTenKCqtLGMYIdLRwnvQG1A5sBLc7JcoEvLlQS0xUTYICbM1S6bdPxphmdTfH1FV4tjRvBqsuqBsRImIgU1qRMy76fXYOa2qLj5mdWhLJe49VRm+JGDb1kzpRtxYAlC+gg3axTMUNpq5uIFAApJNTASeNOkO3ScF4lU4N1s47qJa+QlkNWgshb7dH1c50l9Y6qem42F8joZh2KuguyDehPZb4QRuoBLxuCSiv6PEs0BaZZw2rBpFRq4JXn2lipBrg0DOejoyODbbRc9k9OEGkHg1F4dQkoDZar4OyUVOVhVa09fGDFHlON7fowKboXF8HVFVTSS9LO+ZmXpY/Kn0MBb/uhf3LcPXmCGiHWOv71Ncor5xdKsbtub+ZsuVx/clz0OtF83r04r/2gu7XmXV06eHGZvtQ7PwOU8qpce/iwDALn+fxfUoQWQhwn3T/+o5/BFANLSfnip/TaGjDm36QIIURlufY//0+9L/0fxuE/czdhzFL6/1xrkezXf0P+5m/u/+j1+d1bBuP1+w+ffOITFqGnX3nlg09/OlT59htvn770SWzU8MPH8e292vX80zMxGdVB7+nvfveDz/xaqMvt1948feHjmhPnyQkcdpDj9z48jO/tx4P1Z77x9Ye/+hlc58H1VTYcU4dPHh2mG+My6my8+0F6a3s53nrmG19/9Mu/Qkm78/0fzZ59hjHNjqb1eLDc3Q5OkYgA8nPn1JWR0gPkXgJLYLNmyJSGOi/HrZkPVQBgUE+uTtKgU/f67BIiYJqJpTOKjSknZrCYNYiKyPMuvSaA7UDTGwwNWO2yjesTx7bXg3U2DwAW6YQNnqjWsXpo6AwjBRd7NLpWjjR2LUOzqKEo3vK7V4WXm+u7frgsggVrdnIput6yTbatkyJeat5LRB3gjMzuOu75eXD/JPnVT3YLBVIv3gZOBp1EyC1sLi+9w7P5Z37ZK6V/w+MD1UkbmPliosjpmb1etp+8Z3XgzaHcLhvjRxc4vqWjvAaroJkI2iCcANbPGuOROZe7dUaj0WPTrDeAWn6NZB8aB7ArbPt1GbjhMcn2zKQ8reZ71VhS3vRm12UYlc7AuUa215QR7xyxbEc7bba2nC77Q0Ej99rqCEgPsGsWOTfzQWfr8uRqbQcTw059sWasZ9gVtL6RoeU3HIR11mPRMa/GktHSOferATShbq8kbbXd98Wpgg5yR1Y/bNoOI+tUnwusNDrw2isFZQt3uaww0VKMIvbRyvqErFNz0UBpq6eH3nRFrLUdWt8gArTY67BHMaRw0C1WNrIGqsgls9JUkB4wdFpoRJr9nnu4MsCQXVfPNE4acscRK9SmLbrD/ZmGNZXrBlWQL8HsjuOltT/l8YHuZBVMwnTLkAr5Kykm0LFl7/riZrwlrR9ds9WBjvIKrcJmXfj10s/T1fqmkS6/QWq7rFDUOYHFfjNJzqAEy+HYNg5LgBhbqyy/oXqrqGnYPcLpLenWDZ65YqJRC/nSLrciQDV6dwnvdnyQ14ct3OLYx/C0Al1iItqeCt41chTpDwp4O6Iy4zcrGXA34MUMo4iALm1PBYmA6DvO+VSFHotc9aixGy7poPa0wS60PUumOXJpORyCRyXe4GLgO+/NzJpLesie1trjaiNgjxPH12LSaT+s8Bjnk+HoJMXSzm/5vauSCVRsCLIMkFHJBu2daUCNGhk6x7gB81tsbXYd1Wk8GOA4lICkm6R3riBo5QYIVzO/zB/tPNtdNrji1WYVzaC2RPTlYHkJEYnX1mFMnAQ0O1Vb+iyj1VYVzUDbUjmWbIGgBGIL4ATSFZ4/xXrzZXOD2D41pTILDXdcIwA4r507pAIeeJDZ57tBkZcXlmxThK0SuEMKoVh92Tq3aU18ez9Bz3WDIq61g3yA6tYuNJjw1kJzKtw9WPIAphJ2KBdNc6q9LYS1LJaUTKgByJwJdxcWXoTei+HTkasrcaLtlosczKY0JPPlyHPOwmJiOc35iSeGxoSGX0HrGNmz/IYDR+RDsn15sgh61qPumV/3gY1ado2Ug4uR2b4+M5TMeuPg3BWdJh+5a48a2QGmp7s31y13Lvrb/StNaWO7NbzsVd22GPG1w0Z7Vo0smyIDcLqBuhet4jjdIP1TiYBxO6u66OEKzm/zaFrTyq22qnBm25bEW6x7pYBq1RYEh6fuxbT55RdMyXnKiu06WlirqFw39L2P2ka1Lz2DMuKsoNitVB24K5zviODRcZsJ9dweLzxUGT5MyyZiKyr2KyGCYIbK3SYJWrfgAAAgAElEQVRY6rbmYtK6KUAlqDesBLxzjPIDuZ5e5+lOMxHEALaEYmSNtc41bTfKivvdxyQ5UIFIeslyOVxrW5/PgRwaDaFzw8Pu2XVn/eDs4cnmbd5qcuWJiYHQsBnQA9CiaryaZf3B0u33H5N8u3Fsw679es0C0jpTmK05gIjogpphloZB/zEuNgRm7fD+oWF48dSd8XsPTMDP7z515/uvzW/dmu3tPfetb662d8qt0fD+45axJ888M3r8EDESb+3ffuP11c7O9cHtZ1/5TjEayfWOukkAwav9O/vvvi1df3nv4PZrry+2tq/3D9bfe1v5gd5e7zw686rs7BMvrN1/pAlZPnXr4I0fJ+ONdG+y9ZN3a9+PnzroHV3wojjc2Rv/t5/nf0YRAgCAMaipfwYBy/j+1X/+t4qf+3mo1L9JEUIItfbuv8+fHBnXs5T9DB3GAP5XlmoI6b29QRi88OU/FN2wM53efeWVdLIxeXJ867UfqDDqLm5uv/IDBAEx+tmvfk1GQXc+e/GrX5PDXu/k9NbrPzSEdJri9je/jRFg0Pz8F7/MHOzl6cH3XlXdoHN5dfvV1zBlvio+9cWvuEoAlzz3pa8aTr08u/vK91qfRcvV7e//AFtLgX76T77pqsY66Jmvfh1iW3YP+M0aaaCjYrTY5bmlwYqc3iLSIWTJLjdcYRxdgOUmaShDmbx+0ck94E356T4WLmIxud5kEhNdqNnTsO5xWuCbTZ4R0Fm5J1tIBCZo6Om+zfvMJCReJ5VnmHCu1twYg3Dhn25a0WkDEV50nYK4agFXa6jqiG7VPYtwOtS9MjjzaNZz7QyVPboaGK5YGno3ocMuyGwIsonuVEE8YeRTDK/8BQDJBqANLUJv2kfe1E+2CPlLKiqjqYvjASWFm2G4nGCUUrPJ9EvIX/k3LkrWoZfQqU/jEbOJX2O72KQkJ6VL5psevaCLAKRbhC1t43jXY2gUNTW92MewdhrBbjYha0hO+HzMdN6vYrF6mqkGQW3OX4IIu6IkN5sUFqTAdLGOtXJUUV/9Aq00ZdX/1UUoGzrdikyMatRMXwIGc1CR611XCEBydraPQIu0pNMtZmpcGzrfoKpluMaXu0yYJiyr+wbUxrNVdoLapHWoTJ5QsFDhsInfIyqHYdRkj7AtjINl9gCahcU+rB5bMG3CkUjfQ3VOaA8270hzo3wqiksiY4BDVD22cKqGg3T5FimvMB1TcajbxIawFOdttcCsg6oja6YmGtXl+22d0CBU5SmSC+t72ikGzqwPvYV77aNkXUVVMPNpPCAoDZYtSLYhFbTyvOuBDWNy1RPL54kf00VA4xE1iVsisNrCKIdVT988D51VsHBgPEFuSlcBWw4cm4Y5SGa/gG3BBCU3W9SL+ZzgeJOhGUl8ulwjtnJKiec7HCREMHq93QbSzpLqyLpWc5Evjz1Wlpy38/sMA+PIKj0kDEtStfkTjJUMqbz6sOsp0+Xp9YMOtIZbmTwmPhSkUatHATTIoXL1gJJSuKFavU+QsQ4yy0cha1vUtvkxxsoCB1b3DcpqP1Lxe0QDiqGuH7SOrEnb5k9w2wA8Qv3DPmoi0yn90y4tGW8TlIxpGRjWONOhu6QgmvtnE1D3ddSws02QbYZmilYDWPaNL53p0E9cG9yQ44OmfNpEeXDTIUXATeysApAPmJva+VN2uWN6s+DJAJYD4sRk1qdZyNsVyzooGWC6ovMRjQcouAzOB6Ac6aAkF3F85jhYmNwkj3HQEfZCZhfU7ShxocsL5KGal2rxhLlMtanJ3oZ9J9UzE186vi/UjanOkE8kzJr4HezYyki0fES9QILrOr9ggVPaFUh/ajhuiRTpIe3YHAk1Owo8X5qVzk5px6n1QuWnhLQNMe3qAaFEu57rnI2CtiCiRYstrCwHJbnecWoNecJO9rGB2FZkusVaQYSUNy9gxTku8dUOryzwEudkB0IHYo3O76Ga4FayxQQrapj0z9adwgI3IScfr9t1xIR32Xcb5YoErraQcFpf+2frbomtu3CPJ9YEkMjgZoAbZLl0b0bukrnuKbrcAvWoDUv/usuKgNuVM/dANrCu4IuBuwqhf+Uvn8L8k4jP+XxIsoihnMc+TAaELqh6lpnnkH/pnfdhOUJ8RacdmnUckHhmB5nnKU/IYkDjkc+P8dUaqMaULuiix9MuBylNHbIcMLLA8ZDGE+zf0EXIliNmkm5Ri/guswWRiF7vUCd1VgAvNxla4MRjyzE2Na91c/US1RVtLbneJaxgBcGLSRdcmzJopi8hoHmr6PUWbwukW3q5i2iOS1dNXyQgIyVi8wk1gluFL7epqbHV/GLbuIaViM3WHLWAgrLZJgTWMRcv/v6XnKrUgXfvW99x6gJZcPvV14I8qzrdF/7oq93rq+Rg98Xf/wOnLESn86kvf2UQLwBjt199LVguy/Hax7/8h4Obm3p39MIXX+7NZvVg8Mx3vu3nGbP64Ic/7Fxepnt7v/S//7ONo+PZU7df+IMvh4uZ7kQHr70ezmeIooPX3+ien5cb44/90deGJyeLe7c//qUvB/P5fDgi77//Z4NG/xROfraA5F9giXG9/Jf/strYgP+aDdaf8y5CiECtO4cnVhhQq/7D49YgU6rBo+NWGqvA4OGRKWUrbffoDFa6bUxwfG5FC1rUPzyxGioFeg+OcNa0GnafnIO8MU3be3wKK2WE7R+e2EIqwKLDE1Qq1ZLu0RnKm7ZFncenoG61BP3DU1BIbXD/8Ala5dKS7uMznpQCMJt5bcUF8VHia9mtsAtSF+Rugx2Y+23p18A1RQAqKiFDGdelV7EQZ46tI4G5KQNTeBIwXDBTOTVybBHCjJUsgDEzZdQgDioHlUxAt81dm3NJHJQHIHdKJwAJRZnXQAYKbnK3AY4pXJizhri2YiDxG8Ss8lDsCeioxoGxowyztYNWTk18XTo09hrATE3ADAnLW+nC1JfA1Y0LE16TwBTEzlmNXSUclPiipUYwm/iN9bR08RIL5IrGA0mgADfGhWmggCOUC2NPtY6VDMWuAG6jIhT7GjBpHbj0tPIUdHEettqtbQhiXynHKAwTXyhXAA8ufaN8ATmJmZasASFMQ2VCZShIw1ZSgQMac6WjBjgwcdvGVcCDiSeVVwMPZS5soAQOSALRBA32ceYZ5SvogszX0lWW4tRT0m2QA9IQlExgXq9gFUMBnSbBqiQa8XoJqwRWLGgXukxog5wypXJlhaUiQ00KBXbKJVYrWPFQrUy1gApxVeImRQ3gdQzECgjA1BI0c1PyoFkBuYINoDIB9RI0kDcpkhmWiFcxlEtTOoGKbTlHNeJ1ipoVFBaDhsOVUyNfNw6OA4ldIzhYeRLwVrsw8RrrKunD2BXQtRXFqSuQo1sfJoGyTGkHrDxlXdM6LOMSeY0K0MpT0LECwTRsWke2Dk64tq5WLordxjiy7aDYb3CoJQVpqDSVMIRJUIOObF0Yu9o4TUuaOWoa3BC/vrG1dBvI5dSKCtfQqxeolk7T0nqOREMawOTMZBWrsNdMrS5RY1i1gFVFJOD1AmpBJeL1AlU5brCnp21V0ArweglkCYWhzRzUJVbULW+gylDJfDlvqwwpwOUKNSVpAK+nRuawph5MmMmjGjJT+zD3BHLa0gUZl9iBRQAyp+SBTRlOvAYyXGKY0Ro4benCjAvAYO6BmFU8gAXDmSOxY0tuUk9CbkvHFoEAzFYeSnBDPJsxkAUNdlvhw9xXyG0rx+a+RBw2AcmcigaqDuDKk4jJBldTWxqnElzOgbBOWdNqBpVlWrNmhpTlpaL11DaKKkHEAtSG59JpZlBaJiSuZ7CRpAFutYSN9YQkampFy+rWq25AA71K0maFGoWlpvUM5tqttCOnVilcKV5dm0ZRZVk9hbrlwvJmBpsKauiB1FfCq4FnsxA0WEAO0lA1QU18nLlGBgI6NvNb4SnLaMa0cBrsgDSyBatoAGOua19AB+UcNExCxyaerbggDkoCU/CK+SjGsOTSMpQ5unIa4IDEgyWtIUMr12Ss4gHInLYKBOImcXTlCUhQ4cCYVzywKSeJ10BmS24TR0DH1C4sfIF4W3gwZSUPYILMkkvMde2CzBOGgpK3edBAt608vIQ19XXpwTRQiBvlgSyQ0FElBSsqDEO1A2K3xq4QHRz7EvFWcJD6AjhKBSCPGhLYyoErLpBjJUZJKFpXGg+ufN16qnVg7IrWEaaD4kCgUCsKkkgrKqFPE0faUFgPxZ5sHd26cOUL4zbGJakDJJLWsUnY6EABD2e+Np6yLo6ZNly3DCe+km4DXZgERnABPBi7SrvSuGjlta0rgQOXrhFcIN49uXBvlsKS4PTamSfKkM6TC7ZIG+Z3D59414uK+eHZlXM1l9AJrhdskUpAwuNzd7qqkDs4OnPPr0sWBCcX/vVCWBqcXvHpSgASnVw6q6JGnn987lzPK+ZHp1f+xVQA6p/fuNOVAjQ8uWCzpKZedHodXc0rFrjn0+D8RmP2F7SLEEJUVaP/7R93v/qHhjt/DsKxCCk//Wn8m39j9OSw6ncshP5sudrathZsPDm6uH0nlHl4Np3v7TiqZqtC9MIWY2+20sOwcKP1x0dXd+76IgvOp/HWFgOKLnPoYcU5nWd6EOZ+d3J0dLO75wLhnd2km5sUarYsdOgI1/WvZm3Pz4Pe/z3jX0z/T+ruq+ey7LwT+8o7hxPfnCrn6qrqyCbFoShxTGk8Hn8Iw/cGbMBfw8BY0GjGY0EwZ8QkskWySUojBolks2N1qK785nDyOTvvvaIvKGloS7Y1gC7ku+fihwVsrIv1YP2xn5V3uy6sZaJU4ORR7E2rxmHIVvawylsRIxVMIUJGBAjkKKiTou2hxAiHQVvZk6bwPOoImAIEgQygLrFbF1kvsKe1YBR62pqKxqLIMyA1GOCk67rzyq2LtB/aM64IKlpePEwEw8AHMNFUgdlS4M1Ltyrqrm1Pm9qyp71uNJs7lZwv+ayW0WhRrrhSUC8p5suxk1W4VrZX1dymtZ6tRKyS0TiZbrSjNDEVSpZCp+Ck4iqEhkMrF/OVmNYinmTT1SjKEl3iumvbZaM5NAGUEtsZ121YQi8eLubrLT9PQY6aNrM41zVyWJGT0JpXTd/OWLB0MExWYkeVIEfaAYoSOufQ04XjxWfpfCnu1SOeWEXPZ6BGKdQ2EDajc45cXbhePMwXXd8BNRnxohsyUKEMAstwm7K58HA2jbvBWZ72AxvUZCrLyKVEwAwgahqPsqnAlkyjMD5J0m5goZpOVe0z4VE8r21eVUsxmZSKUeJDNCoax2Y+1AsJtQZtixfQL9JipUWmlcao7kTe2VQxgkOkFxJrkC/F9rxwiqxYaZNJATAqu5E3mCmEIq9OSptwma917XlGqqZZjvzhrKZO1Qm9SaqNRjFRuSZlY/quKCErq2o5CpLKcCMjZASyMr5YiWktonE6XW9FWQIKuFiK7YrTotEh0AKxXKo2qqATD5LZRivMU5CBqufadW0apH0gNHXnpeyQCjmtwWK+GvXScVG7vOuwpgEV0r4R2HYmueqQEjutUTZfCrymBKlpOg7lDSnMvBsbZNqHg2yp7aAGnOVNv2XBxiQa2lA6DE5r4prSD/2Tad5vO6iGg7JpBTFIs8ImNhAug9OG2rpsxe7BpFxqOagxo5qHDrOMXkhEgAgdPK0YlWm7HRxPy3Yofcc7GsnAxg40C2EYrWPfHcwoVVm77R+Nq3aUttrdwYgYNO96blp7RZ4tBXQhIQBZxw9HqSIIBACk2hJmuhy6SeVled23rTnn2Mo6fjzJJTAmgqZEViXmK7GTVlbZ5H03miQ1dnVg7FQLA0AIdQWtSvAOkQ1z8ypf8oJp0kDHhIDmSmtoQiBq6mbFcHOlNztViW6WI6ssdWVgRKRCZFHhLiuQG5yM0+1lr1iYuRT9gPFaFsCzm4J4aFainlUQLzoeJxs9rynUXKieTwU3hYY+4tiiowT17Jy48fE42ej5Ta7mEsTMknVVM+KDmjr2MAEdVjhRsD/IN3quLPRMiNg1NrOntQezedzyRlXe9hhu6ESWoUcZhylAxHCfkLkimGetMDzN8pbHKKcT2XgM2RqmxlCaR3Y4zggSSTuKzrIycmrfbp3OG49BR6OFgQSnLfdXhofUGVRZK6x9uz1IGgsDH8LMQACyth+OM0XxrNfqDGbAaM8p8sZjXM1XYietnbxKl4NomnBoZR0/mFdASx1CUEJWCtEloqFuXmV9L5qlNbB1hFgmtIYmhLLGTtGILuHC8pIiWw6CeSoU0xGgpVYCenaWwLazyMWS1UgrmBfpkuenhVBUR4gWCgojIswha50tZqtxPxuVpVN3XUs2oEDGM5zYzrTQLVQQtz3MZsuBJ0o413XXo7JGOQSebojlTCrHLUfBUvt0Pl+JXVmihaljmwCBcgAcXVuOP8xVC2ZO2DmezZdix5R4bnhIIDIkM3VgC4vEZwsdwcSLukezxVJEEHdGM4RA0W07wznFarK82t87SNudvN1eefakDCMdOWycAExmS8udk2OC1GR5rb+7l7XbWbu7svusdhwrgHwhEYTT1fXuyaGkrOrG3b2DpN3L2u3lg/3GYqId0GnGRJOtLDmjuca46kbd/cNFqwtCyx3MJCW8HeB5SZQ6w673e39gP3/6f4kI/wnPNzj7n/7n4t6L/7CIUAjvw/v2k0f/v/g2gBDfOR+6wWu/9wdJb9k7m9z+ytdPdy51nu3d+Pq3Fr1VL8tu/tHXG8QkpC//mz9M2117mr3wla/Pl1f848mNr/1JEXVp3dz+wz/WkDbMfuX3/7BxHJqUN772p1m35wzmN776J7UdAGPu/Pv/CBUo/eiVf/1va9vDlXzh//haFcVklt/46p9IZAvLeunf/wdUNlUc3f39P9IIjZZumKPrqnYRzNTZy3hBeKcET+7psiXCkuxeBk1gTFFN7oEyACRXp/fo3M77lfXklir6dZDD3YtadA2sxOimLDraKvXhbZaGyXJtPbrWlGt5WzjPt3S9pFEhJtd11qkiA/euknlc9hL27KqstrK2dHbXZbmC9bSZ3tDpcrKkvd0VM9yer2l3rysXVxEeNfmmmVyuXWWSdXK0oaOJOj1nZlfmfeGe9prZdUMXZBbx6a3KUTJZsQ42y25JTlfV8GrW5+y0Jac3gZ3hLOSj24LVKl9GR5eqTslO23J0QwZzOF0S49uQZDh3xeAFzUpVdM3xdeBP9aCr5neNM+EihId3jBCCMfL8tmBINwgd3pC2BLkjhi9KI8Jqno5+i3DRWAQ+vcUpg0KDw9uaKF0QOXzRCA6BUMefgZUUroFP70pMjQbm6DbVRSVcMXoJCGNQrY9eIYUuI2g9uc2hI7XRRzcRBpJjeXZPcqyZ0LsvEsEWLT77JU5zx2bq6Glczhl15cEnET9DaIud/syeTV2yhAb37Tx1bUcfPYuzMYNt6+xDuzrFZIuO3rLGI09v+IsPYDJ1LEsc77fSkW261vhjOz0i4bo4eCeYTSK16iSfkPE4cC0+3nenp55a9sb3aXFEyTaZvk0Gg9DqmtFTdzIMnDZAyRY8Ple1CnK2oobX8iVBT2M5uaVpipOAj1+oLSHKZetgp+xxNuzJs2s8StV4RU9uapiSwuGDu5qUquqTwytlq6ZnkRrf1P4cTDtifEeD1M1BMf4MIKWs2+DguogrPAz06AXjzPS8JUd3gUkht/XJHYNKKVpo71rRRnxSHHwSIgCo4c8/7MNKmZAd/MwRgAIIj+77EENVg72HHSgBRXL3w67JpRWoJ293paEGguMPAwSAlmj30zYQCDK5927bZEYtOSd/aRfaNgQefRQigwxAew9avEaq5Zz83JYZhkv07K+shQq0Y43u26oxBqK9hx1ZEb7hh59u8Wwj63P72bou1w1KxfSKSpaqUMPjS2TSLnspfXZF5TtJT7p7KzLfwmbMZ5dUspnHhpxcQJN+0c+sZ+d5er7ocXK4KrMdiBZgsi3m28ov9dklPFnLVgvv2Va9uCjChRlsq+QCIgszW5eTLekXcHARj7fy3tx5tsbTq3WU6FPx/GEX2TrL7LMPHLREmiNw9ChiHV2cWftPWrajdap3H3Qo00Vhn3zgOS2Zn+Ljp12nJYsh2XvctqjSpT74pE2pLirr5B0HtUkzgPsP237Y5BOy/6jDsFISHX4YU9TUDTv8IIIt2ozMwcNW4DVlZu89aFEMhIaH70XAwtSP8fMrREnOcTN+GXBkcK2PXqGZKdraenJb6EAgYQ6uA8CkNHJ4Vza2smqz9yKt7awt2KMbpelwS9P9S0Dayig5vKNru4wQfXwDlW7VKtnTG7VZamxNDy7qxsEirSevmNJPu8R5chXkYdkp2OOrlVwVrsSHFyRvVZ6gxxfgoG86Q7l7w2Tn513uHK012XmAF2C6qebn8kijky06XC6WMvLsnFhcFNHCDLZlcgGxFMzXxeQ89wswOk9GO3k/cZ6v8sVV5c/geFPML2I0B4sVNT4vvAKOttXwIoxO1MG2zm5qd2zGa2J+BeIEpH0zuCi9XI+3wMm5pp+gUUuM7imT+wXPx59HqBIqBHvXRSjwzNGDO9pO9CKUoxeByqHE+vhloCthPLh7g3sK5Z4c3HXAaVb31fBFoGutgTm8B3TNiU93bzQu0KmjBi9glvPSV2cvSq00QmDvLkCgwRZ+dj0PHVRQcHoLwoILR52+ZLRW1vz1/+UP2Cyd95Zuf+UbVlVXVnDt629Yk8V488Jrv/cHraOz/Vu3X//X/5YNZ5O17Xtf+ao7mZVedOVPvhcMpyc7lz77B/9b/OxgcPPq7X/zFe90PFrffuFr32KjhbDdq298P9w/Pbh2+/X/9fd7T/Z277306u//Ybh3OFtZu/zGD/zTcdNqX37j+/GT/bPLV176d3+0/ODp05defeXf/VH0dP9g58L/7S3Cf+Ij0PPXP/f/EhH+gxopyPk/xT8klQJK4Yq3dw9wXkMpOvtHsFE0r9t7RzirgKXjvUPnxbLgqrV3hPMaMt16vo/KBpUy3j1ASa7tqL13cHRjAbY24r1Dq7irmB3vHsCiRkDGuwckqwBX7d2DwflL8LJq7x1aaV65frx/RIoKQSfePaQvZFqY6MnubHNrIlVr//js+hUNLF21INJaAlBEFGccMZr72mCuCCpCZnMiDagjZaACjBQtgriExGS+tqUAFJeRMbWWUNUxhikwAFax0UoiCosASiLBDJa+ItJwYKoIgbrRmVf4WBlObC/3JPM5nCkeUq2pMooHQjMBSlB6oGwrONM5BVUHaqy4Z6pY6akRoV9QowltGM9aEs90zWDVVYoYaekiNnJkpA8LVwJLlxYpYwmGsLZ01RENpg0FRQwlAdyCeSCgZVe2ydsCWKgipuzp5qlWtilaSjIgfScPtSG0IU0aK020wKDsKm+ijLSSQPVsyLjJYiE9bRQue4QfQwRM0eL2SGjO8tiITOECZYFqEUCgKrrAH0gJURYjciagzbJAtTLJalZExmaUa1F2lTeF0KZZm+CZ0LVJfRWUyi1w2VL1WBMFqh5x5gYgWLQ0STmixUIzBaUQRUIYk4EmxcLCQAjMqsRAAgwgKkUFQT6XZUaZgVTjOoWwqQRy6oWqEWOGVHODIIuVLHMLcwGQk6XQL4UC2GQo1ZQg1mS84paSZZXRukRU4zKjLFMC4zppGkENkCrHRU65lDZ3SR4J6FiVDbKg1jTktqm6mj/TEpuiBdVYCw9moTCWKi1YtADApIKq7Gn1XHNiyraUx1haJg2koha3TNYWxtLCxlWfiEdQEFPEilMAHFpE0ji4wTqLjYBYUFX0gNhVmqMsBF1igA2LSKtScJjObX+5VArmM+ZZOdVOuaCsVqxp6hkSK5gwkM0tp61qQNMJ9alRAMs5KjoUK5AvaNTDpDHZwrLD0gUsnzGXNgTQaqFxCwAF+IJUEcY1yBPLcjXXtF4oChoL2vVc6ZgRYfIZ8m0KJcoSC7MaIQKLEEJLgikuXQ2YEUbXIZNG6EQXPq4Jx5ZXBBL6Ak50E2BuU2U094WIBMhkHdBcCcxgYQMdN3oYNa5sWkAgzX1cBsoQUPkgYwIxndlQtYDGqLRU3QINQI0PylgYhnMHZb6ADFaObtrCHMEKVgsKuZANqueQG2JynU8pN6wpTDq32xXHGhdz2jRCQdLMsVIYVCabkkpTXqF0bneFEAgVU9LKK+lYxQLHSuq8ruZECgQ4TmZ23AhDSD4h/RWoASynuJFQVbCYMyEplyaZ22GZQYcWU+KVWmiLFpFhFrYKVbWVnUDAaNYm3kwCqtNAW0IahquWcVLtVKDqEJobgEDZ1joXyIKpb1ypTAaKSONMiwxUbYiaRnOTh1jXHNlu5ksHcpCpOiawpBqIuqUgFEDoIoLMCMRQ7gLGhJrBJjKgVCBTdWhXjQKY1XatWhJPTe3opm0kNo2nc1/rmeERzA1HllvaWLaEwai2dd1WHOPag2UINAGVq1OXIwYKR/O2AAyVDJQdKBASni5ipZloIjv3FSSsonXdUobAxjZVB0qsuQMT10gIuG+qttbECAbKHpVPoUSmiEVjGYhoHkllI65BFhmOoGCq6IJ4XyGOshDERGnC8kgpR9cG5xEMCWm0KvtKniDjoKwNooGU2iSBFDaQCpRt1RBNsCp7RJwaQ1ARi2osAwizQEpJpNJF2wTIYGKqruETrUF0eFIHAVIwPh7wODRlEx6eVn4sFWwfnmqDBCTx/nFNHcOVf3AMGIGNDo9OBaYcW+H+EV5eVgC1T4faIGWwfzLkgECpw6NTU2sFSHxwUscRR9Q/PrNaIdAoHE5QJXTJg+MB8UMDSXRwIi1LYBodndaWDZSBQsCmAUr9E2yn/kvbvv/viBAqNfgf/kcZtwAASCmNEIAQaWUg0hBiJTXCBkKktYEQQPi3BmptIDR/n8FKaog0Quj/wVU5B+oAACAASURBVPyqgACov88QJRXCBiETR24nYEVuGDEQoarhnmsQchaLKo4dXmqupe0w1ShhAMMGYdgITExte95kkne6jqh0o6TtUN1oYRiUmlDFDSagcjx3NitabUdUoBLc9ajmShqEoSYEVfzvGuk4nsgKYyMMhOPSohKWRYFElWgcj5laSQygQRhITbwm5a5jai0thqGCjRKWw0ytJIJGQ4akJlQ2wrFIySUlBGnwN0YAhoVoPJfWnMpGuDashMZY2pZVlIpSCvjfGtJw1tTAxqBRnFqN61Le0KpuAg9JbWeZ9i0OCK2bJvBJXQMAA50V2IdcNoGHlLayvI4CpyqkQjzwSN1AAzBWAjJalnUYIP03pi6FxoBhIrgEGGMtkEXqBjFQM9ebzYpO26mKXxksuDLYB0VOA1xzYOOSeW6eC8tiqlYSQQIMxkAYAmXleN5sXrRanWJcaJe7NlONVBghbTCGXGMoa8dzkrQMI1uUoNHcdX7dAG58laV+y15kRRzbstQcSMtiupEaEy0Vo0YACkVtu06SVb5v6RrUmrsuglIJwGQjHQcVlWKMEAAqzi3HBpwrRKSAFuGaMFkrxza1MAhJx7HSVFqOBXljCOZcBAGqG6sppe/9rWF5rihtyWROol8Z3DSIc+PZNC84tZTj0DxXhFpI1oCxsoAe4xpjzpXvsqpWkGCsFCS4FrXvIq2trKijwKkLpRB3HSw41ABjJSHDDUfUNMR20qxsRU5dSEUMQ1gKrREmWiDrV6Zmrjebl+2olU9zGGgLYyWVRgQrgS1a1tACFfO8xaJoxU6Va4WVRbCSWsHGcQyG3mxaRbEjKl1J4bmWbrhEGBlAsGkUhapyPXuRVFHkyFqXUrpOxJMEegRow4jihkHZOK41W9RhaBuuai0d29KcK0i10hbTjaJQNq5Hk5x7rqHEWqTCcy3DucLQaOE6NC8ZkLXnkySXnsst265rzHnje6ThjDfCtQDXAADh2FZRKkIp5AKwXxnMhVWXwCagUYJYwrFYWWr814ZwXvse4QI3XHqWVVUCMUQMbKSkFoWcQ5vWFbCRMJTVNfcdVtcCMYw15EpDhLHmyCZNXYaBXyRCIWBRIhppECFAIIpqThiomOtOpmW349TFXxvJhYI+rCrqgUpgC1WW585mZbvl1oUUQDsWVkIqSLERmOKy/jXTdppCSEgoZLIujEWxEYShmhMKfmWqOLZFJQTQtoUgQFx5Msv9iCVlGYa2qkwDhG0x3SiJENSaYiMhNaJ2HDstGs9lmoNKcddlphaGIq24bbOqIUY0rkuzUjiOotjOSmkxCvhfG8dhZUWMBBSBSjaupyix80KyvzFKcddhZWUQrDzPyXKNcSwXCY1xw5vAx1yQppG+Y+W5wEy4DitLjQmFooE2KwvokAYyWlUycFlVCsjwr7YME4oVhxapa2ghjiw7z5oo+FsDuNIQhyZdsDYrS+CQBltWnvPQt8pCQIYoAFJrAxExDXOD6TRvt9r5JAe+ciiRXGmMkZKEkYojairL8xaLIo6dptACSof9Z4MZ4jIw2czr+rN53mk7dSEV1owQLbSEGClBLMQlwaqyPW++KKPI5qUWUNoMG2kkVARrQlhRYmoqy3OStA4DKmsjNNZK28w0mgJRuz7LcmHb0rLc+Zx7HgVSKoiUanyf5QXVovYDmuXStqVl2WmqGAtVlqAAK9n4vpUXGkHICM3yxvWkZdlJoiyLIC00Zk2lPMc00kAIGSZVzalNkQK1UIRiAoTGSMmcufpshKoaGvOrc/+vj/tfK5BWAEINEf47fch/LrQCwChE/q75tXU0AECjv2cdqrhE9NcNsKzuH/3vzicfGUz+MSNCqPX4v/vvrxwe3Lz//uyVO8vHgwsPH6YvXL/y5OH6/tH8tbtXPvz48sOHz3/7S+eePL79wbuzl+8uDSdXPv747OVXzj17cv7p0/mrdy88fXb1/feef+mf7+zvvfD2W+nLt9eHg4sfPxi++NL23vPLT57MX72z/Wz3+nvvPv/tL22cnNz9+c+Gr72yNTy6dP/j4d0XN89Orn/04fzVu+v7h7fff3/26p14nr340x8P795tDU6vfufPi3aHZvnV7/3ZYHPHmab3vvnts63zTpHd/vqfZt0+kOLat79ftVqk5te//5+KXlfn4oVvfnu0umUZ+cJX38hdzxB67Zvf1RZDylz67p+Xnbas9Atf/cZidQsadftrbxSOq2zn5rfelJYlILn+3R80viugc/uPv562loxNX/gP31CWoxm6+J2/gMZMlreyj5SUBHsmu29EocCSXb3L+VyDvpV/qknCoQfHj6moMfPN4n0NG6h7rHhfNFONl1j9WKupMjFNH0GRA+qr7AFu5hqsOfy9ms9QveKLB4WcGtW28qdALUzd8+sPKjlTZt1tPubFqZFrvnhYy7Fmsc6foWYGi7Uo3iPWoDVbQfYZcU+7PCp02XOOgiLUZBbaZwEICzBps5PWYgVZp9g5Wy7byp5CfNbPQ4CTIDi0qi4gI8c6ac/7yB5j93CpbClrgelJX/gNmjv2cVR3IZta1mlfRinPIuegV8baLRA9Wm4cjnOHHsbQL0Hi06NlFRe5tvkva+giCWH2vtAWBRDn9yW0gVBk8b6SgdVq5qcfuxpDQ3HxTm0YFQAWH0lgI4nY/H0NfIaQSt4zCmjjWdW7jTJIUZJ9AnxTFMRJPgDKphaVi3cMwli6pHivERohh1SfKGMMt+3sPaEooUwu7hMpDOrY9m5kV17tCnyyZCobOKV9tEYLUvZA8DSCc5d3pXXUYolf+9I6XSK5lUXI32/hlFV94D8LcNKa96B/4FqJX4eSjpbJnOUt6O3FLLVBnMPjFTKN0j5wD2ySdXnQuKehSYK0hcPD2B7TYhna+wGbRiIuybBFpx0e8+awrvcAWLabI1k+ls1Wy5w25afK9C0wbbJHQLRtPkPq00ovOfWpqp4A1tXFFOefaLPk4FmdP0KwReqZKR9K07P0DKQfK9oDVUKT+wq2qTdLB08cGGCxMNUnAvTseqrKBxr3UFOw5F1B2hao1eIjbTwoK1R9WFfdgGjqPelJF2FdObubHEDloOBpW0PKCXb2ehApian9fFkQjEhjPd3E0CBa4d1VYKhiwDvoYQRKTNzd5QZjQmp7fwtJU4c4eBwbxYStnYMlpGHFqL+7bADIPBY9bgNBmhh5z9paeqUDg70YK1i71NtbBhLM+z56Z1EPQbMeyoeFGBjQYcUuFGPd9P36k0aOpFl3+aeiONRiM1CPK34CrLYuDlA1gqLv1g+4Hmu95tSfNsWh4VuReV6VRwB2CN8T5QCgNqyfanGs9KZXPeT1EaQ9kx3B8gDiHlX7vDrFsEuLJ6I5MnDDlns6fazNhufNoXW0IrwG5rZzEDctRBPmHC+pIOdl6O0vlbFxK2AdrnFboNq29tvQrWHussNVFVcid929ZR5p0hhrf7VxNBaW/zzkAQCV7R72lVsq6TnPlhtPUWns3VVoNaiB5GBZehDVxDtcAX5VSs9/2q8cw7SyDjYUNTWUxcfGFVVNrcVHQBFKLZW8ayBAKqT5e0LUAAW0+kQDoYRjZ/elgpjacvEhEbUGXbt5t24446HFPyoNB9x3io+0lqDqBPVbuaqM6bv1fV6mWLbs5mMOKo09k3yCBYe841VvFaaCesmuPqjLjKiOKz+pmxLLru3tR3TuwSgDp0tkFCXL0DlkbN5rIu4MHDiL0xh6p5FzRotlaB16dBTzdolHkTXqVqFwRh6eRjxs6Elojfxy2TjHLh31ZZzKWZudtetIeDMXDVtNKNgwxIMAxSkYtelpX3RyNYmss44IOZs5eNiuI8XGvnvk1l1dzeHifWnaLEwXZ49c4EJZgeojbmKLp6b8RKE2Eg2bvyNQ10ZcJveNsaDSpPqgMT4VFcw/gbGdzVWYvS9NaBEt0g8AcjFXKL8vjEuMQtl9hT1TAbd4l2uPYCMXH2FFoEGE329q3xXc1B9y7JkSe8U7QtuYuub6t77vTmeL1bXL3/8LJ0nnnaWbb7wJFRisbLz4x1+jaTHd2b7+7R+44/lgffva93/oz+ezlY0b3/kBTovBzqWXvvIf3Vmaby7t/PDnwdHpYPvCtTd/aKVV3u1c/94PcFKe7Fx66ZvfDo/PBlcuX/ref+rsHUw3Ns/9+Bf+YJL0+ze+90Nrns+2Nq994zvx6fD06pXLf/6X3SfPh9eu3/jovY3d/cHLL1/59NMLT59MPvvyuQePbnzy0fQz9/ono5vvvTt55aWLB893nu4md2+ee/jw/OMns9fv7Rye3Pz5z2afe7kzSW79/GfJvdtXj59sPNzNXri+/ezZ5U8+nb3+4sr+4Z1fvjV9/cVWVt/5yY/mr7y8MT279P799Pb1zdPjq+++P3/t3srp8N4vfqFv7yBOXv7JX8zv3l1Lx9c/uD9+5VXw6JG9t/vrAw3+kSJCrW9/7at2nqWby0tvfxQf7ou2v/nOeysfPzh59c6NN7/nn519/OXfeeGNN6LpqFrrB4/2Vh9+Otvc3Hzv/fM/+6uze9dvv/lmsLv78Zd/98b33+zu7Yl+FO0ddD9+MtvaWr3/4Y2//Onp3Ru3vvOn7f2Dx//sN2/+2Q/6Dx8m66vLB8+WfvH+Yn1j6cnjG9/77v5nXrz95vc6T56Nbl5c/enbGx9/vNjeQgze/va3eDvAvLn13e8Mzl9YPT7a+sXP06UlROW5v/xLpMXg5rU7f/qGjl0D0bU33qg7vjVOdt56q3Fdsdnf+fFf4Lo4eeXuC9/+1iMqGte79oMfiJa7WYqdd96BrjfbWb7wox/ZWbL/xc/f/vrXCJJFq331zTeBBdbx43Nvv00AHNy+fOEnPw2no/1//rk73/j6s9/5rbP40njf9ScNq9KDo15wsFhdyj940mehvNCZnXwa0VB36kl+vGENZQjK2WnUOZzHbXX4uEt8E/fT4we+F6IemA8PYoh1gPnkIHSLpLNV7z5ylUf7W2L8mPpWswQnk8M2AtjricmRa2fZuY3x0SdBZnvtCyB5ZvlGdOV4cNqrjOufy+zRJZNHTv7QP10Bhd/ZPawM08mGPT9BWeQc27bzQJ6cq/k2zZ+7kxZIwt7u+yRhVX3enxyAMiTDtrP0c3e/w+tte/EsOPNV2o8PPrFr1sx2PJZrEZPxebf/E3ev39Q7VjS0p77ONjpPPnAQKsbbIcmMDs34iufvmjNS8nPOaJDy/OzQ6coy3FaDp9EqzV02XzxpUac2PJ8N/C5eUHs+PljrlUl4WT9/3tlgeWBnZ09aK6Qgx83iJO6Bgvay0WF/OZ34BO8+6SyZMsySo6et5bIgQzA5W2qrSm03o8N4ZTEJ7qL9x522alhdDJ8G6zxhs+ngNPIbHpzno924M0r9bdc6WSNu3m3G9awPsEZoYWYbfpmC+D06vAuodCe/xCfriJZtM+OLLgXQDYdwsROkKeq965zeqBj1s+f2cJ2ooqc+rYvYKJf1z1Cy4aYVc+83u3cbt+Wk+9Z0CSodyBM9W4Km6/eOwGyT5dhb+5G/t1VZbWeawMGykbY3mJ+OreoR2F6e5Q/wdBp2r4nqqUlO3Y0nKUrK0VG/G0vOSfbQ3tqqzKfNaByHy2l9gJKBt/FgqpUaPPfWrUwpcvo4uLg818/4dNiNlhfqDMwH3vonE6CKycHKFp1LxE6fRtdWZ3KXn511o84EzuV06FsPMmU3k+fxDp5Kxzt5Gi+tFW0dwGynvfccuqKZn4+LZwbO5OizFnzuFkdqsIl16U6bJt2O1RHozPXiXDh/gM7l2fgWNvuWONWjHZs/6o4LmW6FcoSXUz3eCWf7yNojo1e0OvHUMRxt0eoZKQqdbPhqXtsLNLsYzU9K/6lzfEesFIE1JpMtK3lilYmYX7Oa2l3Zmz0iDWErl/j4EbGRWaXT8UnbCBb05PTYYdPs3Ob49BN/gbzuHMyeMatRfTAeDzspd6MVsDhxnXm1uTE+uG8XNOgPm/QpbmqyZS2yEzxJ3Z1WMTxyxBicv5ql92UC47C/KJ5gUdLImqUTNh2jc1EyPvazEb15bjx9H05Vd+W0DKa0nO54tNKIkck1p/Mz5yzg2Q4Lx9bc09l25+kHLjH5eDtApaHETG577gmYgqrcsUcjJ7F1dq77/CNDgJjstMB97TI1ueu1foGmRCbnfXbic9Tk51v7D40n4GgrqE9kSKrJbTd82xS2np4LyAmWwOQX4qPnMJJosBmi51mYTh/h9kpFp2J6shLXNThfjw7j1fHUt8HB4064IRxUjJ74S2u1nU4HJ7GT80Dx0V7cOppHveb0oUu2aWSL0TN7pZu49XT/KPJSwyIxPvDC46zdKQ4eBM2aE8Zwvucse9M4WRwcruEpcpfA5MBrD+qlzmT8sSvW7FbI5/uMujLoGZRuezPBvA+a57c461v5vj3pQI575hTMV6Ba87uHcLZG55a79mN/b73CS85sgYYrqvZahzOSRqJY9+wZSNZp0vFmP/R3r1Zo1Y7OnGGsy1b32fuYd02yFThjM12B6WoY3ufPz5d4xxucgHFg6qXu7kTylpmu2/aJnG+oxXqY/Hn20MyG/uonMwSz8d7aBpzDgJw8jS93F2BYnx50zsczmInZMFj7ODV+Pdptb+kp7tm7T+NzrYVJ1GivdQ4kyDiDQav/oBB9OX7ub4gpWrYGT6LNsJCVGO/7sTshSh6fhi1Y2et6/MReqVO4RU8fOnEbwlpND7wYndk+ODjxWsSsydN73/zG8c1r0nevv/m9ydXztK633/p57+Q488PLP/oRj6PpjYsvffMbh1ev5ivLN/7sz7L1PlF65xc/X263Z5tbl370I+X7P7+0dPdrfzw+fz5Z37j8kx8na2thOdv5xS/WrA+nly5f+uEPIGNHr915+Rtfm68sV/325R//qPQ8CzRb7769KU2x3rv6F/8JY3z42p27X/vjRbc7vnDuwlu/CB7vTc9f2H77re133n32xc/efeNb7mQ6unlp64c/7R3s1b12f/95/50Pi7XuuXd+ufHWu0efffHOn3zDOzoe3rm2/dO3u08e63bQP9lrvf1pudHfevuXW7/45cnnX777p2+09g/G186vvPdg7cMPeSd2k9n5P/txsbWy8d797Z/81elrd669+b2lR4/Uhq/3FufeebtpRbQub3z7u0e3Xpji/+IB7P+AGyyjF//qv01u3kz7vSe/9YXR8tr0/Plnn/9cHYV7L74yvXRuvLkzvnr16YsvLs7tFO32w9/+4mRtc76x/ujzn699/+juvfH1K6PNnfGFC89eenm2uVn1O59+6YvJ8up4+/zj11+v4/j41q3R9cvD85emm5uPX//sfHu7jKOPf/dflO1ovHPh0T/7fNnpnN68OblyYXDu4mJ7a/czLx9fu9X47odf/jIP/PH5S4cv3hltbc82tp6/9trptWvCYh99+XezjZUi6nzyu7+Tr/RHW+eP7t4eX7682Ng6vnv7+d0XjWW99y//m2xjpfCjT7/85XSlP9y5MLx7c3Dp0mRt6/Te7d0XX5UWe/93fjfd3ijd4MGXvpSsLU93LhzdfeHk8uWst3T88r0n917RFrv/L/9VurFSBq0HX/qterV3fOH68Qu30svrhAp6wSYbjBpOr7ti2Q/swt8AciNgIfDajbkaG5fRHeKsYwY5vuKolcDzuLes4bbnhsbuanMlhAw45yjdwA7i7Iqtl1zbF2Ff1Oej0K2sjjbXQmhBd13rLTcwqXOZqRWfBdrtCnPO9Z3K6gBwxYM2tteBWIsxmyN7MFvHgI0wLbLzTRFizKbZWgFRLqOZ6ossBtqeZGsK2BNEitkFAT0h7SxZLyEtgT+olkEZCGRNki2tghSCPLlQA7uSTl5tVsaupTOol2HTFoRNm9U86WgKstkVYexa2Xm5VUm70e7A9HnSQZplfL3gK1GgMv+WrVs08Gu6Y6lVz/KUuwXllm9jAe/EfsSVRaybrmmzyC/JJhHroeVIewur8zGFnN72aBdRKPDNQHftyCusLarXPcuV/pqqrixZSOIbjtM1NpH0hq96ju/X1jokm7ZjS2cT8fMxM9y+5dAusLFgV20YQOWkyk8WO0Ax2fQz3i8oyIuNrOpZgs5ANC7XjLIz4OXplpJUis4sXVW2nBQrCe9TaScmmMzXAMYjEJaLHSWplvGoWBZMTevlBLRV1jLanaRrEpGp8qtkWwDKm9YiWQUOmPDeuO7TyquQMynX68ZrtFtUG41NuL2kzZaP+izwK3UpdFvSooLcCZFnWKDRNY85qtWu4IYNe1YQcnqOgnXfRjW8EzmBtnxjX7WhD8MOh9ue7tuOx50LVKz6LuHoXuS6jY4YvuKRCEbtUm25etl17YZdtsVGaMGG3guoD4irwbUQxSiMS3G+xXuK6Hy6U6iOhKrItoumQzA5a/pZ3kOGJGKpmK5DDKp8O9NxA0CR7lQgMo2d8k7aLEmIM9nPZ1uE6DLbyUxYEphl20XdIoqOVCetlqQhqerliw2IdFWtTvIecMUo3SxEhyhrJlpp1ucETOVSMdvAyFT18iDrBJGdxX1enm9FXsUiBa+HwMXestI7rg9y9wLVaz4NtdcRasfz3dqODbzmQpdYy0DvuAHO3Q0j10PSwWHUyEtB4HMSAnLdQ1Q5awhvW67N3XVtVhzcoVHU4Es27NnME/hmYFvKXYZox6WWiNak2gpBh7oBh9dCQApt5cVWbhwunVG9opu2ImzGV7KkB5hJp5elcWpN82q7FA7XzlD366yLFc35Wp4vAQyKyYUSBFLjPNuWMpTIPi1XVNlSEC2qrXq6hBnI0/MZ8GrJynKL60Byd1YvNaLTQJg0m+VshVoqWVzIoZ0DlufrNQoB81WwouqrPUIMuWo7PWATSa57qu95Xu2sQbzluI6016C4EFMg7BsO7UEHc3bNMT3b8bmzAuSWF1glW0X6UkSAdC8iueJGam5dd/SSx3xpLxm4yXxW0XUMLniIQOsCkqtuDBLrAlarAY2B21XwvOvShq5TsWFROWv6c9CWWQyMM07WBGQz4xSLcwIyzsNFsqZsNJPtUd0npddgZ1Ktl3XIDc2znQqSWsRJvSYByXl4Vq9QEdWQzer1Igs1tvLFBQFxJaK0WleKFjo8U30z7yBIk3qzKFrGsHx+rkaMN0Geb0Jg59A/y1exWrJdzOG92PMa7TN6zcURjuLKbNp61Xdsbl1gYjuyQE3uBiQE1FboeghaJIoqvGWBFcexuH/OZBdXXNDg264bacvV+FpgOiwMGrJJ6SpxGGcXaL3Vckxt3/FYqGxHkisuaNMwqMGmRVeJS0p6kfHtlq0q9zrhbX+4uXN2+9bxndvzlfXR9StPX31dWPajL/zm8fVrxrYffOE306310da5s+vXd1+4U3bag1vXH7/6GeG4Tz73uZOrV6XrPPrNL9Zr3cOLtwaXL+2+/HLVaR9fv7H/ykvCcR994TePL13Wcfj4c78x314fbJ6bXr74/DOvLXrLZ9dvHL58jzvO4y98cXzpXNZbevbyy7NL50bnr0zPnX/++muN555ev/X0tdfqVuvw3r3Zxe3BzqXFzvbuZ149u3ilaLc+/vKXeSs6u3rj8KV7aadzeO+l8fVLp+cupauru7/x+vH5y2Wv++C/+m0RugdXbh++dDddXT2+dfvs2uWTi1fzXm/3C79xfPlG1Qo/+q//RRMFZxeuHLzy0nx19fT6jcGta2cXrxadzuFvvPL0xotNFHz85S83ndbw8tW9116z3/q5tb//j3+DhbVqn+zG82FrfBrOBvHkrByvtuZDusjzYr03OPAXSTudLJ3stRaj7uTMHo/bs0FnfNJZjKLxuMjWu8OjYHzSyqfLg8PWbNgfHQeTgT8ZdmeDeDHsDE6zbH1peNieD9uLQXd42E7G3eFRez4KJye9yUl7Puyd7af5Rmd42BqftOZjezhvJZPe5MzPF+3hYb0aQW26o5PJfEgF7ySj9vTUl0V7epoPD5Vr9UdHcjmUOY0Hh9Vm20qSOBktD48oM+10ko2OVegunR0kLexytzMbqEVEmqY9Hy6NT6AN28WsHh2XsNMeHpkWs2XZmQ/1LCJat+bD3vFz3XLjxUBNYgm97tkhs1WWLNO0NpA6sNalIYvaDkGWA1jXbgvDBLiopomoE444YDYQhbQQYjmoEmMwsPO6SAABgi24yQCS0HGMySXkwOmSfKEQwWGWwEVOtKBzrjMMKKAZxEWFG213cJUYqCArLZzkWEp/IUxOFSK4Im6OWIXDqoxzQavaSfNK2nZJZcntSlkZsJzMyixYuk2l3MLYZQPzMsiELG1Q1Lhmdg5IkeLMpqUdltpNlF0BmFV+wVUhaFayiqLS4WWKUocUAFaCVpVbYZTkTiN1JWle0RKiIrDcjDUIVJYsKs6xzks8F46rykTbXoUEKnNFCwQbyVNOZsJVhVs5bFaRGOapsV3OTFEU2slUbTTNaz2TDstEie2kJhjkKaC0IQbCHLq00vMFzDSZURarJlPOoiIYlKkmBGKKZaaJJT2icM7JrLEjKXNFLERanpVDBiVNC1xYqlaGcVYJzBXzEpq1EQI0z1FOqJQoL3FpQah0YdyKoIrzKMVZGxoclk2YGyo5zQpT2tAYXSq/JNAox0pwEkFJRSm9HGFp7KxwciUB06WxKoqkpkVC0wALSosa5AyXsipqWdYo0W5Ry7mWhaZ5BpMalQ2bCTdLZWbRBdCVAUlpF1wutEi0VRBQCJFzthCkKElBaQJJqUQKnLzSOSxzRUvsFUJmwpnVblG4he0kRgvQpMDPBSh0VQE3lVJrOy/BTLhNrgvsJJUxsEqUk2cuRn4BcM4ZLgkXdpopLEnZZYS7sqBly7CKQOwUJU45sypcNh6tLJ3QoqNBKYiglYVxDSFwKooXJfYFKziCtcallbc04ILWpLCwqYCF3JJySxhXm2NeoQAAIABJREFUuSW2QSXsiuZtAiAquF9QrErIgF0ygWXT5CwpoEFhnuB5DhvhzBuVUaA1zSAuS5wrq4frxPAa0KokSQZK5S84zAhX1ORI5xWTyi6qZip1jUhuobR0Cu0sgCnqmjMvQ02mVKXdqqlnUhfAyome17pQzrzSOdc1tTOAcgUa6OQVT0FVaFM6QVGq0qd5TQTCBeJ5CiqLFgDVEjeFXyOY5g5vTC1JVhFhcI4tJ7elMrWt8pIA6ucFzBuHl7hx7TQ1iOAyonkBa01rov5P2t6rx7IzS9P73PZ7H39OnPCRYTIy0pJFU8ViV01VtzTTUo8aA0kX+gv6I9JIggSoJYwuBoKmHMt1+aJnkUmfZDK9z/BxvN9n+/1ZXbR+QI+AuV/AunqBBTwLzxvGGsZOhGBADZXBJCdRjAGnkcftmANi5bqKUoigmyLgxxoRWsxglOVmhkJugwTNsAogtrAOZB4K00CaCdJAIgU1G/NAaZBpJscxx9PMBJxHgiBoFHA8FxDqRqhkEDucipkAsYRzbHuKxAnBme7hdC6Ro+MYgCAyGLM1ziMdYaLVCIxSHUCjFGdTIUxdhRCFsaKQx9DNCKbcMgIcFBDVacKdCGqp0sPEjjDnjkqEmWg4l1o8J4GLM53EmQoMLRFamNoJ5pTwKDYSAFJC4zmeeySVOGXmPDETpUexmROW6jyK9VjA3LKSOQ5tlGEeJjS3rFhqUW6lECaIBnMYeygxcRziuWIhNWa5FcdWYlkzpRBMA+XEFDKZpsAOuYiUEcVqKmwR/lOCgAbjuTSLmZQAJdCOE0/5eZiTqa4ZnATSmiOogyyQehEphHkkzIADqmiUalNqGIyH0phniMMsFDghOJUsTL0gAwriJENz7slkYdDKWIlOS/X+Ga14yaRb8YdgYGdVrzLpESzEaqneO0mLpdqsXx+1BXWSca88G0ATJX69Nu1bkgYNXOkec8uYTXrVUSfzCmhsl6d9glW4vlQe96zYTzfrC4MzBEE47jXGHaZpcuqVpwOiOF9066MuIySO1qqdYyhlwR9XZgN9Ek4mveq07w76Qbxe75+Wxr3SbOj1e5X5uD7sFKd9azSNZs3qdOBOZmG4Vh93irNByR85vV5lOlgYdsrBBE2j1B+WR93CYBhEG9VRuxJNy+OeNg4r81G9e1qaj4qzQeoPi5N+uXsShutev10OJ9wf6em8Mu03+meeP6n0z9zQ///xF//POLAU4BBvv/NRsdMar51b+uLmwsmJv7C0dON249Gzg+9+d/vd66V2+/Zf/93Wh59XDp5PV9aqjw7Wbt/qLW0tfH138+uv9195def9jytPn939z/6r9Y8+X3r8KGg2a0/3m9/c767uNL95ePHTT45efXXj/U8WHz2+/frfnPvkq5WbXw9WNhtPH6x98U1/bbdx/+Hlt996/p3XNq5/uXznbuvaC2tf3Nr85Hp3dcuS9Oobv0mdAqTs4q//0Nq6VDs73Xrzg0FtVdflzp/fZ0AfX9i+8sZvE82Umn75H/8Y16pknm2+9Ze5UxMLxZ3fvy0y2b+6d/WXv3/03/19Wiju/ebPmWV5Am+99WGieeHm8vnfvoX+RXjyV6+98MY/3vtv/01Yr+/+7k2uaZY13nr7Q0rR8MWLu79/pz0NT7//0tVf/v7Z3/7goH6t/8y2KlLfVO2DgmvnS8uqc9/Ui6rZoN3HRr0oazgfHVjEFthm3YNikaSVZdB7qEkXrS7k/aeGZeEyYYMDR7cBsvP2YcGUWWNXH9wXwjXrq2T+3NFwVtXZ6NBBCFgV4h8W7Dxu7GiT+yw0nMqmHj23dMAsnE2O3Yzr+o5W7zRFVGObB7K3wMMK0WeA1uRoQZU61C8ZLZMVB6C7yOM1trEPhxUxLiH3kZoiPtvlxZaMys6JnTTuk06dh+t84wANbD5qAveZTKDonZf2E54WtLMVVr9tdmvCX5e1W2hQ4+NVbD6CAsnuBWA+4amttTalO0KjBp3sqEKcCDh95pWVKKyi1gOjjqhp8+4Dp2YJxdRgv9AkObfh2RO3EmeF86Bzz6hLZhdZ+5G9AFM1lb3nhapg+gJqPSvW46RwGZ7dNcoXhc1k57HhbcUslt2DQpFSuAXaT526nzgvoc493dtVDufdJ3ZzK0aR7B14JSrQtmo9dYuNrLTtGp0NaSQYHon+OsBMmcd8eIGwMF18op3tCBuLyi3Y2xYoA8apGGwQIFRpyMfnjGieLz8pnpxLDZcvHYpeHSuBtOd8tAqFxis9Nl4z45RVW+hsnZISWz5S/QUpMMGxHC1LXhILXT5ct+c0Xg7N01WGK6B2G/bXRWaC0rNpT2WPRXMFRAdi2jFLO5o4ltEhaZY4H8PesVctoyQi6T3QXIfpMZudGqsredIi031jwUkpI51HzqLFeKrN7mtLzZwdyd6hs76UhS00O/DWnCTJSfux09QyqfDwLtlo0PRMDvatzdI889XweWHRyKVGOo/dZSPliHTv2YUF4kKHD7awOlXFnPcuguiEW0NyekkutZQ7FO0LAJzgJOODXcS7vCFA/7KenPHlGWxdEgs9BYeyu4trRziN+eA8pgOJurx/GVvdrNjSzy6KxamEXdnfheVTqFI+OI/yiShEvL+N9Tarts3jHbYshN4XvRXinWEQisEWSON8feI/MCkxGltkum8BThasbHLsSgqcKpkfFcwgXjhPpvfZHDjVXT15bqGc23o6PbWjxHIXdf/Yc2dxbQdNHqFQ2OVtjR0YaaCWzDQ80aPIWanT/rHFR3zpMp4+yaPcWlvO/EMtmYCmkcQ9IxibK5VseGKKEVzZyv1HYBJajR2o/Jro70nzmZA6OjvHSveMvidHm7J6F49LbLCBjccQWKy7B/TnQiHc3pWWD4MKG16UbkqiEhtuEv2p1HTZvijBE2kSfLonnPt4pvHBeWhHJPf4cBeSA+4YpHMJqvtSz0H7KrcfwNxi3U1dTyEz2WAP4yPhhah9CWiteXE4fazMtRTmsrdfdGOGNNF+5tSGaaECO/d0awOUNNZ7YtRWEiOX3QPXiyXWVOuZWyik5TW9d0/iDbNoo9lzr1KZ60D19i3Lh1qR+M/KBSeprJDRPZmveF4Bhc/sSiXRed4/tLGHzCV9+rRQttLyChzeJ/mCWyrhaN8mrnAWEB/vEJ+yahu11piqstUD0G/IzCD6IzWqyXxBNjp8smyMVLx0Tz9b4WoBNG6C/iqPXGA/k9OyCBak90BMNvHYocs3nbNlzhqydgv2F3lcxUamgqqaLSr3vphs4FmVl05wZ5kmi6Awx+O68OtYf8yTIuw3hfsAj1bAuKEaN8JTPt4vrFhprnDrsbsAM2zD3l1ttcjETHQfWRtukMVy8KzYJBS5uPWksIQSWCRnd/Ull/IEDp8YDd3Px7JzUCzjXC5onUf6okxhHffu6ws251SNntqaFeRK9PcLdSLkAmg9Mmo8N1Zw/y50XFOXfPyc6CDLiqD/zC0qUNqml3/xu/61K36pvvPn9ycXd2LkbLz/SXGrM1jY2PnTe2GjcfbCC1d/+bvO7oXhwtr2mx8E51ZCvbDxwWfhUrOzurPzx/eSauXG5f/+0m/+PF1Z7ixvb7/9ob+yChDe+OiLuFg5vvTy+d+/zR3r4DuvXfz1H5OF+mx1ffP9TyPPy01n7eMvM90e7pw//+f3BID7r79+6R//mBSLrQuXF2/cqe6fts5fXfrs5vrtbx799d/svPtR+fT07NoL6598vfjg3ri+1Hz8oHnrwWRlbfnLW+u37z37zms7731cefSo8+KLq5/dXL17J6o1GvtPql/cH6+srNy4s/75jePXXtt4/9PFe/e653ebtx5uffapX2yU+u3t969P1jeWvrqz9ZfrJ6++uv7xjY2b39CNBe/Y33rnI7/UcKL55X/808mVV32i/SdBhMHf/5t4cy1p1k++80rYXJpvrndevEoL3tmLLwYbK/7S0vjypfaF3WhlMW02Tl97JSyX56vLZy+/xKrlwdXLs+2NWbM52dluX7ualQvR8mLn5WthsznZ2jp55SVWKvQuXpjsnour1enWZvvFa/OlRdqoPP/+90SlMDm3efLKt/JKqX9pb761NllZDc6t9164NF4/R+uV59/7K+mZ43PnetcuzVdXgvW19reuTdbXZNF9+sPvZ41KUqnuf/97rFacntvsX704W1rya7XRtUv93T1Z8p7+8AdJrZhVa/vf/yta9vzl1cmlnfHW5nRlbXR1r7N7QRXcJz/8F1mjHFv2wfe/ny3WpmsbgysXx9ub8dLS8Ope+8Ke8tynP/hB3qzE1frh698VNW+wudu7vBetN0o40jY0sqiXYUh2bVUiJStxmhwum3ZBVEoxO1ewTGauIrKoW+EAb5t4wShZidfkaMmwXF4oZXyrZNGZtYzwklEkkX6OkAqxnazUZGzZcXHoOJHaqzkktpeVWrFryLc3EGgYppUXahSs2QUrKZYyuenYWm6tIrFY0OCIOP24qbA1t2EnXFe5LUzcCVaEjn3gDUGV5i7FZj9ahhLPddGebRJDD7A5iZc5wT60e6whmJlD0k/WkDICR7ZnW0DHc2hOsiUGccT1gWhw6uWG1qGLLPeoqzqzTYhwhPVRssyIFiJ7ACpJVEYE9+gyy6tOUw7JRcdwRclLrRUs6obnpNYyBE2rjnx1qWS7uW0yuOfqNreVb64gsOQU7dRcRnzFrYMZuOhqZeLEA3y5hAqo7MbWEoBNo2Qn7gJLN2qO3yGXC3oJFkmEdk2tiMtO4iwBuGgUjMRcVmytWFNTsmtrZVDUIm1LRx4A+oxYo2AVG8gXFZ/VuSlG8WIkSwDjASpNaE1II9LNQbACkZjRwixdgi7vsfqE17AiU+CNkwWF4AQao3CNEOSrwiheALbo0foMl1igc2gOklWooyEyJ+GqMuGUV/y4oSw5FOUBr0FlBJo9zJqM67FmDuNl4KHYrnO0YupFUCmmasszPN4gU3ap4jrUc3O17dgmLVUSbYnoJnfK3F6BqKbXic8vlS2bek5GtgzTYF4pI0saqJKSE+nrOvj/ZkoeiWFJ0zeI6cpSJcVLGqgZNSvAmxZfsBpwKi8XLEcW3FxtO6bDS8UErLusJIqs7W8IUOCW6IcbFDgUakNR8fMa0LSJrEbzJvJYb76aIZepbBBvcN2Openzsp/XpIbGohIETUNLOskahcVcF4NoNQWeQGQAK35aVUpNZNmPFrFHO9FyQivAo618KeAlAPBYlOd5jVmwryrz2ZLhsl66GGQlo6zPyguMrTgFJy0VEnm+aGrUbXC55tTw3FkDYME0bFqqZmrddklkVwRZ101LWIsArFolFNhLAjZNy6R2TaA107LzspfIXc8jib0ISROZOvPqmbag44KslhK8YegFWPISuVvwSGw1lbasu1ZWXshV04JlVHFDtl0y8AyRSbpCiRYCa8BrNC9wnfToYp4XmCc7sy0ItJiQUbJCAY6ENoT1JKlCjAdskSYlURDtyTaARq7UOFnl2Iqh2ee1NC9LC/fSZTGvECs+mW9K3YrhP+0yY2mNeS3JytwCnXSRJjVUZO3wHENmgvEobabYU0U7KzTyfKNcRCHZMfUKLKAI7ZqkRMpO7CwpsKh7VmotSHqu5AU9Y9chVVQiobalaWXkOqmzqOSi6YmJ1ZBiu1yRvralgwVzAU2NLQKrui4Dq8rQplvRArsp1ZpdQIm2rcOGXoEzfR3ihuG6zK1zuWaX8NxagvmK4/A+r05QiaU2JXY/XEIaGWv60F+DFvJlcRYtKEONZLHP64BqCTQGbJlxIzZIP1hXJpiKos8XGAABtftyAVA7NfRutsSFnZlkMN9AUM6Z7bMFTsgMFfqgkodlpONOtiqoQ03S9zegiQNVmGULEmk+cHu0LlRVX0AzfqXsWanhSXjetmxeKqVkmYC6WbVCcs7gi04DTtTlom4rS8zgXkn3ZKmY6EsYLug1M9CXQLpaL8xOyYtVwxWemcJt2/BUuZCaywhUNTcfmOdttuItyAm66BqeKJgJ2dS1Iih4KVkmpI5reGZsavlSoSHGaM+GBW22uja+vDfc2QyazcmF7da1q9J1jr797fH2pnKto++9Hi83pksrw8sXe3sXkoIz2d06e+FF6drH3351tLUpC87xd79DF8vjhZXh3m73yuW8Wupf2B1c2ROuc/T6d8fra8wgxy++GG2tzpZXZ7vbncsXo1ptcPFC/8oFaVtH3/1uuLaY1Bunr74crC9Pllen57c6L1wDrtnfu9i6eplVCp0XroYby+OVtWBro/fC5dnqWrK4cPD6a6LoDC7sDS5fyEvFwd6F6c654dpGuLbce/HKZHU9W2ycvPaqsrXO+UvDyxeihcbg6pXJ1tpk41y8stR+5dqsuRQtNE5fe5XWK8PdC73LF+JGY7C3N97dGm2cy5aag6vn2zsXWa188L3XWckd7OycvvyK8fVX/0kQIVCq3O6YaVqcTZ3JxJpMnOHAms+NWTChO6Xx2B6NdZoXW2dWFNpzv335ytmL37KDwJlM3MFAjzeL47E7m1mB37546eSll5c6B+5kZE+n7nRiBUFpNAxGw6OXXqav285sWh729TgpjUfObGpPxs50Yvl+ud8b0d3yaGjPZvbcNzpjPYyK45GRx+XxJApDyLg7Ghm+b0WxHsfF8dgWiRVFxV6Hlb1CrxfVymqrVv/edkIQ6ff1MCz0uroFzSCoDPqdK3tf/df/TTUarjx44I1G6XINxcyIo0q3C5ecyg8vUqJmQVju91m1AJV0xqO0USzlwoii8mjAue36c9o+o6u1QqctHEJimnETxUDTM1+6YMLckhwnpm4qO6VxpFOAyTROaBFBZI3G+ssmn021vDyLLaxJN+M0wgBb+nTGdiEUKQh5Qi0glM1onJgAAyIoWYgRAGDsJ9KDqSIpT5gFqCinSZAaEiKYiTwmQOien4bUoEJHlGFZhBITHsPUZljT4w4QhENPy1MgNMWIlo94agnpERqvgydrjfweXQJ5kasqzmIoNKSKMB1sW0f1pfTzZAOlVUaKVuxrjDBVIfFozTytLyRPZT1LVyQuasmJZEWKLS3ydQYZqOrpGAqTy6pOJ1oGFKpr6ZnGaAwKZMZUAQSJZWWSiDzJTSOjgIpIuXDGNBEHfMXwKa7G+iWFQcrGIE7NYp6TeR4DVwslAhPyXRvMRyApR6llU4kjGmZmAYQYdeXrLglmKjAz6ehzhmwwT23TEjiiKTV1mmlBHitXBcqENKY2CpXeUCR3FCRmFApZVlzpUV/AqplLykIgV7gUOB+AzFZAL8SdjcJYAvU43xayCoQgeQ/LZaWgKWYX7OemTveT5VxtAAG0PJOyhLgkcetq8bFRgJ8nO5iXFARmPFbCVcImNIPSgxIS2ob5glAEp33JHAmxns4o12iOCimNI8SoTXIBQhnAghlSmaiI2SgSIkc0MYtBnq4V6AW3OGoHcz1TrhEynMmA245P5ZIVbRSKyRQOaC4cK0lVSiLg6rMMlEi+09SjGTyjYWKVcq4CEXK3miSYkRh6ZsChFFFuOfNUQhRnFqTSYpjhmh7HBs4YOkeiEGEJeQWJQEtTKcooH1pzwXDVzBMHdi8tjYeKTLJaxhegnOtZomSJMFHJ+hfrgw4uTOKSQItmFjM7AaLJaVLmp1vVSQTxYbLFcJ3QHGVcgAqkuUbHSNWxFDinUnqYTuyYc1zFOcOC09RUUiEukxBJYRpBnnIDcklSnjATUFFKkyjRhYAky/lOGSNFBv0s0zKlw1QyZkAqyvNgsteUhBizUMSQSdsOKKNazLRSnPELZQGJFU5AhBJmFaIsDW3ILSfMJddoqhkpyzItj40KpWAGEuWSJNdywkGFxBMlDCkqiA+0DEtU1NKW5IUc21o0N5lioKrF06beXWmEfaUP5tsK1rTsTEpMSdmaTxZIa60Rn8HiMN8AokT4EOaIgbIWtRat40sL40eymtGGUhVj3gIEc7GAxMCgSKCKmbU4JzkuwTghKFawqrOQQZokWhFIME0j6aAIEZxGytHmDBVAkNoaEXrM8lxHRBiTkXjFgulczo2Y2SiUVpVHiQk1ovGEbAuUUjKLE+DiCJBchNTGgSjUInCOQVtn0yjJTRekup/63BYRhlRRZuJUkSzzIx3aGCcspzrESE+FkkVElZ53ae4KpWs0xswTCFnxWAlLiALOUyhdpDBO2nveievx+2E1z6scETueIGkBXkBx/7xzbDnsYd4E2bLEtp6cMVphUDPCwTnr2NLYIa9TscmEbeY9LUAS1/XkFFJTQMOajwDTJCtq2QDxAhA24X0YqhB4ZJqhNIto1QmYQiBKrELOQZqFzKkkCZE0Bp4RMAgD7RUNxmORlZLMLjCOUhYyp+QHWHbUD4toNpHMyrlthFQ5KExsJxWGPaev2dpsjH0jBB7yhWHxOLeMSGIIk8RCDCsmqHC0JCaIRsDTIqnhrNztxbWSXa8URiMmvdJwaEZRcTyc+w3L95VSwbnl8ngcF4o2yKrXloSU4XxshmFxOJiurtiTCaZ0sl6+96/+FilV6ba96RQXhBiPzDAogK65tVF5ZZ0hNQ3DUrerTM2ZjJ3JhFkWm4zNJCl1u+ly1Rn0Ld0Y053aeMgRsWdT259pk8AOfHs6LQ5HA3ax0u9bM9/2Z063ZwZBeTS0o8iajL3x+PCVV3NiVMJRtdOyg9Ce+/ZwZASBMx62L17qF5fL4ag4GTvdvpGfL7bOrCCw5/Puxkb78pXSqOdOJ26/78yWncmkOBqOsqTY7xlz351NZRqbQVgaDOwkKo3HVhwC+B/d4oP+OYhQQrT7/oeXf/U7ZzBZ/fyry7//s9ufbH7yxbd+/muViwtvvvvSG78CDG599OnlX/1Wn0UZsjJoUKifu/7Zt37xa5CJC+99+K0fv4EimmoOVUQxtf7V7au/+p3Tm6x++fXLP/2FOZ4nuptDnUm8/uXNqz//daHVa957dO1Xv3W7o5Wbd175Dz9FUb710Sev/PgNPUhWv7p97Y1fuZ1hsd1/5Uc/quwfV/ePX/npz5z2YPHJ/rd++ovKkwPvtPPSj99YuffI6U9e/elPq48Pquns9eioSMPa0/0Xf/rL6uN9rz956SdvrNz4JidmSuxEd+r7Ry//6Gelg9Pas4NrP3qj+Xjfjvwf8MFm0DVGwas/+enCgyeF084LP/lF+elB7aR97Sc/X779QJ/MX/rxjzdufKP50cs/+dnyrbsygoOHenoEVCS6d7X0YS4UDm6D6SMoUzC7q+aHGE+zwUM93AdyGkvX0aZTqsj0Dpg+wTyD8/tg/ExT/amwDJgmKqTjB1pyl+ZQD2+L2UMimYBxzA1Ddv3RUyN4CniOxw9wfJdRqGV3+fA+5jmeP0WTp5o2zSbPtfkjQBkwTyvawTkuTbNbAGe7mi/JqIRPN1Goq8mis7+kcmy3i/rRJhP2Ztx/Fca2P0ajIj7dQqEOJg3r2TJLzfNx97v5AOeZ07dB6xIZAzTxtOMtPNNWefC9vGsoarUccrANMt3om6p1WZsA7Nv4aAtPDTitmM/WZKpbHRMf7IBEk2PWuWWyE57FeHQTRV0kJ2r8DYqGSPZp647N2xTOaPe2mTynnAusISUE7eajb2Dch2hIW3ds0RJiErCiC8eTPCGzmzBsIT6Ww9sk7QLUHvB6FQxHNIDdm5o4YHmGJ9+AoIXlTI3ukKSl1JB27tvsiItEDW9r+VMmoKUfNnF/WZsL0Fkj3QZMMW5tWvtlBjTzcEk7XVWM2CcN0F035tEVHlzKhyhn6GzdOahQqNv7dXK6gSS4lvReVLE1m6PuqtZqSmrg9rp7UKccfis8e5XPQA60Vh11zum+gL0l7WxJ5hpur1r7TQY057iCT7dAjo1WGbY3UQyCM+x/LfMMpwdidBPlOWEnon3XBoM866r+XV2MRdwn068Un/LILETEZVATJ6xzz1GDnPZk/7bGBjLTrMAs5Zopj3nvppYHkJ+x09uOHLJUs0PiUmQkZ2r6NaQRpie8841G5wp02eltB3TzdAIGNxHv8WyEJ1/DZAq1CVKnF+2hDkMIjneNjiO5ae6v41EVhjo5WAeTgjVT4PSi3nWM+fz7KN6enjJmmftraFhHgUaONtC0XJ5Pvg2TzagPcqIdb5snHheGtb8Kh007m7+SDzeymT7OQXvP7noi18jxlnVaYNK0ny/DwSJMdXK2gYYVwxewvWt0ihxo81tieg8LiuOHYvhUR9N8+pz4TxBP4fiJFt7hFGjZfTa8gzlHkV6IkSUo8Pfx7CESKRw9IdEtLqkKzHKMHcpw/Ez1H+lwlPlH2L+PeAxizfWtCkUkuU+HdzWQqnRf9B9bYJwHR2h2F4kYzJ7j2TeKSsyfsN4tTcYSTWxyct6Y6mBetp6vy8w2erZ2sANSQxuYoHWFjBCZG/j4PBpZizL5Xtb2eKx3TXywrRLD6Gny9Io9geXU/ys5bSYTFZTN5xsgdrShhY7Og7lWn7Rf12g9GMjQ1Q429ZEBQtfY3wChpw0sdLSL5xoZIdC6qo90mNjG/qY2crIIjG/jtA3gMOvctfJjziLV/UYXzxhlePINmJ8QGajJHRycINAZikoRTWfynxL0mOVSC27K+aEG4lRBxRlT/bD70EoPJWVkdBsnDzmTQMsSZWh8wscPtegIatOs/8hIngvG8OAWTh5wKrXwFh/vayxUk4dk/hzKTKHumnuwIARyDsvayaZgut4uw/a27kvUb+hnqyDTUHfFftbMhX41bH07H8EsNzsl2N7BUwUHDfNkFSbkApu+nnelVO6xh492YIb1bkG1drVRtpH7L2V9m8aou2jtrwqGnRMHHe2AFOsdF5ztGjMFRmXtcBWmGhnUzf11QDFv0dPbjupROWa9Wzpt83SEJl8B6mPWZr1vMPUV7NHTO45sUxHEzDaBP08nePo1SMcob/HebZ2NBGpbNTPBAAAgAElEQVQPeLMBu/10hvo3MW/xxEfjmzAaa3geKM8G81D0afuuI84oD8H4G0zPRBag2VcyGRPRF707BusLMOKtuzY9ETDnL//sl1sffmpNw2u/+f3KlzfdTv/yb/6w/flXMKIv/uyXe++8zyR+6We/3Hr/I5Oz76TdC3TiDUeXfvfnrQ8/Bpl88ee/vvKnt5lCieZkSDfG84u//9O5Tz53+uMLf3hz573rMmffjs9eCI5orl747R9233rPnIUX33x36y/X7e7owh/fuvTuB4DKa7/63dXf/oEpfPV3f9r745tanK19efPKb35vjoOt65+/9JM3BAd777z/0s9+QYJ0/fOvLv/mD267v3Lz9gu//K3TH6fESjQ3w8bux59/60c/02bR2le3r/781153lGlWik2KzHMfffrKj38GM7F5/bMXfvRT4kcZMqjEXOLmrXsv/PQX9mC69vXtV370Bg6zc9c//9ZPfmGPJgv3Hl/92S/LJ+36gycv/eTnxXaf4//ohpt/jmiUn/37/2dxPjaiIF2qy5gbSZw1KtbUV0ymK/XK00Mzjvdfe73Q79nBPFluTAsNhaBGabHX07IsW6zos1gLw9H2toBI6lrN72tBLFPhr6zYs5kRx7RR9AtVphlGkrj+zJ6M56ur9e6ZMZy1r1wleW7PZulyDc8TM4yy5RrLoTcazNbWrTw2BtOsXlYQWcPJdHWFSFlqnU1X1hwaaWejZGUR60AfzGilKEuWkyU5QqkwSu3OdHnFAZnemSSVMqsU5napnEwc3wfTlFfd1HCK7fZ8ZdXEuQ1VrFkypVZvQsseJ7rZGsi6mxQqxbOzYHFRR9zsjGi5ZBiMTZj0rLBSpzMFLeyQJPaJMojnZGFgASAMS/hzp8R8rQICakMdezjgMhfYNDEMQhsBYbs8jA2d58gFCmQKYqJwHumKINfLwsCEAMAaIfGMSMGRQzMNIcCKluwyhWGxFOWJKYUEDRPMmM4y186jxKDEEDXTDgDOVFqHWgK0WBI7SFCJpIAWlMo0lGs1chjBuqRaWgMlOioF435xWY9hLgusIHEOcAaxFdoodOKgVV5H1CKhYgXupGkmi8hKi3xgKB5obsoqMCe644eohuYYlFI3D1NaRlaCJGDMraCzEFYEtTRnHmguGKbExQbJo8g0TIYxSENsGXmsF/iAwYaxkRx1RBPZ0NEiChRWQAozTTTXzKhtixFTFdPOp8wASmlEkjTRdZ0iDSaR1pCjqOAqlXJsGQwmEdFsjDCfzy1TZ7oj8xi5JMkdR0y5KhgFGISBBU1oeECGlqEyYXOeOYogg8xlZAuCdctP4iZUwnIneVbSeabcrJD7EsGR09R9TUGoWTOelIFStCIaUc9i2ciqUVoGStECNHwFEK6qI2kQg9KzwroWYigBslIQahTbokQNXwEAdWtG8xLigNhBJoqIKeXmKqQ5010vz3JNpAo0LZRwOc9JVXenk2wK2bmyBFiG3HNSZhlc0714PldF4OeoqtlpGGS25Qhkg5wYFk1ootEYeIUswp6aUa2CG2zYKy6ZeSojmVLTLVAZcDnKzVUSmSU1ylBNN2kUxqbpSgRVGmuiYugQGj7gnrK5T5OysKRBIpaUsJZJovK4ZJMpszEKdebAAu+VRBzqFmZqKjcIjgHhInUNHAhX1addv1hVgtC0LInQjIilZYxyYvgejQAAY2MRhoZ0IDUUmZhQ54Y55XFJapI5UJ9DA4S5o8HQEIZKixj3YggAqBtgmmuMkiJMEg0qoKqmcTQGCuorOI4tSQVaMLBkGEo7ieLUyJEJKjqYZACgoh3HtqsQhLkUMVBUGAWAQpog27VSYWocEpvGYWRBLt1CnswJDjK0YoGEp8pyvDxPCBOoUMjSxOSZUE27HAY5L0M7RVxy5hIzkEIHma7bsxDX0ByrUubl85RWoJUXwMgUmVTKB4ucOro1S0hRC6EoiBLtWZKGZiFnrshtYgYcGCDRDWPGLdCcdvu1FZyAPCuaRoClmPFly5xIgEGmm8Ys1AtGgGhBOSzI87IyqcQs89ECGMVeMQkw8DQPhqFvEBsTwnzfNjRmuiJPsAFy4SIiIo4tnYM0NqABbTuP5gY0EHQVyQKNsdws87mCFlEFDfZToGPXjqiQihCKCmrOHZhgAvyZJVyDVCAaJMjAjpOGgQWwAkUdTnNAoKjalq+gQjV4PAFrkIO0AvQIQA6hnaEI5dATJWqEEgpILN8BsZlGM6uayjrOpXJyPRYUecQMSvkQI+CTAqVlRCFxgwSUSKKUk9aynoIoNjyalYQy6vBwCM7JjNiFcQoKJAXAzbWE59LT7FBRXXKD2H6sLDmluKothu0zsWQ6EgOeJIbl0IxquY8L5ZTblhrloGZY2ZQaCCkCuJ5lxLFyBgiN0aIajMsVwkKGHZ2CNNEMDyDE49hwLAo0ISU1OQ+NuppQUDZMGs9njlaQpp7nMYFFDUCgZqxgJrFTFMMMlgwPR1ZnrHSSV4v6YIY1FVbqXrefFYthpVp//JR5HlssWe0R1/VgbbmWzrBgPnHt9oDZ1ry5uHCwz03LKIKz0gZA0AqDwmDATVN6ptMbprY7X1pqxkNOcCqJOZhjxZNGVevPJEKi7jn9EbVdXnGszlgSkjUrRn+mpJyvrdZ6Z4KC2eqqNx5pcZKu1LVxYCRpulwlo7Dc7fQuX7SjuUolqxWCUjUneimZGePQ8GfpaoPn0JmMs8U61OHIXSgkvjMYooxnyzXkJ5bvp6sLM7fKLMsNZsXxAEY0XaoX+gOnNxxdvSATYfszo0oGWr046IcrS3Y0x/O0d+mq8w//4H18XWnaP180+s/yvkuIGg+fXnj/I5nL2tODrY8+g7lYun1v85MviR89/9Z3bv/wb5lAjWdHe+98gFJ2/qOPf/h//jttFjQfPd396FMOtMXb9/be/zBH5tZnX/7tv/1f9CirHp7ufPgxyHjj6cH5Dz7KkX71rff+7n/8n7hE5f3jy2+/DzLeObdz54d/O6ksNO8+3P3oE8lhY//44h/flByWD08v/ukdmFBzNN179y/2cOr0xxffflebJ3Z/cuWPbxnzWJfpdxYyT4v1SXjx7ffs/sg8Guz+X78wzsZWd3TlD382x3M0j6/8+Z1ib2gPJ//qf/5f127f02fh3nt/sUYzfR5f+dNbTm8M5tmFf/hJ4eYTHKV7731YOmoZJL9Wmbkm1+bhlT+9VeiPAQeX3nqvcniCE3bxvQ+r+4dUaOxJxlqUMUgf56KV50jnj2N5wrngheSJLrsgouJZzlqMIjv4QsseoYxY8mHIn6VcIn5I+RlXAge39ewpgUhnjxN1lOXEgs9i9pxmwkweGfF9DDlkh4wfUxZTSxxY/sME2fxpxPcZV4QdU3ZAAQX8iLJjmkHd6mL3xKMSaQPkHhcAw2iqu6euoOQif/yD4C0NJGYX2UcuVTodNIP9b9PM1QPdPXZkivDMLhwZHBh5ezF88lIGytoEOUdFSCEOdefYBSmKR6vBo4uJKOkT4h54VJjN8NG/zt+z6EhFjnvqwRyjwCocmZLp2oy4hy6XJksBu0/ZRPAUiDsBnwgWQ/YgSQNdH7WL6ABPz/IM53czPpEsN+JPcdoxBMP8UUp9xeeC3s/FiAmlh3+BtEW4QvxBLEZK5pA/zqQvINXCDzHt6RIRej9jHZpLVpzf0fJONpfsCeVjKXOQPKCgnwtgsLuR6lCBdLtr6T1XMGS1bbOFmdCsM89u4wTqr0fv/RX9NM+R1TWMnieZFp1dSM+2qXLMM889gzmxnFNst71caNHpVni6x5mp9xy7a3JFjG7BPYZUM/Pn67PDaxSa1kAz255MsxfTmy9H16FIzJ7nnZBMs+0Odo5tCTS9r5ktD0gohlI8iDKpw24u7ye50EQ/Z/eZyFRaK/ovbqSewzoUPoxzZYp9mvxxljEdDPPkbq4SyWaAPaIsgfSQ0jeneYTFSNB7KZM6myl6J4WxZH2RvB3mY8ADKB4mYsqipar/0jlmaHzM6L1MhJKFkD3IWAToDMgHiYwBzJF36MFA40Jzjov6CApG3GMHTSyXdf86eXuZH8oMuoeePkcZLvuPrtHukhSkcKiTqa0Ycc5cNDfzxA4OXsq69RyYzr5rDjGFlnuk60Od5268fzUZrymGnOOCNsISwf88fPPC7NNM2O4h0UaOyIB1bBlTk1PknBaMMc6xiQ4S9jTLlMbPWH7AVC75CeeHuUxEsLs031tmWOfPIv6cUqin92n6SQxzxU8ZPaRcYH4i5KMgtlz1hZ+/FybIFmc5e84VlbTFxX7OJUm/SuV1P8M2bGXZvZRDPV6pBheXKCKsJdjznDPIDlP1MM6RyVuUPsg50HGk20dFEGM8N7x9XXCTzIh74FDh1KMnf5+/6+QDFZvecQGlKA4Wo8fXaFrW5tg9dLh0cIS8/YJKSZrWosfXslkJRsQ7dkGq4xl0jwqS6TSszp++KuYlySzvyAEzUudn/0X+dk10QUjco6JMNZgg93kBTxTjtrtvawFhDPGnGRtKmCv2IGVDyYFB72f8LM+kKs7vGlkrCxR7yuSQK6bNPtPZKYFEY/didZZTaMDHMT3jOTWjb7T8VFO5ZI+pbOe51Nj9FB0nKfGSGyB+rHFF6LOcd4SYRxY8cWfPJEL8aS6ehxmx4ZOQnXDGEN/PeYtxCfWe5x6jHJvOGTRPCxTqxlCzzwoiY5fTO9+J/oLFXO973rGWa1Z+tBI9fYFBRx9g+9QDDOhT2zqzJEdRazt8fCHDrtkj9pHHhK53oXPiKq7F48346Arljja2ikc6Bdb54Kt/zT8ELNOnmnviSYqJb3lnpqRQG5neqSMFZnNF76QwEiyG9F7GQiUCIB8m6ZygdOiBpzz2+YTTu5mcC57p0YeQjjWRKPEg4RGUoaL3UpUrEGnz9xGf6lJiej9lE8FiJO8EfCayiRZc19hcFylI7mRwnAMWFeQzI2xlsa4exNkU8gjk9zM+lzxk7D6VI8oV3vnLx8u37wEqtz/5ovFkH1B5/i8fNx7vc0xeLIersEcF3v74s5Xb90RA1372ZuP6NyIR5z/8eOHJAYPazgfXV2/eZkj//r//v1/++a8YNM7d+KZ5+4HKxfnrny3dfUQF3v4Pv1v77YdUt899+dW5z29ADZ8vxluFHEi1/dmNpVv3OdC2P7i+/vmNHBvrX97c/OxLrlDj8fOtT7+UuVy8df/8J58zqC09fr7357eExK3tvXs/+Jex4TYe7+9c/1Tm8uof/vQv/7f/XTK18PjZxbfflxKVjs4uvvsBSej2J5/94P/4d/o0aDw/uviX60LA+vPjS+98ADK29vXtv/sf/q09nJaOWzsffWIPxifnr9z+m/8ycgrVo7NLf3wT5rzUHV168x1tFhVavd33PzSnvkT4PwkiVACc/+j60p27zafP1m58tXbzVuPx04WHj/fefdech7luUWJIoXY//Gjp9t2lB4+aDx6v3rrb3D9u3n+0+8571ni6e+PrrY8/s/vDneufLDx41HzwcPHeg3Off9l8+Ljx8PHlv1x3Jv765zdWv/iq1Btu3/h68dbd+sFRZtiZYZOcr967f+HPb2l+sHX9k40vviq0Whs3vlq+c2/5zt1Ku3vhvffrzw+aj5/sfPhx5aR17uatpVt3l27dWQrau0rbYVP3rL373geNJ8+aj59sff5F9ay1cefe0p37m19/vXjaWvnq69UvvvK6vfMffFh5tt988nTr+if1g8P1G18v3b63ffNW/fh0/etvtr684Q4nF955t/HwyQqLXpB4lcfr9+4t3rm//ukX5eOT9S9ubH95wxqNL7z5zuLjx7CXjPtufIZJLx0MivEzaafZ8NAZnZqAcbpcpstVdTwZd+34GKh+MphW6BHUomRy7Ix7HpzR6ZE+G5rqNJ4PrVnb4IO0Pyj5zyCkvPfcHnZdLWDzEz0cmbCVTPvWtGXj7phd3jZqLgqi5MQadL18pvwT3R+Y4CSc9fT5AVExJbNV4e9quW72F1i85fSEPSuy4LwxwWuSvQBzN/bxoA6CC8RXeLws8+3SCJCRLedbpu+AeAGML2iRMvplme5pc2AOKizZ0fsGnhfFfJtMTZTU8XRPC5Q+qLBoT5+jKyDaUaX1oKv7Hvd3yMRCfkGO9kgEjK7Lwz1nyFhf9HrF7FjKQPVPSvkAwn4+PvOSIQRpGL64ZyYB7Ge9fokeMBbB3kmBTjDsZYMTlw0VOMl7oxI7FniQ9kdVdshIJPoHbjDQWJ8Pzhw+gvwgHMyr9JjLcd7rlfMDgZJInF8TVU8c+qMzJxoSdRCOB4XgCPBx3ulWwgMFOAHDVRgvFQcS+kvYX7ZmQMTbuL9mxtlLRFwQysipGq2BcFEfEBmvaf6yOQUi3iS9dRKnZLwt5ltaYhqTFRAtFfqCBE012yQzooINMlgnaab1l1C8TaZATtZBuNqYx+cVvAp4kXERnMOjHRKnervJo11tpozJogrW0AjNRoZ/4hI/D49Bu1WCU5aeab1hUfQ51Y3YdBFXeRe09l0agegU9gcVMWXxsRqNinI/zMdg0HJ5jyW+PmiVlM/FCe93S3xM+fO0Py7zg4iP1LDj8Q6Lx6R34OIp40RPdYcaFj/m3XFFHuVqxPotD7Qpm4DOgZPOlDEwaXLe61r2RPFw1+yVUUbA6DyYLzRZcg2QNZZUeoIlO6RXNkdMZhetfgnHQo32kF8lUyJmW1pQKnakzLfNSdX0FY/2SH+BRAwOzqtoCc90FWwSv6D3DZZsmaOFQji+Zhdf1qWaMzy5IKNlI3DgfBNNCqWBEtEGGq8jIXvPzUHb0yMeHBK/b6JWMuub05bFY5BrVmK4JE3TU2vQKeZzEJzos5EDzpKwR/wjjU7koOtEp44eptNjozsooSkLW8Z44ICzNO6i0YmBZnwy8tqHBUD57Bke9EtoEOSamemOzEEw0MYnJpiwWd8ZHTkkSum+6g9KcJJrU1cEW2Rsw6gExnvEB/qwyMOL2hxfVuGOKm/FXWPusPkOGdkkcOBsDwXYHDg8vGiNuNEt0HS33CHGTJPhrjmyUFQAw/Mots2hx4PzxhR5XcL5nt61NZ+I+Z4xspp59IISi4CZkwILzltzw2tjmu1a44I+J2K+h/wKncvxsZWNkDiMBsMi3adimPX65exAomgutpf5YkUeTUdndjjS1FE4nRaDU8hHWbdXDveBykT7ueMPLDzl07aT9SFs5cOBNz/T8ZR2e2X/AGphGp45k7GXj9S0ZcddYnWH/y9t97VsSXYY6HmZzJXebO/38afqnFO+utrBEiRmBuQMhzOauZJCEbrQjR5AN9JrKCTdTEgjkDAiDIkmAAKNRhuA7au6vDnebb937vRmZa6lC73AzAWf44/4v/TKKtdwFoLhmZmcK4IXTQ+k+UhD09wZaMsBQTMK/T4er+I4EUYd4G2JDoDzFeav1JfxRkGvA1ahWeGvoNm2GCZk2CiSXclh0qxBgzUyJsCr8fkadjCK+nixLQSJOGrm4VXJ42RRL/w187IgQRku10UHYafBpldxQHeSyTZSqsEMj2uFv24PAHRLYLomuRi7TT7ZElJc7MfDWZm+CvgkHw/M/JRGDh4e6HzGZVwkt3bENCpOsstZuTjKwJSORzY/zwqHD/a1eIHSs3w4sOCUpvvhyKvTo5TO6PjCKs7zYlGMT6zEwfQiH0+sZArYK38yLQVHhZwn0Y1NLrB0mF0cGNQV2EUxHlrFRcYui+HEDs+wulzu/PJX7QcP7aOTtQ8+bD151n74uHX/q40/fmw5o+ui/AYp8NzZ+/Vv2g8e1s4vNj75pPXoycrzF52Hj9c//Ei/GK7/8eOr772Hg2jz3ffaXz2uvNxf+eiPvafPe0+fNx883H7/A9P11z757Or7H4h+uPPLf2zdf1idDbdpuhb6zbNXzQcPN997X5k76//0ye6774l+ePU3v21/9bhyet558HD9g49qJ2edR4+v/uIfUJRs/fbdlU8/L52cZqKcirIQJe3HT9Y++kPl7Kz59Pn279+XXP/qBx/1P/msdHax8dEf218+7H55v/bqYP2jP5ZPTjtffnXlH36t++HW79/vffxp9ehk9f5XjQePug8f11/ub/3u/dLpRUrkRFILIGx+8EHviweNw+PuJ592vvyq9+CryuHx9u9+Xzq/LP7rR6P/RRbh8P/4PzcXx7Wjw/N7d9XRXFvMxns71cNDQFnQb8dEBZxzhFTHaR3sn71+l8yWpdHo5Nbt+vERSRJno68Mp+ZsdnDvjdJk3Hr+9OLN1/TRWJk6p7fvli7OFc91NlaUuWcNLg/eeMuYTtsvnh++9ZYWe5Gg5JJUPr/Qlktna0WaL62Ly/Gd6zwBvYcPDr7+dW25KB2fuqs9AGDp8Ohy9xpHaPOzTw7eeEvNvK43ODF7ImPWyYXfa1OMywfH3kY/0uz1Tz89vPe6xNPWF19d3rwpgtw6vkxqJpUV+/jCXel4hr3++ecnt+8QVDQfPJnsXOUCsI8uonolNtXW8mJp1pdyafPjT05u3RYIaD14PFtbEyVOzudptTxvNqUTNzclwULkcJlUDWyh4iLGGEYlGZ9fqnmSrq2hWcZ0CZcQOvapqQhljEYJACBvKcUkk9KE9kvoLKAaESoCOnKZLKCWAgZxzlGyWlUu5lKSJKsleB4hGbuNqvn4QYZEvroqOz7IeLjVUC/mYhiDFQ2chbksO6vtymRJltl8w9bcyBwl/irOY1lfJM6KUQ0GauQnuhFHuuSx8VZZ82J1kDpbquX4yBOcFZ0sMznMaB2hgJNFMdsuSUFqXybzdcl0Q+DiqCtJQS5EPKsCniB5lic9xIp8c/7qeeOmGmfCHERdUQpz6EPBihKuaVPqrpBQ1MwnZ+l6RYIUjlJeIjnBaBgVJTmCqHT40lndrKEkHeR8xYYC48OUWwLQiHARwIoY66p06CZ9UxZyfOClHQtLjF0m2BSYScBZoJt0WamJLxd0rUwEyo8DWtNzyMjJCbPtvNmUBkvBRGnFIi9m8UpZlnJw4BVlOW/Y6jgWMxp0ZG2YUxnnJa6eZZkqshIvO5dCkY1qm2jB1Sj2u7I8BkyEblMrH3uccFpFwhzAjDurpjUO5DB1ezIZA4SY2zbKp26BoFwK46VKMj5dt81phMMibAsrznEKxYvamnGZIshoFQgLJoQsXhGhi2U/d/sSmQbAT2Fb5WEhzEL3apf4kXTixLt1M1pGgloQEbiUOAFsKyzI4SyBGwYtCDmYpjsN2fPyacZWLTFM+TIHTSlPgTAJYV/LgET2p+nVatmd+nMB9E2YUragqE4YhgxiJY8CbJCjRbZVVtIYDNO8a8CMwlnirbQwZo0j3+nIBKfaWRE1CRcZmfBCg4WSNBcXoVGeqs3yaeI2JZFk2gkNa7Ii+JlrILlILCwPCqCzsKSUj+NlS8EkU07ypCwCjZEpyCWU2Uge5FjJvLJmn2RBXYpseffiwYLYgVkjE0YlIazKpfMAStSt6aWDKK6Tab1aPTwtchBv1OWBI3thulHiwxQCntdUBBjKCxGzYpbBuAiutNShIzgBXNfBIMohiVar8tkCsRy2FT7LsJ96V7vSxBWWcb5uKeeLFBLYVsAwQgUHHYVNEhRkYMOkFEHAMOR4FmUFRm1FmMRFDmBbyh0mLsLF9bXmZADnOOjJUpwLPqAVziiSp3nawwUrtmfPnzVuqikVxjzqESnJoQuxGadQVqdF3EExkEsXqbMiqVlCJjxuiTArhCXPK5AjrA5p0oIRlqsn8XxFUfKEjFhWx3oxU5LQtWohsrXzJOvAWFRqR+FsTZOKhAxY2JALCYinS01J/XoNHyxp1xalAh75WUXLRUiOj7muZ52uNHZFmWd1W3w1j9u2pDG47zJbQhUCLuNMlmjTVI5nRCqiVpm8WhQNPa4Y1vOL3FJgWeSDiIli2ikpR1NRYpGhCS9fprVWttozD4ZAwbAugWHCIQz7NfVkxglerjYbxwvAcrkUxZ4qxWyyWdbnEVnSoE+6zinn/Ky2oQ1TzBmtQcFhos+iVZH7guZkXk8wJ1HGJNpA0oLhnKd1AFwkuzTuCywh2iz1Vog+jXkmpA0oLxlLESkFLErq6eykvs1Sos2p3xN1J4EpSupIWBY4AVGLFAWUX03jK7VqOPVGCKxYMM/5LKVltYgj5ewk3tiCuqIeLeMNW8kTeBFnXQOxAkwSVJMYxuDUN1tsqtfVF5P0So3kCbuIi6YmYgYnGS+LmUyEoyXuyIlmyM/G4VZNTCO8fxhXG0KpTGZ+0rC5hNWDKW4T37C1p6N0s4pFVt0/KkTsrPbLLw+hhMcbWytf3J/1Vxbt9s7g8VIwQqNkH50XgjDa2u4+ewpEONy80v/iC6/dmfb6m599Etg2aJno3MGcX+zudZ89yYjsrXZ79+877d60t7J6/4vMMLx+0zq6FLJ4srNddYYMi3O92nv4yGm043a1+uKwkOXlSks/HeIkPb99Z/X5VyDOL65drx8dkjhxtleUwdQcj0d3b1CKAeQIsPLwEvnx4sqmPRwJYeJs9sS5X7k4H966hoKs8fLFxWu3mmdHLCjmVze08ZQsveX2quAljaPDizs3QJC2Xr08vXunPBlJC8/ttzMsUkGSEBXduPH82fLOFZdp/SePTu69bs9Gynj26s1vwf/9P2kffvhflQj/Sz5YPBHleFYU43QhVlgckDGd79m6C0lIL26v9T/+0r68/PC/++83Dj7OJ+kc2WYU66Nkiit6cI6Gs+GNdms+Ll1Gg293ZW9WTLIFMGDsiKN0gspKOJbPg9GNduPQtc+D4Xc66HBeTNKZUFGGo9bLZw/+/M/FZKZdhqObrfp5Yl2GkzfrymhSTNIZrhQ8q5x6Ts8AAJTO/NmNqhBHbBgvcCUTSf0PXy7/5CpURfv4yaK5VUBSvgicVT3OtGIQOtySWNS4jKMNIbNt+3iBAggAACAASURBVPR5YNZiUTMuI3fFdJnFLyNnVxNMsX7h+30xrtqlo4ee2fKtavXzR+4bKwtiFaM4oFqhyc1hHFYga9uts+NIrQ3lNk90rkm6xOKslOeKasIkTkTCcFVNpw2t8KCqLKkoFIJOaJRYOSGyLefHMcIcKVIcZwqgUDZiRlkGdQKSxEYSESxCjyMGUKZVSKYSlnKiZwXFCcv10oK8XohIbUjJICIcJkqZZDJiVMYoYazIhFBrFLyqFfzCEEpukWDskSUtbAvyOclD0BMSiOqjLLZ1KA/VwvK5ishUSlMxQdCakkIhSF8WgZ7gRFYAHipMC1mCJEdN0iTisBSQUBGQSKVAj3AmKJwk0ozScpjcWWiJQSMMrEgIJQGiQsXKMEpKGlRTcbwQ7UkMYa4rBqdhBmsSE2EexnodZKo2w5XcMKX8/Dw1JaBgBWR+BEsyICJNEx3zBCs81AtuWFLqp1XAZEkFWRQTSwASSbK0xh0HNXJqYa7pUuRHJV5IUofkw4Zo4FwWstgwSiAhVk4NlimmSYO4RDjBihIySUA0IFQSLIY4ExcKMCnjiZ04o20MhUD1hLmpiamnUiKaDOSuwnLYLSBI7FCdyAJAlxpKOZOEYiklkmAIrJjLjONuWjBJHyROS4bCUMkzDgAWAznIij7l0lQDNQBgXoR2IjmaBHgoLQGwZUEM1ABzL46AqCm5X/A4ZWoNOT5ITUBKS1eLjiC9aWOcC7EvqCqPWRYnOhE9X8KZgSRbxZoXcxGpAsrzKBdUJUkZDGNTwmGq5tRASKOAD9OSQiRAaRbk4qqcRYgfxOaNqktVHGkcmbqY+AnHWEZiAcI4JjUiIA7FhZirxPFgPUIZ1yI9kTODJjp0Jq8B5HoSyaDqiZlMFiGq+yDVlQmddwGOY1KoXAfI9SQpxYaPM0Gcy7yawTw1U22ocCUPSaZCQySuo5IEawFMIxXysJfIjJqRPlCoApcSz4CAxGAh4VRUUphNDDXMGMtgqtdIpoksgaKWFgXMc1Yqo4fLAgD1lpxcRISCWK1KVIFFqmCcMkYLIZfLEtVolsuWll6mIM2ZWkWpwgsuijLmepwRRRIKmjIKREstJlkep6okeQsBzzPphoImbhRjWZJBkRUxFyw1nmQ4SblYxrIEYNkXIzkHhMqhFiMfyVzKxHnG7Ci+tdBSnfkCKEc4FAkXcw3J45jqCjQyMom4Rbk8I6kBAwyqEQkFVCgZieSEcUljUibNAmTlkLhSomBPgLVUcB2hyZ1KVg0LBjWgUTwNlBKFxBVSgjyJVxIhTaUkzeyKtAxQNclKAlN0KfEjm1mS3CX5sIY1xFUxiQxDYZlk0dxkVDKlIkxsAcrYFOhhVBC5kDUhU2Upp8QsMgNlYq5Zk0QsFKKYYnYQYYNQSRMyWRQLoqGIvMGJnOklLxYzjGRTSY5iKGNGdJRKnJCcaCKu5Tkn+iBxGwoUh0peBgBj0ZWjlHeLTJmqvIyBELPAiiVXlQCMJIcFtorFQAtSwtJETxRfgwoqUGBGxCUSF2JpwWhZQYIvB5HAeKbGSqC6ImeyqJ4FWXvui5E8yYqyBkVPCk3CWGrEaiD7mKQoUJM0gHmmA2QywC5TWxZkCPLMz4SunEuYTfqGBX0u41Bj0NRJ4iccQxmToghTsUM4wkmWdODyErZgpgnAUMXQDxmEsiijIkrEhphxnMemTbAnWiBTWa7pRhbhBilLXEfFaUSFEsBASDUFgUi0QSKhXFesonz2MCtb082qOXqeN+2Z1GjP88jko63u5sfvJj1y+fWeefGcmtZIrDfHX1JLm+Bya5rHGhxcaW+Osyyji81W4+Ii19QxqbcmNLS0KSq15iwl+cVub23wIbXEwbW+NjrgPJ8KVe3lIdWNxb1Wc/lVgvPhVqc2eJwr/PJGf3N0ArJ0Sir1QCDTcHS7brjHwjwe3O12gyW/DCdv182T0/7zZw//7b+Ro4k8cibXqpI7Nsbh4Ha3dvC0fB7M3qip42llki0KwwoxHkeTG7WWOyPnwehWu3x+nF+E8zerQrxoXEaLO7aYLoXzwXC7WT066R08evKX/8KOBsUgdm/rbiIXo9RhBkqX0nkY3pVkLP4zjEYBgIy9/qMfKmGQtavVTx+Wz864Tvqffd559HR0c+fOz3+mX14+/5PvvP6zn+jTCa3Z5ouj9vPnQaPR++yzzU8/md3Yfv2X7xjHJ0++9e2bP/9p9eSYlRX76Kz66IXbanUePtz94P3pzat3f/6z0tHxqzffuvOPv6o9exZXyo3Tw+YnXwaNev3lyxu/+uXwzt7tn/2k8vLVYm+z//4nnUePwnodEnj3xz9iGhHS9NbPfjrvrzROj7feey82TUFFV959V0qj8bWd1370A0ggQPD2T3/CTEmeeVu/+10Ocd6v7vz2N6rnXL5x97W//v7L//Dnqape/dk/QAms+PHWe78Tinyx3tn79T9ak9Hxn37r3g9+8IT+VVQu3/jF34s463Fx6733iOeP7l3ffeedytnx2Z+9/cb3v//qz78zkrrnh7o6pqXAOTlpmIfLsll88aSumPmaOb/8yoIWrCWTs/OeKOUaDUcnpUq+1GvB84dlQeerpjd4oucmq+bT4XEJC8DE4fTUNBKv0UmGXymFLtba8eIp1iTU4LPZaQlAUbfp5EjR46DR9hZPjImslPpsti8rrKjS8eyyHjDFXPH0yy0WlgzvhXrWYrFRBx8GvA3crmJfcs9SzmRJfplfrKV0TfIO5EEFuEbz6IHkiWG0bRinMLakS5tX/qAcVrJ0TXX2rQuDu7XS0VM5VmJn2yJPeGYKgw2t/IF2WEnSq0rpE3lqAL9Te/5AgkI4vaoLD3lhwOGeQY60UTnMrqja1MvcwZlVppGyVpw+MlsstEh48tzGJOGpPz4xGnQhq4vxSacauqXtYv9ptcsDS3YOn5Sk3Bd4cn5m17MAtcLRYaM5n2k34cGTWjMLjYUzflGqdRNlNDk6adhRpK7Ew8Nydz4jWDh5aJc3U8MNpq8MOXGEyWx4YhlBBreS8aFVm7pWT5NOVwXNV4r9ZH4FogLkS+5slQLHtx+Ig+8ysTDMz+BFH4thiR9kiyYGgOpjuFyzPE+wP1POv5VIxFwekMsVkUet4nnkbwKmKJVBPu+ZfkK0h+T0biLbsnssjlqwKJrFI7BY4axqVM7hYkXzIay9p+73Urmqz+7DYZtRyVCfXQ7E6AVcrTvLx2g6N8sblL6Cy0ujr8+hn47O61UjjRIheqiutOLkUTaeWHrLz0/YbKh1yRQwOj6odYkf58LosbZZdqX97HxcUavLdMDnl9oKnCIWjo/bK8jJITl9Yl6rLthhdjGqWvqYONnFudnFbq7mk+f2WjHJZPXkidmoRZXCAIvNen6EtTSdb5eW+8XGPJ99Q4IHWnCWD65h+kIZJ9S5ZiUXqLZk86u1xXOh73nDWzw7MuJLPtpW6TOyjPPZih5NYGMJFju2c+xLC/HyXlEbWvklGG3J0auSH/LFmpk6QHLgYrvkXIbSSL64Q9uRJk7xpKe5+1Lo0cUuSVO1fjR/SDIBN9Yi5wkkSOzA6fyywnJZL9HpiaIug3pnunyiT5BS3WTTF5KUsRofOaPyMlPsBhudKNI8rqzMl/dFD5vVVRruC2kMVuF0eSnOfXHdii6PZTqD6xv+7LNiAa21ppc8l9NIXIETf0rmM3nV9If7WjwTdjvT9Es4LKqtlUifS+H8iiU+40DEgx3V+idtoCX+jmx/Js114K1UXjxUEQxmV3X0hIsCHN3WxV9ocx5F25LmyK7M3K3m/mMoADraqNAHuUaKyeu68Qe8gNS7YpCxlvB8uWW+egksFY23qsknhYbi4W1Z+RT6Ml9crUgzknG+3DYODqFF8WjbwAee6U2finabKvPx5UnL8GJ1LR4el9ujuaKiF1+VzB6t0GD+0iBtlyxnw2Nbcygs4vGRVb1Ylkx88ZUqrgg2SWYvpUYlloPZ6amtuEDS6ORAK5NAL7knj62sK5saXxxqNW1RWjoXZ10+E4waO99XSyQp12fT+zLtaCU1d49kUc4MixWLDX1BJf2hdHozEauSd0SGDZDiJniAnEaS94zyGVp0lLkI6r/X9ruJ0NDmn8PLBkv1Mn6C3TIPe0R+AKZt0atqtX809rdCvKJOP+bDMkzsOruP0mrsrhrSF3y2jry2qXwlHa7HeFPXxmxusrjZYA8Ytdm0pwqf5c4uc7ql+a/PX8DJud4DUwF5o8NOlznIwKdP7avWEo3j09OqIc8EL7s8NzvQ41Y2flleSaawKh89Ka2ry9zjo2NrDZ5L+fT8tFJjgVijk31zlTp5nZw9NHtiWMTp+Ni0yRiBYnBqVFjC23T8SmunLu6RwWPVMjlJs+GRvpVfijo7PzdtxFobgzf/+vuXN69xWbz99z+fX9smjrf1u/eanRdUN3b+8de0ZC+vrLz5gx9c7O4EpdL1v/v7YK0rJ+n2hx+Ejx559ebVd35RGPo/bVbf/PEPJ2vrXqW6++tfue12OVxsfPD7HlGd1bXdd34BJWly6+qbf/PXy04rL5vXfvOb0DSN1Nv48KN1WnzcKu393c8RxoO7e2/98K+dSs1d7W3/8Q/WyyO339/66MO1+1+ev33n3g//Rp1Mnb3N9V+/Xzk5znW1fnJQ//whrVobH/6h/8X9y9s7b/3sp/rZ+WJ7ZeWDT+qvXiEJ1AYn5U+f0aqx8vGn/T98Mrt3/c6Pf1g+OfXWWu2vXrQe3GeqpLiLzd++nzVL3c+/XHv/D5ObV3b/9u8aL16SrsYOF2ufflIQgaTh9V/8enrt2uyfzyJcbmwErebzb39r1u4te72jb3wtsazDe2/Ot9dn3f5sfWP/zbf8bsevV5//2Xfmjc5ytf/y699IDX1w69b42s6k1XPW11698dZypR9Uq4ff/Y5bb477ay+/9e3MMC72dsfXdiYb27N+f//try077cS2vvpX34tqpVl//dU3vhGW7MH1G9Orm6P1bb/VOvnam4P17dTQH/zrv8g1Zd5bPX397nhjw211D998fbh9hRnSg3/55163FRv283/xL/1GbdZZOb13d7K5NW/3Bq/dOr79Gkfwq3/37/xuK9Ksp9/9rtdrzVdWRreujXb35o3OxWu3j+69CSD44q/+fdRreaXq8+/8qddpLXqrF3duXe7s+PXG4LU7B6+/DQTw4C//yu+3Y9V4+b3vRc3y5cr2+Z1by6urEoyFTYX0RZFlyjU1beiWsFQ7nK1aRGdWJeM7JU6QuE5Ij5Dcxzsaa+uanBi1jG0YssakKi+umFjItS4nPVEDKdxRQV2WNWpUkmzTsgVXrPL8qi2IXGsXbE0186W0TYq2AZXMLidsy9ClSLFzYdeAApd7gPZLUFgK4mDRQ5BMMXL9rdzXERGGbi/DyOXWrGgUkcUhGTsrAJIZxMvFJkdqmouu148h8rk2jjs4sqgkXCx7jCmeCN35ZobkhEl+3A2BGGT6NGmByOYIzdNe6JcLUiznu4wrCSPLoJ9xNYHSIG/QsC4yuExW46humdyXdyRUFgwpFNflvKVLJFZWcL5uq7mPXyubds4IFnZVWCWm5Ak9gfUtWc7kFZxvlhQawDs2KQECInLNoHWtpASkL7KupsjU6rJoqyblPrxhSjVEQIKvmawm61KodiHvKYqUkD7KNstyHis7RKohFWZwT0MGyCWfG/5yFXCUpjWPNmNMHb8fp3WSCw5SBmEXcslnuu/2cyamtLQImlSlM78TpHUxF12gTpwuEOAQ6e58A+eIMnPitZhMx2ljyaosMgquTbxOIaBZrkXuOsM4TktLt8WUfJhWZ3FLSvRMJOOoHaRyylU/6FMNJaTK+JqBasSQQ37Vks1MlRJ23RIsIGmFcEURlcIqRbCv8Lqka6m8JsA6kVECXiuLGiM6E7ZlojOjnKJVrWioConlbanoGWrhCfcqupoxW0TbKraQZUZ8VUUtVSGJtC0lPVsDAbptE4MTpUB7FraRbYZ0w04bUIrnznbK7Azw0Fvx0woW4GVa94M6BIJX1MPliiCkrruVcivm3I82EmYyihdZLYgbDGIvq4fzLpbp3FsJeSmB1PfXk6yEGJ4Cax43GRddWvWXHSTmftReRFWmpWO3H9OqwIVpbi38BiVsVtSD+YqIMz9tTfyqbsmeVUrSrbIlhWKZ86sGUpDayMG6ahSevCEUXQPqzC4l+aZuKpFiUXHXABKSWoCvqxb3xR7gXR1XBMOI+JauKqlsFGzPFKRCbQDcJ7KYqH0AWgqoElON5C0CLYHoBbxuilJBmhCtK6oQG82sWLNZRdaUmO3ZkARADKJeAEhUKJO4A6ISQ2icdqKgygl1FrsFUzMuhGEvYnoGpEFRT8KGyJCb9kKvIZBkPtspkBozcen1aW4USBxErTwsM4SdrBsvW0SkC/dKBpWwQEG8RpmWUjJPW1lSzkQ8j9rJoikoieNuh0gLCxiGvQSaXFYLu10kV2sCy/B1Xaojkgfomskbqi5HapvxFVVRctKGdNOWilDZFkgDqzAF13RYk1Q1VRqc9RUDuaSDsq2ywlNlE/KuamcLcVcpmrogJXq9AOuqjgKlA9GWAUEubQusrVSyKdwQQMcSbaCVM7Ch6ijCXTFdUZRsQmsOq/LQZEAaL/sc4wmXls4mwkKSGkuvnUvFPCtPk6YYmVSUxnHDTzQKieesZViIC2MZt6nAZ0lpkrRJbBRImCX9KNIpxsvlFcaFJDW9uFNA0efaZd4EQU0AcB6tpaENBDRZbHEBR5nh+T0IxABoQ7+DeEdTqYffqOp6xnRR2FGxjSzVB6sKbOuKlEhbYrpa0qgHXyuJBpPEBO+ZoCzaeohWFdCUVCk114C33tTSBbxbkm1ABAr3DFAWTSUgKwQ1RVWMhU2SrZXULFBvKJLNFZKDXR2VsCEHqEfEFlaFWNomcb9k5IG0J9KyPu30x3u7F3due/X2aPfKqzfeYqr88hvfvNjZQZL4+E+/66125+3eaGf38O5rYaU8ubH78s23M0E8+NY3z3d3uET2v/0nYb9xuX51dPXK4etvZJZ5vnft9K3XcyK9/Na3L7avFJb58utfc9ZXR/315cb64Ruve5XaYG/v+N7dXFf3v/6t6fZmbNiv3n57sb0x6a7P19ZefePrma6Oruzuv/lWpmtnd+7Ot9eH69vOSv/0rdfHKxthpfLoe/8qM4zh9tWz1+8Glerp7buTvSvDtW2vXj/69tcvt3dT03j+r/+C2ubx7q2z1+549cZg7/rg+s54ayeoVA7/5JuXG9thpfTke99LyvZ488rpG/e8ZnO4szu4uTdc3/Y77cEbtw72XstU+eFf/mVqm+O1zaO33pY//fifyyIs+XMjdkvLieHNzNi1nPFkbzvnWKVheTnRErcULozl1E5825257eppryEXkR0tNW+x9BeV0NETv+xOonr58E+/aYcLy5srsVtyJ0bslqNlvBgvuo3Zes+Ml5Y319Og4oxsf2FGy9J86K73/ZW2RsOyM9LSwPQW4tIxs6AyH2mJb4ZO6M05xkbkWN4Cq6D8Rr+ez4kbmNHS8qZcwmbkUGc8Xl8/+vbX9MwvO2ONRuXppSQBM/FK7gRY1doVAxMqLUZWME+ChcALLfFrzpiWlP0/+zZmeW10YXhT6huCyMzQoe6MqajyZr8KF4WHjGiZLkYFkK3QgaHi+0uaxDDIiFIotBC82GJuoGVYltjSQhGWQIQ9KicyEgUlSNXKJM+YELbDmGEBkCAXYo4Y4/NQUxYACcwrc8pFP5UjnEQFA1CMl6pwjiBI5nUhxbAAQixiniM/lQOgoxGrKKEr4TgRs0TxijzFmDOYBHqcSzk0MqbSQi0o8ZZ1OOjK8yf0Cs9sJWVS7KcxgblihQGQU9rzjLBQQ1AUSp5mEoVKwpAfduhFXVpE2R0pEVXK8zBW4xRmkhhGmV5kFtUTX/BUkqtJkgk0UwrIPK+Vn9ek5UnaSeI6yrGSBFkGeAbzhGYoEJMAhaIAAC84CQOR+cBwMbewD7I8ETxXzHwlI9hPJZxwewm4xB0TJ1xKIfIAzmLsQQyW5brLYpHjZpZmYkwxhknCkZ8oeKzXHOotOS1pWSYFCSQozriYIhS7hukSLgQuL3IK4pwIeU6Z7KXYUuWYSTBHfiKkYg4BiOM95ZHHxAu/KyZVwDiKA5Rikuco9BMrKQRBywQpE2WW09ATYxNyZGfxNn6ug/SFv4IoBozlSa7kEBVM852gJHMEbReqmSyklASemsgCkmnKtqVHmBUHXlcIbVwQkOYwy8SUwijmNBVSLoVp5rGCATHwkF4IVzmMlySMQSryGIG0QGmiugnVJbpL5DTIo5zkGXK5HIUgRpIPeYmkZUmlPvBonAE5xpwymqey54ok0ze5yCLgZ0kGtJiyoEAZlyLINchvCIgGJAxZChQv5hymCVPiUKFIpQKMcgIinCU4TaV4vq5cOsAchB0pwzCJUZArFPIwl6TA7zEEgOF6YVEFaUyjTEohjGMiRDe1k+O85iYlJY8lP83FRMxsAClgSdzMjMQ3Ik2nUEgoTxDJkRBnIJhtSAOfmPvpplqIQhyzIFVznqRFmgZqFDLKceyjJFFTKgaxWJc5QkXiwyKDfqGESIp5TrkQBbhSkBKTgqBIMAKkiERMEzHP5SULKzprEhB6IElAlot+iuI4KWTVz7O+whjTg3nsoSLnJBakBMlZQbxELBcCwUrqpjnPUqAHThHyNGM0TvQ4hpmCkkSgAFFRCIJKcd5V5vvZipeLagEK19dpAjNJ9IPcpGFfq0We7BdFhos4E3ik5oSHcYVersqPj4uGlzWEFIpxyApMMsijOBNgss6lAmgRRVmMQxdylhUWiRMdj9akwWnWZHlXYyCPMhlSQkMxymKZCnFGUKh4Qk45DoDCA7k2LRKMg2aUFgKBQpCJCceIC/PQLs+KQue+JdJc9lLJwEmcM0xgnCnqOWA4WQJGKYxEHAsiz6HnyRYzpElumomLYJYqIFV8llLMY5D7zKxNClpAv4kDziSBRZGQRgQgIRbkHAlFoUQeTkyYi1aarqNDW/JeBKtyrIkc5hlTcoBTpiznvqHHJjfiKM80OctBGGkJLDIgxNGadKww+irswcAkOYmSjGRUzjn0AqhlzIxVmsmxznJJi1whlABVSRzr+XBVnT32u0JqE4pJHAqpBFMJxwGKBZqnsrskYaAmEvGAqETM8mCmF5GCUiAliCcezWLBhSRfSBWfhWoBK3lK5YgyBrOUw2Ws5ZdSc546AcCWlqWynzACU8qluOBiKFZ8nEiAQV7EIGSE0DzjohuLRsKsJci8LLZYVigRZRCDPOIhN2Bc8uYp1ExvZvozitRqMK7cqGeQhsFcjb2qNy2Cku3OUl3T8nBw75aahbY3s9KAezPfn+uJK3giTuRyB3FZnTkjI1gQmBeLsRF7uT8vu5PDr78h5LQ8H5a9qcAKy5uboZNnhDrDyhUbsiKLHT1xJZ4FgVNyJ5Axy1uUgoUQhrY3MxJP951gOfFWms5m3woX+nJqpn5lPjJDR46cyBnNdrdyKBipV3UnRuJZ3kJeLLTEtxcjr1NfbFRklpSWEzWYW6FrzYd6Fpj+3LUqB9/9jpKGtjvT/bnlzkx/YXlz25k6zdp+5xsd76K8jPU0qDhjM1zakaOlwT+jRXjlnd+WLs8XjVb3k/v1l6+cauOitx0RreqNrv7sl6XLy4ff+Tfb//Bu4+hgsLpx9MbVXJbLw8Hqux+1Hz06uXHz6i/frTx/8eLG6+d71wGCmuP0/vBp46tnk2qv9cX9vT98NFpdna/eyAWiL2Zbv/tD74vPp9V2e/9576NPx6XW6foeQ8iKF2+/8791njwb7e123/vj1j/9cdxelVh29//+wR//x/8BU3r7P//osrra2IZvxXwaneCXy52fvMNSNttav/d//fDhf/yrFzfe8DWbOcPrv/pw5+9+HQg669d3f/xzsAyd//Z73+b+P1Hufbl/42/f+VKW7ZTv/MO7uaA/+G/+YlJqK0m4efrZ6//5x1/9x79yq9Xrf/PTR//h3ypSfI/htBiORvjaT945G0xPvvPmvf/01y//zXePSjvTfUOyCwHQyxemLCfy1YitNxPAwXE0ftxI9aLEk8krRdKguDam11bA/jHJ8eCBDHXcK0eDJ6ogsUoxSLdKQpTCEFy8NNUiqK6S4ZeMGUp9G0SmDgQMDrzxeZdAppTx7JmhxF69w7Eip816vO9Fz0yJQxk4swMtK4iwiuuXXe6X6cYBOrMKt0TE4armXPeDgbIcLu7qZzjXJvisRaNVXP5ystoqBNGaLewhpN4Vpp/ngY1O9Nh+cp056xB8lSfieJVOmkh+AkKDTa4C/HRZ5Z5e54vj8nmLBmus8jkeVYr5iih83lKdW3EYoumxc1c57zBpAkcN5u3A0qdRCpfPSnaW6b3i/HOlej2zNlnaaSpBGB9k45d2mcdUBadPzPIytG8mrFkTw8R7QafPyg0QI4GND0oWz6xKFN5cE/fPuS8O70vG1Vwt08FjReilAnW822vC0xOaVEbP7aoTqrfR+RfE2AJ1M/HaTX08ZqfxxfOy7lN8hZ890e1KaK0Y8vlqocYiOiyGqxAzEQ9usiL0BsdSRznZzghmpS/xxRYACbUPJo0GR8geL4vRjhz7Qf2pfLJFJQ2tPrzNAiVFlyz2xhuIwqI0KEarUhTle8fjygoEzBos1YtVxrGAn/BhBxQlXjneSyMjnjzDTflwleIysx9Iw5U8koH+fHYOspe80WTe83Qx1NvldLnTyQmxxmPpKDo7NcoGpqEcfQ47euJ2W7Gk9UYH3iu0PDbbcpSlwvC50RBj3rYdrdbwLuh+MDnTes04uOSLfbsp+8L10mV1veKM4Oli/EJfqWbBEZ0eqpua4101wlJZX8y1c//8id1CESJkdF9Uy1AHh63MLQAAIABJREFUGptcIfCYGSkY3ET+Cdk4eTuePuXFmb9JTvdY54jEMZ9eF+l5sJONan0pjRrnDjq+XrSGoBgXF9dQ/bwGP76bYpksPqEtOrwNF+fUuFCO9wprnFWPx+3VLBgbZ5AO90g6o5rHLrexeFHsXrzpXR4h/CqT+fkm0I4xCIvxHgj8rDt170u5KFe2ROepQnJUq7Bpo12Ion0xnD5TxSBsrouLR8DP5doan3e6pMgaR0fzfSHMFLUmevum6sb1Lp2UugwhfTRLXoIkAC0SxsfE8ZSOkSzbjVRQWouT+f00LIy1Zrh4IWZLuWmny+26q5ZXT55Nnkqxg6R16j3i80CrbgIwLRXj61B4URSicLKRqU/X6Se3GB9mXnSxm0/XBfwIiSq9uAHQS7cRzO2umgfGZZ07e9z6QljabLolS4/qlvsmLfJ0+ZV/g5ytZ/oj7EhsfAWQzyJLDspl1fPss4hf3gL5fS5TfHwzkfY76P7dJAvAdOltZaMbAthntlCc3+ToxLfHzkOB9OMiosOXZSsoSG+Z7K6jF0coFi6+kNVVUELZ4Ils1KlRO/duduThIg/wxVPN1AO7TgZfELyq1pqh36gKCy+7jCcvy1qFEQtfPjJM2a92IDK02LbjF6H/VK/YBU6D4cuqaGMJumitLVyMigBMPkO0pVsaCl/oSCuMGmCTK6KT0tsXwmEvg3XQe7jnXdiFesHcbLQN0iorXbJZh8xgWvp0arc5wrJ/KA16NDCw+IQtKtxvMe3hLky6LHqVM3TSp6wJKp/j8wbzK0x7utis+HKpPTpQhiuCV2X6KTzpsqyPKh9u0cs9Kl7kM2dxXRhVufYETPtoVuf1T6KDYvbKbgh+AtHZY7MWLfVrDLTqOIicV2B+bK0YQRbyyUG5KkSoQdONnng+S2dkel+pSSkLivEzVd8JWOIs72yJD44SXp09UWo0wjV8+YVUv5cb3dBZ7dsvD2lQGTy3SkUG2sL5Q7my4ht3WFEr4wx6+3y+b6zn88jgs+e2mTNzy33t+z8a7O4EZmX3p+/Mt9azsnRHFI6JO87Mvb/9hddoXP6v//Pt/+dH8/X1p699fdHoGPFy5emzrV/9btloDNsb137890mt+tH/8j99LR568urFYHz1579y1tYBYxu/+l1o2kcb1+b1JuRMn81uff//Datlp9668sv3Qs0odHDDElycvJtU9v72F1wUj167d/Nv/jYslS+2dnsffWa/OBqsXln94NP+l1+c7+xOu+0CC5rjbPzmg/aLZ0u72n76uPXlY69cPVy7nogydPiVd35be/FicO3a6nsfr375+bLVPvrGWyOjU5+er//+j90vvzp//Y2NX77bffxksrlxemcj1fXa4KzzyZf9Dz9dtjorn93feO+DRa970L9GiVQK56sfvLfz3u8Co6x7zo2f/P3plZv/jBZh0mtH7cbpm/e8RmvZ64yvXYUYCnku8yyoVqdXr5zv7frNetKund+7zbAgp2Guq4WuDq/tOdtry0bT2do4uXtbZFRJQkHgUaU8W1s7fu1uYZuj3SvzzdVCEAlNBFAEjXpar+x/8xt5SZuvrp3fvglEJKWJRgO/0fTWVoY3dqedflYt7//JN5lCFr3+8PZ1t9N2u93zO7ccu+bH02f13axSisuVV9/8Bi0bTn91cPt6bJsCzdQ89Nqd3NRf/el3opKelsqH3/qmp4qXRTFV7Fmt7fRXpteuDK/ucF179s2vi7jgHIqcQoLmq+vDG7uz9bWgUR/f3BuvbrvB+EV1MymVk1L54O038ro1Xt0c37jm9+s2DkhfwC1icg9d0ZSqyJeuEKe83ZBNZtshXbMUkst9iOqSMR7lUATlsqnEeovhtqiouVVOijVb9Zag4EXZLgmhtAZxRdSURK/ntKnIi4WSZmmvY5JEbTPW10rcUfoAtTXguqofxp2uqUSWHRfrmiZmUhfRlinDqSAN/DZEiq+Ai2AVu0Ra8viwvIJhCLQRr2aZkiF57K4IJI/0cM6JIAk+kBZBjxHgIHWYt8AC5rMsOq5uFiRU2aW/xongAtlJmymGsZhGapExjRLx/5fUmMKH7gbKYT7H7NjqAFIAbQJLoW8jSRjFbZqXlUYxEa6oxOSGnip9zGSkzeZAUvOmXUHLYsfSjUQWKdzRicLF8ZRLCmtWDTVWeyhvqzW45Fc1QReN0SCTNGiolhEpbQgaxJRjrUH9hm2Ph6lZFmy1LPpgXRQqxNRitQO4ieTJDBMp7TVKbIm2VVKGNg7IhoBsjMQlksZeF4vIyytuVkFxNDsymstyC+EFMGdZLWdyRKRBVINCFsk0oirR6DCrOXkVATzH2thtCmGRTVl2WulhFAB7Fja4mo+yyly0kgwiJXYhwUScI2nm94AEHFr2gqrAkumZoDq1PpMCURom7TwXI5GM4w7XcaRWKexIogVsMyp2q4QlZrDgEtHkTNPSYlNXSWLbodAjAi6U1FeKpCjJNbzkN8qanBpaKqyIBGWExgqNCw1W9EhYIbxKatgtdsxyumAMEkAVQs0KRU3EakpJ9vCWVsjEiBZIQJLGdSUBm5qk5aaVwr7KSsykA6eXAptJdOSv5UwunDy9UMpuuSKIM1YN3BrU87G/khIUClkkskznYapGrOLSci6iWWEvphUzjYZP9RbVJKmYRN2QmxwJc1ZeZjYjaaSlIdUFLRv5rTCpIDM9jdtBXhHcNLmQzVmppvAhr7huW9SyUdzyEptU8NKsUdrTNDmumEG+aop5pmQBM1UbuGofoIZEFFqqJNmqqSW+HnuYQJlQuQlYT60gV+4UuCaIjKqpjxDU1cQ0Er5tGEJEmlCsYwIyOYukIpMsULJj2JeQAW0zBuuakoVingmEq4TqLYpaCtd5zQyzTUuFCyguom6GBR+o07xJfVmYp8Gg1I9srBaD5SaEQiDgWdLJMEowja3UizUkkGnaTJMSM4rL2UqRo2LO432rw0SO5TGtxUkJSOAy7VAqIS2cY8gEOUN4HrcTJEe56rKqv1RJxKJzo72sm3p26a9RQY4EPItbEdSBoSRmPU1WyhYKhG0FW4IxHGZEQWXLVGOtzXFLNOREaxZ5r2yOB5mgwLJexj7ZwLgsamqitHheJvpkLGIx7TQq0BXXBN5SK3wurWFUU9DcIZRl7XqJ+GqjYD3VgJGwQYqWWTo/LRgE9aqiUb3B4IpSgkuljbOuqtIxLc1gOU+UFCljr43CIl+i4qS2TpBX2G7YZEoxZvaYVjkqUjlxJUBTlRJhFPaZBJzCcuNKHlP3FIpjs1GoqUSGSZtSjRJxEvWYnHoCTQigghAW1hSWYs9EsjiM2mwhiE7mnfb2MAgL002auSAs/z/a7nJJ0zQx8/vND76UbzJDVRY24/BIlrxrKdZaf9wNR/ig1uEvdkhDGmlEO8zT09Nd1dXFXVVdkJz5MtODN/sU1o7QcVzxv36w0MsXtKmwBTSWN8oFL3MCja4WHMDJYGQLJbtYqrgR23XMorsIx+pGkVEQ9HvcK5ICK5VzdxXCRafsRN66nVXn5tvNrLLglFjZi8EOYxVcLGTeBjYODNttWy7L1eq8GoLroVu0RSellxzsIbc3UI5nlstVMnF2mVwpLOk+uuqDEpsur/eu7/f2L8WLy+OrexeXridyelJa61dXre+efuWDeHNlurzSvXG9t7fj5knIo6RSNoF39sH7/d1tG7hnH7wv1qoN5NUQay7vqrly59p+5+Y17XsnX/v6dH2VGOlITrEZrW2M93dbr91IKnP9a1faV65mJjksrM6CUlqp1t9/b7K9NlldH1/eq7/xmg1Y58rV2uuvqWLQev3G+NK2QZjJ3DX5cG0jW106+ebXVcHr7V8dXLmkKaFSBDIeLC0nO1vNt26O1jfypYXau2/4MpMAM6REudy+eXOwtzHY3E5WlxofvCuY6/IEOkSXwt6ly+2bV5OF+e7Va4Nre8L13Dyt8HFr67KsVo6/9Q1VLgx2di7eeYc9uP/vZRH6wwHMM286caYTynOH57u/veOPo5d//ZfebEajGRV5cTRAeV4Yj5Yef7lwfvbiL/6S8DyYTSnP/MmYzKZUyku3by0eH5z+2ddZEtM0C5KYJLE3GgEAXv/dr6u12p3/8r97kxHI83AyduOYxbEj+PqtT1dfvXr+N/+LF0UoimgSu/EMijwYDgjP2WzKohgCwGYzl6dOP7J3DwvfWvZAjtI0HPR1JWTTMRKrG8+fbd3+vPb1D4QTQs6D8YgRDbPMGw8FLLp/eimvb/thwOKIpWnIjcmzYDLGEr/9g39uvv3mZHOVjoZsseJjzNLUzRJ50UG3Twuvh3ohxElSmE6kX/KmUx6HOJORdjBHQZxMkI8SBebYoL/JmC7O6SQnAhInySemgHLohqDTWSMeLEg+kS6ylgmdcupBRYUdDVexiwpBPtOMCMS0iqWrEaQSRpNVZnInUDPDoIA4ldw6UFpPisloPQKALGElaWZMMeYz4yhJiZRWBxwSpHKaU+74NO1M1U6SXDc8hhBr4Lu8r7SnUQBtXmxAb1CK9jKgicJlmmcKeNJiKnrj+Po4eh+KKZYopwXKI2gpByVrJmEXV/petsutYjkMkWhRSTlxWT5L+Ooweg+6PWKgMiVHdZlAHJcYb2LJpyCgqWXIpsr1pMbA7fe2ikQgrWfAZ7mmkkdozs00oG6/v+tj4/g8MszjOQJyDDyWaOaYXnvLWbCuK0fcDYSiXM+0W1YzVzudzhZdwUUUj4zncouEjKTnao1zZ9DbLFcSQFUEfcIBzMRMu46EjrFaehpBL0sUrEBjCE+O078AWJJSV5k5BC1WmRGewMDJE78dWASjLa1AARlNeNeYZY4QFLwzfmOgFRVjBX2kARHCoAIwFtr2wktKFOveAFpRC6GTjjQMoPEcw8+yb2IFsGpAFQiEGe8q7QqMiJgKjXPBilwKjpX1EFeglcnziLxuLTeJ8WmmpMZCuSEE+ChSTanfZIiDGfLdSBhhZtoLNCRNDmu5vUGgwjPjzuUccDKBXpgqO+H2izG6RAQgUY4qSuNMZjDwsxSNkTyKydUAAZFZL8yksjDmDCtLNExxEUvu6Imgm06eaBoeJv8JoomLIwNKROZODlJcQllKLFp8WZCV3JaRBiWgZpDnAhSY5YR7z/O/NlnGQC9DZSQSoLkyRYAylqRhLaAFFVVBTkpQKZdLAcvIcC3z0+w/SKypn2pYwFo5qc5wBSuJtcolUxZRoXUOE+D5s4zXAdAAXkPcOkBZR4okp0YTIix6GemEox2YGSosQ9IkmkJpHavBoynItP1wMRfEWFRIONdESOpZbZ7EILXgXSY4kRqXubLSTTQLZzloJiYV4CrmhiiFylIAjqfGZSlHhuawhPkUAKZNEanBNN2d5e9gXmOG5sRz8phKy1EZyEkQ02I3oMvEACxQkYlIaxTjIhFxrhZezN5BtAehye0cVF0mjSIVxut+HhQvSvGGQMRIWGA8RQgJW4S6n4uNL6PXXeecQZTTAuEphIDbIpIJxFmqnJKc0VRMgIcT42HUam+4ZeCXRKQ8R2mWm0g6RZVhiXrdTbZIQ56MtesISLWKBbOKEEHHw82CnzJPRcBDHMNM5NZFErpcTYYbMMS0iLh2kLJeLCLoQY4Qpr3uOvFhcUEk0gUYkUwn2kMSEK4tCoECjLe5Dg2iSGWD7K1hTmjWs5ZYG1LBBSxQAyDozh8SmHliXwLlCoypmBjoSVNghrfyD2zmoGIdSieDDuYdK5lAzMnGfp+GiSs3ubIeMD5RDSoJRx7Loyi7ciTegrMeMkDbwOGJMkVlfKLaINUT4PuZRLmIQTXkWoLitF8tB5IqmULfz1Jj6Qj4bqIRQN32VrhmIDUzzkpKA6lj4C+IKWXzzdaWu00cE0+0FwhrsUmVFyolZTDtBvMksUDNUMAyC7SMlOtLoLk7Gezg0MHWpDDwshRjOYEhyQFh3B0NAJpz08SZjBQpFSYR/OzUWUn9Nys0jgLXmeS5Nx6nlUppNLz529+Icnjxxlsgz/3x2M0zlmX+dCKzeffWkQwD9/0FOpspAAujIRY8HI+I1V/57velHxz89V/4kzHAwI9nLI6U6xbiGH5+7vpD+uEbThyTLCOC+6OhRsiLIyeJSZT4aUyy1J9MAIJv/eaXxUb95d/8VZDEKMuCwYCmKZ1OoNGv//EPxXrr2X/+q8JsCuLYjabueGzzPJjN1k9f7tfHZ3/2dRzHdDhgUhQnYyKVN5tcen60/uzp0Z99m2SZG0VeltFo5gwHAMJrn/5p9fnzzl+8Z2QI8zwY9EkSs9mMCQHAv5tFeP1nv3rzR/8Wtvu7n9x5419+Umx0Nz5/ePO//8wIe+Nnv/rguz+03F7/3Udv/eO/Fpvd1UfPrvzmI7/V3/3ks69874cwVTd+8Zv3/+4HIBFbn927+svfl+utrc8fvP2P/xzW25u37374d9/HMd/79PPXf/xLNEku3br79t//qHTWWH/09N2//1HporXy5MUb//oTmMr93/3xg+9+j43jnc/uv/2DHxXPW+WLxle/8935w9PF5wdf/e73SrXO6pev3v/HH82/Oi6f19//3g827j0KW/2v/913Fl4clU9qr//bTwoXreUvnr/33b9fefqi2Oh+8L0fbN6573eHX//B91aev5o/OPnwb78/d3Cy+OLwve/9cP3R06A7uvmTX2zce+z3xl/7zndWv/iyenT2wXd+sHBwsnh8/u4//mj94WOnP/7gO9/b++QzZzj72v/zd5t3H+qZHTxl6YEFY9F94sqnXBoyfmDHz6FK7fShnh5T1M+GX7LkpdFT1fuSiSe5tGR2z06fQpWCySM7OnJwPxu+oslLY2PTe+om93MB2eSBnXxJVY6TL834iKJeNnrpRC+hTODgKcseSQFo8kgMviQiQ7NndviKgV42PqDRMyAEcGqL7sllpZhXK5KLG+5Ys17Rre+gKYW95eBgw3Li1eaco8tGMNSbV+P36Ai4vSI9u0QmFA2X/YNtJdywHgTnuyCnXquA6m+QIcCDEj27QkYUzFZw85pUgXcRspOrIKFOnaHa627P0JHvn+2QEUH9ufBgF2SOV/PYyTU8hXogm48CfsRFhMaf2bQBVU+PHsCkD009bz4OzTm3Q9F+6IlXOU/Q8C5IG9D01Og+ztoANvPW41CdSjAR3S+YOUhVhoZ3YFTDqicHD7BoA9RM2888dZKr2HYeOOoF5zme3LHxGVJ91X+MsyZAnaz1NFRHSieg88DJn3ENXfdkhXS22EjT+ha7WEAxcBrbxcOSADQ4XnfOty3H7tkKbm2TsdbjfdzfhDlhzb3wcEEaHJ6ssItdI0hQm3db685QktYGrW/YnJLatn+4ojRgzXXdv2Fz4jSWcGvPGSnWWnPOVm1G3OZa4WhJWFI4nmMX+yhDbmMRNy6jGExrZPK5FSkWB3Jwj+Ypzi/s6emy6QneBL1Hju6aqMVGn8M8IfEFrL+qqBioE9l4UrCtnLds56EjmyLqse4zP0uJPOTde0xMoTrJm49D2MhEHzRflWRHJW00uIP4DOnjrPeAmZFSDXV2vGRaUvTB4D4xLZn38PAOykaQDgmqveY2HRJBenYjOHctZ4XDDdavkDHyjnfwqOz2LK7dZO0QRkD33/JaZZuT8HCHdBfwlDqnl+ig4vYUa+z7DRckmJ5e809LSjnB4TbtLsOIge4NMF2kQ4saN7160XDqnl0JTstakeLRCu0twYiy813WnWdDTRrXWKOqAJ08AuMnREqcfWkHL1044IN6ODrzZQyHz1l2X0pL00eq/5hxQSYntHNRRv1kekgmT6CIweAFi+9pYUl8gFonFRXb9BXoPvdgL5+e0MkX0ER2dBH0X7ocsPSx6D52TWzTV7r73Ie9fNgKeq88GYPZCzj93AqN+RdZ95FrYkMGPj2/TgYOGleDw12bBX7T9U52QMqcOkW1N9yuxWOPnV2jfcfEVdK4bvPAbTjs+CqcYtZ2Ue1Nrw3phIWnW24XwUnJP9gDceg1KTu9SmbU7VMx+goZOGDmOcf7btfB00JwsAtnBdah3sU+G2O3DVDtTa9Fbew7R/usF+YzM7oPszrC7az9xJfHUkWm+5ipLzMh8fSOjY6QGunBQxLXEOpk3ReeOJQmA52HTvZFzjUb34WTI2KmdvIUxecQdrPul0F2aESG+g8ZfyqFJtF9NTqmYgpGT8nsGMFe2vvST18oyXH/C5I900Lj7J4cH1A1MePnZHqIbKpJfTM4XFEaBacL5HTPCOo0Kk5rh40U6yw7pxsgJbS16R9uSENZaxX030AZ8ppV2LhKhwb3ltnZNoyJ21oqHK9KS4PjEj2/BlLk1Aq4ftXpKzBdhe0rIKW0sRQe7RiJgpMCObsGZ8ZpBqyx5/Q17lf9o3UcIdZeco8uAUH0Wd58HIJarnu6d99R51k2QMO7Nh8jc8H796kaaHvBm49De5HroRo8Iqae8zEe3UHZAIsL0X3gmIEG9bT11DdnqZiA3j2qzkQ+QuPPbNZFoi7ajx3ZVaCZN74omDMhZ6h7j/ITmUV4dNfGfaLbuvPYk21l2rz1pCBPFeLyw+/+8MrvP/Z7o7f/4V+2b98t1lpv/Pin+59+RqL8/b//p9d/8gujwAff/4crv/nIGUZXfvPRxsMnxXrnzX/56f7vPkIxf/d7P3zjX39ipXn/R/909be/d/rT137880t//LTY6r3+bz+78fNfw0zf+Nmvr/z690qj9/7hn6//7FdBf/LaT3916XcfB63ezV/++rVf/Arl6t1/+Oe3f/QvyuK3/+nfbv7452wS79y6+/aP/sXrjff/8KcPvvMDLe2lP3xy47//gk2SS3+6/eaP/rV03ti8+/Cdv/9R0Oxu3Hnwxr/+1Apz/Ze/ff87P2DDaOfTO+/88J+qp7WlZ69u/PgXXmew+6dbX/vb76JU7P/uow++8wOvN1579OzqL35bOauvPn72wfd/GHYGu5/d+8p3vkOm2dbtezd+8suwO9x8+PSdH/zj/OHZ4rOXH/7d9ysXDUX+3SzC1UHHjybR1hqc5e5sFm8sh92eVTbZWvFafZblzes35prNYNiPdjZMIoPppL+9U+x1nSSNt1ac3pjFcev69XK7Uxz00s0lEqUw4oPdXX808mbTZGvFGcz88bhx/UZhNAq7neHebnncQ5NssLvrTaf+aBRvr9JJ7PeH8d66yUyp2ehd2ffz2GsN0uV5AECh1e3v7iKt509PepcuByLy6r3Z6iqmwG/00qU5TWjY6PLlSk686tn54NKeZ3Kv1p0tLyEH+fUumPOk49L2RCwUU784f3wy3NlxkAzOWtHyMvCIX+vw+bL0vLDeUQvFOCzPHx4Nt7cp1uFpM1tedKmwnVRUgsniqu5K49OCk2UDYEIvDLLZkCFsvbKOJ6wsx7DiTGcOYMj3RT4AxnfDQhb3KcLGLdto5ngmgxUqRtYy4gci7wPt0rAsopGDgDFLPu5lTKVgzuUzCDDScy5tRZaSoCziMbPW2uUA9VImM7dk+Qxy4snlwJ8CmptkEToRcCIOy3lqizS12RxmsUICl1gztosghfEKIRl0Z4pXbSFKuAjzKsbC4hThwkxzj6QwWUIkR2wi8wUYJokQRVvgRBide6g4UyrEMcDFWQaK7kTzBeinCRdlEOZYaC2CMmvM0AKMMSpGU6eImjEoM59kyYg4oQEO5iPgh4IzH3QzvRyup7VmvADL1KM8GlE30NhB+QgEfp75BdBO9WJYtNN4SFGZuTSfjZjra+iifAyX8WBSXlAdYapegJOkT1CJuk6eDKnjaeDhbIwKbsYLoW1lpuqHJIl7GIaYVbCdeq7OZNGAyDEYEjeGU084jPpjMytCaGEhkWnJlakoWZu4EMG8BIOB1hiRYKKSCjA2m4fu2LoikyWgMxdAlJdh0JOa0Co+H5ktxFWyTJwpQMqAkDtTzZGfV5E7ttYiGo5VWiTcwHLCRRlzpSraTnORk6AiRU7sTKmNAkwVGmVg2fPjWZIwuxQAocFU+nOKC2qmKpxXsQlRP7GrgZ/OosRjFYCFzGPizymuHTORQVUlNkD9FK1489N2WyywCoBaZxEpVCTXzIxlOCcjUkLt2K4GQT6LI4fNIahVPkV6KSCEhB2ezdHQjPW0IAuQ4ljFJcRywIyNPMZmWRCwvs3mWGBGelIwBVQ2jbHcwiQzrrWx75BZ5rtsCHiRuGhqpgUZIMpmZlZEVBjP2Jnr4CgtenSEZYBkAMKOUh4kbqzismFGBtAdGgfHScGlEyx9kpYRqc8QAHolwP2U5hmaZ3mMgQVm3iOd2CIUVGQ8YVYZuxaifsryzC0bEaEcumbJh/0MGBDOiXTKYC7VegFNBMgVnUN4nObA98pazIDUuDAv4pmDEuEv2jhzUSLwAsHTPDVeUFZyZrVE4byMEw/EQm8Wq9MJ5yVTkFQpnfmoECnj4QiT4jSFRW+ks0UUZJHIy6YgqJIqC4usneKyiSktTCNWDfoiW8Ahn4q0ZAoSG6XTkASRAg6OEA1nsVPxejKbx76cybRM/dhRWSwXsR9J6KKIOP44ckp+X+dV7OpIx0VVtAblYmAX0TApV/K+sWU3IGnaR6DIfC+PB5Q5GoUoHeOQZbIYmA43ZTekadJHwCdeUUVDhhwASg7qph7JRSnQA2VCZkuM1GbAJ15BxiMGGLQVF3UTF3Hmm2yMZeibqgMbCWLIL4l0RAGyet5H/QwQqBaCsK8sJFVyPjKbONfxCmERJLkyJeVNRQ4CXkVsZoFGJBzrtEhyAytJrso0Vaqs3UhwW8BhDFOqFcWlqczLNNWoHOWyyFKjKtpJJDcFHMYgIca4c+x8CDdppGA5TsGcO5NyznhxLkwBhYnNmZEOKU5j5aBuClaD5ajRzBZoBWGr0hkJSlJDpkYqLIuIllA7MiuFgpjGU0bmCLYynRK/pCSkcmKf6P9eAAAgAElEQVTXnW6nsA5asVkKApMkY0oqmACRTYhfVAIzOTbFIk/cImzGdikIbByPCCkizGw2RrDCIIawmwQFGfsl2E1N1S/gqHDaNL6TLlT9ehe5cLK0Uj6vZ+XKbGlp7cXzLAzzlbnwvCNdZ7K6Nn96ShgcL6+ULhp5oTBZXV378kseBHSe6U5mMRpubVcvzpXjqEpYuqinQWm0tbX86qWiNNtYDC662Mh4Y9XpDDUmcqFUqjUytyCWy4XzloYw3lnzG32o1HBvZ6l+ZnPT39srdjpOHEe7a05v4o3H0d4GiETY74/2tkuDnk1VurbgjSYkzqLdNTqK/eEw2l23sSz0+/HO2ly/leYkXV90xlMapcnOKp5mYa8X764pDkut5nh3J5wO0SxPNlfYeMomUbKzCma80O+TVb+P5+dq56OdbT+e0nHcfPMd/7/9t38vi7B6Ud+791BbVDlvbD78wiq4eHi6fv8Lacnii8OdTz8TkBVqrUuf3VMWleqdzbsPjYbVk9rGg8cC0oXjs0u3PxeAlRqdS7fvKoMKrd72g8dG2OrJxc79xxKQuZPznVufc+KG9dblT+8YjcJ2f/v+IyNt5fRi5849o2Hl+PzSrTsaYL8z2P/kNlCAjaO923fZNHEm0e7tuyAWbBRd/sMnZJahROx/fMsbTXGU7t664w9nbJZdunuPxRlN+ZWPP0GTFObiyqefBYMxSsXurc/93piMo71PbrvTBM+y/Y9vsUlshL388S2/3bdC7959EHYHKM53b3/uDKcg4Zc/veN2x0DDKx/fKpzVrLTbn90t11oSMFHTemg4duWx5C2RYwec5aYhpEH6QsiWlsKKc6X7RgFiz7VtZDkNzHnO61ohCprStpU2WNS0akkNiDqX4CLhxCNnkTzlEhDRUrpplMWqbnRTGExFTduLNHN8WEvFqZQWmZbQDWUMVC1jWiLHzBsir+ZxQOkMh82iVAhHjt/wrIR4FoQNIrDvjJDfCBRidAyCesEoBGIWdEKtCRy7hQaR0HXG2G8EGfLI1HqNIuAWRsRvuIADOHMLTaasx6ao0Aw0dFBK/UZRcQQzp9B0gcQocYM6U8aFUxzUAgU9pRA84ia2UhF5JGSEVAbBMdcpNhyIQ6kiIzlUB1yPtBIQHGd6YvMcyqOcZ9RIyI+1iQ1HnnrF+UALQ9FJpoda5UAfczkxMjP8UJqZUZbaE2l6QkDPnuR8CKxG6FzYiTYS8COph1JZqo+47eQKOX6PugNXakx7AR27CjKvF/h9xKkftrHT8pRBbo+wnmukdbqe0yMcUtoN/Q7KnCDoIK/lC0DcEXN6gTLQ6ftOhyiLWC8MmyBzA78D3U6oIGV97HYCZRAb+u7QE4Cwjhe2Ueb4Xhe57YBDxgbIbflWAz3Q8DgVwDF9IY+4QI4eKfFKaIV0BNSJlALJobGHidDUdrk+yJWhamrEgTQS6hSCE24EEjOkjnOhqRlKeyyVJia24kDK3IoMqSOuUmBmAJ7kQhAxMfIV58A1qeVH2nDAJZMHeR4DHUNzlKsUwEx7zRKKoQBeUAvRjErghE2PxK7S1Gt6Ngs1B367RCZWQi+sh3hgJHSDGsFT12gcthwcO0ojr1VEMVXQCZuBNwQSeWGd4pFrJPRbLpoxy43fCOgEZsjxmqE7RDnxwyYmQ08Z4nd9PHW1Rn6jgIeQYxdeJPI4l4CIjtZ1rSxWLavrUkPCG8aepxnzYT3Tp1JabLpK1bS2RHWtaUplkKoreJ7mzLf1VJwpgV3VEfJMaYt118iGUgCrmoDHMUceqCXqRChAVV/LM2U1sn0D6lJbLFpGn+Y58Wwrt2dKAwxSEtQ9yCGInbDhGO3SCBeavrIuSqlfL+kcAc4KDQ9yDFInaDhGOWAGw1ogQUAj5TXKIAdKBUEtsDkF3C20XKMcm9KgFgrj28R4jTJMrNZ+eO7aCNqc+TVmcwZzUmx4RjIjsF8v2YwC4xQaLoutVFifcjk0gltxJM1YK0DsiTIdwbEHTnPes9pgcCHM0BgJ+bHUfakAVUfCtjKBHHo8U00lDJYXSg2tMlidKN2TCjNxIm0zz6kPT1PR1EoBfS5MX2uL1ZkyfSkA0ccCNLOc+uYsE20rLVXnXHWNRpgNgrCFMifwe9BthQpRNoBeO1QakbHn9T1hCe15hSbKaeD1kdcKOWRsCN1GYIQlI8dvU60hGfjFFhXY94YwaAUSOiRy3FaoFKJTN+g42mA69oIGFcjFAxQ0ChJ5bAq8dtFqDOPQbXnCMjp1w5YrIbOJFQdSpUYmQB0KFVsdW3CcyRznUytfca4dk1t+aGxuuXHkAeczqzIIjjMVAz0z+iCXKZCJEQfKJlYaao+FnmopqD7O8xjZDMJjblKjOeCvhJ4qpak+yPVYaYHAQSymVuRQHUmdQaWQein1RClItx88XnhxYBTYePy0clKzwu58/qByVssg2/7s3uLRKafexuOny1++UpZsPHsxd3ymNN66+6hydM6xu33n3vKro5x460+eLz97pQBZe/R0/vBMGrz14IvqWT2HbPuzz1deHuXUX33y5cqzl1Kj1S+eL7040NJuPniycHwmId24+3Dj+StOvdUXB5vPXkjszp3V1754pjRafHGwee+hhHT+5dHe7bsa0mK9vXfnrtWw0mhvPX5qDZo/PN2690hCOndyfvnOfQ1woTPYu33H5LpYb23dewS4rl40du8/khYXT2v7f7qlIPN7o73bd62yYXuwdee+SUWl1tq5fVcrUL5o7H/8qc2k0x9d/vQzzHU4mOzcf0Rn8f8Pi/B/qCIEFlz71a/Dfq995dL6Zw+rJyfjzfXVx0+WHz87/MtvX/39R6VG88F//T+u/+KX1Vajd2Wv9Px469nT9pXrq48e7312++hbX7v+p0/LL1/e/U//27Xf/X7h+HByZafy6mj5wdPGpSsrT56+duvW0be/sf/b3y8dHj76q//16ie3lr/4YvPK1c3jF6u37jf2r608f/X6T3/66n/+8/0//HHl+Yuzb39146NP1x483HrtdYLMjZ/+bLq0QJS68qtfn7z2xmqztXn/QWfvMqV66/ad3A+6N/Zv/vyXyeK8xnj/l78eb63RYbR27+HlxWWzu7758ScC4+a7b978+S9BSHmhsP/RH+PN5YWYr927Hy+vjjcWtj+9DQA4+Z++9dp//4lyaGF+/vIfPsrnClW/uX7vfkqd/nuvbd26TUV26n795k9+dmDF2eobnUYQRKpkp2edatibLW3r54cBKYKNpXh4FNiiu0hnw+aaOzKBk9SbpflOGuzE54cBLuDCetQ/DN0QLdHZuFFCVAeFrN0quXm8dj0/f+nrorcy071D6jt2zot6zQqDoLTM242Sn0zWr6W9I29MvfmRnp66DsQ7Tr/ZmJeCONcE7O7YuEKzl6xezrP5YqPLQUlNV9jw3MTzuOvjpRZqL+XpNpm9oN1FkSyU24/oMODxvtM/hdm87ZbI2l3WmpfJDhs/97uLMln32l/gzNeTa07/CysqtrdNVz9jzWUe7dLJbdot8WS7ePHAo4VsdNUJn0BZAv3LdK5BemEe3/AHt4ZKtRuVeZLRbd08Ki57aSkUzbNwqZJZwZvduaXDiPlZvbW1BCK6bxqHxVVXeHFWPw3XwwT20nanvOTlZDGqtxaW1KQU0NODQgXKQs7b58UKmOLZtN1bnKfc2+G1WmU5i2ghax4UikCXtGieBitWs9ms0y2XIQ8Qr9crlSiav4Jhe8uGvNI64MM1TBV2IjHbp4OELT2EvTesg9j0Du5uW8KD1oGcbrjQesOGml0O4hnb+AK0Xreuw+JD0l7RVpUaL+RsVeeULdVVdMmLU7t8ahpv526VTV7g/iaApNwc2dEyVGW2VAfRHphZtnWbNvY4rbrDO2SwpkRAB0+HPUceo9VLfHZoBt3KYo+nZ2TcraycZHIsmrWFpWWZZ258YNeu6vQE9Ftz+5en2akZ9+a2DiaZRY3z4no54Rx3D4s727E9g/WL0qWtiWjCbm9u63BktGrVShvFWWZJ76B4eTvTR7zVnLtcH+hp0OpUVg5iwkTrorhVjJTrtI5KxS09p0MRXym3j0ww5cnNoHbMncz2biBS82ULDC8Tejg3y2S87zQuiGry5GbYPISkw/uvI9J0QVMN911yUkwiEe+zRhvZmE9vsuRcFc5B/z2A+g6umfEVqo89GYt4P8D9OBzK6U2W1NncCe68BRB3Ry0z3KHpaQVMVXKJqBnebvUOfMHctVj3XmEK/eXCbNCoYsGKK6LfLLij6fr1ZHjsDLQ3P9HxMQMKXw56vUY5zvzCupm0wmCSLd9IW8/RDJWWu/ns3E1TtHE8nTXIKHJ2l7JBO1Q9tfGunLxAkS3u9ybRscujYLM8G3XdWc/ZXopHjSDv2P2bcXpI22lprc/psCin153uMwtc0N8nS3dptyzG+3R6h/YLPNsrXjwM3CCdXHO6XwJEQf8GKXULIz+Pbrqjz9SoJNKrleYXGhk1vRZ0nmqPmu5NVP3CHRI5u+F1Pw2EK9JrXv05KSZqej2k94yH5eANUn1MUodPr3qTezDFPLvu1w5wVcnhDeKfiFKnfxIUNmKaz847SxUrfMsv6uW1JHYWsvqrQmHHzDt5+yRc4HGYTXvdciB5QHmjUSkPZgub4uJlyC755TnVOfWWFqRr40a7GOYmmOPNeqXcmSxtpYNjP13xyhUzuAgXKtGinJy2V9yJIctmUCvN+VllO2k/J2otqM6JYb0AQz23onR01R9Lu3IG6ms5WabTF7i3bpRXbj2GwyXAV5ylCxBv2T5hW7dpY5vDJWd0i/ZWVF7xOk/AdNHGy075IZiug1GBbn1K6zvCLDvj26BVVUm1VH9MxBIfrDrlR2iyAUZVd+UYN6tcbvmDT0ynqrPVSvOxTMqwt+HPPUKjVTtYohufzY5Epze3cTiGQDYb5fVgqgLcOijuruagmzXq5f3VkUlsq1tZOkicMGvWKxvezC6w+kFxrZrZSDTq5fWwb3XU7M0tH6Z0UdXPi2ssRguoeVhcnuNIyHattFscIjOr96sLxylbU/WLyjKOHKMbh8W5VQf18lajvO91YMlpdCrVQC+hzms//XnjtevR8tLl3380vL4nIV6/e680GA9W1nc//mTpxcvaO2++/m8/bt282du9fPl3f5hsrYqgtPHgYeX4uHNpf/dPt2R1bvjW5Zs//slge7u7d/nSx5+Md3cxsWt371e/PKjdeP3yn25Jxk6+8eFrP/35bGlhtLO999mdNCxo31l98HCJfNm/srP/0R8hpS///Js3f/zjuFxdePOtjfsPyq9OazffWH/0xe69e8/+5q+v/+rXpXbn7Ftf3fvoj8tHRzt7u8sHr1bvf9Hf39148HDz8/sv/8OfvfbRx6VXB6dffW/n49urT59MtzdXz17Nf/Z0cGV3487dvTv3jv/jX9z4zW+XDg7PP3x39fGLjc/vDTc3S4POpV//fnDz2ubDR5d+99Hxn3/98p8+2Xj0OH1ta/FkuH73wWB7x01m13/+y5MPvzb497EIVf3//tvNYXupUTt9+w2/PSgOB63Xb6wcHFplBlf3yuctd9g/+sa355r1teOj0w/e8XuTSrNx9N77K6enLEuHl7YKnWHYaR9+7Rtz9frG4auLD94u9AZuf3L61tvztXM/iob7O35vVK3Vn3/zW6VOZ+Pli1ff/OZy/czvjk7ffrtavyh2+72bV5zRtFprNN95DaRq68njV9/8VnEymDutjfd2jLULx2cXr70GjLn26Scvv/VtXyVrD7+8eOtNasXc0fl0e0MytnRwMtrbjMLy/u1bBx9+1TfZ+pOXF9evY6iqr07FxnzmepXD2vjS1qxU3f/00+OvfJVCuX73cev6VeCzueOLeGk+C8PlFwfTnY1Jdena739//OFXsAM37j3u7u04PnTOBul8abi5Bc8SU3acogFHsay4sELFhYZI03UKuqKgYrXqpy1sChSXoDkWtoDpEgQdaYDFq8z0jKMEX/VNTdiAsCoEZ4nxCVpiuiG0AdmlRXI2dfLc7AW4LQwF+XrZPxogF5oqky2juRFXq4XW0Mk4WiO6rQVj8Va12s/oRPcvBcE4K/dNtM6VKHgjM94kwVg5iWDlmeA+npLefsGd8EIXTnZNaZzCxButU2dq3ETJBYVj6Exgdz/0p1nYtuMdWJylcOpmK5omECVULgjEgTch6bLgwC/X9WgHFKMMTv1sSZIM4Bi7pXEiXXfipJsmIkHpVZ+v+xhr3VKoSgxCuqnxElQFlx5OxV5xNW+NWoFa8QBFoqHIHMYhQM0MzxNeCtBBpHcLLszNca5XXOpD2ZSwAGGB2gavBFG3vAQOMrlbKropOBdgHoMQ65YEAdKhY2qSlbRYCOjBVG8WHEfa08zMMTPvuwPgKjldBE7fUQ60pTxoUeFqVVHOgFllxLLGI+JwFa1C2nUtMbNVZ76WGaxlVTtjZjgY7Xjldu5ncrIKnKFjgZmsutXzDBDlFiZpUkUZHF7yi52McpQuy1JXC8zGq26pJiAGqprTMaIZTFc1jBiNQbwBcC+FiUEr1CYGDHh8fZlNEnaRiBvlYDSRI5jtVeCEs0GMNlwQa9DnaNfjmuHTmbxWCaOZGkK97sBY2onGq9TmBvUk2HYy67Ljib5SmB93RqMQbLhIGjPSeIUoAXEzRbtujn1yMFHXym6a6Jaw6x5SRvdEuj3PiK2c6mgdUZQGdRJVAfIN7Ts6UMrVbhfbokqKrHiOZiuAkcyrsbwsQifi06r2jCxYr0+gl09LTuUcTVcQpWnQYnlR6cCyPjVMi5J1OhR6eTTnlC5gMg+SOW/pJBah1UXD+k7OcLaA5hoCsnw675RqKC2ZyVKxeNg2wvDrS25zwmaZ2glMxwKrs62KdzrEBMAlYntapzq7tujWx+4sQ1tU97QENNuuOGdjAi1cIXagbaSTawtOewZnCu66TmMmAEOr1NZSaxDeckxPgplEl3weYTzI7K7vd2KhKVghpiuABniV2JFCEz25ubLY76Gxl64YmlscETmvgITuiObLPEfe3IUe7sBCmpCxlywZzC2eUrc0zjRxxl6+pjh0CnU82zZenrMuTZYMAYBMiaxIrWHYR/mKzIlfOAXTHRPInHWJqqQMGhmVVEVKhAs9Iubj2A2rZ2a0jXyZOX2WVrWgENbTihvPFuf0CdfrBUKlOpJwntI5YNoSOABWGWxJ5qlJpQRfJXrVLxQ5OM1MmaIq0TWuPKbWi+7phDGZrxTxcaqqjC8Vg+cdWKamRGVdSULMVhhejBwqwTwx54pXvWhhzv2yR0qMLALY5QphvlF2zsbAJcnWXPUswUC4xWmWlnEC+5fDsMfdGMbrstSTyjrjNafQEthaOS/ImNAU5qvSpswZg3jDFAdCK8YXNRthrCBflDiCbkTiVam5FwxNtK6LQ6mlwxclHSMkkFceRFnBn9FkU6ncc4c42xTBWNqU8EXFEoxyxBd0rgk7nMirxaVpezgM7apnJJA9w5YxRAY3M7Tl5MwnB1N1pYTTXJ0Lu+G6xOieQAvEQgSa+fx83CmukINIXSm7OjUNBZcIwFB1Fapijh10kdNVwAsBezHS+2XH5qbG7aJjCJY1wVeLrAC9kzFdBnGp7D6f8J3ApXLx1Yl0yHB3Z/HlsfVIa+/ypVt3ejs7/Z3da5/dmi4txquL88c1CW3z+s3tJ08AQ42r13bvPhiurvZ3dq7e+jRZWDBLPqlNkJLnb7619fSJ8vzR9trOg8eDlbXu7u7lW5/mc3OTnbXKSYOJrH3t6vzBicZ4dHl75/7D0fxyvLOy9PSV9rzBpc3KRZvm/OTNN3dePkWpPH/jjZXDV06c9m9cDpu9uWaz9sHbZJysHB0ef/jBSv2czPLu1d25swuW8t71S15vslCvXbz3Jppma69eXrz39vrFoZrp3o39YrvrzuLBtT08jFaPjy8+fBemcvPJk6NvfG2+WXN74961y+Fg6Hd6g9eukHGydnAwfmtvZEs7z54cf+Urc/1OoTs4/OBrzv/5f/1/nQj/x36wELGD2HYmOXTcWKHu1CTq1c13BWShSYrjI2eQTFlxaZyh1lAC53x9/2LjssF4ZZy5w1Fy/WZxcEGboxgFsrp28R+vLw4afiRwZ5xpqiPFGgOxs9fZvXGyexMaU4ol6s+4wibXpD2yiT65/AbcNR7kC+cDetHNPvjATSa4Nc6B6ylMmyO+vgMh8hp9eRVnQeGT//xfWZpgznFraPZV7jlOYyiW1mXV8y+FXd9VicaNoZSEAwobA7PGJztrF1+/VJaTuWYbdWdyw3KJSGskM2s8GL4+bzzGpePU+pPCnPAZa0/M2noMvU//5r+4s1lgU9Qa27XdLHRLzWHiBREpqhgAzy1BnsQQFfyCmAFnYhBI2HaeSQUQJt4ox4Ci0E3MXEQkSoJNNYkAgYT4SawZEAS5ybVlwnkBJHEENXZ9j6Y5J8AK5oasnlKt8qLgRayNRoGMMqgQ2XQxOrM+SO2KTn0k3QCAhAMjYU5CR/quBBGBVLIZpxKNXBate+cDcDNWc1IgSqFI5ylHMQJl29outj5H7zqWmryUAqUAsRwoM4kWXLXMkFYQeFa7MYoxdKyqZma6zs7n9ejQu8z5okyRwAPBC4Q7OUyrzng5rB06u0lScVJHVU1mq0I4Rndi4kezIhWu62sxyVDJZUFuChOD/FQUVRQiWMrQtJeWiSJOUSDWUu58jqsyYUFFZzaQM41UKCoek08n3r5PHRGlMKAI4jx1LUPCQLiecw4mrCwmGFZCl1o5jjFlBEpbjCzFqanK1FIVWD+bTDEKXY85UvjCygxlWpY01FqN98OjEXLOvUs6cyGAKU2JLAqjMhyvhufKgD66kuXzxoGZl4iOjyRMoOJzCLjUyVKhqxDoBBpX+sYS6Zh5UQ9teqq/hnVANMnhlCosJZ0acsV9AI165V13+iHNQY7HxlSRITmOrARyBpxN1wplp4RDX7lm9O6ym0TWhtEMIOsr4MAMBRLyRZ+v+fOzzkwGOrIEBtroKMKuwHCxkC27AeA8MXqWFhGJM8dEBQcWAj9qb10u5lPQyfMZcFeZsVDGpARgghzxzq6bRta4ceIx7AGlRMw5DCkhjkSJ1QEyCZ/PrSRsdJm+6NFqDe1Z6UA7zYhLpBdDE9j2tbmDGq1GdM5m84LkHAPJC9iZxgQNb/iE5wVtdTqfFixwEj/3DVGGjrYKpzFy2nAf6pIEPMM2T/3cB8Ydb9GXU6f8ytxwOMAkTgDBqpBaETNH5AmRhtOCirnJsQfc6XaBSGEIE0mAMCTbLo+5zg1AnpwHk83lyrQfcaIU1DCIc42VdndcAYeenky8qx7XihsPOpmwuWCFTCaX1rVFoU1mGuMoKWIWZ1SmJLA0X3Uz6AQwlyk2maa7hUxaMEstchJUtmpBgBkFlqZOgtUcrG0XO8/ANhdBzL0MZhgSIOYTky7T82W326PVBG5y6RnTjeg8yXEChPQmk+vzPp/5E0TSkFdTazDkjoYjjczlhecP4dUEhbmcMyxyVGqTuXROhLS/Gb46QpsZKMTc5TK1COVZNUdSophnFFOQ4kKcMMi9gs1VNSKW5e6aGs9QhUDoprFk0BoJ2EpmgYlsOZth4LuuQ7Msw5Tlbqmo6tKlMSmrFJMCUchXswAwh65gjM+MH6S6KtKABdYFIMoBzHAOqeuPFHRyb1vHqSFIQy/NpEHUApZKBgBVzGTTFcJ1iswKPJsPkmfwBtVYSTeBABuEhc2cfIu+WgySh+AqsvPCkATPEIBKhVYNRquugiTUMTIVkQKOh1qVgKAZjFa9E4/mx862EkWSM8VEItaUgAp0fRpvV9pP4GWkfCuDjKZSMZKSjMZpDkwUUlRK4ayflJmhzJsi1ufeiuJFGdOiBZEJ9VQiHTAR44XEYJbaso0Sssg0xDL1PGiHfmUxejr0vlGOQTTVZCmgUKpxRiouhompxtay2C7oVDk6MAROpwzPe46bIW+EIJJZOY7C0jKYmXCaaqw840vaHOq5Sg4c3Jnw+WJmnbv/L23v8azped7pPfnNXzzpOzn36XM6d6MBNBpgkkQliqOpsa2FNy6Pa0oLu8qb2dhea2zLQWFGBAgSIEgBYhCjGEGCJFI3u9Hd6JxO98nhy/ENT/aC/4BUZd3b+17ev6rf6rp+90+cZCBo6Ow1MAn7c9HoXkOGUUxdNs4UIhkHsNEHfjpAAdlrIuv0Jkf2jy9DCNxeHzUGKoLCMlTrUJrrzXrhXM4S3Ge5kWqXiDQ75sF2qhmTAl2/+LtWag/JqWpXOFn/+IlS9SEcDLJzHuhzWu8mp3zQU6jZlxI9nV97fOT0UOeA9BU87GbQVX1BD1pqevbx6hkFiAeF2z1EO/X0WdfrdtFhVxmyM3PkMBoPVJxrb9D9RnzsRGHQofstDp1eVNz/nc95WWxjxWq97LgfDg5ptStWcH1sdmP++NLBHaUI3m9JCWWi0E5DnYEUoX8FDpYx3c99/g//6v8Zf3DfFMK5d99fuHK1Pz1RnV9M/TBMOr/3V38zc/nyxgsvvPTqKxO3bw0qI3sLR3gQFqqHz735tWNv/2z7/NkLX3tz7tKl2pEjB8sr0NqRw53T3//u3IdXkpGR2atXzn3/+7XV5YPZRem4ThJ/8iuvzVy+DDx3+sa1Iz9/pzs+vnt0TTmuJ5Pf/Zu/nbt8ubMwu/L2z+c//FD5TqHTvPDyq9noUPnJ+umvf4MXCtsnTknHIVKe/dXP1r79fSdLMMUX//PfpVE0WnH/UIMmMZUf/vLo279gRhXT3vG3vuHFg40Lz/fDomPFqR/+4MS3vsuHCpN37h19++cuT8NJ9kcg77Y3k63WJ//6b1QuyNdrp7/+TcDQ/vJKkssbSo+/+/Ozb32j0KrLYnDhr78AHXwQzdfuubiV5VR791ERbd9yf/cAACAASURBVHeH7QNx7qgp+fTKk9q9Eo3TfL+x97RgmrbsHqZnF0CnHmVm84Ni2sNDYffwCsMDFZVFXMzzMCo82tm9nWNbcWFeVN9x+j1WnuIkb62L3Fv7zZ0yr0MnAvVbBGxlpXINFkiyMkvvVdv3crbBR/lhY9Mb1H13XIw8moGtaTvSDB9VcK9SUvdO2t3njV8jqFc9mdssB+5ttjlt+8tquPf78vYzabmebpm9Bd1eQSwl/XK0McmcJ+3RnHBcxtPSg1Hcmgh1I2gRUz/q0sazbOdFlexkgtybsP0V5uy5uyOoO5fLdk+YexcVGyT1TuskPjwRkZvu09D2jzl0l9fj6m2PDWKXyc3f5AMsw3wtmR/zZSpu69pmLhy0hnsH9x9NOp1uvlCTp484SU9drh+uF32c4P20uhkFvX5otv48VdnmeocUn3yQ85Hw0nj/tl8SrTDbip854lcPTY0c3onKjSbLy60Pi9jq0nAvnR9xu3WzrqrredTjLuV7H4d+a+AvBuHNWVfSKKuD+hLp5gr01p84dLr55JEJncfPw6zkuFtkY5Wl3pi++knOZ4ncNiWzdSLYIzj/KLx/2ibDKNppjw1RxYuHibMzS/sjNkzd3flw33XBh59H3VmUf0g89/Eq7fv5bJ/Ux21nio7W/ljWT9rezY7K3T+qxbTjHJKdOdYrEFRvbJvBHTw80otvq8PtqOx2+/MVgxEbpPBa62Cr4FGZtnDnBhoqdpPJMne9KO3E10xzx8vrDmukuw9yEYrRtNsPS7msA99vH27li7m+WDf1Hb8gW3AxbAdDQRbrG4Ptu4VKuS3uJYfb+RFUTydK3A9oxsnt1u79qKQbKkY718OonFWaDqotB3HPS1q6ej5o9UbCB5+TA2xUdT9P9o5R2/WbEjRXg66o2Et/QnJR/cF+Wta7v0OAdLMu3lnNya4IBnEUaeZM3quZ2nP5Ws/mm87D89aSGXT197kMRLfVKoHqCSdGxhXOkxWvwQP/9n+luy4Ld0SFrS+Fad9NE1hbcdJQlqrdt3WvxfKjqnPZyKaJojgdLmZRLtxu1K5TvSmHp3rN90i36ZaGk97kGNEyt1fr3oadeuCWUOcOtE9VboF/Yn39d4R8LyjKj0BSBUOq2d9GnR03N5Slo4XMC4rdevyztNHJ5XNx7x4aHOAgyOREPg6LQ4e71WusvR2NTzaz99JqK58v8krV0fU1Zjs09cjOcct2Xxpc/QQr76qW3FqFnYUwq0cDqQ9Ourp+wt/4tOb9uJs+Hgfd4w7d9/ci0FkK0mp/GiZuzpVJ+Ylr9p9xne1gh9j2MWZ2j2eXf0/NDPijdneZ7RwtDDapRPrgIqTNs9mVl3Q4kPvJ7hxsrXoiI0qxrTXfdLum17yEi7qLG/HeRoF0eSmoDs4u4/q+20Nbl4pA28gODm+4vsgKyZPs1Kwf90QNHdyK/GovnNAH77ipZZNw57/ptHLEbu/yxsPQ9gCN4OEVQqtJcbhmRiI+OQzvdJoPIrfbKw4a+0/yacfJR3V5dsYddOlBv37J7SnfY6Z/G4J2FhWod7gS7fp+cIM+Omb4LB6q/xF/vJIFzV7NHKyBzjQIB2x/KtofNs7tf8c3VlB+UwzM1mnSK0ei5jSLsDGD/Vp/yMm8KDdoDH00ovmiQ3ednUnUGy+Kh8+C/ZMZb4o0OXiWNBbz7BJ7MGX4ckCePCcfPs9L9fbTpHWK7U+G6Cmqz8HakTC4N7gnqtthIa7n2q3Hjythr+pPCL0259YO08vJ4Xax5HTUhqju5Apxy8910hNzQa0qtuj2jbAcxXaHHzwOpsE+iBv/28Tk/a3tpOtUb/rDWcMYsPVRLvJ5UBgkR6eDrSdi06muRzRJCZAH1/1cv+mP9sXyBFOK35DVzVxZ1EFbHz70ibDjzt7v/cVfOvFABf4zX/37fNzdXznaLQ1nUW7h+rULr75aeXBv7+ypP/hP/yfrduXJ+T9Me8PGqvX1o9/50djWZnt27qVXvjBx/+7eC2cPRucsxhMP77/wxlfyzVagsiM/+dnovQfNT53+03QwDwa3D5I/+Ku/CZt1nQuOf+d7I9tbjZWl1th4EubmH9+78F9enrx1c/Pic5/9P/4yqlXj4eFjP/vJ/PsfdqemVt7+2akf/Wj7uXPVqXlNSLFTf/Zrb05du4YInLl968hP3+5NT+wvH+VeECbd3/vbv5366FpvZmLt7V/MXb7ES/n948d6fjHfa73wla+s/egn+y8+f+71N2Y+utqfHNufPyJdN9esn/7B91b/6UeDqcr8+x+c/O732mvLO8trmtBI9E6/+a3F999HEAztbD7zj9+urh0b7O15T5/8izhY/yzQaPvf/GlncTEr5u//zmfao+Odycm9UyehMVgqAnV3ZKy2uPD42ed745WsVHzyqU9Ii/xBn0eRKBT2V1cPT6w1h0bb0zP3Pv3poNMmxhBk2pWJ9uT0w098Qvj+/vLy/unjQFustSW4NTEuff/Gn3xOlgv18amnz56HEDlpQqFuTc30hoefvnThcH5FOezm5z8vA7c9Nr55/lxrcalfHr736c8gLbHREIK0MpwR9/Yf/EE8NtKpTG4/e646MnKYNrbc4e0jpwxCH3/+37Qrw4o4dz/7WRV5JBVhNuiPDLcqU3vnzzw5eRZAdP2PPzcoDnXEwUflhe7QWLsysXPm9P7xE0mxtPPc+UGUx9bSLI2nKsLx73/mM/HkaHVqYff0yebKrG8SvOCwWTfQCV6L4GRRPtyCHW6OLHi+jPKZOjYEKXDnMBx2yNM9I5mcrORIPzci9HzO8VRYFGo60oB4vQ4qMV/FbMUzY35AE39I8ekc3a6iAeeLcw6VXkXbeb+sOnSW2IWy2Wngg1Y6N5d3B15e6dUCQToYt9lsGdkGofvNaUxZk5p6awFXHf8wrT0oTBMYm7ChKjqJLMWH7WnbSGVTH96eOAtpgkirOyMh7AJnP5mABloqJARWBokra60Vg+nA4E5vXPZNvCHFzujcIELUHqYzGS8aKhqNVZRAsGfjh0MLmmrE9lRFdoc9aqvZdDoYLhSyJjkZ0QLOOzGdp9JjZLMGiGNmyoHqg2eG/ZzESDsncyby4M11RT2zNBWyzJvFfL4Qir49lYcT5Y8Oatsrq6gU5GjfnYJ2KoxI4lVMb3LC2T5InDwd9kIbq9UcHPPytO9NwizAYKOKsCMWxsOs554I6RD0VUzXApCHlnSA124uEAgSOdQdlFk33b8dDDdHp7GuwlytPwEN7hG3XpvNN8zgscF7w6NuUleT3bjiatCAQb0163qDhCmlXaOIBOFhb1wHWTUb7dpJum3oU9l7Ulxy4IF2+615QE1PFjrNMot7Ww+I35hY1E4M8WE8wznjiLbiORPYgVuUdj5CeZx3B+LsqN9uAUgwNn7OOI6wqznmqGLUx9OOJQgL5crEVsJIdsC5MvOU60hyxIME4lQSpGXOybGBs0T1eBiqHjxXKnRrCQoIUNQ1+Si2cyGoBHkyIGuBggRZSIxwCshzlV0roAgW/b5aLPAh46f15pIxZUnS9uCI7EWs2m898cvNsVEKW3Jk0Jxibtrqzg3ifNjODu6UFtJ8ZFFDljtxBRDd5CODpEAsJF6/q4YRSVu92VQOYWwPTandHPIbMn7qFfYqo17a7E3102ET9Pez+TQeCw+SwTokh/lyoA/EUNyYcZyszUeq3ZFcCTb9spBLxZDFUZ6DhUgC4vd6suQXwIBNQT2Tw44sFrLsaNnvdIkxlGnoIX/EmPkgr9qsYtBU8Bi7t5SSRxYD1PUDYU8WfZjRUcjGoIWIZhxTiEo0cvt0xUd54ruZs+SSjCOhgIdDR0ajXM3lVUQK7sCsFjCNrW31ZzliCST72SSoBe5+1tyIZuNhwni9uWaNkyHb7M/ILlbbWXpQGGuNFAmo88m0W7aeaDaWTZh1laXYKssEoAfJuMpGAFW1dE7shpV2fO/m8EnkSQC7gzljQmFITYxl1Shoq8aj/FxrPOf3q53lDPgDaNr9yQwXUOjwcFSkqxVHpeyYDwNM1vc08s3ESJ70/IrRM6FHM2fMJvPj7t4hNw4aCSPdp2uBHXYCGjtjoL8w/KDbO8wFfHk6TLtsAdspb0g08ZJjpor26QHocTUzXsA9pwL0Ut4xPFjCaaUY3ryrFNbz08wRwZCyi76ve3SKZrN+mNay4Y4esXFgKD1oTMJmHB+g5NH4GrF9E7U6k8YRdZk7yGbDPSmqiD8pLgg/pbDZWTbE9kTYUeXEAkikxBikvqLwIJkTMlQItZqLLDHpBsEbQzMADGC4L8dBt+w49mAwI3dwdCgOHk+cQjjRUb83iy3uAXevP4XNiBuJLnhuxPMEdiw7VbLWgDtPVXkETA/nSewsO+lUoSC66mwRGONsHQi/bMpBye/iWQeNexEaBLOgOrd06aNrtefOh65wEJereVKmRbdPZxkHBm8cgDAv5sfySYudzTl548MUnyiYgJH7Wwp7ZrKYRz13xU1my/m4SY65eihqj04cHlvbPn2qXyzVjq/W5hagNlG7WZ9fQFo9/uzvN+en2+WRw9XV9dWTvUF1hzkPFs4ajJ48/8LOsTVC0KOLL6aTwzoDRKssCtN8bnd1beP8WYvJ45de2ppYTNqP73lDrYnFVmWqOTP95IULcVTYO3a8MzeFtHH7/XSowKPCxvPP11eW6qMTrbm5x5/8BPCc/cXlRxdekIG3d+J448iisZAJQYE8WDia5qM7f/zH0nP3l1b2T5+0CLMkJcRWK1OD0dGnn3jhYHY5K+SffOqTwaCjDIEMpfn8wera7vHV+sJSNjT05NOfVNpSrQyjfKhUWzyydf5cd2i4vnRk9/QJbRGRcmhQe3TyOe2yO3/0h7yYr07NPnn+gnv1irO58f8/aJQYnRN9x8W5QTNUiYOUo9OFa9dxP91+/qxruMtgIe24IqYMhJ366OGD4d3d+xcuOoY7NgvjdoCU48Ao7VbW708/fLD+yecDmTAgg0HLh9JnwLVi/NaV8u7ejT/6XE7Ejo8L3brHB4xYx8rpu9dK29ubF875aY+5OEq6OOkzD+d7TV+mHrGRiC23lIKA99lArv76V7c//RlfxczDOd6HBjKsA53ovYzePvBP5ABTjodyvbpDDHNJqBK/ujt57XZ/rpKEoYOVxwcaYz8gxbgNFbTvPY1WHTmS97AJdYr6TQalKwbFXvPEe+89OPeMDSmjIMh6KOUOEL7OwrhLMFdKO4lCVsjM4AKG2TgGkKR9pZUHuIkHylCgMbVWNwrGQ0G/w61GCNK0Ry1wkNBJzLa62iWsAiiVhiuWEAkNADDJuBoUApnSUgqAAgBynjEoqEpZX6TZEJSGaenozLGcJhmzhmvXywahlR6ykbBUKQ8okCVSFhtJmSnjmZRp46R9zjGGTsC1NuOHvUm/nAUythZ5EvvGuFBjnYZ70unS6grzdeZh4KfKlSlABGrOeaWeLLBB28n6AWGIcy6pj5GMucmiameCugk1KYSum/WhQA7ygFSeTX2qNB9QaDg0RCitsG4WiQcBzTBVot91DPchshnCAHA+yVILcVdr4QgAEsiYSHkcNtPMVoKBCnBPYIOsNqlSVuZAkslA7+XobBDp2FKleJ8mMAOGGKUlk+0SAcpLE8SUzGKHSkwlyHpQBr4lFAiTaKat0sBq3uge48RGfoeiPAAgTAcIQM+mKrW9wTLAwBXWI9xyheKEgRBDGHKT31V+mvWmDQMJgNYX1oGKKeOlvU5vGqv5nOUe1BSlKEt9ZTi2OaN62XFoYBi0sfQowUBIaDMGrM4kBtohlvC+4ApgjbKY9nh4a0Mcyzs8MRhLEWOrEVYUAbJVtzVNjjo0jamjbTLwJEcYQW3hvqY148xhR1hENZJWq4QSofp91ueFx1towSXIamqoGGQpBJazjLjGeuu1dDl0ZGqBRYmkxkpsgUyYQTkCTaoIyRxETGwQMo3sWUOzCHUc41nJbazy2DXaEqn322e5ziKvmsKIWUlT4SADdGpSOvkQdkcAdblPKRLSJJyhnDGSZ7DWWyNuN3BSHyugIeDYJRKlkg5Eq7cmAxSajAGNZRxlKkQ0ATDgAwdID9mMD7DNPJCZBOb3B8jabLFAgQBKujFXCACsTZbRna7XT8gcwcZklgGeelghZGnSJR0D9FCu13Qsh1iBAWAmBcAyRMijhhaALROSSUgtzlItMMECc0D2B5T3yBxR1iBr3bjjCIOp7Uvp8QRiAoSgCmDiQdHTcb7aHmV+z+rEx1gMRKBSjAnUwspiLZl26U5kOhQGWAoAWJ5oyaUrbeFpHFcEhYZiyESiNHEhRVmaoaCefIqIzNExhSlLexRIiQKiYqXCvdZZSluB7ruUuVx6VjiEAyk50daknhGq3yNIGwGZB2WtiIooGLQ5MNhClGkNtQtlLKjai8yomzOpZgpkPZoSibQBQCYYy4LX57DbwkRoAwSnDEkqJO07WTaCLKZauDZzjURJRoHhEjLtgu4wzhG335ZaWOqoLHWwMFqn3KVQO0p5aR8LH0MSCJKp+eYABrnY11KaIBAqBBpCS5NBGs/spjQIe8BQF1qdcE8rApBAKtoTSBg+PkBC+oSAjGuZ+RDaNE3jsQEPXK/PgACAeGnXJq6LiRFKy5F2WnGyvqe5VRbFiWMYxhRnccIxpVJ2u55MfYBhBqDCgk+5CdC2C6xgHIcIeI5MstixKKmVkINKrKMIwCqVUjCQOZrnmx0UHi02Oi7klGiTaWSBgJbJhEPX1hB1jZ8MsKNlOnCowETCRFlDRVyx5ZBC5ULupCYghDEpVBYkqQsEsiSXtD1stMpyvcbKBx/WZ2Z3KaAedtNulHR9pAiShW6L3anagOaPjfgMcpvlBm2CjWcyyAfj124YRp88c94jBiIl4o6DlKvTvI7du20QqfBMyVMJYiAXtz2smclSPjj563c6xeH62gJFEgAeDdqhSjCFUa/pqgQhk0vaPlIuEBTpo9c+LB0cPPr0RS9ruw7K9xq+4Qwp1/KZjz7wmu3Ni+dDFXs+DpMe4z2PmrDfmtx6NFf7zc4zp11iPWJyvO8nXQpl2GsWm/35e3fvXnjekwkzaRi3Q5u5xLiGlx/dnrxzp/rS6VxW+21pCTT3sXZlCoH913IRrn3z+4W93XahNPX+1eFHD1PXn/rlh2O3722cOn3srX8sb+/cffaTx7/5vZGNp/0oX775YPbjjw+HpmbefX/h8uVvr/zvJ/7xn8r3H9w6c2Hln96euHtnMDQ0dPdB5cqNRn5k4uqVY+++t/d//8XKD96u3Ll799wnln78zvSV3zSC0tSju9O/vtTJDY/fvHn8+9/fOHZi5Xs/nrzxcX1+burt95Y+/KBVGHGMOP+3r3zwH/491vrZL3+lVhgd3ds78q0f1IMS8+DJ19/Cn+vXj8w//1d/d/l/+O8UJue+9PqH/+N/GOrEx976eiqRniif+Mpb5Pc/u3/m+DN/9+Xbf/b5rFA89dqbH/33/21RgpWv/r36r3VrevjU174RfOZTG5+8+Nz/+3cf/9m/642OnvnS1z7+s39byJUX/uHbWTernz566otvbH/i4sann7vw1y8/+JPPbvqLmzeCoKTHlviTu6XQiaf+EO7fYCQPxgvZ3jWvmLNllezez7mhGTkuNh+EZZY4U2j/CrMhnsqne9ddFrIhk+7fDxHVFaY2bxdCHQ9Ns51L1oTO8ARq3cEJc0tM7D7MMaiDHNy7W/Sz/tgM7n8MujTMz+LeLY8AOmOae+slpSidxXBrWvaLavYx28zxwQij7xk5omtjMNgU7bK35aqwS7amZDKnxu+inWHRLLv+x7Dpy84xFD0V/ZK7Hsrix2x3VPZm1eRDZ9vjjSnMbtokNNVVhG6btMC2J7PcNW9vJGvP2cJlWh3i9TkGryEbyd1jgNzWPCJb83q15VSHeXsV+JcSjXZuloYGWTgDty75w6dkEKm9m96wq8CB3ruZH0ljE8pHt6ZHuoNgGe5cdUZOajcSW9ei8ZMcUbV+PT/UF7RiH98oDE/06Wn65EOaX9VBWexez7OFFIbJzsPikJDZHHpyrVAZj70zeOcSiVZsNCr37ngjiymNxe6tfK6ZkRX45HqxNDTIT0Xm6Zz2Ugc80nvLEEsAH4uD41QO+sN33MdHJcM2uAo3VhTmBD3Wh4sOUDY4FPtLftKNR+66j48KxzOVdbRTEVY77L5qLAFFTLQjD+f9fiLP1Zz1Wc7KZvSh3ZmQGrv2pq4uQJUzpV11OMu6mo9eL6wvCDwEclfI1qzkPgzuHG778oEdLevefdDcyw/NkPghbW0MVSJu22hrvTjkgix2ksumUkb8oa1v5xfGkt4T094sTOEBl2zvTjCOUpk5zet4oizNE737JJorpoMdU3tcnLadRMH1++VxEmvLGlfw7B/IeEMerpeWct20bZ7cKY6jzDh466o/peKEscMPSRjCMegPto464okJpdh9lvaf8vk6e3xcT+7DXNVunjDjT7xBGu+cxMk2HOZq54wfrqvpNnp8RlcObPFAbZ0go0+dQTc7OEWzPTAh+eYzJNiL/S3v4Qk52oRq124fg4WnWMTZ3gmW1DTryu0zzN3tRVvu47V0UgBatTszNtx0bS/bOwV6XVGpdy5TQb3RWVK/7hBAx3Cy+6SAhYmKaP9+we32xmZx+yromFx+hvI7LhRsgTQONvNp4vplXH1QCNrJ8CRoXAd9HQ1P4u5dNx7gCdhvb3idnj8biv2HoarJ8SXS+ch0RbRYHjRvQ94NJ9GguRf2amwqn1YfBPJQT0+p/i102AvGpgBsDMmDkxjc0cYlT+e19zE7zKvaoi1cYY0Sry468DrGrtg+beE9CwlZXzFLLdYt8vpJ6/+Gdgv9g2UP3DaIir0zBN3VDkYPj0p2m7apPDwK2GVPhMnOGjP3bUjUxmk2fs0wBTfOaXILJkzvLmPnKuOM751y9ANTYPLJKWS3+tF+81qEZgToZU9vl4tNUVwGT++Go0MxLeOdS643C0pG7F4PS1PcH2QHj8Kgw0MK168XS7l+fojtvA/ogpdjsHEjGB6xTOqNu4VcWbEA73xcynn94THcvgbSiSCXg42bUamYDI93n94fdSOLy7hxL1f0M1xB+1eQHIvKnq3dDmFocgWjDo8FbS7P1emTeQ5HdeU+3KnozHXJTXM4AfgoKG6L6qTbQHz4Wv7pjLRjtniJ7UyJuIDxTdWeBL0xyD7WB0u4HYjhK7nN+VSMwcJlsjsue0MOvAEHI7Y5Yb0ben8eN8v61J6zMZ7xWRi8j2sjvDPioZtmUILVcRjc0vvToDFiilfiB+LwfnFK9zi067fL46oPfFK9SqcjKep6715xwe/xgXlyK1+RHEZ253Y4bgaDEtv9gI5elGCg9u+W87bPjdi9HVUk18Nk+2o4sZaAIbz/IR29oIDUh/eiGbcnqnL/ZqGcpHAKb14Nx47EeJIcXIG5gGHBd26VFmUjjezG7XIhM9FS5/n/8ure6tH0Tz9/6o2vN1YXZWIXvvPj0pGVem7k1Gtv9icmfvK//sfz//nVg7W1ZmHs1De/05kZl8g78q0fdCuV6r+fPPXGP6Tl8rv/y/909rU3mzPTteGptbe+056Z2X7x2eXv/igplPdnVk78/Tek524f/4vzX/rqoFwchIW1b31/EEX2s5+e++HPheu/O/Hnp157EzjOP/7lfzr3+j/EUbg/NT/3i/eL99fro9MLP35n+urVp6e/sPat75c2t/aOHl38/k8n7t3ru8HE3VvjV272C38+88sPZq5ce3rm7Inv/WTo1u2DmdnZn/165qOPMuKMP7pT/vBWa3hk9lfvLbz/wd4X/mbtuz+a+PhmY2q6cu3uwju/aLlR8WB3+Ydvv/sf/+fZS79Z/vk7+//XXyz+6Bdzly+riWLhcfPYT3+SIjfoNk5+47v7M0fa/3ouQjE+2p+s7Jw73R0d601P7Z85oaJw98TJzuL0YHSssbSwfeJ4OjwUT4xuPX+uXxrqTY1vnDsjc2Ht6EpzZaFXHmoszO2eOZWMjaRDxZ3nz8WlUmNufvvZZ2QY1JYXGysLvZHR9vzsxtkzyVAhGy6vX7wgS/nWzNzm2TMiCupHl1tH5tuVSm966uDM8db4RDZcevziRePR9uTU/unjvanJ3vj4ztnTnclxFfqPP/mJbLSYFkpPX3whGyq0JiYPTp/oTk70xirVE0erc/Mq9B+99FI2lE8LpacXnkvHhjoTk43jR9ozU+2x8dqJ1erSEeOyB598KRstJoXikxcvJsOF7ljl4MRqc36uPzJaP7Gyv7xiGHn86U+lY0NplNt48YIYLtSm5w9PHOtPjwQgZjMEVxwPpu4ihUXkOSIYN2SM0BDkiymcDbADvBlCh6EPUzpP8BAljgxHNa0Q5tuoJOyM7zjam0TOGPZR6swRPER8V4bDUk/5ocOjsrKznoeFN6bNVJiDfW8K4DEHUxEMKTLj+W4W5jmZcykz7hQ2YwGDTexW+xULvJjhZjqRct9SUksmJcF9EDVRMRahBW4jmQKYdBisd2cMpokO4rjCCewivyqHlQ4EdurxpLW058DGYFZgMrBeX1RSRGIdtuWI1F6GaUOMZWnOOrbRndOExoB1eSWDTmKDFir2kzyEpCknhIhYGfXoIqWRdQLlTCNcpp7H3QkIxv0I9PFqLvIT6CK25LCccX3pjAMwTH1PulPYjPk528VHPFoiHk7ZkoNywPeFN27xMPV8lR/lfKYUgIQe9VjO+ChjSwwVMfOkXzFkhPqeDMa0nopCGNMlxy0BD2fOAsV5AHAfhq1+BUCUyHJfDQmq22Kko0rAkp6NGmpEWpYgv5VWFMKJKrSTMeCqhhzt6KIBuAOj5mDMEtZDXmswrgFKQa6ZjVlHNeVQh+TjLLAgbCTjhsEmcNvxhCZowMtxMqw9Vbeluhyyxs2Q5a4W4gAAIABJREFUVxdjmaIp8tqiojyUuiWFJxiJQJTnZiFyfF5kA7jsu6FxI43mPZeJfJHTcULyOIoyd4baEsuzGKxGvsO9SLNZ4vjGLWo6SVABh17qzFIw7EWoj9dyAY1RgTpz1PG1XxBkHMEiDf2MzjFddgugA1cDFgLPl3TBpYENcgLO+LZkfX7YnVEwzCDoJlMZ9FOLO7rUkSVt2cCWuoNR5IhGMpOBSCHd5pMJCTLhZqbUEQWBcM+Wur0RzEwnnkhgjhPTzio9XbSAdEyho4ckIn1T6iQjwFWtZKzHS8CTh3x8YHIa4pYpdrMhRVHHFju9McxUS4520iIr0H5umKupIPREVBRw3mNUBcPKzAQ5FHsTFo8x4qigLPCsF3o8CDO64BDHOuMITzkR6LtjGlcY8WxUFnYu8Fmay3G86HmucscQqSCfZMEkoCMYhzBXyOgsQxEOcgIu+j7JnBFIx5HjKmfU0HGKQhBFmVnMubBnWZ+PJ5Amxm/LEa59TmhdjqVpZH6bIMQSSLq8kkIvNV4blXppHgDaVpUsKwBXHnbnFHIzS3vpRAad1LotXe7zgkWkqceSuIw9cThYkNDNAO7ISgJ9Lv2BLvdlJCluitHBoExd3ehPpdjPEGrz0QGIrO/J/HAmZ4sezNgRjxWMjzK2yFCJUFcGFUNGqeepYFTpmTBAKZtnbhn6OHUWGCnjwBPemLYVJ+ekwbg1k34IBmwGmUpQtF13DqERSpjwRwyddEKa+KOKTLqMaGeRomGWtx02DdAoczztjVow6UZ44I1ZM+m7uqVKHZKP08BCv55MWgpbyGn1JxTBsSjE8ahyTdMWamrIGD9Dbl1WuHQ4Yc10SlLYV/mBGskQ6qtCSw5rzRLs1HmFc18T0hpMKYIGOuypYQ5prHMNXBjEOYRYQ0xKGUgK6/1pjUkso4EY4RAPTNhU5cQOeznQw8dyIYthjrB5xgLj5wQZR6BMQ5+zWaqGvYLpoKMBzUHPEXTBIaH1I+mNQzjEAo/nxkUyNZyzPbwWOr7yHU4XHZRDbii8cYDLNPK4N2nVeC5ne/So5+Ws63A2z2gehkGGxgkawgUWu3NYjvoF26WLFBWd7kilunqkubI0GBpqHF3aO35Me+7mM2cbSwvGoRsvXuxPjvSGR6qrK3urR3mp2DiysHPsmHKd7fPPNJcXtUM3n39WjJfqE7O1o0f2TxxXUXC4cqS6uqSC4On58/X5WeA528+d785OdsfGm4tzByfW4nyxvrpSPbaifG/z/LO92Yk0l9s6/0xnfqpbHm4uLe6ePmkCVj2ysnPiuAq9wxPHOoszndHx7tz0wZnj7bHxuDL65IXnTOjXFperaytpsbi3erR9ZK49Nt6bHD88c6I9OZ2UCpsXnzOBs7dyrHb8aFoqVFdW6stz7cpEf6Ky98ypZmWCF3PrL16QhVx9YeHw+GoyVKotLTXWltqViXh8tH7qyP78UZGP1j/xgoz8xvzC1vln6dUr/1IXIfpnughpkhgE3TTGQgBgnTSBwBApiJJEcGAMlcJNYw2hG8dESQCAlyZISawUkQILDq2hUrAkNhh7SUyUhEY7aYKMRlIQIagU1mgnS9yMa4z9OCaCA6NdnkJrsJRIK4dzCyzlqcMzhbGbDLDRSGuqJEkTaDQRwkliRSmLByxNFEZOmmCtkdZYCioyYA2WwklixaiTxkxwBQFLEywFVBJLSQUHAGAp3HigHOZkCePcIOT2e1QpZAwxmvEUAIO19gZ97TAWD2iWGoxZmlIlsJRICiK1ZK61mGaZoI5VkGgtIBOAQmWEIQZhkgmFqTIICcmpqxTGUkjIFCBAWwGoRAQJJTE1FiEhJGbKEKxlBphADGmjDRYQ40wL4kjoQq4EIAY6RHAJHYmpVUZYqhChqVSQCEOQUhYFkobUWCSQZC5TAFgqWQAVspAa7GKlgCWaekhZYBl3AyIBAkghh0hrMVM0h40GmmriE6mQpdz1sYQIYEV8rKBFWJMAGQ0NFU5IlEYScjdgwkIDOQ2QghYQgwJkDNZIOQEWEiudUE9LCCyQkAIDgdQcu1YbkCnOPCgUkTJjrpYAGiCQAwDCykjsICkQV9wNoEaEZ5x6UAFsTAYdDTBQNkMONJakgjPXCIuEypinNUJaScgswFaBDDnWAsgVZy5QAAkpqKMNhhZo4CrMKFeKOBZSKpSgvgEuUcKgwGCGlQXQ0cRB0irsGOQSYSxxjHGJEobkFPGJBkYRTRmT1mBHYxdqaIhjoUOksMBR2EUKGOhK6lFhLabGUqqMIZ7GEVHSGEdTl0oNEZPUgwpoiDPoQqWMxRwxJCTQOGU+ktZKw7GDpDEQZ8iF1hgNBXGRlFDalHmQK2BsRlyroLbwt09lJMioB4U03HDHw5nExmbYAdJaADl2oQVQWU49yjOoAPdCLAFSShAHSW0t4MixBkABhRcSBahUyg2wtNgATSOIGVbWYAcBSLmWXgAUwVIqJ0AKYiU18gwgWENBfGsRFVo6PjKYSMVZCCzDSikcWESwAhp7FhAsgHB8YB2irKW+NQwbrWleIxcpJJELIWHCKMczwCFaccA4cpC2WiOOKMqUQkxiB3IlAFaQESklYBIxoKwwRGFKUqkBFoYAaQSiHDpYSg2ogBRJbQHlxEVcSYukxVhphWgGHWSUNpgTBwtpNOTEwVxqiDmgQANlkEIOlkopnFEXCQkMVMzDCgKINAmgsdBgziKsDNRYeAGVABnw2wQBizX2kbFYQ8V8pDRSUPgRVQBro6hHhYYQaRohgLECyvGJ0lhY7kdIQKSMpj7WFhqgkQctRBpxFlFtsbDCDbDBSBvBQmCJ1ZZDBgDAKRfM0dIioTLqaYWwUhIy89uUQWYAhJni1AEaICEEcZRGSOsMOApSrK0EVGuIuObM15YiLjmiBjIipISOgkRLyyGzFpFMCkylREBZjhwBGJFSWqoBxtIo5ChAkbQaOxY5RAoAmCYeUsBAR1CfSgsQNeC3CXI1ibCWRjuaekQoCJhkPpbAIqaRixQwiCoSEq0AcIQTYKmhxtLxmDQWEYk9pKyBzCIfa4U0UY5PuICGSjekwgCALGRMKIAdTQJsoeGGOz7iihiTEcdKCwDkyIUWIGk5dQnnSBjuh0gBLIUgLlIWWsChYyw0wmbEwUojLrnjA6Gh0hn1rATQGIkcC5BRICEu0AZwnbk+FAZKKbFjNbDGZtC1AGENMuJaYTA3mRNYg7EUWApkDOMZ0hpq5fT7BiEqOE1ThCFOYqIkUQoa46YJ4hm21ksTizERHPMMAkA5x1L8tgCwNAFGQ6O8ODYQ0ixxpADAYMGxEjhLILBOmiBgodZO3DcI0jShkkNjsRBESSqlAZYlAyQlUtLhGTQGS4m1YoIbAFiWOpxrhLzBAGkFtaIiQ1IQJbFSNE0NQiRNaJpYQrxkgKyBSjKeIaOhUkRrJ8s0Qk4SuzwzjPppgrSERlPJiZRIK6IkSxODkCO4k8SKYGcwQFr/dgX+5fPPdRGuffefzn31H3L79aVf/PrU338j3N5f/Okvn3/tq4abk9/5p+e++JrJzNEf/PiZN97Mb+7NvXv57N9/PbdTXfjFu8+//CXUz45/74cXvvg6iNXKj39+9o238k+2Zt6/fO4rb0ab+3PvXb74ymuon67+4McXXv4S7vPFd9499/rXCk+3py999OyX38ht7E6/f+Xiy19CA776s3cuvPIl1uzN/uqDZ7/0RvHpbuHpzouvvDJ899HYrXsXX34l2tgZv3bruS++Nnp/vbS598LLX5768Eqwc/DSy6+M3ro/cvfxCy+/Wni8MX7z3nOvvFa5eS+/tX/hlVdn378S7Ndf+ruXK9dvDd19dPGVL5cfPq3cfnD+1Temrnwc7tVeePnV+XcvubXWiy+/PHH15tCjzRe+8KWROw+G7z585ktvzF3+yK21Lr7y2tLPf8VqnRf/yxfmPvyNaqraDTa4q21L1j5ykmvcGNS4hHo3oU5B/6ppPyD2MKt+TAe3je3o+nXGr6bC0t4l27mJVQLbV3TnAUU13rxJO7eB7IHqdcavZBw5/d+Y+sdUJKh73bbvEljLmndo75bVMazdYL3LUlo8uKKaHzOZ4v4N27jL7GHavIM7120moLcx7DxaFsr1Nkvk6SptGHJQoBtHcMeBtXH/zqzJiPO05Dxakdzxdkto8wSrW3qQZ+vLqOOBeiW8O6Mz19sssIcrMvWcnRzZOO40MW3k2eNl2HZBfSy4t2Ay398KnYdH7ID6uyF5esypIlr3nUfLpMlwveTeX7Kx624GzuM13MOmofYvO9lDwfuk9T5KdqA80M0PYHaIzFa295HP17lpyINLrn7A+QA33oODLaQPVeNDmO4ju5nsXgvkw9R0TPUy5fcyEaPGezDewLqum5dRum31Trp/zeMPuO6Z6mUmbqU8I533QX8Dy7quX0bpNrT7fP8jN36g+AAdXmLiNtfADR5V6M6U2zJsa9bdHIN9RDcWcw+HhSXBgym6saAz4qyP090ZpwPJ7hzdnDQJxRtL0cNRYUn4oEI3FjVn3tNxsjVLGxrvTrubUzamZHPBfzCuDPIejLGnSzql7tY42VlgLUN2J70nUzpz8eZscH9KWBI+GqbryzambGuUbi3iAextkvYHVgxQcldVPyAyIekjvXstsAdZtm2qv6HZoens0PZ7NoupeKgOL9Gsj5JHeu+qB/aE2LOHl4jYk/Eeab0Psx6W97PqJao6Vj3mu1d9tZWJfV29TOW+ivdw/V3Iu0g84tVLVLWsepxuX4v004TXQO1Dwrdlso8a78GsCd0GRhvH3T0fdSF9vOZv5gx3vDvzeH8Id5j7YAHXC+wQoo1j/m6A+sh5suY9zZmMeveWyMEo6lDv0QKt5t0GJBur3k7OxoQ9Wgmf5qVygztz6GDcdh32ZJEdlpwWxFtrznbBZow+WgmelKSk4b0pdDgJ+46zPk/2y7Rh0eaqtzUkARtcMbVrVGS4c103b1FQ4637tHMT6T5s3KLdS1Jq1L+qGteYyHDvY1O744LDtH0Pta5D3ketO6x/WUuN+ldl9SNHpah329RuObDGew9Q8yMo+6Bxl/Yu2Qw4/IaoXmFiAHp3Qe0Gg1XRe4xaV6Hq2+493P7ASo2z61n1qgP62mmF7PEKani2NRzcW7Rx4O1GzoMjdsD8XQ8/Oe5UCa07zuMVWnNhq+DdW7b9wNnxnEerqMe8XQdtnHIOCG677qMVVvVgK+/fngf90Nn3nYersEOdfQY3T3sHDPU879EK23dhO/LvLYNuju177PFR2iRejeGnJ7x91/Y89/6yW8uJPmhdhvEGNLvZ3jVP3JemZ6pXmPg44ZJ0PwC9R0S2TOMyjDcQOOSH19zBPS0G8PA3TH7MuaLdD03zIRUd0PoIDdYBqGaHN9z0npYZrl6mg4+EVKh3SbcfMdUFreu49wiCw7R6wxnc0kKQxlUaX1NC4d4l1bhHVdc2P8bdB8gMDN2e8+9NKoO9B6PsybLKqLc1ireXWcOS3VFnfdbGLtqeju5NCU3D9WG6vmJSyraHyNYRpw3p4W9vGN6dju7NCk3DR3m2fhQMkLddJBtHnJolB0Pe4zk4oGxnzH2wpAX2HkdsfQ0NkLudw5tHnTog1SHv4SwcULI75t2dt4Lq9Wznqi83Mnmoq5eY3lZpDTfeg1kTyQ1R+5DIptXryfbVUK8nsmFrl7DY4Gkd1d+DaR2rbX14mamqkhvZ7ke+WheyBeofEvGEZy3cfg8mNSK25OElqqtW7/C9K166rtIurn2A5ROVdXDrfdCvMbGrq79hYlebfb57NRCPJUrFxS+8euRHb7sHjXNvvLn47qXizuGZN7+5/NNfkE5y/ouvn/nmd5SEL3zx9aM//Klb65z92tcXf/7L/Obeqbe+tfrjt3GPP/v618689U0j7bNf/Mra937kVttnvv7tpZ/9Mrd9cPrr/3jiuz8CqXrmta+de/ObSqFnv/zVY9/+gX/YOPGt7x75yS9yB41T3/reiW9+18bymde/9uxX35Ianf/aWye/9V2n0V16571nvvKmd9hc/uk7L7zymlLw1Pd++Nyrr5NWf+HtX517463Sxs7sB1eeee2r/n5j+ee/vviFL9lMHf/eD5//4utuo7v4y/fPvPFm6cHTqUtXn/vyV4P/j7b7aNL0Os88f+zj3evT+8osX4WqQgEECNBKIuXV0qgjRj0zvZuYb9Cr2cxHGIWopkKUWpqWpUjRExRJeFMACiiDcunfzHy9e7w/58xiYvZSROsT3Kt79Y+4fr3R1s/fevkbf4bj/MKPX3vpm9/SBtP1N9+/+d/+tv54f/Gj+5/7r98yT/vrb7zzyje+if1055dvvfjNvzA7g6X3Prr9F3/derw3/+lnL//pn1cO2+W/PRH+a4ZGWee/fnP77JnhudNz62QWq743W1lo7e+TOOvduKJ3hkoUH1+9Wmu37dlkurGiDGbV7tnhrdvmZKJ43mxrRe+O9CA8unattbc3d3xweuu6FEbUi3tb5+zJRJvNpudWK3tHzmDw6EtftQb9yqDX396pTAdkFvXOnaufnTndTufWNWXqq+Ope25FBHl9MOhun1PT2G53vOU5IIRz1u9tbeEsbz3b7V25rPPY2j8drywTCq12z19q4bxoPXoyurQdqXbr6Ki3samC1Do4my0uQIVYe6d50ypVRe1NkoV6QrXmweFwdU0i5coHd3vb22mz6uy141Yt07RKu5PMVSPJaD7dnWxsIA06+6dRrYJ1LJ3N0rozbizAQcZUqul5MYalhFWrdHsqIUxt8WwKzdIXjhQHspCxpJZJB3CZGHN5NqYEMVIR8QSpIiuqej4EjGLdydikBBQrdZEMEQQgmzNEJ1OKDMxR6BUcYdZUaTdmUKg17vdlxAVf1aRRKBcpcUDpiowo8VLFGedKImYLSPGh7qdFNU+ZTQOYNIUSMZwATevHeZXkkjsPjWlpd4PhtmlESZkbYR0pMScxEE5QJhaOaLRQSrHQvDJqIiMKi8QoqqXm5iSg6ULOcxlHCFSiVNi6x8Oa0OIkiyugEtOi5Kmuy71Y2CiReCX2iS63Z6ymqFKRTgk2BRcgHUKtWpaqTEZZVlMW8t7Qd0BFIoSFPSI7giqsnAHVKhKq4kFS1DVJZMmACBuZZp6MsWQwqOBsImrEnegN3mdlXbGlIB8LqGNqgXRMJKUsJJL2hG6VhaOSYZJbiiEnyQAiA8O6LI2xxJKsAlGoMgyREoqpzikh9hT4DuKM2RGLLLWIswpQ+kBAMFtQzAngCEDLF55JOPCaWJ4SOYuLaolCCUAUVaA5EUwAS+ukoyrlcLip6TOOC1bYperxDGlxDahDLAQk1QkPLFoI5kRlatKcpTWBZkkZAbnB8xCCCBSrBhlFenscXlnQ4jAJaDGnkYxBN5daQEwKOEzQth6WqjxNy3lVj4PIxahJlSgtRwyv0Lyg3C+1Bg+4TkcxaylNt++OJLFmQAgzF6l1lucI+EKtl3EuG7v96PycArNkRrEjEAC5C/IFS4LQGhaxg1XgQddKTUFoXE6rSE2AmiNPI0oQy7I6RYmDFehrbZRUsKJ5YbKE5aSUS+QqVA4iw5DGSmYJBU/gVGcaAFoCXRtJaSmXahdgLfUaljKGqYFyHVgDUCgcahGfVoUsMpsZY05xFFqKOgaZQb2arLYniIN8UUeDRM0T3pDgWQKFSDbqeJhyzvUmi8cYlyJbNuW2qwaBWNOZL1KolHO6PIgYE2qT5+2cpmV0ZR6NYpIWoCHRaRwLRa4DfBqxXJBzajZFIinlFowiWUoLUCdqZ5qlCK9pZYjKHGnNInEJSMp0rdbyZkVk5BUmFSVIdGEFLFeBT0l1lkFLd3lQg2bqlZFVOqWSZHgq4cosJjaIZWQHCTT1KQ9rUCviIqhxJ6E8BYEGrKgEsuQTYHmZMGrH4XTdkEDMAwOpERFFljeQ4WdCxVMqOdNItoyJiCpQYT7wjcIWGWWgn1eIF5oWn4LSlhSSR2cIGFivF9mYULkEOsxHQlOKyLBEtyhsydLjYiSghmSbJ0MMFJzZKjxNFZrxhoKHcWkovCrTdshlKNsi6ElIhqIlyYNQwjk2QDEWqaEXVY0cRFxBRjPPJghiVNZl0o+AhJKWUxkzwLipdfxknjLktqDsERIzVss0v8iBHtWgMWWAQVHx6VCW4zJcBWVqyCnLqlz3spQbwo6Rr3Gu4MpQBBrJAK9EeWkrMU+d0h6HPJHTeQ4zmReKqZ74xRzJIKiESVGlAWC1VIuzvDCAHaGYgoywWpJkhI6Toi63kuEwquAaASWPx0RrlAAI7gmtWoZQo4OoaOk0z+IxJTWg0DJzkVJhJaBsUja0WUdeJL20nNMs5GVTiG2EJJjPkGyxmEmsXxhNnuqa1IvymqrjOBkhYiOBUTLEsE6gBKRhpNhlpJn0LCiahian9d0jpkjefNNqd5FKp62F2pPdqFp1l5dWHn2W6lqwMlfdbReqOllcXP/krtCkzrnz9aPj2LInS0urjx5lEhWLNtobCVkebm3MHe7niprW7frxSahbo/WNS2/8IlW13o0rzv4JhsBdntM7Iy5JSatafbIf6Va62qzttUtJcjcW7YMzAWF/69xSe08kZW/rXK3XlcNwsrPeeHpgDgYnL91C08iZzYYba854AP3U21huHLXVqXv6/DVlMNVns9n2GvQzezicrS7Onx2Cadq/dlFxfckNvZ1VaTCzZrPp5godBwu7T46ev62lIR373tqC4gbqzJturxndUXN3d3rr/EyYjV53tLaiR74ynBzeehl+8y+Mt978H24RihJROEudh3seMdEoMPdOc0Yf3v7iu7/2+4PKonw6sh/uuZKDvKx6/6mnVh5de+ndr/1+r7kmn43NvVNfcWhv6jze97D5+PpLb/7O/+xpVTQO7SdHETZQ3zOfHmc5+uzWF9743f/kEUP4Re3eM1+ygJvaT4+KDD6++uLbv/Efh9UFOAyq9574siNyVPvkUUjMMhP2Z/tFBssUVu4/S7mSm8rSKo1VOS1p9dPHRQrSkjhPjuA0Pty68sbv/S+dhc2C08YnjzIgh0Kp331URjwx9PoSxFUti3jlwV6ZiBgo9U8e5xma6fVf/MF/3r14i0XcfnIsvDwTivPosIxFZBjzS7hw5Bgb9U8eCTdNsObstvE0CqnpD0gU0kBxwjMYpVpaIsqPIRwEQg1HxJ9SX7K8IYl9GnFM1RETA1+rlUMejUkom6ErzUaSLwiSRqDselj1uyQeQ1erJH3hDUkiyYR1GRqFSPHGUjRDkWIHPZhPkGvUQN4mqOcDKXKJP0AhNbwJTaY4kEwWqcXEDiSzzHU+tCOsWrB3WbxfkDxNHT7WfLmSxzoYGQHWu/PWk5tLrmpkuQHGZoKMKLP5RHOlZl0c3ABvxYqcZBofOAHU0sxgYyfN1f689exqc2oaSWKyoRPITsY0MLASrGnEvY7eA8jLMluM9RCbEavAgRUTw0OG1wYh0yOopx2RpGpMOaHdAmcpl/228IHhY9M/BlGiJEjI4rhEgY+sqIf8woiQ5p7iqFBCrMj0DDJ3BCtRR4SJEkAjPIOz2EiKTJK7RT6ZIts/w7NEC6hddFkYKRkVROoz6CdACtowyBRftoNTGHk4kq0sdHLfCajFXbMMjUg2LqFPlotPXK0KJpR5li+ZeWCyqeFT/fBc43TVDjS9mFrAlWd6jU+VbGZHiryAHp0D9yMi5b5d+oYvW8w1+USJsXayXd+73Iqgmod6MbNDapS+KXzDo5VN9OmO+NBVHeFK5cgKFDML9XJih1gNIhp2hUftJJbDUxERy11sdl654puVOJWiDoyh4UZK2BFRoYy2VnovX8owTUrVPxYh1Lxc87s4KZTZfLP//LnQdrxI9k+xR6yskP02DKEWVa3uy5fdVjMOSdQDvtAjbkYnwMOOX6uefeGqV60GzIg60EeOn6tRB4RcjZDOR5W41H25CoZmXjo5gdfwOzV8EoAqmFkxqwbY5ONKUhiBpD59bqG75KRC5RO9iPUAm2zmRHktRexy9nODHYeyzUZOkpiB5ICxnPmWp8kHl5pnKw0X63xop7Hmqw4bWCJUY0W9CD6YQ7sBrvCZlaVmSAw2copE9VU7GQi3h0LJTHwaDlCkmIMrW8NL66FZCQcoG8OZXk9GwB+QQDbG51eHz20lVPWmNB6jkBjumMYDEGJjenGtd3snZjQKJK9PIqpHLg4nNMvx+OLK4OZ2IJmBS4IOChQ7TySvCyOijbdXhje3I8NMJrgccFetub4cdHAkGUmhs5EdATMtLDBSfFQzxclN8BZTUcY10LdSrAfC4UM75mav5exebbqWlmQmGFgRtiKh82E1BjqSs+vslwiOIlYRIz0EVipMMbRCUptp9PGtxbGlx8ASAysUlRSYcKxGpU3B4Hn8LiB5AnXYs1KuhcQph3ZcmDHUgh7yI9mnlncGk0SOBZTUfinGgVwteizypYiawYhMfTUSgMp9ng5mxPJPYTzDrlKJu9ybSTmGFJwUOAix5nVwHJBQcYIzkHnYMyxUHAI8CYQSDFHgkYAabhdnIQkApfhUsK6n1dIBCKYklO1gRAJfDmWNeQabaK5WZZ4mxkYoq03+9CK8G1NSRJZwjYgaaeSImRpQ53izvnttPuMoi3U2sgOoZrFVuLYLzGX0+Hz5ViDraWyWIydQnCLTxdiKid5Zqu1eWZgZGvd1NtEDyY6LKhpaoWRa8PQyfzenIslrwLUC6mSxASZGSMyUyf4R8LHlcz04AUGu5CiVxWFKyoBb8Sn0sB1z1W3jmCshgio5LUE6Kcy4IwKmh0wNTsS0tLI8lJROls9c4Phn2C2NWOh5R0SFVqKMKr2cJzFQ/GPgM83HVngCw1hJqKDlPitmWUHDMxgzNcSm14ZxKkXUMHdPpd4sxIaxe0q6U19Wm8syqRAAbdlKAAAgAElEQVTXajj3nyp9z1Or+v6petgb1pY/+OrvfHbzFV+2rUcHdODN1Frl4Z58MvD1ir1A9AXJJ6Z20KWdSSDb1uNDaRyGSH/71//woy/9Zow046inHZz6kqMdduX20INybUnS5xTfrJn7Z9pRz1Wr0slQ3zsNtAoZuMbBWUAt6WRkPGunXPrkxa+8+bt/FAMFRdx58CwgJhr6xv5ZmYIHt155++t/4GpV1PdrD3YDYgq/rD54FkH14MJzb//GH3Zb60p35jw98rEBg7J+70mgOPvbV9/43f/Ub66gaWzvnUTYQCPffnKUF/jRlRde/4P/LZDNsiC1Tx/HUBdBYT1tlzng/3Yq51+VCAWAl773w6U7d5fuf7b21rvrb7xjn3RD1QpVi3Nw8bWf77z2C82LLvzwp0vv32083UskzbNqZOavvP3+5e/+QB67F994Z+snP68etUPVClQbZOXanbvrv3x77uHjuU8fXv/+j4zBJNTsSDEzopz/2S8XP7i7dufu4sf3tv7lzdrecaDZoWZlRD3/2s+3fvFW5fh0+2evL7330cqdj5zDk8vf+2Hr0dO5Tx/s/Og158nBVta5The3pntz9x+tvf3BuTffsc56l//pewt372VICnQnkfXlt95bfOfO+s/fWNw7XH37/a03324g+KqmLPF08f6DnZ/9svZkb/PNd5beu7Pz/oehboeqFctG5bB95Z++N/fw8fzde+d+8rPmvccr/c+eV5cvRGeVg/bG6+9ceOtdvde/8u3vLXxyT/Tyzll1eiCL4+Co25ze46jbzS5vxedWysNhd890h4Z86A9OreGBikbe9OZlZkokjPafOCfHDhsX3V1z0HfQ8cBfaYn5KpwVp93G8IkKM97bdXpnNVjCfLUVrbTgs96g6/SP7KTP2t1G/7GKXA8tNtybF8DppHdgjwaOdOiPu2Zn32JhgUZbuXsLJio4XYrTm3Yv28jca9BYyoLU3wKjq5JXSoOF2L+tDLmvVz2jxktMB5XEu47GFovWxPAmm4DnRHJDrdjpTB0txekNra8Bv1n417Frx4ru67UCEbnbysPn0Uwip40wvqG1wWI2uyak1WSW+8tsfFsKkHFqxuHzdETYWX40WPSewWQKdp82ZqeYiMy7ssUVFO/nh/15/jSVeslRf8F9XPIsCp+/xC0lezRpH1TCjsifZe3BXLwL+KA/evlGoUA0E+29xqyr5t3y5KSe97E+Go2fv4LKtJzw49NW/gwwN999UhueaZiz4NpOqSC+HxwPmvE+AuPy+Kw13ZNAKcHBNk/OGWcgm26xwaY6da9o1Ruli2ceGz5XuFeoD9FgO4/OwxBHqjVzWtgFRXBZnG3jMEHDK5l/XU7EjSK8iKzWdMrC7XJ6EbtqElylo4s4yj2rEao2T4kYXSij81YHMm8tH16gMdvBygsSw8MhPtvMoufkGaXTzSK8II/JaGj09mvC4+4ePjyZEweub1RdvVrmIDnkR2fNtAv9mXG2WysmLJZ0T6tkVA13wdFgTjwNkx48OWmEHZioumvUMiqVh6J92iwmwNsFx8N5eJQFVsUzqgUgwRk+etYQIY6fZvunLXAUpFhxjXoGpOIwOTqqsaMkndDj3UYyAUpHjaPnjL4jD1gUPU8PbCvzX5DoJixxT+TDy2Bq212axteVTiPDqq9XA61CZ4yPbxX+mjRVi9lV6M1v+N1r8tJ1EiOPZuEt6WQexqAcPY+mi6KArt2MJVWZKEl8TZmsQk9Ow5vgdFnxpi+gYluSqYvS6VU4aeldlKbXxOAcLHh/z+6e1XmI+rtat+PwQRYphus0+Cg+O3X6jzUUZtMD67hdw5PUNyq+XmEhcM+0zqGTTdBZtzbZtWBUeGbNV51UyON9rdOvgYNo0tH6+2YWkkC1Pb3CBZw+ou1uiw+K8Z7UPauwXhHq9sxqwijvHOr7T2soSIpdfNxpgVGJp9U8vCEPDRE2ivELYoh3osFNrVaJJ+SkGiY3lSOqemYW3qRDO8fUM+upLGsDOwtuoYmsnFSi5IbdVxeT8U2pdiFxS7dRTD+HQlNqG4l/U+qjlGi+UU+oIQ3l3L9l9GXkavnoxSJo7OTTS8iaT/ryEQ3SW+S0AnyzdG+SST3y4OlBJR1Quhec9hrTPYI8f/zida5AMUv3ntT6Z0beLc+OqtOOhtudydVtYslwwtqd1uSZJFLReVYd9R0kQHRpK1Mw2JueDVqjAy0dsaOz1viJhOOQby6F2yvloXfabgQ9VTrwu4P6YFcDw9nsxeuiaYmh13mknfUqWYf1TyuTE4WMWBZewoMLkhfT/kYc3JIjcgPG29xqTvpsupKPLyJXK4LzYHCdBEWoVwK1UgJVmqyn8WVtoDJ/hU8uKQHaQeQllahxJJ8uZdFNPCGws5D4l/FY8k3HN2uiROVsC4xvSmFmHNei6JY85pfy8U1lsdU/KGfzZf+CPOR8tp5PXpRCEOyCw8E8ehrCfnHUmYuexKUEo1uXACzjh/7BSaM4S5NnxdFwPn1W8igYv3BVEFaM8NFuIx7h5Igfd+Zhn2nuZPjSTRwH8RCetBvskMUj8exJ3esRhMHshWsgD8u99HAwn+3zcgKOTprBbilY4t+4AB09eZYddueSo7zcT46Gi+6RLLvelW9/d/mDj2qH7Z1/+eX83Qcrp59dFfZF7tpn3Y033j3/zz+gfnTtO99ffed94keeVQtUq3raW77zyda/vG6d9tZ+8ebln71OguBzPLyiWrWT/c2fv7ly98Hy3XuLd+6e/94PYZiGqhWpFnWDy9/5/vIHd+f3Drbe+WD5/Y+XDj+7BM1r5URzva2f/vzi/39r+YOPa7sHSx9+cu5nrzcOT5bu3r/+3e/LUy9WjEgxSkC2X/vF0nsfrb1/Z+H+o52f/MxqdyLVCjQ7w/K1X7659vo71fbp1i9eX3nvo/ndA8+uh7oDcrb69ntXvvN9PUzP/+DHK6+/23j8NKG6rzswypbe/3D7Bz+pHxyvfnz/8nd/4ByfJrIRqlaiGOd+8frSO3fW3vug/nj38vd+XDtsl4T8e1mE673jxkn78KXb+unAGg6Hl8/nksxLQamo7B0Z09mTL35pbn+vebDfful2CmSBYCHLS8+eykEwvLxjt7vWePLo1VflKIaAm7mvTTy5Nz548cVW+1gfTyY765FiCoAYwfZ4tPzw4ZMvfGHu9FDvDNvXn0s1DRelKlKtP6nv75+88iKeRSv3Pn3y1a9a3qTx7HC8tSYAnHv89PDGLajiy9HRA31dTeLlDz5pP3eNYFF7tOetLwf1eg4ljcc8E1vvv//sC19UWLxy59OTa1eRLbW8QaRoaQKd3fZse923axfeeGPvcy9jyiJiIMHM0K3vHvlLc7FpzT987G4sTWuNG2X3EatCia6899Foa4MaSH3Wjebqo7VNuB9wR1atojwsuENVEsVDFyIA1tfEoNCLACyp4RkSBiE642cdTlS62uQnGYIcrahFlyHGshpBwwEnMmpUUTuBjkRbuDwrAQfRui3vHyDO2PwingoogWzJVJ9NhITgvFQenCHAw51Lat+T41RbgnFHZLKcrFervVQN+PCcag5DdSrlSz4V5ULUf+JskNBQ/EyuTLPYQLE83pKlKEBCQACMmHNf91eAFCLFK7ImM6PufOF9On9d9Uu1T4ONwvAz4BlFLSxlUAKiiAzFsjQh2XJaZJLZJ946a6SDWjo90edZ7hAPys4kKzU8U9KVIiCa8mgKlyQscdbOQFMRMOW9Ia47pVWFu6HYtuay3vDUhMsKQCk4PAPNOrAc2M1oTWSWCZ8GbNNiUUj77dKpo0qN9FPoIGiQvF1YWjjTDWnQyaw6dUx0FKN5GRmgbGfYxIUBRadPDbVotvhujJYURefZ0wTOq6gmyUNEGYtapTxQGeGlk53POpMMTCsLuC9hxrNFhkayxEFajQpABYK5qtaOCk5Z0SzpUEYMTdbQymDPweWeuiR5NgSlu0Cd0xKC0tD7Q6kGIGBU0VyAU5y1YmMAC0hmS8rObBcWrGPP46kkhSBeKYGvkBjHywUZxtzjZIkyn6NxFl9s0CxBEOAiJ0mRDJE4Z4qQo3EkzWOBQEkkjSVhoYrjGF00lCDKhojMI6CiHEoyyIUvyl4mbckRV+gzF1/Qa/FwoM9RUYCQsxknC4RlAPRyaRWmVEO84JjKUZx3OVmSOAd8WMbrNUpF/YB7i0AGvtSxi1qC9GzJ6/mKMcJ1ZSgJM40tyW4TbxHq5TjCCgKgkgeJWxcaL2wuDRSop5HBr8RHB7hRKIZyouRVxo2S9CiUWVrnrAB6HsaaZXa0pJKHFbmxlxVmKSr5ot+PJfXEWK2cllhJvBo1T+W0Us6ahrY7wDlLLzTUozFIGF0mOaAAwkzVlANXQECWSdllMOfJ+boSBoQVGk/jCU6hXGzYypELGZfmYQYlAGGJKZmkwmN4neJOnHOi1VmqKIIBjaexR8EklXa0dCrEtJDWqVRmKVYIBfws4QUkazKfCDbJ4+vzjfEYTq20GdMcIJfkLW5kw2UQHmjzSa5bXexuCD2J0cTM6rEgZYaVSjFliYo8I19IU6RV2mC2xp1sMJ+OT5QGEzU6w0W94CVThko5H8VUhrwEEMlM0IEsqhHlReHZRZ1R4K1ngxO15dG6dQSDpUKGpdST0xYvKGfttKL4SauS7eViUSMo5adnXLfpfJ2fJFgFoKmUpyVSRaxjpXeWGRVateBBhFoKqcLypOQqTWuSfHwEMOELi+QkAQ0paxjaoyE3Kayh8uBM6Gq2si6feBrN5ToMDwWvymHDNh8/YLJKVhdAJ+UEF6uWdOQzCSdrleppjotcrUwSvwJzOt2ki8PDOs6eacvWGJRMcpeJ1eeo4LwappAKhBSRlYkpeTheTo0JYJmUN7PFtEuytFNZgp4ieShdyXggqx6OFzNSpjmghHDiaSgRSnUUxw4NpHQ5UdJoDfoPcVOPZJCQopVhn+IEp/NFzCh9MoMXzLrfH44stKyAxAO9EVieB0iFvUxagrFkkqdecd4W0xkZ98pGC8sWnmSoiQXC5WnuVMI+tfR+O6ktSJqKzhK8qADEy06OW1IuCtzv41olr9TBkwBtajIuysNMLGgAxOJswBsNqFviJDEWRWDY5InPNzSZ5q37T0pVGm9vtu4/YZbW29o6H58MSKVvzl16/Rd+a85dX2o+3GWKfHz1mpTEcpmWRFr/8OPJynJ/c+vCm2/EFadcrapJBBE8cDY2HtzPZMXdWl17/8Pp3MJodZUjRItcK2PruCun8dlz1xqPdktCppur59LuBGquWV344NNCVUdXdmq7xyRKDl763Mb9j3GQHt1+fv7pMykMpxc3UyCVkmzkIZ6Grb3d/VdfmT8+kGbhaHs91k0GiCJSqzOsHR4ev/IinoZLjx+dvnBLKeOpXKWI1Y/b2tTrXTuvDN25g4PjF28kWIOcQwQaZ211OBtcPm8OhvpwMrh6vuAkV5RF/3QKK2uffLT/0svOZGCf9p69+mXpT/7038siLHJUJDxBKi6pliNPdVbuP0BR1n7ltpkIHpaeZDtMKjMQUkNv9+vto0df/dWkJDgWgWxp5ZDNYp9ay73TxYcPjn/ti6iMSMxiqKaJUMPSM2qVvXZjf//O7/9Paj7JwyKBWiIkOQGuWm0eHjSOj4+++BLNpiIFMVYJ5CyDIdIkEDIvS4AsICpjERKdpiz++UH0tR0kChbzQsgZIbWIRUQXCd+88173+auhZrNERFwCFBVhmaegVCXn7iA+v5KblnCzlJOE0TzIE6BgUG7/4LX2rVtB06n5eSqkCKqllyVCypAZ/PRZ/IUvIkXiblowKSOyFLGMkUgyU04wpFzOvILjEiUr1WzaEgQoKgkLwQAkiA6BThC2jMwrDcUiRMepKAASqkwyITDIsSW7E4eq0NCyACmMUVVFcZ4hhAvVyaVNVDJq0dkEkpIzyYo5BxCrOsqUnTJjQjNyxglQBSmngDBGCmoqQC0B8SQAiFIQvaQgy+qz6IJfTxQoFQjmcpkWdSoUnzIzk6Wpk2zORF5wUvVJKiHKeJkqcZxe92Y4IIwhxogVIBehXMBarlISYuSqwWpEUpIhg+FuIlc50X3iIigNwhu57VGGoVByqcy4hYDNUD9BesS4igmQRCKYRDBRnUjUdJkXkh4BVUJKBbljXqUcSYaREEdWCZBgnCsmFozqEaMyVvUqGnWuSCqWDOznEhYIIRwzoRJWWpXJmSNXFFOOxqWsAwplmPKCYChpOAKOKuUFNTKgECQJFAVAA5yolGRcl1EZkyghdSExJI2eDl8spSJTEwkaBLFEiigycwFiRULDGsZFZDKKqcCiUAMJ6BBiXxJn4vYwEWklSMM6LPMAcyIowoLpWTldxCmebpdsIiBWGZ1wohSABpI4iJ/HrEznY+IaEhIZmXFUxVjO8EwwmJdYlWmOBWdZqTg0kPLDVLlWkTM/ABImGkcc5khR5cKFvFuy8yTiaiYUCasMQq+kqoJ5xIsel7dViEVYFBUEIqGngBiYykU03FX0TQ1gXmRMkWhegCwvKhQkUI+fJsZVJSPEL6FKCOIgz4sc6RKCCBkpjhjkKWimKEFyloZrhVRkGsuAifEsIYThagSiUuP4aK6ohb6GE28O0izHORWORGaBjO/3tpJWqpAxw/OxyICSSMAQJC9QJo2dWPd9AhlwChyHFCqokpOSy3EareYQhZRhTDEJYgoYqpbMDyUtLyLMYS5ZKeQClIpCgjOMOefbZsgBJFBVcSZYmZWCmPFUQL+obsoepDknABthzgkEikGSYw68srxVJaOg5FzHmEOac5IbMDsqWQHTS1Y8LjHQdcwDrJaI6TKCoyTNJHULZkjjXKiqxACLC4aRHCNbwGZOp5gjAtRUThJWcccmN3qxUhFE94mPCGGilVGPcIC6WtRAhaRCUeF4EAFTIDshrijx1L+emyGBQhZKKiUM4AzqgPQTYqlHdrjq6iCGogHJhIiiRI1CigC3w9FVZPY8YkFihnLAmZ+huRRGBU4SpspQRMiYCihhxdKjWXFR10mm4ZQrGEOZ4phxCebc1oOORQxqKZEPa4ATWYZJnkFV4bqaQKpoBdf1mMsSQ4VsxWUBEFUNlEpGiTCS9ZiBnBY6ZiNAMSSFYWXiPFGppOMUaEJAQA3IAAeYY0UWJoScSXmEW0QQn5SAX+4nRuFMAKElUH1aAogpL1NLIX1D8kGwPuFZJcNqiqYQEY60XHZPZldELmVqJHlyhrQSd3O5kRMpkaZW6JShmSy5ElQFIKWcRWmdCL0k/SlacidqtjrhKeLCyKVAJhIVOKFhwmkisIokHfljVlUgQpqWk6asUZaLNFcdKlJiJCVWiSY7cDSsKTrBFGaZLCHMAUpLbtEitxrhqSVVVSKCWamqmHAKMl7KFGGCpqxqq0VErFJQCcs6igJhYkSwYubQ4baNKS+FWqI8pmZeIplLmlzwmJWUxMTgGShKFEMteq+TbutB1SojnmcoUOx6mOdCipC2+vEdZijt6zeyFBRh6RNT+DlTYUgNfK/HMQ5fNnO/SCtmiPU8h3mJx5WFW9//XmLopy/f0h+dlRxHWKtksOAopkb8bo81F8PnLJZDhmCgOk4MYMoTomWcSlkWYLOWIyksXKPeePis0W4/+fqXNVGWGYyhmpcYxtzXq40nu9pkdvTFl9Ryyt00RorEsiJiATVqh0dqf//08y9kGZCDPJBtkPvlLIkkk0zj9Q/ee/Lrv54KifpZSHSJEdVLQ9nUjvs7n70z/PLNqJDKBMRE1zjlIcsFJRD9OwyNAgA5f+lbf66GAdZI86OHleNjhSUrH348/+Czf95e+dzf/q3Z7bZfePGlb/25PRlRGZtPDxefPOKGuXznztY7b/9g5f96+e/+xjw8Orn9wu2/+9vG0aEmMfvguP7gWWEYS/fuXXrjje+tzb3wV39ZPWr3Ll+5+Z1vNx8/Rqrcah/MvXe3VJTW7rMrP/rhbHvthb/77/Xd/XSxtvjGB0ufPWS6hmT48p9980P+v+I0fe5v/zG0zMXT0+2f/gRChFRw9dvfrfTP+lcvf+FPv/Hxf/4jAMBzf/OPH4k/Usbezs9/LodBvtq69o//WD876bx480vf+JMnf/ibmaZd+ocf3OO/T+N856ev2WE03Zi/9U//1GofHX/1i6/+8f/92X/8D1G1ev1v/v5x8nWAlPM//GFlNOxdv3Drn749ePbo6GtfePWP/3j3N7/q4rnRZ7phRnTVc581TOYuGuzTj5qSWW7J4+5HtmyXdW8WnywrSo7Ohe6zSq2cVVV2eqdODG7LXudDw7R4IxoPDiuICPNiPHpsq7FXq8HBR1phkLl6PPxImEphp/6oXUEA6no2fkIVP2haiXfH8mWlNl+695AhcskfZZ1GzCWj5VvH2zxyzIXH1sEiigwlPkqBzN1loXaQb9ttVeKP9M5Gmq2pCwfm6RxyTYMPJZck6TqgbRBZ+nGN6G+rB7UiXVebe9W2wb1mnblKKmfTdbkMRWHT3jmqv6HvN9J8UygjfWxwb7mVTFREouG6xh4AZoLuRQ0cGt1qUmxBaRhn7uCRUp3GznrZv2MvXAp1Kew9qMgsB3nhHxiNYErVyfjZYmPo2Tvs8OPachSZqt+7V9HShMDE27cbgUeawfRRa64zMp8jRx/W5rYj0/LPHlQWFkNyKoKjJpn6cCWZPa5qZyP1Bm5/UK1uZaQWj++ZK6uJ0k2Hu44xjMG5bPDAqpk++e26sb+E9cAMJum0BhAHC1PQXbODma9+qhzeKEhJlDto/xKisRZPC7dOAWK4L/XWDd8P9LaxfzmWqFXbN49WMAutYJhFjmBaafSk3rIeJPTSJ/JnNxOtYtaPzLMWYFwOOmDaykWdaydyd00NELF+YXy2mshVqn8CT5u8VGQ06bZR8hRUjUl0H00mVn05yx7mXpcs4xn24slJo46SPKPBXbpWS/L72XDoWGaUHAuvryyzMWDFeK++VAasJP0H2jl1Vu4Vs0G9Yri8W/pd1c5GgPmzozkrm5VI6t0zL2ozdpDP+vW6PEKzzG+bZj4Dcu7et51kXKpa967Z0IKmsFBv2Q4PoJ5n3S1jvM83JuzoZZocWMoJa1/GzcTqZflkyXBPRX0multa5xHc8NHRVVA/0o0OP1lTg0TtxsVkSZkNQWsGTrfm4GFwaagcvpDXO2p8Bttr1DpQZiEfLJm+y8RMPt22zs6iKwN9/zl/PsJipLZXFbqrT71yOM/1SlY7mn4EC0rnF+PRh0zBrJp7406V59BU0+kzSkdBw479O6YL1fpK4X4K5ZzNx8O8X/NzyzZLd48qk6RenblvST6xmvNxdJ/lCazlE79LJj61aOQ+1Yqx2FwO/HeLCapsGm70BBQJqeZTfyRPhnST+OMnRjCkV2qjydtwxmtz86EzpvFkXckTjiR6cpEq76pnRh5ucmmiTTXurs4lnyoURP0NNX8EKBEn1zX+IzgCSbIJpLHhKsxbsTNXEFj0Nq3kU6ZL/Og5i7yPx7j0tqjo2hnKZit6GnOjgO11bdZjFkk61xVwBwSymG5qRZeWAExXq/EhcAp8tK5l+x3bc+/ixkJMenl4PGcPA7Qez55UleOx/RI8+aBmreRKGI0/MecXMmWYDPccrRvb2+ngoVMhM+fXSO9DlaxCWyTDT+B8I1Fn6eCppTtClrPhQ8nCfuOr+eSOlS/KplTO7mPZdKWBHx0tII1rRjl+JDs0qarh+C2tWDQdnHgPoaymhiZL/XPGNKfknvzkWkobxtyBedqCOaJxF80qOVvialvuLelTmdi/MD5bSsgc0T/EvRZLDT0fU98swiVZTMFoWfLq1H7N+mwnIsuSPDD6Nk8qVtjHRT11VwEcgfE69BdsdE/dX0/Ipir3rJEhkpaTDFheEZNlCD5gs0tituQY/xI/ifwT3UgnCLmT/YWlaIZM3P3Q3qFu0U9n7XqFTLCfzY4trZghI509qK74Y9xQjz60N6EnPDY+rGyFMwyU2XGlGbmsUUzum8vuGM0pgzvWEg+LWEz2jXoxlUQx2rUqYaguFsO70vzYxat09KHiSIwn2eQRroWeYvDhU91J2dL66Zf/9Btnly9JZXb7r/5qfOmcPRhe/PGPogcPxO/xW//w94Vj/+T//C9f+eY3Ty5e4Jr2wt/892CpZU0mF37047haySz71j/8PTP0d/7L//Hlb/zJYGuTGfrt73zbW1wcDC6d/+FPMlmPq7Xn/+b/gZL0/e2VL//pn8wW5iAFl7/9/cg0T73hxdd+Kkrwrvm/v/DXf40x/u75tS/92TdmlVrcqF197TXr6UHq2Bdf++naxx8Nr1945S+/pY3GyVJ97SdvNE6OJcibR/vNj+5jFW288fbKBx99Z2vl83/5LeP0LFltrb7+QXN/V+dJq3NUufP4I4OuvfvB2nsfhOfXb/y3v6i2T/KF6uInj+Y//VRmpeZNt372OjGk5Y8/XX/jrWhj8fK3v9d6+mxfyzaOvY07H8hFSrP4yj//2FteHv/bE+G/Cnt2f/f34o31tOY8+ZWvuvVWsLi4++rLTNNOrl4fXDrvN+en6xu7t2+H8y1m25/92lfd5kLUqD/+4peEIvcuXuxev+I25ryl5Wef+1zYajHL+uxXvhzVGt7C0tNXXi01bbiz3b1+ZbqwHM/NP/riF8NWqzS0e7/127mlz5ZWHr/6am4Y4+3t3tWLk8WVqNnaf+Vzg7VNoOn3vv71Qlf81sLxC8+Pl1fiWmPv8y/3NzcJBJ/+1m9Hc02maQ9+5VejuWbQmDt5/tZwaytutM6ef659+TqF4OPf+p1gcY5L6me/9jV/rhk05no3rw42NsPGXOf2zeOrNwkEd7/29WBlCcjqZ1/5irs0HzfmT288193eLpzK2a0be9duSADe+43f8lcXuao/efXVZKk5nl/tXP4iFi8AACAASURBVLs6Ob+m85isy3hVMYsY7qj5glkBnrnAs1VLlXLbjvPzNUqAtEqkFWKLVGwq5YptodBsFGDT1NVCr5TFxYoMC3kJ0mWqs4SeU8SirpHUrObJOdvGoVZhxaUKFaU+V/J13Y4n6ios16uylBpOLjY1E0eaXfILloS4Mi+y1aoMxxIZTFcAJmMFuNNtluhIEgN/OabA5+qwmOexxjGdBMs5JFOZz0bnAZJThD13OSEwIkovWkK5mlM8nK1zIftqOZnscEwTSL1wOYYkAVI/XoK5zWQ0zRYD3wZmORhcIEiKEfbC1RzQBNF+OZcGNlaxG6/EWatSTafKFQU4xCYh3VCKecOSM3UVFYu6nc3K61XDzikS5JIOalKVBGQFZ4umRTN1jRRrtpmF6JpJm8QsY37REE21ggN5mbAVwySpsVCG51pWGcNLutoAJsjYeV20dBt60jxEq5qJY2WVpJtVKw/QBUWuQ50leEdFFYSJBzV3vEEwSIqal7dipQzC5ShuSZiPhD6MFgUnAdG82RpAIC3sibfI9HScNP1kngI4g+pktsgkMcaqP9mCAORCH4QLhZpOstqMN3msMKSMveWc8DFQw+kmkERYWDNvQRjZpKwMokW5JAmRhtFSnJMEy364WmoiVisl3zSRQxw5FhdMTc9NEfJbNaIyTSnEeV2WWdUIwZoGHGrLMd2ivKFURMhu1lSD63JGLuhE5Y6RiC2T1WRHTuRztGzpFRaUNyqmklIdwwsGNUBVC9m6xupKRU7ojlrMm2buwxu2bANNKtl5E1uopobluUreEEY6Ga2lrMq0PHDXkqyKFN7PG77fRDKYFc14vED0zPXXE1bJ5NJ3l1NYYRx6Rc2L54QEpkU9HK0oRjqdrcbADuXSD9bTtE4wH5QVN24VGHisHk6XiJq4wYIbNoEddYLFOGsRBMal7QfNTC3HZSMcr0pq4iXNod8ya3BmOXmybds0MsyivFIhmOu1HGxqJgvVZcDWHUnKTSvj5zSTxLqRi4sWoUJpCL6hWqWvL7By3cE6cPSEXbAMmmhGwa45MsjUpkDriiYic4HxFQMZqKInaFtBJjaMUly2NZzrDSE2dRUmlWZebDnCxBU9YxdsRBKE/GAlAlKK6CBe5FlFyGiaLQR+BVhZv3eRQiXB0A3WMiFnmPRYK/ErREJuupyENaGn08G5ApoFFf5sreRaIaFeNF9EVaDxSbSSTerESiezcynUUozCYDkHRg7wKJ3L8mqh8km0ms3mZDsajrcyrAYEBeFSKmxoyoXVzKPtps5SfF6VW9AWGdvR2IJpCU+bE2LdMEiiLaF8u2rkEd6W5BbSWUzOa2JONUistEC5pjvQlxdgtlPT0lDdRHxRrSZT+ZzEli2ZJFqdgzXZBoE8D9mmKeextEH4olrPp9IGLFYcSWZmjcFzmskDaVku1mQtmWaVCW/xWGFYHnvLBQETTP3pFqAoYtrUXWR6OeN2P1yUCimjZBCtxBlNKfHcTUZhXJpevJhjEQhzEC1JhZ7LZBIvhYnOJOyOdyCFIde9cJlj4EOtX8wxz0Q6HQerWWpyKmbjLSbRnGu+t4Io8ZHc85chr8uVwi9vVk01IwpEF3RsopoS8DW1qKsODOh5PV807f/vgxyow5RdtLCDq3KI1xQ+rzo40teFtzFfzTxw3dIcbtKSnzdRldg0JCsSnpdsGEk7arpccVIXXdFlW6giQjsaqku2FMFlCc/jCvfxtpKtVc1oqlySi7oVVpr9izsnN67HTm18YefZiy8jCJ+9/NLplauyYE8+/8r03Lpfaw12tvdv3uSaNrp4/unLn4cAHrz4YufKFczZ3sufDzaWhs3Vyfb23gsvMEXpXLp89OJtBNDe5185uXRJwnj/5ZeHO5tetTXb3Nh/8YXEsPqXLx6/cBthtPvyK8ML24WstV94cXBx22suuhvrey+9BGQy3Nx++tJLTJb6V64MLp2fzi9F8/N7r7w0WVxO6/WHv/orpWWMN84dvfhCZpi9S5e6N66MFlbS+bn9Vz8/WlovHevhr/4K0NXeuUuHt28l1drw3M7ptUuj5XVWq+1+5Qv9lS2maQ9/6zcK25qsrB0+fzNsNEfndjrPXRusbTLbOn7l9tH2NagqD772taJWma1v7L/4Ev3oQ+V/vEUIAeYMw5JJSIt9CgugYSOPkCRACW1/3Hnu0unNq04wljHLdWrGriRSoUt64iHCkYqNxMeEF4ZkZAEFRWlIZuJTkHOV6HmICRP/L23vtWTndaBZbr/3b45Pb5AJIBOGAAGCMPQSKVvdFVEdMTHR3THX/RhzMdFmuqKip7tKJVGW8t6LEilRFCVa0EIk4ZFIf9KdPN78Zvu5mBfojph6iO9mfRFrhSTKhwwZW+TFtEugciGJsl7/6Nze0gkGPafOBTiUQwq0LfJIjjCyJiZx1mde25gJlXgAbIFGOkEemKII5TBECXliKSpT2E9MSVCbImksB8xkIUK6FMRqJLCxBcZcDrVyBcaA8oiZiFGVBBnREQ2BosqomHGosUp0gVOvIq7hpfkgRAWTmpgKOUCUaoG4QFinIKIYmyAfQZJa4HhmDM4phjzP88B6YNDIIHqIYQ4zHmLoAKEaaJjFRLAky6kFBLF8lCFPbU4kxjAHmHIDMMsNsGEmM6Qhp1gmhEhqc59CgrQnxCVpEO95B0B3FhNjrUNaEpxznHOJJHIICS9H1GnIQKSZ8BKjLM4TZTGhnlsyy+4Xon5fxkNPEJZCcwotJXkkXWBSh0FoMYUAYRvlKbCUYhxrLqClOA00Yi7xCIRWUwgANVGWeB0gkFBrJtjuicp2po5yxwBJhVHUOUSsUCOImYeGa0VAxnACPOZay8BRM0SGKKy8pNj3gtIWzBy0VvDcOciVzZnh0AbGaCq58U4hwhNlKc8TTzVHkEmbcYudAUobogXUsUkNSa1DxGgLZQy87g1R4dDj0CdjhOTUeCExIimDmBsFuHTeEwMQtBTkkQQMGAugswaBPrE0ToYL8QF0elMvQoSIz0MNKXAAgdgQigwGlqQphjGkKDKEQUWBinKHgfcECk0YUgCBKB8CGEEEA80oNhTmhTxZYJuSMGWPI0qQNnGaQhBSBAKjAVYEZlx7gDVmnufWOu+4QnkKoaEsc/koLGlQDQTMADCAay6HygHAFbM0AhDR1EpGdEqRJwYgr03ged43kBqcEsOEhZimQmPuNac50g5ZY4QP5EiVhakyrhOtIWGp1UzIxGNHnUPOSqaFSZlHGJoIYK5zSPqB8zCXgALqdaA789H+ABaMnSdYUgO5khAMIgpCOcxIAXkFNcDIIp/H0hBEIgeJsxgNqEEsTTCOCTLIWoIgtkmgBUU5h7io86XSx11WOEimMAopwpEFFFtskyjnlEiGYKRGFOYwIkgmBEuCMpQCDLyn0GUJOomAR1HSkwRo5pCWBOWMZEIiBR3CnOgcEUWBY6lR0HlmoMwo1ghlMAMEpohwoYwOnMl1kDkMHKKKawsmAjjhoUmwyxymIteGWuA9T3qSEIdSa4vCJh7BwBsGLCQuzBOvOAIpsyYiB6eqG5k6yixDJOHWUGswEUKPEEIAO64yhzDGWeSh0BkmMDIaOe9pIHTqLEKYCp15ijF2TENucwSHEZBlv1sorzZBtWMnIVRc55GHmMLYAgY0BgNqoMfKY8mRjk2qqLQOUGMczGPkdSehxYZnwg/HCZHUWp8TQFKPIDMaMOm8ExnMiaEMMZUiIoVTOCWAZgYhmGeC5wA7lklJrcUOG0aIDKE2A8DFLoY1LAWmikFIEpUjCzDWilOqgNNQMYI0Au7/WxDCINCcIktRHst8nmx4hpQ7jjHH2sRpAn1IMAi1clgxmAXaUa8gcqHOGbFewzhJnSsgoJk1xKUU+YI0FFiDQaAyCjEiNJBJTFszhcGKXMgdpdjF1jKnHdaxzDFgntIwT4DBmGXcUO41ZzkyHlujhUNSYpfgUtMOJ1kEOU+kZlwnjlriAbZWckuNxY56lnNvCiYBLCOGEqgM1Nw6LI0MHPPWOGZxShURGBGWOcCoTmBx38oighUgNEISOgBZHinncop5roEIs8SGEEU0liMgEMImzvs+xIz4ouzhx44FNIiHXUAdDHAxHwABMbaFtOdDSrCLk66LOWYgyocwQA6DOOtDgSi2xbRnA0yQLeUDHTMMVZz2oYDAodim7OwU9j7Oe45CjF2c9X059NAWkh5AxjEQ5kMMDQhwSQ5xRL2EUT4gyNgCi/PBcHHu9tE5biWxueMglAPIIIhYnPQph5rDUA4oyG1ICjpB2FkBIznESNsCi9WIUyAjGiV9aHMb0zDrE5u5iJSzfmdpYf/UiYIecS91QYQ6V46YiAZqRGxmqafAIO/+RQhW59/8b5/94pcX3n63vbh47OXXl157szszd+KlV06/8NLGI4/uHDmRiALK1DNf+drR197oLSzMXv3r6d//sbFwfOnPr1341fO3P/Xpx7/5vZMv/vHjz/3to9/7wdLLfx7Mzc299+Gp373UOHZi8c23r/z457c/9enL3/vJqRdeuv3Upx7+1fMnf//H/ROnDxaPdYqTWJpHvvO9h3/401uf/dyFH/7szG9e3Lx8efnlN878+rfNI0eLrdYn/9sXuvNHCvX9x7/yjfrZh+f/+vHF7/+wOzZdKeT/iuNU9/J7nc/8w//oTs1GjdbjX3muc+xY7d7mxe/+MC9VRZ5f+eevOUhkGH32//5vaa0qOoMrX/tOb3q6tLV/6bs/UCKC3j35j1+GyoxqY5//j38/CgvB0tjfyHYPQf7+yqVvfs94rKLwk//Pl7hUw/HaM//lfxhGN6bO7bwXmj6MebJ6rWYPdHwMrb0osh6PFqULHa6E6F5va2VKdUBYMBvvleGuZkto9yU+aLN40u68FWQjKlC+c7OgWigs6Y13i3rH8jN89wXcbYfBHG6/AdIuCancuVtKG4Tynjs9JpJ+Tsq9P8FGu8jmWe8tN2rRMhnurBQHO9wtibE7C2B/eTA3LN6uus5pjrd1bwHundSic4XtPprU1yFTqw+D5tnhVC9enwSHJxE5FM2C3j+vg9T356KVI9lUJ1id8o0H08levF62jbOedlmv4HbOe9ZD/Um6ejKfaIv1Sd8478qNC3btCY320l7ae8BtnQOsB4ZjZO0srOzi+pRtXEThbrcH629HxBjE4fYrIQ4hyFz97QjGhOVb+ZVT0eom7dOb12ZxnsMi3f5TADhCym69E7PAuo7f/KjMjOYoW3lnnCQ5rpL1PwSeEWjd9juFEknNCKx9XMVSs8Dff6OM+zma06hCjOD5XbV3o0aQBYnZ/KhMckNjv/pqyY8MP1EOPlwGqkJBx++cRf0i5D2wdYk1onT84N/mK9PeXXeT+N55kNUA6ru9M6xbloWcrJ0N9qNkvhN/fE6PjqTjw/jmPBhNYNi2hw/g9kRWTMX6Gb5XctN1cuMhmxxPq/1wZcENZgvk3pOgvWCH98WEu3dJ7I2NjjTjD4+b4QlY3Edby64z78N2cw11P8DiGB5e93t3ingR5zfd3t04rFg9GTUqc1jZ5APV+ICx42x00+3cKhZmTLoC63eKMUt1F25+XA4CnbRp410u5kF+127dLBVnzOg+qN8uFWmGe/mdj6YCobIB232LF6qytzDdKUwwI/MP9fbNUoRyl7rN98shl2nC6m8IMMNKSYS2LmCdYp/b9afYMFNFw69f1oDOBLc+p3oKuVZzytUfAdJCkqK1J8VIu7ADb3/CAgKARKsXEdQo1X7nMs6NYxlYeYIOfF5Lg48uWhB4mOK1C8g7azyoXyApdJWD/8M0yrp/zR4t3riQo4rDit87SbQDTvn6JZyK7rzuPC87B2FwlHbfcIMmi0W2e780qhM0TQcTEyNRjpJe6yV4uFdgx3n/LdPf52U2OFgtdLYCNM0a16hesfwB2nnFN/bLfB6NPvCHu2FRpN37tL0RxZN298OgdwcHD/H2n2xjp1iZSg9nFkZhiXbT3oewuRpG47pxXQzu0vCU777ud7fL8RFYbER25xIgA5iW6coZWeuy3arfe8iW2ufc/U9YtJ920/6S3boI0RDkBXr/PCw00EHF7V0GUQMdVvz+BeI7SBK/fhmBnjMldve8KQ/4Qeh2L6LgALcKbu8ScAPoMF67gk13kd/7rBseENHdOQ53LhLegG3hd69QN7IO4pUrgKqOT/de5RFSILdrH9dAZnjoVt8qo7ZEcwqVoIkDtSL3b1Shc0jq7Y/LMHG8YFdfL/uOIcdE/QWS2JBF4OAtCpVF3m9dL9s+ADW8/6qwh5oeY40XUdcUSQEeXkUuhWW5nVxaht2WCqZ2/0h809FjpPlH1M2LLAbt90k6oKyGw80Heb3qpuv41oNucGI03o1W5313MWSrj/m9EzZd4xW1ejnYmUiONOOPj5r+aVjZxRtHTecYJB3SnHX7x33cxDsnyc5CNncQ3Vg0vQdAaR9vzfrWMkUN2Jnze8subuHd42RnyY5tPd67cSks3JNO7TwID5cJOoT9Kby1ZAsttH+Mbi/rqYPBHVW/WS7QlA/Sm3+dFkRJzXZeF7SCwqCbnZ4n+618M9y4Xg6hREqvv1cNkFSWbr4q2BiSB27rw/J0oTds0Y2Pq9wriODmmwUOlYZk5y8BrULZcNsfleJYqrbf+rAinIyLvXRpBvT7Sbty8Ca31cB37M61qEiGWcY3rxWx9eXi4G/+z/8c9Ibd6dnHvvQNnqaSBBe/9+PiXnP33Kl/7w4n8vZHqvx3/+m/slavOX/sqS99Jep0s7h84fs/Lm/vbZ868zd//w/jd+9vPvnYM//pv8f7h40jxx//xjfDZtfG0YUf/rR2f3P98uOf/4//ZebW3Vuf+ezn/6+/r2xsd6+c+YTpjCnZa7Qf+s4vJm7erV+6+On/+t+nP7x+67Of++x//ofq2tbembPnfvvb5Zdfqz9w/uyvnj//8998+Hf/5skvfm35T69uX3p4a+H0KCyF7c6FX/36xAt/Ojx58tTv/3j+l8/f+NznH/vm95d//3L98uXlF1859Yc/JZOTRz7668mfv3hw8tTyK68/9MOf3v385y9+/yenXnhp78GzR19758zzL4zGJibv3L3wg1+0Fo+unX24X6hRlV9+7rtnfvvi4OSx6rV75372y+HYeG1z+8pz36+fvzja3QnW1/6XCNb/bItQT44Np8Z3rlwcjY33j8ztPXxOx+HembO940dkEFGrmdX5WHU0OVZ/5FIWF7Kx6sYjV3Sx0Dy53Dp9fFiutI8d3bn0sOc0r5Tqjz6c1GrthcX6lUs6Dg+XjjbPnNBhOJycXH/skVGtnI9VNx69QqADAEDonWDNk8udU8dH4xO9+bmDhx/sTc9k1dLGk4+bWHSnZ/YuPDicnhxOTtUvPtydnTJxuPqJT+SFeMjw3XAqD+P+9MzeQ2f7k5P9UvXwwtnm0aMmDu4/80xaifNSZeOJR3WlmEdx69yp9uJCb3L68Nzpw+PHTRTcf+pJOVlNMN988olkerw/OX1w7kxzYryP0R4r7U3O21CsP/lEPlXLCqWtRy6riVJzemH/zKnB4kxAcjaPySQOkGTHBa4AJlw45fAYgUnOc2knxnGExIxn05SolC1SMkUDYYMJQ2coCUFUlmgxIkCJWUyniEB5uIDJGBZMRVPOz4oAS1FQ+HhIqA2nvZsr8eah08BXqiww4Zghi0FA87Cs6RGKGeAzyM+ExHdB3MmmNGIppp18Ls8CQFknmbUW54eedMJKGlAfdrM5A3DiXC9d1ISlNkizmRySFIimGZeGGEO72YL3NGGonc1nlI4MT/V04nCueN9O5kYoxNtmMm9jlIPR7eIRz5wTIz2ZQJq6aIAqvayIAevpqVSWRdEP+AnOCk4EJjiCfQXzQIk5bCMuet0sqEQRQCFiy5wXPMFazEM0ToNAi1nopsMIpfhEQMqI24ydEKgERGiDGUAmiBCmOC7lkSqTI35CkAriKKfHKSpQ1OtDQOBYOQx1NGX9XByCjC9zWgYcyfAYQWXoSQqjdjrtIJGq3Dc1jf0wH+u7qk6B2aO8EYx7IVHYkdPaeKmL/XzaC9PWta4Z8w6PfLGXTSoAUy96+ZxxWIJSN5+yzPZ0tUvKyYBiKzpy3hDU92E/GbcWZtssqhcmiU9sqWnHrMcZiDt6OldMI9HVM5JjyaqWzlEawaiQ+6UCj0yBpWRZCJATpyF0lNpCRbJZLIiMg5wfpaDKIpLiUxHnmkVOHGMcq0KQ0wXmCySMpFgkoEQiluFTUREPQYDFIqYFIMqOzULIkVBJqFNTDAo4QadCHjsRObpIcQyCgsYLApY8t+3hnAJFifwon019lDue2lovifQQmD1eaZQq1Pfz2RTGyugkn05YPJLC2ErXVAwgiSt3RpMEyGE2k6GyRK6fTw59xQA8cNWeqlgNclDp5pOe2n42PUyrAqTN26Q4LFQ9GZjqUI1JCnuuMhhNQOJGerKbVWgJDaJJ5+ZExGVYUvhogLgPxh2aZsJl3OTMGyR8OKbJYlCww6Cg2SJBAvIphOdZCFMxZck041CGReWXC4zJqKDJUc6JYpOITkEBZLGckllOAh+XFTkqAPKBSjD2AuVsAvFZzJiJpz2eYTBEUVH6pUIA+palajpFPHOiZ8YzE2jEO2YyaxOk/PBmccFT73kipxNAckkTPNaTReDZQE8msgio7wwWDBRKgVTNpDDMbDCytYEuOEx7ZioZlhnKWnJR4iDzZGQmhlbofUJ2WGkYBZj0zFg/GWPCdpIjGQglRH05MYIFFwhTGsv0fIlhxY8SWsMM5vQYRWWK+gPoMBirBJEJxw08UmBqxJc4raIA5eFRQqpICC2mIZjiAUyDCecXIoGkWEB+Kiy5vlgkeIJgqIJxxxZFRLJw0poxAQdDSyNXKcd+xOcBmWKMWTHhwZEgQKmYAm4uYLZvyl1STjLhfdTNZi3BAyB6wwntQL7Dgq14CvnMFQ7tmNFIWd61s7niGrOunM0IGuliYsczB3MVdtykMlShoKWmpBYW8146bx1IZZj5iQTg1JX6pNLviLBH/EY4qWKMaTeZM4SkJh6p8QxwaeO+rY18kUQkJacLMU99TNkxxiMnSpbOYEA87vR8UHSThQIe4VMRiTxHih4XpABEbMJZgGosCmRhWqUz40HWo6dDVvKcSXqU0TIKIy1mIKpSBrJoEbqZQgSG7GTgqYOdvmUxLUdxIcezlIzhiCTBAjKTYehTdoKiIhnVxg/Pnm4uLyXlSufU0v6DZ40Qm5cfbi0eNzq5Q0uDQm1YGz84e3r/9CnJeevE8Z1zDxoh6pcvtpaXPCdbly/J+fFRUGwvH9t5+IIqRo2Ty4enT2ghtq9cOTy+aL3funBhsLw4Klc7J47vnzo5xGgvKjXKU4bxrcuXBkfn82Jx58JD3eWFQaXWOrG0e/4cYOhwaXnn/IO6EDYeONVdXhzVxnuLR/YePqcZ41oiDD3HzeNLB+fPOkq6R48ePHi6Pzbem5s5uHiuNz6ZTozVH7mIKGjOHT24cDapVQ9PnmyeODacnhlOTe5eOjesjJtCuP7Eo7JSbC8ea549KaMQAh/J4ahaS6Ynm+dP7i+ckOV489FHdSVuzc9vPfIIvXaN//9/EQIAgMcI6kLMrPEQKs6IVpZRRIwJggsvvRC12+/97//WAa+iCDm7cenS+iOPECUBBJoQrDUk1IQBzdK1y5dXnnh8or0LINCcQSUdwV4E0aB/+6lP2Gc+zZOEQJjHkZBy8u6Nwsbuvaef1oFwjCJnIQQqConVwDkZRURJCIELBIIAeK/DADhDIMgLBWoUyWX3vdvwwQd8QSjGoLVktoD+7hKCAvVMHscwzwh1MhDE2vbU3Kv/4T9U09bYxobmDAJPjZGFArGGEkz+/dPEIJR4xxhkmHTy0Qer9oFjtEhkHGMIoDU6EN5q6LmmxFNMnZUBJwQQYxXn2HvsnSMUcUCsafXmgO8HwhnOEAUo6ZQuUt3rYl0aEgaRR04DwhzmLE+C0wgYBSQxgmPksNWGC48QAZrODAjGPssdLyDkEMGD5jRCvgJkxpgDCBpnKGXAQZs5xjSmwDnPA28hdMYibARD2mEEDWfI263k9G5Cy+yeJ1gLBlxW4/fnF9VtV3OgqAUDXkMEXSCATaYq6xMV+YFegKigIkFsAgC0gkALpkv1SjFfR2WJJ10giGv19dF3Bg85fsh9KhlBzjtCjRACeOecEwx6T4GXkRDeQwCk4ARY7KwTnCJtULldr7I5SN2mEkw4h1AanfMYQJ0GirEA5tjaLA4CCECexI8w3e95U7aCMGKhdZpTYwFMO+EToR2NvGQuEgwZDHC3uxiUbFi2SlBELDBOhQIAhKw1AUfAUeABQdZhrK0mxBNEbc9ygrAjENwefQJCh4NdR7CFhJp0ZnzPQ7/qFjQvIwexHQLGvUcUpIvj66HN100tpzXrPXRSc4o8Rkofn7pDvfurXfIkAgASj++nFxQKvRsBihwKsB1Yyhym0DiAsRUEmZEm2AtGrM4gloIT7zCCaRQw6/FB4g81OhkZwbzwkZRyqehPMzZop16oKCDWYwx9wLHRfl6o4yGTQ9h3NhRYZxYyHQfUGlCjYLkGsxE41JYzwCDZ6Nsdi05DDEVWCGLgofeSceY0RMCFAkKInFNRgP0AG6M4c1ARCBylkOTKTXw0OsejBkFeRwJBw11/aWGz5WjiipIJRA322jKMEYph/8TRjT0SDk1kA4aptdZqHgKchqAxN7Oee7yj53TAEXbA+A+Gn4Y8pYU24BwQBx00nCEEqXU2oA57Yo1l3HmEAdCYEO6wdZ5yiB1ECHzYJc7BMxRQ5iDERrsHShhaNOh5gjWkxEHHKEKYp0nvQg0CQDPpKckDElptKNGOQpODc0UDcZD1FSEmoEQrtDNyXUWWmeMUQIK9spTmDleMykEHAAAAIABJREFUBADZgFOtIIA2oMh7B6EVAQAjhKELBHXtoV24OjzvRZPDoWQUOlCNDyaita7Fh2noBcXeIQBUHFCTVOH+9OJWHYZ9PeMYRQgaiBRn2PpysHtisbVCa1qVDacUmJ6d3R4eo5V9CoHhhEMPPcxD5r0k3tiAIayA94YSD6HXRocBQACb1IWCIkMh7HYXWWzjMaMZ8cSjbBBeRi4dAsl0wCF03FnDGCSYgIyeyIgyRnEjuMCeOKcEpxiEJgNLqY2QTKXljBNLKW/t1lyJUgdBQAED2OiEMccwc84K5iiE1htOMSLYGkeohQy6zGEGOaYOb+QPSl+0xRGk0IMQ6sH8+P0SNPdk1ZCKCSgxA4+phwRavzi+wsbtqq16VnBeENuWhCjBkOzMFfZobHZ8WbIFC1jo9Y46v5PFbGITImACTk3fQWIoQwACjBynEEIHsI5DbCxwTnHGvYEQWM4CbPK8mBxUa/Mp8S6Nw8g7BLLoIrHJwNmiCyhEClqnObPSId6Onua2P/SS+1AgoAGAknMKLcN98qig/WGmQxWF0CMKou5+NZwwjJmMcYyhd84EHHiNnZcxZxBhaywhhmBilBXMYUTzTIUCEkKkGr1x31Wr+NIE4MxTQjmkn33IaclNrsPAQ4C10kIASpA17/y7f4etjbtdz5hnlClpAuEgIFbhz1/ACKA88wRrQlCSZzd2LGX0xBETBZBgbLRmzBKCrUGCW+epyj3GmjHkrKPUMYqdgQiqMKRGL7//3sT9ldWnn/aBsCLnWXrnmU9JwitJC3lnwpAqCSBQUQiN3nz4YuOTM6WkU63vOEYJ8F5rGXDk3M7ZM1sXHgpH/UpzX1OiGDvxztXqxvbq557GEMhAYKuBUSqOsTMAQh8ECEIA/L9MixCgh37260e+9f3y1u7Jl1658p0flNe2Hnjx5aee/RpI1QO//cOlH/4cpebBXzz/6De+He23UholLHIKPPDb3z/1xS+jQfrwL3/ziS88yzrDoSimJPS5OfHn1x/7xrcr6ztLL7/69Je+Eh00ExYnLNYWnf7Dnx772rdq9zdm/3r9/E9+WdzcO/6XN5/5x3/Gw/zMr3/39Je+Ghy0l//458e/+lxtdau2uvmpf/rC1PXb0x/eePqLXyqtbi2++9cnn/3q9Ic3a/dWP/Hcc8ffeqe4tfPpf/7n6fevz6SHf9u7NyF7s+9+8MSzX59/51pta++pZ79y/JXXMhIMg1IiinMf3Xz6C89O3FqZvXb9iWe/duztD8qDw78dbT3Y3Qh3Dz/9hS8ceefa2N37n/j6c9PXb85ev/PEl79x/LWr8V7zk1969oGXXhGNzqf+6Z+WXr/qeq79HspvWtdRjfdY9m7iDey9bnsfeJC79AOZ33d0f9h6HyYfO7ffNYUAtQ+tge03XOcDYEZ++I5t3yB+u6kJBMnIdrL2+2RwVWpAB2/q3gfQSwuTgcHAb7U7H5Phh86NQO9jkL878h5kV3XrXWQSNPjA9T5G5CDtXIeD962SIF4dj24tO83DtSpbPc0PXbBX5itLpMPZwXj15iRMQLxaDm8tOxmc6ax/KutWuk26UxT3lnGb48Zk8fqiHoqHept/kx2IdBhvxvT+A6RJcaMU3D1Bm/SE6vxNfy1WSbRWDO6cQkMcbAfh+rLY86IVB3dP4JYgjVr88TEw4sXNUNw5TfrY7ev9t7i+I7MebP4ZJBvA75v2GyDbA3A7bV9DfnVID9PGVS5v5k4pjLxXUq2PWm+AtA7Qdn7wNjMr0h+0ZSkAzYbuo86f3WgVmAPTfgvJDY83dmS1SPYOVMcdvEnM9dxnrv+mTjecO9Ttt1Cy7sFu3niX5Tel69nWWyT5SDkvgjuzoj7PW5atL/C1SdyHYm2peGtcO1S5MxmvzvgMxXenyObRqNO/kjUv9+ssUWz1WOH2pHa4cGdW3D9GtHu0s/5Y2iq0e3xrXtyfgSnja0fLN6dMbp/s3H9GtuBQidUpvnFUNG2wPR7fnwQJ5esLxZuz2uHCnXFxfwmOULhWo+vH8QANVuHgVauHMPlYHr6O5AjLW2r/HeZ3ZbIHd24UZAMkG7DzF6cPzSAod8Jx7Ym8ne+/zUBdqrprvoXNlkpZ1C5MZlS4O3njDay7Tt+T+1e53cwyHnWCsZwE+appv+JMx2frZutmRXUdWM33r3K/lqkD33wd2M1c7oL2Ky479KJB+MrpuB6QtuMrDwTrRZDz4o2jfHeMdEnx7jxvRNE+4Cungq1C3Gt/TjbPt1ZMyuPrR9nuBOry4O4SPahNNHc/o/pnOltoCIN7p8KVitWseGMB12fKaffpwfap4b5oGbp6urBZcgkp3FssrhaNpoWbC3RnFg24WDnG6lXR9Gz1ZLRW1YD23jLdd6HN4OA93fqIgEbeu4H617zp+f37hdaHyFiYvaObV5HLQTeq9nnZpn5ww/ff82bg2x/i5E3tU9MXtV5Q1TlMPjStawQc5P3boPuON107EKV2PKEQHb4t2+8Q29X9e3BvpQQacngP9t72tu+712H7NWcVtNfyg6vU9y1pxOLOSXbAUKtauH4MjMJwuxDePgkHVGwH4doJvgtEKxB3T+G9YFH1/nVvbSzvRVtBcOcU7rNgi/OVs/EumkwOP58dHB0dwFa1eP0o6keiHvK7p3GLzDfWP2dHc+0t1I3C2yfCXY7bQfnmPOlHfDcU907zNuV1zO6f4/UA9UR0+yTbL+V9334LyHULtkaNd4i8rU3PHrxF7Ue5V75/VSf3ne+YzlUwWEFgY08WY9xq6q5uXyWj96UypP+aGdzCYJQ6r22W+e3B4Qc0u2FUig7fwqN3c2MdSUeOIrObtd9FyW3HGsPuDWBuZFbCwzdR/r7SGvZfk+0bxLRt733Qv+FB4vn6QvnmjNWgcHciuLsEchqsjbH1JdGyol6L70/DEWFbR4o35pQkj3VWPyObdDiK1qt0bZm0MNmdCu8cQQN8MWt+rr8OlS7erYh7J9EQio0KXT3JG+pUcvjJ/mZRDtnmbPzxIshh5a6IV5fIAIqNEr+/HBx4sl8L7iyiARHbE4Ubiygn5r7cv8rdauL35OFbVG/obB+2X3Gm5eHGqPMucB0N1/O9q9ytZq7TlyEF3U6+h9qv2OwAqnXduErtrkYb2/lEFdf3skPQeB3ZVZUdguaf/WgHocFQxwI0W24r33ub6buZ7+rBe06vSdWF3T/bdA+rTX3wFlF17ffU3lUu72qUyqe/+OUzv3kx2j187OvfXn75L9XVrcvf/sGDL/yRtvpPfPPbF3/6c6f8J7701bO/+F2cJ8+0Vx4a7NZ26pe/+6Nzv/4dGeaPP/u1K9//kfZoxEsJi8O95uXv/OCB3/+psrF98Xs/Pv/jX6A0+3R/7anmTZPoJ77+zQs//VVht3Hp5788++Ifyhv1Cz/82aUf/Rwl+WNffe6x575jDHziue88/OOfBe3+iZf/8ug3vh3vHj7w2z988gvPWg0u/OhnT33lG6LRPvrnN878+sXq2tbxV9984mvfKm/UEx6PglJOggvP/+6pL31FNNonX3710a99q7q6lfJ4xAsSiwee//0z//hFkuRnf/O7J5/9WrTflIAlNHLKH33j7ae++JXy1s7i2x9c+e4PSD85/cJLTz379crW3uJrVx//6nNTN+7OXvv4k1/8Sm1t41+oRWi2v/6thf2tYqfdOnGMdEZhv99dnCsfNGCu2yePljfqPEnXL10eW18rt1rNU8dblSlHCU/TybVVliS9E4vxfitotzcfvqQZs5xNNOvxYRv30/2Tp4qNg6g/GBydademFWUiSap7e5Xdnf0HTlfaDdoe7p06Vei0i41G64Gl8KAdt7vtkwtgoMZ2d3bPnAnzYWltu78w5xEqb+7snTxJjZm6d2/31KnIJqXVnfbRBUhhea0+mJty44Va0uvzMEvg5Mr9g+UTAbGllc3u9FQyM9GNx6ppu3Kwz3dayZHJjEaTd+8cnjhFYzeh8k5YVCNVubuRzE7kUVRZ38pmxtKoPHnrdvPYcRzi6u3V0cwUjiBZb8mJSmtmQe8pGNEwkukBAgIWxkxvn2Fkw3E/atGyG4Iy6nYDGJA4HNl8YAgPwnBwwCE04bhPO4wYBSoe6oEnDEGuO9QTGE2YdJ94DMAsp5197IzhNTvAAEM7GaCdxANYmFRJSzhjwZEYHmRc5aJish5RmOv5uNTyJAODOcB7IBhaWx4pUGZDkIx7mgCS4aLYSuykz+hwDk5kO+P95kbtKOsjo4vpmKc5ZENgS4Oqb1XSwd2xZZSEog+ScRunmclLtpSMm71Yp42gludjUIa4eJj6atB12ZgtyFRlFVsYEo1MFpfC7cyVXRrDSrdHY7w1gmOUY5k0iSh7TH3WQkFJSxbYPemnggVZrw+nUNGHQaKlRIQ4G+RdEpa0FqHZzcBkGLieAZlHAqMoayEeGRTCvE3GUGdU5CDvmbhGDM6biJUhC82wQVlocAHnTRgFUhZCsK90hRdZMjrkKAKshlEn4FDJWINBaAmiQR8OSw45WOqBbhUC70oDNypxo3R5NJW1LITbxSOiGUBkYbEL+hXgQDptjnQ3Q5XWi/M2qQHv0hqMDj1AuERXOdAC+LvFY7zLiAO2mIoukChIx0HYRsg4UOm6YRkb7Etdk5eJBKqW+06mcxqOaznCdujcYgENFGrlYE4Eo9FoyMG0gNKBjopqOi9FlpDiqNfPYtLK/KwI09FoIFjNoRhmLIrkyPSA7IF4Uicu9gcpmWEToLVTXQjzBLRlOmSFCaNyaPsgmjAJjP1ugmcCYdNRm4oxDzzI2tDORgLhsOFlDQSu6/o1HSoWZK5dAmHuuIH9mPJBHnLaYrIMC3BnVna6NNKO97MFzFInNBwWKBvZKD/W3dkv1CQMXX/cMomiEehVAFUk6kwkLY3wXrjAW0IVQB6DaB/6wKJwCLtlI3xehEETMDzMSoy3uQr8cByztb4H3h+JwF7GtMJjWPYx8N5OhXA/hR7EEyptCycNWAwB8gS6uN/L+lQC7qcDsJ8igArVdBCXPYIoM67rcKbxOMbdPHVBXJGyEDqIo3yYtCiQIJxQwyQgqcYTmPRkZlgwbmUbWEMKUyrvUZ04d6xQ641sVjallGgHstiX+kBRkMW40ExAJey4bNwV9EiPqjpOaqhRzIcZJl0/79ICKndSUBJtoCZszdSrMmnGYyNVBoMQlQbGUZqEMG57Ipd6e6u1OSCFG5Zo2MVeJ/kUKfSNIzQJcdwekmLYglkNhG5gRzUbZ5oo3QATpD0sFrMGBDVRYMnwgLAi5AUzPGCEa1pCeQtTom3Rk7xjRYFYKrsUChBWTbpPfIhhFZB+g1gvowlwaF1M3ZhAG0PAYVyTKk09pYqNwUMV4JzGLm1REzM3EaC1IYpIWJbDQwII8hMC72eAAjNdiJsAOVTia319FCk/nAGiC6iCupwFPatAlIyDoIuI9q7UHTPtQp7Uo+lcTbLMyYoKh1a7yJeG09kO9XY/GLNJDWiOSq1cV9nImlo2nW1T4JvhmBpUnBbVaLWvZ2HOYLWb6zIbAVPNRWq1ikFpALMA5BTU+iPF/V6KZsVMsreTTrGKxcAmHRqPWWO86qB4XGckdvUEzoZCdzSUEIfAhnkXRFVjANZdPMmb7UJM07aOJ7CEqot5zSPkkhYRZQMD69O+QHjIp9BuZiZEASbDNqclT5nLmghMcA8B2M/jikrDIqynbkIUeDJ2e02HfDA7XV7fAQHuTM+Or6wltbH23OyRv16T5fJwYbp2e1UFQfP0ifnBPnD2kFXG7q1lhULz2LHFax+ouGDnStu1YwCCuNWa2FhXUZyPlcbW1pOodHhy+Vh/WwLUpYXK2g72trM4X6gfOELSqdrY2kYaF0fzE2O3Vy3lnZOL1ZUtB8DBqZOz6ys+1funT9fq20F/0Dy7XNjajwaD1slF3EnLzWbj5FLl8ACN5ODIVHdsShFeTjql+n7cOGydWUI9Wdrb6Z445mLeKEyX0u74yiodpp2zS3yvXW61mqePdQvjMooL/fbEzhZpD3vLi0GnGzTanTNLtD2sNBpmsdJypYnN9cPTp6JhN2x0Nq48zr/85f9V0ej/lDjLQUS7w9r99ZQK0hmUN+taROFeI9ptSCqCVq+6tpUSwYdp9f6awuzItQ8f/cGPvXS8M6hs7+U84get2vp2xuOZv15/6qvfAAaQUV7aqEvERXdYXttMguLxN99++qvfkIjjYTZxd1USjodpZaNuEGPtfmlrR0OKR/nYzTuKR1i5sZt3c8xAbmrr20BZL21tZc06ZA2cuHXXQKIgG7+zAqT1FlTWNoE0tpmXf/WGa6bW46mbdxQk0qHJ2yvAQtYdPvXctybvrChAKmtbQFpNxPSte8YhpWjhJy/79bYBuLpVJ0luHaqsbkJpJQkmbt/zDksqxu6twUQaQKqbddofScJBQ9mR1YTZhtYZTlhMGxnqG0m47jnZc5Zx0NZm5FRQSG/w/KCcsdgfpqitFWG6a33fGBGNViO9G8AgMPvSjnzOQtDJXdPkSMidON8IHBe2bV3PKMzcoYF9k4ZF28x9x2pMdN/pjrOE2o7TfZcTjkaAdmgOMMgBbXGDiZaUdbD2yGeMdGDGIpAg2mUSkt5wfn/34hCUoSGkTbSHPiWsAwwVrf7i7tZDCS55CckhNRBaTUmHWoC7g7md7QcHsOYN4wdQIual5Q2sETKW8zaykDlFgpbVgGkjaItILCSkbldqBa3HaD/VOciAsAfKGKwh8bs59EgSoeq5tkT6SF8nqhtKFtmmMRprgMCOdAZaFqXXeD4oGspII/OZ14iZhjYWWVbMPuT5MPSC+t1c5iTHAh6kIHEKENfQOocKU7mrsPSOc13PTAY1YWSEYZ9lhOE+JkMvKaVNBBOS8Qh1Pe0CgzEaEThkOY4Omieb7eWchLSHUc9nIqI9T3o0A6zROXHQPJOiCAwIGSKFEBoy1PVZWGgdLu/snc1JSBKIuiSHGI8IHiIJEekT2vMZj9AQsia0lMIEoS6xGLnUu4M8w4FLAdhTEjOXO7enNSRaQdfQ1iOVAtDIcxrobaP+1FWOY+1kXSmAlcZ2X2pPzK4BrzZlirWhfjc3mGsLQT33iKgu0q925QA5i9lBqjxTjpl9rSEzGsB6biBRBttDk3tuFKaNzFlkEAoPsbVYUUGaDBoqARVtDzNkICVdaq1QmPIG9NonvFbffLiZLGnCgqaBKTKYsg52ivZhpV4/1xnOZESwAwgUzWlIWp4kIPPF/YPzrfSYRJS2CMigopS3CBrBnAa07WBCtQe0i1FOM0RJi8Ic5iSAndwdGokZGFrTNIYQ03OuaxSmvmVgR6dB0bSka1uFub4r9btDC4ntO921GhPXc+4gHcVldH3o3uhnLPRDY5rWYKIHHnS1Icx+lLp3ejkNTQL0Tm6ZgImVDaMx0QNvWlZDCrsGNpKchirHfjc3hFlHSZtaj6FCpOmV59ZwfgAl5kBb0cAKQGkFbyGHaCeb2d8+O3QT2nHaphIF3sOogRWkfTu9Xb/QlRNAw6DtraPOEnxIDOJDP13feijR4wYx2qbKB96S4NAaSxyg/BBLIBTE4gB77RTkvIG8pQZS09J5hh1lYF+a3GnG/W6epyQjgW8kYGAVoqapfW41L46uB7YTopiZemYzmJMANDPTcxKE+Uqk2pElzB5ol1hJmN9XYORSUcruiWwr1JTrltFD4AgxDa1TYDB1LWObWRoUYEe7rtOIupa2Q68QRkOGuz4TMRlY3GM5JiiFuENySHBG8QArgPCQ0K6XQXjYOr63fV6xCGYQt6nB0GcM9amBuNU6vrvzQMLKXlLegBJTmFrWxhLz9mj54ODBESyhFAdtk9PQKsHaJKcCKiiaWBHqJMd9ojBDIyA6QCOqHQI7OYBYeq52pfLUGswOUm2J9MI2tAHUGADruQPYgCj7MJCyaD2ijcwabD3Vu9IiYmEpfY/lKgQU2708N0w6CvcTr0GWhemHIjdFhYisS2yBoczUc22I8gQ2cpUj55Hd19Zi5ZHbVU55SXhhayc+7GgqivU91hlkLKyubpD+KBWFsfvrvNXNWVCq70eNdqaJ+MvH7OPNFAXV1U02SFMSVtbqwUFLUvHwz3919ne/T8Nicf8w2G/mLKxu7rDeKEVB8cX3oqt387AYHjSL27tSRPHOftBoZTwqbe/xZk+ysLyxE+3s5SwMmp3KxlYmQtbplXf2JeZho12s7ynCaT+t3borRYGlqnJvNSOCdQflzXpOwxOvvvHod79vHST9bPzuSs4in6mxlXXFgrlrH135wY9wkrNhWtvaybHAiazdva+JGFtZe+YrXyWpQqOsvLWrIGO9UbW+pwjHuRm/fU+SAFowdve+8oQMs/L6NjDO/YuIRj0AHlz4xa/iTqs7Pzv99l/HtrbSamXh3fenr9/avfzQg7/5bWln9/rn/vX5Xz9fqteHM5PVOxtzNz5uzS8uvPPe8bff3rlw9sIfXirdX73x6c+fe+GFifU1OVmprm5M/PVmZ3ruyEcfnXn1LztXHn7gxT9M3Lt36+nPPPjSH6euX2/PH5lavT1/9VpnZm7q9q1zv/vd1iMXz//6+clbt/YvnT/y2rsL77/bWjxKsb/wk5/KYgxzef4Xv9w/fnJqa+P4X17rTs4wZpdffsUb1zx36uKPfmI49YQ8+LvfZ7OTvDs69tobo1LVTleWXnoZSLl78dyFH//0Fvg7VSie/fXzNmIz0i6+/qYOCt3jM6dfeTVMs/+XtvcI1vQ6zPROPufL35/Dzfd23773dm6gu9ENIpAQxRFJyaYtb6bKNRuX7fLGW3vjKk9NuewpDTUilSiJQQwiCeYggmBCzmgA3UDndHP685fjOV7Qs5+psvbv9lm+z/Pg40+c+/Z3rxafc+v1kz/9OeaoRe8uvfyaAmj/7NrKc8+7/d79T15+9J++c+vTz2xZR3c3HW2cm7m3u1F3tv1GLfrwpksdOVsN+tc04NB6OTx42KGiFCja7bWrB57dTbZuOtgEM62gf8PQTVRD4/0dmzLQ0uO9Tce4H7Tn4cY1ozRZez4f3NEpJ019vLdRQQBYtWJn3TFif3pusn+djblRnQPBTaYB6ajezlY9yqixkLC9ORk0RHxd36oXoetuvhrldjmaEv0NOKmwTY1YW2pzMUkXmXdD7E/JwG1svscmZuIfN3oPVVRFOzZtvy02ajJe4OMbxk618Lrm1kciFml/lYsPUebgw3k29br20CmCJT56k/caeTBTefgBl1rcO8nFNZibcv+YYf2M7mtJdMLYe2VcRJsb1RpI2JzcvFVv0sgU3u5Ns27kMgkPd2oNGlDd29noNFKfrYIH99pdEZveZPumPYUCBcPt3XqdJHon3N1ttvMxO4NvfuTWy9wIk93bZjUdq95oZ9B1bsRiqdhar3Z8jz8Kdm5U3CI3s3T3rtktAzic7G65Vp7ho/nuw0p17FenbbR9FAm/Du9m/RWES0LvlJNV/WBS1q6SnSeUALT6FtpeRCS20b18vERVqVV25WhZ872i8wHdvhBznft3xP4UUkl9/UY6OSZLjbkbarBoeBFy7xUbZxK9QUe38cECLFUdXQX9eVJU9camHCwKr2TTb4r7izmvsf7bcH9GpkLsf7S7p6U31fR0NL4B+vtuY7UM7+DRVmW6GmajbHej0XSLKGYHH9rT80VyM+vtOGI+9NbBYKc6dWOSl3LnfmXKCJOcHt6y5maC8m6xvV2Znw6SLTnarc9dG5Uy3r/bnmFegvj+jepy21frxfZm5VitXwzUzl6jez3MRb53y54jXsb41kfVSqesACMNlt2H97E1SscnjOReuhSrwwsYPdSTXbh3jKrbbJgWwUltcxdXN6V32k3uwNmN+OA8VFtc7snD46K8U/f8Mjpr7O0DtVWOTpnRVsAfoJ2LRXnA1Dbsr4n0Lkji3FvTZC8VIzk8rk22lXWP7J6TIBNsH/bmaHCvkU7K8UmSx2Rqe+8jPaO8u5wPP6IAog6fDLeqMoNOrdjbsMU4mJkfH1znI2hUj4L4JkGFqKH9g73qOOJuSx2uW+Yg7Bzz9q4hD9it5XJ8h6WRmBZjbxNPfG3BjXqbRt6Tcycy/z3pFe7inO/d5oFnzIiJd8hHfW2+FhxsmukhXFr2g3fAQVpt7af6vhb3j3N+HQIK947R+ttiz5SjY3z4Nh/UsmChsv6BgCoenOL8BkAI9E9Q63k2ZrF3QrNeL0MnCZdq969BgvPBSZNeLTVdHZwmlXf4gMnhcd14hacsT9aMjTtIV+DwuCPfKTWRHV4gzhUYa9lo2T54k+UkC08a2/eA46veCaE9HJs7vY80c8YH49HOdsNMU+1ovrVRbQ093QSb1yvmXFEhyf5tszkdMn+0d9gwypzAdGe9Ujn0qw21fdUiC6yq5wfrTq3uaaW3tWlrvtTtYvOBW9kJWg2vf9uIpw27Ir07etOZGOHwYLMDe9Cqgb27Tp2FtRlv631SdMyaU/QeGkQvnGoGhifFIMPuffnwdExbdHST7s+DhNbp+2jYxmlXr2+o0SzpQzr9pvFwPsdt1n8d7nZlbJtbH5FJI/fa3HgPekfowC3nXtLvzeaqK4ZvgN1uGVWr6+/jsJFO5jTrXdifk8OmWX0W33NSNW/svCxHjdJvVh9+UKZVeDBt6u+CwaIadkX3xeGNaG+30f1wDGG0e78zhb3SItvXq0vVEPainfvuUWdYeGp7v9n5KKR2urdZm+VjVWMPPqrO2HHpp7sPnBnel+VkezxTux7yjtq568wAv2yRrZu1rh2Xab6/11qqD1U+2d2pVWliT5W795y2CtQs2f7Qdl3CsvTwoXME7SMLb25VXVI2jh6c/9a3986eikzjxI9/OjixjNJi8eVX6+uboVtd+eWvo1q1t7b86Le/s712fNJon3zueW9+CkC6+Mo7u6YTAAAgAElEQVRrres3B43O6i9+Udj2K8e6Z579Xn9+ftjqrv3zL0fTM7pMF158uaNb/aXltV/+Smpi8/L581//ZtBth53W2q9/G1o2gXLhlVdnFA7musd/8ZxUYPPxi49+85/8Wm13dW3xldeqd9YP55cWX319/p137j39+CPffdbe29t75MyRX77QuHM7aDS692+337nmTbUXXn9z9t33Hjz9+CM/+KFz//72udPzv3ll6saNrGJ3Nu9V37gWzHQWX3198ZXXty6dP/ujHzVu3+2vLXfe/Wj27beCWt3s7x99/oVgtjv75ttLv31x+5FTJ3/04+6HH6nFenlveOTlV8JqVUujU9/78c7Js/8yolEl/T/5k3hpKu40di6f97udyeLM7vnTuWNuP3I2nOv4093DlaP7J1eDTjOeam5ePu912v5sd+PRc6VrHpw54S3NjKY6o5WjuydWotlu1Kxsfuxi0KwPlxZ3zp8tKtb+yRVvYXo8Nzs6Mr9z7tRkup3X3XtPXS5r1mBxYev8ubRi9U+u+oszo7nZycLMwdkTg6XFou7cf+pjhS2G8/OHp9eCme54bmbvzInB0qx09PsffzLr1qKK++ATT2QNZ3RkqX96dTLTHc9N906tHqwek6557xNPhZ1q6jgPP/5E1nBHi4vDE0dGc9Pjubn+qZWDlWPS1m99/KmkU01qtfWPfyxuOMP5ud6p1dGR+aDbOTy1sru6oixx9+kn004tqdcfPH5eNuz9peXemePxXNUivjELRAu4LNKPQFTBphbUOglrE9tKnEqEFjWulc6sNBqlyyKxqFgVO2ZYbSW4QwwzqVQjsSgMnhrThWhDB0+cBUldWLFCq5GAGeGKiVtL8SLXeea0YzzFGrDvLEhaw9xMm/UQz7A6H5uVhC0xzovKVAbbGiMHnD2MuoCRPUYO0+kwNzKOd/JuyGEfmdvEDRMrEXwr7pac7lF0EM4llHpI9JNOSPGAaBuqmkDDJ3Q7nioQ72voIJ3xOR0D3i86HqETYmyrWiJ1j7Id2Q4LK9TRYTgfY+Zh2pMdjzAPGruk4iV2LsR+3gpBndfUwFiGwpY1yzemFapBx07sTsG6tII9sqa7Vsh44R4DzAIVw9OmJG0S14rNqRJNURf5fIXobunyWDuKsaVc07e7hWhCWw/dVpLPOw4KzGPEsHNXRGwJcBdW7MDu5KSFbBE6nZzOaQ70tGWku9IhE3sJYEtRsg+NftLNKPFAtSdrEZMHRbMHnJLzA2jvlo0M0j7TDvNORLCnnMO0mevFtmocAKeAvEe0raQrBdwhej+azgDxqbmVtTK72M7r+8yOEysR2lbSKTS0A/RhOpUwNFbuYdIs9HIbVXalmxN9hPlO0U4UnzDRy7uJQyKrHtMW0iqyZntwXphGWOE+XdVMIzHtlC5SXUtb1bHWArpbuFagzyHWgFXiaSd0U08MM9EWkaFn1Xoo2hBXVNWJzOmStUiFePi45opAOMpaQpqeu25Au1A0UNUMxRxELVIhHl+lppk5dsYWMDPLmuOROR3aoV7uhzMBMiMGh/n0ABg5xtuqOlDVjLC+qo6iZqGpQdYZICvUwEHSGTEzKcSIVA5lNaboQFWGcbvUVT/rDqAVU7mXdwbIKhjflc5A1iKCerjSz5qZJgdpq59XSzNbL7sDYBWQ7kGnl9djDeyCyjDsSF72YWM7cWiH9516BGZ0R3huJaZLXBOF24jxDK2joTNX0DrmZtqsBniW1TXPsmN2hDFNuu2MTJMGGRvdhNax4RS1ig/nNUfzHScRR5nNEqNVsC60WFCfTmgNmnZWcQJtngpXOmYsjnKThlYr413kaFG1m9AGJk5Zr8RggRu4D+kg704I86mxDaqRMgNCd2QrLOzQgAfBfIy5j3FPdceE+0jbJdVJ6mScH8qmlzmxBg6D+QhrAWH9ouMBEWK+peqhckJOD8uWF1WlXh4k0yOq+ZQe5q0JNiKoH4KaD2yfwYOiNY7ryiz3k5kR1kKM9ormmJu5q4duMy4XLAtH1lFsuLnLI74EmItcK3Q6GWlhSw+dVkbnNRsF+hGgV5WDJ/aSYjaomIHRLnCXVNjEbudwXndQYM3lqEVb4NA8AkgFGUZUbaa8i+p0bLRLNksFzaqLBWzQDuprcwWtYstO7XqCZohLJ2ZXllOanW/J6h5z4tiKhdiMu6WGdyHvJzMJQxNg95JWJuQesrdBJYf6hPDtsh2XwuOsl0/5HA2l1ZPNmOIBdndUJVdiTMRO2YmUHnB2GM0kBE2QeSibMSMD5exSN4jtTBd7eTso9IjRXjQTcTyGdq9oJIgMsbGVN3JWgxU8oSd0R/e5KZ0jkBu56wS8C2gD1cxQmwWwTSrIY6vUsArHiNkiZmZZsX2jK426srXAnUrj2UoVjo01qhupYyRsAQpbVd3A6CjWAA737FlJpg0XjLVVolu5I3xzAWq2rFo+nUK8XlaZZ8wrNaVX1NA4qpBNvcWF/bWjw5Ujfqc1Wl3aPXOqNMXDyxeGi3PK5OtPfixpV0dzs4erRw9PrKaNymDtyNbZU4XOdy6dHy7NKcd4ePm8bDv7C0dHa8f2T61ldXt/7djhqWOlqa8//thgYSZ3je3LF+NufbwwPzw6v3/6eNCoDY4f651aKw3x8PHHwtlW1GzuXDofTDeHszODY0d2z5yEljhYXdk5cyJ3jf1zp8K59mh+brQ4s3/u5HBhLm3X15+8XNj6wcqxw1MrabO2e/aUv9Dtz834S3MHp1dHCwtJu/bwyUvKEfvHT/ZOr8b1yt7JtfGRmeGRI+FMZ/eRU/252bRZvffEpbJm91aOHR5fDpu13trK6Nj8cHExatYOHj2+s7yW1+yHTz9euGbv2PLmo4/Qt9/hG+v/f78IFSgQiaGe5thnNgAeltxjtgMFQdQXjo5NAIIxd01suAUJmKVgkkDN06s24DAnPrcZMo1yPNaqJjl0c+IzGyOPlHSiVQx0SJTmay5WIz1DY+4yMklKGjA7gjou2Zg7FJsa1H3uIBYnJfWFjX0Y5cRnNsFJCkRITSWVWxCP2UCppGQ+dxXGtYJ41CZExjkOsFEwlhU0InpI7ThFHrEETJKCRMQsOXUAD7ERM8uULKKmp1USoHnc5kymCQigngkzLWiIjZBaSYpCpE+4k5Qs0KpIwLwkPrEIZViyCGk+s3IiIEWSZ4FSCjNdgzEtGFVEoymRKaSUsRHROQNKpBGQBaGahhOYQ6w4JzGSKcgpExMCIUU2zwOkcYaxhgtSJApI4VKiqMwwEyFGlMlCmAopBIGukZhJAspUczlTZVGUVPoQAUoSbjNSFBSMOZVM6FjLmYxBXTLosUQwSigQooiKqqB8whRkOaXWmHuQpQWuTngqEixpmYgYJZxSNuEy5UXJ7VCMQJrmpJbyCc0JxjwXIUoxI3pODlNoZ8QNyVBSDmi1YGMiiaJaztMQuSKwClZG3M5AqVGWMJXAklIMOMpBbpKy4FpAdMS0EEyGuOZwLDWVkRwJBjlMQGGRIqMiJQYmRDE2URBTxjSVkFxwhDUS4JIREDArJCZjRPHIV1BRygVMYE4IxJxEqFCkyKgZEQ0xqngywbrBEGK0pBqQKqUBZE1Fc0T7BakWtMy1OMd6qVDOPcjsApaJFkDRLGHm8xJRCnBRaCGjBgJoQgGgUkgV8QCxOlWZxyRlKANA8sQnNS6RxyRmSgIt4wNFeArpmENMFFZlosUx0THFGR+WvMolT/kIYZoQxDSSBWUBC8UdptEMZZzyAisfUsCtMi0FoVRQlaoIFw6DvhQpNkzKNKLGgOmUSFrmUHJO81wplSumolKPiC4YFTQa4WohUKnKjEqm0VzBXOUuk4EUBdEZxbliHkCMUYilolnKbU4UFtaEhwVWCDUyFgCRCcwLVuRCUWwjSgOOSl5L9EAQWJJmwBPJZEyqmMU5ywh1AZ1EDBWknnCf03HG2gWNSpFxbBS0yHlKqcMpCQQqeD0SfsQxo7igmdQSzoyMQZ9JQAil/oSjkrlSBAHTAK2mSpXCoURhmDFGA0IpLkphSQwhkIZGYyZpUcSamzGQ41wyGSAkESmFQxEqKdA0nOIiRwpwm1BUIKUTqBBJMNc5TGhBEKCClrxMcGlzNCGsJNLmQBGclEznKMNlDpShsYgrDDLAjICrgjYSPqEKUSQyEYIScWIUpJdgMyfVkA1UQQBplHSMIIJEpyyPpSmwXfAygpbi1TEflYAA1Mi5j0rJEM14JktCkQVY6RMLcCfSRzIPEWlmgnKZFaRWsgAAkCJb0TJglqSVmA+EjDGr5zSULIuIgQiKmeUTpHH8nwgiQsMxygiGRJAQlZLkBTNCoiNKFU8mSDcYhBrNaZ5QBriOMcxwVjIrxpoQKOWmDwuMkKaRhBaQYqkZkCJCcp2pEcSMkVhzc5TnjGoaSlCeY4KFAQgGCJVEE9QsESh56pOaoNjjkjCpcj1mI0VwBrUxk4AAqspEiwnVMKU5G+TczbieaVgxlACz4JOE6KhEmRYSSikRGenlrBIRM2W9kmNQajn3OOVFySUPJkWjiFjGyxRU88SMeL+kUOZWrnk0piRFCU8KBUOiMcYMao1QtRSoLGRGSqrRIoe5yh0mIyhyolNKSskmCjFGEZYFKbggCEIPSYuqgNkZ4YJRQxFfIUwI5jCFBWdIUhzDHLPSo3ZKGGWsJLGPgMGwEiijecptTCWAVJIsZHaOiNBYydI4p4kmPO7YSkCo+cKNlfCJNRaVpGQxEL7mVIDIsDZmTlQQJZnHnFTykNljUckUD4CRckspDgCZiEoC9QjqPrPTkgZAG4lKJlkCNE9z7IJipTzhGsiQiPl6JVUsQIYv3LggOWG+5lrIYDIKqRkBgZUcc9dGBoLSFw6nY1EyX9g5TJ2C+sy2kVaWacBtHfAyCz3uKOy7GQqEyyiOJfOZFSENSuhzhxEDwNjXXCmjSgJ8bkNGUsl87hhwiDPkMQsSjyPN11zAQFqSgJleqieS+cxmyOMFiZHA/3Itwqf+/kvCm2CTtd76oLKxoafB3DvvTF27/uM/+78+9tUvmzu7e2fOPvkPf+8MeoxD+/b61PWPACHTb7119M3Xfzr7b5/65jesBw82Tp66/LWv1dYfGihzH2zWP7xZCj519eqJl178ydT/efGbX68+eLi/snrhZz+tf/ghZKS9frf9+ruSkPad26ef+8UPZv/9Y1/7Rv32nbxbnfnd6zMfXoOaQAI//sUvvv0//BucJOe+/b3YMDubGyvP/xLnOTPw2rPfr+5uH5xc+/gX/uLdf/OvFcJn/+m7V9R/zw/Hx379a93389nG6We/U99e3zt/7uOf/w+3/rvPpoax9uzPPvjX/y0OstXnn6/0DgcLnQvPfrd77/aDTz79B3/5hWt/+rmwUjnzre/e/NynjyK+8rOf1zY29x89/ug3vj5z6vj6Jx9/4s//453PfHL8TG3rqmFYCZvzBjcadjmsf6p47/Ums8sFPNh53WzYuT3oh1szhZDGSnxw3akVI+tflVtv1IkpF+hk723TsFQ96EcPHIiVsxaNrjtW7LU/B+69qiuTNl2/95a0RNmKe/2NagKgTpLhdWIEXstNxq9afaHV6tngCtJkbowO+rvNQDKr5tn3lmRYq0zdMO90cWxVk/VQMjWZMegO8hx7XQh5y9haSPIFvXVPv9/GE2u6vEInLIwWMV0HsWM8qBDtNe1eLU8WjNqd2oYrJ02RT3giksGiln8IcofuHs3Ei8Z6M4mWhRgYBy7wp6zwUCAc9I6I7CooLLB73Mp/aO25Ub4k4F6Qj4fXeX0c2fPF3htO50TosGD9A5ecK1Xsje6bjcGBpveGd9pof1JZAQ9ed2fWQlN4+1er+mrIYLR/z20NPdIM+9da7c1D+zTaeL3ROBpazqR/tdKeDtjDfHe95R74ciHbv+p0Hg7EebzxWqV2JLVr0egDy5gP0FYyvuOYuwE8kgw+cJDt2X/c0G7PU82D/jAZ1iAs0dRQ7iy60cQXV9jdT5a0lOQtdG+W0lCPvHRUowpAuC92ft8ifNe8tRoxUandNe/NUhU6YS/xXSX1VNvD2zO1SSSOv8+un0v0ql55oD/owLLk3rYctWjZgMYm31nQfIStF8xrM4nW4OxdtNNQuRCyv7UBkltqXgzSa2jYt6vdpLiRj7bRDByhSdzfbNRhWKbk4Aqes6Lkajo4dB1jkq/n/r5wiz6UxeC2puV+XpD9D/WjdITu5Zv7NUP3061ivKvP+vtEjvvrDSMeF5DtvG8ep8Pwfr6/X6+hPpsYhxvmTDRWvBhedRa8/ULTH16xG3RSBS7enZke3QN6mu4uWYd3y4UReHgZhPeQ2JKbJ3AzdmCcDbvaYA/VRnLzaHvrJp4dwwefBvUH3NpVm0ta5aaxH5X9rj44RO2J3Dji4g3/+EN+50LR2AX+NtxcEPZdMorVftcYDIfFSNtc5nA7PHlXv3Xa7ySoPNQ35nV22xyN88O1Ypjq9oP+qzKntNMK+m8VHMtK1h/sVJMMGTQe3yDawGtVRt5rVg9q9U7WexfxtOx6e+PD+ii1XT07vE70Ydx0hr9vEdZbcfJ+GUeglo0nW2TkMYcEvY/0og8XW37/hWIIXdf0kutFHpF6PPb6bHTIXDzp3TDjHmlVDtOX0H5Zazdit8fCwZJIrwPI8OYqJa9pe3buLQM20gcOmEzr8cBAwD88AtPrAGOwecZIf+4MyjBaYrCve4YcT88EQ0VAvrtA/fdLk6uHjwj8Oh6gYrJI820nVdloRngRtHPwcLE6eLMwYLxzGpRvgVBTg2VTHogMguGMFmTILeH9IyC5v2vv9d/ElW4otqKdjQ7YjrWj6f41t6X1tSfg+ms1ZyarB5PxuxafStl+PLnj6JsBPJYMrrmIjSt/gLZf1ukCcIugdwW2a4k+Sg5vOcV2yVXSu0YrZNLg+eBVO5lmNsy9D3BDn+g7o+2N6WAdWFq28x6r8VgXUe8lkU+zCk78DwDRE1ujdPeo08/E8ff57dMJbRiN+9qDFkpRNdqFw3ZSzCB9g+1Om0OGrRetj2Zi0uLiLbTTUJllJn3suSSYVvI9cNBhXj2xnrc+Wo7wrOB9uVsDiVsNdlBaj70Fod4BvUU26bjqA/3eQoSXON41Bw6IGhVvr8gqqjeFim05XiuHM5rxy+2bYLRpTHv7DI/695uaP0Y22XnTPgbG+CDZ36hX1IAG2v5DayYcQzMdvF+bG+7jhth4o7JQTNJJ2X/oHg1HuCCj9UpjMilasveeOTscyjbbe82ZvRDISA3vmrV8gmQ+umNWR56aygfvWrzvoTm2/wqvgBwkaf8j4np9YcmDG0Y1yroLW3/wF3++ffqElkQXv/q14Ylj9s7e2k9+GszMvPHf/OnFb/9TVnGe+z/+9z/8whc2V1cBJo9/4+vewpQ7Gq387Oex42b/4//06Nf/sTT01/+3/+XpL/7F4dKRkrIL3/rGeGrm4ODEys/+OWV6Xm9c+vo/QsZ+/P/822f+6ovjbocieeoHP41se6u/vfLzX4JCvfq//s+X//FrGKMfLf+7Z/7yC+N6I665p59/zrl5vzDNteefW3z3yndWvvDU3/61dthLO5Wl515obKwLUDQe3uu8c/VtphZfemX27Ss/XJp5+qt/b2xuR2134bevNe/csaNxc3ej+tZHbzE1/9obC6+98fPFP3vkq1+prG/mdXPq6u32lSsij/Vh/8ivXsAamnvnvcUXXoqn/t2Jb/+weefOfTOXdwdLb78pooDl8akf/TzsTh3+S7UI/6vPhVPd2HVuPfOJcb0VdNr3n3xCCrFx4vTeqeN+pTaZm7116XLcaqSu+9EfPDNxqkG1evPJpwpdPzh+YvfsqZFd8aa6d554wm82pWXc/KNPBbXauN299dTTpWD78wvb505Ppme9ZuvWk0/5FafQtQ8+/em04njtqdtPPZUben9xcefMyUFnOm7UHzxxuT89X2rig09/OufEd6vrF8/35xfiWv3eY5cOF+cxkFf/+I8n3ZbC/MYffSpo1sJK/eHFC72FhcS0th47v7l6AkNw9bN/PJlqSwVvf+pfjac6UaW6e+50b2U1dKtbFx7dPHmGFtkHn/0Tf3aqpOzmM8+Mp7qhZW+eO7O/sppZ1vb5c+unH6V58sFn/2SyOKsAuvOpPww6jVGts33ubG9lQaQemmN0lpEy11b0pGs65cToqHLB5pq0K5laqSAg8Rzls5RmCThqqGmTw9BuFGpRp7QQFVWsOgQURkfxOWqUsTqiqSlD4MSqpvmS7dCQVVV2zEaq0Jq5WtDNwtOWRDFnI5ybTgqP6gYMDadEJxyMFO/IfKFK1Ijgg9G0hGTEcDA5WgYa5qDnz4YY+NAYZd0iNSHEA28uR2RIpXd4DGItV9ifTIUIhJAdRtMosRTH/clMqpjHyvFwWQKWQBLG0wHGYcl70QyKDInBKJkOQrsUxWS4AkpRABhGc4liKSH7eScNmxpUXjwfBXXbSidilaIqMXFEF3jetijOzTmYz5hmPEbnqmZVKgDYCR1WqIM8MovLGYfRQsyidNYyorE8abMGomXGT1pFQzhgwucomDE1ktrTMj7a1GIPnLS1BiRZCtds0OQaCvVpAKYFRxmbwemRihF7YoXxFtKLRK0YxIUAhYURTBYgVEle87JWhNIo6MZxhwI5JtpeNC0RCaURjOckAmluD4JOYWbjsOnHHSqlB8RgPFUQMEJWNFxCUuXAPAy7mRYPiqaXt2SmAcD6k9mcqX4potESpDBL7dGknetJP3cH0RTNRMl5L+r4Bcsg94P5UpMhcUp5xMYVbmiRWjapSHQZyrNVYgKN52RFUFbYZggWdFnhFo20oxRWqC7D/JE64SUlBV0RTJemkcAjRlbVNZJoyyxrGk46QhfqhpErjZJVnerKEX65YMCWzknKl3nUNK1kJM9WqIM4LdAJBxmwwv1iuZI3oBEcjpbyolrgMg1mgqQKcXqQ1v2ohRGMimY4nkIiGk8WEllNSRYlC2nplEqN8kYQtxWSQdaMRlOIJ14wG0o3hmnsz6VpHaGiD51B0i4wCNN6NJlGLJ6EnXFYl3Y68KbjpIFAMSrsSdCMRT4sW3F/hvHEz1q9Scty1MRx4+yo69CQOapYtQEstWqqFnWzDLQFWszZCuWWk4BlwyShaRTolIsw4HUJlzQz90lLyiWXWEQ3YrVschQKvVAnXYILoyHxHNNVJNqlmjOlRRwt5EcZ1hHXc3myQkHOqgovaQIlRrOQi05ucVMk4IQNSYJgEM/4kEaA9sNpENsKgXE25QdOKfLJ8JgqjRKoMJ6LpMgI2i9acdTSIPTTmdCvQj3q9ZclMHOAEm82LUSO5F7ULeIqpMBPZmOvQbWwP1opgJEgFcfzWamlCg+TbpZVClz40Uw8blI9HnlLIdAjqZJoOoG2FCy3OzJdbvA0gmum1kI0S+CqBdqaBgOzq8CMxlDGuyg7WtHSQDtCeQfrRSxXDNjWNBzpzVLN6zYMaRcliy5PAr4A5LTmZGN+TCumLYISo1HiBW5KX+9AeMyhRc6WEJgSTjyEc0TNOVxXolbAea6XHumyYkFo8bCsj/OWTHUASW8yW1AwADQcHIGEZLkxnnRSno1z8zCepqlWcNqLu37KM4wnoyWJYSKNSdJJqJyk1iCaIbGQGA3j2TDRCkL80VGgUCZ1P5ouCPSR2Eu7MqwLBEbRQhIbisrB8KjCLM+NyJ9RSgWQ7QUzKK9rTjyCFxqmmSuGyZqBTegyHyxoqm1ykvEjNGlbVjQqz7jcARSX+IQDbOxSHy8I2NEMFFuLMJhvWuEInKvorsQqBysmrFKThmyOoBblMKVHWDLr2uGIn9REFQqYyRUTV4lBQjRDWZsYIKJHWTTlmsGYrtCy6QS2219b2Tp3LrXdg9Xlu5cuYynvXrq0dfykAPLmE08MjywGunmwvHz//PlSF/0Ta7cvPY6K4v6lS9snT7Iie/DEk6Mjs6PWTG9p4d5jjylKd1dW1h+7iCC6ffnS1uoqlcX9CxcP11YmtdZkaf7hxQuJYe2vrD68eAEB8OCxywdry5Ky+4+cPzy5OnGq49npO088KTnrLx25c+myYnR3dXX/5Nqg1Y1azQdPPTFqTyWOc+1Tnyo5683MP7x8ITXt3ZW1vbMnB62puFq5//EnD6cXCkO78ZlPl6bYXVxdf+xCYru9hYWNc6dHs4txxb331BN73ZlS8Gt/9EeZY41mFtYvXYwcd7iwtPHouf7cUm6ZG09e3DxyQlJy7TOfzm1z2J669/jH2Lvv/Je2CP9zNQ2NxKNRmDaqKs5pmmQVh088VYKsZtNJgLN8PDWljUZa4Cf1ispKHoZesyU8j6RpVrVJkJAoGk9Pa5OJ5k2KmgOTDKSF32gI36dpmlZtHCQsikbT05rvifE4aLf1YALiPGg2WRSxIMjqLgoTFkZZwy0KaAwHXrutJEwSKkShAExjhCwEAZJeDi2s52GQalQHVGVhKgTPAQBpjA2epkwrvRJa2CxCL9aIABxnfqLZOIBQeZlhsCRjovAltrBehH6sEQ1wlIWJ4DSHGMQRMVhccJF5CplEl2EQCcZLG/iHZZ2TlAhEQlIwSFAEI55TRGlYZjZQJWJxkZlG4RWaynJLUUhgBGImKUY0kJkNQQlYUuYGK5JcAzClkiCCI5CIEiHMApmZAIDUVCwEtMhyHcCUKgQzHQpfSQgxC2RugVKmNqARZEUGaSJzUWAWWxhNQpjkqmGCMIdhIiwYKw6iHLgCJIXKZI35njSLVIK6AaIchCmucBpFSU5/vwFpIQyV5VjGBagbMJNgEsGKYEkcZ4RZCGRllmHNKPMcyyjXbBghDY4jWBEijcMEMwOBUqYZqTEvUHoeK2HDlJvaRJYawCCRuQFJCqAEKUckzpigIUgNVI/2PNUuuSIwlrkJSaZgCROOaJxxzsW55dIAACAASURBVD2QWJiXgYr1kiuME5lbECYKK5UJW/Y83cYRyXTEZaBiXQqFUCIzC6JUUQlSTmCUCkYDmOmES1+lRkmB5Gk2UUxlyGJpKBUhGisyr5SE6kYZhRgqKUwQxZgWKTJZFimIkbI4GEYAI10voxBBqVRNB15KshTbNI0hUAA4AowigHCDjnuFC4oSVHXgp6CUzIDKz3LMgCPAOAYK6KaMYwzyQtgozghIc+xQnkIpOWShLDnOUWxDkgEWlZkFRJIUpZ6ZAJUQpxCyUJUc5RjyIMUmD8rUAloaF4WpRIZKJUsNskBJhhIERZQSkwUyN2UlHHqwCXiGpJSljlhYSoITDEWUUkN4MrUhz+Iy0xXPEAQqE7EBlcrhMIS2pqk48gDUmcaKMECUKURh6iuNFYWmFeMEWJoOkshTWCMO8vuZw0iBKEpDpdMi0zQ5TqSlGTCJfAg5FryMQkyxRAKlgRI4LwxNjhNgakpg0I8gx0KUsY8AhcDkapwIlJWGVkxyIGhha6Ynf08QDQEr8kKTKmcQwFSHPFQKQMwCWZiwULENWAxplmMWlzkvMM90xUIAIULUL3MTlSqxAYsAKlSpFSyWOdIQi0DGFCCI+WVu4AIAEeXKIJkstYInZQ40yCKQ899vZKGjDAQuNrxRlBJmEVgUaYqELssSFWGp2yBCGhyHoKJrWRRGmJoYyTLOSJUGCeBJBHRLxVgD4xhVNJ5FUYSpibEqkxhpuswVLoLCMGVCdTmKoKuLIo5CpGuSgHyS6ZpWFgqXYaEbMqI6HEXKNbQyjkOIdEwYhTGx5DDUdRCzXPtPBHGAcCRzG8JM0QKkgoA4FYRGONMwV75KDEkAopFMTUlUwRULIQVxolESopLDXEBtokoCMA1lZikoMx2yEFCVQJLJTM8ZyTSlTYCiEJGgzEwAVaYDHiiFQGwSOAqBgg067pcOyEtQM0CQgqykNoZ+kkEGHAG8BMj/jyCVlZoN4oKBOCU2Q2GSAiZ0VcSykFg3yzTGKsk1F0Ulg2GKHUqiNC6pMFSRgrzETTYelrZMCs2BkeQwTInDcJQkBdFMUKSgKIAwVYEMHqjUVNWw58G24jlWhSwMRKNSYZRgJKKMGnwiEwfxPJSpoXiOoJS5jkhYAgIzaoP9od7kAUhNyItIpbrkBYSlynVEohITFGNCwpjr3FepiXkRyMxUrIAoB6mea1JBwEJASBgLnXsqMzACYRQLgkrGyyhmAiRKJ1kAoSBQgHJcIgp1lgSxBpFCJi59KVQCdJKEGFKIDFSOCkRQA/X3ywaCEhmoDCTEUCdpGqKSU2QgOSoAhqZIgkRDsuC6jFMGoNJolkWoZNSgcRhxgJHJ4zARUgFqgUp/vyxA0GxqkzFJ0rTuEi+iSZzWKzKTwptEjYbmT1Qm84pNwghleVpzcJgx30ubVVkAMR5l9YrpjZKC5NXfb4qs5sAw5b6fNqulREa/H7RbIvRglGdVB6cpiZKsZoOoEL5HXDoijjnoh82miAMcpL2jK9bnP2++/NJ/kabhP7dF+Jn/8Odnvv+D3sqxlR8/d+7ZHwzn5h75zvfOf+s7N5555pP/9+cvfOs77/7Jnz79hS+c/6fv9peXjzz/4sVvfGt35eTpH/zo8j989fbTT33ii3974Sv/+N5nP/exL3/1sS9/zVuaP/rblx79yjd3V44f/9kvPv43f3f76acuf+nLl//ha1f/8DMXv/3spb/9h/6RY8svv3Tpb768t7x27Fe/e+bz//HWJz5x6Stff+JL/7B+6bHlnzz/1F9/aX95pZ+3+j/KmFWWh3n/F6VsC/ow3Pod11kkJsHD5y0z9yTEh98vqKFgPzn8JdScotzNN3+nGShwkvDOP5taFkAK936gdBSxUbT1K66ZOTyIH/7WqCqfZuGDnxta7BMD73+voETCMNv9JTJoxEbZ/d8YduYTlK7/hDuTPtOL9R9SrIqKhumH5/gEYnRIrl3StrO8O3FfOY37lnR6+pVVfURYvi/vXWJ9imiPX7vo7hRBZ+K8dor23dLpmVeWydgm6hDfO8sOOOKH2rVH9HUczE/qrxxDvVbY9CtvT9NBjcADeP+Mvm+ElcS6smKss2hm0HhtSR3MBE3f/WBW7FcM+RA+PMV26/50Gj4/Gb+FyUkzezU4eJe3rOF4XTt8g7AaHN2myYtxbTqcvK723xZozcjfDHbf1htukN7Ndt82eAVM7pPot6lYhPEbae8Nio4IeCXYesu0jUBu5ruvarYR++t49DslFmD5TrT7pl6ZSqJbcvs10xEe2i82XtBNM413ce9XoN7y42v55ht2tRXi2DbeO0/jSUmB/cZpiTIeZcZ7pxQL6BDSG5dQ3m94O8XNP6KBB3huvXYagxzFiXjvFCKh8Ap043GRDrEM2JWP8fFY2qXzymkgFcoScfWsk+zKUKGbT7DYR8gXVx43x15k585rp0CBcBFpH5xmWQTjnN68zPwQ0YnxzmN8lI2b4egneX6AbBZsvWEU28q24u3favBOyFdp7wcyuAet2XL4KxluYYsHO+8Yal3CBu79GqgbET9Bxz/IJvc5OSKCXyXBfVjV/L0rZnSnhFOi/ysAPoyrR+O9n8HBHY0tMf+FZHKP17RJ/wrxbyE8w0e/VekHqXYcT36aDG4KdzodvQYGt4RTi429BefqTNro67fq4vaKNx1U7pj47hlCtrQDgW+fKfgYD1rVd6bjtmfessXt48rZYutVeu9RBHeMIcU3zmHcx6OK8f5yVh04DzRy66yyt/m2g2+fZ3Kj0k+L208i1McT23r3WFkdiPuC3jqHzC1tz4S3LolyG4WEv3cWg0MS2dY7K1E1K7YPN39jOmXEi+DeLxzuh6yG955VUAIs853nCAeZCNP7L9hGHDGaPviZafZHejV/8EMNZIDBfOdXzChTFKfrL9gsTDSRbf5MYwc+mmKHz8oiAoLm+7+iJMlInq2/aOuTJKuKw+9LchDjGdp/tohjjeio/7wik1jHxcPfmaiflEet7ouzdLfjd73q22162KVoG66f0HaqcSUyry2b9/Roptd4YwHuzPkd3/2gy3c6hrqHNlbIdjeqRuaHRyp3zWCm5745i3eWwvbYvdaCO4sMbomNWboxrexD7fqSfqcZLAxqr3XozlHgrLPb02R7mcFNsTXNHsxKuyduL+m3uvHMbuPdpto8mVR31a3x9sumKaLoEA+eV3wayg/j3Vc0t5PGd+TWq6bNfT5M7//GNHiSjdDBL0ClFua3081XLKeZ5Pfz9ZftBvXKcbbxa8OkcRHgw39WehNk99Ltl/SaM5E76t4LjgM9lZTbv+BVMCoCsPUcFzVVbOTbL+kNy4sPwObvbAOEuCh3f0YZToVVNd9ataOhilN462M8iBGZiCuPW4MgqsXOq6dhwhDytfdPsrhAWUxvXWTjFLGR/u4lcZhH7aj+0qpM7JzH9rvLPJRQhuTmBTGWfrWsvnZC7KukPam/ciwP67kWW+8f08dSy/fB7ctsSLxGUXt1zdrHYXtUeWVZBq1cC+yrR/DIiN3w4LdKvhfXl6KDX8D+dY0e49GLSf8mrxve+AMw/oiSLh29BrN3Um0Ne88l/Wu8Mpd5b8mDD3VX9/zrcPIetprZ4E0YvCP1NZQ+H+5fM6tT0fgDdPCeqIpxdBcdvE2tZja6gsevq/a8N3pZ7rxv1TuhdxXsX9Eb2sS7B3tvYKddTD6Ao9eAvVgYWxV88yIpt2tDr7j1DCkHMBHW26vKmrAdzK6fQ9qu6HF467Io93Ei+ZXzJO+hUrPeOi71gB5QduORSnkjmzjkxuMs7+Oi1N49T/NhqZj95vFSi0QPsY8e4WhXTRi//jhJ+hjkxjvncRaUCFdeW4lNTIdSv3ZGyD2VUHrtMk39xJj0v1vkA6mZ+eEvFZhkXKVbL+qiH+d13vtBTjYCeoSOvptGA0pqePRcBg4Tk6SbLwq4l5RT5uB7KX3gu3PJ7g+Bf8BYHY1+lec9UIHDnZd5uZWBOWP4vUTeS7RlNPp+HOxg20l6L8FoF7p4svcqBw8SMa32f1Tm93L9GIx+FA22RL3hf+wfv3bm2z9YP/XIpa998/KXv3r1s5/55L///IVvfufhYxdPfvfHF7/+rcH8wonf/Ob81761f3zt7He/f/nvvnL9Dz/59Je+fPHvv7Jx8cLqT5+//Pdf8aamVl964ZF/+Nb+6urpH/7kyb/80q0//OSFv/vq43/35a0Ljx777asf++Jfj2fm5t56+4k//6v9tbWT//z803/xV3eeevLst5598m/+brK6UH/z+if+/Ave1PT01WvP/NlfbJ86G25v/cu0CP/4vzaLJOq2eqvLOeVhvdo/uqgo8dqdw5PHsJSTdnvzkbM8jpJWvbe6nOpGWnH211YgRmGt0juxjBTwO+3Nc2dYEqeN6uHq0ZyzsFbfPb4KKE5qlcMTx4CUYauxfe4sLfO0Wtk7dRxhEFWqOyePK0ayqrt/Zg0q6NfrhyeWM8Jy19k7ebzQBaZI6ygkAGKIzWBEAScZn6Wc55Bg0gbcKBXGekcSAyKGzVZW6ozjgk5hTeQlJsY0pKaSEDmtDOugFNxuF8rknJakDbhRAMx4R3FLKYCNjsQmQARZzbwwGSOSdbHQc4CJaJSGXSTYMNqSuhgBWdgh1AOIaGEFsOKXJYeuJx1fQkr1YWZnBaXSjpDpQYRzK1BuACVXticdTymEbb9wMgBBbsXQ9ABAuRODykRJVtpBWktwIZHhlW4CEJRGmFdSWpSFFUF3pKSQdpTVIlqUSJuUdqAQzq08rackz7glyQxGRalphd6W5f9Ly3vuaHoeZppPDm/+cn0Vuqq6ujo3m0FUIhU8ThhgPbbnxwIL7DENFrvAeLAz6/WsPLbXI1mWlShTgaJkiqSYQ3ezu9mhcvzSm5+4P3YOwP7hg7iAG7hwX4IzYdkKIdgS6TtLTY0ESRBfw8Q7KQxfQRRZGFC6jChxLPDhsrMQ0xjSCwR6K4SVa5hT4wXhI48ZgBJHy9ZhTCMULSlFhWCarWBOjZckGHvCPBS4M6paJkiM47ExUchcq7M5ChsAkOvOnWgAQSA71wJjrNpuEeFpxSPXy2FQA4htZ+5F6wlG2URLiJA23RKz0mNqsjmMa+eJ705doACGIjwsEo6x090KixwgotM5iGvoievMbdAABFBnqiKIgG46FQ5K77HuFD6tsIMyM7SPPEa8C2SmIUR8iMXQWkN4z8uh0R5HHUuGGBFMM4+GhFpDejAcWucp63oyRsSYINFkiDzBPAV0hIg1pAu6/arwIe9CvkKosyJxYgAwdLhDyBJmVrMuDJeMNlh0gRhag6hMnBgjirxj1nUnFmEgtBoW2FlMKjWsCGwth22/glgj0rreDGDomPLdScs4IZUelIQqS6HtzCE1jjnfnTjoYeBAdqoZJ6RR3ULispLSdWeIGUsc6E4cgVA6mJ0qxgludL/AtHEU2+4MCOuJa4YVoIYjQ5agCLVHjI+BSKzzOBg5mnhAcDzUNqYUe7aMZKgBpnzokqStUBQuWdaBEMOor33GCHRsCQdh6yCRY8S6zlkihxZnACAU9bXvcgosWUKwi7lWdISCnvUO8xHEHUi9iTrK9zmGjg8RHDHZWhMW7bAhxqMgN50aEORkpTsNsdZGFexMvRMurtt+RYzDcmGThcfIhFb1WuGUC2vXmXjPfVCrQU2shbJy3RIDowLjO3PggYuV602hIzasXTbVRCBR2m6JsDPS+GwOIDShBt1zb4mPdDvIuWs8J2zkqfBQkGjZQoZxAOMlpbkQVLMVLJj2nIQrgAgPGe4Ma80Zikg6NlZwwSwZAyYcZESOPREeUhyuOM8gFSha0koKwaxYQVwYQGk40DQEmstobLFEhPl4pE0UcKTIMpZCOUrFCqIxgt6x6KSMMcJAZzUOFgARkyxcWkNHfDq3YeUhxNlcJR5503YqFBQeIJWVIM2Bo7pT6kRRZ2A8M4lFwNq0NImmSpu0gMnCAWHT0qQNtRpEMxvWHmOVtTpTgdY6LX069467tDJZTaz2WdVmCquWpqA7rAsYsC6k/z9BkRVLgCAHU0KXCPOGZf+DIN4BwZLViIrQyWXIiPMRFkMHESIpDsfGesIzEA61xlwElo8hxRbGJFhyGEGU4P6oLHDIMxQvGcuFEJaNPKEARThYshBBHKJgzQMiCK7bbhGgRSVD15shrjwGrjO1zEMOYHaqOCW41b0Cs9pRYjszILXFwHcnjjtAnIj2ZnHKsGq7JeGFx9hkCxAqAKHvTg0ziFjYm6iAYdA2vQrLygFougsYtABBNSgcMwQqkE7aiBDXml4BZekMEQPHOs4jFPUM6DGMPB0i1CNcKTpE4cB6R/gA4D6izkQdBXsUIcAGkPQxVYoOYdarCh/yIWIjTJ3mPS86DkJIB4QMMW1bsYTlwGqNZd+zvncQix4I+h4j4Dso6BvnMB/gYGScI7SP2MAFqloMRwc3r2Nvy/Ho+Jlr2Lj5eHR647LBpOn3Tq5fAZwUvcHptW2AUNHrHT97HSlTLI9Pbl01hLW97OTqNhJoNhif3rgCKM17vZPb17z1zWh0cuuKJkxn6dH1Kz5gdZqd3LhiKan6/ePb1y3Cba+zuLoxCzsujY6uX3WRbJL0yYsvgo8//pcqwn/WwJr/8Z/4LAYYHF3ZVlzaODrbuoign47G1VK/ZaLp9U7X110YQAyPrl1RVFghDra3MfRVv5+vLLVctFF0unVJS0kQPLlyyTJeR+nx9jaCoOxk+epYBZGNwsPtbS0EBm7v5g2MQRMlx9vbAOOqmxXjQRlELpCTrY0q6xPg9q5fY0AToHAPI4kCUPuRBAHqlpN2PeXM8Kb0F0LBLIct7iAXEOFq0icu4Vk1sxsJZD5sS70SMqolVEHmbCYJMLSHVCrSaqbWUyph2BZuOcDCMahxBnzCIleTPmm7YVZO1WpIJeCqBqthwBXBniReZYw1eZtYl3hZzes+cNJTU1he6Qx6oASaVgOGdaPDxiaAVnmVORABpheelTrzHmgC5kWfsqbwUWsSz9uySVofe2BKxKqqg4XNKZzlA0qbwsqmynDUTF1YmRQDU0JcVAPCzILCRT3AVNc6bMsOlzqXtLVLoSA60Qu7FlqCE1/o5VC4RgrFMuA4CUirxjHFOlHzej0NQcOxtuOQAiVpA7sUUx/C2qwmhNi0nbfrqfANRQYuCwm1YBp1iWUo9CVY4kCQrJ3rjZQCJZB2S4IBLZkWmVdxKF0FR6wMg3R2VC4hjBtiCxe3bYCRzl2i6wjG5XkxxoLWQLXFECLSIlO6uFUx5qZwcVtkLMrPF0OAhBX1ohoizw2xuQ3aNkFU5yQsF70wLKdFz0HpSLWoeh4KR83Ch02bQKpzFzR5h8X5eTPwUBralG1H+wBiaELaqHESmobGHvSYbCvbIyQhzNYydqDHEAYhqfVyLHRNI++GYVwvWOZBh2HXMmHtKJCuiUjTLsfUKB5aPQzjdkFTIBJvKQlYq8aRtLVArVkJI12SCJp+IFXJQw16HAMT0haNOUIgQLVZkkyVDheqCyHSDMyLPvZYxdXpbJlyX2JbVkOMfYPB3CRWE8vcQvec4TCqzmZLmPoG2aIZAAIqDBYmdZpDqma67xsO4+qsWCESFMbrugcQaBDMdea0RFJNVN9XIY3y0/kYU6ywLesBhKjFblF1sAlgt5zotZiEOGoKuxxg6ZnXJPM+5YFvaAe1vSitZno5ICEUbQlWQsk1xZ4mzmZM2IYkXg/itJyDZYFiJJvKDBiNEAONSAHoMO5anrh2EKfFzI84yHhazkgfwoRS1+IY+h6PVc5TXw/ipJiDIdVZKJozwqqii4UrmJ8WQ4JU43lZdknUzH1Qmgx7WyKUVwNCbcH9rB5gYlormrqLWL1wPDcZhr6hKF8MKLYVt9NiiEWbW9HaxBDbGF64xGtgOJi3AwCQ5W62GCDeFoa3OrPEVoTNVYo0AcxM8iUZuIp6BVcCgY0gCnWI4ThyJVziPqBZMzMbKUZauNaOJUNKEiUyp5NAugaNmI54Vk2bzYxTF7jGLQlCHCMKdZALaWoLMJZtIjvlpLkQM2qZU2AsAmYodTiDJuGxyv2I6UR0i4m/EGFuuWncgAGJiCmoyIt+IKt52TUgcKTMy56H0lGTe1mpFBJTel7lXRYV07bnQGhYXTadFgQemsLLtklg0M6gKBY9HuTTuuuaCCbVRKetSRBUueN13UFBOwOiVh3E66LuGBVDUU5UWNsEYVtBXpVdIpuZE3XZE1Gds8Tz1DtKA9rqccR9E4JarcaRqUjg7TAQpuJSgx7HyESkRUsMYBSBUq1GQpU4hGhApddcathlFoMIVmAsEYWJL/WFmNuGSA/6jLk2DAxNfRtFoSvAWDiKIlfqC0ngWyac7zMKDeca9EjLYFKd5StUotJaVQ0ggg0CuUmtDpBQU911RUST/HQ2RoRoqopqgCBR2BUm1m2ApJqCVC06UZKfLoYAMYPaou57RDRxCx+rNkKimqqOLRKazk+qMURUEVW2Heu5QTZXsbWhC6tz02mLRCTz03wIIHPcNzL1oMeZ1yI07VIcl3PQp74nk3JKexCkjNiWRN73RaQLEdl6lERVgXvYdEVSzUgP8dh7Qnhg9EBGbYFDAAY8agrUJ6YjoragiQMdRoEKpAYjQaxmoXcDFlQLMBQ0hdJpnhjf4c5bybVayajVZX94urmBEFoM+tVSvxShC8PzSxtN0kEE7V29Qrwtsu50fc1jUo6WivGwiFPL2eTSRhUlCOPj61cZsLPeaHphxRJa9geL5WGddiGlZ5c286RDvN+7fg1jWKW96caqFaJMs8XKUpF2McHzzeXJaJ06u3/tKiCwCuPjy1f4W2/+SzMN/yxFOP3jP/3d//R/br7+q9na2tarv7z82uuL8fjyK69e/+ErD77+9Zf+7D9ffvVnd//NH7z0F/9166c/z5fHa//02+s/euV8dWP7Zz9/7jvfffDyy1/5i7+88sNX7v7O733pv/23yz95tRoP195858oPXjlb27z4+q+/+Lf//cHLL33hW399/Qc/uv/ll5//3vevfv+H8+XV9bfeuvH3Pzxf31z/zVtf+ov/5/7Xv/H833z75vd+sPfs7cs//9XNv/vuZHntGKwe/hLxyOpze/gbBpek31EH7zMiPJtXj95OqLcQgt3XOA6cn9mD3zDWAfrQ7b0fCKRE0z58MyHeYuh2X5OStSDXO29HNDDu1O2+JyVQ1LSP/ikidYMk2v05wxTAQu+/xTk3YGp23w2wNgTbnV8Fsm05ax69llnkOoyhh9dwxQmYgs+fY1Pqe2f0g+dhmbnonN25ymvG3Nzs3EQLCXmBHtyOzmgzPAs/vO3Kvo/O2GfXUBlBt4A7l0keA5Hjh7fEcVCvzqJ3rvlqWXfy6M46mEfEz8H+FTzvNXEjP7ssD4N65Tx7d0sX602/iu6s4Fkq7LE/2Ibno3JZla8XJ58Jti2qN+uTB6Ib5LM9cfopxSmZPSH5u6a73Jy/jw/vBXSTNx+Z07ssjZV+3O59GpEUzffY4m0j12H+njq8G/INpj5uj+5wERpzaPY+DGRoqiNy9DYO1kD1oTn8NIiXbXEfHH4SRLx1527nvVCEujpCR7/lvVFZ3Ie7HwVJr0G6I+7dQM55rNmnzzuiaO3Y/esuUGRK4NObxOZZPasffx03ynPNPn4WYENbi+/fALgkcwKf3CK6JbYCD75Aa+1lTT96wSMIjcUPbkZ+DironzyLlYZU4TvPy9LquJIfPusQgUaTBzeQ16gy6MkzWHtAa3z3BVrC+aA8+7lvpygkzeGHvDlFIjJ7b0vzVIdbcP8nqDiT8UgdvkHrMyiYPv5YtIcQ9+jRm0g9bMPL6OwfwdlxyNfo7FeqPEYxrY/vynKfoAE9fROqh7q3UR+8xk4PArpK52/b/IBmvDz/FE93OBuzk7dRc98EV9Dkp/r0IMxG5uxDOnlKwq4Vkw35YE31p/LzPtq/UgyL6Gkf721BMhXH1O0/40UN8n5wb70ZLoLHPbR31XTO+X4P7l0B4JwvJHh6C7IC5il7eE13JuFeBnau+WxC91O4f42BSTh37c6LEM1RmdL71102EfsJ2LkO5TE5zuDudeIWuIbo4S2C5rAJ6b2bTVfp/dnBbzlzmrv28zcinLc0hTuvcgAhMXr/DYaJw7nefTfAraXUPn09EKUK4+rzX6TaQQLt/puCeYsqtfteABtHmHnyukRTxUZo/yfEOEqx2f8NR9pA4w/eEaDyviMOfgrAWcuW8dErMLchZuDkDY9rRb3be1fqHIGLov/Wul+stYN5cmcNTLsYTMDBJTwZtlHDP9+Se1G9Msne2zTzzXqpiu+M8aQv/CE82gRnq03a8kdbyV5SrZzFH2zY2cW6XyQPx+BsTNA53R/D03UYT9HOFba7VF44Tz5Y85OLLj1jT1bA8SoGZ+RkGR6tg+gcP73E9i+0o4P0zpo9vdT2pvbBfOeDWEjTTPHhG1SMXfuZOfhQhkuu+tzvfxyErIEz8+SdiHOjZvDgNyLr1c1TsPt+EHZV+9TvfSgj0vrCPH1TCtyqkhz8ExN9rx/rvQ+DJG3Uvtt9P6DQeO333xAJqW3pnrwR08y1R2D/XZFFTXPs9t6LKDTeuN3XBaIuSHv84+3A1rA27ultVDvIGnz3+WDh22wRfPCcdRLAity/jixArUJPbpIGAVahe8+zGW6GVfjOTQMyz5S4d4lUDhoFn9xATVCHKvz4BpvidrBI373cuJETWt7bpDUSauJ2n4V5WHVt+OG1YMKawTR+73KrR0604f0NWAVtUB6/Dep7pr9RH7xOT54GbJ0t3rHzpySV9fwzcPpI8hE9/RDXn5rwMpz+Qp88DbNlM/mYnj1mgVTFI3jymYh75uwOn3wMo8t+/ktz8jiIx3r6MTj7nCeiKZ6ik7s86uqzyDnhLAAAIABJREFUT+n5p3R0IT97hx7el1mvnn+GTz/jsWiKPXjyIYm6evoZO/mYJhe0POjD3RsETOKFap5+BfkCKcHu3fTxgp1K+Pg6FKfkLAFPbxKX48aiB89SnwNLyd1nbFjhqURPbiZ4384i//Q2tDUCkN59hrrKesTv3rZBjacCPb6O6RzOCX5yGzsFoSV3nkO+9QjKj282MeILih9dpebctwI/egY6aMT5wU9I2zAh9f5vuK8t8u7gPeEXAHbZwc+hO2zFBjr5MZw3IY3h2a+dzy1HZv8DqWcI9NnRz4E71J2VZvenfJYHJEXnbwK9gKnP9z4I6lNMV8TBT6HdN3ILnr5iF3MZJvb4PVaeo5A0Rx/w4ogkI737K1ns+HjTnr3qJtOolyy+8J2/3frpL48uX7/5ve8/+53vfvqHf/C1P/svl1/92f7tZ67/6Kfbr/yk6A8333zr6vdfOd+6ePXH/3j7u/9w7xtf/8q3/ubKD3588Oztyz/+2bUf/bjp99bffefyd350fvHilVd//uz/+51H3/z6s3/17Wv/8IOTq1cvvvbGze9+r+72lj755Nm//vb55e2tX7z+3F/+9ZOXvnrj775/83vfr7Yv9N69e/tv/rbKOoPPH33xz791eOvZenfnX0UR5n/070KoVCc+uX3dMd524rPr2xj6fGk4u3IRO1stjw6evcVMq9Pw5NnrljIby8PbNwjyzag/ubIJEaoG3YPnnuFWQUGPn7vhBG3S7OiZ6wj7tt+dXLtIvNdptPvCc5AAH/L9528TBpo4PnrmBmJIdZLJ9UsAQ9VJT5+5pgUHguy/8CwQhCEVjp0QhmPD1ogQRriWr+OY1cD7eMWL0FJgwiVLQ0+Ri8YWBUB4LdZgECgEQDB2MrTYu3TU8tACgpKh9gkRTsl1HMoGWxiNLU88gS5csiI0FLl4pFFKmVViFYZBi61JxjYMW4NxODC4i5nVLitxOCfW+nQGkxI7B8IZTHPsDJcTk5YOIRAvUFJC5WE2h0mOjQXh3HcK6C2WC9ctifM+KlCSE6N8PENZjoxF0UL1G9Y2mBeuVyDkQZSbThO2BYrnIMmx8yDM9aAJVIFEAdMZhNBHpepppqswVGSdhaYU3CQrxnImmOKrSIA2lKrTbzwhItDsAuW+lUgF60iiBnMoViHDRoo2WnIEOEEV3WQSthy18gIMUIOpj5Y9Q5pREy1bQr3gOlrxkAKBtFwDgmlEfTh2nGgmbGdYQ4Yp99GysSGVqrb9CeY1dgpkUyRrCBTozL1omWv1oIjwqUIcZGcwaIEDsDNFosBewXSiIyBcrQYV5XNgMOhOcFhDZ0Ayg0FJvBLRUZ1AaKHtzkhQYaNhbwqDijjj45mPKuwNSiY20cwp211gWRKtfDZzScO0ijLNBx4Bzzsu6Dlqlei5cGCxdWFHy6HH3oZxK5cg9CDoOLxEZFNGPR0MHXAgzDRboYGqg9DIFQAgEJlhSzhQZdi13V6loZCx4WtY2FrGLlxyxGueOrpMhK5Ex0ZDg52TkQ6WAcJeCCVXEPMGshJ05gRqTIp2qaGgpqjSg4VAlSfW9HOCFQNz0J0hoCFpYX/isaWgMqOckRpi47tThDRACnQmmGiIa9CbeWwZrO1wIcGiJRx2JogpSBrYmSHSENyC7MxIIn3ZDkuOcwQN6E4R0xA2alBj3kjTiAs4lA1yIB5pngEMXDi0MjYU2mhgcIdQq8QKjGIFlUuWbRTVGrGor3gHEGCTgUZdTLXlyyBINTFaLjnZtdSacGB4ByBrk74ifUKMCpYB7qKgysMlJ3oeOSeHnve8NHXUNbhPsDXByKEBDesChQs9aIQqCc99NwcEwKDQ3SZUBQoXMF0g54HM1bAVbQVZjbIJgMAHle43QhUwXMBsDgxAolKjktka8cp158wrJxvQXWDjnChQZ4aNxiL3vTmEltDK9RcUah/UIJsTp1CQ+2yOgQGsapZUrGeIgHjZcWYYM+HYYuo5M9GyhwxIqMUFIJlCGIRjy5mh1HeHFeKQMBCPlQuY9K3cwAFtKYTRiqWBo9RFS5ZRzZiPxxrEjGkVrMOANwTadEkxaTxB0ZIl0gtikrEGMeFOiVUgA0OBkcueh46ZmofHTeKQdy5boKjCWoPuFEYVtsYnMxBXyBkcT22isAW2s8BhTrTy6QwlJbYGpLlNaqoUjmamo4i3LlvYtI3qBcxmMC6AQzYpQacUqsTRDMQFAAhkM9WxYbUA2RzEObYWxIXtVMJWLi5U10pVhqnudmuDWBAbukaFq2Vgg2XAvGKxpytE2FokJlxy2NhA6mAVIOwlU+GK56ClEQiXHLVaRCYaOwydCHS44iH2UthgBTCgifTB2FJvROJ6g9ISyqWNxsYxIqkKVoGASgQuGDmEnYhsOHaQeOZrM1yEcKIIg9k54hqiFnZnmNYEtSA7NxJJV7ajkuHcOwi6EyRaCFvYmUFSEaRksl/EnGtl+nMiKuwU6E2RaAhofTYHQkHvUO/Mhp6b1gzmmFfEtr4zQ7LGoLX9CvKK2Qp3ztoYCqv0YEF4Qa2J+oZ3PXY27irSJ8SaYORx/38QJHseOhf0HRsiqZq4Y+gIY+fE0NE+jpo8HLhOp1aAy47lS0jYWvRA1DPIWdn3eMzCumQDH/UNti7IjBwCAqxIbdQ32Nmg54KBI1qHXROMHPZeZI4u00gXRb9/cusahbYc9ibXLiHvmn739JlrhjEXyaPnbmEKql7v7No28aYZ9E9vXAIEN1l0evu6pdRF8viZ6wyaMu2e3dz2FNfD/vn1S4ASncYnt67oQGIG9164DTlus/T82iXPSZOl5zcvW0JcEs5ubi3iLqLw8PYtL6mKw90XX4Qffsj/hYrwn9V1cAgfXX1GQ3q6ulkpWoroeHWDTnLfX83jbO/S9fT87Dwb8Ks3jfHHK+t6sPnk+jPz0YjMyjzu5mlvf+tqxsJJNjAvfPnR7eexRBWSBYyOxxdg2TYiqWnw0dd/F39JVVFysHUVz4rj9UvI6Mrwo5UNa2ALWZ507NZVC9h0uDSVPXY6OVrdkIsSD3DbYd4DXzZtEvDEmFPo+lHdWthFesgscn6o6y4nsAlXzm0QtWHHTqpqFBOLbAJMXxREs5WjNg0My1wFVMZsFLhJU/eFLTXfLBQhVTwiw9J0qJLYLNom400a2tMajYWCxHdd2ceYYz9kuoNMJnWOdIxxJPnMNSlxcRbElWOwTSJSea9BlUlniRYOSH5h+E4u0iJe0pmBCLRxwCpjHKgybnkNoCOCitAoiaxMWGaQt4uIZ4MHArvzdJVoDIgqYs5D4wTSUao7FhpYSIozwzQyvZA4rLArZSAGIcF1GYVgpKy1RV+2hUS6Vp3Aa4UIqRLUIumwVmkUWAWXRBlg4aHzuO5GCGjsfMuR3sZowxtG/FJkilr1Awg9NNSk2KcOjWErgYMQW2Vi1EgJirochX668DXWXWaphs7WcVxxYVrfZrySTERWZdBRybNWpcwxyioHAlhIGpdw1hey4FWt2x4jUg/Hby+izpyOZOlAwqqYuByXPWy8XFn+zWkyLllHJqVJoQ4krRwSZJ6xDfTWbryJIWaxbXvMhlinysbIJowVGqZonrJ4DqseZZTy2LQdbBn3A6yNtgOpSo8k0pnBHe0SWkTEjhrgQJVyNzS2wXVK6Xrj29ZiAVOiJC8i5kcaKVtFIhweIwTnQaq7jADdpCHKtBN4yjFcqpixi2jMB85WoOhjX9QWkTqJZEc7SMuQtUua5LrJuNLIEdukFCkLvWmjCEHCnS5C7p1ytS+6AiDgnZ1njBUQZG0dhhGfdYJ7+3ytldTWcN6ToKiANk3CI30a0EenndWCMqGdDVAhadqieVdGaD9O3juOR74VVBVtJB3HsjEmkzXGvkBFn9iKw9o1nYhYx+q6CAOSxK4H677AFNvU1kuhSRAZNLZLyoTouWozrzvSnLVwLBYGJZuThrG57IEhUTHzXYbmuk6YTgi9MrcU1kGIe8aPWBkhO2p1jK1UcnVhhajTyHRaPMB1Eolu1fSpiygYtT6kdRbgvlMR0/3InjZoiGoRlVlOjZ1FIerUrEZNLwAeY2eKmNLQAQJ1lKquQ40uJU5H+5GeH4VDDIlxPo8YiZwCto06nbWDqJ4/FbdxqiChqiudwso7JYPl3l1v/SJcb/tI5rqKiIWEIdj0ZGyB1UYlkjQOadXGWd0ihlwTYtaJQEPaPgWVda2pEg45ILq1CVRx6OZlOYphPvcd1HYlaHNmJ0Uc1WFkGt9m3GFpp7jsCTNXdD0vo9CTlAzqNuUmBLZWXpiC4ejyvM46GEau54suZ16DEW1SgjECudJdWvIYnORqHBJdoD6wPd4IbBIHKZr2Al6jqk8Fxzy2TZe4ONWZdhKalLPSwADnXbYB3z/lfcMpj0ybYRdEJlVG4iICvaWPLON5sixzqDNXRCIIte5Ql4T98R3I5bHYQKnDjNgspC2oYlCEPIyVCb2Os7YHPfJ5THHstSB1wnDauC4vIqCWYzdTJgnpwFniyh5FFbEaN2kkOs7HpKXA3KDQOIsaPQqNV02PobqAIdMh8NsAOlgHwC1DRJRJcAs5BKrsUiYqcEG20plKw8AXUVq70HrbdqWTwpi26lPsaoWQ73LoFCG2TqSSxNdw1gvkjFXWqC7HztOmVBG3Hova2lTUjPgcFT0CWjhEbx9nK61LRF3pmBoIeWNwSGcpLeTcESchJoltu9Ijx1JlYu7RbAW/dSa60yD1U1D2KHeYJqbtMMCITZoy4I56P7egI4tYwImvUgd5akcNFNB1mB9ozZHtReZMoyFq05h367ZDfUzBkgIUNyGNVipDUBNHpuNIB6gkJJ3ad9hMYLJSOkrLZMh6TnFa9LmZKNihlQiy4RPMeBmP1VJDrW26XJXeY9x0jTtXKKVVTHyvsQQUsQTLGrag7sZHK5skaY5Ha3xtPk96VZjsbd9oRTwZjnMY+ro9vLCFlV6IbpH2Pn3pd5yHWKLdzcta+elgPAcRaMzx8oU2CiYv9HzEfOMLmc2TbnvxiivV2fJaPdra3b7WpImDqDb0dLisLK6gnGX96hLHRXs6HJ8PMnlyfrB5eXC8ZxVa9AaR9/86Haw/+pM//A//YfWjj3wYbP/slxd//WvA6KXXf3Xr+z98+pUvvvyf/vPW668//cpLX/uPf7bx3rv1aLC7eTnvD9Ozs69861vP/MP39p6//fK3/uriL147vn7jcPNSG0SDw93nvvv3W6/9uu73N99888vf/vbJ1Uu7W1fLJCNt+83/679s/erXnrGL77175Uc/qTu99Xfe+fK3vvXky1/80l/+1aWf/WKxeeHq91+58trrLogO6MbBP9FQNOioOfxAggzDJ/rkHotsxU4XT+90adEQp3d+E4egCaKTYmuUNEX5ITi6H0XFPC7zzz/q4clCphN3e0NMTvVbs4MHPcEav9se3o/SckH9fv3itjg7Bcfk0RuxxIpPiv0Pghjm5Kg5+Czi85KZZue9JD45C3l1940RA+3II7J7RcyRbM/84XPRgRfJDrr/DVQmAj3FD24FtQ8W5/bkBp9HXf3OH1OwfvrZ5yDjn70My4zRffLwBiuFF+fTflrGyfjxGdx9Pjhgrr8f3nneVqO08+Tfl48vKXd6btTRc3yWeqn44+1gT6LsYXL3tq6WXVKHDzfklKX5vj+5iOcXTG82/3Vz9jmLLqDyt2b2lAz1SXnITx8KHOB8D9fvmeXO6eQDevQkTqOyuLCkA8nmpX9ndvAk48Lnh3j+Lugn03xtVMUpbZrmbX32iMUu94ft/r04wJVdC2fZMGrn9S/rg0dxkpb1PX/2SCTVhE7VzidxjIrmFO++F62kJ/Un5uBxlgVzWXXI0+usMhg0+OGXiSuiWUF3bgFSBacCHV8R89lwflyefpPNmzH/+E/VFNv2bK9Hd25RsAjOETy+GuTtqv3t/8yj+Oz+nlrDD14irhVFhXaeSarTDf/e/2R7wu4cz8foyfPxWQHYjN5/GTvLqgbt3JS6FDOHjq7RhRcgB49fCKZ20Z+f/9T7wmf17PiuVMc+ItXex5H/vO6sVnuvRfkZ6cb50VtcT0FH7bZrIZYEHqmDD2P/sOmuN8e/4JNTka45JCoX0+CD3fPdXnFAcEYm72P7QA2CXd2h1dU18unR9NNEHbm+Op8+QPNdwbvw9H1cPQCD9Xz6M392FqVhOfmYFgcola3ML8onGzh4Ih4OwflV2yvix310ekHq8+gU2ZNbGDW46ASfb2t+8LvVb78B5YHL1ePr5GwzrE/lnNn9m9TPvhTs/VtYH1c1uLfsz28QdhzsD+DpVlTsvmDf/5pjOj84nzxDdp4T8mn4SPrzmwLsysMUnF4OiwUrHdq5FTWHtA3hky+gcGoenhzdkUleyHr++KMuP8vTuP3stZQ6E9b5/rtS+oqf1gefxWxWh/io+MJFfnLITvRnbw2JtaKtdt8LRNME84fN8+v45Mif0d13I3pchN125xeRbX23t2g2unByAh7po/sJXngnwMkbhB2V0VAd/Fw0ljHkz95BfL4Iq+bgbqhnWKyY/odXXbGM0klwd5PlYdicgZMtNu0B0bLdrfBphNIHyd1burhA+qd/vPjoegXn00l78CyaX/Bhy3c20sOuxh//r37/OpB3LeQPr+NZL2pO+PHAnW6G2eEftAdfxPOPc5B8csWUFxk9Ik/X6XQU1SfkfOiPNjg5xAeXydElEn3S+XRVl9uen/r753ufpYGr2yk8ejfodEt9p9n/LIqTurlvTz6XWTVh8+bJJxmfnbIVA64sy72d+m1z+LSTBkX7wBx/HnabCeLn7c0L4dlZ9Zg/fTfspZV+WB8+iEfqgOqz8ouXg4dP9DE7eE/26xNX+4fv9DpxVT+xh3fDPjpXO/bofsjLmmiz97YUvpZJl9/dTupzOrX+9FaQA+ILuPN8dlI5eUo/exkZzPQCPbnJWrfu3v13mmf49GTR80++EB5b3zkLPv2CdulqeO9Pq/NBvTifRO7gNi+kDbS8f00eQx58+r+0hxlAO2rEPr8RL5podmZPbtG8YxIV3L2aTLiXj6OPbxnfx6gNnqzTnClaHn8IzV21OjrZfZ1PTiKxTPN3bH0A+3qyeOAnO6FIwdmnuLwLR4PJ2cqaCoJoMjl/l833cGoX9Q44uy+SqKpWe0XayYqz2av6+DDKonLxCZo9wSGv24u9PO5k07Ozt/HJg2irt3f2Jjo+SrpyXtwB0x3eU5PqAJ7dYYNwevZAHN4JxqNpvLcMTy4F+Ul3llcnL8lmRluCnnyRs8NwB4GTWxweyqMInlyNyvk2+OCPMUOzJ8eLK+Txi4yc8GMBjm4My89OB9Gi13GIDu83YP/5MD+FFtJHX3KkvGXe+UNDdP6kOt5CJzdo0XJv0MPbssohdOzB8y40/JzT/e2kPSQ5RwfXuSYAPN59LVE1CH15+K4kZcPK5vheAGfeC3DyJiF7VbrcHP5UFDVPl1rXx7Ca84f50cNMnWPUIaf/hNBeM4j2mmHUrA7gR5OzT2M0093i9OizsDyiSTg1z4xxm5PDxfmv+KIQIWrOPubtqe20s5OHQbkLs6x8+mbc7uPheHb6U3BeRCN49JXvf3vzZ7/MV9du/PiV5/7uuw9/5xvf/D/+4/pv3srXV2989wdbb76JMVp/992r3//RYnlp99rNPOkKXf/+//a/r7/xm8Xa+MaP/vHS67/SWXxw8+ZpdxyXi6/++f/9zN//w8GXv/jin//Xi79+I19ZOlzfLrJucnb6wve+e+N7P6yWR9u//NWz//3bR8898+zffufSL1+HHTn+9XvXX32VAN9/9PmL3/rr4ytXq/29fwVF6Nz0T/49iiJAyL1vfr3hoQrCRy99hTg/WV7dfeFZaGHd6d352tcdgpCSBy+/XAUxstZDSNt2MRrvfOkLljAl5Me/+3u0VYYLoRvDRCvDz156GRlb9fs7X3iulpElhDW15oJa+8nv/4FjpOXBva99DULUpunOi883TPogePjyVxdJl2n1yR/+IaSAakU3mM041QpvSRpaUVbgekxTgCofXCEohqxu6JZwAtCdY4dZ0+3yvPI3E9bBoHRkk6II0bv3HaJuNAbeB+tYDySe1+RG7IlDRwcaBjrrRapgq9gOJdVWrkEzTtG0wldC0YduYcRlQToQaifGyKxKVNumV7cDxUpf94pyGbO8seGsWvbIABosFsvGGKqyoh4RPH/wedw/7F/AZWPFrFlR2Dgoq6pb1zJibYOooi1pO2W9DGjRgqCYLGm/ODwG7cPhNlTYBbP5kpF5bbK8XMNQOUcW+UotmhKyulptgaMmWMxXSFAuJNPuSkRdI12LbwQqiISpyCXKnBbc8E2imWSwBbcybJThnBpNkcPIwUuCAhuhCl+UhhFLKDaWIMNVC28mmDgCvFwHJhTOQ+Y0ApBgF1xCTRjIsgS3UyYgdB5fZIR6BptgHek4gtYGV1mZhEneVCtzHRlUVWpUtl2ClNX9ednxdKEWGw0PFlqH9VLeptZM957K3mm8RI3Ww6roI1Lo4kKlAm8WD+51LpxFQ17mqlvUPUe0w93z406KZnc+ClfrUNISNqtN3QW4rHSWq75m2uhOPh8Rnrf1uHBJQRtS93PdMah1MoPuUuSVJz0o1hBuPF+CbiNmteKBotsSWcdC57cSP1/4ycInCW0tGROwGZKq5bGzG4Lu7MOyacZrEDMetHhT8EUh+oBuBO3ZjJ3Ny+GKsA0JMbwSEm1xCv3FIKyqMGrdduwtkLwVWxRAwLhHW4yqxpO2XLMOO+zK2bpCvhRtO92sIbXA6HylhFAzM60vuIaaKt9/kFysGOVNM79kMG6xd+Uot/ZsNjt60F2rww5SbbsyrQLMynp+scVgsTCLz4eXSiComZcruhGQaV2vlVVCWV6XmyWQBimbr7ZGONrO52vepIhOC3w1Il2ACoe2OBgJUdV8BdklSZQLloFZTeCsIdsScsD2d1tHcZZha/gI4GVOlWVj0g5CdrDnDEHdFDeOr2O3EvCyFgMPOh7sHiHI1NKYlYpfQG5ZBrMcb3C4EqBGsz4EK1RUpRhBtdUhuRZj16x14/kM8Hy6onmbI9bM1zSw0LPFfFmJonZxXq4haJxHi/lq46vF3Mwe97ccEJYX+VLNi9pFebnJqsXR3Cw+Sa9w1yDULNYabK0VVZHWpNnf13Zn+bJ1iNCmXpooCBFs842WGuNEWy23sNGAz6o17wmBpp5vthQaZL24TBF2wrf4UgAlpsCFW6CJQ1nW/nbCQgSVx5ckcRW+/8hHqe30kLXhZdomgixqcivxtoUnJ4qlLogSW+BN5lLOjI5X7VQG/GDPRgOcCNBasc1xCLF3dJ2SBFFtxTYr+7Gclegql6kHCpB1ilNIlMHZdDqmuHLluHJpyQpQr7Z1H+Ky0clCjRRV1qb5WU/g/P5d0pmHIWlIPSiaIWBlY9Jq3lN+9nSXiafDDVa5pjPLBzaaFu2gqFewnhwcMv4kXAmb0iZlPdaopW1nVgx0OC1VvyyXIWyNCctyqZVVpeKmXnFsnosu4hewRoGgLb4asKakHIBrETUah8BvBUFbh0EDtkIDsaU0aApLMMcWXQuxsTQCYhm0YQCtpd5AiAlxwTZqGRde0S1GoNWIU6hxrZnUYhO3IiJOyxu8pZRahW9EGDokILgUEu8iXMHtoJWY5/X8YiN4oZyslgsjLGkW9bKqI0S1blaqKiVsUS+2ak11U+7c611csIw183rUtKFj1oLR6SyKmiAM5lMkPW5AdUHpyKOm0IOyDo3Knz7uXTrpDsJ5U6zlQJZYk2qp0rFnTVkO2zYoZV0343I6EOG8zZempot52YiuI+sCKcv60G3GpNB8GbpVKWcFvsDAWoBrzXrADxF7uustbFbXcOv4wKALUkwWYgXjFa6OJqhs295QqhqPOFznuNJkGarVOLh3B1RWr28A7WSs+QaFxrMeQRsclkaMEb7A8KwOh95txdACGgF4OSJKLbqD+1/5KlO6GI12XnxOQWqT+MHLX61kDBD69Pd/nwBXdAcHt29Wceod4K61LHAQPvg33yiiLrTm0UsvQQIrEhJvaFkV/dHjL32hFiGi5P7vfENBqmTITIswqmX06MtfMpS3QfT4q1+ug5hivPO1Fw+Hm7yt7/ze73vBVBA9/NrX0fvv/au8CIs/+iPYjVws8tUlI5hJw2p1iKCdbayDiKpMNJfGVZL6kPuIF2tLndPjtScPi/GAUFCsjEDM2iiol0fVsBcX02vvvFGuDj3DVa+7WBtjScqlvumG8fnpxTsf5GvLNg2hIJONVUbb+kIvH4ywwMXSwMe8GfRNyJtxr0lTSOF0eyO0ZRQ0PLaC64gUqEt56FMzgWMOFkU8+8QtZTE3klcisYxTXkKaMBzhVJ+jVSlBk4CcrtIAOWKlDBALXcAakViY0aE68ysy5p7MEBtEEalj2dDQO9eG8/sw4ngghurUr0qJ2xTM6QBFsCSBD2LjQxCpU5c0hLXcnOteK1BD4AyHCyRa6R8naKdOI2bnIKmwNPmiO42HDDUIzSmfocAilDMy0RkZPj0XusGh59Wx6SsBa48LKucqplUdlT4zCRBmCqPSRXhUvwvEKWEYogUVc51iBqYSnPlYM7ewcW0DkKAiCWqfsZDUKczpgBBsMlaiLgkCJUYmcwvIbBgp3xNRUw7vPHYjGYFScIX6NEJ1FDcs8qKquvef2FEieZv6ORzzyBeSN2RAwvNpdHLOmKMxTOEMdzHlLlq2VNgA1JGoWR9J1ERhG8vGhzgjC9QjRuC03lE9w32F0QSFNeaK+XMUNobrTfz+LBUJyJ1d/H+0vdezXud1p/nmsPP+8ncOACITBAiAOYsyJcvudnva46mpmqq5mpr/Zaaqa8apLVtyW7LlIMlKlmQFiqIoMVOMAgGCiAc4ByfHL+zA4nNfAAAgAElEQVS83zQX7r6cqZ4L3z+17n5Vv1qrVj12oDBwEzOueICkomhCZKF9l5abTd8SaPbz4dQLBKoIniC/hFIxty/YNI/C2bw36yYMN7zd1d2Wg7kPbjB/biRh9gDLQgfAq3d0R3HasnpLDzTD2hdNSHLQZZ7NZGxYCEM1ZWNitSXzO5JNXZIEogp5CfuC5oBJ3/bDRE28nhOkFUIFYetiLgzyFCID7rvCSw2IaAzmMmpTmiuccEPAMIhoEdJC+4Tt3sE0Q90oRZnoaEGVECogOekRyoxPCtSl0s4hnxBeQ9JwtKsTwkjhq+2mC6JmLcQ3yjQQsER0n4qyRNG07NqYOK69ZqsdOGkzAicu1VqzHTCygkLecLMFY6OFS8rVdgBZa9b14SrkEmaETZBsEJ52wW90yI3n0mqjGkJpMoymLmoFLDCdqggw3w6aHbDAJWklyOUYC9iEsma+dU752RKUEPaDgd4FC8ILHD9wtBfHKMOeCyKFAxjg0gsb2A/ljoKjjpQm0hM6ZhzWnDde0OKQ0ZIIj8IOi9SEjImhJNn4EIeAShrJUkQGhTh2cy9SLqSJntAxARRxu8PETEeY2wOOJya23M6RNzchGdSXIN8iHEOYMTltI1oqr2wiEzqm5yasTIQCuwfllNF6ansTlaoeF2DK0cQkVuqJEwUJ2lKl22TAqAK8EWbLdgAjBz1wNY8C30yAzJBshZ0Ab0Z5C1nN7VY5kImZe7QiHeShSviN8A3zQQSnNmTWtcnBJXVo7MMqZgUbEekAhn4oHQpgRHLSQTgkg3bXHfZDZElJeVcEpPKDhgeWedYHczLkoJNEm406lPqgDEmOU4yqGdGb2MM0ZSmc0sTBgCTNLjkkJKoDlLEBwVgxsCfItImx1+zprmK04e2u6bYc5B64IbyJ9hh1B0TM65DPZp1pnFCmebVlBpqDEuA58bJGsqJKKhLoAAbNru00QICw3jBxzZCa2v4cpzaG0uxROrVcDe0nVVA5T4Rm2/kZIy3AOWFzk0BP7yGZtyFJQCaDOiYlEC4IlUt5yKsI5ziBgV/TAcQSRjD3EsWklfOic3sZDj0qbIAyPKChzUSgSYqDjR1vMuPSUd/FaIK6lFGd4AyOZby1F+xPiQclbYKgCXllI+IvOs+VOCJdMAEj4dlchkqEzse1HyviA8d1VG+0AxeaqYE56CgOCsomWNaIzLrgkvWJ8mBabVQDgCzYq4a5FALliM6wrKCnuNrmfl4LfuLjlabHOaqo3bNJS2FB8AGVZcP9edlvPKal86ot1dcSl6n+uJU1ZRaTAxcoKFpP7+KwMsJF9aYaGOEqzmvfb0gAJSr9oIUpDfWMDZERLFn/iASGShZ6lRcqEAtRQY8S2OGxnvIRhhKnYMoTnZBKw5gBhgYyQXMhWxo6T2esh3DMwtwRzxeslbz1aKUDzub3GJyiThi7Ge8DxrSHCt4xkhshtE9z3Q841MVwUA67WODZ4hD6tOr3TMDLhV7TjfBYHvSGlIJ8OCgXusPV5e7WKvJJ1e2YyCsXelUcIYnnJ44srC3Fm1sgEjbyi8WRjXjV6ZjYr0ddqtWD7705PXqYIJOPBm0vth2vPD42AS97fSOxHqezTg8xPDl+mGJTDPqTo8fEW2+xlZV/gw3Wf/zD//B//qfjr75edHunfvzyA794tUo7Z37684e+8e3rp8/+j9HBo6b6gKWf/+Mvnfj5L+o0ve/VXz/4wx/NOsPTL73y1N99/dZzn/nsX3zl3Hf++dPnP//8X3zp1Mu/0kl89M13z377+7PB4slfvvaZv/7bG595/tm/+pvz3//xzUefeuofv/nAD39S97sPRuo5YrYRuu+bLz3/l1+69sLnnvryV89/5web5y+c+9HPH/zeD7K4ew8ev/uypJ5ttu3yOwnqYnvH3LmUMKDCg6vzP3g+3djKdr07v0wQt3rXLL2dsNA298ydK32vnPFGX35ngPPWEXj755HEDZjqG+8OOFftPX3rct9valrnn741ZpPceeTmi4EFWCaT+uJ9OJvbW+7Gx0OS1Ry0V98cymnFRHvtF30FYIdwd+dxOPGk2tX3XvDWiRkd4I+ed/NRmF7/w+LOyNa7e6zc/Bw8iCiamuXPehte093ilz7jigXrbcNrj8GyR6qp2XyUTTsIH5iVz/LVqDq0F3zwpM7u0/HMv3IGZD3aTs3mo3B3bPny/8r2z5utD8AgvPR4k52q49y/chJnQ7/eVFsPwe1D5UK990q7+kngnaTz19r1O9EQ7u+uhqtXfOyB8khnkgzTcm/nVbr8acoW0fwDt3IrTUneLKmlT7pcur01b+cN5N9n99+x924O5AKoL9m712IP1dWGu3upI2mdbct7bwf+ITN9z9z5tNeT+3tHjszjLiqUuVrf/LArWDvbZGtvBd1BMbmCb10ddoMZaobw9tOoMI4Y+snzzrW0APD2M5rqc+7S7yNWHtx2k95s7T+SaWuYpZeftxjQUsGbT2FX0D3abj7H8pY3s/bu73qTVoeG/OYzFmBUGXD76dBkcAqbjd9CmaWoNLd/m++DsLv8v9ktTvHy7mF3+yliDJwat/6syBzGub79O2wfzXrz9RdpMROhmt37OC43qcfrWx92q+ua9TM3ovbkIrq6u/pRN9tl0lb3rsb1GiWhW30/Kj/VwWlw78d8cz3kC3jvVzbbYjEsVq6nkzsMR3DtHdlcB73jxd2X/I31lPfd3tt4f8NP61V1f2ITBhRdeSOcf4LC+936T8D2RreTVlu/8baXZRS0fPIAv3GmTfe82wtu6/EyPgiWFsz2Q9xtP24+eYHhA9fsbj8dfnqmGsz4zZHdfBIG63xlrHeeINUOzSK18jS0M1AM6I3HdbTj3e2rzaeRt4WW+2b3Kb/a8ee4WP08VlNX9sj1J5qofKZ5/3c53VVFcee02n6GVVNUYLf0HLMT13TIp0/UaVvd3l/6KJV1y5v86ttjspPjlFz/SaBb6nenzQNjpFtwXd28PILTRoD66ltDvld7Ufnpy4NaUWL00psRsQ7uVUsf98BUM9p++vrAbdZ0hJZ+5BvNqFV33klgqUBhl37TQVMgxaR5+iTc31J5sPpzb9oEBLr1NxmZt1TbWx916i0ITon+O+ft7ETTPQgvn3KzBdbume2LcOeI42v/M999xGx9CLv+5cfVwZmymwWXj8LZEb9ZNzvn3ObxNqzJnfPB3VE53gw/ekDNLzT+vn/zhDk4wdodtHEU7ZzG/r5beZgtH8uP7PjvnVbzC8K//dn21sMG7mdVsfmUWzvl5A5au0hXTrWDlejjU9X0Ee1v1tez25f6AjbVhC2/EcihzS+bpctdGba0WS1+9yn//cv1bnrzg5SDtpixldeCNC2K2+jm5WHsZ9lNuPRpPzaFmba33u5JXRQFu/OrgHddfUMvfdxN/KxdRdcv90VVgcbefKvnZROx2DRnj5gizz6Ed670Uq+u7qrbnwxEURoLb76aQqdk1CcfPyxNS2aq2XwezhBFuV76bbGLWW/lf7cbEUW3J4vgxlNYYTxv7fpTYk4gnpulL7BtWg8y7/1nlO0BVPPrF0mBcKX1+lNoGjSiEteflTu07k29j56q9KJBLb/+ICvkGHz0BWRHzdp1foZdetrbCcreXvjeQ4057EDOb58HedqS2b1fs+oK6B0r7/0yWLsbszGcvAN3lr0oUfNxZ7+zIGbl+qt0cpmE97uNF936+rDbK3YueRtLgQ+rgyW2cTUI42rrarz1GxGf0tsvu5XlXpoWex+zjdthx04nG97Kx6GftpuX5c7H3uHO9p0TD2ZBQst6+qZbvZ103GyyyjY+8OK02bjqrX/gR/c1fHnBbj8tqp0oN9m938VN7lRIPn2q9dvHzPv/nsCpKfM7x9vt51iZkdrYpRd4m1kXkCtPGVmSncCsPpOCe2Y3rnc/h8qKOOdu/harS2MIvfacERXc8829ZxiYgD3uNp4TZdlnn/4vtCJqcvvgAfnp03WA5B6FK4/Jetfmvl19FleoEXt3/sVvK8Fpe+etyBQO1frupdTuIY8eNM+cgJNtXflrL8n9PKQSbL1O3NRwo5c+7pTryHXIvZeFW7Xp4erOi9HuNCbCbr7NqilL68ndy2mxCtkiX/qZqO5C/7i990M8mSXd+MAei1sfohsHy1cXshUcD5qlV9PpEkmOq9Ufka1pd8i2n/ynr5/9l5d2Tp258J1/fvJr/3j59/7D5/+P/+v+F1/eevjiE3DzcUez/bWjP/jV+W/98/6R+8796MUnvvaNay987re++OX7f/DTnbNnH/juj879+GfO845+8P6D//Ddg8OHH/jxS0985e9uf+7zT//lV859/18Ojh87/eIvz/7gx4ay0eUrT371H3YH/Yd69t+R2RUQPvqXXzv/vR+qowvD1z586JvfUVQMbi09+2df3jxzNt/Y+P97IvzvkxdCuPL4Y+NrV7dPncTzCnK6c/xYvLysuGgOL941y3M1LZG//PDDh5HbPnNGKciqfPf0qXR7855WVZLcO/dAy1nZSdeeeILodvPEcdMqWDT7J46F+7vLjzxURdHahQvAuezQ4ZUL58X0YP3U/R7YYSbf4Qk5tLD6+OPK9zcvXsSqnY6GaxcvRBtru2fPCKO6/YqkAljS6cxgZ+RQ0/HmbCTreuz95tq8QTCh/eFcdJhDuNuZkUTgBPfW5+h4pN28G87EmBIfdAYFTLCRuNPNSQIBIZ31uR0wEwaDNKNHBPJcf1jQhDlIghsrFst6INOVOVsgNqaDeOb60IWiM6pEDJrISr5h4qoYcH9vVQVljTEN1yBv50Jcz5iP0USGoNoHPG8CEPDVmuRNGITxPU1BFWAWbUFYNz3N813N2yYhHr0HRaY5R+FdQETtWZfsIGuKnqPFFoBNlabLW4XWWPd9469S4luvRdGmUW7eg7g+gAhpjsJBw2zl/GFwH8J3inYhwHuuM8154lfOsaYGANK+GbqZ64yjUQHnpR5JCky3KskwlKYJhnMbRMFCBdTUdceoW3QPGjZiFNs4yeGAMWwGowyEHh9VvXpixhFta4AhRA7GIE1z0sUe0WaoTcDgiAyqme2KltcxWSnTHEpLw2WVNCV1gbznvPIejpeyu5v+IUAd2bunO3PrGegvG5GpwHnBepuYmprgYLVIMxUxf3O5GdQNc9RfBnKuIyXEaiOKrOd5k402mbvA+Xy5ig4U4p/UzSpoGs/4cs158za14f562c1MTAOy5OIDyEk8LhFUZiGMDgxmDvS9NMoo1abTideuw1k97Q2CueKqoiMW79cAaNPrJdEBjdrWi+PRVDiF4l686EDV6qH0DmrmG9fr9JIJ8Fvl+XLByHbuOv2kn9cFbMcp3911DhSHh3FSSFEqL00WZ1U+b1PB0ppiAFMC1J4NstbHJMlYs6RT0s53vczMOnq9jkMz32GHLT9Awa5jBkT7pAR5hJEp/GxtNrI4y5BYbVLFq4JEd1WEXVl41XLhWdvJRLY2HSI2a1G23nZapCzwVzRrVm3cb/a2/WN1vw5n97JuxYAV/mrRccBU1L9rRYtHuLuUuT7TnWCQZOyoANL1BwVJqcXUu7XmmF8MvWQt4wvYddgwnoE+sKFMx5VIAE5xt1uQQINDYXetEAsUdGUvnvoL0PpsOM5hQnVXhJ3S7xg3Fp27Ge7AMo47V2+3easWvM5woiJL+n7aL2kP6gGLvbk/xjntoGjZOa+WwKT7pHFFz+F2BxJbJsHKXoUqoPqB9teom1nZomTblTTrQqSnkFXabwnfUGHVelKne7iu2g414RYpg7Jvwnqqqa09K/kmDq0jCCRbuCzyiN+dSY2qHW9Msh0HKh1C7u1ge9CEfhFNeO32Qxx3XbKTwSFDpR6OMhxJOjS9+RT1fDgX6Se3iih1GnTijA4IbUwyLG3IHCD92Qx0ORWosz3XA0aA6/UKtCCodd1hgX0MF+lgmruebBvaXZnTRYII6qUzN2SuzuQnS3XS54dpZzZzEcD9sL8+Z0c4lrbXmckuc6xB/priWTbEfL6lkmnjw4Cv1OGBxfxq027buhY28NeBN2n72j/YrNO5jmlA7oBoogXBwZJhpAl1GGyaoCj72t9bV+FMdSgmd1SUK8Gsv0I4d2FFvLVW5nt+d6NeuUvjVlgqlhs+V7606SbivOmAcGPDSqU7pJtkgNTK9/hID/O5TQfxIKv2nI0oUZo1tRE4TAtC69ZLkkNzOpmajiRpnRpARyQoKuOA7QgvbXyndBB44wztT13f8zNHbKWPhGSpTPoAJyROa2JQ6wtRly3n2Gk6xnFbNGOfI5U2Sqfc62rWNE5S3cnldHUyJF6uCV9r0wogAP1lzasNki6Xm5vRsbpXB5N7WaekFHjyXtXRmlrq37VeZRlkByulp/IAxgerWScHUkuxXEaliwAL7yqv1j6U+/dMVLVMJLurs25NZbo0ndwlCUgd8m9rboDXRjurTcdUQZturBTRrvXFcFy4lJqOCHt1ELd45KUrBemAMg47ny61s1rdF6TDAxUa3I2SYcF9pwcivJt7A6QTf5geQN8o3/PGre8D2IvSzoEJaLsQROs17jDtk0468WWr/DRdnLUE1JLTlS0mPTiI053MRNxEIu2VBOvGD8LxnFFUL/T3Tp6sWbA/HO0ePoIhNIKvPfZo63vTwWC5sQxma50j9Ni2U3Z6+PDByRMIkTqJ7549dwTh6aDPLpz3s9n6yRO8mbUX7PS+I3sbW6RqSsFXn3icGH1w332qBuH25vrZs6MVvvHwxdmh+7Zhe62cFD7buHBBTiY7o2HDR/3lpZ0HznS21+89dHHW7/+/taj/r+r03+kiTG3N88x0wlZB0jQ2FDQvDSAwIG6mYNvmh8Y8y7xsppOgNYTVVdHt8izDrQIBdY3DTTMfDHmZy+kUxAK0RmtYdLuiKHCrQEBsbUndzIdDXtdiOil73WCyrxpXjIasqWndAJ84BVBVw4hVjsvZrOp1iFZVyznTDoC2wS6gAEI0b1zE2LzISwFTGoN5rj1BWghB02CfVhULbK5dyKJmltcc+YS7Otde7OYAw7n2A1xWInC5tgGL2lnWSCSRdHWhBEVKAVoXxCeljQQotPNZqOd5LTB3HX2wCUcCNZBj11BLobSZaqVixIfzyibYaYKrXHei5gBKXbjEYSBBZhqhCRY4q0yCgaa4rE0gdGUk1EpYjCSYKyUdwhzPaxsDAJQHSelEWxoPmpY7DLVEYI4BhAHdq20MrW0DTCrHm4rSWinecln7lOQ1bLRLhGssyUvhmQIFsFYmlnxvhrImSZp92jUamkSg1qK8BjGTRVY4z0UcNcq1wGN1Y5hRzsUcaIeKFgVEVllmPSYdVKa2PKBla7lpnC/qSgu6M9cLEddVYSSTFmpXWjFy21OS6gZ6osllIqat8ZAwZWUjimoArVFcoLzkoSmY892hbGkHH3bCCVvWNiKoBtBqJQTMay5xCZSHfTVrVGS5466obcJg6bBTSvbazUnQcTXVEgVq1qjAUUdRXagehwWmrWq4hHklPFghLXGgp7WOILaQtE2DuaqBT+qWAgx9VDYVtoRxWE+rADoT+k0DuGhK+K8Mgs4jcN4AhAJaFsoD1tqYg0LzukQBqVsCITQ+xVljIV60mxtoBK21EYeVhkojDs2BrplPUkSLysH/NscYj7cZ8JEyVDrSOuUEx7lywmmkQoQ0oIXWAZRlldvEho4YjRTiOFOOO00EmeckpqU1vvPrvDIhEgoZ2zqfo7l2HCjEaV7QiJTOeqafbe/QQ4Q1+L8xNfBNzQK2X7KQlkZ7yGvyyoaItdhZbWTrIeAsmtfWZ5GaZbWAkviwzFtJkTYAlyXzUOkiBkrzrwkqag4EGrS762gsYIOpq2viwarxPJNb57HIzPOaQ4YlrjPlMdhiBuqaBK5s/H9laIup220RRwEvWiMABlZSkLW+K1rfN5lxHlMe83LjnFM+xBUQTeWkU1pACJVEIMcA/NcEIWOakJDasbpmtNKaN1QqD7HCOIAEntUmgta1ASItQK2BQrNSlTQQuNCaW0gFmjY2hAYwks9hF7WOiZJVbY19RgpjhAZUomntQqRsHbN4vp8Zj3gAK1VZ4ZNKOWoaIGkzwSmYtKhDEzUptKCew8aURvbdbk6CpqUBq3IWwawFIfXqea49Jhy2utbMo00Dma5cyKqcRzBTIKR+k+VKCtKiRu3abkgLyImtTMDrjEcgUyCgQZsVrcASYgJ1wzrtdu6HupWaw8DM2ta3FDJU5qrPYEVoqVpPuLyRAtRYc+ybaaNDiB1DRW1ih53mkBZWmLzxOCyxkVhTwHLrEBA4q00MoFUS09JyU2CsyzpWPgNC80w7jDia1zoGyGmJaGkcBLVPybx2Di66zQ00hMaaWKBaw0ZT6cT2pAaiXUhgbYAFPisLJYG2nmhyGMJGUQ+QoiqxL1mrWqQdDWlRGgla44kmQyGsNfEALasKepK3WuEGsEW3cVCFOKvxWMxJiEqFfUiqugKexxutkLLE43WNQ1I647lhtrFDDiPeEqNaFzKUtUDqmgfsoGQ+La3ykN9mtYkAU9Tp1gYMZhpT27CBvrcVHiIl0B7023mjQ8g0BroxAUe5xsTVWOI8FxEpnPKQp7Ki7VDWUFQpHRjuLII01xLluRfSuVUhEXpetB6BijJbNcy3hfKlKgAQRBFqd1vIUciL1goAnfUZyFrPlDqQOndOUCswntUAo5Hd3oBjCK0LGMpbh6DEjS5cKyXwCJrWAGOflnnrYasghXktIXShqF2lW88LYF40EiAY0LJopQMA+Sg52G4AKzsdnmVYG+hj2wBSVy4SdYv5bKZGPS+ftQaDkKOqsRrAgJoWsLJ0iagd51lmIxnN9+c4AgFDtXLKwJCpFvCicLGooZSzWZXGXpkpBV3IYdnCSsGO1JawbB7yZkuOvdm0SWNZ5baxB0dPRP/3H/1buQh/74//9JFvfXvn7Llz3/3RI//07d2jxx/55nee+sa3r77wud/5k//8xNe/8cHv/0+f+9M/f/Lr39w5dfrkS68+/fdf3zj1wMVvffczf/3V6y+88MJf/vUzf/O1j/7dHzz3la8+/ZW/3T954tQrrz/5lX/YPH3u/I9/+sKX/su15z/7mS995Zkvf+Xyb//7J7/7z8/9xZd2Tp05/cbrz/zV32yeeuDMy6984U///NoLLzzzt19/7otfvv2Z58788Gef+7M/3z79wI4a7f5A08DpHbPzU2dGAq+Uay+zUDZhPl96JQxsph3e+p7BEoCJ2nwR8cTZ9ebuLzwfFbKubv4k9FVhMd7+nmWoRrN27eecBwbsqJVf+KEriKqXfuTLPIM+3f6WJsSRqt16GYZehSbtyi8Cvy0I0fd+KMR8Tj27/D0KjE0EklceYgcAogNx+WlvTbcL8+SNi2wvbJODzgcn5JRjswNvPsH2KOIT+fFj/qrNF7POa+fobqqT/eCj03SSIrBHblxk29zJiXfpkXQFTI4Ww9dPoZ1R0c/S9w6z/S6G2+T2RX/Ty5Mm+fB0chflR6b9t06SrcVsWHQ/WqBbPe7uoTsX+HpnfkjXLx4cvIvog171y/nWB2IQTQ7uyoN3EE/d/pKYv2uio3ryltn7NYFnPPdusfZr0YmL9maz+pYUqcuWSPlyQU+Q8u16+02KTnL3Ub7+pkjjUt3Ta6+JIGjmd/HkZc2PYfV+tfE2jw+1+Q1354M09jK7bVd/KQK/KVfh3s9tPK6Lj9X6m146rkkd++8/zuq5ISB+67xDCpcm+vBBK2q2b72rj6NmJ6n33eXfFUWmJOi8dR5Ag8rW//ACwBWdKHHtWVruQdd4Hz7LpnMdmM6bF6EBWDX+Rw956sDmjn76HM3niNb+e0/ySdEkpv/GGWgQNLV36SLTDaiVuPI0nedWVMGvHw8mzf6onv2gLTZRyIu1N6VeUUHcrr4s4I2cn2OT7zbViouOmt2XbHUPxbJcf1eoOxr2yc7PHLia0Qdl9t3q4BZjx9nsxbK4AxO/2PjAa28aMGb7Lxn1cZ6c1hv/Ymc3GD7O61/W+7dY15tNfwOLG5YdYfsvG/VRRc/z+feL/assWFSTd9z0Ko0HrbdxNLl0qBpMg2tdee2B2ULRuyHZzUcgWfW3Jb9xwfAp3h8O3l8sRll0PRLXzuneDl2K+I3HCFoXE0k/eQSTfbTfiT480XSnwZLnXbtg022+4ovrT2C7Gh44ePVZgvfRPI0/OK06M3+J+9fPA2+Nb0hx4xnUruFaeB89jMAeKqP0/TNVR+n1/bWfc0+XzNa3fxTwaYa7dOubGhrHYLv9EpCkIUW98kogy4Ixfe+Hku3NRMfc+S61NWBQb/wMU92Sur3zsiezkvv27g84383dIt/5RqtLxIjeeAnzqiVWL78s2bw2PX7wvYbvzMlRfvDtpsgFFm7np4bMa4bN8ise26/bE8GRVw/h9cV8Yd55d8x3FjDawHfP+WtJGZfR5ZPpbZodmfZ+fZytHp4tFt2PhnRtUYC7ePkMWx2XnTq5fDy+FWWH97tvH8arJ/PRrH+lj9eOE7Qmlo/Su4dMvC8/PZXe7M2OTQfvDPG9+21nQ1wfy9XjFN3j947wpcM22hPXTgTXx8Wh7c77Q7Zytupvgxvzu7/yfFGVW3j/p4YtIv1Jtf4qiw+15maz9qboiik8UMsvewGv6j208xMbduvmplp71YuGjbpj7v3SS2hmp/rei9zHpcrw7g81G0B1u1l7RaRpodbMvVf8CGWudus/oSGcu9Js/Qx5fetWm5VXRCfK22179yUvcDm0YPWHmOHWC9P4nftlXdqqIZ8+w+YVZKX//lNir6p6Tf/1M6hiABfeR+dZA0BbsatP0kntZBG890S43c7H7fDV+02daFbFH5wWBYAm49eelBMzT0H3jXP+litGRf+N+23Za706+fAkmyGudsjNJ8Q+yltiAR8AACAASURBVAZ2+Pr9cpvmo6z36nGTj7TMk0snyTSo4nzvZd1+WCSnzNZP7eQKxadF+1q5c5V1wnzvU7pzw6OH2ORNq98tyTme/bjY+ZhFR9T8fbd7iXXDYvYJ3P8Q+X29+w4q3lHiHCl/lm1/7KWL9ewDt/UB63jz+U108C7y+3r2AZy8qXvHq913yOq1TmexzD4G2++LjpjNbuP9N1w4NLOPwP5rNjjtguVIXHsC6fVo3oArn6XuADZ+8u4ZHeZyHQdXLwCxybeYuP4cbddhi70PHydqHxiZ/vqc8UuxQ/iVR3x0R01C/ukzpNlFDgbvPYLVzDqevP2Alg3bR/zKo5zuuCkTV54m1T7ELnn3AldzhXn3rfvLALOJCy5f5Gbb1URefoY3s8Ivdr6hmgMoArP5M4RmmkN171cS7zV2wA6+3/L1jJ3m02/W8wNBErj3Ew32WknVyqse2W7U2Nv/TuvuFOExu/Y9m+8I0kXzn+tqByZ4vvKaVCsKHQt2v127pZqcYZNv5dk6DzrN/lvAbLmumK++4ek7ShwC698H7U0lzuDZPxf7a363N3/ua3/38De+s3z+kaf/7u8/87d/f+n3fu93/ug/P/61f7j93HMX/+mfn/n7f9w/fvKBl195/G/+cfPsuUe//u3nvvyVT37nd57/i79+9qtfu/PUU+d+8LNn/+or00OHzrz26hN/9feb58499L0ffvaLf3XtC1946stf/cyX/3rlkUdP/+L1z/7Flw/Gi0ffe/+3/viLm2fPnv/pS5//87+8/sILj33ze8//2Rf3LpwZvHnpt//oT6bjxcXLV377P/3J2oMXy38zF+EfxNm06qW7Z05pTJskmpw6LlVThuHmQw/Sosz73dXHHvXzWdVJds+edsYCAjcePIsQaLrJzvkztG6yJF17/BGR5W0UTM+dtpSUUbx17ixwRoX+9oWzoihV4K089ihvqsaTW+ceQAQ0Qbh1/hw3yiG4/thF1DRNJ909e9o51Ab+5oVzSnjKAG/BIQEhxWIMCQXIGXIIM6o0JMHQct8YR+SCQxxoQKKx0pwQZPkIeJ5pAOUjwEKnFEkGDfddTUQ40lZSCLQ3RkKo1glvYGjsGoXlyOIAOEziXgsC7owjY+L5bWNoPDQybEvneWNLU+aU01GD5dwi7qIcJHPd+DqqQDwxTnB/4vxCIWaiFoYZKiGISpfOrfJckNkks4YBP9dRayCzXkn8KQS0iRRIpqb1XFjobilKjfm86SoLsPVqFddMGRu3INl3Kmh8ZXoZVoDIDIczi6jyTNNrXNFiacRRCmvFPBuOrYGEmZocZhhZyl2vX7RIOh9792FQKc8U/CijyBhO+BghZCC2waK1Gghq6H2EOYeIYYsYIw0o9kf/+onj/MNOWQQ4TMZtjTijWoyhwNpoJw8hyCAgqDcsLOFa0mislfCJMSaeQ6+2hth0BoVSUMDkoJUEOWWTLCIHGe+YeAZFgSvokonzlUUURXutJNCqttMQnrcwBMkMBYU11CQzK1uDqedv1h6mtWr7LfYybblLcufn1giXTK1sjOMgmqgAImdsnBMxB07WaWWi3FTQSw3vAAMQSaDfMUphnCJ/bJqGytT6A2M1prHFfWQc4RGgPUiNISnyh7qqCEkcP0RgZWTk+AAagEjo2IjAVskUdTpZ6SIYI3kIuUazwMq+g8DBELMxxpVGIQoXVKuJiEEwUBZx7FkxAgRAQ63r7DvEHNNtP4MGOaR0P6eoNRTrbg6xAciY3sQA7hiA8ZZFDJG27ZYENQoTm0wdcQoR2N1HGCkGYbrXUolADTtzD5ZzkbpkAomyCIDOHkDQcIijrYaF1mrTLwmrDCYgnUBmLITVoNSwIVrJMZSyVZDLvuGJqxsih4BG1kIS9hUOqHMWD7EX6kqxeGiDsCqc7w0dS6C2OOxrGxBoIRliP2gbRdgAyJ5VLQkGmifAWiK7xiQMWisHACWItpr2oeiauiB0AGkXYq2DnoYpA8bhLkQjxgrg/FL1CqYc8EqdlApyIxqVVFQbGyiQ7kMtWq9V/QK3CImKRBOLaesZ3SmRsk7WLt03NjSeUt051c6yyqY5gs4IA5MptLSRzvX2rfKcrGG8ayEHotFJCQBqOHTJFFloPAM7e8gK5dm6l5G2dJQGQ0OYtRQHi8YA7ChKFtW/JoiPoRS2QVSOARbAQtwZlJAzxWk8Vi1hGGp/ETKmNeLBSCMJFcTBgnEMIY6TYW2l56ylC1hIpQBNRy3mtsUiWLSWEgdhvGBaRgm0fIykVK0lfAxoRIBBIthWHnAQqU5DZNYa3yUlCGdGey6eWr9yjoBw1gaOVY2NCuxnwIkmqUE8N62wSW6iFhkEgpmKLDCujSodNbwxJq1hNCUFUUHVdlqiLPGn0CsM4iqqTaeBBTRRDeN953wTlib+r9ZInSpQtyKG3V5WgwBGUB7GMG8krtkiwcACH7MxxkpBDwSLWlfQk9pftAZSLCwbQQgskigYGlwrJGFwxDYtRT6IxqoGjEsrx5BZBSiUh6B1EHu4P8xa4FsfxcNWOSpcKQ8RRCwQOBhpDRCUMFw0hgbINS6dhXieicQmU8CUchR09gGxliIUbbfcg65pexWhJVLIdadOauuQTSeGOkdgEKxmXggcUP2KskI75tK5E40FFHQPLLa0Va4zqX1GrLLpnLLcIKHSworGGK67GWQKAYuivdpjyOi2U0GZK0X8npEday0SXeNS5gyQPYhTSJUhfej1TFUR1nOkj1Frgo7BXeIswB3ABwSWjdeHSZIXNiZdxAYINi3rABkbACBOER0RlLekS4KRUop5Hev1jAUEJ5ClzlpEY+j3tFEId1EwNnVFcAfJgfPreZF2tx88y4yu4nDzkQfl/oGOw82L54ABTRpvnzmNoCnDaPeBU6yulO9vPHaRaZ3H0e7FswZg7cvJ2VMEuWnU3Tt7CjpXRdHWww8Spcs02Tt3GlgArd68eN4K2gTB3vkHAAB1EGw/fA6VNWB49tD9cxFZwbbOPWAFNpSvPPmku3r138hF+Iemk1hfbt1/uvaDNoknx+/bPH3/zWefg5w00isGw51jx5o4AoJvnr1/88TptYsXp4sLGIL5wmK+MKxkUHU7O8dPtEd76L54Y3xESb9Iu5unTwGM5v1+Oezdffixu08+2YRBHQSIkNXzDwJOy05v+9jxnRMnbj39jJO0itLW9/ZPHZ93BwjDtQfPcddI2uIeQQFhRJEOhrLqiVU1iKhE0jboEKfMcdLiLiGpS/1tGEGdBkGTm2MREUCqEixwTOvY3yQR0HGEiaFdaFPpl1N9NAJUJ/6mG3LHuI9L2kEg4QwqGeo6Jj2yYgcCB4zqGh/ijGvIIU+BSgitZyppTYpIU9Q9bZjukQ+DaHvqpQ41lM6LAXFWmaiGoq66s3zYUugMqqAsdGwcagnNy54dgcsw3m8iiduqTRrrW2RzxLO8A9vO3MWzJqBUldbLyw4WKtNhaUIYwUtptLoT9ZmbE5yXfYhsq8K6TJjnioDX7ShkngldZQ9J2+fB2OiO5FbJQNMEWkk512bk0Y4NF0zT8TlsKXdm0eOg8WWDEwwPe+KwMwGnzAS2MEd8gRSjBvUp7UF8ghPukEAeKvGCMCELVG6ORcJT5BhDKeZWUc/wxLWxL2CDxqz0RVTuln2FqAKw0LGCYTbAH+nIFqGQ1WS+iDkplTVNt4Gizsa5DhtAAECVjVSVIJnvFyOHvXrR/Tof2oZ6yE5dULchQLYkXn4w9FWyWycKUUebvOprJx1wGfAqkBR9fMmF9SwJvXJaDTT0DFRF22mcAAxpIbVaDJhtSQfBCCRiiy4g50uGWi/QsMcwc1IoO+Q+3WVxCSJP2IYNMIwwQ63nK9UXId0KwqxIY46MiKzpSd+UvOOQj2Awj+Np2en6uGZM2QVP2gZ3sEpYStdlt3BxSDgUrMEjBimQtLUjTnVlaa5SYKnGKCv62BPrh9y72/2UYINQmfcBBhVBByoCnrfRA+9mSVz7WDaT+QKksIagrFId0bVUXqlCXgtK7Mx0deNBWU/mh1AE1yJ5vepR4wBAUxW71gfUZG3PqaA4ZV7fHsUAQwTKumcBbhGYVh0EIxwWE3MkID6ipkGHJeEqFJs8tSaOONY0hU0v8MsZOuJDpOJoG/QQogwJIBPnYixAgxOseqxLVlyPAN+TuiSLDPmY45bHDiQu8PZYZFUnCtoCjqhJZdBmZIGhAPhym6dKxVEAax67qh8ETU7HtI08r91DfJ53KUEFQ3nRcx14Q4ZrszRiqjRBYSLobAFZWXVdh97soGv7aQKBMV5dpZC1cydnKqUxuZ7yT7e7I+xyCrNyAJmttFc3vunhKyJcKWWoiSZwqvpOMUvdvBiCHrwpg7tVxCFQWmQ2RooZ7Gb5iHuw5lihBUaJYUKjLmWJ7vhbNuIm8cI208djSgyHDRgwTjPf38YRaeNQoBYPiY1EUE3V0RiTKgp3TN9DGHusIl0CYiZgIxJbhbhP75pBQCShUOERJcxiAWiKcKw6/g5ISNuNwmrm7vOZsMw1ZMSBsNCVRGazvqR1Xvc19NQh93bZrxoRApBBr1CRQ66CXpl3kY73yp7F3OK2bLoNkBa5zPpNm7Rj+BHxDg6SWNTzNq2bGHvtrI0r65tsUKg0txQwMwNeVXcQUXXTbWsPHie/tv5GGfYcrJxXVyng7dR6ddWhvql4YlkEjKRCGjP2RNfIBadTKW1DYqQH0jOFiDWOCLzPk4sOcwA4lqh2i1K6moYAJ5iOMT7BKdZIYA+XaEE6QTxY2UOeiDU+JrAHuFM0NDx2deR7sIaHhOtRf8HqRDKgiQ/BgDPYSqlgjykPeOXB/DARsGyRabraZ7tdfqnyXOVLYjPdtVUAvXy3WISYFvNx0aQOmxaizESt8h3WGYrLeRedVK8e9LglEOuiHhjHNIQZkmXRd03nAPhtFbKg2C/GFnENddl0NBPTPvuoCnXhCV9P2q4qQhoUe+VAIQE5amVsQZdRZHhkTZcmbB2mFgZcmoqOCAoQhw2PnO3ilG2yqCk6ia8rOsAmYb7KWR8hD+Jg6idlk8aBK1EEwYBJ1cAFrn3SJ/dYr3ZRRKjjQsEBJ0CTCOIOTPg27igUSwYU6ziYMkotF7o9nGIEZqOFnePHAMazhXHTiZcefWLl4Uetx7LuwHG6du4swnA+GM0PjdfPn7/x3GchA1ncVZIfnD6epT1H8MaDZ3GXTC7ePx2NFeHzwWC+OMo6fSPF7v0n14+fXn344eniAkYg6w0O7jvcSJkPhuWgu3Lh4eXHHoMe2hneh6Fbv3AeEjTv9rYeOCt+/c6/iYtw+gd/+MJ/+crxt389X1w4/srrp3/56nRxce3E/bmMeFu98CdfPPXLX11/4QtPf/VvTr722t6xYxuHTmZp19ufPPmP3zj/o5/cffKJp//hm6d/9vLN5194dnrlkWk1LfcO/+K90y++PFk8cvzNtx//zvfWz5/fPHQs92Oal0//07fO/PzlrD88+uEH577/4/3x4ur9D1bCp0Y9++WvnP3Jz7YunD/x8189+KOfzBYWd8F48w3KAqv3zdZ7HEsn7UG+mKDlLbxPlz8MiTXA2rU3PUo1k9MqFrRV5W2ycdnzXCXq5u57IahbEmYmFago25tu43LIpLa7Zv2S9F1L1HZxOCV7s2YebbzJIIWoVJvvi1DNqNqbH+2htR1QyntvebKoOGuWXk8gdhGl+O4DpOAEzfCdC3wP2njtD6a3R8B8WvXlzbO8pKydmPUH6dyz/sHWwpGahdFkT159GBRd5++xW2dQEcXk1meznb7Nt13X3n5UbrJqYRL85izIB7qzvddb0JT5uy1dPk3nSRNpeeu03OBVZ/N/OPjkKEBX8Mi7fpJOA6k23eYpeDAsR03+RrF3S/BTvHqv3b9F0yA7GI1mSZ/Mq+wjnX3sOuNycglt3Qr8Tp33utO0zyc5+Dhfvx7SCGbrdP6BC3rtbNifRj1SNfqjZvO6kKLRm2rzsi+kqg6lB9FQqrJ6u9m87oUjnd9yO1dFIOp2Md5NF0VR1L9pNj/2er28uA7WPw2itMJtV956AGoDseLXHrbQ3OcufX66VSCQ7RyGqxdwM++Uk2rlOVK19bDc7h2D0MZ3EVk6C0lF5hitnkdtO4DXfr+c0Xp/zRyXn150EOCmxUvnIjWdRW57fMRZlGxU6PZjolDKr7wrD2nHxuLaC9Nt1JYH0yG69xAqLWQ1uf4Iy+y8V+z9Cqg5DGC1fUU0uyhik+pQBCYThOj2K3y+w8J+s/2uqHahj3arCCMIwBxsvu/ru613Au6+DCa7vncUgHJXC0aX57tLabGBYZfuvY/NUuP3c62KatRVG/X8E6/4f2h7ry3NrsNab+W1dt5/rlzVOTciATTABJEUh04Yss/wI/nqHNtD9vGQKStHHooQkygCIJEIAiCRgUZ3o3NXrvpz2Hmv5Av5FfgQ37yZY87vEMUknd5Gs11B/cr2KCgyIMH0bTw6cINWPf+CTveIHysx2RIP1nRj5Dxs4cOzWTt/tvji+UlxaCrb3wQHFw0vUdJy727lwfy5xSfX6nKHc7V9Eh+dw3DKFwLuXoI4vYIefjMbHBDHPNyAB+dtOCGHMTo8z9TxRfnFs2meQT2ZXBL3z4No4ux7cP8CFMNT2c0Xk2pi82x+gt47T8AMlz69d7mKK7U/PfqEO1pSle/8LkBJyaO5jDxrTX1f9j/1KJA0lfufurhSxAzzzSbtj2FCtt8NrUHIqqMPBNKWZXvFWoSGE134+791yKymXXj0K1LXxG8khU/NYqGO0PF1H+dGhWz4JgTDii8XGqoqCuptPfmE47RgWh986qgEgQ3W/mgLLNbqzsK7uY5nLQ/dfTY7PqmzHdqF9664+27V60fXT+vFpl4afWvw2Zm0HJVGHl4Fo5UqqvmjU8F+mLb6fzy5ebaub/Cee/8Umi5hMGVHPTDYgMHwxWrnSjG4jmL/5ik7OwX9AdleheN1B24/Uzw6X82PsKO2nxQHK7KzH91c1eNTVWNm7s8OPg84l8UEDT7gTkNiMy56kZhni7v8+IYT4BzN1O4nPlI5alQmYGSR5jfI4ZeBH5fVLjj8QoSogHZSdH0yy9KD4PgDLlq23lFHnzttOLL1LN1q492BXASHv2UhyHRmdt4PeWgZmmW9kA1G5Z44/MxjtobaHrwrCNUiaIgbpzyVgUyB/cdQiUJ85z9mIzc5fkhOBl9ctZoDWLAHF2kNCr8c9lYkcdoHM/jgGTEGVTv1PrsiTbMR3v/uaDsss37WgbuP41xUofZunGdjVC2N+q0TNeVOHzr3zojcinJsDh6DaWCag/9l+mCpGN80y9EX56TqGlZ697dQ7pVOPvgIyjt1a6Pov00Ge67fq+ftbho0nP4s/0SOtx3aJpMvSHHLBJty1FpJ/dgfT2efk/E2c3mZPQTju9yN5Wxtae42/Hyx+LUabLthT85vwul96gV1ttoZBz1/Nhl/AEe3+fLaYvQB6j/wmo3FvNWZxx0xmRc39PgmCZpydpcObvBoUzqHHXRwCctRnFbl7rNAJSfZw28l+yNI8r1NsncRiREbevDgMjZZ0VH99hZRsrFD8IMnjJOSsYN2LwbwsKvufzepc1SMshP83iWqUmOJuHOV6um8S0fddV4XYkeQnSuoriAy5PZVUBTLzv0/mu/N3GA22GQPzzIzsQWn25eBhkqMj39Fy4IIro4+ECBVTPXzJZ8UpVmQg9+69rgSG7D/KkxK3+2WGua2qvGxProR1jNkW2zwFoSHldtNa1MVzVjuyOkXjpzDSC+OrjvFENOoMh0Ks9QWcPwWXsyF58nx5ywbo0AdFksOlAZZvPdrrzoA/gk9/pWezv1WsHj6Jy+d+uVbg9PnLv/8F1d/8rPtZ54edNdTP45H/as/+PHZ19/KWq2TH350/ue/nK6v7Z+6kLoRl+U3vvcXZ1/55dGVy2deeeP8K69W7fA8yx5fjAfMPfWDf3vipZ88+vpXn/j+Dy/84pXhuTP97sas1QuGw6sv/+LKD38yOXny9NvvPf4/fti/cP5g7dQibrdm/ZM/f+Oxn/6sjOLW/QfP/P33jy5eKXZ3fj9Ho3/8P3OEikZ8+Njlint5Iz6+cK7yA6CN0BVRdt5bfvj001DWdRwfXr2U+TE2GkDIq3LRbvcvn1eI5p3ug6efRtiaZryD3Aq7edjYuXoVMJa32/3zpwvhI60ghMxo6fk7Tz4JKEqDxu7Vq2UcYa25KrE0eat98NjljLtWuNtPPa1cB0HEVpF1CaCYLCFqMxdaLRGMAoUdvEZwgDQkYgURPcNFRRHN3QhiitYZ91QNHWcVUq5YXVJta+Yr33eWgQoFJgyuEWZzAYypjfYaiBK2gkBINKJBqzLUuByrGqIwUNAhPSsim5FQLCHbpAiLKq5MkFnkFEFhO5IxeMyDQ7ejIUdeUsSyYqGMah3kGhpkDDNSW9eElWwVBnDrl2VcooAeE2fgxMY6dVioVgktl4FMeoZUBa9zBIDBrvGrtG2J1LWX6XbJORw7wX13GWkAvVI2Uomdwtd5R1OpmA/Mlge1IhyINSgdQbW0EAENkQvDnsyJCz1iT3gYG2ok1BZbrRwB1zmmkHPAl4FmkGgNLDAIY4roJiPYSMdhywALAIzlqtQa0YDxFVtxB1OM1ynl2lhIjAQGaZdHvSrnjnW5u2rrwCUGVa3UeMZYIls59ErgsH0ej0UTIpZ1SofOUtKsWpkJagkw0zXUUBNqWmnhQcJ41sqBU9KAbkN/6rYNoLqZ1b7WiJJgmAbEAg0t5LZGxC2jREVKW66bhfIL5NA+84/dLoRO3i5MUAFLq0au4xoaTGOEl0iNOG5RN6xoXZppiuJGDV3chHSF1JDxCLJYUWCINDXzLGK4hfgyMprSBgYrVCxm3NhKBMqLaAjMqoOlxk0UBLmtawxx7cSYUhxSsowtgLbBcZe4xZSkOfSCCrkkwnQN10RAD5NVQhBUzMhWJiHW3CYrmqgMx84d1jRY1IxmncowiImUrRRyJSPvEYkTFkGKi26GSS0Jk+0U+qqgbM9tpcDFLq6bs0IwSHDezjyyyJrtHRrnLDYUqHZSEag51a1Z6WAWi7ukIZmnMK2bqXaRISjvSSkMgQStEhoAaTldRo5b0zRHACkRKOY4PatijgiHq5Thytelzmvs+TkLRA+RJraI0jYirBYEgNpKL8JU0C7EHaw0Y13IvJoWBa5t3WhDzPEyUR2HlZL0kBPWeJ5AxKQTQYhEy+gl11rCekh1XUcZGcikp5HUwK3KRk5CNmXuttsFkig3U61CAW59uVhSHFRl6O+RoCJR5ZmsY7jRKqhlMyOCzIPwjrtCDNCurVsZhLDwgGwVnJUj5h44HQMc4+uqnVaYAweWjZQ7au74+05LGl+6SnUSaQgIYNItua6kcMgahgJZzpxlgLIJVYZAknkNjDHaoJQbRR1nGTJcs7qmBuROZDzHWTWV6xJG0SriJuNWK421EyGB+RoyglhKw3YhMfQ4VjWEYaAwFz2NfFxx31mBuF4QWVNl0riHIMYbhPoQYM6WAAypsRj740VMDfHKVmF4Kny0x+Ox2zaG6UYh49ogZsO0DBUAGktJkLZAVHGmGzW0rGzUZVRzqsd+dOC2AXJknKctxEpVRRkI8xozomoLMQbIhmkdlhK5RVQXLRvY9NiPj3nLWCZjVTYVtVZGMm1bVCkcoahTLaBPImI2XQI11gpbgzQwEQdrghrNQsSXrEaIaMVlWTAfO4isYYygjoToGEsh1FqoShrCYsZWbEUFdglZwZhqYAE10hhqQxZ3iwX2UcCcVVt6HtESQYC01Q5jK9ASgn3C11DNBMI46+aCJCmL61YKvUoyduB35ii0jOjWPHcoZSxtpYiX2liqJLZYUaLaWc2BJYRExzNX8JjdEZ2SCIhZ2Ui0Zw0kspOXPgBGeUVaMQ9BmnUy60kLSN3MQFBZinfC5QkOMAC2Mc1DiiGt2pl1K2kZ7SDUJQox0QLY1QIaWBnphgAQ3MWsh62mpENgEzppQpUtw6YmDutAteTSosYdHHiZLSUkTDohxgR1GG0DZQnqUtwi3mKE89oGYY0c2kR4mSrIYZMJvxS6tqXSfiQNIx3Kl4DSmHY4WsJcVrN2b+/qVQhB2lvqXzxXMgdC4NRFhagMwr0nHgeUJI328PyZPIyAto4sbK3r3tL+lYuF8Iwf7F26SCIxiTp7PDYaJ+3O8WMXK4CrMDp88rEkaEJgMTBcySRsHl2+oITIm+2jxy5mbggBaKbDOY+B4DtPPG48t/Ki7aefxp9/9nuoCI2Z/U//pQ5DWlcHV69ozBXnw3NnenfvhsNx2WlIwmrO98+dB4Swujq+ekkcjZbu3RltbFEpyyCab65WkBrG9i5cNIsafHl//+R5i4lk7sHlK1jK3PWmJzfCw+PVL2/1T52uhWBptv3009yoCrODS5eb+3tLjx5VzbD0YyDV4OLZLGyKPNt98gliamsM6FDsUSKlXAuUcOttIE8uC8dWGSCrFHPAtERtUnO/Oqag5eumizJtN13CjUoNXSPAcfQRRKGn2741ULSsirjJtNlwmEPqHWSX2jpkUEvUJCjmpFaoS/N2p9xBeqVFfWgLa5cZd6wFiMSwbjBQoSKU0C9piapGXYd0OOsMbaNuMaQ1RnnRNtq60q+gyMWxAzU0EUKytqwqm1ZrA3GdNtAkW+qThvQtym0dKR0AoJQkMmmzcKCcHJdNqxWRTpE3gJeX0pO6iftJZyCbi7ZwZG6xrlu1VVS7dd5gWFUYqmot9GAJS4XWWJFTdJDbtiAAUKZ5A9RMcKSLE00+S+WOhD1OjAIQ2DUHaINxbZccnBq7X8iej6hBhYYnHAaUMpCsUJhoPKhAQDUjSGm6grXDcKnBpiBJKfclDjGkiGFFG0D6DlaGLaPE4XwhLaOh0QAAIABJREFU804BmUWyUpGci/h40UsFLwLCKpwtSQfOaunJTmkJdo8gxFD5kGhjgiKNMMlR2q0tg8Pp8rHfUh6CVan8WoUA1ha58yxwvH5kSQUcBQqQtYx1Aa4r41cLzxkslubcyxqM57Bol1DUMLd1U1rHWGMQtXbVsRLhEOIOLXdB7TTBUkikpI4FXW4NoETD9SAbixo41XqLZhVrEttkRhvEjVwJydTKnOsTTakQd7XquW5RoBA6LZDksU1pcbLH6kITBFcYLhT0kFyLzEFdG8+uNRCGGCi8SiUkCAG0QnBdS6xkE2oEqK4WPbhQ7mjRG3Z8B1baoLyjjLXYprppp7B9PGtnEa0czCuQLVXY1kqjqlPn0j9O24vQqxiBstZxWrmUVTBbrkGN9sqTk8i3UFtdVE2oOSTS6FY24L3xbKnf8jmukIFlW2sCkM7TNrWORZkyaw5zLMo023CU5xRHULohWo6I1CSGdduzmUFL2AlwuYOqRkvE0EKCI2iazFaGxlYtNeUuku0WbWCTatijMKRUKRJBFbnFEcG+r1Zjm2rQIyYWYpayJaYjtxzQCvtgNWRVhUNQ93y7ULyHysgXVaKwTDqEywwjlXXgoFgemHgec6eslVOrJlLKQFwt2jTPg1HZShvYGmKEytuA5kXNa9NCg6w1zFuTjoN0aQEoOxWVpuZaxmqyaB6bXt3kCkGo67pTKYyJNUVHpnm8r5fykDElLS2rJq4QIlrmXS2QljUgK5Qgy6CCPV4Brz7GYDVUvsC5sidcjLSuAFln1lI5Jrjl1rGPleFLSHrcZtpsuRTh+pjalZZxMAIKthkOGClrtCbSsF1tQ7PRYtwaCdESoxxACFEbS+aWewSuRFUjJGmNNhhhRpcGdil2AFQWiyRpUVyzslkYB/YnS4fekvUhrivtyjKytrbGKWoPesc+wMr6Bma2birjQyBlLdQiEvNpPMPBosFRDss4LwPkZ0UVKhUS3sdU4rIBWFVpR8pYg4qpuEgiNxn6B7ijG9QqbZlM2xaXsnZN0cJuUWIf8MhUxOFEl5sxPV7ooYEdhmsFBVIrHi4r5BjQFXgiwVGJu6QmDBlDNjiupWEY9wgYSTqtTYNrjJBRdI1aSqG2aJXCYWXGGjUotoZxw5qgcj2qFVmlVYb0oUItQrQmDIElYYDFWIGeqDlgFcqWascklXVVt86Ad7zozINQO4gqrRp54jGaw6RXuqp2h57lVgqEVCljrV0LS0vCyXG4NpktD3yBaa0rkLctYBarsm4YjaB7yDEvs4iJDGS9AlEJKiDbumBuf9Htu03tKFYo28jmoWCJyTo5FJipmvgQtJmtrfCtWmkU+6QKwnqlyRc56zETMas18KHp+GBgNXKr9YbNDW9Z2fL8NIUtImKYJJHRTrXepGWhAorbCKYKt0m1FINHZUUjsBIjawnVcIkpBZEL+YpTHqCCRbDjklrxEOiW0AAhYvVGwMsq86PDc+dpWebNxnxjNewPV27cmJ3YyL2IKrX3+GOkKnI3HJ491d7dbe/u591mJnxkzODCmSxoUCkPrlxh/eF0oodLa9aC3AumW2uVF9G6PrpyCSXF1icfTzY2CALS8UentzTEteONTm+5i+zEB+/Xq81hZ1MU+d5jjyOrSir65847v3uPb/8eKsL5f/7j//Qn/8fGZ59a3znzqzfO/OZtK+j5119/8ic/2XvmqW9878/OvPHG3rPPfvPPvnfig/chI6d++7urr74qw+DMr996+kcvHTx59Rt/9w+nX3tt/8mnvvq3f3vyjV8zCrc++ODca29UUXTy3XdeeOmHB09eff6v//biL395dOnytR//6PSbbyJBT7z/3qV/e6VuxCff/93X//qvdp57+trf/cO5134131q7/PNXzr32K81Yn24O3kERz8FBdvwxJ00EduXwtuOCyhvOdr8IeJIJU+2+47q0YOP8+HrgelLtyOFt7qezRjp79FnE01zA6sHvWgFMvMFk7/PAJbndl4MveZzM3GLx8HrHmc49Vu+8HXiw4IPp4SfChwvcrw7uhDyrHJnsfeQ3hqMALb58r8NMsWwM3TntzYFX9MHBVX9fM38b3/o6TmIXb7PbF7xcB7OhPT4nplSAKdm50jkwINgWt66hNBB4n989L3LsFRO4f5ZOAmEnZO9ivI90+yC4/gTMGtgZhV+eEFPqFSPcP+lOY8AL58HJcJ+j8F7wxWWTboAgi+6siglpzHdhf4vMVlVzmr+Tju/R5iaYv1smu2hZ98t9MrpNObf5Hsw/qtbi/vQTfPzQ9VdA9olK78EemOpH+dG9wGW6PETpR6rdy9L3y+E91+sYdb2YPsCRTeBhcXTTCWFaHsH+h6zVWahP8+P7bitIs1tm/IA0igkdFvvX/Qgkdd/ufexsBkfl9erwQdjkU68I+PYFN6mYzcndrzC7CKcL+uAxTHNnQPHRaXc67s2Oiv7XnHnOUMa+fEbo1JvOyKOLTE/9EURHZ8NF4uVTvX0tGM0Zm5LbzzNTiSQj2xda5SEZAXx0WSykY1P08LHmKIF8wm9fI1o6eYEfXvaqTMwUPjzHF0bYBXn4WDiuFq359HULF6pZTAa3mDxSIc4PboZ4p2ysF3uvi2yIO8H8+D1sJzrQ6eCeJ/uQu2D8GdZfJu2NbPQGmQ9Z0AGT3wE1sq16PL1HkwPiBGD8MdC3i82l/t674Xgc+B27+NCoY92thvN7cL7D/BYYf87kI9jeyKav1tOB2/SS2XVc7JsGK535ZvhonTiP/ActNDijo3l7pw0OtgJ57B0j2L9IbUIXUXDvJIgOwvsROLrI+ZGz08KDTS/r+3MIDi85akzmIX30BBHbrYcMDC5Q1vd2I9w/ES4OG9MqP7jmVgOS+nT7MY8/8h9x2D/v2G3/KAKHF+J8xBODH13wi0NWcPLgCexN1YPh6AsazOd+Ntn+POJH0zCoH77b4kD7i8nBh9zVKetn/S8FnxeeSR992gyG85aY3HynS2TtlenxxzySCZkUB7cDPFcByvY+cMXRzG/Vu687oNZCFkefB16d46Tu32BkUlFhh+9CvL+I2sXx20FlHWb1+APrzudulg5uOXICnVXZ/eQcnLdR0A9vbbKZExR91D/JR13ECrGzGe26MLrfuHneLFZNlEa318kwaCXbeLCOpmvAKcTOZvMgBOHd4LMzenEC+tPmg2UyicPiUBy10WDdocd477w42DDtB81P1+xiQ+BDsb1KRm0/P+KjLj484ZADfHCaH24R/0bn+pJenLLOEH05PLjlBTpVE3v0AY+bmblTHd2LGnFefKnH91lcTNxJtnvdD3RqErD9Sbzqj8zt/OBu0ODz4r4Z32WtdISSevd6HNUzmJmD9722P1d3suMv3SYZ6z1zeC90qwLn1fFHvJv3YVo/+KgROYk6MMd3w2U6q3fq/m1HpDmtq6P3uWtyz4vFrZNxORbjyvYvODPl2AV48ExzkAPnmH/5PKmBU8/YwwtumYlFRfbPszkSdkYePR4PahONvC+eBIozk4m7Z5154aQ5OTrjJMKy0rt9LhgY7O9Hn182qomJ8u9v+rM8nAxs/xJNYhDk7o2LjYkAzsPw+kVtmgwWwcN1MUeAzPufWHmz3OwcH/1GjI+coAuSj3S9a3pqnN03k20RhHZ2E9a3VGc9mb0mx/tuK0rn11G6Cxt6Xj7S47uk6STjW3TyBV5am2W/rkYHXstfzD8Hix3UqsZy3wxvsbYzn9ymg5vsTHNv/B4YHPhtPkluwfQB6tSj/NCOv0BL7mR2j41uidXu0NtZIscng9lRa7Yoj59z8zGtGLr/jIP3g10ADy84di849uDx6Sib8rTEjx7zkj5TEN9/mtGhc0TQ0flueQcMAnt01ckyriR7cDlKB0hbdv8pSibugNK9c6EesBHDR2d5WgpVsfuXvXwKgeVfPmV45Y0I2z8ZpYcsIeTgnFsCCHd2X3dVbn2T9D+kTpazsjq67cOZIR4cvgfR9qK5lPffoEXOHG5HH2K2KIIiGXzJ6xHmMRz+xqKdfLU9fPibKKl8wc3iY0umspUO+7dFcQSDHjp+l4Jj0FxJR6+oLGERSsefUz1SjXrev+tlh7gTJzvvufmOXV6azH6lZgtvzR48++Pvn339rWx56covfvGVH/3LvW9+9dt/8t/PvPXW/MTa1R//6+l33sZWn/j044v/9nLZbV16+ZWn//mfd649/c2/+KuTv3472Vi++vNfnHnzDaGrrU8/PvejX5Td1rnXXn/qhy/1n33qmb/6m9Nvv112Gpdef/Pyy7/g0C7dvPH4D35YLHfPvvXrJ//HDwZPXH3yH79/6dVXUdtb+c0Hl195hWrZe3T/ub//x/HZc7+fFaEx0z/+LwwCaO3tP/imAsQQ8ugbXxNJOu2t7D7zFCpq6fs3vvktZhSuq9t/9N0KUgDBra99g1dlHYXb156zWalc99a3v4NqyWR151vf1JgqSG5//ZtUyYqxR9eehQYCQq5/5w+tMbzMb3z3jzAGxsDbL/4B0ko6zs6zT1eEE6nuf/vFwg34bH7rj/4IYojzkmxxEwme5/iUQ13Dpgm6HBLfwmntnqHIBzTJ6SZVDQdnpbsJQUTpYI4v+SDEcFKL8wIFyJnOvDUge6EtjNhEuut5g5k57/MWAoOCnPNgg7qLhK1i3fNwWrlbpF4K3MEMXvBJE8FRic94vGHRomLLUK17Yl6WjVJ2azo1VbfKe8CdJMCbJWsWVoCyebZmYA6rIJO9kk9N2lblihXjheXzfLWGlYUsW6xqkirtJ7qbspnJ23W5bMmiQDyZr2svTS0vky2LcmDFbLGsgklu/cVii6C5hDRJ1gq+SBAts7UaVViy+Xyd0iRxQGEu+K4uaZaLy15GuCdTfFaQumKwEqdYaYkAlb3gc1uyPDeXQgEkrBU5L5CqHZvDM57FxK1TeDmgoGZZga6GGCgstTjDiJXClOiMKxnnZeGcJ4ZjPl3gqyEgANeannewlV6dihOk8CNcFuIcTeIwHOX5SqK9ik+Tup1kTUFyqbrzLDDBJJtvKeZlOhXZWq4DKWb/7hnEJFO6kyZNHIzT2Ya0YUWnplivyhi404VsZXkbkEzjxnS+xMW4TJdT26jZxCSrSja1GM9lnJQtTXOlmsm8g7xJWSwlNsrZ1GZLpWoqUijKlT3lw6TisWGbFE0KsCLQpkOmuePW5LRAlaFcwbM+zKXwFTzhuqMZ6VFwykfzkjsSnfOcJOO4hhdCVVjXr8EJR4znJLT8JCsK5LDKnvOdIsNEo7MeySoWAHDKYdNE+Aqc8U0NPJDxc9wqi5FC54QoK6PydEMja2idTTdroDInL2anFMESFjJbzyFQvJ7mq7XEmBV5vTaVFPBZPj2jEJao1PlahkBFs3GxXpdcsKKo1hc5g+E8mZ2SDBe1pPl6AbHk+SxbqaUgNC/q9TT1WDiZT04qzGpY2HxTGiJFNp2tWxXB4HgCzrqsQ2C/IOdc2KbONOHLUC97OKncVSRXQ2e4QGdd0kFoVJJTHm8Dsqhp26J1FyU17QG9FtD+gm9R2sVwWKCTLlpibJK6XYBXOU5r1oN6MySjVKwjsOp4R2O6Key6h2c5bQGyzvh04XZBtdXA41ys2mq96U+mmC5mG9rLUoCr9IQGJYR0tliR/rywYp5uYriQCKeLjZItFgSW6XoNJNY4TVcrZ1FoMk+3IM0Vhul0oyZ5ikGdbElSGUvycjljuTJ0nq8brRGXSbU+N9piVSYnFam0IVW+WtK0wnqQnETSciaT+SlJTI0KJc4wQjSXBT4plMNZXjhniHEpH83xlRBwiHLFzjsYa7dIxBYqowgVFT9DVOz6o5m+HHIXgEWFz/uYQ7dI8QlhAk7S3Dsvytj3h1N4JSSegYkiZ10mNMlKskVhg5NFQU5T2fL50ZRfEDQAYFHjUw5qYJwpHEzny5RPZdZNdaviY5Muy6qtnfFCBYuio0imdZQtlqCY1WUnsY2MTUGyXFUdzWaFDrKiV3uzRMfFYhXzqaobs6Sj41EqG2m6ivGs0n6Wd3Mxy0yQ58uGpKgMJ9mSDoZp5S/ydcSS2jhZ0ivFPJehTFesGM2oZ/gpVlTYIaU974sqJ1bC8z4tasINOO3QRS5EDc6GVkHPpPwcMxZQU6MLPipryiw7xWhROayGZ31pqKNycZ5pCHmZo0s+UJoyi8+4NMt9XLDTNGMRkxm/IAxnbpbaiwHVEiEAz/q4qjxYwDN+zWA0TaenFGeFLHm2nlsmRTrLl6vaxzSr6tU0CVg0XkxOSOjUKAX5Zq2EcpJ5uVSUAWRpDXvjWTvwBuliI4V+TeY2WVfGqfl8XnXTKjAsk+VyMm+ycJhnawn0Cryw2WqlAsWTtOiWxsmcaV5vFPMWD4ZFvjKvI0AmmdPUdFOgTNGWNSdCPMn5MoBrrnc8wRvCbrpoWrDY4i0hZokTa3kyRrPS6Vmz7rpHY9KBfJNVGXQCBU8IMU9AjNkWh5PS7QG14YrBjLesPeXb3Li8YicZzCWKEd7iaFaJLiIblE0zEWtwOlAVFEyCy7GbLHI/vPP1bzppkjabu88+pQ1C1t77zh9IwlFV3frud6msKsd7+MI1XFWlF2y/8Jy0GFlz79vfyokrqvLOt7/FjExE9PCFa8iCSrgPrz2rqCBVee87f7BwIi/Pbn77OwhaRcXDF54HBijhPnzhuUJ4Is92X3x+1Npwx8Mvv/uHgBJt0b2vfR1d/0w8+v24CNVqRy53sqVWFYf52nK21NSBMzpzCjmo6LRm66t1Oyo7DdVrJcudOgyLlV6y0rWhMzlzCrooXV5O1pbLdqNqRWqpla22VRQsNtaT5Y7xxezMSejRdHk5WV8pus26GahOY7qxih08O7GVrHSNwxZb68BnWbeTri3JdpD0eqYZTrfWXJC7sRaRcYX0IwUbhHq2ieewxzxcRk6OlqmDa7dhnEgzRwWh5KGBMWvw1K44Hiojv0Jt5JDKaVkvklRoN1AiMjDAAc3gmhCkjtwSd7AgNYktjzR1dOiVPDI2oiHJ0DJ1SRWJgnVMgAvcZSKywLUIpyqusKg5HFfNUuDKuBnwFphLyBYOH8sAAJrbRklEzfBEN1MOK+BlMEiRo6HIuJjK0HK8sI0SCcXw1EQLDkvIF8ZNTIgYnXI2qQNAUWKizHrQRSPdyDgskZtDd6ZDzMhUiKkODCOJaZTagYEog6gCEXNd2XYz02bMsbFf2Rb3SBG1VcBy1kBupECDO0K2nNx2uU8Kv6Ftm7uk9JtKOMr1lB9L3eBcyAZLYJeFKHdDiZrYYbXbMdzR3FNhKGkEUEQbPLXLToALP5YkBIJVThcEooQ+iP0SNakWKIQHZUsJVFo/RUGJWIH5BLmFDoCA46prQjPVTqrCkqHCugsYVoBVhE+RX8oAEDSv24rjkpJJHVccldpLkZtCXhM2dfg0j6hAk7pnOSowm9s4ZagEfob8wrqK8RlxU+UDB4zqnmGoJHRqopQQ47oyDivboJ5T8y6irvFFLXpWoNqNtNM01AUi0LFbgJh4pBAtAEIS8dzrWo4qHgE3kiCioVtGQambPGC129QgJCHNgmUbgpS2qBdrG7NAlGEgQQNHNOddaAMa0szpWQdXbmC8WOEQEteGbo4ahMEEhAvKS+gUyJ2pEBGeCTyuGtYBc+zOqiakMMViRHiJWYrFFPhK+dZFw6ptXJggd65DyXBmw4yICoicsjHwpXa1g8ZVxwRmorzcBhXFqQ4S4tSGl5yNbKiVZyic1l0tYIbYVAcVIYV158oHTKiIpHCFC1qHTom7/z9BItbM0YFfi0ibmAYkx0vEpTLiBenaEOaww5zIYA8Efu2FykY0ZhleE4JUvqdEzzq4dhtaRIYEIHBLL5Q2JDFaoGWGHNgUmegBhioRGxEZGOKmkzmRMhGPSUp7GFLkwmPrJSZElM4dNqpDQEkG/EQHwIFj20wZKpGXQ2eqQszozGFjEyqGExMV2occjVEjYai0boHduYoQowvC5jrULpjouICOQmgOwjkjFWIL7ExBYIwrOZ2qyDhwrsMMi4rQ1DYzSmsoUsonRZM3wMIPatgiLq3dlhGe4p4OQ0kj++8EmRUnwEUQStyALq2dDvDdErog8ivWgNBHIc3gmnBx6fuSNAFnkjYA9zV1dcMrSAOCgIQkRcvUR2XgVbwhXS5Jl4hAQx81vZzH1gakyVOw7jio9AMlmhahmtKJ4NMyIhTPZEczXBI203HKUQW8DAU5cBTmc+YmMoAOGMuOoqSmZGyjhMMS8Jn1CxMAQcbETWofCjiRrRIK4MKBbhYMFcBJoJsaHzhkRNyF8QzHE9VRgAOBhrCZMVRaL4NepgPIyRT6ufRxRNKwZwKY0RZxIwka3BNV7Je2RSOSiw6wIQto5natQyo30F5D4QBiD0YiQy0S4VQ0AfGt40i3ZzlTItBeKGkMSIhDr4AdFuHcaVrqGi5qdwkGJLchanglbmLsAs+tYI8FJHcahnmGuZq1AXe09YyAw6oLAj1Wbm6CiuHMBAl2K8MrxkYglMqzFE7rjuYwI3SiwoqR0ngL7OSWVYyNubNII+7CYd0DHOZIzECYUVxZP0VuaVzlkCH0S+0CF/SrJcthjvkUBBlFBRAzHSjga4/0bVBKF7pwWHWUAyo/Vk7DYB/4Xu0HNQhpiBLSo9DFDZG6/55asRWRhiGOndyLlIl5RDLWhdiBMUv8FeCDjHa4E2sQ0pgXTsPgADRYhlcYcFDMErEEHVR5ofZiBUPkONINFA1BA6d4iXCqhKe9nuVEilA7kTExRy6dnDqZdxrKF/OTm8hBaa+XrS3V7TDpdlU7nm+sIJdNT27VTb9uRJPzZ6CHk6VesbpUdcK005bteL6xTAQanDotW34deqOzp6DPFr1OudLLl5tpp101gtnJDSTwZGujbgZ1M5psrtuQZ+1WsdozbW/cW1GdxvTkBhR4dmJrtrnhvPN7cxH+5//6v5169bV0eeXsT189//OX80bj8s9feerv/un2N1/89n/7P6/85F9vfec/fOO//e/nXv5l3mxuvfHe4z/6yay7ev7nLz/3F39974Wvvfi9v7zy/R/e+tq3vv69//fiz/5Nd9pbv37v8j++NOutnnntzW/+5V/fe/6Fa//9e49//4d3vvL8tX/650s//HHeaJ38zTuP/+33p0vrp9/57Tf+5P/+8sUXr/0/f/HUD/7l4Mrl8//yb4//4KWk2T2AJ/ZeFcgxRd8++rVvm4Q8qu9/EgtYo3F+6/0eL0oF4MOXPSyAGtud3wTcV/m2evhJU+QLX6nP3m6TrLQEPfiFz2CFpvLOb9uc13JXPvi0FdU5zpIb7y7RRYE8tv2vHFIEFurBb0IOc9yXdz9ukUXObXXj7a47zQivb77csgA0qMD3nqRzh6uhfPSis4/N0pR/8DxYdK03pF88yTJf5KNq/zk8CSkc6wdfD4/donlMP/4amC1Zf8BvPmHTFqumZvdJOAgpGoNHX2O7jXJ16H74DEg3ZDgXn52F8w5Wc334hDNq125Gbz8ltoNyZRB//Hg1P1uFC+/GWTzv+dWBPHgcHq2lq/XwV9XohkNOsdmb5f7dqAtH08Ng9xOHOnb00Jm+j5rL2eAj59H1iG+Qxftq/1bQFll2p7p/vYWFne87w7eRs2lGv7U7n4d8BdjP1YMbsYvL/MBsfxx7tJju84O3hbtmZp/g3c/DqJ1ProPtG41IzcwM33kvcliZHvPdt91mO0lu4Dufd9rRHBcddOcay6VGhn72ggFS5ADdvWZJRgfc7D1Pk3krm8y3/5gnteGaf/IChIZkGt5+FoOcTandeY7nNavm8v4fOuPCBop9eM0AgksN714L5cxMoNr/OkkUAam9/S1nZmsvZx9/TRuKpYR3n8VS4rk2uy/QmUYkA7f/gIzZvD0/+gXKpjzU6aNP/HQP+6J8+H5c3VbuabD9UzHZ52G3OnhLTI+ZZ/Pdm7HcQygAO++46U3lnYe7P2OzAw8v48Frer5PmyDZ+zIe3yOkgfffFeVt2NpKH77sHuxGTteO34XjfacFZgfX+eA2xx0y/B2ffQ69C2b/5/DoURA3svEn4vCRH/g1H5/zbp+vG0N6ZxXsP5k1Z9GDnjl4HMGpc8zl7jWIUpgsezcvFd0Fu7eC9p9C7j562DPHX6H1kKSxefAMsXMw79Avn1ThINztqL3nkNgnuz1z8BWRH4dzlO18i5iZTVvi5lM2nvKHod57HpM9etSxe8+wck5SZO59lekpKmN24+mqIcsHo0cfRn5esCL54t0lMi5okz76qbAGYakevOURU/Nxfe/jJpyWDixvvN0Vg8IJypsvd5REWMuHv/a5NWBS3vuoaSa1w+SdN5vgsKI99OAnTlVhBtSDd0KQSpjp+x83+VxJFz981an3a76G937KM+lBaPbfomBWuUrd+6hRHVtw2un+9oqdbsnW1Pn0lJ2tETnWR1dZf7kWObn/mHjQLJcH8adX6/GFojnzb5wC0/Wg3leHF8Hx6cov+P3L0XY3Wzp2Prygp5fKcBLc2VLj01QN8f6W3T+N3SHZvkofnUo3BsGH583kMmK78N4pOzrL1RCOTqGd08AdooeXyKOzsrMd3zxXDh+X7mF+O3/4UdNFRTKku286Ts8mt+DOx1HYKmc3wPb1RiDnOAU332kIWOUzuvOGG0VZcQ/e+bQTh1l6xz663myZVI6rO++1uS2rlO6/JlgblvfU/Y8bsZjpHXDn06ZX5rbQd3/TjFRSJuDumzGPbflI3/8wbrhFvq3uf9piaYYMuP96BIB2wzb77HG3LvFcyb2vkTkgcG7ufCuagjxc8I++amoPgRzf/gqqEUkqs3sNTyDFc3v3W7Qvqs7C/fA5Y1oWFuTGVZxhVCu784yzcAun4tefY0esbs/ij7+Sy1WDK37rCs+pmw7L/a+RYZA1pP/pV5yhn7dJMQw2AAAgAElEQVRG7vtP1tWaRalz7zKYhZItdt6jxQ3Y2ky3X3P2H8RiDc3e1YNtr43mgy/wwZcebqLRJ2LyMfTP2YOXwcG9sNHOZp/x/fuhB/PRPda/IfyoPPzMHXzEgrNm8Do5vBfGzbz/MT664zfAfL7n7HzseHE9/MI5/FCsbiT9d/iDu61OtBh+To7vhG07Gz8iex+7XrMe3eCDD5h/wriPeubgOZEP4kQmO99lMrG1z7/4ig0StueYnRcoPqKDht19lpYpy6W+9w1WzqF16WfPardgfVfvPN8023U/1AdfJ1mKtQZffoPVhdSUfX5N85yNXf3oOQ4naMjN3ldpmiFg4a1vwFIahMVnz8oA0SEBD57h9QgWnn1wjRWg4qOHP3GzhDqs2n7bl3ODq/rhpw0yNtpHj37plNtSnEA7P+ZZ6mEHHr6J5Ej5pn74SSM/AKjFdl7lZtdEq9ndn/ujacAdc/QOrWYkLib3PmmkjyxccY5/SZP70D9ptn9KxiM35MXhh/6sz3yTPPosLh4Cvyfv/zJY3Mf+aXPwb2w0DLti8Ow//f3Fl37WP3X+yR/+6Lm//Nvr/+E/fvt//a+Xfv7y4eVLl//pR5d+9osqiE6+8+7j//DD8daJyy/95Jk//5vbL7744p/9zYUf/Ev/0qVLL/3r5R/9zPjeyd/97srf/PN0c+viz15+4c//5u63v33t//relZd+Mjx16twrbz3xg5ek461+9vmzf/qXk9OnL7z82gt/+ucPXnj+qX/4weP/9M/V5krnzQ+f/ofvS+52b9/92p/86dGFy9nB/u/FRWghPLh4acURo7U1dnqCjJ5sbC7Wd42B0nWOL1+qHSd13IPHn+AIDk+fgYXxp5PxxmZra++oKMowPDp9RtUybTUPr1x1yuJwY0NnJZnm462t6Pho/8KF3A/6Fy4SpZPl5cPTp/3hcHDqtFNnznA2Xl8PhoPjx65K1+1fvuIonXY6/fMXOnt7o9OnsNVxS/IGsxo22qVutCCsQq8ibQwNaYYl6VHg2bBb4xhjAhutEgaY+jg4lHotqMi81ahJB0MPRa2SxVgxGzZK1OAA2ei4lg1MIj8KSrhEDQdBV9IQgYCGLQkbvOY4CGrew7rpNcMStqD1RdStaQjqwAp3If20anL3eFxFeUmI8PvG0dJDKJoBtEhjaMoUuKkMkOdMcjgpfZ+HI0uN9BAJpwRXaROSLLM4rWMa9geaLWohUHAMqCgc5MYJsapuGpJPpSmKkPjO2NBp5bjGPUa4kL7R0YQokDQwKVKDSslRo6cypY3wvA1KdC27LpqBMCxRyxelpe1CBg5t6agqTBy67QRkVjUYqlRca9IRupJ+twC+53UyWxWy0RGNPJzWtIMIsnGzhi1OjY07lfG506maeal9zpdJMJNyyaNp3mjWKCYEg6hbg4CbNoqTUjfdiurIGaZRYV0AgoEOVUaML/ra1RIZdzZdNE0GIJv1i7i0jgVBXztl7VrXH9cRkER543keFTIUDu2XjVJRwIM+cLM6MMIbSVHlEefJXDYK6dPAGVTBXHrcCfvWy1WgXXdk3LyIoTuZVWEGfBLywyqYWE68XsmZLduCNzETWsc8DGvGbOW64VJZI6R97vUs0tp2uDvWHEjZ8P048yItnaC1lKTKAD8OVjHMtew4Ym6EK23D86PUDcrK9f1lA8pKtxqiNdIVqDuuGGkUQxBR7i0cJpXww96CVrVseqypfQJNjI1aaE/WLkFRSnVfxqQezUTqFXEtgUHFOI8MT3LoHxhmjD9n1WESUqMqt5ymHYDTjLijLFS4qKDfVx7JvEyU/dRHFpRONsva0Mk0TIdVqIkuQdivmIEN5ZSDsslUWnizWd6oNcJsOCxCg0H9/9F2X02bnodh369+XXe/76e/z9t3F1uARWczKUqyKXFkS+PYzokmE88kPlGOMpPjTE4ycTJWJEsaO4pFxWqWbJFiSEoUSZBEJUCCIBoBLsousPXt5el3u3q+gpOJPsd//vNj8bHhCg9plmnbxbob54lEY+oETvqaZtAVLOlZVDCZwDjVYoRMJ+okLep6EwdJX9IUgpxmPYNiZ/tBuOf4iLgUdHIZDIGOgmzY4hT6jOWFYgVEA5qmiua27ER5Pkn6wIRB3i/bEMFOkA4qWmDVpWmi2MBKQkFyBEFYCxR0ykDBtuuRWjqkZYbicObBUgaBjs6A0zoCJp2TlqwKTFTj7UzF1vGpjisVBGE6xQ7KnNpwirVvC8vq2jEsYxzyOU6WjhKfnVDlVikBRU1XrO26WC9MAlQEg3gJgFRhWIczroSKcZiDvJCoy3Dts6H2KeM91Zk3LmZ8jcQTrcaxaMuiq1CXQA3SvgIptxbms9Z1Akpgeq5UlxIA8rxFPQIhTAYaR8gNWX4uXSfUIc0OFB5iIHCetS7HOEBpX6MYI4o7pxLEHmRhcqDRmoCR6fSkKLxhFiQTK5qyAHS10EWtYxyLsypc6EiE6akLax37MJ64sK67UMwWJqt1xjJxpMREBxQnR46hJgJRPPNJKzsmmMxUMJUZw8Gp4zMVcBMcAR6qxMB4YsO66SC2mjdpY0LgxKnhUxUGLj8HYShTEPNzlzhV8ChvAyZVFEUj55aty7uis0RLoLuCz3QaAphTHldiKHUYZ8MFWba6iGjXJU6TPk4qjT3yKRepDazTYZCOKjxBNuXRCDvrVT+k2uRdBTPKMpcbJwPm+yAH0nSioARWatUTxIGkcT5lPPewNiAgutMGq1nZBZF0dHVaZxpjj+JjzbTLUVietjnVbRPP5lXROsbY0VmTWcQ8S45doAxBweq0CUwjQLKcNUVrAhyHJ3Vc2xjD9MREznoXzs502DZBHE+nTdagCCbioIorG3oSHWoiXW6Ds4kuvIxUzCYynusozEYSZtBnIu0Znns0YPEDTXLTFHmWz+LcmSDuDss6gj4Lk4HkIVJdEqeaZNZlPM1LkWoThOmaaWLrekFUzFFK1TBKDizMiItIEC+i2MsoykYrLbDphqIwlhPQZXkiQYhsyKOuFtzrMChGdUVo1e/Mdi9oJGaj0Ww85k8/bQU/fvQ6jKJVv39y9WpUlieXLkXlkl6Vy+FovrVFWy3jZO/iRajUotc9uXotnU6PdnbDcuYvN4vRaLZ74XBVN4ztX39UtHK+tQUWqvPg/smlS4DAk2vXZt1eurl5dP16k+cnly5l55NZr9fAzsadj08uXuyeHJ5cf2TV6fzdWITG3P2jP8NC8LoCEWsdpUpBjgyhDQ0ytTStg9ZXRcHrWlQrkIqGR9zrCgdssULWkQBoBZGxVVF4CLkzoVyg1khP6yxjbYuUptwbzolzKxrRVvKylFmSVPPW0jZNHEYOolQtjYK4lTRCJYqC1bLJM2Hk0kUBagGEraYUGSMwMbVBQaaqhYlRAENbL10cohYgqwCMbVuS3DfORzRT87lNMAexKxvEU7XUgM99ksFVJRJfWRfRuJ5JhjxkVLsGhgFsPYatZh05WSUp8NKgINPVQkeEg357uk/XQ9hAiowWjsDYLaSOtKCJmdQeIQgowhXIiubcMbCCHYdh7KbMqZYKbF3lcwScgNUK5qGpDXMeAINwZFppYotxDOeVzwGAVthYL4OmLEUuXQKRNwzS2nkEYz8tEYPAaxpjjYRqOCqli1oWWA5Ba6DxPiZAOVLLmLclCTG0BjKrgHNwQx+e0Z6x2MeUWomg8w4K2S5BDCICjXfap3ClKIPeSRJ4C1GtQYzjerUAiWCaAdViFpq2hLGVIKflHGeo0SjCsVw0hFNvjcU1CNf1wUT0tUQZLReiQxrgmQtNWYGMg4bgFjuDnV3QLpRIB2h7fueQ7zruEzvzBAHnpQ+1FZFf1DxCLdIC5e3pkoXIw8CZEhQcNB47ZaNhszdPskCXK56ESlWu8MyFdlX5nIGWUIWsYkbOeR+22Aic6mnlCkCsANXSx0I3mINGM49QCpYSQoNo7NTSJhC4GDdzn4SqRsJLADzG2CKnoMU4Q6uFSyHwPiTUqsA0CpHWCAiBFxiW2iG0UT84SvrIW8MSqIG3ThCNGl3TEHBMXO29j51auhQaG7NmAVJkLBOWaNDAMAILCQJvsQ6hAE1Uz5dBN2qaCmQmANABqGEAVwAq5gwAaE57tHUmAHG7KlFBcYN90yIaWtWi1Gsco/mS5rR1ntv11d1ZkFvMrOPSRyFcSBwAiSM0r0SQ6dWCxGGrKpBhKrG1EkSWew88WikXslzPVjryAsduWRHOnAGeNIYnoGxE5GrvAho305ZCiFl3NTsK1oVvMfGVDRK7asJAeFsjmsrVQkeQo8SXS5swoAgzrQOibWSQO4U8Q4AiVCpAYAzLClGAsIECNjZxZRuGpoWeYc8xbRwA0AhAJOCqhVwDYBH0K5bjGgAIIzgvfQ4BsAKEehG1pcVMuqghoRUQS+89TP10RQV01pIQWQQNIKSl0tYkCeCSgdZAiCxYoS42LsDLFS6wdojJVE5bFkAPpY0tIBGclbiDlLMhSKrZ3KVcGGJM5YIE1wpD611i5IJ1YGVATGK5XPiUEk1soxjrltMl7ypDUlwueI4r7SKSq3nFQmINtKD0YYIqiYSRsKfPJlkXm9rhIFLN0sYRaQJdn5JBAitDsXE6MXLBu7A0ICKxXi5swpil0LY2HjYHyyjVJjQMZeYMe9vSAFlf+4wChZBufBLZpeIoVMtWpFzJyuae+BCsKl8A5Bwx2MjQNC1NvOGOQEsBrRzAPgJzh6AHcMUK0vjAVgy2tUuVYJb6TnumMYUW1D4H0BuOiPQAAsMBrLSFaEc+2OMbwHoQEWolgABbh6StcAhCAqV1DhRgUdMAeSuMnOECKUsDR2u5wlFMGgicwiwy9RxmUNqUVQuYQWWYcELXLebcq8YKBfiWur8fbqFGJ6yuSICAQx4gZSoURaTVlihPM7Rc0Zw23gZ+vNg7FLuIKW5WrccCWEUSp0kM50uek9rrEBbtiYNAE+EdbX0SwJXCzCk2bu8eZmNkGod5rNsSFJgoamUDkgCUgAMqK+zdlA9oDUyAEj0vXYGxZKBqIPGYeihwC2IwX4iUtMhwkJj5wsYUGEZMaYLYVppjB4wlnCpvDPYEpbBcuAQi4AXEsuJto0RqDfEEAY7gSgEMN/TBUTwm3tUkwLX2CMWwUi1SXAAGqa2985E3C5dSqzgzpQ0BBBEsnZGtSFPbLE0MAEhxOQcZtI4JFy+mLeRNlvG6Qtow7pYi9wDk9axCEa8rlUZhvWoAJxwgiiyhRDatYbSpaYRLEIiq8hENTX0Sr8VqhVptHaQCSM9ZWeIYl2EWetM6EK2WraMoxEBZZwDnbslTh/DW/O5BtBEslyqNAlkpS+q1teG//J/TH7z4/79FuPjH/+Sf/ub/9nN/9qeTKw89/Zd/9XP/4U9Xm+vnG1uSB3Gz+mf/4//083/yxze++MVf/d3f/vz/9Yfz65c+afZ/dfbhkfKf+f0//uUv/f7tn/vsP/zX//oXv/QHH/zyF9d/9s6vvP6Tewz9/F/91ee/9IeTSxc/8bX/+x/+7u/cfurJX5u/8V8sbr4Ls8//yZ9/8Xd+e3bp4qPPffcLv/O7q/HayYVLhjKh63/0m//7F3/v9x586uknv/HNX/mt35xd3F02efmVRSRacliu/qaJRZNNbpEuEW+8E67Y3jdQIafMqOWXqwDLzNylchqsluj95vi7sOcmcV0efQ3k9YRu4Gk2itQqfP3o7BmXsIodVeff8YU6D6o9nrjo1i1XpfVXV4Fvwvli/k3Vk+fh9GM4EOlb72AVnHzVdScnMW/Ov2xoWw2E67xxKT2XgTvI3nq8f29hOyeT/qbHpLc36f94Mz9HgdyP3ns4O7SBvfEb+ubO3rsfhRubz22nBwLGh/3XNtIToendz7390/joWFHee+16745sNmebz29E+ynp3/6v9l9+vD49nqLwg0e7e1DHi/FPtocfKts7mHbGlrL0dLX2ei/dj2P1cXzrUnY/aMeV/9uj9uUqukrxc0ezV+xGfr7sFJLHoq7Mc3P3/Ky/XskfVIuXVbEuZRYrFkTTGXvtfPKSy5NWfaj9M+edfFUOuy2Pg3JJfnA6eRH04gW+XZ59z3bJzI7jVdiJ5Rw+c7J4wXbHNXh7fva8HwYT0OeLqJ80C/DSefW9djBY+jdXZ8/7fnfBazb64bV4dUK9HL+4i237kHvtv53eaNtKPUjzd55I6v3O6jh46+fi6fEguPXfHb/KTTt50B2+cVG4WTKtircfTaojk5aLbIS8H96dDV+5zJUUzbL32kNZczZS7/yL5gE4vrMyw97Lj3TPzl1Ubzy3TSu/y376z6fv8eXpcpLmbz2Zzc4wXvRfeTQ7W5W9pf7qzN+XXTybvGDJ7bIDDmAo0w9uoLQo/3LlPmjytWb5t5W9o/v2YwRW4fycL9rFdxx/5zy8Atsvz9QNlV2yD7/66iN3bu+RqH7RwPfLYACqv13SN87TtSo4uAWYhcete0XKn6khPy9/pOy7TRAuE38eP/jIs8B+Y16+7frDsvlBXb9jOr2yc38wfC23vbPhu6J7Y6vuTb9w/uKvlwcHy5I/GGbvXKL4ODxON17tttn8lw6/++ty/7aF4Yfd7MY14e9mZz5/80pg9qs+rKJeIOcbr5POOxdRup/eRvnPHkn0zafUT//p8k5dztrDa2uvjmBy2vvI5+9eofzew9Of/IadnB3ft+cbvZ9civS+WLDRDzdt3qD7h7NvmbyZJGZ5/DWYnx6LzjI7u8+b0n/crr6tIlMFs8X5Mz4p51H9AEUyv/UxbsTsq4os6whUi7+Rcb2CZPnPXvnRtGmggWdftunhKR/C1Z8v8Ex2wyOi5/ToiB6b8++i5GxCClT/xSJ8cB5uNMneRxhZeLctvyvj83loy/Pv+PBkAXb5pWe72b1c9k63fpRHe50c//RXq1tPL+488Enx+pXxe7bemGy80Evu9f3awX9593uf15PT85rffCS7m9lsPnhzNLqBXHdvkfctFfF0tf5mmtwdhfBufmuY3uzA6PDX67d+/vDNd0lv9Ooo+3gDJfe6N4r4443Yv/fF5r1fnH107FDw1uXhzxI1Ptz4UZx8tG26B+y9s5NnfIfO/YGq/qYMeyY7/wATGZ1N/Lv69FkwIufstD7/lk3kOcen6eKQr5budXn+POh1lvCjevJd10PnaMAWcS9QNf/J2fJbbVJI9uF0+l23jg/p6V08FOkb7/oynn5NDswZXqjZ11UYq2L6AQltuH+A7vqjb4G+Psdaz77SxH6ZiHj08mZezXi5SN59PJ3OEnfjv29vFvsf3MG7289tBkvP/LT32oWgVP3m9f+mvp2vDs7avPfKY8VhqQfl1rObwQS1/UkT51y3+VHTfeNafl7JuNl+abd3vwKDvd/Yf3FTLu6Ww9FPttKJjpq96Mbj2Zmxxen/cPDiQ/M7P/Mbuy8MxCxDbDL6ySg+wy6ZLb+9JK+e9y82q7+p67d0tq2bJDGYZqdn6sVV+2abDF3zQoV+OC3G1bw70FR0j47aH9SrN10/msm32/pHbSdZ1qNuw5OsPHffOFu+5vprpf7Rcvlj209mqh+XIu+szutvLfQr9WhnKZ8tJ6/60WBe5bniYTyd2FdXzYtVt1u1r8v2+TLflb2PcPbu9Vjd7kxX4U8/GdX79dAus7Wona+/7fJ3rnB6Pz0ExU8fjeT9XfXuvyhvmtnxfPrQ+isbmM+TA915+2rHfDjLeJ12qVTDu3X31WtJc0y0H7+0BVD9hH7jv17cKudn6mxQvPl4Uh1QKwcvX0mXZ/WoXaZDbOX4A9N546HU7LHS9n5yPW7OlJhXf7FEJ20iqunftHzWZosPcOrj+/dpA+df0+HtM7ED2/84M4c2707D6X2+nLG9cvYC4fsLNkDlf5yJm9PuRXn1+69sHR3eyzb9X0/AgRz609Pve3F3SZMq0yfx/Y8cisxX5/KO7SSrxbPS7pl++QGJTfzgPsdk9hUJbyzTS1Z9+by6DYe9xRf+/f/52T/5s5NrV3/xD7/0hS/9wd4nn5z2x47QwdGDn/vjP/2FL/3BcnP9yW9/8xf/3ZemO1tfgA9+7ei192n/l3/r977w+//H/qeffvorX/vlf/t7q+2NxXi0DDths/pHv/Vbv/Rv/u3tX/z83/93f/BL/+b3Tp68/kR565+3ByfH+1f+9oVf+e3fnl7aferrX/+V3/xXZ9cfPl3bMoyvnd5/5K+f+bV/9b82ve6Fn775j/+Xf3l67Vqz9+DvIBFCAL0/vfJQmyWOk9mFbR9QlYTMSmsINe3J9avLYY8if3pxVwvqCJ5AdgcTg8jk8gVEPEL29OErTRIRYEwSTi7tEOin25vaQxeK6e7WvfaTiKFTMYAEQuBnG2t3/t6nTMRXa8N7n/qEzGJmWuINs/r8ykMeQUjRbGv93mc/bQKOheW7kMYWWMgvIBIaH6bOAF90IMHZrkIFJtyJC5BG1vFAc8KQ9UVQ7GqfUixUtGNgl1JnQrniRoKcRBeRiF0LWbylcE4AphYSl+bYO7oNcOpRgMUuQKk3ee6Nl71+SEFywaEewVSyC4h1nSEQdCYulJQC2zs12CDsg3ZJnMbEmUFp0cIF2HTOHbOc0zs4PY8QdboZ1oBriL3qlxQ1BMNy3NfeY+RM79xiQ62S/VITiDE6DLrEOCeEzScKGgy16UxKoyEGoVxB7wgytlsBI2FIdT71UGBgyBri3GNgQA+nyGhGuFdUOWYVHAANKUEGjYIkQBi7QNcWEIwAyFG841BAaGHdDoIhEral3lBvXI/mqoUhIblKdgyOIdJNqFbEWTVgAfSYONBjhdQupMzpUJfYWdhD3CDCHOqTBHjCgOZI949M7hDT7Xjlo7am/BboVCgwKQa9A5044KDqH4PMaohusd4UMB87OZyhoNGUuO6RSQBCPpBLajSiph7NfVSCAKjhJBCNEsFth+oiQM7I4RmMHUa6XatcVtWY3ibZEnpLMOocqNwSZNTwFCSWAEs2CKfaCxKOHabIR8JB0nb6oZJsk0MCEPZ8HXJsXZJYihGBWGI29hhQ6hTZxAB46K3qpjXNKfHByHoDEDTxJoCKYmhV3gMsRBSSAQyV8YKyoUUWQQEc5TLvcK3RZhBKi7CnIxTXChHoopUcQ4JkWxjCJgTJCUs/qlvNCAga3T/1gQekkiNPUTNL+zehcxDJzHGz74V32JjBmY891zKQC2aVLqxD54honQGk912MaxTcgWnNYm+qejhDVKkMI38KqF6y9H1p6iAEqFWjiYs9QG0znHkqQYjjTYULjCgIdx3qUQRtm/eQdTiCYheS1IOQxJsaFdhxBrAwSRo6w3YR73vAkdjyNAeQwNnOOogiQnyw62DBKNB8h/C+81x4AiFTjrF0Q+IeRV6zbYATimwr08ITjkMUbzicABiQeEOhDEJvVK8ysKHItZ2a+NpRfOQFJRwAYDuTMrTMtmrYGOcQ8KdhrzXeRaHJ5s4rjK3JZzoEGLlAlR4CjJwuWi9PAccuWWgBMDH7JojTdQqA6iugzhC2spCEH0MKzqGouTCYms6qFYZ4LXvGqDOEHU5xum1gjKjx4iLmRGsSW8IdB6DHix3lYkqdCbYdyghkXPOArUrUoykEjPkmJfGWQillToWqJF7DDLIdiIV1BQkuOBhCmxTOANXrUeCiiwDlmFDLLyIaWhnmFlDHhE9osatczgST4gIBOYHMquE84LUNkese6dRySm+R/CTiFKpmrXJZ5ZhXgxmKKhcl962bUIK81YNTEBriZDtY2bjlXltVMisdp7o4drEiXunBeUMM8u6u6M5pQqC0vRmiKx8S2zlxoSVW3uadpTMU6HIkbSIhsaY7t9wA6OJ1ADJG/RJvJrEGBJhA1x5BgCHtgyAFkHg2cEggxEBgGgg8wp4McRooIIjoKkU8YV6oGnqHgQdjFmYOYY+HLKXGC8KtDEyFvBNj5GJMkENjmsWOYMucIspg6FgXCIAh86TrmYMIOZ0jqPZ1BL2yanAKE8e1DJs5tVpmDPlzyLSi0HUPbAy0CO941ECGdNMMZ563nlA1OAWBpV4LuSTeAqba0QQkDjLdrK1A2C6p+NhlDQSWQNfbU5mnRMq1CcwdtTqQS6pbHVA0OMGBtqHQ3QOdWAoM28a88JDBcNOLjnNp7gD2SYaMZ1sAh4x6RbYwypzHxPKQAIMSEa1rHEHsdbyLKGPUVnLYVUnMoAZjgGNnBY02DAmJZ9AyoYseswptBwE1iIJgDAA3Pg4dwDbNMQBiGyCPqDdwiwXMQuRnu9s1j5xgp1cueUEAw8xI7xAk8PzCNgTWhny2vWmNt2lwxiIPMfb27PJFzYmneL6zeVd+Sichc5qZlnlzdmlXI0woPL20C6C3gk6D7AMLpIDLjdHdz3zSxOH0wvbdv/cZG3LmFDIAYrAcD+987jNtJ1tRf/fTn2izBDn7/zYR/mdahP9Erw1dwI8eu95EadPtTC7urt/+qHvvvuwXq7Qo18cnF3abTg4E3X/4ejk31cH5wdYVH7Dpxka1MSqLXt3vHV26xKypV0u9sdYmadnpH1y5DDhbbG6WO+uzk3Zx7+TooettJ4eM3n/ySc9w1RscPvzw4MH99Zsf6Cxe9Pq60zm/fGHeX0OU3H/y8RC2EZOoT0GEE1KbjUTSRLx8r752OYh0YkpwIWbUREziIVEgwO8tcSexo6hQc30xx4FPTAl3IqwVvbsKbOMLzqjFfWL7QUfO1MUChCF45UCvjUUOUtbQLjZFEMMaD+iiOwpfedDubIsUBLYGm0IIQwPPukgVLNBzWUhdIFFPV2secpsfmKAysgMdbgWZLIeMuEbnteoEx8fxHbrpC4z9xIel6kAIK0IXchidofVl2oGxFWrW5A2IAXAzGC5nRXwyz85kNFvLuZr7aFn3adie62xlcpodyHhhyy6lfk7JvBpiZgKrXfIAACAASURBVCod12U3SHyZBbVaS7iwmVu4rQTOrTiY25xzaoJY0g4CIQ6ZlDsdfrYUD5agYEIYQZXdTELUJmHj1kJ0Wov9lekImvhCzdVuFsImYBptBGTSsIMlDhFKSI5LuMZcxguz1LtZOF3i/RqGiIQgChXLvcyCGDV+I2jSIFvu1WMPqEZg6vJmSjv7k84izaucZO3pfEw4LaGuVutIUX5vOjjjme5gaucu12WHpPXpcgyFlekexEapAjEwtUkjU8jsDCfLg2z97DS6m49xqLmaV30DQo/9xKf1TKQH5/k06pT9MKlOqpGFkeVqVnZaFELBbCRauZGFvqU9AAYJfutMowyN4pA0YWJ9nzPhE1Y1mwNws3SSqM1+7pe4B2FHCCzjVMthNkP5CQxJPxRABbmxwzAxK97zPEPLE0L2W/vwRopWAVd6K41NSTrQbHTYjXO/RHC7Q6jJRWvHAREgIo1eD7lderHSBbK0pWBSjdhMZmeT4HBjk6EVBvPVmGBYY3TmOu7YDPfnRdmLVeSS5mS+zSlusJu1QxeULjlSiLkm8sJNVNfWEcjb0+k60wrfWo5Pih6jJSYznfs2hkKdqRE4oevnJ2R/tM1Yg9yiGkBEWoKnVQeBDPXkVO9mKEGJXtndBBLq318BzP1mEaKG9pAapR05d1sBzmLw4xNZ9OLCs8CxAsAuj0DDut50i1s610XKA5vYGm4EOEYhbkQB2ihy7y8xC8x2t9NO4bbQRdBRMzgiPgn9rRpIbHa7mV3wDmjWi7yZk02q0ihQRzhYrvqcghmD0+koPyuLI5Mve0moJjpZ2pxCVBI6n3fFedWZNNFskDJX2rCqujjSExsuZIfGByqe+aaLKJhTsFiNKddzE9WmgKeL9GM1sj1hSCPQpBlARxUH08V6vCjju3JQ5wGFK8+nukMl18KfL8ZhBKqQSLgVcqSiUIEhV61gP5vCrZ7rsEIv1KWcUhuhBu6ksPT2rmS5sIMowg1Y47bDu3ImLxbBqkR3a0Q9TWAiWtSlNheZW/qNcBkPopfvtZcvhKHhSMJ1JrhjkSM9pGCMXz+GWwMzjLvt1F6MObcxlmiNu8ATNyfhYjYIYzmrhkbH0cFhci/cRKnDbuLiWueA2gVMqrNicHoS3A9HMHFCzepOC0IP3dTmrQxZ50EdSFV1WNScyV4rCxrWJ6rT6FQ8mI+OfF4ORKDOfFi1PSzaRd2TVZacHwV7eOR7BPglCMq6hwJ1ZmNZ90Wsl6JwLIM+wKGQcqsIj+b0pIIdFuCWpcCM4syvwlTbYYz3FuKkxgWCCUlBqbeS2NUsB7hPyVHNTlYwIzhCBa3AWIAIJ6h265E4XuLTBiSEcRelmuWgTcMUVWYrAQsT7C99SjmzNPF+GISkjWPl+kEbgaw5nW3SAK2sb8ox5K3NDxQirs0QM+dqAOoUZ83JbAO3OLx7nO9nAxq0BM1MqtsYBPrMd9vWB/0D55gFkaNmXg8s5BrDic/lKe4cnsazXr/KaVYeLdcB5pqaedU3xIPsQEEEVWrD9kT1XFnwbHlQjS3gKMJtkDnfFyFRQayr8Rp5/VjFhRllHTuDIwxzHqJG5L4pcnJzDiVUl8eZXNA1bDtBpudsAGkE78QXT8NCpCD1K5JDNwpiuURjpgcFf/PIaQa3C0ZkGmkzDgVSLHF2o4ffPm1pTroshE3YdbYbMO6TUDWbBUBwvr5x/NBlINh0Y2OxvT74+PbWRzdX68PZYA0E4sETjyOK56Px5NKFZn9yctyu1sez3tBkyflDF+a9IQjDwyuX1u991Lm7J/O4KTqLze3FuD8fjG0gTq9dmTZc3bp9/+JjMGTL/nByYUtl2XK8Pr2wmR8c7f7sHZeJg+3LhNP7TzwGOVsNRodXr/1dWoT//o92Xnt9tba2++IrF196uRwMLr/w0pXvPvvxZz/72T/+80vPv3jzF/7Bp//kTy/+8NVyNNh8452rz/9gNt68+NIPHv/GX9/91Cc//Z++evl7z9363C988j99+bFvf6fp9zbfePuh770wW9/a/dFrT339G3c/+fSTX/na1edeuv30p5781neufP+5VW+w/fZb1775zGRje+uNtz75l1/5+HOffeIrX7/2zPeOH3n44vdfevg7zyyHozMwPv0RpbE35/bkDWYGET5R5/c6XBixaO6/k0HrkPMHPwxIBFDpzm/mrADy0BzfiKhrw7a992YGjUUEHL0iONVgqfbfSnhk7bk7vhExqwJdHdwaUae5AHsvBYhBUKnj1znlhiztyb0+tZoB9eC1hK+qQKg7Pyw88ilj9N4VVAkEluj+Y/QMg/5MvPcErDMfTtnNK7yhRC/80SN4FUOyIA+eCKdMdc75jSdg3XXhhH18FTcxcmVwe51NOGQrfP9RcRq0w1n8s+uuGumijD7conWG/RIdXSLLbhvb4PYlfijU2iR577Itx3VPxzc32DLh5hQeX/TLjWoo61ebyUeCXAyq19XsNk15uTgNFx9gWPDFA1K/4zrDavouOf44Jjui+cAvPkAideZufXgzhQmpjujyLSA2QfmOndwUcCe078uTm4EIjD02h+8nLLT1MT5/iwabvrlhTm9FYc8sb/nTm2GAG78ARz8NeGybU3zyluh0V6sP/dGtNC1aojr89sNAO4g0/fAphzVvLb//mBMSzxnZfwTouqimzcHncWMQk/zWJxB1uFbk9jVAJF4SdvAI0AYBST56DDcahZK9/5SHEClN7j4S2blvMD56EmuLUENuP8VWRkcNf+9JDzGyhj54lEKNGo/2HoYNhLTBHz1FS7js1dOXvFqgADQn7wXNKQwCefZx152BcNcfv8CWZyzu65PXmZyigMjpg9QuqO/Q8zewuqvDS3D6Ip6dhWRM65drd78NmJx8FFaHFHTZ+RvI3HP9zfL0jXi2KtiYrN5y5TGNUTX/EM72Atwniw+FPqTRRTd90U2Po6ir5zfofJ9GuQvnW/zuti4m/E4XnVwte016UKCTK5jOgzPmj65brtGqiD7akb1l+KBApw/77Jzs99DJZQgXYsXB/nVAKrTK2J0rOp9Geyk+vuaSKT3q4MMryJxnC6uOP0PwEjcJvfOIT6dsL8JH10Bwzs9TevwoAUvUYHz7EeTnSMbs9nWZKX04P30nYEZTXd9/PQUrFeTg6CcZ4gRrefQaI9CQpTn+WYSkDXC7/8FQVG2aNh+/0nEeYW+O3hAUGL9Si9cAWCnG3cGPYzRTZAAPn+faIErs2YcZRxZpd/huCBtgInb2A+gnWqzj6Y+DhqaQwulPIK5a6t3xu6Euid9i/bd2/Wrc9lbR+xt0WWC0QKeX6GLQJobfvSj2IzU6T957yC226kGbfDymi35gD+HJtp9vt5kJ7u1ER0UzPA1+tg2X21W3Se6M4HyMwYwfD/1kB0ZTcnBZHK/V6+fpOxt+edHH5+T+BppuEDjl5yN0vgvCKd67iA+3TH8/fX8dzC+12dzdXhy8lzJq5AyfvCnE0Jn77vxBN+zr8mN/+mEoYIOXZu/tmFNlG3L+ftbtlu0de/B+GhVKPgAnH4QCtq62R28EjBlbo8MfB0HXt/fM8Y0gSqU7BacP+gJJoNzBa0HsVq4F91/Pee70qT+7303jpj1yxzciZDS04PjHAhHL40K8dzHQlW88PHwMtQijitz7RLByKlvxG08BKwBoyJ2HiQVYabL3CJYY0hrffpLPkeyU0c8etT4z1IQ3d6gC3lmyfw01YRP76IOrdIJMd5G9d035geE2/GiXN4DLuT98FNRFU+jsw2tiFbTFNPrpJe1Ghuno9g5sYxk0Z29C9bHtrpfHr/LTg4hu8vqnbvUAB4Eqb9rz+yHusfkNXN+E4UU/f8VO98JwzS/ex5MHPGSyugfPb4ugsNMP2PxDHD3kVz+05w/isKfm76HZXR7StjlE5zdF2LGzm2zyAR+tzc9ex6cP0rTTrm6TxUdECFUdgPMbJC707GN29r7INhU/6pPDq0hP82XbHnwG+woZzj6+7pMVOeFk/xoQc3ye0MNr0FREeXLvSeZWyHP60WMuqOhUoL1HErDXVh3x4JoDGltHbz+GdO09YTcfd0FLViE5eJTSOVgGeO860hoBRz96HBnpMQw/uCZTjJac33uIgrlvBH1wHTqs+eToedE2hAlz9LpAjSXOnN0psIK6EGevIHdixDY+exaVMkQpXt5gRDvmzel7gVoQ0OWnL0FwYvNhdfo92Mwozun8TaBWONLL0xuiPKNkjc7eFHZGw2179ixYrQIRmsm7vJniGFWT+3m74HG3PfhhWB3ieNfOX3DTRZLFy6e/+Y0Lz79yevGhR77zzGPf/PbNv/8Ln/6j//DQSy8fPvbolW99/9KLL9VFZ+fNty4/8+xsY+PK8y9d/9vv3v7cZz/1F1+5/NyLJ9euPvT9l658/zmZJtvvvnPpW89NL1y4/OIPHv/aX9/77Gcf/4u/vPrsC+cXdnd//Ob17z7f5vnwg/cf+8rXJ7s7F1/58RN/9bUHTz995TvPXv/mt8uLG9033n/8m99q4rR/585Tf/GVw6sPtw/uBXfu/B1chAA8ePT6qt9b9Pr+ytU2SZeDwfHlK4tioINg74knpmvrbRAcPPa4zIv5aM1IaBGaDfrh5SuWMh2Ge9cfnWdFE8f7jz6mOV2sr1sLaiTmozVx6RL0XgXhwWOPN3mnyvP9hy4752Zra8y2RqP5cEibhn2uVVF4+OijKorrLDu+epVqNR2vO07gLnCZAZ6QixTkFFCDdrDre2sV3hF4oG2E8QXqO857jbaoz7xPKWyYG0jLHNkRvi9dAtEF4roMUUwucF94j5nZpG5oAFFwW6ARMJFHu9R1kA8Q3BWwY13AzTp3a8TGHu8w0gtM5OEFDvpQRkj1jROGREANNQK1Y9QOG8+UC7AeNgRZm8IVsJ5bEkLTayhsLaPVqIXM2oDYfkOI1gnwIw+QRQGw/RYB5SipxtIioARuB9Iip1PorYVAyxA1/RZr7Qip1qSCUAtYD1trLEuRBF5haQnwY45CoAOKN5kW3BcaRAI45zIKtMMucKJCGwGJqQkZGmkPmS800AGiISiwhwDucics2iQ4pC6ieMAwoK6rPXXUclhAQCAy2IUGjiykzMUOrAsIsO1CrzTaFS6TgCN0gZiYk40AMuZSLbmjo1ZnCsbErFUu8wQDPapcbA3GSmnbAVqDeqxcJGFAzGjlMwewV8MWZN5ip6UxhWUBlGsKB9oKokeVy60TUI9kIFwbo9oY13E0gLrf4FAZQcpxAzLjQmCHDUlsHQI9sjY2REC71gChIYZokyJGbI7AZgAZcCkEYwqhtwTAXYagMzGiGxQCZAsLNgIAjIsp3gTYAYed38YQEBNStCOAxCBXfhxAa23M6KbDBlqywjsBdkRHDIy118h2PFyH2HMbU7jmQAEdMXiHYk1c5OGYwRbb1DTQKFcbgVzfoahWIa5zraGSCcAYaKJkbBGz1bgxAWp7Bga1E8B0rSFW5hBLq4etjT1yXunahhh2jaO1D7xGTnkNCqgaX9PWpw56L0eVDxEqtCQSRlBTK612mSMQyFELc+8w0KOyjaHDgdkEduQAk3BH4AG0gQfbxHWhzxG6wFHXuYSaTeHWnE083iSkI4zQaJeBgbWpwVsMdK1NMNgNYN+4SKJtBlLnOEIXGOwYkDq6RX3OXYehHQ462sWUbDOcIcc82EG+R3xG0A7wofYFB1scFs5hXI2V80Zx0g6ldcgkwAKDtJcBavutibUjuBpJ7ZAOUNNvnYIsQwp5CaQOQdWVJjaOkGbdeN+oEJddjQx1CQDWK9OCENlOi2LnCa5HyrvGCWj7DkrnU+S90WHtQmz7ykXIM9aOpJKNDiDpCrItQFd5BfAF4mIE+hY56mILxgJa4nrIO4h3hesACDDaISZmGHOMmM+M44GpsR9gaBy6EMAedBDCXWITCJGDOoCZdKkwK+oG2BFJdhkeBhZ7fIG6DDjuyRbzGbMohBWFQ+UDDXc47GlLdb3Wcu50DCtrXOZoCHS/plxaTqu1xifGRdAMGxqZJoZqzbrQEgHdsAFMW4brcSsTrSLQrGkjgEyAHRorlApwO6o9Mpbiaq1pY6IjXw+lI55EqLHOhFILXA5qgpWjuNrQKoIyguVAeW5VSNA6xV3g6ArtBEQyHVE4pj5DNodgTLAKbELBmgcRcNSg3RA3zEUerjEUE9tpgbY05i6BaIxRgh1TcAPDmrnY+o3AB8h3PEQQM2YTAEcECqwFozshmFOXUDcSGkPQARA6jIVLFRgSiJERXnectsZ3oGp9jaVPLaRQrVU+hghqBSSMgRZWG2MLxwhoRw3qWC+wGlYuhU5Z5GUU+TZA2hrbcZR4PahhYXWI7Lj2CXDO2FFLYtdQ6NeMyQ0JvR03LrMuhPW4bmMHnMZjQ3PQCgjXtMwV4Axd4Ci1LsV0i4HY2Q5G2wIk2kcUb3MUQkcA2KUgpT6haMt7anxBwKaAsTMRYzsUCehojS+GNmImoWCDuwC5QsGtAIdIBwxuaECRIxZfQIATn0C4wTzDNndwnVEKTRSQbQowtFTBLQA5lUl0dOXqLB+uut2jy1dlnBjO9556erk2rrPs8OGHjeDT9XUu6wrxstc9unatFpEKxP7jj1dJXBbF4dWHnXOzzc3AyynKl73u0UMPSUhlGBw++pgK41W/by8BXFWTjXXDMPy0KgeD44sXjXZNmuxfexggUvb7Sz4KF/PJ9raaBrc/9aly0P//cBH+51qEsuhAgvceu96y0ITBybUryJjpxla1NqhF3EbxwZVrLgwR9HtPPNaSwIpg7/qj2NpybbzcWKtZoJNk7+o1FUfUuaPHrxvG6zjbe+QR7H1VdJaba3Xec4w/uP6oDkNkzN2nnkYEqSDZf+QR733V6a42RmXecxifXntomfeoUnc/8QnuWwQBHDIQEQadGwYo9Eg6vy0Yt85gNoaIIwqs71OYMuA960NfCNQYcCEm3FmD6AaBAcbA8sKDnFtEaB+aTohaC7Y4Dz0wkIwxiCgFhvQIyCgGnveg6oawdXibk9A5DckIk8B7gkmBdIcCA1SuQNRAhXS/VQkBqvGRbAvogKO0brpWA6FS6RIJFWoKZTMMrIK8arvQegeFKbsGWGKjyqYSatAW0sbQeuNoXfaJsDVgpupa55iNqrqAoqlNYUwKtXOQtas+oK7BxKhe6wBvo7bJCYFGYK02Eo4k8YDusIYIhqxfZ8S7ILA09zYQhHi1EXOioAV+W3AKPIJgnWPvmLCox1xIAmj1RkiIBRbAbc6Q9QTjESLQYe5hj4GQQu/5GrSxQNKiiyGC1mNM1gikkBLLC2DT0APIxqiMuGhd0ylBALGpVeFkBJ33Lm2qFDLpqzUV4mULoqZXuxBB06hE6QxC71xc1RnGCjbDFtPGa6yGrQ4wNJVNjUy9944Ei0WXY4WqfouE9Bq3XWkjBE0DYtmmHjjgkmbVwawFslOCUCPt244EgQcEY+79euAcpjliXeAkICMGcgq8iwLrBgwiiAPo15h1mCbODoJQStTFsGAOQcatHsfCSSog2Ai0xSx1ZhAEsmUdSBOnowgRZzciDhRgBG0w7ABKoR2GXLaogKjDHMMBtXiEDCaQIriGiVGGGl0AJyByzWoEIaqwRauRIlh5AOq+8tBDVJnMaQ6g16ZopABYg+VYEaQtRG2nQcgC2OgukBwA72ynriLMNKjX2gDUNRWyKwEB3leq8DYCwFlblHXMqPLVWkugdBCqgXHYIVCXPeoTRBoDNhiPADCIrmEfU+YN7RKfUwI870DVC6D0aJPTyDsF6ZgQ4R3GJEegw6GHJId2FMPa0g1KY28lIOsMxAR5K1IPugwCSAqo+yFoHBlCUwRJ24AhBQmF0OME+R5nWokOkoPIN46NgExjakqPq9WA/j+0vVfTbudhnvf01cvbvvfrdRfsjUIQBEmQIECCAFhEKZHjJJNxMslkJslvSJlx7IydiTORLcmRRMuiZEoUSUmkKBIUaYANAAmQwEbbve+vt7evvtZTc+CTHNoz1o+4T+77nuuydAGJKnpCAcvYRdExVlWpSMgICqMxqdMeJKbASPFupSFrHJ63gM0r4XIZAU0hhFXah0RXGsNqribaSJuLWAAhuMdVaCQ1EHLRqSSGAOpqrsIGcCZFW2JRa7sWHdxgAKAq5rUNpUaYLiCCAaIa9BjwKADGWoA6sHEt4BkPY6UhJosEUkSIstpAR44CiC0gEbm4VnDNspgBAOEFAizEsEI9C3iYAGPNoyZyca3xhkWY1gbTPkIWBBSiLoEhBRrQPuJtnxacrjPMtDaIzlNjawUMtdO0Q6AgRbeGTgMErjpc+RiqGrhVHQOjgfHrrA1pDXmcG08AAZt2ozyoNBc2r1vAko326rwNscA8yqqIuHkuOkIGWGqlHV60FZUcug1vCaOsMi7KEPtVXkVChdAgrVlTtDWVvPF01UFu01gxoKGRjoOoliu+DRqAMVxhBBjkQrXgMN7gEKIO0xZ2iMJ9JAiBGKJlSoxBLkJzmCoFfQg7DNgEQm0tYcUohgCtO1Bp4BLUpwQaZkvWhjJwDQTWEuaOjY3Bq4xAAx0M+wxCYzvGzFmVCxk35ULtwLzCbt2tDQNQF02spQ+A0jrOi5BSDsr5GuPaGMjnhGQGmVLEmrsaaoD8ybTjs8pkCxWmQivUdLm2IdKlimTtKqSRbOVZyOxKV70MWgooXXeEsoFRdRkB7XEmlGwVWcisBlbdFDgYGuX42nQZhIBEUPZt0wDShapte3UN5yiMqIGAeFD1bSYbK4Ji3tUNsHpAtF2vLMkcor4RjgMdYOZtSwkdUtrHgEPag7zjuWWBugS2maHYsRSYwwogEBLSQ0BC2kWwRRnnMIawYxmKIYN6JcDaFO3e0bnzUIpkfqFY6BVhRxNycvFc4cUQgJ0nniBGZVF3eHYTIJz2+unqYu7HmpDThx9Kox7W6vDxxyhQk/bi6MyGolYWt2erS3mnDwA4feShJJ4jUu1+5AkEQRl1xmc2BaVlqztbX87iHtJ6en7tdG6d8WbnI08gDEs/On3ogvvmG/+hE+G/V4MFtX7uy7/vpYljms6lK529vZvpl1beead/8+4P/vH/9umvfMXdPxhvbn36D/8gPD1xRB7e2Vu8eYM01cp7726+887L//v/8tSffiPc3h2vrz/1Z3/a2b7fKifR9l587TZtmsWrV86/8Ysf/sP/+SNf+2b7we6k1/3wD38wd/Ompfnc9t2Fty+Tpurdvv3IT3/60j/9hx/5xre6t+4AFy2/+d7SlcsIqFvLTw3fgIuPZ5DLkxueB2o4rif36HydMDMb3uzPnVbunDp6M5j/UElMc3ItsJ6oZVZPdv3e5NQNqwdX53snBVvSx78It85OHFRN7i0tPlIgUU/v+wujCQnT4dXFfmfinsO3X48XLhYurY5vxM7ZlMLi5EGrO5iRhXr8dsttjZ3N8sbbUedMsbyszcFZwBIaH4vTNcZP1GO31d0XoFO4/D14/6zLTsPBJMl6mnEyP9X7691mkD96Dd16AVg10+/DBw8RO/OmmRh3IeRwMTP763GTZh+57t642FhAO/e9W2sEZ950ypMWBG2jT+3ddbdMy8ffc69/vLSd0NuP7iwyVbinu0URUdjW7m72VjUek26/zN7kPMW97AjW/ekxC3VTnMLijlj85Gh6Kz5NorhTVVdVdgrXmoEe1uNhPxZVnWB5U68+P6uvwOHEbwUlv13PTtgyn5msHu3Gy1le5XR0x9p4Pq2vy/EoDknR7PHkxFlOjyk043ud5Sxrajq45S5+9LDaKceTuZZOum7IttetEQTdAbzzUbpwy0JjffiwXrvrcAxHa+74ZgfxZrblntzX8yN450nQv0vwqTq8iOZv+QaoyYo/eoACpQ622P5tsTEAd54z/Qc+G+qjjdg/xYeiSZat8Qnppvr+ZovdKzeO8a1Pm7k9yx2Cww27dce2ODhddgYY9Utz72xgH54+vJ/8HLq+7E3Ho+0WxMY/X06vhj5P4y+o45+HiOF1Mhu87VuW7KfVeDdulPJkMfwAumna+pIav+bU1OqE5fRN4UCxMM1Gx91KohBVow+gl9drzwz3f4EzGrSiOr+klWg6s8lkz0prN4LF5DKyMtH5QpL8hJTIW0NZc9utK9Q23Km26EGH67fp/oIq1/3gQbQ/D5OeU112Klolm1DsGOkFu/2KvOfuhSLbhOBtPWqBdHm+vOYoXI423Ow60A48PMfMm95xWBebEIydSYCSlXgyjBVsss1gdsVgx+xfQOCX5NjhxRatjv2S6nTFTzJIsT7aIJMPhO2i/Udjer8cjMc37P7phLbT0ZXFbjhpfwje+HlrbquO4+LkckQ3Motlpw9araMZXSzH73Rsb+xdTG788ky81sT9/PiDYHE5Z9vp8H7s7ZXeZjN+J+4FM+cpdPiaH62qeCEdXQ7m53OrUw1v+UFL0EfQydsotGZzz4DBqy5cI35Zjt/WVmvmzmfDOzGPoPOZtH3zfINwFNzxb65hKPxk2GQRVHPGHNt7K15alY9fcq9/tCJBGO9Et9eYEP5ouyzWKJiH+MjaW/ATWXz4bef9hznrBu6D6MESEFYrPUWzDufzSFzWh2us8DLnl9EHZ2vUg2ZqjntGBFE2xHnQFAuAvwdPHoJZrNGP/Vvnc7Si7GvwbjLa6S4leaPRyVV37dOZ2pGTwyiARXPIZ8fuQnZiEbl7u7s4yyXCx5e93oeP5IBMTuZjmaIJn+65K8kI0GZ0o7uyOVa+ffhesPVUwk/l8KAVFxNSVKd70UKSgIBP3gs6K0Pgo6NrS6sfzVQKRg/83iNJXZYn21FnnNCumr7t9R4qozM9dnMt9Gf0uK6SRXswInMzvb3ZhrvFuW1887OmfWJle2Bv3Yp2HCfXgyXXGaH5Qj84E6Oj4uID+8ZjZbegCl+8EAAAIABJREFU4sS9u245O044U8N56PpATezt85Y5rS7edK49lfZAZAb+vUUX71n2cZb2iW0QGbL750M4LS5edt57ouiyWB4F24uGFUgPjq9Ae1SvPjs8+hWcmaDVqvJ3NS90O52kh2hSRDEoZtcRHjTtLybpz8hIeRsk43edIqNxmRUncDx01z6SDe65xQCe/dJ0+qoZizgis+quXac0ng2qnE1P/LUn88F9Nz+lG5/aHb8nJrzTBjNzILIxayXTIiOzPXvtY/npfjA7ZBetYTA5h6Yr/mTcAqZJNv3Jde1AuPMhT75DJpAnm7Qc+DVRyao/vQltqA+3yPCyjCHY/rDdfAArrMabneiW3lUqWWXZDgk5uL9JR9frWKD7T5j1K26DzGjdmRtZEKHTVWumYWsC753123d4p6K3Hwbnjgi36NGaOxxYnoInKywhTf/m4Wuh19dzZXL8XtDpls6gGN4OvECyx8rTSyhCSfy8Hrzq6nk71OXkl7oXZMEgGd5rNR7yP1aeXMItUix/bLj9GuVzXgTq5JJhXun0RsP7LU1Q8Gw5eAd7WLQ+m49/bJnYmm+K4qaLLOksJ+N7gVZm7ePlvUshAubM86Ojn9A0irfkzhPf++vw/j74H/6nh370yvq1a9/+rX/63O/9vndyYjyy8dM32zsP3DLtb9/vfXD93f/2v9r8xS+X3r/8t//nP/rEV78R7O2hgC6/fql/51Zndjp3sB1+cPf9//6/WX3r0sov337ln/0fj3316+2dPULl3JXbi1euhMnYm442Xn3j0v/43y2998H6G2/9+B/9r+e/98P+zVs78otbu+nG228FydSu8wvff/llz5v9/wAN/zEbrOlv/meOMrQqb774POAaYnz3U590krSMWrtPPWnNMmXZ1158gZWVVZa3XnheQ0aUvPmZ59005Za9/cwncVZRxj74zHO2EG4yu/H8c0YDZNDNZz/tVLUi9P7Tn0BlQ4W8+sVfR8YEaXr181+kvAJc3/z0Z1gjAITbT30MSk04v/P8czWxgtnsxgsvAItYWU3WmXIpSSp8zqO2ciYFPO9agQHHFT5n0wDQaWmtYh0yknFnA5uI0UGOHg5RANFJaZ+xSQisWWkvAt5yYa7cVSQ6jjvMwTmPdIk5qfFZD3WoO83ZIlI9D01ra43KnucMC3TOtloAn9ZmzWZdCGfCXQBiwXIS3cRlMy/Z0JRzspzXzqiBTpova5prjJN0SaOKaj/lc7U/VHmLl8vAGdbAzsulBlcQwyRdUlYOpFc0c4U1MTLIixXojErAymRVOUmFYJGuA1YCYKXZovCHjXCyYh3TScNoNV2sgzRHoMhWOSmQYdl0jbrTxAGNeThkVe3kOXo04Njy8xSdc2zOLVnQM1bToAAIc95zRW1NM/XhNlECVwI/5DEtfZGDc77imtU1eDSionKzCj7sWkCYXODzNtHKaUp8zpUA21XjXGCaEDbL4WMxQopknJ2zGZCsrpwNnFPPqRr7PC1DPzrKy9VGu409a3gvLyJqp1L2stoXwZTPljlxczO1mqWSR8obl3WnKNuUpFrMFUVsoqlMlwrd0mxk8mUpIu1MStnO6zZgMwHiyWTO9qeqnE91xL2hSvucd407LlWrqLuaJVK387QNwzEvupls12wEeCfjXU0TTi2uz4YwlSw29jpDE0HaEGz67DS1fI3P2DpXPqnhOR9m0vEl2PLoSU5DDc5H1ri0XWXO+3ZSWJjrhyKQS8/met0JJjm2G3rW5onxbQPOWm5aUCTNeZ8kFXEBOOe7s8K2Gv1QZGacQWGdo7pWNpTgDHNrgUSRrWkjAa3rZKMmqvTSZnZWYNXgUqarNdLSLablUmO0IVVTrSQcwSBtpusNIorkslytEBCsLMpl3mDiFnW9mnGsg6SZrtcU1bqi5WoFoLSLLF/kjWWxtK7Xq9Il0ZRP1wpkG5zqfEUaqpw8my0r4RvvNAVnXNyj5qjCWx6aY944t+ahXPDRtLGWsFzw7WGBtiyrh8hJo1ctew7qqfTnDJi38bRm81Ath2RQsg1mzWN0UtMlChcta5BZXQAWLTjl3pwRKyE7LawlaJY9tj+jCxiue/akZF0CF5k9yqyu4Zttepx6i6BajePTKcTVbE3ZWUV1nq5r3GCEk2yJu5NGsSzfQHTW2LgcLzVBlmFZpmsNqhHEebLAo6QGOEnXIco01lW6UTtliRVP1gTLhcJNOV+wXCMwK1YVKjWWVbmaAGmYqNN1QStlsKiWKlZKrGf5JpCcuLyYbElH1mbW4PM2QdouC7JlS0qssnHOU20RNs7hoxFiBs0a66xNibLK0llHpe2xgjvnaOPb3ig3DwfEg3oq0Hmf2NDLc7xhKd+hk5xddHnouaMcnWeWa9BUgE2LuICUwlonxqNsVpCzjLd86yRlF2wWQjTldI2iFmVTDv3ZtE/dBFTdVLW5NzTZXN30jTMqdVTUc5KlCoRZ2gPBRJTtTHQqawREOy8XoDssZVjnfeFOK+3l6TLxp1JGs6yvwuO6ifNyGVnjGnlV1quiSaa8Ip/X1hSIOE16qjesGj/JVxCZKuPUxULlzyrh8XxB+eOMsoaetZsMhEzqc65bFETU5pGIFDUhBpz3nbR0SWUeikwiLMXtC0xx7QiOHnJZWQNkyFkbZ42HK/hQIGtgq8a5YGkNWdPARwJUCwo0OWNZZcNMbZ8lqfJ8UVsXLIGoU5Tm4QBJCYxB533WyEAV4LyvsQmnfLLeMFKqnFUrlaHKzvJivq49xtKmWa1yH8eTZrpWQE/Tqc5WtbKlk+bVfCVdY80a0x+P2mE0EflSAlxhT3WyxLWn7KTkcwWPkDuu+HKZ+TgeN9lCqgPOJrrplzwwzqws+lx4jTcqmoUi6zmtk6JcSHkMrEFqdQBYseG08VpKrYd0XFl9YNY8tj8jfQi2AntUsBaEa449zq1I8zMtMii9OS2X7Pg0gZFkG7aYGTeCcI35kxS1MFyz8bBkc9hsuv5p6gRcn4vguGaOIhsUZoL6kG4wPKzQHKVrlA4qL2zUuUhnyrY1uBCEw2FtebeeecafJY3r7zz1UVrUUOvbLz4vNbLr+ubzLzBeSWw/eOZpOy+47T149mlTcVvIO89/hmPLLYqbn3nWaiqO3fufeprWXDLr3tOfUBpaWXbn8y+WThjNZtdeeAEbZaR58MlPIm0gwHc/9QmAqDseP3juk6NwvjWZXP/MZyBBkOs7zzyLrl2xt//DPlj/HqBRpY6//K8W1aR1dDjZWsdJZRdF1YlIzQWm2IIkr0lRjlbX7DzrHOzPNtelMETy2guC2QxKIWIPFtxL05ONTTvP5ib74/6qnWaoqEcbG+FwiDmXsae5MQAK24bGdPf3js+da41OYF7PFpdoU0OtMUOgls50Wi3NVcDq7zw4Pn/eKXKcNTpgxgCUVDzwKkOiq9emjz3WpiU8zqrlOUtUJpPAo1WtyP4pWumJVhTtD2YrfVeV6CirlubMrICnIxpaoBXirIEhKdwg3B2kyz1TSnbtRrW1aQeMlbWxsbIYGpXMUiOnHV+9MblwIbY4Pc3qThCovGgYskjtO/64rFyGbOOeFHkcUCrQZIYo5WEIc+XVedHxyVQ1HqNYKIk0gMQyINMEGh5SkxuH12k7iCeDynK1Z1ujmtsUuRDmymiQdyI7L6gR0qIk04aiLA7ag0lDMPIQTHJm5Ki/GkxnVlk3c641LLltjXr9aDpmRZnO9+y6co4nfDnmEmMpuOdaWYlq7rqykpSUfLY0z6oSCaVt5lSFKWS+2KVZResGhFQ3RgOoLAYA8I7H2VrfT2ayULrjYimFIQxJDiid5aDnVtCODkfJai/IpiV2MQGEC10p1xYZ8e1xVi/EuRX0tw/ThdhWDcq1crCGxkkmKvQzN2odTKcLrW49ElNczgVUc64JAdJgQlIOXZi7XusknfYCG/Dw+GAazxMLo1QCCwqX0XHj0LpgjjJYUcKIpCNRRTZBCuYKMMQZ9KdT41izqNs6mGS9gCFBRrzxLRE4NK1omdcLHTbIpU2Vw+jNB4UXukshLoTWGrRslWunyLL+HLq7rxgziz1/lkqLYB+bQkOhZt0O2Tm0Z7P60Yfc6RRhlHfbwclQEcyQSGaS5kX5yIUwHcOS84WWfzIWzEraXfvmXYWwtdoBtcRlbfq+LA0t6mqh5SU5aJQKqRHAKZrxfJdWVX+8f7CwFRcpyFWy0GZFY5WVDilopJuNy95chZz28XS80vGLDKa6mvPcYgaKSsWRgJYzrUSHVchuH0+ni3EvOxWlyNtzTCpQae1jgZg3znmXVdDtn945XTjv8Rwlqm47RHBSyEm3B7Fp7Rxmi10HcniYiX5shNAHIxi6aC7GkwI5sAoCf3+UL3R0o52bd4r5fiuAjaSYAhk4aFQCChIvDt+7nJ0957iITVLeChhRIBWGwIYyeO8Ahr5YXwwPTqtuxD03PDgVvm0wEntDYFtyczk6HSACsk7b2zkRc9E07PYGx0CZdK7tJpmbF2k/9pIpAHDa7rdGM4ER9JDJFeNystD1k2lYTpJO356UgtCsG4ejRAMDAmLyyuXFYGHTTTOr4Fnfj4azBlsqou54qABRrUBXgJWc92zJsZeUaT9sT065hjwKWSmVgiBEUmB3Vhytr/bHhyrhfKFFywrWygRWMavto1NzblU6drQ/nK3Pe2UKpk3da5txAieJ1XZkGFrTXPfckjjh3iBZ68Nxjrd3+da6YyFSc+MRhRkepDSAA9LqXL02/tCjbVDCSa1aliWahhPoklwg5+4Dc2aFx1G8e5Kszrm80FPO2wGg2BkXDixnrZZ/UiTdwMLSPz3OvA5yCcw0oqbxLDLlhKrKdWCjADDYhnRYc89CNkCZlIQUodcZHSGCJq1+fDwrW27lut3jUeUwZAMpkEFI2CwYJQhpEdv2UV50gtL35453GuYq34G5QgAmnTAazhQh47le++gYGB04POMWrXmyNG8VOVAGWMRJMglx0WtboxkGAAZYVdpAZCFeQcea5vVS2x2OG8iIh7SGykALq1pTOy3UnMcFdidpNRfaZVFjmzCDC6GE9m0+Q6E7TsVSwAUhghub0rxUEoKWjYoGCi1jt4GkcziZLLW7+bDJrXrOZbxCWaECl1PPHReyTQvidE6SyXwUlEkNbIQMQgbl0nhYUGYPCjvgp3Z3afjgqLfhmQbNVBNbREtUauMgCZHWyDFl6rU6+6PZQmzrBk2ECC2MFZ0lZRg3jt86mqiYpF7Y3RvOFlsYKDKrDAY6dMCksqCctnrO5RvVwgJped40Ea6FXQQypSFMw8i6fR9jxM+tBwcnsu1XQRgdngibUcOzCUfGNOc2o9FAIAxj2zkcN60w8WL/6nXh+dZSCxYNllJ2fThtDIIi9Owrt7JW11kIUFojgoxPZWUQ19lCb+HgvlRwsrwSDk+J0iL2pARIagZ5wYLO4eHgzGZrcKwkELEPpNYGUqyEIvHwNF1b5AJ3jg4m66vd8X6lcN3u2nlpGinbvlQoPjnO1hYlh1hw5VhukYJS1r2Y5iWpuOgEQiGoda8cHLbX53a3T7c2o2SCiubg/GPst3/f/fnP/+ODRse/+Z8//89/b+MnvxitbKy/9JOzP/zZydqZOw8/edjd0Ao8/0/+xdnv/PCdL/79p3/3y1svv3pw/uLNCx8btJdNrj725T9+9Bt/ff3Z5z/xR39+4a9/8N5nv/RMfftD0Brlk4WXfvnQt394vHxm9bW3Pv7Vb957/MmbFz42aC/Vmj31x3927ts/mCysL//y0sNf+87x0uaNJz512llpMHv2X/7Rxa9/5/4TH9362589/M3vjHrLD9hDd34U6YBNx9atN/vAJwxOTz7/FLh8W+bdS5c2dGMq6l1/uSMoJa18fHEdKTO5bl+5soq0Ihy8/daGTKWcl9X5RaP17Cq6cXkNOTAfWpc/WGYaaHGcfP4peDqaFAu3fxTXtlvn+Pqv5i3VEDE6+uInzZ0dVUfvv7EGEg4C/P4rSyVijhM2O8+qog1MUhz8Oj7x8vnxP+A7c6h5t1zBd59VTQ80aTJ4Qc96VVzuLG8NWkv2NIVXPtfkG2WYmtufqJvNkN57BhZ9WO7KdrH9G3jQH61X9L1n6/RsujDdWzyTOSFMLLH3STldySIl7z0Ljhan8+P/Ut5eo/htuYjvf4LnG6Q5zUdPi/HZySo4eRXev9Y2D8fD18HdBwtuUB1unR22FnmNjt9xDt/3vWW1+1507/YimgPDpdVhZ6nhOHtPXr++rEP76Kh19KZD++DgzNlBe7nWNHkL3Li9ZLkmHTrX3ptHNkjOLB/MbSqCxz+Ht64uOgtmcMO+cWOR2mp2dvmgu6kUmFyCty71gwUxuG5du7EWxpXQq/L+C7qBlWWZay/k2Ft2r/8azmfKDI+2ypMvyqoJ6mx4/F+jTA9Xzc7CxcJ27B1f3n+OE61zvzx8UXPUJvf+E1ezenwDfghefb6ioZSUP3jO4uXBYri3uJXYLX+/rnd+E6Y0jQH64MXctPrRvS/gQvH8aLxaHH9JFJawOL/7G7D0B91q+xV3krYsxG9e6Y+PPZfORp84p7NEWq3rP2idnLTZErr7ejSadV1zPL64rENXTcy1S0vjuww/bG//wN857JLztqJ1eW4N3xzf2dk63Qtl39t+uzW5bVlLDW/Zk0fOFHezw+sLhycdl/AHN+P9By0T6OqxPidaKLL703j7YMHryZ0P4r39rh1DkD6Otj+S9TKzfa4+fnYy13ycX34W2zt5WgwfL46f40zW5Ra+/eQ45k/Jy88F5IbA5d6HysFzSiag7pb7LwhYXfSOPqdH91hQ3nusOf0sDyf6YLEcvIDk6AK5/TQxUyD3p8/Au5+ouzXamyuOX1TWaJ1f/4IVnKQn4+zxevt5A8pGLKhbz2ZzJD2qbrwzDwWEUrzzq00xFnBZVpt9btHkur71zrJGWKb4ygcroiZInkxe/AgcjpoyvPyThUJ7wuCbb87Vymb57vALHweHp2UdX/35UjNGasm99b1oJkK3XyXnVzjnxSG7dnlNZKRueTd+3MlPMNnSZcfNVhfz2/rOpX5TQGPQ1Q9W8qlTn4+DSx+ppg+nCzW7+khZXLTNrafQbIvUd/B8fv9z8GA5XZ7Z1z9RZE/ki8nnk8tbBp7menbyPB8/PGsBs/txerQ56E3+XnPrgk1/pXpw+6Nl8igCMzF4pD79UONPPmedPm6Gb+JFdvPj+exj3D9Vu4+Wk48Q9eDj1umjuNzGQfrgBbD/aLo0sK89kqfPVNG4eCA/eH8FUTNNwjuvx6CDAUtHH71IJ9nJ9eja9WUGucjY+5dWDG/4Kmk2+6aqh+/bN26su7EY7/tXryy5UAqaZk8/CibpyeHCvdcj07dnD9D1D1ZiPNMyPfzSp9C1e2XWu/rmIjNNVjtXX5vXLRuyZPT0h9Dx6WQnuvLBCuC6gda1V+cFJTSc19c+SZThBc5Ov6gLx4K3/4tQ2/XRB+Qx+sFzle42SMn7n9YiGM6Rw4X1mdv2jsv6wW+iNJosKPruc6lZi7q7X6xPAiP2ysXs4Is6j5MQmRvPm0lrtpI8WHo4cSI8cMCDp3UdomqWDX5NJXN5e/IP1MGimb4Bz7PLT2fqbGNJ8OCjdb1SuuWDt8PhdSvckrdfbe/uL+BFeLq0NmrNy1QdX7J27nbVgr/zQTy6YrN1uL3+8DieR2mz/05wf7dvO/p4J35wvU3a6HRz47izBow5/hG5c2/B7auDK/79e3M4huOttcPOOi7qvTfc+1e63c165w3v9s5qK873V88O2ouq0KPL7P7lDu3B/Vut7XdCdgaR/ZXy9EXTJG4BJkd/X/Jq2d/5DX16ZHnjnYvN4fPKmqjJQnn4gtZ8sOrs9c9VzPYeBPzeC2UIzbhVHrxo6+Mu2vk8sSpY7WXn1L3PaalKFIPrzylTH66Gh/0tZTTc7dRHXxCN4ZjIu7/GG9jx7/89fTKIuqeHZ/n+Z4HKK9mu9z+vOU388vZL8biMgItuvdGtuE/LveGzj+GyanL78s+XkgOCzrp3/zY8ynt0WdULvnJsta+u3dhMRm4zH9z7aSvdp/ZaU/fC6cZyfrPaubw4y0NL19evLI4PXd015aOLQvO6Yvd/2j6cdqmn773fG05abrE9/uQFA4FE3s0f9wbbPjtPt3/o7E7n22HyxF9+a+tvfrT70OMX/+qlJ772rdtPffL2mSeGrUWWFR/6N98689LLs1Z/+VfvX/j6d483z9969OPHnTWB6ad/6w/Of+eHO489eeZvXjn/nR+W3fa5OfJhU+05rY0/+e6Tf/z165/70hN/+GcPfev7hw89evvM46e9VVDpD33ru4/+yV8cn3tk/ZXXn/xXf7rz+JM3H3py0F6ymmrjOz999M//Kvc70b3dj//uV3YvPl4cHf0duQjBcGOTajWbn7fXNqy6qeIYaoOlUISMtrYEhLVtDR+64DRN2psjnCvGGtdNVlcdpZowHCwuwqqpWu2jE6fjw2kZW8tLk0k6XV5pHx6drK7Vrgu1hhAZBIfrG62Dw/HigpcMg7W1vD8PjYFGY62H6xvWyaBotaara9md25PVNcyM35YwpFSKVrsBsdUAYu8eST9SjPi+sLoQ2TBoc9ixNHWdo7FkDLWw6wnYZQ0TnidoD0OArZ0jjTHsRM6Eq5YNIXQ9IWJE7Ajd3RE1RwHyOpJ6AHnYjyWMWGW71v4JhES2XMfjOEbAJkFb4BBLT1t2Lr1Su4jQRAelIPYJs0roSMcRQUZRKUKAqoI7HGIDjSa8kZat/CmgUjgIewWmYsbwUOkUkdq1EJtJJ9GUan9qEKmZhZVERiuqsFVqVNUeCawZITPp2EeFQwip4pbtFw6XMgKoqSusJYWspX2uNcOsj12hSGwhIQzC2rFgrNys0Q5z2qaUQrYDCAw0GgPNOtQrNIwobQzrKGBhpBRCShMCY+DMpGlRZKQXKRhgBQE0GhptxcZLhfIobSFnIkFMSVNjKhGCqEWDNjcOoW3kZrxpe5o0xMoqvzGWMc5IuU1K/JGuMhaULeQmuYpkBRCmkyYW0EBkFFIa2AJ6M2krYQnLKhq/mgA2Asm+YYIB446BXXFXMScRjlTERkZjKXhkETqTQS0trLwxsMsUO6fNNHHi0rGtcQn8XHrEYhPhFcAiTodjakRssQhQaoRvs4MhrTVfCv1ubZCBDvG7BkDN44AklZJS+q0gUpYWyg7sbg2NkpSRpMQKFK6FPUNtpUOf+IVNpPY8PjiyNUi7885YmQLLiNoRBC5QkWMNh9YobTbOOr3a1FKEDg4NI0bHxMhCWLCxqeUXWIy5RwamNcHpLFwDooFV1gQa5I1xx9pDo9I/4WUVhMorcVHUHUhKDp2Ue2ZGyIHRBXEav6H1VDhQtrRTZE2gZ9AZQjGDFnAq4I4bYmDIcTmRHhziXsaqGe3zRtvOlAdaGsG8sbIEiqDjSR0jFdtByFkHauawg5G2LNCL/FOBIiJ9aHuStCBwA3JvD3BjHBq2OQ4xjIgXKxzAxvPp/rHSUAeW53O3raTjOV1uB1AhjE8mUBgTW44nUWRk6MRx4kRKuD7d39PIaVqBEyurjWVELV+ztuEIa29mtFUzpOMSCV60gylqsKgzx/WshJqUO05tzQzSDUMnyJ63SSEc4FbcYOkbYCcNb5RnH6Y4lrxuBY6bwQbXobbLBuCce2ysSC1dadnST0HFhEuwmxvoVm1vChyleWm7wE6NrIVt1X4B6lnjYSfEfqhIhFQNvLbUbQ9yy9k75ZjSNnQGwnQt0dRuIFCb4boiO0eKWbhNnaxpWi7G0PEEj4nFbHhvTwpIAuh3FXYxaGk/Eia2KwKd3SNILOXbTsBhiIgF/JaAAUaAuLsnkligazmuIG2kPeSHwva1YJp6qbCFCDVM8zriyG9N4PEQh8pCyp8YqxAuwE6unBJACbUCAIrAomwi/FxTrP2JtuAE0FMIJhoURFtWodys9ihkQ+wkBmIkJQBGOkB6OaaNCgHMiiqqq6A1rtBYucAhKJhAx6k86Nm5cTgPGA6EZ6S0LK+jZSVEy4faIKAwAVYLOQwYjzh+TZQ2BCMlDUKKERwB2ygdEzJTTqyNhwEAyGhgjNM2LpDaJzSGFjfIx5hzAgVgGMc6lFxYzOohZyjKboSV0AQDAnGMg1Jhn1iBNl0ILVLF2ktzHsuGG2DN6hhWLDhVs5w4OhA0n0oPClZbVtF4FRESaWkAaFzA/ImxBEeGWlPumorFKUkPS6R9CNhU+kLa2rhj5SgAIdISGlC3kTcsjF8qFxFrLCOZe/GJmqTY5qFxnFT4gjvYYkXjFcqx7K5kPkAB8VoaBYDHkXU64Y02gRtFwvaktAO3VcrAEghZw5lShrst5mmrpU1oM39q20parjg+ZQLIXtcdKOUgHTMn0DhA0nPs4yM2K+vlRa9baYZAyFgggUd0r8VGM5BWei7wQgGIkbbtdytNVBWH08Ul08Cs203neoPz56swxEopAg3G07VVdzwcr63F01GytlZGITAGAg2NGaytsSTNu+3B1pn4+HjcX+qYDCGYYTtdXRmOxhWjw3PnvGmSzfWQNkhrhdBkft7b2so67dnc3PD8+SqOqBAaE6zkeGMjv9GfrKyYAR5vblZ/ly7CP4Oua9UlslEFbMIFsqA/GulGi37UGAalKuOYVpXVVNiCqlBOns4WFoE2SCmLKK4J4qKMY1pU1uDEmguUBjWwGs+jdQ2VZpamo9TKstMz55AQdlU1gedXaQWsxveD0dBJUj4fl9jFXFhMZcizy7IJfYtXmfFcVGuEG0kx1gpTMMxAP4zqcQIiTI0ri8z4Dm60hlWBIrsq7ABUSrm01UxnIELE2KJKpdfWU8jQFMWRSUvmmUprl/pVkpUWim1HZgXwHFgbjCptt5tx7rXEpIFtNxbTmQ4YUZ16cMiWXVPFFfTkAAAgAElEQVRChoR0NAa+TmvtS4IDNZnKHgYyYGkGW61qbIhOccdgEPCpyZkigHg8BzE2yoJFjmJHlIaCsnEBQSGcNMaXGAV6loMYAqMo8CYlU7yOGQeegUYyRButEQr0NBFtow2MAOLAkrVr8gr6NbE1A7AWQBrjUSAM4sJHRSlsktei6yuIjYZL9cHQmpMKa59a0wwWQncdWzYpCqCNgFBAGp9UZsZ1A6rFFjQA1Qo40K3zFIcO5jQtRI1YDArqKwkjmCU0RrWANgrTaVEz5mlpWaVxlprDsdPlisYwndktUgNAtSvzHEQWrIwBZe2GJKkdFzZI2Gh9dn/fOwOwDOtEpRS6uvEY166nk4o5sMHSgqFIZ2WIAhSoaQ5j21QGmRoEC+VewiKYIWUbizU5bAGsXZXlMLZMZRAsKieCs9pzEceCoUhMctACWDs6T0DgygpSUykbIBjAokiQdqyI5qkJkFYerhIY2qJCDJYFghhhG2phDMGRSVPtAwOMS02pWZHhiNbGAsAAC8NKaoxXit0DsmyEgh0H1AIYYGMOuKqIC2wMpg2AMLaLBIZISJ9UKQyg1IxJInSFfc8kNXKNxNIBRmCQcNxCQZPlOFLMIGWghC7MSuPLCsZ2krGINkbaxq+zDLcYqoAAhQoiMuHEVooGZpKyFmmMYWpudrxH1xxXUFVXwPdAWmNHK+qDaULaJlU4QgFPMhRRWBOtGuAqBgwwqBTaprGYzXQAGQxkPq09h3JsgUI5oUpry1U1MDbxeZpnlASk15wcOiuOrhAxpXY8kTeOJ2cchE4gZgX0IQG+KVLtW6ZBBBYF9kwpQk83wDCkKUaFgBT5Ok9qF2AAAwZq5amc27ZskKHIMExrDQyQNkQCWrKBWBaNCyAEPqSN+ncJylEMlZE2MjVmRR64ZWOcirqaGtwY8+9SpjuGaxBBzBXSAJOGcl1Q3zNJVTsCWS06yVGElHFBkpEWlhoRjnKZkzBkqdRMQBaYaU5iKIGydFAmCYpswrGUJXB8VNaaiczEfp2wEFXSuDhs0hmMGBaIy0I5C/o0c6JKWzFMUxrBSgIXO0WWC5cGkMimAk4Ai5rYQpCOGE79vh6VqOsGfJaAMAC5JaohnQtBVmFXznjsNokToVIahwQiTUFAsaBAVzDsFwepG3DlaQYDMZsVIfJQCKYZbDHTYCRKEPgqlQDhHEsL2qwsQKyx8XWagwhAIyk2BfR1Jj1qOFEEKgZppQ2GvprplCqCy7aDOHBlwUBVwEhQqgkwCYAERGSWwRgCoxgktTYIKIZgLTTCa+XOvrsKpDEuscaZERrFFClVEg8wBBoJDAhxIcdSa2i1YUIiJDShinBeEN9HJZjUSiGrDVMUAKFDXCQ4glxSquwkrwWxQ1NhpwHWWr1z6K0aoSOUFw2FpcQxgcDkyPdRIRThmsQoy1hMaqBss5TsHfhbFFZYyExFAU4kpUox38wyFpIaSNu0srEoHeNp7ZBKey7IOLaktpeL+/vRhk4MinDIxzlsYdwwxUsQOLCAXIOKWCyfhh1aG2HBkCcZahHEsRQF94GtIQVYwEDPUitEDdTUBDJJTMCMoETmxvNlLmybTwTwbUyU5gBQHJo00T6EUNsEpMIWhY4cKbDBEDAMSwEIXMwPDsgyABpGDFQSIOiZggvCmQUsDEcVoDiyigSEVHFGZKkdA4GHaj6VjR/GKMmVZzAMYZ6AEGhDmfKKtEZ243msLKFSzDb0NKGcNwutDPtWVQnfdsu8gjaxoX90YoThi60SuaSumWVy5LOyQA4Jx8cj3KKxDYWSGllMVcAmdc1sIyvgTidFv0eAqg2l1BhltATUNnhcWGXpBmqnfcbJU+m7dl00hlb9/sI/+cfB35GL8D/9rf/72T/9k/H5M09866+f+bOvZssLH/urv/rot799/5mnf/3/+mfP/vFXbn7uC5/7nX/x2T/+o9HZrYdf/tHTf/7nwzNnn/rm11/48u9vP/3UF3/ndz/z5T+49eLnP/sn//rFr/zrfHPlkZdf+fSX/3C8tfXkt7/1a//v72x/4mOf+pN/8/RXv3rnM88987Wvfu53f2e6vvrIz37yud/5l5P19Ud/9MqLf/B79z/1yWe/8pUv/PZv73/sw4//zfd+7Z//P+OtzTT367+ceW5DjvPiOymco8HdQfpTEJKZN56Ov6tjPiVcFH+RuRZngzT/YeP5Dd1Pxz+QkZlFxWzwNyYsxjYS2V9lAcq8cTJ5ift27Rwnpy/Jnp54TTp6yQT52KEy+9rMAY2dFel3ywBlziQb/FvQq0cWqEffrLuzgcPq4V9ym1ddplpvb4TDmqFR+63znd1EdSerr661TjH0jufeXOoMjMUP3asPxaeSsmH4wcOd46bpjhd/th4cWsA/7v9qMTolDBx3b2y0DgRlg96ls927db08W/3JQrDvi8547t2+PwkoPImvrbV2oYyLxV8uzt3mzeJk6/V2cNSWncnyW3Fr1/LNdnBjPdp16oXKvHTYvF465wh5dVC81vSDUb2N8l8Jt6341Ya+ctpZKOXrWfZ645xl8O3J7C3Ui1NwfZa9Kv2wkdcb88Oht6r1u8X0V8DeQO6vjpNXdctJ8P00/zFvOVmzrarXubtmyBvD/Ce8tVCBq+nkR7JLx+S0Hr0GA6802031/azbS+B76eRHstMvnIIt/vyMW4yIrhde26AqD/Ns4dIaIUkwalrvXHDqo1565L/7lD8bElp33rpAQe1k6dybqxRm/rhovXMxKE6YyvpvXWgNj5BdL/103ea1VaZzb222yyNYcP/ahx2ZUjPr/fxiazTSbrX003W74hZP5t9d93nCqrx36YI/HVGS9n9xsXWaZJ1MfGuM9uoIJ9OfCfIgDb189mPj3Th1zmL+FzN5vWgtNvUPMn2njFiS/RLY92a4h/KXMu/ywHqI6G8Mq2vcXQPmlTG4U3adWfGmgtdSu4+q76feu4P2Vl19Ly9vKm8NmJeH8hrv2ZPqF5W8Wjlzpnk5pe9OvAvIfHtQvi/bC5V4Las/EHE7a+/2Ft8OVXvcvWJ1Li81C7PFq7p3a4vinfYRbL2/gckwOPJW3oxld9K/Dvs3N1C0F92h3atnGdyNT0373S0Gj/2BtXipB6JB/w5ov78Bg4N4h8bvnfPF3e6oDq4/ZusDd8Tm31yAwbB113Te38TuQbxvulcu+GrbTkHvjTVHHjoJW3hjSUYl2z8YfZd3+NTlyfDbypuOXV9lX0/spnZkmX2n8HjqZ+np91U7n/ikHHxHdYanQVgPv8mtvHJUmX2vDOrMytPpKyZIJoFdjf9SRIentI+qr83wpA5Qkf/bxitzRxTTH6hoNAYxqb428Q9GdBGJb4xUChyLNy/NnOE0BNXkb6V3mpgN69wrcfgg5HOjxTeD1l7A0H7r3kp3h8gonX9noX9DNkuTzVdb3nZXzE9X3vG6e1Go7gS3F6PtWEVJ/5255eugWhqv/DyId/tNd7T0jh09mLPAbudOJ74TE/e4d3Vh/pZTrQxXXnPj+8so2G1djVr3Fyy409lud27H2D9qX53vXgnkwuHqL93g9qpoH7Lrg+Rl2aKp3mua76fevCLvj7PXdDxXgBvZ5GX5/9H2ns2WZQlh5fZ7H3/O9eb5zHzpKivLdLVBDQI1AqFAgpkvE/N/RjExoQkJGRgBEgK1QAxtaGhDm7Jd1V3edFVlZmWlf/a+e++79vjt5oPmL/SPWJ9WxFoNMnfP1uffVzFeonGVf3fdStbkzuL8h6rRzvHnq/EPdI/M6Cyb/YOK8QKf19k3l0FDwXvL1Q/KRrSiB+nkR7ZtpySrlt/IO2YKF9XiO6UfVvjhYv2Kbblze1zMvqcSPWNVuf5m7uuV7/i91/qN9Yxk6/CD6+FqQeGs9/b11ngqw3Tw0o67NMTOum9tB2lOVJp8et1ZppTNu29cbRyv6la6/ZMBXzJM5ptvd8NVxeSs8cF+OM5kXG+/tNE8SOv2auPnm6iIMVsO3uolk8KrT8KPrkUTVSXZ7quD3klZtuYbL7b4PIJ0Nni75U+QCZbpP6ycN88au2X13XX6nnQvEP3TeX4btr15+XZWv185bV29nOI3pt4VUP8kXX6Mko3SvL7K3lUNZ64+zOVbeZRk67e1+rD29gH87ih9UzUGuXwnS39WtcVM31Or903UKqs3C/nqure9ki+vFm/odj+tflEt3gVtd1HfqspX1kmrqN7KyhdXya5sPmHxe1dc/aQ9XXofPCvqUzeFG28OsTONDmTrvT3snEZHOn7/mi8PqbTRB9eEGosCDt7YxHQRHKv2BxcTfRedk/YvrgfykFdV5/VLfnGGJez/dAvjhTO3ySdXODnhc9N555qbnjJT9V6/5GVTAvT26wMgan9a9N/ecfUxW+vuW5e9bKrcVfmXC3BahG6efjfj09TX6eIVEIzObYsVfzX3H07YDpFfn8gT7UZK/sNSjJcJzuY/VM7RknRJ/lez8PNJvFWuvpXJU+vFSn53hk/KJpjOfiTJ45T3UfE3C/753L0I1f+Y1I9UHGbFj3PzpEzQ+fqnlj1YBt1q+TcZuZN6F639m3F+3zbbq6/92Z/8yl98/ezq1V//0z/+2p/+8d2v/cbX/vCP/tFf/veDLz3/pb/869/4z3+y3Np47vt//2t//Cfnl/ae/9a3vvrnf37v13/1n/4/f/yb/+4Pjl549vnv/P1v/sG/KQadZ1576av/5S/mF3e+9Nd//Vt/8G8f/uOv/tqf/Olv/dt/c/L8zasvvfZP/uO/X29t7v/sp//s//rXs4u7z33n737nX//fT778xWe//Z3f/E//aXV1Z+ON9/7Fv/o/8kFv7923fvdf/Z/jq1eLX0poFAJo7XJny3gcErTutU/sVc3IbHdbEYqgmWxvla5LkFlu9A+xRUCvB73R09chhavNwUl1nVg939tRBGFdrVqNw+eeUQQte2301FWAzGp74zS/SaCZbwyQUcTU2ebw8PmbltMsiQ6vXTWcrDYHRzeuAwyWw/7BF56FCKy67YPnbloEqLBilxJHaWnpNuW0BiFzNhV0ENCEDqD2qKASbzPMJcJIbGMu6hLTcFsTBxCOnQ2DE4qxYtuceQYQwLYJ47kENNqxKKCIlO4WIAEkRIs9xkIDCaQ9jAQEDo23jQkIAbmz69gIU1TxLUIiaTC0raUWigCpWwtlFYe66uSWVQjaKq4oLSxFoDFXVEJkTWtRAc1MrZqZYYogoxo5ZjXEWAVzjRVCtm4srbZcVkWrMqymGKi4ALAEGOk4tUBTU9dJoSVgqizbWqIUGV1HBTSVxUiHKwlcZBXuMpdbDJSJkLepIUEsAmIIIQXclaCHMFKgJSghRJe2wfxaYYpQAMTQQmJFAvQOY0hWIRKbiNkaNIVTKsgQDRDbgIADFtpqiJmtiiZ3DGa4zHyedAooEKVGDC0W0AJAtwmlxkbc2cEYacOJbM+spxGRdTcFTmmJkd0Mibo2BLZmhhsAYNWcGl9DpKrOGngVxFq2U0KzihDQmSgfClCp9sI6lhBV9nLjVoCYurk0rrSMgdZECU2xVd0FcCyBddUujFchalRziX0NEZCNifQ1RaZqLYyw1Go6oJxJTJHXNZAigozXNxC6tK50i2MKgC1hlzMoIUG8rTGiGCpvAHVNeV3kG9xDCAGNG4AZCwjmbaslwVaxLgBKULU2PY8BiLW0HUqUgRiTxGKFMLROH9qIM1Wu2lRYiKxEHVd4GiJkvVp2KwIqGSiDU2IqHQlkJoATU1V1Yw0YAKgquwTBWgZMwwVCuvYNUmOArfWAai8gNwCVdSfHuJIehd0lJrp2rNMZaw4t0nVzboWFVtbdjDCpPKrbGYVV6QW0c6YdzE0qO2vrWEjrspshWiuGo22DQoKxcbcsjRDGWuwxmmiILe1h5EHLabxtbUiQrdxtZCJKQcG3CI0V5pBvIBoCy2AwVCjCFNXeFgCxw3SFhpQ2DaCWDiH2jcFUDBRsC6IlGVLp2UBXeIuTFoDIsC5gAbCUuEMDQkyNLJqVRYogq+IKmgJgZMJMuYBaKePccstUWbWUBhm0WgYVFLmlWIeZtARCaxp57SiuyqqhLFxzUJuGMNUSUKK9tOYEYauSXHNGTV01jLULhEwVasKWACMTlopkCFkT5ZJYamQVGxMsMLLEQ3wDWAEpMXiTEChlwtxtSIm2nuMNAWCIYCQ2AfIwMpZtU0pV5TNnBxGkSt+Ndw1wCbG1uwmIsJBavseo0NaF7ibAzEJC410DPUxIzXe4dREhFm8wSCsUUrFhsIDEwHBbAw8zWvEtDiMIkKwba+2VlgLQOpeuIsiozgJxQ21dtwoTVIgY1VzjQEEEZDyVniLQVK2FIZbqMu9UJpGIAJmkRGSWYt2YG88wVVatNcSambLsKOPXAGgZrxGvAMYyWUqhsTWqk0Isma6qjrJBSrDSzVwLDa1hbQBcTuzadn0mEdEVaFPP1YhiJyw1URhZtwc050xXaZM4ISBGwo4rqIYUsYYuCUZY8TasCGKqLHqOGyCKNIqpbw1iiMVWaIixJS1IIaJQ2xj7QCGgcUNExmKGeGzEBsFYshY1AEEsJbekMzZUW2jq1swKA6kuuxnmUjvE/E+CXEG7Z9rFHK5Ue2lci4QsuznktQGoas58YSwxoD1WHFCiVG+lPYtJLTtryCtgkGzNsAOhMXVzLD3LsMpbmXEBQlXRLZBbA2llc4aJspyr9tSEkOoKDhhrGIgsGWDiVQZT0dMo4dRIMqSSmVCVeEs4MYTQsA5gDrAUuwMDHUx0xTYQEJzqFA58FloENBtg6gFIkNe3RmAKjLsFEeZUVaZHhWsBsLBLMbaQYNGzmhAMSrbJEbRMltlQOAxgo5b9Tg0wpHB2cRdbhYGab21oQRG0836PXrsKEVhtDGClAEWL7Q1c1wSa5Wb/0L6AEVi3mydffF47bNXv1teVpXixvXlSPEsoXA67h19+gUCbtRsnX/yCEbRoN49vPmUEXW0MDp+5iTBYD7pPnrtpOC2S8OhLzytGim775Lmna89Bv5TQqDXL3/v9ctjXrji6cT2PG3mndXZ1HzB+cmk/7zTz/c30uf2TsFN2e8blR889mwVx0UwOr18Hjpjt7KyHnVXcXHc7o8uXs04bOPzw5o3KD9bd3vGNG5qR8+FwvdFbtbplr3t8+XLaaABGHn3hC6AbpDf2ji5c1pTNt7ayQXfe7dXN5uTS7nywBRl9/IXnXFA4vEAdigIc8sL2PRDBVjmtLiUYlg331Gz43EGhKFAb25D6JCcdoluiWc/VpRgKE5pU73hM2IAXomV1wxVM0g6uun6rnhV7AQtQoFOw6xMHBDyHCZYRbDgjmLCqG7XqmdwLiA99k9st13E19zRrwrLJhJyXjUomwC1nad8oF1B7rtx13bSB83nkHE07TWJyGWcqNrycp63KhITaqXWXVQMBnGE8W/UoV0uV5Coyop4XrVyHGNgZcperJuVgRuliNWCsXmp/sW7wTfw+jE+ysGHsAvFZ1iXMnmOySvuE6kxGadoQIVr7TiYHoefKyK7kbmQ4islKDT23aZwdyIU2Hg2dst4IhKhjs0ovJB6tPV7rbZ+jMvRy2xbMBxHP662QuLqh5vV+08WlQ2uw6TpU+kEFWtx6MCKpHTq2y+O+rIYRB5UrarPhCVoHQUUTWDf9kGRw6KQ+i9eH6dAgVhM41XFVhgiZmY7rMi52yXuTniBcAp2tNyzgCoNzHZZFTJid66ReN3GYny2GFrhaVLP1BrBMETBVQVk1MLVz5KfznheUk1VfQVfRap52FfLqJvvI+st14FM7036+brMgO0uHGjiS63nRqawLuVCeKKvN0IMVawHYZqFOQQ9L3/HZadTMyzhyfe3zot6JXFPQhlH9INFL0bU2cQQtHb9SgyCkhS+qcjPitnQTJXteYla0bVkIbUBDr6yHgY8zwet8GERoylqV7ESRXTtNaRuOcHXoFHboIG4CVqpNn6m5YvO6iQyvOJqsuhSwtVeczbepT0+bzu1Zzwe2xPhMxUC6koNp1dJVAPxytNimGGTIzvMuwCjDdCoTULqamWnVNkVgw/JsvkMduNY4z7sW4gLR8yqCMDgf8g/nTS8LSViMZpuI0AqDZdZHiJQYTdctahLSrmbVjk8iEqo12PORAwKWkwaSMY6cMY5R2YuacqF2PBphX2dg1xWO5q5hTatbwkcFTawcRo1qAXYcHKDApGbIcIADVogGMG3Hw4VowWoYNcqZ2eSVzwf0APaMDsKQpyS2oOfGYMWaMOtHSTVHG6SKA0eeYGexajGO5gzN1kOG9do681WTD/FHODrKw0SDFaLnWYcSNGNokQ4IsYX213kbiWosvYVMqCEZo+fLDsVgTuF8vUEcvdReVieG2JX2FzrCNSsEmhYdI7xxU9ydtEMmV8pN64YlYAWdad2gFa+YnS43XA8VLi7stutQ5XsVbDPro4hkuuOoQHfEabmRYGYDWphNl3MVeCVvQtn0fVKgAVdNp1XP8ouxENbHhd3yqNC+W8ImAQmP4BpsumXD6dTn5cWIC+3iyvYF45UbzkFCbcNtgCUYcNXyWtXMXAwYtx4ubI8CDxC4IO5q0ffcapZ2JfAUq2brjoSebvKPkH++ikIC5tZZrzvMLyd5T1lPcTUvOpXxILLnxsvzBnL0BAbZqiu8/Kxo5kVEw/KkbFUqRNbOrLPOW9BVE+PlRRsN4Ud5O81Cz81PizhVMUVoDt103UKOHCuvyDoiMUvesiyEJqChW1TD0Ce5R4piOwoaiuxwmTiBWjmN2jYd7urIKcBAAAdGKJN7sWcyHlnQ4x6p3FjaloCOjWlmhi7yYEzyajdybMEja3vCwVUQS+brcieOhhoGyEQ8hutyN/JwJUJre45gtR9Wtilq34TF2WKHCpwaUGQDAEmJ0bSOrA3nA/HRssHTkEb5aLYJMZfUzNM+hLTCaFrHqoqgoyamUc3bQVycLjYsYjVS87xjIK8xnOhIVrEV1bhqyrTJwvR4vQUIz3vio6xRSc4xGJeRlqFyq5HqqFXCo9XRaiChwD7L3YbVHeHi0olNtRnF9QIMaRk4A3qAuloHsc9THmnT92K4FrHNB2FYr/AAq6bb1HPSwywAwCduqGTfjcwKxdD23VCtyQaTTSdRc94xNhGuUJ4vVVs4bM4atWwFkV6TIYMRCUjpNJVuupgp362LnRZkdL65dXrpkhXsfG8v77Vm3X7dak7291YXtu1+99HeVUDwfLBxfmFHBsFsd3c96J63enUcja/uz3oD6zoHz97EFI23L0wv7lZBcL6zu+q3F4NNHQaj65dn3SF0+MPnnwOCLYab093teqe7fHZ/kTRm/U3ritnlndHWPhL80fPPWofNu/3Tq9d/WS/C+e/9L//4z/5866231/3+9qs/23vj53mjsffya5dffPXhhUtfg+PrVfqJ3/3Sf/lv2z/7edFpDd79aP/Flxe94d5rr1//++89/uILz/3VNy6+9MqDf/Rrz3/z23svvVI34uH7H134yauLwebWm28/+52/e/zFF57562/u/+Tlh8984ekf/vDSj15KN/r7YP6cKs8wGX7/tS9845sPvvLlm3/7vf0f/Ojs2tW9l9+48sMfLzvdCRic/Jwwz6pzc/I+ty2hj9TZJwwL6Mwf5vsdvsrKmXv4psAuBCt18p5gEahG9vRThwLllsX99yNsDIbmyRsOJzVc1Qcf+dTVagJOPubUSFqXj94LWVkhDg5+6lqEHG9RJRRkhT5Cx5+4UFoK5cE7Hs8Kwar7byYGgphS+Ggf5ZyaBTi8SeYMxFN86zmQJ1708J/MHvWtOluK8ugmTj1EU/D4KXfG6uY5v/2syZvWm5P7l1HuYZCB44toFUCWwsfX+Llf9hbux9d01q3j3L27hdY+Aak9vYBWLROc//P8cCsb3cEt7/ZllQ/rlvI+34DLwFFjO7oAloO8W6dvFuN7gl10sveq6SMe8XRxyqd3OQlR1o+XfrOZjidvobOHHt5xy0/1+B71PSWflMefByQkq1O6/MA6G2D9C336uUOHrLwjJ59z7lkzkkefusLR2RkefcDdDVvcUiefe40gnXe6s2aPL1LwoDj41HdcVYzRyQei2UzX9+DxXd+PS1xH9P5VZCxAitx9xlBFCkMfXpXcbKo7XyvqXM7AuZcdfxXXBlBJ7ty0yJBKokfXIMrpmpiDq0QpWmfgyRdoVltR4jvPAgSRsvDhNU/NbYbN8XVUA4QqcP8mT43rH/xueYoRPF1voIfXiK1QCeHhVVghhEtw71la4FUjO38d1Evkomp0WxQT5Ljq6CNHHmucFIYWuhmYJ9n4U7+cIcH0+A4vzhAKyfh9XD9S3h6YvIqmY5cN+fxNmZ1Bl5bjz0V6xlCTTz9AxUPd3kiPfibOxx4e8NUHZj1iAZgqX2oKAPZGb7PqoXUuwtmrZjJyg4Y8/5Qtj6ibADHbpI82VWPBH3fg6GLaLIOjpj3dJWx+o7jzBSknGC4m170H23Vr7Rw2wfElG87JSQuNLlqYOiuqj56CJIdpRB5dVdHcPYnsyWUQzuhJYk8uUr30F6o4fQGhFGU+fnhN+umN9YdftWYsq/pszxztU5CRDMInTxGwBKWH71+vEqlOVqcfUqI0V8XD90KyrlgInrzmaIXdcFWHENTSHICTTzxUW4rlk3dcuqo8N7/3s0QbhK05eV9Qq2Cmjz5xTAEYU0/edNFS0jY6eoXXBhOojt8TxGgo4ekvmM4Rc1PZE3A2VYU3/TldaxcxNHkHoEJiY45/IVRK7RZvfrCjV4OqmXqfbcBlQsDCnO2hec/4s98qDy9kp3dIw7lz2ay2y17tfT6As4arz8B4yy42q8iwx1vuaaPsTd1Pd81yq2iUweO+nfUoWpLTnpluIW8BDy+y0SAfngefbun5Ng5HL5SH14t6Uqt6dBWMt4A7w8q++h8AACAASURBVEd7eLQjWyf+Z0M9v1DHS/NwffCJT7kp5/j0PS66oLqvTm47LLaoOiu3Ev7wpJ6FR5+4nOt6AY/fFVGcFU/A0W3fi+ryCJ98yh1Ug7U8+MB3UFVn+Oht4TSMfKKOb3mBm1Wn8PgTh0JtK3PyruMUKxKui6Zvyjr/HI/u+JFbFGfw5FMHW2OVPnpLIGpF2CR3LrkyA7nRx9dRiTApwf1nxBpQ/+BfVicCgKN8iB5cwdrA2qDDK7CgkOTw/jNsTetG6n58vbaRoYrfu0ALi7QCh1dw6RWe8T7bx3Mmm0v34/3ato0DxP0dnJEW+uyrReqr+UPnovPJZbH0qsbM+3i/Vm0ttPtwCxZe5VaTD0H+uWkP0+O3+PTEI5si/VDPD6gfq3U7Po+77iqfvg3y+8i9AOevm/GRG7Tl7DadHzDH1dkjML7PvVhNPuOzz2iwq1dv6tGBF7bU7DacPeIerfIjeHbPCRJ5/hkZ3xGD5vR4cHERt7zlavUxmt6nHi3TEzS5zfy4mt1j41s82qjEacce72O9jFZlfvJlBHIkOb7/lHTKq8VHv6rqc1VX4219dJXYgpYaPL5J9Bpohu7dsF6B5y48vBqCI7WMwPE1qCUyFt17iprCWELv3dROhZcOPLyC8QKuXXx4FSrV5o9+O5+bOj3KL7r3rhY+ImuOn+xzNbO1Cx9fQZZINjt5hRclZUydvMtBZZC1o4+5XCPmpKrHwexcS+f8DbosXBzg6VvWrBUF+vQTUa0pbIrJ66A+Vc1u9uSn7ip3cUjn79l6jQOTHd9y8ikhfef0NaBHQOzA8Ut2sXC8ONee0bbCo3p0J0zPUNBSB2862TEOtuT5a+B86Tf81TPf+87OKz+b7F688sMf3/jeDz779V//8p//991XXju7ce1pe34zq2fr6dbLb1/88Uvzzc1LL73y1N//4MGvfOUL/++39l56ZXL1ysWfvHbhxZdV4G9+9OHeD15ebm1deO31p779d4+//OWnv/mdSy++PNvd2fn5e/vf/wcZRt3bt69/+7vnnc61UH+lmtxD/pVvfu/yD39SbXSSDz679t3vla7fevzk2f/xjdHV6+XjR86jX8KLEAAwuny5dp201Trf3bWUpq32bHurFL4OgwOmVghWVJxduKAJWnU6ZlXzPFs3m7PNTaK1FmK8v19xUTjueHeXputVu42KytRg3WrNNzaObtyUwjm/cBECWAbhZO8CW62XSfOcrwhBK+qx7a2D5U0l+GRnB9ayiMLznR1vdr7q9izHZIhMoKAFZICMRzA2cEBAgtTCMbVVmFuf0CEBEbIIkyG1fg1djPrUNpRhlPYpbBjjITZkMGaAatqn0FOWUdhHoGWQULzPQMsaD5ANChKrMINSaeLYRMA+Ay0LHYm7GHW4cSQdUBiD0iO0qZRjqQNVWABRKUZBq9RcWiqO3IhiUBpeNQzgGjlQRbVmRlNSJrllWgmsk8pgXfsQxtYSDQU0cQ1hbREqWtJSWAtSxooAoH1oEgOQKRk/5lzRlqG0aCuJ6prBMq6o0tZDUmpFKoOBbTNKgeIUtwkCAIXUEg4lMAGmsgbGAmNRh1OHGEFMm6EC21CTmiFFTECghmjIDDewQ6hB2qWozWAJQGCAwajPbKQggHSAjECwg6khOmDQGjddGEZsRGmf2hACBNiQGJfCFiOSABfXHqibUoYSCQRaufYMYFA3C+WqhUgO6WJKAqyATEoVVFZg3apBYJEANsmBq0tOeUuVEXAY1GFlIwsEqVuFDrRxtE5y6erapyaGOrZEAJPkNtSGuw91OGVeTaFuViywlQ9NU8tYEwFNUli/QhjiHkUY6lDBDofMGs+iDkEEWoGtpFpBEwekR4Gx1ge24yCktc9w20DgGqxtH1NElCC4y7BEMJSgI4CF2qWkVeOIG4LRwCGGGIeALocVUL4DgTKQSMpYz4DcWmzhEJMKGZfCLgMe0I6ppJJtqThCsdQo1wIWkWJS1y48r71DbJfUV4Eu26VmoPAVVBIIqGJtlKpDQEujmpX2DaqtbpaKoyo2VhdWABUbLbUJQZ2DUkvrGWiBbhXaBee69cQWa+QVkRWlKgPgGGsapfUBwNa0C+kADTjsE9Cy1pWiT2HbaAfSDQaaVhMOVaUxMQ0H9rltA+hI3KawY42gdEhhAm0IaJ/AhJqIwK6AbWNdjfsUNoBhkGwQlBjjY9RjIFQmJLCHUccqn5BiZhA3gsIhQi1qfIz7HAbShgz1KGhjgFDRkgAZyUkV11pb7SOlDDSy5PRYC4ybhrKipbWpaorKSBKOrAeVNhLWUoAqklBoS3DdkFpLJWCZSCCI8QCMdC0r6yAU64pJi1CZ1FZWkOMTFhtSrJGrI6VFaVxkw0pTpBkpm8oUhRSQhhgNOIwkkIBsYOtq2EK0ItYzBnjYIumHQBHapyBCUFk6ZNZjEDBSEuAiTRnqA9DUyFreZyAxAGO6QYwPALA0pSZg1ndgD9umRkjhPoEdxzIJNDRUoBajBTEhBZaiPgUNBRyLBxTGRmNZx3nt6DqAuqF1ZLCAOs5hoJBwH9rwnHg1gKZZWd/UIVAJMJHFDrRJYUVlCMrbtYxU7cIiUUaA0gOkYVSgFId1XEBRa4LzlilDIB1bJTUlOuP+CVUjSGpKykYluTIUV81a+kY6tmgqTa1yMG5i5DBDCe5xKvH/JAgJa1xJZS2KTDJCetx4yBAL+5ik2LgGdhigEHg1SAgmzHgQNzDGUDMF+5T6xLgatTnECEQASkMA0S7ATcgwVg6ndYmNtBCYNofW2hhio6nExmOwSShA2mEmMqbSMoJ1YWWj1L4GBOpWoT07Q40nOlshLw+haMkyAsACFUsQAOD8/5RZKk2jlK6tOLC5qSNDuDWNwkZWC1A1c+VaY02dWOaDilnY1HVkMuoduNkUcg1A0a6kD4zRKKlhCErPwpapIg0YxRuURNb6EPUpCJQNCOhRlFgdUFLONKSGMzBEOKbGo7jHsWtsiGCPwwgZh5AuxlwYmuGhA32qPQp6wjrABjVqU+wTxQkfcEOwxRptCIChEsxiYDA0LoFthhyoBaA9AhDWDKIhIpTUvjvb3smxl8Xx+e6ehdBQMrpwseKiCMKRcCkplpq7mxtKw7yRzDY2oLRK8NHlKxKAPAynu7s0z5fdXpDNqn2YJfF0awuWSrrOZH8fSZklCdjcDC7tz3s9aNXJtetFqzXxHIp1xcV4d1dkedps5LA52r+8HAzMgh/feDpvNKD95b0Ie33tucdPXc/dsEgao6tXIGXT7d1se7hK7XJWHm5erJLEcn5046lZqzfeuzDd3gGMLTY2V4NuFjezMDy6dj1rJGeXL2e9VhYlWdQ6uHbNIrgaDvN2c7S9d3ztqXW7nUcxwOjRC1/UaTlfyIPhRUvoetBfD3qrVldF8dnli/P2EAH4+Llnua0It7jLgE85M2rgMd8Sre2uywUxZ0S3IugRSjXuMsCkh6aoxVQrYNaqHZcKgyAGQ6Y5dMEpibFshIhh1kGy5VBtzRa3QAs6szFUUeywGneIcgSYIhy7dctn2sJdTl1LIAJDQl1oHEoaqG5SaECZ1CaqibFps1YhQSZTQZVFdFR3l8BfNBxFeR0Vtaub7sM8KuvAg7DUXlknFmAJnKqIVeWWdVQY3xCr6jhXIbaosrxYNVFIH4vGfBb7FgDjrldNsVp7h6wjI8egUrMqbUFiMuiosl1bRCu/yGPqEsm4rAaB4JJjC7d5LRxBpBk6YG3YsmCOlaFDOKg2Q4cWvA1NwqiA2MFm6CBoXF/hmICEsC4wEeeuxdaCPZdhTVxCOggFgPQxcQF0CRcQDZnSBJ8p3WCMKMAxGjBEAXUNb4AydBmFZICzgAtZFa1cuxDCok5U7UGAlImKczecFN3DRsfBq5q4WbOAvvbZfRXVa8+DSOqkzmKINUy7pWWyET1KE1s7HIJURqoKDEIK+6tZLDqNR+chhEJDC8tWWXrOad48Y3EdQIykDspVgxBl824JHIWsrpICOAAxxD2gBw7AhLYwbkAKAO4S2HbsApuSwq2IMCA8oDuUsoK6pY2YAzXpYJtwygFxbd11PTVhflm1Q0gpD7TqukJVtIuQgyzJmJ/VGz0HFMhBaNNFK6yBV7YjoUreAjbhiEPhAjhggFMsIOxTqCsjCpUAJSwi5bKLEVoja7OBzoF3pPqjRgMijclKxgD66zh4sHRZGWJizLovKa4sNkVTcmfhuo+LhBcMIKxko8w9zKxd9moPnfPOfBVAQxEkqWyYCW2Mi/Y88fKAUg3SQYGotthkbW2ZhThLW9SGkGttNzgNEbHADhnwKSOatLH2BZhh7LuyE1Bj4RazBHhoahoQ+a71CEkgaHKKLYpxHTGXL2ACjc9dAmyfwYASanCCQWgdNEcxrroRVkb0QdlqsCeF9iPU9hjVMMSmwx1Qsyaqez5RhnZBFfncLi3Llj1OYYqYzLpKI2LEet1kq8w/op0qci2qNC3TDnTpKExOZj63jNZOmTYAlbnx1zKmVIz8xunITyhYG2bLTo2trn1ZhibkRyA8qwJPMkVpmTf1mW2fqfZ5w2NQKbeuEwOhNF5RN0hNFKJq3QUOVoRjtMEhhdS1qMWgTzgDtA9rJ4YnoNruCK4gRXbgAFI7eIITXic+oZAOsExcprXZFsBWgs1Uwo3vOkLCNjMB5cS6iUxdEjkL3eIsIJggPCSI0HLOYezjBqfI0i7KW6HQGu5wJixjCHQI4BpgTdz1rC0QIHmrAE6NLCxaRR04o7w5IkkVIoSk9qtljLvR47UotI/+J0HaRwCW0qvzWDfEfZhUi9hDytbRKk+YU63LVq0DKPgBbqYzx2EmN2GVRmRc9g+cVhY5vFzpRiEDbIjUnkoTQ1UhI1M0MZcVayEWGhm4RFg59AQsMYdgwMhCgsyUictMLRoAhRg0COsA6gLrc0Qh3OIMGBpT1IA4gnSACQfQp1wA1MfaF5QDMKDM0bDHYEwQMiwEtAGrAuNzQ2JQRy5DFmxyRgHysekKQgH3rO2I3AXM2GVfOjAtGM+bJeAAolQ29JQnZ0V7HvtZSKkCab/ErIyiJ+sEa0YhSmVialdhqGG4OG8E2ltUkUJCQ2TyljQOAjiXsalF3oofph5aRQ6TOuuVkrCTojNyG8bHAGZFBGRQc1PXrSKNOalU2VoDhxBqcIxAm1MMeARk30faii4oOwl/Umg3RG2PUYMCpJvENxMU6KobYwBY28qm59Y5HRAoACApjqXqNYQpUJOjDiXG4j4pGn5UPmGRNJ0YU8BDALtCLqmGAe46DALWI7bBKDCshWxTYA6Zj+thADFZ9DeOL1+BCC62t9Nee9XsqjgZXb44q3l5Mn6y9xTidNkbLjYH5xubT55+Rgdi0ehoPxhd3V8kHcDY0VPXZBTcu/lC2msp4S77w8VGf97sWs87ferquLcx3rtwvr0FHLFs96aXLuSZOp+rRbu3bHaBcGZXdk/6FwiCj55/FnK2bvVP9y+7P3vjl6IIl//i9/7Zf/h32++/Bzyx/+IrF3/6KmR4/9VXb/7d3x++8Oyv/tGf7v/wJ8df+cqv/tEf7r3zVtlqnOztp61WPBn/ytf/4pnv/O3Jzeu/+vW/3H/xpfPLl8fbO2UQtEeHz//td/ZffLmOogtv/vwr3/jG9NLu8d6lNE54Ufz6f/7TSz99DTF64Z23r37zuzKKdt979yv/7S8OXnj2y1//q8s//vF6e+Pad79/9ZWXjOuM4XD8BvJpAUbF+H1KAosfF9NbOFRrMV0ffNzgWU5VefJz19ep552VF1phvio/NePbPEjnUbp6/GFIZ3Pfm4DrfXcx0e8tR59FHsngYTm+zZJ0yeuD6oVd7+zUnpGjnwsHlM4yO/nQD8yKj4rRHeEu1q7MDt/1ovHUJ6t7P2twW/YMZE/23Dn06gk8uhEcKcc/pJ99ia49Fx7QuzdESvzVGJzui4XbUB/8nl7uTW8/BqG48wJeh5wcsntXxIpBfr5qupXn9p+MyaMb3jEDjcPgkxsgbSXxg99f3r64Wi5nWp88LeYBEjm/f9V94uDoYfzpFZP1oL8K7w7dOY1Xh2i0Tc97urnIXs9n90gwNOU71eoh7FYn5QmdfY45N+ljUL1T9RvT5Uf47IFoeat80NAe58sUvnd+9sDnVOZHIH3fNINFNmhWoc+KXL9Tze7j0CzBcXl2S/gwBUNnHTeCalm+UYzv8dhfl3fM+S2c5FN6Xh1/4kZ2Lcdg9IEY+KPqljq97zedhVMk/OFFkdXUZvTz55le+vOUP7qK0cqdcHB43V3MeutxfvIr3jLrkU/+1+KQyXR+mIgnVzlYuhOETy56q3JbvfP7qm4s746KIbv7BaYLL83x46tRNt3RH/1ObgL55HzVJQ+fDqcpIjNy56tIWlHk9NEVp87chSQnl8RcCrsiD552ZypL5rOXNViqOJ9PblN5qgKUjn7BwYOiOUxPXvXTMW37s/HbRI1Voz5Qmw7jip7kZx865vO8sVGMX8arMYkGNaVLFFP/48P54yg/gtw38w8A/Lxsucc2AnJ/gD89Wt1y1alq19PF52h9wJ1Qzz6A9V3d2lzPXlKLEU+c9eoTmB3ahOfectN/MCTOE/deA40vmWgZPmiw8cCtp/4ZUUfPMLtmq9C/t6PZ6W/m7/5aXU5sZu5foGfbQX7mLik4usr1+Rfp/d+uz87LAn0+RJN9Rk7cowYebYWrg+fVB18tC1ieLadX+aNrLj/wHzhm9IyLDp3jEI92/WwhMkUeXgnyU14K+vAGcuf2wXj6MY7WazedPf4oFNNV6FQPX3W4roMyPX7XdVXqjLOz21zMc98eV1/YcsZHzln14K2EKSnKbPQ+E3nun38mn98QoyM4IyfvOny09uPy+GVhc9mK52orIvMxflBN7rhsUSNuJj+jZFRGrXL8EqkkEVbN3wViNvezfHSLm5kVA9X9YA+suiQYB7eGfOF4xQSfbYlpgkVOH13yHvk4ehDf2rfrIW+c/svpu9dXWbFcyMOrdDaAbu4+2UqOAkXv/m/V3WtSPrDY+XyfzeKgOBOnDTja9P2Tf1o8/Io+uVuS8OMLYLkpyAl9uIkmw7Ac82kLnw45OeHHe+R4i7mfNm8NzWoXsDPw+fnpp56v1+pcj9/lSZyqO+XZbR776/q+HX/mxMXMmeeHH/vO/Ex0S3ih6Z0cle+rswdBgy/lfTm9y1r5OcUT+dTAH58Vj/jpeyzxM/MgH91x+sUhrcfVF3fdu/fsmJ2+z9vZGKTy0futxMvrJ/X4FmuCmTqsx3eYyDJWV6dvUkcXvhc7t/fifMZnFRxdcVeGmRV5dCM+K4A4o7e+iioq5Jw+vMIKvSvf/51V2ibj81WCH9z0zgwKx94nN43yt7zbv7c86KzOljMfHl93VhyI0vvskneqHX7rfy8eNlV1nDec+5fCZRktRvLkWb6MTVAEty9FUwTcJ/4vrhiZUJv5Dzf5ihi8nHxg7J1yozMd/ZTMT4XfAfl7sjqynXqa3lGL+8z11eJTWN3WndZ83u8pX4TT6fJ9tD6AsVqUj83sLomddTmI8zCK0/P1y/X5oUjcVXoLrO/bgGZyO8rCKFlMlm+D6V1+sX16/iaaPBQtd5HdNuuHsF1Ns0M9u0VazmJ+l0w+pd32Ijzs4NNdf3naWmXl6Qt+MeMlog9uCnrqH2B79KwLT91TD4/2/Gx+WX3wuzLn6yfni4vswdMMj70zCk8ud7N754lIGwGAqHc3gwfPeKspUZrdew6g9U373m9nuV4+qs62ycllscq4kfDu8yLLqK2duzegqJwJ5ge7QTFiK0yP9t0SQHhw/LKjUx3Y9dl7jCwzJ8untxmeKcz05E2Kj6qkV4xepEVG4mYKG4bVa/fecnw3UBPLAjN9A9CDvBWeqBbTgwT9Yry45eJZ1UzHZ3d4fQICPgfXYlav6Ony/Ke0WOIQZ/Nf8Hpsm3J+/hktDkzDXx696chD2+4tZy+axUL04OmX/+5vLv3kpWzQv/EPP3j+W996+Gtf+Y3/8Id7b7yebg2e+uZ3Lr38GqFo9713rn33+3m3dXzl2jpuuHX+W//+P+699tN0o3fj+z/af+UlE7ija1cmzWGYrb76X//rzW//7dkLz33pz/7s0muvFf3WeLiTtlrR+eT5v/v209/6dtVrX3751ee+/tfjp689841vX3nxRRKz/uvvPvWjH1KjOw/vffEvvj69cKE4OvwlhUbhyY2nh3dune3sgVmGjDm7uJ8cnWiDiyCcXLigOV+F8fHNZ9gvPjzf2AAWIK0L15ttbkEA01b79NKlTl3Pul2eZVXgS9ed7l0AhZ5sbfmj09H+/qrdgRZahKHWp9evB9Pp8d4Fois+WY53dsVqNb68X/nB6eXLPM0Ww6F9qmqenowuXqRaRpGyMUMWRWFuQ4dgkDxagZZnrIgfrViXIde2wgVpcoUc9/ZDFQSoS+LDJegL6cKIr0mbaMj4mx+qILRbvSjPcUQMQt6jtRk6EkTuR3cUEDIWzXBFA6ZDN/JymDDpcffRGnepaToBW4OBoyPUTVbMg1mCnNG8DouiwcKTs7KZl0EYswODZB4w4Z5Dss66FFVz7dbrZvfg6PUZDYogEvSJxSgPiOOeQ1AqBgwipJZlRDw6U2Jdep7HjjAVK0YfSclNMWnuk/XCorJ06gif2SjNo0R458RmkpfImSCl0g6n9QrConIoj2sutXHboq1FVlYbMViQcJaSMPBTjZtVFQWsBRKZ6WZEqqpyfcMIbrG4SkESupVC4VoGDjTGIGwBZE0VT0rQdhBSkZuTCNUQQ201xLQNw7xACaMGxpOV2fBsXkcnmU0IS3UnWurIhX2epGvbFCUzkT1dJ4UV1kePSqeoPeZNp5rXMoDh5HTW0bkhYj5aR9VKBE/O752FXp7QeDGuXFM6IJhO02Yx9brHo48eJtuFoAl9VLJVGQJ3MlaBPAkGo9MPD8Vl5WvXjqpWXfkiIg80zavQeHRsnCKNbTw+K5K19LWDJzJKAcNeXHAB6kHgTirkYdjkvqh4qAo/aMZrCYCNHb8hiZJ1t0kOz2xZm9ZG6BUk1EUYuEnKrVLME/fPMcVZv8uVZlWFgyRyC8dVVbujjx/782zauugFualwMQjdeY5NZUIn9lfYZqUXu72cZjlMqBvVxFodE3A2A6LKQ9eJCzY9rbnU0UyswDQxGCmeHa/DiqWW0EwH+Ekd8PGDefwVmazD1WjeMihTlIxWvh5X9cP55HwwKNtWHJ5lnrUwDeajZY+cVT5Px6Pkqq7XgjxMPQxbxj88SUOiRO3PJuu2dDRg5CxtYmylix7WTNkWcVFq+0KGQcLWsEurgDTDFQ1o3XDDoMARkS3hPslIh0jhee99oiGvk6CTLKmHQUwiL0MJyZO+8+EtqRno8EDkvG2l77ajJQpYDSC9dR9Sp+760UHKm6SMaOwueQeUgefFmXEhiETkr2kDl8MweFLiCFQiBvyYQKcU1gvOSWnmbUrrFCFZupVPJjbM8jgR/ozU5YLqI+ok+WSaXEV5apWqeeGjsXJk2Wl//vhOC5yumjda3pisxbph4yLD5CyFdlwtF0WWX4xhvCQlzAMN0zlf1mkb+CpDGuQhcqZTSmeLMCStkp6P6hA6MYmcFLcIylQ7WtrAIc06nBcoZhih5HRph45ROBIp7LpwdU4efyRbLbjhx2VqmwIR6B2u1dC1ci0+vVeJGMa4Ga1syAHC8STVbbfAhP3sXRUMsMsCJ9N9j2LViRbQJ5SA6CQFHccAJzlYsS6HLvW9EkdAMcn5RHkybRF3PSmiFQi0a0Zlt648z6EPDamrALhsAsR6FPRGp+8/5r3KNw6eqGBducJjB4Tx6f9Hy3s9bXoWeHp3Dk980xc7Z6klIZFRIgywtVPjsmv9T3m8NWXX7DALzABDRiCiNINQACSBhHJoCXW3Ond/+Xu/Nz3pzrcPsNf2mcu1c36dXkdX/X6Y31SzOU0nvSTfPzDpQgtP8Y4v5l1vcO36pRmSXUlythOSpl7ivN5vU2eoJWjbJG1bljzbR5jr3BRszzLnM1AmShDXFkW+FtGiQSmXRRci0odKUVWRdrHslWmDVa3yDFtnOQcYyoFD1sQVwZqucAolOECEnPcY01Wf77VoKFgdUdvBEiPjIoGOsSTvSHBtlqE13N+q/EiSNsiqM4dzuqmzWutBJmeOVJ3JExDb/GBnugpVC/lipy4dgj7B19sEBAiyarstsMlscbBfjew+6d/Zv3x7+YzWPsfXO24Cxclst+ujCJGnLJtM1BDz/R21BJwAObmuebuN01v1lb3h6QaFwd52PWyiNILu6r6xIibkVmDeCpPvbKkyzHO8srW3yKc2y5bKeUwQHPCiUKwAfkXmtxvRR7qX9ZIJ70eV51m/9imwBKU3tgGA7fqRdF/jIsBM9pMmy33XH4Gbt+WknQ2P5ls1KHi3VhabnRsIlcrBmx+AAMyJc9lyG2hAA5JlnefYLsvkjoo5C4XoZTWnoctztqrTqPVqf3LkqIH8YP3wYm1tT3VGJpt3n19HH04PHaHn703bdvPcXaKt0UnVDIbQe4hCAHDjvvuOtN1s7RA9r9LJwfjoMWw0EsFhPFlbQ62uynLjnntFp8aHj2JjVJJ5QiZHj/Hd6Xj9kBhP5KlTVX+we+5cMT7YP3qiSezqrZvbd58f3bkxPnWqWln59zka9e7OP3+rIKG3tzM/vAoaK5q6XRnl+2MlUihQ6BxXarq6ztqmv787P7yGKpVNJ7snT2UHBwAAmJCgAlfddHWNdt3a9asHZ0+wqo0mTNfX09kUGxtT6h3I9vb3T58hWg+2NvePH+9N90Fnp2uHkmqBtQEpMUjk+3t6uddCObxza//0qbRe+C5AiQHGcK66QQkwzLb2q/XlTFdgYsxSzkznuogkDoyiucIStDJPNvaqI2uZJHi4ngAAIABJREFUqcCB0Us5Mzoqx6FxXLgWEBmbtOzd2Z0eWklsE2faDTNqTOw85MgLBuaaiNimZXlnd3ZoJXU1mFrXT0o1WZgEcajyNBvXXSox83Rmu1RypEETAQIhxV6hop13gxTMgpUMiSgmbVvkFBqvIPI+5iQoJIyuh1m2eaATAXLKJrrLU4a00xj7UA/yZF5xa+pBzmfGUdQVab4/t5ww6v7CVMNCVm3SdrYkZOE6IepeKWcLrrp6aUiU4uNFXE2NxbxqmpUhr1poXA/XC5BC45qlIeva/q3NvbtPZ/Op7aBeLmmjsDY4QTFAub0/Pn0CW5vuTuvDS9li5hUIfcnmi2hiLLkhkk1q0KctTtKt/frYalbNfAtCX2BlvI4laea8R+YqDHglyuHWfjNMuVWhBUCiQAmpHOK+TvPexni6vrTWbFZNpkaJcAq0ATBoOMNNwMzXSTbcPDhYHaa+wZXrcsmCBW0EFDhJcRUKMBuXS8X2wWxlJIPCM9/1UxaU7yBGwSWUVB5RXxVFuTFZLJdJ6NDUqUEGYQDKM9WppRKPm8AplpActKrIODK2A9g5WDKnEetaPSrQrIsE6yyV+1OfCkacUQh71476bFGztjOjAs9VxEj1iuRg5hHqx/kUFcS5ejTkVU06Y0eZGM8N5bqfJ5O5B5DJaDxl8yqMUmMxbTqzlMuq9Q4hGS2grFLVch9bW+4vpmu9ol6AFixGOdOOKAMFcJjKWeNK0uK03JlOj4yKeh47ZErKOhUchBIZzMWsCwVsaD7Y3JsdHq3MtxYut31OlQUaAAk15WxmYB4blo0298eHl7KuCh1yOaHGQhWqfhkIHt7enK8tJ6ENY22Xcu5UaCJkwEsOFobQ0OZFubE/X12SvkUzZfpZ2U4qn2AKXMLhwmIcml6Zbu52S0MeDZooPchZ1K6NBIWQclAZgkLT6yXbB92wdEKkm3uul1JoXQcAwapI5cGcQVf3e3Jnqvt5l2T5dIadr4alXNRCmXaUkIUHMDa9LDtYWEY5sU5j7Fw1KkXTZlVteoxUXlHR9tJsWgUAifAmUN508+WRaFremHYk80mlEQMJoK21kGARTRRyXtsBM47KRduu5NmsMpCBBKDaBoixjAYIOW/G60uD+b6vox8mpO28hThBFjM6bWBJWpqmG3vV8bW8nvs6uEFClQ7ap0g1okALA3u4YXn/zs706FrSLnwXfSGY1lEHJJFhAh80NANV0htsbE+OrGXdwtcRFVh0TeMETqBhgo4b1COVLPNbW82R5VQ1ofa2nwIY8cKVYT7tD+VuVY1KATSZuq6Xsqh8BzH0LqWojhTZqiyK7Uk9LDgw/zejUMSoy0U2WRAcqrLM9qq2lEaK3t5USc6IDQoAhOoyzyYVjdanmE5s1c+MFMX+3DGCubeGYBeaQZZNqoBRNSiz8TRGMETzCSixtc3SkDYtq1q9XMrJ3ECqB4WcLWIERMZoodwdq6NL2lM2r/VKL5nOLGAwx+xgEQCKJddQ8EkVR0J5KqaLbn3Y29nxEdthBhvrHeizap8uyVkdRkIFnh1M67WBXFQ6MpxC3JpoI8hJR9PBxu7k8PLaYmuuCzvgzGigABDQEEYXFmax4dloc398aCnTFWyDzgW1FqgIBDCM85nJ6Xy7PHTogw+3zp1MbQdqYEpOvIktQDxazsSsixlayLJ/Z3++PkhNA6poeoJ4GztgUuEJzMYNyEGVFP2t6XylEE6F1iPgYyZi7Siwbb8ndme6TG2S/J8GIes6CCHoermczpm3zaDH9+bmL8z+gae0F+ZTWCAYu14pJ/OAMMwx2a11kZosyfcPHKWUA2sRbVo3ymLtIoQwo2x/ofOMEhcWNlBCJLSO4NY0q/3RzqYLeLaykk8nwEeYEgNZsb/frfS1p+Xu9uTE8d541wVse5k4mISIQk8aKIq93W51oCLvb20ujq4vb9+sQaZW+rRuoQ0hZwqKYndXrw20p+vXr945f76cj52BrpS01dD4kDNFkv7WJhnSHb462rh1cPxYMZ9EG/dPniv/9m+zF1/87380Ov0f/9Pf/O1/vuvXTy/WDp371dP3PfFvzWjp3p8/8YnvPnbpc5/90t/9wwOPPX7hP/zNl/7+H+/5xa+q1bUTv3/p/p/+YrJ6+N5//c0j3/r2hw8//LmvfP1j3/vR+1/4D4/+0zfv/eWTZml48vmX7v/eTyaHj5176tnPffXrVx555JGvfesTP/7ZxU8+9OkfPvbRH/ykWlk//cc/fupb358cOX7qdy/+1d9/5dLnP//gP33rk//y/Y0H7j//xG8++d0fzJfWNsORjacYTaPejVsvJGBAwQ197ZWeJI4e1B/8cYm1XYDk9lMCcuD23Y3fZzz15ra79sYo0ZVo2vdfXKGtigTfeFIIYtFEX/lDnwrvttyHrw1z03DdXHp+WUwXIaHXnxAAIzTXt17IE6Tg2F58bcRaRaK59NwgmVRM2MtP9V0AA8bAlU+RWcLdLrj6hXQT2JWZeO0hNF/22ZS9+4CoM9numY2H0bQkeAKufC67w7uVg+zVT6H5ukvH7ML9sB5RPfZbn6GTAcZjcPWzye20PjwrX/14nB7W/UX+9hm0WKVuGu58jO4MVdKllz+W38yb9XH++gPh4EQ3bPMLp/DBSqLuhM0HyNZas25nv+223hLiHG+f7zYuFqvkYP9msvV2QhNwcJXN/hj6a+3+6+zOWxk9wdvX7bXLy0O6sNfczbd6PIkHt/jkhSiOwdkr/vYHS+wQtu/o6+/0Cq71HX/9lTKh3Xxbbv5RiqOoetXceKtXLrWL98HNdweln7m9cOWVfiLMYpNsPc8HK119AVx5a9DvVbgdkIuPkEoFCpO3PhOhI3WkH3wqMkX3aLz9Wd5M+vVBffM/0YXyPMg3H4oQ0tbSDz6FQ82m2N/+HG86pmfx8n+Uk9ZlXrzxUAAMKo8ufrowMziL9s6XSW0xasGfv5gcWJ3p5PUHQWBAG3zp08RZXLl487Ni5hCu4AdfFhMwG1W7/wabfVr4+a23ymYDZaK9+nLPXNTyDN74FVlsiHTZbv6WzLdZhrvb7/TUzYh7ePslqT6w4izeeRLt3UjYYTJ7zlV3aA/Xt94rF1cRHtKtF0j7Z798orn1tNy9lvF1PPtjGN+UIzDbu8AnlxldpVsvkOa9mJxD+0+6/Vu9Xr/Zf5PvXk2K0vC9M8kHZ/TwILu8Rm9/rBrMyg8HcfOTBOzJHRFvPQhJE+frxYW7upVKfrgEbn4Kphv85nLYfJCabTbLwrXPQDCDixXx/v2mN82u9uKtT8N0h9wexs2HU72ZTWB748vYz0AzEu991PdnybUM3foMxTfR9jBsPCrUAWkwufQQD5PY9cV7H1eDYG5Nrr7cS1QnTX3xhWW+uwB9dv1X0ntMtb7zu4RGR2f64msjXBmG9MXfDsVenZb60q/7xhACwvXfJcxZNFdXXx3BmRFUX3xuALdavEbu/JxaRXG0N55PUeeQCVf/1CNT41O89bT0m4auw41f0kWXUBK3fktwZalzV1/ph93gT6erL98F9o6rpXnv3ZNgcpSHvbD5EbK9qkUjr95fXCub9b38zfvA7ulmqSnePQrHJzJ9O27fA7eOmbJjl87n15ea9b3ea2fBwb1tMS0uHgnjs9zvkI0TePMUzMbo6v3yxvHq2EH52smw/xEsb7Frp+Pe3cxu4Z1T8NbpkB6wG+fF1ZNqbbt845jb/5jNt+3F6uorAwm7dsI2f8f4Cmwv2BuvlsVQVRfBjXeGhZuDibv80kAi007JxnOiN+i6y+HK64OibNur4Mqbw36owNxce76fhbqp2a1nBB9C+6G++VpvkDXmdvzgtVFqO6D9ld+WhV3YBl59rqA9oG+Ga6/0+qJWG+7Ka6OkrYOPV54pcDQyG7F3HkhVRxba3vkCWQCCK/DBX6V7Ufer9NXPRJvH2JEPPkE0wq2KNx/lU4jwDFz8ktxD7XJXvPIJbwcRK/n+R1hDsNHxxkN8ytvE5Bc+nezQdmmev/YJo9Yc08n797IFk82evfM5ui/akc9e+5jcS7rRpP/KR6w+4mGdXjoP6r4R9caLqL0QRseaO79Nti+l/ChevOR3rqRDPJv+mex9INkIbb/Gqrdichbu/SZs3lruDZuDt9nW5bxg7eQy3XmHZ6XeeS/ffYun5+DBb+ztS73+qBm/TbYu5gMwmdxObl0osqHfeZuP32DLR+uDP+DrH/QH/ergPbrx53wQZ4vbZO8VWg70zgWx/SrPj7vk5lLceDTptou5aW/+NbELoHPx7id83shNjq8/SNEm2inC7c9yNaOdhxc/z9QshFS88ymXarYr49UH+/FG2M/U3t/QtkIB4Pcfpar2gct3PxW4xvsCXH2IggM04eDm52jdoOjQnz9PtHaI5W99vEuxOEDkyqcSMw6thFcfZY3t5OzOL5haMMbsrd9LX0Xm3bVXe2jfhQxuPSvdTcuO481fkOksYRnceQ6HaRDBXHu9bzYDWBEbT2J70w0Pd9f+NZmOM5qB3RdxOyYDM73xRk/fiviwuP0k0dcjPwV3fx7nB1nG243X0mqH5bC++UbRXg3pir/9bLq4grPTcfsXfme3t5IdPPjD79//+BPbZ89//Ac/fujb373w13/9pf/l7+7/yc83H7j//h//8r6f/bIt+2deePGj3/nR+Pjxj/ziyQe/8Z1LX/zi5/73f3zgsce37r///M9//bEfP+6K4vSfXrnvuz8dHzt23xO/fvir37jy+S889PVvfuIHP94/e/rMMy/c/6PHdV4cfuOtR776zcnpM3f929OP/OPXrj3yyMe/89gnvv09feLw6otvffqb39Fpvnrx0uf/7r/snL272dz8d0mEAMLp0aNMdc1o2C4Nm8WsGgzqfl8cORw4r1ZXoGoNpZNDa8lkvxsN69GwXl5qlpfbXjlbW3NCzNfWRFU5Katjx7qN2/P+gPd72epKNxjqfn929IgTYrGyku3smrKYHT48unqlWl7Su71qZaUqy6bIZ4cPBUrq9UPV8p22KOZr62o07EYDIoDMLZYQWy+khokMBnDpcRpZRFw4WGLAoMgclRAimCQGpZhRkEiP+zRwyoVlWUQEJP0AEugZlpmjEgQIuPQ+g14yKQ3uEcSR7HkiI86wzGyU2ArMpKd5xBJliQ0ZiJyIMvAEeOYIa33SxRQzWgeiLYRU1IjpyAIRlafGZCg2BrLG08BYbUDtKIVJBXgELIKkAUT7xENuPFY+IYxWiMwiwZDPHOOGgJh2CLggdGRdhCokEJDaizpiEsQC0xBpQLIJDpgExsYA7gIBPA+uCZER0oO89S4jyCOReJJzsQg+t55TkIQ8dyAhvADswMeCxNrx1KOMkhbQ0gFGZWbU3MZExDQKEXAasQEs9TDHxMW0cJAilkcpNUwZL6BMPOwzUFuRWCIjc5EV3jPkMpgmFiYkMItI5VINSYDJInLnWMCiCtQFbCltVRoVhAhXSLaWAiAWjhnIgmC1k9EyR0nnZAdTQsjCJ9ZgwEQFqALcYloZ5lSG8EwB3gYSCJtb2QaGsJw5bqIMgNeAm5B4Sjub6CgJJzPIK0iQyC3kIBaM54DSCDMmpBPIBZpmudEUBk5IARgKNINMeghCTDEVJqUmkkQWNgKPJKElADaEFBEZKIswxWmuAXKOcZ4HBLxLE551QQdfUJxERgMQmKWGQx8oywptlLUpA0lgIUAOANNR1p5hLxRuF1EinylcN15aoAPgtREuRhPFDGAPRQvoTEuCU82aViUuaAdoDYSDxsBkERg00pCu0jRE0RGquiRKFBGtYmKjN1EuPAFeOlwvTEICNnjaukRDFClbKOYd9kwsAnEoBVx6nwCXMCkt6RPEoCgdlRGlROQBSGQyyGT4i0F5YkMGAie89DyJmIckc1ACVBKZBl5AmDEpjegBQHFaWCgJSDDLAk8iyiCXIaYRZYyJWmYuMClL65OIUixyC2X0CaDcyRIohCCfeygNAV622BPHTWQdQDGkEJDGizoSEkWNAIgsolSFLugEhM4A2jkOEO9CogOlXjbRi5CgkHRA8SAtpCqA1lBAace4AghCWQEtTUJI0mGgrHSo7YBoIAuAdzAJnhKTdNhWgQGZQZY4kGOgYZI7xBDNYpIYmFFewER61GfARCEdSQD0kRc+ChwylKYWJ4QYwGXwOcHAi9TCjBAGZekQB7hEMrFOYIuwkAHnETKYpNanCPGY9ALmACZBCAsShCVJE096BKUxSQ1NIcQWs8oyp1IImAaiDTRQVlnZeEZgsoBMAe4BbyLVXnrKlJM6JJTTOeKLSFFMFo54wyMQbeTGSYtI65MmSITIzLMmYBT4HAkGuEei8dzEFMF5G1PtCcSi8qzzjAReASJCGoNoAVVBgjQPwFrPGU5dzlxMU553xoeQU5REFgJICEs0LVykLC20raz/i0HKkwxwGWgeQEJoEnDhIyaiMDIYkDGRA9dFUDJYA5F6xKNIvcuhp8TnIDcWJoRmkSUxlAwayPMYBKF5lIWDHAPRItqpFBgbEamA0I7iKBaOeMQjpQsjcWAWTzqXKEQhZQstvSWIiQUgDgiPaKVZ0BRw0jmpAvGIzq0wkUUk5oC5QDyhdRTOI0xpZxLtJRZ0FlgbGQhibrGF0jPWeOG8NIQ0QTaRkbRwQJKYYJpFnkSSQi5DTALIGBWNTE0kqSyNTShKmSg0kjGkiAiflDAKnOcOM+8ZE4UPqY8pF5mJKfI5ZdLTDEROeNokwkYmk6JpOXEpxUnAHNAUCuEjB0BgnjqGfMBYFtYL5/JE9Qfz1bbNsrrfmx0+FDFaHD7CqqrNi/naWn9nu15aavv9xdqq6pXdcDQ/fsxxPjm0ns5mWop6baXt96eDQa/I2cqy6febQX96+JBjdLa+3t/aaopytrbe9vvVykrS1dXaapMkVb+/WF+3UjZrK22/t8hzt0zUsN8sL2HoJuvrXZb9/1gR/n9KhLe/8S85A3yxiLnQkRKtY0JpqwxmlATvEXSuLXtUq2Sx8IXUgVKjurJkTQu9Jww4h7BzTa8k2iTVAqYY2GgiVkXBlILWExadR1ibdjAgRvOqVr0ia+bG465XUqWxdYQFEylRGkvYoERWC9XvMd3pwCmyESJvoUsYgJBVrc1lruadFT6liWnbKBm2EUJnkUSq4xmqlc2TQs1ay2PKpG2bIItQRQzrmCag60SKWmsTXphFa1iUVPqujZJBEzFyGkqkOpGh1rmEZWbROQEpGJjJHlnmQAdGosWBIBkar4kRPAmVAjkCnkDVwazXHnhB2pAFgmRsnKWW0gTUKmQIeIK0gpnUtZPUWxIwEqDxhjpGZagVyACIVhCivdCtS6g3GGBoBWWNDhBLWKuYQgCMJFh7YTpCrLdEMakSwdoOueBSBn1ki5qmsEEJ1salAhkfXVz24xkpXcA+48h53OiQ86RZdEC4TGLrgA0Caw0YMMEVErpAG+VzlrRVCxIsADLWAC6R0pADExKiKlaQWoWcJ23VAkl4hM6rKFbc7pT1g4WcmlqUctHZlCWmViAl0EQEvKMCtC1Paet0yg8tbu3jVZdQaWsFcgpURMBbKmDb8QSraATJ7UJb4QUWvlUgp1FFAp2jI7U7zYegAzZhqVkYLz1DInQqphToSKCzRIamSzLcOJOwzC6Mk54hAq32VOg2JtRaFDGWoLMGOcpT2LQ+QdFzYmqQJroJkjqLIkJOMFp3AeEUt62XEESXcKScUHVIubUYQOAlw62JAK65nW2yAmP0KcfaQuuRgLTpOpYEyXBnQAQJ7tqQIGc58w1MsLFIQmK8BZzB1gIOHTAZgz6yRpuMZl2tojQZx9YjFzlsDRLAAo7bmha8MTanaVt1IEE8EOsMkAy2BnFoosBdzQraOS/h8mJnj60iHohzJkoOaoMF0pGTvzDWSpLqSocEsoCDt1EYQSAItFY24bldKEV8whLftjGh0EIMjMESKC0T0HovWWYXyvHI0JLa36GrHGhAoTEkCa1KM1Abl4jcVZ1lkWEZVRskjQYyZCxOfKuTFDTWS+4pposOUCyg6rwAGHpBca1k6FSWwdp5SQ0XslUwAiMJNkF2jU+IcxRAYCVljQkQ/b8MMoGrjlLjHNVUOoGpciBCAWsFUuSDzjjRDlkPBOCNalnKkQqOeEQEqDuUEeMI1S3KiXFARNYqRTOGuuCJB1TAqkUZsb7LRK8+aEKCJKRGKyAE0hbSaKIkqmYFqTpfyLSbt0FiAbGzXZRLbr+iufUkwV3NCtJon7JUV60TWADinYpCwM5gDjovqa75/4MJUmDNnZ7ifgI7TQVoncSqTnpk3vpcprpqA0cMQgS9JSO1u8h6QWMrafp/2SFiq0JGgQEkWMelb5SUqItW0NRVxgnPkAytimlEwHFCWyt8o5IEdcFx6hnilQkEcdDqmAIELMe0c9x3GDunaZcmgSLW2oggh60OCQDRSsY6EyFUCaeNihGsud1dsgxi9ClHxiFtYYJ53XY08cl/M0i1QSDnObMNSokyMMG0bTuScmy8hQ7SFHUtlMg4QU1NctxpmGDWth1OBTHOYQvomt3aEav4vzHKQgFZ13VIcmK8xw4QibqO5aw2NqWri809tgZ4ZM7omHDQWMyAAQJ3NctZ54wkmamUl4ABGqwGCY+NpTxqsGy2t8vDrLYmY6le6JAAGkj4i4mtoRyqKFBbi5LVxqQsNX9hIg3WBu4ZChixWnPU1bJgtbEpE67pvKDRIhqVZ6lrTSJj671gnmJadZHgBKnWC4CAFww3WvpOp0lsfeA0MEIaHSFYcfvbZAXF4FNOWh0Q4tiC2uo0/QsDIJRYNV5SbxAH2lMIIUcmdl7JLI21ciwinKC2iQkMMUpSzMca8q4oeNuiEAn1JlKqFJS4g4I3jSmzpF5oyJFA0DgfEGVBR8qUggJ1OGFtCxJSVpMJ62OOoPE+QEq9Bow1LcpIgxLRNiZLkrY2kUCOgI/RI0pdhyVfVD1Sb6WHkmphilx0tQtovn6097/+5+zFF/77J8LJ//Q/f/mr//SxH/1k9/z58088/fEfP7534uTHf/rLjz/208uf/ewX/+Frn/zhT976j//Do1/958/86MfbZ86eeeb5h77/o80z9zzw818++C/f+fDRzz769W899O3vv/3lv3nw2999+Fvf3j995vTzf/jUt763efruj/z66Uf/+ZuXH37009/8ziPf/PaFL/71Jx//+We//o2tU+fOvPTSg//8na2Td939u+e/+F+/dvnhRz/5vcc++0/fvPbQQ2f+7dkvfuVrW+fOT7ve/FcKpcAehNlT1qxm+E578CwUqWPzbuM3DEcbPZj8yqIExQM7+Y0jfRR27d6zmDFLlbrzr4Q5HRA6+IVlyMSF23sG0zSEAzd5FlNoiLebTxCqVWRo9jMFUIxt2P9NJNzHuZ88g6ltEQ7jJzFe1Ez4rV9hEGMqcPn2WTKBgNX5u/fzrahW6uEf7xLjVPUWvdeOJ1MRwJxfupeOiZd1+db5ZCNWh/XSH06y8cD0p/lbx8U4i6RKLp5jO8zlTf723fktMD+u1148gvdWq+Vu+PqaHJceT5PLZ5JtWQ3D4PWT2XVUHVNLf1wTO+vzFTV8Y0nu9BHaFVfO0I3e/BiwT+3Xr0d4d2peqqavgrLfza+z5iWHBri+CM3vZ+kxsHjZTF6G8VwGXpsf/AnJgfNX1N7LFA9wdwWY5xbolFCvtLOXoD+T4XeqyR9RUlp/y+79gbLCt9dB/TtLjjP7en3wEpKHo77s9l/ASar8tt37HRK5727H6TMmXzXNe/7gJZKuWGTK/it3IasiAcOXzwXoYRv7r5/0wqEJLC/cF9yi0DP8zsOkbQMHo5fOxhhh58q3zgFi4cz3LtwH1Rxil7/+UbTofBqGL53DHgBr87fOSb+wHcwvfBTpFlBbvnIvnWndi6M/noGOxmDyt85Sp4MNxbsfQXXnE9f70z1iYqcrpn6i1ZtRpG78EvYblvfj3jMQXq7QXen8Z21zNfLDcPI7726EJDMHrxJ33boVOX/axIsVOp81j8/qaxSe4O1zjbrqZW4PXif+Q+3XZP2M9hcW2Tk0flLPL1F4QrjfV4srOMvV/DVfX4LgsGx+a/xbFbo3a55czN/H4ghsXvX1n6Fcdvnmod7by+1Km18ssounpkfs8BJNL56zfJzuSfnB3Vp07GC48vqoXlXFpSS/eEYvV/xaUly8J/BJMqbyvbsBW9BJ2X/zaDes02ui+PMZPZixO0nx53sC3M+nkbz/ACBzOi/6bxwzvUrclNnF874Yk03ee+8+H8dUi/zNuwNckCYdvn6i7XmzM509DUjQDNiNJwhtFMrx9HEFbAQxjp8KCHrQ+clziHSKML//rwSNG9Fzm7/AQAME/fgpj52LDuw/g3BrSAp3fhHpXhMPydlPOldDRMP4GUA7jUDcfxajyqhhuvhJC3cbcCRpfrzoah4lmv9Gk8pAAvaeRXis1anyxPNDurk+X2+HbyzLnaVADsTVk9mdvO673rvHiytkcawbvbya3Dk0W++Gbw6SjSVEtvn14+zWUjO0owtHig/5/Fg7fHmJ3zm+WFdL75Ti9qFIx9mNdXZjzZez7OLJ/uVieqJd/lOf3z6phxN5cTm9dQyw/eTGsri+5spZfulYcWlYHZn1Xxvkt07Pl2b48mznecYSr7fi4hlLDhH3fnvwAhCHkL1q93+HZaLB2Ow8g7l0ZgymT5l0YLqrbvwCkcve3vST3yHBuliFnacg59bOwPRJTUbQ3bL7z0E5dHbLT55FHHVBg8mvIQetr8LeU4j2obtj9p4DaWnsBO79BnGoY4TjX3oMHc/7/T8dk0Y57dP3H8CNCUz3Xr2HjU235Jf+cAZ10pMuf/MMUzEGm1+4D82Mz23vlXvSPVut+bUXj4Wur4UevnGMtdDHLr9wD5+FakhW/nBK7oT3l/5AAAAgAElEQVRmzSy/cBg1Q5WY4RuH2QIhPxUfPIDGpF4Gh148IbZhdcgsPb8O6hWTqeGbh+gsaUq9eE65NxbZOXTwaz1/F4MzSXhxMXsfp6Vp3rbz9yBaZc1Lzr9aoXvS9qnF7B0sjsHuDb94G2albt4L87cRX46zV4J+zZLzXD89n75NksOhfdtPX0dpprpLYf4aEMuxeiNUL5v+STP5Q5i9SZI1270fFq9Cmar2Gpi/5MVKbN/yiz8YcQrK23nx/n0B7BeVwe9+EsEFUnL46kmbtWyL5u+d99mM7JDehY9Ef4Adyd+4N/oKWT547bRPOrZP0nfulmTLzPLywv3Az2BAvVfOAdcGKEZ/OuWEwVOSv30Os6mvWO/t+6OdA4J7L9+FbBcwW3r5RJtSOgP9t88hOI0d6b15H9ZNk5v5450eA5LHg2cinhmCw/7vMTwweild/KyDGw08mTaPzZsDAXt48bQCY0N43P89RjvarGXVTxtwvU5Oor2f6XqPowHpnu30Pkxpt/c8spshHk3rn7XxSgPuSuufVc0mYf0wfyHYbZDybv9FrG97vg73f+X9VUvO8vrni8WGyJb1Qz/44f2P//L2Pfd/5vuPPfT9H773pS997r989TPff+zqQw/f+5NfPfS9H+ydPH3+mec+/t3Hdu66++M//eVD3/zOB3/1Vw9+7VuPfPeH1z7x8bv+9blH/+mbB4eO3PXii5/45o927rr7/l/922e/9o1Ln/v8J7/zo0e/8a1bH/vYqd/+8Qv/+LXx2uHjb739yD98fefsuXuefu4L//j1Dx9++P6fPvH5r/zX8b3nlv703pf//isHa4cPXXjvr/63r2zefa+6fevfIRFCgGJoe/nB2ZPY6bZMJyePURiapQFuWxLsfFhGcFx6Y4bl3unjJBjdy8YnjhCnmmFvcuIYCbZbHuycPi6c0r1879xpBoPpF5NTxykK9bA3PX6ERtstj/bOnOLdQhfpzpmTJBqdJ5OTxyiJatgbH1mH0Lf9Yu/0CeqNL/Odc6eQ14AiuyYx0zHGuC4SqCkDYA15HH1EaI1wrigJcUVAahGMYVVgorxHaBUhqChFeJ1BYQj2aJVh2XEe4SrF1ICI/TIiPJBo0aEcc40xcqsCSoexQ6uMMQUIDiswCEKBccuEFxgjD1cISByC0BQqyIi9M2WHoeHAm1ID4Yh3rrSBWQZDmzeeBQqC7lmGoVBN1wuQGhyczx2kHkbg0gZhy731pfaRMKfbEgaueLAxNw5qBKDLjYeQGm1LDSzgtnN9qKmjIfgikGgYBDbXEUDudddjEUISXRCRrEAUPZHRLCGIIk4jWBU0TkkvRZFwp23G8HIgMCIR0Agi4GGK3DJnXukco1WcuA6nCCxHDyEQiIwiAyYmuF3CNGib4biMMDSBIrwcEAw0RWgFAWyJhHgFE+R5zsEywjAEhNxIAxZgNF3PRGoosWYUIHUwItOrMbUURzNUQHgErOobIAwlzvYUQAphpHs1EAgHYwYdkTYiZ3raCwdRsH0Nhaco2t4iMECBVwNLckBtq3o2MoWAdz2DJYAg2rJCMgiv7cCEBBPvYB/TJBBgceEjBcg7NOQBMG46PySYABYN63EAoYsolgBBxLy2IxgsE7qdL8lAEAne5AADiKPHvegtlcHpASK5oHaCl0oUEfNG9ShxEcdISggNIN6YPnCEc9NWA0YsEr7risSgACJ0MuhhpNGEDBhgmGlDkupBhzGK1Pl+x7CFXHUDhoCzaRKdZq71Utp+BaIPDPq+AchHatqBw1HjlKqhI9A7AW2vothhHsxAI+wjsrpvMfFIONPvsFNB5qpfYQYhVLbfUWYjBrpvAPEQwTgKRAASLF5LkAgEAr/CQOoJdHiJMeExRmAUosQEaD+CvEQEOLQMQRIRimiJkAzDYPEIQh5JtGhZhIIwr8MSIXlg0NMRBhL6CMEoohRLp9wqjinhrpsuS1hABDwaIpwADCJZijZnLJi2RBEpESxIjQMAQeAzazyizrpCIwq563wPGOhoDCEPwSmOoM1MEJbGoAoHMeROuR70QFPTuVwGrhEEPjGRaAZ9zFQLBfVGlQR6xYKJqXW4BhGELDpkUfRWKgcIMwr2QaMVBcEzRJYiQ9ZLGJcIAcakCKxgAlRHCF4JCHoqEV5FkARCIlrBBHueEbiCCPKQ0TgKhCMaNV7lhLjAaFwhmPjIAlpDHNrAGFgOkWGMTVxGXEJAPFyGiDrPIF5FiCGoDV6hgEMMLVhhIAkIOts3ifQUe1dWXkQKnRo4kkCmW9VzUXQYON8zIIkgRltUKAnCaTvQkRHqdNuDIVUMeF9qIAyCyPRUSDA1tRkYRABzyg6AyRwG0ZaOc8cQNGUHRGTBtwNHMeCmNQMQUkWctT0XqCXegz4iTDA7oaMSGcyd0SXFBJAYcA4gAjg60APWc27auk+RxMI3Ok+Biw6gmEccIguGlFR5xGzXljRSiIEPAuGhxzCQAgEFIfC4QGQFk+B4D7UQ0uCA5GAYKI5eRjxEFDhVULACSbSQQ9tfEOIwimaoIPEAedU3iDrMoRl02HeBC9WvIEcIaDNQlFtPge7rSHwMyAx1QQNBzvYXgUGGnBp6LEMIWg0sJAZ65IYaEYRhtP0F5hEHbYY2SgCB6foBUY19MIOOYAcR18POJow67Zc463kGLBnRmGEXYRxhmEPplF2lQQRu28lKGnsIRg8HGGUIx4CWkEs590atMMIjtRO8NvQFItHrEaMpJMHhJRYJIM6pZWKh7Jt2vsSxhAI4MkociQ4gMCIYAeY0WpEORGZVHIkgEXK2GZbw2GEGfbM2Opiv4uiaUX/v1HHmlBn29k4dx1Z1g3Jy/AiMbjEo944foU7r9ZXtpqIgmF62f+YkpbAb9g9OHMXA1cPe3omjFEc1LHfPniZWuzLbOX0CI6Dy5ODUMcxwNyz3Th5Dwaoi3bnrLCVYZXL3zEmAos6T8anjQfJ/rxXhrW98uwR2uLmxfc9dbFJl0+nByaOj23cMk3qQ0Xkr5ovNs+fSxXzt1s2Ne+9iB1U+Pdi46+7hxh3sgx4VpFLpdHr73F1J0xz68PLufeeSgylZdJtnz/a3t5g23VIJle9t79y89z45n61fu3bzow+M9rfZtN48fabc2+Vdp5ZK4NHg9u3x2ePGk8OXL1//2Ed7kz2xP1dLvYhxuj3eP3YEgrh2+frW3adT15bXdsYnD1On5e60W+oFQrLNfbXSq7Peyvsf7tx7NjF1cX13cnSVwiD2ZjGjPhFke6GXi7boLb1zce+eszKo7Nr2/Pg6BkHuTEyZ6jzNb++5UT7v9VfevTQ+f4YCU1zZbI8sC9+5mQ2ZrEc9vKl8TlgW4oaNBYIJAQcKEhx7NMx9ZjqzxPQ+jRmiaQAbJhQIZdRPHQ3eD3mcOhqCWhF4z0cGUR7wjvUZgQmKcwddrA4Nkv05M0GvcrAXIA3dUpHcmUUOcYHB3ESLmkNlclCxzsIlFMfRcFKPesW0po2dHCqTtk3Gwax5Y6SYm3pd8Lmn2gpR65CgBhwcLpK25fuhOcKyRRtbtlhlbOFEZ1wJgUds6idHC9G0yRgsDpOibkFN1ADQLkANXQmBA2Ti/QrQSCRbrj5K87qJFTUDQE2ILRSy0USiCbIroGZZcWNilwlBME5syCmkAIwtKpHOBL3RqWPZWrM9m5duiAgCcWJAiqPAYOxwiXTKyY3OHpECmrDp4whDDMHEwJR4ieEklGQxGQ7wDWOPSAF02HRgiACncWIxBy4jaN+RNDZlmtzu1BLlxMFt5wc0coyqwFxolyg+QJEBmFm+E2yKYgJxHbF2eomiCnAT6mVMJhgiXw2TcqvxAoEkoCpCB2ZreTZrReO6FQSnGKFQDZNyp40kpnTR+AKpOD2UZ5MGKWiXQTp2hpBqKIvdGkLgCwgVpAunV2FsMFWoXUF80kHtY58GC/FUV8dHrKnZjuuOy7xu/Qw0hzJSW9a0oMdiAGRswipRgMkt3RxPs6p2MxiXMdYhNB70GQwh7ju0hhUW7GZrT8ql6f5MlWGIsAmxsqDHIgRw36FVrDCjNzp7UgqlwySAEQE2oMrWy0PEYnZTtWskiS3co2YIEQp0FoKANsFkChC3XcH5RtBrlIGO7CA/iNK3rUoxjSZBdAqQcHUvSe5YtUwYVHQPmhICBskiABhMSdgEIOGqQmZbXvVil6f923NTUsg8mUdDaTfg2Z6i2NQDkWx604dVXhS7+9D46vAoOVjw1qk1Hg8ABq5dKeTWAlKACgzmBhhYHe4n04ovDFxFcRINxu1ykWwvEPSgR0MbUe2qYwN5sEANdOskGXcGENBDZKw8RrCkUQc8c2GNWoXJwtl1Jsad8wgMMZ4aHyAcMNA5MA316eFgNoFzqkaAKg876EoEAmAT75aiIjLdsNUxnjU1mBMzhMQG2AAuO0M4mGI/CppKedt0R0miujhndgiI8aiOvkAeIzoGcGgbJpPbVh2jXCk0xbBnsXVGcZBDi6AcxzB0lcyLW6o5RIRTeIJsD3kM4MwXqJoPe/COs6tcYBM2XBxiKEiYWEqiKygcOyJi20/EhjIDypiD2873KWQwzmwgpBumyXZFWeyGkt4xvge7Xp7fnPiM4BTCqXGYqaUk2akp9rCEcSvoIWuLPL89iRLCnMaFCxGr5TTZWXhKm5W8t11DFBK6aH2Buzg5nGfzBldQr8H0wDhAq5FI9jsSgy8h1JAsvF0GXhFegXYN5VPtPLY9wGY+AuRLCFQks+DWgHWCT7xaR9lUOUtcH7A6eAvTpG5gRqbArkXrONt35hCUc+st9j1A6wB1tENiMGbXW3MqXZ7tzNpeGCLsQlxYUP4ftL3Xr2bZeea34s57f/HkVKkrdVVHtigOyaEkWLZnRjIljcYjWIAv5D/AMAwHjaWRZgCNrDBjg+aMSAWGJsXcQR3ZgR3JLnasrurK4eTvnC9/O++9si/aksd3NmDdrYsfngW8L14sYD3rwaIGQTiSaAHX1KKbFT/quoLroQJzBGgDEwEaVGGEJqrpJ/3GonO34Mc8h9d6oMAcNhDDWKAQC4LIUOKuybwg3MqrZds2AoyV6VADAIp53QyUBb1ehbum8Hx3q+TLlELjjmYQmGxpLjyYQAomS4tzt3azuVbanVu5dr3stpVvuaNYITxZW+3u7BGopuur7Rvb1Vwz7s6t3LzNIt92lMih0Wa8sdrd3pXUYguN7u3dZK6bdLrLt28Lz2HtyJ7ltCzjjWX/cKIRqZaa3bv7eSPiLT/a6QvXYZ2IzjJc8uHpE8s7W5rr/tGj3b09pDXrRoCD9v7e8NQxU+v5nZ3d+87N7++ASqZrS8F4AriqF1qGg/b21vjeexRHi7duHN5/bqm3VQkrW5nzpjEu6mqprRXubm6Ozp2UHC7duL730IPtwQHO6nx1wZ4lVsHK5baQaHFrqz46N7S669eu7Dz8ULu/T5Py8N77m3/0v4Z/Hxbh7PO/+sgPHl/98BJrNlfevrj27vs8CNbfee/Yaz/pnb/3/sefXn/r7e2HHnngiSdX33ufNaKFD68deetC1uquvfP+6Zd/1D996tyzL268+dbuA584/+zzGz99x4TewuVraz9+O+ssrH7w4dkXXh6cPnX62ReO/vjC3qmzZ1557ehbP62CxsqVK0de+XHWnV++fO38s8/3zt97+rmXjr75VryxvvbW+8feeLNsNBvj0YN//b262Qx6/fsfe2K8tLZ46865Z58pvUZz1L/38aeANEjrTzz6nToM/cHovh/8TdluRXf37nvuWWFwUBXnfvAkZAIQ/PBX/xpa2JsmZ594hodBtD889/TTBllWmd3/vcdxyZTrfOIvH1WWZaXF+ceeVBb1h8m5p55CHCCgH/zWd920UC594OvfQ0YPo43Da65KjQfK3euR6bNoXu++E1QJCQPWv2jDTHq83NlqiRi5pN6/HsKhcrti750gTyw/5IPLDk+hY1h/06+nyKX84EYgD5SzZAbvOMnMsTpk9oFhMXQBG2751RiZyJp8hMWe9Jfk7H17PPFI104uAzYGbZUc7vjJyAXzKNxZAeP1qsu87TaMj9iiZ/JlNDkqLAXiBbe3YFt7sL+upveUbe7td9D0KAETe2TL2VlFBMzmnL1VHU7pXhdM76kiFhwEYHYPAIkd22p8BqIMZh18cEKHU3uvCeIzwJnAQQfNTmI2dgtbDu+FIINFCx2cduke7rfl7F5ChnkCDz+yEVcQmf0PAgwVKeThVZ/YWs5g75ZvC9bNxtduL+OSUwvsvRdgBGjJelc8iqWJzf7twOLSkvX25YhmteWj3XcDCABmsnfVb4pEZGjnTgMzbQO2ezmiKcO+2Xs30AYioQ6u+pbipjK9WwGotI3F7uXQJNJe8ujtI4RHmM3M+CTMIwxSMDhrDR3ZmtK753UxD90J3jsBywaWmZncQ9KI2ZL2jttDV7Ynzt1TqlhiEXO3l3HWoGJqZvegtMs8ZfWOOKPIcrfA9jlZrrGIuztLsJi3xAiNVnS6zDxBe0esUVe1BtbtDV0eQfYM99dNvoxoHO+D2Q3La8v8Fhhs+XDBZrf1cNvxiNB9tn+3QW1QzKzZR9ieB+VtfbgdBhHPd2h/2/MQMxO5fzu0sCxj0r/q+g0ut83+3SAIWblPDja9SBXONL99Z96FjKek95EftiTbEr27YehV1QHu3fV9w3Suelc8D1a8pAeXPdDAUebAwRmL11aZqcEjOBfI5mDzfmgQEgzsnSGqJoUx4/tIragqzMGDTsIImYq9TwODoeSwd5YqYQqNxudABTCoQO9BmmgVlOTu/UbbUDN4cIYwCYQBw3tphSsbOFsnaYpUUFh3znDQ1Ehbe8doIbAQZngvqr2yxdK3dDxy7AWSfKDzCQlgNdz1iz4BEZ1cJ3xH+otyetEeD12y6KSXdTHAHR2Pdp3JwEdNa3Id8V3oL4rJe2g89FGHFtd1PLBDWKU7aNLzwlAMbjvpjuWtmfhdMxoEYchmd6zJoRPAKt/H413HD/jorjvbspsLdXEZHh4Gbgc0RpYcnYOggHWE9k8bP7b6gRmfgU4Mhi04O43qiV8hMbwf6gKxEO6ftdEhHgVyeo7QCZo0wOQsFQnNjerfj1UGhQ93z0I3xyPHjM5aYIDiwEzOY1ESAeD+vQ6bQm6pg4eAXaCpB8dnKRjr2MOT85CXWEK4fw4Zket6eNEOWG5Ks3OnAStgw3r3o4jGworU7juB4JgAcXjFR1yiWvVuBzoHDuG7H0VmLO0OGLzjFNwhFI4vYVgJLPXBLV9mSEfO6D2oh8LrqvG7TsI85NDJZYwyHrJse7NZJxS0ndE7UE+g2xHDt2lS+9CCs6uYpZBEln14zBk2bXcL7p6R+UbV5N7uAsyWqByR0bxKNrjLaX/NGiyo5sC6s2byo9CZ4MGqSdexnpFp10w3II3haAOONnSz79xe0sU90B6jwRKM1ykfWWlXjY9BK4bjdTQ86jm34O6Gyk4SMkDDJRgfscQEJS3Q38DWBE7WzeAYDgfZNuzd8XxVhbP45p1FR9eS4d5l3w+U7PH9W0HgVryP9+/4rhawEnsfBZ6qlcT7l3zLM2KgDm+HS2CYTp29O6GlFOJi73LgSK4U2v/QJ64SE3B4yw9RyRLSu+kTLrHR+5cCzAWAuP+BowJHTfTguh2YXJS0d90FEras+JN/8Q1vMq3D6PwP/iZIZkrjs88+H46ms/b8px59tLW1Ozx98lNf/qo7mqbt+Ye+//1ocKiIe/b5HzYOBpOl9Z/92tfm727Pjq3e/+j3g8NB0l544Iknw+EYAHDm+Ream7uDI8c/9ZWvLdy80zt/7yf+6hutvb261T71/MuNvZ4i9PQLL7Zvbc3WVx/65ncWr1zvPXDfz3ztW62t7Xhp5eRrr61deHe6vHbijR+feuW1nYcfvP97T669+36ysbbx2k/X335XeN7q5Y823riQz3U3Lrxzz4uvHpy/9/yzL66/9dN0fW39J+9t/OQCsK21a1dXXnu7mJ878va7p374o8F9508988MjP76QLS+uvHvp+Bs/Vo7bvXP75PMv54sLq+99eOr5F4dnTh/70ZvH3/wJaPvhla17Xnld2W5re+fs0z8cHzkm79xy/j/+RYj+X950OYxF0ylA2C6KYDYFELpF4cUzYICdJOFsChC0yioaj4wxFqsbaYIh9PLcn04NhHZVRbOZMcYqi8Z0rLShVRVNp1BJtyii2cxAaBd5NB4hAB0hG5MJgNCqqmg6QdrYdRlMJlAbWhSNycQgRBhrjIbQaCJEI02wVJizaDLFUiGlo+EIC4GVDsdjrBQSIphOCGNI6SiJsVJYyXA8QcZAYxrDIZESSRXGMeEcCB5Op5QLoFVrFiOlAITN0ZiyGhrQyFJLCiRVNJkQzoHRrVmMBYdaNyZTVBYQgHAW21WlAQzKinIJIPTS0jCtIfSKyi5qYwwuOC6YAtArKquuIQBeXsOSG4y9LLfzChptVcyqGILQTkta1ggCO6tRwQxCJC3sooZA05LZeWkgtIvKrRg1yspqUjJNCC2Yk9dQS5pXVl4JAK2itvLKQGRXLCwY1oIw5ce5hhhL42UlVsKquZUxjTCupZNxrATl3E9KYwAWyksKpJRTcy+vAES4ll7GkJZYSm9WIKWIUE7GoDaUcTctgDFIAT9nyGjKeZBWwAAkjZfWyADKpZdX0BgjgJdxCCCCwItzIjVWyk0LLKTWxs4qIjWSKkoLKJWEyJlmRhiotZuVlEupkZ3VRGpjTDMroNIAAC+tDNfQGC8vac2NNlZSAqGMNlFWEC4ggEFWGq4gAF6a0Zpho92sJFJBA9xZRhhHENhJBUsBILSZsgsOEHTK2i5raJRXcCq0RsjJCqdgQCtSK6dg0Bg7q2nFoVFuwWnBNcZOUfslg1paJXfySiFsV9wpa6yFnXNacEUIKZmTC6SFXTM3qwxAVlHbeY209ArmFpXGmBbcyxkwkjLupBVU2qq4k9cAIlQLK6mIlo4UfpwDADCXbpJDbSgXVlIArSETTlIiCDDn7jRDWhOtnbTEWlPOnaQABigmvbTCBiAhorQwWhtl7KQEBmAh/byAWisB3KSExkBtWlkBDMBaO2mlJCBS+VlJpIRKRlkBpVaYBkmJJQTGBEVNuTQGeTkjUmsAo6wkjAMNvIwbZiCAQVZYTCBj3LwmXCKj3bSkjCGjg6zG0gAA3aykFYda2zknTAFl3KTAQkCj/FyQkhuE3Jy5NYdSuWlJuVQQ+2lFS24gJGlh5zUEGlfcyQoNoV0yt6ypUXZe46zWhNCSOWkFtfx4ygSAtGR2XkMlnYI5aakJQXltJRU10qmZm5UGQlrWVl4Bo+2S0aQ0EKCPdSAgFXPjAgJAGXdKhoCxS+amhUFIV9LLamQMFtrNaqQNYdzLCqg1UsDPOTKKCO7HJQIGKOAlNdaACOnlFTLGCOBmAhqApGqkpTEAABykFdSISBUUNVYaahQklTEQaNNICyQFVtrJhZGQSBVkFRGSGOMlFTQAa+UlBZYSaeXnDGlgNKBpBbnU2oRZQRiHAHppBWphIPTSzKoYAsYpKsolhMCJc1IzBKGTVqDkBkKaFFbFIDB2XtKKaQC9pLAZJ1raSYUrrjGxipoWDChpZyVhXADkJiWpONTSzWqnqBTGOC1pXlMjvaK0aoGMtAtOc6YwISV3Mo61sGrupZUBiFbcySqklVsyNy81xrTkXsaBUYQJNy2h1lYlrJJDrayKe2luIMTSBDmDQNGaeXltACRMugVDRtu19PLSQASYcTMOACSch3mlIaRcu4UwADuch2UFAERSRGkBlNIa2rNcK4Cl8rMSKa0UdNMKagCMbmUFNAZp46SVEgYr7WclEQIq4MxypQFQOkoLLDg2wM8qLTXS2ktywiVRyk1yDAzU2p1mSEpkjJ1UhimglJWUhAuolRtnUGpjgB/ntpQGwmA2dfMcGOClqZXnUOtgMqE1AxA2ZjMnTTTCfpb5ZQG18pPEKUsNURAndlkCYMLJ2ClyjYmbJF4SI2CCLHPLAgDgT2d2nkMAoyRxi0Jj7MWzIEsNgG6WuWkKjfFnsVVVBoAgjr000Qi5RRFmmYHQKXJ/FkMD3CL3pxMAgJ2ljenEIGTVVTQZQQDsmoVJAgF08zyMZwYhmqXN6RRASFndimfaGFqW0XQCjbHLKkpTA6BVFM3JBEJIpWwliflYZzIBUjmMRXEMtbbrujnoawMI563ZFEppCxHGMZLSAPj3lSJsy6q7s92/76w1ybw0mR5Z6/T2OSBsvkknqVtWvZOn/Nlsfnuzf+9pkpTRZNw7c7azv4e4qBfbJCn92XTvzFkvTpY274zO3uNOY5KWB6dOt/qHVlmVSx2U19FovHfvOTdNF+/c2nnwwe7hPknLw5OnovHISdNqsQMq2RoOpsfXuSIrt29tnz/fSCbuaFbMdzSEjcPh8MhRRcjRSxe37nsg5Fnr5tbhvWddljvDuG43hGU19/vVYisJWhtXruzce94Xeevm1uDkSVvXzjCBIWGO6xxO2VwzaXQ2Ln+0c+85T5Wd29uDY8eoEV5/yptBFYat7X02F81aC0cuX9o7fcYxrHVnd7a2EppCzqQMnKzbcfYTHrnEB2Q3q7oNy1FwzAwCuu3IWLfqWbnYNCMpfRsHAO/lVTOwAwNHDCCgO46eKZuX1Uqb9jLp2ziEtJdXgUdDqCcMAZivdN3hzC6rcq1Ne5mxcbXQ8nfG0iGkSXQsEFfZWtcdxk5R6SUf9QvuOOnyXGM0cwoxWWs5eR0Oi2LVkdwKpvlsteEmnNTC9YtKuKQE07WGXdRRv5gcazVmCShQshI6KbOLWrSprqGXifF6i9asvZ+MjrUbSQpyVC9YVs5RBWQLSUW8CRMLsIJu5zAfrjeiPIMpquapXQpYAdfLU9ywJ5Iv4sxtNK/t1EfmLFWDmQABEY6FhpNBha0AACAASURBVBVtoMIPoq1xvDG/WB0UQ1Kstl1Tm4mAIRGujYclDlEZhcHdYb4+58Ia7pX1UsOCHE65cZAIXDysmlY27i76W7Nste1CBntlPRdaVMExAy6WoYMOCxqCrNUO7w6KlbaDOdovq7aPA4JS49RVtuh7IyFsZHztDUTpOyhQZCIBRKIFQYHdokqXA3fIlIXSbtg5SDlBsAHQTGIFpsvNYJq7RZ2uBPZIAqSzubBxmCkCQyfNWESZmq42gklOK10sOVE/49TO5oLGYQqgUW1sUujWoprHuiJuytIV352kmgPQtlVt6KzKjy5YVeVuj9MzK+FsqhJVrndpzmiSg44tJLInBVh2S+CEO+P0xGKQx2os5GqDlpXJFWhbXFN7mMFlp0RutDNKj87NxwdJ7oulwK4qkCvQshiynMPELLsldqPNYXpiwa9yPZFiwbc4hwlLF7qAgoXt2Xil6YLSO5DJgm+Bmky1diEPbHsioSvTKOjsprOFwEa13ddV22rqWVmGxgY8JNZYIUemjbCzl8Xzno2Y3ddF2yZU4pkGFmARtccSWTJuR93tJFnw68Cd25mVAUW+IRMpbZq3vGY/Q1TG3WZrJynadtZqNvYPsQLZ2pw7Tuw0r9bbaFghoMvljrc/UQTjFjGJxBVPN+adceomuVoJ8KgU2CqWWv7BDEADO5ZMFC14dmzBHcc4qdmRtn8wYdhGbYontdQAdSxRACcp1ZrPS+TMinq95fWnHFiga+FZDYQ2cw6vkT3OkpMrnekIxrBctO2KowLIBpSAuhMm52CJ3W4vG240wzLDU1gu2lbNUQEct8hJQKdazcGSuN3dZHikHdY5mZpi3rEYw5mWDSSIEwxKPodyO5jfno7XI59XcAp1C7iiZJWnQ8Mtx+9Xcg5mdjC/E4/XQp+XeAqrjqUIwqOyidLZ3IKzmxSLTYdwuFdWc6FtKzhmwEaq4cBBRW2dd9v+1rhcaDiWQPtl1fQtz5gxM7ZVzUXe/oQQlS90/DtDPh/VzTDc7IvQISEyU6ERqhYa3v6EEm1aDtrN6rmobobhzkC5FmxSPeEQwGq55e+NlEWz5W6rNzPQhG6WsZDWZroW+bPCSWW66jeGqYA0XQiiYQ6VUm2ic+iVoppHilvBtIzXwsYoFcaSbWQlEggjO0TV2J/V9RIWwg5n9WzZa04zqSzRgjRTUADPy2aw7ce8XkRcWNG4nK2GjVmuJOVtZKccMsM7pEZ2Y2uUHJtfTA+S2GErDYdVIJWgSRmx7cMULtiF5Ud3B+nxBZ8XZsD5UmgJBhIBIsws1z5Io2bdj1Yam4P0aNcTlRkwPh9Sw2EsQESE7dC9GC05mRO1bveSYwueqkCfsa5PkIRTztqRdoi3PUJzVhq1mtf2i2PzFAr/cGwsnM/PBfsDjNRw/cjK1euT1bWs3T5y7WrSaOh24B5ODEaj9Y35zS2M1PDI8aWr15KFxXRubvXalSpqOIFhscZaDY4dX9i8KyktluYWr92YLS4n8wsrN28w1+XzDTqIXcEmG6vBwcgAmK/ML16/MVtY0ZEd9oYSk2qxZY1zuyoPTp5c3rqtJRgcP9456KGascU2yFhrPJrcc0RXem5vt3f2TPdg11QqW18KxlPIRLXUNpVu7+9PTx2VDCzs7hyeOr7c2yqlna0ueJMZYbJcbCkO5re3x2eOC4EW797ZP3e2M+zDnGWrC06a07SoltpC4cW7d+vjc2PSXd28vXfmdGsyJHGxf+6B6N/+278Hi9Do2S//6me/8Y31d94t5+fW37hw7Cdv1Y3GsdfePPXy67sPP/Qz3/zOsVdeu/upz3zym9888tO3eae99P7lk6+8mrbnjr3103NPP7v3wH0Pff+JEy+9svXJf/CJJ544+tobshUtf3DpxI/eyBaX1y+888DfPLX3wP33f++J06++sX32/PkXXjz+ymtVd27j0ocnn30xnV9cfe/iJ77/g51HPnH+6edPPffC6J7jx16/cOqFF8tOJ5qMP/nlr5addnO/98ij3xqvbqxeuXL+8SfrsNmejO775ncREwCAz/yHv6gajXAwfPjRbxXzc93bm+cfe1IjEpT5A1//NhYSEPLp/+NL2qH+NH7wW4+xKGhu7d/32BPIQJtXD3/lr52y5L7/2S/8mbRtN0kf/sa3ledGh8Pz33scM4kI/ORfft2fzXjk/+wX/woZddg63r/syBRGJt252lKHLFrS26851Yz4bTV8h6jU+LLYudUUU+hZ1e6lEB1Kd1n13nDKKXUbov++VafE1fXhLZ+NoGdVu5dDdQjsDTB83UomttOFs59qNsM+rA/uhNUAoQBML1nljg7W1eRNNB15ZI4U7+t8jJs6Prwb5Ic2WCHNO6uwv1EtltGtppmdsNUhTJfg8Li2GJ4tOLsr1NuHvaNofLJqZf7enBmfsNDYGnpmdNbQCiQL7va6aE293XkzOs2jONhr6PFpjHMn8dThGQQTnHbJzj2iOXN2OmB0BnkjctAyo7NUTt3clgfnMM5Q1iLbp6h3SA66ZnzeIr00AQcXPVwLTEzvLQ8QQwp5eNElrjJjtXsttHg1V80+urLiFCVw8P6bNsSA1Lz3gediZmZq93pEufB4tvVhmyalFcCdNxwAEZG696HXkKlM9c6NJiylQ9jWuxFNOY3MwZuOMggJeXDJJ1rCXOxfDUEhbVxvvdfEubZWXOfaBqqblpjp/imcRgTHpnfeHnqiM7Wvn4fZnPbG5O4JVHcoyPXgJJ01lFWS3VPeMODdkXvjjEpXRKsMby6BYs6SUzM+gWdz3K3dvRPWoEmjbXjnPpivszD3ttdAtmirMRqs48mCjBjdOWYN5/jcyL9xTKcbxOrj/aMmXUVOMt0B8UfEm1fJVTC56+F5WN9U/c3Atxk8qHdvNm1b5RNr+gGyV1B10xzc8MNmXWzCgzuBhyo9kXvXGg7h9QQPP3DcrhZ35f6NIGrX1TbYvxWEoHCmxa1r8wGuyoQevmsFXVXfFYe3opaXlj28fzP0TY1Stncp8mDFc3Twjm26NMpc2Dtr1ZXNMrn/KTetlCfozfsBwITXeOssNoxmyvTvR7WxTQF2H3EyienU3PkU0ARogXbOUqVoIXT/PloCgnK9/QkrASIqnev3G21DVcOde6kGkANzcM4qkbC4fee8k2IeZu7189w0EOL25jFcA8xrcHgfzp18QWQ/ErO+7S6j+IIoxlaIyv5WUBwS5Jv4qlXc1cG6mv4Ejg89smxX78t0QFsgHm57Sc/BHWt8GbEt4K2p0U9gMghoWxdXwOTQjXCVbOHJlh02eP+ak92lzgmUXADDfb8RlbMbdLrnBCBL9q3xpuNHfHLDmt60wg1ZvKMP9kN/XjeHrji8D5kMlRHdPi2j1DqIYP8s8sbksGmG54iMvRKJ3oMY5qgKyOYZ4gzpsGGG91PSh+M2GJ6zxMwqjd5/0FIJED7ZPGuCwu1bqn+fjfpk4unB/VhUlGu0+4DLp5BTsP2gcisydc3hOQeN8cwx/fsxr4nWYOsBCFWhi+HblicqWIid601TSJewrfciPOV2xxy84QiOCZQHFz0gFK5473poUuWSeuv9Boo1WYCHr5FSOJTq4XsYV4pItX89lCmAPpi8bbGh8hb18HWc1S6x0fQDZHLts3TnVktNkZl3hm9CM4HWguq/QarKJZaeXiZVhu0Qer2T1mGHRttw8xxMj9SNzN9ZNsmKbcZ4sIgmazJgZP+Ic7DI5kb+7SM6Pk6cQ9xb18lRAhM6WTCDdWjP8OFRcrCuOn3n1pqO7yHuAO8vgelRW41JPKcHx6AzI4fr+OAojTbJ1lEdn6JkHw5XwfiYo8Yw7uCDI8iewuE6PjgG2uPytti/Efi6COLsxtVF31Ssxr23bbdh5D4/uB41nJz14d6N0NWMFtXOhw1XVkqivQu2FUE11HtXw0U0zidk93qDCIG13HkvdFStNTr8qYM9I0e6dy10SaWnevdaRLjASuy8F9laaQMPL1gmss1YHF7xGibjJdm7EiChG076c//ui/54UobRw4/+dVDkxuBzjz3ZOjxM5hY/+x/+bGFze//8vT//v33RHYyK7sInv/5oOBxobJ174qn29vZ47cjPffkvFm7eHp858TP//ivNw0HSnf/Et78TDcYYmLNPPrNwe6t/+t5/+IUvrFy7uf3QQ5/74pc6dzarTvvcE880d/aAY5996rmFy1fH95z41Jf+avXihzuPPPLpL/55+87m7MiRcy++ePSVNyfrR0+98NJ9z/3wzmc+/TPf/O6x114fnzh+4qXXj7/+Joui9Q8u3vPiK+ny8olXXj/31LPbP/PIw997/Pgrr45P3nP81Z+c+NEr2vfXP7p0/LlXstWVY29eOPfE3/Qe+cT5x548+aPX4o3VtbfeO/XiS9IPFm7cPPfkM+nq6pGf/PS+Hzzee+jBs8+9ePKFl9Riq/nh7bPPPsc9v7O9/cB3HhudOMm2Nv8eLEIDAIR2WbUODoVlW2UVDUeSWG6c+uOxQthK8+ZozBGhNW/s9Zjr0bIKx1Nhu06cNiZTRWw7zRrDkaAOZLLdO5AQW2UdDkYcUaso/dFYIurUvNk7EJaDlWnv9ySxEOON/kBBbBdlMJ4aAGleNHsHglhYyPbevrAcpEFjPEZCIaGa/YExUGvY2dnTAEoAWzu7UBmsdGM4RFJCDRr9oZEKaNDZ2TUACoS7vQOslFGmMRgSLqBQYe/QCKko7eztK20Upu3eAWbCABRNppQxoEFrMIJCckg6e/sAIglgtLePmQAGNoZju6g0xiYHkGmNsYy1VQuGMC6kqYyGwGRSlwBAoHIISmkwNonBBZeY0pzrVAIEYSFNYRABskCwUppikxiYVRJTWAiQa4EwrI0oIcJAFQDVRtmWTpSb14I6IBMqM4ASVSmZQwCNKgFgQCGMmbErrDHGXOHcxVAqjmlJFTRAUDvVChFSK5IiQS3IJS08qTVVAOeWAQYrQgugsAU5sFIiCYVc4TLQRgKuSW5DAJDApIAKUiKklRGFMOICFz4wQkpkFxRoYzi0cqABQlzjlApEIAR6ZpAQ2kAYMyiBNhDGUmuMtBIxxAYIDUysNVcGIlBoLQDQACZSKgSA0QmESmuEZWyQNBwglCktoVEKxFIKhCDkCaJCakzATBEmJMQw5qY2EBiTKCAMIlAkiAilCQVTCcpaI0wrgGuqocK1RWqgoKEFtSopCKW1IRUGCGKGYUk1YIhbsIaSIKskuNSSWKQ0Vo01RogjWlIAJWQ24UhZBBSIZFoSyyokLqigGNcG1Y6BitQEVUghRBmhpZHUJqWkOZUEIwZQ7WqgDQNmxgWkNhdwIiSxqNIiRhhqBbCKtQFQcwBTqQyCNQexVpgQqUSMAIJAGpNoqLVmBmVCQwSFhrHSiCAuxAwho4WAJtVaG6gAzqXQiEqtp0JBTIzSCQLGKI11LKHSSkKTKa0RhIqUnlZAY2RnBAljALIKjAQGBtgFhppqYFDhIW0kADiluFYaEbsAmEFotF0SKKExEuUu4kIiaGeUcKkwpSXAnAAAcGEBBgHkpPIA04JabgZRrRWmpDRIEI0QKm0qkAYalx7WRkFsCgVzzRHGTIscIgJ0CWClDaUq1W5WS+qATJjMAEJ0rUQGAdC6BLqCGiNdAJBwTh23ZDpWklqQaZVBhI2qISi0hgiUxuRKI4KK2sRaUYJqxXMECDK1gYUCEOhMkZxLTEAlQKw1xkZqkjvQGKQwKaACBAtlZUQjggTHhQ+NVBo5BYVKQQHtHBiDoTA4JQIRZBQufK21AdjOMJQaaWgXCGgElXELqgzSSuPCB0Apg0iGgQJQYzuDSAOklFNY2migFS58LLgExM4wkVIrAxKpmIHAiBQSJg0hINa0FhISFDNdagCBySRgBhEoUoS5VpSYmQZ5rTDFuTKlUQibAkgBMdYyhYhpYTkq1m7JBLZNKmSNAEEqN6oyEGqZQigAJNgkxmRcEtvNKpUbg5HOtamBthAo/+8JIhkWlCCmcekoIwnHqMQaAsIxLbWkFikVzYgkGDKNKlcbAWuIS2ogJAzjEnJiW7WwcqowwkzhytFAAE7sEhkIYQWtAiiEca1xbgmModC0dBTQWGIrh8AYyqFVIAMhkoLPEAJAKqATrZTRGqFMSQ2J0mYqFEDEaB0jaIwCWMcKaiMVRJnSGhkuTaylhggAPkNUCQ0JmkkktFYQzThQEGqlY421BhjyGSJKKUTAVBouFCI4lZJDAoGKjQYIAyVmCCutEQmmMy9ONCbBaEKTTFhOsz8gRc0cr3XYt7NcYepPZ36aC8vxprGXF4JYjcM+5ZI7ftA7cKczRWg4nXnTmbBdP8nsJJW22xgMrbJkmDTGUydJJbWCWRKMJ4I63mTmzhJGnaA/sLJSEasxHDtJJjHxsjwcjbnt0qIMR2OJsJdm3mSmIbbSrNk7FNS2atY8OOSWbRVlNJpohJ0kDaczhQgp6mbvUBKKatbqHSpMaF6G46mE2C7KxngiALJr2drdU5hiDVr9oSIWqVnUH2oNrKIMB0ONMCrKTu+AI4KVbu31NICkZtFojLT5+7EIpdz86jdUq01ZbUOREx8pbUEBlK6JG6q8RK4BUFEKtbZZZSEpauiUedqdMxACAAJVFNiDSkvbdrM0iGe65XFsldhTFsVSGgB8XZpKW1k2W1oGCBEhDEE+y3IcKIsipYAxgSxK4hllQlPEdtNiTFnE50VGAlfXGkJhPg4uI6uquOM061kBPf13jKo1QkyRSOe5E2EuuG3/LUN9lmfIb/GZoiRDYaiy3P6PGOVo1/LF3+pgJDQJVP7/YKAHKZrL+z1/1VOVxlgYojCORFobi9tuk80SEmGjXV2lNOoUI01wShoao0hkpXGEZbf49GPG01VCI4cVhmBpqMbY4ylHtiK0IZKERBACjTGUxq5LY2GBqEHQIISF0Ag3RBLTBtZKUQqVdnjlgroGdmV5BiOoDdRaEwwNsFhtQ5bSiCglKUVSaoiW8/2xN68MVhYxBhImgQ2DOs1JqCiGWgMDQ5kWONQKGBsBAyjn2iJBnWYktKAAClTYi2RcWr5RIFJpbLco59rCQZ19zCCtS+wt5/vjYF5LGOg8dluIC0BQwPOUhK6uDYI1sCOZFVYAuZSOvTHb3PfWjIWjYiYKjW3EQ59pGsqssEMolLSsdj6WueKRbxGdksBRNUSwAvZS3ovdNp1kVbPpoypFIaAoYGlKIkczCE2F3FCkleUDoaVlNdksRQHE0JdFajVcXgIMubE0QoHKamMLy26yWUoaCGhPFSlteCw3FNfGARAaAv+2HXFCmxAYjTFQwGEltAADFoBQY4yFUBhvJFvb0XGklLEw0goqTZEkXJSWrzFCShsAGyJOaAMr6Zvq4wVBEgtZEi9UeYVdo4GiFABAhNAUh3Wa4UBbBGoNNPB1oSuNKkZDNPO6lDFl06BOUxrZUDhZJhmgPsz8JuAqNEVstwhjgOLV0eYIdUBADSEldkOZVdQzwoQ6j53WxzphnWQ4pFhhKSrsAowMRJQxYVlNNiugrywSsjSlDVvVEBoGnUBkpf13pY4zEBoLLae7++GGoyoETYncUGSV5WsOlE1bbJKh0FAcsiSlDVsxCE0JvEimle0CYZRFDISYC4NRKLKENhA0GmEkZSDy0vaAAB8zSCoAgCYYau2wChJQIRcCoDHCQmqEGiJJaAMppSlx4sSpK9iwamRXxDMEIyE0Qg2ZshrTsswWF6AxUGsKJeUst0JflzguJCRWABPaQEoGpkxoA0uJsaaC51YYyoxBS0LakEls/0dlJBFBikpREi+QWUU9rWCk0sRuEcaVTcI6zXGIsSJS5iRcKvYTryk0DVUWuy1ac2nTqE5S1CBEWpKVxPdlzqhrJAhUFjttzKS2caOepSRyTeXxYuQuBDJj1FEaRzJJ7CZhQto0YklKIgol0aKE3mJxkHgtromyrCabZSjQlHzMWJpjqArkhSLj2LLHCYtCD7MMBR+3IyURQAAa48SpY1jWamuNNMF/17KIp7wwyKhsYQFz4YmSAlECh9uuQRBJZSCIRJbQCGmtKUFKAQA0xlhIhcl6srUfbUCllEWhNlhKhI3N64IGmiCktAawKeKYtpDUoUkTu0mE+JjJrdDXBTM2R1ZTzlK7iYXwdfF3DOU8p2GgcwEJR/Z6urXXPEIY90D1MQOJcVid09AzhQSUI6sh4sRuEsaVTVfjnT1/nWBlSVaQIJA5o46WIFR54jQJ49K2/CIpSYAIoKKuLD+QOSOOUmgp2ztsb6BKSNcKyriwQoK0Lesc+74qOHGEwg2Vpk7jY52IxSmOKJSW4gX2AYIGQczFx3thISUlTRanJKJaECAr5Po8Z7ZrpJGUfvzEWWMcySyhEQRAY4wY90XJXM9IoyjVCBHGDMYr2d6uv4a1Uo6NhTAQuqY2AtS2pzHCjBsII10kJLQko0iXyAEAuIYZYUrHb/CkwL4BIFR5bDWxlIgYpy5L6iv6f537ocwzEiClAlMmVpMIDgh0eJVboWtqK06NNCiyYrdFa+bBOraaVHAK5dy4NyVNGFiMOgLSUGaZHZGaeaCuFfXiuOq2iREF8R3AtEEc0VDmmRURxpfqg53mMcpqYGGHlQX1ZbO58vu/G77x2v//KcLkv/iV//wvvvzZ7357fPLEA08987Pf/Xaytvzp73//k9/73u4nHvzPvvSlz3ztqzc/93Of/frXfuFrXzk4d3baXagakcXFL375zz7zzW/sPvzgL3z1q//wL/5881OfqsKoajY7o4OHnnnmM19/dHL06EN/88R/8rWvzI5tDJfXilbbzbN/8O1v/eIXvzA5ffLs66/+3J//5fjo0Qd++Ow/+tJ/2Hv4gZ/97vf+0z//cu+B86dffOUf/+//bnzynsU7N3/t9/4V67Tm725+/g//cHTkyNGPLv3av/79bGmx09//9d/714Bgp0j/6e/8rnbdbsT++c6rOcKdD27809/73bLZaGbTX//d30esslb839x/mWruXrjyy3/8J6IVNg/7v/57/1LYjudUv5VdtKYH4iD7jd/+bQy1m2W/8gd/oBwajCb/7Pf/pbEIweY3/of/KZiOTWh//nf+lV8ko4Uj9Q9zZ5iGbpU+zeztsb+k5WMx2i7CLqufS529xMdZ8qZ2epkPBx7rB9cuwXZXPh7r20W0wOULKdquAnXgyaHX20QCyxdr+/rAOm7BJ0fqWuWv1K3bFz2ekEFev23Izam1SPnTM//qwDpOrScOyuvSXYXo5RG+VTScuLwg4K1Sr1vH38PdS41iKVm5huc+mid0MzqI5i+3tBc3dqK19xHqjLtXvNaVuWql/MzBq78+vbKfi2C32bm2bJxZtOVtvE+qVvXzh698fnbxJm4u3CCdj9Yo3W0egLnLyxCOPlW9+0u9Hx8iq3EtXLy6qMJhcw93PtzA+KA9kN0PVwkahn1r6f02DQ6C23juyhqMBjJm4vE4YJkFRfVY4kLmJJl8NnMsZo3y4ofM12k3G4x+SLx06i6CyfwqRcr5qF89X3iUW3GaPccbMvYCNl5axVo1Ng/rpwq7rm1WVM8V3XKsWlaysEh57Q/j4smqMRrhBuKPzUhe+6Zkz2dBmVLB8mfrIJ25rqwez93DsbUeLP/Ybw0IBoPW1dVGX2M6Wby42tks66XiyJutYMcDjcHiB+3GvuPQO/9k+sHZyfUe7Sy9szJ/uyxXy6NvesFmpOcPfnX/1Ufim8MMNm4ebW9B0SwX320v3xKqM/yV3Vc+md25QVeX33Vauw0L7XWud6Jdn4fp8jvN5VuqWM3X37TaO13ZGC185Da3WsTv15cK69WxuwTQO2P2Wmnf4zhvHxSvVt1WSbbS8tXaCSW8W1o/7AdRWc01ylY7ysbgUlb8qG6Ghbufli/VEUzUkp905gOeOW8csDdF0C3x7bx8mTXcxInUYH4jYBl9fyxeyvwWh9fi6uWqMc/gVpq/wFtWTGJWPZP7MCWjWj0zwy3QTdjSu0eccmCrbP7CPV46w3a18vqiUzMqi6W3F21Z2GmyePG4V4xDuPPfDN5sDW9PzeLcW+teIYjOl95Z9Mqiza/+VnzRmm7nqrl84Wg0mMkG33itS1I4hy//s4Mfd6pJmntz7x1vTidVCE683m0NMt5mR18NaWJhK199pxvMCqLixQ+OBOOsWDT+93fER4WzgcDLE3Qtb4ZldYGhj6b2AuEvJt6Hfes4tZ46KD/kwVHAgkB5dms44G/V+lplzwP+UtL8sO8viKTdZVHoTWf4taG4VHfDXL2Xi0tVFOT54lwdhO3ZAL8wkhdZsCTk25l4v24HublYqPfysMP4jwv9duof1eTFPn9f2at6cUfOf7hK4dAd0ZV3W7qRdu/KxUtLOhiF+3Du4hGMDtuTuntxA4vROfHhr/Ver5Uwt9sLl9dhOIr25MKHxyy5fby6+l/F76t8Wo3WVn7aQV4S9PTiB8vAGR1PLv9m/0JeZTqbX7qw6Nc9t0Rzb68QK/b6eun9JQf36AwuvHvMUQOH8ZW3lhwZlyKXTyeNbIp5lb2ogixxaVU8WUcHAzKH+Q9maFKFVs2fz9w4ow2dzc1BBBrDUfl07e4MrGVifjCC+6W9hsqoYRnuD7Ps2cIdzMi8I74zCnZG9hEQtxaVa3s7E/VS4g7SEMTZjxQd5GSO8G8Pm9tjsobh40N+oN1Q6OfG+LC25+HiB93la8IsjuffjqI7HbaUrbyPOpttSvfatxrtrYYIkvmL0dp1ky8Un9984Reza3eU37neat3uWmSns+l3bjaUNfzl+M3PHb57hzaXP/C6txZ0dNi+47WuLVB493Pp+58bX55A6F9ZX7zq485edNGfu7UCGwfNO1b32iKFu9F+0PmoAb1h52a4cDlScxN4PS5eYBGJo9F08hIMQUJmTD498wKONuPqhSrqMLSbZc/zFp55V0gL0AAAIABJREFU2b5f9pyDAz1y9bMTz+V4P61erLpwDPqbnl0Ed7dE7stnkojPkNDsydi2VCO+GZZ9Oh2BCc6fKiMZW0ayx1OvSggw+okxooZOC/5c1iSpVYrk8bzJk8Av//n//C8Wb14rF+b+yR//6fzuJguCX/7Df7OwtzdaX/+v/8f/fu3ald4D9/3Gb/+LlYsfJg+c/I2DN9ay/WJS/9If/eninVvDkyd/87/7b49duTx+6PR/uffaMTEexuCX/uSPV2/d5M3wH//pnyzdutU/c+q3dp4/Pbl+G8/92h/8wal3Lsw2Vn/xi//+yMWLrNP4R1/4wpGLF0enjv/K//I75y78ZPNnH/n8v/mje998fe/es5/97rcf+f4P+idPfu5rX/m5b31z89Of/IUv//nPf+PRvQfvu//Jp37+K38ZHz3ywMsvfvrr3yjn2oNjJ/JmJyjTn//Sl/9P2t6zV9f8vO7797s/z/20/ex69tl7nz5nZs4hxWGVqULJJYhjBIkR2C8S5BME+RoxEKTIsWFAxbHkSFZzyGHRUOSQHHoKOZVTTt29PP3u5d/zQnmRl1QAre+wgIVrXQu/3/z937146d79733/t/7Vv0yvbZXjtcVgw5PNb/zrf/3rv/u7p194+IV/94e//X/8zsWvPMyiftnvB2X2yrf+46/9y3+12t1++J3v/Nb//jvnv/Lwc3/+F//gd/634mBz7d1P/vH//C/y9fH+h+//g//lf13s78vD5383K0Jro+Xctk13cuUtl7ipO/MZrkpvNiWce7MZrWtf8O7FuZEinM0AggBAN0udZOWuFk5dRmlK2iZIEkMwABYZHaYJLovObErrxk9TLIVFEFqDjO4mK2Nt9/zcWy1JkceLucOFM59j3vpXV7Aq/TSJZhNjdPfy0hGtUxZOsvLSFU1TL03iZKWt7c6m0WKueNuZTmjTOGnqLOaBasOw50EdXV0oYOPLi7BtTFNHq2VEaN+LPWA7ZU7q2s2z+PxcaTVIk4goKL0BQ07Tsix1qjJaLXGRe1nSXy6UMfHlRbhaWs6jLGVV5SWJUxaolNwQxQlJ6xo6urSu1pV0akFgo5qWCENpIYXGbYtJlovr22A8cKoqbyjXFDaqEbRVFFcN70Vg1KOtKIxbth7jbV67DXAdC9TGoO13TNbUminrgZRXxikaxtqmUUFtXVCrpiHCECeXQuOmhpgLofqa9qlEtvJa4rklQMrhqEMaajXTaMQaDk0kSY9UaqQbbKOOKWGLuQ1wTRD2OBzDSg4YCrw40A3RISceanwj/RZETOKQObHfCY0yqtugHuPGVK7Ejpdpap0GdBAnQDMBepgrR7ot7rJKwVLn0FU1tFyX0lWVJq0ujGu40bmpAYWFYq3JgWsqAIFFRmOrYaMbzUwlUQEaRE2FNCXIGoAxUbaQjhQI1LrSzAqAjEVGW4LdrMitqwTFTVs0TAlgS10qVyoMEt5AKhtEyia3fs0doozRkYa+XwGjsJEuawyHIVBdp6m56mnrU66NCgQI/KL0DYkI9euag0DqPmsrrtcM7XgKdAFwYdAxSgNPgi6tgYaRhQNSV7FLY78flJm2PQ0drwJaE8M92iJLI6GHrK2V7nEQuRUHNpDAQzUSCpU6wFy0NSqsB9JaSdZil2dWl7LUDOXSGrySIWgNwBBag41VhW6QA1ZcCFwax3CArEbGAGiNdjLtwlrLwtaQwlwaSgC0FiHZgkI62ABY6Ro4KGtsCRpEdQ5QyUvj4EZbBXPlGWFJTVvkesp1C8FJRBtKFJAgBsZlLZA2gi3wG8yRBxq/V6eu3wuwIq1UNrbaIS0QNkTWHeiW2mjkYMpti7pIhLTlwvSAcSKoQjfqKOFWliMf265b8gbGSnQo5wKMLQpJjaX2gcB+gzn2rY6JVmnr1cBjXLclqA3RiWg05dYDGW8UKRvG2rpVfg1cVHOLEQQWaCsErFsMStVaVkkfGGMRREZDbYVyG8BkKrmATYsgN8gaCKxmJKnd3PqkaEQNuMY2FY2mtWSgVq0kFWesabRyUsNQpYDyGtQhLUEaC9vDrQYiaFCPCgtrR2DXzTSztAERFqzLnL7X8axy1P/rIFy7LfZCyQKiiHJiDKBhEnSBIrTBLe6wUgXVPPA2Yt3g2nDco5IhbiWIocSkJS3qYO3QHAjiopJiAVvYAzoA0tSSWoVorhpAeAlJLXPrGsloVectkwrZWpfKkRwCCxCwAEKWlbl2q9YlXGStpwxxlLQIAgRVY1vIhHRBWpfWrRpKlYQQYKstN5WgWiFayMYSngPQqBa6tfBo0+Q15QrDQnLgCQ5wLjQMDBw6dWV0V+GYVtqYWCDfK6GVWMiAtBjQsDVjUtVDj3puHKgG6EAgHze+MoEwkatBwIKeH/qSKz1oUZe1RjeugsyrJQPAM8AX3Bhf2Njh0lFhgztOIUDjSeQGNYICKhNSDo31JYixgrKwNWIol6TVhXVsZZSwhXSRBrDSJXBA1sIKNpiqAloj65t7MHKBNIVwtQC2NKV1YGtdApq9bQMMqUVuPM2xrWUhHFMrHPrVrX3Ka7hsGuyoCsJaZiYQLbGNyoUPAQaFqiwzuZSFaYnLOXOqyp3PWV0HecrylBV5/+ICCBEWWbRawqoMplPWVO50yngbiTpAzEesX6dAiWAxD5dL0jad1ZK0ddd1uk7USaZOWbK67l9eAME752dekwZut0uJk6z86QyXZWe5YHVNi7w7mdi28RdzpyxZVbqzqdM0wWwCmzparViasDTppismuDubIimCy0tYVUGyCudzIHh8cU7LghWZ09TQGmiNwbBb5Laq/DSNZhMjZZilmlGLMdbazVInXTlaxdOJlapzdQmtgcBSzr3VkuZZVJVOWThp4lRluJgbzsOiiKYTI2U8mdCqZGnqNI39WzeEv9yK8Ozf/F7PtMPL8+mtA5qWQZ6v9q71T085cVU/AI12ynKxs+NX9fjkaHpzH+YN42063ugu50Sbth/ivAnS5OLO3c5kykRrOy5qJS351cF+dzKhLRfDjuGKtiIfDLGUG0eHJy++NFhOYKtn167FV5dOXfNRbCToXU2S/S0BnK3PPj1+8KCTLtxV3gxji0k4mS+2tzXC1z/+6PjFl0NZ9C6vJjt7XlM6y4L3OwLB6OJMrI/z7truhx8cv/hyyIve0fns+i7VrTOZ4o7Pg8idpHytm3aGux+8f/LiyyEvuqeHy419Qi2bZyryeRTEsznvBEl3dP29909fuO+Btv/sbLW10bGVzK12abE2wOel6rhOYOhZ2fYC4lqQKmuN6rg65ZEo5Ti2idYBIxGG7z8WwxHdiGAqgQFmwHRmWdvW6zF7dKTDAI5jMq9N6KAQgkQaA6v12H926vK2vHtA55VhqIy7wcUMOASGDDcScF1tD7xF5jaNHjl4IYTr5qN+tCpYJdOtjlu38bwuNpgUbpA16bofVTXjNWW61j6p4XK7S/J6ND+73LkxqHNY42Q9dApJW2E7ULc6nE+nN++wto4nfHXN7xQlqFA9ZH6aglarYaQNcVKjhkYgOppmV1v9qGhhhdoeZNwADny3LFDoJqYZk9LpxM+umq2uYzhIFYiwpgTOW9zFbSeMjmfptfFGcVmsXD7yXcMFx4gByzBcCtxBVSdizxO+2+sVi9wEmALMgE0kdIAOHLTgXVYmnSFMuXUQCZA9b/XII9TAUgFspe+heUt8W/W6/uGy3e45sLWXrel71iU4147SxYh5C6NcZAPtzKz0APCBW9QQwDrybI2Dtk1HgT9fakyK8Vp/UmoHGN/CEkJps/UonK3COlltbnu5sVDng7A7KSyxESuzArumnV/bj1Y54ageoeFyxQlb9fvRrAbI2tDCBrLGtEOoG+KWqtpw3FWpBYQxQQqglGc7I9q03mVW7Q+7+QqWOtke46xxigrGVEkIW8UCW7sd73hZ3RgFeaZyYIYOablUiHpAAYxnNdp0G+z7x4tmfzCen8/REPsQKqVLg7tEUeJPM73mlyx0nq/EjX5Ql3Yp5ZqPlQKZKNcGgILhaZFs+D5o3Ikp+oxibXMIHGtd62eNcXEadeKzNh87DKng/KrsjWJc1k1kKRABYglAjko7nY3Dx/O1Hcc1dA5FByAGcGYtBdyF7jJFPst6/d55UwxZG3ibV1eNT5XjoNwqSprYjWclIirpRf3LpuriohvHlxOrQLUzDJLcyatmK7ZLAYFt1rqd6RI4yLpYlRY3stwZ+vMEWUN9CwojMavGsXeZWAxJBEULkbai45FakELIrTCYpQI7KISm0lZZGlipCM6F3XSVxM6yaLZ63izjkKEY45RDY22PyRbQZZ3e3h4uZzBH1Yi6rYIN0B2gLWIp0H3NKRtfJpfbw7BqcIaaAaK8QVnjhqBiHZZaOUQVcfvnzfJaEJUZS4pyNCLG2ArCyEqIgqWUI5jDYOv40cX1O11d4RXQXePrRkkiPdJgN1xq1dOFGw5Py2TH90SDEiRiaAjGy6aDymw0wqcFX+86WNjzRg99wgysFIBWBS5YSso09x2UC+W7jqftRWN6HnIASIRkTMYBSwoX8Cbu0otS9D3RDTuncxsy4FhVGkOpDpi7KglUJqbupCyG/SYMwpO5DhgJICiMtrBe6/hXqaW4WO/1r3IAdMjyUkWEg+VmN8gqVoFyTIarlYJ4ORwEixZaAzpAFypM5tW1TSWQn8py0+muamGpjJE3W2pAzDA0HLHcyDWrlOOveL7hdZcLKa0cRKyyQIPALQrgdgqejUKhnGApiy3ayRolsIohqi0Q0HaBII5/uKhvjMbJZVb6cuQRKW2pbYdxA9miMINARb7zfMVv9N00I8/Om4NdzDDKBYywIpTO68Bv59Fa+NbPy1956FiNlq0ZeRhokCkQEq4Ne3aGbm5VnTh8Pmt2+7htwbLVsYcJJBlvehHwSHC2hAOaB93w+bzZiR0gvHlmEahGg+BqgYmdb13b+vSz5cZmOejvnD4vO7FhmC0KC8Fyc2t08oQgM92+tfnsWTocFcPh1uPPmrjnM14vG4zt/PrdtZMjTVi5Mdh88mw13sx6/Y1H7zdeZNdHJG3cukh2NoP50kJUjgbrzw6T/sh0nOBibhynHYQ4a1lZTm7d2jo9NBpOr+32L86xUnzUta3pTaarGzvC0OHZ6eTmjbWLUyNBPYpZURltQcdVCg7PzhZ3DpSA46PD+a394fwih6GOA5blqJF8vaclHJ2dLW5d140J06QaDoI6RyWvxgPCJaxa3fOVwmtnp3Innjlr208enb14P15MSN5cvvBy53/6F39XLMKXv/tXO2+9M9/bX3/7w2tv/zzd2t57483Ndz6Y3bhxubE3H2/TrLz32l/vvv7jxc1bow8f3frB61d7N3ff/NmNH/749MGD29/+/vU33nryxa/deP0nL3z7O/O9vbWPPrv2+pvT3YOt9z66/doPTh4+vP1Xr9/97vc/++Kv3vjpWzf++oeTg1vpYHSxuQ+Evv39H976wesnDx/u/vjtW9997fKlF9ff+/jmt/9qenAzmi9e/vd/lq2N3Xny4E/+YnL9Znx28eKf/eV8+3oH5F8Us4oRc5Y//MM/yeM+K9uX//TVbGPTnaxe/OM/S4ebTlu/+Id/2gZdTeiv/Ns/FWGEG3nvz1/NB0Oa1A/+w1+U3QGy5uHv/5l0fe4Fr/zeH3HHR+vBl6qzGhN7lj78k7+ogtgQ/PAP/i8IkAi8l3//PxiMLtdule8b2RLm6KsPXFlAvMmSN0xdOGAMQTFFluuCLp56bYFICGZPBoL7eIuufqLrlOIxXX6IRI5QgJbHXSkCt2vn75M6I2jXW70N6hUzW2H7CJdNyLpo8YjwBGvfQDWzZdpE4+IdlS9dsx0WH6lmBlzPLJ7RfEb5TtQ97OHJerapg9MOuNyCQSbTNXq1XoX6rnj+QnEyc3x+fhNNNtJ14E27YHJXdWtv4ajJHo8MSHreybAeSTodo+mtfKzciY8vd9qOZqkDLve138JiiC8PmqFAswE93xK9aqM5/1p7eQZck43hxZ6MFMwCerRlO7lJxuDyuuqUeUtW71hDiCVo+ZbVDlWaLj4gxIUicK6uH4BGRbPk2Uc9Tan1WfJTayhRkMw/IMiBkpP8fasZQwxO38AQYhvg5E2rqCMRmX9Afchb4K3e0sJxqQMn71Clid3yJoMdxai9aGcfOxBDbtnyPaANpi6YvM2EpngzYM82YRVqR6CzTVsFxmvp8XVbxXWv+ur8gy3bPseb9HQHlF3lSzC9Date3QH+8zHI4npduI+2bDYq17R/sgHLDevX9mob5nERQ+9oHS1jO1iBk9s63y1Gxj/t22QIvdXD4tlYJGfhOn6+SxfdalPQJ+t4OZRxBa/WYTKWsShOTPYZQltO9cyunlB1LZRHYvUI4SFTAT3bvgGEFk9V/gjZba86x8mHkG2R6gItPoGox/RKzz5xUJe0K1S8a8CGW1/g5WeUrePyEqW/ALhDcMbPPopATGWO048QC02+Nppu7vplUZ7j6iNrI2ZLPfnIxSHiJco+QHwtZDIInq0Lgojh4PkdC4hyjf/ZroJO5F18OT/U0KyasXt8TRAHQoGOPgcAg7SAj28pyBTF5Pg6AEBbBE9esIhaJunTm9p4vKPYp7vGOsYB+OyetURQRo+uGUOE3/5n2S9Qk554B8GjbWE70oXs2SZRsKWIHO1q42Vj0rzZVjNqdsL2vTabERaj1TPaTKGNabIxzoMekzz9mckmjtmNyk90e2xY1yaHNL2iZhzkn9j21KADv/yZyU+IvN7ln4r8ktIuKp/b/IySNZx+AstTbG9F2YcgO0Lehl12RqvxOs3q6olNzxgZ4uwRLJ4CvO9mH8PkhKId319ScH7DuAJWATnebnoKrXr0dFvEzTq//Hvt+TlwdDEE5wfSN7AJ2cmBjWpTDOD5vuzUpoj94w3uaiKwPXtZexpw13u+JToKFhE+3QVBaeqInt4XgUUC4eMbGusNfPr59jzBTpntkLPrxq9lG7HnO4poaBF9fiA8VkC5fBczJSRk8w+wMIR6cPIzKgWB285ksCMcF13V84+ZMVAwL/mZkYZS307eYYITsO6u3gStcG3HKd6WWmLtuYsPoZJExF72ppIZMFte8lNdN44aBNn7WpeWBvZs71YLGWzt6i0rEwC3veRNU1Su6TrVh6ppmBl63tEamvfMMDGH+2C1lo+td9aDi5Hx05eqZ9vt4jwYgZM9No3LTe4e78BkX/YyOFlHyw0RSjKL7WJThTWe7aL5drXe0JMNMl0XvQJOhngyUn5LVmt2dUPGnF4M8OVY9bP94vgBWB2BjlzuupcD6Qmy6oDJtupyetV3Lsd8rSnObPoRQCFx8urwkx4IiG5x9j5UAYMmUTY3reF5VH5gbOhgbS6fjklEtYLJzyHoOU1OZh+xfljnPFo+69uAYgSmP6fWodLg5c8gCLHg7uJwQDugap3Vu8Yy4pBU6sIIWYph/r4VndAUdvY+dALbKG/5c2AYcz3x+X/z77w0y9bW7//pt1hZlWHv3l98k1btfPva36+euuXilI1f+YM/cufJYmf/wZ/8xyDJi97o3p9/002Lq+s3v/x7fxAdny/u3bz/718Nrlaza3sv/eU3nUUiut27f/5Nb7q6uHX3y7/3h/3zycmDhw//7R/3D49Xdw5ebi47us5bcOcvvxuez+YHB5//P/+49+Tw+POff/BHf95/enR1597NH/5o682fX964e+P1n+y98ebhK1+8/e3v77/+44uXXlpEa1c7+36SHPzkpztvvDM7ONj78X+6/b0fHn7xlZt/9aO9H71xdf/++tsf3fzhj5PNrWsffbTz12/ODm7s/OyDW6+9fvq5z+/96M0b3//h9Pbt/sfPXv6/X53vHqw9fnLjtR/na2vnu7cWoy0i5f6P37797e/lN655z6Z3v/lqMt4cnJ7f/tZfTW7e0Y8f/Z1UhBCA3tmpl2W92TRezIIk6UyunDQbnp06dQ0gwEpYQnonJ15Z9s7OOou5n6TxfO6vVvH5uVvk/fksWsz9LBtMJ05ZDS4vO4t5mCbd5dJPk9504pVl9/LSWyyjJOnPpm6WdZcLgCDSigoRLhb9s3OnrkZnp16aBKtlfH7u1dXg8sKrys5kGiZJdzYJpxM3S3sXF26eDy7Ou23W99f7deZmWXcyibK0M59Fs5mfpYOzM7dpB2ennXTlpUl8ehzkWWc6DZJVZ3IVzqbhajk8OWJFMTg7CeczL88Gpyd+mUdXV/5q2W+rEY260A4uzmhRjE6Po8XcT1a9s1O3yDuTqzBJnLQupScriNOGS0evpFdXqzYqa8cR3I66atQFSV5LT9QE5qISDkyEU9cljyrh4VK0JZEcs6SWgjYl1oWulA+XirVNUzhF68BalyXVDcSLSnDaNi6uanuwCzs+yxJZkVp4pgayYqIlZFmKBreth1uORajNkGkC646BXZZqh1MJBkziLqFj1wuqnNbUwBHRFPGuhp0g16jGxvQwd4ENrRqyVjo5VLaPW0yaQKG+l1vKsdI9UhOgfWNGlCs3swr0aYvWiYrIaMiXtIVKx6wESHsSbrNWu6k2tudUmuZtpTydGtjoTHR1i1he84aCjGvCiOQAApLVlfLgSqJWrdpQtJimNa+pqTRatQnowFTCrGm1q5ctacSy6YgGkZw3DbW1AouqQLHJoS1lJT28EkQJaDUC2ja2aRioDV3WjQ1FAU2lK+mhpUAawiaExvNzZW0Xyi6rkQKxkxMnzzbibp+6HtKm7ULt+7kGOoAyoi2RYEgKhzUVErG2fSIp5B2gPC9VUPtG9algwPYID926RE1o4QBLCkRsTRSXZZ86Q0J8ZYztQ9ljTe1UroRD3GIkOtZ0aGlaQXnj0pqrzJSNCxprKlrpAOccIECUwErLGpSVh7hCiailZyotM1uDCC9rVKlKuCBTmqNMdlArSdLU0rOFBJnJYAwWDSxEIxyctLKBSRNCabE1RHLpujhTCeziVWsr1bYUJbVpwaqJQKPd3LZ4nRWEFcrAvptBKpQAm1aFvaZcI34MrV+oFo9ZyVitNYjd1LJGSLQDdMQqY2RMBPYybcgAtQHlSIKemyHKJTRrwES4Rlp1GcdebjTq4SYMTB073W1GaFYANTCmgzkDpgcb6qdSgy4SPSp4Wzh566JWlSVTnOBFJVrStC7QAFsNgHXLXFW0Ep5ugCppKxldlLJGLfdsA2vuyNpx6kpkthQurI1snEYwtqp1DcqGgVpX3KlLl3IOVrpSPsq5oYRKjqQ2DWhqikvRcjeXXdo2aCVqFcCCuw1UpodqBrRr7Drj1s2Nsj3C8Ri1EV0biYQ1QJkeLRFSTMBt3AI3lcbGrNJOjhu6HhSQVMDqjpspJKkAW0QgJ7fG9klp3Ay0dM3LMWmB0bGbqY4RQxZ0kaE1VrbnNsZNraIj0vhYEK1jp4ag1W1FTG3gvCxBpHMAClEpD60EVQJZjYCx3DY1M7Whq79xEDKVrpSHFpK2vC69hlNTmYp7plBkWdXS5RUFja24yzPg1JWoaCNcU8O29XRpkVRUtNphuJZV65qasqpqM9RwBmrQCE/VgNbGgj7hkVtVtPUNGBJFkIiN7cZl1Ud4yNxAG2NiKPtO07AcS9DHLcY80jb2c4OlY2RMG2hMB+gha1s3QxL0aItwG2o89HJDJDYiciplbSzBplO3W7LpsV5Qp6R0BRoFhYaSQR66Jbe2K8Cmw6XNdIZiu2hgzhvu4ITr1i6byNaGQW1v7hGgUcJXoItWrS1k2xK4qmxjVk1kGotSUQsXFdzM6wJ2bWZMpWvhkrSBjSraUHNkclVxhktBlk0NujKz1Gq+vwOQgRkval9yKErYKB+tWpK0pQlkiZ2mHlxd+stVuFhEk6soWQ4vz/0s7V1chLKInXjHc1my6l1eeUkSLRed2TRYLvsXZ16W9S8vgzwLF4vB5YVblr2LsyBLo+Uyms2iZNW/OPfzrHd6GpRVsFz0Lq9YU49Oj93VKiqTgRv1sdvLZl6axmcnXpEH83nv4sJp6sHpiZunncU8Ws7D2TRaLrxkOTg7Y7xZOzl207QzuVKOQ6QgUkbLZTSdduaLIEk6sxmrq8H5mZdm4Xw2mFw6WdY/P/OSpDOfdhbzcD7rXF16bd0/OfbSNJzNBpNLWhb9izM/TTpXl16WAgiANQCj/smRm2Wd+Sw+P3X/JmOkSXc68Yv8b0Z7fyv9cqicf/Jfzl64Xw/6z7/6ldXmtdW1a8evfIF3Osef/2Kyu+VUbbhaFYPhYn+fDwZP/96vLsZb2c72ky99WXSii/svJTeuX25fT27cPHzpQXLtWtuLn3z9a9loPN89ePrKF3kcn927vzrYne7up7u7T3/lleXODo97j776tShf+cus6PfLtbXJ/ReT6zuTg9vZ1tbZ5x9c3Lgju91Pfv03RODO9m5e3L+3ur632tk9fvC5y9t3gO/94rd+u3DCRbn8ZHyvGQ3n+zevXrw3272+2tmd3L938sJL2vd/8Y3fzjdGTRg/+sY3qrXhfO/W/N6t6cHBYmtv9tK9w5ce2iD44Bu/VW2ut53eZ7/xm8VoML9+cPXSCxfDzUmWnnXWn995CHzvF9/47XpzrYpHj776VbkWn998aXL/Xnp97DgWXXfwJnOxRrcCMQz9QIRbgA9DMONOq+XBBuw49DqlY+RSrQ98Mwz8UPprVu0EpAOd2JibHeJieg2TLepghW96pu/6sWEj0O52I7/1ekDd7EAHeZum3Rv7z6bKOmprnXRtNNTyWhj4nMYA3Aqhi71tLNa7lqYoWC43GHATjKt8T+Udxugq3bALQ5/CqIj7RRdCL11tEewsAOHzfYAcrtw63ZKAtiactyPEYwVZklzDMqgYatLrHDqt8NpqWwCHm2DZruG6q5GTtevtIV2fQPBoeAP4wjhVvSWMp3Qw1wOVDinVBTIkAAAgAElEQVR0ynazrdfiEDT0rge7JAwl3nH4OHAjS68x5RK6bI2Lva7WjuPc80yXRZFAW1RthG5gnR3SXuv6xJg7AR1gl1pwJ9Q9L+wIZ5PYTZ95xhub+saaQyy96+EYOMya2wF0MF412BowcGhonQ0o92IXKXLHJQPsUIVv+SAwyq102KQ7wDLbDgsx5AjW+Uat1sPDhh3RcNGNlV9Bv8x2jKGa94tsHTo64Rs57zvCKWyUJhsEk6UNmuV1JJkF3UW+Bh2b1KMcdEHetTBI002IyVJGcrHlLA35jI0u+2sMJLy/EkPGoxa4ab2lmkABv6k2VcAUGQGzGcA+DTtC7HdZT7tU6TuRU7cwUypkoMt6Qw7WXTRgXqTInmPWPJ9q+2KXeoZECN/0cAiDgbZbvhh5UaTpNSy3Oh5R9sWu5wkQu+hmQCIY9qTe9oHUNBMOkvVm16fS3A2dDmARNDci0CHdnpC7cdOXDmyWu9L0NARNeb3lHWrpXI7qaS8+UuwiGE7XuwzxYqe0kYCoSXYE6tjWL9SgqofWkEIM22SLUMDzndJ0JIRVvt3omBi2VP26WrOW1mpQpZuIWJ5v5avuYFk1vwg2+TBWLJG9phxpChM5bJc7DrZcrM/LuNMLK3domuu9MOB+15jbHeMRb2zURsDSmlW16Xg2Rt2hFrtRGHDWseBWCHzirCO1E0SkIVtAj0MUoyBW4qDjBpJ2oL0VMmbxJgXbDnNMtG310EVD5kUSHISwkKjWts8oM3SDgB3P87W/bvRmpPpO3NVyL4Sslk5b7rTAUcZftGuw6hrkZHy9PWLjCTSfDG7agBvaNNutDrRx52YosyEFrOIbbTYEDPLZnoSBMqwpd43ytXHnzZptBwbiot4Wq7Hrgjq9XkFPaKctt1XmuJ/p6DIaVl0LUdls8WTNcW2VXi+tLw2tqnGLO4iG0B/r9uaIUkhuOzQGDjXmtg99glcNURoMXBIBZwz1QexgTW8xMiQOUfi2b2PmxxqPidoJIrd1N0i7HzNsnH0kx2EPFujA1aOAdKw/hmbLC1iLNynuYZsZLEW53u/D3O4SsxaQLnBHUO8GPhNo02m2fMckzTADsc26FvnpahMTulC+mF9zEksf0f55f0xQLuMlHzLe4cBJ6i1Vhwq5TbHTQsKbuG7XNSSVilftiFWhQF5eb/CmayBr0+vKEqE6Zb0BAKtUZ2EG+kmwfaLg1Wiz6Rnk8NWuQlTIblNuYs1qE8zqMTUbvoeUfbnr+dJELrntowhHsTTXfIUd57xQwz7f6YVU6buB00WOD+ytyHZp1JN4i6ENjznK37HFwWaAJL7v08AwH5ibEYpp2JV4i9kN13U028V8t+8Djl/wrcPQYSqjHtroRF2htgK8QULC2YFTbfcCy+kdqjr+bO/W9O6dy3v3kvXt5a0bz7/winKDR1/71enGXrk4+9Dfqjc2prsHszu3j++/WA8Hi1s3nnzhy8r1nn/1q1f7ByYKn3z5K2I9Pj14cXHr1tGDh22vd37n7unDl5XjPfnar17u7au4++yVL5Vba5P928v9vZM796d1e8L8870XlOc//trXs92tYm3j+EtfyrY3rnZvJnv7x597qKPo/M79w889bLvxxcPPZ9sbVzfupFtbJ1/6Ak2r7mKRb4x5t3Nx54WLBy9Ww9HZSw+S/d3zvVvV9s7p516+uHG36Q8e//rXdeSd3n1w8eDFcrx+/sKLi4PrVzfu1OPx8Ze+cLZ3W/Z7n/zar8lePLl19/L+PVbXUZIZn03v3Ku68cUrLx/d+bzoxp/+xq/zbufq5t3TBw+Dn77hnBz/rS5Y5Jd8cr///dc686lY68WfHcYX55bB7Y8/6Zxd/uy/+2cv/eWr4WT62v/wP9597bXB9Krth+HJdP3wedONNz/+xdrjx++N/5vbP30jPjs7u33n1l9/f3xybGM3OrvsPjmuo2jj2dPtTz/9+dp/vffjnwxOzy7u3L391pvjR4+q0WD9+Ong4yfv/xf/ZHRycv3dd98e//P9H/2n4eFRszXsfvLs2ief5KOhL+s73/3u49/+DaD0zR/+qOh0O2m6987bZTf2EN//1g/hV5J6fXD/1VeffePrGqFbP/zJU/L1tdbsvfUWZwx13f233qJaZtc27r/6zcsvPRBBsPOjd47tl2N4tPf2WxoisRbtv/EGK4vV3Rsvf/vV46+8UsXxre/84PRXvxAHZ/vvvA2EqHbHN974STS7yu/t3vjma5MvvJjBKD9mJJBkvVqch/SqXn9ozp761LcDp7w6jLFTBCpbXBLiAX+Hl+eeAyrPA5MnHnLsIKiyZw6gNkBleupBbDwlynOHmtb/Elw+IooRf9CUT62HTYBycREaaJjbLk4GVLajXpI8YTWmdCiLp8az2jV5cRU1BpFeHU36uPaD9Xlw2SNVENMZM5Slw8Bf6fxATYm/83O49Ew7dkdzbzogWWfgHvkp4vV6GCxIEwRXTus/J1chrIfuaNGdRyTphU7tVY7I1zE+wa2Hl2vAf0KnEaw3sF/gspetbvfWnvoKiWQDkxMoXTDf9O0U59g0m4QlFWyKMxya1hma5VOnI6XDyvSZx6wG8zZ7SvzdmuGyOB8C3kRbdvLEja8J5lXzZx7VEs+K4tRjokRxvTrpRFkR7qPzx35nWzh5Wzx3R6OW5Gl6EnqidQe8OPHDrMQHcPm2G2xrZyCrQ+atK1SV2RljTettiuLU9ZLG/VzgTNYwbqm9MknEsIYyx8nYUXXrntqzz7fIhOgJnGwx2yA4sWkHQaNYQZIxETXyT9yLA01YMFi6kxE1og8vdB4i03G8HC2HYdt6147M6b4hkdtbeLM1oHFgFmpxYGwUOImzWmONaaIn7GTHwtALn8NlDDkFTllOgLgA/aAoj2G1dNwtYU5kfYW7YWXSNr1y/BekaXH2FPXDlp/oYu66MRczw69I7JWgFdWZ49BGC5gd0jW3slc6XwRuyFVSNeekh1PcNtnVYAAqA3D6jK57bTvV6ZUfvNCQGpXnNIClISJ57g91bgldPWVOUMeAscWgrybUkWa54fGpXUvo5I4Wc10rfvFF3DvrO4ou+pBrEuRwuRnnl+6gUlcPYbwk7QrNNxi/wJXEy0EoEtKt8HKLFTOxs6BXd003hWqFZ2PHk4ArsujHgljA89OvUbaE1yfe5UHdNwaVdN5jziWUHK+2TetWUVE+MgLSYL2pnmhmQRfnahoZZYkvy48sqXnwFVM/JZmhdCzyJ4ZJ0wdZPQtLgVkoqnNEK9XpFstHiBvX2xDqUIsGBaTgl6Zs6Jjx6pya0gwGbfuxbpTrxU194pgCDW62YgbqwgZOlZ8xnqKNXi4+g5kInJHwl5RnGwifYY3wdAs6z0jiwmKLeI9J1UmXd/rjZ76BYrFO0BmAGE52XPMuqaGuNgjNkHTZcjiAJQNWz9cDewIpQZNrhB6CiqJkg5KEGMCWw9C2lEk4W/fbx5D67eIB2X4eCoRWGwSnEAK8XIugIq7G8w0CZ6mXlE9RbyBIlaxOuk6t7JosTn1/WdJ7aPmW663pYEvUh44zUKgtylOH5tzbFsWp683q7ktw/hkFa9DBTfEc4q4goijOXCdXmIrkmHpuGzq2fkxVH2Eq8uewF3JSVsVRDCJAHoj8hHiOcvy2+AzrPvM83h5b7EnHF3g19krpXTsG53sadr3h3JuNIHcctNKLHaWGAVu6q9gpYBM9YSdb1nS86BlcdVHtIZQ7WQD50OBDvFhDdQCjT9nZhrV97Oc6DXEVxWDFqkDWa4QcodXYlt0AHdvp3VI/iOwHsgpYGVO7wo1v8hHBhzgdwyKmncfNJa/PSAenRNXF+YDYirgoecLWSKUKMzkbbuJMXRXlmePaCnhtchT2RU46ZPbYHcJWtaA4dXd0jWZpfuoEsLWBLI/crshhl6weOz0ogFblCeuiCs3y/Iy4tnF7dvF83Nls2bBKHhNClNUqP8WBKUiA83PsAxGM0xe/9a1kZxNCc/v115utIRLy+s/eia+uPvvKVw/+8Hvrg8HH//QfP/j2q8nWVuuHt370IzHqAoD233u3OjluKbvxo9fbbvf4P//6je+8Wg2HVRDcfuONfDRainz3vXcHzw6bbvf2j3+kXO/na//V/Ve/JaNAB+7+629y3/M/d//ae++N3EdP/tFv3vrBDyxzfrb+T1/+3nekHxTj4c7773kXs6oX773//trR4U+3/9t7r32vM5vXO6PRzz/eODz89B/+/fHRs87zMxCw7Q8+HDw7fnvzn9/64eu9y6tys9f7xbPNp0+thzbPj5ynl9bHm58+Gn365Odbw2s/+Mno5KTe6EeHl9uffCw81k0W43d/Ydx/tP74yeaHn7zz3/+zjXd/sf7pZ96I9qaf7n/wnvRoUGa7b71bbmyo/0+0+iX1y7IIw9lscHwCpfRXy/75OdW6O532z86BtdF02j8/h8ZGaTo4OYbK+ItF7+QECRnM54OLCwBh9+qqf3iIjA3yfHR0SLQJ03Rwfk6UDubzweGhtaCzWg0PD6G23nI5OHyOOXfKsn95SaWKlsvh8SHU2l8uR8+fI2uDPB8cHSHBaVUPjo5YWbKmGR0dU869uho+e07rCrft2umRX5WM8+HxESsLVjeDp89Y07KqHj57RpsGSzV6/txNUizk8PjELQomxOj4iNW1w8Xg0WOnqoiQw6MjL8uQEP3nz90kJZwPzk/duqKc9x8/8aoaaTN8+jQoC6j14OTYWyXQWD2tQcaBsWbOcak0xHDRgFQAY1HKQSGxNnYlUCGwtWIuYKUthGDRgBWHWoMlt5m0SoNUkkJQq+1CwExaiMC8QasWG40LDQoNtAErDlIBtdaJwBnXhMBloxJNtMKFBKnE2oJU4lxaAElhWAKhUqQBToahBriGNEVIatbaMNcAIrcGTk6Q0aQGbAmAAoQjJ8NIGlxZtrLAQNoiJ2dYa7eSbAWR1JBDJwVUatpYd2WAgaRFbImQBaSRXmqQMlASmiAoLG2Av9JAAyYRWyFooZbWzjioFBTKzhrQKKCgnLamNVgqs+S4lUgDOxOo0UBqMK1tJaGGYCFso5Ey8rLCtTQW2kmLWms0QNMSlBIoY5cStAYrbactqiUwVkxaUGtrAFg1oBSQKzBvba2B0GDOaS2IMWYqUKUsQCSDpELQQpoDmmtoLU0wLbBByM2EWxislFtYVmKgLSmgkxtsFCuwU0IDkZMZmkGoFK0RKwnUlhSQ5gBrxUrs51ZjHJSAlRQZRUtAMgiUZbV1KgC1JjmkiTUQORVkOcFauYWiGcRa2VKZaWsMQpXWCwmVAaWwk8ZKDYSBK4GlxpWAs9ooYCttppwYw2phZxwIZRtjZxy0ClQSzBurAeBWXLVAW9IqM2mw1EBaOxdIGlBJMK2MMIhbuxBWAcSVOi+R0EYCMBNQWtgoNK0A11gaurCEG2Sgk2LSQKAtXQFcWaSsmwMiAFGGLCxtDASQLbEjELDQWxlaW6AASyBsEdSWpphUClpDl5DUyALoJprWlkrt5QBzDKRlKSSFRsZ4OWQNsACyBKAKIaVYgUgDoQIsgaSyFiKwaNGyxVqjQttEWmNBykHCodI6VyiXBmOwaORCEaNJKeFSImNBJlEmiFYqUWDeaIxRLvVSQq1txu2CA6VBoVAisNYoacGsMRCaTJmZINaQWoKEW2lsrsCSQ6VNIsCiMQCBUvFJgwCAEnopZEKR1riJARpgjugSQwNxq7zUQGmgwixBQADCrZdYqCCViK0wspByQxYWS4MUYilBAiJunKVBwmIBWIqRBoxrvIBUGqQhSzDVCEvrJQpLDVvAVhhqjIVmK0gahZRlS4w5hNqClQC1wcqYOUeVhNbKSQNrYy0AqwYUAgoFFtyW2ioD5i2pBLXGTAUslQEQzBqQcqgMXElbaas0WLQgl1Bpu5QoVwZhO2t0pqjWKOWwkEQbkCpcK2K0nEmwag0mMOEmlVBqu+ImE1BLVuEgtxpjr7ROTpBRpIQkQ1ABpwFOBaAypIB0ZSxArIZOTpFWTqloCqEyuEZuBojStAJuag1EtEZsgSCwuDQsBdAA3BKWQqAtq4C30hZAp7VOYgGAtLZ4BZC2pEUsQ9AAUhm2slAbwpW5arDQ6G8cJAxoNZw1VljIjZ03VlgklLgoCZdWQbCQSFrANZpWttWQazvnUFkslZlLzKWVRk455NYKA+YNrCVsJVgIIwwUyk5b1gislJ21sNVWWTvnoNWQKzDngBvAFZi1oFYA2P7pSXc+w0oNLs6D1QoJ2T85iRZzCMDo7LSzWloAeycn4dUVUqp3eRUtV1jI+Pg4WCwQAIPT095kajAeXl52p1OsTffyPJxMkJC98/NoNgfG9o5PuudnBsH4ahJfXmJt4ulVNJtCKePz83g2gxb0z8+75+cGovjqqnt2ahHyl8v+xQVRujOb9o+PgQXhfD56/gxZEGZZ9+ICChEsl/3TUyxEOJv3To6BBWGSDB4/QtoEed4/fE6U9tOkf3aGlQrm88HRITA2TNPhs2dQKbcoBo8fEyG9NBucnhKlvDSNz06BNv5qNXr+HGvjVuXg8JBw4SZJ/+iQcv7/oyL8pVmEourNposb13HW+EVRbI27lxdA2GpnjaS1U9dXN276WTa6PF9e36LLwq2q+e71IEupkO2oS5PSzbOr23fjq8totWw3h0BqXLTTvf3ObMrq5v+h7c2aLT3LM813/uY1rz3vnZnKOZUpgQYEiAIDVW3siq7o6P41HV1R0e3usI1dTdsuF7hsbKqMmQzGCCEQaGCQAAmNmZJyzj0Pa17rG9/vnfvA/guc3/HcR8/hfV18rRuM5izPTy5c9qqyf7B3dPlyZzrEhRxvbiaTSTSfletLxqDmcJidWROaLu88OLxyuZHNvEla99qWkHg4nW5sGGU33/n1wRNPx7Zs7g3Gp7d8XdNJJjqJdijZ2ZVbq3mju3b71uHlK7EqG9uH01NbTAu2cwS7oer12MlcdeMs6a69997x5SuhruLr780vPEwD6J3MdCsWcZwcDXUnnoftzTd/ffTwY4FvmztH6cZarDOVWhP7RaeDj3MT+yxybiB0K/CAyO9lmCF2sWczE4lc90IxdS5kKETyzX29vhIvI50a4ixqYZUDolTVb7kbB64V+2sBngoTeTS0emGRc3m/je4NWZHbR06RWQkoEt3EO8mAh1EEywc5qrh54lw4W3h1DXoUTLT0vKLfiqc5q3W6EjOuGnPFe67mLJrl2VY34IrUOvJSbiNYk8VKEE0rJIlq6EArW3tZjwWlIpVyLSdy6M15fn7Z4zyeyHTdT4rKcaoahnJnawKaWjvqpdr0bAWi5ogvNsJokZup1utJoIThOPLTgjbpAuquybxGsjNSS6EHlEodiAmCTs1tGCrebNCDRbXVWyuPpmkMuj4zvE4xCR0IiZ1bGlkex2xnKjY6iczKOSERRDG0U4VC7EJqZqZNs8xviBTiELLQ6BMFegEmVi8M8YD1sJ65iPKi22ZHmVqKAyzU0NiWj3wIc+dpXfYYmzlNIIotPajKJPGb0qWIWGtaznIWSJV1GD3hhmC+2mhOuCEQBdLlEBmYLkXeoIqqrNpq09QBaMtuEE+kQ7ZJ55MsYcrkZzrRvMTC1T2czERNvKLtNwaFUo4uO8sZrY1paV0zxi3vY7qorIC4jY1CIJN8o+NPUls53EIUGJNDvprQQuC8Rh1iC2sqF3RtSRLveM5PdaMi1SkAPYoqLSvAWsgg4iY1WyIch/7+VJzuLI8PBrpPWxA5Y3ODWkRDiiY1XiKqBjoHtAEIcXruQI9hIUGhy6UOJLC1n2YrcQgqPMV1CxMg7AjACLgmRSnEniqjIDmqiuUwkakqEuipkGY5b2FmlA9JCrGv0yhJHgyzlW7i13gC6wYlTIEUYWq0j2DOPFhk7TAe6rJFReK1TriKECTSTiAICO96yUQQItJG2DgRvEWKZpIcT7Cx5XqHzUuv4qZNdQqgtWKlwcYlJJAkwC6MVU6st9goJ1L5TaMqrBAV/ZgOc+ws6hI71tpCvZrQnONC6tUwmJY18rxQ68wBYcgSlQKTtEarrBaUpNz2PW9a1NajHehKqw1kLaQEgvO6emips5iC0qvbjgltawwSK5TnjwqwTiocNgd8vhEmvIQpFh1AKq0XNmnyOmigFLmWqkjUOCrSjTisSjMQar3p2xoUCCRWEebNnWupDDXa947m51YbtgAZAw3piZrLBMWaA987ycCqVwZh+7BKV/xIV3CBRYsA4txctWCWdbvwsBBLzYbOihkmIUJNrGaGUutiYmeGelb6zM4t8gFLgBkq0wyop/XcOoZFM2LDIkA17zbQoDItXzbC8GhhI0o9q8bK+J5rMzrjDGnQwG6oRDsyHoEjTqAmPaZy5xyU/SgYpIbiqt9sTmroTMLmuW4hCdPlgI1rPy/LU63WohSAFR0vmXFnLGgBmCKkIGjVwoR+rstlkiyEstQ1NJgaU1u8Rq2gfq5U3wkdhKnkPRQvhDS+aypSQ61gw5/Pcc9Pje7ZqmLxOC3OdBtFqTWzDYMLAyS0LSdw4O9N+ZneSno8qRqw59l5KQ9ydroJGyEac9JFFQ393Ul9ugczbm+euIe3fKJgrlADa8rARLaC/CjcYK+8Kz9+LXalm2vY86EzJrM0AqX1wHv77PKS6jTC3alYbwaWq6lzHd/yWu6kbq2N+lEwLEjLFUHi783lWsOD0h/OEEFFvxMN5xjb8dLayrtvpWun84219Tt3yk7HRsQbzi3Gs5X17u2blMHJ2QtL97fzfi/v9lbv36vjJAgUPywwBpNLV3p7O5rQeqm9tLM36/TzXm/9/RsV9c2ZFTKtvLrM1leC4dQhVC11ezeuZ50Vu9qOTqaKENVv4mlBaz4+f37lYMcYON7c6pwcYynrlY4/mHllWZxeE5p0jg8HF871hse2tnylE46miEu+uWyVaxweZedPaYm6+7uzc6dXj3ZK7VVrPVpwXHC+1nMKtI8OFw9twpTHs1m2vuILjgpZrva8RUarmq928aKMpjPWowet0ysP7g8uXmhOR7ioTx5+tPGnf/rbchE+/Y2vn3r9jXKpv/nrN86+8mrR7+1ffHi4dopq8dG//fvzr/7y/lMff/Kb3zrz6i/np0/tn314trJOy+rD3/nu5RdfOrp29cPPPHfuxZePrj46Wt+arW4k6ezSiz878/IvspW1rdffePS5H81Ob+2evzpZ28K1eOzZH5x96eWy29+8cf3icz/Ju/29K9emq5uAwCf/4ZuXXv7Z+OxDp195/eILL+RLK63R6CNf/u9Vp9M4OnnyG98aLW+s+/NPtVkq09bt/Ue//m3igDP243/9d9wLm+vep6My81jz1zce+e73FaKx4I999ZtIKnxx/XfpUBEQvHXv0W9/j7ebzYOTR/7pnxH1WMt+pgM9k1XH5dNf+rLxPZaXj3/9H7XHokh8ph9oOXOj8iNf/vu4LGUcfORLX0HQnrTPjq4znYEYFgc3EjSsG2RgNyPUJva4nLwf4VTFqji42xQL3LRDc61PsgGlwckrlE+R39aTt2i1oJE4MudijCVe6PH7SX1kgi0wfAWXExasG4IWoO+h7flovysHDiVw9jbSR8ZfET4t3emWPuH5Ta8ega5dDB6ExQkB66R5f5UNN+qlMr7bhLMzIdy5QI4+5k4WmJQnl5L9Pg2PyNGWG54B0XjeS4pWQGrd3PfN8IKjFcr64c46j7JPqOtP+7P7zg+OVu34PMY5m/vw+CJB6WKFzfpdasrobh+ML+LgBA06cHyBmuFlfP8TeFgjnY4veHunWXDiDTpmeIX4gyJ1g7c9KiQgcPArRrUAW+F8dZUipXb08QeRL6slPr31wSquBNmk07V16EF4Lx9c9ylWdq5PbsdESNZHs411HfrxwezwN6EDCCo9vB50xLxaaWZrSwbj+Gi6+06b5oLE7uQVhipJ1sh8fRVTZ47V4EaAChkQefRWYhba3wi8W6dI3aBq5k7Ooazh4f3fC4626u0d1PHvXUNZ20VTb/uMK7oJvPdxfHwajI5Bi+5e8UaB7M6CWxdctoK6w3+bvXmBljOO1PAambdUorztrWjUAMH2p82dh4Pqlm6Ge6dhtuTZMRmsw/GSSvLPuVsPu927phHcO4eydciG7GTLLdaQv5jvuvR9HC6Z/Lab3vHCpK6X27zfgtKAu+Xx7dj3TTWji3dglNTF1lLR70RlVt40g/tRQGswUUc3kwAJvZUsllcYEOa6OLkTNdp1dYAGd8MGLNEyHW6cok6pm/XoRhD1rN6Rx7fjDp0XS51qtQeMQ/ez4/fjGFeqJMM3qe3SJPfI0WVfcSZScPgkyeqkcfgf7B2P8MFs2du7RCxnlQGDa4hb1apGK33h05W9od5/CliCnMJ7l7G2fXPr3wdpYKdznoDdJ1jmVFL5d66SCvJONl5ZhljSqYcOLns1rD2T3LtI5pA0D/5n9X7TUwflsrdz1qsNMRIcX8GFVy6p7KciH1BvjRS/kcUAh22dri2LZkIX5ewtJPZduKEXr+PZkRevu7zX1g0/nGXzm2RxzEiHzG4guesa3WqysSlaMcoFv25mx15MqmwHpbs0iut8c6nodX1Zzl412UnQaFTz23h+6AeJrtea6dJSnM9nb8LFA9rY1NXbdrAfR8u2OfLs8WUKMlgmbO+C8MoPmbc/7U+H1sqjDTS6hPU0qqA9vgZsdTnc+4x5UDmpTk7ZwcPIH5FJAw4v+3a2Be78T2wETD5Nz9Od8yAq6TjAg8sEDjfk7d+PSs4nZblO9h6O9BwIAnavKV9fBu9/Gg1qy8vhMhpcJaoiRqPdRxFUha0mvyGJLGCpDm41QWVYzy021kUcxgejw9dDIyDBZviuB7mGXZqv9WUQJoejw3dbeqLoChr+AusS0Q2SLXUxsW6oTt4LTOpgi01+6dzEhCJGo7sAACAASURBVBt2sr5pIg8MxPRdjHPZUNn+7aaeObjCypWOaMbRaDp8jZY5o75b3EAiRV6b+Hunw5MWSXbJ9iU3W7e9+e/U713G6UxYd/wQHK2aiHtHG97xMgoOhuurZdOPs9I/2HTzTYpSMu7h0TrwF58kD55we9uowe6dcYuHsH8Mj9bwdBPgWb7kzXrdoC6C7U062GLJjre3ZWbnPbZ31W0/7eeLIlOTs/hwDfszPNogx6dAZ1rumJNbUWzyRpbdvblMVeH1JV5i2Al5W5/cSZpeKobo+HbiGe7bsbjaY7OpnCXjtwiOoR6Zwc2ob2e6HIpPngvu76oyOXk38I0wGg5fZ8gHAZubK0vx8KichCfvR0zW1Onjt2LA66BXuT4lyJj75uR2nLhcVPjkvRBJ0wiLT/3FX0XjCW+1Hv/aP4bTqeuxT7RxjPNJST/13/6m8+DB8cNXPvVn/zU6GRUPn/k0OOn4Toyyh7/z/fbB4fj02U/+5RdXHmxPrjz0KXi83I1HJ+mT3/puPJ4ggh/+3rPd2/eHD1/893S8zrIHpf+JL//33u5u1e1c/f7zzZ1912FPtt0STocw+diX/nb9vQ/2nnj86S/+Tff+zuTUqSsvvvjQz16dbp259NJPH/7xC4Mrl/YfujJZ3WjMxpd+8JOHXnm1brVPvfX2uRd+Viwv7V15dLx2ihrx0W985+xLP5tcOHf61d9ceOmlutc7OX9+f+si1eqxb//TledfOHri8Ye/9+yFl366OLV5vHVhtHma8vrKSy9dfu7H2ebGmdfevPrMDyYPnd4/e2WyfqpZTDd+/saVHz0voqS3u/2h7zwzPnte/lZAow4ACOM0b+/tQ0SjybxxcASUNpAoTA0gjXna2jtQkHizRW93D1igIVaYCi8Mp/PW3r51MJzO+4dHClGNqIYEKEsXaWvvwBoXL7L28QlURiOiMLPGeVXd29mDAPlp3to7dNJoTCVh1sI4yzv3tzVluCj697aRtljp3sEREZJVvHPvARSKGbWMKlfX2ILegx1QCShVZ2eXVTXRqltNkRRImf69B0gbp0z37n2/4Naajl5EBLqSd3b3GBfIuOXdfVRyZOEqqKhW0ML+waFfciRV5/42LjjQaglxJDiwoL93gGcLYGx77yCYpdBamyHEjQNQz4DMrEGYFTnmlbPIzS3PoAKQZ9hlWhtLaw7TTBHq5kYuIETIZs7kgDjjWUfzXENUTV2w4AYRlhmZQe0gKktmLTTGpACWDlAq58BOhUEEzGZYCmOhzYxIoXZAZFCkTgHsVZDmvkEEKQzzGGrjW9kBXCkFpY8WWEHqSsgyv4bMQKKIZwGENUZl6ByGkvkFtoAGVq5YbhFxApO8gbUiEqAychppSAQNNMK+sCwPrENUOZS3gHbE6q4tbJlBSUnuG0A1xzQLnUXEQTkGWAFrkZpYI5EkTGLPAAy45BOsFRQG1SOAKqswkcRXmGoJ1NQZiajWak6p1AYgSTztoNLOLCCoHbbATC2vobVQIaqIZyABU2tzpSE1M6MraDGRyDOQQGX5FOsaSEzrkWG5sIh4BQalZ62DPMacGWlaKm9kc009lmFQBgYRwD1SMShF4kTD1hZCkPksBRpTPwes8hzGDQJbUDoDIA8ZZ9ohWnoghQrTjq1btjCIUk5QFQFjYeUh7muHE1m0eaYJY5mDqa8xATUlZQCsdTUEC6MdBrmWY6upJ6gnMbMAucrKGZAaiRrDuYEKKEgk8ayDSrhqQog2UEM9dVgaDYmgvoNIpwpMnXUIcVlNiDLAWSCIrwkFErq51QbJCqixU4gp4knEDEDGID22qgZKQjcHwABqDK2aTmPjMFpEqCbA2LZMA107hXEaWMWsgaRqUWmdg4r4GlEDCM59WFOoHc08UCPgQBdLarWFhOY+48AgShfY1Z4FSCOmEMPGkCJBkmhE4MIjJbAIdeos1LW1BBc+5Ngai8rEE9ABxDIjU6AAVqnVC+AsUJhK4inflwvoJsIg6nKLFlZgprCnIXHa1jmQC6cABjlwqXEOSkwl8YBxoAIyRcg4kwO5cNohiZmgvnXQL1U9AZpQWTkxR8hYjahC1GGiC+jmRkOs5xLOnXOISYOL2DpiFaMLYqHnV2VXplYqqhwsWlADoyBJQ6IhNHXPlQRDyRHNAmAx0oCUbScdsbAPBDQaaOynBBiMBELzyFiKtFwi0vAKGsIyXwnkDCWZDxSFZbYMayBqYBHKW1A56yjLPKIAMMBMbV1BZWE1J6BUUFlBPImYwsxOjSogBFDPHeDAIiIx0wgrwvjI+lltICYLIwpkINaQKswIsGqBPK4dwnLi3Fw44AT1JWbWAD23dQmVBfUcytwZgCT2JPathW4qZQ4BJjaDpgQaAFoxmEFFPJBDmgcCIo+nPayc1LBiqPItRIj7QYksJApRSXygDRCUlDHWkgiMykA70rR125UaIZ9bWoTOISohLBvAAUWYwsxahyuCck8joktEsshCRKqiZStnrRM+LkIDCKmxlzELEahkOSXawFohOXaotkApWmQQOFlDPbYKUCy1nFEktYUwVgrwSipk5wBoQJWVY1drZLMiRshJaQ0EE2O51RbriTEK46oknGspobLVhGgJBKRyqAnX1lg/WyCtjLByCqx01BoxRb7QzoL23n44nhgH48PjeJEZZbqwjlSlMW3d324MxwbTzmjSGE+BF3SwioG0DrT3D+O00A72Dg6D4xNDad9ViSkdIK3hKBzPLECtvcNovlCQtut5i6ca0/bhcXx4rCCJBsNkPDVCtx1vydJC3Ng/bB2dGIQbk1lrMHQIx9N58/DYWBCOJq2TIVRGIaIQdRawNOvu7CEHg6JsHR1jqTUkijDnEBtNlvYPnYP+Imtv7xllnIMKMUW9aJ62D46sg/5k3ru/ozCVhBmIDUD+Imvv7Fuh2CLtHhxhbTWiClPnIC6q3oMdrIxX8M7BIRbSQfRbAo3+XRhgxivkIQ59aA0DGmorMfWdqHEAnNOeB43xqxIxyFGAjdb/6hICgatr5ENjlB9QzrEzARAKkBp5OvCxlP+SUZZAa6QfOgRZVekwCHnGka99n0iJjGFQCRwAY3woC5ZQznXg+7KSgBFoAIDGQs0ooIQu5rLRbPC0NsSEQSBKjnwKtINQWxA4UXkJ4bUKw6ROa01s4Hm64shPdOEgLkEQOl76Ca0qGcWJSCuNge/5uubQZ05ZjIyGgaursIGzTDWaMU+FZYChNp+N/SVmhaHUKmcpDXUpDVae38nH87CBjfYQKFHU4jPLaAFDS3CkS6msZn5s6wLH2BoPiAoGnuDG96wUlnmeUdphS2hkigLHyBrjMShrJrj1Q2ugw8hSioWwCMemKJCPlDRRA2rt1xUlRmlce6HyPCoV1kr7DDjglSWhlmOPiEqGDaSdA3CpHs69rrVA+4zUHBttmOcrzlGgA49I5RwMLJcYo7qWccsBSGthfBrxvEIhJo7xQjtECeA0htr4oM795r9mqkXtEKYUAFjjcImfLPyO1cCDMg+aXl7YgPmqqlDEgMJGGoc8K8uwCWulAn8j3R+wZeeRSBbKIAKNYr6yODRV5ce4FioIm9VMWmIJItBVKGBOAgSlpf16lLMEGKcJ853gznOU+IaXOPKsIFZphzynyiCBQivfb4i0AgHAiAIlAfNlZTyqLbEYh6Y0QmovCKwscYKc8VxdkTjghfWpERJQoryI1NIiHJm8xAmyWnsMS+7XpQljZTBASDNCa2kQWasOh2EfiVombaQM0hpRQOq6ZpFlhFaZw+RfurBRHpQljZHSiACspEB+6CoBfWet8Twqa2y0ZsyXnENfBz4yGhgQOG4tAA74pl7EPcJrG7CI5xX0MQFMcGMBIY7TGCgTQJH7TcJr55HVxcEoWMbQOIQFZKHlNfGBsiGoOQ6olppQz9QVDAh2yBoJmaYUIuiVpQyCRKS19Y1HQpnXiBJnAKbS0sBUtR+hWqogaFYzYRHAqKXzQbjuGQ4RUIYEpqrDCKcL1WxHoqitZykNTFnihFlBtFAO+U5WYQMKo33PIUhq6TAOTFFDCgBQfkRqGeiqDmJYK+17ilAmBHDWMga18esSMKQdRtZIL0BKW4hjW5Q4hkZbj7GqpFYRZAVgNQ2sR0n9r1+mDELW8LABAMBSQoYo55UXh7aGShuIKDIlSZDRPhQFTbBUmDhPcAEZg1ogTyMam7zEMVJKRFGrnHIYQgaJkjUOA1tpCIGUPnaZ36Zc6IDFPOPQRxQSWQlEOyrNvbY2OAA895uMVyoIEj6vDYIeQ84J6PuWS+IB5QLAyyBh84lsdyNeVDD0ofB0PWfdwJSKEMi5T1zmdyivdeDFdcZBAAmE0CqLe3ycRy2lkPG9Jp9LRyyCFNoCx8wKDJ2wNLBcIwKtdQ4ypCvoA4wDU5UogsBCCKiofSvKsOGkMb7nEMK1cBhHulCOOABFGCGhfM0JtkITEUYQAI8XAEEC7b/cMR4jtQAQCj9gde0gXq2OhuEqNEb7DEuOlXRByDjnNLQexUI6iCNTKA2hMwyaNOoSISGDjHPOIg9Ip6RGJACqwglSykOyYA0iJSTA53mNPQatQp7C3mp5MIjXsFA+kiUNaJHZRoPxmpPQA8I4ZCD1XVWymHBhA7aSHg38FUwcVZxD6gOtiO+UC0Fd+A1ScRUGYZkJACFl2FqFmW9rRZjRoF+OR+0Vki10qxOUhUAMYUCc5tAPLNeEGiEjqIughbnQgZfUWQlDjByxdQ0IdsAwHwkZgroImqTkKgojmdfAJ04hDKQlgapEGIGqNlFoIaacW0xCW1UwhMBqz8Oi9nUtgwhypT1mCaGcO4yX+GgQrSAtdRCRunYAMqRhreogsoSQmjtCQ11WKKRaQgIkYAAABpUTUgRxpCoBmEX4X7usMT6Ly5STUPseVgo4ENqqBgw6EJgqC9ukrp1H/bqsccCgdtpaCxgxHEdISQ/rgiWkFoTYpFpM/b7nhIHYQBI4XpEQC+lhXeIQGwMRYFpw5DOkrUMa4NDx2jEA4Up5fNA+Q2vufObXpQCsWFpt/9Efx6/84rcCGv3Y17/14X/8ztHVRy688PMPff+5wbkL1777zw//4CcPPvaxj//dP3zom9++/unPPfWd7z7xtW8cX7126uevPf7Nbx1e+dC1Z3/42Le/++CjH3vy77/+5De/897v/LsPPfejp/72K4NLl7d+9cZj3/zu8fkrl3/y0lNf+/r9pz766D89+5G//9r7//b3Lv/wR0//9d8eXP3Q2V+/9uRXv3V8/srZl3/+9N9/7cFTH7v2/R8+9eWv7Dz11OnX3vnUF790cPVDnf2Dz/zxF7Kl5ebR4LP/758fXbzavXX39/7kP49PX2jMp5/9g8/zuIG1+dwff6Fstfzh9DN/+meLja1kMPvcH31+unGa5vnn/q8/VH5kAfrd//tPrOfRtPjUn32x6HW9cfq5P/qTvNlDAPzeH3zeQcK94HN/8McaEyTtZ7/wX+pmE6T89//wTyo/MYz+/v/5h5AL1Wx85g//Pyrl4eqF9AVuU8MSOPuJQWNhT4fzZ2pzbOkazV7kZiBZDMa/RG4oSQMsXgbmoIbnYvGDQu0bvEYWP1f6SHoNsPg1NENLenj+45rsFuZiS/1gnm0DcCoyPy/lvvFaYPZraw6kWW9WP8rgduHOJ/y5lO9SdyoyP0+rBzpM5PwtqHd0faax/C6L7izNT5vubT+6vW6TKRj0G3fWREsHh63W9VD3C3av3bi1PNuy7e0wuLklWnk88MM7W3VD0eNW992wWDWNu2F4e2t+CiR3UfODLZ0swhHzPjitotKftRs3V4oVE97zog/W65U6OA4aN7Z0UrAJDW9eMBH3RmFyY8W1UnTQbN7cVO20KMDiWY6wQwiVP+SQQlnDxQsKJNSmev5jRSIU8cXeT3xfCxvQyQ+EhgQZm78okY8Mt9PnFYUaMjD5EUTGmZAsvl9CCyAAsxekbyuBwtFPHIQO+3D8rES5dL1APJtbAQwjkxcNcdYBNHteYWNdjGff4yzl7nSn9UaXjiMdVvGtzWBCVSKjd8/4Rzjfssu/6rJhq+qV4Y2VaBCZIPPvn6WDoOyBzptLyT5YnAK9XzXI8VK+rrs3Ov5JRwWZd28jOg6Knu2+3Q93iNoogrfX/OPV+apZuhGTo44N0/jesn/Synu2eX0t2abpGdf7TezvLcnlwr/VifZ6qp1XN1X9SuG2Qv1eVbwizOWeuVnMfmbYCoYHfP5LiLu42HXu5TncCtUtMX3FoIdCu6PSlyXrQntST34BaBOqISx/ZdxGKG5W2S8NXUNiR6YvG9oBeFQevuKzBpBjl71Y4yVs91T2C8NWEB+A6Y8VbQBbuOIljUMnpq74SWGXw6hE7bdOayAxVI03zxOppW9arz8EFLbAtN7dhNBq4ZrvPARVbalpvnnJ1QiGPH7jLK6RQSp5ZxMaawFo3riElNaBS351ystV1YXLv+wD4QvfhdcfYtJoAprvnAWlrDre8s+X/NQWfdR9tW9kRwe2+/YyrZwgKnn7jF+66YZPvnec3nbgXOxeyaq72uu66ZtQPNB2s1m+VMI7uTuf1D+a8zvQnEv0a2V2y4VtlV6H4q6xqyF/pbbXU3elxX9aLj7A9lJH/3Kev2+DruXXFb/t2BKcvmbt+8JeaemX54t3DDvjyeuyfEd7HVu+p9KbiK4R/htl3uXuYsR/mpY3IDjjd48he/+s8SsvDTvvdHlXefte9P5psSz9odd895ROKroA0fVz1uMkC5J3T7m4RMMoeX9LtnIyYo13zxqWk5okNy5YJkDhtd5ak3HtTVnjxpZuFHbmN29csjhHEjffOu1gRWoav7OlA4FnLLlx3oQZrKPOG6cAraxljdfXITGFlfnznIraYDp90SKucIzGP3RmocB6KL6fmxy4CM1fVIgr4NHJC8YaDJpk9gxn40pvNcQzi3xBQMcvXxamcCDA0xcUypRcbVffmeGRMFtR9v28ykLYpvzFws4U89T45wjOtFhrVN8v9JF0ZxPxg0U5IqDn8xczNTZg1e9dX4ruU7VehO8uRbv9+abpfdCge6s6TuOdjn/QK7smudlv3cHpadt+t+XtbtQb3LvdjHdWbTSP9lpke1m1ymR7tXG3lW6p5tth8GBNrFTRdiO+t6qS3Dto+9vrqlNG9zrBnb5dnpLby417q3V3wfZa0f2HdJD7w1Zypy8bRbDTTW526+W8flAvXtS4Cb1ZdvhT5ntaFTj7icQt4oYq/amhfSRHZva89EIDlMtfBMRzkoPsRyVuEjtW05dMK8lLHs1/bCnTyLnpcwIhawEVz5cwwmoBZi+aqGFViefPCeo5DVH6TMmg1tivniurRoJLm74gfF9LjWfPCoq0l9jf/38+3947mK5u/Js/+2JrNEq7q5/9wl8EJ+OTC1f/l//9/+jt7O9++PHf/YM/6tzfGZ699Nk//6+d/YN5f+13vvAX7ePR3qWr/+t//E+9m7dPHn/0k5//y5UP7hydu/SZv/xia3uvWFr+5H/5UmfveOfah//Df/xPa++9/+CjT/+7P/nCxtvvDs5deOqvv7Jy6262tvFv/vK/dW/dP7l27Xc//4XTb7x99+Mf/9R//vOt19/cffypx/7pu1ef/fHe5Ucf+/q3nvjuM7c/+amPfPWbj33tm9tPf+LCS68+9dWvHV+4fP7lnz7y7WcGly5f+8Hzj/3zs3c+8YlH/+FbH/3GP+58+LEzP/3Vx77yP8Znz51/5ZWr33pmePny5ed+/ORXv/HgIx+9+sMXn/7yV/Yef3z9tXf/zZf+avDQha133v3oV785OH/h3Asvf+xv/8f2Rz9+4YWff+Ivvzh+5HLjzvFn/+IvJhunV27d/uhffXlw4cpvy0UInevtbFMpugf78fGRP593jw7DLG8eHnhV2Tg8iNJFXJf93W1S8/7ebnM0DLO8s7/nz+et46MgTzvDoT+bhmnavn+PSNk/OmxOxsF00jo+8rO0PRiGRdE5OgiyLD45XtrfJ1IubT+Ix8NgPuuMBlFVNo6OaF01D/aDLGtOp93dXVTx7oP7YbpojoaN6SQej6LpNJlMlv9lyLC/F58cU1H39veDIotPjsPxuDUaxbNZNJm0dx7Auu7fvd2ZT2ldt44OvKJoDAfRYtacTqPZLJrNent7SMjeYJCMhjTLOjvbUZ43T47jdNE+PgrHo2gyWT4+QVL19vfj2Yxl2dJ46BdZ8/jIn03xQnAbqZzQSZ7ZsJ7aQCkuwkoGoJBF5RnD2KQSklYVxblKTeRqz8/zWcYy4aNCVCUTNnBjUUmmSswWVemSeRkywedFXNuIVbrMSK0YnAhhgpL7cFoWNua559VciGihIluaNKfaet6slpKkBYN1jcyyxqusBjCPJOnEc01VIHEfFZ42iXPLjEtaxzVZI4Wzsq1YN8ogLbF0XZpj4BILNr1aO54ouurl2lfNmnZw4TsZGdT1CwRUIOwGqRQRDUXXWKZQ7tekE4wV075CPVxRa2Jrl1it/IpyvMIK43JboIacOcv1lEcyN7RQhY5d7fTcFjiBo8orVQlivoCOm0ImQBOUq7T2XS7QSJY4NgvIZuXcxmqqPG0qEUtBdGZyFRGJ6KgsYSRSCAuVg6arCMnLcRHVNQILWepI1siM6hxGdmFZxlPXzAqPKOdUG6BmPDdGhUYmNFMa95Fe8stSmFULukGlsGwp1MKFp0ETu06QW437VvX9qgB23aA+KYHlTeviKHUANrTr4xwJ2Meuz6oS1h2Blr3Catt1qBXPLTSBEU1WAQu7wG76VWlFR9MlPzPEtBVs4RxWkhYiZlyoDGauBeeFTElJEjDVJne5DkBqgKHzumGlLXOSuhbNuZrZFCV2wA1HpYncXMkaTssIZRxWIDUJrrRKYYFjb1zhyhY61Auja1TIiCpn5mpuIjovzESXpOFmFmZqLkOUKaRgIRPNLSmYIJ2I0yAVNe7TBaEKWbsMbeIXwOgO5rhRAE3bmMe0cpyshCJgvLZ6yZiY1NTAPrWhNzECtWAR0FwpuopEm9ZS2HVooqBwDnSsTkjGBG57us0yqegq4E2/rJzbdK5FC6JMGwsWZcjQjpFdquSsirmNGNdVSrjx4FgK5ZUiBNOqtGFVeKyuJA/mOnKlSVMqne/Nai1wVnmgNJkO6zryqrLOaO4acFbUlc9dCMZC1LisGK6MtMGUN7CSae7loEkXvM5Qrn03FkJ7FfdgKgrhTYvAy3PL6cJEpFSgDgzuegWGyhN2A1eWiljRNZprlHmcdf2x8hVTuEcq5nRg7TIV2udE4BVWWFJGknWiCrPCCtBjGcTKM26FaEZyVqMlv9RJRQTrkiKgEkm8HHKMa6fNMlSE1oGk/UB5dGw465HUI9xpvAZVpIUrRAAlYqOydEGdIVSohY1tRWlWTPKgEhSkspSBlNSO6wKEJgUs5ZlrpoVPhJhXibE+znVee7pycCwqFFWFh2Z57pp1jlldc9XIladyl4nASexNa+6CfIFxbeY60dz3yrIsfa59OKu5jmrBSKol7CHTZ1WJRbvGKzR3RnctbscLh6RvVIsV0IK2c1texY1oK7riZ5qatkAtXHjGNIDteIW1plnbda+ssOopusJSCXgkQTOaaOIi47q4Qtq2oel6nHt1wMmyn2lUNzRpRqlBgmjZ8isLdWLAClNQLWCOEzYqKbeFidTCmhoUIkIG2oVe6IjMSzNWBWnYmcOZmKrILRRRsJCJqYFIQe5ir3Z4WOQ4UTMLKpu7JiqBy8S4jHVlzVyXING51aM6x4mZGq8UC9fiBXaVmdcNqLGd69yGYCHcVBekUWcsyNNkfz9KF83JuDGZ+It5Z+cBqaruZNoYHPt53j7YD/Ksd3gYLRbNk+N4OIjms97hARWicXiQjIbeYtEbDr2yaB8e+ot56+Q4nM2iPFva32Vl2d7dbiwW0WLe2z/0qry5vx/kaXc0bKRpMJu39nZpVXUPD6PFLJqMk709vyx6h4dBlreOD6PpJB6P2rNJVBbNwwMi69beXpDnzfG4s7+Hi6L/4H6cpvF4nAyHwWza2NsL0nRpNGBp2ljMW4eHlPOlg4OoyOPRKBkMosW8cXwcKNl9cJ8UeevkqH+4T2uxvL8bzqbhyUljPm+kaWM88dNF+2CPStGdzXo791HFO/t74WLRGo/DPP9trQgP/ubvuoYv7e0ePnKFjdN4MpleOtff3ZE05EstNk2jvNi/fDmeTFZ2to8+fI3Oi+Z0enDl4e7ONlaar3XpoowX6f6Vh8N0sX739uCRy+FkRhfV4ZUrncEJq6pqpQML0To+2fvwY0FRrN++uf3EE/3jfTbNDi9faU/GLM/5atdJ0NvdGV85pyTa+OC9Bx//eGs2CkfzcqljEW7t7A/Pnzeed/aNN+4/8WQs8/7Ne8dXrniiDE4mfLmrg7C9e1CudrOoffqNN7af+mgsi/7tB8cXL3iaB0cT1/ZlFIcHY77ambf6515/ffuJjwSq7N68N7pwnkITD2Z1K6qSRvf+Tr3Uni2tnXvtte0PPxYA0bt5f/bQmUhndlzrRpitrvg7M9mJSATAbq47IYkgHtcGQ9D17cIkVVqvNcyR1IlPWgjvZrrXgKGzY4GMAauRm2smBN9owf3cRpS2MN7LZLtBImdmCluQb3TC4xmTst5q4WMOfFwutaIHQ+tj3KFgrp2y5VYvGKd+UbkVDwyECPx8tdecpEGpJpttLyuSseYb0Eg/WtSzjTicS8xFGFVc+aSE09Mtv6wbAzE9EzYWhctJuh6EhWaV0C3kFAxGfHyu7/G6cSxmZ8JGVsIc10uI5grWznSwcyQYCrGGuPNbh+X0XNJMC5yjaomwUoPSBnGVoziYGr5Oc6/ZvnMotlrESDuqQdsHAcWDErYIj6Pw9rC4sLJSDbIhVZtN4hSYChgTHTAw5KSJ61YjvDWo3792IgAAIABJREFUzi/5pgaHpezHlFo4ETYiNvHQSd30imlvyb8zqc70fKLgbqHXmhAbO66xh2w7QCccx6DstrxbQ7nV8YhCe4VYacEA0tT4Ui1WfH9gDIOuZaMjJZqeDS2eGaStWqYwQ16t8lXPGzlHXLYUdXdmKiKuAVHqiAbTtUY8KYJKFavUmyKLXbYUtY5Lh2zsZ3kdEwlnW41kWlLuyhWcDGuBab4cdQaFBda0ECyBtxB803MlYYUp11kwLZxwrkO1AGRcFedXvZr7e/PywlK8mOup5qe7pFQ0q0CHGkvpIAWbIXee/2DCr67Gi9TOtN6IccZtaWDPc4DgoxRsRRwG8d1BeXm5PxukRWDXY1QJlCvQ8SQk+CTHawH3ovjWSXllNSgyO5Z6JaZKwVSmq33H4Oq9+Wwr9hz3jmyx7DGs6cyYAKqI0LGBoS1aUXe7mGwkAeLBgeZLXmLmVR5CH4om88cG+mbRiVrbZb4SeFQGh5ov+YAYNNWQAtmi3sRBz2SdsLlbV0u0SsL+/UndYcAHZGENo0XbS44rQlXWi9r7Iu/RotlsHA2wtvlmPxjOWFnLUy04khC5arUd7Iyth0mbgIV2tSlP9/1pHsxzt+6DsZDY42vt8HgOoYNtqnJL0rq4sBrOMjyv5alGcDyXgMC+hye1Bgh3ieaYjVNzOpElpNNSne2EJwsNiOsxOCwtgKjvuxriYTa/cqo3HaEF5iuYlgqWznSwgyQYCrGCOPRb++X0fKORF3QOy2XCag1z60W8ohGbWLmCChKubGejc42oKtEU1MuUKU0yq5tIERIeC7WMSj9aepANzjYSUdIxMH3Aaq4Kz7SQ8mg8UKIP8iDu3cvmp8NQ13QM+LJnMCCjqoGKxVKf3p/zjbbHDNrN9GoLUmPHAhHgeiEa1pjZaqlN74zVWtPzDNrN5XITe8ZNlWOU9+LwYEY8x5db/oO5XIrqVhLfOTZNjzSIm0qDSb3SDI/mDCrXoW6fi15SdxuN3ZENCGgQPdPQgnqrEx1MLcXZWrd9nAOrkiDPRUxrMD3Viueln5lsnSZjrhzJV6LmuALW2BYCFfBmot5ipmbBXOUbrDHhxhLVAXSmnIW2i4HEwbiuNonmNJzJ7FTQmpTGENGB3kI7YcNGtXDNeKb4OpHGaw6rxakwmnEtselCL9dIONGjNfSS28fFldXlxUm68PVGQoSAqQItphBGgwqveDyM45snxZXVsMrdoFYrMbUGLKRrMu0xelQ0WnzYXI1vnhSXVgLNwTE3aw1gNJjWqElN6NODHCyzIm6E7x+LC33f1OC4VqsN5BScSdltWArZ7pyu0CJpJndG/KEOtTI8GkEEss215t4JpGB4+uzmO+9OT53Kev0zb7yerm+apucP5gCj0dap5bv3CDaDh85vvvfBbHU1XV7eeu993ky8GIiZJtacXLi4ev+eJjTfWF67/t5sdWOxurJ1+zYPQ9FreKM0yBfjyxea+ycWonRrbf39D+ZLq7odNu/vyzCsV7tswf357ODatc37t2xtTi5c7B8dIin5agdWtre/O7x6wRZqaXt7/8nHl/d3YW0Wp1aToxOgLF/vQwl62w+G1y5ablfu3j368CPr+/cqzbJT6/FkSitRrPWMcP3t7cnVi8rgjVsf7D7xRO94H2U83VyN0pxmZbHR15as3b1TnV8Zk+7W+zf2HnusPRl4abX/6OPJb81F+L9d+fmr6+/cmG6d6l+/vXb9vcXy6sY77y2/eePgkUcu/uRna29ev/vk0+de/tnmO9enW6fbt3c233hruHFm7Z0bp15/6/DqtXOv/mbz1dfuPvmxs6/8auuNtxebW927O2u/eXe0eXr1nRsP/frNw2vXTv3ita3X37z/2EdOv/HW6V++Pt48s3z37tqv3xptPrR08+6Fn76y/8ijW6++fuo3b59cvNh57+7ZV389OnUunk4vP/N8trziTdOLz790fOpc6+Dk8o9emK5uhIv5pWdfqOKmdfDqP/2g6C+ztLz0/Evpygodp5d//MKiveQbefmfn6+pp1lw9TvPikaMuDz/o58W/T6eFpee+3HZXgJWX/3ejwRmMm5c/db36ihxyl764YtVswlLffHZH9ZB01Jy9TvPWoBFFF787o8NpcOls+PbTCmKPTP5/2m7jybNssPM78dff+/rXfrMyvLdXe0AEASHAQ41u5H0sRShkTajCAUpDoczHJIAAZEwBAjbhGuDNmh0d1Wl95mvf6/399xztFBoL0WI++cDPKv/70DLY0gHbP4JS0JCumT5nMoMQArGl3ZRKEwX85d6HSPQp8vf0SRWSI/5hzSPCdCwe65mEaGGmJ9Y2QrALTX4BIWBWg/0+KUoIgJMurrUMh9XbT18DgsXoU01/IL4SyaGevJSZD6hhlhdq7GvFGumcdcjy4E3EOrYIe6g1qI6HaDZIDUkCNts3hWNUK7WxHI9HAJ2ayBvszQL5jLh7qQWgFGLTgdpp8LTlnS3go7QZjpebOYGp6EiF7uVVsrIwdONrMvpzJb+LrfCKuzi2XqpcjVj9exeqVQydeB0U9qR8NpytVfbUVRQ73MqGeGEzH+vSI1JgZYvFWCgImfLY7VSmFMEF6c9LqFU8fx3VGgKl8h9yYCGeUkmhxpgFKN6+rleCwlsdfE7yjGtIV4eqhrMY6HNj42aKoTUky8MIFFt0uWntMQMULI6VCAGBVDcA6VClLJ6emCVucRDk5wPIXcqUoLZZl05gGVwvo8iLelWytl2nTTLVo5v10HmVKyCyw2QmokJ6HgDRlbWq9jldh13w47UrjsosbnGpbsJAitpQOV6gGJHND0xvieiXtyV7Kop836tZHTWqZNe3ADK3ZC4TjIs6cVQJCNuJ3A2AFG/tIroVgQXChoqySXwLpVq066v69WFCtpMLPniwqgdNfVZcgjAmp5cSffaVDoinFL/QoUNKv16fm4iC6cBcY8Z6rFqBmdnGuvAeKkszxRoIjXIri7aUId5iFcHCuqzdCq9U420Ye6x6bGGLSIyOT/QoA7zXFk9J1XXUHIV3W3VFEFRortHXCCuA3x+jxOFI4hvtyUWQlA52ashgriEN49kJaVW1DdPONJqLPF4BxBYCILHOyWgmORg8gQUMHOAcrZTAY1Tie+2JEAlomS8LSSNNaJebMhKzZuSXewUwM5VpN4MoYAlo2iyUxc06NPywyJ0lXrdTF7Umc+gjVfXerbCVdsID2Exg2hTjV4Qb8rkhpke8HRFmSW8WzVYanVXCw5gsaBwk4WfA3+hiXUzP66DmYItmNwgb6HRFvLPaDKmcEcPPxP+0lLatXfFoplGHJSMSTBTcRt5ZzS8U8gmTY/g/E5HI6Z7TM7vVWopMouMt/KWwK4JlnvciqqoTaabhcaVHNfT+xWtRGXBuy1pJnVggeU+t1MR2GC2IzQOcwSn90tWy9pAt9uFI2RooPmu0KI6s8F4t1YErBG42wc4F7VSTx6UlgSxBuf3oB7nRYPebhUMYAnB+EFNZAi495wxWWWSzU4MjhTK6skXBuBINOnyE1YIhnSyOlCkQBzT1ZFSckK1en5oFTGAAy34GKVC5wYLXwBRI66oyyOlKknR1INPZJUSOFT835MwV4WtRC+AqCCicn5q5CXjHcP7UPCcgQENfgei0hCOkryUac5kW1WuBzhsiqYnJ7siGEZ9wa4bIBlyPaeLhgiHsQPYrE9XrXhQksuBiNYqJ4HzHgyGlVaSVVP4w9LKwWyA3UE6yOltX0ablRnU3git+rWa07DBl+uVVcDlALhroLmqZ2vA3y6dsF52gbsmtBwFDliulXYB3T5crBXdPJ6h5YkqDaLH8cV5FyiwLMjyOYUdJfOgd6LiJsxDNj1SsUFAJacvdMBAJZXF5xQ4NA/Q4tRw1HiZ28tjTeoMCjF7oSMVF4CtPqfSplVGlscaM0RSaN5LVisEQTl/aXIEa8L836OyYRY5Do8w0WQmjeVzWlOqGPzZN75LgijoDx/+8Oc0yxPNevDjd3BaLgZrb3zrHxU3mDx8+No3v8tW4WK4+fSHP1ajOGr27v/kHeoGs+39N//m7ww3XO5v73/v59p8tVjbevyDf6ZJUZjm/s9+ydxwvPfwrW9827kZX7/22tNvf9+cLbzBaPedd7W5lznO/s9/qc/cxe7uk299t3k3vX722pPv/NC8mcx37+188GH3s4Ppzv72+x9u/faTyzffuvejd9Y+ez65/6D/yYvNjz911zZHLw9HH3++3NoZffjp5gefXD97tvubDzfe/WD68FHn85OtDz4M+4PRyXHvt8/djc3hx59t/+bDm2fP1n79290PPprcv986vNj61bv+cL11cbX1i/eX29v954e77/z69tXXRr/93e67H0abQ3Kx2v/lr73BeuP2bu9f3p3eeyhevvhXsgjl4MULczJuj8edy8vG7W1zMnbubocvX6hx3Dk7a12eG2kyOD6xx+PuzXXn8rJ1edmaTJzZbHB0qAde//SkeXNjhGH37LRxc9u5vGjd3DRvrhuzmbVYDk6O9SDonxx3Li5tz++dnVrjcff6qnl317q8bM6mzmzWPzxQ46h/fta8uLCXy+7FhTWZdK8uTd/vHx8702lzfNc7O7WWy97NjTG+615fNe9urfFd7/TU8Lze+Zk9nTjjcfv4xJpO+1cX9nTaOz9rTCbWzXXv4tz03d7xkTOdNO7umufn5nQyuLq0F/P++akzmziXV/2zMzPwe4cHznTSurttnZ1Zs2n/8tyezvsXZ9Zs6tzcdo+P9DDoHR81pmPmpUVE6lVN3CyOaD2t9CyNAjVPVBal6QrmgcTLJI9J6UrgV3Gi1NNSSZPEY1nEcJgnC1TEiCzTPEBFgEEskpDwKWdZlrg0jSjOROrSIkLKMilCUAQYxiKJWT6HahyXC5gkqsxQ7LMyhHQaFQFMfQVnJYvNOuuQEtHEEWVLdzkLsSg7pDRAaaOoxdKShqosu7hUaNGqi6bpVjQismwrmQoKE8VtJa0Un8G8QwrCUqviLc2XLMEyb7AIw9KUxYillRIwmTZxhvWACd7RVzWNYJ221ATjQpXFiGVc9WBdtEkMsV+GMa3mNU6rKNTqUOBlkrqIx1IuyijTwCJHXhaErF5wmpZRoItQMDdNPCJCjuZ5ljK5qICXhyGTC64keeBrdQKxX0QeEUGJ52mSKPWCi4hHkSImGUnyyNd4goFXxS6pQ4lnaZyyciVlIiKP1PMKCYhiB6Sa4VUoN1liKikSaYP6ppImMOsC3qWJQLEDcl33ucxMnDVpTmXZYr7JspRFjiyarMQ4aYLMMlYlTlVUtnGhiqpNQltJEuxZouySguGyDTPdXFUw1XHWZAUReQvkA5alzNdl2SY5wZktCpvGoMho5hIaJtWijkMFpqJ0YRxRukihW0aRIkLJUxS6DKYVWJaJi2QiuCejTEXzVIZ16FOxKkUCQldDcSGnaRoxEFX1oopzDc1T6ZeRR4FbyBSEgUaSAs6LMGTIz8G8SBNFzgrhl3FAsZvDuA5CA8SVEQJQdzQfqiHnRUv1qZpVdT5Cua6HHCQNnFHdrQFvqSGlMeBlh7mYpYXMRyg3WILqtEVjZKw4qFpKrJMMyazBfJWlOcgHsLJYjGTawTHVPV5XDZqYpCCg6DJfY1lO4jYoHZYzULRJzMxVCQqbFG1aFIlL4lDBuUhdkkeILZLSl5lPYSyyhGULqMRxuQBposoMJh7LAkynUenLNGAygVnKigVU4jifgzRUYMJzD6cJpauMBzLxKEhEFtJoSUme17MqCTCIReGjNMZkntShiBcIB0WR0NBVlSStx3mWMhRVLEayaLCQ4lIFxYCmtRIQmbZQRvVAEXVHWwolEiJvKjElORPFGs2k5kORt0gM9ZAI0dZcyWIps5buS5phkQ2VHKo+qvMWTaThwVp0NR+RFIO8obicZFgUI5ZjEms8a7NQGstaiI4aqjjDMHFYQkUuYhfXQYUXSZoqfMlFVEeRIsY5TbLYV8qYQL+MXcIjiRdpEtPSgyAWoU/qWUWyPHJpFmEYy9hntVezRZJGpPQRTEQSKcVCKHFcLFCesjpBUahwT5BZnEYkXxFZoCRS+EKyOMkWKE8oDOs0pGUAUAIlb5HQUdKU+KYoO7hUSNGWmWWuSpyoKG+xnMrCgWmPZRnzdVl2SY5JZonS0fwaZwymDRYDVDVh1mdppq6YyBokxWqkyaqpuxzHVKZNNZGwcEA+VLJc8RRetlgMtFgFZUt3a5RSGDt6WKDMFPlATSs5L+Ncg7MUeEUUMLAqUSrC0EBxhZZ5FFAYFGiep5kK5oX0izhkyC1xwoNAB0kN3DIKCPIyNE/jTBPzso5EElAwS1BYRL4mUiBWdRgyEFRoloaZVq2kSEDkYrAsYcojX+MZkp4IQwW7BVumccKED7Uo6B8dNqZTZzptnZ3Z47vOzbV1d9e7ujKXq+bN9ejoUEmS0dGhMxk3VqvOxUXj5qZ3eWGPx93ra8N1m7e3/aNDJY6HL5437u4a83nr9rZ5e9O9urLH48HZqRkGjfPz3umpkiSjw4P/Z3PTuLlu391Y02nv6MjwvNbdbe/4SEni4csXzt2tPZs27247F+fOaulMxv2jQ5plw+Oj5vmFvVx2Li7s8bh3cW5PJ63T08Z03JqMB4cHahz3D14644k9n/XOTuzppHt1aY3HnZNjZz5rzqbdk2M9iYcnx/bNTWM66Z+etG5ve2en1mTcPj9rLBfN8bhzfm4Efu/01L66bEzG3YszZzLpXp5Z83nn5MRYLiX617AIpQj++/8xfnQ/azfO//Arfrcfbq7dvfks7XQu3/5SOuys1je8/XvXjx9Fm2uFY1987SvecG2xf+/mtVe5oU1feSXY3Vyub3pbG9evvhqujbydrfGzJ2G/561vXrz9VmXp08eP/J31+d7+/OGD8cOHwdZGaelH//brlWN4G1tXb70Rdzu3bzxL1vqLre1kbXT7xiuz/ftCUw7+5E9qXVltb8+ePvLW14Lh6Pb1Z4vdHTjQD770R/latzCs4z/9k6JtuxtbsycP/J3N6N7G4sH+zaOniJEX/+7fZf1WadonX/83abt59/hp8GDH3drwRuuLpw+vX30GCXrxp3+a9zvlqHn8pS9nvba7tTN78mCxt5u2O/PXHl89eooH+ouv/FE+7OSWffbHf1S3rcnu/dnD+/FuT0MZ3aasj3WRag/V2iSWnOndXGw2NaN0GgW4Z2EFqBuQdpFezOkmEQPL0VOzW6I1zTAqvcHBjs4MbqwD1scmSNQ9CltMM3OnU+Yjw4YLxczFvabCuDGs5ZrS5K6xh+uOTlFgGWG922gpodqo0Z5KmdSHdT2wCFkyZRyOMMFTioN0uyyt2mDnYaegKJTWAjXLzKoQncWDGuE7x7hbrDOqJpL6ySgjJEL6uGqLuFFx5a4YIai6VPrRbkFIAliQjzKqeFi/LDqoskuK59UgTRtcFatgjxMWMX2c9ErAMqiMYbtMmhJhrxomVc9q8MB4QICFGnqirGHYU/QmN3u1XFNNkOJXLMvMMAXsAUUOttu50gegqxpaoY1gvW0ZdUKe6JrJFYuzXSYd2tQjZQjhQDGUwhrwartp6CXZo1pTGihjDzXQoI6eqANJhsS0Sr1X803LrBP1kcIcYaKU7TFsSKD6UAuidYFwXrV87qQWucv7XtlUJZpha1H0BVB8rAbxWq0oK2HP4x40qlnR80QT14qL9EU0gIRMke0nw1qQStrzrFPp1bzqetjhhR0r+pXXJQzPgR4nGxWFCXfcpAtseQ6ad2VLrbSIsnkxzGs1RSwo1kodZUaHg5HKWrCpJ/WuaVq57lRsm1JLqHpF9jVVrZqNhHQJ6eJGq1AGEA1VU8ToFVvTKk2vtD0qLakNKjpguE1MNVW3qewpFkjIU8NmCewxusWYKZuNBAxV3KMWTdkuAz1FtwpljzFTaFqF7xnElE0rrjftql1p1crfzIBT0tpP1+PaARo6rtth0iIYr0QnCQZS4UGymQA9t+htNMywJTlzRcOrOpwgt27F4RBh7OajCJo5q91kI60tANEENMKiw1XlTjaCYEiUKiiGbt6SVnGXjmLpQEyvpLOMm1KVM9mJgxFkPCp7s6xhNBW/2crzTbupxZpZ4T2NmtLsV3JNbYjA2IKyp6lmZTm52NIdLTbaNdmkWEN6twbrig1Dc62WXVVtA7tTyHXNMnLN4OS+ylChDyTrI8MsjE2JHUxbsGGmZIfRJtbVgtzXdKUwBgKOmE7z5qAQQ520kKWl8r5NkQ9IWKzFUMmBMi67orZiXbsumnnaqrXaDe6VSMkQCbO1FKoR1c5Bq0yaEBGfD+KkLVTue7s5MMqULPNtiJQMK+OywwunpHBVruVBS7bp1XJYETUDOCzWC6gVgs2rTs7tyFJusl4VdrFeufF2hJUEEz8bpNiQhl5a3aq619JAQfeZaVWqxdVdKhvM1lOtL/CIGWqh9mW9oRtaoe1i1gYmTNV9BhvENDK9L8VIdYxE69Vyy1BBru8A0VPafKXdZ8gharPSBgL3kE1jZQDQlspgre8A0aYtLdQ2JWgqqlObzQpsaRZJlCHmI6aVM952sVOldoXZLBlwhm5Ue+mNMMVJbXjJoNbksm7MeAtzfWUaV0kPcD3BJEg3cwKT2vLqTg6UoGpMRQNWdkbxohxmhVFS7EVbXKEesqdpV0IUAHMMWzxuCIJX1SgtrNzQx6uRZDQVupcNIaYBNCZZD8IOsWSMX7UsLUM6ovsqVriNZ3BNw0PNpImyTet106wj9IqJaa2DFdw1UYM0rZiMKO4Sk+Xmhoj2R2q9wK82Lb0wlBLf17GNGmZCRxg7wpJLsqnyLdvikfpUVQxusBzvKsgQFpzhPlFGxEKRsk2qdcvksfoA16a2Wt9YPr4/f/Qg6vW9/Z2rV5/Jnn7+1tuLe3uQ0ZOv/WHeby3Xt5YP9m8ePiiHTe/J/vkrrwuVXXzly8udbamx869+VXT0iydvzB49mD7Yz1vN+f37d68/FYydf/UPFpubsqdffflLyaC32NgOdzZv3nwWtzuL/Xvjp49lRz//ylfDjWHa7ly98Xq8MVzu7AWb69dvviE0Nntw//r117htjV95JVnrTfcfrB7uLx7em+3tl455/Cdfry1tvv9g/uSBPxpev/V2st6b7e4n3fbdl9+Ybe6UDefs33yVO8bFa28uHu4n3c708WN3d2O+t5+1GjdfeWu+vedtrV99+e3KMRe79yavPo56vdmDfff+znzvvru9GT3aPn/yutCVw69/vW6Yy3v3bt54Q3333f+vFuH/q4MV/vv/4Y2f/HPv7LRqmKMvXqwdHpRNe7a96/WHepW88oMfDg4PZ0+ePPnZTwdHB1mvs1jf8ocjPfQf/fJfNj/7/erezoNf/2b4xRfe7m7Y6/ujYcud7n78yfCL51m7NTg82P/ow2i9P9/Y9kZrrMgf//oX659/Udrm6ORw/Xef5U1ntrcX9vtMlE9+/JPRF59HW+vrn36++dmnRaPheMuHP/5J6Zj2fHb/F78Ke72BEr7tmFG2dC7v9n75G4QAEfXTH/6oVJnThG9aICbQ+exw74MPhELtLNx755dQ1qv9vfnGNqZy5/efbr/3IbdUezq79+67kBLDrN5u64gH9Sx6+oN/FirVo2j/V78WKtO19A86zbJckpn/4Kfv6LwECnr4g58hIlxzmJ1KmNamiPxLRqPCKa7rHpO6ILMkuWRKXhoi9W8YjGpDLMQ6JamnIRQ8R1UMTLPwjxBIuWLxumPUJjOXvn9CgFua/Tr4DJQJMTo1Az5USjRJ8rnGQ0l0mZ/UcFnoTkpEKPsKWBb8AoKAOzKI70jlQdSFjVub+E3ezMxrhYYNE95ug/EbfBxAxL0NY6Zpyh2Zt3HY4072RvrFG8Bb8ZLObeD3IStobOpTR1hhf3n5xLu+abYaE4u5DYxjI4DAG1IaP0YXb8lbXwpw18FBD+kBXWjMa6tguSfP3hSTnKdFsM6WA53d0pWJgiGlyzSS6Yn8vzME3kuswhx3Ud62FFSK6zK5hLoo2ql7d91kear1QNxtMlyz6zC6wCrOZVjnVwDXldoUeb+BKDRn3uJIIaCmVRGek5YIa1sp+yag2Ji47ilTs1xRC+8FoWWhtGTacxRUwmkeXSBSVSouvCNC8lLpq9plSykxEyFZdklKVXj3x+qsH1/OpaLcbpPCQMqKjgckVyx58ba4G4HlSppsPNJ8BK2Fej1CmS0tL3UoYIURVWzeIakh1VKftFSfKOTibX62z5IraWnjLksUTbhsaaHYgVbyR/zogRzfZFC/HaCsSVWPLjo01BCL0mldXEmzUVQXZTzBup7zpgZbjCQZuYiCsaqoogpBdlJbelL1zKqlGVmUX8j0Duo4Q37hXzOky23oP5zfZg2tuBTxDTKcqhrz7BaaNFcaIhz0VJHL89S7UhwnB+M8uqNN6peWLnsaznN2HQQ3qoUSkErvhOAGcFKiTtuKKLQiwottliaOevcn8kqBqeeZ6nzIYKZkgC56uBbt+vzfKnM1vIorE03vY1ETkZPZSOVZpZVZE9YUN+cRnG+zrEJKoF5vI4na9PwP5diqVmFq0/mIciCU2rzusKRW1euv16c2q5ZFS7vraGVGREXnA1zRspGVH6d5RIy2LF/kIqgNLa+6Ru2oihdlRzWYl1a3SF/IxMdmR/CmRkxguEF+BTIXERtl5wLOud1I05YjG4wkZX3Ki5U0YFqNebbAtpYWA7tqGGYapM956hHDLvMrWc2lruago2Qt04q8+Fjmc+J00uqIRyuqtFFrKYC7TnBCM8pmI0C8R9kXX9aCleBi3mVelwnPyAFYriEQ32dXXxI3ZRVVix7yR5QsSWCyZZuBSC/Hz/y7tIxw0aWzdcx8dQWJN2RoNSrP/jsaxvmyTLt0NjCEx4oazvcATff4wVcIe92VAAAgAElEQVTgPKmiymuz5RDXGRUCj0cYlYXI0kPZAAnKeXBFSV6qjTrvO0LB5tL1DijgQoGlf4pZlWMbip5eK9SauqsThuNCa9bh51CUQOvIqq2rpALLMjmHspBYldlLTsLC7ORxtwUMguZZfiKUvDCryL9RZCxZE9QdTerYXq6il7DklCqiPOMy4YqBjVlHc6mmXuO7EUpbwA6+nD5/AHw/53TegVED6IW6bKorQyiTPyqPHrFgUmGyHJHAIDBSfQ2GbUr8uIWyhmqmvn7ZQkkTawGZWSy0dTB9Aq4eVXeRFGK5xfyOoZyTaRclXUYnD6rLL+HQjXwYjtjKoXRJ/RbxesSYlTdlcoMslFmhP71x1DJkRgQbUOGpvOLxmJlKUi5Bfg2oLAhfwTWiJlHtseAM61ol3Sq6oQPiFoL/exlMylLGIDjGuswQr/wjojJOSSQGWF1OpU/jS8xARevKP8ZKmSqaLxypygrc8viOOiDkGUzOIQKyid1n//g9NU0qQ3vws3fULEEOeKOhqThJEvna97/XmM+CnY1n//AdJYqqncHbJGpQWc1Xu796z/TdtN169oN/akzG4f7G7cb9pN3q3Fw/fudnRhRSWO+8/74znfm7a39Io4FajP3qtR/+yHKX3LH23n3PnC+QUb/aVhs0Cyv09J9+2L6+Xt3fffrdfzKXy7jf2//o/cGLw2Q43P7dJ9sff+TvbszXt73BsOEv9979YHjwkpva8Pho9NkXRcuZ7t3z+0OtiF/52TuDF8/jjdHGJ59uPP+Ctxxvc326sacV6aN/+ZfN333qPby395t31168yPottzdyNzf10L/30W83Pv190WuPXr7c+eCDdNSfb2x7GxvNxB3+7vnWJx8LlTVnk73fvLva2eVHR+rFxf//odEaInWybB6eco6UuedcjUFex6oV6Y1EtY2bSev4IpdMWwaNk4tK4lBzYtXOiK5PV63Tq4Jo+s2kdXhWShorZqg3SqiwZeCc39QcajO3eXjGaxSpdmC2CsiUedA6OK4rSN2ocX4NsjphZqQ6KTOVudc+OK0EZkHaOrqoS4mSov3yBCYliov24aksAOZlL1wBXou8bh8cozCTpWgdX6Awh1J2vTmGEOV16/AUpVzkdevlMQ3SlOmR6qTUQEnZOD6HaQWTqnVwCqOcA9RejTEv61K2D06pF8tCNI8vcFyAqu4ES1KVXODWwQldRVzg9uml4kalZMkCFgEsgJLOYBwpHNM6SxAvS4FTj+Y+qoCSuyiPcC0gLwvAy5zp1QIUAS0hTV2UrUABVd9sJ9TKEUsXMPVQRvViLtIV5ghDXsm6qiXJlqBwQQFYusL5CuWqLvME1GUpUBrg1IOFYJmHUh+lWIUBE16jABQUBvStUjBaFK00qgHkmQl8M8d6nerAszOoMl52oqjisi50FFhcKFVmwpVWQr1KKubnKTXqTIWuUdYqrzTgmUWl0jJrpF6FSJUbcmVXQJGFgjybCxXzup1EAEhe6NjTS6BybkvX4kDNBUvv6jSnlaDFAlUJThXTs7oZ1ssSp1OYl7SQSjqVaa7mWA20dkqNvKTJHOcl4xWOJpAnKIfMNbuxYpYc51OZJzgXWjZHWUZzQF2rm2K9gEoygWlMcqhVc8BjXBDFM7uxauec5DNYpaQCSjqFeURKoorIEpFRSQ1EBkh0TrRmmlp5lFED+aqM7QoyEVsy1EtArTw384QjIiIbBmrGdOwqIrBKQCKtGRqtAigi0kGoFoDJyJKBkRHNjH0rCQrAZKSKwCiFIhMDxEYOdadI7TjIqAZCTYZ2BZmMNRkaFWBlBNOxSKGWxzSd4QqwlBiu1U2pWWQ4XcCS0zwh+VTmQolVZ2X3uUBVTvIZzEtS5CSdyFRodQXgIiohSVOaTGAlaJHjdApKTnOkuHY3I3oVg2Ii8pqlpZZOYS7VDCuu1c2wVlRKOgVZreUFzaayqAivKPRMXiglMoBr1ZVTQ9wpE7XmhTRB4FSVWQoFeBZPKSe0GXiMlwUysK+LXC+FCnyrzvVSMtceZFivoAJcu07NHOnI0+rcBAg1spgVeVkz4Jl1qpVIlV5DxmpB1XYcGmVRSB2EZp3rRa0CX5eJkhOtWIhsiQvAipBkK1ggxTfaodookJK6JF+hVLGKhcjmsAIkUm1fbxVQSX2UuriULFuhdCoyrAZGy7O6haSpj7K5LAXNA5R7uOI41Bxfb+ZUS32czmABlCrC2QIVgoZ6wzfaHNHCR9Vc5kSPA5pOIZe0qnTomVWt1ZWGPKMABqvKbuBLQkGlQtfkQqsqDbhWVWqgKFp5AKSsKku6VgV0UVDoWiVXSkiZlxal5JWKllrFFc5N6FklMGHFm6GPpSyFDly74iavNeIbVa0jWXeiEEJYcxV6JqhVLlXoWrwycqCkS5QnpJIsX8AixZXArt2LVacArJqDMqGlZNkCZBHKkepavQxrOWTpFGYhyYmWzUTuoQLSQGsFRisHSjqVRYgKqKQLnPkoVcxAb4eaU9Q0XaEsQrlg2QKmEc6h4hvtUGtmWCuWMPVxIZV0AfMAlgDLyASBkTFDRgbw7QJRrcwbcVzVSGQmjM0KsCq2katmzNLT0Il9TtQ61oBvFEARqS4Dq+Is1hqh3s6oxhNDunYFGchUGNiVZFpRNLK4RlCkBva0HKu8cKRrcqiyqmhEgQCIF5YMnRLqdWrAlV4BpShwOgVFxXKupDOQFVRIAbOUS5lWajJFRa3wHEVjyAtUQwB4KSDIK5pPZF6SrFKzKci4kpZSCcusgoVg8QTGmVLUSrWAZcWAFKIqJJB5RfMprHJSSpZOQJ5TjjDM0rrmRUnSGSwKXHCa3okyQRWkjdNLY7biAtnnN+rcrQVsRq6aRSVRW8fnxu00o1rz5MK4mnDC7HilRn7FgXN2rY0XOVIbxxfm7SRjRqzake5wjuzLO3U8rzl0Lm71qVtCpZO4ZuxlRHMub63rSS2xeTnWxgsuUCvyrSIuiepc3Nln1xnR7fNb+/yaA6LMXPvqruJIH88bp5cFYolqBVa75kBxo8bpZV1IZe46J5cy44lihXojJ5o6XrQOTmuBqRc1Ti5FKTOqh3ozo7o+WTaPzgvI1GXYPjitAE2xFmpOgVXmRc3TKy6QsvSbh+eiFCkzfLNdCYTjonV8IUtJ/LhxeAaTQiD0r2YRVllrNl7sbZMw1+M4HHSQlBnVVZEyN9KSdLy7ZwZ+Z3w3398pkMrKIjHMzvgOl1Xeb2A/0YNosr8PALDiEFCgBiGOq+nOtrNY0CwrO3ai2ZTXGWNaGPVur28fPewspzjK5xubEEIJkVqnMMoaKzfYHFQ17V9f3jy4b0e+sgzSblNKYM9Xy81NwdB2OL+w+2YaOFfT+faWWhfqMswaFjdYo8xzSiOor52d3ty7r5dh43o239jAFBRIt6oAFSXxsqplhLqzfnp6u3dPB2krT2ZGg+altghKW891vTGeV03Da7R2kuW10VbLzLqehd2OBdM6lJXOwmZLncWlqVJV0GmSOpYiE55XEKPaMEEGmrmXdm3pylKlkNQk8nNmMEMhQSURFA6uIqgXSTJo6mlcEQowpLMs1VTFAjwQuJZhz9G8pVJkcbuve2lNSdaxrLFXMwxMzMOICeG3R1YYq1nCOypZ5bmiR72m7UZKyv2hqaS8uYyiAcWCd5LFZWtLjQAqakMNE2HSVLhD28pW69HsuHuvESQgI17fVJOSZJW0QVnlVhovRjtKXDbn0WKz0Qh9mdK0RVvFnIoyVFt5pRlhVbVBDrTWIl6OrE46M6p0ZnZwRmABdSUMcEMNeNUGvuq0z8fxsKWLTIYSarDSCeEcgzompnXnBevdYXqX+Erca+gwAxJBWZeS4KDCBogNy7p1w2HLlAktqkg3lKIQEYAqrAyFLHObRG6z48Sha7c0noNZkTd0ldXArxGVpcNoUWFQB6pjX7tJv6niHM6KzNGwQVAM9CINO7ru8YpioNWDZBpgJVcbKAAYyNKBIqVGloQ9rRsvOUIzZ9SYphWS0AEyRITLYGCwLDKzKLbbildLCKKO0ZjHFcYN6nJZq7y4bO2bfsYKnnSVxiLOqRq2jI3gggMQay0RI60QRRvWGVWzMuprmhtVJURNKgtJozJaa2FRNUN32ew5vltHMh61aZqTuIA2qhlVyxxAGSPLGrvBVseMAxHKom/qVQKLqta1vCKKl6EuibHRuFuFG+1hdBsSp9AUGhUih9BGJaDaKhZdljKjHbkru2UksQzqsq3TqgIRj3tNiVF/7K16ti5Tbc6Dtq6hpFH4GVED3FQDDtU6tMz2OF51TAOGvWi+0hy1llluSyoKgyi+QLQKHLPpTn2nrQvOljK2GGUchwASyW3ZStxagom90b6LgpZRmqx9F2YaxIawU7ciysxYa85DTCqv5bTuoqihxQ3Tmi5JVUfDlubFWhylowYtKwBAZNjmxBcYIQfVQY0Lnq63lCK1sihXNewVBVaznq1P/RpCasMaY6UsPaOh+JkSp9maY83cHGvIBFTwkjAs6zxFWpLXfZXnRAmTdGg7aZATBWCAAg5rIJo0z5AWZf52v+0tUYSSrqJlBcgRtyCWcTcPV0Y7hnZ7Hi9GtplGOIJxS7G5q4tcSOjhLgtl3RAJMbrTcL7mmKVPgjBstvUSwhQKU5SIWau8bKOc4nvu5VlrWyk4CpGwpMazojCELmrG16L5Sm/5rN0dB6uBpfMU+TB1qKSQuHkDhl67q0ziuGXpKMO1SJnKOAehgAxWJoVuRUkVt5uNYBUZNpMVmha5rTFNcr+GBGVt004CAmrfaJpXq6xplY5m3ywrQ6G6EAICCBNm6vOA4qq2GZ1mcdOpbM1JvApTLOs6BgDCpGMbU08wEvWarVkoILSZF/IGy2t3aLbiWbOIrxsbjVVSQSXoGOYqBUJIB2i5a1fZ3BpVhWJGmT/UG6uoBKowBeJZBRHFuCxUMy6KLuIlM6PC76mDaAwg8DUHxQRyaKi+izp6UFQdAGQ9SJaXjXU7rDgnpQ1oKmAFa1umSG3croKtziC4CxMjaxusTEBWSF2tia6uYtCmMTOdm6W/0WFRyMo0020qIUoqaKGCqto8Ns103BxtzG5veut2HterumyqKqxBWEMdJgRrgSc0NbNazatFuN7R60SueNHQEKxgFFeKIXTDnPmoiQOr2TibBmsdHRXazIMURe2mMV0RIhfD0SheeYoRaY31o8PItETLUGY+QGi5udGP5oSXU7M7OL8Mm62w2107OclMQ9frqT7Eoq4w7tzdcUrzrtM/v3Zb3bDTXo+nKWYVxHQZqbz01/r61BWEpB17mHqe4dQSWHfLkpCi6+BlxHg139ke3lwIDucbm63ZDFZV1TYj1VGqkomcV7g1HU/2djvziShl2m0ITDliap3IuHRcN9waVhXsTibL7XU79Vd6DyFuL1ao4Fm/IXPRmk297bWIWkaeZZrSWU5hUkWDthonJMrKju3ZbTNOnHq1ZP3h1cV4d8cJXBym40dP7f/lf/3Xsgj/4Ft/v/Xxp8louP7eR1vvvR+3W49/+OPXvv+D69efvf7t7+z+8lenf/C117/97Z33P0i73a33PnrtO98Nhutb73/w8Ec/Gb/y5NXv/+j+r39z+taX3/rm37/2ne+m3fbg8+e77/zKG462Pvn9Kz/68e2rT9/4xrdf/+a3Lt7+yuN33tl75xdJf7D+2e/v/eRf3NH67m/e+9pf/Zeb1197/JN39n/6zmJ/d+39jx/+9OdRf2gtF1/+b99MWy3rbvz6N74939zqvzh++p//Nml0Gsv5q9/8R1kLIOWX/9N/zRqOOXOf/cXfxI7TuJ48/YfvFIpm5elr/+3vMURQyj/9n/4DQIBF8avf+m7esO3J4pV/+McaM1oWr//HvyJJwQ3jq3/+l7WqsiR99nffqnRNW/hP/+JvYI0AlG//l79Vw6CyjDf+/K8xlIv2zvwLJiNpkOTquVNPM3MD3/3GrDymt/niEyxCoYrs5tSuA2hqxeSzFpzWypqYfaAkK6S3uPsJLBOqo2LyeyWdAl3Nx18YfFKrm9B9F0VLSgbM/xAUrmKrxfzcKOeANPHyU5zf1sZ67f9W82cmHdHodzxbYgcGs3MznhIwoq3LAZ2txb3MunCEu8PAhPubxeQVoVbQ7WrXPWxN4c0aXOxlnVS76SeTZxSvlJVaLx/WSk6CtnE9qhq+ej2CkydZIzZvNbl6QJCrBqqYPUAo4OGauHi1bETmpAHn95A6w8uOXNzHwKOhntx+CeIExw663ifaFE8bYPkYK/M8gdNPVVpkkKHJBwwyiKNi9S4hipQBuDswcV200tXBiyHLcszA7KdQSoh4Pf5UY5SjsL49MNW6VER+8SuDhBl1yPhdihBAJZ98rtk8LBN087GBcqHh4u5Tm0UZaODZe5RLhKtq9h5hQsBK3r4wRM4VUt5+YsiQq2u6fjhCmcOkJ6b3UWRi6ObXbxG3U7Q87fihCNvCcNn5JspbGIT55FXh9gqtUq622cqq2p59eg9E/ayRNQ4GZNXByAfLPey3KqNQLreVpYWdu/L4zdx/WDUT86oHor4i52i+Dvx+aRfy/BGabZbtpXayJYOR1Fd4vAbDNah4wQ2IX2K1y8NjsDhR6DoTL7PJ7xXT4Hxa3h3ZWJPpikSfCjrC1Yty/glVuzy6QrMzw2Al8vndS1OnWbpis080pS3ERbk4Nc1Gnt6iybGhwlRZJuefNFRalTFZfEr0jsgv+fjIcuysupO3H6kmKWXCbz8zNJxVMVp8SkGLNBIM7x6RMmNFKO7epkkpVF6//BKvVVwX+OohkjlJajl9ysoC11l28XUcSKIE9cVbSBAkS3x1n4mS5DU+e5XkCIMQ3jzTUlCamXb8mNdMiqo8f1OWFPFKTp/QAmaq0I/vkYRyK8Uv30jrnsSFcrmD8xqLEkyfkATHvTr7ZR5MKVln4UdlsmIWzRcHanYhSZMsvyDZVW2s18HHxJ8oaKSk7+XhJXZItLzUgrGC2jR6AatLoW4I/5fCPWHKBsu+KL2ZbtE0uMCrG0VvidXvYPpcsnsk+6he3GpWI3NPqDdWbZYlZ8h/gbWmWB2z1Sk1Nuvik2o1NtWuaCxZPX0CZQgym17d43bI5t389k2pLLDblLOHCPhGLOvJKwRFddqqTt9Eik8WBpg/wcqCeJacPSbcoykEN1+hIoWVqlzsAyOlSxXMHmE0Q74d336V1jGqJLl6ysoV4Iq8fkXqGfHN9ObLBK5AYoDxE1xnuKzI9VOC66hOlx8RrUzqXNwcOqSQKi3Gv9LIIoM9PHuPlQViuFr8nuFakpLffajyUKhKefuJIdyK9fHy1ygrFaTC1a9rEHIK6vGBieIaNtj8fchXtdqT3s9B7DNgIO9TCJJar6Lb4wYPoOyq/k9LMeZkBBbv4zjRmCGDz2EZE+Yg5XpbmTnYuQVn+8DfyFupermZLu8rYEEWPemtV0auTda0WadoL/Hxw2zxBBgLMh6AYJPBFXW7YL4mNJ9dbenX63nXc877wt9GyhTNR9LdomAFVqNi8hhoIZ2tgdkmNS/R1Tr095E6BfPNdPoKq5coaMLJFlQ9Mh/Q8YZoeeklHx8ZCsiMIDw56Ou45AWdfWSoTVBNqvGBYZtZvQC3h6aFClLwq4+bKiiAQPOPqWJJPufjA3MAZ6GnXnzeUuuS8mr6qc5EzgGZfUSxAeSqnn7RdIyyDuHtCwPWJQbi7mOV1Bwgsnhfq5uWCPjsuWqgVOTo9rmpCG6o8df+97/QlsvUaTz75v+pBz5H7Ml//ZZ5t1j1B3/4f/yn9uX1+MnjP/7zvzIWy6Dde/Mv/tq8HXPNePLdHzTuJovN7a/92Z91zy5WD3e/9j//x8Hhkbe588a3v23OloCRx9/7QevyZrL/4A//w/82+OLo5rVXvvpnf9m8vIq6ncff/7FzO65N4+E3vtv69GC5u/32X/716OXB1RtvfO2v/651erHa2Hz0zjubv35/sb1z/2fvPP7ZO+df+fLX/vNfP/3H704fPdz6xbt7v3kvb3c2Pv987+e/8rY2nvzwR29+81sXb7/52vd+tP/rdxd7O1vvf7z3zi9y29r76MOn3/5euL6+89uPHn3/h+M3nj34px/v//wX7t7O9m9+++Y3vlk0mt2T00ff++dgfbTx20+e/uBHd89eefKDn7z1N3+br3XMl5eP//lHheV0rq9e/c4PFrv75enpv5ZF2BhP20fHMM2N2aJ7fkXL2l6smpdXUgDn8rp/cgYktqfzzsk5Krk+XTRuxqCqzemic34pJGrcjdsvDqSAuh82z6+UkttLt3N6QQpujKfdw2NZS2OxbF1eiVoai1Xv8IjEieaH3dNzmnPDC1pnF5JL6248PDxGEtorv3t0DIuKpUXv+Uvqh0qc9Q+PcJIbSTY4PqFegJO0f3BohjHNq8HBkRrGzA/6Lw9omtMo6R8c0SBGJe8fHGnjGSyq1sWVEidKnPZeHpIwZlHSfXGgJyksqu7RkbbyYC16Ryeq6+M47RydMD9Siqp3dKy5PqhE7/lLa+VBXveOT/SlK2qYrHCdAFHDbA6lx2tIgCtyH4Aa5EtQ+RJXPF3hMpA1B/ECoIALiPOFKDxY16hwUbaSIudpxESMoYDVCslVzREpZ6JyAZBAeJyHABQ8W8EqABzgYgXFsq4xhauqcIGUsApR6UGlEqWPKg8KAWDAkGcJRHGCcdiElUQFw6EpOAKlofoq4FBNKPm/aLuvZ9u2uz7wI80x48pxrx1PPufmKN2WhJEwKrDd4bn/m3Z3NWDchMJYBhWmVKYaY4NASCgh6SqgcJXODSftnPdae8WZw8j9cKG7iyf7gfdPfefDqN94mL9R9Y0DiSjKIMqaUBhQUpzUoEKmcvHSVZpYOSJxAAyxKwzTjq6MLi0raRCFEaM0cowmKIE4bBhAQAlR0gGlQoLgKAAVBsymoQcUsjKAVjWlsS5ldg1VDoVA1QzwHBgJ8yUuS6RLlc4IKDVispxhkRglURESzbCuNJtqXkIodDojINdSgXxlmUQpjdUcsAxpBsoZ1IVGQsWRqwstBEwnQCdaSlhNDc+gEiiPLJYZVcpiQVCmoQZ8BkGsFLRg5KHCM0LDLIC5YwAiaR0vkUKYxC5JXQCRnbowqxmmUe7TwgYawNi3IyKxhVYUxoFGBFQeqBqIK5j7OHUUICgPnJhqiLyMkMxTBsPUhnkdCgNzDyeu0ohkHllZChIaY5zWgUFWZoGsbqRhKWBTLTUxieZTIDVSBUhCRxdSF4AtIGRGF4hfGyURT00ZWlAjnKtibulKiVQVM6gYEiXkMyUlBLnJJlAICHKVTi1aclPpLKSyBDwD1RRIiU0qyymQEkAOotAFlRYMsmtjCqM55jPAGYQMgqRDGTQKoqgOC2IAsWIXMltLjCIflJYlNUzbsCAKYBQGVg41IFbogtIxHKGoBhjVzGjWN4wYSHDYhBFSgJDQxaWDFbITDzEbMoPSFi2Q1pBEPk2xRJREDixcYAjOAlQ5WGiYNnFBDUB8psQSAAPVSrEQAa6KhFQpUQbzEOqFlMhSc1nMoDaQF6RMLJtLGUMeQqNAvoRiphS0ZGTKiDAFZWzyBQSlFClUSwAVECkpl1ABLBaCLyAyUCe6XGLAZJXCIsJaQx5DOVfKIBjr5BoqCUyFSdywBILCtkLXKAumgIQBABYoIUw7sFBQYhTVTUEgp3bkAIVJAdAqUIqgApqkS4UxDKHIBxxBaZPI15KACqGwDgSgwsCkY0psJIWrADEEhGWFPhDE5ATHNaiQKQ1Mu6CCwFASBiBHUsJqDlRuMJfZnMhUSQGihSUzoDSuZoYnQEtYziGLtWYqSxxQAKgAm0MQKgmwnGmZQCABjzAvMGAqX2CZAgkIm0GzUhJacgWKjBiNqhCr2FhS50ssY60NTBdExFBBomayiqAQsFpBttLKQFQETkSNBm5qkbSmAEYpQXkNSgMKFye+1ghkPlo5ChI7QSQJAEB2acGsqZkxuUvTABsMKw9lgYIERwhFTQMwLjBMG5BJzGwceUYgULh26BqDaAJhWFcawwyhrImkAQXFoQs0QqWLohpQEBQqmVqEKVjJcgZlpnVl5EIbgUCuqmsgBIRMJ1MLFEpJwGYaFsoIxGeAVUjmppxBww2sdDojoFSMwewagsJIBqqZEQxBBssF0LmUpcpnFs41EJrPoMm1UkjNlWYQMlPNka4M4CCdEZkBoEHv4KhxOcHK9I5P/ekcV6x7fNJYLI2GvWd7zcm1Qrj7bLd+fgU0bF9cBtM5FrpzdFKfTBXE/YPj7vmlhqh7NQmuZ4CJ1tFJfXINmWwfn9YuJ9rg3tFR+/BIQdw5u2ifnSMF2ucX9fNLmJetk9Pu9dRA3D04bJ+cK4iaR6etw2MDUDBbdE/OsDDNyay3d6A18K/G3dNzZGBjvuweHMGS+dNZ5/AIM+EtVs3TM61hY77oPnoMufLnq97uPi2Zu4oaJxeo4v7VpL9/qA0MJtPes10jtZuXraMTUlT2MuzuHyIu64tVf3cPcOVNpu2TUyiVE6e93T1SVvZs2dvdt8rKoH+yLkKfQouVyEYVtJGU2IKICU5sBzAOLKAN9zwsxId9hRW0CefM9wljwEAHMgZtJAQLgg8NtbTSkAEqPRf/I+MHWAqrLETge0VaQVt6LhYCKmVDwaANhXSwSO26naU8CFyWc00I1gYiLY10HIOQk8RVrV6rYqaIcmxHlsxQgpSBSAtjQ164dSvNWK1WYwkTWNvU1lUF7EDlBqFC2w5k/9g41FaMGcuCUiOshHE+zMkyFvy9AZQ0+WpBu9QISSmolKTU04XgSNhOoLMcelgrikRh3AYLtU1z42hCPF0IDqXt+jrLoY+1pEiUxrVloRxbCaOJ5emCSywt+8McaIxyKGLCFqV2bCkAQFDZNilKRaxAZzkKkBTSc64RfSMAACAASURBVBCXlJcUK6EwsxzuOpRxLITwHKS0VVbEUgXxCRPCtbEQBuBeNQ2djpFGeDZU2qqY9GyvSCvkSpdiLoE2DqgYdqFQwrUhADTPhe96ZVpCjxCNpOTIcUzBsAu5dBBLnYad58J3/TItgYuJRkpV2O1X08hua2koFqnTsNNMO5YjyxK6FhQQQiGRa6rCreG85IG/EZ9NSd9Qy1VF8aEBUCjkmqrwalZasCCosbhUlqGWq8sCelQzgBDTuM/mYa1nSiE8v8biSlmaUlcXOfRszQBGXCLPVKUboKL60JTG1oRQIISxbFkq21ISaYJdXSoOOXV9nRbIR1pRyEvkOyxXjqUkMhgpyyJlpbH190Yp4TpISJuVxiFCYQCAsqlVVorQteJy4q1jKYTrYC6g1sgCpGLMchW1SFkZhD2T57hGBKdY5NgnggOKMWMMO54pGXKAUNJzoNZWUYjA88u0BLZyHaQUkNoFFUOOkdqDVeo0aJaJwPPLpDQOtgBWskL/kMOli1nqNGmWKd/pJ5MpHRCisZIVsj1TMWQDrj40TpKyeuCXSWlsbAFsFDeWoDbE0E4z4XtBFTNFlU1dlZfAtYAACApFXF2UboALxj2vxmMmqaGkW82unTVbVxBCrv/BlIx7Xo3FTP5DDvItzQCGQhFXlaXr45IJ1zUIWkX1YV9hCT0IjLQprrirysr1YCUVtTi1aVl+OEGQS5uXwP7wOIxybFIyhcn/f4I+NBQroTEjjrYtXDKNSaCzDAX476dMICmhja2yKi3PhUwqJJHlmzxHARbcxiKzAlIx6BCrLCviOpBJjf8/w1lVC5p5WAIXUUAEZ8h1TMmRDYT6+wnKch54QZmUwEEEECUq7HXYPKM1KbGDqsxp0H9ktGDAdkDFsQO4clCVOU2a5TzwgzIugWsjbssqslquLjh2PvzWP+T4QRlXxjEUIaOFQl02T/2WZEZ6bo0nlbCUTT1d5NCjmiMEmCauLpnrw4ILz63xpNT2h1dcAV0AgaYWqrirSub6sOSKUm0Rkpfaop7OC+hDI5Vjo4o7qiJIM0W47RoLk7w0FnF1mUMfGakcm5SVQYi5nl2UGpO14mrqjZAQwrM/bPOEFNGqrIj7YXGhgcgzeYFrSHAb8dyqEVYBm9CqLIlnQ6Y1lsjydFbgADNGicxpjZQVcAitygp7NmTKIIHsYXk1dUeYc5uIjNZIWRnXssuiwq4NuTJIAuKAKrcDmuXC99aSy2trgC1gKV5BxwFMIMsI7UKWuQ07yVg9CMq4MA6yoKVFBWzHVAJTJcBQTCf1DSsveODXyrg0DrSgpUUJHVdXAltaGA9WmduwspwHQa2KC+MgAi3NK0ANwQYjXLD/1wjP82TOtYWNRARyhR1VMtdHBZOuqwixs0xZ1DVlZWwAjHBdqyxtWTHPh5VQlqUo/dD0q9nUGSAlhOvRMtcIW0jDijPH+9BoQhzASuBasoIUc40BAP9gfFeXXBOD0IcGKik9J8jiCjvCdQjnUBsHshK6WHBKVG4FVpErz3XKjEHHIlorIxFxASuRS6rKoia3AqsokINrWbiyOxQrrYyExIWshC5hf2/sLBOB92EOIdoYqDR0IC+RS1jV0dFVbcMpMum5TplxQNPh+j9hF+FHP/+Xr/7VFy6ef/Hmt37w4he+NLtz78W//tKDb3z76M2Pvvmnf/7y57/wwad+5dUvfOnN//LnV8+9sP13P3n1L79wce/5F7789Vf/+ktHb7312p/++Wuf/+tHn/z081/5+sc+95+mz7+w/aOfvvRfv3D+4MX7b3/njT/7r0cf+ehLf/U3H/nT//LoF37p/je+9bHP/cnlgxdv/uynr/7ZX17eee7md77/0T/78+PX33zub7/90T/645OPfXzz+z/9hc/+x/MXXm5fXn7qdz+T9AbB9fyXfu8/XN59rnV09ul/+9vT2/e9JP70r/920ewgLn7pN38nbXWdVfKp3/39cGPDvZz9yr/9rdnGTbuqPv1//mYV1BUmv/xvfke7DkmLT/y7z6bDIY6rX/31fxP1N4BRv/JrvyURYY776d/8HUkoYPJT/+4PikYdVuZXfu038s5A2Pav/utfhwbIwPvF3/oMVPJqdC9/u9SxJoFefVOiSIANJ/tyLmcAD1D6fQkmlV0Hyx8AEQHSwtE3OJxW5kZQfCniFwqPaPIDocYSNWD4IwOuOe2R+BsVvGbqZp1/Pc5Ptdyuy+9n6kLguo5/YtSVEMOg+FZOTjJ5v8m+GhVnyNwI+A8zecSDpoh+ZopLwHYaww+c4Giw3BSNPbuxv6aasZl2WvvdrCnpuNN5bMte7h7UnMON1ZZpHlm13Y2intantru7XtS4PWt1H/npmqof2PXd4XIL1o9A7ckWa6be3PZ3N6SfWde1+uNWMZD+iV17MsrXGD2jjSebrJbboe092VRBScJ684O2bsRkHPhPN1if5ZnJv1IBZAAC6VcyYGHNQfrNAtapKGDytcr2oc+zy7+1MDaAwuzrTAMADEy/VUEb6NLE3xTQMgir1Vcl0UoFVv6VAgBkMEq+xT1TKmAtvy6MhbCLVn/DEJO65RRfjDUHxsLJdxTWUmGy+ltuKYXrJPxigUoNNxuNhx268IVX+LsjOiWsLmrvb7ozmGyA9o877rhW9fP6k549rwu/8PbW7SlNuqj7bi8Ym3gTdN5p0GkvGvDWo3rtus78wjlc96ZO3IXNDwb1M8BHefCwa02G0Ui2nwb2VUd4Sf2gY101kq5sPxnUTki0o9sPA+e8Xw4Kb6/lnndFJy92RfWjQm/75lGev8P4nRZ4mqY/UFYPoqsyfAeZDi3Ptfm72Gz6+qDKvleRGw4/YqvvSruPzIRHPwC4AcWVLL6bw3VHHvPke5xs2+WVSb9Zeh1IZvnk+xapQ7kwxXcrOLD0hYi/x60h4hOTvM2tBtSJjL8tSA2xlSm/U6q+51ak/bN1aUkIRPNnNwFj0tXtn20ChbQRzffWDTJKw+bDLQOFtkHjnW2r4sCtgp9tG0U1kfUPNqAyAujWwy1TMRnA5jvbVi6LDm7/qItLqqmov7+BBeBYNR7uYK7SlrP2/R6JZdFD3R+2taiXtuy827MZKC3TfG8bl2Y1cvBXpvm+lLca+oepOJS0peN3oTwRYugX3yvJfirvN9k3ouIA6ts1/k7Gn/FaWyTvm+wY6HW/+kGpn6T6QVN9K8yfanGrrn+aFk8N7ejykSgOgNXH6TvMPC313br8QZI8FPSGXb7H8kfKbpvqqcyfamuA8p8w8UEF7gX8x1n6cwXv1hsT4D3d1k6OQ6/+XrvsaeeK1h+NigG3Lknz0Rar5TQjwfvbyqtQ5jff6wE/xXPXf7xZdQVZWs2HWzyorALV39tSVoEKt/n+iAeczHHz8SZv5CBx2j/fUl4JJG38dIRwTiriPdoSgcSxVX+0rfxU53b7Z1sSZhDT+o9HwFIZlvk3SodVxrJW35TKIOyj1ZcZzqQeuMUXI5kC4OPkuxKVUlO8+qYglSRNK/xiQVKpRj77clImRDdt9u0cpsJQGH9HmUxW/Xr1xZgsKrFTK/8mrBIK2nb57RyFwnXl4jtGpFCNgvJvEnNZ6Nt18eVVsSC6Y7PvZmIJ9JrbfDxoHEE+yoP32vRyLdzQ7WeuezZgQVw/adLzdtqW9f1+85DGW6b9nuMerxWj3D1oeMcDUU/986Z92hWNPNhr+wd+uiUbj3zvaFQMcufQrx0PeSNzL5vucY+3C+ek39yryV5Ij9rewagYlvZFUNsd8FruXteC/Q5r5M5Zp3HYK3tpcWWSr5duB9Iwm3zXsgIoM5B/m4EW0XMVf4eTLhQLkHyT4wCYSsXfEMQFvILFN0vUJCoy8du8USvL0ll9lcEagQaGXy0xhVKh4qsJ8LFKQfIdRQPFmLX6CnNsBTCO/6aABEpIqq+lVeCDXOdvM9sWFUfJ1yQkhtbMP/+N326fXay2bvzCZ/4oWCyi7vCTv/vv3Sgb37z3v/xv/3vz7OL85Vd/+Tf+r/bxxdXOnU9+5j90x5N5f+NTv/cZfxFePnjpX/3r/2NwcDR5+fmP/84f9vYOr+49/88++0fN44t4MPjkZz5bPxufPnj5f/q13xg+2T386Fu/+Ht/sPXw3en959783J/29o/CjY1P/OEfd/ZOJi88/8u/8dujdz84+tjHfuH3P7v9zk9OXnvztS9+8f7X3j597qVX/+KvXv7y1/Y/9onX//wLr//J/338sU/c/vp33vizvxjffXD3u3/3/F9/5fruvRe+9NWXP//XBx/7xItf+MpH/uRPT956a+Pvfvrxz/2n+d37t3/4/fuf/+r13bv33/7eG3/2F4cffev+195+63N/cvbqK6OffPCJP/7c9a27mw/fff0/f35y797tt7/3kf/8F6dvvnnzOz/8+Gf/4+LFe/7B9ad+79/Pbtxee7b31h//yeTec/Jg/59gRQgBNGa4v2fH8eDkuHtyVFstu6fH/nLR29vz0ri7v9cYXzWSaLT71A7DtYO95nhcm8+H5+e1xbx7dOgvl2uXl43JuDabbTx5RLO0f7DbuLpsXE8GR4fBdDq8uKjFUf/o0L+edKbTteNjmibDo4PWxUX96rJ3ftZYLbp7u06W9Haf+atVY3w1ODqkabK296wWx+2z0+b0un1xXr84b0yn60eHdpZ19/d6pyd2uOrtPfPCVef0tDm5al+c1yfjxmK2dnpiFeX6/rPedOJEYf/wIFjMuyfHwWTcvrpsTMaN6/Ho6WMrzzcP9zvjK28xWzvYD5K4c3JSX8y7lxf1q8vGbLqxv0vzYvD4cevq0lsu144PnTjqHhw0r8fWLEu5x2NE50WqatXU1PMsTvw0pSjlxQoxTuFVlla2XGo0z2IZqBVxkyTLaxkLaFIVsZVzG41zVuAssfSiiGQ9m2LKysXKS1nNy1mxIkVO8JQVzMkyh4ZVyvwkpF4ci8xLSl9GsghJxSga50VpsSUiRYF4T+g1lyM7bTDUDebcr2xmBjSmiHuGD2nOUN7QYOhEHBY9jtrtyOAES9D3chvKupKbTs5wGlRgw02Ek9cZ6dGlhUub6z5OLCQDLTftjJGsxvC6nwinDJjVr8+VWxEBBlZCSGkJsUVLacUOR6NgWZhQxtJXS41KFVctnRkrYllVk6lCV3kK6/yqJMsq0nV5LUypV4kPCkjDKi48kCo05TGombmiyyKSdTmTXlGtUr/MkFmypPR0ZtRVkYAamDC4KiJZ10tD0zwpGqyiKORpYfMcocu00H4xB2ZVrGSzWhgsjK46wDQaK2VEHcp2kEiJeibve0kEyoEAa1aqZNnTsm6vkFQtLNpBLDnow7zvpgkS61L3nYpYZVuqWn0pkAwUH9AUGtOBVd8uMlN0tOk5kdRsYEy9uVJGBIh3/JJI1QViw00TnDYFHHqxJKwpTQuHpiitsgjcJK9WYMEaNC5FRGIdwAU3Sx5XLlwKzcgsroFSVDO9Ek0YlXwBclBD54nOTcxcuOKywnHRIGmFFjKWdRwxeFUmqKGvMpCqlHlwXvEchknglFLPRSTq1qpESxGBOrhmKBZxFaAlg4VZJb7IFA0tRgbBCvuxqNCak/iUQcXXEA+cDCvVJ6XVmGtBeiQO/GXJ8YimgVUwITdx5VkJELJncbuxUBz3aNXwY87xCGUtJ8sNW9eqiVMsVA8Xlh1aHHftounFrILrVtZy8sLITaNaXk6B7MGcNldKwhYo+hZny5Ubs5qXV+UK56WFp2VZWUXh0rDKmBuH1EtilblJ6atY8jkohIMnOStwFRMd8rRyRe57SZyF1orX7aisUjsVDr4uZQrS2LLiKq+8+conrMynKNENtCqqiBTMRuOMVSRLbRyzgnlJ5jtpYhYwVnUclTSzuOnjxMbC02rLTrmVuhyte6l0Cr+yBvW59grE4YAmlJRYiE1caDuhAo38iNGVV9FBawnsFHAzdFKKua35GmHESRwGh25iaiGq6JAubSdVEo6czIIlUnzdKiBJHAb6Xo5qC1PRoVv4dmYE2oBVYDKRZrYsgL7KEh2Ya45WRaTqammcNEvyelXZOGJpZlcFQZdpqdx8icyqWKlmMYOkquahX1a2HfM0tWVi8Jyn0itj6kRlxGvZCvlJwnM/Kx2xklnuiAyCcZZJV8yMSmVY+qpw3TiOYyerXBryknl5RsiigqoFqoGd57DoaNB3ImGqgTaNVqhh5QLR80qiVduIDTdLYdriaOhFwiqbArbtFQbCV6JnpUCbrhYbTprivM3xmp9KwpoCtOszaQlHyZ6daMwCLjbtsiBxjaO1+iLHSV2hbmupUeUo3nMKgHhdsoEtwIcTJM8zFIuYB3rGZA5WiW8VCs55zGskYmAqIlgHM4EjHrEamHNS6DANRG7UXESyRhIpLssYNeBVaSKW8DpcCpTyuGzqCuqlirgnE4MukhzWqqnSMVvxhlpKk8tZUgcMwaWMmQeXDC1VbAK+wn6y6u/vBbNZ/eqifn7amF4P9vacKBycHncmE282He7veWkyODoO5rP+9bhzeRFMxpvHR24S9w/265cXjel0cHjgZGlvf68xnXaPj5oXF81wuXF86ISrtaODVprUJuP+wb6XxMPdp95i3jk5ao2vauOrweGBG666RwdBuKxPr4cnR24c9/d33TDsXl02xlfNs9Pe5KqxmPd2n9EyHzx57EVRc3zROz1xonC096S2nDevLlvX49pi3j878ZJ4/WDfXa2ak/Fo/xlN4sGzJ7X5onN+2h5fNa4u20dHjTxfe/bUWy27V5drp8c0idf2d+vzeev8rD2dNqfXnaNDb7lce/bEjaLe1dVw9ylN08HhfrBatC/Og9Xqn2hFqCZ/8Idb2bh/fHT55uvuLAyW8+n9u62rKwkQ67e92cqN4tOXX6kv5usH+6dvvorjsr6cj+/dHxyfEM6TrSGdr1qz+eEbHwniqH98MHv+fm06cxbx2Suvds9OrTxLd9ZRIeuLxfn9B0EcrT95sv/xjw+vTp15dPbCC/Xlyi6KctAicda+vLp++TlVmZ1HH+x/4hO11bxxMU421gyE7ZOz8b37klh33//J/isfCXg2fO/J2auvuqKsXUyzUV9Q0jq/yNYGaa17+50fHbz1P7giHz58NH7pJQuK+mTGay6z3cbZONtcCxvtO++8c/jGmy4Qw93d6Z17UMva+XXVaVTNZnfvMFvvr9r9Oz//8ckLr9hQrT16Nt3ZdrAk05S3G2Gv656Gou7gBnYOV2W/YRykxhnGAIzq1qKwWcY22/qSmbqtfIzPwrLm2x1qzQsMDRv4Zl7ZVZVvdtFZIl1KutQ5X+nANh0HXedKw2Rr4F4sKKvYToeOU2jjfNRuHE6US2TLl9epxWR6Z6M5nZO80js1dJYq115sbbSnCzdm85tdP82akzLaoaZCjUU82+n7K04KRlqSlZaXysmtnpdl/nW1ullvhilJ0GK77kTcyUvWs62y8sLq6ua6nZedcTW/6dWjDMYoW7ftUqJKyyY2FfSWrFy3OLQ7Z8X8dhAkibNi6ahGSwMzjWu8gF59VkXbXkGD5qOT8lbPBhLPKtmgyiFoUeI6Yb7v7s3T2/0enxenUt9oA6TBstIBNY5Fr1LQoUXN9w5W+XbLxYJcZuWgjpGC1wWqWaruoovEC3jU7dtnMVtvUCjgacL6NeIiNC2hh3nTQde55YGy3XKfTYvNNnUUPopVz2etwJsXlIlk0/cnQjhYNkDjIqp8WzYde8GNxtUA45UJyipcd4JZpQhaDdvd00hRIDsWWQrMwXK72Z4vvYLN15vORCKkw/VG7zziCLpNxlPkMHG1s96cRrgw6ZbTGWeMWOGw0bmaAWxYJyChoLnKNh2YQC8T4ZbnTROQM7VWg6W05/nq3qZT5PQqym/3a2GoFzK91ScJc6NE9z3NNY4qMPIKY9cO58Xza24UqVklt5uUCZMr3aRSAPcyVDebHNnu/iJ/0OtE0yylZlRDWWUirgeeBti9COVOvUS2dxmXWw23LNF1UW02UcWtVRlujhBRg8Nouek7mPunLB+5ACt3nkufssB1p0wHMGm43eMiHLmUcG/Cs7YTmEwkDvRgWbPcCdM+yDte9zgM12rYMt5pVXZt4EF7LhRFVYs4c4moSFpB+7hMBnbR8IeHi6pBVB27i0xYdtxpdC8T4KhVr9bZT8sBnfcGg5NTLWF6s+9dh26SFzfbYM4w0Plap3481RSZnmtmBS14dG/DX4RWXOgNH41zhaxsq1c7nSoIwNCHCSeFCLeH7iwkMeO3Wv7lkkFq+g5eVRAY3XbAiuGkkrdaMjXOIuO3WvQ6lNACXYcuCiWMHngyke4snr98ZzC7wkuUbLp2Ka1U8Q4xUgWLLB8GjDidk3x+p+blhX0t8nXHloLEHASmRH4wY8W6VSC7e5rPbtVqLPfnZbJWg8xYoRQdogn2r6pyZJWEDk8X0+22J4QzlUWPUCVMZqkmEhZsXMb5ml84fu8wXdz0HcHssciGnrExvQhdmyWDHj2I+FbTIhJMct7yiQ3QtIIO4m0XXWaWY6ph2z4Nq45veQAfRbrtmjpF17mw7WrYCK6WFlFZv+3sLeQwKNqN9u4Fb7qgScGiAIiUg5Z/OkPUmL6LD2I2bBTtRnAx0w6GDRvNSgBgstkLTmfaJuH22trJXGvpNHmZ2m4pJ7e7zfnKTfhiu9W+TpWxVuv1xiSDRoquhVJJC1UNqc5xEIlokzanOdeUDYgXlliKohuA1PgRzzep5lZtzsJtx1/mRkDRtdxIKWZok2UyaC6KeNvRDDQW+WKrWV+VgMGqb1mRxKUq1jwNkLc3y+4Puuk0n0K13VIFB8vC9ANIiXcRqs2gII5/uMxvd3DByDhhGy1ilLUsQNfR1EInoTcAE29Y25/ktwe+KdE458MaRQYvKtm0C0LJ6QqtuapV959Mstt9BzJ0lqp+oBAE12nRbeOA1I9nYOSktWbt8RW70wMEtM+uNIbh1nr78BxTOL55e/uDd+fr23F/ePsn78TDNdat186nBoHJnbsbR/sImcudOzs/+Um8ubUYrd95+PO42SBd1yxyos3lrbsbz54y2453NnbefX+5vrkYru08fa+qN4te27+cO1V2ff9e6+jcYBzubGw9+WAxWOfteufwXDhOOuq64wUtq7OXX765+8gwdfHgweDklAiebg5gWtWXy/D2Nkj56Pjw+M2P9E8OYMGjW9t+lAAmq34DxWX/9GT82kugVKPdZ+evvdwfnzFpZZvD2mSKsyLdWYeVGh4eXL36omRmcH52df9u93pMw3R1c8tdhl6cRTdGSuHu4WF1Zy2E9Z3Hj07efKM1HTuL8PCNj5Pf/6z/vf++FSH5b3jkbirLcT44904v2P033KOlc3ou2juP7r1VUq9ZrF7+1vv1q/Hs9U8PHr9r75+X91+/3HrF7EC7LEaHP25cjU92Htx8/KhxcRm+9svX3d6zrZc3Lg8ap6F/eFHceB1c5a3dvfHw5nR0j910nTxr7u65jy/EA2UuEu/JEe/fe3L3dQ1gTaTP//TbtccHB/dere+f2++fsec/RhPT/v7j+ad7UOvmD5/tr73ot1RzsyuVFgnwH57Axk7aqW999/3lL35C3x8MB+5Tr2YuEvfdY731ckVN8PAY+6Pspe07tevLwJfnae0nB8ugb/LCe/8M9u7xncZgqxUZNS+d7e++f/HWG5UKgh/vhZ9o6EC1tnvnSuTCDn52tOJuemd063s/mL36wqx1v1g1sEZNj4dhCwBr0J/lTQdiBFQzm3drILZyaxZ6lsINEvHNjkoq6g/TDwSmwB3i+FpTKO22MkOAmNCykS+atITOFs2umSEE3XF9LwK1uk69PO3CTMP1+sXSxQTUNqD2uOrUKtVYrFzMWD1XSdIWOZW32qBccwo6ccvWNS6FxdV5z12tNcoT5GbmOScDuHvIy2Fe1RcgVM1etVkLokjzoWb9lSkcYwUJSobhc/buZiN/guuk3GbcT00oTaXYkLFp2deFVauJyF8GWdHg8EKWXca9VC4bVrzRLN4nnVDeJGmDNPeqtJPzjpSnCxNcLrHVsr22ya60gwwcuPFau5NOxRwWqzqKHQzKg1U/aCJ6A4XDns9TdJWns27Tlwy65apFm6QxYNPXX3bLrDZbLScDDxvikmTeHZlF3mmt3nzeSZJmtFxOuxaG9g0rH0u3p9GARi90m/lSprAIAxTQ5lCsZl2XIHvNK7MGQbyUScYH2hjgnz1ozlLN33W3vXQAICzJnBT9XHNmLjfcSENwQm4rdg8KWW7O/dOuMdbSCW+Sq0ZdnuhWoreI1EtcAXlXCkW7e3f9qWOxh6iv+A2gcSUXSnicewtS3fXObCPfsXfsfIgrq4ITUPaopAVcLMpacQ38DcJCza8VvdMw9W61+ZIfhlFmxwuKNgLDNJwU9ZbK1jvVTW80PZkv63pVd0vPrehybgVtADa9jNSaLGT7nC27nR2dJV4VNtyUqDo8vPtcM1vgaZaMSX2IRAyLeac3Ukm7WXz0hdpy4YbZcob9DgHaVFcCjJoesIB4PmG568xjdkPkKe2c3LXOxlbvXN53ii50ZyVHUnRzlmFnev3appel29cXWfYKJAnTpV32oL00erXhn5TQm5pBxrZ1nhXtzEv6IMhzaxXe69erCJUWV72yKrKuguU9QSvTv/yIdTl32wdgW1Y+xstEGmk6vGILD0RTrQ0Gd5tw7lslsxWN7vawEJYUlysXGVPbIulMIIEAqan1NbQFutNxEre5tsFWA6Y1VYrWyMTrbWlTmmQq9HVm/AqZ1MuY026w/GaHY7tVLspDw0LdvIGSFVGhrHGsbqxltN7JZuVCiRzWtkl2KHU4oLJmAUfzUS5DqoEVN/JedAM/2WnmP8WdpLzDuJ+KmMsU8I1SrjasvW26PKa9VXoz410JzivekKKZ8LBG9nfq+SNQ3lrOYwAAIABJREFUn4J7blJjvblhdly1jL5w9Hyrnk5Ua2G2SbWm+IQgoZMt1lx1weF2q3iEaiuxI1g9r1ILJ7QcCZlkdlbMm4PmqvAbcdi2A9ocZJPXXrGLoh6t4gmiXYjrKJ11Gh1ujBe9ds+N43qZLmYdFyBrQLIxgy1KtpyL+9u1MgaZzuIGshDs1S+nFHHUHMHVvQ4AwJmlYuW7PvMCHcZdQ2zcss9evGFx3oiX0Rgan5B1b7J0QQ1D7Vty0xSaDvbZ9U1bOQsSbViTXlucmYYUN5Twl5gxadPcVP1xNKpLSofTS17tZIKWYCkNYmWrNLPXrfd9k77jbJNxL6t8Bi912c84LeV8uq4Tr92OJ/WiJaum0/0gD3dKEXBz3EXTUZBd6f5M7oC8wdGMyJqV2NXWNFvAalWnKTVFdbwY1NrAqsdaa+QFbFUr5u3uUKWlxxcN2rc8uACbLSNJqUZizIIm1AJny+66t4qBTu52dW5CMCym2g4QruHiSnk+xjgSN9peyZK8y1Y+iexanUfTvu8islaYljTU4mHzYlVvNnlB6meRT0PaGFY3/u5r2dog697c/PlRtdGv1k0waufUnMFG8PMjOaou/sdPb/3w7aTXTx680WxSTihPjf3smubWavR88PBYNZuX//MnHrR4AWmSE+/Rhex0WGNEn145c7X4V8+/2nUqCxyjzms//YEmsNh4bu3xBa/Vy/6ottlVUD2zOjs/+aZ0nMP/9YWXf/ojDWFx93V4GrrjRbr96uhw0RqPL0e3Jjeel3fp+vio++yc7l+IW6/ByzQ4vrxurR/ffl0C0mDRnR+87e+fZM+90Ty4cD44Fxv3D7dfHDc2msXqpb0PmifnZ7deHD1933v/ML//2qSzubv9Smsx7V7u1h7vXwxv+WdR+/Hu1XDncv324+3Xnj/5GbyInHdP5PpzclE1frqnbn/EEPrf+wfrv62L0Jidhw87p6f90+O13Wdre8/qq6XBCEBIJN949MHoyaNaFG69/1736Kh9PUFaGoKdPOvv7++8924QrrZ3n/WfPGpfjxWlyGjKq+HRQW/vWefirHl2uvP+e435TFkEQoCU2tp72j08XDvY652fjZ49bV9PNMYGYaz1+gfvDXefta7HG8+e9I4Ohgf7rfls4/33OleXvbPT9cePmhfnGyp6AHubxbhzctw7PNzcfdKYTbfef7d9ejLMwwcKd1U12nvSOzwYPX7UH1/2D/bXnjzqaP7AokNZ9U6PR48fNS/P158+6R4cbO4+66TTu6i+nc4bs+nWew+7l+f946P1J4/bp6dr4ckD2NsRy8b4qv/0ydbu0/pysf3w553TI3uZV3OoryWZpdnKAmc5nV1bGz3Yb6CrWTaGMobuNOELUE20laRgrU1t42ZJOibpDFlxWU4RizCdzoFvgcAiGctDys+FXZb5Nc4X2GYcNWwQUDKZVwtYLTGIZB5aaizpamn72Kx3SJhkC8pC6E1jHoJsbuG8dJIGyAduaWgYmLLbWqZbZXxXOz3JVNG04o6blX7kymzoJlq6DjLaEsoNIch7bkJwFZB0aEXljXJ1FzhNHvqxD6qON9dWQmDes3MLIKgsC2vprSxYDKwcOXMLlN36jI1YepvDYToHuYfSkZvx2sKYrEdTiBdFGntqImDCwrGllgpBDRCEGKg5T1eWNcntVZ5FDrvkxEiDIcAAL4ryGuolt8ZZvsRoXGlCEDCGECtj0cTmIYCzPJ0TuOIQQKyVoRaeF9HKJuPKSopoTPlUWIIbjADBcFFlK8tMJYpZtqLiiiMFSNgiRa0+5yQJrDAI8vgWsO+VsRdHOBngrO2mwo6boGi2o9UdAW9DE5QSlR134TtZSuMuKLq1UtwR2U1FOtGCZDWUdWhFVd714pYdhvdYehs6DZ7BtIuLRmPBcebhpB1IcQs7z0FlLxfuwkNl18mgm9ZQVnciXSSouCZ2koNJlS0ta5pK18FKWlWF5ixZWHDBRWySiYUTBoGGwACM4ISlkY0uE7Bk6co2K4WM1gQjoKzrKllSHUkxZtnKsmaFdB0IDFFKrVQ4ta2kQhdJtrTQLEVGEymgNnCWFzNMrxMUiXDqgpj5S2CKbn0BvRXHZc+bwGYVPyB000hnWdC4aSe4PpOgaHtzG0CItdIWtnNB0hHMAyfWMO3RzFlLru6h7k0d2zkExcCd23ZRkXSNJh6RSlsEQuNEBhRtNw7sDKKi58xtPw0fGLWFiMcBTDtubDUWApRtnLTtqiqmVjbHVsariSlDZMc5VkLZFMdVuURirN00kdcwmhM7LrVNETCk5HKpk7llUp0sCb9GbpJImwIIkNBijsolsq8TsdLVFMFSG4wMQpQzcc6ykKKQiYmslgiFFcDIEGQXeXGtkzFxkoRcVXFom1jYEYZFP4iQlVOcjqxYbmWL+9pp8chZEFh2g6n0UgCKgR3CNcPvE6tbJbWFAlmPZMhfEFN1m3PZK8O70F1PZzQloNhycuhPNcm7TqhHyflzeG2TL2BOYdqtL6WbGpiuo8zazBd3lDssl/5UgqrnzgnNMMr6dmLJVKbXCMTSuU7KFeZjbiBEWmuLWFkZj61yYciqyueWWmrEJdGKNer2PMlCKs4Zzcvs2uJzTbjUhEBkUFjlKyzmCMQyW1lwLOyq0IQgoHVp0iXVS+VO4yLCxQwjpZAxGmOaFXysyojAUFYhETNFUqOKjhO13Cz1oprM+g5Hd3V1g9FefG2lLko7bg5R0bSSvpumilIAAUDYjWswb/lziTMPp52gktuEPE9xEK/8hQPKAc2AvXRx2rQTo2wLQEA5B1mLJEMny2tzrPOem6qbLLxnGv1obKWOFTaCVUGzGii2/KyUY5aGlj1OrbBKV1SepwQKfHNEZQlPVvnCgouCjIs0omRSYlWK9TYFFVlV4dQxsVCTKg1ta1W50UKt9yjLVaTSOaXTCkYsGltqKWwo0ahDRY4neRbZcCpQLPKFpa5yi+dms2s5CF8XydLGs5xM8jSkYg68NNp592Hv+Kgzvlx/+rh9fLy2Orunazcga86mw729rQ/ec9Nk+713e4cHnXRxW+ptLkaXh93j47XHT+qrZe/Zk+2nj904us+LG5B2wqvB7u7g+Gh0etw9Ptr44IOGSO8h775h9my69fDn/eOj7vhytL/X290dXh/fVPYtntfj1dqTR5vvv+umyc67D/uHB53JVff0eP3p4+7kqnd8tP3wZ14SAQihMUjrzaePewcHw/3d7sXZ+uNHjdkMAqMsAo2+8fhRf3+3Ob4aPXq/v7/Xub4Sjg0gtBnrH+5vvfvzWhpvvfuzwcFe6+IMQoOlpFXRPTrcePRBezIeHB2O3nvYnE0BNEgrYdubTx/3D/bXDvZalxdbH7zfXMz/CbsIkzs3q3738GMfiYZr6eZo8uIDP02cIscWWG1trW7eOH/xxXRjVHaapx99g5ZVczEtOm3Waly/+EJ4a2u5sRXtbB+/8WZzOWtEK9nwkuFoubl18tZHVM2fPP9gdecGEaKxWvBaEG6uy2bt2S99irXqq83ti9dfcYusHi0JMfMbt7O14dXrL09v3NK+++SX/7l2rNXWzuSl55bbW8lodPnaK9dup1zsPt76aDHosEbz4JO/WHXqy83tyWsvLRvt62g5CfoXz70MMXzyL361GLRZvbn/z36hsK1xml07teXNe+Ha+uzFB+evvIagfvQv/2Xu+Ivl+dO1e0Wrvdranj5/f3rnTt7rzV9+/mzjuWL69PHwpWLQq4L60Sf/mejUxjfvXT9/P7s59E1Gb1rWuuXr3Lvn6GFLn4corsydTT9gzVZl7tQx0fYOQR2bHlwjKxDdRsPN631uNjzf40Fb8J02nhWmMqTn13Fh36WwSYIaq7eqclizLla0NPzG0HdEMBRm022r0L2BzFoDTAs6iapbWy0vCeqC3PUJlu6GkcM6wXNqXcQjgumUwjC9ASYwmPLyrL1GUQxrM9ORrC4wmYS3nEa49AqlXY28ElqrbJ0REEFvLEf4GlqXJTvr3gRu5JpldEMQK0N2yAe5xYWbFQRrXucWnIpRWbalz+fhHVAYdKHMZWcL0BI6l6Ajqi7AYFZtct4NOiq071qkgZpuam9gTI0dZY4o9civiQi90qwHjFBD71JCjBXlVHGrSQKXOetQ79QDEaOX6r7McFw5ujIBaQS5O4JgaNecqr4mWSsgq8JRJe7RwOTWAxe2aMPL3HUIasQOc5/ncujXVOreo7QF6qgg92zkaEBD7EfJJsAmk72obNNVEu7V1rJ2F+AZ9iZsCCCNsLtarftLqY+xt2g1AjYth5HsWNpaYOc6HFoLpscGXgzWECpAfZr3tS/Gor/CHXionCsFrjobDpkAN023FQWpai3jFsmKZFfZ+do693MHT6r/h7b7atb0Os/8vvKT3/zu3L077A5ogAAIkNJQouTRyPaUDv2tpspVrrFG8rgsa2RlS5RIiRQDSABCJgkQQKO7996de+c3Pzms8Ky1fKBylT+Aef4/vavuo+u30bRujd203m4jVAdjBbZ9OsQ9r5B3RlG6cgoBQswiHXoC77mOI4e9Em07rFVO3jCs0BrtgMK+1nc9FfjSuUKIbr2swsSgEYtY6VxlZsvvtjF5rdtP55ojBgSNQL9Xg22XrrGIFnTPswg5qxJ7lnWs7wp8MySh7XcLfbmjxiaop9k1CSNB2ri8UvHIu6jVidevhgFBSzsqs23s8lVxnXu6YIV225o5Srqx7udiaAha2UF+MR4lVXoQbpnQUhVXlyvTgRBNQT+RHehn3Jd1M4BemzQbCe+biJ9Vl6q2557X+oyGq47vg5kd5ukOcUUsN+Z1Lxi5SadXqyudvlv4XQUuuag2QZ7qkd+FuX8FmjWfharfa+S1XrRaOY1gESCe9TYMuOwOQOrvWLDmsrpxi8pGLPQbP5TgVuRREaxpNoa4Fl5VIQeRMe55FbtKSRcEgSRXHLconaIGIfVd0VmXZjskQxw5hb7VozhDJOXbFSIlcCdq0y5d/7ysJoNL9dD4Yp7e1NBpEE6bzaaA8IUEq7BfjxiCS7nZVGPo17P4lq4hPpHi2XjXuC2mZ3Lcip5gYKG26/P+5VUxfTS+id0GwURcFsAT2pnrNb4Mommdn/d38nU3bObF9Qq7NYIx32pwYKJA9NaU3Bu6bUVeDnwkcC78tkY+jqImWNdw24ncxtu0ZuCyjHu8xCMWgpredmGXdMLGXQd67IWLlae4HnshqP2rwK67I710blDQZU5eMylQF3ecMli36IrPrHT3kO06YZK6ooYR9fs2HCi763ZQ7mxiseOG8kKPV6BreCQRm+ZbaNWYC6BPN3YdnOsoLje0187b3qwdQtrwoGyQo2RQUxrnlxVDBYhiPtCxkE9alq5tyZBTNJXbQkSSwVWzI8OicioBHABJDsILO7bN0BAway7rCzyYiOJoe4+hQodJswkgyZB33mxgsOl2ZYzf7Hc8TgJIbgdIA/RsrvtDuh1GtGRXmd4JOyKGr/eBsfTFUnX7ZMCG3ZxsM7xGu04VbJt0vOM8upCjsTdAIavRTZ90UT8onR2sMWbPl7DbVbvDnkzcV1wnNJHDye0AUMKeL5QT4i2vQyvnOlE7nX6bOreo7njLncurW9dnd26X65urW9eP9l5vkqOH3tZ8+zJy6KP/7nebjWG8c3l5c+/0+o08W15g+vTG65bRo9/+1vzqVRt4z3/7t9q17jMBJ9ocX3pFjbqTWzfP33zNUvr0d35nNt7i6cUhjKpLlxa71/LLOyevv1YOR/PbN89eeqWJzw/DrWy01nT7L775jXJ3a3H5anb50ovf+IbxnfmNW8dvvK6i4Pzrr2W720zwThJbn8yu74lB9+F/+Pc69Oc3bs1fvunVlV/mhIF/swhPf/PN2fU9GQUn3/7WYDUlJQcOrNbWpq+8sry+u9i7VQ96p9/6JlK6P78Q/Z7odxbX96avvVKsr81v357duUmbppsmkSmevfyG8ZzD//g/tpG/uHb95I033Z///NdlEb7+wbtrz5+pXrRx+Gj96VMx6N366MMbH3y4vL136/0Pxk+fzF9+5cb7720+e9J2w50v7r704QfVaLj18PDS3S+T67s3fvHpxsPD2e1bL3/w3stv/7Qd9UbPnm4cPGyGg40nj659dXd58/qdn/7szrtvT27fuv7FF1v7D0Qn2nrxdPP+frGxcfXuF1//4Q+WN6/vfvrp1v5+eXlr/cHBpf0Hotft5OnNd9+T3TCKV9c//iTb3Fg7Prvyow94GPaT+dWPf2EpJkbdfufdthv60+X1f3mfD3udi8nVX/2qpaQr62vvfwSRxQDc+s5PLCV+XVz5+BdtL+hMZ1d/+QvoeV5TXP+Hd4hsgc/u/Pgt4zlOU13/6BPts6iodr/3NgCIAXXz3fcdo4AL9372PqIwDdbq54DI1gNNesxQ1gRraHEvVI0f+WX9FFAuXdMkFy6qteeo5aMOKVHYq5NDrASO/KZ8DmHTuljmz33duB2fF8cEpjIYqWLfyoa6A1w9wjbHARP1BTIVJD5sXliwEuFQlYd+UXScEdLPJCjaDsiLCWlzjMawexrQoq+GPLzwWNF1wNxWW3Jxw3qGFZ6/iDx2jpcDVI51tw6m3eikg93SW1lYbhomnSrwZhHyl3i23sSvmH4drRyadBEuvRyidB3Tykn84GQdBbE7c0g+RkGG45BlA2JSt/Gr+asQ1U7DyGrDo+d05YF8g7ppXRr+VFPQUqiSJ4yy1mtE+YUmzMBSF6fIAaJfLSdnPaoFRW3+qXVBS5XIniOHtajRxTFkQDlWJJ9Zp6m9ACQPCYaaKVEe457NtLDZl4YA4FiePyOeEpTJ5CHFyNBWVV8Z2gosVHVKoNYeEvFTSrlgY8c77jktprrEqyEWjOFczV41aReHC+9sG8qQuDGdrjFBkK3k4gasR8aRznzoFQgGC+90A8lO25HsdKfN1z2Y0GyEa994ypv33YJ57FhdvFrVe6Bbehcdxl3HJs4qBHXHRgKcXbXJFoomwckAiD72cice4MKDrOCzVp2DsCvksarnlI6gOdHiPmde6yzrdOpSH5gC8hetO7b6magOdDjQfGb5FHu4wZkozqnrKr0yxX0TRALPZXFBgkjqua0uUIhrPynn+65LpKltfkS6XWkmPJ+wMOB6BcsvhUs0bmR+RALcAA6y5wR1YadCznzoIcF4AVdXmJIEC3n8dWOZ01Z0vkFg7SpLlmu4VQyJ4PHloBaUJnZ+mwBNDGezNRdxzLWYvGoNc2BFp9u0kdjN3fNLAEJqRfBi05UKGuMsx8yClmrvYs3lGjiFPX6VgyGh0pv2mWioVXQ5Ri2VXd7erURFvRHkjwQojE9E/cS2M0NCKI6BmfHOSMrHoMoxGZF2X9iTpkuraopkRkgE+bFBSx2sq/oLxU8BXsPmqFWJCXCjp20V07BvmkOtnrf+tpH7os5o2JHVGVQr4BOujlp+bMNAVBeknqH+qDbP2nJF2BD3VwYnGxhXhDO23MDuCi26YvUq9nOc+iwdYZNGwoLVJkEVFJGcvOqQJUspTDeJm5HUpcnQxTWpTDN5EwPOWkhmm9jNWEpwuk7xyqmC8uI1hKQrWzpf902MpQWra4RlIAvk4lVGC5RBlqwjK5hu2XQDYSlt0zy2oS6BbNMzxlrNqMp+qYNKkE6bHBAIgANE+RxRI4lq67sKcONjkTyjtBJeV+UPgIGUUdN8wVnFMTDVCYQKYg80jw0qhN9rxaetyCEOsHzaMsH9tkrOHcgB6DP+cUVj5Q1UuW+EJoQB9aK1dctCHCyGbuZ4zjGabkPe1x1OJlsq23Vx7Cw9WPeNL71V14k90F1Fz0fOfIiizEkGpAgJyt3EQ0WfOrFeXG3T6yiYeqcd3AyQn9JVlxYRhWmYhHQywk7O0j7ORwF7gadDWI+JE9vVhlrdcHFGc4aTAXVSmnZxMsTBsp2o8hwFsAnzdHre9WBjOF497UaRgcu6OKeBL2xqixPkIslUu3zUCUnrAJ08JZ6nYCLLM7pBVlXtJY8DxyOebvLnOIQNaNv0GXU9pQuYPA09t4VVW51iDBUzMnlKXMuhhclhCHshqBU/thGsgADVEcQQ9FHytR/+hIlaRcGNDz/yeAWAvfLjj7yibrqdl3/6VidZZZe3v/b9HzIheLd/+/tv+4sVdJwrn37mp0m9NvraWz/urRbVzvjGd99xJ7EYD25+8L7fNBjqK5/9Klgui8uXvvbX3+s/O4tvXHnlR28FSaz6nSuffR7GMWL48js/j05m1cbo9tvvDiYX8d6VV37yVpDE5fra3uefjp48LTc3L9+7u3v37uTVO699/we33/vXbO/a5pf3th4/UoG3+fTxxuHDan184+OP77z99uLW3o1PP9s4OCgvbW3dP9g82DdRcPXB3evvfdSsDbYODnfu3k1v37jy8c83Hz6sN0bb+wev/ssPZCccXpxe+vxLvj7aePzk8q8+T67vXv3Fp6/95Eft9jB4fr771V0dBf3J2dXPPo+v7bWHh+7Rr8ci7B5drB88ttJEk8Xo8ClqVHSxGD18KgHtPj3eunsgAeucTNYfPARCu4t0+PQICBOdTtfuHQjAekdnm3f3haXB6XT86JmtpT9dru8/tpUKT2fr9w4VIOHR+fjBo9ay8GKxsf8IcO3PVuv3D2Hd+rNkvP9YGdw5Ot+6t29bGEyWG/cOUaNI0Wx/tU+Khq7yrS/ug6p14nzz3j7JalzyzbsH3jyBldz+6pCmNcmbrf2HpBIsLja/OmB5Ayu++cV9f5kBrrcfHLpxjpJq494hTSuS1dv7T5x5YqTdfLDvTZdG2u37j7y4wDnf+uqApSVKyp1HT71FapXZvH/onU6NBFt3D8JZLA1TK8JTIgxrLlAeM4WpnRudghbSeo6bGDeKiDluE9QaYhLIFy1nvpwAPsUK0GZBmpgYjXUC2sRK4DRzIiaGE0+cWz5HChC5AjqxrcZ8icUCKUDrGeYTKxxPL1q1BNKSegb4AnGJ+RI1c9hABxUhifvSUlAFeBnpltnGpWnYKmaqLl5EHPkg8+ByIC0zlYeyfqsYqD207LSta5qQrroCBSh33DjiwIeFC1cDIwloXBz3tXQMD2kcKeDZKiTxQBsGSwcmw7Z1tXDpKjKta3lAV73WurKMSDJqtaMkbS4wL4hqaXOKREUqTpsZlpLKGqkL3JSYt6w6hXVOpaZiimVJKumU51g0lDdYzamqsDCsnpIyo8JQdQZlQYSk9TlqKsobLJe0zaG0jE9IuYQCuvLM8ozKltZzIiqqFakvEM+QgE5zgZoFUMRDec9mnVZTWHRRFmjDSBaSzOfEp0mIsoE0FBQ9mIVGYZz7OPMEcFHaw4ugYT5OQpz1hWUo83HmSu2APIJZRxoK0h5e+DXz8YqRNJKWoTyEaadVFGYhSjvSMFKELPEbGqDYJ2lfAgdmAUy7WmOe4eYMNMATK1ueQGUdlcNmTqSkssTNOeSS8oLwU8C1wxPYTInUlGeoOkNaYNmQZoIlJ6IiYgKFdcsY1RdEGCZzIKekqXHDaTVBXBBZU3kORUuL0qlPodBMVqhdOYpjIVh9jquayobIc6Q4soKybGhKJmFIln1TRhI4bhIh7ivt41Vfy07LKc2GsGTaMJQPVRkp4NJlBzSRNi5K+qb2jWQ4jVDBWsNIMgBlKJBPVxGoIqUdmPdB4xqBYdyHZcChj5d9mPic+Cz2URXI1sWryNaBah2c9G0RCOyJC9tMkbCsWmAxg1KRJiVyhVvLqhlqzm1NfT619QUWhjZLwJeYS8RXqJkhrmkzJ/zCNsTnU13OcAuYjEEzJ1IRGaNmjqQmPCZ8Yjn2+MzyCVaG8hjzKVSKiJw0SyIt5Ussz22D/XIKminVGoGGobivhWu4T1ddZQPQ+CzpaMNg5aBkaJTbChcv+0Z5RrhOHGrtyDrEyUhpFzQOS0e6pspENI609LX0nbivtad5SJd9ZQJdEz/rwgpr7aJ4JJvQKI+u+qb1gfLoKtLSMcJB8dA2TGsXxwPD/UbT6gI1JWk4FjOiMigNFUtaLIFAnjgHPCZK02aGRU50i+sF4SkS0OEXWMwNRx4/szwmvKX1DIsMSUWaKRYJUtApz1AztQ3xmymoE8oVbqZApJgrIqawWSFuaDMnYgEb7DWnulmSVhMxgzwlymKQdfEiaKiPYh/GfQkoLDySha1ioAhg0pHGgXmHxh1OA5S4JBtI68Dch0lPSwxKHyVd1TJUhs7SE8SDeYiTgbYMFh5M+6p1dRXArNdqBsouXXYkdNq8S5KhMi6qmJNFraSGd9CqJ4wLy9BJ+i10ZAHlhDQV5oJVp7BpiFbYzoxuUdU49TmWLeMVbudUNbg1RM0Bb7AURJ0hyQmvSXUKG8lkA8wSgdqKlvELXOZYKMZPgWhoK1C7AEYhLXF9hkSBhXX4OWoypDRVU9MKxAWtTqGqcSNIc45FjhSkm/uPey/OrTTDw6fh+Qw2avTwSfd0KgzZvP+w//A5J97GwePe81Mt7ejxs/BsZpp27eHTztG5MGT9q/3+o+eceGv7j/ovTrUCo8cvOscXgOvhwZPuizNp2cbDJ8Pnx5x6awePR8+OjbLDxy86L84Ab0dPj/rPT1qD1x48Wnv4lBN/9Oj56MmRMSg6m44Pnhphe0fn6/cOJGCd52drj54ZDcLz+dqDR6hpg+lyfPDUShOdz9f2n0jkRE+ONx88Mi0MLpabh09Bwd1ZMjp8ZoWJzufb9x4qi/2TycbdB1YBZ56uPT9FlfIX6fq9h6ZR/9ZoDYPJcu3+Q1ArZ5FuHTwhRePG5cZXhzgrzf/ntfr/2SIcyro/vVjduIIy4aVJcXmrO50pgPl6PzqZUCHOb7/k5cVgeh7vXYGFiOJ4urfXnU2xkGKjT9LaT9PzWy8FSTy6OE2vXXLyEhV8fvV6dzmnDW82BqjnNaQlAAAgAElEQVRowlVy8dJLfpIMz04nt2+Pp6coa5ZXdp2GO3nONwawVtFyWexuyhaNj08u7rwU5bG7SOu1gYEwmi6Wl3cBBFuHD8/v3ImaLDx4trh5w6XGmWd81NOYRJOFWOuWXmfj8eOLW7cDkUcn03h3l1rpLHIYYuU4dF6oYViE/a3Hjy/2bnimjo4myc4OQdqbp6obNIR1nr5ot0bZaGPr0ePZtWsuEOHxNF8fRbCRqTahVwwGbFrK0KW+RedlNegHDteJAQjqgNTTtlPH4MbYJq32GPUNntZ1GLihaWMNIcB9LDPoyYpv9vC0Uq5DI4CntfBdFtk2NtCCerPHlqUvqmajh+e1JbhaG6D759Zh3o4DSoWlrraHzjJ3eWVHLlwJwbxyY9BZFW4lk+0Oq3W0zPkGU8rxszrb7JJYwgb0OkmtA1KbdLvDahUty+RSt5ummtNiPXILThtlekBL4hZtvN1hjerM8vRSp5NnpmFiSFktgcSmY7SmXiraISh0x5/k9dVOt0ptTcWAUK6sICFLMtJ301aNYOFE3ecXzebAtY1MAQ6hpgTFgnagANRkEAZ2hOIsdvhG37d1mwHkA+U6cClYaOowCk7janPgg8ZMOB93PcTb1GIPaI/ZleyTbDXYcM+yZqPvwRpMJe95DjVyabAPYUTsUjBPV4OB93xRbQwCwu1EiJ6HQ4IK5PKyGvssMS3FyFfOsm18FwdKrVwoJV43pnGCOi/WAhZrQ1AxDHvTvKUYRi3MEGlNstnxE+7XVbnu00RbCMuh35mVmqCuk2SyT4TJt0I/aQhvmzXXOysaGopNtzfPLQS2a3VNvEqKMdYNdSpRb7hOXLYS4gFRApKUN7sjXHF/EpdX16M4rhrHDhkygGQ1GpBWIZI2eI0WIIjOltX1tTBP29y26wFphK4B7iGhibOq0TqtkB+exeXuYCO5iHlkxj4TXFcAdaHiSJcg6snC64Snq3J35DeFSqwee0wKW9p63IUEDs/yZD30QE3nphyFLihhToBrOGNwYVmg6p7fuyjzke/Cmi5h1XMGepGLIWa69TFOLKG86HWi86ocBR6syBI0XQdTBXOEsJERIYmhhOe9Tue8LIchD93wWaYix+lylEBNcdX3onlFiMgH3e55UfX8shdE5wuobb3dp6vKrwqx2YOxgNaKcRfNKiwE2WJtbjFX1aURWxV+WZo1FyZCIJevd/1paiAkfaQKgGvZ7I6cZUEqLre7wSyukUf6CMZSW0gGWFbQKThYd3hqjSZ0AJy8EsBBfWyz1rSADnDTYC+r8qsbw3gOSlqPHacRgGPTMdoQN1VmYHLbDc7z6mq30ySwIHzEqBCW04ClBemwzJi+qWnYuyji7SjkFciQGDEmOayxDbXAvr9szMCWLBye58lWGIgS5ER3rcN5UXdpR2jX9WeV7tvC7/VP02wrClQJUyT6TBMAlmIA02S0Ri9KPu56mIMJb/qBS7VaGORC1MUmbhlV9ajvHMX1uOszCSZcdlzm2TbW1iF8ELmT1CWyHA28k1j0Q9UP/KOFCh0WAh1rgFAz7niThJGWMFMXrqFQbHajk0XrMdyBKjEQArnW9S5i7dBqvd+d5BaCrpvmske4TrcjLxVuJcpNP7goGuvxbb+7KoCxtmd1TbxSijFqpeNnTbnpdVaFgJ7pWFwCqwHoGi2ol3M1RqL1/JSXm14U58p6pmNwBUyLuu5y1m55KQfbQGoWJE2x4YVZpbRrupaWLVSo7ZkGuuHpqtgdb6ZnSRW0a6GjuC4BCFHVMvh04ez1VOh1Tpfl5ZEvy3ap1ThwNNcFICEQ1KXzshdWk85OdLKoLo0CVeplqwYuhUbnhoRAMpdNczwkRdALn07Ly2MmOX9Wwq3I8QHMper5LWP++YoMUB72u8+m5fbIw9ybJBCDcm3oT2OC29XmzvjJs3y8VoxGW599VvVH5tLQu1gZjFc7l4YnxwTp1fal8bPnZW+Qr61tPXrYhKEXAZEYCMzq8u74+Fgxxse94bMX+WCcr61vPX4ofF+Mu2yeuZKnWxvei3PLKL+8MXxxlPfGpuv45wtNqVjv02VBpZxfu7p++lwruLy8259OsBBic4ByHsVxdmWLzlOvrJMrO1EWG26qrbEXp5ArsTmwpewtFtm1nVaAwcVFfO3S2vSk0l69OfLSHDey2ehpbvsX5/n1S0qh0fnZfO9ad7UAlaw2Rt35kuZlfmVLWjo8PbHb4YytbxwfzW5c76QrklXnd77W+V/+c/TBe78Wi/Cb3/uny7/6PN/Z3vrlF1c++7xaH+9+9PH1Tz5b7l09vvnKfGcXc/nmP39/9xe/qDY31r7av/bhx8nW9tVf/ur2v753/vLLL//wJ9c++PDFN3/zlbffvfbBR/V4sHH/4NpHv4x3Lu989vmdn/704muv3PnJOzc++Ojo1a/feP+DvX99P969stzdPb98w1r4yk9+cuenb5+9/uqtd96/8bN357dv7nyxf/OnP802tqLV8ut//Xf1YNCZLV77zndXu9fGz55/7Z+/Xw3HHVZ8e4Mp09jz4pt/9bd1t+vHyWvf+cdyPOpNFq98959F1PN5+erffMcCaCn9jT/7K+M7TlG+8t0fiF4vmC5e+d4/K8ejpn3jr/+eGCs99xt/+ueKUG8n+p1Oo1xCnpx/7R++ZwAGFL/5F3/rcK4i/42/+Hto9Wx4Zf6AtZXtoOriIEJL7q3b80+YjIE7lpiVrAvRaXFxPDS58Zzm4r4PFsrZhKtPqUyx09OLu1iVyMVi9sQ3KXQ9ObnvgYUml0jyC1gkjKzR6gslUhQQPn3m8xhhWtEd6MlYQq/4HOZLRjcZv9fWKxyhYnEUiAU1m7T7YowW2+W66L8IQHqVwanJ19B8t2Xl1/Hj3zLPYmvl+R5ZXanGjX8awdU1jJdu4oPlDeNwm4/8kzXVy7yzPphflYMquPDBcg/T1M0dPbsFaUHKHjvZUf3SOY/g7LqN4hvm2e/iWcIrk27YyU1IMlx26OkVEszxvAsWN7E7qysw+dLBWhEGV58zSAHi7fwr5kAuNrvl5sBC2D+bPj1cdwSHLpz+ggCCmFKL+8wl0pTm/CAkRvmmPrvXcThHAZr8gkAIsDGL+05kCivR6WGEWsWwPPvCp7XEl9hqawu5AJxU032PWWmVuTgMWKsJUedfeKTRZNN3H64j0aUws/M9VnmQZPDsJouZiOI/aO/tseS5GZEXVzDvEpSbxXWSR9rn9PiKt3LkIHOfXDXluujz7vMhrAYYJGZ5jSYdGUn/9BJdRqR7AV/cxOVG02+8ozVbbPjg+bfI8TVwMWE9eHyTLTpyLQ2ebJlsG4VLNNmG+SbwsuzUZofYGdvmmU2fMbxJxXO1eO77kTIjJ93YQFaLR21xiNgmki/M/LHrD1t1DidPfc8TIJbnDyPXUSpF6QPKRlYftbMnbtAX8gJOHgcd1jhxcfx46DMhMjT/igShrHf61frAaTl/riePAg8LXMnpvu8TIWqy/AKBIY0qgi5uIS08lYHzN5y6Mq4mT18ylmzQh/8BvGCoztK+nb6MhKSwgievupUgTo6OXrMGWSPJyR61EisLJndY2wJUwxevsFKprvIe70HrWCDwyR6zBmgNJnewgNBf/U/mwRDMj9p17+l1BTqWSO/4MhUGGAkmd0gNq7FpPub5nNEt2nwp6yWJaDM/8us5xl3QbPebcT8q4tWvcD5lZNuRD1QxIxEp4hO/njtwyLJ9IE6tv22zL2AxdfA6rg91OnUit8mPSXZGg55ePaLNMXSu4OrzdnnudYZNtr0hBhGrm+qRiY8dv6/zJ7g6It4l03zZLs58b932EqYmL0Fc4iakJ7uqUzkzH073TJDtmWe/R6YJL20+NpM7CBaI++z4GvZisgrA7BZ2F3AZgPlLGOSsMfjiDrWZVQE+2rNB7SwRmt4idAZzH0xfwjojrUWnL1GZXfVf/A48qoit5htwetuhC1R7YHKHghq0Br+4DZFqgEi+wG7boNaePuxA3rpEnH4Z0FyRS3i1vWV9gs7K2QMXty3SenIYEGEpk+dfeKTQaI3En8BKOsQD6V2AhSZWXzwMTG1thLPPoEktWwerT2AtHBqi/D60pWY9k1zalIGHSrH6BWgT4Kzb9JeoaRwcwOQBkiUkPRSe77J5F3fOwfEeybbrYR0cD22248Ljb6Jnt+F0QkJ7dtOZ9eU4DZ6sm/QyiuZ4ugnSSwTHNB7a5Q7wcjbfopOxHMX+07FNroBwSedjkOw6aEnjvp3vQD8js3U624bhxTfbx78RxqeV1ouraLVLYUyyHp1uIzcGiy00vQT6MT9RFw/DgPIgy54/GvtIKIFmX2AUoBAtwfUILWO5YJOHoYMFbcTF/dCHQrd4/itMI2BjPXvoj8mqqdyzg5ABCU07+dJ1YGsBir+gOABtrCeHfkAqXcPz/cABrc9iveXAMhFZmNxFbeShTC32WQfViqPzBwECOvTqf/cn/5efZVW//+rffddv6pZ5d/75X/wkS3a3/2OYDml1Jrx/92d/6a/ibGPrG9/5h2i15G545wc/7EzmiyvXvv1nfz48PkluXX39L7/TnS2Sje3X//G7wWJlHfbyj34yuJhObtz+1p/+6fjpi7PXX/3NP/+bwYsX1UtX3/DKgWd4Ur70/bf6T5+vbux94y//7/XDh6dff+0bf/G3/WfPV1ev3X7/vd2ff7a8cu3Ge+/fev/Do29+49V/+uGVDz+e39w7vfbSYucyq6rb//qvux/8PNvZuf7JL2+9+/7xG19/5YdvXf3gw8XNvZ1P7+699z4fDXcOHuy+9/Ps0vbuJ7+8+dbPJm+8futn79384KPVtd2NLx/cfPudejAYP312+6136vHo+cuvTS5do1rd+fHbN9/5V7GzFj2f3P7Rj+pef3B8/PIPfry4cUv9WoZGLQAQjp4+X3/4iJZV9/R87dETVjb9k/P1+w8QlwqzFhEN0PDoeP3wEa2aztlk7fFTlte9k7Pt/UNg7PD50eaDA6tB7+ho4+Fjtyi7k9n6wUOWFdH5ZOv+AWjN8OR0a/8AcDmYzDb2D1lRaYBaRDQi3bPJ5v19IHX/xdHmvQdYtL3zyb81bl7u3HvgxYm/irfvP3DyMlwlmw/2nTh1RbkL6o6qaV5u33vgJ6m3Sra+uu+kuTeZbzzYDxYrVjZbDw6jiympmp2v7oXL2IvTzXsPnFXsxsnGg4NovnQavnl/v3N6Rhuxc28/nMx9xS+p1OOVv0w2HxwE8wWu6o17D/rnE8LlzlcPOpMZkKBZYB0DKHQ1J2DZWgPFDDQrDLSGmkMCccH5EquVNRJUc2IXWgMoLkwzM7YFYgaaGKOmbVZYrgCUtp5jca40wPUMNnNkW6BXoIkRaNpmhVWMbKMsgabKtYXt1NQLolrE55avMKyViDFfAK0ByX2a9q3BOCMoGzJuYO3gYmAF7Fu1pVKsJctduOpajVHp4GKEOKA1g2kPcYi5T+OuMZSULs0GoEWkckCxBhoAK4azAeIINj5J+0YTklOUDq3CHZFfRq2rasAdkg5QQ2DlsrgLWkxzipIBbKxtTDmjOtatgM2JVjmwjeUT2JaghURBphCFTVtOaRsrrXA1xW0JYa3FBLYVgIWu5tSkGnBdzihMWtNCPkGiwKZq6wnQFQRVWywYSLWRoJoQsGw1wpI4LaK6MtUUqxLiqi3nTCcGtKCaEDlXBhKSRrjqIG5QHsHMBxKgfMBi10C8pdJ1VcMWkjyARRc0BpZdXERWY1L0aBoYAEkakqJnNEJZgIsO5RpXEay6VmOQdmgSWgOc1Idpz2qECx9XPSzaMRDrMke6xUWHJpG2iCYeygewhaQKQDEA0vIcyQujNbKxbiZISQBSW8yYrbUBSGLWIqYK2EygUkit2mpCgAIm1cWcgUK3JaimWJdGFqA5t1pCnbb1BGsJTaKKOUVFqytQTZHOlaxRM0W2Mi1iErEWYZjrYkZN3uraNhMEKqMb1ExRyyEUGOZjWmGkIM4GNCNAYxZ3UeV7Lb+ky1A1pNYoH6PGAQKgZODkzGr0bw2SGKc9XLu4amE+xCkBCuF0SDLXGMziDqoCJBHOeqB0YWNhMaIFs0pdtnxNZkZjJ+2gOgQS46xLag9zi4shrEMDUDODcoG0wXph6xWBTctjLJfQaCgxE8S1FuiZqeak1UjMTb2isFIygXwOWmnrJW4vdAuxnbfVHGtpdQKqJQFVK1LEZ9BIIBNcz5AGWMx1PcNQW049BYlpgUhQM4NGGLEA4twYg/SirWbYKABKgvMRbjDkLo17tmW4oDgZWElCUVxG2lMVFA5JB7AmsHHYqgsVIQVG8QA0EDUU5mukBlhinPRQhaBgNO4DyUiBSTaEguCawHwNNhRwjOM+qZmr+I5MfKtQSUg2xBzh0sJ8DecQKEaSPmlYy00zsW0BYKXKObWp1hJUUwIWSkMssKsQNY2pp1DmENVtuWQ6NrAF1ZTIqdIA11MoEwQkkEssMgjqtlpQk1itcTPDYNFqg9TU1ilVEtZzJGOgDVSISuxADaoZNgurATYT3cTYciuWSMQQtBZkIYlDa6CT+TDtW41g4aOyjxo1sGKjLYmWuOrQJDIW0cxD6QBqSCoflAPILawCnHWhgrgIadrVgNLEQdkQKIhSivIBqjSsA5J2gUQoC5ykYw0eVMtt22AhYOnAYogaAxuHJCGQkFQ+iXtAI5O2/3ZBpjbVBOtMygY1E9Q2CDSVYQhIifK2mFObaduYZopg0bYNrCdYcdSmqpogwC0o2mJOYd5qDuoJBlmrGticmrYCbWnLKbEcgEIXcwYzjVsFGYKyaTlqJkhVCNamnhFdWVvqesFAZqE2W/uHg6Nj0oj1h487p2ckL8cPHw+OTqwQl9piQyRGmc39g8HRCWnE2sPH/bMLp6zWHj/rn55CqdfuPRg/emKN3Tx4OHx+hBoxeva8f3Lu1Hz8+Ong8XPQms3DR2uPHhuLNg8fDZ88p63aMHxNNaSuho+fjZ6+gK3ZPDhcf/jYWLh+8HD05BmSqns+XT94SGo+ODnfvL9vDBg+ebr5YJ+otsVMAwxa2z2/2Ng/pEXVPT7ZvL8PtB2+ON68f4C57J6erx8+8vIims43HhzQouqcXWw/OADKDE9ON+49oDXvTubrB4/cJAvny+39AycvFKaKMI1w9/h488E+bURnMtvYP/RXabBYbT3Y9/Li12gRhtS6ZWEiT2pMBTehy8pKYIdSw7GrMUHGoFb7Rdp2A5gLP0uWV66ypgHWEgaUwUiqptfzk2RwcVpcvWRb3ba46fUob6DWhAGcN8FyObn9ElHKLXLe7bqyLrwe1opIiaXEDlSW0LpBAalwECRxOR75Tdm2EDFoAUS15J3IIhQtFvnaWkdksJRlv++JulUAEagJhaWgzFR+J1yuivFaKDLdGBmGblurBvhYaMeRHDKqy6AXzBfleByK3GRS9Luu4UpCTGzLKBUtQbrwusF8UQ2HgSpN1aoo7PNVAjoEG+kHuGik63mQ20LxqBPYqlEUWWMQ5LyNygSsjYyyLWMuUqbUPIxCW3BFkLXEAVwzV1YyDFAhW4cxpECtpOv5iHNFAQQqDEjJHV7JXgi5Mphw5uHZVDtuQFFLKWq1jAJaNa6soEds0wrqN1HocEGkFJGPpfbyzHYYBy7loumEQZnCVkWgSZ0R4qrudYAGbl7Jvh+VqdCUd0MiJNTWwYIDj9SK932kjZ9k9agXlqnU1AYUiVYZ5tKGE5+WDXFNTd3ecp5ubAVlIYwDXESUlIYOzCr1RrgUyAO51/XjVIWB11aiJQQbQ6nhxkVSIqpcFwu5JuYr3VWdMOAZWDYgckU3tLV2kKiCnrdKqsEgkrlzvMi3NxnRXBGKWsOoaexQr+JwrfvsNLl8yUNcNUCFgWcqDh0KtLWwlcCDogq77jJtet2wLVUDWt8l2LYtdmWlAg/WrSaEEgUrzf3It4XVFgmug1AYx+eF6IS2MgZjFTp+mreMubBprI91K0IfV8oTleq4VhiLkPRdPy0Uc9bldMq2Cee8H1AhsVQ2IFGyaKhXd/qsEgZhD9aN9RjnyIfcupRz1fFo3UhIXawkoKQWTb8DtXXzXPSiqM4Kvwu1BgYgoRgzoFY0LcxGt2AdP07q8SCsUq4Z8KmfpbZUYOBVXkTKhjmmZB0/Tviot7k8Liom1noQWtVih6gWEeW4UZNl7iBYLuvxMKpSoYgNGFGibZEIA4BRtIirYT8Uma6B6IR+W/KWEaQBsZgLimweDt1V3vQ6gSpVhVXHW2vOl3hMgTKMGAldy6uww5YV70WhznSDROj7bSlaRqA2jOgGuaCuOx03znkn0hT1llPu+QRDYVwArXJdpygdIKqw62Sl9APuuV6WQQBkFJKau02huqER1kKoGUNQw1Z7htfAQ22rwsBbJUGWiO2h5VpgV0YBK2uDkIeEKbST5/HeVcI54cKErlvkDfGpY9k8MxqAsV8DlzUN8WADHcMoVYIK0WDfIa2RRlvk0rbCISvLajjoF0uliA4dKrkylGIlqY8rTpmuHa83nyab22FViJZZnxAphXYHdlm6XVC31NG5148Wq3I8COtMSMcGmLWN1IxhKalHSk4dnXsDf5E2w07EE6kodmHA0wowTFDjdHAlGVOF1wsXcTXsBSJXira+g4A2jRmqVdodobThnSgwVVsDGQaBqRroEGCgtUohF3DuRd3nJ/nmpkNVW9vWc13AhSKWYO040cWEYpNubNK8kr6vGfHjTHmuBxqbCGhtvrPlZAUDkgFZBH0oW+l6rKgNJS7kXDtYt7wTOWVlEWyiTpBkmpA1NVs467Thdb+LpaKct6HTTRccsao7ZI0wAHmoqW1IG0F83SDfKSvZ8/0i59gnzGgBtcUebWocOVWJPPD/NoFb1g0KGJVA6RaQsV6sSCfgtQr8knS8PJe90C9zjjzMjFXWGERpW9PAX8b1eLCVni7gwAYObblsCYaGIwKSlHpe2+8Gi2U1HoZVriTSkUtlowx2sJbUhZUYmGTWv+TP4nptEDS5apHxHdZy2WKGW0F9VErHUYXfDearejRwyrwuBQwCFwNrQes6BhM3yxizRdDz4rTpdX1Vam4xstahhmvH8Krbd1eZCEPluZ1kJVyXgVZqDAAQfuAmqQtk1RuwvJCer1w3XC6V5w3beAl6pFX1cOAWuUEYM8jSvPE7MvC9VaIpdZiWLWaS69CzQgOEgINZktV+18HKcGMgpA7ghhEpebfTTRcCMt7tsrqGqqUOEJawuoEBqZHfOo4japfXAjLk4mCxAELL9X5NfLcoYEAb5Hp5rvrh2uS41K5Y61sNjAbUsQ3yvSwFEVMKDU9PFjf2vKYUliEHaYgkZqGqGux6WdZhfBJuB6sVH/a8ulCGZDu7vf/0n8IPfj0W4Zs//MnrP37r9JWvXX3/5y+99bPZjVsv//Sd62+/f/TmN9/8+396/R++e//3/+DlH/3kzX/8p6M33piuX1mtb2tI3/yH77301jvPv/nN1/75x6/90w/2f/PbRWcw37nm5+n1j375yr+8dXrzzo33Pnz9hz+e7u2db+/Ndq7isrn53oe/+Td/d/TqG9c+++yNv/7O2Utfu/bRJ1//3veP3vjGzZ+9941/+O7RN39j41f3f+u//fnJ62/2zs6+/V//z3RzK1zE3/6TPzu5cad/evF7f/RfJ1f2vCT993/4fxS9EZTqd//oT6re0OnB3z3+JOkM2VH8+//rf5nuXHUF//3//L9XXgg2ot9f/koCCJ4sfvvP/qpYX4el+u//yx8v17awZ/8guddCVKXm9/63P5GuZ5X99h//Sdnrawn+hz/846wz1L73+//zHxrVyl73W3/035BpLy7dLj6UpgG4h/N3lUo12nH5241YQrqO+GfGyQ2MwOpTZAoInQblE3RyIrZ2xdtVNYVsm1a/VO3UWqdB5Qyslop06k+MOW/0Xle/m1Un0Fxh7MUhlJwrt7yL1ESBS5H8WNKpsC917btZcYz0ZV//slRnlg1A+SUQE1Bf6Y0PWfBktLwMekes+3STdzO0GkWPB1Vf0+la51mPbyjvRcd9upnsopfmn//u7N5z3GeLgf9iu+prOukNHvnpBn11+dlvr+7fH+yFx174+BLv5N6Shs+2S0/cEYffmt89icbkeNh5ulmvcbToBI+u1VHtZzR8tCVCweKg83Ck+zWYhL1H282arARWP620gy1G6dsSuMQqyD8UNiJKkepHVduhgawWHzBjDdhwL3b2WpeCo7L4SMGQaQ3T9zTysUPF0Y2vGUTYvKg+bLWBGqLqI+Xppun5F9dv2daQui3e0VAoPfCbH1dSYuRi/rFCVmtM03clAACHuPiZNKUEl/u9ez0vCepA+k+2SOGrSHUOdpwVzLdA//ORTTbFsIweDp1FpMLVb8T3Nquzo86l7r3tYAbTy2jwRUQno3Kn+Z3F5zeKo2Mw9I6uOHMnH8Leg7VwipsN/hunn9xW5w+6L/f2PXc2FFHtv9jAq3E6gr0Hg845TXbB4K7vTNfLdR487zoXY9Gr+GOlPufmctAe1sXn2twa6Mdl8YlC2y644PmHql0P5Kkxv8zRkGTra4vNXbcpmhc2/0jRdQqWsvzUYtfUm/3Z1hVqpLlX1Z9bvM3k1JYftWiIcIhe7N6hRoJHdfMrDdZZfWb5OxW+5KmVTT/QdIh1BbP3hY2wLUDzMRcj3xV48OCyRMIS2PtiV1ujAjD4ctsCIjEK711uCWm16R1caa3C3v9D25s0SXalZ3rnnHvuufPk8xDhMWVGDgAygUKhABRRRbIGVrGtxaYka5N22mqh/yFr00LThtayFkVTi+qm1BKNRpGimqwBBSAx5ZyRkTGHh8/u1/3O873naCH9AFKm/gHf5ls+72vv4/3+6FckC1Z4S33aZ1SoCDVfdQFDGC7/Pedpnic+aRlPdyjmqmQAACAASURBVHBcRDXc+KZRZbIo3/xg8Y3Acrus66/2YVG5NbnxTR8nXNjCta/NKjNShTZeNVAGU4HWXg3KFDg9if/bRXjBqltG/k1UXlWohf1nrBgWdKCnvwrBeVTescCnfngK2IEc6lbQaIl+EL1g6Q0ou3L6ZYouYrjFrxp9p93DTpi/yOJTgFsof5WF1xxvMHswcGtNIYvLR2HyBqBdMXjNsicJ3+WL4yI5BVwXR8+q7HUODtXiKz89RnRf0WdMOd3O5RR5qvG6kTSYuCD6STdpZmitqscHkZZJAdKOt1O+GNDzT1bfehwfLfrmm37SLPEaGW/2Mzlo5cOfbp6HPA78Het1KzMq5AjGq15Sq2rZ5CeXf7eW9SzrGc+7FEaoFMnr/VxjMBJrR51CS4pUtJ7vpFKJATKf9hhfeIClv06FLM047HwGIIRIgsEvKhDmVVtN/jrIQsQZOPmigBmrDG6+c1AgXowS/28L6mZ0Sy/+rZ9sIN1T180eJTzzqfernKas6ujRXwdok5UH+qI5iMwaWkTZoxQGFdBQ+Hd5mXBpR8v+OkDrvNzV878NIkegTSn7TZh7iHYl66ipTfikn6lHNX7ccQfMOBbkcTvTQ3lYx8uu1wDam5o5IpsB/nj02XeCs1NlWxjW5VEr13x1pAmTeqBkv+U9fmfz5sQc6KeGMuzE7VScGNJVJ1Xch96r+87JQtHw5Y56rRftAFx31KvtsJMJY0m56ud6JCxM6bKeWJkytrSrRtTJiknhfVqiGiJ+bH/BAwnQGKafZ7COyzXLvmLCQChd5v6qwiYC0Zr50yrOs8jKPouBjnMHJF8URMiTOOTiOXPihDWyX6eAByXgw1+mVBNxOEHuHJRVlqju3+acBDGPkt9QDgNGSPJ3YaYoVQ6TX2dEYwkV/b9JgMAJKv3d/+aPtPF0vTX4+I//J2W9caz2D/75vyBhMt49/Pk/+y/0uT188OC3/+s/Mi+u5995+GP7iZ65y0L+wT//E9F2Rnfe/vl//s+syXz+8N7Ho6/bpLyprO//8Z/o06XX7v7Wv/gTZeUMb93/R+NftoLRhX7rk//uf+i+Pp7dvvOdP/03tfNrp9P9/r/8n43RbH54+IP/9o+23pydfu+jj//4Xw6ePr9677sP/uqvD37x2fD+uw//97946xe/Ov/o4wf/6188/Mu/uvreh4Nff/Xxn/yP43ce3nr05b3/8xdOu3tzcH/VHvBF/tb/9n988Bd/efX++/2vn3/vX//Z7ODQbXdu+ncQo+/++V8++Mu/ufzgg4O/+eVH/+rPLj/62Dba8+19nOSHn33x4H/58+nh4cEXX3/wp382fO/d3U+/+v6f/uvVnX3hYvHD//6PZ7fuds4vvvuv/s309t3q5OTfQUQIAGRs6/lTcTbrnJ20Ls71yaR9dqJNJt2XL4Uo6L56Wbu+NgN/8PhbebVqXV4yACrMK2u7fnnReflCXdv9s1Pr6qo+GeWiBAAjSdq+ODeHw/bFuT6d9I+PFc+jmAMAAAQHx69Fe7X14nnj6qp2fdW+ONeXq96LF2Lgd45easMbazLuvXguuc7Wq1fGZtM+OaldD+uXl7XzM2syHpyeSOt1781x6/JCWsx7r16o9qp98sa6umwn7n6tXQf51qtnguMMnj9pz2fSZNw/PmpifKjXG4i1r8+sqytjOh48+UZYLvffnDSLTZu0D1ikrVadVy+t8ah9eWkNr63Rzf7xsWjbWy+e1kbX6mS8fXqirFedo1e16YTMQz+SMxsJE3ddGNmk1JNktVFdR8Be6q3FKBbESRAmUrzmyWwRv/cW61qy5yzWahDInBO7tuAGIpmvs5022O3gMF0mhrcQhDha2JqT6EpZ0d1OttXgx6sgliJfActkE2vunEi+HzjKOlaBV/mOFEVEvHGDiPdXBEcJi3ol2BVSxNm1CPa0RSnEcsa2iSezwqLJQPIT7FsZ2CMhPCg2LXGrly45R8zKvuAIkFpFeoA26R1Md6ztRroRglqCesJGgLEaVztKhDsiuW3WGnmMHTOmO0IAhLWccD1tzsRUSspt0cUw0/L8Fh/miitGcFf2SjBPZpmZTysYFLZn5E5FNsk60AqP0kmxrgwwTcVVNM+tYsEgAgxBBADYFE6g0E2ORsmm0OmkyEUJAkYJFuJyvtFjn0d2agcq9SiiDDJWShKZOfPEKJYAe+F8oyUBrpbZwlNSD3FD362MbAGhk84TK1pinDMatkrasFYVzSwYWZJLU9pD66bke1W6S8uuEJQ07uVVu2GvBpyyQ3jTj/KqB/yu7Ps02q3AlpaV+xzrcmYncGhRL9MtMcAF66GwL9r2bYnb09uGZ7O0T1nDXJQ00auoJYUcZb0i3pN8HwWdjG7LTsXFzaJqERcGMfE8nfhxMger2AR2kq14h5rVOKGrYh1rcFnkGZlvTODlFHMQAo6xfFI4zOJu/NyFy0Ar3P/3nwAB5qBFZEA3K6fVqjLhLK5EAhCiACUbMN8YfEThLLNzC80DNik2pV7cZMhO1rHOrxIWgoVrlkElrviY9XRXUlZJjHYkWxISmqe7MNUVl1ZFjwuxZcMUdPHG6DiT3eb+duEQPyvL2zC3BBemeR+n2iBadqWtO2JFPBbTHW5jiUFc5Pu0qDeKeE9rbpepahcJ7ApJU3bLlO0gty76Ecj2AGtKPl/kXRRIlg1S2EFhl2TpaqVuEkOMimCO/ETiboIgFMNABYvMjVV3IUi+F2xkO9KQmxaiCADjwix2kevK1Km8RAkdhU8zijEEjDIUb0Q3VeBNlPjYXQsgKhGjFGMImTsj89RCdpwsQZAq3E0YRMLGkdAm8XxxtdFE36Nrfh7rcJ3hUE7YjuCKKJPKfJ+EFXaUpNohIRTWUsxtaTMqJySqtqWAbAv8oVarFZG6wTHYFb2KX6kR7NUcvl35Pb42KEOUiHm2y8W8vOZTOBDXZc8+32k8GORL3oMZHSgexiGq8gOcCsKGRGwghUSdwRj3pZXAhTgpd1Cos6h0PLkIEZlEbqFlM8Bt0nmiMxsRP1hs9CggdJUtXSXZAFZCAEEpCmThzVMrWmA+ime2nkSilOYVxoyD0M29Qks3AmcndmwEcySmEUMIAlrFYOMrmYfEse/kajzjgF9uEiNei6LnOwviRxJaZn6kRRvI23nBujDakgMfeI0MDIQAwqRbgqaxoixRy6QjBhxj7SLe59fuWxK3bWxZyQaHjYx1xDVf5rU83dYy2pPVO6ZhxAFymjEbiB5FrpHTtmXHTQi2AaxFHs1bRXagRLHs6SHaVTY555glaFs2gLFYJR3Fq0Bay9MdIablpFpTE40ifp0uEpMty9yn843BRQzNUzs14SJk43xT6eU4RzSLP3hAJAS8auGYRQDYIl8kJtoUehaE338f0ozZ6TzS2apkbrZyjWJTEVWIPn4XRy439DfMyueUufkiNHMbUL+YOwbNeTYvVqnO5ik3TzbUKlac4qy7R6/M8bg2uqmfn5rjUe/1K3mx6F+cN8Y3xmjUf/5Mcd2tF8+t6bwVb3Z4uYf4ndmlslx2X72sTyfGZLL98qXkbg5VYUvSm5OLxsVlbTbdPj6SF/Pek6dGthmozVsykabj/ssX+njcurys3wzNm5vu6Yk0m3dfvFDtZf3qsn30SvGcradPlMm0ObyuDYeNs7PmaGjM572nT0kc9Z4/125GtfGoe/RSWdvbL1/o41H99ERbrxhCDCGK0O7ZiXwzrN0M+69eSYtl82ZYSBLFmMuL+tlZ+/WRHkWDF8+kxaJ19ub/IUokiZsXZ/Xz89Zs1rgZto9fK5t1//hInM9a41H/1UvRXm8dHanTafP4WLdX/w4jwk7stq+vrt9/KC02um3P3rnfPjktIQ52u8rUltf2xYcfW8tF7+xk+P5DEOS4zL1Wuz0c8lnm7XTldaBPJicff19bLUmRcwSQKBFs7+rhu63htZikQb9RUA4VZaqofJoOXh+d/PAH7dGVvHSv3n3XGg1Vx3NvbXNB2ri+nr33Ns3h7tNvj3/42+Z6YYxm3s4WhahxORzdu0chvPP5528++YFahL2nR9cPH4hVZgwnQb9biLg+v/Ia/UBt3vnsN8e/9YmaB/2Xpzf37nK4NObjStdSrBgXY3+371jNu7/+9eknPxBTv7+5nurbkOeN60ncrMWa1nlz5u/0nEb37i9/cfbhx4Qrt759Mb+9r/AlN/FTU3G3t8B5wGqCoDPu1M8bMjI4OMsqwFBXpPNcK4O8r2UTQHUBmwi9OC5UHe914DylFICeBOwCZ0XcVoThpCI86zTIOKw0AuuEzVJasXivoZ2e86yK9nfRPAOEJf2adraAMmZ1HtoFjYvosCOPNyRJYZ+waVGIgrfVsOxQcPLVLVNxQ3NZeYOKZoq6ye1tQXFKkmREj7Nc5F24OLQk1932hmetu/UoQgFZb4uiW4lxnjcgTiLLnV/uviNGmTGtNgdY9yLkkbCD9MgmSe43GyDjxDVKemUGJOumWB9g3Y+wT8IW4BOGQ0CMMKoEaY3jAQyIqh8vi4HKgYotMljjGeHwLAF1PtEUcrxJD2vdeOpOhbIrE77Kcg7zjCEEZymq48xU2WuP3Ta01E0KAgjHcQwscqoioBE4TkwldMwGiysq8IRQeJXQlgAkBBYpkzhmCniScBoL6yY53pQ7OuELeBlVTakyRHFTCmXltjlpgUsRUj1XprBQWGlAYgNY0LSLuDUQ89LtCjV3VVC46mw3R2FFWGlC7EKYss2O2liMtTKYNnZkh2OsdHtK/TqghMpqnHuZBIvr3qGxjkiKwi61ZnmGeaerWKMIYFBaDHuMRDTqQRYQIaThDifMQ5gy1hZgQtEq9u72SRDy12H+dk3ZuNSm8UENeJmwCVFbqChkOSMSTTmZP3PTtxqq55ZrALoEVlVZQCwyVkC8SOmOlEBJOF0X98z2errEDSRBlFXAKUFbqCjkRyHYlxMsw2MHvGWJUcimWbmtckUFVmkwaEHCmmepN+AFmCg3wG8iJADehpXCSglIC8Z0GpiieVG5fUxQ1p5dbtS2JLDcUysR5DqSVwCIuWPIe6uLsdLFIlKnXGwxKkFhyShhqUqtzYLKsm22rKsyaKKkpnTOnVRHlQr5NSgwjlqkNk6QkDsNuTakoUG9Zs28nLK8iu501JnD+0m+r9NZCUGVDGrKpY0IZHUCNyUNsvBuV7IdTCuRL0oXFoiPBzXhas1jiEyQlTyoaKmKnJ8hr2AHijhyC0hQDVY5BCUlIi0ihJwMHChFiKCd0ANVmvoF40GHgEUKKwa6IvBKbCfrd7abqwW3IWEPCUnFhTBvQFgy0YZpp0o4qX5drG7xehgSh/gtKOW+5PlAl0KgijZO+izlRPOq2hxgLdqYkbvSWhAI2AOlxSoGtAVNeyxC4sHs6Lx7Xy0zYQ7zRi5SWgRSYYECI20B80YaSGrzoljvYblIxSWK67DgEZrGJgm9TgOcxcVAF0jBXURFS4QqB2YZEyCri9wswyLNFUITWsqCyJfwIqzqIjQwm6SlyOddQ9x4Is1SS4eXcdUkacM0jifMEoEKq5AWImEEC/OIxyVo8uAqy5pK3DTU4xkyBGZhuMoqhuKBJV1tmMiHg1r90kWoFNUoSRQ+hosDQ7NjKYDeFrOWWUn5TV/WpwkHWFEHxPf10F529liKZYf6A2Ass6oiaQuZ9gQCzm80uJAJLhdvVWUuqqvS3UbNzQyUzKs1iAtRAQQzCFJZdVG4A8pMUJY02IXaJmMJSluQDwFKYN4COeSF43V+v9Z2p85arvoyKiuwKWCTVBDhUQh3pERQ0JFD37L4zYbcTJJ+D8oqZyegzlOO4yaxaqYruaa9ehXdvctjjOcJ7YgAArDKQI0UZUauRmi/HxoN6WhV3KkJVQJGSdWWAYZwniRtA8pYPl+jLg51Q3i1Lg40nqtql2PKo83uoH56DUQ0PTjcf/TVcnfP2d6+8+Ujt9tJ67p+M6eAzW4fbo9eQ4wmncP9p8/W3a49GBw+ehS0W5zJsbXPifimfWdwelwK4mavv/ftU7u/vepv759+k0pq1Oyq46UYh7O7h7WzK8pxm9u7u4+fOe1e3LGaxxeFLHmDjjrbCJ5/+f77eycvYVIN3367c/qGz3LvYJvfBLWb0ey7D5CfdU/fnH/0UXd4gaLC326DoqIVhDKHw6x9eTX84CEI863j1+PvPKxt5h6nV4akzxYkSp39LRDlWyenNx++V6ZUCoLUNMzNSrBd+3BP2Xji0nbu7IG46J+c+u8M1qi29/zp+ccf1+cT2XYuv/ux/F/+V9qnv/7/3UUISoitJ6/1yQjv31IvJtZwaPcG6vkNcYLVzk7/6Kw2mx9997cbL8+Mkzf8waFyetM7P3nyB3+ono8ao/Gq/wf958fW1fDl+580np8Mjl4c/5PfV64mxvnwpn9LuF50zk+X/9G/v/v1t53j4//rP/3P9s9OzJen5PZbymhRe3U22roljdfto6PF7l7nzXXz2fPp4R3jam48PyH3vsN5ceeLp5GoQga6jx5P2jtynJgvT8nBWzxIrSevN1o9r+ndz58kP/w+EKX2Xz+Pfk9X7NJ8cqxsH2JCzW9ernk17dT6f/Xt8qOH1KSNb1/FsiKFwHp+qvYOmCk2/vzb8PvE3+n0Pvt2/L33qjprfnuUC5KUE/P5qdoc5B2z9vgoRWI6qG9/9s3inTuTxq1kwVDJ11C8XtVICeUa540IkKHWgu5czDgomnixkmAMDKHywrc4zKsIhEMO8FBpYncsSDgnFr8K9jkJWaxYLQwWEaOD3JnAcRDuaWm0h1ghpti1Cccz2BDjucHxTG7i4IYUAHF7cjjPuVIw9cJbSyXkqp4gewYLcMwSwVODQADJJElaIBTSPIYJRzeEqme53y98Ma1SGFmTzdvMWpQRKb123oxZTtCGS+orLuzTzcN8EPCxFcZKnqyzVKrCbpktg6yBNnpaX3KBUjlK2RyVcS0KlCra5KmQeL3MsGkCqrVFlYsiaZaBCaKrOCfe3OJFTjFpcI1FDkMVh2Oii7TIcTw35ZaUVfhm0RAFILWg95wquwDrMJiJBp9njMRzjVgikpXVMyzvcnwXeiNR6EOMkb+QUJMmouQ+LfEtYtRze4JFRMQ+C684occhHgdTwehkJcarpclLgtmCqynBkGh1Ql2rhBlN3DRs07Rk4ir221UOstqKrhsQwrRlc0GrokWVuSvvQ4yqoopTr0F5kDWW4qbFKEpYGnkP86IC2iYK2xCAtEzjsAEghOpxEj4MM5B1kyyoFxlH40UaNXImZc08iZtcAZLmQvSaNIJla07jTpXgPLfDAGYzoDW4ckmLCaH7Irem2QpLLqG+uJnxYlvKI5FNsNIhmc3yi6zxIYw3YrSyFB/TQNxMBdngUFSml6XyPsldmEzEehuEAc4WltYX/Eia3cjaXcBKEN0w04KlD+IZ32iXQSlkc03qElrwm7miWYhWIBqLrCXKlZCEchHHGC3CcL8S/YKEbN2tYFBxCfSbiKyqGCdhrQgzqCznm39EcQD5m2K9X5phLhbUbWJzxQiYLn5StAKKN4GzXaKkkD22blIzyeVk7b2N2aoQaZg0qzjJ9CJ1GhksCm3DNu3UoFmWxV6HKJtShmHYSHGWdFgy17mKsdtKPs1oxitN3l1LiFLYEqOFzjEqt/hwVOa5wt2S45sURpV1yAcbnBUcbEvO2sRZqXVw+qwoA8Z+aMFVUPpMb8LQUbOUaBaKL8osRcbHOLikbJ3Xt5G/JNma6C0UuWoUEt2C2YqrAqr1hHAB8pVIDnGe1atoq0hWIAd4YyS1DYrEylHLxrgKzTDQaLgpUpx6W5m2poWVreu8fFUUehHUQXKdwmYSKlXo5SVaLt7LyRpBQNf1Ql2BHEeBBdJhxdXG9h+Ulpcxrwj6UFsAlpZ2P1c3ZUqgV0PasKq0KDCp56Y4Kpx+roUx9KO5ACyWKfxmaWDCG22wnhoEEFkB4TWHG4iXeH8i6PWMilJ4VPB9YnYze2pylKgW8qYCVydcXQreFLLFM0EMVzwPOWqI0cziEyjex8FJURoCPpToHElKoQulbZug4Ghd8eaWEEK5xsU3PBU52BDClc5khFo4jQao5IB2nLn7eYzSKuWDGo34Kl5moVnkWtbKkqRFEpA0l6U3KHy+ao1o1AljUqTLJK2XqVFZCz9+nyVS2prxXq30SNEeV24jDkSWrIL4fh5ZpTlngcQlKjCOy2gnCMUqGaZJlwv5KlrnsUp9q6itWKgjTy6aU38FsoWp9MU4IqN5XdEYh2BwQ3UVsBxGM75eL4NQSOea1BVUpsyCBxqDMGP+iKgKKimM5tKhtMh4fR2+LyS8IqSbka5IPCfS+ArLAi6RFkT1VppGgLeXllgTVIWtx6IkcJzJxRMBGhqfV/bMbCpFSIm9sgRTMBpR6/PHcbftWO3G41dxv4X1jn58WZR4Xe+3vn2Bt5zzn/7wzqMnQb0x6x40fv26aBgrvqu9OiudZNreq3/7Suy0Zz96f/Cbo1SWwc8PrOcnkWXGqma+PAWraNLea/7qKDWM1R/u7X/1nOPYsrdjHV+lkujXmtqbKzjz/B//oPXkKCdkvru39/ULUBTo7tvq5USa2TeDQ+1yZkwm81u3Wy+fNE/PpvfvmydD49WZcHBPGs61y4nd/Ong8Yva9ejb/+Q/Hrw8Mo9fD+/eNU6G1vPT5e6t2pvL9sXyzT/+Pfly2ji7XA52Oq+v6s+PJnfuKDerW5/95vk//Q/E8bL5+nzd29YuxvXXJ6v9g9rJTf3J62S7qYYL68mxtH2LLNetxy9G+29XHP6HEqy/1wEE7ODrL1XX2bxzp/nyVf1m6N8atM/P2qcXVz/77YPHj9XxWP8P/+nON1+Zk/HmzaFxet17/Xry9jvNs7P9J0/OfvLJ3pMnxvW1bq/2Xr5onJ24p/vW5bBxdD598LBzcXb70aOLn/72zpPH1ul5bT7defa08+rV8q17nauzztNns3febp6d3fnsNxc/+91bj79pvH5zM/l+/9mL3uvj+ZtjDIuDR1/4+1tcnu98+dXFBx82RsP+0Sv7zh0igq0XzzNTd+7fPnj0RbDToxzae/KNd3eH99PuybH3cq9s6dvPn5aKuIBv73/9Fe1omazsPf422u02s6vuyXHY64Q7nf6rl1QiI+l7B59/ltf12HF2Hn+Tdk3tZto9eZPXrOU7t7dfPiewmkjvHzx6BGRu2HuwWQhinCEYRasa2ETNrep6ZIpa2exE2UjOlUrFYbqQeIlychLamrkOxR02neqcVBluko5ErFBFCouFRklF9SKyRXUekztcPiSFzNW8LBxDgas0KS4WRgGR1Cn8FS/lSe0wWQ/FVOT1sErmSKgqUfDshZUwUYpTyW6C0BDTK8G2uFCWVjcgx8htSP4CJ5poC8SM8ZKUaY+kQ3mtY1/V7angE+a3RH8GI5WsNNYZCzOuiptCFAkrmQtqur2WYpy6dU5bc5nEbTosnMhTUMRt5E94R0JRU1s6YgFTp4GVFSwV4O2I/jFe0jxtoWDIl+Fmjk2cQ0TdqdI0MyX07bGsWiWoWLQRxWsfkyBa9AUU84S6Y5WomRjH4VhTlZLbBOFaqV2HsBEHqzphviIhZ6g3SSqWaTRRAMi42Isc0xjnEKTRUlfLgNeRN1FNoZBBGk1Ek8txxuKVpOKC8mW0kGpFxN0SxKXBS5HAr3NPB6gCog3Cpmx7oBsJzlsVz8RwDtc1woVE3BSuDAEQahmIWnIcgU4oLe9mhBfiCXEsnqb8apm7MqCi0FriuCWEKbECtJBzXhXjsbg2QQUwmXCeSitLiufYr4sBoNtX6lTMOFMMFpWtoZyHzirYgPQG1g/idAyduWj6ZTGh0YLTpwFz02gu84u8SHA65IzDrBoWzkquuUk5LeIVNkYhytNwKqlWUhacO5N1P+Kvc3uuN9YBXIFoTcxrD+RJNOcMMywBdm+k5m5YjKi/VJurELoo2IjKdUDFIpyIhuZXvOjcSPW9zKAYRU11OiIKLf0WKZOS5MzZ4vgbLvfYpg45Vw1LEDW02Rw2AIwaZBzw/Zh6uxDfYLiEm7oEHZTlILLQmkHkwKAlJuPYyIjTK7kVwil0TZE5iOXQtziGcjVgQUdOF5mWiMtmiTJB2vBuQ8gijUXAMxWax1uT8AZkiG94aTiGHAOG5hdzgxZI6Za+zQtBYt0NnRsSA6IHVbbAMCkFxXXmRlKKql+lS0RcJt4PvSEOqGi5RTYFecTqepAvYBiKRi8LV6RyQPPdFJ2XXqVYmzifcKkHa4afrLjYJrVu7K7EdCM2Qj++oFFuyOtEcXDs1pHicoDjNn0S2tKcln4L+TPeE1Hc1JauRFnsNrC0gRwG/rbonxGnyMImCkYoADBuafMNx3PUb5LVqhJ55myJtSXnQeq3uM01LABM6vrUhwpGblOAYyAjGuwJgUdijLyGolwyCmHUlNc5UDLObRAljvQyGUFa5Dj3Y7umwgJyabjSW2lAmsibagrIBSmJxpJWFXxVJWsFwpKKZbiSa1HEb+FkyKMKakYezrGYhgRUyUKmOSP1wpvzmpsY2+lqSKo+wi5N5lhUIr4KkkUTxpy0W/ozjhMLYS+xr/miKRpOlc4QlgqxX+G4JTilYIVoIRfIEpIbeW2CjBOkKe8ZLGsK4QwHlrjBdOtKmfZy0BCDcWmrKJYEe0k8MY3qvDlHgYlck4UX6riZgRbyxsxWUayT1QIHGHqmYI5g1Ad+W/K/hmNUsjbvX2Mbo7iuLZcg1bh1nRijyuuzTUcOLr0JDDeCdu0hGkUzTldCKHPujWR1o2qTBwuj3glRCENHkq8CoKThTNJEH1jEuVH0VoRiEM5kpCRw5YUbVRiloJbHM6Um+lVLcKaS1ElZVsUzARsJ4lBsEzLJq1YVzwVVSjiAwyEvdypUVtGStGSXV7n1oOee3QAAIABJREFUgiClUoh759EX8/t3g3578PhbL7lVSVL/9WvV99eDnd6z59boZvK9d29//vns9m371q3Bs2fxdjs2zN7r19ZyOX/r7cGrF/mN7n5w+9aXX2wG24vR/Z1nT71ul1dI5+SNMZoMP/xw8PQJlaXLn35y+PlnYaO2ub0/ePokMU1UU3tvjnOIN+/d337yGAB49vs/Ovz8N4FhTm6G3TdvjPPhzXfebx+/3nn58s0/+fmdrx4p09nN6PuDZ08ap6fO4a3OxVnz6dH67cPOycn2i1ev//Dnt779yrgejkcfbr143n7zOtjvN0YX5rNz+927/ddH218/vvrHP9n76lHz/Hwy/E7r2ZvO2anz4oXi2Ltffbl65077+PXuF1/e/N4P9x9/0311lD8c1MZh782xe7Anxv7BN19f/ujH7j98aPTv6yJ07x5mdfPik+973Z6/3R9++EGpSDfvf9ff3XL6W+vbB6N33va2+0XdOv+dH7itTtjvXHz8caXJ07fvO7f27F7f29u9efddb6tXmMb57/xW2GhuBjuXH32v1JTZvbvO7V17MPD7/esPv+d2O6Wpvf7pT0pLXw92zz/6MDe1xVv33f3t1f5B2O1MPnh3cXCnUqTj3/splchmsDN9+La7u+v1eqP33l3cvgUFfPSznyWdeqZob37yo7Rubnb2Zg/urwcDv9ubv/vW+O0HkMevfv6zqGXlsvbmJz9OW7X17r799qG9v+/0tlYP7l+/8xAK5NVPfpJstTNFO/29n0R1c7OzN3/n3vLenajZXDx8a/jOQ0Twq5/9LO02U904/cEnZduaHdxdvH3P3++YKMR7PO4Ti/n4nlJZoiX4RreotlVVyU09Lu/WCV9JO4h0OIsF4EAALdkQAr2Vs21FUXJNT+kdU0apvA35HmdQn9wmoEZ0OVIbRbGjmkJgWFl515JhonYLti236VrYgbSjSlJaM5NqRzGFQNUzeluXuEzt06JnYTgT+ZHb57G4ksuZuw8yuZLhONzKCdsAdcGaNFVzwk+8PuT5hUgX9m1EOB9xK29QErBB0ihrc1SPeTDxB4hKnlpMnduM53yI7XirwNhH0iRrw1RNRTjJtorULOVitrkDEI44tIwHFSQhFMesUQQNLIJJtlWkDa1VLPHbCq8BS/bJNq6aoiFGcg+wHdWsXPBuXVUzkcvJWwrUUV3ySJ+jPdWUYmkL5Ttao3LpO7pYgwYM0X0V6FxdDuQ+BF1JJ6HaLtPDll76/FuSYAET+vRQ5mrElAOpQ0FPNEkkdVl5YJqFQ96SBYsZwOfvSFBhkLc5aePsYsy8ou4VrUwsllHXL+uEgzOkLZM2AHjDC0t/F2LmleYm6jIlGZddN68Txi2wtPD6SIRzntibPY5jAdLmYZtp+SRrbpDFYjUlwtTrQxFMoeh6u1Cs1qWx8dtQKaalNS+ahMke5qbJVlkIEc/b0YAaMJJrGduSeQPUJZ/e1iUtq0O3fFjT5FyRUnqoSkLeNAPUFXCds0Sf3xNgm9Sow75TF4VMlVLujiKJhWlFqC/SOt8QfX6P0K5kUZd9p66SRNQoOlREpawZIeuLqCPWeY+/reRduUWd8l1DUUpNzuihRlTaNIJqV8/qhZFOnb2SmYWQLcKDvFQZQeOyESRtjofLqhF5XU7NF/5ugtRUKBfeoOD0kvIrZnlpkxEwL2qhv80r6TzaSZiWiMUi3I6pgTCcMMtNmpSwBbB8b4tTk3nYC9I60+PrbCfKTQ6hKTPcuF0q5biqh5stoqbzrL2KalqLrPValu/qhhRaakzvmQLOtUbGduU6XUvbgLUVQc4aZlztqYYY6kpC7+oiX6itkm5LdbaWehXrqkQrLDWkh7oiRIac0vuGhmKxw9AWUXFs9AvQFEkN1CSfuyVhA+lSDN8yZBApHQYHkkFivZ1WPRUasCn7xaHJY5eDq2iQc3yIhEneoqmaCWia97KkXqrZbHMXQBLzYBENCiCmmIxZIwsbHIHzvJdFdarlM/s2wGKCwSrcY0xMeH6UNcu8XgnVNNsuvRZnpBP3ds6TECE72q6QnABhXjSzzMrkchptlWEHa9HYu5VxYsSBZdRNsA51KVYbeXanqdKQ3BNJDVrAp4cyrAum5CvtCmxJuhDL7aq8ZRqFR+6KQh0YzOPvSdDEphRKLVoN5Dp21C7Ib1lm6Qn7CHTFdrXCBxg0JUWK9UYBtsUa50ptSvc0lUXyLURbYrtckF2OtWVZSrVazvYUA7hiH6VbspZOisYaWixRUoFMvT4U0IzDm80+FJhDtXXQBXI5q4xZ3iJU8TGaJFtlIcUiWnp7VGBOpW7SbkXgGujTrE0yKRS5abJdFWpK4NK9BXjqAdVO+gCjDVOnrMGCOieBUbJdpVouwqWzzwToU3UTbSHEOZx0E3d41CFW6bL366qQCFLJ3VOJUtW0EPYJ6Mp14vP7Qt5XW9WmfNeQdaoLCbirYQ3UtRBv8VybmJwvD0B80Knla/xAFdVK56PqUCUGrCkB3+VQl9SQx+/hcseopzZ+VxXVUudD7lDGOrSUAPUI1+UaYCPcEtIto5WuuHsiNaT19o59/+78/t2w1d4c7l9+9wPG4/Pvf391eAsI+PxHP4q6jXV/sLpze/TgQV4z1of759/9APD81ccfLQ/2gSJefPxx0bWme3c2t2/dPHxQ6Ors7t3xe+8Awp998slib5ep8uVHH0ZbHXtn39ndHr3/XlirL+/embz/kPL8+Sc/8HZ6Sb05/Oh7/nZ3ub3j7e1ef+8DoAizO3ev33u3MtTJuw/9QW+5fyvsdkbfe2+1c5DXjDc//lElC/M796bvvZPVrPGDd5xbO/PBbtLvjb773mL/MLOMsx//DlPEyb13Zu++Ezeb87fesm/tLG8fpvXazW99NN07qCzj9Y9+t7K05eGd2YP7Qau9un9/dedgeftOZmrTDx5c33u3UuWTn/640NXF/v7w/f8vLkL89yy57z97bMxn/l7fOh/ps1na0FvX18p8GQ5a2y+eafZ69N3vDp4+bk3G/nZLntjt4bW3vd26uqpdX4U7nd03r83x5Oo73xk8e9K5uowHTX081S/GTrfdGl53Li/i/d72i+e10Xj0/vu7r486l+f2m9ft0ZVxfuP2u82bm96rl/5uZ/D0SeN66N3dUc9Gnctz++S1lCf7T58wTYAlHbx45vR7ShR2Ly/s4yOZpd2zk8xUs5q+9/gbKIAKop1Xz1ldNstJ5/LcO+pwGumenZS6Enbq+998tSjv5Yrcf/EMKliZLroXZ3GjnptS780R4JF7sHPw+JsJexC5zvbzZ0gA8mrTvTiPDT3rWP3jI77KooPu4KtH9jt3Xbme2QhlBSkTZ8ODODYlsJpznIwUPfTGnMBDMnHdlcZFJYJF6BIc5pKROVMOCpxuhe4MQ44TZkG6xswvRVwGDuHcVLNKd4ooj0U/TWcMMcbLfrjhEQd4K/M3BNPMbAThAsWAAD9PZ0wsoS66nk0yyLhuLHs6joichqItw4Q37FFeGVzcErwIR0RcIyItOdsEaU0MPOKbOMXWei25sIwaiheRWBU8E1gTvNRQ3BCCUNuIXCoqq7UQYho1FGfJpRL2LEkfwrWGY1MKRjjmuNQy5nOp4Kq4KbsrLhOg25DElzDSUdwS/BsOZrEjkGmGjdKZEwXnopCGSw4rFIZx4hD+yuVxEq5VkSRCk3kLUeFKnAbeDPFcTsIwc3k0jjkldm2Bh5HWB+sZliDj8iiao1pV4tIJHIInGarl4UYgNCEIOFMsVoxncbjieFDxlR+5BN0kqFX4a0EoE8HgRc/CMDVXLvA7mKeAC3HakuOiUKeCt0sRlPwR8vq4IsrSQ2FNQCz3Ui5tcEEG1ZngbDOIlCgmjoGLzLJdEJmMITFIUFyX81KQJmzRA0iWAp8P67hgnL0kgQyAqkQpH9dRxlBtSBZtwCTRnyJP4RIkOOvMQ9UCac0ontJgLfBeBhdV4hEyjZibRLbC2ymLYTjl9EZKp1VkE9PLqw3NPUG5cVlaJTZRjJhmIJryphlDm8U2b3RK5seJS+Shw6VZuNA1JWIUxXNi1bLESSObiKuI5DT1CJ5EkGbhgqhSwEEUznjYyFTI46Rh2R4kJYr7ytyj9Vjwtjjk82WE3TaAtpUXfNJS1hEv+Shu6ytHNKLEvQOYh0DAhx0OrgBNuaQj2THWKE46qu2XeM17exUNZejjsMOXSwWWKGloXl4JOZd0xLnPcRvRG2SgkvwMBw0uty2c82mD+pXQSOIJopATwzSbUphDceInaxFVCNfTeEO4LDMbQTRHUUFAUGRTShNoKa6/InGJQSNPXYFPC6kdeCOalgJxUroERcjJk6C0q7QANT0NNlwVUaObxFdFmgvmJgmnPAiQNvErF2YRMPQwtLkqBLW2z8YwignyC9HhqrilODZXEuw3RG0EfQnHdTEccSmHspo+W8gU0riluGtIAXLbAn+MMgqjDvRvQEm4VKut1pgyGDSUlY04hL0WVOcgAVzYFN0Rx0oubagrlyCEgqaORwjRwu0hacqXGCU1xR8jL0d5W1k5hIc4alPfC8swnQLDopg50UbEOOMaue8IfJmKhG0mmNSByEfBgsMW4IEXuTxEKeoW/kYQslgUOXeKUZ0jehovEK9kPAoSFyNIeTn1V4QPM1UrggUqdAw2ebKAqlyJ1HU2YlVWQM+TjUgyJhq+OwZUF/l1Wqw5FlFiZSipC3EpiBO47ACmSIHP+zWSQMteCr5QlKYSJnxs4RjC2g2ZN0GliP4YegoXc8pqxQcSzC2kTLmgwSWiZF1iu4VzRfZHlSfgSDTncyEWy9iC7hQHNRYpsjRm6xbKWqI75GOJxIK1WuJIYJ6BlDkOTC5QWTwuViBxiXjlcGUWrXRFiSCG0ZyYep7HWbDk60ZKKExdAY9jxKXhgsgkIBJ0Z7ymFKAoEhtjOeN8J3EFYZpBsUxsXhYipEJvxmtSwblVsiaCnvGeF/kifxNDrYhWvIxDZOFgRniDsTCN1rwkRHzEx74A56Vacw8ef+O3m4VCtl+9yFpWgfnO5ZmUp4Ui9U7eGKvlWe13bz3+2m+3o3Zr68Xzoq7lqta5upADPzKN3tGrmqGP6x/ufP1lappeq7n15nVcbwgCa12cSWsnare3j48yQUi2m/tff1lKQtqpb5++yXiCBNC+vNAWyytT2Hr5rCIkHLRuPf6mEkR7f6d9ekJWjrfV715eWDcj79bW3jdf66ulc2/POjprD4f+Vqd1c63ezPO22b44M27G/n5v98Xz2mS8OdwxTofds9O0YzXH1+R6lTW0zuVF/ewyur3defKkeXXpHe4ok1Xv/NTvNk17UT86zetq43LYOr0Id1rNo7Pu6QncsxT7unt2GvTbmuf0nr+w794rEPyHEqy/19Aohaj58s32V49RmJpnV/2vn/BB2nhztv3l45Jy7WdHu59+UVDcenO+/eVjLsrN8+v+14+hn5gnlztffFNSrvPyzeDTL0qK6pc3gy++FvzEvJ5sf/sUR7lxdrX7my/LCjWPz3Z+86jKmXl+Pfj8a96NtMl88OU3vJ+Y58PdX39Oc1A7udj99AuYUfPyZvDZl9iPhbW7++vPpcVGsp39T7/AXqRNlju//lxaOsQLdz/9whjNsRft/eo38nwt2d7uLz8ja1+ernZ+9bk0s3k/3v30kXY14sJs79NH6nQhrf29Tx9JtiMtncEvPlVnNvaj3U+/NC5HMEp3f/mpOpyQtbfzxdfybCW60fYvf6PfzFBS7Pzys9rVGCTl3qePjOtRkWPnkkUzlhfYucLZhOaQDy9BMAJliaIRihYci1lwg5I5qErOvkT5lOVICC5ZMARlieIbGM25MgbhCCVTRkvOH3LpCGScGF3QaAiKCsdjFCz4KmHBGMVjVpQ4GON0yFIsJVfUvcFVhdMpCqYcSFg459MZlwGeW6lo3iwqDF0drPssQcxXwLIBMsJCi8xrJRV4V+OWnRIQ5Ehs2QEJBwIF2p0qFWio8xOzoCLnqdyiU1CBcwhYdEHMgVjhFm2YEuAr/MTMmYQ9BczaVUWgK4J5m8UYxDJatFlMQKTys3pVCNgV4LRDCyGPoXfFpyuQZZx3ChIXFQnnnoE4wFUAnAtUuoDGzL3icxtkGfJOWeKgIob+OYwDXAZgdQoKm+UFdi64cgWykvfPWGTDIgL+kMtdSCPgXKJ8DfIcrU5RYYO0IsEpjZewjIF3hRIHlhHwrlC+YrTkvAuULkCJBLi0oFujKQdtC9p6VfJs0SRLLeNEPDfQ0qIVh20TOA2WccCucSu9Knm0bhHbyDgRzzS4ahQVxzkmc5ssgWBjoZVZVTzctMhCzxERbBPa7YryyNaYXQcJBG4NrutFyUPb4qdaxon8WkerdlVhzlaA3YA5itcoPKdpJWRL6F1waUmyReVc4TKkuc/517gIUeKg6JSmBUlX0LtAVcEVNnMvURHC3IP+Fc4DmDgoOKNpwRcOXJ+hPMelAzbniAYs94B/zRUBSlzon7E0w8WaOecoy7jCYcsTULogiXnvHJUBSgPOPwNpBFCC6bQFA5HlHFx0gKsWFQ8mDeoqVcZzyyaLFJjwdNZCnlhRAczavCsXVOBnNehrVcbDeYNFKogxnLfRRqwogdMmWis5lPipAVwN5JhbNECogYSDyzbaiCUV0LKNHS2HEh7r1DVpjtG6BXwNJIgtW2ij5JwYX1XhFSgpTiYwmPFVCsIpjEYsL7A/5ZMhS3kxuabOEFcVTucomGCQsnjJxVOOlpw74eJLmmIp/b9pe69eSbPswO7Yz7vwEdffmz6zfPseeooAIQojEdCPEqB3QU+CQEjCUENBpJpUD8km2d3V1ZWmsirtzczrTcQNH/F5c75j9DB/gAQ07/t1P+211r5W0QVhnBQ3Mr7GolDZnKTXmNcoG8H8VFZIL4YqOseS42qskkvEc5DPcXqJa4bSMU5OZAX1eqwWJ1hwDHITTXsw11RqkWGDcwvHFhp1BNdhpMNxH2RUFQaa9mWmq9ym47ZkOkkMeNMT3EQJVTd9mFFZ6nDaU5khchON2qLUVWKiWV+WFOeaGvVhqglmoXEXZaasLDJui8oEiYmmfVVqKqVw3IcRFbWJhh2YWRVDyQWuVlAVIDxHbAG4IPMjVE9VBbTkROZjwAsYX6B8iXgGowtUTZXkODrD5Q2ogJGdiHSCWInTK1ysCM9UdInLsWRKiy9IOQIlMvJTGU+IqHF6jYs5AqWKhhqbQS7J6hwXF6IkZn6h4gmtK5Reg3wCJIdw3damPkOatvThvM8VRStHLTqgRCAK4LJd1xSsG/TGr5BBVh6e9YWkeGHCeReUSMU+nrVATeDKpzd+hUy6dNSkJ6QGVzacdVSJQeKhaUvUFIa+NmlxRenSBOO+EBpcG2rSASVWiYtmbVFrYOXCm7bgmIdqdYpkokQKwnPKIlgkMDpSRY5ZCMITVJaE/+cNWquypNEJ5hEsc5IcqzKBLAThOeWJEilYnaB6raoShSeojmBRkPiDLGNUR2B9hqoY8BRGp6heKs5wdILYGpclSY9kHpMqgtE5YSmqU7A6w9VMCYV2f/O48+Y9yuvtp982Ti9InG8+ft76cCo53PvN4/7bDwzQ3d8+7bz5AAq++d3rxtEZiYuNJ9923h0LSXa+frbx4i2HZOfpi96b97CSg+cvm4fHJC42nr/qvnlfC7T926ebz19WSN9+/mLw4jXK68G3r9vvjnFaDp6/7L96JyTa/u3TrWcvKqRvffOy/+w7JWDj7HLn6Xcw5+23Rzu/+bpWpPPm/d6Xj1Gtmh/Ot77+hkSZf3q1/fg5Tsvmu+O9L7+uJe4ene3885ew4o3z662vnuphGpxfbT95jtOy8f5478vfcg7bH073fv01zJl3Ptz81Vf6MnGux7tfPcVp1Ti73PvVb2QlG0dnu189oXHmjOc7X35tLCJ7ON75zWM9TOS/ncH6V1iEnJ//L39hCWGmSd5rqULoWVZ2GlYYCg5YJ6DrhJblfG/fWS3tKMr6LVkIM0vX/YEVhlpZVN0GDTOa5/P9fTsMndWS9ZooL1UposHADEM9z6teA8e5lmaL3T0zjpz1OtwYuGkIsjoc9M0k0dO06jZgWhpJWg1adaXc5XK9tenmkbFY5+2Wgsidz+abu0jwzvBiunvLrnN7PI36GwhKazbP2y2JsDOdlp1WifX26Gq2tW+qyrmZRN0epNiazZVjcE3TFlHVCnLd7lydzzf3dMicm0nS7iqNOJNp5fvMNN3plAVeavudi9Plxo5GlHM9zFttjQq4zGvXioJOGUqoY0dnaYSgjhyzTiMMMbBtGeeayxNqo2VhYQ06GksiiDRs2zyOEALKcVWSU00y7OA8wxBD12BZDBXBjsPTGAMAYNOQEdMEIw7OMwQRAL6hViWEyvFkGkGpEG7pKmakZo7F04JyTFWgWxnEAmduTSusVRhpqwL5tEKVw0lNMIMOvMlhUwkz92qtVKTSmJ3bZV2poLIqUiPKIDQSKUxUa5nHaalopdVWbrGaSR9oCeJYChMZseQ6rA1srArgaKXOrNxirBY+0FMsEee2B4YZDlRtY30dY0OtKmwjE9VxSgxTYgKzBDomrzW9CgXx6UY5vqramoMMxJKEGIaEFOYpdIy61vRqLYhHbVhGMdYsZFCexFg3JKYwy3APrlInyEKAPeKCIoyIZkNdE0mEDU1gA2UpcklV22a1FsjGHqnCEBMDWjbglatJxvVKMk8hSUkMSk8gifVUlh4EAOlRzQON19woZe1CIEtLmhlVUGAtkaUDAC6cWi+JVguu55J7EMjKlnpGoZIeGMZwEyhcOLWeIygx0BJaUYb0yuJmhoFSyEgEc7DEUA8Z97DEykhFVpcMO56sSliXAHYtlFUyE6RBjLJIKh0HVDGpstr2FKsRy4Hv81TqIuG0QY0ySyrDcBSoRVFixxW1wFWqfJ/n0KzD2mjgRroYy7bpSMhFUWDHFZXALAO+W2fQ5CHTGlQXVZIR0wFQyjKHMNAJRnqqM5NZMuFVi+tMgxmvAoQLRbhinoaSkiJS2LVRGyARZVPR0pPzSG1jXErM/vNMoSFS2FyrDZjIsiEIw7SQlYcgU5TJ2tNAUuqIFDanjJnQjokgNSaZLF1BYG1wPdc1lZQGxIUtKS9tCOeZVAC1TRVVpK41F2YFgVKCwFAhg0o6nswSKATEHUPFDFe1a9d5SSqo4UDja4akcHyVJkgKgNoGSCpVA8NVIOMF0F2rLjMgJHJ8kaVY1srzRcx0VUnTAyBlBdAdqy4KJDj0fJEXpC4l7ZpeXjDRAHqCBRDcRnoiJYXMxPq6gLZe6pVVWHVZ80DpGZGKc9cFNwV2RO0SfV0iS8t1ZpdmXdS8obQcKyFrF9OohhpmFqHLgthapjOrNETJWUBpRCXLVJfQiAOCKovQVUZtPTNqszBUKSpfaYVALE9xR61Lx01iTBzsoDIMsWZBwxBxhDUiNRNkGbZQJWyjiiQysUerKMTYgKYpswgqDUNHU2FlYiYtrYoU0qHyDDUvEAGWI9MIAoKxR0XITMgMXUSpBkwKXE3MCkyU5ao0Qoog5FIQVQBD6OtGrkEhPTiM4QaUJHdrPYdQEGWkeokqaFVWbRQYCQXNRNQO4gQZayY8zIk0Eq1ENbSwFgFmSUWQEQlmI06xsS6lR2oizNSoIFMW0mLITaG0QF2GuI9qDRthqRzCiDQyjQEGHEwjUOtK6lhfZ0qvw1pvkHY2G4mOYSsseZ5j2xYc4DIFnlMX2KzXjDY0U5ZxQgwHYCDyHNum4AiXKdwki7ndY+uaBtQSRZxSwwEIyixFtsklJkUKfbMsDKdeMuITG1RRQnQLECzzBAJXgwjIiHtWlWsWX9XYxS6p7Jup0kjWatnTOdRQ2Ow1R9e560ft3ubpu8Lxqmbg3Ey4pq16G+3RFcZq3R40Rtel44Xdwcbpe2aYyKMqrBRCy8FWe3hZa3rtu8HNKLO9dW9j4/yIa1rW69jTBRZ10uubi6XApGr4wc2osFzW9NzhjaA063etyRxJudzeDeYTWYNwMHCWC1JVrNdAYa5nWbXR5jm34jjp99xoJStVtT0tSmDFWb+JklKPomqjw2tgLVdlrxWEs0warOVpSYYKVvUaIGNWFJWDVs2gs1wkg76VJyBjVTugeYGzknUDVXArDLUGmWttbzpNBj0rT2Barm/dbv+P/4P3q3/5/z80Gv63f373u29vPXuy2tvpv/uw9913yfbG/vPng3fvV7f27jx+vPft86tPPjv45tm9J18vb+32Dj8cfPt8sbu79/LF3ovvFncODp4+O/jm2fUnn+29fnnv179K9nd6x8e733y33Nvdfv3q1vNnyzsHO988v/306dVHH++8ef3wV7+aH+xtnJ3cevx0tb27+e7w9pPHq9v7m69e3/vqq9WDO83js0f//E/z27fqkoTHFDlIMrQ+1UXbwmm9PrFwgPSqHJ/4xMFQiOWxgWyqmIpOdOLjuibzIxv5yBBsfuRIDVGslkeWpknI1eTMwR4GHM3eGTigmqwm711lUorE8oMBdKwAXJzaxFAQ4tmhiR1MiZy8tagGdVJPTn1FsGmo1qVvcImNsn3eNEuhvKJ9FjgFUVYcDINmyoGeu+OOxQDU8s5FwymFCMrWsecVmrLj5sj3UyiNvDluWDnAZta6bjmp5I2qf2Y4qVk06s6VFqRE6llw47kpqHzRv3C9SNRt1jkzvNSsmqwz1PyIakboTgIrp1ULwtc34mSttix4GfLDtRVU5ZzL90vSwvw6BccLt1Gxy6L4EJIdE1+uqrdLrQXwTZgf56gB1U0KDqe0g9RVUh6u4ZapXa/Ym5XWVHga5e9T0+Nyktbv11obyquwPlwZPVDPqvLlQg8EXSfl20izuVrm1bvQc4t6VmWv11ZXQU63zwJCSqrq9kWDkFqTonuvegOSAAAgAElEQVTpEo1RobpnDWiUQbF0h7sEVRSW7csWRTWRdfsyIDpHgvfOGogUlPDOSYuCElHevfB1IBBgrWHTFyHHqH3eohojsOqetUxVAZ13Tl0TKIyqznXDULnCsn/RIpBptGifdwzBKrusX67wKsa2zN8lIEwNV1TfreAyon0qvl3U49jsqOpDqiaR7snqQwKXCWxp9fM5WUa0i/nzGZ9moG/Adys5CjVPlscpnMW4rbHvFmgaeS2WvSuq64Ru6uDdoh4lhsfFUcgWJWwS9XoBrxd0U5OHq+oisbqKnWX8MjaaMlgazbEugsqdkfbUyzqiMRfdoc+dxE9VMG5xu3RiOhhS6Vf2ArXHPgwSPaS9YZO7qRfVjXET6amdkebIAW7prmF75AMv0zPSvWhAO2nEuTXdxFpq5bg18oFdmBHsDAPopaQg/bNAWZlZw9aFj/TcrFDn2uOuhHlaPp4SS+qySJ6uEWQ6YcU3SyxKhEXxMqQqwwRkT+ZUqzVaJ18vDFWYWhm/yFCdE8yrlytd5grJ8vkKw0rXefF4QViGfVQ/m4Mi10xZvoq0OoVUVd+uiCikR/lXY1plOED86VRWNbIhf7mkRQxMVH27JHXBO9bOMfBjM2uxzrXWjDRpJP7E9RLMfNG9dvy1rDusfW4EoV22WXtEg5WumWtn5tmJWXuse201loB1WOtCCyIva9fdIQyWljLj5tx0IgtYSfPGDlakbpXNa9pceKAR21OzuXSkFTVmmru2oJUEU9efa6KVN0a0OffKDtNv5tlharhCLdLq7Zo2ILiJ2OuV0VF8xYpvF1og9DgtXkXUrGFclG9C1yrEskxfhWZbyYjlz+Z6E5AkTV/ExOYoLYoXS92uZcyKl0vTY6qU2dczPVC4KrNvVrZRoLxM3yS6XYuwZK9Wlsd4LqpvFsjgmqjKZwuiVYZutC7cgKeCguZlW8M1RkX3tGUKJq26e+raAiBStIcNizNFeO+yRSXXtKJ90TYrLl3WP7V1rnGD9S5th3OJWecyMHhdeXDr2LEKLoK6e2IZtS4s1rm03YprOPGHXV0CZtdbx46T87pRd85si5ncqvpXhlki4Yv65QLeRF6LFe/z8iKm2wb8sKgvYiMQ8jSqxgVsYfVuCc4XdIPK9yE7ja2e5BcpO42MgMOLqL7OjIBXR5G8CLUBEe8W9Ulk9CG7LqqjtREIeB2z88RoiPo8qc+ioFmUlyx/t7YHoL4pqrdLsynlMK5OE70h5UVcHa3NtjBzrXvegHbaSBN7vIVpZlaqdR1AszQy0LkOoFeQQvXPmsrIDKla5w1CC12AzpWP9UIrQXvYdMmsVlbvtImtQuO8cx5QWhKp2hc+0ZnGZG/YIDQCCg/OWpAWOmDt8xZFFQWyf25LExImBle+ThOpwMZpA5NCGjx5RwqmGa5an1uSQ2yo9YkpFZK+Fr0hSiDSgPFbUNQWCnB2RmQJkY3Wp6aQGDW18A2SjLhBtTi1k9KiDZwfg7I2DLNan1mlMkGD5m8BzzDtkfQIZZllNFR0qVeFbppVfGEwpps+X7w36kzTeih7L+PU03vw7rPHG28OF3v7d5483n39an7v9v7jp3eePV08uNt78+7O179d7e3svHm1/fJ1stnfefVy5/l3i/u3dl+8vPPVb5YPbndOL+/98p/jva2dd68HL97Gm/3t16/3v3m+uHuw9fL1/S9/vbh7K7gePfqHf1jvbPcvzg6+fhJvDzY/fLj1+PHq1m73/dHDX/6y2O3SefLx3/883N3uXp7f+err1cEBevnCuDj/L8BgSfXJ3/2dOx2vb+9uffVN6/QkH7T3Hn89ePX2/Pd+/OhnP2sMhy/+/L//5Gf/T2s0jHcGwduT3Vcvl/u3Dp48vvX48cXv/PDTf/pF4/2H7/70zz7625/1jo+qvX7z/XH/2Yv5zs7ut99+/OWvLn/3Rx/94z92Dw/f/vGffPTLXw6eP5/v7uycvtv88uli/2DjzeuP/+avT3//Jx/9w98PXr6++cnnO//05cGzp8s7tw63frp4QzftCnK+fmu5/QqsqtWp1jETQtL1cU8DOeny9RvbNHIIxPKdbTUKnFbxmb1FQ6tRnxy3ujyH22L5ynFlYWl1/L7rWimocXhh7xkJ8bPwuDMoIvqARG9MQphl5Mmh4+HCoGx04Wgg0zeL8KTlZit6J1u9cNo1a2MM5xskjQififmWJmdGe0yG35N2juwxvexqFtLqebpoQ5oBsACLHV/OZWtsjz7hlkDeGA/70CwCGcppSyEG6ALMtp1iJQYTevlAOdQdHOnXfY1kgVzWyz5R3A2GeLqlZ5HYeOGPbie6Q/sn2lVXQ0yvz4tVQISl74yjY5QttP4XvHpTRSut702TTFuc6c12lU+QPAQbg6Q6tJZLv/coS49ANjZudRN2k0dDq9MqkyUt3uh7B3lxKJdTp30/Eyd8eaVv+LFM+frYcpy0jGn0RvN2k+IIzG6cxl4MzrXw0mi6ayTR4tjbclKWkcUrfau9UKfmatRobaxsp6Vme7Y4Ed1SH91C+D3WYnSzB6wPeinhcsdRb32dzWZ7bvmBbWfacJ/AU6CX6mZbx+8shNVqx4XnNB3y+b6bH0ma4asDIC+1rISjLas14hHjiy1HXKleBGcHbn4K6cgcHjB1Q1mJbrYpZzQO1XzTKq6hCuFkz1lfpK04fQ+FhwcknJ96hIKmmYwuXIOXWwfh/I3OdNLcSuJDV6PQpmF8FhgQGq1seaHZebp5EM8OKTP03t0qfsshAA1jvbpqpiXxu0V4Tu2EGbtx+sJLqd87yMIjDCXsmuvZuZYXsjGo5ucaWMqDh3H4AiTAaW5G9Ymdpshu56TYsW9asvuNPmzI5MDdfu3cNMFqw3UiN67z1bZrn0Du4asW6b+0r20Z71vtGzG2QLjVIlMEbTHetWkEpa0ND1TziTO2q9W+2Z6CmQFX2x6auApVsx0brRQyteE+bi3QjSXW+55zLXJXrXZ9PSUkl+NdH62kQclw32ifZrN0fWVuazltJ6uzbi+N8Rd49dZsytqvksmhbcjCWdejK7chy2AnX523jDg0PokXr91mWft1vjqyTZmZ4Xp6ETQqpqlqfuY312m7GU/eGOa+NGC2/uAYSQozvrpwOwVHdj4/MZqzpNWqJ291dJt6epW8h1qncoWcXTpGJI1bhXa5X+umt/NOv9wgUAZgUoVdypDrX+LZwI4ysfmdPzzIsE+jY/2qTSUyxGkZerhuasEILTa9mNXb3ziX2yXpuv3X5mSDM8fDc33pVGVPs6dwsUFWlOw9d842KjywmkM59BELmugGJgMZtbE9wdMuDS25delfDjK5ZaxeqatieWSbRloLvH6ruxtpfq5m546/k4ARDS/NPTekBMxPgk0zFRAtXhs9bwmm2vqi1eyv5cJYX5nNIJa4Co8a21qsLLo+1O1+BSfF+sRrBXOtqEfXjmXGuCnXx24LL4ClFq+7dpCrCCxO7UZjpRXV9MIf0Bx1xPzI7hjMNYk23DCaKz/J2HzDrm7UxhrO94PkAtjn5vCzurskIEGjLdgCjWymZgMrnUK4gpM9B13LxgpffUo3Cs8YkZsNw74EFQPLLauMmDdGkwcWGIP2B3f0gHeAbg61m75uSpLcFPMeDvM0WJHZvokXovPGuriTdYirX2jTTWgU1LmaXxBjwXZ2k/yVF0q3fy+Lj7AoYMeNVhcwjbTmoFpdaWIoDh4m0UsVMbe5HdUnVhgSx4mLMVlPkRek6aXFx6hxL4wOUZRbzd1YHBnpgnSddbbS19ea18qyMyMZklvbMXsN12nQ2VpWF0421btBFK5IeopbnXh5oYeXeuNuZk26cLXjns09pKrprq0iZUFtuE/8CK6wmO259liVrlzt+lpOjVBMdj0ZyYbC17eU+QJnSM52HXVYKAFXOy4pVFDhyYGj3rKg1EcHyHoLGQCTXZMu6VSCxY4jC9Au8M2ew96VvRINb5uNMaoKON1xxUg6FCz3LDyq4JvpK5tsANdIo0OXtku9KsMzr7kW2M2Wx7ih526/mrzW4DZpNKr1W478qgmq+bmHZ9Jp5Osj3CCF0Y2TbzHva00/D4+R3RA2W8/OAkJLp69NPhANir2DaPEdFoHuO0lxZCuNN0QUn3sQ1I0gDz94Fqwb++HqLck83G/f3Pny1/6H88n9B7ceP9598vj9n/7hZ3/7M384HP3k81u/+MfB8VG62Ru8P9x49jK8tX3rt7/de/rN2Z/87qf/6T813r0b/fT7B//wq42XL6tBe+v8Q/OrF9He5q3Hjw++/Or6T37/o5//vP/u3eyLR/1vD/e//HW2NfBn4zt/94vo7t7e46d3fvEvVz/54tEv/nH7+XN1t8dP5nd/+cts0DPT6NO//r+vv//D5b/dIvzXhkZ3Fje90fX5F5+Ys9Cbz0cf3R9cXKhKLu/seMOpsw4//OhHwXC4eX52+b1PjGXSnE5OP/28c3mux/Hq7r4zW/uz2fsf/TgYjXbOTi6/97E7XRjz8Ozzz7vDazNOlnd2zfE8mM4//PSnjclk6+To/Y9+3Btd6ovo4pNP2+MbZ7Wc39035lF7Mh59fF8WYu/w7fuf/lRPczSpZE9TAMBJle23sRTm8bq437TLGFzzctelkskpRx2qgCLTCvS0TDeN06Q48B2VqfOy3vEw4nIqTacSFPEFQh1cWJZ7Fic7rgErdVqwTUvXFZjUwMe1QdCwIB1SOJZ1muR7ngFKeFnxFvVoFq9t6KE60BsTmNtI2qVzBVOfIr8yZ0QQVDY4XRKXJ3GXkIXNLCSdMhjDRAeyKeicQghZu6ZLogsW9qg9w8xAwmP+GORUiZbUFhpSar5t+ze5VZXhgNoLLAlY983WRc6xEB2prQiu1WzX9SeFXVV5uzYXpKD6csv1bpZ0lYcPN7RlYl2H8q5TlZo+S9PbLTrPSVZ5rSorDLWuk4d9fZ2ZoyS733TmKxbB/HbbWOVaXMgNXcYCrark4UCPcu0yrh407FXEI8S3DJwwlSrUJzIV2rISu2YudPMqKW97TpqIhRTbFsq5jJXXyGNukgWX+2ZG3c2zdN1DGEu6oNzjgkh7ietApJbevlLzTdyrxmLejLsY01pfUmHzUkfmFKqgTm29NwKzHtJRYV7jpIOpUesLUtuKmcCcQstcz4KgMcTrAaYo929I5AjgArqgypTMlvYMKbuKAqt5IcM+JVrhjXDiyLpB8KSkrKy2PTVi0iCkCfTLrDII6lE5EUhItamJpTLzvNxxwU0NCMy3A/tkAbGEAx1MasRkdLdnDNdGmrGDAI5rAGW207ROFpCiIMjCtQUrkd7rWTdrlAu+a5qXYUmMYrfpni8ggqpP5JzjlMkDpw4RiYr6lusuGS5Q2RYwhUYMZrc9KykbEz7bo16UoMRYbplWLsywLjsC5MqJcbohKm40x3K5h7w4pZGe9BStAEpw3apVBf0QpX1eQqs1FIsd1YsXZdLKekrnSotR1eSME3cmyw1RILN/I8abyGElXZK0DXQptQgvBrZE3Dta5luOQZg6yauBbVgKjGvg4tomcFjSBip92zxL8m3PwBW8KOumFuhptLKRDZVH4KgkjswannaWVhu2qdfkPK8aOgqwGgtoKNHU0ajSTJ51fHoU84Fdthz3cMwdQtoU3lTC0POBa54udYNng6Z5HJZtKxs0elcJ4XK667jTwsnLaICNFYVArgZWc5hLJXhX0jXRSjXed/1Z4WYs6zJjRSqorzet9ihTQNVtjhZIY3B24AbzXM9BNFDBtCyRVbe4tYRSItap8QraJYoHQhSGnaikL4JFxaVetoW+xooD3qlBhL0cXt3yWtMbsUT1jkWyUkYK9agshTav5I6RKdO8iMo7gV0kcszFjo0qIdfSDfJMGXAuwTYtsOWfh9GBZ1WFGDG+ZWlCgCUHXcIVxtcZ3DUKYjhncXbLN1mhbjjsAF2xLDJRGwlM6FWCNkiie+ZJWB74lizgDWMdAzi6NZEWDZdN1xvRqIsJLfwRjm2hfEDnVOmKecKaI6ixsGU0rkDcJtgovRFKLakCpc2p0GHY0ZtXBaHlum22RioLUNw0eydJaSnVUNoCc4yjvtEaFphUlc/tsRYFetoxB6dRZaHa53SGFKVRX2+NCq6hxYbjnMwQVEGjCEMT5jx90DenEQ2r6sC1hqtKavl+y7leIyHVgMolR0mtDuw6wXSe8VuOOYlqTtVAA3MGOEQDLJdcj1m9b5cZ1hYF37OteVwzDAY6WHNQKr+VrnJXXzOxbzCmWZM037ONZcZSALd0EtawAGKD1sDsXPP5Duqlsypspj2oK6GFuAo4U8SZqarPC2IMRnK8ia260KYk7UINcm1NWcBrhJ2p0oLlxOl1r+Rih5g8t6Y4DgTSEFkR6QtGkT9RVbNKbLdzXi+2qQEKe4JTX0AdGguUtHWmwdZFwdtV7Lj9i3q5QRCpwJhDomRHB+NaJ1Xe98lpyltm2XXddxNuIdrV1A1ThKTbgXW+1FGdbzX0s6QO9KLnO+8mwKW+m6+XhkKo3G3YFyuhUdSG9CQumk61EfjvJtKmoIPlmGEh+I6rxhwggDpIv0wLxyItKIcMaBh1kRwzpHC639j78EaV4uqjjzZPT2lRLO7u2TeL5mJ2/clDHOaDi4vT738xuDwjSTm9d9AY3pC0WN4/0Ker3nR6+elDlNZbR+8vvvhk8/qMJ3J+7yBYLPV1PL+3hxfJ4Pz0+gefg6zeOzk5+uKz1vRGn4fzO/veam2twtndfZyUmx8+rD89WEt3/+2bkx/9oDWf2uPZ0Q9/avxP/7P7q1/+FwiNIiJXObqeFd839XCCJyH7yFDTiBZ19uhRY35MR+P4d7zOMqXDGfuBhaMFup7nH1MYlsZklX30iTs9185v0n/ntBOGhwv2GZFpTSdrpnS5yrXhOHvwwIqZNZzl2PaiEl1Mix/qqhD61bR6RHjM9KtZ/uCRmc7pyTD/7HMji/HFpPoxZcoq1ph2TYxQvc7Enqm4jMMKShsjsV5zNLAMqtXrAjdNQXC9zu0OEcCKI4GQC7hMQoL7pmapelXWNhWYpjHwmiLnerSy5JbXIGmSYqqcEslqVRKbEqyxjFgtkAI3DCUqDWipOCQ4MKCJw5BiHSnqwtIoDUXhCotGJQHX07pwIKqKvtCZjTgtIVe8y2sO0TrMvBIjbhZuaSqkMlIQ5tSiKJCQzJEYSBTCzOEuLs3cKnQJSIqBUZlAsIJwwRwlRAaVXVlcg6WZ64VJa5kjZDBdMslQImqXK5RTk6UFCiEjjpWzpAhMoCXMwLEEQocMgpjCnhaVuopZhW2Ws3XsYGALURcZBNLMGEJrqO3Yigu+yiWy8gqgSGBoK8mjEGibFqkJXzGy5VRcqXXuHOgVM+JIUuyKmmch0LYsWNdiLWAbJ7VdR5WnUA6sKHdSKahWmrld+YwRIHJd+lEp9ai0CixLEJbloJRMkYrHBrOlIBIKS4F1Ds1VbqRAACgB71agLkhppJbQitqBsrIhqUvsxaWTc6brUmbNwhFKZ05u1horkBSFhYwwRwYt9UpKDpVKG7WjCpJXCdIExsguCg0pZGGwjoGU1DDNOkqRAoRaWVwbDBHsZDkDGABklZFFLExMo4wzUCtBbJ4VWYkhsIpCEMEEMOvMlhiTHloVFs7rCplVUopC6cDMmRSMMmwXaQGlMvZtXlQiqhxkJDlSKdKgQZiuMqscVBjqsIQ5lLLWUU5SVOsAyTwolOQCq4zng4pAXaaogmuugixDBWAYYlT4GSxNpbRCz7RKVVRluIarUrhxqZWkKmSZV/0C5bUQJDGrTgmEhrlWw3kGglVOclgRBVnZKnBVKWUkOBtoQhTZ2pIbXgNlSYJp32FEluuS6IT4Osup6akcuutIwY4OXRWHGHoGdmEYaRgjFegswpYua+KGsSIdW1lVukbQ0nVNq9cpaWAFjDLELhUM2SzmqmFBbFYrm1om1EkVpjIwILBYWmpEVsAOE0FtmmmeV2DFaUqAXhk54yVmrHaREBlUVmUJBEsz124MrZA5xibTE6YqFHNuS0VzjNJSBwKUW5UmbVnKHCvM9LKkOco0YbPKYjgFlQ5qlBmVoUyeqZLEgAcghwXKCLdrZlckAzWFJUnNAq1tmUqmeAbccK3opkUF4auKbNiVAGqdO3taVZtxyAlyJedpqOiWhSWvw1o1ccKtOmL+DkyZHq9scOABBdJU15Cd1TVbM72rA4XrFLmQRMqN1wLVBlAyjnSjg01QRWustbWaY75WzgYusJtENZWWBCBdEdIydWyAyoRAltgBzM85o5qUWaOwlDQqpzAE5jnmvLQIjgukkdIohcJordKgtgDTCyszSoRzqEzmEJIVmESFUVo8R7AszUIHzCiM3FSGKgAoKgvAksNCsqCuZUI0N6MVAKxfG9yWSuVQaKVdI1giKHNbSoB7eF1YKGEVtlhSiIRSYJU1FxWpkV1kDJbc2Hd4WYl15SI9LohKgI5sxkSaEnNTV3khM0H23aKq0bq0bxtFRaIEG9gumSgjbGwZMitlCtAAxYmnotxFJMz1dQQodFgNqhJrVK/LUqyEsWuqyowKvSB1KbO8GpSwYIJriVk2K6AIrnUO5hkIlhnNQI0hELxdoqqE3Eg1FpQSEFCZlshLHEQFLUSlgGJ5s2jVCAsr1yu/qBSGuSGbixx5SY6ZEgoCkTWqJpe4lpketzAQImcBQKsUuVFGMl5Du6rXGXYgRGYRVY5LObKrWCnbgNgsVxbVDGTQKsqUSRW06rSgpqyBvU4UpZhhiye2RBpqoXVqIgJqYJaRKRzdArAuIcz1kjhlZEFB9G2rzgrIax3qWUoUxQ6GUaIYsuwB5lGmKCC7bpGXgCtCDBBX+mxdfGSoVa4v5unHH7vRkJyM8i++7yUxvp6V39NFXGmjRX7vYStm5niZfvqpHt2g01H52edGnMLRin3PVCmjk6R4YHrzmAwn6aNHzXSpj8MSmyQt1eWk+oTInGs3q+reAx6V9Owme/jITxIynNePbnOJ0XBefwZZzr2rmfgcyH/7q5x/5YlQ/s5f/K96kfNeo/P0VfvqEhlo58mzzddv5x/f+cH/9VfO6Obk9//wJ3/5H6zZVAR2cHS5cfi2bLW2v3l29/Hj5cNbP/6bv3bPL45/7/e/91d/1Tw/xQ4Kzq7ar4/yZnPj5cuPfvnL1cd3f/RX/zE4OT37/Hs/+Nuftd8e1q2ge368+eRF2Wx2jj58+vOfzz69/4P/+JetD8fx/b3d3zzbePEi77Re3f6vZm/ptp6qsp6+df2+QrNqeeX1rRjheHreb4nC2uBXr/xNkpkimhz6mlmwVMwunAFYOl52ct7vlKm9J05eNe+yqQUWF0fbOixhlc4v3D5foVY2Pu4PVkvvATp92d7iiUGi67eNbR4aqro6DzplLrbLyXFzZzE17uTDF3fad+PdupCTe9YyxK0pm99tXI85uRDDnwIz1s2n8uKhbmhWfhonm4DksIrk+sFgMg2tK234Z1zLPOMbcPERtUKdn7HVNkK1FAlYP2iMVmHjjXX1e5UJvO6xOTzAMOpUJ1VyD0LC3Ruw3A/SOGk8c85/klm2GV8aoz7mcZDdFOktjBrO4Gz9FiQrt/uIrV/CKjIfaNMkbc1u3EYDFDONH/ItZzF/05hFQfceD99p1Yzu23O5rKfjTsupi5BWr82DfrT6Ri2ioHe7rA/rcOxuOyHPqtl5a0dP84xO3np3exF/UY9WLae9zq5FOLV2yZyoenbU3qVxybTRW69vnYDjbLIeuN6iZQdw/UnAz1W/hDdfmPDQBzN18xGjRzqDYHWv9fZ9E+Xj8JFbnKitTI1/SOEhhTd8+BGq35lC1eu7veoM+YLN7jdX7+tbKzn6XU0cmclMjO+57hytqiL8LKiGsJOA5aON8H2o1mT0U1if6+lUTe7q1SFdM7W+67AZ6Gdw+ciOr1Ivib4FdoP25Wx21oRY2iydn/teHrUGxdELD5jodjsevXIsg/TgMrkIoASuWy4uHTOM9jrJ/KVf6lp7jy1eUwPJDTBfDZt1bfttMT2zaZjeCmbnzzZT3ens8vBIh1y0wWx+ZUQFbXfY/Nwm66q1txp9jRLN83pJemjXhbbVKHB5z7xua/5v9ctmne8bW8f+sAHDZvf4hZ7rVXw/sM+5cPVhX7Wf2GcGy+6g5jO08FS03Th6bUmSLx4E6I2CFh4+tJ1fOUO9yB4R/7G1tGV80Hn93BOgiu8H6jtJfHXzqe7+CxkbLLvvkJmVUh7f7b1/Cwxdju622PPacsDN517rMB1PJud2l4WkHU1ONzvzVftzcPKy3c/y1nI2fNkY5Il5XVyeN5tpKfeKyUlzczK3P45fvrjX2ssaaTh65+/noTGdTc4bXlLae9n0tNkfL3WLXL7w/L06KNPFe1ePQ7Jejy9dJ+E6ZZNTt3Ed9jU5eenBA91BbPHO3Gyt7Hh+ce5ra+xuRtb1R4wQd/3eutqHoGyJwyq9DYXFvRFY7gRhnjSeOuc/zDXPTC6MUQcz5hdPynyHqb7TGKL1lheKtPNr52gv11rW8jgY9+qK2uoVjDZQ1TPMV2pxi8a2HPwqeLeZ0741fYpuOrJyW/IFzns42TT052B6h6Qtp/n/+u/aMdkzp6/kcTo7627SjAE0eukdtFNyXI5umnYrKm7k8sbZoXMNl9Pj3jaKBCVXL/w2vaCjZDjdcNwVWKjZtbON5tKop4etnWoOXXP40r/nrOpRPRm1PGOipWx04Q1Uyppi/s69k98AF1+/3N431mKtJufeZ+a4LOvZhd9VGWnXi3d2V+X0YZMObztugsOqiA78bAJ7sVp9tLE6DsmMjn6CGqOgvlCjO7p/qiUxX932yqXsJ3D1kbO8jsyVef192Ut9OtbG+5p+2klCsXpEy4LrUzC/15xPMuO1efFx3FeWMyOzDQNdaMt5FrwY3j8AACAASURBVH0GUmX7M7R80FqvU/epdXwv7mLXvLHmm5JEwpgOz01tlh3486tvBhF0urf56oMBCrFH5tGILGKr1WTrSxOO2a2d5ehrHEPf34iyd06VmNv6Kp+i5Vzft5PpmZXc4Ic76+KJnIrWrc4qPbHzWNtH0zyki5F52w7Hp040Mfb868lTNuddtzFnFzILjV04SzK6Ose3yWx0HayG1qf9CV3eleHd9uvvPFmX8YOAvxI2VTc/8KyvyErV4T0LL80c8+h+7/0htBAf3Wtm39VNpG5+SI0nIKdi8bDBTuSY1+HHwfExDGo0ve9XL9IWwMPPDe2lUSK1uG/D3xIIYPTQPbkEjRpPH5rssOwKbfSp6V5rrEaLXav6GttSRQ/84byqv7160bA3RA8l14cu7jIrXY0vfWvFTZ3Nzl3vPNp0+fSFLzf0wKmnh2bXj5x8fnke4Bl2Az46clyY7pvzk2+2ed8OfLE8tSyn7iXL89OGILDVrW5OXBuyxmB1/YSKtrVh5eEHmxiqWa6nl14tQceJ3r/3kZT3NubxExK2W3fNywe/+Ifg9DodDO79yz8fPH8++vHnP/nf/sKczeMH+7f/8cvm6Ykyaf/qovfkO9Ww93/9m73n300+f/A7f/kfnOthemd779dPO+/faZh3by6aTw6lq+9+/WTvq8fRFw8+/z/+99b5RbrZ2vzmTf/1K0KREa3v/P0/i4a1/ez5wb98GX1859H/+de99x/MrgZOV3tPHmMCKSs++Zufh/cfzP7tFuG/CnJf/3d/vr51O+u03/3xH4W9jXBj8+QP/qA29LMvfjB/eHfZ7S/v3P3w45+km5uF777/r/900RnE2xvvfu8PhW3dfPzJ+OOHy97mYnf36N/9TtztlO3mhz/6w6jbX21uH/7e71euM75/f/LJo8X2brK1cfi7v7fe3Khc58Wf/Td1K1gOtg//6I+rIJjcuTv96P5k9yBtd87+4HfHe7e5Ybz+9/9eJ6VtMXjLhD3T0wt4y9L62BKp+izQA0EVMx4ZyEeekdE9nfVdi2TGPoHbll0l4PtNFQATMvyRBQLik7W5S+qdhqZzcxdUtxpOGYHvNc02MnhCPvFk2/DMlO5Ste3apDQPNHanZReR+l5gNpSuCvSJT5rIMJmxhbI9B/E43SxZv6JlmN2usraG5Yw1V9kGkjgm9iq6hTniRSdk/YpWq/UOq3o6qm+kN0+3kKCZcqJoD0KVVu2I9wpcRclmxrqYozVy5qttbMgxcMPVbaJAxYNFMhBOMS0HYdHXOEmEtYg3aipnwEnifQAQY94i3DAtvQzcnN31Pbe0UY4+9UrfdM0c3rcsmFkdQXcN0bQ8I+H3fcOuXJLVn7ZMm+sWhw9sTau9Zin3XdIknhbJRw2jIU2Vqi8amlkbFif3LU1jvpepfbfuOS5Ntfsm7Oi2TNUPmsiUhi3AA5ua3PdyuquzrZaNU/2+sWo7ZrlI9lLhcSxuqn6WdHUF1rwXJ12hl6v1Q0XtpJYs2U1YEyJww7pZNtAwXNfdZL1N9Xy1vM+VX9J6ldzlpY8QmFTdNO8oCEPYXs32Ha1cRvuFbJSoita3RN2EmA/rVpL1FYBx3UvDbaLl6+wgk36GqijeyYEnNUeZQV0/8Anm5iag+4bJM/XABQPbJpnX5fLA1ixh+Uw88CEW9oYC+1ajDo07Gt/2NLN2m4zfdn2aWB4Tj5qQKqvH1Z7lV0vjQCMDTXi6Z0fifsPSC92X6pFnwMrsq3rf8VSib0q142o+du2M3DORgzSnVvctImJmr/MtWNsVhdPVnpJGqKn14p4kZqFQFO3XiuaIDouBzH2BwZhtZXkTaNVi9UgovZIkTXdKqKWQXOdbMGlgrGblbp4E0mCr1cdKo3Gl5ckuExaDdJxvyKKlqJyXu+W6rxnFfPlQID2DOI7vAGExRMarbcQ3dLdcg88aRl/T64x+5oqO4emZtoPljmdplbVDqtsts0rU577ZkAZP0ScBaWHDZOYmEHuuSRnZROxOwyoj7YFO+9QQmfwkQG3dpam5CcG2aeBM31T17abOc+MeVRtmmy3pI5sPbNOo9D5Au6YPE2MbFXeapij026jsNYgcI3u63KE6mEEjCm8Dgbn0FvEGt6t51VsXA62mqTIW0RYnaoqNKDqAEtfcXUabnPI1ay6KDV3oCaXj1a5SKFR6Gt3iFKTMi/KNmqhl6c2qAckthtG03M2ZWUMShnclRgXz4mxLYLWW1ijbMXJHYTQNb0Pd4LpW0wcmtZTnFmjPqruOQ1P9ngF7ul0n6gctZAPD4PChTSzhW4m2p7Htlo1z445W7AReFaoftE1P6riAH/vQx76TwX0LbljO/0fbezVpup1lmsuv9brPpHeVWZmV5WvX9k5IWxIgdQeBYGb+2gBCCCQC6IYeiKYRMJKQkNhe22jb8llZlT4/7163/JqDiTmHiOE5vs+fiDvuuC5cRtfierOdVQP38nycWkYNutUkTRAnilzkYC3KcA0uC3uxndYT+lzCmoBRDa+lKDYATuDceLgTIZ3nW6Wbk0hOxztOzyNsT+3cpFgBAeV2oRhvYqFGxYXSz1VITmdblZlHHg1ta1IsW+E6brEYbDFq83plWC6DRnlWXMjrZebRyDbHxbLipmcXqvwCxK6Wy4PZEoxkt1oe1ys8kGlIhtN1g9y4XqiLDdJUI7FJ6DqzragR5+5aK46kSK2/1RRERwve7aYJqMSq9VtN1gSZyMnVGLcI5zLcbjKsxEJAl+IIVc1FaXdaYSHKeE5vJLBNI1qH59oEaz7vw+VMkLq1qPFWpNfmGnhGb0RmKUtwGV5oR1SJBeivNllkm/PS7TSLphVqNHoGMJ4rrGcXa5s6hM/rVVsvQuIHcqMerzFR9Yc3HIpKBMezy8CkHuHzatXUbYPBOKxOOlutuDofX1YgrYOfTS5BlxkETtVyXc4ZHIbyQj1dY1ExyC/XICmCm+ZbSrcCQN1qWem5iuu+vFgPVnksR7OtsWuxjBbJKvBbsaA6Wnbmaos5GV2mfiOaU0N+I7FrqeAqWvJhO27CiVgJ+uo8D0rsAL8WteseuxrjZeZTnLQqf7mR4AKvErQbM1enO1CuZ3NmSDYB2GyyNKRNia9EhDuyhODliHkVbWOwHmWoSjecudgGTZJmUj+7apPkfPfqw69+zWbJ0a1bg+tXOps7+dLSk29+rbd+sW41v/zOd3QSnV++dvDaK3W7fXrz1vntG93NS8Xiwv433+hc2FbNxr3v/K7LoqMrtw5fe2W2uNi5fvP09o3e7nXZbu399jePd6+ZNPr8O79nG2lv9+rBV17NF5c7u5dPXnj2bOeKbLeO3nj1yc2XLSVffOc7stXsbW0//o3fEB9+wP//H7lDQLzLXBUx0MyHqco5tmk5Esgir7J6mriaAdmuxtzknIJ01k/mQZLNN8xEeMWAyqppEiTHtp0Piq3Vh1vLjXoa25pj36wnia8jbNNinC1jMbfRUJNE5oKGVjmKVMGpb9Tj2OSxl6nMMzljAmblGJuas9CYDb1FGBimDfDeIgtNTa0NoPYVFrYGEEDjsXcKem48tFPYHOOwUBrIqfJVnoGKoWCUx64k8yPmqJ1kMGDuMCynnmqkZkCN55dGqooCWmbQEGtBBSwwTCOQA081qKeCaQgMLesEqBom2EFcwwhGxHqoVIRIqKxIJIUN7zyrS+J8FMpQ2uAiEgKqiuvp50PHe+Umw00PA1Il9YEFjar6UnxeQtpTCxwiZpypJHNpCLCtii32KILuUbXFPPTQG40Y8sAZUtXcJzZgrVEUbOSNljPqgcI0USDylQcKq5JaSbCklWlaC0JtFYPAUCgzKaEzijisa2ZO4va0zA03NiBidQWspkCC3FPdIQvVtE6oMhzWsMKRrjBAoLQUjnAj1+VyVFFAHJXO24CwBGWe2opCBKUN1loEElX42lgcmOWprprQQesQ1JRkxEpR5QLG1iqAfIyCKW2kKgBLoHWkhhvxyTi0+/ma8MI7HSSIsHDKJmF0tX3vWDcnaJWCQI12tWHOR7pu1Z2bzcO9sC5VGpOa1rWnlpIG8ZooEwEHTBEqFkPutadIC1QSbWoThFccaFd54j0ARMjAiA7G0hJJ4EMIorbEeepNyE0cjWEAdrgQgENWsarSAdjgicoTckoTI2cBexyCt4oRYpnXdFov4I5bREXVJLaCwdNaI19r5LGqsKsogrRS1jhHPFMgGBC8RVWIPCbBsrpEmjDMUk2FtQkwWupIlRBRaQIzIAoeVXLed1bS3mOzU1oWIZSWKjESA4y1WiZnc+3ZkVlWVTMiUa0UciRG1ubVvDveTA4OzYrWCxQKqgsncQQ51grCIsLUSC2sJqEKRU0BRCBKtAe+BERjlVMvE2xA7RmHEnriLKpq4y0zAFXBEI3qKTJqYeHE1pyFjEGBHcC19dASHdS4jpd6ymWgaDaIhvUY10gCh5yhMm+k/RA4KbAPChqnFWJYY21JjQ3wxlmvIYVKWI0lcaHyxmLFEquQB01lWHAxML7WxGPovdGIIw+dJWXFXBIAbhhwQey3w/igWsMeGMASjRMfaHC0KNbZedaqp+p24hANCtdV7IICAOv6ItlDJBxWF7BMKKJIGe+kAABUVeqgDZ7VBQ0RwgRVEyJrhkWucaJKBBBWDlhnUeAyj2QIxFFlgwuQqFDmmS0pQq60CExpe8ZqDoy2CHBLG2oCmIH1FJXDubmJypsAtQOyVDung0c2HY2smEbL51VpBaAwmLiYERQUElxbDglEiusAAoREBcNiUFPkgHSQisi72JSq8DzAYD1VKiY1k7WtLcMNFwxWWgQPbY1Ldat5eOzbuWoLVFJtXK2ZT7RD1FS7yWONma230uCxc15jhh0yRpSzTXKoeGNPXYlgoLY0smaQBA9SFeLggLO6qpiLLSaxQUnwzgarGcWWBpXKUntvqSe6IraE3hJpiasw5ERX0FcMej+13J2iBReqlaBZCBqXntqaBQNmmvEOQkZNXCQBpAHL4C1FUKLCRaQTRQZVixrQAEEmc1W6gB3ToaEDxJrUOXIKOsUq7X1wMFDpnI1j5EwuY115UCBVYwgIipkprYECCGw1CEVEmJG6bXtbzXuHcr4EywQIomrngfAuUtW8PH6utf+l23EuFrikVQUYJDhFVjd9dyftnOp5AC9EkFrlGFIclVSpAAP1wlvsgY8RcrJsUJQAao2FdaGBRV4TqVlw3BlTIRY01kZJxLHGxpIKG+iNx0G6ND6FAMgSiWCcoVYTRhyzMz4rF0VpoqiqIuIq6AivPYPKBAjrWSM7chChfMW4AJDHdYDO+wBFCYKX1lkuYQ0dhpCXY2O8I8SVRWQrjF2jGgtbJl6m9awhZ4zDLB9zVwrimrN+HBTHrlFOsq00vsAaepbpnMckK8fMFIK4bNqfbq2Prs7zoDJbC2wbsoiqESUhLSdEmPSVCw01jEzNfZ3lo9hVHJmsmoh6zIlPi7G1WvDQqCaJlzFxkalh8P8pDdbo9/+P3/zj7298+PHw4sWLv3zv4lvvTlfXLv3y7Sv/9NN7v/2tr/zxn+382zt3fud/f/VP/uziex9Mdi5sC/usro8h3v67nz3z9//w4JvffO2v/q9LP/u3B698pbN5qYwajWH/mX/88c7P3uxuXdp678MX//4f77748huo9yLwH/PW83/9o52f/WK0urH16WeXf/ST7vaVCx9/9tJf/fXd3/7Wc//976785OfHL7+8/ZNfXvvJz0ebF0/IxdO3OG4AMwbHHyV+JQKn/uTLmHJLpvLxF4s4uIDh0dspFR43i+LCKpFq9oie3G0RoOIQ7n0wh5TC7dJdWERSlw/C0b0F1gyy64/vNhJkoevOnr+Ch9M6nzt5W6AYAemOP85oZFAODj5vAGUY849/1WZSssQ9fGsOMNiKOHh6C9Ycw4k9epkMqVsdk89fdnXbpz2y9xxRHLmp7jyHyobA93/b53OT07uNXfb5S65e9I0efHzbyXZL7H1NF3OwOnUt/eQ3SC8qL4yjT54xaoOsnn5r9njdwYMqrU5fIXlLNWqyf4udR+WFUfblzbraqtpFvLeN8hYPfdu9AcbL+abpvymH+wm5KooPdPcwaUWzSa9xfl+QNh6fivxT0NzS/c/58eMm3+VoelLcuozvn9THyfGjJm7haTcafgzRCsRoml/b9t2qfiBOHyVRrGUXnNxvsVD6C9Q2hJFh+hk/2Wtm62b6CJ0/SmNS+Zw8+SyLUlsO2dnHUXu1muzRg0dzzUUJ/Areuw2c99Ti+69YqoiBaP95G9V4KvzZc9DmDTUtTn8LatPInvxOfVr5cDLYxkfPA1qjkvmTZ5Ezi+Lxt1QN1PApvE7vPm9xgNbBw+eZz+fxo29qhEh1Uq36vdepBLIh2Z2XLCYgWPD0eRA0qr0/fR7IAHnt917HBZgsV91forrkEVHHd5N6iKPMHnzakKcg2oXnv+CjYdxY1ScfJMWYprhXbLQAQm6Gjj5pqUMvrpHjf6GTYcZ3EQyFmmuRx9Pzw6XxCcbLtPMxN09DvFFrAavtC/LxdHQ3ywe8wareQzY6jdEa73/Cp3sgvQG7v4C9biNb0sM7YnAmkrZl00viYFsujuiTFd99plyaJsdLvnPZ0mk8ZKrzvIskKJfFo528NXnd3n3Z+T3G1ZPLoX8DwwGqmvbkOU/y28np1/XkMWD28Ko9v+2bXdJZ9J1bBHSugnsv1DYnrjN6jj254efH5KTlu8/CpAuH8/7sGRAKrLB/+jwiM2Cb6NFt2a7L48nZ52kELHLy8cfzqDZkgRz8PA4EYeCOP0wRdaQKh180fG0jMixuXwKjMSjE43faARGM3OGHKcKY1sez2zvwtKd16+n7DThRZJ2e/DySjmXLVbG6EMpKnaLjO3NYI91kZ2+yMHbkAjn9GStghjjsfYBBrSlCR59n9RSCy/H8hzu22pIrU37vIsznIZzY3jU8XtKNCh9cZydZuTlo3L2u8h290vsv+f31wnYNqs9fDOMLVVOywytxZ2682Pn9cu8y4R+jBb5/zU3WIBqRzroZbIPW8Ovm6KYdfUyW+Z3rprgKGufwYAtMNjEahtGW7277xhCeXEOnF+TqSePRrhxfle2+fFwe3W1zYcsJ7XwkovWQ76Oju4101c2ewLMHjQhXSOK9T1rEFnDJ+dU5lBeTO/xobz5bNNMn8PRBo0Glp9Pi2haZVdOz1vkHgi5DdeSP7jbbbBLsbPLidfzkXM+aBx9lCa61Qk8/bEcLoOqgoy+yRtPUXXB0p4GDRQjtv5sg7nl7id67zn3lZLDnLyINAC3d49eiCpXtgn3xkgNRIDV48px3eInu/aYyGM9Ozarb+wqeoGq5Ep88J/FCu/3420UnseakWjJHLyMlZKbY/efJCLvF49+fPo548qheo/u3iITMTFXnJZSnszmb3H+Gz0S+NBGf3lJhxfFaPL1s60wl5dnHWD0Kzcv65E3eOW+JbTr5KEw6UUPUwz3cP0zRChve5dN7gG4jaCf5tUv8Sa//aK5/HMWRmhyy3pOUo4m91LQU6QqMPorOjhqNVTO8S/oHcQqHboWUzQaVrvtFc3CfL18qzz8Wp8et1pIa3cP9w6Qp5OiUdO5G6bIf7InuXZHs2Ph8xXeeQWGUlLY4+zqChTMRefRcaM1QLwmnz8F4gMYtf3obhnJT7H9dlqUvz6pnycPbJpnhSepPn4/Q2RLa/y3DJa5Oyx34+EUElIGYPHyhjuwuvvOGNqN6PB3v+rOXoFMA+/DolRCkJZjde1a1MZoxdHiDhYE1GTh8FgCoxeD053GpmEjs8Ueplz44f3IngyW2bXr6Nnc9y3bIyU/pTDX4mrYZch7AM398f74cIbTMTt/koefjjVImvF5Zkg/L/pdNU9MkFMd3smpIUFa5nXknla9I5y0xLpK4Ybufx+WYRFie3s/yLk1XzJN3Gvocxrvg7Be0P2vON6cv/sP/3PzXd05u3L7xzz+99aN//ux3f/f1P/z+9lvvnbz80uUf/XTnzXdnK6sbn3x6+Z9/3r20/dIifanu/5osvPKDv9n+6b+evPDC7r+8tfuzX44u7pxeu95pb/Cqeu0Hf3HzRz9++O1vP/uX/2P35//WuX1ry3VfUbAnx0tvfXHrb/9X98at7bd/9ezf/N2jr33txo9+cuXHPyt2N5u/fnjzH//vcmG5dXD4wl/8zdHzL5YnJ9GT/f9Qg/XvchHOfu9/02vL9erKwasvFvML+drq8csvulicPfPM6MqOTZLh9ubBc89Wy0t6ob3/+mtTJkbQ7y/uOC76168OblzO5+ZHFzefvvwy9VbomiInm83xxubTV16xadS7fGlw69qQ4F7wT9o7Rbtp5lt733gjJGy6un7w8ku6kQx3Lw2v7U5XVvL1tbMXnx2tb7gsfvjNbzCi00Txi5S0UENUcCfBTd/yY3Q9JQnk1YTeTHnqk0iKizQIyPsDyLhfbrXcGNxsktSmqCZXIyIC73YRwmGxFcVabGKwKhpuBq6nWIC037UsRQ3RiCVbA26eC5uzTWrX06aZ4Bsxa+PU53iXRa1A4xCtAbvCiJ2qpVIvOiEHxUbtmhbDiW+N9KK1QFLRLS9ACCq7MJNL1NS9/ebatLWEwig0R2ZJA1LTpDdaJNJMD2kybrSZmarlqZsLCI59NpotQamKPgRH82sUlbY5KlegKAd6fmKXQQDTkI6qVcPBGEfDat0iWOu5Wb6IW7TMGpW/mMSpnSMzcC0JCWmKHO6mzFVJXPF1Aps4SWu4m2GjGoNevbQaZyBJLNyNBVbtuRpvRcTJZDColtZ54ht4hq5nCVdRavBuzFVOJzPfmqdzrB2XaIv5Jk/zXnhxSTAZx4buMCZs2tTJWjBt0eAVuRzJBmmoTrlRgVgiNLJLuWp6jMZ+IS/nICt6+W4QYuxBXV2QNrZTU58li5Nmm6KRXSrLRRjJXn5RK+akGj1obpZRTNDQzk3NnMNwglv93tKCKU+/TNd1Ipgbl+uFa3jgprY9NQuGoEloj/IVEtWD8kIFGoqZkVzLfTNE3GZNabdTgVS8Dsk6TUMV7SC/IISrRVORi5GIbaNZh0stUhRQajc3l6E62QJgVSRCJ3Pab8TxdBJVZbW6IphLlwzcShpumqw7vohcUbGyVitrqajjTPtLGZcFXUJhK26EWXPDwjUhhMuaNbnIsfBpqtC2oCF30UgtWy9qQnuzTRjoLAa9fNtiUocwy7csQjPCOnbV58SNZX7S3qpSlJjuZDdQmmMyLVekBnnHmpNkuYwxI329WsuGj3VneskjX/Yoftq+YElAfKgX6zIJ1A30lq6bIdadfFcjIREcFxcM4BWig3LVojnYtFNwLaHzOHUFuhaTBki5EmvAzXPuCr6G3WbWMFN2LcItHp8dexqzjPPUR6sBr/CUa77s9Wqz0e84nuF22oAlv8zQPE1pHa0G0IS0P8A0cmvzDZ/jbeJW0uakI7YZXBEJk3zJow3eQnm86vVmltpc7GC7mEa249NhvgoYmjLWz7c8QrVPB+Uq5PXQtMd2JXg4C/GwXHW1t1OgjtsbkBjbmFYrVtihbQ7sMpqYsh/c/sIlAkdUjMoLhoeZa87kvDF2eoB4v73hmaSsK9eMYh7DYbntBZjYRq4XFQZ5aPTMclBMctabbuIMl3Gk6S5nwmYNTS4w0sYtUZFN6ttROuuGF5a40KlQ+ErCgCS9Po5iP59lUc0ucbDAm34abjQYNNFwaKMGTWmjIfkGAvOsSQtxkeTNtHl2rOZWWIMmpCI7jESBUMM3MFpmbVqyLaRXszkzwjeTKHMxqfkOAynAYEwa/eka43ZSr1ehobid5GuFa4Xgpr45MYsaoxlqDQZzkVHdB2KpyhJmxmpt5lsegpFtzWZtIOXklMVnC8vCTurlqVyEaXWmV6dmHudankbZeWsl9j3QmpQrAMuiXK30go11Ty2MzSJAaBIak3LVMzfwc3m9ijI7S1etWEYww2lTgt00plWaSH+5wUzF2wFspxnIm6sGbsS0LqPx2C0soAxnUYUvJwmuo8WAtmI+G9CidovLURO2kxJvc9zADVaGa82onsFSgWaTUd9Y1GwR1BHPwBRdb5AmSlkZrmcx0aId0BaPuMnmNdmgJgux6kx3vSATS2W9LqGoERvphapMA3VDvaHrdkhkZ3ZJG6RzU+y1NhUjiPTtQqGbnoEBXByeL6zY4vjz9IKPMQnjar0OicZo4BeLXIDSzo5am6OFdlqdF9sKRgqqsVotQcMgNKgWpWnpxHb0WpUvkFT18o0CZCCldbTiyRqLmE6WXNhK0lCQLezWsmzSjbcJWBEJV2LRgSWeDfrYh3p1OYVVtAngWtyyw/giYHPITQtqvFleatKCLUG0wbJQ8h1uV5Ps9BCZEFZXYmHSOQ02BDYVnQ90J0pDybYIXcICq+aaA2uRSFzW0ma3DQTp7V45euF5L1j3xvXx5e3p8kq+sX760nNla04uzO1/9SuukQwvXT69ca1E7hCxbjw/Xlyu1lZOXnputLKm243DV1/iXllAODA2jnrXb3Sv7oxX1+XS/OFrL03S9szkj9eu2zgZbl08ee6WTpPp+nrn2ZuTlTW5NN994frZxashjfa+8YZtJuOti09feYV99CE/+M9xEWJZS0ZEVcDgLWdcVh5BCGFU5V9861uW0CifCS2l4HFVws5En52LVzYAhhBBahSyxlBKvN3+1QfL+48Pv/4aCs5SzGXpMQwgkGDwo6E8Paf/9RrzVsWRqIqTmzcmr74h6hIH7whG3qLgDadU1dToOkl4VUgYKcaJ98EDzTjxDgaosoR44OsJ+2qCdAkd04yT4INJ82GjKRRwTsURdY4EJ6OIeoBhNJpcmlsoGfN5lMSmApibmFPrOeHD4wtiGXJmpoRxFwgo4PMA11VQXKURDhjpSsZxggJ21rIm/7TQXwAAIABJREFUhgZrbxkFwPNa6igjyENrNI0CcXHory8cRlY/NWsV5x5YosFd/V8C9ESfOdoE2CLjLaIIGWzZQ/1VEDwzXc25I4C40oh5CD106Ei9jp0kJreUAESoKi4v7xvgn8gVS1c9QMRrj7HmkKrcUuoQo9YaiD3n1DtvgYzFnLZlADaKQaXJisItg7Qxbs5wgo1VbkUO56ImxmEmGaPGe4Ik4VmQUi+Ww0BWiQfepDG3PkAoeZRCZ/T8dLrYmHfOg4rxpLIIVuQN7suxs0gJjq0HACoqkKsQgHUcJVIS5GoRQ1sh7C3jEGjsnWEC+LoJO9e2Ro9Qi1g3SxLoc+Nad+TvETYSvLYiIy6niuooQk5DID6VvwN4QenU8DlAPPZKiyhDAKrkE/N7TpcCDiXnMITE93bW92Y4OrIXFOUcYmKCSgRGllinoiggBX1pIMaMMG0koxBALFVFCQiYFTJs1VwwN1UaRYB75lA+XcEYYEo8DxaBhpI1op5B7EBerlItowzMKAmBIu0spQ7AxZAP1UWXO7KILaKAAz4rwKYlpEZOWUadxU2tSkQ8Q7GxjggfIeKCJ9SzGNsSwsQKgY0ikMiYUTVZpEfzF8I9sFTjdRNhAgZDdX1oXqb6GAcsE0ZMCT2QTFAvB+ZSL38JJR0EsGIcWQ8tlElErS3d6oG6geouRkCzCLh6lT/d3JFPTDwC2yriRNfYWhsJ5hWEyIgYQhscNLEgLnCtijii1hNnc8qYDySU7LZHsvaS6UTAgGnwo/6mmIecDjTNODTIuprQJqopjEa9DbJMUltWnGGAUmdKyhkNwJPJdCtLKhSBOokpIqyY0Rehr3KQQ0UZoAQZLwljyGFl61gASIi1VmTIB+I0gkTzQJV2GCNEiSovLe4HYJ+oZYdXA8fAhpP8ma4J3Jx6jCxg2GnLWDCMuu6Zev1ME6oLiKlmmKnKU+w8Yz5/ql93liF/4kikBGJustJ4PCf0nmlbOGcQxzb3BAXPiZ0FlEnOkJUBwFpEiQUQIUlJ5qy3RDKe1A7CEf06d+XYGSSFiEDwtjGbLazGY4xgFceJrDzkLhbMegyiUb+drAUMXU4YcxZbUAsBSIVwe9RPaMagK1QcM61gJNkzPDjnKqQET2yFgVFJxKz3wSkuWEAiBMMFwIi7oCMOvMbWSc4B0LHt767tlYg/NRc04QyVwMafy297rrnpKhF5pJGXNmoAZr1tPpTfZHRMtdeCQWixtlaIgA209SP9LVMHGleG8YBtrHs7ayc9wvb9ZcNjSDHVpacNTwA1xnPuoUPKOUo8JEz1NUgc91g7gyiMMMsrsKIx9dBXllHnUcPVhVrzhW+3rEPExVFsnaNEOdYgtq5WdIWaK7by2DLeUtq5KEQ89l7V83kZNZo64KApA7IS7cquYzCeGtgGglGpAQyOkcSaGiFFSGSMtUAmEbUl8kFHEXISIKxYDLxcpgdbF+Whj4Z+W8YcG6ld+7P6GRhPMFZGxAhJH6CK40bwTs59pH7fuZyHqeIceQOcN4xjUFf2wqfy2YgfYxRkmuAgUzDc3to/J1HXbZkog0gx51UkmJ9S7WUc46Cg1RXhlAbogqKcYgeN13FMMabFjL6EfJGDHCnCAMPIkfF0PWGSZr4SLGCCjTOcO4jmfdGvtoEOuIkM5RAjpmUhmHeBETIZbyIOm07lmMLguVJ01/ugQmmViAIkyKlASQVB06oqEEdQLGsAAkBQqNpT4jHC/9/f57J8+uILe6+/3hr1oDMWeB6cud81vRH5zRVireKMyppqqeI4kuX63Qe7p5PDr77qCHHAU2uotyqORV3iQaUedsSr6xB4z0hcFfsvv3z/K2+0iwFyVsYxs4ZZXccxL3NojYMAOwv+4/fvchE6iG7/3T+8+sO/bBx3Lv/s3178b/+jeXR25cc/++qf/ICNZiVLaxxrwJ75h39+9c//W+vwbOet917573+dHZ5d+fmbX/vjP4WFevZ//uiNP/guLPTum+/f+l//1HxytP3me6/98K8aR+e7P3/r63/wx3hW3/6nH7/xpz8gs/rKv/zytT/5YXrcqUlUY6EAv/LTX3zj//wuzuUz//STr373e2ww2X3zvde+/8P23kE5Yv03YX0KzLHrvU3qIdb7+vhXiT/S6PzMrbXjQbcek+GbqDxD5lgN3g1VF/gDdfRR5p9q0Nen7wn1QOoZHL4T5ClwJ6r/DpLnIOyXRx9lZk+Coep8zP39Us9Q9y2UP8WknOEMI1P7p/nxR6l5UPmJ77zP9ZeVLuDobZg/gWQGydOr7HCBTDw9uJU9aHmHovuX+eML3MjfmB08a8dxtyQHV/nhEpwB8fTS3P3UBJI93BFPtkFNoocb9HiT9ww/2kwPF1CByJNrzfsLCrLs3gbdvxQkTh8vR6cbom/J4Q5/shFm9nXdf6N4oj1K763y/V0vWfRkjR5t04GlRxfj/Y3gWP6Z672Dlabmjjn7OA2dunocOh8yd1QEFgSogtXl56b/HtGSyLv12a8jeFZVT8PgI+56fvyEjt70ukLqS9X5gNoCyPvm5MMEnil1HDrvE3Nuy2M4fsfrCuovqs57TE8hH3T81pI4OqyPYPddYs90cYSGb0GdQ32n7rzP/dCSAaSPn+VHCaiS7N5VPJinAxbf2w2j9ur45Js+3xod0RFge7eTgxjUvHl3k/bmaJ/ED67gYZOfQ3TwvDiKyDQkT641nnAvRXTnCjlfICMWPbhMu43o3POjG/GRgAVmD2+meykz9W/l+1f0EA4of3iVdFqsG/DBc9FhFirKHt7M9lvKocmv7PAuxd168AWbfQndxHU+EfW7lVaO25IIZIfV+KMwvMNwXw7v4Mnn3uag86mo3zfG49n7rv8RNjUqP7PDewx2q+GXZPIJsAUcfsZm71nnYf6BGn5KTAlnn/n+5xwfDYCADEhkzOBTMn3X6UCrD9T5RyLkfvZ56H7KwcSQ7kp656KvRXTQjh7eCFLwwwbZf4733FbZ+aYbLJUD31tNv9xxMkkPk+TRLihpfJygpy/y80B7CXt0nfQ4G7bmHm6FOkoOYv7wJpyR+Eigpy9E55AOePZoR/QIGszFd666WXZ1cvQt22+VE3HM0MFL4hSTcSQeXufnDI3ayZ0rKM/CUX3yUaIfqjAwp7+K3JeVLnHvTTTdw6QqSARQUOFpfvxxqu/XYWo7HzHzeeEkHL4NZ3vY9F3/PVQfAHRanX0WmQfa5eHsfS4/LqWho7fB7BHWA9f7ENWHAHXk2cdxddeCvERzEZ32ZAnHb/nRA2JHfvgRKfYAOK/PPo6qL60OOL2/zvYue0Wixyv0eFcMLD3aYk+2wNS9bAbfKJ44G9IHK2LvstcsO5gXRxdZ35LjC9H+Fqg4fXoxu7duPGk+mouf7gZJ+NNlcnCVDxw53Yj2LqKCiKO11v1VDVjrQZs+voGqcLs4/YodN4d9fLYhHm6BgoijzezBJeNo836TPr6BZs6c+rP3uT4x1SkevQXqGdH36s67VI6h6Jz67RVxdKhPw/m71ByqqgNH7wQ9AeZh3XmXuqF3D8ujjzL/pDY91/uQhMO6Oke9N1E9xH5Pdt7nZhDs4/L4k8TvlXoAeu8Q/bDERnJqfDDqYXX6nnBdGw7V0UcN/1jLIey+jdW+RnWUPNimnXnRCfjgtjhqwByxRzcbDzNk1Dfz/Zt6AMaEP7xEzxd4z7Gjq8lhBirC9m429trG8+zOFj65gHKc7m9F53Os78jTG+JgwUsqHl9PHy1aR1r3lsXpGsxZtH+JHq80h8PX3eTZ8b53lD+4nDxcsY5k9y7gkx2UU7a/zY5XURVGX9Lpu9ZYWHxoBh8wU8Pic9f5RODjIUSWUIekGn5JJ+8441H9Ud35mPnCF1+6zq8F6qh8D/U/gHbiZw/Q5D1nHJYfVecfcDvz6r4/+yRCZ3W+D4YfODd207tw9A7UEkXjrl/KYG8o75qzX8fwROYHZPI+BGM3vY96byNbg/iYoYMXozNER1w8vCHOORo3kztX/HRuNz/9lu3NVxNxQtDTl6ITQsYk27+cnBI4bcZfXoHjhjgT4tFNMqDJGWCHz8RHGM6EuH89OhGgyLJ7V+Fkjp+z+PEVNqC8g9DBi9FxlNbTb5vubnVqp1lyZxeMF2hXsP3bvCdYF+H95/lxpgwevwum94gd+/6vYP4Yo67s/FoUXzowLVE7otOeUnDyth/eIXYaJp/A/BGAner8E1F+7oxE/Q9Y9aF2Ds7e0+P71EzC8Nd4cg/DTt37LCo+9Ubh/ocw/1BrT4p3Te8LgfOKMo+wJeej3mdi8rF1Gg4/JLMPgXG4fFd2v+BkUl/9+b+9+ud/FZ8Prv/TT7/+h99zBrz4t3//le/+KRtMK5oqyJwOu798+7Xv/3ly2r3+ize/8ac/CNI9+/c/+sp3/1T0Rru/ePuVH/7lwsMnq59++ezf/kPSGV796b9+/Q/+CJfq1j/++Kt/8oO4M9x5+91X/+KvFu/vbXz06de+92ft/cOSZwXPJBbXf/Lzr/7R9xrH59vvf/za93+4dOfB6idffO0Pvze/f2AJ/U8BjR59/4cXzo/S0WB8dQePymg2m21vZCdn0Ph8e23YXrGMRXm+cHiUDbvjK5fAVKaDfvfatWa3E01n4ytb6XFXTKcHL708f3LS6JzNLl1g05zMZPfK1bTbSUej8bXt+HwQjSZHL7zQOD9vnZ52nrnlEjaL27wqF58+TUaj0bWdqD9OOr3xjR1QurnDg/NbN73HdR+I+QAg0gMf1uLgAT7J/WaDTLtWQIIEA7gaINYOEAE5AMmcVTTx53VYT5pmOu0RuMBjqvI+baUlIHA6ibKmqlnsz5VfTZp+mvcpnqOc6lmfsCRAYZzX1AIt2qBTg0XRwOWsS2gTzuHp+WyeJwY3BB4xKyBnEzBpGAFpMjGTBQQNbY3aapaZ6pQtV9WSp4GJKRilniPUmPjJPIQONAo7awpX6BYAUxEYwlEOh7EXGDamYdQMENZLIeoD7mrVCmAmAsJyzu4WTwAm56Tpp3PQh3IFxUPATA3TKhSxJqJYJvBshgrjLzXwUMFxFS3DwkZwrNxyDEOBtGp5NTPtUAe33YAjBUc1uhCL2ayqeFhLQmHAVKfLXpbET23YycLM4n4FN5Mon81mjC9hLG2do2TJqQq7qWcLQUGAQ+1BLJSrS8KXEFSuyulqczhxTT0F6ZItxVx27lQbcVy4aQsmVcAOTmKUFCCRW5PucXO5OctHattmnpDCTVowkYBYOEtINK15zIZItlESRm4651KHWWknbSzKIECYxnPgZNScx0OmWjCCYzdu+cTzqL+kJjVifbwGpxFj0yqLeReqNhFoEkYNlwKb1rITIl+jRVGPACA4zrTshsAIzqzzGoEAPdeKR6ZCS1wOgUcQLkfwuAAEp4s67xLgQ9jK0HnNdA0XmZxAEGBYjfFZ6QDcaAxO8gVsndvMYKeCxqEWgHJqGfeNeXRWAx/SFVcOKJQmXgWznOPakDUmcuClgK1ZUDGucLEGSQHF2MqVsCoPU+8OGyuyaolZAO086AjmCLVnCjb5wNXLIK1yXbV8WxPjQ52AxjQYDnOCW5MatvgomEW/ODsfua3QrLHxtkpRlmdwuGjKHs1GcE0Mg1oIsc5t2Q6NGoYQ8qRagt5KdFL65aQJpnmXoDkecTntURoFlFjnFLHBinboKjDHG7TKuxg38AIdnU0XeGRQgut+yCKpmpk7VWE+SmlRnCOYkbhpiw7hkQMZlj2QRVK1MndS+3bkE0jlOBhPaGLGEMQ4tAU6KyMmdTu15xpk3KwkrTMHAiyXQzSEXNW27VwZgwDlnNupDiAAPdqy+TyyoVhD8QjQWqJGGUphUFwtwKjvoYeoPXZ5GxlQrkI+Alg5N6ejqVMgRY0c5FFwBM5PQt5ElYcL0zTUDV2diDkyFf9vBpXCW4paI180oAT5GmyMhrMJpUuUGl1NcLzkjUJ27Nl8UDBgUDuQxMbVM0wXCXa2nJLlxqQIiZygxqLJQQK6dVhPMjXLx4wvYOhsOcHpnLWBmqFtzOspbaGzAqxEqS/zIROpobAomEABOpeAsW4smJy34FEe1tLY52Wf4AVCE4ZGvB3Ops0GGEc6gwJPwqjh4sDi4bIeK0C6dA1OY05mVcZZH6smEWQShplPIEzyMGw6EXQDRH3AcV42Oe0Tk2DdCtlZcBzAdOrH7UCDasNoECioaDJdkZOhSM6yzfTYBwZgYxYmbY+DmoNR33sSykWCTvLgwHpzcFbMI+3dxQx2a1gbNI9hNbac+WwedVUwPlux5YiByqRrYVpGOFd0jZFhWdgoXvRu4rVC2YotJxSUNlkNpYzDTLEVSsZlqUW85M0UaE2W4tEI0kAC0djgDEwUW6Ekrysl4gWnZkDXqLFiTWiKYZCLYCU/GZqt0NTYaVs0UKPIyHhR5QMSD+E6HwC1AGI7tcVcyCSEzs0yks0s4mjK5snTbvMC7wE5ByI/c9NWaGiIDZg1UJJrzMgY03SaR8244+UiboXTZVv2aTTz82iWyHYAEEZDT+PpLGnE50HOE0In5Tli3OMWqvowpdLOpeZM+Yz7DFM18soznugxBAyFpQidVQLVdj4xXQMiFpYEfpoDjlbS4cl0ARPglyJ4VgKGotTZnjFxHFYi+GQGKEyXXNGj2Bo8D7VTHiAOaRg5FUXNtq460COYLrmiTwIAZJVu7D8Itetcu9Y+PhKzfHRzNz3uRsPR+PrOsLWs46Qx7i8fPgGFnu1sJv0Bm5Wja9vx2SAaDCfXt2FuGufn092t+d6JqvFseyPu9dkkn97YYb1p1u+Pr1z0pW2fngwuX8pmYzos8s2VyeJKTcVcMYzOh82z07A9N0Bz80+fDHcvxeVUdMdHr7wm/ui7/1HQ6L8LnOUhSgfj9Qd7BvNkNFt89ER73O72F/cPJEtu//OPf+u739NYsNFk7d4jjUg8nKzsP7WIZef9+b0nGvO0N1x7+FhiTvuj9Tv3DaLRaLZ0/5F2MOuNFp8casyj7mD17gPF4miSX7j/0Hq4+cnnb3z/z+ikiMezpb19F3DSHW48eGQwJ6PZ+t2HDrOgAj6pnUZOQXAiladBBnCsvfYUR/59oOrEGgDONdDQGkzOZLDYmwCOTTBAeYKPJVTAWoTOlS+DMjicSmuw95CdGlBbiSJwIFUJbcD8rEbKWxebXyNXx8EBdmqABgYyeKxMHnQg4LQGJXAQJWNGCqiIiHoElFxi3uj7OMdVSAan1/qdbQlFNCK0wAazbMzgBEgax33HJ8RCFE0An1GHiJhyNMUa02hM2cAqIvgoRGOsIEEzwibEQMwnVEyDIaJ/fKN7sC1FGo1DPCYaIjIBfEQMQGxC6AQpwtFYo5PaQuJKD4+kw9iVAHe0I8ScMvs5dCyBY+WPlYYEVA6faANxqAA4UdZjMHPkrFaIg6kFJ0oiAaoAjrWzIZQed6z3yMwC7igDeSgCOVGYYl8K8z4MJAk64DPtHfIlwKe18UQXAR5LDaiHNOtRJIP3JOlDKLGxLB5g5Fgt53tPn5+WSxqK+AwBhX0gjSEiFdSBsx4NJnIeNPscaKhJnHSJk9wG2uwDKol1OOohr5lxMO5zpJDBotGnoIAVnBs+vTmdXAwAJQOMFfUBxgMBSqAJi3uETpzFFI9dGDgDMei7MLEeU9w1cGRAGoOHSN0jKKFg7EBP2wB936KhUYj5noN9WfME9yTqaItoGNvQ1RYTMHRopA3CrmPgeVWLGHUrd2YspnBqQc96LsABcY+phQz2NOlKyWI0lKFjFGZw6kDHWABoRdIBNDhCBRA9YjAjtU97TAecl6vnT25O9QKROOoDGyjOfdanFjNfwaQvAkDAsLSPgwVIkbgXHGCuRI0+tUgADbOeMBY6y6MOspYgjZt9AAIalhfPHz1TgAWgQ6svnEMuRFGPGMugwlkfAYO8h/TMgMpKHIFDqYtgIGVnNa69C5H5jLhZ5ANkZxqqoBEFx0rPvIEUnNUgBwFg3NG2hC6gcGZC6Rxi+NzAmdNIkK60E+8Dgj0Tcuc8RKca5kaLTH8YwDn3CcfnVRg5AwjoaDcLBhJ8qmFuNeF86KIh1oigHIsx0RCzGYnGwBI+OL3eP9ypRCbGIRliBTGcgGhIDEB0RugIaYCjKYlGQfKEjZwYEIMJy308phZiOiN4QgwkfErFwGss2MimI6aRGPY3e8c3FWjQAkdD4CCiMxL3vaIRmMLGkBnMQ+3wmfEWmhLgjnKB+gqQU4UJDjI278GAE28BOlXeIl8DfKqswboC4FipQIMB7MQE5ZXj4UhJjbyG/Ew6h43G4EhJGAHp8LEBFhhHwom0FjnJ9ScIKYEDQifKGOQtCCc2aO8Dxec61N4HKvoI1Mx4FPU5qqHFvDFgYAYr0ho8uT4eXQoQxSOMa+ohjkYCFlATFvcpHVuNeDwMdEYUIGxEcElMQMmQssIbQnmf4rFXLE6Ggc+wDuj/oe09lyRLE/O8zx5/TtrKrCxfXVXt3fiemZ01A1DYhQiCiFAoFME70g9S0i86kJQohASuAJBYLLC7INbM7PiZ7mnf5U1Wen/8+axuAWQEruN53+cxx5AkONPO8PzGbLJdAOJNiLkAheGYY0kjKiCyp5AmRCCshgL20tx00DBTHSYQhaGAfS5NE3apfEkEMOGY00GWGy6aFrrLC2zCSIEulwqAhURjITUSE0WHRUEdMOOkwySiKpKgJ4RGcqHRiEuN9VyhTqYNUgxs+YXWrgtyQHucayQSJLssBxSF0uznHFHAoTewBIdCmk4fK46hIKUxgAJNkrXuwZ1QNRXXlbElJRLQcQaECwtwHIwh4FQz5PaQ0CZn0B1YUCIFLX9EZAaVNJwhAJwAAb0R0YpqBb2hoXMYgVr/8GY0XwaAuCMAC8IVsftECyIU8ocGKjTDJhkWYqokwHAs9FwIBVCPoQVnlse+ArBjSM/CvQyOuYBED5maKw4J6nEYCgYJ6DI9zHLTQd1UjoSEBI+4niuJCegWYCEFJLibkwnLDQcNMjWVgpjikKozCgnGQ4bnUgAk+4JOWEFtOCrwUAhiuMNp9eScaVLuDpeOzzikfne4un/IqLX52Vcf/st/TQrhTOaNozMJcdAdLh2dMmxavdHGq0NObDqPVl/sc4Wc8bzx6lBoGEwXK0dnHBBzslj99iknJo2z9VdHEhBrHi/tH3JA9j75/Hf+zb9DaWHNwvUnz7nGOEzXXuxrSOwoa+0foST/7xCN/v1iz//kjz78t/+28eJFslTf/O3naw8fZcuNrU8/3/vlr/d/8P0H/+f/vfbw0avf+9GDf/8fVp4+zZfqrW8eb3/x5by1uvXZZzd+/vPjD77zzn/6s/VPPtv/7vff+fGPVx9+KxrV1uOnWx9/NlvfWP/6m/s/+9nRB++/9v/8eOuzL47ffPv+3/x87fMvkmp1/fmz7V99PNveWXvy7LU/+/NXH37/3n/6s61PPh28dm/37z7a/vSTaKnRNba7X1u2z9RMdJ/7uElIT3SOPZvmVpydHjccUGCizr/0LZ/rKbt8Fjg1wbqie1qyRRRo/upZzVCMGur8C8+zUxpm5y+rrs9EX1yclAJUGEV08rxuF7HpgZPPfGIrmmTd/cBzUiuUZ0clynIb5sePq34eu05++OUSMmWNYN29iQpiyrEcvmFOJGkO4f47igXI7hintwyF7WKcT+6Q3MJ4JnuvBSHl1TZ59bbiFWh30fltLRyjmOjxVZS4hjlR3fvm1OGtnv3ivlRNXZlZh9s4tw0xkdPrOKsIJ6KX162JKZYvvVd3Mr7Gy6F3soFyz2eXYnYVRst5K5x/mk+6vrVH4y+zychbIpNo6vVPbTPQ8dBIDlCrNZ+8MLudsr8Fwmd63HNqdlpc5JcXVcNX8cSeP4Wl9SJ6pnuXZWcd6H3Rabu+mbGR7p6UfDeLJ3Tw3C6v59ET2bksV5ez6FCPBqUSnJMMnb4KPCfPZqT3zG015+khuGjX6tUIFTXUuYMlAziHJ28BGtsFg5170k6MOdbT2yabVLNxNPnQyFNtZuj4bWgVZpaC9h1EQzOEfHKX5qkhQtl7x4ojUsrg4VuKSFJkoHsvgCMQATF9jbICoURdvu0WhXBm5OhtTRUSGejdRToz4kKO7xEmDBrJi7eNTIdLi9HHSHAa6Lhz4BQz6LvZxctS0ebVneLyt3ac2vVm2vvGzhPioqx/6skFpSU4eGqKS17ZKXofmfMkcFbA/EuZhbiM40G7tBhSqw4Hz0zV0a31WfszdxKVvGU1eYyT0KyhxeyCzoe22cKT52Z0Buq72ehjNJoH9Xo+37emY7NU5jTept1tVe2aZ0tqfjOrTvxuQ8x3qB44M8Ln97AVobxmnm6zxsg8r8r5TRJ0SLcuF9cM3ieJLSZ3MZmBJEDtW7rUKfUrbHYL+j3ar8j5dZt1gkQlkwcGHIMigO27qDqwLm21uG3QM2NS5rNbBp/SXKvuXRMOsXDRxd2iHBcX486B7wFmsvD4xZIRJ3ZFn33mI6pNnnafe7ZVuCk/OyyjnLkkO3lS9eI4KCWHny9BAqku2k8815R4nl4elWDGHIefPCzDWe4ti/ZHrsDEQUXnqWdohgrRPiyZAmlPDx/aKGb+Mu98ZGeGS4kYfUspy20t24dBESO6jauP9yRflfWJfbAJ85IpB3J2FScNYUekt2ePXLHc9vdv5fkmqy2CkzWQVQPeltMrOl7jQYq6u86kyhvn9vMdzq+w0ti93BBx05Q9MmnJcNNwh2BwjYyW87WO+3SLsR3qtXFnHcYtU/ThYlXNt5Ddg/2rcLSu6oeVoytptitKA34StY8rnpXnEek+dkqtPN0XnbNyuZnHJ3LYKwdgbjFw9KLimjlPUOexu1Sbs3N9cVatVJP0DFyelyo003F28aIUoEhyevHQ8+qMnRWdk3LFW4AROD8p2SpFUrWHRDJ+AAAgAElEQVSfBFW0AEKdPa17Szzvis5xqV7K8kt2eVoxZIahPvvGJ1R4lTI5uOrCCCWMT+9TJhGK5OVbbqakP6AH72hMEAhB5w5U0ogTObmNc2AYc9l+24wgX5qaL16Tho9ISk93DcEwy+X4DmUWsxPj5I6xgKo+dF7dT3AVGLl1sWsw6WYjNn+NZk5WYebhbTs1eaVrP7tZ4CVAE7Ozq3NTWGHvGRXnoLU+637pDWeBt6anj1E0tWp0EV6g6cAzG2h6aC0OYW03n3wKh5NSvZmG+9ZoaAY0XXTopO2Uq9noyJkdG7WdbPap6k8r1aV0+hJO+3YVL7KJ1TtxStV8cmxNTqzNjfH4ER3Mas3KbHZIJn23SuNwACcHRrWeTU+t/qFdX0/sfkPOblqsW0rzaPI+1TMgTXR+H5Um1gDryW1K2+bUZ9PbRjGnjKnu66YYY0TQ6evKnZlTQ0zuVOGJWPhydo+KGGgJ2q9bYKG0RqdvKHdBFqYa3SFwRBdETO4TkVDMwPkbRCeaKHJ8n1e0sYBwcMNRPZ3benQXK6Vo5/Jjt1DUt/LOYxdxSSW7PA5IgWEJDB5acM6DdX75GzuFnunK8UMMc+5C3jkqZSE2G6j7uYFnstFanH0SRMo3PTl8YiiOy2x6eRykU2ism4PPjWyoq1t57zckYm7ZyyaHbpESH8adEz+foHIzP/sqyEaoupUNf40norzsjF776X9Z/+zr0Y2b1/7ulzd+/otnv//Dd//1H698+3hw/+7u3/567ZtvslJp9fGT7Y8/nVzZ3P31R9d/9rcHH37v3R//+coXXw5u39j7u483vvhClvzVV883f/nJ9MrWlY8/ufHXPzv9R797/0/+dP2rr6c721tffLP9m48Kz62fnNz4q59Nr1/d/Obh9Z/+7OT9d2791c+2PvmM765Vvnm59+tfF75Xvmzf/YuftF//73kR/n1bhFd6Z0vti+MHb7qdUWk4vLx/p7V/oLka3d6rHLe9yeTFD37QPDlpnRwfvfuW2x1VO53Dd99tnp7Yi8Xw1tVyu+cPhs+/9/16u7367Nnld9/2egNrMD1++53G6ak/m/ZvX/PbvWqn++TD36ldXq7vv3zx/neavQu/Oz56863qxXnQHw5eu2EPZ0tn5xcPXoeLbOvJkxff/x7JmOoUeNkAGKlOkW1XFZNoPxY3SjUwz0812jARkqIjUJMqjcQFo+tEOYY+iPWe7+mEn0qxYmmEikvlVZlpq6QHaYsw19HP5uBGmbI0PQJ43bAcwdqM1Ah0ibgorCUde2XxeKGvlctWUhzkVovYRj4dOqQMVdVy2qTwAQoyu20yl6mawl0HEC3qOR6ZvorTulKTGnc18FP3jEpPFHVJehZCgDUKY2QQLaOmMvueMBUoJ07HYJYWdUYHFlJqsmWX24WpVLgsrKGlsFysWLVzLrEQS4yOLCjQdJsGXWYWUtUjNPEYJdN1xz4b43mW3l0xRxHqZMYVtIhcNMzE9bIxTUhcVBp5nFhiKpNbTTTM8EUCbvnBYpYuDLHro0lGpylct9gCqQEv7tTcMISXubru2/OomEC4boqZFAtgbmCSC9DP0Z6dSQseheC6R2dZPoR4k1Im+FxXl9JMWHlXGNfshAbLJzxeYtDQdGDyEoeGpH1T1Hnm0OCchmuymbXz6UZSLbClaM+UZSFNbgxMVeWpQ4NTHK5pk2T0wuPVFLiADEzlCW5Lc2R55nBSKbnnTrimKE7sC5NXuXA1GVjaFcKV5sAGfhqVaPnciJYktTKrY+eBKMoAtjNDC7Fms7aENiY1JF7lyifmKpJdAYUkWybvScI4X3Pyc4CIklcC+3CCbYSXkWhzIFRydRmdRzTO6RVDDQWAoNiqGIdTRGCpnIwmJZUKcbvmdmcgFWTDgJcZxzTdqJL9GQLA2MZgLEAo8I7JJhpESu/a3lSRGBRNARNihni4a9mz3BuQ+bYIohSF/nwV0AQ4M1EsC5hCe0KyjYJxq9RD020RxBmeuWmzwIzAhSGWMlooOjby9aIATnAB4y3emI+SeCVr5ERAMsO8yUGhzYnJVvMMOMEZDq9whxVkaBaNHClCp3i67gDI8NMx2Cs5MMmPJFy1tAmzE04q2KxidVkYZcVrvnwe6isBxSJ9kVstWvLi2cihHgAB1pfMLOvQ98XLTK275SDXBylomLCMeVsgS6uanZ9Ky+Oy5eLDWDcNtuRbT0ewbugAFydceQZYd82zuWUy1grUqwQ0zGS10jhNMFPjXbvUyS2mo2VOJi5UcrFGK5cSAMEbhTkkSODhFTvoF3asZDNEM5dBY7FGK5cMS14sCzS2cEEmOyToZ2aG41YRjAFXJmsUdg8qTdhKgafEinGyXujItiMUr+beCAhu8GZuzqkqoGjlxgzhkPavu9VhL+8jvGmATMiJwquUJ0B3GN0zBKTgKAY3PDdNWE/LVUsVoBiBaiPVEGddZe4aObTBixm6VUJRll0iukWJ5mLAjRWqAJLtwt5EC1RWT6bwTiXQUXHO3RWIlIhmFm2gTGB0wYxNyByb7Edyx7EAV+eFXrW1Y9h97NHJrOqYHT9pKGKk5pktK4UoKdIzga14wK2hCe0irJDSuRnXFHZSp23lgVRlQXumtEC0REuXGptZVEVBm2QlFTXsxlHBHSkr3OpTbpBFi5TagpJcBRkeVNKSjpbMpaNCmZLVmTE0JSFhE1Y6QhpwumqbhxMCdLUWzxaeikV6o4naEZpyetUwB1GhTbZdMs5mWCi4ahQDqOfcvEpkCMBMwB2L9mPOCFyh/JJLga0thCZMTzm+7uQxgf0U7ti4l+cJNrYwmjGRwaVmHCYuG3Ljur2YW7Adm7cccxryhJB1KidcRpJuWxLYwRmMtkUz7CfRWlbPsYZ4YsqlAkhpjEy+kmfIKp+i+bayZWF0zaJRAIzo2BA1JgCwR5YbXA78lnduhZvclqnZpWyJS4zoiMoqlxh4fZM14sh2aqdkvipMVBh9q6gwbUCjT5MmkURUukhUw8j1aidgsaKgyeQFR1SjZUO0hWGJpB7I/RzUqFrznOcDVDFwDYoLpgySrVfQUWQiBjcdeJLospGvlKxnQ+QT30tGw5KEAOz6ztlUGdisKXnGZcmMW1XzyRA6xFxHui+gkHDN4F2pCcZLUBwy4ZnuqpYnmbaIsYJlh3FJxLXyleff4qg4fuON1VcvzSjp37/un/dr7cvz77xFB/PWydHhe++2zo6NedK7fa123jani97rt7zBtH50fPbBAzxLV54/vXj/nbWTfb4QgzvXK52uPZ71X79l92eNo8OzDx7gWbLx6OHBhz+o9S7d3nhw94bXH7qD8eD+TTyJ1l6+mr11da6DrYdfH33wQaXfKXX6r777O8Z/OyL8+7YIea55LFLkIE7cRKbK4Gse0Sr2yx5rg0W+MMv1TBcRS4kz2L1ztnNLElqND81YxlbJSjveLAuNUtnvwdcbscKGIDRkCXKKAljzvBB4tHfn8NprQAPOkVgUCXZySeyQpZz66zXsZ0lQwp0QzLKUulRwHrGEOBSSjFmUmhIixQsj01JmxkZaQC+G7kISDyKAVJZzhB2LRKiecMNPgFtISrENAZowSDUlJKVWN6d+DmuhhC5SDHlMaop9LHLcnOXQS3BTi5RiAhDKlNMsFsxkdJvlMkmJsxCWh4gkcZSbGBBkOhq6BVYUDjloSMy4kVxzXyhAXhm3DeggaOQk47oqoDJ4cX69DKT0ZAJBlSiRmzkFPkEiQ/GKfxgha0rqQNcyhIQdm8BFUkYGLJYRBRILwWADApZgbQisDMTtcNPcdw12Ct7FWgsggKEBqBYaJ4ZRaI4LoyBewbhQRtXQzBVGLWLKy0GAeU4oDZUlClGYnlt09CaUEIWktGAYAwdoigTV1DdhGwWLDDckCAQ3CHE51ImkBiDQGuFsHJm7sjAEy8oYRdziCtvYdM1Y1QuFgxQHsuAGxYlyYyFKCCXYWwArQxGk3BBOTqXjTdbKB0NcH6MtJJ0E5Yw4IWjkKIF2nOx4BmRWrAtUhmCSYhsBNyEcgO526eGZt7UwWq40GBbMkAUICM4i0x3c9uwoNKnNdZNRpuzMUI6GHNqzNf/lwijFeAdBPwOJgbWS9RRJTpNMGgaQlBqRBlBBT+ekkWuFEnc15cxkBaUuU1JpSSBQS7EqOMyMhLmFYbg2zhWDTHFquGAsajSVHtMelkIQj3CGAAKUSjxFnkjBspAeE9o1DA0p41gg268OYJqk7nYy0CbLbYwSbXCpDEIxgEo7hZkSZjMBY6Q9c77uv+jR9xE2gaqkKMOIaAU4iNIq5g2zko4L4GlYinGIsaFBPYNR07xYsvunzm6sli1tSjLIdBkrI8VhaC/aK4FbKDOyiLZzIwcKF8DTsCuo2qw9fGS8nigfwSozYiy1qYwEU4WYloIgHxIwVYQKaFYVKU914Ie4VAjLx0IQL5MQI5/mY9yKGXHnqBxnCPkUUpILaSottLJXU6FUBMoJMzGwTAswVlCqENKwEhUIZ8CWAhNNJbEZ87m2XU8ir62CIAX1JOe+bXHiZwJRRTMjcIBFlQwpMbSVQ1rQxZpzjpUckBuRtCBFzA4L6BAJEqx4WesGKeccggrTZoyVoVwFELPTNWu/QqJT9T6Fdg7MjCwAolIakI9mG64E2AEYoTJXkOGxAKUcOQWZtexX0JTnzjafBBDQ3E5w6BiA5rgIoRdL6iEsAc+ZpqZj8jFspgwbsfKYpBaxFUYzjimkBp0ia5yQMteVWMoAgxR4QmqD+BikaGmewqoEFS1yk1IuEFNyNZvmNrN3WM6z2PQioRWBWGTz1DCIiWGk15hANMI+E8TClgbJjDkGMk1COPAx4imxuKomWBqA75YfX5Y2JtaKpS0IWGFwBkoIxSlk02se5oUHlNb1FGtlpaZ2hAYpjbftp4xYPXQNwDKDWYJhJDyBgTRngy0PQE0KHeuSARNAMZCVTKsQwnvOw7FRGttXTWArBVIiDEAYhCnRTORQQ2rBxdRTBeeG5aoLvmYq5OWonKVIIwcLCbkChmuiExDkOVhNtSOFMImBAGTSwIAQpw3TZGFel1ApXpQwDpUhlOlCaPmRximjtUIHgAnLRIs0yAQrYahgTDZYrh2OyolANsECSCmkZRhSeliZKUkKEoe6meG4ji9XnMNzazMpmgZ0AeinpEyAlZIc6kH3RtnkqZNAodyC5AIgDgJC5rkBb5U/e6pvJdgtdJ3TTFJhKkvDNLN572apFrEIlyhwMhgpDIWqJ1S6ZnclODpydhNWR9qBSEfYp9BJYarNnHFGgSDEyqSiQAiA6GouBccZjVjAlenYKBcFwFgB4NhTRmihq1wQIhGjXsFSJKk2kKJjYNJE1wrhcWqWDJkqDgRmxDJrmZYqdrdTwW1eUEJShQQgjkpwM9dSpEa9YIgT03FoqqUSimIrY8iOeII9nmma8NgI3OUIBpUImD7HLBYJdhhHKGSMo8Nr9wUkNmY0m8mwSIhjSKYjngGL1R20jPOgxE77VsQjMyAiEhHPkBU165d/sGfw3JPEikVCPFNMQcQi6pWWNDRrGcCZMmTEE2g6isiQcY0J/IdBhIs/+MMf/Yt/vvr8KQjcvV99dOWzT8WS9U5z6Y1F9xDY3/0//uXW55+dvfved//Nv9p69DBrNTpbe3kQlPu99//j/3Xnr396+eb9D/7jn1z5+OPzd9/9XXX6Dmumk8frP/1k9ze/zer17S+/fOfP/3x4Y6+zc51ZtpmlH/6HP97+9FNoW1cefX3tb36RrS/dWq78TjF5mcEH/+rf7fz2k9nV7Zs//fnV336sXL9j7fQ/JSUrJr2499gyHF32o+zulv/osTE2z58HJC8MwC8+93wQe94w3lv1syh7rMZHjpctysn89FnZnM296lS9ec2Zjfjnk/5xzTMzfZ4NT7wgWviomz644fR7qIfOvvA9ktnTsPfEKRcTn3eid244F+dgQjuPPG80LBvxyy9aJshWlKadq85c22IE+/e8jrDdZ/+zXLQwOpwQ6/iekwk3HKnxLWtGVXUaVkrMsuvDrv3qPZQFJr2gRzeNzFjXX3/I5DpKBrnPL94sXwLZaLtPXgNZDbun0+UmBrw0ZNb5FRpVtZ1Z5zvlDlH08T9T3S1c3keudXDTXuBSdKHHV/BiVdbG8W+TyblZ3obRFzxpo2V+rj0udlt4Vsz23fTbYq02mD7Cg7bvrELbmPOra6Wvn/NT0j8NbFulAxx/qyvWCNuL4s0bznk/f06nZ7SqI9jPe/uemw6MaxbYqNNOX35WDDtBpRQnL9X8zKjkQ6MS5VfXg+l08crsPvE2g27yJO9dlOv2zMsC2r5upoUBY3L8gKj0VvHR70uYqjS+WEGTm044rS366fC7TpTy5cm8skxVXjsExvl1Ay28IQDTG94iWgdf/VNcCcb77XwVH32H6sSJIty+XUm6UUXFQYlb5trLCei/XR1PtDWz9t8DWu3AL35YSJPN54MqHNw1E23gGJ7dD2ZZuDSf/1eFElnOZ6N9Uwx0k/aSN7bAfOjHxeVv/Hhu1MuL7hcGWOi6PGcbvumbaCg6jz18FC9tRINfGYu5Vd0S0C1AYLqPzmeX9bRPjDKcfIv0cd50z8SSnd3ZMZ51Zy8CMVJLxWhxBBcdJzAm8v4SgczqjqKP8HjmlUvZ/BlJ+qDmFHa07Z5tUPvIOa6h6VVeDj8UX383a02LU93e1KMbmGQkKbsnVwzjdL7s5rYbJDPnaJ1M1oNs4C2A6t+24fiBe/mPNOumGX7VgrMb1OjaF2U023Gyfr6uJ37T5mnlyNOdNx3r2Dsx4eSGBS/vxL/9fb0bhk/jyR69uO5lbZLb6PxN6I7FQW+0b/th6OTziydlczQrBT1+d4fyVH0+HL4qOyCmvXhw5BuzqEy76bs3rO6FOyj2v2lRwdwi6j+yfZ4G8XH25o45m8gB6j/2vN7Ua7DzXzpagnp9XmzW8HwMj9joMEChID7qf4ad7jxozcSyz5tV8Hgxfu564dxJ08G+y+bIW2P1h9d0vgxLI2//ihHay/Lhd1W+q7ILXQLn94ILikr7pZe3VbKCvPasVcda1rpz63wdROvAy8z2VrlbkvTxP5OXN3D1ldbW8S2yKJXyrjmowekWckeLup07Xnk+qH2zIfMtw+obF+t0Vq/zFx/AyR2WjZgo2u9Yg03qflt/1hLZDjD76GB8uR84MOULMHzk1O0ZNsfJnZ1Kr794bMxOzVIxtefJxYvADfvWpgK3trz2efJ5PmhXq85CHLDhmVtJJ7Y7y+9u+4MBOzHa37qNIFSHaf/QXRZdUwzjBzfdw2MxsgeP7FreB7k+flir2LEbRMn1Nff8tNgnoyPHShMs+OBry9WxG1Sdl1e8YmxOuZ7eseayAT//n8xyufv0WK05+w+QAiaf4/ObfpYUQRyX/MJ1N14OZPdBeZjJ8sB59qaA3pr19J+mYY1n07GtevfMzJFW5h5eD4ZC1s6mtVUAlTci3ulVN0r9+VBN7uC44rcu/pdwsSFGT6dW5dUdCasEZd75hhEhQRaDJ1DuF5vNXue31mTs+Wva8hLZrAQPD6Jje9p23CqYvULFga4GfWCl7I2blecnk4NK1IV1Nc8v9PjUKhdt8Poyqlj0rMc/leNxUPGj8BlML3G16MB1zJbL/iwcPnRGh87V2vngEzUZlRruBBix2G2Vnh2FF970BWr5s8mRNXhlbzSH7kUTTffcsFeeh/nwPSsf3nKOf6iyRZFlhy08ummhS7fjgNl1L56EWzB2K4bKa8cItB+YtGcNDTS4Uc2P9uS3v1esS9weTbbN85tB2odS09O3vaIdtWDqlQxdVA4MPLhh5szQHJ/ctWbDTfvlH6k8tuyov0HbO37eM0OM+jdMDhE4vviVJzjyUNL9xrSyopyfZLfWXMnUCF4+dKx2VGklvV9aKTODVgaaBs0i82AxOqnkU2zV0PATiC/TZnBRrFTYVgs9Hk9fBmTBqvFodGBlQ1J2J+y1VVrE5sV49jGNctunxeQZlVNQTw/Y7ZaJpBmy088D2YXLrcnib/msCNZQ972f/On2bz4ONzZu/uIXr/3VT47eeP1HydEbdqsd9+/8yV9e+epLQvHW44fX/+YX0fpK5/qt3PHcNPzh//6/bX7xZbi5eucnf739+eeGJW833DcKNeXZ7v/7l/f+83/ufufBW3/873c+/zxaW+5eucZtuzQevvUX/9/tn/w0WVve/c1H9//sz/r37z1IDt+na8nsZeunX1z/9S8NoGpnJ2//6Y8Ht27H3c4/iGh0/od/FG+uF5Xy/u/97mKpFa6uHL/5IObTI2L2GlthrTnd2tp/9914aUlWS/vfeZ9T00pi5nnSc3vXrvVeuzOt1pOtrVcP3p1lbCo6L5v34/rSYnnl1Xe/pzy3v7PTfe0uUIAKDhCYNVvadb79x/+Y++58de3F29+Jxfw8XvRW9qZr2/FS/eSDd4fr28Bzn/z+Dy2UBWaKrth6yfLNlG4Rprh1fpkub1kNw+EJvm0bASiZqbFnM4roWU9TC6xXyyICb1ZxoGyRWfc8aZv08Stt2HJzxbGYuwX1ZuCzBb9fQhhYZ5ea+GypvIQnxiZRG4GHM28DzOp1u9PPiOcsO65MwQ2f1rBlMXcNpmuWwWZJM2YtaReLaDNPmvZ0MTzScLi8RcgcW+PZNtaw4M0FNmOODTNNkYkwmqJgFK4oCGbEnfXXg2k+PED2pFqm+SJfi/MljPUYeNPZOnHCyJBSmRLCXAWDaEX62Sipz4ttb1xkpzw9Lm+7ugft+WJbY53z8mixansorntxdrXk23mAY3W3kipqtvs8KPmWdEsF2bSkT6tuJq95cDg3BpNofdsKkGMV4Lrr4KzsRfxanTJOT9rJ8gYNdBnG4M2qY3HLZOBWmUynxnmPrW7BmlWioXnVRMtOWcf8jSWSpaA95l5gByTwU2OT5kt+iWbmDTOqO37YiTYjVVIWGBQrybTq9NLJmduYL/lOPhnvKeqGWuT5dqRMBTgweS4dgEkoluNZU3vZeHolSwI7TM6f1vYW5bKhOnIpzJYhVRNdn6Uultiwk1CXtVHMZusFXyKG7uvaYlIzJ/n41K72VppuOg3XI1XJjGyaryeqrF2LW6VC3qrYRDnrUDcMfHopcyg310ok82sFuOIYliyXCr5X0+NQz1NZLwUwNbap2vBtnFWqebHpGd0xCfPiyhY1ZKlegC2rLud0RZPNgI8j43KQXNmp0Nj0uLhdcWHuLKtip+bsH4JZJne2sa3Lfop3TWBpP+D6mm2JmTDH8TpijjDRcLHFpxkfs9MnrTeRWSA4m24UiKQG7WTLQkGFpHJYEpehLyajGxLRBOnpYi0PVXyexuf1zYVvmGDI1uKsquxiPNsV1WgQ04DKAuMc0V6yqrIqsPU43cz6bnMUPX+2/AZ0FULhfFsrl5mwvViHoGWUi5m4UzKXqctieq8Eqi54cqgB1TvrHs3sDZzvVHweGnd9YVPv+IwjBy5VHLvw1jRYdzwU4xVUrDfo+aVCDl5yAxnSm65u2j6OvVUgyxicDSC05O5qwCOyS8CqXc0m+JqtWx44vIQxE2vNKpibayi9WvPyhb8L01bV5ZfQGU/XoQHG1JwMNt2JWpwp3am3nGKe12bZmsn1zHaGoy3iRDFWiuBCEy69cbjCAzbKgmG+7Y44a7Nwf+kmAQNizGc72tAx8+eimmgAaFFgAqTLMOwnmxmzhGnMxls44bMTQi5rawZIlNePN4zUEjbqz3e0Y0kL5+iGYzkq8DJ4zedMmOd96QZovVQWoX6rRmzh4JzerYCiwC+OQaXGV+slM7WuGqLllviC369izs3LAXOroGQumTNwxQYtt4Qi8wqdlGreeTt1Kt6SYeECXHOMAPgeN66YTEl82gVeWe40q/mc3vdMX9g4p3sODCTRU1iejjctg4XZZpQE9jQ8flbfCyt1U/d0dR41BJETtRRmAZSQerOxqGMjnWYbSVHHRA9FeTFeMsJsfGZ7F601O5/lS6N0WfvxIGqFoo5hIZDSCGWmGvJqFG0AWsRFczx0rTw6fm7V5iubAC+gO12sFGYxZPU0WUNVvjCbkq5bwsH1Ui52HNQZoyhNN9YdU9o1KXe9sph7Szm72jAWKTnvsNYyDHBgF/Bu2YGZWVHqetU4v6CDRb69SwJUtkLjuo2rhmfm8k4NT+e6H8pq1TelVy3ohpHXgqqZkOtWGitjEqbNluNBuwbkXmDRvOpHYq/My9LOxuPr2jQjAbNsmy0w60az09pGVPNtMM7Ws0UN+NlwvJs7IhaaGLwAVBDcy1s5qwqDj2RrflLdCqMnjyq3oSuwmM82uSxrE/by1YIbCkrgJ/NoyfSS0XwrBn5C+Cy/IqIAd+ejA3tp4Zs+72dr6WzF8KNBvD7jNSvAsddUetN2cB4sy3yjTrojnmnU8AMVkz1Tr7kuiv2GFA3bOu9CRbIrG65Mgi2tV8wmm6INhFc90ZniWcK216tggZax3vN8kVrbKFuveo+fqVyr3W3DFKVKjq5YlHC7BdF2Cbd7RaL0ci1QabAu+WYJW6pUytmNmrLs4c7VVw/eU7bVu3Vr8NrdMcbdYnRavTFe3+K1ytMf/Uha5mB7p3v/tiKUZrkBxaixkjWaJ9/7zmhrj/vu8w9/t1DxMYbHjZ3Mr/Sv3+jcuzXa2uG1ytEPvicUQkADgkSlPNncPn37zbi+NN3Za7/1Wt/yJsXleXX3dO8+dOynP/oRL/vjlfXjd98zv/7qH6BFCKAhiooOHZovzS69fOjhpCGm3n4bp0XTCMpiZtN8bdGuqoWFs2bc2z54Wmu3D957P1ALh2bNRbcMItPIW4t2rTdwXhwsfa/m8ZlLsuW4G8i5T7Mqn+4cPK6eXzz5vR9V1Nw2imbYqfCpg9NlPqpc9PzusOF13GJoG0Uj6hFV2NpTrAoAACAASURBVDhtLLqKQwVSmhlAKymLHNSVRuQUGFZWzeauYjIvGVBIwHFCCkBEH5sG03pI1EwsMhsWnk51UUKFzJOSbXKqukxImpJcJQiEKs0J4MUFQJuyRi4FLjCjOCZSZhgQKD1wJMwrsBSPfZmAwvZhbkATc6SS1BHQLSCMc4/N7BzrKI+jHUhVy+mQzHBUbGYYsVQQqKyi9ThlLhZLmcUXGkEr5kiapsiNCPP5GnBQLQ0tEctYBaE0mCEhRjEttZnF8sUaNotYUY0To8pzrRWb82JcRwqtVEclnpt5wRJOC4MrQ0WZI0YAFm6IDR4RFaLYWSwsNIRWpbDyEdJFKRGOoEDzOEJAUXyW+G7oZxOmIE+Z5oygFCcJm2s1wkYzslhC1ULMcjsNHQVA4alEqdArLQaqUADmNLNZERI5l3EKE5H3EHELM18woMtJYRUm0kynLpW6KmLKqRHnhjTdhAmG49muDbO6HJWEQimt8hnjKM8A0HK9E8myEI62GZYJs5XpSYAzZIpsOtxDALREG0tLFUJG2iiITWegKO+8jMNVjI3MVInNAVgUpjBVIbMIhZMdGqTLaBBwTRklSWqJBUu1kVEkYioLEOeqyBSiUFDWVdIx3WkHSCY1ILFRCGkVKU9KeU9CimWd22qEOSBzg2shlbJjiENEkwz6Y80ZBKCIhcVHAIEgEYPIJRmky1NHzoFgOHIIW3BsZpnQM0IUsiZdzXMlJUldX2jEchWWHA6o1H7IcIYNDmXsmMwNx7fr5XkpDQF3UGbQXDucF1wsXUBzgrIdEQgRFAItoJ3lUEY200Zmx9Fu2Y5LRWEqyLKCqawkJM5gPUz8s9NiTUGhkaJ+XABJHAacmOXaDRf3y0Hu8CllMydVSGiqiEhdrTJTT1SW2YJxncE0UgDlC4dgYeCulhnJEItjoCMZJ9SQrA2MZVgn3RkwTQZ0YgiVmwqkRQmeK76BPDYRMNF5ZIaQA645VqnFBtjyM+X2sVhI5vEwNVUfFQaYGNncVAIE7tjWE6MQeZRrtQCFQyNYYzOlMIwNjzGnyI0Y6VlDal1LogobAajYnFNuQIGbsVnqFHZY5BuMFLEEBk7MgHMsJZsXYhKEWblV7XtCGExZsQyyogCukKx1FCIBsnVGEkolsFMJWWpl0oqVmldyaVWM0GYJFNCb57CwDaWzzKvFXVcBkHm4YApIHNIsxqoLLa9QbEDVTCwys8gKxUCeglTkSbmcxIHOgeQkNixtYRCqrMCyEB0EjYzoucKSpolWUMmMIFNziE6AeU0HyciTBc0oBSIBhCQ415a8ANYeCxYdqmY8zQxQMJ3rwsOp6ebShguYpi6fJhnGOltMbyClW+rcFJYqlJ1qQxKU5UZq7D6bhk0De6klFzxVMpQmNwpG7Jgk0zVqJA08cBkoBDIjoyrSgAEeCqtNhU0WK7TCcqQLEQuDhzajlFnRbFebskXPzYJKjGlqliSTOQCJ54ohlKIacaJMLHkUAZRT2E2pNbeLKZMGiznSIQECRVExVyrEZjWRPMI81XPPKCJXUZSbIqY6J+XFpeASwoJmDssQ4YlMAz0TRUKsUkR4XmhYjzPEU0OlIHJwYeC+wNcSOwslhyg0BRcaSxQXQBglIVGKq8UkZ8BLFARoEd/07cwHbZvBIuEetHwFUAYslW28TOMVDaHE0vBSrgC0OLLYRKd4Pr8flKSpx1RHTq4QF4YwecE9QdyLiWix1CrKXGFGDZGaYsFiqQWMo+u2gy3aLzOmktRESVlwwggIC6AzJRFMDCELO+dJVlZnQjWo1oWtBpgTPKdMF1JQllbIGBs0D/whkCFgVhEzV/aVJH7Ch6GDEmBXJpaaaIbNCAERCe6mKQczk5rInF5qkWmtUeoKwUGmUUpFVxUl6i+GEhRYADynhRBKI5nJsphBVKzEnZIObRg3427p6NLudJe+v2zp0IXx8qxdEnNC8zKIr371xJpH7bfu+SCyLbYU9ow8cknWyIb+eQ9MsubrXgBCG6fLUc8RM5sWjUVnZRqtP3t2+P57JT51SdZMh56Y2TRvRl1rOLefHlc/cCnPHZI24l7AZ55Z+CpW6r/ZNfr32WDpgpi7f/rzcq/TX9pqfPRF8+Rk4iw1f/Ow9Xz/4LX33vzxL2rn51+9/wfv/sUva0cHo+p69enB5qNvzpav1z96vPv554f/4n9987/8uvZq/8s3/oc3f/LRyotns6W1pZcHrS8eXVSuVL56euej3xz989fv/eVvlp89/+qdH278zafrX33VrW1XX77Y+PjLTm2n+mj/zl/9ZP/OWzd++sn6V1+f793Z+PUX27/5Vbuytb/6YPiNWqYMcdF7ZJaarjFK+2eVek0IpPcP6g2dOi3d/tJovM0pkt0X9mqlkD3QedVYxXGlRJ6+2lgSqb+uzh/bG3fmliEOn9ZXzVQo3HvVXEO5W0leHa+2eGhdI+1vzNp9Ydv55ZPllXsJJuj8rFajjK8aBy/X16J5bc94/mWjfJO1TIuP76l0jsFBOn0fTwes9sq8/K70sqL8xOje4/ZY4+N09C6koSSn6ewtZzKeNl4G5w+ErXj5Eem8ltqZoIfp5DZaFNI4SCdvGWIx29ovtV/jDglXD3B/I8WZtI/S2X0ERFi91NPrdjafrz2rd+8vTG8ij8Fg2dDMoA+T2X0pzcXusPMSxEPSuOmyZ3QxWb4RjIdRfXaKysso7VfVi+T60rz3whpPgupVMz/J0m59ezWbd6uX59WlCowmVHybrLZkcsxHPa9x21RHxvC8uVIuyMJq7werdlKEZPYEbqzy+EANL729Vrw4JaPTxpYTYWEdHwWrpYQl5dG35O7vDqdHZvuyeq02Ff7KeHZT46OkMbHO3mRbh8iIyOW9wj4AC8nGtwV+xWk+nv4h1Idsfep035LkWItp1r0P8SuRKDa5q/CxDC7DyQ8IPyx2xtbF22zjUskxHt4D6itWgMH8bQ1P1cqQDN6j6Um82/VO32ZrfaWGeHQfwiOWh8X0DSnbYO0yHb2Ps8vRcnv0xHd8vWwkZ4d1QvTy/fzsvOHIdHkXnX8NgU3Xm1n7qWWYasnLLzuBpbnbMvePAj8NG9u4+xBmhr20YyT7BoTVtUp2cb4sCuA36PykbIfxtX+yuHjqp8SvXkVnLx0g0aazGJ35WWqUWs7kvERn2ep1NXrEQ+XvrsSDAzuO0eoSE/FV57IWNh7ZvUa82J5tvyj3S2yxyiffkkW9mL6elw8VL8uL2mLlqTcpR5MrYvUzNFjKZzvS+kogUw3vKPtJKl3zbDupfOP1S+nkOq9/DkdVNtkDxtepsGeTH2jnscauebHHq9/gnhOPb6LSxyJZ4pPr2noqcap7b2rjiSKmeXEtbl4ko8vRq0pL5kEjfflqozlf+K+R82dG+br0dNH+ttG8lZkROH/VKBd8aSPdP1ltRQtyTz/5slnak77i7Wet1d3ESOTZ2VaFs+pV4+DZSqUWVz/A518gdxdVYHF51GiuRwjTznGzFhc4cAcvgkoQ1T4kl08w2HWCQM2ee0vNyAbk/LhhzpWxFwSX9ySx5puvcHcl10Bbz9nsHuYorF3I+a6zSOZrT+qduykuj7cO4bCecmyOPs9mtzgvT5tdMdssLdhk/VHp4nqBlxbsBeovx9yD1qPFrMWKhi4/zmY38cKc73xT6eyFsi4bn+HOdpyWgfmIRxtqURelR/noOl040cpnlYu9iVhNNp7mg/T0ZXXFTJVEo6/w2u8xdqH6x60r60ncxoOjxqYdmcg8eFFeMWIAUf9b8/p3R2mfnp1U9iqLxcwavWpuWSki9HS/tGrEyi4PvqYrAZP9onOwulea5Yl1eVpb9vLEt86fuVfUBNn6+MvqygeFiEH/pHm1Pk0z0n7VaIHUarhn39o1wOrXlnh/V1WfcpGNJw80a6u1Hhm9Z8QX4c0z//QBb0wFuSD9+7B2ycUoX7yu1UCvn6ej98mis1i+KF28nrWK3LuEgyvUPS/Qgs1f13w6Lw3R6DaddsPGcbX72ryJkrwDxuum1cHyKJp9R6XxuDnHoz0bT6atl6Wzu/ESZZVzNNoUdppUOientjGKr68vjh95oQpquzg5cWSCN+vh5flKOLcrTWt6WsW9dO2Kmrzgs8Lfu5KMDqzFjKwGSdYtzXrmZikdX5hZV27tyfETPYu9vVY4fmVG4+a2EyZjb9C21pvZ5KyWtOEb/2Ov/cQfLdauNaeDCy/5/2m7syVLr+tO7Hve+5vPmOfkXJVZA2pEoTASICnJVEtmt1tDWxGOsN/Al77wU/jCjnCHpZYdkmyJkpoCKTUJgiBIiABJzEUMhZqrsnLOPPP0jXv0hfQA9gVfYUX8rtZa//9x+0xtcTzqTh6zzVo52uWLPby5lnu9dTl+xvLPSodHw9937CvrQ3FwWSdfoinKeldh+IGuanJ02fK7mk3mw1chu60jx/evleFdlKN0eKXNflzNluTkOUvvls2MHL8A3F1Zq7zdZyt+HyhQTq7A8FY1hXLyvCEPytaUnL7o4KOqnQcHV2f1U5rmZnieow8LGZjpdcP3i9WHex83vDXUFPLgjtfsFByBo8OlWiZZ2z+9Fza8RWOFHN5CdsWvN+D0kVer54kPd3eWSGi9tje5Hyd4sfV7i50vE9MJ4w6Y3Qu9Oli1o72dNmYuWo1PHwYBLjvnwektrWveWlIcPfCdcKskPTlZciduo1Pu3O5gKzc2zPGvUR7H5xrDzs9v1R7t7a5eWf7pJxuffnr7L3/r+df/vLGzs3v5xuqPfrnyxRc9v9O4f3flo88H9dX2O7c2P7n11Y1Xbn7/neadO7tbV7pvv3/m17dmvNF4fK/9qy9OW2ud925tv/er7/3ZK5de/88rX355uHque+vOxnvv9qLV+Hj/wps/6/3P/9PSr764+NN3Xv/f/pdzb/xi8+OPv1rr8ieTrR+/OaKNIJ1d+7vvPd14dszEb+AGy9n5H/yRbtfzTmvvpeezZmux0j1+4TnD2cnly+OLW2W9Ptk6u3fjukzislnf/fpLWa2edpeevvSC9cTw/NbgyoU0iqdnNvZffL6s12QtPnjtpSJJJqvru6+8ZANvtLnRv3ohrzfm62tPX3qhrMWyHj/5xqs69qcrq7svvqBCb7J9dnTpfNpsLzrtkxefmy0tyTh48tvfZFR7nhQbGCUoYCXa8kgNRG7BnxEi1Awp7xwVkRFciU1M6tjjpVhHrkkisECXIxJoDynvPKUJ4EIlKxo2KRWar2PYEbGZg2cCFlsOK3GO0dgKob0Vh5uYM+WvA90NQr3glzyWAGFzdpYEsUY+9JeB61JiFlVrYRoV04tyOVU1jWHm6iPVKB2pmNevVoxDUjZT26qomhWd1DQNdHMQj1RbWlphf1gsawRLXZ+5VoXVXHbmtqYInNtomnUMAzPoTbIVjWFh43G+5AI11M2JalvrMhiOy64kYE7EWHYLiGTVzPI2CGEa1STYEFzIiBXsAlceSfycbAtOZVivwq4DIfESic6FoSgDUtgLfiAU9TXe4oLJOC7pOiUBCP3cXUiYUAFO+SVPMMkC450lnCs/0WydkhCGXsnPYBeRCGfockSZFL4RZyn1bFhTYUfbuvC9kmyLKsKBHOWrKfRyCGeqk6rYQDi17XnRgkIOyzMV4zMFVbGWIT9HeCGbC5lojOa2NcsaztfjbL1wYYXNvFjNbCQxWtjGRNckhClJ+lmXUjXL1woUVVhNy+XMJhqCqa1PZUMhlILaKO8gpmZyNUVJhdVMLi9crH2q/VrlzniMaNGxdIUKV3gbEHcpJzJoKb5GMAdRrSJnOMNKLDmw4Ydm7q9Yssw4kWFLgw3ukzJKpDvrYWzitoIbfmAXwYoN2s55xGtovB1GJOORwmeYhyVdAmCNhyCLOgqvMMZtmJT8LAcCBJFkZyl1qRYT2ZWOV4QOF2sGkQUHo3SjIKxwJMtXS4gLyvummVshER6ZlVwGhtlxtiUJyQDNquUU4RyIiepUytcYDc1yXiTQ0/3ibMXxvORKruSI5VDMqlauAkPRSHXzPHaBHi3OlJBXEM+rbuG8CrNJ3pGo5hIzgxcDmlgB/lUQ58ZfcbiFBNf+ijMrQWhSfkHQBhQ2F9vEjzX2kde1rEsFVV7H6LUwMBk9S2gLeTYPtjBuUY9WwbLhXcKp8pctXPUCm9GzxHVEosbeJsJLTJBKdB1aJgHKw2Wn1kLf5uFZoJt+aHo2nKRdw8AMiUm+KiGuQDjOOtZXY9uYqCVrXAaDUbEsMUwpH8nlAmBV1bKqIz0zUeFIdQzABfKGi1VL4AzzabFSEFSYKFWtDMHcJUPTlI6WiA31SqmpRGxarkkKUh2nulUgkNporDtaMY3pIN0AAc6ZsN4W5p7xIsU3CIlQIEp+hrgEx2iBrsaUS08YsUVp4PxIxcva1bknKrbFYJPHbu4uh9zXgmmxRWlgvEh7KwDVic8qcQbpVhCZGbsS8MBwXIkz2AstDp23hnCDBKTkG0B1o1jPyEXOIuCRyj+LYQwQnJNokC0TrBf5So6SCstZsZzamoZgamsT1VQQ5TAZ5l3AzEJ256hWITlTKwsXS4JmqrYomoq7qa1Ns45lZiFb46IFw7IvOzPVtM4tXG1atSpmpyCZ6LaEtiqWM9VQQTUs62O9ZBFYuHieLSlmJ6Y2L5aRb9KgY4Il53zq1xU8F0Yk9wIJt7hHJGsCuM4ClIdtiVcZ5TaKSr4lnAdCrxTnuUAVazixjhlW/pKha4x4LgxKdoYCH4ReiS6GFElRd946plRHLRV0rE680C/IOQ+HOGA5uBAIIkXd0TVMuAkahq+gMsGe6hdbFSeziqpqNcU8h3wum2kVGorGqpvmCQjUaLFZQK+CYF6u5MCTkE5ta64ijcAENwezjs/lMN0skSiRnZUrhQsqgCa2OZeJIXBm2+O8TYUclRsZDkrkZnI5A35JyLRoFTqquB3r7jyrQV9Ni5WZi5xHq6Bj+AphVAdLGq97ns35JnbLXqIm3jokXeaRyutYuEIDVAQdo9Z97spw08JlL1YzfwP4Lec87C1ZsunFMCVtQFaJByq2jm2HJ3YRrBrSpYzZsFHRTY6J9jtOrGHhSr6GeQdSUAarlqxyxrRXV3Y7QQwNz27v33jWenxw6eLk/Jm00V6sdE5evLlotopmfecbrzmPjTbPnDx7WYVB78KF4ZULi3or7bZPXnpusrQia/HeN151Ap+cvXh640qVJMPz53qXthetpbzTPnzxxnipq2vhk2+8akJvsr55/OzlqpYMz54dXHtm3lku6rX+C1d66+d0IHa+/pqO/cnK6u4rr9Bbt/7/rgjR/8cuQkdx1qhzXVlOy3pClNRRqAKPWKUZyWsxs8YSmLZbVEnDaFmLqTUyEGUcEa1c4OdxRJWCGKatJq1Kw0hRi5mWmtMqiYnWSvCsUSNGAYrTZoMqaSnJkxgbZTxeBj5y2mFQLLWJqhyC2VKbagkI1oFwGCAMdc2HwGIAdE1Y46CxNuEIAYigjQVEAAFrY4GhhRibRgCsZcDqmBsLELA6EJZgBKwOBYYGaF3VfAIsMdrE3CCIMTQBAxQBiEzIEXTUGF33rbVISR0LhAGEToae4xgZK30COMbOlAGxGDCrlU8kxcQ6w7EREGlT+dQKQrSSPgUMEiVV6KmAYmcth4ZDaqzxsRUEa20iaikmRlWCax8TazXHxsfEAeURKwhxTvpECUplZX0qBUbGWY61j7EFysOWY2yN4dSEDAEHGDE1DpUmDMmAO+sAQ8bn2CjDiQ05MsZArBJBrbEI2EQgCADDKuDYakuxrnlISYihq3lOGYicixgEzlFsIoGcgRiaRBBrICOm7kNtCAY6YM5YSIAMBITOQaAjQYyizsoQYwQgglUsAIYYGBVSCCwxpky4dRAbVcUUAoiAKz0KKcDOSA87BCgERcQggshZGTIIHXGm8qnBEFqnAgKxBcaVCQcEYa1kyCBx2GoZeYZjDJzyMMAAW6di4gjGSqmYOYygtYpi6BNijPWZ9QUx2sUccEKUNIFwIUfWQYGdR6AyxqNAEKSUC5gNODFK+cL4FANgPWZ8SowGPlGcQmOcR63HiZba5y6gSGtNiQ0ZNsYK7AKGAbA+VT4nWmrObOxBrQHDNmRAWUuQCRk1yhJURRQ7BwguI4IdBBjKkCAAAAWVzxCwDgEdM6IVQFAmFDmIoZUhRhA6jqpQIGsgAjJkSGsEXJFwZwEERoUUAWcRrHyKgYXAlgGFwBCMipghACG0MqAQWoRB6TMHAdCmSnwMHTFax9wg9C+CHEMAQh0wiB01WtU86/5VkMMQAVuFHhDEaat9ihnE2pia5zijWtmaZzEiVplIOE6AAyZgEEOojYo4JAgpZWPhBMNWq4A7QRCENuKWQma1jrnGmBhdcq4DTJ3VnOgAEwu1wMYjxDnlEykolZXzqfQwts4xpAKMrVMetgJj6xRHJmRUVZoTGVBqjWVE+pgYYDhUHsLOGYGkx4lVBiMTU6Kk5VhHFDvgONQewcBpn8pQYK0AAVVIkVLAORczBACkyEYCAQcRMIkgVkNKTCOA2lDkVEidtRADFQqAAIBAxx42yllb1X1qDXZWh9xBgCjSPgMYOIRMzLE1xFnVCJzW0BgTCQydw1DHPsQAOKdjxqCFxphGAAmhTptEWAiBdcojgDhobBlzQDHWSkYMUoCNkqFnPIKd0x5yBBIDVIgdJUgpnTCDIba6EtxySJxTPnYMYgerEANGibVVSDUjVFUmoJpB7Jz2sWUQWVdFBBCEjas87HxGpZS+0AIR57RPLEPYWOcR8y86PGYDhrXWhJiIE2MsRzZgCADrUe1zqqXhTMcCSgkpshF30gCGQECRdc4jJhTYaEDRv8wcCGYSgZRCDGpBgTGAYxl42GqHoYoFUZWFQMceNRpQqDwKgQOCmIAi4Ij7V0HIaRlR6IBDsPQpQg4BW/oUAkcQLGKGIITQypBC4BB20mcOOOCAiil2BlhQJhxBhK2WEUXIQOhkJBxFCAIZYAQcskAlFCCEjFYRBxAA6CqfQQwgcFVEIAQEoTLCAGNilAmE8yi0zgUUUAiU0RGHFGOlbMytz7BR0hf/KihkjiFmjY2ZxgQZY0NqBSNaaV84QaDW0qPAI8QYEzLLCXbOhlR7jGhpBHUhh1oDn1hOgNTap5BjaKyLhAs9opX1uAsZVUoLXkQBMUoFXuV72GpLYN5uE1lagvNWg6nSCF4mEXFGhkGZxNgoy2lWr5OqRBikzTqWheGsjAJqtWa4jALirCU4rdewM4iRRbNBjTKcFmFArDaCl3GEjXLOZu0W0coBmy61sTOOoDKJIYIAuN/IF+H4D//Df/1//NmFd/65d/Xa9ls/v/STtwfb25ffePPqD9/86vd//5v/8T9deePHn/7Rn7z25//nlTff6l++vPnLj6//0w+Orly/9OZbz3339Xu/+62v/8X/c/Uff/Dr/+YPX/7O3177x/8yunh+/cNPrr7+g8Mr186/8+4rf/v3d3/3Wy/81Xee/d4/fvntf3/z+//07Hdf7126cvbjj2/87T8cXb6+9asPXvvLv7rze7//wt9+98Z3X3/y9de23n73xb/9++PLVwdgefS25XWnxmbwHgJrnB6VBx95UVjQrNz7Zey7zBDSfwvSGkBzNfwF8RrG9M3Bx0HAslCVj95NQpg7QU5+jDyvwml1+L7v1Qwayd2PwjpOiS133k0Ck+MQn/4IEB+gUh39ggZBSeZq96Modikheu+dIComNAa7bwlMXT0k5ME1njlEhuThS16/lKuz6MMrOPNNfeDdvihSjOzAHrzA5hjxIXv4fNyXi7VZ/Mk1Mo90fRB+dQ7nIUZDuHeVTigUA/bgWe8EpWdnjY/Oo1kr78yTz1fJIsZkgPaviLEoGnl4+7x/RLLNYfOTLTdbzpem8Z01Ok443AOHV+ionq6X+c/n89uIXhXqo7x/h7dr08WRGH5BvBaYPiXlh0X9bDW5hQa3Obss9K/z/lei3VgUu/r0y0A0XbqPyw8qcQ5lt9TwS0bPM/dlenw7qCWZPjWnn3phUqTHZPoB8M4h/WXV+0LU16rsMTj+zE/CBRrrg4+8qFZmfTL8JWiuF8VddfRl3FzJiUr8u88xNVXCRb++YlhJKxncu2K9BZkjvPM8soN60Zc7/4bJqfF0dOsKIBWSJb97FfCUpRruvCjMGIGU3P0ayycmMeEnVwDWyFT8/rXAnOoSoKcvU7mANBV3XvazWV5TyadXLITI5eL+FQozIDV58jwuUycy8cXzLK2my+n8LSWnKBbZ4We+GdgoqU7eF+QwJ5fo8A2QH8Fg04x+7oohjvzi5LZvTy3ukMEvgNspxBU6+7FZHBF2jqbvyewY1oP09E5Q7Rmyxka/APZRWb8oT96G0wPhnaOL96vFEW2F88Ftmu1iskGnv7TmofSu4ulbcrrL43U5uQWmu6zWqbzhZnR/pewMvScttrudrk/rjz14cBV7h2Io0O417U/QrFX/ajlfmYY7MXt6wTQOyWGdHFzH5EgsOH5yHfEBXNSDu1tle5TsCbx7zdUO6EkN7z5L4F4803r3a4gOcBYFt7d1e8QPPLJ7HUUHfBCA3ZsCHqEK03vXMenDKgq/PFe0C3s6PHzfi13GYPHk54lXZayFT38IEAYEmeN3CaeKFXL3wygwGff07s+CcDYVbff0TQ8CyKg+eY/6uIJS7X8UM1l6vtz/qScWOVxj/R84YyBjqv8LSqyiTu9/GHplpVpi8IYl05KeIZMf2tJwGoDRu44UhUf03ocBmVf2Qrj8/ioZd9LVWfJ5l0zbhB6j40uiH5eN1L+/HRzwbGPYvHUGDFfTlUlyZ5kO2xztgOMLZNDJm2n44Gyy56cbg+SzTTjcLJZHtXstONxg9IAebeDjrqkN+aOtYK853x41P+2iwVlb2ydP10j/LKWHrLfK9Za8xgAAIABJREFUDpZNfcB3tvhut1g/bn3Zdr1nqtaReZIefRKEfp5PyPg9IDahfVT1P+W11arcdYefBkmQ0rnafT+IwqpakP7PQb1TyB11eCusrVR63+x/GrbEzGTm4Jd+5BeqIMN3gL8K1Z48+thrNFPTc7sfhw26sMod/5zV8ExV+OjnTHQAOFHHH3vtRlqM4cEHQUBy7OzJO0x4SiR1//PzvppaqeDTl1mZQzbjd14K5/O0XSafXAOGADL37lyiTkNd4J2bJCuhmInbz4uJylbyxocXjQ2Vl4dfnaOVQSDDT54TmcoauvbJM3xsy+VZ8+PzUte1n4V3z/HcUNN3uy+xOZovqdanF/wxTJcntY/PGdk0wTy6ewbnokwW/V9afbdsXJS9d9D4KecXWfFhOX1KW/FifBfNH1O6SmafAHWn8q7i+TvV+DFPNtX8Mzd84tXjdPEYT++goKtGn+PsNvCvourdcvDIb6wVsztocI/Xo0Wxh4a3SbSsx7fJ7HPXOZ+PP3SnD8LWWpE+gIM7vBXMpwdk/BmKV/T8Dpr9GkQXrH9cx0+fw+AwWWTq6TcJGkIlwi8umNqUnRL65DoKj/hIgKcvCHCMlKN3bhIwAE4EX1w08YKOCNm5HsH7ZZqQpy9RO0BAizs3KRgrxKPPL+kw52NHn1xn7NRljD5+AesRwNr74lnkUsNQcut8mTg6t+LhFYZOgST04QvMLvJwPvghkDkQse6/i0FpOFYHHwc8rfQSH7xpUb9k59n0hybPGWvCybsWzGXA5P7HIRpLsOYN3jDotEy25OGbOFt4fAlNfmnMHNbI7OCTwA0MOuNNf6TcieQX8fiHKhuzoCEHH5FygmsiPbnluWMl1sHxT7A+tP4llL2lxgO/1Z6/9N3/fOFHb+/efOm5f3j95uv/+Nkf/eFv/cf/dPlHP37y9Vcv/uCt6z/40eD8hXMffHD9ez84evbZKz9487m/+4fb/+7br/3V31z93j/tfP21C2/98/XXvz/Z3Nz69OPL//DGybPXLr310xe+83d3v/3t5/7672+8/r3dr31t6xcf3fybvx1vbS/f/urFv/jro2efPf/Ouy//9XfufetbV7/3w+f//rujaxdrnz185f/+68nG5tKjx6/9X3+19/yL2eHBbyRodP/P/2Lr8Gk4HQ8vbtPh3JvNRlsbKw8f4kVx8MrNZP/Em04fvfK1zqOHtfFocHELTcqo1z++dqV+cuxNp6PL29FBz59MHr/0cufJk/rJ0ejyOTZb0El2fOlS0u+F4/Hw0nawexr2Bztff61+fNQ4OTm6cqnRP2bDxcGVy+293ebB4d5rL3in42gwHF7eBrNq6fjw8PJlA2g1RKxprQVqBOyy0Jqg/dyeCRp2lA0YaGBBZD6gvOmMRUWfhK1SMk5OC7Xs1+x8MWCwSQUsqh0TtZX2STrzg7rMqQ9PpW0zZor8VMA6CEJVDjBPjGVQDVAQ5XNWc4faLvManRYDhj1VF9nJpCkijRPGJ57kjrAZnSYFMbQ2V4MuoAYnYzuvx2pURkYVbUsBFlMwbRmscW0Exy0AtK2nbp5wV8rYwFmkCST+CE8jhSGsT+Gk6ZxNu0D0KDelbki8EBbCvAnCHjTAofrYTLvOwGK58kdIqMIFC5iFFfHnXYiPU5JKtRXBQUlnJV0C8yoCY+U2PZKWIDXd+mScRyqH5mxkhhYPSnyWhYtpnguz4uFMg7kWXWN6Gg7L/LllNJV4VLl14S8WWcpxE5oFqFIWrlS2ACAF/pKc65AMcrPsiTRfzHzetUSqckG79dG0CkxGREfnXjM5gWWiKc7ApO6izEIL5nUSjivKvQlZ1NxqsTOvtsvIEJrr8RL254CWeBbhYJ5z4Y9pXgOem4DZshQZ8VM7bUIvN8ygeVgH+4OohcexTIxHBmjWVKyEQQHHTSByLayb1Lg/LX3ijWgWAkEncNxQnjZJoU4Bt6VrUjmCjmIequKEWorj5aLoM2CU6LpsRD1TmSbPBwQiAJcpOc4BAmFHZQMOjdHrAXsyEWWhtmI5xc4Bu+zBowIA0I2HB+MO0BBuC9rPoDKsBeGwKqHQy77br4BD8WpRDDBUVnRsvuCo0niZeikGpXC1iS0DmtP5qiVzwqeoXK6iNDVFkrYskZAvkG3MTB7C1CON08LWwhnIWzqsMp3XdFLgCoI8dvWhqygpfJCMClf3pyBvVEuLwUifA8mcWA3SCCYTJZnIPZeMMtgQY1o1K18vXFpX4QIDCNNwsQSsLslhblosQmnR5yaCPHCLI85jQ0KthtAPq9LzQE/bJuewzE448nUnmpxOW9zXKIRyAAO/LAJf3B1ma40wqrIeQyEUic5OKeHKBSQ/Jb5X2pYHTkqXcFNn5GkKfMRrNj3iwIegRchJ7tNCNkJ7LE3E1UrQOHZAu8Uq8PvAU7KqK5MmwMKyrcIhsBai+gjMGkDDxYrjQ8ZLiZMRzHyJgqzt/AFADoD6iJ4GJHeDS8IfASKdrFf+1JYghMnMjWMLOW71wDQiCtnatKrqonSyXvI5qmyIkjGeC2c4aA7cLCKGT7s6HI6yOcctgKWp5tRb0mXG1BhGy2UBPNrP9aoXV4vFlKMmdpXLx6zTGJeW6oz6Syp1IeqVrkt9lS9GHmsaglw5xl5bVwq7CQgaxRg20GHl1ngCZ/mI+WHBkBnNYr+pC8tNz0VLZSECcpSpjheBLB0y3IQk4HQkauBoEoZ4HlUh4GRiJl3LSxxN4bgFaGmCCs4iztIsoHgYVoH12QBOG4oZFM/huGm4qSJX35VYFLOOL4ZUerCou/gIaWpQPDPjrqFWNlQwQAwtnCjgolZ4tGi64IA7ZnDSd5MmIC6vg2AILHWLJoIHKTBurT48njWAgnozNKcap5KtIT5OCyvsqk/6hVVALOm0x11uw3VVFAxnCncpneS5El5TV2NoFQ1Xq2JMUOnEkloUPp5XoEPxWGaVFywrO7OqwmvNwUnawKXjHTvJYzSq6Ar0iqLIqVgyeoF0gbxlrU0tmMC0oZbTo6k+q+MCOmBmdVIbWQ1YFoB4nOM4nKCsboVJ9axrwxkl0i4SGM01IHTu1cmTk2DdG3tlXQowBYum9nNINJwmIJpLyNA04skgY34wxGnN+XDmpg0TFoBaN67LhgTIBCOEg+nCj6IezBsQkUlxQjHVpA7KAfFpWdRCdWBcTFEb0f3MCRS0dH5KAQWy6bsD6aECdJjra+NTtyTgToo47Eajw3EbUgS7DB4XkEERKtMz0hdqKQCPcihw1C3yHibAkBYsxxgSyCKrjl3Beb2dFycIMuIvVWWfWoTREth4fN/l6ujy5dberpdmvesXO1/cT/qnT7/xChkuasNh78K5+qiHZ8X43Ga8d8Qmi/4Ll6P9XjibDZ45i8d5rd8bbW+sHTxxo+LouWv+eOqNpoNrF8RhvzEe9y5tg1nV3D/oXb4Qp1Pem47ObfjjqT+cDq6eDw5OVh8+Gr14YeSSzv5+7/x2UCyCk9GTl17hf/pnv6kuQpwW9QePM+aheZYcnUIL77/02qff/oM0roveIDk6LRGnpa4/3Km4L1psaYtXcSAGk+SkX3GfjWfJzl7F/MO1s5Pu2iSq03me7B9JIvhoFu0dVUiIFuyc56XnwVw27z8sicBZmRwcYWmeXH/+gz/8k0XcILO08eRpxX1sQOPB44pwo4HplUohY5DrlbgyOD+N6QOnxhoQfVgYh63DcFjoCgI4D8E9BBfQWndcQYgKyMBxIRWUiZe9vCK7gdVID5TUxDpoDwsFiHE2wvcJmRWGuUEpS6ABdYMK5A7IaYjuoeJEEa6PCg25RpQPMpMbRQieAFchyRgZOmR4DskN8osL4OPcMpgilFPNGJ1CkFlF4E3z81X365IFYlKRhdMY4xSiKZxxQsxT7Y5KjskEwQxUzMNjSVJikNrGnzwDPpFQwymCGSwxolPAUlvw4Jx89xp4t4IEpxbNsSIULzBOrSQcTAvdlxoTVBpzUgGCoBsE+g4wqUkdGJYlFTa34LRUhNFiz0f3NJBOQtsvAUAqB66Xa42zc+3F1zYAcE45e1woRFQFTU9WGhPXS/RtiWxVQHOUS8qRsu64ggRBtPDBfQAWpgSknynINOT6uNSQVpCyE+sMMg6LsYaScDJ5zryd4L52jvUABEhSjw2gVoS6xQvo7Q7eLUFAp8gaohChfagNXIQeX3yaw5FEHh8pW0CJKBlDbX0EZy+Bt2K4oyjHp8BorpAQowoWmLHFTfRe2+xUENA+xBpoytgIggJKzPTcmInRlIGJhgttnQ3wU64eS+67YQpn1lLqFlaNtAbGA7tc7lBdgEEJplUhAjfM9VBbSvJnlrJrXcWYmmgwlQpTO5RgWqU4DNSjwD1QxrqFNgOlCLUzDWdKGRjYHVHekYy7VLte5QQzmTUDqTACBcBjU1Efl4APXQVJgzx91f0EuMwqTEbAAuQqTEa6Av6SufMK/ilkBlvHeqBCSClC+05hoeEEZJ9XxFjLSc9Jwq02vA8txiEevQDeZuhUS8rGWkLmoEcHTmLu4f434E8pmWjHyNBp7FmD6VBrgw2A9qjQFkvu25PCaqSRjqoviD1WgLmhVIpowsxxqSpXKR3A+wyPCuDzYWpSrRAxQylzoD06f3lDLweaCXecycxVmINBBgqIqAzQY2T6CgB7UtnCWMbtaQnmUnIsytvc7BsDwLAyBZCY6J4EuaooR2NJFlhCRAoIp1Bjew58fgF8ZLEkM8TmpvBCNNN4AjW26+7WNfsLDTXMCJ47hQCaIzJWCvLTM/zwiqeNczkkIyAxAhkmC1AafIH9+or5mcLYZYCMYEUZKR0eQQnsGXvnCvoQkQKlMJjKivrWCNxzknKtoT4pLSRAQTjIlWPI9iJ414ESaeuOKoBJaQg4rSqNgRkn5ra1uQJcn5QSMWusPcwVYjogHb2rPKcksv1SaqwNcqcFlA5Ww4jch+VAOWZOK4k9axAZZFojZEYBugdgbh1wRxVxUEJqT3JVQQ0JnkKruGGUj6CrrCHuZfOTDrxf4pCPSlQAgwmdQJMTDfOb4GcdeF9zwoYQFEASQcYSpajg9uQCG66JCiM6hLaCFcJ8BEjuJCeX1E/PwVuVI2RmQUE0IXSMYOkqRG7YH59RH+Y0EjODF0hijKcApUAR4sYSjKuC+2ChXa9QBPPiiUBPDDQ2c3AiLcJqZuEgy0hE1U5CH1tgXW5NTypM1NyakSxFENFZxAaV58nUmqNMMQ5yBQYKYkTxwHMPHarsXNNhXhJhDdVHpcaEFAcBemhhZSrkxkpiZjPr+oVC1BjLesAhrJFgI1QZnoDjF/DbARpq4NMh0JApgNkplABRsnjevuHBfWUFH2qtkHKUDaFC3oyZKL0141IjgXtOW2YtEWNlNQ358GX3E8/2JYT8FCALNKZsCLSkHprcdD+uwQNjMR1Cp4GCkPWh1lASAYYZyJwl1E6UzpwxZYAe0XKXGmVPSzBXJfPcINczB4kO3CPiTiqI9ECBVEnCXL90c1UJL0T9hE5yEbhRqWbGEGKGEuVaWReDh6y8L6kHptJNlGPUTpSeG2M1h098s+cYhjMNh4uKBeXMmH4phc8m8/i4J7HwxvPg8Ngpe/dr3/zgj/87AxCpTP3J04J5dDSLDo6lRv6aaJ4XkguUV/UHDyvuw0o1Hj9V1Nt75upH//aPJ62u1x/FR6cVZlSDxoPHknm47a2f92Xo4VkWHR4rItgij3f3YSmfXr35iz/570seAEAaD59IwlFahE/3nbIW/sa6CH/3T/+0c+9esdTefO/9zU9uTTY3j7fPZyLyi8Xv/K//+8ZHHz/61r959S//cvmLL8qVxjnPXQO4Z+TG99+++tZbT77+6ivf+fvN9z949M3fWb9//z94wReHuzfe//DMe+9PV9bWb/365hs/evTcjd/Cg+tE3MH85t+8vv7xJ0WzufbFZ+d/9u5wffPw0hXJBNPyt/70z8/86v3e1Svnf/be2V/8Mm23j/nW8Uc8iKUZqaOvIj+ykZgvnr8g7j5CE3/nfpMaSYnd/SjmtAiSSXFunekqu4cOn9Z8m0eyeHinRa3iZ8go6gqTw6/m+3drnl+Zvj3cSUKde/B0/vIlNhiokX/4SUg9S+b5/ldRzU1905u/eo0fHLnM3/s8iYvU59n9jzuA2jYm4PgKySnXfd1/kY8Aih/9kZm0GflqGvDd60xjvxpWw2soC2N4+/c5isdPH4sue/A1oxrIP8FPryIVaPXk20b74+nMCXfykhgKudLz7tzUquO1dn5vsb9q2ckCF/0XcFZXfkoPLomBJ2tP/rjcX/caX6mQPrlMiiCsDsz4Aph3y+XF/IOyfxD458ji42rUC5boqa3BanuZTKvhTpLeccvd6fA2Ozms+ZvQc8Pi2YvR3b3igB/u1Wjo5gM+u43qS/liuV3wEEspP5Wnh2HICjs0h4+TwEzZNlcrNTBZFLfQyVG9vlTOH8HTgyiGU17L0wub/mw+2YmO7oTLS9PsgTvYqzeSGVUNfHydKAlJiXZeNkSecZ/8jjELoGcna2b8HJOTVjGeDn6HZWk7fvzvYZlrfXK6hE+eRWjOptiMrvOqNObx/9CbTvqDKlqHD1+G1BJZgcPrkZt2zee/BeoOjHr5mjt4IcwKHczIo1cUwRv+V7+ttSvmo1FLD18k0mCSur0XeW4XS9Phe0iXpObSo8dhMcE1f1Q8dwamcyHt7i/jbOHV2/nJLVGltA4PyzN1Ggk6Nvtf1dSBqm/L3rt8Mg2SdTlrLzkIvNPZ6A5fjARrgP5tpvZtu9mzNZZd2oIPB6MHtXJKWmA6fMompwEP5vBaB1LHJovBr9honjQaxfCemA5YLVYi2+b7G65+LPaWzPRSXp9+3dy+WcSnRU+ebpvpNcRSkLf43vkqOPmGvvMiDnZsKQ+esdNnqOqJTJj+dRnKc9Xuq/d3Dzc6fHfVji6h8JQd183skq+OroIvb4IgNdlwegMfXMXxMT8M9PgaIYcX1e1vkfXTdHc+3gYn1zzXgzKA+zd1I1MH4+OHUaAKX6ePbjfpZB4uzeTlsxAD9cX0+F5DMEmm1cHjBJUmRkfp1y6z3imbgoefdhwGHFSHX0aBk7BNZo2WJqx2//DRV206Kfyu2X83NJA0OovyzBJazM0BOHrSxLlFCTn6kMFxGW1kuhOr5ba6m/XvhkyWQqvDh7HOCD2LGl88Y6pl1xgEDzdd3qiD2y+6dBObHVBXeze9XqCXDuJ7F6U8Czunv7e4uynFKMuKwfNgvqLikhxth/26qT8dtFcVFWImk51NnXWF7uFh103P0OTot+3wCso/K7n/8Ioqt7B/TI827HwtUvdecL0L2B05kh++iIebpvmodm9dFud13LePpwePGx6rZIoPPwuiJGNill+74I0n03vieD+OYcpS9eR+Q8gpXTV2e1kMh9mX8PCgVYvTch8d7cYxXLTB4N/1pr3FrJ8uHX/uRy2pnpaHT+rLuIftZP7qdfFkv5rHR1+GTTgzpXn8eduLtYjm2dXt8Pg432EHOw1uJbT64NcRYyqoNcnDi5Gbk4WUo+dw6Rro839LOR49fULO8ocvWUgxWMCDa8i6FXT7txWlcHRadu3+y2IO1NJU3HneGaFao0ljiVgZHUF4fIVUvvRyvnOdzQCuPfzD4jgKmo8XDbJ3VSgT5MNqdAOmEW0d/Ldq3ihHX+pOcP9qidqI5N7BFqiE5tOTr2j11K6uTw4/9AejJNq0jKVyezW8ezTZDQdHgb+EBvdZ/gRGnRTSrLrxTPz4pP+oPu57Ec2yYzLY9eAa+d3+3oWD3pNzG7OfVr1RrdHKx/fw6DSowwFYo+VSI8iKk9txf8ffXBsObuGTXq1VGzuSV5fPho8Pp0dJ/wGt1/LRU6//OGitpf7xkplcFaoXp8Wi/xpWw/Ph3u/C6tS4xd6aG10n9JAPYz2+5qnJFr3/O46W5fA0v4r2noPhlI6FGVxrwr35vPc/En57NCW2i/af9fTEAoD3XlReeZl89Q3LZ1lvMTxjRjeJLjBQYO95XMzWxL1vg2roRdP+Bjq9xNUpLITrXycGWHq0+/NIO+Kz8uizAGkXyv3sxllPKzRFu5/XXL9KNvTRz8XChOFyYVqUQMD2iqOdplwQWoenH3PXV52t7IXPHm5A9CnrLD6FoIR1OTl6nORjymtzcG0VWYUHae8DP5VB5JW9+0GxIM3qSXltjVJMS7v7YW3Rw+3NbPDPaCJrXdG/+cb3Nz74dHD+wjNv//TKm289/fqr/eVNSXl91L/x3e+vffJpFUXrt29v/ey92fbGzbZ3U89uu/Brf/E3ax99Mrh6+fxb75z58KN0dfnkmYuTsBXOJ6/89Xcu/Pgnu//Vb9/4m79b/+Cjyfmt82z2Kmz2Jo+X3vvy8htvTi6cO/OrD678lzd6168cbD1TMVHLxxf/6a1z7/yzCvzG0dFz3/3+8Y2b2dHhbyRodPEHf1Btrebd1sFrL6bL3dnW+vDyOaEKP18EOp+vdEfXr5xcvpCvtPNu+/ClF6aeGCL11O/aety/cW1xdmW6tjY6v3Xy7JW0VX9QzeHWWtmsjbe2Dl98TtbC02uX0/ObY8pPod6L1uZrXbVUe/LN10wjGp09c3rjKnUqymdRNZ+tdGdbm70bl0dnNlUrefrN1wSvakkmVoFft/U44ytQWhc82Suby7WW9WmRnIc81PUo9TeQMwY/PsJM0K5okIV32aORSnjpn7UerrwireVjHji/rsNljZZpA83YFc9AFDx4bIjPGl69kfrLxluC9aiMO1WW1PjjpzJoJE0U8yLchl6ovJqKulp3iQAD2Zm5eu6BoV4e2hqaqHwXkEW9TviQBb2iqwDNXHMoI1ehxQPWqKKE0lMYnZiWRHRE/L7aZLuqPGnXYUI8c2o6AxhJRE5Jcpo2UaarAYeHUYOyhWv0VVvFat+2B7BuxxAdOnmSdH1yTIKR6uaY5K7Rqxog5otWcwE3vGawSIJcbPO8gGL3yNTqtUg2lvKwbWkDNeop2PTBYOrtH6vOSq2uw1h6WyTwikYz9Zac0GW8GBNkg5qq0zl/hkUiD+NSbAs4mdODHkga3hJqxQuxgWkDNFnKnvFhmpOdE+eHYeIarTxc0rhNk6jwNlFZw6E6UqtD5FeMHIH2JPPwFOljniwS7IFevlkIPnI0MysjFcBemR35tTLmnPVAc5IvOd+eZhs5i8BXGI62NxhVhB27+gjUckYHNDk5SepSzO/wFes7ak/l2gwFkrJjWB+mHpgBecTjSSvy3UCujFFQcHeiuyMUmCgoas2SbLJAyGhF4wYhD3dBZkmnXovyRqckyyQIq1o9B2sBPJ3BuQQ1v8HT5KyhTSxi2Vwq7HpQm/STfEKp5YGpLRVklTbxJNk0LIHVIOcnA332TDOYhTVJz9KQlcmycusxf/AY9eZ4pRPVdaOWsQ0iIpXEBTlDmRvB6NQ2S+gvOD8sV8y8VIWfPQjPcC8FfFStpITNGH8K28UUgRE2h35HRZUPTouzBWUzxEaoPZpT97DdMAFDforJse1kKsp92Ms2Kwf1CYM7vOWEJv6hbWQwTjk9NZ3FkEQ579+l68SvKOvL5Qx5OeX75ZIiTdOEE3pJeLFOeBFfRE5Qcvsx0lBs1OtxHiwbuObV0MK7iAxn3v3HEHm8xqJmFXUk7eLYz8MVjUIYpxOvSmmCamTunYO8BpIoT5aVFgg8OYYG481mg8yDcxC2yBIYxOcA8gm6f4AzRTaaLW8WdrXb8GOcRZva1UXgdml8knecQCfUG+RdmHF3YEEvqkfmFLR7IFGOT7F/lHdcWlWzABxHHUQzkPTlkvTNsWuegpr00llUTCEHmA2ZGFRrBUczmwxMU+V2seP4ImlaMePekekU1ptz0c+XjWLmALG+l1A8xbUj21A2nGN2kq+DOp0FQRVuI9+TjWYu1ohLK7pzBOOEdUiTLvhlj/tVHJZsm+GiojvHxA/Yqp/ERbDm0BJukBm7Ejgf3RMs21gJuIobubcM/KarR0W4LOd+PXy8W8XNWsNFQRFsAi80Yb3yV4EppXi0D8KErIVNNBPPEC/UiZ+KTUS9kpEeiU8Xy5CBsVweawFzvHgoOjLwGD2GtaFplIQMUW04qXmKzR+QjgzIvwiCYYXpKWhNdGRq035SzExgOBzLzsDUdU091d2xS8DQgQOMxkkSoGOQjFW7JGhhO70sZlV+/MRrVlFMxQBF/aJTcXjq6pOyi5pwXFtTYV2TBm40UrfqoeMB60/d8lItlPGSxOusRue15QI2BRzPxP6JW132GyYJMrFNQppFSzpp6xNOHndblFge62ay4JtEJK4eFWTbx4MxPBrDJI4D2Vgug6ZGbVaPMrGBywyIo1PbXqrFqr4k6Sr2Q5W0crZCTVT44DQ/U3hkbEVql+clNceyPA2aKgacndj2Im+60B4vzpQKmQkpHrOODQBlB64+c3HKSR/VT+eb63emvfTcGYHmGPXU6gL5BaFHoDmdErjA2T5vLdpeYE7K9Sn2coZOzMpchrCnygO/WflWuGPYneZtF9h+uTYinqmHWa0raRdHXpF0K92O6e6h1QTXRZPNknOO1P9f2t67x9LzsPJ8cnjTjXXvrRy7OpJNNjMpUtLI8no0Y3sW+4UWmL8WMLDYAOysPWvZHu1Chm3ZVhpJliVREoMYu0l2k+xUXd2V61bde9/8PnH/mC8wA4y/xMEBzjm/g4KkaY+Ua1G+s49qZ5f7LV4mywqP2ACeJusgiPTd+cX9iLM27/KpHAGxjEJStda0HraDW7fhrCZLo6St2t2SLtNQ1vFAo/kA7x663JJe1BFZd0nhOSLapt2r7GYbROLk0vbBM9d0Lzl85qlidcSMjoqpgOpsfbUe9R+98YppBSeXtk+uXc6g3vVkGrbP1tZmywtHN65Nlpaq+f7h80+3inPcKAFq47b1AAAgAElEQVR11WsfP3t9ur4421zPlxYOblwd42jKs3ty2fRb40sXT69eKOYHJ5e2z65uU6ujMp0rT/YvXNa95OHrr+lufLq9+eSFF+h77/HdR/+9QaMeGEQUjhooCt4CYEq8OE/mlm/eQqXae/VGiEJiq6nsJiSKLUlFy+ydskfn6TduxGAP2ioVbY7OuSMpa83vHMzfvn9//o2CRNBnU96SMOAuy8IOuZ+ye0ez/+krIZuVhmaiHSOBPBu3hnMPH/YfP378lRcYiUNLcxEjRkpNUhZRaxV0iFPnvIHKttsMmOlxn20GqZqlmHBGMPEVNJQTErXNZBQENuehRg2iQcf5M4cjQSvgq/sWDBIvwQywiFkrZEEDikjURccHLTbPeAzNEYOMOIFrp5tW1MRBc9KXXZbTagYgh95yMkEBZYBwbljHMIWFVbDfEO2D6u7hyAtYy5qQxJppxWrDBoYrGvrPnmzWsjFS1Tj0yFleUBIpYhtYKfuUt1aTsSMdTb2RJacBBOBcYKDa1LqqXdZZH+MmZ55i7Jg3Qb5zNu8tSaWDFHLgnbCI9RSGGaOMOQrrgicNtQY6K1xlghL1YDsqmgbigPBiygIFrRMx7W5mjzVv8+k4yzEDPMREY8yahDWnFhxr/UKPVUWDpWRcEVggKkLijGjykWgzOPMWG0/d1IUO1ZSLQIJ0shi0iMmdxp7wMrWiBtAxX7OwIMKw1DBb48gw5WiUn10F/WkmmOPdGS8ic5bCnuIC0aioN7RUSoIGRpDhmlNHezXLODKw2VSq0IkSWFquNbcEJR7ls7D1wd5T+SAPydTQQY0LJw3DgePaMnn7dBu301QQSzqVqDg7V2ROUWVZpTEvjaac5xgCilqJag5D2CI6IiVW2AMhSEU8topGLDvvUQbsMELHU49BIKnCBjlXsRZ55HBD2SYpMMbAWh57BBBBvDWbVSOXWx8nZgwNBiEDFjMNiYkTQ7Z87FlMy1ODsQk4yQk1xEqGASWIsEaUUAmGyIx7wLffO0rKzZnTjSO9lDfUEkdtLavS9MZnQienDWobEmZshjT1pKfZzNsWroXGVY0hI7EjRzlKHGtl7GxczD9IN/ViRjTQiDWCOUAFDCz3DVp478n1YnUq6hqQvmIZ9oBTmnOKAFWUYkS8wFMHKCE8QYrHOCQgwJXXEXGayZoIwalM2PlRQgNI4/L8PCQMcE5KaCzRikXNbYVXhZdZgQLCSSNQiTQnEIe4Bi0ZahNEJRKS4ppHFCpPiIxJE8aOUxcGAKGAasvCGgnKYMWTgHLs0FQgRA2zsJH0y7Mt4vRMOkAIxF7LnFMBHZ0RtwNuHBXIDk8Q6yjPptxBiogzdczJYYgLNLvk4MxVNND8zFOqnLRi9mDyrDekkSVjXBtes1PD+6RiOiSPTvtOy6abYSqRx40syYxSGta4mJEkx0ww7I2rkRWSwU5iCh1FbuYiC2vCZKz8xNFAUoeCJh30W3WpWAmQ5V5BWRLJMdUazsaJaDMinIFGC6owUk6ZblTicHbSC/o011UKkMaWEXOOuOAI89bsbL6b2ClLDKScB4HNShxSTjWjikQe1ymTjI9KXkgBbj2+VCWlC4wioaXKsZqiFuZ5KuGHJxeauZrSc4XnNFNWNBxLTWDKQPsANzwv54DBXUuKlCOOkeHWStmcLCuP056FlBFWeoEQ7VdUpZJ8Xn3NStMEqqahxbBghhBiGCyZK5BBGHJBJiyxpfFBiyQXzNTJFq7SusHEsYQhBIhlHVrNLsCZCSOS19RgGzIEMG4wkxypU6krH/SZZ9YiE1Iw4dIjLQOCCKooC2LcZNZpwDk5q2IHTSJg3R1lT1TQQrVqSoelpDDTFjsjmUbC8VbKpiHJZqinBStBMqkuNm1lGa5B6AWoSQhZu+JTj+YnB9ebUYaxZ5RrWVuAahQDMq5tUMI3Gj2rKMB0rqG5p54SYUXtQXjz9BqcP5mx2NOkFJnwaUP6jagzmuTlhVkbO2Y9lZD7gocAJw1NvaAl1oxAynFFnELKtcPiqC1ipAYxeXzmCZaSKKQBxiqKKow5NyCSFXKUAs0TCA1mhDI8Pk08AGg+QsRbQizTFbYAYxW3IN62krCIlFBT7JigOYEeo1aiq6MQJtwGoELGURgE1BJjAERUFFAQ4Kai0/ICODZpDXt37ncfP773B18XKUwMSVmSkBBCXQTt7M6hPZ+lX72E3TTRuOBxw0zbs4lsr3356dLp7t6rz5csRrrMZMu7PKlcJttkUk3fuZ39/lWJZ0jhlCUIZhTJWdyV++Oljz8++cZzMx3XjqWyxXEeeNFggf/bI0LyX4VpcO7V//v/EukMCzx671b70aOoyVbe/2D+1mf/sPG/fOUv/yI6PNy7/uxX/uzPWmenXKDk84cLn9/xnK+8997Wb3/9g6V///r/+5/iBw/3rj/72nf+U39nJ/JV68Gj/mdfOi4Wb9289uab/7j8P7/07T/vPto9vnL1ue/97eDzzzEjo0f3R29/4Ckd3r379A9/8L31P3n5O3/Vu3vPjNqLv3p3+dNPYCBvbn7t8B2yrAvSqONPoyBU+ExN75KBnTI4Pf18cW6SBwvu+N1kqSmQrY4/ay0449P0bCecT09Epxx/tgimeXvFPny7HVyexbB49MWiUDWo68nDZGk2pe3s7Pbi6GQiL8NP3uouP1WEtDz8tB0XaQCL4/sdejZDi/X4Vm/l0SG/rA/e6owuZaMaucfzkE9Q91AfbiQ7hw7t2kd/5Hga2Hft3WuRJPH0QZoNMK3d/MTvb47qoxTfIvf/rRUVwO/De09hmYbZQ3U2dEjjhXN/sBVnkzz8JLx3vZaw27oX3l9nZBaXD+vJHAbQsiN+uC7StGYP4y/fSKWMu7vhowVhUnn+pMg6HPV078H5B6o4Y8PVqvjQV+dwqI6LZnC6z1vclMfY36nXvzoef9Iep+3uqik+dtUx2oDj5qQ5Pxn2SJ1NSPMpWo9n9Yfu5LzVHZbm82Z6SEI/8UUzftRb1GldsLMv5FY8LW65w7P2RjDzj+35oVwqjzF24y/7y3pmGr5/O1p6/QDfZePzYZuftaIO29/i2SM3PIEPnwH1Zwyc+P2nGnt/rkFuvDo3+aKHm2ayHUzug8UDd/91sXCHwAOzdw3UdeydPluO010Yj/WTrfbh5/bCuX3wGhjeRezY7G+1owN0pFW6EpwfuEEOH19IDr4stg7I/VfscAeGx/7xBd79gtPani7zyTFeSP3jS9Hh7uFz6fgdECWunR+fPOwiAjtXiuPbrbjKFv5QH77bcRxsyOnhuzHnblieVY+7GgABi/EdGqfpXD8bvx1VjA0H9eQjyJzu5Ofnh32lSRSYyR0azIrom+PiV6QUcW+kZh8BZO2wGB/vilxFvag6uU3Qud6aO5/9Bp+ReC3IyjtBU8MWq0VzSTzuN/S35FHPVuvx6H70sAtnc8J9GhYkn20itONtFNwfouB34cNYlRcFn8jTBKXLgbrFvKhPt4T61PsA7l21+JfRflyVW56Og/PEzVaC9LTtUDPbjuqPHJbgyTMB+RXeZ6q8QOCRL7iZrgZFCZh2T9bb2Qc2jNzOjUTcLg/Pzz8PF85mrJee3V6c259Ez/qb7/RH21W3Mz34sCW3ioBlx/d7+HgWLtenn/YX7x3JG+XBW53BRiUmxelHyfJqKnbz4we9+DCDW/Xhrfbg/rTzmt97uxOtqs60GH8cjxZqfqqOvkjQiYbXyuNPeF/MxNfB4dsRWsct04xvYtaaxOPi5IsO24NRbxbfvaoZ7XW/CO+tUlgn9VmV9bFmlh3yo5XgrKzZ2/GXr+Y4iQYPo515rppgslvmawzMu2CPHyy2ZjaTvwo+Xa34XNL7Mn44b7Wg1SmZtKpmEcKPwN46mYWo8+vWJwsVn0/o2Ox3kY5pdQqLQZONuP/AH27DrM/lD5Lbl1KyFkSfgLvZ6cPhQp0ZgMe3wnU+Uw/s4UF7U6Rg353tBSv1MWF6fGe4VKeekoOP4uFLx2QfjY/mW/TcnjTne/F6eYaYOv9sbrU48xF/9F5yCc3soT573OnDI5FXx4/aIpu6tj3/IOmdHro2O/iotQVm9QScPIz77oBUzclOa65I3cAcvc8GedG+NM/vryfxhJ6oarYYnp644Qw82Z4H99NLO+T+y7Z3AMtH4NEma+3Ik5kZL4nxGZmfuL2L8e6T4saD4N6NclhgfygergfBw3CWmuMFdFY4POZ72/HuUfP0Xnj3q9M5F5OTYHcpIDtUHOWzec6dC07o7qU2OsvQe8Fn29mQIrwX7i55lml0cvAFFSdF9M3x4zfRDMb9RZV9DFzl55qT8ydkVrY6QT2+g8Gh3epPsrfQxCUbcdp8GRQpStR5eYLPT/kKSGf3guaEzvXPTn+Nx6a1FUztPZdOabs4qnN+ts9bcHL2MJ4e8At85+R3+tT0O3JsdlQ2lb3qNMvpbDeYw8cHu+3zJ+Kp1ikfb7vJ6jA973jfTLej4hMfOP/oRQnfwmOnZlsYnMYVNZPVsKiBKO2Tzdb0Y9uF9sHzwr4Lc2wm293pbY29n63J/KFrabyzFU9uVT2P7z/r3Meohu70glQT7qAbr7H8Ee4ruHMxnH7eDCt+/2nD9nCtyd5anJ9K6d3BqlCnTf+D/bf7Yl4PqvT8g7jbV9F5fXS3BfcMtOXxJ6yL0iByx29FbgF3SXN2E3eDtDPJT+93cICkNONPaBvm4Rvj7BfULsZtoc8/RlHcRMcnp/d7luJuUu5/zCXUa+3zs9/QtC/nQZ5/FmKue+eT2YOWt3oFFrsftjn0g/b44E067XQuwv1nf/Kj6ItHOoqv/PQn6++/99dX/8+v/sc/DU7HZtha+8mv5nZ3JbBzOw+G73+MiN1487cr7773vY0/+dpf/j/h47161Fn71TuDu3eTcjY42O387vb7DKy99c7a27/74YWl5779553dJ3rUXvzw9vzHHwVlIdPzrZ/+8l0JVj74eP2Xv/7xwr+/+jf/OPziyweydg8nm++/J+uSNuVTf/+jbDgaE/Iv0sGa/Lv/sVpeNq3W7d/7V7PeoBiN7n31K06KvStPHT19pWx3Z+vrX7z8St7vmTj67H/4Zt6ZK+bmbn/ta5bRk+1Le889k/UG6XDwxeuvl/2ejcI7f/DNvD+Xzo0+f+MNy9np1tbejevTwXw+N/js618v+nNeiFv/5lsqiWbD+Ttf/ZoJw/P19f1nrp2PFurB4P5XXj1ZXoOc3fw332JYRbTBG8L1pcQV3gpYz0cqA890cAdxo+Q1hls4JiXeEGAgA6rlKjTzMqoz+1yX9gkzmmxT1KMRyuUKBIsxIj5Yx2Y5lmUKnm2THuJWwysB6LMOnLEVohcjARu5TtRaWxQZejrhc5iohl+LUAdRquQyrpcFrrNyVNTzjtdltpDl85ypsU3OsmUMcUWiyXTFW2/ruUwPS1pV5WpdDCky5zYaV0sW4RLIbLLmkVOmN1NzOTVNOZ/XI4z0BMTn5wuWuTMf5pM1gFxjktPZvA2qs2YwLZaEh4Vn42xJMXuOgjxbNxA4lZxNF1jisyRuzHYseB2TClxPdCuIUY63mcRN2DF0jdm2DESFLoaSliHW5kaXC8OF8ZcCQU07LMBm5FskZDW4EvFAR77yz3aocIJbflkQauKo9lsxbhPJVLBNml6YmMy90GMh4NSibcGkj4KSr1A9iDlW4rLMOjIpz/P1mY4sMuN6oayGFPpcz03TgeflbHJBszi3HuYrqWoDpk+aUZn1EQaZHlWzBSjS6Wyr1B1L6iJdLZoeofpE9fNyCAjIYX9yuiJolc/WCt8qSV0VW6ruIGTGupvWI0Ndofr5+TKRZVasZC7OiWvyxdy1HCM67AF9uY2BZQuQrxLhtNigZjkJYBX1FNwKObcicf5Ki0AbDK3fCMM6ZWvUrcQC1lFb660oRHmYWHOtizAMuzXaEGGTBSsILzIfizCu4cUwBDlrQ3utJYBiA2/Ww8QWyVxjNlpQwjhU7AJFAgSRA9uc6NQGs2IFGdkgeDZZNYCm0szOLiggNIT5bLmApGb4qFx0TWgpTuv5aRnbQKfjyxaxCqMiW60cqQE+KZe86iBsZ/VylrUBryeTi5rxQjOXLdeONQQe50uuTDz3s2K1yfpIZufTS5UPNHBFuqZNYIk/nCwjO6RhMYPXW2QOc92gK6EfiI6b8hWklmMOlVhBZqPDywxdi8UIk7pm1yLcQ4ToYBH6JSmRkiuk2uyEVY4vCTEE3Bp4QYCFIISFnPd4TUqo2CLSm+2wKdgmdotBXEzoJQHmwwCUfATdCo9dJpdQfbEv60quuXKxFzVHMDwbL3nqz6DIJxseeO2C09miEfW57k+LZe5R6ek4XWqoP8c8zza1x95Ek2y+FvXUdCf5EjO4gnR8vmqxnyKaTTcUgtpE03JUUVu4eFwuIs00YdNyIVXCYJJPtgz1lYuyfMlAk4PwqFihOvLITaablnMniOZXBJU+Cmq4GaIulVTJC6iZC5MmNS/0eAI5MmhbkAhEvBCrxI0ShpW4yJtBENYZeK5LY8+wBVciEqMOTcGG9AMpYcW3ebPYDooZutHiLUCgYVdCFEEhFFnjdEBC1LCLslxsJeUMXg+DtmPI4Q0OE49hjjqT8TIlTZWt5K5T0qoqNpuyh5A5M+1pPW+wL3Unn6xgUeflYmbaBTVNsZQ3c4iYc93JZgPF9Znul+fLSNRFNRinAx/mp9ViVi4w4DMTTcphyfXEdot8xWOjy+FZ3jdhMa7ns2KBeZjbaJotKKEnTacsl0FUp3IB0GVmYxmFFbwcBTAXEbBPtwXSrOfdVhD5qtWr7XoLBDAMarFNQQADocFTLQa17HlyQXCvor6ymy0WQim1uEh1wiNa+We7gjqWOHghEEhFnZqucd2LJa341UAnTKIaPNOm1LI2sBcSyUwnztxmUraAKCfnlzSTuSYgWylNYAg4LhZM0fbMp9Vykw2wTM+nFysbW2SKdK3RkSf2qJxXVddxkNv5yfFCLIvpZKtEQQFNXWwaFRnkTpphqfqambxcKCbzLMon6UYGghyDOl+udMtRN86HTZXkUp+Xy9X5kMXZNF0a656MYBEMLF6XHGs+gma7LVXF15BfCaJySre5XwwlrOSct2si8rkcweZyj2sdLFmwIpJyKjcpWaA+5EFHow0e2QwvCr8VctPwNWiWop6eBotGr7Ux83Fb0w1GsWEjArcCplW4juGKEE0RLTu93iYSBokxV7uW8tO1zS9ffQ1wdnLx0uH1q5P+qJof3X39tbPRkkmSW9/6117ys7XNB6++0ghxfPHSwY3rk8XVOgrvfeNr48U1Ldjtb30LhOxo9cLOKy9VQXS2ubV74/rp/LJpJV9+4+snKxuO4M/+6I9UHE1GCw9eebnqdM+XVnZffP58cdkl8aOvvbyzcQ1g+Om//UPdimYLy/dff4O+/5747/9FCAFx1oe0HHWlKYFETT8WrjEh84KHKtctWYBurDIU0mqhF+gShFiNOoGrQMwb0A6bzAWkXF2IVO5DVs33pM4RBdWwI3zj22Ht2mGT+YjVi3NJk8GIVgs9YSskYDPshL4GES1HHalLkMhiriVtBQQqRl1pKoQs7VgMLPDAxkr5EhOAk0r7UvoaJg4Cx5yuY0eAQwDZsKEAeQRJUjaAM1MlkXEiYFaTtotQA5yyCeEQQIRZUijEhVMuMYRYqp3uOExq77RqaYEA8jSK8oYyZqtWS2NMI5s3rZBS4wwSvAYEIV1ibhmHoq6IUJh6oDXkFqtUOuDouSYIOs34qcY0rnMoNPEWaYMoYKgRFhNiEAYAWIxPqeC0ylEgoQehZZQrrpU1kFHjKAwtYawxFMZliUkDCRKWY26oq1BTYuwtl4G2ItDYaqwrwhSOam4sAI4kTYMMYg2MmlCVBhvbgY2uaOhplBuNJK5hhI2rMdW4bajKGEUgqhobUm5AVHrHBK5RBKk1kOimBUQzs4yIoKSWxwjQuNCOCVDCGCDEIdWkDSKbOwxYyxJLua8xLwlE1FY40MgbYCDlJYaNgYCLPACYOdXQY04AMQ2JNAIae4Cloq6yVkqZN4gJU+LAc+qJakigEDHYA8w1hUXgiOCNQpR7TcWph4iqEknFsG4sJNxAVEsrMC8ZghQYjE8YI8goHmmmG2s8lY3nmDpIeGUDycu6jDwxhjYOCkCx8hYSoRFF2NY0bCCDvKxV5L0D2JSUK+ZroCFlFkikbENiDYCL6qxkHrSg0jWLLcorpDjiFYbc2JqFDZSOlTMqIVQ1ddJLBHSDLGDMQ6HCuvKQUwlCIzi1ROTCIeELz20AMMUNCGygG084phkDGuAa8VIYxEAFuRPOMGiBZEjXEHAqUuAah2rBp9pD6ivIxhJoghyOmtA0GlMk68AUgEAZ6AoQYUsYOOkVcZ6ERPrAQSPjQiMmbONiQ6ijyumux6wBxqjEcOy1p2FcKMqYqVstBYmJdF4lEWUaeCtizaADDuGoNBRRp2NZgyAkdVnFjlEHjUWRFdQ2juCotAJiW7GkQczRulItZyW0DpGwEdRglaMwN2EImhzJAhkQWc5ow7wNtMPEQQoCRzhTDoK4LDEWKMTSccw11Q1SJULO0UA4wmSFrI3KCtCACh9azrhhOpfWMlRALrBtKLOGkKguPbeknnJgLSkIyaTxFBnImkDXjFUOk6iuILZUWO7DAFYwhsQ5DFWTAG6sJViEJXEsgpAmpfacuwpEHhKBrCZtH/mihB61HLfYEcSTQkEhoPKJZ8AgYE3XE18aiHhLCY88oDTOFRbCVbDlBPAMKtsSHBiNGY8q5nAEEItLjSh1TRwawELkHWYNgbn0XrBTgynzmooTgDBpSiQ1IxpZQLhDuJaWYVYxDCmwhBxTRlldwEAyigKPmTQEVtowymvLcWgMkzWgiJUlZg3gmHmKRc1gQZoGU4gZDxxhYW6hi6oaC4khChylXCMMqBIsMtCYqM4q5n0bKFWzyCFTQmUJqzBm2tY0qBGxrJpRiWBQMSecoECWyCLK6wZirjGTSlEUVDMlsQgKYmVMLAlLbzHDBYoJsdoFxgIf6bxiWLYU1TbmjEaFcxiTikSIG+2Y9R2PTVkjxMU08Ij5pmYnAloKDA6bwCqNMA4aaUuPgAxUBVlgcxg4AQ21BoU88EpjCmnNfBP4gouUQ8a8ouIUOA+cwkFDfKM9I7yBoJYWIpFyiDhQmJ5yDJBRMOQCEos8ERV1ubaQyIwxiuusji0jDhkHQ8u51ZaAsPRCYlOxuIbcsqpSsXPca1vToOFEaU2grEwotKpZW0OmozovpAeUGKd4oh3OmAYgrBQH2lQ8UoBBVk5pCIlqEAhgaB1SzOIgLBvGZeNRoh01stRKQoesMQREVMNW5ErbliVuh03qu1FRtAJbopCWg5Y0pQ9oPdcKdA5askadsMlcSMulwX+xKGqhL33tBFHDjrAV6IY17Ie6gDHPFwehygH1aqHPXIU4qBfmAt/4kBTLg1DnPhbZQpdbxRirF+eYrxED1aiNsEfe/UuBRr/5v/7vG2/+drKyuvnTX1785a/ThaWL//lnT33v+3e+8c2v/h//4dKPf3bzW3/8xp/+2ebPfzldXFr57ftXvv+j06WNCz//xfN//Xeff/3rr/7ZX175hx988nv/+tU//4uLP/2nfHFx+d0Prnzvh6erW5tv/ublv/r/Pv/6N1769neu/eOPbr/+r57/67+99IMfTxeWV373/tN/84PTzYsrb7372n/89p3f++bz3/7Otb//weMXX7j4szef+ru/Hy+u7KG1J/9EWQvWY7/3TghH3O3q3Y9jzi2dlXff6xFnIEaPfxHiEJiJ2307ZF2gD/yjW23hK67N52/3iDGe4d2fBUJon9mHv+vw0KhD+/BWN/CKmurub/usqnxIH/5EQIF8YR6/HQuh/MTfv9kjdcOwuffrrihrGpp7/9w1GLY5Bw+fgSmn4MztviZOsJqf0o9fAXnXxGf0s6dpE1AzUQcvgFmI2dQ/ekUe8XI4ljdf9OXQxKf48+uwakM7BXtP0zT2Ygoevsz3Rb42i9+7bsulpjeLP9vyeQeBqTt4Gp11qrji964HB0GxfB5/dFVn62Uvjz5dwVlPukNzeA2ejvIlNf1ldXBbisu8eLs5ehDNBbOTg/joU05ieP6IZe/59qo6/ZDu3YnpBis/NHtfRO1YqR21e7vDW/BsT5y/7YNNNH3PHHzZ4muk/kTv3YkDqZpD+/hmIkOdH/Pjd3iwBmcfmt077da8yu7Cx3faLZKbCXrwfkuGJj1mh++I7mI1vYMf3e52ujkwc+TuC0BpTw27/bIjGpUA33veyRqPqd9/EeusVc+K3T8gRa2lY7detMzj2uC7z0FS4Ak2+y/jpsa+8Pe/yvNKtyz9+CWLINAW3X0uBDNXYLP3Kqw0IhW49wabmSap5K2XDCbeGHD/eQw0KCx8/AKpHeAl/OJ1OvPTYXH8U1jMWISrJ59E9QmWkX74fls9UPIy3f8hycdBOK/3fiuLMQ5os38nrg8Q6aKDd0V9TwVX6MGP8MlBwFfZ9Nc2PyEJr5980UofYdQnx+9Qfc92ttXez8XxXswW8fQdf34oOyw/vcMnjwRdYPvv0PJzH1zDJz82p4ft9qg5/ZifPRZxR/PJBX53ox5MggdDcHg9n5tFD+f80VMQj/kpd/vPO164bD6+s1XOp/x+H+w/4ztH7MmcP3oWumOaRXb3eYBnoOjze0813TP5qGMPngPJEXrSA0fPMXsaZLDefR2iiS+79Ivrpj+VjxO/9xyWe+iwa45eoHoCa4zuv4jx1OkOvfNM1VPV4/PdDxNhNXfV3bf6JK1Qlz78sXAQY2ce/zYgxMLMPvi4CwslSXP3tz0ya4Ku+vKfuhoQhNzub0OKPMybnZsdWFoq3b03O3Bck0X65IfMeE6xffxOhJUC2u982IK58yLDx0MAACAASURBVG26/3PuThVdIk9+SFMTEg6P3yK4UgSAhx+2zJm3F8PBOxdsulYOpsln6z4dIjD2R9fQ6aAOC7ZzNXwS58tn8c0rerKVD7Lk00UwXZB+3x9f8sfLdadiDy+Hu918ZZx8tGnSS2X3PPxyxU3WCDjBh8vwZAMkZ3D3mniymK6P4/fXzewyaB2wR2vubBO7YzRe8/sbNj4lT66wx6vVwn706bqaXGvax/puuvNxV3BdT+nhW0IsguKO3b3VjudNcR8+/qwdo8Jl/t57bcF1OSMHv5GtQZ0/RDu3ukmnynfgo886Ca5cbnbeaUtclyV78ivJh6B+aB7fbLfbZX2AHtzqClcB5XfeasWoMjV8+Js26cNmH+x8lLSTpjqwOze7wisH0MNfRZhY2eqxT64KV8PK6f1XYeERLcC9r7JzX3Wz4ObL1gcWVfDuc9gCUDfwyfOkRIBn/svX2RkoF6rovRvKdxyt5OeXUQOBMf7xcyjjVaLCz57jZ6gcZtFHz9R2aFgtP9+mNRHqXO29jCey6Jvg5jPiTOSjWfT+NeUWDC3EvYu+imuZHryL6ju2c0Ht/1IcP4rpKk7fdaePZVsUky/J6f2ADenRRyz/xAVX8OnPzNGTTne+Gd9kJ4/CSNSTB+T4Sxn19Omd8OwmiS6D8T+b/d1ue1iff0aP74dtls/2+P6nMhqYk0/5+FM+XM+P3uX7DzrdXn7+OT24H7dZOdsnxx+LaE4ffy6Obop4Q4snA3DwArHjuNDlo68jl3oT0ds3TDsTR8I/fh7zI3ScmIOXiJ5B5eD9V6lNLQzpZzd0VJJTCR6/kODHetp2B69ClWEA4N1XsCkMoOLO8zqswDQEu89Reu4mHD15iegSIIc+fwXp2hAqb90oE8SnGO08w9yprQPw6EWkbB1O937IlRIsME/eDl3psHO7Hydg6kGX7P1CmIOGb7G975NpGdIWOv0NBpnlyO583FKnwA354c+IPzDJut79aTBJIxLDk7dxldEWyB7dTKojRFfE458QtefFNj78kU/TKIrUwYdBcUZCXD6+Fef7JJ43u2/G+S6OL4KDH/mTWWcumrzwN9+9+JNf7l955qm/+4fn/vrvPv7DP/7qn/xv2//0iyfPv3j1+z+9+J9/lvYHa+/+7srf/uB4++KVH//sue9+77Pf//3X/vQvLv7oZ3s3nt3+wc+u/PinVb+/9uEHV777/eML29s/+fmNv/ruF3/wBy/8+Xeu/OOPjq5d3fj5b6/+w/eLdm94+85zf/ndoytXt/75zRe//Z0vv/a1Z777vWt/94/5pfX+e7ef+e7fFK1e/+Gjl/7Dt5889UxxsP8vsiJM/+jfuW6r7HefvPJC2e5l88P9565rCE82L5xdu9jEydnayu6zz5gkqnqdxy8/X4eRasU7r7xsQ3m2sXZ6ZbsMw+nayu7zz+koKqV48tqLVRJP55d2X3rBSn6+unz01GVLWNnvPnjjjaoV6SS+//prNhSz0cLO888rjGeLi6dPXyy7vXQ03H/++mwwUlGw8/pXMPeCN2KV4BYWrKHrEoY+aFJ+JQgihaBhW4JFngsTLEOUYIaaYBWjPpe+oldjIjWBRm5SGjqMTbDgUZcQ4YMl4Od44Gu0xWkLYd2IbUkSz5mRiwAPKGM2XPDNXMjrnF8SLHEMKrKKg7bzAYnmnZ/D0JW6n9u+Ia6s5ysbVcZZ25q4YeWRwsG4GHmDvevOXKtiqmyScz0PCShda6r7tUcaB2f1vHFGq25p+w3Wueudm57FPtdJWs5poVIsp+W8Aki75Lyah6KZmN7U9IwxjQtnzYLiPvViWo9qh5zp5OUABiAPkgZtBEK6gFb0AjeMhKRi24GgSsQ6XPBG0Cgx6EIkmOa+wJcDxi2LANmSjKg4rskyxwxwVuErLYybkNb8Ig9ogwPA1hkhVoaarjIUgDBo5AaFMQtIha/EGDWEWrlJubQk9slIq1hEoabrRMVMNOfNYg6DBuHCzOV15JwvXT9VbUNdlq4aQc4VAmoxg2FNmtR0Z03XIVzZubzseGrScrEAiXV1VS9ULqoBrFxvZloKwgq1TtIeZ6pQg6lrKeLKajBzXYtRYbsz1TbW1aA7qQee2SKfr3zSYJ2bwdR2HMU2aDV4LSDUyxFgC5Q0pVxCZIEKrPnAkUUKGUhaNV3nyFrWtWgtkCYPFwGeZwwr2TV4PeBQiVChDYmQjXoarQWRK9icDYegJjyOG3gxClHJY4PXBUOaDwlcEaLJ44ElS5wSx9uarVJHfJgYukqIry2f6WHjmIZkUqwCj0tk03K1IqRyrC4Wa49rQsdmUGGQYzBRC2UTQeZn5WqNce5orRZKgBtLczOqFDeYpnqQ69BTkGbLtfQTjZ2ZT71Qnue6l5rIY5yqhbKKEFRZtVpiWQNS1aPKsgaStJpXMLGhLdEm5x1IgBEXBG15Aoxc8GREKXfBgtdDKVzNtyhpI6YqukqDjjOCxPOWzGFKjBwYv9LiZS4uCNpDzJbBOiF9zLGm8wANKfYqGVm4JLmryQqEC2HYTOUaxkNGXCOGnq6IANTByNrFgJkqWgVmEIZ6rONZOTIElkhMqgWlgXXhtFn0XM1Me2r62vsKRNN61Eg9RWTWjEpDnG0V9VBzk+r4zAytBsaxSb3msU8hT+v5kqBaxZXqFaTJsTxVAw1Q7eVELdSWO8jSeqmCtmqixg1KjGqXzNRQa6ixmGWLNkYF4p5tMiasjAxfwVBCQapgi8EWDVCFr8WUKsqdWCeMW8xdtOBsiwWBEWvIdXgIK7QtaAgJ1GyL09CJ0IpFALtMcB2swKoTBvWMXQ1YaCk1bAXxBKAIBQsOtylzlVwFdr4V2oJfFDT2DCu5SmACoC9wcjobcuQrNUp9rLyqq7nUzxkCCtuZmY6CsALtc9UzrKnqXmp6GuncDia2Y5HPmk5e9Qw0hW/PyqElplBzk2aIZTlWg8x2NK1mrpU2w4a5mU0mzZzx3uhh1vQsLdO6O3VzBsLSJlk1b6mZ2VZWzaPIFqJvo3mgGI27Gm5FAawpa8h2wLFmcwitCu7LuN/gJUGw56JiF4SjLgy12CACNbiL2AolXgcdQ1Y4Zi6IldhgKMAyUHg7JKaiLRisYMYs6YJkqKsobEUNXScuYAJW+GLAmWVdSJcwYT7oWL6IbQipT9NVLfCkYd4s5I4rjSrTS03iEM70qKhagLlZuVxg0RBT1POFDZQnuZtLTWQQzFD35GzYQsWsWSlRUEJQ1IMcJAaR3PQzJxpiUjc8L3uEmzRbqkHYIJeZ4czG1rm8GlS2paib2lFadBFXs2p+BmInieYjj+cZJrY1p9ByQFVNliBaDIImDdcwGlCGGjH0ZFlwV8mecysBdXW05OGCTHTKFnwwBxtMw65B6zIEBZ0DdIlSr8QadSMhqzRe8niRMWREz+IlhpCRcyBYRFjVeJmJESTQRvMWLzCCjOw5sBkjDE7XNx4/c90xcrZ9YXz5QhVEk37/4JXns95c1W3tfOVVK9j5yurRs09DgmcrSwfPPpVFcb4wv3/jqaw3qDutRy897wg4Xlg7evFZFYXj9Y3jy1tFf1DM9Z+8dKOIExuIB1951bSi6dLSwdNX6yiarCyfPHOtSDp5EJy+eO1oacuEYue1V0wSToejRy+9TG9+9N/6RfhfabD+OKkLj1E96gNtLUJVNw4vDM36sGScFI1hdLqyLNIUQVDMD/e2rzx+9oajJJiee4TKYY8q4wiZLC/LOSKe3iy9hcoZTLPRkGoNMNbtcPfqUw9efhkbLZsKOlvMD4WqDCLpoC835/CFuYwJUiqHULkw5xuLnMtHA4+J9pwlDkKgAaNcE1HJLe+14hCWTpIEUmwry1kEsCjoog2IUkA0npEYCNCUPqChx6IWS0aQyhiqcCAj4xjXnuIEUT2T2xihxgNmHaExgATWliUo90wFl6lzlYCwdpJEICT11Ccs9IRDD6SXjuDCusgwS0WxljyU0TjzgYMRwRVgTYMSzx0l6WTJVwMY1KV2ieUA89L5ABKLZbYYPZHRRAFkXOw5QKJwLjYcurCuelp3auK8AyHg0EqDDHXUY54ttx4n0cmYtrClADvMSgukkVRHDtUWEIR61DXOQxgEtY2tWHIWAmeZR7AVlKWXllLQJRRN5BYGVUMcqLBALQw9gBBh4UlUijVojYeQOoh5AoizFZaEG5rUfGQg8MAyg2kQaQW5AoSGRoocLUEClfdcI94KixoJjViQGMsk0gQECmOlfOSFTsTxcrLjWFXByKPAyiaxk4wMoVQuqM6XoQ8brKABErHCEQhQYKUOwcl6ZwfyooKBgzGQDaTGgFDiaRnDdMmoGIa6Nj7yQiHaaBc5biU/XokfEZrmKHYgdFIzVBofQ2Gs0LZBmAMa+MpxIHAAM7bsoSgZpVXDPccyMo1llHrCFR5a2rVYW2MoFFhGRhsOBMUdwGQq5qxT3qAACgIiDLT3nEQo9x1Nl5GzBDjsGBaB9RoowZH04aBCSUUgUIo7jmVsGiAAxSwGyGNLBZK5s9QSrmM9R/ZWW49mGCJLGxKY0ECEIKIUz2ZDnC+ZUKUNjDxkIKiZNwqFUNS94HDU2W+wVy70iGKZGcQc5EBWTEwPNiSgmimoSYCC0nhiIcc8lWyy0d4pmPWOaxh4USIMHZY6ttBbZzFOEId1aQQOAOclW3QsNlbBxnEhDeDYeEYSQmwmNiCiFbMwRxEPPWFeWUq4R7jiGxBQhQ1QjpMQ0sDVmpMAClmykcFMG8S1ZzghUEBXAxxAHlSk29ABthpbgzjXICDGUpRQH2KmsOHIRgYpjLDDPB3FB614nFHpjfDEY5E7H3sKVKzLntX9ilvtfGAE06ElGnmOIcsWgr1B53BMW9gQTzASNbRYcU5pUYzMdAEFqvQusIximSsfeEIwSxfkk3Z8qDBQLtGEYj5zXnqMdGJEU5dIsthD4BsoeOQRy/gaFKaqUaA8YbFnQNdQUuFIrMSCEr6qfWAwC2KtMbeAkhgwkLJ1CJFxjluAReIdQMrTDpoa4cKL2JmaQVx7IUKNkS1gKCKHeMnWAHelQoGyhCSQwaZxksaACOys4CTVDBkYemk4Gm+0HuIwq2BgXOS5gUxZJzGtm9ini9rERqrawAQKA3nlXKwl5OFsTd6XYlKQyPkQhN4wizRzzGI+O1vhTUdjC6FjiCqMGwMjG0In9UXxmWxPMy+9DY2EXmhsieNAS+dq7xnuyCL1oWcUdRBjU76CUN0AhxsmUAsD7SElmFscFWINkbpuSOQJ5pED1ismCVZ0ruED6y3wjjhKg1jVnhuEuWh4u0YDS6A1ThhKOkFegMhiHMTa8SbcwLCpgcMNZCJ2zmODiWw5D5mHwskqMllO54CoEn601N6xwlU2clAgnllMARQuaAAvz1aA54orYEiERAkwNFAGZAyC5kLrfhl6b4gBIZA1xE77ANOiael02RFcWyAAEC5QDNYGRpCrgJ+utHdM5EsfQkcIywynwAkfWwTLWjEYICptYxkXDpKargESaKysNhQHiP8X1QooiY3oljRQFhDjOYwoDBFovA9whDPfN2QeeUe8xUBiwZQxxAYMCRcNSxg1BMJacygQC23jOZKEoZwuOhI2FMNKCSCJDJU23EvKIhvlM0V4OhpS1XhKq7kOmQ/5jQup9b62EMJy0GdaGUxVJ959+vrDZ58T9v+n7b2aLDuvM83Pbm+OP3nSu6osXwUUAAIi6CBSo+4ONod9MXcTMb9mjNgaSmppRtJMSxojR0KGaoGiQQEoECBMoXxlZmWld8f77ffn5kKt++6I4V94I5518a4Vz0ppnCqM4kZVCQAASKsFd87Nr61Fuo7jXBAaz1ZhnAII4ka1uXrh8EuvK4qc6YQDFFdLSCmASVQrgappvbrBeBpJEwsezVSx5ErB4coKuX9fPzr6//vZswICoNv/719/+Q//j8LB2eV/+Kdf+8P/WNjdfzVu/evOAyjVl/6vP//1730fxPzlv3r7zT/4Y7vZjYkV6q7I1I0f/uit//X3cJDc/osfvPW97+Mwe6W7+Zujw2r3+OLPP/jK7/9Rae9k4yfvfvPf/45z3gnMQqD7uUTX/uGfvvIf/rCyvbd69+Ov/c4flJ/vXos73+091Dm7+YO/+/Xvfd9qDy79+Gdf+53fr+wcxF3S+6lMTlR6JPp3VHqm6Gkzrxbh4TE/Z+d3Nb6Vpn04+KkM9xUOJgArkaXsedi8S9nzGHbz5vs0eZywMEaKyTjiO1H/PZCcA7GbNO9qYCfhrV7umarTTvq4+1MZ7gF+nPXeA2wv1w6O87JHTs/EULbfI/JhxMZy8BM+faHoGBk76+ZuhfSlvnPZf1qUGf9Xk92vhad8Atxni8bxnNnm5s6ytVsTGZ1YtbFdAzF3Hy1bz5dUjO3NeXK04vcHX4u7X5qcGEFq7mx4T2sZoO7jRWtrWTIwduuBUQQh0vdWzJ1ZNaX69rr/tJEx8Ov97W8mbRgIa2fe2F+yO7lxsGhtzSmuB5/nnTuQpyT+PGl+pKHzEAaBpBAPJ6PnZPwu44FM72fdO5DFmLaaecEhJ+10V3buInbOJ7tk+DMeDwDqdpiJ5WjKnubNDzVxxuID2b2L2F7MBQNZmKdZfD9pvUfYSKZP4tZdqvYDGUyVhsh0GuyhwU85Gyn5OG69T1SP0T7Vt2+Yh64KLPf+OjqvzIbtb0cni3HfOCP61nX7gJp9ZW5ds/bdFNGhNxuYvt7W3CfrtO0Yp0jbvm4dWt508O28d3PwQsSW92CVnlVJT7efXbTOXKa0kTMTU5cOlbl51dn1ZaJ599fIaaMaD/6b8PRi2DbaUn9+1dp1YIDtZ5ftF6WU4/EvxOgxRs14+Jma3hOqNRGWTgd9zsD4Az78WIlQhh+z/kMKT3scAZhEYBAOPkXB3SyXePJB3v0YiVSC8YATCA86o0d4/JnkU9j/FEcfZHkiybAnDcKb4+k92b+HYSsZP4ajj0TeTgUGNByzXIQfpf2PsBjz5D7rfYrBkGuthvdwRUW6vV+yHm+I0Lwy2P/XyaA8atFz39y8pHd00q76D9bwiIZGYejO5orY+6a+fV1vEtpxzc3LpKVvsNF3p3vFfGrvOeazq2iqGUeOsX3dPIex5oydRooN/dTzHqyjwHT2TevZVdwny729f5uP57qHoOfaTy6ZTZ30Pe/BBTR2+XHa/JDK7RR0s/YHNL0XijTBPAN5mh9Eg/dUcqDwUdz+BU2f5qo5yAsW7rTysRr8lE+2IOuK3l0U7iGwd5wXHXh+zoai9QGNP0/zFA9/xidPAQpChZUMA3kUtD+i2eOcTVTvAxR/knIuSTIVUGXHce8XMNpWqJm2P6HRfZYr7D5ZsDbXVEqdrTrdX7eH09fC9pvjYyNK9J01/8ksE9h/NmNuXoCJmliViVWBMTAPZs2tRRjq2t6K/3ghS+E3Ri9+M+uiSeTs1rXdC1oX6Mez9uYSGqOJVRm5jQwSd7NmbV3CU2jvFPWdi2Y3vxl335oe2lmk7y54D1cEw4XNor59FU1Besg7H2BxlEdHsP9TnnQBGQ2kRWUQ5c+S5l1NHsT8jLfeJ+nzWGQpEhmPY/E4bL+HeV+J50nzrgb3IjYcMxODwSA+Rr2fyKQD0+2s/T5Vpwk9PE5nSuT4NOuA7ntQ7aXpuez9RERNTIdD6VmgPxA7UfMDjW0nrAvaPwfxLoOx7TxdM86L1pkwti8bB0VjMvk2673We54nhvdgRTtqoBG1Ntf10ypndGLVpmaJjIW9ednZLjOhew9X6OFiIZ58a3J8M2ib3VR7cdnaLcuIWk8vettVptDIbUyNIhpRa3vNPK5aHaa/2LCe12Cc/6vw7CvDnTQh3qM5ur+ERtTaWTYOZ1EEB1/A8L1M5Cr5KOveRSJRdNBnlkZOutOncPALyYdq8ACP77A0hqTTyi2qOuPkIe98QlQrn2yh0S8UO425EiiaZkke/TJpfkjFSMQPs87HRB2OpMiByGCSjh+B4c+5TBX/NGrdpWCY4V4vdwxyfB7uwOF7XPXZ5Inq/VTySBrHlr51wz6GZo9am1fpibmQDr872Z1JR8ahaT29TPrEPNa07VvGsZYSY+TOxrprtA3vwToc+saZZW1eNXrGQu/g23y63jsAE8d+esU6tuHIch9cME7NBFsjp5EQU2tp+tYN60BHE919cokeeJVk8J3R7hKb6E3D2LqqNU2tTfTtm+aBm2dw+K4YP1RyIEa/UNMtBI9azDZIv6+GWe9DHH2c5IyM3mX9h1gGiWIJTxN4POp+poVfMBbjzl2YfJSxjOPpQBDEjqfDT+DkCUStpPs5CT7J2SiVFNBglIVy+n42/ALLPp9+psYPETrscJPQ6URlcnBXju9yJvD0g7R7j9BBeOWdn37lD/7IPe3cePtH3/re9+Uk/XL78XfCA2MyvPZ373z5f/vjyvbehXc/+Orv/e+F/ZNIc6dWMSHmK3/59lu//XtWZ3Dlxz//6n/4w8bW9qXp+b/pbZby4Mrf//g3/sffImH20g//9q1//7vOWStFRkhtkYO19z5867d/t3B8fuWffv4b/9Nvab3pK51n/3Z6UBg01+58+LXf+f3Gw82FT+9/63vfr77Y54T+Cm6wlAy+/Z18ttK9fHG4sTKem+9d3hhfXAmIfmhWQ2oO6zOtWzcG6ytxvdy/ujFaWxJUo4rpIssqhbNXXo4W6+NGo3njWv/CakTNNkO9QjWo1FpXr3YuX0iqpea1K9PVBUkpVbkuWFL2++urneuXs2qhdfVq98pGbJinyB0Tc9SY7Vy/MlldGM/ODzbWW9cuE1NpZWk2AC1Cqy7NqsqLDjk/z8uzpgvNsjTmkWkzvaqMWQgMJMIcUU2UvGIxxku64UitKPVlirDgKSMA0aKlzWK7xkXZLLhTvGwAE6nBUDhF3cRGTdgNSUrIrEGvmsXVimq1RKFm2FgvSmsJ2Vaq6oYzK0GRQDrMqzFyU2kHWSMkZjxU+JT4U8vhXkadNitLZkZ5NaZmACAz8tiSaepzUO4pN2F2Cv1+VgQBF0dGMdRtYARJIyFmkNk5LHVZQWoidrIJwkAaIStPRIFTfZA3QmKGQ6S1sNNxy1APsdfhZa7MKKuGzBeaydx6Rmao5spSLaNzNIUanIaiULEsbs+pQiFRFdOaEbiuSYxRpyerDduVxgzAs9S0MnNGGmUgEARBJP2qVkCFQgQXDdNmZk3pDQwUFynH1EBlw6kzqyZF2SqWIrRkYsV4lEO3oNnAaKhiMZEVy6wJY0Zlrkm0dl4PsRlzL5DlCTNVV9Gu5o38AqXn6SyztUns5Vk90kgIAbeziOCcFSNYGKUliul5MiuQmQ8yvuc1mEaEHcjSSHi5cKa6300dgiCzkkjDOXfivB5hM8q9BBb7uYv6THV0d1QsE9rJZhJiRNKeJI0EmanmA6+WwxlN96Q5B3GZyPGU646mE1Sm/myulwEpIreeyxlbhLGEBFm2U+D6KrXcHBSI28hBRVdxisJEVapWURkNhavUMWNzBTtGHFIbxJkslKyCdKsZmaGmy/UFDKs6ikIpkWboqGr4jcyoAFSkbiXDMxTTMCtOkTdlnlSFPi9lqU6GYXJcXUIa44VJXsmwHgh/KIshQkzjsc2T3MXEbCUzAusRc6eyHDHMTqXWo27iacofgsI08YlGT7KGcMSAIWTlCaYpK4yhP+WekP4QFSYT1wuzfL8wC3TFC1NRiaARc3+cVTLlgJI9Jcu64Qq9DIxlQgyQJhxATHzLnIFOnauK6dkRWtaxBeV4LO2CQwVqaM6spCWkl5Rd46zi4W6HOyXNJFaRa2vUdBguIb+Rg4LOJjGUSNZKvptoSwT52HdjbZXqpsxyICWCRc8v5t4MFzXTsxOyRJFPoDaGxS4rCmQEqNDPyiAU8lxzem4VaWM2ExAryB0uij1W4pqILR4aKgdmmpVDVmSEDvPqmNjBCJKm0lpuBRipcrqsyrExzSshcqaAcDONLJHmrgJ+G/hx7gro9vKazHl8ovkjuyz0NK9NsBPkrlSFflIRJk3MGjAa0HC53oBWVXCNqmmETEuU3FIpQkuGaXOtorR5AgHjudABQjXXnJFWXfKiVXKncNkCSMppqCxft5E5I62aQGXq1IVXzoNKFZ2fi1LDdIBeBs6c0m2OG5pTF8yxVH+ILIdX/KIfo2XDsnKtAoxFgi0h7YnutaMyhXo/beTYzMdc7DqNnNLcS2BpINxcOCEojqSRY5DpLDNBJqxpOptgI8ydBJQGqaumGetgs1eoQjrM6rF0OdH7WSMieoQkM3iCkUDWBPl9URTAnCQzsXRFyrJdrZzqJiswWRzyIkfakBcnrAgcIzaXUcGOeM1xGgzVdMkFmASgUrM9oc8hXCO2mejz0PAlgxAGEfV8WNG9UoLmdMvOjVlAK1jxnOeC6CauGm49N2tAlvRCNUXzJkpjxjF2bN0B5jwoelFed+06N2owoSbsD2S5ZntAn0NGVVFHmXPSrKrM0zR6ks4Kk47DEuPVSFLWAmZb8xJXk/4YFiZJUaPkJJmTpppAxSyWEpwxPwDFCXOFcoa61+0XSpM43vNmpQa4M+W1CJgJ90NQHCtdUJn4ySRzDI2ep7M51UPhBmwmljTrIPPMrEQmJVZPlaZxgRLSTBoZ1eN/JoiWoF5UToOzug97A2b5xNCcMtdWqeXksIDtWQ4KGpgEiClVLTteri1gXMKuGZurxNSSEJkw46pYcHxmzUhSQZbHtWUCSgaaTgQguqXjmu7P5LQCtQIwZwSqGWA4znVXN6jhS30ZWz6DBWo3BJzRedE9v3mzf2E1rZXPbt2IFusju3gK7YHljRvz/UsXulcuppXC+dWrk/VFaWhEcjcLpvV6+/rl8YWl4ez88MJy/9pGQtC+WR8a/rRcOb95PVyeHS0stq9fmawtckoRgXYW8qJ7duvW+MJyyQMT4QAAIABJREFUWKuc3bgxXZ2bFOtnOQpspzW7Orqw2rp+Jav4zctXzl96iXzxxa+gwQJAQqQEtEfTyHAUk3oQpU4RPD6SH++mUMcZN6ZxotsSUWswTnRr7vGTl//27yVTgAM6jRPdhgkzxkGqWflxgH/+RYwcKaE2CVPdRhnXJmHoFpY+/+KNv/jLHGlcYXc4jg1bKmRMo9T0xPM2vPMsk1QoZPXHqe1xqjvdfmxYUgIcMaEgR1QFMnY9pZzsiSG1AiMUjrkiWCACIy4RyoWXbmucO1DDciw4oalmogmTCAPqsB1dpLawDDEVDFKlETESOaXI8tNNm7GC0jEKMsFhTg0ZiMRwcqeUPtaE8pmuizHjiGbUQDEDEjIKYQyVUIlpoAgriWLDO2xdOxlcZLquUqAYzXQN5BQIlWNa3HK8Ey22fRQzkEJGMMohzmFIvaPxtfZkLaMGTDBgKtNNmuQogzk2/CPDOfVzTFAGUSoyglBGYCpTxzvrX97vXsmJBplSGc01qnICGMiIhtIcxSIjVEEkJzLT9Cxy+J4hkCkEArFINUtxICMlNMpjm21qqeFyiVSkOKWSIxxmyqIsLeTbNnN8JBWYSkGwkAhGAtq6mphs3xK6CyGEEyZ0A0AgxoLpOuOF7LmRclNJAKM8I3oOqRgzpukZIXoEBUAcYhxLqcBQze41b/X5vCQARzTHOKMaChEHUHKz9phqfSPVDRzBHJmMABxgrsSE1l60Xu2kaznRSMyAgDkmMIYMaoqR8hNHm2qpbpCpkgBlxEQRUxxMReGo91I3W8kIITEBAHBdQzFRQnFMVSplCjPDFAlQTCrHTl9oedfOLAfFGUiU0DTAgEwUtxzWtGXbTAslliKQy8ywSZTCRDKssVGBn9vSc3iqUCYkxSIBMBXCd0TXTvZM5bkwFzJWmabJFKhccdtkRxo/1nPbBUyJEAjTgLmUseIEAQ5xJDLNklzhBGUU96K5ve7tQBY4wjRBOcFSYC1imW4YXa3y1JHQkAiREGcECoW1GHCMuvHsbuvmCNYFJCRQOdEUFDCkGUEkNErPHBjrHBIS8gzpDFEcwEwzxryxfXJzImoMazhEOdYYIDTiQmFFiRjzDJNcM+WYM6Ix6rDnmA0NbpkilFyS3ND5REAEVcFPnmgi9oWlw5gDAXJEQMAUwsIpJtsWyzxg6WDKgAAZNUicKQ4FMtmhJVJPGroKJJIqNwwRKKBgptlsj7K2oUxNJZJzlOkGDxQSMqM6TRhJQUYIkAhnKMbW2WTjtH8xgibKCUxlYrowZjhDGdXdM83bdQWighPIAMMQ5ghFLHW80/HGXu+lRHcUkzRFGUGAU8RQZmiFQ6P8nOTUUlzhGDOdAolwjBKstcKLx/3robIRgyhmqWYJhXAAmKZxSGAgJKVSYhTmTNNY7KRPNUE8iKEYC0ZpjnU0ZUqnMrfzHV1gh1MqxjwnuiKYjzijFGIv27aFcgGGKEg5wBwROWahW2B6MXmoSeJxTMWU51jnkKKISYSyxMieapwWuUbBREgIONHhlCkFOcIggULqmU5hogElYurvnr7Uii/kmo5iLhliGJEUKoE50Erbrt3BuWHgCCsOcmrSmCEGAlg4Ht9qZhdTopEYgpyllOAUQ6ZSwytvE/vEzjUCUyAEzTQKEw0KlRBrr33zfHghs22YCMhJhhFKAWSAEyRSABORmDbIpYgV1zQ2tvM9IzNskUOVAUapygGJc+kabFLId2zhuohJFSlOsWAApAp5hmwZ7NiWngeEAlMmDAMoIALJLItNvPzAyIEBmIRRnmoWl1iMWW6YPHHSbYMZHmNQxirVLMUUCnKGNYUkDGhGcY40FECB0IA19s9vDMUsxxSHMCcmRwoHmAEpuFN+6pKpnlKdRkwAzCFCEWREG4H6TvOVkWwwhEkIGKYMaShkOdZwqNWeOjAxOQEkJAoqTjUYIwnRGDYOmzf60QxDiMaIEZohpIVQCZ5rJokzlUOpaSBVgkFuuvmeBSZmXCqLQCkBMs3CUQozxYmZn3ty6jHXVpFEQgpCRKRALqXrinM7ObeVY8GE8xzmmiZDpSTklsV2qGiauePCTIoEKI2CRMocMNvNjmzRt7mmg1iiJEt1G2RCRTIzbcCkFqWJ4eCEa2GSWQ7b7qEff57pvkTE6o8i00W51KM0sdzVTz5/+QdvSwG5QlZ3mBqOQJrZG0VWQR4N1d2tKIUoF9okSqjJJXQ6/cR0Soenv/anf4biXCioj4KUmoorfRplupkeTcg7n6XIzjXL6vYT01FMaUHEEQEA/gpEowoApV7+wdvOsBfXyjOfPiwf7HNTW/3lpzNPNtuv3Lj1N3/rn51vf+2tl//8r4rtZlzyS5t788+eTmszy598svbpJ93La7f/8R1/b2/nza/f/Lu/m9nfA9VCYXe/9uDZtFRZePTo6t0POjcu3/hP/1jdebH76pdvvvPjxpMn03K1cfB8/qN700K5sb11/Z1/PHv15st/87f1za3B5bX59z5efvBgMtPYmn+j+diY1VLI0/Yj0y4Ba8A7vUb1RQRhePqiXuGJVecnj/xZklCVdw9Ks7VURFnrwJuDU91Pd3er5TzxFsT5i+oy6VndcXer3iARYOL8qLCAQ1yIOs16HU7xBXjwpFiVmd0PO09dIqY6Hjf7M+XN1GokzRelxfEQr0cnDxerl5NZBWDvohFMcb4rext2q60bW6j9LWlFtnMfHK0Qq1dI96bDJV1LlUhUf8Nr94D71Dh9S5hMd+7j03VkBEVxJHorGGaA7KveZScZyeImPb6VW8iq7ZLjJUQjVxzywQoF0ih2QGfNCCeyeN87fSUwPRoc4NMGUbGTPkgmG1S52tz5+LkWDKyZKzJ8pKKBdUlvT6Nq+6xcmBFRh6ptVvVH4SOnMy6UVxnbJVG3trI7ZN2s1axWXB4M9fyxWphLsy016JVL7TzeBr1Tb9YJs4Cf7xfmaBgHdLRTXl6Loqey0/LW5sL0SBucuMv6CEnVPi7NFcIwoe1nfsU5FYeq1S6vl4euWxPdDVMcq2oXn902wDZBI9i8kZn7dkeJ4SVPbntUxL1rTrIn57vw/LYBX8BxT51cxGDHVFKMrjrgmJaaYnjTT17w1QiefxnLQzvoqvNLZjz1BoD3183sXNZasnvND/bDCy399HXOT1HWRd2rVO6haST6l4y0qebOVeuaNTwN3dPeI2J7cEYNujs+1qCJ4m6rbJ8k3nLYfGRJHa1UJp3Hnq6pKhx3znwdKr3C+ruWHYWzs1HrsZ4ZZn1ZjncNTYI5u39yUuIM2UU52rPsadioTI4fFwO9VOnmvW0dMLmERr0DLcpoocR6pz4dZYtX48FnIIT2SiWcPDejiM4XcxRf1E9KqnhPO2rIaFWffeE2PTGpF86emSHKRpct4xByF55UcfWBdVwU0zVa+BR3imKyVDp8QqXD2lctugmESVsXQPGXdtPIJpe14j2tX5SjZX/3vimNcHDJho8VMkHzilX4hLQMPr1imx+bkSWnF/2Tbayn4GzD54+E6amza3plN+n0m4f+rIphOWzu1CqjwLwBznZrFZD7edh94tQvhGZ7fH7o+VlWXky6zdpsMtauT08fLpXXmBNnzS17LpnqveykU/EhgyI7f+FXu6FvsZMHrrPMCiztHBYayQgn07N9pxRyQPLzbbt0Gpa+Ds+fe2jNci3Z3zKrxaETDw8PPGMEjLmUHl9immHNbZPDJQi5L5+z0TIRxCqdg86KOYlk8b578lJIimR+j5zWCZdm3kynVSCrWqGpeovOKBX1z+2jGxmtmqMdozkvc6OAnpFBVWZ1S38Eemt4bMClz9ydpQzX9cJn6rQOc7+EnqFgDgY17DxA3VVt4qnKHX9nLgTzWm+THWRnL8oNHHGJeg+9pUrMD0T7tLK6GqXnsnfoLWljiMXp81IDRxCj8xeVYulctUXrtLJaHLO+aB8Vl42p0vLuSWneDphJTp6UlgoR78atA882+1osjwez9WcRLoj2puPkPWaBk4elJSvOx6x3Xluv9FHOTve9OkxZhTc3nSrMChctcrKmF9LCOMkGG2baUTNnanSzGBwF2kg//RKv9JA8hucbpHDih2PVWdOmPQDOZOea0zsLrEN8fJvNxLbWxK1VzTkB2UT1L5nJNPWGoHnRAC1gbVsnX8pqkJoteL5gGCd0PIyHq9o0p34fdi8WYHdSemruvxJVNUs7xe15ZETEbrX2TaMfNSrB8b3CGHjVZR7sGzzSl3cH42M8CrVSUfQObNTCi6vx+AmY8PJqcxrsmNOxNacHQQuNe/qCGQ7PrLyNV66EgwdwkPgXZoNoR48G5jIZRCOte24vVcP+rjFt0vnqUfo57MbFi5V+duIF3fLyziiYkOEhXban3UNrdGxvLE313owcr/svHjoQxb0rtnyqLIDaL9n+PTREYnDZND43YkuML3nHL7A1Ua1LDnsiC1Sd36bmIz1CsnfVEacFheTkhne8L30AW1c9vpmVoHZ2GxpPaY5k+5JBHoAO5cNrhtoH1TE8v2Ylz1ktJs2XaPFc7+eiteKwe8rU+PCKjtsMPDt76Gozsobj1jOvUp4a0bjdLvophw5rP3cKNKiV+NlDE8xZJU8ODt2iMymAwfG+TzrQcOXguVPCQc2djB5X8oZbrLLBjmW6ohQNjvYK8BQ6xbx1VLJhNrcedj/DvGjOGXF/xwKanM3G3bYPOtAphKdbBR2IpYWgfw9FJedCob1x505h52hYn11//4PlL+4dvn77tbd/6DXb/euXVt+5Uz/Yzzy3sft85osnSblw4e6Hi188aL5660s/eNvf3+9dubD87kezm5vK1hsn+6VPnmQzldVffLzy0SedV27d/uHb1Z0X49XFmQebi59+whzXHXYv/PjdrFJYvHd/9c4HvSsXrvzDf5p7/ERfLLDdwcWPP+KWrcXTG3/zD50Ll4e/MtHov5usrU+WV/Zff60/t9JfWzt87ZW4Vju8/epgfWXUWOhcubb78u3h/FywuLT3xuuD+ZXx6uqL174Ul8qnN270L633Fld7V67u3npp0pgN5xd2v/LGuDHXWdt48aUvJdXq2c1b3Y31zvxSf+PS7quvTedmg/n5ra9+La2U2msbO2+8EdbqrWs3+hfXukurg43Lxy/fbK5uxDON7a9/nZrSKQKypMmKbhaVXHVRBVmmQFc96kPiUHLJQEViFBVZ0mFFc8pQm0f5jGWZil/xaAlRm5gbFJR0owSMOSjqJirp+iJh867noHzDIhVCTITWdVTWrYLUl4hoWNRF+jJJ5jzPQeqyRUqI2hiua7SIQNUwF0lS05Apopk4K+cA55PZKK8QZcS8Mo5qMLcEcMeTOZC7WjoTslKqGfl4JsgqlOkxL02SupJmogrRZE5wEya1MKsySJOgEbISlFbKi5NBg0JtogrhdE4oU/HyeFxHGARxfcKqKHMY8MfDGYjoVBWicJ4JE8blKKhgx+Z+VcTLvuYJ2xVgw0krnucztW5RCxozwKjDvOpYVZCvFxwvNzyUXfapA3AJizXbsIFf52DOUCXNLst8vUCK0LKVuuxqtiJVDa4Z1IXGDIRzOixRrwrxIsmrtmlBftWlLtBLhK7pyCdGHRoNENcco4zpCg1LhkaieHbMfMT1aVpPkhIAbsqrwbQKkCaHK4zYk9jE4ULMC0BZ07SehCXAjZTXo1EVaiYfzUfS50pn47mIFZE0A1aLogoQZgpKw96cDXUxWYyUmxIzH81GeQEzPWbVOK1wZWa8Mh03KKJsshCJIgc0DmdD6Cm9iPU6YisOdDWyQHADOzYiq0TWDeJjswHAkoWLxKooteYgV6OziC05Ds20FQpquu5jp6GSRc+0crMC2AUfuLrTUGzRcbScLhO9pLKqZ9UAX/VcO6d1Xaw71ABonuaLjq9n9jKUdR0WqV1TcMWQRV2vILViYBxnpTCpqcTh0pmMFhA0JshMRkuZslhm8/FcJi0OnEFeZYnHoRfGjTQsAmyko2UGzUQ4edyIhC1zZ5LVReQr6sVJLQrKkOjZcJlRIwxdGM1n0uHSmyR1HvhAmmk6F49L2LDywUIEHM7sfDqXc09JZzKZhbxKCjZkF0xSJdhE6IKOKobtS30Ry4ZFXGwsknTBdVwMNyxSxtTCcF3Ti0iVTWMBo1nd8KC+gNMlXzcAvKjTMrQ8QtaIqhhaARhzSC1Yugv1WcRXCqYB0BrNZ12fJOSCASua6UNtHvM50zJycwHHKyXDAOYqTCse1caiMBrMaZBOgBdO57mwgCiMxw2IUBRXJqyGUodDfzhsIKhNoR8F8zm3UVyMJzPKwOOkHqQVmFq59KajeYS1ifCzYD4Deh6X4qTGgJ6llQkrycThsBCEjTxyoXRZsJBALcuKUVLnUsuS4jirw9BhWjEeNqRpClrR0LqOPWzUAJrXYJm6FUgWcF63LRPwa57mQ1og2roGfWpUodkAad3RS0RbJnnD9V2UXbJoARMXwzWdFKldkXhBlzXT8BRd06KG59oQXLaoD4lP8ArBBYrrOl2kpKYZPtCXUTRXsnUBLpu0gM0ioUtEeJCbKSgO+/Mm0FWwEAovo2Y+ng3zIsn1hJXDtMalmYnydNxAUOPT+YgXGaRJMBfyIlRWmFTicQ0iOuGVcNKAWGdpfTyuUk0NokaQV3BuZ7wUTGqSkCmrxOGs5BoK6kFYxRbohfNJVoKZkbJyHMwISqdpLQtmiEVzfREbFZBWXLum2Jrn2jmtUHbRoSbAM5Qt256RO4tQ1nVQonZVoRVDlAyjgMAFS7cAmdPgoqnZQJ9HsKGDAinMQLhAeNkyikhs2NSGxgxBS7rmIX0OGXUV1opWGdE1jdccp4TzCya1Ea5RtWRqPnIaCiyYUQkSPRusMGqEkYui+VR4UjmTuMaCAhBGljWicflfCHK5MPPpXMZ9IJ1pWs/iklJGLKuD9lyR0nS8FEE7g3Y2nUlyH3JrmtWypCyhmeS1yWjGICSdLsfS5UJLo7lEFCSwptO6iAs5pkE2G05qmkaSeHbMPUJ9ZM5BtWBTDxkNyFd9w0RohbI51yMJuaDDqmb60JjD2bxtGrk5C5O1EjWRtQzyWc8jMVnTtBLMK47RQGrJck0GFy21aBAd4FUtn/XKWmStE1HTYYHYMwosW9DVyJxOFqluQbKigYapW1JfxaBu4AJxZnGy7GfF0sm1m/u3byeV2ulLLw/Wl7uLa/0rV45v3+osrkWzc9tf/1paKp5fvn7+0vVptXZ84+XepfXW4sr0ypWjWzfaaxvR/PzzN9/MSt7R1ZfOb10PZhfObr3U2VhvLq1NlpYOX3+1tbSeLi49e/PNtFY537h2eut60JhtX73WvHals7w+XVw6e+X64cVb8czM1je+ltcqncvXD2+/on/+2X+taPS/9LdOTgiMI04JAAoCIDUNZJlEiDB2cvXqs698heQZwASlidI1LhVjLKcUQIAAVAgLIXiWSYyBaYI8EwgrqaBSSjeEECLPJYQQICiERCilmooTTuhgdvbF669zw4SMKaAUQlLTQRxJjBWlSHCJkYAwlYpBhDGEQiIlJYacKwlhFoSGdYTjPoAIS4GA5AgluRQQIAylgghDgWCeC4agQhAJDiGABCMpMZAQyCgTkmAIARZSNwhHMFcqVUSypGCdSR5LhKJcAiUwVEBIRBBAiCihlJIU5QIyiCCUGCiCgUSYCcG5AJjV9Id1c0tBqBRQAAAks0xwRBTCGEgAgYQgB1AIpaCEQgKoIBCASUCQQkgoySQAUEKggAQAAyZhCiETat59UPf2FcZcAC4VRBIjKSVUEEoJEZIQwQyAVCqFoI4VF1BBIJTKuJJS6WRg4jMmlQIQKwkR4ADkuQRISQAgAIQABkAqgUAII6CYQFBKoLhQkECIIcsFAAACgCWHEHKI0kykEiMxLTQGkGUJJ4wJAaECCkuuEMIEQy4wVAIonqscIwUUAgpCIDDJOBBSIRCtFu/oNABQYaEwkgIAzjgHEGJEIEBASgIzBgVBEAosJcZQIMS54FJCAolSQCmIFVAIQAmgzHIpMCUkazj3fOOIAcy5EkoBJIBUEEkIBOASYighYFIxpgBGCkAIISQozQUDMBWaCc8ts5cxIoUUUkEKuZD8n1PgkiCgEMoAyCSABFKkuAAKAs4VVwpigIGEACiEAQQYKIUBkyrnkueCaiOTdiAQuQKpBAohDJXiAiEllBJCQQSEUkwoCSFACAMAgMgwzDnkCJqwvVz4TNczLoBSUkGgCBZMASUhUkAACLgEgHHAMYRAYiAAggKRNJdAcgWVUhBhCYAiUiKkhAKMCw6xY46W/Q8wSAVBWQ4lQUhJLBXEiAOgBFcQAKjiXEiCAISYC8MgAqFcqUxhwTLfPAMyEgDFuVRKYqgg55gghTBRAigpMGJcCgkQgkpBhADAMEsYgwgghKWAQEEEBJcQAohBxqRAEAjhmueItxOpZ0oxBQgBCAChAEAgY1JCoCBSQDEBFVAQSiABxIopmEHIJJizHzf8XYChEIpLAJDCSArxLwRBhZBiUjGuFMIAIgiAAiJDkAkokQJSAaUAUpwrAKRCUBEkcgkhL9ithcLnQuUCIKagRBBCSYBQGAtMs0xgyCFBeSYUBBAoLDmAgCH8nwniQWF2gHgac8xyISAAEGDJAYKYYCgEggoAGeVCEQwBwEpRnTAIM6U4QpQiLCQAQGGU5gJCiYEEUgmoAZg72qmUaQ5QnkmBMcJACYUwUFBlGecQIiiJkkApiKQCEEAF4H+eWojwhvNFydpnCnOhhAQKSSgAhBJAoZhUBEuIuFRcAogkVAApoDBkHGYIAiWIUhAhhRAXSiiAkIIASAUBRIv+pz49VEgyoZhQCmEIkZJAQpkBlCsoMcRAAaAkQhACDBRAkCvFuJJcYTKx9A4ELFMwFUr+M0FMQCilAkIoTKECgDMJIIBAEckUQoyQJBWAIIKBUhBhJCRgXEgElVJE8VTohhzY2kESS6VkzCTACEpFEUAazgHIlZIIK6iUhBBLABQWCmEgFGBMcIAda7pS+JDC6F8IghAIrBQkkAPIGZdKIaKwVAAoiKVSCCgugcxyxQmFECAlAFASSJ4rCQCEAgqgoKQyXPE+dkiTA5JLJRSASCoJMVACQ8ZhTiBACCv+zwRJIRWAkKCMSQGhEsI1m4Q3U6lnCuQKYAwwVEJCAFXOpIQAIICVBACof8lcIphLxbhUEAgBEQQKw0yCVCiFEAZKMMXz3NQ6Bu1wJvMcSKUABEhJKoVEOEc4ywXQCJQScq4wkTyXECgIlaaDKBKEjGYbz776VabrUkrJOccEYgIRUggLREQYKYQlIVJIqVGoFAJQapoQnHMmEEIaJUopQnNKWZpwQ4cAEAgoF63lla3X3+CUKsOAcSQJlpQCKRUmQkiW5xLj/+oF4X9hgzX59nf+29/6n+efPFaudfFndy58+CHQycW7d1/++x81X7reXlrLTcsIpv/me//L0v0vgE7WPv382p13me9ffP/9V3/4g9PXbn/9T/9s486dk1df/eqf/Me1X35EKVj57PONn91Ji8X1X3785g//+vTVl7/8J3+y8e6d1vUbb779w/UP3s9r5e7a6qRYs8Ppm3/2p6//1V8evPn6l//kTy/duTNeW7rxo3c27rwrdL2lr/U+hEUjkudR+76ulSHaSwbbxBGRM91Ov3bLHfb5KTz+yLL1xOhMO/cNx8r4Yd7b1txwVIomh48KZhjZKtn70C2AkdUZnjzyLBqrM97f1gvToRWNDx8WzG7f0fPju64l0qLfjS7U9eEAP017zy0jSAwRn35uFbo9j06ef1TVZDIvlX607oyVnXbU+S33lGnevrb9Jgm9GfPed0ZnSwIMWoydv2wOqQmG+PR67Zwrb9/Y/DIOHBOfGC+uGAmyoyFqrhkDQ1cDfHrNP8OieuI+vo2iAja73taKPkFO1KedFXNYduy9/y4ZXA0On0VacfO6TGaBG3rbC+YIFyfHsLNKRnOiPEg+DIe7tLwCRr/IohM4K9vZKe7vGbYY4os6dpHfOh9+qncObW8OhA9EfAgaciiOotaOa+oia8Lonqg0gvCTrHdouxUmHqejfeLLAJ4n7U2zAKdJGzbv6dXaRNyPOgd20YooaCdXF7zPH4Gm3do2CzDI2+rkC2vZbyaP0vMDr2yOnMQzjq/YQaqBkOy8RuXEH4+Ng6uKZDflp98OiiB8qppO1PmqOY51EpCdNwwxcUYjcnBNk0OvB3Hroj8NzHikjl/1uiOqDbXnX9ZUZk5DdHi1nDZpT4H2ZXOcGSAk+y+VuqOSufnfq7EpRLdX1Q6vWPnYGAl8flGfSF1Nyf7L3iCdVsajn0scsFIy7GxR3uQeDltPLLoX2MYZXHL4QkX/dL//1AMj7mXBYEcT59ywRP8h4ltBbSns3yHTrubV5PBDBnp5OR8Od7XonFqe6n8BxHa83Gifvm8Oe4ZXVdE9wVuyHu+xOU161Ihh7xMqX7DqSjj4p3zUsyrOdPwYJ2eqaKT2dMXfnyf2gbtXgZ0LojCuHJZJZ7bIjr+cPfoq0yb5YNK96e8ug8JZYddDnYuafm4eV3B3yUq77hjA86sWG5CpQw+uE+OgsqfB3hXNbNlHBdRZ9ibN4ihN2q+ZaZtGtnZ4i+Ljr4zu/gYqjINzebKGO6t+2NMiiQ+vOMkZTXS6/zKyh3yv139K/MnYjkeHjwpas19wk+P3PD3LKsVuvFKhcUCehL3nlj5NTR6c3HfdZr9sDp99WKc8c9Kgfd/0eUC6UXtLJ4PUQfHZPdtqjZxqfvQzEzBhpmHnvuakIQ6y7qZGh7mvt9Pbi3qnSdrZ8JcW40STrP+ZdKdjKwy7WyYfIGue1b64hIMqclr+5rI2NpykQzpLZr9mWQffzXovBUfbEfS3r4NgQRSmhe15fWiUg0PcXUDDBeBE5tFS5cxT/vPC43UnD+2IAAAgAElEQVQVLgJnVN5r0KHvJi29XSXdeZO26Nm6dTYranuV+4sgWHT1/W+xvdcmJJi089Y1vTVnkjN8uq6dLxP3ce1RQ4Rryuyire75luOJQIzk+WdGqRKCZ2Fnx/KtWJOt5May+9kD3DKbW7YvQznkx5/a83aHb0dnL/ySOc72wOAFLUd9NElOH1h+0kOxan5i17wJ25y2nttV2lMHefeFaScRSrL2Pb3UPTErk+zGKu1108/z/r4zi/r5Yd7ZMc0g0lnW+lSzVWw7Jf3pWjEb6IMMtK6YY27AKT58qdwOHePZ/6BGPufNYVnbu2rnoTFJcfOiPgaGGuPDlwqtTJR67uNXodA0GVq7a+Y0tKOItC7aU0MaibV9yesI5B77T64LWcSE2btL1jBdER9/K6Yz4uwQr9rPrpaHRFl7/qOrUhY1mHj7C9YUKjxu31f8Wbo80z6/q/dbpjcDpp9L3pT15EiWBa87Rij69zS+xWpLweCnbNixK950+hiFp7AoJumRGO6QijXpb2v9TTK7MIzfSwctu+JMx49AcAQqWY+dy95zo2aNB9u080xf809TPUlvX3afvJhu29EhqOX9qKn6j2HDGkxe6INnxny96x3XUXvVHTfL42nSfsOK+zQn2t5tTNqvjz/4TWWHUVucrKDOBT8Y6GEKj266QYtyoO29pmsdvaXB5uV69hy1bdi5bIaRIZi+f60QtqGQ2ovXKO3bXUqaF13R0noabm2YYbxiPPp3iaDh2eloxd17CZiR3Sf66ZoTnhohJeeX7FQpeHj8M1sm0lNB63PNiGM9SnpbGuoxn7bSVxa1XpP20uFHZpoQU+P9XypzPHXjoPfcyHvYLKj2hwofRfPV7uHPrTg1bENO70k6zitRr/NcT5rAr4PO+wqfsdJ82HuHR4Fe8cZq3gQyMXf63Rd+dsJrhenxx1Z4AuZmB5Of8klkz6uzN/72zzfuvB/Mz914553X3v7hzje/8a3f/v7qJ7+M5moH114WVKs0z15/+wdXfvKTtF69/E8/uf3DHxy8+cY3/+iPVz/6aLo8f+tH/3jh/fd0kS8/eXjp796J65XL7777yl//oPXml17/4/9z7e7deKZ8/efvXb7zrq7kzOazl//iL6OZysn1W5HjW3n09e//7sbdu7hsz9/99NpPfkylqB8d/Nr//f/0rlwOfiWiUSlH3/luVqty29r61jcjtxCXii/e+gYC4OzSlc61yznRoVSE5XFjRmK0+Z1vT20vKXib33gLEjxaWTl95aVQt8OZmeff+EZi21KjW7/5rdj1g0Jp661fVzodzDROX3s59ktpqfTk178ZeS4k5P+j7T3e9DyvO80nhzd+OVSuQhUKiSBBMEiiAiXbstuWZbsXPYv5i2YzPe12+7Lb7SBbNmXJCpQoWYFKTBIpMAgCSORcVaiqL7/5fdIsPNesZxban+19Fud3nfv3m9/7PLVaQ6IZhQhN1lb3Ljw57Q209G79zmcnS6vIqMt/8ieU1AKWaEugjghBirc92gJBNgMXmhhCdv1m5TVxQ4YuoRtE9QJhUrGJ0YD78wm60LBNwoucn/NAhONq7K8jtdIkUIsNbNbCeD4xTzZ4j7HFHD8ZwxaNzQKvsUoidv22o361PmgspvCpmHYQzzN0LuJNKEHJV1Cx7vN8nvWKaqh5khXrZdajPD128Wi2TJMqGTl9d/kEUlXZmtXDmqfJbEkVQyIW+zacpCsGugLIZLoJaZmq1kwNC55m6TCrBghXEygn0zXg18dOprMdgqrKRMfHPY+ld69Z8Xh911UJlJNkpaTVCIpsvuWgMbV/PF8VwqaRyPRuGPLS0wl5Ks4lj/HCnW6QvT328NAOlo3PA57ZU6HPc69O6gsdj2mGK3zGI7AKZO62I+yjJlzYJ5pM1r5KwdNNQpXEip6WDKuIp3A70A0ZugTviro24ubtfHUHeMynFTnrY6KadMG2WNlrCpPRM3LeDqPZKNlIbFDx/HHdTxZ9gVViBvMDRrG+/8vGKRBiU9l0I1WRFvlB1c2yASN6ofrpdICj+Wh0QrmGommSnVBlE4r8QHXTtA+JSmF7OlmTcjGZrae2VbM0nW7qooPz2dEd4j+O21QvVDebLZNgNsnWE9fMaZot1nLbNAJrFmhzKmZ1IfuObnKeZ+iEtCd6+MPr5PG03tjwpRG+smdjqiqvo+EJP06ncoPqzZiZwo9rcDII7cITlTnfAsYE7cpuyiCb8YEjG1IhGHi5PR15LqGeUac69OAATvNyMAiq1OtquxVRCiKe01MCUsd57U57vF5oMkvXEGAVdePJhgZ4Lsv50S7MXTWvplf6u9ZZCvfzZVMJw9WoWk+KwPjpeHQaQFYjm6brGcQZ1Y+KVZdGjOtJsZYloQ2T8fiMpayogUo2ashLpvezVTfycFU+vtLZnjb8eHF0fMoiUSKdJ9vOskro/ckaND3Smo30uYgvCT5PyJORa/NIz9kqLkNKb9x1jlYbS3E6h0+EuE/EYgHPRqyLpSnE0IHNgJuSLiGzGfnTCTvFyYDwNINnQtTjoZp7Q4jXhVAFXUZ6u+ElC76N824cvvu+drze3PLtgvcB2eBBtZBLqNjpyDTxTrhiue3l+0iMJ+vQ0yNIk9lJCLRy3vFoIGj+8Lpj+2u7rs6QGC/WalIfI5LOt6wDRotJslyJYlZ742yDQ5sRcjxd00hPMclm25aqwsp5vlyxeqHkYbmCa6q5G2Vr+bh2UzO93N0lwFh/ka3UpJ5j8jDZ5AVH3I6mO5YSJW3Fz0rKTUgztCV10/NdRnZ4bZy4fiNf3XEh90BJzvqYmQac8S1aDZrc5uwUNwO/NR+riy0RAl7n6HwD+zBGCdyStuf59UKc8ctB1JoeuWeaLHLMVPh8E8HaXr2jgxZaavk6RSeFXo7DyTF7yicxoLrEux6MHTYpakzH65wni2Ql0Z2aJ9lsQxV9WkyP7mKxF/eoTXQrna4QfzHJlxPTLliaJ2tF3YG4Guk4yYbKy49Nt5isM5EmeX+c9lxjflwNFukSh8XMxLOsn4tsZNrZuOfZ8tFvePuw3Qnmo6w5zVc50Qvrz2fDghfTqlWlKyhIp6Jr6aZQlISysKfDEKec1vXZLj0egcNZ2R94uvBbtTkRU+4inLIzEgggSGmfiLmrvNjibemZPIpys92wPg1RSs9I6EHfpe5CCzstQwN3A67zVpSBrcBkily/pQerqOeFeqGfakqkWGjtbsxhFfuZ2YnzwIaL0fi0o7JQxiSbpRMVUwflkhr5uCwOP2pvjZtBPD86PmmgX5E6TU5Y7Wmh9othlbccL+d2aT5eCsPJ0Wy7BGGJy2y26axfs3K/6udFU/Nynq/ksy6Jp8fzrbz0SZntX5b9tNOj9eOsV9lGJtNJsVVP+zyYTRZrU90goVr4fQc3pHA17zl9siGyVGzCYtCIP7isFaq3tj2byI5FWzIsZ17XVac6LM/8dWdWZDMZ0TWCV4UFzmvWbtsP1Bx2Cdr2eZoFG7Be9aLFSCw7txkzpIOwAisc3tu3hamWl0Rd+esYrvKgSsJ+rU80HYWBV6on+wiA2cra9c+8CBE42t4+ePLcrN3TYfDRH/w+qDRA2BEECJourd7+5McNY+O19UfPPj1t9nTg3/j850adAST4yhf+EHJ8ONy488LHVRDMllfvPfP0fLBsGbvx+d89Hqxi5D74oy84jy8Gyw+fuVALibQVKp/3lyDG9z73wqPNs1jXl//4Czrw563OzU99hr53Sfw2RKPpH3/RIxYGsmxFEAIbenUrItBWcVR3otWPPlz/6PJ0e4MYhXyet0LY4Gi1mfkBNVo1IhNySKkKZdFulO3m3tNPerYkCKowyDtNAoxphCbgsBfYrU7qRcwZzEna7yw9vLNx9XLZaWDknMd1w0MQmNBXrUBTRghMBl0CDCVOBIajmhAHYkKx9XSOGphCqJMGaAYSlYhCEVgB5x455AGyQkpYgyYJQEmRASFidsH9KWcKQ8ioE4EBBBCnSIeQahH6YyQthgRxIAKDEDV5zAJGPEhtjWPowZI5wwMVoLzmkvsWc0eccb6muKBOaVEKkjsMAa8RUdNqvXIhEMpB6DxNSTGAV7CcAYQBIVDUmNcQWk5zx4sBuUO9xBCMnYGiYDhDxlihUKC67nZD7GdEUmCArKHnFslwznocLRBygCvrA2ErTlLEKwaU9ayRwLMlYxo2KLMqJBUKAcZQYOUaHFQSgEaDZkRgzB1qMq4nMX4EIl/amgjoYsqdEsIQohkYSzGyccyBFrbEDeTrgjCLfMjtmAcLRDFD1hNWhEbhJppJ2JFNlGLiqG8Y1MjDscgddIIaFgNHSaAXOtQclI4i7FUUFBgpLHLFg+PZahnJhhorjJysfDRp0TtYFgoSAiosS8shdUpHJnDTgXfD8ApA6DBAvMCoIq72yFxJs0avlhHFziJoscwQMMfViZwGmFcUKsozSx0zhW44DksCFBQFpAZbHfIa+ZAAiyPEhabAssBKUhnVsTjyYkOQ9WmFpJXuMfNKAgFDjgVO0gIjRIUFIfLM45DPjZACWSIdCojUpR87aRJCExmlLgi5VZJpEiKccs06MKDSFjyAkhUYOCot9S2CUGBFQ0itQlwxliFoEFUmdBQoAXMdWqT4kd6sfMZBSWGOaRHgx13yUeVJTSh3pQmVZ3NMtPFUCx/G4aHlDjqDSY1l4QjirjRR3TUPQ/nQCgscctxRmVYmPi7WoactgQzUOtLC5hAqIAqKlCXGBI66kpmatolwBYWWhE5S5QjkocUYmyzmviA++g+CqClCdkCkks4aT3DfYWYZNFIYy01E9kBMMEAcOx4aiSuEIfMtpYVER5wrx5hUJW5SJziaeSiIA5YRAogPoId8VwqpoIelrWBMMId+lVimQOioqTlJDTcUaCgqELgk7c9xj6HkPwhyvu3A+116u6KMQuuktR7gpsSypDiN2X5DPEj8FneK4sIK5enceg7xuutuCv/YYkyMIqwCUie2N6uWQOB8m1tuMCsxMM4DjKTY1ZhUVYSiakGIJTGgQGMBpacp1JJZEWhFm2jiuY7XwikmDnqAgRkP5hJriJBklkcWIEihIi1M1dwPpogDDBGSQHgaQy2oDdHCcOfZO7AdelZRaKRfUkcKt8R9TLjzcS0DYwjyYQU7TIKSIMsjS5EhppRkrgQiTpvIClggaLCXYVgfl1spiYkoCdCU5YCWa/TafxhuiVNAFAznyBonDRHFAN0QbFJQKWxlAgOZlbpwXi3grIXvCG+S81i6krEUMjsp1heyCzgQJkdeyVAGoQNSOw8IW0FeawmFLvzQxnjhGKfCwgblRklS0wjjnBragRGXtuABIFAxOPL9MfY9iJGAFYuAZwosAWKOgyMaFwhhhq0nLA0twEgQjSMQuCPK50wgjAAOcYMtUtNBVcy7GEFLiWEx5LbC3HGpMHbIg0JqiCF3lY5UXE9riqAoKawtBcTLahceFxtAWssgs5WOdOxGPe+2YRpAZInDPEdAUVh6dFZLt0EupyGnziJksEwJrB2FSNYcTQbsQ8usJpybwjQdtuYo26q9gMMUQuN8A6jzXAZ5bhjyTKpix12GMaCeo8JSqKWooYBclyQmTnIwE9hvBCyjGGAPQM+G7kCwwkkpXY0ijCWUqvQbQOo5YQseVUD6wtbcc4xpCSoXMyRgpO+wSAkCMATMM8jHqogdCvzIBa4AERashhCwCEpWUWCZD4EP/Dorm82iHVOnTCBNw0MA6ChQLT8ajc68/Xq61OPQ1nGkIsl6fr2zrAlyEDpfVJ3QYYI4LnuNste8f/5pjjXRVdWMTcu3AACfF73YBpStRosw5FbVUhaDdv/R/bUrv9bt0CHkfA6aIuMepShd6jGgTejPV1fp2+/w+/d+CxesL/7n//Rf/9v2j3483to8+cqrp773w9lwePaV7z/5la9f/b3fe/G//9Xu91+9/Id/+qk//++nvvfD6fbqaa/6uEnva3D6H7/17Jf/+dpnP/eZv/mHc1/95rUXXny8slkIvzE6uvC1b575t28fbWzvvPrTT/3jP1955mO/5+6+AMpfwfi5f/jq2a99Y7K8tv7OO2e/+vLRxs72G7/41F/+1dXf+/xzf/uPF/716w+efWbn2z94+l+/Nlnd2IdrBz+kOHTlsXv4c+H6jN6v7rwbCVbxWXr7Utu3uYHo/vcFZo4H02Ktz7IiuYruvtsSJvWNvvpaiyQp6pRg2EBFWX7o7l5q80DrfX3v3UZkCljupxd38IO9IuvsfQ8jAXGhHr0lfVHgkb59qcnKgmN97actL82op69/P3YINCTHNy6QhFMwsrc/JQ6AWpp7bz8Dk5aNj8VvnuC5YPWofvgcmocSfvhnbDY4vn0l2JaXnoXzromP+NXzNuu08NXf1ZOhne3phr3+abrn52vT4FdPgWQZD+5/YXZl09iHqVc9+IQ4bpRRIa6dkw9Evjpuv3u2nm2VrXlwZQvPO57Z03tPoMNhsqYmr+aTKxzv8uSNcv9a0OWT2UGw/z4XMZjeZ8Ul3Vovjy+RB5dDts356E767Dn67s3inn/3ckxCsHjEp2841AfMjNIzG2AvUVfInStRKMt8zz58L5J6blep9qku9OIdtnc5iodles3sXQ0bZO5m6PYvQ+nV2SHbf403h1l6Fd3+dbvVTqHps6vP0KrSxPH3nzNYsdKy688alpEJQQ+eI+W0WUyyu39I8yJu3Pvf8ju1tY8O19nNZyHJ6JyAe8/RqujBq39mE5Ye3HJngl9dsAghpdH1Z6VedO2VP8IAVdODvI8//AxPVBmV3rvPWIeQrcm1i8hWKDXg/sfxooKiwFc/jWdo1k+Ov+eKOQ9Qdu+9oDiAnl89+GVU3zH+abj/Ckofk8aK2n+Nzx6zGOynmx3oNJi6+2818htKnCUH30GzPY+ecEjNyl5LfHi4f2swvwNJl+6/QdVN6y3nCqpia7W6OZu958/2RIfOjz8ks7uYLJHxL0hyxcpz+PC75vBeEPeq6Xv08T0vbNZivB1cO1F2x+z6AD08n/Tn8a2W2zsPyFgecvfgacsWaNEPru7Om8mL9Xsv4vqmxerWNti/iN0hyUJ36yJA06e8e3+QPbiLpL19Ety/6MJH5FEHPLrI1aMz7sqnVFYAdXh8wf/wjGlP+T0fPnwGy4f4cRM8eJaYCSkwuP48BmNUxfzyk2VbVfdH998OA1VQnX/0WpckBWmz+y9TBxCD+tHrgpOSLarbv2qRvGLmoLi4Ax8duFTe/GFkDcJQP3hNEofY9Eby/Gl8856u4vs/C+FxhZbpg2+xqsJRe1EMWy5J9QN39/2OSJRu0oMfYjgq2SY5fBmnykcMHPycwKSU0Nx+p6FHxp70u2+dRLO1ojfzP1iHsyUEj93+E+ywUwY5v3Xau+tnq6P2e6fVaMcMD744//VJbY5SUzx8HhyuFXEubp6OH7SmvaP/snj/HIW/0i3/2ikz3sTgiO6tuf1NFx79vrt7sTi4xIb++2fs5DTwH+Db62h0gsBDdLSJHm7a8BjfOU3ubVbDh80rJ+rR+TreK27k9y41fFYmY7L/My5WYHYNPLwUhMMqv2H2roQxXuAU3HgrptUcDR3oBCjJk/fp7cvtRrvIbtn7HzSbYGHhNN9dwYfj+aPW459g1kfVXX33Utwlxy45Wnz2Wfab2/Usuv1a1ICprtz9n/tez5WP7L13omZU5Hv2zqUmUxkB8N5PAoS1jLv8g3Oezl2qzMMXSGYQXqCPXvRnJmtm3qVnneYA5fSjC07hAbjyhyaXZrxXD9CHnyHHuByk4dsXlW03Gx99MXkQqfJx2tV3XhAJzaLa++AiPUS2++B/Tz5qSu+jxZBfe4rnSFQj/fBjZCrTdh2++1Qw5mlvFrx9tq6Hlqbe9V2bR5Wf7L2Jqg9dc7t89CO6fycSm3Txph7f9zpiMb0Gxrc5GaDJu3TxgSMbjpbHyZkT4sbjydXG3q0gFMX0Fjn+UEo40Scii22dg/kb7OBmGPeL0fvk8Q0/Mo9tH1dxQAr1+FfR8Xt06UQ6fhs/uh33esn0Cj68FrTpYnKfHLwn/Z4ZX6Gjd6i/Y70HXfDgWVo/jlOV3f1dYudA+fLykzZO2D5H95/B8oAcx+Dec1RP19FHX9DzqprvLZ70Lp/XQU5H0t19LgQPBub6H2NcVPPHiw364ceIzpRj4tcXS2lOwff+QBfzYjYbbcF7nyTlAiBLr34M1qUhxH//qSqCZIzQraepHYFKglvP87rOvNnDl1meEenrB6/7JjHE6LvvxXRmbBsfvErcQSl2yOHLeJH4sp/rEFprycPy7gfd6tCgvnj4fQL2tVzJag7LpX714eL43VAtSGSmd9+N80cANwq32URZVhd0//t0NpZxVI7e49kExzi9935cPXTe0Nz9oczuYX8XHn4PjY7DTjR57iv/cupb39s7c/7pr33j2S+/9N4X/+Rz/8f/efa7379/8eLpr79y8kc/WfSGW2++df6lbxxubn68ZT85v/2+WPrU//i7s19/+eHTF069/IMnvvHydH19f+fUYWOJ1dVn/vKvn/unl679/h8899d/e/7fXt5/8okdvfdZDI7nx0uvXnr2f33p8PTpnR+/9syXvnzjxc8+9ZWvX3jpa8nJzfabv372y/+SNzvdGzc/+Rd//fD8hWzv0W8nIvzT/6yjOF1eunfxQtpoz4fDRxcvaCEPTu4enT2Vy3C8sXnrmWfqMCp63TvPP7cI4znj1xvrmsjx5ubBE2dzLxivrN76xCcswdgabqrKD6bLa7eef14JOV5dO3jybCL4mMsPGxsVl0W7feuTn9RhMB0s337u+VrK6erawfkzWaM9X1p6+MxTs86giuKbn/6k5QQLxDcojCn2oDsRYt9BAsiOYKGzlLITHIYICcy3uPEgyCtAuGo2CYFuN6C+BpzjTYZDBIoSAKTDyAWMr2HbEYgguM2RB7FSxnEbRUgitoJch0OOxSqquj7CiG1T3KYOIrqOeAtWnseXsetRg2DRyeuOcg5kg0I1KoNx3UxVt6ooQf4k7ZmSy7qdqS7Qrrjr9Y6igUGwbmaqWyiKYbSYt7Ri7gGPjvyWAahqL1RXW2jquJz3jHF6xMW9oGcYq6JFMkDEVqq1qHpWQ1vGZTbUENXAz4peoSktOirtOQ5rHDmw5ROJhLTkBKl8n/gObknEIYuMtwyUJ1CMwMnImZrVqpIxiQSMOdzyEAUyNnhdQqewMmnURRwjCekOp9y6kOF1BonGpdJeEwaCRZCuIR37hAF4UhJuXMjpOoES4ZiEyyYLfeJjfoLkAWegSgeZC5Uiru5nZWAstaqbFA0EkE1WKk6nKfezYWEjlTr3wOuNg45lVvfztAEgdOkwq3hlBLwmelMZW+rqTlI1jaYQR4fjdmCY/TVtZn4EEMp6c9UElri6k9YNbZkzzVnSIwC4fJjaWFlny95CNS0gRHacXhGWc7ZE6AAjhOgKhkscIER7kK5QKxhvWrjmWQesAlXcwgTLFYSWBSEQdBBYk7QssTZl0LCBzzrAbfjUKTEAogdKbQHAZWtAmCNNjNYZohANpVuT2CpvCPEKRwSRNqIb1FCCGpiuE4BdLcuqX2nurKzna87BHAqTLqeIayXBYrk21DiW1ktVQc0cgLv+UhIEkOh0rUCkVBJkvcywYozJPdlbUAk8XfWSIiCA6GS5IDAZh/FNr1dw30hVdtIiQICpajnPAoyQzVZS6ztNbD4sjLSK62SoQOQQBOgEx20CIMbbnDSRpYSvINdlkBOxjFXPcwjxLYpaAhaZsRQHng4kW8KoTyGnrO9USxKnLRAmighFdIPAHkcE0mUMmsgVlSNCtVsQY7yK1HIkdCU3MOoLhAEYELgsGLFiANRyABBka0j3AmbyOi4WQwOJgX5WDEvNiArSdAlgq1RjUfWsRraKy3RJK2TnGN73ekp4ZaPOBho6paJF3deFU8dcXmtuQqBsUJfDHGFbNG3dLh0qH9DgUbSksbNBWQ7ySjLrqXyYY2KKWNfd3GCg4qLulSWGLqjnQyVwBXzONinwMI4xWyMgIDSAdBXphk8oALselRZ4HG9wLJzLK0RoFcU4IHyTmIZEFMJtjphDWhskbRDgEIlVZBqCCOitgnkUMFVpGqLYdxSxNUhiXAdSrGLbYEwisWLKXoNAh3ckDR3khK9jGxODLQ6PZz1qCC0GmYlrgFDSnuu2s8jV7axq1YYC25hNYuSYvsE7syC2zpW9hWpbC3XRKpOWNaDe5+HDqAcwzlvzrE9oXVS9Rd1xFXD3veaDeImA0saLoqMtpumgzDsO66puL6qONQTUrTrra+iqulXkfYiN4n3nDVwlPdxCcCckSJEYow2GGYQ9CdY8BLTXc3BVwrrC1hk/1L4HI8Q2KaEOthlcZshVqLIqamGP8Saia1gHHgkg2pIEaVVD5/uWcNol/lAt/JCHiJ6gKpBIQrjFkUCgyegagRzjFhGrqPQpRDpZLRlZpNIrhqn1tBa26qZFiBzT9TBPA4SQTZcTxepSkBuyn/DQcKW6SRFCywxuHBy12o6q92in8iNLbN5LTAQ0NXUvy4WuiLsXLI2aHWhNvpS5UFtoyl6iYmuwTnuqbFiCatWfJy2CgU36cxMBSBAbILxEASOi78CK5zBmy6haiYWu5BpGQwERBAPiBpwVBTCgbLYhY2wZmVWfqUquQNZyVW0t5lWzw4hDfYKXCMQYb0g99PliYhSw3R6iiPYQXmGaEdSnfIgghmSF0wE2iMhVjJYpooh0iVn3EcVHG9t3n75oKD3e2Tk8dyoP4tnq2qNnLiya3bzTvv2JjxtPjlY3H547U/psnwUPZWchomRl9cHTT847g6LZvP/M05DhgvjMKYvJ0eaJvXOn5s1O1uvf/8Tz87CVUnwjXq2FPx6uPnzqfBnFo6XV/Yvnk7id9rAVUCUAACAASURBVLpHT57aW97RYXjrhRfqKJgNV+48/zHy/nu/hYjQ2tmf/BlCqGYsW+rXiNVCZr22dS6N20WvZRwqwmi8ssqUqinNh/3iqC7uTubdIXC2ln4+6GgNyjAabWys/ubyybfezIc9hUkmgtnSkkOo4jxf7uu9LL05nqxuQAg0ofPlJex0wb350pJFqBIyG3YVYhXl2VKvRswQMl1ZBpQUWJAIWIo1prDFNMYWYdhiAtUpDmCMKHcFkjS0BvuL4yYJiOJMQUKbiBKbQo+0MUJkOu4ISWBAMhoIX1fSMxC7JuUcTw6aLo6ZMCWRNASAIYWYCE0thYXEtTjDKkOSNIhHyjltksBBDyvItWcJyWvo6cAhXlYw0J5zoiqRT2lppClopISBTN/Nnjqkq0SkykktLRBliXzHtPLJ/eL0IVpCrKqhrAMIRVmhUHmw8PFxvn2oBjZwFZLaA7UHoEFGOiirwvnaR3XogMGWGciKCnulJMp3VjsrKOxwZ4DByItsQgPAMGhSB6AhJJJ5gkPFGegwrcL8sYeGAXMqYx5qUoexpZg3bK5a6SiAw1BDaAlFTUyQzZjP2kgX3iLtsiZSmNWUytiUSBiMaRNB6Arm0Rg6jCrGI5kXPKwIk5EuuW8s034NiaqxdLK0HFZYAFnUlBhEdaB9O1+IngqUwfR2fnHBIyhUjQUSeS2IQVSFBlJybXFxJluYFxUOrK8MczXxOJvMRffG4onEa1JWFcAzoQW0qqF0nrLclUhCWdSCGMRU4DCuShjWoYW8LqGAApAQVZA5ibhncsdtSLlvCsdBQFloMyCxBLjB57OmchFoSgeg8Sj3TQak8ylsinwemIyTriiQAD4FEbXaGY82vfSoXM8XHlrynYOGUxo461DtCRBTB5CVRIS6dNwKIiKTYh9ISiIAEC0YA15pEFeMVKGFCBuMdaAwMAWROtQGE0sQ9LORWb+bnwOBqok0GNWBJlDnNAB+OVfdO+V55UNNWYUp8rOcSIuwCZTT4jfmE4VkgLiKcucVhlBFKRaLkvsWYR0oDFVJPRfUlkBFRRVZTSAAyDWoJCoHAjUI5bZEkoQACVRCJgKjPGEgcU3OqB0/bkM/Dnk2py0SAOShAgnmadtszh/6ttESos6BBA3KhCmAICGCUkwmHYKJjaWCFMQUegQo5yJCPZMDCSIKAqI0FL6tPa4gAzFzAYEWKw9VgcMWGWKtpyrsGQkrDwCLjHDQKwvga4nLEI2S1YN6HcuqxqKQVAUOAFwzgILyoDxzr9pWsQMQaYacLB3AFadI5o+K3Qd2l/hZhQNFEfTyGktNkfUqB1DBOZC5gUxxCL2iwn5NkYoNsyqjPokhIqiigodWYVoRJmNTYWEQpi2MkM2Jz9rIKD6fd0Lf1J5XUiEiVYjAQQRalEAyPW6jpocZqJngoTOEaExFbPNgmD2UrhtTXBfE45EjxKUs5qExnFWIyVBXTGiASBNjYjMoUQMhjmokGF2UHquxp3xDaF0AT4cW0aoC0kptuS6RD0VR+OGd5OyEdwktShjUgUO8KlGoAlcIsZfszmGsPKwhV4GrBYAWGWmNBHez8wdwyfrGOuxYDWld4LDygfYgtLj2ABSlcl4tgfKsg0RLoLz/Z6E1RDrFDSsJaDHrkOUEhwACWAoBG9QBbAURsUnKbjH1RRen2HOckBg6gErpiRYsZmFSdWQL1JDXFMvYFkhYilgTq8xLqi6NkAbYSB7LdMFammER6YwEgGIcQwRczQWPnEZUCy5CXWFpMDGBkjady44JakBBSaTzSkOwIgyLtOTSIqxCbaB3LbuQSQ8yXVPP+ZWiWBEh2ehIrN+cnSnCkJCyQL4NlCOmQhx7+YL0b2XnNXcVFxZSFWqE6wIFOlCOgZp4VWgdcQYQLNOcCwtpHVmMy8JJFGLigQxK5lkb8BoyF1LoE6idDQn3TAYkiKgLZXbsI8dtU9aQgZg6nzrlTEBjWRxm62UlUc9zFhqfSqFLQG1AQcvL92SN2yI2hRXQQyQABZIgoMyzNeB1yIWnSyCMR0RoCyeVJDSCtCpzL5ovDQEAhRfkg44CtOIiXeopSBXji+EAOZPKoFzqqbuT9GGWLfU1pCXn2XK/gkwzlve7K9c/7P/6w7IdKyYLz88HXQOZ4iLvt4sFrD7aX/RXILSlDPNhz0BcBWE27FaEG8pVL1z4HQvhfGkAECyYnG5sind+8f/X5P7/8YvwT7/w3/7rk//+3fHuzrnvfO/CK9+Zr608/Y1vPPOtb9347Gc+/3/9+dPf/MZv/tMf/e5f/sWFl78129k6+fPXn3v5G6OtrQvffeWFf/6nGy9++nN/9T+f+epXP/z851946aUnvvfKYmtt5/U3nvnqv412Tp77/r+/+KW/v/nipz79t3/7sW9949pnPvOxr3/tY1/+8mTnxKk3X3v+H/5ptL196mc//Z2//qsbn3vxhS//88f/8UsPPvbs2e/94JNf+vvJ1ubE9hbfzYOoxof57Cea94G4Mzt+Aza8LFgsHv2YRnpOgZ6+UkuvppNs8jMTNUv6KHv8Jm7DWVBlj16lkZpzoiffrWOW8ll6+HMcyxQflUdv4qadBCbd+zFpFyPqu8nLtWS1TNLxT1zMUjbODt+gkZp7KH/876i1OPa86vErkLqyI0F4dcebK+n25UdPNfYS0x+13zolJwRGj1vvrgULKOpH9M6ZYGwYP4qu7nb3s6p/1HtrW44lCPcaH6x7c8btvn/7RHRsCHscXjnT2jPF6qj/5oYYhaZ1NHyv7U+ZgHviznZ4iHUw61zeaNxz5cph/80lPu7o9rj3fss79nxzh98/ERz4xXKmfzzK362D0wT8fDS7jJaC4+weSn+lg7iubhrz5ry3kmbvqMUHwN+B7u3Z4jLoBVN3OxtdwnGjKu8Y/doi2tT2l4vZBzDa0Oi96fhd3IkS9KgcvQlafqIf6ez1orGh7KXF9Feos5zaK8Xxr3CPj/FRNXoDNoNEPdTpT+v+0kJdLo8v0X57yiq/9cFprzjGqOq8vYNRIrOi+cEJzGfeqPavn5XVXjs/gtc+GSRHUFTdtzc5XMgsiS+f5Ggk57V/7ZxXHUs1Cq8+Gc8eAy9v/+IkcSWvs+jyTqPaJ2np334iyI4pWYTvno9nExNk3bd2qKmpWkS/Ocn1jOW5f+2cn0wwnUbvPhnPkqQ3L76buiPdIvPROwg9yuMwO/45YremwS6Yfas091VzuZi9WrvHqinTyXsQ3U9l185+otn1qb/rsm/nxV0bbAL16lQ9Uh0+nXyA3e1S9Fz60wp/OO9vZ5Pvq+Ih9Nesei2p7pqemCzet/WNOlgG6U8LeDUJzoD6lcXiFur2k/Rtnd2wrU4e7w/av2mZzmHzWhjdWS+6h72b3Lu9xen9+BCKGycpPWSTqPd+t+6N2rdEdGsdxnfDe568c5K7u/4U+R/tMrTnTWX3N0Pbety6Tf2b2zh+ENzn/q1TvrnTmFb8xjkJH/GF13x/FTYex3exd+sU5ze9x8K/cdqzD0VqwiunhH1EM956b023M/Dw8fhnMFbT0CQPf8SbixFvgvE3a2YrX6eTV40PC7lIDt+gQZnEJDn4EWlMjuJm8fjbiNSVB4vJj21oU5Elh2+RIJ2FfnnwPeSPJmIIp18vSaU8lI9/aiKdCVsd/hwG8zltwtm3Su94Llfc4puFKYDPq8WrhZcsPFccvY7lJIMn+MbrPXHQUL3DwaWWOI4lesDvbUb7QnuT9kdrrduwXHnc/8VQPO6q/mj46yg4ikJ7kz9Y9/Ybpjlv/mbYuUPLlYPhL7vBuK9aB72rDX7Q89D94F7fu98kwX50Y7V11yvWD4bvdPneMgpvhzc78tFQgrv+fje424H+XnRjuXGrWS8/HFxq8f011XpEbk4PX0MxX9hjk7xaBssaXF1M3oLtYeau5Udvkx6dsEl5+HPc5ImbqOTHda+3sNeLo1+SXmtu7tbHv0BdNGJJfvQz2CFTm7r5D+qwW5Nb8+O3cKe5AA/z47dZE05pWY1+CDpgjDI9+qHzWzV8mI/fgP1g6g7Kw9dpy06JVaPvWY/kURC03lmLyzHNEn7zvL+YUzoL3z/fGM9Vc9578yStHIGT+PK2rHJWzsIbp8LFDLNJ9P75eJRXvfngjS1cUEwnvQ+WgzyjZu5fOx3OisrPhu9sh4el6o2Gby0T5SE263yw7C9qr9pjt877E1N3i/6ba81jW/WO+m+s4iJCbNy+siTm2AaTxc8L+OtF70Q2/ZHJb4DgBNCvL4obrudNs8t1+aENBjb9RQneT6IzVn1/Pr+BekuL/JJKPgKdYFFdU+n7qtXJsvdM/V7ePK3rH81nV3FvKS3fLRdXYF+MqzsmfV81u3l6SZWXqqWtefZaNblCh91pfkUnH7i+mJR3TfZ21e7m2WVbvF01t6r4vvRvnvHUncYiw9eflWaflrR1aR0Fx+GeC67vcnbXO6L+jbOefuQVeXzltF8/Igq1Lm0CfxocueDabtNdQ1Ma3j7rVXvUqca7pz11hIxr/2oLiZk31vH1bYEesBkIPzrrlY8JKJvvnpVqAqHpvr1pvDIcl9GHO6F9iAsQXjnnVWMlJ7NvVHBRB14xftXIvPBgefw68sZz1gaz71Ty0czbBMk3czUFQUNlP87YLAtRevwWZsc568PkOzl7sOiuZ8evaL1AfkPlPy3QRHXA6OgXmD7KxBpZvJLRB4m/Y4tvJtUhaIXp7E1jD3Wbzce/gOBu2lyuxt817nbe2NH5t5P0gAw7k0+99KUnv/ntg3Pnnn/pXz7x0r9c/aM/+Pyf/8XFr33t/seeufD1bz3z9X9brCyffu1nz3zlq8e72xe//fInXvqXjz7/u5/9n3/z7L9+5dHHnnniO9977isv5cuD7XffOfuNV47Onn7yO9/55N/93a3P/84n/v7vn3/ppccXntj92eufeOmf0+XBxnuXPvU3f3t8evfsD37w6f/1N7df/PQzX/23F/7h7xent4bvXP703/x13u8vX//wd/78f+w/+WT+6LcRETq7+OKf6F5zsrU2Pr2TdLujkyfGu5tFp7X3xBPJ+lLaae+dPT3e3S5a8XRrfXTm5Lzbm2ysHp7erdqNx6d3F5tLSbf3+MzuaHenbsXj9dXx2ZNpu324s3N8ZrdoRoendxeby/Nu7+jsqaPdk0Urnq6vHJw/p2Pv8c7O4bkzRbt5dGpncWI16XSPd3dmJzdmg+XFxsrBubPQhyw23tCSCIqGZksENXFDzNGmJz0tvIpvMeFr3nD+0KIY84YJBsa2ZTPI4IYUvmZezTa5DDRtgqhf4xahbRgMjGnLQCZkUwrfiECzTcZ9jWPrDSxpERFrf2BM1w9kSjaEDJQILV/HgV/ZNveXLGgQh2d1OwNhieii7OfYzxWvbHMM4sr4NfUO6g4wslCdDIalI4uyl2E/07zW7SmIS+1VKDiqOxCRueqkMCwhnRX9AnuJ4aVuTnRbI7JAwVHVAY4murlQDc3wuOokOEi0VDYeq5YmZI6CI9PWluW6nVZNK2ju9xReYlzqsFHQJQIj6sclXuG+rGXPNpqljkXQN2jImGfiKMXrkotadBxZoUzWXlt5HQNDEnRqvMxICFrBAm54kle8afgKFr4RXed3DYiI3679gTNN2QxTuO55UtGGYctE+Ib3QKNd6pb020oMYRV7DOxVSxURuQ5y15pZX2lvAZqzqoEoPcwHymPzQpZqWCKRKZna9tyGynmpa8zKBob4qBzUSBaOL6p+jryiloVrTm1srFyw8DBpC0xH+bAmIjN0UfVz7OXaK21rqmPt+Bw0ZlUDM3SYL9VE5I7Nq0EOvYp4LmqXaMC5b8Qy4m3IZU3XiRcb4jt/ybAOxCGMWiVcEkzUcghQjwY046vYa2roQW+oyYBKUYWtEi5J5mvRt7hHQ56LVdgM86rhRwONBpTzKmiVeIkLUfEhxn0meSGXrNc0IMBhrxJ9CCMStws8YARnupHARqI868KRatdOVJjslUuW4kT7i6qvIMtcMIbxQvkG+MegmZQR5nSvGBjCMy3nulcikas4BY2F9qzxx7CVVBGm7KAYaklmRVSZXgFYrqIENhY6cNAbu1ZSRBTjg3KoCcuMn+pu7mSp/EXdrYDvIj4nG5IHRgSabTEeaBJab2hJG/PIBn1tup4vc7rOZGhEaPgGCb3StESwZHAb80D/x0zs5WiDy9AwT7EN5oWahs5fMqSNRKiDvjY9L2QJ3WC4gUKesnUiI40CJ5Yc6ZBA5n7P2L7vi5ytEdigFE5Nc6LbGtIUeYd1x1qWm3imWobicd1e4CDRnjbxSLUNoQvoHZqOsrzQzUS1DUYT25qhIKk9C+Kjum0gS4E8Ul1D0KxqpaBRWJbq1hyFSe1Z4B+6dl77jsjDuqcxSapmChqF47luLVCYVAFwwXHZtz7OWUPzVSp8LTrO6xkQEa+l/IEzLa8VpGBDep6ikWFrVPiadUHcKU1beC3jDZ1uyshL8JYnpeYNy9YI8wxtGq9vYIv5ceUNnW75oZfgTel5NWs4ueJEYGCP+H0LmtQPCrkMVMtv+SncktJTNDJ8lZDQAbag4WHa4ZBNy0GFvdzSRdnPsJdrWdnW1Ma1EQmMJ2UTM3JcDCoic0fn1TDHMjW8UO2ZiTUmExhPijbG6Ljs5zbUDI+qXoL9THuVaUx1UxM8BvHENK2ji7qX68gQcKw7cxhktdS2OVUtTdDENBZlCwY0E0ugGRV1Q/4HQUKqoJHjFSFEJYYQD5ngpTc0fkuDgIS9SgwgiGgc53hVSFawPhQ9yH0jh8BrGRuyoFPJIXQxixslWhFSVLiHZA/wwIoBbDTzqh2GnVoMoI15GOVkVUip2ADxPsSeEX0ju0CFlNH9YqglnRVBpfsFYJkKUthcmMA6f+pai/+XICwyI1PVK6Asaj8BzbmOABAjFo9m7YDSx/mSJjzTPFG9HMlCB5lrzlRkERu5dlLGhKP9fFlRnlmxUP0citzIVHUyGyhMDkFrkbU4dXvFUkVZTgPnLxnaRiwwYU/ZvuezjK5T3CQhS/kGFpHGPhBLFndpIAq/p8DAk6JkKxi3cMwzvg7joKibXjg0qEs9nns9SzpEyJqtU9ymPk3lKpCxgSEO+zXtYRY6v2dIF3usIKtMNizzrb8CRMu4gPh9jQbceXT/9JnjUztFu/H43OlkYznpdA9Pn5yd3JgNl2aba4dnT9XN8GB3d3pyM+9398+dSzaXF73+0e729OTmvD+Yba0fn9nVTf/R6Semu5tZu7l/9kxyYnXR7492ToxPnZj3+tO1pcMnztSt6PHu7mR3K+u0DnZPLXbWFv3heGs93V17vLSx2FjdP3+mbkWH29uPLl7E77772+oiVDKgyiSNtqISKpuGTYioAbhiQkGKHcplmMuQVHoRNhUR2KEsahuHNcCFDGsuKcCpDHIqRK0XUVNRgbRNZAAwA4SXXlgxgUqVhY1KRrw2SdSsiAAGJkFsEXUWVURUhFPt8rBZCI+WKo2bCEJhtCGspgxpoyhzDNPSGII1JbQ2llOLMFHKEKoZF8YCQixHuDRaskpKXmnAiCaUaW0RriiHtVWEaUk8ZZXgpfRwoWvKNBO+0QDjkgtY25qK2hO8do4jIzgqTU1oTQVTNYCoFIRZ5zAo/2/a3izI0vSs83v39/3276x5Tq6VWVtXVXd1qxct3VoahIZlBgzDYk/Y40vHDCZAeAJkyfhimAGGYRcCIYkBIaklJITUEgJJoKWBFup9q+7qWrMqK/c8mXnO+c63vqsvhD1Xdjjs4Ln+xXP3j/jH88T/eXwPNaYhpOQBMzV0pubMGQssKARFGlhgG064tgrB0guRrrDRDafYGmRh7VFoALBGCkI0cFY1zIOqxkbXjBpngHalYMgAbKXkGCpnraq8CDrDjK4JstYgA0qfImOd1TUTCAChtSbUEkIrKUNfUyqUcRxbA7AxtecDDGFjDKOEQtaYOgwsIUQqzRkBgBvdiMBghJVphIAU4doqShxC1FjDmQWQWN1wz1IqlDGcOQZRY1XAFWVMWcuZQ5hppShvCEWNlUIoTnljLAGSEiIrg0DFBdHOYtJQyGrbMFxzxmrdECQpZbpxCBTcA8pqQitBfO0kQ7Xv4dpUjDVMMF07CBqKsdQak0oQ1jhNnORUaKsQbLjAsnTAaYap1g7j2iO4cRYaKShV1jhjKCMQQAvq0AcWQuiMoEJbhFEtAmwN0VZzhjDExilGoQYYOCkENMA5W3kRdUYYoxgDACJtm9CHDjNgDSdAWeRs6YXIGSCNYZRBhw1swgBaAJ1VjDJjqLW1F1oIsDRSCIgAkE5SjIAlRtXc19CiRjecOCR5YytOJSFI6YphBwGWtcTUOEeVUZxqZLAEjUcMJVRpzZCBkMhKUlZTwrVVjEoCWO0qjhWhSJqKUoch15WkrGQM1ab2vIY6X7maY8k4kLZi3GLMTKMRMoJw5bRgdRB8R0GSeZ7RCKGa+0BZRXgTeFw5x7H2BKqNwlgyj2kJILKcUWMtwjrgSFpIoRHM0xZQ3HCPWI0s0JwT6xxEWlDSWEhgIzysrIWg9kJuDXVAMoqUtQjXUUClRRg0lAPdEK0qRg2wSLvS49ACYqTkEGhnjar8CDjDja4JNNYQ7QqPAueAMRWG1BrodOVH1ikobcMRdIpoUAlmHXTWVoxgY6CsGyqs1VTbRlALDDSw9hkEkBgjOUHWYlVWXiAhEMpKTg3GVFkjmAOAaKUYt4x62lpOHYOoMToQknGmjGXUIsy00pRJQqG0DReWY19a6fGGC9iYhglHSGCUobyhHDWm4Z7yuGis48hQBqVtKDeYMK0NIYBRJrVmzPgM1wZw5Bjl1jmCJcJQGQNxJQiVTmMrOeHKKAhr4WNVAmclI1Qbh1DjESydhab5joKslkxA0yBrK06A1gbASlAqHQRaCgKlMc5UfoSsRtbWGCKlHYSVT6k0BpoaI2G0A7ryQmAktFYyQIxyACqGgXLY2coLobOgMYZTghzRoAl9ACA0VjHKrKXGVF5kIMDKKsEhhkhaRQl0EAOgBQPWEatrL4AEe9oawQCBSDkZCgsxd05zDgFkVtXclwijxja+BzgWxjWBsAg77SruEwg8oyX3JHW8thUnklLSmIpSQxA3lSKkZBzXpuGiZsBXtuZIcoEbUzGmCGW6MQg2GJJaN4xWHPPaSuo0JVwbSbCkhKjKIGQYolIbThuOaW01tpISoqyCQBGKdW0hrBkmtaoprRnhjbHINNwnViPrtGAEAACgFhQrizBsPIGVsc7VfsSsYc5pRqG1AKA6Coh2FDvNKGgMBK7yAmwN0M5yyq0FEDWhh6UGCEpKuTHE2coLnbNIW+1xZJ2zQHGGlcUUKc6QNgjYSoQYIW5c4/sKMQPwLEgAINZhSUVDGDWuiFs19bC0s1bXEo4ArsLEAGwBrkRYMp9rV4ZJJUJa62mcGkiAcbkXGeYhzErul8wnZVNErTJIPO2ypK0wd9oVPDDUQ4jUzKuZ4GWTB3HhhaRq8qRtKYcAS8L+CX8RPvDxT0THR1WnNff0i91bty0nq99+anjp9f3zpx/488/FW9vX3vK2N37yU+nOdt1O2q+vL156JQuTlaefOvnUt4/OrD74Z3/eurl+9cE3P/C5x/s3r9uQt66t916+nLd7iy++eO5v/3a0tvTAZ/6sd2P95j1vuO8vvjC4dGmWpIP1qwv/8FyRtoeXL1/88pf37z57/59/bu71K0erS0vf/Pbyi89PW53Lyw8fvMjnSW2l2Xs5DDuAH6md9XbPryEsb1/rdnSN5tytF9MhqrkrNl8K5ljTzOqjm2lPT1m7vny915FltORuvZieuGvkwfHuleEQlUCV6zfSvp7htFi/1u/mM3EGXns+Hcqa82zz1WjRTBlWGzeSVtP489Wd11sLB4fwbHXrxeXuXdXQOrd71htPQXHTHN4T7x4Yvuc234X9PBQvwFt3Y7HXaW4V45OC1U7fUKO729uHmbgEN7/belJ4L6CNs1BkLXPbHJylSFFwXR9ciJqpbb/GN99ce8jrXqcbZyDJW+CWOTqNIGTRgT044ecT13nJW39jJXwx2CDbQ2xn/fqlenIKoxZf2Jy8RvLDtHPGHb/o1HF6kh9Mq/7hZpKkMN/z3eu2G+6PLsUH46R9BlaXSb6dLkVHcq/a3O2nkZmOPXUJLs1V1cv2aJS0T+jysh1vtQZRUc3M1nqyQMt8Ro4u+yf6pXzZbB3EJ7p5fstlO8k8PLbQbVxJF1gpS7L/WvIG/w64ae6MeifTcRj05OH9wm267p7dfBOxVzx8DDfvseR6rLA8PJPqKylR+4f3R/KWXdh3W2+m7loLj9yduyG4nu67ZnQ+kbdAqzAH96XZdXMyh5uPEH0rnI3A9gUvydtHTh7dHepd0NvWe/em4/Xi1A7YfDtSm6Ic2Z17uLzeHtf66G5f7oP5Lb13XzDezuPNo+eRH4muPty8nkDq5ky9fSMOmmKur26/FFgGV9v56IVAMK+LZvs3Iwqs8OX29cjPZ/OLs+0XeM1Fe8mOLnFmzQCOtrfaTjE/BaMbkT8r59PDW892chZ3lsHBZWGVW3Cj8TrOGhG39eH1iI7l0vLs6BlYomSlV05f43UZLLQ1LNbInY4XP0vXB65cIws3xGZfHnfbt14TedCML4bBBtA+3uzj/ovBrdTM1lj6LN3vqclisv6aMIHcu0eQy9AId+cMC//B3/X07C4WP0uPW/JouX31hcDS6vjeGF0CKLKb573w2+EOM9m5wPsWL6P66FQbXXEMwK0LiX7ZeiHcvMhb16uD443rSbcpSGe2fn2uNZkF94JrL6TdMzLKsjuvBIMq5zvjzetxXDT+UnXnSmuwc4TvzW+/eKK1JqOy3LoUzK/O4P5483ocjCU7LW+9nnS2suSt6PbzcXhCp6rauhQM5zM8m22uJ2lhLykqowAAIABJREFUHJQbrydpMOsm1ebzHlzzAmQPr0Rz6REvjrZuRmKM2LAWm3c3hPnzV9nGKQBkG11Tk1NQUx7tuoMVb5Lbzov++gM1ifniLbo9B2XTV8/V2Yp1c6K9a0bz4UTLzlP+9bsl69HBDbK15CRvodfI0dA2AyJedgdnWeahpaejqyuS9L3kGbC16GTUQq+SbGBnA8FeBHunwDQRna9HV8/WcNE7eE2uqzvXWgNcNYbsvRSstCt9U29vRsutvNgEk610CI4hN7dfbw9xCRDaeSG9m2zCXbW101+Np+bIra+nQzi1rNm83J53ee3RzRfaq8FM79fbt5KT3ggV+tbNVh+UMjU7l4LVYl9G+NaL3SVRyEl9cL11ih+Z2ty+kbZ0Befs1stBV9Xp2TbeXBOxbE+lOjof1CMwd0fv39c+vJ3R2+DOd8HuvqfvuO0LtLXRmmXm8IJXHoGFDb1/X3y4MwvW2caDaq4I6C7cOcOCO61mqo/uYVVOvGO7d8p3uzC6xtffJPvO8w/A3oJgG+H4ZnV8Hy8BTQ7U7gUPHevwZXH9/mKOM77tdueZKLi/v7seiKNq2Dq883xn6uLOSXD0GpcFWySHs9vuKPfSljm+GaK9ZnklP34OzHSy1s+zK6KYRvNiNtnF45FYFsXBda/aQydXyuwZt90kJzrZ5CpvJv4iOCqnbLQjlvzy6LqY7dA3pevyabDbtE+1x/WGPxqFC3A8m+LjW/SENx3dCiab4bnhhIxONoerbftyBNHo8L4YXAY+c5v3CfFccIzs0d3ce46VQX14puWugkC73XsS+aptIbj1IOUv8QKZg/Mds1tD3BzeE8NbMBnb7YtRfVm2pbvzEKaXsML64IxPnweANcd3BXADdo7s9r1BfV33C3LnPhzu0kaaw6UAvAB81BzfL8heM7x8+4VIDGwPVduvBL2uFlW2dTOOjw0k8s6VJCZ5v5dvPu/ZeS/27OFrYTscR83h1o2Y7UMegs0rcUqKgT+5/kxfDcM0dsevhzy2w+lo62asN3GcmtuXEx/K+eFs9Ay2bW/o16PXBeJgILOdW6G9DVd4deu1iAC7ujDbfxrN0nQ12L/rG99Irm1k/eHpv/n66gsvbD507xsf+1S8t3e0unTyr77Zu3lDCj68cXXuuVfq0Dv9xN8tPfvC/rnTb/7TT7du3z46sbDyzaeGr10C0Aw2b7aeuSzTaO1bT608+Q+jc6fv/+SnejfXs8W5uZdeX3n+eYlxcLh/+qvfkEm48vyLJ5/4+8NTKxc+/5cLL7+MutyuH5/+1rcUwl6d3/25L+2vrI3/6X4RFouL2XB44+E3j/uLk+WV2w892CTJxj33HZ49Ne0ND06duXH/A3l/rhwMrr/14XFnMFlcvPqWh5u0tXfu/MH5s5P+/Gjt5PWH3pjNzRWd9q13vDXr9feXVq+/5eEqjg9On9k7f/Z4sHi8dvLqWx7Jut2y23v97W+vu63j5bXrb35LlaQHp07vnz9zNFyarqzceeC+g+W1utV+/dF3QA+hCOFlBrscxwSsBSR12GfgjGApUJ4fnMKgzXAIyQqDXQZjKpagWwgM4+S8IAl0nkfXMOxwFEBvAaCB3yS+WEJmPlCU0XOCp6DxfHGGuhaDPqDLBA48FzJvCanFSBLG7uK0jRRj4VmKUqQSjy1hNaANJUW/0t1KE9YMJ1WPaCybdlb1TSMcTIpy0ORBVM2Vul1qjKq5adPDDdW2PavmtGbGtupsKBtPyM5MtQvHUN7PVcdpbnQymwwBppVpydmwUYI3aZYNHEVN3Z+pHlSekUk5HWhIS50oOV9Uvl92i7wLCTO8bfVa7MUGhpSfIXUrphGAJzkUwOtDbwBVJ0IdZE5G3NcwJPishwPsOh4+JZCAom3QEoctTlNoT0csNChi6IwgAQCJ4CcJCjDpIrLEXUJIQsQJpOciJwg5L3CIQMzwKoERIS3ozQMziHVIvDWStQTFuhgc6xRqWtVzddOySljVy/OuVYTkKyX3J9OgVQyOVQtqWjW9qmlb5QHVL2ZdpyidLc5sIhWl9cKkbmFF66ZXVG2jPEBah8dD1jBRLGUuqjVB1fy0aWNNa9Ur6o623KpuNp5DjtFqMDZJbRgqhjMba+gT3sNu1ZO+z5YoX8CAYrpKwZxwHvYGDq54KCCkg+Gq0IHHB9Cs+BwbvkrhHHceZn1kVkPmGdIm+JSvAp/OAbPsU6S9Fcy7TnZS3IVuLWBcww6na8x5DC9yu+wzrNkCRPMCJpx1EV0mJuKww+AKBsTIqKzndBMBE9WTJWtoDnwwWSpBoFWIZsNcC+uCTPaaOnQ2UrI3LRNkPTRdKqAnpe+q4UyHQAZFM1AyMsq3cpDNWkgzVK3MqCgnsV8OZjoEysvrOdVEWvlALsyylGiGZ0sTFxrpwXqhkBFSIp/OA9vFFkNyl8c7UHLhnWGuwyB3dInAec8F1FtAeilsKGVnBO0iRUh4F8cpkrFgiwgOOPIxX8BqOXSc8dOMdaGjhJ2ioCtAgLx5iOY5DCibx3opgIKxFajnQwEVP81BlwEf43kKlzjlQCwSuxpbQsUJIHsRwYWOs/EQYlqYxMzmy9oXKsmyocW4aboz2QPatzIupkMDWGEiLRdmdeCX7Xo2JzFsZHva9EDjA5vW4wUNcKESl88XQKCmLete6Ygu24XuKhlYk8imn9URVjHI53PHXZM09aCx3NbpTA6s9K0M9WxRMmFs7HmnvqMOhJc4SCmOsTiBzCCyjNDzHg4hiBhZIzChOAXeArCDSEXUW8WyH2mK2QVBIqAjj59iLiY4cnSZg54AIQlOoGaYaIL5eYFjYAIanKIuobbF6DJ1HUoDzE+gepggTthZyhIEfMpXiYlcwx1uHU8GqOJeuTCzSa0xqobTpo01a3Q3r7racqs7+WQOGM7qubFOastRPsh022mmmnaZ9wymUnXL6RxwlJS98ayPGKzq+UJ2nBKmblV5TwIqdaeUc00lwnJuMutC5oq6N6t7QAZAtetZr4FEVj2Vz0GCtLeEec/JdoK70J6MmFAwZeQkAx5FQw5WfUo0G0C0wEHKWRezFWJiBlqcnGLYQ6TP8ApDApIhIQvMhoS0MV/DuhOAiJIzHPsYdileocBHpA/9AVBzqYmxd5I07dAGWJym0MdgziMnBPAh7QC84pUp0hQXJ2ZM5JM4KgdTHQHFi3rQNLGRPpDDbNZChpLZcuYiLQWq5/MmRoqXzVxdJlr7EHd2R4NIU1osT20gFQP1Qt4kSPOymaubRALhZH8y7jJIcTk8spEyHiwGuUqA4VU+p2RSI+7kYDrpMkRAPji2MQYBEUOIFzgMGB1isxw4j/MlqBdCDg0/zWCPOZ+QeeKWBfMAGxK3Fmou+BJUCwFDylulrANkO0EDApYF5RbOC7pMtfD5GlELgYCSrRA44CDhbI6QRWwCDuYFXUSAM75CwDzHDNBliobcBBT2mTsZ6jDcP3P+5oMPNlG0c/784dlTx4PFyeraxgP3HS6cyOfmrrz97U0c7a+d2Xrgvrzb2zp7/uCeu46GS+Ph/MabH9pfXMvb7ZuPvl0n4capC1v33ztrt3fOntu7+9zhwko2GN5661v2F9eKNLny6KN1Ox2dOL314Btmnd7uqTO79969P79czA323njx9ql76iS58s531mk8Wl67/ea3sGef/qdKEZa9nonCo/mFRgR1HE2GcxaT8WChicPSj4p2e9rrN0FgfO9oYaERQZMk4+G8wyTr95skKoKoSFvTfl+GkWPkaGFRcq9odcbDeYvxrNup0rhIWlUUT+YGTRA44R2srABK86R1NBhaSvJOt07jPO3UgZ8N5mZxGzBysHLCs1WEZib1nE9Td1y3IuXz5ezGZGkeUThXbWfDLsEmhLmLuQ15Sx/p1CuTZGV8bbKyiCjsFtvlQgdTEOIchlhGfmLGJvXKdmvl8PJ0aR4w3K926oUOwi6CMxhTFfttNbKJyNqdlcPXZyfmAUe9YqsathF2jEoS4SoKbHWkYmdDpuvjpgUtJ9Jk2lcmZArWDudVJ25UbkJrQ6aqI9mCVlAJK80qE3EFK0eqqhVaOW1CayKm6olKreVEgkrRqkkD53KHi6odApUpX8nIQ3IsQ20Fka6UtJYtH9rCgFnTCpSpZGBkwH1iPA9UqY84Somr2sxRFjBbdjwPQ+YD4EEVeF5AqkRgDhJo8q7HCOA+q1qCI+sFzoYUeNQTsGp5kIIU26InOIZcoCalhCIWYhtgFFCfAtnmKvBa0JRzHkWQcahbjDJMIgJ92Pgi5Fi2WS0EqfbKNnUUNrDQgTMelmaqQyRjz+X7Vd/HxOSq0F1qCZBupn2jA6pMpiPYRJ7LduSc7zhS9bHsEEuhdJkJnAqIMhnwddZKbXEge9wxJKsj3cGaYQkL4xnlE60zHYAq9mGxV/a440g2Y92CjmPMMfNg2fJ9jGBKdUgC4GCXGY4pxzTEMqSYY9+DRcvzEYQhqmMvgha1ieGICcwCWCa+YC4QME85h5DEuIl4gh2IEBJAhr4fkDIRHnWCgarFfQxBwptIhFDxBGmPIJ8KAZuUQUoCAeuUO1s1pDQhkdRJkDct3xKFm4OiH0CopJtVbd+52uCZCYjmVumxbotKEFjt1XMhhEq7TKbUIt2QyoZYcyjVkW4L6VOQ71WDCDlZwNq0qYFawlwHSHtIq2PVFo1HQbYlF2ILtbJT1WIaG4VyGVHrk7aVTZdbTkICdZcBihkHKCY6YB6DICJV5LWcln1hOQ4hVB2KCAKhRyOsfCoIgDGpEj9xTnaZEziAwHS545hzhCIiQ+ZRiGNcxl7qjGwz6YsEKtdhliHGAY5oE4uIOBzCPPQSZ3SPa86AmSpS1q3AgRLArO6EVheG103iIzWRvnIelaBqcClbAQSltdOmHShbS0/J2HNyokVlAiaJliiXbR+ACrhp0/GhKSoudUiNKaXXWJ9IapSZqE5QY+vMWHYCaMqG1TpiCtSS1y6gklqlj5t+wpD1OKpblBDEAuxCDH3iU6BarPG9DjRl38cECgp1mxGGaUigj5qA+wyrFlOBaFtV9jxMscexalFEofCRi6jxaUiBbvEy9LtWVnM+otAj0LQIYISEHIZY+zQmVrV4HfltYKq+QBR6BOgWMwwqOwNC5e3EVEeqQ53AsjySHWw4bkBpPKlDqkyuPVOnASgOqg51HlH1WLegZbgBhRKqjj2gJ8bTVeKj6qhOofYFag6byFqOpSskVyoRUI01a5rE1zKTbaQ8AZpDFSrrMQmbhkqVeEhNtJB15EXYoQhCAWXgBwEpE8Go84kr28LHECW8jniAjEig9gnyqBBQJtRxFjFXtrmHIAqJigijmMbY+BgH1KegaQsjeExs2fU4dDjAOqaMYRwTKEAT+hGHTVs4wWKky67HCSIh0RElBPCImJA2HnPFbjWIEFA5KE2bGmQkzLUPtU+0HusWa3wOsi05H1lslZqoNtXYKZSbEEgPazUGMZokCch3m4HvkNNqrFpUESBRYQMnPWzUsUpZFQiU7xRzASBOqqlpY02cAnkTIOkT1IxUSipf4GKn7AlMQAAK5psyjSMz5YGZdnvD2UbTC+soGpZ3zFxoBA1wyYQqOu22OhRcTrr9frZh+mEdBoNm16YEeBQy5/M677Tb8pBwXXTac+WW7AZNGPXqXdzGyvc4VSHMZv1OpKZCqLzb7lW7qiV0KGI9JbFToc+5jd14OhhAgied3qTXt4Rm/X4Th3nSrQM/GwzyKLGCHy6tAEqyTi/vtKXwp/1Bk0Z53Gk8MV2YL+KWYfTwxApAcNxbKHqdRvhZt/cd/yA9f7o4LILEEnx08pTDaNbuFt1u5fmzbr9uxXnSVp6XD3pH6Rwg5HBtzWE0a3UnS0vh3//tP8mh0ekP/jc/+kv/8e6/+pKN/Xs+/4X7v/gFGPpv+MLjD//RH2289U0/8Eu/8tDnP3fr0Ue//9d+9eIXH3cBP/+Vv3nTZ/60iZN7v/j4ox/58NYb7//e33n/Q5987PbDj7zr/b/1hsc/zyN+1ze+/uDHPtG0Wvd85cvf88Hf33rj/d/1/t9702Of2Lx476Mf/9j9f/pJ5/FzT3z9zX/8MdnrXXjim9/7G79++5E3ftcHfu8tjz02OXfq/s9+7qFPfFyH3nDz9g+97xd05M2/+toP/Mp/Gi8v3vv0U2/7wPsRRvO3rrzz138rzTPelP/y599jfD68dvX7fuWXVa914ttPP/qhD7KynCuOv+c//HIyPsLO/Pi/+zmG1WDrzjt/7TdAwFZeevkdf/DBqK6jbPR9v/hL7Z1NEPo//u6fJcC0t7e+9z/9KmZg6cb6O37vA8nBvmfqf/7v/8Ngbwv4+Ef+l/d4VbYbrx08icFu1ZUHd54M6NXj/lKz+Tmkt3U3mu5/xbFR1c93N5/z3J26A8ebT/r8xqw9mG19DjZbthdN9r+G9J5Ky8P95wi8XfXJePtJT1+adU7K/T8zzQ5qd5rJVyq52bTro70XuF2Xvt8cfMOBV6bdE9XxF+x4g6QdOf2b0t5RS/XW/kukuKz5khm8uBxsnLTd3f5zCd9Z61RX6GjBv3WSu2Ox0+++Ogz8K8nlJbpxziXb3ctdvnUqVndaG5jcOs/csXc413plGcV3kld73p1zLthZvBHBjbsCuRvvUrF+IdY74iANX70LRxvp5Q67c4/PrgdXI7F5Ps5ut6aMXLvXN/veKEpeORuz1+MbHXznvhhcLXab7W9gfzrzbLH1RezrPBjnO0+wCOf4Vr7zrGhN9pey3Vf+vhceHvu03v4iDmwTTKZb3yCJHrPtfPtZ3z88Tuvj9W+G0d5hFOqtxxGTtZ/Ndp4g880B2ZneeTbko2lqpre/HgR709DLNx/HtGqCMt/5WxqUmTeabj0tgv1JG2W3/9pHm1m6IlpPn/KzIM232I3Twa6I7Da7fn+yLmDndvrUfd5h22M341fPscM4qvfY7TOd/RDh4+jKueSGgN1b6VP38KMFFOx1nh0Goygpt+j22XC7Ddk4vHy6fyvk8aX42QtktIK8nfarS3zUS4vb/vq8d6cP/XFy5Ux8vQ17N1rfPilGKxG6nq6v4t15H25NXquzv3OdflF/u9x/jsa9Cjxb7r5IO+YY35hsPStauNQ7YPzXMpmvq6fK0fNsITmWL1Q7L4pOc4C3y81vi5abqi01+prrtKbu5Wr7OW8YHTcvV/sv8GGxE+8eX/uHpK2P7b7e/xrs90rzwnT7OW8Bb9vrevsFr5WN2HG+9QTv6GN0bPa/CkhqhiPs3bjYmk2SbI9ce1u8P6M8S555UDTan038V++JmnF6VLHb9wdHRVLvsStv7R0UHrztP/9WXllRZf7li7Esw/0pX783GFWhG7HXHkl2FQoOWk8/wCsQ1cfe6xeDrPBmFb95sTMxFmftl+4LdzQO91tP3Q9lxGSWvHIqGedBmbP1+6IRruam2adm5QbuzMvZl4vytu7Jg4OXqbqmQ1GOngTuhax7opx8yRxdJ62hyv86b27Z1Wbr6BKavmqjVB096eyLdfdEcfx4Ob5O03ZZf0tOroKeOshfUdklMB9Nj57Bs2dU+0wzfryc3GAL0WH2tB6/DnvyIL9ixy/DXjA5egpkz5j55Un5NbV3RXQ65dwGY+sXY7kvxl546QLxdpIbMbt10We3wmu+2Lwnmm60c4CvPhA0R2LCkpcuhPh6eickt++PwPXwdsjuXOyUO+Fxza48GOe7ohTxyxcZ249vEb5+b6qvtbYZ2XhDOB1FReW9/lC72PYzJV55iNJRuMX4zXsTu+VvWLFxfzgeBbLxLj3k6aKsDg+/bLvlsb8/vvNMyPezlpve+kbgbWVJkm99DsJMxSbbeYKIrPCPJjtPc2836+Lp7a/5cH2aDpq9zzpboAiUh19T7GjqZ7OtZzjZqYSv9v7S0puT1rAefd6VExKi+vDrMjjM57Ld28/6br30+2Dvi4bcKluDfP+zTXWEI1SM/97qnSYOZHLtXP9GLOJX0xfOob1VFG63X13k+8O0uh3e6oiNRSSO4xtryZU+7NxsPXNC7J8M8ZXWrWW0vRw1O8FmN9hY9skdf301vLqMOjfaz66Q/TMBuRZc7nvbJ9LiVjzq85trAm8FN5fiq8tx+EL08greu5CYV4NbQ7F9sl1teLuJf+WET+4EG8vh1ZM8uVU9N919Tszlu72jw9efbKX1MZqqva/CTlLB1ydbT4kB3kc3m63nvGR66E+nd57wWtURrfTeX8HQK+HNcufb/DS4I9frzWeDcHLsV/mdr3tJOcFFtfOXOOIFvFNtf5v37CHaLLae8lqTo0iXG1/lIptyo/a+BFkA+Z1s90nUrXfBkd76FufjYoHt/Hc/+VP9O7dB6H3fr/5a785Nryje9sEPrq6vZ93Oj77vvWv/8K3dB+79V//2f+7fXm/i5Ad+6T/Or1/lRr/twx9ee+GF4+XlH3vPz51/5unRfad/+N+9d+G1S02aft9v/trg5o1Q5m/90IdPPfnk/l3nfuJ//bnzX/vaxlvf9OM//e4Tr7zo0vjRj/yXxeef41Y+8od/eO5b3zq+a+2H3/cLF776ldtve/jHf+bdyy+9lK0sPfzxP37gM5/NT5x4y6c/+eiH/uDa9zz6L/79Lz7w+OOTu06+8bE/fcOffxYReOEbf/PGj32yGPYf+syn3/7hD2088qbv/cAHHvj0p2enTtz/p3/24J99hhN34VtPPPhf/qQa9h/4/Ocf/f3f33r7I9/12+9/8NOfqua7F7/812987BPEmbXnn3nHBz5YLs+/4a/+8p3vf//O/Rcf/ujH3vzxj7HF1spXn3j4Tz6KrV659NK7fvt3jk6f+f+QIvx/tVN0EO5dvIiu0sOTp9i4pNYcLC11FhetRcrzRhfv0WGUBcHW+fMU2MOTp1Fp4zw/WlvrbG/vFnkVxdtnzwKAZmm6fe5cUBZby8s6r/DhdLS0lB7s79x9dxkle2dPs6bJFpf2T59ODkcHZ856pgn2xwfDoXd0uHv/G1QQHly426ubaa+/s7bWu7M5OnuudbQ3unBhsrgEjBudPTvtzxkmVhYXR2fOCFtmKye2zpxtWtH+hbtni4uS0MO778nn+k1vvnjuuYPzF5wPs5Xlg7vuqjrt/XvungzniyQZrZ2aDAYVD08vLW2vrjaDNFtZ3r37niJJRvdcnC4sjOfnj86cnszPT5Lu6cXF3QvnZ4PB5MTq1onVKkl37744Wzlh2jxNNWlb0w/acQViUhHS6kkSQhtyv2sYV0Uv9EeWJ06nfC6pHVNVHHd6BRDYJSLpaUYUGvrRyAJqq3YYRxUjUvOkN8xLAuogFt3SgxbMefGh4kjpTpxGYxYp6SVBNxPQ6bDldzTTuuz53pEmMVKeh8IR15XkTnXLYO+gaCNVFn62O40aJnPjTVTAnT+hZrsKSRIcshIep4YYQ+Rhk6jATJg/nQaUJFOutqqYzsI8mh1M0woCh8RhHjvmKh7u6IDoaBqo7UkAQacW1dG0j+CsomKvjBQpSxfuSR/LuBZyr4gxBaQTlt4cgiHqdivWJlKAdlqSGFuMWluVHgQlZe2gQH2CfNjp1SSCMmZJW+KOsAz0tiWeQ6YbdKLS9ZlmsN1rvASBFo3Txoaw7kTJVuMNkE34IK1wx6g07PZKlhDU4e2O9FKg5vx0o4FdWKV+Oy5EXxsKYHQIkc5TKPLC0rqJYOyNDJtK34/ifQ1QFVIRZ0LXeSJ5fiyBLhIS0gMQZ00QxuE2QkHjO5tmqNFZG7J6QrBqQpuww8bNnB8Cf4SRqiPU8vaBDoo2jGc58KwMDBEjYqvGD/14hFUzTbk3nTDQZJEJI806UgcB61TtInfdeb+z35kYPGCE6vS4US0fZa43V2LPC7oG5kXTCtxMtcYVXgrYOO8kNelSOLPdXu0SYdNqblKYmBKN0lFVDwJdVGlcuQ5jCnR6jeHI9Vl7Upm5BHDQ35dgQBGlaVqrFkMAtvrSRUJCGbHDSVSjxAu2d6tuo5mNwp3SmzUJCIJDGZnac62jozyd6ZiEbC+PcuZzF+5DPzehEf7IsLxso3gyrtqljkgq9mR0LH0WBjtOVGUEA//IBYVKm3g0qvxJFTNMtnU8loFIgjuUkSLW2D/UYZ2nNj0Y6eBYY9Ib1oV1tZ/yXsGkdnNeNDVUStvrJPGMMdl4id/JmLI66IiuIYXJex6bmoArHQXteIZx2XhJ2J/SunLdXtQ6YITAgednylJTRUzEKoDS0aA9KIsMNS2ftEwCNVoIgrLBVoKURakOnGnCELVmA1KpJHZFTrLRLHG0aniwY0KoollQ7WY+cJ3GKw4ncxBWNeV7Vdwgg0C4pwOknGTFbhkhS+o0OzyKtUCIjfaLDiCupsFO6SmNUDDbK1pMNrY7PpykpWXO43tFqAlxKNwFvjJYB5N96TV1zFrTw1lXWg9E3nbpz6CPo5aCoasGUbwjxRzSCRumFexomYTdXkkSAtqi1VFBYs28n2xK1AFly2/FhWhr40X9ubwOoEq8sFV7LeQGvLXZ+B1dd9rtaI8mVnph3MlMhGzix2mBYlf1/GizRn2qfTKXjEmspR8mvUwlGvb8VjoGMVEpw+xQeqXzfeePqDFViFNvH4Ikb4GkLCEZy9Ak7JCEsgl8Lz7Elc5SwfKp0LZIa1FNtCNVgHw2JXElfWGiw6AB4xDhdkmOJ3kHBQcZ9kkdWOpPbaMbTzRJ6eV7sxZDtgjk8TQFHmxQOC5DFOYTEpjCR3RAWntVMwybpkijEnYopaDTa5yHbJe1R5Xrha4A/V0J5wgIcSupTJsiH6V9yUMoGU6jsomJpGFruxLzFPqwnxauBVGLd3sVjYhLUHe3pi3ceH7rdmW7uElYO8l5G+AI93qlDriJaCuucU/QEHWDyuvZKo7373vDeGF4PDfYO3M2Xx6MTp8ul1d2VlbGvf7xiRPdcTNnAAAgAElEQVSj1bUqjPbuvS8bDsfz84dnz5bDzu7qybWFhcPVtWmvP15dHQ8HTRBt3XW26XSPl5ePT52azA32Tp5eXZgfLa8V7fb49OkybdVBuHvxomNkNL8wWlquo/Do1Kliaem40y/idLS2puO4Cfzte+9DhEzn5qaraxaww17veH6B3f+A4WL33nud5017c3urq8FsdnD+QlQVdNbMFuanKyu8UXWcbK6epNKMez129mxydLizuuZXU3v6zHg4nKyuHkzzirPt8+dFURyfWHW57W/v7J05CzbZ6MyZSat9tLgU3XOxbrcP7jqbHuzv97pKRwvr66O77rI7mwfnzs1arf87F/X/UPAHv/nk/zXBQmXZ+9gfp3/xuOXivxJa3/qjj0Pf8/IZCFltGVYKepiUdYM451YpCJUpWymraq+YgZCVjtOmrpKEFyU0hnKnNIZKl2lKpfRmU+pBq10DeBXFtC6x0t9hcCPzdptIKfJZk8RRMa4dr+KY1jVWinInLcVVTT2Qk8ibZXUS+03eKEwYcBCCStdR5AAIppMibaXNRFZARoFnKikxpsAi5CotmMlZJGazMk4SOf0/mbrWJHKFRagyXGCVi4hneRXHsZyqGmjf40A2EhNiLSGg0hyp3It5lldRFOuZrKDjuKUnh6jNkFZc1Io6ghI7qxTXlMYgm7iYOOPjOnNhuzx0gkxg4hBM3KyUTDOWgCxzMbLaJ03mQl/mThCpqcU4ArOqYZqxBM6mNgLOOp/C2niyAB6pFXUIOUFIXluMYzibugg6Z32GGs2b0iOyNqyivhOESAu10z5G2vGypqzJSYKlVT7GyjqDBs2dYzZnHFE+hgbQSlkPhtWsQJHyCVIWGuiDrEYBVED6GFpAK218FFazHMWUSKxNDYMATCvkA4UClE1Zi9VKCxjWeQFjQhtkXAXDYb1xzHvG0ABNJzzFeeM8GupZZkOBJYCgViyCeclDV1nj0xOT9U26CDwSmHxqIw/WCLla8wjkBQ9tZZ1P02Y8MyFg0HflPzIYlJrPV9vHUV9JZAVN1WSqfMhR6MrMhgLUgMBascjlpQhAZbTHUzWZKR9wFMCqAIknC8uBMsIiFLiZ0kISGrlxAVLkjEDlDCa+LByH0ggHgeGIltpiHLpxDlvAOuMj3FivLq2AjRUAQs0Rra2FcLFc3/ZXoQXKQ1g6ZCwhkta6pIERCNcWQBiCSQ4Sog0nxQylRBtEFZSqAl6EihoIJ52JuDOOlI0LaFRNMxDagEFlgHIhLmsojAQxKaY0xWXjfBo2WeZCxg1WuvxOn+8wOJ+yFFUaCTjItrfEImcaaV04P0Z5DYVtXEyKCW+hQoKARHWWuZBRjawpnQc5AgjS0iqBU3lcmcgI7Osshy3mKoSMNF5gs4IHUCLNcSKPSxs74uaqnR1vlbsSItMYP7BZJXxYQ8VJoseFiSyFgZ0VIGGuhtg21g9NVv4jgx0BrDCWQt/OCpA45CxDpLaBnpWeB2pkODEUoFIB62xAYWNEUyKBKsMBANajpGgsRDHKMxcC62zAkDSsLn3SNIZWNLCCwEoCgBI4y1yItNERh9IgZSi3pGxyFkaorBXViCZoloEINSpgdYZj1GjKLambnIQhLqUiCrEEzjIYQaldSJMqy0GCqaJaVTD0QdYgYRUOUZaxlFZaeShu/pEhRpcwmmu2MpYo4wVoOmUpra0WIG6ywsWYKmxNBUPfZZII0EAfZlPeorXVAsbNNAeJB3JhqkM2DNy0IQI0KIDTjKe0MsrDsZzmLsFEYaAKLQbVbha2GsUMJy09yZTvOI5cntmIuYZQV2gembzxfFs5LVhqJjPpA45DkGcmAsg5TlClA5M3nm8qZwV1FOFCAQRCWGQ2hAg4QWGlfF0yrAotpOcBimHeQIwimGc2hMAZn6FSAgSsx2htLUCL1fqOtwItUB5GymFlEDO8akoaaIFxY4GDIZgUKMHKCJxnpEWkhtyIWuYk9GBhDFWQhWCSowQr6+FZRlpUasAMb5oCRx7KteUK8oVqfddfxsp5eDYlLSItZEo0TYlCgQoFuAIscuMpiXGlnEcWpnc2+SLljmqZOz+ChUTMSBDjYspTWCgX0LiZZjak1BCrchdEsJCEqwYuNZvbyQqstPVoLKdTE1JmmFO59UNQSsp1DWKUZyJFhTQ+S+Q0MwFhjjlVGAEYAhjBQiYwn/opmjUmFImaNA0m0ACOVQN8W5dBTPJSCqGEFx4fK8EFVo3EDkEZhCLLPFMVUYrzUgshPd+fTBzGPX24T3oQuDoMvVlmEabUkqys/KgJAm8ydphwZmqJuW6sz5SEAELKHJ5VpRf6SKoaOIwYNbWmSGsT+eFsXEFRxzErCmgs57Y2lNY18WEOA14WOvL9IqugIAKCRmuHOTe1Yf/IoICVFfRQOjs64l3CgZPGWMS5qS1jZUl8NCMRz3Md+X41qx3DHDptjUGcmQp6PJ914HQzXhH5TAeeVxeNIeVwMPylX4z+7glH6X89sOD5uz//3uKBB6FS/z9WhD/0wz/yq7/8yJ98dHz29IOf/uwjj308Wxw+/McffcdHP3rz7Y/80K/8p7d95EOvfe/3//Pf/s1HP/Lh6anVC1/68qMf/tDx2trDn3rsXb//e+tvffP3/eZvffcf/P7r3/PP3vWRD333B96fLc/f/ZWvPvqhj4zX1h767Ge+/3d+a/2Rt7zrd3/3ez7wu1cfffRtj33in/3mb0xOnbz3b77yzt9+//jEiXu/9MUf/LX/vP62hx/9wz9812/8+uabHrz/C3/xA7/+n8cnTgw21v/bd/9s020vXHn9x9773oNTp04+9/SPve+9xWAwt7/9L3/m3RhYT9b/+qd+WsV+d2Pjx97znmJhbuHVyz/6C/+bDoI4O/6Jn/oZoRsM7L/+Nz9JKEj3dn/kff+77MT9G7d+7L3vsZT4Tf7f//TPhlWuPf4//k//BmOQjI9//L3vMx5LdnZ/4j0/z5zFyP4PP/lTydGBTfx/9W/fHZTTncGZ8ZcasZt1yHj0JRhcPwiW7fHHZ2i9TPt18bkZ2irb7mj0BGEb05aX7X/RBlcOgjVQPJbZG1Xar6Z/UbiNuouOj78JyHqWhMX+F0342gG/i9SPTeorMl6w5otH9kbdY+PR3wFybeZ1bP0XBXtxFJwB5SeP5FUbLgL75SP5ej0H9w+/BcGVEp9AJ55JWpcGzeLh0nM0unYyAlfEVrd1aQHzw+R2e/7pALc2k5c7ndeWyuHR/EskvnzKRzdaGyx87TSlo2Dz/6DtvposzQ/zsP/zm8N5T+xz+nSa6Znunp6cFlhkMKEoWpSq6AurXCrLliyXSxdS+cYuOcgWKYNihEiCEM2ABQgiEBkL7ALYBGCwOc3uzM5O6Nx9uk+Ob/onX+gz4EM8V089v6fQeCmQ5c7cm2bh3eW8clJ5FxbubFhkO2wR943TBjz0j7y5V6oyapduk+Cd0zjci+7TwrsbtnzgtXHwxhkTHngtp/ZynXk7wftW+M5ZZm3zXtz+Gg/jrgH5+O9iWyfGcDL8VuwZiXE46jwFo6xbmp48+q4TDdvMUMMvTEyZOJPJ+NuZC6dmd3L8pI7SvifG7W/AsNumjhx+YWLlqZNMe9/KinmPjmYnT+Fw2LZp2vqKCNodI9TTL45pnNl82vtO7qVjazbtfB96/Z5jpq0v8/C4ay679WeKTtt11F7w7mrhADLaCl5bLz9I80a/+UzDPwi0f1B6qRG0XAseuHdXw12knX711eW5e3ky35t/fs7ZjVSlN/9i6B2Eltp27q1E26YMRvWXG6W7ANX2ij9ZCPcqWalbf8V390qufhTcmw+2fFUYlF9p1N4jabOz9Fzo7DSQtxPergTbNWbtTt+IxdMjb17onw2mz+XWCjRe6XaeUXPegN4fdZ6BoTXNtoT8Xt9pavTysP8jXpyL0dvj3rOgStr4YNp+ShfZUO3m6XenXi2Hr/X7P5KlygS9O+7+GFTQiX04PHyaFPFAHvLxN8dhJae3+/0f6UphAB/OTn6gq6hHu7Phd0UBDWSbz74+YWVd7fHSa5v29NhJe8VXrxc7x9qKa8+uWmnqpIPKS6dMPrGG48KbF93RiaWGxZ9fDE56htUpvXDOmuaMD4ovrzrpxB4PCm9d9Ps9QobFn14IW31VmC38aMGMIRWD6OXVYDI18nH4xgWvO1BO0nzudOlwnEeT+WcWyNS00KD2YtPvz0g2CN48H3Xj6VwGvthKbmfestbf7eR385rR670IwLtTpyhmT8Xs1Y5zFqRf7Sdv5+4ygk914rfzOXwyfEXLd1K7omY/mNCXe+6aFn/fm76pvUWln+lN35R1s5O8nGWvp1ExHj6TwZfGzjpU3+wOX5Hl+pTfmk1fFTXWmb3Fk5eSqBinP8nkT8feGSG/0xm9CsJmVt9Fzhsbljp0etbci3Ud9Arvw+itVewfhI9w4d1Nmz/y+tJ/ddOSB07fqN1qGta+v0UKb5+l5p63C8O3zzvpI2eiSq9uOPmhMaHVW4vY7pa2efTWuoG37SMQvXnJTXfNVJRurbnpoT3VpZ+vYtbzD0T4xoaNt50TFb152Z3tMSFLPz1ji2GaDyZfm/rToTWbnDyNvV7PM5Kjr0r/oG3V9PQLYzTIXDztfye3xxM3GXWehk6779rJ8VdksNemTRJ/YSg60jPT+Ntjuzv29PT4KeCcDGkE0y8NzUc9awHMvtCXXei6efLdCW2Ni7zT+hG1DsasjidP9KxHI2tRpV/sZS3ouUn85BQdJk5Z1V9vlt9BqLob3ZovPKol1V7jNcfZqrvgYfig7D8og6AXvTU3946RzZ8s/MT3Hi2gYDt8JwoeLdhoO3hU8u5UqHUQ3Z2r3g7yuaO5W673YJkE29GdIHiwaMsH/k4UvlOj9mF0p1J5u0iK96PXiv79FWo/9N73wveWHf3QPQjLb9eIfRDcK5ffLMPKoX6z330alMGJ1+7vf98oqR4YivHXxm6QGg8Gw6dV0R2hndnxk7qie9Zo2vu2KvC+nonJVyeulxtbw5PvqyY7Fi3e/j4sZieM5+2v5YVsgDM5++rEtjJyMO48qcpsgNpx53ugkHaZyrtfSQvZEGk9/fIImcA5Hgy+x2u6gyd561u6kA0L1vC/+x/+Rf3h/Wmp+I/+t39bO9hVlvVf/e//R3X7UW9x6Z/9y39++o3Xd25e+6f/8n9qvPvOeHH5t/7t/9p4/27ueL/xf/27hffvtdbW//n/+N+vvnjr6Oal3/pX/2blrdd7i0v/+N/9n/N37gDX/PXf/p3l1984vHL1n/6Lf7Zx62cPP/qh//Z//ldnXrw1XGr+6qf/46nXX+Ol6FO/89unf3br5OLmP/nX//riD5++//GP/Nf/5n9Zf+7Z1uWLn/jL//zBzz9xvL720c999lf+4nPv/con/8Gnf/ejn/2zvceuP/alL3/ic38+acxd/P73PvLZP++vrnzoiSd++U//5P7HP/orf/CHn/zsnx5evXj169/65T/5TFIpXnzm6Y/+yef6qys3v/S3v/r7v7/9sQ9/7M8/96uf+ePWpc3NH/zw137vd+Nade3WTz71e3/QPb185etf/41P/7/bH378Q59/4lc//enJuZXFn77yG//hd7JStPzGq//g//5/OuvrycH+L6AihABq3T21wn1XMdxfaioKlcn6Z1Yyz0NQHZ9ZnQUeQaC3siQJ5JYxrVU6a6uKou7KElECItBePZVaJkRqUCma5zeUbY6bjb1McJP0VhZ348sQg/7SApEcQjVq1HZvXpeWMWrU969ckq49WlrYvXZFE9RdWkAfuAkpHNWr29eucM/OmN69cS2JAijVzgduCs9OZXn78cfSgg9Auvv4B2a1cu7a249dn5WLEpPd69czz9Wm3rp+NY5C5Bg7H/rAaL7OXWv76pVpuZT53s7N67MoFMjc+cDNtBxJx9q6dqVfrSiT7t68Pq2VE8/eunY5rha5Fex84MZ4rsottvXYjfHKkjTYzvUrg/kGotCrS+IjSKAxr5BBKJT2AsUOgEiiJjFpiixi1wSysWbYngfEsKgSuIGxBSDSRh0ZhpKmYVQVZghj7Ta1IhaVOW4Sl2pCNK1hjLgwmF1TCEIEJZvTSFKiudFkkGiEFCpDQyltUqumhEYQKB7miAyx5lmAMTohBpQ4keW+pkrak6RmmpDnBcGNAdF5FjICutJEykm57mlDQGeaVSWC+awIMR1hwHWIhTqSBhIiV5UeNKQA6bSaQCx4AUDch5Dnvtb8UBkQ4JxX+8qGACVJZWxhwQMpcQ9QpaHhL3EYEkwlW0LYU9RC1jLGrhSAefM59jHGyFlSqsQIlm5d247GDqSLkDhKMCNc4DAgiAC7kuIiZlTZS4QWtLaQsQSNAEibhNEYFhgiubsMcNGgSJAFQgsKGtBeACSE2iJOI4cRoShxloEqWkjzrJoSnGmGhd8HWACkVLGfaUkVjyspQAmGUhanAAnBiAzGBHCEtYz6UymJ4lkplYhDINMwJUpqE+twxBXCUOSFgXS1Lfm0LBUcE81FMQVCQAOpYJQrgaCUhVFqAyqyWVUC2cNQ6kIq8p6miBWAXsQEC1zGFoQYchgif0FIk5KQ2POS2NhCii9hQpUIkTfHGZZpaDrzufIMQpWzoICNKZBmVVEMVYk4OcAGVAXDn8+QSynUTo1rC1EL2CuYMsEL2F5UmGrg0cJCrh2CoWCLAPvYBNpaRtjSEhIVHUFPQqazUhu4EiGZV4bKTTTTSXmE7QQaSEdHMgCA6rQyoo4iUKSVkfRiYEJeGVEnlSZWYVv5EkMhKj1tawzyuBYrdwqpEqUx8Lg0MIyOgS8wlLzSSy1NdJZVYu1zSXQWjQwrATbWxXbmcqKFbBAHaIoVqGEipTCZVVGAQwiVOadwgRKVswbRGiIoQSA9QwKTmhWtc42wsuqQ+IQoTqrQzzIEKCkjx5XSZKyiTAcgCr2SECYgWoA6cwJAkUIlbDEhLGYUNacQEIDLWlNItSB1ZocaMiQsoSpdYEmtkqQ6ATgXAVGqD1Ceexqkh9IEQAtR7SoXapnHlalJhPSlAH1AuPARTg+1iwBI4+IQ2BrQjFeGkOaZR2G5S0wBKBJRi3uYYp7XBsyXGmVpZajNXBCmKx1qaKEBKhz8l7coXhsKT0IDGovQLGhpE6eRo4hAIt0ljQODAEGahBU1oNCaB6QAlc38aIZDQiF3lrQKLKo5XSRGWWGG7BKngZKm6c0LFGCkBVuAyCRU5/YckGEOqEXntGEDYBKnIZALMZDWEiImppqTRYYCCRGy6ho5EAGdByPFgKV4UuZaDqjOeZTBtKsNJL1JbhGIpApHGcFE8VkF6HyIgFSFXJltaWDiTiWmkCjuxxwjDERe0iDvQSjzgBN8og2kvDRHM80gD6YSYRvKpChl1oNYiVAheKwNqOU0KQ0BUdKbaUAgUjpkXjODDqESmotA+YQwbS1jwxJKY2NBY0MBTAqLOfAIIjlrQhwSYmtrGVNbcUydJU1siAlxGlyHJmKZswxxgWBD0kVMbCkVdhY0dBAg1G3mMKAGTa1lpiLKmDSWMPMVVshuSu1hZdFgUYCAQAh2rl4ZLdS1xY6uXJw2a9xk+1cvjZtNjMDejWuTcpFocXjpQr9e11gfrK9l1UjZxuHVS7NKGQOxdfVy5vlEi/3z52elEmTo6MLmNCpKyzi4sDkrRAiIg0uXhOMQyfcvXxS2CSg5uXAutSxB0dGF85lhQ6j2L10UCGEtj89vcMOAGHRXlhJqApP1z64ig2KgTk6fykwDYTBYaOzyi9y1R80GyKS02KBZJ3GMoeyeOS0Nqk02mK/vXbkkPGdaKx9unBWM9E6fQhmHGPZWlpAUmuFpKWpvrnPXGs1Vd65dVp49Wl7YeuwmILCztEAff0zbxrRc2rl2KQ09hMT+jWtZ4CMlfwFMg1ajf/ibWXMu993jc+szP5iVS521VW0YJ6tnk0o0LUSjRqO7tDgrRsq1Dzc3WqfPHFy+1G82IUG9paVZozIpVSZz1e7S0vHa2uHli9N6Nff8UbXWOntWWcZgaTGpFLcuX3l040YW+LNKSdnm7oUL2qKjSu1o7ay0zGFzPq4VR7V6GkX908u9WgMwunf5EmB4VKsPlprTcmVaKLRPnx7NVSFG+xcv8sDhtrt/5bK0jWG5NlxcmKzMx+cWRvVap76IENi+fo2HLrecg/ObInInV1YnjcqoWJ0Uy5Ol+ZPmIkFg69oNETn5mbmDK5dyaozL1cFSc9hopH4wXJw/WVyhUG9fviJDl9vuwYVN4LJObXG4OB/XCo6aoRqFJcMRU920oIttkpqhgEXGTOXbSd7wDchpFUEPRE6bNKB2LItlRiBh2TQtZVs5n7MDq8OKufZtDyZ0niAHGywzAyHKpme0vTBNygUbZUZJy4rjyZFRhSA0TENYPpcVy6WJa2e64ZgoZxWclzwCBpT1ZkUK2NQW7WmDCEMbujOrQAKmyBogj+eWgHQQl4llH9Thq8fliOAM4tGkiimcQaMnQmQ4B0XyerdS0yS2eHvSQBTOIB4lJeCRHce7l3pm7kAKuqKkEkc76clw0SAkRWiUliRGM2gMoJfNfExVL6uCPHAi3sNLDjGUZWSsjFXBcFhqFKEqW1E+yE8VbCMlSKEFk9nCOIVpCUmTWCyjZcxrbpAO5SnfsDhbhHrBplrYZkYLGkSGSzIn5NNm0S8net6mJrBBipYsZEPLSFkBwJLhkNSIQF7zw6yPlm3sQg8kaJ4hEwA8IcZ4XKNEjEUQq4KmYpSVUu0ABIfKHIkIaJoQOpyVYRG9h7zWqOAbWZdHM+VChSfIGsdFGBr3ffKgWylikCh7lBSxxXsqGCNXMns79B91ClUCBoSOpzVi8FHuxXGELTUQ7lAFSLCEoF5WgYKkBA3TMrRgagYKlhi2gWMkou4bjgr0OF8JTJpbRi6aLsPctRPiQ9Q0/UYOPSxDIxQDvlowqbCNHM8xoyCNFYxdBH3DozNco7pgRHyQnw5dK0anHVxhTGeOx1GJyoD5aKIbtoisQj4QZwLGpEky1bCooVw7VRVHuMqLW6M6BI6iWW9a19AUEAyUH6cBQmCk/HRSpE7SnlQlcXgVvBjXJMCY06l24zwABIy0n83KcIH/bFLTyqZUDNNqDkwFYV85sYrSiL0Dvcmw4NhJNynnmU+spJWWM+BAjUbajZMAmLKrvXhSMuysm0Zx5poBHFq+4BXHZKlnpLzpM8TtgpRVx5UTowJAaBiWtNxcVS27It261EXLxJxFUJQtF87skgYBRQvMqXPpWabFHSMVTc/WMYkQDJE5B+gyIUgiD/nGDNRNaEOXxnI5sGHKCgCXqIlzuyBAZKCQ+niSNgsGGCM4TKsKoUTTgQoldzUFXVGUqQucpDVcYoTlBAyTonSM48C+rRw19S2s+7yiE1e7yfFogZpsENG3ZnOGhAqSvghF5gEmOlkJZF6yLJ7vNHxEFNTDrCwwSQSbqIDnrjJULy/LSci86eGkCQnLMRhlRY4saNPU9fO4UfD4GC461AWOjtGS/V8SZBQ0Kps2y81Q5nXfLydgwSYWcEGMmwxb0KCpEWpVNZ05bpZBGrmemJAG1ZHpyxGdwzAgdImac1CZ2Mczq6DlnOPoGM8b0mcBmNAKAiFjpmC+VHXXg1NagFnFtnhfeyPsytzk2BhOywSjMUPDyRwx1Fjas1kRm3oonaEMsGnvFOjbo2KRs5zpflyDTE2kPc1DXcDvmeFW7HrckQx08jLgBmeqN27QEG1Z3lYSMKgTYPehJyYeNnQnrSFucFN2x/OUqpmwZmkRITQFRp+HAIS0yPv5mcglMTEAmjcp5a6d4zIVAfPQFNYtXrQKWZ+vBgSnYdTjVQtR7NopiRAsUB/PaARnFadGtkTDJwa2WYYWLGJqy8pIhEkgin4XFWle8qOsB1Y9aioHp7jOsKktM0UlBgok1CNUZ3nJLWR9sGQDk0xK5WFzfrQ4n4TFaaN6srwCMD7c2BjWaxDDgwsX84I3KpZHC83OqWV5qjxZrLWaK5DSw7Wz47kaYPR4c1NHdvf86uTcqZNiQznWcKHZW1mECO2fvzCKIrheObpwNre9Ubk2q5Q6qyup440ajf7yAqDk8PyFrBTEjtdeO5tWCsPa/KxcPlk9hTDsNRfbp04py+gtLSaVaFirp8Wot7oyKNekbe1fuqBN2l9YnDZqh+vr9z76MWjgYX0+D7zu6eVRpaZc+/DCucl87c5HfiktFwRjg+WVSb3Sr8wJ32tvnD08s966cKG7tAgM0q/Ve0vNNAiG8/PTRmVYqwvPHZ5uHjVWAKP7Vy5rkw4azeONc+bPf/4LWREO/uE/+tj/91crt14cz8+vPPvTU8//ZFqunHnm+fXv/eDR4x/44F99YfVHz77/sV+++TdPnPnJT7vLy0eNU+NCye4Pr3/py5vff2r7xo2bf/f3Z598evvqzUGpNi4Ui63Dze8/vfr0c/3GwsrPbl375rePNs+15k9N/CKdxje+8tW1p340rs4tvvH6xnee6jcWF1957cbffunRBz94+SvfWH/yB8cXNld+9ML57/1gWG8Ex8cf+k+fm1Sqwd7hY3/1+aPl1YV371794t9No1J4cnT9r76oAdRaf/yP/mzmB2GFfLK3OzDNwq23r33pqyk1vTS58ed/BXOBzzZ/ffAgk8p45d71J748KxcL2wdXv/h3yrCMEvylcY+J8eQw/sTv/WHuuOZ4dvMvP5+5jn/Su/rEF4GCkOLHP/NZczLNQv/Df/BnEKOD8tnWa0xNgE3Sw9dseZT6TbD/jBH3iFcUx7cIGEtTJXt3PD7ENuzEcx5sdxFzj55lsz6zS+L4JZr2iaXamY+AEGpKWq+7+U5mncLdH8FB13IWgc6GkkC1O+s8LGQnEEas+zIW25m1AsfPg86xTeps9JLM2zBA07xYPgUAACAASURBVPY9c3xkqEVWvNegx0vj2tR/VCEnCxQeoeEcOVrObYl7c95WFYYtY7epWmdntexm/7Xrw7gFNDiahycb3OK4X3EeNqf+6CPj2zdEctcsmNsNeHQKkrHZt8HBumbTS3Dn8bi9Cy29tQKP15TfJUclfHwGqr41NfXuBiATNC6y7XXsHJD9Ejg+h5zOdKiPf06BUJjq/RdMzDSZ8eOXDexo2dOHb1tE5lE8uPdmFcc5XaCH5RWAEbw7OnmVUVOpodp/y2GK0xI8apzSGtl7/Z2fuQABwsXRK6YvJnlktxZOKQGsvcH2Sz4dJTiEh88aCmKkVOtVg2kOYnn0lq0TaZhq72e27mfGcmC9vUgSH6kR3F9FkwCyKdrZtE7MtNJ339nUk6ryOvThWTQKbfzwA7OjppjskBp5eMFqmencwH17DUzqqtL5pd6d5Thuc0MeXiT9Yupz89Ep99iTwc4vjR+tKHmH1KyHK3hUorpLDxuwP58EufVo2TwqJfWO/9aCHi9rp2vsLoDenLZGvS09fQtZDT29LU7u2WTJSt/NOvdM0xHgID2642EXxW0yfEU7ER9XS71ozp0Mx2+rzj3bZpnqq4M3bUZF3gw70byhMv5y3LpjO2Ueb6PWHdOmGaqxveqqkSb5O8nhG45b0+n97PiO40Z5sqcP33FMzMFUtF4xLZKJKTl80YBlXBgbeOccFSnNZ2j7Op0J7eTs3csaIiIy9mAdgZzFGu5fwJkooIe/Ou0Ycacl5vCDmwAQrTh9uI6Vquj3f3k6IShu51V8/6oxlHkhcW5f4MouWe9/dHRsp7PurAJ3LrKcxLZ239swhjCLYvetDQ5CRaTzYJkkHCuO986h2B5Xs9kPkn7LshbZ4JZIT5DJss77VnwAYcT6b+L8QW6fgpMXQHvftJtwWKwkjuu2uv238eDAhmXWfwPJR9Jqyk55fuxHeBBPXlXDA8Nm6ewB6G9bdoH3G42xE9npZPxs3j5wvTLvv4NGu4ZjpJNt3H3AnEj03mWD96m/JGa3xPGOY8yh8NgE++cgjuHUMx+t8XBkHgboYF17fdyKcGsN6qEdE71zXoN4je1+ZHLU1yrZX4GHm9Dpk2MXHm4acLCgH3xsMuAwPxmuGw/WlTdjxxbe34C0v5i997HRdATTyWSJPtwweB9whrauKWeK+i7eW6ekB3sGPbwAVIY1pO9vIpQPVdL9KbZFrGO1/44PUs0o3/u5S/spKYOjZ4ycE0pl62UDZRKGqNNYyhHz99rbLwX6JGXzuP0jlM6osUA65QaEGh1mx68ZcgJBwWg/D/VJbp5CrdJiyiy0N+u9ivFUWGJ68I6XDYiump3nAOpIOg86P9aT2MEWGLwGsyGkEbO3V92jQEcHxntn5OBUXE68hzXUr1HQZUdl0FtOvdzcX7T2K7NS51Pd25taPoQ22j2LOg1ERuy4BI+XtNP/CN+5mPbfg75175TundJel+7XUWeJwf3L+fa5SeeEmPn+ZXq0TMMtcn9R985Ap00OKqizwkAP9Yt4fwnYPdxawAcrutKZ3hdHty0Lpc5gcv+tkglzkcDDnxt2EfDd7Pi2bYc8O9IHtx0T5kR106qHBuO07bdeYizQvCVbb9sFNFJJb7oQ4cNuPAlPXmZMpVKh1k9N4kCDjuOiQ45P0hO79ZaNpcAQ7P/MxCIHGJ+8QHTBEj3VfYPaKOYxab1mQAVDe/TLv/P7Vrc3LRYf+8sn7MFAQnLlS18NO71Obf6Tn/lM6eHW7rWrv/bvf9c+7owurf1K72EhnyWdyeUnvhbt7h+vnPml3/+D+sNHh1fOfWpwv4zwSSd+7K+/4J10NUaXvvaN0r1Hh1cv/Gbn3kLeuZv5v/xHfxZt70yqtctf+Xpha0c4zsWvfbN65/2Ts2c+/JnPLtx+5/7jj3/i9/64+P7D9unTF578/qlnfnJy6sy5J79/8dvffe8Tn/jg5/7q9LMvHG+eO/Pkj84++/ysXF565bW17z09bNQPVs9NvIjx7MN/8ddnnvpRa2N99ZmfrP3g6Um9frK80g7rdjy78fkvnv/Ok9sfevzSl7669sMft9fOtouNXqXudbrnn3568+vf6a+snPrZi5e+/LXdGzfOffsHG09+P12oha+9d/k7303CqPjo4bUnvtxaO5fubFvbW78QB8tLEh74rY2zXOPMcU821ojSs6DQunDOnMSZ4z78wAeM6VRa5tGFzZnjYyEgAPZkkvr+8fkNkvLEdbdu3BSMUs7tfIZynpru/sULSClp2e310xM3REJACMzJVDF2eOUyVlwgunv5CgRAGcbRpfOIS+F6hxc2c8QAhPuXLmlGYK5Ozp/LXAdIuHvpsjZNLPje5SvKMVAsjs5vJqUIJvx4fS2tFrFtHhr+RFsk53sXL8rQRYk42lhPFucwxR3DnXAsEWtvnJ1EJcr5wblzWeRaUXAArRGyaJq3189OikUU887GmXFUZjk/PHc+qYRsmp2cWU3mijLRndWVXnMxF5QUsVmQCbdIAZg1FCeMlIlRhSk3bV+RIhjDABWIWRDKoiLhyPOT1IIhtmow55T4GIVSBxZAVGJTS4YDaNQRTxkKCJynJE8AoQqaOfGwC0HD1AmkAbLmNE+p9BlaMEGmiIuMskygLX0G64YlsMBqWudEAE2wLPQzw5MIJ+VMQaqwJsFJij3OyKTOHT3BvvuAhQo4uekkpUQDiLES0dhkufSc91gJKlNQMy+NMZaC2Dwam3aWE7pnFlLta4OIwiCjtkBOXhoSIjLi8miiKeKY4bCdYSYMR0a9mWFCjlgF4gBl3DDmEHEAh6ZRA8KiuTBpHYVs2kclWlasCAXATOYgUzkwrRrQLuPaYHPY8JTSEElJhUilaVQR9FGuzSiK05IHtQIQGTzNlcNKEhdxmpq0jGgBcWVYJS0ClgqTVjALNOeMlCCtEp2bwMkyP+Y4FJ5Q3gRqmzuZjGacR7kjVXGspK0dwcOJ6dGh4ezaVZhbyk5FNIXCySyZVFNPxZnn7JMgQyVhibgkMcfSzrHbJjabOsH7ZhVyQ9hahWMJnMwhSTlDGQK2EMWRyk3hIFEcC2hzi+TFmZBAU2rUNddEGQwuW4gLiYm9gBFQKbJIk2mCMQRWXUuLYClNkSXAEpCyeUyJ4tgyaxq7UGlg8lSnWpiWVVcpMRRmrKZdliTYZIJrgSQx7IYWEOXIdJsyZ6ZAhtGA2ASpNs05DSwqAYELJrYIBHYWTqGdcl3IgzHwuZSuDGfcznMV6HCS2UojLy/EwJq6Hj0C5pj5MSjxMJFupoCjvRG3M69g7iJnRF2tPO7NRJgJ7gk/Ad7EYKhnesdGWcBA+rNZAeHEEEGswhgIKwlEHgokCHBnqa+U9nI/TSKJpwL4GC6YIFXYwUYVpdCCDoQNUyeAOMCchyKnwmFoycJAmlnMRJZKS3gGbBg61dSFVpln1GQ81RDpTAOHWjUgFRKuaVW1phAqbfGE5xS51JzTCbAko9YcgBAp0zDnQC6pdqjT0CLHyrNQk1hKcurywlgyJJApS2MNoGJUFAYZMwX08tKI4Cwnfh5NmZsqSjvUGYJQmo4s9HPD1NjKohFlM+rbe9gdkUhDJotjjoHElop63NBuwX5IgxR7Etja70kLT3Eko4lkAGBD+53YMjVysuIUGpkCNo+m3OIip4VClodGDDxcQUYgc2EbRYnLJElMWoKshLhkRlHriCIgAQCGyHNh4iJkVSwyCksUzBlIcCubaUSznJISUlULzCQtQqMCBCSUZxIQzZFRAKSgZ9KFRarrFhhLo4RoRWeZASIDVSnINCpSUDMxp9LkxG/HMOIWnNVynANpIFkYKWAltpGWM5RDYEleGNpUZp67Bb0cFYRJRGEMERYG5oWJY/IpZftmpJSjLMQLg5zYklEejhybZ5ZzYEapLggD0rA1BaEwbVXs5MSVzBCFocYkp1RGY40MyZAqDWNNODRYHXgkGdKiUVPQpRwwqw4kxTmyvYbMDVMAw6hDxLg2meRQUU9CbDaQYlhgs1CYzjwTFH3FkaIOANQoK+DTTBlGHUJDaMaQVKlVzIXB6oi6kEuLVgAOiZAUNhiwoFLEqQgVOmmOjToyPEkH097yYn9lGSd8tNDoLi3TJO+trByfOuWfdHvLK92zK9Zw1p1vHq+fdageWU4LB3SWdRaXjlfP+N1ud3l5cqqOHLvnRnvQs6azYbM5WJyncdpdXGptnAsQ71n+EQ6MaTKu1ztnT8NEjCq1walFlot+o9k7u8JGk2GjebJ51hjFk/rc0dqaFU9nfrh/8SJL0ywIjy5t0lmWFwqHF85JiQCCBxcvYART2ztZPzPzAw2gncdsPMv84OjiOQ6IZqy1sSYdM0UGU7k5naWO17p0XmVCWPbR5fMTN0RKES2teJoa9vH5c0gpzozDq5eU0Mq0BhsrA7uIgD7Y3NQmk5jtXrsG371tbv8ioNHf/MdZuQwg2L90PjNsYdvH59Yxl725+bRSOFo5c7S2Pg2CPPCRlIdXLlonvcrOdm9hAQIwq1RHC/XYdFLP39s8H54cL7z7zmihnhtWYnt7588DAGdRcbDU9Dr9+oP7ncWlLAiw4DuXr3Df3V0/P2g0IELTQjSZn5t4oSL0ZOPsuFBhebZ14ybCMLW83vJCXCwqwlpnzo4rFWsyeXTthrQJWrb3z17ijOWm019ZGhteutPuBeXe3II5nmzfeIz7Fs7k7uVLGUTxVndkeqO5eo6M4dL88cKyM5k9unqdG6a8u71TXs4CNzfcQXN+2Kiqpj2Yb57UV6zx+OH1a9JzYMKPLp5XrjG1CqNmfdqokFzCMmUhAIlk84YIDKYE9mEeMIOf2E6aVyMBGSlCUmJ8Ryg7BJHNRMY8DSomxJCYGjT9vE0VMFiJokyCMoWRobVkLsyqPukKlTK9FEqJWKh5yfZmO8RPs2IZYo1tLeu2ITPEAJ6jUmJcQKLsAJkQPBtXCdMjqHReS2bUwjAdR8KHu5a9J10yo6YGyaSGxtLtJtWTyDVQLoCKKzmECuCpKOouKB5Pok4pACTBQk/ncqJzAWFWyibcO0hLk9DLLYhkxktJbqQN9+5R4GKiFAJpOQNAAjgDQTZzTCBzUZzFoc9mKVkg0ERM5aSEZcFCWpESVAXGIgXKhsPSVJp4nmhG0GGKIICRgbViERBlm6RSL5gWyLJdyQwgPcYAR0WiQoqUsgs89T2xI7AFiY9AJlHTBC7BFUwjAG0MNCQR4FUXxYI2KfWAzhSsMexCiSRgeVySWiLhJCrMYKaSSEgfu/C+6R0PvUBDAUg+KZNOOncCglFoWmnKw1z4kGuBjHRYZuPY74jCMDIloNKazYrQSGbczanHd/jCcR72yy5RM83gNMqLeAe4nUHomzzNnUSGUBINQZaXUo6hQiorc6oEtBAoG9hADHI+7xqGAFyDJdOmGawxFTGIoUlyUDXVTIGjjBSQdA2USXjaJkhqiEmDgJnErRi4RHom0pLVsXAYziVeNp3JeNYmyIHAJIwKEFEdMVpFhiXzyKMpB8umgQUACDUtSCHFIp/zuAvMhI9rHFkcpSCtZtJGSMx4wLmXheSe9PgwdFimx5VcGbA1rLb8KjEFB1I4WR4CqKRy04HvtkflI9NTjoKZjitC2gCoRLv5xDOOp7U+84eRQVKQRLOpQ5fgG6k/S7xAy1S4Mi5IlmfAScdFQjMQFyZpYBt8ZjggbXg2SBHVcNHiCjNP6yIzfEnqFJlIYkxMlc977Hii+4IUkYAUe1DWLCPPcAB02UTdHHVTXvUYFpogvGBgKYGLcYXANtfjHIZUMEqRwPNU+YwVAYgYkgp6GFUoVsJ0parYyqIYCrnoUDWTGmXlBGKtUcwLKrMglDkvppmRNZ13j3wHM6UBTMvpRNpH01LiGLFnAClENJv4zMzkaI5zaBzN5tpBAJgGepYVFLc1k5yX0q5d7o4rR4WA4kQqkJUlM4emuZX7ILUwkjIvJrFHaSLT6hQyoaVOixwYCgFgezyrByqDpEGoD0Eq0bwJPYLKmBYBsrGGkAZAhqbc5ZhoHGCQSlhjMKBQS1SgMjTp3gRLxcu2TgGrQV607XhGq0R5hm5lOgOyYjGeYx/SAgQlg0Q6jTw7nsIyAQWDQIVsqOdMKoUOKa8wFs+Ek2E3m1EGUTKuQqxiiMG4JAp4jzon/YLLRM6tWEWwLcqtaTQrUM4AADqupFgqbsg8EoMk3OelJHJyBpBK83LKCYRAx1U+5cGhnpsENpS5ZjHwOfeSIrvT9/zEplSJWS0jSijKs5KGQGg8S4tA2hinEp2yHZSk0EQNAg1iYC6LFmDcAS0R2rLis4TrFYthyA8gLIfQJRRxXaLAw0Qqr5KPinXxUIC5AnMUVAA1LWhABnJcJoKZ+T7AZTcvBnia0yWGGDf5kSpg4fkMclmyWAHhNDMbJIkcGnOygLXFMmZN52q9pUUJ0KxSOjy7bkxnR+vr3cqcOwdbq6div5AwezJXazWXwc7RJAcHS2eNJD06e7Y337Smk5PTqzJyusf5cCYPls9ioEaVWv/0CpvMWuvrnWqDPNw55HYyP5dafuY5x2trsu6NF6uduQVjFrdWz07nKkCj7uLCpF6LbT9zvcPNc4bIJ0Hx8OwaBnpYqUwb1akTKMZam+u92vzJ6dOD+XkE1CQsdVdXooODwsFRWgrHhZJGuLW5NvEirNThhfPVg237sB2XCtywZlG5v1ifFatQyuML51AsFt+93VuYhwTPgmi4NN9tNPc3zuehOwqKVKreuZWT8iLLs53rVwGCsV84Xt+wb/3sFwWNfuozf7Tw5hvaMVd//NzqrZ8Bg5x+4YWL33uyvbHWWlhJ/MCcTn/lj/9o8bVXoYFXXnzl/A+fzj139ScvXP3G148uX3j8ib898+wzh1evfeiJz689/yw1ydKrr5358XN5GJ568ec3//5rh1cufvCv/2bjqaeONjZufvMbq88/nxcLnaXFQanmTCePfelvb3ztq3s3Lj/2pb87++Mfj5Ya577/9NnnntWMFrvtD//pZ3noVd5//+YTT4zm6quvvHT+O9/BFDeM/q+QACW76J39j/3Jn3LfLe/u3vyLz2cFv/n66xee/J6RJsXp4PLffMEbjzDUH/3dP4IMRYcHV/72y9y1mnffu/Dtb9mziT0bXf/zz/vtE+Ban/yPfwAQqFTZp0SudVJ84aWL3/hmMBpRmT72l38TnbSUa3zkD/+Ead5xF7tvYdJN/ax/+K6LtofVyvTRc67ogXKxr4sI24C91Wo/DMFJ7uPZ0dueeZCElcne807ag0V7dPwiRuM8SEedu5Zsax9PW7dNvD0LF/L2M2Taw17IBy8C1eFhNurfZ/kxsuWRvlAy4wHpJ6NbeNw1nCKcvMT1SVqZtnqP2PSQGTVeu98gJ01d7Hv3KqxXCeJd0i/TkyWD9j+K9j4xO+hMhuDRBuydVoWRv1VDR/M+P3SPqW6fxShhw8h/VMfurn2vrE/Wgdcv7ISkPW+LntOj4OiMJXtkEOHtTezshvc92DnNaOuD+s4nRjCJt2V7GR6esWSXjn26e87Hd60tH3TXDXyUt5OT1wkdzWyY7d2yLZg6g9HJ64afDfJGkBc8Inj13e17d6rGYOowvvucZanYHo6OXjMcMCXHafs9w51MzGS095pnt/uunW+/4Jo6Nybx8ZtGOeuAdtq647BhbIPk4FXLaw/NJTCMygxy9N7o5A3qiQnuJZ27jA5TC8SHr1pGd2ov2t5bi0bMvLiLjpbMgW2qHj5YCw+Qxu/9N2B/JUsOtYffWzMnzM368Oi00QsQTaydJXeP4mDLv7sJphXojpy7C6znuskxPlkwhmVgxvbWondgO9Zteu+yHC9Cbxw8qtO+O8dvfzI/Wc+7x8iFjzaDwwAUtrw3F+G0YeIjY2/B6BcM2Olvi+kbOipNkzey9gPTLGlxh48ewiKegDIZF0tGmorXZ723aFBLxdtp5z0z9Kfxe2rwiHrpwOjER+/arp7kHX3yqhUVxuBOfPK+WXCn6X3Vf0CLs47ZHm29G/lqrAbg6FWzYvbS+TDxPFNz/fKkc5+F8RAN4tZbRpR1xAQevWI5YV7tG3R/xR2PnWkfHF5xO1NGh+T+Tczlmnz5N9IRE6P4MIBHG+40N8QEPLoedqYW3NdbH6eCW8mYbK+5+dgacr2/aU6AqUdk60LQiYHTs+5cxUKb2RRvnbeT2Io5OThtjbHvPPgn03YzPXowdIMHl5WyqMzth4vueGJOZ/jojBl7POxOf8jHxyQo8/FPM94WhbQ/fMSyA2gWJC87mW2Xuge95+DwxHSrcPoiFwe8mh6PtujwwDADOHxbq/fTqDkb/lj0Dy2rqPK3ZHygirw3fSjHW6Tgxt136OwujFaS2TNZ/8iomp3BQkMz7HSH6dv54CEtWKPuXTZ8n87N9ZOf5J1DKwzzaovq1obJh3Rq0a0NbB0G2wY+PkNI+wPg3V8aIZ7uiE4T7K/ZeZ9OLbR91YMP7T1DtzcYPnZbIT5ecqcdcyL11mU77rCU0Z2LJjly9zA6XnPErnXiqMNNP5mQVLKtdW/S2qD3PiUniUzjvQVyeCaU+/TEJCer1iyhnLMHa6acjcW0dwsW0j7pxq33bdJLXBgfvGo5raG9qAbFCkWK3B+cvErtbMZGcfu2iXupg+KDVy2jNXGr4vgZmmfIAnnvRWCMJnacdO4yMFDYlN1bGh9Mg2ra/SFMUtOieviqsjtDJ8jH1VLqef5+9/jnhB1lYXnS/qGepaaJxeRNiXqZ5yJn/7S367rWbeP9c3rcBP44eFSnvaDK73w02zufjzrIlDubwX4BFLbcNxf18LRpHJq7TdorubxntUN43DRxCx+eIgeniX+vcLumR0smOWQ7c6xX8dOW2Y3A/imLHNHWImutuOTNj/ffetz0WoMO3N9gnaoXH9O+T/fmLbhH24v06LRl35++m/Xus8K043X7D94redkYxergJcsjM8foZBtz1v6hfh+evG+E8YhNZvtvBkHaR5ncf8kJ7VjtZcd3rUV9kByC43ueNZsZIjt+mYXxAHB58KLtmYlsiZM7TgkM1DFv32XmdFa22uO1mtU+EQe485pBHQCO0t5tHCVtOJQn7zCWyBo+/LV//2mv11EGu/nXnw/GfSeOL3zrW5Wtrfhs7bd4spwdb3ezX/+DP3TbbeF5H/zs56LWEcuz89/+duPBg0m9/vH/9Jmld273z536yKc/E+wdiih8/AtPFFpHbjLZ/M5362+/3T9z5pO/+/un3nx779rlT/32fyjs7sDT5Y+BbD6Zwd0H57723ebrr49XFj78J3+2/MrLh9cv/+rv/n5heyspRxe/990zzz43rc1tPvWDq9/65tZHPvjxP/vz5Z/fiufKu2c2M9eNTlrXv/WNtaeeTsrRxlNPX/vq1/ZuXv3IXz+x8sILs4X65vd/uPrCcwYQy2++tvHNJ9Nqaf255y///d8f37h6/a8/f/onP0nLhfVnn9/8wZMU6tp7dy5/5e/TYri/sTkNIovHH/rcfz773HO4YNVvvbrx9NNE8Or21o2//VJvdTXe3/sFQaNo9+r1ubvvtFbPqAnXjB2fOesdt1M7mIUh1FoiBLTevXp9/vabx+vnYm2SeHa8ctrrdveEnEbFvbW1EiGjsLB/+QpO4v21jQQQmcjOqdPGoL977ty0UNjdPK+oMZhr7G1ssl6vtXwKAACl4oS2l5dZHCd+uHvpEsjFYL6pLip30D9a2/DH3YP19e78AtT6eGNjMN9MgmDu3TsHZzdsL90d7e1WTqX1/OjChUGzmTGjdfbMsNnMF4y5Bw/3z1/ADq435vcuXEwqhaNLl/qLi7Mg9NfXR83mxAqqb729c+48KLm95aWDK9eGxfLB+fPdhaWxX7zfvnvCltpr5+def3vn3Pm8WequrOxtnp+VK4cXL3eXltOq77hjXGay7kW7U11xph4sFsfQhDPLJvs7FMmsMefmGfahLhm+PZEFlvhWqTQGFuEFy6kKA+R5ww+6MXCwLhkFewJCI3WMoDQWhKgg8spTqoRoBtYgpVSLUujd3cJxNllZsMojqiRwjaCcgSRPFotGLAwSJ16V2x2WDnNTgLANs9mwauSz1JvuxBY/4BnIs1FpKZnNzNFububSPWbDcb9CQihIfiCCmAihjG7iGrA4o5393BWxNwyGWbcMYD9H4/2kIIzRyDDeT2w8LQnz6CD11SMeBnprz9oAmuPxURwpYzwxjPuJR3OMWOsoC3RG3ILRJvMu8EW1NGIFmni+FaagZmEpBKaGEEk1KByOjUVbuLIU9mloZFEYtjJapLlr+QcjUHd14Ba9iV72M0+Vov+ftvt6t2w76zs/4hxj5jlXXjvvXVWnqk5OSkcSOpINMtCAMcY25qHtxqHBpg10N9hgMDw2dhshCeEACAECWwQJCaWDhLJA6Ug6OVTVqbRr571XXjPPOVJfuPvpf6C5/96+l+/vM6ce1QM3PCxNRJp+FI9Sssph2wqcVA4sRSgwQANEIhxEBYyw7Hud/SUcOqBFYzfB67YiRoRToOu0S3k+0ZaUseGnJ3lblb3ujbN9bfIFwtw/A0okA8rykQKi8pVHT3WrKsKIOofQ0MYVyD/WNcj6jDRzrMY1lyE/EVjoKFLBlOlkwmvNj0DFx53O3nhPU5o43MVH0mkqz6OdJcmKIlAwGVs5LVvQnVSkpZrQA8OmK3MVctIVaJSLYQeCxhhogCGxabeXxvXAOomLBMbUMqg7n8ONUC1NeJbAgc2TptdZmNCuV6CfZrBlEYTak6XYDMGyipMCrnooq9vxXPkESqUZpmVZrfLwdK7XAyxB3KrEegs1ohPNVNBLiOwe7S3agnncHh1XQylcy6evCLs5DDvXxjdOrcGiZ4fTgyQsaQRseph2FQ9dzK5ruyxiZI9PKqfJYxzNDotWDkLD0WHW6qyMVAAAIABJREFUqYXLbH5LsbqMtH12KPwq6eHWyUHmVznv7MoXD7DfdCN1fAcQJP0as8ParxcDK5yeCJbXtuN3RrZGyu94A4XyWm6ErBCkEYBHGmLa1JUb8F5JqiVwWdhv9LwqN1qkVpEqYMQDN8O4qfyArBTdItdhi8cpNkqsh3aaAKs2EfH8AiAl7JCs161xKno+S9IqDITLabD0cKXaThhXPlRVEMg1GZ6lqtstdYUX+0WrscqG8xuVY7K25sWhDNRtFXf0jV3rsvEVnh7lbWXVgrNXKh81jFjHJ3WgS16E0/1FDzgGWbPjso+Aqh3ySu5oRAkvjssOLaRuzXbnrZJjRflJ1iEjy7mWnJ7Z7aIl/dl+GqKK6Hi2t2jnlAnLOi6DBvsk7NYwwtVqHE1ysmKBNg+dVK5ySTDURkMIA+y3KhQCuea3DxM45KBltdwEr/PGpVE7Ub4NWm7YWrIWFOthsJ9YbUsGvBdkKkJV4DjdRDsAhUEc5chDhgGFCakaGduxN6EhKsPAHuZuUMKW63upCVkV4oidikDqKJLhjNV5ZTeuc4SFPe7E+7NdBszCsS18rB1Rea7VXpC0Ln2ls6kjkmUPBk0KlClD4KRjWk9yzwWd0poflpGB1cKe1kmPuKPCyNMixG4yxvWiiLybZV9Xp9P47homTlkt+8Sb1bCZFh2H1RNK08zjeFW1Rwu5FeqiaE0zvOYB1XSimY49aSh/9mpjt9WaHR3P9VoAkWnFhVqPMJHdaAY9DgMnnqRN25Zu2DlbkDUHOcDz83rgMs/0O0sSUEOs+HihOlxzp3dnhla8AlnOi7dqyzEdpxUttRuTCEf7KRrYIvbjVyas6+VhfPDQQ8vVwWRj8/TipXxj5eDS3YOnnj2+dGnu9u+kN06AXQ5X9u69f9ntTDY2z+65p2oHhxcv9Z95frq9M+32ZhcvzdvtLGrt3/+giMPR6vrJ+Qtpu3104WJn9YXl1rllt3u2s1N1OmUQ7D38KAD6rDXcnd+qET5au9BfWZn3V5bd3tnly4paeRjdfuBBpNV069x0Z6eg3mRjc7qxAQCoXW/3kUeGV66Md87Zy0URx5XrjTc2Ggknm9v+yRgAUsTx7XvvWzNmsrGp72toWezf9wBBqqyt6daOfTY1eZUE4f4jj2KtJzvnS2Cz8fjg3vv7uzdO7n9gsbKClALYQKWOHnnUzvKzjc0y3I5GZ4cPPNA+PTq8dHnR6f7VDI0qefCe341h0z49GW+t46xxsnS50ovHkxoQGTCc1U5ZHm9sOnnePToY7WziXHjp8nR9I5qMYSNk5KBa28vl6dY2z/Ph8eF4Y8VOc1w0p+vr4XRChBKRrUvhJ+nJ1jYviv7R4cH5853xCUmrk52dYLGgZdmEtmpMnCTLflxDPty9fXDXXUG2YMuiijxljDdLZsOhpHTj2tX9uy75dRKcTkbr606V0aSqfEchFMyWdewunXDt1q3DnXNekwVHo/H6OpclTWrCTEWptShF5Cz81ubNm/vbO67Mg4PRdGXIgLTSSnJacRaMpyJw5kH7/22K4HiyaMehyZqSSk6yMAgmWelwyDQ7yfLIZ5ZEmVYQNLYlUx1VSdXzaapqhyGm7XGZ2xZ1oEkkAkCFFGSa11Xai9i4kA5FNmDTqmCU2lDnimiw6MXudOlURdJv8UmhGVoEsXs8VQQSn5BGwVolw7Y7S5yyqDu2NS0rZi87rXCxsPN6MmxbZRWPFunQU4p6y3zaj9ysoI20aZEbl5R6NohpWbUn2dl6HM8XpgLzQexmlVXW0seqMW6hxsMWber4NJushWGawAoVsWWXja6N9pBUyMtEFeMKWq1JNhmEfpGjDOQtxmqBGmSTZIl9J9VlG2dW0Nk7ygaxpWqQKWMjhbGVCOii1PeHNw9PtlYH5Uk5t9JexE0FMo04qi1KZiV2YeYHneP5tB/busAnZdn2GFYwV9qCgjM6qwKSTuK+f5IsBrGtC2vc5AGjRKNFhRiqfJdOK8xMEsXBwTgfxAxUdCoKzwKcklzaTb1su/ZCCoqMo72ZyC2CbIBKbSlZ+gxUyCmLZdfnM6EpTFte62wpkIEugoUh0kz7kbvIvDyf90NnKTQ0SSuIxonGyMeLVEekkbN+5C1TVum0zePJvLRY0opb46WGQLtQVdCtZRET3RCeNWnPthcZkBD4WNbaysVytWtVVXg6n270ouUUL+rp5opV1LSojIelgk7aiBatsO0fTpcbHT9LQKarjmtVta409HBjCF+Uum0V1GkdzxarcW9xklZu3XGsuoG1Bg5qAA6Op9ValFO3ezwbr7a8MoNLWcW2pSQs1bIVGwyHJ4tJx+OmtEfNsuVwJHAODAU1Q3xRQw6WftA9yycdl5vKnqrMI7FZFiLQFAiO+FJCqpaBH58WyzZnsHZmKnUpoQbmhmBduhZbSoTlMgpax2nScSqb945HpU0hxzjVipI0csPRgmA1b0Wdk2QZ8jwIw9GYKL3ot5x54mZZOmzReQ2hSbtxfHgqCIKBZUoIK5GsdOxl6iVZ3XOseVVjlnVD92wOCYIulLmitU5WOs4ysbI6H4TBeFZjpgNiLWsBEfKQqIBbiqrNdAO98Xyx2fcniwZaOiQkbbQyMCBNjey0mmwOu/MRymHW5nZVmxoqFyoFvUzWESow64zS8TD0i5ykJmtzqxGoRjZJE+zyzIgIZtQZnKanQ9+vcrzQeYtxJVEFlA1qRINpWbdxRt3/r0mg8LSrqlpyxWFDLH9aNiFKudc5SacD1xMlTUzuU0WJNS8DkMzaXfcsTzohAxUb17nPqKXRokYEVqFL5jXBKm3F3uGs6AYWaqxpU7gWYQakGhCSxZ53OqVYpe12cDQrW27peeHRuLExsTHItIKwaIf+aE6RbAIWHkxn3U4ZBvHxSHJqbKRTCRHOO2F4NpOELHqt1mgBIPDxIjUhrdR0EDlJ7uZi3ndbk2mDrEUnjqYp0Fp7SJfGqXXRwlJa3qJaDpxoljSQCR/ZWSMVND6UNfILkbeJkNRfNsseD5eZ0FT4iBYaaeCS5QyGQd5kbUtoFi3KWdcOlrmUWASYVQpKIDxUUt46nM3XWv3kOM+cousxUcNKa05yQ/kk1S0uPKd3PBmvdtw6Q7Omih3LSFBo46CaMGecul55Gqy0DqeztbZb53guypARoEGukI0bZtmjVEc0daP4zmmy0cVFiZay9iyLaFqIOnAbZoUHZ6DFEjdq750uV1rUNNaiJEhncWhPEwLkZLAyuLU7Hwxy31/dv5O6LrQpTSoAzKQ/bB8dWkCOVteGd/YXUZzF8fDObuX7Li6KhiMpJiur3ZMThXDRCrp37sw7/SyOV/b3KtuRLoVp7Yl61u8489QYULTD7t7BotUFNnTGS0GpDGyY1LaSp2trw6M7SuPx6mprMgZCyshWpYrTdDFoSUUGe7sHly51zk6UgkUnspepkVpGjqxNazZdrPUabQ2Oj842Vwajw1w7RSfkWYYb00S8kbh9erLYHDbGGp4cH25utGZjUMiiE/CyxnlVt73aWL2jQzl0x1Zv5ejgcHurtZiirD4+fyl4xzu9v/yL//+HRuff/b3f+fZ3nv/UZ/Phyl0f/sTlj3+iiFt3f/SJR/7gA9cef/wtb/vVez74kStv/Z8ef/s7L37y02Wrtf3FJx/4wIcW/dVLH//ka3/3928+9vo3v/t3H/jAn155/ePf8hvvvvSRj8tWtPnFL9/3hx9c9Ffv+vxffsvv/N7N1772db/x2w/9wftvvPr1r/nD99/9/g/mcXfnS19+4L+/fzFc3/6Lrzz+zv/0ypsef/3v/LcH/9sfHj9w/+UPPvHgH/9J3uqGZ2dv/YX/kLY6rVduveWX33m8ef7cN555zW++p2Fu5+T4sV/5TzSvtAbf8a/+TRlG0eHJm9/2q8t+f/jsldf+xnu0QX5dvOHfv4MVlUD4O3/65wUj4cn4DW//L0UQ9K/eevV/fTfRiBTZm//9O5z5vAij7/7Jfyks5kzm3/K2dzXMau+fvfo//4ad5ACCt/zi/xWOJkUUvPWnf4HIZq9zz+2vuPXIBCC58mRP3SzCHfTKR+z8hPgdcfhZqpcmrJdXn+1UR8Dj1fWvtsAdyTf07Y/ZyxPLa4uDL7Bkzj1Z7D7j5wfAs6trX4nFDWlfQHsfppMT11khJ5/SyzPLBfnuS3G2R2gED7/Mq+sqvEuffZKeHfmoj0af18kJbYnJ7kvR9JZltq32y3fBg8v5cBm+MJTjB215ByzOgf17Na3M2bZzbQcG+/j6BXX0aNGZhzdXxcmDNjgjx746fK0huZmvO1cv150Je2VTHz5ShtPwVlscP0L1xJqF8s6rMZiZ6Yp17SHRGju7G/rwVcg+wHsr6vDVtD7jmS93X0fgAs76+OpDxNtnu4Pm6DGL7i+X+MYXPJrXhqLbf+ZABHCmdv8ywLYW++Ll53osT9vZ9Mknt8k0wy659XEbIIiy5uZf+AwLcyavfrNFs8oW+Ytf6lvjlLas6x+1tYYkb258KQhkKibyytNdNK8ZFVc+3ybTGsb49hNerS3ciFtf9rExcFZdf6oFphVn8qXPxupMOOd9++l7QN7nzVjtPYSmLYom5vbr7ZN21Tu1v/k6OVs33gl9+UGYDC0xlYcPWuN2Y+XkxiNsv12unvnPvEotzpVR4r9w3ixWbDVWJw+R0VplF/T6g85+G7Rvo2celYu7m3DpvXJBzzYdcawPL8Pjc3VQ0Bv38v31YuXY/ea9cnYZ8kPr9iU1OQ/peHQDT59k9oqaPgX3ngvZKqqfEzdejDyrau40V5/vW0wtz+yTzwJrA+fP6JtPh1G3Wjyvb73U9k0qZ/j6k6FL6uTMOvyibfeVeFndeiYO21XykrnxXBzUczoun/vGimuKYmbtfZ57A5O/KK690G95SXkbXX06DmSpFuLal2IP5EXC7nyGwzZuzR19+7V2WZIiEbffas8a6UnrqddrTXHTwGuvg0rYiW72Xs+XDVULcf3bvGkD+FQ//21AWEjW8JXXEqXJUqi9x8gSEJjoV/46n9M6SPk3Xy+FA2UBr7+GlgYVSu0/Zie4sBrrxTeSiV23lu5Tr6t11+iaXX2AZMYStdx7zFp6y2E1/pPmbM9xN8jpp+Ts2A5gtnclSm8T4oPdr3vpy8a7ACafpie7Hl4j08+r6QHvqvH+Ve/shoPaZPQNXr+o7XP66M/x0a3QHprlk+Zo1w9gMrpCj684YVvsf9OdPo3te/D0k/rOjTBu56dP0cPdOILp+LZz+gLz4ub4Kfv0GRaeF8kX4J0bcdAT0VnQ7D6G9dykLevlR2Uwsw8Heu/V0D4m+z15+DqrGtkFq299C9EJSCP88qOIH/ODVnPwBkwO6XFbHLzWaaZoifStb7HkUtchfeFVKsj5gav2H6PmwBoHzf7r7XwOGwReeaNTTkzFzZXHlZ1ZI1/deR3DYziy9f4baJFhCeDVNyFkZlWy/2lm1yVYVFee7pqJsFlz9QttOqpxF978uFdUloXE7l96RkCSlDeeaqlxbdvi5c/F+qBh63j3Q1ZSO9QyB5+nOtW0kdefbqmxgQG+/SkuDpSzDo7/zJplHrTM0V8Qs1RBPn/xhZXiCJkhP/qkpQ8MXTN7H6fThc+ZPP6qlUwpDyC/9bC924Pt2/i5h+T03iqe+9fPqfF5Rx6Dwx1zfFl6Bd69ZN/eKocnzrN3y8m90N5nu+fl6LKlRni0qQ8uQXYG7txt3b5YD479ly6J0f2Y7+PbO/r0Mm9O8GwD7N0D+ITs34VuXSbRK9aVS9Xo1Ra9g/c39fF9tjg14xW8ew90JujgHL55t+xNls9WN56NvWrhTtOnv7Fui7wu6Z1P2U7HyNvV7afjdpBWd8DVp1pOXcCsvvKXbbfJhWS3/ty2AiN3m1e+2V5Bo+UhvvZ0l+UFUuaVL8SWqIUke59ykAfksbrx9TjgdXWirn+zTdLMwub65wLaNIDg3U9wHTrqqL79ZOCppEnxta/FpNSxPf/ef/FT3sFxFrff9CvvCk/PYKNf9e7f7d05GPXWvvNf/5u1p567+dhjf+vHfyrYP160e3/9V94Z7e8bwh9993tXrl4/2jz/XT/7c5tPPXPwqge/9ad+sf3KrWV/7fFf+y/x3iEG5pHf/r21bz53cP/D3/UzP7fzpa9efdObvud//5neSy9n3f5rfuv3uy+8DBh75L3v2/jq14/uuffbfv7fXvjCl6695S3f8wv/vvf08+Nz5x/64/ff88GPjs5ffuCP/+R1v/3eF//Gt3/rr7zr8oc+enLf/ff+wQcvf/yTjR/ufOWrD7zvA9Otrfs/9NHXvOe919785jf/5u/c84E/Pb3n3nv/5KP3fPBPle3sfO1r9733/fPNrXs+9onXvvt3b77lLa/7r+++/wN/Or1w/sInv/DA+/5IOt7Ks8++5tffM9s5d/FTn3vs199z+3WPPfy+P37gD97frPd7X3zqkd//78qye9euv/HXfv3k0j350V+NRQggHG9uElHP+317e8upimQ4KLvdyTkpOJ+dOwchKjkfX7zk1NVka5tOsrTfnw8G2XA43TlXef5oZRUmaRW3xpfvDmfTxdqatUiXa7PFxmb78HC0sV663nx7x0uztNUar6/H+/uz1VUvX0brp4vBIDw+nt51Qdr2eGvLn0yLOF5sb1e3b423NqP5eLK+nrbbAID59nbW7WKE6k5nsrHBiaoGg9nOTh2F83Pn0sFAELJYXcmiWITdqtOebGyAgBUrK+PhsGq1ZjvbRadbtNqLtfWs3694UMbxeGWl7oRZr3e2c77yg/HWdtrqzFdW52vrRa8/bw3KMBxt7ST9ftofTLe36yCYnj+f9Ic6ZJajrNA0bc+2BfdNiYkXCuQh4BGnC5hldJtbZ4BHUAeUs5o4TcNdO5bIwcomLNSWpXTLskLILKADFnjCpkpwz++UkjSSMbsNKAKmxfixpFjI2Pf8hDtNYwfMzzXExA+8LsS1wS2bjQ0hQDOi2QxRIRgUcc5kXoZGFKVDF8KTVl1iO5ecYLcgaix8quylldqpI70aoCatfWXJ0rBGcmx5Ka1nTcjkJGVWvvQVAZqyReEZomtkj4VjWWyOsZ+7xNSVm+dpaEjdYGtR2JKiGtkTyRDwK1xN64ACaXxboBACjtxYIB9pG9pOATi0AsJ3Be7ZClmBL2iPQI68sEE2NB7yfKFCTphhTKAY1i3PYaXVIQ1FfiSIC1VkuZFGLgJdh92RvIu0Sxxe0VApzuxYUgcaD7tuQ5jUfdu+rXkLGs8K/cZva4mhZgtCdOYCi1eaVo1HHJxoNGkcx/VmGMDKodRNIARFYGhaGpXXPg6sBKK55Layxho7wgEyLKzKlK5GeW6QlAHCbIFALhnHXgIBbHyk2dxIL/Whn9SaZTU3Hkk0qyXjwJ9hCSvfYmyJEJI+sBwN/EbajMV1kFUq6tAg91mDAoiN5U0UigiZq7AlIWdWWEfzyrjU6iBnKnXfQYvMdQXwEcbAjWrtUBDUNq+Ug1mPsBOBugxkOAgkahHYACcUwkI4gt6oqVseQoBbjQmNsGzXrWFEIYBeJIxjSywozkqnog4iZFHHQlrAsIm0CuEBbifIUZUj6SgXQWVCzvFE+YWxGXRmxi4aWzvWXFlVHhhvUlR+KVzq4qlik8ZhrjsBrFQuYGyh3aYJlTvJBFvWASN0pvyFsLlmI01g42vg5toRlac5yZS9lJh4bS1UI7ntdCAURrcYG0mMhYxi1y8YahrbJ35hC0g8x+sCs5SoxdkcGtvggDAnR1rUtuvFKaqqJoiwlzsQgRa1ZsDFQLvEsRvQ1pradqvSeWN8xmOkgFFtxhZC+gY4mAca1FpwbrVKrmsReCKrKVuUnkKNQs5YuBinSwuNcwdrWbtplgQGKkWsecEbAgGyp5oj7Te4mNahBXBF5nnhCmYMspI6MAgIw8aKCBEYvlyKgDRCsnFehwpxxOhU+kpbEDoTY6vaaA/PaiZKm4XTvPAqZSOXjCVNsIO9tiYelB2H7an/cUE2K7EvJXedWFgeNi6xfUmZNF1uO4q3kPGs0GuctpbcDjqV5kK7zA1qK8S6RRxbUl+KOA68qRMIYfuun5U2pRH3YkFdADs239esj4yDbafGTi0cP2jlta9EaDE3wy5SgYPYEtulZIx4CRJE+ETzBVAm94GXNQBntaM5TQ2XgnPgzkhFsoBZswRru/AVKUtsLxsXM5YDVzWOrdgYaytzKfJLS5VlYFxVQbYQtsE8xzYQzAJBjtNZFVJUFbDMa18zLYC9bBzIWAHZTBPJeoQdCNxlpiZ+IFGbYAydUEgLohB5btPELrYgswSKYOO5rl2hmGoO/VhAG2lC/UAaj0DHYXeE1aPaMY5dUt8ohziRwDY0HHl2A7mGLne4ZC0MXBgFjd2C0CFRVAPLghz5nkQdjlzi2QIHqvL8ycWL+aCf93rJ5lYyGIw3Nsp+bzYcpt1uOlwZnz8nHHe8vZN0Osvhynx7u+jF0+FK2elMNjfz/iDZ2MiHw8Z2FxfuWrZai+HKYm112e6MVteqXn+0tpl5/mIwLFuxsJ3x5cvawsvBYDkcVkEwWVkt2u2kO6iCIF1bq12vdpzRxrqGaNHtJatruNJJu511u5NLFxWzRjs7VMi8FS+3tqL5bLS5FUzOFmtreRyn/f5051xj26dbO3QyzVvx9PyF6OBgtrYeJxO8vZV2O8lgMN3aKikd75z3xtNFu0s2NgdXrkxWV6FsFptbabud9Hqz7e3S96abm93Do2W7U6uweOmF6c6OPtidra8XYQjNX5lFCDzPqitCTQE5FpJQIxGuMQ9EUhkLCVn5Pmkaqy4JNQkLITAAAFLVEACOmspQJFTteVBJCEDQJFrDEnFh26SugdYOrHPqGg0UpVgKWtXK4W6ZFNAWNjcQQmN8kVbAgkLZuEmJz4qi8Ry3zkrDCNEAACWA4DZkmFdZaTlhPi80MxaxZVlqi2JlLGIgZqL4H1xR5Xlhvfh/GlBV3PeLuZIgQ64DytzyaFnXth3KZU0diQkv80pTCpXBWErogDJ3A2Z0iYhfLAtlIQza9WxEOwwIyVilLINRKBeZcTUlnfRkFsRYa6bkkkRxOcXYzHFsEAzVsgJQEubLcglDrKWDqiUKWFNCrBUGGhG7rktoa0IivVygECptOMaqsqpCY1YjG0JgKIKVBBiHapFihgAQxAHSMFF5IM+hWxEOKEQKImkUA1ABIrUD0pJYTBY5j41AAOBhsTflfWmosgCRFTTCIMsRTUYCTQBSxhjk6kQjjZXIeAwUQhIAqty6SEjEQUFEXiNiA1WQUGkS6tmSRqTRmpmwmjSYGsK0wCV2hsX+zO4JwwIzX7DI1BoT48osAT4HFUGqQdRt8tQKTWMUp1uL27vuNsHal0mJOTVSAVIaHqiksBwtkaGoXY4Lwg3EVMsl8m1QQ2Ry4K5kh4kXG2MUQK6q5jDE2LgqS4Bvm4og2WBqizKzAlMbzUgoFgsQYmxck6c45qKEWDbG0Qh6eolVXVu2JWUKI2yUDbKExFxUCAugBcC4pD6qgUYwUPMEt5BWigEucrvJa+qWwIfQaAJxYzRC6+mtSdDFUqQsRhICAyxQYmkK6mgCfLEwEBKpEtoiUtkgTUiENLBgCYXKsROYrES2lsBwTFWDjFKQ2E2ZYB9aCEhtNPRBJiE2EDiinLMWqiSwkVulSxJxWHOR18hiQObYlZpEerGwIlxLZIFhenjqDZhsFCQ5dAKTVsQWEkd6mVsuMVJBbDflEocc1UipAnBoIQAhaYCkMGpmKYwB1r5cCEqw1sKwCrqeXJaWDRSRBMbluIDYEKtXTA7dba5LhFQBfU8sG4twmZeW69ZlgloQaVclKYy4rhCsa4BcWRY80srS2BgMSa0NBp5aSIQ0xAXxkQCeTCuLKWUZDDQBsNFAKcMJFMYSFcWyxhYyWhBmamMwivRyAQOkNWCYqNqSNdOiAHZJHGAhVEsNUawWFaTQ6JL7QAGgDMMNamROvUCnCkIFkKOqBYmBUgHIEhJCoSwimapKwm1ZlYY3yIpMssQBUAAyEFRpQloWKIiSBfIckxlsiKwxMAurQ2qtGPDrdElaDJRcZ4LQqJjPrW6F3EDNEivCAmiio3oiMFHEAhKVyHN1UmOmtBWoecYdr0ly6jtNvcSxp5ZcFBM28HSqGKJVRgCYsw6pjbKgL5IExxassW5S6A3y49wJSsW0RVrVtLRsDZEtqgUKmK4J1ilwfZVqTAw0ChJPZAsYQgx9nS5ggIxGFFhNacsy50EjKSDQEIhKCQgK1TInDgSmQcxI4MqcgzqBnqAWRIapygBgq2aBQ2S0tjBsNIAAWBg3RkO0nt0+8raAAooDrgpal43l0kbnxDUUYGE0RIGclQZBozg0S6uDlCGoplLmxHd1gmCjAKRKp7QFFPD1IqExlhrhhjVZQbijRQPdBrG1/PaxtwkVdM2isLgtywZz0uic+I5JBGASMl/NljRElQQcrSwP9rwtDhtLFyXhjqxq5DSKRHqZsBA0BljQKeYVYxAR3KgKO57JG2xVxlpL9s6iITaVhNyuyowGDAlLNRlwPFMIiygpPFUnvA1LaTgOmmSBQooE02WJKFZGYa40jNQy4aGpDbBQKBalsghQiMBKU0cVteMirSRlwmCepYpQB1aFZtCYxnUs3ThNUWIOKqkolYzxLNMED6rT03gDKlVDalWlhpjDBjSqolxybpvaQGRVeQE4kzWksNbUQMiRQEaX3HOqtBZYQ2QTURgOtAaMOGVWIruxOa1KAKADq5T4BqGwXqTYs+pa2Rav8gLZDEtlYGU5oUgKw5GUNhYp9qy6QhQ4Mh/bXU9kWiNpkI2bAjm4bjgRSxYBCKls7DovkG1haZSRhjiozoinCN6Y7x4EW6zIlc14U1SA1d3O8N/94l+VRfg9b/uPj//+eydPxuByAAAgAElEQVQXzz/0gQ++6ffem66tTFfXasu26/T7f/4X3vie33z52976Hf/5197y7t84fuDeeauvqOUkyXe8/W1/7Xd++/brX/ftb3/7W37rt26+8Y25H2pKemcHb/iDP3zju98zOXf+1X/6oW//1XeML10YrW4Jxp0k+Wu/89tvfcfbZ+d37v38Z9/6znctV4ajrR1pWXaVf+evvuutb/+V/Vc9/PBHPv7tb/vlyV0XVm7d+Ls/8ZN1Oxpee+Xv/OzPjja3L6Ljfzy7Opudta/u/cCP/wSChlflD/7Yv2g4H3TVD588mQDU/+xX//bP/kztB0Ey/fv//H+zZGNtRj+y9zmsquALT3/vz/2bphN17ux//0/9H4ZQJ1A/OnuGn+2Kg+yH/tk/o8i4s9nf/pf/yjAasvSf57ea45twXPzQP/mRaDrSgf13f+wnnSo77Z9PP1bwk2WMFqOP6eD2OHBOnfTMTqdmJMpPVdbuso3mo89BcpC1wRG2cu/6K9gN0/ct4Y3c79XZxzKzW8f1TYpS5/SQ5GbxUeldm7C7cPXfp/XVKtiogr2XmCzprUn6JKWvLK0OzD+ceM+f8W0d3HqREqHHwnyhMC+lq3Q8/5KWVyq0hc5/3W8/3ynXZsNvkM7VTRe/+Gh5428urp9KiG5e3P46RuFB9/kgeHlTRceNwxru+rPFyhUcvLiN2NjfDzee9Mto8e1Hn/me+dWXaav1vNt68Rwje/ExaT29xfRh1qOZ13Wq2frXrfjZLRgdhtdJ/MJF29x8qHnp+6cv51VR711Y+XrXcvfa10j0/F3E3xeTcvn+ws9mDIr0DxOnTPG6lbhtv1iQlxaTJ1RbTHvp6Z2PWFEyYStgGg2YqPxnjpZPVK5J7Uk6eUK3yonVAdN4BRoTXTuc/0lpN5Vd5cuPFJ30TPXsJGhjKcP909H7ZTg6oyFI37d0Jku2Bhd+125ya3cx+YiI5hOH1/M/rILRjG26K19sByeM6cPwxe3wADJ66x9lL146ev6auzr8/Hp8x4Hhce/r/fAo8OFzf3N55b50/8CEvSfPD66U5fp843M9706M+nt/Z/czr87PTmbAuXJ35w6RYTr8xnD4slSdg793/KXHssMXdHf12Z6/G9tmt311GN7wmmj2Dxdfe+Pkpedod+1Lnfj2AAQHrWdb0e0u9Q6LZwv5ibm7puBX5ulnS79fS5/X3HXnc+vZ6fLTMnDL+mZjnphGYVb0opL7rdmp+fpy+hnVdpbWfjL7MxHBuVrzl37bq5bkM6fLT4vWsDRXsvknRZ+OrRYYhWtundEnJ+nHc6/TsBfn8082PX/WBE7uhXaSsJcmi4/VLbzQp3X54YR2QH+uuk/e5aanrpi3v/pAcHYaBLs/evY1v05OjlsrT245IrHnSeupu/10Cp3ZrL2GjNm4dRg9eT/PFROL3te27bwY1k//cHLdnu7NRKf7xcud0byJyrXPbISTqmnPFtGAiSI8bjpPXgrGC+XVm1/YCvcT0j34kcO/GKh8b9Fb/epqvMhJs2g/dTmcFtmwRu87Kp8r/W1o/mwsXyzjTp5FsSKWvVgWH17az0zsu1D9x2PxfBPuqNz1AULt45PyC1X1Yu32Qf2plHx1FK2Vs85QWLYzmYPPTMtn6q4zr75RiW8WLT/Juu2K+/HyzHxsln6taQ0L+eU0+4ZoRYmI3YL5rflZ8fFEfDUPL0jzxDj9mgq21PCOiZ++YOsjd8z7X1tp/PRNsy/8QHrzQELrlbj13GWu7wQT1frmRSpOHtbP//3TbyRawpc7rWcvEPco2getpy474vZdzYs/NHtRLMf5+K7VLw+xMw13Zefp8xY9uLB8+kerw/nJfp1t9P9yOyj37Ay2v3ZeW/mj2Vd+cHF9XhbqqNV56h63OLSE7H9xx9WLolkWf5LEycQu8rNPAne+CMJi2h5CbaL9k+X7CzotbVwkHy3ceUI7IA1iYGDr8Gj0xyI8nNJVWP7+zByVzjbM/NhSDT+Yzz8m3LM5buH8fbPw1tjZUrOgByBwd6flJzL7aN7V07NPQXyUO12ZR7GGpHV4VH5w2RwZ162bJxboMHd6Zvj02trzyvQOhl/pebdW5Mrp3zr84pvz0+Nl41/diq7HOph1nuuuvMBAuDfrDARzW5NR67k4urHCyZ34Rit+qQX46fcsvvatJ09foZ2VJ1v+lXUc7kUvedG1dYveKUOrtKMwGfW+Oeg+H5PWze5TQXBthwQ3Xn/6te+rz05nc7p7rvtMjN3j6Fp78GzH9I7VC4vpJ2QfjsPR5PAJEjYTl4z8ySFpSvR0sfhz0fIW5E4+fkJ2zdgqj5hZuvsH4tiuPrxwvIbuJ4uP1z0womc3cYf4z7wk8yD7YB7Vc1jL/P2JbYkgv0VJ7R4eqBM8/ahoVxOqm+SPiiBfcG9ij/etusIvF8s/V7EakbSZflhG1Tx00//5H//o6vVreb/7ff/65/p7d+TA/gfLa4Nk76y2/9E//SfbTz+19+pH/5f/9UdWXnhx+fCFf7D7ua38LD+Yfc/P/+Lajetnly7/8I/+04tf/crxYw/+wzufvVhOTs6K7/ul/7D+4vMycL/7l35p6+mnDx667yfOvnj/7NpLIvzBn/7py1/+0mJz7W/88tvOffOpuu/8QHnzrvHVIxT+wI/9+P2f/tSNN7/p7/2fP3350586evihx9/7O2/4vd8/vXTp8d/89W99z2/tP/LgvDPUlHaPD97w3v/2+G/+xmJz49EnPvb4r//mcm042rnQWNzNl9/5H3/5W//zfzp6zcMPfugjb33Xr6aba8lKf+m03Lr4jne+41t/7V23vuUNj7/nt9/6znecPHDvIu5Jxvz55LE/et9bf/Vds/M7D3/oQ9/xjneM77006a8Iiw9He5ee+PR3/YdfyvvdC099/bv/7b8bXbpU/pV8EUKAtCrXeyOqGRSi5U8ubkNi3DqDEHhVmqz1FUOOropOMLr7PBelrSvTNG6TlqvdsSy4yPOt1QkxXOS2KqA2TNdFO5jftcVMVfWj2b0XEUNukypFuCybTjB+4DKFSrnW6OI2osZrUi2xrcqyF589cDcHTRX7o/svcV0Z1xrfd8m4lqbg7PJ5bBkFzDH0gdGUmuP7L4vABhyO7jlvXAKkOqahUQJ47PTBu6GNLQuePniPbPkYgWMrqjUynjW+/xLgGEEweugeGLucoZM6ljYktR7fd6lpeYjD8T3nlc8BMnuCSa5Y3Yweuqfux4iY8d3n607IVOV3EfWNhWTQrzHTlpbKDRQEdl55MXO5QED57YragAAhiG18xxaF0yfYBtxIt2sRLCingGLs2LCW3kBY2DhNXvUgs5QLAPY8gACxUNBuEFCWLExXQAXsJle+Zyi2m9wKLKSEAdqJKmq0UQy4GTDaViVxDTDHBKgawZEkRNaMTpsYM10Kt8ZkaovcCKEMtqCApIHB2IIN5olu1bZciCCeKperCrs1FscUSUqbJhwRUvtlAQF0RGkcB3YmtimE46DwiGIhETwDXCvJrIVuEQ7KxvZge8xUQYkTDirbNzaq7BVqexKrIqzmtqpqKuMhIJYgWIXDxralraqomDkiQ9w4feHwRllWNCiprZlpwnJGhXCocFeY7UiGlN8zjie1yHU5g0ozXUYrlhUC1xTOkPJIMVUF9dJrskbLuK+wq5mqnRVoBZrJAgYJJhVESjsTTQw31RjQnNpevoDB0hBATa39jEJFgFwaogFgojDuRFiNU6cgyhBquJY585VCnAPlTaCGtsixN5OksUU+t5ySOK4qoJsRmRGsIJ9B6rmmnArYQO7lC+2WAE64LICfw0YSJRy3IavAM3nqAXcFeTKvJDFVxUxj4crrUQvhwMPNQNuwlE3KdMNVVTisNSi5kRaXwaDmXME6MRA6ohAhCFcbDkphWfGKxkTbognqBVM1tJW7gn0qK1t7w4bjxlYlKY1lKodW7oBSKik2YggJaTCAKDqxnIaaWkZnyBW0qU4kyxvNcWWiOSYJoxqFJ5rVFhRBOSOyobApowVlcwRrGE4pSynFo4opl9u6hO2JsYUjEhMtkJM5TR6Uc7dJLWjj8AT5lSNyFU4Mre0im2A7lcA1OQymgCaMSBwcG6eyRanbmkDpmrIMBFKKGOCJzEBo15lua9hor07kgDRKUAH9JmGqJkbw0AAJqUSwJQgDDDZBtQAQUF3hSGNWEyNdV2loKGiCalHLiummioFvC66rMuARKJkWtE4AhEzVdhchrlxZqjYOeW1paBFRBWeQCoxyHS8cOWsgOkE2UTWyBQyPKBYM1yI6Y7yRiJxiHyiNHGFaE67S2mIoPmJcYYuMlG8opzozrTlFOWRUR2MLlhqB/RoDhrlMYHuCuYBYinjGUdIAc6QsYCmKGxgeIS4pzGFnDpjgsPG72naVgCrqldzWFhRhuYBGM9Q4A8JDzbH2+8D2BDR1UC+xEEzV/gqjvnZEXg4QaUlHFLBCbpMCJKNeYQUAi8wMJeCG6yqol0QKCmsTK9uTBii/XUIPc21QOQcAUCRJh+KwcgxGXQkdQJoau7NGS1sWtV8ROLHLLDdopAixjGZzgDQzNfYWWiMKG79cAGAcUQKvhOQUQEX4ogkgx2UO+Mxuu7rRXo35hJtKuRU2JxQKRyrTYCZq7MwVBI4uK08gfuaoorL4cWMQNtha6BhZoMFOqkNDTcMs0RpqTKQFVTBsbNoQBHXgW1IpLr1Bw4nQnLZWSmJpC2rjOrhQrq7VENpUAGWCYWMzKTwOAdSRb8syWKHMUQhX1QrhTCJOISVUSwaq1kBhW3NQ2ys24zWTonEdpjSgwu+XjClATTwomAuYrGcXt9L1HoFycmGzHLSoqsfEyaCxRXZ26Vzeih2Rzy6ey4Yr3Mg5iwtELI5md5+vey2nXEwvny89jzfF2Ilz6tpVs9xeKXp9C4jJ+c3CD11dHiuusPbK5XJzpfYcpuvk3EZtuxYQE2DnnDNVTS/uGIzcJptvrza2zWVRt/zpuXULNMXm6lRV3NSuyI1CDDRFN5zefZ7ruuzH08s7hEG3TqiqbVUWa/2RuttSjYy88X13IaxtUboy57LIh+3RPXfZSJad6Oy+SxwKW5W00bauVcsfX9yhRFfDzvjyOYIUVyVtFAVSudb4vksYaxF743vv0jb9K7IITfpd363We8rlJ/ffXUZxOeguzm2s3Hyle7gvYy/r9dL1lenmetHraM8eX76rdXS8dvNq3u+K0J9vb5ar3aTVKof9kwvnVw9ub770XN2Ni0476/dP7r4MOZ1urCUbK73d2xvXr+aDbj7sA8faf/hB4FrJYHB29+XB3u21m1eNby/7vbLbXlzYWqyuQtvaf+hByHA2HC62N/JOu+x2JhcvzBWXL720t3m/avuSsf1XPQxclg6Gi7u2E8MWN0+WcXeyvU0wvPPqR0VgS86OH3lYCjm9OVladrK+lrc7yc76aGuLEHjn4QcbTLPnru21NlUnzIbD5dZasrJShf7y/MbEX5UvvrDXv6zannTco/vuBj6brm4kO5tVP2iBhA4QatNYzNCWg0Oury1pIvH5jseKgGdyzfdhwfvA9DzrqVMBXdplgZX6cQM7lsOa0E6btQ59aawMo32nBRZsYLCPA5q5YV23PLybsLNM3r0am4XbkbrHO3JiDwyMbH2n4Ee5urzaIovAytSGH5jM7ammE9j6lNGzooWINfeaw2yVTURrlNqTTsc1E2SdolAKWtj4NFlh8SyLjwodQEpSCkflwDCwoNaJaZvjItxfuEk/RmQeNMfpKmRgieGk6Ul/WUenBbGkdGtuTmRHNW4TlofLDausrIO0NW93OZ4jeob8ugqgI4+qPpIBHxSH6LzLLBGwzOojLDXfnXHYgC7ryYna8RzeuCYjOw7Wwr49YaJCbSukKe9C1eM9OdPbrldn9NaSqRq2SGwldtvANg1RYreVopTfnjFTk5hEegk2GHVMYGW8BxCFfHfGVQV6drcZoy1u+SYySzrEwEUETimeZEPK5VQHSxlbJ6fsNtuAEaHgDDszGQOIlw46XfSiceIe6jBvu359pMMFCBCCY8qnacTGqT+u7Xk/4mKMnXHZwmF9LL2pFZib6eCk8rK+5+hjgqb5CnWbkfYXVYvMpuyO6pru/03bfTXddh/mYf/31evue7+9n4KDRoC9SLKtxDOeKHHi+3yJzCQXSnKRyJYsN1qUKI9kCaRMmU2E2CmAAAEQJHo9AA7OOe95++519bX+JRf2B0hmog/x3DzPzPMzIZ5peFg1FECRjidpE3kqtuwU1plmiZq2KrcDZzI3TqbYp5ZRODSRG7aFstBawbbOZolxNtdNAWu0KSbVvmfS3CUxWde0RaxfzBkVwMd1PMdtDQakJcZiy/amY3VV6JRrNvfNGIUYBLQGZmjTRAW37o+JCTVHuSRW65bO8tCMRduWThUkp6u2QLbU06t8TXBNO5nXJrqvapDKIfCypIa89CLuVobK/PNKLzLoCoAm2I0qTzExhE40duvjITyxW8SqtGKY1xNsCAYG0kuUhvyLzOZ55kM3u8zqaeUiNz3j9Zi7xsm8NoRe1cB2dQGdJGpobnTO61HmWC0wctyiahouSwMWgZbJriJrNhd1KwQLsylQwHSShHZWtG3vfGQM5jTABinssJItoyZmelCAmq6fzozRCteobeQejeWm7culUZO0jrWLhTleYo9oRuXrMe7qzBQ+i1TP1AcLbRxjl9gss4MchhqyZAPPs63QlFMqR3lHaCgidKBCPgTts4mR1t3S4V56vtokBCVMDKsWj0p2fxnkplZ6QBeXVVMWjvTTs0UPZUo/mdijoMm0iOK+9EruSL264C05Yu3xUF21NjQWMTEsmoKQBNEJ9NMRbQ4m5sIP8hD50Wm8LijNiBgVYUZM6JPI9IpyzavxBdrUNFyx44WWpTikDovMUKA6c1BshhzoyDyeamWO69STS9ZF2EEejbQaEBa1j0dWEomGGfKZ1kPKZ20+Yj2ETGIcj7U4A00thHPD47Jr+eXc2CDc0Zy7QyOKUMhsLXPdUrb1EMz1QBVNyy8upTXGjqxobNDRqsFmmT9K9EUrtMQIGPO8Diw+RtaQ14h3lXjjHDoF1zNLDeM2NPlEGQseqnFkHic+rxlCSwzYLxtSapkhh3kLeqPEnpbIUpQsoDbEDk8dZYuLrEWGoDOak2mnY4AZ0Oa8pihcEG1Y+gj5uF2N+a5r0cwkGd51UCLgnQV0bdQ2QzijXU2GrC0mfNfBkKLXLqtW03R5aES0BnFAAjXT2mDp97zn7lS7G7Zd+Xil1jTd4K4Ra3VQQUN75RxutGHbbBUDsGPqhnRRRNc0AAj+YC5NC6/bDT6mPVo1rHY2QDs60HHcakUbveVGN/P9dK016u5WH358zupRt4sZurr1UFVzVs1WtLU2Dpv58dWiUv3tA4xV/+aNVadJDHZ5dAA9o3+aLhI+3jsAtj5f6852NxFGV488HDWa1Qf3L7hVbXSieiPttib724XjrjZ6880dfuf+iXTLul/4/vjaUd7wlq122m6P9vcIA/ONzfH+DjC12e52vN5uPri/9vGHWbu26K1Jxzp/9CHI8HxjY7mz3jk5bh/fk54xa3d46E0Pd5adjnKs4dFu78HHtZMz6Rh5LZxtbSXt2rzbq1xzcv3AmUy33387q4eVby96vfn2eu45q63N+c5aOOhvvPc2sfHV7jVosvPHH4EmW3S7/WvX2a9fZaen//8fjU5/559+8mt/3X31zdn6dveFV9d/9fq0s7b5i5d3n/vlnc98/rG/+s76L3753pd++9FvfGv95VdnaxvNtz7aefbF/ub+xku/Pvzh39391GdufesHmz9/6fYX/sGN7/9k9++en69vtt58f+PZXw4397qvvHXj+z+5++nPHH33R3vP/OL2Z37j8KfPbj3zi3lvq/PWu9s/eX6wud9+671b3/3BR5/7/LW//dnOz54/e+jhtRde3//pzyedDWM2/8RXn5p318zz4SNf++b5/s3mx8c3v/OjRWfdns1ufO3bBdYrRD/5lT9b1NvaIn7kP313sr7l3D2/8d0fRIarFdWtP//PHGu5Zn3yT57KPI/N4xvf+N6q2TbPJze++b1Cd4EQj/zFt4HCsRd+6t9+NbF9FOcPP/XN2A+1aXzzm9/n1KyY9tifPoWzMgnDT3z5LzihJ80bF2+baUIJ5cfvhtkEaNvs5PVaUnm0Ac9fsYoYYyQe3KlFK53a6uxem0eMbOKTF+zF0sZ1ePaGnSQ61mH/opmmFvPByTte3ifgwLz6hT6cu3Ddmr2nZ4VFNXn+sbucmmXN6r9pxBeQ7tPpG9Ykb8k1e/YmXk6YifOL++547BVblne8WQ33FxuSHffEdA/QRRltydFh5mM+X6dnG8Ify4vdanpz0gXaVVddbpV2SSZOPnk4dVG16rGT7aSZkrMunz68bHN20eDja5VR4aWbDx4uTSGTDjg7SpoJuWhX45tFEMtBR46uVyQjmVP2H+NmyZOmPLsFnJHsd8rJI9xZzmJ29rpZKiYZPvmVX+lUlOjyTUt6WrqgZ7ddBaCdR+++ty4FFpZ29pLNDU1yePKGo3RcROT0fU8oLAk6ecWtOIIevfeiWzKdS3LylqvDPK7M83e9AuqAoftvBDIFMmQnLzo50itMz9+0AMF5yc7e9/KKQRPdfyUQGYI7IfrwUBShhIXoX+NJnRuFuPwEXgVxe0U+fLTI1kt/pR7clFlDalU1uqHiZuxjdHodjYO4l7APbqTpdtSW7P6OjFuKpNX4SERrq5Cg00M8rov6QN1/pMyPlk2pnW4XUVfhBAy2ytVOVAfw7IhNO6vekn1wUCT7lR/Lyz2+2uJONjwlo/dNtM5mH5LLex7fcuIHdPCBjmssvxIPPq4Lz5jPzPFbGtjUo7vo4iMHr7HZiX5x14UGqhbw/u1A2SRe6JdvWWiNxvfR+cc+beP5Kbu44xImVSTuvt+ENoki4/RNh3ZodALP7wR6IJcj8+p9Cxm4SNHJ2z7WZZIap2+4vG2ama/ObnCFoKyqs0+rHJe2gPc+VRFdSCBPHhYYyJxVVw9zpUki1L3HVUmUsSqPv1Qgq0RInj6sEOIVEYMnhNC5JsXxJ0BuJKHAHzyWQ7dkUJ3cBAqVQOcXDwlurVyN3rklEyNtVNqHj8S0XTCMjo9AhSuA+eVDXNRnXTn7uRqMXbBlj19Fy4lGLDi47yyGZlm3++8Z0Qmie2z0stYf+XzbW76FlldMN8XggTUY+FXHHbynRacEH+iDX5LRwKu2vMW7eDywNFNNjln/wqNNcvWhvXigw2v69Ff4sh/QJhzdMUcXFjHB4kzrn9ikAQcf2aP7lr6LZq/hi6sArxn2xMn6j5WaEHmoTm+lYY7HNT56tAxiOWrK0c0K5yTXi4vHK03yMoTnn1D2TE1q5fCx0knkrMn7NyWpQMH45acUrarKlSeP5m4BpiHv35LGSq18PnhCUiU5EyePA5UIYZZnny6cSsU13n8UWnG59FT/kRJAAZk8flToZAaqy18bRPGyIA8+DIuSApucvuKWCYQt9uAFJ5WGZOjsLUcokgP96l0r4Qa20L1Xg2qF5IZ5/rwxF65wzeGrtCoRJ+zsfSfNzTy0L1/WsxlCG9rwF2zG/cp3Rm/QMoVMlscf1ZaxXa4FgxdRMSNgS7t8jk7LkLv6+B22SkxYt8jFNTRsidpA3r9VxIfztjLP1svFltISOOgVi/04ROBilw7XVt0VvbNTLQ+yZqYut6vFjtALMO0Wk4PSTtXoGpwcJJ0Z+3i7WBxV/kpcbsnZrqApXHTEcL/wUjncF4NDGJyJBwfl6kbpLsFog0+PAI3Fsi2vDio7FuMdObxWNmbT+/jsQxcRyaL8/Q96iMFMmhcf1GibpVfg7AOPeioeaycfeAhDJeHJ3S7VQSG141ddGGrJhJ6879eMaBHZ5xc9QLGE6Pj1ACGQQe30FVe5NEu0i5OW4aloqZ2951aQCEyOX/UAQqVhXr0b5N1aviKX71qEVGlpnrxtV8gwrOzzf/BHZJksGp2Hn/omS/PccK99+0f6Mhms733mj77qHp8fP/npL/zhH+HJcrK28/hTf63PVqkVHH3nB1Z/er5/83Nf/pPg/vnFow898cdf18erUW/7kW98m46XlaZf+5sfOg+uTm994jP//k+bxxcfffqzn/03f+Kc92ed9aO/+bF1Psy94PDpn3r3Ly5v3Hzij/+i9d5HH33ui5/+oz9z7p/39w4Pf/bs2guvXuzf3PvZz49++tx7v/mbj379O5u/ePnkkce3nnl584WXl/V25533N//uxcHO/vZzL+/98JkPvvSbt775/c3nf3l589b6c7/e+vmLca3Ree+DjZ+8NDg42njhlcOnf3Lvc188+s73d557abB/2Hz99uGPn1k0uuGde4dP/2S4e7D28hvX/+bHx09+eufvXjz4wU/nh7vWeydHP/hp5De808vr33z64tqt4v+7RUj+XzZd060tqbPUc+frawDC3PMWa71SswSjo92dxNA5Y9OdHYhh4vuw02FZkvrevLfGOBcam2xvVZSUhj7a3MR5mteCVa8ncpn4/qLX61+/Jiid7e8jTArTmK+vmYt55HnLThsfHmVBsGh3Lm/c4BqbbG/DNCsta9HrDff2knoIiqR/40YcBNAR/Vu3Ms+DQI4O9lf1ug6L0bVrq26ncJyr69ejWqh0fXh0LfM9YXmj/f1Vp0NsOrp2NO92cte5unEjaTZzy+4fXYtqtQIbo4ODZbvNXWN44/psba20zP6NG1GzkfjB4MaNpFGPTX98sD/v9TLXHRwezTY2uWn0b1xftDvKJLCFoaugVuAW1UwlscItgi0lNQA7FGEKbYCaTFlIGRLVMWIEYIk7jFCpdEKaFFGgLClrlDAJNEHqGDINQgA6GsZQMkyaBAImTX1jMScAACAASURBVAGaOkRKGgQ3MVU6QEK2dQSJ1LFsMlwqZBewoQusEwIzTxBccoIqr4JIYkOWTAElSwNIR5I6NwisAsX1Umgo9ziRmOtKuKpEvNKVEiKrVZKA0heYpoKC0hFSiNIEVMmqVkldcMV5rRAUVq5QoJRMcUfCSlQ24IUsa6U0JcJK1HKsIeFWFa6UAQBApGtgvwIGoW0MXQB1gDsM2pXCFLYQDDiAGHeYqlVAV6RHocuRDWiPIZsLnaC2DgMOtRz1NK0ulQZJlyFPKEuhDoMuRQ6GbU3VADAEahHcUIAp0qHEF9BUpKNBlwuXyibGoQSaIC0s6xoAovQLgiU3lQwkQAIyoPxSwBJglNYFAoWkUPoFgLLSpfIEVEIwIIKiqCoAQVpXCpScwtIvoRBCVzwAUAmuwdyvpCkNBMu6rGDBNZQHAlQU6qoKVKkqTqHwqkwXAIK8xqEsOIPSk6gSXEfQp6AHAatIAzNApE5wQFCXSFMAj5KOhmwMIIBdCjQFGhopMdQF8rFqYuhymGHS0ZFdKKJIlwAdoBDBgildqpoGVwB6EmcYdTXgFohD2iPKqFgoSEahAQGmsEuUV0LJSY8ST1YKky6GJq6QLGqichRmUtZLZeSSobKWS1sqXahaji1eaUqFsnIF0GFVr4heIgqLMJeOkLoCYQVsyU0Fwoq7AjNVhRU0uSQwqXNoV1JTZVgpW5WGVKGQrhAU8louNK4wyOqVcESlqTyskC65rlQDCZsDAEBbwwJIRmiTQg6VWaq6BoWSBsZ1TD0NIKHaBhJYaoQ3dWpDYuWwrkupS4OgBlUWAIjTni5LKjUCGxQaUJpKhQhbDBgAhlCZFGIJOxqKMNQRqhFIELAVDAmhDOgKB5hQpIjETQItpjRYWrKscWkIwWEWFoIB7gjAC6kB7khYisoGnKuyzqUpoMKyliIdCMVLVSoDVFiCUHJLIcllPVNmpShVtVLoUIoC1kusi5IpEPLKFgABXiuZJRBVRS0XBgSkkrWK6rKCIA8FtwVgSgWlNDnUIOoy6BHoENRiqg6BLmGLkIAB+l8TBExE2hr0JXCIbBIcSqUL2sIw0CACsKvBkAAdkf+SaBPDtg4DKXWCW4g6GsBK9Rj0iTSwbOtA59jioG0InyKGaYsqA0PE2ZrBHaJMgpoaNIDQce5VigoDw6ImuSy5hrOAI51KXXFflZxzBrjHU6wABHlNQF5ICipPYia5obAjS1IpDQg7rxBWGFVBJYpSUSBcIbGUFkBSVriSOpQORzBnFPOg4GUFNCg8gYFQhoKKS1VIHUhXSFBIimRIYAtAH4CS4I6mPIAwJB2iTEABIF2GzAJoFLWpCioIK9IhxOdSQ6RLoS2UBnCXKYsghmGDylABQ+I2QYEEJiQdAm0AJCBtBsxCMqZaAAUCGIq0iQwA1iHqYGViBBXu6tBLpYVRVwOBEoxeXb8e9dqFY40PDtJeM/a9ye7OfGOjMPTRtWurWk1h1D86WrVamW0ND/Z5w888d7q3t2x3SsO42t8vAl9hPLh+Pfa9zHPH+3uxF2RhON3fW/m1UmOTw8PKtgGG/Rs3FKO564y3t0vTSjx3uruTaJbQ2Phgv8JEETQ6OKwwKkxjsdYriJG7zmxtDQGoMB7uHxSEVpa5WOvRPI1rtVWnDTKeO858rSe5lJRMt7eVArljTTY29DhK6o0o6WQFyRxnsbaGkoxrdLa7i7ksHGfR640ODuJ6nfB8cP1G7rmLXvfqxvXK0Mebm2y1ylwnkd5oby+q1wCFw6Nrhe9Bpf4eJkIpF7/zP2TtltD1i1s3E9svwqB/7RBiMt7eizuN2PGTRuPy8KjwPKXpZ4/cygyn8NzTmw8pQubrG9FaO/ZqSVC7ODzKPQ8ScvnYw7lpRX79/OZNhfGyu7Zaa6/qrczxzh56KLNtQOnxY48DnSVB/ez6DUFZ1O6seu2o3qocd3Rtf97oIELuf+ITEMPYq013tqJGM7fdq4PDZatFpbr/+Ccq31KQnT32aOWYqRNOd7dX9WZu2bPdzeHWPinLe5/6DHdNibSTW7dK34mdYLXRXXZ7qeXNdjeHm7s0z+8/+akydKTCp48+WnpOavvzrY1pr1dp5nxns7+9R7Li+MlPloEjFL569BFha1O/vdjaSDs1JQBuElJHUkC6xoSnESSoD1VThxSZtlBdPcUGbRIWAimAtsaEp2ECmK9UW0cEUgvIng4QoXVEQoggJB2iXIooYI4qu44BC2pjtWYoQGigeNvUqoJ1qfIY1iC1VNk1dVRRC7EerRAjNcxrFgIpJMmqRRCJEQRZJ8t1HcEsrhcIcqCn0C0zgyharFoIggghEHcriDlnIG6UEHDAoioAXJcQV8s2hDjBGEXNnOBKUlXUMkCk1LIqBKUmIK54Lc0cDCWIOwUmlSCgCHNFlaQp8MrMJQJJXs8yz9HKkm4QaFFCBWoy6GuQAFaHsmGoSoEdw9XSnLq4h4BNCeawQVWgAwxIA8umKSpANjXiQCkhXaPCZpQIXCMqoJggJyjTTsA5olsas6SUiKxT5TBCFakhUNcwArQGqpZNBCBrhNoK/RenTwccc2hUcV1IRCsvF24OFcz9VLhYoQJoad4ACnGo87heSUSlmcQ1yKq8DDLuYgkLqedRAxOYAE0kLS4RE2YUh0DjWeVlyCozg0otjxoAy1gZMGuWCIDSKeIQUp5yO+UBEpQDWpW1nBOldJDVCgYEcRFoaNCmhMlq3WGsxBDKLZ0xhTSi1hgi0DQrWGPQJlQDtI2EqxMI1bZBqIQU0TUCNURNCZoacilEgHWR8E3AJdk1TFwUpo07BGiI6gI0NeUyAAFbo5VvKK7INsM6VATjrqZ0TDVedVzuSFyqtFMAs5SKpI1U2ATIpPLK3IUKSenmSYiUwGkrA2apBCybGdRlSqV0i8KVCkrhZKs6RhxlrRToJVAoqyXSxgDl0iqKQAIopZtHIaIC58Eq84hRJnkjFxZSsCjtKg2lpnLhFVEDYw6LYJG5pglzzZZF19VRTk2k1nSJKXWk6BhMVKxFVMCQDqkhq56lk5JokK1TThgKsGjpuii0OgIhQxbBuhQbtgZLqBO4zigC2MewjgmUJEQwoNDETIeki6WpIQPDdQ0DRTwEWwxjRT2gGgayCSRKbpgEZpKAop4qqhTNqhBUmoSo4mGaOQhJEHULTEpJYFbLAFMSZ8ArchcLDHgtTRwEBUy6GaSiwihrFFIDCsVlKAtTQSR5mMQ+gwKmnQRSLqDijVxooKAVD3hhCIxUFSarQGccpJ0E0UpiVfoZMCCk2PGrvONWgpANjTlKCkjXmXQZoYqGEDQ0hAD1QdU2sYK0S6gDIISkS4BDMVE4wKKpG6CiIeJtCwlIW6BqWEaVkTVNupQwgD0smowBzgKE2owrxrqoDAyrSkmbAp8hA0MHqY7OZAVCVjY1VmXczZBVZiZVLF81IFYx0EDaLhFUpVUlNUV4zs2EB4gzAUlZ1PKKKsBgUs8IEtwoy5BDwLmVVQEqqUC4KhtZbmKAVdrKMVSlXpUBB4gLPYVuldhUIVE2slTHCoGsnQGkSkPkNQlQJWhS1KB0NVBJvGfYOM91B/UINDDROGgw5TGAAOuSKjRUpcgOIyZQEOMekyahTMAGUy4hGLn1Ytmug1LRHY1qUiGE1zRlUaZJ3KA4oAgA2kZ53WZcki1KDIUIxl0KTMKYkA0dB5gpQTu4rNmkkmQNApPFbm3V60y2tyrdjnrty90DItXlteuT9XVaVZcPP5yHbmqHi063v38AmBZ12xfXbmAu+9euTXprVKnB0TUemrP6+qrdvtzfBwgv1jdGB3u4khcPPTzpdImU/b39tN2I/UZaDwYHB6VmLbq90d42Fery2s2426wwGx4eJe1G5NezMLy6fh0juGj3Lg8OAcbz9fW401jVWpXrDq4drMKm1I0Hjz2GEFw2O5OD3dIw592N1Xp32ehUmja4eW1Ra0uEzh9/FBM4am9O9rYLTV+0e4uN3qLZ5ZT0H3loXmsrCE4+8bhidFVvTfZ2CtNaNtuL7fV5rQV0fX60fdndRUqdPPmkojiqNf9eLcLf+Udf+febb74BbH3/uRf2XnwB6nT/hRce+tvvnz/x6Bf+7M/3n3vu/MknP//VP9l+7RWo0+2XXzn6+bOl6+y/+OLj3/5m/9aNz3z9rw6eefbq8cc/+7Wndn/5okbhxiuvHjzz89L3d3718pPf+mb/1o0n//IvD599dnB07cnvfHvvhV8gjWy/8dq1H/649L2t11//zDe+fv6JR5/8+jcOn30m2lq79qOfHf78WWDowejq81/5ahV6jXv3PvmXTy07nYM3Xrv19NMIodblg4e/8U1rtSSi+uKXv8I9q3Fy8uRTXytCd/31N259//taHIfR7NGn/spazjGGv/lv/h3UYHh18djX/7N0jPXbHz709NN2llnLyRN/+VfedKRM/Tf/4A+hTv3R8ImvfQ1S2Ln/4Nb3/sZbLTSef+rP/mNt0Je29sV//RUGqqG1OX0b40nu8mX/PRMfL9vN5PQXJp+phrEYvozpInXj+eCOCUaVA6PBO7r2YBXUV2fPm/kUhMZy+CpFi9LLVtMPKe9zl8SDt3V1Lw3XsuHPSTolflDNXuJqWnjlavoxq64ks9T8DSU/iurdePk8nI+ZHaro1xUcZPV0OLnLkguqtcrGx2t4uKbCqfdhnU4aXnxM5k02WMe4YNOad79uk4+0B+tgvCP9lfegwYZNJ7+yLxEYHRCU0EXo3u9h+9S5V0OjHWUvglMHD3smH1sTgq72dTGjC18/2SfWqXvPQ6Ndyvr6aUhHa07Ud1YYXBzpfKqtHHZ63cUfGA98NT400UU2KiZvIrLKdJWe/9I0ZWLNlqM3mE4zcFlOPmTOatFcDu/cbhrzlU3zsxctS2XmfDF4g5koRf18/CEzV5FRRpdvWsZo5lnl6QumLkszjgZvsnoxVqNyeMfUlpkp48vXDGs0N8z87HkD81JLkuFbzC1jOkvHHzAyzyyUXbyq01FsbxjWOxtGSp14hC82tZmhyzm+OHAvCPRPrduPoCjQ6Rm7d6CtmJnPSH/TmluApPrJlnvBQHDm3b6uoga0F+5Ha/qUOEmfjDa0WQ3qmXG84V8YhvE++/AGiLrQjtx7XTYzvfiSXTXwpAP0yDzZdK4C5N11390Gq65GB8bZOp36GhzNH4joTVkLV8mb+fhjzayL6r1ycQ+GcoXO4/6HpoGKagyXr0m3lZdvpeM7WuDGyUdycY8E1RwPs/5tw5JRNZbD17Wau1S30+HHemBFxcdy/jEOk5E5Xh7fDn2xkHN19Zre8lbV+9HoY6OmLcpjPr3DvGIFl9nwDeaXUxXJwWuG4RbtGaPn2160NOI5OH/UWmQamZM7n9BEaaRLenyol0tnznF/31nlRrnEJ7f8caSjS3D8JVZxLYvogwO7jIxxRvr7+oJrKiLHN9xxAc2J9cGjuAJ6EdGTQzNJjLTEl7t2BAUprDv79lgga2y/f1NKk4nSvL9pLVZ2kuLLPT02S38a/axYDUnQKOcvVXzEg2o5v8fyc6mZavkOELfjei9a/ALMB8xpwuTXJb8qWulgdg8vz5nhyPk7QN3Ja53V9JlqPtBNj6dvlvmlqlWz9J6YH9OaFY9vs/QOCLbS1TPFoq+F1mr+jkovYFDO81M1+xiHZjT5gC4+xN3WNHupnFzqrlc0+xRcHunVgsWm/uAaMS6cEwMP9ikb6uc+HW7Yq4EbK3V+XS/nWqKxk5sWvGeeG2B4TYdX1pVLhlt+MtKWFTq/YRUTllFy/yGdDcwrDff3LX5mj0zU37XTlZZzenzkR1csh+j0CUZHxkDHV7s2v9QGjAx29ShhFdfuHekyjfhq/hLw8zmeZoOPTDbPTBlfvWHa/YXtJKfPmyDnZhUN32BmnujLdPwBQ9PcxunFawa5iN1GPvo5rUpkgXLyitSiWE/T8W0GZoJYYPqSwqfLsJ7MnkNxTg0ql69KY770l9OrOxYfAS0A4xckvcr82mr0M5VmTIPl6i0J/6tFuOOdWZb+HrtzHSzWgLvy7rXpxHWTS/3SQ5MNwCLjoudcNJB3z31vAy7XNa1vnHXJtGaVY2Po48GajgbkatO4WkPeXf+9LlhuMHKlnXbYuO7Gl8bcR5cbOr7S+uvsatunr2t3NsFy24JnxlmDjttu0mdTh52vm+icDDbo1Y5p3Y9vF9OPaZBMvcns3u2aVyxQKi5fMepWDO5Gow/0gC35Az65w7xsheO0/4bupVNayotfm56RgrNseFtblxfpBRx9bNhJQops8Bpzkjkqq4tfGRZLxGU5vq3VwFwNivEHTI9STeSXv9asZMUAH/ySsf9qEaKwnKi5GL1HSco75Oq/+b0/sCcjpWuf/I9/6U1HZpo+9L2n2+enied98Y+/sv7+u/2Hb/7j3/sX1mjIXeezf/5ntctToyxvPv233Xt3o3bnN77877bff3d2bfsL//LL/uUVd71Pf+2p4OrSjhc3n/7b7tvvzHZ2fuvL/3bn7bfPH7/1j37v92unD5RlPvqd79aP7+uyvPH9H2689dZys/f5r3x197VXz598/Lf/4F8Fx/fTZv3hH/1g/5mfx53OzZ/8+PGnv3f8uU9/6d//8fbLL8ebvZtP/3D3ly8RDHdef+3wxz8t6uHR3z3zyHf/5vLxhz/3F0/tvvhCtN6++eOfHrzwvKGqrXff3P/Bz8pm7ei55x/59rdHTz72xJ//xf4LL2bt8OjZ54+eeUZTvH3ng0e/+e28Xd9/8aXHvvXt0UPXHvnu9w6ffRb7eudXb9742U+Zks37Hz/xn74x2d3Pz0+N4+O/D4sQDm8+1Pn4o9HuHpolhIvRzm54NZAS5ZY93NrmTIv9YHD9hnZbDLZ3eQHtJJ5u7gSX/eHhtTisjXf3oUJLz+vvH9jx6nx3r8UBWeWTbs8ZjyaHh0kYDg6OKJerdnt884a3XFztH1JemGvz8fqmEUXDvf3csgf7e0ZezLtr4DAP+/3h9o6/ms4PD+ftjqqq5fb2qt2pHGfjzbeG29sGyNNWu3/teuHb073dZbNV6cZydzfudLMW3n7/g6vr14irZa3W6PAoDsPp3n7U6caO09jcjNrtWHe23nrr4vCIB0bWag32j6JafXb9+rLbm9frq7X1Zac7q3e3X3/j6uAwadWTbu9yby8Nw8nh4bLTLRp23UllnYiGVdNi6eHYtltBrAyYu4YZcIPyvO34ixK5AIRaw8yVhVLfb3kLZVDp62FYMMJ5x3aGGTKhCrXQTKUncsty3RhqlDuBW491WRZt25kWhEjh6r6eEktmnmc1MsxTaTLXyzVepV3fXlYMpZndBMbELqMJ49yd+1m6bOoqye3V1dKKjUwCHSSeCZKYLfpcK6Q1sZbZpI4R4Hp1tbJyu5JMW0a2BqzELq9GJi+dwlmAiSsY5CzqRx7XlitKHiQGBmFpD/uxIzhf+av+qIlQxrV5P/YETROdHhc2LbjQx8PURQobDTahLQ06tOMumEdy2/KcjLm4Irh1sVJtPSd2/XQBWpowZctbai7JXcd3Cubh0tK6V7Ho6MoHdbZSbb2wadtbERuXgeFZKXRJVbea/Qg2KPT1hpmAJuWu1vEWxCa4pjWsmAaoqBnBgxVpMBCwupXAGhAUImeOQL5qUDOZC1qVLveGoyIsc8sx6CkjeGUh3Z7iqlgGSI9milSFwwMy4maa2Y6l9y3EphqHxojm1bxtmudLIGSlFxQPpVWmngfsORVFpedIH5KCrprMzyKseGVWOh4AXWaOS8yZIcqlzZk5I1GS2srRS8vnlWeTRl4rE+joVoMbk5i3PYrK2nQF6r6+4LobKd3CdVFPEugzrQDheClbJklAfRDRBiMRb7sL6Rm8weurFPkaAbA1iqquJeK8No54wyA57/hL4WiiRerLWNV0YGi94Up2DCZgYKVVw6IQtLxlZQcJrlr9/jTgxNCcq4tVUEmLBuwk05PUo+5gKKwqdlF9Nlw6EbChjUaxX+m2oZP7JU0zD9ijoTDyxMf16dXSjYUtfNyPw6wwNYs8kLTIHGWxoTSy2APBoJ87aWkpAw9Sa5Xbhq1fMoISs/DxlbLyRUuvTYYJS0tdt92RA3Dl1J16zNIib9vWqiSMC0/zjBQTnrm+U4tJLqWp217OoiLu+kYmYZkK2w/NVANF5npGI2J5grwgCItqAcuOYywjhTPhMd/KuOBcd7RmzKIEhLpVk8YkkR3LzDIkCxVonp07Jc99TzWyxiwFvseLXFtcrfyKpRUlx6kOVCicfBBZnKsoXPSHDYi40sdXkS8IFyZ9wC1YQKUXw9SFFcvq8/6oDjSBrfHl0ocUVDY7XhlSAOktLlOP5EA2Z4OJl1IiDDqKA8RwqZHjhJUihO78KvdABkVzejULYqpVJhusnAIaxHVz5MC8bTeGKaxjEOoNPZZNUnpax5sjh6FQq7uJ7gHeMYOTCNUpCFjDTECgStNw3QiYpvCtwJ7ptijbXu00JiGpbDM0YhrAzPPsWsI0AEzTt2LmwbTrBYOEB7owzZYVM0dmnm/XI2nlJDRq9oq7rHIAQSNhlonnQ2vOyorrBTBGGteWDeYVGSn6lVUFdIx1kTk2thZ6IZcWJ/ZCnxVLX/l5hAVOHGDPx4TMl6aBw9Rc9SNHqnRuFeW8SbxJRrVR4kAjnjOapI6TB4W5GkYeATx1ivGsju15ibPJyjPcfMbSdG5pqMnbg5XoGFWu1waRaBoEiY6/Ai6rIAknCQw04Gi94Uq2DURAzU5508ImbHtLbJFK12qDqAoNoZmdqxVsMGKxupXKhkZd3HHmzKbKI41+hBqawnrteEGbOvJozU5AjUgL17wI2IF0cf3BQoWM+2bz4yWpG6njDff3V73uvNub7u3FnUZ/c3u9Xu/vHcw63XR9fdjtZI47PDpK2+1Zq73Y3s6aweX+4Xr77fHa2qpeX/W6Ua+XeMHg8Khw7Fm3u9zaWjWa/f2D9bffG3XWonoj3tyOPDdzvcHBPjL18fpGc2Oz0PXx5vZGozFrdaNafbm1Veh6Zjv969exUvPe2rLblQLNut1VrzfIi9Iwro6OeozNeuvsYGakSX//QM8zklSz9fVg7UJL8iQIL/cPNjhfdbr9w9RdLs8PDjWeiY1y1um4a+tGlEaud3V4zciL+dqGyHFjNLo4ut49vT/f3p61OubGyp4uEz8Y7h8Eo9FofaPQq+75WX9vrz4eTPf241oNSvX3ZRG6iAdXF4vdTRSVWpbmgcPSrAIEGQikJcvLaa+np2lweb7a6PEKkqrIHddaLiHn0tVVJvQommxuGqslqSodlAJglZbz9Q17OsFlJT1dlgoJmboekqL+4Hh4dBRMBirjq3ab5DkUglLABbRns6wdZshonp0NdnaceI6XaeVZEmFjvlq2WlyqteHxRXvPkYl5Opjt7hh5hOJSWBpXyprPeOCt7Fr7+Li/vWNXkXU5nq6tUcBjZvvFAgkJF7ly2MoJW/fuDXb27DIy+5ezRs9AEi0yabLStqzhTDpsbnjdq3tXrR0HFnp/lrQaXjlPCg1oOHMdNlxVtkF1RYZJVvP0LFr1Y0IQ2WqqTHjpLKv7as4rSwOMyLc+ypsdt23ApAIQKIfxROl5UtQ9mUugFNUUmaaZbWlM8EQgBZdBDZ9caqtl9dCBOZ1LRrKab/cnXKNIQ8n5gmZZ/vB1aznT01TVDDDLSt2Ia6Ezm9Ocr1oByzJ7GudNg1fQWUzG7XUzyVAlXRpHwMJptWrXcJw0plf99b1gNZMFipouS0qa5cBGXGAkQKlThWBwNZ1uNt3lUqWgrBssWqlKAUcvAdPnmayRFJvhcD7t1txohqI0roVaJVQJbRKvdJ/MclFjke4F98/TtbrOMxlLqCMFARfYAEViO9bldNVrdZLLKNKzhmfwNBeUgkpRAiOOdZXajnk2TnoNJ1/mQoNQYgZULCAF3DTQPPdwtNJcLpDQmIFKNcqqusNUJRMBGVQE8woaKksc37qaxe3QUDkcZ7nvIA3CTBpZGjdcfZZXGgE6tKfTCOvE1WAsAQDSwTKHRpqu6p69XFYIR2HNHy0qSrAhVSIRV6tmoMWpVuVKwzJHAKokdJ3RQlASoMU81w2VT9sbxnJFM543THuyKihLQs/rX0iAYWDyHGl5UQWMV1iPsrjlGotIcIBtwjmkURZ1m1ocKwGUTsw8EYlMOzWSFjRJoU24QEACE2RLK3Avx8v1lh0vxErIuoXLsgBUx7xClEwTWNNSajoXo2St0Z6cDY0OIRIJrnKJLFxSnY1WoK6VFQYKKIp1mcuV5DWTFoVKeRwGiqL6+WjerpkyZZMyrjsaT8EiATrljkuWFWZ85bjB5WzZ8nVVGMPx0gnrKF5VDiSqshhdVJiKTNdhIQXBGqnotEg9i6EKJAoQUJlMmy0xUYtaK7iYrJpeqRtBf1KYOqEcLBLJSFJrOuMFxmIVeu7lIg3M1PHc0QhJFTdr+nKlRUnZcHiFkOCFa1vjhSCYmIBnChdV0q6zVUx5paOyKlBJWF7zjPEcIEhMuNR9BSAGksUJW8RFLzRH84LqmiZKjjiiOqkKToxVLJpmWRF9GZc1m6VpARjVFMiF4go5uJSUzqLlZi9cjECk8obBshIUAFioKrm9WhUNP6VW2J9NenU3XcFIpXVTz2KVcZNVsRHgeal8nFAzvJzO1urBappgGxJFBFeZgiYsmW5OU+GjJbK6o5NBe8cpIxUp6EK9yFJhQh1kEIeTYV4PI92tX02mndAuEhiJ3DMUQXiR+2o5D1vaYJk0AqdYpsrAokImVhGHBEqbgVlBicgcB2QVQJAxicdZ5tka4SIWipI09KUEZpkISo2zcVH3SttyLkelpTMqeQE4JcLQtfmKAg4cDY6StOZxTWdxjKXCOqgygJRKG4E5mEhColbdG0wVRgFaHGTseQAAIABJREFUzJVP83LZDvX5wkmXs/aaN5uXkCY115zHUApkoyoRVhHltbAUxJ5Hq67vTpYlZFgXXGABMcO8lNRYprzBCsmcebJsef5sVFWgDBySCSCgS1dTraFPY97Q8hKF88Gkve7FMZdUOgilHJRSuSSnhns2XG2026vLRW6VNQfNluVgQds+CF1tsgI+TTXbPh1Gmy04T9S9E7m7bWgQ5SU0UKkZbBR5VjYlvhJA6MyQuZxXvGYzXshUIgPFkuHbd8ler2jUgtP+cq1hlYmal2VgqzSrLuegHqCmZwxnyCORE3j3LuLNpiYKssohBoXn0MmSAT5tdJr9B3Onngdh8/QkdVysQ7jMAYTLZivon1HAp53N8MFp1Kjnrlc7PckdJwDRQjqS4Nx2nPFQYlKGbnB6tqq3Utern35cMlMFNogqvcqiZp3NIgBAEbiN/vnUrWGd0EUiEZauphJO83y+vt64PK0AWXS67nQCeSVdXRQKS0Uxj5kbDvrjrY1g1Occ8sCGpRAAUiJLqLvDQdpr5JKG/avVetcqVmOzZchMn6+AANLVSqgFV5fxWpPnCiopNWalkSxVEbo0zWFWSN/gHOGK18vxWW2veXE63tp05xOQ89HeNe/3/+DvzSL8gz88+ukzq0734G9/+tD3vr/o9k4PbkzDjpan//j//OcPfeu7t//Bf/ub/+rf3PjhTyb7+w92bqzcGptHX/yTP33iqb/6+LOf/cJX/sPDf/2t4yc/O+htRW5oz6eP/fW3Hv7W9xa9jf2fPfuF//DnVzduPNi/ufTqIOdf+up/uPWtv4mbnb1fvvz4n39t3uwcP/TYyq0DCH7rD//d40994/LWQ9e/95PHvv6NVbPt9/u//bv/V9xoNu4e/9Y//1f93ta+vfgdAmfTk+C908//yy+TJAMA/ZP/9XcTatY3jX+KplNCGz966TNf+VOFqBNHX/i//xAXfHZ49Fvf/UGua413b//G7//bLAjCj+5/7o++iiqlN/A/MwotHqxO4//uf/tdrmnWePalf/GvK6p7bvE/mXq1OFYX8W//H7/njkaZ7/3j/+V/x1V5Wr9+9rJdTYAHlnd+3RD3M88aqJ2a9Jm6u+i/7JZz6JbLO2/Vyz7yq9PkM/vGYgSpdfa32upKM2vVxXNaMqZaQ81andR0nMvxnV8E5X2hHdKL75HJheVtV4xlVdOh7/VPP+4lpwg5YPCSEX/AzV1u4DQ96MqPpvPX7eUFCfj09H0vOmZqi9Vu7+GTo3gtCt5piNEjlrxzC539lliMpIouP23f2YLOKbl3gK8ei2qLf7R6+YsEXiUrcXYALx6TJAXTDfvDfWAMBr3Gwq2Bgoe3G3zwSQpm2swVD56AYPFJ7f4/qc6OoSZvH4KrJ5B1xY7r4urTenlxk979bTGLeTobPGF8eAs7F+x+U119lpGTxVTdf9GnUQ4IPP2RgTnnB96otoahqN7N775a09Oons5ffXlTn8doR+83thRF6p3o5EVHR4Uc8buv17U4AxvGsLFW6JZzZ/TRT10pMS7F8Qu2m63SXjBp9kqiO/cHt59v0nGKQnz6fQNEHG5qg9Ymwkrdze7+KkDzwmDl7WdreFaxbcd87RAlLb0Yy7PH0Dg00P1/Zk73o+PbqKm//Vk07Ql7SN+/BaN1X7z1D+X4QM1Puas++rxz5qbdsf3G42qyXdRHk3qr0HV9zun9W2TcK8zUuPewftqQ7t3/vvroFq7ezH3r7sNgvqGXfXRxSK42C2/xP8q7n+QXb1ae/d6jcnod6+f03pGaHWBtMryDJi9hY01NX1P9dz3bihdrndgNYC7UW9HdN0Od8cVAGz1PHC+fb3bnfsNIo+Wr8vid0AZxNZL3X63pKC+2g0nQI5AXv8iO3/S9Rp5+qO69XfNABNaMs+YekVXxen7yom21ZPpe+eCtWoOMZ73OMmioSpB3Znd/XbdUnC7p6d9pqsmCuQUffEaPY1bE/N5vm5PUCs//5+qYIX427Gof/T+0vemz7Wd15/fMz/Ob97z3OfvM95w7SRdJSLoIhBgMdnvq2E7auKs7rk4qValKiriddvUQ2+2mgyuOO6bpWBYG5AEDBiQQQkIISwgNaECzruY7SHc494z77LOn3/x7prxQ/oBUqni/Xqw3n7XWm/X9nEa64hOlr34Ex7ZlX/vnflafXL5cLZDXPm4Vh7rC507zUk8b9rDVLanT2hyb878ohraoF+4LHzRVMOe89mt6LyjHg3FHXfk5MQElL8Vrt7pDDBuX/kV5tsbJxfGc8/YNJIW4zO3mh+nImS7I4T3l4LLrLqHhw2q6Jfy6HM7106DG9qd7T4jZG8Y7Cg5+APcu+v6inbbaueuLg9neC3z0jkM6bPcZWrxmyElx7dPP3vTamy+snSofq3avBHWcHL7NBm9wN6rGi/1p2HSKePhDe/WdWr2eDl6iu+c9LyjjfntYn/Mnh3uPk8EZJzxqxg/LKxca4byu7wTq0oeQnqKkyd66MXfiW6vnf8WtNqVU76zp7VtZse8kTL17G5LTa8WFX68uT6xOz2/A7VsJ2SJ7bXP1Vl4djPts0FiwwNQuBPTcB3SQOpvCXPkQgbsb1Wu/SclsdHESb9Dzt4rsEJQOfvPWwlE3qud/GWVTleSbC3rzYyKdQQvRm7dBoGYq3XpYuGUOp+XZF9toVOE+GXQXUz/yrh5c+mFQZRhDefnJAKTadti4M18yx7+0/+YTHbSn0CLZ/C6dpV7kjT729HNemW2l3uXnm/rA2gDu/MgpL1fecbzdXauYgJez3SeFmRg/n5x7pa13IFigk3Yv8SN3f7T7IJ1MfML04Kc8HRERQu/i9c6lHmxcJK/fCA9Pysber85euB7bvVmuL7+f7KzJIKPvnvAuLcX13d9Mz3yAyfMZQu+8Tw+vYfaQ7C+DK8cAPxzMNydRhxez6IU1M7wBuVv0nUWz9z5Xv/tBunlrtTuAuHz3g/ziUdx4h72+boc3c3ru5vztTzpk72C3GNxM3z0KnQO8vcHevcZ09uPXygsvN4Nq4o7iM8/2vdkh6Uu0EBJTxc+rKy816+4svwIvvNz0qpSCXXX6qLu/N9uOrjzCaQ2pTfnO8/V5MDhY6E9aPWWJODs8+2TLKTOpydWHBHKQy4bZzRvupXfKq86551s8zQgwZx9tsDhj3cTMeQSD/GV18eVmw8zKGT7/bJNkqubNfuNf/tvo6nZca3z8Tz8fDQ9tA/2qA5v6YG9G/vG//cOlM69f+MAt/83v/W/hO1fim07+WnKhC8ri8uADd/xN781zu0eO/foffWb52RcufuRDFxeuyZygffb8Jz7/541LW4jg03f+7eILZy7fdMNvq8vHzPDMzPmN//jHvTOvx53uB7/0lc4b53QdfdI3q2rvsol++d99Zv3RJ85+/Od+9fc/M/fK64Njx2769ndO3XXv3pFjN3zj7g995euXb75pc+XkLGgEo8Ob//rr1957XxbV1598+oav3z1eWLx0/Lpx1IHW/OKffO59d393533vO3XPg9fddc+02egm6ckX39zzw9PfuPuWL/7VOx/7+C1fvPOGu757sLF+ZeXEtN5hSXr6rrtP3/nV0crasR8+9OG/+NLgxMmL66emUStMJ8e//YObvvK1yvG6Z89/9PNf2D1+Mtv6mbkIJwsLIk1m7XbYbgfjcen70FqotaZsPD+PjCmEmCwuuuNR0mggpQCElesmjUa8sCBdf9rpsvE4j2pIawsxQChtNtNWe9ZqZ1Ft1OuVwoHGAmgtRpPFpfaFd6btTm3QSFutvFaHxgJkobXTTqfWbGZRbTo3V4bhtNsN08msP59HEVQm7nSyWn3K+ajYnfEIN0URhtP5vvS86dx82m4BxMeVzi2uuj0Z1Sa9no1E2WjMOp0K4812N/Z8HEZxu50GYe6EVRiOej1J/bHePjBcBuF0vp/X6kmjmXU6Wb0Wu40ZmU2Qk/pBUq9Nu3PSdacLC3mtbgLOXUsCqALBaOU2kHEctDMwjJkgJL7BzKqAURcST5vQo5u7MC1kizmBNAIDB/NAM2oBw8BaCID0hRCac2UIZ2FFmS4xIdMUUmwCQaeWYasDTlkacGU4V3sxgyhp9nBgYWltQKgDLIaWYsNT6mDNkHZzWBXGhTH1pyafMtewypJCcWpZjqqZdMkgc5epSWFEnEplmXI0URUQM00hNBZaCwCUTkHTIncUqyyguXJAgum+1hkkxs1IMSscBJyCpoX0TAzYBKYxYoAWUEw1g9qRMI8Ll0CABZMkApAjx1fYQ9ZCpDWWCrqWcY1rrCTEFRIEUBEKrQHGMhe6nrQuAQ52hEYRxlJCa6ExpSe8UGMXWAcKRwEHAQQhANBoGTiekCwEmhHHl8iFECOsNTDAhpQKy0IoPe4KiV1lMAAiBcQUrsFcAlQUlMyMHlWg8n1XTC0hhkPj5BDAXPCxNQbTwnVckmon0ZQbEQPoSIcgo6G1GlvEJABaesjizNAUcD7KmatVGXmaJ0B5xrOClRYjyVmsoFRA1gPDY6RZ4RIkclgxKSwWgHpKCe4FhR6XVcNHxhhrITAgpFwYEGBkoAikcSiwFloLraUhotyAOsWJEo7CHlQWQmCBsTiAnEvrUhwAxgwMMFIFsgYAAF3kRlpzzCIjWCFrHrQWAgsBUDWHCwk8AhByQo04UkRxmpeuth7iJJZOVVExsiZBWAtEWFw5oOSAk8L4ZYzFVE0n1DUUYzEFtDBCMZZqXln4Xs+mdAnDM+tkmmHoTAErc+rMFJxCUjqW0UKKovQ5JVMlUim8wwTHCFYuNaKgTlW6gONSB7lBiIWWAFUxh0dAlcC6BFoDNZCBS0XuIKm4T4KMSqNdD1gArYEEYBdQDq2DiGOoUhY7gyBEEAJOaWCZNSAkyAFMWcghAOA9YxjzlWOlDjgLQFkB61FgAVHKUkIc44eVYpTWDNdSO65kFSC5cTVU2vKp8sg051Otciy0W7K4zH2NrMU0LYRKABpCkGGmHQWypHSJRhUnReEapAy0BlpruMI8NlQrF1KalgLGohaj2aRyFQMYz5SrETOQTyGTM+zMzCwBgfSNGFWVW1gGHDJTLAcUcFdZDkHEqAAkAEQrACyWUnHmBpIIBF3MXUUcAAgC1gIAVOC4XGJXWua5tdI4pnD9Xc/NKEMBpdzQ0KrQ5WwcRtoYBI22EBmXEE9BAUBIGLe0hizG0FpoNEDQDVThWhAy6hbIAconhuaGFpJxyGJgWeGyg1hQYnPqYV4oy6QAjsgN10bwwVgRVmaOb0WOTVHxCuPC8lyz93amBQBaN0F5UjoIigKrshIwBXgMYIappRkQSFJi3BJkSe6xSenEWOVeA5QVZLHilrEciFhTgH3LmEF1pgriOMrWOKhKfHVPBxGvM7Ffao9Bgh1uYIgBc8jVHQ2wdbETSsAhsNAVSrsEAvAeaDoQnqiIj6xAjq+Qg4FUfHPHUmbrjDFDIyRd6vISBdAAjLf2VdimDYfvaOtjFCDGDQmh5GKytJg3GnmtFne6SRTN/PoYjMYkSNxG1miM+33F+bjfz1uthPsTQhJCx0G9CMLpwkJWq8f1etJpW4Sg0QCAyg+Sbi8Lw7jVLoJwurhYUGeGQFJBFYaT3rxyWNJsxa1WHoQzvxWD8RQKJUTa6RSOIxmbLC5pANKontbrtNNJoyhvNcf9vhQuNMYiBCCYzc/Vdnemvbnmzlba7RZhAIAF1iJjRv0Fsb+fh9G012lHUdruQFMmYVSEYdJozBYWCsZmC8v1q9tpswmtAcYYjON2O+l00yDImq3p/FweBMhoDQhSajY3X4bRrN3ByL6XRfAzdBHaIHCyFDooA/+vi9A/GCqDTNPLkQDaFr5HKulkKWZA5Ybn6azTBcZCbRxcFYChSuZhGB4ciCy1NSExLwArPY8WBTBGEI1mGUuy4dIy0prlmfQ9P5vmUJSeFwyHLMt008uIh6QSRM5IILK08j23iAvLKdYGIiNt5brAWL67V87P1cpxoZhyuSOz3DKGtDFWzSrXBYlX40maB0GtGBeKKoe7MssMC3WsKc2McGEZi4AnWe77UTnJx5WpRw6qCs0oUpoQW1oHljEPyN6g6nQjMysrYjhpFsMB7XBYKcqkxIbiwCSlpCUXrfxgCBvESNdRM+A3sqFhZAYDS1Bg0mxmtedFKJ6BAAHtwiKGgVslgCCVGICgcFRmHY1JaOMY+BYAIyhIpMhiUHcrQwAAhhOcVwahyMZT6QGpbNNDpRJVJnBVaFZQx3BCKg0MUBwhA3lRUFxOQYhyZWscS2kBmsu3DkVbG6IEsQrBSYaaLMhnKQ4kx0gZZIADE5ACVIG46QGIaCG1QH6RpDgguLKlzYAfkWmJhNXIA7MZr9O8Ug720niiA9dR2Koceb3s6qHbsQp5MJnwGslKI4gnkxgGApakLGUKXF7NwgYsdOXwlenFbbZgBQ7yaTkD1AFV4FaKBDZNuQdyrR1eT4dZRiGD1AUx8IQtAYaF4XPZ9pTVTKyNxxxapto1nPg6mYGAg5LKSqbAJUVca9jCKIfVqmlsPYCBB/IEhW6VGoYqwy1Cnk3SlGnOa2icwBAC69g0wZFbJobjtOQQQeAiWkiDUGCmCYqABZoCZ1ZwJSsfl9ABACiOSaEMwovZ5ct0FUoFahRXChmLiaSlyqmnGAKJtRDW8SRGAdFaoDxGIVYaU4lKlWMnAGkOhVFAe4JPY5grEHFuigR62mFQaaiNBwuTKptpEdqx1yR5ZVwa5LMY+oQZHieygCwAKQ9AaXyUTnmN5JUVqH9wZQ+0qWuNYLkVvk1y4oLK+CjLFYOphB5mRCfWJdQQpXIgLEMAQZapSpComuTGU4J4VTw2DQdmhOpSC1/HmXBRCSQnoZxNUge7eL7c2naXuckhsqUVvopLwtlEVi4TJMtAYDDwTJLAiNkSIJvlPABx6buwBJphTRDLlcHANelURRBZ4EJSak8lmXBQCSXDhiBcyPcIgpV2qgxjW+UIGl3VAyi1gTCycQx9oI0RjI9nRCnh6hw5OXHeo8wiFNp4jOrGQsIMlBopg5mhRZlS3wMZmFQaIB6CGAdIaRflUxwSqQjRYjIrLOe+zbGrAA5tPCUhKaV2WZhPUxRiorCWBfQ8G2fWkQWq8TgWEctl5ZAonyYoRFThUsU2nFdbMyfSmnkwnrKIFkoJXItHVeVYx1qGCuC6Ni6IAyvogXjMG2BSwjqPilEKQw4zp8oORdczcYGcKgF1nkydiOeycmhYThIYEigR1JkRvWx36telJEqwRjYsYgw4oj6cWY9ZSbDOjfB0IjEHk9II4nKZWNdi5Ns0Bj5AwFCiJApUbBhREhqCAcUoKy1GoUmqidWUlM0AldrVKUMqU6IUDsBQ7I0QhSTEM+Aha7WgOK8AgprT9whaSC/veEtQW+kQUECYlTQCoigy6iuKsDTQWt/GUxOBUoVOFpOQSAWp4WWZ0sCxCY61toT4MsEh0saFyYzWSaUAMyQpEhQENNWAVogvpJe2g2VaKoGzMW6ApOQBEGWekkDAVBsqIQvMZMoiklXaJQuTzS1nkTDLiiLWjk/zigpbAR+mU/5eDXOzWVpS7BFmqxy5nk0rwpVEC8XVfd5D08IEXJAqNR5ilukqBa5ns4oKOSojt5q5NZyWyuVRNZ1Zn2DNjEwqTojVgqJCBTCZiRrKpeE0lNPccAwUIrDSxFVp7gbgYKyDQHmukySaEBcWmREAWskdejh2TF40WyivFKOSc2cWa0rnit1ZygCCk7l5lmUWYYYlymUuPMk53z+wjLmuTQ1nqoIMVZoACBjWcDDOwkZAq0JTgJBAZWoFMsYK6qazHLuV69CigMYKItE4Y1kmO9GURzzPlcvdPMmQQ5l1hiNQGd32E+yzPKfcxCTgeY4YDNPRgWgLLI2BymIHVQl2eZ4zZmQBRDwr6xGGNgecUAM0UBYLUoFpIeLE99XF5oaTJsoTbp4UkOftzvxn/8PPzEX4+c/d9o2vjdaP3Pid79769a9OF/u3fPe7N3/7O+/eesuvfu4/f+zOL7/5C//ok3f8+c/99Z3Do0dOPvLorX/3dwdHj37w7rs+8cUvXLrlA790xxc+esftZz/xCx/6+6/93N/+zejo2rUP/+hjX/ry4erqjd+5+5f/8o7LHzj94b/9ykf/7itnP/5zt331Kz//f/+X8cb6tT/+0Sduv2O4uvK+Hz3881/+4sUP3XLbV77yS7f/+dUbrz91/w9+5c/+r8P1I/MXzn3q3/ybotXonj/3T/79vx+srm2cefk3P/uZpNdrbV/+rX/3BxBDnsb/7H/9Vyr0m9vbn/rjz6bdZu/c+U/90R/mvl+Px7/1e/+algWG5p/9zr8kDIZ7e7/xR5+pmmHrypVP/cHvW0Z5mf7z3/8DnieW09/+9KcxAt5k/E/+8A+NQ6Pd/X/6H/8DwoAg+9/+zu/6hwMbOp/63X/tZ9O93nryQMb3ZiFPZ9+X/MLAW4HFt6ZgM4+aeXJfzLfiAM1Gj1m+OQtEPHvIBhf2xQosvjU1F7KoWxY/mKErecDT8VMAbudhUMQPlOL1fXGU6LsO5duF17f6h4d4s6iJePZ4Rc5OeBcX35s4r+6JDULu2U/OaX8BmAeH6FzWpIezpzR4O7Or5MjzTvvVVjY/6r/K2m/OU3Qh3K113+oCcRheri+9wFBzt/VaWHu9X/Snc2ds9/wyI5fqV3DzzRXLD6NL/vLzbtUeN95y6xdWkt5s8Q1Zf32Nks3aNmydWaZ419/xF19q6tZh903UeW3Rhtu1i7T96hqxV5sHtvXqCkV74a7Te67Lgq3wLG29dgQFW3pUJN9OwmJKgUzumrkgZ0kWPyyZq/hePH1Q+mrSTgbb32f+bCS4ntxXCSDZbJbfn7qk5MPZ+AEVVRMHFcP7rEimrqvyb0xZWbhFknw/bxRDm6vho9grU4cU429XtcGA1ED+zQlJCg+Ws0eU0DnPi/EDlT+deKKc3Z0720Ox6s8/Vq8NODW7jdeXo21A6P7cq0e6l7N8frr86Jx/xYO1nd4L3WjLIWC7eW6xu2mkn/Sfn++cLfPF2cqj9eBSrWyP5840ox2Pwqv1txfql4iMkt5zvflz2vR2e0+3oq2Oao3nn3fqV2oCXGm+3aldDqpg0n+xvXAeZAvjxcf9+uWuiXa6r0bRu03i7mSvJPChQ2/BgqcPi8cKcYTilw4nT5lGIyfvTGaPSM8v7TsFfODAWbDwlWnyUxAsSPvqdPYj2XBn7Go8faiKWGx2dPrjyp3T7KWD+Mcy6qTwrWT2sGzgoTuY7T4hAieFW0V1/zRoVeDVUfZIVWtl+nIx+jGouQkd5bN7swBO8EFV3DclDdsdye4L6166z+W09+w17miMebL8+KJbFqya9X66yGXiTabtl4/78ZCB2cLzJ6L9feZMWk8ecxNJ9az77FKQz0iWNt+8hmcJw5P+Uxvh/lTX8uVH+nxmmB3Nv7wUJCkrZ+2XjjYOx7mvVx/r13Zi2cqOPNZiCUdotvBsLxglTE06L20EgzSZV+ybV+WZ3F1D5oED8FZWC9LpC9aczXgXlT+MxQt74igh39mfviy9I1g9PtNnZcsZpc9J9VohOjb94Sx8YZ8fJfa7u+rlwlnB8Ef7xZmq7c7k82n5clFr5cmTEp1J+QkM79uvXqyiXi6fjosXy6Y3k2/I/FUddMryidQ8PQuOKPiD/eRF6y2o3mXbfmWV2X3ngPd/2jHRuP2unX9jCfi7tSu488o6MVv1UdV4YZ3pfX9M+s91ubMTXIStM+vI242umPaZY051WWS2eeY4hofuBM0/NY+8cXjFdl5eJexquC3nXj/pVZtObnrPrHr5lpvC9k9XMRsHe2b+tVUHbdJDNPfChij3nKpaeGqFq0kmZ+V3p7V4RPJ89A82mIyFqIYPAH84oW1bfGOKRnlI8uyBVEwToYvRI5bHqe8Vs28X7uUBW8Dq74d2IB2nUvcNveHUhcX4gdLdHZMOK78+dC8d8EUs750mh9jzpfz+xBnMIjMePWzZdkx6ZPbtWbib0AWov3VQ7QDPr9QDh3gnd9qg9+J8/y0LerudZ1rBhV7Vm/RfpK3NNoeXGucbjXcaKph0Xm7238DZwnjpKad9ZU7Xtruv+dH5HkeXG+/Wmmcb0Nn1L/Tb5xtF/2DxWad5vm+DrcY5v/n2HAVX2pt+8+0mdvebbzU7r9ZI81LtxahxbgUFVxrnRff8ArcXo62w9WoTOnuNs/XuKw3dHYDXRtN/kHV0GB2M9h7EkRnjaVXeHwdRic6P84eKqJ7jS7Pxg6oBx06eTe+vQj1maZXfM/W8kl6ZxQ+Wc2QvG8HxIzBACSuL5J48LMao0vl3po4ond1Z8oiq0zEcVpP7y0hOmJXJXbGXTSgw8jsj7CF6kOQPJHU0JbNydE9eK6aRN/vtT//O3NtvZb3Or//vn+1eeVcK8Rt/8ifzV68Mlpb/h//lf14+8/Lm6Zv+xac/vfDqmdHy8m/+yR8vXjxfBuF/9cef7b/91v6x4//d//Q/Hn3lpZ2bTv3KH/3x4htvjFZXf+Ozn1k8+7YKvX/8J//H4quvbZ88+d//7qdPvvDcOx/64D/9vd87/tNnxsuLv/T5z6++8LysB79y++0bL724d82x3/rdf3Xd44+d/+iHP/X7//6axx69euraj331b2/5+t/vnTjxiTu/+Im/+etzH7/tE1/44q13fWvz5htu+ubdn7jzi7OlxRseevAjd/71aG3l9D333Pa1r174yIc/eccdn/zSF7duOHXD977/j/7i9ni+c/2jD932l381PrJ6+u67f/HPb7/44Q/e9qUv/+IX7ti5/tTRHz/x81+4Y7K8dOynT/3Cn31utLp84/e//4uf+9zVW2664Z57P/nlL8+OL80//eKv/+n/mbZba6+8+Kt/+p8GR48Wm5d/Bl+EEEBr03ZF4sl5AAAgAElEQVRzYCqqqqweHq4sY6Bn7QZI57DVs0bNHFsXRhZRsH/0CKmKPAoON9awkUkjGq4sUSOTbgseP8ZkIUNv99gRblQZBQery9iorNM+XFslVqXN2t76GsvjslnfP3EMybLy3YO1FQJN1mkOF/sI6Cz0d9dXsZFl6O8dP4qM1L6zv7GuOIEY7q+vAQKNy3dPHgfYQgi3j60rTg2n++tHpMMsRntry9ph1qrt4xsWAyTozrH1ynMsQYNjG2XoA4ftH13XnAIDto6t28DD0OxsHJG+Axne3zhSRD4gaH99VToccbJ9bENzio3aOboum3WIwMGR1aweYaNxi6AQYpXblkAEc6tNhyLHQluhBicupMDSJgACYWBhE0IhWFXohkAOhKaydQ65BMbSusEMEVPAFlfGYbJUdYYcSoEpIoSI1gagBoZQIFnBFjFScJnnbRcjioyGEcLAYAhIHWgNqZaFRyhUFFjFTFFLMYTKsWW9ghBap6panOhS+1AiiWWlPb80GcbYUpmHOQQA+DptcmJK5QUaVEwWxnN0LbGIamZlrQTYWlHldYVNIYVbNSSBumIQ1hICNBQor1UAW8NV0dQCVNYFebPAVlqIYQ8bxyKkbYdBWlEKYJdiWmhAcA8SZimyaA4hByKobYciz1AKbJsgXADCSA9aQYjOUM/BLqTYmi6DrgLIgjYmLiLY0o61DqK2hF0BIkZVqVsCe9pCa1sYCQisph2CPEx0DrrC1AzWsgwNE5JAKN3MUIOsLaISS8SrPI+MIZpYU3kawQpBorwSQkCNKsNSC8yrLKsRQxQFQDoKmApDpEIJFCJG66iqXMSrvKohhSqiKhkG0JYIIuUU0lBira6rjEJeZmWELayoqlRgDS8AQMhHZt6loCp8YnsUVwUIGO0BCYBDLeogjIz1SdHmGOjSo6ZLqYmBw9kchFYDCmEHQ6oht6aNqS1MSGEXY2CtwKQHMIXUANSBiFiLrOkyQhVxIehipDMsOOlZSzAGJewxyoEhwHQIIsZaWkQpdBExMqsXlmuCbd5UxtUYmrJWIlJiiotaYjiiWqa1gjkImzJvKOsqBHUZldy1GNgijDXV1OqkLjGHVBVZXRu3wtCWUYWFhohUUYpDwlSS1yvAEFVF1sA2rDAEZVgyLjHCRS03DqYmVxGDhDCtqhAiC7W2MELIY0hJ2IBacC7zou0SRbFWOkRYaAIhiSAUEBlNWsR4HpOFqVOrMa5y0+CEA60NDADCiFiJm6zCqKZlHHLjUmIKEDgEAGCtDRA0GAEFQmIMpirHLQdyiIA1DGb10hILUJU3NAalZiSrVxiqisK8lmCoEAd5vbQEAKiKluZIIgHyRoltVVEvryVYYKiLvFZArAGHeVMBbAAFRSPHQFrh5bXYUExBljUqwYHFVdFQgCigSF4rGMNIqryeGAIw0klDa4ExMqiFsY8ssrRjrYuprWBHwDpjstAth/sGAG1aDHkSWkPbFnmI6hJ0hXI4U6VuctzABBrURNC1ygDSRaAmUFmAHjNCc10mXZfUMQQWNjHxIQGAtCFwITKKzFHpQFcVuslQZJFRqMMB10hqHVYVRazKqxAqW2FZqcA1oiAIa1FIXGJrTV1lEDGZlxGy5j2CgMG5xVw62gCJgTFemVvKZF6E1IoKA11xa8KUQGs9UyoFgVGe0lb7VhYBLHhOjKo4AFFGCTKsVJHE0ChfG62QNZYh0gOIQgoB6mHEAKDWdChhCghguwTZHAmf9IBlGNsC9QQRAHNgOgxSaRUEPUw4osjSHrAMESh1V0BPIyRth2NaaYNABwGGIdR0DkOBqCltzweexVCaDhcuBNLiLgbIGIzZPNA+JjoeLi3E810MzeFiP23UqZYHR1bSTpPLcu/IatpoUJkPF/uzxQVm5Gi+m7fqFJrh+lpWq/MyPzh6RDYarComS4txo86qfLzYj7ttKouD5aXMC5iqhsc3FMK8yib9+TLyqK4mC/MFF1ir4WK/FC7RcnhkFRDMq2Iy1yk4Z6pMGxFY6jOrZv25YTwlRqW1YG9tmegqr4eD9TWsq6wWHq4sImhmrcZgaZGqMp/r7sZrxOgy9PeOHiHI5vXaweoyhCZt1vePLBMMikZtZ+MIee8OObqOoCkDd7i6ghnOmrWDtWWkZVELd48eQRgpV+weW4cEVr57sLqsHf7/44vw/4uL0Mx+7deSlSVZD69edyputNL53t6pk0rw7VPX5b3mZK4/Xl7aW11NF/syCq7eeEPSaCXd9tVT1+rAOzhxMplvj1qd6fLi/rGNaX9eB97V667NarXxwtLWqWu07+2vrSbzncnSatKf2zlxYtZt69C/dPPNOnKnC0tb115bec5wYz2daw+X19JWc3hi/XBp1Qh+6fRpw/F4celwfS3uz8dz84OjG8OFBcjJO7fcUrZqpRdcPn2zDt3xwvLoyMqk35+1u+MjS/sbRyEh73741tIXZVi7fPpmGXmT/uJsbXHc7087c4frq7vHjiMEz3/wFtmqFUHtyumbq8AdLSyO11bGS4tZqzU+ura9fhRBeOHDHy6bYRlEmzfdYAOxv7w+Wl3O5pu+TfA8xW0aggSvOiYgHi+8trZtV3gq9HK5FHJcsXmE2iywMVjmMGKBW3h1ZTuCu9rzKrUcuDAXPYhazAEZXyEwII5bujVV9fyak/q+VCuhAwvR1rrrNeHUXUCmxrGjwrBUfS8QuesWetFzcMnmUNUKGDpAdD9tE+xMhTlIFnHJNIf7yZylcAacAxiowikRGyZdCtlYmMPRAuE0g3SSzlkEZ1AMVA1qL2NoP5ljiieuGsyWIMUxoOOiazBOgXOgakD6JQGDqqXKSHvl3mSNIphhOi66CpEU8QGKqjhCHAzyHizrXlsO4ZpLHRO6Oeth3eAeS3kXm57bkCNzrO6KghEN1hzigsDJcQfZlghEwbq4mvNrcmw3fBrAEKVwzQMO9J1ctMF7NW5D5ovNmpyADZ8G0AcxWHVwgEM3501r28KnBW+DciGqyzE+4pAAejBjqxw4ANIJ4tPZHCZ2pmuJamgqx0UnNQEG6BD5o7IBNUsoG8d9TGwsg0nWpr7c1+1Y+9jSMRCHWRszckjYZNZn0KbWP8za2FWDMpoQX+dOicRh3CUUDhGbJXOI25mqJXkTMX1gw6GqU+PEjAzKDpAsJ2Scz0Ef5ayuQIuzOqq5mVwMmCfrcGY2IkfIwCnNik+JrEUZahFSxwFL8TyDTVHTI3m8IZh0RQkWHcFkWCtQR5gaCUlMFoRpi6YaquMNF2fEA3DJ4UJ5QQFbFDR5xBK8IKqWaMixPh46XPmitEseESb0czUfyJoNsp3JggWBYeog7UvLNcAHOkyrGsBoZOrltEO9fD9eUNY3XB2kfUl5pelEh2lZBwSNVZDNeswrBtmCtJ4kapTP5cYDCB8APy4agMCRjdJZl3jlwaybVxEJ5E7Vy4wHLR4aPyma1rEDW8vHHe6Uw7Izy32vziZeTZZzQc0tfLfQqxEnlVuXat6rw5m7gG2NIUeFYaH6gecUvsjVkieIFG2g59wanNG2sQ0uajDy82oxclnuO4VZC32Uux0AOszBhdtVsMZYHQQ8gwuCBMhnmV4NPZixJkAd5tE86EjTckEIaywp1xocThAaZz1FSAbEgaobGUgCh1VLljXjFbuTNQpwQdCo6ElIc0QHMCqSCFJ4UHZAVgd+vnu4gqmoEBzlPQ1IAdiBrkkZaW73qx6I6yTIdqYrFvMKwVHRU5gWhh+qmqwiJexB2aqSFgvS7WTZIFpCOC7bBfKQL0qvLovFRqhncNUlEQpsbFcECkno5qJpbEe4tBQNUy1GkZ6QFYEj5MGMrTLoE88taMPqrtugsdMGciGM9IwsYtNyWmAslqgJGXMqr67snFMjCasbNe/6ICPL1DScJhiTeQjr3A2NW1Nq3gvhjHdw0fNduV8FI/ze1OLDpEsIGWE8mfaJgLHxZ2kHUX1ow6GOiHEThgdl25ai4vAw6QNuJ8qbVU1L4NT4Q1Ujys0IOKi6RrkVA8PZEiEmtt5UtgzCM+gOQKDiCHCwl83h0pNcDyaLhOHEuLOqBQCeWWeoasC2eKsaquMNl+bEMXDF5UL7QQFbFDZ5xFLc51VbNKqROR5xxwQsByseEjbwc9rGoMlDmoqOzfqtZjm0xwLmWg+nYMVhHgi9nLaQbfEQJ2SeVHNBszwAGz51rccKssyxB30vt21G26QOpmRJVB2/KQ/hEWEdNukvTtaWR8tLaaszXVncOnESMHrluusmC33riasfOF1F/ri/NFpZ2t9Yr5r1eGXx6rHjltGrN1w/Xpg3vrd56hSoObtL65Plxd1jR6taeLi8fHB8w3J++fTp6VxPes72ddcVncZoaTXpzw2OridRfbS2Oji+bjm7cv37i049bbZ3T12bd+rDheV4rrd34jhw+HB9Y/fohvSc/RPH827jYOVI1m4OT2yMF5arwLt8803W4Qera4frq2UUDU4cT+Y7B/2FstUYXHt82F/Wgbd50/uBIDvrxw831vJ6bXBkPZ5rD9fWq2Z9/9oTg/6i9txLp2+2rhiurQ/XV7N6/WDtyGypP1xek1E4Or66s3rcOPzS6ZuNJw4Xl3euuVY888zPJGh09Gv/9W1f+/ri8y/Fc73lJ3668vRPs2bzyBNPrT/6xOXTN9/093evPfHUuQ999PQ3v7X81DNJtzv/4qtHH3ti3FtYffLpa37wwys3vv/9d9+79thP3rnl1uvuvW/98Z+Uzcb8S6+uPfLEeH5p6bkXr7//gSs33XjqO/dtPPLYpetvPvXwj9YfeTTuzi2+8vLGg4+M5xb6L5254Z57L37g9LX3P3j8oUf2T55YffyZow89PO3ORcPhB+78StLphFe3b/rqN/ZW1+ffevvUPfcm9VZ9sHfd1++2AAFjP/SXdyb1pndweNPXvpn0eo3zl07dc28pfC+Lr/+7bwILLSG33v4l6Qoxml73jXvSZj26vHP9Pd8zhNEyv/Fv/p4UVeUHt/35F6Xrsln6/q99qwo8b/fw+u/cCwy0ENx859+JNK8C95bb/wpgsNM8OnoF6QQ4MN98LTSDypu320/wfMacht59joKZ5qbYedsrRog7eueMY/e16Nvtx1kyom7T7L9I5QxSpAZv8eIQcVftviLkruFrZP8JEh9y0qXTZ1U2RoKog3O8OMC2zg9fROUV5SyDw5/A8aEL58T0RV0OQACSwQVnvCfsImtemEN7S9l87r3TBoMlhvfBuIf2lisPwFHbudTFwS7aXDIHR5Je5W220d4ColMxcNT+Ce0qNGl5l+ar5oRfasO9tbRV+ldDuL9qecFH3O4cgzRFkzq+si4bE77ZRHvrNhzh3TbcXYEoETNqdk4AlqFZhC8dI94O2m2bwQnoHCYzu/8ChdogArae5pBBlKn9lwT2YHVgB28wbHSUjt9+rQOkIQJsPyUwtqiQOy8LSrSZ2OHrDBnAVH755RCnJQ3g9k+EwRgqu/MyD21SxXDnDceUllG9+byHE4lDsPUTYQBCxu6+IijUOrd7r3FTAMrU1Wcdmxi6HPC3VnHmYZvA7TUYh4gmePMYO/TL9pi/dQ2IW9qf0IsbKA0hzuHuGp6Gpaf5xRV6EBTdkffmURPPFc3SOd9H04iAKdhfQZNOERp2cZnvR7i+Cc5do5OFrFF5l+bRpEnBGO/2zWi+qkl+cZkftIre0H192U4XrD/BW0twMmfcZHzZTl/DzryN37SDcwIsOOq8OjhPHc/o7WL7rI88lA3p+BVEF0h6zhy8zd22Ti+iwVnGmLYjuf2mz4XOR3j/ZSa6trqg9s65bsvEl8Dh28zBFTtM33mryZgqp2TwMvfbprikds56YVDk2+jwTcaJNrHaOeMKWskY773IQZsGqc8vr0KlWJWaKzfSTFtR8LPvAwBBVeGLxyGocAro1gbQGNscXrqOpRqTkbnwAQMIhBpfOk6tBAVAm0dhiTEu8LunSEaqMONvXmssB0Diy8exMtZgtLmBS1K40H17HSdMRrH35okKRIZY9s4ay5U1Gm8eBTJI2tX0UTnb57TPZs+qZIBdJocXRLaLQZ2NXsX5Re0u2/HT6HDfQYtO/KLOdkCEksOLfLjtwrYYvYaqd427Cg6ftIc7Lppn+et6us08Xs0u2NEV4db18C02vUidDTx+xoyucq+lJm+R8SZ1WJVcgaOL3G3o4VtsfIH6Kzp+Tg+2PDZHavtc71wDcQ5TH186JsMZ24vQ9oYNxnivgXaOQJQ6CdTb1wJSoszFF09gMUAHNbN3DXBGcNigO6sE5ji1YPNaBFJUOuziCeMl9MAD28cxG6KRT7bXLTS4svjSNUxPYYHt5Ru0l+JJiLaOUToyU59cPQqMxAbgd08CCqYmHzyLXZnp3Oy86enMcq43X/DQVNEm2HqcS4kJsXsvc6iMrez+60ynkAm19ZxrxprOk73HaS454ujweQByjS3Ye53rFOuIHzwF9IHic2DwBJxVnvXp6CULZtKR6dbbfjqhoOsePGnNEPA+GDwOp5mLPDx9BVQxog3Or6zynQaubcILJ/R0KW9V3qUeOuxQNCa7HTNakmFFtxbFbqfsDsVbC2C8ZIMR2VoAoz5iKR00wWAZOFO8s4R2F2Vn4J5bAKNl6x/irXl00Mdoyg7rZrAK3CneXUTbyzS8hC6vmNE6cA/w7jwdzBM4Q+MG3FmBzoju9enuqm0OphfM4E3GkXQns/NvtClWusD7L3CnbuSW3H7L8/2i3EPDNxlFBmbV1ss+A6WRaPc5QWuoHNjd150OG0+HbO8NgaxBFmw/7zCrpEE7P+UkAPIQ7L7heKLKx2jwKgMGYGC2nhMYGI3J/jNMR445VHuvMg9nMqO7rzBgceRMP/Jnt7vjSVJvvP9r33LimYbs1D3f8w8OD3v9j97xxfrFy1vXve/j//kvxHA07cyd/srXgoOB4s61370/2trdX9n4yB1faJ89f/C+E6dv/yt/d3/S69/0rbv8/SGg+Jp77q+/e2X3+LUfvf2O3vkLF0/f/JH/ckf90pW407n23h9Em9vada657wfts+8cHN340Jf+uvfq6xdvueW2v/hy/d3Lw9W1kw89tPr4Mwcr68cf/vHJh350/rbbbv7at9aeeGrvxPGNhx9ffeLJrNFcfOXM+sOPTxYXjzz6xIkH/uHiB295/133rj7+5ODYsdVHn9r48WNVGC69embloZ9MlpbWfvLMtfc/uHnLB6659/vrP35stLqy/OzLxx9+pIhqnbNnT9734Hh5aeXp509974GrN9549B8eOfbQw8VSr/7K+ZMPPFgEYfPK5vu+/b39YyfLSxd/Zi7C/oK1Jg/CeG4OG5OG0azZBNIaQqZz8xJAyfhobo7kWRkEs0ZrPDeXRVHcah0uLmpKRv2+MbZiPO3NTQb7aRjSZhMvLRdhFLfbBytrirG43ebTWeU447l5d34hi6K80RotLKRhmNTro8UlTWnc7Q6XlkrXnXZ74+WVslaDJdlfXUujCEs1XF7O/ABqM+r186gGcHW4sDBttQrfHx5ZLxp1icmgv5SFoXHgaGU1aTRRyEb9haTdKl13f209rTUK3xsuLRdhWCAxXFyaNVvKd8fzi3GnU3ru/tpa3GgUUTReWcmiWuzXh/MLs1Zbuu5oaXnSbleOMziynrQ7hiPZZDACRlQkwtBlmgLVFsSxllekQQwhxgWq5XHXAJahhgAcGaxRmyNmNVWgziy1yDWg4RhujQCgTjGBFiLUxBJDJahpCGRL6xgdMUCg4QRFGBhmkQEtCjEzjOA6M5XRDjURw4RDggvfQqAlpZVnrFSIwQIhFUAlAHJtERmHoMIHkChNkfKMzaEUSDpGaiU50g7MIqMJ0q6VGiiKCx+aEmpmjGOLwGiBqEU20obhytVAScuR8gGoI+nACsPSA1AAC4FqGMRw5QJrFGDGQgYawITGco07FATaMAUbDIgKYaI6TIfSQAwblIZAcwAaRPmIuYo0CHSVdmjZZk6gjCA4xCBkmkHd5tbX0NWoQY1LtE91w0M1Y6kGDWojaxnAHQZCbRxra9R6ErsYNJmpGcMtaDIcSQtBFSiDjHaACaElAAqkPICtNhirSCsMNMPK1xxoy0zlQQCxZLj0jVWVRSgPjUJaMlJ61khgOFIegMAohnSgC2YwwkUELbSaY+kbKxESCLpGAi05zn2gKbAI6UApazRDOgCEWU0B8CloQ0MVCC0siGEYRNxE2rgW5xjUmfKothA2NaAW+wTWsRbWRAy2EfIri7FtUONCAAFqYculCRFqcksVDJnqYO3nBhJUo9izlUK6TZQDQWBIHWtBDOJVExlPAqBwg1rXKERsG1tBFbRpnUgfQYZAIK2nJSMqgsCFSEAQKOCASiBdo9qzlFrla+tayEhRt9CzilrtS+ZYJYCNqAog4UgHCrrKYiRrSnlACaQ9IxyrhAURUD5QDFeBgkIZjNIIFC5UHElfQgEkRyYE1gcGQVSnEiDFqKkzUgErtAkp0MBwAkIFGbHI2AZBhmpKSI1qbLRLTMiwZYYTWKOWWoMVjBCssOHU1LnB1joGhcBypgTSAcQIWmRxjVpKDLemzhECyC2A9//Q9mbPul6Fnd6a13rn95v23t8ezj6zpKOjWYAkMLOHdrfdiZN/I1UZ3O2kXU4l3ZW03WnbsdsYjMFuGxuwwYABQzBgZCQQyEhC0tGRdOY9D9/8veOac0HnJledVPn+ufpV/a6eiwd5zxwDMMcII0eU7wDEGCDQBr5NrA0gscjnxnOohPexgxybGMAO0gHQFqjYOuGhJ6YDgSDGAZsYyLwGtupQG0DukIu9F9AxZHLnBCLOu9QABiVFJgt84BHyMjFEQESw6jgnkENOR14E0HivOlDFiHJoU+OExQziLnUxcRE1/TDoOE8t7jCfeUcBWmEgdU4436Ug1jhEIOcu94570KU4Ax4j2McuITagILcg1DbALme2AxEnuIN9RD0GsItQygCnqMOdUC4wPue0QyyjqEO9QA5b2IEwwU5Q1+VOOEuxjZ2EFmMsMw+cNQyr2DuIMEcodIpZzYmMHADAYexia7UzHNsEEAQccyZ0ElrPkUsQht4RLCPjqfEc2wQ4CG0AkQLGWccgiJz0nlDcRgAS46mXkTdWwABR63ysHUcmgdA5TwlIoV0NbNI6SWCX4dg7gtyAmkB5a0gHe0EcY2pAfKwhJbjHQOJsiNyA+cBg7GCXuoA4zm2fodwA2oIe8wmAAuIV6iPkPQIdCmOJEfZ97nNnhYc9iiIIBAEDZCMKAURdAwS2IXFd4lJkKJuevzBfXZNRNNnYbPv9NsvmK8P56poKwvHWVr2yYjE5PXtutrbWRvFsfUN20zpOpmvDcriuhZhtbddpYjGebJ2tel0ZxbP1zbLbbaJkMRwuuwPFxWhjS6epR3i2dUYFrIni2fqGCgKVpvPBaplkmvPxmW1PiCNkvLllKFVhWPVXrARNFM1XVljTOIKXq0PUtDIMZ2tDXhRNnpfdLtzaauK4HKxMt2tL2XR9A0olhVgOh5PJdpnnZZ7b4bqKo+VgcHLunOZssbYWzBdtFC1WVmbDYZl3mKynZ87IKF4MBqOz57Tg5crKYnWtieNmEE7Pnqu7fYTBaPtsG8f/OC1C74pf/Od+tQM5Pn34ARPHOo/n5zfrlcHxA/f5VJhO3q70xxe2TRrhgB5fva/o92cXthdb6xSDYmOtGfabLLX9zsml87Kbzs+fkatdmacqS4/vv0QpKtZX5SBbbGyM77tYrfZ1J6EE7D32COag6fbGl841g+7o/ssgYU2vZ9N4ct+5st8TyO8+8SjBTuf5Ynu97XVMkkwuna/yJL4c7mxdAp0IIXzwxCMwpCrLltub7UruL3WXa6vTja3IyJ0nHnUJxwgePfqwy7k+26k2BlWvb9KsOL8xO7OVeHPnsUd8xOmF5PDifS7gOk2LM8NyfdVzuji/NR2uR+fZztnLPo8IY4dXLuGYlr3VYntDDuLU1mLgRO5i07IhYFhH7WESLuFalpA6ETXaYAy4uG9x6LJqz0ct7UcJLJNM4T6KSBty6bskdtOQl7hDMldHQ4cjkNM6TrTu0rTaT0RlV+MEtWkuYR935SwYaJQyVh1mfOaHeQ/OgkCxDcy9jnLlukFkTmI8qnue+1HklnK1IUGxEtwoQhf6ipARTRpDWwFnzYrn/gTGYxcrjisCl22vDn0RoCOXWhGMe+Fb8zRgZJ7YpVwtBSgILkG2JGTukgnhDaRNgOY+L20gY100a00Ex534dh0aRhTFIxJXKrQRXJhu5RPRaabRNqLcZqgSHUdSEHV1Ere4gxNd4W2W8Qp7H20Byk000EHHEuEjIsO+hV2cm4puogTXQdcGfYcF6NAq6liawxg0SacFXR6nkg+cCHRqK7oOWQgTVEa5xV0YZyqNG9Djia7FhhexTUzJh4Bwy/2U0EL2NHcSxAsn6jVxwyVTFzLmp5RPbUdTuAzossmajtij/LBJcdZOQDYHsWN+Ivi86dgOuttJ9+eZYKDh7FTnOpMjF01FpDA76ma7s0ik5hizWvbbyJQgmDepX0dv0mDXJgKhQqCp6UmMGkGWptdmro6ilvQw4zqjFdgMYlqmHYm7MKIqpApv4BDLgShY5nik476OI4kSlNmanOcRrAMi41UnAhUPVBAaLFyPtXxgcYZTU9Jt0nNTOOSi40JqOrzEPYQjl6OGD6HPSJ7WZAAjpiMs8RBzZvu88msBDKqOXNQrNeF1IEu9UuJAdvB1nC5VSLibo6RoOy7VperNBSpcMHVZEZAW4ALHhYslNzMUF03mtoLrVdRgoQK1MN0lDh3zExwVPmkQH1MxNamJVWW6Mxu7vDq23SWKTEpu8GRcBzDWYxQXTc/HskDJcRvzIZwkkXRrPIeLlEu0hllk426LuqSjFmFXkQ7hpOkHtVtjGfZNowcAACAASURBVF2meSsyT6kLE4UGuG/mQa5Y7INUJ/0WpyjDVcQk3cCRb4PM8NRGiY5XNWc6CExGazbEjJmYKrYGI14HK57lMEFNHLe4SyjTA96aDR65GYWV7Zcc1hSNXWYImwySt1vhXKRiVdTDhsGSg6XrVQGbpuQ6ipQMYQgXvlOaSGemKFeqkCx60c06cxgr4Y59Jn0ghZm7XqUCvR29OgudoI0Ahe5WjDYYz0AmPZ2uZzcbYWSG03bRDuaMNtzNTVYy4WLUpGnjhmGkpdgEEalF1/FVz5hLUB11DOnCCDRxpnAHhZmKO5KlMNFluO5JCDqkErnDXdgJiiRrYYdEtkkHCnZIv53yVcdDx3suziWLbO6XQW7oKg6MDFcMCkE3KcWqE1gLruJYkzWc22WQW9unWTMC4YRFrSatQLOmbxJ7t9/Zm0UkAiVkc9nRqZkSfgoDDcQcxmPKKidUBAs9KANbQbG0mc7BLRHf04mAuAjQzHUqz6QApe6VDM1sOgexFn6J2ZgkbStsBOamV0E6X01vzmMUgBqzpelq5hYcn+gOYKlPVUnOswyXBPtgExNXZWoPDxjJcIZqsQZAirumxFsYONW1R66DaUwGvGA9IDIfuzroqVbQVXjsukQIm+GarCEmfEor0QUAVpneJz2CV8JMLvkZEAiT+JoNMYFN0u7SxIsMdHwp1rzvsLxdxhsGBFhlWbW+Mt/ecGEoN1ZOtraCM+zkzJlibS10+vjqAyYPVZo06yuT7U26HrTn1442zwVOj69cLlYHITAn911AHVFsr8r7N0aDDcbgcmNtduGMMPL4wQeW/W44JJNL2zpL2zxXg3xy+YLnvN4cTjbXww12cv6s6uaQ0enFC3KQNlmuVnqjy+cFMsVwbXTpPKWwOntG5+Fsc310/2XbjYrBgHBy8NjDFNpqbbXaWi2Gq8dXHoAxK7pdnCfjy2erfp9SdHL1Moj53tVH2tUuiES1PiyHvarXRaE4vXJpvro2P7e92F4nHNf93uzcho3DdmOtWh8shmvjyxdxRk+2Lgjk9p94mBBfD/on998nXnjhH0ERejf/hf/ywx//+Pqrr5k03X72e+ee+17V7+3d/9Ckv87b+r3/4WNnn/vevaeeeeoTn9x66eVydbB74cp8ZRhPpu/47F8+8I2/PXj04Xf9xV+d/c53j++/cnj2QtHtd0ZHV7/69bPPPl91emdefOmJv/7y6aULOxevzAdD0shnPv3ps89/3wThmVdevvS1bxbd/u7VR+bdPnH23X/wifN//9zs/NmL3/j2hWef01GUjcdPfewTdbfTubv7+J9/djwYXupV7zXpstnpvXzjwc//NSsr6Oy7P/qHLee9Vfp+JwuKV7/9gwe++jVoQbacP/SZz7OypA+c+Vk99giEf/+jh/7qyzKOVt6+ff9XviZaHa/CD5OQV4fV7uK9H/mYFTwcjx/9zOcsxp2k/CDsebMHb548/id/no3HKgmf+t2PI+gOkwun14SdushXO9dTv992/F3z0JbqcvjG8fG1HJYmrIr9m2k78pk7rJ48Q+qZsGj/+aSc0SypD38k5ALH+kBdSAizbuwOXk/NPRlv+/1n+XwWdDY1jJVNMXpzPNrtNaeIROjkx1TfM8lKgRJfX153Nybja5Edu047ObkTzQ443oCdm2fB+LweLONbQzDbzM2tK+jwXdVigtDi5JF4Z5UFd8nuOTO7H8H9ybDbxILWpntLmOmDkGq4XBH3zkl2/AF57WnsbwAvdi648VlsFnwW2JMrCFaLVTjtDpgp0zeHbnofEiN6uArG5yN5+AC69Uw9r/RiPnkE7V0J2W2239fTBwk+KWb++FUBa0OR2f1hQp0C22LWX6PI1NfkydsRL8teMXnzzXW4aMQ2Gq1sYALgm8XhqwEj2p2Yo7cjWkqwLWaDVS14end884c58A6Xzf5rcd7O5SBerK1IHkY3T+++1mWTimV293spqhUboulwHVFgbjRHb4R2rjnRuy8lcNLysym7fpG0YSBn/uQiXuQJuf0zfn5uceu2i+mtp121gtghvvMgKpPcXX+3mp5Hi33b9TsPB4eR7R2J6w/beoP2jn6uuH6xbeeFVMePosWqFJrsXgyPU8yu/7w/OG/NLR/Tew/iZR63J+hkA8y3YXf5M9Wtx+zJ9RIGNx8y9TZmY3Zw3s/6CM1Gu3DxOk17zfKaP7qb5KJabq6oLCZF666Vu7dyRlwzpacv4TQui62VstsN22r2sh/dC0Nb+qnbezPlSNqteNZb467VP2z3bydhJosbfnQnikwBt8Pj1S3qjHq93X097XYaeUPt3057eFoNOnW/g6TFb853rqWJLWyF770Uk8x2ZhE6vBxUtWgLc/hOtlAdceOfqUNmm+PTAd5/CLs6WHh//ABZumZFjvt9xenw3km79yHoIFIS7j1EtNl2135G+gSNTsuB330imAMbz8nb78K11+nkZH0dYBMcC7R/H6+ZpFbcusKniIu3/iu1GyF4KDfp3ftE2YhW+eMroM7b3nL8LTsdBcmqH33ft2MYxqoY9us8F8fF6UtU3nH5RjN7gZ0cR8mKnq+vupCGJ/PZNTzeD0hORteoue2ylXq0caZNE7RUy5fg4pjGtlzeRaf3wihV1Wa/yLtJtZg8a0enSZrUozfocp8EoWo3O/PeSj4+PX2JT+8GvfVl+YI7PE55D/SOuD59mNgaVhHefcTg0VP1j94NzMi3ev+yH10MmnFYYXX8KHL1FXb7g2ZUq6q+d9ZOHsJ4REc9cHopbscX4Zsftsq349P5/WT3ESCm/Chyp1eIH11SP/4ZSWu5N19eQHtX42YEW2z2n7a0vip/9D4paz1pj7f9yRUmFbYA3XsYQzM31ckPaKwqP5P7N1M/M2yIZqtrKgjjg/HOC5nRkHl5+OOQtMb3abnab6Ikvnl877WOP2rDVbfzbKAqFG3A6eoKIt7vqePXhVoglNKjHxB/pJIz5nR4xghq9+TpK5Qt27iY793Kq1NMN0k16Mooik/GR8+zQsaM+umrSM8BjSk/uBwedFhyE9+4asrzLhv9dP3a/bWZt43bv+RnZ30k8eG2OFwH6c7x2nqVhFFRBzsX3WyNmjmZ9P3peSpO3w/2n/TTmzXgNx8w5UXMTsnBJhwPMZ7P1qNZpxfPJ+zOBX96ORbXyO3zprgs0L3HzNtPVqZsZ83pA+hwm5JjNDoHTu7H6d7smj69HYX1Ip4Xb7+1Shczuta6S6usWjQvq6PbecpLtQcOb0a0qQM6qR7dCMYjdRTuvhwFoWz3zeGNpK+nQJ7Wz1wiu/vNJD55NQjayju4/2LCmAk7bfPgerB7p97hRzcSWCrs7f5LEVks+aCS53scGPW6PrqVJmahF+jozdBL2GejD/zGbwfTqQqCJz712WgyJl33Xpp16GJ6Kp/5gz9cu/7WwWOP/PS/+y1+OpVXz31YznpW+bu7D3zha4Pbd8fDjfd87GMb16+fPnz/PwGTnATzg5Mn/+wv09OR0PK+v/m/Vq+/PX7H1Z+vxxdQ8dZIf/AjH8/29nUcP/Slr2a376HUvEuEZ2h53OCnfu/jWz9+9d47nnz/b/9etrtfrK49+I1vnP3Oc4v1rfu++a2rX/7KwSNX987et+ivprPxI5/5/Nkfvugp3Xr11Ut/+3fl6sru/Q9Ne2vUqPf9wSfPfO+F6fbWpa996/xz35NpcnD16uHqWaHad/7Zpx/4yteO3vHkI5/5y3PPv1BurB2cuTwfrEWz+cNf/5vLX/56NVw///0fXv2rL03Pb+9cfHA2WEuaxX1/9bXL3/kOhKhzb+fxz37+9NJ99e7u/1dFiP4zSoTAQXhy9kKb5EcbZ8brm8XKsOitakSch1KE480zy7WNRdY9vfxAk+Xj4ZYF2HlUxtlkdX1y5tyyt3K6urHsD2aDVSRNy4M2SmcbZ4qV4XjrXNHpjtbWl52+wVQSrjE93jrXRsnRmXOT4Waxtr7srxqIHcAa05Pzl+q8M1/bGJ2/3Mbpwfb5Is6ma+vz3sp048xiuLFY39qHyXj+1ijaPN3YbrPO6NLlsjeYbmzN1jZGWf+4nc0xOzpzvhHRwfmLo/WtJslGm2fnnpzW81NtxtvnF4O1xcrw6OL9LRMH5y+dRoOj8dt7Yb7sDSbDzXlvMN7YXgzWlsONw3B9Wt8e4c5sY6tO8pNL95d5b7J5Zj5Yk/2EEIUSILsxwxoNuOr14Zu32K09MxzQHCIB9TCj3JIcqW5XvHUH1rZOchq3nEuVBDxxmPuq14O7h+Z46lMRMI37tI7iIFaCq4pSsH+Kj0dqkCPuSGBVyqmwrIvqbg8cT8T1m6rXCTKHBVDDDg1QmHsdRYYtAZ7VFDRxg2A1z6MjgyZ6Nhex5y0QizpNbCQRWVbdACnT8kgT0mTI46IKtUGNw6eyk+xpfzS+Nw8HKiyRW1Y5aENA8MIKrbHQiCkimsRAt2gTXIctdMWsJ0aQn7bTeb5qucT4qA5F00XAL5ucyTwIsYQdbAIqRO0jqBFThDkDcJ9z0Li1UPUTQRQecAOoQdRA4mPGQ2djZlcC6hvfo7jVLQ8NIHUoElGRCJpezEPrM665aEWEm1YOMopatxo0USyCGoVeM6owcw7qbsCJEl0EOzygCvWZpsjwwhA16wWGtJotF0k+KU/3RFJ2Vxw9RWgqYwrCwlM5zVfGpjyo62XIIKlU2lRxatjMoWJJ8X4rx3J6Evc0lY4sVGQBrXywrPuDe0U9aafzsGvZwqBm1g2ssIYv5wDPoNytZDUYqlRCXMoYGL70qG5ThLkjgarSFPU4F6YepEiZloeGUNsLI6F9l2EBkqhVSWghkSywALMBEUjKldjELODKp1QRrjBTRJiBIKS1mcBrgvvGroRAO0W49RAFPgmqJgrsIBZENf0cKNPy0DvX9lMhjB6kNiR5WLmYN10IzLzIXNkRECyazM15MlL1FOEyFY4WKoTLLvFm0eYat7LloYWkiQTCR4a1TYwAmssI7IcrJ8tb9xRqEozcvElMHcUAHztWSyE05g7QJgLQlw2v2gRDXLSpWuaDvaqZAjcnyJOljsG0K7wvjVg0QRQkOuRKRkL0EBFApqHC3FvQpiEOPeuAMu/B0FBhVCy89i0LdRKimASpAyml3KJQl1nHQCqpMISSxFJq5CCCMQ5TBzmSmFuPJBWozwLc+m4gckC5Mwk3gBhIlOA4sCxsqiR3q4LA1mZBnSCCFioxlhuAj2QmDpL+SXkyDzp11EJXzPukSDn08yrwM0+OiukY8baHAVw2GW0yA9Rs0SUnPN2f7RyEuQocQEeKg6oDAVy2OTtgg2l5Y8xXVII9KuouUyFC8FgLO8qHp83xmPcWHYTcsk61DCEACxlYEDGeeJ9Qs54xpNCAQmNaHjoHZCB41GBuTSZEYHyENaEtC5wBbiUJiIID3sRxFEsSuJpxg6jCQqWcEENSr2LGuCE9UkWpAUQRAQMcRhYmVA87nFoxQA4iSYSBpA0j3gGYW50xJjSMoE4QIJUPFmWau6iCZNlE5ASIiTw9CXITWsuKKrAeFZaM6jAyiEoqLIQ6KAAsyxwZbhFbVIyMbLs3Hi+zXpMoZBdNiltRO1TXIQAWGEgk557VBB8XSSq7DvpF2Y1OcbYwp8fRwATO8KrMQssqiI4bwfBQCF+7YWI6oeDar2deefrj69pBP8w40zKP9ErAXA0HXEFKX39bIiETlgaVj6jvB4Jp04uarEt+9GOjvc85YcqsxDrmIqh9hLVy7PpNF2Z6mISw5X0McyaYBquJRkxcv2OWjRmEghnTCVVXMCBRBsu0Ozpztuivzte3lhtbi5W1k/7Fcbl3IP1kbavuDo4uXK7i9HT9TNHtj0R2VE1GujlYOVPG6emZs/PV9aK/Ot6+WObdncViqopxb2u+ujbr9I/OnG+y7sn2hbEPZ7Daa2013Dy9cLnqDiZb2/PV4WJldbx2YVaf7DRykXUXa+unm9tlmp9un6/DZLS+NVsdLje2JsPNor86OXO2TLvew5YJg+j43KUqyffPXVx0+vPV9WWnpzHVmBlIDi9ersJ4vrZxeun+Ok7HG2eAdRYSjch8sDodbs7TzunF++okGw+3oNSKcEXZZHV9MdycDDdmK6uzre1lp++tkyzASh1dvlJH6eGFS0XWmQw3lr3+/w9F+J/bIkyIS2bTarXrW8eaVnaTeDxpeYgEdNITpRaDFSplPhmXKx3funC5mKxvRMslBAAx4BQgTb1YWaOyHRzuL86ssbJ12i8GK8Fyjo2FAbYOi+VyOlwnUuaj0+nGRracOg0X/V40nyFlYEQVIEFRmjyocdQ5PppubkTVArTWB9RhQsqmzjKLcf/kYLy2kbRLNqsX/V6ga9c6wLBllFUN5KgQWffgYLy5mcglnZRFvydc6zXgvjVMuMoiAYuo09nZmW6eiW1JiqbKO1zWvjaAIxMEbFFBjpZx3jvan65tRqoks6rp5nk7WbgYEy/DiMwqHQacGjDXOgmorFtFIPQkIt7CuF2qLNQltIIiYO1c2jgIidEaEW+gQFpCplWVJHDRGsYEt7RSOhQcKqUQcq5Ic1JUvJW6E4qmtQS3cRzMlo5TAoxsoLdW9fKorXjT+JTAwkguqiQNipJLWXYzrFWyaFWGtCVhUy86OWsMMjbz8xInQIMqj2lTdU7Go7Nbabk0hlZ5yGuJlEHcOYhoq5d5B2sVz+pikMRVYTUxMRJl5YwHEVOQs0qBGFQkTibFciVJq4XXQEcUK28MzuBiyVNceR+DQmTp8UjlIbdKKwQpwMihRQtDUsZZcDqvVnrr5f5Upi7jXDegVIBjHYa+spTaOoyD42m90otMScdl20uRd6YFmALHiat9x82n+Uqyd7xcWxFAmYX1mSBOa4UIso5hNqugQMu8G5zMm24WudqWzkQCIeelF0Y3mcCFtYQQplnRtCKg2CmFqXNeAG1oqGSdclYph0CVpNGithRTJK1GyPiykwR1GbZtmca4dB6BJg3DRe0w7NnJHGfYumWWiaoh0qqMRcumpbyJRFrMLYCYAuUFkRqEXlnKGiNzxsrGOUS415CiWjfdlMomPzgeXzibFAujcN2NaSNxq7AA1sFgUZhuWNEkHM3q1W5ULUwLfcp40zjlQEg14XDRkhRVNI5OJvWwuzbdL3WguzFU2ipABNSE46XEMahJ2L23Oz23HcrSNMgnBCvtpKvzHGCYHU/LXhq6GpZQxpS71ksHCLSUwRYQbKowik6XZS8NXcWmrUx5asop7FJoLEOwBoSYKo7S8aLIU+FaWHgZcQaVUZhAaxhki5YwP8+68biqU6EFj2elEYQB6bVziFRRHC0bClURx9G4blPehHE0m2Frq27O6iaoG9kJ2bwCwBe9Hp+XnmKGtVXYa9v20mCxjJeLZi1HhZGEyTRisxIjgJnzBpCqmQ/XWFmTRppuFCzKlgjMnZguFeEowq1jrGlhQlrHaVXrbpxOxgpQnzAvnfGYcacBRcu2HA46y7GTVMWQa6MNxsxZCHnV+oiUNEnGxXIlTZrCNUjHiBttDYhAVfEU1hAGtmJxcjIrVjuxXALpdcSQdk4BzLymjJYWBrZiYed0PF/ph7J2EiNhmW4rH1NqFUFBIV2ICpGlp8uin0SycA3SEUPI+8blZr7Ie2hayywNfY3GpckiSKCSiELrOXa1o8g2aZrtHhSrAw71Tx7EgNItBBjKJEpHY4LcotcLx0WbhEqIcLKwIeNewkobhOpeV8xLBrQPMVjYNgm1EOnRkefMh0QbCqyTWSRmS49xlWfxogTAd91kirtY+7ITiaYStazyOCoahVidBGFReQ8Is1YDUVW6G7WOi0q1XREtKwkZFI5KbTzEDErPedX6FDU+CMqm6QbZdKodsilHCmqHemA6Fn1eap8ACXi0WJbdLKwb7SkUDrTOW4AC2NAwOp5U6/21xcHUZj6moNVGesIhEAwVkkSgYnF4NKqHA1LUVnqfcG6l145waCgDhc7gcj/eiE8nVb+b2tLXDqQMWmsVoNQ1kMGFxBFUSRKdTOtBJ1KlaYALmdfWtM4xBiIaLiocuDJI09Np2cuEbX1tMXI6FLjSzKtFp5eNR2WSqSDMT07aOKZQO+kB8FWnGy/n3Kp51kmPR3WWyTDKR6cqCDp2PiM5tnaZ5clk4hCyiUhH02XWkUGQzScGE0SBMYS3tcwiVEoAoUlEMp0VUUKRQ7U2CGMBjSG0bZaDQW9yoiAtOt1oMQPWYYGkI8ls1q5kLRDpZLQYrmWzsXbEJIKXtXUQRlgiEY4nZpA2QGSjk3K4MhgdLHFqsgBXLdAOxrTFQTIZy17Set493J+cPZsup8ZiG3PUSqAcCrGkYX54iLvkOFzvHB8t1ofxYuIMnJy9mP/6r8ff/e4/Sovw5//9b1756teX6xuXv/rNh7/wparbf+grX3viz/7i7fe97wP/x+8+8bkvvv6hn/vg7/7e1S98sVgbnvvbv3/8c1+crW1e+euvPvOJ/3jzmaff99FPPP6pz7z5wZ95zyf/48Of+yu9Mjj/7POPfOovZptn7//6N9/38U/efObpZz76R49/6jM33vHM03/x+cc+9enlYO3i9773jk/8yXRj++J3v/+B3/v9t9/73qc+8SdPfeozBw9dvfKlr7/jT/9sPtzsHRz8/K/+66Lb6966+7O//psH2xcvvPTjn/qt363iTu/w4H3/9jeZds7YX/wf/+cq7+T39j78b35jORyu/viNn/oPv28ASZr6g//635G6tVT8wn//PzlKspPx+3/9/6y6vd6Nu+/9nY9gRElb//Sv/W9iupBZ/gv/3a84yvh08aF/+5smDPKD0Xv//e+QSjrG/sm/+l/zybRK03/6y79GjN4ZXNl7LjCnNkLlzec66E4VXST7f02bExKv6P1vMj+yqVzc/FFXncAkaO4+n7Gdlp8Fu18kxR4OB+bw71g9wjGsD15J7J7Owmb3+bR5S/L76MGX0HJfiA00/6ZpTmCC652XU3nXoy45/DZW12V4CUy+Bie7YbBFJt+x1Q5ccZPdV5PFLezPsv5rZ9m9+8r1RfZyHx49JNxdPzmH7151vEZHW+n1LZTu8ZsX4N0H67VlcmPVHr+LgUNxmPg7j3tc4MlW8vr5djAL39og9x4t81nvTsfuPoHgmI8SdPtxAqZgusluPNYO5tHbA7jzJAr38N0B2nuSysNgGbtbT0FUoNlKcO1BHO9Hd1fcvXeSYH8xcne/HdGqdRTtfJlCBHBl7/59igPvTuztF7KwWgzqySvPbeFlDQJ8928iiDEq5b1vBxRrf6Jv/7BDq4Z5+cazK2xa4w7Z+RKzFkNpdp8Nu2phZvb6j9ZAZTiSN76Zk3GNOmjni9QoRIy5+3yGtAGlufP9nE5rwfTtb2b2WIoLWfQP95GiT+UI3H2EjDKCx/Dme8ROWq9Pk394Ap+uueQkvHYFzNaIm7qdR8PTbiua8K2Hw7tJvT5JfvgImmw33WX8ynk0WWf2BBw8zI5WVS7FtQeSex3Qu8teedyeXJbdZfLGWTjeFuYA71zEe5uy0wbXHwzuDKvN0/TFS+T0khcH4Z3L7uQCDiajN/3keRxsusU/2MMXBdnA5sf65rVBQiuzJ+/+IBWBLY74+NuebpPiZbf/Sp712vJ1e++VPMK1OTJ3XsgC0i5P2P6zQgy9vqbvvphm/bZ6G9z9UdYzMzZuXntxQ+C2GtOjb/FwzcvXmzs/TDvxQt5Db/14NTSlLdy9v4sSVLcLtv9N5vq0uxD45rvCpqTtEr79ITZpbWz4PzwDDINasTeeJKYRSw3uvoeUmvrCv/XTyVQ7sSAvvdcbAZ1m159g2qC59Hd+Ci8sQQvyxvv5yLfdJvnhO4EU2DX4xlNYYtBaePtd4RJWwmavPMlGuOlU8QvvULrvoAquPcgLQHQLbj/Fp3S+Yaafb+d3Od8m879V0x2S4HLnWl7cIaiDj54j7WsqvAhGX4cnNwJ+jk2/68Y7wRCMD94Ix29zOiDH3yPtaza45I+/Dg9vJWwIqx+44xthhovxG2T6Okl6+uClZPoaFVfo+G/U+EaSd6vTl8jJ21GKl+Mb4vj1IOjqkx+y6Y9weskW37Y7b6bpus0PYnDrHczN/LIbXHtE5YvgThfeeRcMj/BOF+28k8ijqKTu5jPIl7DqiNceQ+Ik3M/cnadQcMQOc7DzdKhP8RLBN99D1BzJVLz6uI1Luh+T2++kaI+PEnv3fUQuiATkjacjOQaNIK+9y0QtOY7hracFPAHHHN19D6nn1Hr6+ruRNzNbHX2Dxk2J5u2NF3tgbgSTb/5dn5wqvIJ2vkBliQg2u98JQW1Qo278aABGKhDqzrcysyfJGbr/BVSXARHg4DvMlp4ofeeF1Bwb0BMHXwFuR7Ez8OCrfLpMaASPvoPgWHfa2dsvdvWBg+ti56tUH0K2BQ6/4ObTgAo/ep5Up5D3Sfjmg8ntAejeFa896g8vyZVl8sYZf3qZuyO6u4V2z6u0FrcvRzfX6/VR8splOHrUB3vh3fPu+AHiR+Rgk+1chGKE79zPb1+u10/Tl7bB8SMo2MG3z6DDK1wfsMm2333IRTN672Jw4wLu3g6unbNHjxN+j+xuw4NHAnuATtfo25dgMGJ7F9jN+93KSfGGvvNilqtZMl2+9v2h8I1u8NE3WTTw5nZz93tJGhVmx998sRuqkmi983dJ6kqjyd7XOU6B2jc730/OkJPyGN95sRvaBmtz+1sp143RePdvKI6AP7a7P0gyVpoJvPVcFlYF9u7u38ZQKkDIydew74TwVO18N8jcUi7QzecyXKosLn7pv/2XnXu7ZW/wod/4rezo0Hr8zO98dPXewfH69i/96q+tv/zq7Xe+87/+b365c/vebHjmp//33+jduWN4/MxHPj68/vbBfVf/i1/+l+deemX/8Yd/7lf+Tf/am9ON4s52CwAAIABJREFUsx/6rd/u3dnzGD/9+3+w+cOX7z32jl/6H37l3HMvvP2+9/3zf/Grm6++vlwdvvsP/njl9es6CN/10T88/4OX9x66+k//1f9y6dnnr3/gg7/4L35140evHN//wJOf/YtHPvuF40v3P/nZzz/zx3/6+s/+7Id/47cf+dJXDq9effizX3zk81+suv3L33n2sT/97Pjc2Yc/96WnPvEnb33gA+//6Ccf+/PPHj780JUvf+PxP/usTZILL7zwyJ98bnzu3EN//dX3/MEf3frA+5/66Cef+NRnxhcuXv7280986tNtkm68/Op7PvLxyYULV77+rZ/6yMduP/X0Y5/+3Dv+9NPtuc2V77789Cf+qInTtTeuf/C3P3J835V6b+8fq0XYDFbC+UzlHR1FbRxX3bwNw7bf8wirQb9eLjRjy7yTh6FMEpVlKk3aTkfFUZ3njtKq00l7Hc14laUqCBZxLKJIJnETRSaJq0HPUlbnmYwjlabloG+iUHY7OgzaJK2juA2Dpt/3GKl+rw1DFcdFr2s4V0lCG9B0uyqJkTZ1FKkgWHY6OgxVpwOJ0VG07HaN4FUnN4EwIpD9ngkCibkRou52fRiqQKhu1xBcdzoyiuowapLEBLzliYvjMknaMFJB0HRyLbjs91UcqziWUdSG4TJNbBw3g4EOhE7TeZJYTqtuV4UhZp4Lz0MPAsKpcQG0CNPQsQB6gghzlgAVMSggphowHDMDqDE04KH1HGOORegx8zREhHrAkY4YJDbC2iOWRLpi2BCMAkewxwJS5jhyUGBMNcXWYU4jE0JjqcDcAutVSCED0HuLEeY1apgj0EUGq9YxZ4HlRDrmIDEWV44SRyTCrSIIMQmwbrnjxAAqbQCw1piXgEJAWg8qI7DihiDlqCQYSip1AKGxjJQAecglIZWi0AsDsLHCa2UQbDzVzmhHW0uRpBai1lLAKGbEiAggiqhwWEBDoWCaMOS8xdi6hLWMYayJAIiiILSQAccJ5Q4xRLjnDNDQAYZ+srlBkMWeCUc5ZNRqhkxEEAGMG8hJRDUJvKOEBwYzhAQizLIAggAi4lyEdMgwNpEwHgPCGgOMEcAzB5F2FDDcYF55hBCuFEGWYxMY6o2nChNlgDECOthCUjlCMCs8so4CzxW0wHDvicHAGuwQlprUiHFHW4CkphAz6Sw13DNsnFCOOkgkZsoTAkllvTACA6axlQYbyryIrKeYMkWJgVFM45pSx0IIKkCpcQx7BcPQYoRjoSpCTMRdBBD2NAAEIM4MC6BxkAUOUOSojpgBDCNuEfEqpqrGhFjEEbGQh9YT6AQWRPuAIe05BTSCCEHKrWHIYSAiRznwQBOqFbOIYwRaL6wnnpKlo62lHqDWEG8FZNR4Lh2FFDc1M4wix1qAFcQG4sZhbSOIiQFcW44JaSBvPUEAF5YYyyGjCmKJqMJYW2EsRx7UCDeeIsoWmADPHSCtxVoxx7BGXALgk8gWHhmCoXDUAswh5Y5BhwNMqOFMOyxoaCJtLEGYO2d9GxBAAXLOMhxyRSLrMGFCSuBgxFjQUuVQgChxKPCWYcB9FGgAWBbbcWFsyCD3GDsWQsYBDxwRiAgAQmsxBlTGzHjGCGk0lSqAQDmI/9ODEKkUBT95kBNeOYtgA5g2DjraQAZr6iBqLAFOaEqNYg5DiLC0AQDEYFoC4gAzADSaIQ1tQLXm0lOAcdNSj4j3rAXIEA4ArB2zjkKItRfGMoRJhZnEGBBqDUU+YYgCyg1kOGIaMmsZ+8mDSIC5cFRAIjwm3kfIhAwREwkNEYsjrbi3DGPuMPVEQMpcHDnDISGWCusJZ6HFwjlGKXOeQxlSTBwQ3lIcB4aE3lHKg1YygCPCeQsFsBRCIjVpEGMO1xCGikDEpHfGMEewdlx56iGWmDlPMCS11bURxDODpfS0JURqKg1HgBhKSoAgEbVva80gEhooY4XXrcakMdhCLB1rDcGKOWRay7HnGhGtGMDSQWYMR5i0mFSeQswMJt4kTCpCsEEcQgZFaD2BVhDGrBcMes8ZIKGHFDFmDEOIQhE5ygB0gGDTMuIowcRz4RHHEVNIAM8RCx1iEBFImWUhVghi4l2ETYAJ0ZxaRGkQGEihI4hS6zlkEWDExbGzGNd53sZxG0VVlus4kp3cRlGRZyoMayHaPHeU1isDnaQqitokUWFUp4kOg7bXV5SqJCmy3HBeZJkKAhlFKu+0UdhmmQnCajCwXLR5LrPUEdJ08jYQdZrWaarjSHdyG8Vlp2spq7LU88BjVK+tGYRVEOoorLOsCcMmCpt+H0AoBwM5mcg4Ljsdy7nMc52mMs9NnLRJXPW6lpBFmuRZpoKwSVMVBou80w2DNo50GKo0q3o9g0mZ5zqKmiytOrlNkrrTiRfTJk1bxpo4artdK7jq9qQQRRC63OogkFkWqLrudpUQ/3iK8I8jDnhZ+JhLwLHWkCNSNwoLjnULOTJGRRE2RiwXPubSM6Jkm6SsaYDzjBjtCNKmTRNkbLBc4BB545WnMklo2wDrGLYKUKx0m6bIaF4UMk/DumgR10HAmhY6R3/CtJIKUNJElIVMk6AttcWYAA+g106FoYcgnM/rTieRC0cCibFoa+kpQc4D6JTlxNYiYcuyzdK4WZjW6zgStpWeRq52CLWWcaQqkYrloknzWBVaQRMKblrlCIHWcQoRYaouaSQWyybNYrXUCnrBcjmbkB4F2nDuWmsZjXyjJdRCRLasfQC9R940mKf1wjOqsLAER65RChrGI1DXIMTOMqRrL4SqbCisho6QwNdaY0to6OsahNBZEwksNW8rGwkrgcdIEk5VbT0KnDKEQ+dMKLDSvK0pc1ohyQMVBKxVWBsVMeQALyssfA1DorSKhdAl8j5rl1Pa8w7qkEEHWFnrRER10cJARZxIDSwQqFWAA+N0JIDzvGx0wqO6aECAmMfWKigEqCXkUFlOVMNF1BaVSIO6bv8fpoXBQJ3MeRcoT4laikwsljYSgakbEFKoAQDaoAC2rYiCclFH2bA4PMYDIKiwTe0FAxoQpDUKQNOIBJeNjqNYLaRmLqDC1LUPGNAAQ2XIQJ3OkoGvrYlErIpWE89o6CrjMQZWE6E1DH1ThwmqlImCWC0bywDBBFntqFC1DZg12GEkfGMV1JSHvrQEY2+9Qw0IhaxtSK3GHkEjKKulQzgAVQMi6KwOOVZWNNX/i7GIrMnDEz5E1qqIY2mQNkiAqF22PGhoTGrtEQp81cAIW0OxqlFElAYBwq2SkAVIKsCAdiYOgHO0qEwaptXUONzEqTceWi9+wigriCp5RqvaxEFULxovEIfEGAm5gK0CzCsbEFXylFaNi/hgeXTKVgj1yBgJWABahzCRLSJoGeR0WZokjNqi8QIzgIxRnphAQAhFUckoSNRSGmYFDUzVgIgAjbBG3lOrC57jSqkoiNVSGeY46Tcnp3yd+RYgYAwRrm7CGDdGBzxSS6WZ5SRwTQMj6qXH0BgS2KoJY1wpHQpPYCLnGlJsXQtCAL0RlNZK2PonjAmE5IxXNbTWRAHSRtSVj5jSCEBgAxGVMw8gwb4CEbTGRAHWhjUVZc5oJGlgA0bq1kEUwaYGITJGxwFSBksFA0LrumGRgNJqaDALQV3DECvJmWsxZ22jg4DWTUNCgZW12EASwroGIVJapnFeTlsvAIfUagkDDhoNKNCeE9nwIG6XhcjCppLuPzENDHtqVPLEaixQU/CMl7VKgrgtWi8gBdhqBQQHjSYcSCdQW4iMF7WKg1guGxdwpEJXljwmxjQkgo0VWJZBxspWxTxqC2k5YAhCpw3qyXEZd0wLTMBjXbSaOMYiVxqPEXCWUGVIYOs2imGtTSBiXbSWeYL/b9ru61nX8zwP+9PLW7++et977Yq20QiSYFGzZWWkWMkkk5mcJ6cej2MnkZxYiqJEkTJxZNkUKVEiRbGAvYGdBIhCgugbwAZ2X2vtVb719fL2p+WA9n8QnV9n933NfXAf/KQrMicAdEZykpVC54Xvw6TSnnAE0zR3hEiQp1Yg6LTgJC+5KSjSoNKZHykqcFo4jD1YZE5CYLUUJM0BgqXv8bRwEC2WJ6d88T82qDK4rIDEQTkpqchYQHLtAJIwzYGPjWa4SnFAi9J6WGR5jj2GK2uRhkyCJAc+rirGdEpCUlZOIJ5lxX/KVJAvFkejoMFVbgFJaUTz0nqE53mBPYZKY7EBmIM8YwFNch3KxenhKe1gBomuSsCJrUpEobVEKx2GdJ6o0A/KWWE45IjoqgBcgFIRZiu3oHrdeJXMc/XLkWmOGCRAl44LV1SEgdxIUiYy/mWGp7MCcwcgB9YCbCl2lJB5JnE5lzGdpiryPZUqQwjQgCCtgDBFHkRkniopNeeerTSAvMxKSwFwle/zNOW6yP2QJNkvM3I2M4S21bBHW8iZ0vN5mjiIMAUkyXI/0pyJ2cwSylFVWkZ15SQxmAEAkFFknuUykLBSGlmEOaoKQ5G1VeCHs2GJZen7LEuBgxyrEnBS5FighEe0yC1nXjIpkCAMAm01IBxXpWOkKLEAKQl4lgFJomQ0Yg1KHdDWOMSQKohH04wImBBfJHMVBjKfl5Bj4px1GmABqhxLnqR1PD8K18R8rgMp8lRZPFtarf+ffxL89Nn//1+Eo9/53V/5D594+HNPHV+8dPGb33/4y1/tbZ956HNffOiprxzed9/h6plZ2KST5MN/86lHP/2Z7oWLOz/46WOf+rvj3csPfuXr7/vUZ65/8EMf/Ngn3vepv3/j1/7JE5/53Ps++bf9s2e3f/L8Y5/67MnZC5e+9d0nP/5XN5588n1/89kPfPyvr/7abz70pa986N9/7OD+K6OFpWG85JR74pN/++GP//X1Dz758N8/9YGPffzOE+8/++MXPvr//Nuj85ebewe/+Xv/ZrqwVN8//M0//OPD8/cvXn37t/7wjwZrZyI0+W/TO/NsDm4Nf+f3/yCt1f3j7j/6wz8er63X9k5+6w/+cNJZ5vPkP/tX/1ojajH5nX/5+8aXvD/69T/6s6TV8g97/+Tf/FHq1TBwv/0vfx9pl3v+f/4//M8aELoo/qvTN3KI0VsHv/W//IEiQjP6T//F79MkLeu13/of/4CUxd7yxdH3lB0a5qnB9yA8yvEm6z1VqHuGrRl6epfp1PZM7yVPH5U8dqffcWAvw2e95Ct5sW/pMhp8X1dHhntm+FNgjjRpoN7TBt2cu4tR9uXZ5DbCG7L8flLsGRGY/gtQ7WkQaan7Yv92WlvJvzZN7hC44ZU/mKW3bODl/V+Q8qYpd8KlV0P/vdXJZtV82/eub0F5Ck860bXVIiwez9/+laM37vEQvrMVvrc+Wqta73r82rb1h/6xL947U4YlOW43X43T5TK+FgTXNifrKnyPh9fOGG8kTyV7Z9d4iTit1V9bTBdy/z3fe2e77CQXx1d/e7R3UDrdX/evnnFyxk6j6PV1VxvQvXrwzlkdD+dTN/iawtZCgiZfrRCBJjXDb1eU2aId9tprMNfRvd7N7/i8LIAkgy/mDkKs7Og7BlNrZ+b0O446TaDufROStLABHTyVAwuRtb2njW8zrcjpdyEuS+Kh468YPM7Rljxsb1uI0Y3p6Q8BNQoo2/+uw4WGATr9YkVHGdpp1F5coMPYsbl3bVv0uPZz77Vd/x6eLST/zfHzu/PeW2jDe3PL64ZOTMX1M+JIZk1Tf2U9uoumm6r13AI9Xpovle1XW+K47tiI3dgKDqOkqRuvroa3mFqd+C+ti3tLk8Wy9UaNHC5ScfAb8xu7s8Nb3qJ8dbd2g023y9YLDX6wqjpTcW3J21/U8Xj2ts5+nKFNUb2aj5/V7nzNvjbr/8TJDqjqfre1AZUpfpHlP8nQhlSvp4NnLduk5no1+JGTsdYnVffHiIdOHbvkByVaZeXVbPgsEMuguln2fwhFqGl3fvBjKaUqe2D0ncqL1Hyp3WusiiIp3yl733dSKDu3o+85JnQxhONvFXZBRnMcvXoemIK4Inz5fpYWpbSN53etIk3yzn/Rfxc62x+1gtfvQ2UGcBW/dB+ZKeAl4c8vwRxbqIJXz2BtgbHRq+dpXmlpghcviLHOW6bzzDqspEZV8OoZXigDbPj6RTw3VTP7709/EQ+OrvHdhZ8um7JmuG6+vMoSp5AKXr8gZnC4ht1TvfF7EJ319Q9nyXUnYzV4CZfvKdQmw8WVuajJfD7/ejJ7D4Mzvv7RdH7NhUE2fA3n71qwLKc/Lu2rM3g5zL81m1yF8GxYPTudXAV+rUpeNek7QCyA/vPAvpLC+8L8u7Pha9DbgsPawjRossk8/VkxfROIRZi8aMpXSnxepN+fj9/AeIs1jwi7etHwlE/CxisrebOQd6V/dads5buTq787unNYWjVc8d64AGiCJ1788iYIxvQo8N/aVfGEnYjgzUsATUiOw1cvQJSAVNZe2lRBLro8vLprgzHsi+j1SwBMUIWjV847lW7j937r9K0R5snRRvjGLpAjNPNqr5xHcG4tDX92BiI1s/n4mxUvcufg6Q8QmBfMB8dfs7iXkS1+2N7WhNHbk9MfQpQp7Fz/+wAmGkWo+2VFu6nZCpOn5vMJJRFOvpODmUIUnH4fwLFSi8H083N0krtNf/6FSTYRMCbz72a2X7EmOFreyUjA58XgqQwdZOBMkH55OjtlqMHm30vKnkOLvPnGWnxdVKsj/+VV7+7KZLVovhHRvTUs731kfv3+6b3bokXfPFd/T043y9ZLNXFnTS1M+Lsd7+4akENvr01ur+p4Ft1Yia7VZ+tp/HJN3NmqOlP/VsO/ue7EiB805Y1VU5t61xf9dxfVwuD99176FdV71wT4zpp/fdPxEe82w2vLKprK2wvh28vl4iR7Nxv8AMjQsMHs7g88SSqVoOE3ChjSoDgkbm5nRXVIet91glWgsIOnHSO6ytHo6zmpYXuiTn8AGv4smYj+05aiCgB0+jVFgLKWzr5RoABVQ3v6fesHVTUGg6cdQ1qQAaymbjAs5q3km0kReGSiBt/VAS/KnJx+wxBg/KD43X/xe839g+HSyq/+0Z/Wut0kav7GH/9f4b2To8sX/rvTlzqnt98jS//0X/1e8/rt0+1zv/m//0lr7+6kvvBr/8eftvcO9y7c/1//s3++/Mab9x5/5Dd+74+W3rx2vLP7G3/yp41be3mz+dE//bcL79268+Cj/+U/++cbr7z63oc++tv/0/+6/otXT69c+kf9t9bS4WiuPvwnf774xjtHDz7427/3hzvPvXjtIx/9x//6f9t88aX9K48+9tnPPfSlr+9fevDRT//9+z/z2bd//Tee/Hcfe/TTn7nzxAd6jZV5UJfj8aOf+8KVv/9i98LFK1/86hOf+sy1j3z0fX/5yfd/6jN3H3nk3Ld/+MGP//Vgc+vcM888/MnPdc+dv++r3/zAx/7qxoc+fOULX3nyP3x8/+GHN3/8wof//C+G65sbr7z65F9+crKwtH/28jjusLJ47NOff/LP//3w8rnW67d+/U//bLi0tvzOtQ//2f97cv7yP5RFiJyzks42VmWRKk7m7YYo0qoepa06go6b0lpFoLUMT9eWZZYAhvNOi+fzMvKzdl2oTMfhZHWJV7lFNl3qyCJzks0XWrTKytjPFjtcl1aQ+VJHJFPIyHhrQ5YZdZqZUpjKxsGs0yRWaY/P1lZEVUBoR2urPJ1DDJPVJYwdcG66skhcxSgebm8RXUiHhqSBKERITRdaFjuMQLK0hIAFuhqsr9EyIxEZ72xCThC0s07LEkgImq4uAYKI06OtDSIIVcVoeclBx4BJlzpOUEnojLescwLZ8fYGxIBaPVpfgaEktpp3GsrjVClSw1gAXlamJhzT0ijSYNADSOemFhtoybjCAQASY1PCuiQCiyIZ+hx5CKsSBQJLB5UCAYMCMFWguqigV6vycSioR5jRmUQYI1QZHAuICKnSshFCZViRmbqwlEKjlU8k0ExrEoDcYKpL50faQaa1RVRFiiulmKtqEBsFmMz8GlEF5KCoQap0xX1Sd8w6DKs8MNgYyK2ue1wXmkemhllVUu5VNQUBtsTZ2FKrHXFpxIkuoQh0HdKq4GGQQcGJq4jVNYsgcNSZyAlXKmqLmqNGI0hIh0KksDO6xgEsBQK4LQicW6P8MqFAM2dJizhqsdWoxjizxGlbYxSWxiDSoQg7pkoTcYSdgBq3OBIWOIOagnNoVYljCTAgqkItjwQQQ+tViTAFxAA3COLKaQubDBLHVQHbnvJwoJUNEGGKaWNE5YgjVWUjCx3zVJGz0GIpXQWk1dhBi5W0FBJWaRNYVVFRJiZaNwRQZRQDgBquXeWbylKibBVCSAkvs1LiiiCmtAmoU0YAU1KJIBfGuMBpLESRKr8DoGOVch5U2EADrECgKYQpUwZAW5AidYyQRYeMY66SVUqtRhKmdYGdKjmBdcx1oRAHbQyUwgSRJsG2MhioANMqBxLBFqUgTzEjbcRNQZHDEQTOIophkzFSEWi8KsHQQQBohyBgiFO6xgksEIOozRGyAGIdK0YsVVUZGQQtA0bHDjIrCMt43TggkbVxCSjB1uYxFBxiXZQBcNwS4EzsqHCkUkWoHQJElabuoEdYlecxBTRhRpsQQI6RRSpSWBJZJTMWI2p5mekaB57CxlW+FVRz62ystaBMZ2UoCMFcq1wgxByqLA6R4xRCIKvUYEKNwjG3gCKtK44p1FQb6oMCY64NiCmEQpZZLgmilOSZ8iiBEFYV8oCDiFjFAlxpUdPl3KMIEVHMqVNMVdhZ7BNkMNGl9akqoSjSIqIAEWqNw9DUDAUGIJjHhJgScq4bkFSFiIIUMkFBgZ2uG4QAgE5FSLjSEL+qOWqqksSmXlJkiVFF4KAzGANdgwRbAG0ZGmorgP2qpgGmCBYqdowZAFAeLhBCCba6bhgEqqqKmgIAEFuZOgQcQehQnTABrapQnQOOsSlRyyMhRNDKKhUqhxiiGsYSO2VghCB2rCphy6t8FKh8XBMswtSYwoeYWlQZ0hJOAFrkZZsDYUWVJXWpJXLW2RrlXoWh9apMIYKUgm2JBRJlOo84ChGuSlxnyHOkNCoAGBBe5spDlQdIZYzPINbCGUV4CSm3tgqctkKWaeV1HEe0Us5DymlgiREAWMuMVgKoiPMqA8GCZo5WpSEO+CWtFBJAhRBZZwSAoeM6LyhJeMO3qmDYxhWzBqKqkpoaDbjVMaDOQoJwBxNVcOxwkzikEHa4LTg21hdFM+DZCAFAFwhCltpC1wSGOSUQtQTGSmOAW5RjQHWFmgIgRaxCbclZAUxZ1ThCFTKINDmEJbAWtCmEmiFYNJtEjU1SmKb0OIDKwAaGziBjySJ1FDJVzBpxEYdclflCqww9ns2ny0suDr0qnfEGJBUv0qLVKFt1WSRpPap8z6vy+fpq6QmZzicrS7oW8yrLa3HWiEWVZ4udqh7LIkmW2pWgXpklS4tGCFGmaSNSYShNmYtapZzIk2SpY7gnynTeaWoAZJlWzVoppUxnOhBpqy50oVq1pN2gpqwCOVtd8YoEQ4NVwYwyoZcutGiVVx7PIp8Xma1H45UlbpRleLq+4qnCeTxr1miVV3GQLHUYsBbD0dqKyFOMXLq8KFRhGJk1GxADZkpXIWqVlmyyvS6cKV01Wl+lunSc5CtLkJF/SIvw3JmqUT+48sC8szBfXDi+76IJ/MMHHkpWOs17h+17B7OFznB1VdXCg0evdHfOHj34YG/3rJb89Py5+crCaHVtsrZ2cm63d/786X2XhttrabM5Xls/vP8+Ffin58+ni629Bx66+8ijydLCZGNdh/7dRx7uHB80Dg7SRi1vNfu7Z9PlzmBzO1ta7J0/c7q2aXxx+8kPWI+NtrZ7uzuzpaXp4mL3/LnB1gZk9Ob7358jPrm5d7NzTjWi4cra8NzZybnt+aX10cb6yfYuIPj6Rz5SNeIyqt15/DHV8Mf3n51srY2X16ZLy6Nz20e7FxBFNz74wbIVZWc6e088UQbBaHNreH7nlIfDvW4/ah/uXkAE3njyQ6odF1G899gjNuAnG2eHZ3fy9Wbo5nSdwQ4P7QxvCxfSWOa8ZUzs2/1cjDJ9aVWSgq9gVMcNfko2oI18XxZeQ8ElPwiUHyi1HoRyxNrKNWQNzum2gCH2gyqoq3JRxqLv14tqvRGQyls05cYC+/ldbQReqfk15UelXfEjWYR+YTZ9gSuxBFUnwnjA+Mm8wwAfC9ubr0MlHUMn8xUyTOitqqFqfhkqRE7TZUrlURu+frpaxyRzdJwsA4pm0OtWdQyjbg29NlxsOTGTuj/bhBTPIR3lC0aSQ+5fKxtCxYaBE7Wob9tOb2zuLm1yNkN0mC8YjBMgeiCushpg7rRcBlUj6OgBPechAQKZ0mUKWjwSKVkmDmG+P4EMekElqMZnJPWdtwlYB9qQBDKni8SshbVqBC5GTFq5YtGmwNiEQUHbAHZExHO/XqUbjahZ4g1OQxjChJyVEDp2OKVVRRZYJAreBmo9rlUjdEbSCEQoITsekBayEebT6QpFdqLqiWlaoYflUqJr7O403rdx3paATwibzJdRiG+C4GjaDrziuOikNiKWjbAcJB3ssxseuzNcrWGY2mCYdZBUp6o5Ib51wb0guDVoNyjqYzEdL8jBFF8HnVGzFriurg10jRgvwfAkX4SapYiM8mUUoEy2DOxwEoHIS9VGzYt1Hc7U2cgbz/BpZn3KAxvHKaljvMZrizmuU9ugdTPW97U8XgVehdcZi4y3BXCNoAat0Tla5aDNm2pYXaxHIiNbAq0wTlRcK92yB7KKH88EVXYlbFRDe7nOhPFEDtYlkzYMc70SqpqNs3vTDWADw9Rpsq6dtBif2kbSl9HexOt79fGCDPLj6ZrBoWqCV9LVClBYsbGL07LpKBraKJst4oXq5/Ml40LITL9YyY0PET61wVy3VUSvgXg+6Uiv7GeFbqR8AAAgAElEQVSLybxW6x6aa6SN2syRvgtnWdNy27NxOltkXtXPFmZFIJt8GtarYikIRBp6pd6JBVVhR9u2EMdTMUtAxFhkwqgwK37QVmFHuQXBqJYdZ5e8GM68FQdigtZosFy5hheEVSBLsxP5KOctBxvYb1uxAzGHtIEb3tys+niQ80EK6zTgpddxcFEIkgdtZTs+beCYTvOtBkdThIfFsoI4dfxUN6GOFYVd01F30EJvqG4vbjGeEDRMF5WgPV++ZWOX1ikBvWrBpnUQ5YejHULZJGRvJmvUUY1YV9V1WTPMdssFl9aqVfVib9PHrEJwUC2pBMp30tbAj1RNS9crOmq2wIP03mzDEp5jNCgXKuTBUBZBrcrWG5GeoTMejWEEEnJWQuLY4YwWJV5gkSxF0+r1MGiUeJORAMZwTs5I5EPfL2TT6iUZtEu5ANRSGLs52yK65TdMn20QUGN8zYklCGNS54loOxggfJzRsqw6YRuOyBpyNc59JWvarocxmbM2LJd9WXV1fYwDkwUVpqfpMkNoSOlotCxHc3JDNwfNhgd6Ju7rGsXhQYzfnHZqSmQUDpI1yN3UBpOiAQJ0g0S3inqso4KBk2LJlVIx15tu0gDusdpe3uQIzGzQR7G+SbePJmS02KqCUtreZBNTMNfBNO9ATGbQ65UNBFu8qQbVpWZAM+Y7tO1RYaIwR4usMIK8eg+sL6N1v1EN7eUaIWUcD+yKjyWOwxR1CGqzGpnTlksWgwV6ALZj4qGIp3AnQD4M/JS0MW6BZtAjC0yt1BtFH5wPLWHg5WPT7LAFVgtS2GGwhetwgtdFtRS21AjtSuvx0er6cHN9dHZ73u5Mt9YPL18GjO09cmXUWcnefu8dWNNrS/31zfH2Vvf82XyzNTm7drh7yQmxf+XBycaaDvz9h6+Aujx+4OL00s7x+pmqURtub3cvnLWM3X30sVGnY8629x+/UkbxcH1rtrLYXd8eH0+6jp3unHNS7D36WLbYnEe1oysP5YvNwfbZ2fLS8aVLTrLT7Z3jc+d0FJ5cupgvNPpbZ/Klhe6F3bDbW9y7nSx1ykatf/bceHu9e/bs9Sc/ZGKvv322aNVPLp0frm6oONx/5Eq22Lr2oV+Zry0XUdi7cHG20ultbReNxsmDlw8vXO5dOn98+aL1ZG9re7C7Uz/tdvbvmkj2ds5Ujfrgwva9zfPWF3fe/4QJZH/nzNF994vnX/iHsgif+NwXVl9/I+l01l58de3nL+f1+ubPX97+0bN7jz36yOe/vPPMc9c//KsPfuGLG6++NllbO17eHrc6YjJ74BvfOvuTZ+89+OCD3/jO5jM/PXjgyqi1MGovNk+Ozj7z/PpzPx8vra394pX7vv/D3tkzx6tnxq0lkuQPffNbWz99ftZeXLv65u7TPxgtr6+88db9X/vm/qOPXPzGd3aee6F/5szacy+dffa5WWcx6g8e+dvPJu12eNR98Etf7a5vr7x749I3v53GjXjYu/yFbwOEgQOPf/Lv0qgWtcgH8sGEsfjldy996zsV5kGZ3vfZL8NKw92VXy2ONYLkleuXv/btrFGP9w4vffNpQDiruY8ig9U87RaPf+yvK8/nafHAU98ooyA8HV382reQcQDBK5/+HJsnZRw88vG/Bxh2m2dP3+ImRQIVR+9Eqq+iJXfwop9OSNzWvXdjYjC32eGtejkjPhgkq7EdTTD1jn7mp1PmN0z3dZFNCbfDso6dc3aGTq7F1aESW7j/PJmMJV8FRk0Ux/aoGuzXigEBNTK5FekRDTfM6CU26Au8KKavu3IIA5T17nrTgWdXaHx3DfXXkoXC3+ug8SqBAzBfgqcbyrNgtiaO11HURUcbdriTLJqHRq89UpRdCN3Jihufq6SB07a4t56G0/cVNx6G5XUa0sM1ONhyOOVjbk/POZrdRw8fU8NDJOzeph2fc94IDxb06SUCpzJl+uQiwClMG+DoPPFP4FHDji4Cb5RMQe8N7rRDFBz+wkcM4lx13/SwBNUM99+gDoNGMnjv+jLINF0jJ50Nh6G7mfTe8jBzbuqO3guosbgBuytbBmB5Mtt/OXIIQWWOr/qRnhV1v7exZQ0Uh+P9q3WSVDBCx89xCxBy4OSqR502uTt91zMF4FQfvBGZqWIbEb+2gaoI65nrnoFZjEgCTi6yvixbY3H7YZUsunBE7pwBWV2Svceq3pKdH+BFuH9R9P2yMxY3Lppk0bbHH57d3NZZT0l1ehlOm3nk2P4GH9aAvPPB6ngH2Wu4w+5uw7RFwRj2Nu18o4wVP9igg07ZGXjvbuhsA/hTdLQGpotWJON9MH2XywU7ew/073hwzSuvq/5tyXznuurkmgcDnI3o+E0kazrpNMatJT+ZTa6hwV2PM21H+uh6RImuVuNhY4m5Sr1entwKRV2l98jpbY8RjTvkYPEMrcrqvfLondBv2+Ku6r8jZV3nR+j4ps+Rcak+ecvnpKwydvy6B5o4mgt4vEutIVXqjq6g3EKvgjcfdBATq8DhAxAZmkF3cgFWMLY3fh0ldH56ahbt/uMWUug0vHcBOdu0t3+1SiAuRlXbHVxhM1BFBb/+QOVkg9/5QH4qddpPOvD4MlIsk4Ddvo8Xsqql3vXLJYwtBWJ/E5cWOgdPzoHcSzsqeUaN+4Ku8PErthhTQVX/rp92sWvK8RugvKO9DTB5mQ1OhFhBs3ojD0LvdDy6zsY9H7T46G1UHSB/sey3V5J6A03y5Cocn3KPVcldNDj0vEhPlhfnQd0r0vELetSPvbgc3GDzAyKlTu7hwZ706npwXQz3ZLSm0ldt9zhiHRj1ue1edKhwWUTvXdC1OTsOQf+C9cZ42NTd+zGceRlSx/c7l5+Vh+8ve1Nrk6MNN7zk/DHux653joL5qrv1pE1LUw3mZ/H+BRukpO/B3gVIRivV7Q+V+dTlSbKODi5QNQZK6nuPAb+AEx+cXqBk4CY+6p53zhAL0MF9EKqJK/svU09nKgeHN2NbIk6qgzdiMq1wAx49J5TChIHumwIpC0I0XFursAgP+vtv1c2goku09zzLC0pX6Ki9iKADp7r7tlAptjUxeAmpofU24GlztZTSHRWDqwylirny5G2/SJhry/6LwI4RWzCDF/Cs9JGHJ1dRMce4zvnhNu83UHwP3r1g5pt5W8s7HThbJHCC+itmvlvFih6tst5yFvc+mty8hO0dy+3xLpysIDSnw4YbbFpv8gF3eNlMbsCA3do28x0nh+h0FY7WCOw9YA/O5aMBYsXJA7C3SaO76GBbz+6Dfo+cLLjRJgVTNK2D7haUYzhYA90zoDmY3XanN30KVTCZ3bi+wIDSBTp6zfea1vR1/3gprFdVFx3d8Bk00I7SpQDO0mIQnrwhWQiqU9u9EdTcRBXTdLuFupNsFp6+5TFbaotPXvGwjzhLkrZPev28H5y+6ztjGXIntxYIcRCj7kvMxF41AcNr3MdlVbKTtyRwOBLTJ//ir9h4ktUbD3z+y2I+dw5f/ObT4WA8WFh+4q8+1TrpHd5/34f+3Sd4bzS/vPuR7Ljmqrw/v/DV70ZHx72NnQ98/BOtvXv9yzsfqbp1xnvD9MoXviJHUwjh+ae/V9s76t5/6R/P9hbN5KYKP/jXn4qOjpN2++K3fhDc61pPnP/OD5s37vR3th/95GeWrt+8+8gjT/zl38T7h6O19Qs/eWbthZcHmzu7P37m/Pd/dOsDH7jyd5/fePn1/s7O5k9e3P3JM0lnYe2Nq1s/fm62sHDv7KVJrU119djfP7X+3M/6Z7Y3n39566fPp+12b3vruLUhi+zBL3519wc/vvfYoxe+9u2d53/W39nqNZf7y+vBaf/8T3+6+90fjTY3N37x2qWvfOPeo4+c/e6Pzjz7fLlYj67eOffDH5dhXD84uPy1b/fOni/v3PqHsgjzuEaLXHleFgSs2Sz9oAjCpNW2CM8aTVBWmpCs2UxmE8UFAI6UlcEkjWvzVtsSksY13O4ozpHWCDiLSRaGtNEohciieNZqW4SRMaQqHYJpVJvX6kp6eRDMW+3K9/MwnHU6hpCsVk9qDcV52mwmtXolBEZ23m5X0gNUzZutSnpFFM0ajdIPAEDzVqsIYyXlbGGhktJCMuGyRLSKorTRzMOQcDmr1/M4UpSNvShHovT9aaNZCqGIN2+1Uj8wiM0DlCigOZt32mUQVELM6/XS8wvfT1rNrBZXjM3qzTyKDePThU4RhI4iG2DnOUuRlRBIarADAUQ+NcRaD1kGHcPAJ86DllGMiQHQYgxCDDmyxEEPOeIcY0AKBypnCfSQA8RBZEOEKTSYEEwxY5ZY4GGHoKMYSug4scg4CRHChmDnE6uJochK4iB2CBlhQKUNRkY4VQJEoYYOSGsYhNQoCRGCSgCrjEGoYN4c2RJRx6HSzjKomdNSAwwy5M9oVSFMJbDSGeoMc1pYS0HKxNT5mnIjLKiUJcAIhzyrGdDKKW4dgxY46BlKoBZAawsxcJg430IBHAUwIkA4xywIsGPGImwjDKSxAFuJrEedc7TMoXWWU+tjxwwQ2AbUSQuAomUBnTWUgBA5iS2DzseOY4AQyzMHoaMY+shJ7AiCMQEedMwCHzmBHcfWJ8AHlmnoISCxg0BLAIgxDBoJAAWQQMuMosAhVAnliLMYG2ktcA7hREoLrSaECFth5yAyQmvgNCYpk4AC7aDmFlJoCNTCQgw4QnPhVZgbSo10VjtEoObOAGsIUtxACB2ElXTOGUMhkAAgYAkAEsMQOoysh6GPHUHQI9Z3iFnLkQuJk9RZAEIMCETG0KoAALiQucA5ZpBDUELAMbSGVCWwznLkAgx/uTABdMIi62iZQ+ecR3GEHTXWo9bHgCInsQ2IE9o6ZH1sBQMIwQg7hh0E2keaGkaB5cZxADByUjvuHIGGaUydps760EhXYTanPAPQElR5FjAHKFTcAGorLqdYZ1xYDR3XVgKLYOUZK0GJyEzIHGILUSUdFM4SaIV2wjkEtdSGWU2QEgBwaIgD3BkPOAhtSJCDhhIQEqscpNZJBCh0FEEPO8wd0sCDiGFDCKkqCrTD0EriDLYEQw9D5IBz2GhstMPYec45CCm0AgFILCfIGuyUgxAGBBTAcQIiZo0DzELxy3Yj6GOokcXYSQssghQa+h8b5AisfGMwMMJBriyBRgDkWc2hMVYJ4wTMmRx7QQWY5sAICzG0wikfGuIqLOYezCGzDGhPGwoBg4YZxGAugrkDOZXGOCcdYxAwoH3jKAAOKqYRAQYC7QErkSVAc20YBARZH1qOnMAgoNZzjmDgISCxJRBGBHrWEQM87AS2lLCycA4YTmCAnSQOQxciKJGFiJYFNhpQ6DwMPegIgh5yAQNAE62wUZYTF1griaPYBhR62FACAmw96JCzIYYSO4qdR6znDMGau8o6jFAlgNVGY2g856ADFBjuDDGGIM0tNhpgmIqAQ6cR1hIaCxx1mjljjCVoTiWh0BJqhLFQWwKMBK4CjsAciwRChYjm1nqGY6yEdUABDIwAVgFAgaXOcusoAtxZT1uMrAQuQE5aWyLnYSsI4gDF2FJjBbUCOoocxDYgTmqLCcHMQeUYhhEEHFsArIcdRYYLwKUWBpQI+MhxBBiCEXECGgcQ5Y5QC7ENMPCdoxZI6DhyFMMQOI6gw86HliHEoPUQ9NAvD3fWaGgu0kYzD4LK8+b1elqva8bTZiup1R1CSauV1WoKk0QGmTOVZWkcp/WmImzebBX1hkV4KvyES2W9pN7Ig7CSMq3Vs0ZDYzqRXgGNw3jeaCpfKs9Lms2Ci0qIpFYrpW8InXc6DmOHUNpsVoRoxrIgAM1mJb0srs3aHYdQ2miyolSCJ63mfNAspSzCIKk3lBDIaOwQdC6JYtZoaiHSOEobzdLzoLGkqpwDWbM57SwYQtNafV6rKc4BgKQsDWO5580aTSVEFoWT5SVDSVarz+JYCZnVRNpoFoFPdZE0W4rzf0CL0Kx2rGQnD1wqw1A1ov6lXYTBYHurXKjn9ThfXuxvbehGCDg+feBiMBovHt1NljqIwunmWr7cSuqNot04vbDbHHZXb72bLzerwM8bjdNL5yEn0/WVZHUhmE6W925lC62800AYHDz2MGYwq9e7l85DQWeri8ViM2k1dS0YnN+ZLy4SZA4efxQTl7Vbk62VotVQcTzY3ZkvLwhd7j3+qIsFQOjwsSvOZ3mzMd1eS2WQnI6nQX20siZsuff+99mIQ0oPH3lIWzM9nc6ZTJeWijiab6/2t7aFLvbe95ihpNg/vNdYM6HI2u3pxvJ8acGG/mx7rb+9LVSx9/ijLhKWkNMr96OATNtL0/WlYqke6RldgKJufZUEaxAEOCaJV6twkwQkj/0crQoGtNfWtEn5/hRLiSJeI0kQ5LhNA1r4foHXAtKriAWsgSMzF0uAhtCjWRyVpi2CQUILh9dC7sqwWcI2aRYDf8ngiAhWBjJDS6wOJp5XsRXMgQoalW1KbgcSdbM2krYrzFQv54Ya6XplM5dmSnifBoXmJYO9om1SK8Yp6TdjD80QnKhWzsGc0C6sm7Hlp3M0ajYpGXvVuFgthJshNNPNLCvxUc6zwHeiFLZnWqUWhV8N89WSwZSgqW5lBMwpPqVhrgLDzNC1chOLRjHyNiDhNsYz2Qa4hgOcypbFTRyVU7IjY55Q6OSaI8jI7kS6ioUwJJnXsm5B1Iox2uaBSdnR3Mcl9GDMEtGwtA5DlMbNSkdS7E84UTwEgZ7zDUqkq+G5rGnaRAHMRFODJRFlE28Tc9+Ees5XIBGW4hGls6KjmEmBPwFxIcqRas6h74jtUT40DY3BlJFx3sGTXPScl9ZELeua2hgFDsIRZ/20gdM5GGgxb3rYFkj2dU2FeRdEExGqIxX3Kz6th8L1KJ1XnVLqORDDquHCqmeDAQwdxFOBBrpVIpxQOFWdKrRpEJS4iaWn62QGVmQo80AneJv7OJekYGvEw2VdzkibsCzzu2MaABbDsJqSXc/DuaSlXEE0zYL+hDJLIxChOV9GOISxmtIzojYdwKFiVHFhYy/BTSQiF4G5WEGgyRvlCJ0VPikCWopVwpiO+NwueCCsorSXLZdE5LIaq04ChGGg58LEBkqAEYrmWdP5+bhYzACHvTE+jVucKQDHyJvYmuJ2hMLZNPbHI3ckIyS0KIdVJ0VCU3tK/GkekGEiJsxPGtTPx7o5MaGtp4dVK0HSQDBE/kzHhVf1cDjLW84rpro+UIFogGEtzPWibKKpxwqyzhlUUVyiDm2Uw6CjcY0IVsZeZpa9+LTPxymrA4p1EFVogdbsNGhWqEbleOb3x64lIpZ5tKAb1LepH2vWhN5wJmZzKgGXukYTukK5MAHJ6CaTNvNjRVrYB0m9VqAG5YGN8Nxshr4ZYjfTnZShjKBTF1dOFtz2bLPQsgjKYb5aUpQyMNXtNFPoNOWFJ3XoiB3BRlqGNsgH6WplHOolrB/GmJYMn9qodDLz3dC20rFsTMeu2+wINCduAtoZpTlEAxDlLsikHplmVkYuTAfF4pzRnNqRauaUqYhkca2wK4FfpWIVisgFesY3KPFAjGderaJN7OPMq2tUo/JgLGBJYxSqmViBzAcBSbymBRGODntSFa7F/CqVKxbGuFX2vWWHPCR7U5bmpEFqdiJrmi5ioctgSbs6bauBXHQkQgHLA79ASyxyM1G3tsXC7AQGI+YVmpcc9Yq25WDA4axYLIWZQTaqmtpXA+D1UewGivcSnNZ9SBPuJtViLvUciImtVfMMHVayin3AMuF6ul1amkk3LpZ0UeAujNLIZ3pERI+EZeVp4UamnWupPTPMlivp5piNTUNTNCWsp2uWBC4uJ2TXi/CcMsdXAGMmFgltIRaBCMzFIgQt3shH8Kz0gGInBal5zHMxn9MmZJGNYRYvVHmz7d0cw07kezpECVtjjJsanYmWQwSxo4w1PNfxatlY7FDBVegSvoyYNDFLcAt7sYn0xF8BuimifMI3IBIoqzfS9eXpxrL2vGyl0z1/jpmqe/+l+XJHAn18/yUT+0mjnq6vDBeX1HF3amF34wx1unv5fLrUpk73Ll9EMTud2mmuemvbUJLZ6tL47CY1unvfxVmzCU5OjoFvmvWk2SjbjcG5HS15srI43N2iRnUvXyoXak7K/rkzZTtO2u2i1Ti9eI4BPV9e6u/uIIbHW2tVO563WroeDS6cyVotwPHhY1cQBvPl5cmZ9Xr3qDE4BR6ZLy6aUA4u7s4bDShJ94GLiyf7YX9oQ65Cf7axli43k6Ul5/HB5V1WqY3rbydLbShJsrQ02VlXYTBfWkhWF+edDvT45OLmYGWTIHPv4YcQx8niwunFi/yFF/+hLMKPfuITK29eVYG38eyLWy+9rH1/64UXz3/vh/cefeSxv/3M9gs/23v8ifd96tOrr71uPLH+81cufe/7eVTb/NlL93376XsPPfjo57+09dPn9h965PHPfHb3Jz+xvlx94+r2j3+a1JvrL7/68Ne/cXDloUc+87mLT3/v4L77H/r205svvKiFt/b6a2d/+EzW6qy+fvXhLzy1/+gjD335a9vP/HR8Znv3h89uP/ucEiIaDB7/xN8UtVr97v5Dn39qtLqx+frrF57+rqG8cXJ06UvfYEUJHXj/f/irIo7ig+OH/+bzWbOx8Pa7F77zPahtnM4vf/ZLPMsARO//v//SCRb1+/c99fUy9Ns3bl/89nd4pUSePfjxz8rhWAf+k3/+McNZ0B88+IUva0809w4vfvPbIskgMI98+nPRdFoF4om/+BsEzUm43Xtb2JHx9ezee3V3XDQWqtvPRcWUREF2/IrAqQmK5PBWVA2cx6r9q3V8bP3F8uA5P5mwOMwPXxE6AV6Vdd/1yyENeHnv7VDf08Gm6z7LJhPPb4LBK6zsA09nvTt+3iNYut5Vru7qaE0Nn8PDSciaePKyVUNQL8e9u/70WMBl2Li1BgbbVWvm3WjD2ZqvjsFsGfa3HFFosuDtLzF5lxxs6sl5VZuJ/TXTO++BkegJNbwISAXnC/LuugtP6K0VNbhi4nl01HTDTWwTPpG2d4GgGZgtuf0rLjyVe007uoBEnxy2wOgML0deJvXJJQxmeN7Ah5cEv0uPWmZ8mZLufGi7VwVIFcNm7+chxZrNi+M3fSKtPtHdm4HM02Y6eue9ZTIrGHcHzwbUaZblR28GHBX21HRvBjQpucruvlKn41QG9vYLEYYO59XRW0HDzezYHLwVwUQxrA5ei/gop6HefyF0AOJSHb3lcVOhUXl8M4SJEbjaey2GE8U3A3Fth5S+KKfuZAfPIgJnoHtZdKWpH7PrV0C2gMQJ3ruIs5Dpue5ewpMFxwp8uCtPfNs4ku/dp7MVHaXsxjacdqQeueE2ni4YUdB7Z+VpxIKb4OZj+fycDWZybwPP6341wL1lO1pxYUHunRHdlqnf897ZMfkmE31yvAMmbYTHgwM0fZuG7Wr+tu3eDUkb5u/a4R73QWYPs+PbNcZdNmbDV7Fcdunb5uRdP2pUyS3Yu+t5KrEDfXQ94rjM+/jkFRk2SnXTHN0Ow7hMb9neXlBTMzrMbl5tS5CXc3z4elCvl+XN6vh2HPNZtg9P7wSiyMCsPHo7imxSpvjeawFugMZU4uNzMit5mZjjR9mkhDwjtx5H1tE8RfuXMSjEBLreRZo74rJq/0P+xBHctXsfARYRVcDDy1yXZKKrk8dwSjDMwL0rYmxtMKU3HgGOYl3ag0dICUgBYPc8zajmlt2+yMfQBSPx3oMKxNBptrctckXLynUvkjzMW8n4R3o08MMFN/q5zYcoUPP+vp8eEyRd/xqvbqtwTY1ewP1+wDtk8pLJj1HDjEf7YnQocUwG15i5bf11NXgG9I8C1obZ23DaJYGeze6i8YEX1VTvLTG9hoMdO37O9nthHOXjd+nkiAZqOj+kgz0R+lnvupzclvXlZP4SOOmGsgkap0L3L2OXoizA+5eB3xf3Ytu/gMSIHNfBaJfnI69g6uR+YhOYRnb/fZwes5PoP1mEDdg/K8sZyVC5/wStZkh5eO9BJEb8WNjBJQ66rB8Xp4+yKscKonv3+9UQFcgevg+xCRmGrnfRc33Ul25wiRQlsgDt3Yecntms9wsWmQxNq3s3YzDTv3wRsl7G62r/+UArzFx59IZPlMKz8vhtz02dIOX+6zHoV2IRnDzLUy04sb2XCJoWuNDdG4GaQOiB019wd1r5C6b3IzLNA8LA8HVEEhWm08PbtaKPUJOd/oygnpEd1X0Wz0qfUjd5C1cTJwPIj8/JkxoPbuJbl3SyraK5vLuGZ21PDehp24w3nVfi7pY86pj6IX9nt5pdZt4pOd4E42VmZ2TUBoMtwvru9KI7Pg/iu/K9DZ3sYN7H95bgdNXXQzRZrk4uYTpEvS3S2/HkNXJnR813KT7Gxyt4suZVIzhtoONNQnqov4VPd3F4b/b/0fZez5Zd933nymvtePY558bOAYnoBggCBAFmKlhlT81opHLNP+IZy7KnxtKURxqPRqKibUmkRNOyRFKiSIIUApEIgoiNDDQ63M7dN997ws4rr3loSJpH+4G79uOn9u/hW5/feti1fr/zZvtqmusqmdcXLqxEpnMK3XorzTLtbqiNS3nOa3kT7FxJRd8Rqa6/N05cDwy88WYWJ0Zvuc217BDdb3fpzfNDqjT2fv3tJFadc+HmG5mIrdpDG+cGMerdjt28lFGlsbe33s6YURDBjddikDO/Z3fPi0zPdYW2LyRAgkW69zO/84fRbK6T9MG//FZUlVSZe//uydH6RrWw/JmvfHX5/IXNj5/+ud/+/Wgy6/PBw1/9er69iwP82ONPLV67MVs58LmvfPXAhQv7H7vzkd/5SnpzuyuGn/zmX+d7E27U3U88tXRube/uj33+y3906J2zNz754M98+Q+LzQ07GJx67InB9ZsYgnueemb5vQ+nJ088+pWvHXz73Qi4hx8AACAASURBVJuffOiLv/cfBjfX66Wl0z986vhLr88PHbn7uefv+8HfXf785z79Z18//Pob8xPH7n7yuaOvvR4wOfLue3c8+6N6Zemep5/7xLe/e/2Rhx/55t8cfvm1+bEjdz7zwolXXsOMHH3nrbu/+0S7vHTypVdPfe8HWw8/9PFv/u3xl19tVxbv/NHLp554AiK8tLZ27/cfbw4dOP7qmfu+89jWx+879dgTJ198GawMF858cNezzwUIx7duPvCt7+zeeXd/44b4Ke0inK4e8s7vHTxajxZ1nE4OHFZMlOPFNsnL4VjGaRVns0PHXICTQ0frwUhRsX/gsEqydjhuBqP5YKgJq4vx3uGjHqK9A0fqbKiiZLZ6WPK4SrImzculVRPF88WVyeIKMG7vyLFmOFZRMl1c7UVcD8d9nE6WVi2m89Hi9NARAODOkRNdknZxUhXDarykRFyPFvZWD0Prdg8cnq8cCD7sHTjc54OuGDX5sBwv9SJqBsPZ8ZMgwMmxk5Ni7F2YrBxss6IfjdrBcLZ0oI+SdjDaO3gUOLB14PB8acVTOjlyrEkHbTpo08Fs9aCK07YYbR85HjzYWzlUL6wYRCZLB7p82I0W2mykhgn1EkXBjLNIN3AkqiRLScNgL/OUiYCAaxcHDFoaBZ8LYVqd4C4dFLRNmTVpRLnFXquFhEHDhdODKLItTkIfJYx0Akkdcc4NgdosJiQoipUdiCj0LHZtXkCsuGtNTJmwOJhuMcfAcq5NnARQQjOTDIC4p7JqciaxwXImIxugBGHepqmmkpl5H2FP56SbVykyHFBb6sS5IKHd7ePI8Z7qiUyxpD1vZ20GDHVYz2WCAtHUbSvB+jjgfqYS7DLNZdkPSYcBUqWMocOauV0Z8X5AsZyplOoiiroSDmmI6JB2RMA2S0mQkEEwZIlq3DjuiyxVdRhyE7EYVoGDfphzpELE/IJgfQsKbIZZbGq3kPZRlKMKEmeKOIbSUGwWs9h2aIBdLoRuVc51nA5py3hwA8F97zlUC3GsO5ojnUWJqVGKLMYeNs70zYBh0CPaqZwgWWquuniA/B4CtYyZQy2yTVMQ6OoAqj7DyNYBl21aAD8BtlICQFIh31UD7pwMft7FELvKg6rJC4cqZmddjACYBd1VOQ1AE1z3EQy2Bm6vS3PPGmJKmWALamRqmWGKNAltH0cwxbFvQy5QEkRX2XEEEiJCHzIKkI9xYzkLGY5drVOBBzhRrRnHLqbcNH7AqYBD2to46jPBTRsSBguSyEYtZjYRcejsMAoUpKBSgplxGtnWFGkoKOvqMOY+i6LQy3HuOc5AFTjp4kD7WZu6dpAwOdM5lIIis+1Q32cCg9owVBWEyFIyKVNK+0mbwT5Jmd8OqO8SDG2psO9GlOsypEpnBPczGTkpYuJ2AVIywcjOHTF1jmg317SXCSZ67mnbJSm0e953OnLEzy1xZc6JKi0qJY846WLYS8F4EqhXZjHBQFMow4BHQDLhuryARHHbmJjS2FOvuoUMIi+oBhnlroOw75KcJiEOvc8FJJqa3iykmHjOjI0JRUrQVosYJSAyjc5jFgfhlVlMEXIUaZtHEXMD0vTpwKZY6MZlXIqA+5lMgCeOuF3FSJdALGc6Ri4zoiv7IWkpxHImY2CJZW5bcdIVDKu5TkkfadbNyxyphDE7lzk1xCGzrSiQOWZq3uVxPQCim3aR0QJBPW9TZjlibsfQINOAu1kTETnEop/bzBkBkZobZr1gAmtLkFrKYtujDLpcCNWonKkkG9FOcG/ziMMekKAWksj2NIM6j2NTwxTJKOK4ZcjYhAmiEA9mIWaqIcLZXCS+JQloswFGPQPSplQgBWjoFnKme5qGEBNhG0BNn2ZCWIGMLwQJElMnc4Zc7UHZ5IVBDbVlHyOA5kA1VUY9sgRWKgrBNcDtdUnqeUPMTCbIoJaossmhDxL4SiYEwpa6zT7JZWRwN5MpDpGipmkG1ACN/LyPMYQNdbttkskcUjWTObeiQ7Iuc+pJgLjrBpEHLTI7vWBgSFLZmMVMZ3HiGjeMPENpqIygepxFrrV5HEaMdXUYMZvFkWvVKDOCZKDyDPkiSmzbC+4Wk1Q1YEh8Qpnt+jwKMS9ICzkOBROq8gk1oyiTFRgwm/DI1j5jRtAYNQEHkFNh25AzO4pEW6IUtMmgHi3IfDBfWG6TXCbZzqGjDqLZaGm+tGoJLZdW2rSoilHPotnial+M+jTfOXDYBjQfjmcLK4ZF5WixHhTNaKyjeLp8sI/TTkR7Bw9bTKsDh6vBqI+iarzQpYNycVnzaLJ8sBNxl2S7B49YgObLq00xknnRDBfarJgtLBtMZ6sH22zYZcVstNSLpBotyCiZrBxwmMwWlucHDnpEt4+e6OKkj9NycaUvRu3icpcVewsrFsJqYXm2etAjsnPkRJcW3WA0W1pp00FTjKokmx4+FhCerR7aWz1oAd45eqLLBn0+mo+Xm7xo0qLOh/sHjwAIp8OFyZHjAeL9oye6JG+ipItT8N///LfuIkwEGpSztki9QVxrlfK47XrECHE2EKZ0VQyp1sV82owy50jUdfPRKGkb4AOm3njMpK5GQ6LNcDaRRYyV9Q6Vw2HcNtB7TIIBhEtVDodEm8F8Nl1aLKqpt6gcFlHfQ+cxcQrQuJcuxh1Oh7PJdGGc9E2wIDDkA6DSdnnmEB7v7e4vL+f9nDSmKgaJ7ryBgUELEZUWsdDwrJjNpuNxpqrbTGw6Z0kUOoOxswRTX0f5eDLZHy1kuqaNrvIscspbBLE3hLJOIRqqeDCeTCajcWoa1DklWGHmNSwgcjKK4rqTQhBkRK3qKObQBAMBBIFBZ/GwmzZ5BiVQnBNkWGNaIQQ0ziIcvOPIWZyoth7kou414xhb1pqWC4GM9oRaWw8GcdPEqquGBa97i0mfJllZWkIItsYRamxdDETbJbLVKWON7njcZHnUNULKuiiwscPZtB0mNrCobcrhMOp74PwgVDXOiLZVUSBji/l8ujQuqtntOETfI+sQDdZDoUw5HCJjh/PZZGlhUM+8xSrhQknrMWJBARL1OkSwJemwLKejQd5W3iKVcKa19ygP1ZzmTFoX41oMFvZ2yqJITOctChg4hLByhPg6zobT2XQ4OtBsdCaqBoPYdN5iiL3FmEqLiK/j/DaT6oZ0poljAYw3CCJvGCW9Lexst1jJZ/WsKBLbos51keDAeIMwcpox2hmCbJkPh3uTclDEvrvNYAyCAYlq6zzjnTaYYupYq1smODLOIuqsi7BxJJVtPchZqzxCXZoO5qXCiFL/j3F0Xdo11XDAWu0h7LI0KytDyNhOJmTMjK6KQrQd07ofJNls2rOkz9K0qhzChFhtUWyMiYlxTKi+HaRx01hAEA0OYCZ1VRTIuuF0OlleLOpp0LAcFlwpbCykwQAs/iGO6XS6OM67MmgoM8GV8h5D6hWgUa9CBBuWjafT2ahYKTcrOFAJZ0YHhxDxCtG47V2MG5aNp5PJeJz3VdBARYw6Cyxo0txjuLi/Py2KxLasvm2Q9BZBFCwlpDcEuyoZDKfzWVHEtsWd6wUfm2kJC4S8YYT0lkJbpflgVlVZHvkOd64TgkNjLWbBKMFIbyk0ZTYs9md1nhvOium0Z5QRbw2GEHRpms1LCs08HxaTWZ1lvUjSpmTGVEUhui7t6no4IJ0BALRZllaVw/jvDTJ1UfC+z9paZZx2RhLRZWlaVRZhSqy2mFtbFQXve9HLdpgO5vOeRIABKo3GjGIrA02kNAnRgcdt2xZpVlcSC8AA0S4ECGmQgcZSTRYWxtVe0KDP4kj2zmNIg4bkHyIr5rPZeJT1JVCgT2NhlHMoC3VNEqwD4KDm2cJ0sj8eD/oKSN+nEbMmOISwl4QldetjXIl8YTLZH49zWXmFAPORlR1MEXY9E2nV+gjVIh9NprPhMNV1UFAKBgkivSnMbDJYTMquzPPIfRTHR10LeMMJko4FXeXDfDJrslyE/h8jcxgB0CZJVlYU6HIwyifzJk2N4PlspjmnyFiLUQhtlqVlRYO2EeWVrLJCf8QwCq01CEFwm/EEVXkxmM8dgmM7ndAR06YqCtbLuO/qYV7MZxKLNs+SuvYQEuK0RZE2LsYSiKRt61E+KOcSc8Ah7Y2FGH8Uh7QxUVCkXVfncd5UCnLPIdHOB1T42R5biKV0Me5xnNd1NcjyptSAeYGw8cEDQEHHk/FkMh2PVsuNOmR9FgutvEOQeI1I1KkgYC3y8WQyGY8zWUEZ+phzb71FCHvJeFx3Kay3BodGk8ntzhYUUIKyYINFCDvJRFy1QcAqGS7s7E7H49uMFpQABzTQgltKB9NpiFCZDMc7u7PROHJd0JAEqyOGpOdBlfkwn87bJNWRKCaTnjFGnLUEBd9keVpVwsl5Mcqn8zZOdBTls6nmfGymEzIi3tVZnpWlx9hFOJ3M6nykBc9nc80ZhcZYFDkjE4F6HxB0Ec5mVZXmDGrUe0sIJk5bQr1rBvlotmcgqweDpGk8gARbddugmCgYZXVZjoq8nmvIvMBUWQcJxlYGGvfSJaTDcVGV7SAel3tzOvQCEe2Ch4h6CVjWtTphHYmLcj4fFnlbGY89x9j64CGiriVxMZ8LprbSg8PZtCwGaV9bh6YHjvy0Bo3OfvGX/9lvf/n0D54ojx6757EnT/3td+vFpfv+9nsPf+Pba1/4ws/99h984i+/9f4/+R+++Ad/eP+3v1sfOnTy+Zcf/Ku/mh4+fu93f/DoV//8yqc//YU//vOH//KbH3zx5z/3la8+8I2/bpeXjv3oJ5/4i2/uHzp67w+f++KffPXyo488+qf/+eE/+/rFz//sw9/45oNf/6/1gcN3/PjFh776X/YPH7vjuRd+9vf/cO1zn//M1//qk1/52sbH77/rBz985Kv/eX7oyHB943/8t/9XPV4YrV35+d/4fzZP3Hn0jXe++OXfb4uF4c72z/y730JKBx/+p1/9tb4oivXtf/Kb/2+5uLB44eoXf/t3jUizpv7ZX/tNrp2B+Bf/5f9uKUl39n/m3/9uNyhG1ze+8FtfDgBjo37h//h3vG5Vmv3P/+JfOcLErPq53/gtLUS2vvfF3/oyMcFD8M/+za+nexNZ5P/0X/4atWZ98Z4bPxZ+z2WkO//C0F3rs2Pw6neo3ITRgl3/IdZ7IXHN2hsjtw1S3l19MUeXO3ICbzxG5zdxumS3nobtPs9Qd+2NvL0Z4lhd/XECLyt8F9v5Tpjc5PQg233aVJtkgLvrb+fquicL9NazpD+n4zvg/uNw/3qED9DJc7q8SUdgcv29/PYuwqUPjvJrd5UH6uHZBb/x8ShcC/tH8LVTgbdg60j24WFYbOALx9G1+5uVerg28rc+IcA63xz4G5/0rEF7B4uzx+XCNF47iK7cVy/Wg7U03HqYgW2xn4HLD0EwAdOD8Yf3qvFscGURXnsgROtsYzlcf5j6nWgmwuVPEThF0yX24f0w2WTXFuH1R7DY6Gbh6jMJrxvP6Y3vU0gAqvXGc4IIB3bcxZcGVHejZvLGi4d52cKE3PweDgGi3lx9NmbE4n194eUi6Tvm5dmnR2zakAG+9l0KPEDSXHs+Tm1t5v78KyPRaI7U5acHYq9xY7b+PWI0okbf+FHEvCWNufjSAJSKCnfpiSTsKnEiz8+cBNUSd7vh2oNkv8BwD619Kt9ImpVZ8eYn/O5Bl++K9+5B1Sp1e+HGx+nWUCY9//C++GbRHZiM3zwN9o5143rw3rGwf4D5nbB+P9telpnkH56Krxdh8SZ96+Nh9045rvNzR8Lescito5sfI1uH5aCLzt2TXV9uDuzmb97jt+8M6Q5dOwF27kBid+8inL2ExAE/f9Pfej3ix5l7u7v81mAYdeq6vvR6wSJXbrDps5YeIeodc/21OFvsy3Pg6puDAe3Crrv8kywhXbXDN54lfBW49+WtM1k+lu1auPT6IAsV3e0+fGUpparbJxtPo2gZdOf0lVcHo6KVV9zaa8MRaEzpLj2XJrDr52TjaQwWyHhO4NqneVsT3YQLPy9myiYmOfNJYAkymp79JLCal8Zf+XzUtMg3+MOfEVMJRYXe+RLSDHnJzj5IjcGd85c/wxqHwByf/WI6C30hB68/ZFUCfM/OP0S6gKUKlz/DS9/GPn3zITFlatQWZz5uzYKHMjp7H2kg1m24/Fk+xeVBV/1Nu3eZkWNs+rSa3uAFa9bfz+o1SBfw5o9J+75J7gCTp+DeJYGPsvlzav8qXwCT9bPJ5ALHS3TvZazfNeIOsPdU2LoQR0dJ8xO1dTkZ0Xr3A7JzlqcLdvNVVr8V2CnWPKlunksGC93uW3T7QjxizfQi23+HJAt26wzbe4skd/nuGbV+Pk9X3XAzDpceRW4a6oX4g/t0UWc3C3T5E0Fsss2FcO1R5naiEoe1z9IwB03BP/gEFLt0fQCvPIqibbGVu6ufEWoHNRhe+Cx1JejT+N37fdbx9RhceRSjDbod+Wufj+UeVIh+8Kjo94JO8AePuKSPtjm4/CiHm3CWgbXPMl1B5djZTzNoZ7rdeJJw2YfGnH91TCsTEXnpmQHfasAKWf8u7WvMsb71LIcqMKnWXhn4iWGJu/RUEtYlOcK2vh2qWqAIbjyDfOWE05deGaB9DRb4jceQuaXFIbjzfTSbRyiGW89BN3WZnl94fcFsB7Aabz8W/A1Dj8DNx+BsGosk7LwA+33MRyg6fzq5vBAWb9K37wOb9/RLVXH+gN++M3Lr+NZxuH5SZ524dGd+ZbVd2c/fuRNs3OPzbXbpCNi+l4ctunkEXTvpowm6dk905US7ujN+70jYOA2jm/TGsXDrXh426c5BfO2uEO+z63fgy3fh4RV27gTceBBFt9jNA+HmfdxuwL2D5NI9IJnQ60fF2l12adJ+oC69Ooh9HU/q915ajX2vO3zrCcyHQF/Vl1/KBllvrtuLrw6HvvWdvfhsHtvWaHLrcULz4G/qSy8NVvHOfJutvTJMbQ+1vfZMwnWvAl9/nKAUhg119eW84J2ZwIsvDojsAQxXH4+w1g6SjR9Ak8duR197MclAExp44YVBJGUaN//8X/zr/Or1arz0s//+d4rNLYvYZ3/vj8ZXb24fPvZLv/Krh9794Mojj/wv/+u/Hl2+Ojlw+Bf+798aXrtiRPbZP/hPK2uXb528+5f/t185euat9Ycf+IVf+T+Xzp7fP3Li5778e+NLVwNjn/mjPzn47tnr933il3/lX5189czFz33ul3713668+fZ89eCn/+NXls+et1n26f/01SMvv75x+r5/+m9+/a4fv3T+S1/65V//zQOvnNm++55PfuObp7/1nc277n3oL77x2a99/ewv/MLPf/mP7v+rb218/P57//qxT3zrb7qllbtfePHBr/3l/omT93/7e4/+6dcufOlLX/zjP3voL7+5cd/pe7//wwe+/l/7Ynjnyy898OffnJw4efqxxz/9H//0ype+8KmvfP2TX/uL7dOn7nzqR5/6s/+sxksH33jzM3/4x/vHT97zxNNf+KM/ufLpTz/4je986it/3h1dXXj5vc/+8VfkYLj64bkv/e5/2L7rnu7Wzf/eQaPov3EXoUlTQ7DGWEWRF5FPkhBFKuIBQMW5SxJAqM1yJ4QWwkBgRaS5cIKbPHeUGSE0pYEQlRee0hBFIIqsiFwUWUJUFHlCdJLYJLEImTTzlGiMTRK7OPYiCkLoOA4QSkZDHIco8knqOVNR5DmTlFoEbRzbOHaUuSwLnGmMNcaOc5/nnjIdRYYyg5GhxHGh4shzphjVnDvOFUKeMZ0kXnAnhOH89usIsXnuCDWcqzj2Qug0NZQaxrQQTgib555SxajhwlBq0sQxqrPEMAYoggQggTzDGDtKrENYiOApBAhBBgn2hAFMASDBYeSABxwGiCD1iAGPMOQoQGcRgBgwBgiFjIRAQEAI0kBpQBgw6jEFlsCAAqTAUUyIJ8R5TChxAAVIII4wxoFxjCnEDEIEHDQBmkBQIAYh6wgI2ANkHQGAekw0gIBQi4h2FHtkAXKOQk8CxCZQD3BwUHkIIdIIG4gB4R5ib1CwMCDsMIcQOYhVwNAGA4ANnDjiIHIOmkBDAMYhGHDASEEUINIhaEuRhx4gBwQJCCLqAQyAoICDhdDTAHCgHEIcKPaBQggC4pAwCBGg2HoCYYwhBpBBRxHBFpMQMGYiBAwDgYgGjDzmECEQWLAYeegARwACSD0kIFAMKfDAWwwwBowDgjyjgbLgELLQA+wMDgHZQKxnGEJvgwoIeWQQ1oAiQl2A3uAQkMfMARwgsghLT4gBJkATKAzUImgD9gD5gKyjEBKLiQYAEGYgMQ4DDw3A3lIIkAvIOgwgchbqgBDCEmELKCTMBewdCgABhF3AGFNAqQsYAgYR8h4GSCGjnlBIKMA0AAg8Cox4zAjjgJAQKPDAQxICI5AAwhyAAAvkobcIQQYgCpx5hD0iwaMAEMAcAIwwCQQ7RxFJMETB4+AJotghCjFDhAOPoSMgwAC5B8hDoBx1gEBATCDekeCRBshB7gH2jgDHSAgaIR0IQlh55BwAHjqPg0EOIueIDYxCYG3QAaGANEQWk4Dh7SucAWKPqQ04QKggVh7jAHRAzjMYiAXQQhIgcoE6AAAkgdGACWQsYBoCggF4xICnGNNAmfeYIOICCgBDFGGMAuOQEoAYgBgE6CENHhNCPSHeQggZhMgHFDABjAWMEaUA0gAB8NgzGjDDjAFEQCAAAAcoABghGii1ASISQQe8w+jvDQIQe4hUQMDcNogRSxxCzkAbCADQWAgCDhBJiAJEBgBtCXLIAegB8wC6ALUjARAYsHUwWOIBUIAALAKAXmPoMApQBwYACQArAL2FAELjKbDQIuwdsoAREIwNJuCPDCICIQwAvW2QBxwGhCDzkIZAMGQgAOf+3iCKPCOBchAQwjwQGhAGlDpMQSAQ4gAYdART6gm2ARNKHEQBUYQ4IgQwgTEBiAaAQkAeEOAxIcwjHByGiAIAvCMIEoeJBgASZhDRHgOPLEDeUhiwh9h6HAByDuiAIEQSotsGeYC8QcFBh6mDDCJkAFIAIRs0gNZzEoiFyDnkAHYeuACRR54gGRCESHmgLUUeeYBCYCBAG6AKOAAaPLYAeMgBQIFygLAnxAMCAASEA0gQwoEi5yjCCQEoOAI/MohARBDhwSNoCCYkQAKxgLdLWAwD8phBAANiHmAQGIHYe+A98hgDQT2BnrFAKAAYURYwBph4ShykADCEcAgEeIxlHDkhQiR8HBnBFaWeUpeljnHHuc5zj4mixDBmRayT2DFuo8hxphl3kXBx7NI0YByyzEaRZdQwahhTQjjBZRzZj859EjBWSeo593Hsk8RRojA2hPg894y5SOgkDghJQhxjRkQmim2SeCZCHKkoAgBIzkIU+Sjyaeo4l4wZCE0cOUqNECqJPUIqzw1CllAdx55SF0Xu9jGNiWXUJYnDRMWx5cwSYorCEqIY1YzpKHaC+zjWcewh0pR4zl2S6iy1jKo4thBpwR3nIPyUfhF+9WtRRGjf+ZjbgLAxgVMilSacAWMBht4bIaD3vG1vM0QrFadUyQARBdZCgrTRSQKd410LBQHO24B0HFOpAAgEeoMYVkonCXSWd63KctE3FhATRdho6AOFTiOGtcIE9CwWdSXznEmpIKfQBoSsQ5BATzBpepdEkWqkZ5BjrvsORyyYgJC1KAKyFzFulU2iWDXSMcgxs7JHUeIaAFEHRBSkFAlspUtEotreM0gxc30PBQsGYGQcjoKUIkatskmUqKZ3DDGYq/mELTCvAEWs14YxAgxSXgtBnXSAouABCi6QWDdGMGexpYQGg5TXXFAvHWTIOYC9C5RZpSKOlXOEkGCgDYby2wz0XnFGjWVGacGRdh5Bw5jolaOUeGmQwMaoiFNjqDGAAmCApbTnEZEd1somKXSWdh2MuQYYy96mGVbKBzDwXUNioM1thrStHQxE32gPXZwgrYD3DAMNCVLKJikIntWVGRS3GcQpMtYAxHDQkKK+Zxx3LGJV+RHjIOIUOasBGriuZWnoFRG040natJpT6rQHFEAfEIQGEGh6EYum69N03O62ILOCEqs8IBAGjyC0gADTiziu2jZLI9MFA61gxGkHCArOYwQszGxVJgVrZZ8mke6CgUZw6pQDBAfrEQIW/kMtmcTCdMFhwxgKxtpArXJxHKT2hHAUfKeMiASwykPkHaFYBcSU9EnspQ4YeyFI2zjCPmKcs0mKlKRG+Th2ygIIvIhI2zjKRqac0gE2+jaDrA2xoG2nCfUiIl3rEeIoSECIVphjBQhWKiQxU9pBgqB1iGFjlOAoBN71fZpEsvWBqIhj67D3CFiLGDYWIdvzJG6aLkujvvWBeAqxcw4SDKxFjCgNie95EteNzJKimTQ4DxQiZ0PACFpLOFEGYtfzJK3qJs9u1/IUIe9AQD3jAQExn+psEFlppXFRzL1WDhAIACFOWwa9ihJcVybNI9tbaX0UDXQ1wwkN/jZDodcfMVlkpZXWRrEIRnlIggOUWu0YsDpOcVWbJAGYkKZ2nItglQMQQBcJ3HUMeBUnqG6d4IbHsZIfGWQtU0pHDJoAANCccak8ocRLCxm2VkaCWku1BhQACyymmlEuVUAIA2OQIEbLSFBjsHVGUNFLTTiCFnrgEcFBGyyo1oAEjTmVykScy94ihpADDnhMcNAGCapUk6VpO9cOQsGw0QYgioGFBErJGOpYzMpSF0UkW2MCEBw7owPKQ9+T2CvDGOxYwsqZLoaRbLUNkHPsjPGAIWAwhVJRhnoes/k/MIAyRK1qIWcwGEyhlJShnidsPtODIladMiEIgSGADqSmauIc90bFsTBdMMD8Y2dzAcHgIA2mj2Le9iqKuO2BQ4Yx6qQDLMBgKOVKE2/6KOKdMoxaQkQvHWPEKQsZ9E5zxpQm3gICoAZKcIux6OXt5ZpWBgAAIABJREFUNvgPzO0U+iiiTRUQGdlyRgdIK5tmSCukVUgS2rUaER9FuO8DhBwFCSlRkjDcI0b7zqe3GUoJ9NY5iD5i+o4I2mNGu86lCetaDQmlyFtnIRq5ZsYK3Hckoj3irKltnrG+1eAjxgdACNIsiuumz5JRs9/g3FFMnHGQIGAdplhZdNugsm4GWaRa77BjBHsbAEbBWkyxcamrpuliVDddlv7/GQ8wDtZigrVD2Pciiau6z1KhWh+II/j2dyxGHmPeydu14qbt44g60yNBncHIK8C4lVbwIJ3jFCIApfOUJLZpUYy8DwwD47nXljOvfKAYIgiUDZQu9Lu70TK2JnAMtQsQMuS88pZziABU1hMcu74lCTUKk6AChQBQ5LzyhvPEdj3kAaHY9S1OoLVA4LitDaJGCKpVAIABqxAnSmEGeyJ425o04V1rIEUEQOctwrcZ2vdI4J5EvG18IpKmrGmGCQDWeYAodIoI2nVI4B6LqKllnom+NYAiAkEI3gOKnMKCdl2K5SRa4E2ts1T0rQ24WT4w+o3fTH/y4k9jF+E/f+CJp+5+9keT48cOvvr23c/9uDxw4O4fvXj8x69s3n/6vr/9u489/+Pzn/rc6SefPv3kD/eOH1s588HHnn525+gdJ3/88j3Pv7B5+tSpx5+5+4mn1h75/F3Pv/DA9/9ufuzowXfev+vJ53eO3XH0lddOP/n05ql773ruxVPff3ztwUfufPnV+77z2O6xO468/969P3h6+8jJo2++c//3frBx+vTJ53/ywGOPb5362MobZx/8m+/unLhDdqz8icIxtHWQr2tbcLir6lcNyAlr+tnLgGEXjGtelCHCrvH1q5qn3sxC/YoBAgkj914CCDgEQfuCZEFh5aZnIBXetLB+STKGqJX7LyNiFCSo+lGPIAgmVK97TnSQsHxJAww5MpMXQWQVhXL6YgDe05jzC6uhY4i0bO0EraAeyvzdZVSmOpf80mpS4oBadPVoqAUUHb1wjFZYjWT03gqdRLro+cUVWsaGa3r1AJkSINp47TCain5JJe8v40lejUN2ccimseYSXz+Ip6LJQXZ+iU9Ev9jnZ8dhb9Qu+nRtEO0liJX0xiqYZtUK1W80/fkQTuTmw647h+KhqbdE94EPI9HfgPYDnS6Ybg1WZ6E/MbBrqv4A0TEG63J2loaCyg2k3lb4AO8v+f4sdAdjeFXN30N4RMGWqt4nPPVqM1TvBrzK5BU7fxclK6G9Ceq3IcohmtrpOxSn0O6G9g2fFkpv4sl7NF5w2hXZBweD1wGF+P1ViwG0UFw45Jm2PUvPHdDELvT79soDwVlPQvz+KgQeeCguHAJQOYnFhcMWOAw0P38HNtZyl727GgD2IYhLRzI7l46K84d1gBhLevYOrJ0RLn5vFRrkYOBrh7G3CpDk/KGgLaQqPnc89LjLdX3G2T1I4lC+h8weEJmbvonNtidHWPmi9bccXcHtu0BvAZyh8hx2W86MIvmGM5ueHGH1T0y7jsGhRL4t7QZgOSzPE73h/XLSnjHhlk1XdPkWbq5jfzjV76punfDM9+d8u478UiTftuqyw8d5+6bVVwBfDN0F2F7DdAGJ7UJcHeuRYhuZuLo0XwrxOqFXD6ukj6eYXj0sY4dnaXqhkGNDttL08pItKrCbRpcO9omJZghdOep5T+YRv7ioB0rsRuTKIT+owjRLLqxY3o3m0t74mGc9qkR8cdEONN0X7MrhEM18mYmLBy1VRCK+diQwiRqanFtqC6pLWb/iMMU8yJ1XBdQOxWj+gg0eQQxnbyACPHRo/loIAHPqdl+iXFvO5N5LNBgEKJqfgSR4G3D5OvAGCOF2XyKh9XDMyh8Z2HnEUf0mgto5QudnAJJBFrF6UbvSowVavWB6yUPC2jOW9s5zUp6BrgPy0GDh7ZzsDsqlkF/M2d7AiBbePEh343YAkkuLYjful2R2bgy3FuoVn1zOo60M8zm9tQT3hu0QiLWFeCvqlmV8cYFujqtFl15N4PaSE228UaCdkUvb+OoCXS/6AzK6OI42xjaf41tjurFo4l5sDNDmgs1acW1MbhZquU2uFPDWgW5RwlvN/B3K42D3ffVGQEtM3nLVOyhe9N0GrN8EMMOk0pO3KI6hm4PmdRenyu2h/XeoGHu5T8rXHE8xrM3kTUaZsw2sXnW4IGYC5u+QJNdmjuevBRhhrNz0dZJQHSozfZ3gHOspmL+B4tx1Ja1fC4hDbO3+KxgQwOJUnF/NZKsC5BePGIcQkfTDO0gfdGbjdw/gnlhm+cVD2AANYXzhEOg9YjI5dyw0VA1t+t5ykImMQHZhifRAIc/XjoAWNQNcvLtMGyqHKn9vyXS5SkJ2foG1EKGWXTrqWt6OcP7OEp1TuWCSs0uhzvvEp2vj0MZ9Ebo3tb9msxVdvouby8gfy8z7qr1KeBH0JVtfw2FR9O97fcHi47x926pLiC+5/hKq1xAtkL7i+suYDX1/zjcXAD3GurdNeQGnq766BNvzkObA3vTVGqEjKC/4/pzPl1WzRmcf0mTZN9dRezZEOVTrvjqH+AjIS6H5wPODCE3z5NyqEWpUNfb6aY8llDT5cMklmpSMXzoCROmqOLp4yBBNDOAXjkOsoEbJhysmcrBh/PLRDG02apB+eFAyT6yj545DZLxDydkVzx1oCL90FNFGmTg7e9AFRYKLzh2zIHiI0w+WpWBQ4uTCAYRq6aL47FEfrIlV/VyPZhpnsD3jQG08w/UZC2urhrF5ofVTg1ZY83wr5wgUTL7WkVLDCJdnbJg7u5TI52q8q5IFXb4a+j0YRkKd6dQMCq7bN63Zd3YlVT9q7LrGR1n7Uu+3LMl8+45T+4BHtn7bh21LFkL7EyXXHT1Km5f6bhOJxXD6mWdO/PjVzZN33/v0c/c88/zNhx6897EnTz/1zNapU4deeuPUE0/vHT524syZk8+8MD167M7nX7zryefWH7j/nmd/8rHv/WDn9KnVM2c//r3vVwcPnnjnzeNPvzw9cuT4T149/fgP1x+4/+TzL9/3vR/s3nly6f3L93/7O9PVQ6sXLpz+/lPTY0eOvvrmfY89vnnq1JFX3vrE336vO74qLm0//I2/nqweWl67dP+3v7938k5/4fxP4RYhABCElbW1fHt7cX198dq1fGO9uHUr2d4+cPFCVFVLV68UV66mdbX04bl0e3vl+rXxxq3R1vbC1la+vb3y4dl4Ol2+dnXh1q1kPl+9eDHZ3l68fGl440Zx88ZofX2wu7d6+XJczpfPnxvevDGY7K/cuJHv7q3cuDa+dWtw/dpofT3d2Vm5dEk09erlS/mtm9ne7uLltXRne/n6DW9YV0WwdngiqwlRLbRz1MgY7ko27atWdLvB9WBWZ7BybNLJitom+H1XqQTuSDZp25a7fQ9rvVdltg5wt2ln2JUW7qpGp2BPwmnfVNTtGNLpus58C/Ckb2bYzQ3c7hqdhB0TpqprmNnocasnVaYbmJQG2RFvY1G6oAu8H6Vta82RoMai0agagE5E+9brIW1TWgFgh9GE8a6B8oCzy7z1qBsiFacTC2Ue5Ah3VJvFaCZ416Nu0ZlFLhFoRqiP85klOmVmxBTzdgFPE1HXtB1Du4CVgHIxyCTaVaCPYb/EpLMlqusIKdDv06ahdKsGM9u2kS1R39J6n9CqVbuuriPXBDWnXcfZbuP3VV0LXyHd066MSCfhtp6X3HZAzXHTR3i3h6Uta6HnoW9JPU9II8mekp0IlXUTUKmYbrVoppqKwZnWNdyvMtJqu9l1NQMzlVTemxFtUiqRM0doL+KZJt0A9zyeYRNWxATTuXVqxGacS+/NEaQSMTWhLUhHkzn0YDGuqCit12OyT0SrrTmK+lhUIXRDVCOxH0xYjuacNhCaYbSPWKeBOhx0zhsM5Ih1JJ045xdgO8A9MWopntFgvZxgWwO+2/Q1USUJc9t1LOw63tTdjM+6FNS63MemQXSvlRWWNQe1L2tutxxrWz1nfSuQBO2U9iWgW5WpYV8L28C2E80OjJqm2Q51I3wdZMlVg/lOree+nTHYw7oR/ZzzrnVbdtZEvgl9yfqG4JlBboD7RdEoPqNeLxNJmRwBN8rmQFTI6QXSCGAyoA5QGZIZNmYZS4br1IalfN/xDkE15DWFKg76CGudmACoR6RFYsYMWIn2Aa6A74e8hERxp4/y3tN96NWINSGdIw+WozliDQj9iO87Ipk1R1kH4Z5qVBL2FJj2TU3tjmGdrOvMthhP+26CbWnhTtfI2O0aMJVdy/SGpE0/qTLVIDRTbUXD3JCdtu4jM4G+sl0r0K6hbV/P46YTdmbLOYWlIztd13JT0lD7eSn8jqVdr0quexpq0JbM7Gu203QN7WeM9bcNWuIKgmYMZZpNHZUJtWMuWTALeJqKuqLtENoFrDnqFrzKoz0FuyjIRSIFtCPaLYimYdPEmUUqKVFjZAbZzOOW+26B9cSaRdQv017FU671IukwbAfADPOJoZKhfsjb4M0YqoOi6cSEQjuiTcBTVTZCzYLsSFWluFZkT3UNd7V3k1CphGy3eCbrioOJsY3frzLYGLfVdjUFcw12daMzuNOHmWrmFO4r1IWyzlBnwY5sShpmMmzLWmVgR4VSdxUJO51r/aTKQuPCxLUtx9Me7falSuyud3XoKwZLixWBzRA2JNrz1i+KUtAGQjMS+4R1EqhD3gx5A6Ec0Z5l+9bbEeoK3BOll5IpYb2C3UowBeuo78a0pfncQ1swPWKKOrOEJ5GoW9IvQjukMgp6ETU82lVOF7gbYc2gXRRdIeqGzAfBjHjHkRrTLsE1aFvR7CLRNN12qFvhmyBLplrCdhozde2cgRb2HZclZ10Xtuys4r4BsiRdz8le5ytfzamvXN2Ibh6xpkV7RnYizK2ZwrbjbLvxletmlMxl19D9eSqaTq+rrmVw0vuJ72SEdlpb+m6CWdmrhpRVSrs+mlIDV8VeoKV3/ZCXmCrizBHWIzYBXg5pA9I5cmA5KTGrvJcjth9oj605SnvM59jJES292IcarsZTSjqM1TCaeNojpw9DyVnDvR6JFib73oBl1GRQMisX4jkkPQz9KjYRrSKnx1EFszkIYSTkgDVNM4+qLnJzW06przzdbbuW6ZKE2s0r7rcs6zpdctnx0MC25Hpq6XYtGypL5hpQN1G7A3jdVDuobbmrQFuLUAW2XXc17iYU96GqhSopb1uzHcpWuAq0DdclpLutqlEzJagxszpRc8abJuw61bNovxyv3xxeuVLsbGdbmytrl1jXHVy7kG2sZ7s7i5cvZ9tbq9euFHu7C9evD7e38q3N1bWLoqpXzp0dbW/nW1sraxfSnZ3FtbV8Y3Ph0lqxvTXa2lxaW0vrevnDs4ONjWJra+Xa5WJ3d+Xq5cHm5vjS2mBzc7i5sXjpUjKfrV44l22uFxsbi2tr6c7O6pXL+d7u8rWr6XQaEPypDBotf/GX5h+7Sxb5tS98dr68Oj966OajD9skvvXgQ+XRA7Ojx/fuOHnr1Kny2GE9Gvx/tL1Hr61bdp438/zyCnvtvPfJ555z7rmhisViFUVTIos0ATUMw264YcD/x27oBxg2QAuCZYuSmVAMIlnFCpdkpVvpppPDDiuHL888pxuE1ZYAs/9g9F5gYADjeV/+5m+Uh0f1yfGLr3/NZvH0vffKezeXZ+f1ndtvP3i/uXWm8uzlN36zmUy25zdff/1rdpBN3320u3NzdeNWfXb6+qu/Wp6fmiz9/Hd+2wyzzc3br77+NTkolo/frW6dru7eq4+Pr7/y4fz+A5clz77xWxx2RSHJOcV7NE8FvhnTA5CHlj1O48IxrPkjRnNQZD07JXCfZYmMThE840kQ6P2cDEIEVfGQggEeZH18CvxREmcuPQXgLElD7x9nfARjrNm7MSjwIK7pKQLHSRrp6Bzpm0UWBH8cRQOfEQ0fJrwI0dClx1AcEgSa/rBxe4r6pjtv1YgiMEfDlTxAkJUw2bU3gCXO7Jdg1DFfVietGzOAF6BYq8MQWAnjsr9hAVJmUrqJwG6nj0szxIitXLZtjhFHc5TU9bkDUNjhqj9wsVnKw8pOqGElTZfNoYvA3KetvGED1m60aicsj7vRoFM38iLvUybRu6kp+Chu6V2WUpntG3aI/YQP897fTkeF4ES5x0UU2yiz6C6PuRqMe3gUkTHMkx7cy6KBybCgj5M4Miy37C6PYjuYSHQc4THKE8FuEnjEEyTgBwWNbJrq6G7EUpfvSXYI9XE+jAW/TdsRieWyv1mGxGMyN/u9GEFEt2Bc9QeGuqa+q3lcaWTteWkzQNC13OvlPsKk9JOmPvSRq6sbLSw0CXV7S9oCYTT1k0ZPHMQlHO1255y4TtysUdqTUNVnfRhiSGZ+VKuJhnAX9ur2BFDTivMSFBL5rTppUBby3NCBCfcLykNyGtgJTmAf3SFhEueZTCcancdR5vJCk3cSwnx85MB5PPBVehuGSRRnKt9T5jwdRF0ysO5hgWgYHWhwysegYjcRH0Mw4cOBCLfTUdyRwoP7aYR1cuTdeTKEdX5mwn7MhyHLBbvDUA7S3KLblIbSZmt5AE0mCZ23pwFEGxaq+o5CvA+0as8lJD2Nrsyes7mEdG0OhRwqHqrmrsK0DaSSZz3gAkQzfRBEYRhZ24NWjA31TX1PcdroSOmzHnCJ2JU4CGrsKNqoU1mPQ+zK6laHYwlY3d1wLjaEXTbHmByiIrT+UcbHMEY6ehSBASmimh+DcJomsY5PoL5VpEHwd3k0CjmW4GHCCxANXXIUyAnLmGTH0N8exEDyB5SPQQp7/g4HI1pkfXzo8SmPI50d+XAnj7xg9xE4iMZ+y+8zOGJpKqIDD055gZvoFKrbw9iL0T0vRkWCLn26aU4Qw3McVc258UT5fNUeutgu9X5pJ1SziiXL+tBHeA6iRtwwgRpfbPoDk/qlHK3NPjVxi/myOfUYbyBvuhsSw94Ot3pfI1iG4cKOoc06xNbqROlUYN70NyQGnc13+shiVPpirg+oSEXE1s1ZKGJNY8vf4Tx2xUjgE473SJ4KdpOgI57AHn4woIlLuE7uM5qFYiiiY2SOsyJR0U0cjuMM9P69LMpDHFvyIEGpH+YtPuXkiKdc8NtMneUD0LH34ji1cWzhXc5zkI4NPSHRAUywoHeoOy9S37LHPM5cwhW7xWBiIC3hcLe7QbEX4qyCWU9DVZ93foghnodhqfYtQDswrNozQFwnTkswkMht1VntCoTp0hR1d+AisPDDtj4B1DbqcC1GINVTcdLYMXF0A/OdmEgeVm7cyRMPg9AHKzEChblqDyo7Jo5XIN91R5qCjRk14giNQRWdBb6H3R4bD3p/Ox0lHUlceJjF1CT73t+IC9TkxxocxGwU8kyw2wwOYBpr9jCOieYHgdzgCZeDIx2OEjIERdaT2xTv0Tg26N2MYZ1PHL4RJZHKDjU7xOo4G8c9vcvBHku4Ao/SmNt4H4SbCY/VaNz7s1wPFXN1dV9zXmtm9FkbIoPolThwahwo3urjvpqE2JXlrQ6lCpGmu2Fc6gi5NvvajRSCJTgoN6dJZKruVk14C1DZnpmQOURmdiLUWJKwtcdNe4i4qbpbJUokBFt1IkJmMZt3E6NHbeTX+qirD1Ckd/3JOuR8kPXxgcNnPI51duDA3ZxDxW8jeBSN3C66T8CIJZlM9n04iwraxgdA3xvwoIa3nD/g+2FL7hI+QnBMspHBN/mQtPCI4Js8Aiq9BfVRMgHb9IYPkzgauGyo6G1GY5ccgugW5UBGdzE+YBnr8zPrDxJW+HRk1L09nefzh4/e/sqXdJHPvvxhc3a4vPtOfXJ0/atfWt6+q8bDZ9/4LZNEi3sPZl9+T+ztXb//QXX3fHbzjjg9vvrKh4u778hh/vx3/oXPounD92Yfvtfv70/ffbS+e2Nx74Hc37v49V+d3rprh/mTb/y2G6TL+w+mHzyuDw5Wjx6tH9xZP3i3z7PZ1z98++BDl8dPf/cbtsiWd+9d/MpXor//LxaN/mdV5cAQ3vnedwbzmTzbHz19PZhNQR6dfPZJcb346fH/cP+j7yaL5ey99x793fdHlxfqaJxczA9evuj2906/+Gz/5aufn47v//Dvi6vp5buP3vno+3uvXrqjoricDV5eiIO9g6fPTp9+8dOjwa0f/WD85u3svce3f/Kjo88+r89Pjt68GD95KSd745cvb/78Zz85/h9v/f0P91687G8eDl9env/i5/XZySo9bV6CIe6h891rREcKN6q/DkneItf10yzFAg9c84oUUKJgm1eUcYucNzOQxlVEu/U0JUhGI1+9YonuImLLt0UMFVh6cY0KWvG4302z3Nb0EK1ek8IZFsn+gmVARFtbXuMEtGgo26uoEDU/6PqXUXqiCwDxakI7TYoN2o14Uzm0DsuHiCnMrtFsLyYhCmtRRoEiuFfiajKuGslmfHk/UEfiK7Q8IEhgtIbrOFDrfU12Y7zpUbqMpzcMxcVoly4KAjTGnd8kAMU27vlulDqp4otoetOQKBnX0WpAnCVuYat9B3i8J8wb0+1QckuG11pvwTgrlfDdLEQDZVcuTE3Mm+bSdRVjN4R6Y9XM741KuO70Oo8HQpXAvPXjopVvvNiQ6LwHF72cAl40vpHiOol46zsoruAob+WFa9cs2dNwG+w0pHlJhdxdJixqfQ+aS3ZIO7Gy9TpJ8jZJMlbtQ7JBeUtXZwwsAOrx8sAhgypEtwNAVoU1ZrcHnfbjHi9uYb/CbQOWE+A73ECyGQyBo1zD7Zjrud3v4eJucFvWN3A5yrKlUwZsc+ItLgLa7o/kyhxVfHnXjkprWrwZU6uhtHSb4eDgoMXbfSR2ZVR3ryyjLuOluIo8BRiYaoZpEMNcbl/iQFBStM0rAqFjvDFXEQaex52chWDlcKC2L6GBJDqS7o0Fzg74Tk7j3gGaif4aIKMSXq+eY+0JORLijfUapKztZsFYGI17cR2wsHvjpnrulaHZuA8X1PQhLixtJ3QVk+iCrAewGaVHVbrMyC7LF33WAFOPB+kWaBbNCpBds2UM6j2cN7ylbFfAaR0pZLcTzKfEUrLcp/Frso1Bs4+yBreU7QrCq6wPph5z1ALA4fKIxK9wzUB1yKM1tYRsB0MqKAxoNYzAlUMMz4+TQavrpr/yGah43u2meaZqfg4Xb2imbCzK/g1NjIoaU16TyHVoTzZXUdbV8Ulz/fIgPbBU6+6SjLSKZCmnmDiHD3R7TbK2T++DzUvC9i13Rr4lyVhB26gpirTgEIkrGFd9RMH6BQ6HICJCXYa4EKlzmykBMrBhH09HFtJif5vNC+w8IbWtYuSSLOl4OaRKq+Qimp5bkCT7VbwokIE0zF0zti41eYd340QYmb3l1ycupFldJqscSsrIjm9zYgeBXaHNGEsahm+Ty33nMlw0YBPhPmZow6rcqCGNLul2hEUsi2fR9YFzY16uwrQXVznlHbBBvMajtNML185ZPFSg8nYairxkRmyuE8b7EEL7io1Rxxq9m6dp0oXeiykcxhWFsrpMYlQ5gqvXZC8SofPtFR8mDTeuuYIZbQBX/Vu+b1vMQvd6OCLCStBPyTDtDQBqChjvYWK6C5RhSY9yvhimyS4YHbYZNQ4PA97tj/qNgptoed8VDQU7tB7TbI2MJNuMWABHDd5OcFsKtotmN8HAItYmqxHha+Q13uVEU08V306ipjFkxqe3xADHvOXrnLEtFMLuzonQNrN4czCsWslnfHauMpLHbbLOHdMmFmIKUKeSqA4vUa8xPRXyjXOtHya9mjotIR8JMQOocnvjpnnupaTZpA+X1NQgSWq7sKJCSSTFDJsS7I3r+pkXgu1PVJh6tw1RLOzadTu+lzTtNe5KciOudm9A2/Jk0Li1MxswjoStglmRPOrrORUrtL9X42bCdsWQNblyphxz2AUK8fKUsAssPdgecLolgZDtcEgVRR6thzxcOw7h4hyTa2gg2IwL/0q2lu4KiCWJENpMWJi71LHVuSEz5zHeTShtYGn5rkBEw1iR9QS4pR1YNj9KeIeD49sx9TvQe7obJgT64Wz7gtBRYLit39Co0MjX+pqwxjIC5TXg6z6N0eYF9EMSpUJfeBbLDNrNlIoGkUi2l5BxET+o3QvsU0oLIS49TmSi2+2MwdKkw765gJyY0cDsngUfkSjp3RviGUBOqjmxa50T011SBN1oUO2eARUn48H2xqc/j+bb/nBy45c/23/z9oc3/qd3vvu3+XzW3jre/+z5wYsXajI8fPUif3Plx/HpJ5/uvbn88Y39e//w0eDquj4/GH/x8vD5Mz9Kji9fRi/noWCHnz2ZPHuhbhyc/eDH+y9edjcPsjfz01/+XA2LwXZx8MnTMIwmT54dfvpEnAyPf/LLwy+eRCfxaBtu/exjNSrStjr7wU92N28pjP5LL1j/WaJRB1F2tRy/vHAqsOl6+OIC6JBdLQYvLxTi6cVs//kbCWgy2wyfvfYqxIvd+MWFVyCarcfPXkvEs4v5+OkrDThdbPeevgTK83U5fPbGyRAtd+MnrxTk6XQ1+fy5Ciye7/aevwG9oety/OyNly5eV+Nnr3UgfLmbfP7MeRytqtFnz7zQWhOxJFIzoXg7I9JwJVmzIEKzzrJ2TpSg2tB2irqeSht3KyoUlQLXUyQFEZb3C9ILJh3tZ1ALLG3ULkivI2G5mEMtmfKsmhPRM+WomiMtqDC8muNeMm2ZnEMhqQhRM8NKcOWZvA6yQTZwVBegjy2IfT1ybSFIQsrMd4WEketGoc+sj2CVuiZWMA7lMPQjQWK/TX2da8BCMwx9oXzsqiy0qYUcVCPXjwRNfD0A3UgFHtrCdQPtOGgL0OYKcN+MQZX1PANV7tqxhNx2Q9jm1vHQ5LYtJE5UifoVVZ62Da0XRDsqWiI2VFrWdFE3RwJGcoeqBRUh6jsqllgaqnpaL4kwVHQnfRu9AAAgAElEQVRULLCAcVeTdoYUjFTHmiURlnWCdSuiLZMtqaewD1z0UTfH0hHZonqGpKJC8m5JheVCUDUNylHZs3qOhY+0T2E1cio3joFNYVSiQx7KkXGZsjHcZl5yGxK4K7Qe6BChahhUqlzmy5G1mTMc7wqvYgNiX+0ZkSuYkLJwKlUhDfXQmcwZDneFEbFGCdiNvB5KmPht4brE2AjUI6szaWO/GwSRWMjBdmjVQCLe76goqbSsXaN+i5Rn3YqIkkkS1wvYr7ABVOxouyPCs66ksqQaRu2a9mssaCJXqNsy5Ulb0W5HlaN9RUVJZeBVGcklEDTpV7DaRhJwUdN+S5ShfUnaLZGO9iWTKyRI0q9xv8QWMlHRdkOUJ15mcDdQKPUiA+WeBLEXCaxG1nCjElAOrI2szkE5lCAxZgjKPQOiIGO/GSgTW5vBamhNbGQStoUKiTNF2A41SI1O4SZ3JrI2DeVQucyahJa5CYnWuduNTUi8ZGQ3cDa2LnXlnna5swkqB9ZG0lIxh1pSFaJqhvqWqcDMAumOSseaBe57ZiyTCygEFSBqFlR1XAImr31fY+l5v6ayZ8LQekFEg1Vg3Zz0DetRXE+hrIl2rN+wvmfS0HaFVUsV4M2ciJIIHMslFDWTjrZrIlsiDRMb0jVU4sjXBWxHMnDQ5b4daBeFLgdNoQJ37RhURc9SUOe+GUvITT8EdWEd921mm4EMkeuGaJf1LAtN7uuJBBHoMtAMjItcl8G6MJ4FMQrVQJDEiiEsRwZw0KWuLJSLnSxAPTCe+TYP20zi2Ith2A00jDrJuiXRmipBmynsLRfi/0tQh+oZkpIIzfol7Q0XkokZ1JZKwes57n2kNRMzqA0ThpdL2iumFVFzpCzrelbNkPxHZoGFYcJFzYJIEytFxHWQikgd9QsiDBeK1FOkOyQs6+ZESKZh5uuhV5mzHG4z28caJmE39GooUew3mesy4yPQDJ3KpYtdOQB9YgCH5djIocRJKAdeDrXjoR44mVvLQT0MIleAg2oMmrRnGagGVgxViH07DH3qHAdVofuBgjzUY1BlPU1BOXDdUAUOmiL0uQm0rphYQEFjuQLliknA+4b2G6IM7WvSrIm0tK+oXCJBkn6Duxm2kMmGtmsiHG8b1m+I8azf4noGBY5Fw7sFUZ6ICjULJA0VLe02VAbeV0TPvEKRqGkzxwLGuqdyAbVlXUerJRGeqwrpJTaBGh3DTe515FwKyoF2hbUxLgvrE6MztxubkHrJyLZwOjY+deWe1pn1Cd4V1iXa5bAaWpc6E6N1ZlVsQAZ3Y2sL5RK/HVgVWZPAemRsIk3itwMnYxcisBtpW6iQgHJkTWZ1HMqRtYk1EapGXmUS8mYORUmMZ/2GdC2VjrVrohqqAW/mVJZYkEQuUV9RaWm7paqh0rJ+S7uaCsCrTaQ2SJC4W8C6ioTn/Y70FdaWtWvSVkR52m+52pKexN0KiS22gYkd6SoiHG+3RFTUQtYtcb2EkiRix/o1MQ5Fs/Xo5YXTMJutR89fS8Tj2Xr/8xfOk2S+HTx56YTl63L04sJpkFwtRl88V5Al8+3ks6c2YLaqRk9fAxX4qhw8e2MNjFe7ybPXGtBothl+/txbyDb1/pNXQHm6rodPXjkN4lW19+yV8YSv68nTV0EHumvHX7yAjWK7dvL0NRbGQ/RP8kV48b///lh1k9n14sFdsm3Tqirv3hxdXmrE5NGIr6q46y4fPCy228n1xfrebbau47qa372XbzdU6f5kQrd1WlZX7z4eXbwttpv+xiFUjlT97N794XLB+q47O4zn67isrt59L+q6wzevL95/PFnN/pEp1quoafrjCZBueD0t37lpDD569uTiSx/GbetqAIcYQAjXSh4PAQTpm3V7e5Lpxs2MOc0jI8zOoyG1APkXa34zU3kRvd70d/Zj04G5MYcp1tK+LpMJCXuF2TgyhH2SRS/W/d2DWAvzy2l4eEqp86XDKfIRgStFBrDPi+j5UtycJED4a+UPkqErqyaGMbDjNFo4lVISq2gRupzi2OItBMjbIQANHeim22NgQ3SEUebRWymHA170aAdgAHYMQMtjI0UBYR0B5GChyYboBMNEowpBgMpJEs97boQ4y6ONcwS042g46z0DoQihwsT66ihJtzJRSo0A2wbJeHVQJKsd7rQ4H5NGxMva30iFpHHZ9WdDXEos9SCTrYlCbbvb+9G69o2HBzRRUjdBno5wI1mr4D51pfWtt7eGUNl4Woo746SqTQPhAYONtk2gB9h6AjeSHBPho/hy19+bJE3jahAOGOp16EKRiwoPyEqEk6iJBgcXVXUQRa6DJQl5sIzSrYeZ6eJkcNmV5/lRP5X1qB0TErRfAFQAX1C29TCxbZYWl6I+SSIv4KUVh3nEerRDIXImxqxEOdkt8/30+aa8uZ+zjiyQ2GMEK1hCSJ3OKNshzFU1SAcXXXeQRLiHK6oHOEQ+7Cw1Wh9laN57TsgAhZl0GWdZsBsLvcf7TDUoEr06GfgvFiHm4eYen1YhpngAbemh8eJkiN+sWVm5D27ClUAoiIMiui4hRYOoWy8xbHvzlbvRsoLCueM4nleKcHVQ8HmFQkAT7GqAWolOItUS0gp7mial8ob6oQcascptzzIqzHAlt6dx0TawpdVRQoWPWuWGXqqIzxp4k2oY5dNudzMvuha0VOwhWglXQ3iEPeLxWrsD34FkNG2qG9lkuyzr3J3GkRagQ34IDCDJxrqJ60g6vOrL8ySTHayIHCNiHa5DfZAFDNLnC3FrkobeX0tzkDFi7CagBISEgJUhaeiHBX+xUWcjhqz76aW/dzxmbdtGgIdQRGFlcBT6vSH7/mfmvds8DmGuwjgikXcri3nwGTPPtnTE9Nl+9GarDgpbRMnLlR9GOAFu5zzFepzG05Ix10xG8dtSj5NuPNib7ZBHu6M438mol+0hpxUCwTeTuFiIgEEY+FBjptz2JKMrnZU7fxbRyivM20lUrGQAIIy83mDWyOadvXTXR51tD0m+6TWKfOHR3BhAyZH3gvHW2QNnBee16o55sRUaRG5gaBlAgH4UrOLxTq9vD0bbldkBfxzTXto24DFxAYO1IgdIoCR+s+nvHyRd47feHcawbN2sLU5onw3RSsBDJkgavVyKd46iqlIvd/DBEQ02NA4PkEUEL0W0F3Z8L/7+z8VvfpjZxqwcmZBY923L8AAZyvBCon3cRXn6Yqlu7XEn7NqFSQQoYruQw2o3HsTXupnEnEo6g2KPE6rgFkAadIFpiSlRu0Gevdj2B0WcSrIkqkCYG1Qix0hTROlVQ7npDotiJvuCipztXbU6RSEJdg4ABeo4LZY9xcZmgC1DM4jlgI+uOxMhkHtUwQBQM4kGy94RVB1kyfUWQTBK2p3MYG/6m5No1+JGq9MsW5YyMHVUsFVLrIP7xK6MVyA+hr2O2K4z53m0qlVgdATdxgbl6RE1CqOdxKdUSsrWrT3P2bpVltEJArUGFg7ybh1G0abzZ7HSjC4qc2sU72qtMRoT0FogPNonEiajq6a8mR81864f92PIjIEt8gNgIY62PoxtQ9PxZb+7kSay9/OgjxMeJKphyJyhNN6GJNlu6B4rsUkViQJeYzUh2GtYQZhagRM2F+gAtWk6flPXZ1nsO7hleggwtKRC3TByFAxnvR+6Ki0ml93uMGZQ+rVBOIAxdxvHsBaTAbuozDg1wyR+tQpFhLPgdj4gJCc5eTLlxMl7p9FlZQquR2n8doNSmpFmM0cAePfuWXy9tZTiIWKXtRhkei9nP30ZipTfSNzWY63DUeI2DiAAR4ReN6LI4tTahQEUozExDQDK67Pi+O0rb8D8zp3J9RVWuj+doEYP5rPdg9uhc+OLN9P3Hk9mV0H69nS/uJ7jXjZ3z4Nw4+vLzcO7tvf7F282D+8dXbzoFW1vHkd1SzrZnh0E4SbXV9v7N9GuH6yWm1s3E9miWjZnh1HZsKrpzg+9BvsXb83N0YruHz97Mn333eFuScr+6oMvF//qX+X//4tGgy//m//u1//0T84+/ml3eHD6k1/c+NGP+0Fx60cf3/nuR9cffvDhH/7pzX/4weuvfv2DP/rjmz/+ye7G+cWdR5uTcyrV+9/883vf/d7s8aP3/+O3bn33+1fvf7g+vbE+u5lXu3sf/f2Nj35YHZ2cffzTx9/6292N07fvvLc8v0178f63/ub2977fTw5OP//szre+Vx0cn3z62fvf/POrL3344C//+v5H/7C+ffPshx/f+97326PjKozXPyUkCbZ0689YGBI0V4tnMeOele3lkyG3ygO4+iknDHC+cWcpM7K/IosXOQl6aMSbz4ZICJL1+JhiK81rO38xiGJjt372PMuCht21/tIR26xMn29/ihEFQNrlp1GEelTb6bOcWRsBdfGLIutbyszbnw0ghgUj6PoOlZSEEswe0y2Ao5I+fwRFDuOKvrnDBSa61Jt3scxi//q/ji5O5Osruk9fPIb9IGQ1f3MDi9QmzeZw0GdRsd6R6WO8icykip/eC/0e2Vt8Q/zyJio3gtnlY9ZkJjX87Q26Tt1onb+67ZoDPRDp2yPc5JFduO1dVI3lvul+rpo3BJ8z9ZnaXkZDWnebeP2C0gx2M9w9DaNRW77k69dRsh/6/aHaL5C04Wm7uChICsSKtF/ALO3rG4ficISU9k/U8k3CufFLu3yeJFTp86I6Ooy80L/U27dpMRD1NVm9jhMiaGWvn2aca7XDq8/5cNjJ12H6uhgOBNI5u3xAtXQYklcPA3aRMvTqfiCCtBQuHwHTTdplt/oNIvUwu/w9+wRjtdkd0uv7EAvawbB8RKzac89/L56mar7wZ+zlOwBCbCy+fpDochhefSMuaSgbUaCrD6Neu0iRF+8CgLE35Poe9QrpABcPkfQk1OjqfdzBftBVPw6uwyns5y8SUeKcidXTJMwsOwGbH7NuheM9W/4CdzuSg5W5kWEkcalXXyRqFpJTv/kxbVc0OoMsbPxhTF9vtrNRNwN0TMpPkJ6CLC1dpMCtkZn27TPWbekAteUr3Mwo3iPN50hcgOTc734CykWUDVT9gpQLHmcmKo/49NQXO3I1xstbYk8U0zSs7yBSxTvqlg88EagfRm9PRdp/1X/yz/hybr1ZnKPtfYKqqMFh8RCB+lH05p+H1xtM4PQorN8B0YJsR2D1DnHLh+7TX+MbF/pdeY9d3YZZxWYcrh4gvmRlHlYPWKiJCPD6IQklMgl9e08PvFlUyydx7A0z7cWnI9jrKIeLHxIIIfZu8QmnQNHeTp9n2LhITO2Hh3A9Iz27/EURAqLArT/jyAZSvTVfOSWrBejp6rMMVwqP4eqHzGiUpJU/5lg2bg0XL3IinYvR5ifI147vhc0PibQcM7D9JUJC8eBmTxNX+3DKx5+fgGbPjLr4+T7q9wgsw+YWrYYm1uzqjC4zN17nr2658tjvbX6r+/kDXNa91ZuHqNo3qaJXZ+lmIJPF79pP30vqFyqJp+ehPqJgSzaHfnOMou1v4ueP4cUrMmIvbuD2DJIZWJyg+pTBHd3to80RjCs0P0erUz9aFK8PbH3HpBt/2c2f5TGRosa7zxifBPvWbl7GeSHbGV69TCIk405fPimg7uhYowOCZaeew9nrQVHIfhoWr7IC9sGt3IMRqyqxSnefYD4KZu4Wz9M9u7DtSv+zu9H13NVs9kk68K2V4PrTnA2AW7nFs3QUd/0WLZ8lOBjmzOKXKYI2SnL26nZqmiCsW7+PDaSgQtdfSlurk46+fA84hqGkV3eRhePw8nf4Kg7rSmRo+iVcITvs42fvWF8M0te/o5+OYbnth2H2mEqiEhc/v4Nriovrf6k+nUT6Uh1El7dZH7jZ2dV7pOcqt/GzO1EfmWzLn951dhioii9PoYxdIrefAHUZ9vab+SfxbhrxU9p9YuolH9KueQPKa05GuH0O+9egGPfb8zM9KZJdVT/F21kUU9VfweotTRLd3dhvD/cS2TQ/C+UsKQZi94aVlyxKjD3Jq4P9RLb1J3D7mh8cttWnaDHNh0NRvcblVTygfbcA2xc8HZj2DalekOTY8/UIrt5Bbjeq2271VRo6aDl9cx8mLd0QtHiA6JbViV8+ZKE5Cc++wa+h3e26W+zNvZBIWjK4fDg0b+Z7g/Ko8AiOZxLMPqSudYDQ1w8s97fg5/8cXVvb9M0xnD9CRhKn8NVjZI1HiL+6ZxKIG4Sn91gooeXw+iFx1rB680OmBOTcrj9hXnoS3PJZAlvnU7T9GLud4wdg/QMqBI/HEucaQE2WevFqYEuP99jmR8CvbVZUNvXgJDMXsnwWWYFzW8+fJ3oLcarRKSKgD5psPiZ9TSOudk95X5MUdsuXiV2GaGwXHzO9JvGJrX4MqzoZJfWj73zr7B9+vL1x8973vv/g299582tf/fAP/vD2xz9d37px8/s/uPWjn8jR+PyTX97+6Af9ZO/Nux8szu8Qp371T75583t/t7l3+8aPfn7no4/6/b3lvftvbz+i3r7/x3/6zre/O/uVDx/8xV/f/f7fbW7fmp3fXdy8S4V456OP7v/Hb9dnJ+cf//zRX/zV7L1373zno/vf+b45Gmefv3747e/owWB0cfHen/3V+t47+vnzf5IuwgDh6O3lwZPnyIH8ajp58Qq5MLyaHj5/GTwYvb08/uKpgSSfLw8/+wJabxA1mGnCB9ezw6fPvIfji+vjz594ByykFpHgQLpcHzx9hqQuZoujJ89IrywkFlEHcD5fHX36BTYuWW32nzyD2mbz1fEXT4Hxw9ni5JPPYIDJtjr+5HPUSaOwnTonoOuDvvbaEiRCM6ews0F5OUewD9ZiOQu6R0AbIgQyBvW2m1MonJO+n8JQexMA0Ao4a/rQT4PsERFWLKlvjBcGgwCq2lisZsH1MEggZsAqAoTrFxS3xhioZkHvjPVIzYKuAXIB1yPUMeAA3A1xzS0g0S6iTRI8RLsUNIm3AbVD3HCn7JHv9tqthYSXMWky5ACvItSmHkKNuSYcAIzLjG+RRYTtOK2SEOC+aQ+wJcahNic19wGhqmAls4jyDSF1bgOGVQzbIliA2xw1qYXUb72eeguIr0K/wEB7W3u9hE4DUSMz8xZQt/ViCkzAGnONIx+gbYFcIm+grqGdOa+DQUySyDsAGtvMCBHGd04vAZDeICZZbBEBtZPTYD2GretWPEhn2iBnAcqgW6in3jlsatdPodEAOYB2Q9Zj6DDfZUiyIBHZZFAzKAKqJ1BhZyHaDEFLkTMntsq89BLjbR4UR8qjckIFAs7fQmqgOudotImx4MAgtM1CT6hRZ9QkTvpA8HbIGuwCiXYJEhExkJU5VBzIgKoJEthDSrY5q5CD2K683ECgndpiXwbggVxAsHUOYT23eotCwGbj9RbCXmHvkdbOA7VCfqEtZmhpzTJYD0DXA+990+stdBtgHBIr6BbWQEbqxlvrPHYllGsIjdMlNFvkAFYr4JbeYOYXRi2QB9hWQKwQsh7KmG+4d4R0jJQDFyjpECwnWAcoCCoHwBEoo2gTecdy2Zy5BgSPFIbVBMiANMG7AiiSQnsjtBQGUEG6KaAjqIW43ocyRE6fI0lkhxSPtnGwBAqKtkNvMRYAlQdIAq8Z2gyCplAxtk2BJlRZsaShNt6hbgp96axDehZsA70GYgaMwEDYfkFxY4yHGADc9iYQNfe6Al4DMYeuBbgTgBLcdsEjPQe+8tZjNXO6wcEYpI33AArTLggsrYFEzqBfGwtpmDtTQmegWkJTQ6CdWKJQOQ8Q2zFWphYx0saozpB2qE1JFfmAUJOzkltE+ZbQKvcAjYM8JhpIhdoM1bn1GDcZ31CLyKGuT31vMSMNg80QWY+aGNeZA3TP9ye2tgHEO0i2WYAE9wzVQ6gd6iNSpsBC3KZsl1hI0BbScgA9AJ1TSxg0NAKrmTcagc7LKbAOo9Z2CxakM50X0wC6YHyASgYfdO26KdQa0c72CxZa7ZXFzoG2MwLpWfAGuSZ019BrBHoJGQ1lYxRSU29aazWSs2AVAD0QMwhlgNJ1C4aEcxqaqQedB47gbQY6GgzA9QQ3KASCdwNSYwdItI1xn2AHWZkBEWNrT7AeOREgwduMlchBwjYctTH2/iT0Q2CJcagekQY7gElZ0IqFAI5NOQq9NRhVORBJMADXQ9Qwgygt86gkFlK+Y6grgEekTlGf2gDFGti5NYj5lRNzoAHxOy9XCGinKqS22AWsN9AtvXdAI64wB9b7KvRLTJSxVbBrAHTQmEkaO4DQzsgZcB6B2vcr6m3QNFIkChCqTTBzbwHSO9fNsDUAN65fESCtbpCcBW+hqYCeBxcQ6gOqD6AEXmO0GQJBoKbRLgGGAkHRdhgswcKj6oBKQL2/BUSqRTCcbxOgKZIYbQZBQg+wJpHBPFjGdjkWCBjKtynUEe/rc9gT2YE+wGofKuRhRNYZbBGwlK9joBnSGJVDJBFWDpYT1iMXkJo7XWPvoF4FWwEkbbvEoHQWErWAfmkconBh9RY47aDSQevQSblGofQqIDEDduMtpKSpnfPOIr2BZgeQcXKNbQWdDdgY3rQGYD/XcosCQGoH9RYgbdQKhp3zAKsNUgvrIIFrq9cweFhM5/vPXwYbhpfTo6fPvQfjq+nxp58DgNL19vDzJ1jqbLU7/OIp6eU/bhoe4PzN5fFnnyMbsvni6PNnSGgLkUHUQTycLY6/eOo9yKfzo198GnwwiBpELcTJcrX/xRMgdLZYHX3xFDpQTOeHv/gEasvL+vizL0jTJdvq+Mkz1vUBoX+yLkIOWd/BmCjAkDGIYyyVxhFHWlmCjFGjAVaaiw5FuEcx8s5RSrQGAXBkNGDQWpWmRCkIYWpbF5CCTCcpURJ6wKFSiAeAAoIBQtZ1NkvjtlKemCLH1kDn/3EO0oZR19Kct7UucqakBBFHJgBoLXQRBQiTtrNpTOpeWQZSUoC2BxGHJqBgrE+C6eMhbnuTpbmqhOU+okz3ipBM9wHQDicp6PsoR720SZz0tYIAEE6dU5hTb4zDWuMYSlfEWEibxLmqe88xBiO9XbBDHlRgxBvoCElcazU2PEp908MMAc+AakEx6DcgRh1IPcGJa4EShqc82B5m2FuKtIBppPtAgA0gABgBqz23lKW+6VCBvLURYarjWpg4txYHjB3FqLOe0DzsepRD70zEsPFcdRRb64jkieaMKo2McQmH1tNeUO57nCJtTBrHuwpJM4jEJp54B1zMo64BBDmEmVESRi7myNjgQQKEAxAEYAh3mJBO+DRKRN3DmJJAjbQBU+wEToNxCVI9iPPZvDk/iXXXg4jQgLyXkB+oZclGzoEI6zYa8rZ3EY1NJ2BKoQYAGE/j0PdRDnrnEn5Wv16SQx+RxNQ6AAKAw7F1OApCxBkSxsY8VztrnIsS6p2AKQ0KIKgd3ZeLshjjplaDcSI7GeJAEPeiCwUPElJvLIm9EHGKhDVxlKtShtgTTKFWnka69xGzBnpCkiC0QY5xqkQTUuxdTLWhLFLCR8RaFxCCAEPrHWFZaDuYQe9czKAWTMnAYhsQgMhxiqUKiOw303WxD41ySQGNQ8YGjEBjJYtQipmWAeEstC3MiNGM2g6nWBvEIdZWoygOvUIRNN4kDAZAe2lTnvRtF9KQEmQN9JCH3iGIpKAU19GYt51J41TWAiSIBmY7AzEFXqE0WBDDvomGrBcuokf15ZoOIKUwIAWjOHQKpV6GlHZtVNBe2pinshYhxjQg7wzkmlEIA217m0S5qoWhPmKJ7zuQkGCcQ0qTCEhfRFhpG0eJaHTwgPJRV67SAxY0QkA7zK3SSQz7xqV5LIQGNFAS+76FKfcKIq894NaouEBSu5jrQFElPScZ7AyJAfCeUSR07IWKUyCMjbhljEsNvbMRQ9pyLQLHxlMAoWME9c5j8p8SZCNKdReJFnFmLJUscQxTZQLEiasVoVhJkY2RDUgbxCEVUtAkghJqaQllwfe4wMYwrBsywFLjGDAh/pFxDltEk9D2KEPWqTQatNsexIgBYo0EPIbKIOZN4EHt6JhVrRkmI11KyCBF2EpF6LivOlaYgFMkG16QXrgkirpaYYQxRT5oyGIgNWJB+4GpdoMJrbZmOE5F24OYBw2V2eFRAvoQU6hdgmQTDWjb2SzNZN2HCFKEULAWT+SyTkfOQBPxXFfSRZ6S2HdtGLCgMLHas9j1Ko5pU+p8EGvxn5ge5hA6zxATDXdGJEMgnIuYx4h20lOauFoDCAKQ6YhIE9keIy9MpJMEEk+EDhjHvu1hhoKzESPKBABUHFEhA0AncjqPjqGzNomQdUhpxFG62apA5WQEnQsAZb7VkMAQuFNVPMZSoQhT0QuSxEBDZ11AFLsOZ0ibiJiW5kQIyEmkO4U4BVZDZiA9VrPSZLhXcC9qeIGlghFmopco5tjYgC0gCRQ9y2kvbcyOqusFP0IUUKcljKPQGxRZBVPcdXFBe2mSKBdb40LgEfJAwjgKwmDmNTg081W0h7z1kETAiJAgEog3CkQ8SIcA1JJjUMd7tJMmjVJZdz7HxDMgVYgCgR4hInQMRRsXtFMmjRLV9CCm3mDipWex7XWcBOk8ZxpSVCrPSQYbQxIAvOcEqZ5pbeIMKuc48wSRXgWMJ918le0jZ2ycY6kBhBzqoJziSaCY9JUnNAmmgxk1ymPU2wgEkDCFtdFRnPpOeB4QSkHfwQx4FyKSNaXCXCcJlRIEyJFSIMJaURY6mLC2cYM8Eq1EnGLvA3QBRVBLGGGtKPUdzagQmMFUNut4P/YyeGABjpCWICJSEA46mkHvcXBcSwUYZiAY7xyMmBM4Jn2/B6ppdhZ1jcnSWHQa0ObwdPi//M/Z9/4JRKO7//a///p/+KMP//hPrx6/f/dbH33w53+5vP/gvT/5s4d/+Tcv3//wt7svvtpffJzf+eof/umX/69/d/3BBzc/+tGv/CLqR5cAACAASURBVPs/vHz0/rt/9pdf/pNvvvza17/0f/+HL/+7/+ez3/2XH3zzL77++//H/NGjGz/8+MM/+JPrh4/v//XffuUP/v2rr3/9wz/65q/963/zy9/6vUff/u6v//6/vnj8+B19/Rvq+lU8vvvn3/3a//lvX/3qrz3687/62r/5t6+/+ms3fvSL/+p//d8uH3/YdUn71x3IiNu59lu9OxmAed/8jUJ7hJbXyC5o21o96P6qAwmyLWi/reE4chtbf8vRAmKhN98CCHsMQf0XOuIhqLD7roMp9B2o/kqhGP6/tL3njmVZYqW3zdnHn3O9jRsR6SqzKqu6y3S1owOHHEnzhxyJhAYcQYDmCfQo0i9BPyQIA8xoJIEYIw6aItmOza42VZnV6W1Ehr9x3fH+bKtXoAbiWyzgW+tbBKn0rxQUHBo4/yEVEus4NJJzwAXNjOyHFGkKYhD+JTWU0AwZ/5BLLgzf8h7PUImlWfuPDs0Cl4O299WeFtlwsPqD9cO7PLpsu8abO3oomc/tR3eMUMvncvSrAdm57aB2nk312KZO7bw6NFKLdbj3aGFuQLYPBr/u6kE/G/PB06G261C7tN4uyM7IB/JPt18cBm9eDe4OvvTJZlDMRP+JY2x8ZcTmu4WxsZMF4T/f1b9h8v2O+jrOHnBvKpMz3DxoNR/k02HSH3ksLb9s84dc3OuK1038C4lnGJ030a8lGpH6FPBfJuDQaZ802S+5uD+Ur4r8C6aPkbxqo18pfQDbS1j8slUHNn1VF79kxh7O/G40W+CyVqd1+mtAupBe0OYXrT1R9ZlMfiGNGeLcGX51CGCrIOh9NZWaUhx1vp5SW+uz43+WPt/VNWps8uwjIJjQof/wtkQYKNB9PJOEC4oGjxYcMQC59/B9KGFryN5XcyCgANJ7srBUSZXuPLkHWC0s3PlygWuOO9F/uf2lIevreuE/3YeSSSg7vznQJOOW6vx6n5Q8Han6Zw2/FpoPsq+UWFFtqKU/pfKqYfu28+6hyYuK2s0j2Z4xMkDJV1BccH7QrX5cgneFutep/zqpToC66bKf5+xU6QMQfw3kBZVzp/5JoY5K546W/S0r3iB12xe/yspjoNkVKpYk23C/X3/BxMscvN+hP47LV8q8aZS/odVLrs+xsxx4z7xyJr13hv9yFB4A91TzXy2oF9/LX/9h/e4KEba5OXxiV2Nhndn268N60lor03223/q5EWn2yxvCrnDodZ9Oq5E0rk3n6bSdtMYS+c8OpZs7CUBvPuJWq2V25+mk6qib8YM/bo7PFGHhePB40Vq1VmP/6YHSG1ia/qNx1YMiLfO/qiGBmqbSvxawpcDT8r+mokWamRrRGeScFUb6I6ZBiQwV/qXUqTB9Gf+NEJXAFs7+liPBAELpjxWkAnko/kELCqrGVvuDVNYI2Dj5qdCogCZOfshAy4UP3PAt3F7X9h7967iudOBq1U8KrebCxPFPuFbx6rA3/4VhrgfxlI2f+/qyw9zceDc3VnY+kH8U/uq97cvXg9v9Bx19OcoWovvcN5ZTaUXm2cy49oqh8J8P/XcouQUGD3zjai9agP4rw7wcciexT/rWst92K+d43zp2sxuq+8i2zsbtLPs0ePpb1cWpNPTzfetiQLul9XrQO3HTfe6/9MzjSbbP8Ukc/FwZXci3qv67CkwNekKLn7dqbpP1kQFjGVc0svJfSs0DIhDV3zJ7qOhKJV8IbYL4RqU/arUuRLXMfyyxJUUOi59R1IV8zfKfCzLVmgimP1LYAapVyY+ornFd7rTNCdcwu9aLXwi9I3iB079m2ORCovSvGkAUsf3O1xOLtQxD9/EN1DTCgd6XB3rBRT/9k+0vfJZd0P3Osz1EFcfcffIeooA7ovPlgZ7ycoxGv+qqxm9s2Xuyp5WIatx/dohKlfXx7BdDK1HpHPd/MZLNiHq8/6iPC0LQ5R8XL61qc+Yejn4xsXYq3wPjX/kw71dd2X/cw7lZ90X5s0K9LMxbWvFFm78Q6r2O+CotnisyBeWgHw5niPP270r+rADv+83PiuyJMt6zyqesfMyMEWhf0uy50qeoegyqJy2857a/LMpHnNw06qd19ViaY1i/5dkzpE9w+ZuGPqH+uFlPDpPx1Eni6ggUX1IyhOxMlA+pNoL1S1l/zbQDZK70zpND6VRO3pIXHwhSo9roPJ42DlgUv/mn5ZtroDXRePBo0Ro1psh5fFdpXDLS+XrKbQZTzX9+oBtBw/udhze5XitkdL6aKUyV0gcPhtwSoNKdJ3eEWQBKeg8OAaldY/dfB1/TNt2yO6MHvcrW9Rp0H+1BLWOc9L6+KSBtHd7+IOMpQh2U/i2DOYM2Tn/CQc5FB7rREVpfNv5++zdxHWlgoNc/rlAqoYPinwmUMjb1qv+YwFVtHWrJD1m1IWBstj/Lm0DZNo3+TsBIsj2v/qtaXrbgttf8VVwvERlQsnuHmwQ2KPkVZFupz3HxI8pPK/CeV/8orS4w2Sef/vt/d/8vf3z+4cef/Pu/+OZ/+Iuj3/ndz/7Nn3/25//u5Hvf+yA7/b1mecLxez/64ht/8f+s7927/xc/+Nb//Zdvf+u3vvEffvD5v/43Z9/7/o2f/vJ7//v/sb37wXu//OLDP/+P63v37v7wbz/7v/7tyXe+88Hf/PQ7//JfnX/26fzB89/7X/7X1a17i8ePP/8//+1mvv+hmfxe+vrImtz8wU9+51/+q9033vfeXP/e//Q/b/dvTI+Ov/O//evVvQ/427f/IKJRoNT4zWszjsdnp92Lc2e7HZyeOLvt8N078/p6guTE8F2Wj9+8tvJsenzUWy79zWZyeuqH4fD4nZ0k0/OLznptB7vJq5dmlo1PT7rLpbde9c/O3CCYnp27STI8PvZ2QW+zmr19a6Tp/OJ0hsFE9yY89zab4dGRXpfDd++czcbfrMdv3+pZNjw+Zi2JCx9kAiQ0bnyZcxWCVPl0J/Vo137+TcsiMuNB4bMcgJjHdUflAux4rDxwXRm5iKjHAiVqFVWdNlMwbpPKAblgqzZRPtoxHJZB68lAgEYkicVyiDXQfOMeIJBsq0z57U6hrE15lwUKlU2ce02jG6kScoQr3w1pi2Ywdu22Fe1MtgOnaRdAeQr5WSF4D9CBlQqOpkbVN/NcsIWQY72QoB0q0TVDxHlPNT2SCAanoJqQthHtgouJVQHVDADz3ABK0Qd04mbpyO3ctLGRpxqdcjBFpaaakRIdO1KS+7IZkVbWmRFXHqwkDXAhPLQqQQ7T2mkrDSAoMdY4a2Kctl2QtEWo59IxAsYiUVBXxbKu9STzSF7TGEWsh5NahiBVHt3ytkBZ44iQNRUK8g4uWxbCiHX1XSY1DQAFFcS5SipbRpw2eph7sGp5IGLqk7A0CoOiMUldrYVcHaLasjKg1B6uyUzmA3M2bXZ2rhow0xMLN4jTOW5tI1OUz0iJnQjXeGZmlhPTBu9piWFSyelCUU8vCZdTrTXtQLR4jMuumfMW7eul7/Pm0LQnGrJLIORYo46xgxRNUOYaOWvRPqyHgIki0hpqGLuqrLQmJzisU+pVIdGCgN/ab+7dNOIsi/WKWmJDi8ZsKlOLmpR5VULMIi9SO6ldWIgq0qpaJ+uKtnqWWTKkOfWKxNSrMo+MnPogarPMaqjhBimc9sXeyGAsq+w8dY2yrBI9Zj6OqiYlGbVUxJXoQrFnlhSXfosPzUyQqk/R2M21OaJjvTNRNWA+Z4eIQVh1KDwgucSZ02oTcwe1xmZyrGcEMZuJA63gVmpwvDALoRV+q02tAOoVZmKiJxi2pmjnpEH7oBrqvYkorcSstJkTEzNXVE71AmmtLugCUZ1taCJ9FDAtKAPq8Z1AtUhSq82RhhT98LZydLIpcuVVW6VlLBVdEQJc1XHmNI0hIpYUpqwxXpUpd3gMSdrGvNsGiBTNNvXLioCY5rVNS41tWCJdHmt2lrWffYh6Dk6LNPOq2gSJKEqLpkrfNiV3ktDAtJXtgomZ3ShV9ZXsuIECogfYxM3yke3ddDSzSLV2zNUMVRqoh0L17Ago5kk61ipdqLHG53aWg2pE0Z6VUtwOhRraMYbMEXTklICzEWc3MGOgGlO0sPN2juVQ68zqUrGubIdGoaAaMrZvFIVRdBneMxLKEpBRjwWcVnCX+6BgPJIR7cK4QUgUn3xo5bGWy7h2ZMhZjcPcVyUTAY9bj0QVXfME+Nq2AXEb1i6IuKhAmliqVmrH4taBSYs3NIE+3wqV87T15arGBuSffwQJZOs2bl0tl2BZxMBXoYIZi5tOmyON64JPMLWdQFA4wlXXzDnDC73seE15aFgTXbcbKNgYMdfYIQpHqPBIJlq0D+uR1lJGbyrRtwos2QjXphNjBoe4GZoJo3hflX2zKDGfCzlEucH5GLXesI596O3xgjSQwzmhYzMvFJ0K2bcSBcQY0a6W8LK189TSqzKP9Yx2QNwUqVkzUy84QEAhZLWsKM0sdfWyqmI9Zh0trmmEM+6yLa9rvShNmLR5pYe5b+RFE+KId7SoYgnOqa2tSt6QLDNUSqvGilLXyEum6wAAapjNVhbC1dcVzWGWmzhnTaGlqYVqRgq3IVN9B4wSUTkxEg21umjnuNH2VDXSuxNZO4leaTM71s1UUDUjGdZbTbAFag1SmBRMjUqzAtWQKSk8o1QM7pu5oZWKyRuYmnpmMTQllU22qCFTLbG6TTq1uzPQwEy1/CZmFslMCidmAq3UbPFYL7ukKHepX1Q6iNustNqK8A3LhENTYqdp++l9OPBwmmepW9QWSGVRmG0O9W1TcSuODBU3Ge1UMdGrMo3NsnVkxLLSEQ3Rt1XFrDJAei2i0i0T26jKIrHyxjHqFtza50NPBXnBnTLWcN5EpZMmtpnnRWSk1LE3QXe97iyX/c3a36xHb4+0thkfvXV22852Pce8TwYH+cZbbzqXl931qrMLBsdHVpruHR05YdRdr4anp0acTI/eeEHQPz/vLped1Wp4fGQ3zeTlSzsKB9fX0+MjI4rnx2/97bZ7fuZtln2l5qZlt+Xk3bEZhqOrq9HRWyPNxuen7m47PD9z4/gfEBEOaDE5Obr6/DNzG7tJvL17Z3zyjgKt3hubSWxV8fntT/0wnBy/WX7rYxLmnc364pNPx6enmNJyb6QnpbvZnn32mRtF86M3q4/vO0Gkx8Xlhx8Nri5IWVX7E5w3vdX1u8++bafJ4vnT4+//9mR9otflxf79XhiYWVrOR7Cig8ur3Ud3GdcOnj89/s537CxVQQP7hkJI2xTFwRhpwHt5nd3fA9uMvHrTfONDz1IwbJWvcULwqkBjo3Fc580uvze2aInOiuZmn/AWBNTyJDd1tuWopzWua73ZlfcmFq/wRdHsdTTA1a6VrtFCqL0+lou52h87r1f1raEFWnBeqLnriLzMDWXjtm+7V7TxCXS4d96UIweYXA+FBID2kRGlPVFux3OyQ62NgCfty7bumtBResgBhM0Aa4kyWprObWfFma1JXziXTe0YoANwKBBE0bzjbzKjodmeZa2EMGA66S7OXzfQYKMeijmmIDrsedvcamg7AkYAGp3E07673uKcFndmVpoby0Tc9tqamLusPuyjluOGdlFeCBvETX5vYaQ5uUyb+xM3CHkG6ptDlNR6VsCxJWqph2Vyb9+oavvdtvhw7oYRj4U49LW8hQVXQ4MxqK9zddNrKSZ1w1zLamsZMr7wcNHAUjg+LTQfLnNww0vNzuz1Njt0iGQkktxDTNeMHZUdUNj2jdXR2fi9abvmoZdODAI5CYXwELeIuaHSU7nv9E6rZN82VWNfsWJuI8hIJIQJqUvMnXD0bDcYdM/aeG7puDUvWTW2sEbtMBKmUXb61qoFjkp7zuCkTPdsglvripc9Q3oYRo1R181BX1uWwiKwi/WzuPFdbhvg6AJLwe/fNvKK5GV7c4ivCmXgcj7wj1fc1HBfl0GtOKxvju11RIqqPezBVQORqhZD+2QDTK1rV2ntwKIt7s7MdYIKWu919SevuWG2999z1yFSAo1NEXO9qPmNjsgUiav6dt/dFaBRfKCBFlgR290ekKbtnRXBXb8XbqyqvJrdshthphXrY8mQHTbNnt4Cs39R7m47Xl7iCJR7plFRXCjWx0Jie9vQGa6ROTivwjv2JNrWlV+OidEwXEje19qWTpJ1Mt4rDHd4kke3HbeqtFDVU1OjTEtZNB0ATXqvV9XtsaVq7Syv97oaFmrTSFtvNYzfnoDRQN6cO2/X9UHfxBSe5mKv48u0yA1gIN418XWJbViNus6bXXXQ0zWmnaT1uEMshXatMrHomuC6JJaqJn371aY56Od2p/vFr+rRkCxGJCy4YbCpb52HRBfldGC+CdjEyYeD4dVWkzBY9LwgM/O2WJhkBwBSyaw7vzqiArNxDyVCq1V4q+/scregzUTpEaSIpDOve5UppGQfw0wZBd/eGThhaSU0PzD8VcmwzvvQijgXQAywKqCTNcXCgBXvleH1+KC/KyggbIjMiEEB6BDDBhlBu3lvNAg3fEvFja5WUZgxNdCZQGSds7lftKj39Gn0+eddXuCgpoddraxhLmyPVcQF1yVc2LVm2ke78v7MLjK4bencQ5SrhKI+EZgYy0zOrcpynZfr6t7Yakq1acHEVmFMN7nan4K+Z52HYGFXlue9Whf3RlZbgeuqnXWgjs2AuigLRj3vnGUTk+jMvKDVyMIGt3eh1LWiNzQ3FBoiGzj98yYbm5pOrStWdXToKC3k0tCzgd1d5lhj6djtntbFUK977vg4aDpEOQBHQiKcT93OMtc03np4vLnc+LNiOBycxcxCqoNwJAGA8Z7fv0wFQfGi771bQwy7bp02Nkyb4t7c3CYobsTNjpkVDdTbnmesEswoGlsi43pa8xs+r5CxyZq7A/sqpFIDUwtHLWRCjkxWKmOXy9sdWiNzUzR3etY65gypiYXCGgrgdtqUOQogk9ASOsYqbd8b2NuEt1BOLJhSUHE1tRgkg7MyuONNk1WTd4oJ0Vum5ZJ3tZazcbLOhtPSdPsnZXTLcdpa34pqZmHOtERwH1FCvHVr+Pm6OxudFMGhbfGGbEQ9NjXFSSK5j6ipuZcNnaDc8cZHaXjDtUTd2W4Sv6d004h50XeFDjsXhRiIzO+M3qTRDVeHDG9rpWPRN8C6JpjXewPzbUCnfub3O1/8uun3tIOJHuUCk3avZ1+GBLFqPtCPI963mlHXfbNUHcs3q7SwlJD14dA+DwTR8JCQ04T23WrUdd6tgYHQwBRBSxgvhj48uQaEgBsT5yJgQw97CG0ahYAcmiLmqOHlncne0WtJ1er990eXl7ipq70xSqv+6nrz0fugaKbp5cnio+n1OahYcuvAX+8wZcXeCFZ8cHay/cYHspbT46PlZ9/cOz+qKUkP5+5mp1Fe7A0VhZOjt+tv3mcMLl48O//254PVlZY36Y2FlYZGleeTPU7h3ts3xft7ARkePn96+ukn/d2aRNnlJ9/2/of/0f3/uCLU/j4ldw61+ZdPplcXuxt3+i9OBhfnidfrvzyxdvGjP/uTG7981rtcvvnvv3vnwZPF0VGwf6Nzej1//Wo9v919ezE4effon//p4cNng7Pztx99e/Lo5d7z5/F02j256L89XY/2u8fL2Zs3X//zP5l9/Xx2/O7t/c8nT9/MH71c3v7QP1oNn71d/clB5835/vNnD//bf7b/+On85evd/mH/+Gj+1eOr/Tsl9JpTQ7cIZ5KdanLoaEWTXY/gVPcBieRnuABQqeINMT4gCIP2krgWrwqcXvdlT9e0JrwamJZGfK14Z/mHLdBlctb1dcFaLV0PgI+VreXnfYQ0e6RXJ4ZxE0kXZ/QbPmibHShWA83VlSvSq6HBWjAxV0eeuVCGbctsXykm8aYtbnNW89sR3kyBxWk3Usl7hQyUmdPspqJUahtV3gJNXd1Ntd1YYd52IrgbCo2qsmT5kNZckS3Lbsha1IPIC4YMqnrC7KCnASargpdDWHHep3n2u1LRZrZzwpGQqOGVE/U5p8Je82wikdHMJNs0aEv5TateMrTsOyNVZFa2RGDiiGsKllD7WEZLo91Z6LbZbrhYYbKHZWQVKwMMDBhheIXNkV5fM7XSxQ2zuaLJaqTtaSCx0iti+hAkqLkEdp+ItWBXemci0sQGRwh+ZqlcpJeO5WNVkPpMTe/nyTVs18PuSABl8eIjHqTCb9B23Gq54K0RDaWxhVyPdv85tzNJoza+L42c+oUWjBnKmKA4HEt9C3JN5IcqLbgZttn7HCdqXOLNRIxzhikOx9J5wzWHZrekVQp3p/J7Cib1vNW29+igYFqtx4cQhZJoLD9gYQW6gZbsS1BXfkrPEYEEd1C58qGBPK2N1wMYQ/MzUlY3IJCOArsz2wA67uN624VAiK5erbpKCW9sNpe64koszOrS1Vui9Um10yGXamQU6y4SQv9Y7k5MySx8w2yubVlbVg/U6FbTGEqabOlADq25Vpy1qDGdCS4uISgNY4ateqYFTtMPtdhhideK3AhdVh7IKmUNYeGU9YqmxPpGq/qJnhos9tjwkhU9mh3IPFU1EelMdALWanDbaboBjojKerx/LtmA5S6PQ1FULP6AuyFiAG381gtkaWbxJ7x3guqOyPdBmjBBVDSn1k5AhLcdPmA8z6rVANgacFF60UeAuHuqPNH1BQJDlLX3PUBpqPLVQDN00JPp9UhrqbYwV0eeOQWahqpz7M1Yi3G27CnN6AzEbjkiDGp3tOpYJ0OAMa4vfGfIua6l6yFCGrutBfQjUAOHkOwcgwERnplf+G6nYbaWrntIoGZiDcJbjMNqTq1dRzIpqhRVI8yE6LdF9n3AeD3bukFfcL3mtR31WC2ke82zoZBuO2hkMUStrEaBuRswZlCWu6HHGkNWoSh6nLmtt9WiDmB6OQ2coENrg4+uZHSQFh8gbydqX9Uu83da1kWlUY035spT1BVtXCZGsvQMD6IG1+8M+1u6jCS91P0B55W9lZ/BlrAGby/7loch06p3anSvLHawuhz0+qKkRrYaogGSUs/PbN3EBJLm2LC+iZsKsrNe1+NFivP1UOsTic303PZBrRvduJw7CMgAJpf9vs+LiuTXA+kTZZD8ckAMrI8dLegK81QQk+W3hV4jf6uKu0Cl1UGFd+9xv6JWQaID3EmkCdrsgOFG9XdafEfypvVTfzukHdm4jEdj4KSyUqw8gLhuPUGT+6KtmzuJF4y4rWjTingCzURpSZz+Pley7jMQDYFJq15ib0fCQbwuRNzjDhCU7jY+rLn+sQxOTVFZ6LbZrB2ZmOZIa86NutLVt8xm5YJKWDNSnFFYmN4U55cIxH1zjFnsFanm+JAuEc+gM9HpJUtjw5/JdG0mG00fYBbZVazbHcQC0kZg8XEaXhgqRJ1v68XOSK8R6YE2csqt5vhIrLEIgDXCqOyy/AaPY1GmLPxA2BGDHG/71A5lS/LoY+afgdYT+SFIUwYxiBfMCIUh8bbPzIg3Whv3dfiYA59mBzLJhJZq6b7QQ+4obdOjeswawNNbyl8C4dFsj0c5M3EWfNCSVGFKNt3WZqIVIlkI61pqLisWICnKflq907QO0E1cXmh2h0tHS9YDSKF4H4fsPqiAa5LoAilHE32zvPQtu5Welmx6qIayZxZXPS0G5AOxe2cpm+Cx0Sw96WiuztJ1l5W66lntuQcdbI60/AQTHZg9rWgOANC8lkZhry00/z7I32GoI3tC2nMmEDEnrP/6zFrtVpMbw8eve8vlg3/xZ3cffDk8O98e3Ow/O5q+er35r/Z7p9f+u8u8Nxq+OOqdXjz47/7s5pcP52enuxu3Bm+v9p48jQbjzvHF+Cx48Uf/ZHB0MXr55uG/+LPZrx8evn67O7zZPV/vP3wezA695Wb8m+fP//ifdN+cz1+9ffjf/OnoxZvFgwenfbuT5YtfP971Zm4SLB483s3vcE37BxCNAgCBOvz6Ye/N68nx0fT1q8nrV5PT0+G7d4dffWll6f6Tx9OXzztJfPjoUe/keHZ8PH35YvLyxej8vH9ycvjwgRuF+8+fT58+9aNo79mz/tHbveOjydHb8cuXo4uL3snJwdcPnSg6ePpk+vRpd7PZe/68//bN3ssXo9OT6asXk4vzwcX54VdfWmmy/+Tx5MXz/vp6/8WLwdHb6bt3tNKCa0MFVAuadKPzRIJQhakNrmu0q4PAkCspcxFtbRVxtC7ia42lQK5YEFvaZa4HVRhZzSWFGd2ubR4LfVumK8RCrpb1LjL1Va3v8iC0wFVNsmZ9bdFIaesiWWMZUHNdBJHFly2M22hL8LLBWROsrCZWTsxkPtBjyw6FqEbWznSLXJUHIpvYaYPDHqpcL2Ag7+Gko+e4zcdeaFtZqrKFqKdWzmE6VoXv7pjK+yTrWRkU9cTcuEZVomSqmrlTAS0dwdL3AwaKLsqHeqmrcmgEvpllVtzn9UxvDJiPYNHp7igoOjCfmA2vlmq3MXEt6UpFiWEsM7njUWiyCFQZKdeanuZ8xZPA5CWo1iiKTWNVyE0ThwaIZZVq0SXR0hJcsyiwQMrkDkaJiZYlC1i8M2DAmwTFa1PLGnlRB1tdJFyuaJiYxkUCIhqudblu2lDsri0trfGyjjZEhY0ZKFrNzdjVSyDzQ5LqXsRUMgalZUU6a8buFjox4/Xc3OpGIUR2g5S2G1OQDrSUODvAqom1I2YseDF1ttguqSxuwNy1I6myCSl0fytYNbYiSyswzadeQPS8Efk+rDwzRTyf41T3toLXYytxjRLLam4FlqQivQB1jK1VWm5BtVYg5nFoNKfMLPLsmiSBjlNarXARIrLO8wC1EUEJSyKDL5VZFuUVCgMTl7Lc4iJC1nXShDDbEZGBLDKaJbSKvL0GaWSJTOYbrYg08zqrdyJbI5iLJLLSK2zmuViqODJRwpodzkKshVQ1fZjOraw1mOQ6rwAAIABJREFUQ1PmE1JpJO3SeuZshJkiWU3sVMetD7J9vRLmThfl3CiQHTusmbpbqScaL2ZGTFBpy/xQz5gfIpZOcamZW72tp50NN1MoipkdKJzrvLhplsrbKlEMjEQ5O9xWM2eHSIpUNnIDbhSaKG6SHMFVFYSmtm7IrgwjS17URlFvVnYdArIt0hXiAbNWRRwZfNnCqA13BrymJC/DlVVHEO7qaE1ExPWrNIpNdi1AJoKdwc5qvSjjtVklGMUs2ZksFNqqiGNDbCFMWBToaslJUaVXWpboKJVpaNBA2KssjfVyR/S6RvFENnt2JbV0qKqeH1CQ+SAf6aUuq4EedMwsM9MBL6d6S2A+UuWgs6Uod0Ex1mudlWOSjM08N0OPl1NSaCjry2LgbhguHFRM7AKpZgLjuV435tYW9czMoZ56quq7Ww4zV6YTK5OgGKh8YWWZH5ptNiUZ4Ls22plgx2kK4o2NskZeVuFGl6nkSxqEOrnIUEjDnSGuGxaL3bWN0la7ruItkVErL6pdZBrXJQrqaGdo61LGfH1tgZShZRWFBgorY1XtIkssaxHzZKORdS0jul2ZMmV0ScPQQmENL/IgNuGK8VRFK41vWq0hIBlrpenvBCvHVmxrBW6zmReYZlaqfAHqrpUCmU9RZrlbIauxmXh6iUQ9t7aWXjYonYN64BQY5FMts9wt5+VIy/qkJrKaGqFrZYURj2Q9IrWpyglJ7c6WyqpPsr7earRYGMnAyjI96smqr+cGKCc4cVAqs9ior5GZZ3Qpk9CQmSzWOIuIucraQGQBgalIYj25xEZRyKWIAhOmjAcoiQi6LqtQ5RsdxTSPSLo2jaJgp22wM1TC2IongWZepTxS0bWGgirboXBlmlkBL5swNGBQ00sah7q5KmnI82tghXkV4vXKMtLCCoy2nvk7YKWKlzNrB7Wc8PyWUSJvJ0Q+0BPg7BCtp84O6wlQ2dgNpFEhkd/UCs0KkKhmViq9rWrquR2ZqCQ8mbghJDngxQ1c6VZisnJuxMjeKtbO7NgmlQaKiR0gvQAq29cax05NXs/tGLg7SZuZnnb1PE/WZhFrKKbZRm8DSa6LJNZFiEHCosAQV5wUZXaJ01hHucwCvd0Je5nmESl3RBQgCUx6rYyiqJYojw2RqGSn01CZ12kW4XKFUCnCwM4vkVnkdAmyyIARK3ZatVVoVSYRaVYYZ20cmMWamHnenPMwIMYum7w7mj9/PrpeDk5PDh98aRTl4dcPJi+e91fXixcvhsdv569fDY6Pp48fDZaXo6O3Bw++spP4xrOngydPe1eXiye/Gbx+vXjzqn9+Nnv0aHhxPnr9avH1QzdNDx8/7r980bs423v+bPDixd6LF93T070njwfX19OT470HX3rh7uDrh8PXr4eXl3svnvdev56/fd09Odl7+MDfbv8TEOHf94swe/+9ajh499vfj6fz7GBx+fmn5Xh0/vm388Uk3D8M7965+Oij7GC/GQ2Of+d78Wyxe+/2+aefso57/ckn8e3DcP8wunl4/snH2XxMD0cn3/5eMZ3sDm6cfO+71HfWH32Y3D7Y3Lm7ff/e1YcfJocL6nsv//Af0X4nOrxx9vm3islo+cnHxWK6vf1ePp8tv/XJ+vZd5jmv/uD3HVz5Xq3tEzjUfacmC4y7wBNb+X7P6kmLcPsexh70nVLb12UHOzi2DjR0YHsqhx939A6wUKvf1YEDfbw1pgjtuabDzX0sDz1X5OibHrShJQJ8xwJ9a+jm+h5SU8OxWmcmm0XXoVt0zzVHyEK1+b6l+cDscGuO2hnBKqunJR+1Gk/q/bzpmwY+hZ1NOdGBnhI7yPelwLSd5KBTAC1t5jnr6xBvoLdrJgrqCXCyZE8ZesAGMeu3ukzqRSE6SJEAurt0CnVtpby0mAqAa9HdlSNp8w2dJWygS3Or2VfpGOl4B52sWVCFKe2F5VB3zXLQLekNz/drz2jwBxbo6F6nwgfE0VtvRI0JhgPD92t5y/Gs0u5wdNsyPGV5HN02Lb3t9is0M7Qh8vst2rfMLne0Gn7oWRZ3fGHc1HRPeBOGxwQPiW9V5i1NjU0PFvDjjmVxYyDJATE80O1V+gypqWObtX7HzHvEYdt2PxGuwPi6nTRtX+n6UvTyfAIMFmU3G9POGKL1fi5dAaw1HxZtT0EtFqOimAGTxvntWjqthS6zvZp1iYaXfFg0A4lwjHthNCdAy6pFAb0Gi7g8bIQPkXatelk74Ca54oMynmODJ9UiBU5FZFzuV9gWlifcPhd3faILex/qM81qA7yPwMy3ndoftPDAsl1u9bi85RBYmSOG94wuzIzbGhgaptN2Bm2773ggtL1G3OvppnLHLVgYPZDZhxB1sHSk7yTyvX7HzI2uQndtU+POTPJ9r0M39qAE+13SVx2vIjd0zQeWL+BNnchYeEE7gdyrCVlncwX0wCOXwaGGzRLqablXAa3UzCs2ktQtbfOiHrOmQ3UVx3eoppdAT9u9EpoZtk7oGDddjtGOzapyCEwe5neooSfCicopU1aLzOt2rJoe1WBI95piAHroPNxnyG6gllQHTNoM61f5DMEp8USKP/T0PrRRo79viI7eg2tjDtW+71qNNQf8VseSpfahi1xgsZ1+19K6xOowaw7R1HCt1pii+qDjsp32nmn2kYMq7QML9XTXrpyJhBNiw1QfK36r68LKvIPA2BiqEN+zQIdYODaHAhy4XS239gC72bFU496Sbd834BLZ63SuGWiF7DzbF1hPgbfOx8riGz6J2ZBwI0XWNp8KHV0Z/q7cU4pQ1omrMTPFlg8COiLcDAzrPJnrRAugmecHVMOV8MNmSE2ylt1r2ifMq4m2becNsxtkpPkBNbUIdIJ6DDCKlL9qJzr1aoSD7AZ0TWbb3Lij6bb0ujVamNIFnhYZtyw0Nz2Qg0+7piMsk+I7FtaZb4ZkRuDctczGvKXzPdeTGfy4gwi3UAxuu6SLh90SzQkcE89s7AWoZh2vXeMPO1ZHWiY17prEU06Pkj0d+MpDIb7liBt+R2TkG7btctti2i0D2AySmPhhvK9hUZSHJXAqCy2zvZr3dIRXqhu3I45xLHp5NlWIpM00UV6ti7g6KIUPgLYT3bQcUgedyn6WzDVDZu04qPrAodfNIuMdzK2I+zHtNRoKVC9r5hyoppkGVRd0+NtmEtOhqYwEOHE5ozoIaL+qZ7inMmch9SGGA93vNvKW45mFPVT4pmmYwh5LeWh1UeHOKByb2gD6Q6rPIeoi26bofdvSmTNSZKFZHnPnHI4Mbah17FK/rcM+ccwG33dNi1tjgRaG7fDOqNEmGpjYjlWR9yzV15wuxXdsw2DWCKhDx7Zpb1jLPacaCJOG2R1qkoTqtN6voV0T46QZy6YnNBSweZ1PgNVG6e0KmZUw02qPSltgfUlHtO0xDUZwEm9ndg+dR3utZtcQJdUhlS5H2pING96tgRGJfhZPNYtGxWGKzBKhpFq00ml147weMdopTRC20yKdaBYNq0WoPN11anfE8cKwLWpOFD1wHR6TA6SNyQAk2j0D9gzHaeyx5DPDVztjqPidnoWocyjVxBio2LyFkYeVw1y/Are8DsnJTMM3DQtS56ZqJ+6guTJmFCy6Rlf4nRrf0A2LGxOI9g2LJ9ZCobnla4V9oMDENrrK7VB6b9h63ubeB2effsI67vKTT/KD6eb23d0H72/v393cutP0u6/+4PeF52zvvr/+xgfJfH7++bfzw/nm9nvlaHj5vc9Xt+/Sjnf0B78vu9blhx+tPvygGI/XH30jvHO4ee9ePRxcfP8761vvpYv5ye/+Nu14uzt3rz/5Rj4ebT64v/vgzvbwZnTndvLhreMPPxOO9eo/+8fcd3a337v89uf/CV+Ef6+Alf3RP/3m3/10cnba9v3py9eTt29pv7O9eSsZTyxW3f/JT8dHb7Yf3r/787+bHb+tx/1gvh8v9qw8v/vLLxYvX8S39u/8+qvp2zfbe3fvi+WdjlPlu9HLd5PXr6tBf3Z8fPPJ42I+2i0O4/me0TZ3v/zV3quXba87e/d29vxl3ettb99MxxNDtne/+Pns+bP8YG/y4tX+sydNv1spr3wHLIPhvKkvkKFRQyR4pvA2tCqYXRADUCJZdowM1No4VD1JIBcr2VwBE7YuraIzQiTVYEJ6whAVuGbFWrdMCjLeXEhb46DaoT2o5QmsjPwUEk3oVV1doA6LYZugBdKi0BBa9g67vHJQGbxzNMw9BIxNX6PKZIkWzsyKE339W/x4Xy9WlWFux3bLTVmgaKw1WDPSYqhRA3bTSF8utJZgIyGbEWmRj5ffBVd9uS2FidZzowTAi+3LCW5N4CVVR4OkMgth7Aak1aXR2uuulQFgbr5Xv7htVlfKt9Z9u1KWCLS4D2uHd1vwtmx2wJhgdVSyneqYZdu1QN/AnIszKq75wMvqK1CExPa57Bi4j0lVk4s8X2vEUiIB/JL7VskHlhxZiHN41rTXyrIYTpp8iU3SorHeDlyHl+K4rTbY7nJ6zdprZRkt7mE6dEzRiIu2WuKhm7KVLLbEcVrMbWfdg4oSWevXcwjpVLz8PrpSoK6yvhkMdFX264jGt3XZKjfNBjZCTWfD9WBMYIFrZAQDJOhInP6evsLNppBDYznTECO8JdtRR8SVi+o+AEh0oxrv5hZrIM6NqzkCckDOvisvDB6XpU92EyKYgWptNdWZoE5F3wrUCAvU1aVSGTdRDkZKp7EJQP4WyRJ4flO+A6BSloyA0xCd6TltLhCMWq/flm9gW2LSF6RZEVdqu5oGusiU5qH2lOOIOnoKUAFHWKVcLBHIhItLvmRtjLDekm6jw9xgtD0GNEWOx9ol4JFwXGEWvhX4yAqtrUnSPvWb95uX30dBxGoSOjCbQKPVK8Pe+lJPP2xffEq2IVAqGJCkR0BqVQDFY4Sr98nFZ/I8Axxte1o6QFaqJZYZ+iaMb/PXH6GdkHVTTo3dULMSK4RaOtJROGnf/aGVRmUA8p4WjgyYEor07UR4FIQpPeUG4labZ+e6XheGkQFPEkzRumkusYGp1vDqAhDJSbWDC6QXiVmp5NxCQBiAludIE1wr13iqUJGAVq9Ood5Sy6HZa4SldJwCuhRXKSxgc4k0IYEB29ccF7XtlhCXqEvUjvEzadMaK1lfAMUBGqDBqYsaR/mVu3T1UnPg5cdguQ82EXT01dhKIXRD52oIGx90s+8Wz+6AoKgYiKao9qTN7J1vp7o0N99tXt0l8RX0rE2PlKYBYjMyYdkhZvwtdXIPra8ZslZDWHrYSsnOI7ljg9U31ektvMsVgJsZyXzsrp1rH1Zd7tX6dZRfE4tQlfPiHDlmY8gATDChLVuqdglsrTGqNr0kRFaanpGuMKpCXKtyp9tuKwLZXkpbp4BGaKz0shCxUV4i26FwW9crrS93si3wApEg0igpT1EH5lpL43PLtLgBIjgGRp6qENUXgICWSJ4dA10XlmlaVx1X5JgJEo6hVF1x8o+sjZ6eJbBnXs01ITGsyXZsiFYYdd2HwsCDqMCbud5IaGf21RQA3TOvf5sdD0SUVjYJp5gjRaiz7FmVAM4u69nSElYG7U3PbluLpTiaAKYTJ/4vxNtue7ERnnM1gNKEGnU3vkahMNrmlKFtO+qm6Ttcp9juCdkxsAf1rFRnTRMh3QNsKeVWdL286Xuqr9tF0VxCFijbaNWG1zvomA2fOLxruU1K39A6xrZH20vBd9KyKBgQ1rPdtqhOFN2iSScqzmAVaR2nYF0X9TS9LNVV266Ab1X1BtYb7HVrI3Gt0Ccg7ZUVS25gld8gF99W541sxM4nyUhHEc4tM+wh2ACnyns6xryzFdpurukFKSGJhj7Y+mL5h/qOibJuOsZmbKgCM66vp6aI666q+tjihRHrxm5IVGMorq3GmFdT7eS3wHlrgDbpmrueyUPIsBUMIZAYRtkrhLi0CC1OIeFMbwM4FLpsSc7KM4yzxu3S/BUUQiN2TVBEVI0z2V5jSRXxYPOakbx1jASQGnaRiqVYQtRwj+fVJZAFQCY1OrUuC73hzbGSNbBN0VxCWUm33cA+1xA1pcyPsEhlp1dXr0TTEs8s7z381eD4pJxNDx4/Wrx4nh3uBfNFtLfopMHBg69nb15z156evpu+etX2/e3NW/F8z6Ll+z//YvzmTbk/mz1/sXj1UnXNhdPuu7iCcPr10/0nT5J7t298+WD+9nU17sXDaXTj0MmTW48ezl68bMaD6ZvX+0+e1NNhON8PbtzoFcHoxdsbj3/DHau3Xd948DC8eUu8em2enf7/LxoVEHnvrvpPX6lKOJebwetjWNDc9GN/nFvd7puTyaOXrSTexfXoyQvJYOIOUrtXE8c/vR4/ftECvfPufPrwaSuJL5tpEkDGzKvN6MkrWDLn/Hry6JloVW76sT9qAHHPV8MnL0HJrHUwfPoKZnWh+4k7yOyuf3QxfvySc2itgvHTV7CgTUOyC1Q3pC715Bw31FRtIwkWTdPWODmHWWlVVM+utLYkFCiFgVSSVlp2rdNKq6mRXuA80zkEAEqpRFHp2VJvatKUOF2SOsNAQaghAEDD9eICVblWUzNdkqoyRUuhQTilLSP5pZamRiXN7FJrEiwYUUlfZS5VNowGPB/Viszb1Gvzhhsw7ou80zJbpAOY2lTqsTdN3VGDDBR1VTGiyhDpQKRdIVAnzztVzoHGkxEM/Io4cOeLZNgKnDrD2J+0wJZJR/2/tN3Hz2XJmSb2cCeOvef6+/nMLO+ryKbtnsbIzlrQnyVtBQgQNJAgaNQGPW2nmmy6IlkskuUzK703n7v2eBM+QguqG7OUAPX+h3f3BgIIxPOUQwlCUMxANuy8aMGqcV8LR0GZmiKVKjBFivKBQGG/hdUZEcDvS7+5wEzTKp7s0n2GQ5bB9gXoQdBuUXuKpER1NN4ODzqSsIq0S49Lv6m89gz2Nizj6S7dF87rClJfYMEwr1B1jhmjLR3kyaL34zbD5TkRmorWa5ZUCq/10+3wsKOJykF9ipny68pvzoiUWKnQbSeaD7WLULHQPKUI7HclcQ4wz2VT1UfSRG47c13a0SRLD5pgKFXq8rmSQ8MCW8xtG1pM9qpioAS3EdhNdDfQOgbZVLNUWi9LD1ovlTZw+UI1IwEjkk81nyCn93gXSm4FgflE96kAockWsIwZDqoVqTdIKq/ben3hG2EdJUZxQSN+ActNwK1fbv12CZW0FjqgpYG4XNH+EvY0ZpegWhKDPAichVZr2Gxwu0LM+N0St2eAkwAYZamnNGw3qF5jIb1uR7ot5RI56JwW3Av4ElSXnnB+uyXtCguFXTeAmxEHsW0GLpsLEMeKLeoaCGFYBHYTKRPJh2A7ZjZJRH/Y1c4Bw4dwNzHCVyK2u6nkMRXsgJUOQNkN3W6ubARb3+VzxQOq5H7XYKcVS8F2Kk2o+ATu5sKlgZaj7cY3RuohzOZSjZQcgO1Uq4j1uLr0eIWYDasz3NWBhhAAa6BjjFSXXtf5nJH6kvQV1tZB37OKcxiUZ5iXmElaneO29qy2kCAohbS0uiD1BjMY9megLgNtnbHaIicFai5JlxMBw+LM4xskPQ9obqETHLcrryuJUF51DvsdZiSEu9SUC+58W09cMbQKpW0zZq2BxNZzmA17LwbZwGUTCejIiGnTGA1MlcJ8II1vqwncph1J5qKd806gwFapLUZKB65ObDHlyh919bzZMRyAXeKKuQSB7UagGBuJB4JPu9Jaa7sZzicMR6aegN1UW8obXJ6ilgdd73fnmOtAGeUossaIjtaXVHKvZ7Q6w6wlBkEAjIGurml9ToT0eI3qS0+0GAAIMbQIsd5rX0AmvL4PyzPCZWA4h4GnhOSc1me46YOW+/UZEYwY5wxB1hrJSH3pyZ5yRatz0tZYu3/eIBmZcgbrwGBv0ZRjwxga4Gxi+UTayOYT0w4YjvN0vyOJcL7JF7CIGY7hZijbKcRoT7BECmOozcauHkgYwmzuikFPo2x4UCYzZUKYDXWTSOXbbATLWDh/1lVz1Xc4gdnItFPpIluMdDNUhnRr1J7BnkTdJWzPsTS4iqe74X6Poj7D7doT1q82pDsHHY6LZJale1qjNiPNEgtGugyWS49LUofjPJkxL6zXuLr0lKW89poVFZrW4Xg7OuDYFxtQnSEO/Crz2wsiDK2D4W6wx1DU1n619JgN+oy0p4g7D3TUZnPFIqVit5uaPkFKHrASGyP4GO7m0qa2822xsJ3fkzgbHXU01jwA24mUsRUDt50qmXhSzPLSd0qq2OVzyVJtY1zMLY96L8qGB3U00jwA2VSxVNjIZnPbxgC4vXZLhZRyAHZTI2ItQ7edui7h0OdnoMr93vj1yuM51lJbjJxgGtHy3GNr1JO4P4f11rMIA+gctErh+hL2W8Ss356j7hJKzwdGWUqEgPUStTnhymtWpMupUMBBZ7XkOGAXsNr5TNNmjbstUdwY5JzRGnnV2pdr1OOovkTN1jMGJWfL2e0HmtnBs4u963ekxWUyK5Op1jA+38zvPIStCs/X01sPUCPqaFyFY46CweOz/a9vW+mSs83s5n1U9wPJ99vMIJw8u9y7fltakjy/XHx922rYeXEZTxgKo02xd+Oe4yY63xx8dQv0qkymRTKVjvjLYn7zPqm5v6sW1+/gqrP/2dXq/8dfhObif/lfj7rt5PJs89brXtHFVVkdHzgIexqHjkeX26jrT998M82z2dmL9TtvMhRSyXmczF+8wEJ0Jwu6LQdl/eKddxLdHtSbdTwJs8qru8vX3hyvV7Rr+4NZG6bIWE1IVFWL0xen770321yQslu/9DLA2EIYWO5nVbLbla9dMxzsPXty+t57IWMul27sAYjgjulpLJwd15vd6GAMuTtn/ZVZpJktjBsSDQ3pWhiGIhzGL7btS3uhauFF3x9MiO4JazDG2ktArcgId1GSPNu1J1MsWNBkfTolCONSwgTp0IfrPoh0EQ4m9WYXT4fUgcteTpMUtm3t4xjJYRivBY8pikxwybtJimm/YGvlQBYe4gYPRNXMI5JDGRIUKiB6HiShM6hECAE5AqD2QtE1e8FeecG9oAtHdG1VQMDQoRwggMqF7/M2lH2fTMKdtgQ2s3h8UWuK7AiM2pWn9bPpG4Ochazncy/YKeEHu/3pYL2lrWyvTLxWRKtMXRspgCPeVuk02LaQyXisu94jnW6vzjAwaZUV40Wa5bpx/cnMqxnpGJxQQ5AnRRsOIVPJsmhemg+qUtXO7McBkBoiBJ3sESkYPgx6F8RnWffqIhGVsVAFPi2YYigZiAYm3q6zx0lD0/2nm/JoGNoeVVjHAAdiUq/aYFTS+eiizQ/SuVirPGoXkYc7bRyCwMHIyzVMTBdF44uu2Esi1Cyy57vxkQIRKYALnYyIvzOR1+Sj1O/qdjCJjKRr241Cz5e4AM4HJtGLei2wvx4cjc+6ah5TwunaiZTI1IM7HoheHI7xurO+B0JAm7IH2BunMBPQQjD3ZAXivmn3h6RqHAQ6HQXrxvoETT27k8iA9nDsZxuf9930ICg7AFG3Px6cZxqBMNKdML6z5ewoLmvMpDpK48tceGEzGablSmuNxmNQK9xLcBTJGtCOs+PhoOSAATWFQKCgUvnJcCDz4/z04fzNtGWgwcXhIOiV30g1hqEuJ7zYDA46Nxwvm+xqOmgbVKFuEQzlKtJ9Fc2YSqOC6QViIBgv2+IoPm6fI6PX6R7iPuqAGTqFwmjT6gXRnru6evh8/03KLSocn/tEKVK7YjF02I6eLtvDaYglOG3E/tiDHDYtoJ4KBzjjJAZsMIheZP3hBEgW90VHoxQD1nkkAGrgww1HPmgHyWh3XidT3yd41alRSGNgtwpRJBOMyxIi0k/n6VnGZqkeRvGTtRkGIIKuKEEQ9oNZus4Itc1sEj/d8Flaz2fz8w12uNyP4kLEbVsfhNN2A6Bbjq6kS2YwtGOAckeVy47jRbMcdcVqdERzJzGtF8lo01trzBQP+k0su8eLd+OcBb1qDvzhpuE40qnb7y+NNcXgwDXY77XYx4bRqObNvn9Un2qIymTqVcQaYCZAdzSu+9VLe/PdhcqtPB4GPbOtdRMqBQ9Eb+NE0kH8Ytu+uh+1lc2UWKREtkgwShAjqVcwtO93OBq82NZX57RrCKt5OvEMRI2CIyyx7y1LOsEZief1ejtYDKE2OwGnga857zycIgZB2BQmTsRgkjxedtcWoe7NRupZDEIabkSM6nwyTlaimYa+x/aL58Vgr8cDUjhEAR9gb2cJkdU0iZqcB4kPLV1ZOfBAbHEGjO/xCTrOnxsAl6Orw4u+HwZsGExPSxF7INFKGwixpIN002Ks5JBES9mMkz4NXtneYUFShnNSOABBPY+Hq9ZQnC+Go7OtsyYdybrxPa6bazNf8YB17WCUrDLpKDsYB5sKGoOmngHIUwJg1HMaVi07GSWbgjkfjwkBSkOMoRMtojWDRyHnXpDV4uokZqXAPsQA5UormKY8N8Mgr91JIpEX93WXDP2sURLDOUW1hMzYvVA5b3LRZMfpot3wNunnfmKygazrYNzCSbzt9Qz1JJxetNlREplaS6WjJGAGtcgOrCJ+uBH+oC2SwSu7R0/nr3oK4syxie9BhWsIQt3HhLY1DIKepLPTKj9MA9eTHZBD4tFu0m3zeL8mk8lFacagSoaLZ3l+mGKi4aZHBJmx77YyxKIcDZNi29MIJoPgsjSDAA2R20pHSD9Nou0SWdfP95PzTA5jPhkMn29kgMNAddwQgpvh3mCbW0LgxAtOCzYetkk8zs4FQHg0AqXAWpu92O0kwMgMcJSt2zANEh9sJSAITKnONTSAnUyOnj200q1ffmV6cYEFZ0ezOhhSKQLLLbOTy4vLt96cLc8ct/3BTNJAIi/RHSr60WaVv/GKZWZ2drp57eV9vuUkrMJBsspxz9vjBWCMfJLDAAAgAElEQVRmcn6av/FK6yUh60QcT1fnuGb1yUFQ1n7T9oezYjALu26sixyNDp4+Xr75ZlruvLx+8cH3/P/pf/5XCBp1tvzv/vtvf/jhwa279fHJ/Kvb+9dvlXv7r//0o3d+8tGLb33rrX/6xdEXXz/6/r95+0c/Pb5xq1ksDj+78fY//lN2ePXg+s2Xf/vp+Tvvvv7zj698cf3Rd37w+o8+OvzJb5r5fPrg8dGnN3ZHVw5u33v115+cvv/+e3/3o/f/+u+efP/fvPTb35/8/vNq/2jv/oOT33yxPb569bMvv/cXf3X6wbde/t0XVz7+dP3mG/Ov77z8ye/Loys7MN1+Q2GCRYvX9yK1SGRBLu5O/KFHuXz2aAaANZisboQuwoZ5y1sjkkLVkIsnKfEBFeLpg5nnjKX04qsxoggKc/l47EVO1fD86ZBgQIF+/M0BcRCG6OLrEFDsFFjeT3AIFPde3Jl7BHvYnN0bQ6FIYF/cHFuCfeq59etO+Bg2Zvst1BA77tuHfyrYoU07fPYyVBGwHS/eBSICpCNP3g22tNuT9Pmbhk1V3IHTa0CPAOTN5rusP3Z+55Zv0j5pF5w+eVWKPTbWg/tHqJo5wnT+muuHIrXo7GXcDMSk1k/+qOvf6icqPNs3fIZdrsrXTbfoFqC9rfJTal8e8Pt6t0zioC8vo+wOdalf7YLqERrM1PZpkF2m+iRS9/TqdhAMrNzY5fnYpV6zo/VDhA9IewdsblJzZSBP7eosDVOnC3DxfIQS0FyS/EuMDzz2AqxOB8HUdCv/8sUgCLTemcuvIi8EbROsHkSDiajO8Pp8HA2FtSN4+ZYz2lCMX7ylfag4Zac/kBSCnursXQ10yoom/1NilPawf/tNiyhwDizfdJ52PdLZe84IBEx9/u+08EygwYt3LcHAArt603etkCF6/gFUAGBhLz8gTPMBoM/f1ohqi9vT71tEjcZ2+44xCHjSnb9HjFcPZXUddb0f+Gb5POW1RyNzfnusdoF3gjdf+XUZkAXc3vV5FwSRWz6ZtHnkpuH2vi+22DtC2XVa5JE7ippbtMviMFKbi7QtfDChxQParsj4kJ/fmOTbMTzyu4eoKKKQyvwsKLaR2w93twK+jOkVmH2Ds11CJy57HpZZHI4BaQ7h+ooYtmS7sNmr3VyB5aLe/tD5nVeHsnhbBtrwWXBxzKccbo+r5ffUsLX53JZvONx4PZW7d6zXa34szr7DhtLbJjZ/w0UFKCe6eNvC1m+97ea/AURbMXCXr5khI7vIZG+DIJPFvNn+W+t6pIhZvQNwb3SKL17rx0hl7fLhAEJIrXh6b4qVhYl3/mlqUIAJXN6NEXFGuPMnI2hB4JkX3+wBjuKBfHpzYgGBCKzuxQQaCL0Xd+cI+B6V53eGiAM7pasvfWGp8/Dy7tQDGGJ0/ijVErmhv/7a0x1Gc7L9PG3tEERkd49aaSGCF0+GRhJ5GI8eHanuoJ3J4Pm+FQuAuyb7gFfX+MCiy5dINRKTipy+YvujemrR82td8wYGma5eMt0hS623uoLKCZ/V7um7Vfk+n3LvYqH7Y4gqWB7p6tjFXb/8QGVvdPtddHokm2smqezuyLZXMGl4/nK7fdskDGyv4uKITYvg+Z7uXhHD2q3l6bMJ9m3f0s29AM+RXHqX9yfhzLGtd/E89UMHWn36aEwDK/pweWscjw3bwtWLcThU/Y5cPBtRaoEAp3cWvg+FJOvbPhljuYMXT4bhSPdlcHp3FgQQGnd5f0iRkAqf3x2iERI5Ob8/S2PJe//8Yep5wBi3vDOAPvKiFJ2+7FkpFVT5+0BDgERz9l+ZJhAjRZ+9rV1kkASXryNErHH42TuWh84X7uI9Imk/lN7TNzgcCQr18w+UTi0wZvc2kJSnkDx7DbJIDLvk1utGznnsvIurTvtYt7L4wPK4GyPw+Lu8nctx6z19RZiFDjW5uGJUyiJVPCTNORoeyIs7cZkNzGHIb5rdUz+MdH3hZevEzoLyCenOsHeFFJ/a7FHgHeDiRZBtkjgx7YpuLmIyhdltr71HyDWvvQ3W60E41dV5uL2Mo1A1z1F2l3pjVJwF2xfhZI9tHvrZZhQPRfmUbu4FQWi6jGQvIn/k8jM/e0qDQ0jysc7fsbBPet4VP8CICTPpn31fxI5U1G7fBkFl68Rk7wDXEYXJ4w+gkwrG8OwNHWvYB2b3dgSXlTysl//WOgOtAct3oeWKhN7pGyJyqPLJs3cI7SWP7fZd7QxEDpy9C7HlKBJPflAlA9xjuHkdoV6rwGzec87ykK+/8Hvpgxiu74XAEkTI2d2pEoGbhesbvqoR3kPbr/xaRnYYZDcjzYnnuctnQ8k8N/GzbygrcbqQL76e1v0Ijr3yAem4HyC+fD7sa88eJptPfVFE9BhmX3tVF5HYbZ7EPQsTX18+nDZF5M3h6mbUFz49BOV1mHVpkrA3PvnN3lc3V9deufa7z1767Iun3//+9/78r9/6h388f++9g99fP/nyervY37v34Oh3X+6uvfTGz375wd/949PvfOe1X3x88vnX69dePfj6zvGnX3TjyeLmnYOffF6eXDn46sZLv/rk4tvffulXv736uy92r7xy8NmND/7qb9rxbHx69tJHn+RXruzdvPvaL35z9q1vvfnzX3/wF/+xvXbkP16++qvftKPx8HL5+k9+uXrtTfn4cfDsX+eJcPT4xezOI9fJ+GIzv/eYcJMsd+MHT5Ujk0fP9765q5w3Pruc3brvpAuXu/Gzc8B08uJiceu+dN7wyenixh0F6OBys7j3iAqbrPPp3UeQm+h8vffNXW1xcr6ePniilEsvt3u37qOG+9ty7/Z9zLS/Kef3HmmDBs/PD2/cdhKkq2x+8x7ohZB+dUEZ93mLukvCtW86WO9C0QLOULkKXYOk8upL0gtfctSuCJO+aMBuHYsaKEXKVdhVWFivXRPDkeWwviAt90WPdqtYtsho2G6oyrQ0tF75Xe/LDtSXHmcelLDchbKBvfayC9+UUBnSXJKmpc5R0+y7fmQkMtUeKkOOfFyOXDsRhupq6rohUsC2C92NlfVlfwz6sYDU5RNbjwXwQTO13dRIbLo5qiNnsWv2XTYQyAfFBNRTCULbz6RYWAFtNwfthBvfVjNcjhgJUZHaZspBqLup66aegqCfmnaqoN9nuFwGCvhs67J1bCVqWFiUA6m9pgn4iihLdInLSypRyBqyq8dSYt56+dJXDLZt0Fx63Pldjas8UY7KEhebiHewb0iz9h3HrPerbdiDkBe4uAy48VkBdutYdpALP8uTXpC28/ulJ43napBf+EJhIzxXn9gukTYE2UyzxKkAFjOlEi0CXZ9g5gGNbLWv2kTY0LRHSo2M9EE+0yxwwtfNMeCJktiVC9xQCSNUzDQbSRnYcs+yEHEg+DUlBtIGpjiEXSxt4LKpZqnWAWoWto+twKo5xn1onQerfVvFHNFqTdoyUByUO7/ZEWlIvfPl1gniNxuv2xDrPLElVeZLDtuSioIo67VZwJaO46Bbk2rja+f1G9CWgdO4zIN25zEQFttArZFGxOaw2nkK+X1OqizSEjWF36yxtqTLabeGgvhsg9ptYCyRhVfuAiGh5kOQzyWIQZO4fM6sT1jo2gPLiRGRq/adDIwYgXwuXeSaAFULZz3cero+McIzPLb1oZaR5hHMx8oEsI9tcSBdpFmk6xMiEJAEVHMlfC0HsFhIG4MusMW+loRwauojwAPNfVcsnPCtSkA+NzqSDO1WkWyRVqheBWJjlKPNlrLeVww0l5h3xHCUr2NWIeG8YkVBaZTz2NKra59LUq4o66nubbkLeek0oMVlwHaQA79deqym0GCxw31HeA+KTSRKzA1tVkG/cRL53Rq3NZUSV0tPNlQLlG2iNscSeaCcgmomQYjqoWnnVgDXTXA3koaaeoqLESchKEa63pPQd/0ItFNPAdCPdTMXLlDtDBYzgQNUxbCZcUtRPzTtnpXY9GNYT62jsB3BYiyQD/IBqPeNJbBOdLNvOHT9CDZTaX1Tj2w2ETBAzcCUh9J4fYvrlW8Z5r3XXBJmAlHBeuVxS1kFdutEtk5wUq4C3mMmvH6NjcGuAfkF7TRVLdquYt1DLWCzIroxQtByGTBJeWGrpS8lBhzm20j1sJO0uKC6g4qT+tJjyucdrjeB1h6vzG6d8AoITcsLyhskbWTLueMRkdA0R6YfSOe7cg+3kQC+y6emGyoTuHrP9AMrsOBXQR9ZR1x54IqE4wjmU9uNjQlgPXV9YjkyzYHrhsz6rpiTKuE4Mu1ciLmwoW73YJ8SBWy7b7uhgL6tZrAecRSgfGS6qdQ+6Oa2GxnglRmVG6KAp3c4W/oSBV1F82aoFGpLWq89pUmTB+2SCOT3hVcVibFElaTchryHTUm7NTUKd01Q7AKOAr5FxTLkhraZy7eRYqBjYZ5FwnhNSfmKSEhc7rJLXxjCW29XpVqgvqHNBdISdRWt1oGyWPNQ11cIR0B6ttyXfaRkjIqZNrHrA1fsaUEgD3R9BfBQSaraQytCY2JYzJWMVRfact9Jn/TANkeGB8rGNj8AIlYqcruZlrFWseZXNQ+s8HR9hTDfWh9Ve6aLlI1xPtFyAERgqyPbe4Z7tjkBLBHAa1ZeXxCncL+mTekJ5urcVxXi1m/XPltbjoJ2Seo8UBq3W6QazyiUb6M2wx2O8xXVO2gg0VtUl1Q52m79pgqsgMU27LdQA6/ZUr5DAvvtCnV5YA1hGa0zKrhrCqoyZIHXrkizRBwH/QpX20BbFF1uFvceQwnS5xf7N+5I5w2eX0wfPHUKDC+309sPQCvii/X89n3Qyeh8Pb3zUDoyenG5uH7bShCusvmt+x5TyTaf37nvOjk4Xx/cuKscSV9cLm7cdr2Md9Xo0XPcCbrOF7fuAW7Sy83eN3esAsHpavrwGeY62tXzm/dww+mm2Lt5z2uZRfhfK2h0gC3lvYuotNgTwsU+6ZkgASVGWUy46MYTyhjtO5D4wnkeZ2w4on0HnPM89y+GCBE2FYo9p6yymA+HtO+BtZ7nlMOEi348xlL6TSPGo7grpSEiTQlnyFhCnXQeYZwEsMdRWJX9dOKLXhjqedZC5LgxcWAh8stajIcDXjJNge8FuueGEs86hLUEIeJdmAZZyabjAa+Yoi7wAsOY9VPbWEJaG0aQdWHqFzUfDQeiEpKY0A8N6w2lSDuCjQAhEl2U0j8YWTFJkY/HYrdFUwqVCQPUa+3TwPWOQRFGkWu4CRGwBGsGohHPdeAJ6WnfDwAzAik/iFzDXES0wtRyFwaq1yEFzBqP+FD8i+lBQpQUg8jrJdVMR77lwGGkQ0qb3no0BG0PEqIUH4SEq0D0hDojIachj0OPC8K4SmOojN80OME9DAkTMo0JF87CuckKb+SkVWkMtaFNp0ZJ3FXMUp3GmAtnQYglhwHuuRwNoDZe0+lREvU1cwGhAEspIA2w5CiEXIVUNTQN84JPR/E/G6i1dN7c7Ap/aoX1qW7CUVh3JqCB7oT1MTYAIaNxAPouTMOi7sfDo/p0gxfW9wLTcRt4UDmCrYA+ZH2U+lXHhoNElEpQHfmB6YQNCFQOY6XJXG7ywcyrWD8aJrKSyjfBH+aE1AlLiVY4dKyPEr/qeBLHupGK6sD3gBCGBIrZkFpujecFUGgOlB8moO1s4ClFfNe7MFC9DakWDiCko8CvGuUHCeg6EGEpVZogJgLemSTUAgAEdRTQptPEO5Srpb+PpZRpgplASqOIeG3HvcjEgdd0hngx7FuY0L4jIWQgwFK52KNMSBT4iEvoI6HFIETG+U0nhnHc18L5Kg6wVNACioREARTaJ6Lz0qBu+GgQs5q7EFGHlVSOUiwkjv5gGn8Ul5UYxot6uaF72LNYK+moj4TEIWaKeqIJRlFZs1GasFKYAAQQa6WtJ6LAIRjlBR+mA1lJjnUUhLZnxvegAQRqiQLA+2hAq5an6UDVXBLne3OxWaM5hRJ4WCocAcbCgfefGRP4ke174/+h8VMqHALOwwTXvYpD5xG/bFQURq7vbYCcVVFAWhY6zpIUV72KQxUEARNYSjGICFO+ZCbyjEIAABX5tGWWeCFoe5gQKcUgwkIHrMO+swpwEqmI+v9sGEywkCKNiFBYKBsTv2OcRBQLJ4H0ggi0PRp4nBOqGYw9zk1CfcYZjnzErQQGegFmPR4QLvphMmqz3gaYQqKEcNQnSiIfCB0S2QTDaJez2SRh1R8MNlJYOjV5Q4dawZCIJhiGecUnw4RVXFMYIGIUN16IpcABYsqnqgmGYV6yyWjAqt4GAVaB6gs4DLEUJADC/POckk1GA1712ocBQdAZhaZiWydj1EiWpomupfR04Iem4S7yrAQeUsYLbM/D2GuYiKLYtv+PsS13EXRGhYHXCd8xHsW4lToMDEV+zYzvBa7/g5FRQDvhG4Y8ZzhicWIp9uve+DRwHQMJNkrGAe24Q5DFkV+3FuMDuVr7e0goOUwwl1hIl9CgbRkOTRKStreY/MsGeSHoYEQ4d4lP+56j0CfKCSsxjTHvYII5933TojhoWz2M/b5jKPSJMhpoR/b1eh0eIi58qlsy8NvWpFHQtQyF1NNGA+NwQFRPB1FZs9HgoDpfe3uIAk9L4agPhSIB5NrHoglHUVn3o3TASmECFyBPC2l9iqQivpVuT62Ww5Mor7rJKOGVMAHwETFCWUqBUF7gpAsRa4JhVNRsmMSyESZwPvKsVMazHrKYeB0PMG/CUVC1Io1D1TJDqfnDmeGFlokogo0wYWB94heNjMLYdb2NkNUqjkjHQsN4ksBW6MC3PqVVa31vT6yXdB8brZLIa3uHECUGtFJEsQ2oV7XGpxHoOxcHogMRkQoDCD1iADfCC2PYS4GM50WItyBGWtuYplUuIBWDgd+1DkDPM8L5lPUoRAwGQduKURr2jQA+pgAKZRzyfMCB77Eeh6jHcVhXJg3TJi+8MaEAKGMN+IPxuxZFpCNxWJdiNAy7RgIP+RgobQ3wfMdB4LF+gprL+CguMjYZR32rHK4Or4z+h//x/2vQ6P/bLsLv/KcfffAPH569+961T75468c/Xb/++jv/9PPXPvrNs+989zt/9bfv/Ognt//Lf/fuhz/6zt/+3en775988sX7H/749K333vzFr9778MdPv/+99//2w/f//sPb/8V/+/ZHv/run/3F+t13j7+4/vZ/+qfzt9599Vcff+vvP3z2ve+++Y8//aO//vtbf/pfv/6rj3/4H/78xdvvXvnm5gd/9ffnb7zz8u8//+5f/+2zb3/n9Z//6rt/8VcvfvCD/c9u/Mn/8R+ev/8t1gbsoxYkxDSu+5jz/RRlov+1hGPscVl95HCEjAbtRxwmnmkN+1UHp4GsQfNrAwbQM6b+ubLYQY80HwnsOcdd/bGCI2p7UPxMkBGBRmc/M8BDgKL+Z50lyBhUfyxRCJzF+U85CRHwcf1j7gENfVj8yjkIaULjbw5gj1RiBzdOcGX7hZl+tYfLmI/6wZ29uMAy5N6jl7yOqFilN46C0jYHYPz5xKtGfNQN7s79PBARjx8f4YKaVKZ3jsOtq47g4osBzqflwoxup2EWi6iPH+7TnFZzNL21F16C6iqcfjnwtpNq307vJeE6cXFFnx55eVIdUffJWtyU4o0JuNt1X9pgYdsLT37F4EHEnlj7decfQX7X1l8b99bY3W26zzU8oO45K78hZuHL58Z9WtqrsbnHmi+0fn2MHnX9ZwrPMbgU7deQTDE/1fxzAa4m5l7DPpP4WshPTft7g6YQ7nTzqUEjqNau/0yGcyCem/J3ml6lRoZ7X+5bLC1CwxtHhjio4fDGTEYQSjT45qoKVcSq4M7bxilL3Pj6kUPAGJDe3FcRBtKm1080EZbi9MsrQEsVw8lX+9YhA+3wziG1LUc0+uZlQJQhbvTFMVGSpWT85Rwaz2AzvHUArVIYjG5es1a7yAy/vuoxVU4d/3XrNgYMUf2FVTtLpqT9NQfnvXl1qH7RdKeWXqHsS63PNJ6g9mtrV0buxf3HHJ319pVU/jTnp1C+PDRf9PKZwVPUfAPsmbRHA/5JB59zeg23v9XtEwBeTfWnFXsG8AzxbxS7QOowVr/t3P3avjHSn1TtA0Bf8tlNzR8avI+S5XhwP233XfTUTx4f7E5g+hwO7x/0aRsVNHx43KWa5On8dtQtbHTqJQ8P5B5Hy3B476QfdUEGB/ePVMRIORjcnbKpCVf+4M4eX0gv9+Obx3rQxoX2Hr6iAkbqaHhnwcbaW3uDu4dsoXDhpTeOVdwBFSXX93QoMI+GtxfdCJqmr37CvIRAoLOfGQchjFDzE2YtsB6pP5YAOYhw/jOBKYQhrn7MiVIoRcUvrdPIeqj/NccQKAjrXxtoLRyQ5scMtsIcDMRPGskQTEj/icJKGw+3v1ZIWTYdsB/VpFd6P5Y/zjnz7dDnv+Ww0y72ml9LKGx3ZXzlU+Ktp/mhGd1J4nUqkjZ8vAg2YTWDo3uL6BRW1+D068RfTssjO3kQRxcjkBT0xb63HbUzO7o7Tc5IeRVMbsT+cn93ZCf3aXA+kYM2eTEmy4kc9/GjvfSFX15xo1tBeH4g9jr6bBSeL3jSJM8HwflYjvro2V74NGmu6OGDKHqyqI4Nfla2nwE8xmJt+O8ZOIzMU8Z+K/CVgF/Y5hMLJxDVuv6NBgmwFex+J4OxU0tT/1bjE19vbPEL4c2Jq1XxsYFDYmvXf9zDMRFbV3+ivDmwDc5+rugIWYWanzM/srqH1e8smhC5M+wT4c0ga3D7S40j4BzuftpB3+E4Hd2Y+VJwgsLbL0NgtG9GX5x4XHUTPP58BlWoqRje3sPSSc8Mb1910plYDa9fo63qZnjx+dCJQZfYya2pJxCnenTrCPWmmvl7n+95hWkP0OLz1LEhT+3k9sTvMKAsuPcy4riagP0vFkFm6yM0+yJ1/bRNzfRmihqfzUD/OwYfMf8a7j7TzX0H3hiar+r+vsULLO+K7hlSB7H6krlblX19aH7fNHcAfZnye4bd1mQK9X3JHwGyh9vrSt8V7rVU/75gNw1+KewfmOYb482geaq725rsYXbH8FsqPnHtLVN9pekroXis6i+Vv4f5M9V+Y9C+p+7J7ktBrmC/jJMbJyrp4lrQB68ryhGnw1v7IjUox+ntQz4zqMbp9Ssq7AAIBl8dGE8AG4xu7ctQw4aM7hzRIOt1Orh+1UbcGTz66sARqZ0/ur4woYU9Gt8+1H6rNZ3cuKqIgBiOrh9DKA3yZl9NusR3DE5vHbigF5rMvj4xkLMBkD9tRAnIxOt/p0GrXYjbTzTqDVuk7McNLoS+OlA/yljl6UUkf8tcoWzqtZ9o0BhzmPKfVGijyDFpf6W6HQZ7ofxNI0pEEtN+amUNxcFA/6QCZ519daQ+qtkak4XXfa7UDtCB6X6vTAnxApW/EOBC2VcG9qO8u8D4qv/Bj/7xtZ99/OKdD97/8Efv/vTnj3/4xx/8zT986+8+fP7DP77yy9/90d/+/dmbb7/2m0/e+tEvVq+9/sbPf/n2P/z46Q//+J2ffPSdP/vLFz/44fEXN7/zf/755vU3Xvn0969/+NHq1dde/9XHH/zdh0+///3XfvrL7/3ZXz7/7nf3bj744b//35evvXl4+/a3/vJvlq+/8dJnX/7RX/3t829/++pvPvuT/+vPt2+/Gjxe/+m//982b7y1d+fud//sPy7ffEffv/+v1UV4dOtmtN3sP3m09+DB8Pxs7+HDwcXF4Tff+G2zuHlr8vjxsCpPrn8dbTYnt+9MT09HFxeHT56Mzs72795Jsvz40ePp8+fJen381Zdxtju4f3fy4tnk2dPF/fvp5fL44YOkKI7u3h09ezZbXp7cvx/sdod37y2ePB49f7b37Olwtdq7ft1vqqNbt4YvXozOzw5v3wp3u5O7d7Wgm2aiK2d3KqtTUxm7dls1UksJ1nzDU36hbGu29cgU2mZ6001MZeClzPSQXDI/F2sx1jvoKrktUlUauOVZNzS5NGe8BBN8KeCO5WLkzjjqzKYay87DW143sSk0OesrN5Fnyts0WzGWl4bU3SYf8BoHBTTuwGPT4YYpeEjKacqYZledXPitc83C6nGyNlZOUTMJKyvhMa3nQdNAccWK/aCztt/XZp5soeVj0E1oCYQ5hMXc7zvVXdX6MGEQ9HtWjOItVHYPyqO4dEodgGY/rGvKj5Q9wa1n6oXRk2hlrBiBZo9yUxfxuh1TZviZK2RMlx0owY4NxcZ0LS12IWlYv3JZNwQZqzd+robRuje5qZoQb3XPglUxxg1nK5CxEciZ3sCtHJpLriqwrWOz4qzzsjL1qo5tyZpPcc7s0uQ69S86WLttO7CZFhXa5gPUcbW2uRrjTee3IYNXvHLocWTFNdKHQQWMuUqE72+owEf+CsW1692JlyeEIy2uEp7ENVT6OKhgvKPCOw7KdLBlAl7x8yhmVolriA+CAipx6LEgurQKH9BsELROoGu0GvotB+IaUCNaE6WPoRwOVoCDfVKmXg25uQKKCRS6KcK2D8NV17WU54Rs+kwMq10U1lW+i4p2gEqWb2nd+eCiLxu/L3204yVL6pUXtE1RpHk/oo3uVqjtqb/sRO/nVWK2ImsH1ZrSviuXKO8GbsvKKul4FC1bVuN2Q2ilin6wyUdB2zRLkvMUbnuW07wL3U4bM0PqWtgIr0mluRJXmrYzhk/CMvALLMxRWPlYpYK9hDjEVSrMVa9yXpEwdDBcOsxTbk5o7SMRO/kS7XRQBBJcpa3zNpEkR+ES+J0n1DEtPCQiLa5FHARFJMDVZCfiPOT0JMyjqDDanUQFoj3V/Bphnr0QpRvDS4l3LJcjeyYIM1k14q2Pd6wuQ1UDct7XbiTPtbdptnIslo7WzSYb9BWBhcq6VNXIv+xKmcgNIDlbi0m/o7Rqdnnadb7bil0Vi9y5c7YTA5lTmNBnJxoAACAASURBVPOMDdsLSOtuV4xbFoNM14XPS+ev+kqm5SbwOFftFWWOk97BdqHVJNkCYxZAHyYV0HIB2sOwqik/lPYK6jxbzbWZRSvj2AB0e6Smyh56/CSqG1TPpT4aVBqxfQEOwx2GLLFsP2qhMXuyu4qFwMVc2GO/tKgZGT0dboAzU8MP/RY4OXfyWtC2QTHm6IpfaV3YTZuopZAd3pYjXDKxRWs+gYVwS5OZlF70uDTbPrWZlRXYZKmrhV6bHR/BTSfPdQkn9ILZQpXdAC9728GsGhsGwUoULIU7Dk/7yo7MKYe5yNjQrYQt5TZPTW1NBjZ8DDLtnbeZHpqVAaXc9BNWEcw9zQ4wj+OlUWDPK9KgcRxd9atR0PRAXgNi4jdYySOnxoM1FHaBq5TWkNsTlE9oz0X/kjV7cetpfoCbIN4RAQ6xOIxKLcwJqudh0yFxVZl9VPuKHUCWhEul7AznM8J8aa964jCsWtAeWDlLSmjNkZVzXJiyjcq173VdvcRZn7odK7OwEXG07HiJmozSUpV9vM5Hfts2K5zzFO6Y2JGcJWDJWEuyLABb1nRRng+Cumq2wUZMSNarDWhE7F90ssV5FYNctJW/zRLa93wJCzX2Vg3bwFan3kXPG9wUPs37vvaLZoS4oetQeMfBCgYd5vqEFj7igRbXQo7D0hfgSpzJKAs5PQ6KJM6kcleiggY90ewa4TTIKTcnfoPCCyC8Y38b0R4Jd42WIe2AVS8hGdAqYvbY6+NkjXp8RPOYtISrK6iIcI+ZepXoQVhH3B4FGYwL2pMT0u/7VZXlg6YL3Y7vipCX0J33GUt4EaCMZ2zYLHFQN7tyXLME5arOPFbBcNk1Mimz0O74th21a+y3bb4idRfZrczagepoeNnVXcCX0O/0phvusjRomnJNqy4EG9bUYV1guOxLHleXmFQs74ZVFoV1XWRJIdL0Yjt7+nT26OH8/Gx0cbH/9dde3x3fuJGenY7PTg/u3onX66u3bw1Xq+mTx5Plxfjs7Oje3bCurty8OVitRpcXhzeuJ9nu6M7t0Xo9f/Rwen46Pj3bu3M37brjWzeT9Xrx4tnx3TtxWZ7cvzu+vJw9ejy7vJw9e753916cZ0f37kSXF3unZ0e3vomz7OjmrXS7XTx4MFiv/xW7CPf68uDsxbNvvxdu6zTbrd54dXZ5oRzpF8NoVyVF+fhb3xqu10ePHz7//h8FRTverp++/d7ixXPS983VA7/ohqvV4+9+L87z4xdPL996bbDNgrx+9u7787NTn7H6yr5X1uPd7tH730nz7OThg/s//OH+xfMwq5+98+5ouw36rt2bkJrPl5eXb71qNL5288aDP/mTuCq9LVeL0FhH1119Zc8aGz/eNa/vT2yBTmV/bUS1hDtpJr60wFv3YO7LQRQ9LtpXZ4ms8CkXV0cIGlgYP5CKILcDcEr6QTx4mFcvj4mU6HnDD9PIt95O2NQTAaHnDZ55eZgOnpbt1XFKGHnRi3mQ4r6vfBtjPgomK9PF2MYqPdX12EexCQprMRIp8Ao4kE25H/i7QMTIhHK24lWAzMj3dhZByKeA5i7QKj8Ioh2QPrKJGV3qPoBmhGgOkHHbk3SyLEIhsqNBsgGG2GIvnb8olQfMBOHKYemy4+Fw08VctAsQbwGjNDsYjzY7P+t2bx4HZR2fFvKNoRRetGuqq7Ng1xIm45Fi0oc5L9889qs6XnfVK9N0l5vSNq8uwqzzO6YXgWPar1T28r7f9uHzsn97nuSFLZ08ikivALNu4tnehDsmr8bMBtHzsntjktQVqKw6iHEnYWvDVDQu8jdcXYsbLz5+UuYHlCDnZU4NoCUmzZUY4jqO907V6tjfYyu7SaoDnyAdFUpFqA/8eK3t0NZJcHimVgeUIhG/4PUi9HwQZFZFSPzftN3HsmVLguZ110uvtbU6OnTEVZmVWd1Udxv0pItmQAMGM94JDGOKGS+BdbVhZdVZKW7m1SpuxI2Io8/We6+9tHL35c6IOQxy/r3C//vZyN60lplu+93xnG8nFgVNsNSRD5GL6F5rCzaODna8tdShFwyvm8ORSXHtL1QWIOExuq9oXZUnHbTiysTAQ+wuq21GhgY+NFi2YmqDSJpFmR939aLWGDTHvc7NqmUIjkwcclTJw9OpvYmsvChPu2RVA6jyk4F3tQUGtqwmyU1U8PT5rLsLUSHrM8+9CxtmJNORd72GBOmxiZPGyHl95qpYk7RpHgfutoA15EMECm0ncvO4b+fZcF0uLjpBnJGY7M5cq5B2Wtd9DGsRpCIZGTWyBnfV9okVJBlNcDomtG5hrto+bjnsHtpsCmtsjR/4+oxM460o7GRkMq5Zppou5Ir4G14dwQoZJ4vi4dR1yortQD6mrG2NVO0mXUDbzrtdehpYiOPLrDryIYZoXWqPKp+xVYEDXHRd7zLOzzpICnqfVz2zb5Z1ZmgTtT4jywLZOh90zbu0Hrsm4cZtXvUt6BK045DCKjDRsiSGamZd+/2hmbl11wveLkTAtGfCVSENVs86wd2WmCqb9r33YTW0s+FgvAiJUOvTTrBJ3ZxHp4ZzUFDJ3bQ7eMgkUm0fk1jTst0+6gX7xM/rZIztEHLEDjN3OE9aBEUPwqQ1GrA77Qb7zCx0fER6m7KBRt2DQVi1EFU9ihLlFjqZYV0zJ+LJEe7vc6lx2WdGBLTUYoh0qoJMPzzpDfdrtVfNiUtLDrNW9g1RKWNXtse2YKZ9FRXPB06RoA2vjwNVCJhK2xcNMdiOq2OzpJb/4ZA87Tl1obZcTB3GBY5l22cNwNZDos/sA3A6N1H2uBe0BVxxNcSW4nXGVJc2ALOHVE9Y4wfO+335pGuLiizLauxo07C33CbZbhj05vAwogTX/VUduVh5BtsrwGDtQ2fXQiajgTO8L6KhjZgM5m3mQe0hI9QtxfHA6i4zTGXctwcPIu/irOdOLg+lg3QHmSFXCIfjYLDIERF1B/hLGHeNrOec3Kwqm3LfIHsJMYmmzmCRSYb3s05wtUEYeF6dVRZKmvjFzN1HJBXVqe/OQ65pfj7w70MElByaIOVG2TYzS+aI7YvmSeCuYt6SdmzSsNRSq5GlE2FFdX3h8Qobu7K58Jx9IiVWQwNHHHLgBFXEXSusxIXDJbW2RXni2LtMVEBNLZY0qNZ8YnLEJvd8dcYmyUYkXjahtG1proUHOSD+htcTWFDz6IEvTw27Ke2tSEcG0cCIddNBAmNvxWk3XXvj8VJsZ9TmtbNVSRchglikZQAF0f1tU3RB4nXGH8r9hWW2lbPWeRcBBo24zTs2Z2h4m4thG7ve7LrcHzFENNnWEAExNPG6YUTEgx67innPEiO/9/6BewbqG2RbtwBHs4F1u2VQ1md97yZqOkY56nZ+WajA9KwyK22lQDnrevd7SSkYEPMqqgZe0uv33j0o19QDRg4VFao69uCmAQiqHmU3SeU5dIDpMpcEw5EJdzWSMH48PL18B5v27sXL6e0tFjw9GuGMdw/77aMTWLazy/eXf/vb2d01KsTu0Ulnt0W1SI8nKC6my+XDJy90A07f/XLzq49ni9ta0ORk4m93LK/is6kuxOzqw/xvPlWNvri5fPfJp8PlAzuk4aMzN07MJI/Opq1E0+ur8mIY0v6jNz+//83fDLcra3e4+tVv3f/tf//rWISIdH94F1x/QBdPnff33bvbQ3d4dfqiIWZQR2fffO6tN68/+RePf3rfffd29eyFT8UkEHecu+8f+vP5jydns+++7l/fvHv124M7XP/L8+P5pXs171w93B89ta4W4w8fdv/jfzjCxawvf+ba/+Um+OpH8uiVfbfpvn63CiZ3j59qDTyZP/3L7wc/vl6cnfu3y+5XP9GnHyfKyW6YbTEMQD43HFtjGBcnkKdJjaz1g2U4hu2Q/IYahGE3KfzG0qCJgv0iMEYGAuZu6Zgmts5xfN4ZFDuwavZzs2eIqsG7eUC6titTOINtk4dqXN8w6xxRhbZrY9JGoJPk07aJw9r19/PAggj11OrGMY40c90k7nIlGV6XxUWLWu6s19MuAm03yVU8xfqgqjJLT1shEHx4ZC4Omv5EXnnRUBJZdyMRD1pQcZFtT3qE150qSQ8nwAbl8AD2PYmIPInP8Iaxelt1s/QI4LbuNXXaawkU3WUy8BXFZsZFcpxzodxdkQ1awpojsF5Wakvgc4ev+H4/6M5A0uuF3ZFRlUXoqp2YeOVhbcmtBc8JZ/7mk1P3EEaxna0pmZhJgsED7rgoH03khLKyqPbmbhlYJ6xN7fCB+DbQU7+gri/S+o43D0ZvgsrU2C8D54hp2w8n46A46K0uNnT6CqWpVczNwbDllpfE49qtiFvAcFjTMvCvnrP5pR5G6TiOerJXtnVUpC+FndHO/Yv2/VyPD/VLnHWBsZbQCOMO90rRObiHq9o7FWwkd33VTyRpQdJH8sZwmqfsqmmmMRpGhyOJS+mVTjjk3cq0bp+y+RrYu3KapSfSa7S7T8NZi5siSIs5MiAzByzdMGJhDyRlr1VFStlpcYch1PaIxQ/aUsQMBPc44RLHbLEKKIP2jKZ3VElAHxvlcIrHyo6T7dbHrQQjs9wGSoPREyjbRASkKdRm5ba19no4jwKlWBtA6HDYcEyCasVELHpTHG2Jjpk9o6Qcw4NbDyJSuig2KlnNxP0ztlyLT0XVr5Mhr2pVE7xDeSe70G+esOTP4qxtTrLElEXS1LQ+jMt+aclbc7ctBue4GscHs+0vJe9EsaeGh0BdDe34h3aUlud863N/Dyuryj1Z33s4eYIvd9UzXvdkNhHdWLag3bpNrxV5Gs197NrIUft13zYJnZaaJBC7beGGS+pB2WBjtwiYwUyagZEy8iRB3fDWoEOAKcnvcTARssO2Hz8z8wJk0eKhwwQyn7LimtAhtu26sWsglYhguPIRMZFrJvOO1WCjpzXKhOWLFJfrjt3hrUvDVYe1uJnYfG83Glcn3E46edPCZjuFD0Q3G9mrskdAg2q8M8KeaGCji5mYjyj/oT4r8iOlzXos87QLJZTBKh51WkJZ2VTJMS+I7B2qvNNIr/bCj+AVA/IL9BFLRklK+HgD0mGe4ra/GZE7s5Wv9VmTHePKKCdbmnaSmMomy3Jr90DsgJGKlNfK9g3QbsuetHhd5p39MrCODJ0b4cI1TMTsihg5VDQPrWJuDfptFpHdPDBGJgjA7uMnfhFV2zy5tnwH6wZuN+zEDLXVZjPFD1FBvMODGRiKI7K/NuyXBOKoHimD13U+CheB1Tcl0fE9M01iGx6MA2LeKceKkzE3KuTcPbXmD9p5B1/5YU/5VW1VKuoRN0Ju/MS4uxWDDZykh5kCouqkYNdrXF3307uzI0cWsERp0msQrzxRJU+FalVv+Sn4kGBnWR/VyRAZmWJJlk5bpBq/eYRuCxh8A1+5Sb81VMObIu5LS3EhdztPNRp/ojcPDihM9Ig0Xr+Zun64LyO/qSicWukugIXodmE8nWqIBvvFYWe3G+xOWHVwspRZI3uUb3Uti5NRseZyZ/aOcL5jzYr4I1IPRpkZ9Mp9tYS8oMef6nhnb1dsMFWx4++ej/3Dvk6cKiTuAB/2lO90d4h1HkSR3w4TWeV58qJ1wnzQFIHnitTYu2XWUZ070XTDyBPd1MD7j9zNWz1eicdy5wrroAHN045PX9cW+/nFxMwL0iAZzriVAauFYbcysx68eWEu38huWJxm6VGbVQ2JVThtjSwPyiIIUMvdHSryY+zOBfaS+LjxK92NixtEA0g9ljzQoCM1rcRIwSrFMb1f9IweNgckvSHaY3SssVW3reaRPqw7tAK6Z1XLDsgweypWk2NNED2U6dIDLvUtuQ97RLLGBZnbACAhmpVzCpV0+yReEmDgAMZVrwV1qsFov+xA1NpTM1uQFmD3CLiXD+Zyfzd74vxy21su4//w9+vJ+d3J08nmrv/LbfDDG3r+zLxZujeLXWfQtWQXp1+R0+PXP3Tf/nJ/8ci/XHjfvjaOT5fj05V/HNTxyevPvbvF5n/+nyZv3w9+ert8+tzDzair7vLUfNgN3rw/DKf21WL0+k383/03++n5w99dvHz4wV1ugi9+sCanZLMbfvX9/dnLv5pFqPSzP/5h+u7d0ftfTn747vSH77urpSJEMoZUe/HVl+dff+Ufwqdf/GX65s306s2TOv0I2LNiN37z9tkf/+CG4ZPvvjn94bv+/R13HAAAbeqTn344+e678eWH8dXViy/+4iwWr9r8Y2UO+OHR99/P3v9y+vNPk1/eXHz77WD+oAhuGQUQPPryi9Mfv+/NHx598+Xs7dvpzz/LnGRLE+0asCiyOW3DlmItTyd2uAarMgutdi5gLMK51a6FKQp4PKBEybsy2xvoJrN2RbYz27kgSkpmaIrQuigeoNxLOG+y0KC3Ga2S5mxMIWdhGc0tvW/JPM3mSK25c9jL45ElCxXydG3g25xFefRgNTvt77iR9N2tbe9bmI29B4qlkIahCCWVptseyzx/2bC4Y+yDIN6/gu6LaGGlKT4cwXhsp8LYD2jWMQoJgWoc14olLqb20mZVRQ8znI/9onxRpa9aMtqHNO0ZydDMGcin7to1ykJYliIEtYRGI5oG/qpmiU+isVEKvgDZxsKZKO9BsaXmPgcYSsa0Qs26ze+gEWXtPT+smLHNK98HAKBW42WdrpjaShHjYm7QQw4QVIQABOWDyg4mmhdq02RrpsIWICQZw1qiuzLbmjBs+G2T7xlclMK1FcZYte22Pdxb9FCAqyRdM72rvbCF1YmzNY0cgMMpTexptPhUgFmTeluNiok3B+6+AcXMXBuDJv01NU5VY68qsh/QmHbWGhfjYEXpavn3mJzfXFuZIPE5y307VCiemIlxcrh5xTvPmq2RIlwcddfYTGscneDUn1bxJy09b/JgU8NyYm0clmFUHNkrQwlV3QFxQOY8rdegvFckysDpmJjaSuL0nqZrw4jL6gFWO2wst9AmsOegUpY7Vl5KM03rB1zsDJaUwjQhBDjjYqPznQkTlWxZdaOM3RZaUF5M2S4uNqzZI2uZthuVzAnaJPp8QvsOC3f1+ybbm+jA+RKWe0L2HDUDMzqy0sbdUJjMaM6ewfSV7B1Ht3RvkHzmxJQWHolOSSSeZttPgD3gob01YT2119CKEMpmJDHOmurfxnGghLclJD9iGXbWDBXjYNEc5dtXFTxL1jRzSXJhZ6231DCeGAd4nlz/mp2fxjc0oiQaeavGTAlKHls5xss63xnkriDrLN0y/i4zkhA9P6Ueg5eb7AG1u8a4S8o90w/CzBN5NtFEGPs0ujebHSCbMlsztJW05kgpYVnGIkp3FrhtaFbG90a1hUbL9VEfQ0EWSX4w20ULY1FsjOaqNrIITztw0gXrPFlQuOLmPC1DVmwYq0p6mKJsZhfa3Pdw0uvtwue8fQVB0LQgn7hr3yxyd9+F1YndwFcyf87JeL9gkUfisZFRWM3ccGQnmTBNhTERykn6pBh4G4lTjx3Gfi2eYuPXoDX2W3dpo/LISJC1d3E26mzyR3X9kZSDMoXxEB+OzTTrLBkqj1gK1KrOtgZcNTLSh3sTbkumK30+YbLmN1W2N8hNQvdNtjH0bUqwgOcjViX6fZiuqNo1+oHnoUEfcmkaGkEIAdiKaG7hQ6NvsmxF0bp00lCcjq0qlWGbLSl7SOGuPjxYesdNKNTZhPJcX6dZaKjbSh9kvmJ6zXFN8WFsZkawECQbWhunm24+gf6zaG6kNT6coKTjRIrGE5p6R9HDR437XEZGBlBxbC8MVjU0OjaSgAolDBMA7UYtKUZ23GcFgdmRt7LsaP9rLZ9C6jYKplMjsvxVjfMBPQw7sPqMBC+bhO5ie9OF+cjODJaPzdg1Y5nsaHGjzDQVtzJeG0ZY1J4HAMC1VCuZLTA8yOrAygdqJlmLiSIYKsDnutwzsijEThcLzAv9a9786zShUIHrKt2bcFfVd6LaYBByaZstpYzzao2iO8OKk/Z9lm4Nss5ag2oIgVRyLbJbbawTvoHx3DKTzF1RVE78eWuHEqVTY08BAsK0NALOBqJoZESws4KonAQreJTvPpXGRbIwE4TjR2ZOzS2G2cyNtMYYAKgodbeK5rPORhkpQIdTkpqn9eFj7Z5Ue2+tYDUzNybLKc5m9hojLSWlGgMztVE6MbYw2ChYz+woYHme3LFiS0hYFUvCt9pardTAIx6Dqcy3Rn3ZmGlWPdAyZLTheuACqth8X+9psTFQ1kZro7mRZpVzywYI6kIVW6PdKnuR1luU3kEWxu2zU+oSutnXV7I4MLjn1QbzDaSrPRx4pGuaRRUvafLArCRtr0UeGtZiP/v59cW3X4/u72a/vH36xz/YaSopVQhj1T766oujtz+f/vjD9Ory4ssvx4ubJxD9GgI3z559+cXJTz8O7u8fffPVyesfR5cfuG23lNCmPnr9+vkf/xDE0eM//+n4+28HN1ePi+WvyPh492H47v3jzz8f3t8dvfn5yeef9zcryQwAoTCMR1/85fj1T6fffTu6vnr61Ze99Ur//68I/z/+YP338fOn9Whw+W/+LpnMkuPj1Wcf2UVu1BXB+nB0Ej57cvvZZ8npCe8GH/7L/2oH8KaIPkxftL63/Oyz8Ol5eHR6OD+9/ld/F4Q7t0iVayST2eHk9PLv/qXw3dWrl9HHzxeAbov4w+hlcnYsfPfN3/870fPD07O73/7GqAs3SwlS+9OzbDqd/4vfrB8/ay3j7b//exvlnlvhc4MMWccu0JkpILSu1sXxmd0DLiiMVyb2QNcu6BlrDYPdhdqw0IkXyAT8qoM85eOKvDAJViytHFGgnmHbwjqG4sLv8kh/1gEImjfbxvTR0Ol7KTvCYGb7rLDPcDoam1cr7g+sAfFggT9yqQ8dtzGnoDq2SHuopoUcCbPeFY8qbSkzq0zRaLPVRszsfXqONKjEKK76ZlQlb4KTotsjeq79XTlGgMXIjcohYJW2i0wF2hBhOU3aHoFoDdzdYWJGvF0CcD+dYZi3nV0xVl6zaCaR6BIjK42Gt7amaI2dJD/TGBais8tGpmeVXS+TF37Hq3yS4xeOEtBKM2hBk3F/xNmUwoERWEn9auzFIa0EMbTptY7ToKe2SeugX8Fji0hh5qUyidOTnk7BZ4Ft1I7LjQuKW8GKiiAFBqxnZvSxgUc0UCn4xPfSmFYcEU3tttsr2Yy0M79DEvrUTHqmVy+K00LbnMC5GBVRx19zuXAG0chwmm30XBlWqnWen+alayxKfuv0i6HD4KYdFskRtutd/ISjgfvTPlu/fIYdRNBc9LO63zKww/3D3WCaNPFPwanyNRP75Ey0ASD6vu2lYdfe1PWD3d1Nu3a5Lc8L6BVU7IujHLnA8aUbNOKpbxFuHwMwMtj1BkCzHfd9M/eHQp3YjsM9v+KPB3DfwEK0o04XZ85T2o5s0238bs3PO14YMs6Bj5mh3HGjj62e3DuPCBlbbdrih7B6ct6xc8uX+pnroNKZtvyib/9yB3KpTmd2RwVOjh9b1NOO3egnFm0PrbvjY9i6pYGW6YkOG7SXybvZK2rmEO3TM4lIRtidmMEdNVainndO6m5rNpvkhUKshCQS0yKE+mfLlYEjPMngqpnVRUc6fBu9gELraw2vBifaEtiYN0PFe9JQq+aEL92jfbl+N36BrBqjMLuA2qwpvU+mSM1Ynx/AJ745xF6b0k885bv47YOGDD4eB0ZuH2HxqOPLGH1sa5MZHxbQ8lHfdr3amihwbPtmxWaQ920jrVjboKHh6xR95KIO7ViZM9XCN/BdiJAhH4/8NjWfEj22RnJnvLRU1wE3O5i36mLUp4k1A82jjq9y8zFo+r6lb5GziY+ogdbEDMMzZ9+qW0XW/b7D12J04APaGjE0NskUHiq5gep+fIpJ1Xr7YtIaYiP7Oz6itOZWWUsbA7JHRpxftIaORXDIuyqpi3fATCfH3MoZXjXHjbA5wfv4Ec2luELWpjPEMALuvJ4YtVsbeBVfEM+oHKMhzy3Dkt2gRGeW1si83akgwMdO0Mb6111iSd9q0KsAVALf7XCvq896HZaxR0wcu31x0L/qOGWKCkmAoC7odCp0zMjU6JDUfGJEnbHzYdEMRnYPuqxCzx3D145fk1MmADJvdzDwxXmvWx/oZ47ptq5R4ac2NBuCtrgTHc4N1kT5Wd54dljGb7pndeAScN9242qkMQp1L1mNh0mdvLUmMoCU74qTvA0QQivRS6su9pLc5lXZxYaMqnFYD2BQ3lfHWTN05gW/xeY2CBy1UP28OAKsTerJ/uDaabL5xRxU4yGkO+CE6VSydsf7RTmjPbF3zxEbENBnXTerXgy9cI+bljjANrgzaNsL2wepdyTA2KJ1YxYlZS3pIYeV+pXnkModttYEX4n2nedhz0J90jNT/NSmXeSyEj8x7SRjVa1NbLGqO27IlLVTv0Ni+MKDTcuKmljANoUzUOqRa9m8M6jaY7fqcKfeRi+1xVKJi/KoJEhZaUGBFJ3W0Bt+1CQT5FSb8IUqKFs15S+9U2VBQh6aEW86jam3epLklm3nHKtSBwrrMD1rtc0JepDDcus4uzK9DWbxxHaLVf6kQmaB1a44rhDTVlYhALRTWO2an9TRmHrVOjtJgUc7ZuaOW3BsekbpjER1Omb3h1YRNfYGMDZfmGpoOU5tD6QYWOx+RxThjyYeKq0LrcfmQGysZ4z5UNeK1TX0sGfmbIzRhemown0Eq6O+/8N71WJ9NnV87gUVfmRZjLORBqe++bBvBWmHfkAK/1jKmUc7wPer+tVYes762Yubv/2N9N3FZ58k58dYSD+NIYPrpy/qwH/7X/876Vqb56/mH3+c1MV1i8Lh0frscTkZ3f0Xv92cPeId//bf/KtOtIWVhAbKR6PVJ5/sn5xvnr2oe527f/13a9YPs/tfzn4rA2/37Pn8bz4pRuP1BZv8ugAAIABJREFUixebV8+QbL0k9kVy+enfKtt4++//nne87eMnd7/9W/PPfzWL8OMvPx8sHpqeN353Obi/bfqdZ3/+/Mkf/xg+vXj09deD66v9q1cXf/l8fHfbBG73zfXkmzflYDi+fD/95W18dnzxww/Dq8vtk8cv//SHT/7pH5txr39/P7y6rvq90fXV6ftf4vPjkz98Pfzidfj86dlPP04/vK+7weT2cvj+MpuOz7//7rP/9B8Pj89Pfvxpcn2VH40H7y+Pfnlb97uVcqsbYJkS5U25QLJv4pxkH0xqQ6vK8yUjWFIl0jtiGRwUIL12DQ+pQtcrQLAIZJ0+EIJbqkT+TeuAkjRNtqSG1YJcNmtsEW4qEb7zGQKOwbNrwpgkdV0+QGIr0KD0g4MosUGdPVCL5y4ut/cOIa1HAAt7tNWGSkk0wUXLvMz5MGW5gczE2PYtIXGbw2SEW8xwXq8+goUDrT3bHsPWJCxm+z6TEIPKve+wwqA4Z9GAlhC6sb0aQWkBT8CHI1X2CCpI3KGcaZObuw7LAfDi3u2QHTzeEe7aMRpk6ginXVTbvCP1ddnsAZ1SfVfLA3CMUu4x/6VGPm5jDday6+V8DfMDYyPS3nL1c2l3NNrkxcHELmxj3S5auy/Vh7p5J9CYkVVd74BpcBzzckcMW7YbyX8WTtCAlahD4vi8DiHfapNyI+H5G2UYUhW6WJGek4hNW4TMCSRqHW/rQSgpaMj+CJOa1JqvXiqmcA1Z3IOq6FUHkV5gxRGU9eJTCCFrSrIfYFIRDlnUJ5A7ZWwnn9lNw1hF1xOMJJENOwxcdVCSlpvPAMAGqFg4YbxGJGfbE4Rb3LZ89RJqSBpBoz5ULYMV24+xbIVbiUtJuDIRL5dAN8A02/TKbTPmdcryCvEaeV5V3UNYtYy22b0FSlN3jeahhTF3u7y6hLxEtI/16xI9FJYl+Q61mcIe4g8Kp6IfJPvroEoDY4D0faML7dJarGSTE9wh5TUTse33quZS8IpaHudLIFJg2dIqPDvxoBWxvYmKfhs0LOpVu2eQFl7MdT4CJmcVs0IPugnc9uv9c2InKLGN1McwtSpAkwHCuVW73uERMWM7QiQfIDPBmW0kHawTu9DR/rcY1QaH9DCkdkJCRLIBMWJY+tXqFSSNWQt8GBKYMIFIOJSOgIdELLSJBW2K9IER2RiGDj/0MKZmW5b3kBJBGlmvMFbCISJ+F2Chema6vXUQ0gbg1QN0iECNrL9rUKsc1GR3hNW1ZfP0EkOgKFTZlWNoTUDbLCCFQBugvmpJ3Zh2m773GuAyU7d3gsqaItXMgZIA91D/3oWNA4LKXDlGTRDMmsPjtpxqS5ihzzIMndBd92DjSY/T1UxmUwvucerDxpeWdPauU1DtHoIPvrX3RadxdxYtTYIyM2K6CUwrFdsLkZwCf+8sXMwDaKYo8lluMZTqeFIn59hIadzFRYDttbu2cdMTbkXXSbmlJuO6VMUCO14jtyS7d+2+FhFoNsCg3KmadElMxkGFk7ug5xT6wIsdsz2hDi3fIpsKlNXld8LEHLWguEeWK3XYVGvM/LbNSHppExNZsi4esKdTLORhYdk2lznLr23HUzpr6xVEVJuSl/eIEmGYprnuOCrVXOFkhLQyYFktfoUagljMticIQgJLI+xRzYmU9eql0paBCrafkKZFVmYtRwBRRGX3g+eUDUQtC7tYY82Ete7SWiEzAYuXRTvBRuPsfENwQyQoHSNJWrcFd89RE0BjbywHGlqQCmfnYolakzcPEkWyF6T5LapyygZYvq/hbe3Yol3xOqPYR3LR6lA5Q8l/rPittPutWAMRQ4s2YC+bGNtW3axMtTH8Xi1uJM+I4/F6i0SoLMbRilfX2nJ4vUN1iMdBUtyDKqGOx5slaN81ti3bSFRb6DoN38Fmh5xuY2SOkXSQTjtlLrMzjCvWQO9uzIySpIDEA8JSWjIadwmsjKYtFp8iIAzdkv0Ys5wWmCV9D6xl5XSuBhBLs+VsP6Iqx6ol4QyxAldmvXlFcElKwKIBANxQnO1HCNZUKf9yCA2AOTTDDtMRUtTYdRHRRIf5JVYKmLSpHhCRHEKYXlkYmG3AmiuJK257vPgAWoWxQ/glJbW2DNUsoeYAu7C5alktAjdPvofyoHCA5a0gUtuqqJdIcIQ6NH/LVGl7nbL6IKXEhiHrBdC1NjEvbu2mND23Tm8JKLUXVM2HVkjqGtXj77/q3j8Uk9Hx6x+PPrxff/T8k//rP7780++jx2ejN28n15fCd8b3t6Or62bUG3/90+Qvr6NHpxfffT+6vc1no/H7y8nle+k7529+fPTPf6gHndHNzezt2/TpxeyrrydXV8V0OHh3M/3d1zzw+6v55PXretgf3VzPfv45enx6/sVXv/q//0FMu8Z8c/r2Z+653XB78sNPh/Pz9s2bv5ZFOPz5/dGX3+Gcd6/uZ1//QOM8eH8z/fI7ocj4m59O/vAX0ZLBz++PvvyWZFVwOz/69kec1t0Pt6d//EIoPP75w/kfvhACdS/vJ9+9plEe3MyPv/wex1Xn8v78T19JhQdv35/+5auWg+6H2+M/f02iwntYn/zlG3Io3LvlyZ+/aiXs/fz+5Hefg0Z1PtyefPENTYq6pMkVKXJaZTS5ImXFRKKzOWxzzTN4uGH1AdcVyS5RnTNRweIB8ZrwBEa3rI5gU+HDjVHvYcNpekerjFYZOlyTsmD8oPdXRMRIcJTPkYhBLVlyhYuY1iVNbmmZElCAdI5EKLmg0RUpd7CWNLtCVUS0YHo/0nHQSlPvJyDsNICh/Ujve1ISsB+quKdrqg9DFAVKUhQO6CFosInWfRiOpKIgHOm4D2oCkqGOglZStZ+iTVBjCy46OhzzlqFDF8Ud1VAdj2DUk8oA4QStOxW10K6ro5GQDB164NAHNQFRHx36AlnZAqa3rG5ZtUXRPRUFqCKSLBgvcB4a+SWotVEtYXxNuWLVFiZLSxSwjHByT+uC5AcjuUKlMqs9jh6MWtByC6Mb1mSoilF0y+oMFxHJ73CjzHKPD9eskazZw+iWNSmochrfM56jMqbZFa4FK/couqZNjXXF9O5YZ76UFlpN2sJT3CG7gaptVJhwNwO5pWqqt0cg9SWndNXXhScbG27GqvZ1aartDGS2bC2wHenEE8pC60mb+qo2wW6qCk+XFIUDnFlta+jtEUo6DXDQaqiybtuYOByC3NYVheEMxk7bMr09QqHXQJbdoXRj8BImS1qsUCtxviFi0TbEqu5Qeo+5JNmcpEvalqDawnqjucTxgpV3oCJWeguzuVG3LF+TdMtECbI1ze6x4DRZsPIacGrIuSxWuNWkWKJsaYgSZmuSzQlvSb0j9X3bMLOaw+iOcU7yJUofiKyBzjt4PeTaAZEHN1OuTJjaKByrmunCRbtZW9myCNB8yFsbxRbcjmVrwMiC+yNdGapy9W6marstXbgacGnDLIDraSsNlFp6NwOlAQoD74a6MtvcJauRFLZOPbiZCWHDzIT7MSiMlrtgM21LR9UeWk1UYzWx2l8THiLekOiWlTssNCnvdBND3pDkjhYJkTkMb0i5AbIl6RzxnWoUza5RGVJe4+SWljFtChwtjHKHGkkPt0a1gjWwyhuU72jLUbGETQx5DqI7Um5Q1RrxDa2WqMFGda+bmIgGp/ek3CNZosMDKxegRgZcdfR+zJUBwh44DFXDQNJFh46Uho7GeN2piE12Xbgb85bhyEeHLqgwiLsoHEjJdDQim15NLLgP1GHKWwNGHX2YqJrBtId2IykoiHp06dfYgttA7Y+UZDD04GGsGqqzDgr7UjIQ9fF60CATH7p6O5GSVjE63LAqxWVM8itUC0MkIJsTKUlzgNEtaxJY5zS6YTxFssT1PRACVyE8XJO6oc1O7a+oSGFVkujBKDNaZjS5JFXNqphEN7QuMch0+oDauK0qergiVYKrnKRXuK6oTEG+wG2NeAqjW1bvQd3Q6Io2EWiVC3Zjlfm6YmA/xYnXSob2IxL7NbTRaqCTnuQm2E10FuiKkmiIIrttmdrO0N6voQUXPZX0pTD1YaTzjqoZOMxgHAhtwO0M7fwa22zdgUlHcAPuhzDxQU3BfoziLm8Z2Q9J6NXEwuuuikdSmCAcgqirJU6WrLzWNbaqOxDdMtHSao3StSFKmG9x+kBqTrMNS65ghc1qhaK52QiSr1B8T2QBsy2J7kjT4HyFyltQI6tcof2N0UhabWB8z3gBi4glD1Q0uNiQ/Bo1gOVLGN1SLmi9VtHSEAXMDzS5JTVn+ZYkV6RRFGUW2B3pwlSVqbczXdhtacP1WHJH5Q7cTqWwdW6p7bEurJbbZDfUud1yF60msrJBbuvtTFWmKgwdzXBuSWGB7ZEufSlcvB61latLm+yGoGS6NOD+CKV2Ky29noHUFcIGq6GoPVU6YDsFpd1WBtifgNRtgFHeoGxDRIOTe5LvSFuqYgX5XvGWxneseoAVtrIblG0MKXG9BvUBthWM5rRYAKFYdEerO1AjI7+H6c4UDcnnpNhRUcJkTosVFprWS1wtWk6M8hYlSyZqlM1xtsKqAvka1RsoFYrvaHWHamzlDzhamEqA3uXd0ZffgVz031ye/vPnQpPBz+8mf/kWNG3vl6vjr79nSRnczI+++BYVvP/h5uzPXwhNRm8vT37/Z1DLztX90RffsTBzH1azb35CWd178+HiD19IiQZvLk//9CUsuH+3OPrqW+OQuvPNyZ++QlnVuXq4+M9/VHXb/XA7/fJ7nJTe7fL4z99YYWovd6d/+tKIMoXJX6UivP8//s9hmXa2m/3TcxKXdhTFFyedxVJoXB8NzF3MknTx8Sf+dttfz3fPn6C0dsP9+tnz7mJB6zo/GRuH3Doc5h9/7O/D/uI+enpmxBlJq/WTp/52Y5RlcTyiUe5ud/e/+pW73/fnD4uPP+5vFyQu10+eevutlWbFyRhljb9eJ8/OZK1HN9fzzz5lRSligLsYQAj2dT3tAK2th0N91veahG9VO3GNtuYRIB3UUgJ2nHVAaTnGdVheDD2Ryq2SI8cAdXMArt1IRpsDNDqgtF3jOqwvhnabtwvOp74Jax4C4oLWYmDbMF9Xrmdch9VJz4WVWHLVM7swOaQOcUDbsY2N4i7FVmMu27xjM7OAEdNIab9VuRU0h7LHdGxIiyC7MbagdE1iV+BAIISqI3Rhmbwo+gY7AMEQtjnbgdo2kFuDmAIF8onphMLiZT40SAQVhmXfCBaFZAgGEqQUCpBNmHMQZlO1HY0T1FAzGTnGLiFZU592SdZYm0id2I0w6KGoj7skrlAlOkGVN1abyeZigLLK2ObNedeJIlGg+qiLsxoVnA6wrCE6VNXFAOcN26TivOskcZ0TOGKoaNoK0T7ikuB9Rae40Lb5cKgeD9ws4RmCQ4ZKLgrY8/IIBiis8cwomNdblkWfMV2CxNCOaBmhEUBOU9qON6+iI3eW3+f5sOozBgqdGNCWwiQ0BMjhhed692U6c2ydkzUsBiZDJUyYNmRrYRzjAG133aG74PnENkGO97TqMEJrEBFotNJGJMaElVngevO6GFgGzsmW1AFRtlKRMkTFRy7Y1Nqk1AdgVQvHYn4r9wooRYe4TrFZl3LstqHQCPJJYN2H2qC0C/hBQ6Ga4w7b5WaZ85mvD1JrLSYBm0eA4L6THipf15Kf9eguR43UY9NYxw2xm7HPVglAkPUhTyDJKzQ16hzjstFT20yFFkwHQjeYJW186pJSevs6ObaCNFWFkY4tWkkjl7ojW8lYJPVQlNr1t2Vy4nh5pgqT9wCthS4J6AipTesg2r6okOsvi+TEnkSrRIxED9CGg5KCQDTAcvaNHKqSusFDkZzabpXrhDU9yKSAOc4GlkLKvtrVp30HlO285pPAxDUPAbaBdqjecuaoKvDZ9aGedRxSt3Mu+tYARWHqEVMDj7V7aZii6ATGzaGedhxSt0shexYzW7lXiCoQsHYvDSaqrk8eUt53Zce2r7Y6MLEHxF5pimXfMZcxpaIadMl9IjpWPQy6qwxIkE1NOxJ2WRUjilIMtC6Gpr8qFYagI0FKEdfp/7uRvRaniCMzHxjOnkOodSB0Smil4hPXjBtWtvUIefuqRjbwGxxDBTDsiLY0zZyLkRK1Zea8mhDnUHNga7/BKdISgJ6UjW0l9f7U74abJsF6bJK6ljmkfSw0QbuKjnEBbesuLB4P/TLhEdQjkzQNz2DHLTLk6lCwCSmwbd6E9dOhXaZir9qxw2QjEkC7gCMDbwo6wgXzrKtd/Wjg8JzvFO4jS1ZpYbEAcGKSbUkGMLcC+3JbnfWdthA7pQYmNAg5gADsD72utWqLgWXgguxI1WGEVeBAIVXSAzihDBVZ13GWvOgaJinJFlc+I1YNItpSVPWYt6kprtOe66xF5dPGp937nDsYuhLGVGFYdqm3ayhqpKtpiAvfqnwSLOvWhMAVMCYAgmxoedtaEZAPHTY/IAB6Xh6Vri4lv+iTsCB50x455ibmwKynPtvmQGnWhyKDKK3wjFUlpUmljhxzn9baJD2oI6EVIgPUVIQcSnLEqobSfS5OAztMa8loH6lEKA77frZVPXYo8LFZNoztM3kWmGFac0oGBGSirQEdIgEdf57Hp840XqTNiPcQFTUoGPBFg0x7L1RfFoYX3BXJmeM0OTyQqo+NtgEZBS4XxGQH5ZvbVXDcuc/TY8eWOYyMuoeI4jAlwBGcGVbYAr/OnE7nPk+nttXm6ECbDkJIogRXAWsZ7qwK3RGpEwTzMhuZDFXtvoVYoQ4VB22ipu57aJHLwBI9177eKt+inuah0giJkWuuEgaaatzBi1y6Rtt32U0IbdK18rDwgNZi6rNVoinBPiCLrA4C3rPN+0ibhHUAjwDltR5b8tBqhGgA0aqsPddyRLNTmCDcRzxFoJHy2J/cXapGbx897s4fKOfFyRhHhb/dxs/PdS66y8X61cv+eqFrlR+Pnc0OVbw4m6Cs6axW0bPztmr79/f7F4/Hq/tSsPx4bIUHWjT56Rjkoj9/CF8+kgINLy/Xn37c2a1RUmUnEytJaZwVZxNdtr3FQp34WzqcXn5Yf/TSi0MaFfNPf+X9L/+r9/vf/RUswv/2f/jVP/zD0U8/ZbPZ5Iefj7//sRyPTr786vib71effPTiP/3j6fc/XP/mX3z8T/90/PU3+Ww6/Pnd+dffHCaz0+++f/KXL1avXj7/x386+err21//5sXvfnf21df1qD/85cPJF98eprPpT6+f/fPvVx+9fPbPf3r05Ve3H3365Kuvzz//czqeTN+/P//jF/HRyfjt++f/+XfLj15d/OnPj/74+f750+nrd49+/4d4dpS1QfwToo5uc5X8QmCfwYPYvTep1RplNX/nMyAhBIcfCbIhKNrwLWFO20bt9sqxGDdFs3jrYS0wBuH31GQSVXLz3mG2lDnYXtoGlpYsV289U9aIof2PFFNAah7+wmxD6BpsPtgEKgqbzWvbarlJm/VrV0PtmBQtzxDHhjqAzXOWK+Vn7OYJaFxgp/j+2GgIBakMn8OSIJqB5VMjw6KTWlfnsLaVnZGHY8xtCFKwPyeViVkKl49JysQgN67PcR3UHe7d9WDlIpiC/RkpnMbm9sOxmbKml1qXR6oaNB3u3HVwGTC1B9EZzPyqL5vXdXaPyZkp39aHhdl3imhvpPcUeajawOpSd3plcsviO4ZPjPpKpnPDthu0Fes7h/mw2uPqvTJHurpW2T0jU6Lmcn9rmo7QYbu9sS1bigNKPyA2huJWhreG0+PlCoZ3lmvWOpbbK9uwBI9g8p4G3apawu2tHXilUp7x8AjKBmDJ7s4BaZHQZH6mjQbmEG6fEVT55b7e/y2UNWCK3j4BSBHZkMU5xiWqIdw+xbo2ZAZWHxmiVmbLbh9rDFEr8eqRo2Itkdq9hKJGqIYPL5gQ0mzM6zMAEdAtWl5QXWvRwu0z3AqMK3j/AgtQBU36nZIFsmkT3pg81pbZ7N6Zet+yGYy/Q3VErIE6vMFNik2Th7dWm0AS4OwtENvWmIHkO1gcKJnQ6jVvIuSaTXhvVQeMujh7A8RGd0fl9rWV7SiZ0PJdW4TUY2V+///Qdl+7mqWJed/fuPKX086halfoMB0nz5AiBduwKEuieGDA54Zvxj4wfBeGbAkELZHjyTPdPV1dXaErp533/uL6Vk5v9gF9AYaBuYsHeID/DxcrSka0eGXEzDjbpnqii5UV9EV6YeVL6naUlY6t6Ui2c2fRIeudsle3li5a70McOzk14Q1gN7DuuFdj0a2sZYBWe7AVkbgNwhuIxnaGzOomwAVuPPt6W7ZLe+mC5QEIYhR5MDqycOzlnIcfQpgA3iLTPRPk9prC8JBYC5gFcHWToIw0Bs1uUJID7tGrfdY1MinWr20HS1eV05dtq2moD1ePKYTQ0nz9klpEIaGXbzwEtI3Y/LnnMOG5zexpoDVAWIcvqI0kEmLx1idcUFvOnzikFqgL1w+Q0RgTvXppEyMBMKs3HlEGBiR7aHSprB6IHyGmbWSB9Dm0pIBAr955mkGzYXfeTEjVq/u8fd4FZRfjBKx3aNFmLvOmm07s8kHqnmyZYlQPWHDeRmXfMiuQbIOsz9rCmm7Yoc8HaXA6gcVG1a1a0w7IRxRFJB6idAj9nC63cTwQo8Q5HYFsEwVrHI5AuonRmkZ9EI2Nl5Ll2FoP+SB2z3om3WHdDE3L+UngOkIWMH2F6RDIa7l+Z3s90SzB6sQLXAZzMX/rW7aUJUhe0lanEUuwPPWDVlWvcXjieRYnFVu+9j3SSIbXL4jbMXopw2On02pEihZvXcsSiMvVCycgDWB6+dKjbSMiE75zum7FU7B467uYG22WzyxMte0F9HzHVyWUUq3eQ0JgXMPLO3atpV+5J4dAY4A4vj4gWgHJwfIWFQaTEl7eIQ0QncY5PjTA05b0LjexNEBztLxJOGpc2TrdoxUSnco53pWmLR1lX21iDm2Z6vUdVOG6q73jHauyRCf3T7aV7Cqr8aabgDnCY9lLzaemPypXL918TskWbd7KfGkFdt1cwXRukQEpjgG/1M62qZ7rYm61hjK/pOnM8lxWz1B6bXkdVZzj/AK5O6B+JuOpHfRZcUHSqd126mYJ40vL7criDJaXtD8q0nd0PXU77SqbWdmVHdg1X4H8jARdnl+R7IwEY0nXPlzfoigNipKFnyCdQuORqwPjVlaC0OoQ0zXKXbg6IjCzGIOz2xQWwNjk8tC4DBUELW624bVoArC6i3SOjMaXRwRUBiDrfNe4ApYULm5SHJuaoOUdAmoEBLy4hQzXhDjn+9yHdmXIbN+CieKULG4hw6VVRN8ixbDl6fAFhcJgpJfvPMwA7JDsEdCZsoYmeYgaZuEA5c80YpoiFR57soJm4OTfSp2Bbq+aP/GqiuIOKl4CXuMuLFfHrko0mtjpQ61iY2+Y/DGoc+r5Mjq26wIHtA5PHBFDb6DiJ6SOoLtl8ocqy9xWu77z5R+3Hz0ND27c+Pre4df3rj77+O4//mrv229Xd27vPni68839YjTaevFy50/30+3N3W8f3fjTvatPvnf713/Y+/qb8NbNzUfPDu59w0aDzdevNr96mG5vbj9+cvOPX84/+vDwi68P/3QvPtwfP39788svq8FgeHp6849fpXu7W0+f3/zdH5cfvLfzzcObX3zF9ibe+fLWb39b9gfdq8s7v/rt6uZt+frVn8UiNBBOnr/cefjYSbLh25Pt757ZcTp4c3z47UMg9dbzF/t/ugeFmjx7ufvoOy9OeifnWw8fO0k+fHe6//V9xMTm81eHX3+DuB4fn+0+eGyvo97p+e6j79y0GB6fHd67jxo+eflq56t7hMn+m3c79x9666R/cbX34KG7jnsnZ4df30Ncbr5+t3/vPi6r/unF7reP/DBmNS5OoUiBiXV+ikWFxFol1xbIFCp1dkn4WiqGqjPIUyBTVZ5DXiKQmPTaMomBuUwuqF5yznB5gVgGdarKC6QzA9csvbZkKE2l02vLhFI2MD+BLIEg19UlFqmRkUiubRMKWOv8kooZlxwWZ4hHAFYAxUMU+7AxOJnYK89ohFZDGgZQQGfVxlkHFwYmA5y2UKVRPHKWjjLQjoY0HgKJSdhBaYeWCiV9ErcAQzAaeaEnAbaXbRANgIB43cFZjxQKJQMUtSFHJBnQVccoQ1dtGvWBQFbSwWkPlxqmPRJ3gSFspvNzJCRWS53PLJVLuQbFFQGlZjGpz4ESoJnr6oooAc1SxVcWzEQTmfqa6sLUEa5OjeTAzFV8TjWHbK6ya4pzqTOTX2KZa5bi6hwpBuSU5+dYNQCEOrm2YKZ0arJzZDLJU1idQcmgWIjikoJa49LAaJMkFpS2vZqgysclpssBLCjJEUw2cUpIBfB6w4kI4JTOB7AKcIlpOEKVjTMI4y2cUNgAEm+StWUExvMhTl1cYxIOUeHgHIJkiyYWZBCHYzukRlMnHOOyjSpEwgEqXFJAGG+QxNUc43DsRo5Shl3IaolRIcsFEUtkGpPPKD/nCuD6TNczpAWorkyzxKRS1ZKUU6gbnU6pulBaw+ZSF9dISSjmoFhZOhflAvIpMAzUC9KcSW1AdWWqOVUCsqku5hjkolrhZgoVh8WcVBdGASwvZXJFDDf11JQzghpFysBa9oHAJPFwOEaKotgF0SYpDM0IXk9giU0RWIu+kdRJbBKONSM0tWC8ZaUGlxSFY1wSVAb2agyEZcWUhGNUASulMN7CGUQlpesRrQgubDofQG7R1MbrLcwwTgmMt3GKcIXpeowzDCqXLgaQWSBm2TXlS2VKlV1RsBRSoPwMNWsEKlNfIh4DHfFkaukFh43Jryw2FUrC8gyxNVKFLi4gjwwsdDazVQRNrfNLwi6Z0rg+B02ERGnyKyQThAuVT2kzV4ab5IKamZIKlWegDLFpQDNFzQqAXKTXVM1q+NvuAAAgAElEQVSVNshetWA4NBKSsEXSPi0kTLsw6mKBYTK0Vh2toBW26XoAJLbSNkqGqDQo7dCkBwTBUcdadbQCNGyB9QRJDKMWTEakUCRt4XUfNgjEPWfRUYB4axdFY8MRTXyUDK1cobxF1j3IkJX2nPVQa+SsHByPcS1NZrILLFIlclydQdFguZT5BZYNgGuTTm2QapPp/BybmIsCl+dINlCuRHGBTQ3AiifXtl5zWaL8ioBU8xwWp1BUEEaiuCS6UCIUydSBa2FKk19guRayRPUZFAVSa5lcUF0akJnk2jaRUaUpzpFcK8Sxveqh3MMFQskGSR3YABxO7NDShtjhkOQ92GAc9lHukkKjZAOnnuYIr8fe2lWG2IsuSDugQXg9IJlPSg3jMY59KDGJRnTd0hrSZQ+nXcSQFfVI6qPSwGRMk5aRlCz7NGxpBcmqB9IB4gRFfZS0oDDNEtcXRmvQXKlqSpSEfKryGYW5rENUXyPZgHKBq3OtFJSXKr60DDPNVBczgkvJI1hcQdWYOiTsAmoF5QXLr4hpgFmZbEZhIdka1hfA1JqtUH4GlYJ8KoorChqtFjKdWiCTTYLrK2gqUC9geQq0ACQjMN4iGcIlIesxLSmsLDofwMamKSHrDdRglGIYb+EUo5qQaIOk1NQ2XQxgbVsZwesJKjHJCYy3aIJhg0k4IikF3HHCCWxcnGMcTVBJcIFhtIUTyzCLrsZWboOG0MXA1D7KKYo3rYKSEoJoC6eOUrA+A02IRG2KSygiiEtVzGg9N4aZ5ILoK6k0rM50uUCagWaG6xCaXGRTImcaMF1dE3EplYHVBahDKmpQXcNmCXUuijlt5lApnF/i+soog9ipLOZE17q4hmyJSKWqBa2mRktYXWN+abQGzbks5wTXfHx8uvvwsZUVo5dvDu/d1wrsvHi1e/8BKevh8enOw++CMO6fnu9++9DOyvG7kxv3HwBpNp+/PPj2Ac3L4enFzsPH7iLsnF/uP3jk5NXozfHBl18jLjdevdm7/61d1r3Ts+0Hj/35qnM92//mWzvO+m9Pbnz5J9SIyZvj/Xv3nTjpXVzvfvuovVi1pvP9r++769j8+SzCNpReGvFhV3JI61r2gs71tdKIbfQVM1iIfDSyy8pLIzHowKzx42h588gpCigV9IluNGEsm0ysovKjUI/akAklYDEYOEWOuQCBZUWpv46uvvcRrWovjorNjVa2lhwWo1FrtXTTtNkcMkjtvDAdp0JeK1xlmxtOU4kGYgdohEEllWthLv3Zst4cEUvJXPFu25UVY5hSRbgAKbc8HQ82nFVcTobtJhWF4p1WK4tkDT3CWMuvlO0QUTote53Wg24/XppEsEEbWaCRlBKlLcuUwkMNQ44zj9igSxzFK6QDu8+iFehTLLVr45wLx3ZgAwrdBL4LyrrxEdTU4Vza3WrNWh5viLSoRbh/ldTtNgw05zbWErqAC8tledNpgUwr23JIDXPJPM9BFeM2AqZpue155LIy2ZnAQmhMeMtxk0IRYuOm5h7WmvUcq2AOK4CPQaUby6vbvpUVtGHNoIOY9OJI9zzZAC+M0v0dwrkRegCTBLUhU82gQ7OyPZ3Htw+CIuGcNIMOqRvIpOUaUwpnnUa3DqFU7jquJ4NWETecgI7txIkpFBi5NXZJ2VAfFLTlhXE1GXTilYkZ2+xBY7gkQxAldg8WHAeocLqt5brptV1RCGEhqjXCqkYObSrXd6O8HPS2s7NY9Vjb91iGImV8xNoeqIyFWem23Kioei1f5Ly0Zctydc6FQ5DQNjE16MtV1Jp0zubJ1sTGjSqh9B0LNIJbFArh2LoADq6rVttdJVW/48tcl5D7LsJSMuixnHfbJueK0kCXaFUJ16EtmGuXaIU9WHPLY7nxbLLMNcH1sEuKWjiOh1nNCVZKBJ6/iqy6qjYGSkIDIe8ETpxJQjfqaSJahPNyYwAUwIzpjuskaU0DY2N/uTYQ0TZMSceqKtQitbatqpZ93y5qqQm1hAAWqXg1aAMO3SirN1rtPBaC1L02YRwxSW3BgIcKafm8tFp+GJfjXqtMOKeqZVEmmLJtq5GQ4pITTxVWx4vSZtAeJ9MVHMMAUt4IRS3KOXZIwYkrc9p1opINvKBJhLC0h7GUSuCm5QIIvFXU9DqBzGWhebvVLUOWQ2IZ0Q04Qw5qyqBrr7Om2+5mK5MI2fYCVK7oiEJpbKJLaSOhsOVOw6bfRS6QlRGB60LWMGJBiaBGUW1BmWxt0ihruh1jEXcVC99t81RkBhBYDfu4YjZgVadrrVLeCljg+UkBjW46Pi2ZWxe866sSAgB52/ayQiFsk4YLGytZ9dq4lE5V4BYAtWLYZW3XSUsDoGXxWgWk5s3IoyWnTS26npdmNfGJLXWNJSSuXTbGs8oaBrABrlVUouc5aVnjgDocNUpCYlHWGI/WTT7s9rIVa5DuuBZrmCQu4aBRKG1oB667m95qXW0MW2XMGmR828sSwXCHFnF7pBttuaawWu46aQadbrhQlZH9wGDIFbEtyYhL8sqlsja2t1jX4z6lsmGUusbhdQLaLmZaAivMrBZajrb9RViNB0GT8gZp34YEgVr3eZh0RjhldTvwVSEKKn3LhQXnNoFCO0RXwAZ17bd7p9N0MrIsoUvAfdcGleC2wZD5rpU0DqjrTmCvCxb40qFenEub2rBGkTQEpRtDNy0sw4CNQKGqoC1daq9rTbFjVYzbyKimHbhJoQkqu203SjREEx0u8ZBwXvc7bpg4WVbsb7ppypDDui07y7WBjqN0Kpw0Z/uDSjtOXohBy02TGrnEQ9YqMRyAiVsZh5Y1aeMKuHZW8GG7M59zgfXI/2eLcGJWC3vDKkoSoIZTf7kq9zacumyATR2guFYK2Y6qactfJ+Wou5lcRGCoAosIzrljW7UkFJWC2rKwO946qQadVp3SVVON2gAbKahFGKcuKvgQhNfdA2dZNAO/xRPREO0TooXklFIhIQ2mazVwks4gWETlqBewVDRE+xRrKRuqPKwpduOMOirzesEiqgZdR5esQRQq4xBZG9c0yrLtacTbAesGNC2573moqRlBwGjH8hZrS/N0a4KzWnie9B13nUjLmlSzRLSwUeW4jytuMMIupOuianUQ0t481NQibZjBls0q03ZkbQyELhL2PKr8Nm1BxonE2LVlJWwkpez6vfWcA1r2B06WIqlgQGlUuPE6v7HXGMtL4mo8DNKIG6rbbjBfoFrUOyOuiV0UpuPW0HaTVAxavfUiRm3YdhBjRhjkE26omya66wkG+9eX4c2bDiulxCagkAnANQqoqWTv6tLsdWf+dhCu6mHfLXMtTLR/s/M//y9/Fosw+nd/9/GvfvvhL391+eFHe988vvWb36+3dqY37oSjbUPI5//h7+/89g/Pf/bXt3/160//8RdnH38ymxyuN7Yldj7+h/989Nvfn3/2+d1f/OZ7v/j105/99Xvp6x8Vp+fA237w4s4//fr81vtHX9//4Ne/nR0dTbePZntHOKtu3rv//f/096fvf2//6dO7/+WX852D6eHt9WTbYPjBf/nlx//wj2ff//74wfMf/R//8fKjT5ocsy9r1XV0buqvmeo4plxZIkqMbTGUfWlUyzbCVH8SoOUIHcF0IVyHZW7xJw17BHJV/kFq2xK0IeGFQKhIW9UDBdtUMcS/EtpDWlRQREzoygz0V5W2qAC4+opTpCrT2CLKSgacXv47IY0hjkm+NBBj6NPe6w3AkAh099mGrB3eq/+r+R9G5fUJvdF5M3BSXHvSOd7GtaXt/Gr/IG91LNb0nvRp3qn6rPV2aKeeaKU/WT/qlvOVP2692LVCnW3j/pO2te4kGzzt9MtuG+c8OJlYKc1GpPes7yxhsov+8vqLw/LiWetO/7Xnhh7za+9khGM/2nbht2vxnPE7ffO6Yg8Z2aVxf5JMNoAx/EmtnlZ412peiPKZ0Qde3Wqvt3dxI9BJmT8BatOX50J/V8ARTcfjcHuPFqU6l/xbDsYEzZv8CcQ+qDb64WSHGC6f1fw7hfYcNjXlfYXGRHbc6e4NogV4WZZPlDUG+RVsHiqyTaVpbTweccsYDLtPJ5zinnX1FxdfVI5d1KPui53KEa6snaeHCoNyaOabe9JCzgK13ky0C4UA41fblaUdK/mb2ZeaV0trf/Rd1xAiAGg/HxEgMkdPD24qbSyBhk+2Ode8QwaPewp6jnP5l4tvoWoiPe6+3OPGSN/0Hm+hRmZjpL5pxEKBvlU9NioBwBOARXy5qPduqi/L8hribVI90eBaqI7SZaSagtnt6r6Gs0YcddUfc3ZpxK0Wnp5p3ZSoxV4jNVds05df1/q8UnuBvj4HWCXBBnjWsAsAR1g8bZoZqoaOnVzp+XW5f1PdK6oTAPdd9kKxYwU2cWveDk5a+QZ0z23/dBTtkaPwyV+Fj05I14p7zslG1oVW4g9eu+mI3o4e/ST57rK7KWe97rutpsvsBPtvNmpXHMg3Pwkfz4KeuBoMTsflWMK11X29XfnFQfnqB9nrNSGi2Ou/alddhZKg/XpcD9WgvvyXl79fOR3ONlsvxpUPaEUGrwdFj4iyll8ybSNtkeI3XGGiA0amZ4LAUvTqb4SmVBvAvxLGxgxLp1qWVaXoIP+TBAoDGzX3BFdYwArxsClrZg/ZF8I0Wo09/ruSMQo73MRLZkAD2vxraRQohx3xyxRViu046N0L5to56KtvasMUDxz2RaMVrHZ6W4+ptRpEW6b9JgiWPg+ST6Onu9XlVWcreLnhzmC6j7tPPW/Zj7b5D1YP3s9Pr0DbvtxCq1YyhMM3XX9uRXv0x1dffNCcPR180HtLnfmgajftixYMB2VL/iy9fzN8+m54q/PcdWfjcoPT87Y/H7Eg+Th5erM4Xfht63Q3uHTSXeC+crvXg/W2sc7j7CEEfUusNH/E9VZbri8gz5jj8bVd3tNwREwiq/tKBpbSGYmm3PHymds8kHjL4ikSXwndgZoXkEU1oiwJ9P1aD20ew/pP3GqrglWuiNJaAtMtvuDABaY2+X0Ne64pFqaJJUSMtcsvBGxjqGH9hdAOxH6786RHtaoIbr3cVRBAN/uvz37tgOrSPxo9agPlcld1Xo2xRLkvZ3v7CkAidPe7bVzIckwHj9uad0An/Jfze4HKr8G4/2obCJAPrNGDPkpBsWUWk9283bZi2XvVxQ0RNgtebmlupT3zby5+0RfhcXDUfxLApld1Tf9lCzZe3jfyXqmPK3LTKb6u62Oob/pFp5tMtkjN9NOyOodyu6Ue1fpdbfbcuD+JtrZb67A8weKVREOk37HylNiBiHe24+GGy0r2uBGvDNp3mhPdvNBkhMpBb7mx64qSP+bstbJ3YfQKiacSHDh5qxtvbhHGxTtZPlNmw9bvhHou9IFNI7f7crv0G6+S1qv9xjFj9fbnq28SL8jjnf7rQdPXJscbr3Zyj2ubX+4dGSPb16b7bswDAwqr83xo/JrK9b+af7GyXSYnvWcbNdYAWb1nA0B4NHQW23vYcJRZ/Wc7NZWG4v53EwmU78z+8vL3ZdCOy63h855xeGOs8ZPNhmoWGPmHsskx6JHqvkSlYrbA9ZrVDbe71RccpULsteXvUpZRuUnI8oJrWeMOu690A5pxoH6T65DpHcdcnysHZ2RoHjaswKQFqq+ZqEnZt93VqVivyu2b8susXmEwtptHUqwh9Bmo16xuZG+QfaHxSoiDtvgyFwsMdqwPf/WrvS/uX9z98MNf/ub2H764/PDD6cbhcuewFa23v3rw8S9+ef3+hzfu3b/1my/Tyfjy5vvLjT0qxe3//H9//k+/Ovn888nD55///f81PTy4JWe36uur9vjod9988I+/PPvss/0/fvP5//mfTn/4o1V3c7l7Awl565v77/3DP81vHO09+O7jX/xqceNwtnk42z/qxwv/5eVP/vf/MD+6Mzk++eg//sP01h39Z7IIoTEH974Ozi+2n3y38d3j0Zs33eVCYywtm3C28+39jadPu2ly9PW99tn55N0xgEBZdrBcjJ8/279/P1iHN548GT5/1l0uPoTVDt26kZ5NXr0ev3mz+/ZN9/R0/9tvW9FaWpaBUFnWwaPH/vnF/sOH49dvJi9ejM7PNcaKEI3x9oMH/VevhldX+48ftc7Ptx89YpU9XQ/USqgFn0dduZY+MeFf/NCNF3qhptVAXXBT6Omyw64ah6jyhx8hj/LzelF35bvci9lVNazOGJJ189NPcdfFF0kYd2Sk5GlzVXbRTMM6i372KaYaxuJi0S3XEF0Wy6illqyzXoY//tTCCi3rWdHH15yk9WLZydYwCEFTb6N0GMxYpW54M9+PlzcpeX84cNPCZNtAjAYzppsJTgYGOQYiTYibZqK8zetdJ5E632mazc1ofdcd3fGtXl7X8gClu1ZT6+yG5Hutsm58TyNgVVDWG6DadSLAxQFMtp1wdaSq24PNfp6Zcl+JjcGMKTaG5a5bi3RhzdIhKHV2auZsgK8ygICiBDWyiug6bLtR1izwPOs780LYNoAQMsGuxSLriJnIKn++6OC4VoQaCBUhegqvm765ZlVEFnFHrZVBUFKKgamuwFXWBxGrzsSi7ujzf04gYmhAFZPFsmOlNb5is7yHVrUd4hzcssIOLmBT34VZexxf3e0MNkXhL1FlDlpz3FvzEt2xwwBppTExGDopktUeiUlrTlO17879rWJ50N27qSK3grx+3xQ9N4Kc3/AyC0JqEJKOHUyrTB21056d1qx8TxWDSZ0cup0dVXUXrNZ7TtQJEl2pWzjdALVYTa28DpzLLF7TZIZJlOWff2AOJt5qPp8G69inSZNd03XRcqZhfTAxh1tO1oRZO762vTQJZ94y7+OCy3GrurXnXy3zzFuFAVqrRdbOl76zWoGDSfb99/HVIlz6cek751mW2OuFS+Zp+cnd5qMjZzFNr+ky79KwbkJrXQRkJSWf6Pq2mwsSd5m4TWJ4oNe73uF2fkFih8mbQWIj3uXVXRSJmyo7am/0i6W7blVm378CKPVqdcvJ7X3X+qjXHzZZNw1KcWSl0A3bFdjvzeWWzm/a/SOem7RV1++7jPgz3Mgb3kJsxe9ujz7ays9piFlz2F4qq/aa8i4ubXPeXBVdONf2NJ7VA/6uwk2R/eRjttkHJ6vV2hehsK/radWtz5QTLpc//xy7iMblctkpYqKXbLYOVEac1SL9wUfGpzDiV1mvODdWWiwW7SKmrhbNJ7eVC9F5PK26bGnhsJrn/fwSOmlUf3qX3dpFl+kqbjUr4F3my6YXTx3SNDo9FPwgKA1JNhjfHobhLrYOKG5lJeN7MN1x04wW+4W4FdTgAyx3SXc7XepqCJs9L7cqsQ+LQ2sVfuSgo85mJ56Baq8RO+2pMnXPFLudhm8j6+N+1wsXMN6s1Q0/UTQbMLbVm2U7GN1yrF6ZyWqLF3fcNO1ko1zdcmJdr9E864FrxjI0nXfAVUJbVvbTT6nh9Slf1F1wnNFUX2d9fpoZn7Iff0yQgOfFPO/BsBHH7KrukZnQSKQ/+NACnEfgYtlVsdSn5azo4WnWyaPlz7/vSKbWcpl26Kw2iZwuujxUyKfpzz6Hhqt36bzpNadMJfJq3a6WgDRU1vtW6XenqlE71rrXDy/vbt3aT6Z2WrHyrqpGdgRZc2DyLhYQQFi3Wr1lWakjnGxaZSPz20psbYjilts+hHC4qku1j7ItO9KNPKLJ2OKVIgRAQBtHshuk7PSnjJk9mm726vhWf+uGyGmUgnRPsG07IpIf6HzgrOUqa2XLwI+icuku8p6zKv65ZYrSugjxauXpUKzL9nzWcpJc2LYB0GjE52RedsA1TxInWrmqBAYjaVGAYHmBroo+COviUoeZpxaNdCxNMJIqjezFouVluTlls6LvXYfStgGEqGL1SkdL2w6LNHIulh0aV+7KK8FBd2raCaj1bXeFtyn4oNMdVkkwVQ2/6a5Ea+nG+sCfO9x1NEKGYIu5TfU+yh1nbdfgdiuGh9XFUf/DD0EOE1KJo07qkBQ29Xs0JEhqjZCBwJ+jEh54a9/LYCVv49Cf1OmHvcEYCj/CtTqy18hf0gzdpMnIStLptJ0lDlk165Wfx7Z7PUs+vk3HbRrXi7yXXhM3TVezdpS3adXUN7fksOVdR+uqE80cnMpp1qsWjpNG6s5eeXcfXoSLuF0nlnWahFU7viBWlGY//oTf2bOvrqOZG+UBXTZZ4mWx455Ni/cOye7QqeowChZT302T9JLOik5wHY5fv956+nTj8nJwerL/pz/50VpR8v/ukMeP2qen+9980zs+njx7EqxCRYiiVGN89Px56/Xr4fnZwaNHrdPTG2+ebSJwl/q7ZTx49nTnm2/aZXnj6z91zs43Xj6HQGuErKqcPHu6+d2T0fXVxsnx1tf32rOZpJaBUNjOwaMH7dPT3Qffds7Otr77rrdc/Rktwq1kMbm6PPnBZ9583V4ul3ePaMOUgjCg/vUqiNZvfvyTwXS6dfz29PufwIIjrfLhaHJ2ZlVVcmPHX0TBYvHmJz/tzi62q6urwa12mtqr5OSzz0anp15RZLsToTHUpmq3vSzbf/nixc9+Nple+Ivo4oMPoJS0bogNcN6Mzi+uPvtQV/LwyXcv/8VfemkGFg2YuAYhOG/U0BFaBCfvsjvvtzEXZxLsudAoM+dg4gDV4PMp3Nuogh5+nco7HV+U+oyLHR8qRk7O4KiLu302F3hi8ZYPn671ez1clfT4hI/HpjfA8xL2KPAtcM18t0rabfvtW7azZ3cc+ba0tm0HNVlkoxYWfce/NE0boRb3LhBrSd3FneUlBCga75IQtGVZjCEIOzwAFkklpwYY5GEUQgwAGxsrhJbW8RhPklkJbNZpB1NU+1B3DVkhLHV42A7WKTFCusSKHY1lsukPzytpGdUzfrSyNT/bvNsNK6dWYlyT0GMWijdbrVmEoyJ7f8eNc3xd0UPccIqkZN0WjhitWKvHysYBMc/e23TjBAllXOJUTZ1idqNjrSsrr9GQCECMAqzt06ImlxV/r+vFmYgA3iTGAKmxjYXhUM8adNOrtE2OM/NeO8jTyljIxbDSKlOtHuPG4VOOb/k59TePm3wsIYF0BUUHQqvpR8ui3Uu9XvcMpHtoUl6yeLPsS0pEYxwKBVKKxJbq6dK3usc62SeOyDbz2cIeKL9jLY0ODHeBsyK+vV63W0BgjRGl0j2HbKiVi+kSANeIlu7HM+74YXvcPwPZxFDKvCtc9YEIMJpXVEmxG8grYRxsDTB5+Vb6Lbg71guOuAR7nl4KzITc9uD5VCOsNyfWtDYBhQMMltw0sjwa+6/eUCXqwwMYSoRMtdf3TtaIQNdn5TwhnKWffurNYlModOCQy4Jj2uwPgxfPjQHw5raJNUoYOHRFbGAh5Q3fDwWpNBtB2CAnMcubLT9Pd9PTN6P3u2WBUzfexlah3ZSzMbLqrJPO1hs3mHF61yC8CdtZSWInn5igidw8y0YbRlC6QHxXNdjtnpv0QG9E50ipsL2JpEVSI4bASO2sCNuRNaC3129eD+/6gtMV4mMNJLJiHW13ANXWk4U+6niw5icC7HiYSnB8BfptPRyZy4q2DRu1wbNYH/Uwr8jxqep13a5fpQR7SLcJum6gD+uW4715wzY2UadFzzIz8UyA9YwjC6gWIOfXIAjqnR3rXaYmNhu0ghcLM3Khb8zJtW4HbG/fPolsRzbjFnydgw0nGw9GFzFhanWz050XDgPJBPSKCGozG+32rhqDtexra20gw+GhN5xddFQedjbs3OGQpJt296pBRvAxclfLQOTvDj7phoVd4WxLdUIlNWUD042ukEbpaAJT6Gag3NW6sNwcZttmEl9rBdLeyI6RFkCOAckUjsnyvfZgNRdLAPYcWAqTKrDpgjzHV1Nw90aFXPI2VR90/TyXcyV2ApLFYLawtgba6YgZJwdOQ1z0PNQfDkgU4atrsb0FrACtGzSxNULwqgl6deh2/Tevmhs3bYvIy8bdwkjKKqVoRBVv6MU1PNys/B5+mejbLUdyc1nL7QBY2FmBgMTRsOVeOvkGIJhtLU/DYCS8FlkAaBveBe4SAVdWHgQCKQyopYNLVPaACYy1hIqibIi35yfGthfdrd6lqbq6GAXjdwUPAPCYZEhalqbIXxlCmOoKOgvKrskH7ZuXzyrLLzt9EgKNSD7BvWsmbRxtB63jEELT7dRp6cFc5HcnXhgDbZCLSMS4Is1+17mIsDF4gLiiQBvLkk1NUcT1Td+e51JgMkRKQWWQg7lqkFk08HZQ1ZTMCnzo2kXJgAUDDCOhGtAd1AX31Jzh2z5nCCqNbEjiWjYIbtlmzUClwK4nkNU7NckNsJHMqmKjGitX5n6yzvsjoV1nBfmmqojTP1bJEQnqpFEOpIBoiGOo+kZA4K2I117M/PGt1ct3nSOXQHsK2dhohGhoVA8IC8JGubpKOv3hiYl3jWO4PcfVEFJSt8NFONxiOOhPlRhWuRcMj0WyDRE1YNYgpMHE1VNGbV31fefNO9brw0HPuij1wIZtZKaNprgZ+8Gbt9Cymv1dfFaCgVVNusHLOQyoY9fVLIaUVHfvumdrRYk1AuCUia5Vbw06Dx5Iz0cHm2YlUCP0jqvnQhOC+sA9Pq0sl+xPwHmtXQuNCVwJ0YDmznDvzUtU8rPvfbT59g1hLDvcUgwgpaCHdam23r19+9Mfb58e46yKDneMhlpDbAMrqwdv3lz99Iew5NvPnp3/6PvbyzdCo/Vwtz1f2Uke3TkkWTN+8+byJ5+rxhDOpO91w7m7SsK7N73V2lsn6zuHihkkZR9mC3fz8MH9d3/x88H1pb8Ij3/8c/d//d9afyaLsPXuyr28RLffsy7D4PJqsX9j89snVly8+Nv/duPkqnN1Xf/orztvLuzjS3Dng9a7y83jt4//9b+1zhb9xXx+eGN0fNU9O29+8Ff+eeE06eQAACAASURBVOQ/fmP97aE1XfvHF+DmB3Qa9U5PZ4c3dh4+HT1/8fv/8X8aX7xzX5+j9z4h8zh4eaIO3pu8O9589erZ3/3rydlZ58XxxQcf+BdL980l+l6dqyBf+F4HawDYwoEbAW7kqulaykYsWcXdTtdARxcL1+lQgFpFMehL0WR2kQzcnGKsplHHa0On62WiNwA1ZmARdTodIYBTZiOvsNtYnbPve0bZAKQr23MgRDAPvcGeK4C/KD6zgdXixSrxe4FqteFiHtgIOB2vbvqCCmzNc7ZrUC2HiS7+SlEpTIjKCYGxahJW70go+QBaZ1vKF7wbWcUYQcFAIqoRR0yKbJr/JaQMBfOy2RDG8FFklyMATGW4G/WNUc1mwpsxRKqRLK/GUBg+Wsj8h1rjeruyyr4SErMpbza5sGqk6kiCpW3e8+ul4Ik94TLMfX3JyCeBKm20aFAHRpHXhL6549eJZucy+L7Nynwd2tbENRUFc9vfpNW1AVcC/DygOSkT16uoqGQWOe2REZFs5tD/NOCZqdfBeF+lmduktFViUZvVmdW5C3RtmjnA7bzIvSjujBrBTTttdnmTAL+W2UD4pcGSJR8BZy6QVTR9ViVarrPmNm8i1qns4zGf5MrhpBpCbyGxVbK+qEpkg/P1p2YQ6aAyxUhahaBcVBMLaSF9er7dbBcahrLel3Wu/NIqhoYUQnGRfoDBgtsw4yNepqYdgXqXVU3dSYu1ZWPlNihKPdTAfpvH1fsI4haB2crFUgQ7tIxcoJUjUMrvEqiR4+qs0BXsTGC+1EBocNTK1B3KRWBglBOkNdj2s1gSYLZ7ei1uCQ6h9spIigZ2GyNy1CiKtuyyuWUkbBFYhAambnvH5JnNchNwhEUPlQGbhJi1ZO5UqkJl+zz6QHSXjFuq2mA8E9oGBWk2ooZvsvJzZc5F2Ul5n9UhE3bVbAu5SuQwyztiHOLGxrxr9Dlj/aJpyWbB1UaYfqy8GVGQFl0+WkNGRNOHzYmAo5PVbdmOmCxZtaV4CAHSZafRQpYlS4duaUNLL5KO7WhvEyb5+05gaAOyyOlajS6sNBtaid1pg+vqk25XE5QtZi17AokD09DrEy59Z9F85mjcA+U0HDkUu22YL6Td1XaLZk3f95hq7DDtWQ7WXb+I+gDhoA2LPDCEwNqW606nywxz04xaNhKbXqtsQwVKyJ2yyyWUIlxm3ydKN726qMYYAjZeymoAOSlhQ/jndQVI67yux8x4lWZWM4YScLzk/IdZSRnIedNhjcPZsmpcJVzZW6XVj5Sw2ebcKnuqpkJeqWokmC/YPKo+Ek1bdReq9KFwGVlYdQeIgIs4rbwkdjtDI2tVL4A/IYL7ZTkeK5Hlbp3SICeG6UVktwcG4s2Sb27ivCmcddwebImqcuts3KosB7ZXbKMFFZSgWLqtARQNrCJ/PK5r1V6Vn3vAclkZxcGowxEQq7kbdKDkQVGONlWTFU6VDP2cKILC0Pe6xLEdWQ0s91TWpGh2WVUZf3UZ/42CuWqltBwDUEldy2pEUFz4yptOZDcH3bBsdljJRSdVxUgEqoZiWR0SEwvBczaWdV0rmVdjg6XorZ2LkfJAvstg7VpWitia1buMqmqgFtG/EL6S/ZWdj6QFGlVXjSuMZlJUSRtzbXXNeuWLEgDtVyvFE9P+AJm8LisLb3lFZkylWzu4eG3AWnY+N0XmNKkKalgUgJVWa2SaM9HUpPODoLrSPPZH0iSRI1Kry2ERkyyi7fcNS7VKoD0wcRTkSWvEVRq74pJ3P8K6xnlIO0PTFA5fm+4OUHW/aNqiWklRZNUt2SwF6tX5p6K3hhzKegD4hZSdknd4nZYGo6tduRlyy1hFn3cipbGsR7ZbSt47if6OdSIoE17vChEbW1lFV3US1UD7elRtXjQsyHhflGvo5KraaVheYcGyD7OuQIZnTQeziwb5OeuzOjd2ki9ty5Vun2aR5wQMtN0l+8jmQLs+iBKjcbsH85VrPAwnfsw/bJEGKSfNbIqgGvnFukcZ3Dg0K/6eghgJN4va2qP9FktTB0gMJk5ZvgcBCRDMF5oa6Y1RmvqKom6Lr5r3EcAB0Flka4t0tlC+0kKjVgOsy5W7iPSND5yrsL2YT+/c3b93b3x8/N3/8Heds1P33RW+U9JZ5F/MF7sH2w8ft6aLp//9v+ufHLfeXOpPPvPPl/7bS+vO+/Z56pytV//NtnMV9s6vpnff2zg5CV6fww8+9KfRza++ePLv/xYvs/aLk+XugTOL+6/ezo5u9V6/3XvyZPZv/tJeh/6bC3Inw2HWfXaM3v/8/0cHi/x/vAhv//a3rTQujvZG3z7rn5/Vm8PN5883Xr4++6sf3/nDH1vX19/97b8/+sPve4tZfWOn8/p068WL5a07Oy+eH97/5vLHn9z94ovu2fnT/+7f3vnD70Ynx/xw1Du+GDx7s755tPPyxZ0vvrj62fePfvfb4cnZs3/1N0dffzV5+iw5urFx8mbj3uPw6Gjy+vUHv/rl6V//9M4Xfxi+fD398cfbX3279+S7+M7tVzs/yI+B366JkOtjz93RZs3LK+S0SwtV5YVPEQvGMj7xN1q1rVlz4sueNHXTXNueU1p+Vl2OXVPjHZO8bXdhaqGyOnbaPsdcVlPPpznsFuW56zWldRPGb9sOqV2HVSetHm3gTFSzloUytNnUF71WEdEjFr8bDGHZRg1c9+wiQ0LpeIRXoe6c6cUPqJNb/hWc9y2L2zyEadfQGhoAs3E7XGa9zF58pp0Gx0s471tOZplYhYHGDqQhTDa7TZJsXHmzMXegl51by7GFUgeuRNRGELLe0ko27DKv+2/86Z3GcZzi2lmNHJlbgrPMR7Dr7Fzlp6Ja02EhijMgIoP9GFW4uCbBpmJzqF5ra5TLdyhLWp0PBTsH9Qz1z1Kzqqq5a40UjxF/C3sHRfMWrNd+N1H8TFTXqNMvUc6yM9drNbzA2bHVPSj0G1GtOnq31teIXZG+n2HF63Or16pkQ+N33v6gUKeiWo/QpGp5mq5HSHM4qnB4SOx3FIZw2RVuZK0RiscY5A4QVjS2WaVNhlYHmLwjODeLIcCxtTYgHXVMjbpKxyNcpQrVZnGIwYld12A5tOvjVqZVOgyMQAOI4km7KgraWMsDBS6pWMFl31VrXAkUDbAwCGYo2uqU19VAsncKt6RFGnnRRcQAi1UzL2CFu29mb1vAsru7RXbseZay7EpctqQx7lBk18QrC+dAhm9dblm9VGTHyhhOrVpddbnE7pYsromV185GWr90G+q1j0RzDo3Utp3Wl6TmVntXpdcYpWJ0q0qf4wL5rd2qOaOiAtaYWdWWNRug3hW96Jlqyy7OglUA4353PvdKXCcbqi2BcJ3F0ExCf4pkNsHDKQ7bJB727JUjcLMaEStC2sWrPdGfelPKi5HuXloJQcmIXswdCdy0D/EMQMssD73eHM8tlW9g/9SqIMiG1sUa2cgsxxDMleOixYE7fFct02pqe6hAw6K48Kzy/6HtvnYuPQ9Dvz/97e/q7ettCskhJUqiJNuytyQbDoLYzvZG4CA5ChAg95NcR5CzIBuBt22JZhNFcjicGXLK19a3vtXL28tTc5AbSA50F/+DP/ArLFtHbxpdXXlVWVwGgeF4w8v7kKoM7ZflXcuLIvu9bHfZbcvKFlV57UFdkYTzSUA51yc8v/edOKVtHL92g2NAcZlcu35ZkVoWExeUkoZ8NyatdW519OKNA0+g58nyBoatkglRTzyVQ/e0cGfnglA3ubIWIwoqF8155GLFeHvB4oEdl6jzxru/qInv5GNn2bY4tzTnqQNNj/cWNO65sc77b5zbI0E7dlrYq46uqYtndGdXfEgbGxh17dQ1hzeNsVPBHo6natnCtevjexxbPG/pYIKjPkobqPcmuOkVsOfsNnhapTeu49ZawfitHe7n+kqU94E+4GaOqilrugnBVXljNezSYBy98Q7CQs+rfN7v9Uq00cXMCb3E0Cq/tps0Mw7dvfGa3VStdX4TgLCAmS5mvmsnoKHKawp1DAMcXbbCdgIiU4xd2sxwbaqpY7NKtUV5Q1y7YG5gzzssmASZFLsO4Rr1AYy7dpqnbmEtjlV7QdEdXHbtOsd1DnctXEOEI7Tdb0bTpFE5sxHol66ztJcd282oKM22RWunau6szR7dLksvdWfvmp6244297tm0Ijkvty1Umrq7YdHIjaOide1MHhZd4sRze91lLK/DzWpK6LqwR3H1A8uN13jMq1ugcmP7UTY2RW41DlQ6J2jBuw/S5CVKlNc4LaobWcWoHeTVzCQbFrSrbGHxFR48SsuXKheN7mGqrhDfIWZHVYTLGe52imTiJHP2YBTXz0FRt/EwBmNTbSj24zpB5RgPunk88aIJHZ7s8LaH4j65W9tK21EPwg2wtVkc02CNd0DtBtgZ25Uxcb8xjpELzKYP9Uo1AFicOl4MSgLWfUtqnyKUtMldggKC1z1Pr6u2oatT6b0wHKJNz2F3EAKy60FY43aNVnsN/brsGnuxx9sLwrG17jn6DjoG73oehRyO09ehPSC2XybXrt0WVMnqzgOJZk2+u6PNWWaN9OK1Y4bY74jyWrt+aeuqngQqhmwgkzHxcWF34uKlq/qu35HFHbZ8QUXMJ4FiODiSmwm1kegelZsXpGzbdquubgmyFQMZn7jCmGa3Sm59irR9GscvYNFqDTqbs6df+5d38/feO3z6zdGzZ2//9tfv/tu/erP55M9/evLpJ/3rq/Job3j5uvPVd8n54eHz7w6++vb6r3/1zr/8c2N8t/zo/cN//2r46ns+aA4nV82vXhcPDo+ffnP42R8mv/7lw3/7L93rm+2PHg2evT749tvscN+P1qf/+nHy4PDg62enH396/6ufPfzs09GLF+Zhv3sbn331VTns20X67r/+y/iXv4zwn8YiTP7+v80vjmQ7vP4Pf54OBtn+4O4XP9Guff/hh8npfjwaROeHkydPsv2hbodv//LP0n6/HHSu/uwXwLMW7z6OHp5sh4P87Hj8o/fz/YEO3Le/+VXZaSf7+ze//AgEzvzRxe7hSbR/kB+Obn/+s2TYB4H98m9+q0IrOTm4/OnPVMNbv/MwOjvcnZzkw+70Zz9an55B33n1N7/2QdJxInJESBu3rZgcW3ZHDeQSvx8wWzaLCX7keKHuObFziFGXtFnkHhh6YPfFGvyowXzZUlv3XccKTM+JnJEGAyd0iuDAgAOnK9bmSeC2QVvv6HseCUyPru09aPpWR8zYAdOnQU+srCeu21BtGOOHthvq0C+CPVMNiSNmdT9WfRHW8/IoE01s6VsaTssBYmjq2NP4kFAYq97OtHOvnmXDVHcJA3csmJU9TeCC2cv8BNp6o1pr3SnceiaGO9UEDEywu8hGxjUTZi2TY2ipNQxmZV+3qre8s1J9ZsDMtafZUHj6jljr/FgztQONeda32nQ38HfyyG35eRdswJMG8kCfbdgZDVnR7BROH6IGHPgxOHVaXtY2G/lB07eqppOhC6uB004zRgeO1dA9e2cufMcr+2CDPggDVoROTi6sgJbddor3LNKCXba1zihro7adooeO7dQtO2MPnMCp243EGkLdd3o0ss5Z2SLN5LI4SrBXu2gsu1ndVAxMYScqO7It32yPgePFWO2q4xR43NHXopvynnHgzHSTrC8b5SQ5KVGD+3yan1TK1za4Bu1I9JStp7i13R2wRjUrTnPspg6fpgclbGrHvIXNbdWRjrwHnTjbg81imh/EKMjdelrvJ8g3LTcP/Fw/aoa4CPeVtQebcutcENVxW3rZDGJwGgRe0fEy8DAIUOH3anjkDOTSOzawbwdW0m3l6tDpsKjpZ/Kdpgvydq/A+2ygVs6+tHsEB3oQpuDE7tDI9wv5sBGUK79d6hOvr1fNUQlGnuvXXT+mJ8xyeDsowBlzxRLY93xojJO6cFwcKESXPp9EDwzDkY3H8Z5kKLLppewJ4+U2mIhRXodVk0+ih4qRiJpZdVAymjB2wwea+7Vr7tQw523eqO+jB8olESC76rAiLGXkuu4rEdaheVvuq6LJm9U4Oq8tJ7fgIjtS0ModdJWNABmgPl+adz23g9pyY73nogbskZW7Z/TAbqmFs4fUWaMr19a7ttvWHROhh7bdMIFXNkcC7bE2ieyhNueNntjY7zC7qdpqYz2ycAv37CjoC7RHWyQJhtKcBZ16bV0g1LcGu0tyglDf7Vi7sCvQHu2DlTvS/CzsVOvGua47YSjfYmeWjYAN7206T44BMTvk3pdD1eBXsrVUA2bAwrUm2VA55s4ii/xUU5AAb172eYNPRHivBgzQrUPusgNlgZmFF+mpctTGBCverWy1hI071QbAiSw4rg64ZnHA7qIj4OgV8JZ8KF24RN64HlJhJR66S09xkxUNltIHzLerbiMl+4x2UIdu2TFFbTzMb8WPe45Tt2hsPXI8V7SDxB4BPXB6LLZPCRzaXbHSH4S+r1o0xY89y+F9d0MPKR2yPtqSc1vtuQO+ZD/yfbdqshyeWRarW2zL9pk1Il2zwmcUHAXDakHedz23bpKEnTHkcRtMSbiJjqjPl+VhioPcrafZQQ5a0DFvUWNZdrWlpqi1yw5RUM3LUQQbuVtN+X6sG4bBsQm2VV+Ecgwau+QA+cVU9BeirVrFdTncqQ5GZkKDVdUtfHEHmrt8BANxV/XWvKU61VXZXekeQWhFnXkxqF1+Z9pJOUIjsfRG3OoTHJhhmIBTp2MloZWax0FIi6DHwSHrgU2zl8E91wlE143IKWWe7NopesdrwMTvcHrEmjjrDnIzclhD9+0dO2d2G3ZYot8LfZg0OzU+sdssb3Rya4BMx+o0cvsIoTZu48i8FzZZEXY4OHFDmvaakT4KVbNslve7h8qlOwCj6rDAdm7h67rHRUME4LLeE3lLNstxdF4xt7T1PD+R0CltdKW6pWpVrpyAQRINrXZxl12UjMVMz7MDjoPaRjeqk4tm4fGx3CvyPmjnd8lJSuzU1TfVqII+Z2jM26Vspo36ph4V2QC10uvyYAtD1rOioFvjfdZgaaPLwUXQkjvnBJiBPdq9JccQDryWtWt2anho9dDG70n+oNkS28axQn28Jxf2sbG6mPmy0y7QEeuBNesbfOo0ZeSfGD2w9sQ0OJJg6Ppe1Wrk8IC6aht0BD5122rrnCHaJ20YtQ+4Hriuz1thJh50jGetHjy4/fAD7TvzH72XHY+2xyfZqHf/sx9t9/fVXvPVr/7S+Nbq/GL+4RPRaS2evBc9OFrvH1SjwfTnH25Oz1TDf/Obv4SeNX/8zuzHT+pOa/n44ebB8ebiQvTad3/2s+XhkQncV3/9a9D0Nmfnsx+9l3eb20fn68cX24sHMnRnP/9gcvEuCNzXf/Mb3XDWx8fjn/7M+uKLP4VFaLK/+7v3v/60M7nlraD3+k33fiwb/uDNq971VdppPgyLkQ8Xln/+1Zfd2yvZClq3d4Pby7rTHrx9Pbp6kx6NTr571rm52pydnD37pntzqZte527cvb6uOq3uzeXBm1fp0ejw+fPe1dvo5Pjohxf9t695r3XkVoctmjPSefnq4MV3ydnh/nfP+pdvi/1+/2Y8+v551W5WxqvGyrM5y8vqXtMGYnEtp5xYmqzv8D6wZYULWN1pm9a4qMupcTwFM8mnwiW1J6rsDhKsbF1mt9AlpcXrYopcqza54jPlEeHwvLwDlqkZ5sUtwNjYKKJ+QURFU8NnmiDp6iq7Na6pfZTHd4xg4WNgrX2qjGMStm4wLi17y2ZdpLBvTX9U/bAHVxm3YNRhQlOU023TEQaztTtvY0GoFbF1kwlDTG6tXaoQxRlbh7QAyI+caRNKhr0imDOrUhQWJPKJoMhK/0y9PISztWbhsqWlrT0RLC27MraJSOST2hINha4SvgP2gOBxpreyYRU6gXyliAdQD4OO1SkW9Z3mEcQdbGZcr3ngSbZKqy22fKhirefcb1RgVtUbYHUgXZRyIZktaFQUS+DaAqainpsgKPWc1xvQcEsZWKBPnTK31lW2gLYjUMqLGWz7qVrreqW9QCDleAuHEGXrgq3aBOW2qK1tE1I+xNOP5FshI7tEcjdioKawZMsOw5UjCrYKKCooB2wXUMRdkdBVh5mKoZwuuhgLJmu2Cn0QIwnoNiCAW6Cg645leIPcf6ReOyTL6gbbhBRUhHNr42OsLJPTZYtIIbxKXNZESJ/UfCpApVxal/cG5oLg3MI70tAoVmJpUMEtwuu5xqVirlHjmqRV0KyrK81LaLcRuslRzn1ai6VRhcEeMOMKpaLTSLNbpAtDGlBNOMqUDxKCI+LUhDBxJ2GswlaprmqRQT/kcq51xG1P2bnnRg6xd86O0NRXYe1FlEUWtdIH8vUTNK+oUUnL3djQT+wNJomH3cRKLWtnY5g4pcFxg9LSqgDbhcTaugkmaYCclKbU2jkEZ80sV3Gf4ZRwxLZNYmUX4tUHYFxAZdIm2/mUlG5ZkW2D4dTihm6aMlBwF8sJd5FwZFbeQaa5TXlxAzA0Nk2ok2HNaarETCMgPV1kY2Nr3qBZNGYUCAZ4OTEOEYzzeiIJ0C4q81uIpAjsqrjUGGoGRXWnbFMhpfhEUmgoKp0gIdUOCybGUkFsUa3HlS1LC6r6XhqDUAu1bykSFgqKYM7swjCUksQntQXt7Ofq+1Mw2WgSLJuAuyrk4YJaJbLBjiYurlzjKXtt+wVGztqbe6h2jF/5a4sWhKHE3SJYeZaV0K1HMwuGW/veJZXL3O1jffNI3efK4KiBS5dZsbXzWW4TZ+mtPFx5PKjt6SpfItuWOBf11PheadacrwF1Dc7ndl+SXUwKXM2BZQmc18UUNp0MRqpaAN+rZKTEXAVMsKKo7oFDasxleQ88X5Jtyec6cCuYSD5TlAq7roo74+rcxhFsKihrswFiqUNPoKiup9KiylJ1PgbY0q5F7antmRQrRbchMZKBgm46lpIenf5CvQpwkvKQbRrUcCoqa+0RaCyY00WLcIGd1J62IUKUVN7CtmWBgWQbHwMEKfenri0ksXbevKUhhVR6K9sSogHvPlQ3IdltSdedeq40mG29mQ8NxUz4a4dIIy2hxgWKeLeR5ndIpga3sZ7UIBahz1ETyK7r1KW44SZWYbNSNxVPYKMp5NyoLbcZh+u6jmDg1mKp1EaFzVLcCp4AvyHlXOoN960aRaLcQs+t5VJWGzCyV/mgZXrMqUo+ETrSoVWDbS1X2ncrsdZ8A/y2cGLGdi5GebtMZTxgKKfCsE2LsuJUv/6xuamR1GmDRgHFlVvndNNiKGNC0nUbWznLAd0GIVmbmrKtj1Flq5pu2hYsmKysdRuznFaQ7QJCMppra+NjKnrg7pfwGpsoK5v2ugkd6dTSXrk22CGJ7bUPqSFgW71VEBib1NWdZqrGRvJ7SbVhqHDCjNRbrKgaS2mQ5QBzWzJeOkjyqTIKYheoy4rUou2nyQ0yEmAHyDtOpQpNJqYSCGCFsL6scan8RiXe1rKCjl1aVopQjqJaLKDhJnSLcgxAJsOwqK+UENj36vNnX7Ymk3LQ2//+xfDtq92Dk5Mv/9Ad3xZHg0O3OvZJWWfdH952bm94rzF886p/+TY+2Tt5/rxzc5mP+v23bwaXr1To9u9v2pc3vBN2r94O3rwuzg4Pnj/vX76pBu3OeDx884MKvfZ8Mnz1qmp4B01z2NQxxMNvXwxf/wAGoTNf77/8Tja85nqx/+LF9uxMvvzBuvn/F1j/Xx4swzFtff26d3P9w8//0n016b5+9fb9j4Lvb/uvrz/7q78d8kUfmhj7zec3/efPXn/0K/dq3v/ji2cf/dZ7Pd3741df/MN/1/72be/lD8n/8L+4P0wGXz67+dkv3etl74vnT3/xN87bxcHHf/zi7/9T47ur/adP/+Wf/mfv1WT46bOXf/4bm+l9UX2OqHO5OPjdl5//4z89/H5y8MmXT//rfxi8vh98/PV3P//Nrh1ur0RnCKCQmyvovNNkSbG68zuHMBSaDwbONC5EY/lGtzuAYLO8xvzQ6NwsboJBT0m/ur0L2wF3HTO7xCbImaUmN2E9kBrg9S3utVWN8fU46NHSauLFNfZcFLa4ajZJLHjsLMZ2IzTKE+NbOsAFaODpZehbqtfzdPxI6hj4gsdPTLkqT1SweSQ9jo7mI80VZry2ivihYalsAzt6j6LN7ky2lufSNuUet6MLzsqqcUOSB7CqRUdb0bsEJFsbdjYX0qERGrOoz6GoW9cwewiAjA+vB1qrKt22O2RzWrIgxncs6SrJUVDI7FxpmrKYz6tijsK/aOi5zqaN8+N0FYXZvQlPMPc7NXUdnm1ncLMJ/F8GapMW98HeuQQpnI39xikrY2wuqXyfVItyO7aav2zBTbSZNAdnCuVsfueMDngdw/iNlu+iaiU31xY5z9fBXu24Db20kt3kzhscCJ6i+Mr4ZyZa0/nYP3pYKdGw44e1d6OtnTt/XHu3ABTW6lHeum/K+1PsvNQ6F0G+/UCxaxFG4ebd2r9TKLI276jgkmttondq/1YQQ6IPFLqq27G/ely5c8V2LHqXMF4TKKL3a3oLfGJtngA9lsP1sSpzEGa84+weG+u6wrmK31dsogJobZ5AM4vJIrq3Lcd0+vX9zCZUd4d6ek9dVNsX0BGxCUM5RZtJkxLV3lOzlWsZaT3woqnl1Rn/mbu5FRWxgw/degqQ1oN9OVt5Rmj7oZctHCfPvA/IamyXwAs+dKoF1TUetJa6HQItOXJ2M58lhfo5S8ZZKlz0rlotSRHD4UMNq0O4DKNT4yUttD2I0A1MbJ3sl/VTBs2eMV8CkPJTtrKjC+znPt4elacSJS21Oyk73ypJYfQOb3+PuOMsDpJD7aY+XJ+Wh8rkTb07Fu3vqhqWu/d4+3toHHd1HO9fnssXxwh+o2GZd8HuvGq/EtDDm/dE8xXAzFmd7lSLhwAAIABJREFUFqfzshTR2GmHRjB8NQ66oHAHZHaNXIwbg7UKQ1wBuXUWYydwjPb57djqqZL24ewycCHwAzO7Yz2rJBje31kNJHnLGd+SRl3zc2t+KVyAg4aeTViNChSw+ZQ2saIDWXU6OJ9XuJlecw19rw/TO9oZFbTFZlNmawN+0uxsuwY7Eb62dl2hkGj/YLILJGB6eNMzhlXpttPqbE84bEZ4zJKWrBFu5Co7FTKM4RJleziqtu+o9uZIgm6ELkncBnUg29+Zoq+qTr0nrfwBjvD2iWpHh7puy7OswW/7BgmNy+IUpo16JOzohCb29mHV2e3X5TAHb0XRuBsHgwHXHO7e6P4FElu5vma9U2DV6/RkSL9fSh7M76z+QBgNNlfIOQDFltyPfX1R5wnb3aLhnsKQTm7dYbvSjGyukTzRaseXN87RSVEV9mpM2n1Veu70hoxo7Ld4ZVMhSbVzdncuOkgLyea3jV5TWj13do2aLa37XbM9RhBWVgWidwWa6RDbm/cgv5d7uyNVKeomsu1E7wA8qdhOpE80XsgGsLfvIX+xO190Vg9KInO9ILsDHE6LOpHpewpso73E2Z1Baxmdk+7qPEckAQsSjYi9wv7dCFEhq5g2nd0FJZvtOegsz7Oulet7Go+EWxdguVlia5P5H5DVnZVxL/i5LxaJiNDgkSqbjdRuNrbrZF6iLVcfsWySxZlH3pfrJUnXeHgsqshN5gyciu0C8ZlUPyXJlEc7B77Dd3OUL8L9A15G3nbCRudyt8b1RA+Ps1njSDC2V15tt7i4g3t7PIvd9JaMzmS0xsUYgR8JO+3q3QVvvaikKLdPePgaWMBbXSSDm0P1/SEEzw0sy47ZPaqarwWzyPaJ8F6pALqrB8XgElaARI9YuK5U22zfrfw3ZcuzV+8q57Kmwp89KJvXRkMVP1KtpxUkOnrC/bcyuD8xaiZFBEad5Uk63GFVgN0J8kROHBS/V4V3RVMuriTbI62+nt7R5qi2W3g2ZaFUbMCrTgendUVa2Q2XQ88/JtmENNqV28PTucVqQx6E2YSGVm4/JosbR3Z975TkM0YbCIbJZGYTD9jvNXd3xKU1/wmLbvLa84anKXA0lwpHZLHsEabxhZzcYAzF8Cd0Mxa56x+cl96b+/br26//6r8Jvx/vffvt7//H/6n17Gr48uXTv/+PYRXtS/0NNM7VfPj1s9cf/YX/4mb/66ef/uM/NZ9djp4+ffq3f9d/O+t/9uzmyU+dm0X3s+ff//xX/uv7o0++/Oo//fcX310Nv/zqu//w163L5eiTp5dPfk7H6+HHX79858Og4zzS5afYPX51N/r4m9mff2TdJsOPv758/CHZxXv/+iX66/8oCf1TWYT9ZBtsVrvzExQXbhwnJwfhdKo1zkedgGoG9S3ptJbLcD6LLo7YMm4sF5P3nrhxZOVFdjRky52bpHdP3h+8fd2az3bvXsCSw6RaXlyEq6WdJOnpfutq7G53lx/9wknj9mQye/yoLWNK8Mxq+bO1m8TZ4QAnlb9YJA+OVAU6N1ezJ09IVfMY0QYwAKqtlKMAGEPuE30YkmgHsJDUdQAsI8gaQEPMx6W7hyvHRfeZOmx4PKnWGHYtoqpiqppNYTyWx8QOVeX6+C7h+w1P5vnbCh+FFuVVgollFBFQ1ghi4bXINJWjwANFNdeoSVsgWuVN6ijQsMkWCxsyluMtq1xKnAwkPkQKeWkgM5/nS2vA81BYiDqVuMWq5TphpCMHAg3CWhaBLQvhKRR5AEnQrEFkKZsgLweJAwBKO5jNDeWlGUGUEI1h2cSnyWUNcWJ3dOIibfI+sXfaqmsUlCaza2KlPdtaJrCU4iDAcYV3pTXEGbdJWqu+Q6EwEHWqza70ZQHkcdOa7/ROwyPLruo8J3rowkLijNMugqtKpEY87MJK4GUODn07TfKcWR0E41rtJD1kXDIdC6dncte365wz194lVc5oF4Fa8gL33CgGDRlJNsC5FTbmompCy+Qq9aDHNYEoptirhC1Oy+WNN+wk0a7YE01ATMHXAQ45dA2KKbGL3HHctSk6xNFZdWebAbWt1KQ+ZLW2jEnsFpxvmz3zhvPjwMcR2NgiQBbbtWTMIdvgHk4tStI8cJ2lqVrEQaneuDKAxuFyC6isTNcSG20ockItlkpbFLlGFQkm2EBbSWbxEvadclwCSuC+h2eZIdhu63oLjYZy6MFJRvMcnTf5TmFoZN9HsxRC1HOiRRKarDbv9ekqNbXWISJlIjGWYZuua2S01QH1DsJKWEOc5wwVHA+ZlWrFGWjUoKYkB/GI0VK7W5kPQK+cekDceSNT+VYmQVjWtQ+W0j6oKug5K1GMsJdnsgpUQ8BUitRmg0obiyYGtqocB/6yrgaovVstshEeKlvVunKBV1EQ92WyxmFM+v5K5gPiVpnOXNVQWGtQWHmbGKjYOOLD0AdptYCwxRjlZUSxZQwVRlQYYuE1ySyT/cCBdfE6I/thh2zXRZMwhXzMN4BZnLca+unMnHZcR9RLDUNmubLcQMoU8khxJxxfy0EIJrlqO9wmbrqUgBDmmBRoC+sGQ/PCZbxsBWBaggarOkFzUUKD0h52d5JVnLc1yG1gQNEmx+m1BCa2Ozr1sDTpgKI1tOPMHnGT0Qo7VRu5W2UUQK2y3voo5dUDx4k4rYxoSSeSNXJRUKollYBY/UKXLi4U7JRUFU2RT52+FSMOXejnKKXGYNgoVemSXMcHdmO7yhNKegTXvM6Q3QZCYB1J0oIFAK5ICqvtcS4yRLoU5FW9MZ1OVTCf77TdAyXxyH0iDhpOnhb3kh552EieITuUAlC9EX6TJ24PfTPWHx56PCk3kAWG6KwACCGqiQ923GnJ3Gng21jt+a4pqw1ELYpsjHa0CZZJM0BrUjapgzOwtoSPmR23xU5AusE9mNkMZXnogmupe5bnpGZjKx8huwSxoxgsfUwn0mZl3bOsDeQeqgMczoS0IfSqeulBrOUQO2vFYInsvKeSLXXX7sifK0UBCioTOQaDok28tTAEZB2bzBKowcDbraqmqZU8apBtAXKJ+sxWZUVtiRlcVkhp1oNmUsoaOkckFQ5KKjy0yDYvte20tFpwUChyalUZgZmwBrCoGU5qNLDYMq1qag0RL5EWcGBvVo0hqwtiTGJ8sinMyGZxUXNmtbQsgKqQ3ZY1CrylKIekn0x3fF81JNXclJ5xa4Kivki22I/o0F/KvE/sOq8WDhxAC9cmc5BbSExRRFrW/Zb12NriYYkdbXa2ahgEuUlt7JQVdswcOt2s8HxvLsseCcy6zeMd9kvUQimpQmIwcNaKeFni+P7SlB1MUM7XABNNGqjcIgdXsu2pWW0aVu0wN11KgIjlmsRoilXLhtexTYXcb5p5BXwiWy65i6GFO1a82AbAaHDWJPNUEewESs6F9GzVc9GLNfSoM0L1BkClQBMrURmAMGFwJ6Xn+H5drADA0O6Aaoe1NmDoDG8vDTfLs/PWbPr/WsZ0k3mbVfzwBGDYluWMBq3pDJQqOxi0b8ZWnC5//A7aFcF6FT88kYVuTyfJ2fHw9jVPze7hCS0qmpXZYR8ldWOxiM8P8CZr308W7zy2ywwnZTbsOR5yoY4kNolo3k/MUWNNe92rt+sHF36yo5v0/kc/8f+3/zX43Z/CIvyHf/zgd7/f/+675GCv//zV/vMXeb939PTZ6OtvF0/ePf/f/6+9f/7k9qNfXvzu90dPn27PTu9OHi1Pzw1Aj/7td4dfP52/8/DhJ3/Y//KPk/c/2IwO5xcP3Sw+ePps76tvdwdHo2ffnX/5x/hgdP34g+nFI1zW53/48vjzL5L9w8Hzl/v/5+93o8Phq9fn//b7+XvvHH36+dmXX60fnne/f3v+6We7g4Nch/FziH1gCp28RqbN6LbevGWWLa2Kz561HWIAAvFziBxs0S1qa6brekO3Vw6lIpDV9AcXSk680gpKpGsxB+trlznKZHL51vWpgMUKXjhotxaqmb4w2AJQovUzz2WKCr1441BQMyAWL2xXFtRWi5cuxMCzEZwfUYkQSuD0nGTSNAv75tiUtnbz6vqJyX0EE7V7gGtK4f1vnctucTW1++z2GFeu8VJrOqIlk162HfpFgwVJzGbnJEaiW1hXe7AMYWvzF9XTMydalpbePGClVQdKXr+jt13Zjv3xQOXtulW7922a2QyudXQM87DsKv4yL+4QPrblFU8npOEWRWzHNwi3SHENxDdF0K6TO5ZMKO1D3vbrvRBxaW7L3dQlDVStUP1W+n6VHvbL/RauKnDLN2PLdjnYit2N7Vo1PwiTw76jSvm6ju8sryvqO737CtkOJ7laXtqWLUSC4jc4bFXVVK9vXb9VI+VYkyNiKo0Nuzs2RBIp6f2hoZWpnPTuZ9rIZrGs1h9CyRv+7Nf6O4KLbdxlswNIKyIhWJwjUIbq5r8KJ6RebOC+fXNgoIZa4tmxK3cOnv3WmwETF8JH9w8sWdY25G9+UhsPA05nB9RUWhmyPCO6RrCg9xekUkWjTp8pUELH4ttbW+XIdXj0hqENZ/so+satF9Tfg/krU8fIQVswINSkMFfxW9tsJdvHyTNQbQndh3Y1RT0Kp3G6btZrA9q0+MGYpXTCwpiYjKw60uUNKre04VXZJSuXHu6z8pUSc2Xvg+KFzlfEbcv8FqUr6oTCSjr2sqPClCxDshmWXe4tKNocQhbj2E9mP1WWxIVr3bVqv36iX/yC3cwB0psu3p0CsnNyANangGQPnds/w2+3hJplB66OgLtFsQfXpxitz+QPH3kLIYskO7amQxCketPOJh8CLyG5C5ZnmMS4Bmh6jHCChE0nIx4qHSWbV5ZHBFPl/HsPS8FCtPsWQQwQgptvPYa0pfXyrUM0p3JLziy0XUBpr35wAIQEyd0rQoGB+RS904CbBZBO9D2jZQVbOPkWAoVsN8EtjWQhd2Bz7VpKq9BKPiO4gGwA42+A0Ay5KH9lMOeMmtUbG1TKDO3GmybKwrpdeeOQ5A1IY7M7oJnHAyHHj/S6L9uJN+7rtCO7yV9Uzx5by02pVHQOsxYPuHXfcyOnDnd/pV+8Yy/emqa76IK0Q/CW7Dogbhk7/gV6+Z41u4YBu+mSbADcNV+eFdtHCMdW3IBRF7oJWQ/Iti1bW+++YaK9upGgabq+cV1H8hRkrxHrQD0T8Q3zO4LvrPXz0PWVW9aLtw40NQ1K6pVY1tUEbm9dt8n52qyvnNCuoNrBA4LStE797JVhTSA2endldfFWpivzQYfMV6pkmx9YQHIg8OJFy24iEMvdJWsFVZnC3aVjMUmU2r6kmEjmenQ88mSitAGbh1hJDHN8/8AWde1J/vonlWhgUtLpPlHKx9PfOncMbDLh0elDnEvRqO3LPQ081538GnzfItGmbsDFBRWg8rV/NcIFQP7iN+pZ1y0mcmhPh6ySFOTJ9CNetapQe5dDq8KikdnXI60CZdXutAdrxn2evdJqLpu9cvuaZUuK9i3+us5WLHSr8geZX0LYtaprIO5l0Ch2R3t8GHrxLh/TeEYdu6pnoJhT1+XFUTff63hFWr/S8ZwFXZHPSHpP7UCIkZ8NO47I89cgu8ftfp49VdsXxBno4h7GU6vh1NUaxLfEbetiivNb4A4NiT24OoMoaqRptX4foRxIyib70M10HGbjD42T09ICi3OM4765+a13J0Wc1AfW3Z62S5QRtDoO9d2y3djtuRqBxqZG8wsCU20ImxxKSw/Vi7+k11yXWdGjy3MCc6BAcf1zZbDBxr7ZFx4ipWazQwp3Blh4dgoBVyxPvkWGQ+aa+BUGXGGkNpc2rbVpsPgLghLI9mDyFPCasVZlWQmCNYzlZhyYXJmOnX2jYCLcsDAohT3GNzq7JLrGHsp3V5bMIfSU1S6piBWy8xeA59gJdfSDW8V2aBXRNTOxYW0TvcAqgtbIFM9NllpBkz/44rP+d9+vT85OvvjD0RdfTn/8wcX//S/Hz56vLy4Gnz87+M//lg729l6/2v/q27LXuX7y4/uLx1TWj3/3yeHnf9hcnPdfvj7+/Iv4YH91enb94AMMzMP/8i8nX34zf/LewWdfnnz+xerxg/nB2fTBIyTlyddfHX3yh/jkaPiHbw//j/+8ePxo/49Pz774ojrs0fH60cefZP1+635y8fGnqweP1MuX9s2fwiIE8Pjrp8d/+MrbxcOXrw6/+NJdLHvPXz7490+BUMfPvrv45DPA5f5XXx9/9oW9jQWmHDMo1OjZi7N/+z0u6oOvn158/ClN85raAlIgVO/7V6effeEtVr3Xlw9+97G9jTi2BGIS4r2X3598/mW4XHcur08/+9xdrDpvLh/97mNUi4Pn35/9/hMWpb0XL48//SKcr0QGi0uoIsPXOnmL6hTKtY5vqd4qk6rsDvFZzTMQv8ViAwCvhEW0lHrJoxuqVwLEIr6mZi6UkNq1jFZiyfMrpBOD1jIZEz2rTJTUTZ8WhUhM/grILYCJzG+hiqVe1fEtgQsJCpXeEDGpVW7i17BaAVpAturQlUtzjbYja+5DZcikZW8aSEBnFZIoJLFB6w7ZNEiSn4rsrFxpDdi8R9YdKJC1aOK4owGtqFMTB1QALrts3lAaOvM2XneJ1mfF5qBIvLwmuzZbhbiGdN2xpoFRxpq16KYHKkyXDbTr4FSjdYOuWkBhfg+Ka6g4EBMV3VKz42Kh8ltkIpXvaDymisP6TsWXGOSyZE5FHGkIX6jkGqtI1xtYXkGTqYo6FXElJHymklsKIyW2ILtBcqcEpiX1BGFyDtIbAjIllmAzd9VWqY1KrxFIdL2B6Wugci2nIrqmMFOkoHi1b20tyBmbDVDqkRRZiz7OGYsIXffxlpAM4PW+tbUsUT4u1l2Rw4ySxQBnNttCvBo4W+aX+QOeHZYrUxE66dDUIzmhyz6KnTBenotqWEewBHQ9ctauEcied3Hs4wzTRY/knpVitB7CtQsqiOYDuvaVAvzW5FMII57dwWpiYCqzO8LHxhhQ3ZjsHupKZlc6n2K0KyQjCgJQinSM8kujpRE3IB8jzTUAWloEr5PiHlZjYAqT3uH8UqtaQ8WFZ+u0qu9hfo9wXFdLU020KXUxwdU10BoW1zq5Jao05UQXE4xyRdLQmvZghcjaw7MhrLG9cdB6xDJMEouuujAlMA2cWU9X9EBEj2XmVDnaenA1YgkhuUOWQxKTjijeqbauKK0VY6sRLgHb2ng1JDvY4vEjkbZkhjKX3XdQia01ZusBzYG1wXg9snYYZxQvRjQmKLPZtI9KC25lOiZqWptURDdUTqUuQf7aiKVBmSrGQO0UWJXJLQYLZfJKNAJVZCrX8RtYLaDJTH6DxBqQbcxbISsKk4n0hsipUQJkb2E2h6CuBcNKShjVyZiKe6lrk93CeqKAMPzKlAuoE51do2qu4bZO77C4V8oge94mq76RBC+acNthKSC7Fls1cQnppmVNQyO1PWuyVR8Lc1Ssj+vCzXK4adBVB9SU7Lr2vKVrc1puz0WGuGCrEG/7NId418DLLhLkUGQX+QIY7SxDvBrAEtKNRzYdK4M4aVrLFqoR3jbJbKAVdOYO2YxIqeTOpNeYr0S1Q+lbJAqolia7xiCTeiGSCQLbWu9kckPMslbAAJtqKeWUR9cEZBouRXxLzKzUecEDB1Ul34D8NdAZ1EsZXVO9FTCKeb+DtzsV6+wK6SWXic7eGpUBs1HJDTWxAlu1u6FiLlUs0yssdxAJxhZdHDskAnA1oBsXl4ZuRs7SNQrZiw6JQlxituyitBGW6Wld7BdbWAO46LNlqBWyZ120bXm8PMt3wzJ1c4k3fWvtYo7wYkBnAdTqUbXZ4ynMNFl2cRTgWOFNx165RhK6GtiL0EjAZm207ZAKk1WHxE1Um3SC8rdaK1BcyuQagxrUU5DfIbjj2RonU6YLU0xheQUN1yXzKuJoCet7nU0ISmS1Qvk1UJmusVUyTxIi7nR2S0Am+b2Jx1imhlO7ZL4yqJyY9DUAElQzs1n4qFB6rtJbBNZluQL5NTCZqqcmfwOUMGxr4fWIbBHOMFmPWEJxYbH7DiwY22K26pMcsQ3G65Gzw40qfVAng3prModNu7i0WELJckhjKCHh2K2pg1NEl0MW2bBibNo3ud8V2WNVhtnWSjBc7cGNDWrC5h2ys2DFrFkP5Q6OLbgc4oTRGKLVyNlZSpjsLUynSCQyuwL1AoJIpneET7WuTTaG1Z2C0vArUEwRrKWhWAONN3l2h/m91pVJrnF5rZVQAChpUx3VxQTVM4MinkxwPQEm59KiCChV6fwSpPdYZ6q81/UCwFTmU1LeKl3p5BoVN1ArIK51eY9JUgxe/nDyyefWNho+f/nw40+0AifffHv68ac0TgbfPT/+49eNxap7eXP+6efONhKYcWIJTA+/enr26RcszUcvfjj+7A/+dC4JraitENl7+erin/8FV3z/6bOzf//U2SUCEgkpkLr75ur895+46233hzePfv/vtKqHL78//fhTb7npvnp7/Onnzbv75t30wcef+suVwn8yi7Bp6iCJi25D14BVVd1w3TTjgBifgtrQNN0dHNhp2oijvBNmdkNjTAT3kgRKZXxqOKBpFu3vQSWl43a3UyCMESDq9dw4wlIBj+TU47ZjlQUWMtiso/29RrwxHES9rp0kREgTWoobJytE066M3d6u16MhqytRIeoCYwAodN3wDITuapcP2kEVmxyWge2oUlYEO0AZoZLE9uzc7gTbKOm0ApGqDFShS+tU8dpBRhPbaItQmdq+s06LTmiXicoiE7YJwFogTJXGEBeaMJU6gbPOik7gyxQWqGawpZOtbhGspGfRRHGbMlTamUktZpFa1jZEGjKpudXku9y1FLckpRQXXrYpLd8QprkFlTKekdxyRCFsWNguUMoTFapwzSxGCiEcIHXdsoJs7fA8DkcwMxqjOrD8XSEJpqQQ0gVSVy3HyrhbF9IFpAQldbPQsbMC5RXvhbjkXpLKJquNTdOi7DZYUQFpOnAXwxBwU7Z9Vhfc91mW27ySghStABc1ktqyBMdU2A7i0iho75Ky3wyyiAsKXAQJ4Ig5ouCSkkpiT2fID3dx0m2EZVJzanxCaiEUaaNdhALGgXZB6jRbqzhv2LYsdU0h0woZVBNC65z5QVxETX8/G+eqk3nM1QkpcsWsioasAojyzAm8bZk1PV/u7Dwq/DYwSHILE6kIxCVqqPUmbDaj5a4xsJQEFa0txBDXFYNIShvjAlJcZW7gR0Xqe65OYUm5jQ3VqgIuL+vQQZlUhBImYKY5YzblgjMoOPJRzanLyzq0VBQBQoAdsEIISm3Ga8EMl7wTkGTDslR2R7CQBkLe8JwoUxi3+GpDXFxXvL9PsorUQjUtN0pL4tQNl+0ygLHNeCUYqzkIUCUYrWvZcqy8VoYRVknNWKXStoe49qI86/lhFmtB06ZHakG4RpZAhttlxh0vw60wyZKWH5SJ5JbwkVPvkNLKsivjsNpAW+Q4COMsbTn76W0O7dLyoaZKUsIqbqhbKuXIjDWCbZZ1vKBKTE1rBxBjtCBF4Bho/GVUtHxfZSgDucscJOqSEqoNAbA0lIjMDexNXrQCm2c63uiw1RJlDJsYSW0TU2gCZOm7dH4nukOmNKqMsKlNuKgoAkI7UMUphahq9611wkNfOtRbx9JmjPC6pgYCGTj2LrdQnYUNtkp56Ne+4ycF5Kpsu1ZWe1VRNGxQQmBAFdp+XMj/h7b72rXsSND8HhErYnmz/fEuzclMJk2RLMuu7ukeYDAvJ0GCdCtAEiRB0EjAYHraVXWzHFlk0pPJtCeP32fvfbZffoUPXegVVPffG/zx4YcsQmqhfMh103bDchHSoonasIHM8prIcXMKECS4gUoQ3izbe3YtCeW8RcK8opYHHeXXKwmQcn2mPJdL7SlmfL+hdWwHZUORh1yBamCghRzGpeMIve6E7XwhGJYRsRlTimBXcY2tRhJfFziKl+u8345oJigWoYObXCoZQ1WTiAgEPVXYUTDPq17kVqlqKhO3LY20QLYrObCcWjmwXAVtZ3LFdg5DQRXH0OKe4ZmKbVsw4pKcYl8XThzP13m3FYlSUEv6GFkIN6Yll8uo4xSqCn3PFLAhzEa2xTRzIJDKQ7CxbEDr0G2vx1nYI0DDGnMHE4sJ4WoAZGQl+RQDnUabzoox3xUejlcVdyyCakxrg8k62ghyahuqHEBKUASh8LG3rI1jEauRwoXA1JHnp40mqIwDf50bAAdmPkc9S8i6E+OKkprKrh+kOUUubQUkrSBCLmHUcqXrxPmyQIld1bLr+1lBkUuIFI6tDApE3SjHphyGoDG+V5Y8dgmUNfF9XukGamP10GKGuj5XKjA1CNyi4m0vqCquCPSN4VBLhH1JrTBOy7wdbOWjAvaoh4gWSjgWYQJYXqONJ0s7DldV2Q1iusSsokHbKEsJGxPGsONVMgKLmdNpkshPcwdqw2zhAAKkZgQSoS0dVmvpeanbT1ZlloShLAyzpQMQUpoS4VkKW3FaA1fkfiua5VUndHWpKEZaaB+bGriAlkliLyseeNK3g0UmXNvFnHLbGC0TjyxnRErW3bTXFfc9Gdj+qlAEt/hiaXlIK9HesNPKYGS5xlk3VRDIwLVvh8a2Xd9l3CaSqYBoCgGCyAV21lRe4GEmSwCxZbmSMYI04InbW94KjbNeL1gtIYAgJIoZv65ZyxOKxHma9ntRngptGQ9T1+eIxLxQTPt1w1seU3aUZ03Ld4yYxVtRk9llDYQ2sSM4iOu6aXkFiXgQhPk6qkvFAY8ciwnIlYlsBki8WjmBmrqbrdUq63WCpgBMzw/vxf/Nf/uXsgjf+uSz+7/91+t3f7L51fd3//z57MGju7//4/aX3w1/8v7D//rPd598/fIXf/3w088f/OM/37z3/s4X37339/9w/fCdu598du+zJ5cffnj3t78//rc4mFeLAAAgAElEQVTfv/r1v7/7x09/+b/97+O33tr48eXR7z4ZPXi8/8XX93/3h8sPf/ro4z/99P/6T8/+6t8ffP3du//l76/feX/7+fP7//K70fHj7R+ePfzNb4fvvbf3+deP/+Gfrn76s/7J5bv/6f++fPd9XuPqC2YSW9Sw/qIWOy2dyvrPtdzwMRXLTwRqOcDA8gumfMyE23wNYNczDSg+46DnGKHzz4QKbIQgfaJt3xLIXX8hTWRJaVWfNLrtWARVnwDlutBF9WdU21hAXH7BTGQLg6tPKuVjaKPVHygIXEJU+URyiGDLD1+1tSQqkO7THgBB3VXJ0wRldtUHzpuuU2AWG/tsw9SWSAz58UCLVjUwrR98VEa0K5yzrpU6VRtGz1t24fC28l8MQI7LbRR97+GynfVNdNIFy4Qmyj3vosLJO7D9PLHXVrpP4qcuWrXzTR2deGTly6hxLnsg99Idz3yzoidS3mvBk7L4UbjbOJ1Y/AWTmwG7kOJ5hXft6lRXPwp23NXnjH+S6b0QjWn2zICBR8dA/VDq/Yi/EeyzQj7o6cu6/oabHc/csvQHjTYcNVL0q1LvBGxk6m8adOjLqS6/4nDbFUtV/qBMzxZTyX5geMOqVl76JcX7DjVx65u29KGBOnrWkTYQlh88b4sQKIHCZ30eA1dR9OKI2kjZyH2+K2xPEsd/0ZGO5oC0nnVqH0Bs7O+PmBsKxyRPEwEtTrD/qoct3hDffnZICdIe9L4daExYAJIfIg1tQaD3sqsRaIgVP+1pGyjf+D9sKIWqLmi+5XIuTdeufpAqM6hL8i84WxtwENZ/zvgSWzt2+VSykQADr3qG1MoS+536SaWnTN1N+J+L+haZg0h/T+WtZQ3s7LlWcyW2Ivp5ISfCuWOX38HmGvM7LfW0rIcG9y36jLIpVNth8z03l7U6brFv6vpMkSO3fq3rMwl3bP82cs+Dcsvyx45/3l7uQHdqR6/bVV+6a4TPBk0boCyIXrv1AIDb2D7brTYVXnv+i17ek3aO3bOBiCXKQ/dNv+xZKE+8Fy26KUBqR6/6rCOdAsmzB3ViWQUOT6KmC3AVeS/brC9U5bWfd6qWwtRyXg1YC6Mahy+ivGOruqk/qXTkQBulf2QKYxOS5s+NgkjapPySaYdITMpPa+gg6Fv5nyBwAzuQ5RPJFVSBXX0lIALKIc0TCIkNY5T9kQmFTM+rPi20JiZwiq8hBJYK3erTRiMk+1Hzr6mmRm2F9E8FFY6JbPpFCYThgVs+YUCCaq/T/xqQrLve0K0zH0892uL2VYesvbwLkteJN7fWByT+0bXm7XQH+Octa9rncWMPO3AVFj0QnCTeBKWHJHgRW7c7610YXLrOJKZt5l6H1qLF2hxf76JRXBxY7knojlrVpnDGERkmdUf4NwGeJaxF3askHHvpDnRuOvZZlO1DeFOm32rcd+QaVN9RPfCaGai/Ydaeq+a6/ELALVcVuvhGmo6tMyC+M04f16WbfsHQtiNSUH7WgA2PcFB8iUDb07Wh3zDTtVkKq6+o3otYbdefA91xjQDpZ9zqOLBW5ddStwirrfpLCrtYMFx9SlGLSAOzPzPlExRE/os2MYq6xHsxkFCrAPjfDgy0aQJa34dGudwz7que0ah2Le/ZvkKODLX/w6bmqO6h+LvA6LgJYPBiwzCbejB60VMAlwnqfhMjauVbtv9Dn/FBE5v4ZYQY1i53Tra0xEXH6nzXwgUotnDraQCauGyD1qvQNE7ZQ/TLQg4ZOXTy72V9qsT9tn5e1mfa2rDFSdNcA70d0Gdcn9bqfsK+qfm3NbkfFqeavhJgy+FvaHEBrW2b/yj480rfTehL0Txj6I5Pz3nzSqNtj79h9IUEGw59xeQbQfZJNnSLbxt84DY3un7K0JYrb2T5WoNNl59J+oqDfRelTvSizzrSayQ8OWhCgKgVvUpoAgCL/Jdt3haSeu1nnSrRSGLy7LCJXCBQ+DyhodHUDV71UFjVqDP4c1RHACLsPe2L0NIaRS9iEQIpfPLigEeGGrf1fU+EWlvQezrgNtTIip/HVegiiYJnbeA2FLrtp33qQeaZ+tNCMQLbpPiaq0ar2K0+p1pCuRE3H2emVHIvYn/Km9I2XZd/xWBjqcQpvuKKAb4R04/XOtf2vl18CZu1KzdD8U1Bc+TEMP+K6QbxrZg/kXoB5J2Efl6xOcBbTvm9bNbAaqHiS0pLTDZw9aUUo1rfbTXfUDoy8MA//vj3e198O3z4zvEfP7nzpz9f/uxn9377+we//fjyZz/f/v7F8T/9y/Xjdw6++ubwkye3Dx4e/+5P7/zm44sPPjz4/NtH/+Xvr37+i/4PLx///T/ePn7n7pdf3P/H303v39//4uv7v/vk8v33D757/vj/+c9XH37YfXH26//5fxk9eGvw8tW9f/vD9MHD7e9+fPiPv7n+4IPNb57/5D///eztR+Q2/+D/+D9vjx92zy8e/tffTB48Ni+euRcXfxGL8P4fft+7uDz84fv9b7/dfP5i5/vveyenDz75NEjXd778au/LL9ur5b0//qF7cXH49Zc7z59vP3++/+PT3umboz99Ek1nx19/vfv0aet28uCTP3UvLg5/+G775fOdp093nz/rn58/+PzzZD47+OyzjafP+tdX97980rm4vPP1V1vPftz+8enuq5cbp2+O//iJl6b3njzZeva8f35+57PPuucXh998Q0tndtvSc6lHdDpvyaVWV2ra9M0VQ+N6VvXqc6lTNR63+cLgUbmYxWJp6KUY1111WvvTapx1xaUAmRxO+3QByNVqOQ/lXJozOm168LwmN+kk78nTGq75eNKmS4Sui9ksMlNKTvNZ0+WXCs7oLO+iN5W1Kq8nvWqFo1ul6IG97EYjxs2xO4ySPBPlW4beDVfcWu2gptcZUlVvk/XAXxhh7sXjxM/WunpH1kdOqlC6B/hGciNNs43STWcBubjjzTadukbFQ1UfhgVA613QbCRDZeg2Kva9NWbirrXY8dPUSfcVv+OUjpXtgWajdU113UfpgVvJcmJP5y1QqOwSL8o2Pk/BVM/XbT1RReauRoG3zOg1uF13rBUrb+xF2fauCz4Ws1WLT3SZOvNxjNOaXsjpugPnTFzDWdOFl7VYmMU60SNWr6zJqGNljTylt1kXzGl1oSd1xzrN8VzcLhIwos0aDyc9sm7ASTHLu+gmj+aAg2PnNsEVFs17VtlKphzVd6wyCCYuBcfB0GpNWSOPw7HvFEjU7+EiiW+Zqu6Q1E2GVm2Oo3Ho33JmHkXXVlBwXr1r1f1ggWR918+d1rVk5q477bgrIPWDeOS5OZX1e7AZeEus6T07D9pDxcxde9Z2V4CLY2/RNUymV6TMPO8yT5feemKDSbPIEnoGvCKb3cSLWWAt69W1UxY+viyydVBOPXRLF1mSXWA/S9cjf7GMdSpXN06e+s5F2qyd9TSEM77MW/m1Ha5X2SWZZx00Z+uJX2a+d56WK3sx9q21Shf+ahR6WV6cgnnWRpOmHOF1FpIJh3zTKu4FKSXzFmMPnZy4iwEF9+MRdOa24vf8pWM1bVM+xjXyF4ngD5wUOdOEwnvJtXHXjmD3nLkDq5aof2IXun2DlDp2UuRP4gbcj66Bv4SyuRtOIKkSUb3nFca/tph84M1kfONV4Di5se0VkvReMhJ24YvmPad21RWf1l1wQe1hOsm7/A3FKR1P2vUc41Exvw3kTNin2aLq1JcATums7IGT2l7nw0mvXDnWpJ7PIr0w5KyYVe3yGqI5m6YdeSZIXs5G7Xxhg0mzXERiCeF5Na/bbIjQlE7TbnVp3LRYjqIs9dVMzecxmwL/KluXyWrkkaZB5QNZ3wlLYC13NN9Jhgo0m7A88FaE80O43PPXqbPe1fyeU9l4taP5dvuGwaoDiwM3dw0/dPI74XqNF9tM3HfXyMu2lNhLhhqVHVAchCnQ9S4s38IN9ecDLu57K4OXfSn2W0MFm54uDr0U6mafl4+9PItHgTDH3krpWzFP2/yaszWajNogZeCima3aZs7rCz1puvC0sKd0smybIRMpurrtgxVHp/ksbcPbWp7JKe3hs0JP6umqbS5KuTKjSVuvpD6rJ+sOHuXovJzSvroQaikXqxY6z+VCDG97IjXqik/SjnNLwWkxrnv0Uum5mC8SOZGkcXRx5BR++1pweeTO2t4SCP0gHoVuXqn6PVNveitkmjukjFvXiusjsui6S8PFsTfrOTUHxWPTbAUpNtURzpL4xnB1ZK+2nBQy+YjMB35a2PkdzfacNDD1ISmS9g2TYo8stpwKK37fS3f9NIerQ8H2gqUN6KFV9N0ZW6VReu0Gq1V5ac3StrVg+cTL8tC/SOs5nt+GcKGydbAcRW5WlGdglnXQbcNurGWZoMuqSe3VNIBTmi3c6U3LKQr6Wt4WPXTbVEM8z2J8looVWkwCPKuylT+cdPw0k6/qWdm1Rjm9AquiRc6yegEWIweP83zuTCYtsq6CSdCA4/AKBgvN2L1wgu0qFNW7boGjG8P5sbsw8Y1TgQfxjevOjRAP4hvlFETU79pVEMxcxu+HKxBdGwqOvdvYzrDi9+MJJIUlm/esKnRnvlTH/gq3r00Dj91p7KRYseNg4ZOSqPJt3ETeMhD62JmRaGI14L636Nl5Nr1ppQsXTpvlNKQrjC7KRdlqRhhNm1nWKS6gl+XLUbReB2qhlrOoWSDvMkuLqBi71oLNs255ZQWr1fraWWcRmPLlPBYr4pyv0yJaXdmkkNNZnI19L8vSc2uVRWBE04Vfzm3rukyrOLvCeFXPptFs0vLX6/IUzstWeDnZfvHj3rffbp2fbb58efynT+yyfPDppxsvXg7Ozo6ePOmdnt178mTj9M3eN98MTk83X7y48+knwWr56I9/2Dx5Mzg/u/v5573zs7tfPhmcnR588fnW61fbL1/e+dOfWuv13Y8/7p2cbL18cf/zz3snb+4/+Xzw5s3BkyebZ6fbz5/d+fxJazq9/+dPeq9fH7x6ceezz3pnZ0dfPOldXt798svezdCgv1gi3M1m/TcnF3/9UTiaxYvZ6L13Nl69Ngqsjw+iq0mY569++avB9dXGyeurv/6ITBbt8ej8F7/cfvPGKavFw6NgsoxHN6/+9u96Nzfbr15cf/RhNJl50/X5z37Wu7jw8mL94NBfpJ3r4fO//pt4Nt//8YdXf/d3W9fn3nh28bOftyfjcDZfPTgiy7x/dX3zi/dBLQ+/+/bl3/6tXVR6wtAGQQCYMW3udAlU8GWu3koiXpTnmhx5EGgwpqBvK2yBESXbhNs2PKnMo5YjSnqh4Z6DkdJj5rWNcazmFtl9QwMfvirVg5avS3GhrG2ibSRH3Gpblm/pG457gCYRfJGZo8CzGLtQeAsHqM7nNoyQHATe0GIJsrwiGNk0NirRzi3S0IiBwQscyLoaGLBIpCNBSzhDS0RIt6Q3hdoAumnwAmOlm03l3LrS1Trh/g3iHlRdTW4NQmS55yWj2ha62tZ46mmiyk2nf14LB8qewguEmFndCVrjGgsoexVeBBKb1W7s3qxITsu3tqLZSk6EfR9XtUdmpbibWEtqVTzp8bJ2zIpXjzftdQFvuH4U+cuMr6G6G5tM4HWFdlxQSDBj1Ttbbl6Bi8o8jr1l3qwQPnBgIWAmwY4rao2mFN/1mMDosgZvxXZW8AUEuzYquMl11BeN9sRE2kck8+L+iWi2BULGmUHZNoIge4Z0R1Dbiod4fYA26yFd7tIeAzawp1C1gHSAN7NUTMvIjy6sYs94pnDHXtPTxjXOLZC+lhF0ZrbvzBfdVnhlFwNJHOFcE9YD0FPuDEoPsNjYExuGTdl2kgur3FDY5u4QsxbgCTK3FEsJ9301lNoGZIDMWS1DQgYE3DTAQLDvqVuBKEdHARtKywZ0r+W+WiAHwC1HjrmRgB/3/OslqBU48vjEWEawg5Z7ugK21W7XaeqDWjQPB/YoVTV0DgkalhpazVHHuVgbrfGuY6YcFBLd98UKmEyA+4E346hBYsOgCnorM70f2CVLxnB91yRZrdOg2IOkQd6y5hsAUuSsINuVQtnxGKeH0q8oXPpsm5Ma2AVkA6MFtOdI7AouSTzG2ZEarOd1tcEGHHFAMij6wCjgzy222VQ4Ti5NfqSDpsYrlw4UUsCe6+VeAi1Onq/V/cQHlThX1hbRniVvGIoQTrC+4VYL8E4EXuZmz/Nszs6ltWlHpMpmDvSA6brqhtuRVv3IvCz0rud4Sr2pVd/FMdI31PIt3XXkNXNDxQaReVXCHY92wuDHW9CxYYfIG6p812x69lmGfc02InNSo55VbPf75wsM8PwwaI1LuwHNrgQLHxpdbJPuNdUIiIHCS2TVZnE/bE0qu0Zio8QrTwGU7rrxDUdAiYG2VwhXZnovCOfMrlCzw6NbzYEt+8JbQK2A2DBwBZ0KNvsKlLaTW9UODeZAKSwG0llAJCHd1FaB8BrNj/3eYlbdAnzowkbClQBbjuAATSi+4zKF0VkFHrdIWbAJQPu21Qi9UmFPMOjyibL3UYM8dFLqxy2vLsTYWLtEKaBmHG/aCAB1I8g+bOwIvViD49ARlI+NswWJ4tXCQhtEWVhfM3cfUidAL3NzP7Q1l9dMbXvYxc4M+WS97gbejVt1Ffa4c0VYF4BAeVMoHcBaxp4S6PKqi6MrXHe15TH3GrMEmFiTW6N8p+jhZMixzfM+Dq5s2lF1x9l4U9EQqbYiM6QsVG7ZrSGHRKqEkknMYpFtRL03pQigaSt7DjW01ttOa8SVDdOdwD+ZI2ja3SbNfFDw+q0N5zYzmbbuuPa4FBKxoza5ySGX1q5j5hxkAt0PRA7NnKH7Hh4XQhC8a4MphdyYPVesJF4xdN/nOYRzAe97ZFpxhtC2DaeNETAZ0JxFesrsu6ShNpxQdByQWalqiHaITpXKJTrwlETJyEqPwGY2qfNtOmBIAbKGsge0Ad7MEoOmdKLkAuSHOuAVmXtNXyEAyNyIrtEIOnPHj8e3yVbrHGS7yoGCjG02MBZSzgLyNhDEuGNHD8rSD9tnptjTBHBnjFnPGGKcOSz7nsYyGkLYK9IoTM6sapsjW8NhjRzLDBw5ZLajxFaoTxq4YdN+5P84hS0Me7a8YcomejdyztcW0WI71GcUJZBttbwXUxDZraRZL10DAD9o4ctU2XYw0OqMwQSXO53g1Uw5CG/ZZsSA1PDQl2OpLeRuAHnamMSx+hhcNpBAs+2ICTcSyQfd/edPYUkvP/hw6+zULsrlwzveZNEZjYa//JCsy63Xr09//VcbZyckq2fvPGxfXNkNXzw4ctZl783J9Uc/x8ty++TV1Uc/3zl/rQo9e+t+fHPjFs3i0R17XW2evLn65Qe6UQfff/vm178ejIfePJ0+uhcuVsFkNnvnAc7p9qtX6w/urUFy57uv3/zyV+3FbTCenX30N97/8D9Gf/zLWITecOHcpqCWaJLi26W+J/E8d9bF+MHD/s3Sm83qX/pkvLJvFrrgeFl7o5VpgFmU3nhMHz7sjZbB1azRLh6vnNHSFBIta2c4l29ptKqTq9HkwYNkuHTORvQjZ2Oek+ECFgIuS+d6Lh4bsKiiNzejB4/i6dC7vBXvA2dW4KuZyVhlwjoLnB7SytAsADomQta14zfECLmoPL+G2NN5FnptaANQF0HEea2DunKdyulAcZu5cQ/BQGdZ3A0aZMwi9ZK21NTJqO8XwDj+LMVey/IcU2fGiaAWsEh1Jxa8dspmEDIkHbzMceAJ04HjLHFd5WsHmIEUDLlKiH1Z16abSjqAjmJwhdgAqBXjBVcHSjGibo3alVXFezlvBgBrBZa46WBLMZlTuWWktPREy03VSEHWDu8bC5aIY94VWjKZCb2JBK+BSPUmZFLghUfbStsloJbsO1ICOeZqixtSYdXUGmVcwKiqjKpkl/G8CWTlWtqXzIOpsHrZsgqbHCIYWbXFKxkxxJsgKzwsXMg0yFx/DzWlloUGJmxqVNd+TC3B9bp0QwEUN3UK4l1kak3LqKVE2gS89Pza8qlcpW6yAbny6wztdLO08Kocd6WQKipVouhShZLRnlI5QFyznlHzWhFgNmq+5mKZs0MpMujWkvelKpXinPZhNBPCquGGECnEhvI7kq6ATyXr6aAUmjPaR1BQFmm916jU0wstD0W9lgGldd84tZGVYgPsLyhHQG0wWdpkLsWe5BXHRZM7yHCP4XUZQBe1BS0rFxvk7KA09y0IIgOLAmLJXWqltU2kMTrIagOpivetqgBQGQGivDQW5QGDWe0go4EJ1xWyqHG6Zpa3JDUYRKiGogYJk7RC3NjQRFYFjDQhgUWmQANiDfLKFrWJBQCmazGPwQUSIZNBgZoW15lqcTmvpafEVqVLS2PDLGatLeEIESl9w2RHy6QWc6KpkltSzRiwLRpLuETU0qKr1FWjWsB0GzZhihXsSKmFBaCkMYcrw2zOOsBcSZ6UoCvErDa2oTtSLCwIhYwbIAErakqcEhnPzFLsRlYQmCrzbRcAiYq1Tjyhaztv+gFFCjeLAnuOhAMzTlsO1r6CWebHhIraKUpCatLB5brouT602ijLAhcBW8KyDEJMNfXz2vMbiwM/L6RFYNhHTe4rZGvlqBJHsAHCK2rXriG3/FAcQQ1LJAlvC2W4WBq1hbVqAEvVpiWUwAuXto1yKtBYousqAOSQqw1uvAIyrGwiZINXnHagIDVsHGEx6Wt5WymP60DDuRIx4ITZC4dFQnpCj6TcpNJR8tbSjuahAHOu2ojZHN/aVYhUIvRq3fhp6QUcGGGqFIRbCFLTFEFbypQFrHT9mkRczDM34VBqU2Vwq1OWjV3kpKMk425B/bA0QgXLlIR9hKFpUuNtQE5hner+Fq+EW9ONmEFm8DrD7ZgSy55mQdQ3SKAs9zc4TXVIS+I2JEJVVkQeh9j3OG0jAyi3hT6gMnfVXKsDUWcyrlnTN5gZXSjWw3ZGudZyk8vKNjMp9xStRbdwWY87pgIcCw9bKZcK6A3NyxIaR24ZTiVOHdbjLiw1tWRAUGbkkqs9KkSJTCD7hjGKU9z0lIOoaWruKcswzYq6g5R2+mBeJKIAFoyaGrLKJEznNWyYA3Vo1ZapVYhhkStTgsSAorZZrVvMAIrK2om0aRqPl6B1iESpeQlaUq4aX1YypgA2oMydeBM03OM5IhvrRRrwIuxqWVCf1kFSK02jIiXJNlSN5jkIEDQ6hLrfsCkTRc6OlFxYyEiaCLNW3GK8B8215GEN+kIsaoMN25digYgmoi3hWiuLsa4lsoYlyGw0cgFRrsQu50vtGtq0VSuDXCkxwHrYsBCpPpVrx0qF2BEyN7bQTas0yigJZWTJoZBBrXpCrpWbp5nnhMBVsCgDX1NI3az2vQZJ4+elRBBEm1ZT+NrHSge6Qr5DLebklUcI0DBaF4pASFpmmrU1QcT4pkJGIUNpXroAYQOjOufAwz4GReZZCPgKZZUFMOwwWjauRiQcmLzwDAbJPmKF5gB7DFizzF7m/G3Lus3Cye3w7Xd649fe1VR+ANzbHA8XJqM4bYLhXN9/aM3yYDy9fvgouTwNLqb6A2MtSzJa6pSiVUmu1/LOPbxqovOrm0dvBdeXzvlYvv+BOy/s8RoVAqxr582NPLpnzfPg8pa//bY/HjtnI/PgANYFGS7AWwxlNDqfwHe5Rn8hi1Cbt//pH4LVst7f3PjyaefyQreCw6+/Gjx/NfrV+2//62/iyeTVf/yPb//TP3WmE7a/mbw833nxPNvfO/j2m6Mnn08/fPzO7z+OT09f/of/8Pj3H2+cvlGDuH122Xv6Kt3b2/3+u7c++2zy08ePf/vPvfOL07/9u0e//3jz6dNqb2fr8mTr82+zvb3N16/e/pd/Gf3q/Uf/9pvBqzer9463Pvn68IfvijuHL3d/nj4HW26NhCxfed6msNeMXkDfqwgsijPfUQ3Z0MXzILAp1Cx7GfqBAlVZX7gBXDtJVV4GRNbJnkqfR21FPaupXzuxywDn9anf0g2Ji+pi028y9xhd/hhtGeo5dHYSAVR6Fp2fRT6rrG1WnLZaq4wc8/WPrQHnMUTmJoFejrpKz/tYTFWUgvFPtVPZ9g0YdgNH+HxRpCGyiVEIzAaJnOfxFRl9qN3GBBM03rDcwpFrNY80EkgBsNgi9boc3ETDt5kH496lf7Np49IzS7GMEAAsnruzDacqmtZpPDwubM/fGvqTvisLmzKaBQS26M6weM2rJeo/ZvWJoAuz7awtamVDHHU4nwLzknutHLwAZRolb0n+RjS3YBDn4rapZm67zesVEs90st2w1zpfBMk9ak4YvbGSsNAFKy/cyK1FiatXdm+7bF6q1SxubzAwBNWQtOy1ZVTxxoucUlOyfuHdiQpywYt5r9+t/aDnzDaN4Lq/sMYHGpzYVglGA06KlrTgbQezVYI4XW5DSs1WCm+OXPMGoUzfdAFY+dCY245LaxgpPRvgLFOwBqMjLc89t9a3G1F4pnKpli23YaaP0HQzTKsKr8joQMkhaBo03SSisDIOZx27kdYmgrMdPx1O2+viuQ5j7cK1OE0QgZbV5Jehz+vWtpq+jDQBnV7RvPSJrUJUicsYQGCHdXYJw4q6R3r1zG0I6R7R/EQRxbu4Wd+0KUf+QNRXwCup18v4D3ZDgvYdUZ0opIxnpfUlrhlpb9H1OUSp6B3R7BksUZAMKnECeANIRzr1tnvTxfENvuqYasvfu/THEVp1iTMLK9SsNoxHoQz8YR92F9411uWOiUf20sXrrmPNXIXZbNO2VkA56OZARONwErF8U0U3ZOWidccVN5EENNtEYG6QA0d3nWhmjYksBsi98GtkFh1PLwFGYNxHaqw9HwyPguS0mef1qROJyu7k9eWmW+QeBpc/xj1Kk6KZnUSxqt15MzuN3apGe7Q4bYXzwrHK9Fm7dyQB4/lJEDNmrxi9iHFVw6SCV9MAACAASURBVLs8PXe7szwI4e3zEO4JV4nyxPc3GWkku/CdSiIC0zPUvi3tLrx96qAjE9qsPNVhUvuczy9CnSJ3vwqH9ySx4+1T/2bbBsKHU54GSBAeT93FwE0b2jmNhvdqFPp718Ftz2Hc4YzlLjZ92Z6Q+SAuFOxe+Bf7DHeD7NwftzVzERjZK69hA+hN4XyAc89sD+OLLYY37Gikxr7FIwTGVh6KckDcCZy2Yd4mndfxVb80G05vCYd1ceYFpFbaqp67nX7Fz2V6E7cGHIxAfU0Se40xL878gFTAQutn/o5f2reoHPV77VovZH3pdXFpWbQ+7batFHj2/Ef/TqtSU1ldxltR7tRyfhr4prETUb0ON3mqI5w+i1pRxTJQnvvbQYWZqS99F1amJ7NXNoaNfxx5434YjUEp+DLxSmkGCE63kiXNnSkZ/VR1pkDlaLKJG47LAsxaTmmsLQNn2wSMymQWDrfqjQbimTseuEFj88JM26AkLEjd2y0MZiy4Dq/fyro68Bfebd8j1MoYXbVwYUR7Tm53PLxG4ZV/eb/sEeBPgmlX2TXz6/QaOEvm9zL11C6N3z7m9YlStfGcPLsCdUXaGzS9BHAqu0eseA4yGba3SnUCeAaRW9FbUC9IO66ac1fMsXuU10/BmsWtATVnoFlaPSvjOSlHpBNX9NzLJ9aj3hr9CIqmNehX6lKxpe2SvEgteg22W9X0MkiHpLeb2csemHdsOY61oestqFfGM3B01/G/tpZKrgfIvvYZ0rOuo1LoAD3pYzZVbWhu7tjkO1gjs9xK2DfNQsNlx1GFiYk13vTZknYMHh0q5znkBE43bTRGAKB5l6ja6mA02fZYyQfGHe2J+BYx4Uw3fDXEuUGznmW02Bg2zxO9IQNMq9eB3eU+F/wysDMJHZidWS1SOttg9tTVWyCJafpGu34TqmZxGasQOj1ZnsEEN16c0+89vemTjijfaD+WXrOeX8baQclWMz+1XMi7u2z9o0XbbhDU/I2HHG2JvL6KABSdpClOQgKNt5/lT608TrrR4v7XX4YnV8u79+598WT/228v//1Hb//jP0TT2erd44N//XRwccY3elunrwffPqv2Bne+eLL71bc3H3343sf/Gp+dz9873v/TV5svX4DY3RqeJ1+/ogebB589Ofji69tf//Sd3/5z//xi/c69re9f7X75pWzFwWp+99/+SI+29r75/vCPn84+ePzwd/+2/fyFdZRsXeb3P/9MtGK3zh//w7+Mf/KT9V/oRXj5P/2vW+tFd3h19fMPvckyns9u3340uDg3XC8e3WtfDMPV+uVHf9W7vNi4vrr6+QfedN2ajM8//HBwce4U5eLt42Q0i29uXv7Nv+sNh5tnb0a/+CCczt3b5eUHH/QuL/2imL197N9M26PRq7/5d53RaPvk5M1ffdQbXwe3y4ufvN8Zj+LFYvr2A3e26t3cjD98F+R09/nzk7/+K1JSPgV4EyvLkmNh9jxsNLio1f044EU1RPYuxkCwW4AHlrIsMVHRgNd2oE5rcz9q6bIYYjiADlHNWIctDl2ST0nYl9Tz1UkJ70WhLoohgZvYtaUcCqtrSZ+IsQ7bnMWhfFWCO1FkVfkFdDdxCPPF3HcSgHqOP0QsgiBsvLFPQ6NazB8TbRk2kGTphaooOgouEuEDGJbeOBCJJeKa3DoQKDHgeB0QKYoN401t5WgTFu48Yg6UXebdEgjAYhslU20rXWxob+YYS6QbuDPU0kayz5yFA6lO91E0VY7QvE/JwhUWWu0G7tUcpZQ+3rAWBbptnHteQ21rkqu7CVwxWMnWhihLRy6ZetxH8xJNqX6r487XzRrqO4m1pnjdoEOfFwbMGvWoS7IGDEv9qOukOV8CcmCDjKtMWwe+LrWa0uDIypSPzlLwuBMUZT0z1q6Dai4WorWlah2yMQ3u2YXdHlzqqs+gJcjcEy1lXOjOiIqK2neTa7jetTbpDcv26zaHRDhTIluKu9Cd2SZpmoDEF6DYRQTVzihsuhK40rvFIlQiAO7S8+3beZJEQ1xsIRvlzjhhfaMdZk9t7QkZa3fma78uWk77EpYDQ6zcnrdppGTE+Ig7QJtdn11R41phz/BLLn3b2SHqqoFS4UOP3yrEBdgP5Yghy8jdGJ2mxsXONlFXNZCGHXetm5w0HB6FYiKhkvowQadrYJNOr1mtfFBS9ahnXadAIHTgouucI6L2E/t8bYwmRwG7lVbBnENC1xCUEh754UpB7rENAUrspmZ5aHm5iObW6tBEBUO5n24qhyI/1c2GBJR4mcV7a26iZAKXd2BU1CgLab+xKECFLQYcKduZWXwzoyBKbnR6YDaLRV1vNz2KmbYzTAdSC8tbErFZMeQllzo/NA4X1tKnA4GVdhZouetqKK3nM3OvE6CqOVd603NDo89L2LZVy5XXzGtp3o70y7U+bEUOq94wvOXFpFhOMIkt0HLkDfdaoGmH8PVa7kSxx5oLpdqO00XqskEeQD2HD4Udat6P4esUbHiiG1gvFqbr/X8b7WG5GVoXmeNrvtUCrzPQIfVub+OqthSY71nxVDoClhvcXrlIqXTLSsZGIyg3mLN0UKXXhyicK68xbKMha0canG3AeGKQheiAWwvbbvTywIrngjS42BbxAkhDRLvylrYxFt0SaO14NaSDTFWhV6FiW8ZLrZTNepQsMZSIb3JceWQFFndAuFzTqSEHDqqFXCpr35PUmAnzD1GhPXiamse9qCnrsYY7NhaSzUQyUAz6dMyDI1wBH71egsc9t86rCbD2PCK4mTRo12cGg1Hj76HajuCLhXnY9WVdDZW/g7Foijmytx0JLDNi7r5VkpC8XMj7rcjU1Q2Am47tY2fhBtZ82fb9G7scIJvkzijhPSC9xpk62haypZ2FB2xadEhyjeqOsezcnbWa0OiEeRMiHZh3QOsWQIfnHdwaWk0syw7pXSgeQdUWztRWSJcDmIyNZQuRcGfi1xEou6h7YWSEeJuTqQ2QKQawNVbCscpNyzpdQYQ6fbpeuSbn6nEPjTJYGXg3xDcpk1gfJuQ6g0JZRwGbSZQx74g0GQJrDu9FeFIIYdm7RI0ao6B16KuFNCvm3cVZ6Vi3lXUc2YuKVojsEjOjqtadHZk2kZo2/rFTlhhNSvwgQfOCVhbZd9GiATlHR6E0futarQ7hVjltyt26yyylnBVmfamM5S2IGFQMe8mFzo6gIyiZBU1fWFA5c8y6UiHLXzpeeDMJNzuXOt1HnirxPKEDBYEgCyITZmzkzlzZLsog7F7obNs4piTLVtOWEEtvjosukpZq3WLZrQo/6F3ofFMZT4uhsrCyNjCdAJtIteHrcwrbWPVccFKChHhdzW+0sYDa8eGwcTFXW6G84DCEZtMDr3MU2904m61aUEuw58GrWvuO25HiQqiEmA2PnOTaQc6OVd9CIoS9BekUGoKdnhbXAka205H0QmmHuLuQT4AWxjpytl6/tkp29d5726cndlXP3jkOrqed6e3ww3ftRT64vDj/xc83r85JVt++ddy+vCJFPX/3YTDPuqdvhr/6qbWuN1+/vPnFh9vDc5mr2aN7yXDkpfn0vUfOdDW4uBj+6qew5Ls/fH/x61+3b2+8yWL29sNoNg8ns9ufPCarYuvsLH3vaK2i/R++vfzoo/Z8EoxnFx/9dfjf/3fxH3///3ciBAAZHS1mmNMoXTn52q3yqEidqsRlU7CdIFu7+dqjVXu9xKyOsrVdpG5TxtnarYowSwvaBMuZW5d+U0XpitA6Wi/dPHOqIiwyl9bBauHUO3GeulURFFmSrTCr4/XCq0u3yuMi9eoySFc2a+J05RSZW5f2OsWsjtJV0JQbZ8+m9iMDwNbpq/PkZ3bT7J1/fxF/6AL+8OLFHD+QvrP15unUemAQ3njzYk4ehtDavXo6jH+CbXD/9Ie5vsfb8aOzp+XhNve8e2dnC+suJ97B5TfD+H3goOPzH5bigA662+ffrfUhDcPNsxfru0c1Tw6uvhx5j1XsH59/k6kD2QkOzr5Md7cn/oN1xRHAEWCLkhMFA9+sCwoJDGqTZsKA0nHQogRIW4Ell1VqKStwdVZwC+oggXmW25CTAqdlYzEQEbXOU0BQGMF1ziAwdi+o8roB0i2srKTIgiR210WNMQoiXayoNsDiYVE2jNPEVVnBBHYsCgOrTTxkBHVlaIcd0EyhCh1vs5acWLFtIZsPI90GXjcTzAWh7bdpWfrGtb0O5TWxQtttS7F0VGB77Uw0rnJJ2KJN4Wnb8zqKphbwgR1LsUTSQ25Hi1kksJ3s8zpzpYWdnhaZBX3kRY66gsLygx3NZliU04raruV6apUL1wFYgGUKAqKgBvOKOYWNJJvmK9tCXigXJfOJsRRcFXWEOZLevOYkd13SzEvhEuhAsyiAYwELwEXGtoLGQWRZaVS4riPn5drFyG7rVU5dYyyEFjmPDMdIzitJMuxGapktsbGcyHFRFyMmq8p1ugYZxNeOMzBAK7pGeMOCXLHMQR3LlrwuIWlDYGrGQ7dvgFFsickAWAAI5sMWcbWsChe1IDKVUKEzgMAQtm6BDeO1Cs593IEA6iqzSUsYt+EscHpQI8nmrulgF0mxxDBBjsV5UVPGMh5GgBewypVFQ1mJWcXcwpZVnRfCSRzWqCbnQQvxnFapbsVaN/VtJdzcVnW9LKTvQ81VndMoBrykeWGSRBtVzysaFFjVzSxfhy6QSmc5jGNjGjgvQadiBoBZQ53S0YqmBQt9qIyZlwA22kM4jHYZpzbUdrCtRAl5g7wDg0pNKXIGFljjRtrRDhUcg7oVbENe2lC1nX2DGs0pdDawWcOKk3BXKIY5je1No6niJSabAFWacc8ZIJijWjj+ltSCchV7W1ILzXNMNiRWlEvfHtgwbypO3IEASgmW5iVUmvCoKepGCj9AZSGgMbjxsrJBAESJyVfUSGPxqCobWtO2r4qcUeBYbS+vaiBNHIMyoyXThIZNRctG+QWsKsGkHQcmL0upragL6pRVjUoS0JRVVemwgFUlam5Hga7KRgor6eh6ybLaYBoGCrt+X7HUMgg4iZSpK2zkdoCcBxI7yQGvc0dC7G4pkVnG8t2Bo4f/Ly3v1WXpeZ7pvTl8aeeqXTl0VVd1VydkgARBkRBHkZqwlsf+K15etsczmrHNkWZpLI0kKsxQlCiKESRAggkgEQgip0bn7sq7do5fju/rAy+fSwdz/qz7Bzz3fV2kQNJY1NnA1Eg4K3EU8gJzOafiCUQMGvNajWhOiLEIsx5UmjurURJSjSpyDqdDDCiWi0pNlCLEqJO0o/NClNbSNKCQlFm9UEGYxP40wjLgBIyDCHsMyXzgTzhCoq7Gs4SbmjI0mmWWkVDCB37BCOaOGs7GuMBSgNEsRiYipph6kcxSRvjILzAi1AAjz2MKC6ln00gLhGI58SOTxiZUIy8FkBNHjn0X5siSwJ9FmiESCTeIQApw2bJZHReaJkclMAcM4qapgcqI4dx3BXYENKM0lqyGIMrTodBlIipFNsCghCTJwpnAVqEtFY8JLSski3SAixIUVZUMnEJQyynCCQOSUgekY0QqSHCW7duZAGZFx31b2cx00nDGkCmppaMxRCUoZJEPsijsh7HhUR3FfXciOYBQDT1oWRrkcOCBqpFqpPtRwlwBdTj0U1NCTfTQg4ahC4X6briKE6KDfphSTyiUDd2xyaFmYDyLTK5VggdeXOYJKmAvSLhLIc9H0zEjCEswnCpkCIRA3w/KLCE56AUJ8bkNop39d1PbGsmtrcNbackcktXl44+9cL5rnL9w+IpfrvTMC+f334kt+8y5uH7wnrKNHt9aOvooqFRapcsXD19LHMfdWb3y4HrKeatyZe3k/dg0fbC00LoRTEqnpYcuHL2eSqNVu3ruwYcQg659cb11P6NkilcWTm8lwhqI89tH72RCtOoPbe5/pIA+nntMhB71QtufCd+VsxlNEmc64t7MDH05HdEocMZDHvhsNjPdqQx84c5mSWwNujQOpefy2YynkTMdszAgbmT4rowDczbmSexMJzSJzOmEuBHLEnvU54EvfdcIPOm70nd5HBrjIYkDw3NVlvI0tscDFviGO+NhAOA/9YH1j9Y0PPunf7778s+7e3vnfvrq7k9eGmxv7/3gxUsv/uzmP/vCM3/2lxd++ON3/8X/8PRf/dXej37Su7C7+vrbV154sXXxyu6PfvrIt797+9nPP/XXX73ygxff+91/+cTX/v7y918Yn99affOdS8/94HTvyvYvXn/i6/9w69nPP/7Vr1/97nMf/ebvPfS9569++7nO7oXNd9+59vffae1dWX/jzU/9zd/e/GdfePSbz1375rf2n3n63M9ee+Tr3+jsXXG63Wf/038erm44rc4zf/rn+5cfWbh999Nf/uv+5nnHnX7mD/8ksks5oV/40h+OF1fM4fiZP/7T/tZW6aj9qT/7i8nimsjiz37pj1JuJpb1G//hS2Glwl3/U3/y15P1NbM9fOq//GVQmwcIPvt//WGBWFCv/+bv/59BqYrT/DP/+c+8ZpNN/E//8Zdju5Ra5hf+/R/AQoeN6uf//R9kQhwvX27/SkA3d4zw+A2H9EJzG7d+QLIJtBaL4SsYzHKHeAcf18BU2XZ08kuLtX26Qwc/gMkQm8tF7xWYz7Ajw9aHdj7QVjVr/dIgZzHZ4+MXCnfA2AbzXsmSIbJl2P7EyTsKL7LBK1Afp8YFOP6xcruSbdLgl1nYRXN8fHbTCVsIbvO5GwvkZGW64ZZvOKi7w8n9fLzKT85l1hQNFu3bdVA/pg9WUGt7ujwtPyiBs4uIH8u+CY73MmsKx83SrfloYWjer5Pj8+7ixD60YOsyEmdibMCDS1oM4LRh3lmPF4b2AxsdX1DVE3ZWgadXCT7hLoP7VyDrI7ci7myj8hE/KYOTa8g5DHzQ+rmURYhM0vsxpibAcdp7jYlSASfZ0btlE4X1dHjjjSUrD5BDui9iIgHN0s5rzDAyOEsP361ayhc0O/yFY8Y+rpL2iwRTQHXe/qWowWke6/1367KIpEiPX7aN0AdzbPBDBDEiKG//kpskRVlx/HaJpTG3i5OfmSKI6JZjvbuKA5uiLji+QicciT6+d9XqQn917Lx7AUyqea0nb21hr4xpF5zs0bEZlQPj1o7RpsHqsPTeNpgu+M1Z6eNFMq1i0lZnF/mwFFQD886W0ZKgeUg/vAimy978tHRnQY8XKTllrU3crQVzgXFr0zi1g7We894KHK2pcosdrMPRCrDbk3t69g4wNkD4cd7/kPNdkX8StT+xK2W/aGdnH9i8qrw+D36Ziy2c30y7H8ryWubdA2fXnZoTgGF+8o5l2XE8wpM3kLGmi1tx+0OruhQlh/rkw1JNzsQouP9uo2RH8YT0X8PWqs7ux+33zfqcn5yC4w/KVebqWJ29Jm0jSnwyfAWjZVYJKbr/CC3GVM3Q7adZMM2d1Hj3soY5BCG7dQlhD4epPnyCZjOEXXrzKeHNoDXBHz8JgQIgYLcvMe2jNIH7j9PEB9yl1x+XfhQ2YuftC4WmAPvszmWWh0AlaP8xEsVRObffv8TdLG54pXd2E+gULDRvnmdpAnQADx5jQTFdycLvB7MWY7vC+0UUdFjF8jq37PQU0AUy+BVSDxJjD01+omanku7w8I3UPaFNOezesWbHlC7Tydsov5sZe9B9KR8dSrlNw7fT8YmsWd70Dpndp85i1n+PJXe0vEqil+L+vllZScYfk/GBrDve7D4Z38bOYjH5gAQ3oLWng1fT3gPb2VTVngAH1wAbgKBs3NxKGmPjxMAHF3WlxToOPH6IkBb3Abj3CCIDGFvy5i6yz1jHgEfXgHNKewY4fozCMxIpfOcRjPogNcXHO3l5wvoMH1wlxiEaG+Docaa7qAD81jWuuyBj5Na1wpnSASEHVzk70p5EDx6nuge14tevYha42h/+HDvKR1m+/35Npok00qOf28bMRyus/wJUOeIy77zGuM6pzo7fLZEwleX85CVTTkK0wUfPqzAXtATHrwKUZBxnx++WqJfqBdF7EaBRyjfR6Ic6SAxaRePXAfKzMpztf1DTkwKsmcMfajDIxBYa/7DwAlNU9eRNmLiIzSPr/q55bILmIb2+Awfr7uKkfHdeD9cIOeFnS7C9FNU8/mDdOSoFq73SR8uwt1HUTunhCuxvEtZi3QXcWilKPXK0aRzPh6vd0kcN0DuvK0fseBF2tig5IYM5fLSmSl12ssqOVlDjLru9DHp7sPSAtJqgs8vwCRk16P6arnRJa5U9WC6W2tHd5Oj9UoV7lju789Z8SYZZjHu/wMaCVqdJ+x2zWg+ytjp8v1ohM5Srk1dNmwa5or2XsZjTup+fvWOtmr3ZhB2+U7FxQEBx9nPDImEO6fAlxGuwGGRnb5t1x09cePKWY8AQU332kjBxUjA6+AkoGgaaZd1f8TJ3iwQd/8oxQeyUgt/+n/+tmLij9bWn/58viyD0S7WnvvzfxMQ73b30u//b/1E5ad975ukv/i//Tgwmne0Ln/ujP7HGE7c+//hffMXpDQ+uPfp7//u/q98/OH768c/92/9kn/VaO3uf/bO/lN1RXK8+8VdfLR+c3nvqM7/7v/6b5u37N7/w67/5b/5Dbf+od/78o1/5eumk7S8uPvHf/rZ++8HxE4/9+u9/afn67U9+6zd+8/f/Y/32/aNHHn30u9/dffGnBw8/fvU733v4e89/9MUvfvbP//ri8y/uP/OZned/cukHPxxun99644297zzfvnL10gs/evhbz13/7d968it/d/m5Fw4//dT2z16/8u3nJutr59556+I3f9C+cuXCj196+OvfuP1bv/nw175x7VvPnTzx2Nrr7zz89W+M1zcWbtx87Ctfa127uv3ya4//3d/f/fVnL3//xUf+/hvjq7ulD+49/tW/G6+tzd2996m//MrxI48FrVN5sP9P0jT8YyrC4uzLf3nx/sf2dNy/tEv7M2M6GW2tV87aJEp7l3eq94+k69976qn5u3cq41HvwhaZhs6g39q7XD07Mybj3uWd8mHLmrp3n3qqsf+g2usMdzflxGUjt3XpUqXXtwb93uUd+6Rjj8b3n3yqenpSO2u1rlypDtqsP23tXSqNRqVup3PtotPqmb3+4OouGgWN1unplSsyCep39kdb60Dr2sHxyd4lmiaLd++0Ll8xiqh660F/cwNI2rj9YLy5qhCuPjicbq2kSC7evdvavSBQWr35YLS+pixRu/0gWqzljFvHHW9jKSZy4fadzvY2EbDxyb3R6kpetuqf3PFWFxPTqt/b91ebvrCXbt/ubW4Bi8zduOfONUBZmPfOgoVGb3FDtVNtENNO4h4uGLLn89kppUTxeR10sa09WCHuRACBLSeJOgAwLBdUcMYwKlhDBV0kUQoaLBkARbBZyaKORgyLBR2eQgChWjVgKxIq1g2WDYHGUC9IeBJpoKylImgTpEG+ZuKzkOYJr6pkDBMskvVypV/wEExWtZwg6RVF1U2KkvChNwe4D3CADKcVRXWcislSIT0kZiqcy0w/zpNy0FAsBMyDRX0GQhMHZLakhA/lDITzuRVGWVDKqhFNIAgNVZ3AkKOAgdokURU5RWE9saIgC6tFOcAZ1KFtmKdRXoaRVLWZS0z8YKYb1JBF2CW0ohBWSR8YtTwmUnfToiGW8k57XCU1zI3cP8O8ohHTSR8ZlSymHHRS1eA2i/wOhTaQjvLaRDgFNFDUA3NiMrPKeU8VNe6IyD9D2EasosM24VahTZh0tO2kkWXqs7SoMMuIgzNEbISagvUY03FazqHnKKyRMcMjUxGkalM4rCGg8qqrZ2VZREm1ADNDQxDWlN3DGmpdncBRBWs4W1DGEIk0jisZ9E0IkF9XTh8VGtj2seeuYIBnzcIaI5SprBIZU5RAw59TTg9oBXV9DCZlnAFV8/KwRFOdNFI9ijIPyAUVu1i7Wm+ZepKRYQRWpfR8f8bUssSxAqNULOnch9lEmUvKSyQZxHBFGpHnjShtIpzreIqNZp75MJ8qa7HwMwP2E9ik9XDQDWpiDsC8iEbYWijiGKuJtpqZn0vUi/WCMFTkjxirKQRAMoTZqi0gsnsgLCuJpnBcT6yMSB8MKsCMCpHjqU3kLORMjHhc1gJPwbhaiNSkPT/YQCIoZIYmNuXTyBJ8JGJbMTKBo6qWWWGHaFBBIs7MDE0sTmehw/mAxRZIHeWckVzkwHHxsFIIHZaA1YeUeL5D5ZClJprME3lvBBQsNgzQikSWoHmSTBBQWi0a8CxWStlLedAhKAf5poXaIY1jUVfpFMZAqGWDnkUqV+ZyHnQJSIHeNEE3QnFBmhgPo0BJc15lA13k0FjVcReqSMkl7buchBltYjyKgowbTZ1PYJZgazmN+lhFRbZVmZ/NMq+cVhOWKeBbqjqFMUU+B9VpAkrGBAeNzIzc3K8V5RArpb2SNM8SZYHQgpVRpB1jjMN6buS+cqt5OcIqBzMbVNxcMeYKWBqFyDFGOKoXQvl6VoHGlIIsDpuwNM01ZROGSuOAl80BCas51x6YlPNKFrOsaGU1Ng1sOxlAXWa2EflnCJuI13VwRpnMoQPjDrDMJCkZqp0VDrOsOGghYkJe12ELAQOrOkenoeRJVjGKdqptphsC7fuaA3O+CFoYSqIaHLVCThNq6XiAEkuCpqQPPMWBnFdhGwOK9TxHZwFgKFksVfoQ5Mp2jj1vmSgyXSzMCSKRSmuxMdUpsPw5ZQ0BzKGqT8C0hBOo6rM8LrNIp7XEmBVxYemKR12hc6bqIzi1UIx1fZalFRaCtBJJv8gyS1U87HOVGra17yaLNKG6Nk7TMvNhWo1EpPPE1pUZCjiIWDHvhSGC3UQ32XzU73g13oAY5OEAG/OqyGA2BtZc5muJurFqCgtGfp/SqsYEhH1kzBU5IGlfLzrDrpiH3VTNcxuFqAd/0QAAIABJREFUfhfTKkACJT0sankKSdHNnbnMYzZsRcUct2gctBGvQihA0oV6nmsEcTsyqmlo2PA4UPOGaUQLH3ySO9Z4Zal290A7YrS0On/rjl+pDNY3tt59J7Kt8c5G88NbqWV1zm2t3LgBJO6vrDfv3AvL5d65c1vvvhsLnm41xc1TZcj29vmV2zdjwwybtfm7D3yr1L5wYfutN1PBB3vn567fgRiOzq2XDlsF58Hy/Nzte4G03K2V5se3FWPdS+fnPrmrEGrtXVq9fwv6yenly3PHR3I26z60V9k/keNx/+oF2p2Wh4Pexd1Kr43H3mh3q9ztsYnXvbbrHHWs4aB/9QIZBZV2e7i9Xht2CrcY7W463Z4YTfsP7cnTXmXQ713agdOo3jrp7u7Y3lR0RsPdc+ZwbPWHvWsXRX9SPm3F5xenmZw/OerunDeDmd3q3n36c+C/ftX6J1KE/xgPli4QzhQRZ4OZLKlY8ZHnm1XoRmjkBswCfsK6I5fbBaCy1fXMSp4C1p/6sgT9hIz9QJZ0kPL+yOVOrpl53PFluUgUH0x9UVJ+SoYzXzg60bI98M1KpojR6rlmucgg7099bhdBSoazCMsiKuRZz5OlFEnztOPJUqoI7U8SyFLIRHsQMzOE0j5q+1hGSBjH7QiJFDA6dNOCRJCz7iiBIuK2ddKJhO0T0zzpxJDHiPOBm+UowlK0h5kiPnfM026EZCAco9XPIA+x5CMvz1DELNGbppp6wjFOehEWIbOM426ao4gZbOzpVEfCTFyQ5DgwStm4SLT0mJ17RRLDgFtpAGMf+tJOfJinKBBWOgVBynxRgl6WBzAWZpKS2IchN1MPpCkOpZVOQeprX9i5V8QejJiRxSjzQSzNOIBZBCNpZTOlI+AZldwrEhcE3ExjmMxUKMzYR0UEQm4UKSo8EjKZawqnOORGUnDl4pCKLGfABQG3s4zAGQyZSBXVExIwI1MCuiimPCmEDmDA7SJDYIojacaKgAnxCU9yqlwcEpnnDLoqomakpJ7RUNgppHiCIi4DYGqXhETmBdUejIgMgYEmOKaGT4x0mCfESBDXszxX1CNWPAMhkCkV2SAPmREwMxtkseYhMQpXxTkJqJ25OgIy4UYy1gnivnDyYR5h06NmPs3jgoZEZhMVFsyXdjpSBaQBt9KxDpSImAXdPM1IzMzUBWHOYi6zUR5pHgorGxVJDCNmZCnPQ+4LU4e4SEkgzSJgcUp9UYJBoSMaMiNNWOFhn/AipHlEQmEWPgUR9IyyjkDmk4BLnRIVkEAYeciKCAVcqoAoT3tGqYgR8FDARZbgwqchN4qI6hhHVGQx1yH4/3KUSwMhs4QojwSUJRnJZipgdpHjfJxFwsoVTodFyM1Uk2JaJFSGGcmmeUiMJEXZqAi5mSOeDfOIikDxdKpiZqY5gbM8wqavZTrWAbUyxLJBHnIjBCIbFTExYsVzVwVQJtgoRkXA7ISIdKQTKiJsFBMVMjtUPJsWMeQh5XpGY8h9WQJTlCIjIAbwYZqziFk6oDGwfCaByyLAAm7CMYq0EQhb+7DIaEik8mmsjIBJNEWZpqGw9JREOY+YCX2dZjKkhvJImjGPCj2laU4CaekZLlLsi5LyQJKwiAkQ4CxnIZd6SvIch9zK/SJydciMPMPZTEXCiCOcBiASVuZpHWrPqOS+Smc6YGaaonSiQm7EISoCHTMjCmDuFZ5RLmKQTYpIWmmGE1dHwsgimIUwoiIOYO4WAbXSCKYTFUlLK5JOdciNMMZpAGNuZBFCfuaLUpDRdAJiYSYFA1McYZkpDj0dYyMEUs1oyK0EMjRGERUBssAMh8TIFAM+jJAMoQGnOKJmhDmcshDzkDjAJTExYyCgDyMkUyzBFAfcCf7/mxgZeoJDbCVIIh8kmsVYginxqRUTgSYowSyipnJJpHmMRDrTUUp9w07HKtck4FY21n7BQ2YjN8sSHHMj9UGY0JgZ2VhFBQuFnY1VGkFf2Pksj0IcUZn5IE1wKMx0otMMRdLKxoVKoSfLuVskIQqZkboqjlAozXiiVYYCZsYzlYXaN0qFp9IARMJMPJAkOBRCR1h7wJOlIsbQRQETaYqVRwNu5CnTIY6oSBOuA+DLko6BcmkojDQlekZ8wrKEFiGJmCxSBrwi4E6cC+3SQFq5ItAloTCSVBQRD6lUKdUBDLiVKANNUcCtWDPtsoAZWSFBRANmZQmBPoqIzBDPBnkorBDLbJjHWEZA5K4KAQ+JlY9VwKyUiHSoUypiZuZjFVI7ACKfFREQMRDZMAuQEQsrG+QF5hE1kwnwoZlAAdw81SymMh0XAZQJFdkgj5AMqZGNiqigEZbZJI8KlhCRjfIE8pCZ6SBPNYmYSach9BJf2HjsAS9xhSPagzzWnl3n7QGMtS9LeBrCSegbFTILQJC5ZoV3hjoqpkZVdEbAjTxZwrOITAJXltHYB27smhXeHekM+KJEBjMYZL5Rxm5ERq5nVPDYh9PQFQ7rj4tY+UaJTnw4i3xRUn5ChjPfqoIwI2PP5w6cRWTkhdRSfibOBp5RyTWT7cFMlosgI+PAlyUYZGwWBryURUp2hj53MkVku+8aZR3lfDgLua1jxYauT2QBmNHqB7KUACbbQ88oFyngYz/gThEVdOj6zMoyZB63fW4lzDRbvUCWihyRoZtpoiD670ARalBAdPG7L5T63eHG5uKv3m8cH7uNueW3Pli4fvPwyacu/uCnpdPTD377X1743ou148Px6lrl5v7KRx92lzYX3nr/3FtvHTz22N7PXi3fufve537nwvMvzt2/566vVu/cb777cWfpXPODjy6/+srRE0/s/PCnc7fvfPTMF3Z+/PPF6x/11raX7t1YeuP93tr24ic3r774g/0nn9r50UvNT260Hn1k/ZW31998o7O6RWFx9R++EzklnKR7337+ePty8/R4/eXX+wvrlBbnfvpKjlh/b+fa176VMK4QvvydF4LGHB27az97dWKUi+XGuR//oihQ5+Er1772zRv/0z9PbPvi938UO3Y1UesvvZoYlfHq3NaLP8Mz/+DXPnP1774J/sd/5ddqu8+9kFFclq2Nn7+WK9J9ZG/7xZ+ZU/fg80889LVv3fntZ/ed3fZRzRjnzdzdP543z/zVeXDvVo2X9GI1OL1lN0qwQfz9w3km1TyLD0/mK6eBWE4efFKGNl5tBGe3bcPgNRa3j8qEg6aMjk/mjCyY34VnN8uZLepbuH/HEoQ0WNg+qUClrDoYHTes2F/Yzgd3Sy6V1S06ulviUKyLUeu0mmRMbCvU20m8mopvg5NqEFSMs1+FxUI8baLhSeJWeVfg+s9RZyWMNnRwG3QaUVAxex+jkYxme3B4lAVV1bHh4ru41UyCTeXe5f350F8Q3Zs6dLLJLh3czOMS7K7q5pv8dC7xt/TkTdSf87w1efQRhzIaX8bOzTwxcXebln5kdCuRdwEP38iy/Ox0qUJTvYoPbzTmUWQaWetutWGnOtXd1mJThNiMH5xs11Xg7ICjm405lJh+cny7vIBDANOz02YDRqQZ3TtsNiK3+hA5uNWo6cyMspMHDUO3sRfsn85XdFo5p/YPm/O+h43i4Hq1vKPMNGs9qDdzj7rJ2WnTUbkA+f5BszoLKlsStM8VIjTO7ieDHYBzyB4k7kOs5yYLN4rOE4XAqP426u6mKBK9g2xygQIF693YPSe9GVj6GLYfj5lU/v28vQBBarbvpe75IqWw0Ypmm6YfgvmXQet8yKvQu1f0l7SGZuuTbLSWFRUwd5pMN003h6tv45OLMavT2btguJ0mnA5vtjswuQOW1jP3Lhl2mrWLsLgvpi1zeT/0x+DoeL7WgEnoxDdp85yK7uvBWeXchu8ew1FrYaXkBjk5Pqgt2mGeyuEnpbWFsNhXR8fzG6u+dwqHrcX10izL9MH+3JLhFZB3bzhbS0lwkHYO57cbo2CEeqcLS0ZQcHVyr7YsfMXY2U2rvAQtZYburtHZB1Y7nD1J9w/Sc2nae6pAJyTr5J2rGj6wvThyr7KzFq6fhu5TRnaAVnt598lCtzDoZMOrDO6LwA1m15DuqKV2PH2ChKeZc1J0nyjAkKDTYnQV5ftSR5F3iaGRb0/jyS7xWrByoNuPJDADo048WBPRodRe5F9Vyler3bNbpYSKxkUyumUSRRfMoHtSVTGwG3p8UpdTf3E7H911JkrULpDJbRNmfMscdk/LXijtRTI5qVnjYP5C0bsuXG3Vd0B0z0xje9Wc9U/MqW+v1ePOaVUNssUrYPgJ9/LquTW/f5dHU7FiuJOePe2LtXrQPXCyPtjYCqPbpO/PN3cLNG7Esyu0fyfXFHW3df0d1imlkx3tvov7Vc/b5IcfCUqjyUO4fyeHCHQvMvMlY+rEkytw9CZza4G3bXduKhQmw0covZFLJ+1eBtX3yYhGk4u4/6adOoF3kbfuapNl44cM+r7mJO1+Wjkf4kiE7jYfv6si5nlX8cl9VfGzwTVkHAalzvBuhazmOA4enMw5aVbbTh4cNufGPitn+9er1pouo+T0fq2+6Ms4OTuZs+JckGT/sFnpe9UV1LpZRRvSKenOvWq9NhVFdNqak54SJXByOF/u+PWlon27nC6apRLs7NfrFW8ud09a83QC+Dxt7c/XeVhZK84+NrJFp1LR3mGV2IXT0PHsijFO0PzLsL0VwAbw7+jeYpIyq31djRbSuAnmTtLZGhsBsPoWPj4f40XsvQ0Hm3Foi96tfFZV3gIuf5iOz9OxDVZeZ0fnYr0Ix2+Cs5UorBqn11G4kIyXcOnDbHwOT+qk9l15PB/kq3jwOhwspsG81bmeB2XYW6LmB8V4Kxsv4OXX3Ltq0FpcsV0Fi/3D5qLwkUnPbtgb9SQdpO0Hjc3qJJihbmtxQQbAzg8eNJaIh2vs+Ka1XE6Ur86OFur2YdGOjlqLDRGb8/jofmMJ+tkcOr1eXzTiIgHdo+aGPc1A2m4t1kVKF9H+g/kmDOgKbt+s2XWD9NLRUXOT9FI7OTpbLrNifWdw7WvfbF+97JarF1/4yfDiVqbY2iu/Kp90x/Mr2y++FNbrp3sXHvr7b7V2LwyaKzvf/9FscyUh1trrb9du7beXNrd++JO0Wnv90vaVbz43XlvtLKxv/+jl6doa1nr11Tcb1o3W5oWdF36sTOPw0UeufOM5b77hLq2ef/n1wHE0pSuvv90kH8/W13a+96LGeP+Jxx76+ne8Wu1s59LKr94p3zs+2760+uZ76++/d/vzn9v7/g+ds7PWo49u//Tn87duTurNxXu3m+99PFlaWn71zbV3P7j3+GPXfvJy6c6d1rVr6y//cun69bAxt7B/u/bGx6OlxZVfvrX5yzePP/OZC8+90Lx1u727s/DejbV33nYrDWfQ3frJL0arq+tvvX/u5VeOH334/I9+tvz+B9nuavFgtP7yq7NKQ4be5W997+TyYyNC/ztssLTyv/h7xXozXF44ffwhb37OXV/uXruU2Ubr0YeDteZsaWl4Ybt38XzUrIdL860nH/Hr9WBt8fjxR4qy1buy520szhaa44vnu5d3vaX5vFZqPf1YWK8Oz22ePvawqjm9vV13Y8lbbHqba62Hr/grC1mjuv/pJ4qqPd5Ybz1yNavYvYs73sbiZGPNXVkcXN0bLS+n9fLBM59SJTnd3Oxd3vGWm7P1lc6VvenGauGYDz776bhZiavVw888mdWc8dpq//Kut7bsLi0OL+8Mt84VJWP/2V+LG6W0Xj185qm07kxWViYXN6cbK5PV1cGV3d7FC9AW9z77dDpXiirV489+OpkrTzc3Bpd3Jpur/tLC8PL57u6ONvn9X/9cPFeOq9Wjpx7Vdau3sdW/vBuvNSrEN9ahmNNlFlrnAC6hkhmVl1I2h2w7rVZ8vMa5yOUalHOwBD1zC9IysM2oOhfSBWqbSbkek3UmaGovFXIe2Ghmb0HuKMtJy40QLvEq95xKQja5ydPycoYX6Rwa26uKVqG00upchFaYI71KJRRrRLDMWAVwXgjcF0Yrmi8E7XI6yJb9TOaCdeLFkJExttq05OdWhM1uvFhQPhBoFC4HnM2gGKULASNTarZUJVJmRGk7WcowGzHYj1Z9RmeIj/O5GSGuNnqgFgE7YKyt5sPcDAzYDTdiQlwsRsX8DIkAGS1amqVOTHgvX4xVhc3hiXVOC6som565pFkNOWZgLSi4yKtwwne5YwRcFtZ5xG1VdiJrQdEaKNuRvZDDZVlDM3aBG6XcIaG5jamtSk5oLxWsDktGUG6EatW2gS92iFEqysQztxBzdMkMnGbG51DZCJ3FHC5zB3nmOSjKuoJnxjahZkHogJjDuJlQ7OrKAFQjrvvFwhDYGaRDbLWKegbJmFijeCFCKAClQdpIrbytG11YKgCfULuTzOUC9YkxiRcjjANU7iZzuaE7qtrlVpiYKbIHyUImcJfIUbwQSjzT5UEyl1u6o6tdUEqBOWP8LJuLNXexGGaLiUPDUiNhc0CUVKPsoXVpl5Kq8Mh5bpmpZadiAxkyqVR9OadFRVXkzFjDtKJL1GMXpWUmjpMa69CUcbkciAXIqqBienIFgTlaR2N2gTvc5442zmHDziu1iM9pXtE1O5ArAC7IBp6QPWnJ2LZjuY65mVerIVoVoJKWsnawEmDDZ3CslgbAyinvwepIlSJCB6g6iRqFUfTiVQ9ZEVWjojlgZqT4GJaHqhpTOgTlUTyvRD5OFibYDoXuFUsjaKaQ9pDTV5WYkCmoTuK51FDDtDnMSoWTnxTNIXByzIeoPMprkQk6qDIO5wupx/lCP7NRXUwr9RAui4pwnXJEN7mQeaWZ4GU2hyfOSs6qUNhpbS5CK8I2goodyE3CRS4XFFmmDTK1l1JWg5ad1co+3JQVM3CcmG5Qiyf2XMYXoE19azGXNSDLqmK7fI1wu7DtmG8xm8fOfM4XkMP90kLCGkiUVNn21TnLQCPMJvn8hDAXyo6uhcAJGe2ohp9boQk64UaCqUvYKG9OkYig7NLSOHdiwvpFM0yrqa3a3lpIqYfYtFiYIBkSo6NrnrY9Rge64UY1ILN+tOFT7hHSKxpTLEMt+7rmKccXuF80pnEdWFk7Xp5RGTLSK+Zn1MzLZlCpR3rVsEkgtpBR0WXsmucRdUDJDEvNhDVxyYhKzQytcgsG1iYUVVXBU+M8FnbhOLE1l9IFUqEzp5nDVVnGgX1O4wZtoqG5AVgZWFbmNDO8hMvMLc+lfIlImsgtRBqkiYfmmmIV4JTS0lyCl1mFe1ZT5UvCVF1Q6TI7yMwI2d1koeCky8QoWgwFngFnmMynJuiBchuUU2UEjLXzuUAJj7BhsuRzPNPWQNUDDCeq1IfVBFgeY2fFfKSkx2k/XokI8oA9UnUf0ym0W7TkJ1ZMeTdbjAsnEagbLkcczZA9KBoR5jNst/NaChq4Dsf8onCEz21lnkPSyivVSDQ1q+qqFRjLAC3KBprQPWmaiS1juYm4lVcqobGgeR04wi8vxOmyU9YzfoGaVlaSvjyHua0qjmc2lWjAsoysNQUWZBlOjfNYWnlFeHKTSLsolyK+CGUtr1LXWQe6Ket4ZuwgbMDpytLw4vZke8Ofb0wubp1d3issefzUo5O1ZeDIo888lTSr05XV4ZXd/oXttFIaXzh3dmVPWfL46Scm6ytFxTp58lE1Zw9X1ocXz3eu7uWN8mBna3hpR9nm8ZOPTzZXdMk6euLReLk+XV2dbq11L+0G9ergwnb/yq62zaPHHwlX58JqpfX4w+Fqc7qxNt7aaF+7DCw22N3pXLmQla321Uvh6vx4fc1bXexd25suLoTNxtFnnlQVa7i7M7i8k5Xs7uWL7rnl8eqKt7rYfWhvvLaazlVOPv04sGj3wsXBld14vtG7fHG6sThZWwvWlroP7Y3X14uKvf9rT+dlc7izPby4Fc7V+pcvTrdWx5sbcbMxuHa+e+58WnMOnn4qr1ij8+dOH3+MvvMOPz76J22w/rGi0RAbhqaBcADxOPI9WS5jA6aZJxxDU6bYVFRK2HIUm4nS2YXlk80LiZQOlAAmniwLMmbRYCrK2dLm2dp5K/cgNoCiPnesglDNEsDu7z1y99JjsFABNsMc+aLsExMD7vISRyOhqC8cDPwMCJ9bWKJEsZkokTBJUhhQU0McK+6xElFFWhCPWACAVNGI2ZqhsqIBsVSznF6dD2zTQ5VUsSmzLSSinEQ58ZvN02c3bO07w1GSohBLl5phijxeoiTJnlgKS9U4Z1VFA2p5zKlmOCCmJ8txjj1kIEaSBETU1owhxUJsBNJJMMGMaSNxdYEx5gbNUKI04IKGUAEMMaMjYhBKtA5JZZwhHhkLCUkhAkKShKgiK5CCtOHmaZopJyNCYSokinSMMU6JtGgLOTzRRkIIgUXBnQgWgGMhcYrzPCs0tTBSMRaAFxNKCoRTZguc5hDNOAacZ9RIeZ6CcoaQj+McswzrVCRJViVQuFRbotWo3TjDVyCXKi67POEJKWAeCaMGzxrF0RH8dEF5Tm2PzhBJNa3EFDfEYUVPT8ztPCtTYhS0G6GKZnLGPZjFBawmhFCFIBYpT1LkQGQXpB9R09OFxWgkdEJywgkRKIKZgVXGzBATwmRFTyeoTBARMgGinchyQSuxzkymC2lGgDJhxH6CF4IC6pwuQhAghCHFMVIMgmmB6XKWoiIFVqQLgzEgYUJSwhEVOERFirOUWynmhHNAAp9IQKigOIGGIHkgQsRqmivEBorUUpxnMmbEwErFIqTUSgsQEXfJ2c8LFdAdQgxNVWYEDEsEkM+LprxhymRAtiGrwTzxqKaQQKYLEZulMzP1j9GTEBWIGrmAmokMEJcDjCEGeWwwQk3BdMQmipYw5QmdAQAziLikWQw0TDLhMIRSwDg3MgYCRBGTRaIJxBLAdI7ncwTms1BYMRGUGyqGLqQGAnreiueZRKkuQADyEgURNhOMDUYNy3cXFgRI9LjItGKC5YVKdF4WIEJWApDBZF4gF0DBCFIgB2lCbEoBpkbEwhwWCFViKpDMJBQZzRNZMGxhOo04KVjFJ6GF8+XyBx1zwSWVhFQAz1KaMegQjiOmNsrvnfCNmBoFrYc4ATJh2NAsV2JUd27EotSl64o4GYt8BiUhiciVjDmSKUY+V4gRzHkgQEErGXADZhUwhFgn3OGk0KQQgvgY4aIouB0hDQgUBk5Jnke54qaYK9IFBxa+S1iqiWZmqAuMoQmLYMPQEKKsiEkpR4UhsCYsh6RARbJZKgBSOAkwRjTTVPuMZ0QZHCNEEkSFwCmVqgDcYIoWIcgRFQG1NammHBKAMJGxjLGGlFiK9kJa0syccg8qXKBahHmNtBrG3YnR8LIaRI6igwDYgNghm1E1W6p+fGKtx2lFQhJxViDMkaF5keLifOWN6/wxBU2IalAgrLKcVAseKgUz5CCRzagDueWbvpl7KalHJMpJHEPBiQq4PcWQCg5oONNCUgoNnJIEMcgFCVGBtFIKyaUgKlSmnZRIRagQMNIxpFIBZTqhz3iCrRQLClEmnBimmjNpaChOlWH6aB4iXdAsF2qMKUY4Y1ZURJgyZuCY5hpBICyIQIGxIpIjjJkuRBTEdZpjlxUQckzNlCPNcI6kyxXEiKAslmK+dFJBnbvwMqAOpTKkCBCslIwJXTVuc5UfmhewLzGVOeslRj2NWUAnJfmAieJMrkC/BCDMRRikVYJkzlScOxkVCZ8UDKjUSqUQESaQRBxHgCYIC8Yt4k1QmROMtcp0zjjLM52orCRATK1EI86NOArhXBhCS5Ga1hElpAAoxYUF1EQhczWNtBEDOwFKMqopSHHGBIHKw40ghtxjdo4x41yj0MMWohQzlYM0pw6mqiBMsTSQTg4QQ7yQPNE8g9KT5QoUGRQ+teICh9h0ZSXWLMCmK8sVRdKczsxycqGRE+pxJ9AszMlMlNMUxVAG3Dp44tcUxTSKgwzHpjmT5RAwn9kTaEUXKjEreaLsaF4A5cqSjWQGmU+tSJGUWK5RiZHMkHSNsqM5VUUgnRAbFOiZKJehQDryZYnrYQqlz62cpyXAPVEqI1mAJCvwnatPFABzkhHiWzEIuE2pigriErPYvDi42KBUibyPFXVlqcBBKdKuWQ1NcbKyTVUm0BCnyOUOIT7Nxr5wiiKOFY24Pc2spuaeWeEzTxYsRsL4p1eE/yiKcPbFf/47f/AfV65/rG3j/MuvnHvjl1rQrddeu/L8C0dPP/XZP/3yuddeO3766Wf+y59sfPBevDB3tr4dlsrlQf8zf/Pfrn7/e63Hrj3zlb/devWV/t5eZ+u8onS+ffzot7+99errUa2++fZbT37rG/2LO2fndlJhkDR99i/+/NwbbyBONj94b/cHP4rqtfX333/qb7969Omnnvqbr2794hfu1vqF5188/9qr0DDK48Ezf/Kn8Xx9/sH9J//mq8PV1d133t574XmIUKN3eu3r3zQ8F0H9uT/648Q05xrwd9LCZWDlez+59MKLMorK/uThv/ma4bkHT39qVqpznT78/ece+odvJxV79fqNSz/4oR0EziL8F7BiuCfRwfDZ//tLypSVfv+xv/07ZbKle/cvPf9CaTImOnvyv/5NpdvOavbnv/RHhOiOs9X/mJFR7GSTs5sOPpotL3j3f17KpqBpjs5+xagf2f6kc98BvbyuD/0nt/G4a4fZ0S/sZAJrpnv2JgNuUU8O0s0SExh30rOPTHBvVttMui9zb0Lrawmwc82huN4eH1azHuAlMHwfqTt+c2U2+gWdTqQxBydvF7Af18L+cF/4bSGW0rl7m2i4quqD0t1FMmqU/AMymaeDdchzMmqUDuZM/gk7WAfj7bTm/cb01aeDxiQ7Kw7W4XiHkIBMa9bheoHufTG5+TSp3Iex2N/BgyUj7VsjCjoXJOp/2uz8BkhOwhjdWAPTHcLa5kkNDdZst217SLUvSdWnsxI9uWrRT4wAP102AAAgAElEQVT9kp7ucdyKumH/Q469yMDx8eumiUJzPOl8IIRIwWk8vCdNb9pwO7dvLfLRpFTqqWvbIpjmbw76d8omCkg76t0zDNd3cDt8YtvodVgfHP7KFiA2fPfsI6MaDuy05T+xY7TOih7pfmTa/aFhJYev2FRlLAjbH4py4dFR1L1j4Els0uTkbZP1XPO8ZX2wyWPieD3UOcfHJtMT2L5YOUW6emDefhL6VcaO2f0L1JPN4t1fT6MtGB+rGtq/Uj4lurZfuvmwCuaM2vG/Cg4uZnQ8drPONT5tKDORh+v2mcHgm/8ajXZx+Y7C/MEVPpWloMU6TTRehqYrjzbMTgOVb5Y/PFeEq4z1xfEqmVQZ6o+OlPchbNSm3nvJ4MAwF2F6PZ0ekop2yWnQumtLmqdDNHoXNErTcKUemXYpmEzfKiYHvJqO0SA5vWkZOIRr0nVqVuYVr0x6+1bF9uLb+ehAlLIp2rKGpaaZhsUH3unHpcXqJLvh9/aduhjn9/PevuFELnKj7keyXgy1B0/eMc1ytDjipL1ddmcinID24/bY42wI7z7DdGz7Y3q0J/KZM8phb9eaJU31xr8WlVL3RjueQ4fP0jyVsUuO9mTin9fv/m5adejgbDqHDh+q9ANt9Y3bn9I52iDvfTHNnGg8GZRA+6oV4JSl5t1dZ5ACu2PdfLSAkunE2N80ZzMzCHFnh0VOVu1OXszcIa82i+FrmR4V1Xw62hdxGwlbjz5G+c1gfmU6eZWM+6I+H82W5jHSdmsw/QjM2lJW0Pg6UnfT+YXRcGElZ4x4SfCrPOrAejqODvTwQJQrUbRYiUy7Mhu4PwqHA6tm+aOPYdRGtWwcnKLhfdqwZoNbfHSHrSz2/VeSQd8ulZNmh+nOJZFPcWCKo8tEHjkHBuzvYt6VJ1U8WHe8tuUVqn1NZINHjePfQdnMG0f3VsDkMiNtq2Wj4VbVb18En/y2MrR31hvt4sOHpWiZpxQNLhh560ry5u+qjTC4MZ3s8KMLZe+IZgScfIrTDu9K1N2x82PWk7i/K72A5AV7cNHQ3rRwR6/DcjaBo6hz32ajyIRB60Pbao/sSnj4Cxulysj99vtCxnHZvxc9tGrEge6C1vsmO5mUlrL2S/8vbe/dbOlZWPk+Obxp53ByPqdP56TU3UKAQFjYgMf3j1t3qu7XmLlV1+NxTjMOwOAE2AYTTUYCAQIjiaCEYnerW919+uS4z877zU+4X8GuuvMtfrV+tdYSWUYqY6EeE6TfJveiozsB6FkWwMOfQ7w5rNYPTc1NJyrwjc7xDc/p9oNBe++On7Uxr+HWzwDdSyrV7uEPYZg7Aqn+GxZ2UqfA3J3lYMuX3pv8nTM2nDbFQbA2ybqBH+2I/QruzCA5ZDtT3t64oW/9X/G987S6ng7RxkXcKbvZgWyV4cGsdLeexJ2rOHmnn/rXl0w4R+muuzGGj5uV+Naj+ODSoH+cRMnuQ/xwqUB/yW/P2XBZoE1vq07aE8Foj3YLdHfWRWv4cAHtrzj+u8O34uM1UQjbQad951azkHVRpDdf8et+H9wbHLzrl1nP3s8O1pwg7DtuOzk76x/t6w269Suv4g7tZrx/2x3Pdpk6Gj1y0l3byA5E6w1WiNpAm+1fui6Ni6UwXJ1y7t1Wa+zwtstGEQP5zkvCj/oUm72fCV5AZi89vo4bqg26ev+mpJmdJFu//vt/IvpdK8XDn/knb9B1wvDsd59qbm/3y+X3f+p/zdy8sfXAxY/+/h+KdkdfWvrNdDSeZfbdW2e++p2x9fud6enHP/HXMzfe3r56aWdiyRAyefvWY//42eLBgR/1T3/36ck3326/9+L/Oewv2ONbB/lH/+dfBPt7xncvfu0blY0NbvPT3/v+9Ku/6izOP/aJT82//vr61Yc+/Cd/5m9vh/XahWeeXnj234ZT06d/8MzFb33z7uPve/zjH5956eXh/PTpb35n4aWXCIazb/xq9anv96cmd1dPxa7vpOGT/+MvZl96aTA9fua731/6+c9U0d85d7YbVIuj3rXPfubct7+7d/WhRz7z2YUXXxpONvfmljLpFFqHD37z62e++3Q00Vx+/oWL//r1/UvnLn71X5eefwGWxeQvf3Xy2WepVrX7aw988cuHJ0+Ge7v/0Rbhvwuwur/5W8n0dFYpv/OBxwfl2qjZvPOea4aLnVNnD86eHFXqvenpW1euhtWKCYLbjz2aEcHjKHNdK8ThysrOpfODcnUwOXXjsfc6g77FmEA1rNQGjbF33vOYdpzWwuLuudMKUQAAzvPh+Lh2vTd+4yOZ7/THJm4+9t5cyvb8wt6FM53GRFKr3X3PldbkLBTy9Q8/qVw5qo+tP/jA8eRkXCzdvXbtcG6OWvvGRz4aNquGireeeCJs1sNCZfPKQ61m4zg8XHebW/NnGDCvfeSjw4mmxezWE09knrQautko9vxBY3z3wcubZy5wBF574kP9Uq0drt2oLHTLY0mtuXXp/O7KivYL2w9cvHvmItfmrV//SH9mAlDxzqOPRlNjnerkzvlz7dV530RkluEZ4aUxXBL5ZFDQg2DSpjMFznXBj7MTJUIgm8agKfnGThbbbGbaB6Ffzc2C57DMLdt8sQY6PT1MTbXg2ZQucDPlOSj2Snk8E/DNAxyrbG5GUO3UtZl3gqgnppGaL1GcBqVMz0sfhrKg7apPoOENm82WOegKun88BTDuCDDoLuvURRwc9aciAoZWHOfjOnEAxJ3hTN5JdM/uX6+fg1Ij1O9OJQyNMN+PZlmb2J20dbN4UvFQ6l57WWOaQDLsNeKR7q3H0XptNikwidvJ+HBYgF5+fLiKEIsRHo5mMksTTA9VMxkUMEf9eDpKGsVy0mWnBCzRAIZkgWdNz6eZO4PyCS9IB+pcyQsyDCE5G4CCAG/e1tRRc1MeSuQczWYKhXwEz/iWQr65k2E3q9eqZMCniJr0PJy4Y6bXqJG9w5x6tO4VYaKWHTvuFuyAjkM4I30UyxmazJf9dIROcF6FUiVkRaIKwngAnP7xHMEgzSqDvBHJbDiajMMmw7ptZSuc1AaFxB0cTbk907tv8HZtzE16cW0QjVMIelS2jiZwLxpsgdHdsRMIauMejsYzmXTTStdO4k2ltnV0q7QiYQvKUWceMBupoNsbN352rIqtcJIrkhLWGk1FOUoRHw5ntQsip6D0YgALNGARWPWlkxfsSF+qEKklV+aEy1heckdw3oXWWIhlFqmyKJjQXKwIz7g85/PYUghyw0yuXRqIjC9RVXOLamTO+uWwNWAFqlLObcmN9JyjKm6JxvSEzJt+SQ3t+QIvQEGUXvFJgMo8zJeLWRUESac1k+iKkWnYm4nTCnL0QVYbDhqEgYGqh61x4iTD4Vw4Kvn9/to7zZXYdS0c5OV+1LTM9PLacKda6Q5uvimnkgLl+Wg4lyRVTNSxLnb7ddZOehtuZbs57sT94Xh/VLfB6HA0ESV1TExbF/uDeibztqqNjmeYkwzj2lG/GZRtLyhk8XLRx6HrKXW6RIn1ypldcPxsyKexmSsQkhWLWboU8GEIEGRYQQpFxZp5p6gHsqnNlGe1BQBSnTmucl2lzxYZyHgNsDGgCKdxgilAPg1kjFc4cInn5vZ0wcGZWwV2wRUgKVSzfKlkHVR0M3MyQDhBcDiYCiFLIGlFkyYtWok6yfhgWAR+erx/EkGREDsYTGd9O9oe9naLY91iwFE/ngpHFeCl3f2FPOT4MD24VV1MJJNofzSWhyXo6m44k+wWxvq9N683LxnHEhAOJlPg5wB14maalXOhO9FM0muIYHR8vJhgZ0hgOJpMbABcqoJ6Fi3VXJ3hFckb0NepXnbMhO/boWwaMOt6OBETKJ2p4939PMeg6nsmISvSjjsejEQDplXO13ch5Nn8pK9iMQfNlFMMu3yR6pmyubcDRlk2NV4EQzaGzILP85TNYT0h62mXzqB8tkipdqsaLslAj9gky2e5iLpZsaObJuYG0fZgJie2Q+igvQQojLTT604oL++Y4DCcc1om3847G958KjJKBr15TWGs3H6/okbZ0Z3M7owt5K4VtB1NhLFjOeodLtIk720RtFmfQyAF8iAbU32fObg1mknjAHDbPV5QlGZWDvszkOARFfv9KajLvJgP9YWy7ySYE3jKpwEs8ZGdlXlVFmDEVpxswi9lA3u+ALSiuwcJL9qyX+VDNMt1UxZQJGdhu1QT23u5LPKqDGiuVjxSZQEe0Xme6Qxt7KFiOZ5uFpI+Ou2IghUmgSdcVGUFMkLTDDVwQY/wkkhmy6WwR1dpXvMjv3ywurJx6ULiF1snV959+Co29s4jV7bOnZN5fvvq1fbKYliotE6u3ls51e/v3Cf43flLGKC1Bx/cOXuW5tndK1dHs+OJ5Ugr5TgqCPZOnlx76EFiwd2rVzcmFrPh/dvQbc2eHASl3vzc3UceSqR3sLq68fADGIC7D189PH1CI7r14MN7p1eHlcZgfv7dq1cgwa25xVvXrlmKD06eOjh7slcbixrNu49dazcn8nL5+q99yPhee25p/+ypjDKSZQzk3dpY2mzce+zR46k55Xl3r12RaZRAgaBRQaG1sLR54WxrbMqUSncef28GKYAAIahKxe749P2HHggrtfb8wtalC8fTc8b3Nh59aH3xDOL87V/7tbxS6k/N3Ltylb76ithY//9bEUJAjFI+iwuc5xGQKKv5EuSmwLWkhWF799KZ7cvnyoMWCkRYDxwdle/tlvZ27z30sAlYBgteOrS+COsFV4fV/c2Jd29tP3wJSpzUi9Im1sFprUCJmb/zdnln+8b7P2AdnFYcocL+/OT+0hLDFgc8aRZlNgIuicbKjo6Bx+J6IE1MsAnrRQpSgGE8VpYgBQLFY2UGU0Yy9Z4VVpF4GIXTdUwVaI+S+xk7keaCRWNlQRSzWdwsGYEqncOVW79Ip2pxoRDXiwRk1CZh2acCsSxN303NiVy4YNgoQGKEUNlDs9Slkqi4UcAoY1qFVR+XPWriZKwMJJb5CLJQE81zTUUoXI8lSV5UxmZ6pDA6IChGasbnqaGEcaT7jlt1adRVnjYIkNxk0tAs0bRghhJ4lFJl3TAXXMRpKjJIKVEpRUUaxxwliqRWEJMkjtjQAKHepHSVTjQyLhUJBzFVhHKVMQHzkMLICuBpyUlCaN/N09wCQhW3osk3XW8QZd4IQyygo9yETm4dN7m1jukYnrqAEWwRzfw0G0TVdNhwxoBkCaMDaSgDoaW5R02UTUTZNEtiYwcQaQq04PvzlftRvsAhgkxxaxnQUFCphpBjSGOuc2q45ANLENN5XtQE5ABQ5SRWMZQNXHkHpgvQGt8PFQIsVzkepxBLODJeQi2wFhExyiD1XZTteWTao6CXBzlEGTDcOKkvIuN6ZluoWU/gRPGIcU/1RtQ9ANC1WQ2LiFmgDSZyqDkSNiNeoqgmuUE04SB1NBY4yzG0NsOkjRHzo3jMP0Qm31EziFuSJNySdrxkKZTGMtrW2JIoQ4QDjKQSPTUXJbmjU4I7FhuhBaN9i6GXDtrpQjcFAUwITZkyTp6Ps7WUiMQuQA6wUl6UQswotY42VoQs17mGiGTU1SxTmgDgJ1EeIZ5LOUxz7hYyXBIGh5Bq5GcEWnoce4f7ZMnxGSJymGvCbAS4RtwTrZ7TaZMJYiTRIqRWOAhQZ2gR5nFWf2MNTDuIZCYwTGlRZPoCFirKIeJimAEm9Ag7BmKJjFJ+rk1EDcQ4lYRyHWN+JDBEaaalxFAX1WDM2QtxkINJRkcUEKbU/eiycYEHD2LBEFJQQ8QSAtNM84P8SgYhByEmLQIoi2MsBGE6zdFOcoqASLoxoyNFia/xbOHNDg86cR1xhglxLKIswyB2lCQ0JBS6KRQ8QpLgfMRkSlQEcpBhDQgk6QidgMBaJ+wqYbTVQHvsoMcGEZummOsMskwLwhMKNDGEbo7AMIvOjDGeYhaD3FA6pEwwStn9fZVqPscRNcRLiRG4zkDdQBMSGEJBaZ4bJ4fEkijPXWzCMAI+B0NAlYMsQxqK2ElTYzhAhlpd5Ifz1XthtsgBwkxJBFVW3jbjTn7sshFksdCZhRCTgUOojeV2fDnPlLQ9LblQqQEYcyhNGllvK3+PNUCaEFHl0jzQ+7K01kWlnq1hOmImkwoQbjxEmM4waVFLgFHASSTLNEyUjBUBzCjIIyZc1Y2oswewtEmDy4hhk7AC7kg94XOSaS/STPEkTt0cuEBBh+QFATNsY8SHufBgFjleCCQjUZrjus4QwilzYkHzPCQS389Bg2SMOIkUCEeZcpTBKNWMywSgDOSc0QgS6yWDEeWEW6lcSlOKtZObMbJmGE7sEmIQWxXEaSeaGEZTspwYHjGSSwspSCETAc46etlm1k1iAwFEOQMGowGjxjd2pCY6uSR5SlAXcSKzULLbY0H3vppLACJEuRBymxoee3mOMbYcOWkOEabOSEMqsPK80AJDTKYDDRTEOsLOng4nWYC4HGWIexJnLZ8XOQaDzM+JzSlk0It9lsRuCW/wfMUVIMp5KomDc6WLBkOlHNfuGV6kDoiYM1KUURW5zpZOK5gUUZARkkIMsDuSFlhFiRzljDlpFDUC47muTrKKDxwibZJVfVAQfj7Mry0h6nhhNy07uZROPNLbKeLICeKs4lqfu8kgaZR12fXS4akbLwAANi9czIoSSOKqMK14JhABSrNtraX2Kv2sFkCVOiC15ycQAEKFacHRRemlw3SqniPkR72sIGwKpYqsg9Nq4OvQVIOEGjcdGJ/HYyVHhcOFqZsLkwxkFpukKIHESzdfF8Pw4OwJW3LDsidUBJhNagFjYGL9brP/ztHp5dyl0XjZzUNUdIb1okxHTrs99fbba1cfAdTEzaKfh93FmcMTy44OocRhs8KhYhxEtYChDGIdlV1EALLmf4si7H70tz70Vx+f/flLvcnJ+R88t/yTF/rN5sozz5761tPrDz28PbMycgowzj7wiU/N/9sLg4mJyV+8tvrMs8cT04s/fu7yV79+633vv/rpf1596pnrH3jykX/83MK/PR8261Mvvrb67e+3pufnn//Fw1/48q33vv/Bf/7Sie/96PaVxy5/7ZsrT//waH5pf2GxXWjiRF357D8/8Lkv3vzgE5c//+XT33xq8+LFE8/89NS3vtsZmyoctd7/Z3/RG58sbOxc/eTfb588N/vq65c+/8VBoVoJwg9zlubt7J3jJ/74z3u1pr9/dPVv/qEzM1O/ee/i576QCs+N44f++m+htql0n/ij/xGXiqLVefhT/zhs1ktr2w987gsac2zya3/5KRYlo3Llyd/5w1h43mLlw3l3iKzzy3ce+OznbW6V5O/980/IMBxWKx/83T/XGK83zu686qgu9PHo7us1vZP58+jO0zLqcncm0QWIKh6+2d64M560oONma6+W4VbK5+HWM3JwLPxavvVzJxoyYaLtG356hBw/X3u5mG5ocZJtP007LdeZxK3nQdQhDk62bpXCPcrIsTk3xuNBgoLuj9B+q8gmaecFM2yxIuxvv1vsbXK7yCq3F8H+idHEMLheNZ1TEmzlvVm4d0qz9iNs92q0vQ5IevcyaJ0b1rve2phtnSDokLeCfP+i5pHpTbt35pNGW94bs0fnklrHX6uow3MW91gv0NuXAOmhfoOunUprx2JtTB9etMHBA+buo4ocxN2wc0pvXYS4B4Z1cvc8LGzjrTF1+CDmO90e2HwxwKlCHG486yIBQai3XvSAQ3iyEV07491Zox381mtTZJTAgGz8SFoGcazvv+gzkZtju/F2iSU5t9Htl+u0H5Eyuft9xxIKc73+ol/EsR6Yu29VcZwxYe68UCadGE2msCaUw+LryfbbVQI1GOT33yzjUDNHv/tcyfYUXynK106AtMJ0W++cwb0iYj2z+TA/8OPq3n9O705ac13X8K0LIKpB2zf7p3m7mHoRXjsvdoN4suW9eTEfzsaVoXdjCo5q1BzrwzOoPRZ7Eb9/VuyWTGOTXr9gRotRqevcWTD9qSK+9R7UWTDDNV7Ttx+Su41w6tB740Q+PIG9PbRxQndmLG8fraH2K1TMouEbYOedIp2C0Vtm+07BDfKs6R2WJ1Gmw1eyvVc4nyeDt832OyVvLBvdQpu3iy6O8g5cf6MseRa22d7Ljpi0yU1z/0Y5aGTDd8HmO+UAjXAnvfnmmMPScMC3fuYUSkl3drwT1KnKkl9lGzdKro3NSK+9XHJZGo34xvMSjNHiwIVbl0kaUx2p++/j/TQLlHjrYWXZpLz+YdXPoWofNtTOVRAbhCO49qgcZka24c33a8CAydDaZQw0HmZm9wEaKkBje/cx1gNJORRvPmSsBDZC9y4iY0xmwfYDdIRtYe//Nq1q3n8tnwmuP5DAsoEZe/ckySxUmdl5EI2c/lTW+nZ+vOe6c+T4edM/4h6Nd+4UhxsUjZFBoz6UJS/sHv0A7+4X+Szr/Vz3DmUJ9/fv+cfrEjfYwa94etvKE/D4x3B/vywmwPBVeLjj+ixq3+XHa25QU1tveJ13qHOOtn6k97dKpfrocGJ26BZpJ+q8iQ/vuH4l239Ldm5yb8V0ngfbm2Vv0hYOfLXzIIADGJbJnfNZucu3q2bvsvGPL9o771P0KDqOekv51sMIDmwcsDvnkb+P98pq72HoHKDDqt2/RFUbJcTef5CYrlFFdvuiKgz4rqN3H8TsgLR8vfcAzIdQY7R2haTdef7uk3Z0RHhva8HuPsjoETwWZu8Rko2MQfDOFUhUx0Y7/yZckIFQ3X2rCoaKS3Pn52XcStFkCmtM+U56M9p5u2a1xWG68VYRDrRw1bsvlExLkXm2+RQLc4cJsPcLBqMca7P+Vll1ASyi3Z86+YHi83T/adzOA+LCw18QE8JSvDG8sgq7x7lobv+A2kPLZtH+M6QTF7hjjl5hYZ/wMhKb58VW1TQ2yY3zZrA6qnTcu7O6t+CTO1fgwaqJ12khXXvY2RqPJo+8txdV/yQOdvD9BdVZhrCDW5N2fxk4LbJzgmzPxhP77vWFvHcGertkY8oerzB7CLuTYHfFui28t4y3lm1l/dH+Ww+6wd1IZVvnYGuF2iPQH0Pry8Y7RnuLZOuEahz0b+QbN8oBHPFB/PZrExKkacY2n3NoETlOLzq7QHYPkzW5fqMkdQKT7O7LFQHiTNP7/+bQCkp2zfobxXG3Nzwg996uMJVACNZ+VpQmzQDe+jeHFmCyb9bfKnpOkh6a9TcrPE/8oBeemgHdbnhU2n1B6pK0Lb31Kz+AgzhkG6+VUGZKXv/D//V3nXa3Xx+79sm/58NhDuilz32ptLW3d2blP4PWRNZ+Mw4+9t/+QBy22xPTj33yU/5xOxXehc9/pby2tbN08sk//tPm9Xc2rj78+O/9T2//qDU+c+3vP+MdtAAX57/wlcY7dzYuPfJrv/d7U2/duPn4Bz78239QvrfRv7z6XtVrZOlg9/DCP31t7I23dy5d/MAf/vnEy6/f+sAHP/Tf/7hyZ33/xMnz3/7OyjM/3jtx5sw3vn3hy19786Mfe89ffWr5hz/ZOX9uY+7UyC26x+1LX/vG6nd/0FpYXP3+s+e/9q3rH3zi2qc/t/y9H+xevLjy1A9Xv//DpFyZeuP1E1976mhpeeVHP734uS/d/tCHHvzMv6w+/czhyRPzP/nFyaeeiUqVxs1blz7/lfb0zL0zl3tBlWbJw3/72TPffnq0OFN9+cb5f/16WChX72888g//vHP2/Gj3f4citKb/sd+0RT8uBhtXH06CYtis7ly+aAhpLS21T8wnro+NYnmmfC8peOuPPhL5hahaXnvkEUhJb27m4OzJWMj++NjGlUdy38t8Z/PKg4nnDRpjG1ceMZx0JscPzp1MvWBUr927djX1HO3Ke9euYWwtgBBahEB3ZurozGocFKJadeehS8Nawwh6972PaZeHheLOxfOjRi0Ngs2HHuo36wiBO+97X+x7ioJ3ZCMWXhIU9y6e7Tcbqe8fXDzbmprG0N7+wONRyTeU3b96JWmUYzdonTvZn5wI/dL+uVOt+QUEzLuPvSduVg3E969dGTVriV/YP3e6Va8mIN9hpb3aJLH67mPviRtVjcnGww+mzfKgUN0/faI3PyVNzCYxGSM8T/g8h1XIoJY1gxsE9kY8inWthhgSTUPHKcsSNoXQOOc2EVVNpxnG1g1SNOcQqOUYpOOE5Yk7hXCTcpg6VWOmhQsTWcjwnIOBdurazBTEwR6Ila7UGMqckqLzUsJYBjmbpRAC0YR2ymOqD3k3nMgIjgjsJ1NRwiHFvdGkhmjU1aDlViLKAO2F0xrjIbGD4XRCSWJoHE3EEMWQHufNDKAUk+5oxlocMtOLpkOKQ0OSbDzEOAG8mzdSTTKMu/lE3IXG2uH1YNpiAEmUNUOII8BHsNJLXIzQMBmPsoB7yUAuMxpYjlM5jUGZMJTyKWwc4nSOY16QHkIY0UXKi4ChXExCVKUC52IC6qZ0VYiWJS1DoRK+yGERcpLLMUvrlCFVaCTpZInlCV/ApIJpFos5CoqUtFrQElApSZq5TQ2mPJFFfInRMuJ57M5hVMHAxkh2RmMagTwvDPJaRvIwK/fyqsp1dIDJrtsEJMOik44n0GQq6EfjQMY9XWhnTQB0aL1+NJZRO0KyH07kxmrod+MxLdK+LnRxJUyxBbIXTSpq+lYMh7UcgXCHiA2/SbPEeEdZw0CbQNHNJpIc54j1s4mE2ZR5iswywoHLY7NSEDxzYExWuIAZtBYBQ6AOvIRPYiKsyxO+QGFAHROS066gGWFGLDDClesqNsOMiyWNxQIDHnZsjE+5Ho2NIHyeUmmFq/gEshJTlbpZqDzu6hFedZhrOM7pPCOOlSIjcxIGViS94WQKChnJomR8ZPwUgkRXepEbJzrZ46WDQoVlYTw+An5CVJQ0h8SLcqx1oZtXcgRiU+yOGohnYTQ2MuVURmoAACAASURBVIWMZKOkPjBlTfTQlAdZOYcmhqVuXDc8HUWNQVgVbLh3C7m9Qh2ZgSoN02rK9MCWBsM6oHmkap2wQgt5160aMyMdlDhuShYEQlZWFBpnDOZMJQwoCJUsKrIgJUykm9E5gjDgVUhmhFChrOZoxqFIOX5qlgMOIulkdJEzkPEyJJOEwcyr5HSCUqwcL6WL0iLA84QgzUzKy5BPYwJzv2rQtIAUSJmAlcCxfQOTbGKEaQJIP2vEiuUYddV41EUGmP5bwYwmEKE4HQshiQEbwko39RBAo3xslAaAZ73BTIZlimwYj0VApJZGqjLIPU3AMB8bjQpEZr14OkQiBTZUjaEVSRvaTVYcSIeBQVbvRRUq08FoKgROhvQwrQ+BryXKg1qspopEZ3wWszqmWcxnCSgz3DqGCoJqRdDcqeVwxuV5IuYprSKex+4swjUiYCqaAI4z14ZOw9hpl6tUzEA74QVRV05jOMaITWQdsCnumEjWtKpy3OsZJE2l5CYDPm7xhKAgEzVgZ6VUI1EHZlqKdGj8LqqEKQZA9KJpRe0AksGwkSEQ7RO27jexyqxzlDeUtSnmnXQizonGuJdORsxGyo1VI8Igsc5x1lQaZYR20okkJ4rSUTiTIRtpJ8obIbQx8Aaw3B9QFmJ91xvLJcGwP5rKKEqMCNN6DEmmnUFeGwEXOTaip32XRYYRvkCZa4RQdJIApEnr2MjA1AJfDdGqwzzLQcYXOPKAYLkzCXGJSJwF42k0VpXZkC5zVoDUJHSesiIWJOWTEFcot4k7DcyY56RDdkIYptFBW1OHFH2Px2iK0jKSZuTMItVwZRbyJYKKNBdu6+TK4cnljInuyvzOuXPY6M1LF44Wl0nSvYW8brGeSudwdWXvzGlASWdpbuvMWWTMzqXzB6urLE+2L14I5yeGbvF4cX770gWA0eHC/MGZVWjs9qXLuytL1Kjt82fbKwsZF735md0zp1Kg96W3Vx5DymxfvNhdnrOY7J0+1Tq1nAjZnZvdunyRWNWZmtm8dNFS0l6cPz61nHrBqFnfeviBnHKic4QshqY7ObV36bxirLOwuH92deT5YbW88/DlUaGUB976tUcAR63Jub1L5zLX7c7O7p9ajqr1pBhsPnK5X21oR9x9zzXt8MHY+NGZk7HvQwjcdJR4flYI9i+fOppYMAyvXb2iAndQqa4/cgW/+fp/VBH+uwBr8NGPBTpTUmS1sjZACZ6WC8iYzHFHk82FF1+ef/O1oxPLLAqNYGmtZCAxjESNOlJKMZZUi1iDXIre+BhNIsNoWi1ZCFMhh7UqNsZyntSKINcZZ4OJCZHnipC4UR2/e2v2lVeTSslQYjGOG2WkTC540igrCw1Co0YNQwMgimsVQ7CFqD/WQBQDo0fNBk2z6I2tSBaspNqAuFa2QgCI0loxYxxaO6zXKLFagbheMYJpa/OKryhVEKe1ouICADCq1xC2RtmwVjGeBLlOGhVtcH59N5aOchxgTdhoQAqttlGpgBySGhJXS1lQyCxFLpYkTSwHEjqOSjRHPmbc9vo1nGEagJhI4FJJ0xxyKJGUWWwldLB0VAYYlYA4MIPMOliyLLMUSCRdlWqBHIQLROeWcktcmEGGHAwDlhy4GSr7fqIBtw6BAdYKUG65VBlkyuMgIMxgQEHua6ghIACxkSLSEmRc3UnG9rIlRocZcXNOc09hgwCBwEmxVimX2tUIAEAwkiMDuWYk9xSx0BACnJQYlTGJRGIhzKlEcgQQMYwS3u/ZyY1wRTtI2CQlEsnEYpQyIWk7gUIxjsVIUWYNYAEkRMdQUs9ibBTi0s1zXhgeFW2pULGdHilSH3KmUiiYaxCxCaDCVYCTFDBWQBwmEZDYg4LrFAjmA0xtClnARqkQOeIwIA5KUiiJByhDvV4TEiE8qxCVntGM5pAAn0icJoBDFzIXWk0gzQHLFBJGAkpHBgrjACKjnfDsgZmmzkBBCagCLNdYWAG00MhiIyDmIwM8w4FyFdIYEg14ZjDXDCppkEVGQBcfh7iiJVKewQZbZpHIW9H0Hpg3DiDGakkxH2jrGAEhDzWSlkLIE2OAZVy6eW5JzggpE2ysRogFUBwN1L0YudgKahESvrYAKcZcL0uQMBhTH3KoMiK5byyCKebcU9ZAzZjnpCmRhhDqG1eFfV7gnkEYKMx4CaDDCL7TF75VTGhMeAFSkMXYYZ5BBOSEwyKlyFpErcyYDVPoaUcTlubQhU6SE2crPBMTBhgwmFs3pyjOoWdoIslwQGtA5ojmCgjCIs2QQUx7lsNRjlzgakTjDPpWZIDmGgnEIkOhRhy4RlG41z3RIw0qhha6WhojDLQEsdhyYCHXDtBSw9QAh+AiMZnBHDAX5IgjgVCRkuttehixEsytsJKgAtYaEGalkyvIcofDAENlkSTSSVPDtcC0iLGxmhHuGmBAJoVwlbLYSO54ea6pldRxcrWv8PqABgghqDkTrlKWZpw5bqoUsYLiApJZljEJZGKRzamDRAQxNowR3h+AifXRCe1iYeMEO1AmAIOUOIJ1M0RzKrAYakw1YUDGxKQJdoGTIWIUFtiJNCYKUSKGmlBDmHVTArIcO5gPExjcTR9QDCICNaJMDFIiLMTa09xGOXGBlxuscoM9FueCZphDn0iUZEBiD3IBer0mwI70jYJEuNZykgEKfCJxklgBXcQdlRqOfIIcrBXg0iCHZJZgjyAH6sxCn0iR5VYAj1gHGQO51FTifqea8TL2EFIGe0SILDXCupj6CGgAHQw8igy2HDr4OMRFJZHyDDLIUoBk3o3Hd8ySciAx1gqCxMBYqQWGYqSwtBQhniBrc8EQjzSkueBYDAAQhmPMhzlxNIaAx9ianDHEI415zqhHD/f0yfV4hTqRRsxQjHgErU0px04MENWUYCdMITMYIw94atBjRe5bgm2GufRUot1hu+wEEDCSISoKkIEsRpJ6llCQYS58bRDKES2x/pD7BhNcwAKkGZLCt4DaFAruG4iRwtT1VUqYhhgVMIW416piRzKpc8BIAQMMDIDSzQ3nyiJaJAIlKMnjciEPPKBNHriZ6wJt4no18Tz06p0oo3m9RFIVFwtRqUTTJA+ctFBARselYlwIWBzFlTIMWKpwXCzElTLLkqRUNK6AWo9KpbhSkdEoKZXScoDjLC36iR/k91pRPze+i4yJS+U8cGCq4mIhqxapskngx5WSTKNYusNmnahMCxE3SjhVmSOzeql2d23pxZ/HjRrGIBNOWikCY1LOk3oJZUoJkdaKymLDSFwpcJ2OZJBWitBaxXnSrNgkU4ymzYpBxGIU16vY6oyw4URz8vatxZdeTMaq1kBFMKg6I+ICjKJGDQNjKO/OzdFXf/UfnWn4934RPvmpvzvxwx8dnDq19P0fn/z+D47n5s8+/f0z33rq5hMfvPb3n1358XO/+shvPfp3nz7z1PeOVldnn3/x/Le+s7d6evXpZy5/+Su3PvCBR//xc2f/9Ruv/8ZvPvL5L5z/5rc7ywuzv3zl3Fe/tbt6ZuXZn1z94pfeefz9D/7jv1z42jfe/tCvX/7mty586StHJ05Ov/b66W8+tXPq3OILv7j26c/cfOKJy//y5Qe++vW1K48s/vC5h/7li/snT5cOD3/tj//seHq2sLn9+Cc+sX7q/PStd9/ziU+2J2f9duvxv/5k5voa41//wz/qNcb9Vvvxv/rk8cJ88e7Gez/5N71KXRr1gT/6M0V45nq//ru/HxeLsjd87C//V3dqKtg9fOyvP5kWqxaYJ/7oT43FUa3+sf/2O1GhhKLk/X/1iVGj5nZH7/3Lj+fCzRz54d/9A5qpYaP6od/5Q0vp1vS5/RcI6eeOE68/75G9gbtMD79t02MQTOStn1oyTAM6XH+tCNrKC9Lt56XYG9Eluv8dEB9Af0J3fgrTLvJFvPsrqQ+VX0j3XpB6PWJnROc7WbRP2RztP5ukRzBwk903HL2jyDhrP6fg/ZF3Bg+/l3d2GJ8jg+eydA9URW/vuhtvALvIm9fH5cZkd6ZfulmguyuU3IPdSX5/LvcH9LBRvl0E1W1+b4psLA1nBoV3A7K9SvgmO/Dx5unc7dNWo3yzEY11vNsVtrE0GO+V1xy0eRqxPd72yNopyI9gp+7fmo2b7cLdAts4qUvbdLcktk4huOUMBbpzFvBj1Ks4N+ZheUdsBHjzPCxsRX27+2Pqm0hxvP80JMKwJD94jrCCgd1s7xXpoUEta9/8eVNmIfbQwVOAcYOybO+nlLuaDfKNX7gBjAjO1p/1nGhAq2z/uwAhgI3ae55VYC+P7eaLBaliJtXmD6UXjlCDHH3PIGwlzPefpwzlKFNbP3d5EktPbf9Q4n7MVwrBK9M0DDA8ROtnSEci0RLvXvD20WCqW3llmXaLWeXQe3sBDcoIH6KtU86xEwWhd2PZ3SGj2W7l1UXUbo7GBsW3xkm7TPEu2jnFDoK4GnnXF4NtqcY2xRur8HgqbvQKN5qk3WRoi2/N0sNyXBv5N+f9TX8w0yq9Oo1a06a0J+5NodY08Pbbt034khZzcPR61nqNsRPSvjXaed0pF6P8QG1cL4mKjQ/I8KcJXyTxm+nx66I4kY7e1XuvOaUgssd690UZBMnwiBy/AJxpC24nO6/K4kQSr9ndV50i6/F2eOflSuDGSQe1f2q9KRvdV5vXy8WxyOzYjZe9shipkdn5KQucMB+R1o8tGKflIeXvXuB5G5khuXWNhiNTyIJXTkNggE2cmycwHLA4JfceYEkfkIjdeMTtd00wZK89gAwAMBY3TzEbgTihaw+SKIKs71x/iPfTqJqUXjplDYFwJG6dwnkOTULuXnaieBDo6q9WRD+LG1HlpRPK+IrF3o0lFmcIhPTuZT7Q3Wk1+sYg3CZ8mfWfjcJdXAyivRtutqFpkx68wtL7uXMKj36QH9+nfJmFLyThBq7L7sE7criG6QRp/xzAWwk/hXrPpO01IRdJ+mJ8vMbL3qh7E/Xexf6Yar+CwxtKnGPDZ6LefVkYj49v0Na2Xw7CwR0yuIm8cd16FQ/eNsFJkD4X7932/DlT2Jf43jlEWnBU9N9eSGs9b8vl906Z4i7ZD8TmWQS2nAihdy8h0oZx4F5fRsE+35Xk/gVb2GMHDl2/zM02jBG7fQnhNkpd/61lVezzfcHunUVii3UcvPYg0/tQY3H9HDf7UHF546QpDESLsntnmdg2XcnuPYh1C1vjvH0ekrhnR8c/gr4egkxtvFTgSSrcfONHjtcb4Cly9JSx2joyP3iOYK2pzbd/6dBR4nj59rMSHoVkQRx/K88yhgNw/GNNkowTvflLl/VTMyaPvqvgYSwWSP+7ahAKVgLt5wwZZEUy2Hrdx50Uzsr2t3N6lOIF3P5ONhgJWQK9F0zaBXwM+7eWg3VHj2/Jt5bxwUw40SvcqJKjKUo22c4E22/G5YF3dya4XxrOtAqvT+KDeV3ZddbG4cE8ortsf5xvTurCEb0/595vhNOHlTebZH/ZFDfJxgTZWyR4S7SaeGNWF1p8fUauTcDGPXmzSQ5XYWGdbdXpzjJDm6jdYGtzungktyace1P5+GF4K9l5yQ1Y3+sPb/+y6rE4T3DrR9Yds3Y73n+VlxoR2Dfrv3TLfAQytfkT4ZPQaHL4AyCqQB3kuy86k+7hoEO3fxn4OMTA7D7LPRynlhz8ANASQK1s55ey6Ed5D2z+zHFByKg6+AkRKAGctL+vbFXadrr/M1oUQ5Pi9eekC1KvEP7mf/lt57jdmZp+/198UvZ7kV969FN/V+j0thZP/Kff+Z3qxua7jz32W//1t729g/2l1Q/8xV8FhwdhpXH1b/6htLW7fv7yf/ov/8/4vbX1qw8+8Tt/Urq/tbdy6v0f/0Rh/yguFq7+7acrd9fvPXTt//jt355468aND37wI//v79bv3DtcWr7yj5+v3F8fNeqP/MM/jb9zZ/2By0/+9z+c+dUbb3/4yd/4gz9tvnVj49KlS1/7+snvPrNx4YFLX/365a/+6xsf+9hjH/+bs997Zu2Rh5e//5PVH/74eHF56We/OP+1b++eOXvq209d+tJXbzz55LV/+sL5r379/tUrK8/85OK/fr07Pb348kunv/ydvbOnT33vBw99/kvvPPnkxc996dJXv7b+0EOLP3v58ue/0J2ZH3/rrYc+/bm9s2cWXnjx0le+duv9j5/5xnce+OJXOmdOlF69dfWf/rk7Nd24c/c9f/uZzYsPjP7jX4T/rgRr+JGPpWPV49Xl9uJMb3zi+MRyZ2lWee7RynJvcbrfbO5eONtZnIuqpePV5fbKfFQsjiaae2dORs3q/tkz4UyzPz6xe/bU8dKC9pxorHpw/tSoWd8/ebK1shTXS/tnTw9mx5NC0F2Y3T9zKqyWO8uLByeXs0qwv7p6eHLFeLI3N9VdmeuNj++fPdObn+w1xzvL80enV1XgHC4utVYXR+PNw+Xl1uryqFbtTU7sXjirKn5rYXH3wtm0VjiaX2qdOZGUCsNmo3VyqTM51Z2f2Tt/VlX91tzC7rkzaa14OD7bObkwmhrfX1npnJjvjo93Z6d2Lp5XRScplrcvX4jGq0fTc0enlgcTjfbSYnd1vjsx1a3Xdy5dULXgeGZ27/J5XfZ251dbZ1bDRkU4KZsivGicguEzmLkm94RoWl61tITK9RTWKCpiOYlIALCAdFbIICNF7E5oXrGoRPxapquCuJA2sShqXgJ8lrhBjgtETllQIURYr6pBk8lAywmAKsRzEzmDhK9V4Ii6wU3i+nmhltE6ogFkkxhXKWEjXe7k5QyIhLgHqpomPtFuP6+mhPd1ecDlcVzEuthRlQyjkQs2R9MI81FeHGbVFIlQF7vQ76c+SgtDU4mAkxHnMKkrwkNVHKlKRGEPyjYoj3LP5KUBDPpJgWCxlzYUEmkcZKY8RDLMyiPhtZICUoU+KA1TXxT9GM8Q4easitxxiwICC8SpaVDlXhDTGVGUI1t3+BQkDgABFeOAlYCsWq+hddXx/AhPMyws9RGdIiLQuIKcMU1LUFRgqRpHlRKVGs8Kx81Y0Yo5wj2Fy9htKlAiyMeypkFdFIKETDPHzWgZiVlMXa1FjMqdrGSMDFVlCL04lXlSi4gzVF6mKx3gh7mvUNDKyprZri1004rFrJs3hlgOc0epSkeVcssS4LfzmjJurMp9VcwJ6+b1oWTHiYBY7iVNA/kQFLt5WSE+yKphXlSWRVl5QLx+5mtb6oDCKPc0DNp5KeciZZPEqShUwN6YIU1CuGrQYzvvSU/xGqATlHvKqWunZhjXQSER41CVRLmc4GkuZMbqiEwghySun7pjAFaYrBmnoXXJCQohneFFGlLf8klICpA2kFvXqICDhnYaRhedmtqzS77jKV4FYgJSH7AaJBMU+BDTw7SZYRFZb5TWh1gmkZObUtf6ifGHqNhNSgTwdtLIiIxYfpiPR8QZjYoAlNvaS40fwlIvKmLDBmkjYXKYe0laGxAZ5X5iyx3jpRS0UbEb1ijh7aQeaU8R3s7GhliEsWPyct8UU8w7sNhJygTJXlof5Z5xZepOGlzDwtPFRgoa3MGxV83BmPC8xJmGTiEHZe6OKdKgHo4q3gBNc1qAfByROnGdhM9gWcgpM8VKDKYc6plCLcNN4niKThBZtQ6K3boWdQsL1B03oqptkRWqKR6nPhzyqqFNRAPrTCKvpGCZOw0NJ7ggoSqEeTVCIsrLI1js5w6ICzEqdNMCwWIvaSoq4jwI88qIogFgbVLspCWUFwao2I8CisRBOq4wj1M/05UhdKK0MAKFfu7lIOiCYj8NmJushdMWy1D5IagOoIyiUoaK3czVyg9BqR8XMBUHcSOjIsyCJK8MkZOLkq3U4rzuiUCxGUodjSRic1z4OSljd0yRMmJl6NXyvOJKR6Mp5vg5r0A+gx1fkRIWExYUCZfGqRtTF0GQ0mmMCijwEjGLuWd0IHnDkhr2g9RvKFzBvADYDAEB4SynM1wWMlwm7rhFNeL4mWxaUOWU9PLa0OGtUYmbUisvZ4alptBX1QyzQVYeqbJCfKDKXeQNIUmgODT1JPMs8ltZNcd8lFZCUPj/aHuPIM2z68rv+ff+/vPpMyszKyvLu672Bg0QDRJkEyQY5DBEUjGKYEih0EI7RYw2CkkxYkjkjMAhOUHODAgaAQSHQwsPEh7t0Y32XdVls7LSf/nZv39WCyq0nllwf3Y3zlncG/d3cs2l7BQkGqvA6tYYJNMqgig4VF3N8dR6I9subFDW7WngH2UN7pIBbGZ5SK030rMS81S1MpDkJqhVcwTi1MQ8Sgq6whuicHM+nwfYg6BFvRmLGzCcscGM1W0/jjK0LAh3tIHZAuKJpV0YzBjcwEHPNTvFpN31vBqver5fszbgi4hFlnVhMKNNIngMRM+4rmgmBV5hvqdoF/NFSAKAGpTOQNIEjVblz1nVCZpRjpYp981w+cTBpbPTpbnh8srg7MnhiZXJzMzupYvp/Ew+P7P96LW61xwuLOxevjBZnteRP95cPdrcmCzM716+kC3MFnO97UeuuXawt3Ry//zZ8fpyPtc9uHAuXZxJF+cfXLk8XZoDnO5fOjs9sThaXD66cGaysjBeXDw6d2a6PJd2e9tXrlTzrdHc/M5j14r5zmhuYffS+dHqsm4EuxcuDDbWypn23qUL6Ym5OoxGJ1f75zbGs3ODzZP9sxtVO9k5f364sWJ80d/cGJ9aHS8sHJw7M95YGc8vDDZWB2c3YMgP1k4OTq9PZ3s7Fy+mq/Pj2bn+xXOjkytpu1M3o51rV6pec//s2fGp1XSmu3v23GR9qWg08qWF8fm1g8XV4frK4YVzdTvZP3N658oV8vrrfOu/bIP1nwXOshAah7zhOPNjqy1Lc0PFB09+5NWf+8U8agBleJoX1HOYeoNRHsa3L1977flPDzqzJK9pJSsR2kqJtFCE33zo4Rf/2a9OG22nHZ3mhQiQcjjNrYEfPP2xF//ZrxUi1IAEx8PCj6yDIi0cwDevPvLiL/1qGjcMJN7BUR3EhnvBQT/zQ+MgSwsDicJUjCYK83o2bp5P6k6zFEHQP66psIjSLAe12jm5+YNf/rXjuQXJvfCgn/tRQUTYHyoqdCTCa7NgLqogpZNMQ1xEjfiwX4oga7S//yv/zb1LD8Fa0aJyDijus0lmIE2bYffReTWXVF7oDcY1oopyVpROWyk4yaXTRnEO09ogAmoZkxsh2VKIOmV1ZY3vkUI6bSxycXCX0b2a+7goaCk1ZU4aU7gKOZ/e89EDyagbV8CCmnmkrlGpNYURe8DpjoQAlRrWWjGCc0WUqYTv25sxvycBdNra0mjBYWmcspJyqBTJtUTQWU1SqxhMwO2L+LuIpUYjVMiae04ZXFoJYNoxOxdQyRxwiORaIeeUI0Vd07Dl3r6Mvmw5sM6SiVYUGABxZiwA43m0ex6VPjEA0kldU2qtYhMlGfTx/iX8De5PjEE8KxQiNWY00zXjNaVoXGqEHUQsK4yFlsnIu43x2BBIRiVkuOY+GJUGE4fqBvmA4OOaCFBqA7AimA4Lh3FtTcO7SYOsAJ6XF8hYzRjMZWmZpboZ3CZ6vyIemZQKEkk5zXOsLaQyFrcpHytO3Lim0FlfgGHpADSEYG1g6UrOUGlQrSQjF9kL8/T1WnigrEkhNcVAWVjBSqCjNXi8aGoOaK5hLWvu46rGpVFILaBXTuGXKwpcBbHUEiNUW1dJRfjhSXRwBmrnkDYotxXFSAJYm8LBTfbaBv1hLQTQmmbKMAy0RbkxDCLjYF5VzIMAkrTWGNlZUT0xpwNiLMSZdARbBUhROwPkWqN6fNYxQDF0o1JRogFG08paqJci+cScSoR0BE9Kw5ghkI4KIIhusurxOdULSK38vFSIasLgtDaE6QTXTy/YCCtAUKYkEdYhnhUOIk2hN5ESQykEmhoHEST1ZfGdWX67xgyV1kBacyIm0iJrgNq9hI4XMLCQZ7mTVjNMC6UsMby+iL6ekFsVZ3RcOwhrxlFekVJVgds/iyYzQBFHU2OtVYzQzABlDYeb6NvL5N0KYFRqaGHBKEkNMFoSTiqJclUTRrR2mTGCyDNNdbqhPYJyRaSp/MiVNS6VJUidSqorPe1xUFknjaYE1QZmpcS8vtiV13oWQag0zJTm1EpLpEbayHOt+lrXQgQAhKPCeB61BkxrQ7E8mdSXO8YjtKhpkdfMU5CQSaE51xCRVBsMgXU4q2ooIrp1jX0DCmmd4mOlKKogY5lyAKQduHsRlRFUjpDMVJQb4vyRrClmfHoBfFl4R9pgnpcGQIMQnirFeO7J3cs4D7QmDBemptwBxNNcORxEw6voa5hOJUN0qCwyElM6lRZCQ4grlLLYepxMKguchTYJbxFyVFOf5AWWWlMKC6UUllBG/BYH+4oLNywARJJyWlZAOkdNLO5hdKQwRKkExihGybTGxklBQ3AjoDsSQFdqo4ERHKbKGieda/JbnrtT8wDVGlVaUQoLBZSVBEPpYKUqEYBa4tJKAubZ++fA9yqsoEaoNhIBWFtc1RqxozWyfwFp5JxxKDWSQaABqmzlyAn21jr4mibIuP8vtaCWNFWKgFHPHmyCygOoMiIvqn8cWaYrzjr07jX/BY1zYymqrKIE1ZoVtcJUE0yHBRCsJgJMKo0pxpMm+QDgTBEOU2kI0xixYWEpka5M/FvAl7VhXl44AA2jaFrl0IekbAU3ke5XSKBpJTHTEJOscBZimiXeh4iXilEwqCiGljMwKi0ijpQhuc70AXQGTqVFRCGMRzVEQDIBi4rUqhYBzisnbZ404s2GXUmKKPFGY2Ch5B4uJc3LcbP7xs/83I3Hn5kGsTccW0hyHvBJ7mpdhiFdD7yNRkF9UilYVHnY4OPUQGK0e/nnf+nHz/2MtQAoQ6d5GSa4qGAliyiJNpvuVLMOQpaVoKgl84C2fDwp/Agqw4q6EgEq+RbE0gAAIABJREFUJaqkM+C9j/zED3/pVxTljjCvP0j9GChLs9Ja8MFTz770S79a+KEGxB8MSz+qMPUH4zKI75279PIv/PKwN09qzcqqop4iXByPaj/c3th88Zd/bdjuAWlIUZc8ANrRonLG3bn68Pf/q/8690Ml/KA/LETgDKDT3EACAPwn+SIcf+rTH//93597972i1znxvRdWX31ttLy0t7aRe7FfZR/7zO+uvPTqzY99/Mk//uPFN94YnljZWT1dBlHcP370C1848w/fuvfYI4//2V+s/PCF+1euHi2vKcraBw8u/t1XTnz/xcn84sprrz/0pS/vXL64t7ReCZ/lxVNf+PyJF18qOp2lN9849fffHS4uPThzvuYeV/Uzv//v13/wwv6Fs6e+9YP1738/73Ti4fCJP/jsdGG+cX/72ue/uL+2uZakHyn9QXm/9dbdC//xb6B1luKnf/cP6ii8e+3RLGgwXZ/7+j+c//JXDPPiIr30+T+HxqCLJz+Z7dcAih+8cfkv/y7vdTo37579ylcJxPeuXhmHbQDAxmsvP/n7ny0biRiNr37hz+s4jqP8OdMs9Y69O3jkD/80zPOikzz1mT8wHn3QOr3/nm+nLoHp1vtNdFA12ZY+uyqbPrx+dPBuExU6kNPt2005gm28l11Zw+mYO/zg+1Ge8Ua73PuRV+ckcTtyrUkodEOw+05iH9TRJtj9FpukfntVYd/IkNMbw8PtXnGMWRMd/JjpbZUsZNAH5ckle2s0fC8yQ9s0g/174WSP4RXYub3iBmtyfhTfnHWT5Rh8eBofXqvtASHjwyvRVo9Gt+nWCT05g9nWcLZdeYGYVq07nh5dgKKC6Yx/f6X0d39C3boK+PuUsnvrdrRO0EiMhDk6B+kkncHTsOPV0+S9GTs6jaIDttdzo03f7Z7Bdx+tzVDlw8FVvHPWC26z7bYeXWB0Ox2Zvbd8ohTj9v7LMWMmaI3z5Z5fF8V1sHenxVTeyQfvX18g0yzojPTGEnVKvpPtX2/4vHbHeud2g0kZ4IPs2oY4PHSpv/VSCBkkdb3zdpiYXMjd9NopctRX02DnzcifFqxpHvwwdgjFM3lxoscnx+q23b3VcqXhTN1/PcbjyluPxHsnsfQ91XeHmyhr+v7tT8J6dnz/BmyyG4+BuguCPXz3LCkC5w+G7WblRXE/Y/fO86NAz+z5719Q1SLp7f7k9OZSTY5LKw8fgtMZHRZse8M/DFnjwwez65IJPjXhnZMoawT6AB0tgfESmBk/l90/49K3curfOG+qFewfkJ01N51BtH+0jabvkmShTt+x+/fiVpylC73SC+k0d+9Md++2vMDmQ9b/MWm0i2yxm/txnI7Hb4PDe1EIUjeyOzcSn9d2KRiHHU8X+sV8924cderslj3cikOYoRX/oLHIZV2/U22/l3RmKnmz3rmTdMWo6DWnzQ4pa/z+cPuDRpNmssD334jIDGhNA3hwzq8zLqd69zExLZPo1s/KsbFm52iGPrhIYCYmxvYv4spUs+WgOWcRnts6rB98HCCATAm3L1Ijl+E7HymwYOl+MQe2HhZTo+Mhvf4IlkC2Bv3uMrG1v8fR7jlWY+VJfvs8HzsW3vgFeYRocL9eYHfP8LIQsrKH52EVFbOT4Tf1aOTHi270ss0HOGrWk5mZMoj8/dHhj1h9XzdPVOMXyGE/jBb1pNczmER7/eE7eLDvsy7pv8vsPdNYKvvdhZp5dFykb8BJnyUond5Dg20vasvpXDf34jgbDr7rjvtx0q5GH5DRHg/iqppvTcJ2u39w8IYY3fdba0X+Q713lASzrrvPVf8SghNUxXj7guIHjxY/fgJ795GR26fs4IwwfT+Hcv8KQnk2aybRbFQdxx+29eAipTvkqOWGZwN9tAqvf1SjSqUH0/Ps/iUXHrMDDxyd99DOuMGmrR5Wpn3HuP2roTyACtmdJ5SXna9ff0KRkcuK/RV3fJmaEluHti5hUo/N9OglGrscTOX27SbITZvuTh46RYcDXPPtH0bGEYHr3Td9pF1Yb5WXTogikyO282aM+qW3jB58i5fWa83ltueBqgQP9N7tlskQbeD9lxnoV/FS7mJWzXf1jez4g5jlZViNt2+1qhEOgqE5P0fqEqbu8Ad0qkPPd6N3sEyB30B894y3G9Pmh/TD86pcs+2D54rra1IcqVrvnAHjFRsXZG9V7Pdsa+u4OyuZl4wmfGvdTRcYGNDBjO2v0nD/abd32ZbvG1/cOGXykzjYpztLYLyMxGA8G0zDTmPUF3c23OBUEL1Dbq2pfFOw21f1nSvSOywH5eAi2Vsm3j7qr6KjTdjaGn8gD+4mvplG4+mNG7NeOeLz0qzN0yKr3lS7d5rNKK0egN27Dc/k3BuW50/4/eNyP9h+I/BaVu3qvRvRLB7A+jB77Czd3pPjeP9NP7SFg2Dn1VhE1msW2aml8P5WcV/s3WogrTC0D34U8jL1ZvN6uceAk+/a3TtJE4xVhnauR8iCrtd/7v/4LVaWeaP58J98QeQZ7MGPQD/Ek/4YPvM7/7Zz5+69xx75qd/4Tf94ePeRx0adGWTt0gc3rn7xLxrbO0frG8/+3u/N3r699+jFT+bbMYmODwYP/8kXg9GYIHDmS1+duXnn1qOPTzpdRXhn/8FHf/O3o36/6HUu/tXfxQ/2XAc9wcQcmGwr7+nf+f25d9+/9dGPfPw3/+9w/2CweuL817+++uIrxydPnf7m35//2tfvPf7Y4cIJyUTj+OjKf/zr5Vdfk3G0/PbbG//w3eGJld2NM6UIhCw/9nt/sPTyK4dnT5/6+j+sv/hyNtPbP3NmGHaDIn30T/+fs1/95tazT1/9/BdPvPjyaGN1b+lkHjeiweDSV798+qvfHG+sr77w0oW/+dLBpfMP1s5UImhkw82/+eapb39HBn5rd+fKn//l7sVL+e7OP82J8FOf0oudcqa9+9jVstebnlgYnl5nRvpVFui86naON9YOLp4tO0k12zm4eo4YE+VjwJGLvKNzp6frS2mvMzm5tnP1UpSPgyqN6jSb7Y6Xl/euXTax3z99crK2SI0MypRCk812VDu+9+TjrhmNlhYPLp5lQAVlGsqs6LWnK/NHl8+PlhZ1M7z3zFMmYJOF+f6lM/ncTDY/u/fQxRGiOq5veEu606yi6N4zj8tOMl1aPL50xnrMz9O4GqcLsy727zzzVDnbkEGw/czjZSSGVh7xYNyZmS4uDC5s9tfXXChuPvOkDyom67CcuoCPF+b65zenqyvlTLd/8XS/OS+D6Q06LxuxbDS2H71iusnxyurx+TPFcjtEub/sRM9FpAzXAYuw2zkmuaLznSQsk0aBVzjjLl4yqEW9B4cmlbQdx0HZmKnILPa8Om5UcMbn4wwUGiVeg2bJimZNEIsi7NSmK8jOIU8rO9cNPRn3arxA224QLxvSZuBBn05yOz/bE+MgqfkJQplpzCk0KzjqU7ZTzhmODxk5rmfLnLMJqe6KrkApCg9oI5WB5Hw3XUHJ9DgsxhxISjPIR3IuI2hExA7olgWBY2HuBouQjj14LBemnIwBG4P2kNvcq7JA5YBlhB3YmdyEpQf7+WJlQD3i7j5rYa6hd0ia4zo2gvXNzNQ2edMOg3Xgxbbh5/6Cs0bxnWNMBJnxE5zRTdEIMsxccwMAj9K7+8hhNhc3wiqY02iORjAVG9gREm/tGMdQw2+GaTynvS6MeBH3yjKJ/aOBwYEX46ZX8BOON0DsF9G8cdjQB0cYULrYjGHunUR+w0QojVcdiR1Fx8g/rudqQnLQPFZ+LdH4Dm3kYUOwAYgOTbuCZMz8vmsWTJZJOrAB9vQR6PRhIh0dEn+3mIG1rqcRehD2AC5ItC+7daT3TKvviyGSMqgyTIyHjpw3knMFg6lLjsumcXrwgPBJ3MEiReLQ9ArHUioGeraMcBm2Sj4DRWxbYepORVExiosRgzr0pB8quko8UXeTSTDjuK3DchrJFHdog6TBGeGL0gtkuGQFqP0qi6oUB6aR1OGCoW2SkIxtsJl0D2rr6dJnKm4UfB7wFmgEpbcCIQGN9JggGwgZhZKvIOabZpSSFYHC0jf9YjFDYc7hSM8NdQhGptwVcRF5jA5Ac1R2jHAjOTv04YRXeZINPVIrLyeNvmuUFA5AazCKhQum13FP+YjaIzMzQJFktK9bIxjWQTZOionztLDjunesmzqoduzs0DXcxMldJkZ+4IMD2BwWM4aZEWzv1wmZwYOoXcJlEdM0adR8DnFZRtkIBqQBJ8miYR3IPNlqV3BZNKaDRj4MQIEDlPQUXcAtNAlma9GyfplG5ZQQG3hlFFViDYe09nsmbElhKr/KfVN6oU6S3FvGPLJRVPmLMKynQTYh1Ia8bszWrAdxaFqNGp5gIRw4OjazE8Iy5u3Dbp4zNKL1Dm7UsQ1AP18pMEsxHsHegMDCK9JWOdJCU3FsuxMZ1R4Y5MuFpS7l9V3etdwitufaKYwKRod6ZoyAjooRc4qznJCh7o6JXwJvgNqTCbaVJ7dRM2/x0B5VC2MicgyPdHfMQpnwvNEtzXIY4CpahzAg3ta+rizvhI0gj2cl6aGQl2FX2VYY9AdaEdrkDTKNVy1vuMQr/BltY8Lv72OFwGIrRnm4pPAM7Zh+uAZwhOHWAaktnm126DjoabZIGFGtFS1nkujmHVgY0mtEQRV3JF7EMZqGs9YuiEju29YRj7PKl0LsFrOmrKtpaLdFl+EcRMd1TwpziKI90CxFkUbF2HeV9krOjvXslMOJDQemUymU7wAyjNqWZYQfmJnCeTlnQ91Nw2LkFSlDhuGpi/usMS0i6/MD1cuGVKQ82xJzjJUoPDbdEpAUB7u6I1kDJDglZ0TDy4hv4w0CnaX3DqDwRc9rBKW/6GCXxnDKNyk0Ltze19ijCWnFWTBn/baNeB7NycxP4v0jxWI/go2wZCvOi20zKvwFYGUldvqABWwxTsBUnMJBpGKWhWsIEsDu7UNAWU80vCJYsm7OS8zYX3ckwsXCfP/sxvDUatFuTU6v7Z84pUN5R4lBZw4KtP34o+VcezrbPTy7OV5fjqfDZnpcJ6H22P4jV4Zry8hn249cMXOtYxEccLbbWrSN4Oj0Rv/chvX4g0evZQszYT4NqlRAmS4vjtaWDy6cKZvJ8MxGf23dhOa286ZhUjUa+9euZMszk15vuLF6cPkC9kn/5NrepXM6FMfnz6Sr80Rrv8pCnQ1Xlupec/upx03A+hvr4801jIxf5Ek9mcz0stWlo4unp0sLda+1f/VCnI+wNp6rdBwcnj412lieLi+V8729q+chdFE+xhS6SIxOrh+d26hajcHG+uDcKWx1UOez4929k6dNM7z/1GMm8YerJ7avXaOvv/5P1UU4DdoePppGHcMrS4t+a27hnXexcnsPnZtEXZmbkd/yg06IDyd+y98bdB5sX//Is2ORqMJO/QYOOpRVw6g3f+fWwltv3nvuGTPVjlXDqCvEEAg5bM3Fd7Y797Z+/Kmfx1HRItE4bPvs2LL4qL3Qub/VPjjYvXZxmvQYHU7DJqxZC4djvwUrlXvNqdcADoQsGvtNAED/O++NP3He4KxBw1HUpchMaTiMOqAwK2/8+PDKmUnUS6E/CjsCFDkJpkHHGjy6UU1XkjJuCj7I/Ma40ctFMoi6HtVrf/+9nYceGje6IU9Sr5EHrQT5qYgH4ezgW++NnzyFQlDScBh2qcCAJ7mXZF6sPAEFdr7MiXOceQutOpcUOdzgamSUpsRnE8/jPgwadXkQ6gjpkNdMQgS4R0pmqVW0Kyb32tCDcVPnB1r4sPKY8VXtoA6bNQVEStzyihRSZpUXAWoRA15Eq+gMVaZsNNwAGKes7zIGISOliBmXWqCxINZjfu5JXx3JtUHhRm0lKkIUEKIsTOJJOvZgoxLkMBqtpqCqtWxMhPQkcVyVAbs3ae+M6LgHuK+NF+UBBrpWVSJjSDKCJyxfLKCPWekpZmocSZ5k3siatf1RQ82nBBFXCyXinDS8PNCiLkWsseEek8JVTFOPOBabgQqEU6GXC418P5fTCWuAEFsfaBiDgIIAS6oCYaQnlOcjj4YxOzyK0RLnDSiPufIRDnHODfdd2e0MPzR0hRJRZhgAQaiHJZeYIxzj8rAZJ6YOo1JI5DPoVVNmfR8hRg3n0LhKlIi3LFMo0u8+eEwLKwNZU88CqHwMvdBIUwYU9jsKybTlIMeAaO1zzjkkZMThTXNZlCBbKHDeZEBOhaMcSeRM0pWjWa7gcA0gzxrk1T40nElIJz68cfQwdrbsSSIEdlh6zsiEA177BLOi8hANqCqNocr4DTEp5S3JLnAlYMaY80OrjfBI3WAudeWetqfbae1JLn2fexKklJqIuMJUR7Zea+oQgLGCni15UHCPh0zIYnSXB2vIRULlVvpMQWiwtL7LjTC3Fdkk1oMpwdSnUBvnydpLFHE4CMZ+ESKLSEv6ORR+cbCoqZUxrFiIBEp9bL1WEWUKW74zkzUrHJGyCpGg2pOUhYDBPIhf3d6suhljE8qaSlTG05wJ7VnFBd2LlRhnHaL9VhXmmUBMQOUp63nvD5clxtOmAxxTjic+0iKxQZaLAAVlJZ32Gsxz2Ejm0WyPUGD1eug4RNB5Pq0CQ4Eu/KZ3OJpMcLLGsxQ5QFTQ4AxqBmSDlFvaZdZeadJJpmunBAKUVpibFqq3FTZQrbdtYKtaWx9PC2a4iUPoHhS1Yt5aIIVSAPg+K0NHitp6QepbnTcqH1GICOXKZ3mZ9PtCLQ4rECreSMXEWgx4YgKANCJDL2u5Mgp5FRuhChQ6vzn2x7rG/eGFeq5AxjJKpEetJYyGzqtTEiUPonppknsE8ab0IXdSsdh4BLr1N/ZCMzfIRGR5UvgjYUrCm1KUVsjSCwhHpR+nAoqABD7Nd2PbxTpgNasxh8QnBTWhr3UvmNxoojkaJXXKhRcg6FHty9rnNvJKR4SnTBJXXPIQ116ImEIMi5jUfggBNo2oFpBwrTyXEsR8Wjaa6O4JGTMR4lpIjQn0AiSQ41gzn3NoHLAiT0XDc3AiwDa58CD188WJybAyYuQBxy1Fpo4YPhI4I+OVoQKxpz0ZAJejCgfahx/2r6AaSr/CgnItKq6VScoqqHwQDBJUe8VcajyuIbMintiGqWjt1cN6aTDys/lRaKA1ofIZrTBRsPKIRqgUHvVFrrMpb4IYG20VauOIGoM0kVa4HAkjPOxTn9GjYZssMIScGQvuE4TQlJk4tEU0P/qg9jYpdUWKIfIo5lAyxQQGOC6PZGvNpUEsBSc+d6RMufND7ATSuFm1YkwdOMydUIWfaE54yK0nJyyumTcJ26FoQiEmydzxd24cr58chp2MRlOvmfqN1G9LIVIcbr7xAxCwe5eudlk8jnqjoJ2LZOS1FQvNO0NI8PjRmdRv5SIYh+2cBkOv2U9mr3zza2XSPLx8WtADgupx1BF+y1E2bS8Ov3en35uf9ppTGmnhTf1m4LdYXU29eMoizMkwaAde02o6bM627u407ty984mP8IIEOBwH7UAkjIJBe65z8w4fjPaevGaDjo+G06hFKx6yvUHSW7n13uL1w53HrkzCtirgNGhpXodQTIMWHBarb715/WPPpl7iSDnxm84vcSBHjW5wnM796Ef9Zy8PVdgh4TjqkrwiLMp5EP+XdxGS/xwRtPajv/9v+XRKPTz72tuNra2wnK786I2Fd97723/1G09/9t8Hu3uHFy5+5N/9QTLoc2LjG/cWrn+AIFx89dWNl1/68vz//syf/Gl09+6Ds+ee+ux/aN+/H5u8cfd+590bjpCFd945//3v/d1v/K+P/vGftO5u9ddPPvLlL3XeexdhOHvv1uxLr0MEZ27evPS1r/7VZ37z8T/609aHt/RMsvTtlxbffxczCgV+8t/87mu//s9JVV354n+qhTe3fX/zm98geckCfPaLf9l+sHV44exHP/Pbr//zX3EIX/mzv3jj13+NH402v/XtYDRWK91LX/yz7tbd/UeufvT/+q0bv/gzdRCc/U9feetXfoEU+vTXv9E6OBiszj3y+c/vfPDe3eeefe63P/POL346bzYuf+Evrn/6pzcQO/2Vr3Vv3tq/dv7hP/6j5Qvntj7x5NO/9W9u/szHpz/xK9tvhUFUsuXJ/fd6kRku/aT+8YszNNZraLD7QtiNVdQ/znaWlLD+6fLgvaStR1EwefBih4RulUz2Xg2DyHXSfnG3CbFLzuaDd5O4nM5+urj9g8CGZCbO+q+4UNjZon98vwUB9HE1eI8F2aQXj8YvxH3htdvq6HUc2HJ2eNjf6+WOx61pfGvd5u3m3Afhh0u4DFvFduGEnSwHeAdNk3hLiFM3g521Sq36vVv+nTk0iSP5Op2yvNjAZAuUiX+3gcWL3u22qlb95q32/aadzAiV8sqrhhu+etfJhO6dlOx7wVavKk8J8VJw2ATpYpgdC0Sz/hlevwV07PbPxSf+KtpvFmqTg6NUjY/f451RwU/ovReTufN5wrKttxroqnbldHg36h4feH7/+OYs3J80T7s7LzSXzuaRmO6/3RKnc4aK/duN2eMp6eVHb8/MbfWjy+idH/Z6G3kcT47eafYWM3ZX7t6fa+5ldk3uv9WYi/viYbL1w1b7ZJW0i+O3Iu9Egbfr0c0k3M3Ayar/ZqMbjeNP9bwP14k3TbJb5bALoUULY7t7KsknKXuD3XrOUGvxK+j2eUpzv7hdjXrUOegOxO56OJ2k/uvhh08VTDRbN8Nba9TlSfp+mZ501vfFPt5dao8Lce4t9v5DVdD0k7venTloTGe6Z0ez0nShf5/tnPJShMLvRu9cLkWb09fR7qxTQtjhg/uougFOsMHoHdQ/jtuztbyuBjto2Y3QpOxv9zqu0DXd/zE9ERTV23X/qBHxqbyvJwdeXB9Dp44/7CzWqdTk4F3/FB6h22r7oLMqptUDM9wLVqYH2E76Wz0/HSnEH7wVncOD7I48OOg2wSGdBAf3o+V0bIU+fruxOjnQnn/vjbhLJi3XwAcrS4PbIKjrvY3o8JZZHYO7n+C925HYttsXcPdGgko5XBRHO7A7tlubs1vX8coI3v1p0LkHol23vek1r/sHhRkse/1DNJva7TPJzlZ6/h7/8BHd3QPTHbB90ktuskFhjhaD4yFcHfH7pxO4k1+46X/wdDpXJabvb6347MNwOFZH5/RAetHtox84xdhcJ+u/bAR2i/XgeLdVKhzgavg+9QbTXjKavhAdQa8zK/s/wqyWc+P94VFnXIuGJw/eZ/6w7EbD8ff5lCSdTlW+YcsStKrBZIcMpjxG6dG7vj6Ga53s+Dt6iBpJMC3f16ok7WI0PebDPlsF46PrYdEn55Kj+rtoz3Rm22XzmKeDDVF/ACClD84p8oLYi1R6ltNX/EEDTFb84u0AwfRoE1bvAYzB9pVg+cvJwOblBoQDfxra8cpSOnYEqr1T0eTHJuRu65pAL+IBVtOTTO4lNZDjVTG+ASMNtjZaxy/rAJW7V4F5BeSeG2w29A+FRGB4QqQWNSy8u+nVd/bi/f5LuDGfs+1ifH8+2c68k/XeO40Zcew9DbdeaCdLqjOdDl6P6ELN9srxrYZ/v4hPlf23Gl06aj6Hd74X0FWQyLz/IzzXyfxh//DDxH9g+NWq/xZrkknnY+r4hbheYDFU0zdp15/6rdGD+4sgAJGQD34s2qwMPlr0vyvkomihevoWJF4Vc0r2zibHUpx7m394uaKdoHPHu7OMatzIDuFottLL0NtieyvhgGXh96J3lkoyw/lreG/W1GFYDvG0SbIlZ34MDpfYtF0F34zePVWQ5ZgN7F4HVI1muofqbjldFeZHoL/EJnMN+45/e7XAJyN44IZNV/Qak76WTddfQupVOz5rhkue/43d62DwIFwY71E07N+dEZMRismDV5JNO1KH1f79TmL6NPMOtuKldAwj2X+ztXJ8iLvi5iut1UfGamL695rr0yG2ZHy/RcdTNWMPXw+W+8duTuy9mCw/nNnC9W+HTdWHRg1uRc3B1FtQxz+K5o8maIXv/UA0nzauqo/fp8lkwCN78F7YytTcie1P/Ot/tXPp/IdF+uhn/3h4fjN5sHv2776SLS2+/Au/+NjnP68aydf+l//5pz7zme0zZxCAT3/uc9PVheZR//SXvlq2mvrX/7tH/uhzJvBf+hf/w7Of+ddHJ09ahB770z8ZLywe7p4//eWvSuZ9+3/6F0/84R8ixv72//zfPvE7nxnNz3Onz//Vl8o43jl8dPOr3wDavfA//vdPfe4PMUJ/s/ovn/vdfzPqdOskuvy1r8bX7xjPO/vNr6+9/saf/7vf+ejv/a531DfdaO1r3+nev+/rurt1Z/ZHb7+G9dr3f7j82ht/vfxbz37uPwTbO1UnWv32S93bN5PpoLd/v/Xqe6+CXz/x0iurL77yld/5zYc++7nm/Qem4S28/eHsG68H6difDE/+/Xdfwf/t0htvrn73B9+Y/ZfnvvjXvZs37/DC3jxef/21oEhpXVz826+WMzNHhPzTkNx//tNVp6sC/4PnPj5pdMpO5/azz0BCdk+f3btysfCjbG7ug6eekc2GDvx3fvK5LEzKKHr/2WcdpYNTmw+uXUk9P+/2rj/70aLVgoy8//xPl0kzbXU++NhPAIyP5xcePPxQOjOfN5rvf+xjRRg6jN98/nkVB0W7d/2jH7WMjhYWdx66POrOqii6/ewzo7kFgPCbzz9vKCpEtPXow8crJ2QY3XriyeOVZVFXb37q59L5HtLwg0/+VNbrlEG89egjxysrirLtxx/Z3jzHa/nm8z87XZxFpbz+U5+cLMzWXrj70KX+mTOlF+48em373CWRpW89/3Pp8jy04PpPfHy8MFczsX31ysHps1r4uw9f3br0kDedvvWzPzdZX0aVuvWTn0jnemnU3rl6+fDMSZ5P8TKjKxRX2t/0qsUorsYhF1LIAAAgAElEQVTBjLPrEaM2aihwJnHW4WUqVijNK7AeuKWQmTxuSXAyoEDxxJqzDWx1MGv5CvHrCqwLtxQKV8VJpU4lCch5w6nNGGotOsqt+WGVeqtMn4ipq72ohqeC0GZ+bPD5BAHAe7Zea1E9Ju5ouGIQHHFQTDZ1yokw/XQlxyZH3lAuaOkBBAaTVYXBkMn08DQgQlmQThYzZAqEj/IlWPuOw8F4uXIk5XIyOGUgrSHMy8UUo8KSQb4MS2GxmdRL0zwwQqbD09ZyDWxenigBrSjYl3NV0fGQSavVPG3H4XTsnaGoRXyb0VWmZ2LqVHAC6sUwyKboSitqamMwP+fBFk30lCwjs5RwqPkKUkuhP524CzHrQKyMOB/ormjICV8hbikQQCaLpjzZE1kGLkZeF+K8hGdj0OO+zYIFABc4MzVdQvV6y8szb4PyWeRVFdgMSAsCW1i/GK9apGXdHMuZAhdVOpuXCxTICfEOikWNYWFFMT5hkK51OEjnVVSN85minMNOTiEbjJY0NSPsF8N16IwGfj+dl346Mt2JnLWKA0dHkxXJ9cCyanTSMatkMJrMqSAf6HiYLxFFNSfHxfzUYAlolq1qT+fE1/ZUjBPu0wKcDikrPVXYqy3iOw8rcloQohOegXXfJLyBCr5JYUw8XeprbcIshYqf4VSYQFTwZCgbngCVf5qaXhAWE/xwK/SU4ZSc8Ynnmnhq1wPYDRiQYpOWnSBMR/ZqkySIQYvPxSgADZTq0y3VgeH4eLQuTUvjWuWLad2CpDiUnTSfQciVeiafzCGRTiZrtW1XJK+r1VI3rNVT3UnLGYtVLnvleB6JIsuXM9soUF6ly0XdxbgegsawmpHUFLKTjxexl03zuVHWBY1smC4UVQ/DaqzjSdYrhZyYXjlYYiLLZO9oMhclahxHpdxsNGDOIqfPxsAYkdRuPQjqzF+hejWGToZBBU6HIcgDT+GLCUSQty1c98JySjrGbjSJwL5futOh51LKtL0YU2j8liUnaKhL3jVuLbQeTljBTzPEIRPGXkyoUTS2+KQnbBm0td1IlMcCWoFLMUIVdkW9NAa0AnCQLdkqcFhP5OI0i4xXpcNNY3wDVVGeyC2XBBzpubLscqjzajlLGzBIB4NN7QIFnUyXK80VUgflvCraiNq8XiqmHepPB8PTEgYV0lV1ojKedG5ULdQyVljm5VI+6VI/m0zXM+AXTtb5Ugli60GVzBl5qsOKCp4NvFmEswKdicGc55ksmLNg2aO2ZrNIbjRFmfvrmM8jv6zsZgDnPc8WXteAE35kMjaPqvUGzwq+4uxyEOdjccrXSyEzldc2eI0HderNI7SZ4FqzExAuevF0gJawXU0YsaKl4Sr36wmZY3pV+NnINidyzigGIDqerCqqhxAXg1OAQqXEeDJfe9VIe/1imUhqBD4uFiaSKAKn45MaO2nFpJwvqUmlPyyWccUsdqNiJau4pjAfnrIAasvTYkkRm2G6Xy+YsuEhOynWy1I4pkfDDYOJ0l6ZLTtgc4T2sxUk2yLKJ+SRdhhogwk96+EAJWgK1gSYCRlQYoNWM2E4HdkrCW0g6iw9F4EIJWCK1zwwK3xbJusgW54J0gm8kniJQ1LCcwls4BBmbIWiLuGmphusXGpG6USc46INuFFuM8AtGoIULTE8SwNdsA1azDeC8YieJmYmLpl/fOb09kNXjQiONjc+fPxJXla3H3ts++KlsCpvPPnU8ebJCtGjkydvPfooIGRw9vSNJ55iZXn3kUe3L13ys+zuU0+NTp2YNmb7q6u3Hn8CObu3cereE49RpW8++tj98+e8Mr937drBxXN51JyuLN154jFFxcGpU3cfe5RJde+Rx/YvnIEO3r185eDy+VyE0/m5688+ixAYL5+48eRTGILDkyf3rlyYNLuy2bz1sY9M2j0ZBG9/8pMAo+Hs/N2nHtdcHK1v7Fy7PG72VBjc/vjHjueWIUEf/OzzwCOHi+v3nnhM+sFwfnHrkYcm88vS925+9CNHvXkIwVvPP6/jcDq7cOfJx6sgms4v3nvk2mhhxTG29bEnHpw4i4B962eet6E/aXZvP/0M/fHr/ySg0exnP+VjDSNRdRoIAhfwuhMTK6tGw0YCQSeTsOi1qVNI4KrbgAi5kBfdNkNGB8JGHGMoG3HRbVFoIEWyGyNgZRBWvRZBxkW+STzgnIn9otcmyBKP5jNtz9Y68PNumyBjAmGaPsDIekx1Y00YJS6b63KgrC9UO4acWErKXgtSTJFJ52YEVA6hbLZDiDOCq2YEBQWMqlakfI9ZNV2c9aAGhGQzLUqBEcI0PcupwkS1AhX6TNWTxTkOFQAgn5/B1BlKVSd2HgMIqGYoQ5+qaro0z6AEzhWzHY5V6UW6nRifMi1pDAQ3XNcktj6pLQQitJwpZHXEShQAAAAPAWeSmcqLrEdqCACLnOCaQBt6EvuAW0lCyIXGRrHQ+bTEwNHAoRgzUwe0xiEUTrIIoAD7pmKe8mmFraEBQDERtha05p7C1pAQwQCFKsesMDHiTnKXQ5Fh6CgqTASYrTGuAjwyCACqbOSYk9ylLpRCZZBaGzvmKoYLynIKFICViQF3tXClCZVncogl8CWBClBFWcagYjCHgXQUCVOYWAYmR7ACXs2ANMwGZGyQha6CgXQYN3WKEiiwhBgKX/tEcih5aLCA3NSohRp6ojGmkfGwhNix0HKiqJMiNIhjrivSwh5WzEgaG4EkIEAEhhBNgI55oX1KreItJGDFrfRiK7CECIjQEGYE1H6gEQe+LmGLClwjq3lsKbPElIJkztPEKOBpwiXTFfYrhjMMLBA15tI5y0nuPE2txELaADJZYlEwlBKnkCddhIStGMicJ4lRUEjnO25KwKsIDmtECJc2sNxUBOeIV0IVzrfOd1wXhJcUZwQYgCsUSAgdxZX1FLeSc+N7EjvFuUEJ4VBFuMYJDEyBqQMNwqHiVHmeYkATrHjsLMWhK0kDBrZgxJDQcmwwA54nBVIC1SxykEBha9KEkZxoSkSoOVSAOs9XBGoBax47SJGwFWmTwJUEaB5qijSmDsaYESXqTDeMsAW2tYtqDgsIDfJKQiV2NaG5FYCq0saagxLrGgRl4CY141QUCNfUloznljuuKxVpjmpmKhSWDOYIGiRqyDRyirLcMitM5UIFuON1hoOKgwwDBXwFPOCZgpLUcstVaWPjmA3rnPoWJYSZOiQVCQF3WvgWRtg3NRXKZxWzhgQAJUSYmpOK+xpbjX0EQ+Tb2veUx2tktfAcSjB3MmCaBs7XJfQR9w12mgbO55I6w6iisYMI+bhmoQtsyXzHhWbIIeECUXOgOaltW0T1FEJpA0WBhFRTljMoKSxgUDuGuM7/0UEYVs6rOawttR4ZO2igq7FfAQKZyk2ihSkgkCisKagg0cQrMVTUFtgrDEVc5rZphC2hrYmXMVAphhnPAdKey5BfOwo8VehYCVBTV8GgRkgRKyNeWp9gp3kDcVRzI73EClwDBHhoGTUc6sDXSADfVLBJBFHIaBFbn1QYOBIC5CNhKt+TyEO+LUkDI4ECU9LA+KTC0JIA4BALWwkuKVPEWtLESMDQlCIwHq2RM8QHOESBrblvXEA9XUJWRXAoEUFMmtAJWzGUQ68SqgDC2gBwUxJeMfL/O0hB6BiqnC+FKSxTWNQYGMglIxkFiqEChgpiwFwBgtrXOSAS85IApbmN8UBCiEGBgxogQF0OIsV1CagkvMRAIq6xqBAGwlaohWI10YTw0AikIHEi0ARpDqSILWRI6Ip2iA8q4hSPNEMaUvCPGgp1IorKF8JUtI09V3KovMgwpBAGXqARcQGsRWggBaH5f2l7zybLz8O688nPP/9vDn07d89MT54BZgYYACRBihRJWVTa1cqqctXqxfoDeL3adZLoIFsOsmjJsiTKFCGRFIMIgGACCDFAIEEAgzCYGUzu6emcbt/bN/7jk/aFv4D9QucLnHenTtWp+p0ElKkFcwSU7SuOcoI1DjCh2tWJ5UnAkK8jWKYOSCDGeSlQvgUAUAUnD1ymRVyvas9mIkmbNcQhJDitFkUx4EYqj2WBR2SeNKvS4czkSaXEmUqpLYp+XgwYUKLoG5dSJaJaVQUO0zKrloxDIMbC46LoQwhk6OrAIlqOa2XoEIhgWikAjwGMZegllaKXR2ngp5UiAVIErg5sgLGyWVYNNSGYwaRWsoDMg0AUXExQ7rsmtAwlxmJZLdQEAwzSeskWydgPVdFBCGS+q4uuQRAwkjWKmlKMTNyqc5Xlji1LHkQg8z1ddBRCgBNQciLLpUhHrTozuXKd3swMu3Ll7wQ02vuFX/nEZ/5g8Qc/6k9OLb74g6XvfX84MXHixe+f/cqztz72sY/85z869d2Xb/zcL3zoD//46Et/M5hozr7yxtlvfvdgcnbpuy9f+NKX7z794Q/86TOnv/6NGx/7uSc++7mT33kpaU3MvHbl1NeeP5iaO/LDV5945gt3n/7wY3/2+XN//fztJ59+7K+fO/nsN/q1idk33zz35Wc7s4sLr1/5wB9/9tbPfPSxz/3luWdf2Lh48dh3/ubsV589nJgs7LV/9nf+fa8+Ea6sf/j3/3Dz2Mn5G7cu/ennxoVKuLfz1H/5UwixAvCTn/43w0o92Nz90O//18OZmeqNu5c/90xKbFfmH/jdzyCpU8v5e//iX+We5xwcPvUHnx3Va+X7q5c/95eacCTyD//ufybjJK5Uf/6f/XZme6w3/OBn/igpFAo7ncuf/TxMhebso7/ze85oHJVLn/hn/8YguFI9tf2mow6BQ+IHbxT1RuotkvVv8ahDnZrc+hHXA+Cp8cNrxbxtbDtbfaNoNgWfNxvf4uN96tXl1o/tuEtskGxe87Md7fli7Y0wWc6dE2z7m3h44PBJfPBDHbWRjZLNG2G2iWCZbP+YinuZc5zsv4jbOx6fJJ1XVLqHy7C/edPvrxC9wMs35+jW0rA18G9Uwf4xZtZVf55sLEkewYOp4O6kLm/y5XmzfSaqHXrLTbB7EuG2tePqzfOSR6g35d6eT+pddm8Cb5+JKv3SWklsnYG4S7s+Wj+PWA8eNvi9U1m1Yy03wM4jINjGq1W4e46IPR77euUiIAM0qFh3TqFgg65N6J1LxN7oHYL1Vx2cC8jg2ksWpgYmauM1j7pG7Mr1d0Mri0r54L3XWyRKkYfWX7QxRTARa686lCnYkWtXizhOOczu/7hE+zErs41vcwURzOX6T9wiikRPPXy3jKOcEvng1RIe5LgE17/DlUZYq7XXPaIlGIvVt0M8zCxXP/hRqDq5fbTgvLME0zKVXbN2Gnd9xA7R6mN8249bHeftR2G/acJd6+4ZMKgg0zNbp+3DYuKk1vJptuHFkx3vnXNgMBuV++6NOdyrENPRu6fpQS3xE+veSX8zVPV1dvWs7B1Ji33v7rzpT1K9hzfnwe5kUkqd+yet9Vo0te++fQz2jxh/11o9qg5moNXZvw+6b1J7BvbeUdvXXDZLshtq44bnWqnYyteuhpZnxm3r4BVA58nwqty5ERQn8sFtsPF+4OJYdMzq26Fti3Gb7PyYuS2d3NHr1wpBLRvdNZvXg0D3aS+7/VbN5lnSozuvWu4kiG+LtauFYjBM1vH69aIlxzoCq695Ds3yMdt+hek6LfYdun6BpzERI738NOnnMhDOu49pQ4DIyJ1HEUitkQabj6OxIHAElz9sD3JjH+LrTxtNjc7x/QsEKDjM4folPBSIRfDu0+wQpOXIe/uSVhYECV1+FGUS5BquXXRiOLKl9/4F0kFpdey9fVGYkoCZffsEjRXSOVy7SIa8P5n3nsv6O449R7o/lP0d6pFo+3YQrRFcRNtvsPxWbh8nBy/jvTWXz7LDvxWjLVbFvZ07TmeZkzrbfZOmt419HLVfVHtrvtWCoyugvWr7NOreJQf3rEJN7l51eteRfYYcvCgP1oNiPW1fJe0Vx6fR4QPWvm17Vbn7Lj+8joOjqv8q3FopuC0d7gRm/SJGAzMu8ttn8lKfr5fhxqMg2MPrJbj9KBb7VmrJlcsIjUAUWLfOImeH7pT11mPI3qE7JbRzkasujhBYvgxVH4qA33xEBiO67YP1C4RuWQeB2X4cpwMkIb532ZJdkDJ8+7LwxuQgAOsXKT0AbQtuPo7TIYII374MkDzU0e73qadTM8pX3i2BoeAsf/BqCXZy0oAb3+J5ihlX6z9xQa5wKlbfCeFh7vjqwSuh3MnYLNt6gaTCITbYeQWBSEIh198NQVerMtt6iajtzJoj+9/GvdjDDtx/FeKR8sXwwdVStqvhpLv5XaR3DZuD2980/ZHLHHPwOo66iJewvXLGXyup+rp147TsHEuqPe/ujOnOEbNPt1pmZz4LY2vtmP2wGU217XcXYPe4CXac9QW5v4hgl+y28M4R43bw+jG2Ope0dt33FnT3FPA2yYMpuH+U6T3SmwIbS9rt0o1Fa3UBlZfJnaOqe47wVbI1g9snmNoFnTp+cNT4Xbq9QFaOyfre4P1881roqYE9jN+/0rBRlidk60eWXYPZw3zt7TDwI7EJ164VeR7BXK38OLRMqjXd+j4nRZBvqbW3wwmnO9jBq1dLLE+h1quvFpjOpEKb37doCPI2WHs78HicH5iHbxWsLMYYrvzAgyI3hOy8zHXJUfti4w3bQ6NsTFbfDFGqiuHoF//vf+a1D4b1iQ/9l//mHBxIQB/73F+W9zs7U3Of+vS/rr5/a/mpD/zyP/on3u5Btzn1kf/8++HuXu4Elz7/xdq9lY2lU5/65789dfPO6hMXP/5Pfyd8uN6dnP3Qf/1jf3tfMXbx819o3Lj18PylX/wXn5668s6tj37s53/zn9eWVwaTU48981elO8u55174iy9PXbu1ef78xz/972Z/euXWxz/+8//k09W7D3aWTpz/668f/9b3dk6cOf/cCxe/+OX3PvWpD/+Hz5z83vc3L1w4+ey3j3/rxXGluvDa66e+/kJ78ejJb3zrwhe/eutjP/uBz37+5Avf3j53bumF75194dtpqTj/ztsnv/Rc++ixE999+bFnvnTnE5+49Kd/fub5b+0vLS3+6PXzX38+9sLmzVuX/vsX9o8dX/rhq5f/7Jl7H/jg+a8+f+5rz8ZL85WfvHfhr74a+2H1/oOn/uRzW6fORn8nX4Ra937pV0zgJ7Xqw8uPx2F5VKmsP/oIhLA7PbNz7lTuBt2p6fsXL+aWlVXLq5cvp46fB/7yE09Kx+lNTe2cPZU43nBy6v7jj0vOs2Jx5bELsR8O6hPLjz2ubedwamr73GmJSVyu3P2ZjwmL52Hh3oc+qD2nX20sX7qkMYorle1HzkZBcVxvrF96pB+UdBDc+/CHlc0Sv7R+8ZFBo5n64YPHLo+DgGp99yMfSWslwZx7Tz4VVYqJW9y6+Eiv3kwdf+eRMwcz8wCTex/+SFwOFLEePPVkXC1rxPfOnDiYmo784vb50/sz84Tx248/HjVrKjcrTz05nGzmdrB57szB/Hxuu9uPnNlvTtpa33vyycFUUyO2fOHRpFUb+NXdc2c6c1MMAtLCZIJBaeCMRSoAAmzVIZ6iChOvkMlpV1KLtwhuUKa0aSI47RJjeB2RaQYJdkoazTjIAFwGaNrGRlsTELU4QwZVkJjxsJZOQYN5F2NkVY2cC7hIWROgKRsYYxWNnPcoFFao4TQ1hKIGk9O+pRNojfsTAjBF0DieSiLOEYxHkznASrGBqUUxQcpO+zMGg8SAbDSdYiaEpUeTAqIMs0E8IQEQEI17c1DhjOBs3BoTkgoLRI1EYS2sPG+mkkpsJUljnHkUoXw0kxmYC2KyichwJSwByv2hQyHPk4lxHjqOzug8w0WMCeQzVIWUIsOnkarZDAJ9xHWcJMc2XuCsiCCAdBKpCkME2jNYVCwLATDPWIUqQNEcw0WIKLYnEagySKBbz8atChaKHXNQEVoQgCkEGzaBhrcwqnMMjDMB8smAagVmGapSohSfRbDGDFDAGY0b0hAiSomoZFDqtDTO6gpCLfwoqWc5JdgexI1MAZR68XACMhGL4iBrAKgzGcTDpmIyhXw0msg1wcYbDpuayTT1B7CaDIEl3WQ0o5kaKzeLmhkGOi3JcUPSLJXBKJ1QGkjtxHEzywg0Vpo0EwIVDQCa5pAj5gF9ouSoMccaL9nUAcDCasFBHNpOTqcpwYozhRetzGEW1fikZ3GJLEznGWHAIpIs8NzCrgvJLFahw4gxRx2HpJIwOs9wQKgLSQvlPufc8KOWtLEDNFykpMgQgWiGIh9jG6E52/iAA9lvxKKokdRRK1VepgzIy6OsogFSohINSohBEzXGIlQqB6PamIRJTLiqjPKiAEipQn9UIwbStBHpICNGJ7VBXgYGSFEcR2UlNADFflwzRKtRZTyucSsZpfVRXgFa5FkpjWuKmFQVh4MqIlrnlcGoTHwd4zIUcx6GyitoNGdBAJ1AqMUC04LXDJqyADJ2Cah5l8nUdhWcJZozVOV61rF1xqsaTttECB4Ac6JomZSFiCxYBBtQZmgSI6XtgsIzHGLDQojmuaTECiFdtJgWNIBkhkKCeQXgWSsHwC8iseDYMBIUjCcSQ6XmedZIBDMAZ1lznHqEwHw0mwGSGWKSZqS4zqk21f7YJYbJtDmOXcSxPJyIja2hEnEz1q4yNE9royzACCVJKxm6jCo5moyMIzTSSXVoXJ3ZUNZHmQuNzmXtcFR1qcqiqdi4EkKZ1gbCBwhBr5rFrQJCiM0xXCFUGDiFUMshQPMJhJocE+TUtZwKUKbQDEMNTqSyZhBqcIYUbBAx4WAEnAbQLYcCwyehnCpYaUwmEZqwgNashtWUxYHgNaMnGIQYzVhqwnGjiE0R2OIIGNpkaMaxdUonmJhyqMwzdwCq8RgR7aWDaUNUKlkWTaYYqSxUo6ZkKjV+P25qBYxgSTqVpARClibNEUEi9VVai5GUwBnFLamQxE6SNOPcpoDn0WSmtcwcKeqxITrzMljuDywH2WncihOLQZQPJ1PEVOaqtJ4ArJWTZI1YeIwjo4+6jpXm3CELnHoQU0inkAg5ocZaZFlgOcSYRU4KFBgD5y0UYGIhaxrJAqfEeK18UC5bMufHOCpghgGYIbDMKTV8moCSRY1yZ2HSKHCVgwULlwgGms4SUqIEKjDJdZlZMHfmsKy6lpZsnoAyy7m7e+zY7onjqeV2l45snzxNhdw4cWLj5Ckm8vULlzrHFgS19peOr585qw3sLM6tnT2PINo4d37r+HEmxOrZM8PFmUzTzpEjDy9dghjvHVvaPXUCGLB24dLO8RMsirceeWTv1FLOnO783Ma5szm12keP7p06QaXeWjreXjqiINk9/8jeiWMZd3uLC2vnz2GMDqZmVh55VBPanZvfO31CYRpVq6uXLw29MKtUHnzwg9pivYnpzQvnACKD5sTmxfMj7matyYcXzg1LNVEIHzx20XDSnpjbfPRc7geH07Nbp4+Pg3JSKq1+4Imx7SPHufvUk6IQ9CuNjXOnUz/stSa3L5zLqC1dd+Pi6d3GnPbcBx/4gAy8UbWx8tjj5Np7fwcTodb9X/xlw1ji2FGjmmCehoW0VFg7febeY08yI5RGw7DQb00iYzLbGU/UVpdOrzx6MbcsLEQShkmtLAAdh4Vea6rfaA6PLEaeBQ0YFcrDiZaEKPF8GTor5y7cfeqDxmgDYWrxQauFgRp64bhW3zpx8v7jlzE2CuDY9eJGJeV+blv9eh1QNObuuFGVlp1ye1ird2emb3/o6TgIbZ1HiPcnmtqxYuaMKmUxWVZnF8ehFyMr53ZvYgIxFGPebzaHE83rH/hIWi0QIcZOIa2VIidIET5sTaGQycdO9uoVCUhM7XGjlntezuy0XOi0pt7/4NODWt2RaUzsUaMOHTqwwqhaTsJijLgOmcVkRh1QYK4ZiEmEagAomFketyR0cWQF2qcWTVTLmIJhBEfYNT6hrhljB1vGFKmY9cCEzXQWA0uXHObqEXKFz1ABsWKCS1IBmnJXBtx4xECkfcZdKQoCTxMJGGAMWIC7JqG2LDggoBqQxEaiAAGAkiNox8wblArbfdcWyJEW5rQX8TBzaRYA39qvFTb6nAPAYscWnhGEZBxjGvWadNjKATQSMcmwCDRGZsQd7Ypi0A7KuykGMfETRpEbp8yRjEgvL7D9sLSTWCbFfmJzi/USy0sthuwo4pYA2JQYpTrmLgkg9oWeUNxWKfFyymCRhnrYcyu4QAhPzaTBvtGIS+5wT+WunWAKy5xyUQ7GqkgMZWNiIx8iB2fUdmgmHEiPEsE0MnhMLFikzNIxdbGPkJPrpqZ2nlFXUAaK3KIiwg4oMuqABNnaktJWGbVzByI6rhU2kN+PqZMiR7gA2lmMXW1p4yQFf48XR0PuKMOEA5GVxCRIHCR9Oa6prJwAiBLi5g7KPQgQzhzkwI5TG3nh3gEvKMQyCwE715gkLs89MOGt8GInQizDXm5jaMcx9aSNlCMFgKnjskALYgnHQgGSdRsscsMIkTJyPVikEmNlUcsW6XRBH/FsGadWoBiBIcJIjyyPBBDWaHaswIgU0Eo4d3yZcFcyggvY87LhqSZxgJYgsh0WGE25si3LFUk1NEc4cBlRMrE8WkCK4ZTboMQMIzkkaQA4jBPuS18xPiyXNqCfRMjPmQWtJLVZhnnqGQsd1svrwE+VIQOnrGyhuE6IDax05EMfr/VDRDEYIzf1DOJ5QlzpKuaMqoUtZA36vJhhK/WBsowxWHqGsGG5tAPDaMAKGhHIs9QmGeGpj4QDlUbCY6BADYDAgjjE2UwgJx2NkQZIe4z7OoKO8BkqENiy8BRjQCXYFqEDQyYAUh53eRIvVMCCozXIMZMWIQHUEGaBY1lCznr5QmCJJKaBtIgV6JR5mhPkQ9Di+VzAgMixJXybezIhbs4JLlIG9Ii72hWAgIRb2s5dp10p7xSOmtUAACAASURBVMTERKwgGRGeREiOuSuc3LPbpeoOxOmQFlOLYzuOLSclRHgS2nF7FhjXQIVGtgvdVFGcMwZ5hNykVVztO8AgFjMHOikiamiH2sq53akVNzKuR6ygCMl8jbEYE0f6CjCdE8u1UuE5KXNMyGwcyRkAfIMJjakDfQRdGBMb2kBTyeaB8QHQMCE2LDDimCFyZcBwAZadEQtV5PgZpKpoa4cYCGHImCNkRaImUZAbyrBtiA0i5uqSbbjhrUz7AhMWE1cFzARUQKw8AnwGIMkd7JDuwKoIF+WBKlrbhXBvSLlGPHG58IBAJLMxZFHo75Uq7TGiYxpKDrWnDERjm2OWDpuq2wKWyhLipRaGbpJSV3Cs7bTi7NjFdspRgoPUITbpjO2S4AjZEXP7lcLWkBEFrdSxoJOllCcOR/Y4JZagFBWJp6O+XyUexF5uWgbbRiBH2pblicTxUkRQmREY4QUELSiglVgW8aG2rZzx0BpHjRI45ojA4jIbUhcXMGIw4Q7xAbAzMGMIzBIrVISCEuNExNTFIUZupicMto2GVFjccUXs+hnCpmIznGeARfVaUggzSLNyoducvHP5yfbcHAQAKjVs1POCnyHWr1bHU018tCmm60Pi5AAN6vWoVIZGj2oNENCrH/zE1slTSEgNwLBclr4jMe1XG6NKmZ6cGBydSRUUkMSlYlwtp4iNS6WkXLz32OWtpRO2THPEh5VKVglTSEdhMarXsMyHhfKg2VTAxGFRFNzl85eWLz9h5UnKvdRxRtUKwHAQFLNSuHr2kbuPP8GAUJCMHDdpVFLupo47atSSemXt+Lk8cI3SwyBMG9Uc8yQsxPXS5sLS3cuXpWNRIcZBKW5Uc0IjP8xLwdqJs3c+8CFfRUO7lFvWsF7VFI9svzc/b11543+V5P4/VbAGn/qln//M7537zrcPl46e+faLj3zzG725mc7UTE4tLx586t/97iPPPfv+3/vUx//kjx597rm9s6cPGlOKUmfQ/9k//qPLX/va8tNPffiP//TSV79y6xOfbN64/rO3bq0wdPlb37rwta93Fo+ceunFp5/5853zp/cnZzXG9mj89F98/vIX/rJ3ZHHptVcvP/OF/sz03sKixtTK45/9gz98/At/uXH50umXXn7qzz9/OD9X2d36hX/1L0cTzdrDhz/3n/7T4eRkZ2ZWcJvH0ak3f/Lxf/tvASEY6F/57d+K/aDSxL8+uNMBqPqjKx///c9kQeino5/7V/+aGLPx6PnIDm0ZP/Ld73zkM38Y1yvlja2P/6f/qGzbrqD/M1mDnc1kK/7f//k/VY7tDYef/Pf/Qfn27vzRxPWJEAs3rv7ib33ajcd5Ofilf/pbBOqtieOHP5K8G5dpb+8V5m4ehv4+JzmVqdmKhq9CcpAUQW/vCid7cRls6oblrawgOxg8l+jtPKwn/e/nYC/3G/k4LBqAint7ey9DtjpwltD4+Thbl8WFzOttAST5vXb3/QCtxbyq4+9n5F7PWZR+ewMFXG9E+etSruU1fNC5SszDDM+hyWtlf7mRTbUb1yx7fTbA18+o1Y+OtncUVA+P1254pLTq3an7K5Nxs/ux9qsfzYYb44SvT9grC9hqW7vF2vUSdjcGZT/nnjUeNW9ZzsOjnK57bWrfWaR45wl4+5PdO6vQpjcmvIeLKFwNHtruyjFH3zut734y2uilSbJ9onK9SYNVf8W1V5aos5L2svaLJsj6nMreC5mF8tDag1jYo6G5J9o/ZQV5WE0Oll8pBIMDpzxwdMLSsb6XDX6MfDqm3Xjvx6SY95k7/Lkf/WQQRTEv955LOMjtPO78QBfkwBuu6qN1/v5tlDp7L8Ggf2iVzeC5FAtZcvegpe3OPtwQ7dcsd9T37HT3O8g77FsLbvX1ut13HL3pLB9x9zGn9/6B2ZjZuXXbbtXfWLTbPgg3w/emnMMghO9+Il1fjPfXdehfO1FZU8nkfv31aXu3IOtbsesjAArtoXdnzt+1VdCvXJssrGJQXv7fDq+dFaOrebF6s2XvlTzw0H3YcjcDUWj/+vi9Rw7vX0W15lstvtfC/sPgXtPeqRN3a3gzyX8cBdNSvT0avWXCepyWfUEtezhkVzvtN0nRS9JtLX44LpZGSaOYcK/U2xfvxJ23SI134X6y/2NcogPTckdO0c1H8JVO9wqqNsfqbtJ5g9RYl1VAO5x0khF8s3v4iio2M3xn0H0DN/yDuBSkTsDHkX3roPsqLrGB6qnh91LWALUBCG4ed5JdV/Tc9y+Eh/t+Ye03uteIzHb2a5X3F5nu26Oxe+e0Ex+W1I3fMDtue31LNAtvnaB5TvUguHbUFeNE7/zKW++0pSTA9q+eCzp9WR41XlvEKZqw3/uV0UNr2OkOS/adR/zBQHlJ/c2jwcEYlx/+xsFbITBrg1rl2qw7HlPRd+6cCvrJuJXKZw/SBzJYAPLlfr6qStVoWCxLwtxOL345Q7f63hKIvzlMlnVhXqaeZyAKd9uD11W+LN2mGf1tgm8MizNJr9xUiNrdgfzxcHzfNJ1edEMmN0W5OB7XyilzC8NO+uKofxvWm8PknXx0y5QLo7zix8wv9tvR99PsvbRwTIqX+73bpDSTV7eJdXuJw21r4JSvTeXe4ZOj135RdVcyjVfq7oMTtlxxh9q+eYqq7bP07i/373Wllvdb7vIStVedHebeO2mr1ayiRl6NZ1HtHiq8vwC9g8KG9u4fZ2xtcfzer4Nx52A7Gs8Wrh31xSZLTPjekraH5+Of/kK6300TsV1175510y2iZOHdJQ76keoNvpsVxIDH0e5PuTccFsGWrDv+ygqA4eC5BMfCJXHnB4rFWTC+j+aK7soqjNneS9Db7/FZPPp6pMagWOg98dM37Czp5nbnR9jujUgJxN9M+FbPnc+97g5wmFmJotcM646K6nDndZu1Yx4MPCd3tzcUcJNvjvMe8tw0eiWF3dytqMrN6eIyw/WV4J05d7suG9ufav/00Wy8M8zZwyPuRtkUuuHtZuW+Hdd3f7X92hNmfDMi7v1ZZ6tlkXVvo+w+qFJ7e1jxMu4H407tStnemsXBg2C55G5MeeDWk/rB5f7DXUDQnROle2VSuRvcqNub88y9fWlw42NqvNPtmZ2l4t0qcbfch/Xwbh3Wt+T7g4Ofkio6CHr9tVe9Qt62i0NHRiRN1Htp7w1cCftgI9v7Ca7CQy53cIk5W9vZrjt6KfaKAm2P26/CGbY7qpVit0CzzLu3t/s3qKAHSKnhtzPHFYHcMFXHW3modmn7b2lBHHKYt79r/LRvlwY8H1IhwM2k8zqt4w6I1N4PUChHJa//a//oN0u728NG/RP//j+WOvtbJ0+NgyLSurqx/mv/5P9t3bv38InH/v4//s3i6urwsVO/tvtWPe2PNg8/8nt/0Fhf3Tl+4u//5j+efv/G+tOX28UW0nrqzq1Pfub3q+sbOnSf/qP/1rp9e/2x8//w4K2F/oMbWfHXPv0vWzff783PfOhPPtu6fevg+NFBqSoxre2s/8pv/faRN16/89GP/NK/+K2Z967uPHLuia986fzzL+ycPn35r7705Fe/svb4xW59UiNc29947K++eumv/3ow2Tr+t69c/NKXe/Oz7bkFQSw7G3/iP/7exa99deuxC6e//eITX/pSd2aqMewtvXO9Uwye+uIXn3rmzx989Gee/Iu/eOJLX9p+5Ey32lKMBYfdy8/+9VOff+bgxLFTL7304T/77N65s3utGUVos7Nx/Ds/ePrPPhvXa607Nz/6B3+4c+Zs/HcEGh3+wi+aSnh4ZKGztDCqVLsLs73FGWVzoqQnxlFY2D998uDYkTxwu/PT3eOLgnFiBFeZdKy9E8eG85Pjcnnv1PH2sSPSd0bTEymHMgx3Fxb3TxxNK8X20cXe0dncdqgSTAnhO/256b1Tx0Xotufn2yeOSdeiWvjZKCkUOkcWe8dmB9X6oNXcPXdaelZ3aubgxJFxrXI4M7176oTwbIwBz1OCVa81uf3IuawSHramDk4eHZWKHeLvU78Xlkf16tb5c7Ls95qtrQvnVOAojMO4BxjZX1jsLs33WpPxRH3r/DlRdPvIWXUrCbd7remDk8eGE/XexET35NGoWAAMW3nCUd6bmNx59Jwse+3Jub1Tx8YTdZenrIVZ0VBbkllm23kCiDKIOA4q0WI5xjWGPMhbkBSxjGJFXG5RZBunpXkFIh/51ZyUCcaGq9RWGbMVnaFOKICLvQkFqszECRonulSxQ2A1DKoQhyV8ElkFk0ugM6nDou1Iv5yxOiYuIE2EaoziBITdvJwBliG+r6pZxHlfmK2goYjU4cDyuomHRdiXlVwh3c/0RrkFkci9UV7LEY20PwCFoSGSyZgaA7nCfD+tS0Ij5Y91OZIo34dOm4WJZwGvC4rDxMcE76ZNDWh+oMmOX88pkP6IBZ04oMrr69I4C+zQitgstVxJA2M3NHCRjHKCqSoGvhPhWSu0Y1VgbJZRS+e5hpAiz2YF7daUKtsFJ4IzluOIeKJ+WK9QDmgA3AmNS5gFulhO41pJdzvSK1GLOoFkc8zyJPKBO6FBgatRgiBW5ZJnp2SGOZ4krqZzlPta8xQGnbykDRmL0ggU8kyrB7wQUU9YShcPTRhrWyKvk5V0YvQO9dteGcNBWo2INxRc6+KhKEmiE1tEBBrAU1UaiaIguJ+Xx47T7VJ3D1q7QQ3yHHpdWRaYjLJSJMpKy2SDeAPq5R6GwYEpxMI1wOuKUs5ZxmrQqSroIbuqcItymFs65XnGmGIFRaaY5Sm7JO26IVRbKnOzce7bBXeM5hxuC1YEVgsxS2IjnTyGNnbL0m4aGdqhOyLTPIRDSZglEkYUr0GvrkFIrVA4DQ04YjDneWIRgQrQmsTc1bxk8CSDPrLQbtrIsZMoOkqaKXTSAWBbLBw4rrYiWOhlRUzJQdIQxEnHGq5YRUlI7Gld7OlQGDuG4WEe4qQWHpYKhBnAoryZYHssnBwUD4Wvhgq2idMp1Ajp5/VE+YrCTtKIkRMPAd1mha4TINKHhV5axoT0skYqfGXR1JtQuE45y71yRmsEY81lgoG2WMpbyClK6BJnwuA65SC3RWTLBDmQNCCuU8fOrKaxKwZgYMsYA8Ns5ZVy3KSWldM6cssKMcBk5ohYBzyoZHYdGJ+6hZQ2iQ1ToqWlUswNqxu3qk3IvYoAU7YFI+WNVSWCPBF+BAu9hIMu4HtWOHYdgnfTpiJWIr1I1mJDRBtah9ge+p7yh6Y4TEPGyU7aVBTHSAtbpATm0o9AoZ/5xliHoDwaeTyVetmvawq1E5nqCLEoDgQo9VMf9zK151cGXkDgXjIhMI+0E4t6DBzBPV2qxqLmWXZOZxjxgIgSzTxuUexqt6VJCbHAuFUlq5457Cq3SBl2fElnqRNI5CF3QqMKk+UgKfmJ67h2xqYILiCXxWwas8DkuVEKgCBwvNyvCVImxNV0ioCShXuHmjrU4jhATlOhKnWczK4DUOEUDURpbNudyGOqcCjKQhjZUWSnWIcozQqRKAuCh7I0RMEoQWgfWvtWkHkEOQd5RWAykoUIhkNDJVGZl0XSRdA70MVx5kDE9vOGVjDbxe6BWxFEycLA8jqj0FZe1xTHY9saGrzhVQCReSHSpQSwRIUDUxjlHg/t0f9ILVnifJogokUOEKYwsK2idBtaFN2SM4azDsRKRbGxA25ju2KcpoYhsUNRqiWZ5zAkmUgtmNECtKYJdyUpGKehZeCYwwG2HFkqBHaEZy3XyUkA+AzBXOepBpChwHGCzJ4AeckN7IjMcsuRw2pt78yJ4fTEsNE8XFoYNBuGEStPEAZZMdh89HzSLA8qtb0zp3pTk4nBbWS3w3JcqWyfOzNqNZJSsHX+jGoUYuZYKsPQxLVS+/ix0dREVCxsnj83mG5lGq9axRG1R/VG+/jR3vzMsFZrLx2NWxVDKVXCkdGoXN08dzqamRhVa+0TS93FWW2zvaNHD44fzYrh7omlwcKUoowa4afDYaXam57cP31cFIP9Y0v9I7PCdZBRfjZMSuXOscXDpflhpTqYbnVOHpVlf396LvdYHgZ7x48PFqaHlWpnYa63NJ9bFobGziNj072jR7tLC0m5uHv8eO/IbG47FKjyuN0tN4eTzd2zp/JScLC4uHnhAn733b+rL8LUC7Eyo0I5Y5YGeFwoL7719unvvawNkoRpDRLLHfsFrMzY8Ru3757/7otGwZxZglqJ7We2BxRIuOMe9p0rbyPLTR0PQjx2QwWxota4UJ65eu3i158ViCRegDM5CEoZsw0ig2J18sbNC9/4Bk7zlDvYwMQrRF6B5GIUlgRhGtHUchPH1wYNw7LX6T3xl1+we/3IC3Cu4rCUMUtBkrt+NMbJK7fTgRwVyiQVw7AY+QUsdewGbDC69PVnq6tro6AAlMktZ+QHKM6GhfIYuOnL7wwPVOL6CtOM2ZEbAoMSy8WpuPzFL4YbW1FQJJmIbDezHEmYJDx3LGCAoSj3XJxLhWFUa+YPab5OdOhio6EBue9AAyA2qhCIu1yOCqnjUqig0cLiBAEEgMTUvD+GK1Fu28QYhHTGbCISqJQglmi7pu3Kcgi1wdBom0GlMTaZ54s9O33Ite9gpDEAInAwBASZnFsaSqJERojAigkR2fZOPnvv8HQPFg1QWGepbQEksJA5Q5vp7N3u2QEpSwqIEilBBgCo0sh3nX1SuW0JxDTKcSJyChQEWGSKw5146m771IBUFANEKsGppAYLHdu0rabudM53QN1AiVWSU5YTSDIhbCYcmwkJLCIZJUoAhnJQEA8cKQPgUpJJ5XBhWSwThhOBvewOzseO8BwklWRMeBwKrR0iqXtzr9SThZxxqnJjjGSUSAUZyP2ieJ9oUEQuQdJATnLLZkYABHLkihWuskD6Ds4lspBwLCYlREZgbIxCBiQONcYYqDPLuXdwfDU9l7g+VgmQImcEQAmVHPNwZbC0OlwcEwcoBaBIbB+ADBuVElbYwIVVnlNqgDEqzykEUmGQZq63Oj5+p3Mys2yjM6j1//ACQGWUPOwv3T88nfhFDQWQKrUwMDnMhaQAGkCNym0HMIylTD0PtTP45jAzVDFCtJYW0wYSmeWuY7ZSeKUvAYMWwrkUDtOcM6W0w+WeJFc6MkHaolgozRiwIM6kdC09MOBKX42MZIQpIRjPGSW5SH1fxdC8PlA5Eo6NpUw5k4RQlWuEcqJhqjMGE8vCucwpHOLyrfaprWRGWAwqKTCJLQwzLVA+squ3tk9viGPSolgmAOicYiSFIshQd294VJiasBiWSmOVc4uIyBg5QMX73ZPbYjGlFKW5giJlBEppsBrbleXDM+ujhYxhrHOFYOpQLJRWSU44VTmUUjAGKMTapJyrBxm+O86ZBQDEWCeOT5CGUueMw5UxfH8suIUwpEAripAyFKrYD8ndobk6EowjDKAyqWsBABEGuWfDOxF9t5NRG0GFpZI2AwRgpaXNzYZEN0c5tAAEDMqMO4BiIpTiVGBI8kxyqIDBIlYW2dVHb+2dG+JyzgDJdGJjwRjOhWRoL2ve3j91COs5hTgTwuI5ljAHqQVxzCq3OIpYZhGk4pySnGGcy4zTAzR5fetkxzQVwygXKcGSEiwTjfWunrnXuXAIm4lFSK4EM5oRrKRGWmOCldIIC8/GQkOOVBCKm0wmxdyxKJAGAGVzYjQmRhVK8h6TiQd8TqWCyOSUE5EAbTLirvVL7bEnHYtIjQjQNkNSIQoyy883rWzHMr5FtIQYidClSiMClOdmy1x1nDxwMVLAQOkwbLRCKLcIUJqANLVdiAWUMqF0LTqy3D85YgUDDTQyJRgabVSWu87DeOnW/qnY8gHIUCYyBozRyOSZRfwNVr9tcuoqArBUwqIKa6R0bFvbycK93rk+KgAjsE5SbgtkSCZSx97JZm62Tw54TSOolYo511AjlaSMAwvhTErPkoxRIbVjiczJ7uJc+8LhJJe5bSuXwlwpl0K7KG/zTIfawkRmCkJNCRVSWlymELx2CAdKOjaWRltMck610ARJ46k7TOKici0cC+hgaVtMScOw0J68RxIR5qHDhBScCZezTEIGM+5IyiUksV/QlOeEKwMfee751s1bI78IFEgdL7U9wWyD6QjY8Xub8Upn6FegAinlkRdCaYQX5sw6/YMfHnvl1XFQ0pAowgeFMjQwdfyI+dnrd6Mb+1lYzJitIY7CooZEIpJR6/RLLy1ceSt2AwmQ5HZqOZnjQ0jGfkFQLgmP3FAhoiGO/MLsW+889tzzmeXGboCFGhWrklqasFGhPPvmW49+85tAgdj1ca4SN4jdAAkdhyV7Zy+4eRsYmCOiCUstN7ZdnMuxVyhu7Fz8+rNAmcx2lUGR5WbU0hqOgsLErTuPf/krUJphUMC5HBUrgnJFmOA2+F/X/xTJHRhw4Yt/5XU7cbnQfONqeWVFMzz/xpv167d2Ty898twL/sbGgyefvvClL5f2tpNCWLqzMnn9+jAsz115c+H1n3YXZy48/43C/eUHl5589Nnna8v3LAIK9x/U3rs1LpQnr7574ic/6S7Onnv2uerygwfnL5577oXmzffHxdLEg7utn7498ovNO7fPvvTizpkT5599rnHrzuH8VOuV1+euXh3WGtiIy//9z6/8X7+BsvzRv/rKYanW2N46+a3vpG5AUX7qG9/kg+H+8SNPfO7zb//GP1AYX/jyV9+y6VRvfPy73xMayJnGqWeft9oHuxfOPfHnz9z61Z/PXO/UV75xHf4fk6k6/p0XsQT96erpF77jdQ7XPvjEE3/6Zzd+9ZfH1dr5r37tdvpJYYUnv/UiH0QH54+f/vrzlYcPVz/6xBOf/e/Lf+9ju87M7l3bDpU7P1p/UHBXhuWPw/X7Jerr2cZo+6qrA1xOOu3VJndMTUbtnXJxZxTUB/ff84kHpwvR3lXH9U1ZjQ/vuQjrBky37nlWNp5oJHvXnNzl9QnRuWs7HJec3vZKiKHxfL1z33fGg1Zj0L5rDbhb7pr9G5YDwEx2sL5eTpRlTWXWxqwZF8jUPXe9KYdeEf001ZPgcMLubsN+ga9zZL8CNxbTeIbMrLi7Bd0rVNausZ6bRmdsbwOMA7ZWEKV3nNWKjmZI/b63WVLdhr95m8VcdZY4uQXSkG7Psuqb1lpBjeZo+BZpl1WvVVi+biM73T/t8JtQ+mjrKGffpW03G5+w/Tf7Ml67ExTjLJxVa+8V6jLx7eHugxD6KdhK91aKVTWizmD9XqM6jArH9MPl2gRLnX5//brXMBlE483lsCxSWI87V4vVao+epyvvhZVjmT+Mt2/4wewh3evstJvF28KZSTdvhbW9QfAoXHs3LBwVfpzsr5Ym9BAPBtsPAm+Us6Ni/XZQ2B6VGiF7OI+cuCAeyP1jGCsDHpiDE0E+GhWu040ncwa4fwWvLUIUh2ZFdY9RYFJ/z7QXnGgoS+9bDy/lnLHqCt2awDIuqnticBQoiwU7pj3jjGPg/JCunE94ifVX+PYsUKAkb6CDKa0qPNwCB7PO0KiJN937J3JS5ME7ZGta547l3dnbZPk90CpH/Wu62/YLs7p/F483acNORT/dXC2XHZEnNHnXm2iq8W3V3fHmK+Pxaj7YKracoZB647bfRInM0f41fz4c6Yd6eyucLY3SXdhdLU6RvlRy5749BcbCkN33giPFWK9k21uFhcIg6/DOQ7cOUmTl2++7U2ooLHvzalgqiopxdXepZB5Cd6AOzgXxmpzbRdsXkVrnyS7cPEGy+5VuqrqnArGJqrume748XIbT28nmRVzftvNduHmCZw9Qdwx2Z0j/ADQivXva72yMTm7i9Uuqtm/LbbR7mgQrYRqZzgkvP0zIAOyecjvbwFqzNi6KZu7QNtyZYny5nIx1+6QVx6Sx07nGE8ybs+LgKmHQquFRe72gMxyEem/ZtbrDyebw4D3aM05lFnZuWiSH87pzsBUMMycowYNl1+tFjdZw9y0wQkFpSo/eRyKxW3gwWEe9AZ9zkvYDW3T01FERXRE9FcxXhv1bPI/cFhsN9ujhPp9x44P7dtJGxyaG4yuwnYb1qdQ68GX7lIXvGkPRxjEavu3uBeZwkYbvkG5RHs4U7l+3GU13TtvwDsQE75xk7Pusb2WDM5b7Fh/4snu0jN+HSKrtk66+qTgBG6e58x7uUdNZcujrtuDq8EQJ34UuBNunwuRt5Vhi8xK1r5HY0p0jrnUF51gennFWV3FhADdO2WZ1EOx1r1nu5AB3uhvL1WAs2Vyyt1VtDPrEBQ+uBt60Kulk+7pbb435sL+1X7fzzNVi404QWMNqaLavemiWhyzrvAWqtcwaqe1l1+4AyzKbt4OQDuvh6PCul7bcoKQ7N91aOKj2DjaW63gX2wWzt14t78elieHGFaKadsjzvVs287UfGHhwzOrl0Fmn98+kuMYmV+y9KsxohVxHBzUjJ3mwYdotu0tk/Yr/8GgOarx0hWxP6Tjw2R3Sq8pxk9NrcH+W9kNaf827O5PrOgveAjsNExWK8DrJqvBwErnXyME87NUc9zmyUsrVtBu+btoVMG6U0XWTlEx3CvOrsLMIug238tPRPdl5WJzAfQDjzbvNlhxBD2/fr83XY7A/3FoJ5/yhHuj1B4U6TGCQ7q1XW7QPKtbq1cI0j/Uo2b7vN9S+1vbuclBVOajIrRvORDTEdbJxNWyQFIhsf7O+EHYBGG4+LBZNiltq/bpXG42tKbp/vxrUOdzPd+8G86qDfbq+HBY0qCx0nvzcM9snlwxG57/81e6xOTaITrz08mBuLrL9M899Iy6X/+a3/r8P/Nnnto4fj9zw/PMvDFs1kqmll16eqlSisHL268+pUum1/+cfPv75Zzozs+NS9exz3+hPTfv9zrGXXp51/V61cfa5b2jL+ubZk5c/9/lRvZq77plvfjv2XCePj/3NDyWicfX/p+29gi3Nrvu+oBPZfAAAIABJREFUnff+8snn3HPzvd333o6303T3dPckBCIRAEFVucqu8pv9aD9JLluUSEtUiaIEEKQtBhGBkCAC4IAAAYIEMAiDNANMwsTunk4353Dyl78d/ADr3X7A66r1+luralWt/+9/uvSVZyGle+dO3fzLz47K5aN2e+H5H5furQ7qrYXvPz/7+utbVy5c/urf+ru7nbmpuW//sLnyKPP9sQfvNl99K/Psk8//eOqV1/bPLF370ldKKyud+amZH7/UfucdIMjY2oPKy3decv7n2Z++MPOLV47PLF368t80Hq305yabr7w1++qrmjK/c3DiOz8oyv7Uq7+ce/4nvaUTp7/97fE33rxXoa2V7uKLLxTC4tHg/Ne+dTQ52yP01xQ0+tvx5NRwamr1yqVuvd2dnVt/7LEsCDbPX+gszg+qzcOFxZWLl0ZjY3Gr9ejG9V5jvD8x8ej69TQI9s6eO1w6OWiOH87MPXrssWG9HrfGHt68Pqw3j6bnH169mnr+4dKp/VMnu+NTnZm5R9eujRr1qN689+RTWSU4mjn58Pr1NAgOT5w8PL3YHZvozcxtXTq/P7OQliv3nnyi8L3uxOzOpQvdiYne+NTqY1e709OZ67779DNhuxEF1ftPPxO26p3WxPbF893pmcH49O7ymZ2F04Xn3nnP++J2Iyo3Hty6ORprHrcmDpfPHs/MdMamdi+e21o8Ix3v7tNPD6fasVe+f/PWaLzVHZvcuXB+f3ZuVGvuXr6wuXBKef7dp98znGjFXvnRrZvJWG1v/MT2ubOD+QnACZ5iZAwDSsk8A02BBRTjUI9bxqFeXYNpK3V9MklpCxGL0hlsmhZyCB+DeIITl4gGUlNCWZxNUjJGCId0msKWoA7iTZhNe8LWvIbAnA1czlqgmHIElmKWwKYALsV1qGccYSlex2iGK8fC41SNOZglyuoPxpHhIbCLaDKOfUuJeDiWalbIYAjKURiQ3I0H48aQkXbUcCrVtso9PWwnmhXG7eeNIne1cpP+pAEslMIMJxMo8sJW2VhY2Cb34qye564GXpLVw7CMDdeDqRSJvPB03EoKS+duCGrhMMDK0VlrFFdch2s+T0GZYReyaQpqjPiUT2LZshAnYNEKvDT2SnSewCqnjiGTVDcEdKmYwrLtQEbwgqAVZATFJzmocWYDNkl0UyCXlFp5NF0BguOTjFYgtSmbI6ZuIQ/RcYyajLqYt1E24QGK2UlGqphySOcYKqNCFCaIhy2Z2Sytx0UlhlTFjVFRhRmTMhhkLVVwqYM0bOe5oEVpOGxBjNKsMcqrRosi90aDtoE01n46mpC5xXJ/MGoaDOOsPoBBMvC59KJBW0McFiUZjaeGw6SWDlsGm1DWhllNFZaWfpK20swCuSeTsQxTTRsYjXNT4ayM1LwnHIVdjE7Z1AGwJMAJC9lIlBUepzCgvALZFJYNj1gYLNrMMaTM6CwFHmVVRKa4KTNWoWwK5TWbWAQtcdsu0opPZikqUV6GaILJqqA+4bOkaLjIwnSJYw9Cj6F5DgPKAqNmfVUGkJjhRCzLUlE4GhvKMtQsy+pRXNaSy6IZD2oAcBSNhzIoFIZxqw8CFTkwq4ZZTRkhZWXYaxLNcDQeaj+FTIftsCjBgudFZZTVleJSVsNhE2kK00Zv2KAUhMlYWJSNFnlcTsK6RDQt6tFgDGuK4lovqjKLFlYTZDO+sBWvIjhnGc8SDaCmHE4Un8GgJaBHSQ3KWZdbklUxmmPKtXCLqimbY2lNIDDGQYnxOlbzriUkrjI0x7FAeFzgcUK4IW1Cxqh2qFWDdJrIioNKFM0xaiHWomiCQQuLcQwnOPAZr1M1ZxOa5rZJ2mFhm8KN80aeecq4aVYfhRUMmO7PZNDKCgfEY1Hh6MKOTDWMfJQ7Jm+FYRkBDoZTkfJ0IUw0FivPaDvOGmnkKSNk1o4GZQKZGU2OtCsLbrLWSPkmdmTWSFNfQavI64New0JUj6ZGwM61rZJmqF0DXRK0inS6pC0Lz1NWhcSiZI6YuoU9zNoYj3HqYtGG2YRrGGYnGKljwhCdo7DBqQvJGJETjrA1H8Nq2oOc8CmYtR0H5fwEhQ0BHYJaBEwIIRRvEzPJtWXhGVK0XQukfJaYJkc+xWPUTFmCKTzBiwkLgzitjWAQjwImvXjQ1oCEylfheKoFSMvZqKUQjFVlmNdkZmvlJ2krzi2Tuzpqx5DJPEjTVi65zipRXpe5rYCfpM0wDohy1GgigVxmpTxtZgVTeWkIylHPp9qVydgoCqiyVTgRaQukXp62ci20CkZJo8jLnAiCloTnZHG5RGYpLlEWGDzJVE0Qj7AZUrRcxDE5xXGAgE3RCQ7KjAeATDJV49gj5fGsN9nAguAFygJIPUZmqakIEiA6SVGNcg+zSZiMlTAFfImTABEHkVmGqoy6Rk1aqEmFAHwaFxMBoYDNI1Pi3Xp799zZvbNnRtXm4enF9eWLmeOvXb22vXRKW9bKjZuD6fFBa3Ln1KmN8+fTUvnw9NLa5cu55aw8dmVn6ZQWYvXmjWi8vju5sLe4uLZ8IfP83bNnd5bPFo7/8PGbuwuLiov1x290Zye7jfGjxcXtC8vDSn136dSvelavXT8+OTcKqhuXH+ucmOm1p44Xl9aWz2vH2V06s3r5cu55O6dPHy/Od5sTvfn5rUvLh5PzYaP54OaNvFQ6WDi1vXwurFS2zy4fnlk4bk8NZuc2L184HJ+Oms2Htx6Xgbdx8sz28rmw2dxbOrV/euFgfGbUbq9ff+xwYjau1e899WRaCvZnT25dPD+qN/bnTuydP3PYmhi124dXzq7Nn0srlftPPpFVy0dzi2uPXWWvvvz/94vw/6tbJ/a8AsLUdXPHKTiVlpCEpJ6vCY1cL/NcSWnhuTlCuednQuSuk9mO5Dy3bU1ILETquZKxxHUlggVjBaU5Z5lt5Y4VObYipLDswhKK0CQoFYxljlPYdkFJIazCsqIgUBgnpfL/W7EtyWjmeVLwxHMzSgvLlratKI0dtxAisd3csnNCM9tSlOaeJznPLCtlLOc8d+yCkNx1M84lIYkfSEZzx5aM5Y6TWZYkJHZdyXniepILSWkRBJLSzPUKYUnHkZatGMs8TzIWe27BeU5Z6vuK0MJxpCU0IwRryCEkEEFJLSQJoUxDBgzFhGqAjBYUEYOoAQwbUGibKkIYzCiWhiDNgEIGWAQRABmAFAKotU0MxgVDiAFAEKXKYGMENsggDgGFhChMgSZEUYipMQRBohUyiiINDWIGEJRjDahUBABaaFMAAnIMDFGKagByBaXCWCJgQGqQkVwBKHMCIdYa5IAoQLQkUmIoGYAwA0jl3ACsJAOaGkWUpBAhDVBqMCwELEyhiQEoNyaDVBcUKlQoZgCUECYGIy2IBqmBUnOsjDYCQmQ4yjHSSnBNtMEAMoiQRAJLRglSUABFMWYaEQg4IVQjpAHHFCvAMSIAQ8lsrDEmTAMKAUMEK0UQEBQhhTgCFBtQaIcZBDnMCFKGIYWMohBYBCMNOYIUGqQ1RwYZTaVCWlMAQA5gbohRpig40BgDnEGiNIaKagW1pFBjLZnRWGOTKWYMxjmWgEpNoCGFhkoTqJBRVGliDEwLbBTGCmsDUo2NYkojIwk0UAGYGmwgVRmRBmHJDQSZQUpSZYhSFCAKDAWKYEQhJQoSCGwEMDAMQo4AlJgCQCDkQGOkHWqQARQhChBQ0CKaIoWMYQgjzVAGMNA2M1ACiiCFCCkgqKGIYAUYNBgSqgxB2MIYSYQBsAhDEgiMsSFEYUEAgZRrQFHGEQCZZABgDXQChdEYARwbLA2DCOcQSsURMHnGoIHSmETaRGOEYAJhoShQuCgIMFQbkAGUawKkKQoBNYYApwBrhY1CSmNdMKCRzrkxSBOQKgY0RgWWgChDjIYFQLog0CAluTIIaw4xBYYgxrRGBghskIEMAoYwUYQCg4miEDFjCIREG2gMhgYBiDWgEFGjKFSYYAoxVhAZIJBBAAgMCMBYAgIRhVBAjZD2KEAaMASxRkhDiygENAWaYUIUg5nGWNnMgAIwpInWSCpiINIApxqbgkNpCk0AQLk2GaBKUqhRoZgxUEGQGAykRQxMDSwyjhBICwYhUhCkQECDDUSxxgYIgGAKsFYcIpBlHBmkFc6lIAYDBBODjGZKw0IyCIk0JgO40NhIUEgGIQaEKP3fCMIcAoaNKbTNNMYMZgQrw5AmQOFfEWQQg5BAA5XhWGMkGYQUIgoxVoBBxRFAGggICCJEYgoVJpoBRA2gEGCtENAEaagJNwZDzLSi0GBMiKawgBQCBjWFmgADMoW0xuRXU+u/EaQkhQCpX9EKiMqJVBgrDhDIDNIFMwYrxYAmRlGtMIQwNyjRCOUCSp1rAgzMDcgMNRIDRaRiCIAMwVQRrC1sTGyQKqiBMJMMQlAgnGmGDS4MTBVBkEEEJbSIooQgCTnUFBGmDUFYYIwVxgYI/CuCEDYYK2IRgxFmWlMEBMLESEqgoAhKJBCg0CCpbQYRYChDSAOOJNCaIWhhDDXkEBBgkDYcS4wAh5BCQo2BBRKooBAhDQVShGaeKy2rYCxjvKAscdyCs9h1JWOai9QPFMaR7eSOUwgrt4RiNONWwXniB5KxnLOUCU1wzoXkPLet1PNyxqVlSYyKXy1T34ssS2Oc+74iOLdEZtuZ4ySulzOaOI4iVDp26jqKkNjzMgwLxykELxgrOM9tKwoCjXBcLueE5JYobFFwmvp+xmjKeSGE5CK3LI1J4vs5wbllZY5TUJrbrmS0sKzCEhkXmeNJQoogkIwpxlPPl0JkjqcoLVxXWnbBrTQoScbSclkxlgpL+m5Baea6OWOJbUnOITC/lgvW4KMf/9i/+7fTb7wOHLH43e8v/uQngNOFHz1/6e++sX7j6m/86Z+f/P73Nm7ceO+ffHrmtVcQBfM/f/nMc98tPHfhRz+89pUvb16//PRnPnfquee2rl595nOfnX/xBc7g7MuvLD33/aJcPvmzn9360l9vXrt86zOfWfre93fPnLn5N19e+NGPkKDzv/jZ2b//dl4uzb780lN/9fnVW9ef+Nznl577Xn9u6vw3/2HpB98HFFc7h+/55KfzWrn57p0bn/lsf2Ly1IsvLP/d1ylBY9vrl7/4JbffI0C//9/9h6zsN9fXbnzmc2mtNP3Sy8t//y0RxdW4f+Uzf+X3O5ii9//Bv4c2qW5uXv3P/zUPnNm33zn/9a+5o4Ed9h//7BdKezvadz74+//WCFrZ3rr++b8ygrTfvbf8ta+Vux2q81t/8Zna7rYM7Pf+wScpkAfu3OHrhBwmQdbZfsdDj/pjreGD73uqAxp2f/dnhA2T0vB4956j94sAR7uvW2J1UGkN137opR1Yt/t7L2A8KPy4d3yXqV3poXD3TYs8DMuz2eF3yegIB5Ws+xMFjrJS3O084PkOYEIevwLNvbA2E3W+awYd4dXA8EVp9pJmuNd9xEZbTIzl7Xcn8P40qB1574yxTqMUr9JOg+3NEJyxTr30sOby29bKNDxe0JVe6dEY228E2bazx8DBKYIS1q2UH45jb9W7W0VHJ4HXKT8q04Nxt+h4RwRuLVnqmPYCtnqaOI/K73roaImzLbFWZ0fTbrTvDxDYPOOoQzoI2Pqyj9+yH/qgc4aTnXwvPHiV0n5owXT9p56FErfb23+ZWzyHm+nRXe73j5vDg7vvtER35LFs/UeuBWK30919VTggpDvx3l3h9wdW3N94PbD2j3wnffRD3wKZGEU7r4lmfgj3kr17LuuEDky2XrL9vWPbT1ef94kqRBjuv8b8fIiP4sM7nHYSG8bbL9nW4ciZd/zX5kRM/egI7czxjm3pDtk+U14Hprpuv3MVD0uCrvP7p6wRddMu3Z2xj1zIYr42768RVH5YemfZDJvI63i3p6w+c6M9vD8leg0kYnt1zt+wXOcN6/YFE09Cr19+MME6dinaYXtj6GgCOwNrZba8XTLVh+VXZkA0JciOtTbDj0sCHB6vqOhVXW0Oo1ezgweWXVfyzaL/ENRVz6wO9++5FsqyIzj8hSm3E/ladHhflL0wvqO6j6ifdNhRvPOW5alBdmz2XrVr1T54M9y7b1e9ML2nug9oPdy3Dvord2olNdA9sPWS1Sz38rfD/ftOnR6rh3L/Hq9EXTBId18TlfxQDsD2L2y3nLU7gm6d8Pp9J+yB7avewYixDr33BC9SZ3BMVs4KOXCPcrS35A9zqkKydrGy1xd40zz6AC0KJx7QlTN+PhCdguwtWQMjdJ+sXCzvh8Y7st++TqQW6YiunnbT0A4l3TrhDIChmf3uKXcvw86u9/ZlrS0qU+fhrDcYWqOQbC/YkZdX9sN/yPv7uNzIuz8u1IGspMe9hzzdBEzI7utQ3Y7qM2H3+7q3J/wxOHoxV7v5eLzbe0QHm8Iqgd7rwLyb1KeHne/Kzr7tlIvstSLd1rXsePRA9x/Rqhcevs3iu6B2Io6+nXT2rIo9HL4Jw01TSY+zDdO9h6tW/+gd3rvHxseP4x+mhztOuZy3dpnZPmMVPTay+KNz2N4sP+R47xSjO2Kzyg7nnPAgGCqzed7JOyzmbOWSj+45axwcnudkz98pob05f3ggIoXXz7vhHksZWbki6Ka7RtD+aUeu27sW2l8MogHNNH90ujzYoAkA608wsuvs2XhnoSQ3yJ6g+0vWKGFFIR6csWTYV8Pjn8BK1qWHo913XXIUuyjeesV2tzpeNV79gQdT5RbDvZepm4a8Gx7eEfg4tVG0/Ypt7Yy8sWL/OyTPkWXS418AqzdwRtHhbQ46mgp5+FOIN4fVZnT4HIozbhHdf1nb3WG1f7B738sPkF2Hhz/UfCcttUb73wZhKiyUj17T+Dj1XeRsLpXXHcd503r3LBhOg6Bfuj/Bj71SvM13q/BwGoqR2Jwqb1Rg+b7/2jQYzgi6ba1P0eOalx9ZByW0O2WhHbo7xzZnSHCn+vqYGc5ZZIuvtnmn5ce7VqeEt2ccvEX35ujOiRJ7SdyeBqMFgTbdzRY9bPvRDumW2PqUA9bo/hzdWbDtO6N3iuN7rDI88I86D243/GyAIr3xol1zB+b+aO+OUyEds1Ls3RPlUY8Mou1fOkF4hHK18YJbcUK1lu2/w6fNVrKh9u879nDIinz357wSHhupNl9wfTtWm9nhbVYzHbWdH97lYhgxle7+wgqSAQVq78eUusBsZ923SSXaxz299yZjsWqTrY/9899zusfa4rf+02f8Ud8Nwwtf/duxlUdRtfbeP/6j6V++uvPYxU/8y99zDg8Kz3vqz/5j5XCPp+ny174+de9ef3LyNz71ybk33zi6sPS+f/Pvg/19WS49+bnPVnZ33Hh4/hvfmPjl653Tp3/jD/5g4Y1frj5+9eO/8y8q21vGFle+/JXmyiNb5+e/8c2Zl17uLcw/88lPz//85xu3rn/0d/9VZX09qVcufePrS9/74Wi8ff5bf3/92Wfvv+/pD/6HT82/+MJgZuLC337j5E9/Soyc/+Vr5775raReOfPd71559qtrt66998//0/yPfxxOTyx/8x8Xf/y8pfOZN3956u/+MWnWzn7/B5e/9KXdW48//ud/sfCTnyTNyrnnfrD0veeElmN3b1/54peidmPx+eev/vWX9q9ceOwLX1z64Q9x1Z740c/Pfuc7TOat1Uc3P/9XR0tLo93dX0/Q6Mc/ISsVjeGdD34gs9ykXH7wzJNY6f2Ti9uXLxSQhK3m3Seflq4LIHj3o785sr3Ctu68530Yoe74+ObVyynlUaV896lnCt8zRt3+0AcS20nd4N33vU8bM2g1N69eiSy3qFTuvPc3Es/FMn/945+Agka2f/d979OEjOqN7ccuhuW64uzhe5/uNttCFm9+9GNSsAyz9cev9ScmNCYPnnzqaHLSGfTe+M3fCidbtDu8/eEPR826omLjsStHk5MKoq3Hr+0tnXFG4Zsf+c1BsyZ6w9sf/vCo1VCE715aPpg/IQnfuXp5Y+GM0++98eGPRFPjdBje+9CHuuNtBcnmxYv7p04jbbauXl47c8Htdt78wIdHs5MsjO899WQ43U6Zt31x+eD0STEa4SlKZ20xDMmCKyccKxw4ldwslrhRtpsUpyq40KIN6SxnvaGZ4mbW95K+U9Vm3iZGWk6uT5dFHIuWIbNcDEMyJ8yUY8UDUSrMgu/mI9vJ9PkaihOnmpl5txwOSAvoEyWSRraTmgXXlyNm5eBcCWbSqevsRJWnXaoPe3NagC7JBoOTMuZQZPuDqZipEOJuNqlSbAToHk9Loo5YOjxaMhhlWA3D2RjKkJi90TQCtGBFp3tCaThyw87xKYNxgvJBOJtgEDO9H02a1AI06yazcWFLO+p1FrUhOSzC0UyGYUz1QT6ehSVb5P10OhzUS8HhAT3vsTL05ZBNk6zu0TSy57BpW+5woC9UeEnBSIpznikhO+6yaaomAqtIrRmcT/p2rwfP+qxJrEGIzgSmxp20x9vQTPtWljitIj7RtPsDcsZiTUL7oVlw0JjlpwMxDs2kJZKQT2I1W/J7XXJK0CYWgxFZsGCNgCJCuNebhTRLdamftnM6CMNmkraxiDrQ6owmJVAJpv3+CYiSWNrd4aRy+z1dHYymOI67kHYHM8rOOoCMuvMQ5jlgB4PxIgi7uT8oxnVqDKPHncmCZ0cAR72TgKYJdPuDCWUNO9o6DGcYVgkB3Wg6KnRK0GA4p6x0xERmFgLAYAVH5pQnaGJHA/1Y3WYFUSk+6xFU+GiA5h3jUVeN+CkOfFqKevByGXPNZErOuIKrAIfwhJt5wi5CcZrrgHrRQF2uCpEjA+gZDzHpgiGcd0zN9fIhO+PkVdsZ9sClknANVzk4XUKW8VRfLpbyknG7x705qepG9EbhfJ6WDB8d5LVRNEZZFhaNqNcmdq/fn4xAJRNhMpzMTaVA2UBVhqMxQONhXg0H48Qd9kYToapmtBcNJ9OiBqzoSJYGUUvRZKjqUW8C291u2BrGTRMcH2fjYTRGaXhU+KN0LHdGx3k16kxzqzdMa4eDthcMD2wvV0uBqyKHJWC5AorC8RJ40inFQ9IA+mQJ54lrp2bRCXTIaGrOBUApO5BwwbFHfatSqJNlqGXZTsFpz1IjjjJ4oWJlMSsZOsdZnrhBCuZ9g0GARvSMjSjwUGzOl0iRUc+Qk7atksCL1UI5p8SFoTpfojjF8TCaTRBOmDyIx2XiIJp105koc5U76nYWtWYFSUajuQyShMqDfCweVmyeDdOpcOAZZ9A9XlLQliSNwnmlWM6yvWhcZmUk4l40VwzLxOl1OvMJdjKSpaOpAtgZlt2knSYlLUadcDoPa9Tvd/pzEXQTFEXRRAJ9Yxep3ciThYYVRnSRszFKO0Nz0kYTjp8MxBgw0zbPUt4C8kTJ6/XISc7aRAxGaNEBY8KKB6wB4KzjRn27BfKlGu/2rWlgpuzqoIumiJ4NWDKyqhrNCS/p0zpQCwEeJu4sUpO2f3TA2kbNl0mRuhWN5i0n6uEmTWftYNTNnX4xqVNjOD7uTEtWHCEz6i5AUiRQDMJpycOe4QfhDEMmIaYbT4c5yLnq9RYNLSJNh/FURvOQkN3RDM2xprITz8WSFFwNuic0kpmiYTgpWTFCZD+dUKHjWEUnno1jqlne6y0YAnLAkuEMQHKIwd5oBmkP+6O+vlwXtkRSszMetJQrB3je1jXHLUK+ZGUN1+l1wKUyK0GRJuBMCXrILQZkloMxx05G9hwYTbX8bgdfcFkJkig2Sx4p40AN6QwFDWGP+nTJkuN+qXuEzzu8DHkcoSUXlrGdDtAMZw3sDLv2KRFPVryDA36G65qtIT1cWti4fMlocLx48sH1m3a3++jata0Ll4Ljo9Vbt/bPnFISHM7Pr964QZU8npu+f+MJ0R+uXr68cX45iEYrFy52z5yMkd2bn7t37XGaZwfz82vXrvLBcOPGzfXlZbvX3bhwce/COUnEsN168MQTWoPj+ZMrN69bvd7apSt7y2dIVuydPbd1cTnFfNRq3X/v+1iR9Zrj9556GmvVnZzcvnIx9su5Yz987zPDch0R/PZHPgKgGdSaK0/eMoz1xyc3r10Z2oGy+MP3v6dXbSCM3/7whzABvcbE6pO3Ckz6rbH1q1fCWhNgfO8D7+/U20jJt3/zN41gYaWxcutGZjmjSnXtxrVRqw21Wn/68e2ZU0Lmb3/kw9KxIsd/8MRT9NcSNGp0+NGPcZepSpBVA2mJouIrT3Tm5w4WF4VOpGVlY42kGhiGdMVLKn5vYnz/3GlEEcYmblaAw5Tn561aUgmiWnnnykVbp5CzpFELqwEVNB1rQIE6MzObF5dZEQObgUoQ1ssE6bheS0v+cLK9d/60ALkmWLYaecnKLdt41nCswbDKa9W8GiiGs1o5qldAIPBCref5whR5uTwab1Ji0ko5r7hxo7Z1+TISUDIKXGvYbjJqiiAYtRvUhulcU1UchXFSqeZVL/Nd4jmDiRYH+f0nnsxKrq2SrFbL6yUpSOF7ec3Pyy49UevXqgzKwvfDsZqgclBpZI2SsmhAC1JClBQC5bCMKskR8WLhxYRTTHXZiqCLODWohCnJS2AHBYXNIGKAB5oKzZl2rEJZ0AN7xFeAUE8o7Gsbp0ggNwDGhY7Zd8WocByPSlpCwCE2TFhJ2zijpO/6kfJ9gfKSnSEbOESCEgE2tkHERZh7CKHcgR3taov1qvZ67NgUSixiB/U1h0CkygXASotWSKG0dQR4mvnEAjEWCaGJRY8CeyVyfUqkQMPCN7YcQTtHPANWFLYKS4cYSW7FQOSSQQuFha9K8MC31xTViEBgSxv3C6IJT7RdGEqqaAQrRKAcW4B5muFRQA+FC6RjlUiqy6Si+9gipoQcMLSsnvA0YojTQrhKW6TGM1WhWCYlsqNdzQi2PcB8g4myWOHwrHBI2Wyomo21KdkKl4yFcywA9wyBHCTBAAAgAElEQVTmuYsPbadQtu3gFNSYhTJbGOgbxhRBKRehsg1FqXIKQtI6f4TsPoJAM0PsGPICUcXp0NA8r6S6lGEMLBAqO+Y4RlQTO5EOCNBWiW8lls1xjtxM2YibELpZII97LVKUQ24UJQUTsbaVrfq5q5SNmvghsw8AhIpoKkLgaMgMJZFytINyq4SEKCgpuKOBT6mj+RylsBAm5Q7QZe6QXNhK8IJ5hs5yrxhJwTyaqir3TGw5htmKOxJMWTaIKVKBq7GjtaBlloIAVlA/XagKklqosAIkbAmxclhGfW1cYs1gzI2lE8o09bVDC+Yj6FNCVQCHeaC5iQmKc69wYD8QG9iOCkwwjrGIlYAuCvMysGV/2M6hHZeyQWoT6GSY5JTEVMRKFGP4ThYgAjSniXJShhPNFBUx4HnSyBnpAQQsGKmKAdRYcCDt1ILDgG0yP4qFw8GQ8Uja2AXDogQM1Q5M3AAYj3CUeTwBHiZNQhsYEWCBlAfKphkT2PGV9ijzpTUOLZUJolGAtUMCHIsysmGMGoRMUCpTwYFnF8aFDkhQgDgraJOgCebmI0S15xvsaMCQbSviGKui0Ti3TMKIxj4UPCfElDyVl4WX95HItKsYSJStEE8dfFQV9wuLFZQIPCp8bcMI0xSKFPJ41Fae7hmoMU+hnSmBSmSQ+SbQhwF/VAhNsEG2xCIxSFIcQjtVXBatENPCMgkiGRKhgHHuICxyBjp1d0VzUzBqgVFe0hZICM+0nSGiBMlLdiwd5lJpKsxCcQntwJK2GEICCE9ToS0mhaW0MCWzCSsUQeTZGnnKxjkR0AogsHQAdpmVK8cukQxVKRTIgTEtaRsnjPZsL1OO4+LMdSUWxiW5qnDITAVu4ZKyCCBMMx8Al/g45R7QLhN6hJzMA71CYCgS5YEA7JTdrZhxW4fKLgoH2SCETsZhmHlpNpa62Ug7lJFQ28rWI+1IJGSA1ol3BAkCWFpWaCypKWAkMnaOnDBpFMLEGEvgFC7qpRRSHhk7F7Rft1Zi2xI61jzFPKGkQG6BeKoZrtBUl3FFD6BDgQ8c0LecIXcApMhmOfO1EaTB06JCmQx9umccICgWAeCOQkR7PLd4ltm4alaLesCU8m1JPC2IxDbgjkY0K5Fd7iFlWT5KTJ1bMLUsgx1lw1CInsULKJhHE+oZLUiVJrrGBMgyz8sbFekJ6Xuy4qUOx3PlcGJMEQIdPmzWiUCFH2TNWhJ4Rwvz8XgDEYAcazTe1DaHDEeTbUGLcH4imWrkXGCbp82qdjkUtDc+phk9uHD26OScH/dT1y2alTywleNmlUA5DE+XhrOTCOrC8UbtBrSJtJ18rB4HLocybNaTakAsGjVr0MKdycmtKxe9bJi6rg7cqFmlFEbNJrRxZ2Z65/xZu4ikbetmLS07hW2DwE3LTtooP7p8leICC5aOtbTPc86Lsp/X/GGjsXtpGTDIoApr1aJkQ1vErQZwab/VWr9+tZx2R14ZeNZwrMGJSWvV/uQke/klvrHxa3ERfviTn1r47vf6s3ML3/jO6W/+4/HM3ObJ032vSvPsI7/7+6e/9s23P/KJZ/7o06e/9e29pVObM6cj2+f98Mk//Ysrf/2Ve08/8/Sff/b8s1+7d+Opg/GZhLvB8cGlr3z17Je/fjQ9v/CDHz3x2S+sX7ywPn8msnyQqaf+8vNnv/Jsvz0x9+LPL3/hS4cT02tnL4Z2AAB4/6f+ZPm/fHnz6mNLX//2pb/+Sq89GewffPB3/02/NVZe2XjvH35qa3pxzu1+NEuPwsPy2xtP/OEfAw0UgB/5nd9TmL177VbPr2Mpz3zruVv/159lzLbz9Ol//Ycol+b8/P/Qv5MBwH/y1hOf+tNhvV57uPH4H/9HQ6z15fPHpXaO+cKLL37wX/6fKbf5IHz6D/8otV3XSz6ui3SwpXfi9//Ov3aGYVgtf+h/+z1ozGrr3OrP7OJYuzS690LNbCRu0MmbfuE78tFo90U/7wFPDu+9Xsv3oWd2hhfn0c4u5KWNb/DRPnWacud51u/YgdwcnazDNIV98PCnZfUoF6fo9tfR8a7lzWugw7Tso9uH6/fa6RZAVbL5Y57eL6wTGqgonBkv7g+OXrZHO7gGOqvvBP01ZuZZ/fY82FwajQ+Ct5vq8IIN75xBO09EnT3EBluPe/cmQHmL3J9XO5ekt9OrlkdOlURF6Z6vdq4pPgKdSefuiUGp/6H0zSdB/AZwxYMZtXsFgwPW9fXqFYh7xxNOpzyOVVJ+e1JvXQLeFtlomu0rTG2cQg+eiTs9U3T3r7K7Z5C/KdZa+dbjWGwMh+jRDxyWppqhtX8QACLL60UTdRTH6W1w/9WalYfVuPvyz2dod8jGEtkoGSWj23Lj5wHjEhzLd39R4lnKks3R9dN0cydNa+vfIhognBWrP/HsPGbhRueJZXpvTSb+gx8GrJ/CGl3/BpcKO6VBNFmHo2Gxoh++2sC9hNvq7nMBPM75ycB+9RSI6kwe6vWLqFPh5OEnwMF0b+1tMeG89rjutFVpj94+DwetIugc1FuRFfBOYt2+LLbL0dSx/8py0ZsFrZ3f6r11Usm9CCebt8hhK/Fidm/Z2QhgZWVt/HRs+WRggrsnVXdK6B2wtQT3ZtNS57eye5ey3Vdg3X3jguqeAs42Wzmpj+eBODx4gDo/J2ISdF8Dm294fjUZtepDp4JHmXwjXHm9wm012BeHPwRuNevOjPfcupWEg5eKtbcqPhnJDnr4c99mWTJbPSxPEJDnP0tXXgv8ZhbeNQ9/Gfh4CCbdreocVjJ9JVn7ie2Mg+R29uj1Ss0djOrVbrmFogLeHjx4sezhKBrxze8x2MSVvqUfXudZhLORevgbvJ/a/sp/H68goNcPJ/mD68BkYqSKtZtslIat/LAymVFrbOVQvfsBICnQKb53FSg4rt/6mMmF7OwmTXDnvWKAknLkvHwdhiRq9Pcbc9Boa5/BR4+LoQltab95nXYIqqz8j8lDC+NHg0nx7mWSaCITtXaTDUVvPOs+mx5uWNYcPX6u6O1abi3vtNqx5bOj0dbzPLqrnFP4+Dtwf9Wx5mC33sqYZe33dl9ihysWadH9X7D0tnZmi92xuUj4cJgPfqp211yfhUd32eFdy6kW3YnxgVsVSdT9R7X50C83k8PX0P4D2/WzsF3r+K2ge7z3I7L/Fg+W1Oh5vXq/5E/o0p4nVx9HoGtGVXH7QuyNricvvx+laxIUGyf15jVa7IlIyEe3MBh2x8lRecrKB8GdWrF5A4odtlfO125Yen+OPPi4DpMi2uleEG+dL4IR33XM+uMUbB7X/V65ZRSormj94Em7ONK5Be/cTO1sWb36/mzYKaJwb96sP0HSIdIA376FiOoVw+3nuMgTOMrvvlJDg8KBu/1Li3hzq4C1jW+wLCWUyo0f20VO7eFK/9oi2zlQsXX/Bz7az8g02/o7MoxtrxnnFWGSRK8XD19v6I4GVbr+bSa3C+uE0lBGzVp2O9p5xQeDws/7d3/ZyA8QFv1isYF6vSKzdr7DugOPO3rvBRJ2Ca8g8fCis1bVjXX25rI8PlM09z/Uf+1MrvZiWWxcgnsn8yCiq4vW6mRe294Zn41E4AwicW9eHS5RuI8PpsHmgrH2PwBWnyiOXocl651z8uAcdDbJ6gzYO2XYYbcd9NyWPew5txfh+iKuPuB3TuaHl7n98EJ0+6ZWe8NBtPcYXV0w3jHemMMPF1X7cPhmdv/Vkq8Hdi98/aUJEfXwhFYVzxT56BW1+suqH8T5JrjzUtlVITaH4fl5un803CtvfY+yMpCbxcOXy3XUlf3t3nse42/ej0fV1R85Que5ppvf4chBgnSGp+fY+ka05Tx4ucKTECLw4Hs+iVLazrKKqzWMXy9W36h5cJRH5N4LAc1l4A5/+3/5p/bu/qDReuYPP+3uH8m2/SGQBvnhdmh97J/987F33n3w5JP/5H/9p+WV9dtPvqdbbSmEm482nvjkHzcfrW+cPP3x//3/mHzj7bWnrv13g9stSrY3+s98+s+CzR3N6LW/+PzYm7fvX715XG+n3Klub33kd/5V4867vYnJx//ir2q37+VjzntxMpXurMLqh/7Zv5j72S9uf+CDH//d32/88q3dU2cuf/lvzn7tWztnli986dlrX/jio1s3t6YWI+F5ve7Vz3zxzDe+FdabJ154cfm/Pns0O7e2tDxwq0jLD37yT85+5avbly+f+do/nP/yV/uTU3sLpw6CNi2Kp/74/37sc//l3gc+cP3PP7P8lb/dO3d2e/zE0C3bveGVZ5997DP/+XBxaeG7P7jxZ3+5c/786olzseX7UX/x2X987AtfTINy/d37T/zJn2+fu/BrcxF+/BOQ87hWXb96JfRKSb2+u3w29gNtENcZgKQ/Mfnw6lXNeVYur924FtkeBEBTQrTqTUzsXjqfEz5oth7evKUIgcDYMtWE9RpjqzdvKkwGtdre+TOjoAwgRFprhArff3jjhnKdUaWxduWxJAggAE4WSUSjRnPz6qXYLxeOc/+pJ7WgCXe3Ll8ctcYy2115/EboB9jGd73J2PI0oqs3b8T1Ws6czWuXu+0JBbGTjlLLhQDev3UrqQQasUeXL8fteo7RJvW7ohI7/u6V5eOpWaD0/Rs3ipKXCJcoSY1MiLV7YbkzM50z62D57FF7htj4ntMauRUtzca1a3G7HrFg9+L5zuyM0pCOYTzGlMbWJGEVnecFAkQGJcmFWy7QOI+py9uYjVGQxFIiU65IjVgD0zaWmPKyMS0CgNESGseFiNIxhCcYMJjWoZngMAyxgbkTGGHjqlEzHisKXtdkiuswBpBk1SYlkPrGnkQZsXCLmQkby1yJJJwoDFaQqqzRSwTNLXq/PJMZBvnIVPs557kowimlsEGmwFoToDOHhGOxwRrQMB/LchBFCN2pnNAGQAbC9hARKQUq6oNCGIWwk8VaayVM1holFoEIRmMhYkVi8TV/IkaW4hJWOrHNcw6L8VFsMaI0ncGkjBXCbBJDD5goRZaj/EABxE5anhWPWJnPEVrBYBQriI0bGE7ZFFFVbgCis5R6WGWJkkR5ASKIjSNQp5pSv57HZQspqQ3TvqsN4lMQ1ZkGSLQxDAxKMkR5WqkDg8UUpjWsDeFTCDWoUsZ4cVjLJBZ5OZG1HJlojQX71SmtofTivJlITLWbpNU4JxhrqTijuZHlYd4olAK5n4Wt3MhkxNgj0cioq9woHDOkKJQf4qDbtwOscmg0hLqwiqQ+goSkJTVsGgcOd6DYrExpALUns1ZUQKQdlTUjAIy2IZlhgELiILUUIAYQUAgYRoy2OJ7jkAFuGTZNlM0gNFaWFJYNKSILgtJCcc6nMPCpgkgUqdZAC2LPIuXbGkH7BLNIEnJXFCnURjuMTSLjEMgJO0ELW0AMkNGAYUMInyXAwZAjOGsjDwCDwuYIerkEIh4LVSlXWG9alQOnoQkylWFYMhrQbGyISJhyTlRhqzRmjqyOslKhIVLlwdDVjKm7tDSwS1qTvNpLa8YYUFSitKYKRKwiVhgjQJNKP2wynuV5fZQ1jNL5jlvfcJsYaFAajqrAGJJVh2EV8SKnVWhmLYwAL0EyRQvKkFHS4rjQoq7xlNAawjJGJ2wADFW5pXOJOWwIMi2QlKSk2DjJuMBGAQMA0NTHbAZDgmFNiBbQFjXG2HksMSVlIqaR5AJ5hM5SSKEBkJlcA4rKiM9QiTB0MTppUZNJTrPmyHAjuUzHs4IVsVHr3vhAeBChUXsEWKEISat96agCEz8bKswyhmR7lNrYQBSNDTRJMwzuW/URCwqsVH2YuUhhoMYGscMhVFgqRGGBkGr2lQNSRmRjmLlQEr1hjR15VQJg2BwaVyoA81ov943BNKhnRdvPIbfmKK0SE0VKUxOUFIBiDKEG0ZiImpJ1QVRRKGw8D0LCxhEaY0BD3MK6hnAYAkCyoAwxpWNATgY8TcU4wi2qR7FhlizVCDJWzbA2TaFg00xPlqxRV6daV2sGINJiZlJgrWgDm0mbSK3dEFYGKbULJxtNaURMSOAju2UwTwMdtXKolbKHqhqmnCNTWFmcUW5sHTeGEJnC01k1wSTex2zdaRUAaltnY6OUMc1M2hhAIiXEXGcAkMLNYaUTWU4hVN4eJYIrju7bLUns3NF5I1QYFlYu60PpcA2hWOAWT0O7xOcpsYEZxYpa2naBoHye5p4AEPI5gm2o0lQBph0PccgnkSpxwEmplY4Cl8pMQaE9FyAopiEoUUCQmERaaJplgNtZpQYNtGYJKWMDCZ8loELgIJLYkn6AGXFmYVZ2sAF0GqEqVYgdLS4cnD4lCT9aPLF9+hxx6CotbZUnrSTZeuzK8cK80nBvcXH7/LIBUBSppBRLvXV+eefcOZbEe8sX+icmQ0QP3NojbxxBeDA3v39qERmwceHywYkTkjFktFBZJtze3Oz2hWUJydHJkwenTmGXrmDvyGuAXO+cO7d37rRU8HhmZv3KZQpBb3xq9fIVCGFvZnp3+WxseQBCO49Ty0vL5Uc3HjcE9xrt3eWzYamiEbKzqDBkVK1tXr8SBrXMtrauPqYsFhObqwwXqjs+sXXxXGp5cam8dvNa5AQGYwQUhGBUa25dvpgLe9BobV25ELmBQag6OuxW2orRlSduFTYPg+rKjZv4rV+Ti/C3frtwvahaC5v10AnCSiVq1Sdu324+Wkma1cgOBrV6r91WlIfVSlIrl/4f2u7r2bLrMBP7ymvttfM+8Z6bQ4fbaADdDSIQiSREihrlke1y6Y9wlV1TssrjGVpVU2NJtiRS5AxFUdRIIkWKOYEUQQgUARCRJIjUAR3v7ZvvPXmfncNafhj/ATMPev+ev4ev6qvf1u7S5XdnrXZmmrN2O2sFMTPCdme4uNS7eX395z9Nu83Y9sbN7qTbrbgIg0a41Otdubb05pv91bVKiMjzJvPzmpNxoz1aWpp7773Vt95Mus3Y9qZ+kHWakXRjzx8v9ADBU68ZzXUy04y8xnhhPs/R9O27g+4KFHjqtia9XiXF1G1MFuft8Xj95VdqS4ReM7Wd0fIK4Hhq+5OV5VLj4fVpxoy02Rj77bTbnNl+ajvDhUWzSNZffBnrOuq2IyeY9Tqp7URukHRbIXPDN7cGjWVtstAOpgs9aJCBPzfrtjPHqTkHHhOsyA2pG5w6ZDTt5NpiAcqEKWQJbJJatnYZteDkyCmsNpdVxiVyMbFAIixiAxiYs4lbUZ9ZsBBcNwSTdcKlcqjyZJ54VcZRW+ZCAJdpiwAMYVMwU4/jdp5I3ZEaI2IBaurckKVnAJtogkoJclspBGtDAbPch+tb6WpscUBVISGnk5m0MwulPrYHlXVAC0shoWPJCkspikoJkZkeolM38zOZxyoCSwFKp0K4jkxRW6U5UvKQKFtnpkwlwkaccEMJXbg6qtwr+f2pZJqrxKScTWJpZhZFRpwIqTgBPqVcFYZBAqiYNZ0ERJDMltqgIKAOiMZOAzUoxng8bmphQo8VUgqzrE1DGRQGjAo8OfJ1o0mMKpeSeBBIUkjDkVHW7M32ZdnpCl5m0gQBI4ZKhaQeUIY5HgeEksIxa05AgzFWZYaEAaOGSrmsZFVLVQheyhqK+mZ87tBYJyJJmKzMCoos4VLLUrGanxgi1LlDFEO5pZDIUy5LS2UWOczX96te5ZKM8dJUpQkUBoWtOJ/RI2mM0KxjaAwKCbUsNUWpiSpT781W7qINaqYpN0tTQZHGXJamVrLQGOa2ZLaqDKO2qGoZtJ+AmxHwGOYwtU3g0ZoSbVHSxOg413di6qNUmkpS5BNCVOo4OMB4kIBbEbVgZpqVIwynSgwLSoJ87AxH0Q7CAtSuKEzBbFWZsrK4tMoyJ/p6hCXCAmSWSTykGSotAwRMMaIYzO2a0CI2jMosFCHX4otj6iOzygQHMs0MWgmS2pqh0tw2jCwBdjk2/coslVCJFMBIZmbj+mRjYHUoyyJhFI6CvEy4Udsl1rV1wFhVJzbLGckdXRlQEVDYlWLgZnphH7SVpWoMoMwySQpGc1tVBgQE1Q7VHtMUIQtiA5R7JR7mlWsoimAguKxjKoHPlMvp3Rk5jJkLM1OWrqEdCgisXEY9hG5H4CAt5lxIsbIocZCmqHAlc7W+FeOTHLZYJqzaJtxWmTCVRakF8H5cHVfYxqXgyhfCqmMmtcuhRyksY9NQZqaojk0GZTJES9ez88pUmTBqrgtPYVzNTFm7JU+gtYsFiiNbJiZHMkkNoxIgt+oC2ZezC7FpQl6lJgNmlnNSSAxlrAvm7jLNFRAqMQxtpJDWU9tCMh7h7nvx+VKCRIqagsKtESljw6jtUjNdCMMystyRpTSUx5gBRgde7TWZrDNhEhcCC2eGye26bgTRkVm7DW7UhWGABqeGSoTUHlW2nY5thJkKZMkZDJiS/7W1ODHQeNYuKwmaQlPEbI0NmEmz8jnwRLLLc9HkDsiFqV0KHKopAh7TNtMYFpY28Hhi+oUFUgueJL07xWrtIMhAbJLS1ArDwobKreQhsk5QGehIyFICZVaKwNhkSCZ76eoNcA45ZSZkJgEykpQZldTKrORQsyEDUmWSpyYy6GhiBYUJoZEegdWb0WrhcsjqxKRAZgWjuY2RkcRCQoPCgJggnXhNEiCl+DhsYpsrW5YWN80ilxaQBDYZwnQ6DGDDRUIVlmQuqCSvTOHLWdhYjXZYNd8RrMxMCwUUclgYgrs65/7kxBY2SSxLcwIblFKVSYkCAhCfTBqlaQNP1AYRssgsq+YUNhmldSTcsNdJA39meUm7MbMb4eW9IXaTRqMSYjQ/X7hWLJ3x/EJuGBuvvepNhpNWJ5HWeL4X+4GidLQwDyXp7+tBqGYLiyVnk0639JzMtEZz82G7c/65Z/3j48nqUizsWaeVNPzIdKfdbuZ50XvHA+1WnhUbzmh+oQic2HAmnU7camqCR+3utNstKQ3b3bjX6V2+uvTWm+Hywsx0Z34w63VrTketuWiuM3/53fl3ryS9diSdWauddBoz00uCRtxpzN96r3H9Tuk5seuPm+2sHSSWN2s281YgT0brP309aTRy25w0u0mnmQlj0u5Gi91ge2ft9ddVII8bi5nrTXpzSvBJozNeXTVee+VfxCKc/OZv//rH/+yBp78zPHPq3m8//cC3vjVdWrjv+9+78INnbnzwyY9+/BMPfPUrV37tNz78nz754De+NtxYO/WTVy48/d2TjVOXvvWtJz7/d7eefPzD//nTD3/1q1c/8tFHv/SlC9/59mx16fQLLzz4918abGzc98wzT33ur2498djDX/jipW9+870PPvXI177y2N/+zWht9cxLLzz+138zWFvfeOknT3z+8zee+uDjn//7x//6r+8+8uD57/3gyc99drix3trf+c2P/V9xr9u6dfvX//CPDs9urr71i49+6pPxfK8xOP7VP/gPmmCs69/5P/+PNAjcweCpT3xytL7SvXr9l//0TzLPs9LZb/z7P0AYaop/52Mfqx3D6Q9++Y/+JOk2G3f3fuWP/qiWkoLql//w/8FalY71P/3e79VSyDD81f/4f1e+7R73f+UTH0eUAIH/9e//WyueFYH1W//7vyOw3u+eHT1X8kHk0+nhc8S8O7TWweBbOeyXTiub/SBlJ4kPJoevMnIYu1Z88jw2twdyHUy+nqm9wunm02dzdFJ4JBz+FJG9mefEg+e0uDM1TsPZ16J8p3YWdf7DiToqfBaOXod4O+ZNPf1hxt6bWKd1+p0o20dyQdc/mtZ3si7uD35OqtsFWsPLv/DtG510adj+hWFvLxroOu+3gjtzyBjI/Ub3LRP72+7VlnVnKekMmrcdurfB2ba7R40b61AM5JHf+YVftY8bV0xvaylvHrXeI9atdUZ3nRNmXl1jeJ+OG9b1lbp13LwmzOuryNux7kjz1mmh7tgT7Ly3wemB6Fv+WwvM2XJuGcbNs9S6k07K4fdKO58yUo2+nQuSG1E0+VFtOjk5ik+eB349aiUnt59z3GRMpR5/txAoN5No+MPKIrEYRUfPI7+cWHp28Bzzwr5w9fDrOQelKJPxs1W7HqAwPXjNcJKxJbKj7wJ7PGQBmHw9Y1Uh6mT0Y+WAhCTJyT9DOxxJWfS/o83RVKyarVc6csSF2pc31q1jRNmxc+1Mc6/I5k5aP1k1j03g7vlvLpoDk+JDdveMHPLamjTeWfXvqGzhZO7lBXEUlO1B9w3fGZpc7xp31twDo3InzbcXm7cBaG9Zb5/C4VIR9LtvB8ahZ4Bt63ZP7rhVMGm+2+vcZenCQfuVjjzoQmvbvdY291tE7s+uZvkLsbNYVT+NJq/V5ioE785O3uZNdwa3o8HLyLGyYlcVz4XWslKXs/4vaDCflpeT4SuwYUzIcdp/HrgiLPtg8jK0Fmv09mT8MgzmEnUt6b8EOmQghtH2zz1Pxuqkmj6TeXMFvhL2X4Jtf1zvVgc/E002xZNs+KwKaPhfLULaBe0pct85bWYnRjm23rpkj/rAStuvrXOVsSL0f7HB1dQII+vKPTKZUDQ2rj5oh2MsBvZPL7CiZGrqvblhFCGPQvfWeWs2InTq/PS8N44Kb9Z+YY1lNQETef2crBJSxvblTXsaVmbaffWUfRyVzen8K0u4ZhiGzV8sWrOY1lPryjl7WkRzafXVQXKjctZh8cy4uFMGMhxeEeVOZTRV+HyO3x3bZ3T63Vl6U5trKH85SXfAHO+P30LZ9crogviFHL85sc7q2T9lk21uL6v6J9P4PdWSk/itIn23DJpJ/+e4uF5b50D1j9Ppu7rZi5PXs+iKbhnj+BYMrwGvnUWvFvnPU/9MWf5wOr2MveWqs4fMK6cpOBRjI/jFovL7jTvYubGK3F1zm1s3znK1bU21+e45AY5obNhXT1Fr17lLzOubxNw2D5h19axUOzIq3ctnjXqPprzxs2VoD+xd6Fw7zemWGNb0JJ4AACAASURBVBB+54LQhzyrvDfO2MUuzaD9xlkoR9aR9q6fknSb9aFz9R4jP6B15f/sjEBhXE5mT6dePmJpevgiNWehZaRHP8T2SV/MgfFXU5IUBk3Hz1Zmnogi7r+E5XhsW/nJ09o6nrJFGH4lriNtmUX0XCKiyNRp/wUohzMc4Ok3Y2N/Kpd0/K2oSrC0yuSfYj6cBfXo4CWGj1PWI9HTCd8LjVUVfT3MhtCyi+SfE3CSm03dvLLcuo5ge8v7+ZJx0Mta/bkrjnnQMuCWfacpt5u1N/audtvXjWz+wH+zxwYr0Nly3muauz1OdpydhnmzieW+c7sX3PSK+b3uzzxjewm7W9Z137y7YIAt0W+L3UVoHbq3Ou57Ldp4z327Je+uEeuWfddzdpYkuC2PPOdqG1uH1p1OcLWlu/vlu9P+i7CFBs5odPfH0kdTnajpD3O7WdI74eB53XCncCc9eh614Iilaf9HKAAjmKnJ93LTK/BePPqxXhKH+VF9/Br367FQxeB7tacmoFTT7+bSKdFhPHoZNs2p6hf9HwGvHDFYDr9bOXUEoZo+nZKAiH40/lEVwAFK1MmzwK1Czwr/5//lf20c7ofdzq/+4R/7o34pjA9/6lPN48P+2vrv/t6/6V29svXoI7/7v/2bxvbd0erKRz7xcX9wnDvuhz/xie729uHZc7/7+7+38s7bew9f/I2P/Yfu1p3h6sqv/tmftra2Kkc+9clPzV++snvpgd/6g48tX71y/YNP/o+/9/vL77w9Xl784Kc/07t2rWj6H/rsZ5fffmv/4v2//W///ZmXfnL9I0/99r/72MrPfrZ/8cKjX/zCA1/75uH584984fNPfPHvr/zKRz702c/d993v7rz/oYtf/ebDX/6HcHHh/I//+aEvfLF/5tT9T3//ga9/4/pHfumDf/GZR774xb2H33ffd773/s//XdzrnPrZa/d+47sn92xe+s63n/jsZ29+5KnH//pvH/27vz26eH79J68+9KUvzRYXVn/20w98+i8Gm2fu/ccffOgv//LOE49f+NZ3Hvzyl8NzG93X3vrQp/9T3GkvXn7nwx//5MG996V7u/8SFqFOfu3XTAvG8+3w9Eply3hpbro2ry0xXluKV7sE1Mlit3/fWUh1stCZnloqPSfrNUenV5BJw5VesjpXO+ZsfXGwsYw5yHrN8Znl2jJmC93xqRVo0Wh1MVqdqyWPVheGZ9egSfNea7C5zlGVBc7JvWeAK+PF7my9pySbrszPTi3mjlXONwdn16FF81ZjfHa19GTSaQw3N+rArlvO8T1nkEVT0zy+sKkDmQXBZHO19M2s25ydWU5bQR3YJ/eeRT7PXXtw79k6kFnDD08vZS0v6bbDU0tJ06+b9sl954DLc9c+ufds3bSLwBtvruUNN3es6dmVpNNUDev4/Bngicp1+udP6YYZNZvjcxtpy/NJbHZq0y1MnBpzynNzQQuvlRtOTWDtORFrAtMorXZpOYWLY9lTjpMJUjqd3A5Kg1cNd8Yb0KaJ3SpNr5A0szql46SClF47F01twVnDjXgL2Sx1mgX1gK1i0ah8L5G89JopbyoXhr4b8ZaWNDObJWliW4+5PFZ+xOHMoHvAnyFeGORIBZHQU8jH0jyqacmsoXJDgSIT7ahWYoAJFhPViA00oUaf2gOMUypOlD8TNDbxQd2MJRxDNgJBKHCIjRG1TxBJmRwQd1gLYMAT3ZxJOsV0BP0JxyGSE8M8rIWmog+9iRaoBfrmErSNxJCF18kNq7KNzG1mQCIfxXQONowZNWq7V1tmZorc6+bCqg2R+p0Mu6jFZrwHbJlJmpg9Zdu5wQuvnRlWZfGkEcTaZwGPjJ62ZWqTRM5raZSUKKdVSK92eOw1U+ygBp0aPWDLzKCZ3S25q7ieCHkIvIyhEDpjaoYCTrQ75nIEQYGcIXFGEKdC7AM342hK7QFwEln3oTPk5gijhNonyo0lGHN+oP2MoSmxBtqOLTXQ9tAyTgqimHWinZmEE84PgZdwFWp7BrzYAkNknTBziEBGzSPsTDDLOT9WbmzByPUKp5lRVnheLjtK0rwtxnweu2rq4ilboo7IXCt2mxmnhesmTitHBmzLqegBhyammVk9bfHUdHK/lXFDeXbstAtkgLYRih7w6Ixayu5WJs9sp/A6BSFlEw7tRQ0t3OVT1gM2T6WRW3OVZRS2lfE5xHjh6EPVmHEWUjjRzZCLKcUhcftQphBGzDnRVinRSdWYGXTC4Fg1JpINa15Q5xjLmOIxs0+UXZlwUDdCg08ZnmpvxMWYoIi6J9CMKZpw86h2alOfgMYEyoSXY+1PDDkkOMbuEFixBH1qHSu3NtUJDCaVWfhq5LVzo60ljBpOKFrQYpnrZ7wFmtnAauROMzdY7Qcpa2kHhg0rFG1t0NwKSt4EAZq67cL2UopLv5kZbWWRNHAi0dIOmhlBZTdKm2VOI3f9lKHSC1KnVTJcdsmQLWKHRLZX2s3CYLnTLL0go0IHbox7zKtHkI50EAoaYjGhdh+RhBpD6g5rAQTo69ZMkgkmY+RPGJliPhHmoTIUEUPiDCpD2+hAtSKBp5hOYDBlLKRsQtwB4jnDx8QfKUNZ8LBqzgwyIXSCvCGlkRI5c04QTwU++v8z4KhqzySdEDLV3gTz1CVRI4iAT1yeyDltm5mDY2NeS1kQpO1WYfq1zVO/kRAP+nQmu8qyMkkza6607VTSwuuWwqsdNPWDhDSIj0Orp4VT+Sg0O4XrpJKVbivnfu3DqddIsQOELowuEF7dAiOnV1l2ykjltQojUD6Z2Y0c+dhVQ2AOTHlcEs2svnZnBpwKdgCCmOkQmBHwIhOOsOxTa4hARuURcUaIlQY71H5s6BGwJtidcjRD1pBbfYhLZp1gZwRYJemhbsQSTZAxJs6YoRBaI1MeZowRMaDeQLPapIc6iDiaQBlib8LxDMsBdKeQ1m0x5T3t0ZiYyu5WlpFZVuG1csihwXK7UxIbdviUzSOHp5Jn5lxtycK0CreVC1nZRtxsxqUjmmIm5oEtEpunZk9ZMpdW4bVz7miPhV67hBZs06mYBwbPKa7sTmWZqS1ze67msgzoxOuUyCUtPJILSIqsto3x5no83yocM1qfj+Y72jGG95xJugHEun/uVLHYrC053lyfLXSgxWar8+FyT1titLmRdAIq8fHmhppzYsuenl0LF7vIJNNTK2mvoS3eP3cqXuhQgwzOrqcLDSXY5PRyvDxXmma4upAutZRkJ+dO5/OBNo3+uY1sqV2bYnJmbbK6KFAxXlkYbywjm0/XFuO1Oc3pdH1ptrFQGxy64uTes9Ck0+WF2caCssVkfSlZm6ssM1mZn55ezG2zmG+NTy8jk4yWlmYbC7XFw/WVeGOhMEW82A03VwrbqrrBYHMN2SycnwtPLyGGsk4w2VypLDPpNZMzCzMvqHrBYHMdOjye7+xdugjefvu/d8H6b6BygK4RHrQX2GDSb/eKaQXyetRZkIeDBMkc89d/9V/DWmkABt1FYzAedpdkcUTHs2FrXh4Na0+FToO5LZSUEycwu4vW3tGgvVCnWk+LYavHJxF0itD2UdCl03Tid0gzdXYOBt2lWaOVPPJLJedkElOvFUsHN7pkEE78NtZmsL3Xb8/7k8HEDiZuE2htW/tTtwXNag6LfnvBhFHw+MZ4fgVFZei1QrdZMGZaB6HTmBn2HLs26C5ykAfEGHQXNENTtxE6QWZahjyZOY2R2+ySK/1mjxigQeWovVhacuK3p1YjWmr5LIqChT50e/jdk84iMXHCrUHQRbYgXmtm+4nnUDqpLAE9A6Bp4VlTCSGdaopDw2DBPlX5zFyopxoYJLURwHFmGsqkSEwBRqlp1kZZlbgKHHAyqyXNHKJRVEkWmRKyaQ1AbprcyOqsygIPDCLMQSJEEOzUAI7MdcaGWMHMlKaRVnUVe47qR0igTFoZDQGoYkMimdNRGbpWHVOi04gbC9a2A/tDp5cW0Myz2BAkzUmtJ44FM42TIjGZyhVBaSxtnEERFYkldJ6xupxZHJcVAGVsclUripLYMIGiZlSmpiDkeDm+/qb9oIo4hXVsG0IXGCSJMBIq7LBMpUi4beph7khICKFpafDC4IjGCTNyVrntw4k7P60doOvSc2qpEI0KzitHAjYrDJa5Ft8ZZp6FOIUwSnxXCYVoWHGuHEOTKBM8bvhkd1Q6IpUE4DSxTCJq29uqpRcZXULDXIjct+n2IHMMYjOA4tIySi4gh3UBQseyJrpkurK4eVzlJi5Mb935GYLgBttkTONMhY5hTLVG1cwUHFQ1V5npmbSCZR1LwVgOKj1xLWMGKCgiaZiwKEldW24Z1qLMY8k4KWCJIleezq6UmAzNTQFKQqrI9IRQJNapyWGiONSxKfikULSKTKdOCxImqWWyXBMdJ66FpM7WTE0xPEg41alha6vS4yS3zJpQdJjFngNmUKMic0yqU8SKRFq5AmwYp5KXhLH9NPE8A6RgDHLXhkkBWZZBmq+ZagXRIsy0KXbSPHBQhjUpItehWYZInBtGTZGt0tCknNvyoIxtDgxt4TxnRNvpSvzS1G73zSV6VMUmqbi0VDEzeW3YapJpqlPLMAa1ZjR0hTgqUolKUxiqSEysDctESUlRahmyX9WchLZg+2XMwcyU59wXB2bz0Fy1SVEgmpjMgHnJ8MS1yG6eMxAbTcH7ugSRNLmZq6pIArcOM6yqgjH0QBtoFaNKsQRrnUnDlFmd5pHnqXGCIExNg9NUQTWzvNpM8KzILJMbBchQGjhsWAKMM5MrnCCuEuHUdqGLLHOtSpLydACRopNJLXBqCcgSWKuZ5WVmacRxZplJksJxEVusTioGk8QwNKIyLHMpIBusxFfetB5SmcFBGdmcJzmFSS1EAgwrLFNTJIAah+XU4rIEbFjHtiRlbqA04aLm3BrWuWXGgIuDIrKZrkquy9i0GmB3Tj1/aCzEqIf6ZWqIhAlzP4stojRjoIwNXHMDsCrnLG748CCsbZ6aRJM0s2TBoe3dVqY14z3M4oLz3HfI3WFhG9jmGsaVKWIpIJsWBFW2yWhScpYGHr3b1ybNLcuAYWXxmSUhm2iKc0saNC4YrSxmtPYTaafSpxSWDJWWq40ZIDBzpCBJLVhqG9VxpkhZm14xq2WWR5IzVtQJmDrmRn4N6nponaODWtMyMj1uABaWqSlUrmCsZ7bAs6xQVW4aeqYQSmLpIJPIrEgsoWqAZ/XUNXWaA6JSS8pYAZDMDJeRnfnw8Ip1bw0RmVZTV9plpQudWraRlAylsWGWWjOVpp6fRimAunAdWJWYJjNmQxx5zeOMdQrTNnbz1LdhjQHOEs+lqkAkzoXUhIKDMJFm7PjGznHpGBlEihSxZXFaExaXXOSGgHCcWLIQJtdJ5rmgnrqtg8QOCtGgdJxRBgxCQZRZMvZdqWelzWNJJ0E3N41xo2vZu1qa/c5ik10feK2x18qePDuRjZnpj/x2aYhRYy60G7XJTtoLDXpt3OiMgnZsOGMviE0PBR3E2LAx17KC1LT6nYUmvT5qzk/cViid3PMjyx+3erTMhp0540JSYdb3ew16fdjshk4jtP2SGzPLGzS6PMsmjbmZHWDFx0HXNg5qH8bCHja7YJaNGt1Db+H6Q48DDPk0LGwwCTr8ZFoWKLR8EHT4eDbx26xg9mG/314wplNks3HQweNEx2pqOFV7QR4Nx0EXAcfdOeh3llBRazeLuXn06AfKJz9qFrO6jaztvZHfCm2rsbUz6C6CY4ydWWS6AsB/GSrnN3/nqU9/pnv5atJqLT//ytLrP09db+WlV8/+0493Ll3cXzoVSwdnxQf/4i97b72TBX7v5++svfjStNVdfvm189/7xzuPPPLQP3x16cWXbz3xoQe/9JWF139eu3bvF+8u//jlSW9p4Wdv3P/t795+9P0PfPErKy++cufSgxe++72ll16ZLi4drq4P/C6P0we+8rX7v/O9608+cfEr31x9/ifHm2c3nn1h7YWfJH7DHI8f+cx/mfV67vbOpS9+9XDj7NKbb5393g8Kx2ua8Yewn1f98vrgsU9/dtZu20cnD/z9V8bz8+23r579/g8rjcwsvffvv4bKquTG43/+6dy1rf7w/Je/PWs2Wte3zj79j1hjnCUXv/AVNouzwH/iT/9TJi13wf5AlicYWK++s/nt77Oi0gRf+usvyNksDbxH//yzFWe7webeu2Y1BVJHW9ea9X7mz5fv/aQRzwx3Li4CrmyOro53tueSKTF4uX3Zh/uFtVjeesGbhtLyst2fm0VCuCr2b7nJiBiy3L7s1XvK2IB7L5jD0BIdfPI6zmZI6vRgy50NDYKH+f3zKEtUpIevy6PQI3Ni8DOcjVFQjQ7uuv0TFy3B4PZaNTqdzEXi5oKarRrVngoX1WhTWdGD+PiRfLKX5sXdS0V4b9TN5e5CNV5jYMAGZj6+WIpaz3piZ71sHInb3WpyPm2m5m6rmpzBaMamVtG/gMhMz9pg//7aP5RbzXJ8rzYHD8A7D+feqOyn4zPlyQWEQhW1wP4DXN4mu81segmKk9lI7b5tg7zGVN18zcdcsyjffdvRgnB9PLu4we7sGIf15VvLMC6IBDdfCRADNEruvmUzXqlBvX3Tp3lBq/zWO006jYWrr7/cgBTDvLr7juuBWTVWW7eaOgOM1rd/EbBxgvywWG+XBs/eivZvtTkqq5neue6qBHBe3X7Dh2HN1x12+TSsbFZMypNzYOZDEqvDC+LEKs07vw36XU1ukra+eT8sPKqjenAWzpqFWaO9TXFkFd1D49qlLF/Og4TfXEexy6tRPTqrZku5W6OdM6IfEOc6vPm+rNhIGqVxd62KOgF479Fqsqzy26xdbL2PD7p5e1dcPlvk60gO4cFpHc0jMTnaIYN3hd0rpu/C/W0fLbD4PXB417LNsujIk8YCzsr47eroqm0sg/iK2tvyzUY5uUkO7joSpnpcb11vcFJEY7rzlmN3ivJ6tbPlW81qehvvb7m2itg4uXpjXuA8nbHtNz3fS2ZLnbHTIrrO3sh37vimyspI3X3HtWCUJWzrTZ+2kR/a6uhekucsj8qjx1iYa5no209WEK/hdz6UFRWsxyftov8+WiCk42rvUWOaEXZSbP9KjQiqC717kYICz1TVvwBTDEmudh7mIci8iFx/rEICqRzsXyB1hQpYHd+HMoN5e/9DGvnF5HLaNG4/nBJfIY3vbrK8RHlR9S+AohG1w+FzsD+yjAUyeBXFE2rB5GjbmRwJ1GVhpxFJ1x33j18Sh0OPLBnDn6NoQFtqdHTXOjz2cFscXRHZHWivVyc/hkejAHfJ9B087hsOjEfb9HjftYJq/z1rsiXNs3r0PDgaeIEbHq2spcISg1l4BR3dtSw3O7hun9yxmktR+CrY6zfMDvBPZN5/H0IJSF2we6nyBsauUw/ur8zpJXTz0cyf1P1ktF4cX0Iw0bmjdx+i4i47dLLRA0AMyUmzGp3nVYgTUB08QKuxriy1+wgwh/TQrIf3E3xIBk45vkjrHFZI7V7i6WDNuPNBBMaEjfc26v59BunrgVWPLsKigADB7UuIVCMVH7xmmDoFYbl1q1HHkLP69psBHWbYnRRrrdKx8remBzfbRCmQ1rs33GoGhVHdfiOAw4otop3nrQiYmKPDNzjMK1KUOze8PKK1w/ZfF+qkMufr/RfkWLvQYEdvMhQrK747ubgOiqzArd0XuRoCY6Haf46Oaw8ZaPAOzyLMA0EO7uFHLeJeR7cvZenppJXL7ZUqnPfgzYeL4/W6vsuDZOdBcdLLW7vy8kaengX2AO+v19EyBVM0mquGp5EY6pMz8ORU1dq2ri7nySY0TuDesgpXeD3E0041OAPEQJ+cBoN7mHn10en1C3JuK4nKwwv1ZJWXQxC2wOEG5odqcEr370Xe9vA63LvjmVUkp9GVG4sMZGVB77zhCUtxcxres4r2T7Itcfe2L+tcp9X2O76solqR2z8LpK+zQ333hj9HB+ERuXuniWoFtb77C89UaanR7dd96qjsCOze8ByeJgO8c8NDRe25k+HmKh6NkyNz702nDoxyoA/esyw9KyN095qvauzL0Yf/45+QKEn94IHP/wPPMliDze983zk4Ht63/ltF1k77783IRz/xn/FkFra6D/3N35mTMYD07NPP+Dv7J+unH//Up+dubx1cPP/on32GD8bTTu/Bf/iyGEyxrs98/4eN67cO7rv45Cc+1bt+88ajj/7SH3/cPDzOzq8+inQzr/Kj/dPfeKZ9+b2Tzc1HPv1XvXev3HjiiV/6k0+Z+0ejxcV7nn12+cXX+msbZ5790b0/ePbKRz782F/+zfxP3xiurd45cyE2bPfk5P6nv7/yoxcnC/Pr//ziuaefufGBJx75wpcXXn19cOb0xjM/XnnlVSXE/JXLyz98cby8vP7iK5vfenr7Ax+4+PkvLb3609Hy0toLr66+8FIlZePGzXu++b3R4tL2PRdD0+dF9tB/+fzKT14p51vN1y+feu7HpZD+zu79X/7Gwb33J3t7/zIvwt/+nbzVTpvBrcfeP3WbYad795EHK8b2N+8dnl4rCMdVpTFJG43E82498djMCZJW6/qjj1XSHJw+e3z+zMwJxkvLNx96OPK80rK2nnws9hv9ucUb739/KeVgZeXons242Y26c+89/kTaCErLvvHI+1md6xqUQlSMDlZWj+85O+7Mz/xg5/0PDbsLhSGvPfVUZcswaO9duG+0sBA12ncefN94aUkTfOWDTyWWFRnFVWslttzQb+1euH+8tJxY3sHF+45OnVGYXP3IL8edRsnkjSefnM21I9s/ue9cf3Vt6rX2L923f+YejdC1Dz4Vz3dyYt76wJNhr5O4zf0L9x3Nz89Yuc2Dvd4pjfDVpz48m+8U2Lj92KPpXHMUzB3ee350ahlBhJa4mEM1Inyd6a6BsDbmUNVicDCheVV22pVp4kXGekgBwtYE6HJIkdlWcEVChnkA4apRYMZ6hPcgBACtCtyhmmLWhGrV5rTGAUFroqKcd2G97NGDvSrTZXsOSCqasF6VmNbMQ2KFFEKCeabnTYgKIOPxvMY0BkLl87PQMoGRT9t5gaIjhPp2MzTt0synC0qRWDEyW4ixqHIJovkU0FoZs7xdZBIpUYULhRKZJipcSgArcwGyTqiYykVWdvLcQormZW92jERmxe/I1dqgpdDZXFQLVfFIN2czh5aGKnph4ttMVfwUxT5hVJNlBloCCEzmUW0QGs0y2eAWygyLnWbIp4QBskhwV0CDiHlYzdsYAHTG4J6uCDdOCd1gmGmxiHWXA46dTpEv+TVkdIPyJlQa0tNG7QlweKKAUJ0WkVj0YLloKw3FOiFNpAHCGxz7oCJK2XnUK0pKiyCpmonWOu0mRQfOymTfMPasbsXq2imibpZjXvpJ1KmoKtJ2VDZUgbVykmlPYZwrq4zns5wZlTcLOxVRWd5IoB/PpFXY2Wy+RDgtLT1tFCVLt7i15S0QXWferGzVhQBAJlk3SjkqzTrtZRgpGBC0yJHLuAPrDYuZClMITwlZxrpSNaWaEeHWZIECl0MGjHWkPK4wJOckkRBIxjYoMRCzAF7h2hPQQMYKKpoWwZpuCi6qzHbYBsMWopYmi1wTgKvSTGZZ0yVIo02DSqVNTjeEtgk3lVq1Kx9gVUeLqbazEtOsNy18BGBWtuLQQ1NS7Aj/yG8ChKKFuHbzQtNsLgRukTJQNOO8UStSV40o7KJas7QXaTdVCqQLSempEpXKD7NGWRJcNaKwrQGAaTccO0LXR9dEc+x3K1LljSxuFgiVqhlPOwhglrbHSUA5zFkDqDVbsAp7GK3xkgveRnCO0TonZUmQKqQhAlCvWYRWxEbGGi4MQ3e5XjaELlCX4B6DlhAeUKctRmtkE7TOAMOkTdkiAUDTLsbzTFsMmZpvcKUgrQpIACQINRlZ4Qxr3oJoyVCWQDYEpySiec5x1p1qXhc8KTp5biNFi6obHmORW7O3jLVKkoqjbG5WGaCkqW7OYheXBijmwsjDEKrZUgJkWXKazUeVCTRL8m6eW1XNdNWdhoHQGs2WQy2LksBiLiosdYjAXbM1NQ1AVNGdhk2OtE4WQ2UWFQF5N9K2AgLbrbJccmvM8CnBmlABxDZE3ZDg8FgporodIpHognrRrDQRa5S1INAAnxK4SQGDpIPhokGx5l1UL5sAYb6C654p61ysc9DmteCsg9Uix0SJLtQNUSdxncN0rmvWOVjiuEuRyVmb6GXBUI3mWLlgEJUXfgyDeGYapZWGCzXEcWWCaSsvWb4jjNvuAoK6cGZFq8oE0kaez4Uph8qoo/kYUJVbZdFJFVSZnZTdMpdI8TTvRbnEStTJQlRTlFp13k0VqStzClrxsXQmLLspFxPP0KyOF2LIVOGU6VyhaK3kLO1WuWcQqOmmYcgyNW1+imGbUKngoihhjfuDygjqXoNAjc9JZqtacH5KaIcyQ9MlqpsMMuTOF/FCS2vMzlDqQY0R3TC0R5lQfJGAtkCMGsswn3NArYwzDFgY7PdrYlbNBpNaLxq0hRAG5hpKuy7RgJ0m0KWh6Z/cc/bo3GZiuf0zp7buv6Ah2nro4b35NYAm11gwCnqx7R1tbt6970Il5Ojsqa37L2lMtx56aP/sJgJ6+4FL0Wpv4vWOT21sPfBgLYyjU6cOLtynKd9+8JHds2exBnfvvzg8vTZzm+O11bvn751RsCfNnWBZE7Z18YHhmfWSGXfvvzDYPD3zmqPFxa2HHgIU91c27jz4UMnY8anT/c3Tk9Zc2Gjcffz9JSCkqjRBSrDB4urOg5cqaZ6sbRzdf8+02Z01WtuPPjToLOSOe+vJx5XBDpdP7z54MXO8werGwX2bk7mluNnceeTBwdySEuLahz5U2OZwce3w/vOZIUlWUFBNWt3Utg8fvn9v6WxpGjc+8IHcIF713gAAIABJREFUscZz81uPPErf+Ol/74uQ/DcuXdNOR6RxZlqo1aYAZJaVtjuqAqnrdre2zOH46uOPh+22FU4zy56eC0421hLHiRqNivGSstncHFC6YPxkbW083+Nc4aBRFSC3rNj1EIBI6+3z5++e3QQIhn4QducSz+3u3229t3Xr0gNxs0UUqCmLG41oabk0jLJJovmFxHGNKpv05jPbVphM5+cz2y7qejY3nzgurtDojZ3owhq26XRhMXM9FcjZh8+kwpqxRtjpRH4AdBL2FmLbrl3zzV//DUsnIsvCXi+37dh2wlZr1gi4TrJ/dS7mrVrj0Xwv8d1Yif6tPFnBcSMIm63I94nQYbcTtzuUlcOFhTQIAEfKBtiClWTIhtBBNUHYrQAGWLKMLHKdYA9VGWUWqDF2rZ2aiJp0kac1BJpi7GhVkQwCpzFQCuTawS7BptYYEVcBpXNYS3aIbFqgBrYxREpZRnnUQxJQiZCnygLUFDIbYoprm8OIMAoygpUoqqqqCSlkiYu8MpCmdZ1lNdd75emhqjg5UWYNQFYTRK3xInv3MlnPDVCrvGIasUrJUhFi+sdt9u4xvFexUlt1yUDNFTDzikEH7kmW9kVXlUjZlaJ6hHtvDM5Ec6EHMiWLkmqgVS0zSrGiWpelpkAzjB2ITKQ4hH6tJYECEEcxU1WyMblV0zXG0x2WUmBUkCvT3FXMrY0mtGtlUiBRbSNqoiSFTmO/1iLFXeJlysCQQeIixXmuSqc7LKmTQYFcRF2oTVmOVhCGzMMqBEBgZWDmI2gDwGoWEGJpDUElCgx1ySGUWlOFGcCWKkmuKL0FL6FCK5ZDmWuFCgrm2leLoh7TJWXkmpSKECUzXROFtbTvuDDdxnNYVqrKaoyUUQBY14RK74YN8xNwvmI5xri22Z3++RKRguKKZ4gpRTAwC4hQxaA2SkVYRQE2IPMAYFhbGmd1wTEwAHYwlKScMrBbw2WCLExziIBWDWR4COEKWIQHQJkY1Bg7CJEatjAOKCIFUgjbQHOEKFa2rk1ACyXPaMShAhA7urKYTuv6SFXLHDJUWwRKqAnHNgACghohV5ccl7jKpSoNTSBCssykxgSVMlW8CrF/pVxALKokKKQqhcJVvb7w6r6yK0xzs0C8rqiGsoRCV1idWnhlqw5qQpFTVizXBGkrq5mqaTHX/HlJxYR2uakULyohLpePljIDrFYyrRmpGVBmXjJdS6iMomK5QhZxEC5VRjG3ACBIS4xtguqikozcjREqy67BXVUmoKLQ6CjSqEuGgU2ZhhWD0IQMQVSVcFEQpYCqoAkhwLUBiaWVQhoqvoorRHGVIYtyTTQDKKzKCON5QixFOdQMaUcjDmuKiatxqTVDNddQZkqoGiFk54ohVZfQrhRVY9h94+T0rDdzk1jJoqDAY8dOcC0TVqqlLgtNQQnqwlCloQGMe93X9ni7qmxgJBUFFYLcLBWDFYxXlp+/QtfqGkCzwKYa1t3D/Ky2i4pVxCwBA4WAyiwLU5MaaCuvaQkwJA7GnNQmVTahFgYMYRsRFwJTlGQFQsg8rGe65LSCOugcF0RkwKIBJrZWBBG3VgaqVG7ZB4DRgrSxjaBRKY6ZC4GLNalNsatsO2FLzIaIEtyw8p0AekhTjGxAbKgIgY5CBFYMYwvWgtQUKlFAXteEVEaJMKwxqGhODVZb9G6+WWqeM8h4jlBVE2I1dn1ztAWWoFEqoCoGlKgBzkuCA/c6BuCEzimRQc0UBYWlQK0yigPrDrXhMW9XooIAKIqPwdogNMu5YZFXQNaVAKRQFShrioBQqk4rirCFc1tXFkIKU49oUdUKIkdjEwDdSKa+1VY1RrVNtAGzUFn+Xq2cWgTYyxVHGgDqkZLxQmVeb5SjoMAcuxBICAnAAVASwzL2nQEkrjZcEVBgAwVlVtnUB5Rp6AItoOZYuLWWFEmCHQ0NXVM2XVpSguaWFc31lMVSy446c7Nms7TswS/icbdXB2zSmy8Yy6WI378KAEhte9bphEFQcB7O9dIg0BBde/xxTZCGYNpqlaaVuO6s2ZzMzZWITh9by4ilCJ0uLPAsyqgxvp1XlGXrdtxsxa1OKYyw28tNsyZkOjfH4riQMmk2M25nhkyaLWWYiuCo1eJ5VhiGNxi0t7ZuP/RA3GjkWCpC7lx6X60go3XYnePTaWEYVasVtpqZaZ6srQ82G9BikeNUc6CibNpoyFYrcZxMNkbzvVpQI/FBqiI/aO7sypNB/8LZ0A+ixaXCskLTD1vtOGiIIpn05nPTNPS/mEX4W3/8h6s/fR1a4uwzz5597p+Awc7803OXvvLVrcce+ciff3LzmR/cffzxX/qzP914+Sdpp3WwdjpxPf/w4IOf/cz7vvLl/QcvfvAzn9185geDzXNH6xulYXQOdx750pfO/uDZ0vdP/+T5xz//t8f3nDlYO5OZFkuTX/7UJ88+9xymZOO1l+/76jdy319/7ZUPfO6vth57+PG/+ty5731/urFy/hvfuueZH0Ap/KODj/y/f1o0/c61a0/8xV+M5xfOvfbKxa9+BUM4t3v7gc/9nT0ZU1V99A//uGK001S/GUcR04tfe/rS178pp9NgNn74Lz5rT0bbj75/8v/Rdp/Nlp71ueDvfD85rLz22rlzzi2pWwEhCQEGczj2cfnMzEeYqgnGkTE+Pj6DwWBjGwM2yYAACYEIlhCyQARJKLXU3Wp12r27d857r/CsJ6f7nhc+8xH4Cv+qq/4vrqrr59QZLM58/6mzX/16aWmTb79z8rvftYPAHoG/HUMnWIxvb73vr/4HVLm7vnb+C1+EDI7OzJ78znfcXo+X6X3/+Pn66lLuaI/+1Sc5yNeM3d03Ed6K7by/dlHHt/qddjD3vFbuyJbeW/sVob3I9nub11S5XtSL29E9u2lvw4qyhefUdAvUtN7Gy1R2y2o0m09bChd0PVl7XRE3/NpkvPUTMtwirYkY2aVkQr202p118hWhGGLnVSjfGTbG/d6/w/4mt6rSezEDa1HDX9++Sf0FqoxkrWuTZGNCVrecayNso+4OZ8lOi61NUJKwrbpzs2mwa8rsBNzcI5zBbw1+cb7HvXwNzo7hjf0EBKxXsWYmCnznt/033yXoHMqVW7vIWsfItoxtCpf262j7Pjb//mRnNYzRtV14cw9jq9p8ja2NW4MVc0jAwkGt2OIDS507YuMr2qwLtw5qaClej7dex2QQajJa/LlmiEDf6W+8zlQag6W0+w6zBv2Gt3H9Ul3d6lv2Bjw6rUSD/Ndb228bBgzQarJ9lerD0C4W4nP79K01vgmWfqlpItGH/tprzA12rGw5vGuvtjQvNunG64q22jX0aP55neYp94O1N5id+Wwn2LxMyU5k4GjxRYWu++a0Zr45pQbUGm6QpUl1W1VklyzsdxcpqMwZl88gr6bQeT5zkPWUdnHhkaC3XwxWcwvPHLUXmKzOO5ePSq+pu/O/23vn0LDwhkGxdEzt1pESqrNT7qJG8Ru/ly8dlnw2h9rMYb6jOsMlvtLGm2NQ8bQ709ZSBVZuOm/uhl5HYWva/ATdchS40Z0thq+LWs0LX483byh6S+QXs8EMcMsBWvRXrugaTrJN2P+1qJv9cKwWG7YTdP1X8t4NXM16eDNeuaTqIETj6tCumvmwfGGwcUOpGsPketG/id2kh3ZrW9VRPQvlheHqm1rbHeSX/c3rWpX3spmie5PasQf70cZrrJp1wRCs/lrR7aSzo/DFXc5woPt9tHjK6Mac7rCr53iRasGA3TqsJn27m5OVvdYgGwWv/m4OajvX18Mqu/UgzzMlGrLZA0oc7Ste+eCQOmBpy2uRmRP2VgqNTf3yWZjh3fzNDwZ9d7Dh9xy4cMjyUYkj4+pBc6OE5oZ16bgQGisT4+aU0e0bQYiX9mq+kVY2vR8lwzVaa2Y7P8/KjaySDXozNF6Qii56F4C4NGxM+IOfgu4KazXDwUgDMmQtbw3fKLwFrtmi95YU78T1kWG33SkUhQ5C/8UsWRa1tBvOlL1bxLHCqFONDdMd7vR/nPSWec0Y9t6U0bysZ91wTnav45ru7bxN+++gsfa2/0K6vaLZTtJa5WDpoJr12FBVZg8TddG+Y+DVvYyta/MuXZsyB6t2IOXCETXZPqvc+e1i4Ae97OY42jisoRVzxaZrU9Vw7RC4/D4/x9H61s5+5fYxla3qywpZ3qfly8fSVz4UumlwLeju5rMH7OEyiyGZO8/Iurqu0eU9Zr6srFK6uk8bBCwvlBuH1DIa5oPuz4UT98hWuHlNZTuxBvzVV1V9uWc5wdxzBopyPRuuv8aUILG9G+mpcSMYgA25+goni0OnlWw8S7MINdqBaCt0sM1mo81rOtgpuS63fiHw7X611QV1pWhV0JtbOxcVbafvDrZWrhr5BlSrYOtngi6GlcZw42kRhkwBqfeGQNuxbSJjfq8zZ+jaJeXaEdAfh3bfmemwLcsJlpXlCt6cgmyoLo5bC82SXf1f+1dOQXUpD/HtY3SrYqYbynoFrU4Y6spvyZX7su0ZLzeu7Ae9CU5W1DttvlGvxtfvLRfP9rcGeZIvnlGXpx32qnJ1Wvb36GBeX2jyzbYTrLEdiy1OaWiRrU2wpT2aMeNfjnvXScXfsXe6M5eqTjrAYbnyktI0h/LmcP2KVmH98na+c53akWewzeTEpLW1Xs7h1VeUihaA+Wj9bd5JV3m+EZ07oM/MZhvK9uvECnooy5d+oRo4sB0/OjJuzFwv75CNy0zxAkUkK7/iWjDkIF//OeMmAMvx1kVYS7dFt9i8RGlYjLDl//SnHzO3NoTC7/38F5ztdT0MTj7xnc78ncBxH/n0pybfemvlzKnf+bOPGqtr5fGpDw82R+IQ3Zo5/vXHx2duep3R937y47suvrV0/vTK6J6CsomrVx7+3OcqK8um1zv+3e9NvPZ67/zR/83f2ZusXt/MPvxX/6M2PydN9cxj327euqmU6fGnnpp69bX+7omHPvW3e19+ee6+ez70F39ZvT0bNhunfvjUgaefDUZHj//oh/d85/GZh9/16Cc+tfuXv/Cnx4898eSBn79AMdz92qtHnvpBONJc3X8wMiw9DT/w15/Y88ILw4nOyR/824GfPi8sfeX40Z7bssPBg1/4wunHH1+79+57P/f5/S+8ELbrG+O7Ystyt7fu+e7jJx9/Iuo0D/7shXu+/o31k8fu+tbjB5/9Ma6oY7969dgzz7Ayb87ePP/lr2zv3Rf+5ixC4tiAkKvvfU9GeWZbM+96gEux0xlbPn1cCBg3G9fe9aDgjFJ8/cF3xapBijzXVJ7nw05n/p6zkvDMti8//B6aJJISLrKcq5lhvXP/AwSAuNFYPHMyYapAiMVxWXFJnl38wAcAxblmXHv3Q5DgyHGWTp+MNQPq+sxD7xq6Db3IL773fUBhkvA7Z055rRZB5OZ9D3RHx+xB//J73hs2a1oQX3v00bBegQAt3HWm124kg6UFY2RtZHfFG7z1gQ+GI0194M88+GBUdTPM9cQXCBZEXTl7annP/spwePGRR716TQ5mr9am+2aDILx84sT69DTPy+Uzp+YPHqkNBm8//MhgbET3gpv3novGR3KkrB49vHlgtx1FZIyiCUX3YjjBignTjnytWpRTOimEbcbZXpvmUm1L0eLGnXkR5MnkhOV7ViUHewytyFSzLHe7bHOz9JKyXjHjlLahnDJZ4NsVEU9o7PY8j/NobEwtcrNayGnNHA7Vtiz2uGoUqnohdytGFhh6XhyweFYoFZHsqWpx34A7W+OCAs/KBt29ZcGglnb74zErIgy7yVieYUHlsD+Zxkkg0pVLzkGsYlb6/bFYkRGXm8Eumpahn25crh4GKDbCYXdXylBCyshrR6Lc6YXe7dpkzLlRevG4n/LCiYabe0tMM5aFwUSMUMKLnbQT+xpTyiiaiOKaWdneVg4q2MV2PKRTLG/qapZoU0Q0mBME5SFTcUvsl/CgiS2ILl0rqZJ3RvUsUaZo3tGrfiAOakIl1u07sWBZvVnNBnQE5i1VS2NtRAxbrjo3l2MT1R0ziMBuDkd0N+wrIwiNqUYSKeM4G7OM/pDupbRFtWGMJhAa4TSLMRt0pwBPMmEP0nZsBklUC8IRrAy7SO0Hoxkuc0r6O6O0zLZWJZqvjVp+nFv9YJzS0ONKf7sjRTrolt7N+l5aSEm3vU5m+kNh9vIJFETDrTK6Vjmo5z2Mw94uoWapUD1vtHDDQaluDycpjCOFDv0xX8qMAH84nql5bLBM7LMQhSZKwEHDoKke+uXJCieFJlJ5yFBwYUkP7tIkwwJhI/aFTq3QF8dtRZNakWjTWHJUAsyLpETAFCnfz4SJ3SgQhzQnGwy0Ks8iXmQWjsAuLTMVu4jpQTWvKJVgWB41FAMqaVbuM6iBbOGL3VZui6rn70ymhSvMQeyNxVlVmkE3r/l+HatxlDfCXgu7fjQY8aKaKrauztQmfd0lsV+4XtQUahyWteF61UTDq++oncDWTT/1R4KkBlV/RzjDYa3Mwo0lw12qdEwvDNtDvyGr/UHUGMZtrAYDaftePbOigagEO+PcGoSxu+ONaJa3bVt5ut828tBiSX7IJllhWRnYrRmBrzVEsddV41jTsnyvSdMMYsRhRgqpWGWxS3NC33SycpdZSiQhUrLYxBnHeXnE0tKYW5KOopxxVAgKCyRLm+fkgIKxNFAqj1hqmaq6RLsVtUgcPc4PViQQBkzlUZuJiGWhPxEhFLN8JxwtclXqxTAZ8xOtcH1vc18JecaTyB/PE9EdDLeWnXZXd5UijiaiUCvcMNicTjIuknDlWm1XQlUt2w5G0kSHZuwFk3HXdOXmG1daxzOFsTTxRyOkZTj34maU2dIIvWgi8quk2hv0pkJsxGqS+CNhbko1ScxWEU+5RpiQXZi1mTaIwTQD46Yb9LQmgJOakcSsjYqOoy4upCkCTdcKIzSGwZiuBkOtjdMKUmfnMeTRaMfyA20ciHHN7veVcVxOu+j6bRnlSbPppoHSBMW0oQYJG0PFmFbr97W2SHe7PEl0V5I9ihN6uEGzKcX0fan3kjGRC0Gw159IlWzAZNDdLZQyA9TzRnMrGQK65e9R03jQL7ozzh6BCl4MB5OpWsaSR4NqgrL19SiZa+8tEdClF0yGAuVaGWxPAVR0t2U+VxvHmSR4Ix3Nfco16ftTcUalkfvbuzIGS4Qib0LwMqBgx5sQUoVuGBTHTFUrUAbAAZ1q0il9OK2kjmpnMduv5DW17g/zIwYEpTG3EFIT2HZFeHiCZq5iJQGbQp7r6rdnM7WKK6aZxHKfSl3ipAM2zYvM1+dXgF1JmnW7NyCHFVbBapSgXQxWiJEM8ZQC6qTiD8gennScSren7KdFTUVZubNv78Kpk0TA7u7p2yfPGIPB/N33zB871uhuz588tX70IM5Eb++e2aMnYLCyisCtzhHTDxeOHlk8fsLd2Vo6faq3fyrEOhICIESB3Nm1e+6uM+bQXzx1dm5yn+rNzJd4dfIwKGQ0PjZz7hwqQXdsfOHsGWPoL5w+s37koBokG4cOLZ08JgCJR9o37r9fTZOg0b5x//1MFINma/n0iYwq0rZvPvRgZDgYoSvvey/GKKo1144djjUDlFIpo4QoUFFuPfSgZ1c4xjfuO0dBGRGdyIzmZdju3LnrdKhblJIbjz4SExUghIAgGCWGNXv+HqEZcaU6d/eZwLA5ALPvPr/enjaL/O1H3wN0NdOs2fsfwL85i1C4emLracNJDTNq1TLXKFTWmxxHGo0dazDeyWyjNJTQtfK6W1lbGbt1Na5Xhca9kSbUSGSaXruR1CuV7bV9F16JRxqFyvutZtyqCZV67Ubh6pW15V3X3vbGO7nOM8cMRxqYgkGrlTSqpcaHI01g8sSxh616aamRY6cVK2g1EJH9ei2vWYWuDpv1uO4WhpJbmjcxSpn0HXc4PoIY9FqttG4XkvhDkmla4lipaw1HRygtA7fSnxw1Em/8nSuGiKJWfdBo5lUjsa3E1LzxDpWFvyUHVg0zMKjVk5pV2kZQr2Z1K7WsxFC9iQ6lwq9W/elxBWXb7bG4UQEaIyhGLlFYxmiK21yHEVQAtUumSqwBU42wSTErsYsUA5YBl/WmBiOkQepKqkioAk2NYEUjCQOWoyqCsQw3qIYTrAFqC2ByKlQqMK5yhmLkIqATA4WognUcSw1yu4C2wlnK1ZTpAJMc1gjSMAPDUguEiSiKIepDQ5S0hNQrHExgIPXYQF6qIKEOSxMnyN1OWnldV0ovUxNpQ4QiqQaMBh5sbBSjpU0QzSjcyWuUS79QImQVobDX4ARkUrAC0D7UilxBBO0UDuYgKJRYmiWGUa6nGvFykwgygHpeMGqAAWgqCkqxBbkpmCqpVnC9kAZTsQ8aWkV6pUlwgypQlKjKNJXpkumloudCZxz4sMYNRZSxBptVEwVAB8wUVEdMKx0tzB2XDUnZquk4JixjDabCGJqI2QKrkPCc64WwuIF82dRUGBOlIHVGcS5oQqgnDIRRUJgF4QUAw9QuVBiVNBNmTHhe0gxzr7C4l9V7SlNohIFuaWUKDATNCj0qdeqn9kDWhUUADISZSh1R0M+t3JbdZbx3WzaADTEKAPOBIYkY5lYudQShJ/SY41goJVQ8pImSlYh6wkIcxdABqpIjBahGJm2GuLDIUNZVXYbYlKjCGMm5VXBTUC+q3l6CLhU6s1goqqoqQ6yWsMH1wcBa2WJcIItyNWGmFDozsA8amh30lPkeMjBRSmRBRS2wgRU14boQGtOQB+uKBiJiClbBDKfYEMClmAEqNjMXKSgSxBduyUCYKhlUIsILyUKshKWGidjOqoiCbC3fNTRqltgJdQi0CColoCFWglTT+8nIwK5xlAg0zO1CQVGh5NCIC8q28rGYaKVJWLmVVgBikoKd0io4DHOWFkaGNInhAClRoSEsenlVAgp0EjFXQoOqNFHUFFuE4pQ4UppURyF2oY5joUJmF8BWjfVtd3EFVhRCCljB2CQKClEFKrzgG4Pq0lrScSnJNSVCDlNRhFxMLWiubOnbfWpAoBPVSIgOgIY1NQE2UWUIXcQ0SVhBXKjSFBlYUeOyqunCK1gkzZKgpNBTyhLBCkkHUMtzFVO0k7tYkUHJQukUudTW0TTFZWlAQT2sJoVKiNzMqhSWcK3clegqRWGuJoRHUBWQDrCeRMzupaNBxVbBsGAhsTIGwkgXWE2EIhAeQD0rOGZoJ61hRYYFDUqzpKxkamFpUWmpHMegrqg4xTSjLaaCGBqIOYKoAPNC01NRNdkQy0ZNJynhOWlQDSVIB8QGyFBxxjklwOEK8GVTgRyaKIBVrKGsxBVEVexyhURcL5gKMM1hg2EFqdiHNaLhFGmQ24UwmUpiZpbCVJnol2Zqy36kEaAOS5NgHEHqSRNS4eVGJgyE4FDokUKCHdDZKkeIXuRcEtIvHULksNBTpuf9srGBxyhPC1pC2oP/UfyRvrBxUhjrdBKoGOKwMDMDDRKLA9JFel4yiVBP2IjLYWGkhCeAJIUWEZYKlepkCOqaKwelzaiLOE6RBblWEh0qasqNUmhcQR5sKDqWZW6SmqnhGBqAGSXRMddyV48Su654qOzUDBhireAVzFGGLciNAloOCChxeGlwEw9FU1dlRHRBXazglJgA2ZgpUmGRaopSZxociqaqgNSv1cKRRmkqoW2ldTupVQpd7Y+NAJVmhjaYGCVU+q7jtZu5pqXbSSJYUnUylQ0mx4TGM0vrj3V0lFZuz9U3l4fjnUJlfrMKLDXRlP7kuLT1dDsdUBfrJKxUg1alcPTINqNWvXC1XGH98THCYVCreqNtpKLQdobNalqxMZH9TjutOoWhDEdbUKeR6/qtWmmrkeOkVSdsVCGR/XYzbtfaC7dbS/PCVqNaNahX8qoRW3bsmkm7PrJyp7K0DEwlsYxeZwSaNK7WwpqbVww1CPe//lLYaUKO+qNjRdXMdKXfbgGbR5VqVHdRRRk6jcQ1g3YDUuCNtAcT48orr7DfkEX4gU9+av9zz/cmp/f96CeHf/iM12ofeepHpx7/3rWHH374k585/sRTl97/oYf+9jOHn37Wm5wY/9XrR3/0zHZn8vAzz53/6teuP/juB/7pn48//uSV937w/q9+7cAzPwlajalf/frkt5/antq797mfPvAvX77x4LvOfenrx374zNV77r/n8e8cf/zJ/ujknldeOfOlb2xN7Nr98xff/fefvfbII3d/5Rtnv/rY4pnTh3743OmvPdYdm3TWN97/53/ptTrVucX3fPxvVvYcmrrw1n3/+Pmh23C3th78fz8F8lJC/KE//eiw2rBWNh7560/1J8abb9+87x8+lymGHoUP/eUnYSFyVfutP//LxDKNjZ0HP/H3Qb3uzq3c/5nPCkRIWTz6Fx9XwjB0q//5D/441Qy15z388U8lpmUtbzzwmc/CTJSUvv+j/13v9sJa5QMf+XMkxXz76NLLhugKGw9nXq6JudCYxre/z4NNarTE2nM470Gr9GbeqhcbUtPT2ZccfCdiu/HyD+hwjenNYuVnPOxRA4bzF+10VepGfPtXTnE7Y4fY6lOot6YqHbj9fBltUQuH81cq8SJCLth4UUmuZ+ohsvUDubVi8gnWf6EcrtCK7C2+YwdzTEyz2tVdZH6/Pz6sXK7KzRNczIvuJF0+WigB2Zo0rk8Cd4ne2oNXT/jNgT3bQGvHKVzh6w5cOi14IHcmzKu7olbfuNXGSyei2o59yxVrdyG4ybsmuHMW4i7pttQbh+JmV79Vh0tngb3K7lTl6jlerrChKe7cjXAPD5r82jHkrLDbdbB6jioLg56883ObRwlgaPFpDilEQbHwgk4MCdbz2derahZU4v6Fl8bVYSBNNvcjhjDAUbbwc12hmdzOb79R5WmileH1X9bZjoer9M4PuIQY58X8L3RH+qKf37jwkhr4AAAgAElEQVTQoH7E1eLm8xW2E8I6Wfo+KwVGRTH/K4vInPj5rdeqZJAoan7jOZf0MrrbNl7fh8ImKzbk4mmybWHWhbfv1ZbUcLRnXLgLd0dye4NdOYr9NpY7cuWEsmlnms9unjEXDX9sx7pwUnYn4+bQvTSBBm1arsu142yrnViRdvOYulCRjXl+6SQa7AkrffPaNOhPqeUKXt6H18bSasSvH9YWW/74tnNhv9g5gPQlOrtP7uxB2vbmTdR9ESmTcPCG2Lhk4jGUXsznrlQcLSqW8ztvuIqaD9aV7Z9JNs2CK/LOlZrdSoKr8s6likX9Ylvcea2i0cTf5isv63wMZm9ndy44TiuNbsjbF6sVMMA7yZWLYwaNg76y/FOmdWRyNV244FbtYTgP77xVtWQgh8WdXzoGDCOPLT3PQIs5Qw3fOccij+aBnHmv1vMzu9Qv3CMhxWnErp2FIGODTCw+QIIMQ5/cfLfZDYXWJ5ceAoIBkZAbd5GioH4MFu7jXgZZCK89pHRFUovN1++SuQZEyG6coYWASS7n71M8EeuZdum82gVhPbReO5OLqkSJevUoiSDKQrhwHxnwwVjR/W6yM69qU6T773l/TXWZv3TV8e9gbIPNV5XoSqYewttPg405k+9WvBfF5rpVg93V62Z/VqFttvZrml4plQN459/h6lqdtaX/qlybNyvM37nOt65yu5YtvWV1ryvKMdZ7JludsZ1GtPMmWb9lONTrzqpbVxSjlm1e4N2L1DwgB89li7cq9phw1wxx5zySfRhUlKsnkqqnzjto/i5gr7N5R6zcx/I1HvDy9v0I9FHoaFeOQXODL9pg6V6krJKNilw8z9N1EhE8c46WOyJz1CunCifQFjlYOE/xMt3U5NL9NO2iHNLr9/F0G2Qqfed8boRsU4Nz5zhex1tcLjzI476QkL5zH4SZVwarP2FqFiM/vXGhQYYpV4qbP63QjZB0yNL3WRpjSor5X+owFyRKb79Rgd1U17Obz7twI0VTfPl7OIxVqoi1nxEUFUzks29UwXYJHLz2HM+WUj6NN74PeqFJDbT5cyQGwswGM282xKYUo9rKjxBYEXQKbf9Q9j2LqeXmr1nUI7wCjdsntDt12Zhnl0+g7sGg1jNvToLtvQpYYStTaHV35oTs9n79zmgwvm1f3AW2DkFjkd3eJbYPE7BONybgwl6pbbGF/XxuKhrdtC5Oiu2T0Fyit8bkxlGlXCE7Y2hxr9S32cI+dmcfrN/mV3bJrTOE38HLU3D1KC9XUHeUzu6T+hZe3stuHyzbG+GV5NabNUcM1H749isjBoiShC89x9QayO+k86+5ruknS+D2hZohQhwlt37p6rmflWzhx5xWcb6Uz71qj/Btfx3NXKirWYCAvP28pRZxLujKs5zYKF3N5153LSUot8TMqzU1DhGSs/9u8zLLMFt6hglHEZvZ/MtmBXppgGdfrtA0t63gd//3j1ira4N68+GPf9rs9TPE7v3sF2oLyxsTuz/8h3/SuXpj5r57/8v/8Uf2wkp3bOrRv/6ks7KSKub5z3+pMTu3vO/w7/7Jn41euLh47ux7Pvpxd2Vjc3Tyob/7+8rCqlSVc5//UufyO3Nnzn34Dz4ydeHS1Ycf/s8f+dPW21f7Y+PnPv+V+vWZ1LLv/uK/jr96YenkqQ/+8cemX/r1tUce/e0/+Vj74pW1I8fOPPHk0e98f2X/kdPfeuKer37j8m9/6KFPfub4k08tnDp9/IkfHv3eD4aN1u4XXz75zSe3du069MMfn/3Wd648+t53/cMXjj/x3aUzZ4489ePjjz+ZVKpTr79x5Jvf39q37/C//eTc57948z3vuecLXz717SfXDx/a/dMXjz31w7BSG7t46dznvrS1Z9+BZ5+/73NfmHnwodNf+/aZr38z2DXeePnyPV/6auRUGjO3Hvz0Z1cPHw3XfmMWIcE4tqzFe86mVIsrleVTJwCEvZHR9RNHZAm80dHZu+7CACSmsXD3XTHXU8u6c9fdAIKo2Vo+dbws4bBRv33+PgBhrvClsydzrg4r9bm77hYIBpXKyukTANHQdWfuvU8SWnB2+/x5yXBo2HfuurvkLHIrq6eO5ZjFtrN4z5lIswRjs/fdK1QlR3zl5PHQreSE37n7nsCtYCFu3XtfVjFlJubPnYsbtQLQ5ZPH/Xo9J2zt5LFBo40kmL3nXFKzQQHmzp4Nm/VS4K1jh7xmK6Hq2vHD3ZExXBSzd5+LGxVUgIXTp73RkVLgtWNH+yMdAfHascObY5Mkz2/ffXfYrsukWD59KurUE6iuHzm0PT0lS0RamDZgnmPWobiFy5LQBmQdlAqqOQVus4gYtAF5C2cZ5iMYj5AyR6SO+SjKAOOOJKMsg5zVEWvjssC0CdgozXNM61iOq7gUxAZ0jKSQ0xqS4xrOCrUG8DjPMwSrBE+poCyICdSOTJEKG1yOKTQvSzULRzMoSsGkbO74igloEbZjiaEkMaz1YqwUqhiO5QgWAsuwNaSkjDmL25EgQJI4a0UCgJIJfyyVsAQExi0P4SJlLG96ksKclXkzEBgICvLWIGUUEBK1BpLJktC04QkKCiZhZTtW1JyhvOVFhgpSoU4hYqNCEmUcQYeUECtjSNq0kIRPM02JAuzQCURcVALCRyGqUAGxMgLLmiolphOEOjDNCZ+ksIpLSVgH4QouAbXqSdqyM0nVcUwrKEuxNoFhnQiB6QjGdZwDordE2dKKkijjGNdQmRE2CnGTyBILI4qrUYH1wkqKSiQES+2gaKRlQQo7Lep+BhWpRkk9KpCaG1HYKkkOcmuYN9KyJLmVRe0MlQJoaVoPU6yXZhy2CpSKQg9QdRADPTcKv5Ojsiy0Mqv5AODUEmGrgCUo9TBrxaJApV6kzWGKeamWWcMXEgAVsQlKCAIqlnsMTCTAUNlNEIMZZWSaS44pE8oEEghCjrRpVCiKxIjvZpSJnHB1EkEFQ474BC4VJijSp1CuMImwMo05zULVViYxUBHgmI9BqHFJkDoJUlOHALJdBGswh4RPEmlgSTGaUrEOoEBRMxBmJoSa1L3SzkvBi6qfW5mArKgGoQMkYGnTF2ZaFDyrDZEZxsjMXT9zUgFo6Q4iBwCoJFWvtBNR8LTm5ZVClCSvRJmb5ZBJexBVBRAsrg7DOmFxmVW8vFqIEqfVLKmksATA/g+LkOe1YVQFOM5ZFcspFZeCmICP4wQr1EFgQkW50GoSj/M8R7BC8C4NiRJrUuuIjCigqoBxBWSCupJOMFkA6BCwW4dSIB0pE0gABBzGx5AAGDlQGcdlCbFJ1ClcEA40okwSCIG0mTIKS0C4i+gELiADGiJ7VCbSlPC86UkucyqzRlBQ+T8TxDEkNGoNhFKWmKUNT3BREAirO7HKcoqzxiDWGYAobPlQKwVkaXNYqrLAKK97OQeC4LzRCwwFQBy3PKAWOaBlvS+1IqG8qHuZBgFCRX0nMjiCLG70pVGUkmZ1L9dLIZFVy4qWkQKFjyJaQ1mK1TGMmkQUmLUxaeAcUK0h4Iial0QZxbiOioywEUBbtMwxbhPQVkAptYYAbaUoCe/gckQjYap1EBrheQZxi5EOA4XgFcGaMAEqHWWwo4IwY01IO1TkCDUZHFNhKUgdy1GV5DBXQ1Trh1Av9GI4lqOyLBSRNHwIYGqCqJWBEggt+p8JUous7SWYCS7SugchSDRYNoYC0kzP8mYgJBK8zFuDlDCpwrTpCQwzFeT1oYA41wpU2QqpVSiiaHkxVyQVcdNHFOYcZA2/xKRQirzhZZRIiJVdVKFpwGw+gaCBJcF8DEmDAozVCZhaGgSIThNswBwQPkmBRQTGyhiUBpGYuK0orFWFxMokJjbKS6xOYmgTgTAfRcCiJSTWuEirpiygMk2QjcoS8zGEKgRACMcVZBMpgdUpspohcqhMEeJAmcvtfXs3D+wrS9jdM7164BBO8+UjR9f3H9CG3srRI1sH9sK03Ni3f/XIUZKm/d1TywcP4TRfPXJ07cAhddBfP3hwsH86K+nWrunF4ydpnm1NTe3s3wvTfO3I0eVDh7UwWN+/f+PIAZAUvYnxleNHhUD/MQiAk3T1wKHugb0oLdb37N04dggUoDc6tnTqFM3iQb01f+YsAsBrtdZOHisFDKu1pXvOJExLDWPu3D0Qw2GlsXrquMB42BpZPnUsxzyyrOVzZyKu55q2cOYUorBbH10+daLAxG+0lk4dLxU90Y3Fu88Ehlsqyvxdd5cKDa3K0qnjOVcCu7Jy9mRMlVI31k4f7lZHJcZzd99VaEqkW3Pn74NXLv+mLMK0WgtaTb/d9O2q12gGrXpi2tvjk2nFCpxqvz3S73QS3QwbjeFIK7Bcv1rtj46nhtkdGUlqTuhUB61Wr9NJVS1qNvxOK7KdXmu0NzKSqepgZDSpV4aW22uN9McnMs6Daq03MSE47Y6Md8fGMl332iNxvRJYrtcZjZvVgV0La7Xe2JgksF9vD0dHIscZNNrdsfHEsiLb2ZmaAgx7jc7O5GShK/36iN9pRY7rNdtRuz6oNiK3sjk1BRQyqDS7ExOFqfVrI1G7Frhur9GJRhqDSi2x3a3JKalzr9LYnJoudHVQaQw7raBSGVabwUjDqzRi293atQvo3K80tyfGgM42mxPDTjt2bMGJqDCiQ6DSvK4ytcxVFVcJMWBmaLqR5baSGxqsMqIJoNC8pjAd5JqGqgQbsNA1bsnSUaRCZYVRTQqVgrrCVFGoHDhUuBwqWDFF6aqCE+hg4aqICFljTJeFpkCbCIdBBhVLQpsIlZVVpbQ4JGWu5rEJCgVCFqdWmahM8CyxgCRlrhecB6GhprpIbShQKXmauhKQMjFRZEOBy1LLgZalnOR6EdtIMiBYmroSkyw2cWEWJROpAaWepgoptUJqSWDqkiWRiwhOIx0VZik4iA3IuOcZeqkKYaaxpjMuQEMhiswtjbio1Kg0GLdEahlEg1ldtVAYOTaoc8ZFbqjUgaVOhcG4C1NTgxzJhkJVKXVe1DlTZGGo1EXAoNLkph4PXRerpGyolBVAV8o6JyrMTQ07EJoU6EyxZGLrhIOyoXJeFroCqwSrIGYEqFFi4YyjzBZAyQsOE0dAJU8Vkpm5UPNEY1CNMhNkDBZmkZgEkjyxSqgWKcepXiQmEEwCJU5MUHCca0VqIIDzzC4VOuwbZm7kmYUkLkotz40SMBk5KNGAxEVu5kDJY4UWRib0IlFZoWalUQIspaswC6SaAiws6xoggGpS1lVKitJWypoGOUIWYhYoNIYcxi0ZGQbTgGgojJSFreIKgSoqHJVaIFM5tgizysi0mAaKumLgJHJd6CKkQGEpzAKxpmIT0wqKdR0rUDY45aA0VVklSEHCVmCFlQoGNI9tgFmecRhUEaRZrJHCyDINpSoCehTpTDIROwKzvOAodIRCop5llkZaaCBVEFCj0FIFLaIKJCTLVJw6JeBlqpDCzApN5gqEWhQbrCRl5pSpRiBJE7uESpkqKNWL1ECACaBFsUklLWNL5CqiVACXCpcDjrkphMtLTqGFZEWFVIoKpZosNQ4cIhwGGFRMiRwsNFZWlNJiiEpSIUyXsa4hh4iKgohAFoYuwVQWVZ2YsFQYqjKmi1xXiYOJDRJdIyYCLiVMFhWN2EiotKxwZshEVamNS5cxlCU6zu1CUJkYEOhppuJCF0BLQkMTNIkqiOA0UlFuCcFEZGLGh76u5qqEZhpqCmB57EhI80hFiSMkLWMNAT0JdV4qhTDTUNcAyxNXIpqnGs7NBDLhWSrQk0SjBS+BHvuWAUgSVhHGWaLhwsylCqXOTS0OHAvqVNRVykug8bLOqQoyQ8UuRiaWOueWzGwNKUjUFaaIUmOw9v/f02XCYlgB3AaZrUMGZJUKW8VUyBqjmiw0BThY2IxwwB0JTCo1XtaVUqcYC1ynRAeprgGXCodjKoDLhMklKjK7UKg/MIxcL1IbCVwKNcssAaiIbBQbAKAit3KgZjGnhZkLPY9VXipZbpWQFLGNhZZnHOWmgGqaqKzUM6Flka6VSpbYAOE8NJHU8oyj1JIKHXRNW6pZaeaxwoWSp7YARIQmLvU8ZzA1JdCSWDeoBvK6aqIwrriwQjCHpaUwW2aaAg1MXRgbOlaAbCiUy9JQZY1iBZa2ym1ZmCo0iKVFvUqDqUA0FUpLYXBZpVjFhaUSGwiDIwMzW8amSRVZNhTORWEoqIKxCkudA5eVBmOqpBUYmSZhQtYVwsXQrvdHR6Jqxa80olbdqzcjw+pOTPqVamaa21PTuWMO7VpvpO01G4llh636oNmKLbs7OhZUK5mub0/vggbdrI32O6PDZiPTtP7oWFRzEtPZnN7lO26mqt3JqdQ1h3bNG+0E9ZpvVwedTlKxE8vZmtqV27pnV3uT00nV8q2a1+kMGzXJ+c7oZH9kJNP0/thYWrV9u+p1RqP/+Pv1+mC0U6rKzsh42KpHpr3TGU/q7tB0g/ZI0Kx6TiOo1b1OS6psfWxX1KzGptXtjCV1t29W/FbL7zQ9sxK6bnd8rNS1nfZY0KrHlt0dnUgalaFTH9brZd3cqI/H1Wp3fLxU1V57bGdqSnv113zxN2AReh/80If+8TPH/+1H3q7JIz965uQPfzAcbZ/9/lNnnnji9gP3Pfp3nzn73e9ce99vPfRP/3D2qe8OpiYO/Ozndz/+7d7U9Mkf/eDer39t7t5zj/zLF88+9o2bj7znga9+6dx3Hvenxvb/8ld3ffNb3cmpoz9++uGvffXOvffc/8UvnnvssVv3P3D+8W+de+wb3uTE/pd+ee6r/9qbnj78ws8e+dxnbz9w7/mv/uv9j31j5fTxQ888+66vfLk3OdlcXvjQx/5b0G7Ub9/+wF9/YmPvnj1vX370U58MW63q2tL7P/5JRCApst/5048mNbe6uvLBT3zSH201b9x6799+KtU0Jw3f/7H/RosCUPy7f/ynwODW5sZ7P/V3SaNSuzP/vk9/CqgqS4MP/cV/53FYWPrvfeQPpcZ1b/D+T3wiN1V3c/v9f/MJDAEm4MMf/Zg+7JcV4z/98Z8zka629nsv5LwbOnS4/QJk8zvOpPC+F8vV1KnF/k9ith3ZYLD9GiFrgamG2y9Ac76rTZTeU7FYTt1GFPwslRupjYb9VwVeDh0z6L8A8Ezf2Aeip/x8sTRGRfyTAVhJbOb3XodoPlRqIPj3iF7vmntB+QPPm5PmmMx+6sGFpEF3+m/BYjZB02T8omPfasYjO80rqj3XUeEtbatmzbSg0tdX3fY7JnYX3Rt1fXYsae3Uryn2nXGFLNhLWL+9C/Cusea0Ljt5Y6vyjm7fHk/q251bWLs1xfCKtUmsm9MMr6kbRu1Ku6xt1q4r1u0paC0Zc4pzaxcrF5wB0q7upnhD39Iqb3eYOWfOasbtvVRbyLpp/9ncTj2Ksv4PUxUl+jAYPF9oakw2wt6LwCr7zWh74QXdDPpcKQbfj3WY8NAfPJ/rNOa9cOcXwMqHugi2ngVGb0e3hfdUzESqpmH/Z0Wt7MJhuv0rrIeeTqOtH0O71+V2OXgqplmqF+HgZ4VeBCyMur8Auj80lHjnaaFsDbRpvfFy3RhwtVwzZqaMTUjppnN1j7tYpO3t9suT2qYBreXq5VF9S2Vy3bg9WVnHhe7V3h6vzIl0dLv961F1w81rO623Ksa6qoBlY37SXFYKy6tdHm3OAdBccF8bVzeaRXW7ccnRNxwNLNizLWPZyu1B4+2R2h2WjG40f13T11vQXKzcqGvLdaKt+u/E4sXAGsvz14f+a6U2hfCFXv91UDV8PD/s/hqaeiKW8+x5T5+Q4k0veE1WWmFxNRi8Cqp8QNaj3ovSUf1iNQ9+GlutlFzxeq8Atx6Im9HgFVDBO+amt/oSc+lQbubB87HTTuU7A+8VWK0MwVzSfQlU8YAO4t7zhYMHqF/6P4lZXTQHwH1nrxlv8bRvv31cH/SQFjRe3s2KlOV+5eIULz3DC6zrB/Swx8HAuXTE3u5Sddt646iSZqzw3MvTeu5z37ev71eHHiOD6luHrG2/dIatl6ZZIpjoVd6eVqOApaF5dZ87HCZaOvLqlLkVFTVv5MUJnHKMhvVLHd3zeeFZ1/bp/SzsxOI7m+lsbk2D5LleOZe5ynBwCcqZSK3J8Bcxebtr7gPiaW9wU5pTsPhZv5xNW2x7eBmkNzK1If1fxOrbA32fTP6tH81AY6yULw6im2VdGcSXs/hqXqnFwcuZuBjoB0H6dC++ASpNP3o9jq+WNbWfXC/iS5lbj4JXsuxC5O5J5fODwTVsjebNZWzc2M3lutLj9Yuj0tl271D35hQwV4xFbt/cw8pFxy+1K/u53FQ9Wrs4ztUlY5GYM/uItmQsY+vmfqNYVIaFc2U/LzeUkNQujEOzay4B5/ouRhbMLWReP6Dlq0qSu5f2GemyEgP74l6k7ugbwL62W6PLbAs41w4oybpSZrULexjwo3wQPhM5yYBG0fZLRPOGOou2nwXOVpc3xOB7CfJTk0beT3MlitQ07P5Sqv2hqUbdH0u+0tfGYfDdQPpCV7PguVAZBipIdn4JlB2fVpH//UBZ7hnjIH3KiwZI1/P4+VDr+a7obb1MyHrI2sT7nm+teMqEjJ4apD1k6knyQgg2E60m6u+MN28T0Fhw3+ioq+2ssdO4bBqrNRUuWHMVfaFWGL3azWb9ppp2NpqvVYyVEWAv1mYq6kJTQSvmkmvN1ZC6at1qV2ecbGS19aajLY0jc8G65VjzI4pccNZsbaaJtTV7tlmZqbHqLevthrE0RbQ75h3LmhvVxLy2bts3mkhfte/U3esN0FzLrwwGLwEXdp1ef/mXqgMHwM/9Z2OrksLb3uAl6To+Wop3fgUryONRtPNcaRd9mpbDZ2LdSvFS0P+VHGUbyabo/Qpa5YDnSfcnwi4GKC+8pxNNT9lG2P+FdPkA7STdn0s79xSQ9p7O9WxIoAh+FHET0E3feyGroj4Ky63nhZWHFX3w+//XR6ory2Gr8b5P/o27sSooe+TvPtPaWN8aG/+vf/SRzrWrC/ec/a//5/9dW5gfjI1/8JMfry/NFab18D/8Q2v21ub+/b//h38wffWd1buOfvDP/nLk5vX++MT7P/03zcUFofGHPvtPnatXV44e+1/+6CO7L7w++8B9v/NHfzzxzhVvYvTBL/xL5+o7hW2+6wv/PHXp0tqRgx/+6P+z/5Vf33r3Ax/+2F9MvXVh9fCh849/88RT3988dPD8tx6797FvXH/0kfd8+m/vfuqpldPHjz/51D1PPuF32od/8cJd3/x2d++u09998t6vfe3Wux946F++dPe3v7V64uixp589/63H4mb10Csvnf76t3p7pk788EcPfvnLd979wLkvffnebz62fvTgwZ/+/IGvfDlsN3ddvHD/577Q3bfr2HPPPfj5zy+cv/vME0/e/9WvBPsn26+89fAX/zmqVcauvfOef/zH9cOHo9+IRShE7/d+vzRMb6yzcuK4b1UG4+Nrx46lhrE5vXf9+OGMaVt79i2ePVtwZTg+tnz8mO/UvLGxxRMnM8PoTu/aOHo4Nqydqen5M3eVmuq12+snj4d2ZaczvnTyVGrZvcnJtWOHI9PtTU7NnzqdWVbQbC6cOp3bZm90YunUmcS2+yOd1ZPHE8PujY6unDoxrDSDWm3h5KnM0AaVxuqRQ36zOWi0lo4dD6q1yHUXjp9MKo7vVBdOnAgajaFTXT12eFhvDCq1tSOHevV25DjzJ07GzbpvOIvHj/vt5tCprh864LU7/Upj/fDBXnMkNMyFs3eHdTcw3MUTx4ejI57prh490h8Z8Z3q+pGD3dHxSNUXTp4KmvVANZZPnRq2mz23uXb40M7oeEkh7KioSgqC4CinTR4ThtoYtXjKGK8D2dJjhcMRBddpjiCZUFENZ5TBFsVtlnBOq6BsayVDqM1xnWaUoA4nTZIiijpMjmgAS1aDedsUHKM2L5sqwgJ1GG2ShKmwSWVHh0SSyv/H3XvGXLNd52G7l+mnn7d979fL7YW8JC/vZRVJFTq2YqRATn4FcWykwAgCAY5iAXboyFYsR7KkSFYk2ZLsIFaixJJtNUahRJmxSN7e21fe7+3lvOdMn9k1Py5FqziIE/gHnefXxuxnr5nBxpp5sNbCXhCtCcWpnwm/FkDoVaqqNecIcLJuZ6aJpJa6mjnPnIq1z4oqCLvUVjNrKbKyrefeMddGvpp5K7wOejVqtaB9bOqZtRw53pVz7zjoAtdOlBagj70aNz0nbWzVpK0D6VhbzY0LYCtBO+2NBE0E3KBoo6ANrZq0VRhirPzFEKZIC0o3MRhwE2CyQc0w8szB7VBGrpQhusBhRnpOyAXuB8wIQjeIGiWeOXAxwgnSFKNtjsa844SsEzfkRpBgDppp5hkAF0M8wAohclHCAVGckQ3iRsIITNeIGoWQebQd4AHqKcUXOB5RTbAddNUIKIbaoVEDoylpRkoPlKasG3RqrHvKbdrVY2A46ke6GgGAXT/SetApzPuxrifOY2/jupoiJWiX6WbkIXHdyIAkL+VADXU1No5Anehm7Bx1zcDXEwiI67JeD1vFhMr6ftR1Upior8fOUe+HnGwQIzkYUrAlXUBpYNx2iCR0KQXbIQgxiCHZpEZwmxK2RfoowIHz2yEMsIkovCC8xC6lbAPbWNoIsgusjyPPHb4UMumrJCRbzEXEJJRuMRMyHyOyxdo0gUzDSxEMoZGUbAubMBNieEG+H1qo160JrWa4nvYmcx2n3aRTsdMM6lFbD7khvpobG5se427eu6irg6gfNV3iNId63FcpcdRVU2MT2xPaTlqVGs1oN266zBuG1bCrBgxQU89dnQKIbDdVKtU9E91EtQMAsNaDphwLT0E9NW0GoDNwjYGtABBAh8Cuh5YTNKN2LYDE4zVK5qTnEkyJvxBCAskAoDXRS+Ymwm+EiDgwZ2SOFVEv6uYAACAASURBVOdwQvxm4DgmqffrEgroxpysM8OIn1CyQRThYEzoJu2DACfAbwaAQzvmeI0bTuCEsHWsZeBSAC7FiMBe+mamjfR9BNS4VQJ3EVCTpg6l51010z6EnYDttDcBaCNoh0UXiTaE/bSpQ2mZrjesla4XsJ0qHcFOgn7c9iEy0nWTtklCQ3S1Zmzgeo7UtDWhr2PWj5suQEY6Ne3KkEOui7mxkes4bqddHzlLcTAH3TS1HMILARoSjSG+KOEQa0bJOvETrgWlM6wmIaAeXpB4iL/hQROiCEFbws8koo7OUDdLIPPgQmBHAmGHtziekp5LuEbBXELq6BSDiVSC+g1pZwGGFm4JPMU943DO3FwCBuCUuFkIie8Gxqd5IVM10NXEOgJ13DdT54hrM19Poaeui3s9ansu+lSpUdsJocOunnpHfZO4fqw1g11m1KjpKO8SrSZdI6SVbTM1VsAm9mqsNEdVZmGWl2HchaqfdrUUWqpmzVrm29j3E9VL0sVKTbpOyvc9iEtXRiHa4j4mOqTsAjMR9xGim7TLEkA1uBLjEGhB8LYAGVOS0E1iYu5CHG2AYjRC3MFLEYqRYphsC58QHVC6gXUaeAnoJuvSEHMHLoUohj1n5AIDGbGSwO3AJQwLSzZoM8oINe5yRGPfyHj/oQdPL1+uw/T0xrWjS1f6ON575NGTC9tWsN3HHl9c2m5ZsP/QI8dXr/UyOLlx7ejK9TaK9x588PTSZcfY3sOP5NsbRTg8vHXr6Oo1FQaHN24tLl5oonjvgQePrlx1jO/dvHl642ono+Mb149vXK+j7PjK1dMrl7sg3L310OLyxVZG+w8+eHr9asfCg2vXjm494Dk7vXJt97EndBCeXrx4+PijnYgX29v7Tz5eZpNyPtt54kmVxKdblw4efaSL09MrV48ee7hLhufr63sffLLKxsVksvfUB3USHm5dPXjskTZJji9ePnzy8TpMV5tbe08+no9m1Wh8/6mnujQ5X9s6ePKJJskWWxcOHn+sTgb5+sbpkw8dTbfb0fDeUx/qs3Q527j7zLP4hefEnTv/igUWAMAmaU7wEqF2mdddt0JILVdl159KCfb2lwCecQ52dytrlwi1edk0zRIjfb5o2/ZMCr9/sLTujHNwsF8pvcSoysuy6RaU6vNF29QnnLv9g9z5BeP+6Kju2hXGbVm2dXPGmDpfdGV5Gkh/cJhbd8aYPlvUXb8kOM8G7eYFK8Rqc6MZDKyMTre3i9GQd/3OI4/aJIAW7t+65SVrwyTfWKtHY0V5sTk/X7/Am2bniSetIMCigwcfsKFoZdzORtVk2vMw31o/27zI23bnoYdcEnqPDm/dMgHvwqxcnxXTqaOi3Fo/mW/ytt157HETS2Dg0Y3rUOI8mVTzaTtNIxSAMUWhjmCA16gJsCREZlQPiBAiFl5NMKMZHFGQ2BiFfkJdRELGggipIeWMBxHrJ1jiiGbYJz5EksyYk5BLLiSuJyKjQAasnyBJIpahbsiGHvIZ0yEmTMQRqScsZjCQzI2sIBEc8T4VCLQIVsWEI1RR77qZ7TAjqKvHDnkDWI0C1XPoUV9MGEIV8bCYOoaNRa4eO+g0oI1JkRGOgrYYU4s64nw1sxT1FgM1UBgaQzudQCOcd9oMlZKQaFuuA+yVxV4NNUDO4xpFupUIeqPGpouDERR2ArDECSNwyGxEQybxAKgBy3ygNmmMKsrHbg1DiSNGQUpARiMucOrVkGcwMuuIMh2RGK4Tx3FICR1Ql5GQSRn29SRKQajXKRY2xJGfERCQiFOaYp2RkAU0A+1IZD7wMwClj2CA14ijtgWNho2esa5vddSjzDdVoQbKR7Qxhce5GbHONRo1ekZa1fW8UUMBm0UXdyAmjasNLNpp4PW5Ra2as6bvjKj6kQD1ogsbHtiS2N4X3TwEZtWBzs6wU3Uru34kXHXWyQakrMWddys7xjVQHWjsFEUQskTaFLNYxBTXc8mFS0nUrqOAwVCE/RRjTOIQ+RSTmEWc+6EDEU9Q0GxSSbyQsR15yXAYUDskPmAxk37sdBoMgNAbMEI9Dkd2BhmnoaQuwzhhMRV+aHUaDFCoNwDHPpCRmxJMUSxJPwlN6FRx3s4gkbZtCj02XmClVzZUNgJKFyA2TUa7MldzAKVry5UaGcpt6RoTKpOiVpU26Lux6POlnnksXFnlemK9xK1Zed6aDHd96aK+HbK+zPuB1olA5XE30CDArc0179UA2/YcRroZ8bbMzbBTYZAwIANcjWVKfRjwfgI5i3mCuhEbeMCn3ESIcJkEuJryiIFQCDc2goZoINoRHUBIB8zGUMRBJHA1EyH1UoZqBiJMWSpsCgQibMR9BFkkQs7s2BEhAyHVDISEkkTYDEYIiozrjCLJIyGaOSGuqk3jJt4D1YLcZlgJ2/Q5GoNewr4477eFd21tajcBFuneLkls2wA0qgAT2Ea4L877TYJx36rSjL3FTtuVy4AWtu9XaAyrCPfFUm8iiHXdl35oPTG1L10KdeDbrgBD22RCLU/NJoJIV33phwYLHAgZBKqZyBjGZo1gaUMU+RnxAYk45QnWAxJQyVLYjXgKAzhBIPARlHideQGF4DgiakgHGLMEdSOWogiOfZ+JsXNkzk2AGRMipt2IJJiyGLmhD1HiZqRL+BQAMOY+QkEoRUTbMU0RwZloRlSoWgcdlarjCOC+nFICaoBgPbYMWM1NPcJUtSbobIIMcwS1amA74hHyzVhTbzS3JrXYKS06m2JNLfRaD5XmEAHXzByyxghrBg547WmDQt1KBIFRE9sLjLxtp5Z4bZjWGfBQQ9rqDJqED4BQGzhGLQ7Gbg4pp6EgNsUoZQkTfmBVJgco0huQERvwyM8IZDgSBAyoT0hCA5Y01Xgw8LLbJJS6kEV+ipGkscBoQGyMYxbBoemG0dAJs+YxBzGVYEahxAFnOuMohQMs4Mh1WTACgZ17wHAdpu14tFpfM1S0k/HRpcu0V4eXL+ezuTT26OpVEwdNlNWj0cn2NsS4nwyPti/Rrj+5dm05mzNnTy9egjFdZfN6MDi6cgUhVI5Gy0vbTJvjazeW0xkx+uzS5X6YVslIx/Hp9pbGrBxPzy9doNoeX77Sj1KH2WJjsxsP6njYx9HxfE0dHi2dNScnVd+dSgl3d1cQLihVp2d1r1YItkXZNM2CMXO2KJU6CQKwt7809pwxtTivOrVEqKnqqupOhdCLRdXrExm4/YPcuXNG+/Nl3bQrDJuq7orqVAh1tqi6/lRKd3xcaHPOaLUq67ZbYtzWdZfnp0KaxSJ67uvk/Bwg9K/0JHeEhv/r/+whAhCA9w8yhQB4DyAEAAAP3m+ACL33AAIIwt/nQO/9NznfvAIggDD0/p+v+sOc5Pc5kXfvG/zjHOABBN5RCj/67PjGrU/8rR9+4Xv+bdL3D/9v//hL/9GfG+/v3vjiF5PlghJ36xd/+e4zH1lcv/L0j/8PL/1b3w0wfvh/+eUX/8yfpuflzd/60mDnnlsfPfRz/+De0x8+fvjWMz/8E7c//ykVRtd++Tff+NOfB7278eu/sXbndrk1fvznfnH/sUd2n/nQs//tj771Jz7XZIOHfumX3/2OTykib/3qr2+89dbioasf/ImfPX3kgf2PPv6BH/m7tz/19Buf/vfh7gOQr0h6qI8fSfWRfehd9+YnoWxc9xK8/VjIjrPTO3n5gKOKzFdu96GxOq0efBO//gnAO6FfBncfo6IKl/fN4goEHVp/z+8+yFVRPf5m8NqHOuY9uxu/cZHgKjy/o/KLCAKgT8Sda0FbNo++FLz6gUYEsdhL37rAbBMc7zfVNkWZofdGb819lcXJ7eTNNdql2fIrFZig4iKwhzBP0n0mLv2WOVrv+u0o3BnsrMM8C6qXRYGa9jpQ91GTxntx/+DzbGfNNVsxu5PuDny5Nu/e5C3vz69Fa68DlcCjbfPA14KdQd9eBe45dxahcmtWvhJ41Jxdi9Zf9yZGh1firV8zp3GjbkX6qxxbfvemONkBo1P07lN87V2Gzv3+o/bC7cBgdHotWLw1RZVZ3QqP77rZGXrnw372HsEndv8xNHs7BsYtb0Wnd3HYm/0Ppvff0dtn4O1nwXQH8FN38MAoPGf7jVo9wBZHdJS7O7fE7p1m+xC/+Yyf7AbhGdi/xgfvSK7B8Q15coim++C9W3Rv//TWXvE7UIZ2cra4f3eAsA+vN0evxZEqp5+1t78UY4a3n1refy7kzE6Wxd5Oip0NHqtOn4eyKte+3e59SfaEZaJe/VMjoFo7W+wejo1ByVPt6fMwqNtLTx8f/J+gJFEm2up5a7XZOlue77KyD5Kn68XzSNRm4zPnh79FzmFwwZbFO7Jr0aZuZHuZ7g/1zefJ3tzVF0J5L90bw3wyb1+TNeyKG6C7502U7Eyah16Su7EtLwL7nFvEsLjAm9cCC9vF9TB/HToJD67xa18NjuO+ugHdc8FK4tWFNH9xYJwtbySrVwGWfvcmvPY1ciR0c4vUX4nb1OfXwtXriEB/eB2fvmxEgHYfBPBuc3p88oaY7p/LQXH/1bVxskwehm9/KRtf7tKsuf9KMr9UBqzYu5MNdnOx1uw9N1iLz6Mb+dtfvZJd6NNZtfdytLZZSVnsv5dFd4v4Un/2e+kkzoOn4L3fCpNNna3le69Es1kZjNq9t6Jk2NEH4eFXUCLy8dPg/hcl3Cbhhebs62BtsErm5c7bKc+g/Fg5eu1qj7CVt5PXtzA00fLNvtxGlgJ9JO5dCcu2eeSF4I0nOxyl8f30zU2qXHSy29brFMwgOBT3NsPCNI8+J165pcgoZvfSu+tAizR/Fa/mSk3RxVf9wTVah9UTX0teutLDMbDP+6MxUHGSv4Lruapn4OLL8OgarDL3wJfCt6/VaNOyN/B7q5Od0fr1Qjt89Lq48Exp75uz/WT7Q6U5bJf7wdryUGC9+854/XphEDp6JRg9dmRPyd7R7PKTK7XsTnbk5vkpJu3xm+PNy2c2FLsvRpc+VOgTc7iXXX3whLfy4F4yP89R1J+9GF/ZOoEh2n99besDtS/ByZ3wxoNL1dbHd5PhyQqO7OLrweRGk1yZ8NcvZWEtDuouf5CfnNHxyt69KdFOc3UHv/FxPziW2S7YvcXTHRlU4OR6cHiGZjv+9uN857C+eU+8+qFmVLP+OHj3Cpc7Msnd2bVAlvDiUr77AIcn7a23xatPVWMP7Wn87gWJd5k4qooHsHTo1kK8eytFq+bmq+Klx5oRy9RhfHfbs5q4c3HvcrhS4upbwd3rLRpH4f3Be1u+Y+nqZbKcdmbduz2xux3muH3kq+L1GwaOuXvBH069SuL6DZIPVTWH26+Ak8uoyNSjX4leu9jCTei+7o4HsBsky5dxP1bFpr/4Cji7jPJxevGXovtrLbwcmt8Dy5mrp9nqZdBnfrHuL7/slldgvgb9b+uzLXx+MVq+PPbKFDfj8zecgHDnIXjpRbLyOr9J1r4aK+hX18Llm4g7d3AjPXrVpBDcewJcehW0yJ3dmiZ3IVJu9Sgt7tG49Xdv0NM3u0TjO0/47ddDhcDZdTF9TkCPj2/w5T2UncPbt+Lhe2rY0ncehteOiFbsYFvGXxWhQkcPs9Uhyb7+Hf/1Dxw/cOO9T33i8V/4n1Y3Lk8efOTWr/zjYnPjxc//Gx/78R/tsux3/sKf/84f+KtHV66K7/yuj/7sTzdb8/EHPnTjn/xaMxp/9Xv+zMd/7Eddmr7wH/47T/2Nn1psbIo/+ac+8j/+vdVsfvLYrZu/+putDH/3z/65T/74j3kpv/i9f+Gzf/WvNZPRW5//3M1f+fU2Tg6e/sD1X/tN49Hz/8G/97Ef/TEA4W/+pe/97F//oSpN++/+N8kXvyhvvwsw/uP//fcH8R+UBO/3X/7GFATvH7b+/uCbU3+I875+AB7C+Pc1xr/QTvJNDvxGj2fUtv4PhK/+ZfAv1SoHVRX41gMkxBtdzGaLq9fztQ2v1GJrq5jNtZAXXnr5aPtyiHU5Hu9fu6mHyemly/lsroVcXLpUztf7Kd566ZXDGw+giFSTyenVG9VwdHrt+nI2b9NsdOFCMZuVIr3wwov712+acVRMJkc3HyyGk9ObN1drG8V0ttzcWq1vrAazS8+9sH/jZj8bV+sbB5evVYPR8dWrxfpWldHseNElfT8WwfFxM3YqihO6C6ipUhEG5xZU5VjAdull38cwBsdVppokTemuZ7BLqIiW3tfVEAerM8NNH/sAnui478IooHuEsD6ETp5525cTLtscIKVCTfFJH/VNkjF5IrFcMIXYke/NcippuyKkUQHzckH7Vgtnk1w0bT6VptFpvp/LJqgNYKBOIl9Uwuwb1ju5kOfN2ZRD3+PuUEVKmALzog+5jzreHanQ9lGRFHCReQI1Wh3WqRN5zcn9RhI/cFIf15Gztk3yo+UYw9oQdNQknlUVxnfbkPYWiaODLvK1IPF7++VIQQlCer+VbSF5cnrqhOoxzE4Ozke+9JyeH/aD3gQ4Ifca0bZxEJwtdOQbCYf5yWrQ04iL/cNqglwgErLTi0qlWJ4c94HLByJZHfdxaUMQg4NyYHQkErqjedNlMDw+MlLVKRwcH1ZRCUIfoCMV157TaNBjpLp5Kk81EhCMRSo7MEBdIAdx6TBWiRQDI01vZlFwpgk0PgtCUWDh2zhJBpUCFsQyGmjSqWY9E7kmzrqADWXjsaqTjA3M2NUu5HHS69q36ylblBHvQRJmcY2JauOEjyrUNC6lLNUUApWySC0h75pYsLgX7bGRxsSreGUXY4yAxuVBH2taVYi2hgMVd7Q+akNndZutDs4GEHWenB/WA0DrVtCdNiRmAOLmsA6BcVVwfrAaI15BXB12A4j7XtJ7lURgQsTOUZfxPrTD4vh8ZIWH9PSkHAtiTETuKW7sVA5vF24WqAzEtEQboYrhNKtYjNQkCWMFUqoHYrBXw5nwAzEUtZkJldhBnAcRdhlPwgbESE+j+H5NpwwMeBbUYIJMwAdRQQJiBiwMG5pANQ+zuzUdEpeSSKzIBPVxMBhWOkRoINKwhAlq19L0TsUGvOYJYAcE8D6ANjwnrSkngukSGqMiHaGzPlBNklF5Ilx1SjXkJ0CB86mk+yVxRjE1IKdW9E0Uo6jEvdWh8XJBnCjHPKlLD2AT+JCcA7bUnKukxJWtY0+qFc/7csxjXUN31iY4OF9iWlRJ4jIV5IeLBLMhTQ4rN+a41sOo8KG0E5yuGpQy4nB6XNj1WPd1Jio04ljpUZzbmPdQZHkFUgY4Gx2Wei3ADoayUbOEQjfLCh8zj0V8WptxZLQY7Zd+FmBks6DtJjHHahznJKSO03S/VJlwgyDdLchc4gCmcYcyYrmF7FRJXYxYUJz0UeViGPuDaqBVHCZ0x/C+y3BwcmJ40wxAenJcRw2IQICO+rjuQhmwXURRH7mIHhvRVROWLU7boNGBpfioDZsmjrk4ZIRWoiPoQMu2mUien0DheuIkPex528QJCZYQExUpRE6UBG3gUnRshK7T1MmlMMqwDvAT0vN8ItO2gf2xCm3IziHXbRyhqCSNqwOHg6XUfZHBpKk8O2ljFC3PMSv6IEADxcvjKgGgrWRvywkPTzrET5sYBcUZJFUdR93Qy/ODOoHOdlF7kk9osFBArposCLucV+0qkl7X2dnB+QQFHcb5QT9wELiQ3KukbwkLy6Muo51zo/z4fGg5IfzouBpzRFRE7tXCeIbF2UGTkELg0fnBKq29cAE5LjMIJOB0pxXKxDQ6O1AJbLAdHx6skgYHVuKjLjEmgJLuOmZ6aYL9/TZF5YDODg7KqNBxenTtej5fW6xvnG5fbGfzoytXL8zmx5evLtc28vnaYnOrjbOTG7eK2Wy5sXl+8XI7zfavXNsYP7+4fGWVDaut7eVoWGWDo8vX+mF2Pl9fbm6txtPDy9fWxy+er2/mo/Fq+2KbJE2SHl2/Dhg5W99abF5QUXRy8fLGcLSczIvR5PziRcNEG6d7DzyAAKizwagscFH8kTTat4bmgP+vV/yJL/3Tby5GTTP5+b+T/aN/6LgA3/LwhDSf+hT5nu8e79zps9hBEJ4ul2trltCNd9/Zv3Y9VFWyc3By5ZLsG5I3Jgk0ocHpuRlEZZRtvP3O/vXrga6Tu3vLixep6+mqARwYLuhZYYZREQ833nnn4PJV6dv43sHqwgXiFFk1LmRKBtHBsR6ERTJaf+edw/c594/y2TSytW6AC0UVx/FZ3QUcChce1WUWU2ZQrhGCKmGotGFXVeOILl0fMCi9PG7rRBLuYGERBDqhoPay74ppEp42ihMQQn7W9ZyiEKLSQI9W4zRalrJr83kmT1vHUDmIR8fnPcUwxKDUzPrz2TBe5rzpu4kUZ60S7Gw8S1cL0ajlbCi6Jjkuq43I9DheFcv1UVC0qFdC6tYw3pqz9bHo2uSwOL00zfIVaHw+z3jVs763EXEKiLI/3xizrs1OqrPNQVoWvgbtRPJWgd75CGtP5KrVI9pCMTpanW2N4qqAhesnknXKd57zviaRPG+aeVzyeH5nL18fCNvD0jqBLcf8vPMRqoJwtL9azAfj/swscD2Pqde4NFYgzTlfdiAEVRAOD8vlNBag58dtNU4oULg0gCMlGVt0krbn2Sg9LJfTVMCenakmlQRbWFjIoQoYX3RQgiKJR3vLfBwxbNiZaiNmQslWFVFdOx/Sk8pwimNMjss+CqnwsNDee58JW7ugruq1ET2rPUH1KIsPTy0lKMagslDbcj4KVwUv62pjRE8qREA1HiZHZwajWPRlx6m2q/VJtMxh3Xcbw+jwTDNejwfhyRIgAGLiakub3swi13hadfX6IFqVoLcmZV6BoOoWa2OqVHq0OtuepEUOS7taG/BGiaa1MbEG8lKZEW2QGB2szi6M4rqEuW1nkWha33kXYQNJuKjURDRIjA9Wi410XCy6lvcTyXoNGutiohALz0o94Q2So6N8sZ6GXYNz044CqhRp9Pl4ArAf3Tso1sYCKbRX6PkAQw0LDTiykqNFgyRqkjjZPSvmQ4E03S+acZLasu4o4VhFEp3VmPtmMAjvndRrQ4EUPm66LKLEokIDDFQakNOGMFcNB/HuWTNO+jBId491JJBEsNQGk34QR0cLRH05GsV3j9Q0OU8n0+MD5OBykgV5FVVNvpayZY+gL0ZpdrLUGMEI+9IIbRdrozAvg6JpZwE/7zQmxTjLzlYGAJAQ13jZqbO1cVjUvOrLeZye5j1mNsE8Vw4AFxPXetHqfsyNwmHRFvM4XuQacptiXvTWQp9go1G4qg8uXZgtDvxKt+spbzrQWhBTBTBf1n4kWiLT+yfLS2thU8BFq2cpVcrVRgjT0JCe1W4WNERm909W27Ogr8BZb2YJ0T1oDAipIYwc5eCfc6ZhX4NFb4eC604pggLci4DvL/w0qHmc3D2sLk6lauB532eh40wuqgA2y8EgPm7yccyxYqeqSSUhFhYWUqBCRpeaUFtk8eAgLwchoYafqi6kSEBcGEtplYbJ6Ypgm4+ybG/VDIM2DMYHZ41kSEJYaIRxMYzTkxVCTqU8OKirUdRE0fjwrOUERgSUFgJYjuL0dGUoWUwm06MT520o+koJocxibRwWJc/7fCPLzlYaknKcRucV8NbH2DVeNLqfcNujsOjyeZwucgWpSSmtFLDexcRoFBRdP+HakHhZr9aS5LywFpuMslo77aXocpREy6qdh9qQ5LxarSVhXjmDbUpIo6H2KhUKkfH+8mwjm1Rnfcnaaci0QrW1EVaYh4tKD2lDg9FhvlhLQ92Qc12PImZ6XFsXYkWpPGl4rE6j8Xh/dbaRBbrDK9NlgjiDagcC2HEeH5V6xCoRTXYX52uJcIqc6z5hGHlcmjYJNcODg6UZ0CJMJjunq/UMAyeWOQSuGQ/F8TlFbjHfmN65m0+nTZLMb9+u0xRElKxaD8FqNh8d7BNvTte3Zu++V86mVZKt7dytgyAQtqsh8f5sfX28v6cx6SbD8e07+WxeJdl4956WoYsZWrVcdcX6TJ6uPITtZDC+c285nqKAiNOVZUwnEpSKab2Pw+AnflrcfvdbUWD9MTgZHH7vX6yf/ADU+v97Dda3KBDqL10ZOvaZ7//C+XwrfG/vI//dT+5cf2T9hdce+5lfWA3mydn5kz/y07YHHZGf/q9+YDWYycPzp3/4J882Lw5euf34z/69Fke8Vx/8ob/tFahp+Onv/4E2iPhp/sEf/7l8NEveO3jsp3/BosAr+5Ef/O9R0a5Gs8/9l1+oRYKW9Yd/5KfqZBjsrx7/qZ/zCioqnvlrP8oXRT6ZfuL7ftB4sL/1jL73UVsPoC+q/e/ER2GzVvtXPqPqrTZtwBtPuX4C+zI//qQtxoY2/e5n6XG2WqvwS5/sq4ttkrt3PqTUljNtc/gRna8ZVqmdT+OT2WK7ZS8+01TX6onCrz3cN9veVe3JR+xqq0qcvf0MOFgrN1bi9Y9W9cOrkWFvP6SqS0Sd1Kcf1otrZxsufPN6v//4YluL1y82y6eQO+qrW+r4Q40E/fIGvv2gGRzbu490Zx89m3Zi52q9+DAkK388aY4/3kjfF9foOw8VsxrdudqdPFOMK3Zvsz57xpEaFNN671lNGl1cdO8+VU1Kcu9id/SsTs/d/nZz8iywOWzGzd4nDen7Ysveftqnx35nuzn7Ns8PWr1m73zK9bBjDLz+6Y5S13N35xOtBH4RNUefNq1K2uLs6N9Fla8iCV79pKLC91jd/oTBzpVhc/BprwAwqt75kyj3bYr9q59ucWw1Vnc/zlzbVbI++pxvkCZW3/4uWJIq9eiVT3coVhaqex93kJkKtwef1o3QrFfvfRdsopNxd+9/j5ZlyqF++9Xp8iigoX/9+bXiLiXX2Ju/mhwdDdg6fO9307NlRrl/683pcl/aIX/3a6PFO5Q8IHZ+I945GIGL8d5v0+OTRFJ9aJqdIQAAIABJREFU+721493EjuXdrw/O32HxZfPmbw33jmdmXR4+F+4fj0Ki7r6V7t/J7EZ852vx+VsC3+L3vyjv7M3lyNx/Jbu/OxQZAPkj6N4T9bjy9652R8+spi3d2WpOP+pQAU6HzdEnFDVdc5m883g+V+T+xe7wmS5Z+P3t5vTjTq9AO2r2PmlQq+pN8O7T1bihu+vt8SdUvPR7G/XpJ5Be8ZKtDj/vUNt2m/7dp7tRi3YmzdGnPD+xi43m8BNQ17YLu3vf5n3d67l7+9liQsr95o3nZ0gDZPTzX7tklsaMwzd+fdDAwFj01lfGFiFb4FdeXjcdocC88NWL/kyTIXzhixuNCzTAb/2ziQNEl+C1VzZUgVCAXvzyhjpHdj14/R+lhYstQm/93thZoix77cU1XZA+C978P4bVCYbb8t1fTU/NSHN++ytJX0Pg0WuvbFYr0V1L4+efbFc3y3nHXnuga655n7dnT5nzy1Vk7c5HwN5mubEUb3y4Lh89n2r55vWuvMX0cbN4sl88UAygv/8UO7x0vrHELz9ZFx8ohzW5c7PNH4ZgpY9v9ccPmaRUux8E+w+cXyzEq4/Uqw+o6MTef7A9fxS6lV49oA4f6qPC7D7p9x4u10/F6w9WxdNtumjumJdf2kDEL/LknS9naJ2t3qVvvTTnM3D2Dn/jjXUGlS7Jy89tQQqXRfj27w7l0Kxu09dfvxCmarEbvv7qWoBsW8BXv74Joc275J3fzsBM5HfQmy9vhFGfH7BXX93C3qsevfbPNpjrqk689uW5H7LVLn3jpbUwMvkhff2VDaC8AuzV351pxmgy869/GFuvGlwef85XVFOt7nwnWopypPHLn+rcWCFj7jxrfWxa0Bx8UleRYV1/+ztRkSynmrz0idJvKA7A20+ZPjIOtoefdFVWxNC/9Ul/PqinNX/tYyt/ueEQ3v6g6xLU5MXpZ10+WUwZfv2jpJgtpyV+8aOVu9oLA+4+2XUbXdiDnQ/C3Yt6um/ffLotPnA+6tjdB9viIQiX6uiGOn20GHp7/1Gye221uaSvPtqsnjLRsd17uFk94XFpzm90h4/2YWkPHrM7D5ebC/76Q/XyQyY+M/dvdosnkDu3+bX+8Mk+rMzBw2bvCTC6695+pC6edeJAHz7QnX0Q+5VeXtS7T/RxZY4edvcea+ZLuDOvjz/tuzyo/fnhnwKub/zMv/VMn1l4lLb7n/bs3C7nzcGngO6cos3d74Cm69DQvfXxOoHgfNAefCr0e8VqrTn6rO+Ugty+91lnbIsS+MbHmxDaVdrtfxKQTq/i5uAzprUKE/Put1uPahrD157N0xCsQrX7cWCqXg+6vc84TQ07+o7//PvZyWoxXv/ID/9tntc9Ch79mb+f3D/Z27r2bX/lr49ffvPNZz7xXd/7l9ne2cna5Y/+zR8PDk46kT388784fOf+neuPfOYLPzh/4Y07H/rgx77/b0Q7x0fzix/+yb8jDxaK8Id//h+MX3jz3Q9+7LN/6QtbX33p5U995nPf999kb91ZzLce/bu/mL690ybDh/7+L81+7+U7j33gk1/4oa0vf+2Vb/v2z/zlHxy9+vbtWw+R116l54s/WOr0rRvl+X+qwfoXvYMH/7rAIdgmcT2f12lWjyft2lo7GCwnEyODcjJbDccmScr1dRME1XxeD4f1eNwOR00Ur2YzLYPl+noepzoK8/m8S9NyOu+zrBoM6yyrh8PzjU0ThIvRuB4M+izL52tKBuVs3qVpPRw340mbDRbjsZEy39ioRqMujvPp1EhRrc3r4UiHCJHW866XEMMKRG1PKCQ54rWm3srW415FHpHOSK0jDGGtWG0oc7yAuNQcAd4i3PWxc8xAVpsAIVQ5UTpCPC88rlsKYdgh1qvIAqIBq/uAQJQzsVJCKphDXGoJtewg7l3kENU66A1FjjWYVZZ4EzQU9yZFHbGI1Dq0hltEV5YTKGqPCh1zywsMVc09iDyijZEaUAV5aQQFvGJkpQKuWItQpwJjqMakccJCohBdKsm8aDGqlEBWtAh1Xex6ahGujDRWOEyXliEnOwSLNpKGdgS3RipLASRLw41izqPSMdAnHqHex7qTCIOyj40jFrNcY6WFI7S01BmpCOqt6LoAE1j4qO8JgiSHrFfSQVpram1oEez62JoAQ1A62WlGPFt50mnhEGk8absUeqyhqG1AEMwtbwAjIraIOJMwKgELnI0oJzaMlJJSJl5yDQQJY0u4dzFhzGFuTCRCaZOkN1zQQEtpHUc89kJ4F1NAPQ+sSSTiTobaMB7FFlPj04BHFhNvIsIl5DFwEgtueWSNEDKxsdQ2EVhYKryLCaCdRauOURx0DC97SS2vMVI6MD7UmLYqdBZrQHNHgRUthrkRxIsGoa7PoKUKk0YL64kGdGUYVLyHoDQC9YnFsNOh0dxBXPWBB1QhmvfY21ARVBiJrOgI6nVktACYFDZwmlhEV44YGEFOjIuQjXnIFI0cZDgIDRUeBkiGBofEJIQRSyKvBiHHPQ6BZyQKFQwgirGQBgroBoxRSyNgAsZxJyNrBQ8Ty7gjIQoCCwLsI8yIhYE3sYyFkpExXMhAUe58gLi0PMYmopR6GjmHMeAFQFVHEYg6RHodWUA1JE0fYERqzldKSoUKgEstYBcaiDsbGUSVkcqEAJJKwVwziWVJSdkF1LMaEdWHDgiDglYJ6FELeG4JcaICoNKSANZAovrYAdJD0hgOIK49LzTnitcI1EoSH6KAGRwhxlwcKRsyGgHJlJWURIAT6wfMcCSYRjEEAQ4DBQXGMRSk79IAJ5AToyMEIhoKRSOIBJShRRzhGAZC+5i5CHOsaYpsLDjtYYCwQDIyICAkgQE3LiBgwDg2OIZWEkl7Iayh3vPaUmNCh1GnImtCgkBleG0Y9SwHuNECQNp60vapB1gjXpsAY1g4UVtOPV153PXcI9F6oXuhEeodq7uAYrikouiE1DAHpFWBd7zzVPnYY6T6SFvhEal6VBouiFgh2nUSAtJC2vURsbRDLDeMY156VKmEeVYApFvpsdCAN4Y7RDvASy2klyUjRR8xQxuIOhUYTxWiteMA4gbylebcixLDWgngeAOJahNkiAakMtI7rjA914x6WSNQN7EAsgaoa6Tz3CFaWmYBaZGoHQV94jDsbGy08AhVfWAB0YjmPbY2MAQVmsP3PciEqheQ4MKGRmEL6QpQo6VBsOw5cLHFsOsT5wSCsHRSawYAXTlqtNAEtZZ1bQoh7EHYWYkQyo3QlnlIc42sDi0hrZFGcUVQb0SjZLDavNClWZcN2umsSbPVdKbCaDUcVINhPx6vNja1CPK1tX6QlYOsmkzVeLIajXUQLtfWmtG4SrNiNtVcVusbdZoW83k1nTZBlE9nJknzrQuNlN1otNrY0CJYXtjuB4NqNK6zQTMcriYTHUXFbK6DoBmN8/V1LUS5vt5FcS8EBP//wR9NEY5/4eeyf/IrjvNvuQf1HvxhkegJKb7tsyf/8X9G84Ii4yEyFlrGAIS8aTops35Vw8BTHOimAYJBrSlrIc1U3tKItL2SMu1XNQwcRkx3tQjTdgUBrEkcurrm3+DItqiCGEIY1lVPOAfaIawdDl1d8/gbdlRee4kIGDWnR2IufOcIVUA4BGOTtyA0hKR6WeCMOCNAU5DBoD0DCOR0ABCMTA6M6oI46NsCZ9gb6ZuCZIFuAHbQGkOosKqFkcbfsAOBdxRy20rV9ER2MAAAOAKxcg6jVC8LOoDWWYaQBVx3gasbFPZUOAygBchaSzH0gGgjfVWyDFnnCILWeYjXq/unwdx5bBkCXUOKhRtMQ9NXJPEEQOs8QLEtagdQVenhDEGElXUMRn1V0JT7Dter1qGI44anzpPELHM+IMo4CiP1PqfFztU4Wq93F8FEe5ba1UoMkPEQuUBXFU65awECHZSxzhsWQg01JxeX7+3GVwCyiVopiDHwGokOydgUDQ2ARZagrD8zmCgiuOornArXAuQbFK2Xu8toQPu2kXHUVyXOAPahrkqcCtdC5FsYxjpvWAANNBSn/bLEKcAgNGWOE2laiH3vhUMwsWUNAotJZlY5TrCzAWhzkkrVYOSc9RD4TkReO09wplcrkiFrPUO8b4Ku7mXY4AB44ClCylhMLua3j4M51qoJU+AA8J4DBY1taQAwQto4hDOzymmGjIl8XZAUWsuwwcY2JIps3uLAO+QoAt4j4wHxUV+VJHUEIOeAg6EtNaOkbyjwKz6mythvbFnGfStMrTClzjYktg4ndpXzjCgLqF/Pd07DCSAEatDgMLJFR4JvcrDx/vfvxUCHne1g4AjwEOBOW06zfpmjBBIY6zJHCfc9hr6GMjFFxwKngOMkrJeKYkfFJD8+iLekbRHyDQxkXynOsOk1l2FTNzQCGMamzFEiXI+w7SEWbdPLBBhgKfYIIWW+wSEp9M5TDLSLdKW4MBZ7DD1GWDvogWEIWiB0h5Bt0e97kHYOfcODkHWWQWY72ZcYohYGHQkcAd/ktFzSvq14hhwADlDYE2NrGkW2QFYbyphWBRtg6wJXFjTD1mGkiTE1jWJTKMA1ooldFTSD1nsK4jbPScqhItbUKIh91SGhPclsnvMM99pxHPdFjlMGtVR1D+lQLZdy3EKR2TznKeqN53hYnXWAOsGAAzUKYld1RBqHM5vXJBRd1YtQmjbHSeQqodtTPo1d2ROhPc3MKucp7o3lJOnzAqcUaupNjeN5tV/IRHlh/4B3RLoocMZdh6FtUBTpomeU900nwlA1FUo9hpHOS5xC4D0FXDdStxVPrGMeAYchUc5hmOhlTxh0vmEJMj4wNfNdjWJNmMMAK+sxinVe0Aw5ZwlCxgP4jS2ziFwobu8lF5H1hiFkPXSAQEW1rmnkMcDm/S1b5Q7RupKRKNgQG4uxoVrXLA5taapSIZowWPAhdD62ec4GWFuEDS9OKyQCQSyUPRZb5Z39ZBtaH7kipwNsLMKGaV3ROHSlAVRBntplwbL3P4wbq93d5BIFPTN9g+PAlQpz41liVgVPkfGOgKxfKMIsoti4BkWBLRXhGvCt4u5Bth42eR2kUd8UJCVQM6tqFIW2MhRh1XFgV3xElDMMJ/2qIAMCFbN9CyOPgUcQGp+YvOAJMsARmKhVBQLmNcK+BSLUdc8lUNZSaghhbesojWxVoRA6p4QAwIu2cpgB5x3GllLWto7gaXGwO9wmzgKIiNYOIek6Z0DPhCUEWO0xGfR5iSOme4x9D5gHUIDeGdBzGZmyc9xhFPq2xBEEAO/vz//W3+S33/tDaTQI/bdkQMvJ4Oi/+N76if/bFCH5w5IFtzdvItV5Qr/Vyq3E22/xe3f/iMaC3j/w5d/5/Bf+ypf//J8Fzj/7Uz/9D//i942Ojz72sz/zG//Jf0r/L+7e8+n2/Szv+/bvr5fV17Oeusuz9z67naLDUS/oIBTZZsZgPJAoxMGGhGRkJmGcTJw2wS9sJxQZSSiyECZGHJCQLGMhSIKwUD+c3vbZvTz7aetZff3Wr39bXuBxiGc8k5eI+3+4Zq65Ptd930x//y994rkf/7H55uAD/+gXn/tbH3798cuBzhF1zr1y+72f+tTXfvo/N93g/f/wl57/0Ide/OAPDIqRAvTsq7ee/O3f/dOf+omaOe/56D/5zt/+O7dPbfMeo8eruEJ/9R/8Ly//2I9ONzbe8/FPvvJjP7xs9r7/F3/pxQ9/ePzImQ/9d//zrQ9+4OAdj/7oz/+XNz/09Mt/9Sec1za0P5Ubafu1XUwOkrek/W/vKLfOHhl1Xl13eF327gUHu9BdgY3jHyuvnaTZV7bff+bb29pS2cVx540BZBxuPvsfgJOZEt92dskbZ4mejN5Rn/+GV1G6euu1v374bUr5H4Az+uBd2KQnj686z61zMRu/s7r0VZLTcPTUYfvlBpUWGuzpozUJyf7b5uvP2mTROnj6oPOGRScbYvfbYR7Zx53ZlQM2aTXug/qxKnpB62Sw//7j4OO/6H3nW7Of/blG/E4z7M4vP6CzOL7LZu/O41/8+/YLL+79k0/00l023k3OveKtcO+ol525jv7g09YffHXxD/5+V74bTzbSJ4b+qORHu/mZFz1Bunvd/Ox1WvnWvZa+dDcaFXK8Uz12gmHaevlcMbgn23LtxXZ++hBZiX19PT+3imBKb10pd242TML3Nsv2reD0/g9PX3uBd1/Kn/Rub1Rn8wgt6e1HxcYtP7j5YTx+LtPfDr9//flmuTkSwXxwc8dpHXXj536YZd+o0Q3nSufFS6B5Nzlbrz3brNandTwZ3NgC/SQPF+7NR+vmPbVe9F+6qMOHR1cr9K8eGheGF+vDl1xmKesxOf967sMV+esB/eKBAND+QVh8famA8d9pZmsDg5C1SJa/lwcgxz8S2L91NzNW+CNuGoaGkHg4Kp9NoVTs/eHq/079MnPetywaZ6AN3ONx/a2irln0eDp/HYGaWe/38j/O2SonP+7av3t3Vdr8g5q/kGQpj94tw8m6+8CbvnXSvavxeO3w+w+btwAb7qQXXvNS1D3ozh95QHK/ccs7eUv6A/VXr8rV7/jngmPXGp5bnX/VzXX3QTc7ff2x8PUnk70vNS6m98/jk7P5YyfhoiQPzqvTz16lrz8K6z/h3XvJuzZvt5PHZ+1RgQ7PVY+OwyQlDy6JM68AinrX2tXpG4bY3vXW8VMTvTw8+jruXRVOMz/+ahWfKdklqr44RxextWGWf4LR+SqK4d53nN6F2gpOnHUbPRhCf1M/sw/PQGtLr75J0I4KnAfgXBu9PpTR6fKPilZ3Rd4V4C8uzQYOLs4MFDAXTDRGz0eN9bx8SzP73DLulvi9Pv3c3XoncC7R7F+XqL1qnGUH37T8vqx/oH3ujyJh+PCdB50XmsR4xakb6LBHKnXy2Kz56sDOluN3VRf+NRamPXnP7R86/EYT6a/Kbnn8TqW98aPD+LWetyqP3l7+5M0/iB3nE8HbGtcHsIqr0697x32VhdkjJz8knm2I9J/FT/ZfCo0apFdfjO9HpOgXp19zh22zDPIrx9adNplb83cdn3/FFcXWydtu0Zsj+VrReKuoCwRfnJK/wpy7o9mbFv2QdIar8aus97bSNmLyfBldycwTzVXQak5nwdceLG8E7IMymib7L/DNd1bmrLWMO1E6pc+Nkxcx/QAFs9n0VWa9I6nOriVhy13M2O2p+NbKf2ylOVLP1uS9EK6m2auW9baVUOnkWdZ5vCQxUF9N/cekfaaz9nzDbZzY0Qzcf6vpPpS9vPPSRRjcX1wse99piE5S9o/W3tw0LUHaL/9NMHlFmlfcy72XHgHO/uwxsfmNYNEGZPf5H5m+foysb6LL9PZbtX98clUMvraOrOPsiclPH3933Nr+F/D72q+uU1uAzqG+d1YEq5MryeBr65TPRk9WF77JVw0nOXPUfq0j3Wp6ed58dd1b5NVbike+C2vVOXrPYedlC5bN+vSr9mFXFfH40cPoZtebqfH7Zjv/9Ufo6GT5j/9xa96E2aA4/ap70oKrKNkdWr/y8/TmndnH/rfdu74pN7PHh+FhjGfrxdq37C9/uvPym/l/8xFX/RA5ccwTb5x7sRbpTvX4C9GRhyab8vRLNGn1hlF+dWjv98hJnL7jOBzl5N5FcfqlVl1ah4Nq603A0ea11urykqxyvLdVXZkGeULvXZWnXu671/4amH5NOdf0+zavtdOLKShL6+Epd3Cz6337b7Dsy7X1gDzefbUvNm9VEVh/rb06JzatP3m6Gn/T7t8SjwU3rhaDN01Trr3SqwZ3y7bZeaFxcmWJdOrfPGt2Jo6deG9eLgd39GD8n/zc/3hy9tRLf/NvfP9HPz66cPbN97zvfR//5NGlS9/8sR//j3/2I+Nz5/745z7yE3/3Z062t3//737ESw61MG5mPvipT52cOfv1v/1T/9FH/otssPaHP/szg7t/VG9t88PVB3/9M/P19VsfeO+7P/G/Tza2/6+/9ZMUzqzJQvW2fvTn/56w+Xd+6iff9pl/nobRtb/2g+/59GeSRvubP/N3Pvzf/g/C97/0D3/+P/2f/nsJ4b/86f9M/zteyhgVhulTb4V/8diaZkzGDfDv4YP/X4NljKE8efoDyw9+6C8aJdSO0/n0J9nBAazKP9/kh1pri012zyiLgboand6BGCrHOj53FmCIIDo6faZmVDM8PrUtCcIWrd2WUwjA8PDiBcgwJGh49rQMA4RAtbHOFplieLx7SnMCjDm59IixOcJAtdo6M2CWT8+eqUIfQjDZ3KotC2E0unxRMoJldXLhXOk7EIHZznYZ+lBrExXaAkwKHRdaQw5UFSvDBdKmdhVlAgJo/EwxxetiysLUQlTWKpCaS2K08gTg0iF0ruzcsjCEtV9KhZgoy0ArJihCiRUSQyhhuV8BAJioRSB0jbkoq5gIJKAy0lYIVAAg4eYSuFgrGSCIJdFKMQDDFQZYWUpENQRQc1m0LAZk5QKFBRY1dLg8dxpTBKgQQQEBhLYUTY5VaTotcWGXGqldWlUJQshwXUalQRo0I3V2hyAjPapMRY0QFlbNHGEMoK6jClKojSpjZSMpOBSNCmuBLFrFOeQGISViAakAyIiGRFTVCqM4M1AhBKswNdQYIcYkrIgFqZZxTVFRE2waK82xxdih9IQPGJJVrLQlITR1UNpcQYaPtWt8hrWRzRI6CANRh1pziZGRYYltiDSsg0RYgBpdRoW2MNUKNyixFAGAxwZRRIzkLa6hw+tSuQhTiLTUoUWhgBBSLQxARFSshWvBojpPWzaDGEnBdM1lCSHEMVSCQCFQjGTFEdBMVVgriBGMKJMGAcgcpagmRtMWArZNRVX7FPsYixJEDqEaGmAsJRqG6kravmoIKivoOjJOAaEG1lVQQQyArcpYY13V3B0zjjGQDFZhgjQwFhZxgRmsqT1xOhBhbSPTrDDUgiDcSDRGirKhsQWhmqm6IShRinHVqJkuK+bjZmIoZSatYwGIgUxWDYmwkgg5fQgtgoDmPYgtiIHCfQt7ChqJYgJ5BTF2e1BzrBmCcaTS0sor1PaRqxCCuIWJrYhnC9eFcciMZF2uItuRpWow5mvMaeV7WM+NRKSlTcCpqFGb1Y72ZGm6NgsR0BrHgDjAIEzbUDuYaFH6RiNJgJGOAkYgjZVXAw6YlNIXBSZclDJCwtRQmYxyqAmkSDil0hxqqAMpCWCiSqO2ApQjbTygrBJBqlhVw5pAscCugYhoVftYmwIbI2ygUY4NVK7WUCBjtCcEoEzVwsPSLrFRmCPUQQZDbBnVoljX2mV4jVJYAmq5DQERxBjiNoQOw1pYdU6lqCyM+xSbomC2uwYhxcRILgusJbIh6VuESk0B7SCEDAbKqnMMDTE16nuGQooVjiFG0tiUtBGiEENjdY2hiKIadT3tIgh0HVS2JSEEOlpJCggwslFgjqiqRaiVLQk0MqygYyjGI+UIF2Cjq6g0mFBVZaHRniSUTllQAoIRkWEOHMzrVMQ1xIgoObcbC4mxzmu3RpaEGtZ+ITnAGqiGhBgyWZYhNF7FgFKhVJbG0ghXFAAyJQofSiBILVTgQpZjiLUjFZUUQuOLGhBaF3p9TcQ+UUL6CMAUQaxtIYCARpr1npSCqlqE3DglNUowDcMUIWDaTbO5hgGUjlSxcrSoXCItRaQUNkJRijAyvNZhhYE2rpJNBSCUGOHGUkMDqanC3FAAqK4aEjOlKdGxYLqsaAwaS8MYZuxI+NqxcCWrhoREGoKqMHeJ0YwcKg8HHiqVbNaaQ4SFiAQgAnJnBjtK1EjpKkwkgxSqLJSGYQjLvKGRpUCNRJRSoACxRJwahxAppmu9Za+DtJhsrOdRQOpqvLOZt5u0rianTqWeR0Q139pIB2sE6CqOaSlomU5ObedRwIrVdHuzajSorvP1dem5jlkkG2tZp81kPdvZKvyAAqVaLUk4L9Jlu1nHIRZi3h+UFmdKTbc2S24TWU1PbWmEeV0k/W5lWUTKfxdeaS36a8P/6u/9hSsvQQCMgXUNlfr/gQj/Qnf17fZv/Hr8L78Iq+rfGixDyOrpHxAf/g8Hb7y+2FjTELQeHh7unpMI7T7/3M3veytJVtZ0mgzWG2LlH46SflsJ1Xj1Wnr13DLunH3ppVuPPe6KrHvt1v6FR2hdhNduyvVO2WwGD4fZoDUPWrsvvXj7ylVbFL1vPjt89Krx3cbhcRZHUy90RiPtuVUUnX/5xTuXr9pIdK7dGm9ueKRCw6KMnHm3Gx9VmUehK4J9tYwt6Ap7pBSGdQv4yTyqs8PGgE1oZUPtG/t+koUhbmh7VBuEyybCc2SJctbx3JNUcA5C7Z/ozKEmNGSsCIDjvu8fJ44sl2uReyI01fN2sDO+XUOcxB0yMVjo6WYQjjK7qLMudsa6YmTaj+OThZ2K0XbkrIpwWCw3map4MMvGm4Hz4D5dzK2dTqkbfKVOtkOe1M6t4+SJ7Xg2Qys03fTspbBWZdUhOsPseLG4ss5WefMwPzkTRIsEJThbY3SWg0TrdUvXyJ/JYg1kadF+8+boLY+FlcZLlPYIzwXJAPOzBIXuVBV9s+TR4PZ0PrCZqfjMCB9WnDpjrSOV2ejMeP9ua7NVztXYn6851JTkuNYRFS53R1KHMvGc7l4xGjgcVNaDNOnE3K75RAsHVh5zRspli+O429ybjLtNl+f2AVw1KbOqeDkqqbWw2+5YGkcuY699P5/3LYZL5ximMVUuAJPaLot8I2bHueIYxpg+TAvXYhFUElCtDQZ1iuw8L9cCPaoAgtUgDvYmikLQ5mhUwlqvTnWc/SmQ0nQdMJMQ6mzQ8PammuDIyyaph2tVrQfWLEOZrDb9IFkW1M4D39ubQGh01zbTmmVCbdr1ErBlVZwK/UkGCiTaGBTASeRoK+B51TwshmeCdnLs56uH/VMkgXZS1C0sCuRMM7HOK2C19vOTU16wSsnzlEzbAAAgAElEQVQCpX1K0xIslOpxo4g3lWVfF9DpHOSjLbszO8kzL29blpI00VUDQ5P3k8mw0c1Q0NvPjrfcoMzJFKUdyoSgCzXtx5ro5q3hcqvpoArfXRWDiFGJRpVyaGFjazwFtlU0m+H96Wo9ZkiRF+7nO92mJ8ol1TYSoYWPcmyDrBW7z94ozm1QD7GHWd60mQfRsIQ2rnwGrp/Qpp1t9vybJ8UgzGynvf8gty0YRny8kpyV3cDbm2Julr2Gf2tctt2002o9HFMNxhtBOM6dtFhsOHxskJHTXrA1ua+ASeIuHitSg8l24JxkXp7lXcuZa4HIou9GhxkwWnawHGmnrianOsF0Za30csOKh6sa8roB+KQStQEDy6ygt5LZmmFVFaXL/fZ6NC6FYXUTWHOJhCm7RK+Qt1THZ6PmZKzHot4MeFaaROi2JQS0RrnZtAtqBbPFot10F0swFvXAo3kpUhj6RUY8OKrROkuJE90ZL860w2SerhiNMYIazSrd5AIxfrA0m3ZuLHqY6I7NidLHNWxil1QpdBHWlWb8IAFrPLf98OZodaZpywIcVWXXhTa1x9JDy3Erig/UvM0orZwDkMSUOnVjeVITNnO79kRhJqaR39qfLJoxt0v3CKYRhY4iY2U4XcR2eDin3Cw6UetBmjZIGrndvWXpUhUYeFgDjtK1sHG4QlhVMVgbHYzC5jzst+4vhY1kA7GJBgjPO1bjKBWMzvp++yDRALjuMitDXsjRVuBNM2elZwMe33qzFmZ56WI8EVBK0SF6Dtk4Ued8mWJvXs+37HiUCs3qNiJHORBAb1p6Bf1EZAOoChrM5WzT9g7ndYn0pm0tJaoBD7PZtAgOj7InLknlxONqssGjWaEqXLURXylc6KKNK8h7D7PhtttPhuUyXPUZqwVdGhEjA4v+cjKO2wkJ+3vZ8Zbr1gU5KrNeYEFB57oOUc1ZcFSxcHnobfQeHA3XOwHM2BClbUKh5DMlQphDGgxXddfOXKd7dzXZch2dtpfjsRsKHFiTOms6kuPmw5Vom4Ub9u8sJus2xTLcOwaczAb9+N4+5Ph4+/TOSy+PNzcmUad1uJdaDvOdxuGhxmi4c2rt+RcgxUdXr+68/vq005sNBqdffTkLIxhgeGeIEDx88sn1G9cF48v1/tbrr0/669PB+uYff1X6werKrrt/YlfV/s5pMplrhFQj3Ln+xrzbr1pB++7DitLVRs8+GNtKPAgarY9+1PpziBBqVZx/ZP8f/cK/uXH1PTV/bovwL3ZX333lZfvGdajU/5tgISR2TjWo/c5f+fiq0wn3j574P37rZPts/9qbj33uC6tGO6uKfDPiq6x7f/+pT34mj2JnvnzLF76UrA/i23tXP/+FkrtOlT/2a78JIAacvfMzvylDz54vr37283kjDvePrzzzeUk50+LJZ34XAyg8512//PEa85NBVzlSF3L9jZuPP/M5ABDA6KlP/jrLiypwn/rYrxmgZ/2r+M4pWFjELMHDy3jBdTwj166ALLaih++e3e1rcbJy64PLKHUgS9Heo/bSqhsz69oVnTWVu8B3dlHmIpzjg3MoDSDP4L3zbB4U3cR5dVdmnapR+re38CpEKANHp9CyIf3lB7P7G9nJDdZz3zgt00HZVu6tNbjwuZma4x2w7CfdOrzZMsPtbK3ybjfAaIuQiZ538XCnogX+w8/zL/4ePL+Npk+A0elVt/b3G3RxwdC5dWKpk/PSVmDece6v1a2V9bBN5hfzRubsBeh4W9uVNbf0/jnDMpx2+OH5urGwHsbm6KyJl+b3/xX5vd9TXtNxr6q9XcBytAzx/V3iHuNhWx+fh85UlS67dc5oAJGiNy8rokkJyN1zgpu+uPW+bJXJFZy72f7bkQSASnbzqiEA1QrfOw9wiVNqHpxDWlOZob2nSK20VZFrVwxCUAN077ytlrrA8OBRpAzCFbx9heTA9g8/lD5EwBynm+jeOQIEqBF+uGtqDEiJbl0mFUsaxexboFwgm4rhG7yYQO7Iw5cscaSsLXTS3kydwB7Oj19k5RRRF07epNUIwpiNXkD1nrJO4cnXweTEJhvW8nlZDgF39OgGz4fUtOzZizC/pxob5fF3+HJogU1v9ZJcHhE3VotOaxa07VUx+i4o7kN+hsy/oSdHtttUk2tkeUDsBuSzAb2/UbcS9rCDj0+tWpV32AAHW9BOHsmuPyHyEWXL0Xn37nrRzuyjFj46r4M5GjbR8WlFcishav8RQwuchvzhIzJaWsPIHJw14YIMI3hwGoGVv9Ll8VOIFyhz8d3dyi8eSV99p65OlKrHW/rhWQwLkkH44CJEKSgdcud8GWsxXA1fJEhppqv7z/s4l8QH+3/ChSJ2sBKWMrWQx+j4VRtKwIl4eK1NK+25xd1vh1IhBM3RC5xCBUszvNMwEltcPPiuDROF2+T4a6RSBGNw8mZIETIaDl+iskDEzlUDmvm8Fv7s2zhRDuJ48qcGlBIacPwKExnVm3bz5XW1XCtauXdzAJYNjFbmZBvOO9JLni7vn1od3+Bt+/oZvVwvesq/04ezjg3GYLipF+tlaNj9dWsYF/2Fd2MHLk9ljdx70DXTLiIrctwxkw3oLdD+WWu8ng0W3vUNPd1A4ejxcv9Cno4ElMe7YLRhvCXe38bDzaozcm8N1HSnaqz0/fThqw61dLnAwxcY68Lynj56wwr8YtlojnrrdJmjvXzvVY/aoE7Q6HkaxFW+Dw+vuXYsy0N89BplVKnCnDzHmKXrDB39KedNUB/Iozcczy2LMZq8RIBFQCkPn7MdU8h1d791impRvCmPXrMCvy7G8PhVjiEwSh88yyEFPGiw66ctUZgSmP0LoMaIluDWZZph4h/8lXzPMvqg3MB3z2FloNL44WVYU0gKdPsyzXjZLJxXz9c60lw6d8/SEgCjwd5ZVDm5a7zrZ9GS163Uu7YrTFfaxrq9ATPawHffkc0cubjvnLZfP8tWbtlInNfP1KIlLG3dXQeFW3vSur/Fjhoo3ke3zunFZtaq3XtdM1uD5T32pd8233quPneZzN5iHzaK3tK9sY6yc8Ybo/11NB0AntOTphluGmdJx6fZaKtsj9ybAz05BcIZ2u/B8TpECV920GzXuEt8uI6Otknj2Hz6N9DXv6FO75LsCjhaQ2iF5w10uG2cORqu44Md1RzToxjun4UmDZIyP/w+CCsgOLl9vnbEbvnau2Q+0aocb+q9XQxLWiq89wQGuVEU37yo3BIlLtw774FDkQbo4CqCNdIG3r6ETWUMYbcuSqvGuYP2LmG6BKlNHp4zWrX43g9kJ1Lmx8Vp++Zu7mGScvzgLNMLJVx0dxcigtDxe3/hY3w6TxvNJ/7ZZ51VKim/8jtf8E4mx4OBbBA4G6GaPP3xT/CTyXJt86nf/pyfzGvuXvr8v4gOhyc7Z977sV9t3bw1efTC933mmXC2mPbXn/zsM+5wDDi7+Ltfatx7eHTh8nv/6ad7ewf3nnzyPb/8iejO/aOzZ4WlpKmD/fHjX/hS+8bt0bndt37y1wYvv37vbU+98+P/NL599+DUGfraq39+ixAaI1vt5OkPfC+W3An4Xh4DYOn5052dyvOgUvONzcq2ijiebayXQaAIFNwmZVU7zmxzswg8Tfl0Y7NyXE3MbG1QhCFyrMXGRh7HwrKmGxtlEFSeOx8MStetqLNYXy/ihrSt5cZmFsXSsqZb23kUG4g0dRDVVRAs1tfTRkMwOhsM8mZLUTbb2MijWBEoQqhsSAmoXIW5MAiJECgOECZLy8MYSYkrH0EOIYHSk4Aog2DlG82UJkj4CmCgqZEBBBhAAmsfICMBMGUEDQOK4NpTGgBJgQowREYSvOCOIhgAUIVIEKAIEp4xEjECpAsFMQCa0scUGkWwcIxWgFItIapCpDhWzWa9uWkxJpnRUGmKKgeYwGgKjG1qBSSD2AFlBDUGwtFSCoWh9EBZE0W0ZLqOgOFQGVMGShOoHGOkNhjKZlNsrqswUFyLwCgGjIE6NIRCZSnpa0CMpqaOoLIVYqgKtOIAMSMCpbipmDMPTE4sU2oRAkVrQFEdG2MDyLTwJWRGMAgiVNsQESN9iWxpCKpjo12oiTKe0lxrZqoAShsQAqWvoKsUJmMnyJirayBCAyyjuCkDLG2ACawCA7lECKCIQgoMEyZihgJDNYwpQhAgTUVFlNCMwIgaCCDVOrIRVJpiGFKjDQRKNWyIiCIYhBQqgJgAEVcGEYZBQKDFIQAwZgAwQ7GOOLSNIRWtKwaIxBg3uC4wBMrEDAiiqQYR1wQYqoUDdQQMhsZWQmpNsXCMblBJTK75nOISMuXAMgIGA8mkDJWhUNmgDJHiQAEgQqC5kRCYQBoCla11oAyBygYmxIYbpbX0paZKUaIjYBjIsT8Bsoa0tiCMkLAA0kb4CjBtCKkjoxk0DtFNDDwFSUmaDHrKUATaHPhGI2wo19oAm5qmrX1gcA1CjDxsMAJNZlwJuEYNCh1hGNQxN57SFMEmgyEAGOomA54GXJmYaltABlXTAYE2FtRQAEQAhqptg4Bpjk3Dwp4AHJkG1xFGAJQhAsgoQmpXEwU1BcLDUKM/UxBBBgBQh1AaIDGqXU0xQMRIFwgIFEW1BzFGEADlSmWkJqj2AMDYUCNdWFMDKAa+UchAAIStNDOQ4IT4NmI1xNIDihhNgHCNhtogKD0tqVIYAg5NywJWbTAETWKYgB6ELWZYaTCy08QQDC2MmhzawkgEmtQwBF0MGxTQSjtENyl0BDICNSmwtKEEtJnhFXAgbDBAc4ipahHgKEggbFDoEaS1VeZQa+hi3KKAV4Bi3WLGEYYp0GTQVQaq2lPKMpqZOkTaAYhAECjoKETo2A5S4mptRKCBDRQHVQiUY/6tggAEVQylBxRFwlfAgpIaHRPtIk1J7WnMNYCgCqHwjKSw8gEjQCCy9P0VghLjOjSKQgCN8LR0saKwDqBmSBNUu8YATQEQntFGK4orDxKCNCey05bElpatgC4VBNDUnjZCGoK0DxTGGithAwGMoVDYWmgNEBS+0UwbDJQDNUCAaWnrWktNoXCMMoYhIPtdoIWxuLDhv5GJUMYThgDlGqWMwVDboIywsowCRgRGMwUYrCOjuSmgNzFVBYmwIIxRbUEMjfAl4FpzZCKjODK1BIFWzCgGyhBKCwAKTSCBrRUFVaAVh1prEBnCjaSgCLG0QY3ZzPEKQLQBdQyVhYGRdQAQB5KaqklrG3KMZxsbWbslOV+sDbI4qm1nsbaWdrvGsgWWCDOM0HRjY9Xr1Ywte726FQvHSfr9Za+nGJ/3e2UUAQCXa2tpqyUsa7m2ljWawraTXi9ttCTGi41NYVkAgsX6es2YpFTY1FS1Yirp9Qrf1wQv1waSYIDgYmNDUqowggb8pZnv7QSrPHN29t731a433j2TdLp5o33wyCPLtQEy4Pbb3s5s0rn5MFnbrGI/afZmZ04t+v0ybk3ObB/v7BKEbr717SLyJHPvP/FEFXpJs7M8tTlbG2RRe3Z25/D0OWbMrafeWrVjSe27jz9exsGq2Z2cPVW5fuvekWlEJ6fOUANuvu3tdSNQ3L3/2KMmdMbdrdmprWU/gkYVzVpFNTYmbZciYNAkMixWET8UrTlyZy1XMlbGuQpqZFTWLmTAAM6Ul5VNaEgFnCrpAoNgHeUqrDGQZSNTAdGkMFa27FCCVtCp057REEp3mTTtReY84J069jTOpVWuWgDDFbBF3hUa48rPspgTtIJktehSTBOMVL5WFjZHaJX2DGhu6QuPo/Vm7mND86RHAEwwksuBRFQoptKuAFAia1E3kLAkYsWijyHNINSrQU1wZZgs25UmWlsr0QCVIzEtq2ZRbJ3Da0+sHj+DUSmpLDoCEKl5CkKx8jEiom4Xq8C2ZV50c+VAiNO6KUsfAlLpKB+70ahu3I97Fl3VlKedXLkIwJWI6yLCENeyUaVNiBVY9UvgKAT0qlcrF0OYiEhUgYZYID+ZdB0MQNorgF1DoLNuWXjuQREds7gMEUK1CstlkxKls34OHIGAyJsFdAC2EfeAXHcgpbSDcQszBPAaNSGDU4HSGrQs4mLuGr3hAEyshhZrnmNq1sMmtqhtqKfrgedQQV2ot1yDCY+06Dm2rtgapr4RkUtdU6+7Nqqwj2Gfk5mApSobvmVKqwNMw0Iusj0DBwzaGDsIDihQpbSzugGEowHNl30M8QpBvRqoDLr7qn3YbAEsEZuLCAhbElKUrboIADYqGQhCKk1l2akM08ZKqyasHA1JVbfKzEcUqGRQWygrbJK3a8M1oMu6aU5487iMp40wCynRKhmUiElN1Z/9Cwd0mbSpiZGlpdm2SYgIgGCLm4BZTJIOlq4NRhB5jugHFBi4w6lnCIRwi1EXao+RJjQdi1FDW7Ba8xkAeItRHzIMwTqDAWOWJk0EupxQw5uwXvOJ0tY6yttN636i3BB2As4VjJHuchsJ2kb1mk+1oWuginwG5sZKFx1G8ApaIu0rSZC2k6TFFrmzR9tlw9e41LxYdRACK2jLvFdrgmuvSJuAiExHqzrC0hLQKhc9hMFKc130awxkHVRVU0FTSn8lIiQsQVietdQRbB+ZxrgZUCyEV1ZNA1CtvLRu0ooJxOqkD2wqqYXwFocccl/DNochsSwAN7jWiJzUMuaMa8wxXGfQQtzVrAXrhkM4ogNSt1xulDllUxciisCmBR1s2RJ0uY4YpwZt8KrhcqDQGYtZBnOEBwRpI+cGMogiyogha6joBLaR8LTFbMMsBHtUc22IIF4y7VoIoKxTALdGRmedogrcgzw6onERUYgr5ZdJC2Ftik6hXYGAKNq58rDBufCrtAkoyHRYrToIKV1Fi6xB7XpZdirhY4Pz2q3zhiIm11G1itmwbj+w26vQsaqlbOV1QBSrlFevWprovIpU1oRUZDLKkSdSDxuWL7sIg8RwlW5i3D5TXXhLeqpPVab9lYiw4ALxouiImmtAVd4tCRLSrauWMqhW7ko0sLAEZmXVqioXQapWPYGgEE5dtTSAlXJTEFTJ6ato64nyQq9wACZy1asw1rUtijYAWBhrVTZR5hkG1GIgLZwWnOWd0lgAkEQ01MhqHFfxNIrSiBKtV4MSWtJgnXalsQzASd3UlScxEihajLoh13LVLxCvAdZ5p9YOAmRVx0oEEsO6bubLBmdSpIO8ItZh2TjwWsonAK/yBqiDmulSdIpVgxMhivZSuTxpdmebG+PTp4ugsdpYe3j+ItTw4MqV2frGxhs3pB2KtW7S7MwHg6PdXeU4y431/UcuIYj2L16cbGxiAPYfe1w17FH/1KLXOzh/XjM229o+OX8WQbx39fHxYAAROrp0Oe80F61+OuhPTu8ER1MqwfL0NqLs4PKjq7VOYXsnFx5Z9buL9lrW748azfDb3yTz+V+OBOt722DJre01A973iY/VUdC6e/fJ33pm2eudfenFS1/5MsSkd3T/+37r8062IrJ6969+SgZe4+HDJ3/rmSryB6+9fvkrX2Fl2UhmV5/5HTdZYATf/fFPQgri4fGjn/uC8p3Bmzcu/v6X3aJwk8njn/0dfz4DNn/vL/8KZDiaTt762WcwA717e5e//GV/ubBU9Zbf+GxjdKw9/o6PfYqCOmtc5A+3rQVwxAQeXXQPpe0fkFtP0tSx0QG5fcVaEXc1Nidn7QWz1BwfXIiONPQe8puPo1XA6SG7u8ty6uQLerzJZtwGc7y/6x5REB/61x4BeYM4M+/GupUQp1iQ4Saf+8gu6J3zzoGNgvvR9V2TdYG38m/3nQWNVgfwZJPOuyKeN250yclANWbxnQ4Zt8L8Pp232ckGwiVbrUdH2451x9pbh5MdFa2iB0067gT1sXtM4PQcQjmdx8G9NeQdeHeaeLytvEXzYYBHa7aYuFMKh2e4mpJFYD08i52Hwb0Yjk9ROnRO2nR12c2HfgLN4TlLTVnis4fnXXzDfhiB6a6FjnBmWw9O87ymOqN3HqN65S1StncO49Secr1/2U5mnXScD59yk4LDFb3zGFe5u1jhvXMcJM4UkeNTTlo49RLuXfYmS4sm5NYTTJdOmuO9c41yiOcAnezaqeA6xQ8uBtMM0gW+8Q4ooVXm9OFZu87spSTHp3kiLJPie5ecucyjxfzrCiYqLBfTG0ScKB+lo9c5uF9Ga/nwj1l+DBtBMn6B6Inw62R+h+kTRbmavwLMnSwelNOv42RMvKZJnxNyWMX1bHmPFYeQeWbxigF3y0FncvQNkkwsL9bpy0Ke6GY9WV436QPCA7V41cg7ujFIF38ilyMW2WlyDWeHJuSFu1z3HvSJvW/fa+DxKR0mwf2IjvqOnLgnVBw9SkHGVr57dxMEQ/9egEanKB/yhw063nDLkbMk4GiXqwVLHfrgPOH74Z4NR2cZPbaOYjracNNhvKyr4aOOmPDUJg8esfi+e99RJ485+NA+9slo2y0WVqbIg12vGLLSIvcvIWdh7o+n10iwWjnZfP81n89WvlXtfdPmWnhVdvyy6+jcmuajGxZfZK7MDl92vZNZZC1uP9ugUlhVPnyJBSIl03x8naFl5cLs6AWbj1I3Ko+/zkEl7Tobv8KsLGVpNbnOaCIgN5NnGRnXYbMYfx1XgnCgli8Za75wi2J8g+u55j3VeWUbrtrYn3jX1/jSdssJHm1Y0whZBXlw1t7zcXA/un7arNZMmAS3evbcjrN9NOqTWR/Yhb2/Hh26wL/nvLEDVwPgzv27XbqI3XJsDWM0XrPJCTncsY67prkXvbYGknVOjumDdTRaD+oRmzTQcI3hIT/eJsfr1H4zvr6mky3DR+D27OSG6+iVnOrJSzyKMnmzGt/igZPWd/XkGgmKmT3P999wXbUyS3P8Iu/aE3W3Ht52YzsR9+T0Nm2UCzTPjl93ArkEK3P0PI+8XN8rxjetJprAfTm6zb18RfLy5GXeKMYgFQ//1A+cvN4X4+usAedqvxrfsnies7oaPkctXXhuYN/YDosZXdTwZNdKNdMJ3rsUjQtgjcn1d8CaWXJB7+/aVWWvKnJ0ii+0BRL84JI71tAfu9cuAWUxkNt3Np2s5HlFj3asFQNW5d487YwUdo+CNy8oHWJUuXc3/FUZJKP66DGaRMYvvRungxkC9r577ZwWEdWZ92DAUwJpZu1thgee7VxnN3dBsg6CVXivy2ZBWBzx6YCm5yBP+cGaf9iE4YPgjQFM1ik9tg8GZNp06rE9jvDJOkfH9HjTPl5HwZ3w+gAu1hk5tg56dNzy82N7HqHjDQsN2WiDHW2H/BVrbxeIxxzw0D5os3E3yId0FtCjDRsdkJN1erTNvfv2foOcbDnJsLXKyuETTjXnFSL3Lllk6B5gffi4jYb2iUOGO06R2EWFH1zw0hGTkNy7yvHEGRF0fLZd3TETj4zOWnnOhCD3zrvpDCtF714leG5NKT4648mRNaXk+LS1yrmqza238LygprZvPwKs2p5itr/lFSdkRejhabsGjrjx9C981JuMDKdP/uYzwXRkZ9nlL/9+5+BhFkZPf/JXN66/Obpy4Qf/1190xyMZBG/7zX8eHz20anHxK3/Qu3Nn1eu951Of3H7z2uz8zjs++olwOJRB8NRvPxOfDN0yfeQrX+m/8eZ8e/u9n/j49uuvHTx+5elf+KXG/h5wrbd88Uvd23dsKB/5w/9z/ZVXkq21t3/q13ZefuXgiStP//KvNO7fH/V6+MabdPqXBBGi7+n8TSOUheGq00kajaTXy9qttNmabGwYy1qs9RfdvvC82cZmEYSrbi+L47TdKRvNotmcbG8rysZbm4teT9rOvNsrwiDr94tOe9npFlGcxfFoY0MhPN7eSbo96XmzwSALwmWvt2o0F71+ETfSRutk55QieLqxkbTa0nHmG1tFGK4Ggzxq5CEBIJF2kQeUyZnwy9TxCBwCMykdAukcorxoEAwTwFfS00TMRVCVjk/AEQbTysGQLTFYlaFBcIV4IhzFzFLbSW3bCA4JmFXcQLbAKEsjDFAGyKJmgoEpsBeFH2qWEDOXtMJkjlCaNxnBGcaTmlNFE44mkmrJE0tOswYTjiRmJnmGQILxqHItY2XUTBWtNJkzvcpiLF3F9Li2KoxTiMeVzZRXUTiXlpQs4fW08qTkgpqpcCXEBUdH0ia1L4lZSNfUds6rURGZ2kUULmpbGpwzeCAcUnmaymntoco2XEykV0lmqD4ytCxDjuDSkFr4pSXGVWwqj3A5EW5VM0DVoWZVFlACZ5LpPNJYLqugqnzC5KwKVG4zbA41zEoXUDhTli4ayKqn2s2UrVg9E6GsLcbMAUIr4WoCF4ZWZai5nBk7lbbiYKacDBDsOjVlUrRdTmvbESBkNqq4LyvHjZzUY7l2qevWNq1l07ZwafEK+NilpRWqynOZU3ms1Da0ncqhdd3xKVc2y4ljPFo4vihc34+Uh3PoAJsXDIuy5VlcOU5lXBzQyuFpbdtWpDxSwABbTuXi0ngQmSXEs9KztVtSMJVUGCdhcpkGUDrSMqPaqQHKKThSBAinomYuba281BLTMtTSUsxMhac0Kik4khauQ0PlTFhGeTmvp2UDCMtQPaldI3FBzUFl4TLQXI1KB5ehxPWiiKS0AQGzIsCCSaoPJFEmxJZMTYvK2PVBCiIsXB7ZKbG1CCyPF8jBquNwlZHQmIgHKENNLj2naa84E8DFPsmhi1Xb8lDOAwV95qCCBVrYVswTm1YooAEviQd1zDyTck9BD/skY5GuXMd2KptJ4AGf5dRDdduzYcntUjALoRMCpjUzgC8pSNIYG1wAvKh5TcEMWbPCDxRPiJlKUiE6JybJGwzRAuOppCUxM03npR8AJyFmLriCfIH1MvMBJAUms9rWGM4JPJacSXuFzVI4ClgJNfM8BIbUmC6FCyFYMHBYuZ4IaiZmwoXIwwHOUEgoV7GVGIfgALqwQD7FMfT1CrS5DrmHMughbDwVvj8AACAASURBVOmGlRiP6Zj6KDUBQwGw6lR3LBNwH6Uq5MCGIV9pG4OI+ShVoa1a3K4S2EDAox7OdcOGLo6tBFmA+MAHKYgt3bZ8lbDIQIc4pCQuUEQRPNdcFg1M1VzbqXY0rWd1KCvbYuAAwaVwDMZzQMoiNEzOwJ8pyMyVk9YWJ/AYoaS2FEEzw6o0QkQnii8FkwxMjZMWvg/oHOuFYiWFE0OLosEsM4fWXJKKmYlgaen7kM4QWNaOwGhm4Kq2JIULxKaF6yE7oWYmWWXQDMM8bRBIcwTHNRcMLQGelK6jvZyCubSloUumFpVvDCowntWOQXDJ0FHt2MIriVkI1ygrpXqRx9hwhfGisgGCC4YOCsepnZJXo8pHNc+QWuUNZFgN8Tz3GSAJhselzZRX8HpSNmHtQKontSMlrqk5rC1YBIDpUeWgIlRELMpQ1A4kelaEqGaG6ANJROUqbqaVi8oG4NVUeYXmgupZHUFpAWYONC6FJ6meSVuXobTExPiFsiSDE+UJSQ0BJwZXwi0tPa5dk4WIy7mwktLzk36/iOJVp5u323ncmK6vK8dZ9NeWnW4RRbOt7cL15v1+GUXLZivvdIp2e7y1XTvuotNJ42YVhrP1jdL3062dOgqXrXYWRWkYjQcbtefP1zfSMKw67aTTqhx3vrlZhuG810/jRt6Ip/2BcOzlxkYWxXmztVzrl6432dyqXLeIIvjvv3rwvXxo9HtwizB9/9PlT/5EdLAvPFtTao1neRhlgAff/c7yHe9s4NQezuaDgV2mIK2U51S1gXf3yWa37LS6d24Pz+761coeTmdr62CRgsMTHtmwE9MkNy5d+o3+3TvHO6dxmtNX3iguXLBtxPLcWLRyHXs0IxSMvI733T9NnngisoR9Mk87zaicZSUDFsmDwBunhWdjpuikKkKH4RrMK4CBCW1dwqBIstiBCy1sBi3ojBdLy7VcqHOAlDIB0SWy6moVec7/w957PWu+3eWdK4dfevO7U+/eHU+HkySOzlFAAYFJBiGPgZliamZupmbuZ3B5kEjGxLEINgxJCPAwY8IAAiyDsGxQQPGk7nP6dD7dvXfvvN/8/uLKcyH9EZbK96tWrarn+103n3qe52SiogTFSEybKo0p1q5G2Pu825KTWVQv87U1vmgsRXUrSU/mhhHKnZsbHOxyfS1aFlFVmxYhS9swnnfa8TLntVoM2rRpkmmthkyXgR0eV5cvyKJC2qUkL0EMlc8HbVQreede9bZn09nEK1z0E15pogyMgK49OTqprl7CqmmN8tl6OyuWvkGmw/B0aUtLB5GhkZhVvo0qHLUO57OtfjIdm5Pcn13njQoqJLRY8A5bKNuhuWj19kdlP+am8RUAElkA2LJGKS2itL0/ma4P1qr9qkyrrhCmMZpg4jzBsAxY+CJOevuTyVovcjU7mi263Yh4UHjAgJGU5D4Di7lsOUcDCowYMrV1N6ZehSogHHTE2WiOJV50++29yXLYkqEmM1e3Y4g9qB1r6mbQwuPScSqx0RqBAHAMXeWRdaDFXINYU5lMWg1hCLqVREdjl0hKnakhdrbsddmyYFaHmPrSAwzrThaPZw7hPpiNQScAYLKYVjWptBkkYrxQVLiY41pjY0gEtad0nodhrDWmZa0HqVxWziEkgQGELVW+0kaV7h/uHp073ylmoALLQUobS2sNYghUcIFEsFxE7dbhbHa6nxWLUCHVYbQoQ+1gxg2VYlb5Fqpo1Nkfzzd66+PdY7mBicHegyaACCrM2ELDFliCaG10dLiy1jJFKIFucaY1rH3ebXuCejv78/Vh5CtwUjfDDq7rZmdC2hKvddG8osxXrXa2e7Jc6wNlwWv3zYWtISkrLzAFNhJwoSEOi7TLv/xK8+SVRDg2r3U3pUG5ymMYGiHtvX0SC3t+Iz0a1b2WESLdO1LtDHtT7Uyh5OH8qXg0psiVvY48mKpuWsVpNplh5/J+Sy6KqFbLQcrHSwBgPuynk4WlmHLnGoytzfstsayp1QzrYLDCtOykybTwEGAZzFKLupydOSuLghe6HETZNG8QpdSYwL2HjBoNuVxUpku1pXJZlaut7PhYQR7aguTGQYQiYACX83K8MewuRq7wrheRqnEaoAQHHZxDEW6WcTveO8nPrKfl3C2970qsVBNYz80KlsLcwDYtWdJ5fDTbWk3zWe0FZgAHFyoHI2S4ICc56jJtkfcIIc+w90uHWkQ0RWU4irAN2BuQwHLaXkm3D8rTK5EqwtKZbgxAIIXN/GLa7kVHeT5ocWj4yXIZJ0ICXwEMvE0oLjzBthICavi1LZvZuh1RoH0NA0ZVGqdHxxS7xWAlPc6rttBSZsczKyih1tXQUaIjFi1rGoyLMZuavBWrOG7vHyjCSJuaBmPny26STHOP0bLbyiYLEEAHzWehRYxbDtp0uqCHx/qpC8l4riGtOnG8KIEHKAoqd3Q0QhfXlcHRsimGcTrPDaAhxeB47iygw9hAIeel6VLtWDQvi/V2tLevLEFrLVoZb3yLFWPajxaV6bFaE/54z1w5Fy1zHSiMAKkssMEnuCaiuzeebg7Xlvu5bqkOY00dSgsTqpmgCwPTUPBksDsabQ6SJkeTouy2ZXCgDkBCzRif6ZQv9uJTa0e7h8P1lithDlRbEKdDFRAPDZPx4ZHvZ8uk1d0ZLzY6kSnREqi2wLqGi1p3O4bT9DgHLbiMs+7ebL6aSdugZYOA192MzkoKzbi9Kr78cn32HO63eoe7xWCFYAsKAxCcZT158w5BoH7y0mD/8bLXb5Kkd7CvuBC2no81tqZ69sneyb5HWHeS9uP9RW9QxJ34lWsuTfiZAWiczOfF2gqdFwCCJmvxl64Xa5vxasymueUMJMwZyKtq4lHnV3/tv7oI/4tAhPrs2RUdvu/DP5b3h/37D7/tX//aoreieci//W3ktTeeuHv/3R/5VdIoAOAHfuJnDKVH59bdhSGw5vJ//Ow3//bvOkCSfPnen/9lOi+2L53HW2lw7vIn//49v/HRvNPp3330rl//bQTwMcX1+5/Bu49Xdkbf92M/YSmLR9P3/9KvYRByDGff9fbo7puDw+Pv+Be/0BpN6nb2XR/+aaTV+NQL6P43kYXk5hhsvy8+gKZ7+D/pW+dh9VrRF7ee43kkqxO9/04468Tmjf82mZyZv3mHn4pffh4uVm0yYW88A/JhN7z+nWhyxoxOGmHuf6vci8uNefbyW8NsEw92/uni+iWkD6ZI772HnXQaWcd335I8TovB8Q9V155h/rrqRTev4NlK1Oz5g2fR0dpyte7cOI13LlXri/YbQ3D8dFTd0J/4ePJnf6rbQwS+I721gbJd8uAifPx0OczTX/kF/qm/tVG35Z71O28NrACjU8mt82qllv/65+Sff3x+6Wp3fNUdfBOBUzZKwKPngZvgv/m/01//zerKhejoObj3XIj32M5KOHyeFdvkM38ufvffAkZh9N385hWcHrCHK27vHZjt+iIhd99FisZTKK+9YAO8yF77fnDUqHJxsBn23sfLWTef5NsfoLku1pvj4ZbDoPWQ0Ptvw75gM+wev4eVasW9/kNRFU3vb4Or8tXnA2RQeXT3hVY9Gw/YuDfUmPV3l/7+t0cTo5MmeuUdzkanxI0PhmOmptPR0O98C1s6DHNw51v5FCx6xfEnYTmiqVvuXMuqXSh7brx+atHut0fH25+U+b6IB/rg03R5QMUwTFfX6yhm4+Loc7K+48RFdPzXaPRQxlt+3l/RUtCT6uBFuXyIcZcc/gOtbvnh+mL79JUqSvFJOf0CmjyWXTAfvcFn9xgeouXacNnuxeXy5D+48eNWq12Or/GTB1Ha0nx0Qd4+r7vT5P4q3X3LspO/p3rxO7A5Lmb+8Am/8zZIKrhYS29eAnx0dKq3aPVluYjur/mDF6g5ovPEP3w7Covnoocf0I/2MHW3zsLd52FyRHb7Yf+d0h1Vg7C7cg5Znd1rk/tvC9ks2k7g7tsJ3L2obvxghKaz3XJ2gd5/O/PTULf5zbc2Ha8fzx58uRU3tdTFnc+v8OM5W6vxRoJYMLeqgy+1aLB0ru693MOFjcvt6n2X0v3HoGAP/nNbKUyA3/5sBFWQk9v1P3qGbj8KuXzzMx1wWOFVsvtXVNes3Znbcz1UzPxeePhKn8yMj9Hhf4rcgZKnG5wCv9rSN8vDryQ419TaBy+23Yn3Z6OVL1+C461msGi9djbMttrw1vvQ0ZNhsuNb6N470u1WtTrKXr0aji+oztG8263jOJmU7MFlcLSls4bdu5JsD4ru0Q/W158X7rU6knevhMkF7o7w/hmyfz6Iyclab9Hq82aRvXTRj5/C4jF7eN6PrkT2/nvwo+fD+AAyc+8F8eiMWj1svXrajt9i0iNzJ3/wUkfCuprSg8/yRNaLs2vzzlCoMn/ZPXqtm7kFmNp7X+rKUNmt7KS/Gbui/pJ685Vu1irLB/DNa512KNRWa9TdQCCYa+X25xLeA+a+2nm50xXLxUp/0Rs6SNmt8f3Pd1t2aQr45qfbktTlmcGstwIxMNeLBy/1ZF0EE978+wx5LdM+u/5s3NQkV2bvfThHbfP6/5DMu8uH98CF9OXngk1CqMnt52gNi5aZ9AcNk4O9ib/37eIE14Mye/FtRg+6ya0faB4NXH48b7vt9/IZr6TObjwvjlm1NjpYO98IIY6JuP00y4ksx2bv3WQc29bx/+Lf3FS71/3p/itPGbXhUBHduQyLto0qceeKfLSKug/ZzbeGoyfqeD/7yM+RL38JZqtR8Q60d86kJXvwRPTwVL0+TX70w/zTnzYXr8qT593xJQpG5HATPb7k0CH/2L+O/vCPine9Pb31dBg9C+Vj+ugMPH6Szm7TT/w7/od/7Lc28eLb+KMLuPNA3LpgT74J4bvo9z8mP/nXgEqO3svvnUdyhHbP04dX3eCIbffC/rtkfZjNdbnznbCeX4of/qDdniBYPdgi2y9QuI+PW37vm0Uz2/I3fpAtXX5wXL5F3HirizQ5keDh29tud13d/MEIqOX+tDyHb7+LNYXzTL72vKb+SXjtA2RZFSfLkzNg5z20LLE36Pb7YNGsJ3d/yG7nQi52N8iD5yMzCZWED97FaovQ4//mn/1o5+Bw2e58y0f+Tfv4eETp8r3PRsU8Ppp9z8//q1OvXn/4/Av/9Ic/3Hnz0cO3PGP7GEg6vHH/3b/zeyu37hxcvPJ9H/qxrZdevf+O50ECyrNrvRdvfutHP9Z++Bhh9MLH/u3mtRsPLlzyl/s0NGipvvcnf3b99p2iP3j7x/5g5ebdnML5u54UtkEGfOAnf+b8Z/7h9vvf/70/8hPr115/dPEyvfnGN4yL8OsbEQaETZpWnbZKYp0kSkZNElulxWQGAFKdjmUs7/Qs502rpdsdpww5PEbG1e0OEKLq9ao0dZQ27Q5AmI1muFFVluk48VFUddqBsTxJoEyS4zEFWEWyard1FKl2x2SpiiINcTydG2NdHLsoWqap4axut22SOGIh0p4pG2EMmkC1IaT2YWEVJIji0iHbSAhIwLhWQlS6LgO2lGJSIawACZA2EKoKwxq5krFGCggbgpaeEEgqh5QipCKkhs5wApACrPESIlgFXAVCSusLUxnGMFceWi0gwBYJEwgCpAKocgx7phHURuAgZWDMpjEiBqLCUuJQzVBlJLbdDkDIJbElHmLleAjUAZJ7AnwSA0pc1lKkwcAFqhC1ATZWYh8lNkkCF4BWEJReEMA0QdbG0EkZGHdphIiCpHYEaWYJKAODnhoUGsA1xB6TClLXIFZqVRHmicLAaAEUhyg0hNThq0ZSAAL2CDaBQystBMazuqJRYZaN6BgEAKkh1ogaAipNPAgOggAAsBwjUFraeIoJKSC1GiAVbAOpox5D55nWkqJQYlwCghh3EAeQUMYhFwBD/9UQPMt5zC2hzjOKBGDEEwpACDAAJxlELmbKE8KFpcwBwQAIyAeIA6SAcw8jIrknzASEoPcAAi8oEx6R4GMCSSDCB4q/9tc4HwmLoHUR89QREgCDEBtIGkeJJxqEKghceuh4qKAAuEFYGWoNdBAVAHnoPQABhuC5Jsh6bgPRCDaO+SqAAgaNuWUWo9riEGiDoFM0YGNhCACCQBzAlSXQM0dArQWtAq1CUWMBWCCwMsQb5BGqPHZIeIq9EdByypHBCQ4AsdEUVw2IOWMuUKQlQsgT4U0URYcj22jHGBGOswCRY8QAClAcZ7OcBhhiypCm0gOKJbOQAh08GE2CC0Aigr1jDkQMQyOZcZiQ8RSXDcoE4x7S4ARE0IsIBIwwzj1qNAaBKQSVhkETVAWvKYZQBVx5TAJpMGksowAA5BwMxhEHubEMYqIBbRxnFSCVa5ooDVwBqD0zCJlAdcD+q7MJPUC0hKDSkgauCDaWBENo460NARGNaeUoMVQR2HgCWIQIdlBiRIFkBjL0tXucZxHgOKCUBo4Ytlgghwn8mppAEgM5IdITHJwkWGsIAIAACcSFQRSwCHFsLMPQewgC9DYIJoixDAKGZOQwBdB7EAAyFseY40ASDCTlWFMOIDQEVRb7RkCIPMJ1LaJSlVVgjmFMK4gNYA7iJiDj8dciIhvJEagIzj3FgJQW6oaKkoASQsMChNrz2kuMQBlIDQP82gOIw6RxxGkBIDRIaC2iXFUakcBoQBWA2ksIiA5YGw4I0QAVljEHS4ZrEzPXbgFMXCQCtoA0jsOADaC5x8inaaDEZanGDUY2UI2w8bixEfNR7JI0MA5ogULlBYFMYeJsQjznPk68ZJgogGtLsGaagipw4tMUYGLS1BMPsbYMAmogKjwGnjYYOi2gZgCDBjNXB5AbrRFxzGNYO4lDZBCwjquayNJWiqUGeYhrgB2mCodGEV/LVh3KJaABOwgKS00gAJMCUt8EZICtofDMYegcN0YQDHJElMa0tFoB4FhASAfmHNMQmEArx7nqdJSUKk7qJLVx4oQUizwsS50kmrGm07GMFf2eShLHpcgbXOsqSS2lqttTceLTpMwyj2mYzHnZ6G5Xx4mWsmy1PeNlp+Mp47MFmsxdHOt2WzNWpFktIyulihMyX7rJzDHapInqdj3GZa+npbSRhOAbx0b49Y4Iv636X/9nMZ1i5ANE3gaPsadUHh7W6+uxWmhLbCykqhrAKPLA+VA0TKIibrOibNIkbRbaEhtJXpdWuQhqJ0QNuQiqEgmtGy2jqM7DvDC9LgnWQEaB8Qh5E2JbFkmHHZ2olZXIFMpTQGGnmY1ZnwFjKfUWeYIjV1qDNeeJzRc2xdBHtClR3KkmjtMCJgGjyFV1iawQGcxrlKBgGdQViqUqrOCqhIHRBJXWYUtZbPMKJRAEw2goHdclzLi3MCBkGaG18gjHvlz4DDoDMoa147qi2BpHFJOac6Y09s5wCj3gRYlFKEmGlXMxRVoFD4fqZMY6PmArCIQUL2uf0biY10gaybG10AEBKoVToJxNKPSeVo2NWFznFYox9Uh7RZIoLBViwHgJ65y3WNXYiEdNWYGYMIesUUgOmsO56HqLOGpy0RJFZSWPdFHDmALtA2gUTnDTRDFWVgt+arlzQlecYEm9CAUAHKqEO4ulrysRY+WMYInK84rBiCSgqlDCggoYGEf7zUlBEtggLYlAqgnCUyxsVaOYeeURqmuUoFonCaqNETzVyyYIRzENRgEmVekFNRZ7ghOdh8JZjGkMKxQjbznUJU5kUwCKfeWhd00nw9oEhCJfVTCCIDhK+Cxn3riUN5ADCB2jWOkA0VpzOFNJgKjpZcg5aBxmgFR1w2PPiBzNQAA4wxWKsLUMmwrH2BjIIDZOIyZDrSEHLhjJgsdgmqNelNSLGggTCWwddIHDJjQBq0CEnie9r8oR18sKRZgGoFwdZIJLQxgwQMIq5y3aaM/w2mRvivpABM+wBkL4UhMBtROoWfIOnBSgFyfNvAERIh57p6EwjEAQaN1YwVO1qC3zgklTFoYzbDFD2hIZ6kbEUFnLWaTKpgwg4n09OYlWWVAQAeMxN7WK0zAtfTuJVa4B8wxHti5hxIIGCDQVkECbLEG1sZJ7hGhVB0KEr0vDIQw+laRqpKvrKIG1cYIZynijYAiGU2SdrEsnWdNggBCIv7pBKPJljWIYgmVELGtmNOZGYdEQ6Rgh2gQA41AufQqMBS2OjUXGQgZZXdcs5kCR3ISAYOJLkhJjGNIFSYg2kCO0rCqSJKxxAVtII19UJMHGNpFsl5MKRohBolWDpIAK1AY0ngs3z/q0rG0s4npZQwkZFGVpa9CixTzpWYclqAuRkVo7QbN8phoMIxQYUYDLUGvCg3ISK+UozZXJJEe6AlIAJWwzpV0JatDYoIBget4a0LKysYybZQ0kJBBCYD3u1yd53LYWW04TlRcVBZIlqKhgQr2GOOjApC0N4aRwlkJGjQrCURzZskYxgMASEnITgdomEjbOceoJppXyGEtfhhIGBMtOSrThtibIacuUlB4jsNCAkRTlNYgh8EYw2pgAgRKCKR0CWGsOjsUqDMAIij2GtUISsLJoaGw5JcaCAGQoK9xGSgvalDgm2gAGWVPXLOFAOUsMjWI3/epycahKlhGlA0esVjVNBKycxwaxtWr3KF7HxnHUFLyLSwVjxMu8JjGH6quyilDWLCWNdoKuLvaO+SpkkChdehmjyjEWdJCwLkRGGmMFjeoiV5xKyKFuYCR8pSkPOqyoo4PsNJiWoJekzayGEcSBeKOgFL5SWJrcZlyVUUarRkciUcsGSEgCtrYyAnIAOaZFLaAqZEYrZSIudaEDJ8FAArUn0lQqSsjJ1LRbASGitaNUhKYBHEJgOOezObW66XZRoxzjlhJe1x6iXj2eoQx6q7o9WlceIoocrpomSi1j0cmJ54Jy3wRBnQIMG48BgBQ7Mp7VSUsQpz3xiAjY1FAgAPRo1vr13/iviPC/CESozl1YCeT7/48PzQcr7e39f/zTP79z5en1W3c/8JFfGp06k01G3/vjP6N4jJX6Jx/6yaLTi49Ovvtf/cps83T2cO/7fupn5v1VXhYf/NBPOEg8IR/80Z9yEReT2Xf+3C8V/X58MP4n//JnC5li6D/4oz8VAtRR9AM//CFLKKqa7/mXP1e223RafvAXflERbhj9gR/5SbxY6lb6Pf/8p4g1hxvPdl89R6cg8DK7/gw/9PWwHH75UjyNVLZovbQVTYSDS3HrST4hThS9G09G+67YNMPPbbFRT3UWrVe3+DjxNE/vXRIn1KRV69rlbCfMt+zaP6zj49XlsB68upJOWxbPo9sXkgOW90HvpTPpQ7TYUmtfHMQn6/Oh6r/ciw7aEJ/Ie+fZXmuyCVa/ErXvbU426/bNNL17BohDdLTSvbVaxyXfXe29ktjhPHqjF985N9nUg5skuXveyJNkT4o7TzRJLQ/6w1fa+ZrKbsXZ7TOTddO9z1q3Ljoxjo9ldOuy5Ut+1O5eXytXquxWlN6+oHtT8ThN37jq6ViMWXLjomcLNmq1XjkN2iP6sJ3evmKzUahl90uXsFMegd4Xn/DIwtoPr5930qIZaF9/yrk8a+bk1XeSprISZa8+5RAEtWu/8kRgFs1t5/rTQC0gdt1rz+KisZEbfOEJ5EAwNnv5CWmXjWXxzbcGrwMxvS8/hWe16sCVz52FlnhgOq9f5E4741vXnoJFbWPf/cIVMTWzFVt9olR7gcdu8gXkHhvag6PPMbetwOW4+Hhd3vN8E87/3ttHVmRu/ApTB9CsRvknFbiVg6tp/eez4k0Czsn887p+GHjbT7+C3T1l12T5H5V7bRFdIsef8rMHApyL3KcXy7soSlX+oivuALAhl18A5pYCT6X1JxaLNxDfxNWXbXkTiBWf7a61r/WLYZ3cTrNbZ2ebrn8Hte9fNmIUH4vo5pUmqtm4t/pyrxzWycNUvnm5Wq/YA9m6ddXJmRyT6LVLgOd82hpc36h7VXRfdG9fbDpz+li2blx1aBznENx5zsmKLZLuS6dVuxA7Mrt52bZmbJ/27jzj/Ihqmb1yycElyZP+y2eqtrOH08XfAuw1gfbgLxEuFUrR8i81MCCAMP0bB6AHlZ19CpGmIdyNP0nRrJRte/AXEDYAIjf5G0esAdZP/o6QsiEZPPmLQE9KvxEt/7/K5AAxMP17yBqFYJh8CsJc1/2k+JMSHldgM2r+bFHXAkRo+bcNWipI4PhTEE+a+lz73Gc7dH99sV53XxrEh33HJvHDs629uOi59vXTrXtksdUMvjCMd9enGyp7fSCPhoGfiAdbbLtf9O3gtVOtu3yx1Qy+1E32Ty/W1fB6Kh6fCnycPFxj26u2tZBvXkx2urMz9cqXWmLnbNOfy9uDZPt04KNkZ5hsr5r2Ir19unW7tzy97LzYzrbPzwcLfHd+/BlKY6cOQvEpjdepvqfHX6JsE9uHdvR3UMYajNXxp5AQtp6h0aeRHNj6vp1+BosVZ7bd/D8jzhtXgYO/ozQGbhYWn2hwD5odM/5PQPSsGuHJZxkVOtRh9teBgsrnfvRJSDuoOQEnn8Gy55opHv8N4FAFjyZ/aTAyLOl0vnhaNI3RLn39GVyqwPTw2tNsoqqBX/nMWVwLR5vslfO8Dha6+OYzoAo2Nd0vXZXHplj1a5897atOI83w1dOihg7Urdeu8qlb9Onq586IQ1es296XzwS9WkvVe3GDLxAMs/jG03hM8iHY+uKZ6Cgs193gMyuwWFWJ6r+6Tmey7KjutdOt20yfmidfWZPbW7P1euV6LB9vOH6SPhiy7Y2yq7q3Nro35eyM6n+lFT06XQ/n6d1B/HAz8JNkeygenNLZIr57qnOnvdgsei+24ofnVG8cPejH988GOop2u8m9dZPNk/vryc0Vv3LAbqylDy7q9rF82G3dP+vwsTgaxLc2dLZI3xy0bqxVqzO+I9uvP2XBJFtq8tpzEOZEy+FLZ0xSs0Paev2yS5f4CHdfe9raI6HPYAAAIABJREFUKfWkc+1qCAXUvPeVcy5q2Ain1y9zcqAXSf/ms8DPgce9r1wKtvJA9r941gpDFqRz4xJmU1/yzitPB70MhHS/cBHpOmC69pWtOuF0HjrXLiK08DXuvPwU0YUT8+//4Q/3HjycrJ/+jl/45e7hYZF2vutXfi09Gj2+eOWH/vcfHt669+Dt7/yBf/Yjw7sPDs4+8d0f+cX+3u68t/Kd/+cv9x/sbF955of+tx9ev/ba3juf+/Yf+9n12/f3z1/+xx/5pd6bj4pu/x/94q8Mb7/54C3P/3cf+tEzr1y79d5v+cCH/8XWS68cn7/4Lf/X76zfuLUcDt//Gx9de+Pu42ef/eCP/PjFL3z55re8/3t+/KfPf+nFe089I167Rr4xEKF3X99Bo8h7gMBi0EPQAxQWa0MSLEPgZHMd6xo7M91YI8FCDBbDfkAeAb/odyH0VDfj0xtYVRTxyal1gAICLl8ZeEYogYuVAUSBWXWyvkowZE092dyAwBMQFoOuY5haPV8ZQgSl1aP1FRicMHoy7ONYoOCKQVdzQp1XsfccU2Wa2FEQRLA6CkFA7LyNoGdeepdL7Slkzldx4IiJqqgTApgmxhgBMAPIACM0wkFqZZJgrWSmqSLhhBfWAQkMcsRCG3kPOW0WOglAU6Frm/CGIuaCiwgGSnrQRN4HxK0ycScASH0ICKiWpzZoEuoMYoADc67DmW18RBRCwmjD2y6zzEOIvUoDccGzUGWcGuVY1rSR1DVhUZU5EJglISSWeQuYqxNETeN53GSQeGcCbDKNrcMcqzRAAALxOgPSa8RBk3nqbI2xaocAAPamSkCAjkPXZAhChxFuMkOBFcHP2h5jgJ1tEghYYNDpxCOvMMJVaiCmRKs69YQ4AF0TA0cCCkFnIaWeO6tSFRBizhUppBFhqipTEqjDzpsEEIKQCSrVmEKmG9WGQTJijYspjT0xDZDMU0C0tkmMPOuoehxRxBEzFZLSIWy1tREiEHCtVIsYzWVTzNPIUBJZ6xkEDBOtYIK0YZFxVYswEXE1QlnXQ0qVriMBOSDGwgh5jJB1LsGNl92mXMYsSCZ0XkWJ8QAYazgCLca8DiJt2pg2tWdJk1niUYDOpk4ED5itkgQFZxhzLShU5UisMg21AxjrFgQeeGTLhBCnqOBFCql3hhCVaR4cQ26eBQxcgK5OEUGeYNeknja1JWmVaoowcrVOPIcuENskAMDgA9JtmOFAVRP6CYQFCaFpCc4t8ca3JcWKBGLb0GJMbGPaMZOKOu1b1HEPffAthoRDyvg2dRgQo20nscLFutYZZ5Fj3sBMegaDAbaNAYNS1YsutyJwXU9aCZCEWucyAhkg1oUObTimXleyE6gXzkIBdHDEAisDZIQqY+KAIBO6dglTEDMXQIQd89IDJb0nkLlQxwgGznXlYmohYHVjo8QThy2wzHkQhHdeusoJ6nQTSRCC0MqzoINGGniJrIfYOs29SQlvKpSsFDow5x2EoY2pqy1DusWIU4og22Y4lNaT0KPQKkqR79AQFMIQtAkJljNiO5S4GgBqOpAgSJwOHYl9GRjTHYGh9sD7nuC2AJBOO8gBxIHWbSkoAMjZNgVQBwBCl0KocFOHXuSBxd6ZjvTUYOB0AmIehHc6UR5j6m2RABpR3pRVRgJz2DkTA8Ig0kDHGjEQqaZpQUg4M6pKuBdKeO+SAIjDFjeJD1LSZta0EEZE6MakTElAAjQpEjwIB+rMAYaoC0WGGGZClTqlVjisrY6hY4HqYBLAABeqsilTAApjtGwBZJmFnnsLPHVeywBTIVRp5ErgUKoGCdR4FzwzAgXvuTVeutpgrmsrVxwFxDkDoY81cR5KoDTAPlgRYAoiq4CANfJcG0NgHWseAsDGSsectQK5DFPvA8FNpliwDPlZC2AYAnB1ijD2BMEm80TXnnTLVBOMkS1V6jmyFsMmgQF6CHHTCi0UWLB1pgPAxNsyQ4QCb5sqRQBa6JHKAoUYGd+0NMEQm6ZpE08JDLpKKUIW+dBknnmLgqhbzrKY6aZoZ007E7apuy0lGS+W02HPcxo35WJtRcdCqLLM0rKViqYs2y0dy6gqFisDI1lUF/PVge+2uapVv1d1W7Ipim5LpXFcLvKVgaVY1sV80HWMiaasW6lup5Gq61675pwXy2W/6wQXus5XBg5CoWudxk2WUaO/MQBhwDh648bXfReh/h//+40bNxYbK57xzvbjg4tPBADOv/Ly/be/I1X56s37u1evcNfEu8fF2sBR1nuwnZ9ZX8btcy+++OYL74htsXLj7sHlyxT55PGR63CdZsnDg2JzZdEenP/85x++8A4Z6sGNe8eXnsDQpTsHTb9Ttdv9+w/rtf60t3L+85/ffuEdHOmV67fGF89HoQRHlemk85VhvGdUilGi5QFWLeAjz4+tp9h0EVrgVBXFAMORNDGEieZ7yGTAZwCNAAFe9wCaYe79coXIE2h5AImWR0glxKeOTAJyYHoqyUaNMG4xROIEBeyWK1F7X3nqfduTiYeWzDdFOmqEcmpg+JgoRqYrWetkKQoz3spE3rTGodwwtonimZpvCrH0rDYiXjYmwhWZnI7kso5PwuwsbS2qUIr5GpFLwwpjBwhozGd+shWJvE5HYbpFs7xGOav7llUB1Mj2gNcomji9bmsvW4d+doakeY0WuB4CrgLIAU/ymqR0jNW6zVlr+Kgo+oFijybQZhAwQEfAt20Vi/YjO90S69V+NR/UfYCRpePgEmAjwkYwZKaMReuhXmxygRp+gJs+ANixkXcpMhFmE5zQyajbSbfD4hQVsKJ7RPcB4ACNAxJBx5CPMYjNsi07OzbvQ0YVPyJNhzjpwlhTY8ypzB80gGHWBWCnMqkgXeyPNXIOrgk387QxejMGhxpiX6+15fYcSIw6KBxr7PzyTF+c5Kxs7OnYHRqMfLPRFjtzSEArqZZlHBpfn+/yo0UoHTgd8YNCI9JstKLHMwACXOV+EdBSoVNULyEqrNuK5ETDGtg+BAqzJZieljyvWidgfIa2ihot2WyN8NKLpTJ9CCwRU6+HugmyfejGZ1m2LNGC1QPHVAAFdD0ILGAjYNdtjaLOjp6dI6uzUdH0Vd8T5cjCmx7yAIsRMKumRlF7u5md47FSeErUIGBl6SJM19uQBn7zyJztxEiZHe3XJcE+HDYgJaHFw6GiCVDtGN2d2rNdSZR92MA1kYLlYiZxjHxKw0FDU1j3MnJ/btaTiGn3qLbDmMQgHGnMgu+KcGRI5Ot+xu7P7VCoThrfH9uuIHEIR8oJpocJ311y4apBSu4vwlDk/V5/b4JtGG9m2aSKar9YgWyKUXCLFZkd6YC873gy8dDg+ekomdRR6ZqhYTNiEFms8OxAo2DMAKElImWYbkXJpGIVytdAe2I1ILZlxMQ7gF0fgJLIhanXvauFyEO+DlsjbRy2Pcfm0FnoBwAWgM3h6GLanYztOIQNjirtcw8HzFmIjip6mlVBkkcLfbkXF0t7bMNGRIx1Mxu3lCKxOTJ0k9Y4ondG5sogqkt94sC6xEqDqQEDZjGFBzVbIyWL2N2xudKXTWUOLV9FWKsqZ6RLDEJoryanaClSfndszrUjV9sD41YjxBGdwBTNJr0s2kX5KuGkortE9wCQAI0CYkFnkI0REnbRFe1dV3Yg5YofkqaNgXRkHBzDeV+0jxSmZt5hrb3QtELZkd3HtY2hTxwdeUtZOaStQ4WxMS0njljRRmVbdrdLL73tIDxFAaB8SNuHtWVkviq7Bw0MNooWVZNhhSanZTKpWQ6KDdiaGO3JYkW0TmrovO1DVGG29GpFu5rLHObrMJtoZ4npWrYA3iHbB6FE8czVp5xWMp76/BTMJsoZYvqeL4FvQNRa5KEjpqA5ZY0S8bEutnAyN05T0/N0YVED9RBrxDqP1PQ8X1scleWgGXhiHJkF04EeYzECZmgrIjsPm9k5EdmGnpBmGJB1dBZsB1qCxRjF8eiwtdrZtrPTJDIVPiZ6CCAAeBZAFgyBcoRtT+dR0n+kFmtQQE1GVPUQgI5NfNkVlsP2gXPdZinj/rZerCFCbLZ9ACmandrobu8Bjg7PnN969dpkfWO2vnHhlZfmq2umE6fb+56QwwtPbNy7iwk4OHf+9KvXZxsb0/VT56+9UrbbpI39SMHgD564vHH7lmF8ubW2+fobk+Ha5NTm2deuNVFUbq5EhzNRLUcXzrW29zyhi62NzRtvzLrDZrXdu7ejBC83V+T+WDTNTqf/DdJFiNDmh/75130XYUek7/qtj+aDYXpw9E1/8udHm2fX795/6i/+fdHqtccnT//xxwMiwYe3f+wPyk43Ohm/5Y/+tFhdbb/5+Km/+g+KR3FTPfv//ql3wVP6jt/6PRcJMc+f+pO/Krud7PHRUx//q8Aiouu3/sGfIGV1mr7z139Hy4iW1Vv++M90EiUn86c+/pfAwoDh237/D3mtVBK97bf+IMAwXX2GbJ+DNccgB3tP0Tn1nTm9+4yv2iCa4wdPsIZim9ujy7CKISnh7lU5Z013zu88BcqOT5fk4QXYJDA04PAczBNICrx3BU/iZrgUty6Heli3lXzzFCoSACpwdBbn7Trx4uEZPo51fxrfvWjKtbpn5YN1ski5m4STc36xWgx0en8FjU5Xa7V80EOTTYpO/GIVH2/p2MPpUO4MUXqE9k770fliqOVuD442EV3ysXQnT1hh0HIgd07p3pI9HsDR+aLTpPutcHw2sIrPhD+6BGgJl12ye8F0F3y3C04ugmQKjgf4+CwES1Fwf3g1kArmbfz4CRodkuO+O7kC5MQ2CXt0CTofsKf3n/LEExXwzmXHDVxKvH8BONWpptXhu6B2gFhy7xlAAlIO71wCSMGCov0noHPENWDnLaQ2QWhy/1kHEbQePb4c25mpODi8EgzEUIHtZ2jtTaTYnac9xMB7tHOJBOM1xrsXgUYIN/jRM7DCy24z+RIyOeZYj+6IZoJkZA5uxPrQi9Pw6POsnFDZc6NrrJ5hRuzJPaHHyLfY9HWidp04g04+j+djQdb5/GXXjKBkevIgKo5I6MvpdaQf+856dfCinJ5ItCby13x5QhPazN9EswOOh2L2OioeIXkOTr8Y5seR7Jj5PT47oLIF2Gyd7Z227QXdG6Kjs0VPJwetcHwWsFJMqTu6bJmBRVc+Oq16FdvroOPzPp3Doz46Ph9IJZbMH1wGpIZFhneesNlCHmX++FJI5/Cki4/OopBnua2OngOohFVKdi6FbEkPE3B8GdETPGvDw4sgNLjBcOcyhgVUMX50RbWcPsxHr1MGALX14+stUFqagL0viAAR8u7omiTIwsof3pTBAAbN41dTWmgR6YdfaTuPIAyH1yUBzlf++KbwTeDMPX4lCQtHBvjgH5h1BCF/dF0ia72FR28I0CCd8slXkJ85OkQn/4BLF0GOJ69CUmsM4NEbUpfEnxLtG6dBvlr1m+j+Bso7CC79yRky7zaJYzun+Umm+5Po/gW72KgHRj5cxbMOD2NwsunmG6rl6c6mHHWb/kTcPePzzbqj4scDP1tDOKfHAz85BeMlPjiLTzaa1Zm8tQ7yMyCawv0NMF3HKCfjARidCvES7W+R41O2dxw/2DDzc6q19Nv5/p2McdfM0fF1wYawfuiP70Zxz+WP0fieYNjg3O7ezAjzOkdHr/Isq5t9uH83jVqm3Mej21xSFwqz91rCsbY12X9F8G5o9sPxnSiJa3OCDm8JAgPQ7uBaFIPaVODxqxltgfoYHt+O0ripRujklkDBQ+8PXokCCSzp0PvnI1P5Boajq0FhhBv46BlWQp2W7M7TwYmANdq+hBwIGqD9i0BRhCu8/TRaUtWt+a0rFrQs8/zhGaJD8B7tX4S1rFMY3TlHc25ay+juZeW6VgT+aIvWgOrcH171dVa3gbx9jhex6izkrQvG9S1zcueUbxKdWLZzWhx1YGsXPbzsFqervo62h3C+QvCCHPf9dFNnlh2cYodDtTIVdzfBYsvHU3K4GabrkBRk1APjTS+X+GgLHW/qwTi6t+7mZ2EyQXvraLKO4ZzNe/7kjJdLdHQKH52m2Q7a2XLTC1CO4NEaHp8iKIezDto/DeQMjzbB4VnQG8OjLj68AEPeypvq6AUYGmAEfnQlJCUZCXBwGbIpnqbw8CIImqgAd57EvgSO4YdPOtmgeQQPLmdgr8476OBSCA65gB49iYPygbA3rzqpUS7h/iWCl76K8O4TwDsEPH74VHDWY8zvX24SQkpGHp9nYW51RHaf8B5jfPDuX/1tOV+Wnc5b/ujPxXJpAXnyLz+RTObj4fq7f/f3O4929p55+r3/5jfFdLFY3XjuD/84GY8tj67+1V9nB0fHWxfe/Zu/PXi0M7p68W0f/XfxwclsdeO5P/nTeDILlFz9959sPXp8eOnJ9/zWR1fu3Nt+/vlv/o3f6ezu5/3B1U/8bbZ74KS4/IlP9u8/Gl248MLv/j+rb9x+9MLz7/qt32s/enywdfYboIswUBq//GL343/2dd9FaLkoez0rJTS2TDPDeJOkTatlZeSRKtrtOootY0WnY4XwhDTdnmXUBlq021oKRGmZZTZNHcZlq6WFUJSVrZZlTJOo7vVrKSxjTZrpJPKMFv2+jmPLeJVmSshGCtXu6DRxhJSdbiWloyTvdXUcB+RdDJ0AEAPHg0UwQKQkgdx6DAMLBgeHoZYEcIMR8BwoCACERkJPEUDICQCJh8Q7Ch3FiGItAHUOAOAiYChwGGsKYQAAB88hRDBgZHn4qjdUSeIRdBhbDhEKAUNLgcEwABAEcCY4TCxHSHmCgOPExdQT7GnQAmAIFIPAeodQkNjG3BLocDA8OIoRQy4CAQFPgifOEawlIRICHADxXz0DPQExCQgG6oP0DgEvkIqwI9CDYCUCDLoAXAwpQYpAzz1AAeJgmfMceoxUhAKDAQHAASAggKBjYimyHhjuAQWAIBMhTzHA0PGAKPA42AQ7HjAKjnlPUMBIRyhw4AkMzBsMLIVesEBBQAAI50gICJkIeoYARYYB9P9T915Bu2ZXnd9ea8cnvuGLJ58+53SfoA7qbnVLjRIyGQSMufGlyzX3vnF5TJDFABp7AGMYwGJmCDPlonBh8BAGLBEkkEYodqtb3adPzuGLb37izr5ogZkbX7vv/7Uv9qr/ql1r7bV+nBAaTEKtQs7QKEK5BwSWscgCoeBTEXn0zENCGfGEEMiYp9Gz6FMuSKAMiKSeMeDMSw8UCSFYsEBooCxKGh1GhKAYCBIoJQodMELAKhYC9ZRCyj2NgWHgMVLmGY2K0hAJOFDoHQbOfSqID4SGKIhLSURwPIKKgaJPuJeMMBJotDI4QUNEm5GIhPCgOURGrURIkGCILDjpIyeO0JBApGhZROUjxsCjTllgYCzRHCgHEsGmEBh6jigjcHQRQiqDIJEEq2KkQDiSDAIDIpGUygsbKIaERkUji1AIolxkMSQYOEZOY6GICoQRmlIrGWHgCx4lQUYwpYSRKNAlIqgYqMMUWYIEgOTMK+8FhowhR8KBJCzkGBgNEkECIMFChoR5zmLiPANgEFMGKRJCQkIcgKfUiog+REqcQGAYGHWSYIiEEJtgDMFRZiUSTyQSJ4iN4CiLKhqCBMBxEggEikFheMeJLFhCCKdBMu4JAUJUtC54Tq2iPCKy4Hm0khCKTgEEiAiWB8BAkFAOMUUnWCAx5oxQSwVgSgOFoLjLMAodPSEJUoHOE5+JwKnjlCSUUABFYyk815RgVBg5JYJCwSKPRMSgMDC0hJGcE+kIJZhRx5AKCAWPPDBJiATCICK6THjlAvWQUiZjxOhEsCw6Bl7RICLFGGS033YQBomEgVcReQAWtGReUs6pVoRxT4D4lFhJHEXHI6WR0OgUkOTbWcvzEIHohAYZHKNOEsoIIrEyOgGe0pCAFiwSYiVxggSBLsHISUD0IvaSZAS0gBiCRwwJCyAiJZ5FR4hn1AviU0KABImeoefUJxQ9ITRGEawDz2gUlCtCCIkyBIwBiEswOAoUPIeQImHgJbEZSgTNgcRAkISEOqcCA8pJUCQyJJJASgLFwKP+dtaKVgTghHA0KY2Meo6gIlLiRYSceREZjUYioZRwIBlEDiQSJ4JF4jiiopHHSGNUPrAYGOoUIkOCxHNQnAQaTUqNooLRqCIwHynYBBynEDwTnlIgjFiFRBEC2I7GbZ47LurBwKSpV8oUZTcceiGaotB5TgDa9Y2+LD0XOs+NUpaLbjDoByOP2AyHXV5EgDrPdaI8F91o3GW5Z7zLsq4oA6PdYODynADpiqLPUitVMxzZJAlKmrLosjIg1oMB4YIQ6EZjK2UAAu/60UEgIaz9we+/+z+5nzvXfugDLpPzsyf7jbV+NJw/cWJaDJph+ejCRdgsAuLDl1+Mhew2NhZnTs7KwWQ4qk4em589raK7+8oH3CAFgJ33PW+G6e6lS/2xjX5t1I3Gq3MnJ6dPSW9uv/+lVqarLNt56rwdFv36cHn6eH3imCvy+uTWzsaxtswfnL+Im0UU4vF734OFWG4fX50+Xm/l0izcqIayU3plt1YxhyK+Lcq9fqA4mXMxN9seoonlPKbdhripi0UoJI8HTE3d2CNZcL7QGzaqihRTKLTs5360IHkUZAJi1m+QEd4u8p3FiItQx3Rqxr7s9sJoQUpQeDdP761KlYc9Kmq3qVnsMZvoEZPhQMBes4kJHCRhqY9oaydy+qo5zpO4ovKQFb1TWsF+txlZf4/vfrV9aqywprjUG50IK8p2SWmDqiHfD4VHvsjsvD/aybgiuPJbPWvux/oN2BIh6UU8DOu9S/vMLNoThve7MHvDH6Wcd5Qd0LI1eVBxGta7PsOiOTDbKyqNDHtx2MakHahbMWt14VO97E70GV2EYOPmAhJT4uVY1LZkksygrPpRSLtFe7RLaOWzZRiuQEYe97CoQmF4XPBiXq2xI+pqVfRc9dzO9WaLiRdkH/PKZ81Y3SZF3Q4x1wuzuWRJL/TUrVdUhYw1KjV4UiVE5+suLUxWGHWU0BQGoinKnm6yRPSF6thRLnNXjHpY5yMzK44ELGki2jzt/PFMJrVSC3Z6KHw3HHa4ydfMTG2FPOnpmBablpcwZJWQlp0UWWyzkccxHaRVsWW5iknmClmLY4xLV4iOHkPplkRM/ciDaCXZ77cicw+PlTcnA65YRWGltzoemxQfxaFHMR3nN7s0hFzndtGd6ASpEKqwVqFsQnHAks4ro2AWR7XNbGoW3QldksOkfNQWljPNcM8NXcx1EqZ+vetlfzy7PB+iFJ2AhdnUSHtJdrp1YKUf9At4QqS5z0ObnKJEkLy9K8eBb2cD2iTrIWzJQlfqNEaIA/0Q1onKWZ7pbOTYGDLSqI1AxywfaLUVZBFzu1LHgecw4nU68qSImd3jhYNjeWkqeSzSIVvrD9MTGBUk7cNkYHErHYdFuh78kSTrq8ExZ4q0dI+QT7pNTMO+oMt+w5TJPhf3mwHm+pCU8zggEeYJn7Rrvox3RoNH9Ygz0mEyMyNTmMOQTkhJ6rJ1cBc2OQ8HDFdmu09cHdO5L8wAb/PikcsFYSsOE7epgbcKl/1mP2Q7PHukCyLjkiYHYQSeNwlO2+04wEbSXp2gCTejrBEjYENSjPsst7jGB3bFnkwV61Ps0mMgMp9v2jLt44CXvFPbBIa0NCs4q0rWJutBbCLnbiAqvo5qBCU28ihiDoNBx49iKkxGO3aMZsrmqRZrIMuQj02W67CVD/VCnWWJtBk0cpsyoTlZsGzebKGylV1fIu825LWurGPORdij2dINLAsLmi2bgT+a3myLBlMr9dSvLSENghzGtLJDK+gOTRd6HJVeutHMFnHQPnbrFRQhxdsi3+9yzO0+yWu/brltyWjS53A0vO7LiS8UwoyqhVmziZmEotbjOOj2YzETubayT+hBuxnE6hou39ZnShUqUPN+3aVmAuk+GYKvb9PVG+TMwNNaxYXdapSvg1qSkdZw0A0nMguR1QImbkNH2Smy7I9aPr8d9Y2wLSSsUB3yQvd5SOOh2+gMmfGDb/RnMkUaVEu/5ilZUrZnx8QnbdYv2pN9BouAJm7UXC5z+rYfeJeHJEzjWtuVMWtn3bFG8mYkr3drkTAryC4Z9iRruV+y0awrEZOJH9VctMzPzaamUkuyFwddkIut7Fafaz2EvJ3pIysuOu6mbr1lSVPSq6bQPtW5nYa1uhvFvJ7qrTkkvF9fr04eXZw+brOsO761c+pMnaS7J06a7TFnZPe9z7pRVo/H1bEj+8dPVFLMjh0/fOpJEc3Bs5dWR7YkhJ2nL7KSP37y0vT8uWZrPSZ8fvzI7PwZFv3uc08v1rdaio/OnI3ba+36Wre5NrlwziWqPnFkcvJkxcSDU2f8sXWfZ9OL5/uNsh6vtdsbh5ub+Ze/zOazd28FKyg1+NxfD/7yLyD4dzuL8NQ24If+9b/pxqPRvQfv/T//r344MoNk8v4L4uqN89euXPwPf87bFmN85Td/xzG6f2Jzdf44kHjpc1+6+Jm/wEAG1fw9f/BHvGkfvPjiwdZJEHjpc5+/9Ed/pots4/qtC5/5C6XtNEtnH7yEi/n2nccf+q3fDkpmh9Nn//CPhLe15AcfepY9fHT84aPnf/f3h/OZKdMPfPrfEeKWa0+zR+d4LaSZxf1n00OA5M5/GXaOYHtnkfF7l1RPk3bhJhdxlRfh2g/w+nh95y6O1M0XST+m/IDdu8i61Cer+XreFdlod4WPnkkOlN2cyivPhG4rHT34/vbmGdtPlsTsP8uqUVBGPDiX7KcuvfdPzM3TGG7Hgbx9TtRp3u6H6Rmy2O7Wm8HNI2R62m9Uxa0tsjyW1tf9X/9J8vnP+nKD8g8n9zZkcpc9POXnT5lRk/37Xxdf+wLheUEuhclTwC2sNtIHJ1DuTbfLepCj9cPb63F6ioaVXCbh4ALaOf+KjdE2AAAgAElEQVTC76s//AN3cjOZPxvn5zGZip0NmJ6WzQ7/yp/wP/1jypAW3wkPnkrkXfFw7OeXGO7FruCPzlMdGWh274UY3Tl443v8QrtmtXccJhdFU6/X0/rwO/hKb6hbHycH4LvJ3hrfuURJLReCHJ5Xre03zWxt5ARb22nDvZcAIusMeXSpMKtt+9b3uKjgcNJuxQfPZysTZC1vv88HdlJd+56wks3B7HBMDp/hLaHQ4YP3yiVZjZvJlzE0WLj24IbShyQbuOXRzWYwGB3u3v9K1sxkXrb731R2TlTp6+21vizkQXX4hjD3THnC7X+Jz+ZZsWFeunP9JUlvTLvqdr7cEywnk8vc3HHbG/PHJ86ZMoWVnb8Geoojv1zcYcvHio9Jvz1sB4NyNTv8IplP80HRz67y1S7NEi+rU+rBCZLtqYcbZPqUGXTvt298yMRptesOTsXpBYo9NmN574yW0+8w3/oID49t4x8/CdMnmJ6pSoaD97DY1JthvrYufDO+PvLTC6DmfGcEs7NJf/BsePMD0Rs7n87fwx5dEsm+epSF6QWBB+f1W98deF0/bBZP4OMLys5Qp/DgWV/0+v788LpKuj7V9f3LIzpZJcXUXzyCaPybi/3rA0kMnendmzlUTrod/fIpOdnlC3Ln9bXoI7Nm93KWGEPGrNkaG5WUN/fuXh7TmZYj9/BLqbe0HFb2ZAn13D0KB7dKWHma4v43BDnQ+frSbSd+q3TX29nVRDSt0nbvRtovKT2OwzfPhvYIKefZzROkHg7D9Q+4w3NkuQvD+OA96V4WRo+LG0+Z5iQZz7579fr5vqvqRh88DfMjLnfs0elib9BtTp+9dePl5eSN8Ym1Oydgua7MlB1ukNkpkex/Z3j4PE6udTS5ccG3p6k4FI+Pk/l23t96JT5+GuuJJ+bx8+LgZBzcHVw7ZuqzJl3G24tHtwaSGLeEnTeSQlbt8XG9Nk5MU79FJndV7mu+MPevDTgx5GSyWN8q3ar7pt65MyhVXd+nh3fSMnTuZLFc32DRh6vNo8tlVlhz3+zeyods1a8VzeYoeuBXZw+vDodmEbpw91trOVT98WG9sUYZ8ZfbvZspNwadf/zNlKFX2ZDfPJvbmlYhHF6iNR3Hyz/E+3F97144ktx6gQRBfUsfXUQDx8jV7zZtEfYm3Tp58LyaoRtX4upzYBI3XMw3xsBccsDo46dYmztl1O0Lao4svf2j/Z0Rg4fdOr9/Pm1dUs/95GmyGvDh7o91D0/D5EpTFDcuurhOqUkenMRGRtHzx2eSnYHMb9E7l3xz2snd7Ld+jV1+lam1xD5HZsdjptnOyWRv05WPs1/9Jfatr8fjT8jlC2SxxcOKT9fi4aluZD64c/X8zuMHx7fyt476+gmmJvzxUVwcE4tb7G/+kP3FZ+jWBpqPwN7pLLlC7zzhq3Pc34U//F355b+hqIR8Pz4+xtk+Hp7G/Sdp/oA/Xsfpk6qbDVdde/Ayaxdn1Z3vi7OVN/39I3B4UeABnwzJ5Emlm9Nw9XtjC93uYX2O3nsWZcUmihxcXDMPD8b5aj13yDfum7jz3kRXEJDde94zfYld/a6o9epxdXgCDi9x7TB4ev8ZWjXH5M0fioeNYPX+MbbzZGYmpElh/wK1TIVbH/1f/7dksTBJ8vzv/UGxnC0Ynbx4XgS39vjg/f/7725ev77zzNMf++VfSw+njy+d77Yzz+Ho2zee/b8/u3bn3uzY8Q/+9m8fvX5t533P3Tt1qRmMNm/devn3/o9yMpNWn//sX21fv/XwxMnmvScJuHT38CP/9ncGOzsuz5/+0z8f3X3QYDh88TxNWNr2H/w3v3PsjTcevO+FD//qvx4+2tk7egzfvvIubhECoLVbn/5VNjl81y8a9UjrbNAXw7ocL8cbVqpmuN7aIL71Vp8Wk82jAXBx9GSXD9q0rEbrlil1+z50erJ9NBp/eOzEbLxBkC+3jjpCDZOO0Hq0rpO8GYwPTp1BHw9OnDZJJq/edCY0g5EejNtiNN/Y7rlshuMmKeTb14KHxfqWRzo9erLNBm1etmnZloq5OnDdDRXouk9CrYaNM411TVYEoSO4eqTQ1Z42dTnuuvl+ZG02IH5K3KpNRaAdIbpTsheZi7QrJLV1n4UuySIsKWmWQk17vWxm8zQD0hNs2hTAtxHrZjieVG3TLZZq4FgPUTdDSaIJWHdSBtqyULeMaNVh13SjRGdD37t2bc1HDbFq89xxS+2qU9itbZPpojl6zMpAbNUqF8BgXPaJMkx1qrDArLJUN20BWhDqG1swS5UL2JfrpqTMVL3CXmlaL+uR1MXY9bbZ2HKghdnTUnQDgbrqctHknJvKKKs5oJk45qayXHarWqRtDqKe64I0ZSr0qi2x5lmjuxpZmyXRN1bSbgCqXerMExNaVfRMdUooPwlo2kJgbKyCg3RD24NdT/uMc111OeuSJJqDGLuFzJbNfIF8NU6hq6w0JqNMV11OvBAiIUhCO8qRWOQ+ktjJzEds01JQLYn2eSJUAAg6SzqRuoC2zKXXmECblVJYBVanyb18dLPVejwmEDh3plAUvVChT3JDZc9Tj1QkAaPvRgVgpNwFij1PHGG9SGkGynV+oKgkFJwreCB9CKs2yzy31FVdAjMnu9XDSXncKkJN1SfRo0Oy0EU6sbCYPa6SsU8sbap6wLVC5lZaRc0SwxJNZZszaiqT0i51sp53JZ0RuagPZqr03HJ/2EreZgh91eXikI+8O5ylGybBaJumSCwnwk8Mi26cpLoOI6WHBe9rP8psWvC3rsd5ZbbWkFgnmFkvUt2QEXcq42/fQAc6z8q4otS5QarQOsW0UK0qgifdWqlsGweiSwsBLaXBU0rvPiSd9qOMux4ytEWibBNz2pVDevshPt63g5QRAwKbUS68oSnpZRZwyWPdSB5ZS7xeDMaVt4u2XQlOQkegrsuRIzWEtuMws7E1y5koIrGBNL0MQDofqz7J7qG4QYUpCo81+q4peQCHUNWUNbadTGZtMeozz3Slcxp4R2yzHA9Xwc37rkoy4mvU+11adDkyXZmc20wKr6EUwEBBa1NlkXcyC56QAnhTmVHaZ0kaNcm4QdEzZQmLg0SGXg+LuCbTrrYj5UPsRGapAA4lNlExO0i4ad0wDyG2SRFd6EeFCL0Zl0HyPK6IAA/YiZQYb0aKtw3m1CVCxT5m3Egk0FoO7VDwbmVkXxXrfTPdB9mnebQHMbRdghCbwPy+Gq2WDw8D7TPGddUVsVOKkEVE00hlmNI87RJKXet526UUXR1oXxejWVXXplslGSG1o74dKjSt481SDhu9OFi13XAUaUtC3ySEhDpQ26Y0xjbSpi6GnrZoqyaTfbkWqr45suXB+ti0IhLSAVl2g1GflaHuu40txzqwbVOiw4DQBknvsOSGzHoqTBaYrroEDO+hq+r1wlJpQdTjtRhr6fabLNcFUl01g7QfrPtWt0eOOnQhdnWuCLQiTPpEdqkT9bwraZdJ5uqmlA3K2rQ1V23Oian7VPRFSJplV5C5LFbLnR2WWEmFnxgeu4JyV7W5MFR0qiA2NKWitm4L2QlK7GGgbqaKarkzl4NqJGmzMon1CaCtu6Fc8XRRL6sAnfCoqy5jbQ5C107pNh2sBiOdFqv17b4Y1Plgtb7Fbt2x0/lqvBEAFxvbbT5ohus6KzuR0vuPyKKabh71VCzWtxejTS3TarTRq8yiMFS0adEORq3KJkdPOGCzje0uL9WVm373oN0+ulzb9EzMt4+1adkUg2brKH/wMB7M26zQSbYcjJtssNw+6ihr8xLiu5hFGJQqP/dXfOfxO1/y3/Uswu6f/tfl/p5PRQRkdduleU2T0eHBfGNrI8zFvF1sbqR9FbsQFe+ojK0VLJg8X3v8cHLiZK5XYt5W45FyXeij4s5SRroIClbZaO3B/cnJU6xqsXddnuWhReOBgxVCVi1jcS/bLA8Olmsba1CJedMMi5FerHyGGPqyEEttEiGoEUvbZEqCIS5gCE4Kb+jArLoi9Q1YyTlzSd02qWJAvEYIgaToHEt11wzSZLIwiUJFaeWNEpxabxAiNIMsb5aJbpeDMa+9p9jnST5feUEZddETbs18uKHqNtGdyxmrXS9UXZZJ28peV8OCGVMuKj2S2kK6Wq42N1XnIPgBWVW0oL1fjQesrfPZfHns6KBZeUPrUSF6Q11A7qN3vOmWm0fQusGsWmyWZbPylpmC82rlrWe5slSxNkDiWpYVk+Vqa1hUM99aN8iYAx9wAKslL2XjXEFXarB2MKtGaWI6rxEliYzwVgOHSg3yw+Vqc3SkftyYos2lipoYHzl1lNM2MO6rrMgOqnq9zG0lZqtqNBQkeoOIwQuKXRzE5WSwNpjNF4OxChpWUZdSROM1IASfMtlqxLDMRvnBsh6XaWhJTWzGAUnUQVijhyldaC84k0EsKiclCHSGEu8wY9bRtGvaUZlMl16wajROp4sgOafOeI7GdaNSdU3W1tVoSCtHIHZlns6XQfA1O12ygnV6sbGlmpZqZ0ZJPlv0POnKfDA5DJyBABME63pScuO4aHs9TlXd+shRBh+Qd7YaD5jTo+V8sr5ZVstgsBqVXFuuDYoYIChtrOQNK4rJYrU1KtpVsNxkVDV1dIGkXNNErjQpSM3ywWRebQ63l48ayPpEURu9QyqjBapqE3NoRDqezWZr47xtgkabMeZ91LEpC0Ix3zlot9YyV+PSdmWKzmlNKCVMUtpbxkJdlNnuvF0fYtezVussWXOLCkskLipO2sCYb/Ni9PjxcmNTEuOrGHLBifU9AHirZKgti96MymK6aIallyI7nLlMQfTGQARwRVaslhxsNRylBytdJm2al4s5BlIPC9W0ads240x0hpBY5YNsUQeGjDnrBdemGpdpu8q7pstT1gZNRVtm2aIOjDFmTSCiN6u1Ddl0UttuoIrFqmcKZJS694QQzkwUsrc+Ax2kqrt+nAzqpWYycCRtiABURE2EqvR8e21tdeg1mlLxrvOOMhVjJLxuQ6FqXuT70+rYet6sfA8+57JvoyMZ0ytR0saRDBuRp7vT5th62S5obbpBDj44i1wES7lcdSSnjSw27t+bnDiemi52kSSQuL7yKePeM5bOV3aYVGpY7Bw222uJqV0HMeMECG39ICxngzUx7dphnsRW1rrJExaj14jE+5RDRwQxdZEOptOqHMpooY4mFRxsMBgo6zJZTg85hsV4I582bZZYJYrp0iaMoyMuQCSrfJgta06sTyhf2rrITJKM5wdaSkRwlkKIzSDPl3WgWJVFOV8FCmt+NmUj3rtqrRSrhWy6dmu9XFYaRVvmad1GQMas7Q1vuzguNUnSumvGSbGqNcqoCHY6ADCBJgrRupiFnqh01bbrWT6fGg+kUKAhAh2R+SFfy5reFrR3mMwX3ZGNvOlsZFERYknwwFRoaTo4nK+2R9vLnVUYmIwpp6OPkaMBIRsLKalUUewvq61B2S9pb9o0pYEETSgPhnFZu5Iud8qj6w8ezI4fTW0fOuoyyoINGigLJuHFqnGSLpPhcG+22hxlpg4d+pTyaNC6TuWGiXK2IilZZcPRwXw5KhPfxtbT6GyZ0drIqA+LjWSyaIqSSRzODvqi5Gi9QQh+UYzFqhLO9OPheHLQFkWfZsODPZ3la242ZWNmTDUal/Opp8xnsjycrMpRLfJksXScp9zGALJr9KjEShMS+yyVi2aVlSXt2ar1QqAC6xk3dtmZ0a/9+rt1ihAR+v74P/+EunrlnQocPf/f/NN3b4vQPHHmSKA/+lOfqEZr41t3v+tf/tLOmaeeeuP1j/3qr5rx2ujRg4/8wq9w7WIIH/+pn2lGo/WHj37oF/+Xdnt9/crND//ab1gUZd997FO/wLTzXHz8x/9HL3ixP/nIL/6rdrw2vnn3w7/2aQpc2vYH//nPpVXlRoMf/Wef8EzIRfWxX/hlk6jx7v53/fKvKGsIpd/7yU+Vk1k3KL7/x3+WeTs7/iK/9qxcCh6m8daHk0fEby2Tr38gzjdjMVNvPiuXSnQH9uEHxDynOCU3PjTYVe36TL36Mp1txnKavnURqzG3i/joJTwcUpywmy+LB0V7bFF+7TmYHdXDKnv9HFkc4WEWHzynDoZ9oZO33qPulf2RyfDVZ8zhuX5UlZfP0Mlmah+FR8+xvSPVVjd667i6e3Z1tB68tQE7zyh/Fxdn8MGzQfV4cDK/cgKGj/nNM3Dv6fZoU9zYhL2XFO6IR0V8+KIXDZscLd8+3a/PkhvH8f7z9dpqfLv0j15EMhWHBdx+nsIM5seym+/V68v85ia7/xykj+ijI/jgBWb3syondz8ItMblprz8Hsx3kttr4eEHqHpE6kxefb9qWseo+uZLEbxsgrjyfk97PuNw9xVsFuur/ebux2WtvQjy1Q8gIbSx7Mr7CPRqEcmdV2TXMleHG98n533IXfKN95HIqPb06vuKfhKW6O59jDeexopd+Wi2MF1u02+8HD1nxrJrLzHjaRvgzitsaShZ8SsfYjNabdTTz8b+kOexvvda3u5Aqbr7XxnaW56dY7t/zhZ3abbl9j8Pix2Z0/b+W6PuAWIJu1/k+qpV53D/z2B5X/ATYvbXdn6PjWi1czlf3gS+wXe/QPUVNz7Z3PvLdPdOqY7R2Rfd/L5aw8Xut8TsOmeb7ODLonozqqfw8DP+4GZSjvvpq3TvVlKUWu2fya6dNaOpvH6E3Xm63mxGtwb2/suMHKqDJNx5iWCFy+38zXPdRi1vbtO7L0b1mD1Yx0cv8XioFineeoHBPFTb/MoLdrgc3Cvh3vtCuiMfr5P7L3G9M5jF9u73sLiCephcfiYOFvJuDvdeRrmT7A3cnQ8qO6Utw6svcT+DbpC8+ZweefNgcu8rRd733NRX/3adLzo5orv/kdIALLh7n09oMHLe3/raCKu+kPrG50fZ4TIZuut/XnhLOfEP/1Yl3salvv71jbhyGdf3PlfiXke2+OM/ZbpBCebxFxPWO2rc7a8OxNzYgXz85zTsGnkMD/8jtn3CZdz9GxoXOvX21ldLv+/CmXTry+fg4ES3vipeP4nzEzwchkfPit0jOu/UtYvJnVF/5HD8+kW/d7Zdr4rLT5D905l7EB5fxN1Tumjl9QuDe2vt1kH+9fOwf6EfLYbXjoaDcyLusUdP4MPTJJ3QW+/hd840J2f5N56gBxeieEDvPIEH50U8wN0n2N0zMZuz2xf4nbN2a2ftzRNu/zmT75ob9d2vDDJs60O+/7dMbEN/xT/8xrAc96ubcO+1Mo+1XNob/2mgqG4X8sHnVD7qzXV76+uj4bhvboW7rw3XYm0X5vqX1iQaX9Pdv6JiDfQde/er5VAt3KN45dWt3Hek97c/lw/sUtd49/M5H4F96G9/bbyWNu1uvP3VgdAds/7h5zIarczX5BvPZF1NKhPuf1jUkcISrnxXMQ1t2apX3090BtAlbz/LNNDehjsfonOguGBXPswPeL/Rll97r9fDQLV8+znWCTQ93H5ZLXhTuOK158SeNBuLwTde6JojQejkyiW5oqqfuHsfFNO0Gdns1RfSw6IZT/OvPu27bcKa9NpTsCx90cprF7M7G2R8l7/5LNl9qt+q8ysncXYxCTv0wUl4fE6XbXr7XHb7SLt9kL95AfefjeWuunUy7F+g8ZDtnGD3zhA1YXfOp3fOdkcOht86jbuXSPKQ3j+DOxek25WzE/jgmZgt+INz4uZZunZXvv2E33uBJQ/pw1Ns/4XE78LBFrt9IWZT9eBUevOcXZ+oByNy7/2s2xsv2u7u9wpXBZ3LN16MeSMfS7z9MoiDdD+Nd19RZk67GN/+CO8XEFL1+gsh7cVhArdfHrpbejaMj76Lm4ZaJ99+ReraRpW+8aKTRk0EufWKiFNYJnD7O1i9ot7yt1+B3noU6asv6pTzGbIbL0o3xU7A9e+Q2hD6+Mf++08M7j+oxusf+/lfLnZ3CeUf/fSnj96/t3/i9I/81CePvfHW3Zdf/rH/7idGd+4tjp38gZ//+e17t2KSvfIbv7n19rWdC+/5kf/hJ0+/9sbjF5753p/81MaV6/Pjp7/zV/7V2t2HkeEHfuO3jn7zrQfvff6/+qmfPP/lL9348Id++Mc/eeRbb622jrzym/9u6/LVkKcf/Le/+eRXvr7zzKXv/+TPnfvSV6999KM/8slPHX/tjXtPnmeX33qXtgiDUqM/+5Pi7/4TOPfOQwWLv/18pPTdOkUI4CjzXHghnRBBCKeUyXICYKWwShHGTJEFLmyaeEqtSjznnnOb5YCos8yId5hnwiMaKYMUWknHuKPU5QUg7ZQySgVEmyhHmRPSS+mk9IhGqT5LAdEmqeU8APZJEgS3aWIZDTRGJJEFx4EEF3jwSBE6Sm0AEsBFEhwngCSCixQgugDWU8qwB3QEI6AjEDwLkYTIQ5CMRguki4gAPRITGEbmCXGEeURPwAYWAT0wGygD0iM4T0kASzA6DgAhUO8pIlpCbEAgwiMJnkMAT8EHGgNEQBOQEuoArKMIaJH4QGNkBNFHFiMEABMYJWARnRcYmQUSKGqEd1hskTBCwAQghBoMJnAK1AAQz4MnHogF9JEEREMYGB5IsI6h54REF+k7g1ka0HtKSDQESaAOIQYeDEcI1mMgEBlqAt4zJMRGJJ55AgS4dwwh2MBjAKCgCfiAkUQbgDhJEEhAGziFaAm1BAhDjRiARSAu0uBpICREGYJiQCwQHREBA4EAAigDzmIUFEhAQSIioqMCgFMhCcWAjABERmOUDGnkaANjgnkkIVBgnFBKokCAKFj0DAACo95zwThBCE5wziLFGAVFSqiAKBhC4DwGzjg6SmNUjHKCEIFFwICkjwjAPKILDCI6IIFwTzAAOC8hoqekAxIY9DR0QbIoA5DoqSfUEeIiRgCPqCMFDw6iAQoBLSKJglhKSLAeA2Cg0EcggRMILlDwzBFCAosOgUTnOBIMCDqSIARhAIT6KDgSHykhDCiLwEikgOAJo1EgAggRHUUkPrIYGWU8EoiREcAYgaAEisAlIYIC8SgIAUD0yIEIRlkEGiknQCJnIfJv70ENjEnmKYbIKVKCDCIHCoHzSAgAaPqOg7gn0UXmCYYINjACGJB+20EUbKDgwQJGyxEwRuo9BQSH0AdKgVlA7wX99p1TT4gj1HuOCI5CFwnhpIWgg2KEB0JCoB7AEfARI6KjqD2lNvY0GsIAMXIaqATgiDREIEwSBO854yJSACLBAaEQKCURCWMBGHqBSIJDVCJSgMAC4QzBf/uqaSQUCAVKfFCCcEIRkAePyMFFRggHpIFAJBwAAlACnFAArghhiMQjjZFEEm0gwQkCSAK4dxwU0AWKHDWiR/SALmLwNEAMUYQgGYKD2EcEID2ADxwAHQEP3AMJBHVggOCB2YAUYg/oHY2E2ECJZQQhBuocp0AcITZSxpgGjIFHAj5SYilE8IDacwHUAlhHAdECCZ5FoBGZj4wQ4hFMYAyiBnSeI2EOSKBoKLFIbWQEWCBgIiLQHqON72QtjJ6FED2AJRgCcRSNR3RoIVjHgFJNIDjqIyWE2EhJxEjQB0o8WgQSeDAUSbAeAmCg2EeIngOJ9u8dBIR7R4FE5wVGiIg6QvAYSXAeif/7rBUZQjSROoCIYBAjsAjRRho8eiCRiBAlhWgIWIIRoYsQIosIntBAmAcSgdqI1AjpVeKT1CdpYNwoRQBskpokiZxbpTxjPs8C51ZKnygvRK+SSKlNM0OZZ8xkqedSp5lnzCjppQqcmzSLjJqi8FwESo1KAmNWqiCEzjIvpWfcSkUodVkSGPdpZjgPlJo8j4ie8XfvZlG+s5N/5cu0qv6hDAT/7YULdz/9WwTh/8/1t/8PFmHzDouQxkgg2mCTJFJUy2U/GBTd0lhwWZKYThPBwL/DEJRg26SQy1U3HBbdwljqsiQxbU9EHpoAtCVSEd0mpajqvigKvbIGnZIy9JpITlxAjCZKNM0/0mhHgbOhnk3EmojWCRlN8JynofWaGJWkvu4wwxgEMS0kw27mE9l6GRhLYucsWiFT3/SQYPAcXQdKmdYlMproGf8HTeabBjMM3irBjJWmc4kMBgJDL7housB4EpsWMgzeJpIap3TLWHAONVc6SbgxzFqbSHBedh2VpMWUWWPShBkbI2zow7kYxUBsIkmMoulspvKu6oh0qaLWkUAU6TVKNM5kCYlE1q3JVdZVPVEoCLXOoFCx11SB8YrqSg5UVZsiS7vV32usRrVuDpdyHFwU1FZqkKxql8rENB0oDi4COE8T0rcq51Wry+LY8v4h3XCJSGzbQSKIJUCcQ0X6Nil42+s0LfRSWx6UkL7rMeHRRgTn6Lo+mJeb0GidZ4Wues+D4Mp3PSgeTGTUOUxC16YFr1uT57levaPhxJrIpW5DKr2JgTJFtLPEcZmFtgWFwQt0LSRKtyGVzpKI6KUQTesZT0PbYorBOyXRWKm7kApnMSJ4KVjXB2RHut295Ai1xuQptRaNA4m863qeBClob77NNMSUGc1FaDGjxoCi1BgDUpHegET7n4Uj76qeSJso9B58lKQ3KP9ROCpd5ln7/4bDgFSgNUqwXuG3NbZIN5e7B2KTikid0yhV6CxVoK2ktkoGsm5Mlubdqo8SONDoLBFGSgJEVZXJskKvtKVeycS3LSScOIBoHU1i36Y5bXqbZYVe9p5Hztf7gwO5JYImDK1DFfo+zWndmSzLTaU9C0Ikvu1A8WAJQ+MwDX2X5thomypCKa+bwEUSu5YkANELTjud/L3Gpcowqfoe3nGQdbLvfMKdpwSIk0K0XaAsiW0LGQbnEoXWy67hPDiPmkkvBdMmAE1j00FGrdF5Sq2jxhJFRdv1PJWgvUdLRRqalmbMGMFcwzLe9yHhsu06nkrQwaNHpmLX0owa0xf5sJ62UaKk3BoNQoIxIIj1CTWVLOWqMoMia1mww8EAACAASURBVFd9FCApt7pHtWZmtSiswwR1pQairk2eFf2q8wIF0uA0SBV7SwXRTlFbJQNR1SbP8n7VRaXQStct2DCJvRYKOvuORi5XpiyyftVFCZwiROdgvZ+silHovEnT3FbGcS944tsOEh4sYcR6nvi2SzPWaJOm2T/WYEpI9IKz3ijfdUlGO2uVChRl03rBk9C2kAEJTgrWG+U6SqO1VCdJYJR3OlCaxLYjKURvE8V7HQnRaSrbLgB9h0VIrdV5is4zbaKisut6qrwSTNsAmIamoxk1RjBbs4L3OiZMdk3PUgE2eHDIktj9ZyHr+pAK2bU9SwRYH9EB3+529tOj3z6HF6Lvg+KyazUqjjYQ6oCp2DUiV1Vtinx7+ehAbIIA7kyPSoV3wuEVNe84SGdZ0S/7IEBQ5v9ew0TUcdMe7A2Oi6rWRf5OyIABj0ZHKWNvuAQdFOo6GchVpcsi75bvaFi0hohIMVDK206hqZJSVq3Os8xUOgoWLHC0DpXv+qygTW+V8pyrqnJCJqHrQEEMTinWdcr3fVpg2zulPOeyaQJlm/pgX26htybLeNdFApwFbLVWmRNCto1n/J1zhO2JZCZQiISxgK3uVZbG3ngaESUxHSgg0UwXw19/V7IIoxDjP/j9td/7XbDmH14p9IMhxCTpnnkWvH/3rWk4c24dxD/5yU8sN7YG9x5+/6f+5eOLz269ffXjP/upvZPnBtPDH/iJn9HZgHr/8Z/8mWo4zPan3/epn18cOza48/gHf+5fzLeOq6b++Cd+znEVCPzwT/y0z1K1qP6L/+mX6rW1ZH/+8Z/+2WqwRmP44R//6eiCzvIf+YmfdpTTpv/+//kX+8GAzdqPf/Jn+qTwjP3wP/uEME6XxQ9+4l9g1+098b7hqyfpikVe569fSqbQbrbjL50Vh3k/qotvnMwWKrJKvH1eHKLLdfHqk9kuqY7bzS8ep/sDs1YX3zwlpqUXq/TtM8lE2qIvXj2XPsLlKb/5xU12sFVv9huvbYm9MoiVunY22U9XY7/+9ePFPZw/4be+sE53j1TbZu3VododAzuUN87KnXJ2nGy8lhc3js5O2vFbSXr9Ccj3yd7W4OqxvtTywcb4jYHbXKTX1rOrp2Yn7eCqKq6dsdmseJyIK2d10Yu9zbXXh80RXbydZ2+fWp7ywxs8u3zGFMtsT6m3z8W0kvvjwevbzVaf3crzy6f1ZiPuqOLyUz6ZyVmSXT4XVcX3i9EbJ+JwLh4O08tPuuHc9MnaV8+D6yOS0ZeeiCxCD8NvnHJpZDNffPMioV3eL9nr7yd9FyWM/u4sIRFsHLz6RKQ2tjh67ckQDUZTfOMZYaxWYfx3Z4mH4H3x2tkkNs7Q/JuXsGt9QgZfPi9r2w3i5hdOEMcDceU3z0Fw0bnytYvMWKv84O/Oi6VfbLnms715GGTmp18i9qHlIzL5yxDutPBUVv+Hqr8D4gQuPuf6u04O4uTviLtt4tGs+mwfrlbkYln/8aq+ifBE1v/VorsVkkE4/BraWzYeSVefbf2VJnuSzj6jl1eBnMn0F+rmOklLU33dN1c9nMiqz2v3Ro3vKdo/Wyzfwuw0W33Vrt4M6gjkj7aHr681R0x5LSsun5ye8KNbMrt81mbzbE8kl5+yuWaT8fqr42bLZjfS4vLpfrNJHmbZm0/aZK6mMnvrfJAVOyxG3zzSrev0flZ867TeaOV9nr91AdQin0V6+dnAa7oohq+eNKM22Unyb521wzk/yAavn3O85g0rXn+K8AarbPD1k90ousPV7M8comfo9/80QquhYNM/ttFEEv3iLz1QiH2YfCZQH5CH3T/yojWyDAd/Em0TmIDpXwZqHPi4/5lAO4sZHvyRg7km20n1h7VvKUoy+azH/v+h7s1iLsuu87A9733mO/33n/+ax64eRTbZpCiKigU9BHESwAkQBMhLEOQhL4kTQEDg+CEGHCiBotiI5MiSI4mSrEgWJcoUKZImKSkMKVLNJntiV1dVV9U/D3e+90x73nkoOYoT2c/qt3OAdXCw93fWOgv7W2t9BmA0/iOPSm2GRfnbKzBq/W6y+t1VU3EY4+WXGlwZQOHoKwAurbza2f7jXJxuLDfb4ff77Ljvozl/dDk+yauu7X5/u/iALq7atW/22NH2akf3v5+LoyFkI/74Ej8aVAObv73TecAWV83atzviYHu+pQfvFmx/04tZ/MF6dLCuO2V6fy99P1le1b3vdsSTXbW+TB/2xeNtF83i/fXoyYYpVtF7W72HvcVe0/1BL/pgb7UlwYP5xTewyLw+B8uvtmiT6gdq9g3Ld5l6ICffgFHh/NSMv45Z7NWFX35FxwMvD/zo656vA30UJl92PPWh9JMvO8KMWaHllw3uQnOg538Mor5XZ378R46IEFo/+aKNqQmlH/+RxwV052HydRDl2s7gxZcCI9o5NP0DDamncbfznd1Ya+dD/IO7pJYu9vm3b4mFrQd++Ke7oBGe6uz717CCIej0B7dZ401si2/f4lNXbYDhn2yGtqsj0//eDqmgQyZ98zYuYdkFG3+6F438ahus//G6rwc60b3XN2lJEFiKd5+jc7wcorVvXkrPQLlth3+6Bsu1NrO9NzbwImm6qvuDnfyh0NuL9I0tvr+72FHdd4p4f9OLWfp4jT1dr/smv79V3I9Xl1XvO53oya7ZWEYPBuLxtk0XydMBf7xli1X6aDu7313tNcUPuvGjXTlcifeL5PElH8+jo370aNt0yvj+sPP+0K6Pove3ogeXdW8mDnrZ/V0brcRZL3lvQ+er5OlG9t5Wuz4XBzx7627gy2xl8FsvB1ghGXf+/LLJJB+z7M1rNi3xTHTfuOlIjRXI37iDUOMV7373ik00ntDsrZuCX9gqz964A8IyQN75s2swSO/Z4Nu7Vji8pNmbNz1bgpoWr99FoPYIdb51HXrtEB18a7eOGa9A8b1rCC2cYp3v3Q1eAzb793767w72jyY7e//W3//Z4ux81R3+1P/wP2ano5Ob9/7D/+q/WXv/4dNXP/7v/vTfGTx8cn7lxk/9/Z8ZHB7Next/42d+tnd4cnj73t/6L//rrXd+ePzRV37y7/7M5js/PL5x9yf/5/+l/2i/6Q9+4n/6ubWHTx+/8CN/66f/273X33jwqR/7t//Of3/59e9f3Lr7o//rL268/d5qc/PHf/YfbLx9//jFF/+d/+7vXf7mt9//8c/81N/7mavf+u6j518Sb37/Q6dFGDDhh/u9z3+OnRwB/JfjRREAoPPFL+D5/P9Zz4dpGL331BmVRMwpZlXbzaluY9WW68OoWrKmrDaGrK2w0zJPcHBctypLqDeRblebG0m1SI0q+x2qGuK0yRPoDdVKdnLqTbycrTaGsWqjuqp7HeY0s1rHAsEQqbbNUmRN2qyq9TVerYSWzaBHrSZW6TQGDPNWmxhDjKJW2xgCiGKjDeeWEWZcECSwwKR3AnjOuLY6ZYAJ0ZQ6jkLKqDaek8ABldBy5Cil0piUeB5x3agodRERxltKAwtcgiCgp0y01qbUiShuSh9FNqZYQ89Y4J4p77l3jDJrIOM6ZswGjKjNAZOeEqxTjgwGFNqEMS0DJjpnVFlIEp3h2ADovU8xszgQZOOYqxYQqrsxkxpBanJINAYQ2gQhCwCBOsuYVQhinTOuNcHUFFAozyAwCUMOAopUKojTCASdYq4UdcikAQFMgrNFAoGnzpk0wt5iT0wHIe2Z9TqFNEBsnREUwcCcMwklwXITZI6Jh0IqlZLgoPDGRBQgQBywCUYAMGVtCiHmXGudMYgxV63JEsAQscDEBCFEJDA5DABTZXTOAuXYWksQjDCTGggIOaHK+IQ6TCNZSyFMTIlSlmEQk9C6IDAUlLbKJMxxGrel48xEDGsbCIECkUbjCGlGYatBxj1nXDWO0pBw3GpLKIgxaw1gMAjKjHWcWMYjWXtGXc5JIwHBISVAek+wSyOhZUBEFyJSOsBIp4gZTJw1KaIOQBRUHGNvAKYmZUQbELApIJMBQ2RiSBxEFOkspUaxEHTOmFYQMVMgojwOzkWQGo9gMDFlwWEPdE6E0tjhNqfcQmaMjjHxnkBvE4ZACNL5LqXOs1bagmPvibNWUIAhtdZGBAXLpAkd5qynxvoiAgBRI3XCASdBWxsRRABpNexwhDDT2hYiIMR128axpxAq62MKMfKt8wWFhLCm1oUAGIm2tlEUKMbagYRCGJi0sCAKEGalFbGNSWS9IyREXrTBM2wZ58q7hNhIRE0FImETgk3whAYRmAKBBScIschzBBIR1yvHIp2JSBlAmYsA0xBQbwUWxjmGTZQyqxzlJiNUGo+JiwFvfWDIckqtBzGRcRS1FaJEZ4RJBXwIOQ3aARR0EhNnoA82E0QpBFDoMdxoHoJNSDAOImjiCDuNvPOF4Eo55XyPU22x1jZh2HkIgokIBh47ZzKKjabGhQ6D2hLvfC6wszBYm0UAAm+9TTANAbUK9Bj0iFpjchECJCC4GEMMqLQmA4EwrrTKOCBMyNqkCRSE2OAiDAkkLXAxCBBTqXVOAxPcSBWngSNmgxEUQc8VtEmAhIlWm4J5xqO2DrHwDCGDXUQQCVQFGwdPGDXAxBREsWgrG8deYKG9jyggkMpgI+wixlXjKbM5pdICKlyCIx0C9iAi3ALHsI9jIWvPhe5EVBqIqU0gUThQ6ASixjlOdZpFsoaY6YwKpTGlNgaidZhCGzGiXRBYxYIZCRDQGRJSAUBMTiPtIfIuwtwFR5ARhDoPIdMFJMoT52wCqQ8wBB1xGjzxQWeUG00MagtEHeTaqJgiGyhwJqLIewyQSRF1gSinCgwBZ1brjBEAqDU6TxAKyCOdYOwJbr0uYHCEGqNzDhHGzrdJShDAFpoMMW2oxToNCDIuGy2YiVhUr0wiAqfpfNJ0coJAtpw13RwhINoycOoiltQrHwuAYV7OZb8DvUmWczXoAoq5rB3DLhbpcu44AxHLF7O2WyDgMt2qRCAMhay9YCpPktXcRdxyEi9mqlsgFKK2lFniGYmaCmCospi3zYdPixBC6F3y598V998L/yrFCf92rxcwXvzNf3/8n/5nUKkPF0VY/o2fNP/xf7Tz9pvzSzsewMHT/fMbtxrONx+8e3b3pY5abnz/3YNXXhG2LQ7Oyq21RsTx8YkZdtusf/M733n/tU8kutp64+2jF18KwPHRBOUiUJ7vn1S7G4vO2q1vf+vBqx/npkmPTxZbOwz63v5xM+zVRbHx/gfteudsuLP54L3RpWsxcRtvvje5fjVGEp/VspPOtzbyQ9XmBKYm2zfVIAqJ60wuXIDLtQ069YUqF5uCjbhKoM/c4Pi46nRUmokLCyFohphMQGT0Ykf0zyeSM5VH6aFrUhZ6gF5YBNBkN1u/OOFGjTY249PgmF9uZmtPllpg14fxfEqVOdq51h1VUavbzSAukGZkst3rns+jlbm41k1WTXGuV5egmevk8GTx4s2kQrRWvNNKKegqjG90+elp9sHp9BPP9+Y1Ksl0L46XlpdSbbBwMYpOJuOPvSJa3T9sL26lnXmJFrjaofz8Ak4X5vpeCHFyIeUuVCDuH8qLG3F2cYqOps2t3chxXHmaNzVMxcjJXbQSxdb96WxXMOj4xOkCe+GK0Vhm6SrpDZ7K0ZVkoz5xo3yxxQnWXkLIYYAkurC+cGUe9x+r2SURgaZ/ejbbWLeARWNjEyRTGp/bhC1Ga322CG2KBDLxoV+tCSIcH1kXI52F7sVEx3zaGw4/kLMtzphM9l3Z5z5HaCSJlPpqDx82LiK4A8LC2Igx6tFIBg/9duQnhlWNvtrzSwtxaPud7MGZ4QSvC3jeAB2qG8NoURKpXC/ypxpD314aRI8uAMOdvJ2hDlS27RfRxQJUzl3Lov2Zobzd6pBVg73FCbZTI0ppr+V27ulCyhud7KIGEuohgS2Ip+biRjcupxvno4OrV4pFgxZ4dikRtYvnjR5gEyCRDiZBgnhwKC9uxEVZ4Qkqt2naLkVdlf2+sVE2UnIztDhZ229H18XG/HSOBiaFcWVxCdQAO0jyE9XsQIXQ9uHR8eW9WFo2htUWo9rwmZ1s9wDx2Q/P6htrEZTkUdnudgg18GQS8tgXHXxS4RzV/SJ6b6KudIlv6dnUDIoEWrkkIMK2K+BRhRIkB2lycK7Wuogh8nilNnKSQHzWBIFtj6KzOWa42ViP3xvL3UJ2s+4Pj2QvJh0Cjkc2TtT6MNmfEuGrrX703sSux6v1teHBBXHg4nK3c15GtVns0ny6BN6Ot3Z6B42jwA4Qnnre2tG1TrQouXIgdXyKDGLz7aR3VHoYzJBGy1nSyKeXbmSTOlq55SXaOW00YrbrkQreB5QgsMLJUlV7xDcsmdvFHlkbnXsAy35XTAG0QW6QsILpXJ/d7PYnI3tu7NWC1hosNBgwTRksNc2gRHHycLK6t56uluFMub0MBucNzFHVhAicSbgXtSSK3x+XdzfydqE18RkjlQQzE9aFRVQcLvxe3EYJHZWuH0VSulOFNhk3Si4x7BGVCDBTPA11VGTvntd31iJVhWNltjIgcHRhU7wcDbudA7/YYITKwfHpYm1geSwujBeo7eDozCHhln0hFk7HiBKbHtiqy0EW2IV1gpZrfOPsKBA0XtvofaCqAa77yfrDhSyIL4BvXABYpaI4bQi1qhviY1z36KKbXD54ICPR9NboyAEA5ztJ77ByHM+2s8H+MgAf51VTp6wJo2t5/ORATMryleu9C2kDmW2nndMaOms2WDg4jaar9qXroaXp1Myv8O55ZRxV64Q/PQytsrevgIYkU9VcQlaKfGKml1n+9MBWylzfjkoEDeBFVbpOPNHNHlLzNts/W7xyq7vQQZN2iPjcYBnaDWoAXXvaXlyPNxenetFZbrFYL6PVqu71NEqzEyk3Qc3itcft5HqUqVUxmc421oPGfGZ1FytKilPDisVFZ21vf/94d5c7x89Auc4ptHzqdAdb5tbOLlaDzjLpbTxsxleE8K04CfWQE9Lms8mstylp1D9ozJpb5vn6/XJ2RRDsuk+OAyfT3e3+o33E4PH1W2sP3l8ON1Sne+N7r0+HQ73Zz58cB4yPb94a7D9BKEx3r1x7/fXpzu50d+/Gd75dDQagJ5rSUmeml67tvvdDzcXiyu6V770x2d4bb22vPXzPxrFf7ydPT2Pdnjx3p/9o3xM8v7zbffRg1R3CXrr24KlmbHV5Kz4ei7bZH2wMfu7nPlwUYcCEP3288Q9/Tjx8EOi/kmDh16IIek8n4+bFl223B73/EFGE+vLVTlJ87LO/Ua4N09PzFz7/xWVn0FA8vbKOjye7hwd3/+ArzgcA4Ud+7bdkml+s9aqcw4B2X3/nzpe/JnmctNW9z/2hM/5oe9t2qNFu68/feu4rX6973fR09NyXvmoxm1K+uLyGZ6t8VH78s7+pkoSU7fO/988hCHNIZjd2wMV0MJm99NufJ6tSF/nLv/o7gcDZ8B45uQKVQKAC5/fgnIVi9Jl6fwDkE7dBD29RSYFr3eQOkAUN+58Ok0F9sS826ePnQ9u1SYkPriFdxOT4I3baC/XU5e7kRbJI5bDkD+/YZi0M5p+cP9iGZqQzN76Lq47MAj28Sma56ow+0z7dBuYB2xX7V0idYr8Ekyuu3qoGOt3fINOdcsPE+wM024HuRH/zG/TrX1VbVxF+lR8PUXoBzi6F+fVy3Ynf+S38za+btb3EXXeT6yoGaNUXJ7uqr/Bv/wb6kz8pb92LV9fg7IqjrVgKN7llUQO/9QX0+S+q65fj8g6Y3XDJHIyHaHLFuQV+/SvgC1/yeYGST8CT61ichfM+WNzx0dzLhB3f9A4EHMjTe5rgLfD+J+Sogb5c7qGLW9a7op4249eCAbLXXqzteQzjY4xObgWqUcPwxc3gQRc8/QwsUTM/A3v0yb2AcbAentyJzHKZ4LPtHe9R56KF56+QxurUkQ+eN1D0yZPX1AgFOW824dkdawgiGh4+DyWpenLxOtI1YciMHsfNjIjETPZ2G5Gly/n5G+lqysQgjN9iuqQ097ONjSbOwULN36LyDPBLaPY9UU4420TLbl8nCRs300dRPeWuH83fxfIU9vurw83rdVbApSrfI/WMCaLKp2R2EYMeqdYHTVIk1Wr6PTSfJLwH5g/ZaixYDvhqi55e0p0VOR2C6bV6YJ+r7n/E1OcAgumWH99SIsCqw4/2XFLOe+l0bSMpF/B0C0+uBCxFRdzFXUfUDXr8UT2+QMIebeH5LZcs4XSAJlcdqF1uDrauU62T0wKc3fFZic5SML0VxHK7ffwJXc6caus9eHIbgBLqDB/dkYUzo2r6fgSdp04dvVPYxrGsUgnxMKhjMH4/Q8j5KkweJcEC6BbtbuGmS9Swg7d7HmAYwviHEXQQNqNmNw/Tla7E6L0clA718fl3I2UIL9o2wbaRZsrGH2ROIp2J2evILgNdN9pL1cnUOVncF0A5CNH4/Ui21G+Lzv09V643AxM93sJNn5GTF+xox61O+BAc3SHjwqzNosc3Qr3VdKtV3qnzNJtU+HzPVTtNDvjpDpsMmnz04/rgKjT3UV8c7oFqA+KKjdfcci+I1WS9V6Y9ruro4Y6vL/t4Ac534HILo9EL/uiqWYxxZE7vwcmu7o/iDzZCc61Jy3BUnz7uIOrlCo/ejXlq6o3ufLiZ1KvlB3jyJEbY0tIcP8gJcWYzHfe2EluV79qL/Y4oXHuOJx/EhLiwxs+He8Ra/1id389YD7ZnfvQoiSLZZOlsZxc2Gh3Xp/dz7mpryNG7XUFlO+zMNraoVe0TP3oYBwCg86N3UkADSbvk8VVuW2sIGN0JlkfgyU+4adxMDsgl9sFzPkQBGnx8gzrYRGa0sa4R64yqcPIyqpnuSfHwtgzdOD39VHmQQTWWQ3B+F5q4TYF4fB2WQg0Wo8GuFILNCDu5ShTCtgmj21b3bWf1U8tHa758CDaixzcMWLPMicMdYFKVOHZ8iU76sDgO+7d9tVcXK/GbnwVvvhHWLnN5xy522syzsy023VSDBfmlXwbff8PeuItX98ByM+CKzvt+dtnQJf79fwr/5Jvy5RfSk+u+vOLjObrYAssdr87xn3wBfP1P/d4u1h8Ho8s43feHl0B1w7Nz96Uv4v/zG6EYUvxiONsJ0RJNdtDoqu9M4LiPR9ccaPNVU09e9UHv0MPX5NmSsPJsG41vA7GEixyPbgKgN8DTHw1LpeqJvE4O77hYwWUMLm7H/qwI5592skJmVW2Sk7vQtRZxvH9Xi7DrHn7cLWvTrJor6Oym9QDDAA+ec9b3o8Mfa0/KJF/Md+nJVYJqbxJ8dtNDitH5a7/0WT5f1J3e87/3hbiqpiKZ7q6BVsbj8qO/9Tudo9OTF57/xC9/NpouD27d1sRIF4qj8fNf+VpyMRntXnnt1369u394cOtWi3w56GdPzl/+0lei2TIQfOvLX8+Ozk52r9R9HrTCKrz62d/KzkbVYHDrS1/LzkYrwSa7a14b6tFHfuU31h8+2f/Ij3z8134rPzo73drm77z1YaIIIUTGFN/4Wv61rwbG/r8k2zMLPJ93vvAHHz6WEEKHscY4BBAo1YwHBD0AmHKAoIdQMQoZ8whrITzGAEJIGMTYMqYp8RAGQiTjPgQAoUcIIAgo1Zh4CD2ELcaBUQ+hZ8xh7BFSjDmEA4SSMocwxNgjFCgLEGqEvIgCgooxizEAXmPsMAoQaIwC9DAEi5BjAkDwrDIFoaAJNhg961zSmIIADMaGYAigx1gjHAD0CHtCAwSWYk8gCMAS7BgBEDqMPMLee42RJdhBZAnyGIAQHICBcYCgJcgQgmEwhFiCAAAOQkWwR9gTqBkBMACEQiQ8IRYhTzGAIRBkKfIQe8E85yAEj6FhJCBoMdYYgRACY4EQgBGkWFMaAHAQWkIhRoHQwBhA2ABkMAoIegIVJQB4gEhgHBDiELQYBQghBgqhAIAPQBICEAoIaoo9hA4gi2mAOCDYUgpRgDBoSj2AAEJkLfQewGAx8BAGBCSmHgbvg6PUIQwQNIxYhAIAhuCAUUAYOwMACAhKiALBAQBDscPIQagxDpiEADQlgCAQvP2Ljj6gEXIEAwgMRo5igCB2DnofEDKUOEYChIEQg0mAEHmHgkMhWIwDIxAAA4HBOCCMnEPOAggcwYZgD6En2FMCAEDeoeBhABYhSzCC/xJoBJF3MIQAAGDUUgIgCIwYggOEFuNnsHoCLUEOIsAjR2hAyCNgGIEIeoItJSAEAAG2JiAECFKMehgcgJpgiGCA2BMCEIIUSYQ8hB7BllAIQ0AQGw2DtwgZSgKCAAWNUYDAUeYJ9ZR5CAyGAAKPoaH4Lx+nCAWvKQEIBgS9EIFSAIEh2COACZSU2meNzBhDCAKChiCLkAdAY+wx8hBCjB0EACGNsAUAAGAJdhSHACAmECEAgqYEPNtq8hcXkAuAKcDYUOIQxMBrSj3BIACLsWUEQOgJMgQDCD0mnvGAiCXQUQQC8ARqTAJEMDhsDYTAEvxszzUiniIYgsfUghAgdORZIAqWIEdwwBg5B4KHADiMLcEAAk+QJhggEDCxhAaELEGWQgAAoFACFCB0EFlKEcEeY8dwwAgEgJwFAASCNSEIBoCQIcQCCENA3gUIMUEGIw+Bh7ClFBHkESLWQBACRppgDwGAQBMCEQAIPfsGHEAWY0SwR1AT7DABIGBrIYABAUNJICggpDByEAIALMYeQ4iApNQhCBBymGpEAQiGYksQgNBiYiACAGBnn6nKWUo8QgAAQ4mjJADgMPEAAe8UwQ4hD7ElyGGIvEfOwuABhJYQhxECQFPqMAQIecYtZTAER5HFyENkKDYQeYAsQZ4iGECg+JkLBMEDZwACS6AlOCBkCTEIgRAC54FggFGgWBMSALAIO0ogRp7SwBiAyEKgMQ7oL2AFwQGEPecAY4ugxRBACDFUEAYICdfZRQAAIABJREFUAuOB0oCgQ8A8cwqCNEEBgPAMDhgADIaQAEIgxBIWAIDAaww9BAADSYiHwAdgCXMIB4SeeVAAwBASEPCUeow9xA4hiRDAKABoKPYIWQgNxoA8i1oUEAhCsM/CEcSGsGe11YaRgEAAQVESIAAgaMoMZQAjy5ghJCAIGQWYAAgUpU6If/kzIgGAgElACASgKXOEBAg1Y54JGAIAMEAUANQIWUICQppzSxnAGFAWMAEh2Cgyz1Dg3GIcAMCMQ4gDhIZxzRgIwSBsMf7LA5QPTxLCDg/yr/8L8FdNY8CvRREAADpHypW6fMXs7P41rHb/N2kRfvqTtkhn1y/V62ty0Dt/7rYV/NK7P1xdu2F7KeDi8NVXfMqr4XB66wriYvD0EKTx7PIVBuzjT33SFlGg7Oi1j3IKiv0TwUl7eafu9RbXL49u3uROP/nka5zDzQdPdbfnu2mzub64urvc2TKdfHbr6nJrZ++td5pLV103dZyffOQlmLLJ9t7y6qVmPYn11PQrkEuhZm596TI2qfA5znRHEDwTdCLXHQqN665sBy8W/iAb+kzQcEHE2PQ9AXMq5qshmzX4BCY6I7GamuEKxh6jCeajss/KBk5CtOgnLNQwneiuT+UZ6M1Ah4waNgrRvJtH4ILTuR0qElqbL3SH8DCK8HkzhAyO4jDTu85tXBE71+XdXREWhF/gTLlIEjJuh55urkfDG+0r1wReITRv1xQHK0rPfNeGnV2+c6N97hqms9jOmx3FUUnw3A1bWvTBjefA9T2QKhFGbiBNolI1bi573u2ivbv23hVCWkxOSd7q1LIwtRu6TVHRnpv1JRKG+9PQras4HtVkkXbqHk6bcbsjY7IIvjWbK4ZcNq25UyAzFE5hUclByNpps9M6ii4W4KS75jlk4BQUlSsMC1OWzWTGumcSEY0Sy/VUbdQo8jSc4XReJXxSs1mUl32eyJkeLnCkhJ7ojYpwn0YySSTci2Iok4HlBWBny8S0KMYxbztZhdZZymWSarRO6bRNqmXoR303j7YDzlAsZCdXZispLkbJqoY9wrAtuhKus65bpEMj8oBWOlqWoccLVsaRwXs8A23aN2CNZ+NZXK1wgnnmu7wkW4QJkwiFtomwcygmvuOhqDi8aNdD5dCsxufDocAlQQs1bAgoGTlxPSuM7IwqJJxNZGKm9a5mqKJoYdaa2sDjli2Ljos1DyO/JlXusvai3VG91YwtAWYGM0nJme2YkLbCj/1aO00Gy4U52dzhqCRwZjc0JJLh03YNkh7qyim+FonEZr6OrmCcMH68JISS9TgnVTIIfjPK1IJdFzwCYn+OkyjKQBTreOBJH2ekiQY2bPWiJ3OUp1EXZW4lrlKSwJTXWVfDjLCLlnPmt/LcrsROgF061GN2CeOUsnFLDICbSTcs0jXntuLMVOmus1kc+2PCLto1FIdzxhbVEM51NILRqohTNYKdacgBxAvKxvU66UwXnZWEmUZQ+XSu+zY1FyCdhB4eKXKh6HzQF2hMyVJuauErmMxCIZNZkzQ1EiBElQAXZk0DWnO8aDZ8o/AJyKsiYX4OowvfRZ6XHIzrHVzgJqVK7ABBdZ7UZJ1yo5PzGRMed3HHLNDtJKIqIa3YxVSrZLJMQIMKktBGbBM44B05wzfjtFqKccmIoZHvpjUZIFaEglR8G3uM04MxyaCIfYprsYO4sHGq6QZhwURn8xg1YT3utjN+nUXcJKgWuwRyzcCUxdN6iIRayM0mRHg6D0eddZBgFs5wMreFYWEG85XJQmekuK1A5iM1NpsVFI7AEUjmVcGWFZzSZNWPYr2w/anJQ94eu7UljEA6rWOtbe4iP8Lpyg00tbUezNuUNUtzQDqhGxE8JXyuBia2Y59XcgBSNYLphKZaC0npuN3EfH2L7dxS97bjsARiJgcusWOYXIQOBHtX+O4Nd3PTk5KjhdxSwq9gNPMDS4theO4FeHk9RLUAI7uuHJdRmLaXIS/WwK3n/bVNikrET0luVKI5nNhN5/auRcMr8pXrUVhCMbMDR+GS8nPTgzo3WXPR7KkYLTxq7WYjITlr2TzthNzyMPH9VnZD3oyrPWkhOq/wqDsARDN8FjrKp1K4CenOzzpb84k8XNtitKFuqjck5oqBU1jUKy4mNZkX3bqL02aiNpeEttxP7FarKL2o6Uh0fCJTO3WDsu3CrJ3ojTmIaDlcq/a2F1d2dZ6Xl7bn165tvf/I8qje3SIMH//IS64TV2vD1eXd5ebG8OAwC2F24xpB4ezl55fbG4SisxeeI13OZk1SVoubN0AiFnvb09vXCAwnL76w2t7cfu99jKjb6C3XN+XmYHzrukrixaWd1Y2rW4/3PSBukNskOX/uth52VxubzcbaaDhMPkRahBBCKTtf/MPsz771/z+++ssEC0CI6hp6V3/01b+GKeS/QYtwHZFP/NI/brud7sHRS//sc+O9K1fv33/xc593UdI/O777+1+k2sDgP/bLvyrzvHN0/Opv/B/t2mD4w/u3v/o14FxHNs/99ueYUgjDT//CP0ERTSeT537vD3UnW3vw+M4ffYUHKGT5sV/5p+ly6dLotV/4JcdZMpm+8Lu/b9K4fzZ65Z/9vmhaDP3Lv/k7+WplEv7qL/06BHbZv4ePr5GKcj0LoxfYBNPuzD/8lG82CD0j+7eEJJFc2vFtXMUMLvXJx9NZatMz+vBl0HYRHaPD27iNqCzt+fOo7HO8Cmd3+Tiyg0n8/t0ghz6t2Qe3QNnlehEm1/Cq55hkR9eji8h1z8XDV5r6ms/q5Mk2LeOkHfnpZbhcl72qeLwBZpd1f5U8GqDldmLPoLoK65cAtXi+Fh2uc7GPj/fc/KYpVsnZJaRfFnDKz5ib3Q5EonIYH+6E9Cw+vQTMa7qo8pNumO7RsBKLyI9uErhEaocuPgbSUXTY9bObMJ6S4x6aX2FqmtiOW/0owTVa5eTkdsQP6OnALu5SfOHrlJ3cRMpjINHTlyFso8b5w495ZticodEN3pb9ZlGNP8FbA6HmD16h0NJGwaO7GNR0TsH4Jm0M12V78hm2sDhq8QevwOBJq9DJndzOUYXQ0T0iAQEtPHoxLq1nK/LoFQAgNs4dfxQZRysHL25RCRlqwuGLvERVp5x+B/oap7a6eCjUBAhuzt+M9GNTbOvTPxPljHU67ehNrpYwDmp8n+sLjBM4eZfap6rY1hffZvN5zPpw+V2nzkIK2+lBvDonNEOTd4k7dMPh4uRb0fQ04gO0fBu2M1K41eIJXJxw2ifTN5B84ItdOf02mE2SIlPzh7w8w1lk2GqXH++BdMQPB2F2Q2er+HhDj+5xP0/G0E6fg1ihphcdXLLdRbw/wKe3cDqlpz04u8rVjFfcX9zBoQR13x99BESj7Lzwk1somtGzLppcTfQsL706+Qh1K9Sm+PguSSb8OA7T2wyekXFXjV9lsmR1CKf3mJsTneDDezaT5mA2fp/HxjBZHr7XxbOax2H/uz0YMG/r87cjAgxZ2otHCapUgvXTHwxEaYp49fiNobeBOX32ThQFg5bq9H4nNIBTc/xWCqcq6rmTb0fBIR7cydsF1QZJd/EgQg0MERp/j4CRjPv25P/Kap9h6GZvItZIatz5g1gtEd5G/XevhGbTFavogx1cZkJNzfg5sNh1rKWnV6Kz1HXP0w9umnrH5nX6aJvPeqk+B9PdsNh2saYnl5Jxz3ZO6Hu3Zf28TZfJ4RZcDiIzpeO1MN8l0YKeXBfHW3p4kd7fc9UlzMfkZBvN14WegNllO7qJyQRdXCXjXZA/LR7vmeqqTRbg8eLkg5xDrZbw4q0o7Wn1wFy8G8UdWz92k/04NiWr/eH9TGCj5/D8z0U/WaijcPKkyJOm3ffjp3EWpF+okzcSYWvTkpM3kyTTZl+dP84yXrpzdPYWF1YjZc7eSTtu5Rt78HYvyow+9Rdvi46Q8tiMHqdESuzD6Q9iBGycFuyDq6mtSenc+C5pIAW1OvqxZImdmJJHrwBHsW/gyR2iHWkcOb7NGspwHY5eFHPkiwV/8HwAGUQOPrlHaoSlgeObqEksk+LpbTHDvphn790zuu+Jiw72ROPjem4nd3GZmcygh69Ey45Nz8V7N00YQKSio0uweaZFeCU6z3nyAXpyx9aXXVbGZzewuxXZETnvh/muiyQ934vOh6FzGO8/58OPYHHBzvbAYp37FZ0NwGQPkwkuX6DlCyDfjx/t+uoKFBN6tAEXm5GZcn05VC8RNoOjPTq6Ekf3ydMrtrrO8BkZX8b25djN4LzAZ5coGcHJHhrdQNkRPemjyTWhZp1SNeOPMlMClYSD13Bc8QsaRncZHJFxASbXmWxY69TRJ6mqsad4/wXISjoXYXS774/9PJfjTxKliXPo6J7QVbAA778IWEWWqTl/lfglmTM4ukW1ocCCg+epkgEg+PhjNmV8RdHZtaidgIaji9vQYOGffPof/m/RcqmT9KXf+Vy0XBDjXvzc59dG48Xaxmu/+qvDh4/Onrvz6X/w89FsIfPitc/+Rm90jiG+/eWv9p4eLLZ3Xvsn//vmB4/nty9/4ud/tXt61nR6r/zu57LpnBl184++Onj/4fTG7c/8/D+69NbbRy+/+Klf+MXO8anp5M996audw2OM4b1//sXtH74/v3L51V/59a133jl5+cUf/YV/XBydnG9u4/vv0emHhiLkR4frv/iPQPB/ZeL0/+IEQ4jffDP9sz/znH9otAgxaaPMY16n3TrteAeavDPN+6RVs95a1elTqWe9Nclih2gbZ6uiZzGTSb7c2qNVu9jamyc5alRZ9JsotZQ3LF6lHe+hjNLZcIu0elr0V711oO1qbbOOc8ciGaWrwXoISCb5tL+BpZkN1qu8h7VdJEUT5wZRw9MmptgZh21dJLRpTERLHkGzRK5ukgh65axbphEwBoBWRoQ1ZY18nXaoW6FgZMxAUMDoJiPYtg6aMmFIyRDaJkqBWQWrW04AaJHVVc6Rs9C3KiFUtR7IOsm9WXFVtRxCVwWtV5kAxkBXtUL40BDVSIaDcKxuqpQrYEm9UNzDoJGu6iQzwYi2bDn2qCHLRRVTyxBVjY5QCA6ZVRMnBmlcr9qItsiwpm0EcNAi2dQRdTAQNVNCtBQS1RiOXRxIo2TBGgBxtdQUAmixXraMV4KxulIRaWPCVpUVwDBKXeUJWIkYysohoIWjTVtHZBVHtKwMh5JTopcKuDqOsZGG0SYGTBnFXZOlol7JlDeEI7V00MmYYdlIhJepwFI6qiQntG1qhtooZa4MOKiYYFV56Nrk2QAqX0eMtI2D0jCOoXfayE6GQyAUhlQwayD1dVJEvkHWyjj2BFCtdcaRtwA4m0VYG4JdlXYY0twaF3PGAHZWFmkwhnllIiqMglaVWQcCExn5TFcEStV2Uuw9Qd7EnAZHoKrTDkOGSCWTyANHjFEJg04juapFZIljbaUS5ogUbVOnWDNCZNMIZIKHqtSYWmywqhtBG4Fo3TQZ1hQS2egIexy4XSgRlZSQutScOKZp065i0lBKZK0TahHActlg0iaCtU2bpVJ4oWQbBxULoto6SSWCWC4MRibnTCotsOwUrG58HqtIpL5FOKgio8F7wVUe01b5hOg4ErJpI95EGdMNQ0FHnBhtELK9lCiFU2JTwaT0FEiRxLb2AbRJhJ0GCOhcUKkcAS6LqFSEwzbJBZAE+JBy4U3AqM0TXDeEOMmTYJbAqJqRgDRWui4EBAbZWiWEGBVCW6eFNRWXdcsRADWQcpkKZx22leSA6tbrVZ3kAUnS1iphIUhsTJ0R6I13qqHIWwPdSrLYhwartk64IR4b3RQiBINCq2KKg6F21SRFhQGtShWzwDGVUqWRQ1i41gihI0atDhFDuaBV6wdZQwltGhdRRCB3jaKiiSLWShMJlzFWS5UwnSXUKFOknuHEtY5TW6S4bXWWy0zwVtqMOsaolFWcaCYi2wCCbExp20rOQC8hlUSFMIJRLQGFlmKkWwXgMo+wUg4rKSivFjUFbZIzVwLkZUyRaT30TYqJbCXxVcRw2zqomihBeuWcawUitg7I1wUlSgfYqpSxtnZI13Ea9BJbKQVAujbBrTKBGxlQ03IUyZVxVZPmIDTAGhnjv3hXhJhugq6rtONcK9qqFQiEGlZllbIAAvJaRhg5A+2qTorga9yUbUwU0FjKJgLAam9lEzHvLdHTJikUdkS2RhDPHdGqzrm2BslKMky8BGpZi7TmhNd1m3BNDaqrMqEWIxBMnSYAaGoWrRCWa1a3VUxrzkhd64hYAomatwg2SUTbpk1iGTmujIy8TGImqyaOFEZYLQ2BRiBaVRVnZcp4XVmuNcWkbWpGDGfMlo4gGSNWLy2DMoa8biQFDadYNoYGxSjWSw28FJA1lY5IkxJeS0d0E+WaxQaSZdHTiDnMp2ubwIYyypaDdWhcm+Z1Vqgoc5gt+xtWJJpGk7UN2Oo2yRZZDztQE1ZmHUmZh3TRXXOQKICnwy1kQ9PpL7NuQLRK8zrraB47D2adgQbYED4fbgDjVlmnKnoOM5MUVdIpkzwA3Gb5X89C8L/6AMvo7h/8PqrKf115Ffzbvd5f3lhbfurHRv/5f+GKAvx1WuS/rouw+YmfQP/Jf9C5OLWcBIRI3TZ54RDuXZzPNjbJsqYPnzR3bmZYBe0DJx4hUisoUB1lnfOL2eZmqkqyaKpeVzjpZWDIOEJ965BAVZz3LkaztWHiajhv215XOBk08BgpC8jBKRj29LDXG53P19YTW6NFo4ss02UVIkSRFFFU1opzQhyd13XRiYB0LUAoeE5MDTK1VL0c1tYIjqlny6aJM46NVQADHwRxGsaqKrtdvqwNY5gFspJaCEq8VQAHXxWFqOpY1mWvI1bSEtymaXQxtYyL2FsFmPPLbiGqOm4r3YnZom1FtOyspatZpM2qk2Nji9m07efW4aiqVoM+WdbQhoI1DeBY2bLfw8YWs9lsfVhUc6/BqteNWoWMhhxaj1gjn9lks/lyfZBXC6ehKmLRttYjxIEBjNdNSEhDMvH0SN64lFYzr4DOBNPaWZTAumI5LWvTSSqWrZ2drNYGkam9hoBCjzGuNeahpHlyelZubW3I86bFbbcrXBsUBBRYSlGlCAt1lOXz5apTxK6FS9l2CuGkUwCTYBhDlcpCPSvW0uli1evFvkEr3WYpC1otHWXA5TFdtYS4VdYtRpOy241CC0urYuE5w40Summ6BV21llLMAFm1iguOna29x5hxJwFP61XV79JV6zFusyydzSyjhAanAfVhleeiaeKmrPpdtmo9Qk2epfO5xaQDqwVIqTZlvyfqmillYoGlNgGbLI6b1oGAObAWMSldxo3FvG2bXpFUtXcBCGQDYrUsez3sbDGbz4ZrWbW0la0HfWElURpyaAGmjQIJaXBcTKfz9bW8WXoJVBFzJZ2FiAEDGa+qkLKaxJ3JdDXoDcpR6SKbx8wobyDioHWML5awHzc07szni143laVrvS0SqhXQfpX3AA798/NVvx/7Fixa2e0IL72CAUHpsTub00z4jU4+mZS93jMbnWeFXpQhISQYxnCtCXZV1slGk7LbjYMEpdaxoMh5BTBwJo5wpSgyZd7LRpO6U7Qkpg+e+l5X5JhYGxBpkjhdLAiyZdHLzsdNpyiTTmcxxs5XRcHbNqlXda9Dag0AqPNcjGeOMB57pwE3btHt8KZJqpXuJXQlFeFNkefLlYUAcWgtYlKt+j3etELKuptl87kkEYgQqbVFmPBgLH4GWauIWC31Rj8tlwoLICBpjQWIcKA8jepqvL6ztjwNbZDdTLSNdRBxZAGmjQQpcW2QJBJeEhJCY3WeMKOcgTH8v6l7s2hNsqu+c+8zxPzNd8p7b2benDOrSjWpBkklUZLQwGCaHuweVj91r37w6td+cNteRkhgBstY0EI00Ab3YjQNGLBACElIgCQ01KzKzMrK+c7TN38xn3P27ocvhW2WUfPWxfcQ61sRZ8WJ2GfvHbHOjt/5F7kXi1mJDZ15See4P1pcSKqZy51txtrWbFBorpTvjWfzNu2j4/HSYlLNXOYw1kGRzSDxtKt9359mkOhZ0GwfHE6XFuM6pdzVcURKebOs4dJxqxeOZmm7HXApp2WRNDyoq4nTiqkdybTWUM9ancbxKGs1fajErK6jQEmikkGIPEniycQDM211GoNxniS17zeHgzp80EagSJtJMhorsDYOZX+WJi3XCNqjcaWV9MDVIIizVjMZT0mJUXuhMzhmhBamE0h0babdrl8WYZalC93weFCDVy20kzQlJvHtCKKGVzkvytK022pOxpXwOZKqNJZR+ViRDvKMGl7JfjydpQvtxnRcg8ehUJW1JJqYDlVX98ditVE6lUxnabcZp7MaNIRS1o4dUCgr6bePjidLC4uzw9T4davhm5IMog8VeXo0ld0g8+POYDTqdZI6pdTWrcSzFdcgPK5U4E1mia6OGsvto/54aTGpU85dHYeaLdUsFZd+GA2nnKhZ2OocHE4WFkKb27HhRqiV49KZIDSe1+wPIFbTqNU9PB73uh4bLGrFro5DkdU+V9N2r3E8yBvN3E+Sq9dMt6e6kbJGoJg1mvF47HE9bfeax8d5o1mFUat/VPt+C7IJRZI5bXca46ET0iV+cjycNdt1ELbH40oJ6aGrQddVHiWwO2AUsL7QGvXTVseTFnNrpZAeWiMkUb/g6Od/Kbhz6+8ARcgc3njj5D/63/4aOfifKxF+u6Coj47M6np54eJb6kusv6lEWJ4918bgQ//sRwbLJ5P7++/+l5+6f/nxpW/deMen/q/B8qm+McPnH9M7R52to/d/7OPj7nJ4MHr3T33qePVk6+7BM5/8xVlrWRfVu3/8Z8ip3Is/+JGfKINYD2bv/OQvjRdPRFvHz37yF3K/SYzv/bGfFqWbNBc++I8/VkKwc/ZceW65TOuVV9981yd/0bJfhPH7//kn5CidLiy87599nBh21p+m28/atMkitfc/qI78fKWEV7/LTVfE4u6HsjdP6Wpz1JztfZinLacze/e9+jAerVXRq++wk5NZO4Xrj9niJEFW7b7DjldMVLhbz/nH3f7pOnjpyWJ6frpowmuXTHaKIC33n6Xh6rQx/W/g3mXqf1OvB6+/vZpeHi3Z6Pr5anpWuKPi8B3UPzVYpeSNM27zoaMNit9YrY+fQdwvJhdo76lMF+73fzX4xV+uH7nojt7H+08erdbx/bVi/x2kB/J4qdp/Vx5RPdwIblycLBf63hm7/fRotdb3l+r955w3lpOFcvO52ivMdEPeeHS6VHp3l832O6rOpP6tf6t+599lfksn76zuvsPqws7W4NZTrjWgrRP24HmKD8uqw7feR4Wpglh/612l57lS481nc0+u4u3vldmsmtbj1mT776vCZpEvXn1npUOuEW4+ZxW7mVdtvp+rioDs7e/F1OWJFK+9pxYhOcm3nlOU51Vcbn8QSjLC0M0PiTHj0uC/o3vKwzvDM3znOQZhKlVvfbcpVB04vvY+VYbHC/X+F7z+sBH47tZri7ODQCbu5tcXinvIl+OttYuTZg8H5b2vJuN+y4/crdcXhrsBL4V3v9pIb0u8FO7+SbCz1XTnmkd/IY72G4Ff335jabgVupVo52vJ6E2dnKdbf9rY3+3aU8ngm972ZttbwsHKicPeuqtx64ve+E1PXA4OvqDu3+t5J8TOa8nuZidYBBxeFrceni5kavO03XpmvGb0vaVq792gj67QnedVdoByf/Cu8MalyYr1t9fs/WeK7tTunTR77yYcilmrvP8eK3Obres3n5guVnprwW4/ZxpDPlgp997reBzMdLb9IZZ5nZ+AN56etekpceO7vWyzpNnhhXrnvWzHbBp0+7ucyKt6WVx7arYg84PsxtcXkUCye/0rqzBzphPd/KM4qwO/V5r1ljG22MTrr6xBxULR9S+vuqGRXfH655ZKGzghbn+lRazIiGvfXOZSuhDf+OKCG0K93rj76XBiG1bL21/tgFWO5bUXlquZcnHlLvaw3x/R4t7nw+O6beNg6ytxnSOBuPbiipmq4ly7/cLlanBptFpHV8+Z2UXGUXnwdtffmCazH8B7j9HRi96qfv3tZvTQ4ISN3zhTja9IOiiPnrD9i5Muy7uPyZ318XoafOvhsv/YdKXWt85Uw0cAB3x4pT68Ujcyd/8JtXN2sJHH37pQHj1hu4fPmHuPiWq3xHz/WbtzoWplsPmY2r44WhtFr5+tBs/knUG16a69uAI+D0fJ5p9HvBakN+HWS724Ue6dOT/sLJfsu9vVtW8sK4/H0/junzf9Ho3vqduvrYQ9mm7rqy+t+NrWqXjza8tK0SSL7/xpBIt+vi1uvLgUt8z4wL/+4oonbGW9W19ekK4qLi7eX73stExfszdeXG7E5WwUXf3Gsoeck775pQ57SjUX5dWnpOWyksXOB6CQRpXu1ofUSJml4X9PdxNNb0434NazTJExXG+91+RxHdZ0/X16Fo+WyH/5mdStlQHrG0+4OrHk6s3nXRrPukq99k4xbk4Wy+DVpzN3KgtB3Xzc5O0m3vygnzbLg2uNR/xXn+VRb7pcBq88mVVny8jKW49W+UrRKPXdx2FzvV45clefcsNH+ytVePtcMXrcZTfw//lV95k/HV15HI/f590/OVpPg6uXqqMnqu7IbV2q+4+jP6bj89Xuw2Ujp+1H9Nbl4do0en2jPHyqavVx90J19KQUfdc/67bfVjYy3rnsth6l5b79yU/gZz+XnX8Yp89UB0+BGLrRadh8tGqkbvcS3L6Sn5iIncVy5/2OZlHq0u2/h1CUbgGuPTNryMf0mx/S073KTQ/PVtvfzXbCNnC3v5tcUXJHXH22SBgnTXP/+QB2p9mq3foA2NyQ4jffy7bOVce/+mweCzdK7P33CC+rZg2z+QFjbFvf+/v6uLKzO+Xj3utPj1sNnPp87zm1gWGBAAAgAElEQVSGrKzi+t4HuHYmHH3PP/qRcPf4ePnku37658NxmgXtp37ul6Od43trp/OH12E4qlz0/R/7Cf/u/v7Gpe/6xKcauwfTuPfkL/5q597e3cuPfd9Hf3zplev3nn36PT/8r9p3dndPX3zu5/51eP+wiFtv/6VfW7h6582n3v3hf/JDay9du/pd7/vQD/+L7qs3bl95rFhtzVpJfO/o2V/+9cWXr91/4un3f+zjp7/28mvv+8AHfvSnFl66eufy2/TVb/3d0CJkXv2XP66Gw+9AB4q/DhxmafPPvuhtbbL4OyFQiCgkIDKiFQLn6JPSAtABEoMGFEKAkIyChCQpgZmEsAzoyALM91hAkpJAgFbsaSa2iFZKcMSeR0IisXUMiCglCGmNcUXuhHTaR2IrhROSjUHtAQpQygKCUoACUTAQOwIgKyQyEQpmRgQCRmSB4KSwShIJRGZAS0RMTggWEgEImUEodFI4geiIGBAABTMJgYjMQEgAiIgMICTUriIhGAQBOyGsUgJZK5bIVkhGZAAQEhEIHDOCFg4lABMQEDMACaGVswSslANCFE4IlvNpTYdCACILQWQlMiMiMAI6QMtzSomRCcCxEIwILJyUPEd4pGSBwIIQHUoBBAKUJHZkhSQABCSpHDKRI2YWyIwOFTFLT1gUKBiZSxDEgIiAQAKZiIhAIIAAqQiIiQCJpEIgAmEA51OyGg0CWiVIaSJkpRiFcxUzgJQIyAwkmFloMWdA0TkiIS0gIhIAMThmJxUxAgJaYkAGZCl8BMdsGABQAhCgREC2JCQjzC3P5AAQpWREArDKM4xaEgD4CqwD1BKAAZEAARCJUSCgYAQSkpAVgpSgEZnRMRAwA5BUgEIAGwYpAPHBIZBQESEgMgIKN/+PoLkGEIxAIBAECcnAjogZWCATPIATWWjhEImZLQAjCASWoqwqEESIUgKzBCUdz20+d09gREIkRhbAAoCBrQVAloKlstbaonAAoL6tbam0dTyHSrVgi/NLEiiAmInRMqGUxAhzl5OogLUAhWwAagZgFAiEgsqKrJ2nBQYEZnIkULAUDGgBGBFACGBGKQCYmYAYcJ4npUTjKhIID/Qy0QglkJViieBQOIFuvs6xkACMyBZYCgBAEkCIEhgkIjAzMCARIyIiOwFSCzc3EEiHgpiIgVCCQCK0QlkGAewJkMAogKVgIZjQzgUaEQCAhWBAi4KEEIKFQC2ZHNdSORTMiEoAIjhi5wAFSAlCEDnBzAKEQGYkKQwKRyyFYCEJEYkdSwQgqR0RM1ogFIIJmdmBcAAMrIRDQCsFKc2EKJhRkCsJmKUClAww9yuNRgoS+CCCmBkBWAgEJGACBkQUaACQ5kqdigGJwQkkRIlMzEIBo5hDdgCChAAmRHCIiAKYSUjDQkkCAK3IMoJSDASITkhmBEcIjEIAAgvhABQCIwhgBDQExCARBDIyOWdZCJqPr1TzfGsRQCABskCLSoNjAawVEAEwSwAQLB9EN4JDBJAKAK0SAEJJi4KZoQJJgAIRhCjrmtERIkjBIEAqx8zkQDALAQCMwhICMwr00DAIK4WTiglZKmJ2RA6ABSIKR0QCGISWzgkB5FDM9eOYAQCRGZkcA4i5TioASymkRK1QSCI2zMgMUrH2uarImHn4oEACICJASVIhgwWwnk/E7HkAKLTnmKz2CAUDkFJATEIwIqJgRBLSCimlZCGYAZgtAzAAICMyg2UmIRkQmVn8HaEIEZt/+vngjTe+s5Tzf1IinL+UIdHRP/xfxx/+vrfO1+5/sxbhB/L/5X8Kh33UyEJCYUwckZCNYX/WW1CjGW7v1+fONLCoSUiFVinLInJFHjSS4+PZ4lKjnJhamDDwTFkqP6lTFtI45QmTxq14MEh7C1E+rVE7zwvL3IJA4Jo1Hg1V7NXLvcbx0WxhqVFNXAk2Cjplfyg7CpwJA5VVJgh8qCC3ZZRELq3IE8BSUlb5nbzvekldgA0CHytZlWWQBLYuSUtnpYcl+IHJTRToIjfa08BcuDKKY5eX4AnrqiT2qzQwRRE2oXQkpYlC73Bc+17DrwsMVG3KRqLLKigzDASXXPphGcVeVXplWTYSdC6aTjnWJQa6KKt2U04yBrEIo7HXxtqWjQSrIhgPysWVpEhr0mUz0UWJzJ6wJYGeTYvFE4IomkzzbjvJJhV76EuZT2tG31eVimVZe9rO/La/c2g2TsSzQcm+8FCaumav54ajoCeLWgQwC1rRZGqjIDBZbZVUTEpRDT5Wmd/w9wf52vLJ6f0hdOo4jGxqAQWzlQHX7GOVxc1wOMk67UY1QWOLMPFtXTmtwTpPUY29uj9oL8XjwbS9GFdZbaQNg8DkRaE85SBUzqDPZR43w9EkbzYTk5oKTRxpMoaVX+c2CjA3TilPOS5cFUQR5yYHQNQBFBgEVWaj0JvOSIqs1fWnMxOEEecFBKquq0YSZLMkHc8WlqgiRrRR6M1S63knisORSHRdjRdWVFlKY4UnZFEZUCaJdFk4pUMucxn5WSYiWYKny8o2Qi/Pa+H7wtSoRWWqRoKOgum07LTi6aTMoV7qeKYS1mlpjVJekaFWqdeMx+Os20nySck+esIzuQOUAkr57SELWkm/n/c6JyY7I69Fni+MMyw9NLmM5WAWtEUaNpPBMOt1knxaO8WBUrZ2TpRRxEol/X7eaSfVzJVQJXFks8ppAVSBNsMi0OSWO/FwkHe7UT5jptoPO2l/5Hc1W9bKWvSoLqI4ng3zuBXUlbNgwzB0eUWeppp8RY61rYu4FYynZSOpRCDu7XG3FXvWSYnMdRR5aRZQlTeaepzVSVR7YZjPhHFlI1FVFVS5jQOqGBDqOPaOJ0bpxK8LEaiqrpqJV+dhMSM/4IorHdRR6KcZKRVwWUvp1dUs6amqUlXtYj/IslKFWlppSqs8zZTJxM8zEWBGoTdN3VIjzNJShZ4wZNiBDGSdyUSXRd5qtdNB5TSESldlDZ4nbSV9URpPO1ujRYUafa4qp9GXQZU5ww0sxlGPC+t5NAtb8WCQ9Xrt2QCKumi3pHM1KV+YSocyLz1NmRf3DnYGq6ficlYbpXwOinTmYl+7MoywNL6ys7CVHB9nvV5SzSqnydcCiA10q/64uaAmed5uxSa1lTBRGJosL5SWJCJhrAyofDBkUStwlSuhjqLQ5RV7AGzCICymHpk0autpXkeR83Q4nVnfC7gwKARBHja9NPepQl+aflm22hzrcJZa3wu4LNkX1laNxE8zFqJIGuFsSlItV4dHwbKqqqLZUFWtytI1I380KUVo27GfpiRVgGXuhJdOVSvJReTnuWlGQZqWKtTSQZYaIQNf5TL2ikL5kKkonM6qdhJMhyVqz1dg0aBaMscHySl93Ne9IBNhMJvVrSTKZqUIlXJs2bHwpM2CRuO4ny72Toy3B3KBAqVtZVh5aAsZ4jALm5BGrWQwTHvdZjEixyYIpbGGpIe21oEobc/1D9pr7cHBuLcSF2nlNPtK28o6qYU1Wqu6VIKnYTcZDrN2Oy7TMgWOta/JWEFKOaX8NPO0nYXtaDgu2o3QFMZJ7Wr2tbUYuDJPmv5kWsVxKaPg9l3b7QYhOimFc2WjoarSq8o6ir1ZasLI+l44mdjA7+b9w8aKctZ6vp9lJKTyQE2zImlarVVZOM9L6qzA0C/S2o/c8ZQR5XIryNMySkIubcXO83ysCwgluXI0bf/sp97qC40iiqI48w//ZzGdfefXpP+0RDjHDp1To2Hx8COu03mL3NJ3KBF2ZfBf/NBHJgtLzZ3DD/6Ln9q+8NDyzbvf85MfP1w/05kef//P/lzdbmFVf+9Hf6Jstg/jSB1uWZZrr9388E98fLBwIqjK7/vIj1VO7Cwux9s3jeX1b91870//bLq0FByNv+dj/zxbWu2D5LIvjyfJMP+Bj/6I1VoZ8+FPfsr2Gjq3H/7ojxZx0/n+9//vH5HGlo3GBz76cUn26OSjjZdOyUy7sIpfvOj1oVypu984qwbNqjVrXT0dTwOnUnXjip4GHPV/YO+F3tGd2wuXOl875Y0Wqtak+dq67jdlsPuh0c2F2eGh7oWvvS3Zw/FJt/TVZdFfqVYHH9r82oV8cN+2/RuXw+Nw1uXO62cau2qy4Za/viQPVibLpvutXnjQEf7Iu33GO2oP13DxxbBxa61/2nXfjOM3N1yjL49WmjdW8pYJDpZ61ztmcRzeXAxvnR6dpvDf/Jvk5/91fu5Kszwb3jyTN2t/v9d9pZme0sHPfiL++V/uv/t93YPl5MbZKhqHR0H85oU6KuUf/Eb8iZ9LH3tb8+Bs49rpYjlTO0n79uXKG4TjKH79jAlyb9huXD3JrZG/3Qivn68XMpMHS984y1STwNYLF5wgYUT7xVN1CKqQydWHWBdJPtavP82VSaL9H9z7hjP10eR089UN56MoIHnpEkAZi73/tn8NZsMddan3jdNEkhw1Xjvn2SKEnf+yf8MUWR/XWl+75Bd13hS9FzaQfce2+eoZYa0jar12hStHkW2+8JA/NcNFl342cwegYhp8DesD0B0x/LyjuxlebBR/Uk3ugn9aTf7C1fdJLeHh0qlZo8MlTf64gjsZXG7mvz/O7ko4F0wavVmrq4f56GtQ37W8Fs++WPKNIl43909cHreW9OEk/1qV30LdhewlM9tU9mQj+1JpX0vx4Wb5J9PRdRVuyPQFN7sOeomT3ROtqwvpikluNeIbG8OTrnVbN66dLaNJMvDDO5fLRqUH3cVXuuMFeGrwzecPr91NlnhzpXH9konHft9Lrp6vvPxhuvXBg9e2/Ka4v9q4ulEs5P5BFL1+CfzB+fKN5wd3ZiimR+c7r52sulWwGzRunK97uT7y41cukjfDKkpe3CCdy6LRenUja7M9nh5/lnwfBJn9P0KoHDbV+PcLdigUz74Cnk/Kwd7nUDKJaujcVOzsWez1/wSoEiBh/HnLVnC2T5Ty9lGtFod/aMUgp9Uk+4OsSKXXmNHwCGZ5PfWGX5JYcr0QFn9ivFkFJ8P8dydZHlLizT5fwsygr/pfYJja4kzn1F801cHqZK3uvNKN9hdMOAlunQr3m7Mutd441d7xJxt28esL3u5aup5+9+4Lj0y2922s7l3y9hdmXdt6fbV1Nxyumw/f/tKTxeHL8bn2tV6wuWLDcePestxeqRrZ+4cvvn26+VpysvtSz98+Y5Ymwa2FeO+UC4fx/V5wb9m0ZuHNk/Gt7uxU0f1WK7h1erpawc3x4MtKN6HYh9mXKrEWmjfyyZ9bfdKb7anp543oohi5/p+CElW13tlbOhOZzLycH/+F8NZ1ve0OvwBhh03H3z5xUVoLV2ejLznRU9W2G3yR/a7Lmu39tXMyLWG37n/WBro2OR79uZALutyz0y+6oMvFAPqfF8ojZjn8dIXSqka39c31oK4Ng/+th6Ul59vW1y/5qct61PvmhjSxlWXr1Q1RyEDt/tf9G1yM+upE44VHgqFNl2Hpyydsteg1t/7ezoutarZfLzRee0hmOOvKxa9u+EdcrUx/8NaXlj2+aTd6r60FqRSYhrce0YU/6cHSV0+Fx3Ky5ha/ukzZiTwxvVeWxTjKu6b9+nrrTlSdGCevrvtba+N107naDLdWTTxJ7i/6+6vTrmm/sdK8EU7OisYP/XD427+fPfd8sn06vLdWB6N4qxdurRfNPPjkJ8Jf+a3p+5/vvb4c3TlVrKTB3W50Z6PGveC3f837td+pL52P+0/GNxfd4lH4Ri/aPF8tTv177ejmaUqmeq8b3lipOnl8byF5Y6VYnvqHcfjaFRdMG+NKXXucRC7yoP3KRt0y3pFqXj9vupUe6Pjly6Rny7z1g8fXsroc5pe7L58yoZEj3Xj9glL9xO79g/6bo8qMzenON84ILC377RfPVIE8Ub3+PfuvTkGl6Ur7lYcclyix/eplibUFvfD19awRYIrtV84KPbPG67x4ianmYPoDP/xj7bv3Byc33veJT7UODqaN7gf+1f8R98f7Z8/9gx/92NLu7tbTT/zAP/7Iwp2tm488Hu7csmnqTcyHf+ZnW9t721ce/a/+yT89cf3WzSeelLeuOwH6MP3u//MXuvd3pr2F9//Mz7Xvbd85fcEbbMntbUp6H/rJT5x86eX+pYvv+fXfPHH/3mx58b2f+oXlG3d3Hnroez76Y2e++fKNdz//oZ/4qbNf++attz0evPzif1x3ewuWCFnKhV//lfiVV/4/lw4V/9m3mfD69cZffvU/ni56a/4kUWBK8rRfl1E2IV+HRdqcjrJ2MxkdJ5NxnsTRsO/VBWgZzCZakDl32gt0YzIsuu3WdNhMZ0USRmUmJJhHr+hmFI4HFAVekTX7h+VirzXsa1O4C2dF4oVlDkp45BrTEfnKq8rO8WG10EtG/Xg0qFuNKJ0GZYaCla393FIcCPTiaUGxh+AldeF0xJ7nV5aEZi2DzLIvEL1kMlCNxUSpME/Bi0h7vnGkAud7DVNpv5nEcVA7G3jEflhktW463284iuKG5zdbxpLvsY7CwpKvWYZxNkUV2TBQNRL4rIU3M6SQwPfqmr1GHcehI2m0jWWUGc1QR5GqFKGyOvCqEkhUYaQL56UjXlkIJmNl2Xg6qBClMlFHF4VmCyu9YDrU7Fex55VCsqh9X2W1VCBaiZ9Pgb2qGfuV0VZWiQoL9hzVYagtIAjrNTxbSydNIwqK2rOySnzptHbkgkgyeGXtwlAz6wJd01OZCwxVkS8NBnWug6avtLZEYeib2i/AtXxh/TaXHC40FYR1bb1YoPQqsp4nAJv1DMLVdqz9yppGiE4FVU5+jEJ6hm3gC6HDlKpIC1K6IhP6RJ6yximPlQjTErRkEDotbBwYEYTZNBe+DQKVF5aFU0LW1qdKk/VnGQXakIrSae3Fxg+CvJRIvil8Y4QWtfQxq2vtG1B+XXiuCkzOShnhQ6DCWQECmdEva+v7hU7CfGZIUOyLtGRQxleiIFIeh0lYFoKkCcOoqLWI6qbv1UrV1gSeX6MUogqaOs8TJVRnPXaFtKpKZJiSBFVHfuAw8v2gs5Kw0wZMI/SNlaV0LU/nLmIjgl5gSoHSBlHorDZgkyDOCl0r1/G10UFZujj0a9KOrB9JB5gaaAYqtyqvXMNXpfFNXQYJodJlRVqiIzUtselz4aSr6OxJ6CR+npXaJ0RVGRP6SBTYym2sq1ao88IkoQMvyGe5jkhIXyKfXmVPiLSsYx+dVFlpI98aCPKs8BustCoMaYXMXlpR7NVW+XVRq4YL/Kh2gD5pDDIm32M/CktHnnYYRulUeJEJQ2040Ch1NzaZQyThq0q6IMAgDmajVqejWyeSOkMZ2dj3C0RkUn7ItfISHcZBNgUOTOjp3CBq52OYMSmPvEDXxNpzQSMoUknaNAK/qIR1VeBxZgCh9GNd5uCwShJVlLJwphX4WaWNrUMfUCq2vik8W4EF04h0VnDuoB3ozEjkwOSaDTJUQSyZVVm72FdFrV0VVpmQ6KepSSJZ1MqY2guFsdJClYSiMn5W2paPFcmyqqOIWClLVnsC0U8tJUpYNY8g5WRQ5OzHMI8gL2Dptesco6VW6HmVm0eQX+WV1wStW8Be3I2VF9VQx56UcZTXNvQBg7BIg2ZHkAryzKgAUejUWF8gedoIG0dSBVE6czJkJaKSKAhZe7oAq30nA7/KCXQdRn7uSCUuVElmEJmFimp2vm/Crp9NlWBY6gbjkcSgDrRfIkpppafTXESeaITBdMQyrhtRkNeClI1UMKuEQuWJYDZj6Vmd+HWB7NVJGGSVIMWJjjMrEMDzwsqg9FzUUI5lIVxLe6nziOvAVwYlgPXjwDrPgE3CMC+8Srq25xnVBANBr4XOd876sWTwaqhDz3PcMjOK1juR0MaZJJI1esa4IBYMLUlecz0px2FGZexJq6ThOgiwRmVtFTQ0Ct9IE3leYf0S66YnRCPMJsIZlBhPhoAsnGkd7VdRENZlYzwwceBn0zCbSQEoIXKlWVsWrUZnMrBxoMs8Hh5bX3um9IsMz5x0q8vRbIxKSMG9/lEdelE2jarMnTvF68vR4NAzJfu6MeyDYCDb7h/ZMND5LJqN2dPK1XE21c6Y0AvSKeNbesFzljK4e7f9mT/8zsXBv6FE+KAmRxRFuz/8o+WFi2/lEuH0Ax+0/+P/cPq1VwZnThLA0p3NrUcecUJe+cqXr7/3fY1ivPbytXtPPBa4qn1/b7x+ora2c/WN/JFLs+7yxb/86o13PhfX07VXrm0++ijYunX9Bq0tV91u99729NSJYXvp4a9+5frTzwRVvvjCS4cPPSQa8eLmTrbYTZuNE9dvzk6f6PdWHv7KV958+llf1OuvXDs4czryrNqdlZ3GYHWlt11nTcVx1bpP047HTYoPnRNQLCn/mJM6Ha/6/lFYxeBi193cmyZdtxD4h04g5IvC63No6/6JuH00LJVPbd3cdXksXVf4RyQIDtabvd3DGMrjleXGgXOKRqvJ0t1p7Qu3INSx0xUdnW11DrKwNOkKx4dYeqq/1u0djqNxuXthIRmnnQMz3kBXha1+cXQ6boxqXVjdyqs68kd2/2LXP54k1+6On3+i2x+IqT4+HSVjE6ZlseLRINf3DsbPPRqkRXenProYtEdTOfGm65633+dhQReXoBLNvklPioqD7o49Puc1xzM1Vumq8gunUlDNPIW4cUzpSZh6jfWbk8Ga1oL9Y6pbwmiMD53rUpqEi/fs4ZlgJdulg9ZkzZOyDg+mdSep/CA+sNR202a0fM8cnfQ8Krs7R4PegohUeGhNIqpExvsUBuODhaWVrZ29pdVQV81tGHeliNE/dBRh0RCNfceJHS0kC7fq8bqvZdHY4rQrTUOJo8KrynKjg9slh0r2hH9nUjYCWPTlXi4IzHoEx7VflPVaYocWJJYnOq1bR85DXgnlYYGFHV9aTXb7wlnuBdR3Alx6eqF564gC1Y7Tft0WxtZLiTfIRGbNmUZ0f2S0l55eaN4+BIV0IuLj0p/V5lzDDklOqvpiKznIsMBqRWEG8bA+uNAJZ0V31xxcDDvjmRzq440oTF08LoplDYVpDkfTE51aRr375eGFoD2Z6aGarOkgm+lxUZ7o2Fq2+i5fo0JGS5vm8Kw+cbxtq2Dca3kOvDGVC8KSbB2YfJ0zEa1uVntn/UaeecdydkJ51vlDe7zWAU2ta0ezc51YVOLNaXGqpXzQewU1dN301FaqOjLtNuMbw/xMW7P1X76VrS+2OroaSY6EbftqO4MIil4reuVmee6kimVwb1osxaKpxF6BgagbWtzYld2o2FgLrx/X642q1+xc3ak6AXY8uZdZz8vX2smdYxXybLUXX+/Xy8F0eXl561gZOjjT6RzMorQenfaCIyHYDtaSxc3UKrSLQg7Iz+zBhU77YNgup8OFdjxig2qwlixszkiQWdainydlvrNxuj1IgxTGp2Rnr6jRKxdF82joHJarLTGiZOqmpwQXQTQ0k1Oic1AY0uWSCIeEhssVxRNuTtzOxfZi/8gdunqjofMaRjWthFRzsJ+6M3GBYXBzmD+ymExGdGDdyVgaa3JshnmOkTyo4HSQqzB+czi73GvNRnnuiZYURLJfuaXAoAw2J3wmKcCTxzl3vEBY3ql4RQdk6pGgRc9qre9O8KSXxe3w2nF5uRvWudguqtUYAi86tKGaHi+3O1s4WpZaFY0tmLQlNkVwaNnHoi2iAxK+GSw0V7a2B+0FlXBzi9O2pAYGh876atQLlnb3RSAGCwsL98u0K2a95MTNUd7U1AbvYIYS+muri9tTVLbscnMbJ11vupCceHNQNqTtKn3khBDDtWhhKzW+6q81lu+NWUDQmBVFw5/Z/Qud5lEazXh4SvUOCsN6sJ70dlPBrlzWvJ/q3WP39Bk3E8nIjTdk5yC3zitOaH1rjw3T5WWcUHNkJ6elK/3GkMYbKr67Z1LnLi5FUxY16vZsYlrtgZ2eYmvC1pHtn5KdYUGlKpZFMLGqhGxZGVRL98zBeW91dFCNWrM1rY31R1T1pAHZ3DflKqU6Wr1f753x4zJtHI6HSwtaoD9wVVcYpRq7xm+PdlvrJ+/f3Vo73YQi3IPJklQSvb6zHVEyLfYHs15j1uqs3Mj658LA5dEepIsSFEZHZrKYGI8X7xdm2Y6S5urNrH/SU5o6d3fAk8en1pdu3oNQbl+4cuEvv3Z05sxg/eRDX/3yYHm5XOm17+6ywO3zF0+88jJqefC2Ry+89FL/xGr/9Malr//ldHERmh7c2QclDt7+9KnXX6uDcHRm/ewLLx6vbxyfPn3qi1+wSTN728XG5n5YZLsPX1l4867Tenj+9JkXX+4vrRYnuis37pSeNzmzlmwdhlV1e2F56ROf8N/CJUKWcv0j/zR67dW/1RzQXy8RfrtQKNNUVGX21DMg5Vu2RFifOduI20/8yq/NFhaTo8Hl3/+j49NnF2/fvfDHn0s7i63x4Pzv/ZEVGoR89Dd+J+t0wmn2tj/+wmxlubVzdP6PP1clrajIL/7upwklhMFTv/3vXTvxZ+n5T38u77ST3cNzn/mc8SMP7aN/8FkhtY3Cx/7v36ijSBf1pX//maqR+IPZhT/8LLMAJR7+t7+na1s3okd+/fecgOHSw7yzQWWoYOYOrmAeUWvMdx+losFJCpvnVamRy+r4IhcRqrTee0pljboz1fceobLt4pQ3z0MVoyrNwSNQLYCfu52LatYsllLv5sW6Wiw7Ltg8S1kPRW6Pz1DeK2Pytk+LSbtaGHu3zplypehBuLlCaVPz1PVPU76SLpjw7hINT85WXLzZdeN1qcZ2usyDk1WMOFlSeysiOeSDU250Nl1yweGytE+wmnijqB5eMBHTbFHtrNadmXe8hvVTRa/099vUP8l+5Y2D6vgi+ZUwq3LyuOmmeq9jB+epOcXBIh1vgFfqma6PrpBXQtbi/YsyOoT+ghlcxHBo6jd1MGYAACAASURBVIbYOu9AoSSx+bD1QBiEnQvON5CFdHCe0bWywez4HejAaXZ3n3JaCUu8exFUxXlg988xgU9ZufsudJKDmu6+zQkAFrR7MaCps0G5/zSwEKK2mw8rK0xk1b2HndDESDsXBFqymvcvAGkQlbv/kLDetFP1XxRlrn1Nh3eCaqq073bfiKo+eCflwQv+dOwHC7D3elBNpY7F8Zt+MdDY9o6uyuoQg3U8eMEbjiJcjWavUnksVIRH98NZ3+NeOLquZnuivVruvxxN9313qjm5ytOhH3p2vKnHxxEuBf3rcrargtM4fFkdH4dxj4/v+JNDz28LNTkh9k6ZdqoOl2FwJl0w3mGPjtfJL4KRVw0u2QAo73jb61U3V4eL9dEj1J6JwSIdb5BXeTNVH10mz0DRw8PH6lbm9dv26CwlUxx03dEZIfJoRtOj58AvuUjc7iVqzuSg5Y7PiWDIk647PAtYqRLt/hWUBZmEty5WbaqOsoM3PETh2XLzehsrkjHeeyFyQiii/euhkEyl2HszJAOBrjdvrwnWSZzfebVrWIDAvauhJwhI7t5bBOFrbTZfSyhn0VP73/Bq1uCp/bsLnpKOxMEbgbWaGv7+NwVlQvZw/5v+DGL29fCqBMPMePBmWJUerQXNG+smX8l7Nry7wnkHMbeD05wulgmr3ZNq1C0Xx/r2GZutZksQba2a8aonhjRYd+la3hJyd1WNFquFkb53qZ6dL3pVsLtsJ0uocjlYtJOTEKfu4BKMNqrlsX9nvZ6epOYMD9fceEXIVAwXzOg0J6k4PAmjU3Vv4N9fraenqlbOu9nmraYMRJ2qg2uBXhLFNu/fif0u5/v68E7gh4Azu3mzKQOsMnn0qp+06/xQ7N5thC2T7qv926HygQs6eDVQPpel2nk98jpgDnjvTiOOi2wS9l9VGEioafdaEkjjKty83pYtyPti/1bciOti5u3fCKSUZGnnWxF4qBpdvHcmcCVZZQ4uslEoK7v1kK513Sz13YcdBE442rkgGJjB7D2BEIEq3f0rqgrKTqVvXiyxbQOhti6JWhMw7Z93tlFG7N89i2Vct1N165ECT9gQ9NY6VlLZ3BxdYtNKWxjcOi3KVt2Zencu1tSrA1Dba66K6pi83VM46MnmLm1ddunJbMF628tutixlJgYLbrpeNlkcrImjxXppEuxcAH4M4j4fnaLxCnqFGHSr0QZHqcyuqPRitTD0756ophvcmIijE268KmSq81Uq38ZxCser3N+QySbvnbbjDYiG7niFh+tCZTBpQ/80hFPqr7ujDej08bjrDs+AKJNplvafRlGzCWj7iktKMYno6AJ6E5523OFZRqNrqvaeRWmIPNq84sISZzEdXozxoKp79f5TKC0Su+3LQjhCT25dsaGBomn2H1M6dWUMe+dJCCDizSsgwAol7l3MG57IQ9w/o2BmTAK75630pTh86pd/Xc+yrN29/O8+7Zelld6Fz3w+mKSj5dUnf+XXm/vH+1euPPVLv6LG6WRl7clPf6aRzaogPvfZLyTHg+NTZ5/5td9obe2Mrpx97Hc+k4xng+W1R/7g0/5oClqf+8KfNfeO9s9feedv/lZ373D78cee/JXfSnb2s6Wlc3/yxejg2MXRuc99sXFve3j+3Nt+83cX3ry99eSTT/zabyVbu/vrJ/1vvXW1CCkIW3/2p+1P/wH+7d72xN98oqDxlb+IXn3lLf0lP4NgFgxILJxT1gpH0lpBhNaCsYJIkUMiaYxwDolwvq0rYa2wVhAJZmEdEqExaC1aJ2qL1qJz0jnpHDpC56QxyCydQ3LCWmXdvC/prCCH1qKxgvk/9MUALAAQaY79MDIDSUaNREwIgIL4AW0FiICCAZnBMTlEAgDJKNGBYEaGB3scIwOxAFDIIBgQGAkAJaIEEMBCEiMzomSQDMiECAIdz48iMYMEoRAQCIFRMiEjgkQQQAAs5ijdHHtjkOBIEIsHgBkCCEaNAEDAbg4DCQDJNGdDpGBAR2AZiAEkgwIGdgiAaGkOMeIcoiOBPEclEQiQgYSHhMBAbg6hAYAEEEgILJAACRglW0YARCEIBBESIqEgwAeKdyAAJBEyA0hAiY6YhQBEYsGMBEAAKAUDMLNDJkBiRjXvBVggITIwSnY8xz8AEBwxAVkGRwxALOaEFRMiMTAQAVvH+ICXAQYmQGYCASiAmBiBQRIBA4EERueICZGIGAkEMBMKYJTESEzuwaV+uy9mQmAGEMRztkrAHFwjmg8QsmChkeABqDofMpZzpG2Omz6wp2NgJNRIc36SkEn8B9f9K5szAKAjAESWyCCIHtiZ8dt+Ndckm7OLGngOLiE4FszMDI6RmVkwIxIDATKjIyZgEOyYHQliIGZCmA8HIDKwJSAi44CYABkE8l+1ISYmR0zMIJgAiCXMBSqZQDAIICACQSSImZFAAgORYFDILOZ4JzGgBFTICCwEETILEAASGGDOczoGQACBDIwKQSCzIodzGgokCyUcA88F6hARgQAY0AGARJ43E0jAIBkk0txtAJjmhBsDsJujswDETADMDEiMyEyOgAGsQ2JgMb8wAoHETMAOeG4fhnm0MiMQIwOzAAImBgYgRjc3D/4V9wnMPHczxwwCGcgQE4B1wECABAKI59EqiP8qOgCVIEQidsgOkIFBMcwDkJFAMAMqtgzMzBJACJ5nEkACEkqAAJ5nLUIGAQiA/IC5ReEYQM5vloQnQCCxcMwPCDc5Nyw/yCQMJOapFVAJnvf17SSDAoQGZrDEbp7xBMx9Zj4uDGCJCZEJ4K/O/OAykB44MMyDghlJAEiYs4WMklgwgAMkRpjnZ/52JoFvJ1KG+SFiOc9aDPNwQxbz1CQfJD2FIB4MB8A8POftEdRct5Xdt8ME5Zw6ZlRAiPAgaQMDgwJAOQdQGQUxCY0gkR88pwQ56Rza+SOP5HzrSFgLzIIYAQQRWoPE0jnJLIiQCOpaWotMaCw6N48vSU4yC+dEXSMRMqNzyKzqWjgriAUxOgfOzh+USCStFc4hk3BOOHorrzLKQqphv/u7v/23X8Tqb5jBejA1R/721uy97/8O62j9/65FmD3/XNVtDi+fS1eWipWVg8sXx6fWlIK7z73TdmJKknvveIYawXRtbXR+Y3ZiJV9eHF/YOLpwQQu69Z7nTDOgZuv+s0+7hj9dXZudPzVeP5EtLY0vnDm4fNkTcOu5d9qFpgvD+88+bVvR5OTJ0fnT45Nrdbc7unR2//x5X8Ct599D7dgmydbbH8fW/8vcm8Tadl53fl+/+9Ofc/vm9Y+PpCiSsiRKbtTYcsXlIIMKkkmQBEGCZJBBgGQQIEAFKaciW41VktuSZLpkl1127LLl2CWXZEu0ZIkUSZGvu6+9775329M3++z+6zOQXHEGdmlWnP+w1gY21sbC/q/1X8506/ziwk62FvpqKru5bQifT6q13ATQgQNYm6sGIGjmOdN8DVKbqXZq6jyUk6KXwghRMITBTLUswTPmzos16+hYN5e2XvlyJDpLGAJCxtiZFF3kwQFj83wDUr0E0ZR3QJ2f6c7ChgjRCWHTsmddO3KcRbmmmc1MbVa2HBeOGO6XPcToJDSTfFMZR/lwkK9KV89xMMI1oYKKwUG+hgmZ1Mx4uQ1clBAyL1YltTF1+6ZhgZd4cJCvI0IXgRxn28qFS0amfEUQlBFnYJta+7kPBrIrZE2EfJTuaoYyRmZ8RRCUUmeAG7yqKd+OxKouItwsj6v1HDmcwoFplSpUDhyBZlG2dI0P0x3l0SW2SbleIE8xPAD1TNaNa8e2VRQ9EFaTbJtjvwrEKN/g0FUOGthGpmrKtWNai+M1J6rG+WZJvMpT42K1QL6hcGBrqWpI105APS16sFYMy40Su4WnR6KXIc/WAl4PSrXth7D0Vyzt4aZNnB2Ea6Tu5mGLox6NvKoeFOBc4MMy7Ei75nXNPNgEsEGjoAzD0m76LSeteaU+FwSwitoVWHVaeh5tWTc0tInqfqq3ggZNQp+DXa8GsqCnzarbUPP6ukJ1EoSyFeZow3E9WfMKuMWYmiN/bJrGuqmLBtkqwnTmy1G+Yx28pGiWrysGE5edmKYCbubBoeoKURNRNUjPaQclDM34Kic4I87QNI3yigAOVU/wmqzzYbprAhBbmlYrArOCsb5uKBXxwAzEqiobJqpG2TnFSM7gpFwzhBaMnhVdiDuwJ+fgoufWQF0v6UUHB7Dp517PojZpsMzrQbXht9QSX3LdQDdBSs5Rz9deKPy2Rl3aZJnb1mojbIqYXWQsMi2Q0HOUBKDhZl7H4B5pkNRvK7MZNETs7iLbYqt67J4jKMKRV7gNRdZoG8ZBR8uNoCVjdxuomh+BI8zGeQ97aOSSab4JiE2hP+FdG4mBac1shBCdEjouV6ADRh6dl+uawtwG86pjAjGE9YmpIcgWDhlla4iCMcPzakP7em6iWLWko6ewPjYRgO7Cw0PRU8YtXTjJt7SvFyBc6LZiYI7CkW5i7SQBGiWbrEGyhlOQHeoy2awXtINJA7SdlK5T0qFNtbBPRR6pmm6JdpjHRCPKvY4BLdZkGd0koOd01MJeDXxHRSTDu67jqWaU0y4iTdTBS7zt6o7TVQt0LfAZr9GCbmHP10HEWRfSDu7iJdrAZsVv8yl5yvcc0WAF26KQCQeOWThfrlJfzMr1Avncl+NiNUeBpXAAokQ2lQsmKFrmKzAqJ9V6gbzKUyPeS1FgKRnZYMnbJtB9VEuyFRTwiejEqgGa1bHsJTawCI+hv+QdFaghrCVVz/gq5p1Y1mFUnah2bCOA6RR7i6KrPTWytazo4rAagOaUBVIEpYMH+Sp24Mgl83TD+GYBgkXZtZ4codrY1gF0YpcM+YqyLHPBtNiQvlkgfy7bkoEYBQPdQNpNfDQUK8p4lWun2Zb27BIGC9nWDMxIMMJ1VUQyNINqXatQ+naSbRkPLIE7Fx1LcEy8oWxCHvFGNUzP6QDGAKfVKsdOyWjfNISMhG+GqifLlo2qUbYrKS1dOynWFGYVo33T4CqqAjNCzeVixWsUg3SbM1YwO65WOHY5wQPbqFSNB3akO0XRho3srNipKM0dOxK9CnmC4oFsCF0rIjlQvTJvo1reL9dS6JF0fSPe3Vqc267a7Wxn/ezppzGGpy88t9xaJy49ee8LshkuN9aXW+vTyxdNI0x3Nk6vPUUIOnn+XcnGGnLJ6XtfhDVntHsh21gdXr1sQndxbmdy9QLG+ORHXliu9ayL+y++W7aieGOz2FwdX73E6/V4d3ty5QKm+OTFF8qVJq/Xh889W/Ua8cZWvr026XTD1157Z94itI7b/r3fCd58/YfsAqFWf1+DBRAi85llTvHu5//D9pV/9y3C3R51XvqN3yjr9cbJ2XN/9OXp+sbO3fuXv/YXkrLW8OypP/sqERwa/b4v/cuqFtUGw3f90Z+UnXbnwaMrX/tLq22DF9f+4Mu0qgBB7335t4xHo9ns2pe/Iuph+9HjK1/7Swagky6f/YMvu1mmwvClz7+sXSdYLJ75oz/RLm0NJlf+7VdpmhGon/u9fx0uYxV5L/zG7yBo0s5TuH+BFtSRCzN6lk0RbczQ/nOoqiEyIcdXHI5ZuZSzqyT3KEjs8Olo4ahwSA/ebXmbsDE+uYx4wHiGJrswDSlagtHTbOrp9sR58LQVPVDL2KNNUnhUZnZ2nmVN45Tk9CIb+6Y5ig4uS74u/DI43CBF6FdjM9tFyUrRLqJHa2ixLTp5dNCyyZYrBjhbBdNtSyVa9oLTVeqf0LNtFV8SzTw46cJ40wNzNmImvgKIQFnXOd6y0dg57tr4UlXL6md1s9jFZuksPTu7ikEC0yYZXLLByDnt2vgS8mZ01Abzc1Qs/NJRo6cITHDeRP2rjB3SYVvF1ygbm6pGTy9haTAU6OjdEHG3VPjsGqAljR04uYKLtJ0vsulLrFQIVeTwOQwVLQQ4u0pgQRMKJpcZFw7P9PAFJ62Iy9HBuwCwrJJwcC2QC5BAM3uaVJbAEpw95xdas5Q8fh5ARKVA/SvYSpYoML2KCsBQDk6fJyksGvn8u0AVONTF+JFbLZBPqsHDUJyZ+ko1fN1N5k69XkxuOXyJHF1MDn09J9ADk7tMHMvauhy8yrLYc7ooe0uXc1iz+fwkSEeERHB2l+lj01lN+m+4s3nktmxyG1QLWrPZ4gleDhzSJPFdkh3CxgafvQ7m8zCKyvghS8Ys8KSTbXmDLRtM2HHHLC6rehIOOmC2jUAaTKFcXIOY46LJjnZUfeGcNO3iMvamuN+G8Xkm5jT3zfgKARkuInx61fqTcBiZ+VXkTOm4CeYXWTWLcl1OX6A2xWWAT6+SYO6cBXr+FMMjOm+C2UUqclYaM3ia6SWRPjx5SoVcny4mDz1fClqmx/dbZFm6oT193UcAMFENbnkYSJaI4UEIMuGD6miv7iyKyMv332oDC6gWwz3fgxot+fBRYAvjY358uwYX0qvL/uu+1IhpPrrrYyFtoUYHAamgdsD4LWZmwm+p8etuZT2CzPwWRnnlKD18FMgEonXc3Nsx5Tpops7+Os7rRC7t/BxLO9apSP8cG4amOYweX1TFtqjlweE6zhqhGNnZJlhuSE86w51w2pT1vvPgvC53TJgEp+s26TIVs0nbxNuMzeH4PB2vye7Iv79lyl3sTNDZBlr2XLlA8SqcbWI2w6NzaLINakfRwaYsLmh/rh6ng8d1B/AqQeM9L2zw6okZPQ7DOs8OweTI93TuZPr4QeRCLlM42AuaQcKP7dlhIwrK6tiOD8OaKfWy6t+v+aDUFR7c8Ny6Vsdi8DiKWAZGYPA4pFWJuTq7G9bkUpfm8HbDj5QY2uG+X3crMVDDJxGRkmp9ejPAUHlBnR6c91UBU23m10iJGMjA2XNhCpQbk8fPA+NQk+OzK0hDlnEwvYxyzFAGzt5NY2xqS/bgGQNrCAt6uMsqjaWG48uU+4ZV9PAqXWBbm4cH17htW6Tc4x23tG6+ULOncRqUNRs8vhikrgrG7oMr0nYh4u7Zti09wyraPxeMGqR2iA4uq/Kcqmf+0RrMep6a4UkHLDe1L9h4iw1XbKPv7e/oYhc5YzbYNOka1TGZd+1iG5EZmuySyaatn3oH2zbbxc6EDNZAvO7IKUvbZrJD6AxPd+B41w/28eGuyi5QOiaDHoo3PTUDcROPdrAzw9NNPN610dAdNsH8IinnjZwX0x+hKseC4pNrxEvoiJnJUxRP2SKy00tUFE4lTf9ZJhJqKTh8BrKULRwwuVo3p3IRgflTRAqsDTp9xpElMAAdPwdYwZYMji8TmNA5BrNrqJLUSnD6LiIqCAF9fM2EkC4JGl5wxQxWHhpeIQY56smP/foX3TgWYfDcH37ZXy4wF1e/9pet6TxpNN/327/dffR4dO3qj//yP/fipGw0X/y//yCcjAnEV7729dbRyWJt/f1f+q21g8fTq+ff88+/FE6mVb3x7j/+43ARMyUuffUv2/uPp+cvfuA3f3P1zr2z5971o7/2xdpgwGvhtT//i/rJKQb28tdf6T3YX2xvvue3fnf97r2z5579wOdfrp/2R+sbeG/vHWg0ahnz7uy1/+D38TL+YZb/oFLZe9+P/n3zXKT55X/Njo/fmeuEGiGBKC0Ed3zhBjQrOfNTx68NxkVYL4KaP56VbigJo3mliMu90E1ySZ38+4c1ao3c8cPpgjNPUJeVXGNWUs+JE0mcNGiE/VEe1LOo4c/i0vEr6pJSKEByv+YkhSDust4K+6PSj4qwEUwXJWac+bSSxsLSd2BhtLVl4LFMKgAy5jFegUIIRpBQkJvKI7gy0GjhYZZIbVTphaQscCW5S5HUpFLcg4hbqKRwMc4klLxyQlpmqFAFwVgqIkAVEMytFaJwKSkkVlURRJBzkgnJAFICl5p7mHCLlOTMhVqwTFQEaWycVJU+lRaSTAiiobakKCrXA1qwjAsHG1XQxOYu1hjRQnIGrbS0KirXA9DQjHNGhFHOUgkGjLUkk5IioIyT58LxLDY0U9IhGlsnUaWLJcA0kxJBawArCslcYa0Tc+4whYybKI2txITmpbaAU0YzbgDSDLBESgdzip1EKGQFQiQtDQAlpSSXEjPuQpwYSWzpe86SC4xKwlhRWmUEQSQX2kLuEpZoS4xkmCVSAVs5PilyKw0niJQaaF35mKbaQi0cQhIOjFCEEiFBaSrfBZXG2qjAhZlyVVX4NVcIXHDpOFBqVCjjO7hUtpLCc1AhmFGFH1ElcCEEY8gaXCkeuoAbUgnjMVwIKqrSDZiSpODad7EQoNDCZ4hroo10GCol42URRNRolHHpOlZqmAnlYMglynjl+BZoJ+Ol40grnNQIF2pCnEwKCrWyNMsVpsZqmkrJiIaKJZr7RCNACy0ZMhaSopCUKYTYknNKDbVsqbiHlYU0E5JArS3NK44pJ8hJeOn5glqyNMIDgjk0FRXBEkCWVxpCxRCYl5oRHgQ0LjUjnDJScmOAcB1cCo2I8F24qACywvdpXChgBfO8sgTaGkZJIZSBle+gJbcQCM/FS0GNrNzQLUsotXYYzgUwwHgEx5XSWvg+SSsCbOmFuOSAa+VSyoUFUPgOTiXSkjMPlznOZU4w1IpUlgcYSQsrUTgEVwpLXgQR4JKmXFCAtaCFqlyMpCVSSmZxIUDFi6CGdEVzUbjMSkFKy32ENMRCcoZhIXGRceYBxVmuhEOAkaS0lY+hskho7hAgNCnzygsNME4sKocBBPGyMg6xxrp5KZkHCcQpVw4DDiaLUnmOwJgk3GBkAHKLUhFHIUjjQnqeJhDOCuUz5TgkrThzFMS05Bpj5VCyrKTjSIfAaQEcohyHxIUgxCDiFKVBGGBIE64IloFD5qWhSFJK0goDoxAipTTaVj6hqQbICIewVClgKtcjRQ6kqRjBlUJScR/T3ADw/QoSQIuKeTTPbKVLighXwKLSwyS3WovCdVgmoBaFH6KyQpWU1NBKAG24R1iuANSSGCettFClH2JeIq4rB6NSYaGUC2mlSFWUro8VZ7kUDgaixLktPAyVxUJVDKJSEV4WfgiNYJngjGilaKKEA6E0pFKSYsiVUxalF0GgaKaFSw0wNNelR42CuJCCYqgMK4uCedJoJxGV52ijcGoqn1qLYKVKh1llcCEEo5paJ1bCIwIimgqJobKA5pVAmFPspLxyXc4Aia10AHdclgpOsICI5aUGQGHIUiEQ5g5yYqWp0RixVEqCJGEsz40BkiCWKgM197CzVBZrRTFLKgOtRJhmuTKQU+BkUlFSOchJjASy8kIsDeKyDGqo4FaDtN72ZzE3YBm1nWWmqVP4EdAAVzING1hoaGzc7LjLVFlU1NvuMtUWVm6ANcSVTBttXAnA1bLe9tJcUS/1ak5WAotKL7IA4YKnYQMWFeQqabTcZaYhLYOI5iUysPBrFjMnzSs/hO+0kzjft6+SsvmVP6P9sx+uF7KW0Nl/8V/+HVuE/z/SlE8/e/KJz0BevfOMRj+a//f/LVvGBBgLobZIUwKtxVJqynyRVcgBCDItOHKoVRZCDZCjRcVcJJWiLBAZR479GyZQuYWwQL5rqoq6SGlNmS/yCjGAINOSI/b9OAogV/OKeUgqTagviwoxBGxNJAunRaw0mAgSAaADvixY0wIT8XnqdpFRnlxmbjuqlsjypduDQAc8LlnTQBtV89TtIGs8EWdu25EVsryiNQBswBcFa1pgQ77InBaAEFpuIWNaIlNVtAaAgVZa6ABgw2qeuS1gIQTcQka18kRcsrrCVENDlIIWGAyQBUgbpouShdAAiyy00ALYLkeJ01SQWvz9WRkIoXZkVdHQIgMMBAB4qhDYMQAbbOHfYkoaMlMBCwR2PZUL7BhAfJXkLIIGQKhdWRU0pKaCFnDsdcpR4tQVZK7OcydkGlqgHFVVNCK6AsAo7Lsy5cQBgGpkV5KzSbgFgXRUWdEa1ZX9AZNxwgBgBhmfpxWtWaBdVVS0RnQFrJEkaBWjxIkMcg00vkgrWgdAM1VwWiOaQ6DlD+JQAJiB1hfJ9xmseQUp1RIjUEEKIAx0WQJmEQp0mWMPWOMbnhGfKY4RqCADAECCrNIAwNCUGfKAtZBiqw1VEiNbIQasRQQbqQGEq+V45PWstYhiowwAwAHKai2IAzGyygAAQlPm6Ae5cuIDYxgyyEKFXUemErsWEo0MtABZZKHyRFXRSCMNLYQWOiqT2LGQuXJZsBBbZKByRVWxiJgSGiCJ58j0bzPIIgBEq5hPg3WqC2itJL4jM4EZgMyVSeEEyGALpSurikTYVMgahT2JITAKSA0JCmSeIQ8h4Kkqwx6zCgNbQurrShKmDAQYhTLPkIeBafPF0O06RmBoS8h8VUn6AyaSeYo8BIGnqxx71CgMzfcZRZk0ECIIMLJSIwg8U2XIgz94C8ZVlf4bRlHmKg0A0kgji6hWwEpBfGitQRZZBIB1RcppBADQyCCLiNZMZQp7gjgGamQQgNAVSUVDCJBGBlkIAMSmwgZw4rkyVdjVkLgyqWiIAGQyLVmELET2B4yjUo1cjci/iyMwCMtlhn0HKmhMhVhguEBEWhzpImeBVQYR6Ikiw74DFLKmQE6Hz3MaVJBFusiZb5XFBLqizLDHoMZGl4j5hktEpYWRLgsWmO/HkWWGvMCUTIs5awSmUogIiyNdFE5g5P/HUKgxhAp5zXKSU09h/29Vh3FUVtE61gJZKUjgylxibKHztyvIUXlFagBaAy2yyJGlJNgCZqC10GKD/iZODVhrkEEWM1USzQWNFEIGGmwQAMBR6fe/hwZaZCAAVhKElQUA9Ir+1Fu1AFr8/eEniIHA2gjiWgSgARYAX2UlCaAFrsoLGkILMBBEm4p4rsoVYhoRT2YlDYCFnsoKFkIDMJBE64p4rioUogrRXtGfeCvQQk/n+Q/i/IBxdKEhVZC6Oq9YaKCDTdHJJ4ParqMyrFXpx8UtoQAAIABJREFU1H2xlIhK7Id8XjJfQw/b0hNF5rSYzolRBav7fKkw4SRcSw4nQc8gD9nKF3nqdpjKiZEFq3si0QgLEkbVtGC+gR6y3Bdp6nSYLogWJatDKwGwBjohn5fMM9CDlvsiFZBhqxCwAlJHc0mYNdYirAmmQliEHMMr6EAANCVIKUdxSZk1wEJoCCWCWwjbfDZ1u9BaTQmW0kLEjLAGSMo0JkRwAKFjRIUdohWCVkAKIGBGWgMEZZ6qBKT2B4wLENSDSf1XfvWdZjRqPK/+tX/b/Y0v4GX87/W+AgBAzhf/6D+b/lf/9d8rEf5N70bmc93uVJcu/YcSCv9uo9GLdS/62Kd+Mel0o+HkJ37pV/sXr7aO+z/1yU8PL14OlvFP/sI/K2pNIsSHPvW5vNHyFslPfO7Xlmurbn/6sU/94mTznFcVP/nxT3O/ZhD68Kd/CTiUZsUHf/WLWadNMvGxn//kcmUDafmxn/+MRFg5zkc//UsaE8jlT/zK56soBML+9M9/Im+vaId97Od+ARlgfPajn/081DptdT/y2c8Go2nWbf3UJz5R7w/Prj31Dz7+82v37/UvX/7A51/e3nt7ub71vi+8XDs5SzY2fvITn+w8Ojh+/t0/9fFP7Lz1vbOnn37pi7+5devtyebO+7/wG739R4vz5z7yyU+s3H94+OKLH/3s5869+uqjd7/3x15+efeN18ZbO+/70m+t7e2dXH76pz/1ye0bNw7e/74PfeZzl77z7f0X3//+L/2Ly3/9zfjc7nt++3dWbt46fNeznb2g/rAx21T1h6R5v23qSztqtR7U8oZ2zurtPWrbqbcf+g978y1Qf4Ibd3pFPWsMiXevm0fSG0adW27eU9EBrd1pz7dQ7Qlo3O6JRhZMiH93RXuFM/Rqt6KyJ4NDVrvdLldKdkKbt3pVLXfnJLq9ooOSzoLm7TqMFqTvR3s90anyzBZ/toTIAKCzrywBsUDC7C8S6CNRoewrMQthxOP+Vw1CFhKTfTW3xgBrs79MIVG6AtlXU0gMRjL+NznRyvgk/8oSGgsRSL6ehzrViC7+PDMEERfEf5LgStoGLf50ZiplCUxfKYmsFCGLP8+oliTCyy/HsFBoI2reaPpTV/p5dG/FGWNZk40bq/4IZhum83o9GISikzfutv1RqMI8fLDqj1nWQd2327W+STds743IG7XjFdG+FdQHoQgL/9FqOHKSDmzf7NSPgV5Noxttt99drpvuPc87baggaew33X6YtE1nr11/gpMt3b4RRk+acqXw9hvhcVO2cn6/LF9LwJZn9rLy1YxfaID7y/xbBelCeJotXpWwTatjZf96Bjc8/ahM/ipl24w/qeJXCtZDsF8s/1rgyKozXn5zCdcc9aTK/ipjW7Tom/yrsdeGbJSMvmVxBPREFd/MYBebU5G+krKe5SNTfC0hdWhjnnw9xyFQC1O+kqqO60vSe2NFUY0tb7+5jSquXd15awNLa6xo31hBUGuAOt9bA1BaZtvfXWdcYKeov70NFbFYNW6uY60UtJ231iEXOrCd19ZZocom6rzeogWxTDZvrVNhJFbttzex1FnD2fp2ky512YYr361DEZWO6l5v+5Upqe3c2CSlWay5+M8H5UMhz9f0qzF/UDgtk76l9EEhVwP+SowfpvpKVP3FrHqgzPmIv56ovSxqyfSGyB8pu+7zb6d6bwmuRPob83KPy/OReXNR7nHWsvxWWTxQtIfy7+b2TmYvhuq1ZfJm4ezS4maZ3ajclhF7ZXFH0C4o3sjljQxd8sUbaf5mAS/VmgNbu7Nh3MJZuM2bzbKl/T5t3upVPU77pHNzjUeFn8D67S3jVTh127c6yFuSqVu/vcbbgixY561VEVUst80b6wYXpGDt26vKL+kEN29v6FoKcq/75ooJOJK4+eYKwTkrUO3GugokSVjr5rr2E1M6vTdXNSowxK3XVwBVOZDFX6RekQJGFl8tNADER/GfJijltucUfzo1SwUDlL1SopwbF8dfy3ElaR0v/58YLqVd88RX5uUC6oYjvrGEy9I6IP16CVLBexH/kwmZFGorrP7NjC+gbTnlK0s8LXxXTr8hZGrMalj96QycpuZcpL4yKUdAtxz5zVhNlV71mrfb9QOrV7LgZsM57iw2beseC540eZQ0HgfOUS1tqcb9RmMfp5u6ddPxH7Wr1dw7iKKDpqpl4VHoPqmreu7fb4T7br4pG3tu+LBd9XL3wK/tN0U9908C/6Ahm7n3pNl44Jv23Dlohvc75Urpnvm1uy1Ry/0zL3pYF1HmH9XrDxtVN+vu3f2PPv5z0/OXe/3HH/jMr6atVm0y+fAv/XK8ttJ8cvzhz31utrMZnI7/4cd/brq2FSznH/3ULxb1hhcvPvLZX0p73ag//PBnPsNXGyhRP/NP/vGit8YE/9g//b+45+GKf/STn6rqNXcy/8lf/Eza69hS/+w//t+KZgcS+LF/8nOKucaCn/r0p9OowZLspz/9ybIRSQH+k//jfy/8MFlZ+cgnP9s6Ol5sbX/w138zGo+XvdUPffZXvGXeP3f5P/65f1o/Oj179tmPfOqzrcfH/e0LH/q1z7f7/Wlv40O//Gv+LD69/NTP/J8f7z56Mnn6yku/8nLn/n7/8rUf+8LLjYPjZKX3E7/+hehocHT12X/4C59Yuffw8MUXf+xXvrDz1lvjy1df+NK/6u4/WW5ufPDzL7f2D0dPXf3QJ//Zxs29w/f+yAd/7eWd7755cPWa99Y7y2jUYsz6/fYf/r77+OCHMlUwxtTrw//pfzF+8EM5ekGt2//qX+I0facJhdiY5mSyevdu6/S08/hx9+F+YzjcuHs3mEx6d+6uHByEZ2ere7eD2XT13r3m8VHnyeP2o/3GaLj58KE/nW3fud07OwvPzlbu7EXj0freXv3kuHN42Hn0qHl2unHzpj+e7Ny92z49rR0dbe3dCReLtb29Rr/fOzzqPNxvnZ1t37vnT6br16+3To4bx8eb9+568WLt5s3W6Un38HH99HTl0cPe4WHtrL/19ltRnq3s3ek+fFCbjNf3btWHw9V7d2vHp2v37rWODqOzs+27d9003bh3t3V0VJuOV+/djfqDtTt3micnnYcPGk8e10/7W9ffprxa+97brSeHzdlk7c5e4+R08/6DWn+w+nC/d3raODlZv3HDS5LdBw+aT540zk7X796tDward+9EZ2ebt28H8RRWq0JuuQI7cauya8FY+oXH5SaLXVRGttikuUBJU+stJ5YoXeV2pbkAeEmU2vAzD8imqXadXJBFnetdd6ncpMHBOpsynPtSbJCEQVG3/BxLBV3WhN31l9pJ6xyu10fGK6hUmyymJHdVeY4Wms19bnbCaWlmelaGcmhgYebLpl4CPC2XSSgTA4/zhazzwwJPq1lVlyfS5HY6DXSK6LSax4GNDezzuaipoaHTclY2xJny8mo6i4ol1lOxWPomAfo4X8i6OeFgVs6qhhpamhSLuFHlDpyJOPbKBMOjNONBNoRmVk6qVjkAWEKb9YBq1mfGVi3Eu0GslVkHyaqbpTbfUmqLpEZlq0Y2nBlRsoPLjh9rqddgsu5mKSi3pVh3SkbyFSWa9anCoqHLDZpgo1ZRts6KHCx7Rq07C22KDaDajZm1VQ0Wq35JtVwF5Y6bpXjR5nbbWWpatLXs4jlIU5bHNWdZFGM7Sut0UYkpnfMIjIQZizgN7ETKgo4mNZOrsm/mZQMsSj6CiYjgk6VKwDwN7FSKgszjBk4qONTzog4X3B5XC9XQx7mNVZwGYMR5iqbT0MmVHshZUSezyvT1TDbsmYALtUgjPOEmtZNpKFLDZk4FN6Ip9ReqMlvOokY5MsUWrOpuSnS1gXO3PrICrJN53Z9V3O44ixophCzPoSKiS6j4Giv92sgIu8ryhr9U3O7AuO1khcl3tGzjhAi+hjPHmTkcrLpp21vKUu+SZdvJS1udM6Ljp67lazB1G1MjdRdma0TK8dCfJaGX8XxE8sxB/TLL2HLpkxmPi2gxZt4ykQt/kYYy0dUQZYWHTvMiIfmUqrmKs4DHgZckyZhO8hqbV8XcTQof9wuxAMmUkZinWTAahZhX6SmKeR3NynJG89xFp1mR0+XcRbHIsiBehE6amgGcVXU8r2jicLWBYxfywPDzTippHAqz6yXaTWsV2qyNrJdTITdo7JCcqfIczoG7cITZ8ReCTYMKbTbGyEmQkFvu0sWVp/MtXDE39qXe8JYgnKAKbdKx5yRA6R13yWBBpThPCowXHtcbQYajEajQlpuGLIVC76CirjMVzx2ZQn2cL3ikTyWc5bOyIYeAJXm8qBe5i+Yijt0iJfAoy0o/GyE7Kydlq+gDXPLhKMxzly1kPHfEwqKhiKsgmzE2r6Z5PRlhN0nLRbjMPDHTycITSwROs6Ty+RmQqZ4mkVz67jKZT7xl5tOZzFI/iSmeciC7MFtnRQ7TnlEbzkLZYt3oTnNmQRHactUrmJY9W+66WYYWbWG2vFixvC30CpsSUNV0tUYSCFQPlDtOmuGkK+y2n2hatKReqY805YHm6yy2qGiI8rxbFGReF2A7mhR40dBmtTm2sAx0vuZlEJVNXWwSgbdu3fCn87Xrb3VOT5vHR2sPH0TD4dqtW43ZfP3Bg+aTJ+2zs627e95ktnVnr3V61nr8ZO3hg9p0tnrzVjQYrNy/1zw+7p6drd644U/nmzdv1vr91uHR2sGj+ni0fv9+NJ2s7u/XT05ax0dbb78dzuart2/WTk7bjw97Dx9Ek/HKzZuN2XRj/2H9+Hj10aONh/veeLx1566XLDeuX2+cHNdPjjsP7zdPT1bv3A0Hw/UH99v9fv3wcPPWLS9Zbt2+3Tg57vb7vUf7jaPD7f2H0Wi0fvt27azfOTzc3LvlZOn69Rvt4+P2/qP2w4ed4WDr4X7YH2zt3W4my9bBwebt216y3Lp+vXbW7xw+6R0eNh8/XnlwPxoM1m7fDuez7uHR1p09N0k2bt6o9/tBHFv0DjMatSB69dv+jev2h7OsQpxP/pv/TjUaUKkfQiL8myZu+Q9+Zvw//I9Q8HeORJh/9CPuf/qzW3s34q11C2Hz8dHgylVN6KU3vrv/vpcCka7cuHP8wvOeKMPTYb6+IjFpPjlKd9bzWvv8a68evO8DrspXr9/uP/cc0yI8GcqmL/ygdtjPtlbjeuvid18/ePE9LpSrN++OnnoKQhUdD6t2o2rUOw8Oso3eotm99N3Xnrz7BYb16q17k3O7DpZklIpmfb6ysrl3d9nt8m5j88btyda2avjRwSmkZLmzHh0Pg2o5vHKlde8g7/TKXmPt9t1Fd0V2a9HpGBsz290ITsd+kQyuPbN6517eqBdr3ZVbd4pms1zrhCcjJOTZtad7Tw7CbHnyzLMrdx+IMBidO7974+2q1ijWWt7JyC/yw+deWHlyEMSz8TNPde8+KMP6ycWnWuOpG/PphU6QZLV+kZxzdMWiaTHfrQfzihScNJUoqZuo4aVukORBv5pdqjUWCUrgfKfuxtzJK9llqNDOTI4vdZy8ap0W04tBLU7BEuWbnpNJUhjZwaaC3rSqNpmATuson16MgiQnM1NuOqxQMLOkJkrgR+Mq3vELFtZvP6kudB2g0KjUDUc5hAwy1GY8DN37o+zSSldMiyNldlsAGTgodMOxAWNnKWixIgz8R/Nip+FgxQ6WxWYDUwMGJYqIqbnoJPVDseiuuPcn/HyHIQkPE96LqAfJuDQeFg0PHyekhstOw703KnY6DlPoIDadgLdq/jRnlUy2gqAvpYd0AwZHJQ+paSBnJo3GYgXDuQmKcrkVeEOjKViuRu0nC+sC2aJkrjAHs91GfZS4OY+3A2+gIDKL9UbnaCEJ9OpVmbhOpccXOrVJijOdb7FGP5eELtbqjZMlxFZ2CFkomup814VL6KVyueO74xRk3K4FoNRski2ubLEs9w7n2dNr0XyhpzK7uEKSyo1Tu+LbUqFJYc/XS8PC/XH5zJobL82Yq50GKzhIpO55SgJnsLQ7NYEc9+G4eKrXjsf5DJudOiq4jYXpeQYQ72ShdmslZMHjeX6x7VclGpZ8qwa5JLMi3tpARK8cxPPNwMEiOOL5mgeIYSOlQ6Qi4g2FDmFa91pPinjNY1RGR1XS9XyUy9RDji3r1OsLG9i0HXQOssWGT6jyDquq7QAfsqnUDPMG8QYSuTJph80nZbLilnW/93DCm1RHmE2kYjTr+M2TJXR03Ku19tOyxybdld7hoZYgP7/iD+fuMi8vtOGgxMBmW53o8cAybLueHZesEPGVzWA4J4vUXqjD01xjmm93g6OJhQasBnDOcVLGV7a98ZzEXFxoBidTDpld89EgR9aaVR/MKpRwc7GpU0MnmbzYcs7m0mC7FrBJYbQ1K55KtDtNJ89eWBmf4RlMt3ynVCTVso2Ngt6Y8w3KsdN6kk0v1fy8YCOdb7lOpdDSoEhU2AvHotigJWTto3xyPgp46Y51vu5grkmsZRsbjIOzqlynBXG7T9LZbuBJ7gwV7xGqpc2obkBBSXhcVhukdP3OfjK7ELmyYgOZr/jGwc7JwnN4stqlDxdiq0mZRk+WvBvRAOFxYRkWLQ+dZdS11UrLeTApNxrMs+jRwrZ9W6NoWAjH4av18MmYODZfb7sPJmolKlr15r0j2QxAg8FBBjApttrB4xFiwPY89DAWa42806jv95VPYNtD48JamG73wqOxYWSxs7b6ZGyNdpu8TF23UKOL3do0oUud7LiNYaYtWWzUa4MUWS07lCwVXapi17UZCuYi2XFqo0waxleoN5dIm6pDYWrdBS93HMNpOKniXT8cZ0AgvkLdWBoBWb3MVVSfFstdV1UknIrltlObl5DDqkdJLHFlijXfAHz+u6/tv/TS6rLvPB6Nn7lGy9wfTLONniLO6sP96ZXzOfUvfu+tR+95MRBFe//J6NJFIquwPynWusL1VvbuVRdWho2Ny6+++vj9L3k8a+4/mZ7bxQhEg0nRbeRBtH5rL758Lql1Ln37rx+/50ccwFt3Hy13dwzDtePBYm2tiqLd628vLu3OmytX//pbhy++B1LYPDo1GMTbW439Q8Tg8MLl3e+9NdnZjVdXL7z+3XR1rerWo+OhRXB48dLG/XuQgP7FKztvvrnc3Jqtb154642k2SRtzw4SbO3ZU09v3L8jqJPsbm6/fX2+sTVb39i+eYPXasVaOzgdszIfP3WleXBsMY7PbW6+fWO2til69db+kfK8bL3r9SdMiP3WhverX/QO9t8hEqHF2H34YOWXP+s+Pvh3j/T3/Y2Ssnj++cH//L+qVqv9e79Lfsg0UKnaN1/JXvpg8a7noJTvlCF3SIqchN876Nc3gbFb158sz7/ozBJ2b1A+jZFl7t2BWrmybITrb+yPP7SiKdy6cThqb4lSsrsDfsloQt29vm2dX/Taa28+Gr7n6VKYlVvH4+YGV4bdG8hNoVoeu3mkw810a3X9jW8l73k+w3jz+uE07FbWsntDtcqrlfru3plFjWS3t/Pm9dFz19KGR+4PbM7S+hq+26e5c/rhD6ze/I6shdPzz7Tv3YBYlmsQPZrDzElaG7t3B05HHf3stefffsO4bLb7THPvbUhBtkvhkwUK1bKzs31/aNr64PLzz91+ywIwfvYnNh+8ChTPz1F4tES0nJ978dL9kXGTR5fe9cztt5gxk+dbG/uvoyIpdiA6WhJSza+81/BVj5MzTzYnoDSksGNh26XGc1T5iLES4ZVDsez5Khiyqi5Rbpw5SJUVVrbnkLuIhDnItmKSBK50B4yHC8utv8SpRKVW3RzErqUsD7LNOU0YEyHHZ4q3K+UlIKlACVW3tAsGMC4apHVQ5u1MNYQ9menoLMYkdb2GLcaahgQSW01krW5F5VdpDRQ+MuLxsuPnlDRtPpY0oMigYtap1wznrpxHsOPVa3y+7OEG9Xs2nUiPQWxpFnfXyCwu22XaYJkb1ot41sEudpq0HAm2hoDCxazV8DRXUZXW4JLVV2Q873gOIht+UUbEisLmielYYQ0YZ7qrC1VuJv5xE0Gcsynl7RzyHBcEtI1QSyq1vmhLU4bz4KQBLRm6gnPiGJnZjIAWUWZGhbUXVKnZ6mM+OscsG9JCcgosKcFCGEdyb8qk1BRXJttduIMmVajEYyA6zNKCLAxPyynwdpgYGzmV6Jk2TDOdtpkKFsJLl8SqGlQaTTJ32zGxKeeycQEtU8emdUeGvnLnC+yuO0TpamzcDSpio+ad5i7Icl+kdVYyyfVJ3Ak3CNCqGBt/jaoMVot2e8cuK18vGqxyfMPjOXJ7GBorxlJvNTxArXpqqUuPTGO5JURhg9TPmtIrBVIOb8NomhksVDPThcOmmd4qq8xv9GV+BdCkAsIVLejNMku4bpRCYDZx5LriZdUpgqxlGryEhSva1JsvEapMsxBlzoyVF4UQwk/CrCkaNsFKCBezOAZQgLqQfByQ5cIaiYzTQnFAS86UkxUISgV36qepj6wNdlk+50RCyVpk6cNc1LjJyzZXzJ5rkDzSlQwuuMUjqVODaQssfZNbTxFQBrlwog3EM2W49S658kiIWNcgjpcU5TIw1FZ+lrFwg6hMqcz651l+pMBSEdCg2DNqJbNLB0Inj/L1BS6JIyKB+1K2KuUtbRbaFMnVSscEWlI2cfOI8yiVbQlPS90QOopNUsIMinVhYoiNm0W8FxvteCJS6CzXdaWjzCYuSrFaV3pMkbT5umjPtcC5qht7ltqm1EGmcgoTxleFzXKUV4tGt7EoqmaWtmnGonYZz9uIIK/LipGgbYSaOJ/JeldJHZZZHca07st40XYZIWukmFS27QLow2Xg16RWYZXUsEvMau1sziDA4TrJpxw1HAN8uPTdULrCJnnHxo5eb0zmruIg2HayCQceQTCwsQciYmFAzYauDHMf8/GOp9mAlVxuY0MzEEuLlQinTFaG0sLkuwtn3KCKlGRoZScFToHnAiFe1TiZeaqGC5rtTJ1xQJVX4b6VvUTTEiwqC42IOJl7yjdl4PTu5+l2rn0OT4TqFsrN7KICwPIGp3NqfJrRwlnUjk6cewN+UfFC1/b61doVYcjqm48G7W1ScHbjKN+6wrl29k7E9ruAQzduHMn6VuU6G997NPnousmNs3eWdLpCKufeQO3yxHc2rx8O/LW81Vh/4+Hkxz/Ac+neOuHrF3IDnHsDvZolvfr2zeNZsJb0OhvfOzj58LbUwLk74O2tkmDnbt920+m1cxtvfrNY6cSrV1bu9vlGN9tw8cEMmtp0/eln7pxVMTzcubp6/bW805nt1s7d6Yt2LVuj5GCOZDTeeu6Ze6OqUQ1/8r3bN/ZUvR5fCs7fH/NWK+sSfDDDmTPcffGp26ewXju48OyzN78HCEzOu539iYiibNPHRzGJydn6xa3b3xGuu3/puadvXwcQFu+/4L9zdDIIcZ7XvvmK9+Ce8fwfZrTdMmf+j/5z1Wyyfr/1x39ILCFQqR8mE8qy9u//bnX5sqUMvDPcwLDWnaMnF7/9rWR3Awlx4bXXjp5/oXN6sn7r5u65c8QDGzdvyMifXL106VvfyjZ6AKGL3/lOcm6DxvnGrZvLlVWz0dy6cV07ZPjCuy6/8g1UI1UYnnvt1Xyj3S3V+s2bVau13FnZffttDOzJj790+Zt/pRtBNJuce+O7cqXWwkfrt25y1528++rWm687PD/z3nPlr74BGAQEb9+4Xh+PqWs3b93sPH48f/HalW++kqytTZ6+dOFb31QbLRV5mzeul80npkbWb1xf84OzH33Plb/6Rt5uDt919fKr36lWWlWjsfX2W8L31Vpj8+YNBOCjD7909ZWv5/XGwQc/ePHVV1Xglt325q0bCMLxlcubN2+4Uh599IPXXv12yZzWj/7o7ptvYKBMJ1q/swcq9fCD7/Xm7wVpLSgeueOmzaPW4CgzDKRrznJos/+XufeI1WVLz/NWrFX5z3Hnvc8+8d5zQ0d2ZjQktklCgGFBEGzIMCzAHnnigQYGDBjwQLIkm4RENt0kRZmmCIoim1SzyVZ333hyPueecO8JO/75/yvntaqWBwQETWjeidmcV9UaFfDie5/1fBZbMlb/iC12inyDJkeKU5ex3Zo+VH2UxGu6e4ISmzo1PZiyaUskG1r4ylwZMupYY5+lSuavWcsA5DpytjV/ok/0PF1nwVRZqTDq14+WKkCJv64vfFAZ0N3RrSdsLtN8TXVPiIiWS6OhZGhTLA7Nnp5ZLFmcWmojL3nizrQuC5gaevMNBML6XjU/qQ/0xAzi+bFtagmFfL602kqKu7E36XRzV6Pw8KjRoamRBd5pbSgLNXKns7aNMnM9d6a1QeEpFj45sBswN3nuT0yLeCTy/blpS45I5k8tlEXqGYPOu0SP6xMndxoQlYAsQDSw0gD2j6lzWRJheGO07CCSWBO/cBsKkIU3h9FAjyM4eKWuLuSUGuGhsmxTmTUni8yvwYqxeF6GAyPOleZdsmhliq3Ex9jrwFI2JxPotWXZNMIxjPpqDKvgUBtvcKWheUvoNmTBtOVq5uDkCDV2wvwQ+guj7ohyDIM5WzvyUZC5k3azm+cZTg+U9TM5f8W9mdVYhfJEenM2fOkCkXvT9qAR5xwvD9TtzQge8sWkXlsGYgq9Ods8dIlI3TEzLb+EZHFknt30qyPuT+qd3lLxy8ncGBzGFeP+qVXTXc60+bHR2s7sioJo0JgcEb0oojXr6FWJsio8R9UDtRhVyw6GnuIXZdQzxxPUgpW/0Ug+ITAOnW2JTvRqAtyhBiOcZVU40OQCdiEMN8zkGNRW2NmCcGrCFHhDpUysIoVRzwBuaQUoXLeSSWpPFOdyQVPVX2G/r+cJrqIy6hGZq+lJcEA5gm0vCw4Bgdg2veW4IUtqDEU4V7Qw6Z5xvWPdBUojrPyRouRlX1+503pQKPZO5cwo86rOBd/UdmmKAAAgAElEQVR/QUJoNlc8n+A8lg3TDWYkinB7kK5mjDuo8Vocf1x6lVlfROUxiCNcN91oTsIVafRjZ6xmK9o+6+Uv4byod5e5tiJJuGYvIykpdLZUf6HPWBGsK8GcugqMh7WTJwYEcbihzSNJCHTO6MZLza2SeKj4EyWkMhq0Rj7CUrh9FS9KXamCs1pwE7lVGa7p8yNWyDJaM8cpMBToDupyJDWZuHu07isxA9FGzT2FvATxmj4+hBbF3pqmH/jc845xm3MtcyezjlHmVpU703o/9lgnOzmwrYI3SBKMDL3KSeEHM9soOFIzf2YhPzQGeHKokhLaduaeUtZMmfTmC1vjQGlzf8bqfqKtRYsjSxTEbFbhlHWM1ODBaNGXKTY2q8VYsVmhbXnLl4z3aX0hkhnFPjf6VRn1dZez5l26eCNHTSU+pm4L5qQ1mRKnI3nf8Eco6jKXVMGxPtkoQFvzF3BVrzLDmiyIb1dxn9QW0G+QsKkFB+a4k8I11ZvIqYFSu3EyQVkr89d0ew6CNvV7Rv0mGZEMrGnOseLoIG43R3OZ23LZZuZx6Q9lMDSCD3ZuXBs8fLC3NrRYunb7Vta0irp99t0fuZf27OPRzq2b3vltfeYNHj6Iup2k19i+cZ3XjGjY33/nR97uOnO8zbu3yZZlpCfDB/ejRiPcHW7fulnVzfnm+tkf/iDc6JEo2bp7N9/u14AyePgwrdVWF3d3blyvdGVy6cK5d3+03N2hvNi4e6ds6eZ4Mfjoo7xmZ8PG+ffemV48n/Q7ezeuuxf2SoCGDx6Yq5W3sbl590738NX4C5fPv/fu5OzZ1dbWzo3r8daw1I3h449qk8nk3Pm1mzf6NTv67JkLH76/3NyY7p/ZvHvb7w+YIocfPWq+PBi99dbmzRtAUw++8cVz778bdTvB7ubOvbuJZSEdDx4/7lYwvLS3df0qJOTFz3z5wvvvhvXGi73dv1EVofb4Uf17361U7VPlMc79n//Psr0zAKH2b/8GzDL82t/62+rHH38qdEtKHIRS1dKLl/6aafe/FHLf33d/+qejXmf01uXlzrY/XDv+zFuzs/tAZw9/9ufiYTevNZ799E+l7fpya3v01uXVzs5ie3fx2rmTN9+WGD365jejQTuz6s9+8htxvz05d2H52tnl2X2nvzZ547Wjtz8Hkbz/zf887zfd/tqLr3017rVXWzuTy5em585Gne7srddeffYLEFSPfuEX4mE3M2svfuan8k5tvHtu8vrF0Wuv5Zr+6ie+uLh0TlD28c/+XDDouIP18VuXZ+f3nfXNeHfj1Ze+FJu1gy9+YXHhHLe1Z1//RjzsOusbs4sXp6+d99Y23J2tV5//XNDunL799vLCPteNZz/5U2m/udzaXe6fOXrjjaTTdPY3nn/+JwrLPn3zjZPXX5eq8vIrXwk2BvP1LWd/7/Dtt5NuO9hYO/zKl1LDPPjMZ+f75zBZUnrqrWHE5hS60R5PDKjASbiRUegCe161y7wmkDL1NyBSFjo+nu8oVI0r6kXrGYE+1CdZD2VmrKOX3joGuqfKlbvHsRJD5qVrKcYrqR3lfZzXBEXzYi1NmqVWLNwLEiqpVLxkLYdqAtVR1eZJFwGwzDeLvGM1K089T3Ed1/WIblHZN3Q109ZhtWOZPMBv1W27gAwo5xlukBpZ0DUMNmyTpeoGErs1k/vojRq1pVEu6XlDtI2WESjrGAw1U8vtQZXuNM1igS9ZSgvrIKYXddBkdT3SBwCtqaaasXXId2tmGWnnFdYCFkrxBQ3plVQ8ZHr+JkAw4W1PdFPK3Xg94i1a0SXRRukQAuZD3fPXK0KdsjaPutIsZsnQ5y1aUgepU3+IKJpAyws3QIUzac+jXqkXU95zQUOmRojZSbCGFTSTWhhuVxREouGEfanzSdGaFx0lNxOFTtN+ytUEqX68ISyUah0BNgzaIjU9lPu2buU2TsrX68wsDb3AZ1WmimYzgkMVNUANL5QzOu4QC8by7QbTS93g5Iyqsqyhr8CmCQa6pURsT5HrpiU89HbToAnWMrhn0Bpq1GK5oZEeM2lM91W+blvCQ2/XVaM01AKdt7ANm3YktmtFt9LTmbvHgZ2T0ou2Y1FDGI54O0y7AGGnakfBBlEyJziTSTNV5TjayoEFBF2KVpi1S4ydshV5a8QUR8F6CmsZEW68nZY1CMgM2qusIygeiXbkDbEm3KTvps3KykbhViIaCJB5ablJt2ByUbVCZ4MqhVv053HTbKlerZ4Wu/WGGmp1UZ2zqVJZnVxu6zXpabuo6hvUEo1aWm4bNbIy9AhfqBNVaoNKbmlN4KkbsuwZqpHWVac83zLUWLeEvGCrtDC7JVhnZrUy+rkcWKSD63qs7CnEhrpZyIu2Rgq9K/GWaiqJ3SuqDRu2iKXG5YU6IT6kfroWIxpLbZr3ZV4TGM75IInbpZ4v3PMVUDOIvWQ9BVqE6cuqy9MuAsDha0nYhVo6X50ThMWl6sXrvNILTE+znsgaXIHLYj3ze8QuXqz2JNYSiLxsq5BaLtic93hezxTkZP3E62MjWwRnEqzGELrpeoZNaRq81hfF2RYrU/yaobagLmNyyZAtVtNjo1/BDdVUc9YHfMc2+VLbxbRDbJTiixpsUMtI9W4l11hDzllfFmeaJoi1XSAHWqtcsrOs6mgK8a0OhzuGjQO9D/CeQUGh7uOqp3arFd3DsqdrdWk2CrCj2SikQ1Jsqlo+LTsOaMrU5ohNgg1A0Rwqgb8rKYqF5YTDUhNzUZ8VPZrpvk5exf2y0FOieMFWQXFUWW7eF4oci9pp3lMLK6Nolq9nmVVQ5HpnJMFuZS3SAcDEA+ao7ICkBSFcZFtFUquoXPn7gqJIWF4yhJD4UB+lfXh64SLE4OEv/pJsGvP+1suvfjnudRZ7Z6eXzs3Pno9brdMvvH30+psQgvu/8AtZuxYOey+/9JWo313snZm9fmF69mzS6S7evPDi8ufSRv3pz/xM2m+HjfbLr3456neX27vz185Pzp1Lhq2Tt986ev1NhMDjv/W3k3477PRffvlLUb+72toZXbo0vXCOW+bhlz5/dPF1TNDjn/vZom4uNrZXF/bHr18MesPF+TMvP/c5oLLnX/7S7OwZyOjTn/ypeNhdrW/Nz509euONqNddXDr36jOfEQp79dUvT8/sSV198bWv5r3G8bnL0/PnRq+/ljcbkwvnTz/3VsnU51/7+nxrM+20Xn7pi/GwN93dD3a2jj7zVtTuTC+cP/nMm6WmvfzKV93djaTRePmFzwfbG/OtXWdne7S9bV298jdCNAohWS46v/UbyskJ+DTloBC8P1j+/f8qO7NvffB+8/f/DZQSb/5v/9j+8H2UJH81wA4hyjPiutm586LT/fS2+P//Ahbf3q71Oufee6eomabvbd29G/T7relk+8G9rG7X/eXmnbulyojku9euQUZWW5vznV0Fiu4nz7cf3C80ZhXJ1o1bksBwczjd2ccKWH/2ZHjvoaibhuNs3r0jLTNt1cZ750pdHR692rl2vdIY4/nWnTulruhJunXrplSoAvjO1esUSaTAzau3oEogwdu3brEihwxt376tBX623j/7znt66GWdxu7NW3bsVhT3Hj3Rk1ga6GKbaJUXIP3cO+/qUZB2mts3b1v+Kup2ltu7UavZXk52btywnaW/vXH+R+8anhue3bkI5p2alnnB8MFjy3OTVvPMrRu16cTf2bhw5aq5Wvjrw/2b1+urOdCU3pNntdXS2VozZ4YSGbxVmDNFjQwFLEhmMl+XmmChqruqRifYtVHUELX8Uvb0NXHkVZI5OkraUitYwrSVWjHvjfKTC2g0US1tZTLfgDTWw4oEDUjjM/joMjyJEIRTU4lspAfEV1lgY+iZmURek5BQSQnxWjodU5ehoEk0P0tkeZBTJBTIwyPCSKHxND+WVC1RUmWjihHeiJeLialUmcZ8bHMGUjgv0jFSWInSMj2VCi6VbIWGkqYeLtT4EFBcKiLPRlDnMa5C3CuV0JWZUpwAvcoVWkQvEcEVrYr8FDCYk4IXpwBBoaIiPMKM57RJ9ZGhloDIiDo25piimK3qNJbYcPRJGwoNq56yaCg50tH0TXnUkXMXWXTR0hOA9aU2bmCuVXZUmABoqR5z1a2RTJG60OemmmBGji/xl2d0fwRsbV5jGVagp3oazA2pc2OhKyHB5kIfWzAziB4yv0ZjCpWoWPJqJs1aLkZ54RDUQmhWiLnQVM7cOF5QqgMQV+KUE5Vr5RS1S73g6ZKIOdBoToM0nSFFplQNqZkCzsm0yudQr/FyVWWTSieJmk2rLqJZyh2aT6BtF3KepnNkmIX0q2xUMcpplienyKApyqr4BKEatFKgzS1GcpYHyBnQKldwQmc9AjmtMrpqEpgwIZVVDQHRFIdfVk5IOsmFDpwtCjmRmbKoK7CoydGXwDHBUcoNZdFWihwroTbtAghr9Pjt6tAu3Si32KqlgKqkpTGtMc6x4mvjJiAUEWEsLIUnFBaqU0cVFlYOnkQigWoHiZcZjoVGsnwOZCyJAcWohE5uNYvqQOYxpG2gZyNil8oi4i4tQkRsJE459QXRMjWf4h4u4wpMQeWXOsngoshCwkBMawmlMcgqeSSKEBs2z2ayciuDZnLBhQcMPcvmOFuBejOBxyJzIW2ThlNgv41oTHKsOE2iusyBSlBDRkgCpvo1BH0rL7HTQjjZIyeX5SGXsXAaKGhRLcChwhxbYUmpZ1GTUsitJcBum7CARYj4TUK8Vnb4FTpKqqRMa2xZ14FLeAm9NaIEJEKqa1PmoxhpbgPCjFWczRuI5Fxm/KC0qlCKKhojBVQqTONDaJQZY3n4EiFYMVRkxxIJoRRL1smVKgcpjk8QyzLN5OlzKQHWrEylLqliEMJiAnEpkQbEK4HSzLRjBjzYItIpy5FUeaZVSTihSEhkIf48ZznXzDx7LsqKYBVUxwXIBbWIsbS0kGjKKVx2UGZXdqbPVZZQBfnMpSizpV5ojs58CtTV5/nHu3i2xBrx2kqkYBJqHkWJhdTgdfDyHJ6uAFXGJs1MpAfEtZRIV+DsXPl8rzpNEQROj0S2qRzhZQ3FNtU84uos1BUc0hCTwKbMVwID+zYyvf7zZxsP7nHbbDvj7oOnlaHocbh982bR69TGo/VHD7NOo7ZYbt69XZpGzQbnW4SDgk3dnRs38qZtLxcbDx8REy/bg9m581hWa68+3rpzV+qM8Hzvxo3CMsy6PGsDIQt1vNq9c5urjFXF9rXrkGEMqv0rV7NOW/ecnXt3pMFolu3eullRRVjahXfepSIXlrF96zbjKUBo6/59VfDUNs998J6RRNGwe/5H77I8We3uuGtDYaqN1XLn9m01idN289x771ihn6y3T7bOxt12a3y6e/ummucIy60H91kYhL3ucnc3bdRby8ne1et64PFGbePhQyP0gYI3HzzUfS/tNnavXK2tVv72+v57HxhhsGy26f37fyNEo1VV+8H369/9E8nYpxk/AYzdX/o74Ve/TsKw/8v/nDgrAAAqa/XV3/v7nzItSYSVo8Pa9/4UJTH4GzDHkwCqc2/3vWvqwtNO59vvXsFubJ3ONj68qY2X6tLb+vCmfTiibrz3/g3rdOYbTdfuRMw0T6br1+4Yo6XihJsf3mgcjGLF9IxmpNra8XTr6m1t7hij+fatB/bBKIeKa7djxVScYPeDG9Zozmbu5gc3tOlKGy+2bj+svTrFUbZ17Xb96UuQ8N33r9WPJijM1h48aX1yiMNs7ca9zr3HBVS2r94ePHha5tXajbutx8/JKhzef9J9/AJl2aa/WPdnGVI3r94e3n5U5XJ451H38Sclh57RCNVaKeD6rQfrN+6lWN24emt491EFyEbuD5OARkn30bP+w6cgq4Y3761du50Sbf3G3eHN+2Va9e5+1Hv0jDhB96NngzuPyqJCbgsvhkXFoNuA806VqzKswVW/zPQyauJxp5AaXNlwPuQla8bLrTSjJa9CC846ZapXUZNMe1ll9fJwr0hABZBny8UQxAoIDbgYlInWENl+7gMppVdHi/VSMOQaYDWUiVolFpwPqtyQcY1M+4Krwqvj1XqZsyLG3pEar2iR0eAFSX0aB8Q/UNJYyV0Qn6rpEuYp8V7RaI7zoiwrKSAKfeIesjQguQ+TkZatoMirUtWKXOSFEr+iiUOzmHgHNPaUIsgKTedxnufUP2bBCGUVC1+RyGF5SrwjmjhUhMg7pskM56XiHyrxCHCsQ6cr3bbMFOl24KpRcQW4A7RoZUglszZcDYSgwOkBpw1T3ouDQR6WkgBngMeNFGt40UarflkAz+x4VpeXDDgtsOpwrki3hyeNDCgbmbtRJEIq0GkBt1tlDDgNuOhwoQK3T+bdFGt4VkerIS9V5DTAqlvlNHZo+BInpZpMoPOCFkLJFsg7YnmEchf5hzSNaOKx4AVOMlZkvNANkfN0gbwjKkKYBtQ/YokL8woIiHOJwwn0j1ieK9kcxCcsdYDgZaFqooKpx6JXtMhp6CjeISsykq1gPNILD2URdV/RyKNZSKNXNI8RiBTirkFP48JE86F0G7xS6XwA4rrIdTQblGmjihh21qBnQAC2Qq+XB3mpkWlfRq2y0OByUIV1VmTbadbmYVUqcLkGVvUCaHTaq4KOIsV2ETeKFKRELgfQr+eVDhdrcF7LoI6nPRB0eK7DeVsGdpmqcDmQbiNDanQA/VO1EIo/IsEp5jGMZko8Iryg/kgJX4EUq9Ex9I5YXlFRAU6UIiyiKYlOcJrTYKyGBzCTasHLQjfyqEymJBgpRYziGfZPaRZUHFPJyxTS+FD6xwrnJJ7gaKSUEYyX1D9Rck7DMY1e4QyqwREMThkvMIh0uBjIWK8Sm8wGXBgyqKHFWsVV6BlgtQZirUpNMB9WiaWX+V7hUcG5V8PLdVFo0Nexuw4ClhJtWV9LqI4iTZmvldysghqaDkRhWTze8EKjiMrchIt1HlpVatDZsMpNkNhgvlalOgg1sFyDgVEWOlysychOOfUPSOzQzIfRKcvnMC9ocKIGJygHaviKRAtWZMQ/VuKVIsI8V5hIsqxU/EMlOgYpVIMXKJyrooQZxEUF8qgKTpR4SnipOAdKdARSzERZcQATrwyPSTzHeQTiEY3HOBWKf8jiY5QiNXwu/alSZCQ6pdGc8hwCt4snzQypeFqHyyEXCnSbwOlVKYNuDS46nKvA65J5N0H6Wupv80wKidy6XPVkTEFQh4sez+iAJ2cKnwOMlk24XCs5Q44FVj2ZkSYvNmIX81wGXTLtcamUyxZ21gVXkW8Tt18lrEpaaNbnXINBU1kMuVTtw/HOzfvayUxd+Fsf3jBOZmzhn3n/uuKGxvF06/1rihOax5Od2w/N0UyB8ow7t3msLIIz719nC988mW99cF2du3lFPaOZIqa40faVW+bLE+olu+9eUSeOKZL90NWyUJu7m1dv116dorjYff+a/fwI+dn2e1e1matNVusf3jDGc7bwtq7dbrw6FhXZuXqr8/gFDPPhrQfN54fKKhjcvN9+9lLkYOPq7cHN+zlim1dvDR48LSrim61Qr4OoWLvzsPPoY5GDzau3BrceZET1rE5gNEHEh3cetj5+pbhR7/aD3v3HRUl9o+la7VySjWu3+w+foijv33vcevScunH//kf9+09KDjau3924cTdHbP3mvf6DJ0BI8DeDwWLHR80/+H1AyacsB9NLr4Vf/0ZlGI3v/KFycvwXSD785gfXceAP/tk/Ma5d/TRJDZalqNcX//C/D772jb822v0vu0UY/szPir/3d4cfPfI3BhWA9aPTydlzEMithw8P33yrGcytw6mzPqgM1TqZJb2m1+mKChllXAq0/tHj48uXjTxoPj9a7J0pdRJRq1Z4ahCyqZ8NGoHV3Hr06OjCJYZ5jI1KIU1npk+ctF0XBDdeHWeDltMebD76aHTuvIJF8+MDd22g0wJMk6JhBZ1O75OXQaMpmmbn8cdOf1i2jNrBpKIkGrRqL090kC92tvXTZWrVio7ZCeaRbqdEsw7HEIJga6ifLrQinu6fLbkUlOoy6z19Hlm1dNg2D8e4kqOLF3rRVMvjqTVovjwSCltub288uA+qann5nHk0U/Ls+OKlzsErM42cnfXG4Shl+uTMvr1ItKhYblksKu15mK4RXqimm7nrJlkJHEur6WZcJxF0tsx6Mh36k+e9i5abyETxh7oW5EosijYyxKoZLT/pvU4TUJtEzrZR8wOZqEkbt/KxWmYrvVMUlu5y3isjbhvjJN7T7cgHkZJ3EE2FTKmhOz5qaE6Z92TIrNrH42yjwUDOlxWuIaEQNMtwHUdqDd89zl7f3BCn4VLJBnUdBiCOgKbntAZmKauBxDSNQydeb6oiYqkbqTZGKnA4MmBpKGCWWyRyG7YdLkOjSSHFkyRraIih/FVCLIwHOp7G1JRJq6F+soiHDYMV8jjjDRXVFewhNYujnqqsQEkRtAptWqU6AzVezVRYlrBfyEgzkjDo0U4wrigdNXbqx7FQsGyUeAWJkO6aSvJEzWOu1xQHSAjDrlobpyUGlu6iMlV59qL7urVMSS6SHtNPooyY6brSmEQSgLJdSZ+oMc8GUMYKi4p4qLBlJDKAeoqIJXaydL9LglQ7dcNzfTZx83FavL6uZwn1kqqtgjJW86gy7AjYxvEqPdczfFe4sugajHuoyIRey0umLRO0psRAN46daKfZcw5jouRGA6cARCVo4DBj+PmMXWoJRs0jN95taWnEF6Lq61Rw6ZXJoI4Iah9Hbl9TYapMQNTRFRwRh1ZamakUTSCt8aSuNk5Sv6dpOBgsD5x6DwsUx02oltyAyhIgJY8byu7i+bg+kJCxKUrqClQ5cTCgVVUr2uGUI2Vqb9WOk6itJbZmPQ+FgWiLkwUsGQ6bWm2aEJr7Lat+nMQNNWgZ9uEMlDLdapFZaERhvtUA8xzJKl7rwI8mEiFtVwMuR5mId1qGO1PzODdqxCszxLJh3Ri5FQSgoaDUU0rutjZUJyZhXmzV9ImTIk3aWEtXoKoKqy2jigZ5OdTCFaJzH13uGbNlARjuYOmIikvSp2kAdS/xzw46qzkIlKRLWVqAlJSNshREd4TolmFlGydxtGfaqYc8mnQVi09NHgrMHDhgHhBtkRKjeZqsNnSDOxnAUmFGJGCiiJrIkW7OEt4BQsfbJ48OBudZAaFDRaNkeRaFDdLMSkUxJplol6Feax5F/rqu8wivSNoiggI4TRvEd9tdehLlPVulBThJ07qGdZy/jLGGyIaB5ikhZdoyLGccazZWNDxOeY1RC5YzXqm0aKumM8EABvW+fugWLZO3TfWTubAUpQGB5yJCA6unTXxGeFHXxYNlvtEDa4Z9MC91ippYzAVAsBjW9KNVqSrRsN4YxQBI0/SitE7Syt0yDSdnURGuacYoyist2VTr8xiUlWhDK5vXEn9c3+a5YXhpuMZqyygHOq9V3XSEQbUwelWs6UGRDSQvdN0twiEdhsdAlo7eQpEuOTKNxbwY6m5RbVZCMHOV+UPF9FLBVdEsWVjCAvNWKTjcevzRweXLQ/8EjOPl3jYTuT51orUOirP6ySja2/D0+vbjx4evvWagtJaEjtlAYWZOV0m/mRO1/fIADI2jzhnCc6GojWhVe3m6Wh8gis3RPO028obdCxc+1XyluXPn1uml1/QyaXxy5A16ecOuHY7dtbVc09aePI63Bm6tvXvnzvjcBaDj2sFYUuSvDeyjMVbAfH1r+NFTb9AP2p2de3czQ/f2t2ovTyUh4/2zmBcaTwuFDZ88C1ptZ7i2/eB+Ytu0rZxa60QUUMLewauCsXjY6T955rX73mAAZAUgtLJAm6wYz92NYf3glCssWu92Xx749U7etRrPj7mqRpt9fbSgQpyateYv//KPXTQKy3L4v/4vxq0bn2p8VVWVZS3+wX/rffMXtUcPBv/sn9DR6V8EFXz5q1/n/T7v9awrH0Ih/uqiECEcxziOs/MXylr9rwfG+ksrwp1ds9m6/Af/Lmk2jZV77vs/cLa2myenZ370TtLuBN3Oi9feFoxtPHty7ns/iBuN+mT65V//jaxRs1b+mR/8MLfrWp7sf/f7JSYYyG/88r8gWOJCnPnBe1mjpi3cMz94p9B0pSy+9iu/quZ5ZlmX/uA7AIHT19442b9YMaXzyctzf/6DklBI0cXvfI+WXFjahe/8eYlRoWr73/8RC6PS1M794B17vlzt713+3X9bm0wml18/2ruUtWqN8Xj7nSua4xY1a//f/Hv75Wh28ezbv/9Htdnc2dg4+70fWKtFZlpv/sEf9l4eJK362T/7QXM8Hb9+4c1/+0fW6Wi+d/bS732n9vworTc2r92ojUbLja3T85dW6xu271z6o+/Wj45mZ89f+uGP6ifH3NLXb91tnkymZ/bUSQ+63aRb1E41EK5TtKjiNnQGgsWvkxefgQeBrPL5LvaHaSujo264fBNTjwWK9LZLNZdxnU1ble3ByVo4e7Noptpcld4WYqEaUbHagUokokF+cpHXU2VuAGcDGN4OOPwimXg8k2G7XO1AEuHEILN1oi2QY0tvm7BFksHlUwVVJaHAfaJABaKidD6hkAKUTsHlljqdqIE4Pm4zUSCVTG9bElNFFN5zxpSiSuTspU6gMEB2cr9FOaRGNXtAEQYYVKvnrAZCCdTTx00EiIbTyVOd5AKZmdIQQJPFqHSOdSoLKeT8pY6lpKSYPlYxl6SnKq/aSBgEhXK1RXIV4VDOdmiocMP5BvhohzlHsolH67gwME3i+eUiHFRaTsdrzGei4SlHm2Xayhu89XGDrRqAhpW3gUOzMLk26RPfUKxJfPyZLDyX1xN11JRJW4PHnyWHm2g6U2ww2qaeJVqedtiTcQ8bDlwMQNyBWhCOq+CAqO0qO4HhqYJ6pDgRzqmqqyku5nLfVpIkXBrJi1sIxJcAACAASURBVIr1AD8l849tvV3xBV4cqyoroM+nB6aqCxEy51mNtUE5LpfHzKjnxQLOjzSL5WqQHX080NSSR2D1McUWMuBS7lt47hSeOj9UVVqQtFi+MHRWFBl2nmLQokaC0WwbAK5yH8wvKTytFI6Oz1QQd+mLr5IjRlI/qMnFPhIFAWl4+jWQEqp4YHRRAgAkR5MtCgtY4WDyBSBUhGIwOkszIexCOdiGkJRAJidvg0oBlQDzPSgQ0pyfB08aaHEquuxkq4AmIIU67hNeScnlcg9nMGnL7E4Wewru0/wpz3xsKPlqrKUuQSSla5KVnoBq9ARFK0qGLHuKwolms8SbstRRUEsJnstiCoxh5T9S/YmlDJX0RRmsFEtN4zEK54rVKJ3nRnRqaFsweVy5C6bWOSWh3NLp6TKaGf6M6rUyOkLJhGqDMntareaq2pE1n/DlGUgSnGtkvFZaieJoYLlZ6cEWOPwymXgikVGzXO4jFFZ5Mz95C6kh9jXp7GB1BT1dursIxWoCtE/OUJiISofjbaDHiofgao8qCxHU4vmXgMypKMF0j5bBpnr4eXKUkipe9cBql9ElyHQw3yUog2UJR7sQiwzkwVOslSms4OTABKJScTZ5qpNUQDNVGhwZsBgX7pFOK44gnDxpoJIxlk+fqDiTuI3ceyitFKIR9wFDOSComh9oVQGAgaJHQMZAaVWr20acmcQm8ccAFEALx8WFBkpWXGsGd0sRQbVdeY9ImjNiwOA55CkidazPhsS1FHNUjXdx0k2amTaqVXFPRadvoVe7ZDajRjXdURxbtDz0ai90X8PWEi26MuoT4hG/XnkDqAd8fi6f7pVNj500ZbAGTQevWiAYKMQtnUE8Pw+1mKxayOljffpm9fJNczVORLlaA/6QIo+EJl71kOoBtwuXA9nw+48f773zTtZoNlaTjXevC9NkYXj+338vr9eOL75+evYihlX/0dMzP/xRZtlaFJ39nT/mRCU8v/Cd70a9jj1b7r3zPmhobBV95dd+XTCGK37+T/6s0jRYVa//8Z8mnaZ+Ot/7ve/mNYsEyYU//w9cM9z14YtLb+eWtfnwwfnvfT/odE3HOf/DH1WmDjJx4U//TDCWtJtv/t4fsCRNGvVz3/s+47lg2t6P3lXDaLG1fXL+ktcbtmajS3/0XSWMolb3K7/5W63Tk8y0d995T3c8Z2Pzs//uD+3ZPNhd/+Kv/ubgxcvlxtaF739f9UOp0L33r9grZ3pm/2f/6T/dePjo9K03Lv/hn9ROT+cXLxztXfQ7vcZqcfY//NAcTfydrUt/8r3m4dHk0oXXvvNdazyZDtbYg/vkx1oRSoxrf/5njT/+Q6kon/KV6PNfdP7Lvwurqv1//7b+6MF/TCmo8+1vAULznT3n7/wXOE0/1fGE6A/uWR+8h7L0x6selQAYC+fMB1fMydweT/evXtccvz6a7ly5ro/nAlEBcIEUc7bc/+CKNZnr89XG7bvayrOOTneuXK+djlUv3L1yvfnygITJ1s3b1mxpTee7H1w1JjNzMtu5er15cqqG8frdB42DIyVM9j+82jo45ohySAvKzPlq++r12tEJ8aOdD690n7+iUbr//pXmwTGL0s0793ofP1fCZOv6zbWPHssK7l27sXbvYVWhAiscURalG3fvD548I3G2efvO5qOPygrtXLuxdvseTouNW3f6j59pfth59rzzyQuaFhs376zfvCUA2bp+a/PufViUmx89Hjx5qrn+8OHjtUdPUMo5IhnVqlJu37q9cecezPnavQfDBx9pjjd89GT97n1Y5MSxlUWvLAlZMrQasKRCgY7dvkxQt8rO5EsscrYy8LRblQT5KvX6OK6UQMWLLk4Qjmts2i5LRXFVddkBHCu+Btx1FAIYqHjZRwnGkaHNW5Wgyorh+UDmuJW651ChpwGINDrvo5jA0GDTNuBEcRQ8G8BEylC4h4zPhMhg9IksHCDDKj5AWYCJ51Z1nQQ+iLh/qPBpIQoYHkDuQhiW8QHkHoAe906YXJQgFcExAfO8LFD8CmUOrgIRHkDhA+Tl3qki50WZg+CQVuOiBBLJXKJKeDw4xIUHUcC9U1bNS5AD/5Dmp0UFKZ3Xsd8icYWcJl7aMAPY6bGpISTey5dbRQA5pMsa9NooKrHfYJ4FOaZeR51bpUTqvEadTiUw8Jsg7ilxib0m9lpSELhsKbN6VQF9YZBVU3JEHBt7XRLnazLbzB0sOHHbyqwhJGYLE636kEPq2dDrgRwkLk5fVqJA1VREB4jnoFqU7okq3VymETAoLvLcJ/EryDkuxoV/iGUOyrlwTlTpc+5J/5CUnsgdmbwQIoflnAcHRKSwnHPvWIVeUQZleIRKp8gCFB1AkSAU+5VBQZqCpXCP1coRZSijQwQ8IUIUv0JFDFFCoLOueATnEC/7yoICQdi0g3zbKJJ97tXzkAYlctZQoIEM0EVXWylSIDZrY99CKaaLLo50Ggji9JUVAQXCiwFdalVJ1VkLB3WUIWXZRIGKIgndNcXRQF6cLaON3BUCq7MmDusgJ3jZoaFJ4oo4A+TbJUDxMUxPcVni8rQMxxRGPJ7gdIxklEuKYOKXEvCjMjghXOBsVEZTAsM8m6HkFPJUhmOSvyoFxNUJj04wTysxBcFIgaFI5yQ7hjIH2RzFR7IEODsW/jGVeYWyGOgUrJxkQeIjWKZVMoLpi0pUiJ/y4IhWuYQBxashjghMTDbrVpwRh5F5X+a0mbjnUGFmAUpUuuijiKKYadMGzAl1KZn3YQJRqMDVBg0AygjwhygkKNHYtANyRj2CFz2cYRYi7AxxSGSq0Fmf+szgydl8YVU59ihdDnGMqSehs0kcBHJGZ30SMp5W0StQuBAFhX+iVDPxF3+QPC1KCVBVSFQJX4SHKHMQCotgTMWshAUIjmh+VPCKRAcwnWGZymwE8xWEAQ9HiphJIVB4RMoTISQpDkQ0JzwF0SlKZxCvfGlrOPZBCYIjLE6kAEQciGSKq0QmY5zMoMwlXDXUWa2qkLo04awjBUauhd0ejotBlW5zT+E58Vts1iwlVpeasupAjqhvQrePYgmDOlm2YI6oZ6uLhgCUzXS0HIAckiVDTh+HJY4MtmyCAiOnps1aVYkH4XRfxiTLkG8gZ0DCCoWGMmvAFBLPprOOLEn98GTnyrXadGYsnd0PrtqHJ/p8debDK9Z0XmDGIeUQ10fTnSvXGqcTbeXuXbtRn8z0lXfmww/1uWMfn+5+eFV3PHs6H957WB9NmRfufXCleXSqOt6Z9z40pgt7NNm9ft1cONZ8tX31WvP4VEDCkVJgxZgtznxw1ZzM7dlq58p1azozZsvd6zdaB8dSyL1rN/pPnilhsnXzTuuTl4rjbdy+23/ySVkhDmmBGU7y3es3+4+fkTjrf/SkdXSiuf7mnfu9px/DvNy8cm39zj1ZyvV7j1rPntMwWb/3oPfsuR5E63fv9+89gkW19uCj/sPHVQV3bt5Zv/sACFBgxiEBWbF298H6/UeQVzs3bm/evF1VcOvGrbW7DyAXP+ZNhBDS1bL9O7/9KbWisCx5t+f9wi+JetN67x3j1o3/1LEA/8dOZ/EP/wf357+pvnzZ+z/+d+3ZU0npX/3RqixNa/SP/ufs0mt/DcqG/49dhNk/+K/NyVjqVAJIwzRuNiRE9nwa9PpWFiyaQz109SIt0wpouCQKCROkooSZ1nzh9/tmEcqwyG2LybyMS41yQZlISsJAZNStySzs9wweQS9L6zVFFlUikIK4rkWaXYvdAjNzOgvbHb1KoJfxmlXjXsA1pMDcMFXHy3WDkpI6UWLXFSJkVAAEgaEEas3iIS1yEVeCMaIA4kWZaVNSyohDKKXByqxiPMvqNepFgjHMAHHjXNWIBstYQADiZlN3PbVIkmYDh2lFSK4bJaWwLK0iEBlAZRm3mrrnszyuLA1FWUG1oNlSUkHzIqtpJC/VMAQWTqFGsyK1dTt2sMg1ULi0i7IyaRqIAzWMsrpuxUEulLxm4IJDIRWScclIytOGAUupu2HStszIL0omdUQ45yVVaJ5hg0Y50XiuKM3VfNkd6lGSlyrQACk4r1hTLly1jaMC6VWg1tSVLyxNL5MsJ4RWkpIylQTwmBlkMS7ag/Vk6kmb27peJUlOFVIChZRxpeI8Nmps6aethsX9IgLCNrQqSXNKcSkZEbHsVCun1kOrOG/YZhnlMRSmjvKk4AIDCTUDcqmhLDFttgiyhm1WURFBoTOqSFFQrYi5yUBclYQoNAcRyHRDBwGuOOI816xcaHoW5rYGUikxKgymubGgiorjrDJQWeaWSuNCy+PC1qoMSIQKg+luxBXW4+M5HdA8j5smTTnhotJhw5+nVA2tFo2EREjFcVYaSpZBQ2ZSp2nGa4wkaVFRTeFFRVGS5y0biIr5Yd6w8XIlyrLs9JRSoLRgaplLBSUFM6qIWOrKz7p1M/ZToSAdE14UHDMmMqzjKGNaGVFLXflFx+55ozlsIwNjwQuOCa5ShGHgUaaLmq0tnKzTMFMvzykwKRacF7CwDUiINXfjpm0Vfpng3NK0Msq5hrFAhLM8QxC4RltdJWndNERQxYhbeiubrGCPoKJSUJUTFSSJYTInyWxDr8IyIYWhqTIsCpUgUSlYppDBNDZt5iW5qVcK6DjjRNUBInmpAyS5qqhBykAWWxYNskLXc40yL0SVLGoGjTKWRWVN5zmAAGaahlfzClONYoEVJASvmSTOWBpjA1VZlWOV2wYJEwmRQbNEqCTLs5aN0wLnBbAUFoQp1hmrylyKCmkaT4RK00yqOJEYhz5otvQ0yYnKFFFlVSmxpvIIGDRK0k6jGa6KQiktSoucC0qVosAaiTlRi0xR2svZoremx0nOValDynNesrpchawBkoqqRag2zKUXt2tW6hWFUhmEiIILRSF5QVQcFVTlodYwFl7Sss3MLwqGVWkJJwdUIhTTBk6EomShWjeXftK09CLkBSsNCoCo4qolVkG9Ddwst01DxjwC3DRQnhSixLJCmg5KqMk0tSy6iv7jM0JjGi6SnEICS12lfqyCLLFq2E+4oVeMqEtfaKqGs4QzCIEwNMWPGMg5Itx1i1oL2hYLIkmJhrOUM1yVec2kQQwwymzTcKMKk46YLOiAZnncMklW0iwvLdLwZwWifq1DYyEBYjjOpaGkOTLKBJgsToq6qodRhnTMBMyBkJgpWQJtFidIL1NosCjlDVWN4gwZRClADkpJ2nLmEd0usljTY9xQg6hoqHoYZVAnqpAFKCtCWZ4Das/n4aDf9089bvCaSUVeZSVWUaoZqWY1/HlKDXs6Dfp9MwuqWBR1i4pCJhxqhFNGvahO40lt3Z5Mw25X57GMRFEzaVnIRCANF4TRIMI6DvV6bTwOu11NJK7dUbNEzZMqKwvTqDAxlwtk0ECvW4t53GwpsoBhDgksdQbDXIE8qjW1lZubJlc1oSi4yK0iFBkAEKa2bTiOAnhUbzLXK3QjN4zadMo1rQ5CrzJRVUbNluG5FcZQxerKS3U7rdnmclESSlRYppXCM24bEbMkwmbmMy9INYsyCcKiAhBaVGSQlCJMi9q/+FX1xycalYxt/KP/Sbt/71NS5pIq7i/+0uK/+e/Y0UH3X/6KcffOf+ojxV9SVfXVi+AbP8XbHUCIcevmX4S4vyphIRTHOE2Sy29IVf2xaRr29g2r8bVv/bo3XNMd7wv/+nePz1ysjWdf+/Vvj3fPaoH/c//4n0fNDhDlT/zGb0ftLo2zL/7r3/XW13CQff1XvzXZ3GM8//qv/WZYa1QIf/lbv8UtCyT553/rd8PBQMb869/69nKwASD42q98K9GtwjC+/H/9dq4blZA/+X/+y9S2eYm+8Wvf9hudUtN+8pd/tQCA16zPfvv/kQB6/bUv/KvfIUkeDAdf/dVv05U3uXjhK9/6V83Do/H+2S9++19vPH28GPy/xL3Zr2bZeZ+3hj2P3zwPZ6pzauyZ7G42KZIiLSVWBPsituPAMZIgN0ECxLGAAEFgBL7wRS7i2BQcJZIiiaZETRRFkSJ7INlzdY2nqutU1Tl1pu9M3zzteVx77Z0LJUhs2EZfWOIfsLH25Yvf+/6ep/X8H39fWlpms/XG7/yePJqcP/fCG7/+25X9w8GN51744+9XT3rTzuqLf/hdZTA2VlZe++bv5Xpnpy+/8vo3f6/+6ZOjl1595Tvfbew9G65vPf8nPyj0Ti+uv/C1f/6rjSe7vc+/+tq3/qD14NPDFz//0p9+r/1kd7q2euP7bxWPzno3bhSPJKmXn7dB7oLVjstB3kGLgnqse/kUT8pyLx9WiXCuCr2K0UXyKdCOa0EhEKeMdF53Cik3VguHglsHyjkv98rTDqOfZ+pR3dc8cYnlXi1SQ2ah6ke6W8mkEacel/1qDOe68GzF1SLRZpWjaqTGjCGph3ma97KJoh7UwmrsESZ910kFBjDI/jAGIpNSGN8KkcZkCQo+ATDPabE1/YTNMAAcCj+KIIejFPk3Q6BwaYbtjxMkY8Ah+6cRBRAqbPRhQAFMIfRuEwGEAeKdmxQKGPLQ+zCBJIElKf4gyhDHyji6EyNIKctaH8YQQyxj690IhnHWzutPVdESPYVIx1XGE4hKtGc1zoJ2HeoPy+miEpZ85SgvLKVQDqSTKmdwZokp7BalOTDbqLijMNOc0ciKh7I4lX0tFE+r3IxzSiC3W1CmKGjE8l6BmRStZqY/4/lpPlJ94aQCjIpZQrldXRkzZgfmngj8pOxWQvlU48eFKO9HxyTZCdOOTA8D7xGlW/nsyPPvEtTkuXns77BpTSKDBGzbWVtJjoLo0wR3BH+Yebdjps7BZexvp7DAxNMs2I5BQ0zPo/ABRU0unmXuLYLKmDGCxX2MNJSaWbRNYI0l4yy+l/KrIjGAc5MwZZb60LkZZRqTuSC6E8YliY9gYbcRsVHGQv3TJoWASCC/U80gjhgkPukQliVZln/WSmCcClD/tJlFEZCJ+LgOUpbymb5XhiANGag/bVAMgZjoO00ckSDPFB/kU8JTnqh7FUgzwlB9twUpNfJS/n4ThoxbZooPtCRSQiUr7eYRAT6fFZ7WaZSZdZF7b+KdZnRdJw89epbACus+yZJ+DNoquUfwIAaX9ewT2++BZE1JHzrJKeXKyN/LogEgdSm+F+JTj1zK0duWewyTjVz6yAmPU1xlkt3Iu8BMjfHvR+A0phtadtcODzN2lQ+OsuQQ8g0mPYrCHsA1JnicxAcx2FDIthsfALqmqONUPmrEUohsSd/PB6WUn3HaYTkoE2Bowt6aq8aCh7SDWiwR5PLqfjHVwmwpqvu1oJxgE2l7nVD1UcSpT2uRlKCI1/dLkZ5Ci9N3K0ExATYq7LUjwUcZrz8uJ4wHYx7vrkYqgj5feFYmahjHQuFxM5BTBgD9cSVjiQvT4GbAkzBiWetWCjFCEvQ/TEBAYEMm74cJ4dgcDu7GiNBUYO2PSUYBo2H7/Rg4cdpU6XtW4DFJQYxv+ShMqcK7N0kaZ7SmhT92kBXRjpZ+YLsWmxVYctuDLuV04G+zIGFITSE/cbAZk64Wf+j4FkvLErnlEAeldTG3l1dGTNCI5IM8NyiZHaAfsNKwGOZ84bwIZ3WrhNRDXRswVgfmdnl+WPFqoXimiINSpLjyQOZH+UgPhLOS1pfMVqYfCtJF2a+G/EgTz0qB6itjWeznw1wonOflc5VU3PS8zvXafo1wI0E+q0Waz8808SzvF2JxmFPO8m6dFA97X/4/f2OwvpWfj1785nesUlm0nVe//Z1Fu62eDL76q7823dri5s5Xfu3Xx2ubfBh84dd+yyqU2DB67Vt/YFWr4mjx+u/+gVcrRl728//7/zFrryAAvvQb33Q1Habg9d/6ll0ocm74hW9926rX0gj+/D/7VaPaAAzz9f/ln8YsRznhjd/8ppUvoyj5uf/rd6xaNQLC1//5r1q5klupfuk3fkcZjZftlVd+/zuSaRql2uu//S0ckMHq5i//k3+SG80urt/4wm9+UzvvDzeuvP77f6hOp/NG5/Vv/j67tAZb1772v31Dny0mVzdf+t0/KRyfDi9f+9zvfluZLIxa47Xf/X3BcM42r3/tG98oDEYnL7706u98u3JwNLp0+XO/90eV/UOj1nj1j/5EGc3HGxuv/8bv1HqnvZc/97k//JPmk93e5hXx/t2flYsw47jcj/489+YPP/sn8crK5L/57wBC+jtv6e+8CRD6/49PCACADLP8m7+eiZL30ivOz30FxvFn/BX1/feUu3c+E6f0Lw00WhiPW9sPi0dHlf3D2qOdwvnZ6tOnan/QfLxTPTzQT05aDx+ok0n7wYPi6Unl2bPqp49yFxfd7fvyYLh6726tP9AODjsPH+jTSevhw/xJr9Y7rjx7lr84W7l3Vz0/X9/ZqRwd5Xu91Qfb+nzeuXundHJSOzys7O0VT3objx+rFxfde3dKZyf68fHq4x15Nmvfu188P68fHpaOj9uPH9efPdPOztZu39KCsHXvXvXJU308bj36NHdx0dnbLfROGjuPy6cn+uHhpe0HkmW0796t7O/nRsPmg/v54173008LJyf1J09Kp7384dHKnTu873U+vlk+Oi5PZ6179/InJ91PP82f9Fq7u5XeSfHoqHvrlmjbaw8elPb3C/2L9qNHhV5vZWcnd9LrbN9XjRm120mywYcIT0p+2tGGieAqEVnlDDmNS5m7Jtgha5QieolzIDIbYdbJzQC7FMKwKywFmJSIt8VZBC/yIdkQzVSwSn7WFeccctUwXuOXPAz0xN/knZiZ53yywVuAn8oB7GpDIPpiEK0ISxYHehJcZu1EWYhhtiEZNB1FfbcQXtDMIpOZHs5TdhbMFlpsZul5NIsK2ZnPz4OxXyR9mtnJcKJHBuan/nyppPMYnvpzX08uEm5ojb1iehYJPhmOdc9gwTSaLdXUBPjEmQW56DxFi3Bo5uIBZCxvMtU9k03G0WiuBkuEevYyzAUDABfh2Cm4A4aJQWbVE1LNj2nql6BdEpdZmHTRrC7aFvXWEtLl7SSzWiSqyVNEohoOGrIBYtKGRlO07cRao8mK4CJg1EhUyY0TGJeo3xEsjiRdZHUky4SLepysCiYEXpsm1dyYpn4htRqCg9OknVjrou3gZSMiK5KRYqdK4ga3hJbFGTONswKvn03MXDYNwxE7C/P0PEjH8dTW4DiOAmE4yVOP+iM0sApgEcXnZB4X0IkdLuDI0Ogojiw0nujQCMA4HZk5sIzIGZ1GeXTi4Fk8tXPpOA4MOBrrnJeCfjj1Cmhkp2fxLMiRkxBOg5mlM+MgteBokiN2Kkw5n3ZzC1mehH62IU0k3k+Jtw69nLzMaNDBNleYoJC22VlOWhI/25CXOu+ENLgCwxJvoMjvcqGmDUGUtpmZxprAJ+vMpMg7PvU3QVzmTSaJVrGrCVM2TNuCWxUNGibreFEWbD9z1wGtCSZHgjYy5cIUhGkbWU02iiZDZW7nBJdYF9i0RXRqWyZvGwoYh0tbNfq8aNveVJpYemZSeyyYtiScGu4SL2dSuqCGrTgTRbRt85yZ2Tk8C7yZuHQUeOp6BrOc8MwiMl11PFRwGC0v2KFTQDPfHwLLk/GpY1n8fCKieWAupclE422bjpmhpcNZxNhKkKzzSxEFchJscnbKztUg3uBsKEylAHbVIZA93o/X+CXPBEriX2adTJ1xIV0XzZQbqwHoFOYcv0wjsibOGcYXE3cDe5w05cN0XZyT/Jz3sq4wVXgLhMmGsmSxw9LwMhPw/FzwyJrocOoA+nBFHPOsw4ThBrJz1KWLuUwsxJ67S18L+wAvwoGdoyPEWs5oonkmB2fhdCZ7BoNP7KWveUMA5+HYKTh9lvH84VBzXIVZkPlMDOaAuXCXvupPeDwNJlbOvkC85RoT1bQlOk/nSyUwMH9mLnzFu8CZncxt3Z+IvGUvLzjTkuA4Mi3NnmN2TpKkjeyO6LhwUY3oOmdB6DQJbeTGKXB16jYFi8mSRmJfEiwXL+sRWROWlLXLcdIU50walBKvy1swC6uJuyHYLppXg2RNMDO8yMekmRunOM5Hboc3IQyrib8peb68yAVgTV4QPCsktFmYAuhJ1G3JJoVeKfHWGB+t3N+Wh6OVB9u1k5Pi4WFz72nu/Lx9505hNm/vPi0eHpZ6x+vb95XBYPXe3dLxcfHwsL27W5xMW3fu6v1+c+9p/uCgfHG+8mBbHo5W7m8Xzs5KT560dne1waB9715uMGg8fpR/9qx8dLSyfU8Zj7sPHuT7/cruXmN3VxsOW3fu5mfzzs5O7vCosbvbfvJY7Q9WH+2IptG5d7d43CucntQe7xR7vdbOI/3srLu7Wzo/Le4fdG/fFk1z9ZNbpbPzyulp7dGj4uFhd/epfn7eefAgf9EvHx6u3r0nWWbn1q1ir1c9OKjt7pVPT1ee7OinZ93bt/OzafXwqHvzpmRZK3fuFI+Oa8eHtYOD0uFRc29PPTnt3L2rzqa1p7vt+/dFy1y5+XHu6FhZLH5moFGEmNm0+O1vffalXCqKy7/1n9BcTtzb1X/yNkyzfy2cgv+wUABZRjV9/N//ivv6F+V7d6r/4hvccJB9Rmu0JJ//r/+M1Oo/qxZh+nf/VnvnobnSSSEs9c4urlylEG7cvXv06msKcRoPd89eeJ5PAv1s6DRrsSAWz/p2o+yohc2bH+2//oZM3Objg/MrlxmYqBcTWlIiltOPB/Zq0yxUtt5///CNL3Igru3sjS5tMDjTT4d+OR+oavXZkd1tGKXaxscfn770Motp6/7OeGNV4igzsMKcsuh0W/cfWo1mWM13725POl1S1Aqn/TRLjbWudjLQYme4tVnc7TnlclAtNHYPzHI5Kqi50yGkZHFpVTufSLF3fvly8+mel8u7tVLz0VO7WPIbxdzxAJLw7KVXaic92bX6V6/WnzwLRXG0ubmxbNiwnwAAIABJREFUve3ndK9eVIczwbGPX3i5dnSkOeb4yqXKwbEvKINLm7mFxxvxbF2XLS8/pVabprGoGtG0JcnLhA8iTvejWGBMOL2UF223MEsnK1zO8JDLz9u8YKaiH8clgH0qWnS8kufdUB+mi3WsWx60OL8OBS/BAYhKGEaZuER+nYZQLJyT+RqTc1zWYewyZHzIuIDXXY/y4oLxutBllfyzadCWMaTZOIYFNkEYjCJUwokuc/vL8FK+4Q/NkUDqcoZhOgxhgYcyYoceKjJhTs12LXBJQySGx17ckAQJwkmYKShTOTgIc6I7KVTRgRut5FQ+RGdhVuYyCYFxlIk4UXkwiFglDSsa/8xIuhrPEnDi0aKQ5kV+SQWSmFUkTlkiwFSNc+M0FNNYZ/g5BEka1iCzBEJErQbm50yKUrsul89cyqUkD1kTwTBbrCj5qSMFZFnnhDmbZcRsyMUzL+OoJLlupDF+Nl/VtKnPhcit09yYxJg16nKh70Ami/KQMQHnZ34DQIdhXeB2MDvxUJCCGgf8FM0DY7MJlx5/4ZCrxZyzpPPMXy9AK+KXblqTEo+CacisShRz7JERXS0ptpkuYNQQU5sAh8Iqz1DKToK0KwZQ4A+X5HJOH09MW85aIp8QYCWgylEK2b4LVmU7lfGhkV7Oa7GTDiPaljGh2Txy2mXEZaWjyG4zHAqUM2iXERIybp5RGRABilOQaqmrC3qPWg3M41AaQF9PVDaILI0KINagNINAiK28UDojRp1jUaQMsZ9PUxFyM5CyWZyH/BQggVh5MXeaumXk56XakRlpiKqAX6QxwzploTgIIBebJTF/mnr5zCznc8fDLKb+5bo8MjjbD7p6PKAgo2SloJ3NEAeyEgfnJHNja7PJ9peiE6I2yyzjGHJ+pyCcLlkGkCJP5zF0iHu5rs6X0KJgXRIuLALZpCzQCy/LINMSGSuCZgTWFeJCNA+TVYUbOHHKoDqH5yFMM1DjgZUwy3h5rVmaT5gl5zYAF2SMA+MShEkiG8CrwACLhVMyX2c01+MMzqlkmGTYgYLiBinHL9mwkYWYz52mxjojhb4wy5wyZjKILZTk0iSD6iQNG1nAivppYnaRHEfcBJJCxIOUOHKSzxIMc1PqF4kjauUeWawyYhyKc+QVIWEgHvg679rVUnbkk7bOYJL1grTIMzqC0yjjICjyaBSzQmLkc8yBndRlWSHoxEsLPNCZbBAmPBfUc0JvyXCUtHT2yKZlPizr+u4wy/OJwqWjMEGYdHPa+YJlk6zEgtM4LklWIS/vDpEuoBLDzIME4KCdE06MTGDcTqF0YkNIRMUNQoVxwXQjp84d2YNGA+amUZJxy4akjwIEMlJIWZsKXubUcepzkpHaHaDPwiThogoQlwSlKCwh7GaijZxGSmNBmSdWG+QXQZowQQkxS4ATwOu2E8qKhZxOlsacvEjtFlKMOPNRVAGcB0CA4gogGd765ONnX/xSc3nGnhmjy5cEEiqjud2qxZBp7O3Prm26kr71wfv7X/6K4luFg5Px5rqQxOrZ0GlWI16o7x2Q1eIg37ly++b+q2+IsVfZ703XVxDMtPORWysGstY4OF6u1k2lePXD9w9ff0OgQXn3aL7WBSzOnZzP2+1Az29s359fWlkWqlfff//o5ZcZFhZ6FykLl91u8fAECHi0dqmz/WDeaput1tbtW2a9HpY0/WxEQTa8fLW1v4+YbLh2afXe9rJen3e7l27f8ioVlGMSI2HiuL+11dl9mvDicqW5uv1w3mzNW+3uw+0oX/ArOWUwE3xvfHmzcHRKETI2uq2dp2a5GlZypb0jKkpmt6aMFrwfHBeqhW9842fSIsx4vvmP/5F8/95nfQhC540vDv7RP+Ymk+Lv/cv8976biv868J35f4Q7tlX8zh+FW1f8F15yvvzVwh//AUjTz9QotK3yN397/A9+Jf0sbca/BA4WtKLiw2eWVsQZKD3YHVRX7HL59i/+TdG2VHOmPj0SSw2iyaXt3Qiyo+dfuHixm3fncn+mPj5m128AJlF2DgQpN71x5fTFjXy4rB8e6k+PHF1nCK8+O+VWrgat4t0v/3XJd2r9Xmn76fCl51DK5R8fhrISao3bv/A3ZcPI+Qv1yZEj6nFTr91/Or12KSu21IPzOEahLKt7J3HMnJYr14tZANlRAFq7JwJHcWNVPuxTMwoF8eHLX0QkyYVG4cEuFbhRd0N9cohZCLuruZrIIGpTqD05yirudGWls3uMIdx9Sep1rzAwVZdz8XjASuJF57K+1xNlZdrqbIjnjMzt+Zm2dyrGHq41pOM+g/je2uXQrWce8iDhHdl2+YwMNGivsf05vepGdWrjRD+JvTpyOD8jjeT8kjAdk+fisJjYxSgOU8JCC3lFezXdXRXsU1pEUdWJpJAYHBHToBpGixpzVMXzx/hSGLQSiyfVIQlyricnkSGjaZubP4FdL2rwpky1s4iUia9kwYVLeWuWYxVW0qg3YDiO4/MB1QMGsr6phlOdbchBwl/Mi5yE5GoARAdyKA71cMpqAgkZPpgrbEXiqQsbCUg8gzbJmGUaGAvYW4g0BwkbZm1KPMPMCv6Y4ThBkDK/j9kaK0hRqgUUZr6bXyxzfE5QC+FizLKIkcpiYmsJjBPi+H4xjWnKjdek3jJFe+xWahdhlobAxF4pSUmcLGctnSEEpiB02ymTBRVDsAqAohA4NXqR45NJeCkJmhBkQRr5fiELMRaeDqpVgCDnxoHfiiNMonng8wQIbpm+yD7DafQAX+O8cupmcTpLw2oUMSFduh6IxkCpsolB6YhLNkTZ68ctGLuh7cvmDHMNiUQCmDG4JAp0gMsko7znCMEiL/o89SVzyrEy4tiA4W2C68SQyYTLN5HrcmSWk9qiQElcITRhbT9HRrFaxKkLgylbqGckNOEKTSzLA6I1E8UylyVpMBZoTRIhF/h8FESYm7leM1YCTh5dx0dDVDhMLgFHQ/yCxFzg62FEMHN+eKXFh744H0VmN9G9SKapU8DIAIm1Jff2korBllKrEXJJpljUyEE18ArOaCWvBwaNBScsJ2EQpUlo5yOcZPnJNbC3ZItTct23y6xsxBFyw0IYxj4Dw4WOkywFcjwJ05CVyxlRIyYI4qQwW2gYpEKN8yeERBBBDiQmKLDYwp6dIzHKGpJlUiamuCCwtAc1GEUZMbXEoqqPXUeJY46VAKcmqe/6qBCZIpyF+RXGWbKRwSvlNJViGkcQ8oEhpw6VWqK3BMk0YrbYiBR8v0GiRZpkrKW5RbcBDi9Ly3u0RYK66ylJaEWEDdx6rCxJIXJKet3pA7MUe/ksvvBRifPEOHDy2XxLmT4BNYus8oYSa0sQM76rZmTksvzshqqYJk642KuB3CKlfmrUA9XN44tLQn8va5BAc10tcayQYYlZizQ/hLY/57I8IJG4XHBI4nTFJZWYDeOIaQV9xBQxVjl3wgt6muGIaSRZZDtRwZ0wCPJSEbszAeU5lEKo+FGSxlYuMnIMi9OCGE40nDBCESA0iRUl8BlvrokykcTUWEoww1RFvBbANKK4HY2YlEdZRXANLZMwJJzvNxBlMv0wdFaQB6Is3EiPSnywSLbCIE+IFKaEjYpsCPyysUWf1ERyk1wCYYNGbETmflxIQi0is3VuX4zch8w16FWJxcS1EXWL0OOSeFFkhzIMdtFK6FeZQExzR1HcST2OxueFzFpXFndJNwyKmatFZYP6MraEsLqQLy7UJz3uykvIDvWdw5leRiu5dhXsSjzfm+cfH87bHc6k2tNjbvN5u6z1vviLUuR09neLnz6z9Ty2E/3J0TIvYzmobhbOYjtzktz9p4aUI6pUvvc4/vyLk8vN/mtfrRhDtPCU3Z7Y2qI5Ib/91GLl6HqnVYOmJnEjU3u0bxeKiMrqsxO52rXWWoUHu36lNC+39CdHYa1AGhsP3/gF3nMgwYXHB8AMe19+ffXhM1/X/a2Xnm29IICYN2358DwMs6ixnts5ZGr25Msvnq+vQJBp06m6f+bpOSZflA9OEzsZ55tPX/45HMf5YFnYOUQpybob8vEg4nlQqT9+5UsZoTqxC0+PCccN1jeaO3cwiehr1Z9Jc+4vloPi453PNPn8hQpZz03/6/8We574eCf35g/Tf9Ot1P93jcUfHea+/7353/8v7K9+TXyyIz15nH2GZzKOUz94z/3cq/bX/9pfvaAQ06R8fHTt7beDahFH0aV3f2o0Gotf+o9ShDKEurtP1m7fzli8vHH52jtvAxH3XnqF8AKN2bXt+92HD4JahdYL6598jLNk8vw1wvOUss0nOxvvv5eUtWaw1334gGjq9t/72xmCCcdVT0+uv/kjxCO/ULj0wftQhIPWagZhJMnN+x9t3L6l+fbFVz5//e239iFBCK/fvVMa9M9h3N3eru7vL7car1DfQMJofHL13Z+SRh5wuH3/bqiq9zfbKcMkDMcv+jd++hO7kJ9f3bz67k/DciHdyN8QNE8MyOC0u32fQbD/c5974e23PEXe/drPzxptRPzcdLx29w5g8KK70rl3T4yj3ivXPp/alCkd2KOtmx8jnCKVaz3YRnHWe+0ladwBrib5x/y4hD1FGx1cUifXqNCP3YFTlYaYUx5zA4FEbd47u0GXV0juzDh2py8BtyktB9BThZFOpNsvMos1rD6Ipsmoic1ifmSKPhss6oxirivBjSwZW3PnpEmiFjbG/EKBdj0/mK1Ik+dT1jH6R9YVvFyT1ANuQqOwhXMDntijCy6fhnAlnZ8oFT5WRDNsFgTLgc88e6qVn5ksb1r9RjG0OGEZXunw0zl5PLLO6xLn47Fpj9UicoSa4X9uQzo4Q1N7cKhWQSD6wfhEqVQiNVyaX7gq7p8nE9HuKw3PZAVm0VNzKdGLgd0qcoM+e2rMhgWQRvJqbA3kkufhNUkalhjJE6ERLYopAgK3+7wguPbZGVPmJs+lXCZZ02xS4ZGb5eeGjkJeKY5MaDSkwAeVU2lwJeR43d95IbJVpA79yWJ2HQEuLk4Yp8k7gSTYSY0HAIgJkSZlkKYyGqJlJUtKert3FcA8iJ9Ox8rJSsQVJHNBx3kYs1idj6coPAKFjhsew8VUyi0IVpioU1JvH8ZGye7nC8UoDpnwgK3kLKT54cZK5eDc7MnOiFOPTEQi61QqkRn7Cs4adfZske3yi0Wx0LThJLUnvLo/xwU36XSl01nYFxaHQrFu+2fUGOaK5bnIL4OtTWX7KPbK5pmiCAbhxPmhWGoG+RRju6H1zzmJEKujhicaP3+RxkLkjK0Qzy4j6OkWSe16/nTkX+UpwyY8L1hRtNxE4IxNx3Ba45KgAx5eD0WOmd52y8BqF9wzTwr4+Usw6sflJSmISWJrU4SNMptYRLQysys5IyRNXs2iY0BPAoQXTcEPc4mbLctyHAe1c/sQRpgpbwV2D+EMFflRsplPczp2k/mIEy0vv24Zh4IH+fyNhMnzqSSo904tc90litqmzoDhzaRYnkI2iS5v8E/mzkmBeLAoGE4fW57YANPkcwXssuxsQp7IRqroUy86gb6NcuwEbKKkrKumP+vnIkMoXDbDp9mS5CoTX10w3rLGCA4CGM9WOe3RVXDxkli8CKfZ+DJyGnrfEbPMnzcYxjFLEeFF4KbKRRa5TaQPWRsBt5k/f9zNTz4HuCTofxpvodmGpN3FJqJmhx0PwBpIMUN4QTkNkkVDpEMgI9/YYtUnG9zFi5noen3fWkVuU5mkmZww8zrPn/p64h6jtB4hZ+GMSgqJ+TXbf24N7R7xC7t/oitxlMee0xO5CtHCgf/5VXG4DBeRNZSKhsvVGPeAh01Uawagkcf9MTqdL/tV3st4PV4MRH3m5SpWXJZpocA8No0LlZdcMbAHgxqw2Zy+iK52pZM+Hs0W+1pc4XIF6p0jVoiUCse6q8KS8JrJXKgxLsjB/gupkw/lUXzuz18HpCxWhqxZkZYs1XuvEivH5vf9uTt+DgW8KMwYUwrdGpebXIVJFSZHszHbawaoJS5HYKKBIF8Ynm6ITp0kSzieWM9juyxr28wJjmFLNHpXmf5Vkh+6R3P762heEeQBNVqZUZfrw83btzqfPvQaVZUNWjc/phID6198NYkNzxQ/+Wjj5if2RlMaLTufPgyrlXv/6d9OMUoZpnp4cOOHPwxbFX5pbnz88aDI5OXg+ahkw4vlvrN58yMscdO17rV33sYiPr96I2XZWBQvf/DJyoMHiawsr6xu3vwYZ8lsRX+NJhakyeMHa3fuwJyg5qrdu3czltst/rUbb781un4lLOqbH3zgbK0Mrt4wC5VA01d3Hq3c/KRW2J08t/XcW2/Out2jL31pXmuI7rJ6drZy717l9NRYXd+4c4uoqvn5TVLmcUa1+Wzzow+tZktBSffhw4jfP3rlFcrkKcbSyLzx5g+dStnrNC/d/ChSFGdzZV5rxby0sn906cMPMgBPfv4Lz7/1Qydf6K39DFyEGcNy/Yvcn/0p8v3PSlCHcP6f/edU07l+v/CdPwKUgn9TOxC//hehFoQoihjTiFZWgxvPwyyTnj6BYfhZRrmMYaSdT71Xv5Dk8vAvJ8T7d7gIl1/7mlcu9V963lhft2v1wy99CWdUdW3KsWG9GErK7i/+QlAqLLsrgxefS0RBdD2RBJNLWxnLPP6lX7KrxVhSnn3964DHvOMrkePUa8tWd/zi9ZPnXoQcu/OLvyjSkPVDDNJIV+ar68Pnr4+vXfXK5eErL3qqprouThO/WQkV7dlXfi6uF0drl8c3rvavXSOKevzaa7Prl6koPfv61+1mbWose6wwbW6Yna7TqR+/8UVPL5y8/npYyvFhxIYBFPGi1Z1evzK8cWXZaNsrrYPnP2+Fi0NGnTRXEkHc/+rPe43SstmZXt7qX7uqL+ZS6NmVcqhq/ReeP3/uBpSFo9decy6tjoL4LPZPateCRsVqN4+/8Foka2cvvzzevIzhmGcvjBbLCnOBjpbrzIRTxoFxnG9y0AbqJK2kgUo4Zmi14Dyic7J81ryBWQcxc7ubYGAg4SLu8kMAxzQ8LG2lkiXFY2MrZRgbMAu3Thwa9Ug2Lrd9NRXgIGzHQSGR4tH8MvYAHFLSK3UBGyKhTyvEKbE8GIWtOChr5XjG3JBZDeZFh11hCc/zZ3PACqCb06mVvVSU1YjHMfecmkoi3j1LWRFsNHTBE9soXtUL1Mqe1wDPSicjwuu0pJZlS2gC0JJ0zpNq1G41hZNJoOT5EpdHTnJZRiU+L7pSA0QSi09nmBXIei1HTO66zBUyLXXYKyJQMsDOkbg01hic2UnJ8Auc4c2fyhW7VMNgCNWpV4OAMRhh5tcgJkC2zTDPieGYNI2oxKdowkjjRYNfxsk4S06aqxg6UB05tUyJhlHFhHmahikfx1SALB4DwTJXoUCXSW6xLHKuPdpnZKvWSUUb46HfTmLeZ7i538106EqFOOvIjA7zspttamBhi8Ol013RclQUwvSKKvCkmLOyFS2NqXg6josl3JJz1MheLvJCrIghupFHXsiez2m5CipSUbC5VTZtyLnUyl4uiZ6XjbwwVxRyqKg7WVuEdbHA2MyW6HCacjJ2KzW1gBQxTi9rjJKVNDdZ1eNiooQjYzXJCgkXTb3N2JXZseudinmrWmDRNCl5RouRo6m16ouZh2LARR7HJ4RfpgXTrwIWTJKS0y+W7Hj+WGwlOubjqdP1kxxm0IAW7UgDvBtJJPDzSIqmbsuJipnqnUUrXlCSh0F0ArhZTlfSflL0lh1OCqZRbeaU1DKzUItxvKrrgpvTgnirRI0IL1xSzZeQzbdB2lB4MSrmfNJR8NjkzICsNUUplStJ2pUK6UJs0qRbZC1fGC6d1RVNdBUlzK7nFBwI1SxZywknQ2DHSbstFrOc6OBNickhVQzg9SIzd7K5T4s5XSJaJaQtNdNRWXbJls4wNoJztxNjzkNcP25kE1EaBVZfa3slKIWjxRUAuIAB06AdIJTxXqgkvpdnWTSJG6FTStV4Ot0EIc8NSbBX6qZCyrDnYTWJyilPR0E3SbNUiimmAVJiBBdOJwVylHHjpBJOFWUR2b1cx6zJijewNkIkeiibec0Qa1AXA6VCwstVmXrsNRFKDH80pIwCavm8YMvVNOtIKh8ItdRfaYjn0xgJTFnMA4u9IsE8q4uuUMvioiScTiDk40uNHLX4dQQaQjWZMRssrebA2Rw7cdKtFRlTqGXpui6nnryBgmpe2zlIMi7r1EUxUItxti5pmcU3mbAjKeEwLi1BMfOVmOMGyyZa+NEIxr36JQ4sU3VhNzKRjBNtGLelPqEjGveL3VjyODSz1lMuMxN16ZVSM3YPUmbRWAlFn0fDoEOIEjNoZqxzXhKfYHZQaiFoAWWQVoFdwAIYeJ20j7VJvOg1r7HYpqrptjHEBhIv/Doabm4Bltn5G38j1YVFrXP0la9YLLxw3IFeHW9c9SuV/qsvn157DnPsw7/+H8qxxwQRQ+NIV2ebW+MbV8eXrwS53OTla/v1a66x+7D+QlgpuYXywRuv+Y3acmW1/9JzkSTJriOHTv/qDQzSx7/8y3616BVKva9+2c5pA8c6YbThyhbV1fM3Xu1df54F6eP/4BdIXlu2utNrV4fXrzrlyuza1nxlRXJcybUWq6uAQUdf/7rdrM4brcn1a6NLG/piJofuotlMea73hS+MNi9BkT984wukogOfCHHoF/JBsTC8euXs5RcAw+z/3JftelV1LD4OU0WYrW4sVzqnr77i5Irj69esblN0Pd73wqIWFMonX3jNXGlP211jfb3/V+8iRAgRUvqt35SePP6M2RUkxHv19dl/+V+hINB/9IPcO2+l/5aq3/87YP3FhZdpQELCy1fCK1e5wQV/dvqZlpEQojjmRgP3i18Cnw0d8e8PNLqi1asbd2/FeU0yjeburl2rlKbjz//pHwc5XbPm9d19KvFsGncefhoVdH06fenPv5/kZMGy2s92I1lSid989CSVBZ4Er3z3T1lImITUdp8RXRYcZ2V/j8gSl4Sf/8M/4ijJZH71/oNU5rkwbD3eSRRR8IPXvvcnlGVYQDoPP0UIYA40HuwAkcsw6j59yiQxZEFr5zHrOnGzuvKjj6S5G1Ry7QcPlcilPFt5dihEQaLLr/zZ90v9M6vbXL99R/DdoFJsP3osh24i8I2bO/zMoqrYfvJUXi7tbmvt9h3Zc+1m4/k3f9g+3ItVtX50pHhOUCisPLivLBdOt7H607vK2dRYaa082NZsI5W5ytGRtFwu2w1lqfK+SApEmmHe5VnWpH6FGOtUApzHiBYr8FNsyCjUiBazVilerGPBU0wC/XwqJULAipaARAMuKt7yUpKLVAvzjphxvuSm2Mtj3kv9YmxchrzFGwwf6Eh0kCsIroKRzfmCN78M2ZgLEbYLEjvGNsf4OSg6cQxBP8FMKsDY62OeJTjKglOFlTGIaDIFGMb52FpOJIwoCzPvLMeymEvCaJTyXIJims0AwlQGsXmoAojyYhicQ5YDOImjIcizbkRZtydhXlBxEA0Rm8YCF/vnCDMZhjA4VTgMcZaQCQQgFXEUDBBOYq7AiVOZowBDn7NURBHHuvF0i3gFVpzziwpMeMybrJHj4wzgUB0okssnYiotJdbLkGyI8wKmAlUTPKwlTpHlXN5VcSJQMZEXIudDQZ5yZyV5LsV5KhsiHzMstgWDA0QCGoGTLrHrWJrxE4VJZCi6vKNyAZvyPl2SzECSFtNJHBsIF5jM4OJTltWgYDq+wTIyRH5Gx4mUI3SCg5EqFUGypOkS8ELMunG4QIJIUpd1B6qsE8aIqIl5PSVGms4zjgmlgMz6RVEGLEniKVI0ki7ieAYkOSKeQE4ErGAh9P0JFNiYjZNogoGGlBgqhoxRxCU+Y5QwjRickMkNiiQ2dbmFhnHEkUS0VIBTDga5wwIfEp41U6PDYMpkAW/kOByiFESTqxQJAnB5s4yThGEdbl6BKMNZrJ3pUhimEEqWyiBA2VSdKpgkkPXp4HKEyohLpJnI0QBjKpoqQGyixOAoSCPEFyG9CLFPGC4NBkLmSYzG0CGBTqzk4nQIkgClRZ6eI7DAqhjSZRp5GGgMnFDGz5Rc4B5zoaHyZYwnUeZmHBvDeRT6jKok/liJlpJWjulpSEMsqiScZtDJeI7QCRMZiqwm8SxLrEzNh1k/oT4GJTZvxMgrIC5kI8jaRSwYYKlFiy0ke8jjBFdFyFHiBDlFyEeSi5SzgoAX2GcYL4cEG4eS7CiI8VAAveFlhKlAU87KI8FjHcjaGuZsNhRKR0rKUT6JWSPPA5MjBDgNhndhIAeTTcwH2AeCkwM4EmjMmQXIEAJCckYVGGRJGk4RhinPUu9QgZSRpNA/hwgBHsfxCAqYoAx4x0KKeJUPoyHCUSSqSXAGIcKQZ8IjLCQpZrJ0miEIkYDIaYziWFRocKIQqkAFgn7Ep7GY+eEUJRRmupA8YxAVJTmIz5IMMowIwYhAmiKZkZcS70JRGKN5AcYy0QizqBO7xbKWaGIYKamSyIbA+QKS5nDU8dw1RjZ5R2V9HjGuaDMoUhneTowucVaxOBNmMhcrULSRLQk+z2ALWoXAWsOcyzky4ykSdwGXGhspkLczt5aaG4hxeQ9hW8K8JbgiG2iZYpd6R639vVAWiu68sHeUqKJoee0PH0SFgjqd1Pf2oqKuLJbd/b1IllTXfPm73wUM4gHtPNqJirq8WDT29lBBREu3/dPtRFYFGnV2nqQCy6RJ5+GnUVFX5/OXf/D9RJd4x+08fRILLAeS7sNHKYsRpZ13bke5kug77aePM5FhIrL6ZIcKApX59Tt3GBonith+8pRLIoqZV/78+7LnhZqycf8u7zl+vbxx+y6bJrGmvvyD7xUmg0RV28/2GBL7+dzG3Vui75Ja/uqf/bByfurUKquPH3FpgljYfLrLRqHTar3xh9+u9Y7M1fZ0Pn64AAAgAElEQVTq7Tti5MW60to/4F0nlfnn334zPxqE5Xx3+4FimXa7tnr3vhwGCz3HPfwrBY1mDKP95Mf5N//8MyZKME2TUnn4P/yPmSiKe7u1f/GNjGH+bR+if1U1iOR7d5VbNwEAi//478T1xmcnn0qfPsh//09TXvirjfagNJxv/uBt+XysXoy3fvRjbmrmj87a730in42k6XLjrXdLz064hX35+2+qZ0NpOFt75wNhYuhH52tvvav1+sLc2njnvcKTA2wFG++8r54O1OH00o9+Ig2m+umg88N3Csfn3MLsvnez+PQAWcHm936kH5yIw9n62++pZwN1NG+++1HhWY+1/I0f/rj25AC60eUfvJN/dsyaXuunH1YfPGHsYOUnH3Zu3Y8Be+nH7zU/uZP5ycr7n9QePpGmy/ZHd+r3dxg7bHzwSefeowjyG2+/2/7wNvDJygefVO/vCFOzeWe7ce8h40Qr791c+fBWiIX1t9/rfHArC7L27e3qo11+btZvbTdvbQOPdD+8vfb+JyEWN97/qPPRLeDT9p0Hzdvb0sxq3HvUubWdkhRNStloNU4FNC/QxQZwMDQ1MGlkvkidKh60CBXwvJiN1wnl0VzPpi3oMJmlp7PV1JeIXUYXrZhKzFLHkw4hIjNT6GQts9nU1rLxCnBFYOnseYUkEl4W6GAtSUQ0V+hoDVgc8BQ4aaWumDp5fNFOYoFZqnS4loZCaKPxoRgMQeAzs6ess2BSH5nHMHAZsoCjAyGaZNROJ0eKPwAkhsYBjAxEHDh7xjkGRxbgYo8LhoBEaNFjkxGNCDfb5awxE1lwdiT4CwZadHnG0SmNQjx8xodDFCb84imyRzhxM/MMhwYiRjY9Evx+RiJmsi+45zBGEhjVU6MBPSad1uCklMZcNmlwf2FA61fBuJESBk9q6bIJPCZZ1OGsRCM+nXeZcTVEArqopNMOSThmXgJGHTqYzmvZpEYJT+cddliLIMdMqnS5miRcNqkksyZ0cGrUwbRJYh4s6my/FCKRHVey6Woac3hSSGctEGBzyi12cUA4b4CnB0KYsPGELi641KL+gpkdipGJnTm7eIyCmIsnmdFjMoKCQTY5FOIF8JfM9FAKDRSa0DrIohjHUzTY5cOQDcdgdCDQRZpYqXnGETNzFsz0CRMEXDgGkwMx9Jh0TsfHOJ1Tz+amz/jYQL7JTp8wvg2hzcfDVWwoqc+m43U4z5OERxf11NBTT8xGXero0BXIaBXOFRoL8WSNWeRIwuN+OzNL1BezUSez89BmwKTJTCWaCOlgBc8KUSYy541sWQYBn007qVMEDptMVtEsFxM+Ha3hWSnKBOainJrlNODBrJuZRegwyXQFzIoREoxncH7Ixglr9ZjFuZQ6qTvGXh/EEZqfi/YBCrDo7IPxkZhQHA2BNeKgm5gDzjzlSMRMzwRzD0aMEJ6ly3OOEGSf49mpQKzMGvHLYz6NkduH3lEaM7xzDKeHQhJi9wzOexy1Un+O7TNMQmj2ueUeDgEfnsH+M55GOHO0bLQGbTG1c/i8SWKFMTUwaidEgguFjtagxWWeko5WU0umvgZHnSSUsCHTwWoSyXApRYNVbImZJ6JJGzpi4mrZWZf4MjDUbLye+gK2xXC6hgwxCZVssIIcmQYquujSQM1MGU46wBOhydPRGlqISaSAixXo6EHALA55b8YkRjo9EvwBTGI0O2boKI0At9zF5pCNXbQ4ZO0Jm9nUOmfIiNIYjQ8E9xQFmbB4is0hR0Non+FgiRMrnfQE9xxGhJ0eie4JjjEXHGXWlKMhWvZ4Z8imdjo7k70LhqbYOGOjExph3tzH874YeXjZY40LhkYgW7TZYT0CHDsqZ9O1JOHQLJ/N6tDFqVkB03YSCnTZwBf1CIrsNI+mHUp4PMml007msKlZhuNWFnJgUWL75RAIzLSYjFZTIqB5Ph13oMMCuwDHDRry2aIKB22Scsw0R0frNObhXKHjGnIZahXAuE1jkS7K2UUnSfjC4enaWz/VzkbKcLr+zvva8YU4mGz98C1+ZuSOzi69+RN+auR6/e6fv5M7OuNm5upPPsqd9IWpsfWDt4TBVD8+X3/nfXk41c7Haz95L394ws7NjR+8Xej1uZm59f035YuxdjrovHdTGkyVi8na2+8Wd48407v0wx8X9k+Ypbv1o3eki4l6Mtx4+131YiwNZqtvv1d8vJ9QtPmjn9Tvfcqa/upPPy4/PRCmy/rN+9VHu5mfrL/1bvfWdpSxG2/+pHr/EbbD1u0Hxd1DcWq03r/VuPcoi7L1H3+w+tHdGDBrH9yuPnzKWGH7o9uVB0+Fqdm6ebd1e5tGoP3+J+0Pb0eQW3/3o9Yn91nTa398t7a9w82t2r1HrTsPUwLXfvz+2rsfRZBff/9m88NbgGTZX+EJVsZx/ElPf/tH2LI+63IwTed/7+8n1SqMotK//G0Yx/+OsQz+w0LhX82+4vDS1vgf/Epw5WrurTdr3/in4LN5oAEAVFWH/9P/HFy7/hlBD/8eWoRf+3ryd/9O48ljq9PIEM7937y9WaylWXqmtcZ//vc8nXP2PvM5MWZkVJWrytV22WU3INFSQ6tFCzUSyNBqaDVIXHIBFy1xh0ACgWgZWy4P7a7qqrKra8iaMjOmHCIyIjMjIzPGM8973v/+52FN3BiBwG0l0OXbpXWxtK5efY++9zk6629vISkXnz07u3HDKcLy7ulkY40Abp8M4m5HYFw5Oot77Vi3F589P7t23ZZJZfdkurKMNOQcD2TdZIapn46zpUZoV3uffHx2/XUDZOWdY295GerIPTzLmtXMKVWOTvN2Jai2eh99dH7tukZE5fl+1FvSKQMXEas6fqvVev4qajRZw2282AvrzbxdruweS40GvUXrbOzycLbcs44GaaXC6qX6i92o1sgWqu7+GQIqWO1a52ONZ5O11erBaW6axUKt/nI/LlXSpYZ7dIE4P796rXWwbxTpeG2tcnAsNG20sdl99rTQjXil7R72SZ6dvXajeXhoxUGw1nWPLzLTHqxvONNCj8Wsa+ixtD3O62kuHNNn0YKuRRKnwLHGCa/ABAddqofcnPKwS1w/5oUTtbARMhpLWWOq0Gmg5j1dj5g1ldEidMJYZI6oMpIImeuqVqiM0kDJZp4qxx7xaBE5SSxjm1cZKZRMddcchaCCAqKaeUAs42guW4aO8mSKaBkiDfER1ysqtx106LPlymJyOphXYV3TSZ6PIXGBMgkbCaOsMtsiB/OiW7Zxll1IUNcMjedjRWwALJJPVEubzWsteZyIpZIF4/xcgrpOTZGOgGYI6FA+kYZZZNUSPI5403K0PDsXqqqTMkYe0UWe1CD2NEEhtlNtjAuDwFIG5gbiQtUKEdk6y7M6wHNNIRk39FK/EBqEbgo8DQvlL+qWp/Q0y+oK+RoEKm5p9oABDEvGIMg7qFDhEjUnDOeKNYQ5kzkx4zoqDXMAgKoVKjJpInizEIlJM5W3JJ4kRQq1NmYJhD7L16okSOkgYmtlOwrTgPBFB8UF9nPawixDYJYbiygSJu1HfLVkh34aUNwkMGN5jIwGEAyLcW4uogTZ5NiXa27D64+yOmkgXDDmA60BCkHVpDAWYAIteuTx9aqRJ/kU4ibBgnNP5QsuprhynIQLhqlCOLHyKqI4AjMTmEyYEPo61ePYtZwzFrd1A0VgZIgSKMFxmHaQVnBTYk8nRhK5ptVXSRWbNMYjPXchsgro6RAz4Sg0N4gWxyXD6qusjLIyLZ0WwoXQzOBUkxpMatQeS4riqGJYfVmUcVC37JMpKESxWiWjRMtS1db5VAKlisUyvQgBBHoTFGMFcsHXqniS0DDR25B5oEA679j4PIRAaW2UTyHIOFut0HGEYgYXDTpNUmUYNSnGTEKiNWE2hzgqtC6JI4rDAi7q2iTKpKHXJZ8rxqHVAnmIwTzPtxoNb8Yjh9c4zblMdVUpFCdkDkEji6HjDnm4iJ00UqFVVAXlXCambUxS5KhAg7U0xU7pPAt6upVFMrB5VRBegFAHpYwhQ5sjWIkTzXVP87CrWSyRnonLKRVZmtaQmzJIdQ+CShLqbulcRB1siRjO9KIKORViLOrIixo1fpqLtmORND/joGZQW6QjQDWJS5hNgUHzvO6C04RXTcfMszOhyrpeksVQKg2xpk36sUGLvGrD01hWdN5w6cFMWVSvqmIgFUWy4+DzUMcFKcNsAHjVElWTHsyARWgN5SOlMBRtWzv3lYbzhZI75FCBkjkIihbKQNilxozRWGZtZXssh3bSQM64gFKpWqFik8ZSNDOWWnqssqaw5owpC5RT5GOpCKhlKjJoJEU7zwvX8HnWknZQMG6pSkoiIjgtmxee7NBIyVZeMMucFukCNv/iToYjAHKqaqkUcOnTT09v3lyYn4hhPl/pUpHb5+Oo1xYKV45Pw/WlhNrLnzw+ef2mJRJ3/3y+uoygcM6GyUKTYa1yfIo7Rr+y3Hv8ydmNGyaPS/vn89UeQMo56WedeqFbtYPjuNfyS43lx4/7ly7pRJR2TvzektJJ6ehsttQtTGPx1U683J47tZXHjwdbW8DVS/tngMKwu+geXUADTZeW289eBK22t7S0/PFHabOZtiql/VNJteH6emd/D2E56a22n7+MGg2vt9x7/HFeKuGGLgcJgGC0udV59bLQjXSh3ny1F9Qa097y6pNPctuOljvOyZAWqbe+4pz0BaXJQrO5sxeV63mnUnl1wEwrWlmwjwdYiItytfrX5iJECMVx40/+sPzTNwCEn2d8hdI0+O1/Y/hf/JeiVKr/yR83/tkf/tU6nf8LIvyLA0zHY2UY+cZWtr2tnZ3px4fg8+mtUVHoZ2fR3/g1Rem/3r/4VyLC9XWn1X7tRz+KG3XDm2/fuTftrVQu+lv33vHrDTsKNm69Iy0LAHXlZ2+ltaoRRltv34nbTWPsbd59J65UTF5s/uyWoJrS6ZWf/kJaBs3ztbvvp9WKHsSbt+8WlSqRbPvntxQAwrauvvFzZtuI863b91jZJWmxeftOYZiA4Es/eRMjKGx966e3ha4xy968846WZsy1N9+6bfmBt7n22r/8iTWbzbtLm2/fLfvTwnGXHnxshFFWcbfeuu3MvMnW+rV/+YY7GnmrK5u33nG9SVIqL3/woTvz0kZ16/a9Un8wuHrp+g/eKA2Gw43tq2/fckeDtFrtPfyoPJ7MFpYu/+LN2nl/eOXStZ/+onx2Pty+vH3vXu38lNUqSx8+ds8Hw411c9jEXj1rZdapjsKORsciqROvxc0CBWVr7BBzhKYN4LfTemGODegvAC0wQyJnXamnOKkYwyqvhHTkEq+dVRJ7qgN/CVBfjyn0ekgLYeyQcZuXQm1iwdkScmbAc9C8h4mnpUhOliGNcWrRUYeYI+w5cr6M7FmWgvkrgpFEBMyfIawDzIS3S6kpZKomh4ZJimruHR01kOJEU95LSjRAhPD2qKYzmIPJkaljqcNstGMakmELes8IIBBK6R1oFRhKDodHNkFCQ8XolUkFQzaYP8UIQYSVt091xKQEs0NDk9zQ2eSVAQtBWwY9aiBuIBSoaRdlGqE+GK3SCPNqpJ92UWoBN9QGbZRZAIdqvkQSk1u5OWjhgIpqYJwuqaySV3LzxCWZg0mg/EWcWHlJGv2GHmjEHsD+skpreaWwBiWVVgmeU6+mwhJ3C3PYpIHDq3PjpI6SBrRnZNZUcR0YQTyUyQk2Gqo4V8EJRh0N9Nn8zDBsBr1icmrpLsgDnO4r2oTsXITnmlNj+QhMzgzLZigoJie2bgkegmAf6XUlBmJ+qluVopih2YlWMjIjiE7PqobOeIL8A2LUgByL2aletuMiorMjwzQ4zNhsX7MMJnLk72FVo1aGyXgFo1TjERhv4yKFBidnaxADqHIy7GKUYi7BdA1IRlACLzaMIiU0VBdbAEIIGBz2KMwVZ3C6jiUnOFEXm7Tg3Mn1kx5UEBFGhl0smVAST1eQUIWl3JMFmCnpZMZpj0NTUG5cNDUpAORwsgIZSmsif5xlPsELOnuZpz629WLeN7mPUBnFu1JOpdEB0UuYeAQt6vmeKGagpsXzgR55GqqR5EAVI2V2VPxCxhOK20Qdi3CqWWae9GEyoXaZhyckHSFjESQvZTTVStUiPsPBVHPMLBtCf6SZFRkdwWyIrI7M9+R8ZGgt6M4RmK0QGoHcoMOOcGM6M9C0Cx0Pzk3oLUPkGbkS4zWIE8R0OlzU9CkKdDBbhdYcRQacrRLia5mA41UMAqRMMugBK8EhRdMupROYGnC6BmGEhcSDFR36iGMwXFFmCmMNTZc13RMxRbN1BCOiJOyvIlKkIPdfIVfGSKrhsY2A1HAxfmWSgpMymH+GgQRYB94exZJDIGdHBuHc1Nl0xwApJ3U4/xQypWEbx68U4gxD5R2ZgANZItFnEmeK1lTwDGVcAzaKdwHNmQvT4YkjcqhqWvSpFDEwGip4CrKcEBdlh4glENU0c9g05jqx+2jQBXE9rRVW3wVxA5O55pWBX+VObkzq2txlVV87q5OoAewp8RoqbCE61/wyDBrQCMisjuZ1UZ3rZxUVdqA9hZMKitoEz0joKq+FzBDN6tSrE+cCj5oyWETmGPlV5HcI8Uhko0kdmR72W3jWlJWgsbu7cfdeWm9Ug/HSvYeZ6xhpsv3WnXBpsdQfrt97L1poOWNv8+69rFKjRbr1i9vMdShjl35xK2o1rNFk4933VcMFfrF163ZWrhBWbP/8bWZbUMjLP3srbTaMmb9x792sVkFRdvntW8xyEIaXf/YW1zWJ8dWf/Dxstqww3rp1m5VdlPHLb98ShhU3qjd+8BMtTtJ6fev2OzRLc83YeOd9Lcv9ducLP37DmHnTjdUbP/iJFsVBq3PpzTftJMnc8to77xlhPOktf+GHP3Rns3B1YfPnd425P+8sXrpzRwtDaRnr7943J7PJ+ubNH/2oNBgOr2xf/cmbpfEo6rRXH3xkTqeFY629/4EzHPsrvcs/ebN80R9cvXz1p286g+FgYUH/a0OEEDoP3q9/59uQ888zvoKCs3Zn9I/+c9bumC9ftP/p/wKU+qtjGfrLTYc/fcN8/hQoNf6dfyAq1c//YH33Ve1bfyo/tyLx/2/wUsC+GG2/fds9H1aPz7bevGUMJ9WD4/U771T7E3c637p9t/JyV594l956u3x8VjobbN2+Z/VHtdOLjbvv1I7PrdF08/a95u6BPvMv/eJW+fDEPe1vvX3HvRhWT87W771f2zk0Zv7m23c7O/vUj7fferN6cFzqj7Zu3S0dn7vHp6vv3G/tHRpeeOneu+0nz6gfbf/8rcbOvj71l+8/7Dx5ps/D9Xcf9D74EAiwdvveyqPHOGMb7z9YePLUGox7Dx4tPf5M96O19x+uvf8BU3j73rsrjx7jtFh57/7ik2fuZN57+NHChx8TP1p+5/7mg4cM0s13H/TuP0JJsfze/e7Hn9qj6dJHT7qPPiZxvvr+B2vvvCckWH/3/uoHH6E4W7n/cPGjT8zBaOGjT1buP4RZRidlMlgQnGrTEhx1sa+wZ+PxIoop9KvkrCUZhsMS7S+JAmtTF0yWSaiwZ+HxEkp0EFT0i6YsiDZ14aDLCx1NbTjqaTEmkQmHizLSVVTRLzqq0IypRUZLKkW6Z6Jxl/qI+DoZLuKQwMDG5wsq0/DEosMejgGI1HRfK84LkeNoj+QzLDwR7ULuQzAqvEMtGwgZiNmeBvuMFzh8BZMJEr4I9hDzoBplsyOdX+Qqkv6+lp8ykcH5K1SMIYhktIeyCWATPjvS+YUAiQwOKD9mnOFkF6UTInzh7yE2g2DGZodafKHyFM32qDzlShFjWMXTOg0BnrW0aQWmEE065rDCFdL7DTheEAWGwzr2mnoC8KyJJ1WZEzXumP0qV0jvV/G4IxmlkwactnAg4axBJ02VIjxuk4u6UBCfV/FoQeSITmpo1iahRNO6NmpKpoFJS+s3uUT6sAqHCzIjaFLB0zbOYTrB8S7kKUjPpLdHJcPsQsyOdOizYqz8A5p5KpqSaAewAouh8vZInqK4D71DHc4En0NvD/Mpz+ck2iMsQ/yc+ftExUr02ezQyEeMeSrYI9KTxRz5LyFPYNHn/h4VCRTn2fTIUMNM+Gq+R4qJyDwYvIQsgnqE4bhHpgZKIRr29IktGaWnTeQ5KKG030GhjT0Ixz3dM1AC6LhLRrYsML5YQl4ZxlgbtvHcNCKMx0v6zFIpJMNFY+IKodHTlvLrMiZw2KG+rccQTrp46oACw/6SMSoJgfXzOpzXUErJsINmDg4UnHT1WUkAkh3C+SHhBQwPQXRKQMDiPoqPocqAf6ZFe0oIkByo8IjyAqVHwj+lYl6E5yg6hDyF0TlN95WUID4E3pGmGApPlH9Ggc/TPgoOIEuBf4bTPcAgFSfSO6B5hsJzGBwTNOfpECX7UiUy7ZN4F3EJi/3cP6IoFTQ20KgrA0PGZe1iEWS6NrfwcFElRJ+baNTTfEJ8QkZLJCAwsuj5gkx1PDPJsIsTpHsaHK8Qj8BYQ8MeDg0QmvSkqVJd83Q86IGYkBmF42XdpyihdLiEPE1FJr1YAqlF5xoa9bAP9EhHox71DBVT2l+goSVSkOzBdAyLCZ8d6fmpgIkMDik/yrnA6R5Kh0SE0t+DxQRDX8yPtfgc5Bmc7VF5wpnAyR4M+phFKjxA2QABn3knWnEmVYHmezQ+lEKAZA8kAwISkBzCqI+4V3jHND2WnOPokORHkgsY7aqwr6lYBYcwGSCVCDxpkoumUBhdVMlwUeRIm1bgdIEEEs0qZNxWhQZmTf2iISQ2hmUwWJQ5gZMymi5oCcRBGQ1bIqfKa5uDtpDYGNhkvIhipXsuGi/QABLP1QZNkCIyq+CLBckQHthk3MMpoBMLTpbIHEC/RIcdmFM0KdHTphS0sXu4fvfd0sGx0x9uvn23dnzm9Cfbb92yRpP63uHWrbvWdF7dPVy7+15178gZzbZu3a3uHlj98aU333KGk9ppf+vWXac/Ku8frd97r7FzYM787dv36rsH5nR+9e3bTn9UOT7duH23PByXT/vrt+/VXu7Qmb/51q3W3qHuBdtv33UuhuWT882771aPTpz+eP3OO81Xu6AQl96+vfDJZ3Q2X7n3XufVrjvzlz941P70GQqTtVt31x48khxs3Xl34ZPPND9ee/9h6+lzazhZvv+o++QzHOcbd95ZefeBFGD97btLH36ieeHKgw87n720Jt7yg0fLHz0BhVi9++7a/YeCw41bd5cefqxN590HDxc+fWH78fJHn3Q/+EhlfO3ee1vv3hcCrr93f+XRRzgtFPrrYIQKY+30pPadb/+/2BwUcvbv/wf5ygrkvPm//VP4OVzM/3dE+BennOfrG2f/5L/l9YbzwYOlf/LffF6ttFK83hj9Z/84/PWvoyz7ZSPC5Ld/i/79v1s/PcxKFgBQD5KgXsdF0d15eXL1uqlSbRIGzYYhChzlzDERK8on53G3HTjV2ng8bbVtHmnjMGzUTZ4652NeMRO3hCMmbZLoTmU49JotS6aNnYNJtycdg8wTaWmFpplepGySUqv34tnF5iWsKToLc8exVFrkWJlaYlruZJqZFjKQ6YWJYUGbVPZOlEa8lS4OMreIonIJR6zQdWAgYzDNTRtUDOznGMrcNVGQGyILazV76jNKpaNpXsh0Qzka8lMMoFdvOOOJydKw3TS8QBASVWvV0YgjKMumc9ynUpxculKaTs0iySqO7kc5NSfNRTtMrKzw6zbJZWnuZ1WtEKYWF3HdMpMMMunieQRcwmBQNVGB7HkUN61S7AtGwqprpDkuBDA5jIHtJ8PVNmHS8aOg4bqxzwvKXEIzyYVBzEQITFMGbJVgp+THQcWy07jIdFmGOisE12zoRbhEc8kdGFKn3B+lVdcEOcsw0ACXSARSt2Rhm848DMtuJx2EhZO5JhEsDwkxFDIUSgTVRazb9tSPqmVHJiyA3NYMUhQJJlQIjahIlFUwttraNI2rbgmFIGLC0imVLMUEixxTMRO6IfKK7Xhh7No2TGUImEGVRUkkDJakZYskihOMNFY6mae2LatQ5CYRXFiCM93KorRigwgqhHKHuH7CCSZaxjONChlWrdIosONw1mvimCuEEtdw/ZRDVEGTqWhToeIaNeOCFEVe0u0gzoiZlszG2VgqyFqEFYbOmLAV47qWF2nF0KNEMEAsxRgimYjqJZEAa+Kni9VaPmUZTmolrShQyoAB8kKDEdMqqqCG44dRvewmfpFCUTHMKAYBlw29gBTHBXFRhG1nHsZluzSf+bykysRUuWCQmrJQhMYM2cBTZXvkJZ1KVfo8RdLGWEqZgahSxghUJ/Ow7FgyRBFKHEMHGUtMTJnUAYoBJXlsOraXRGXHlLGKdGaiihiHqkFwwTSEI0VJHjll2ytiRzdACEPATB2TQuUGhkVuEBhhHaWxazuzOHEspuOSl+Q6JqTgiQkJyBxizxOK8tAtObMocezQcdzplEiVVB0SZnYWJWWXzxUAgNdNJwgFALol8oxQLoJaGXqFGcewRvQ8y5CRVxxrHgkANEuKSWFk2WyjR8NEy/Kialt+kGJT6VAFigFsOFwJSLIClnDGNTPPU9ei0zgFml5WlAkukGbxrNBolvmddjMYiZTkZd0ocsk1YDCYSnscpkt2RN2yH/s1x019meCirFEuWGE6aJphE+UQmiwmruuFYc11U18f5WHbhVipDCODF0g3IgYsHpKyM03ihuUUvsgI1KUm8kyVkF5IBkt9L1u0fatamYV+2bZZqFJcOJrAkAR5SQW+U9ECFruWJnM+ldw2LCsXGaGYcw2pSOiI+3ZVH8epazkkATEXhqZpnCUIEpxbeul0SDTlt1vGNMgcm5uaPQ0ZRZiCNKQIAVYznCCgoFAmQX4RO6XMMI1ByHRi24wXEAOQlizTCxVBQbVemYdKqWpclKIAACAASURBVAqazmVNE9KvWjRRepLlNer6YY6MuGw5QSoVwEaRM1vLJXDzQhhGmqUVwwnCDJrKBs4kURIXdci5TrNcOTADlhVnaUkzwzRTFnIYLRQXpAQnc1w3ciZslSjXDLO8SuwkKpQBTIlyBTiSDuAC1CfjWaPRjEdZTuNqyYkDqz+JllqZZplBxMtGgs36cDBtL9gsxrMkrboEKRTl0tYY1kzPNwzh6dXezqvTrW0NcTxP8pKDMCRhLi2SQ2oPJ7LpBmapORzOanUT5GSeZSWTcuaeDeaLS4nrVodDUdIDq9I8P5s125gC3YsQAplrET/RkAjK1YWd3aBWn7fb9cEwM3RgUzxPASFRudQ52IcEDpdXS6NJajtpqVQfDHJKbJ1lIUAIea1WeTIWhHJHd8fT2C7FlerKs08zw4h6CyhINSnSsk3CXCHEXbOz88ov14tmWfcSTolwdRRkAMCRIPrv/aG590t2EUKIkrj5R98s/+RHin6ubIPSNPg3/63Rf/IP2eJS8/d+t/b9732eXqr/ByL8Pzvjx4DS7NKVoreM57658+pzpTwIcRSR2TS99ppw3X9drQ3/KkSYbmzjVu/Gn35ntLSGwuLKd35wsn1j1OzuXbqZYKs0GF374+8OF1Y5JFe/8+OgVN29/uWD9esMUWswv/nH3z699Drn6vVvfnvaWjrZvrq3+lpuWtVXBxs/eHO6uJxK/Qt//O2L3rZfazy7/rXArpbO+le++5PArWZa6dr3fjxvdM5bK6+u/kpEbS1Ob/7BtyOjlNRrl77z04yao3bvyvd+zDiadldu/sl3OScXa5u76zcHC6vmdL7+kzvu+fnZ6uX1H70NIzZaXf1Ssk+Q2Ktfev07P3RO+gevfWH9J7fdo7PnN76ie9kMmUGl/vqffEcbh3u/8tVr3/qBu3P86td/8/VwvybjfWNh/Y07xsX08LUvv/6n/8I6nx68/sWD5atnvW0Y5hu33q093znfurr683vm2WT35peMQ1f124M10+g75KgZtnitGL8W7exVFtl4mZ640SLkFw1y1j5bt5f9V78S7b5wV8GsCs6WvLbBvLp25AZN7aLTOV9a5oUC4xI9XJi1EfYtcdKbVeiyOLjivzisdtPRIj7q+F0swoq22/bbuMn7r8d7p6aTBmvwuJnVeRIv0L1WuKAm0vXvZWm9wqkePFKFY2dGAeYDZuhJUZ4+VGGrpil28kBjls0dDvvHmU5C0PE/UblrJ8icfACKsp00nObe8diwQr3p3WeZ4aSmM/tQMqwlqEDFJM5ZYnZmH4CI2EW9lHxQxMjhJS79oYQixI3pByqx3KJsjd8RGdCK5Qbab8qgFDQ0ctTmeSk32cH61qTalAqaL6osbnodpI4XkeeG1fx6uFMqxgf1dfJiEc3peM3Wn5eZ3xr38LjRmdYbMgfqdFGF5fGCbe3UxcyOlsCV6fPF4uxJ84v6UUlMG2EDaCc1ETZmNe18aWHU7EiAjN0yH3f8LpRnTTBp+G0YnoDgpUpX69kp9J6q5OoSvDhGMvCRIXxj/Iyki7X5mKZP87DbYd4Zzia5Uw788uxDwXqlbA7Hn1FRtfyFxll3XUHoHyHvOWYr5WSuTR/JZKmsBYO0CDLdDvxq8JnMFtwksOaPJVutpv5UB2GQgxzUZx/DtFWNY+J/IqOluoS28WIxsLXcNPRn3cR0hR39Df8TRuEZ2DQP2olpBcTUdrqhpUs7//XJw0KJob1CdpZyaAaWgfeWcmJcuLRzfHziVoVRJk+XU2iGDcN6Wk1gDZaDL/kvpMpPaE9/1YuJPm+VnMcNIfWoDX518jEibN+4au/UuNLnrklfLOXYHC1V+HvzaV9Pt1rpcx6fA9bELPFkGkTNrv9YFWMYX2rHH+bzgRlv1fH5PudxSsvzIyMcaVGv6X8msnNZrJSHneXB0oqIRXQA/RPCl5xkR8xGVlwxiX8u5qPZwkbyUnr7hG9V41My24WiRWE6LtKQVWv+jpGfqGS76b2AwT5Ot5vGhMij5aCuycSlB/WoDPsLrdPeOsozETf1V515m/DUhPu9edWsieMb86czwx7HW3S/E3ZQmpX1na5fQ6mF9tavZJTSoY4OO0Fd43mZ7Hb8tk7Z9DdmHx/b1Vgs4p2l2NWkNODBUq7LQbt6vLqppGB51Xy2ENa0AlvoeTd2jUAj3iMhFIzKtfEjwAxLaEwl0yTL0sZK9kEWCTOrudPHMBZmbgGcDNM4iUqL3n2ZCS3t1YP383lmZWvV0cJy6pRSpk0/kBkwkqVWcCcuCi1ZL8u959zAntZOnog0I6xqjT7CuTKDmm2O9/MsChc2koe5F1nZUj18zMLcirsVZ6+mxnbUg3K/jcbNwZp2afZsM+/v2T100ZRea7pgwpMmGdmjNefa6JNLxenz6ibrt1G/OW9BMqjk08XcZufdxYv2olICnLTgedtfxmpcw+dNv6k285Pl7PTEbciLZTWopwtFMl7Rj+t+V7XSi+vBqwN3ic+6aNDyW0QNa+is7nWJdTq8+cff2rvyBWc+W//uz7x29+DSjYO112LDabw8uvL9N84uXxM+v/lH3z7duq4q+hfnr0blFuun1/7sjVFvHYTFtW9/3+91j9qbO1d/JUWGPfWv/7M/m9QXGTZe+94bk8aibDlfyU8vtFIcaV/+/T86725kun3jm9+KrMrujS8frlzznHrluH/j2382Xez5eu3Lv/vNcWd11l668c//HPnJxfLG9vd/RsL0cPv1vc0bvlnN9PJXf/f3cSwOXrt5/Z//ud6fPfvVb5wtbc4rTZnJ69/6c8nQ/uWbX/v9P8Tj4ORLX0wHCSfOuNp57Xs/gF4y7q1d/e4PYQoON64dblwbtFdxzi5//+fOwenJ1ZvrP76l96dnm1f2tl4fV5eABNf/xQ/t4/7Ol7+29f2fl/eO9y+/Zj365bsIpXQ++rD5zd9XhH6uzUHBi25v8h/+Tr6+aX36pPUHvweL/HPFsr8i01S/91398AAoNf2Pfoc3Gp+zR1RhbL54Xv3B9+HnbkT9/140ynnj6Gj9/oP2/l772fPex48Xnz4tNEMQKiFcePastbu3+fBhuT9cf/e9zvMXSilONE617gcfVPcOVu/d6x4cNHf31x4+UgAyTS90Y+HgoPfJk9rBwfr9+7X9g63HH6WOKxDmVKudnq6/+257b2/h0yeLjz5sP3sOpBIIF4bVefmy/fzF1scflwaDjXvv1nd3F3d2Fp+/WH30aPHp09qrnc233gQKMN0QhJiet/r++82jk41Pn3Revuo9etQ637lSW9mOR6bvrd26vfDx48rJ6dr7D1q7+5WXL38D09eDsHl83H61u33nrpakl957b+n5i3o0v4LEllHb2P+s/Wp35aOPG69etV683Lh3T4sTphsCYS2K1u7fbx4crX36pLm/v/LgA2cywOEKz65qCYaDVgIuV4fJqgi3SW05C1naBdG27SWG10jYdd3HN8B8mfSW/CMyK6XFNp2YMG+D6Lrhc0Ewx4QTavutDFxyhxoOy6LYdud4GaMvuG4zD81hiYmrmgfJRTkGl51TvljMN4mzls1F1JTxa2Ykyhc0k9eMCYAX6SDpJAeSz/nhRS3sAwNk4RcvQwvlR/lF0qJ7oTUI+0kn2mcoi5KvfxHVbPliMBiWiiGTe8kwbuSHkl70/7ZUK3v7JGL9YSOaUNXPhrMamIGSN/G+9gWKhJoU/XlDHQk0jw9Pa96IUiXiL12TNkEH/jBuFCeKTrK+34xPMSkgmi8D0a2cMxYvqdkiLJBEODdM25vx6JrINk2f03mP8dX2ZLwFzMs6KYU5E5tgtmr6Pg4v8XxbD3hmWhxjLcMoXxLxuuGRrNjSwnVtMn0NZdery6VwpoJ1KZarF1KmHe6vohQJTJimG6GPJ10mLtke0OKuYKvWFM59czaoan4WncLzeYsNc1QxZ7/2RWs6Zee8P2+Igcxjcziog0FIbBz85lftNM6PxCBtot2Aj9XQq+UDKSlhmqEwgudy4NXBjMWHcpC2zP05Qjz62utEsWSELvp1GgG2n5zNG/DU11g8+tpNLY/YeXYxqaCTQAboYtBgM25f0ExslSa2PchieU0/1qv5/IbtrEKpD/Ii2ERzozpAhdw0R7XO7OxqZXUrPte9jMU3RLJgekRkW1pSs86G/26pstrvax4oxFV9WNeigkWvy2ShmUwuafY6L+ojnqltK1igHsr4VThs297oho7WDNucizzZwkGl3FcF2ATeMs2z6UVpOq/hSHgn+sQvacfjvNcqLi2DIB/NK9Nj0wjCaOBeTKooFrJTyV7bLp9cJDNjNHTFVIy9SnBR0qIkNyyBMQc0ODOmQZnsB8FMnw4c89TLXt/KX9ug41F4QIZhAw9T/4RM/TI9mBSLtez1bcLZeGgdn9U0P4Cnqu/X8TjXfJeJy9ZYg0m5SF43pkpCwAmVGNMLNwKX7WPohnourhgTuqaTL1ZrTRZVBiQXV7UptC6cFFwqT83UsTnRFEQ4dFn6RRobxpmWsyvGUK4H+1drNzbjcxxYvLhSGQHqkyK+SX1LYsIJzS3bOIERvKKdOzjQRHFZm5fzQIyGZTlHxp43DqvBMdIC3/v1LyKX0Fl4cFqdTkzVz8ajSjzRtJOz+Reu6A0HTfJB0IyOCUmK8UU1npo0E5lhc0rgIJ9Etfm5wYbZhd/0j4gZePj6RnJlFR7NhtNa7mnWoTeNqrMTg4wm/m98CXbrdDAZHZuTeUlc5N6sHI2oMWJ5sYGDdduba/5Swq86oXqNFMuw1pmdg6jNonVtTmW+AoMrdDL/oi633HY1mhrhUiE23AFRaRtFazgzOCGM6lBKc9go5BV9otCokRYblYt4URZXAezEcxavgPiaFcSlCzuW16xxcYl7V0vrneGeCqtivmKPChgt8OymHsu1+/crh8fbjx4uHB52nj5fePKpoBrTdEG1pQ8/bD/5tPlqZ/O992pHx9v37/ey+bVKdzUZN07PNt57v3581Hv8uP3iVfdgXxJNIMyIvvj0s6Unn61+9mnl6Hj19p3K7v5KOty0Okv5dO3Dh5XD461Hj5qnp0tPPus9eaIQ5lRjur7w5NPWi1eLn322+uhh7ehk48EHRuBv3r3XefGyeXi08tHH7ZevjCAQCMflSnNnp/3sxdqdO5Y3v/TOuwvPnlnePDMspmn1s/PGwdHKgwf1k5PWZ0+3PvxIy/OvXvS/Eobm6Wn3kycLu/srn3xS3z9cu32HxhEnlFPN8rzNu++0d/aWdnd7L54vPnvuTKcCk8QpueNR99GH6/fesebe5bt32y933MHwr6FolI5Hrf/1f5YYf66IopRC2P9bfzu7cg1nafMPfg/F0eedlP2liPD/SG0i7/ZO//v/URqG9emT7n/9X31eUCilKFfG//AfBd/4LcjYL9VFqP7+31v56MPp2jKAsPlq9+TG64JgmuUKIQ3zpYefnNx8nQJe3TkKVpYy21GF1KjIkLHx4P7Ob3zDZFH3w0/PXrsudcwZMlFOs8w57PsbPb/aunL3zs6v/pop4lwQRbDBs+rhWdSup+Vy5+kLf2XRrzRIkglKiQa6HzyebK3ruqCHk7RZna6srnz48XxhMW1VVh49HneXedXKlYYEJ0RZZyM3nfevX69/thPWakmjsp72J8qKqvX6y0Mo+Pj6per+qV4kJzdvyouRsB3DpssPHvqdxaTbKu8caULu37y51N+xQX5UXV96uZs7zsX29tbDDxLHjbtNnkOJETPN2slJ2Z+NblxtPn0RW6WzK1crw9gM+XDTLU0Ca0rTboolaIX93eqqGWtalBvlIM8sFJPxplMeXazK2fPqdtWPVGDOe8iIoB4UosqYAgrg3Db1hNkjMl8VbpCBuZW1ioqYG1Ewq7ZlbugTkHd5wWj5AnkbsJ5My6l/brcQM3Gg9LKfCZN4WrYsI+pYzyagp0Ms5VkGm4aQTB73cacm6jW8E4jtSifrT84ssGxJWYCXR2CpDetVdJ6QGsgqJfJizjYrOimiYQAW2k4Wyz4DZQwcIs8Kx4gmdkU/PMpbC1rVJEcxWDShCeVZCm3CLAQOL0jV5ksd8CqEXVM3BdtJwIINKlSfKMpl1FHaUBdEUisqlAY5QxZGE4KFzBchmmBNgKjJO8m0gPSiulg/TKUuWQPQCcIcjlct2wuIElKD2HcgZPMFo3KaA8itko9yYWT+ztKNyiTBGck6mTsEDOK4jhDjmHONcBXpWgySngKhRmKcLAttlKhQwgVNRpJMU//yAjofG4PzZGu7xuNspMRGGcSCTOOi5aDhEM1m4Oq6AhTsh+p61QxCNoZ4AUsMuKIa4SoBsp+RdSOGpvZyqq6UjbOLZBaLxTbVLeLnoK1JDtBFTlbIPILmyVG+vFIyVHHB0ZIOBFATFi03sCabe2zexYYK9As7bxTQFK3pIDZt36gZQ6icIqoYlSM0X4IaD1bT4QUpawTlM1dZoCgBY0SBmc07ZXB4xpeWSiI0T0heV9JUZAiBLllFtsNxgfCg1Kmc0aTG44rR3onyKlQlXp+OMtPql5ZqZxkycr+ulU9oWmXzZsXeH1Amou2WfTyBKRdLZnE6Q5zl2+vu2VxhgBY0OWQoF/56U3+xh1hBlts0FCnUipWKdeQBpUgLMY6BAswy6DxVcwHXdHoWcoVZ00GnZypKxKUNLeFomqItq5gDNS3AmkWOz4tckvUOneSSA9gz1VyqSRbdWGxMxnDmZO2c5grNgWgoqbhUxEBZjNzyGfQ2kR3HeGYnbe5y30nCwjIi7pKZni/xDJu1Qzlbg3YeZsqARGkFID5idSWFMEeUdbKI6DfS46dk0cCIDoisZ5pgLLB4HQjApKK2CH23UdmXQU/qgGkDkrUgp0qeZRUtTDo1vpuJrk1AIXaOVLVOuzV1lkAdwpYhzxk0VGTrxsFhVmsYDQcfRqpt4RLk5wU0abbo6kFssjRzbHCYgaaWNVzn+UCUdVUm/OWJsoxie9M8mRqEa02UHAhZ14NGzf3wY2472uYi6KcSQbZS0Y/mQiPxSq12kiDBrNI8iUs4x5N1qzk4aeDisNQtjwsuyLyrlwYMcVG0lDkbNmRyUlsFKdV8nPQKZyIl12Q5YRAriTRcgFTTPZgucxFRa47CZbUQDiFjE7eBIwOlwqj6ceboPkmWOU6KZTV/qbdLEQA5LpqChAinMO0AzsGl99559Y3fWpge6fvjwbXLCiMuEKGKJHl7d390bTMyK1fu3nnxm9+w01k7HpzbCxpT5eNzf2Wx0M3FT55m2+1+aYnmBdOoKfLOp89HVy4BqKp7x0G3k5fLy8Hx0Gx4VuPK7Vv7v/o1DfH2J08nW5vcMpmAzDQJ46sffzS7tjWptK7fub331a9CCuov96VGZhtrjee70tGniz1YcIlRXK1du3t7vtSLFurV3WOp0bPLV+yZp6kicUqrHz2erCyPl1e2338vqVbVgjMQNioK1Wz2nj0tqDbfXFl98Gi62PU6HZplWHBKlXkxNZPw/MZr9Vd7HNO418ZJXlAdOFrn8VNmmt72avlkQKN4b3G59j/9cl2EMM8X/of/znnwvvrLutf/0lrR6Ne/PvpP/3GxsNj85u9Vf/D9z9+TQP7qWZR+fFT/1p+O/+N/kF677v2dv1v9wZ9/LmCJEJlOKm/8MF9eyVdWoRS/pByqABSxsHdPJs0FAKB5NCy2oZ6n137yo8f/zt/RZG6cjrVeXLimu38xb3YwSzfuvX/09a9KnRpnUzWJgAH0oyFudHm7uvWzW9Ob23mpbO+dzZstyENyNMSXksJEV/78x0df/Uqy3HF2TmLDEYjpexeq1lCauvL9N15842+Kum3tn2nlRr5Yruxf5MQQHUDPJhg5rFKjh32d02nvK9e/9+PMNg9+6+uN/U80XaqIkfMp5YS3Ovjnz6zV1eGvrfSO+kKj+VVqnk4Q5GCebT76LK3XB69fNvq+iMFobWNp/xwQmr5uGp8NMc/A1zdx39PMLF2/Tk8nppX2t7cv3buD4vjDf+/vdU9HJAnBeoGHvknz/DJJ847IaYQYZi4XthTHedEJ/WtJLRBcN3Is0H7OOzizY5BD4byYVXl5EHNdiHasIiY0mYBswcNTh87M8GoimRJFKWVTyi3BWhyMJ1kTBaWiMaUZZbwq1VGmGkC5KRsHzJh4X+bWiEoCshKvHBRFGeRNyU9SZfhh1U4xcmUYukYFahYIZN2lnHEjTA0jpVVGL6KmHiizbgX4pkUR5DL0tJIrVW7OY2qnmqOp2XHb4kotlCJPUA0QDQa+s4q5JHZfvK6blKKwHzbsCCIbRYGt6UCnKFBVB6W8sJJEN3LsarGXOiiAdkNjSVnHrBDzQiwoLJA20E8WBM3jmq8VTaxEhmakqHGlcukdh9sYsbSShbINcsHoTE9rSuEYFppfA0zli1MgFiBnsWJaYQAlQHUvjrZgQRMc07QMpSHFAAqHAcOzWO2AwkLMtzw6qxYcFWCoWBMJPReTKEG5h50llGWA+zZADqFkxhuORrwI+LFJuSEKCabEXiKFucrASg1mYWKlmWnlVOZiFhhuB/JApqfKuWaBTAa+XeciKqwkJaUMWtQeqM2SKbNCZlPLaaMiUcncbnR5YThTVik7dJ5G3py4TQgBTKZcLpomwJ6oMOYDLPN8pchCaKd5eEmQhOlZkdeQNWEMeaKdpYGy4Kvj12FtZpSGGdtQKuEy5VmDGhMWQSv7mgii3OSSrxRJyEuxkTcUTQqQnvpXsDPLhPRZQ7A4BjIQbZ7nHAd5uJVBkJYyyjuEe4WQvmjzNIgJST0XKqigGycCZsKGcI6qCEuo2XGMIFRuDyahKmKJiJvqW7BgLZyOEj2XFEnbDySVwF6G0SslQwl+tUqiMI9VqQA8x3lOSx2VyFVBkOui8RmHiVPmzM8slsmSVAotp0B3iJxljkiASxAPZRjYOtcyXpN8kckRUxgXpVybkxDTcTVaOc1kFapyUkw0RZOsy4oJA/XZvGroR4yZIG8pcZaykifqjI8TScHZIlvyGAZa6hbQFwIXrArlCZflT/tXs+UREHGeLyLeZ5CzosvhDHJE+vWkexKzEla1VM2B8lnRzZmf4Tj2TVKSSabPUldLabmcjPmNEoEFgWFg0gowJIwC4MBcVK2hfM3QkIaSadJCPrRqMJwxWDMgN/w9YJUosM0wMjWLyKYdBBxh7LRBjK8whAkww6DqWLmb80FiEIOIRTsB13WKDQqi0FAaRtJUIVAmgkJPiioSCpAiKZZRTmOQ4eKGlzvSuYhElXMngjnkmpbKjM5ythxFhDXOJWsU3CzYGHFNZg5rTrShA1Ij2JroU7sQrpDHhWpzaeR8OM4u51FFlEZa4cjMkGgnzRbyoiTkSc5X4pkjV/qxxCItczjVhE1SPUNTfTbST0bKS1HEjP0LsLBCoNx47+Hhb39dJpmxc8LWN1CW0pMhmqdCAONnL9Fv1rihOa+OZ40W9EPjaJAv1hRg13/4w0//7b8ldWAf9FFnObcN++DCa7ShyOjbL9RvfAUUmXk+xbOEO9Q5Gs9qiwLQy2/e3fn61wvT0s8mstsDhJGTEV2eR6sdd/8iadR4D1qn46xRzlv46s/enFy6PLvZ1k6nutCH/ztt7xmrW5afea20c3hzPO974j3pnnND5W5XdTu13e4eYzzMMEYzMkh8HARoECMYBB9BjISEhGSRhwE0DHa3K3dV3VB1K92q6nJ35ZvOuffk8+awc1h7r7X40MgSCNt3Bre0v/61pP3p0fN//r+nu9h9eBaWSnQVb93+s6yge08+i/pzRSjxwrp+0AP1fNKqbL7300xV7/5aUz4c5JUKbzEydGSozy9cev61fxab5v73f7168DUCLN8UyvkcKnLaXth8//2gUj95/mntbCKp6tnF3dbBAOUZbyz9su2r4o3r1gfvcV1/TLeIdjrO7/yQdjrGp5/Yb9+ESQIe22Mjf3UD4isvBc88E119cv43/5b+xefKyfGfS8u/fFD/8ovC9bemf+8Puab9knqwMWOtB/efeuVlVrZwkjzx2iujpaVW73z944/DVgtLbOudd2SajHa2nnz5RWDKAoLdN99Iq5Y8C9bffy9X5KxT3XrnppqE/aeuPPnKSw/wDxNdu3jtLVFUccI2b39IZHm+3Nj64H09CU9+7fknf/Qnd/i/GpbLl29cu6eBFtm/8MknBMDhpY3t994tTYYn3/uVZ378o/s//C2Fs+1333W73cP82c2Pbsf6F9HO2pXXXnHbrWB96cqbP6ELZSTyxQ8+SkrFfZytf/qx+PLz0cXVp158MaiWvZXO7muvJM0qJPjCR7dTVUMqWH/vFmTs/FtXn/rJ64Fln1+9uvvG66JgAFNb+fgjBITX7W6+/64ShoNndp76yWuhJJ08++zOjRsYCVmB3Y8/IXF2cuWiOfgW8Cxjac88r2LfkPl+wjFwaqKQI7dgnSqy/KV2rqVJR106Modl7NhIOpUcksYNbmQwsPTzEqwOtSOZxQvKwkHh3BBOTZInWkzorMnxDFBT7i+R8rl+bGRxi1mn6lSFTk3mQx3AeFQHZAxyHfZXNfmeNkA0bQnrNKS+d0BQmthLbP6N0RCxJEfRfduQU5hlyaGmMY8orn/QkUNfWefzuwWVx4YWDh/YNk5IL00OTSP3SS0IjyqaOzckfHKnWKOJ5CXhns3bEZ6i6KAiR77oxt5B0ZjNFIzPvi4U16gcUO+BYSeJMs8mhyYIE3uFzveU0jDAixXzrEq0QKcjOrEE5iAfwmlDixxYOtHPr1KcG9YAndUwiRU2zmemBGBujORxSw98v3JknV2MJclsHeq9GmGRnvcyXxdMpeWRNGnpQUyMn5MDO1ULhndijGogF1J2giZ6Jip58VweNdUQwvqR+WgxlUu4MEFnBZGrRBsOBzDZ49WGH36D3IlR3qDZXhqewYLljAKq/wAAIABJREFUEyf0j9WimbCEhHeR3U3BndQZ2eValp7GUU8uKA7IqPdQM+SQZ3h2RytWPXaQhYNStR6IPk/OlBJ2QBaER6KIfQ6l2R29WvXYIQ175UYpQA6Lj/Si5CI5jfbtCpozVZvfMyr1sCIsPKphGkAjyycLenDA4VScrcL8CAWu6DVQ5hmzlI0repyCioDThjxz8HIAz9ZE9RjEIeg31dSVZokYl6SQwTpCw5Y1ob42Vs+Xs0pfymLcqxHbl8IIjqooJlT2lGHHnPRCY2CetWEjJmSi9RuKHKixz4Ylbmhx69S/LygmtbV4doepKFdJlJ8VRQqNcuocI2kaNNqp943uA7W8kfkPuJJSScxZvxhTyarn/ilW5kmtG06/kGNkVNbi6AHLYqAQPztDni+VCkl4pGRTVrmQiK+oCwrFapo8QHmIZBIEI+JPSNkOZwdaMCaN7nz+BQjzsrESWROSTBqycASUpd4qtj/Vekrut7DdU+cKnNVkMdSRiIZNFTkAE9hbUaV9MFXTsC3MnuZJYFYzxQgSwUYtCCZck8T5umT8DM2QmLewdGSkgE0rhpgKg6BBU41OuI2T8zVFcUEog0lLw0eIATyuERZBSya9hgrj0Hai+4g3Y+Kw+LBOvBAtR9FR0RzNDA2dfl20OpnGUve+rrWp7NP4wBRzaq+nzkOlILtylYR3VbwoLCVx9oRRTRSajY8M5GSylU4fYZMEWimffq3TNjGtPHwIdTOWfJ8eNvIxVGu5+wgjJSmX48nnSt6ybCMNHwpZT42KIk+bxpwS7efSgZWSit45MkY1lGDEKZkWM1bPC2fKpKbPZVg7Mg86KaqR8gD3Sjw2NDSUXS0LGrIyhKOq5JdR/cA6aqagya1TbaCDqGjk51JqErcm1DMwLiOvYWmfygcrFLWBdWyMFRAViTjlqSGmNaCc8NkCmLf12nD7nbfXb9+mBduW4sV33iUKSkuFJ15+KVls2kfHG2/fSpYaxsjZ+PA2MIygXty9dVOSRdBZeOrFF/N6URtPN96+eWrnNt1f+/gjrsjO6sLO9Wsyp+O1lad/9CfCUkgQbd96G1X1JpPWP3gfCD7bXd9583U18XpXLj/x6itho47j+OKN66rMyuXW5vvvq4J/Y//W0y+9eH5xixvKzptvzLbWJEov/PSTRq/n2/bFmzeScnG6e+GZV18+X9+IGo2dGzf8hRoWYPP2B1G16iwuXnz7ZmZZn2zUn3z5pcnKitdZ2Hn3HbfdthN348MPYs2crK5t3bgONK337JWnX/pTr1FP6+WLt94OLUvD+drPPmUcBUvN3TffQJJ0/tzVp159yS8Wj1ot8cvpg/m/80sP7lf/6f/8uOpKCCHL/q/+RvDst8hoWP7TH0uTsXhsHildXPwLQu7/D4MM6HfuBM9/J6tU8kbD/PQTmOeP1VGIsfpwL1tYSJeW/3+Gsf6SLkLn138jbNROn7wy63b8Vvvo2WcG6+tIIV99/wdxq5aa9je//b2kXp53Fs+fuDJeWw3aC/0nLp3s/qL+6W+ErTo17bu//VthrTzvLo2uXByvrbrtxcETl44vPwEJ/uL73w87TWYW7v3mrweNmru4fH7l0mB9Pa7Xe09cenTlSULIVz/4QdhtUrt4/1e/Q5vlwfLGYHf7bPdSZhgHzz033tkUsnLv13/D7TS9eqt39Up/e8Ntd8OVzv63vhUXSkdPPz3e2WSafu9Xf81farnNheHOdv/yjrvQdVYW955/PjPMk6efHO5s5Zqx991fDbqtWWthvL15cOVKUqu6S50HL7xANf3s6pXTy5eQTB4+/7y70p20OtPVlaOnnoyrFW+p8/BXnssM6+jJp3qbWzIeSnLPXUBYHmtg4qyL2BAy6AXdWIYuN4a8xmIzw/LI7zAkTRQxmW5CIodImnmdhCAP62dJE1IjkXHPXQRCc7R8NFsXRAqhNAu7CSIB1HpJE9JipqBB2oqDIjPzwXgbYzlEZBYsZlAKkHLOa2lQggoeJ500rRdq6Ui5pGEblVRfXpZZ07DUROtC3jXKzGVXipZFVZnjizoqkrLikS7KF8yiEmmLmC7bxcyFl22pigrcBzsWKMll1VcWIO8YRSW2Wnm4Xi8xD+4YalXYMEIXLVFRi4qntgDqagU5VLs4XS0VMwdvq3IF2DCUNlVoQyjNkT6brhAC/aziZLVEy6dhJ0yrEoJDaI7iFheyQzTHWYIYBllhGrSESftxw6E1CZAx0kZuWyhoRNT5bA0BEAtrEDVznQ7T2gyWeaSnWB35nZyAEdRcZwXIwssKM68p9HzIyoOkIeeKL8v9aCHN5JAo87DLDRDq1VwsGlIZlTSfb1i6TQvA5U9WFC0ztRRsGapCK4UAdlRUlkqaL63KoK5UgMueqGhmbqkJ2dRljZWLIVw2RE0pKqG8JvOGVuFufrVkq4lsQ7BhKAYvWQFf1EBDq8o+WdfztlHO5+KJgmZyU0nYpi3Zomr5dKWYVbmZDKYrlJeYkU2c5TgrQhmeZ1U/rEEZTVktnLWQQSfeSiwKqZpPvaUE2TkjU1Z24gaX0SSrhrOubCUjZymGdqjwub8cZ0WEYY8XnbiRS3DKK4HTJUYyDtpuVAN2dBp0oryMIB4y2w3qVGcDVvVnXVlNp2lzHFStKpkVKzReKZS0wDLT7GJRIblZo2JFL7K5sQTYgqUYqWXHfMUoqKFlJnzLUmSu1Rlf0grcNbs5W7CVAi9ZIduwTTUyTMp3CzpK9AZHS7KFInshEy2dFFHZCtEFlRRQQYvZpaKJY7PBwZJukKjUTPPFAizhsh7mWwWCfYxnwWKE5AgqvbTJ03KuoiFtRUGZWXQw3EJIiQicBksUyBGWz0Ut9stIwZN0IQmq3EhH4/UcGVRCU3eJCS2VpbO4nsWlXBejqJvNa8hOB/P1FGkRxvOwm0EjEdIorSe0QjU+jLrUbZBC1JteSIkWYDwP2im2REFP7XoWrVdtEeJtTa1BC4Rg2wA1raj4WovjJb2gRGob0PVSIffJhizXkA0CsqXBqmKrodYQeVerYE9pAbpesjJPvYBFS61mE3Vd4i1TV2O9kqNFpYQ9tQn4qqmLRF7DvKGU86m2hvKWpRqZXcngml4AntwhtKvq6TArT0CFRwYlytDvMAxHWHLma0BBPjNnXptpbMKLvaQhZ1ogk17UoZkayXjqrnIZeMyaJe0cA0fY/aQp5Vak4kG0kCRmpuDxbANJwOPmPFrgBDnQOMvr3CsBDQ/DxSy1cwlMZmtcIZEw5n4HYcnF+nnUBIPVC1CRv/gbPxRlY9Za3v/uC2GtOl1Z6+1uDS6sp7Xa6XNPn168BCX8xe/+K2mzEpeqD777gt+ozVbX+rvb/c2ttFIZPnFx/8qzhJAvf/CDuF0Pq/WH330haNXn3eX+pYv9nR1aLBw/9/Tx7hUJo69+53eiZjUq1x49/22v3Zx3ume7O73tbW4ZR996+nj3ChHs6x/8Di3bTrs7vLh5fmnHrzWm2+sPn/0WkKS9558fbG1hGd//7nf8bnu20B1tbhxfvkyLhcn2xv5z3xKEPPqVbw83N6As77/wfNqp9pbWJ+sXDp96klpW/+L2yTNPCknaf+E7g/V1oKkPX3jeWVqYtrvO2srhM08F5cpo5+LJU1eBJO+/8N3ZhcW4WD5+9tnZ2tJ0ccVZWz1fXDI//uiX0kWIEPHc9j/+L/B89riCBMLoytXJv/VvC0kuv/Sn9nu3wGMmy4UAGA/+wT8kj/MGGQ6q//s/7f+D/zDaveT88HfLP/4T8TjYUwhhllX++J+nS8vp6tovw8RCjNnutHx2FDVLQIhi/6w4GytJYs7G1fNjjcfmaFDvneW2Vu6dxnWbY6l4ehgtlBTgGPNx4+xQ0rA1GVXOjrOSWTk/wUWiplahf5rULeL65mxU758BHRujfu2sFLZrpbPj3FZkGhX6Z2nNRBkzpsPa0cO8VrB6p/WyRaFZPTsCKkhHRmHcB0gQleujfgOjvFOu9E6prnpLjdLpkaqK2qhfGPUJjUFJMYc9DETaKVdPD3NNs+fj8ukRQqJyfmKPekoSiqphTwZy6IUX2tWjh1zVy8602jshjNbOjguTIfPk+WjBngwMZ+pvdutnRyLL7fm0cnasZEmtd2pNhiqamlsX5NBCGVFTqvoEZsSezxJm4ExXoowkSIkkJYpwaAGqKhGVYxXnyHJ8LcjytKiHlGQSiWUtHEPfQNTQ41QLCcqI7sVyLFiiamEiUQJiUwuGwLdBrMiJh2IBc60wdxUKONW0wCEJQomhxgOYmSJR1dDzkzgNgTSjspF5AdLcWM5o4AsQcpLGaQzUWSDzNAkNMk2kIvN8qFu5AljgC8VNSI7yhKNZLCvBLESqk2iYzz2oKJmEeOQJRJg+8/1I4HmMGfV9oc0TBeWeD2WFYS8OfCErmQR9GgnJpRJIowiiOZWqRIo0AoTtBChScQaAxkAqywxic4bDBsJQjwIQ2yhLdT/GiYEQNqIcUVVNMhLOSdgQAGsJlUMJZ8B2ApAYAuhKlGEqSwnX9ACEdcEkJcrkRCVUFBxPimUAFTPJcaZiyvVwiIMKyhQ9CkWqoAQoQZolgHlCD0I6FzSCxIuZy2ACpYlPvDgMZcVNWAISD5pRzB1AQ6iFVAohjYA08UGUJgExnBjEPPGh5fsoIFEgtIDiCKQxMOYBDjPqc80NQSaSABWiJA9gFIKSGyIG81TgSYSziAbE8CIgROhCFCYakhBVrDCRMeVZ2XADgRKUNqQ4wCBCcRnLsQ1zRHXZpbKe80Q140TlUZx0EAlhQFFiYZLY8whmRdOniAuQqIqTSsTFcZOQGIYxSjWMUt3LIDXVJGdRjqih+RHXAhI2OAF6SHGiSCK23JRkBTnCahrTOQeYGGHEXcaSXJ14gU9yAbEbswhgluuhHzogo5DEGXdyFnNr5gcBYRxJYZrHAOXcCP1wJrIESn4sfCFCpk58EVCeIzVIohRlASskEZ1yGgMrSKkrZaHQJj4Pcpogyw3iGIgQWIEbuSQNBI4yJeI8VbWQShmAiamGYxDrIFblxIepALlWmHkaEyzT1cDDucCJoUYjxDWeqkrgCqGjTLaCUM1ymKqm5wAIYVJQ46lIEKCGGrgQ5SDTVSeWIUeJasUB5lmaGko8zQWGqaKFvghjwGzLiwgGMFaVKGacUpdBwfR54EUQzRMMqO8LVaOqEnkelDCX3NDzAIGZNPHTkBM3k0gaRRDNUrVAXUcgDLQgyQOh8ESZeEnMhceJFWcRJIhqpSx0ATMACmjmcyWn2ix2Ykk4uVylWQQ1xNSSF8wF0yTiJTwCkKWyTwlVMOW6GYCgBjNJiaicqFIGCm4gRyhjihlnJJNJJOvREPsllKl6FPJYQhRqfkoSCKiqhaGUaSKStGCA3YpIiZr4LAA4lQtzV6Y4S1QtCElioFTV4zMR1Xgq676TxiahxPYjKYY8lo3AR7GFEk2NJ9bZkTEdNXondjw1B2flXgViWDk/ipbqJEqtYa8w7hs5Mp1J7fRAQrk5PK+OmlyVKscHcaNgMmANe6hjZDE0psPa+REw5cL5SdSopLpSPj9JWkXdc+1BLxw3sBsa02G9f8YspTA4iyp2XCtVzk+8TtuMfGvcLw+KMGWWM62eHnu4XT55lDRq8Wxc7p3R1Kr2TqzJkOlqUi3b4z7kGe1Wa4cPw2qlNB2WeqesoNd6p/ZsAgjym3Vr1JOyeN7WaqdHVNNKo35p2EuSAuyp5mQIOHeXu4VxX/dm/kqrdnYEMaxMR+XxgBKSlXVjPISUZk27OBnmEM43F2uHDwUEaq0ufhm3cRACAKr/6z9RDg8e1yETIq/W5n/772SNhvXRh4W3r6M4fpz1HQAA0mz++38zfOJJ8pjSz/zgvcLO7vz3ft/9/g/UvT39y8+ErDyO8yQfHVb+z/9j8O//B49ryv0L/jbzdHD5R6941QbM80t/+urRzlOV05OtV6/1OhdkhV989U0qG7Ot1St//FJo2rks77z0E7fTtYNk69Vr82onr5rbL/8kA2R4afvyj175Wv7XokJx5+U3omJRg/LW69c9qxqsti++8gZM8kP1uSt//NLnf/C33Fbz4itvUNMgZrD1k+sx0sZPbF989a3DKD3+ztOXf/zqNz/8bd+qXLh2a7ix+bBgbV571+l2j555+uJLr3vt5snlq5tv3mQLZa/cWL11263X/YX2+vV342pt77lnd1+/7hcLp1eubFy7lZSsaX1h5dZHSaXiLCxceOsdqhv3fuu3L752PbXte89+Z/3tD5iMpgvLS+9/LAg52Lp84fr7AIhvvv/97devZwDcfeE3V2/dJiLzG43F259Cyr5+4bukX4NOObtwSAaqcJvImIC4hIZ1UDoRTlk6VvJKD/crwOvmq4/IyBTjFix8geZFOFkDxSPmV6VDJWyFZr8InSW2+ogMZD7swuJd4CuwdwGYd0FYUI4abiPQ+jacLvFmQIaGGK4R40tACTy/APU7ItLJ8RKze2Rks8lmXp5njE8fWFWUwUUy+RJViJAMNv5aqloA5vngvtFQM6aL/j2zkkRwS558BUoI6CU2vqtCOQY+O79vViHTmmD0wKwmMTSl8ZewsCM0LkZ3ldImZpQN96xinsrreHBPrvkxLyizL4W5DTXBJ/c0tBFhmg/3jEqeS5tkcEcqtlOyqetnC0KlWN6Hoy5ESa6HYnRByryo4xZOFoAqpRVHOm9DlEPjQIw6SFBeGYvhihI5wVJQPm4mspV3D3GvDACE2gM+XiAZYNUBHC5JUZQ2TqSTZoaLefcA96uCqVh24XSBUIO1BmK4JHupv+yVjys5rIq6h/pVkBio+sAbSuk3OVkm8RF1j1VzW+JHmbtvtupZMoHjB3q1hkKPpF+kaFVOj5P5gSYvZemJGD8wWqWEJ2h0V2vbNIuI/yUkHZAdZcM9TV3K0hM2eWB0i0mawNFdvanHjEnTL4DcFtlZPrxn6A2Pzvn4vtXU01jC47saMdIMSeMvhL6ALaiIwTrCh6IQg96mFD2MDKEcr7FOD+RDfLbC4DGisRisE3bCGzEZbEnJUd4dyccrSXME0BierYHGAaa+GKyT7DRfSEBvnZhnQTnRjxaztitQH56vwOoJRKEYXiB0kBdC0LsgaedePdeOF+MFJuQ+6nVB4QxKrhiu4WiWrgb0LsqIUtogyT5mmdYqpvNjA6Rcb6LgQFP9AO9q/jeRx9TSjpQ+RCzVTCNxTvQ4lIwl4hyohhOQi2p4L/apbm+R/BEMPW2hGPunUujJejOfPSJsROUntOBBEEaavEzDQxhM1HYh9s/kYCKrDeo8wnwi5IswvJ9N53ptFyLX4r0NoN0TXJEPu0k5UoYmHK6yekRGhuivS8ZXgBN4vg6V+0IgcrzGjDF2TDDYyIoe8TXeX0faXUEwOrsA0V2mysrRGrVTeYZAfwNYHkp10d9C8gOuQ3K2AWEiZEk6WqUGRYki+htQ/xTnquhvSeR+bkP5dA3Jp3Ehnd9F1gqBgg/3rEJMFRUPHyiVWQTrivO1kJdgQeHTe5JYEjLPR/t6Mc5lAw/vyoVygpfV+VcpXJULFnIeKKKeyzIfPDRMj5EK9u9pph3DVdX5Mkk7ql3B3gOZVbgC6ehhQS0KaVma3dMKRgyXFf8bmtY1q4qCPRkXhLEA4HhRcmjaOJFO65xX8uVHZFARsYW0L+G0jqMqb56LcUceM3/ZK51UGGuCpoP7FREUkXkXzCrQrfNiAMdtaaKFS459UhZpkzd83C+CoImNr4RTRdOmKPhw3CCzclY5kM5KLOyy8pSMS2Jah8Y3wi/hXp3aPp40yKgaLfi1bx5svf7WpLtiCWf75Tciw8ot8/IfvzxZXin0BjuvvDFZW5HH/tZLbzjFBq2Yu6+8SXU7qFcv//GL81Zb9oOLL79xr/EHuYi2Xr/uGSV/fWnnxddzrExXl6/++OWgVhUp2371raDZwJKx9dq1SC9Ptld3X3ydCdy/tHP5R69MqwsA4+3XrmWmmdfB5mvXUqzNF9q7L/5keGVn2lzYfPPm+NKma1bXbrw32t4+W93eevXabGnx4Nvf3n3lJ72NrfPlrfXrt9yV7rTUWr35/nxx8XDryvbr14N6/cNndi++dm2+0D5Z31278d6826WWtXbrtl9v7j37nc3Xr1Pbuvub39t+9a2kUhptbK++/X5oF8JqdeW9jyLD7l25vP3atUwi9773vc3X3kpt+8767i9DYHFFKb36sv3urX+B/SPGzu/+XvjUM8rJSfG1V6Ve7/FD8cnGxuxf/wPI2F92Rfj/UnNZs9X/R/9pvLFpv/du/b//IzKZPK6ay7Lp3/3Dyd/7Q8j+JdPuf2kX4b/R+upLb7ENACgenfYv7qieu/TF54dPPS1Lwr5/ML1wgRBuHvb8ThPTtHZvf759wTNL7Xt3zy9dUalf3Duerq4ogta+uhcvN4NSWT0dx+1KqBfad+70traNPGx+dmeyuhzVy4VHZ3G9RA2zeHCStCuxpC/9/LP++garmMUHh0G7qagMnvlpyfTqjeqD/bBcYWWjev8gqFTjZtH+7AFXSLC7bhz2TBbMl5bU42FimHnNXvj0s9AuTi9vlvdPuODu6qJxPJBZMllZLXx9PysU0sV65cGjxLSDpab98AQC0F/fWPn8M8Lp+aVL1vE5U+Txylrnzje5pHhrC+ZX+yRN+89/u/ZwX418f3nBOu6nit7bWDcmjCTQbyPN44rLeTUKaFGa86wLf9FFaBnDkJZgIvkLiHgQjThbFrYXZIkZ1yGOgRRwUI4zXwcOSi9gOcy0KY9awgyiLLZ5OQUBykNVqvsikUmAQDVMuKlPedyGshsnfoHUYinLeaJbaj+EJeDLoBw4sikduLCpqShJJkguQoZQfErNJsx0VZzFvGMtxKe9WRnWZBVT/1QoZYgtQsdcs7JYN8RpJFqGDuPwUU7auqxlyQRKOocGTsegJs28SiO558Hlgq4k8TlHRUkxWToEki6ATsLT3CzkWdUSx5Goa7qcROcC2RIpYzBXlTymZYFdNccCm0l+rjJNUioOmBtYCF6M8sBWsji3MzxRARR+R9GHDCABSrGYaQLgqAGlEZKSVDRz4CkIiKiKjREXABSMfjwpoxzPViXV4YhCVkq0OaBIjWqInEPIhdz0haMRCkQtppFJEp7XcjBL0ghqDZgFELhZtloCTgqOXLxbNlwn8CWwYIiYoXkq11EWwHyYmmsk5irshWBJVwM/dGW5KQE/pS5S2yinkM0yow0jpoBBjLp6ZTbsOQV1gSDGqI/UCsuZxKeZVmcBKoC7Y7RT1agXz4hURZDxdA7ytkkwsvp5WMUG8MDEpAWBSZSOK9iIsEGRoxM1CA1VHaGkBHXhkp7JLKoZjh8vACnhOgeOLkl+ZOvwGLMG0iUXTPRMF9iI4dyAciZkSkaqpAZuVVNGUlqA1MbWacY0ASxKBybXoKhwfSwkHAYFWR2RzAJ+RdEOZ0KAvGtKwwjHCW4pUV8AIcSyTXoBBEBpgGQkBBVixQbnEfJio4uyOadIFW1d9CLIuNqA6RByP+W7VTQKUJiTFsHjKAGaWhFpn3EGtC5O5wBGubqA4lAGPiUtCfTCmKlWW2RzJhhWGiJ2ifBpfqFcnc1oWGBlKlPGIxUUIprqYoqUdpACzRiLcAHpocfCQl6kapCiuapUpqFU5IGKil6MLW3EwybSkjidWKiaSDDjoYlMnwGZOAQV/YRr5gDH1YTInHsmNkKFJ2FSRaafAEOMJK06jzXD7IuogRQWirmeFxiTWDZiNTwPK6Wkx0BZ1rXce5Diuq6X83QAkcxwkcQjoEppVrXz+x5asDQ9Tc8ZtCS5wJOhECrhFQ0c+qqc8aaZn6eiIPOyKh26UMdyCYTHOZAxWrLgWSDjVLFENEKsoLGaLr6eQpOYbZQOBccwb5r43IcyypqmOeKQi4Led5MGoshfQNIEAl+gBWo4GRVaVIPKDOCMg0qUTS0QAaXjp7Eph5zWMt3JU2GiQsjmKqNEbnrM16QYiFpMU0MNRFpneMrSzFIqHgqxYEpBO3V4GwcAVIIoK+NRJpaE7ic51UAhBLECqAyKHoiz5oP7vUuXm+4p70fz1SUSx/rD02BzEWC5eHzqrbZjbLTvfN27uGNHTvXuw/H6KjUN67gXdho5lkoHJ6itD6xO8/ZHw6eeUmRu7x27ix2g4MJRL1iog4w1vrk3316dVdudL74Ybm9LKC/sHXvddk6wfffR7MIqt8zmvXvRUsOxq53PvxhubgBDLu6fCFXyFpr2ozOoE6fW6H72hVuvD7YvLv38Z1GxGHYbhb3jTFWdTmfps58LQznf2KruPYzKldlid/Hzz1LDwk1N//qEydLRE0829vYyRUlb1eqD/bBUGS8tbb33Xqaq/acuFfaPCWfztWXjuMclidaL7c++DOySs71S2jvOJNlbWzAfnSMIelap9Ed/pD766wSN/gKD0Pqv/ksynT5+S0zw7ef7//A/hiwvvfjjyj//Z39ug/3Vy0EIe//oPwuffuYvBo3+fxJEw4A4TvDMc7TTwb6v7t3/K4t4/nxWfbiXdRfTxaV/OfToX5TBoqurerO1e/NGWKspjrt6+2OnVh+urJ3uXMqI3Dw5WPngE2pZAID1d95nqnK6e/l4+wokqHx8uvr+h36ponG68vZ7XNf7G+uPdp5kptq8d7/78c/iSlEO6doHH8aFYlCv7l19LioUGydH6+9+mJkm5Hzl3Q8zUx8srx1fuhobZmVwvn7zXQEBs7Tlmx8yRc4se+3jn0pZltrm2jvvSX4wX1u6antGWZ76YvXDn5ruLCwU2n/2lRoEQaP26Mozk+6S6Tnr1942pjN3eWnl9ieG48Sd2mZLNm0Y5HD1nQ/N8Xi4vbH91tvWeHx6+YnB0oqzsGD4/tJPf2bNnGl3ceOdd+zhaLS+dtXyyjX1jKowVQLdAAAgAElEQVRrH31cGg6jcqn1xdfGcDxYWzXGVcWxwmqmjhTi1gjpdfH5FXI8QjjzFtWJRswpmJehWw+rdD34/FvmcJjmyCsLr53piRQa+riQmvFFcO8Z4+wI6tLMQm4byY4SEOC0heRvakdPyIcTTMC0LLlNaM6Eb2B3AaDpMjh4Qjn2WZREi2Rak/Qxc0zkLABjnufY24MazoAE5g8IkoUizZRSLsE0nRPnVCNyXkjmp8dlIqhshMSMCcnpDLlHkqzmOAfuiaoqHCVTZZnw+YiDQrgPMRFI8PmhpLNIZA7cNJE7FVT2D2SZpciEzn0MINIURypzWYTUx7NjHSOuKPnsgYSyTGpo6lkRpTLCnpgvQKpgOHhe2a9lBwOtJp+1QaoL0yOjGonVzAjnTSMsyYgyrV+XfJxVQqPXwLGZVfzn8nsX5OEsQdxdwrGaWLkyqEiBrJHTk+VOWMYkEfq4CCOL4DmZl0VSSM30efHlKjo6IwWlV4NJmRsOnpdRWASaF41BdgaUikj6wu8RtKAYzom0acHBTPhk3tOwjTIfZkc5tJHERvICJmEYzjXnTNJMJkXMOVMVnEIjUvQg54L1RDBQzCJN5sg7VxU11rMR66g4ClNHi0+AWhLZhM1PFbOYIW8gbdtgOBKpMjuQVTXjFIZHCJSJmQIy6UIUERaB4Srk1FAHz8OvVBxM4xoZtSBOIGNotkxExLVo1C2kBirNPDreABBgRPGwiQHVUP/XzFPBZ1GukcGSxPJcp9LZAmIgLYbTpgWUTAoRmixiwBJNGKcNTDm2xi+wry01HLCGOqiSPAM4I5MuykVc4vmdOPUJaSnpPs08aEgerABFChmU/QPMZ0xtCLoPIoegJlKyoVRFuDcLXTt0CShL2SFHMwYKQs4GsC2zQMAB9OaSpdN4AEJHJjiRKqlCAgpkccj8mWyWc38ghWOiy75SpEROiGDuqRqNkd7k6aMsmKpKDVoOEE4XSgGgKhnVqZmtZV8/ox5PhciCGnI6AjtqwsRkkeAgqIPJgq0zF4806HSB7uJQQs4ShtMWOPkV8ywVUZC0lGFNmBEKZDRpIWXmllS3KXMMjDlH4w4RcyQwnyxxNWuyu8/rJ2EWhWkdT7oABRLPybCD5CwG1N+DmogYhNNTAwGhiYnclZHTF5Ll3EdcQFVh/iFGAMr5DK3IKJiBXJo/klFCSQX5d2EOFMkMFcUlKAERcs80mTNeUPxvGEyYUqWSmImqnM5hdIJJxlSczk71PMdApXolVtMZg1qwB+JcUWyYHvA8w6hElGFV9hRijcWwDaJSWgkvx1/uaONJItC8waNSZlNtXFA8I7W8J/idy8bgjMrAbaCgIpGZ5JnAL3PFf1p5tCWfnUFdGVVIUAPGFLol6NchHOySg3V46EDAnCXsFiWjn0+q2G9Ac7xA95/Sek6WMa8J5xVg+LJbJPNSXgrLB4/WPvrYrdVK7qR1++eJaaodc7uc+lZRu3e0/NEnXqelzb21D29HjabTauxffiY1jPbeg5WPPo3qVW06X/roE1Y2YFF+crngShCPvY13P0xNEwCx9s57uamdbe4c7V6BErZOe5vvvhsVipDA9Ru3MkL4cuWyFQTVBj4aXPjwQ1q0hcAb79zKrWJUtLffuE5oGpXLq7c/UeJwtLR6tHvVabQUz7/81luqH0wvrOzceFfzvNPt3d6FzahSNlxv7YPbcpRMFjqXfvKGEQTecuve5W9NO4v2ZLL+0cdyFOemvvzRT6UgGi8un13cHS+tFCfDtVsfGvOZ12wu/tnn+nTmLrSOdq9O2l0jdDduvmcNBsPtza0PPraG40GzqXz+2V8jaFRgIk0njf/hv1WOjh5fXbFiqf8f/SesUNC++ab+T/4n9NiXgyiO53/777i/+VuAEAAAgnn++K/qn/+89MpL3LTmv/f70dUn4OOhRwFCKElq/+N/J02nf+3o0cJ5f/f110uHx+WDo+03rxWPTjiHKVEzKNnHp5vXbra+vquPJpfeeKO2/zAlaiLrOSKVe3sbb9+q3XtQGE23b7zT/PzLDEqJrCeyXj06vvjW9eJpr7r/aP3mrfa9/ZSov/jswejS62/W9h7ZR2db129W9x6JTKRITolqjCZbb91c+vwrZe7vvv6Txv19fTpfffeD7mdf6uP5hVvvr737AaTZZb93MZtj19945/3Fz78qnfRXPri99OnPYZqnRE0kHUfp1pvXVz+4TVx//Z33Oz/9WXE62I6dC8FIddz192+vv/0u42j7xtvr730IaZ7IOiWK7PhLH/105eNPFMe/8N4HGzfeZhnfcU4uZZ7keWsffrL88ael3mDx05+tv3ebRJE8KJKzBZATeWChYUdxs3bqXozdYhqiecU4baBI6ANbOmvDTF6ORttJZMUenuik3yKeBJ2SctJgkbQSjS9TX0lCY2TgQQe5MnQM0mtLLmnl4dVwqNFY6Zuk14UhlCYGGnSlGaimzm4yr+QhdIvKcQtG2BrKpN+VQphNstmenJ7mNADOHZgMIMxSqsuMUj6g84dSfppK89TZl+lxJniODFUAxnqhfw+kEwBHdLZP8rMMzrywbIEo5CHwvgHxADKH+XuYjQQeTtJyATtO7rHJA4mdcBYD5xsYnkOQM2qqLI3hOHEeEXqcCYf6+1J8JDgn5Lwqz2uyB/CwhgdlHKVbLNyORyLN1fOa3G+ABKpnJTyugxQlspFKGkg46deV8wpnQDmt4kFLytmG11tPAt0P8KQmDao4lciwYZxWQMZjxYoVi2VY6hXJuK64Ao/L0nkVJmAj83fCPs+Y0iuRQQtHUOnbaNREIYr6ILgHeSySo8y5j/IQ8TiKygU0deiQOw8wG+d0gr27KPUgTqLYUqEfZifUeSiBcZaNmLtH6FnMOecSzCkVp9nsPsk8nvfYbJ/w8yiPQmoqOIqiEXK/BiwA7CR17mPmMOA4YaOCJrNsBtz7kA9pPgPu1yBxAHFkPFjQx4rkMjJclAe6wtIn6bTDfOgRqdcic1WfQTJoyyMjQ3Kk2IliEp8pJ01pXgSBRPpt4hYq3mQ3i5fDCY6ANOhK5wWRY+2sKY9sJlCkFRNJhZGEhgv60AARknpteVDAWXolGa3yGHqA9JtkaqguQKO2PCxwgP37INiHPOHhPvOOCZwFGUEZBjxm/iEO72U8F8m+mO9jQQVO40xX8NgNT2G0D0TAnSMcPwAsYSCNmaWJ8Tw4BP4BFlManaNwH/JxTAmCCOQ09x5w/6EkvCw+yLwjAkdhBnimEiFEcIy8O4JnQhyy2b4kQgZdnfQ68owgz9ROWyJUOsn0Su7rNCQjDQ+60hQrgUIGHeioKZEjtZhKsj5RpV4HB0ieaLjf0WeolMwvJU4rmUPfVE8aKFCksUIGXWkOmCCxYlOsyg6Seh1tLkFPUY9bIrTa1NlJnGLqSmOEhotkrOKAyOcdPDNpKII9xAccDWJnH9MzLuZeWrJAEvEEOHdgeApzh3t7KDiHfDBJixaMQuZm/r4UPRI5Q8E9EJ0ilFKm4DzP2CiaH5D4WOQxcPdQtJdxxhHgQpXFNPEfYXrK8SxxDgk9YNyNMlUGgOcJ9+9y/wRnc+4/guGxgJGQRnX9tArTXO2XpF5bomItHK2noR4FaFKSezUQEjSpqyf1nMKtcLiTB1IUqUMbjdrQk8CsrPQaJEYr1LscDnBGtTMbDzooBGRk40FLcfJW4u4kU5NRMq6qxw1EhX2u4WFXing3nmxnse1O8MySew0cYTIpKCcNmJHmvf2NG7dq9x+WT8+3b7xTuf+wxNOngnObRvWv72xfu2H2Rs29g40bt+oP9lNJTYlKJbV6dHL59des835t79H29bdLp/2a27uaegtuT++Ptt94q3XvgXk+3Hnjjdq9vRzglGippJSPz9Zv3mp9eccYjHau3Wx88Y3J0qfifpUn1b2Hm9ferhwcFU97GzfeaX35FUzz3beuL376c20y27h5q/35VyBlKVEzJMmOt3797dUPbgPKt9+8tvTRTwEHqaQmRNEcf/X928s//VQO080b72y8+34uUCLpKVbU8Wzt1nudL762zwcrH360+t6HKEoTSU0kFVC2efOd5Y8/NYbj1Q8+Wvzpz3CYpESNZQNH6ca1m5tvvytysHXt5vKHH5E4FfCvDzQKIYCg/OKP1Lt3Ht8Ag5wP/51/L2u2pMm49r/9L9iZP+ZiEVIaX77qfu+3haL84jn493/lee3ON4+7mOScdrqjv//vhk8/a376ceO/+a+l4fDxZ+OdS6f/+T+Ggv91rQiD3/xe9m/+3crBo7Rkc4zVmevVar8gEWu+Z2a+MvbcRl3hKZlHuaVFlpXIeiGaU07K5+fTTtfgkTJy/VIRqsTViqVkrsSxcFNeUEPVLh8fzxaXFJG4Rplk1IpcMvWZpVFN06cONxXPLCaGKYehlQXK0EnKRUOEsQ+BLsWFonnWT2wbGsToTwKrgEwixRFEKJE17MZ27vvlMpqEVDewhlyzBHNmZT6ZRZDnaa2I3USlsdusWaGXEpljrI+dVNW5rUjTAAAxW1gQAiiMSlmqzDxGJK9eK5+fZVgSRQ2niYTAxKrb47GWRknZVuZ+KqnTZsvwqJQwv6ZJca65CS8ywKjlz8aNVRwClMMCHPqoAhMR1DQjcUrjk2F7Qw+SLFPjsiLHOUkY1CjmkeXPe80LKMWmm/gVxYwClmm5ya14hLMsNmzKLRxDZMQhtvVZHNWUctQncRQUGzwlea6W0DAgBRBhaGWuYuv9eVbWVBanIZE0IHDGZzPF0CKjhkc+rRc64ek4rXATKyDMvBBrCldMFgBFzRPNxAMvqxWUzOfOhBXKBClZDCUpBwpmISwyxy0U8Olh2lmRGRe+ELYsI5qEhBAGNcAmE0WVokJTGYdx0TRBkPpEaBhrAIRYFVliIBggJhFJjgrTs9gq5ZLKEh0JIXSaU12jSaaxWFEB5wJLmis4FpIcsUSDTEQVueT29diblrsgUQAQsS0b84QRqcrOBnqNYYQYJBSRnGcm0JwkJVpSlKvTIyEENQpZbpJMAD2luS6neVpAxI8yLil6nlKC4ozWCygNlPOjdHnDCMM0IbRi4DTHIUUaYIKi+VSplnxU1GZ+UreMyKcx4RbB0ZxnKdRtgXXgZ0qBB9iURh5rmOXZsZtjUK5CCnOKVJNlHAOfKQUeYlk9fJCsbhk0TUKMLQi5yEJAywbG2ByFUUUzclcEaqpDDfqqP8sVLVIrKCaEJJGmK1OaFDUrmziGTbK8Fs2mok1wKmQBYkkmaaST1uBoXG4SiFikM5lLJGWJhmGWayySNTt2U2JpDossJVOxMaNMETLy5chjkjq329o8lWEc2qY+zxJdiixFH3lC8KxqYidR05iVZDabAYxorS1PQgaRYdAkVWFC04YtuRPJmeFyFaQwRQot6fI0EIQQQvMsIaGftFdIQElCWUlR3DBBGlI49CYCY6RZjMswyWWbR7kmRwkrK+q0l0AiFWwWwEwQw6RppoAgixeKFW/OUo1aQqYZyxSkpggkpjOKizUPV/VpFNU0K/XzVGNKBnEWSXotnibAErGEjCQiljaN47pWSIZqFPiFGqcSSwjRKIWSHCOkxSGRc4XIcaoxlsca0WIppz4rSUoCcFaY95NCeS7XzUkUVgwj81hqMJVxxLnPK8Jx7SKbZnnRMKGfD0ZZparJUhJICGfIILkPCaCprcnnh7TalAHiERIqUpSM+oTLiJlEGp4RiJNKS5lF1FRzQ1FGHpOJpqapHwGEsnJTmkUKpEgDzAWJqTNTUnpHQtZkU09iCQCRlQxlFgoE44ptzWKBcEWcT1GbpFlYVe1grPmzWW1J8yiFalxUNC+FHCA1JnlsBI5XaibcViOaFLHhJynUoE4tbwSAiI1Clps4EdBKY24qQfp/sfZewbKdd3bfF3fenbtP98n5nnNuBC4iQYLkaDjRksYjWXI5jCXZKlW5ZMt2qcrlcrnkB1VZdslBEzSjNCQB8gIMyIEIHIIkAILIwM0npz59uvt03HnvL/nFYawwvlOj5/2yv7dfrbX+a7ESLo2bEqDYKcpY40qrwJMBmSChArlUSlEatLoTC3okmdCRnamUKAaxkzGBS0cH/fn5hn8SRzQs5TXMSRwzw2QC2/0hq7gRccoHe/35RUMlQ7uks8QOPDyKRMFimNqDkWmJ08JkvdfqVKfN2NN6flQpYSDJMFA29fPl2LTL406ErerRcX9y0pQxHURJwYE61JM4sPJckWLrhBdt3y5WDw4Hkw1CpN7zAQZJIacNAoq4ny+GToFkiYKoenyUOI6yKe0HCqNhvaEgtLJQAeS2u7Fth6Vy5eiAWbalpS13WiGgxYkzHAhN546eb3d9Jx/ncpxSqGQ+9kCQaSyJykU6ihSC0qGRleOA5JKR3hsLTHnJxuMYC9FTOP+P/+DflkUoTbP40guVJ76OguAexR2YZaPf+M3uf/q3IGflbz9ReuZ79xi9AlIqTev8l/+1/8XH/59fxbP/4B/mf/gG+OPW2598UTgc4jCM19aT1XM4CI2d7XtsbQAQ0n4PMhY+/MifQjb7k2salpb1auP+7z8zqjewH194/uXmyrrdG33ld3+vtbpO0+TKtWdGtQYE4MKzL42qNRqlj/6Lb47rDZCIB659p7m8hhm7eu3741pDQPToHz6hdI1LvPbia97EhADaA996qrOwDCF47Lf/gNluallXnnk5cd0MaeefezmsVDJs/cL//tuj+gyzjPuf+E5su9yx1p97jVNtMNG48vxLiiu/Xr/8nedAJtqr5+771jNO6+x0de3c628Vms3+3OLKD34Eo2Q0M/PF3/9nTm90sr5x4XvPlZqnJ+vrq6+/VTxpns4trTz/mtnzhotLl659j47D1oXzF55/tbR3uHfxvqvPPj91/Xp7cWX1zZ/YnbPjlfMPfO+Z3Gn38P77N773SvXm9t7lB1d/+KPa1nZ/YWHprbfNZvdofSN3aBin+f6ksk6pc5xPy0nsTWqtKa+ESN+xjjRWzcipTU/Lg0mgt23UXBpXlDOA9KTsFxUZOO6RHk5A0Cmgo5nBjGZ1kblXCKpMG1N6XM1yKRoW1NFkUCN04Fh7uaiWwZHm7hbCisChAw8XozzEnmHvW6KUcr9oHJTjGvO5xt/1haMhDJIPY25Sju3oUwQLZgJo9n4Ey4YhktOPkHR0qJPsI8BNO9OM+LMMWpgDnL4fi7yGKfA+1IRhQ5eID0NFCDd0/1NmUM4MJ/iYMMeiNvJ/lgpNFzma/jyAEDHTCD+nUNNT0/Tfj6mBcR6P3k4FoWjCtvZc4ulhXpkHeRTiNK/IzgJPi0EdWnccfWzEFaYdlfDYjIrS2inqQ8OvkNyWizwcTKLcpoW8/LCm9IMKGNWioiInZW1MvRKyd/O0j7J6ZmyWtHZxOIlyhzruOUk+c04c4FleCRj7k3hQ8GaIuWvpp3ZSz9CJS7puVs3YMUu2uJhxQJOlt1myVARNltzS5LQNeql/B6iamZ4peStQ01baJuFNTOZt2VPjTzI1aYIRC25KVdXUEMW3kZrO8QGMP0/QjBGNEfsoIpMG9Xnndg5WTegLfjMVdSsbo+h6Rhpa4tH0E6wmXRmJ4LoQJUNGQF6PkwmXKFK4nUscCIjU7zSkBpiB9LuzGXEyHRs7JamBjOLcnSKzADdR/uMJqQgwM/NuWQCS2cjZKUkEQ53SzbmEatwG7o2KxDTOI/uWAxVhJnBvVYDAsYnszZLAMMzT0vWcylRU0Yzb9UTVIlvltnNEgsDCzlZVAjCcsPAHveQUpPN5cCdMTwWsauGuLvo0a+T4ZxHqc7ZUSD4Oszbk8256V/ATYtRxuKOCMywaNr+ViuNIniuImzI+0tPlktoKkiMFazTdY1kH0AkS3cWsRdRKLt1k8VZGF6x0nwf7Ak3o7BBGJxas63wzY4epXMwn+yDeyuRSwRwAejCRuRkNNH3PjcoAekW0PxfWFBhTd7voV6QeY2O3khQUCo38bVc5GYtzxkE5rggQ64XNYlhSINPlzkriQJRidy+XFBSIDX2nxMoZZ071w1xcQkASY7vKLYkzoO8U0zwQmUt2F6MCTIVevFVIHK4gsW+XmItjLKPPGFFM2Gb0UZaZGnaI9x4WxJUlPfm5DzjiOT38jCGlmGsGH2NMDVrCo3dSAYiqmdkHYZJSXrLYZ0oJKlw9/iSTCqVVV7wzVhlg0/nkA5GlNi9b6ecxTAR10egzlUrKaw57XwgP8YUC/yhMfMKqpvwsZDFmE6a7n9e6OKtn2n4O94uDBjSaeXA2ExW5c2qioeuVgH2cMzvEm0FkvwxOp9MGR22XttyknBldG53ls0KKT2q8OxFMIf0kZx47YS3FXdM6yUVlgQY11Z2OSlJrOdaJxiZScVY1m/mwzsCghE9mwwrQBybp5JOiMNum3bKDujRO2g8+9d3WxsX88GzxuTcG03P6ONh49vXe1Kw+8C+8+EpveRl4ycPXvnO6sq6F/iP//Em/XEUZv/jy66PGJImy88+8GM7UE65f/adPdGYWsAJXnn7Gq9alUBefe8WbqGM//uI//8aoXk+A9vA3nhjOLUpNu/zk0365KiC5+PSLw0IVCHDf957xG40I6o8++a1hdTKoVi5+9znL8/szc+svv06iuN+Y+cI/+0Oc8PbC8qNf/yaK0s7q6uXvv2AMxqezSw899XSx3elOzl567mXE1cn8yiPfuqb3Rr21pUvXnq9s7TXXLlx69TWtNxxPTl188VUYpscrG7/wB/+kuntweOXy+ktvVHb22mvry6//kdXt92fnH7r2HbfZPltZvvSd552T0+aVK2s/+KPK3a2D5XPWR/92tggVpdad2+Wnvq2dnt6jwQeFSBeXTv/ufwswtm7emPj931X3OKcDAGRs+Bu/Of6VXwN/THLCy3/770jXdX/27j0m1gGE2smxtJ3k3Fp04ZJ18zrtdu/9zcbuLmtMpvML92ov/omAxWfn6hg/+MQT4/nZwsnJhVde7axvzN+8Pv/ee0k+X+mdLr35FoBAGtqDTz7lTTVy3e7GKz8YL87WN3fm3vs50HQ3DZdfeR0qxS3joW98K66VnOHg3A9+GDRqle3d+fd+ThGmItl48Qc0S+NK8eE//GZYq5iev/Hqa0mtWGq2Ft55l7AMUHTxuRftLAmrhav/4ltJqYAgXHv9h9bYU5a2+tqb5eOjzkNXH/vd3zdHo8HK4tUnrlk8QVJMv/t+rtPJyrm1H7w5cXi4/4UHv/p7/9QcjXqry/c9/T0jjhBGC+++57ZO43J+/bU3Gnfvbv3iV7/6v/22MRx1Ll566No1azxSujH7/gflo6PR7NzFF56v393c/9KjX/1nXzc6nfbGxgPf/16+10UUTn3wcW1z6/DB+4zWBRHMyEbf3avzpJHLdlFUl/4CMCLoV8zTmmnfRSfzLF5hpZ592pBhI6dO6NBg4To2IxBV9dNpVToyDqs8WeXFvtsq8GheV2emr7HxOYwHMC2h7jlYPLCOiiw+R50T2q3xZMnmp2ZEktFFgnoozsneJdu8rZ0U02TDwHvhWLa3bU2khiEOr5csm5mB397O6XkpO2m7XTQyvxaf3TmeNaLQcOTuZyXbZk7oNbdc10xAn520SwZPbRgebFfcyHMKfOvDIrGUlsat7dwEGbEhO+5UaZqaNDveLLqBb5bY0cclZEGdp62dnENTNIo6zaLMuEHT47sFzQ/tJVvbXMKKOllXjFZx6mh4IAfnzSFV5X26e1WqMjKP0fEa4ZouBsJbprGjzAB1V8yerqoHxu6lTE6o4tg+mEHMcNip8hdVPKEsj3YWrYFl5G6o3UtMTYvcwGzNw8zJsRMwnALRlCoEuL2kj0qysmPeWWJwlhontLugkjIh3bNTFOzQyoTv31XtTsGdBumO7PWcghaAbnJ6UrJtHvla7w7Nz/P0Tto9KxQqsXeAer1cXo2gL5tHxZweZRFt7+RLjVBsxq1OqVCOoj3Z7RULbGB6/t5xI0/GSawdb+YnJgK+nbTaxZo7jJvotFdyZYCS7HQnVyEjnuKD23mtIcuepcZrduqZ6ZANHjSDANg+aD4KKNPTMexsUDy2x4wFF0jKdDEUvatuMKZ6Kzn9c5BywgLZPW9Cz/RTFpynTFHsie79ZpAqt0v2HhIaxCIA3Q1DeCSVwls3OeJGTI7X9VCAfFvffyAxLYoyerqoZ76VRWK8BpiT1vqDHysvtkrTYvixSmNcAP5ZOxeNdDOvejsGO+GVGW/8kd4NXXMKhZ/wLCI13ul37FHP1Cuot6OBU1ie88/eQ/2kkJsQ/l3ke1oRjoMWHnTtciluHzh+SyuvJsN35SDKF4vBcFfzR0ZRjYKePuha5WLYPXTGp3Z9dhC+zzthya2xWpem4/MYjhC3Yfs8zDfNls2DdWq3tLMyj1dtfmrFMh5eIWCImC47Vwx92+haWXjRwAe4X2bRqpN1tZiJwSWDd4EyQfs+6LSNriGCdRPt6wOHRRuU+ZQz0LmY400oEOt/AdpdOrSEt+biYzrUWHhBExEBTLUvEhqNxaj3qVZEvvLS43aFRJmppcdbJXscOBPp0UclRbCF49Zdx8Sc+mHnpChSZWppc7NAh5Ezq47etZiu2xo/u63pKtV5dnqclwnU8/D0cx2P09JE2PvA9IijmWp0mxhZ4sTD41aZjaE2STsfUS2S+XrY/pkeYMc1xXCLihS4eaidrZo9x8zfhHsXMzEr8n3rZBakxRw/gaOqDOdALsBnc/qgIiu71uY8UwvEbNLOnEzrhuiScVl4s9g4heNlNJiFlU1rczqTC5rVJN1pmUy6/IT4RTZaIHoLDebleDlnfYT35zOxbKId3JuW8YzLTmCQh2dzunEMxgtytALLhxuvPj/3yScS48q4O/fm29LU9SS+7+nv9c+vTd28sfSTt725qakbd2c+/BBgovNk40JpcaAAACAASURBVIVXlG0SKe/71rcHq0vVu5tLP30H1gv21sHC+x8AJREGl559SZm6Iuihbz89WJqvHBws/fgnWb1S2DtcfvdnSghA0ZXvPwcoYZb58Nef6K2eqx4drf7oR9IxzKG38qMfEaWGC9Nf/ke/B4CKi/mLz75IAUdCLv70HXc8Gk9OP/jUtdLJyeEjD33ld/6xkjKaqN//zDN6GhJMFn76Tq59era09PC3nqy0TjpXLzzwB98gWRpUqpdfeklPUwrF/E/fLh+fdK5ceeTrf1hpHu899oUv/6PfpWmclktrb75lDodQx4vvvFvaPeheWH/g2tPVvb3dL3/x8d/9AxrFJ0uL6O4d2u//WQELITIalp/6lv3Rh/cqQSmlKDn5e39flEp4NJr5e/89TNN7JTPO4/MXhv/eX2XV6h9nG/jn33oHpmnjH/7PzgfvKULvkfJ4qdT+O/9N+ODDtN2e+e/+Lun17v3hrDbR/Pv/gNVq935U+CdtEf6V31z44Of9c0tKweqdzcP7ryoI1t55++6Xv+pkQe2jz4/vu0IVq2wdjBZnGca1O1v9teXIzq+8+87WY1/SZTzz7kcnV++DGJTv7saNYmZZxZ3j4dKsZxdW331n65Ev6JhPv//p6YXzUseV29v+dCMuFCZubY7nJ4fF2vpPf7J79UFiwOkPPu8uzWsWMHa78USlOz298NGnw3o9mqws/Pzjs+npcKpW+/y20rXe+kpp68DMvNb589Xrm0GlEk5WZz/+fFCphvOT1ds7EIL2+kph68DKouZ9901+8nmQy/sL0zPvfxiUq97CdOXurmJs/6GHZ25ct+Lg4L77pj6/lZrmycbG8gfvJ47rLU4XtvbMONl6+JGZO7cNb9C5cqF2/U5quYfnLxbaoT5m3XP5XM83+1oyHWRZzukxbxYZY0UDppWCOLINH3VWbHcU0a7mLYn8IFaBNZ5B+liZ4zRtABQiOoD9VdMYJ26XenOp66Xcc9JGpoWABCirCRBDbYCyGcY5zbXxYE7ZfoKGZtbIaAzBGOulUcRtc6zFjTgwcuR6D86blCh1koIKFRSBFtOqILNMtR1my/mptNU5ss1ZXWhAHiWqrCGbyGamlVXqmGorUguWhdNsT6iGhkwgjlJSwDBH2WFWcINRrSrvxmjeNEiS7UtRJdTFoJlAF4u8xg9TqyBZNSduB2LGNE2RbUW4qqmyaZwpyFXckHrXFFTIYqofY2ECXpFWV3GB04ZEPWJmMm4wfOZIxL1JrbifASp5TZAzhFLVX3IKrYimIJzkuGdRkY6mjdJRypCyC6PALxmJPFs0C+1EpjRtJLmO4ggPp6z8UUKATCck7mMcgXSGg7GuRTieyWg75r6AM4YKJOzE0cW6NvDBcarOu9bQi3uQL+ZAIMwzH84YKhS8k2nLZsap2g3h+ZzpeUEbkHmDREwNOZjSeaxQh2lzKAGm2gnBml0ZdXp9V5/XZMRFj6FpQ3EATzMyi2Jowu0AnLONJMnaCk5rIBOwk4aLNYJ48RB6k8CAnt7KpeVUGko/RcJV3FFGl0gnDVzdPdLCaqbRyGxZUV7YupcMyshiWU5pp5pykrCo5Q9pUGVYz/QjygpS5ZTeRsyALC9oW6NmHBSJc2yEJRaVjOpWyPOA55XWQcygYQXljznSU69KnQMqc0lvslzaOskykGzU9IMBiRlcMNNThSXP5gv6bh8SiBo6P01pLML1mtUcCo+ZCzg9lUqhdKGoH46llGRal10GPBZfqGmtMfAUXtRwM0wlpdNENhOiFJgxZJfJMSOrNhsBNcjIoolafsYxmdZRJ+UZwLM663EyYuH5arU/EAM3mcy0FJARYjWhUkj7iE0zJmmhhfvzwA4j3LOzRoozAEeUFrxU6vrIyCbCGBi5E200L+wkIn0nraWAK9pHvCaVgvqZzib8iDiFQ+RNp6ZIyZnFS7GmuBjbvMwlhNqpzhtBpDulAzieZppipEWzKpC6FEdJzgzCiTLbydCkYRgp2xO8TGgBg2YCTSTKOj/KTFuIusvvRqKhm47MNiNcpqhMwVGSmLqYdOjuSDdF1nDlZoTKOG0UzBttUNBRBYvjFGqETbt0d4QMqVVQdCBJkYSNonPnjFmY1DV5kkkIs/k83R0Bk0Zzxep+KJWwCqPQzxsx6CzbhW4MQhpPZ7kuF5KOpnWnxTQu0rrEA0x8kM4x4eumR+Kp2O7JjBt8ItMHCDGY1gUcQM1H2WwqIlMf0WgmsXoCpBqrM30ARYLNyiCI804Ag0bCE8scauF0Yo8kSLSsluERpCGIpiFgYuXdd+58+SuTo2Pzzkn7/ss4iQq7R4OVeYFI485W9+JqiK21d9+++9iXbB7Wr2+2zq9hwYs7B6PlOa4ZjU8+jzamTwsz6z/58fZjXzR4PHFzs72yBCku7xyO5yZD05756NP+5fVxvrr+R29uPfIFDYuJz24NVpakRsp3d9tLS6ljL33wQX9juVduXPzhm3sPPSItMnFnl1HUX1ms3tiEjt5aWVv82Xvdubne/MLaT97y6pPBTK18d48j3Lp0cf6TT5SBj9fOL777nj893VpaPv/2T8fFgpoukL0eRvDg0uWFTz+JTGu8PDv//kej+mRr9dzKO2+zfH6wNFPaPqJxcHLlcuXutqTaaHl+9v0PBrVGMN9ofHZLGObZ6kJh+4AytjM5V/zt3/2zWoQQAiGKLz1f/cN//qeQoLK087f/q/HXfhlA2Phf/xf37Z/cq+qkpHRzZ3/jb45++VdRkvzLUzlK13v/yV83trfIvYW5FMak2y19/7usVk8XFnv/8V+b+Me/A9P0Xoesu5367/wfJ//D/6goBfJPncf6/4AXRCLDenvM5yAQQmsNkwsYc47OAhEDX7MOf/Ev6ElYPjvVmn1enxYEa6djvqCE4vB0JDzOKcCdMRhnacHSTkdBPs8QpK2RmJoVBKKzUISCmYg2+2AyThpl/WTgu6XeTPXk0el8MgQRg2eBilRGqdYeqUqWGI7bGoZOgXGMegEwkqyK8ekQETdaWCQdT9rmmOZOLzxYTEaG74FeBHGa1TXaHmFpBitO42QoNZys67TrAcVYrEAvgkJLZzE6CzRl+mtuo+tzJUNlom4AWSRSCIcxSSFP4I0HHodC5LhPuyHOkhDaaBChccxjhfohimAMNQ3kuYQ+UVC4XJpCNWNQA5B6ICxbuxU0aJNCIBtMmj7OkMpRkItkjyqTi5oP4wwRJVRCxliYWNk+SjjISZhP4BmGWSYnMtjLgEa4ldEh9ikTOQ5app7NVbbOwFoESkBWheplkCqZ40hGqiR4kYGjUDpJLE1ONSr8wDGLEBKQhtLIA4ESe44kUTJiViepFDIITBDGFlUYSxgGKpdnnGlRZmsplCbph0RLkOEAP5ZWDhIAvQQiC4SZ6THbzpCgZBRi6CDqStP2M11jqZskdlFkWWb5mUEzLKxkmNhOqoimC+5CJTI5TtSkUgypDod1xbKUjKeMuwSjm2gNyaJUPIVjBRsKZgFkBBiIM0ZHuigCgH3MkCgaUsZyBNUEhdxHTAMa5wriNMwaTBEfpVSVBDAkOK3kdjikp3QNwSplPNVGlOWhogx1BKhqUM9gTwotSZCFURKLLHYgzGmSJCk3GeXMU1UZcMhViSaaiYnkMkhUEUovMxizbIYZF/1YdxWSQiYhsCHKOOChKEIeZVbALJtDndudpFIQSgAZJ4BInbKOU5ODRM+ImzHTzGAmySggJkcQqCyRErgGBAQWQukpohI+x/lYkYizAleZRtpTzn5fy3VlVYGJDHoS9YSciWUAkYzFDAIBA3EmK1ANEgGkamRyrIEBAzOS+4xEGS9xlXEUEVkxVC+GUMJ6DLwYpA9Vb3dpYR/PWqKQAhBABoWhyWEkgVIVBcOQYJGWEw4gyqFUkiQzBPGYhoGSQs8XhyBKElBhqYNTyUghZoBlrMwyL6NcUYXzOMVSKIeCKJJpAjHMYYazTOW4lBmOuOFCFXELZdDUEEtFlACbKQU8fQJGMWZpiWXYhSBOrSwCeYKiFKhIYGjGsJTJyRT0U4AodxkeQoGJKgp1YmjZfHXzDKzHqqBkQ6geQgiKggaPEuUyURbwOFZ5iCqRPIOAKjbFVR8hQJnNkC8ZSVgJgqNQ5hAsJ7AHYIDEFJNnE/pBI9c90utB0khVGarjWLgDVU3kmYAhUg0OxgwlQWIrClNmDrOcxaAwySjC0IKkACzLZ4aZpW6ayJyeSWZ6zMQZzsN0mNp2CglFcWJnmoagoRLo4FQKO8hMQyAG7VEsCAV2AwWxjRBVyAQZ0jDTh2NYA3HGfJKPYyEJtggIA8F0SoElGYEYKmmEMC8YADgLxRRTNMAZVQWgrEy1550dBGCHrkFVY1wkdER4DgGdozYDZQ4tBjsQkETmBOqlMkc5SWifcotJm8EWU5VUOVyeAkCVcDnsceBwZQrE8qRfzw2vg9UU1jl0E3UKFBEsJ1CfQAsLmtKxPuyjbiB8JmJJ2mMRKSmQ1h6raXU2PXnyxbl8OsbDiHQDGQoOAG32xCzjOtHbYzaHAVek44klJXyGuoH0GNOx1uyLxrxwNHrcyyYaSknaHok1JTyGuoHysrjsfPKlXyZATDQPaXuUzWAeAdIey1kuLYC7Pgh47LjacY+VcgmnuOsLhViGYD9SRcYS8OkXfxkqWQz7enssHSdiGjodi7zJU4h6IXCzCJjkzMeMeLON0ulImkYiNNyPoEt4AvGZD3AQnLNp11MJDNaciWYfARFDY/P8gxKhfObjQYRQGC+Y+HQEjDS84NS6ARacNTAA/xY6ye1PPyl/5ymF0L0bfN4v/KL/5a8qSosvveD+5C2laffMc8j/4uPjX/ga+lco6P8CNDY52f+Pfqv2e799j80LStftjz/M/+jNwV/+K+Ov/Yp562b+jdfu/RrRuHOr/NS3er/11wGEf5aGd8L51J3bD137NjAxTtP7nnumPz1dbx6vv/F65jp3fu1rie0gIBc/fP/+p777MRYK4/u+/12Q17Set/7DHwJCxFT5wuuvWaHXvv/Sw0984+5f+rXUddeffRnpcjriG2++rksxnK9ffOO1/ODs4KtfeuSpa1u/8osnFy8mjksNcPWtl86/+Ybl+Z3718+//GK5eXD01YceffKbd3/9l/UkPv/Ga6PpmYPk4bW3/mjecd8+N/vo0095E9Xmg1cGk0uRJmduXb/02qtBubKt/tzqm2+umuarG/MPP30tqJbD2cbV556NJ8oIwdU/eiMzzLuEX/jhm0ip9uW1h779ZFgsnq1t3P/8syLvQENb+PFbRMru3Nzp0gqAoHZ0+oXvfyekWvP8hUsvv4SBMBGbffvnMGYnG8vu4YPKL+bnN53DColcQ+5GAqvhZM7evqxH56PRD8IQHF7k2awxvW8dF/EoZ+KWPiZxMAmsIxTmrP08LHxo7pdFPGNO7JSPHDWs6LBnxBrpNwwwgJlFTpeI+66577BoRnPbDxrbDwa5MLjRGT+ank0BOATMgidLDrptd0CcTFH9yGO+v0NoEllzYvi5o/HY1aPejZySqlDsRJfn7I83jQHx92qaF+RXQe8zdyqLLTPo3My7PKQ4CTadapyQajjeqk70B/ZFePxpCa0mTiEd33KnpkOtLaLdCvFiaz7rb+Zqrb52NWRTOZRJ+GEraDbcKEE9Fm1ZzjDBy6l/16XtUJ+yjIM6NQLAetmZC5BArKtO60bi+3Trl+A4y6wT4Kv9c4RGuhiyfo4oKWnX7DSswPdKh+7uuZga+Yk956iuqcjh7XRsA2GauQ5uT7lepOkjultM9IJZOzCaNcBhXn7yIPAoET1tIm2tmCGElSNnZ4ZpJc3uopM8yHSNtk+bMt5UpaKX3IZ+1yksJuJOFhzBgtHDWju5tOJ0gv6BGXwO7Vqc3cy8tlsuR/KQhy0tT3zImb9pujgUHA9umIX8GO7ys1YxX87SpgyP9aIYU+57B7Uc8CSk/U+tOW1ES954Ydr+bB+cof6e6coIaMy/nSuKntDN1id2Oe/nQV5rVp0wgHbK2pN4EPLFGDUf4mlzgf/sF9L0lp5kXiXtrOsBw2WgWjPVziae8eDBoqweaVEMmlNGMTQGseqWTE+i+kCdTOn0ODD6+v6CrJzKNETNhp6PYJTAk5I5RoJ++nioTrPdFs05e+fBRILgmX1SN8zQjH3RLnPfMcpH408lw6g2EwyvMw3JEh72jwuMYZc3k8s5/dgze53k0/wQ6KWFdHQL6Cm3eG/ULnopzZXT4Q40h6nVCEefaAF0SnMJu82SCNSQ7x2hwKMlJ463DTYAhfkw+Ij70i2UQ5BLWN4xP9uLunPBEJacqL9txmd4cmqYfgy7rFCbSd0zEvUmDTUGgOKjBWKfGacWH00Js3vVuvWF0E3Cmyfe1aQzDZQHEIaHy5bacQc8Cqeo3tI9HfbKdtYBWImTOkq70tZA85xmfogHUI4aBO07qRT9qpaOkUPh8aQ7OlmeOn0Egbfiwe2TRdidsuERZQCfVYwsQPkUHk0BxU5dz7uBao1Y62XxbhX3ub2UDjZzleOBjgI25QKA8fvN4HjKnMzoOI22XKub4NXE38wRwzdLtPURpXMih6P+bWCVUzPOzrYd0BNUT8abMK8FVkkMPrPTSeoazL8LK3bs5prDx1bJ2VAbp2e3UdFIzFLa/4RmDapbSXyHY4s7BUo6C7kB0/SPtd18gspG48BsVmFCq/D6/aqfw3Cg9bzOqj2EsNJ0dmcyWBVOG7VyIHEt2SYjh4R1STqoXSJeGZYO3N2JBEzpbku2XBjncXZEklziTWm0g7oVfVyz8KePZTsz9tRZfDA4W8FBPp8ei9QGZ1WAj9RwCgwbpPzO2g9ePffWjyTFrpHNvvpHFPK0mH/kyW9+rP9Wa/lcotsm5g8+/8ram68DgqJq/tIrL+ki8acmH3nym+/ZxOwN1l5788jJyuHN82+8hjkbr8xefuF5Owk6i/OPfvvJ943fokG88YNXqUsTRTZ++AZJk09+699P3JyRhpO3bj765De5bdE0vfjaDyyYlktHa2++aaTJzb/wKw9f+9bphQ2kk6svPDc4v6J5wdoP35i+e/u1/+LvhIUikjJ3cPfRa99qrp6Li8Urr7wUzE9qUq7/9MfRjetetXr+xRd4zv354t989Olrvfn5oFK+8OrL48mpdjI895MfJ6YzXlq+8uILyjDaV8499L2n42L+bGOld/4BmqXVzevnfvwjDunP6vn7X3wBENJ+4PzD37nml0rHteqfdYsQQu3osHLtSeT79whJkPNsdm7wV/8DkcuZd25XnvzGn4KulEpWVod//i8CCP5VmMHn/trfAAAATFitRodDY2cb3NvzFCHWzZvp4jJrNML7H7BufK612/dkWEKIGKOtFi9X0sXFewlj/RtD7ssro698KSwWjq9e6c8v+I3Jwwce6CwvQoqv/+qv6SrDGUcQsKI7mJ47vnrfYG52PFFvX7l4dPEyJOjGr/+636gx3dj82tfCWnk0Pdu9tHF27ty4Vj+9//LR5fuQkp/96q+lk9WwUNr+8uNeozacmDi5fDEqlWgUu+Gos7iMlbz+7/z5YLaR6eb2L/1iUi10pxdPL1883tgQVNt96KGzjVVBtc2v/dJwuh6Ua60rlwbzMzjOCl4/rpSDQnn/oQfPLqxJBO48/ngwPz2u1rsXL7Quro+r9dH8zM4jDye5/MmVy90L69y0737py/5sYzQxebayvH//fWmxMFqY3nr0C8wwjy9f6i0uGlmiJ7GytV61PlxZ2nvggbhU9Gen9h7/IrPsg/uvttY2MOgR0hpNAaT3DTj0VlRoKgO2RpMyEuFOnAwqU0kOIdLxZyUgfYOfdFZ1asaADL3pGKkxNFtJAyZ2ZoADbxYqY2TKs/6ywjSCdJRMhRiNhNZKpnCa5wR006noWGpednq9flEZAqJhPJMAPULaiaylUY0g0E9mk7jqlthQXye4gPN6SGepqNumnloziNuGtn0Q1WecMtY0qZ+3UA4VxBmdhmquaGuJMYPYrJNLhupiTishKx5oF3KiZJS0sTGDwbTp6EluUsTLtdzwBFwqGhVoqQhvOKro0M0DoAiar7l6rE+hbKmQz8b6mq5XgQtieM4krgJkDOzxaB5h5bPKmNUSko2i6TBt6Gf9wQHFndwEMsbA8sZzAMkhy/XChrTZWVz3siqRsI/M7nhSaqCLHW84jwFIgNuJGsxKu6I6EBUQGzGiTW8W6aADDO9s1gjT8SbSm6W6nvZEsZPWKbMTTWvH9YjrMdJH4ax0QKgXuZq3aVnLW4FcdnU7yQOfXSprjONmLy5WTAsUCz6csUAOFtSZvm6jMs0Dn91f1k1uGileNXSa5PQhXHBlzXS10FiiomEV2RA+ULa0BNMEr7nUVkXHF/M2V8DYPuLlCTGdK6R9dV/BcKVpcrTuYluW7YAv5bOKdMLWcJnJAqfCC+ZDlgdYnPCKN6w5ncw/sMqdesWMu95iogopTTrhQgZyXOAer/hJVWIwYLVoOEmc4NCbS1UhIdkomIt5ASDQhoV+UuNEdbNy4E0TM+mHk8NRoRCPD66blbA+CWRb5MdhLTXFmaiG/RnNSAZZ48yvOGUydAsxW8wXzdDMC37OpYTblTRbKNvb+zIDfHpWM9NiMVZLdg4Mc3oALpWpLs2aAPNmQXl6g4uZvG6wHO2J8xWL+LbN5EbeoMyekGhGc9jAqqZqJoeLOG8EeF7PvFQ7G8Xzs6YurapAc4ZNY7eWyTlXFUjOjORGAeEA4VE8HWAtBHo7bqg0JwjosqnwCOh+evLpxAVlKQQG0WystBDDQzmRxjUCwTCbjoKysuPT3jmFzVhpQTidCiNFsJnWeVoSGhwk08m4Qlx/p38OYTtCcJwuMB/zw5QdWpUwj6kaxFOxN0GcuD1eTpAZAjCOphKck7aR5es8XSlbLMDnHbMKTBHhDUdVXLJ1ADMAFhquEet1mC0VXe9UX9G1OsnBGK6bqERdMzJqAkwbRd7TGiBdKTnc1xeAmjRLYmCcM8SEqcmxU87Qsp0Hnj0J2HQRHrWVoKJRKosBmUNgyjVc6ZSYmtNd6GlTlM0aZtIRlYGsytThiHa8GanBLqLjs3kj4sEORIelSZ0NZaGT1ElqRAY6SKZYZsaEDEeLkqJAOsO0kVLRTZ1OOqWlVkpxN55OUjuleDhchQh4wuol04rgIbRaoi72DbsTD4/qq7HLNXU2XFaUxNz1wikF4AhqzXgSna6sYASv/8ZflGVnUJna+crjYaXcn5s/ubie2bYRhW48PllcxVB9+uu/nlRLYbGw/ZWv+BOV4cz86eUL7XPrSbHYfeDC3vn7paHf/KWvxVMTUS6//aXH/Mn6YGq2c2mjtbqSOtbhww8cXrxCgbz1q78KdQRTThXnOXs4M3d84cLpxrpy7P0vPHS0dp4qceNrX0uqxeFE4+z8WuvShbA60V1b3n3oYYDJ1mOP+fW6GQYaz5RF+xOT3bVzR1cupYV87/zaztWHFEa7X3ysvXYOUrz7+OPRdLU7t9JdXT66ch/LOa21taMHH4Aa3Xr88fbCvDT1/YceGi3O9mfmhytLrQsbehASydNSTlFt57EvDpbn01xu94EHR8sLvcbUcGH+eH7R/bNtEeIgqDz5DefnP1P31vEJhZC20/lb/3l84SIZDib/p79PBvc8VqiUyOcH/+FvhVcfQP86E+//BiylpGXxas28e4f0e/fEWBBCKfWDvfjCJV6ppEsr9kcf4DC4J8ZCCIe+1ukkK6u8Uv3/Zax/E2Cx+XlzYa6xdScrunoSl46Ognot53vl1klcyOW9/v3PvwAMCjRc2duTBh3Mzp5sXKCI5zrd8tFRknNsmRYPjrhOo8na0cYlYNPK8aHTPOV5m6Zp6fCAlQpZ3t6/dCVz7Vq7Wdo/ZEXXjIIrr7yMNIQgLO/vMdemKitv7ylLw1Tl9prCNpROqvv7kCBokvL+AZKc1YrVW7cRUKzobrz55lRzNynmnWYLAwBsNFU3gQ1TqE3c2SRKJMV8ef9Ql1lQLZ+unvMmJgpev7KzS1kaNSqTt+9gJcP5qRk9sItGmmaFZlsTLKhVrr78Qn1/x5+dbGzvEpb69Vp9d1tPI2BRt9WmWeo1JixPx1znBWmOMU0pQQPETJrpyhZZMJFFiybtw8hWwhFONi/2luWOR7DmU8Bz0hSUUS2ikHqLancJ7Z/ZBSMwSWIoPTFjATIb6ck0OV4kRzECwLdxZmLL51ltPFyHemKzDMQO0iLKIEjyJu3CWFepjQ0/4wj2MqgpHbFwQKnGTZWxIdAtxoEVNl1sazXe73sOgZmpeTCfaloGfJmNkW5wJEE6ApoBSDqktQSrFHMtHiKiSaJYOkAWiCFKcDHAIAMZYUOoE6FRPu7WCNU1wtgIGpQhKfgIIA2aOA36lEKhuYgOdB1KjCIU2hBASiMSOFQqYoyi0aWQTVFrBIMc4Uoj/VVwUNT6Hs0Rz6VCEX1IRzkINeUkWV5xJzYSRkMLKSIMqfsGEcjEp7PqYNrsndGC5ttEQIqD1JsJ+Ly0pRFohEFiDLWRDaSOzYAkDmIE6pEIpAqA7WR8yFmMSREhX6hAUluiEAaDCs3rmAk1loQyE/VJMTaUSiNDBEo3uJYkmY8JyqjmEydEQGEPsBCYLpcRYB7Q9MxMO6IqiRIyJGmAnBxXHgl6JbfAIUNsrDRTaixLhsjUMyRUPEYwj20ONM+EOjdZoMIiwhnFMQoKmHIggT88DyAiQKLAhprM89NLdBuAoRQ6SxqYMKRSEtgUpw7uXQbb0GSZ0JFfoDDDJCB+UVHg0M6q2nXgIJQ5LbIxVVITUfc8RyVCBtQrKB1DqnTfoDKGSNDQBARzi6FmpBTRSlh0GGZc1zj3IZQAuSTpujKy8oUI9AGThBShxto0l5IgZRFlHBMXy4GkqaBGRvkprnAlxjHv+QAAIABJREFUKfQRSKWuZXDMMkY1mGA3IGaEGAADIBiy8yIa59jYMR1Jw4ynyLQy7uMsQTk3BgPBEoBLNOdnIHWRnmAOYZIjuqdFAKcWtgLBKqPheaClDktB5ECaztCTRbSnUJrFBZA4xPAR03FoIisFejSawAQx21MwzWMjJClCiUWJ58jWfWQv1oBMDRg6Oh5LYfb8+5UucQZobFA9hCnUEkfQzFAZCXJIT7lK2Jl0cayAisaEaMqASdJHJua6xrzOBEKmoWd8BDEUWHl6wSeahBmOh4QobltZ2oFQo5qdarRPQaQYkT5EBCETiq7AgjtmoGseKCIZIzECmsoMzMftsqB57CLV4ZoSppUmbSAJxgYAQwGVRBYxQlPLoEXaMigpYEonM3ydcEhIwP26z1akrfSI0hRBbXhO7szg46HmojiPOUUkNiIImIH0aAkfzGgtHxA6thHXsRXAxCKZRvBwQWxPomZKsQyLkluOdpL4i8NgnVojlJgk1YgWkhjA1KB6gBMDMQvZQb5zWmweJYV8MezrJ2fCNTWWFg8P48la+fj4yisvxRMlI45LRwdZqeDkYL1mpSYhw6B8cJCWc0bgFU6axKXd6lTz0iWAYaV9XDxqctfGilf2D9K8axTxRNXgOsbDsHJ0kOZck8dXn32OKK5Mrba3F1UrehxWjw6EYyIua0cHzLGVpTXu3IUUCccsHxwgKBUhlZMm0miUdx/73tP5Xjecmmjc2YQYjGemewuLcTnv+F714ABBkOTdyc07VApWc/fOXRxPTeZ7Z7XDfYQgoqB0dASljEuF0/X1caNeHJ1Vt/eISEXOOffOO9WDPeGYpZMmEoKV3erOnpYl4VStvrlNBB/l89onf4aiUaWKLz1fevb79zrNpxRAaPgbv+n9ua8BjGv/5Pet65/9Kdo6EfJ+6VcGf/mv/EvRq//3+x9XydLFpeG/+5dELn+P8XNFiH54WHni6zgM0oWF3t/4zxTG92j5Karpuzvlp75Nz87uNUr2r6O8wn7zkW98u7R9VL6z8+AT16zjTuPG3SvXvlvZ3Msdnqy9+mbj89v2Sffhr3+7fv2Ob+XHdinUndr1u1e+++zEjc3cSef+a9+b+eizwHBHTikw3MqtrQeufb+4e1S7uXn12ZcnP7mRYN0zihF13Gb7kSeeql+/k9893njh1fzeUeXOzn3PvjT9wadGd/jAd56d/+l72sB/5Jvfnv70ptkeXHjljaWfvmd2B5e+/8LaS69zgR586pmNV97Afrr+ypvTH32WOzw5/8qbKz96Wx/0H+SD+/vbmSRXn35m48VXUZhefPHVlR/+FEbcN/KBnkMRu/zCKxefeTGF2n3ffe7Ci68hqa5EzYuZ53a6q6//aP31H9FRvPzGj8+99IMUaFeeffHicy8jL1l7+bVzb/7YaXZWfviTjRd/AOJEb9bNvWXBqHZcxIeLeAhRr0QPF+BIR72Ks1UTTNOPCvruIs/MxfHxw4DnwhHu5Mj+PBwbqF+zd+aTxDkXnH4BRiSOjVYOHp0jA4L7Dj1chkNjlnlfTE4NnmjHBf1wFUTUODGM5oJ2hvHQpYcraGShftnaXlCxQZo57WgNBUT0RfszMztQqY8HP4dxCyVdNfyUJj0MWrG3panjRI5491Mj2hGMC6hhBUB0ws8+JnEH8WY2uO2ke1wMw6xWleNRGpLxhyg8xllf9T8lcQup49N0dgaMRpkHBp+b8V0uOA4/5klTyZHqf07jEyjPePeGkewwFpPBpzS4zQWyjMNp3J4mI0hPprXjKvQxbS0Zu9UUas7+hHE8JVOiH03h0xlj5K1n4wvRGU4lac4bW5UU6tZuwzicl6kc2VXPqqgEk9akftSQMSXHc9Z2LRX4gbh1VYUqAcZRDbfm8AhqpyXjsCpjqjVn7d3pFBn6TpkeLahE05sV3JxDIQ6O0OhDmCY02uK9D3EaU77DuzcsdSZkh41uQ9EX4Skdvg8jn4ogzGoV4UXJbta5bogOz9qgd91IjxmDAOgkkyrc5oPPzcyD6R7r3bT4USLihJUKKo6iUzL6EDMfpYfp6AYCvmAHWf+Ww45Z2oe9T2hyItMzPPwAJwNIz4jWWtNbFggoPVilRwWR6tb2Ej6rAE8zd6fQMKedIb21pjUdI40ekcG612SJYW3Nk24NBYZ2uIR65fz47CpgC0ELxFg7WtUPS0ya1vYcajfyWfCQGM2zQB9JeLxqtAosNcyDGWPPyZRlbc3gzqT0dLI/iztFMoK4uawdlzKojT8BgxuEpdi7BQZ3qeyx8Tb1byEZQX8LpJ9HHNLguux/RhhDkrM0n1Otgb+DRzdQFqDhHeJ/BmOuyTTJalXWC+O7oH9HV33u7aHhDZy2Y2YYGKhYIe9z1r9lyEhlO5m3jfEg8w/I/8nZewVtdqXXeTueHL4c/pw6dyMOMMBAM+TQpM2iRFuyXKIolX3hC6tsyaYvWHa5ylWqkizTtmwGMZgeEeZwOHkGaRBnBkCjAxqx0UDnP6cv55PDPmdvX1BiWTJJNXy/6+x97la9a73Pmt0iWQCn95HzkUg58W9lkzta7nMy1qTDDTxRwbSoba3miUF7tnx4EoSy0paV1qo0JHimkcNTaGxU8+CZtG+xELcN6fAU9CSpI8uds1KfhpI+MxsRUehI17Y3hG+Qnk73TghXabqdJ0FWmx4Lz5APTuKBhhzV3FlEvk5GJjk4jWcSGcro8LTSk0GgSQcnyMiOfDz5BIctmHXT8R0l2cmZBya31PBOlgsSXGfRIeCemHxG/CMC2sOkXgMzh0Vo8pkU3GIxl5yPoLNLecIyVWZC8G44uqNGmzyN6fA68W6yBGKeMabIcTeZ3iTBPkSTyN+GyVbKGBrfwMEdkXDqfpRON2nmQuc28nYxjwRpL2rbjRRQdbcs7a/mqaQcl3F7HTuIdgvyQU0ElHYW9N3FKFcedo+/iGIURHKngo7XyBShflU6WhE+OcOmT8ddnuXKQVlqnQQhlo9tcrxGx9lK5j8eD9XYw905fWeZMyIfmGp7DfpI7thy5wQZYzQqSzvL3KdSr67urOeZMvfJrce//0L19v3CQesL3/p+7dZmcf/46a9/yz7qVm/eOfPS60Z70Pjs7uPPv1K/cdtK/C8nvQW/b++1n/r6twr7rfrNe49/6wfF/SPGsasVIqxah+0nvv2D5vXPjPbgqT/+ZnFzvx6OvswmpWBc3j549LvPLXz0qTxyzr/0WuP6La07/uLXv13aPizf3334W9+v3N8p7B4/+t3nlj/4hDPwxLe/v/72VWU4e+j5V5bf/9g67p174ZWNS9eQE51+9acnXn+T5fjxb//g9I/fZjlx9GIgGfLYOfej10++eQn47NHv/PD8j15LAXG0kqsW6MS78NJrq1c+MNv9My+/fvr1n+aJcLWiY5TzVDz8vedO/fhtZTjbuHh18f3rRrt/+tWfnHn9TRBlD3/v+YeffzkV9OEfvHjmjbdQnP7/7iIUhJrvXi1/7ztclh9UQOR5+Njjzi/+Um4X7DdeM69c+hyZJSGi02fGf+dXAftLsVPwly9e/TeQ61FU+fqz9o9ff9DmHQAgY5P/+D8Z/hf/JYzjyne+Wf72N7miPKh4BGD6t/72+Fd+VSjqXxF4/8u2CN2f//fDv/+fzd266SzNcQALB63O6dM4Y/Ob91tnz5vMsXY7g7VVCjPzoBs2K5O5uZioVjRFAWvc3+ycOaPzwN4+niwvxWVrqpXLwdDs96WBF8+XfcWc395pr64RFU2MCgK8NmzrR4OwVow1rbR3FM+VXa0wv7nVW1ujUl7cPHJqZVUDsO2lFdstlap7R75lZRWjcnfXKRTThUpx6ziXiLvc1Pd7JvfHC/NqaxKbZlrV6ygJIPWgXNg8AgQ6qwv6YV/hSW99PZB0AVEhnlbv7YaqFqzNFbaPBReth87PxwM1Z12hFw47nJLe+omF27cyjP2NRWu3hfP86Oy5+s62lobuYsNq9SOidk+dMKaIRGjW5LKPVJeDwiwURdUHfkWQEJNQ2FrLTysiVb05UEnac8zblpuaJ5K0GFZyKQRyCHI7sMW4lAV3jVUSyIqLo0psBWHCKpnpV3lXg2yIrTgqS5EMCuNQFBSHROXEDIM0KXErIAxksWVL+y4o40hH9siRLHQUwApVcOKPJNnOARZsgjU7jWWF93hWk5ajw5ZbB0VchCOepICQCBUih5p2GskyHIisTArhAElZmoBcLcWuJGsMqjAa4QoeR5hLKkwZElTPppTrQrFE0MeyxpEO4hE21TCyzbyV8pJkKnHYxUIHWglyx5SzKLEZ8G2OOVVneGYxIpDtiVkBcAEKDgvLehomll8RLgDgSFvU+kTAHBV9MDWgwF41BSBX0wACCQQWEtwrQ32EgMiL8qFEcgLAlr6uTwTMEbcCZQZjooel3BwhKLgoetwxCIO85LHIJinIiwGcpUlI1Fqe+CjzAF+z6CwEkwzOy5rvup7KmwqKczRLpQLHyZSiHAM0Ig08ZnBRMTzH81ViZko+g3maUz3MTRRAtZr6QgfDDFbhwmhrZhdzKGW5noZUKyYRo3wqrHrqCQ0Pc17HehZ5E4kWOQE8dgivyxQhfSyFFtOgIybFxOIycbhbhnKYKzlwTEl2QpUqUz02mUEGc+lkRnSeoGm6SuUwlxlwTZk4sZ6uZuMONjhQ4LTEVIa0SMwKgCTUnFYzl3HYlZrSWE/0PLJwoc0zJUdmCCdGJqPIEvoYScj1LaqOlVTjQQlLRy7PYb6kw36sJhGq4tilKM+zpgE6ERSZUePhCIMM8HlZm3blnOUcp8KOgMwbKulFUAjJYjiYYBn7chkECMSC1iAaxz7UVOorwENZnqqFKJRRAtR67gQKjnK5BvEkCrmmlVkyRTmDRoN5E0JjkC6pFcdNkwqzI5pkPDaR7fKUkkCB9iSEljKlUSU1I49FZWZFBdi3RMQyMMgXUGJhaxRCU3dkvxBrYOpKGgFA90QWF6AxY0ChjoSsKVf4Stjdl6uYyblfwspEFqnP5og+ZVCijkz0kasWtSGJS6mW+9wr5UbICIuHuI4GXqEYjwgsEoP6cV/KNaAVuN/HVOHUFNGYKDgBNJFRnKZAaMV0jIAG1AKP+pgrBFmZ4Q0xyz2zxidI6Civ6nDPg7Kw7JDPHGHoPimBqdBwJBl5MJKYqfCKQg98KAulxP0BBpSAmkw6gZBxVlW0EYY5L2pH02gOcclrCn2S4xSnxVCfiQiZUSnXpxBnnBf8YjYy86RNylFWk0KYFXzNEzGwgO7U8y7BsI/sPCzRhIjCNMoKUogz25vPugCBMTLysJpncknaHvFFOaGiMI7zohLgxA7UkCfMgtYMR7JgCipMeMDmtrZap0/Nee28F09WFyWR662Bt1jPM1E6bHknFwMhL+7sHG9s6GpWRHwMKHASsz0IFmoM4OLBMW7Iu3NnM0nCeV4dt+2D3mSuDhRq7XeCuWpaNhsgmaW5A8zFmze7GydkSdjbR06zznWlsNcaLi8xSufu34+W6lOzsnz7Vn9lVdhKYeso/7MuwsMulsFobrG2ue2VyuOFxdVProe2FS43i5sHmSS1Tp8RCBmxK3LR2NwJCoXB0vLqjU8SXcMNda9ykmRMiuPm7k6iqnGtWNna8YqV4dJyTgkAoOwOtMMB5WyytmQc9TilUaNU3dl3zULSLBU2D7giO+sL5m4bQ9hT1drv/Y68u/N5twgFJurW/eZv/FM6Gj0oeJ2xdGWl91//t8FjT/w5OP3Bx1dc1Vr/5J/Fp8/ANP1L/cp/ZRH+a8XDVZU155SdLTocPCABAiCk7O5kzbn4xMl0foH2e8rOzgORJ/7MZNzfy+qNZGn5r7juL+8iXJcXFk++/dOoWlZdZ+299ydLS8Z4tPHuVWduzmDh8sXLiW0RJNYvX41KRXvQf+pPvpEVTSlM1t97zy+V5Txee/tKYpsKS77yL5+VQQogWvrgY1aycJiuXb4clEoqi372a38k5VliGRsXL2WGRgBcv/Z+amoiE+tXLiemiSS0cfEyoBRqZPnqR5yixLJW3n0P53luqutX35fDaLaycOInb2nO1F1aXHv/Q8sZRbY99+ltKQhYqbD8vdek4+FsffnUxUvGZOIszi29/5ExHcWFwmMvvlQ/2A+rpdUr13TPG2+snHj7kj4cDFfXNl7+qbW5FzTqc598pjmz2dzcmauXreFwdHpj/e1Ldr8/2jixcf3jQuc4KRWaN2+bM3ewsqwNdDzV02pqDyCeFSQ6yCNbmpqZHsu+qg0o1YZwasuOHVVi3C243dNQcTQXIrcKtBAFqjKQgO2JbtnvnUnKqTIG0rQEVUcNkRhXiOJkXjVqrXMjojOKJiVsTokrS9MKlCZSJOCohqUpjWVpaEnKAHkqnpaJOomZ8De5LDGK82AHyHKG8szfE6rGYML9Q6zIrJCMB0e2hhJM0ehTBVIq4Tw+RKqcoBzM9qAqZxpKRjcMAjE2hb/JKc4lzMMDaMMZIOroloEJUlU22UIyYJLGo22BYU5w7u5BGcUQ8OAQE8gUKXE3ocwZLROpZZAEY+qSaZUkAEsu6tdImPFCoBwXaUKRNiODIo0JUqKgdypyS7me6l1L9bO8GMjtAoi1vJTWNmW9rwHZE06FhphZzOwZkgtkc+genY2c5bQUaX0ZBxolE2lq4UjNrFTumZKHeNHTjlUcWkif4IklhQbQ3GTA4hZXyoJ3s6wHUIPAQeIfQ8NO6SSYdSTZ5KkD+GGqVjjvE+e+otQhd1HYhqoa4SCdHhHdZMIh/rYmVxCfZn4LWqU0n/GwhXQtUt2ot11WdJDHPNjnajGH0yxpI9MKswC5B0DXGY3i4BBrapInItrNUImoTMi9isCBkrl0uCBzH0op6dQQYhRGyqCMYSh4Tgd1CAIJJO7RF7IYKXSKBgtIZBBFtF8hMBAA+a1Hco4pCVGnSVgktERtFTHkOeT+0VnAkMA5HtQhj7mWmUdFOY25EUvtEocUSbHWtWQW5SjFwwbJWVzg4q6fuIDWMNiL+Cw39djpU+5xUoD5AUPjRG7AcIulM0AbktgBYZvYduoPae4jWqbJfgrGqdbk0T0StRW0qGYtFk+ApcdpN0tmyCxlwY4U9RRlAbEjHg6BUUmzPkyHwFDDdCiiMTKsNO2KfMTVWs4Os2AIlRqwfcGHdSK5UkLkvgUMHzsYj0vYnBGfSpMapBM5ycCwQaQZj+3w+CymLgkpnlSIMhEhxcMaoDMt4PZmU4K+EJQMSkj1ZF9I4yqRhklkB61HBI4VznC/JqMpzXI0rEPFxxHAwyqVRyAhyqiOkEtBhjpVRCMOk2RPGLkPEPSOKRWJJqXjLSTlqWzxaFMgziU58/agDGIioeltDUNJ1pi3jWie0yII7vMsh0RDwW1MEkENNDvABHChI7GZkJhJRejclJJQoyXKDjKSJBqOZscy5gKVpPhOiKJcqYh4M8sYphZI9plIOSlic2BJMyjrAzCoSoERl2K9T6FvUmkiTzUS6rmV0KEhOVgUXX4w541PInuKJjr1TKw6kqMCzyTaLBmuJb0FXnRpX0OujcwJnerUtbE0YZOa11+Hmi9NDWmmynoXTorUsbE2Ao5OZgVMJ3IgyzMTq1PkmGRqQmtWODpa/+B9r1otesO5j26lJVsK/dUr17y5mjmerF3/JGhWZS9cu3QpbDRV31353uuxpGPO1i5dDWtlLQhWP/goL+nUCX/22WeZomAMVi5eYaaBgVi/ei0pF6Sxu/j9V+NiASb56StXYruAED9x8Upm6oKg9UuXvXJZieOT197NCkYGyak334zKpdTSTr35jpzGYbm4cvV9OU8j01599xrMc69eP//jN9Q4ni3Pn3j7Mo6jqFp95lt/Wm0d+sXKyrX3aJrM5hceevMnquNGi/Un/uQ78zvbg9W19WvvSkGQm9rKBx/KUTxeWf25P/jdhfv3e+fPnHz7kjEd+3P1xeufqZ6XlAvL738gOV6w2Dxx+arV749OrK1evWYM+sNmU73+0ee2CBHCUTD3G/+T1Go9qCHGObesya/8Pe8rPysfHVW//qyyu/Pg6gqlaf+/+kfBF5+GjP1Ve3h/gaZbXJr+zb9Nez08mz1gaB1mWfXZryULS8nq6uTv/KrUbklHRw+isQTGKIrK3/yTZHEp2TjxuQ1Czuu7O08+94O4XsUs/cJLL7YvPFRpHT/86quz1TVK80defhlSPDx76snnvp8WrYyS8z/+8ezMGp2FF15+ObSsbL788Ms/whh2Hn/owhuviKJsFYqPvfB8VjZyQR567bVc151TS6fefhsQmOnSF3/4vesEuM3GIy++wC3J1uyHXnsNUqn36JlHX3xhfzbe07705Pe+c/Nv/nKm6+d/8uPBiRO7Ojr/xmvu3Hz3S489/vxz7tLC4NHzF156iS1UUsM489OfePXG7Xrhwls/iQul45/70mPP/dCvVLoPnXno1VfiWimuVNbeez+2TG+lef7HrzNN3/6FLz/+0kuhoe9+5WceeuunuUqDhblTly9BhLonTp7/yU9Qnm/94lcfe+UVRsjOX/vKuddfRyDLyuaJy5dRyu8//bjRWkKTYrg+NQ+NPKjJ6CZIKB1UhTYjU8081KDFpJYuvJq8OLE6GpiVhbKpz5RkUoHaWLiavWs69Y7ZVcGsmix0ih2dTxtEaSs+JZ1qTo5RJMsHFbd2pB3LwmlmhU3cQ2Jal/m2xKW0VeNSG8aKvFfFGlc6OHWaQr2Pc+7dlfQ0gGsw+oRrMFP1LL6pcp3hNPbuFszMUYxgdgdofiDOSf5dKMu5EafhTVSAESVxcK9gZwGth7NdS489qUDDT6j6MAdJ5t7UlzYSbIjZnqbkKaGpf0c2mgG2aXQdkzNCTll4Wy+cTOUg9e4VZDeQUOrcprQR6euqeViAWgbFMeuYCEU5TFCnKiVoNt+xDss5hbG9Lx2YQBICd7KeSfMI2hO9W5FDyhbb9n4xISqbmyjdGoCQKq1kaJIMsqKnHJfkMIaluHhoMGwljb51XM64QcQW6Rs80VhppreLqp9PFo8KR3YCa5m5idt6zopIO3KOs/Q2x8tC7GRBS7dPRmI7CvbVWjEU4zTazAp2kgRy/Glqr+HkMJ3uabX1QOxxd98sKb5geXBbKSkRS4l3G5irGdpJ/D0bzcfgOHO3zBpxEUunW9AyYyGk6BNOmhk4SLzd4nzBxzPh3zWr1EM0C27JZSngWI5uIGMhNYAitetqPKSFlB/PEX3iy1zbq7GFFJpTul8RCxMZe6JdFZEDyylqVRVtxAkjOzU2n2W5Qw6ruD6T3aloF6EzyhcSfNwkSugVZtZeLWmMKJuQowKpTHCWwHYV6QHXXaPVQDLLy+PCbsWdSxl29aOyZLkoD0C7gvTcWZrFt9MUUOt0Gt6NE4Bqesh2FRjlcplHW0B2InKWiFtZKDTtbB5tp2mKqRGwAzkOhd2E8XYmTyJyFrKt3OdS8VTEN5MolKkWeAckncS0kkZHMhsJ8wuC3Yr8SF+cD5xNHsyUquKnx3LSR7ASx1s07YnKqRzcTv2gWDwZqwMJteoCtgGQ1b26V+rJbQpGzczewQMIxnWZ70oQpMcNjrsAEmWniGSujlE6aXJli/oY9uoa36eSEIMFSgepSpTdWqT3lBEQwwaU7stxJjoVIY6gDshBXWoe5DKWduuxNoA+hu2mRLZImqFuQ47bvMjpQSOHXmBN/c8QWEnlkPl3FWU+JDj178h6OcB1En6CtVWo4zS8DY1lpiS5c1BEQWxrsXtHosUALsjxpwIsQ9MQzg5QG1yTw3CTSuWY2mB2h5taiJZJck9kS0iusvBOKhUTKXGDTcOwEnlemtzOJSPFqyD7jLMG0Y3YuydoAWg1oLRXlVkGS7F1aOW8JM+N9FaRsyJGW3RQy8NCVpgqHdsYw8nCkX1sZHklK97GbS2Lq4IeSSNdmhYzZZ92dTo2Z4s71r6W5U1R2AQtBQRlxDeRX87HNrMF7Rh0XMaFlO5LadYU2h06lJFbQWILRgXSK8bqPuoZZFgNmocr169fePlHfrlkk+jMi8/nupwUrCdf/KF7YqXY6T764gvO+pw8cB967dWkUk4r5mOvvgxszWnUnnzxh8HKvDydPfzCC/frf1dNuicuXvSq1emJpcdffJ4QONxYe/KH342rNozTR15/NVuuFZF64ZWXc0KGD5167IUfYpi3H77w5HPfd+oNAuFDL7+MbFmqehdeew1RcrP+15947ofth877tfLDr748fOQcU7RzP/3p8MzpycraI2+87szNHT392BPP/7B18tRsde3kO+8460t+pXb2rTedpaXu6bMPvfSjoFK+/MTJsz9+Y7y6cnz+wvmf/mSysHSokzNvvxWUKoePfeH0m29nxcK9v/4Lj7/yiley3dWlc2+96RcK3FZPXXwnkdXxw6cfefWVDOOtX/zqYy+9FFvW9unTn7+LEAIE67/zW/L+3gOTq4Sg1PuZrzq/8B+gKCr+6AXts08/x31p6vzCL7o/+3N/tbr6/1iE/1owCYyrX3+28MqPYJo+eJw+Pn269c/+N4GQeflS/Xd/CzL2Ofj0yyutf/I/57b9ebsIs7//d6vbm1G1KAA0e8PR8rKAsLG93T15ykhd47A3WVqiIFX607hkZ7JsdAZp1Qpks7652T95WhWhcdibLi1SweTuGNlyqqq0N2MV09OLc9vb3dV1Fcbmbmu6tIiJULpTVtATTbOOu6xiumZ5bmurt7ouo0Tf74RzDU0E+ShlluaXy+XD48AucFMu7B155SovqurxUEgkrJWkwcxO3VmzKbfHsWnmRc0+aAfFclZQtKMBwDCcq0lDR86i6fyCfdROND0r6fZhO7QKrGTIvQkSYriyUj46kuNgsrJitntMlmfNufrOdiZJSaMo96aI8+HqavG4pYVuMN/QOv1Y0QfLq/qMSRE/psDqAAAgAElEQVRzm5rkM30cZVWQcE3xmVdTZTfBCbDkkc8LMBTuvE5jbowjr6GYjpclclBXpDAnIQdmnDNZ9jJnTidJbgwjd14zPY9HMisIGuc8IdBMMqFJDhNFFgHD7PvevK5HQR4pWYGTKBOxZCojj5TRjIty5siWdjjOa5os4nQqiA6FKuWTTDLySNHl41k0X5qP26OZyaq6CmM2A1gFQiHZOFOMPNQtrTUJGiUD+Ek/F1VdhkkyBUQDuUrzSV4hs6ldxW0vbtg6itiA5yVVQUkyEVgFwpDycSarWWRZ8tE0rv+rM6ygUh0gD8ssiYuEjEUmY6QxOgWJphLZF66EAOAW46GipElYpthBAoOwKFu9kFFC9CD3dcRzv6aqk0SNoqgiIQ8JjMKiZPRDjkmRdqesgdPMm9MVJ6VJnhagPo5jqkRlWRsnACBkhFmoyjHLSjxLVSlkUZWQWZAlSLIFixH00mSxBGOmDJxkoah7TuoCNl+AEcNeTAuQZRROY6mKAqCpnWm4UjZ8N/EQrFAcp5kPpCJIgQwnsVRBAdTU9iRdLFbd7jAuwLJE05T5QCqAVFA4iuUq9Ihltkb+YkWP3cSBqCJhxnJPRDUbElJpT52KqQEPjlBUVmQQAk8GcpapCM0gkZLAMOxe4FQtVbhgIuU2trO+m9QgZbmOkIsJjQNdMzqRX9UUFOAJjS2J4kC4MpLyTANoRghNfFs3+klYkFON2G0vNSQiR9xTuQRji+qDWMKxV9C1IYtsObRkpTPDeZ7M2dLQJ2HIm2Y+ywAAadWUuzMBkVLkkYNRnidzRTryJT+gVZrNsgTLWd2iQ09woBby2MEoYfFiic5CHCS8rslDN4GyVITcyRlHSlEkIcVeRGokSiTqhPmcIY/9BEi0APgs4wzIFRglMnGiYKVSnk1FIKVFSNNMRAQYaQYVycmFnUbIMLueO28aiccDhRUASZiIJEOeBNSCDgKFJKR2setMm5aRuNyVWBlRxkBAgZGmWFYmOSikPrWKXXc6Z+uJK3wZGkxJwyAtQD1hVJanHFhpQA2r7XlzhpoFwJXTIs5hzsesgmZuoQS6YVqxVBpn/SwvagpNkpHACgAmzaZcpiwumPTYSSqmThPWz5ilqWoazxCUYFbQaNeVKItLNu76WUHLTEU5GnNVks08mSFAESvptOvKiGETswFLikZW1JX2lFOsmHk8hRCKpF6QB64gKKoVrL4PACrS/jSvk5i584bsMTlgcRkbkzCBSlhV1GkKc4DMiEWqErK8lKeZrnhJWJOMWciELEyGfcRzBK0kY7rsJXk5T5imTqOoIetenHIFmAz5QGTEVvoT3JRmKa/kCTeMaeDXZM0NWa4AO8cBAAxzm2WZaG5tdU+dbvhtNmLO4pyUpdLITap2Bond7oTz1ZAaczvb7Y0TOvO19shdnMM5k4dOWrUSotitjlQmPX2uuXm/d/KkwmOtPfLm6ohnSn+SVuxUVu3jTtwseVph/s6d3omTioi11tBvVAFBan/i1WpMkmrbW/F81THLc/fvDVbXMBV6ayBkGlZKWmeIKJg258t7B1654pfL8/fvhZbFyqbSHXOJTptz5f19ivhkfrG0f+iXy16l0rx3L9F1xUbJmEEIxisr5aOjnNC0bBV397xixZmbr+9sM0lO67bcm0ksdhbm1MFEQBTViqXDY98s5EXNaA8yhOK5Kh15hKV9Sfu8XYRc1+u//y/s11/9XBV8yYkTrX/8T7NCsfDGa9Vnv4aC4MHlCpubO/pffzO3rH9nYAv9hVIJcj7+lb+XrK1/jjg9hPLOTv13f5truv/U07P/6G/B/EH/VmAsH+zX/uUffl7uKIeITryNS+/RWUgnweql97jPlJG//uYlPHaBm6y/fVUZOcBL195+Vxk5ZBqsX36fujGZBRsX34UzHyRg4+K72nGfh9napfeU4QxP/PWL79JpgCfeypuX6NjLY7H+zjXjqJ+lcO3yNbU7Rm6yevl9aehAL125+K7cHuQMnXzzsrl7nGdo5coH5mGHCTL34afWbisF0urVD8u37/vUXL72Uf2jz1JBFj/4tHpnm6dw7vqt4tYB43T5veu1m/d92Vr68Ebz41sxlJs37tTu7uQMNj6+Wbq3myBl6YNPGx/fCmRz8b2P5z/8NAZK7dZW4/YWY6jx2b3G7a0EKosf3Gh8es9TC/Of3Fm89lGUS/Vbm83bWznH9bs7jc/uJ5CiiSl366GQ4UyXBo2UUxGYuFvOEsy9ktQrhMTAjk66zRjIcCzhXpPlEo8M2q+licK9gtwpRMRGjoF7zQgoZEZRp5nHmAcG6VY4k3LPUlqFGJnANaReMwWq8BU0WGKpxBOLdqosUXlgyp1iAozcUWmnmQk1zpRgB8UzEmVKsAVDXw5jyd/BSShnIZztSolPUkbcbZIMQZjL3n0eOTRIJH8HhqGcJmi0p8Q+ioDibRO/DwKoxdu5P8JJTPwdFLskSdB0T0qngAnJ3aXJAPrYCHdFMCZpJkUHOJkRlqDpDk3HIEWKt0PSTp4SHQ2KaFZOuSymNTgtMCCRUQMPLV82pa6JeqUESnhcAKMSSzEYlsjQSDgF4zodmJ5aUAY6HdQiIZGxhaaVlFM4LaO+mXAKRzWlr/uyKY0MNGzEguKBAUdVxil0SnhUjLiMhmXaNX3VJiMTDhoJIGSkgn41z1A4wvG2CJGRjJG3Q0KhZGPgbkthSmOX+Pskimk0JfH9PORqPBD+Dkm5xKZiuiuxGEeRHOyiOJZCh4bbIuRaPEbeDk1ziTliuiMnMU4C6m3j2MeRR+ItHmZqMMXeNo64mgZouKemMYpS2dtCgU+jUPLv52FEeAjz3koeyglXSXcBTPUQm6RdzP1CkmmkU8ujQpbifLDCZySFqtRuiLEaI0Nq28K10kyh3QqICmkmwdECcDUGFdprkrEaElNuW2JqZ7lMB2XhmVmMQLeJJ1IEZNyfxxMjkGy1a8NZKWUSHZSFbzEu4X4DjpSQ6skuC/ZgDOWgh8I2jXPi96S4hRinXktKjoWnFrIj7u2ShJO4C6I2ZZxGQyk+hkmG/SPC9nNPKSQd4e7TCMhJDzpHUpzRcECCFk6EFB6jdDsLJSM6zPw9yoCU9IFzSFNGwokUHuFYyEEHR/sioEbSBu6elHGahxrp1HgiZ4Eht4qpMIVv0G6DAZX7Mh4ss1TKmUk7tSxW89iQ28WUG9zVaLvBhMFdJAareUxSZpJuIwv1NLVop5TkVu7ruNNMMj2PqOitcA+y3CDH9TxQGTOkVoGlehbqtNPIcz1NFNxf5oHMuSZ1a9BTY0aCXRhNSZKg2b6UTAQT1NuV4h7wqRHtiaBP4lwOD3A0pWmKZ3s0HYIEyf4uTVtZQPR4Mw27JM7l4AhHYxpn1D+Q0j5Ioezu0bQHAtlOdrnbpkmKoyMYT0gqaHBMkx5IBHG3MWsJT7HjI+j2lSSn4SH0BjQVCI6r8sDyFVMa66jfjAFFQw0N64xT4BbJsBznMhyXpK7lKzYdGWjQTCAlExn2anmKxMwm/RLjEhgXlI4VSiaamNJgLgUycHQ0mmcZBX6R9MoJV8DElruFiOhipNFuM4UKcGQwWswZ5UEJ96qR0IVbIJ1iRExp5K6/fQV5CZ1F6+9ckwczMnbXLl5FXiJ3R+tvXQFRjmbh2luXsRPBMFt/+wrtjrEbr128ikeu2htvvHWFzCLgRhtvXaEjFyR8/dI1tTuGQbp+6RoduXQWrl9+n0596MZrb11Whg7P0MY71/TjPgjS9bcvS/0pnkVr735Ex54I8xNvX9U7owQpy+9+VLm9leV44frN4s4RD7OlKx+Ud49DqC5f+aB6dyeQzeUPPq3f3EwzvHDjTnH3KMvw4gc3Sre3YqiuXHm/eW83kIzFG3fqn91jGZn76NPS1kGa44Xrtyv3DwKorFz9oPnpHV+x5q/frH92LxO0eeNu5fZmnoiFD27U7+5EWF24+tHi9duBbMzdvLt4406K5c8FGuWKWnjjNeviW59LXXHT7P7ar2fFknb7Vum5H2Dff9BAlBACwe6v/Xpumg8Sh/83M1j/769IUnT2nHX18oMj2qEQdDgUuhZeeJjV6rTfkw8PHpCqBRCS9/eELEfnzn++LkIBn372j9z5ucLx8UMvvNTfOLF287O1y5eZVSj3jk+88SZhjEv4qa9/w5ufs3vdC8+/5Cw2G3fur129inJusOjU8y+TjHFDf+rZP05KljUenXrljaBZa2zvr12+IkEoJd65F1+VWcJKxad//w+TSll33bMvvZwUzOpxZ/3iO0qWIQrPf/8FPfCiiv3k176eFm0M4LmXX7UmY6FJp155vbqz23/qia/89u8a0+nsxMbj3/iWHrkkz5YuXSv2eomtn/3RK3O7uwdffPxn/4/fMSbjycn1x77xLd11EILrl68Wjw7Tkn32pVcad+7sfvUrP//bv68N+oPzF774fz9rzCaQkpUr12p7u5PllUd++MOFzz7d+5ln/r3f/0O92x2cO//Ed79d6HUoAYvvvt+4t3nwxENq6wJ3Flh9ZO7N8aBhJTsoaHJ3Dagp9Gpaq6Zq92hrlQUnWM01Ww3uzZl5SxnS1L8A5RD4ZbW9BAoH2kEt9zeYPTU7pTxYUeGQzuRsdoagEQyLqH8W2vvaYZm5J7Dapt0q91e1qKNFJB2dl+AQx0UweEiT7iidUhqeU+iRP0k791SSJKrC9q4XVDnWXbdzz5ANxgd5r2XpsVMLB3f2l9Q40HW2d72kq0z33dY9Q1cTPGKdQ1NLYz339u+XDXdqF/KtD4uymmtx1LpvVuCYj9NWq0yTTMbR0W1bmbl6Mdn/oIQkQZO4fc8wcIzduHNUgEGqS8nBLZu4kbGuyXdXMZf0sM/HGygyKJrywVltREV5X9r9Yp6ViHKIj85CpqjZSDgbNLJzOSTdDXUoicq+vPVomlV5YaofrKBANpMOdzdgWONmQjur6lhXjc/gzqMpX+AFXzteRLFpxS0wnhP+fG55pLumjMuivKXfXc/yBaJ0SX9NxBVMR6MO8LZoue55t/J+11IXUXSPjXtakXiwFx0fFTU1iz06uUPt5TS4nfY6VqkSOttgNDALwIWz7GjXMmmYerh1zyxXXbEZt9t2oeiH+3jQNYrpRHGC7cN6kbppgI/uWvW6zzbDTqdYsWbRPu93LCsLUJS07xsVNM1jcnTH1upZzVXF5JSZuEo0SSdPqkFA1Zk4+BImiZY4oHOOgqnhZvnsjJokEneywePWzKFSO23/PKKcMl90zqnAUZwkn50lMSfIzTuPaE4i7CHde5oTQTNP9M5LmS8nmZidVjKcyolyeFp2M2D3le1HE0kjOKXtdTlytTjMp2cJM5LK0Lkkpr5WmufD93kaoiJ3hy3dH2G5AMdbEttPasve5AN55Br6Ip7dAIkDG1l/2FInY0uu4tF9Ilq8sugMrsBJYGsN4N7Kw5lcAjOvjUYdvVwMe7u6cyyVT0beVT5wrVLRH90n/kwpAcfrktGRXCr4gz19fKTNL4z9D3jPsYxKWh6pbHqegClKNNR7CFjHetvMZqew2qW9MvfX1bBrxCIZPUzFBGcq6D6mSltqV02CC7LUUgaFzDtppgMpYPn4YTUfoEwSnceo0VP7cu6cVsG+NtGZd0ZLHcIy0b9gJccwQ2zwDNaH0ljj01OaOJJGUu6fo3GAQQZa5ykOXOGOrlMbuHgWtVplHOcqiQ7v2PrENcrxzvtFiJEGw9ZtXQWJHETdI1t4mSHHB3dsNA7t+ax9zUiIolHW+4zIWawmaefA4CGiBdS7TuAgLDXDwXuqjzVZBcPbRImiYjA6OC4xj0hN2v8QS05m17zuJRJAXZay8X0iQqEXJXlwUhtYinkLbZ9n+VJedvXjBRgWreQYjSp5uMYNjwyWlGFdlLb0+6ssXSZqm/ZWeNRUxJC6pXy2QqUuGK/j6Qoo3TfuLyXxMlHapDsPgoYRt2hQysarktxB01Uw3bC0j6X9tYStK+RA6jW4v2CmbejZYLwqSwdovCImJ2np4PyrLy2/dw0QVB13V964KFRFCcMvfPM7kzMnFz/9dOPtd7yV+cUbN1euXCUAKklw7rmXoEwkzp/4+p/ONtYam1sbb10EVcO+v7929SoVAvH04e/+AGAEofjiH//pbGWxsrN74idvskqhtH984p13aJoiAh755ncBhlxTn3726+P1jdru7ukfvyEMxRhNT73xhpRnk5WFn/vt30MsSS3z4e8/R1lEM75x8R17Mp7Vak9980/Lh4fHT37h53/zt0GaxrX6Y9/7nupOCYDrF98p9vrDldWn/+RP6ocH/UfPPPW7X6NRHNaqj77wguoHKovW336nsr/fPXf+mWf/qLG3t/fM01/9zX+hurO0aJ954yfGYEhkvHbpSnVza3hm48mv/2l9b2/3y1/66m/9rjSZdFZW0db9B+wiFJRq9+9W/68/+BzkKgAExsN/8A/Dxx+Xup3KN7+h3rr5oGWFAKA4Hv7n/yB46qkHHXf9BRbhn79DkuzXX6t97Q8+hzbknNUb3f/uf4jOXdBvXK/9n78nHx48OJgVZlnnf/zH/pNP/Vva8C+zCJ2f/4XwV//T1Y8/Gp1cEwBUN/d2H32UZOzEe9fuP/NlIw/mP7559OijGGblu7uz9aVMkhvb++P1BVezT7577f7TXzJANPfx7da5M5DC8p2ddLESq1px82B6ctkxKycvX9p5+ksSzRffu9E6cxoaUuXurjdfD0yzeeu+c2JxWqidunRp9wtPUBUuvvfJ4MSGpAl5ZxDWS+PV1eUPP5k2m+F8ee3qh72FpXi53rhxN5fo4MyJ0t1dSwTtM2dKt3a9cilcqi1/+Om01vBWGtVb2wDwwYXT5a1DJYsPz56d//R2WCy6y83ljz917KJ7YrFycwvl+fZTTy/fua2H7v4jjyzcvJcq8sH586evXUssa7qxUNk8JL5//0vPLN25bYVu58Lpxu2tQNGPz54tjDLJBf11yZxG+lRNmkPGyvIEOHOZ7BHFZ6o9iNMC9JThOtXHoeaYwaJnOAkPCrM5prhY8/K0HsOAUFcarBF76tNJwVvwbC8Wnp3WPBwSFEhZLYYRUj0tro5jYep93V3wCn7EvWJS9XGEkCephUHENOwWs+bIkWx5K8BNCCWRHOe0ipEEso6QqjxRJLbLwaq0Gh8e9uq4DqgG2VFGKlioMG0JrZKnppru5HiZ6DgMdjGqA8mA6WEGbYRMlLRBUx9Py+VoD+FFotMwOiC0CoAO01aODYALOGsD2Uyjos62UjpPVDVN97goIFohcKIqeR5UIjSpCMqgMVWHxVRK81JCBhrMcjaX4LFCMhBWQzQpYxDPmrjYgoCkeTmhQwVkaLSMC/1MTkFUC9CsBPLEaeJCCyDMdL3nRw0Qk8kKsnsMcTWrTLWhlCIya6BiWyDAWS3CExnHlNVnwDNEorGGiwYxDyBdwLnH8xGPzpa1iZv3BDgp61M3nEj5mgadjIxCvEi4L8SYK8siyhR2lKNTkun68UTC8yAPeT4V0jwGEWdDoC7lPlf4QU43UGPaazlVaR7AVOSjnC4QloC8K4zVPIBavsvoCaLEUdDBdAGiTLAui9dKMs6MthVVPQV7pFuNbB/rudRTc5OlakaHBjL9SIdyt5yUXJn6pFPNzJkuu+GsKTSWGUwaW1hxHIvonUJcDmTiSv1qYgTczEhfFTLLbUYGJlZ9v4CUdiEzXbeqVndFZiTCZvJAjiQSVHihg7HsB0WkdOxU8926rm6PAOPJmbLScnGQoWUSDSnJ0mTZpPs+wTlpYt7L0xCkZ4pyyyFBri1k0YAwgdMVk+z7EsjxAs4GPPdBcqagtp0sInQJ4HacCio3BW8zxrG8hFk/hz6X1mDgqfkkl1eAPEjSjJImSHs5yKC0iPgoz2cgPWuVpo6YlVjNhQnGrpzVEpBC1dHTyjgCut43nXmvEPliVo2rHmYQzyTVGsVchm6J18cxUJV+MZqbqiyBo2JS9ihAZCKxSpJlQJkYvD4OoKr3imFjquUxGpaAPZSAiL0SL8cZANrEFsXeTLbtth40XDWP8LjAiiEjWdIGdWXiVQrBIUUNaKhxtI9pGQIbpsc5UgCp4KwNZI3FZTXd5bQGVSNNd7mwEa3g5DDjKsnndXoQyFKaN3W2l2ObRw1buTNBNiQVlLfyhFI+r9IDX5EzWsqDQ8qLOGna8p0xNRGqIdbJM0zzBZXu+7kqsQW1cMwJYJrR96MaishojVi9hKZa3JgZI8y4PGtCuydIztNahGcSDqWsMROBBgMjbszMCeRMYrUQTxSY4aweYYeokRrWxmliy44aNabWFGSpyqoBnkqIEb3QdUNbDu10bsRiS3L0pDHWXJRHCquGskdRhNNGzOLs9LX37v3Ml5emh2Rr2L5wRs7S0u7h+NRaKtDirbvdx88FRD97+crdr3zFYF79xt3W+XMyyIr396YnlhMiz9+6y07VjwtLZy5d2XzmGVVEzc/u986cAgiUNvdmawupoi98dmd4dnVqVM5efHv7mWdklDVv3O2vrwpNqt7Z7m5sRLZ96oMPhmfXJpX582++ufPYY8Cg1c+2MpWOT27Ubm8KjbTXTq69/+FkcbG7ceL8pXfcet1brtfuHaQIHp2/sPbpp0BBrdNnV9/7aFKv9U6cPHfpnaBR43UNH7kgzw4fenj1xifMMKdrC6sfXh815zvrG2evXomLxcn6QmnrkKZx7/yZyr0dhsn05MrKx59MyzVvfaHxye1MUcZn1oo7LTnL9kql5u/8pry3+++2CBHCzqz5z/8X9dbNB50/AQAAmP3S3+j/w/8Gu27xlZeqf/Q1/mA80j9LqPtf+mv9f/RrDzi++gtC7v9Wkmv2y/+hdvsz8/KlB+duSf1u9Rt/3Pn1/z54/InZL/2Nyje/gQL/AdWlIKT+W/97+hv/PF1d+3fahZDn2r274vkfdDmHezsQgC5Gxv42APAQY/3F5wQARxij114VAAwwggc7UIAOQnD7vgrAn585xgi1jgAAI4zAwS4QoIchPNjXgDjCWPnRiwCAY4xRtwUEGCAEd7aIEH2M4OGeLvgRJvJrr/zZGdjtpAL4gifZOqnVdB4w5tGQSIiZeUhiVwNJhoQROypKaBrriaflgWAUxaqS+7qIROQquY8gNCJHzQI5T8zEVXgIUixiTU5cDWp57CogJgQUw7GWODKP7XCq5j4GvBhONBHCFOixq2QeoXkxnGg8lLPA8qdKHogc6CyoDjft8Side7IwaTf2tlvFx8m0X2639iuPl4bH6myWyHVj0jGG46z5RSuazO/c2q58qTxtaZ0BtB+1pwNzOBwZa8rELR7sZ/UvG+FsfvO1nepXSm5XP+r0jLPmeKgMJxNtg4ZpdXuzbT6iRZPlzU+3K18ueAPz8P2efkZzpmp/nNKanMLKweV28WGQgzPtyy37PExFebjnSE1mWrXB1kyZ80Bxo/3+bv0JKU8e7bzTK5wFAFVnB75UCYBVG+34cn1GayeP392rfIFQcXJwq29scKhUp3s+KftSqTbaFEJnhnSh9eFR4SGo01PtG0P1ZAKV8mA3ZZYjNxr9rQiUh9r8eudaxzrLkXqydX2snHCyOgwmhKVawRKzTSETLFM43aS2rsQwDcY4y6RIS/0pZqletHJvF1FkpzaZDqFKFJ3kwRhwYMcl6js4ijXb5M4eIMhKbBJOBRSKlEReBzJgx2U5daGfSqZGvD6H1C4USORAzqWY5kEO3ZiWzDSOgBOTcqHmHyqT2bi4Is3ccr+1vfy0kcyWWp9tLnyp7PXMbueo9ojuzexxe1xeo3FSaW91Gw9r8Wz56OOt5Z8phUP7/6HuPYPkOM973+5+u9/OafLs7szs7GzOAbtY5MBMUZYsyfbRcSjb5VP32PckB8nykSxZiSIlBjFJzARJkCAJAiRBEkxgAAEQBEDktIvNYXZ2d3LonO4H1nHJknwEuu49V+7qD13VzzvTYabeXz3P//0/mcUluYMtF5lstiAmcdMNLY2lQz2UU0stHJ9oWC9o+d7ViVV/O63WhOJKXk54phNenVgNd5Co2rpwdDy2kauVmrOzK3ILaZlSKT2vdBoMR+WmUMFPAcMo5xiawTHEVVZwgEMMt2urEKAY5IncaZTzk57llS+iJEshqlGdBR4GCdIrrxIeylE8lpunKIFkXK9wERICwD2zloWW55GUU8kSHsrQPFFKA5oXNRMtr0CMAhB1ahbJQExjsXIRMq5HC3hxCYUca3pNpQvQtqe1gWh+nKuWFiM98ewMgmGLekdj9rSFw6KckAvzlK5PaYPByrScW13xt9Xn50yUWAp3xsqXEMfN+5N8Ic2XihONo8HavLi6vBDsjRauOA5SkJOB0qxnO7lQM69mxfR8un7IV1iQlxbn69ZEi5Oe4eSkZKIwixpWLtDMqmXf8szFlu1Qr5jlPBRYoBl21aY50nOAl58nBRZxTCI3yQdDpKFY5QIjsJhh2TUbQstFKKeSJSQKwRw6O4UGwpSuGJUsy9OY7XiKhTPQQTCvtEgLFEo41OqU6w9TlupUsgQDcduwqmWSJmycwAppnCU5xMNWr9Cin/RMt7RKcCLt2aH8GMYTrkB0pE8uMW0uxrQtncqRzRrB+VcnHY4usrFQdtKSuFU+0bh0bJVqtgi+NX2yQCRVXW4sT1s2u6IlY6vnXI5aFlPJ9CdZIllQ3dbVo2U3WGPDgfyMxdAZfyqWv4BQeJmOplYu5UAsJzU2l06pNlcW6uXcLArQxUBXonjeUKmFYFf9yhnEde1EkF05zVSqdnQkUF2QltLz0kBk5YprOojUE81eAYaZF5r47DK/nFnyDZLljDw/s+AbCWcnUNXM8c3+5XOobuSFVrZc8s1NL4hDUm4pMD875xsJ5Wewmp7jU2I+jdcUk4rCat6/ML/oH8Cr+ejU5VnfqL7z454AACAASURBVC83i1eUAp8ScytUubIitNoOSiKaoJcJSydQnXNUaBsUsFizRnoYBBavVQB0KEQVjDJj1kjMEswyjng0bhmWQqAuheiopQt6mUI0Xi+RiEm5KmvVPMSlXJXTKzrqUp7KWqpjlEnUELQSDlzoKKynuaZBAot3FKi60NNYo2pVcxDRWUfNryKFi+cQAoenzxQxDPE87sSpZQDQ+dnQoYMZHKCz0/jHXh7DEMTznz5ZBODTmBUA0OmJ0JFDyzhA52YQz0MwFEGQwNnTZQAQz6M+dtMAoNPToUMHVwBAZzzixPEKQBEEhRcvljEMQTzq9CdLGIZOz8BjR/MAIAhCnDlTBWgFxfzFAigWvd8ITBiG6Xrwicc+G125rt7anv1Pf4lpGnvmlP/pJ6/WUupT6VUkmvvDP3YE4eq1TP9KifAXkE0ZGuY+OYH/QsruN14JsboKDFPr6tK6evB8lpqeQlz3al21bJs7eaK2bqMjCP/s8P7rS4QeAopF5sJ55vIleuwyPXaZufTPBxd/5eASffkyPXb5F4Iv/tIo+tIl+vIleuwSc+kSPXbplz/nn4dfvvS/PvBXYy7RY5eYc2cdivIDuPHRx/nZtMlz6+5/OHLh8symzbf8zT8ExieXOju3/eiuQCbtQqr72T3ylRk1FFx33yOxU2fHNm3+/N9/Ozw2sdjVvfmO+3wzcwbJ9O96MTI+VYlG19//cOLIxxdvuvmLf/fN4PnLM2tGr7nrnuD4FYMVuna/3HDidLqpbdu9DzQfPnrxhhtu+db3IydOTw+MbL3nvujFS7YgdO59NfHhxxe3XHPtvQ90vfjK+es/t+Hhx9vePKBEItET5/p27y1EYo0ffjS645mF0aGBp1/s2bPv4oata3e90LZvf7k+3nT04+7nXinUxRuOndr04KMTmzYP7Hyub+++K+u3Dry8r2vvvkowWn/27MDTu6vhaMO5S+t//vjUuvV9u17seeHldG9v84FDfbtfUgU5NDU9+MjOaigSPndp0/0PZ4Z6W19/r3fXnlxrisrkt/7kp5hm6rxw43duVTmRKlY23f1ANRyWx2fWPPGMg0FZLw3d/RgsV2v+wE3f/K7O8IRmbrzrfl0QxOnFoR3PYIaDYMjmH99HF8vFeONNX/+WjRGoaa+/5yFIojBXG3psJ6hqtsBtvfVuYSW33JT6/Df+yUUw10HW3/sgiiB4sbb20afoqqb6fdt+8BNxPn12880rb7hKmeSQ2vw5QVvFGcaY/sRvTJhsG5h/lazmWD5sLBxmlBzBYPrCRVFP40BC08c5dcxiO0D6dWJliSNjROGgW10hRFydH5dLMwTwgcxR0pzwAil14R0mkxZgFCscRQsZRgbV1UtkfoYhokT6KKVcQrhOZHm/t5rx+YL66ik6O8+Kgj74xktrH9s5tW79wAsv9T+/9/K6zYMvvtS5d185VB87c7p/54vlaDx4YWzrvfdPbNjYs/vl/uf2pAf6Ww4c7n1hb02QwxMTg4/srMn+wMTUlnsfmh1a07H/nf6dLyz3dDceONz//IuGIEXnJvsefEYTRGlmfvPd9y8Ormk+cHDwqV25VKrhyMn+Z5+3IM1msxvueViT/Vxmddsdd6fbOleqwZmTImVZpKWMfxTGixomg8n9rIsA3DLnD/E4sLGyM3EmgNYsBmhjh8OwqHOiNv5OwHRxgDgzRwQCcdGyMXPGj9UcSNljHwaQrEHUgflXKdsjCcSeOyYA3fQMb/qUjFU8T8AX32XcFZOsB3OvUmWLwwkkcwTiiol73tQp2cohTrvwB3/zd/XHTo1v2r797nsjZ86pkq/tlbcaD3+caWxZ/+iOtnc/uHDDDTd/7/aGQx9dGd285d77G06eMSWx9fUDyXcPpTt6Rx95vOuNd87ddNMNP/hJ4tDRicHR9Y89GfvouCr72t94q/nA4WxHx+CTz/a8sv/0F75w3Xd/1PThR5nevt4XX0u+f0gVpZb3Pmjd91amo2Pw2Rf79uw7f+NN2+7+WdPb708Or1dmsOkzfgoaehGmD7N01K1dcGbP+cSgXruCzV70C17VrXjjx30UbqllfPEQK/sVZQqbOheUxFp1Ep2+GBBRza2YU0f9DKKpCpx7n6FCiD5hzZz1+/iqvohNnAvQloaY3uRHPgFRbBWZPOQj/Igxj0yflmRW1xacmXNByjY8F5k8KCI4lqiMbfnRPbRngaoxsONZMl8xZXHzbT/1zy3O9/R+/u+/TSi6wvEb7v45oeuIag4/vpPPl5VQcOutd/rHp2ZG133xf3wDVNSqP7L17vvoYtH18KEndkrTC0vJ1pu/+8Pw2OTkunW/843vkMv5UiCy5Z4HhFwOBaBvxy7/2ORc39At//CPdZeujG/cdMv//C6TXikF6zY++Ci/kFlJNK1/6NHuN96ZumbTltvvT75/aHztho2P7YgdOa4GQx1vvNn81sF0e8/Q07sG9r5y/qabt/3ozpaDR5YGBvqef6Xp/UNVf7DlvQ/aXn0n29rav+fV3t0vX7jxpq233506cHCpp7fz5Tfa3npX8wWSR4917t630tbes/vloedfmtm2YfihnW3731lua2t7/UDH6+9osr/h+CeDO3evtnd0vLx/8OnnJ7ZuaX7jvaGduwxWiKwsDN6/wyQpulC65rY7Vlo7Yp+cGXnkiWJjPHr83NBTz7oAkrXqpjvvt0kG6OZ1P/hRLpEMn7888sgOt87HXUmPPLbD8QDuuVtv+6mH4q7t3fDD2wsNMf/Y5MjDT2gBHze9uP6Rx1EPeAS45ns/9lzPJqib/un72YZkYGZ+3YOPOAwFs5X1DzxEKMZMMFh3793UxAQ9dpn+l5Ma9Quz26enqF+MufxZYi7/ixj6V2J+6RRz6SI5O/NLy+N+TYkQRRHP8+15Qdr/2mcw7HQcOxReuPU2D5Lk3Gzkrp8AVb1aqnFdjyRX//K/aN09n6l78m8ALARBEILQW1v5ox+hun5Vd4KiqOMQmYwjCHoqpXV2UVOTxHLm6sXymKqS87PK8FqXolDP+1cB69MnC8Bv3Y4iZiKprF8HPSfd053vbCF0c35kuJCoRxwk29ay3NftYnitPjTf32cjYLWzI9eaIix7rqs739UKECzf0pTp7XIwotoQWRgZRl13uaN9taMFGEamry/X3owiWK4xPjs4CBCvWheeHRlGHDfXklpcM0QptUxX12pni+e4pcbG2ZFhHEOrQd/iUL/rIitt7Yu9vQB1awH/1Ib1Hk05FJzavEFlOJulpzdt9Ai8GvTn+toVilUDwYktW1wKujh2ZetWj4aaIE5t3OgQQAkFlwb7LIZTBW5yyxYTApQAE9u2ITShMvzc6IhNk7VQaGmoV2M5Q+AXN6xVfEEUQ8auvdYhcYNmF0bX2CxTk/253taS6Lc4fm7dcCkUZWqV6c2btGjQJcilof5iJOwQcLmvKx9vJGxzbPNm10dZNpjdtKFWH3ERsNTbVYo3eATM9HevppoJXZ/YtEmJRYBhz6wfLUeDLgKW+7qKqaTroYX25EJHD6HrM+vX1RoiwLKnhwYrqUYUECtd7dmWZs9DV3s6Mp1dpKZOja6txKKYbi4Oryk1xaFt0JwBmhgc8eiQBxsIXNPoqAcaSNo2SJ8D4hBBPIHTySTEPBdKFprkKKXKhl0QpwhLpwUbtLC0Z9CMiTbRiOPwPhtNMpxeJfwOV4/oCC4IFtLKsY5K0BaeIgnXIgMYmqApvSb4DCxBE65FshbRBF3EYRkLT5HAs8vBUHqw12Q4TRQmt241WBrDkMvXXuOxpEFzE+vWWSQ0/XJmoNfkOJ3n50fXVH0BDHXHr7/WYaBJMnMbRx2eUfzB9Jo+hWFtnp8fHqgGQxjijm3djNFYlZXnN4yaAqfyUrqvUw0EHYqcXz9SiNThljG+fbvlExxIza1do/GcQXOzo6NGkGfUGmiC0I8RhgmbSUJCoGPRdR4IQwJ1mDrECtGkrpEpnJAxaBh4DGMCiIMCNuLiIZxwTTpkIzGeMjSqiSCCGKGqTAKAMEHZBhlB0AgEtilEbKyeJnQdb0CQGM+WC1QMw6IkNFUyjBBxirY0JuQ69QyuanzMM6M8Y+n5eMPMmjUYhqhB/+y6tS6GFRrjCyMjpKGutLWtdrU5rleOx6dH12IErsnC/NohBwX5ZNPc4ABpm6tNjct9XS6GV8PhyS2bEczTfdLMhnUY6uVi8aXeTtR1c83Nqz3tDkHWQoG5dSMGyxkCN7NpA4q4hYbY8mAfgoFCU9PSQLeJYEYoMLl+HYtpKOrCFEmQDk2asJFAKY8GOtVCohxO2xrewxPAwBGHThGQcgDhcA2ezUEKmFQT8ASCtjSslYIcAhEHNkOCckncomIoJkESNekmXBco1lRgGyRYl3AsGEchj2IQZRo8IOHQ1uiE50QEWq1R7STOI9DWmUaAyNBDsWJLfKGzB5jW3NqRSiyKm/b0QH+ptQnD8NWO1mx7q4dg+Y6WpZ5eaOizI8OlRB3QjMXhoUKqEUGwTEvzUlcX7jr5tuaFvj5CU+fXDK50d7OV4vzQYKEl6bperrUl3ddHuHa+KbHc2Y4Z9vzw8HJbG6vWFnu6sh1tnofmU00LQ0PA0rOtLQv9A4yuriYby13NCqBq9fUTGzcAgKoiP71hHYoixVh8dngYt818Mpnp7/ZwshqQ59etVQloifzsxvUYhhSidUtDfR6KFpPJzEC3SZCqX17YuE7nBItnJzdvRFy7Eq7LrOlDcDxfV1/saS4zkuWTZ9cNq7xo03Bq43qEJKrh6OJgrw3JajS6ONirSD7ctce2bUNYoBLs/MZ1miwaNLfU11UNhx2SXBhdk69rgKYxtn27EZA8FJ8dHVZl0SDpzFBfNRpxMbDa35Zu6oCGOrFlix6SPQTMDA8q9RGHZjN9XaWGBg9FlkYHs43NpFYb37hRC/k911tYO1yrizg4nOvvLSdiwLEXR4aWU81MrXxl86YagvCHD3kU9Vs5gaK/pO3+F4CFoojn8YcO+l547uoJCXUch2WX/+7rRjyJFwvhn91LTU5crfTK8xAULd/yO5Vrr/cg/H8bsDzP9gccQeJOHPv0m66mUIgpCkynrXjCSDaZjUnm4gVQKl2tDA1FiZUVvFpV+/o9HEc9718FrN/KDXUdM56wBgdQlqyFQx5NmJCsBIOuyFiQUsIhU+IsSFs+zpBEleaVcNDjKIuA1UjEFWkTI6pBvy1xFkEaflGXRIsga0G/y1EWpJRI2BJpi6CqkZAa9COIZ/oEXZYsklJDQU0SEcyrBQKOzNoErYT8tUAAwRBDFi2JsyBVDYdqAT/uObpPrkYiKOIaPGsGZYNmTYGvhoIeDjRRQmRaJznV76uEQyjAdIFXIiEcsauiXAuHEEiYAm8ERAcnVFmuhIMogRs8p0RCACA1UdbCPofAVUEyAqJDUrokGgHRgJTBMrVQABCoIkh60GdTUJFkz8doNK9Jkh7yGTTrEVi1LuJRuEoxSlB2WMZgWCMoGSxnsEw1EqYxsyQEanVRl4EGzamRoEVTBkXrAcngOJtlKnVRgHs6SVfDIY+jDIZTQ36LYw2Gdf1sjRMNmqlEIxiJGSRTrYu4LGkQlBr02RxjUrQRlA2WtWiqEgmjFDAoRolGTYH1MIzgPJxBTAgRHtDQdAgCEyFNmRpOowJGM7YFSci4gMUsSGIigTIoChBMwGnKdAgalXCMBx6CELSLc4gDISrgKIehGIIKhExUa6SAigQmAAygKIeRjItgiCWQgAcY5iECpGnDAhQi4DRrmySFcQByHoZ6iuw3AqKNE6okVqIhgGMGy6qREI44NVGqRsMuBQ2eNwKSTVKqKBlBySBIk+eUcBAnMIUXtLDfgYTC8bpfdChaFwUjJJsUY3KsEg6SmF2SQlrY51CkKkhGyGfhhMbzekA0acbm2GpdBAOeyvJqyOfQlC5I5WjEJUkUAkwCNKprJI0LKEU5FiShiGIkYhIExdoIjXsUBBJO44ZF0piM8UCpMBIpeDjlORDSnO1RuEXgmIQzQDchBSQASdsgaFxACMqzSUiztscQLsSBD2Kk52EoEHGKsiySBhKO0pgHEJKxEZbwCBz1kyiF4p5TDQeVUBD1PFMWdJ9skZQW8Guy5GGo4vfZn/6Dgv5aKIgiriHxpizYEFYDQS3gQwCqBEO2xNgorgR8tXAQA5gmiYZfRDG0EghZPt4ioRIMmTLv4KTil82gZABCl0TDLyE4UHwBy8dbBFGVfbaPsyClyWKlro50TZOloYhguGcyFMXZKIG7DGRY04KkSwHAIxSwTIYmeQ+DiM1SAq3aJLRZkuEsm2YQCgAJkIip0zQheDhEHIakBNclgEsRDGvaFIlSOC7jFGaZDE3xDo47KsvTnOMRwKUJlrcsSLokDiRAoYZF0biI4aRnkozjY1VBMhi2GgmhNG5CuloXdTnSAFAJ+S2BNSnKCEg6z1kkWQkHUYYwSFqJRm2esiBdjYZ1SXABZvpFXRJtSNbqogbHIChSDQdtkbFJphoOaj7JwzHdLzo8Y1B0JRoxJAHDkEq0zhEpGyOq4aDukxACKH6fKgko6in+ABAIBQpK0F8LB1GAapJoBGQU86r+gBr0owDTAn5T5myMUAJ+IyCYFKtLoh6QUYBW/UFT5m2CUHw+y8c7OKn7fEZAMCCl85zmk1AIaj6/4eNtEtb8AVSiahSvSaIZkg2KNUVBDfgQzKuJkhGQXAgVWdZDPpsgTY6pRcIUZhXFkBoOujSp8aIe8VuQ0FlWD0gmw9ocU6mLANzTaE4JBxyWMnhRD/osmtY4zvMzVUayGKZaF8EIVKNZJRJyaahRjBb02TRtMKwZ8ukUYzFUtS6CkkCnOTUSsllKZ7laNGpR0KGgGZR0TnAgrMRiZrnKf3jw6sXd/3/Op78CWPSli8EnHiWWl672+l0XwYncn/55ddMW1LKCO58S3n3Hu3rpleOog0O5P/wTOxC4yi6CnwWwEAT1PKOxEdRq9MULyFUaeQFAFAp4sai3tRmpZtsf4E598osS9d9IdXBh3iNJvbPrU23Wvy/A0pqaRU647p77+cxqNRS8/tafBCemJrds/vJf/314fGJuYPC6W+8MzUzXfIGRh5/0z8wX4/Hrbrsjemn88rZtX/rrr9edvzi7Znj7T+4OTUxVQtG1jzwRGpvINSWvu/2uxKmzZ2+88ctf/2b9yTNX1m2+4faf1F+8VKprWLPjmdi5C3OdvTf+8EepY8fP3XjTF779/fjR4+Mbtl5zz73xT07W6uoGdz6f+Pjkha3XXXfvfUO7nj/1+S9teOSxoed359rb64+eGn16Z6a1s+XA+xse3zG3fmTgmd1rdj535sZbRnbvWfPUzuWOruajR4ce37nS0pH86OOtP/v52Jatg7teHH3y6fNbr+t7/Y11jz6x2toRO3tm3UOP55NNsWOfbH3gwfFNm/v2vjr6+I6Z9euaDhza+NAj+XhjZGpq470/LzQm689d3HbPfYtr+lveOrj+0ScWhgZhrnrLt//RBVBnuS988x9VXoSqfv2tPynX1/GLK9vv/KnCCpyjb/3+HaiLVX3+3/3G/zQYDjPt63/4Y9UnU7nyNXfeYxGkTZGf+8fv4ZpejCe+9Hf/4ADcRfFrb7sTpXGkam2/46eeh+k++ZZ/+A5Zqa2kmr/0N3+PuJ4FqWtuvwshCMdCrrv9TlS3a5HQ73zj23wuf2bLDZV9qpbHREpJH6ftJYsXjYWDNDKtwE6YfclWFlA+7uTf87RlIDBq+hTtLNhYEOTfQ70rCtlNll42SnOQSuHldzR1EZM5Zekca047oA7m33PdMVVuNTNvoqUZSDRB7ZBemiV8bCV3BlSvoDBOFg661nmd6iHKr+vFKVKoN8qfoKUJQgxbQ6/tXf/Ijon1GwZ2vzT65JPntt0w8Nr+0UefWG5tT5w5M/rg46uNLXVnL1z/03uvbNrU88r+9Y8+Mb1uXdOBQxsffDibbI5MTm786c+LsXj44uXt99w7MzzS/s4HGx9+dG5kOPHRyc33/6xU3xCZm1r304fL9TH/9Mx1d9w1N7Qm9cGhTQ8+lO7pqT9zcdu9D5RCETaXu+62OyrhqJhZvvHHP1no7M7rgaUPIO+ppKdPv81T1SrwE5m9LooguGdlDgASt3HVnD/MUaZGUvbcmwxTqtA+e3Yf6TkIAZyl9wgSMYFuzh1iKU2lWGfmDZosKGgDmd1j2yZKQjvzPgFNE3jO3CGGqumOn8y+7IKsAhrJ/F5LNUic9rIHXKjpJO7MHWaJomG2SV/92teSHx65eM0N1//kjuTxE4VEsu+5vamPPp7v7N169z0d731w7nM33/L921reff/CNTdufeBnLQcPVeLxnj2vpD44PDswsvX+n3W/feDMzTff9MPb295659KWazfteLL17XfzjU19L73S9ua76f6+9Y/s6Ht1/8kvfuGG2+7sfPX1mXXr+57d0/36G/lkU9cbb3a98vpCf//ax58a2L337C23bL/nZ70vv3pp67XGrDt/hOFpTcuC/LseFUfNS0bmY1KqN9UJe/FjRqIqWNmeO8hyjG6UQPYdTwxp1qS9+DErRQxz1l34iPGRVa9qL7xH8VAxFXzlTZeOouaMuXSE8vlqdsadP8IJoIZYXvodKIKKrSKLb0MyiLhpa+EIHZRq+oo7/yEroDUUQRbeJAjKbVBnrv/ej0nXtGxsy933A81QQsFbvvldbiU739f3lf/xNapSrQRC19x2J2maJqC23XE3Wa6W6+s//81/8s+nJ0ZHf++vv04WSvm6+I233c6Wyyorbb3rPmE1t9Da+eW//VpwZmZyw4Yv/f23uKXlbKL5+jvuFFdWTI7beO+D0vzS9MDa3/vbv4tcmRjbtOkLX/+mNDu/mmy99p77xfnFha6+a+65t+uNtyeu27rlx/d1vvH2+Wtv3PzY461vHcilmvtefqX9tbfnBkfWPb5j8KVXTt9yy7U/vqv71f1T69cP7N7Xve+1XHNL5xtvdb/0WrqnZ/jpZ4de2Hvm85/fdvcDfS/tm16/rmvPa/0v7i0kk60ffNj/3J50X9/g83uHdz43cd3W4ceeHXphz8zo2rb9767Z9XwhkWg6cnR4xzOLA4N9e18Z2fHU2PXXJd//aNs995ZCdaHc0sbbH1ACQaZQvOnWH2XaO+pOn9963wMrba2hy1Pb77636gtAVbnhB7dpggg143Pf/0E22RS8MrP1rvvUZB05u3rdHXepvIQh3s3f+YHBcIjj3vLd7xcaYvLswta7763GY1Qmf8OPbtdJ1iHh5//xuw6kDJL6wre+k21Iiunla++4S/XLWM268Qe3ugQ5HW8U33/33x9goSixvBx8/GH68iWPvGr5lGWVfucLxS99xSNJ3ysv+Xbv8uBnaFZo1jfk/uTPjda232gr+m8ErE+TUkZTilxcIGenr/KteDgOFxcww9A7Oo3mZpei2JMnPkOh0LbJ2Rnb5zdSzQgA/84yWLGEMTxkBaVMZ4dWF6iFQ4tDg3rUr4jSUl93JR6tBEOV9sZSIlYKR5a7OtSGsIcimb6eaqKu6vMt93ZVGuuqvkChLVloanQoMteaqiXq1GBgob9PrQ8porjS3ZlvTqIEWkk2rLS3KT5ppb21mIiZspge7FMaQorsW+5szbc2mxJfbEmWU/FaIJDp7MwnGx2WzLaksh1tpsCWmpO5juaqP1BONCx3thmykGtr0RvD5XDdcltrtrPN4mglGlnq67JkLtuUWulsM0Qu39RYbGnUfb5iqinT1W6JXDlen+npsgUm15jMtbdYkpBvShXam2qylE8li+1NFV+g0hjL9HbZMpeLJ7JdbbpPWm1KKc315UhdNpXKt6eqkqz7pPRArxGUSpH61e52JRws1kcLHalSJFqLhpb6e3GJWI6n0n3dhl8o+cKr3W21hmgp1lBobypH6mohf7qv1wrwFkUtrRlU60OVQHClp6MSDZfq65TWWK4+XvUHFgcHrJBQCQSXBvu0iL8ciqz2tFfrwuVIuNDeVGxoUEKBTF+PFRRq4fDCYJ8W9gPS4QMWFiII1iOjGC27OO0QDTgr2x6LC1GbCriABWzQRsMQpxA6jOB+wNIGjANWtBwaZ8IWHoGQdPmAAyIEQXtkyMNDOA0NOobJbM2QJCrkEVFAkBbrt4kIoBiHiAA8hDOEBqMo57c9juBDNh10UZ5gZBMPA4/GV1taSi0JJRjMtrSstreaAltuqMv0dtsym08ml7s7TJ9YaIwVWpOKKBeSjaW2ZDUYLCcalnq7bYH69HVokpCNNxU7UmrAn082FtqbKoFQtSG63N+N8jCdaF3tajUCUi6eKLQ31UKhfDJZaktWguFKNLjU32v7hHy8MdvbofukfEM83dNlSLwIK0ScpFgLFzEqhlK8g3IoG3UJGSUklIvYiI+iKQ3EIM07CI1xcZRlNUtiuDqHkDHAeVzIcf00Q1t4HDKcCRgPxnFGtFEeFyIWlFGCRZmwawcpltRgDMckjKd1GAe0YHsMRkddPICzjMmHbTdAsZQBGgCQcZcmVrs7sy0pS+TyrU355qQiy9nWVCGZMH3iUn+vEgsrsm+5ozXb1mKybKkpXmpLKoHAcntHvrnR4plMV3utMapIgeW2tlxHiyWwuWQi35YyfOJye3ulKabxQrq9vdrcUJPkXHtrqTleCwTzqWSuPWWK3HJra7kppvnkTHd3talB9QeyLalMVweJW1BG6ahHsg4ewNiwg7CA8rl82LEEWhQ0EIc04wARpRpQgvFwGcgBDZFJ3I+xEdsSKIGpEQmSpB0g4XQ9QnAukFA27KAiTvs9PmQ5MsdAFSRpmjZxEeXrXMg6iB/yERsRACU4XMQxJVrgNJAgGcbEeJRswDABr4TD1bZ4Pp6ohkPp/l4zJFX9gcXBPj3qLwdDqz0dlYZINRTKtzUVYzFF9mV6adWkygAAIABJREFUe6ywVAuFFgb71WigJvuWe7rK8QaUxIutyWxTUvVJSz1d1bqw6RMXhwfVSKAm+jLtrcWmhOGTiu3N1Vi0Gg4v9vVUY3UGS6UH+tT6UDUUXunuKDbGdIlfbW8tNsZsjlnp7bbqpGKkIdPZlmtvMXim2JTItTWbPnG5vS3X0mTxbKajrZqs1yRfoa250NpY8weKyfhqW7MpciutreVUwhSElc6OUiquSFK2tbnYmqz6A8Wmxmxnq82z2WRTsS2p+uTlzg6jMVQMN6y2NhVak1VfoByvW+1sN33CamtbqSWhSuJKW2uxpVERpVp9ZKm/B4gw3dS60t2u+8V8fTzfnqpFwoVEY7GtqRIKV8PB9ECfIzE6L2SG+tSQv1DXkOtoqYQCpURMaW3IhRs8ipgdHbECQikcWe7tUoO+Ql1DrqtF9csWx6z0thfr6pVwcGmg1/bzpbqGpd5OLSiXw9Hl7vZaNFiLRvKdzaVwWA3IiwN9iu3wBz/4dwZYKIrVaqFHHuSOf+xddTtnTNdqGzfn//hP7UBQOPhB6LGHPgNFuK7LcoU/+Gp185bP5LN1VasIfyml5Ehy/j/8R2J5GS4uXC1jEYTw/rtWXX3hS18pX3cDkcv6n9rhMsxVjcUwvFj0P7vTEaXKpi2oaWC1GmboCPJbBlgo6nDcr/4mNIazEaCxfFX2ey6q8pJGMgZJ25DWGM6kaBzRKv4gg6UNTjAh+dF/+CMHA4JR1mkWA0BjOIvhUNco+QKrt3zRQxAWNTwEUznRgqRJs5jn1Xjx5C1fIC0dOLYPww2SqfGSQ0CD4Qya1QkKeqhKczrD4bZVkQMCnjZozqBog2ZdSKkUU5MDgYXFiihDDXEwoPAyS+RNitFozmMNChQ0hp+vq89xpMKLXKVI4bAqynSxZNGMDpiZWEOap2oMx4gy5nhVUaKVCotDheKKEXyBIU1KwHnFQxY0TkAcCrUul3wB6FgcIFSWB6Zl0oxO0i5PCfasygkqYAEKFEFyScqElE4xBifYODRJuiYHMdut+gKUByjHU3nJ8YnIpqQOBdVlHIzQaa4cCMUtpyrKKA8/+NO/QFHUn100KNYiaY0XHEA4JF2uCyXWRLRgwEYJD8FqomyQtEHSFkGqnOASlEEyVZ8fcxFVkk1Bdj1M4UQXABy4mOeZPIuu6ChAbIYkHM1jCJOkIaYAD3FJEhAuMF2HxFk27bmehdVjjoOSwCRpEq15KGYCj4ELBHRqIOYhHo45FgQY4uLArZEiSc6zhFPEumjcdQ3XYGgCqWE4YpEERFwC2CbJAmgjmuMyEKu4iOdYNGsRpImTOs0SNM8DQuXEmiBFEKwqSrShcgDWRB+umRbFWDipt0Y8n6aJAoJQwLlckX2kpQs4VEgWj+OOaBkS6+mOjGA6wzsAxSyn5AtytMbK0AiHkLlVC1I6Qds88LuIwosWg8cMuyL5eKPqokDhRKgpFkUbJIuwBDBNi+ZMSAPLdFnKJl0KUVAMt2kaQywXx02ewqwCwjC67UryrMvwKs6SmAlwYEMAPdMG0ISuwE6rwKdBP3RUjKQsSFGoimGYC22Oz6Ak7ZAs7joYxE2KhE4VIwkL4hRSc1DaJgGOOh6OGSyN2iUCAh2HJs0SpqkxvC5IsGBXJB9HpF1A1ATZwaFFEDrD6QRFA0tjeaun3qvCiiix+KoFMJUVbIJ0PEynWK09QJq0wnAqw5HlakWSfQSJMKQCKKc7jLieRZAqJzLliir5Aa+x+UJF8vmItEuhGitYFOOarkHSNV5kMss6xxNVDbqWx5CoYkHEsAkapTzoaDZFIRgGTMvhWEtzSNe0AA3dvEitWoTfJEjMNmwKIgQOdMOgOVSriNykSUU8i6YQxcUpFyeAY6AoYuOKxM+oWMIhKMwxLZrEbY9ETQ8nXIBRjm4ylINDYKoeRToYhK6JEYxJ0jYBEUhV/KGwi6iS32RZz0MVSdZJ2qAYE1IaxzskZUG6Egzgg2FNCmm05HmowosWpEyadQFRCEWOfvkPGFt1bQ9BMJUTFUFyAaEzvM4yel8IFfwqw1sEtAmyKgeCCKaJskaxLkHqJK0znEExNk5URdmGlEVSKivYOOECqNMczvK4oqmsoLM8XSyXRdlHQIQCNU6wCIgxrArYyaYErsoYyQBW4L3VqijLBInQnoIzU8mEq6k2zvCCzOXyqih7FUvKLJcF2YjZVqTOkXwCuWJQhk7SpiBxC4uqILs1z7O9iuxnC3kAqRon0hQDIW1AyvCTqGVXAiG+pHNIVuVFaBkWpHRIexALIKjGCSqg46ZVlXyAxmb+9C9wxAnPTpoUbZC0hUMXwXSGyzS0riSaUM+VK1kXwxVRcgncJGnPQ9NtnXMt3YJZsRwAbFeR/RgNXBRonGCTtEUxJkGpPO9hQOGliujDXKTGCUih/GvE07qOWeZnsfn8P7K5Dqaqnx4Gn97BHzn0GWyrDENv68j//lfNSJS+dDHw5ONXKyX/53Y6m7dUrr0edd3PJL367ICFIAiC6K3tha/8fvjn9yO2fZVtChHX9b2wy6yvr63bUPjil4nFReGD965ybaQHAFxK+595yvb5ip//ojK89t92k/+f0hWmKtHbfvhLpVmHICLj470v7ys0Jmeu3dT90j5Vkg5+8+ujj++o1EXVoH94x1NWLIgretNrBzTZd/wv/qgaTnoI6s9nNj6+Q/H7q5HQ0DO7LJ+wkmiabelyAR47d7Rnz8sOwPf9+IfrHn1cE4TpwZHlugbGVBLnz3a+/iZC4FV/qGfPy4Rl7mv7wcann1EJYqmlY+DZ53CAkLaRePdDXDNnW7sG974cHBu7snbD8PO7Q+NjlsBwsyvJjw7XODl6/lznBx8c/Ke/6dz9avDS+HRXnwbNSldS+mSi7fCH0WNnVJYPTEwMvP5qXgpmI4zak8LTxYFXX284erQqyIGVhea3P3Bq2sXPbcF8uFVS+nbvjZ05U04l6o6dTX140GA4Xqu0vPS6i6FCNtu9Z99H3/pv9UdONRw5VqsLGSjVs3sPtZpdXDu08ZHHzvze76oB/8iTO0/+8e9HzPNdL+/zFIOMcW3P7xZnZuf+/HevN4rHUGz1+MXhZ1648HtfiJBs16v7yVL1kz/7wyonQ8sIzc5vePDhsZuvz6aahp56ZnXbCE0omy1WVOYmMlj/87sj41dO/f6XNj/86JVrtzKdnYPPPje3fQM/t9T18j5udmHumk0Du56vb2/b+z+/Vz5lMDwadgqrYwIGURrRl65wrK2FQ2r6E9olsYRUXTnNkzgatpdqTQLpISCrLFyWaEONhuzMScIgmUiTY/PQpkn28mJ6JWlbkBPc4hjDVmv8hgLKgFoigV5ayY75EMuJI8XcFFEzcElylq+wRNFoiBu5o14N4RrlWuUSXVOIBr6a+uhoy4EPK4n6zldej508NdfZ2//m/sTRozVRDizNNb1zUCcoLp8b3rWrKH6tP2r2oqBs5CN732v56IjKcIJSan35DcS1/Rva13nKbkuL73ut8fCRSiwSOjPe+t4BExKRAanLwA/js+6xS0O7ntfD/1D3yZnUe+/rAUmYW+547XUHJ1yRGXxqJ2EaHoGvfeSJqihOCv2LE1LUUVG/kr4Y9OdqZA8yf0qUWixJUVdOMaEWlc6UZydESTWkaL461MSOz6F5Z+FEnZyyuIi5dI4JxHRGmK0MN5JTS84yv3hRCCwp4gZ7/jjNxL16vlhLRvDFFWQsnx73+0q2h9mL51iZr/m2YIsnaCzF8MDNnaODvgJTK85d4agcQtQ7o4/vsHFivrt/6MmdAHg2AI3vH2aUWtUX7Nr3OlfIr7Tfuv7ZXa6uz4+uXV+aFQwKK5yK7T9CFSsVf7jjtf3B5eV0e8uNegYi3Ex+cWDvK3Sh4EDY/NFhdi5j/dc/G/GjQavwUCm47okddLlUStS3vPKWnFlyIGw8fVIan9Z9Qudrb4SuTL7Y27Hu6WeYdHqqf0RZIRfHxQiiOjaaPcPHJM2cdpZn+WRANdNebkqI4WUEcxYuyL5qEetF0K4knFuyLoDMSjDJVey8uzwpx2EFCZZrHQl2IV9ZpFdOSzFGt3NaZoqXtVXaZ5aGOsnT04bTkDnLptScxSBzp+Q41PSynRvnU7CImvbiuBByDTPopM+wPhSJwcnhHU/nN/aTc9muV16V5hZnt4727d4Ta2k5/H/92eZHHp0bXgMsa/CZ5zKDfRxvbTLxKWPpwnKp77kXS7GGD/72v2548JGlnp7Z3sGCP4ioaPL4J537Xo+mUhonDjz7XDUUevc7X7vZyC5aXG38fP+uF2qpeCafbd/3el0oUv2r/3tg1/NWMLg/lVj/yKOrqWaD5vr2vFSORku+UMdrbwSW0u/+6Btrn9hBVGszHT0De16m8zmHZVKHPuQXliuBcPub79RPjM8nUhamaD3NwbNTg/ve5mdnDYZpPPVJYGxS/cofLGwfwLSKnSuu3fkMv7RUbE6m3joYGr+sMvTcSIdNEHSt0vHq65FzF99Pfav76Rfk6ZlKoj525FTd2TMuy/pnJmKHjluy0HLg/fjRY/nOlviHxzrf2G+hhEQZjbv2k7WqKQnrHnzk0N/+d3Fuvv21N2yBoVdKXa/udzDi9H/8vSoncXotfvb80KNPffRX/4nOFTpfe2PBvn7S31IOhulqJX7i1NDOXWyplE0lNz386IUv3bLQ1FERA0TV6Xj97a6XXsHK1Xxvx5odT3PXXbM80LvhoYft//yXQDf6X3ieUis+Odz1yqswX/pgy9ZfzfQUv/iV2uioB/DfrknW81yG8XAisOtp4cDb3q+o4P83jgRWJJL/g6/qrW1ELhd65EFiOfOZKmBmPJH/wz9xSfLflr76DUajv36FoGH4dz3j2/PC1VMk6ji2JC/eervZEMNzubpbv0ddGb96AT/qurX1G1f+81/ZgSDyGSVm/wcAC1QrTX/6R7/4AlDLrG7aWv3qH3VculgIBCqJSOrwsbw/WOhvT3543GaZxb6O+o9P08Ca7+kJnRmv+f2VZLTMyh6CCWY5fuSkC8HC8EDd6csEYs0M9NcghzkO66rNh48pDJcZ7mk8esZznXPXXEOoGoFYpKHXnx9zIHFlaE3vO28bEKY3DjccP0dY5rlNW5qPH2NNbamvPXpuXANwfMPGltOnfJmlEzd9ru7SxfqJ8emtG/CiWj9x5dL6DaHpaWkpU1rTQszn5cWlMzfeTM9NkiTQABWuFvnp9PiGjcHMUnBhfqq716vmIepWgg1Sqdx4+vTF7dtCKwvizNJcZ6frGIRaVQPRwPJqZGFheuMQWtCaP/nkwjXbfeVV/9js/Jp+tliQpxZK/U1O0YhMTM9uGq6RQt87B+b6emyBjh09lW1L6RybOHd5taOpTIvtHxwaHx0NgLJ8bCzd0WE2hZqU5RwtlfN27OzFQkdTifN1Hzt+pbvbCXBFxofblr+crT91oZRoKPt9DSfO2nE53ZDoJqsTnmRbXstHJ5diDdV4NHHweCUeLcUb4ifOVhPhlWi84/2Dc52dVtSXOng0X1+33NNpT9sAsUEdUNI4AhEuaJgLBAIcMokqkwBxHaYZVRYxaFmkrECl4tm26ouYyxDBEaYJNadcz8XsFkqYHiMxryKEdU3GbdNo4sGVGoo4QTFv5/IIQ5XCKSzn2iagYo43b+oIdFt4crKC2Q5swdRZD9ERugVVs9CtubAZJK5cZJfy8yN93OxiaG7+1E2fi8zPxi9cuLBtW2B5QRqfG9+4XiiW6q5cmV/TK5BWFFPGERld1VKnT1/cvt1fXPZfml4Y7OV8IKAV59gwMbkcm5mbWjfgKE7bieOXNqyPIascR88gIpkpBidmFtb0gHyl4ezFue3rLN3rOnXy/MgI52jhUxcX+rtJx45cmrgyurZKifiU5URRnlTLEwQeQinZM6Zc3IciAqbOoFzQtDjozWFOFGPUFcEsKoZD+MPZnESLLpAQfR7FeQ9HCoxbMyzM9EWcJRLjLSKCKZMIwXkMW4aFvIuAarjRWgBYADWiHH66BCSUrvPMCdcWSCtCYhM1ljGsKGdPurjkKUm5/4P3cF0/c+11qWPHBKUyMzQQHp/GbWt8eKTt0GHHdZc2rak/eZFWaqevu761OucDxrRGBsfnHMu7Mrqu/ZPjqO3Mj/TF7SIHnGOwMXX+PFsozAwPxi5fdHQ309eRwIrAtqa4+vCZK0I+N7V5LTexGFjKTG8abRi/jFX19JreyPlLsKLMbh31X7gSzBdObL+Gz5YrWQomUEyzjVWUTqJOxfFyGJ20qybtzLp4G+D1aiVDkyEHWjlYLeI0VUTDeokSGk3FY7B5z415QmkROqoCORNGvFUXxhHLwow0EvHnFMsUCLviQFT0V+cJPqQRqJNfFdi4iziItoiIcaMCOHfSAo2AwU19DsciHkvU6o+fRcLsYqy56aMTi52dVkhoPXJiORIttSYS7x9Tw75cayp24pwekjKpZDeWX9DwGimkPjhaDIXz3c3JIydrkjSxZg1mWqxVQ2yk9ejHhYbYbGfX4P5XS/5Afk1XSkkrkJ9y5LZjHzsyl0/GYoc/ydU3zA0Mrnn7zZrfn+luqT9y0mbZ2cHBtmMfa4Iw2T/YceggajvVgSR9Kc0Vi6dvvLnpwjk+l5sYHUmeO4Mo5tjGTc1nTlOqOtHbB1cWcBJHWJYq6oH5ucn1axuujGGKmU41A72MmoYarI/OzgWyqxMbh+mFXHhudmqon1RLtmE7wWB0eo4sVErDzehMITI/P3HNRnKl3DA1NbZ2TXhxjl5cXVw7EJhb4JezM5tGkLLR8fHRCxs2RtUMe25ucaCXwJDI+bGlgS5P0RMnz8xtWas7RPeJExeG13gSU6Ik0jFCi/Phy1PLfe2WjcTOnFN64lN17RYkgeOECkvxUxcX2podmqo7eS7fkco0Nhs46aus2hbW/d57V0ZGAEckD36c6WgzAnLsxNnZ3h4DwrZjx1a7m4tytOf9d2d6+4ulUv33vv2LHpuYqq7+l/9euulz7m9f3dAjCPGdt8P3/xS1rKuv7nkklf/qHxZ/98sIhkXuuJ0//OFnStA4vLD4w9uMROO/ma6QX9+L8H+fNKOo0ud/R1m7DjPNqx0EACiX6n70A0zXbJ9/5a/+myNKV2vVhaIehnFHDvmf3wXKJdR1Ucf5LdvdX/ugTIZWcVxhWJNlXQJasmQThEaROoGbFD05PDLf0+0SuEkzOscZLNv+4cHwlXGbogyOsyjaIkmVokwMs0iq+eQn4dlZmyQtFDUE0cVxlaV1CB0M1I9dTp45o/OiDkCVYT0Evbx5y/TwWtRxVZIyCMKmaUsQTBwYNGsSuCUILoQKRasQWhCaouAA3KBoAyd0HDdJaiUSmk7Gqjhj0KzNMDrDAEaoPzumh+ssCG2KNDlWh7BK0a5IcQRVP7mgy36NYRyAGYJgMYyJYYZP5B07mK0QDNQE3kCRKs4Vef7KhmEVUgpGmxim07RFUTrH2RBaomAThE3TGkVbnqtwvEMQJsPaFG3QjAJwE+A6T9vbG4ywbNKUS0BTli0LrZ5d0VY0WxAchnFIUhUFDfEUUfJwfODdA77sqs6yKkkZkLA4zqYoGxIGLSmHZ1QdN2la8zyN5RyO0kYarCafRpA6DkyC+PSqDFG0KNJCUYMXERSxcc9BEI8CKI5gBIJSwHEsG6KaR7FEmqdXdA8iwPMwzw1IOS9WhAk1GPQAQlCoi+MWRC3g2Qyt0/GiXY/4OBtxPdxDIEbgLgZRTCAr7P/D3HsGWXLc156ZVZnl3fWuvfe+Z3psj4Ong6i3T1pppdWTQutEUY4Un0iJFgQIEJYgPOG9Jwbeg/CDGdixPd3T3tzb19+65avyfdDuxgvtKmIgMZ7wuSIjKqM+1C/+5/zP6ag7aVeTAjrwIHEBZPiywOUA7Xs0cBnoI4RYCKEPMQgwITQhLLIxY7CMh7EniB7HeQxTVVSbAEOUnKgQbG/wIppLU5bA+zxrVEH9rXmX5m1V9TGyRdFhGIemDVm2c0b9aMG1gCXLBgAuZmxZdjBjq6plQeP9ZStgbJapM4xLI68l4e5udAXWUFWTEEPTPJYNeN6TJJthTJ5zGJYwlE38gEMAUwj6rEj7mPYwsWk6AF5IXgOU4THYhgQggmNSXm8wwx20SLHIAwj4LPJhQGHfSyWK1QYj3IwV2vMdj6UDmsbYpzGx5XDOb3HEFFA4HxCfhQQQVVrFVN4gnIuJRxPEQoQBQVTA0C4JAAsIRVuCYDGMhxlHFH2acmTFopHO8xaFveGEN9YYMIzBshbGLmaMDVs/vGSJioOxK0meIFgIGywbMFg/U6m/v+hyfE2SbIqyZMXH2OE4VxSNmWJ9thbQ2FJVDwCX593OpLstY7CMg5DJcTbLupLsyIqPsCkpNiEBx0KOCkAAMKQQYJAHaOizyAW+RWEI6+FMAQLXgIxPvECiHSFaJS2eEqFlTFM+hSHEwCI+EbEfipaNJhJJAx54OPAwjQXEUL4VUo1Uy2qxwYlnaAQgIg7DQdpR+cUA2g6mfRIEmIIcDSlCcRAg4BIvwFTAcZ4geBjbsuJynCXLDsebATEk2edZcyLtdkUtxJgYOwiZnFx/d8EwsctyHgC2qvo0MgTBZBlAQMcHhxKLS5ai2ABWZdllGI8XXFHyAF05UTZni66iuKLoYOzyfLAtY7dEPJY1ETIw42PsCqKHaIsXDIYxEbZ53uMFWxB9hB1B9ATB4XldEBwALJpdTmcW25stlrUYpsbzUGVlglKzy2YsZvK8h2hbVV2WtRFyYqpWM2Jlg9YEQ5IsABxR+uenViQq6lYkW/IkyeZ5W5IswBU07VRvp+4hSxBMClqS5GPsKbIrihbL1XnBw4ylyA5mbEVxWSbgOFdVHYwMlvUJKafTH114kSVKhqqaJKhrGuM6I6+9KlUrpiQaHOdgxtUUj6YdjqUJGH3hBdY0LF4wIGUKoseyjijZgsDU9aHnnsWWaUuSz2BHUTyMPz7n3GxbGwh8g2cdhBxR8ijKF0RDlJyA1FT1/x81ggAEX7Tfqw8glA5/kLjhF5+DrgAAENamp8tf/krAspF775bef+/zNh3n/+zP/5109XlM7v+9GUtWvEiEnT2NyqWzzemCkK5W2fn52t79vqq4DQ3yW78524gwCAFFCceP+dGo3dr2uULx/0dIhI4TeuoJ+N99PBj4ZntnhpBd996dXJxHxB2/977mwx9sTIxc9L3vp07PLO/Ylk01QRa1Hj40edc96ZMngMRtufPuniOHFyZHv/y978dOn9abGqavvyGyscY69uATTzZ+9lkgcVvuua/rnbdn9+y6+Lv/GDs9U+zs3H/dNfG506zv9D//QseRw2t9vcVU2hHFWHb5Sz+5NHnieKm9Y/rmm9KnjguO0fPCS23vH5qfmDj/xhv6nn9uZeeu6Rt+2f7u2zSPW458NPj0MxbG2S09WAJcrrj3plu6X3ppdWx8x733dP3mDUrk2t9/p/+pZz1Fa/noo+k7b1+dGN1y1119L7yY7e3d+sRj3a+8DBnUfPSToUceJyLX+OmnO351x9rY0JaHH+l/7rlKQ6PO+8Zgc+jdQyOHDo88+ggRuIZjx7bfdEulr3Xgyad7n32+nopFltd23P4rLb8ZsPicS3/mczicz+269VeOKLRGjYvqMVg/Gjl0dPCeh7X11UCVz/vRTyGD5MLmtltuDRi68eTM8OOPh0pF1qxN3XxbeHnRTMYv/MGPaAqIpdLW2+8QKSc0e2b40cdDq2uIgTtuuDm9uKAPd/2xt8lBhN8/MnX73WzghpdXRh99JLS8DHm065c3JednP911buV5m6q6YaOYPcZ6a55C6eufCni2JrIrsEVym2Psu7Obnyqk6ClOLX+K9VYDVvDzH9H+sWqspZ5/GVVzjBILCm96waYXtfKl07i2ggSZ5A8D/4TRmtpYfk0o5AUlSvQPfG89SOhzbgYHKmINuPkO4884sZZa4VmntClGxUr5Y2SsghhVmHjpie33P7Q+1Dfx8EP9Tz+z3j+w/cknul9+kZLZQdHY4zE1O5d47p29t928PD4y8fBjAwefrrQ2Djz9fO+LLyASZM6cHnnoEYqhk6dO7bzxlmx/99gzzw4ePFhtTg+8+HLfs89wlpFZmBm471FIgdj8/O4bbiq1t42Q1XOhWtI3Op98aeDgQcm21I3VLb+6AyIqsrY6fd31lfaWQimc/xQplbJULy58rDFrBU2y5l+VWcuOaDmjLYzrNfSpnj0hsGWT9/XFw4qyXgjzxaO/SWDHFs3a+hFe86ooV187IdBFS6aM5fdFYa0kx5yFF3ng+Lxd3zjCS7ZOV+3sMRYVXI1Zt8abmI1ltGEX3hYcD7GBl38PSJWSUNNzx3m3QHFN5Pe/863MJ59uDA0duPbq5MkTCPi9L7/S+d77Rnv8QgTHqvPHLPTly6/KfHgk29e/56YbGz75mA+crldea3vn3WpDw5b77xt75aXZXdsuuPRnjYeP5Lu6pu+7t/HDIwxFel59pfuVV83G1NjDj4wcfObEefsu/MlPmw4f1ruadjOVrTXG2TjW/tSr/S+8WG9Ijzz88PCvf31mevv511zX9N576509ziK1elRQvKpXJKvvc+GoDj6rZU+KKl9ngjVzuEU+dIRe49aOiapfC8r+wntyg5j1j9eXZ9QwVzZnSGGGidZyVNVa+lDSrAKsk5V3pbhccU7oayfEGMoF8072lCAYdcqwNg6xkc1FLlq1httxLmsecvKzUprKOwt29iTP1+qMY6+/y3CU2178dNc118ueoS6tDD7+ZHRpkeLRjptubZo9lR/u+S/+pkKo4NPj22/+legYai478vCjkTMLRMQ7b7q18fjR1dHhi//hu2xNByw7ffONan5D1Gsjjz+ROTOnx2IHrvx50/HP1kcGv/KDHwmFYiAIO3+9UBKGAAAgAElEQVR1W2RlWY06+zwtDdazdXz+T3/acfL40vDAl374I2kzDwRh1z13R5eXzHh86z13Dz33THZqZM9Vv2g6dCg7ODh9+69aPvrQ5phyQ8hLyFLFnL7ltolnn56d3nne5Ve0fHCo1tI4/sjjLe+/R9NU59tv9j73gpFODP36qZHHHpvbs/Pca65tfe+9emN65OHH299+E0PS/tHh/oPPGA2poV8/NfrII7mh/ooMzIme8JGj2x99rPs3b3CQZD76cPjhR42mzOBzz40//Oja1rGRXx/sP/gUZ+iZtfmBex9lXEspFvZec52diC6MjNclFSGw8977Bn/9pGIYUn5j6pbbWccUjPr+n19pZJKNHxweeexxRkbxj46OPvaYVCrzrrnrhptF10K2eeCKK41EtPnjT8YffpiSuOTREyOPPiIWiysTI6VIioJk6JUX9137i1oy2Xjs6MRDDzKBHVldH3/wQa1Snm1r/xdbhNB165Nbrc5OQNFfoNkVReFcLv3j79Nn3Qfzz6RoDI3k/o9veFpYffnF8GOPUHoVQOrsIaf0td8pXvy7/066+jcBFgAwCJymJgghf+rU50hegJBZW6Vsuz4x6cXigShKhw+dfU0hoSnx0Pt2R5eTTn+BFgn/FcByGpuKe/ZzgTc/OrE+MsiXq7N79mW7OqBLNvr71/p6Ah9KRiXgWI+gpeGRtfFhrlKb2TKV6+8BTrDZ2bGwddLFXDWdPLV7mrbd1f7+1clRrlKb3bot19/tQlRuaTm1YxeCoJaMnzhwLrSdtZ6e9a6eANHI8yCHXEKVm5tO7NoNAlKPx2amd0MvWO3qWRgfJ5CY4fCx3budkEp57vEvX1QWNV/kT+zdBzZy7NJ6kEqbgmKGw0f37rM0jfb9jy68CAicyYtHzznXR3QtGl3cMmFoUV8Qjp57nh4KYc/78Ctf9XjG4aXTe/e4PFeLxJe2jJdjiQDj43v22i5hZ2frDZ2eKPqCcGr/fpvnDC2yNjmcz7QECM/sm97INKul4tFzz62lEwFAyxNj2bb2AFArU5PzWpJ1599Xup1YmNT9o+efX2lMBxReGRne6O4BgFqdGF0YHJUqlU/Ou7Da1sRW9JPnnldsyhCIl0dHVvv7AIH5oZ65yZ1CoXT0nHNKnW1sRT++Z0+hrcktrp1B/FKkFVL02sjg3MiEVCqeOHCg2NXOlWsnpvfUmtNc4DCiF/QoyLb4OEGtLFMzYCsfdMap4yfRRslpahG4gBVc0qfQritEPNAuKZUS14T9VgU7piC7pFuSPJ1nnWAwTLxACjtBmyDVSkwc4FbOgbTIm0GPzPs65jy3O4I31klBt5JJwazzES/oUCkEZVpnejlIE4Z1/cEwIkExHFucmjTVsCtJnx44py7L2PM+vOjLFvTqTuGT5IBLMUY0sjw5Vo4mA4Tm9u8pphqZWu3Dr/+uI/Ee4k7u32fLkiFrS1snS/FkQKO5/Xs30k28Xvv44ouBytWxfGr/PiukmYq2uGUiq6mesXYs0r6e7pD06pEvf82JqAFEM/v310MhW1JPbt9pNoRDhU2/X2ZTLC7qaFAGMVaxKrgBWTLCM/MkQHZTWq1W4YCE4jRTNah+hYlB3jXZJAEtEutYKEV57SpfLLNdLErSuGrAXomKs4JVE5KAbuZY12LSlNeu8ZUq146MmCJ99KEfYLulXXBrbBzQLaxoVPg0ZXVG2aoutBKzIUL7brmx8cSu3TSiTU09fv75gU/yrS0zQ1sYa2U2QKtN3Y4Hqw0NJ/bsDSC0Q+qpfXsCQOWaW2d27GQda721bWVyzMVcPR7/bN9+n4aWqh477zzkufmWtvmpLZQfrHV0ro0N65LmieKp/Xurpl31Ch8lB2kA8o0t89u3AgKKjc1LWyd0QfVE8egFF2DKZn2H7eMR44mUQbfznsoLvk53sa7vszOnzUx7IHICsXC/SOFABRW2DVvxEOuZTA/rJ0StXHTHIpwCsG3DQRUJQCFV2MaTCC9YNW5AtOKKViqCcRVLBLsOPRiioBUcm3OFEJ0M806d6uK8tCLm88yQiFWAHJvu5IOo6EFc6m2fndrNlasnpvcUervYcu3E7ul8V5tXXFugmIVoG6BRtrd7dss2oVw5Pb0739fNlaonpvcUuto8iLLdPfOTk4xtZwd6Z6Z28OXK7LZti0NDWrk0v3Vqvb/X90Cup3tucgty7M2ezoXeUWIuHcXaYqZHqpTPDA5tjAwGFC60tp3cOkW79kZPz+zEJK/rG52dmyN9VUbWY7Fje/f6LGuL4onp/XS+ALLFWrqZd51cW9vKxKjFy7amndq3tyZpnsB/dv75FAlKmab57VshpPLNrcuTY7oc9gX+5IF91VCUUNRnF1wIYFBON57ZsZ1AWGloWp4Yces+mpm1E816QxOE5OPzzqcwXY2n5nZsdxBTTaXnt05uJhvFSvnji78eRCQLsCfOOVCPRmxBWh4ZqkZiAaS1Um4z3SxXyh9+5WtWIkq5walzzqlFoy4vL06OZ9vaaS9Y2zE23z8p5jc/+epX9HQC2t7J6elaKuFibmV8ZKOjk/aCuR3bFnsHlfzmZxddGAishXnGsQIGW4K0ODq22d3N6rW5fXsXegaUwubRc841XFd547UvOmBRFKrVGr73HbyZ+1wzIDedyX3jr+zGJuGzT2J33cGsroCzJg1AiN3Vvf7tf4C/DT/SvwWw/tk+ZnX34FyWXZiHZz95g5A/ddILh63ePjeVpow6d3rm7Bt4AEXJ771rDg15sfgXHLDcpiZnchQLuJaKQ4EmHFNOJzELbEkyYhoUmYZTx9O5xWom6XOckY4Dng54rtaURgxxVMWIRwOVc0UxCMtmSIaYrmeSQKB9nq+lkzRLfI4zYmE7ogIO+yHZDCkURkYyakW13iPvqXqJiNhVFCuq2WGFYmkSkZ2wDHiulkrYioQwdKKaGQ1BhiaaVI+FAp7zFaGaSoYqhuTRPOe74ZAZUa14GDIUkXk9ncSUb0Wj9XiYxtCKhojC+qJgRbR6LAwxBBzS03GW8s2QZsY1iGkjHg003hN4N6T6UQkisWlxvdaYkTnfCoWsiAwZqp6MIwWZguipspUIeSyHeVRpSGJMLC1kx0OBwLiq4kYkR1S807lyIiWTuq5G9FQMocCSJTsZ8XnGk0QnpliqDBFVbs5g5HmCWE7HMQNNWXbCUiCyniTCEFdTVcjhcmsTQ3uuJOkNSRr49bxnQdYLSb4kulHFViQaU+XWZky7nizX0gnAIYZ2FcGBMsXQHtYohvM55HEa4WgbkChBCq8FLONLrAklmiM2VgCUkEBZnEo42qRZyIo+VBBPWzJvA4XmKJeRASXSIjClMFFgFUosIwaUinnoSLxHqTQ2GMJHocxI0GBVICCTxQEn+UgENCYCtpEMaCowEolA4TxJtMKqGQtDDkGRraWTjGHVi0EtHKYQdFUp0ISAY5xIyA1JlixRAlNtSLJ0YIdCRlyjEGUkYn5I8EXBU2UnIjmiSAlMpSElErMaiVsxFWJoRMN+RPYc2l6rO2HVlkTAUJXmBob2bEUxYhpkaVNVjFiYxkAAJh3DHLR47NEa4JFDCZBVfBqhwFYYiUcKzUGLDlECbQuUizVPgXVP5jkpQBzhGE+UPMJTImXDOMNTDosDTvN52sYcYeQA8YBDriA4gKfFwKCjmHAsqvG0okrYYHmCJUKJlEyZnOgBEUnQpEM0xQKGDurRsBPVAIuCkGSEVcggKxa2oxpZK+uUAlngKYoZ0eywSrE0CUlORAEcqyditiZRCBqNKYqDjqxYEdWKhimG9lXJjKgYgVoqGchswGIzEQMS9jnOjqhuWLYs6OYMMxnDCOqJeKCwAcvWM0kgYUcQ3LBSS8RVt8pxHq1RLOWyEuAFj0EezwW84rtciClzQUyKIJ3lAloiHG0jmVY4E+JAYD1WIQBDjnZQHPPAZFkfywGLXShSnOAh5Imsi5WAcDQPLTpCi8DisMtLDgOQBVOsgGieKKzNyX7A0TJtgQTLQ5tlA1YNKAR8gQdhoa4qhGWqLY0I+Z4o1hpTCAb1vGcQxg9Jnix6EdmSJcRQ5eZGjAJXkvRMEmHi8rwRD7shGfKMG5EtVaExVW1IEYGBLF1tytAMcVTVDqtOSKZZ5EVVVxa8daMixQOeoRiq3txAMcBRFDOq2RGFxpQdDVmagqnAbEizjGuEYlZUs6JhyFBBSDbikWipIkDGDak0A4xkHAjIlyQzqrlh0ROEQGCteBjTRI9FfE0IWKaeTgAJewLvhENOVHIFgch8PRXFCNSjEV/lAgbVMilGBMRlkkXdaYj7PPZl3sjEEU3qkYin8QHP6vFIoPKuwNM8Ljel5aBeDcfMeIjCwIhodioq1Mq9H7zrRiVLliFDlZobGMpzZFmPh2mONlTZD4k+z/qqBENcVdFoDpVamxjoOqpqpCIUQ1vhkBuRPUkgiujEVFfkEUMX25tFr971wSEaA1/hHUk0ExFPEqDE21HZEUXEoWJTxi+WpN/85gsNWBRFmUbqsp9yc7Of65yvhTb//H+vj08yy0uxu24XPvmYMMzZj768WHzlJ5f9tgIs/o2ABQCAABiDw9zpGebsa3AAAISIx45aXd1We7ubaWDW15jV1bPXGYHvS4cO1ccm/HD4C7FR+K8AltHWEWGEPdffqC2tWqq6+4pr0x99cmZ678Xf+Lv48Zm1gcE9P7s6vL7mIXb4jgdiJ2f1RHz68mubPzl+cseOr33zW8lPj64Mjey/7JrY6VmHl0bvvD/xydFqKj191fWtr7919IILLv7r76Q/OTo/PnXgkkuTR4+bsjZ83yOthz9ea+/dc/V1rW+8eez8C37nuz/MvPXemdGt+666puHjTz1VHXjg0bbX3z46feDcn1819vDjH+8/f9cNN/c/8ZSeSjZ+8NnYXfeUU02db/5m+s47VraPTdz98Mg9Dx2fPrD1nvuHHnmi1tza+sZrI3c/VEg2tLx3eO91N5zatWv8jvvG737gxNTOLU89PXTfg9VYMvPRR1tuu6caT2YOfTh97S9nt+8YffiJybvuWx0Z6Xr+xS0P3G9pWnJxcct1N9XiifQnR/dc9YvV0cHeZ14eu/3ezd4efqN4zo9+ii3XEKQvf+e7JisKNWP/ZVdVo9HwyTPbbr4tAEizq1OXXc9V9Ho09pVv/4PHCkg39l76c0tTtaXs9l/ejFwCaLj/B5eJm4Vyc+vX/vpbAUCU7U1ffg3GEGUrUzfeylYMW5HO/cefKLn8Rkv71/7628AnANDTV1xLBwEu17dffzNXKBvR6AXf/VFofuHjPRdtHnSMEisDfeGIXF+FomgvvKvap32xB6w8jarrWEvbq2+w5Q1GhPWlo6q3AqkQXH6TNU44fB9a+zUqrQp0Ayq+6pdXcIjWV09opVkKRenVt1jnVBBqNxeeYzcWFS4NS2+R4ioXReXcUVyawyiF8u8w1c+I2E+tHSQbC7IaM4sfsmvzoqLak089svOWO+emto3c88Dknfee2L5n4rEnR+57sJzMNB45PHHbPZVEOn305IGfX3V6+87hBx/bcse9K6MjHc+8PHH3fXoolpidnbr+1nokFjt2cu/Pr14cHht84dXJW+9YGxxof/nNyTvusSQ1vTQ3ft2vTC0UmV045/IrV0bHO194ecuv7sx1dTa9+9HUrbd7nCRt5qYvu8ZUNW0te+6ll6939+WqkYX3Zcm2GEc/9mYClQ0UYeafYAihGOAuv8mxlMWWnNnDYbpq8cg59nqUKxic6hx/JuT7NB14C6+LHPRB2Zn9IETKDs+7s69oIGuhNJp/grUdmoHe/FsSMD1oeWcOh7iK54fo1RcwyVlsM1p7EtVckUJk9Q0MKxZP/NlDmpMjfo/6B9/8q5Z33j+1a++Bn17W8MGHeijS/9hTHW++s9bavfuXN/U++9xn51/41R/8tPWl105tm9577XWt775vR0I9jx3seOmNlZ6BnTfcMvrs8x+fd/553/th18uvzUxu333bHS2vvVkPR/t/fbD76Rc2e/umbrtr+OEnjnzt4gu+f0nPy6+tDA4O3/tI98uv1iPR7hdfGXj84Fpf/+Tt94w+8PBn519w7nU3dj/5zOmJbZVFfOaQJmKrlscrr7FcGtRPgKUjshq39FP+8mdqCFaoGjz5lsoh2ywzy6/yatgwT8OZD6NayNBPk4WPQmGou0Vn5u2IQFuWzqy+yDBxaM15cx9omljxl8DpI2HeqQObnH5d00jNs8j867IQC+xF/8whNSSZxpI/dzjMWHVE4JmXJYCo5uqp/T+6nPdsqmJtvel2YbNkhrRzv39JbHVjob//d/7y79hK1VRCey+/mqvXKdObuuFWcTWrJxPnf/+S2NETZ7Zt/91v/B1f1quh+P5LfybkCz7EUzfeGl1aW2vr+uq3/yHx6dG5bTv/07e+yy9vlKOpfVdepWVzkIITt9wZO35qYWDs4r//r01HT5zYtv2rf/1tdX65HEvvuf5G9cxSrrVz9/U3DB987vS+XdOXXN393Muntk9P33RL2+tvmcnk0MEne597YbWnb+ru+8YffOSTL1104IeX9j77wtLo2PiDT3Q9+6IeiXa/+PLAE89ku7pH731w4r6HPr3wov1XXNd38JmVoZGBB5/se+oZIxztePe9oXsf2ejuGX3o8cm77188ML3tljsGnnoqN9TX8+Tzw08etEKRprfeGb/zgfW+vqHHfj11210zB/Z3P/PK1ttut3k5nl3dcuXNHsvzxdL5P7k019nTcOjI5G13FdrbMkeObbvpVh/znF7dc8kVPmYZ07ngBz/ON7WkPjm+7fpb3FRYOL6448ZbgoCiA/+cH1xKaEwc78J/+mEpmY7PnNn+i5vsSESeW9nxixugGwQY7//x5b5PPMx++Xs/KGZaI6fndl3zC4fnubIxfflVjBPMtrR9kSVCQlGU6yZu/KV0+BAA5OyzmQjLFn7vD0oXfQVv5iIPP6i+/OLZ1zmDICA8v/7333UaGn9bF/m3AxYghLCs09kpfPbJ56rBgbbNnzph9g/YHV1+OMzNnqbLpbM/TlmmcPTT+ui4r2nwc9rW/sdJhE3N+tYphgQr/f2bPZ2MYc2PTeQ7W/maudnRvjI+6hNKb0wuDQz6AK/39292tQm6udzVtT48QDvBZkfH2uiQD+hKS+PiyAg27c32jo2hPtr2Vwb6swO9lA83m5vPjE2wtl1PJ+enpmAAcy0tCxOTfF3f6O1bG+rHVaOUTs/s2k0RUE8mlsZHAKHWu3qXhoYZEtRCoVM7dgWCQDh2ducOgxN9hpnZPe1jXI1GcyN9OifpofDJvfsCChKKOrF3D+GwqUZmduz0acqKRBcnxn2WsyXpxL59PsIE41PT04HAWpI2v3XSw9jUIktbxk1RcRR1fsuYHo5DCE9MTwccdrEwP7XFEaW6Ft4c6SvKYU8QF3ZuK0YSgm3Pbp3S0wk/oFcmxorJpE/otdHBzeY27Lqndu0KQpwdsHPbtumZJF23V4cHsy0thELro8PZ5lbGdma276g1puhKfXZqW6UpTQhaGx7cbGsNICr1tq22dfN6fW7b9kpjCnlgbny81NYMCL0+0LfZ0QF8sNHfs9bbx9SNM1unqi0NlOUtjo8VOlopL+BDgd/IeZBl4xRO09AmbBLCJg66BIcB04wDiFjZx61cENBYA36LhGxPTBDYxDO+DxQI20VMApb3YRvnQcxqBLSKyHa4aMBloEM4SoJUn4pcm1YouhlTANAxjrTwVN2SIoRq5Wk/QCqF21kPULRIoXYWBW41El8eH3F40VRDJ/ftdyEEAJ7cf4BIvM3LMzt2uixjq9ryxMg/Jwktbd9S1WIUACcP7PcF1uHlM1smHZ43BHVl63hdlFxZXZgcq0SSNAlO7ZkmHFXD8uK2KSukmaKyNDpU1cKE4xZ2bSuGYti2T+zbZ4dVj2Lmtm6xNNXhxNNbp+xYCLs+1crQEUQciNoYFKZ8l2IbIIwzBEAuQ7kxnvIh24qoGA50wjRCLgZtyDEpik5gCCk2HrgpCZiAbWdwBEKX4pogSHKUS3CSQkmGuIBLBCAjQA8yGWg1qFzdFBogTHGUHYAYTTXxjO/zceJlRMoDbAN0U5JgmYXGxtNbtkIIzURsfttU4MNiMjW/bRtrW5udnWvDA8QlpUxmZucO6BNTU+e3ThIK5VvazkxMMpaVb21dHRkkHqjEk6f27oGeZ0Rj89umaADyTS2rI4PQcjabW9bGh30K1ZOpxa3jJis6mja3fRskQTGeWR0fBoAqNrWsTow4hLbi8VPbt7OUCxBi2jFkIc1CphVDlkIUYdqQL/MoILCbx6xHEIObacjRFA3lJmKKLIVorgMHCgsDQHcwUETEh6idoQQKYsg1U4HEIApwrZSpiLQbcN0MpSDiA7YJ0ApyMcM1UYHCYkKEpsCKKcgNUBeLZAAJxTZRQMMBQOWu5qXuPtoNzmydKjelsO6cGR4udHVAH2709mR7uwlEmz2dqz19fKW6NDZW6mxFprs4Npbv6YA+XO/qWu4fRLaT7+lcGRpmLHtpcGhlcEislFdGx3I9HbhmZNvb5ycmsOcXOlrX+npphyxMTK709QmbhZWBwY2BXkCozc7OxdEx5DjZ7p6l0VHOqOfa2gqDnRYtVBPJU9N7KM+zZHl2+3YKgkJT69zkJLKtcmvr8tgIJFQ9HJ7fud2mWUvVzmyfohBdSjWsjA5Dz680Ni9Ojnk0a0WjZ7aOW6LqiuLp7VM0INVIYnlyLKDoUqYxN9xdYhRXVc/s2maIakCjU7t2BAxTjSWWJkYDGlcTqeXJUV0NI88/uWcPkBgDy2e2b7U1lRB6aWy4FEsShlnctrUQSzKud3J6jx1Wacud27JFT8RcLKyMj5QSyQDjzeGetUy7WNdP7ZnWEzEA0JnJcT2V9Ch2ZWy4Fk8gApbGRzZaO9l6fWbnLiMVIz5c2DKhp5K0G8yPjGw2t+CALE9NZJvaBNM4NTVVA/CLKxFSFOW64YceUF96Efru5/BOQVg55/z8H/8XVK2Enn06/NADhGHOfusQILT5p3+uT237bQLC54tp+P++FcdJHxxKXv3zzwFJAIAgMIdHN77513Zjs/ryC7HbbkaFwtm716HnmX0D63/3924y+W9Ir/9txzTUWv/0j/5lTMPO6fKf/Fk0v2lKMpSxNL9qaGEvKhbEKE0CrZbHG2XF06vJBJ2rOTwPNK6gJUhAwkZBWMwSmjIa4sxakQ2cYnNTVdQAAWE9p8yt2LxgN8b4hQ0I4HpnZ4AQ69ucURfWNwOEC5lMcvZ0gJCdDhfkGAAQW1Z4ZZm36nYixObKFsPn2tqji4u8Xlvt61c31tXsht6ayfEKRREAmdD6Bl+t0gnerfm4oq8NDAauFWDI2H6ymA3KVr69na9WxUKhlozXGERYBCCnFgvq+sZmR5tWycNivZaImZLgUJBzPbmiC/m80ZpyLRBaXcu3t0LKqbESF/havoBKdTrOWh7DZQtGa7LKaqmTJyuZFM0AfnnTjqh+TFGBZ3m+btPhheVCc3PSXHdzbjURc6NKhdN415BLJX694EcEXQyF5xeLjQ0s4ygMVSEosHxxOeeEZVsUxJVNiXOWmrrrvEz7XrSS5efXzVjUVzhxYcNVBCcTk4kbOFaJSJHZ+UoySQtQOrNmhDQzFdPLDOvbWCF6jQU00Jiqg4jvEZ5jKkUeAqKoVlEXedfCsuMgQCBNBdgr0wENVM2qlHlCIJVEqF5Cvh0EvOXJEBAQwSRrAwAy9NomJ0IaOigEdRK4QFRsUvYNiocaxQAdOA7HMHqFIy5RQnbZEoFDuHAQyq1TFdNoTjBlnSuWV3v7FGSHPXMdClouS+X1fHsbYxhybrPeEMMMVBCs+MSpgfDKSq6jXa0Xqc1aPZPgJIqlgB7QpGCIubzZmrQdOrK8VGxrSQWbphiqAYyKOpuvGA0x4ATSWtZqipmKkkLBmgulYhlny3pDkvYcPlvKt7ZYjEiyLtCoEKyWiiItA5Gt2QAiAAKPNWqswtUtnneLEKq05BRsDtCEVkxz3U1yjIMFUquyAjQCnkDO9zyadYleFZBIJN6ulHiW8VjJtkjAGKbDRewKogQAVAxXLcgSSTYtzycM6wZiUPJVWrdl3i7RlED5YTZ96gQVBOudXbGlRa5eq7Y1cAxNgSBLK7GZOQiA0ZzglrK072/09kadouw5uuszGxUH4nxra2RpkXJcszkhUB4LyBIOh9bWWb1ebUqF1tdcn7bSEYWHge/VAoSzVb5S0dsysGLxlWqlJR2mrACxdUJxS1nKduttaZSr8eXyen+fWKkUDVnQPMrz63VWVu2Acn0YiK5bAWGvRJgYJTh6qS6zooupuoehZug1EjYMJqTVa7QaFDwqQglO0WVpGFDE5SwTy7LlBNiuUgm8WZRkGruBQ/MBKFcFRTI538raEUUyCfJ8KhBcp0pH3XzARKBAjFJZ4DQiAp1f3pQYOx/LSEvrtViclmGIoauEchwiLq77Im9FNH4lRwl0PtNscCIkJFpel86sm6riJDVxfsMRhUpzJulUKMfKAym0uGqEQuV4PHP8mCOJZjpaEsIQAFbXo0tLNCJuSOJX89VQpNTQ0OwWHEjVbSIub3oMLjY0RhcXPJYtNjZF52YpP2ASjJP3aMta6x8AjhEgSrDs2Ma668HNtvbQ6iq2rUoqUacAYBnNqsOqK1Qr5eYGLbfh20SPhAxZ8EgguL5UqvLlstGWDiqOWCqVmzM+HRiIE0GgrmxQlouSrKEjvljU2zNFLBKW5gw7ls8Fdc9oinPFKqoZRkvStUhkcSnf1pqqrVgloqdiVljTGUm2q3yhIm5s2g2ROpKi8/P51laaJXklwdtGeHOdyVWsVNgDtLya5TUw19DvMCx2nVhxnVktGKkYoAG/mvcicimWrPNSuJ73XDp++mzzbiAAACAASURBVHSxqYmhXX4xZ0ZD9WSsxspUEMilUnhhwUuqZTESn5srZRq8M3OZH3//X8Y0/J/fKF9wIUH4P5augOdpzz4defA+WtfP3qUNPa+678DGX/0tAED+zRuJX14LLetsrVeEwCAoffXi/B/9CUHotyiO/TsmWP/PrezmZsKy4odHQBCcPWPhXJau61ZXj9nXD2iaP37scyxhUhTOZXF2w+wfCGT5P3KO9a9tEbZ1RCTxS1deEV1ZtsPKRT+6pPHYsfntWyuhOASg6cSnF15yacPcSUeRdt1wc+bkic2ezlIkSSg6srn2e3/9ty0ffbQ2NHDBpZe1fPbJ8vBITY1AQhLLZy7+7j+2Hzl88px9v/9Xf9P57tvHD5xjCRLy3eTC/DnXXtf+4ZGNzq6LL/lR3+uvz+7dWVFjBMD44vxFV13Z+e5bViy87c67u95599Su3RddfeWOu+88dsGFe2+9edtddxY6Wg06EGiDzZZ2PPzo3ltuXts6MnXnvTvuuuvkvv38ykmclNSPT0y++vL2W39Vamvref31866/dmFwmNQ3BBmCzfquRx/Zc/ONxfa2jg8/2HvNdVU1VMtoSmnJden9t9+x95abV7aM9b/48t7rf1FNpywRsrTN5YsTzzx37tVX5cYGBp5+fs+NN2YH+4TN4n/+zrfowCcs/s/f/paLGLmJ/8PCJ7rjam99dOFlP/M5PmSVzvunS3hDX9g6WRc0HHgjzz93wc9+5iuCkstfeNmlFE2jCP1n1hk2N1/Z9H/vb/+GBgH2vC/9+CdiYC519tdllXa9tuOf/s63vxMq5ovNjf/zN/6SrRu4N/F7xWO0XoQnli/6ySV8vW5H1P/0t9+K5jaO7dhrP14g624ElfPvAHrRCNNrIEKJc6dpQak8bvpnHC1jlp+3g1U/BuYDzhP1Mluyiq9SzPGS2APMJ3TrtK+2O8L6DM1Swsn18mcSmalzcVJ/yaQ/K6uZGldc9WVMLevuocCa8RJsofqB5510eLEm8rqQXYEUdp7WaydgLFnT37XrJ0kkYmx78v79N9y4MDU5dd/9e2677eT2nTvqn32tsr5aKXS89Pae66+vNDS2HDn85ct/tjA0NGXPXGwun/bp3see23/9ddWGTOPJY+f8/Kp6PNoZJ7+z8f4CUgbvfmT/Ddevj490vf7mOddcbaTiHZL+pcqZqmUmX37/ossv3Rgc6Hvl1QPXXZfv7Wrxlv/YKWyun4l8fPrCSy51wlp0ceErl/w4191drcvFl3zVKSpedfU5rBWzfEwXzSJjG2TGrLxGRFDnKrXca7RsVkV7icSxNjtHmVz+YIBMi4dG6QWPtU21fJI0qcLJGWjx+acDKVdgU7D8sEHrbkTOUrSNsll63d98g5FLJRyClcdMYa0kNhlCYZViaDBn1F7zhUpFDIzcaxRXqJFO6X/95l/0vfrK6d3TX7ns0u43X691ZfY5a0PVhWWXveCSn408/+zx88/5+vf+sf+FF2bP2feV5Te2WKVSNjtxx4N9r766NjR07tVXbnn64LG907+//pttVv4zAx+4+bahZ5+uNTVuefihoacObva1XxQs78h+/CFKfemyKyYee3RlcmzrvQ+OPfFYrTmxg87vKM0uU/yuK36x7YEHjl9w3vlXXjX58IMzu6fBnL3xGgyxFbLh6i+YUtSV9Dkg0WJ20z8W5N6m47jAFMzcK1D2CixfEp0yV606R7z8uygWrgZzVv5NKgpznJdFMuRW1p1lQX/BkCM2minn36QyeJ0pLQctYfnIZ0Fdzj/rx4I8pbuF53xBcRV9jgqz/NJKMA83XqMjXhH5buEpj8d2a/Xkl//xh7JTo2zv3Mt/Llardkr439x5ee3kqi394Te+qRQ2bU256Cc/VSrF1Z6+uqzSntc4c+zr3/pOYnF+eWL0D//iL5XVNWO86w82PojUi+ZS/rxLL49urGWbW/+Xv/2bhmNHT+3fUxNCEJCWTz/50s8uja0sEg7vu+qa5MLi6nDvX+TeaS6eOWEpv//3f5+YnS03NFx05RXxublsT+8FV14x/tSvF/dtP/eSK0aefvr4gQPS2gxSceTTk7seemjo4DPrg4N7br5x2yOPHNuxm18/yYVY+czKjgceGn/8sUpL8+jBpyYeeiibTHsRLOcWXJ/96tVXbX3wgZWt42MPPbr1wftLqaSjYb6ex7Z/4Pobdtxzz8K+7Ttvun3qvvtWJoZJMcuEcPjYzMQLL+y4/Y7cQO/kI4/uvvW22X27e19+7fyfX25GI+nVMwcuvZKweGl01OQk1rW333/fOddcW+poyRw9ccHPLrPDoVxXh8HJ2HfHnjn4pUt+Um1qTB87dv7lPw/iylJzty2IjGX2vvfmV/7p+5BBjGV9/Xv/EHDM4uCIxYmCVet86+2v/uD7AGMK+l//zn9lPWduaqvFijAIJp5+6sLLLg1CMlfRL/6nf6IpuNDY+EWUCCEEJJDf/E30/ntQuUzQ2UaeUpZZ37o9+41vBiwnfvpx/OYbULkMzvo4dB19+878H/1JIIq/XevRvxew/m/De2cX9Dzh44/OFhghBEHArCwDirI7usyeXsq2+ePHACFnz1jM6goql82BwYDn/8MY61/dImx2xkc9Rdzo7TIyMUtRVkaGjEzUoxHrWYJn6uFotb2h1NpSC0ezvd1mOubwPOeagmeYkrwx2FdubTBltdTeXGpt9hnMeLboGo4org0N1ZqSDsev9/UUO9toELCexRDX5rlcV1uppSkQ+bW+7mprY0AjxrcZ4nkSX2xpLHe01BUl19O92d4OMCy0Nm309wYcrjSmN3u7qrxKbDcQZJpnii1N9fZ0VQ0XOjvWenstmgGWQwSV5ulCU/NGf68jC5XG9GZHuyFIAaEJK0KJ1VPx9eGBQGJLqczGQJ8liC5ANIWByJebG4u9nXo4YqQSq0MDLseZLqQ4jubZfHOz3tlYjcZLjQ2F/i5dC7sCtzo2bMVCtUgsN9Bdj4arFJdlpLIUMqKR1eFBWmPy0dT62IgdUQMIRUunKVJNJAq9HZVkwlCVteFBN6bVKW6RjxiMoKtarr+71pCuRSJ6V2MpmfIRzfiu4Bt1NbQ6PGCkYoYWyg72VlJxHeAc4gt8yFGVlZFhN6oYamhtZLiWTtC0L4dtOsEgjnBJCkVp3zJciuMYBrIUn/S5KIAcJYdsmOR91wMUQ7Ecxj6TpoWwTxBk4wGVYKHjYNsjsopFyEYIHUcibTEpIMmWiTlIoM8rDEcEzcFJxGCXSSCQYJFjeIRiESI8I0Q8PgmIgCXVxkmaYki+uaXU3WrIcqm1ZaO/x2Gx7qNFMUQQU8g0bAwN2JpcySSLPe2WohaQmGXEmqgZydja4ICvcKVEKjfU58hSgRZzWDJ4qdzQUOhr10PheiK2PjRIiXQWh1eFkIXZYipT7Os0IuFqOl3o66iEInUPnJEiPsNXk8nsQK8V0crxxOrQgKPKIq3jJoYTfYojbDNGQuD6FIGI4jksAzHukTDHMhZuxJRAoOv4NM9jEKiskAroEI24QIj6XoRHtukyMmYxw3lMI+JVj2KAkPKhhnzTgT4MwiERm7gR0xrFQ4NtoBnJczwAKAYIAs+5QtQFcYHBFpNBMIwRFaz39Wx2d3osLrc2FjraTMTlsJjl1IBj17u7am0ZSxBz3e2b3V0BjYqA2RTVuiBvdnXmO9s9jtns6aq2ZhwsbHDqGqd5PFtqyhS6OzyBzXZ0ljubLYQ3hHABCbYg5Tvaij2tdS1cbczku9s9jNdZJY9FixNzXZ3lzmZblAptLesD/SztYtFnG2hG8JECxLjvIkBsF7G8J8uKYFJNLM97UARMGlE4CCDNEBgoIh8mUjJwZU7k6qiJo2iPeG7AiIhFWPGFZAAVzEieErX1cIjWa64U5niKFomYIazoBxojpgJXxEQ3aMR6qqpwJtXMCYJL8YBtoJFKG1qo1tmQb2yuRyJrg/1OImzQ7Dyj1lnJUNXcQG+lMV3X1FJ3WzmTBphmXUvwDUPV1kaH6umYKYrrA32VlibXB5tI2JQjlsCvD/RVM6lAZFcG++uNSQIp1rMRDBxFLHS2VpvSdVVbHxqoNKb9gCwxapkRLVnJ9fcU21ockdvs6ii2NEEOZ7s67OZ4WQ4XejvXu7rcABI3gIIIBCbX2Znv7nQ5nO9sL7Q22yxPCMII2aFQpTG92dflC+xma2u+s8PmeBdiTGMiC4X2lkJPR10L1RozG329Dk1bAWZYhgh8trPDbE+VQ4lya2O+t7PCq8D2gChRIrPZ2lrsbrNEcbOttdjdZimKEY+uDg/RCsqlmrLDA44mAUJkqxowuJJO5Xs7q+FIPayujQz5Ch9QtGDpmHjFRCrf11lPxKvxWL2nqRSOAwRZ3+ECqxKNbwz2mfFwNRrPDXTXI2FAU6pZ8jBjh9TlsWEvLOvh6MbwgBkLE0A4x6RhUI9FCr2d5WTKlriV0WHdD/5FF+EXA7Ao8ZOPYr+6BedyZ+9MpyzL7B/M/l/f9GIxdmkxee1VzOrK5zhu21Zf3+af/rmbSoHfNkj8djKlYBCULv7d6oFz/9/OoLPSWW1be+pJ5fVXISHFr/9P1f3nws8Jj/Kbb0Tvvxe6LvlChWMBAAmpy6qrhfVIohqOO5xQiidtVhh+5ZWud9+thiK1UMQR5WI07siKHkvYgjj24gsthw4bklKLJQxVMxStHo3biuawfO/rbzR/+LEpq44WLkdjLsNWIjErFLEYvv2dd7vfessQZEvRzGhCj8QsUarEEqYod7/zzuCrr9dktaZFHFktxZKOotVCEYsTapGYE4oaolxKpH2Wr8ZToVK9760PISMYklKLxAxJqcYSpiQbshqqOyPPvu5GYrVw1JKUSiRmy5oeTdixcCxbGnjudV+Uy4m0z/KVUNSUFEsL6Zl0Ymmt5+2PeRpWUylPDdXVUCme9HihnMrINWPwtfeZgNLVUD0SNwWpHI46olxTNT0a80WpmkhbkqxHovVwpGqi0vsLug5K8RQRxFI8WVVDPi8WYwlkWuPPPJs4M1+KJQxFM2StEE8DWSnF01VGLb76aW6TmLJiJFKmEqopmq1ouhYGjrf1qafCy8t6KOopWjmasESpFktYWqjmceVDC7USKScyHi9UYgk9FHVVrRyOEESxAsAosFUBM4Tmg0CT6NNUUNVsSeI4j8O+xzOMBBnkOZoWrHH+JlsPRVg2oPjAFkSO9yXW9VjWK0nBIuXEI5gBHO8HPEK0h/mAaKJTEIMZikRUnvc55DuaiJgAsT4JSWSJAmuco4agAATseiKLeMLQni1ythIyEilTUqvRmCkpdS1SKePau8dLbLwWDluKWo5ELVnVIzFLVUs5r/z2aZ2WKvGkx/KlZLoeijiKVosmqtl6/oMV3UWleNLVNEPRivEU4cVCLFGpBqV3ZsoGMrWwmUiZolKOxG1B1EORkvPfmHvPIEuu80wz3UnvM6+/dct7b7uqutrDg+SQklbSaHdmVyM7ZmPlRhJFSqQ4okRPQrAEQBAeDTQa6EajG2iAILxptEVX+y7vb926Lm96Oz+ojd0/ikXHChL/5zmR52RExhPf977fy+vvTZcg2ZBki+WrkmxxohlL2jQfcoDFvJAnHIGlYcfnCR/no+tIqFG+zAE8CCngijSD+TCHwooUTMO2K0EcThMeSkARg9O4j9GQpyaCC1AQCQiPcZiHcJhHkBTpATxyMcafA7DHhxJDIi7Kwh5DUsCDaMhlxGiZCPOYJzIEcBEKtSWWRnzAQD4GqkrMESWD5avxhMuwZTFZnStVrpVqlGQzXDWesClWSyQ9Qa6RbGnBqJ1fLjExR5AMUa6xvCdKtUTKFsT8jFb5cEbnY7oa91i+pMYtTtCVmCGo2pXNzdMbLslU1LgniLqkarLislwpXldbqpbP53WEtiW5psZsminFkz4vGKIEEQgNvIjHEQDTpBtwhBfI6CUkJHiYRynYDUTKowkGc1CFhDw6vI5FKBsJOI76LkNEAsYhni/RASl5FxAPlQAL86QbkcDjSApxTFWy6ThyNvApBaIxAgtchogIjCY8iEQCWIqmIR+XApGkYBfmUYjCOMJDKMSkWFOQTE4oxZMhzVYTSYNRqu9dKa2HNsv94q9lSIotKaakRDA6+PLL8euzNSXmCWJVVl2CqqlxR5QqEFU8t1JZ1spSPGB4PZY0JOUXd+5jeM+bbzafOlUVZVOQLEGqxJIBw1WVWI0Wq6cXy3OmK4jVWMJieU2JOaJiinKNlyya1WIJi2Zr8aRNczbLJ/PlzndP2fGkKUiGKGuC5ApSLZbwFSG1sNH12ruOrFRjCY9iKpLiMJyhxK1MMjd9pfXkRZSlqsm0z4u6KFdjCYfhqslkcmGt7dQlCAGGKBtq3CaZsqJ6NGuIsqi7fW+ecDnJ4ERDlGucZMmKFYtbNFeKJyOKKSWSuiC5DFeOJYSNzZGjx9itUjmWcBjOEOVyKgszXCmeoivayMEXhJVVLZaw4kmb5aqS4jFcVVKAbkw+f5ApbNXUuCdImhKzON5Q41oswZbKY4deZCpaJZ4KCaoSTxmi7AhSRVapcnXo6FF5da2UTLsMq4lKRU2ENKvFU790gYMQBCEIOXs99vCDYH390+MR7LpOU/Pm7/2hm8lglUr83rvx+bkbWO55bjpd/PXfcuobPwvb3L9Q6lAUhSS59b/+R7CxTk2f/5RJ1xGKorquPP2EF4sbQ8OF3/4drLjFnD4Z3UgZjH/9eCAIxV//9xAM//IkFQYAS1292vv8C8W63Pzu8e5DRxp5/p2v/PdtP3lUSyVNVRl77AkvI6OG1fjKW5aiXLljX8+BFzyaPd6cm3zgQT2mGpIw8viTTlyGvaDjyCsOzVxG7ug++CIEwS/9wzd2/ORRXRTyDS3DT+8PeDqK4NbjbyAIrNF8z8HDmG0daf/m+GNPOABfa24bfvYAikakY9a/+R7iBIttXcMHD8WuXL42MDry1NPxuVmPIZnVreaPPrRSqeT5Tzrffvu9v/o/2w++Ert8daFnaPiVY+lzZ836uvTl85mPzlgkE5ufHzh2rJL9Wt8rRzPnp5fHtvW98Xr9hx/pnKhuLDX+7J0IhqSV5e4jr77c2dBz5NXsmbPldCz1wZmWEyccCGV9s+3oK7BIc4VCz4tHPvyL/5J+91Tm5FkjLjsQ0XvgRSpfWBnp337v/Z/86hfNWGzsyf1nw19Nu1DX4ZfCik6kqLbnXuCWVhb27tj+wIMXvvj5Sjo18uhjF3/183FAd714CCuUC91NAy+8pM4tXv3S56fuvf/KbTcXmhqHn3gqv3eEkld7X3iJWV5f3dbf//T+eHf32f/li1N333f9pt1MV+fgsweX90zy86udh46wCytLeyYHnngm3dZ86Mvf0E7aNEcqQWnlGg9jUcKz1tYS1KLJ5Mzl03SAI/WikT9NU4BSYK2wKgA4xCV/+SpPGbV0ylw/hds4KabD8jUGD+H4lcL6khj6BM3DW9c4SjeTfHFxWjGAJBfDwgUycMN0VCzPoDWX4AV3fVkBVTfTbpRORCYs5CSzeomwDDYj6M3vvd/8xrva1/6y68UjdWc/WWzr7H/99foTH9XEeGxlrvGNd12cYre2xp599kj2653HjtefOKXVpVMfnWv+4D2LoHhLaz38KhT6VLXa//yh1+JK04cnG9//QI/J6vmr7W+96fqBHFazh9/GYBh47tATz77x1T9Pf3yq+e33LIUX5tc7X3vNDzGPBsOP78csEyLwbY88bvwZPSP0LV4RYraJyvr8ZVUsGnQfNHMlroQua9eWz9CJVoNYryxf4QXdpbPWwkoqXithvfrCyZzY5LGGtfYJnWzQsUJpbTXOeC7V4i1eFOQVnZ9Elk5yTL0vuE5+Xo5bGvC0lesirwUR5C9eEARGl3ln7bIANVI0Faxe4GJSmTKKK9c5ooigqWDqp497KLLc3jP2xNMoHAY40fD2B1StVhNjnS8d5Qqbh9v/YftPH4ccZ7Gzd/jgIcrWMTjKfPAxWdarSqLz8FFlY+Ng299NPvkUsJ2Vtp6BAy/Q5VKIok0fvMeu5D/mf7fr2GvqwtL+bYOTP32M1LRKOt760uvS+mqIofXnzgpXZh2R6zr0UuzqzGZn6+QTT7H5/Fx7j5HHV64JCdiyA3TzHFXHO+G8v7KUzOYMfSXSFsUEVIZAsDgtJiE7gtGNy7EubjXa8JdX1AamFpSg+RkpCVcR4KwvJZKMYZPY6sdiPWF6m87qrNBElDATWspn1UumKwSr5+iGWsFhkYWzSgaYbtXZWk03qeXADuavSZJnwbFo7Swr+lAdOTv62BPFiV5yabPz0FF+Zn5xx1jfgUOZ5uYPfuc/TN1z/9K2EWzKGH7ymY3Rfnqt0PvC4Vh759ytu/qf2l/NZN764/+284GH5kdGfAgb3v98sacN6G7nkaPJ+ovWb/7m4FP7a3H1zT//k8mfPLrc2Wnj9OD+A3pTVijkO48cTSfStT+IDe5/3lGUVxszUw88uNnaamNE33MHq6lUTVA6j76qrq29+bd/Mv7Y47hWW+zqHz72Ml3YChWh5b232aUNTVQ7jh1PXb+e72qdPPgcXSrrva1tR98QFxdckqw/e0q+MudTeNdbb6YuXX1+vH/isSfYjY1iJtn86s/jc3MBBKXnridOT/spufP466lzF97++z/vfvKAtLxSzSTrPjiduTAd8oy8MFP/3sceS7e9+Xbdx6dLzfV1b3/U9dpxx/UEwml47jVg6J7Ijz/wk3f/6L/xC4udr7zmMTidr3YdOxpYrpWQhp98mq7cptdlt997/wd/+Dtksdz18isL4a2SQ/YePoxqRqUpM/zYk9StNxWbG6fuue/E7/xH1HJ6Dx25GP1KDCI6XzyMVPViT+vIo49x+/asD/RNPfDwO7//B4jjDDz3PG3pspzqfvElvKS9NTH5y0VXMAw21hP33IUvLdwAHvl+IIqF3/5du70D9oPEXT+gL134lPjxCy14wHLlL3zJGBmFoOiz4Id/gRbh//O2DOM2NjFnT6O6/mkV6wiCGgZ1+aLZP+DFE2b/AHP2DFYuffrBDXAQkNevhSRldXbB//qA9c+3CCs7dnoMs9jXv9XR6qH4/PhkubHOoPi1nq6NznaNV2pNueWB/holLPcPbrU3uyi+MLat1JTTGXG9p2eju1PnpVJj/cLAoE1zq729hfYWn6Dnh0fLTTmDk/Nt7Qv9/R7HVRrr5weHHJpd7ehc7O2FcLDcP1hqbTQENd/cMj805DJMpS67OjJkUexyd996R4dHkMVcbm5k1FBVR5Rmp8Yr8bSuKNfHxm2WKzU2lTsayvFsIZu7vm3cFARTlK5OTXk8W07lZiYmbJYt1dVt9HRU1WQ1lb4+PmlKkiNKV6emXJHX4pmF0RFLEAu5po3ezoqa1BKJ5ZHBQl2TR9NXdu12JL4SSy8MDxqyvJWpL3Y2F3ItVVlZHh/eTGRhFL2+fbKWSdfk+Fp/bylXrwvyWl/3emunh2HXdu4K4nyFjV2fmKilE5qgrPb3bjU0GLy8PtC71tLuYeDa9qlaLuMCam5qqppJ1nh5tb8n39xsiGq5q2Wxs8/B8Os7dmnZpIOR85OT1bq0Lqrrfb35lhabFzd6Ohc7uiNAzI5PVHNpl2Jnx7YZmRgEECKGRjnCo2ksixMpJEIxLIMiScLHARmHkByBkABTUDhHeDSFx5AgRwMoIHIASYAAx7AYEjYwOBFiIoo1Ei7FgBgc1NEY7JMZjFQDWxAQBY0aGYAFsIxjDSAkCCRFhDkagwOQgNE0HtI4UDE8hwQ0ASs4nCMCAmw2tm50d2pKvFqXu75tQhdFRxCvbp9yFLGqpq6PT1iCWMlm13s6tXhKS6VXhvq20jmP4y/v2mWLvCbHF7aNGjF1K1230ddVTaQ1WV0ZHdzMNrkkcW3PnlCk8vHcwuiIrijFVHa9r7uUyhqysrRteDPb5OHgyq5dVkzRRXVxfExX1VI8PT88YifEAEawVoIQIxunyGYMkkEIYDyDwgkipHAqDQdpxsFw0ExiMuwigG0BmAjbDI1nEDgOYAIj0qiXZkKcwJsArsAhBvBGDFKJkEDJFIwkQUQAIoV4GQbCCTwH+0kWjzy8mUBULMRRJA3gDIECiEghYT3nozieQ7wYE5JkvrVtfmDQ5TitLjs3PGzRfL6leb5/ACLIlZ7eYnuzLqqFxua54RGH47RkYmXbiMnwq22dCwODIQDrHV2FjhZNipeamq6PjTkMW01n5kZHfIZea+1Y7+l0aHatq6fQ1lyT49Vcbnmov6omtURqdtuYR5EbTa0b/T0mL623tG12d2ixdDkWmxmfwPEQYkmiEYNYDOFRkAURi8EcRuZgX2VDHIB2AmPgkCawegTmAcwhdCqKVNZlcLoBcRXWAyjeTmAM5DEU3gQgHoNpGNSBSCYjGjD1kBPnPRQj2gmUg0MKZxrRkAe+QBB1WCRiKIWR9bCVFBGAEa0A46GIxIl6xJOoGq9UO5oWe/ptjJyZnKrWZ1yUmJ+YqNRndF5e7+3Jt7danLTZ2b7Y1Rvg5PzoaLkh65Hs3LZ/ema1q3ulsytgmc2ujqWungAQc8PDa+3tCIYtjIyWm+t1QV1r71ju7fUoqtDettHbZQFqdmx8rbUNRpHFgaFiS2NNjufbOxb6+n2KXO/oXO7uCTFsvbtXa8qUYnWb9Y2zY2MOy1aSqbltYz5NbbR2zQ8N+zi+3t5e6GytSvFyrm55eLAqx2uJ1Oy20YBhNhtbVwd6bJpba+/c7GqrSrFqOrM0OlRO1BmyMrt90meZjfqmtb4ekxfyza3l9oZCuqmqqovjoyU1rYvizNR2j6HzDS1rA30mL+RzjRv9XVupnIuDq3v2RjJTkDNzY6N6XC3H0+v93aVMnSHJy2PD+boGD4Aru3abyZjJivMT41o8VlaTzOvilgAAIABJREFUa/09hWzOkmNb/e3zbX0+hl7Zs89MKAYtLI5vq6aT1Vhqvb+nkGtwBHllqH+1sQ0C+LUdU3oqbvHy/OhoLZ2qKfGF/v5CfUPAsssjg2sNLREGrkxOurbzS+QihGFE17Pf+Bt8YR769FHTYQhh2Obv/2d9YntEkqkffIc98eENOO2iCEIQbe++0m/8FvQvKmz/DCpY//cb241Nm7//n1Pf+QcoCD4lJEUoCjY2Ut/99urX/4cvK2tf/krdX/0lWireAGNZlvr0EyHHarv3/ZKkQcNR5LCcocR0Ne7QjCnK1UzGA6Aai4Us41K0nkwhvmaIEqWomqJYHGcoSjke9wGoJZMIHDk0ralxKnQMSTIkyRIEm2V1UawmUiGGlZNJ1LYcijbi8dDUDFE0BNFlGZdhDV5wFCXAQFmWSdexKVpLJOAqZoqipSg1ivMBrsXiwHVcgtAU1ZRlS1Y8G9ZlxRQEWxSqYeARZE2NIZbrUpQWj0vLyybPW2oM0RyT42mOqyZTPklq8ThtmC5FVWXF4HldlFi7VlMUi+NI0yinki5J1hJxurDlMEwAUaYo6rEYFVi6LFuiiEVRNZ12KcrHBTIet1jOBLwpK5qsohRmqOovTq3F4g7HaaJkykpNjTF+hCuqHo9DFK6lUibLWqJUS6YchtFl1ZDkWjwB8MCQ1Voy5RF4NZWyRMkWBD0WRzhKlxVLVqqKgrKYoapVJeYRRDWecDjO5PiaopoCb0iyIQhaLBYylKHGa8kkjECogER+FLEESkEoEUEkChEhwoMAgxEuIj0/AjjCRrAHhwwKkxCEQBAOEywUEmiAQTAfASdwAYKSIYYgAQuiSoQgUUAgBBViGBQAgAooqYcOjqAsFJhwyOCA8SAocnEEkEFEIQGGwAJE6CGEwxENwVEU0MDihUgNXBw3UmlKq3k0rcfjhiAYkkT7BqnGLEEEpllOJDyCqMVidKHo0EwIUYYg1GSFCixdjRmCgIWBnky6NBPKKKOqNsu6JDAluaYoVlDxYr4pSRCGVpNJhyTdBKWrMZeiLYo2ZUVX44he1BIJU1ZwQ9NSKYdlQxoQtBfSaIQBig5QGg5xlORchEIjEiWZAKUQj8EIKoI4LMIxnIFgDg1xjGBDhIQhGkNYKCIRiCMA7iEsBhEoTEaQgIcAwrgAECFEAYKLEAb1GADjQUQjEIApJgxZNAQwygUwHkU4DKgQMKhNApgIEBaKELScTBJa1aUoPZ6AAWKIkiEIMAI7LGfwXMBwAQAVSWIA7pCklkgQoWcKIi3LDozbDGPJkhlGAYbVMhkYQVya0WMx1PMNUTIlKeQQh2EMnosEKcSwSiqNuq7NsIii4IZpSJIliBFgbYa1ON4FVEDgFUnCNNWlaYbSERqCKBgLQ5wNQwKDBBSuhRCBoAAhcS/iyUB3EQaGWYC4IcmGEYFCJIqzUERiEYyRlBvQKIxiJBPAFAIFEMX7EAlgHCLYCKKwCENJyo9YAHkoSkM+hYHQI9gwIlCEhGEGikgMIlEAbJgFUITADATRqIsxeiIJBFSXFVNWqqoCMbShKNVYzMcJLZn0BNbiOE2NeTyvS5Iuilos5jGMocaqyWQAQDmZtGna4TgjFnMFyhBEUxBq8bhL0boo1rJ1PgYqiuIxtMlyejwO08AUJVtWqrGYS5KGotZkOcDxWjodhaHD84ai2jTzi89hw6gPQCWVZhHUpmktFge2YwqCpagB7dsMbUmiy7IBBqrJJIAgh6KqskzUaoYomYKAYJRD07YkmhQTAKCl0sA0HIaJZImTJUNRjMIGqqIOx5qKGkDAx3E3mcQrFZvlfBXhJdkQBFuSghBzGEYXpShAPIJwFMqSFF1VrbIeqjFDUUjX1pJJm6JCTtRjcZeiDYE3RbkWi7N2tRaLG2oM9W0tlXJYzgGErig2wxiybEmKpih0ZNViMV2SIQzWYnGHYQyS0RXZFHidUwxRrKkxAIJaPGGoakiAcjxuU5QriIYsmwL/izvXY3GoNvdLVL3yg+zXv4ovzN9ADl4UwTC09Vv/obZzd8gw8QfvZz98/0YhyRwY2vrf/1MEAPSZabj/JStYv8AdL5nyRZE99TEEw59esY6VimBtTZ/cHnC82T/Av/0W7Ps3MFvLdekL024u56UzvxQuwubWTBDsePzRxOwMgLzRJ55qOPFhYXToc1/56+TMtVous+eHP4qvLdK61v3ikezlSzAFtv308baTJ5dGB77w5a8mrl3V6tK77r4vtrJIOVb/Cy9mp8+HPD32yKMd774zs2vqV778lcSVq6WWln0/+kFi9hruez0vH208c0pXlR0PP9T1xs/md0z8u298MzF9vtzauvf++zKXpmnXbD/6SvN7HyyMjtx6793dx44ujU/suffulvfeRdGo/vS5/pdfCni++f33Jx9/tNTTMvLkM11Hj6329+945qn2N34G02Tru292H37ZZ9iGM6d3PvLw6nD/+GNP9Bx5aaOza/yF5ztfO45ReP0nZ/qffT6iiey5czsefGhtqH/bM/t7Xj5abarrPXyk57XjwHEyi7NDTz4dUWRm+vyOHz+odTT2vPBS95GX7UxCmV+aevDHQrEAYfDN3/xWRKBSYXPHjx/0Bab+7CeDB59nKuVEZb3/kafl/HrAUrd/4+8QHBUKm9vvfwAi0MylS4MHD0pbBcI1t//4YXVxzkrE7vzrr2NQyJTLkw89xISWNDM/cPAFeX0dAGjH3fcllhaqmeTnv/o1LHDpmjb5k59Sni2trg4/+6yysgLTYMcP/zE1e316+02VYzZc8SSjWLgA3BWPQ/T8ORK9UlNyxuqruL6JJvjK+vsg2vJEVyteBv5KQFBB4SQMT1djDUbhOFzdxHnVL7/tRuu2YhVL1wlnCWb4cOtjKLxs1SfWl18HpTzJyKFxwnNX/Zi1Wb0UlucwRgyLp2DoihfLVUqv+KU1XGF17RPEWoqSoDzy+gsTjz+Z7+seefLJ3peOrHd1jh880Hn8OEyApjMn+w48H1BU5vLlfT++f3W4f/TZ5/oOHdaacz1HjnW/egxEQd3s1YEnn4EBkrx6Zed9D2x2tQ0eO9b34iEjl+k69mrvsaO0a2fnr/Q+eQDF0MTc7O5/vKfc1d5z5EjfoUN2TGp/652+Iy/RliGtr0w+9BMECeWVlb0/uqvaVF8qClvnUL6q0UZp8TRPrpVF1pr5GUf6DlurrH5EMH4NrOv5iyRZ0llfWz5BMytbClW68nYMc13G1jdOAM7VwIaWv0ihBZ1BrLWPKGqlzKjuyis45Aa0Y6ydwFmjilftzfM4VnIwKsq/BRFLFSFubbyKuT5GBG7pw5AulWjdzF8gggJE5qL/7S//e+bs2Xxv700//H7m0jSAws5XX2v96ANTlseferLv2NH5HZOf//tvZ0+eLPT17b3n7twnZ6nIbTv+s5a33tZzddsef3zw1Vdndm2//Wt/W3/q5FZz887HH204cwpHoY7Xjre/9jOrLj28/7nhl49cvnnP577xzYYTH9XqkkPPHmz+8AMcDtvefafr2KtGOjF04MDAiy/OT03cetfdTe+/l2/vcOejtfMkF9S8YrT+Ia4qtei8nr9ESqxuTLul67ikb1Jla+kMzQc1vxSsfEBl6Q3vkrV6mVXJcnDN27wMVLOElPSVU7RkFSM9XHyfjnFV/7KxcYGKIZvBrJu/THK1KrCc9Q/xuLEBG/7ch6LE1Pw5d/M8nsaK7qy1eYUiqhruuhvvAwJyW8rTu39wF2dr4vJq/8EXYkvLKIlsv/eBzLWrhbbGL3z5q1StgkLR9h8/xBoav7k59Nxz8fkFiCWn7rq7/uL0Wn/Pr3z5q1StGgF81333iMVNplYbOnAgNTtTU+Rbf/D93NnT64O9X/ybb9BbBYiidz74Y3VtmasUe54/lL0wXW5tvfWb/6PpwvTyUO/n//KvuI2NiCR2PPZobHHBjsXHH3t04OiR/La+3d/5UeMHH+Q7u3Y/+kju5EmAQp3HX2l//Q09kx16/rmxw4dndm2/9R++3fTeu7Wm+pEDLzR/8D6OY23vvNX18lEzGe978fDo88/P7Nlx63e/3/z++7VcevDZ59veeReEfsuZU72HXjLSyf4XXhx+/uDmWN/4PQ+2vPuOnUn2H3m54403cCjMnT45tP+AUZfqO3pseP/+tbGh/heP9L10mLTMzOpc7+P7Cd8VC5t7v/9DvTHX/M47w/ufdWW+5YOP+g8fYnWNKxe2P/gwYRuMUbvp298zUvGG06eHnnkGZ9DE6enBF17gKmXaNnbccz/tWsA2bvn2dy1Vrjv/yfD+/SgFEhcvjzy7nyuXidDd9cO7GENH4Oj2f/iWkUzWXZgefepJ0rfk5fXRp58SSsXZ5pZfEhchHIbZv/0qefXKDaUMw2FQufMLpV/7jUAQpCOH5IMHEMe5oWKQW5db/4uv+ILwmTrk/qUBC4IgFPWydbDrUpcu3kAAEIIQS4uYVjWGRwJRslvb+LffgqDoRgaQ2vTFC3ZHp6+ovwQtwlxl127WcxcHB9cG+7nNwsz2HRudbZjtbbW1LYyNID5kJJRr27ejTrja17c20MeXK9eHhjb6elDLKzU3LYxviyDMTKhXdu4iTGets3NleIAuV5cGh9Z7OhEnqORyV3dMYZ5nqsqVvXtJyyl0tM+MT4pbheXunpWRwcgJ7Gz2wvbtRBBYonBl107ccvOt7XMjY3gQuKJ0Ye/ekCTxMDh/5x0mK6AIMr1nH4bCpiSvjvbrgI0k+fyevT7NkK5z6vNfiHDgA/LizTdDALiSND8+5uBUQBDTt9xmMwztumduvxOmcBcGV/btiXDcEuSF8VFdkCAArt+0uxTP0HrtzJd+xSfxKEKu793t07TFCeujfUU5gaLY9b0785kGeW314h131tJxJIAWR0Y26+tRP1wdGVpt7xQqlXO33OKkZKxsXb355nIui/rR0tDARksr6niro0NLPf1yufzJLbdWG+voQunq7j2lxno0iFb7+zc6u4DlbPa2zvSNCJXqxZv2VZrq2WLl8tT2UksT4kUbvd2rff3AdjcGehZ6B6Vi8fKOncW2ZrpYmZsYL7fU07ZFMG7QJQHbpeIR0UQQmoWm4aiRZ2oVRvThNgbxfJ60o04B2A4teVALS1eqeDwKW0XG0hnWCzs41tFp0vV6ZMxxOd4JGmlZr2KCi7YwvhtKrB+2Ubyrk7jn94qEaZJiBLVzvF6lWdNvl1HHYXAH78Ch0Gdwz+/gKd/RpfjcxJhFsRBBXLj1NlsQGNs88+++GOJoAKMXb745IEmXYRdHhixeDFH02t5dJTXB2ubZz33BYykohK/efLNPUzbLL0yMVQUJwPDM3p1bqTper52543aIxT2IuLZvr81yHs3Oj42UE2nM9WZu3rORaxGrlTOf+4Iri4gXXt21y1LkkKCuTmw307JUKkVdLJokQFFHujgkjkt6icwgbpYnTJOsx7x6gatocBeFx1CqZIQtNJHAMNtmUxCUIUnTBGnEb5Soska0k0QKIyomaCagFMnWKlQSguoI3DCZFOw2i0xZIxoQqI5hNotYA4DqOc6sUXEYypGcXiVSsN2qMMUK1wCZWYk2dC2TubRrFxqGLs9evulmNITKdblr23fwlfJaW/vK2HDoRW46dX5qB0BgjyIv7d2NeEE513BlfEKwzHxT49LoUBAijiheuOV2CEV9mrq4dy/u2FuZ3Py2EeB4hfrG1eEBHyMclrl+8z6LoCGKunDLzcB3K6m6+e0TqBeU6+oXJsYsBIdpavq222jIwSwL7WZwPOAgHWmmAgplfIvqIiABZ7QKNKIgZEhaFtFJ47jPhjWiETMFjvUtooNwZUqoVoIBEfAIZjlQN4/TkOxX4CYqkmlG10AP4yqsUKnAvQzJRoTlRG004CA6sIgGDBJxWq8iHaSXFNitEtHPECJMmBZoxiOVQp2w2N5wfXgbW61d3bWr1NbEFcuXJye3OlpRx893dS4PDWKuX+hsmx0aEcvla+Pjha42Zqs8NzGR72xDHT/f3j4/Mkya5lZry8zElFgszo9PLPUNKPn12eHRjb5u2PHLra3Xh0cY0yg0N86PjjDl6vy2ifnunni1PN/Xv9bfA7thpaH+2vadpGVttrZcGxmRjNpmrn5juMeMCC8WP79nH4rAPkGcv/VW4LnVVObKzp2U45TqsovbRvwA9nj+6r7dJk7DBDF9002475XiqbmpScwLynW5pbFhC6dDhrp28z6Dk2AEnr7zTsT3DDk+PzmOwGg1kVwb66tSMkKSl2/aUxVUHIY+ue12GIENUZ6dmgxxwlTU2cnxcjwpaNUzd94ZilTowNduusmQ5RAnF0aHipk6zHHn9uxca2pXSsXTn/uCHVeB6VzZvduIxyIMXxodLucagGktTwzNtffL5fK5O+4wU3HcsC5PTenpVIRgKyODmy3tpG7M7ZpabWpTiluf3HyzVpfGdWt2anslm4EiZH54ON/cTGvVuR2Tyz0DsbXVi7fepvv+L0WLEIaTP/o+84tyzKdfFAT6xPbCb/+uL8nc++/FHnkYrWk3tEPAC/k//jOnsQn+jFtenwFgQVBIEG42C/IbxMryDWApipKXL0UYZvX0eomkn0xyH334T2L2Tynn0nX6/CfWwGAgCP+mgBV6dTlksJNiESchkLgPsbiRjXORke9u15ozkln0RRoWiVBiIxp3khKN2uu9XVZdQnJKvsR5SRGjooAlIJEIZLrS1mjmElxQg2hgZxQKcwOedhI8zOBWXcJsTtOBCVGokxAREgYUYmdVFraLbY3FtnrO1xEKiUQClikYh+2MGjAkgQdOnIU5HKMRiAOhwukIWpFZSGIo4LsJgSF9n2cclQ8EysLgskDBMsdjjqvygcoDzHPjAkEGZjqWH+zhvBrGoBAL3IRIQZarcL7IWShSSKoYhqIc8BUW5bGQxhEOdxIiFHmbqgyJFE2GVkIiqdDnGF9mfIWFAALoyMoqLOy4qggpFCSQG72dgIYQHIZp1M4oAmxYIm9nVRqz3QYlkkkMgwKZhWUyogHEATslc5GxPDYcSrQQaI7MRwqFUnDAk6hI+DyNNbJWTKJD0xNZJ63QqOupQiTTMINt9rTDPMBAhFCIUZ/iYMPnKTujAhziMU3GKyQTynCF5R0CdkR7DlCGhLkcaXOsyeI2A0wFL6NUxJqLFKmziKsgJV5yRUijcZtndILwRM4QFYvzawyic6xJUKEcFDjBYZ0qhmksuoFxlIRVeFClmUAOi6zgYiAUjRmKqomoyxGmRGks7TOYLYIKyYYADeyECCgIZoAXFwORxFkIaxQsmmBQ11U4Py4AzPdjPMYgsACiFhWmkJChYBZzUhKN2I7MhSrjq/xmbwcFPAyHPZWHBTykcJjB3JTMk47VnAx4AmChFRcxDoFoECoMLBEwiyENtC3yNGy5Mh/GOBwEjsrbikijTiLME1IkRpoIVzg5oJ0yieQZyuJwT8I1gTIIwleiLVKKMMtU3Gs444iQTfE+T5sU4fGYJtIGgxpcxmdJiw9qPFqlpUCKNJayWMYmYVNwFinMxBk0FhUoOSQRJwYXONFlPQ0P1mhCIxlMRQoCZeBkpPqblBKiaESSoZ3kERZHSQgSyVAkMRX4WRHCYUDAdlalMTfkSDvOwzyByRiSE0IKIACyUzLC4TAJ2SmZgR2tIVvo7+DtCkojoUCGMk1gvpVScAZGY7jdECdgN6RxL8YjAhYyZCQQoUxRIuLUKxgWIDRuJmWSCCKO8lTWUsSYk+fxGi2EQlRlWZsDFu4WFXiJomGSDJJwAUiwHFV4QsfZiLQKHL7JwBZNhzJeZRgHx3wVKRAiJGIal41Y0hSCKsPYHGmxmCWCioJUA9RPOldChVbgmgA0jnMEqEZyPkdYaFRTvFmagUgyzMB5VEHEoMLhOikEBBZALEAlwhdZiAZmWmUjY3m4309KkltxFSGMsSgFBxwJySTEYHgdbaYVOrJ8jrLTCo06vsi4MQ5hQbW9IYyzOOzBFGplVYD5KIPZWZXGnDDDe2kRwhGUhCOZRjgAkahZnyARf7OzWW9IKcaWJ7FBnIdYgJGQr3IhR+Go56RlHrMdgXfjgs9TBgZXRQaIFA08K6XAPIHioZtW4SioMGQ5pVAMhrAg4olAYQjEtZMyYJFaQ3qzt0PwqxGNewqL8lhE45BIeDLrItFmXCEYBNComZIZ3HNE3lfoSKRqOFEVaEiiGeA6SQXlMZiArZSCMihEogiDOimFhwxDkcIYGwn4el8XQQQACwOFhWUyIlGYx62kRGHu8vAAxiI0ZNmqiAg4RGOBQAIZD2hydbQfpVE21F2ZD+I8CQInJqI8HnLE2nAfhTkwif3TXysyXJENYhzCgXxvO8QTBOIhLBbFuIhEERrR6xJuSaPffQ8C2L8ZYMEwBMOJe+7i3/r5DbERFIZ2d8/mH/5XN51mps/F778HbOZvqPoVkmTx//gdbecuxHU/61N+JoAFR1Egyl46Q8zPYVtbN3B9GMacPePH4k5Lq5uti3Ccnj5/A3UsBEG1CnX5ktU/GAjCv4ap8J/LImxu4Whx8r6fUKtFTVLHv3e/emHm7C13LCbbq7SszC5MfftecXnVIPnOxw5KV+YXegfn6nu2+CRRrt7xp3+jTF+bGRyb+s598szSpba+kbkNeFOrIGDXt/4xc/rix3d+8fN/+texkxdO3f6llWRTjRSY1cLwjx+Pn7s01z648zt35d75+Oytd8xnOsuMSqwWd979YPzcJV1Q2w4cTb9/9uze23ffdX/PM4c+/tyvb7v/kfaXjq/XN65m02ZLKiyYfS/9bOSRp69PTvY88WL3gZfP7L7NM8vF3WPE2etdb33Y9dSLa/XtyRPnt9/78Fz/yKXubVtC0orwscf2dz/zQiHXkrh4vf+hpwqMujzcE0h4CeYmH3lm4PEDM2OTDa++1//Ec4VYdr0+obXloorZ8eq74//40OLIaNPLb/Y/8fxyTx+yZe77u+/5HlLl5Nu+/Lc2Qp7Zc/uanPMQvOn4u6P3/UTHOMYxh7/7EFRzS0Pdv2HP6RRvfzS74/v3a4KC52ujdz/sRcTV0YmleHOVUerOXrjpK39vYbQTEZPfuy+CkSjN3IIBPzBKG/6+r32H2KottXbf+UdfgWvWuZ23LCdbHIxInLw48cMH4IpdTqT3ffVbTKF6Ysfnlo9hJU0kMP/SJ4nSOkOT1a3RlrBScRn12lFxbV0icujM2/xWWaWRjWJ7OqApvwpfOpEtzWBIHzP/CrO8LGOdzGqyUaPFoALNnVE3V3g3xc5/JJWv4US95zJYuaPZmDPXphOrGypNOAuXpJU5ORIjuyvmRZ4NU8s/4+eXk0wyWDgnLC7HSCXqe+XlsYeevrR9V9f+lwYee/bcjlv7kNVdGDFXLSfePNf/yHMbmWb5wsyuH9x7cWC8j6rsoaLzAVX/wtv9j+7fUjPywurwPY+u17VgJD/w5olrHb09+w8PP/Tk/OBY5s0Tgz99RpPjyQSyHfI2EZT++YXd3797dmSy/o0Phx58crW9WwXVOwh2rbJBfLI2+f0HKmqKWCvt/tYPF7oHV5zcpRNxOIDhKDj1QYNfdqFcaGdVF4DKZejqqUyIYZ6OfnKmLnAxOMyXdg0g+aJti9OvJ4yI8RD08nuxMERrDfG1ZGOFUvDl6um3c14Z9nLslcN81efppFltzjiOa6wTF87VezpmK9yV12U9j6BtkMUTtWxWuxbNnky4FhzC2PTZOqNKmt3xX/vjv0i9d+rc3jt3f+euxKnpUlPdEA03Ida1SBr/9n3Nr719+nNfuu0b36t7+8Qnt965r3alCUKLW6WWp47XvXVipnds/MHHOo/9/Ny+W2bqe8tsDNLc7fc/nH3n41Iq13bkeNOxtxe7unYIQb+ff4do2vvd+5qOvTEzPN61/0jjq28V08leGeuDjGu42vejR3sPvHTy81/a8cMft7z8s4vb91VWmPOnMwgRlav89bcEKIFDQNsa7EBLRv4Sf+FCHQE8pwbOnchGnu3nMCerhra7dZa6dLGeUoLiMnPhXIbEQwLSdr1/fhEhFmv1198UoBRVnUMuns1KRCXwtLVbJpHpGcNITr+XxCGn5lAX30oECoXg1a2xXni9UFoSPzmTQ4LQRsiLP094FJ3S53f83V1oEHgGNPTA41jRmB0cnU93lBlVml+8/c++jlTtkpiY+u59kBn4GXEfBgNI3yij+/7628Ji/sLkri/8X3+Fr5XO3PSF1VjOxBnx+vLkDx+gV4sLLX23f/lr0qW5i7fc8u/NSyTBLi3oU3f/hMoXHUD1P/yMcH3l/Niu1UxLhVHZjfzNf/VNaqmwXtc6ed/D1EJ+qa134oFH2o+8dvHmmya/dV/jzz84tec2LzLKg93MuWsDh15tPPrWTN/Y4GP7+w6+fGL37T7q6INd4NrqwHNHG46/Xcw2Nf38/bbnjl7etqNldi179sr00Njeb/6o7fDxmbGJ9gPH2o68vhHPrnc3WjLne+i2Hz/V++TBK7fsG7j/6fbDr80OjZaQqDzaTV2c7/rZ+z2PP7/YO9R++LWhR545f/Pt9cffG374iQqrSIX8wF2PmQQ7Pbl3Vam3CHbg6RdG7n9svaVNujA/et8jNT5+ZWRiRW00Sb7j1Td2fPuuteZO+fLcyL0/dVT5bO/ODTVnIEzT+x/t+PbdNmDtENz8N39fY8RPtu1ZV+p9CMm+fWryRw84EW7h9L6vfTcMsdO7b1uT6yuM3PnSa+P3/URnhUiPtn/vHj/AZhqbxX/DFiGCQGEY++lPhNdevVGdt9vQuPkH/9VuayfnZ+MPPUDOXP/080h/ofku/8qvlX7ztxDL+lfAyM8EsH5RxPNS6ZBhqatXEMO4AcZCUfrsWS+btVta3foGxLbI2dkbmBGPolihQCwsWN3dgSB+5r5mJvX9AAAgAElEQVTCfzaLsMEaGaU8b72zs9jcAAx7dXCw0lhn0jwWeKRn+zAwUrHV3t4ogNc7O6sNdQbHI0FABTZseaWmxtX+PtgLa9nU4kC/g4JNRbEVnixra52dmz0dmOGUcrm54dEIRfDAQ6MAWE6xsWmxf4DRa1sdHZudbSbFwVCEhgHiB2ZM2Rjqg/1oo7llqa+ftExLlq9MTaEQHJLE4vZxDadQL3BZGfMDWxQK/V0uRum8cHXP3igMYT8IUIqigElzMxMTEYY5PLfW31sTlQhBkDBEPBdGkGs7d0YAcQE1PzRksiyk6SHFAxTzSHJxfNSkOQSCru/a6ZIgcoKIoDEIsRhuc7BLpwWXpOa3b6uKMVarzo2NGekE5IarA33F+lwAY4yte4DAbXtu+/ZAIgMXmZucMOvSKBotkFIJ5SEE3ejr3srWE7Y9t23cUXiT4jDfI1wjCpH1gb5ifQ72o3J7w1pDC8mBiyGp8ypWNZZHh0v1dYjj57s68m2tHkZQnuURFFmtLo2OaY1ZUDWWBge3WptgJwQ8hGZRH8KBAoNEBNsW5MGhGgssiJAgqh4JIgzwEJZBwzAK/cinecjHaDXE6snIhjEJQRpJGApB6CGB5/g4LkFQAw2ZHikFdBoyTAsLQkeIAxiGaITIQgiMwjKAciSwNUy3g1gS8mHAIkQj4kYAZVG8mSA8u0ZzS6NDEU44PH95aidEYBEWXuRyIYx6ODUzud0DmM8wy2PDHk3YCDzLJEyUgaPg6p69EQV8BF8cHa6JYonmtKQK+aEH8KXJUU1QQODN7NgBU4hBkrOEYsOkS9BLQ30WLwYAX942XFViDE9M4zEXIUMEWxwbsTk2AOTctnFTFmAnwOoxXIJ9H5BZCIthSNWIYDRgBQhB6TQUyEToI0QOBWwE2TrsIyhNezAJMggew8IQpRNRpBAhQNHAB5Dv2RibjeAUEdowlYQwOQoMGwlhnxcjHwUpKMywpG6TqQjJknC5FgDSI3k0jLhk5CfIwMPIDBQkaa5aKWUys2PbCN83VHlhfBxhySIgZ7gMpemV5ublwV5EMyuZzPWJCcy3XJpeAFIYoIW6+sXRMcIwiqnUen+3SfMwFKGBjwWBJYjzYyNoGFRS2bWBPpRGtlB6kYpBAWQIwuLENh8GNscuTE5iFFrCqUU6huhONZVZHhsMvdAShGs7d2Jw4ME4XQ8jBAJhMN5CIqiPWA4EcJ/gIAjGWzGEiAIYJ+sRDHiwriMo8HA+BIBowQIaQCGMNSEIi2gIXqhLQwCHEYTOQRGNhggmZjydobHQjQIQibzvIWxdCFjEQ3CqHkWICLFsGMFcXoY8iGpEUB4JfYTJQbCEQh5Uac6tt3Vhtjc3OmolVYPmMd8jfRvyws2O9nxbK+KFWx0tqw2NgAjmCLnMSHhZXxroL7U1Y6a70dq63N0DIQjlWQGMULqx0te/2tHBV0ob/f35rlYs8DYYdZ7LANuuNOa2OtpRy10cGMy3tgYEgYQB45hwhBSamxb7+gnbKjQ3LfX3U5pWyWSKfe1+CCrx+LVduxDHgYIwwhkKgcvJ9NzIKOE41XR6aXgoMIwQRmCC8hHc5bm58W1IGGhKbG2wT8epLVkxYhLmeBbHLU6MuhjpMfT8+JiPIaETRQyHO14tniwMdlgYa7Ps3NQ2GwJwBEUogWOoLqorI4NRBOtqfGV0yGB44Dgzu3ZBNGrD1OLYSC0RdwFJ2zoWBD4gVkYHK0qCcJzrU1Muz9g4DXyXtAwP0KvDA1osBoVQob99PdscAgA8B4cDyA0Xx0b0uBqGyNpAz1a2PkAB4+gWxVFadWFqykzFYNNdHh4o5+o8DEd9D4tCEIYrwwNb6Ryt67PjExr8b5ZFGCEo4rrywQPSkUNwENyQ789Lpgq/94f66Ci5tBj76cPM6ZP/7yN8mh30nbs3/+C/ILb9r9Ti+v+ZRfj/cRyCEF9+SX3iUUTXb+geA47L/9Gf1ianwGY+9sjD3Dtv3cCQdwiCHcfq6dv4kz9zM9nPNqzwn88iLP+n35O3tmyWhQSCWdowOSFU6MTZSwHACr0dYL3CBzVdVeAt06NpiAOJsxdNSa621pHLWxAC2SkF29TI0KnF4+T6VghAoLL02pZDUl5CIFeKEBRt5XJ109N45G20txOFSoQgpbpsbG4+wDA3LcWmr0G+Pz+6TVpbpWzDjQmgqDmA2srWKasrhKatd3RyWwV+q2Bm46HuC2ur6z29VLVC1nRcQh0LATVjvb1DyG9Ic/PrQwNCeRPSvWKujqpUmUrFrE/wC6tcfvPaxHZa04TNfKGhXtSKcM3VUzGmWCTKeqUpi1gOvVW2czE7wKWV5WJjg1wpYGulams9cBy0YuIyaoYkXqy6dWoV8InZGS2RwEBErJccRUCjQL46p+eSVSUhLSxt5eoz5qpVgXRFRnEYv77mJ0RP4Kj1rVCmdVpQZhdKuRwTGsonV6rZjJmOU8sFX6BcliXzJQ6382KGu3hNa2nByYBaLpiyHHE4ubIV8FSIA/nqnBcXNtMN8uKyFosBKiKXtyxRcBShVsWpyEHZyNBJCIN4yqqtIzCJ8YpTq1JQGLCCq5kM7tsYD+tFDEEhRMHCso+gMMvZegUPIhiJ4WBFA66LpPCay0BhCMkEtOXAMJzB1latZOBFSD0DFa0oRGjWibTAQilYxsNVC44gMeHUdCpyI060azYduRAhBuLWBmR4TlqBaxZV0Tba2pnNAj83VxzoE/USXDGL2TrCNLli0cyqSMmkVjfsrnrXR6WNjUJjg1DbgkuWmYoRpo4Va3Y2FnkQVSg6WcWJCGlpqdxQn9xa0LdCs6UO9x1QrDkpOXQjulB0sooVENLFi+W+XsbXsbxmJhUs8MlCZStX5wIqLPiIiAqRVtZYhIU4zCptkhTjYxykVwke1z2KdEowyqMMbFTXMEJCVLSw4qZo4KJkpOskD3QPAG/NRwWU5rxqlUHJkKZdvUICzMNoWC+gDG4FEuWVYZiCAp6AN2yUgmjS0fJ4RCJIDI9KPgvrPkvaFRQhoUAiUzPX4SDIt7RIqyu0Y2qpJLaUR8Kw1N6qLC9BEOymJXyjitrWZlu7ML9Ilwt+Sx1WrrkwKGaz/5O693yy477PPTvncLr75JxnzsxgMAAGgxlkkABFipRkydde69pbN9h1Xeu7vrrOtrReKzCYQSQlSiJFimImQRIiAQLMOYpiAElkDDD5zMxJfWLnuC90a+u+8S2hyqJ3+w/oX9Wvqqs/9Xyf7/NIy8ug7xvJoHD6Iq7rF6amA7V1XNcH8QjfaloeYkd4cn7Vd3xjOIU0+4SqaqmQ17MoVe0nIvzykm1BVj5GtDqA6RjpENzWiV63Xi7R/UHPYknWAS1HN3CGN00dNtqAELUGION1XViCWHPQ0VmccSDbVQZEjGpqCKPouMCpCsg4bQ+VQNLSGrrAMTrq2YqCsZxpApg1gIN4u0sGrRUDSxGMM+gqNE+pmGs2LJGndQPAzborBo0+zvmyC4kQ46k9hcJpl4R0vNZlEa0lxuiV+iASwRE7dPxMPx5TMlFyuemRmClweK0Dk2BPDPOnzqvxGBLAyJWmwXGOSFMrTZOiVCGQOnnS44haphBYWdN4rheNxs6fN2naCbLE7KpN4P1CTlxeRhDP4WlsvaMExF44XH73bZNl25UCXm15CNKNRYPLKw6Ot5PJ4NIS6PtEANAHEKLrtfJQcHGRbLflkTLfqNkeIieT4uoqbFlqKsysyaQsd8eKfs8kB/1eIhqQm44DmeEAVW+ClqdmI4iskr2emom4mk/Lcj8VF9ernuqohSTe6YO6jQfhgYGT3a6eiXh9m19ZkUeGmF7b1V0zJhHtHqzoRiZsOYi4uCjnc/HeiqqgakSilR4/t9wZyhkYRddaVlzQYCo4P9/K5XitzZ9b7OQzFscQtbYd5m0QpRptmnWaWChx4kR9eBigYXy1rUUkCAawWscO85DpSLNzSiHZEmPhhYV2PE7ANr7W0UIi4tvSubl6oahxnLRSdUNMjxLDc3PtWMyb+7fpIvRh+NeShPjEQdAwLmlt0OX5xp/+196BK7DVavDB+/mXX/Bh5BKowLKV7TvW/+bvfRT93CIzf1sK1v/rRjKGK6DtkGdPX4KYBIKQYZDnz9mJlF4ZsdIZbG0VW1+9BD8WgqD1GrZaNUplVwr+Fo1s/3IXoUgzX7r5RmllxQzyX/qnb6dOnlzZPvW1v/tm5OLFxljlmu98N3PhjMUwO++4K3FhVklErrj5+8mLF+dmpv7gz/977vjH6+OjX7zhxvTpE7ok7b3rp6mTJ/qZxDXfu6747ruzl+/7+l/8ZeFX7y9NTV9x2/dTZ08rkfCue35W/OBXtaHK73z3nypvvj57+b6vXnt95oMPlrZOffGWm4Z++Y4RlqZ/fn/57XfO79x1za0373zwgdMHrth77z0777+/W8iU3v9w+4MPyLn8+Asv7L/rzrWpjTMPPLzj5z8/d9nlM48f3PnIg51ycfiNV3fd87NOJlt5442rfviDhZmpLYeemn744fO7dm878vTeu37STadKxz/Yd8dPBolY4f1f7v/xj5e2T2174tC+u+6sTm4afuW1/T+4vR+LpefO773jx4NYNHf84ytvuqm5cXjsuZf3/fCO+sQo05D/t7/5a8T3PAz5/b/6G4/AKF274pZb1Vg4uLRy1bXXOiQhGu0r//E6QlftAPsHf/u3IIkRyuBL//Rtm6eFWuOq666FERjAoau//T3C0LVY+A++8d9R30Fs6+rvXUvCNtpTv3jLzbjnugz5e3/514GO3E0lvv6Nv8ANzUexA7f/AEJBTDevuf5ast+zgoHf+8u/kVZXzmzfZz/VAtbtANyV3/XhZTXADNpvQuTFNlUClUOqfdEUE4byku4tmTza6x2HsEUFDfqDFw3stEwNg+YvevpFj8oAxluavWxLVL//IeifV4kIqLykESfbUlHvPmto8wCT8t3XOvYFJ4y3lV+Z1lmLivr6Gzp0ss+M+ObhjnLGl6Kq8Z6unfECkrr98GP7f3LX8tYtWx99bN9P77qwa/e2w7/Yd/+9nVy2+OH7l/3wjm4yUfj4o2tuvGFx29bJI4cvu/dnq1s3VV54ef+P7+glEpmzp/bfcpsaDac+++zKm25Z3TS+8aWX999++/qmsdJb7x74/s1aJJRZOLv3jruMkBg/f/aa666rjY9VXn31wK23NseGcx8ev+r2W81wUFxfuebb37VEPrS09JXvfqdZLqkDuvWiLbk9yuqtPwuxvTbBu91DGu6ZpKN1nzMZV6EHSu0VUNC6DKzWnofEdpPl9PWnAMwwSUDrvmCzjoJpav0dlDQ1FtebR322LmMRsP+4CikmA6nd1xzaUEhLa70GcN0ewIP9Qyq91iaSoP6LvquDJGZpL6hku8tBZusVn2z1/SLzH//bn1Vef3Vuz56rb7i+8vab/URs5vEnRt95qz48fNUtN298/tlz+y/7/X/6zujzz17cvefKO24ff+t1Ix7e8ujB0ZdeXh8d/cIPbps6dvTM/su+cOPNI6+/Njczs/+en44/++wgEdv25OMTzxxrjQztvefuqWeePnX1lV++9vrJp35Rndy09YFHJo8cVhKxLc8/N3nkcG2ssvvue6YfeeTcFw5c+YMfTj3y8OzO3d6c3n4dEPC+u2arL+pMzIZP9bvvg1JE9c6p8puAiHRIWWu8AgTQPijbg5f0kNSHzg+ab4BSSIUuKPU3wCgiwx2t86IdQHpA1+kf1biQA14ctF93Jb6HVPXGW1AIkBHNkI/ZYb8F9C35OY8VTGhR6b4LBZmOt2a0XgV4t4vbRueoTSJmpnfuy//4Hd7sg6ZzxT/fyCiKKfHXfPu7QrdbLxe//n/+N15uGAHummuvY5Ue5ANfvP02vtXUY8Hf++u/i16YrW6e+MM//0agXutHY1d+/2a233UJ8qobbwytVhvF4h9948+TZ06tTG3999/6prSyPIjFv3T996LVJY/E9t9ya2R+fnV07Pe/9Q/JuYvzU5O/95d/FZ2d7SUSV99yU+TixfrQ8FW33LT1yOHFvdNXXn/LxJEjF3bv2fezu8dfeamfTm194rGJZ46uj4xe9tO7Zp44eO7K/VfedPPmZ46sTG2ZfvTg5C+e7CcTW44dmXziUGO4tOO++2cOHjxz5YErb71928HHqlu3bD701PTDDyqJ+Njbb2w9+ESjUp55+JEd9/18ee/0zp/eN/PA/etbJjYce27m0UeUZGz49dd23ntvY7QydfDxPXffPb9vz/Brb1z5zzdoUjC1Nnf59bc4NMnJ8pU33SwPlbIffnTlTTd1yrnUidNXXX+dIwik2vvCDTc6HMv0e1/71rf6qUTi5MmrbrrZiQeopfUrbrvVEgTCVL/2998EMRQz9N/9h39QwkFpuXrZHT/SUrHA8upXvvsdAIIQ2P93f/13sO/6CPzlb3+vl0oFFxe/fN21Ns+Qnf5X/+9/RHx/MZ3+/LcIfQiGbIt/8XnxyYOwolzCDpzv+zjR+o9/3LvqarTVCh58NPDCcz4M/+Z0Bem6NrGp/o2/8Gjm8wwk/+0C1q/9WPpwBRn0ybNnLgFXQRAaDIjFBTuR0EfG7GSSWJhHms1LGjViq6vY2pqZLzjB0G+Lsf7loFF78wTMM42hkhaTXJqpDZXUXMLF8Eal3MvELYIc5JPtTMoIiK1SQU2GXZquD5UG6ZiLoK1KuZNP2yTVy2c6mZRFks2hkpKMuhRVKxUH+aQDI62R4Wa5AMJgP5eUsxmHZdrFnJxNgRjcHCr1swkPxVrFfKOUBxBwkEt3skmLYVq5XKNUhEGvn8usjQ6DMKAnY82hgh4Q9LBUHyq7FNFPJpRiQmP4Tjq1PjIM4IgWC9dGKwAB92PxWmXI42klGm4N5y0UU7KptdGKS5N6UFgfGwFovBuJNColh6V7sag8VDApSkkl5JGiTjFmOLg+VvFZaiAFm6Nlm6X7sXh/KK0w/CCZbFWKGi/6JLY2PmaGeJPhGqNDelA0BKFVKQ5EwZGE1dEKHMB6fHB9bMSIiAbHN0eHlZBoBAKdSrEfCruisDo26gi0zXDrI8NGImIyTGOkrISDBscPSik5mnBEYXVk2A7zFsOtjwxr8ZBNUc2RISUeNlmuM1TohUIOTa1tGDPDAZtm14bL/XQSgVxWtOAQgmIeFgYJ0cNRH47ClGB7IEhHfTzo+RjCBkwohMKYT4RAKIhQsIUGAVqyARShwj4YQTDA4gULCqMwDqAiAIVgHDDxCCTwioHTeBBAojAOu7TgoCEIRx1EguEoSsMWFgLooOMAEBPyiZDrkxjJ2lgERBGvl0zJ5ZxNUf1UYm204pO4HpbWx0Z8Ah5Eo7WxUZtnlJDUqpRskhykEq1KQWV5QxJqoxWPowahcHO0bPOMEgm3RkoWTSuJWKtS1GjWjoRWR4dhBm2H4o2RshnglEhUHi0bNKXFo3KloPKCEw5Wx0Y8jlBFqT4yZAcFJRhaHR8zeZbDTCSB4rSN4iAWA0negwmIivkw66M4wIQdXyBowoESCEFbMA5iMYBjDYeiqKiLBCAUB+iQ4wdQGrOROEyyDoICaBwmOdtHQCbmIwKI4AAjWa5AEKiFpTCIBxnQRKIgLTggCpExABRgCrbYoO0FCQKx4CgKBRHMNVuVofpwGYL8fjohF7IWy3ZzaTmfBXFELhb6heSvv6DaUAmEACWblPNpk2XkbKZZzEM42i7mO7mkg6DdfK4+MgwQaC8Zk4t5j0Bb2Wy3lHEospnLdcpZF0Y6xVyrnDcZVolHWqW8SxNyJtMppF2S7BRy7XLORdBuLrO2YYwAHYQBsTiEky7MgHTEAQiI4jwq6joUzpMGnEQo0oVIEEtAOOEiLCSGNIfEUB6mI45NEzxtwUkUJ2yUBPEogNMAxkF01ANwkGI8Kuq6LMmSFpRACcKGaZiKOCTjeRxOR1yQQUjSZqKey+IsbkAplKJshIDQOITQvkVRg3JajiccQVirDJkR0aGZWmVITYQdgmiNlPvJmMWynXK+G404FLk+NmJEBJti1obLSiZmo3hjbKSTTvo42i1k2smky9L1keFePAqhUH10WEnHXJxoDBXbqSRAoN1SvhcLWxxXK5e6mSSCgI3RSj8VdVBcHi61CzmfxFrFQjudhGBALhW0bEQluXY+Ux8ugxjcTyXkUt4nsHY60ywXQRJrZzOdUsaBoF4p3ypnDZYfREOtoZJHE3I60y5mXJqSM+lOOeugWD+XaQ3nDZLWk7HGcNGniU4i0S7lHJps53NqMT4guX4uLQ/nDZbX49H6SMljiH4i0S7nHJbpZNOt4aKJYXY4uDpWQVi0LUbrI0OWyKvBYGu0rHOsHgnJlaISEN2wVB0b9XjSCAi1kSErLKmC2BwpagFeC0qD4YwsRT1JqI5WHIkz+ECtMmSEBZ0PtEbKelDURUGuFAcBweaY6sQGW2QNjl+vlI2wZLJcbXxMF3g7wMrlXD8c8TiqOr6h7/ufdxchBEG2zb/0gnTwEbjfuwSdzPcBEGz98X/pXvNlWFXFpw4Jh5/6H0au3/Bk0zBKQ40/+3M7Hv+c616Q3/oJnucjSOvrfwT3euwbr/oo9puzC7YwLz34gEvSRmWk8af/R/TWW7Dlpd/8Wn0EoT49Hrr37vp//YYdjYK//ZWB/xkrlYDYF0Q5mlDDES0gNDN5DafkWNLCkYEQbKezjD1oJ9L4eq8XDPVDkQEfaMUSBsV001kPBlVeaMeThKm04klmeV0RxX4wNBCljhQySLqZSCOA12f5djJNa305mWJW6wZD98SQEgxrJGXQTD0aw3SjHwy1k2mm127FEsS6rNGcTrPtaALwfJULyLkC0+vJ0QQOdQkhKEfjqKb7rq8ynBlL+A4wCIjNZJqt1xvhKGqooOp2Y3HYtOB4UmM4KJ3FTUeRQrBpCcvLrUQKgTxQVruRKOQDYMrVWc6NJoi+0hckAGGDq2tyKgfUlpFQuBOJuRAMJ22N5a1MAO+pAynYxXiN4VrpDIgCnWRKCYZVhmFCYUUQu5wUnZ1rpXOgJ6NCo5UruBQqxxJ9QVREsR9LKEKwGYrHzp5vJVIwBami1MoVLIqWY8l+QOpGE2yk7om8nMpGzs+30hk/QA4CQiOZNhmunUwrwWA3FOWDoQEfaKaz0TPn5HTGFZm+FGzlCj4KIhwAmZ4pslhH8wjf5UgMVXSGMEmaYgcACDo0ibAuZPhWkEG7CogCNosT2ABkQZNiEHrg24DDCBSnQKqnCxzQMzDQ1RmOJPsuDOkUDfEgpZsqzVP0wFM8Q2SJjkWArk6jON4HEdckOVwyoL7lcQSk+6jtmEJwEJAcw1dY3o/GMcMaBEQ4ZfMry61kGoV8pK12IjEfAOVESmeYVjKFK7oSED2MD69Um6mM31jFBLEdiXMIgqYyOhcwfZRodfpSyKGl0PKynM6RTd8O2Z1Ykux3sURKoVktRXCtzkAK6yIWvXixmcwE1PYgEuvGkpihE9F4nxdNmuIWuxpPYQRDoKob5CzCI2gVJFGXp3Ba8xnMEEmiKuuBgE2SBGp4IcYibJoxABJxOBKjVI+EHIkhVltWAHdZkkBVg6csEqNYBaIQlyMxUgNYxA7S5HLdolGPwSm84wu4SeIY1bdxCOJQnOz7DKIHaBxqOhysYXgzkSIsS2E4OZXl5bqczGDNPgj4HSmsBEMWhOgUW4/GyH5fESQ5nfHXV1uxBCr3HQDthyI9Qex5vk4z7UyObzV7giRH44Lry6k012q6PNjnRVYKAYxgEWQtnY2sVgdi0Et4iOO2Emm6LQMYMxCC/XDYpFiDZurJNOB4AykoWutYx/R4FlZMmjE9knIAEO8pLk2CGEysG3owYioujpsuz8G6wdCGS+EOzeCq4TG4BxLkelMVw4jp4YTli6xnWQRlODgJ4Cje6Ts8ZUIEvdLQxKDnuBhhmxKL2ipDGwCFezBGNHoWS7gYSa0MTJFyPQ/DTZtiNFLsJFKQgMupjHRxuZVKOxI/4AP1REqn2U4qowlcPxjuhaIGxzVT2fC5i3IqbUnCQAo2MlmDpFuJlBEI9MRgLxKzBUZOpKLMuWYi2Q9ISjjSCkc0mmlEoiZFdSPRbjTuMIQcTwYurrQyeSUgdgRpEArpDNfO5nwYbgfD3WBYZ7meFBqEQj0P0CmmkUgFSLknSK1Y0ne8ZjTONBoA4/WD4Z4oqSRtUIycLQBrqwMp7CoeomqtZJrqtGGEGoihfqitk6xB0Y1ECnLcvhS0TZDUdDmeIgcDzcf7wRAh9yyU1mhWzRcQ2+lLYRWgqE63GUviuu64yEAMoT3dcSGNZpU0FZ1fkDM5vAt7QbWdymCGJifSCsO7jMDVWgMxpKBM/Pz5ZjLNOMogFGkn0jDgyrHEgBOsEMmtNfqCJAcy6vnZZiZHgFZPDLZjcRCDO6m0JogqxQb4pa4gdQPhxKkzcjqLol4vHO2kMg6JtZKpAcMaLBcWpH4s3hLjGsU240lgrfZ5csave+3Yt9+UHnsY7nYvwTjl+6DjNP/0z7pXfRFwnMCxZ4SnD4Gu6//G6hdoWVYi2fzPf2Lm859/md5vXcH6NW14BKGPjBILC9hqFfjNqwYBAG02ULllFIpmsWRlc/SJTy/NMg8AaL2G1daNoWGXD/zr61j/8ogwiONTjz3Or9d8FJp49Mnw2fNrkxP7r71RqlbbhfzMT+4RWnXYdUtHXwwuLtkBdtNDB9Nnzy1s3XLgupuEhcV2IT99z318ve7DcOXo86ELc4bAb3r4YOaTzy7s2XXVdTcKcwu1ytiuu+4SlhcBDCu98Er87Dk5npx+4IHMBx/M7dl14KbbgrMXayMbtt93f3h2FkLA4suvJz49Obd125777iu9/Orcrj3b7r8/88v3bYGPfXq68tJLaiia+ujjzU8eao5Xxg4dKb76+sLUzOTTT0FJGYMAACAASURBVOfffMuUgtmPPyg997ISDCVOnJx6/PGlqa0Tjx8qv/zqyoaJjc8/V3z1DSsgJM6erjx9zBAC0dNntzz46PLWzRuPPld+4eV2uZB/+Y2hV151CDJaXRp94rApCpFz57c8+Gh7tFR68fXyCy91Mmmm1pq+7z5c0x0C33PbHSZNM73e1P0PaZIYmZ0bffoZyHFEVa48/BTR61lCYO/Nt9kkQSjq5H0P2jQpLa2OPvU0blqIbU7e9xAty0osetkNt/gYihrmlgcewVGPWmuNPn2E7A4AEpu66+eCLMupxP4bbvFBEDatyQcfhRCIknvjh56i2l2HJqfv/JlQr5/afln7VcdRQM5V1s5RehOicXPlJAMsm2zOWXqd6rdxQdRXf4XbPYgBjdoFymhACAvWP8WsOTOQsxpv4nKbJmNI6z3XkEEOGDTmKaWOIyLa+AS25pxEol19h2rJLBFDOh/7WgsRgX77AixXaTyMNj5BjAWAL9itV9xmi+EDZvM03qthAqVtfOP5iScPVzdv3HD46PDzLy1t3LTpueeLr75mimLqs+PDR1/QBTE4e3H7/Q8ub92y4bkXhp99vlUuFl99u/zyyxZJRpYWxg4dsVlGmpufeuDh1Q2jI2+8NXzsuU4pX3jzvaHnXwRQNLo8V37ymE1T0kp128/uq4+OFN54u3L0uV4ymvnw08rRYxCCMo3a5ocO2hTB1RszP723VS52BtLaSYIwdMJS5j8V0Y5Cce78WzwE+oShrnxMYpCJyGb1HIvqLgaaix9zZHvAU4Mz70UBHyA8s3qcogAL6porZ1lf8XDEWPyQw9s6EfaXX6dtD8Jcc/UTCnNs2HDXztC+4oMsuvYuAjQMOuqsvkGoAAXDQP0jCNUN3LRWzzC2gkIZ7Kvf+0741JnlzZO77rordGHWI4jia2+mPv2sE0tsfvyJ0ttvX9i76/Jb74h9dqq6eXLmvvtjp05BOJp/453kh8dbucKmJ54Yefvd2T279t32w9gnn62Nbth26BexTz9zaar4+hvZt9/v5TNjTx2pvPr6mSv2X377jxMffSwXcqNHnk998KFL4rkPPiy89lYnmx47fHT4hZcv7Nm18+77Uh98UB2bMFag5bMcAZlmD1r5iGIitnPeXD3PsqKlXATX5hjGV7GBtXAyQMOm3QeXPmZDXNe8aC/PBjhONxfB1VmKdVWwby1/xtKe4ujw8ocsJ5r2RaM6ywlYx17xq7Mc7liA6ax+TIt2x9XcC8eDJGfbVXf1HBPC+/qaXz3PorYNOnb1QxpBgfRgdvrOe0nPJORu5emjTLvr0cTUPfeFVldrQ+UD192E6rqD4VvvewhzbbyvbnjqMFtvWRy97af3hheXViY2fvHaG2DNMFlu5t77qH4Htr2xpw6Ltbocje394R3hi3PVLZuuuOEWvK+YAXHbAw9ycoOwzOEjz0pLK7XS0OW33paYm1/atPHADTcTcscIiFMHD7K1ei+Rmnzs4NBrr69Pb5q+877ER8dXNk1ue/TR+KcnXI4rvvF65u332oXShsPPjL386uy+PXtuvyP50fF2uTD6zHOpDz92WDb//i/zr7zZy6Qqx14Yefb52cv27Lrr3vT7H7SLheFnnsu+975DU5lPPym9+Govm6489+LI0WfXpzdteuDxzHu/7OQyw6+8kX/zLZck4ydOjBx5tpvLDL386tgzx1ZmpoZefr1y7FkfgiO1laHHn/ERmO71tv/k7nY+lzj+6YZfHB7EQsnPTlcOPwNDENVpb37gERAEUcPYeced/Uwqeurshl8cBkWaP7cwdvgIbDmoa2+75z4EgkDH2XXHnf1YNHzuwvgvnralgDBf3XjoEGLaEOBO3/kz2Pc9BNl3+4/a8WRwfmHTk4cABKRb3YmDj+OWs5DOfH4jQhAEAIB9563wvXfDvd6l0ZVttf74v3S/eI2PE/zLLwQfvA/W9UugK9d1eb71n/5EmZq+tDCI/x8B1q8ZyydJfWwD/eknSKfzm9fgAACAra0i/b5RHrIyGTNfYt5/F7Ss3zyAFPB9bLWK1mvGUMXluH/l2Nb/RRfhjl0Qhi9v2CAPFQHTWZ6eaRWyvo+sl8vr46MWRiqp6PLEJhMm18bG6iNlWDUWt25rlXIOhDXKpbXxUQOje4nY4tatvgfUhiuN0WHA8Zc2bGyX8waEy6XSwsRmEPAH8fj8tmkXgNbL5eXxjZht1cY3NoaKNoS1U8m5qWkfQQbRcHXzhAOh65XR1ZFRFwD68fiFbdMuQ9skubhrR48VTZaZ3bHTxbFeNC6PFvtcUIlEZqe36yzrw8i5fft8Gu/z0tz2Hb9uoqiNj2hCUGe58zt3GYGAi6Ln9ux2KUJhAkszUzZN9yKx9Y1jAyGocfzS9GQ3FPMR7NyePTZL6yQ3Pz1lsGw/GJHHynIkqdNsddtkI5bCNe3C7t1qNGzizOrG8XY6Y8DE2sRYvVACXW92914nxBogObdjRz8esRB8fXxczudslFjfOLpaLMM+eHbHTjUZ9XR3fufOXipuYvTa2Ggzn3dQvFPJLw2Pw6ZzYdfuXjoOmM78jp29VMzGqNrYSL1UtgG0Plapjowhlj03s72bSfg2sDC5ZZCOAyCACwCYJhwYR6MwFodcDwaTBBLDfAAkghCcRl0EQTgAyeImRGAh2M/QiGURcQSK4zYAYSLo5WgcdFAWRvOEgRBIEALSFGabSAShI55KBNAA5BUoBHJBFsGyqA+CcBjxMjTmuZAEoUnUwzGE8fEc4uIYSMNQngAgoBNNro+PGgFJE6XZme0qz/sgfO6yyxyO0Sj2wvYdJscporQ+PjpgBJ3hl2e2dkMxD4HPXr7fZimd5BZmtpks2xfD6xMbeqGoSZBL01vlSBKAwPN79/oM1qOkxZltWoAfiKG1iQ2DWNLC8JXprfVYGvbAM5fts0TeIJil6SlVklROmJ/cqkVEyPWQEokGAMBHsTziB3EAgrAEDEYwEIbxGOTGaReA4TxGBlzLQfECjou+jRJYDAKihA9BRAx045TnY1gewYKg48JwFofDGAACeBSCYpgLIkTEd5OU68J4DnEjJGqYRBYDI5gDwEgUheIo4ntEGHCSjOvDaAZxwxToee1CYW7zJADDg2h4Yft224dbmdzils2I69ZHRhvDJROj2snkxalpD0G1QGB52xYLwZuF0sKmLZDr1kvlxuiQgdL9aHx2+4yNY6oozs9MAwjcSGXXJza4MFbL5hsbhjWMViLh5ekphQ1oojC3c4cPgM1kbm3TuIvicr6wPj6qsqLG8xd27UIh28YwooBAJAxTIJZGXJYAMZjMQS5HgBCElDGIAj0ERfIoRMEIDtEJzxZZD0OotG9ILAAA2DCJMYAPY2gR8xkEwgEkjYIBFERgIoeYIc73IHQIwynXgRAij8IM5BEYlkY8gYAgiMhBVpAGAQgrIggPuRCMZlBfwCyE7JayS6MTvgfOT890cilAseZ37OxmEjZM1EYrtaEhC8GbQ8WV8Y2oYS1MbesU0oDtL2yZ7ObSBkLWRkeqlVHA9VrD5aXxCdiy5ycn18vDpGmubN4iF7M2iNWLxaWJTSAAyIXc+kjF88ClrduqQ8OYqv26ttVGqWahsDixCQDBerG4vGEctZ16sdQbyvQosZdMzk7PuDhucPzc9hkAhuR0bn5y0gfBRibbGBsyaUGVpMWZqQEr6jw3t33GhyE5nlndMu4jWDObr42PaDSvSMGlma0DPmhw3NzOHT6KtKOJ6pYJGyPa6Wx7tNAW4zrHL22f6ghhlyAu7t7jEVgvllrZMmEQdDuRWts01g3GAMA/v+8yL0AoRGB+ZloVBZ3m1zaN91IZC8OrU1tqySwMgmf27DFDoufB89tnlEhYYwJrExvkRMJF8eamykpuBLPMc5dfroYlG0Dnt88o4aDB8GvjY610xveBlW2T67kSYlmze/ao0ZADoktbJ/vRsElQS5s2y5k04PvrWzdX80OYrp/bvce0PscuQhBk3n07+pM74MHgkiIVIMOQ//f/0L36yx7Lsm++Hv7pnZCmXYJzy/MABJG//kf9/VcAEPT5y1efH2D9mkY9itI2TjDvvQPr+iUxFrEwD1mmMVyx43F9dIx77VUQ8AAAvARKq66grYYxVHFZ9l+Tsf7FmIb0YPtOwnY68bgWFknDqqXSDktVC0PtdBqGfAcmAJpoptOgC3ajUZfEVkvDrVQKhn0Pxo0A30vGABBxWbqdiLcS6XYyabMUbjqdRNKQeB9ALZpeLxQVSeqkEhbHgraniqKcSlOa2ovFLY5azxRbmYxJM7Dn+hTZLGRRx+8Fw51IFAMBD4JqxZIHQSTgrxXyPZzqhqVOMIT5gMmydojXKN5FsLVSWXftbire4wUURxyEWMsXEMf2aLoTifUxohmW+tE4gOOYrq9u2ID6jkswzXS2ByNNSVCliEuQKIzIuaRKC5RlrAxXXMhrSEFFEmEEsSjGjAh9ikN8qFXMqqzAKmpjqOyQmO8j/URMFzk4y/TFYJeRGMNYHq7gqOs6aCOXsVl6pTQyiEVcCAJBRElGO6Eo0+2tVSoejuDDfCsctzES8KFBLKKIIuwCVijQjCXXSkOKJPkkitt+I52xOdoHECUkDUIilmWUsCQzYVZR1srDLkuitttOZWyOAjwAxT0viHkeAnMISPu0XYcwHRA5yAUQEgCCKORDOOa7AQiDFRhVQQLBXADiQJBHEB8AEMAOEaRewyjNoGkIQhHcdYIkqlsoD8KYD7hdmFSscAS3dAiFwBCMOADAILZIkXqVRgZekPddH8UgMIz4IALCIBjFEd0wGW6QiNoo6cLwWqnsowA1IS6TMRQGPBhbKw9Bnu/h+CAWdcIcNCzKhKhyEqXr1UoFgXwfxFrZjCeibjowCEYMCIdBqJNLKRRHm+bK0BCJWdZ4psWHPRh2MWIQD6sEjQFQq5AZUCw3zi/TEQjHAABuZNMehjko3kpnPAZHdccPIRjpgxbkSjBMAbi2CvOgw3GQ62MiZLMYbAJQFHV8n4EaHu7AJOUiGMgAMI/ALgjRkEWAJNEBcc/FcdKHIBEEKARyfZiDfcImfRmkQSvAgYZHiIAZoCjNAAMQxICkXvN53wkECMvCONARSUj3kIBvcxTmODZJrhdLiO8DGFpLp5Eko8fEphAjDX0QjmohwQdRC8XqpRIqIHBJbFBBGIIVXmwmU8RgoIUjWlj0woRbClWZJOq5HoI2h0qEritSpBsJYWFMyUY1JmAjBIQTrVzGQkkIhOqZDBlGtFSwz0ug7WtCcBAL2hAGoehaqUS5OgRhUAgGIQCGAFDEQKfPQi1PpGyKQh3fTRA4YHs+DEgYAOqYX4cZzCRIGIDRMGRTBGL6XgTxLR1DWg4B+wSDQi4kIA4GIwDIUKoCARzddSgUIxHQh2DBRzHIgVFQQABfZWEZYBGd43HTA+IYBjuwB4ECBJAI4MGOxNYSGdIwa7mcRWBkkWmG4xZJ+yCiSsIgGoU8SA9J3WC4kc13Y1GPJlDLbafSVoD1fVgLCO1ElEigeioiC3Gi32+n071IlB70O+mMGWBWs+VOImGRFGZbekjqJuOkYddT6b4YZOJgNxHRqYALoXog0ExlSFVVwiE5laZ6PSUadQVSJQIujq8VirrvdpPxQSBAAIDKi/V0mlBVIxjqSuEeAjdjYTsoWgQL+X6tXMZtSxekTjjWQZF2KKQJYQ/DERRt5lIGRqEQuJ7LmRjcEIN6UIQ9QA+IVojr4RyKII1CtocS7XhkwPEYhmgE3U3EQAA0aaafiCoUy+r60ugYBZgmSLaTcV0SV4ZGTZG3QBgBoXYu1eclpturjo2CCDQ/NqFJIggADkYM4mGd5REX0BPBWji5Xh5WAwEQR2EfktMphyJdGFUiwW4osjZUAQikxwe5waA6OgbgKORDnURcFYW1QrkXCjsEQZhWO5fshWN0u10rD/ntNvfW5+LBgiDm/fdit38fNIxLYCMAgDW1/ftf73ztd92AQH/8UfTWm6HfHBt+rX4BQPd3vtb+d7/n/9a6nP+/BFgAAACAy7LGcIV7923gUqoGAQgiz5wGIFAfqjiRqDEyyr75BghcwloiAIL4wgLcbRtDFZ+i/tWu+3/RRegDu+//eeTiRQQBpn7288L7v1zYs7MdjLk4lj392WXfvz28ukipg7Enns6ePNGolBupnEEzkbWlr/zDt6JnTvey6b23/iA6N9vK5xuJjM7yQ59+sOuOH5dff2Nhz46vffNb8ROfrU5NtWIJH0NSZ07P3H9/7vjHaiy+584fV15+cWHX9mY0ZVN0ZO7CF26/LXn2FG3rw88cK7/97vzU1Bd+cNvYsaNL27ZdfvdPS6+87NKYHA86GYFdrW998qmdDz0oj5e3PfjoyDNH18Y3+rCujWXCH3669blnx44cc1g2++EHe++5ey2b11lf31Jilur7Hn5o5LlnQQTOfnp88yOPebbdnBoBoqRf73zhzp9uOHqkl01uOPb86JEjoGt10pI2lGBa8uSRZ7ffdXdnOLvxyLNjhw/riYiwsLL7Jz/img0AQ6767rW+70lJ/Cu9lukZiedf3/zYQbrTjvZqE/c8KNbW53fMdMQIhICbjx7ZeeddAIEmT5/b/PjjgWaTiUJfsyGhf0Gpalf/07dRz2F73e133i3o7Wqh0o7EHRzf8OYrO374k+jifD8e+fI3/xHVVKYsXN3vEYrMv398y0OPiYsLEIXuueW26Oy5UzsPdJ7VgI4rqnL9BOJU3ShQVSfTcLfOKvrqK/RgDQwx7do7iF1zguainaFxwkdWtbUPCP+sGsxqtefhfg0VkiaG98EAzn+yJM8F1CWAYFz5AwA8o4fYqitAdiWNHJ/vfcbaq3bYaLTP+IM5lINa0AYBBnW8LrdfhdtruET2ep+AypIfgttTLz2944GH6mPDWx55ZOKpp9bHxndjS/uauNI/U3j27Y1PPOmTROLsmX13/GC1kN+F1PdqTg0yRu89OH7sKAoCyfNnNj/wEIgC5az4Ja2+7oAbf/7Ixl88NUjHxl54ZcPhp3DHKEjO7r4J2M3gi+/v/dGPusPlDceObXziCSMcHEKrB9o4aF4Q3j01c9fdKAQE16qX3XJrP5fqtrj6cVjo9aiBPP8hT9Q6HLPmbsggjuq/X699xlFmj64raycIojXggRVtukjVqtS6PvuWgFk2oSvV9zFS09jOOWsyQ6ytQC1k6V0CXxuwor78POmpVlhqWzkRlevIrNY4SaMdC8Lc+usAsjIIhJpOjAaCDPBxp3UcJ1syq6jrJwiv6eFp8A//9q9SH3/UHN+4/8Z/Tp46iYTgHb4/5vY7mjnzwzvHnz26uGvmy9fdkPngV83pyS93T1e6LlA9VX7s2NCbbw1SqemHH9zywnMXNo7/oTk/asJLRm/Pj36W/ehDFPBGXnpx6NkXrHJ0D2TusNdO2tQXv3d97t131HRs08OPld59B2X9SRyYNDuybU/edf+mJ5+Y3739yu/fnnvzzXppyFgCq58QjN23m27tl7hEdDCsqW7MSdXq4FOkfhoJDJpEW186TlPtdTJuQENRemXReN9eP89JRNc5ZzXOoGFVxrC6vTHD1uvaBbT6S1xiFW9WXTtJxvUV3Glo28rMmXPuGrr2Ph5Wa/7Amn+PD2AaQ8rqhhQ/d8E5B6+dwMnBALPM6lsYDtjF9sm9t9zOGT1+eXXi4BPh5SqURH7X8aOts7Ue+Dvf/L+obhuCwV0/+kmg3agVh9qxhEXS42+/uvMHP0p+9mltYsPX/v4fyFYb3pj+ans9NOign53a/NDB+IULg1jsyhuuz3z84cV9e9qhuEMQhQ9/te/HPw6tV7lue8PjT6ZPnOpsyv9Rr1G2ahdqxle+e21gddWjyJ0//1l4fkGTpO3337fl6DNrMxP7rr8l/+67q1u2eIRpZcLJX3449fRTQy+/NojHJ594fOqpQxc2bvIxTZ8cin1yeucjj+XffguCweE3Xx975piCE53towADgtXWV269tfDG64NMYsuhw+WXX3IhX64knQjLtDt77rlv8xNPNKcndvzk3uKrryjZxADz7OFE9JMTE8+/MPHkL5RUbPzIM1sfeWRt25aJZ45tPHSI0NTk2sLGnz+Mu9bC1NZ+IIgA7q6f/2zykcessFB655cTh56kdH1h22Sfk2AYmHzu6L7v36bHwrmPPtry8CM0ai8WRvuhMACAk88+PXPnPaQ6wA3tC9ffCMH+4sbNfTGMwP7mI89sffghttkgAHvvTd9n5ebczh09Ieyh6LbHHpt58AEMcMTF6rYHHxS67Yv5wucwIvRhmH3/vdjN/3wJSZb/w5Zudr94jfz1f+9IQfLkZ/Ebr4c07dJmfL4/2HtZ8z/9iY9h/1Z09W8AWAAIOsGglcmwv+6+vpStQPqzTx1BMPMFOxa3k0n23bcv7cZhmLwwCysDfbji/0/5H7+dEWG6u3sP0+stbZxYGx8V1mpzW6fa+YyBUYhjE47pe5AeDV7YuQsbaKtjG3qZhMLwiGUjoI1qVieVWpzeClueFpKWNk6YOIWapsPRgbV6ddOm1YkNeHfQT6bOzWyHXBd1bRdF8b7ayudnp7aJ9dr66IbVTeM2iAGAD3su4vtWgLu4Zw+um418cW7rFDkYuAx94vL9AAQTrn3mwAHTdPHFZSWYpFzH4APVqQkTpV0UO3nFlUC9Sa2saJE0TOIehJ7auw8CARfDLuza7Tkufva8Es16FEn3ep9efbWPwQAAn9+zx+73sbklI5qGMRz2gdkD+zpCiO+0P/nSlxHPQRZWTVZAYdgiyLWpTb1AGDGM8wcul8PxUHX57P4D/VgYVfTlrZOtZNRuzy8JyfVoUVhbP3H1NXYkgDX7F3fuHMQiFkygrgl6Hmo7q5snlkY3CNWVE1dd3Y+Fsfa5U2K+xYRwRVsbG62Vy7iitTaOrKWLJknTHdmRAoGV1QuX7Wvlspii10Yqq4Uc0FmosqGFZEWqrl68/PJWKR9Yrc9v3y4Xs9hAozjfG2Yh1SJCABiFifl5QPfMdIbu9UnaQoZo2HIp0vbLLFyr+42uGxJx1SEjvlvi0b5C0p6XJ/D5BaSvKvEk7EIEqcNFiuwOqIADlTi7uk6vNQfJPGWpCA74Iwyhm2gAsHIsPXsObXXNfAG1HAox8REcclwMsoARjlQHOhOY3zljERQIwyf2X2EaKqjOfhSe8AkS8qGTlx/wYRhAkcWd2xUCseSF86HhLh/h2vLxr3zFJ1DI9s/t3G0jltZdOy/lVEpANX32sj2daJJv1D/56td81NYN+Vx0yHAgD8Hmt2/rCRJhmLOX72mSPN47+XFoo0VzmGqc27tH53nA9c9v36lHA3itD4/RUBjDWhpUIuEgCn5yGvAgJ5bANQNPI06ahRoqPoz5NEyfP++CBCAKuGXiER+KYbiiY0nMjDHM7DnbQf1IEOuYZAbwkgzT6xEREAi4yIUlCMLNaATvaGQWtlMM2+gQGdjLsNiZi97AMiMRVlOIKGgVBbSlkQnPSIsBuTmIRk/v2Yc5jssxp2d2AVar6Zhno5VAs9ksDy1tm0RUU+P5U3v3oVZPM5tno6Oo7XcSifPT2zm5JafSS7t3OO11ze1/GppAAd9D4NOX7SMsq5dIzY2Mo3a9ppvz8SHAAXyCOHvFfgslQQg6ffkVoNNuWtpcpIh29X48vjgz7SA46Hknvng15luoYePDOAo7jKtCFRawTXJ+yaJ4N8BQnQG4iUVpCFYMeIhCbQU9dc4PiG5AQgwdH8FdGkFkDd7MAr6DLy5bBO9RDGv1kALhsyipamQJGmA4ceaMJcaRAAX3DHSIRFkQsy0sh3qeTixWIYpXQ0G61cdGcJSH4b6F5WBAxDDF6Azn57bOcLXmhT175HwBrp84z8cbUgZXtUa5VN04jqt6a6hUz+VNgsI1zRa5wGp9cXq6PjJEdJXG8NDc6CjavriOk+cSo2KtvrB1cmnDxnB1ZWnr1PrYiOvDoO9BAEiYejufXZ7aysjdxa1b5/OVQPfsRQ+upkZQw+4kk7MzO9het5lJX9w2E2g12tns+pYNro+bLHvqsgPo0grebGmxDOm5/VD07I5djKb0Q6GLO3eh3R56Yc6KJi2MhgD/zBUHMNNQpPDctmmwWoXWmma6CMOwD4Hnr7xCxynEdc8cOIAN+uBqywhGKN3qS8HVbRMqzkOue/YL+w3Fwubn9WiGgECdYud27gB80KTZi7t39qWIUKt98tWvAQwGqO789hklFLRBFHd0HwQxy764e+d6pihWVz75yldQwFHoAGaZqG34PrQ0uakTT5CqVt2xtR5KOjhBd9tugCXl7oXLLx+Ew7Dtrmze1E4kHRhj1U5PioRWls9cfU0/Fqbl3vyO7Z100vMgyHV9DGcH/bmd08vFSmRl+dRllysA8FsfEUIQ+/578Ruvv+Sfqm0rM9tb/+E/O+EovrQQ++frkG73Usdl2sTmxp/9ucuwoO8B/3bP5w5YAABAkB2NuYEA/cnxS2IsH0HYX75nx+NWJmtmclYsxv7q/Ut9A3n2DKRpxnDFx4nfHmDZqbS5bdKNB3vpBERCvURMLhZQzE+fOSX0ZS0a7CQSVpg3Ra4fCncyKYDDisc/xHwbpNF2PNHLJgEG78ViaiJkClz21KeUqYI00g8H6yMVHLLlZKqTT7k0mZg7F11fVOMRXQp082mXJfSw2CrmUcyLzF+Mrq300nFDCmixkB2gNSHQLOZthrRErlPM2gHWEDklETHDAgYigu3YYckSGDmXxnGvHY62ygUzwJAEFur29WTMp9BWOq1FQw5Py4U8yGMYigYs14pIZoBVEpFBIgpRiJzNGvEg5zkkQhA0NIhHe+mEGyBVUVJiQSWTYB2TtgFf4i2eaeXzKAN2I5FOLmuJrMGxeiTYzaVg1G9lslo8aCNE12RsnFBFUQtLOTmc3QAAIABJREFU3XSShvX1TKFdzjOWkjp3hrEUNR5pp9NWmDcCvBENdzNJFHSbfaLDSCgByJmslgxbAttJJrwgZdLsyHtvGlIAxoFOOtPOpxHUa6UzeiJkE3RPIwyc1AVBCUvtXBqDnXY8Jg+XUNjFeZ+mDYDDCMJERRBjYcgkvGCYAjSAB0nJQ0kfZgGGMwGBhDwKDgg+TzO4isRRytdhHqKCDsjiCMrgIA5LBIGbeAgCGJQlNTgESX5HZxIgzkAiSdI2zRowhxCYAQdhRMAwH/cYkYZNSEBozkBpAOBgmrcgFnE4ulHIAzSqhoKdfMYSORMkB11QiccgCpHTKTUSdDlKLmQ9nlRAuqMTLkuqkqhEQ/1UDCTgdiarJ4K2Bax7gk9iaijYzaVdiVEEUY0GB8kYbugrtqiynMfTrULB40k1HOxm065Ia0RAbwPdaBzGgWY+b0QljyFaxbwSCYEUEiE6XoikYANnXSyOkpAPoEGMpjEWwAMuydoegwXJvhulacIDXBaQAgLQdYMYGfBgGiQ4j2ItQOQwHfETMRoxKNYEEiQJ6IAIUbwN0SQAchiJ+wLBkioQxhEMYPEBHMdI33IxCaZZhEcoyiBZx2MwllShGAEhvhaWOvmMzdGGyKqJsBkUVA3uIAGHpc1goFXMY7DbjUbbpZzNMooGKjpsS7wucM1C3g4wZliUC1kU8zpAoGUSVkgwRG6Qiplh0eGoVi4DCNRAQ+poCMHBbirVT0adAKkEpUE8rIdFV3FacNAnMD0sNYt5mAT70Wgvm1RCEgcquOAhAoyjNhyCCNLySQ52KYxHPQbjyb4fYShQxQUPD6MYjPhkiMUdgEcpwcVp1+OpCNWxwzSD+C4QQEWKQXU8CBCUA3MwyVmoADliiDNQOxZk/h/y3itI0us80zzn/N6k9z7LZXnvuqstTAMEQEqUdsQJTcyOYhWa0EriihqNRFIiJYoOBEh4R4DwAEF41/DeO8J1N9Cmuru6fGVWevN7c85eUDuxN9qZHlFLxu53nzd58z/xft95H6hyAcL6iUAZMEwLsgNkH2VxlJ/DHtbPNklCFqHGB1w6wmCBbaYTblhSgyElGq515WjKbXX4WiDFsm41nVXTUccntpJJPRkyfZ6B998iAktxoJGIVwb7GeRUc7lOJmnxgqogjRbNoF+LBms9eSDQWiJS6+3iiZE4tRho1VqZhB4J6vGQ4xE6kXC5twfKfKcJakKMEWA9nW6n41bQa0T87WzK9MpmyFfrysmUUU5m6n3dtk+SKejXTSMRdjx8JZ+3g14z6K309TAiYGnObztuxNuORpRUXI+FsFeodHfjiOy1XZ4XOQm1kol2LuX6RCUYUpJRPRX16ypH88jDqZFQtadb4JxKKtPqyrgBkeXEqGEaiajj4Sp9fcQvGAFftdAHJFr1+7V4qJVOCrS52V1od2f8rWrm5AkWOZ1kopHPOkFZ8/vNVLSRTcm2kj9yGAqU7Zer3d12xKuFg810AoQEixXH3npZScUpHta6ujrpGOHpaj5nJMKca/d+8gGQWTUYUGLhRleGod1aNtPsyctqM3XyBAtdLRJUExErHjT8Xi0WrvXkQbkiv/XmvxVgQQgglH+VXZ3tT11XnZ4t/+lf2Kk0u7mR/OH32FLxbLMrq7un9Jd/Zcfi0HXAb3R+E4AFAKBpM5OFhAjHj50dITGM/O67ViZrZXNWNo9FUfzsCDibun3CMMLRz5BuGAODhBf+jQBL6+kNcsJ5V18bWF4zfd7zf3BF+tBnp/fs+Z2/+mZ08dTmyOgF//iDxNJph5Omf3ZP9NhiJx7bc+UN2Q8PHd9/zlf+/K/Snx5eGx07/3uXx04sGpJ36p4H4scWm6nUeZde0fvGO0cuuujf/x9/k3n/o9NzCxdcfkX8+AnNH5q+876udz/Y6Bm8+NLLB15+9cgFF/7Ot7+f+uDDpekdB35yVf79D0yvd+K+h3pfeevonnO/cMVVs3fec/ii39l/480T9z/UyaTTb304e9e91Uy+8OKr+2+6ZX1+evbO+2duv/fzcy/c+fP7Ju/9RTOdL7z11uxt99TT+fwb75x/1bUn9+yZu+PnO+++7/jOPdOPPjF7212tVC770ccL193SjsVyH3567pXXnVpYmL73wZ233L4+NdX/7Cs7b7tT8YcSi4sLV9/UjidSnxw6/7Iri1NjQwdf2PnT24pDQ8JW7Yvf+kdKNUxZ/v2//obJS3y99YXvXtqJRgMnl/ddfT2GjE9r7f3uFWyzowTDX/r6tw1RYlX9C9/9kSWKnrXS3iuvo2wMIbjo29+X6o1GOvu//MV/AQACwz3wvcs4GrCVztzNd9CqZXo9l/ztt/0bxVJ371f+4q+RaWMMzrv0J4gQuq2f+5NrhEZbDQa+9Lf/EFxZ/XTfRZUnbKXCekF75WOfvoEkUT/9jt8+rvOD9OZjVGudlxL21ktMq8R5aG3tkM9YxigAi28J5lGLG6RLT6LyssBlmcZLdmuDDtCdlSO+zilERZnia7T5uRvq1Vef48pnJDZNNd/AlRUxTDXLh/nGCZZNMJtv0PpnrjBM1Z6wtpd9/rBe+ZDbPiX5AsbsEw/tvfanSzt2zN774K5b7zy6c+/MI4/N3XFXI53Lf/jhzhtvayaziU8OX/SjH5/atWvywUcXbr51fXpq4LlXd95yeycYTZ5Y3H3lDZ1gKHH0xIWX/WRlemb0iWd23XDz5vh49ytv7/7pz0xfIHX61M6rb9F8/sjJpS/84LKNicmBF17de91NlUJv+t2Pd9/4U1vyeEulA9//se4P+Nc2L/6n72/1D1WU8Om3fKKpCY52/NUoV2mDIHPmcc7FiLHN9ZcFBjh00zz+fphRTZYyT7wY4CuKGLBOHvTbFkUD98wrIuPaVNs49UEINQ2Ot469EERFFaXYzUcoy2JpYK+8JlKmjWx8+m0/3bRcL731LI83TTpDbTxGtXWRYcHWyxTVsRnsnHonQCqu1e//X7/61Z5XXj++9/yLLr2s6533lEhs/IFHC6++vtXdf961N4499eyRL1z4O3//3cKzLx7ffc75V13T98abZig48vCThWdfWR+e2Hf1dZOPHTx88cVf+vb3hl94+cTszv0339r3witKLDH+yGMjTz63PTy886e3TT/02Mdf/vIl3/re8BNPr09NTf/84eGDzyqhyNCzL4w9+NjW0NDOW++eu+cXRy655MJLrxx55PGTO/dqS+DUuwGB0rUau/Uyw6Wgdtha+cDrjZrqcbz0SchHWqDuLr4VEmhDa9AbL7H+sG4s4lMfBL1BTTsFFj8MBUGHtKwzr/tEoqgqu/o8x0Whc9Jcft8f8CjWOjn+y5DsasTEp1/yenHHVuHSi142AMxV5/S7vpCoGBvuqQ8ioqFgDE6/4IEI5FonLvj2DyVDoxRr17U3idWGHgxc8nf/GD19Zm184it//ldiraHK3gPfv5xXVGDinTffLpdrSjR6yTe+Ez++eHrHzj/8s68JpWorkrjo+5d6KxWH4vdcdX3ozMpmV+Erf/ON1OHPT+3c+btf/wehWG7GUhdcenlwbR3Q1M7rfxY7urgyMfsHf/m13KEji7t3/8HXvh48vVqLJs+/8trg0mqxd/DcK66efOzg6XP3nPeDq4aefOb4nnP23XRL3/MvKbHExONPjDzx7MbQ+M5bbp2//6HDl1z8he9eOvr0c2sTk9P3PTL05NPtRHro6WfHf/FIqVCY+/mDs/f84vAlFx/4wY/HH358fWpy/IEnxh95XA2FC6++MXHvA6Wh4al7H1i4456l8/Yt3HD7xC8e2hgbG3ziuYn7H1LD0Z6331m45c7S0PDYg4/tuvm2xXPP7X/ulT3X/9SUvbGttYUfXW9zvFhrXfDDy0v9g7kPPj7nyuvquWzik8/3XH29ywp8u3PuZVc7nMAq2pf+7h9q2a7E4WP7r7jWSYQ8J9Z33Hybi1jkupd88zuAAODgL3/z2814InT81MKNt2qxqOfM5oHLr4QuxAh98Rv/CBxss/yXvvVP5VQ+fGblvMuvwJLI1pTzf/AjxrCXuv7NVoQQAgDkd95KXHMlONtzZ0KMoeHyn/yp2dPDra7Er/oxt3zmrETOgBA7kSx97a/N3j5oW+A3Pb8hwCKEsKzZ1U0pCn9m6Sx3hVB+520rl7dyeaOvQDhOOHYU2fZZ9GNxnPD5EaTpRv8glqR/la/wX3YRWpNTkqqUC32N3i6x0Sz1D5QHusVKvdHdVRodkmt1LRbcGBmGhtPoztd6c1KzXerrLY8MyI1WI5cpjY/QmqEkosWxUVbV6rlsZaCPrzcbfb3FsSG5Wq+lk6vTU3K7pUeCG+OjtKY3c7nNifHQ2kornd6cGRfrzU4qtTI7K6sdze8tjQ3RhtXM5lYnJ6ROy/RIp/fs43QN0mh1ftZiOEjB5R07acexGWZ7asS1sev1LO7dyzoWJO6ZXQsUcG2aXd4xDxBwBX5jZpJ2XYumTu3bBx2bxu6ZXTtpGtoEbczNEBo5HL8xM4khchl6fWHepjjWtk7v2kU4irhkY2aSsLRD0eWpIQNxhGE3dsy0/SFfpbQ6P6fHwqxqFidG25Ewbdml0UElHOXa7TPzcyTAg7ZRmp1upxNcSymODLXjMVY3SqNDjWRa6LRW52a1eFistzbGxxuZBN/qlEeGWpkUq+n1/nwp2y20O5tTE2oyJjZa6+Ojza6s0GyXB/sbXXlG1WpDhVouLzYb6xMT7WxSrNa3x8bKhT6kWKwH8ynkuAwfIEICODoQgy6TZkiLAC8UM8C2kSA7YgpYDk15MJ3lYMcWvTabo4FKKC/kejikW6zg8ilgYUbwAjrHsZpFSbYv7SoGJ/gQ7OJY00ECEVLAMSH00nSeQ5oleFw+h5CKsQjFLmC5iOaBkAWioViCvDE7AV3sMPSpffsQBJRtndm7m4bYdcGZuVnMUJCCGzOTAFIuRa3tnLVZgbGt5V07iUATh2zMzxCWwQy7Pjvp0DSk6Y3ZcUuQGMNY2jGHOGBjamt+xhYFTODmzIRDs9B113bM6P4QqyrLu3ZhWaAtd21q3JQl5ODlHfOmzwsMzKYI5yW2TokZwISQqxEhAZgA5ZiIj2IcpLFFiSki+LGrIzaO5ZCr2YIcw2yQYAuJUReFGWIiMQn4MLQVICcBG0VAwUwUshHoWMgXsakI7eiQjWGYEEDFlKOYTjJAc5koYhM00m0xZIMo7ehITgCc4MOlrWYitjw3J3faZsC3OTUJTUuJx9enpuTKdiuR2JoaE1ptNRY7s3OnZGiGxG9OjCLT7iQS61OTcrvZScQ3Z8b5ZlsN+Jf27OFM3ZSEjZkpQddaofDW2DCjqmostjkzzimG7pXXdswChzgsuz4/Q9uWGgyVJsdYw1Cj0fXZSUrRba/39MIOnjgmoaUchAxAgEhZABEAAHi6AaZYYEOhF9Estl3Kk8E0RzBEvpRlsgxElDeHMc8QFwo5SHHEdSkxA5CECAZyhiAOEgylrGsKHHCgnMOMQLANhZRLCcgivJjCFA8Jht6Mjb08tKCUh4wXuAaQMpDzEK6jNXszpe5erqMWx0fbqZjYbG+MjtT6uqVas1rorRZ6GVWvF7oq3T1iq7U1OtLsSouVenl0uNLfK9Uapd6e7YF+ud1qdueK/QNio1EaHSkNDgQ217cH+iuDBaHeavT1FIeH5E6rkU3VervZZmd7eLg4MBAsl8qFvtLIgFhr1HLZzYkJsdOuZzObo6P+Zr0VDlYnB0nbMGORkwsLoq5aorA2M82qmuIPrc7N8mpHiUW3pseEjqbJ4urueWy5mOfW52ZY2+x4/cXpCdrQ9XB4Y2aC1i1TFtd3zRGMCM+tzc0gbBuSZ2tqnLUtxestTw1bOsG8sLow5yIK0NTq/AwC2BKkjelx5GLDI2/smHURzena0sIOJCJsgs35GdPngZa7OT3usByn62vz00ooyivt5YUF2y+zirE2NaEGvLRpb02Pmz4fZVrlif5KNCV2Omvzc2bIxyja2vSEHgkxpl2cGFXCYcYwijMT7UhMajaX52b1SEDerpSmJlqpOKPpaxPjWjgkaGpxcrSaSntqlZX5HU2a/jdZESIECPG8/Wb8puuhYZxt8mT0D5T/9M+MoWFuZTl203Xi558B6myqOjF2ItHyn39Vm5z+f802+N8BhH9TF+F/N0xiytuR22+Vz/YeixDCMNtf/av2/nORaQQeeyT46EPINMnZPFJAmtb6wsW1P/yPVir1P+8r/BddhHu1P/qjaL2sSzKQWc/alur1uwFRLNYJhZRkuM36AlqNsS2qoliSiL2cZ6OsCaIT84mbNYiAGguxlRZnG61kQipWbJZ1gpJnY9sUJCviFYsNAnAlmwtubPC20UwlxVIVM3QzFscIEQC9ZovbblHYLefz/q0t3lSNaFDYrhssX851hzfWRbVT7O2TG3V/cavVlXYN4K1Xy6msQdHAskJ2m1YtXlGL/f1yo+Hd3q70dPvr26htVLu6xFZLbDbVZIRu60KzWSwUxHYrUCxVerp8zSpsG0oyync6dMfQkyGkmmK92YrHmoxI0YCYOKLW2LaqJGOcrlEtnQ5SusMKjbaaCiucL35ysZVO0hTmSw0z4HUYWizVnKCsBX0RV68gIdrYcspWOxFDLBKKdcsn2TwrFWtOUFJEb2h9o55IcpQjrZY6kRAUGb7UdGTe8kjCVlXkrUog6V/baiSTDOVIGxU14CMBUaKB42LdhGKpBmS2FQiHl1ea0RjNQ2l9WwsE9FDAagLO1kCQM9sI0kgQTKsBMEsjxsXYQgATwjpAEAwVBmhToQCEdlCgt1VIQcHnmA2EIXIjPGxYvK6BEGsqFATEDfKoahKCE0K9ZAShS5y4RDcM4BAgEsZULJZzRC9TNyHGvN/V2zS0XS5IVJ2lLReFKF+ljFRLiwfpji40m6X+QbHV9G9tlXu6A7jDMNQ26+MqLbnZ0OJBpNtCraFnoqYFQ1ubld4eb6tGNVQlFed0jWmqejxAdFuuNbRU2CRseGOjmsvEWluGApVklDVNtqHoYR+QWD902jbRHTa8sVHNpCVbZbebSjxCY5evt6vpjE1zdFlzA5wMFK1KAT8rUIbWYSkGE9YFxKVt1xI8sOkSHyMBVW8yRIRRWC8aEZ61gQDNFhJp3fR6SMXCfk6GqtagoEhxgqM3aYZ2oITMNiVRmuWVcMXGXs5ioGi3HQfQNEdUgHna9bCooouUYfpkXLWBRBtBOXlmCbn2dld3YHNT1JVmOslVW4iQWjoVIyrBWMWQK7UY2yr19PiLRVFp6YkwX21akKlnMuHNDeBiPRlit5ucoZcK/b5SiVOUdjrhLxUtwJgxv7hdAwTpiQBTV/hOR8nFAU1JxG4S1lfcdghlxIJitQEtV0uFqJoiKspmX8HXqiu6QPkRbVmmzvI+x7YRVggjYx1CDlkG5iXbMg2GDiDatjWNC/N1BUqWRkteU6VkVLNwmJUsRW2zdABRjm1qDC/bFmRwwxX9tkp7qLpJgozgqLrC06zFEkVjBYShS4mgZUseUxX9sKiRiCA6itZmKC9iaUfcqki0UQ0lvBulVjRGs0RaL6s+Hw6KMgNcF2smFMoNxKNWOBpYWesEQ0ii5bVt3ed1/JJYrFk834pEwmtriEXNaCywsq4FA+1INHHihCFJVsQrbtUcjm3G4qH1NUQBOyB5GdhghBYlJ06dsnjBjPr4zRphqEYyHVpbdTiumkpHV1eA43Jhxmi4nGEUCwX/9javdCqJFLEdi+MIx8c31yjb1lJhttzmNUXJxIDiSK1mM5vylUuOSxmxgFCpIZtoqRBdV4ROR81EsU7karWZTXur29iGejwo1FvQsIGP3eJDFI1Y3eAA9lSrzXTC06gBw9XiIb7ZoVVDSUdcB4XXViv5fLyzabZAJxFjXJutte2A1ApGXIA8Vse2YXh9vZrJiq7Gb9U78TAFCV/tmGGPgxh5a5vzw5InGVlZqebyvKvxpYYaCUKGErYbVlC2WN6zUbSj/rbojy2drqbTNIfavJcmrrdV56qtdjTqsEx4ZdWKeNueUOzU6XoqpS2vx3/wvV+vi/BXJhzPm69H7rqdarXO6nMMMDZ7erf/4i/1kTHuzOnoHbdK7793VjIcgLHr91f+5M9aBw4gTQO/HfMbSrD+OenBrt9v5vPsxjpTKv23dPF/BGug64qHP3VDYX1wyMrnoetyZ5bOLsdiGGHxBLNdNHoLbij0P9lB+i8XjXol7wXX3ehZ31IjoQM/ujJy/OSZXbt+/79+M/nZ50fPPa/qTyBIYscXd99wa3BppZVMHbj8qsTnx07u2fvlv/n71GdHV8fGz7ni2viJxXYouvO2u2PHTtS7us6//OrsLz8+duDA733j2+mPD52e23XBT65Kff55MxKfv+u+3MeHVkbGK/GMyQhyu/nFf7q06933T87vOvea67o++liJx6bueyj3zgfH9p57/rXXzT348KELv7hw590zDzxU6evLvPfxwt0/L8XTnbjHX11xKGHPrXfN/uLBI+dcMPfwo3P33FcqDPa++87sXfeV870977y//6afnty1Z/KBRxbuuvfonnMmn3tx4bY7yvnezKFPF269u57N5d7/cP8Nt5xa2Dn+yJM777xnaXLWbZWohMQfPd176NjeG29upNOpw5+fc80NxcnR3hdf33XrXZtjo1y5/aV//A6GtCWIv/v339Elme2oF152RTsY9nudf9febDTL8npl//euRJarBoJf/vq3HF5Ahn3gR1cYfp9Qql1w5bU2oDDPXPwPP2A6aiud+b2/+TsAEMbwwOVX0hxF2tb5V15LHGJ6vV/61nf5WrM+M/Kftt6jANSObp535XUIAmziAz+5ilJNJR790je/4yltf75wXucpxSpDUbDKHzDOpiP43eLrDDitkTQRlS3IALuCW79kzA0siHblY9Zec0GEbrxK8EmVHeJaT2jtVY7Os8rLmrFGJMksH+Lt0w6Jc83XXHxM9fXj8rNua5mjcqzxpt5ZYWS6jHgDGB2XkpqvUfYRjR3h2s+ozVOsnHI6H4L2SUqKuDNPPbZw611L8zvGH3l84fa7P99//uTBpxduv3O7p7dA189vb2xBJvnc+weuu/70jh2jTzyz67Y7V+fnu19+c+8tt1Yz+eTi4q7rf9ZKpuJHj5931fUrU1P9L7+++5bb1icnsu98tP+mm5uxeHJtacfVP+vE4uHTZw5cfuX60MgA3L5E2Vh1qdirH5133Q0tf8hTq5132VWdcCSwWbrw0h9vDQxW9WDtVcQRkyXm6gsCo2iUH5Ufd7ENRW8DUQbUdHcDlN/mad1gWLv4Asc0VMFvrz7FARtQyN1+GTLEgaZTfIOlVJOV8eYzNF3VYIqrPOq4JmRot/waxRgWIqT4BksrDiWqlGwz6+s6iHSeMlVTQByov+zQqklRpPgmA+u2VfD/4de/3v3mO8f2nnf+ldd0f/DLejI9+cCjfW+9uzo48LvW6ljl5MdS7pLvXdb/yqvH9py7/4ab+t98s51OjT56sPeVN9dGJ8+58acjz7342YVfuOCHlw89//Lx3fv33HHXwIuv1NKZ8ScPDjz/SnFkZOH2uyeefPrTSy458KMrR595fmX3zrn2qflWZbulDj309PBTz2+OjO64+77Jhx797MIL911/88TjB48v7LNX3K13eEE0zTKsv4LZNLKPGpX3aS4G2Mppkgkwx5etmrf0Js3ztt1AtZdcT9gwl/D2u7wUNa0VXH6HkTiDtO3NVxiBNZwOqj7vclFgrdjbbzOekGlvkfKbLA81YJHtlxnOavNyC0HLsVz9KCi/y/n8plXGxddZHhmAgNKzELEwpS5f8P0fC67hWHD/NTfSHV0Lhb747e95N0vF+an/VHpPdp36Uv28K67jNc2GzHlXXcfXWu1U6ovf+qfQ8tryzOy/+6/fFKqNeix14eU/kRp1nZPPveYG79b2emHoD/7m6+HTS2fmd/7+N77tXS9WUtkLfnJVYKtkJ+WLzYq/vbFEJ7/y13+bOL64uLD7d//uH4JnVsvZngPXXR9YXtvoHz7v2utHnn3+9Hl79//4uuHnXjy697x9t98x+MLLG9kuh3Nou+MA7oLrb5589PEjF1103o+vGTv4zPKO+alHnxp94qlqLj/8/AsjTzxbHBqavff+6Yce++yii/ZddcPE4wdXZueGnnh26uFHa5lc/+tvTD7w6NbQ8PRDj8/8/P6lhQXSKNJRES2Vpl54ee7+B+upTM+7783edd/WyOjE4wd33HHPyf378m+8d9611zdD0Whlc/ePb9B9fqlav+j7l7UTieXBiZYcZC2j8OLrF1x9reLxc5p2wQ8uN0WZ0/SLv/vDejYbPnH6nKuvN7Jx7kzpgiuv0UUP5TgXfedSm+Ohg7/4ne83kyn/8vo5V12nJuL8ZuULl/3EZIXi4EAlmHYhKrz99sU/uLyazPg2Sgeuulr3+6iWefEPL8WE+rUXjRKKQrble/H58N13Uu3WWfVdQccxu3sr//l/VyenuLWV6B23ye++Q2j6rOiKCELlj/9z+8CFyNDBb838JgHrVxdtTjhs5bqE06foeu0s/lAIoW0LJ467fp8+NGJ290LX4c4sIcf+50j9f+zmnd3YYLc2zHyXHYv/31Oof/2K0B0fZRGu9XRpyQhGqDrQp+RTwDRrhZ5GdwZTtMdscY7pUFStt1vNxdlOuzbY3+pKAdupd2WbPTlCYCebaOQytGM389lOPgUJqfb1tHqyyDRr3bnqQJ+gK3o0WO3rwRRq5jLV3m7IItY2PWYHuW49n60MFhjiqMlYuyvtMEwjl9seKPCm1olHiuOjtG0akWBtoNsSJNMnFweHdJYhGPIQ0BTdScSKo8MUwLYslMaGEUPUQGB7qB9zjOH31oZ6AU2bft/m5BiioOERS8MDgEO6z1cb6CFCnVo9AAAgAElEQVQco/m81eGCyzCa37M9ONARJGJZgJMEhjJ8UmWw4IqcFvC3+7OW7FX8vupIvy55aOCUhgfNsNdBdGWw14yEbI6tDfQY0TAi6IzkYwlp857y0IAR8WOAKgN9ejzscGyt0KWFQ5iCpdFhOyAxhlEaH1VTUYxBdahPiYZsjld7U8140iWwODrsRL0uAZWRQT0dARhUeH8TMJhlm335TjQMGKo8PGhF/ISA7cGBVi6FHEcMYjYECCR0EIoBF2DMRBHrdVzXIjRHI5ZiEe9zqTANIOQCgIlAFltMGHlClk1oJkjYJI1cV/S6XBQCCtJBQEcpBltCGAb8HYP2UgHEJRECLudxmCABjuESBEQv5ThMEHgilgtozoflmAsYhpZcLgZYYqihYG2wBzOMFg5tjQwiGlmyUBobojioUsKG5HcxsjxCZbiAKdrwemojBZvjLa9YHBoAAm1IcmWwz+FZVZZrwwVXEjWvXB8pmKLsCExpYpTlYEMK1Ib6HI+geTzVkX7bL5sOKgsenRYdjiqNj7oezhLFysiA5RVNUdwcGzE9HhqbfAoJvOnSlBgjvBdjgPgooTzYtS1AKCRJCLpMihY9jo1pT4rIsq7TghhzGR+AFPBEMfRSiBAmgUSvTTDhk0jw2y5AUsTlAgAiJIYc7GcZ6LAJBAM0UpqA0BTLAxoxEUCHKQbYnqANgywFXCpOUxFabNfr+Wx5eJB1DCURqfd2uTTVTiVqfd0scKo0X2VkynEb2UxpZJBxLC0abhXyDkM306lqfy+F7VYm1SjkoUNa8XhpZIAmrhIJ1gpdkEHtZKLVlyeY1OPx2nAvcrESj9SG+zDH64Qp8R5AQDsea/TmCCTNVLI+2AMpuh0ObE6O08RBHBTjmGExEJAccyALEUfEKLFZiAwVsx6OYyEPhARgeAI4KhDRoEhjgfLEHcxzFLKEDMWxNmYYKYkZEQAWSnEXcZASoC9qYomH2GZzLM9bmELemIM4x4A0xBRiOVogviR2OIpBDpeieMEmEHApxImOS1Fad6qZTLk0VRoetGIBF5PK6JCajSEXV4VAHTKYZpq9mVY8RgCsDPSbiSDEpDzYr2TjwLYr/b3tbIY31XY+1chkACKVQm8rnWAdszrY38klkOVWeroa3TkK4E5XWs3EXEiXOF+N97OGViv0trtSCONaV67enaWAW8+l61051rU6uYzaFXMQ38wkt4cHKIDVSLDc22NC4EAacYLgGO1kvDHQjTDRouHqcMFlWDXkqxZ6IA070WijkEeAtBKx2lAvJKATCdVG+m1E6UFfZahAsZQSCNb6uwmN2slYqyfXEj22C2iMKIk1PFJleBCzSAmFGgM9LkN3YpHaUMHhOMwzxYlRVkRNOVAdKjh+yRSF6lBBCwcRcT1mBxLg0rA0Poo9LHJxaWLUDPoslqsO9RlejylLWn+mEYyypr42PeUGJZuiK6MDRsBjs2x1qNf0yBTAleGCEggQji6NDlshn4so0dYkvW0KQmlkyPJ7MIMa/d1KIAAR2RobaSH0a3QREopCluV7/pnQA784O88gANC2jL5C+c++qk1NsVtb0Ttv97z95tnSFUBU9Y//pHnRJb8Nd1e/RYD1K3p1IhGzq1v87AjVbp0VYyFdE04uYtmjDf9fjHXqFDybm3eAEFMscmtrdjJlpdNnvSv8l5vc27t2C6payeRa2bRYb273DXTCAcpwOpFoO5FIH/ossr1Vzeeh7tQzOT0UXB6bWh8coYCLHWgEAtXuLoChIwmbA4MbgyPbPb2m3yO01HI2r8QjxAGmz7deGCh391T6etVAkNHNdjRWyeb73nvPW6s300niAFMUNgeHKdshHFscHGA0sxFLlbN52rIIw64PDDksKxjm+uSEiViE3fWJyfDSanh500lHdMmHIbU2Ot6WpHLEX8/10BR0Cb02PIIwdjiukslXg8FS2F9P5lyOEzrK8swsBTAh1FZvf00St+KRTjzl8CJt2+WRQcrAva+9U56aoRgAXFgaKBCathheSUfb/ghlWNvDg01f2F+tbg4PW14JWLiezarhCNTtVj5TEaLMe79czY0iiQaquzU0bIU8VIKrpRJtOUJZTiubqqTynu3t1Ykp1yOcnNlV7u6hXAtg1Ewmm4kkZTpGPLTZ1esNWGu9g6bs5Wqt7aFhVRDNQytNyDXSOUa3Wsl4qbvgLVc2B4eNkJ9ra6XeXjPgQ7bDMNhOeYDhIg+iwzTs2DDIgoTHXnZQG8JuP+ViiiY4xnKghgQNewXWcGCAIWGeMmyWxVZSlK1Nlu/o4RB0IM27blxiFYP2AuhlXFIT+ZYajfKOCRHAWR/YsB1DsFNBQdMYAbsxGQDIQQckGBdDCmIn46UNQxfkelfO4iWAwdrYmEMzoqouTU6Tpmotbq5m+gFCLsM0slkzHYIxuhzLK3JAbLWXZucpBIALtnr7QIh3M956PKVzPko3ykMDLX9UrteWp2cZ3rLyoXJ3v+Mil2LrXVlFp8ihE8VcXzMQlSvlMzNzgGeR6W4PDDgsSyBd7ClYAYmvtnDWQ8kIdByQFoBIUZpBBSlbkp0ll5JEN+5jGgrJyQRYAtgkQQryIsCE8VGun6MMG3gZ00973HXip4AkMqqFYzzwcYxl0TJ0/YDD20gGZjTEtHUUofVwgD7UtOQgFRcZw4QywlGe0yzKh8yYh27qdJTSgj5e1y2Pd6MwQBECaGp9eIQ2bM0fKPYNsoeOV1WqnctgF1ocvzkyQgUoEGa3Uj2UhdvBSKmvj2+1lXCkmcs4YZ5kPKuxPgQhgNT66ChvGIo/XE0kqRBSelItf9REHKSoYqGgFVV7eWOtMMZbhuoJ1HryyHYVn7/ZlbVoAdj22uQU7+jIJjDJQUgQcUlMIBxNGyaTpk3oBUfaVn+eZR1kujjOU1Dh8Qbw8Y7HB02HSnKWLLD1jtvtI1gVqJITlgliOccAEd6VOcp0RL+rishL1klYQiJLdJdJc9iCxgqAET/wc4xuogRr+DxcUyFZkeEw0l0S47DMQZuYEf9WV0GoNbaGRo2wn680toeGFY/HOrTcdFAj100ZlhINl3oLXr+12dWt+4N8Wy1292qRILCBGgpv9BWK/QOtTLoVjMj1Zqm3r5rJeGv1SndPJxlxI4yRTxYDGcE01HComsxRn57Y5CL1dFauVeuZbDuVwC7Sff5iX4FX1XYkWurtk1rtTjhiJEIa63FpZm10HLkuIGBrbCx58rS3VK/2FnjTUP2BSiZXDfhL0YAejZmMiDBeGx/nDEPz+MvpXEUWi6mEFkk6NIswLo0OGbRAO+7ayKjCM1uZrJJK0g5WZa+ajXJ1M/vBx43ZKYOXGF1bn5hkbNsQPdWeLuICixNqPfl2IOKpVs7MzLG04zh0cXDQYVkMqEpPF6to3R/80vR7G+Gkp7x9ZmbW8conZxcaqRRtWy5iGtlMJxRhdKvTlVjpGl4bn2gmk4ClaMXcHhg0JRG7SElEi739K+NTRtDfiCb9pdLa2CQN3e73P8Q0pYZDwAXVTLYZjUvtTrnQW4mlA+XK2sio3e543nzj1wNYCCHbDjz5ePCRB6l2++zoyjSNwuD2V7+mD48w1Ur05ps8b795tptBSEj1j/+kecmXfs2alv9vANY/51iRqNlXkD79+OxUgxAiVRVOnXS9Pn1wyOgrINcRFk+c3UUXhEylzK0s24mklcmcXY71LxaN9oYZdu7hRwKlbcLRk/c/Ej96bHt68rwfXeFfWWv2dO24+fZAeQu5bt8zL0VOnd4eKJTS3SYrBKrbBy67MriyUu/umr/truDGZrm7dzuZ0yRP7sTns3fek/3k06Xduy/44eWhU2dW5nbUogmXYRKLixMPP55YPFXL5nfecWf+ww/PLOy44OobgicWSwNDO++6K3byFAVJ78uvpT49fGZmbu+dd/W9+urynv3zd9/T9e57jkdKHjnW/8JLaiicO3R45umDtfGB0UcP9rz8+srkNEa60pvyfXZq/I03e194VQtHU599Pv/Ag+vd/QqPtcEcKrUWDj7d88prluxJnTg2+OSzOmKKo30kxNtte/fP7+9/9bVGobvvjXeG3nmb8Gx8fWXokScNrze2eHLmnl80hnr7Xny98PxLzWxaLlbm776bUzVH4Pddc6MtClKzNXf3z7WgP3pyaeTgc5TtBJXa0C8eF+p1ezj3O1bFRjT9/pHZ+x60ZCG8ujHy5FO8om5MTlbCKQJRz9Eju6+5ESPEqvr0vfez2JKDznkgSMOKvVybv/VuX7lSz6QOXHEtgIi27Omf309BIlbqY489IVYbjszvuOWOwNbW5wvn1l5xXAV4rHbphGBVociam5/LeNUK5IytDwKKIvoDWvEjwWpAHy5qCZESGFDBxcOyu2x5c1bpdbZRF705AEDb8fDc8e3qeqRdZGCQqx5C9pIdkLctGam9abjUrB/z6BUYJo3WKt+sesUYXT1EK0sw0GXUX8XVquzxmY1jXLPI+gV17I3npx59cmt0ePTg0wPPv7Q2NjH5/Au9r7xm+f3ZY5/3P/WiHghGTy/tuOvujd6+cVLay4qrwOl57KX+l1/FvBBbWRp+9KDtEXP58Plua4OSCw8+Ofjci42eXPdr7wy89BKkUS5EFjCtGC3vO0fm7763NjTQ8+bbg8++1EnH858cGXjmOQShp1qZuvcBV+B929s7br2z2t3VaAVKx3jRMHizvXw4SDdUyeMsve2nABEdbetYQKQtpmFsLnpQ2xZQWR3LspUaUycnfxkGmDCOuXlIZG2b7yzroxlUr5Mmv/6Jh67qQthZfV1yHOQLKkbCS9pNd5UUF31IA1hClU9lpmlLMWfzdV4HIk3D6kcU01EF09o8Llttms4yv/u970aPHd+YmNp98y2x06cxz/e88kb2yGeNRGrq4cf63nv/zML8+dfdHD10eHPn/PnKmX7CG6Wl7oOvpz/6uJrrnnz4kaG3312cHP89a6NAiacdd+6+RxOHDhNB6Hvjjeyb73YK6d0iGnNrh23pnOt/mvr0UCufHXjqhdzHh4gkdP3yg55X32zmssMHnxl64eWl3bv23nZH6pcfbQyOWptw7biXR5bRRMVPRW/EtE5YxVMef9DUllGlGPa6La5trBz101qLjjs4H+UqFfVTsrkc9MqavkxKS7LP6UCqbhQSQq2pbUjrn8regGmdsbYWPVG3RDmt9kQXe2bTqolbnwp+u05seOZYwhe0zQ1cXJRDbNPYxKVFD2NayMXrv3IRtk/uuPkOwTG4SnPo4DNyueoK3Nwd94TXNrb7ey/48dWMYbocO3PPfayuixF4AHhEuqVtNOd/dmd0aWlzbOzCH1wmNFtrM3P1UAxCkjn82dQDDwdKpUYys//662OLi+szk1/RVz2ct7lenLvzPl+1whna4FMvBlfXy33951xzTWrpzNrYyPk/vlKoNQyvf+7BB72bxWYiNfXggwOvvbY1N7HjpjuSH3+6PjYx/8ADqcOfEVkcePft7l9+3OjtGz341MiLL50cn7aBog51+xeXp556Pv3Lj1xJ7Hrv/b5X36zLvspEDxCQXTMuuOOOzAcfNnu6Bp55oeu993VJrPXEbb8kKsb0fQ8NPfN8ecf45P2Pdn/ySTufLrz0es/b7wCBTxw5PHTw2VZXrv/l14affHpjYb7w/CtDzz0PAImW1gcePAgRkprNnTff1uruSn16ePyRx9qpRObIsaGnnoYArk9O1fwxipDh11+bv/XOdioR/+zY6ONP0iK90j1UjyQoyx5645Wpnz9EEQJtZ++Nt7g8tzo80QjFGeL2v/TqxCOPUoaJEFm46Vbkuphh9l13YyuVjZxamnzoYcjRUqU+8dAjrG6sZPO/nhUhhAC74QfvDzz+CKUqZ2vCMQaHS1/7L2Zfge504tdcKb//HkDorK6xIcbVP/rfmpd86axq4v//BVi/useyIxFjYMj77ltnoRr8b4x14rgbChm9fcbAEHQd4djRs3u/ACFdq/FLS3YiYWVyZ8FY/w8J1sJuGsKN4eFKoYdR9JXxqWp/D7FIdaB/a2TIxaiTjKxOTdmYKg4NddJJm2KR6zIUdhxY7e4qjo+5gG4n41sjIzaiWV3HEkerRnF4tDzYRwBTzWRXpqY5pcM4FmYZgmG1p3d1bJzrdIqF/vLIoE2odip9anaOcrESjaxNT2AXlPI9G6NjFAF6IHBix4JL0y5NL+1ZaMsBLIqnFnZhADqhSHW00OZ9mtd3cvceoGgMxWi0iEROFT2ndi5ghtEDwdWJSRtRjG6pvqjt8UAATu/bRwRO4+XluTmLphnFtDwhIoiOKK3OTTeCMYDJiX37HZG3GGF957whiB1fsDrWXwtEHYZbXZhrxjJ8s7G8e3c7HnEItTU+Vk+mMMUUx0e2cz2UYZ7Ys8eJemwdrCzsbKZipmuse8IVMYIBtT02XOzuY017cdduxyvagKIti4LYpoXS2Eilqwu7oD7Us57px4J2TEx15ABjOstT041cxnXh9tBApa9AMCyPDGz1DdKqdnrnznY+TSnmxtR0K5+mXYJkgvKCAXkYZfg4AQ5k4zRM89gBlB+wedYFFCthKsMDALFFHFHEDmJiFJXmgItYL7FzEt3u0JDWPEGHE/kABBme1k0qyvBxZNkua7pqOMEjgiWK6mKxS0CAwTmRsSw+CFCWdQmFJMB30xbDUzygunmGuK1QbHNiVOdEXfKc3LPXESUCwOLefS7P6rx0amGXKQiWz7c5NaHKgsI4Z9hIwx+HGJ/cf47jEQ3Erc7Pq36xRdxVT6xFi1iUVudmmpEEcJzFvfugAOoityQndFY2PL6NybGWP4RpZm3HbDWaZB33xP79pt/jAObM3KwWDFqy98zsnB4LIouAbo4OUKZJsV0sirDIcdgkRaKCgxGXgE5cwi7F9nJEQB7T0A2MvD6TYfkYZGIMcCEdRTgiMsS1MAtkEbqQz7IkymIXMHEKeTHUTAg4JxwiDmKyjBsX6I7OpmkUZ6EL6CiFkzwybS4C7IzHtZCYpayYzNlOLZU6PTcPCdGDgeXZGRfSjXRmZXKa0bRyV/f26LAFGCWZXJzbAbHZ4sEZMe4QpprKLE/P8I5dzneXxoZMFtU5eNzbA2ha8/qW5+cgxpV0fmNomLDOJqS2AhmXFlR/YHlhh8p7TNmzsrATENKIJremxl2C6qn01tSYjlg7EDy1a4GBjoMYtochPEIsYLMMEBnEICFLDK/kYsj08xRPXEALPTxkCdVWASfqniBgKCkP7IAMMUC9HESEtR0DCa7o4WiHybOOh8MA+ZJ20+uVCNYpkfXwNqbZHA08lM0wXIaivQzGROyijJDXcSDbx7IyIZjiczTxM66Dmn251eExaDgrc/PtfJo17OWpqXpP3nXA9kD/9uAAIVStv3e9awBz6inaXwvEqI6+MTlV7e0mgC72Fbb6CqymCZZmyB5GN9cnJjcLA2KzsTY6Xh8q2MTaYIQVT4bBbrUnvzU8CA17bXR8Y2jYqyhrwyOVgT7XgdVcfnVqmnbccnf32ugYr6qVfHdzsKtNyUo4dGphD2QYTfac3rkTYrcWT52ZnqVtu5HNrY2NA9uhLdcVZdUXtkVxZWEnhLAeS65PThHHoXTHCkQBy5k+38r8TEcOWAK/PDcHECSqY4syJKiWTNVG++ti0BbElYX5jj/ictzJXQuEotr+8Mb0pEOz7Wh8Y3qiHYggyzq+/xzg5XXCryzs0MIhU5A3JkYboShGzMbcdCWRYS17cd8+LDAOhoxtIkB02b81OdaMJ7ALtmeGa4EEwAQRDHkGE2p5dkaJRU2K25wcb0eiAANJbyu+MKOpi3v3afEoscj6zHQrmXQpbmVsvJZOU46zOTlW6urlFWVp9x7VcX49rwgRitxxW+DpJ6FlnRXiIMPQB4dLX/trK59Hlpn80Q+lTz76VYHWWdCVZdb+w39sfunLhOfBb+X8tgAWAAAS4gRD2vi49403oOucHWNpmnD0MyvfbaUz+vAIwlj87MjZ9WdASDcbwrGjdjxuZXIQu/8awLKzWWN+xmd1OomoG5RlQ2t3ZYlI045lBzxm1E9DgkS6k0txlqXHwm5Q7Pr0I97SgJdjAHADHj0egAQjgVbS8a6jR0RLdQOSoLQ76Tjx8hQgtkdQk9Hs0vF4aa2dTfJaRw/71FjYr7bURBQHBIhdIPPtdEJ0DcijTi7BG7oZC6vREOdqkEWtfJpFruAazUIeQMBis9GdFR3D9YggJDo8T7NUoysb0JT84UPNgT4BG5Cmmt0Z1jWxwNgJv1/Xk2eWO/kMoolXb5WHB3hHAxAovelwqx6p1Sm/gFmKAk6nK+1KvFdvV4YGeGwggNv5FAMdIgkkLJqyzALcySUNUQg0K9X+boYFNIJGPOjIPEUcMxbQAwG/2qgO9YuUiWzc7MsjgMFSWUesE/TwEBvxgBIO+RqVykAfT9mFTz5CAkUEGmHXCvvsoJdxLByVmuGEdOhENRBDIsVqSjOXpkSaQtAJ+/SQjzUNK+JTEjF/q1YvdEOBFtROK58mIiNYLYkxmAgt6ZosmbwXC0qL9zqciL1Ox8PpjA9wtuZBChtjYRlTGICIGFAaHq/FiphzVJHSUIST6garuFyc5QzNyyhUgAoqNdFj+SUdGzzfdEBc8Go1Hhp8BMqGIvEWFaQ8Sl0UdF4mEtFkoLBhxGLDg1U2inhLd0TWjPoAy9AUaXZnAU1krVkeHhSJiQBp9uRYbEGOtuIBrLvg9KaSTDiy6NPa5ZF+3tEgwa2eDNts21stQ5Ywx1DEVjMxSxT9eqsyPCC1G9ZKoxMK08AhNGXE/MQj8tju5BKGLHtble3hAR7ZDAWUXBIhDCnYSiUZDvuaNTqKZMaQdc0TdWnK4W1FkB1Rcr22Iso29CC53eTD2MPbaN1CXiHAaTyxJNFkZMwbiuw1qZDALuuMn5dER1RaXMDheddndyTRoiUKbTsyQ+ggI7VbXMChZRRqbktBl+Fc3lY4weF9xK81JMlCflpqtbxByxVZyVEdiVMyCcE1aAY2e/Osrlo+UUnFvEpTj0dwQITAAQLTSie4Ug1sltuZFGebdtCvJqOS2jSCHjfswaUO3qjWc1nWNWjoNgt52VAsv9dIhqjVoqFBxy8RiqJo3O5KIwZx0G305kStZfo9RiLEu7Yd9FlhD6EomibVnq6gWWMcQwgTieg+qPJezEJTtNtcGLIsDigNNsWIWOUsVYi6vO3STdcjYZoHHlflgy5FY6ndYFKMF9rUNub8LI8MASucF8uc7bU7TJxyJYk9pTAxXoK66GhcAHuQIQBD8DgMa4t6Rwy5SKSC7ZqQgDwyBKPD+gnFU6xrk6DQSqe8SqvRlYEyy6ntZi4NJYaBwAl79bCfsUwnKHeicfnwibovAiRWUNrtXBrIDALECHjskLfn80+9VqediHua1WY+bQc8AaWhZJJYYshq1daxmojKpmoHZD0RkhSl05UxAx7P/8neewfLVVz547f75jQzd/J783LOOb+njIQQ2BjM2uw6bLK967z2Bq/X64yxMTmJYKKEhCQUQICECCJJgAAlhMLLOcybPDfn3x/Y67j+Wv5u1deu33Z13Zqe+nTfO3O6zv3UOafPyaXkwqDjZSAAjofOF0VZOasHvPnCAkHKaEE/6sUNgkEpLFNeTFoqhliZ6jJOF00PLxZHGVXUBc6N8IFcPjo9rZQXuRBQrpGuqWDUvMGxWmmoYGHBJ8kgQDsQoJiTryhBcJRyjXRdVXR+Rkhm7JAPd0zL5wECoXNeEtj58pgLIWkqqeoK1lIdnlULAqhr2QypRf0mTQlSNtFUz7myA1CxvBiFNgKBFvE5HpYCllwUVjy8L7u81Fjn0XKV77+HsCRCAoACPeSzPCxhakaR32C45qMvq0E/SiGYoYslhSiJojiqFgQhcKrffQt4SMUvBLLJREMthrmEoYklhYBCAXCUSMD2cbycU8oLJL9fSC3G66rtdIZ77f860SiA0dtu8r70PGLbF/W2BbqufcCuSkoQCGM//B5z+uTFsSsEgaaR+ejH0x/9mMOy/w+L4fzZEKwPmiX4tYZGz6svX6ybD6oqe+qkVldvBUNKSytwHOa90xdnNgQAFfPMmTNmUcwsjP1BMvtvE41WRwDat3W7f2rOxdC2R7YXnjg139e74Vs/CIxNJisqBm++yx9fxDSzZt/B4LlhQ/B1P7Cl6t1TI/39m759XWB4LFlZOXjnfb75RWDZjbufip45r3o9XY9srzjy1rn16y//928Hh8cW65tX33ZnYGTUdWHdsy8WHz+dLizpe+ChyteOjKxaefkPf+o/c26hpmHwnvsjw8OY41Q9d7j4yLHh/qG1d99Td+DQ6NDaofsfLHv9qMVxRe+eqX/6gOTxFx97t2vr40vtTa2P7ak+8PxkZ0/39l3VL71ie4TSt4/VPfWcIoQKT54efPDhyd7eji2PNzx9cLa1s3Pfk5WHDusUW/j++027ntJ4T+GZs333PDDV1dm2a1/dM4dS1VU1Bw/XHDjkonhkYqJp226d5yPnhvs2P5hsrq95+oW6/QfzsSg7k+h/4CE6kzcYdt31N+o0y6WzA/c+JAcCodPnmnY/hcl6IJdsfHgXu5RQBP8l3/uJTVJ0Tuze/IBJ0eGRyaY9T7EZEdpm750/884vikXF6799nQtRTFK6HniUgQ47HW/csY9ZSjgs3X/H/YG5xeWysk3fud61XUzVux/cgrkuM7fUsmsPv5g0GXrgjnuDE9MnV22KvwAMEecNeeYcLy9hNKFPvufXxyy+who5xGdTHCco02/yahYnbX122KvO4ziPzp7i5POmp8qZe4GOJ71UBE0cRaUE6jXzC+PexDyLeeDcSVY5b0VLcmOHvfMJPxUEyydIKY4LZm55lFqe4eggNneCloZRb6U5dwguJfyCV42fohMLtAeX2l54pnvbE0Vd+isAACAASURBVLPt7S279jXsPzDT1tW578nqQ4dNhoudON6491mVF8LnR1fcfe94T2/rnv2Ne59JVlXVPPdKzbMHLQSNTIy3PL7XpNnAyPjg5gfnmpubDrzQuO/pZGV51Quv1j3zHHBBdHKs4dE9Fk4IM/MDd92faGyqeuHlht1PiYUF5a+/Xb//Wdy02cRy1/2P2hTlWVweuvXuRFXlQjY6+z5PyRqlK+dORJBlmfCAkVf8iAtQWZ1420MgFkyak+cFJ+eQ0LzwdphaVjleOv1qzLIhZlqTJ3y0bYK0MX7W7+ZcEjdHjgXBkk5E3OHnvaaNQ9uaelfAdNOS3OmzPjeLAA6dep21FnSmwJ16nhcdDgCw8DaFixqhWVPnBTFJgCr6mn/9t4KTZ2Y6elbdflfo/AUbxWoOvVL+5jvZaHHn9h21zx8eXrN6449vib5zYrq5c2jzfQWn3ocorHzx1dLX3kyUVXVs29Hywsvn16xed92NsbeOLzS09G3dVnj8tINh1S8erjh8JF9e1rJ9T+PeZ9+7fNOG628qeuvddFl58+6nS468aaNoxRvHag6+lC4ra3liX/P+AxfWrF53++aiI2/O1LeKc+TE2QCBGFoWn3jLw4QM5ZwzfcHPC0Z+GJ0Z9XKGCDPWyJkwgRhaHht7KxDw5JRhZ2w47KGl7AQ+NyZ4VNGW7dHjQdqSdYWYPCZwPlMatSbP+wVK1KeRyQsBTDNtE5k45hPMnKEi545Fad6UZ+HMeSGAycqsM3nej2mma7kTb/kAhpZmzg3ceg9r6/RSuuGJ/fzcosVz/Xf+LDo2Od/QePl//hATZQsj+u57mNQ0KpFu3rHHO72o89zAXfdHzg1Pdvdc8W//iUuawXiGNt/LJpYxWW/e/VRobCpVULL2pluiZ89Pd3Zd/p3rseWMwnn7f/aQb2mJlJX6vc+EzpxfbGi95Mc3lJ4bHe/q3Pid65h4WvcIfVse884s5MIFnVu31R84NDPUM3Tz5qK33plpbu9/ZGvsxGmEoitfernypdeTZVUtT+xrferp8+vWrbn+5rI3jqVra1p3PVX05tsmSZUfebP6wIvZ4pLmJ59p2bnnwrq1q2+5u/ToW+nKiqa9z5a9ehTBybJ33q178kC2pKRh/8HmnXvnh3o773m07NWjuVhhzYEXq195HeBEwanTTTv3ZcrK6w+80Lpr33Rfb+3TzzXsPwBMJ7ow3fDobsS26ay44pY7sxWVxcfebd7+hBQOlR472fjk04Rq0tlMz90PQNshVH3lT2/NFhcXnDjTsnMv5qW974217tiNizJmO4N33o9Zjms7a2+4LReNRs+c73hsh81z/rMjrbv2EjkZOPbg7feiumli+CU/ujFTWBK5MNa+bQdAEG4+0b59F5sRRyur/69chAABjlNw4/X80dcv4vj/f0W119QvffVrRlkZAmHsR99nTp64aHOMaeU2bEx94lMOx//Jsqs/RYKFAGCGQlp9g+flwxcnOQCgqvBvHNHqG6xAUGlrB3+UHQsqMvvuO0ZZuRmN/p/v/t+eIiwRB4YCy/G55uaF9hZhdGKqs3uutVlYXE5VlE/0dlM5WQ8JIytWssuppdrauc62wMTkdFvHfFsLN7+UKyiYGOij8ooaEM6vWMXFl1OVVbO9XcHxienW9oX2ZiaRUaLRsytW8pmM5uUurF3HLSfTZWXDA0OR8bFkVdXkYD+9uKwEgufWXsJmMpaHG1m9kktnk8Wlo739XDaLEMSpdeuB6/Dp1NkPXa4CgjKMMxsuJVTVIYjZgW7bAcBFTl52BQCAz2SOb7qCQGxENc5s2ICZpovh40P9DkBJVT115UdsiHmW4yc/ciUECKYao+vWQte1IDYx0G/QHCWJ59evk3hBmJ09+ZGrHBwlFX147RoXABSgs/0dkjdE5fIXNlySisRi42MXVq/JR0NsIj3T2Z4sLWNS6dmujqWK6ujU5JkrPqRHfPxM/MKatdniGJfOzre1xssrmERqvqt9vr4pOj525tLLcqVFwujE6Jo1qdJSPpWZa2lerK3lU5lEU+1Ea1doeOTC2nWZqrLg2MToipXJ8nJmKb5cXzff0MAl0wvNDZMt7ZGR0dGVq1N1VcGR8dHBoVRNJZVXSNKwaz0go1AeE6ugsCWJLMTdCg5P5mnaJGppqFokroN6DuQNirfdOg89n6KCiFvjIdISRdtIHUfkRQZobqPXEW2GM50anlnKMF4bq6FdySFR3Wnx0aKEAxPUs3hOwVjHrmLptMgxqlPvg5LBOjLRwrq6Q7g6aPFy+ZwB8LGVg4iL4Jp+4vIPOxD1pZInPvQhQKB4Xj67YSMCEIiAyf4enWRpURzeuF7hBe/s7PGrr3ZJjBLV4dWrHAwDhjU+OKCyHjqXG9lwSdYTDM/Pnrz6akgAKJoX1q6xGBo17fGBXpkXmFRy5NJ16UgsMjVx4sqrLJ4lk9nhtWt0jsMNc3hwpVwY5GcSsMkDgxiczeO1LBrGuJRIFSJOjEUzCl2MWSUeZi6D1tNoBMfn81gtS4YAlpKJMIIW00RGxQugWcwSs1myAoUFJD6XJ6oopIAmEjk6iGAlFJpVySjilvH4okiWYU4pz07GiRLcLeOolEiEICwhyESeCbtqhY9ckqgSqJX4/EsLWih0ZvUaXszbOHZ+/QYqm5cjkbMrVwVnpnIlxRNDA1Q8pXl9Z9dvIMU8IPDhdWuovJQLRoZXrBKW4tlQcHzlIJ3J2wRxYtOHKEkCKDy3fj2by0j+0Hh/L5/JZSPRqYFeoNuoY124fKNrA2AaZy67jJFE1SOMD/YzmZzMe8dXrUAsFzXN9z70YdzW0bxG11MYsAhVwqoZhMKpvEjUky4NyYU07PS6JCBzKtHAoKjtlfN0JaZ7PUROwhsYx0tyCym3w4dRCJqUYCOHUw4vS1glYXMkmcpTTawhsOxcErbzGIvgaQWrZwnSxvMyXo4DH0FnRLSGMPw0PZuimmjggXhSxqoo4MeZtJipKR/v7PVPTI0ODCbrqoOj42MDQ8u11eziUqKycra1hU1nE9WVY53dobHJqb7+5aa60PDo+MDgcn0Nm0inqirHO7qEpcVUVeV4T19oanqqq3uivSM2fGGhuWW2s51dWEqXVYz39PriS5mykpnuTmFucaq9c6qto2BsbL66erq3m1tO56LRCwNDvng8VVY60j8QnJ0Vo9G5/g4gmhZFnbrsckpVgOueuWQ9n8uKHuHcqtXedFIOhiaG+jFFh4h7ftNG2wKoaZ7ZuJGW8hrvnVwxSOfyks8/MTSA2AAzjXMbNxgAIyXxvU2bCFUxGH5s1QoiL1peYXqgU8c5XFXObtqoURybz53adDmhqwZGjq4awnTDotnRlQMi74/MTJ766DUui+Mp+cK6dZrXQ6rGZF9XLhhhE8nxdauTsbLo+NjJKz+iBQV2Pj6ybq0s+ClZm+rpyhTEPMnUzGD3bFVj5Nz59y6/QomGPPOLo6tWicEAmcnNdXWkikv4+PL4ioG56rrYhfNnN26SiqLe6bmxoaFcYQGTlya7ulIlxf6ZmYnVK+Yra2Kjo2cvWZ8H8I93EQIAdLPoB99hThy/uFAcBAGWpVXXxL/0Vb26Bhp67PvfYU6dvFiGBCxLGhxMfO7ztsf7p8yukP+3iUZ/v3uVf+NI5LZbLs5X+EE+DJKc/951am0dAmFw2xbhiZ0Xa3tEHAfBsMV/+Ybc1fN/yJb23yQalQZXip/9u4CY0RgWJRAmlRdZHiccRLagbdlexpUtr5mXvF4kp5s0A0mEzkoKy6OYg8gmtC3Lx7qayxiK6PNS6ZxB0pACRFbWaQYlEUQyEdsRI2E2k2EMJRcIUFnJxnDZ5/XG4yZFoYTrmiiqyrlwlM1laV02PCyRVzSCzvmDnJgjJSkXieCq6kun5KDXcnE2n88Eg7QkQsvhMFUCLC4r2WgBrqrebDYdjXhzSddAssEgpciYYSIMZtuQFMVsJIrphm95KR2LeaWMo7mawJGy7JqIyxEWglF50fZSCmSE+GI6FvMqOVdzNQ9NKKpjIRyu5TGezsu6wImUN7gwlw8GKEtFRMNlSRvHCFEDFJAoj3dxIRMtLFQWFBWXPR4CMX+OwTBCVAAFRdbnnZ/PRQsZWyYyiuT1ENBGRANgiMnQeF7loZzwRvml5VwoQrsqmjc0jsWgBSQToK7BsWROgbib5wXfwkIuGKERFc1pOsc5JOZoCG3pGk8hedPGcZK0HMmxSILCdFPDXdvEWGiYGK0rupdFRNNFUd3LUum8i2MkbmgWCU1LFzhcVGlN0QQWKo6LILqHIdOiQ2BhK5nAglA3tYAXlzTcNE0PQWdFDaMMD0vkZBdFKVzXLQLVDMhB3SJwVTP9DJvLuC7mMphtI6QoZSNRzDB9iXgqFvPmU4jm5EIhQlFIRXE40nYhIcqOh1RQVlhaSBcVeeSsqzqawJOK7Bquy+EWgjOiaPGUjDLC0kK2sDCaXxAtRvcwhK65hotwhOGibF60vJRE8ML8XKaoiFdyQDY1D4c5FpQ00R+0MZyLp5Sgj7NFV3R1liSBqWk4htkAB67iEpglsxyVEFXBw7qyKzoGQwSsTMr1o8B0aRxRERLqEstTy/kPMLYKLQojgWFoGASWS+OI7BDQkHkPlchrXt6icDqZcxkCh6Zu4ghATI6mMiIBDNnro9OyxpAaxwqJODDNfDTK5HKsIorBACaqCIJIgt+TXLZxApLAtSAmS9loAZPPs3Je93G4pBooKQoCn04hGIEQrm0CQpGzkQJaEklFloN+bzqpoZTDEnhOcgCGcLhtAlKRTC9rWigj5uRwkM+mdUj+CgazLJQV8/FYsZBPWhpqeUhM12wLwynHBBgiOwRjSRjPJrJyxOfR8qaO2SyKGaZhYQLMSxiDKC7GIRLGMYmsEhE4LW+p0GYx1LENAyNJywQYLhso44qkh41nlJCPM0RLhZByKNvIORyBmwZBo3mDYGyR9LDxtBISOFM0dcyhUQAcIq94ECnlDTPLaTEQJIFOZhWZ92DQQiQTQsfgWVwycGDlPT7PYlz0B0hoYFlFZzkUd4Bo2hiq+AQ+lcShnRMCnqWE7PXpDCMsLegsh+IuoruubUlCwJNOYcCyGIrMiCIvaBznW16yWA6gtqM50LFFf9CTStoYlgsEhfiSjeECyGeAl5DlbEEBJUmMoogBnyedMiAh+v18Ju0CCChom4CUJcvHGjbG5jJiJOTJpg1A2Bz5q+KgRdEUWN3GuGxGjIY8+ZxpQ5sjMEm1EdSLKyno48W85ud1G+eyKTEa5vNZy4Y2R0LVQCzH5UkN0sLCfKYoVpCflwxa87C4qSOajXCEATA2J1oeUiI9wtxcpqiI1/Mwr6teHnctRDIAjeoYSedljlAXPTFhfj5TEGNNCciWwdOoYwHJABSq0QyXztscJtJe/+xcprCQNWUgGQbPoq4NZV3jeZMghOVlhyNynD8wN5sJR62zw5EfXffHJBoFAM3nYz/4Djk+drHsCnEcvbxy+fNfUpqbsVyu4IbrmdMn/whuoDa3LH35a2b0j8qs9P93C9YvQueM4hIrHGZPnIDWxcVjAcvij7ymNjVZwZDS1AIdhxq+cHG5GwBAHMfz6stmcYlRGEN+z8mI/74WYYDxXHrjzcHJOSXgX//tH8ZOnJpcsfLjn/liwan35ptbN/7ndbEL53WS7d38YMH5ETFasOG7P6o6fvL80Ipr/uErxe+enG/vuOx710fPvK96/P2bHyg8fipXWHjpD39a+eIr71922ce/+PWyN4+NdQ1c9v0flLx7QhZCPfc9UvnWO3O1TZu+8/26554/u/HSv/j6N8tffGWsZ+DSn9xYfvRNTfB1PPRY9YuvnV25duMNNw4+/OiJS69Ydfd9XY9uyRUVl7/+Tt8996VKKmsPvbjmjrtne7t6Hnis996H3l+/qf+hR3seeDhbVln10ku99z6cLKqoevXIpTfeMjIw2PvglsHNPzs3sLpnz96+e3+WKSgufeedods258PR0jffXv/TW0cHBrof371y8/0zba3VB19ecddm0R8uHhlZ+ZNbxEhh0fGT66+7YaGtuWH/ocHbNi+2NDMLqSv/7ZukZqoM99Evf90gKDYvXfbtH4gBv398Zt1PbjYRzKfkVv/Hj8m8LPv9f/HFr7kYjkrqxm/9QOd532Jq7Y9+iruojcErvvZNNpHOFRX/xT98GXFcaDgbvns9hZgwKa79yc2EbGge7iNf/Td/PLFYWvaxf/gyqhkIgl76gx+jloVK+iU/+imVTMuBwFVf+4YwNnF69abMPk2OYx4ojR/j1TmEZ9SZVzjnvIrVYvN70ewExhVa8UNIeo7kMWXmuMeYdEAAnX8R09/XqXosvhfJjBN4OZk6ZGYmMT+Wnz3lyV1AsAgx/xKmv2f6K9Wpp7H4KEeVotnDRnKCDqKZ+eNk5jyGFuCJl4F8wqYbYOJJK36B8YS11FvowgjrEfTePY+vuW3zeE9P58Pbhu6+79zq9d1P7Om/675MUVn5sbcHb707HYkVHz+96bofjw4MtD32xKq77p1ta6058OKKu+7LR2Ox8+fX3HC7HApH3zu74fs/mm7raD34woqb75hrba06fHTlrXdKHqF47MLAT+5UhEBodGzTd6+bb22tOfjiqptuX6yrKzt2avVPbzE8Ar8U3/DtH6pewT89e8W3v7dUU5cQ/ROv8rwq4Zby/qEgnlWIAD77BERsgDr25HMU6ppkVh0+4sdFhcbN9w/42KUc6bcv7OVsAyUQc/oQSdkmkjdGXvchOZMl9dFneXxRcQuJuV2oLkMCmlMv0ohsobo58rpApTXDT83thc6Cjhdj8ScQSaFREiw8j7oZnXGM4de8zpJh1Qmf/vwXa148fH7Fukt/8KOqV18Xw9H2LTvqXnplvqbpkhtuatq7//2NG6/+5ndr9z97fuW69TfdWvv8i2o41Pz4nrpnX5hpaF13822de548femll3/j241PPTsysHL1PffXPX1QDEfbd+5u2P10vL5hxeb7ux7bceKqj1z+re+373t6pqWlY8vjLfueEaOFzfuf7di+e7Gpue/eB3se3vr+ZZs23nR7y7adF3pXyBNg/DUPj8r5BLn4PEoUAu19a/4o4w1q4jAy/pbXA/J41r5w2Mtimpwm5g5inoCmn7dG3hC8AU0ftUfeFIJI3siaIy/6GFQzRXzxGUCEoT5qjL/m8fKiM2VdeCPgsSVbQ0af4wQ7p0noxEGWCABrypx6jQ+yorzgjL4ukIaMmc7UswxAkeLcyGXf/D6n5F3JWPXT29h0XhWED339m9GZuamGpo9/9otsIq15hY3fu57O5lwTWfPTW/i5RSkUvPJfvhV+7+x4X/+1n/sSv5TIBqOXfef7nviyhbOrf3JTeGZ+tqbh45//csGJ0+MDAx//0tc9Y9PpgqKNP7w+ODNjY/jQTXdFz45MtnT85Re+XHri9HB//0e/8LXg8Hg6VrrhhhuDF8YWaxvX3XBT5xN7R9etXP+dG5r3PHV+9YY1d9xZv/+AVBBr3bGzac8zsw1tK+/9Wd+Wbacu23TZd3/UumvPdFdX15YdLbufzEUKm/Y/0/HYrqXaut6HtvQ98tjpyzZtvOGWzh1PzLS1tezc17FtpxSK1r/0cueDW5fqG7q27ui/78HxS1YN3PlA5yOPzbW3Nz75bM8j2+RwtPzwq333PLTY0NS5c/fKu+8fXrum9tDLa266VWa8BXNTK354s0Gx3HLyw//x7WRVddFb76798U2Jisri0xfW/vhGm+XJfO6yb3zHwklKlK/8t2+mi0ujp8+t/fFNVligL8yu/8mNDkqilnHlv3wLgZirG1f98zczkUh0eGLd9TeqgQA3vXTpD693TcdF4Ue+9k1ENy2MuPqf/jUdKfKPTW647scWy1A5beO3voOb9nhp+UW7CAFAAMCXFmM//B45MX5xriEEQVxXr6xKfPYflY4OPJmI3nbzz6PaL3IRrb5h6Z/+2Swo+NNnV3/CBAtBEACNoiKXJOgL5y8qfejPOdYbR7TqWiMWU5uaXZKkz527qDzvCAAIhPyrLzs+wSgpcQjyd5cs/J0Ey7b1snKjo5PL5+M1NYmqcjaeXKqtW2pr8SwupyoqFtpayVRWKi6Yb25CZT1VVpasr2ETqeXK6tn2Fl88mSktmetoo9K5fKxwrrmZyEvp8orllkYulU6WlM72dnkWl7NFxRN9/Vw6rUTCc+0dhKzmCgun2zt88aVMadlMb7dnOZWLRMYHBph0SgkF59tacdXIRgvHe3p86ZRB0+fXrKMVGUHh1EC/hREIgowPDEDLsklqvrcDMV2ToM6vXUdqGkDciaEVAHEdlJjo74eOi6BwYqgfVXWHJM+vW0eYFrSsiRVDAIMIgk739jgY7gB0aqAP2AgCwMTQgI1RhK6PDQ66FAl0a6a7y8IJF6Dz/Z06IBAXmRzo0zjet7Qw39YiRcN0OrvQ2iQHg6QoLrU0SD6BzeenujscHw0z8mJzk1QcYzK5xaYGMRIm8/ml5gYxEmHSqdnODjnoY+OJhZbmXFkxl0zHG+vzBVEiJ2bryxOxEiaXm2tukmNRbim+2NyULivhU+nl+tpccTGVyyXqqlOFhVwqtdDWKpUU8vHEUm3NYlOjm7EID0JEXc0kKR/ChF1dxUg/QhZBM4tgAsoWI5aOYRzCRB3dJCiPi8ZwV3Ro3ibLUCcPAA/xMhzIFs64dAwxbBLnXLQIc0WL8rieIkvRGMACvIIEsgVoyBU6tgUBj+MlOCLZGOvS5cDJO4DDuDLXtHCXAGyRy8tZByNmujsww3QxbGTVSkpTUdOYHuxHge3a7lRPL0AB6lgz3R2obgAMnRnotTGC0JTJ3h5AoFAz5jpaHQKDjjvb0wkcBNjWXG+XiWKkpkz39QISsTVnob3FYWjUdma7OiwIUdOc6+s2aIbK56f6+kyGwEV5vrPd4llMNSZ6e3SPz1YBU+jgXqDKOB1xyRDUJUhFAeZBDBVlC1zoRQ2DZAsczOtqMs6ELTrsSipNhRFScA0NY8M29KGGjnMFDiW4qogzERuLoEYeUBFABYCpoUzQxv2ormFc1HUiBMhYTMjBCzEnD2AIxcPQVRwmYKMhzFAxMoSAQio8MylFw5N9fXwqafi8i20tuKIpfmG2vc2bXJaikbnudm8iLfuFif4+Np00vfxCaxOu6ZJfmOto41NJNRiY6WrnUmlN8I4NDfG5jMHQix2tpCLLgn+ho4XJ5WSvd66vi09mdJ6d7esBpmPj6HxHO2noGsMutDWRkqx5vdN9XXQ6Z9Lk+NAQNG0TIfgSxCVQxwFMCQIQYLuQK3VtDHMslClxcdxRTYKL2QgJHRv6ig0Dw02X4EocQKCmjbPFNqSgrmNskQMZaFkoU+K6ENoO6i11LBI3TZwrtgGNmBrGF5ooh6o2xRU7CAFNGxWKTJMiLQPjil2CR3QFpWMuyZpUKputLkmUljP5/EJDvVRcwC0uL9XXJ6rLvYlUsqYqXVlBZ3Op6vJkSQmbTsUbG/IVpXx8ebmqcrmp3rsYT1ZVxmtr+XQqV16SqKpiU6lkbc1iTU1odjpZUx1vqvfEE6my0nh9HZdK5kqLU5VlVE5MVFYuNjYEZ6czFRXzzY2e5US2pHihqZFPJzPFRbMtrXwiIUUii91tWF5XvN7h1avZbM7g2OneblzTZSEw2dPLZbOKzze5YoBJZnSfML5yEDUdCydmentw01R572x3J64ZqsczOdRPi6pBUZNDg4gNXBSd6OvFHMugmNmeTlTRdI9vrr/DUWyLJCeGBhEEcxFksr8PIK6FkTP9vZhhGRQ9vmLIdgAtK+MrVjg84cr2bFenHghgmjnd060xLKYb04O9isdH53KTK1aqHo7MSdPd3WowiMvaTHenxvGoZsS7m5OhYi6XG+/tVcNBOp2b6e4SQyFSUua6O1UhgCnqXFd7NhDmMunJ/gGlMEKlc7OtrfmiIiYnTvf0yIEgLUrzXe2ZgiJueXmmpy9FkhdHsABAEIQaHSm48Sfk9NRF50RwXa2ubvkLX5Hb2omFhfC9m7m33vwjPEt6dU38i18xSsuAYSB/Du1P1UX4gVAgCg1d2L/Pv3sXVJSLrW3ksGz8q1+X+gYQ0xSeftK/aycqiReVqANBEKiq6Y9dm776GisQ/B1pSH+ni9Cy1Mam1NXXWDyPOC5h6RaKOyhGqZJK88C1SV3TKIYyVBdBTJRwISQMzcIJG6KMKik0hzo2bugayeCWgQDEBtCF6M8xKMbIoszyqG1Dy7JxHLNNxEVsiLoQQstyUeigGKOIMsOjjgMt08Zw1LYwx7IhZqEYahoWQQLXRU3TxnHo2JhjmRB3IQCOC1zXwnFezioUjwAEmpZFENC2getirm2gOHAcgCAWjtOqbJAUgiDQsiwMh64DXBezLQMnMMtCEMTESUqTDYJyASANVSdoaFsIgJhtGjiJ2iZ0EZ0geSWnkqzrusAvYIInODOVj0QIx0Rzss3RNoYT2bzLUTLD+2dnMkWlAXXZShtSOIwhFiaqNoXbJEWmsy5DSJxXmJ/PRgso1yDjaSUYgNDB8qpD4QZNk6ksg5lpf9QzO5+NFJDQIuNpxedDcYDmZIRAdZalUjmEQkVfQJiZyUeiBLTJeFrx+wEB6WyW0JVMQZEnvmiQtOHhhYU5hfMaPMekU6hti6EImc9TmpwqLOaX4zaG5YIFkdkJnWZ0r4fKZDDTSMTKfcklWhFTsRI2sYygaDpcGJ6fslAc5TBLcUhNWSqu8GYSpCxnC2P+pXkTJdKRwsDirAuhKgiEJDNiNltYhMoKI+VTsVIml3V12/YyiOlS+XymsBDTdW88ni4p5qUszKv5ggJcUQhFtTy0YwMmkzZCgg4I7/xcurSMkzJQ1NRQgFAVqJmWh7FtwGSzRsinAtI/P5stLQ1kFzUVjnCe2QAAIABJREFUqkEBN3So6DZPOwjGpFJG0CtjdGBhPl1UzKp5NKdqAQG1DEzWcoEgQEFs4sJyrIxAbN/cTDYSczHIplMGzegM60nETZbJe4OR6bFkYQkGHO/8vBQIkdAEec0iCcXj8yzHLYrIC+Hw1EgmWgww4JufzwdCDkWyyYRDELJX4JfjDkVkA5Hw1Fg2VKCynsKpEUkIWDRFp9M2TuQDocD8DELi6UA0PD2eD4azvpA3mcAMIxeNstksJYtiNEJk8giCiMGALx63MdzhKZDXMMvIRQvobJaR8lpIILKiCXEpGOSTSRdBHC+DKBYpS+lYjM7lKFmWomE+uWxBwvIwRFa0IebwpKs5dD6rRYKObtO5rFgQ5VNJE2CWlyVzousglo91NZvO55OlZYH0ApXJ5qIxUpUIWVYEv+u4nuSyGI0aEI9MjS1W1bNSlslksgUxQlVISXR52oAEm0iI0aiG09Gp0cWyGlYT2WQyGynELIPK51Sf4ADUtzQvFkQVnCmYGl0qq6F1iUmlVL+fsHQoaarXaxKksDAnhUIK4ykYO7dcVk1YOptI5IMRk6TITI5FtGwgys4v5kMRAnXIeErx+VACoDkFQYHBc0QmDzEgCgHv3LwcCmGYSy6lFEGABEDzmoNCxefzLC9DDOT9Qc/CguITdJb1z8/rHOfSOMwpCAolv59fXkaha3lYajkteQXN4/EtLhgEifAUFDXgOGIwyCeWHZxIcz6wGHcs2yul85wAHQdaP9da0HUg4pooBi3bRVEbxRglr9IcdBzC0DSShq4DHQe6joERpKnbKGahOK2KKsVCxCUMTSdoaNsAQaBrGxhBmLqDYiaGe6W0yPig4+CWoRMUcB3ouNC1TYwgDN3CMAvDGUXSKAbalgsho8syxaG2Ax1bJ2lSV2wUcwEkTF3HSYC4LgJIUzdwEroOtC2dZD7AOBCymqSQLHAcB8VIQ9VJGto2Zls6QZOGaqGYAyBhGSaG/zrGwWxTI2nMNFwAEQBwyzBRDAHARYDn1An/9q0uQf5BLkIIEdtmTxwPPXg/MTf7q7TsDyIZtq00tSQ+8zm1sYmYmQ49+hD/2qvuReW7+uCtWt8Q/9JXtcpqqGt/FuzKRVHsT5r9ObZDkpkPXwVsR9jzBNC1izBBQQhlOXrbLcufkXMbLs186CoXIwK7tqPZ7EVxLIemhd270Fw2de0n9dLSP6RGt4th1Mhw7Ibr/2sDuQgCfnH9lSFAEPc3tth/YX45/I2vEMRFkPCvoH8N84sPv435xeGP33yOX1v+l9NB8INJH1wAAlzEBb82/ZcTfxvzO3+yi7gAANf9LQwAiItYVupj1/L1jevuvOPCurWp+qrBO+47d8WlSijUsW3n2auv0ADRteuJ0x+6kvEj1Y8+NbJq5XJ784pb7x5dvyZTFOvYtnN041rJE+zasePsho1iWaTvnoem+nunhvrW3Hj72OqVicqK9m07M0PN89Gq9sd3Dq9YmepoGLr17rnOjuFLVq+94dbp/p6lhvr27bvmB7oWS6t6t2yd6Otb6mldectdc22tJ6+9puv+x1wGP79hQ+3+AzrHj122rvORbTrref0rn+u/7QGHIt787N92PLYLQuf9Ky6vOfgigoJ3rvl4x0M7XIC88i9fGdj8sOs4L3ztn9sf3UXp8pkrP1zx0muYrr/xqb9uffQJQlZOfPVvOu/Zhun68//09dZd+8i8eO7KK+pfeB7K+pHPfK5ty242n3v+W//Sff9WLpN+8/OfqXj+FT4ef+evP1nx1pvlR99+9Z++UPnK6yUnTj71H/9Zd/Ro1WuvvvGZv49Oj5W9fPStT33as7DQ8tzBl7/+5dK33ql8/eiRf/q8/9xE3QuH3vr0Xwu5RNWBl0597Gounao78PzrX/nH2Omzla++/sZX/qFkdLb54IHjf/WXIWmh4OCbp//iKlqWG5985s0vfMY/OlHz0svvfPbThfFs+/6n3v3YtRg0m5/Y/95HPwxtu23Xvhe/9CXcMPsfeejMpRvlskjf5gene3unVg6svvGOyRX9S3W1XVu2T64eSEeLux9/fHjlmlRjxcAd9y22t82v6ui67eHZno65jo7OrdsXetqWyqq6Hts+1dO71NMyeNt9C01NZ666YsVP7lpsb57u7u7ctjPe3jDT0Nq9det8W/uZdesHb70/XlN96tqrV920ea6paXTl6p4HH83VlY119ndtfSxeW/vK331m9e13Q8s++K/f6HtwC6mK7111Vfnh10lVfvMvP9Hz2GOULL30ja+t3nwfVNXn/vXfO7ft8KWWL3xoY+nRY2hePfJ3f9+9bZsnlX7+W/88dO/PmHTmwD//a9eTe7mlpfeuuarm1ZfpRPbEp69t3rufiyee++6/DzzwiDA3e+Qr/1h++Kh/Yvz0X1xT8e7b/PT8u3/zicb9z/qnZg5979+7t+wUJqcO/Ps3S06fqDrw0ulrrqbFXP3Tzx394mcj50eqD7/y5pc+y43PNR04cOxTn+INsXbfgdMfvYrQ1ea9T7/3N9ewc4mKw68f+9ynqcVs+/4n3732WhQ1Wx7fd+aqDyMobNu59+2//zS7tFx/8NCJv/srJGd07d51/KMfs3m8c8vO0csvcUisftczxz99LSFpjfufPvOJazQL69n22JkrP6xF/d0/2/L+h69YrqkauOfBfHvlbGVTy649k13d8b62oVvujjc2nL76ivXX3bjY3DA52N+5dUeipW62rrlz++PzrW1zQ10rb7lruabm+Cc/tuHHNy7U159bt37FA/dnq8smuvs6tz2eqKk5cfkVa26+PRcrPPa3n1x7463Jioozmy4fePghpTC00NHauGd/orj87U98Ys1Nt6uh8JF//Nu1N9+ejRacuuLDA9u25mKx493d/CMPEQvzLkT9v6K1PlAu/53W+qXa/BWt9RtK+PdigP8X8dW/b51fKr1faNTfVvjubyrP33gUFwFB1/0N5fkbL4jfqWB/a51f3gtB3F9lV7/XzAGBZXleeyW4bQu2tOQSxEUe9zPl7p7E339Oq64hJ8ZDjzzIvXHERbGLY1eGoTY2xb/0T3pl5Z8Nu8Iwcnoa+xN/SmDbDkmmP3IVMAzhyT2IZV0UxwKKHHr4Z8Aysxs3Za+4wiWwwLbHsGzmooxhLkl6Dr+E5vPJT/612tAI1T+glqTrIr/qNPyV628Pf/306+8b/s9iLnY6uBjM7xn+7i9NE9q2i2GSIBgU5aCoHAxaFG1jmOQPWBjmQFwMh0wCs6EjCYJDkQ4KRMGn05SLoYogWCRp4Wg+EDBJwkFRJRgwKcqFUBQEk6YQDFV9PoMgbRSI4bDNsAjiiv6AQTMIBEogYLCsjWGyz2dSFEChEggYDOsCIPoDNk0jCKKyLMJRLoSaz2fSDAKB7BMsggKIq/sFk8ARgCgci6MIgFDjOQRCBwLJ68UdG7iO4vXZrusCoHOcS6IuACrP4ySJQKB7vTaGQ9fRPF7UthAIdY53EOACoLMsALgLgOL1IgAAx7U4VnIdByA6y+IeHriuwTByIOBCYFKU5PUhABgcKwmCg6IOgcuCYKOoQVOKT0AgsElK9PsdCE2KlMNhBMMskpAFn43jFkUpgYCLogZNS8Ggg7g2TeXDIQtFTZxQfD4Ex0ySkPyCg2M2SSp+vwOBRVG5QNAmMAARRfDZGGZjqOgXHAxzbTsfDFgE7gAgB4ImRTkoEAN+nWEQDFX8fpMibRTmAwGbxBGAyAG/TpEuAFIwaHC8A1zZ4zEZxkVRJRQyGcZFoej3GzQNAKIFAzrPOyiUvB6LphEUKj5Bp2kEANEvWAyNuK4sCCrHORCoPp9O0S4Ass9rcRxwHd3jtW37A7G6GEAAonKcTWAuhCrPOxB+IDJIUQiCWDwvm5oLgcayKEoiEKheL+a4wHF0mnG9DgBA5XmoKAiEJsu5muMCqHAcphnAdU2WEf1+FwCN5VR/AEGBzjKox+sCoHt4JRAECKKzrBwIANdxCELy+SwMM2lKCQZcDDMJQvT7bQy1SVKOhF0Us0hS9vkcHLMAJQaCDo6ZOC77/QiENo7mg0EbxxDoKoGAg+MOgYl+v41jDknKgYANoYtj+WDIwVEXQ2W/YGGYSxJSKOyQpKlbkiDYBOG6QI6ELZJyMEzy+22SQFCo+rwGRdkokAIBi2EQ1/1AmgBBFH/A4HkHRWWvz6BpBINqMGgwjAsQ0e+3GAa4iOLzqSzrQqD6fCZNuRDIXq/KcQgAik/QWA64rurzaTyPQKh5vTrDuhAoPp/p9bgIIvsE3esFjqOxnMayLooqPp/OMAiCILYFLAuBzh+uCcH/BOYPV4ng96rfPxoD/th1/sAT+h8UGfQ9s1/YtwdLJS+WXUFdF1esWv67zxrFxdTYWPCRB7g337i4OoMIAnVNrW+Mf+HLenk50PU/G9tVPhe8f/OfcAzWf20D13VJUq+uQRyXPn/uD9wZv4zH0nX6wnkXRbW6Rr2yygqF6HNnUVW5uBg9CPGFBWpi3AoE9dIyYNu/JwbrV0MC/7f/gR24jtrYvLx2HYBwfGhI93K5cEGipjpVWCiFIumqirnaBgCx4aEVrkBLjH9iYFD1efLR4uWqimRxiSIEk3VVs9X1KIYN9/VrkYBJ82PdPZrfm4sWp2oqE2VlikfIVRZNNrYDiI8MDqoBr8H5xjo79YCQDRWmKsvjlZWqR0hVl882tEAAR/sHlGjAYDxTHV26j8tFi/OR4HRrq0lzC3X1ycoym+Em2zq0kJANx9LlZYnqymy4UImEptvbTZpbqq1dqK0DODbb3qmE/JlwYa60ZKa2XhV8Sig40dmpU1yyunq+phbBienGZjvELxRVibHYdGOz4fPlwpGp9jYEx+drG+arql2WWaxvlAuCyYISKRpZaG7IhArVcHi2pQUhicWKqkxpcTpSIEejUy2tYiSC0NRw/6DlYXPB6ERHp8F7suVl2ZKiVKxYCYUXG+vixRWAIC+sXGGxdD4Qne1o072eZElFprwkUVikhyJLDTWL5dUAxS6sWAFYLBkqnmtv1QQhVVKeKi9JFJXqPv9CS+NCSQVGEOcGBy0fLwYiMy3Nql/IlpQvVtckSktxDBvu7VMjIQunxwcGVb83FypI1FQly8oUXzBVVTFT14Ti+EhfvxoOmLRnrKvTFdh4rDJVXZEsK1O9/mRV+WxdI0Cx0Z5eJRI0WM9kR5ce8GWixamK0uWqKtUXTFeWzTY2Q4iOdfcmi4ohgU92datBIROJpcsr5qtrLJZOV1ZMNTVDgE719IiCXwuFUqWlc3V1qj+ghoNTHR06wycrKufrGxACn21qUgrCiXBMKimZrqvXfD414J/pbNcZbqmqZr6mFqHpmfp6pTCcDMfEWOFUS6smCEowONnR4VLkfHVdsqpM572LjS1yJJAsLJPD4YXmxnSoUA8EJru7HRxbqqpL1lRoXv9idW2+uGA5WmxEopMtrQ5Hi/7IdFe7wfOp0sp0eUmqsEgJR5frqxfLqgCGXxgaMjyM7AvOdnVqPk+ypFwqLVgsqVACoaXm+sXyapQgzg4MWT6P7AvOdHYoXm86VpKqLEuWVuicZ6G1cb6sBsex8ytWWD5e8kfmmhp0v2+5tCpVWZYoKTdZfqG1cbGsGoPwwtCQLnhlf3ihqVHx+yV/OF9aMNXc4WDkeF+/EvabjHesq0sPCtlQQaqyfLmqUvYGshWlM40tAGLjPd1yQdik+YnObi3gzRaWJMrL56trTJ7PVJZPN7QAiE13diaKSwCBT7a3K9FgOhLLlpXP1dRaDJOqKFtsqLcxcrKzOxErgjQ10dKqRgLpcGGmtGymsclmmeWa2jxJ8m8chZL08yor/9v/wP47TE2/HoMFIdS0wI5t/if3ovmci18cu0JlObvpiuTf/L0Zi1HT0+G772BPvnuxtiuoqmpTS/wfv6hXVf9ZRLV/ED4ObSd4712FZ8/+GRAsBEGA4zgkqdXUIgAyZ05fdGorXadGRhAE0Wpq9YpKs7SMOX0KqupFltNBsFSKHh2xPR69vOLnMe+/h2D9b7tIEStNLUWa1vP4dkbMAxwO3feAS+F8OtX9+E6DZ8IXhpsOHiRkOZKP1+49wKUSDsesuOseiEEul2nfsROh8PDEVNOBA6woUpbWtm2nN75kBYQVd25GXYvOZ9t27WERjZtdbDxwkE2nUczt3rrdv7QgRUNr7tyM2SapqR27dlOOycWXm555hluOAxLreXSrf2423lA7eO/9gcUFHHFrDz0fHR11GaJl31PlJ08stDevvWNz+ML5XHGs67Ed4ekJiLjVLx4uPn9e9Xg69+yuOPbWXFf72jvvily4kKqqHtiyNTI+gqGw+rUjxadO5UOhzt1PVB17M91a233fI4XnzqZLSnt2P1EwPIyjSM0rL5ccP5EvKencvbvuyOvTPZ0rNt8be/99NRpqPvh87ORJQ/CWvfNOx1NPp6orGp47VPfS4eW6urbnn6t+9VWXoorfP1X73As2w0THx3q2bYs31DQ+d6j2xRel0ljl4VdqX3kFoLBwaqzh6QMIhYcmJrp37kpVlNa/+lrd8y+IRZGK147UHT6MOVZsdrz66UMIjgZnZ7se2y5WlFYeOVp/6Hk9LJQeP1X/wiHcNHzJ5dYduwEG/ctL3Y9uTZcUxy6ca31qP6fItC627XjCsxw3/J6Vd99LWCahqh2P70ARO7C41PTsAS6ZwFyzY/vOwMK8GeD77n6A1FXMNNp37qZsnU8km555xpNKQgJ0b9kenJ3JlhatufV2UpFQ2+rYtYeVc1Q23/zMM4HEssKyKx58IDQxka4qX3f7nYQsAxTt3rHTk0tSmtH8zDOBqamZlpbLbrqx4NzZZF19/0MPRCfGAESqXj1S9t4pJRBse3Jf1RtvLnS0rHngoYLTpxI1tT3btsaGzxPAqTj6RvGxd8SCgrannmx44+hce/Pgzx4seu+9TEVl1769sTPvoRioPPp66RtvGYXhxmcP1B0+PDnQvfKe+4tPn5ILQ3UHDpWdOA4xWPHu2xWvH1Gi4caDz9UfPjzX0Tq45bGid99NlpfVvv1G3YFDAAOB6anux3dkKktrXj/S8NwhORateONY3YsvQAgiC7ONTz6NoECYm+3Zuk2NBUvfPlF36AU1JJQeP9Xw/CHCdXzLCy1P7EUh4k0meh/dKsUiJe+eaDx40PBxpWfONTx3kFBVNp/p2LGLdE0uneh89HE1Eoydeb/xwAGXo8LDY03PPkvqOqOKXVu3QxTaKOh7+BHelJl4ouHAIc/yMiBg92OPh6ensmXFa++4i5LyAAWdO3aymsyks43PPuudn0dovHvr9sjYaLK2au3Nt9GShBBEz9atnlyaVNXGZw8EZ2fkcHjFz+6PjI+laiovufteOp20KKprx3Z/Ks7I+brnXgiMjOZLy1bdd0/R+NhSXdXqOzfzyaRN0117dvvn59M+H3bmNCrmL/pI2v+230OwIISKEtl8h+fwS1DTLi522XWhqqU++an0x661QmFyajJy283MuTN/BLtSWlqXP2BXtv3n8k86NBl68H7vSy98ZNM18M9G/LbtUlT6mo+l/upTF2sndCGEshTYsT2weyc0DKmre/Eb/2EHAsjFsSKAQIgvzIfvv8d34Jmfh/79b/sfbZLHC11XEgSdogyKUmlG8QoWipoUlS8sQk0jHy1QAgEEQaRQWKcoi6J0ipJ8fgcndYrKRKNA1/PBgCQI0LHlcESlaJOiDJqR/QGbJA2OyxUVoZaZD4dVrw+xLDEUNkhaZRiTpBSP18FxnaIy4Qh0HDUQ1AQBACgHAjaG6xznYGgmErEwXGMYORBAXEfy+nSKUTjWpmnd6zUFn4Nh2VDYwTCDYaRgCMEwJRAyKFplOYegFOb/Y++9g+S4ruvhFzv35LQ5510AiySAAHMGxaRgW7JlRVuWZMnKgcqixCBalEjJYs4ZYABAkASYCQYwE4GIBBdYbJywkzt3v/f9sbIt/xyKYH1VNqr4/tmtmq6umRdunz7n3nNVIxwGhJaSaQ8hR1GsaIwJghuO2ormRWOcUjMat3Q9ALCcSDFJclStruk+paaieKJkJJIAYyMWt3WdY1yLxXxBcATRVlU7FmOUGHqoFo4i36/EY2Y4Cig14nFPlDxV9UTRiMUhRHY4aqbSOAhKqbSlqBwTIxz1BMGWJEdWa4kEDwJbD9Uam4nrlBubnFCIE2rE444kebJsSbIRTwIAjFC4kk4jy66kM5amc0KMeMKWFV9RHEmpphuh65bjSSMSgxzU43FHkl1RciTJiETmv1I5nQGOU09ljHgCMm6k046iuprmyooZiTJKXUUtZTLI92uRmBmJAhbUozFXlh1N82XFisWZLNuqVkmmION1TTdjcYiwkUy6omTLik+FWijsS6Kr6uV4ggeBGYt7gujoGhMlS9OcRIIRUk6lA0ocUaqFIwGhdjRia7olKQEmth6y4wkIUSXTGAiSFwqbkWhAiCXJtqbZsRgHsBaOmLrOASglkp4k+7puhMJMEB1N8wXRSCQg52Y84USiHKFSMuVRwZNlS9c9QTRV1ZWVWiIJPc+MRo1QCBJixhO+rDiK4shyPRLlnFvhcDUWR55XTGdMPcQhNCJRT9U8VXVkxUilAIRWJFpNpZFllVMZWw9xhGvRmCMrnqy4klyPJzhEdiRazjRgw6g2Njl6iCNkJJKOormq6ihKPRKBHBjRWCmVxp5bjSdsPcQhNDXNlxVPVj1FqTY1QxbUEgkrEgGeV0smHUk2FcUTRVMPBaLoCmIxnUacWfGEFYtDBI1E0pMkV1N9QuvhSKCpjqqW40nEuRkKG3ooINiKxWxVswUxwNSIxgJZ8WSllmlEAFrptK1pASaWJDuqbodCASG1aMyngiNJniAAwD8Inv8/K1zFucZLfq6/8Dz0vKMrL+Mcem7+M58rfuTjfiIpjo83XHKxdGA/J/R9oqvOLnDs8BdMVeP33K1ufuTcU89rb+mC527dBo4dbDhvwRB98P7krTcyST7KXcMBBKXzPzL3V3/NJEkaP9x48U9JPv8+/Dy4IM791SfL557PBIFUyp1/+8ljhr38PzIQ5P+xwAL6XvEvPuF8/ML04bFKOiUFNjB9JtJApLRYQwqp6rHE4cP5tvbG+qRdQ/VEXLbrgc2ggHxZpjUTiqiqx+KHD821tEXrc77NmCgAicCqDUTiqTItGzoyy1IEWa5PKZEQzZbNdJIyl5s+JsCRZbFUQxIshxOJiYlSukEGNp2rm5EIIAAZruIYlVRKLpQ8UQYKVvIlQ9WpyJkZEBb4uswcrlo1K6SjusMRrMfi4ULelWRCA2YGJAgqiaRanBN9xwnpyHAZxvVYLJLLeYIQgdW6LXAIjWhMtEzRMOrpRCSftwSlHo2ECnkOIFSJHxCpVnNjWuACuVarZpJaqcQCyDUacCJVa6VMBrtufHoq39YerhWh6ZcyaaleF0yL6QKyfGQ7QBerciQxeSTf1h6uzcG6a8XComkyl0GFuoIs5wpBUq/TUHL8UKGjrWX23TkUZaqAOIOmxzXBpbJWmPNiap3qicNjhY4u3SqjmmvFwtS2kOlWE0lGaWrsYLG5RWGWmK9Uk0kxsGHNASL2VEUoG0gA1VAsdni82NwiM0t897DR1BID9aqnYApcWRLKdSQiS5CpYQWEIAWL+aoRiVAccMMjiNuyLEzPYlWopJtjk5OVRNJVlNj0jB0OEeiBuscIrkdj4UKeYFaOJGKTU9VEwlT1SCGHA7+cSsu1ul4rV9JJsVjjCNbiiXA+54sippzZjNh2JdOgFucEz4EKgXbgELEWi4dyWY4JUDGq2IDzWiIpWqZcrVYbUpG5gk1kKEJct6DPWEj2AFXKJTsZ8XyslUuVhnSkkHOgwHSRGrbPIVKIy6laKOQ6OhPlLKp79UREtkzmMKAQDxClXPHnp3riSL69PVQv4qptxiOiZQYu0IhdUyJisepH1boQShx+t9DeGavkXBcGqogggIYLVOoIilqY86Oqy4hgmL4kEMpxxfYjqmTXrUBEIrKppOcLXlyrypH04UP51nbdrqCKZUXCgGBaNUKsVkg06hMz5UyDBBxhtmSkkgJwueFjzF1FphUTU24JslC3fEqRSoS5mhWJEOgBw2OEWJquFosidyrxZGg2X4vFHFWNzczYuk6BB2pOIFArFNLKFQJ9N6Sqs8VSIukoSnR62pNlKCHuABT41XginMsGklQt1RO//S2dmf4PkZzzD2Ly0YVk08x99eulNR9miiJMHGn61c/p9PTR4wsGOc9/9guVM88KQmF5147Gyy/F5dLRkovIsa2hkew8uuLHDHpmihJ6+qnQH69eM7J86eLjCCHwc5/+bPaLX0bHSO7Yv7FZ0YceSN58w58b0b5HcAT9oHzOeflPf5ZLEinONf/4ImFygh+tqwcA0PdL511Q+NRnIACx++87lkDq//5RRnRmWnt5G2Ds3w4e9L25v/xkor3j9N9eeeDUk6dGR0675Dc7Pna+lcksv+m2tz7xUR+Ko/feu+vsNXKT0nf92oOrVm37/N+WtITsmaOPbVpy19q3LzzHFrXRtesOnHzy2xeek4s3U98def2FE674/d4zTsn19Cy/9c7i8QufvvBv5hIZwbYHd7y8+jd/mFqyeOf5a9Zc9PODp5wwMzy4/OY7x09amW3rXn77HeNLl02csOzUy6+aHuh/4R8+f+YvLvPikb2nndq/fpOrKPsuXLPiulsCQh/7+Q8v+O6PPFF47uv/uPq6Wxhiz//DF490DiAWhGdnzr/sUsDYI7/40Xnf/wkDYP0ll5USScG3Gw+/u/SOe6ltPffFL530h6sV2972zc+/3fMhyFniyPjJN96AHWfPhef2b94szFWf+sY3Trr2Gj2fX//Pl5zxq18r9fpzX/3i0CNPybMzr/7d5zpferHn6ee3/Og7A4+iVLIXAAAgAElEQVQ+3vLa6w9e/KuFzz7T9vzWlz73+fT04a4tz2777GdD+dzi+x/Y+rUv7lp9ioeFhvyRoQc29T737Muf/nS0nOvd8NiOv/jojnM+XBf1kF065Yab257Z+tz3vhbbd3jw0Ufe+Ou/njp+8Xi0I2SUVt9z58CmLc98/5uZ7bt6nnh625c/r82WhzY89NbHPsYUYdltd2//xMchhMtuuWPTd74nGfXjbrv1wNlrSl2Nq6667vDSJQfOPu3MH/3i4GknTw8PrbjhtrEzT6xkmkfvvOvgytWFk4fOV+39leo+O3HCJVeNnbhqcsni5dffPHPcspcu/ItSMoP8YHDnSyf8+ursQP+rn/nr87/34/HFo+OfWnOKOT0BhQOzYNlNd2YXLXxtzbkf/9lPch0d277w6fO+96PDS5bsO+W0E667tjTcM7Zs5dJbbit0djz2je98+qtfhhg99JOfnfG732Lf2XnhBZ1PPqdY5vOf+vSqW25Sq9VNl/xkzS+vQNXaQ5ddXkymKfM6xvYsues+XDGf/fJXjrvphmixuOWibx5uHwIARGenT7n5Rimb2/nxj/Q+v1WbzL72ub85MLrcJWKilj3rJ78KlUvP/tOXuh99Jjp5ZMdffqzzlZcjBw+/9KUvLLz/ocTBsQ1XXnbStTfpY4ce/vnP+199vnfTEzs+8Re0Vl10//onvv+t1lff6Hn62ee+9/XYrv2DTzz56ic+oeJg6K77d1547lvnX1BVIg21yRW339f21HMvfOMr+uGp4Y0b3/rU3+49eXVJTah2bcX99w2vffDFf/qyfmR6+LHNb3z+r3esOMUKhSTDWPzMo4tvX3vgwjW2Ii665b6XvvL3cq4wvOHh7V/4lF91l61bu/OcNUZH0+qrrt1xwXnTC4ZPuvL35ZXDE629w2sfHF+6bGr10pMv/U1uYODVv/3Eud/90cSS0XdPOn7F9bfkFw6+9BefKGSaUcD6d71y8pX/km9peeEf//6Cb/1gdmBg8ze/Xdf0sFlu2L131fU3zHV1bvvk33zkB9+vtTRtvui7hXAGsaBj+1sn3HBDrbN5ZsmigfseLDe1bf3C333029+0Ghuf+MaXz7no53OtbW9deOHxt91abmh8+aST5DtuE6Ym/x1gcR6Ew5Wz1hwrbkn/J6Ky59VWHmeOLNTefD3zz5cjw3gf6IpTWvjsFyqnn8lUVX3llYYrf42M+lE/W13X7h/Ifukfne6eY4i74oIg79oZueo3x8caTjzhDEmUAQAk8sjDXipTOv+C/8Lk6f8wgVn8yMcCXc/8y9Xs6Dw5ICck8vB6bNazX/qqH41NXP6bxl/8VN6/92gxFickuuFBms1mv/aNwic/9cH5PIqpo1R79WXtlZf/80e+HoKMeXqYUSGgJBBEm1CGcUAFBwvY9y1NYwIFns9liUMIAAQA+pgwCANC7XCYQuiEw5BzDiAMmIuwL4lcFANZAgDYmHLOAQcMIVeWEUK2JPmUBoLACPEVjWHMJNlVNeT7lqrYihYEQSDLHBMuiIwxS9M4woEourICCfE1nSPEKPUJ9kXREiWZu0wQAGMBwq4kB4wBShkhgaIgz/NEETIGOQgIYQhxSXIFCiC05/MeOOcQ+qLEZAU7jq2oAEJfknxR5oQySWaEMCoGQcVRNEcUVQhdSeaUMkkKiOALIuDA1TRHD2HGvVCI5SmH0JJkGeFAkTmhgHGAIeDc0lTseb6iBI4CIGKSBBjnEELGbYwRwT4VXUqRFxiKggIfAMgwCUSZCWKAiSOIiHNXVS3VpQDamk6wzzlwCEWAB6LIBcHDIQKhLYq2JAPP82Q5ECUoSkySfEkCgHuYmIqKAfR13RKUwCtyIgUE+4LAMPYFAWDiUcoRAgBAzlxZAQj5ms4w8THxKfExAZj5EHuKjDl3VN2TZeb7gSgwgXJVA1TwZAVAGCBsyQpijOlhACHTNO44HhUcKmi+66kqJyQQRScU4hD6VGCYepIsO447X83OgY+QjxDHyBEEKFBPkhjC80sGOPBkVYbQVTWAkEepKwgcIg4gCBgLhfjcnKtqjiwixjxd54LABCGQZSYIgSgGGFuERgjxRIkpCoDIUlRkmQEhHCFf1SClARVcTce+72g69eoAcKaqDCEOIPIDi2DAQUCpo4cpB7YowsDnAHAAA1EEghiIYiBJwA8cUeIQcAAhCwJJAoSYhHJKAkoZQkxRIQeeJDlQxr7v6nogCByTQFY4IQAhByJD01DAfFHwBREh7EiyT4VAEBglvqwwQpkgMipwACHnpqZxxpgiM4yZqvqUBhhDDgAAnqYhAH097IkS4HzexolDBADzqcAw8hA2FAUybgvUpQISBVeSAiowKjCE3HCECSITaPBfpQcFkWjuC/+Aa9UP4u1RQYTIow8nb7rh/QBTzpmqFj7zucrpZ3FKQ08/mfrj79+Lq9F/Zi6sBQuzX/6q09Z+DHGQHGNx/HD0tpsXYPG4lSfLksI5BwDAtWec/9L2bdlvf6+2ctUxxqkyFtn8aOqGa/nRGsICAF3HWL5y9pvfDjQd2VbmN79WX3sVvI/7eJ7T0zN10U/8eOKD8/nesan26iuNl14M/qx/0bxE6H7sArla9hVZ9kwLyRT4AAA/gBJ3LEkT6nVb1RvrkzmSYJKg2jVQc4GAnJAe+FBitinr1DAcVYsYRV73fEkEMrGBSLnHMfICnHQKdaKSmmWrqkCZExAmCJJvOpxSEASUzN/HUnRsOa6s6E7F5gIjhALf5URyzECRAh8yjCVmBx73qKQyw5RCmPnUs02syHadU4JMFzBmRqPYdTkiCjNMpCLm+4Kgzc0R7jvhkMsJR9AXRcGyAkwy1mzNkSALaskUZAF2PShhwXUsUfUxpqbFEFGZYWIV+y7FgYkV7HlQwMRxXCRKwHGhAILAk2XAuVivu5qmGyWXEUfTUBAAxkXoQdPBjo9lWAolBcPwVEUzKzYQoYDkWg1aHguJpqhB15eQV5PCgmkwWWzMHy7CCJcJE6gLqMQdD1Js2VQAVTkiGjVX1TS7agERUYh9zwPUFSWAoFyrOKqmO1UnIIEkyp5pQYkCHwLOXC4Cz1BDxLIcRdWdqlVxga4m/FJWzoiBAzD0fShxx4dEqNY9USIScAISCIIcWCZSBO4CCBzDV6BnhyPYsDxFYRir5ZIvigLwLCgDyH1RpJYtBZal6PPXeEQQbQsFvqso2PMlqx7Ioh/AeTgrVysMIhH7JlJQ4PuSpBaL1He5JriAOlT2RYFaNkdI5jasOcj3qqkMZAF2XSBRwTQsUZO4S8s1DiAPiSZWiWtTwkysUNtmsihYhk0VEXoBRz6icmBaWMGuY+mhSH3OBiIQCfUcG0kStz1IgRdIyKspYdF1XEHQjIrNBSBipV7ntqdStxRKBh6QoFuTQqJRd1U1Usn7LmISZSJ1ARW54xIJur6EXNfHomHYui5AzwaiiDwxcMtKXHbrDhKQ7YnYq8kRwTA8RdHsqs0FQDGE3A9g3C7U1Fjgck+SdLfmBDgQBMU3bS4Q4HOKvQBLgR0gQusmIxTJcP4EyYFlIXk+WUotlSgKqtEEdDxPkjgh1DQZoYpvgLrDMDFicezYkm8TyAKHWVoooJRaFsNEZpaFZMiZJ0nUsgClznQ+/v9IhJy7be1jN96Ka7UP4u17zL0BjMXvuSu64UHoee+jg00QjRU+9ZnKGWeBIIg8tilx683Itt5Hs0K7rz/7j//kdHQeQ4wPgBCXy/GbruvftXvNuZ9IpzLBvypa+A+f+kp5Lld/6nFreMRPJuGxo3cChJzOLqbpyq4dfy42vaeBiXBkXN63z1q4yI/GjOUrSLkojh8++vtgUijoLzxvDQ76sfgHae/vceGEqSn9ha2A838HWIyZwwtCWPzot77rE9EXpAt/8BNXUnC5dtbFl7mqMtneV0g0Advr2Lfj9It+hXxuRqJrLvq5J4jIY2dffJmraaRsrvnJL1xB8STxwz+9BAagEotf8I3vIZ8xSM78+aUE+g5Uzrj0CheJTlg/91sXKbm5Q8tXZGPNPhHaXn3jlMuvxIx5QDj3Rz+DPqjH4ud/47vhbP7IgkWn//LXLW++WWzuWH3NDY279hbb2k/71eUtr7y+/0MrPjH5TH9t+h0vdPxV13a/tK3Q0bv01rsa39oxMTR69i8v7X7hxT0nn3rOT37R8/jT+44/5fjrb+584cVsZ/eyW+7oeX7b4eFFZ112Rc/jTx46cfXK313f/9Qze4478eRrb+jd/ESht+s041Bv+dABrfm0i68Yfmzz7tPPPP2yKxc8tPHwipWjt969cOMjEwtGhx/dfPwfbxhbsnTBQ5tO+v01b5985tCmx0698rdHRhaXU+lcvJkFcPE9a0/7/R8PLV7a98Szx11z/djKle0vvXHG5VfMdvZl9uw+6YqrS02tmX0HTrj2psOjo91bnj716j8eWbCg6ZXtZ116ea61KzN2YMXvbig3tyb3vXP6JVeUmpsnOgbm4g2CbXY89/KaX/yylGkOZWfPvuSfq/Fk5ND4WT+/dLpvSJ/Jnffjn1qCwgV63nd/TA27kkyd/50fyqXKgeXH5WMNAML0zj3n/PQXLEC2plz4419IxXK9ufHDP7hYKpUtRT/j4sux6zqifualv2aQ1uOJ8775/dBMbqav/6Pf/aGWn6vpkdN//VvVMiwpsuZnFwul6pG+4UKq2cZSbGrqnB/9XJ3KllNNZ//yMr1QqOiJs3/2y3CuuG/5qo9f9MPexx7fc/ypp//+X3qffDrX1bP0tnu6n9k62TM429xpSLpgW2f98rLhR7bsPuGUVbfe0ff4E3OdnYvueaDvyefGh0ZPvfoPww8/svu000+86pqhR7bsPun0lTfesmD9w9nuviX3rRt69MnpgcFlN98+vPnJXWececpvrl50z9qDq47PRxtrWkwulVbcevvQ+kenhkeOu/mOpWsffPu0U1ddfc2H7l23b+XxPa+8csLlvy03NGX2vXP6Fb+bHBrueGbryVdeNTG6uM2a/Fhhz5QdxLa/c/olV9TSDZFDR0783R8LvV2NL7916pW/nx7sT2/ft+aXlxQbWrRi8dTLf1dNZfSJ6bMuvizf3tH49t4zLv9NrrdXPTR99q8uLaWb5UrlnJ/+ysYk1iB+eGpbgerR53ecccmv59rbtZnS+T/8sRFNYsc55wc/cRXNFeSzf/JLxajYRD3z4kuJYZuJ+Ie/fVFoYvrQ8g/NJlo8KrW8tePkK65SKmVDi57x698gl1fS6fO/9b344YnxRUs++u0f6IcnC63dp135O6VUqoaTZ158iZqdm+oZ/quvfT15YOzQ4sVn/eSXkYmZXEvHWZdeET90aLarZ6q52xQ0qVz/+De+1bhr98EVx537w5+ld++b7ew77Yp/jo2NTzc0i9vfwLXan4fuIBIpnXcB+kAifI/4oF6fL946uqa9/4quvIbG/Of+rnLmWdg0Yg+uS9x1O7Kt95HcbPf2Zb/6daer+1jSdiFEphl9YF371udOPu281pb24M/yhRAA/KxTz+0KRZNXXylMHHkf2Uj/e0wI55SW13w4/5nPM1U72jJOLgjynrebLv6pNHaQKUru779cOv8jXBCO+j6E4FKx+Yff1196AbrusTSB//eGbNbNVEI068S1HZEi15YDzwtr1Pco8wTfwZArrmMmYti1qGN5IRVTLLq2rUjYc7VK0UwlJMfUatW6rhHHFAHzdRUiQM26HdIoRnqlWI9FqG1KlulEwwJFEHIhcAnzSOB6uooBU4uFeiIm2KZi1pxoGHBGPRcIJAjpocqcJ1AGmFwuerqGEVRci4kaE2OKbyIMXU1RSnlPEqAsadWSpykcAtmqIwx9XVErZcg8T5VD5WKgKgHFSrnoqDISBck2EOS2pqjVMiPIDeu6XeGCRiRNsw1flRDBkmVwELhhTa2WqEAcXVUrRUZQoEiSZ2PmWdGQVJmTXaueTmnVEgKMBp4QeETAriySwEG+54RDsmlIRr0aj6nFghD4TjQkGVXAA5cgwTIkxK2wJnmOaBn1ZFKvVwTO7EhIMGoIMF+VBR4gxKnvQIyUeqWeTqlGRXBtU5VF1yIYeSENB55cr9YTcSXw5XrNiIYE2xQ9J5BFjIHgOzTwAOSK55qJuODakm0akZCEAXUtT8ABhqJteiGVCFgrZOvhEDFrUr3sRMOY+YJjuRgyDBXL8DWZQ66U8kY8KkJAeEB9h4BA9GwgUCBStVb2ZAFBHq6VrGQceo7g2QxyPxxSHRPxwNWU0FyeSQKghAYeDRwMGA1cRKmtKbJRA77rhkOhSpmLNMBAMaquIkFJkG0DQWaHNaWYJ5A74VComGMCYhSptsEVKcCQejbgzI2EtVoZQkADV3QdIFImUsmo+gR5GEhGXRCwGdIky8Cu6cQisllHIHAFQh1LCHwnGhZr1ZAm+2qbjj2R+VY8Sh0LAuYrIg1ckftmJCRapmTVa+mk6piCY1maIhp1yn1fkQQWiGbdCGuSbequXU8lZbNGfdcK6zJkmAMQapCoIDmGlYipniMV89VkXK5VqGO64RBiPvZdV5WxQPRKyYxFsOdKZt2JhgWKIQKC79LAI8zzVQkSrM3ljZBOPVs1qk4kDDgTXdOnmKuyYtV8kSLAQsW8FY+JgaeU8lY0hIEvujaUJV+kcq3iSRQLWOA+DVzCfMWsOWEdEyzZBqfYlwW1PMdUBWFIXed/tFL+YPyPxBWEwuRk5qorw09s+fM33vfOOTkdnbkv/WPltNNJIR+/967YfXe/T3Q1ODT79W/ZXd3wGMoIhxB6XmjLo82PPbL6xLN6uweD/4gfYPZnV0EIq7XKhk337o+FC1//ttvQeCxphQgBzsNbHovfeTupVo4W38Ag8DIN2a981Rhdihw7+vD66AP341qVH30zS8jZ3F99qnz2Gj8eP5bozf9LEqH98Y8MvvTCkQULaGDpY1NGY8rT9fg7Y/WmpCGGYhNHSpnGGKwq+6amBgcJ9MOHp61k1AjpiQNjVkOimGnpeX7r2LIVInAyb+6a7e1BEg4fnrYjej2RSBwYI2E80dzT9dK2wwtHiQga33o719fPCZdmi4EiWLFY4uBhJ65nm9p6tj4/MbIAy6hhx55cR6cbVvXpvGpWJwaHUm/vtTXNbEk37dxdyTTZcU07OEYBm+vuVWcKWrU4MTKS3vuOJ0sz3T0dr71qRqJ2Y1yZLkiWdWh4JHVoLFzMTyxalHznkI/RbG9f665driSRhOAWPa1cfnfpsuT4Ya1Umhrob96/3ULSTN9w68F3AgjrLWkxVwnPTM0sHpFy1VAuOzG6IDl+mNTtWlsDqtmxI0feXf4hxTDat7+577hVqdkJWLPzrW1atRrK56qtDcBlqbFD2eEexycdb7y2/6STE/lpfTI3M9irFfLCXLXe3sg5Tu/bnx3pc7jQ9dor+1evbs0dArNGtr9HqZaVfLnamgl8rhXm3GSkEkr2vrB1/4knRUq50KGJXE+X5NjaTH66q8dVlIEXnju0ZJmAvIY3dk73DyAJRcZn7IhuhnRltsA1sZRs6Nq2bXx+Od7clevuUZEBpuqBLlcbMol3DnlRbba5rfv5FyaHhrFKGrbvznd1BzLWD00ziVZamhL7x/ywMtva2bNt23T/gBHSU+++66qqH9W0yawvSbmW1pZdu5guznT2dr/y6mxHRzHT1Lx/j+g4h4dHkocPRYqF8dGFiYPjHONcW1viyDhDyElFldk5uVIdWzyamJiIZ2dmh/ti7467SJju62vev48hXG9JCdlyKJ9790MrElNT4ZnpI6MLm/fvdTmpdDQlDk/6HNbbMqRkJqYmphYO0nxVMoxyW3Ny8ggLULm9KTJ2mDNQ72giNTcxdnDP6hMbZg6HD09NDw+qcwVprlxrbwIcJ995Jz/YYziw78DruxesTtjl8LuTM4N9SrUsZUusQbOoHn9nLD/cYyClb9uL+044KVzJRw6Ozw70yY6lZou1xqSLhYY9ewqDPTU50v/C1v2rj9etSuzA4XJ3u2hXUb5sNmRsOdS0e29hoLOsxvqffebgypWaZ8TeGc/3dHuUxMeOiHIw0drT9uobU/2DUKdNb+zM9fQyEUqzJS5gM5WIHZoMVDrT1tX10raZnl4QEhu37863dQQhUR+f8SUp29rWsvttrgrTXX1dr72e7egsp1LdL2+rplJOOqofmWUIz/T0tOzZAyRsJqPKVMHQQ3PNLV3b3zIiYSsdU45kEfOnh4Zbdu92NS0bwOSVV34gEb6fRydjyq4d8bvukHft5JJ01OgiCMyRBbkvfNEeHKbTU/F194Yf3QQ4P1p0BYPAXLho9mvf8Boaj7m6BP3pJ1uvv3bV8uNXLj+RMcb/owaIv3PS2QAASZQaMs257a+b42Pu0EigaceMcybnf9IKdV06+A42j9qiHddrys6dQTRqd3ZZg0OBrotj72KjfrR2pgBA5e0dpFT0mlv8eOID69H3IRFGsLjs7nsDgHxRWnrTHa6qUdNeuPYhMxKVCuUld91X1yIRq9TzwGboeK6uL7vuVl9Voe0tXLfeCelyoTKwfhPHAoB89K77sWEZqdSHrr0lwAQEfGTdBkIAr7oDDz8GGAokcfT2+4S6WU5nVv3hehCwgAgj928AGEHTH96wCVq+rymL7lwnF4qzAwNLbr0nnM1Zqtaz5enw5IwViw5tfCy198D44tGVN90T3fdudqB/5P6N0fEjph7ueub5xLvj+YbmBes3NuzZe3j5shU33x3fu//I8OjoAw8l3h0zo/H2rS+ndu/Ltncvuv/Bxrf3zi4cGLlnY+rtvRNDCxc+tCHxzpgVi3U9+1Jqz9hsd//Chza2v/bmuytXLLnj3vTbewudXd1Pbs3s3F1sbm19482BLU9PDw52PvN891PPHRpd1rP1he6nni01NDcc2D+w4bFSujE+Nr7ogfWTIyPtW1/qeeb5fHdX5rWdfU89W4unEkfGex59wojGIuOTIw9snO3ra3t9e+/mJ/OdHemd+/u2PGGEY+np8Y5Nz1jhsD45u2DtQ/m+3sybuxY9uDHX3ZXcN9a3abOjRZRycXjdRksPq3PlRXetm+3qC01OL3hwAycSYP6ie9cjwzbS6eXX3MwRDjBdevs9AaW07gxs2IQ8wEQ6euc6oVwx04nRm++BAfOpuGDdekgwrNvDGx+Ftudr2qK716n5Yr6j47jrboG260nKyAMbsOcxhoY2PCIYdi7dfMof/kUtFGf7+lZdfxs0bFOPjt7/kGAaPlWGNjwizZUPLVh8yrXXJ/YeGF+0dNF99yfHxoxIvO35bZm9+2bbuhfft67l9bcOfWjZ8rvuT23fOb5w6cKHNqT37ncikdZtr6V37ct2dA+vf7h9+86xZUuX3H5vw87dR4YXDj+6Ob1ztxGLd770cvrNncW2tr7Hnmzf9urB1asW33Fv087d+c7O9mde7HxxWzXT0PbaG80vvznX3t695enO57cd+tDyRQ9uatr22uTAcOuO7b2PPW2GIqHJ6QUPbsr29ba89lbvlqcLnR3p3e/0bXzCiCeihXz/Q4+amq7l5hasW19pa07s2t+3+alie1vi7QP9j25xIjG5WBh+8BFL0+VydeHda4vt7cm97ww8+kSlIRMbm+rf+Ign66JZX3Dvel8SxGpt+O6HK80t0bHxwU2bjUxKGZ8d2bDJFWQa+AvvedCVJFcQR++8TwxcZrO+Rx4nju+p8uida9VSNdfevvrq64S6YYWjC9atJ57HfDC08VFcM92QvujuB7Sp6dnBwdXX3IQMpx5JjK69X7Qsj0iDGx+Ri5VCY+txN98Sms7ODg0ed+OdtFiuJtKL7n9QqVQ9URq5f4OSK8129q667sbYdHZicGDFjbdLhXIlkVm4fqOSL85FYsKONz+QCI82CEPPCz33TPKWG6UDB7h89J5HnBurjs9++atOZ5c4Pp6449bwE1v+hNuOipjw/drxJ+S++GWvofHYIiY4JtrLL2auu+ZDg4tWrTyVc87/U4YV+je1LZXMnHLCmW37D4Tuvh3XaseS1MUYQKh81prCZ77gxxNHW9jJESKFfOr6a6KbNkLfr6z5cP7vv+Q2Nx+1dSyEHKLQc8+m/vh7ZfubHOMPUrLex3AoRUEAIAhEgSEEGWMAcsABY64sA8455x7G8/1VmUDn//ER5hxAzgNBAJyDeaIWQQiATykjBDDuIczmNzvCnHPAmEfpvMmKL4qMUhQwNt98Ogg8QhnBEACfkPn+sQxBHyHEWEAIJwTy+XpECBkLEAxkCTLGMA4IRvPfGSEAICNk/hqOUEAo5JxDHAAE/SBAiFEKGPcIZRBBzjnjDELAOaM0wAT5AcOYEwwBYBAwjOarIwNKAYSMEJ8QxBgjxMcUsj99NF+iFUCIGeMAeIIIIGIYe5SA+UnjHHDOMPYohb7PEfIRRowBCDnGEHDOmEfofPtaT5QgZ4zzgAPAGCDYF4X53e5QATIGOOcYQc44Yz7G86KDJ4ocAoSQL8uMM8C5i/B8zWeA8PyEeJjMr28gSRwjwJg/H7jnlwwhyLmHMQMAcu5jAhCGnAUIMzg/55hRCjif3x6IMZdSgBFizBMlDiFijEHEEJ6v2eQQId/3KOWYQM58QnxCAQAc4wAhzBjggAGIOPcFgc1PLIS+IALO2Xwv8yAIIPIxBoz7VGAIw/lJgxBywCD0McGMMwQDTCCEDMEAIsA4QMhHCAAeUOoJAgoCDiBnHLGAUxJQAgFnjPmiiDgDAHiEAM44xj4h86Wd7E9RDvqSCBnjnHuEQAgB4Gz+DwAuJiAIEMa+LPMgAJx7HELGAQQBJhAAwJiHMIcQcMYoBYBzxj1KOeccIl+gAALIWQAAYByywBdFABHgPMCEA8gBCBBiEADOAyowAADnAcKAMcC5L4qBIMAgCBBiCKEgYH/aTvMnCELOfVewcE4AACAASURBVEIZxhACDhHgHAfMx2T+moAIHEHIGIeQYczny2whhL7vCSIDkAMYzBeTMs4wYZggxgOMGUQfuIweNTJAGFlW/N67kzffSKemjtrtiDHIefm8C2a/8jW3oVE6+E7qumvCT27hEL4PdFU5/czcl77qZRqOMXRFqbLjreQN1y5s7ly58pT5Ltv/+bI/MVjzYmIkHFNFee6VF23PdvsGwL8+fo4JHgty7nR0uI1Nyu5dyDSPTktGCFmWvG8PdD2ns9PqH3Db2sXxcZrPHT2PBYTsrLxvL1NVt7WNY3ws1Q38rzJY9tBw9cRVIvDHj1vha6KRTJZ7OqqN6Xx3t9Gaybd3UMQOrT4Ohqkth48sX+xG9dn+gXJ7s5VK2NFopbttprdPAP7BVcdBjea7umeHBoKQXMukS52tldZGLxyqdzZku3oqLc0zwwNuMsIEYWLFMi+s1NOpSkdLua252NZWb81ke7oF7o8ft8JJhANRnFy80I/oRjptp6JTC0e4SHO93VZjotzcPL1gOAjJtUSy1t5S6mwx4gknEckODVQzmVxfV6m9hSA+vXDES4SqyVS9pTHb0+klVa+3YaKrn0niXFf7XFcHoWBmeAiHyWTvYL6/r9qYceIROxGdWjSCKMz1dFeaG2tNmZmRYaDRUmOTk4znhvqMeNyJhmYXDAEC59rb660NtYa0E49ODw9a8Wixq73Y2e7HNCsSmVqykKlStbXZaIiXWtsKfT1GU2quvZ0gdvDE47kqWNFYdmTASESnRkbsdKza2OhG9MJQ71xzM8H80KqVVALjQwtL3R1eSKm0tFbaGirNLb6mFEb6Sw2N5fbWXH9PEFGtaGx2wZAb0aqNmVx3V7m1WWTu2MoVXiIERWF8+VIvqlptyVJ3e7mh0Y1Gql1tM/19hPmHVq4Aupjr7M4O92ONFhtby+3N5Y5WLxwud7ZMd3bLzfLY0AI3GWWieGT5Ej8s11PpamdLub2l2NZWb2vMd3VREIwvW1JtTAsYHFm8yI/rtdZkuas539IGFKHa3Tbd30+Zf2TpqB0Ju2Gt3t6c7e70o7obD2eHB6qpVL6vp9jWTDCfWTDspiKVZLrWkMoO9FmpeKmrvdre6KnqXGdnvqcLCyg70OcmwoX2ztxgX7ml0YlH3GhocskiiMFcV5fRkq40NU2OLgK6UM40usl4dqS/nkx6YW1m0QI3os8MDdSb056qFvp7zcZEuanZDWtHlizBErTDkZklC5kml1qa6y2ZcnNTvq/Hakzk2zqEf10yJxqdWThkxiPTIyNBMlRqbnaikbnBnlx7pwD8d05YDcKS25Gc6un1ouFqc3O5o6nc3Mw0NT/SV8w0VjpaZoYGWFi2wpH8wsEgohSbWssdTZXmxrmuLrM5le3uEQP34PGrWEixI5GZ4UE3pNrRqNWWmRoeQYCPL1sCQlKhvWNmZJCF5HoqVWlvKXe2OuFwpaMp19VZbWzIDg846RijdGLJaBBVq6lUpaM139FWa2motjfluzoR5BOLR6sNKYrY9NLFfkjJdnUV+rprDWmmStX25kJ/F4RoYsliIxmrtzTODPdzXao0NNSbMjND/UCicz2dFVlRX3oJ1z9gsN4rMqCFXOq6a8KPb0a2zY/O5whA3weCUPj0Z4sf+XgQDqvbt6euv0Z583Uuikf3zOUcuU7pY38598m/CUIheEwZSXJBkPftTV595QIlctrp5ymywv4bWuffAdY8xorHUwKAxeefsSXR6eo55jgYt6XF7epRdu3A9frRaoXIcaSDB0ip5HT32t3ddn8/nZ0RjhwBR0XmQQgQIuWysmc3cmy7r49T4QOM9R4lwkbLXrxuHTUMLtAVN94KEDi8aHEu08IF0vvM1oHNW3DAE/W57vWb5XJ5cvHobENbIEs9L7448tDDgURjR6b6tzwh14zsQN90S5cnK+17dy+/8VaIIbXskQc36MCZauuZbutyJaVrxxsL1z4Uzuer6eTqa26QjNrk8Mh0SxcXaesbb448/IhSrjKJLr7z3nAuNzM0uPKmW6PZLISw+4mnkwcPVlqaJ7v6q9F4ND+z6oabk++OFdvbR+9dFx8/PNfaOtPeU4sn45NTy++5p+WttyaXjB5/7Y3xA+8URodP8qfbiGjNTnQ88VLTnt3VdGbh+g1tr79RHOo+1D5UiydD2ezStetSB8eQSLq2Pt/0xvZce+dsZ081GtfqlZXX35zZu89obex//OnMrretRKLlzTcXPLI519/b9/iT3c9tLaczs5095XSDWKuNPLml85kX3FA4PnZ4yX1rKy1NE71Dc+km1aoNPfpE99bnA0LShw/1PfZEIIkTQyOFdDMGbMn6DV1PP1ttbWp76bWe57ZyRcn3dk609GEWDD/x+MiDG8tdnW0vv9rz1DNeWJ1p7iq0tBHb6X/huYFNW4AkhHL5xXfdW+joTB98d2TTJslxRdceWbdezeed/tY1Qh0JmL21f9GDGwFF4Wx+4LHH5XI119c909rjKkrH3h2jt91HXAdxPvLAesF19ThYLesyqXlHCovuWhuZni61txz/x+vVSnlyaGimtZuLpHH33pEND4fmikY0vvLWW6LjR+aGe89BOVmLOGMTo/fdr9SrxAsGHt0cmZk5vHD0jN/9Lrn/QG5gePldd6UOvlNubJzs6q8kUrHJiaX33df2+hsTSxevvum29NtvzwwM5VrazEgkNXlk6LHHG7bvKjc2DT/yaO/Lr04tGJxp6aokUtpc8UMPPJDevYdT0rltW+trb9YaMhO9g+V4SraN4264JbNzV625sffJZ5vfeNNIxGd6+vOppnAxv/jetT0vbhtfMrri3nUNb27PdfV0vfFq75anAlGKTk6Orn3ASCWO9A3NZZol1+7ftLnnua2ckERutn/DI4ySyeHhXKZNBO7Ipse6n3qunkk0v7mr9+mnMcHxOFwtUd+ryW++s+S+dbWmhqa3dvQ/8ZQX0bNN7bnWDshY30svDG18hBCglEoL73nQ1+Qj/cP5plaIYe/Tzw1sfhy7nuCai+5eywj2RXHpHfdowJFKtd4tT2qFQr6vZ6qt11T19Pi7x113s2AajNCF6x5U67Vca2e2vdtUQx27ty+65/74xER2oP+kP1yjFEuTCxbmm1oCWWzYs3/Bhocj07O1eGLFbbclxg6PL1sy29RhhCKNe/YuWXt/qFyUHKv7iWfih8cnB4cKzW1GKJycGl9xw61yseiq+uiGjaFsrhCNkV07/59ehB8ArP+ae5IVddfOzB+uUt98HRwt4QQAdB0/lsj/3Zeqp58ZhEKh559LXfsv0thBPu8Vd1QcGGNzf/O3xQs/xjQdHFPPR06pMDGRuuLSPoDXnP0xXdOD/x4dov+IKTlCaNHoiuWDo4333K299MKx9csB5zAIzOEF0xf92G086lR9jjF03dBTT6R//zthctJp65j92jfLF3wEmeZRfxFCcLUSfeiBzG9/QyrlD0oL3+OoRmK0UquH446sAyJ6ouIQ0RbkgKNaNEENuxJLVUMxYtr1SNKDxCGSB4gt65xBR9ZKiQyp1CuRRMC4JaoeJKYgBYg4SKhF4oABi8ouwJaoBgxV4ilUN2uybikhTiRPVB0qO0TyEK1EEsSwjUi8FopiyzG1sEtFm8rM9kqJDAfYV3QfU0tQXCSYasjFIkO0HopYVAY+M7SIJShBAGqRBPeZI8qGGnKIwD1WUyJ113aqs3NKwveZx3E5nOQ+cIlgSZqHqCkqNpVdKnHLKUaTjEFPVF1BdojoUNkSFFvWoetVo0kLi8ANSvFUAInnc0PRbUVHblBONrAAmqJqC4qlhqDHquGEC6mPiClrLhZNSWMBr8cStG4Wkw2mpELLMRXdQ4JNZRfTuqhC0za1cDWWolWzHE0C17dExaGSQ2UfYENU6tEkttxqPO0DZIqqh6gRjkGXVSNJS5ADgC1BqUQSpGaVwvFaJEEMux5NmFT26kXb9+uhBOfIpXIxkcGGVQ1Fuc9NSfUgdSSZEdGjUjUUYx63iVDAmlM+VDbdejiGDMuQdUvSfSz4VHIE2cGCi8VKKIbrZl3Wa9EEtF1L1k0t7FTKrl2tqHEOsEvEYjSJTduQNFtQHCpyhqpKyFZCwA3qoahNFR/gSiQBOPIE2dAiFiTACarhuAepTWVbVDyAXSJWIwnmMZtDS9IcLP5pG1AZmk4p2eAD4hLJpZKDRZtILhENVce2U40mbDUEvaCuRV0suEiwZS2gYkBESwlX5RAybSMUNWQdmk41EncFOUCCJWsulkxRYwEwkhmhblbSjQaVoeUZasghkkPEACBTjwLbMfVoJRynFaMQTVaR4JQmilRyRIUxaIlKPZpAtl8PxwMGTFFlDNUjcWB7dTViyxrDgiOqLhFNUeM+ryQbhLphpBosJQRs15I0W9YYwA6kxXQTtt1aOM4CYEmqj4gtqpxKjMr1cIwFwMHUElVT1LjHKtEkshxTDVuS6kLiQWIRyUOCLSjVaBzXTUvRKuEEdgNbVi1RdZHgEMmSNR8RF5JSLI0s19TCLhZtKrtIsGTNFxXAUTWWCjzmIuqK8gcq4Xt582eKEt3wYObKK6R9e/m8mn9U78W25XZ0zX7zO9UTTwp0PX7v3alr/iDMTHMqHCW6CgDChc98rnT+R5iqHmPoChNSKqV+++sW0/7wmo//z+gKzFcR/qeFgJzzx5/c+NbYvskf/dQaHALHHAEDIc1mGy/5hTj27tFSoPO0itPROfvt7ztNzdg0Qo8/lrrhOk7I+zAIARA6be2z3/qu09oGP2in86/o87+rInQ+fkF6arKUSCi+CRzABeRhjN2AYFaT9WQul0+mG+qTjiWUY1HZt1lAEAp8hATTQQKoqJFENldIppTAoHWvpmuKZwEXAAwcSRTrdohVs5GGcLFcjMVV36BVu66qIvKBAxEKbFmipkswqyqheDZXiidlZtCqW1cVRBF0AtWuV2NRuWZ5RAACFyzHEBQJOMwFNPA9VQw8qFnVSjwhGHaAkKXp0ULBIYSIwHeh4LnlRFJxTd2slfSwVDEZQvVIODxX9CiNBaWCkBA8txqOKPW6aNv1eDhSKFiiWg/pWr3mIyJA1/Og4jhOWPY9JFtmLR7VqhWfISihgCHJtMuJOGA8NpefS6ejlTlos2IyIZkWcV0oQhuLsm0DARhEj+ey+YZM2ChBm5lhTXQdxjAkzAVYqVuBTuuCnsxlC+lUpjJdBSFbk2Tb4j4EInQh1Wp1TxdqQjhRLOTj8YhZhhazdFnwfegG1VAkIDgzO11IpFTfkEpGORqVgQMh4Zy5AImGjQiv6NFELj8XTyiBiU3PkuWEM1cFIYSYI4lC3cYoqISicbNekjXZM/9tybjNCWSWKhPLo9Cv6NH4bLYSiXqyFMtmTVmiAuCAAABqghIulijyi9F4fDZXCUcsVdMrJcH1yvG4Uq9rRrWSjBPT5RAamh4pzvmEYMp9DwqOW04kBNsKGVVHl8WqaVO5HgmHisUAYSxwB4qiY1fDUdkwZNOoJmLRuYJFZC4j4gQuphR5ToB107TDss+oWqtVEtFQrWJhCQpAMF0OIJegExDNMPOZhnglC63AiIZky2I+ACJysCA5zvySJQvZbCYTMsrIDIyILnpOwEiIVWpEpabHVFIT9XQ2m81kQk4FB6AuSZLtcBdAEdjk/2PvPaPsOq4z0Uon3hw759yNBhoAQWQiEABFSaQkyxo/2c8eWx75eRyexzOybEGimDMBIoM5iBRJMYIgARJEIgECIAESJJFj53Bv3xxOqnNO1fsBWcvjJAHj9WZhxPq9e697d9216uvv23t/ki9fdHxSUfJHs5lUJBow8sBgTEaKa5lMgYSVFY+vUGIqKUm+WDKZicW9tAQ1x/AoQMBi2Qw4hVSowp8t5gJBmZvYdHVZUbjFKUTAtRRJKFsCtPPBaDCdKQSDCjPEgql5PCJ2mAUhAKVAwFssCMAu+MPhxGQpELAUJZROWbJMsGszAQKue7z+bE4ANvVIaracD4aponiLeZuIIqTMRpCzUiAYyGZdWSqmC9EvF43+Ou4AcF7xyEbf/g+Qpl32QwYAsky9b8bk9/7Mam1lklSxab1/13vIuIJVoi4QxNSlhe+X+mWvojIihC2r4t474+fP/+F3vhcKRviv+/z/k0T4TzFWS1N7cmLE3vamNnuuGwhcfVyox1Oet0A5d1aYGL9sjQ8Aksv6PjpodvfYFRVWc4vV1OL76OBlryG9lKqQ9+/dY9fVW/UNX44W/vsSYYULb7jzbkEzbUX96srbqSSpucKK+1frfn9ocHz+2o02xwE9P/e+9XKhbAX8N/39TxgAgm4uvn81VdXARHr+2o3E4QCCFbfc5Z1Ml6qqvvr3P8EuQ6a96KG1ErNwtrxgzUZJp7Yi3XDH/cHEZKqu/ua/v4UYJnD5kgfWiMwlGl2wZoOYyRmh4Ipb7goPj472TVtx76rKk6csf2j6089XHT9VjkauW72h7dDhM/Pn3/Tj2+sOfTzWO2Xhw5tqjx3Xg+HpP3ux6aNPEvVNi9esa9/7wdlF19105/1N+w9enD5r6er1dfsPGuFo7ytbmj88MNHSuejhtZ07dw8vmL3ojofa39s9MHP2wieeatx/0AyHp77yWuvu/aOdvYvXru97e/upJUuW3/NQ23u7Et1dfT9/tW333kxzS+fu9+c//vTI1N6pr22d9eSzF+YunLFl6zVPPlOqqqv/9MjMx58tRCuqvjix7OG1gzNn9r25bfbmJyZ6upt27bv2qWf0SLz6/PnZGx7Xg6GKU2eWPLRmdMqU7p3vz33y2URHe+P+I7MffdzwBGr6z1279gkaCEYvDCx+4OHJrs7mvR/OeeSJdGtL7eHP563fyLxBbzJx3QNrLI83MDx2/f2rEq0d0cGRRavXIptBwJbe+YAnnS1VVt30tyuxbnIOlzzwMABczhTnr98kaZYjkhW33hMcGSvWVC5feZdU1hkRF61aJ9q2oNF5D65VsyXLq6y4/b7o6Ph4a9s3/+GnUiZLBWnJQ+vUXBY4aP76jf5EerKy5uaVP4mdHxjrmfKNH94iT6R1b/D6VWs8k0mGlfnrN0WGhs9eO/87P/pR09595+csWLr64cZDHxvR2NSX32zb+8FkS8f89Zs633nv3KKFN9z3cMeuveeunbtk/YbWPR8YoUDXlm2tu94fb+ta+PhTU9/efmbRdcvveajrre39186f+/TTrbvfNyKxnjffbN/2XqqtbfbjT894dcuJFStueGhd+9Ztie6unpe3dG3fYURjPe++1/vKm6n2jmuee6HvpdfOLFm8ZP1jU97aNtA3o/3QodmbnjA83uj5C0tWrx/v6uras2/+xscSbW2Nhz699pHHdH84PjI8b80m6g+ELw5cf+9D2ca6uoOfzH3kqUxLc+3R43M3bLZUn28icd29D9uyJzCeXH7vg5nmltrDR+dtfDRfU11x/NzCNeuYpEql4vV3PwgwVNPZxfesytfV1h4/vWDtRq0iHuwfu271WoZFomvL73mISzLF4rK77vcYJVCmszc85smXLJ/nxtvujfUPjnZ2fOOHP/FMpk2Pb/FDa1VNQw6Yv3ajdyyhRyLLb7+n4sz54Wl93/y7H6uJyVIotuzeBwKJBCPy3I2PREZGk41tN//DyoqTp0emT//GyluDFwfzkYolD68JjY5xBK999OmKcxeHu3p/9x9+VPfZF/0zZ3z1J3eEz/cXqmqvW78xMjCciFcKx774UiL8d/6hJdls9QP3eD86iCi9AnQFbac8b/7kX/y11dCIHLv6wft8H+y9klSMMdWT+t73C1/5GrjqVB0IIecVD90fPnn8D779nyOh2G/01v0bzAuHCH3r67/XEK2oXfl3JJ2+glv5335cv3/sJ7eV5y+ANr2CauJMpvbvf+A98jEXhPLceaN33OMGg1fmPYlMo+ru22M/e+ZqLOP/r1fGGDItgDHnAHDOOWCYAEq5y1xMsGkySeYIEdthl6a3GIeYMIiQ7TAAHVnFluVi4hACbcdFmCFMOACMM4SQ43IIHVnBtu0izABCms4wvjSiyDnnCEPmMtuxISKUIiJAiCXXBQgzAIFJEWMUE+S4nHNHlJDjMkovOeVhx+WChBGCtm0DgBwHYcRECTsOwNglArcsYNuuKDGMEeOOIEKXQZc7gogAwAByDggmCAAHE+46yHFtQYSMc84dIkDGuW1zhAAhxHaYKGFCMLUdiIDrQsdlmDCICGMOwowIxLJdzhkRsMsAERGEl6bPGGMCY1yUIBEE26aEMMdBts1/VXMAuSBCXWcAuoQQ0wKCwAGAjusCCDjHjHGIXYQQpQ7GzOMhFnUAcCFCtsMJgQRjxhjnLsbYNLkkuRAhStk/zv1xCF2EgOMyDpiiEEoZhA4RkWUBQhjGiHPAucs4siiHgCIsUNtFyBUkbFmM2hxh4LqQcyiKmDGOsIMxoTYUBJcImFJACEMYUBu4zBVlAADggIkitm2GMICQOy4BAGABQIQYtxEGlgUAtImAOIAYMywg14WUckw4EbHrIFEijEMAGBE4tQDjLiYMIcS5AyGAGFuWgyCECAEIBAFDyNml+T9XYi6XJIwwdhwbE+44gHOGEecAcMYg4oIANY1B5DJGHBdJMgQQuYwjzBwHWxYTRAdjgdoORgwTbFHXYRwhxDmEECKELctBmHNITJNLIkMQ2o6LL028AsaBCxGkNiOEKQq2LAdyxiGiNr80wQcAh8h1XGzbjAg2BwKlDiacCNg0meMwCJF7aWBQxpS6EDGIgK4zTDhAnDEOAEcIuYzbNoUQOw4iAiBEdBlAmCGEGMccMEwg5xxfujLKEbEFATMGIGKYIAABB0ySIYCYc6ioxHFchDnGzKKAcYYFACFizIEIcQ6o/WWD67+HrgRBPX6s7taVnqOfXqGEglBxxYrE3/4draommXTN7bd4D+y/kk2ZjLnBYOr7f57/2k1Xo8AKIIxvXh/+/LPvfP270XD8N/27fykR/lPEZlnWC68+Nc6d4ftWOZHI1agVIl2PPf14YMe7/F/zBP31CVx38s/+a/7Gr3FCpMGByrWrpIsXr6z3HzKmT5028YN/cH2+LyXCf92L8DvfVAoFR5EVRzeAJECXQ+g6UAaWrvrEUtn0+WsKwykccWVJcX5pbAcgdFwoc1NX/FKxZPgDPqtgOdiVZdnRTSgL3AYY2Q6KWumsP4400/L6fhkjSYprGFAWGAUYX/I91FWfUNIsr89Hi6ZLmCgKzLY5kWzdUWRmM4aJDCizGJVUj1vWkYKZK0DHQIpsaa4iuzbnGDmiJOq6SwSPq+lIQa5LFRXbVLZ0V5EdBwAIHUkSdd0hYrU+MaFWYduiHg+hFrIdIBNRN0xRuRTDMFaZoWGvYJuEMB2rxLK4LAqWSZEkAYsiCdk2VVXImKSVLZ/vks+grSrIcaDLZGBZSIa2IyNakgNyqfirGChA7DoUipdikEUl4lyKsb2eeGE8JcaQALFr/08x2CkpAbVQMAL+f8yDMHNsTqgkA4yUQs7y+n1WgTrYkWXF0U0gEeAAhH5Z6l/aR/p8tEht5EpizEgl5QqRWb+MYaah+ommWR6vzypRB13KYyBFcCkgyHaRwkxD8RFNo6qHYyxqmiuIiqsbUIaA25IkmKbsWobqxbrpyDIVJNnUAWO2rGDbli3NVSTXgQBwW1b+8cp0DavYptTjwdSWLJ0I3HWgRWRHlkTDYAipzNSwSmxqeTyEUmxTLouioRuCKgPqutDBgsp0jXiJZYqEacQjmAZTRMnQTaxIyHYZdBC5lEewTN0fCGoZk0tAxIJjWVCSALWRCC37UqnlYsHyB7xG3mQiFDFxbRNJEZotCT7XATK2S/KvYgomE6CIiUspECVAKZYgdSRk/5M8BZOJInYlx8gLQYWblEjQciRES0pQLhYsv99rFE0mcElAnLkuDFvpkjfMDcfyeLy0ZNvIkWTF1Q0kC64NMbRdLDPTUL1EMy7FUIe4kqg4uoFkAIArisS0LllDEt2wJZkRQdQ1VxAVputQQb/0GTQlZiHMHRtasuoKgqRpjBCZmwZUIXNsRRF1nUuiNfalF+G/2aMS2LMr+uxTuFi8sj5gJsuFG25M/en3AePyxQvxzRvls6evhHzi3InFUt/7fnHxUmQYVx+64jzy/LPRt7d+Y/k321q64G9MlPzrEuGvDiFCR1vXxROfwUMflufO46p61WEsLklG7zToMvn8ucvW+AAACHmPfIQ1zezqtquq9ekzxGRCHB+7ElcBCIVEwnfwgNXW7kQiAOHf0t7Mf1si9BLld3+00iKSi4WbVt5mKaqgWzfc/YAWjQmp4tduv6scrvRYxetvuZ9DwfB4br7lTgYRc+GKex+0VBUXzK/deY+u+BhBX195B6KsFIne/KNbEeMOEJbft0pAjuOKX7n9bovI1O+7+e9+IhW1TG3dzX9/i1jWTdmz/P7V0HVMrHz11jugDbR4xTf+xz948qXRnu7rH1xbf/TzdF3TvM1PVn1+Mt3YuPy+VTWfHTu3YOGNt97dvu/AwKzZ1218vOnjjycbW+c88bO6I58Nd0298f6Hmg5+fGbR4q/ccW/H3v2nZy9cvHFz1779iaa2Wc+80Hzo8ODUGcseXN2x94OBhXOue3Bj545dZ+YtWvDks5079ySbWuc8/2Lzng8Hemdcv25Dz7u7Tl6/bPHaTX1b3uqfNXv6z1+d8vY7I33Tu3fsnPfYU4MzZ07d+u78zY+fXLi0e8eupWs3jnb2NHz++YJNTyQa22oPH71+/abB6TM7d32waO2GwdmzG/Ydvn7V2rHWrqpTpxat2Zypqa354sTi1RtGpk5r+fCjJWs2DM+cGf/05I333p9ubq++cGbu6kfzlVWx8/3LVq0b7+iqPfL50tXrxrs7oyf6b7znvnRVg39ifNl9a4oVlcGB4RUPPDzeu7JHmQAAIABJREFU1q2kczf/9FbDE3QxvunHdyDdKMbiN6+8DZuUEnHFfasZxtwGX739bpsolt930w9vkYvlUmX8Kz+9VyiWTa9/+b2rkG1bSP7q7fcA3S7HYjf/3UpPOj/R1nHTP/zUm0pr4diyBx4WNU1T/Dfeea9QMhKNbb/3338QGB4b7em9eeVPvYlMJlr1lfsf9OXyBV/0q3fdK2dL52bO+dbtd3Ts2HVq4dLFmx/t3L032dgy4/mXW/cfGu6cumTNuq69+04tXbLingd7duw6sXDp/Mefnrr9nWxLS99LrzXvPTDQO33Jxkd6tu84uWzZ9Q+tm7bl7ZMLFs/92fO9W7cnW9tnvPpGx469493dcx99qnfbuyeXLlu06YmZL77UP2felJffnP7qG4nmtqlb356yZftYd/e1zzw//dUtp66/fu6Tz8167oXT1y1tOfzx4tUbc1W1FafOLlm1bqxnStOHHy95eP3wjOlVhz674YGHJlo6o8PD19+3Ol9ZE+4fuv7BNbmmhqqjJxav3jA+dWr05MUb77w73dDsm0gsv3dVKRj2jyWW37861dgUP3Zq2er1ybZWf//Y12+7M1/TIBWLX7ntXsvv86YzS+5fm62tC18YWnHfQ+nGBnk8d/MttxV9YczY11beZnoDVFFuuOsBj1a0BN/yux5gFBiRyE0/vMWbyoy3d3zjhz/xJ5L5SPz6Bx5WCoWyGrjhzvukolGsqb3pBz8KJlIjPb03rbzNPzyeqmn8ygMP+ZPJgjd0w32rlFR2rKXz2z/4Ybx/qH/GjJtX3h4aHEk2tC5/cHVkeNQIhZfev0adSA91TfvOD39UdfzkhTlzv/7TOytOn59obF22Zl3k4vB4ZZX0+ZeLRv+FAlPIR159JfrcM9CyrhAShcOZ3//DzH/+E2Sans+OVq5fI184Dy63pxkAwLldUzP5l/+tNG/+VYmuGAtu3RLf8tqK+cu7O6dCeBn0Cvp1lWEexft//c4fVhbLFQ/eh3O5K+OB/ncexlxVTf/+/535T99linoFGh8nQvDtrVWr7hfGx+zKqsTf/I/CjV/jknQlciFCQmKi5o6fBne8g0zjy+nCf3YC+bStioFCWrXKGHPV0vzFHIGuYpaiyVHb7/EnRwL5rKuKgWzCo5cEqquWHipmMXRVU4umx6lfDeUmg/kMl3EwP+m3ygKjsqn58ykEXcUyguPDriqF0xO+XJqpQkAreLWiCF2R24FCGgFbMbXY6ADzKZ582peZYLLgyadly5AtDQkwlhrH0JW4Hc6lgYA8xayvmFWMEgSOt5ASjSKCLDI+jLijuFYkkwSIqVrBV86p1IDIDZUyPqMIgBtPjgqES1QLJkYJdBWrrOpFqZwniIeSY6pWgCKqnBzH3JYdM1zKEm4rZtlbznsLGUZAKD3hsTRIeDAxItqGDF2fVlBLOSiAUCEdLGYdVQynJnyFLMA8lEnKzCJUV4ySNzsJBOTLpXzFjO1XYuMDHrPMRRgsZCTHFIHt0Qv+Yo7J2FfMRlLjVsAbGh/y6CWE3FB2UjE14pg+qgfzKSbhQDEXySWpT4mkx316CRIeyGdU2yCuJRvFUHLM9qrh3GQwn3YkHCxkvUZZcC2ZGoFiFgNbsbR4atzxqf50IpieYBLxFbKyWSLUUBzDm50EiCkujSdHXY8Y0IvBYoapkreQ9mp5ieoCo95MEjJbNUqRiRFXlYJ6MZRNMQK95YJPyyu2KTArUkgT6EpGKZ4edz2iP59SqCaV8xjxUCnr0QoQ8VhiTOCO5FqRbJIgJhklXyHrdSmCLJhJ+otpSEA4MYq5LVMtlJrA3FaZ7S/mVK0IMItkUx5T4xKOJkZF2xAsLVjIiNyRywXVLHnyKSYL/mxSNUtARPHkqMiowKxAKScCV2LUUy74y3lXIYFcylfIMgH5sknJNgRme8p5XyHNReQv5kLlPA16Y8kRv15iEvbnJlWzJNiWrBd9xSyXsT8/GUqO0oAvlkp4tTyELFjIKFQn3FYtLVjMMhH6S7lYNmn5vYHxYV8xC0ToK+ckoyQ4lmoUfdlJV0QBrRhNjVkBb6iU8xVzQCKqllf0EmFUdqzg+LDjkYL5SX92kkvYX8p5tYIIbAKcYCmLkSs6Zmx0EMrEU0j70uNMEby5lGoURbMsAjdSSAuOqZhaPDXhKoKvXAznUlCAipb3l/IeZomOGZwcFxiVHCM0MeIqQjCXCmQnMXC8ruUrZkWqI25HcykBcZFqoml86UX4z8Qsqf9ixab1kReevwLjGsA5YIzW1iX/5r9nv/0dnMv59+yuXLNKHBnionjZn4dzq6V14gc/Ks+ceZWiK9/7e2JvvHZd39ypU2YgdHmLbf89ifCf6mypdOK1t14cam7O/Lf/4YRCV5NZ4aVbRhgyN7j9rfArvyD5PL98jQ86jtnWPvn9Pze6p0Dmht58I/TmGzibuQK5ELoux7i4fEX2279nV1Rcsh34UiLM/Kfvkm+s6Hl/9+D06RJ0gwOj5YqIFgjK6Tz3iro31HrwwPlZ10btrP+z84m+PiYAXDBdReACiZ3vL1fHM5GKzo8+vjBtGlFw4NjpTGubDO1A/yiNBMvRaOzkGRBRxmpbwkOj2ZpaCdKqo8eTTU3crwT7x2y/kq2u9kykoELy0cqO/fuHenqhT6j+5FimobEci4QHhyW9NDp9evWxE4Y/oFdE1Mm07vUjBfmHk4JpJLvbvCPJYCE7OG1qxdBFWxTH69tbjxw2/IFyXdw3nJB1/cL0GcFUylMuZGpr4qfPA5GMdnQ1f/65Loqoxs/KlqdUvNA+vWqoX8lkhmdMbzx53BTViZaW0GTSIQL3iepE1j+ZGJ05TRnPBNOTg9dcU9l/geSLuZYGsWREhobPzZkj6Vp4fHyita0iMewfnrg4Y2YglfKnUsWGKgoFNZensYDlktZPj5xevDQ8ORoYGJuY0iU4NjQcxych3ao8fyHR12NBufWjg2fnz6/IjxkG1qvi/kxaTmYLjdUuJNWnTiemdZeJWjEykqyvD5azofNDic520dSDY8nhjm5bkabs33dhxgxRBDWHjyZ6prgyJnmDK4IrCpFz/UY8lK6s6ThwaGDaNCzC2v7Tk1UNEqdkNO+GfIVYLHbqrBkNJJraqg8enOydJgluzSfH0vX1TtgX7B91JSHbWFsxPGAr8kRVU8eBgyMdncV4ReeB/dnaOjMexAWDY1wOBhtOnrB9ymhzW+f+/ZPtHRNVDU1njom60T99esXAQCidHJgxXUnlEWeZmprmz45ailqur1CHJgLF0tlrrw2kU558tlxbGT13wcHSSHdP0/HjFoLlphqgu7KmpWrrIuOjwURi4JqZ9adPUkAKDdVSoewigfklOZkNJiZGr+kT08XYyPDQzBnBxDhFohXxx4ZGAHXzzbUkXYgPDx+/bmnt+MXgxZHhvl5/NqOk86W6SkOQvdkCDXstorYdOnBqydJAJhE52z8xdYpCy2ouS4N+kwnxC4PJqZ0aljsPHTq1YIHPKJFcWauKe3JZbyJdrK9yiFT5xbHJvp6y5Ok8cfhszzU+oxw5059ubZCpIU2WtJpY0R/yj47bEX/ZF+7au/vcwkWKVYqeOp9qaaY+X/z0WexFY43tTR9/OjqlF4lcTuZK4bCIHf9ggqlSpqYqduYC88nJptbIwFAxHIEeUvvJ57naejPqC/aP2aI40doaGRvHhGcqqto++jjR1Jyrru48eDBXVWVUBFFORxCVopHakyeYImSbGuo//iTZ3J6rqIyOjhiqgjyidziBXGe4p7fh2Bc0GBjAvtDadeL42JcSIUAIUsvz6afR556R+i8wRb0CPgIgpPdOm/yLv7IaGoXJyeC728MvvwgpvWxuhXPAuT59ZvK//qVdU3vVmQxeUgY9B/ZXPfnY3IaO+QuXi4LILpNV+TUS4a9K5fF4Y9F46rOPjeFB2tPLriKzwku14hwgZHR0sWBQGhz4Z3zyb0o+pVPKyRPM76d19frMa9xAUEiMC5nLx1gIAcbk82elwQEnErUrKy9ZVv+WS4TGlKmyEup+/W2dqLoa6Hlpqyb7zk+ZOVzTbhMh+sX5pp0fFJWgYmo17xygQBqYMn24ql1T/fHjZ5u37y0FwzBvNe3aZ2BPIl7ZnDZTFECAe36+xZC8JpKat+0BGA7VdpzvmG5wMTw03P7Ge9zikzUNU59/3XHAQNe0wfouU/L4Lo62bN9NHawFw52vboMmG+ns7nxlu5QtFoKxmn1H5PFsuqbufPv0bKBCKOs9L2xRhxOjXb3Nb+9Rk+lcfVUz4X4BjJJI2xvvBi8Mnb92bu8Lb3r7x87OnJ+I1maiVZ5Epu6Dw76BiZGW7vZ3dofO9I9M6e4lWlgQzuBQ2479nsGJQrSi+uOjvrPDwy3dQ41dmVAlspzu17b5zw6Ot3dVf/hp4NxQsqk1fvxM3fsfD02ZVvXR5zUHjgx2TU9GaoaaunHRqP/ks8oDRycrG5WRybYdexONrf0tU0erWwXdqDh0rPbAkVS81j8xWbPrYD4UG+jsG4s12ERq2nOg9sNPJto6/KeH6vYdykaqMvX1p5tnOhzVfXS0cce+8c7u4Kn+ur2H0vUNo/HmgeYeYDgVp87U79hfiFSQvN7+5nsjrd3SZL713d266Hcwad3ynmvx/mkzhytbS55A9NT55m27DY/f1VnTzn0mkM2od5YKmW3kNNzxi+0WEsuit+XtPa4gDYWqOh0yBhVBNzpefQeV6Vhrx9SnX6Yu0pvrpgLNFYRiymp65wPX4pPVzdN+/grJaeevmTtS2ZLxx4MXRtq37+I216RA83v7eNk5P+2aa372krd/9Fzf3K63dvgHx8aaOi629GYCFahsd7+xPXx28PysOT2vbAtcGDkzY14iXp+K1/knEnX7jngvjI20TGnZvS96/Nzg1OkjFa0TlY2oaLTu3Bc4N5SPVVd9eixwoj9V13CxdVoyUgtc3vXGu6ET58c6uisPfhE5cW6yrnm4qXM81kgMq/2tXbGjp87Nnte2bU/02Lmhnr74+YvVuw/lIpVyMt+8bU+qtr6/bepIdSu2aPzwifr3D2ZitUomV7/jw3wwKlcFp7iFtBJQPzpTs/+TZHOr9/xY494D2XDVRFPbQH03BULN0WPNb+9JNLd6+8fqdx9I1TUqAp0mS5PMBmNay9t7dK8PGXbz1j3FYLS/pXeosRtwEDrR37zj/ZIadl3evuW9ciBSVv0dr78LIDBtsW73AYfiyfrGs20zNMmnpnJTXnzTcUDBF255axdkcLS29ULbNI14guOJttffJUVjqHPKtGdf4Zp9Yeq1o1XNuuL1jqZa3t3Ly3aitnXqC6+I6dKFmbNHqlpT4Sr/0ET7u3uQZlqCXLvnY5jTB9t6h5q6Ct6Iks13vfw2KFqpysa29/agopUOhaXPPv1SIuSiKKRSobfejD39BMmkuXT55s2OzVVPYcWNk3/xV3ZFpTQ4EPnFC6HXXgYAXLbIyBhAqLRk6eRf/r9ONHZ12eD86p1Sj3xc9eTjsyrr5y9coUgKu3zM85tiUs55Q13zioVfeXf/joFnnyr88Z86wRB0rqqqMQYhzK+40fUFos89LfVf5PjyIDknRBwbjT3xqJBM5L92c/6Gr9jxePjll7wfHbzsLVkIcYQ8Rz8l6XThq1/P33AjU5QvF2UJpgldV9J07FCilYllQsgRdyHn2DKxaYq2LTCOHRfrGgQcMQcCLliWYJrYsiRsYtMQqYWo5SNI4Aw7jmAayKHENLBlCbYNMMauCwEglCJqSbaNHVvQyoJhIMYg4BACYlnYsiTbJjYlpokNAzGXGLorY9EyiWlwACBnl0anEGfENJlIiE0lywQECralCsTFnNhUoBZiHDuOQC3uusSmEHDIOWIusSm2bezYkFqi42DHFqEtY1VwbWxZ2LElamGbEsPArgshAIwh5iLTFCgVbCpSii1LsExo24JlEccRLZNQSkwDYESYg12HUIotS7RMYlPRNLHjYNdBnHGERGphyxJMA9sUO7ZgUcgYBBxAINk2pha2KTYNYpmCY3OMEHOJbWObCpaJXUcyDGxZ2DQRd4nrQM6IaQqWJVALAk4sEzu2YJnYskRqUcsklAqahpiLGEOMEcsQLIvYVLQsZFmCaQgOlQVBsDXsuoJlIssUqEWoRRwbO65fFCXuEschlilaJnIdwTIF2ybMESEWARcNA9tUpJRYlkApMU3s2pBxBDi2KTItgVLJNKFtE9NEritaJndcwbEl00A2JY6NXRcggG2KqSXaNnZskVJomcS2ketCzDFn2LaBRbFNoWVK1CI25Rgi7iLGRGphmwqGDikVLRMzF0CAXBcxVzAMQqlAqWjo2DIxpZdGvrHrEMMQNQ07tkQppBayKTZNYtuSZRLnl6VGjCHOAASCoWPLFAxdZDa2KTENGSNFEAh3RccmlompJVjmpV81QxAxFzOXUCpYJnFsQdcF2xYsCzHsEVQZuha1sGWKtuMSQEwDOTZmDmIOE7Bg6Jhaoq4xv0BMk1CKbIotkziO6FBiWdjUAeCXviZ2bUErE9MQKBUoxTaFnGHmuqIoUEswTaIb2HUF0yTUgowhwCDghFqIUolamFJMqWSZiLkQAMgZZA6yKXEcybaR4xDLgoBh5kIOsOuIlklsW6CWYFNiGtBxf9slQgi5KKmffxp8603f/n2cEH75nVLINGl1Te5b385/7SamKJ5PjkReflE9coRL4uWSEdBxmCTlvvGt7O99l4nSVad3AQA4xp5PDsefemJ6uHL+dV9RFY/rXsm3+A0ZrEuXCIPBSEDxpD/+0CzkaVc3k6SrzgQGOg5tbLSamoVkUhwduWxgjjHWdfncWSGVovWNZnuH2doGMFFOn/olqXiZiI3k88qZU+LEBG1qcoNhyH4LMNa/7UWYX7xAInxw7rVO0Fuqrs50tEARx0ZHJEZTrS0YOBcXL0J+QfdFBmfPEpEbmpiQXNOoiGmRUK69aaKrmzB64bqFOOTJjY7k21qATy5W1+RbG3MNtVYkXG6tzccrq8+eBqpoxUNMkodmX+OEvIXqmmxHixX0xUeGVVtLNzaKGPTPnU1jAcfjGZozy/Eq5cpKozo+2tvDZCk5tceoisZGhiXbgiopVFUVG+tyLQ3likqzIjLa1WPlsmNASlXVYYJGZk63o/5iRVWhvmayvTWWHI+mJoq1lbbqyXa0TrY2I1kY7+uFUe+oLWZ1c7ymzY4G9XhkZOY0LOJET3e2sS4+Puwr5YGH5OoajHhksru9FI3ReGS0txtJJN3aUmqoKtbV6hXx4Rl93mKuevCiFo84Ia8ejQ7P6HO8cqGpvtBUK9o0PjYMVJJuaMQiOn/dQuCV9Ghsoq/Ha5YDqUlCWKG21gr6U1M6U80tBLj9i6+LZ8Y8qRyQEA0Hck1NhYbqXH29HfAm+6ZwQurOHDdiYTfk1cPhRF+vFfIV6uuSba355kaB0QsL59vRgCvJ/fPnEMJDE+OKrZer4no0WmipH+/pJszpX7SQ+vzl0eH+ilbgEdK1TbnWhnxjnRUK5VsbCk2N5TOnMm0tLORhsjww91o34ClUVReb6zIV1aVsIS15xps7RAwGZl1TrIpjEQ/OmQW8Umhy0qMVirXVTsCbb60f6+nCEAzPvcYIBmg0VGioSXa0WeGAEY8ku9rCyYQ/lylVxaEiTkybYsaDhcrqcl11orM9mhqPpiaMqij1eSfb21MdrUgWJqZ0G1URT6kUnRwvx6NmJGhFQ8PX9EGJTLa3Fxuqg6mkv1xAMso2NJrx8GRPRzFeQcOB8Wk98cSoL59zAioNB5NdnXpNLFvXYAd9QzNnQpWYkejojKmuV840NeZbGgTXrhwZggpOtbYRyM4vvo55JSMSHevrtVx3omyXFTXT1GoGA5O9HanWVsLs80sWBYqZ8Ng4l7Ed9GXr6/NNtdmGBsfvS/V2JEN1tP/E+YbpLKCaoWBy+hQ76Ek1NGc7mgGGVSNDAnYmW1ol7lxYch0LqEY4Mj6txwoGjFjUaKgc7Z0KAB9YOB9LMD40KEDGvVK+tq7Q0phrqrfCoXxLQ6GqsubiBQxcKx5yFWVw7mw3oBRrajItjaXKeNVQv5eWM3V1BMOhmdMLdTVIQiOzZrh+JZBO+0r5UnWFE/AVm+rSXe2MkMHZ15qRUNVwv0IN5lfy9fXF+pqJznauiOnOtrzq8Rw6+FvLYHFCIAChrW9EX/q5+sUXXBSvYCE2siy9d2r6j/6kuHQZl+Tgtq3R559VT57ksnTZ6IpSNxjM/MEf5b/5bS4IV6NNHBcE9fgX8Q3r+tTg0uU3ez2+K0NXlwewLmGscDjmk5TsRx9aWpF2dnFBvOq0Lei6dkWF2dmNdU05feqywT5C0HGkwQH5wgVaX281NVttbXZVtXLyBDKMK0Bs0HHkwQHl9Cnm8ZgdHVcj3v8PAVj6lKmVpjn99dcFy2IEzXrmeUeWfMXSvCefoR45PDTasXMXdFmkmGrZulPRyo4sLl2zgWAs6vqUV99wVSk4kezYsVM0LdHS57zwcjCdtipisx9/GgAumsaULW9J0JFT+ZkvvCTrBsSg76XX/MViKRKe+9hTl5iw2c/+XLIMJV/s3LFTKhSBIvW9+LJ/cjLZ0XHtMz8LplMQoZZdeyP9AzTov+b5Fxo/Ojw2s2/+E09H+vvzzY3TXt0SGhlmAml694PKs/3FSKz3za31x46P9fXOe/ypyMDgZFvHvGeeqT32OVPVxkOHK4+fKFZVT31za+Nnn6entPU98XLss9OZttZpb7wR6x8EotC8/8Oqz49lGpvnvPBC53s7h2dfM/uZ5ytOny7V13bs3Rc/cdKIRuo++2zKjp2p9ra2nbtb9+2faO+csmfv1De3mF5f7dkzzXv2WYFgbGhoxmuvjU/p6Xz3vb7X3yi0NdcdOtLy4QEmSbGB/vade6jPU3H23JxnfpZtbmg5crR1995CXXX9kc9b9u3jmFSPXJzy4htMFgPjE9NefaPQ1lL30ZG2PXu1iljN8dMzfvELiIg3k+p5c5urSIFMtu8Xr6TqGysuXOjZ/o4MoGiUp7y+Vc3lrFhk6YMPi9REjE199Q1AYCBb7NjxnlwsYQJ7n3w5kJg0Y8EZT70guA7krPf1rQRDNZm6Ztt2fyrNFKnvpVcCo2P5hvoFjz2JKYUQTfn5FqlcFnSz8933AiWt6PfP/tlzoYmJbGP98gdWKYWirajTX3tdKeUFhjrf3REYmxia2rds/frohYuT3b0zX3whNjjARbHj3Z2Nn31WjMWnbH+n4fCR0Zl98557IXbqVKK9c86LLzYdOQJkqe6jwzVfnMhXVXVvf6f1k0+HrpmxcOMjzQcOJdrap76zI37yJBeE5sOHaz79otxQN+31LT3vvndx/pxrn3qu+sSJUkNdy64Pao4fY5LY+uH+9j0flKor23ftbdm3f2Rm36zX3qw6ejTV0tZ89JO2nXtcVQmMjvW9/mamtbl1777pr76Wb26oPXSkdf+HLsbRbKp963ZXlgLjyWnPvqrVxKo/P9Gy632tuqLm0y/a3v8AS5J/YmzWM89DAXsKxekvvVKqqao+drx91x4zEqw8P9j6ytuIA1Uv9b62FROo5PNTX95Srq6oPHH6mhd/4UTDoTPnu3buhrYt2rTvpVcZIa4szXzhJdU1hHy5dedeT6HIZHH+5sfiE4l0fe38zY+Lhu4KQu/rb4mmLpf0a176hS+ZYl5l2i9eiwyPpNpbF258VLCoo3jnPvlEIJNCjtvx3q5AKl2KROc893xkYCDV1bFizXrv6KgZCM14/XVfLivYZsvO98PDo6mGhmWrH647d26su3Puo0+qmSz1eKdt2+6bmEgHf3u9CJmiiMlE7Jkng29vFdLpKyCuLtkClhYtSf/xn+pT+6DjxJ55IvTGa2IycdkeOAAg07Rra1N/+mfFpcsAxldj3wsXReXE8fja1b2Kf/kN3/q1Zji/piCXiXQ5Qqi7u++6GfNqdu70v/4qtO2rcXkmtG1aVZX63vfTf/TH0LIu+3eAEABAOXm8+r67/Ps/cAKBwpLrx398q9XScgXGhQAhjrF84Vz8sc0VG9cBCK++Uc3/oKP7gt5c0ZK9VPKIhu0IioZEopmOqOj+UDBf1mRvWQ14M3lTVCzZQ2zgcqwJiqSZLpFL3qC/qBuegO4PefJlE0sGliTT4ZDoslcqGQ4kBV/Qq5mm6te9AU/ZMB1gKD5iu4Aj3RMULMclYikS92QLtqCU/UFPSXc4oqIMOSIlveAPEco4wLovKJm2I8i64scuEExHlz3ABaJBy/4wMR1AWdEbkg3Kqat5/ABgSbNKis/FAjHsvD+MTRs6vOgJCJrJqVv2+DkDoqYXvSHOsaSZBX8EWw5wgeYJYNMBSNA9Ac6gqpklb4A5QCoZJV8IOgAbtiF7bEikQln3h01RUYtaKRSzBUkuaprqYw7EpkOx5BBJNixD8VqSx5vJlQNhR1SUkmGqfheL2Oam7DNdqBTKpuo3VF8gVyr6AiaRpbJhqH4XixJlJhYNQVbzJdPrL6s+n2aVghFb8ihFzSCyA4hkug6Wyv6wv6hpWCr5Q95MngqKISqCCzmWdNknaaYLhYLq8xVKpuzTVK+nrBtINEVVsBzAsekJSAZ1HVYKReWibipew+v3FHVbVEzZS2yAbK55/AJ1GMclX0jNFi2IS96QWtRcF+iqDzIEHV5W/YJJOcd5X9BTKNlEsgSFu1zSrLLsZYBIZb0QikIGocO1QIToFne4rvqZC+SSUfIEHEAEg+b9IUxdSN2SJygYFFJHV7wOx4JulkNRBpCsW4VwHFEX2q6meDF1uQuooHKI1bKhefwMErlklEJRyBF0gO4NIJthmxmq37KZp2QYqt8RJLmoG5KHcyRaDhVVS/bKBrUUn+EJeLP5cihmcqQWNFPxuUgQbIcKio4lpaRzUE0aAAAgAElEQVSbkqr5QoF8qSh7TcWrlHVd9tpIkCizRdWUvJ5cUVd8JUkNFcuaL0hFVS5pBiQOESXTdrBkeQNyydCJpAWjnkze8IWopCplwxZkSkRBsxhDBW/QV9So7NF9AcWwDRcYip/YHLrQ8ARE0wYAF6MVUtmwBbnsD6klzebIkDyQushyS4oXUZcDVAxG1aLmQqIFInKx7HKkK15uM+TwkuLDBgUMFPwhtVh2sFhWA5JBXQYN1QcsB1tOORghBgVAsCXpt3HfDYRMUb0fHap64N5LrjVXMJMOXZcLJPud35v8f/7CaO8kmVTVA/cG396Ki8XL7Z+5hK6M7p7kX/9tad6Cq7SrmIuicvZMbNX9nYJy443f9nr+l9AV+A2nCP8ljwUAOHL04L6P3h/77h8Ubv7m1bqgHEJkGr59H1RsXMcv31r8Evxnqpr/+jfSf/w9SKmQmIi++Hzg3Xdcj+eKrpdzQTBb25J/9TdWUzMyzf9TOe1/Z9FoZGS4FK+Qbc01OZSQSwgpm1gEZU8wkEjkKquqSyOaLpSjUcXRmcmhAB1RxCWTiLzsCQQSk4V4XHU0lDf0cEh2DdcEiHBHlnHRCPLiZLjam0wXY3HV1XDe0AMBiVPH5BhzR5FJQSMCKwZjwdGxQjyuMhPndd3nQwIClqsYJS0SEQqaIwhYgmKuZHh8InFcEyDmco/gUOTRC+VoVMyXXCLowaA/laKSJArMMblg24VYTC6VVKOoRaJCUeMQacGgL512BCECihkYECyzGI8rxZJgGWY45M2mTUHVAwFfJs0QxgqkDpZ13QkotoPlclmPh9VCwWEIKdjmWCqVi/EKQu3AZCJbV+svZpjJStGYZBjENKGCHUCkYpkHpDLxBpOJfE2Nv5RhBrPCAdHUXRsgGVEkKvkiD0hlwRdMJArVlZXZkQLz2kGvaJmuDbAETKKouTz3S2XRF0okclWVPqPAdEYDHsG2gMW0QJARHBkby1dUeJwyzunlcERxdWZySKAji7hkCdgtBkKBicliNKq6Os4bRtAftjJ57seYubKMSoaAnGIw6p9IlqJRlRk4pxsBvwht1+AEutSj4pIhQrsYivnHJ0qRqC3LwWTS9HpE5DCDcYx0f9CTzfwyTyKpB0O6x+vLponrliJRqVz2aAUtGiFFHQCohcPedMoWRFHkNoWiZRZicblU9mh5O+QjJd3CshYK+bIZF2MiQepgSdcKFZVysSTrZS0e8WXSJlGhiknZtCEmMqSMKKWSE/JYLlHKJT0W9mXSFlaAhyCduhwSGZpA9BQKmeqaSD7JdNcKB0VTdx2AZGRDQS6ULpU6mJjI1dT4y1muu1YoIFq640A/1Eqin5QM4BPKkj80MZGtqfFrOaa7VtAnUZNRjiRoCYqczQOfWFKC4fGxXFWVzygy3eFeQaFGmSlEBJaoKtks94olTyg0NpavqvKZRV62rYAPIIjLRtApZMKVSipfjkQUZuC8rgUCMqCOyTHiriKhkilCuxiO+ccTpXBEhhbJ6YbPJyLHNRjAWPf7PbmsCJxiKOqbSOrBIPWo/kTS8npE7DomR5yXQyFvJiNAx/XIQqZYDkYsVQ1OJi1FIQJ3LICZWw5HfOkUk+VSpvjb5kXIMYa2HXrrzdCW13EhfwXdKZfQlevzpf7kvxQXL2U+n/r50fjjj0oXL1zhW0qpPn1G6vt/btU3XLqCqxFdSf0XKu66vZXIv3PTd1XFw/n/6iTf5UmE//TU1TYiCPPbttiqYnZ0Xb2PPW1otNo7vJ8eQZZ12RgLQmjb8rkzyumT5XkLnEBInzKVKYp6/BiA4Ao2kULGhHTad/BDrihGV/f/mW3v/7ZEGGHwK3fdi0zKFPXrt99ji5KULy19cI0eCgZGEgvXbXIE2a/n5z+4WS7rhtd/48pbOSKCSZeuWmeriprMLFy3kTMIMVp+94OebL4Yr/jaytuQ7SCHL16zQWIWLtvz120iOqUez/Lb7/Gn0pm6+m/99E7BNF0GFj28ATMGdfu6dRtkzTJ83hW33RNOpkZ6pyy758GqM2eMYGTmz16oOXmmWFGxYO2mxo8/Obdg4VdvubPp8CejU6ctWLOp7vhxLRrv+/nL9Z9+lmpqXfzw2vb9B88tWHDDnfc3fnho4Jo5Szc90vjR4VIk1vvKluYPD463dy3duLl9996ReTMXPrCx/YMPL06/dt7jTzV9eEiPRKe/9nrrBwdGe6Yt3Li5+933zixduuyhtR3vvJeYNq3vhdfadu9Nt7W379oz7/FnhqdN63v9rdnPvXh+1rwp27Zf+7Pnc9X1TZ9/NuvJ54oVVZXHTixdv2lo+owp23bMfvLZ8d6e5g8Pz37iqXxFVdXZc3M2P6GHIlWnzy1Zv3m0Z0rnzr2zH3860dVZf+jovMce18PR6oHzszY9YwaCkXMXFz+0JtnV1fLhR/OfeDrV0lz56fH5mx81fQH/5OTi1Rssnz84Or70/lUTrR3hwZHFa9ZyFyAAlt3zkCebL8Urblx5K7EoAHjJqrUAQqmgLVy/UdAsx6OsuPN+XzJVrq644bYHpELRJtLiVetExyGUL1y7QS0bhte7/I57g6Pjifb2m1beqhaKVFKuW71etUzHRQvXbfKmc9mahptW/jg6MDzW2fW1W+5Q0jk9EL5+zbpAKkWJvGD95uDQ2PlZ837np7c27ztwfva869dtaPzo43Is3vPq1qYDBxNNbYsee6J75+4zi6674e4HW3fvvTB7waInn2r5YJ8eCvZs3d72waGxzp55jzzWu33H2UWLlj24tmvnnoszrp39/Attu/aUK6p6397W8c6udGvrrGd/PnXrtlPLli1dvaHr7e3jvb1Tt2zvemt7OVrR9d6u7i3bJtvbZ/38FzNfe/P04sWLNj7eveWtgemz2g8fnr3pCT0YjF0cWLxmw3h3d9uefXMeeyrR2dn48WdzH3lci1VEh4bmr91s+ALh4ZGlD63LN9XXHf5s7qNPZVpaqg8fm//IY4Y34E+lFq9eT1WPbzyx9P7VmcbGuqPH5m9+LN9QFzt1ceG6jbaoyFrp+vvXAEI82fySB9flqqtjx88s3PioVl0VGkouWr2GCTI2zevvfsAViC2py+9bpRolYMN5Gx+TckUj4F9+x33R4dGJjs5v/fg2byarq95Fqzco5bLrogUbHvFmi1oosPz2+yoGh4emTr35Rz/1JNPFcHzZqjX+dIpiaf6mx/xj48m6pm/ddkflqTND06d//ce3+YfHChU1SzdsDI2OMoTmPP5sdHB0uL375pW31B4/OThz5tdvvTvSP5StqL1u86PhgeFkvFI49vlvj0TIMRZSqYpN64PbtiLLurT46gqoAbumZuJHP9FmzeaSFHjv3YrNG8XRkStJxTkAQJu/MPnXf2tXVF6l4/BcEKT+ixX33tlK5N+9+fdlWfkPoUWvXIpyXXfB7MW6ofMnHwcQ5b9+89VaWULKs2aP3nZX5ZpV4ujIZctzEELX9Rz9tOFv/3ps5U9pTU3mu39gV1dHn34CFwpXZqqD8/nYo5vkM6dTf/JfmMcDfmssoqHjirqOLQpcF5dK0LYR56JpQJch7oi6ThhDHIq6Dg0DcC5YFnYcxLhomshxsMtFw8CUQsZEXcccQMZEw0COA22blDXEOGJM1A3iOJAxUTNE6gDGcLkELQu5TNB1bNuIA8EwMaUQQFHTka4DAIlJAbUh48QwGRGg6wqmeUlkFyl1AQccEItC4DDHwRaFEELGRYtCCDgAwiXneQ6QaQq2hQEQLEpsG/x/5L1nlB3XeSVa4ZxT+ebbt3POje5GBkmAAAiSonK2NfbIT+PxLMfl5/AmPXvkINGSKIoUczKTSYoRjCBAEokgQRDMJECQiN3oePO9lXPVOe8HZY/eesteS21pnqGp37VOra7v1D279/6+vQnF2A50XUIoFATQdSlC2MCHQcDQNAgi1napGKMgQK5LURR0Pei6NCEgioHnYYyZMISuR1EUG0XIcUgcgyiGjsvEMR1j5Dh0hNkIQ9elMWZ8H9oORTEgCJHjMlHMRBFyXDoM6cAHtkNhzEYRclyKUEwQQMdlMWaiCNoOHQQ0jpHv04SwUQxtm45jEGPkOEwUMQyNXJclmMIYuh6NMUUIdFwQxwzDQMcDfkATivtkJC2KkeMyhDAxho5L4ZjEMes4MAxpQpDtsEFIYww9j40iOgig4zCeR2EMbRd4HkVRyPOBH9CYIM+nPZ/CMfQ84AeYorkwAn5AEQo5DghCChPguGyMP7mZjSOKpoHr0XFMUQzwfeB7NMbA95EfUjGGrof8gKJo6PusFxCKYh0Heh6DMQhC4DpUFAE/gJ6HGQYGAXRdEmMYhtD16DgG/idFpGAQAtelKIp1HeS4NMUAL4CeRxECggC6Lh3FjOtC1yUUBYIAfdJdEAbQ9ZgYs1GEbIeKMRsEyHZojCnXRY5DhzGDCXS9T2rHuS6DMRuEwHaoKKIIho7LEswSBjouG0Y0DZDjMjFmg4DzfTrGbBRCxyGEUGEEHIchhIkjZDs0JmwUIddjYowDHzkOE0YUxshx2ZhQcQRsm40iNo6h44AwojFBjguDkBAK2A7jegzG0HHYIGAJ+aQcNMUgx2Ec+5OtC3yfoSgUhD+tr+vCMCYUDd2frgM9jw1DCmPgOKzvMxhD1weeTzMs9DwUhJhmkB8Az6cxRr4PPP9/KxNBihDpw2OFm2+ApdLKfapp2lmztvyf/1skJxjfyz7+SHrn4/SKcqApiiIAGjsur/7+H1I0c+FiAG52pvDD73X44Vd+7be4n9/h4hcpEf7shSB68eDz77/z2vKf/l/GZZdfuHudAMAtzLfcdrN44sMVblxCsKJU/vCPrYs3YwjFEyda7riVOz+zYv2UDkNveLj6e3/oDY9R5FcHY/3LEqHcbPiKIgRWELEsSwjLAtNhOdqU01K9YeTznfqCGit+MiF5BnYiigOhKFBOxDGhqaTkasPM52TfgHXTzOf42AtiAOg4Roh4cSZsNpKtieWS2tYuxU7sYV+WhdD+5B4MAWs4EBA9lZPqDTudlkM79nAgCoAmEWYEz/IVmbH8GEIAMVfXXCWJ2MiPAUMwi2gfQ9E1vUSCa2gRQk46LWp6yHEC5bk0z4ahpyjI9wXP9GSFdgNCMb4siboeclzBq9bZDGcZensH9FzgB5EsiLrmIcmXJKVWiQEEPO3SPOc6lAg9gjjXDZISZ9sBgxAbBRSEjuekU2wQpCqlZme34mhhSLup9Ccj8RBgHNPQsklSsJAiNxpWS162tSAGRECcbeKQMALrcgq0bcBRFkrIjbqdz7Y1F/VICFIKjMIQs5CNfSgiw2RFxoJKdnGh2dMtuUYYMrHEw8CLY9qTZMKyyVrVzObkwCBW5CaTYmQFFGKpGLMs5cUcFVhKWqw37XRKimxYN6xcLufV6yADqQgjhAPMEd9WkqKm20pSDq3YxZ6iiIH105IhyOo2YmI90yI2mk4yGUMoNZuBKPKU7zEcTagA8YJpICqwlLSgap6k+LwgmjpNiKco0PVE1/ATCaDbhKbsTFbU9RAhgQpcmkOua6fTvGlKphZkk7Qb+EDwZYmzbcywAuUFEeBNQ+voAJ4HfS+SRdHQXCgBSIDpYEIzIusyAuc6lAg8iudsK0gnlEbdYzlaACQiMWE4ENlAFgzdyLekjVoY0pEsosANMYBsHNGA1wyc5C0+qdRqRqFFsbUwYmOR410rDiiFcXQpy9g+y9MWn/jknoSjhTGIBA76bohZxMY+FJBpszxl8qnc/PlmZ5cUWGHAMAIj+LbBKBwT+oATGypJ8IacSVQqZj4ne0YYspEoMFRMeXE6aKqpPFRtN5kUYwc0TTuT5YgXRIClY4IgDiiOeI6USCyXjHxBoFzs4kAQeOIFESAsEwqC1GwgKjIyeU43PEn+p5IJxIs9wkah0VLgdQNRAcUByvIdORVxKFkuBZIEIPExZKPYTSZ408QcckuN/00kQsbz0s8+k/3JAwSwKz5WCADa579Y+0+/RwcBaNRzDz2Q2L/357YZ+icijBfUr3yt/lvfuvBc2n9GUQG1ausPv99Vq//aV7+VUJK/wLVXLhH+lMfC8cjguO379rM7/UIh6O27QPuxaIyjTNZdsxY0m2hpcWVRg7TnyW+9yYSB3z8YdHbamy6C1TKslOmfQRI/T3FYWK8rR14jPB90dq0kpuBCkwglMfHlv/6Oj8QYok9/5/uEBWdXb6hlOxiM246d/PS11zWTedkzL/vBzTjCixOTxda+gON73nhr+w23eakk40RX/eBaW8lY2cxc3wQmbOu5c1dceyMdxSFhd9x4Gxf5ix2Di31jJIjT1crnvnsNMJ1mR9fn/vb7QlM9t3ZTpaWHoun03PKnv38Njhknn/vM3/6ArzaKq1Ztu+nOrhMnau1dm+59sOXMTL27e7F3zEEy8ryrfnRTz5E35tauu/juB/veeGtubLLYOWjLKcqNPnPND3veef/U1q1X/PDGwZdfOb19xxXlt4f1pWUPrX34mZ633p2fmN5xw839b75TXD0+1zqsZQtSub7pwUeGXjlS7R9c/+gTvUffWRoYLfYM2WIKec6lt949sXf/zLoN0zt3jb60f3HV1Oj+gxf95PG5NetG9+y9+L4Hzm7aoiVztdYeaDqjr76y8e4HS33DXe9+sPXv76n2Dy51DdVbOnnb7D10dMcttxaHx1vPnr309nvMTHZpaKya62IoMvXUrkvvfWBhaqrl2Kkrb7yp1DNkt2TnOsaYKB7fu3/z3Q+UR0bb33zvslvuKK5aVU8Vyt0DrOX2Hju2/bqbtXxLuljZceNty31DQtP4zDXX2lIyBuxn/+Z7lBdafe2fnzvA4NifM3bceFsEYRwzV137Y5/hjELrXN9ERMOOmdPbfngrZ9lOIrXjhtvYMArS4leaH8W+bUXC5/767wTVqPQPfu7qHyiLyzPrNlZaejHLKovlT11zHXDDYufAV//y28lKbWl61RdP7ZFJ1NDYK265TWw29WTLp667ETX1s2s3fuEH1w4dePnklssvuefekZdfnZtcXWwbMJU0E5Arf/Tj/tfeOLV1246b7hh96cDxLTuMRNZI5xPVyoZHdvYeeXtubGrbnXePHjo8u25dJdfZzLWJlcaGx3aO7dlXHhpZ/cxzAweP1Ht6FvvGdCULA2/zHfet3rV7bsPG0edeXP30s8vjk5X2nka6DQXBpbfftWr3S6e2bd9w38Prntl1ctOWvrff2nrX/c3WjvzJM1vvvLfR1b3UPVRr6eIcu+f1d6+8/sfLvcP5pcXtN91pJVOLw+OVXBcX+cN79l16x33FVRPpU3NXXn9jtas3hZzPVo+ZkBM/mL385turff25j87suOnW6sBgNdtR7h5gba/r5Mkd197oiqKoGVtvuN1LJBcHJyqt3SAKcyfOfO7q72nZNhTHV/3dtZ6s+JC74kc3iZ5lM8IVP76ZxLTW2jbfN+4BIVMsXvX966RKzWhpu+ym20VDL3b1LXcPhxSbqtc/890fyAvF5cnpz1x9TXp2/vSmS9V0IWahVKpfec11fMMoDY9/+S++nZ5bOLN5SzXdYSSzmXPnt99+V7pYNlOZbTffJZXrC0PjlY4+h1OURv2y627On50t9g1ddvNtqYVSudDGvf/Or7JNA03TcYxKpfZr/i6576UVGFP9VMij6Sidrf7pnze/9DXGdfnzs63XXysffZ1w3MoWjFOp2u/+gfrVr1/Q6AqWyy233tRbKn/lS99MpzK/4OX/9UsEoX/Vjs+tW3dJ5603Kwf3fWLhekFu4zAMs7nyH/+p+oUv4ZXFWjMMHYaZxx4p3HwDmp+LUqni//1X6le/HmWzK5P5CAC067bcdlPh9pu587MrgX0XUgGoVLkU01SqWhIcA3qOoGtc6KHIJ4BN1SoBBJlKMaU2IoZKNqoMTVAUABzJps5GvmDo+eWFELHZ0lKMEAo9miGS73KGxplGslljfVdwTIgjFHoEgXS5GEdhqlETLB2ZOm+ZvGPBOKBpki4vRwAk6lWlUaOiQFEbyPcErcHgKF9eZkNP0FXedVDo0wyRTE3QmyDwZK0paA0mDmRLgzhkKJxsVJkwkNSGZBpSowZwnHSMFEQiyhasKgwDztDStQqDI1lr8o4NSIwiH+BIVpsMhVuKiyBweU0VTA2FPktFgmvLzToVBqlqWTI1GkfJSgl4Dm9bkqnLjRqN4+zSHM1QXODyvpOo1xgcZatFLvI5QxdNjaUxCn2W4FRpOeBQbmFO0FUqCiRdZSgMcUhTJGkbJPTlZj1TKvoIpivLBAIYByyOBV3jDF323VSjGrN0qrQE4hAFHozCZKVIkTjdrIuuDS2Dd6zM0lzEkHyjlq6WY8CkqyUxDlLpgsLxSrMKAk+wjEJ5OQZ0ZnkBc5ALXIbCnGNztolcO1GrsK7Fu07Bb3JSZ0EASqOKcZwsLgmWzhk677m877AkYiicrJUxQyU1NdWoUCSSahXJ0DLpvKKkU3oZuDZnGS2VYszSiWqZ812pUWXjKGXpSr3C4DjRqMHIYymSrJZA4IpqQ7R0xdCZ0EtoDYplYByItgldm7ONZKPCBJ5s6VzgsjRBoQ9IrOgaTcW5hfPIdXhDFRwLxgEgMUsRuV7FcZSoV6VmnaKpZLUM44AlkWgbnG1zhi5YRsZQcRQo9aqkqzgMk7US5znINmW1zpIIRj7Acbq45PN8fmlOsS0SBrLWZCkMccjQOKU2MBXLaj2zMBsgtrVWSVABLxSyJBAsA5mGaOrpWonQJFmvQBygwKMBk6wWaRwl9aZg6ci2eNtgSYRCn2KZ3NJCIPCZ5XlBbdKBLzUbvGsD1+ZcJ19cilgmVSlhBFDgQRwKlsEZGvJdpV5hHZPXVRgGMAoIBMlqGUeR0qzzjik06lzgS77DUJihcUKtEZZO6s1ktUKRSG7UOM8BcQjjULJNzrV5184XFzFNUpUSwBEKPYADwTEltcEGXqZSQlEgmCpnW7+yRqM0TViWNU3l0Ms9f/bH/KmTmF+JekXHMYHIHV+19IMfmhdvBromH32946//kj99CgvCSrAaIX5v3/K3v6NffsVKBuf/zbxeWCzm7rp9cGHxC5//Ri6TJ79oCe5fKxH+0wVY8PLhvW+eeHv5W//RvPIqimUv0M4hwjAUy2aeeSr17FOwViXMSnLIGd93J1Y1/t1v2qvXxslU4tCBzOOPCmdOk5U1JFIU4zpBV3fjN75pbdgUJ5MXtFfWvyARel//yuSrh+ampwEdZs4tmh0FTxSFSoMkeT2VHzl8+NQlW1rdsnxiYWlsDEAKNK1Y5kOBz5+asbsK9ULn6P79Z7dcqvg6rOpuJkNxTHpmwUkrVj5f+Pgsk+NKLT2JxZKZy9Ey6nrr/crIcCyh9Myin5D0Qou0XGUFttbaOXL48PzqtbTAdrxzrNrX52eTiYWS5BjzU1Ptxz9yUym3kJGXSgEnBC2p1OwSCP3q2LA8X1IsrTg6KpbrGIJGV/fge+84yZTVkVMWK5xhzKzb0Hb+ZMLX5vtXF87OxCy7NDbe/87bgaKADLA8jgk8rb0zXVxWVHVh7eqeD497nFjqH0iXimwQ+K1prm6kK5WlNZN8sZ4ql89ftKF15ixneupAFzDd3Nz8yUs2J2pVpV5rdnSmHC15fnlm/QalXEpVqnp/Jw6w0FS9jpzPiINHj5y4/Ip8dTk5VyyPDUPfZS0/zMh0gFtPnlpeP+VT3MiRw6e2b++ozloudFsyvGXK5bre3xURpvPEx8V1U35EZ5aX1M4uMXKzp2aKE6O85yoLxaXRsUCWJ/fvO3vJZgji7qPvLo2PUyKbmjvnpbN2qqXl5Bm3NVfr7Bndf2Bmw0aRuKjc9DJZmXHYZT2QBb29rfDxWT+rlNt7Rk4eXWgZptNy51vv14YGw4SQPrcQSbza0S4WawCSelvX0OuvL41N6K2tY0cON7q7/YysnJ8JJbnWNdJ3/IMwKS73D4289nqpf6De0dnz0YecY5/bsKl9djZZLy9OrhIqDYbgam9//wcf+JJodrYoy3WxUT9z8SXZ+Tk+8PxcMrVYChi4ODHR88EHGEKrq8AYHut7antnslpJl8vnN67v/vhEREG9u41vaiSM41yCUe3c0uLixrVc3cjOn19Ytya7vBQQ1mnPZ2fnmYioQ11c3cyfP3/8ssvbl2fS5xYWpycTWlOsqVpvJwljvqH57RkHKiOHXzlxxZWZRil7brE0NgwCj3FCJFN+DPJnZktrJhwgjR16+aMrrkhplWx1sdQ2gMJQWa4aPW0BAztPfFxZPe7SXGZ+Xu3q4rGfP3GmPtInRh4sakZXqw95uVyNckozVVh1cP+pbZdJnp7/+Fx1ZDjgYO7MLCfTC/2j/UfeWJicRiDiinUvmcYKl55djASkdnW2nDwXJcVGS1tiuWRls1SC73zz3VpfX5hR0mcXApEv9w/m5+cBwPX27r633qr0DTQ7O8aOvKZ2dPgZBVXUkOetTK797GmaY5s9nV3vHi/39uu5fHZpETNMmE8qyzUmiuYmVvUe+8BPpcoUyF933a+eREhYlo4ibm4u89QTiZcPYIRWdnbQURSl0saVn2p84zexLMOlpdTeFzKPPUyx7EpON4wplrXXra/+0Z+EmcwFfBLRNCwVc/fdM3zm3Kc+9/WOtq7ol/C3/Gslwv/52gnu7R4gQaAdeDEQhaBvgLDshejiShNCE+JMr47TGVitwErl5/YOpWkCACoVxRPHKYYJCwVvfMKdWMVaFiouM0GwAoaPQMgahnz0COO5OJEIC200dcFGRP/zEqHCyxPPPBcTJhTEyceedpUE60fTjz5lZzJcVR3c97InJRVP797zakyxvqKsv/ehmOcJpkee3+vJElKtob0vR4AjAKx74FEKU1Y2O/3wzhDxmLBDL+xnEROFzPSjT4Ysink0+sLSUIMAACAASURBVOTzIIi1lpY1Dz0eMyCmweSTzxEA4ogaenE/FVGhLI4//ixwg+rgwPjOXVKj6Supnldel4o1K5edfOLZ9HxxafX09ENPJJeKtcHB4d37lVLZzLUMHHgle36x0dkz/vwLmbOzi6unpx97Jjm3uDw6NbHrpfSZRTebazv6bmp2odbdP7bnxfyps9WpsfEndre/e3xuzYaRfQdSswteKt39xlvp07Ol4fHpp57tOH7i/MaNEzt3tZw6Ux8c6D7ybubMWbWzu/XDj/sOHSmPjvQcfbf7yBtzE6t7331/bM9L9e6+ltmZrlff0PPtiaXSyL4Dy+MTnW9/MLr7pcrYWMuHZ7sPv2bmWtPF5e4Dr5nZbHphefKpXeWRkcLxkz2vvF4f6Mt+dK73ldfsbD5bXBx47oCVzkjV5vDufY3BgdzJmd5DR7SujsRcafqJp41Cm6g2B1982c1k+aY2uuulcv+QXKmPvrA34CQWRyPPvcQEsZPJTj/wNKYRZsHI83tDgWOdcOClA5iGviSuu/dh5AVOPj326C5MqAAKw7v3UgiSiB56ch9FgVASxp7cBSy30dO9+sHHqZj4vDj5xLMMIQHDDe49wIZEyxXWP/IY1zRqvX1rH3qGuJSjJMef3wN93wfC4N6D0HIWxiY3/uSR9Nziwqo1k7v2JOfmrVxLz6tHC6fPVTt6R/bub/34zMLa1VNPPJs6M7swMb1q94sdx47ZuWzbO8fSp89XegaH9h9sP3Fybv36NY893fX2e7PTG0YOvZI5fc5JZ7refS934kyzr2/khf2db78/c8nFY8++UDj+UX14sPPoe/kPPzbzha533ut8471mT0/vq290vvP+3Lr1oy8cyH/w4fLYRMepU90vv+6mMlK1MbRnf2VosPWj02PPvVAbHMienO07dNjKFRK1au/eV6x0VqnWph972uzuSJxb6j70utrdlTy3MPDyq3YyIxrG4JMH3FSa083R515Uu7rSMwv9Bw8bHW1iqbHm0SfMbAt07eFd+yJFRqY5+Nw+vb1DXihOPrvb6mjjKtrwC3sDXmaiYPTpF9xEMuCEsaf3IDqOY7Zv7yuUH3vJxLr7HuFtt9bTs/YfHqXD2BXlod37GII9KEzvfIp1Qz+pjO/cxelWdWho9UNP0H5spfNrH9/J2baHxIH9hzjLbbR0rn30cbnaLI2MbLj/Eb7ebLZ1je95kTdMAkH3wde4ul4cGtt8192pUnVpYmLNw09C1dDzbWP7DiDVaCaS3Afv/YpJhAQhoGmJQy/n775TPHGcrAxdYUzHsTcw2Pj3/4f65a8Sjpfefzf3wP2pPc8Tnqfon/sYosMQK4p+5VXVP/jjWJYv4CF3hoGlUu6+e0bOnLvyM1/rau+JfjlI8RdraEkuuWg7Q9OvPfZoEWPz058jAF6Q2S+EMI5jXHZ52NqaeexR5bVXV9ADiHmeVdXcA/ej+Xn1K1/zhkfKf/yn6b7+5Au7ubnzKyBmCQAUIelnnxZOfqx99vPGtsuwotC/WnPIyLUFTeVNA/q+pGmc60aOw1sGdB2ajgTDEEyDo2NB1yRDNwNfUJvQsjnbEjUVua5ADM62BF2LchKybV5Toe+L9QZv27zripoWWpZgGtD3RMNwXVcwDVFTQRTJTZW3LOh5vGFwjiWalmCagmVC1xUMQ9Y1GmOp2YyJzLsOb1lMjDnXRa4LooiJI0XTAp5jg0BQVYZnRdfhbZsiBLou0g1AYiaOxUYD0zQIA1HXYOwjy+JNE3ku8jzBtjnbZqJQ0DQ2DKHvy5rGOi5n25xhsqYLfR9ZpqiqnzwL6jryPF5tCobJeS5nW1KzyQYBb+h8s4kCjzd0ZFqcbXGmJaoqsk1kWXK9DoNAMg3OsaHn8qbBGyZvWZxtC5rK2Q5yXdE0QBgKhi6YBnIcwbI42+Z0A9k2b1m840LPlpsN4AeCrknNBvI8XteA74mqSgmMYOicadIES4068jzecTjLEtQmTvGCrouGDsJQVJucbSHXkZoNzrI4TuUsS9I0181zriOoTTYIRU3jHJdzHFFtcrYj2BbvWKJpGr7HG4bUrLNRpKiqYdvQcTnD4F1bdGxBN4VmAwQB32hIySQbRWKjwbWbyPVEXQ/NJGdZvKZLmkZjrDSbMcbA9wVNhYHH2TZnWZzjIN8TdE2wbSaKxGaT8X0YBKJtIcfhbZuzLGj5yPcFw5QaTSYOBU2DhoF8V9A0Xtc5x+Etm9M06DnQMmVVZeI4oarcP+4lUdN4y+JsizcM6Pm8aUq6zsaR2GwKloUcV3BtQVORbSPPkdUmDAJe1zjLgp4n6gZnWoJlodAV1aZgWSyJOdNkfV/QVdE0kOcJqsrZtqDrLE8LpsbbVgyBWK9Dz+NtSzB06Dis6gDPkzQtZCTR0Hhdj2Ve0nToeciyONPgPI/yfd6yBMsiEivoGm8aIAgkTSVWmjcNzrY+2SHIsUVNY6NIVlXTNKHvi5rqWqZgmshxecuGnsebRkJTmShSGg29JQ9dhzMMzk7yts3rOq9p0PdEXY8FgY1Cqdmw0inedURNo0TIWZao676SgIHP6zoNIRuFYrPhcQi5jqBqIcexv1pZhIRhKJoWTn6c2v28cvgQHQQr8WenKDqOCETmxouaX/91d2IVa+iJgwczOx+F5TIWxZVgEs8LenrUL35Fu+qzFMvQF+xsO2FYVC7l7r5zeOb8FZ/5WldnT/hLy6L+hTFY/4gLmbb2LuAH6uGDAQThwCBhAX1hEi10GEb5Fnd8nEKccOpjOo5/buaJYShC+DOnubnzFADeyJg3Ohp2drKOzc3PffIh/dzcGISg2RA/PIaqlTiVDjo6P5mH/xVgsLyJVfq2LSxiZ7ZdGicEvbOrPtqvtxfs1lZ1pL/e26t3dpQnJ3BOslLZuc2bw7RkdPc0hvvUrk63UFBH+pdHRhGPTm/ZHLako0Ti/MWbwpSk9/SqIwPNni4vmzUHOuYnVzMQntu2Jcgmw2Ridv1anBQqQ8PqcJ/e1eHm8+pg7/KqCQCYmS2b/ZZkkErPb1wfJiSjo8Npyy1OT0WyVFk1rvV3Y1k6v35tlE1q7fnGUJfW16d3dHht+cXV05EsVcdHqsODNA+XNm4IsorW0Wn09ZRGh9x81m/Nzq1bE0tSfWy4OjRAi9zimtV0Viz2Dpo9ncWxkSCbstsLi9OTdBIUJ0arfX20Ii6vnvTz6WZPr9XRWl01ore2ea355akJWoCV4WGzp11rb7M62oqTE2ZrgVaEc5dcRCX48vBwZXQoyKS04QGzu1Xt7HLaCrWJoVpvPyNyZ7ZfikVktbUvr5uOEnJtaNDoaW92dARthdr4UGVwiOHhuW1bWAk0OnqKa6fCTLIxOKj1dmidnbXhQbu3fXlwGEn8qUs347RktbYtT0/4maQ2OFgZGWr0diMenL1oU9CWjURhduulYVLQu7obo4N6V3ujv9/o6ywPDQGBO7tlc5BLRglldtMGOsVX+obV4T61r9vN5ZoD3cWJCRaBmUsu9ltSUSIxt2ljlFbU7h5tsLcx2Ovlctpg7/L4OAuZ8xdtdLNps7OttHoyyChaV5fW31seHooTsjrUtzQxDgAzd9FGN5302/LaQG9laMAp5P223MLa1aEiN4YHG33dTmu+ODUR5pKNjk6rr7s8NuzmM34+s7RuKpLl8uhIdWiAlvml6VV+IdPs7LC6OpanVnm5tFfILaxfQ4moNDbW7O8OU8nS6tVeLtns6XHbCpXJUSNfaA72NUYGY1mojo2pgz1BLlMaH3Pac/WOzqCjdX71NC0ju6WwtH5NnJBqQ8NGb3uzo91tK9QnhitdPXQLOLvukjgluvn80oY1YUqpDw253S3VvgG3kK+Pj5SHhoEAT1+6BadlO59fWr8mSCnqwIDW39UYGAhSierUWLl/APHw9PZtOCHahdbi9FiYVSqDvVp/d71/IEoq5cnRyuAQhMyZbZdGScluKRQnx/1cxi60WL1ti1OrKQ7Nbr7Eb0lHycTMhnVUUqgMDjZGBs2ONqu1oPd1LU9OspA9f8kmtzUTKYm5izZFKVHr6VEH+ipDA2E6oQ/1Lk5MAMTOb1yvdbXTEj+3fk2YSzY7Oo2B3vLwYJyQ1cEetbdT6+hcXj1ptLfRsjC3bnXYkmq2d+h9PaWJUZwQ6mMjqiDKr7/OWr8KDBaBkHHd9J7nc488KL7/LkUItaKRdjoM42S6+WvfaP76N7yhEX52Jvvwg5mnn2QNg1pBTAghdBi6k1O13/4dc8tWimEuXOcgAgAqFvO33zI0N3/V577e1d4TRuEv73G/YIBFURTLsG1tXcD31VcOhBQJBocoAC5QMYvGmIiiOzoWtrYLp08ypkn9/C5ZFACwUhFOnwLNhjc24ff2+UMjmOf5c+eYwF/J98MwdBzzs7P8mdOs63oDg4TnLyS29p+XCAtxvPqZZ2HgEwTWP/RoxHOiaU/t2k0gKPUNVXoHqBiPfPRu/+4DnG2FAr/xwUcIx3GOM/H0LsyjZLkysv8g6/ko9iee2iXoepTLbbj3AcLQwHUndu3hAEbV5siBQ8iyaQ5MP/F0QtMW1qwpdfb5ktT7/nvje15kaCI09dF9B5CmYQGtfvxppVqtjgyvf/jRZL1GQTjw8qvphcVIEcde2Nt66nRlYuxyVG8XuarlTj65J7O8GAPQ+9rRwvy8mc5O7t7deezY8uqpi+5/KDt7vjY0svbJJ7Pz85Qg9r75TuHkKa3QOrV7T/fxY7WpkakHd+bPnqv19E698GJ+do7Oi5My3SOSGi1M7Hym9623l9atWf/Aw4XTp42OtpFXXmv5+GM7k+k6dnzi4KHa0MDgocP9R14vj46PvPZa79E37Gy20d1V7B4EmEy8fHBszwuV0ZHBVw73Hz6i9fZ0Hn1r4OjRGHHZ5YWh/YciWcwsLk7u2tMY6O17+72BVw7rPZ0db703+NoRDNhCeaF376uRwKdK5alndnmF/OLQWK2zF+Jw5NUjQ4cOEZZN1Guju17APJ9sNKeefLrW3ZObOz/x0ksIU8B3Jp59XmqqXiaz6R8eQr63uGqq1NUfCVzfsQ/HXnoJ2jbDkMknn1WaTT+bWH3/oyAOaYpMPLcHMhSvGSP7DvCWTfFwaufTiWpV72y/6J77ge8RmprY9QLvO6ztju47kDCtYv9gtbffTiQLC+c3PvgIE4QYoulnnuEtAxB6ZN8BpVpdWDW1/c47cudmq2MTax57LD93Pkaw+8132k+dbnR2lwaG9XxBNtWLH92ZP3W6ODi0+plnCzPnaA50vvVu67EPtbb2iX37Bj44tji9at1PHi+cOl0fGJx8aW/LqdMYgr633m57/5jb0Tp84FDP66/Pbdqw/iePt370kd7Tudw7pBVaE436+Msvd7zzgdVRGDr4Sv/RN5ZWT617ZlfbsePVwaG+998ZOHg44rnU8tLkc7vV3u7ud94deuVVo6NtKOGvFxJu0Eh/fH7wpQOxwCfKpaknn7Xb8+3HPx54+VWrPV/48OTwK6/SHCc3qmO7XiAIyro2vfNpo6O97fiJ4YMvO7l04dzc0MGDhBDRMVc9s5vBYaoruVEMbEnOHnlv+MDLYSaZnp0fPXCAIhQfuFNPPhtzXAzB2sd2CtiHqjF44BVe14mIpnY+k67VFtZMFzsHvESi68SHE7v2wMjnLHtk/8tCvR7J/PTOZ1KLi7WhgYvvug95QSRK008+KVo6G8TDBw4mag07l9/wk4cyS8v14cHNDz4MdcMTpennnks263pHx8LQuJHKpZeWL3rk4ZblYmVoYNP9DwqaHijJyRdekCu1RjIFj1/4WYQ0TRDiz51tueeu5L69oFohEK6w6SoMveGR6u/+gXHZjjiTSRw5nLv/HvmN1+koWllaCUVR1tbttd/5XXd4lKaoC9iMCULu/Gzhxz/qLVc+//lvtLd2Rr/kHrJf/LgfIQQAsGHjpVvG1rTtfCKx61k6ii7g2TeMCULG9h3F//oX3ug4Y9srqyurqqnnn2u/+m+F06e8vv7GN36j/Cd/HnR0rjAPh6YJy6L5uewjD7Vddw0/cw7z/K/AgKEPuPTCUhzRIWHEYgX7cQiQtFzGMYkI43KSi6QAoORiCWMmZKBYqVFe6DNQLldxTBwoJGZmY8I6gpKaXyKY8WhWqtRo1w8AL1ebtB+6Sio5txixyINc4vwCFRMfIIeTPSRGNJDLddoNbTmVXCySmA6ERHKpxEQkBBCpJt/QbV7mairQTFdQ5OUKMBwbCshsQqfm0gJnu0JTd+Qk39ChZjlSQq42oBe6vMzpllBr2lCkHV+oq6Ygo5qKdMuTU0JDQ7rjIxF5oVRruIkMsDyhXLcYnrWbtFaxWMTXNU7VXV4GQaSUa66UYC1XqjQ9OcnanlCu+4Cn/UgpVhwkxhFOLy67gA9YzuFkG0m07UvVRkRDEtHJ5XLAS5iwqblFW1RISBLL5ZCGlBvw5VpAQ0IYZW4hAFyExPTMeU+QcUwppXrIcsSPpEo9jogHeJeTIorxGZg8M+OzfMjAxHI5ZGAcE6lax5j2eTk5MxdQrMfxyYViHNM+C4VihTUsH3AuEkMGuJBPzM7HFOuJSnKhSMd0AHmpobGO7wNeLtcoP7QFOTm/hP3IFeTkQpHyQo+ThIYGTCfkZKWu0gF2pURiqRS7QUgBl5M8JIYUEOoq0EyHlwTVYL3A5iRlsUiF2IcC0iypUrc5Cbqh2NAsOcWpBtLMgIEuJ7pQ9KEAVUMsVV0pyYZYrtRdKcXrFm97rpTk6iqqq46gANuVylWbl2nbk0pVS05Cx+N12+UkvqELmhVAgXa8RLHiyimf5VwkeSwHDZtv6h4UUFPnKw2Xk2g/VuYXPE6KKTaxXImhQAexWKkHLEciKjm/HCDRsW1Rn3cIGxEmuVQOKUBhRmxomGIJxSYXigEUI5ZLnZ1xoRARRilWAhrGQSwWK3FEQhoklishAwNOSp+ZCZAYMVBZKsYUCuJIMspxFIUhTiwshyx0AZ+emQsxHbJcYqlEwjhmoFjXGNd3lVRisUho6HNS4vwCFRKfRS4nuUiIaFaqNYHtWVJSWS6RiARiMrlcZkPsQ1FoaFA1bCRKqsEEsSXIiWKFcnxTSirlOrA9h5e4mopU05WSompAy/Oh6HJySEOPk6TlCtBNR5C5psE1NJsT+ZoKTCdmL/jkVsKyhAWZJx5r//7V0huv066zMlnwk/kq/bOfL/33v7Q3bqIIlX34ocItNwgfn/iporICdAWA+rVfr/7+HwWd3ReeWvKz7wahT9xEuxrNL3/p3xfybdEvv0P/FzZF+P8BADTG+PU3Dh19/2jpN7+pf+krF7w2TtOouJx59OHkvr2Y51aMZsKWgvqVr6tf+SrjOGhxIffgP0hvHqVWIBf+4xdFMUycSje/+nXts58nLPtvn8r6l4xGv/6lRK3qKwoXe1FA0YglLEM5IYKxIWcEQ3eUZKe+oMZyKEuibzGag0XOVxTKDSDAtpiQaw0zl0vaDVr3MYJRSow9wgAKA0i8KB1rJpeEuhVxPCUBYoWBLCPK92ge4YAwDOVGiIksJSXWG04yJWKXsgJfVlgWY59woesnfmo0CiFmDM8XRI4JI05gQ5/CJIwBH9gRz7GmR2NstuR53Yg4nmPDMGKYKHKVRKJSgTh0M+lP/u/wlISoNmOIslh1TZrQtNFSAL7PBkGsCGLoekjwACdoOmEYCLGPEWdbJMH7EYt8P0jJnGVFhAWICgmAjmtlM2wQioZmZ3OSa/gE+rLEfeJQz7Gs6QDPJylBF1JyvWG15BVbDUMGi7xoaLTtR1nFQxKwbFZkLS6RqJStlnxndVYlChFRjBAOCYDEgwLjB4jFNicr1ZrZkpddPfJpLHFsHJKQOHKCADZVKZvZnBSaxIoCWeaIHwYUy5CYR1FE89h3BVmqN61sRgptfG457O7KUrpKJQAdxxyinAiyccgATjdjiCgFETsMRAnRYRRQkIQRx1OlBhIYu9DGN1VPliOe5ywr4jgxtMOIJTTty7Kg64gKLSUlqJovy64oy4bGhJGTTiHbETzbT8iUF1GEeIkEcpyYBSJ2g5hlw9BNphLVCogjonBxRPmA9xKKoOuEYQEiqKoRitLbOtjAg54XJmXR0D0gAoi5pk7FJMwlfIKQY1MyslkR+R7mkWAZPsMDRFFeGDMAIuxjDjmWkS+kjWoU0JEsIN+NMMtydEgBYLusyFpSQrFNU1RkS4t8OpZ4yTIo2+ckYogZyglZkbG4hFKtmi35tFbFdhRLXCzwcUAAJCHLActlRToMWcHQfVlmOTpyCcvTHOXbfBKEgU8gtB1WYEwhmahWzVxO9s3YI5Ek0DRNeWEqVLVUC2oYdiYjxjYxg0BWOMp3GR7GAc3Q2MOIDiMGIMOOIKQTiDIDX1Y4yg9DhjB0DKFSqwOA9WwLNKxAFENBlOv1QBQ5yod1I+R5O5vlTAuREAuQciNbSWGWldRmjDgA4jBkGRy5iaRg6JjjnIqau+ECNholHMfNzrbceSt/9gzjeWSlJkd0HBFOKP3JnznrNsSKAivl/N13SW+/SUfhCvrZP1EVsChWf/+PzEu2EAgv6EARgjhu9mzhe9/tDONf+9I3k4kU+V+CFH/xEuHPABKmt6efZRhtz7Mxxu7k1IUOseJEwplaTRAUTp2kMF4ZJGJtWzxxHBWX3FVTYUvB3riJ8Lxw+uQKeT6apiiKcR3pg/eFj094I2NxMvlvventX8giJMxnr/4+FeIIcZ/+66tDxHGWs+NHNzjJlFKq7bj2+ogTFd/ccs2trO3OrVtXah/wRGnotSNbb7wtEAWppm698Raa0NWBgaWuIUdOdH380VXfu5YJIxLi7TfcIkXuzMh0ub3PZ7m2mdNXXv1DSdWabe1f/K9/iWwnYuD2G2+BUYh9suPGW5Dl+Zn0p79zTaJUXlo1ueMH17WfOmVm8uvu/0nr8Y+N9vatN9za+/Z75zZv/vS3/67nlaPFNWsuvePerrffXpiYKnYNmIkstJzP/vBHva+9fm7L5k9/94cDrx45tXVHI9/mJJKJam3jP/yk7423l0ZXbb/lttFXDlcmR84OrDZSOalS33z/A/2HXzcLbWseeaL/pUNL41Nbb79r4sV9p7Zv33HDrRN79y9PTq15atfw3v2VoeGRg4cuufeBhenpVc/t3nTfA2c2bJ4+cHDTPfc3O7q6jx/ffMc9RltX2/GTl916e7O7a25kqp7vEGxr/IUDl9x9X7Oto/3UqYvvuM9NJM5Pram29hKW2fDYkxf9/b3lsbHOd45vvv0us9De6OuZbx8hDDO9a/eW2++ujI71v/bGpTffXhvsbztxbsstt3rJjFSvb7/uJi+VTi2XL7/+pmLfUGppecf1NxDAUTS58uprkWHZufxn/+q7MAgxxVzxw+sZioJusO2GmxkvjNLwN3KOUJ8zDObKv7se2U7Iom3X38QH3sLwqkp7jyMmW+dnPvU3308VK5Whoc//1XeFetMbav0cvSwxgTNb337T7VJTr3b0/Np//4v0ufOliYkv/I/viLWmmc5dduPNyWbT5+StN96aWC6fW7fpi3/1N4OHXzt30dYdt93ed/QNo9A29fgzg0ffKvUNbbv5tvF9B05v3XrltTeM7Hv55JbLGvl2K5nKVCsbHnqs7/DR5ZFVW/7+nokX9y2sWT3fN6ZnWvimtuWhh0f2HtBb2yZ3vzD60sFmT8/80EQj34F857Ibbpvc82Jx9fTUE89NPvu81tq26sW947terAwPr3vwkamdz5zZtn3zPQ9MPvHU7PqNA2++ccmd93nZfO7Mua233FEaGxs89Nold95dnpjoPPz2lhtv0/KFluXlS2+8LeD4uem1pbY+yMTTz+zedOe9leGhtmMfb73lNrultdzXX2rvjzhhcu/erTfepvb0Fo59tPW2O/WujuWuwXpbV0Sz/W+9uf26W4gkStX6tu/dpLW3t5w4demtd1jdXfLM4o4f3xgiAYThFVdfE/GCD7nLr7le9i0SUJfcfAeyfV+WP/WdH6RKlfLg0Bf/818kKlUnmdl+wy1Ks7E4NFHu7DOVdPvC3Ke+d212Zm5xcvLzf/k36fml0xddWi90RghlZuZ33HBLslgp9Q994dt/3Xr63LmLL17uGLTlVP7s7Lbb7sguL1G8cMnNd2bOLy6OrfrSt/+q7cOP5tev//Tffi97eqbZ0b3tzr/PzcxV8oULNYuQYQjLZh9/tHDj9ahcWqEf9Se/n77vTkwtfud73sgoQUj88FjH967mT5+iMV4ZuqIwDlvblv/H3zir11Ase0HnERHECR9/1PKDqwcB/40v/5Ykyf/LHv1LJVcJxmTzxm2YkNcff4SJwvo3v3Vh50YRgkWx8c1vha3tuQfvYzVtZZaqtO8nX3qRm5ur/N4fesMjjV//d+6qqcJN18FyecVgliJEPH6858//z/pvfUu76jMXqNcrjCJO15FlshhLpoWCwHc9UTdgGFC+gSyHNSwg85yui45NKAbTDKEZ1vMEXUd+EDIOZ1nQtGIWEJomFM36PtdoItdDcSQahmk7dIwJRUeIg2EkWBavGzQhkmVBPwBBwGk6CgLO95FhAl2jfB+qqmiaNMaCbQeQRp7P2w7DAuAHyLYhJhTGUlP1ESS+D1QVIAZgTCiaMDQTBEDTOJqhMBFMk8QxHYUxC2IqouMYmRYXhcB1ed3gbJvCFKYZmiKEYUXTZCwbui6yLGQ5TIQ50xR0g8JYajaBpjO+C1SNs2wYRsj1BE1nw4A3TEHTAUUhTUOmCf2AsyxkmoxlMY7D6wYIwphhCc0Qmga6jgxTcD0YRrymIduhKBozDGYYTtcF0wJRyBsWZ1msZVE0hRkGswB5ntBosoEPLZu3bBDFUNM40waWzYisoBvQsimaEupNEEVcEHKWDQ0TBDle1yXTojEW6g1g2zAIBd1guzTPiQAAIABJREFUXReaNjItwfVYikK+LwFMuRFfb3CWDYJAsCzOdiiMCc1gmgDH5SxLsC0KE6nRBKaFqFgiMYgjRGhkWsiyGc8Dmi6ZJh3HgmFA24GeL2q6ZZnAdTjTEh2HomjJtuMgpMIQNVQOh9B1oeNylgFijCybt0wmjnjd4ByHjuOYZRmMSRgC04S6w/g+bzuCbtBxTGiGoig6ijlNg5aNXI8zTU5V6SAgNINphiKU6DhQ1SjHYw0TWTYfhNDzeU1lw4jzfNm0KIKFRkP0PBBGfBByuk5bJuu6UlNlg4BzXNGyWN/jg5CzbMHxQeTwugHCKGJZQjOEoqFuCKaBgoB3HGRZyDAxAJhmMEWztiPUG3QQCo7DazpwPULRhKIpiuZ8X7AsoBsEC0KzAVyP833BtKBl0Rhxps15XuC5vGlyrgfiSNR123ZYy0aWLegGiELBtgVNpwglWTbyfDYIOU3nXB8DgBmWiTHxfdjUpFaLxkQ0LWjZFKEwzWCGZeMYmhZnWWwY8ZrGJ5MURWGaYWgGeJ6g6X4mQVsWsG2u2aDDSDStUJSoOBZVzUkkQRBymoaUBHNhxogRALi586233MDNzPyryCFCKELq/+E/qV/6MoGQIiSz87HsTx746WTlyhAbxs7a9aX/8t+wIH6y/gV8aEMofHgsf901g0j8+hd+4xeYM/j/p0T4sxeC6Og7r71y6IXaF75U++3foS78i3CccOJ44bZb0ML8yl99HMdKov6t3za2bccczwRB643XS28e/VdqfHQYuKPj5T/7L2Gh8G8TZv1zEmHj13+T/8L2XHnZzGSEyCURQ1gKMzTjhSykLF5JaZqaTBfsihciK6EIoRvGDGBwDCDrRyzApqCk6nUtk0u4mkvzLI4YxOCQZhkSQADcSMaWw/AeFGDgMwLDWL4vSpCOcMSydBRABGwPAGzK6WRTNRIJAbusE7sChwFkPV/0HTuVRLYfQsgADE3H50XERFEMYBxFPBvFrORavshHmGXiKBAlybICCBAThxGNImwqsmCZNEMRlqEjghnGkZSEoQcsmyaGGQoxBCHHgThCnucmlYSmukh0JVkxjJClIcABhrzvRxIMMeB9z0ookm3HmKIRFWOG80MjmWRinGzW1ZZCwlZJQBnpNO95bBRRiKLdKARQIJ4hpNKa2sxmFVsjPgklHoZBHNEAEp9FyHEpCVpQzqhNPZVs0Yo1oQWRgGEYHFE0IAGLBMfFAmtBOVOvqvkW2TNJQIUCYuOIiYgupymG5GsVNZURsQNN30wkhdiNQ4phqAhB1g0BG5tCItVUtVRaxA5bbbrJTIqyTVoBDA4hYO0AABxSbMRCEAYM//8qGaAiHwBWNxGijVQ+1WgYiWQEYVLTPJ6DdBQHFM0wjiTL+v/T3ndGx3Fdadar1F1V3V3VGd1AIyeCAAkwi1SiRCvZipYl2/LYXu8ee9Zjj2dnZnf2x549s7M7Y88425IVKclUliyRkkiKEkkwU2ISc0DuXNW5q0Plqrc/QAXHnQPIIonF96sP+p6Hrrr13vvq3vu+W8ZRo+J0s/lCzcXWKBdXLeKmWXU6SUVxSNUax+J1FQGg7nS6KhUdBQQOdQOQhll1uahKBcFREqqICVTMJjmczmrFAAAnIZRMC8c1mw0zTbss1d2sSyzJOI1hFqKb0II4CRSEpFTVYHANkrQs11wOp1hSMTtiR3HNNCGKEZYKcUpWsv5QoMJDFZFdDKUqpoUBAuoISkoyQl90R9HtcSoVKJuakyY11TBRJ6zV7C5cMaAdrZEObyFf9HpdtZKEMwTUAIpaBsRwRCHsdKVmMbihA5204YZGoBaiQMuG2g1FATSKmQphp8sV6CAqdtZbyJc4N6PXoGyqDGNhmE2SnUa15PQy1XrF6aIs+UN3mCaGI6ZO4KikE6hhAFzHCFxTUQrDJFO220hgWAaGAETDcVxV7ZZaZ1yMWKk7XTpJsuWyShI4ZpkatDBcs9vpeo2AukHZbGK94nJ/YIPjGDR1gCJIzeF0VsqWjcyUDNcvHyLTqSsoRQh0zb3xVc9rvwGqOqtVGkK9sTHzl9+R+wcQCIlcLviLH9MnT86whOsDFO7/cuGBvwC6fsXv1BhOnzru/+XP+uyOz936Bbud+pR/wJ8xRfhRtNYyW5vb7LQj+85mkM1Ky5YhKHZFuw2Ypt4Qqq6+2haPE/ncDEO7KAo0zbF/HyGKWkur6XRVblxnBIL2C+dRVZl5VxwMIwp5busWgKFqRyfEsMuOZv0xmYb+AcAGr//BT0psg2R3rv7546LLr2ro8od/XQiErTqy6mcPpxs7SUMd/Mn6Ou0uc/6rf/xwjXTIpHP5I78W3QFFJ1f+/NGCO1R3etb+y0+rtDfvC6354UMyRlXt3NJfPWUSZN4VvvZff17wNFU535p/fVDTMKGl89qfPqLroOT0L33k1wpmF22e1T/9Vc3uKfhDa/7lp0jdivcvHnziBff58WRn/8IXNjlimXR7z5KHn3aOJc+vvv6qnz/uO3lhdGhl33ObAifPxroXL3zxde58dGzxVSsefsJ/avT0dTeueOgp/+GTp6++afClTY0Hj0b7hrpe2+Y9NTq6aNWK9RsCh0+PX3113683tuw7dOzaWwde3hR693iyu79301b/8dHRwdWLn3qu8d3jp264een6FyI79o6uuq7tjZ2RXQcnFq+I7Du04OU3x5Zd1bz7cP9zvzlxw+caDx4bevK58cGVgXOjfc9tivUMuk6NDT3z0tiSlYHDZ4aefO7C6us85+NLHn1qsnfINZUcePrlVGcfN54YfH7T+OAy35FzgxteHh9c7hhNL3/4yVjvoDueHHj8pVTHAjqWGVz/QqxvMTeSWPrEs9GBJWQ0v+KhJ1JtC4hSbdkjzwiRToIvLH3s2ameRaCqr/7ZwwU2KDPsmh8+pGFMNtC45kePqBZRcfhWPLqhTrlqKLPyl4+LjLfm8q7+yWOGiRebmpb//GlLtopcw9JHN6gIUfA0XfvjBysOf9EfWvODX1g1I9Hdv+ZHv0JqWs7XuGT9i9AAWS606hePqToe71584//6F7IkTywcXP3jR2BZyTR1r3zsaVBTM97Iyl8+DhVwbunq6376YPDg+yevuWXopY3hg0diPYs6Xt/uO3F+fNHKpU9saDh27tR165Y9+kx435Hj197Sv3Fr6869id5F7Vt3+Y+dHx1YuXjDS5EDR06vvWnxht90vL3r+Lo7FmzaGhk+GF+wuHvL9oZDp6MDSxY+vymy7+jJdbf0P7uxfcv2C6uvb9l1uH3Ljlj3QNuOfeE9R6IDSxdsfKtt+ODJ69b1vri5a8v2M2tuCJ08t/Cpl/jmLsdUevGvX57qG/QcH1n89IsTS1exo+nlD6+P9i2lUpmhx57jW7opvjj05Et8d7fjQmJw/YuxgSFyMrfyoScS7QvxmnLVL54QIp1YvrL0kWeTHb3OaGbpE88m+habOfmGf/1ZvKMflbRlv1hf9gaxmjr4+ItCUxvOi8sefirVuQCpWGt+9MtsU5dhghU/fbTsDkiMe9mDTwHdKLpCyx58QkId5WB4zb89ZFX0eO+ia3/8CChLmYaWJY89aylGxt9yzU9+VSfYXLh59fd/hpekyUVLV//oIZCtJjoHrnr0KbwiCQ3tyx5cb8lWtGfoxn/+IZkpjy1ZufrHD9v5Uqyjf/kTzxC5ctEbHnzsObSsjPYtv/4HP3ZEhQsrrr7q54/T0XS0d2jp+mfxTIUPhu3HjlwZQqMAANO0j46Ef/JvruGd0xW0Mw9c4XjlurX83/93rb0dVRTnkUON//Q/yWQS4vgMBwTAZFnh7/9BvOW2K7pZyMXQHYoyRw8HH/5lP+X67K33UhSDIJ92KO5TOn+hadrSwZUEQW7f+QbUtcK3vm06nVewDuwHmrapf/zf/qeecA3vwAsFOAPBBQCg3c5u3Ww/dzb7rW/LCxZUbrhR7u0NPPYwdeY0Vq/DGYmgQBRFIPRueNrx7sHc174h9y6waPryPwACAeJLJwnTCEyNW5zdwacdOR6HurNScoolR66AWmYoOm4P2EhD80+NyyGPK5dlqyIipKhi3pET7CWRsPRQdLJEGriq+CdGqj3NXD7rLOZ1nqarIlsrh8cvYJYRmBqDPsom1YPpRKZccCTjdHvEk4ozFdFVyjdMjmGmHohN6hEPJUvedMImSx4hrfm40NQ4XS4SUt2TjFGyRCdiTL3M8SndQbOlgk9ImRQZjE9R9SoOEB+fsNdrtmyGqZR8fEojCC7Hs4kYiiOh2CQjS2S14uMTtoroqEtOsehJJxHL8glJd4bHTTMUm6RqVbIgcpmUoypyuayjUvKk4pim+TIpXylvUyQ/n2SqoqeYd4pldzphl2osnwzGpjAEhibGuSxvr1f9yRgj19z5DF2r+FIJSpbc+UxgfBQ19fD4BUarUVLNF5+yV6vOdIIp5YOZlF2qcaW8Z3IMs4ymyTFWK9rrVV98CjcNLp9xSTVfdIKQahyfZOICDs3wxJhJY/ZK2ZeMIQTG5jOOcpGpVnBdDQmpot3CFdk3dqHU1cTlMq5yQU3F6XzWkc82IoAwtWB0EnrspCqF0om62Mxl0uVS2JNJM2KJq4rGyDlMU7zjI2oDa5elQDwar4mckKo2N3gzaadYksvFcHQCN/RgXkjzSZtU5+JTzkrJm+GrjSFvcoou5JxFVygRIyzTm4zZlbpXSJs44RYLnngMhXpoYtRRrdirFW9iyl6vOYpFplr253OmqnCFjCcZs2lqKDHpqIpEvuzJpOia6MtlmZroTcYxRXGnYl4+bdOU8MQYXRXpTMGTExxS1Z1J2+V6IB61KbJPSHnjUVKqhyZGmXqVzWXcQoou5NhknKmUQkLKJte5XIbNCZRcD6TiuKayAu8oF/ypOF2vc1nePToGDL1h9Jwdh3ZZ8qfiAJpclneVCxyftMt1VzbtjAkYNBqTMQM3yWrFG50wHDSbF1xikZuKkfUqJ6TsJQmFZmhyTAm6aanqz/Kqk3IJaUYs0vmiTVN8hawhxlDLCIxdqLaGHdWKOyvIQZ+jlLNq/vDkKK5rvvik1uQmpXpDOhGvFJ3JWCXAedJJR6kgi8WGiVHM0PzxKbk9aFckXyJO16s+IS2FGryZNFPM60U6ND5Kmro/meDScbJe9STjjqroLeRlt9vDJ5lSAbjsocQUoave+BRXzNHVCpnDmUqJ41O6jfAlppxVERbsVL16BRygBgCBkODT7Pa3uTdfRxVl5kEmCBEA9FC4dNc94s23QgAIQfC++By7bQvEiZltGYhlQZxQensz3/meFm6cC+wKIsyhg4H1jy92em78zB007bAuheb5p5Ei/IjN4fjI2Nm3d25O9vcXv/mfdb//inckgpgOB7trp+eF5+xTkzNuNQgMA4Ew/5WvVq+9Xm1pRXWd2/yG+81N+HRV1ozPlRgGxLDy5+6orLtZbWqCBHE5kNo/cYpQ/cLd/bt2xoeGUMTwjExVm8Oag/GfGxNbw2XO37Nv7+iaa4KK4Dw+mRxcjOLQPRqTGnx11tVwfqzaFCw0RBbs3jW2ajUJ1KbDJ9O9PZAmvWPRuper+X0NZ0eB1xZv6enasy86uAQwROS9Y9neHoOxuceiqoupNIZDZy7UGzyZxtbe4eH44BLEQTS9dyzb1aV4OS6WouRafHBx6PhpxeGstjVGjhwXG0L1sJcbjeKall20gJ1K0dVybMlQ6OQ5naZSvQu63zsoc55Kc9AVTdslaWzZ8saRC85SPrp8efDMBYskEn0L248d1QgSNDlhVqFLxdFVqxsmxh2FQmLZUPPJEypuT/QPtJ06aaJouSPCJLNcRoivWEKnc6wgTK1aHpqcIEv1UleEECXf1NSFNdcw1Ur7kSNnbrghIMTZyeTkihWuXI7l+WJnCyrrwbHx9NJFug463zt49jOf8eV590QytXghU8jT2WKpswXVrcDZ8+nli1UT737v4IW1a5v4cTReyixeSFUrTj5XamsyLTR85qywtF8inQv27D57w41cKeMZjfJ9PXZVcSXSid6FKkMP7No5sWIVjhuRg8fSAwOQxr2jMcnL1Xye4NkRKejJNbX27tkzvmQZTqGRg0fzPV2kzcCjRYNlyo3hhrMjsp8TWjp6h4fjixYjTlvkvfdz3V2a0+4eiRpOqtzcFDw9ovhcfGtHz+69/MKFpWBD355dxZY22e9yj8U1hhbaO9reP2a4Hen27u59+9OdXfmmSMvpk5SijC5f3jg66irkoiuW+c+NIRga71vYcfSwRjOV1rAzxtPVyujKVYGJSS+f4Jcu9o1OaAiRGBhoPXXSQjGxo5FO5thc9vzV1wZjUS6Zmlq5LHL2tAmxQnd7YHTCgIjY1WzLlH2JeHzlUlu27I1Fp1avaho5D3Wk0N3mGZsAJix1t9oLFe/E5OkbPxNOTXhGo6mhRXSpSPP5clcLMGDwwig/1K9aePeBfWduvsVT4H0jU8lF/VRNZNI5o8kjI6RvZEJYMiARTN+e4XM3rHNV8t5zE3x/n02TXalMuSVsoETjydPCYF+NYvt2D5+97ganLPrPjJZ6222aRKZKlZawaqcjJ04JixeIDs/C7e+MXHMdbUiBM6OZ3m6dsvnPjxFOkOjoaz3wXmrBQoujmg8ezXV3ayzlHo0alK3c2hw4N6ZzdLq1s3vv3nRvn+lmmg8dy7a16R4nNxYzGCbd0dl2/H3TRSU7e7v2H8x0dxZCjX3795UbglLIx43FTQJPLuhrPXECsWPl9pbGQ8eF1vZ8S0vvuwerHk+9KeCaTKKWFVs82HbiuOJ08ijp/+lPiPTleooQAIiieLlMnzzhee0V6txZk6ZnXsxumZadqg8tLXzxy/LCAbyQZ06d9D7ztG1qcsaMDRiGybKV628ofPkvTIdjLrAry2IO7GvY8PQST/Datbc5GKd5iTa+TyNF+DGWbPm8wYA/WDh0QL9wVuvpNd1ucCUf/kQQBNU0tb1DGlyCi2WCT8+s1SCCogiKOg4fsiXiptOlBxukRYNqZxdWrxO5LKoqM0yqoiiCIPSZ09TpUwiKGl6f5XRe+jOGf/wUIUM5e7a+Y+CkbrP3vvm27HShqtm1fXedY8lSrX3PgTrlZOVKaMd7lomoTmffa5s1hxMasP3tYcXlImpK6659uo2BCOzcvB2aUPL5+n7zpk7aoAXatu/BbKguI217DuqEHZJ49+btqGGJfv/C17YYOGmiRNvOvZDALQ227z5gAcKgbN2bd+A1OdfV2bvpLUqsyA626eBRqliR3WzbrgNsIp3qX9i/casznc13dHRs38sIGdnJhY8cdwq5QrCpa3i3eyqRXLRo4aatjng6tWCgZ+cuV5KXOE/D+2fYqXgu0ta9fdgzFc/3dbRt28dOxlM9/V3Du5xJQWHd4WPH2Vg609rVObw7eG40PjTYu2UHOxkrtraGD53gxifFxqbg2QvN7x3LdHeHjp4MHz0RHVjS/P6J8OGjlWDYG4uFDp0oBULOlNC1c7ewoLfh2OnIkROF9lZuJNZ8+FjFG3DzfOOBI3WPj8nk23YdyHV0BM6PRQ4cKra2cpOJloOHK5zXl0kH970vcRydK7bv2Fvs6HBfGI/sP1RqaXbFhMjegzLrsZXLrdv3Sm6PXax1bNuZae105IodO4YNygmh1bF1J9QtKRDoe+UNkyR1lGjfNqxTFKZaLcN7TUBYONa9eTumGpLP3fn6DhNgBmFre2e3ZbNZitm++4CJECZl696yk6jWC5Hmgdc2Ixai2pmOnXsRaJmQaNu9DzXRkjsw8PpmslrLt7UOvLrZNKDk8nTv2IWqqk4yLbv243U52Tuw7OVXnIl0cuFQ99s72FS6znkC75/mYslCY2vH7n3esanUov6FW3e6RicSCwd7hndx0YTKsYGT59ipZK65vX14T3B0IrF4Uc+WHdx4NLlgoGPfQffYpOT2hk6dYUdj5UhT5MDh4KlzseXLet58xzc+lW9rDR47678wVvf4Gs5d8J4ZKzdHmg6933DyXGJwcdfwPu/pc+meBaHRkcYDRyXOQ+VLHbsP5NrbA2dHI+8eLbQ2s5OploOHKqyHFcuRnQckJ2sXKx3b91UiIXYy2bT/cKk5wk4kmve9K3v8NrHcun2vzHJkXe7cNlxqbnZOJJr3vSc2hhzpfOvu/bKDJSWp/e1dOsPgstK6fZ8YDjsTQvO+96RQkOCLHXsPSA4W17WubbsUhlFJe9fm7SSAmom17H3PQjDTbuvesp2Q1UJjU/+rmxETKoyzY/teFFqGhbXtPQh1RHdQXW8N2wpitrNz4OVN0ECqnK/7nZ2YpukE1bL7AC5phYamRa9uspUquc7OgY1bkZpc9TV0b99hkyQLJ5oOHCUr9WykfdErrzqKFb67s2/jW1hVEn0NXcO7yUq95GRtx49dnilCiOMAQvr0SffGV33Pb8ALhZkLFkIIDENtay/ffmf+69/QGxvtkxPuNzb6NjxFZLMz1s0C0FIjLYWvfLV0z72WzXZFp5U+ZFfOndtDT69fHm67du1tDH3J2NWnlyL8GMcyWyLtt9501669b5//5U/Fb35b6egEpnlFn1MAqqoHg8J3/4Zra+Pe2kImEjNoNYgAYDEMdfpUKBYTP3u7uPbG+tJlWmMTu+Md9p1tZDwGMWxmOg6WzUYmE/71j9En3q+su6m2fCUkiMvuNQUiCAC0VPfxqVIuqwTcgehUfqAXxVBPOiXUa7aa4uF5VyFPWdCXEaqFvKRG/MlkuVgwbYQ/nSrVKpgFvAJfzGURF+HLCvVsQFS6ffF4pbNV83A+gS+WOaeN9fDpciZjNLDuDK9mfLymBmLRemNADvi86ZTaEpQZzpPhxQyvNPs8fFp3MSi03BnBVBmHWHZnM2qtWqh1ubMZiyAxywykkgqG2RSF49MYifK1KlfIowC1K5JbEHBDx6Dp59OmadkVxZNOk7rMVMpcLkvIkl2Ruaxgk5WoaXgTcUzTKE3hcjlSqmerZXc2i9VVuyy5ed5REXHL9KYStlrNJkvubMaRy1KS5BBLvmScVBVO4H3JBKUpXC7jFXimWnWVit5UkhZFR7nsTyVITeOKeU86ZZclTkh70imnWHbURG86xVTKtFgKRKdssuTKZjzZjF2RuEzWzfNsIc/IJZ/ACxWRVFV/PGaX62w2488IdL1G50s+nnfl86gN8Qu8UC6hGOpPJChZsiuKN5MtZDOQAf5sRsplRVXxx2P1tibZ7/ELfL3SYpGUJ5MRCwUzxHlzWY13VbXWQCyqhry1esjLp7W2kEw5PBm+IqTVZp9HSFsOijD0QDyuup3lasSTThl+Z6VW9WQyul8gVdmXSgISIwwjkErKLEvXqr50CqPRckX0CGnLQaMI9GUylq6RuuZOJylTY2pVLp+jZIlSJHcmQ4ll3DS86SSo1WyaygmCs1LMV0U2myEqsl2WuHyWLZVw0/AkE1StRimyO8MzuRxTr3L5PCkU7LLEZXg2k8Msy5fhmUKRkiQ3n/JkM46qyBYLjnTSJkvurOCOxQhDc6dSrnzOrihOsewT+JRYttWrgWjUrsiuYsHLpyip7spmPALvKhYYoHmENFOrEobuj0WFeo3JZb0CT0s1pljyZjLOfA4Qlj8jZMQStJGBWDRarzmKeX9GiNcquKh5MgJbLBhu2icIpULOcjGBaDRZq5LlqpdPCxURl6GXT7OZjIJ5felUpiLWNNUv8JUGylkqugVeCgaViM+dESzWSRh6IBY1XHSpXvOkk5bf4aiIbp6X3G6pq9GTTgEUwS3Tn05rrIuq1zypJM4QpVrVk+Ghk7Frqi+dIkwdN3VfKqETmF2qewS+jlv1aoObTyEoRuq6l0/LlkkYui8RwyyTrlc9Ao8DgFmXZS9CFLVIm318zHlgn2t4O5mIW3YKIWZYfAwMA5KEeP1NlVturS9Zhtbrrl3D3ObX6dOnIILMsFmhZUKA1lZeVbz3fnlBHzDMOcGuTO6NTf5XX1ndNbBy1fUURZvmpdzpPtUU4ceePVQQUsN73jpDEaVvfVvp7bvSOdb0VUEAHEcOuze+yhw/ZhHEDFN7lgUglAaHSp+7s7p6DUAQ6uwZbutm597dwDRnmGJHLor86g2h2sqV4s23yd29qK5dEu24P5IiNEp33FW9657Q6Ei5sdHCUDaZrvt9ho10pQU54FVIyj85mW1pdVh1OlUoB4MWRbqSvMyxGk1xKUEOeCTK4Z+YKDY3AwL1TsZKfp/poNgEr3Cs6mDYlACdRIn1+yenSuFGSBOeqXjN61OdNJtIaw5adru5ZFplHVXWHRyfKDU0WIzNMxmX3G7ZyzJCnjD1YjjMpQWdIGW/25NIqRRdC3i5RBoxjXJLE5Mp2HU139jo4jMQw4rhcHBqSieIWjjg5HO4qmbaO9zplF1V8s1NTj4LEFhsivinpkwc14Msnq2Siix0dXFpnlDlUiTiSadMBC00RfzxGETRSmOQ4XN2WSq2NtsLok2qFVuanbkcWZcrTSFSrDoKxXRPL10RuVQq09PjEItUoZxtaaGqNUe5JDaFiLrszBeKLU2IavgSCb67m5GrdLZQbmok63WqJFaaQqissrlcqaUJatCbiGfb21iphBfq5XCI0DU6X6yEg0DV3ZmLuULf1FSuo8OmK450phxqwCzDkS0UmiIWQQTHxwqNjYBEfVOJcsBvOCguwcusU3EwXEpQ3a4a4wpMTZUamxA75pmMVzxuwOB2vqQ5GJlj2RSvs44K6w5OTJaCQcjYPNFEze1WOScb53U7Wfd5uJRgMHbR4/dPTNR8vqrXFx65ILGs4nO7krxus4n+gD8RN2ib6Av4JyZqHq8YCAbiMUxVhM4udzpNyVKhpdmRyQHLLDZF/NGohaKVppCTzxKyJHR2cYJA1WvlSNiRzUEICk0RbzyOAFBtaqCFvL1e57t7uIxA1mrF1mZQKzd/AAAQaklEQVQ3z0MTVsJBNsUDBIiREJ0tUrVqoTVClqpMpVJobeZyWUQzK+GgU8hiulFuDtvzZaYi8l3dbDlP50rFprBNkqiiWGlqwDTDlc2Vm8OmjvjjUb6zi1bqjJArNzUSqkLnS2qQMw3EmcuXW5pMC/NPTQrt7XZDdfHZUrgBs0xHtlgN+aEJ3bxQjoRUQDRMTmbb20moO5OZasCDI6YtJ9aDfhPD3clUJRJSMFtoYjzT0koA05XKVAJ+00660hmEwspuvzeaEINBi7F5pmI1j1flnGwsZVC2ut/HpniDtoseb2BiUvT7TSftmYpJLCf7ODbB6wQhNjT44nGLspUDQd/EhOTxVny+8MiI7HRKAa8rkTZxvBwK+xJx00ZIXrc7lqhxHjEQCI+MqE5nPeB1JnmIYcXGRl88pjmcSq3qe+RXRFb4SPDpUqcILbsdE0V297Bz7+7p7MGs1m1JUhb0lW/7XPWa6wyvl0in3W9sdO0exrMZSJAzLkqxGKZ05z3iZ27SQ2Ggqld6rQ6CoogFvc885Xtr6/VLVg8tucpG2EzrElPGS0OwpjlWPp/ZuXvrWUvLf/u7cv+iK1qG/6N0O0BJPuV+YxP7zjagaTM+0AEsy/D5KtetLd15j9bYRKZTjiOHPC8+T/BpSJIz/4GmiSCI2tZeW3lV8Z57Lafz059af5BgIRAaPr8eDJoYjlomgNDCcHDxA4aaFoIgFo6hpolA+IdsfveDieGoZQFofXwcYFkf2VjQwnFgWcCyLBwDFgTQ+sM20AKmaWE4giCoZVooBhAITMvCMQRCzDRNHEcQBDVMC8M+tEEQiF60QTDTMDEcAdM2KIKAD2wQ1DR/3wZiKAQANU2IohABf8rGMCGKQhSgpgkBClEUNQyIAohhqGFAACCKoaYBwW/bmAYEwMIwzDAgAiA2bfOh8Ucfpm0QBLEwHDVNCJCPvkIxzDQgAiz8YzaWCZGLNtN/+T13/NFbbeI4On2rcQJAC1h/wh2WhWMAwg9s4AeOnvbm/8MGtczfv40X/TJ9qxEEM/6kOzAMAdM2GDLthY/bXBzw32ODIgCghgExDIIPfIdiv3Orf88dwML+iM1Fd5gIgkzbIACxfttlCALMizZw2mUIhBf9iyDTD8NFG/PiV39qBv173WFaOP57Nhiw4O/YIBBBLdPCPjYgnJ5uOAKti9PtAxsEQfBajUwmga59fP28VAQL4jiC48yhd7m3tlKnT2K12gy3gIs0SEcAKN92u7juJqWnF+K4a+8e7vWN1Mh5oGtwpj2CgKZpbe25r/4HaXDIstvnQCU0RFFgGIGHH3Tv23PLNTf39y/BUMy6DKqPLhnBQhAEACBWysPDW05XC9m//lt5cAi50kOUHzgbkyXHgf2+5zbg2eyM+RAwTYsk1a7uwn1fqq1YiWoamUx4XnmZ3b7NIsnZiC8Aw7AoWmtpKd1+l7juJmDon+Yc+8MEazp0d4UX5M1jHvO4BK+1GPpbWcJLQrAAsOyULRF3v/oKc/Qwns8hMz3z9GHgSu3sKnzxgfrQEsPrxasV39NPOt49iBfyMywXmR5WlsVbPlu87361sQlc4SKiH24omKwEfvR9z6lTd9x0V0d7DwpQeHlc16UkWNMcS5ald3a+eTYdzXzne9U114A5wbEQFEVM0x6d8q1/jD5zGs6m1SBALJoRb761cP+XLYbBymXq7JngIw/imQy02WbxVEIAoelyKV09ua9+XVnQh0rSpzPZ/ijBmsc85jGPT2CJ+bQJFiQIoKrurW+yW7fguRww9Fm9AJsmBEC85bby7Xdp4bDpcLr27fZueJpMJYE+85GBaSAoLvzVd2tXrbEcDmROvM1CgsALhcC/fd8fnbr7tvsijS2X1c/7VE8R/kEQBNnZ3iuVS/U3XzOdLqV3wVwIY0CIAGC43bU11wDLtI9cmKFwKAAIAoCmUaMjjncPas0tWqRZb2iorL0RVTX6/JmZv8oAgAAANI3ICM6DB/BsVunpuSiX9ednn79/inAe85jHPD4pfHqnCAGAKEafPB7+0Q9ce3ZjtSqY3bIGdF0PhzPf/S/lz91heL2YLDc89DPPSy8ShfxsRkZVRe3sTv7j/5YXLYZ2+xwIXCEIAknSFo8Fv/9PDULm/ru/0hAIg8tsQ7n0BAtBEBRFuzv7NFURN28EhiEtWTpnUkUQx+tDS5XeBY73jwJVnTkZghCrVNjhHVi1Ii8etGhaGloiLVpMnzuLVSqz1GpHVZUaOe/av890ubTmlj876ZknWPOYxzzmAMECACuXG371C/+vn8TK5dmyFgghQYifuUX4u39QOzshSTLHjoZ/8H/oU6dm1bUGQsSySnd/QfjrvzF8/iu0Te0fuCyCpM6f8f/wB22K9oU7v+J1+8Dlt5tcFgRr+tHq7uhFMSy37Q0sl6svXzFnpjqAUA+FxZtutU+OE9nMLEejLpx37t+rtrXrwaAWbqxcfwOqqvapidnObQDQes25fy919qza2mpy7j8j9fmIYFnzBGse85jHJ76dmJz7z0uwIASG4Xn1lcZ//icyOvUJsBYU1YOhzHe/V7zviwiOYxXR//STvmneNstb4fZkv/O90u13zqqk5PKLXDDHjvh/8ZNelLzzc/dzrPsy3f0vbQ3W74DAiZNn39+2/fXS0JLM3/5Xi6LmRiRzmsFAgnBvfNX3/DNAUWYVvIEQWGbp9juL9z9gOhyWzeY4cti34SlbLDr7qqbpGrjyrZ8t3XOv4fFCHP/EXQBxnHn/WMPPfzxfgzWPeczjk4cFtUgk9rMHsVrtE1/GEcsCuu44esT39BOEIMyyrfL0RmAxTOWa63Pf/EuIYags06dO+p9+whadsshZVdkiKCoNLM79p2+pbW3gcmvLOBsXQOh494D/sUf6PcHPrLvD4XBal2vK6/IiWAiCYBg+MTXy1jubhEhT9m//m+4PIAicMzQL4oRtYjz46EO2iXFUVWcujnIxVd+Y+9o3pKElBufGKqLnlZdce3fj+dzsU2+oohhud+GLD9RXXqV7vQiOf8IVkZaFahoyT67mMY95/Hm2Ycv2iVYaAYAAgNbr9rFRz6sv08ePQQyfZeAKmKbJMFqkJfvNv5QHFqH1OplKsm9tcb+5CUHAzPUdIEQQxPR4xJtuKX7+PstmmwNCDB96AWiac9fOhqefHOrovfbaW2g7fcnFrq4kgoUgCIpiQia15e1XY6wz963vqN3dHz40c4Jj4agkeV963jW8Ay+VZn7AcLqDIYpWblhXuv0utaXFZDnH0UPu135DnT2DVSqz0V+5yIFkWe3qKt79BWlgwAg2QASAT/BRniulAPOYxzwuv3X2k3stBwCiGCbVbdFJ9u1tzt3DwNAhQc5qSNOEOK6HG8s33VK+826LIAhBoE+e8L3wDBmNWgwzm3UbwXG5d0Hx8/fVVq9BZXnuZIFQFK3V2Lc2h195efmiZatWrb0cpESvPII1fScLpfz2tzeOoDD3tW9IS5chGIbMmcp3FEUAcBx+z73pNer8uemWzDNeR1BV1SLN5c/eXl19tdYUAarCbdns2ruLunAegXA2QTIEQYBpAE2rL10h3nSzvHBAD4WAriPzalXzmMc8/n/gaTgOdJ0aucAcepfduR3P52feTPCDFRvouuEP1FauKt1xt9rRidZq9Lkzrne2sTvfgTgxK2FSXTf8gcp1a0t33aMHgqgizx1HYBiRz7EbX2vetnXFVWtXLLsaIIh12e9El0uR++89hNBBO5pbOvREvH5wr2G3661tEMPBnCDjAEKAIFprm7JgISRwMpVCpfoM2zkDAHEcq5TpE8dtqSQCED3SLA0tUbq6oc2OiWUin5u5lAOCICgKCWJaR54UeGCahsdrulxzRK5sHvOYxzz+CLVCMIy6cJ59e6v35RecB/YjpgFJcnYSDBokbfUVK4v33l+65149FKbOn3VvedP7/Ab69CnLTs08rm9ZwNDry1cW772/+PkvQBuJ6trc8QVJ2qJTnl8/2br/wNp1ty9ZvBJCC14JZOAyJVjTHIuy000tbZgoVg7s1lVF6+mFBAHmSsATmKbJcfLCfi3SjJcKZDqNoDNV/kUxBEFs0Slq5AKRESyWlXt65UWL9aYmhCDIVBKT5VllDDEMgdAWnaJPnbSlkqiu6+FGSFHzwuvzmMc85hq1wjBI0fbxMc+m19xvbHLu2YXV6tBm+6jX4QwJkKF2dJU+/4XivfdJQ0uxWpXbstn7youu3cOoqswm54gqiuliS5+/r3j/l6RFi1FVnTO7JIIgFs043j/mfuxXPYnUjTffvaC73zRNiFwZF3j5EqxpjmUj7eHGFgbC0oFdMJ1W+gcsm23ucCzLQlBUa21VehZYLpYauTBzoV4AIE5gkmQfHbGPjRKlotrWoXZ1yd29WksrWqvZUkkEgJm/IQGAYBjQNVt0irpwjhodhQShtbQiKDpPs+Yxj3nMBWqFopBmSD7teel5z+sbHQf24cUCJMjZFrNrGqTp0ue/UPjil2urrjJZznlwv+/ZZ7htW8h0GhLEzKmbZaGyXFt5Vf6rX6+su9lwudDZKGZddhsksCiae+ct5xMPD6G2dZ+5s7mp1TCupAu8TGuwfoueo6hhGhPj57fteSvb0Z773t/rPt8cS1FNV77bx0Z9G56iT56waHpWgWjDsBhGbe8o3nl3de2NQFEJgXe8d9Cz8TdERrBI22zFESwLQKj7fEp3j3jb7bUVK4GqAdNA5jGPeczjCt3LaZrg09zWLczRw2Q8hmraLAtYEQQBpgl0vbL2xtKd92gtzbrHR6YSnldfcbx7EM/nEIDMJioGVNVyOIr33l+5fq0RCCJw7hy3n974IUF6XniWevXla7sWrlpxHevkLvOS9iuSYCEIAgCAEAqZ1OvbXs3QduEf/ofa3g7mElVHprviIHix4Ny/z7/+MQTCWSX1pk/qsqzctzD/la8pXT14RcQzguf1ja53tk2fYZntD7YsBEVNl0vp7i186QF5YT+QFWDN12bNYx7zuKKolc2GVSrcljddu4eJjHCx5cYs30IhRBVFbW/Pfe0/yn19ptsDDIN9Z5t702t4NjObloIXB9dUaXBJ/oGvKl3dkCDmFLVCEIhhwND96x+nt79961U3DPQN2e0UvAKv8cogWB9Ckmu/eeOFRE3M/s3fVVdfjSrK3JvtQNdtiXjg0V9Rp05YFD3LSY6gqOHxiDffVrr9TothUEWxjY/5n3qCOn9uVsXvv/0vLJqWFg0WvvhlpasbnZZRncc85jGPy3wjxwlUkV3DO7jNbxAC/0klRoCuWwydf+Cr1etuMB0OSBD20VH/U4/bRy4Aw5i9FrRFUaW77y1/9naTYaaFN+eWU3C8VPI+9rD78KHb193R1bEAm3UocZ5g/XthWuZb218/NXG+9KUHivfeP3ck1H4bqCyz27Z6X3oeaNonQoO05pbc178hDS6ZpnGuPbt8T6/HRPETpIaQJGsrVhbv+5IaaZ4/ZjiPeczjst78dN25f6/ntVeIVOoTaykBIYIglbXrCg98xXB7EABQWfY+t4HbtvWToUEAqO2d2b/8K6Wzc07K5UAMs0WnfA8/2JhI3nbrvZHGliv6cv4v3uC2V8a7r2sAAAAASUVORK5CYII=", + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"core_diagram.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Distributed materials\n", + "\n", + "In a depletion problem, every fuel pin might need its own unique material. We have a feature called \"distributed materials\" which makes this easier." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "many_uo2_mats = []\n", + "for i in range(72):\n", + " m = openmc.Material(name='uo2')\n", + " m.add_element('U', 1.0, enrichment=3.0)\n", + " m.add_nuclide('O16', 2.0)\n", + " m.set_density('g/cm3', 10.0)\n", + " many_uo2_mats.append(m)\n", + "\n", + "mf += many_uo2_mats\n", + "mf.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fuel.fill = many_uo2_mats\n", + "g.export_to_xml()\n", + "openmc.plot_inline(p)" + ] + } + ], + "metadata": { + "celltoolbar": "Raw Cell Format", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/stress-test/lattice-computations/assembly_diagram.png b/stress-test/lattice-computations/assembly_diagram.png new file mode 100644 index 0000000..d138411 Binary files /dev/null and b/stress-test/lattice-computations/assembly_diagram.png differ diff --git a/stress-test/lattice-computations/core_diagram.png b/stress-test/lattice-computations/core_diagram.png new file mode 100644 index 0000000..c658a91 Binary files /dev/null and b/stress-test/lattice-computations/core_diagram.png differ diff --git a/stress-test/lattice-computations/pyrex_diagram.png b/stress-test/lattice-computations/pyrex_diagram.png new file mode 100644 index 0000000..3d96045 Binary files /dev/null and b/stress-test/lattice-computations/pyrex_diagram.png differ diff --git a/stress-test/mc-performance/build.py b/stress-test/mc-performance/build.py new file mode 100755 index 0000000..1cb8946 --- /dev/null +++ b/stress-test/mc-performance/build.py @@ -0,0 +1,656 @@ +#!/usr/bin/env python + +from argparse import ArgumentParser + +import numpy as np +import openmc + +parser = ArgumentParser() +parser.add_argument('-s', '--size', action='store', default='small', + help='Size of nuclide inventory (small or large)') +args = parser.parse_args() + +# Define materials +fuel = openmc.Material(name='Fuel', material_id=1) +fuel.set_density('g/cm3', 10.062) +fuel.add_nuclide("U234", 4.9476e-6) +fuel.add_nuclide("U235", 4.8218e-4) +fuel.add_nuclide("U236", 9.0402e-5) +fuel.add_nuclide("U238", 2.1504e-2) +fuel.add_nuclide("Np237", 7.3733e-6) +fuel.add_nuclide("Pu238", 1.5148e-6) +fuel.add_nuclide("Pu239", 1.3955e-4) +fuel.add_nuclide("Pu240", 3.4405e-5) +fuel.add_nuclide("Pu241", 2.1439e-5) +fuel.add_nuclide("Pu242", 3.7422e-6) +fuel.add_nuclide("Am241", 4.5041e-7) +fuel.add_nuclide("Am242_m1", 9.2301e-9) +fuel.add_nuclide("Am243", 4.7878e-7) +fuel.add_nuclide("Cm242", 1.0485e-7) +fuel.add_nuclide("Cm243", 1.4268e-9) +fuel.add_nuclide("Cm244", 8.8756e-8) +fuel.add_nuclide("Cm245", 3.5285e-9) +fuel.add_nuclide("Mo95", 2.6497e-5) +fuel.add_nuclide("Tc99", 3.2772e-5) +fuel.add_nuclide("Ru101", 3.0742e-5) +fuel.add_nuclide("Ru103", 2.3505e-6) +fuel.add_nuclide("Ag109", 2.0009e-6) +fuel.add_nuclide("Xe135", 1.0801e-8) +fuel.add_nuclide("Cs133", 3.4612e-5) +fuel.add_nuclide("Nd143", 2.6078e-5) +fuel.add_nuclide("Nd145", 1.9898e-5) +fuel.add_nuclide("Sm147", 1.6128e-6) +fuel.add_nuclide("Sm149", 1.1627e-7) +fuel.add_nuclide("Sm150", 7.1727e-6) +fuel.add_nuclide("Sm151", 5.4947e-7) +fuel.add_nuclide("Sm152", 3.0221e-6) +fuel.add_nuclide("Eu153", 2.6209e-6) +fuel.add_nuclide("Gd155", 1.5369e-9) +fuel.add_nuclide("O16", 4.5737e-2) + +# For H-M large, add all minor actinides and fission products +if args.size == 'large': + # fission products from all actinides + fuel.add_nuclide('Cu65', 1.0e-12) + fuel.add_nuclide('Zn66', 1.0e-12) + fuel.add_nuclide('Zn67', 1.0e-12) + fuel.add_nuclide('Zn68', 1.0e-12) + fuel.add_nuclide('Zn70', 1.0e-12) + fuel.add_nuclide('Ga69', 1.0e-12) + fuel.add_nuclide('Ga71', 1.0e-12) + fuel.add_nuclide('Ge70', 1.0e-12) + fuel.add_nuclide('Ge72', 1.0e-12) + fuel.add_nuclide('Ge73', 1.0e-12) + fuel.add_nuclide('Ge74', 1.0e-12) + fuel.add_nuclide('Ge76', 1.0e-12) + fuel.add_nuclide('As74', 1.0e-12) + fuel.add_nuclide('As75', 1.0e-12) + fuel.add_nuclide('Se74', 1.0e-12) + fuel.add_nuclide('Se76', 1.0e-12) + fuel.add_nuclide('Se77', 1.0e-12) + fuel.add_nuclide('Se78', 1.0e-12) + fuel.add_nuclide('Se79', 1.0e-12) + fuel.add_nuclide('Se80', 1.0e-12) + fuel.add_nuclide('Se82', 1.0e-12) + fuel.add_nuclide('Br79', 1.0e-12) + fuel.add_nuclide('Br81', 1.0e-12) + fuel.add_nuclide('Kr78', 1.0e-12) + fuel.add_nuclide('Kr80', 1.0e-12) + fuel.add_nuclide('Kr82', 1.0e-12) + fuel.add_nuclide('Kr83', 1.0e-12) + fuel.add_nuclide('Kr84', 1.0e-12) + fuel.add_nuclide('Kr85', 1.0e-12) + fuel.add_nuclide('Kr86', 1.0e-12) + fuel.add_nuclide('Rb85', 1.0e-12) + fuel.add_nuclide('Rb86', 1.0e-12) + fuel.add_nuclide('Rb87', 1.0e-12) + fuel.add_nuclide('Sr84', 1.0e-12) + fuel.add_nuclide('Sr86', 1.0e-12) + fuel.add_nuclide('Sr87', 1.0e-12) + fuel.add_nuclide('Sr88', 1.0e-12) + fuel.add_nuclide('Sr89', 1.0e-12) + fuel.add_nuclide('Sr90', 1.0e-12) + fuel.add_nuclide('Y89', 1.0e-12) + fuel.add_nuclide('Y90', 1.0e-12) + fuel.add_nuclide('Y91', 1.0e-12) + fuel.add_nuclide('Zr90', 1.0e-12) + fuel.add_nuclide('Zr91', 1.0e-12) + fuel.add_nuclide('Zr92', 1.0e-12) + fuel.add_nuclide('Zr93', 1.0e-12) + fuel.add_nuclide('Zr94', 1.0e-12) + fuel.add_nuclide('Zr95', 1.0e-12) + fuel.add_nuclide('Zr96', 1.0e-12) + fuel.add_nuclide('Nb93', 1.0e-12) + fuel.add_nuclide('Nb94', 1.0e-12) + fuel.add_nuclide('Nb95', 1.0e-12) + fuel.add_nuclide('Mo92', 1.0e-12) + fuel.add_nuclide('Mo94', 1.0e-12) + fuel.add_nuclide('Mo96', 1.0e-12) + fuel.add_nuclide('Mo97', 1.0e-12) + fuel.add_nuclide('Mo98', 1.0e-12) + fuel.add_nuclide('Mo99', 1.0e-12) + fuel.add_nuclide('Mo100', 1.0e-12) + fuel.add_nuclide('Ru96', 1.0e-12) + fuel.add_nuclide('Ru98', 1.0e-12) + fuel.add_nuclide('Ru99', 1.0e-12) + fuel.add_nuclide('Ru100', 1.0e-12) + fuel.add_nuclide('Ru102', 1.0e-12) + fuel.add_nuclide('Ru104', 1.0e-12) + fuel.add_nuclide('Ru105', 1.0e-12) + fuel.add_nuclide('Ru106', 1.0e-12) + fuel.add_nuclide('Rh103', 1.0e-12) + fuel.add_nuclide('Rh105', 1.0e-12) + fuel.add_nuclide('Pd102', 1.0e-12) + fuel.add_nuclide('Pd104', 1.0e-12) + fuel.add_nuclide('Pd105', 1.0e-12) + fuel.add_nuclide('Pd106', 1.0e-12) + fuel.add_nuclide('Pd107', 1.0e-12) + fuel.add_nuclide('Pd108', 1.0e-12) + fuel.add_nuclide('Pd110', 1.0e-12) + fuel.add_nuclide('Ag107', 1.0e-12) + fuel.add_nuclide('Ag110_m1', 1.0e-12) + fuel.add_nuclide('Ag111', 1.0e-12) + fuel.add_nuclide('Cd106', 1.0e-12) + fuel.add_nuclide('Cd108', 1.0e-12) + fuel.add_nuclide('Cd110', 1.0e-12) + fuel.add_nuclide('Cd111', 1.0e-12) + fuel.add_nuclide('Cd112', 1.0e-12) + fuel.add_nuclide('Cd113', 1.0e-12) + fuel.add_nuclide('Cd114', 1.0e-12) + fuel.add_nuclide('Cd115_m1', 1.0e-12) + fuel.add_nuclide('Cd116', 1.0e-12) + fuel.add_nuclide('In113', 1.0e-12) + fuel.add_nuclide('In115', 1.0e-12) + fuel.add_nuclide('Sn112', 1.0e-12) + fuel.add_nuclide('Sn113', 1.0e-12) + fuel.add_nuclide('Sn114', 1.0e-12) + fuel.add_nuclide('Sn115', 1.0e-12) + fuel.add_nuclide('Sn116', 1.0e-12) + fuel.add_nuclide('Sn117', 1.0e-12) + fuel.add_nuclide('Sn118', 1.0e-12) + fuel.add_nuclide('Sn119', 1.0e-12) + fuel.add_nuclide('Sn120', 1.0e-12) + fuel.add_nuclide('Sn122', 1.0e-12) + fuel.add_nuclide('Sn123', 1.0e-12) + fuel.add_nuclide('Sn124', 1.0e-12) + fuel.add_nuclide('Sn125', 1.0e-12) + fuel.add_nuclide('Sn126', 1.0e-12) + fuel.add_nuclide('Sb121', 1.0e-12) + fuel.add_nuclide('Sb123', 1.0e-12) + fuel.add_nuclide('Sb124', 1.0e-12) + fuel.add_nuclide('Sb125', 1.0e-12) + fuel.add_nuclide('Sb126', 1.0e-12) + fuel.add_nuclide('Te120', 1.0e-12) + fuel.add_nuclide('Te122', 1.0e-12) + fuel.add_nuclide('Te123', 1.0e-12) + fuel.add_nuclide('Te124', 1.0e-12) + fuel.add_nuclide('Te125', 1.0e-12) + fuel.add_nuclide('Te126', 1.0e-12) + fuel.add_nuclide('Te127_m1', 1.0e-12) + fuel.add_nuclide('Te128', 1.0e-12) + fuel.add_nuclide('Te129_m1', 1.0e-12) + fuel.add_nuclide('Te130', 1.0e-12) + fuel.add_nuclide('Te132', 1.0e-12) + fuel.add_nuclide('I127', 1.0e-12) + fuel.add_nuclide('I129', 1.0e-12) + fuel.add_nuclide('I130', 1.0e-12) + fuel.add_nuclide('I131', 1.0e-12) + fuel.add_nuclide('I135', 1.0e-12) + fuel.add_nuclide('Xe124', 1.0e-12) + fuel.add_nuclide('Xe126', 1.0e-12) + fuel.add_nuclide('Xe128', 1.0e-12) + fuel.add_nuclide('Xe129', 1.0e-12) + fuel.add_nuclide('Xe130', 1.0e-12) + fuel.add_nuclide('Xe131', 1.0e-12) + fuel.add_nuclide('Xe132', 1.0e-12) + fuel.add_nuclide('Xe133', 1.0e-12) + fuel.add_nuclide('Xe134', 1.0e-12) + fuel.add_nuclide('Xe136', 1.0e-12) + fuel.add_nuclide('Cs134', 1.0e-12) + fuel.add_nuclide('Cs135', 1.0e-12) + fuel.add_nuclide('Cs136', 1.0e-12) + fuel.add_nuclide('Cs137', 1.0e-12) + fuel.add_nuclide('Ba132', 1.0e-12) + fuel.add_nuclide('Ba133', 1.0e-12) + fuel.add_nuclide('Ba134', 1.0e-12) + fuel.add_nuclide('Ba135', 1.0e-12) + fuel.add_nuclide('Ba136', 1.0e-12) + fuel.add_nuclide('Ba137', 1.0e-12) + fuel.add_nuclide('Ba138', 1.0e-12) + fuel.add_nuclide('Ba140', 1.0e-12) + fuel.add_nuclide('La138', 1.0e-12) + fuel.add_nuclide('La139', 1.0e-12) + fuel.add_nuclide('La140', 1.0e-12) + fuel.add_nuclide('Ce138', 1.0e-12) + fuel.add_nuclide('Ce139', 1.0e-12) + fuel.add_nuclide('Ce140', 1.0e-12) + fuel.add_nuclide('Ce141', 1.0e-12) + fuel.add_nuclide('Ce142', 1.0e-12) + fuel.add_nuclide('Ce143', 1.0e-12) + fuel.add_nuclide('Ce144', 1.0e-12) + fuel.add_nuclide('Pr141', 1.0e-12) + fuel.add_nuclide('Pr142', 1.0e-12) + fuel.add_nuclide('Pr143', 1.0e-12) + fuel.add_nuclide('Nd142', 1.0e-12) + fuel.add_nuclide('Nd144', 1.0e-12) + fuel.add_nuclide('Nd146', 1.0e-12) + fuel.add_nuclide('Nd147', 1.0e-12) + fuel.add_nuclide('Nd148', 1.0e-12) + fuel.add_nuclide('Nd150', 1.0e-12) + fuel.add_nuclide('Pm147', 1.0e-12) + fuel.add_nuclide('Pm148', 1.0e-12) + fuel.add_nuclide('Pm148_m1', 1.0e-12) + fuel.add_nuclide('Pm149', 1.0e-12) + fuel.add_nuclide('Pm151', 1.0e-12) + fuel.add_nuclide('Sm144', 1.0e-12) + fuel.add_nuclide('Sm148', 1.0e-12) + fuel.add_nuclide('Sm153', 1.0e-12) + fuel.add_nuclide('Sm154', 1.0e-12) + fuel.add_nuclide('Eu151', 1.0e-12) + fuel.add_nuclide('Eu152', 1.0e-12) + fuel.add_nuclide('Eu154', 1.0e-12) + fuel.add_nuclide('Eu155', 1.0e-12) + fuel.add_nuclide('Eu156', 1.0e-12) + fuel.add_nuclide('Eu157', 1.0e-12) + fuel.add_nuclide('Gd152', 1.0e-12) + fuel.add_nuclide('Gd153', 1.0e-12) + fuel.add_nuclide('Gd154', 1.0e-12) + fuel.add_nuclide('Gd156', 1.0e-12) + fuel.add_nuclide('Gd157', 1.0e-12) + fuel.add_nuclide('Gd158', 1.0e-12) + fuel.add_nuclide('Gd160', 1.0e-12) + fuel.add_nuclide('Tb159', 1.0e-12) + fuel.add_nuclide('Tb160', 1.0e-12) + fuel.add_nuclide('Dy156', 1.0e-12) + fuel.add_nuclide('Dy158', 1.0e-12) + fuel.add_nuclide('Dy160', 1.0e-12) + fuel.add_nuclide('Dy161', 1.0e-12) + fuel.add_nuclide('Dy162', 1.0e-12) + fuel.add_nuclide('Dy163', 1.0e-12) + fuel.add_nuclide('Dy164', 1.0e-12) + fuel.add_nuclide('Ho165', 1.0e-12) + fuel.add_nuclide('Ho166_m1', 1.0e-12) + fuel.add_nuclide('Er162', 1.0e-12) + fuel.add_nuclide('Er164', 1.0e-12) + fuel.add_nuclide('Er166', 1.0e-12) + fuel.add_nuclide('Er167', 1.0e-12) + fuel.add_nuclide('Er168', 1.0e-12) + fuel.add_nuclide('Er170', 1.0e-12) + fuel.add_nuclide('Tm168', 1.0e-12) + fuel.add_nuclide('Tm169', 1.0e-12) + fuel.add_nuclide('Tm170', 1.0e-12) + # minor actinides + fuel.add_nuclide('U233', 1.0e-12) + fuel.add_nuclide('U237', 1.0e-12) + fuel.add_nuclide('Np238', 1.0e-12) + fuel.add_nuclide('Cm246', 1.0e-12) + # other reaction products from (n,p), (n,a), etc. + fuel.add_nuclide('H1', 1.0e-12) + fuel.add_nuclide('H2', 1.0e-12) + fuel.add_nuclide('H3', 1.0e-12) + fuel.add_nuclide('He3', 1.0e-12) + fuel.add_nuclide('He4', 1.0e-12) + +clad = openmc.Material(name='Cladding', material_id=2) +clad.set_density('g/cm3', 5.77) +clad.add_element("Zr", 1.0) + +cold_water = openmc.Material(name='Cold borated water', material_id=3) +cold_water.set_density('atom/b-cm', 0.07416) +cold_water.add_nuclide("H1", 2.0) +cold_water.add_nuclide("O16", 1.0) +cold_water.add_nuclide("B10", 6.490e-4) +cold_water.add_nuclide("B11", 2.689e-3) +cold_water.add_s_alpha_beta('c_H_in_H2O') + +hot_water = openmc.Material(name='Hot borated water', material_id=4) +hot_water.set_density('atom/b-cm', 0.06614) +hot_water.add_nuclide("H1", 2.0) +hot_water.add_nuclide("O16", 1.0) +hot_water.add_nuclide("B10", 6.490e-4) +hot_water.add_nuclide("B11", 2.689e-3) +hot_water.add_s_alpha_beta('c_H_in_H2O') + +rpv_steel = openmc.Material(name='Reactor pressure vessel steel', + material_id=5) +rpv_steel.set_density('g/cm3', 7.9) +rpv_steel.add_nuclide("Fe54", 0.05437098, 'wo') +rpv_steel.add_nuclide("Fe56", 0.88500663, 'wo') +rpv_steel.add_nuclide("Fe57", 0.0208008, 'wo') +rpv_steel.add_nuclide("Fe58", 0.00282159, 'wo') +rpv_steel.add_nuclide("Ni58", 0.0067198, 'wo') +rpv_steel.add_nuclide("Ni60", 0.0026776, 'wo') +rpv_steel.add_nuclide("Ni61", 0.0001183, 'wo') +rpv_steel.add_nuclide("Ni62", 0.0003835, 'wo') +rpv_steel.add_nuclide("Ni64", 0.0001008, 'wo') +rpv_steel.add_nuclide("Mn55", 0.01, 'wo') +rpv_steel.add_nuclide("Mo92", 0.000849, 'wo') +rpv_steel.add_nuclide("Mo94", 0.0005418, 'wo') +rpv_steel.add_nuclide("Mo95", 0.0009438, 'wo') +rpv_steel.add_nuclide("Mo96", 0.0010002, 'wo') +rpv_steel.add_nuclide("Mo97", 0.0005796, 'wo') +rpv_steel.add_nuclide("Mo98", 0.0014814, 'wo') +rpv_steel.add_nuclide("Mo100", 0.0006042, 'wo') +rpv_steel.add_nuclide("Si28", 0.00367464, 'wo') +rpv_steel.add_nuclide("Si29", 0.00019336, 'wo') +rpv_steel.add_nuclide("Si30", 0.000132, 'wo') +rpv_steel.add_nuclide("Cr50", 0.00010435, 'wo') +rpv_steel.add_nuclide("Cr52", 0.002092475, 'wo') +rpv_steel.add_nuclide("Cr53", 0.00024185, 'wo') +rpv_steel.add_nuclide("Cr54", 6.1325e-05, 'wo') +rpv_steel.add_nuclide("C0", 0.0025, 'wo') +rpv_steel.add_nuclide("Cu63", 0.0013696, 'wo') +rpv_steel.add_nuclide("Cu65", 0.0006304, 'wo') + +lower_rad_ref = openmc.Material(name='Lower radial reflector', + material_id=6) +lower_rad_ref.set_density('g/cm3', 4.32) +lower_rad_ref.add_nuclide("H1", 0.0095661, 'wo') +lower_rad_ref.add_nuclide("O16", 0.0759107, 'wo') +lower_rad_ref.add_nuclide("B10", 3.08409e-5, 'wo') +lower_rad_ref.add_nuclide("B11", 1.40499e-4, 'wo') +lower_rad_ref.add_nuclide("Fe54", 0.035620772088, 'wo') +lower_rad_ref.add_nuclide("Fe56", 0.579805982228, 'wo') +lower_rad_ref.add_nuclide("Fe57", 0.01362750048, 'wo') +lower_rad_ref.add_nuclide("Fe58", 0.001848545204, 'wo') +lower_rad_ref.add_nuclide("Ni58", 0.055298376566, 'wo') +lower_rad_ref.add_nuclide("Ni60", 0.022034425592, 'wo') +lower_rad_ref.add_nuclide("Ni61", 0.000973510811, 'wo') +lower_rad_ref.add_nuclide("Ni62", 0.003155886695, 'wo') +lower_rad_ref.add_nuclide("Ni64", 0.000829500336, 'wo') +lower_rad_ref.add_nuclide("Mn55", 0.0182870, 'wo') +lower_rad_ref.add_nuclide("Si28", 0.00839976771, 'wo') +lower_rad_ref.add_nuclide("Si29", 0.00044199679, 'wo') +lower_rad_ref.add_nuclide("Si30", 0.0003017355, 'wo') +lower_rad_ref.add_nuclide("Cr50", 0.007251360806, 'wo') +lower_rad_ref.add_nuclide("Cr52", 0.145407678031, 'wo') +lower_rad_ref.add_nuclide("Cr53", 0.016806340306, 'wo') +lower_rad_ref.add_nuclide("Cr54", 0.004261520857, 'wo') +lower_rad_ref.add_s_alpha_beta('c_H_in_H2O') + +upper_rad_ref = openmc.Material(name='Upper radial reflector /' + 'Top plate region', material_id=7) +upper_rad_ref.set_density('g/cm3', 4.28) +upper_rad_ref.add_nuclide("H1", 0.0086117, 'wo') +upper_rad_ref.add_nuclide("O16", 0.0683369, 'wo') +upper_rad_ref.add_nuclide("B10", 2.77638e-5, 'wo') +upper_rad_ref.add_nuclide("B11", 1.26481e-4, 'wo') +upper_rad_ref.add_nuclide("Fe54", 0.035953677186, 'wo') +upper_rad_ref.add_nuclide("Fe56", 0.585224740891, 'wo') +upper_rad_ref.add_nuclide("Fe57", 0.01375486056, 'wo') +upper_rad_ref.add_nuclide("Fe58", 0.001865821363, 'wo') +upper_rad_ref.add_nuclide("Ni58", 0.055815129186, 'wo') +upper_rad_ref.add_nuclide("Ni60", 0.022240333032, 'wo') +upper_rad_ref.add_nuclide("Ni61", 0.000982608081, 'wo') +upper_rad_ref.add_nuclide("Ni62", 0.003185377845, 'wo') +upper_rad_ref.add_nuclide("Ni64", 0.000837251856, 'wo') +upper_rad_ref.add_nuclide("Mn55", 0.0184579, 'wo') +upper_rad_ref.add_nuclide("Si28", 0.00847831314, 'wo') +upper_rad_ref.add_nuclide("Si29", 0.00044612986, 'wo') +upper_rad_ref.add_nuclide("Si30", 0.000304557, 'wo') +upper_rad_ref.add_nuclide("Cr50", 0.00731912987, 'wo') +upper_rad_ref.add_nuclide("Cr52", 0.146766614995, 'wo') +upper_rad_ref.add_nuclide("Cr53", 0.01696340737, 'wo') +upper_rad_ref.add_nuclide("Cr54", 0.004301347765, 'wo') +upper_rad_ref.add_s_alpha_beta('c_H_in_H2O') + +bot_plate = openmc.Material(name='Bottom plate region', material_id=8) +bot_plate.set_density('g/cm3', 7.184) +bot_plate.add_nuclide("H1", 0.0011505, 'wo') +bot_plate.add_nuclide("O16", 0.0091296, 'wo') +bot_plate.add_nuclide("B10", 3.70915e-6, 'wo') +bot_plate.add_nuclide("B11", 1.68974e-5, 'wo') +bot_plate.add_nuclide("Fe54", 0.03855611055, 'wo') +bot_plate.add_nuclide("Fe56", 0.627585036425, 'wo') +bot_plate.add_nuclide("Fe57", 0.014750478, 'wo') +bot_plate.add_nuclide("Fe58", 0.002000875025, 'wo') +bot_plate.add_nuclide("Ni58", 0.059855207342, 'wo') +bot_plate.add_nuclide("Ni60", 0.023850159704, 'wo') +bot_plate.add_nuclide("Ni61", 0.001053732407, 'wo') +bot_plate.add_nuclide("Ni62", 0.003415945715, 'wo') +bot_plate.add_nuclide("Ni64", 0.000897854832, 'wo') +bot_plate.add_nuclide("Mn55", 0.0197940, 'wo') +bot_plate.add_nuclide("Si28", 0.00909197802, 'wo') +bot_plate.add_nuclide("Si29", 0.00047842098, 'wo') +bot_plate.add_nuclide("Si30", 0.000326601, 'wo') +bot_plate.add_nuclide("Cr50", 0.007848910646, 'wo') +bot_plate.add_nuclide("Cr52", 0.157390026871, 'wo') +bot_plate.add_nuclide("Cr53", 0.018191270146, 'wo') +bot_plate.add_nuclide("Cr54", 0.004612692337, 'wo') +bot_plate.add_s_alpha_beta('c_H_in_H2O') + +bot_nozzle = openmc.Material(name='Bottom nozzle region', material_id=9) +bot_nozzle.set_density('g/cm3', 2.53) +bot_nozzle.add_nuclide("H1", 0.0245014, 'wo') +bot_nozzle.add_nuclide("O16", 0.1944274, 'wo') +bot_nozzle.add_nuclide("B10", 7.89917e-5, 'wo') +bot_nozzle.add_nuclide("B11", 3.59854e-4, 'wo') +bot_nozzle.add_nuclide("Fe54", 0.030411411144, 'wo') +bot_nozzle.add_nuclide("Fe56", 0.495012237964, 'wo') +bot_nozzle.add_nuclide("Fe57", 0.01163454624, 'wo') +bot_nozzle.add_nuclide("Fe58", 0.001578204652, 'wo') +bot_nozzle.add_nuclide("Ni58", 0.047211231662, 'wo') +bot_nozzle.add_nuclide("Ni60", 0.018811987544, 'wo') +bot_nozzle.add_nuclide("Ni61", 0.000831139127, 'wo') +bot_nozzle.add_nuclide("Ni62", 0.002694352115, 'wo') +bot_nozzle.add_nuclide("Ni64", 0.000708189552, 'wo') +bot_nozzle.add_nuclide("Mn55", 0.0156126, 'wo') +bot_nozzle.add_nuclide("Si28", 0.007171335558, 'wo') +bot_nozzle.add_nuclide("Si29", 0.000377356542, 'wo') +bot_nozzle.add_nuclide("Si30", 0.0002576079, 'wo') +bot_nozzle.add_nuclide("Cr50", 0.006190885148, 'wo') +bot_nozzle.add_nuclide("Cr52", 0.124142524198, 'wo') +bot_nozzle.add_nuclide("Cr53", 0.014348496148, 'wo') +bot_nozzle.add_nuclide("Cr54", 0.003638294506, 'wo') +bot_nozzle.add_s_alpha_beta('c_H_in_H2O') + +top_nozzle = openmc.Material(name='Top nozzle region', material_id=10) +top_nozzle.set_density('g/cm3', 1.746) +top_nozzle.add_nuclide("H1", 0.0358870, 'wo') +top_nozzle.add_nuclide("O16", 0.2847761, 'wo') +top_nozzle.add_nuclide("B10", 1.15699e-4, 'wo') +top_nozzle.add_nuclide("B11", 5.27075e-4, 'wo') +top_nozzle.add_nuclide("Fe54", 0.02644016154, 'wo') +top_nozzle.add_nuclide("Fe56", 0.43037146399, 'wo') +top_nozzle.add_nuclide("Fe57", 0.0101152584, 'wo') +top_nozzle.add_nuclide("Fe58", 0.00137211607, 'wo') +top_nozzle.add_nuclide("Ni58", 0.04104621835, 'wo') +top_nozzle.add_nuclide("Ni60", 0.0163554502, 'wo') +top_nozzle.add_nuclide("Ni61", 0.000722605975, 'wo') +top_nozzle.add_nuclide("Ni62", 0.002342513875, 'wo') +top_nozzle.add_nuclide("Ni64", 0.0006157116, 'wo') +top_nozzle.add_nuclide("Mn55", 0.0135739, 'wo') +top_nozzle.add_nuclide("Si28", 0.006234853554, 'wo') +top_nozzle.add_nuclide("Si29", 0.000328078746, 'wo') +top_nozzle.add_nuclide("Si30", 0.0002239677, 'wo') +top_nozzle.add_nuclide("Cr50", 0.005382452306, 'wo') +top_nozzle.add_nuclide("Cr52", 0.107931450781, 'wo') +top_nozzle.add_nuclide("Cr53", 0.012474806806, 'wo') +top_nozzle.add_nuclide("Cr54", 0.003163190107, 'wo') +top_nozzle.add_s_alpha_beta('c_H_in_H2O') + +top_fa = openmc.Material(name='Top of fuel assemblies', material_id=11) +top_fa.set_density('g/cm3', 3.044) +top_fa.add_nuclide("H1", 0.0162913, 'wo') +top_fa.add_nuclide("O16", 0.1292776, 'wo') +top_fa.add_nuclide("B10", 5.25228e-5, 'wo') +top_fa.add_nuclide("B11", 2.39272e-4, 'wo') +top_fa.add_nuclide("Zr90", 0.43313403903, 'wo') +top_fa.add_nuclide("Zr91", 0.09549277374, 'wo') +top_fa.add_nuclide("Zr92", 0.14759527104, 'wo') +top_fa.add_nuclide("Zr94", 0.15280552077, 'wo') +top_fa.add_nuclide("Zr96", 0.02511169542, 'wo') +top_fa.add_s_alpha_beta('c_H_in_H2O') + +bot_fa = openmc.Material(name='Bottom of fuel assemblies', material_id=12) +bot_fa.set_density('g/cm3', 1.762) +bot_fa.add_nuclide("H1", 0.0292856, 'wo') +bot_fa.add_nuclide("O16", 0.2323919, 'wo') +bot_fa.add_nuclide("B10", 9.44159e-5, 'wo') +bot_fa.add_nuclide("B11", 4.30120e-4, 'wo') +bot_fa.add_nuclide("Zr90", 0.3741373658, 'wo') +bot_fa.add_nuclide("Zr91", 0.0824858164, 'wo') +bot_fa.add_nuclide("Zr92", 0.1274914944, 'wo') +bot_fa.add_nuclide("Zr94", 0.1319920622, 'wo') +bot_fa.add_nuclide("Zr96", 0.0216912612, 'wo') +bot_fa.add_s_alpha_beta('c_H_in_H2O') + +# Define the materials file. +materials = openmc.Materials() +materials.default_xs = '71c' +materials += [fuel, clad, cold_water, hot_water, rpv_steel, + lower_rad_ref, upper_rad_ref, bot_plate, + bot_nozzle, top_nozzle, top_fa, bot_fa] +materials.export_to_xml() + +# Define surfaces. +s1 = openmc.ZCylinder(R=0.41, surface_id=1) +s2 = openmc.ZCylinder(R=0.475, surface_id=2) +s3 = openmc.ZCylinder(R=0.56, surface_id=3) +s4 = openmc.ZCylinder(R=0.62, surface_id=4) +s5 = openmc.ZCylinder(R=187.6, surface_id=5) +s6 = openmc.ZCylinder(R=209.0, surface_id=6) +s7 = openmc.ZCylinder(R=229.0, surface_id=7) +s8 = openmc.ZCylinder(R=249.0, surface_id=8) +s8.boundary_type = 'vacuum' + +s31 = openmc.ZPlane(z0=-229.0, surface_id=31) +s31.boundary_type = 'vacuum' +s32 = openmc.ZPlane(z0=-199.0, surface_id=32) +s33 = openmc.ZPlane(z0=-193.0, surface_id=33) +s34 = openmc.ZPlane(z0=-183.0, surface_id=34) +s35 = openmc.ZPlane(z0=0.0, surface_id=35) +s36 = openmc.ZPlane(z0=183.0, surface_id=36) +s37 = openmc.ZPlane(z0=203.0, surface_id=37) +s38 = openmc.ZPlane(z0=215.0, surface_id=38) +s39 = openmc.ZPlane(z0=223.0, surface_id=39) +s39.boundary_type = 'vacuum' + +# Define pin cells. +c21 = openmc.Cell(cell_id=21, fill=fuel, region=-s1) +c22 = openmc.Cell(cell_id=22, fill=clad, region=+s1 & -s2) +c23 = openmc.Cell(cell_id=23, fill=cold_water, region=+s2) +fuel_cold = openmc.Universe(name='Fuel pin, cladding, cold water', + universe_id=1, cells=(c21, c22, c23)) + +c24 = openmc.Cell(cell_id=24, fill=cold_water, region=-s3) +c25 = openmc.Cell(cell_id=25, fill=clad, region=+s3 & -s4) +c26 = openmc.Cell(cell_id=26, fill=cold_water, region=+s4) +tube_cold = openmc.Universe(name='Instrumentation guide tube, cold water', + universe_id=2, cells=(c24, c25, c26)) + +c27 = openmc.Cell(cell_id=27, fill=fuel, region=-s1) +c28 = openmc.Cell(cell_id=28, fill=clad, region=+s1 & -s2) +c29 = openmc.Cell(cell_id=29, fill=hot_water, region=+s2) +fuel_hot = openmc.Universe(name='Fuel pin, cladding, hot water', + universe_id=3, cells=(c27, c28, c29)) + +c30 = openmc.Cell(cell_id=30, fill=hot_water, region=-s3) +c31 = openmc.Cell(cell_id=31, fill=clad, region=+s3 & -s4) +c32 = openmc.Cell(cell_id=32, fill=hot_water, region=+s4) +tube_hot = openmc.Universe(name='Instrumentation guide tube, hot water', + universe_id=4, cells=(c30, c31, c32)) + +guide_tubes = [(2, 5), (2, 8), (2, 11), (3, 3), (3, 13), + (5, 2), (5, 5), (5, 8), (5, 11), (5, 14), + (8, 2), (8, 5), (8, 8), (8, 11), (8, 14), + (11, 2), (11, 5), (11, 8), (11, 11), (11, 14), + (13, 3), (13, 13), (14, 5), (14, 8), (14, 11)] + +# Define fuel lattices. +l100 = openmc.RectLattice(name='Fuel assembly (lower half)', lattice_id=100) +l100.lower_left = (-10.71, -10.71) +l100.pitch = (1.26, 1.26) +l100.universes = np.tile(fuel_cold, (17, 17)) +for position in guide_tubes: + l100.universes[position] = tube_cold + +l101 = openmc.RectLattice(name='Fuel assembly (upper half)', lattice_id=101) +l101.lower_left = (-10.71, -10.71) +l101.pitch = (1.26, 1.26) +l101.universes = np.tile(fuel_hot, (17, 17)) +for position in guide_tubes: + l101.universes[position] = tube_hot + +# Define assemblies. +c50 = openmc.Cell(cell_id=50, fill=cold_water, region=+s34 & -s35) +fa_cw = openmc.Universe(name='Water assembly (cold)', universe_id=5, cells=[c50]) + +c70 = openmc.Cell(cell_id=70, fill=hot_water, region=+s35 & -s36) +fa_hw = openmc.Universe(name='Water assembly (hot)', universe_id=7, cells=[c70]) + +c60 = openmc.Cell(cell_id=60, fill=l100, region=+s34 & -s35) +fa_cold = openmc.Universe(name='Fuel assembly (cold)', universe_id=6, cells=[c60]) + +c80 = openmc.Cell(cell_id=80, fill=l101, region=+s35 & -s36) +fa_hot = openmc.Universe(name='Fuel assembly (hot)', universe_id=8, cells=[c80]) + +# Define core lattices +l200 = openmc.RectLattice(name='Core lattice (lower half)', lattice_id=200) +l200.lower_left = (-224.91, -224.91) +l200.pitch = (21.42, 21.42) +l200.universes = [ + [fa_cw]*21, + [fa_cw]*21, + [fa_cw]*7 + [fa_cold]*7 + [fa_cw]*7, + [fa_cw]*5 + [fa_cold]*11 + [fa_cw]*5, + [fa_cw]*4 + [fa_cold]*13 + [fa_cw]*4, + [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3, + [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3, + [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2, + [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2, + [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2, + [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2, + [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2, + [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2, + [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2, + [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3, + [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3, + [fa_cw]*4 + [fa_cold]*13 + [fa_cw]*4, + [fa_cw]*5 + [fa_cold]*11 + [fa_cw]*5, + [fa_cw]*7 + [fa_cold]*7 + [fa_cw]*7, + [fa_cw]*21, + [fa_cw]*21] + +l201 = openmc.RectLattice(name='Core lattice (lower half)', lattice_id=201) +l201.lower_left = (-224.91, -224.91) +l201.pitch = (21.42, 21.42) +l201.universes = [ + [fa_hw]*21, + [fa_hw]*21, + [fa_hw]*7 + [fa_hot]*7 + [fa_hw]*7, + [fa_hw]*5 + [fa_hot]*11 + [fa_hw]*5, + [fa_hw]*4 + [fa_hot]*13 + [fa_hw]*4, + [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3, + [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3, + [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2, + [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2, + [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2, + [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2, + [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2, + [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2, + [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2, + [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3, + [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3, + [fa_hw]*4 + [fa_hot]*13 + [fa_hw]*4, + [fa_hw]*5 + [fa_hot]*11 + [fa_hw]*5, + [fa_hw]*7 + [fa_hot]*7 + [fa_hw]*7, + [fa_hw]*21, + [fa_hw]*21] + +# Define root universe +c1 = openmc.Cell(cell_id=1, fill=l200, region=-s6 & +s34 & -s35) +c2 = openmc.Cell(cell_id=2, fill=l201, region=-s6 & +s35 & -s36) +c3 = openmc.Cell(cell_id=3, fill=bot_plate, region=-s7 & +s31 & -s32) +c4 = openmc.Cell(cell_id=4, fill=bot_nozzle, region=-s5 & +s32 & -s33) +c5 = openmc.Cell(cell_id=5, fill=bot_fa, region=-s5 & +s33 & -s34) +c6 = openmc.Cell(cell_id=6, fill=top_fa, region=-s5 & +s36 & -s37) +c7 = openmc.Cell(cell_id=7, fill=top_nozzle, region=-s5 & +s37 & -s38) +c8 = openmc.Cell(cell_id=8, fill=upper_rad_ref, region=-s7 & +s38 & -s39) +c9 = openmc.Cell(cell_id=9, fill=bot_nozzle, region=+s6 & -s7 & +s32 & -s38) +c10 = openmc.Cell(cell_id=10, fill=rpv_steel, region=+s7 & -s8 & +s31 & -s39) +c11 = openmc.Cell(cell_id=11, fill=lower_rad_ref, region=+s5 & -s6 & +s32 & -s34) +c12 = openmc.Cell(cell_id=12, fill=upper_rad_ref, region=+s5 & -s6 & +s36 & -s38) +root = openmc.Universe(universe_id=0, name='root universe', + cells=(c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12)) + +# Create and export geometry +geometry = openmc.Geometry(root) +geometry.export_to_xml() + +settings = openmc.Settings() +settings.batches = 10 +settings.inactive = 5 +settings.particles = 1000 +settings.source = openmc.Source(space=openmc.stats.Box( + [-160, -160, -183], [160, 160, 183])) +settings.export_to_xml() + +plot = openmc.Plot() +plot.filename = 'mat' +plot.origin = (125, 125, 0) +plot.width = (250, 250) +plot.pixels = (3000, 3000) +plot.color = 'mat' +plots = openmc.Plots([plot]) +plots.export_to_xml() diff --git a/stress-test/mc-performance/generate_tallies.py b/stress-test/mc-performance/generate_tallies.py new file mode 100755 index 0000000..44bcfbe --- /dev/null +++ b/stress-test/mc-performance/generate_tallies.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python + +import openmc + +# Set up list of assembly positions +assemblies = ([(1,i) for i in range(6,13)] + + [(2,i) for i in range(4,15)] + + [(3,i) for i in range(3,16)] + + [(4,i) for i in range(2,17)] + + [(5,i) for i in range(2,17)] + + [(6,i) for i in range(1,18)] + + [(7,i) for i in range(1,18)] + + [(8,i) for i in range(1,18)] + + [(9,i) for i in range(1,18)] + + [(10,i) for i in range(1,18)] + + [(11,i) for i in range(1,18)] + + [(12,i) for i in range(1,18)] + + [(13,i) for i in range(2,17)] + + [(14,i) for i in range(2,17)] + + [(15,i) for i in range(3,16)] + + [(16,i) for i in range(4,15)] + + [(17,i) for i in range(6,13)]) + +# Write meshes and assemblies +tallies = openmc.Tallies() +for i, assem in enumerate(assemblies): + x, y = assem + mesh = openmc.Mesh() + mesh.lower_left = (-182.07 + 21.42*(x-1), -182.07 + 21.42*(y-1)) + mesh.width = (1.26, 1.26) + mesh.dimension = (17, 17) + + tally = openmc.Tally() + tally.filters = [openmc.MeshFilter(mesh)] + tally.nuclides = ['all'] + tally.scores = ['total'] + tallies.append(tally) + +tallies.export_to_xml() diff --git a/stress-test/mg-criticality/make_library.py b/stress-test/mg-criticality/make_library.py new file mode 100755 index 0000000..ad3d25b --- /dev/null +++ b/stress-test/mg-criticality/make_library.py @@ -0,0 +1,609 @@ +#!/usr/bin/env python + +import numpy as np + +import openmc + +GROUP_STRUCT = {1: openmc.mgxs.EnergyGroups(group_edges=[0.0, 20.0e6]), + 2: openmc.mgxs.EnergyGroups(group_edges=[0.0, 0.625, 20.0e6]), + 3: openmc.mgxs.EnergyGroups(group_edges=[0.0, 0.625, 1.E3, + 20.0e6]), + 6: openmc.mgxs.EnergyGroups(group_edges=[0.0, 0.625, 10., 1.E3, + 1.e6, 10.e6, 20.0e6])} + +############################################################################### +# Create OpenMC mgxs.h5 files +############################################################################### + + +def check_it(scatter_matrix): + import scipy.special as ss + groups = scatter_matrix.shape[0] + orders = scatter_matrix.shape[2] + Nmu = 50 + mu = np.linspace(-1, 1, Nmu) + f = np.zeros(shape=(groups, groups, Nmu)) + neg = False + for gin in range(groups): + for gout in range(groups): + data = np.array(scatter_matrix[gin, gout, :], copy=True) + if np.sum(data) > 0: + data /= data[0] + for l in range(orders): + f[gin, gout, :] += ((float(l) + 0.5) * + ss.eval_legendre(l, mu) * data[l]) + if np.any(f[gin, gout, :] < 0): + neg = True + return neg + + +def plot_it(scatter_matrix): + import matplotlib.pyplot as plt + import scipy.special as ss + groups = scatter_matrix.shape[0] + orders = scatter_matrix.shape[2] + Nmu = 50 + mu = np.linspace(-1, 1, Nmu) + f = np.zeros(shape=(groups, groups, Nmu)) + for gin in range(groups): + for gout in range(groups): + data = scatter_matrix[gin, gout, :] + data /= data[0] + for l in range(orders): + f[gin, gout, :] += ((float(l) + 0.5) * + ss.eval_legendre(l, mu) * data[l]) + + i = 0 + for gin in range(groups): + for gout in range(groups): + i += 1 + plt.subplot(groups, groups, i) + plt.title(str(gin + 1) + ' to ' + str(gout + 1)) + plt.plot(mu, f[gin, gout, :]) + plt.show() + plt.close() + + +def set_it(name, groups, order, fission, nu, absorption, scatt, total, chi): + neg = check_it(scatt) + if neg: + print(name + ' is negative!') + xsd = openmc.XSdata(name, groups) + xsd.order = order + if fission is not None: + xsd.set_fission(fission[:]) + if nu is not None and fission is not None: + xsd.set_nu_fission(np.multiply(nu, fission)) + xsd.set_absorption(absorption[:]) + xsd.set_scatter_matrix(scatt[:, :, :]) + xsd.set_total(total[:]) + if chi is not None: + xsd.set_chi(chi[:]) + return xsd + + +def create_1g(): + # Instantiate the energy group data and file object + groups = GROUP_STRUCT[1] + + mg_cross_sections_file = openmc.MGXSLibrary(groups) + + ########################################################################### + # PUa, Sec 4.1.1 + nu = 3.24 + fiss = [0.0816] + capture = [0.019584] + absorption = np.add(capture, fiss) + scatter = np.array([[[0.225216]]]) + total = [0.32640] + chi = [1.] + + PUa = set_it('PUa', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(PUa) + + ########################################################################### + # PUb, Sec 4.1.1 + nu = 2.84 + PUb = set_it('PUb', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(PUb) + + ########################################################################### + # H2O, Sec 4.1.1 + nu = 0. + fiss = None + capture = [0.032640] + absorption = capture + scatter = np.array([[[0.293760]]]) + total = [0.32640] + chi = None + + H2O = set_it('H2O', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(H2O) + + ########################################################################### + # Ua, Sec 4.1.2 + nu = 2.70 + fiss = [0.065280] + capture = [0.013056] + absorption = np.add(capture, fiss) + scatter = np.array([[[0.248064]]]) + total = [0.32640] + chi = [1.] + + Ua = set_it('Ua', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(Ua) + + ########################################################################### + # Ub, Sec 4.1.2 + nu = 2.797101 + Ub = set_it('Ub', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(Ub) + + ########################################################################### + # Uc, Sec 4.1.2 + nu = 2.707308 + Uc = set_it('Uc', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(Uc) + + ########################################################################### + # Ud, Sec 4.1.2 + nu = 2.679198 + Ud = set_it('Ud', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(Ud) + + ########################################################################### + # UD2O, Sec 4.1.3 + nu = 1.70 + fiss = [0.054628] + capture = [0.027314] + absorption = np.add(capture, fiss) + scatter = np.array([[[0.464338]]]) + total = [0.54628] + chi = [1.] + + UD2O = set_it('UD2O', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(UD2O) + + ########################################################################### + # H2O, Sec 4.1.3 + nu = 0. + fiss = None + capture = [0.054628] + absorption = capture + scatter = np.array([[[0.491652]]]) + total = [0.54628] + chi = None + + H2O_2 = set_it('H2O_2', groups, 0, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(H2O_2) + + ########################################################################### + # Ue, Sec 4.1.4 + nu = 2.50 + fiss = [0.06922744] + capture = [0.01013756] + absorption = np.add(capture, fiss) + scatter = np.array([[[0.328042]]]) + total = [0.407407] + chi = [1.] + + Ue = set_it('Ue', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(Ue) + + ########################################################################### + # Fe, Sec 4.1.4 + nu = 0. + fiss = None + capture = [0.00046512] + absorption = capture + scatter = np.array([[[0.23209488]]]) + total = [0.23256] + chi = None + + Fe = set_it('Fe', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(Fe) + + ########################################################################### + # Na, Sec 4.1.4 + nu = 0. + fiss = None + capture = [0.0] + absorption = capture + scatter = np.array([[[0.086368032]]]) + total = [0.086368032] + chi = None + + Na = set_it('Na', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(Na) + + ########################################################################### + # PUa-1, Sec 4.1.4 + nu = 2.5 + fiss = [0.266667] + capture = [0.0] + absorption = np.add(capture, fiss) + scatter = np.array([[[0.733333, 0.2]]]) + total = [1.] + chi = [1.] + + PUa_1 = set_it('PUa1', groups, 1, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(PUa_1) + + ########################################################################### + # PUb-2, Sec 4.2.1 + scatter = np.array([[[0.733333, 0.333333]]]) + PUb_1 = set_it('PUb1', groups, 1, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(PUb_1) + + ########################################################################### + # PUa-2, Sec 4.1.4 + nu = 2.5 + fiss = [0.266667] + capture = [0.0] + absorption = np.add(capture, fiss) + scatter = np.array([[[0.733333, 0.2, 0.075]]]) + total = [1.] + chi = [1.] + + PUa_2 = set_it('PUa2', groups, 2, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(PUa_2) + + ########################################################################### + # PUb-2, Sec 4.2.1 + scatter = np.array([[[0.733333, 0.333333, 0.125]]]) + PUb_2 = set_it('PUb2', groups, 2, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(PUb_2) + + ########################################################################### + # Ua-1, Sec 4.1.4 + nu = 2.70 + fiss = [0.065280] + capture = [0.013056] + absorption = np.add(capture, fiss) + scatter = np.array([[[0.248064, 0.042432]]]) + total = [0.32640] + chi = [1.] + + Ua_1 = set_it('Ua1', groups, 1, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(Ua_1) + + ########################################################################### + # Ub-1, Sec 4.2.1 + scatter = np.array([[[0.248064, 0.212160]]]) + Ub_1 = set_it('Ub1', groups, 1, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(Ub_1) + + ########################################################################### + # UD2Oa-1, Sec 4.2.3 + nu = 1.808381 + fiss = [0.054628] + capture = [0.027314] + absorption = np.add(capture, fiss) + scatter = np.array([[[0.464338, 0.056312624]]]) + total = [0.54628] + chi = [1.] + + UD2Oa = set_it('UD2Oa', groups, 1, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(UD2Oa) + + ########################################################################### + # UD2Ob-1, Sec 4.2.3 + nu = 1.841086 + scatter = np.array([[[0.464338, 0.112982569]]]) + UD2Ob = set_it('UD2Ob', groups, 1, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(UD2Ob) + + ########################################################################### + # UD2Oc-1, Sec 4.2.3 + nu = 1.6964 + scatter = np.array([[[0.464338, -0.27850447]]]) + UD2Oc = set_it('UD2Oc', groups, 1, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(UD2Oc) + + mg_cross_sections_file.export_to_hdf5('1g.h5') + + +def create_2g(): + # Instantiate the energy group data and file object + groups = GROUP_STRUCT[2] + + mg_cross_sections_file = openmc.MGXSLibrary(groups) + + ########################################################################### + # 5.1.1 Pu + nu = [3.10, 2.93] + fiss = np.array([0.0936, 0.08544]) + capture = [0.00480, 0.0144] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.0792, 0.0432], + [0.00000, 0.23616]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.2208, 0.3360] + chi = [0.575, 0.425] + Pu = set_it('Pu', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(Pu) + + ########################################################################### + # 5.1.2 U + nu = [2.70, 2.5] + fiss = np.array([0.06192, 0.06912]) + capture = [0.00384, 0.01344] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.078240, 0.0720], + [0.00000, 0.26304]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.2160, 0.3456] + chi = [0.575, 0.425] + + U = set_it('U', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(U) + + ########################################################################### + # 5.1.3 UAl + nu = [0.0, 2.830023] + fiss = np.array([0.0, 0.060706]) + capture = [0.000217, 0.003143] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.247516, 0.020432], + [0.000000, 1.213127]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.268165, 1.276976] + chi = [1.0, 0.0] + + UAl = set_it('UAl', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(UAl) + + ########################################################################### + # 5.1.4 URRa-0 + nu = [2.5, 2.5] + fiss = np.array([0.0010484, 0.050632]) + capture = [0.0010046, 0.025788] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.62568, 0.029227], + [0.00000, 2.443830]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.65696, 2.52025] + chi = [1., 0.] + + URRa_0 = set_it('URRa0', groups, 0, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(URRa_0) + + ########################################################################### + # 5.1.4 URRb-0 + fiss = np.array([0.000836, 0.029564]) + capture = [0.001104, 0.024069] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.838920, 0.046350], + [0.000767, 2.918300]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.88721, 2.9727] + chi = [1., 0.] + + URRb_0 = set_it('URRb0', groups, 0, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(URRb_0) + + ########################################################################### + # 5.1.4 URRc-0 + fiss = np.array([0.001648, 0.057296]) + capture = [0.001472, 0.029244] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.838070, 0.045360], + [0.001160, 2.8751]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.88655, 2.9628] + chi = [1., 0.] + + URRc_0 = set_it('URRc0', groups, 0, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(URRc_0) + + ########################################################################### + # 5.1.4 H2Oa + nu = None + fiss = None + capture = [0.00074, 0.018564] + absorption = capture + scatter = np.array( + [[[0.839750, 0.04749], + [0.000336, 2.96760]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.88798, 2.9865] + chi = None + + H2Oa = set_it('H2Oa', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(H2Oa) + + ########################################################################### + # 5.1.4 URRd-0 + nu = [1.004, 2.50] + fiss = np.array([0.61475, 0.045704]) + capture = [0.0019662, 0.023496] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.000000, 0.0342008], + [0.000000, 2.0688000]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.650917, 2.13800] + chi = [1., 0.] + + URRd_0 = set_it('URRd0', groups, 0, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(URRd_0) + + ########################################################################### + # 5.1.4 H2Ob + nu = None + fiss = None + capture = [8.480293E-6, 0.00016] + absorption = capture + scatter = np.array( + [[[0.1096742149, 0.001000595707], + [0.0000000000, 0.363390000000]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.1106832906, 0.36355] + chi = None + + H2Ob = set_it('H2Ob', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(H2Ob) + + ########################################################################### + # 5.1.4 H2Oc + nu = None + fiss = None + capture = [4.97229E-4, 0.0188] + absorption = capture + scatter = np.array( + [[[1.226381244, 0.1046395340], + [0.0000000000, 4.35470]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [1.331518007, 4.37350] + chi = None + + H2Oc = set_it('H2Oc', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(H2Oc) + + ########################################################################### + # 5.1.5 UD2O + nu = [2.50, 2.50] + fiss = np.array([0.002817, 0.097]) + capture = [0.0087078, 0.02518] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.31980, 0.004555], + [0.000000, 0.42410]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.33588, 0.54628] + chi = [1., 0.] + + UD2O = set_it('UD2O', groups, 0, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(UD2O) + + ########################################################################### + # 5.2.1 URR-1 + nu = [2.5, 2.5] + fiss = np.array([0.0010484, 0.050632]) + capture = [0.0010046, 0.025788] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.62568, 0.27459], [0.029227, 0.0075737]], + [[0.00000, 0.00000], [2.443830, 0.8331800]]]) + total = [0.65696, 2.52025] + chi = [1., 0.] + + URR_1 = set_it('URR1', groups, 1, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(URR_1) + + ########################################################################### + # 5.2.2 UD2O-1 + nu = [2.50, 2.50] + fiss = np.array([0.002817, 0.097]) + capture = [0.008708, 0.02518] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.31980, 0.06694], [0.004555, -0.0003972]], + [[0.00000, 0.00000], [0.424100, 0.05439000]]]) + total = [0.33588, 0.54628] + chi = [1., 0.] + + UD2O_1 = set_it('UD2O1', groups, 1, fiss, nu, absorption, scatter, total, + chi) + mg_cross_sections_file.add_xsdata(UD2O_1) + + # Write the file + mg_cross_sections_file.export_to_hdf5('2g.h5') + + +def create_3g(): + # Instantiate the energy group data and file object + groups = GROUP_STRUCT[3] + + mg_cross_sections_file = openmc.MGXSLibrary(groups) + + ########################################################################### + # 6 URR + nu = [3.0, 2.5, 2.0] + fiss = np.array([0.006, 0.060, 0.90]) + capture = [0.006, 0.040, 0.20] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.024, 0.171, 0.033], + [0.000, 0.600, 0.275], + [0.000, 0.000, 2.000]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.240, 0.975, 3.10] + chi = [0.96, 0.04, 0.0] + + URR = set_it('URR', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(URR) + + mg_cross_sections_file.export_to_hdf5('3g.h5') + + +def create_6g(): + # Instantiate the energy group data and file object + groups = GROUP_STRUCT[6] + + mg_cross_sections_file = openmc.MGXSLibrary(groups) + + ########################################################################### + # 7 URR + nu = [3.0, 2.5, 2.0, 2.0, 2.50, 3.0] + fiss = np.array([0.006, 0.060, 0.90, 0.90, 0.060, 0.006]) + capture = [0.006, 0.040, 0.20, 0.2, 0.04, 0.006] + absorption = np.add(capture, fiss) + scatter = np.array( + [[[0.024, 0.171, 0.033, 0.00, 0.00, 0.00], + [0.000, 0.600, 0.275, 0.00, 0.00, 0.00], + [0.000, 0.000, 2.000, 0.00, 0.00, 0.00], + [0.000, 0.000, 0.000, 2.00, 0.00, 0.00], + [0.000, 0.000, 0.000, 0.275, 0.60, 0.00], + [0.000, 0.000, 0.000, 0.033, 0.171, 0.024]]]) + scatter = np.rollaxis(scatter, 0, 3) + total = [0.240, 0.975, 3.10, 3.10, 0.975, 0.240] + chi = [0.48, 0.02, 0.0, 0.0, 0.02, 0.48] + + URR = set_it('URR', groups, 0, fiss, nu, absorption, scatter, total, chi) + mg_cross_sections_file.add_xsdata(URR) + + mg_cross_sections_file.export_to_hdf5('6g.h5') + + +def create_specific_library(groups): + if groups == 1: + create_1g() + elif groups == 2: + create_2g() + elif groups == 3: + create_3g() + elif groups == 6: + create_6g() + + +def create_all(): + for groups in [1, 2, 3, 6]: + create_specific_library(groups) + + +if __name__ == "__main__": + create_1g() + create_2g() + create_3g() + create_6g() diff --git a/stress-test/mg-criticality/make_model.py b/stress-test/mg-criticality/make_model.py new file mode 100644 index 0000000..44a249f --- /dev/null +++ b/stress-test/mg-criticality/make_model.py @@ -0,0 +1,316 @@ +import numpy as np +from subprocess import CalledProcessError + +import openmc +import openmc.checkvalue as cv +from make_library import GROUP_STRUCT + +MAT_NAMES = ['PU', 'U', 'UD2O', 'UAL', 'URR', 'H2O', 'Fe-Na'] +GROUPS = [1, 2, 3, 6] +GROUP_FILES = {g: str(g) + 'g.h5' for g in GROUPS} +ORDER = [0, 1, 2] +GEOM = ['IN', 'SL', 'CY', 'SP', 'ISLC', 'SL-NS', 'FENA'] +INF = 1.e50 + + +class Case(object): + """Stores the model data for a particular case + """ + + def __init__(self, num, name, mat_names, groups, order, geom, rad, ref_k, + params): + cv.check_value('groups', groups, GROUPS) + cv.check_value('order', order, ORDER) + cv.check_value('groups', groups, GROUPS) + cv.check_value('geom', geom, GEOM) + self.number = num + self.name = name + self.mat_names = mat_names + self.groups = groups + self.order = order + self.geom = geom + self.rad = rad + self.ref_k = ref_k + self.mesh_dim = params['mesh_dim'] + self.batches = params['batches'] + self.inactive = params['inactive'] + self.particles = params['particles'] + if 'tab_leg' in params: + self.tab_leg = params['tab_leg'] + else: + self.tab_leg = None + if 'tally' in params: + self.tally = params['tally'] + else: + self.tally = False + + def make_materials(self): + materials_file = openmc.Materials() + + mats = [] + macros = [] + for i in range(len(self.mat_names)): + macros.append(openmc.Macroscopic(self.mat_names[i])) + mats.append(openmc.Material(name=self.mat_names[i])) + mats[-1].set_density('macro', 1.0) + mats[-1].add_macroscopic(macros[-1]) + materials_file.append(mats[-1]) + + materials_file.cross_sections = GROUP_FILES[self.groups] + + return mats, materials_file + + def make_settings(self): + # Instantiate a Settings object, set all runtime parameters + settings_file = openmc.Settings() + settings_file.energy_mode = "multi-group" + if self.tab_leg: + settings_file.tabular_legendre = self.tab_leg + settings_file.batches = self.batches + settings_file.inactive = self.inactive + settings_file.particles = self.particles + if self.order > 0: + settings_file.max_order = self.order + if self.geom in ['SL', 'ISLC']: + bounds = [-self.rad[0], -INF, -INF, self.rad[0], INF, INF] + uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:]) + elif self.geom == 'SL-NS': + bounds = [-self.rad[0], -INF, -INF, self.rad[0], INF, INF] + uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], + only_fissionable=True) + elif self.geom == 'IN': + bounds = [-INF, -INF, -INF, INF, INF, INF] + uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:]) + elif self.geom == 'CY': + bounds = [-self.rad[0], -self.rad[0], -INF, + self.rad[0], self.rad[0], INF] + uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], + only_fissionable=True) + elif self.geom == 'SP': + bounds = [-self.rad[0], -self.rad[0], -self.rad[0], + self.rad[0], self.rad[0], self.rad[0]] + uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], + only_fissionable=True) + elif self.geom == 'FENA': + bounds = [0., -INF, -INF, self.rad[-1], INF, INF] + uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], + only_fissionable=True) + else: + raise NotImplementedError + + settings_file.source = openmc.source.Source(space=uniform_dist) + + settings_file.output = {'summary': False} + + return settings_file + + def make_tallies(self, r=None): + if not self.tally: + return None + + if self.geom == 'IN': + return None + + # Instantiate a tally mesh + mesh = openmc.Mesh(mesh_id=1) + mesh.type = 'regular' + mesh.dimension = self.mesh_dim + if self.geom == 'SL' or self.geom == 'ISLC': + mesh.lower_left = [-r, -INF, -INF] + mesh.upper_right = [r, INF, INF] + + # Instantiate some tally Filters + energy_filter = openmc.EnergyFilter( + GROUP_STRUCT[self.groups].group_edges) + mesh_filter = openmc.MeshFilter(mesh) + + # Instantiate the Tally + tally = openmc.Tally(tally_id=1, name='tally 1') + tally.filters = [energy_filter, mesh_filter] + tally.scores = ['flux', 'fission', 'nu-fission'] + + # Instantiate a Tallies collection, register all Tallies + tallies_file = openmc.Tallies([tally]) + + return tallies_file + + def make_geometry(self, mats): + # Instantiate Universe + root = openmc.Universe(universe_id=0, name='root universe') + cells = [] + + if self.geom == 'IN': + left = openmc.XPlane(x0=-INF, boundary_type='reflective') + right = openmc.XPlane(x0=INF, boundary_type='reflective') + bottom = openmc.YPlane(y0=-INF, boundary_type='reflective') + top = openmc.YPlane(y0=INF, boundary_type='reflective') + down = openmc.ZPlane(z0=-INF, boundary_type='reflective') + up = openmc.ZPlane(z0=INF, boundary_type='reflective') + + # Instantiate Cells + cells = [] + cells.append(openmc.Cell(name='fissile')) + yz = (+bottom & -top) & (+down & -up) + cells[-1].region = (+left & -right) & yz + + # Register Materials with Cells + cells[-1].fill = mats[0] + + elif self.geom == 'SL': + surfs = [] + surfs.append(openmc.XPlane(x0=0., boundary_type='reflective')) + for r, rad in enumerate(self.rad): + if r == len(self.rad) - 1: + surfs.append(openmc.XPlane(x0=rad, boundary_type='vacuum')) + else: + surfs.append(openmc.XPlane(x0=rad)) + bottom = openmc.YPlane(y0=-INF, boundary_type='reflective') + top = openmc.YPlane(y0=INF, boundary_type='reflective') + down = openmc.ZPlane(z0=-INF, boundary_type='reflective') + up = openmc.ZPlane(z0=INF, boundary_type='reflective') + + # Instantiate Cells + yz = (+bottom & -top) & (+down & -up) + cells = [] + for c in range(len(surfs) - 1): + cells.append(openmc.Cell()) + cells[-1].region = (+surfs[c] & -surfs[c + 1]) & yz + cells[-1].fill = mats[c] + + elif self.geom == 'SL-NS': + surfs = [] + surfs.append(openmc.XPlane(x0=-self.rad[0], + boundary_type='vacuum')) + for r, rad in enumerate(self.rad): + if r == len(self.rad) - 1: + surfs.append(openmc.XPlane(x0=rad, boundary_type='vacuum')) + else: + surfs.append(openmc.XPlane(x0=rad)) + bottom = openmc.YPlane(y0=-INF, boundary_type='reflective') + top = openmc.YPlane(y0=INF, boundary_type='reflective') + down = openmc.ZPlane(z0=-INF, boundary_type='reflective') + up = openmc.ZPlane(z0=INF, boundary_type='reflective') + + # Instantiate Cells + yz = (+bottom & -top) & (+down & -up) + cells = [] + for c in range(len(surfs) - 1): + cells.append(openmc.Cell()) + cells[-1].region = (+surfs[c] & -surfs[c + 1]) & yz + cells[-1].fill = mats[c] + + elif self.geom == 'FENA': + surfs = [] + surfs.append(openmc.XPlane(x0=0.0, boundary_type='vacuum')) + for c in range(len(mats) - 1): + surfs.append(openmc.XPlane(x0=self.rad[c])) + surfs.append(openmc.XPlane(x0=self.rad[-1], + boundary_type='vacuum')) + + bottom = openmc.YPlane(y0=-INF, boundary_type='reflective') + top = openmc.YPlane(y0=INF, boundary_type='reflective') + down = openmc.ZPlane(z0=-INF, boundary_type='reflective') + up = openmc.ZPlane(z0=INF, boundary_type='reflective') + + # Instantiate Cells + yz = (+bottom & -top) & (+down & -up) + cells = [] + for c in range(len(mats)): + cells.append(openmc.Cell()) + cells[-1].region = (+surfs[c] & -surfs[c + 1]) & yz + cells[-1].fill = mats[c] + + elif self.geom == 'CY': + surfs = [] + for r, rad in enumerate(self.rad): + if r == len(self.rad) - 1: + surfs.append(openmc.ZCylinder(r=rad, + boundary_type='vacuum')) + else: + surfs.append(openmc.ZCylinder(r=rad)) + + # Instantiate Cells + cells = [] + cells.append(openmc.Cell()) + cells[-1].region = -surfs[0] + cells[-1].fill = mats[0] + for c in range(1, len(surfs)): + cells.append(openmc.Cell()) + cells[-1].region = (+surfs[c - 1] & -surfs[c]) + cells[-1].fill = mats[c] + + elif self.geom == 'SP': + surfs = [] + for r, rad in enumerate(self.rad): + if r == len(self.rad) - 1: + surfs.append(openmc.Sphere(r=rad, boundary_type='vacuum')) + else: + surfs.append(openmc.Sphere(r=rad)) + + # Instantiate Cells + cells = [] + cells.append(openmc.Cell()) + cells[-1].region = -surfs[0] + cells[-1].fill = mats[0] + for c in range(1, len(surfs)): + cells.append(openmc.Cell()) + cells[-1].region = (+surfs[c - 1] & -surfs[c]) + cells[-1].fill = mats[c] + + elif self.geom == 'ISLC': + surfs = [] + surfs.append(openmc.XPlane(x0=0., boundary_type='reflective')) + for r, rad in enumerate(self.rad): + if r == len(self.rad) - 1: + surfs.append(openmc.XPlane(x0=rad, + boundary_type='reflective')) + else: + surfs.append(openmc.XPlane(x0=rad)) + bottom = openmc.YPlane(y0=-INF, boundary_type='reflective') + top = openmc.YPlane(y0=INF, boundary_type='reflective') + down = openmc.ZPlane(z0=-INF, boundary_type='reflective') + up = openmc.ZPlane(z0=INF, boundary_type='reflective') + + # Instantiate Cells + yz = (+bottom & -top) & (+down & -up) + cells = [] + for c in range(len(surfs) - 1): + cells.append(openmc.Cell()) + cells[-1].region = (+surfs[c] & -surfs[c + 1]) & yz + cells[-1].fill = mats[c] + + # Register Cells with Universe + root.add_cells(cells) + + # Instantiate a Geometry, register the root Universe, and export to XML + geometry = openmc.Geometry(root) + + return geometry + + def make_model(self): + mats, materials_file = self.make_materials() + materials_file.export_to_xml() + + settings_file = self.make_settings() + settings_file.export_to_xml() + + tallies_file = self.make_tallies(r=np.max(self.rad)) + if tallies_file: + tallies_file.export_to_xml() + + geometry = self.make_geometry(mats) + geometry.export_to_xml() + + def execute(self, quiet=True): + success = True + try: + openmc.run(output=(not quiet)) + except CalledProcessError: + success = False + + if success: + spfile = 'statepoint.' + str(self.batches) + '.h5' + sp = openmc.StatePoint(spfile, autolink=False) + self.keff = sp.k_combined + + return success diff --git a/stress-test/mg-criticality/repo.py b/stress-test/mg-criticality/repo.py new file mode 100644 index 0000000..4dca3ec --- /dev/null +++ b/stress-test/mg-criticality/repo.py @@ -0,0 +1,1205 @@ +from make_model import Case + +# GLOBAL OPTIONS +batches = 1000 +inactive = 50 +particles = 2000 +tab_leg = {'enable': True} + + +def build_cases(): + + cases = [] + names = {} + + # CASE 1 + case = 1 + name = 'PUa-1-0-IN' + mat_names = ['PUa'] + groups = 1 + order = 0 + geom = 'IN' + rad = [9.4959] + ref_k = 2.612903 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 2 + case = 2 + name = 'PUa-1-0-SL' + mat_names = ['PUa'] + groups = 1 + order = 0 + geom = 'SL' + rad = [1.853722] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 3 + case = 3 + name = 'PUa-H2O(1)-1-0-SL' + mat_names = ['PUa', 'H2O'] + groups = 1 + order = 0 + geom = 'SL-NS' + rad = [1.478450, 4.542175] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 4 + case = 4 + name = 'PUa-H2O(0.5)-1-0-SL' + mat_names = ['PUa', 'H2O'] + groups = 1 + order = 0 + geom = 'SL' + rad = [1.317862, 2.849725] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 5 + case = 5 + name = 'PUb-1-0-IN' + mat_names = ['PUb'] + groups = 1 + order = 0 + geom = 'IN' + rad = [9.4959] + ref_k = 2.290323 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 6 + case = 6 + name = 'PUb-1-0-SL' + mat_names = ['PUb'] + groups = 1 + order = 0 + geom = 'SL' + rad = [2.256751] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 7 + case = 7 + name = 'PUb-1-0-CY' + mat_names = ['PUb'] + groups = 1 + order = 0 + geom = 'CY' + rad = [4.279960] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 8 + case = 8 + name = 'PUb-1-0-SP' + mat_names = ['PUb'] + groups = 1 + order = 0 + geom = 'SP' + rad = [6.082547] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 9 + case = 9 + name = 'PUb-H2O(1)-1-0-CY' + mat_names = ['PUb', 'H2O'] + groups = 1 + order = 0 + geom = 'CY' + rad = [3.397610, 6.461335] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 10 + case = 10 + name = 'PUb-H2O(10)-1-0-CY' + mat_names = ['PUb', 'H2O'] + groups = 1 + order = 0 + geom = 'CY' + rad = [3.077574, 33.714829] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 11 + case = 11 + name = 'Ua-1-0-IN' + mat_names = ['Ua'] + groups = 1 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 2.25 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 12 + case = 12 + name = 'Ua-1-0-SL' + mat_names = ['Ua'] + groups = 1 + order = 0 + geom = 'SL' + rad = [2.872934] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 13 + case = 13 + name = 'Ua-1-0-CY' + mat_names = ['Ua'] + groups = 1 + order = 0 + geom = 'CY' + rad = [5.284935] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 14 + case = 14 + name = 'Ua-1-0-SP' + mat_names = ['Ua'] + groups = 1 + order = 0 + geom = 'SP' + rad = [7.428998] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 15 + case = 15 + name = 'Ub-1-0-IN' + mat_names = ['Ub'] + groups = 1 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 2.330917 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 16 + case = 16 + name = 'Ub-H2O(1)-1-0-SP' + mat_names = ['Ub', 'H2O'] + groups = 1 + order = 0 + geom = 'SP' + rad = [6.12745, 9.191176] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 17 + case = 17 + name = 'Uc-1-0-IN' + mat_names = ['Uc'] + groups = 1 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 2.256083 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 18 + case = 18 + name = 'Uc-H2O(2)-1-0-SP' + mat_names = ['Uc', 'H2O'] + groups = 1 + order = 0 + geom = 'SP' + rad = [6.12745, 12.2549] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 19 + case = 19 + name = 'Ud-1-0-IN' + mat_names = ['Ud'] + groups = 1 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 2.232667 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 20 + case = 20 + name = 'Ud-H2O(1)-1-0-SP' + mat_names = ['Ud', 'H2O'] + groups = 1 + order = 0 + geom = 'SP' + rad = [6.12745, 15.318626] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 21 + case = 21 + name = 'UD2O-1-0-IN' + mat_names = ['UD2O'] + groups = 1 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 1.133333 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 22 + case = 22 + name = 'UD2O-1-0-SL' + mat_names = ['UD2O'] + groups = 1 + order = 0 + geom = 'SL' + rad = [10.371065] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 23 + case = 23 + name = 'UD2O-1-0-CY' + mat_names = ['UD2O'] + groups = 1 + order = 0 + geom = 'CY' + rad = [16.554249] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 24 + case = 24 + name = 'UD2O-1-0-SP' + mat_names = ['UD2O'] + groups = 1 + order = 0 + geom = 'SP' + rad = [22.017156] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 25 + case = 25 + name = 'UD2O-H2O(1)-1-0-SL' + mat_names = ['UD2O', 'H2O_2'] + groups = 1 + order = 0 + geom = 'SL' + rad = [9.214139, 11.044702] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 26 + case = 26 + name = 'UD2O-H2O(10)-1-0-SL' + mat_names = ['UD2O', 'H2O_2'] + groups = 1 + order = 0 + geom = 'SL' + rad = [8.428096, 26.733726] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 27 + case = 27 + name = 'UD2O-H2O(1)-1-0-CY' + mat_names = ['UD2O', 'H2O_2'] + groups = 1 + order = 0 + geom = 'CY' + rad = [15.396916, 17.227479] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 28 + case = 28 + name = 'UD2O-H2O(10)-1-0-CY' + mat_names = ['UD2O', 'H2O_2'] + groups = 1 + order = 0 + geom = 'CY' + rad = [14.606658, 32.912288] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 29 + case = 29 + name = 'Ue-1-0-IN' + mat_names = ['Ue'] + groups = 1 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 2.1806667 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 30 + case = 30 + name = 'Ue-Fe-Na-1-0-SL' + mat_names = ['Fe', 'Ue', 'Fe', 'Na'] + groups = 1 + order = 0 + geom = 'FENA' + rad = [0.317337461, 5.437057544, 5.754395005, 7.757166007] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 31 + case = 31 + name = 'PU-1-1-IN' + mat_names = ['PUa2'] + groups = 1 + order = 1 + geom = 'IN' + rad = [1.] + ref_k = 2.5 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 32 + case = 32 + name = 'PUa-1-1-SL' + mat_names = ['PUa2'] + groups = 1 + order = 1 + geom = 'SL' + rad = [0.77032] + ref_k = 1. + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 33 + case = 33 + name = 'PUa-1-2-SL' + mat_names = ['PUa2'] + groups = 1 + order = 2 + geom = 'SL' + rad = [0.76378] + ref_k = 1. + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 34 + case = 34 + name = 'PUb-1-1-SL' + mat_names = ['PUb2'] + groups = 1 + order = 1 + geom = 'SL' + rad = [0.79606] + ref_k = 1. + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 35 + case = 35 + name = 'PUb-1-2-SL' + mat_names = ['PUb2'] + groups = 1 + order = 2 + geom = 'SL' + rad = [0.78396] + ref_k = 1. + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 36 + case = 36 + name = 'Ua-1-1-CY' + mat_names = ['Ua1'] + groups = 1 + order = 1 + geom = 'CY' + rad = [5.514296811] + ref_k = 1. + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 37 + case = 37 + name = 'Ub-1-1-CY' + mat_names = ['Ub1'] + groups = 1 + order = 1 + geom = 'CY' + rad = [6.940205668] + ref_k = 1. + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 38 + case = 38 + name = 'UD2Oa-1-1-IN' + mat_names = ['UD2Oa'] + groups = 1 + order = 1 + geom = 'IN' + rad = [1.] + ref_k = 1.205587 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 39 + case = 39 + name = 'UD2Oa-1-1-SP' + mat_names = ['UD2Oa'] + groups = 1 + order = 1 + geom = 'SP' + rad = [18.30563081] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 40 + case = 40 + name = 'UD2Ob-1-1-IN' + mat_names = ['UD2Ob'] + groups = 1 + order = 1 + geom = 'IN' + rad = [1.] + ref_k = 1.227391 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 41 + case = 41 + name = 'UD2Ob-1-1-SP' + mat_names = ['UD2Ob'] + groups = 1 + order = 1 + geom = 'SP' + rad = [18.30563081] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 42 + case = 42 + name = 'UD2Oc-1-1-IN' + mat_names = ['UD2Oc'] + groups = 1 + order = 1 + geom = 'IN' + rad = [1.] + ref_k = 1.130933 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 43 + case = 43 + name = 'UD2Oc-1-1-SP' + mat_names = ['UD2Oc'] + groups = 1 + order = 1 + geom = 'SP' + rad = [18.30563081] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 44 + case = 44 + name = 'PU-2-0-IN' + mat_names = ['Pu'] + groups = 2 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 2.683767 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 45 + case = 45 + name = 'PU-2-0-SL' + mat_names = ['Pu'] + groups = 2 + order = 0 + geom = 'SL' + rad = [1.795602] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 46 + case = 46 + name = 'PU-2-0-SP' + mat_names = ['Pu'] + groups = 2 + order = 0 + geom = 'SP' + rad = [5.231567] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 47 + case = 47 + name = 'U-2-0-IN' + mat_names = ['U'] + groups = 2 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 2.216349 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 48 + case = 48 + name = 'U-2-0-SL' + mat_names = ['U'] + groups = 2 + order = 0 + geom = 'SL' + rad = [3.006375] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 49 + case = 49 + name = 'U-2-0-SP' + mat_names = ['U'] + groups = 2 + order = 0 + geom = 'SP' + rad = [7.909444] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 50 + case = 50 + name = 'UAL-2-0-IN' + mat_names = ['UAl'] + groups = 2 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 2.662437 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 51 + case = 51 + name = 'UAL-2-0-SL' + mat_names = ['UAl'] + groups = 2 + order = 0 + geom = 'SL' + rad = [7.830776] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 52 + case = 52 + name = 'UAL-2-0-SP' + mat_names = ['UAl'] + groups = 2 + order = 0 + geom = 'SP' + rad = [17.66770] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 53 + case = 53 + name = 'URRa-2-0-IN' + mat_names = ['URRa0'] + groups = 2 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 1.631452 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 54 + case = 54 + name = 'URRa-2-0-SL' + mat_names = ['URRa0'] + groups = 2 + order = 0 + geom = 'SL' + rad = [7.566853] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 55 + case = 55 + name = 'URRa-2-0-SP' + mat_names = ['URRa0'] + groups = 2 + order = 0 + geom = 'SP' + rad = [16.049836] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 56 + case = 56 + name = 'URRb-2-0-IN' + mat_names = ['URRb0'] + groups = 2 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 1.365821 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 57 + case = 57 + name = 'URRc-2-0-IN' + mat_names = ['URRc0'] + groups = 2 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 1.633380 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 58 + case = 58 + name = 'URRb-H2Oa(1)-2-0-SL' + mat_names = ['URRb0', 'H2Oa'] + groups = 2 + order = 0 + geom = 'SL' + rad = [6.696802, 7.822954] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 59 + case = 59 + name = 'URRb-H2Oa(5)-2-0-SL' + mat_names = ['URRb0', 'H2Oa'] + groups = 2 + order = 0 + geom = 'SL' + rad = [4.863392, 10.494149] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 60 + case = 60 + name = 'URRb-H2Oa(IN)-2-0-SL' + mat_names = ['URRb0', 'H2Oa'] + groups = 2 + order = 0 + geom = 'SL' + rad = [4.686230, 1.e50] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 61 + case = 61 + name = 'URRc-H2Oa(IN)-2-0-SL' + mat_names = ['URRc0', 'H2Oa'] + groups = 2 + order = 0 + geom = 'SL' + rad = [2.461903, 1.e50] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 62 + case = 62 + name = 'URRd-2-0-IN' + mat_names = ['URRd0'] + groups = 2 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 1.034970 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 63 + case = 63 + name = 'URRd-H2Ob(1)-2-0-ISLC' + mat_names = ['URRd0', 'H2Ob'] + groups = 2 + order = 0 + geom = 'ISLC' + rad = [0.0329074, 9.067695] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 64 + case = 64 + name = 'URRd-H2Ob(10)-2-0-ISLC' + mat_names = ['URRd0', 'H2Ob'] + groups = 2 + order = 0 + geom = 'ISLC' + rad = [0.460135, 90.808010] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 65 + case = 65 + name = 'URRd-H2Oc(1)-2-0-ISLC' + mat_names = ['URRd0', 'H2Oc'] + groups = 2 + order = 0 + geom = 'ISLC' + rad = [0.341011, 1.092034] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 66 + case = 66 + name = 'URRd-H2Oc(10)-2-0-ISLC' + mat_names = ['URRd0', 'H2Oc'] + groups = 2 + order = 0 + geom = 'ISLC' + rad = [2.719087, 10.229312] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 67 + case = 67 + name = 'UD2O-2-0-IN' + mat_names = ['UD2O'] + groups = 2 + order = 0 + geom = 'IN' + rad = [1.] + ref_k = 1.000221 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 68 + case = 68 + name = 'UD2O-2-0-SL' + mat_names = ['UD2O'] + groups = 2 + order = 0 + geom = 'SL' + rad = [846.632726] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 69 + case = 69 + name = 'UD2O-2-0-SP' + mat_names = ['UD2O'] + groups = 2 + order = 0 + geom = 'SP' + rad = [1695.337621] + ref_k = 1.0 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 70 + + ###################### SOMETHING WRONG WITH LIBRARY< MAT_NAMES SHOULD BE URR1 + case = 70 + name = 'URRa-2-1-IN' + mat_names = ['URR1'] + groups = 2 + order = 1 + geom = 'IN' + rad = [9.4959] + ref_k = 1.631452 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 71 + case = 71 + name = 'URRa-2-1-SL' + mat_names = ['URR1'] + geom = 'SL' + ref_k = 1.0 + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 72 + case = 72 + name = 'UD2O-2-1-IN' + mat_names = ['UD2O1'] + groups = 2 + order = 1 + geom = 'IN' + rad = [929.45] + ref_k = 1.000227 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 73 + case = 73 + name = 'UD2O-2-1-SL' + geom = 'SL' + ref_k = 1.0 + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 74 + case = 74 + name = 'URR-3-0-IN' + mat_names = ['URR'] + groups = 3 + order = 1 + geom = 'IN' + rad = [929.45] + ref_k = 1.6 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + # CASE 75 + name = 'URR-6-0-IN' + mat_names = ['URR'] + groups = 6 + order = 1 + geom = 'IN' + rad = [929.45] + ref_k = 1.6 + mesh_dim = [20, 1, 1] + params = {'mesh_dim': mesh_dim, 'tab_leg': tab_leg, 'batches': batches, + 'inactive': inactive, 'particles': particles} + cases.append(Case(case, name, mat_names, groups, order, geom, rad, ref_k, + params)) + names[name] = len(cases) - 1 + + return cases, names + +cases, names = build_cases() diff --git a/stress-test/mg-criticality/run_cases.py b/stress-test/mg-criticality/run_cases.py new file mode 100755 index 0000000..c346c89 --- /dev/null +++ b/stress-test/mg-criticality/run_cases.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python + +from optparse import OptionParser +import csv + +import repo +import make_library + + +def print_case(case): + bias = 1.0E5 * (case.keff - case.ref_k) + print('\tCalculated keff = {0:1.6f}'.format(case.keff)) + print('\tReference keff = {0:1.6f}'.format(case.ref_k)) + print('\tBias [pcm] = {0:1.1f}'.format(bias)) + return bias + + +# Command line parsing +parser = OptionParser() +parser.add_option('-r', '--case', dest='case_name', default=None, + help="Run specific case instead of all cases.") +parser.add_option('-l', '--list', action="store_true", + dest="list_cases", default=False, + help="List out all benchmark cases.") +(options, args) = parser.parse_args() + +# Check to see if we should just print case information to user +if options.list_cases: + for key in repo.names: + print('Case Name: {0}'.format(key)) + exit() + +# Run specific case, if requested +case_nums = [] +case_names = [] +biases = [] +codes = [] +if options.case_name: + if options.case_name in repo.names: + case_num = repo.names[options.case_name] + case = repo.cases[case_num] + + print('Writing MGXS Library\n') + make_library.create_specific_library(case.groups) + + print('Running Case\n') + case.make_model() + code = case.execute(False) + if code: + bias = print_case(case) + else: + print(case.name + ' Failed Execution!') + bias = 0. + biases.append(bias) + case_names.append(case.name) + case_nums.append(case_num) + codes.append(code) + + else: + print('Invalid Case Name: ' + options.case) + +else: + # Run them all + print('Writing MGXS Libraries\n') + make_library.create_all() + + print('Running Cases\n') + for case in repo.cases: + print(case.number, case.name) + case.make_model() + code = case.execute(True) + if code: + bias = print_case(case) + else: + print(case.name + ' Failed Execution!') + bias = 0. + biases.append(bias) + case_names.append(case.name) + codes.append(code) + case_nums.append(case.number) + +# Write to a CSV +with open("results.csv", mode="w") as csv_file: + writer = csv.writer(csv_file, delimiter=",", quotechar='"', + quoting=csv.QUOTE_NONNUMERIC) + + # write the header + writer.writerow(['Case ID', 'Case Name', 'Eigenvalue Bias [pcm]', + 'Eigenvalue Bias Std. Dev. [pcm]', 'Successful Execution']) + + # Now run each case + for i in range(len(case_nums)): + writer.writerow([case_nums[i], case_names[i], biases[i].n, + biases[i].s, codes[i]]) diff --git a/stress-test/multi-group-xs/mdgxs-part-i.ipynb b/stress-test/multi-group-xs/mdgxs-part-i.ipynb new file mode 100644 index 0000000..b311465 --- /dev/null +++ b/stress-test/multi-group-xs/mdgxs-part-i.ipynb @@ -0,0 +1,1507 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multigroup (Delayed) Cross Section Generation Part I: Introduction\n", + "This IPython Notebook introduces the use of the `openmc.mgxs` module to calculate multi-energy-group and multi-delayed-group cross sections for an infinite homogeneous medium. In particular, this Notebook introduces the the following features:\n", + "\n", + "* Creation of multi-delayed-group cross sections for an **infinite homogeneous medium**\n", + "* Calculation of delayed neutron precursor concentrations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to Multi-Delayed-Group Cross Sections (MDGXS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many Monte Carlo particle transport codes, including OpenMC, use continuous-energy nuclear cross section data. However, most deterministic neutron transport codes use *multi-group cross sections* defined over discretized energy bins or *energy groups*. Furthermore, kinetics calculations typically separate out parameters that involve delayed neutrons into prompt and delayed components and further subdivide delayed components by delayed groups. An example is the energy spectrum for prompt and delayed neutrons for U-235 and Pu-239 computed for a light water reactor spectrum." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": { + "image/png": { + "width": 350 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='images/mdgxs.png', width=350)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A variety of tools employing different methodologies have been developed over the years to compute multi-group cross sections for certain applications, including NJOY (LANL), MC$^2$-3 (ANL), and Serpent (VTT). The `openmc.mgxs` Python module is designed to leverage OpenMC's tally system to calculate multi-group cross sections with arbitrary energy discretizations and different delayed group models (e.g. 6, 7, or 8 delayed group models) for fine-mesh heterogeneous deterministic neutron transport applications.\n", + "\n", + "Before proceeding to illustrate how one may use the `openmc.mgxs` module, it is worthwhile to define the general equations used to calculate multi-energy-group and multi-delayed-group cross sections. This is only intended as a brief overview of the methodology used by `openmc.mgxs` - we refer the interested reader to the large body of literature on the subject for a more comprehensive understanding of this complex topic." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introductory Notation\n", + "The continuous real-valued microscopic cross section may be denoted $\\sigma_{n,x}(\\mathbf{r}, E)$ for position vector $\\mathbf{r}$, energy $E$, nuclide $n$ and interaction type $x$. Similarly, the scalar neutron flux may be denoted by $\\Phi(\\mathbf{r},E)$ for position $\\mathbf{r}$ and energy $E$. **Note**: Although nuclear cross sections are dependent on the temperature $T$ of the interacting medium, the temperature variable is neglected here for brevity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatial and Energy Discretization\n", + "The energy domain for critical systems such as thermal reactors spans more than 10 orders of magnitude of neutron energies from 10$^{-5}$ - 10$^7$ eV. The multi-group approximation discretization divides this energy range into one or more energy groups. In particular, for $G$ total groups, we denote an energy group index $g$ such that $g \\in \\{1, 2, ..., G\\}$. The energy group indices are defined such that the smaller group the higher the energy, and vice versa. The integration over neutron energies across a discrete energy group is commonly referred to as **energy condensation**.\n", + "\n", + "The delayed neutrons created from fissions are created from > 30 delayed neutron precursors. Modeling each of the delayed neutron precursors is possible, but this approach has not recieved much attention due to large uncertainties in certain precursors. Therefore, the delayed neutrons are often combined into \"delayed groups\" that have a set time constant, $\\lambda_d$. Some cross section libraries use the same group time constants for all nuclides (e.g. JEFF 3.1) while other libraries use different time constants for all nuclides (e.g. ENDF/B-VII.1). Multi-delayed-group cross sections can either be created with the entire delayed group set, a subset of delayed groups, or integrated over all delayed groups.\n", + "\n", + "Multi-group cross sections are computed for discretized spatial zones in the geometry of interest. The spatial zones may be defined on a structured and regular fuel assembly or pin cell mesh, an arbitrary unstructured mesh or the constructive solid geometry used by OpenMC. For a geometry with $K$ distinct spatial zones, we designate each spatial zone an index $k$ such that $k \\in \\{1, 2, ..., K\\}$. The volume of each spatial zone is denoted by $V_{k}$. The integration over discrete spatial zones is commonly referred to as **spatial homogenization**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### General Scalar-Flux Weighted MDGXS\n", + "The multi-group cross sections computed by `openmc.mgxs` are defined as a *scalar flux-weighted average* of the microscopic cross sections across each discrete energy group. This formulation is employed in order to preserve the reaction rates within each energy group and spatial zone. In particular, spatial homogenization and energy condensation are used to compute the general multi-group cross section. For instance, the delayed-nu-fission multi-energy-group and multi-delayed-group cross section, $\\nu_d \\sigma_{f,x,k,g}$, can be computed as follows:\n", + "\n", + "$$\\nu_d \\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\nu_d \\sigma_{f,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n", + "\n", + "This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for only the delayed-nu-fission and delayed neutron fraction reaction type at the moment. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](../usersguide/tallies.rst#filters) on the energy range and spatial zone (material, cell, universe, or mesh) define the bounds of integration for both numerator and denominator." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-Group Prompt and Delayed Fission Spectrum\n", + "The energy spectrum of neutrons emitted from fission is denoted by $\\chi_{n}(\\mathbf{r},E' \\rightarrow E'')$ for incoming and outgoing energies $E'$ and $E''$, respectively. Unlike the multi-group cross sections $\\sigma_{n,x,k,g}$ considered up to this point, the fission spectrum is a probability distribution and must sum to unity. The outgoing energy is typically much less dependent on the incoming energy for fission than for scattering interactions. As a result, it is common practice to integrate over the incoming neutron energy when computing the multi-group fission spectrum. The fission spectrum may be simplified as $\\chi_{n}(\\mathbf{r},E)$ with outgoing energy $E$.\n", + "\n", + "Computing the cumulative energy spectrum of emitted neutrons, $\\chi_{n}(\\mathbf{r},E)$, has been presented in the `mgxs-part-i.ipynb` notebook. Here, we will present the energy spectrum of prompt and delayed emission neutrons, $\\chi_{n,p}(\\mathbf{r},E)$ and $\\chi_{n,d}(\\mathbf{r},E)$, respectively. Unlike the multi-group cross sections defined up to this point, the multi-group fission spectrum is weighted by the fission production rate rather than the scalar flux. This formulation is intended to preserve the total fission production rate in the multi-group deterministic calculation. In order to mathematically define the multi-group fission spectrum, we denote the microscopic fission cross section as $\\sigma_{n,f}(\\mathbf{r},E)$ and the average number of neutrons emitted from fission interactions with nuclide $n$ as $\\nu_{n,p}(\\mathbf{r},E)$ and $\\nu_{n,d}(\\mathbf{r},E)$ for prompt and delayed neutrons, respectively. The multi-group fission spectrum $\\chi_{n,k,g,d}$ is then the probability of fission neutrons emitted into energy group $g$ and delayed group $d$. There are not prompt groups, so inserting $p$ in place of $d$ just denotes all prompt neutrons. \n", + "\n", + "Similar to before, spatial homogenization and energy condensation are used to find the multi-energy-group and multi-delayed-group fission spectrum $\\chi_{n,k,g,d}$ as follows:\n", + "\n", + "$$\\chi_{n,k,g',d} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\chi_{n,d}(\\mathbf{r},E'\\rightarrow E'')\\nu_{n,d}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\nu_{n,d}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}$$\n", + "\n", + "The fission production-weighted multi-energy-group and multi-delayed-group fission spectrum for delayed neutrons is computed using OpenMC tallies with energy in, energy out, and delayed group filters. Alternatively, the delayed group filter can be omitted to compute the fission spectrum integrated over all delayed groups.\n", + "\n", + "This concludes our brief overview on the methodology to compute multi-energy-group and multi-delayed-group cross sections. The following sections detail more concretely how users may employ the `openmc.mgxs` module to power simulation workflows requiring multi-group cross sections for downstream deterministic calculations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import openmc\n", + "import openmc.mgxs as mgxs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to define materials that will be used in the problem. Let's create a material for the homogeneous medium." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a Material and register the Nuclides\n", + "inf_medium = openmc.Material(name='moderator')\n", + "inf_medium.set_density('g/cc', 5.)\n", + "inf_medium.add_nuclide('H1', 0.03)\n", + "inf_medium.add_nuclide('O16', 0.015)\n", + "inf_medium.add_nuclide('U235', 0.0001)\n", + "inf_medium.add_nuclide('U238', 0.007)\n", + "inf_medium.add_nuclide('Pu239', 0.00003)\n", + "inf_medium.add_nuclide('Zr90', 0.002)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our material, we can now create a `Materials` object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a Materials collection and export to XML\n", + "materials = openmc.Materials([inf_medium])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. This problem will be a simple square cell with reflective boundary conditions to simulate an infinite homogeneous medium. The first step is to create the outer bounding surfaces of the problem." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate boundary Planes\n", + "min_x = openmc.XPlane(boundary_type='reflective', x0=-0.63)\n", + "max_x = openmc.XPlane(boundary_type='reflective', x0=0.63)\n", + "min_y = openmc.YPlane(boundary_type='reflective', y0=-0.63)\n", + "max_y = openmc.YPlane(boundary_type='reflective', y0=0.63)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now create a cell that is defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a Cell\n", + "cell = openmc.Cell(cell_id=1, name='cell')\n", + "\n", + "# Register bounding Surfaces with the Cell\n", + "cell.region = +min_x & -max_x & +min_y & -max_y\n", + "\n", + "# Fill the Cell with the Material\n", + "cell.fill = inf_medium" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "geometry = openmc.Geometry([cell])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 5000\n", + "\n", + "# Instantiate a Settings object\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': True}\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings.source = openmc.Source(space=uniform_dist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to generate multi-group cross sections! First, let's define a 100-energy-group structure and 1-energy-group structure using the built-in `EnergyGroups` class. We will also create a 6-delayed-group list." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a 100-group EnergyGroups object\n", + "energy_groups = mgxs.EnergyGroups()\n", + "energy_groups.group_edges = np.logspace(-3, 7.3, 101)\n", + "\n", + "# Instantiate a 1-group EnergyGroups object\n", + "one_group = mgxs.EnergyGroups()\n", + "one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])\n", + "\n", + "delayed_groups = list(range(1,7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use the `EnergyGroups` object and delayed group list, along with our previously created materials and geometry, to instantiate some `MGXS` objects from the `openmc.mgxs` module. In particular, the following are subclasses of the generic and abstract `MGXS` class:\n", + "\n", + "* `TotalXS`\n", + "* `TransportXS`\n", + "* `AbsorptionXS`\n", + "* `CaptureXS`\n", + "* `FissionXS`\n", + "* `NuFissionMatrixXS`\n", + "* `KappaFissionXS`\n", + "* `ScatterXS`\n", + "* `ScatterMatrixXS`\n", + "* `Chi`\n", + "* `InverseVelocity`\n", + "\n", + "A separate abstract `MDGXS` class is used for cross-sections and parameters that involve delayed neutrons. The subclasses of `MDGXS` include:\n", + "\n", + "* `DelayedNuFissionXS`\n", + "* `ChiDelayed`\n", + "* `Beta`\n", + "* `DecayRate`\n", + "\n", + "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. \n", + "\n", + "In this case, let's create the multi-group chi-prompt, chi-delayed, and prompt-nu-fission cross sections with our 100-energy-group structure and multi-group delayed-nu-fission and beta cross sections with our 100-energy-group and 6-delayed-group structures. \n", + "\n", + "The prompt chi and nu-fission data can actually be gathered using the `Chi` and `FissionXS` classes, respectively, by passing in a value of `True` for the optional `prompt` parameter upon initialization." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a few different sections\n", + "chi_prompt = mgxs.Chi(domain=cell, energy_groups=energy_groups, by_nuclide=True, prompt=True)\n", + "prompt_nu_fission = mgxs.FissionXS(domain=cell, energy_groups=energy_groups, by_nuclide=True, nu=True, prompt=True)\n", + "chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n", + "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n", + "beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n", + "decay_rate = mgxs.DecayRate(domain=cell, energy_groups=one_group, delayed_groups=delayed_groups, by_nuclide=True)\n", + "\n", + "chi_prompt.nuclides = ['U235', 'Pu239']\n", + "prompt_nu_fission.nuclides = ['U235', 'Pu239']\n", + "chi_delayed.nuclides = ['U235', 'Pu239']\n", + "delayed_nu_fission.nuclides = ['U235', 'Pu239']\n", + "beta.nuclides = ['U235', 'Pu239']\n", + "decay_rate.nuclides = ['U235', 'Pu239']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `Decay Rate` object as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([('delayed-nu-fission',\n", + " Tally\n", + " \tID =\t1\n", + " \tName =\t\n", + " \tFilters =\tCellFilter, DelayedGroupFilter\n", + " \tNuclides =\tU235 Pu239\n", + " \tScores =\t['delayed-nu-fission']\n", + " \tEstimator =\ttracklength),\n", + " ('decay-rate',\n", + " Tally\n", + " \tID =\t2\n", + " \tName =\t\n", + " \tFilters =\tCellFilter, DelayedGroupFilter\n", + " \tNuclides =\tU235 Pu239\n", + " \tScores =\t['decay-rate']\n", + " \tEstimator =\ttracklength)])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decay_rate.tallies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Beta` object includes tracklength tallies for the 'nu-fission' and 'delayed-nu-fission' scores in the 100-energy-group and 6-delayed-group structure in cell 1. Now that each `MGXS` and `MDGXS` object contains the tallies that it needs, we must add these tallies to a `Tallies` object to generate the \"tallies.xml\" input file for OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=3.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=5.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=4.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=7.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=13.\n", + " warn(msg, IDWarning)\n" + ] + } + ], + "source": [ + "# Instantiate an empty Tallies object\n", + "tallies = openmc.Tallies()\n", + "\n", + "# Add chi-prompt tallies to the tallies file\n", + "tallies += chi_prompt.tallies.values()\n", + "\n", + "# Add prompt-nu-fission tallies to the tallies file\n", + "tallies += prompt_nu_fission.tallies.values()\n", + "\n", + "# Add chi-delayed tallies to the tallies file\n", + "tallies += chi_delayed.tallies.values()\n", + "\n", + "# Add delayed-nu-fission tallies to the tallies file\n", + "tallies += delayed_nu_fission.tallies.values()\n", + "\n", + "# Add beta tallies to the tallies file\n", + "tallies += beta.tallies.values()\n", + "\n", + "# Add decay rate tallies to the tallies file\n", + "tallies += decay_rate.tallies.values()\n", + "\n", + "# Export to \"tallies.xml\"\n", + "tallies.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we a have a complete set of inputs, so we can go ahead and run our simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# tie geometry, materials, settings, and tallies together into a model object\n", + "model = openmc.Model(geometry=geometry,\n", + " materials=materials,\n", + " settings=settings,\n", + " tallies=tallies)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-03 23:19:19\n", + " OpenMP Threads | 2\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n", + " Reading Pu239 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Pu239.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000 eV for H1\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.24970\n", + " 2/1 1.21100\n", + " 3/1 1.19756\n", + " 4/1 1.25555\n", + " 5/1 1.24295\n", + " 6/1 1.21186\n", + " 7/1 1.25633\n", + " 8/1 1.24522\n", + " 9/1 1.21656\n", + " 10/1 1.26028\n", + " 11/1 1.20898\n", + " 12/1 1.23185 1.22041 +/- 0.01143\n", + " 13/1 1.25350 1.23144 +/- 0.01285\n", + " 14/1 1.22167 1.22900 +/- 0.00941\n", + " 15/1 1.24549 1.23230 +/- 0.00800\n", + " 16/1 1.24976 1.23521 +/- 0.00715\n", + " 17/1 1.18269 1.22771 +/- 0.00963\n", + " 18/1 1.23822 1.22902 +/- 0.00845\n", + " 19/1 1.23413 1.22959 +/- 0.00747\n", + " 20/1 1.21361 1.22799 +/- 0.00687\n", + " 21/1 1.24244 1.22930 +/- 0.00635\n", + " 22/1 1.21414 1.22804 +/- 0.00593\n", + " 23/1 1.21809 1.22727 +/- 0.00551\n", + " 24/1 1.19780 1.22517 +/- 0.00552\n", + " 25/1 1.24190 1.22628 +/- 0.00526\n", + " 26/1 1.24078 1.22719 +/- 0.00500\n", + " 27/1 1.21557 1.22651 +/- 0.00475\n", + " 28/1 1.26431 1.22861 +/- 0.00494\n", + " 29/1 1.27196 1.23089 +/- 0.00520\n", + " 30/1 1.24033 1.23136 +/- 0.00496\n", + " 31/1 1.24532 1.23203 +/- 0.00476\n", + " 32/1 1.22646 1.23177 +/- 0.00455\n", + " 33/1 1.23791 1.23204 +/- 0.00436\n", + " 34/1 1.21230 1.23122 +/- 0.00425\n", + " 35/1 1.22857 1.23111 +/- 0.00408\n", + " 36/1 1.22386 1.23083 +/- 0.00393\n", + " 37/1 1.25504 1.23173 +/- 0.00388\n", + " 38/1 1.24488 1.23220 +/- 0.00377\n", + " 39/1 1.24251 1.23255 +/- 0.00366\n", + " 40/1 1.19482 1.23130 +/- 0.00375\n", + " 41/1 1.20078 1.23031 +/- 0.00376\n", + " 42/1 1.24233 1.23069 +/- 0.00366\n", + " 43/1 1.29614 1.23267 +/- 0.00406\n", + " 44/1 1.23726 1.23281 +/- 0.00394\n", + " 45/1 1.24222 1.23307 +/- 0.00384\n", + " 46/1 1.24097 1.23329 +/- 0.00374\n", + " 47/1 1.27425 1.23440 +/- 0.00380\n", + " 48/1 1.25510 1.23495 +/- 0.00374\n", + " 49/1 1.23654 1.23499 +/- 0.00364\n", + " 50/1 1.23369 1.23495 +/- 0.00355\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 8.7385e-01 seconds\n", + " Reading cross sections = 8.6672e-01 seconds\n", + " Total time in simulation = 1.3402e+02 seconds\n", + " Time in transport only = 1.3397e+02 seconds\n", + " Time in inactive batches = 4.7414e+00 seconds\n", + " Time in active batches = 1.2927e+02 seconds\n", + " Time synchronizing fission bank = 2.1579e-02 seconds\n", + " Sampling source sites = 1.9879e-02 seconds\n", + " SEND/RECV source sites = 1.6605e-03 seconds\n", + " Time accumulating tallies = 1.0377e-02 seconds\n", + " Time writing statepoints = 4.2323e-03 seconds\n", + " Total time for finalization = 1.3320e-02 seconds\n", + " Total time elapsed = 1.3492e+02 seconds\n", + " Calculation Rate (inactive) = 10545.3 particles/second\n", + " Calculation Rate (active) = 1547.09 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.23445 +/- 0.00332\n", + " k-effective (Track-length) = 1.23495 +/- 0.00355\n", + " k-effective (Absorption) = 1.23293 +/- 0.00238\n", + " Combined k-effective = 1.23332 +/- 0.00230\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "# Run OpenMC\n", + "statepoint_filename = model.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the last statepoint file\n", + "sp = openmc.StatePoint(statepoint_filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. By default, a `Summary` object is automatically linked when a `StatePoint` is loaded. This is necessary for the `openmc.mgxs` module to properly process the tally data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the `StatePoint` into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the tallies from the statepoint into each MGXS object\n", + "chi_prompt.load_from_statepoint(sp)\n", + "prompt_nu_fission.load_from_statepoint(sp)\n", + "chi_delayed.load_from_statepoint(sp)\n", + "delayed_nu_fission.load_from_statepoint(sp)\n", + "beta.load_from_statepoint(sp)\n", + "decay_rate.load_from_statepoint(sp)\n", + "# Close statepoint file now that we have the info we need\n", + "sp.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voila! Our multi-group cross sections are now ready to rock 'n roll!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extracting and Storing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first inspect our delayed-nu-fission section by printing it to the screen after condensing the cross section down to one group." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[5.14603413e-06, 1.16498857e-06]],\n", + "\n", + " [[2.65622446e-05, 7.58694377e-06]],\n", + "\n", + " [[2.53586263e-05, 5.74154841e-06]],\n", + "\n", + " [[5.68561321e-05, 1.04823410e-05]],\n", + "\n", + " [[2.33102114e-05, 5.45999869e-06]],\n", + "\n", + " [[9.76456653e-06, 1.65254173e-06]]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delayed_nu_fission.get_condensed_xs(one_group).get_xs()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the `openmc.mgxs` module uses [tally arithmetic](tally-arithmetic.ipynb) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](https://pandas.pydata.org/) `DataFrame` of the multi-group cross section data." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell delayedgroup group in nuclide mean std. dev.\n", + "198 1 1 1 U235 9.479196e-08 5.863577e-08\n", + "199 1 1 1 Pu239 1.600306e-08 9.891470e-09\n", + "398 1 2 1 U235 4.892869e-07 3.026598e-07\n", + "399 1 2 1 Pu239 1.042193e-07 6.441782e-08\n", + "598 1 3 1 U235 4.671158e-07 2.889454e-07\n", + "599 1 3 1 Pu239 7.886975e-08 4.874928e-08\n", + "798 1 4 1 U235 1.047312e-06 6.478393e-07\n", + "799 1 4 1 Pu239 1.439925e-07 8.900152e-08\n", + "998 1 5 1 U235 4.293832e-07 2.656050e-07\n", + "999 1 5 1 Pu239 7.500220e-08 4.635875e-08" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = delayed_nu_fission.get_pandas_dataframe()\n", + "df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell delayedgroup nuclide mean std. dev.\n", + "0 1 1 U235 0.013336 0.000061\n", + "1 1 1 Pu239 0.013271 0.000059\n", + "2 1 2 U235 0.032739 0.000150\n", + "3 1 2 Pu239 0.030881 0.000136\n", + "4 1 3 U235 0.120780 0.000552\n", + "5 1 3 Pu239 0.113370 0.000501\n", + "6 1 4 U235 0.302780 0.001383\n", + "7 1 4 Pu239 0.292500 0.001292\n", + "8 1 5 U235 0.849490 0.003880\n", + "9 1 5 Pu239 0.857490 0.003787\n", + "10 1 6 U235 2.853000 0.013029\n", + "11 1 6 Pu239 2.729700 0.012056" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = decay_rate.get_pandas_dataframe()\n", + "df.head(12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each multi-group cross section object can be easily exported to a variety of file formats, including CSV, Excel, and LaTeX for storage or data processing." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mgxs/mdgxs.py:749: FutureWarning: As the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.\n", + " df.to_excel(filename + '.xls', index=False)\n" + ] + } + ], + "source": [ + "beta.export_xs_data(filename='beta', format='excel')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code snippet shows how to export the chi-prompt and chi-delayed `MGXS` to the same HDF5 binary data store." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "chi_prompt.build_hdf5_store(filename='mdgxs', append=True)\n", + "chi_delayed.build_hdf5_store(filename='mdgxs', append=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Tally Arithmetic to Compute the Delayed Neutron Precursor Concentrations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](tally-arithmetic.ipynb) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta`, `DelayedNuFissionXS`, and `DecayRate` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n", + "\n", + "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n", + "\n", + "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$\n", + "\n", + "First, let's investigate the decay rates for U235 and Pu235. The fraction of the delayed neutron precursors remaining as a function of time after fission for each delayed group and fissioning isotope have been plotted below." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Get the decay rate data\n", + "dr_tally = decay_rate.xs_tally\n", + "dr_u235 = dr_tally.get_values(nuclides=['U235']).flatten()\n", + "dr_pu239 = dr_tally.get_values(nuclides=['Pu239']).flatten()\n", + "\n", + "# Compute the exponential decay of the precursors\n", + "time = np.logspace(-3,3)\n", + "dr_u235_points = np.exp(-np.outer(dr_u235, time))\n", + "dr_pu239_points = np.exp(-np.outer(dr_pu239, time))\n", + "\n", + "# Create a plot of the fraction of the precursors remaining as a f(time)\n", + "colors = ['b', 'g', 'r', 'c', 'm', 'k']\n", + "legend = []\n", + "fig = plt.figure(figsize=(8,6))\n", + "for g,c in enumerate(colors):\n", + " plt.semilogx(time, dr_u235_points [g,:], color=c, linestyle='--', linewidth=3)\n", + " plt.semilogx(time, dr_pu239_points[g,:], color=c, linestyle=':' , linewidth=3)\n", + " legend.append('U-235 $t_{1/2}$ = ' + '{0:1.2f} seconds'.format(np.log(2) / dr_u235[g]))\n", + " legend.append('Pu-239 $t_{1/2}$ = ' + '{0:1.2f} seconds'.format(np.log(2) / dr_pu239[g]))\n", + "\n", + "plt.title('Delayed Neutron Precursor Decay Rates')\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Fraction Remaining')\n", + "plt.legend(legend, loc=1, bbox_to_anchor=(1.55, 0.95))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compute the initial concentration of the delayed neutron precursors:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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celldelayedgroupnuclidescoremeanstd. dev.
011U235(((delayed-nu-fission / nu-fission) * (delayed...8.785604e-084.659397e-10
111Pu239(((delayed-nu-fission / nu-fission) * (delayed...7.154286e-093.857161e-11
212U235(((delayed-nu-fission / nu-fission) * (delayed...9.534897e-075.056780e-09
312Pu239(((delayed-nu-fission / nu-fission) * (delayed...1.303974e-077.030245e-10
413U235(((delayed-nu-fission / nu-fission) * (delayed...2.355637e-071.249299e-09
513Pu239(((delayed-nu-fission / nu-fission) * (delayed...2.034167e-081.096700e-10
614U235(((delayed-nu-fission / nu-fission) * (delayed...4.723668e-072.505171e-09
714Pu239(((delayed-nu-fission / nu-fission) * (delayed...2.627951e-081.416833e-10
815U235(((delayed-nu-fission / nu-fission) * (delayed...2.829997e-081.500873e-10
915Pu239(((delayed-nu-fission / nu-fission) * (delayed...2.432107e-091.311246e-11
1016U235(((delayed-nu-fission / nu-fission) * (delayed...1.478618e-097.841770e-12
1116Pu239(((delayed-nu-fission / nu-fission) * (delayed...6.998687e-113.773271e-13
\n", + "
" + ], + "text/plain": [ + " cell delayedgroup nuclide \\\n", + "0 1 1 U235 \n", + "1 1 1 Pu239 \n", + "2 1 2 U235 \n", + "3 1 2 Pu239 \n", + "4 1 3 U235 \n", + "5 1 3 Pu239 \n", + "6 1 4 U235 \n", + "7 1 4 Pu239 \n", + "8 1 5 U235 \n", + "9 1 5 Pu239 \n", + "10 1 6 U235 \n", + "11 1 6 Pu239 \n", + "\n", + " score mean std. dev. \n", + "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.79e-08 4.66e-10 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.86e-11 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07 5.06e-09 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 7.03e-10 \n", + "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.36e-07 1.25e-09 \n", + "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08 1.10e-10 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 2.51e-09 \n", + "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 1.42e-10 \n", + "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08 1.50e-10 \n", + "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09 1.31e-11 \n", + "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.84e-12 \n", + "11 (((delayed-nu-fission / nu-fission) * (delayed... 7.00e-11 3.77e-13 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the precursor concentrations\n", + "precursor_conc = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) * \\\n", + " delayed_nu_fission.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) / \\\n", + " decay_rate.xs_tally.summation()\n", + "\n", + "# Get the Pandas DataFrames for inspection\n", + "precursor_conc.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the delayed neutron fractions for each nuclide." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beta (U-235) : 0.006504 +/- 0.000007\n", + "Beta (Pu-239): 0.002245 +/- 0.000003\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0, 7.0)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy_filter = [f for f in beta.xs_tally.filters if type(f) is openmc.EnergyFilter]\n", + "beta_integrated = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True)\n", + "beta_u235 = beta_integrated.get_values(nuclides=['U235'])\n", + "beta_pu239 = beta_integrated.get_values(nuclides=['Pu239'])\n", + "\n", + "# Reshape the betas\n", + "beta_u235.shape = (beta_u235.shape[0])\n", + "beta_pu239.shape = (beta_pu239.shape[0])\n", + "\n", + "df = beta_integrated.summation(filter_type=openmc.DelayedGroupFilter, remove_filter=True).get_pandas_dataframe()\n", + "print('Beta (U-235) : {:.6f} +/- {:.6f}'.format(df[df['nuclide'] == 'U235']['mean'][0], df[df['nuclide'] == 'U235']['std. dev.'][0]))\n", + "print('Beta (Pu-239): {:.6f} +/- {:.6f}'.format(df[df['nuclide'] == 'Pu239']['mean'][1], df[df['nuclide'] == 'Pu239']['std. dev.'][1]))\n", + "\n", + "beta_u235 = np.append(beta_u235[0], beta_u235)\n", + "beta_pu239 = np.append(beta_pu239[0], beta_pu239)\n", + "\n", + "# Create a step plot for the MGXS\n", + "plt.plot(np.arange(0.5, 7.5, 1), beta_u235, drawstyle='steps', color='b', linewidth=3)\n", + "plt.plot(np.arange(0.5, 7.5, 1), beta_pu239, drawstyle='steps', color='g', linewidth=3)\n", + "\n", + "plt.title('Delayed Neutron Fraction (beta)')\n", + "plt.xlabel('Delayed Group')\n", + "plt.ylabel('Beta(fraction total neutrons)')\n", + "plt.legend(['U-235', 'Pu-239'])\n", + "plt.xlim([0,7])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot the energy spectrum for fission emission of prompt and delayed neutrons." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000.0, 20000000.0)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "chi_d_u235 = np.squeeze(chi_delayed.get_xs(nuclides=['U235'], order_groups='decreasing'))\n", + "chi_d_pu239 = np.squeeze(chi_delayed.get_xs(nuclides=['Pu239'], order_groups='decreasing'))\n", + "chi_p_u235 = np.squeeze(chi_prompt.get_xs(nuclides=['U235'], order_groups='decreasing'))\n", + "chi_p_pu239 = np.squeeze(chi_prompt.get_xs(nuclides=['Pu239'], order_groups='decreasing'))\n", + "\n", + "chi_d_u235 = np.append(chi_d_u235 , chi_d_u235[0])\n", + "chi_d_pu239 = np.append(chi_d_pu239, chi_d_pu239[0])\n", + "chi_p_u235 = np.append(chi_p_u235 , chi_p_u235[0])\n", + "chi_p_pu239 = np.append(chi_p_pu239, chi_p_pu239[0])\n", + "\n", + "# Create a step plot for the MGXS\n", + "plt.semilogx(energy_groups.group_edges, chi_d_u235 , drawstyle='steps', color='b', linestyle='--', linewidth=3)\n", + "plt.semilogx(energy_groups.group_edges, chi_d_pu239, drawstyle='steps', color='g', linestyle='--', linewidth=3)\n", + "plt.semilogx(energy_groups.group_edges, chi_p_u235 , drawstyle='steps', color='b', linestyle=':', linewidth=3)\n", + "plt.semilogx(energy_groups.group_edges, chi_p_pu239, drawstyle='steps', color='g', linestyle=':', linewidth=3)\n", + "\n", + "plt.title('Energy Spectrum for Fission Neutrons')\n", + "plt.xlabel('Energy (eV)')\n", + "plt.ylabel('Fraction on emitted neutrons')\n", + "plt.legend(['U-235 delayed', 'Pu-239 delayed', 'U-235 prompt', 'Pu-239 prompt'],loc=2)\n", + "plt.xlim(1.0e3, 20.0e6)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/stress-test/multi-group-xs/mdgxs-part-ii.ipynb b/stress-test/multi-group-xs/mdgxs-part-ii.ipynb new file mode 100644 index 0000000..91d0285 --- /dev/null +++ b/stress-test/multi-group-xs/mdgxs-part-ii.ipynb @@ -0,0 +1,1208 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multigroup (Delayed) Cross Section Generation Part II: Advanced Features\n", + "This IPython Notebook illustrates the use of the **`openmc.mgxs.Library`** class. The `Library` class is designed to automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, and/or nuclides. In particular, this Notebook illustrates the following features:\n", + "\n", + "* Calculation of multi-energy-group and multi-delayed-group cross sections for a **fuel assembly**\n", + "* Automated creation, manipulation and storage of `MGXS` with **`openmc.mgxs.Library`**\n", + "* Steady-state pin-by-pin **delayed neutron fractions (beta)** for each delayed group.\n", + "* Generation of surface currents on the interfaces and surfaces of a Mesh." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import math\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import openmc\n", + "import openmc.mgxs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to define materials that will be used in the problem: fuel, water, and cladding." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# 1.6 enriched fuel\n", + "fuel = openmc.Material(name='1.6% Fuel')\n", + "fuel.set_density('g/cm3', 10.31341)\n", + "fuel.add_nuclide('U235', 3.7503e-4)\n", + "fuel.add_nuclide('U238', 2.2625e-2)\n", + "fuel.add_nuclide('O16', 4.6007e-2)\n", + "\n", + "# borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", + "water.add_nuclide('B10', 8.0042e-6)\n", + "\n", + "# zircaloy\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide('Zr90', 7.2758e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our three materials, we can now create a `Materials` object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a materials collection and export to XML\n", + "materials = openmc.Materials((fuel, water, zircaloy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. This problem will be a square array of fuel pins and control rod guide tubes for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Create cylinders for the fuel and clad\n", + "fuel_outer_radius = openmc.ZCylinder(r=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(r=0.45720)\n", + "\n", + "# Create boundary planes to surround the geometry\n", + "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n", + "max_x = openmc.XPlane(x0=+10.71, boundary_type='reflective')\n", + "min_y = openmc.YPlane(y0=-10.71, boundary_type='reflective')\n", + "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n", + "min_z = openmc.ZPlane(z0=-10., boundary_type='reflective')\n", + "max_z = openmc.ZPlane(z0=+10., boundary_type='reflective')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a fuel pin\n", + "fuel_pin_universe = openmc.Universe(name='1.6% Fuel Pin')\n", + "\n", + "# Create fuel Cell\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", + "fuel_cell.fill = fuel\n", + "fuel_cell.region = -fuel_outer_radius\n", + "fuel_pin_universe.add_cell(fuel_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='1.6% Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "fuel_pin_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius\n", + "fuel_pin_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Likewise, we can construct a control rod guide tube with the same surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a control rod guide tube\n", + "guide_tube_universe = openmc.Universe(name='Guide Tube')\n", + "\n", + "# Create guide tube Cell\n", + "guide_tube_cell = openmc.Cell(name='Guide Tube Water')\n", + "guide_tube_cell.fill = water\n", + "guide_tube_cell.region = -fuel_outer_radius\n", + "guide_tube_universe.add_cell(guide_tube_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='Guide Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "guide_tube_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='Guide Tube Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius\n", + "guide_tube_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create fuel assembly Lattice\n", + "assembly = openmc.RectLattice(name='1.6% Fuel Assembly')\n", + "assembly.pitch = (1.26, 1.26)\n", + "assembly.lower_left = [-1.26 * 17. / 2.0] * 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we create a NumPy array of fuel pin and guide tube universes for the lattice." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Create array indices for guide tube locations in lattice\n", + "template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\n", + " 11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n", + "template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\n", + " 8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n", + "\n", + "# Create universes array with the fuel pin and guide tube universes\n", + "universes = np.tile(fuel_pin_universe, (17,17))\n", + "universes[template_x, template_y] = guide_tube_universe\n", + "\n", + "# Store the array of universes in the lattice\n", + "assembly.universes = universes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Create root Cell\n", + "root_cell = openmc.Cell(name='root cell', fill=assembly)\n", + "\n", + "# Add boundary planes\n", + "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n", + "\n", + "# Create root Universe\n", + "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", + "root_universe.add_cell(root_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry that is assigned a root universe and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Geometry and export to XML\n", + "geometry = openmc.Geometry(root_universe)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 2500\n", + "\n", + "# Instantiate a Settings object\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': False}\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings.source = openmc.Source(space=uniform_dist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll tie the materials, geometry, and settings into a single model object and export the necessary XML files." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "model = openmc.Model(geometry, materials, settings)\n", + "model.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also create a plot to verify that our fuel assembly geometry was created successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XGUTmGzHcDjg+w9D8IQBMfjHbC51qYpmisgcy3A74LgyvDzOiOI4yltkz41rP0FbwYxCZc+B4XSu8AsCMW3N6+sGQMmgSbAxDUSRDpTJJAubAXfc+/mGciIwQAG6PGzzLGqFpERja4+JNA+V7S9RdCpLEvK/iN1CViOQ1nUbkc2pOao8oCvdZYUxnWf0YGGmaZqX2AtR84Ma3RHU6bTJPaxK+FamtI2JwapoTg1PTpAPnN6OGDG/VDsb6lD6W2SPrmfGY7nQ0dI6BLJJN4xnIImuaVici8V/WtFczuJl/mXEyB2QQmSMAR2AQmStnBoBTBykjYqmDlNM9dZByuqcO0pd20/PD/nDd6npZ4GS+BYPIfF8GkTkmA/e3mQq7ltRmWXsoVdpzWLbaxfxu14/Bow+kGYzIbgH+fAPnHgCWlu2ds6ay/tT8zBEYSHOEgeuZtQFgPlAG1pXG3/D1VPzcRBLlaVtzhKbxaUSQQaSVA2fetPGlqjLDaZJlHe5dw4BLE741mZTPUqDzEwwcYva9JWqQHGkEmWdZXRV/4CIGgHflSAPmV/FEZATFHzivADD/s9khhNVzIwIDLlHTWgarJ0CEpi1jQMzjALCotOjcyOsY0kxeG+nnBMnglGUVhft60geAnZrmNHBMoHKuaa9Xd2WG89tff+ijoT86f6CnZyYZetHQ1gyWtTIXqmmkzY/Br2magWMqkwytDSzLBYBjfjG2nmFoTobWHIGhNWcAOHWQMiKWOkg53VMHKad76iDVAeBzfvK4haHMXn3uMn8yhskAMJ5T65XtcYsYSvNlMmMW5fhiMhSLpkVgKJKmKWfa1fnGWIvb3T17gQecoVenx9BbAI7AsLJpERjIOn4z7eocB4CdAkzSLKsmfMvIKQCsZHNiMAl+BRk48lhDBuhSdTqn5nduOEORRBsMk4D4J85qlx8DoxZvy8BVwObZW98A8C4FDADfS/EbGCIAXO2KnyNdxsBMINHKwxoFGTjz1PQrEcnU1Zy8SY5UGUIWvaczwBoGkdY3rW2RVfh2TdNAhteru1OWVWT2YyBnfG+1hJQyT2vFoGwaziDqcE940/STB2EY/OW9YnEPico8LCtiqMw8w3UvM8+UDLz5RgwlzMCBM23IQCQiH48Bnz2u5mSYMCcDY84AcOogZUQsdZByuqcOUk731EH6EAC+XtjyV9beZvyafQ0DY47QNOlChx9wBAbGPLglqlMss2fuOQt1hpGBD2Qo1Bf70YBfH2aUSUsTMyny4X4Moljb+qbhYIsHDmRbP3DXja8AcI/PKcvaSpQjNTmikmFL0zThWyuGFmn7wPEPXxcABh/oqi0RTjzLGqFpQVKuEQPAqdRNBU336UjQlhwpeee09RjTWdZs2gQD2LTxLVH9sqwVIs+wZhKIGPjwrQYjmzYhhGHw6q6PZTJmENqVATSLXguyaVJaV+Cr6Fuivn/9Dq7nV8fjzc+yIjPCgJvNGeaAn9rIsLFpEwxXTTNkIjJ1kHJlJnWQcrqnDlJO99RB+tJuel4ZIMtY+E+qJsqamyMwlGyawqwsy90S1eqn2W1cTvPrWp5hC3AytOaYAzcIAPeqK+Oprub//uPf7cawwGEZSil//8+/ljHogZGy41uittJnentmJlgHMhRqmdYKGGcgqRY3TcnQo/IbuFbmTRtfqq7MkU7v5Y9okmWVMkw3TTohTBiUTZveO3dE8ijIw41viRohy7rxuIzwpkVQkIEzb5rxLVGDaEuokBeeiIyggA2sNNc09wCw1VhOM5Bb9EcZMkyXyqb5McwEgE1Oj4yGihj0c4KclKIs6+IX5mzatc4Ew+vVvZfhJCf3t+Yv1ffMorIic9EFX3vdcQJ+OCvzY0s2TcpAYiBl6QDwtURblzTjX86Jyt6OQWSOzyAyx2fIAHDqIGVELHWQ/gcg1yPxJKXdEwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot our geometry\n", + "plot = openmc.Plot.from_geometry(geometry)\n", + "plot.pixels = (250, 250)\n", + "plot.color_by = 'material'\n", + "openmc.plot_inline(plot)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the plot, we have a nice array of fuel and guide tube pin cells with fuel, cladding, and water!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an MGXS Library" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to generate multi-group cross sections! First, let's define a 20-energy-group and 1-energy-group." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a 20-group EnergyGroups object\n", + "energy_groups = openmc.mgxs.EnergyGroups()\n", + "energy_groups.group_edges = np.logspace(-3, 7.3, 21)\n", + "\n", + "# Instantiate a 1-group EnergyGroups object\n", + "one_group = openmc.mgxs.EnergyGroups()\n", + "one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will instantiate an `openmc.mgxs.Library` for the energy and delayed groups with our the fuel assembly geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a tally mesh \n", + "mesh = openmc.RegularMesh(mesh_id=1)\n", + "mesh.dimension = [17, 17, 1]\n", + "mesh.lower_left = [-10.71, -10.71, -10000.]\n", + "mesh.width = [1.26, 1.26, 20000.]\n", + "\n", + "# Initialize an 20-energy-group and 6-delayed-group MGXS Library\n", + "mgxs_lib = openmc.mgxs.Library(geometry)\n", + "mgxs_lib.energy_groups = energy_groups\n", + "mgxs_lib.num_delayed_groups = 6\n", + "\n", + "# Specify multi-group cross section types to compute\n", + "mgxs_lib.mgxs_types = ['total', 'transport', 'nu-scatter matrix', 'kappa-fission', 'inverse-velocity', 'chi-prompt',\n", + " 'prompt-nu-fission', 'chi-delayed', 'delayed-nu-fission', 'beta']\n", + "\n", + "# Specify a \"mesh\" domain type for the cross section tally filters\n", + "mgxs_lib.domain_type = 'mesh'\n", + "\n", + "# Specify the mesh domain over which to compute multi-group cross sections\n", + "mgxs_lib.domains = [mesh]\n", + "\n", + "# Construct all tallies needed for the multi-group cross section library\n", + "mgxs_lib.build_library()\n", + "\n", + "# Create a \"tallies.xml\" file for the MGXS Library\n", + "tallies = openmc.Tallies()\n", + "mgxs_lib.add_to_tallies_file(tallies, merge=True)\n", + "\n", + "# Instantiate a current tally\n", + "mesh_filter = openmc.MeshSurfaceFilter(mesh)\n", + "current_tally = openmc.Tally(name='current tally')\n", + "current_tally.scores = ['current']\n", + "current_tally.filters = [mesh_filter]\n", + "\n", + "# Add current tally to the tallies file\n", + "tallies.append(current_tally)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can run OpenMC to generate the cross sections." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# tie geometry, materials, settings, and tallies together into a model object\n", + "model = openmc.Model(geometry=geometry,\n", + " materials=materials,\n", + " settings=settings,\n", + " tallies=tallies)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=1.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=2.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=5.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=6.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=17.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=23.\n", + " warn(msg, IDWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-03 23:30:12\n", + " OpenMP Threads | 2\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n", + " Reading B10 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/B10.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.99225\n", + " 2/1 0.99354\n", + " 3/1 1.02644\n", + " 4/1 1.06300\n", + " 5/1 1.03396\n", + " 6/1 1.00753\n", + " 7/1 1.04194\n", + " 8/1 1.04023\n", + " 9/1 1.03320\n", + " 10/1 1.04267\n", + " 11/1 1.02172\n", + " 12/1 1.07125 1.04648 +/- 0.02477\n", + " 13/1 1.04987 1.04761 +/- 0.01434\n", + " 14/1 1.01403 1.03922 +/- 0.01317\n", + " 15/1 1.04432 1.04024 +/- 0.01025\n", + " 16/1 1.06785 1.04484 +/- 0.00955\n", + " 17/1 1.04639 1.04506 +/- 0.00807\n", + " 18/1 1.06538 1.04760 +/- 0.00744\n", + " 19/1 1.00283 1.04263 +/- 0.00823\n", + " 20/1 1.00930 1.03929 +/- 0.00808\n", + " 21/1 1.02698 1.03817 +/- 0.00740\n", + " 22/1 1.04975 1.03914 +/- 0.00682\n", + " 23/1 1.03265 1.03864 +/- 0.00629\n", + " 24/1 0.98957 1.03513 +/- 0.00680\n", + " 25/1 1.01457 1.03376 +/- 0.00648\n", + " 26/1 1.00560 1.03200 +/- 0.00631\n", + " 27/1 1.00933 1.03067 +/- 0.00608\n", + " 28/1 1.01275 1.02967 +/- 0.00581\n", + " 29/1 1.04347 1.03040 +/- 0.00555\n", + " 30/1 1.03126 1.03044 +/- 0.00526\n", + " 31/1 1.05165 1.03145 +/- 0.00511\n", + " 32/1 1.02594 1.03120 +/- 0.00488\n", + " 33/1 1.00047 1.02987 +/- 0.00485\n", + " 34/1 1.04045 1.03031 +/- 0.00466\n", + " 35/1 1.01414 1.02966 +/- 0.00452\n", + " 36/1 1.03056 1.02970 +/- 0.00434\n", + " 37/1 1.05870 1.03077 +/- 0.00431\n", + " 38/1 0.97655 1.02883 +/- 0.00458\n", + " 39/1 1.05223 1.02964 +/- 0.00450\n", + " 40/1 1.08089 1.03135 +/- 0.00467\n", + " 41/1 1.05155 1.03200 +/- 0.00456\n", + " 42/1 1.01221 1.03138 +/- 0.00446\n", + " 43/1 1.03906 1.03161 +/- 0.00433\n", + " 44/1 1.00455 1.03082 +/- 0.00427\n", + " 45/1 1.00183 1.02999 +/- 0.00423\n", + " 46/1 1.05348 1.03064 +/- 0.00416\n", + " 47/1 1.06321 1.03152 +/- 0.00414\n", + " 48/1 1.05862 1.03224 +/- 0.00410\n", + " 49/1 1.04610 1.03259 +/- 0.00401\n", + " 50/1 1.02808 1.03248 +/- 0.00391\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 7.2704e-01 seconds\n", + " Reading cross sections = 7.1697e-01 seconds\n", + " Total time in simulation = 6.0982e+01 seconds\n", + " Time in transport only = 6.0103e+01 seconds\n", + " Time in inactive batches = 4.4142e+00 seconds\n", + " Time in active batches = 5.6568e+01 seconds\n", + " Time synchronizing fission bank = 1.0706e-02 seconds\n", + " Sampling source sites = 9.8511e-03 seconds\n", + " SEND/RECV source sites = 8.2068e-04 seconds\n", + " Time accumulating tallies = 8.5453e-01 seconds\n", + " Time writing statepoints = 8.0261e-03 seconds\n", + " Total time for finalization = 2.0600e-06 seconds\n", + " Total time elapsed = 6.1735e+01 seconds\n", + " Calculation Rate (inactive) = 5663.53 particles/second\n", + " Calculation Rate (active) = 1767.79 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.03096 +/- 0.00285\n", + " k-effective (Track-length) = 1.03248 +/- 0.00391\n", + " k-effective (Absorption) = 1.02246 +/- 0.00327\n", + " Combined k-effective = 1.02749 +/- 0.00267\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "# Run OpenMC\n", + "statepoint_filename = model.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the last statepoint file\n", + "sp = openmc.StatePoint('statepoint.50.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint is now ready to be analyzed by the `Library`. We simply have to load the tallies from the statepoint into the `Library` and our `MGXS` objects will compute the cross sections for us under-the-hood." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize MGXS Library with OpenMC statepoint data\n", + "mgxs_lib.load_from_statepoint(sp)\n", + "\n", + "# Extrack the current tally separately\n", + "current_tally = sp.get_tally(name='current tally')\n", + "\n", + "# Close statepoint file now that we have the info we need\n", + "sp.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Tally Arithmetic to Compute the Delayed Neutron Precursor Concentrations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](tally-arithmetic.ipynb) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n", + "\n", + "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n", + "\n", + "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " mesh 1 delayedgroup nuclide \\\n", + " x y z \n", + "0 1 1 1 1 total \n", + "1 1 1 1 2 total \n", + "2 1 1 1 3 total \n", + "3 1 1 1 4 total \n", + "4 1 1 1 5 total \n", + "5 1 1 1 6 total \n", + "6 2 1 1 1 total \n", + "7 2 1 1 2 total \n", + "8 2 1 1 3 total \n", + "9 2 1 1 4 total \n", + "\n", + " score mean std. dev. \n", + " \n", + "0 (((delayed-nu-fission / nu-fission) * (delayed... 0.000101 2.537729e-05 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 0.001282 3.179682e-04 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 0.000813 2.001150e-04 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 0.000641 1.556332e-04 \n", + "4 (((delayed-nu-fission / nu-fission) * (delayed... 0.000023 5.409817e-06 \n", + "5 (((delayed-nu-fission / nu-fission) * (delayed... 0.000002 4.317338e-07 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 0.000105 2.667910e-05 \n", + "7 (((delayed-nu-fission / nu-fission) * (delayed... 0.001332 3.344146e-04 \n", + "8 (((delayed-nu-fission / nu-fission) * (delayed... 0.000845 2.105053e-04 \n", + "9 (((delayed-nu-fission / nu-fission) * (delayed... 0.000665 1.637544e-04 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set the time constants for the delayed precursors (in seconds^-1)\n", + "precursor_halflife = np.array([55.6, 24.5, 16.3, 2.37, 0.424, 0.195])\n", + "precursor_lambda = math.log(2.0) / precursor_halflife\n", + "\n", + "beta = mgxs_lib.get_mgxs(mesh, 'beta')\n", + "\n", + "# Create a tally object with only the delayed group filter for the time constants\n", + "beta_filters = [f for f in beta.xs_tally.filters if type(f) is not openmc.DelayedGroupFilter]\n", + "lambda_tally = beta.xs_tally.summation(nuclides=beta.xs_tally.nuclides)\n", + "for f in beta_filters:\n", + " lambda_tally = lambda_tally.summation(filter_type=type(f), remove_filter=True) * 0. + 1.\n", + "\n", + "# Set the mean of the lambda tally and reshape to account for nuclides and scores\n", + "lambda_tally._mean = precursor_lambda\n", + "lambda_tally._mean.shape = lambda_tally.std_dev.shape\n", + "\n", + "# Set a total nuclide and lambda score\n", + "lambda_tally.nuclides = [openmc.Nuclide(name='total')]\n", + "lambda_tally.scores = ['lambda']\n", + "\n", + "delayed_nu_fission = mgxs_lib.get_mgxs(mesh, 'delayed-nu-fission')\n", + "\n", + "# Use tally arithmetic to compute the precursor concentrations\n", + "precursor_conc = beta.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) * \\\n", + " delayed_nu_fission.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) / lambda_tally\n", + " \n", + "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n", + "precursor_conc.get_pandas_dataframe().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful feature of the Python API is the ability to extract the surface currents for the interfaces and surfaces of a mesh. We can inspect the currents for the mesh by getting the pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " mesh 1 nuclide score mean std. dev.\n", + " x y z surf \n", + "0 1 1 1 x-min out total current 0.00000 0.000000\n", + "1 1 1 1 x-min in total current 0.00000 0.000000\n", + "2 1 1 1 x-max out total current 0.03118 0.000831\n", + "3 1 1 1 x-max in total current 0.03188 0.000783\n", + "4 1 1 1 y-min out total current 0.00000 0.000000\n", + "5 1 1 1 y-min in total current 0.00000 0.000000\n", + "6 1 1 1 y-max out total current 0.03172 0.000610\n", + "7 1 1 1 y-max in total current 0.03070 0.000660\n", + "8 1 1 1 z-min out total current 0.00000 0.000000\n", + "9 1 1 1 z-min in total current 0.00000 0.000000" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_tally.get_pandas_dataframe().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cross Section Visualizations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to inspecting the data in the tallies by getting the pandas dataframe, we can also plot the tally data on the domain mesh. Below is the delayed neutron fraction tallied in each mesh cell for each delayed group." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Beta - delayed group 6')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Extract the energy-condensed delayed neutron fraction tally\n", + "beta_by_group = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True)\n", + "beta_by_group.mean.shape = (17, 17, 6)\n", + "beta_by_group.mean[beta_by_group.mean == 0] = np.nan\n", + "\n", + "# Plot the betas\n", + "plt.figure(figsize=(18,9))\n", + "fig = plt.subplot(231)\n", + "plt.imshow(beta_by_group.mean[:,:,0], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 1')\n", + "\n", + "fig = plt.subplot(232)\n", + "plt.imshow(beta_by_group.mean[:,:,1], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 2')\n", + "\n", + "fig = plt.subplot(233)\n", + "plt.imshow(beta_by_group.mean[:,:,2], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 3')\n", + "\n", + "fig = plt.subplot(234)\n", + "plt.imshow(beta_by_group.mean[:,:,3], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 4')\n", + "\n", + "fig = plt.subplot(235)\n", + "plt.imshow(beta_by_group.mean[:,:,4], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 5')\n", + "\n", + "fig = plt.subplot(236)\n", + "plt.imshow(beta_by_group.mean[:,:,5], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 6')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/stress-test/multi-group-xs/mgxs-part-i.ipynb b/stress-test/multi-group-xs/mgxs-part-i.ipynb new file mode 100644 index 0000000..1257424 --- /dev/null +++ b/stress-test/multi-group-xs/mgxs-part-i.ipynb @@ -0,0 +1,1204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multigroup Cross Section Generation Part I: Introduction\n", + "This IPython Notebook introduces the use of the `openmc.mgxs` module to calculate multi-group cross sections for an infinite homogeneous medium. In particular, this Notebook introduces the the following features:\n", + "\n", + "* **General equations** for scalar-flux averaged multi-group cross sections\n", + "* Creation of multi-group cross sections for an **infinite homogeneous medium**\n", + "* Use of **tally arithmetic** to manipulate multi-group cross sections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to Multi-Group Cross Sections (MGXS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many Monte Carlo particle transport codes, including OpenMC, use continuous-energy nuclear cross section data. However, most deterministic neutron transport codes use *multi-group cross sections* defined over discretized energy bins or *energy groups*. An example of U-235's continuous-energy fission cross section along with a 16-group cross section computed for a light water reactor spectrum is displayed below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": { + "image/png": { + "width": 350 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='images/mgxs.png', width=350)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A variety of tools employing different methodologies have been developed over the years to compute multi-group cross sections for certain applications, including NJOY (LANL), MC$^2$-3 (ANL), and Serpent (VTT). The `openmc.mgxs` Python module is designed to leverage OpenMC's tally system to calculate multi-group cross sections with arbitrary energy discretizations for fine-mesh heterogeneous deterministic neutron transport applications.\n", + "\n", + "Before proceeding to illustrate how one may use the `openmc.mgxs` module, it is worthwhile to define the general equations used to calculate multi-group cross sections. This is only intended as a brief overview of the methodology used by `openmc.mgxs` - we refer the interested reader to the large body of literature on the subject for a more comprehensive understanding of this complex topic." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introductory Notation\n", + "The continuous real-valued microscopic cross section may be denoted $\\sigma_{n,x}(\\mathbf{r}, E)$ for position vector $\\mathbf{r}$, energy $E$, nuclide $n$ and interaction type $x$. Similarly, the scalar neutron flux may be denoted by $\\Phi(\\mathbf{r},E)$ for position $\\mathbf{r}$ and energy $E$. **Note**: Although nuclear cross sections are dependent on the temperature $T$ of the interacting medium, the temperature variable is neglected here for brevity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatial and Energy Discretization\n", + "The energy domain for critical systems such as thermal reactors spans more than 10 orders of magnitude of neutron energies from 10$^{-5}$ - 10$^7$ eV. The multi-group approximation discretization divides this energy range into one or more energy groups. In particular, for $G$ total groups, we denote an energy group index $g$ such that $g \\in \\{1, 2, ..., G\\}$. The energy group indices are defined such that the smaller group the higher the energy, and vice versa. The integration over neutron energies across a discrete energy group is commonly referred to as **energy condensation**.\n", + "\n", + "Multi-group cross sections are computed for discretized spatial zones in the geometry of interest. The spatial zones may be defined on a structured and regular fuel assembly or pin cell mesh, an arbitrary unstructured mesh or the constructive solid geometry used by OpenMC. For a geometry with $K$ distinct spatial zones, we designate each spatial zone an index $k$ such that $k \\in \\{1, 2, ..., K\\}$. The volume of each spatial zone is denoted by $V_{k}$. The integration over discrete spatial zones is commonly referred to as **spatial homogenization**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### General Scalar-Flux Weighted MGXS\n", + "The multi-group cross sections computed by `openmc.mgxs` are defined as a *scalar flux-weighted average* of the microscopic cross sections across each discrete energy group. This formulation is employed in order to preserve the reaction rates within each energy group and spatial zone. In particular, spatial homogenization and energy condensation are used to compute the general multi-group cross section $\\sigma_{n,x,k,g}$ as follows:\n", + "\n", + "$$\\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n", + "\n", + "This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for most multi-group cross sections, including total, absorption, and fission reaction types. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](../usersguide/tallies.rst#filters) on the energy range and spatial zone (material, cell or universe) define the bounds of integration for both numerator and denominator." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-Group Scattering Matrices\n", + "The general multi-group cross section $\\sigma_{n,x,k,g}$ is a vector of $G$ values for each energy group $g$. The equation presented above only discretizes the energy of the incoming neutron and neglects the outgoing energy of the neutron (if any). Hence, this formulation must be extended to account for the outgoing energy of neutrons in the discretized scattering matrix cross section used by deterministic neutron transport codes. \n", + "\n", + "We denote the incoming and outgoing neutron energy groups as $g$ and $g'$ for the microscopic scattering matrix cross section $\\sigma_{n,s}(\\mathbf{r},E)$. As before, spatial homogenization and energy condensation are used to find the multi-group scattering matrix cross section $\\sigma_{n,s,k,g \\to g'}$ as follows:\n", + "\n", + "$$\\sigma_{n,s,k,g\\rightarrow g'} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,s}(\\mathbf{r},E'\\rightarrow E'')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n", + "\n", + "This scalar flux-weighted multi-group microscopic scattering matrix is computed using OpenMC tallies with both energy in and energy out filters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-Group Fission Spectrum\n", + "The energy spectrum of neutrons emitted from fission is denoted by $\\chi_{n}(\\mathbf{r},E' \\rightarrow E'')$ for incoming and outgoing energies $E'$ and $E''$, respectively. Unlike the multi-group cross sections $\\sigma_{n,x,k,g}$ considered up to this point, the fission spectrum is a probability distribution and must sum to unity. The outgoing energy is typically much less dependent on the incoming energy for fission than for scattering interactions. As a result, it is common practice to integrate over the incoming neutron energy when computing the multi-group fission spectrum. The fission spectrum may be simplified as $\\chi_{n}(\\mathbf{r},E)$ with outgoing energy $E$.\n", + "\n", + "Unlike the multi-group cross sections defined up to this point, the multi-group fission spectrum is weighted by the fission production rate rather than the scalar flux. This formulation is intended to preserve the total fission production rate in the multi-group deterministic calculation. In order to mathematically define the multi-group fission spectrum, we denote the microscopic fission cross section as $\\sigma_{n,f}(\\mathbf{r},E)$ and the average number of neutrons emitted from fission interactions with nuclide $n$ as $\\nu_{n}(\\mathbf{r},E)$. The multi-group fission spectrum $\\chi_{n,k,g}$ is then the probability of fission neutrons emitted into energy group $g$. \n", + "\n", + "Similar to before, spatial homogenization and energy condensation are used to find the multi-group fission spectrum $\\chi_{n,k,g}$ as follows:\n", + "\n", + "$$\\chi_{n,k,g'} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\chi_{n}(\\mathbf{r},E'\\rightarrow E'')\\nu_{n}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\nu_{n}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}$$\n", + "\n", + "The fission production-weighted multi-group fission spectrum is computed using OpenMC tallies with both energy in and energy out filters.\n", + "\n", + "This concludes our brief overview on the methodology to compute multi-group cross sections. The following sections detail more concretely how users may employ the `openmc.mgxs` module to power simulation workflows requiring multi-group cross sections for downstream deterministic calculations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import openmc\n", + "import openmc.mgxs as mgxs" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# create a model object to tie geometry, materials, settings, and tallies together\n", + "model = openmc.Model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We being by creating a material for the homogeneous medium." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a Material and register the Nuclides\n", + "inf_medium = openmc.Material(name='moderator')\n", + "inf_medium.set_density('g/cc', 5.)\n", + "inf_medium.add_nuclide('H1', 0.028999667)\n", + "inf_medium.add_nuclide('O16', 0.01450188)\n", + "inf_medium.add_nuclide('U235', 0.000114142)\n", + "inf_medium.add_nuclide('U238', 0.006886019)\n", + "inf_medium.add_nuclide('Zr90', 0.002116053)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our material, we can now create a `Materials` object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a Materials collection and export to XML\n", + "model.materials = openmc.Materials([inf_medium])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. This problem will be a simple square cell with reflective boundary conditions to simulate an infinite homogeneous medium. The first step is to create the outer bounding surfaces of the problem." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate boundary Planes\n", + "min_x = openmc.XPlane(boundary_type='reflective', x0=-0.63)\n", + "max_x = openmc.XPlane(boundary_type='reflective', x0=0.63)\n", + "min_y = openmc.YPlane(boundary_type='reflective', y0=-0.63)\n", + "max_y = openmc.YPlane(boundary_type='reflective', y0=0.63)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now create a cell that is defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a Cell\n", + "cell = openmc.Cell(cell_id=1, name='cell')\n", + "\n", + "# Register bounding Surfaces with the Cell\n", + "cell.region = +min_x & -max_x & +min_y & -max_y\n", + "\n", + "# Fill the Cell with the Material\n", + "cell.fill = inf_medium" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenMC requires that there is a \"root\" universe. Let us create a root universe and add our square cell to it." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Create root universe\n", + "root_universe = openmc.Universe(name='root universe', cells=[cell])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry that is assigned a root universe and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "model.geometry = openmc.Geometry(root_universe)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 2500\n", + "\n", + "# Instantiate a Settings object\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': True}\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings.source = openmc.Source(space=uniform_dist)\n", + "\n", + "model.settings = settings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to generate multi-group cross sections! First, let's define a 2-group structure using the built-in `EnergyGroups` class." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a 2-group EnergyGroups object\n", + "groups = mgxs.EnergyGroups()\n", + "groups.group_edges = np.array([0., 0.625, 20.0e6])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use the `EnergyGroups` object, along with our previously created materials and geometry, to instantiate some `MGXS` objects from the `openmc.mgxs` module. In particular, the following are subclasses of the generic and abstract `MGXS` class:\n", + "\n", + "* `TotalXS`\n", + "* `TransportXS`\n", + "* `AbsorptionXS`\n", + "* `CaptureXS`\n", + "* `FissionXS`\n", + "* `KappaFissionXS`\n", + "* `ScatterXS`\n", + "* `ScatterMatrixXS`\n", + "* `Chi`\n", + "* `ChiPrompt`\n", + "* `InverseVelocity`\n", + "* `PromptNuFissionXS`\n", + "\n", + "Of course, we are aware that the fission cross section (`FissionXS`) can sometimes be paired with the fission neutron multiplication to become $\\nu\\sigma_f$. This can be accomodated in to the `FissionXS` class by setting the `nu` parameter to `True` as shown below.\n", + "\n", + "Additionally, scattering reactions (like (n,2n)) can also be defined to take in to account the neutron multiplication to become $\\nu\\sigma_s$. This can be accomodated in the the transport (`TransportXS`), scattering (`ScatterXS`), and scattering-matrix (`ScatterMatrixXS`) cross sections types by setting the `nu` parameter to `True` as shown below.\n", + "\n", + "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group total, absorption and scattering cross sections with our 2-group structure." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a few different sections\n", + "total = mgxs.TotalXS(domain=cell, energy_groups=groups)\n", + "absorption = mgxs.AbsorptionXS(domain=cell, energy_groups=groups)\n", + "scattering = mgxs.ScatterXS(domain=cell, energy_groups=groups)\n", + "\n", + "# Note that if we wanted to incorporate neutron multiplication in the\n", + "# scattering cross section we would write the previous line as:\n", + "# scattering = mgxs.ScatterXS(domain=cell, energy_groups=groups, nu=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `Absorption` object as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([('flux',\n", + " Tally\n", + " \tID =\t1\n", + " \tName =\t\n", + " \tFilters =\tCellFilter, EnergyFilter\n", + " \tNuclides =\ttotal\n", + " \tScores =\t['flux']\n", + " \tEstimator =\ttracklength),\n", + " ('absorption',\n", + " Tally\n", + " \tID =\t2\n", + " \tName =\t\n", + " \tFilters =\tCellFilter, EnergyFilter\n", + " \tNuclides =\ttotal\n", + " \tScores =\t['absorption']\n", + " \tEstimator =\ttracklength)])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "absorption.tallies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Absorption` object includes tracklength tallies for the 'absorption' and 'flux' scores in the 2-group structure in cell 1. Now that each `MGXS` object contains the tallies that it needs, we must add these tallies to a `Tallies` object to generate the \"tallies.xml\" input file for OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate an empty Tallies object\n", + "tallies = openmc.Tallies()\n", + "\n", + "# Add total tallies to the tallies file\n", + "tallies += total.tallies.values()\n", + "\n", + "# Add absorption tallies to the tallies file\n", + "tallies += absorption.tallies.values()\n", + "\n", + "# Add scattering tallies to the tallies file\n", + "tallies += scattering.tallies.values()\n", + "\n", + "model.tallies = tallies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we a have a complete set of inputs, so we can go ahead and run our simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=3.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=4.\n", + " warn(msg, IDWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-04 12:30:35\n", + " OpenMP Threads | 2\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U238.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000 eV for H1\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.18505\n", + " 2/1 1.17297\n", + " 3/1 1.16184\n", + " 4/1 1.14929\n", + " 5/1 1.09928\n", + " 6/1 1.18675\n", + " 7/1 1.19772\n", + " 8/1 1.17470\n", + " 9/1 1.17208\n", + " 10/1 1.09993\n", + " 11/1 1.14342\n", + " 12/1 1.10127 1.12234 +/- 0.02107\n", + " 13/1 1.19914 1.14794 +/- 0.02834\n", + " 14/1 1.18411 1.15698 +/- 0.02199\n", + " 15/1 1.14556 1.15470 +/- 0.01718\n", + " 16/1 1.20337 1.16281 +/- 0.01621\n", + " 17/1 1.13853 1.15934 +/- 0.01413\n", + " 18/1 1.18208 1.16218 +/- 0.01256\n", + " 19/1 1.11842 1.15732 +/- 0.01210\n", + " 20/1 1.15248 1.15684 +/- 0.01083\n", + " 21/1 1.14903 1.15613 +/- 0.00982\n", + " 22/1 1.23456 1.16266 +/- 0.01110\n", + " 23/1 1.18876 1.16467 +/- 0.01040\n", + " 24/1 1.13591 1.16262 +/- 0.00985\n", + " 25/1 1.19559 1.16481 +/- 0.00943\n", + " 26/1 1.16947 1.16511 +/- 0.00882\n", + " 27/1 1.13198 1.16316 +/- 0.00851\n", + " 28/1 1.15329 1.16261 +/- 0.00805\n", + " 29/1 1.16538 1.16275 +/- 0.00761\n", + " 30/1 1.18229 1.16373 +/- 0.00729\n", + " 31/1 1.15060 1.16311 +/- 0.00696\n", + " 32/1 1.15460 1.16272 +/- 0.00665\n", + " 33/1 1.13875 1.16168 +/- 0.00644\n", + " 34/1 1.13479 1.16056 +/- 0.00626\n", + " 35/1 1.21125 1.16258 +/- 0.00634\n", + " 36/1 1.15914 1.16245 +/- 0.00609\n", + " 37/1 1.10457 1.16031 +/- 0.00624\n", + " 38/1 1.17215 1.16073 +/- 0.00603\n", + " 39/1 1.18462 1.16155 +/- 0.00588\n", + " 40/1 1.15361 1.16129 +/- 0.00568\n", + " 41/1 1.14983 1.16092 +/- 0.00551\n", + " 42/1 1.14087 1.16029 +/- 0.00537\n", + " 43/1 1.18725 1.16111 +/- 0.00527\n", + " 44/1 1.19094 1.16199 +/- 0.00519\n", + " 45/1 1.17371 1.16232 +/- 0.00505\n", + " 46/1 1.18552 1.16297 +/- 0.00495\n", + " 47/1 1.14194 1.16240 +/- 0.00485\n", + " 48/1 1.12045 1.16130 +/- 0.00484\n", + " 49/1 1.18476 1.16190 +/- 0.00476\n", + " 50/1 1.17063 1.16212 +/- 0.00464\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 7.5024e-01 seconds\n", + " Reading cross sections = 7.4182e-01 seconds\n", + " Total time in simulation = 1.9857e+01 seconds\n", + " Time in transport only = 1.9835e+01 seconds\n", + " Time in inactive batches = 2.4766e+00 seconds\n", + " Time in active batches = 1.7380e+01 seconds\n", + " Time synchronizing fission bank = 1.1568e-02 seconds\n", + " Sampling source sites = 1.0777e-02 seconds\n", + " SEND/RECV source sites = 7.5224e-04 seconds\n", + " Time accumulating tallies = 3.8921e-04 seconds\n", + " Time writing statepoints = 3.0584e-03 seconds\n", + " Total time for finalization = 1.9732e-04 seconds\n", + " Total time elapsed = 2.0619e+01 seconds\n", + " Calculation Rate (inactive) = 10094.3 particles/second\n", + " Calculation Rate (active) = 5753.6 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.16239 +/- 0.00461\n", + " k-effective (Track-length) = 1.16212 +/- 0.00464\n", + " k-effective (Absorption) = 1.15435 +/- 0.00325\n", + " Combined k-effective = 1.15666 +/- 0.00304\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "# Run OpenMC\n", + "statepoint_filename = model.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the last statepoint file\n", + "sp = openmc.StatePoint(statepoint_filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. By default, a `Summary` object is automatically linked when a `StatePoint` is loaded. This is necessary for the `openmc.mgxs` module to properly process the tally data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the `StatePoint` into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the tallies from the statepoint into each MGXS object\n", + "total.load_from_statepoint(sp)\n", + "absorption.load_from_statepoint(sp)\n", + "scattering.load_from_statepoint(sp)\n", + "# Close the statepoint file now that we're done getting info\n", + "sp.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voila! Our multi-group cross sections are now ready to rock 'n roll!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extracting and Storing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first inspect our total cross section by printing it to the screen." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\ttotal\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t1\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [0.625 - 20000000.0eV]:\t6.81e-01 +/- 2.69e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.40e+00 +/- 6.00e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "total.print_xs()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the `openmc.mgxs` module uses [tally arithmetic](../examples/tally-arithmetic.rst) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](https://pandas.pydata.org/) `DataFrame` of the multi-group cross section data." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell energy low [eV] energy high [eV] nuclide \\\n", + "0 1 0.00e+00 6.25e-01 total \n", + "1 1 6.25e-01 2.00e+07 total \n", + "\n", + " score mean std. dev. \n", + "0 (((total / flux) - (absorption / flux)) - (sca... 1.11e-15 1.14e-02 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... -3.00e-15 2.57e-03 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the difference between the total, absorption and scattering\n", + "difference = total.xs_tally - absorption.xs_tally - scattering.xs_tally\n", + "\n", + "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n", + "difference.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can use tally arithmetic to compute the ratio of `AbsorptionXS` and `ScatterXS` to the `TotalXS`." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy low [eV]energy high [eV]nuclidescoremeanstd. dev.
010.0006.250000e-01total((absorption / flux) / (total / flux))0.0761030.000658
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" + ], + "text/plain": [ + " cell energy low [eV] energy high [eV] nuclide \\\n", + "0 1 0.00e+00 6.25e-01 total \n", + "1 1 6.25e-01 2.00e+07 total \n", + "\n", + " score mean std. dev. \n", + "0 ((absorption / flux) / (total / flux)) 7.61e-02 6.58e-04 \n", + "1 ((absorption / flux) / (total / flux)) 1.94e-02 9.26e-05 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the absorption-to-total MGXS ratio\n", + "absorption_to_total = absorption.xs_tally / total.xs_tally\n", + "\n", + "# The absorption-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "absorption_to_total.get_pandas_dataframe()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy low [eV]energy high [eV]nuclidescoremeanstd. dev.
010.0006.250000e-01total((scatter / flux) / (total / flux))0.9238970.007833
110.6252.000000e+07total((scatter / flux) / (total / flux))0.9805590.003729
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" + ], + "text/plain": [ + " cell energy low [eV] energy high [eV] nuclide \\\n", + "0 1 0.00e+00 6.25e-01 total \n", + "1 1 6.25e-01 2.00e+07 total \n", + "\n", + " score mean std. dev. \n", + "0 ((scatter / flux) / (total / flux)) 9.24e-01 7.83e-03 \n", + "1 ((scatter / flux) / (total / flux)) 9.81e-01 3.73e-03 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the scattering-to-total MGXS ratio\n", + "scattering_to_total = scattering.xs_tally / total.xs_tally\n", + "\n", + "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "scattering_to_total.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we sum the derived scatter-to-total and absorption-to-total ratios to confirm that they sum to unity." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell energy low [eV] energy high [eV] nuclide \\\n", + "0 1 0.00e+00 6.25e-01 total \n", + "1 1 6.25e-01 2.00e+07 total \n", + "\n", + " score mean std. dev. \n", + "0 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 7.86e-03 \n", + "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 3.73e-03 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to ensure that the absorption- and scattering-to-total MGXS ratios sum to unity\n", + "sum_ratio = absorption_to_total + scattering_to_total\n", + "\n", + "# The sum ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "sum_ratio.get_pandas_dataframe()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/stress-test/multi-group-xs/mgxs-part-ii.ipynb b/stress-test/multi-group-xs/mgxs-part-ii.ipynb new file mode 100644 index 0000000..a32106c --- /dev/null +++ b/stress-test/multi-group-xs/mgxs-part-ii.ipynb @@ -0,0 +1,2742 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multigroup Cross Section Generation Part II: Advanced Features\n", + "This IPython Notebook illustrates the use of the `openmc.mgxs` module to calculate multi-group cross sections for a heterogeneous fuel pin cell geometry. In particular, this Notebook illustrates the following features:\n", + "\n", + "* Creation of multi-group cross sections on a **heterogeneous geometry**\n", + "* Calculation of cross sections on a **nuclide-by-nuclide basis**\n", + "* The use of **[tally precision triggers](../io_formats/settings.rst#trigger-element)** with multi-group cross sections\n", + "* Built-in features for **energy condensation** in downstream data processing\n", + "* The use of the **`openmc.data`** module to plot continuous-energy vs. multi-group cross sections\n", + "* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n", + "\n", + "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. You must install [OpenMOC](https://mit-crpg.github.io/OpenMOC/) on your system in order to run this Notebook in its entirety. In addition, this Notebook illustrates the use of [Pandas](https://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn-dark')\n", + "\n", + "import openmoc\n", + "\n", + "import openmc\n", + "import openmc.mgxs as mgxs\n", + "import openmc.data\n", + "from openmc.openmoc_compatible import get_openmoc_geometry\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# create a model object to tie together geometry, materials, settings, and tallies\n", + "model = openmc.Model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to define materials that will be used in the problem. We'll create three distinct materials for water, clad and fuel." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# 1.6% enriched fuel\n", + "fuel = openmc.Material(name='1.6% Fuel')\n", + "fuel.set_density('g/cm3', 10.31341)\n", + "fuel.add_nuclide('U235', 3.7503e-4)\n", + "fuel.add_nuclide('U238', 2.2625e-2)\n", + "fuel.add_nuclide('O16', 4.6007e-2)\n", + "\n", + "# borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", + "\n", + "# zircaloy\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide('Zr90', 7.2758e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our materials, we can now create a `Materials` object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a Materials collection\n", + "model.materials = openmc.Materials([fuel, water, zircaloy])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create cylinders for the fuel and clad\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n", + "\n", + "# Create box to surround the geometry\n", + "box = openmc.model.rectangular_prism(1.26, 1.26, boundary_type='reflective')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a fuel pin\n", + "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", + "\n", + "# Create fuel Cell\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", + "fuel_cell.fill = fuel\n", + "fuel_cell.region = -fuel_outer_radius\n", + "pin_cell_universe.add_cell(fuel_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='1.6% Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "pin_cell_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius & box\n", + "pin_cell_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry with the pin cell universe and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "model.geometry = openmc.Geometry(pin_cell_universe)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 10,000 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 10000\n", + "\n", + "# Instantiate a Settings object\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': True}\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings.source = openmc.Source(space=uniform_dist)\n", + "\n", + "# Activate tally precision triggers\n", + "settings.trigger_active = True\n", + "settings.trigger_max_batches = settings.batches * 4\n", + "\n", + "model.settings = settings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are finally ready to make use of the `openmc.mgxs` module to generate multi-group cross sections! First, let's define \"coarse\" 2-group and \"fine\" 8-group structures using the built-in `EnergyGroups` class." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a \"coarse\" 2-group EnergyGroups object\n", + "coarse_groups = mgxs.EnergyGroups([0., 0.625, 20.0e6])\n", + "\n", + "# Instantiate a \"fine\" 8-group EnergyGroups object\n", + "fine_groups = mgxs.EnergyGroups([0., 0.058, 0.14, 0.28,\n", + " 0.625, 4.0, 5.53e3, 821.0e3, 20.0e6])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will instantiate a variety of `MGXS` objects needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we define transport, fission, nu-fission, nu-scatter and chi cross sections for each of the three cells in the fuel pin with the 8-group structure as our energy groups." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract all Cells filled by Materials\n", + "openmc_cells = model.geometry.get_all_material_cells().values()\n", + "\n", + "# Create dictionary to store multi-group cross sections for all cells\n", + "xs_library = {}\n", + "\n", + "# Instantiate 8-group cross sections for each cell\n", + "for cell in openmc_cells:\n", + " xs_library[cell.id] = {}\n", + " xs_library[cell.id]['transport'] = mgxs.TransportXS(energy_groups=fine_groups)\n", + " xs_library[cell.id]['fission'] = mgxs.FissionXS(energy_groups=fine_groups)\n", + " xs_library[cell.id]['nu-fission'] = mgxs.FissionXS(energy_groups=fine_groups, nu=True)\n", + " xs_library[cell.id]['nu-scatter'] = mgxs.ScatterMatrixXS(energy_groups=fine_groups, nu=True)\n", + " xs_library[cell.id]['chi'] = mgxs.Chi(energy_groups=fine_groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we showcase the use of OpenMC's [tally precision trigger](../io_formats/settings.rst#trigger-element) feature in conjunction with the `openmc.mgxs` module. In particular, we will assign a tally trigger of 1E-2 on the standard deviation for each of the tallies used to compute multi-group cross sections." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a tally trigger for +/- 0.01 on each tally used to compute the multi-group cross sections\n", + "tally_trigger = openmc.Trigger('std_dev', 1e-2)\n", + "\n", + "# Add the tally trigger to each of the multi-group cross section tallies\n", + "for cell in openmc_cells:\n", + " for mgxs_type in xs_library[cell.id]:\n", + " xs_library[cell.id][mgxs_type].tally_trigger = tally_trigger" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we must loop over all cells to set the cross section domains to the various cells - fuel, clad and moderator - included in the geometry. In addition, we will set each cross section to tally cross sections on a per-nuclide basis through the use of the `MGXS` class' boolean `by_nuclide` instance attribute. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate an empty Tallies object\n", + "tallies = openmc.Tallies()\n", + "\n", + "# Iterate over all cells and cross section types\n", + "for cell in openmc_cells:\n", + " for rxn_type in xs_library[cell.id]:\n", + "\n", + " # Set the cross sections domain to the cell\n", + " xs_library[cell.id][rxn_type].domain = cell\n", + " \n", + " # Tally cross sections by nuclide\n", + " xs_library[cell.id][rxn_type].by_nuclide = True\n", + " \n", + " # Add OpenMC tallies to the tallies file for XML generation\n", + " for tally in xs_library[cell.id][rxn_type].tallies.values():\n", + " tallies.append(tally, merge=True)\n", + " \n", + "model.tallies = tallies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we a have a complete set of inputs, so we can go ahead and run our simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=53.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=21.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=2.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=3.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=4.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=41.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=15.\n", + " warn(msg, IDWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-05 23:55:59\n", + " MPI Processes | 1\n", + " OpenMP Threads | 2\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.23300\n", + " 2/1 1.22712\n", + " 3/1 1.23403\n", + " 4/1 1.23023\n", + " 5/1 1.20250\n", + " 6/1 1.20565\n", + " 7/1 1.23034\n", + " 8/1 1.21931\n", + " 9/1 1.23826\n", + " 10/1 1.25090\n", + " 11/1 1.21111\n", + " 12/1 1.20786 1.20948 +/- 0.00162\n", + " 13/1 1.23784 1.21894 +/- 0.00950\n", + " 14/1 1.22692 1.22093 +/- 0.00701\n", + " 15/1 1.20622 1.21799 +/- 0.00617\n", + " 16/1 1.20024 1.21503 +/- 0.00584\n", + " 17/1 1.20624 1.21378 +/- 0.00510\n", + " 18/1 1.20491 1.21267 +/- 0.00455\n", + " 19/1 1.20434 1.21174 +/- 0.00412\n", + " 20/1 1.22590 1.21316 +/- 0.00395\n", + " 21/1 1.20102 1.21206 +/- 0.00374\n", + " 22/1 1.23117 1.21365 +/- 0.00376\n", + " 23/1 1.22446 1.21448 +/- 0.00356\n", + " 24/1 1.22469 1.21521 +/- 0.00338\n", + " 25/1 1.23859 1.21677 +/- 0.00351\n", + " 26/1 1.25129 1.21893 +/- 0.00393\n", + " 27/1 1.22524 1.21930 +/- 0.00371\n", + " 28/1 1.22082 1.21938 +/- 0.00350\n", + " 29/1 1.21838 1.21933 +/- 0.00331\n", + " 30/1 1.24252 1.22049 +/- 0.00335\n", + " 31/1 1.23912 1.22138 +/- 0.00330\n", + " 32/1 1.22704 1.22163 +/- 0.00316\n", + " 33/1 1.22330 1.22171 +/- 0.00302\n", + " 34/1 1.20966 1.22120 +/- 0.00294\n", + " 35/1 1.20998 1.22076 +/- 0.00285\n", + " 36/1 1.21069 1.22037 +/- 0.00277\n", + " 37/1 1.20250 1.21971 +/- 0.00274\n", + " 38/1 1.20573 1.21921 +/- 0.00269\n", + " 39/1 1.23647 1.21980 +/- 0.00266\n", + " 40/1 1.22719 1.22005 +/- 0.00258\n", + " 41/1 1.23410 1.22050 +/- 0.00254\n", + " 42/1 1.23220 1.22087 +/- 0.00249\n", + " 43/1 1.23399 1.22127 +/- 0.00244\n", + " 44/1 1.23143 1.22156 +/- 0.00239\n", + " 45/1 1.23144 1.22185 +/- 0.00234\n", + " 46/1 1.22653 1.22198 +/- 0.00227\n", + " 47/1 1.21239 1.22172 +/- 0.00223\n", + " 48/1 1.22858 1.22190 +/- 0.00218\n", + " 49/1 1.20952 1.22158 +/- 0.00214\n", + " 50/1 1.20497 1.22117 +/- 0.00213\n", + " Triggers unsatisfied, max unc./thresh. is 1.2584438813928658 for flux in tally\n", + " 53\n", + " The estimated number of batches is 74\n", + " Creating state point statepoint.050.h5...\n", + " 51/1 1.22506 1.22126 +/- 0.00208\n", + " Triggers unsatisfied, max unc./thresh. is 1.2360657756693463 for flux in tally\n", + " 53\n", + " The estimated number of batches is 73\n", + " 52/1 1.22892 1.22144 +/- 0.00204\n", + " Triggers unsatisfied, max unc./thresh. is 1.206696013966578 for flux in tally\n", + " 53\n", + " The estimated number of batches is 72\n", + " 53/1 1.20425 1.22104 +/- 0.00203\n", + " Triggers unsatisfied, max unc./thresh. is 1.1794033562761703 for flux in tally\n", + " 53\n", + " The estimated number of batches is 70\n", + " 54/1 1.18761 1.22028 +/- 0.00212\n", + " Triggers unsatisfied, max unc./thresh. is 1.1745090363258377 for flux in tally\n", + " 53\n", + " The estimated number of batches is 71\n", + " 55/1 1.21624 1.22019 +/- 0.00208\n", + " Triggers unsatisfied, max unc./thresh. is 1.1488291964971802 for flux in tally\n", + " 53\n", + " The estimated number of batches is 70\n", + " 56/1 1.22056 1.22020 +/- 0.00203\n", + " Triggers unsatisfied, max unc./thresh. is 1.1253755542228503 for flux in tally\n", + " 53\n", + " The estimated number of batches is 69\n", + " 57/1 1.22213 1.22024 +/- 0.00199\n", + " Triggers unsatisfied, max unc./thresh. is 1.1013090664425782 for flux in tally\n", + " 53\n", + " The estimated number of batches is 68\n", + " 58/1 1.23192 1.22049 +/- 0.00196\n", + " Triggers unsatisfied, max unc./thresh. is 1.0814142069475086 for flux in tally\n", + " 53\n", + " The estimated number of batches is 67\n", + " 59/1 1.23142 1.22071 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 1.0688144272705478 for flux in tally\n", + " 53\n", + " The estimated number of batches is 66\n", + " 60/1 1.23744 1.22104 +/- 0.00192\n", + " Triggers unsatisfied, max unc./thresh. is 1.0845694915740336 for flux in tally\n", + " 53\n", + " The estimated number of batches is 69\n", + " 61/1 1.24681 1.22155 +/- 0.00195\n", + " Triggers unsatisfied, max unc./thresh. is 1.0650160561418593 for flux in tally\n", + " 53\n", + " The estimated number of batches is 68\n", + " 62/1 1.23159 1.22174 +/- 0.00192\n", + " Triggers unsatisfied, max unc./thresh. is 1.0628835462503399 for flux in tally\n", + " 53\n", + " The estimated number of batches is 69\n", + " 63/1 1.21588 1.22163 +/- 0.00189\n", + " Triggers unsatisfied, max unc./thresh. is 1.0440750211689802 for flux in tally\n", + " 53\n", + " The estimated number of batches is 68\n", + " 64/1 1.21867 1.22158 +/- 0.00186\n", + " Triggers unsatisfied, max unc./thresh. is 1.0258844139280563 for flux in tally\n", + " 53\n", + " The estimated number of batches is 67\n", + " 65/1 1.22410 1.22162 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 1.038412688234605 for flux in tally\n", + " 53\n", + " The estimated number of batches is 70\n", + " 66/1 1.20786 1.22138 +/- 0.00181\n", + " Triggers unsatisfied, max unc./thresh. is 1.0213633609643367 for flux in tally\n", + " 53\n", + " The estimated number of batches is 69\n", + " 67/1 1.21064 1.22119 +/- 0.00178\n", + " Triggers unsatisfied, max unc./thresh. is 1.019182247265982 for flux in tally\n", + " 53\n", + " The estimated number of batches is 70\n", + " 68/1 1.23857 1.22149 +/- 0.00178\n", + " Triggers unsatisfied, max unc./thresh. is 1.0128609683027328 for flux in tally\n", + " 53\n", + " The estimated number of batches is 70\n", + " 69/1 1.21603 1.22139 +/- 0.00175\n", + " Triggers unsatisfied, max unc./thresh. is 1.0113107304662599 for flux in tally\n", + " 53\n", + " The estimated number of batches is 71\n", + " 70/1 1.19730 1.22099 +/- 0.00177\n", + " Triggers unsatisfied, max unc./thresh. is 1.025023774532644 for flux in tally\n", + " 53\n", + " The estimated number of batches is 74\n", + " 71/1 1.22459 1.22105 +/- 0.00174\n", + " Triggers unsatisfied, max unc./thresh. is 1.0084011441757024 for flux in tally\n", + " 53\n", + " The estimated number of batches is 73\n", + " 72/1 1.24564 1.22145 +/- 0.00176\n", + " Triggers unsatisfied, max unc./thresh. is 1.0083223109528934 for flux in tally\n", + " 53\n", + " The estimated number of batches is 74\n", + " 73/1 1.21948 1.22142 +/- 0.00173\n", + " Triggers unsatisfied, max unc./thresh. is 1.0051824390021815 for flux in tally\n", + " 53\n", + " The estimated number of batches is 74\n", + " 74/1 1.20923 1.22123 +/- 0.00171\n", + " Triggers satisfied for batch 74\n", + " Creating state point statepoint.074.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 1.0770e-01 seconds\n", + " Reading cross sections = 1.0300e-01 seconds\n", + " Total time in simulation = 2.9592e+01 seconds\n", + " Time in transport only = 2.9528e+01 seconds\n", + " Time in inactive batches = 1.8603e+00 seconds\n", + " Time in active batches = 2.7732e+01 seconds\n", + " Time synchronizing fission bank = 4.2199e-02 seconds\n", + " Sampling source sites = 2.5148e-02 seconds\n", + " SEND/RECV source sites = 1.6948e-02 seconds\n", + " Time accumulating tallies = 1.1797e-03 seconds\n", + " Time writing statepoints = 8.0299e-03 seconds\n", + " Total time for finalization = 1.1328e-03 seconds\n", + " Total time elapsed = 2.9709e+01 seconds\n", + " Calculation Rate (inactive) = 53753.6 particles/second\n", + " Calculation Rate (active) = 23078.2 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.22070 +/- 0.00153\n", + " k-effective (Track-length) = 1.22123 +/- 0.00171\n", + " k-effective (Absorption) = 1.22336 +/- 0.00157\n", + " Combined k-effective = 1.22204 +/- 0.00135\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "# Run OpenMC\n", + "sp_file = model.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the last statepoint file\n", + "sp = openmc.StatePoint(sp_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the `StatePoint` into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Iterate over all cells and cross section types\n", + "for cell in openmc_cells:\n", + " for rxn_type in xs_library[cell.id]:\n", + " xs_library[cell.id][rxn_type].load_from_statepoint(sp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it! Our multi-group cross sections are now ready for the big spotlight. This time we have cross sections in three distinct spatial zones - fuel, clad and moderator - on a per-nuclide basis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extracting and Storing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first inspect one of our cross sections by printing it to the screen as a microscopic cross section in units of barns." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t1\n", + "\tNuclide =\tU235\n", + "\tCross Sections [barns]:\n", + " Group 1 [821000.0 - 20000000.0eV]:\t3.30e+00 +/- 2.34e-01%\n", + " Group 2 [5530.0 - 821000.0 eV]:\t3.96e+00 +/- 1.53e-01%\n", + " Group 3 [4.0 - 5530.0 eV]:\t5.52e+01 +/- 2.15e-01%\n", + " Group 4 [0.625 - 4.0 eV]:\t8.83e+01 +/- 3.51e-01%\n", + " Group 5 [0.28 - 0.625 eV]:\t2.90e+02 +/- 5.28e-01%\n", + " Group 6 [0.14 - 0.28 eV]:\t4.49e+02 +/- 4.05e-01%\n", + " Group 7 [0.058 - 0.14 eV]:\t6.87e+02 +/- 3.36e-01%\n", + " Group 8 [0.0 - 0.058 eV]:\t1.44e+03 +/- 2.54e-01%\n", + "\n", + "\tNuclide =\tU238\n", + "\tCross Sections [barns]:\n", + " Group 1 [821000.0 - 20000000.0eV]:\t1.06e+00 +/- 2.64e-01%\n", + " Group 2 [5530.0 - 821000.0 eV]:\t1.21e-03 +/- 2.83e-01%\n", + " Group 3 [4.0 - 5530.0 eV]:\t5.51e-04 +/- 2.99e+00%\n", + " Group 4 [0.625 - 4.0 eV]:\t6.54e-06 +/- 3.28e-01%\n", + " Group 5 [0.28 - 0.625 eV]:\t1.07e-05 +/- 5.12e-01%\n", + " Group 6 [0.14 - 0.28 eV]:\t1.55e-05 +/- 4.08e-01%\n", + " Group 7 [0.058 - 0.14 eV]:\t2.30e-05 +/- 3.35e-01%\n", + " Group 8 [0.0 - 0.058 eV]:\t4.24e-05 +/- 2.53e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "nufission = xs_library[fuel_cell.id]['nu-fission']\n", + "nufission.print_xs(xs_type='micro', nuclides=['U235', 'U238'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our multi-group cross sections are capable of summing across all nuclides to provide us with macroscopic cross sections as well." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t1\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [821000.0 - 20000000.0eV]:\t2.52e-02 +/- 2.51e-01%\n", + " Group 2 [5530.0 - 821000.0 eV]:\t1.51e-03 +/- 1.50e-01%\n", + " Group 3 [4.0 - 5530.0 eV]:\t2.07e-02 +/- 2.14e-01%\n", + " Group 4 [0.625 - 4.0 eV]:\t3.31e-02 +/- 3.51e-01%\n", + " Group 5 [0.28 - 0.625 eV]:\t1.09e-01 +/- 5.28e-01%\n", + " Group 6 [0.14 - 0.28 eV]:\t1.69e-01 +/- 4.05e-01%\n", + " Group 7 [0.058 - 0.14 eV]:\t2.58e-01 +/- 3.36e-01%\n", + " Group 8 [0.0 - 0.058 eV]:\t5.40e-01 +/- 2.54e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "nufission = xs_library[fuel_cell.id]['nu-fission']\n", + "nufission.print_xs(xs_type='macro', nuclides='sum')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although a printed report is nice, it is not scalable or flexible. Let's extract the microscopic cross section data for the moderator as a [Pandas](https://pandas.pydata.org/) `DataFrame` ." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellgroup ingroup outnuclidemeanstd. dev.
126311H10.2339790.003921
127311O161.5669550.006599
124312H11.5892230.003224
125312O160.2863820.001478
122313H10.0114050.000244
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121314O160.0000000.000000
118315H10.0000050.000005
119315O160.0000000.000000
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" + ], + "text/plain": [ + " cell group in group out nuclide mean std. dev.\n", + "126 3 1 1 H1 0.233979 0.003921\n", + "127 3 1 1 O16 1.566955 0.006599\n", + "124 3 1 2 H1 1.589223 0.003224\n", + "125 3 1 2 O16 0.286382 0.001478\n", + "122 3 1 3 H1 0.011405 0.000244\n", + "123 3 1 3 O16 0.000000 0.000000\n", + "120 3 1 4 H1 0.000015 0.000009\n", + "121 3 1 4 O16 0.000000 0.000000\n", + "118 3 1 5 H1 0.000005 0.000005\n", + "119 3 1 5 O16 0.000000 0.000000" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", + "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we illustate how one can easily take multi-group cross sections and condense them down to a coarser energy group structure. The `MGXS` class includes a `get_condensed_xs(...)` method which takes an `EnergyGroups` parameter with a coarse(r) group structure and returns a new `MGXS` condensed to the coarse groups. We illustrate this process below using the 2-group structure created earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract the 8-group transport cross section for the fuel\n", + "fine_xs = xs_library[fuel_cell.id]['transport']\n", + "\n", + "# Condense to the 2-group structure\n", + "condensed_xs = fine_xs.get_condensed_xs(coarse_groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Group condensation is as simple as that! We now have a new coarse 2-group `TransportXS` in addition to our original 8-group `TransportXS`. Let's inspect the 2-group `TransportXS` by printing it to the screen and extracting a Pandas `DataFrame` as we have already learned how to do." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\ttransport\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t1\n", + "\tNuclide =\tU235\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [0.625 - 20000000.0eV]:\t7.77e-03 +/- 2.18e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.82e-01 +/- 1.85e-01%\n", + "\n", + "\tNuclide =\tU238\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [0.625 - 20000000.0eV]:\t2.17e-01 +/- 1.32e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t2.53e-01 +/- 1.91e-01%\n", + "\n", + "\tNuclide =\tO16\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [0.625 - 20000000.0eV]:\t1.45e-01 +/- 1.26e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.74e-01 +/- 2.02e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "condensed_xs.print_xs()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellgroup innuclidemeanstd. dev.
311U23520.7154410.045146
411U2389.5797570.012606
511O163.1559660.003977
012U235485.6564820.899766
112U23811.1919610.021372
212O163.7906990.007656
\n", + "
" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "3 1 1 U235 20.715441 0.045146\n", + "4 1 1 U238 9.579757 0.012606\n", + "5 1 1 O16 3.155966 0.003977\n", + "0 1 2 U235 485.656482 0.899766\n", + "1 1 2 U238 11.191961 0.021372\n", + "2 1 2 O16 3.790699 0.007656" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = condensed_xs.get_pandas_dataframe(xs_type='micro')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verification with OpenMOC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's verify our cross sections using OpenMOC. First, we construct an equivalent OpenMOC geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an OpenMOC Geometry from the OpenMC Geometry\n", + "openmoc_geometry = get_openmoc_geometry(sp.summary.geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we we can inject the multi-group cross sections into the equivalent fuel pin cell OpenMOC geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Get all OpenMOC cells in the gometry\n", + "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", + "\n", + "# Inject multi-group cross sections into OpenMOC Materials\n", + "for cell_id, cell in openmoc_cells.items():\n", + " \n", + " # Ignore the root cell\n", + " if cell.getName() == 'root cell':\n", + " continue\n", + " \n", + " # Get a reference to the Material filling this Cell\n", + " openmoc_material = cell.getFillMaterial()\n", + " \n", + " # Set the number of energy groups for the Material\n", + " openmoc_material.setNumEnergyGroups(fine_groups.num_groups)\n", + " \n", + " # Extract the appropriate cross section objects for this cell\n", + " transport = xs_library[cell_id]['transport']\n", + " nufission = xs_library[cell_id]['nu-fission']\n", + " nuscatter = xs_library[cell_id]['nu-scatter']\n", + " chi = xs_library[cell_id]['chi']\n", + " \n", + " # Inject NumPy arrays of cross section data into the Material\n", + " # NOTE: Sum across nuclides to get macro cross sections needed by OpenMOC\n", + " openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to run OpenMOC to verify our cross-sections from OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Initializing a default angular quadrature...\n", + "[ NORMAL ] Initializing 2D tracks...\n", + "[ NORMAL ] Initializing 2D tracks reflections...\n", + "[ NORMAL ] Initializing 2D tracks array...\n", + "[ NORMAL ] Ray tracing for 2D track segmentation...\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 0.09 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 10.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 19.94 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 29.87 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 39.80 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 49.72 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 59.65 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 69.58 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 79.50 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 89.43 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n", + "[ NORMAL ] Initializing FSR lookup vectors\n", + "[ NORMAL ] Total number of FSRs 3\n", + "[ NORMAL ] Initializing MOC eigenvalue solver...\n", + "[ NORMAL ] Initializing solver arrays...\n", + "[ NORMAL ] Centering segments around FSR centroid...\n", + "[ NORMAL ] Max boundary angular flux storage per domain = 0.42 MB\n", + "[ NORMAL ] Max scalar flux storage per domain = 0.00 MB\n", + "[ NORMAL ] Max source storage per domain = 0.00 MB\n", + "[ NORMAL ] Number of azimuthal angles = 128\n", + "[ NORMAL ] Azimuthal ray spacing = 0.100000\n", + "[ NORMAL ] Number of polar angles = 6\n", + "[ NORMAL ] Source type = Flat\n", + "[ NORMAL ] MOC transport undamped\n", + "[ NORMAL ] CMFD acceleration: OFF\n", + "[ NORMAL ] Using 1 threads\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0: k_eff = 0.423006 res = 1.792E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -57699 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 1: k_eff = 0.475905 res = 3.879E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 5289 D.R. = 2.1646\n", + "[ NORMAL ] Iteration 2: k_eff = 0.491392 res = 3.463E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1548 D.R. = 0.8928\n", + "[ NORMAL ] Iteration 3: k_eff = 0.487335 res = 3.478E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -405 D.R. = 0.1004\n", + "[ NORMAL ] Iteration 4: k_eff = 0.483788 res = 8.318E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -354 D.R. = 2.3913\n", + "[ NORMAL ] Iteration 5: k_eff = 0.477110 res = 1.437E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -667 D.R. = 1.7273\n", + "[ NORMAL ] Iteration 6: k_eff = 0.468745 res = 1.966E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -836 D.R. = 0.1368\n", + "[ NORMAL ] Iteration 7: k_eff = 0.460108 res = 5.656E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -863 D.R. = 28.7692\n", + "[ NORMAL ] Iteration 8: k_eff = 0.450367 res = 1.612E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... -974 D.R. = 2.8503\n", + "[ NORMAL ] Iteration 9: k_eff = 0.441143 res = 6.685E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -922 D.R. = 0.4146\n", + "[ NORMAL ] Iteration 10: k_eff = 0.431749 res = 3.902E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -939 D.R. = 0.5837\n", + "[ NORMAL ] Iteration 11: k_eff = 0.422685 res = 2.208E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -906 D.R. = 0.5659\n", + "[ NORMAL ] Iteration 12: k_eff = 0.414237 res = 2.964E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -844 D.R. = 1.3425\n", + "[ NORMAL ] Iteration 13: k_eff = 0.406456 res = 2.238E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -778 D.R. = 0.7551\n", + "[ NORMAL ] Iteration 14: k_eff = 0.399125 res = 2.117E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -733 D.R. = 0.9459\n", + "[ NORMAL ] Iteration 15: k_eff = 0.392814 res = 3.569E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -631 D.R. = 1.6857\n", + "[ NORMAL ] Iteration 16: k_eff = 0.387174 res = 3.206E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -564 D.R. = 0.8983\n", + "[ NORMAL ] Iteration 17: k_eff = 0.382416 res = 5.263E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -475 D.R. = 1.6415\n", + "[ NORMAL ] Iteration 18: k_eff = 0.378489 res = 4.537E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -392 D.R. = 0.8621\n", + "[ NORMAL ] Iteration 19: k_eff = 0.375392 res = 3.085E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -309 D.R. = 0.6800\n", + "[ NORMAL ] Iteration 20: k_eff = 0.373239 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -215 D.R. = 0.3137\n", + "[ NORMAL ] Iteration 21: k_eff = 0.372107 res = 1.210E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -113 D.R. = 0.1250\n", + "[ NORMAL ] Iteration 22: k_eff = 0.371724 res = 4.235E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -38 D.R. = 3.5000\n", + "[ NORMAL ] Iteration 23: k_eff = 0.372330 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 60 D.R. = 4.5714\n", + "[ NORMAL ] Iteration 24: k_eff = 0.373804 res = 5.686E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 147 D.R. = 2.9375\n", + "[ NORMAL ] Iteration 25: k_eff = 0.376130 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 232 D.R. = 0.8511\n", + "[ NORMAL ] Iteration 26: k_eff = 0.379307 res = 3.509E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 317 D.R. = 0.7250\n", + "[ NORMAL ] Iteration 27: k_eff = 0.383324 res = 3.993E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 401 D.R. = 1.1379\n", + "[ NORMAL ] Iteration 28: k_eff = 0.388118 res = 8.469E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 479 D.R. = 0.2121\n", + "[ NORMAL ] Iteration 29: k_eff = 0.393671 res = 1.452E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 1.7143\n", + "[ NORMAL ] Iteration 30: k_eff = 0.399962 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 629 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 31: k_eff = 0.406957 res = 3.146E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 699 D.R. = 1.0833\n", + "[ NORMAL ] Iteration 32: k_eff = 0.414600 res = 6.896E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 764 D.R. = 2.1923\n", + "[ NORMAL ] Iteration 33: k_eff = 0.422881 res = 1.815E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 828 D.R. = 0.2632\n", + "[ NORMAL ] Iteration 34: k_eff = 0.431752 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 887 D.R. = 0.5333\n", + "[ NORMAL ] Iteration 35: k_eff = 0.441164 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 941 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 36: k_eff = 0.451113 res = 8.469E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 994 D.R. = 0.2917\n", + "[ NORMAL ] Iteration 37: k_eff = 0.461526 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1041 D.R. = 4.5714\n", + "[ NORMAL ] Iteration 38: k_eff = 0.472392 res = 7.501E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1086 D.R. = 1.9375\n", + "[ NORMAL ] Iteration 39: k_eff = 0.483657 res = 6.533E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1126 D.R. = 0.8710\n", + "[ NORMAL ] Iteration 40: k_eff = 0.495292 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1163 D.R. = 0.5926\n", + "[ NORMAL ] Iteration 41: k_eff = 0.507260 res = 3.388E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1196 D.R. = 0.8750\n", + "[ NORMAL ] Iteration 42: k_eff = 0.519528 res = 4.598E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1226 D.R. = 1.3571\n", + "[ NORMAL ] Iteration 43: k_eff = 0.532058 res = 4.598E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1253 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 44: k_eff = 0.544820 res = 8.469E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1276 D.R. = 0.1842\n", + "[ NORMAL ] Iteration 45: k_eff = 0.557781 res = 1.089E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1296 D.R. = 1.2857\n", + "[ NORMAL ] Iteration 46: k_eff = 0.570908 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1312 D.R. = 6.2222\n", + "[ NORMAL ] Iteration 47: k_eff = 0.584174 res = 7.864E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1326 D.R. = 1.1607\n", + "[ NORMAL ] Iteration 48: k_eff = 0.597549 res = 9.074E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1337 D.R. = 0.1154\n", + "[ NORMAL ] Iteration 49: k_eff = 0.611006 res = 2.662E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1345 D.R. = 2.9333\n", + "[ NORMAL ] Iteration 50: k_eff = 0.624518 res = 3.085E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1351 D.R. = 1.1591\n", + "[ NORMAL ] Iteration 51: k_eff = 0.638062 res = 7.501E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1354 D.R. = 2.4314\n", + "[ NORMAL ] Iteration 52: k_eff = 0.651612 res = 6.654E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1355 D.R. = 0.8871\n", + "[ NORMAL ] Iteration 53: k_eff = 0.665146 res = 2.057E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1353 D.R. = 0.3091\n", + "[ NORMAL ] Iteration 54: k_eff = 0.678645 res = 1.512E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1349 D.R. = 0.7353\n", + "[ NORMAL ] Iteration 55: k_eff = 0.692087 res = 1.210E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1344 D.R. = 0.0800\n", + "[ NORMAL ] Iteration 56: k_eff = 0.705453 res = 1.028E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1336 D.R. = 8.5000\n", + "[ NORMAL ] Iteration 57: k_eff = 0.718729 res = 9.074E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1327 D.R. = 0.8824\n", + "[ NORMAL ] Iteration 58: k_eff = 0.731896 res = 6.049E-10 delta-k (pcm) =\n", + "[ NORMAL ] ... 1316 D.R. = 0.0667\n", + "[ NORMAL ] Iteration 59: k_eff = 0.744940 res = 1.149E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1304 D.R. = 19.0000\n", + "[ NORMAL ] Iteration 60: k_eff = 0.757847 res = 2.057E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1290 D.R. = 1.7895\n", + "[ NORMAL ] Iteration 61: k_eff = 0.770604 res = 1.149E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1275 D.R. = 0.5588\n", + "[ NORMAL ] Iteration 62: k_eff = 0.783200 res = 3.146E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1259 D.R. = 2.7368\n", + "[ NORMAL ] Iteration 63: k_eff = 0.795624 res = 3.448E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1242 D.R. = 1.0962\n", + "[ NORMAL ] Iteration 64: k_eff = 0.807866 res = 2.964E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1224 D.R. = 0.8596\n", + "[ NORMAL ] Iteration 65: k_eff = 0.819919 res = 6.715E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1205 D.R. = 2.2653\n", + "[ NORMAL ] Iteration 66: k_eff = 0.831774 res = 2.238E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1185 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 67: k_eff = 0.843424 res = 1.633E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1165 D.R. = 0.7297\n", + "[ NORMAL ] Iteration 68: k_eff = 0.854864 res = 1.633E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1143 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 69: k_eff = 0.866087 res = 2.359E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1122 D.R. = 1.4444\n", + "[ NORMAL ] Iteration 70: k_eff = 0.877091 res = 2.299E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1100 D.R. = 0.9744\n", + "[ NORMAL ] Iteration 71: k_eff = 0.887870 res = 1.875E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1077 D.R. = 0.8158\n", + "[ NORMAL ] Iteration 72: k_eff = 0.898423 res = 2.420E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1055 D.R. = 0.1290\n", + "[ NORMAL ] Iteration 73: k_eff = 0.908747 res = 7.864E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1032 D.R. = 3.2500\n", + "[ NORMAL ] Iteration 74: k_eff = 0.918839 res = 1.633E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1009 D.R. = 2.0769\n", + "[ NORMAL ] Iteration 75: k_eff = 0.928699 res = 4.174E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 986 D.R. = 2.5556\n", + "[ NORMAL ] Iteration 76: k_eff = 0.938326 res = 4.779E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 962 D.R. = 1.1449\n", + "[ NORMAL ] Iteration 77: k_eff = 0.947719 res = 1.210E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 939 D.R. = 0.0253\n", + "[ NORMAL ] Iteration 78: k_eff = 0.956880 res = 6.049E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 916 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 79: k_eff = 0.965808 res = 1.512E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 892 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 80: k_eff = 0.974505 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 869 D.R. = 1.2800\n", + "[ NORMAL ] Iteration 81: k_eff = 0.982972 res = 1.452E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 846 D.R. = 0.7500\n", + "[ NORMAL ] Iteration 82: k_eff = 0.991211 res = 5.445E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 823 D.R. = 0.3750\n", + "[ NORMAL ] Iteration 83: k_eff = 0.999224 res = 3.630E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 801 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 84: k_eff = 1.007014 res = 5.445E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 778 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 85: k_eff = 1.014581 res = 4.537E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 756 D.R. = 8.3333\n", + "[ NORMAL ] Iteration 86: k_eff = 1.021932 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 735 D.R. = 1.2800\n", + "[ NORMAL ] Iteration 87: k_eff = 1.029067 res = 6.049E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 713 D.R. = 1.0417\n", + "[ NORMAL ] Iteration 88: k_eff = 1.035990 res = 7.078E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 692 D.R. = 1.1700\n", + "[ NORMAL ] Iteration 89: k_eff = 1.042705 res = 2.662E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 671 D.R. = 0.3761\n", + "[ NORMAL ] Iteration 90: k_eff = 1.049215 res = 3.569E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 650 D.R. = 1.3409\n", + "[ NORMAL ] Iteration 91: k_eff = 1.055524 res = 1.149E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 630 D.R. = 0.3220\n", + "[ NORMAL ] Iteration 92: k_eff = 1.061635 res = 3.327E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 611 D.R. = 2.8947\n", + "[ NORMAL ] Iteration 93: k_eff = 1.067553 res = 1.149E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 591 D.R. = 0.3455\n", + "[ NORMAL ] Iteration 94: k_eff = 1.073282 res = 1.815E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 572 D.R. = 0.1579\n", + "[ NORMAL ] Iteration 95: k_eff = 1.078825 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 554 D.R. = 16.0000\n", + "[ NORMAL ] Iteration 96: k_eff = 1.084186 res = 4.235E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 536 D.R. = 0.1458\n", + "[ NORMAL ] Iteration 97: k_eff = 1.089370 res = 1.694E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 518 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 98: k_eff = 1.094381 res = 2.299E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 501 D.R. = 1.3571\n", + "[ NORMAL ] Iteration 99: k_eff = 1.099224 res = 3.569E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 484 D.R. = 1.5526\n", + "[ NORMAL ] Iteration 100: k_eff = 1.103901 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 467 D.R. = 0.6441\n", + "[ NORMAL ] Iteration 101: k_eff = 1.108418 res = 4.779E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 451 D.R. = 2.0789\n", + "[ NORMAL ] Iteration 102: k_eff = 1.112777 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 435 D.R. = 0.0886\n", + "[ NORMAL ] Iteration 103: k_eff = 1.116986 res = 2.480E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 420 D.R. = 5.8571\n", + "[ NORMAL ] Iteration 104: k_eff = 1.121046 res = 1.573E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 405 D.R. = 0.6341\n", + "[ NORMAL ] Iteration 105: k_eff = 1.124962 res = 7.320E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 391 D.R. = 4.6538\n", + "[ NORMAL ] Iteration 106: k_eff = 1.128738 res = 5.082E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 377 D.R. = 0.6942\n", + "[ NORMAL ] Iteration 107: k_eff = 1.132377 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 363 D.R. = 0.0119\n", + "[ NORMAL ] Iteration 108: k_eff = 1.135884 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 350 D.R. = 48.0000\n", + "[ NORMAL ] Iteration 109: k_eff = 1.139264 res = 3.509E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 337 D.R. = 1.2083\n", + "[ NORMAL ] Iteration 110: k_eff = 1.142519 res = 1.633E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 325 D.R. = 0.4655\n", + "[ NORMAL ] Iteration 111: k_eff = 1.145654 res = 5.505E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 313 D.R. = 3.3704\n", + "[ NORMAL ] Iteration 112: k_eff = 1.148671 res = 2.178E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 301 D.R. = 0.3956\n", + "[ NORMAL ] Iteration 113: k_eff = 1.151575 res = 9.074E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 290 D.R. = 0.4167\n", + "[ NORMAL ] Iteration 114: k_eff = 1.154371 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 279 D.R. = 0.4667\n", + "[ NORMAL ] Iteration 115: k_eff = 1.157059 res = 1.512E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 268 D.R. = 3.5714\n", + "[ NORMAL ] Iteration 116: k_eff = 1.159645 res = 2.843E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 258 D.R. = 1.8800\n", + "[ NORMAL ] Iteration 117: k_eff = 1.162132 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 248 D.R. = 1.6383\n", + "[ NORMAL ] Iteration 118: k_eff = 1.164522 res = 3.025E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 239 D.R. = 0.0649\n", + "[ NORMAL ] Iteration 119: k_eff = 1.166820 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 229 D.R. = 16.0000\n", + "[ NORMAL ] Iteration 120: k_eff = 1.169029 res = 3.630E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 220 D.R. = 0.0750\n", + "[ NORMAL ] Iteration 121: k_eff = 1.171150 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 212 D.R. = 2.1667\n", + "[ NORMAL ] Iteration 122: k_eff = 1.173188 res = 1.331E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 203 D.R. = 1.6923\n", + "[ NORMAL ] Iteration 123: k_eff = 1.175145 res = 2.057E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 195 D.R. = 1.5455\n", + "[ NORMAL ] Iteration 124: k_eff = 1.177024 res = 1.452E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 187 D.R. = 0.7059\n", + "[ NORMAL ] Iteration 125: k_eff = 1.178828 res = 1.149E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 180 D.R. = 0.7917\n", + "[ NORMAL ] Iteration 126: k_eff = 1.180560 res = 3.388E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 173 D.R. = 2.9474\n", + "[ NORMAL ] Iteration 127: k_eff = 1.182222 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 166 D.R. = 0.5714\n", + "[ NORMAL ] Iteration 128: k_eff = 1.183817 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 159 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 129: k_eff = 1.185346 res = 1.875E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 152 D.R. = inf\n", + "[ NORMAL ] Iteration 130: k_eff = 1.186813 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 146 D.R. = 2.4839\n", + "[ NORMAL ] Iteration 131: k_eff = 1.188220 res = 1.270E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 140 D.R. = 0.2727\n", + "[ NORMAL ] Iteration 132: k_eff = 1.189569 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 134 D.R. = 1.8095\n", + "[ NORMAL ] Iteration 133: k_eff = 1.190862 res = 2.843E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 129 D.R. = 1.2368\n", + "[ NORMAL ] Iteration 134: k_eff = 1.192102 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 123 D.R. = 1.6383\n", + "[ NORMAL ] Iteration 135: k_eff = 1.193290 res = 2.178E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 118 D.R. = 0.4675\n", + "[ NORMAL ] Iteration 136: k_eff = 1.194428 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 113 D.R. = 0.0278\n", + "[ NORMAL ] Iteration 137: k_eff = 1.195519 res = 2.420E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 109 D.R. = 40.0000\n", + "[ NORMAL ] Iteration 138: k_eff = 1.196563 res = 3.932E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 104 D.R. = 1.6250\n", + "[ NORMAL ] Iteration 139: k_eff = 1.197564 res = 5.505E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 100 D.R. = 1.4000\n", + "[ NORMAL ] Iteration 140: k_eff = 1.198522 res = 2.420E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 95 D.R. = 0.0440\n", + "[ NORMAL ] Iteration 141: k_eff = 1.199440 res = 8.348E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 91 D.R. = 34.5000\n", + "[ NORMAL ] Iteration 142: k_eff = 1.200317 res = 3.993E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 87 D.R. = 0.4783\n", + "[ NORMAL ] Iteration 143: k_eff = 1.201159 res = 5.263E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 84 D.R. = 1.3182\n", + "[ NORMAL ] Iteration 144: k_eff = 1.201963 res = 6.352E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 80 D.R. = 1.2069\n", + "[ NORMAL ] Iteration 145: k_eff = 1.202733 res = 5.686E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 77 D.R. = 0.8952\n", + "[ NORMAL ] Iteration 146: k_eff = 1.203470 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 73 D.R. = 0.1170\n", + "[ NORMAL ] Iteration 147: k_eff = 1.204175 res = 3.448E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 70 D.R. = 5.1818\n", + "[ NORMAL ] Iteration 148: k_eff = 1.204849 res = 2.541E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 67 D.R. = 0.7368\n", + "[ NORMAL ] Iteration 149: k_eff = 1.205494 res = 2.178E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 64 D.R. = 0.8571\n", + "[ NORMAL ] Iteration 150: k_eff = 1.206111 res = 3.993E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 61 D.R. = 1.8333\n", + "[ NORMAL ] Iteration 151: k_eff = 1.206700 res = 5.324E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 58 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 152: k_eff = 1.207264 res = 1.391E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 56 D.R. = 0.2614\n", + "[ NORMAL ] Iteration 153: k_eff = 1.207804 res = 1.210E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 53 D.R. = 0.0870\n", + "[ NORMAL ] Iteration 154: k_eff = 1.208319 res = 5.021E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 51 D.R. = 41.5000\n", + "[ NORMAL ] Iteration 155: k_eff = 1.208811 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 49 D.R. = 0.1325\n", + "[ NORMAL ] Iteration 156: k_eff = 1.209282 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 47 D.R. = 1.4545\n", + "[ NORMAL ] Iteration 157: k_eff = 1.209732 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 44 D.R. = 0.0625\n", + "[ NORMAL ] Iteration 158: k_eff = 1.210162 res = 3.690E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 42 D.R. = 61.0000\n", + "[ NORMAL ] Iteration 159: k_eff = 1.210573 res = 1.149E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 41 D.R. = 0.3115\n", + "[ NORMAL ] Iteration 160: k_eff = 1.210964 res = 7.259E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 39 D.R. = 6.3158\n", + "[ NORMAL ] Iteration 161: k_eff = 1.211339 res = 1.010E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 37 D.R. = 1.3917\n", + "[ NORMAL ] Iteration 162: k_eff = 1.211697 res = 5.263E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 35 D.R. = 0.5210\n", + "[ NORMAL ] Iteration 163: k_eff = 1.212039 res = 6.110E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 34 D.R. = 1.1609\n", + "[ NORMAL ] Iteration 164: k_eff = 1.212365 res = 4.356E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 32 D.R. = 0.7129\n", + "[ NORMAL ] Iteration 165: k_eff = 1.212676 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 31 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 166: k_eff = 1.212973 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 29 D.R. = 0.2187\n", + "[ NORMAL ] Iteration 167: k_eff = 1.213258 res = 1.815E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 28 D.R. = 4.2857\n", + "[ NORMAL ] Iteration 168: k_eff = 1.213529 res = 5.747E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 27 D.R. = 3.1667\n", + "[ NORMAL ] Iteration 169: k_eff = 1.213787 res = 8.530E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 25 D.R. = 1.4842\n", + "[ NORMAL ] Iteration 170: k_eff = 1.214034 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 24 D.R. = 0.0071\n", + "[ NORMAL ] Iteration 171: k_eff = 1.214270 res = 8.469E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 23 D.R. = 14.0000\n", + "[ NORMAL ] Iteration 172: k_eff = 1.214494 res = 3.206E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 3.7857\n", + "[ NORMAL ] Iteration 173: k_eff = 1.214709 res = 3.751E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 1.1698\n", + "[ NORMAL ] Iteration 174: k_eff = 1.214913 res = 1.089E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 0.2903\n", + "[ NORMAL ] Iteration 175: k_eff = 1.215108 res = 4.537E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 4.1667\n", + "[ NORMAL ] Iteration 176: k_eff = 1.215295 res = 3.267E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 0.7200\n", + "[ NORMAL ] Iteration 177: k_eff = 1.215472 res = 3.690E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 1.1296\n", + "[ NORMAL ] Iteration 178: k_eff = 1.215641 res = 3.085E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 0.8361\n", + "[ NORMAL ] Iteration 179: k_eff = 1.215803 res = 2.178E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 0.7059\n", + "[ NORMAL ] Iteration 180: k_eff = 1.215957 res = 3.932E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 1.8056\n", + "[ NORMAL ] Iteration 181: k_eff = 1.216103 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 0.2462\n", + "[ NORMAL ] Iteration 182: k_eff = 1.216243 res = 8.469E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 0.8750\n", + "[ NORMAL ] Iteration 183: k_eff = 1.216377 res = 6.049E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 0.7143\n", + "[ NORMAL ] Iteration 184: k_eff = 1.216504 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 3.8000\n", + "[ NORMAL ] Iteration 185: k_eff = 1.216625 res = 4.053E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 1.7632\n", + "[ NORMAL ] Iteration 186: k_eff = 1.216741 res = 2.238E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 0.5522\n", + "[ NORMAL ] Iteration 187: k_eff = 1.216851 res = 5.565E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 2.4865\n", + "[ NORMAL ] Iteration 188: k_eff = 1.216955 res = 6.049E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 1.0870\n", + "[ NORMAL ] Iteration 189: k_eff = 1.217056 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 0.0900\n", + "[ NORMAL ] Iteration 190: k_eff = 1.217151 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 1.4444\n", + "[ NORMAL ] Iteration 191: k_eff = 1.217242 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 2.9231\n", + "[ NORMAL ] Iteration 192: k_eff = 1.217328 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 0.0263\n", + "[ NORMAL ] Iteration 193: k_eff = 1.217410 res = 4.114E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 68.0000\n", + "[ NORMAL ] Iteration 194: k_eff = 1.217489 res = 2.420E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.5882\n", + "[ NORMAL ] Iteration 195: k_eff = 1.217563 res = 3.569E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 1.4750\n", + "[ NORMAL ] Iteration 196: k_eff = 1.217635 res = 1.210E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.0339\n", + "[ NORMAL ] Iteration 197: k_eff = 1.217703 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 6.5000\n", + "[ NORMAL ] Iteration 198: k_eff = 1.217768 res = 1.149E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 1.4615\n", + "[ NORMAL ] Iteration 199: k_eff = 1.217829 res = 2.238E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 1.9474\n", + "[ NORMAL ] Iteration 200: k_eff = 1.217887 res = 4.235E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.1892\n", + "[ NORMAL ] Iteration 201: k_eff = 1.217943 res = 3.993E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 9.4286\n", + "[ NORMAL ] Iteration 202: k_eff = 1.217996 res = 2.843E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.7121\n", + "[ NORMAL ] Iteration 203: k_eff = 1.218047 res = 7.441E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 2.6170\n", + "[ NORMAL ] Iteration 204: k_eff = 1.218095 res = 1.089E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.1463\n", + "[ NORMAL ] Iteration 205: k_eff = 1.218141 res = 1.754E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.6111\n", + "[ NORMAL ] Iteration 206: k_eff = 1.218184 res = 4.356E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 2.4828\n", + "[ NORMAL ] Iteration 207: k_eff = 1.218225 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.2222\n", + "[ NORMAL ] Iteration 208: k_eff = 1.218265 res = 1.210E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 209: k_eff = 1.218302 res = 3.146E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 2.6000\n", + "[ NORMAL ] Iteration 210: k_eff = 1.218338 res = 5.626E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.7885\n", + "[ NORMAL ] Iteration 211: k_eff = 1.218372 res = 1.815E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.0323\n", + "[ NORMAL ] Iteration 212: k_eff = 1.218404 res = 6.291E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 34.6667\n", + "[ NORMAL ] Iteration 213: k_eff = 1.218435 res = 4.356E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.6923\n", + "[ NORMAL ] Iteration 214: k_eff = 1.218465 res = 6.836E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.5694\n", + "[ NORMAL ] Iteration 215: k_eff = 1.218493 res = 4.719E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.6903\n", + "[ NORMAL ] Iteration 216: k_eff = 1.218519 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.1410\n", + "[ NORMAL ] Iteration 217: k_eff = 1.218544 res = 1.210E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.8182\n", + "[ NORMAL ] Iteration 218: k_eff = 1.218568 res = 1.573E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.3000\n", + "[ NORMAL ] Iteration 219: k_eff = 1.218591 res = 1.875E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.1923\n", + "[ NORMAL ] Iteration 220: k_eff = 1.218613 res = 3.388E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.8065\n", + "[ NORMAL ] Iteration 221: k_eff = 1.218634 res = 3.751E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.1071\n", + "[ NORMAL ] Iteration 222: k_eff = 1.218654 res = 1.815E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0484\n", + "[ NORMAL ] Iteration 223: k_eff = 1.218672 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 224: k_eff = 1.218690 res = 1.815E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 225: k_eff = 1.218707 res = 6.291E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 34.6667\n", + "[ NORMAL ] Iteration 226: k_eff = 1.218723 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.1250\n", + "[ NORMAL ] Iteration 227: k_eff = 1.218738 res = 6.896E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 8.7692\n", + "[ NORMAL ] Iteration 228: k_eff = 1.218753 res = 6.715E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9737\n", + "[ NORMAL ] Iteration 229: k_eff = 1.218766 res = 1.512E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.2252\n", + "[ NORMAL ] Iteration 230: k_eff = 1.218779 res = 1.573E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.0400\n", + "[ NORMAL ] Iteration 231: k_eff = 1.218792 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.2308\n", + "[ NORMAL ] Iteration 232: k_eff = 1.218804 res = 4.840E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 233: k_eff = 1.218815 res = 4.174E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 8.6250\n", + "[ NORMAL ] Iteration 234: k_eff = 1.218826 res = 4.114E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9855\n", + "[ NORMAL ] Iteration 235: k_eff = 1.218836 res = 3.630E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.8824\n", + "[ NORMAL ] Iteration 236: k_eff = 1.218846 res = 7.804E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 2.1500\n" + ] + } + ], + "source": [ + "# Generate tracks for OpenMOC\n", + "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, azim_spacing=0.1)\n", + "track_generator.generateTracks()\n", + "\n", + "# Run OpenMOC\n", + "solver = openmoc.CPUSolver(track_generator)\n", + "solver.computeEigenvalue()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.222044\n", + "openmoc keff = 1.218846\n", + "bias [pcm]: -319.8\n" + ] + } + ], + "source": [ + "# Print report of keff and bias with OpenMC\n", + "openmoc_keff = solver.getKeff()\n", + "openmc_keff = sp.keff.n\n", + "bias = (openmoc_keff - openmc_keff) * 1e5\n", + "\n", + "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", + "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", + "print('bias [pcm]: {0:1.1f}'.format(bias))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a sanity check, let's run a simulation with the coarse 2-group cross sections to ensure that they also produce a reasonable result." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "openmoc_geometry = get_openmoc_geometry(sp.summary.geometry)\n", + "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", + "\n", + "# Inject multi-group cross sections into OpenMOC Materials\n", + "for cell_id, cell in openmoc_cells.items():\n", + " \n", + " # Ignore the root cell\n", + " if cell.getName() == 'root cell':\n", + " continue\n", + " \n", + " openmoc_material = cell.getFillMaterial()\n", + " openmoc_material.setNumEnergyGroups(coarse_groups.num_groups)\n", + " \n", + " # Extract the appropriate cross section objects for this cell\n", + " transport = xs_library[cell_id]['transport']\n", + " nufission = xs_library[cell_id]['nu-fission']\n", + " nuscatter = xs_library[cell_id]['nu-scatter']\n", + " chi = xs_library[cell_id]['chi']\n", + " \n", + " # Perform group condensation\n", + " transport = transport.get_condensed_xs(coarse_groups)\n", + " nufission = nufission.get_condensed_xs(coarse_groups)\n", + " nuscatter = nuscatter.get_condensed_xs(coarse_groups)\n", + " chi = chi.get_condensed_xs(coarse_groups)\n", + " \n", + " # Inject NumPy arrays of cross section data into the Material\n", + " openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Initializing a default angular quadrature...\n", + "[ NORMAL ] Initializing 2D tracks...\n", + "[ NORMAL ] Initializing 2D tracks reflections...\n", + "[ NORMAL ] Initializing 2D tracks array...\n", + "[ NORMAL ] Ray tracing for 2D track segmentation...\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 0.09 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 10.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 19.94 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 29.87 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 39.80 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 49.72 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 59.65 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 69.58 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 79.50 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 89.43 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n", + "[ NORMAL ] Initializing FSR lookup vectors\n", + "[ NORMAL ] Total number of FSRs 3\n", + "[ NORMAL ] Initializing MOC eigenvalue solver...\n", + "[ NORMAL ] Initializing solver arrays...\n", + "[ NORMAL ] Centering segments around FSR centroid...\n", + "[ NORMAL ] Max boundary angular flux storage per domain = 0.10 MB\n", + "[ NORMAL ] Max scalar flux storage per domain = 0.00 MB\n", + "[ NORMAL ] Max source storage per domain = 0.00 MB\n", + "[ NORMAL ] Number of azimuthal angles = 128\n", + "[ NORMAL ] Azimuthal ray spacing = 0.100000\n", + "[ NORMAL ] Number of polar angles = 6\n", + "[ NORMAL ] Source type = Flat\n", + "[ NORMAL ] MOC transport undamped\n", + "[ NORMAL ] CMFD acceleration: OFF\n", + "[ NORMAL ] Using 1 threads\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0: k_eff = 0.366644 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -63335 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 1: k_eff = 0.390955 res = 1.210E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 2431 D.R. = 12.5000\n", + "[ NORMAL ] Iteration 2: k_eff = 0.392706 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 175 D.R. = 0.0800\n", + "[ NORMAL ] Iteration 3: k_eff = 0.380770 res = 8.711E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -1193 D.R. = 9.0000\n", + "[ NORMAL ] Iteration 4: k_eff = 0.374646 res = 5.324E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -612 D.R. = 0.6111\n", + "[ NORMAL ] Iteration 5: k_eff = 0.369186 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -546 D.R. = 1.2727\n", + "[ NORMAL ] Iteration 6: k_eff = 0.365104 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -408 D.R. = 1.4286\n", + "[ NORMAL ] Iteration 7: k_eff = 0.362589 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -251 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 8: k_eff = 0.360985 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -160 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 9: k_eff = 0.360771 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -21 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 10: k_eff = 0.361478 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 70 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 11: k_eff = 0.363179 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 170 D.R. = inf\n", + "[ NORMAL ] Iteration 12: k_eff = 0.365787 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 260 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 13: k_eff = 0.369244 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 345 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 14: k_eff = 0.373421 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 417 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 15: k_eff = 0.378350 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 492 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 16: k_eff = 0.383902 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 17: k_eff = 0.390057 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 615 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 18: k_eff = 0.396756 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 669 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 19: k_eff = 0.403951 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 719 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 20: k_eff = 0.411603 res = 1.355E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 765 D.R. = 7.0000\n", + "[ NORMAL ] Iteration 21: k_eff = 0.419673 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 807 D.R. = 0.1429\n", + "[ NORMAL ] Iteration 22: k_eff = 0.428109 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 843 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 23: k_eff = 0.436887 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 877 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 24: k_eff = 0.445966 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 907 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 25: k_eff = 0.455315 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 934 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 26: k_eff = 0.464905 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 958 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 27: k_eff = 0.474705 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 980 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 28: k_eff = 0.484690 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 998 D.R. = inf\n", + "[ NORMAL ] Iteration 29: k_eff = 0.494835 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1014 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 30: k_eff = 0.505115 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1028 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 31: k_eff = 0.515510 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 1039 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 32: k_eff = 0.525998 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1048 D.R. = inf\n", + "[ NORMAL ] Iteration 33: k_eff = 0.536561 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1056 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 34: k_eff = 0.547180 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1061 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 35: k_eff = 0.557840 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1065 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 36: k_eff = 0.568524 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 1068 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 37: k_eff = 0.579219 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 1069 D.R. = -nan\n", + "[ NORMAL ] Iteration 38: k_eff = 0.589911 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 1069 D.R. = -nan\n", + "[ NORMAL ] Iteration 39: k_eff = 0.600586 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1067 D.R. = inf\n", + "[ NORMAL ] Iteration 40: k_eff = 0.611235 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1064 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 41: k_eff = 0.621847 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1061 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 42: k_eff = 0.632410 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1056 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 43: k_eff = 0.642917 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1050 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 44: k_eff = 0.653360 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1044 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 45: k_eff = 0.663729 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1036 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 46: k_eff = 0.674019 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1028 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 47: k_eff = 0.684223 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1020 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 48: k_eff = 0.694335 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1011 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 49: k_eff = 0.704350 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1001 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 50: k_eff = 0.714262 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 991 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 51: k_eff = 0.724068 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 980 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 52: k_eff = 0.733764 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 969 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 53: k_eff = 0.743346 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 958 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 54: k_eff = 0.752809 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 946 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 55: k_eff = 0.762154 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 934 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 56: k_eff = 0.771376 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 922 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 57: k_eff = 0.780473 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 909 D.R. = 0.6000\n", + "[ NORMAL ] Iteration 58: k_eff = 0.789445 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 897 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 59: k_eff = 0.798288 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 884 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 60: k_eff = 0.807003 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 871 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 61: k_eff = 0.815587 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 858 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 62: k_eff = 0.824040 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 845 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 63: k_eff = 0.832361 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 832 D.R. = inf\n", + "[ NORMAL ] Iteration 64: k_eff = 0.840551 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 818 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 65: k_eff = 0.848608 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 805 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 66: k_eff = 0.856534 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 792 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 67: k_eff = 0.864327 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 779 D.R. = inf\n", + "[ NORMAL ] Iteration 68: k_eff = 0.871988 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 766 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 69: k_eff = 0.879518 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 752 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 70: k_eff = 0.886917 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 739 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 71: k_eff = 0.894186 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 726 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 72: k_eff = 0.901326 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 713 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 73: k_eff = 0.908336 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 701 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 74: k_eff = 0.915219 res = 1.161E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 688 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 75: k_eff = 0.921976 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 675 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 76: k_eff = 0.928607 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 663 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 77: k_eff = 0.935114 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 650 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 78: k_eff = 0.941498 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 638 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 79: k_eff = 0.947759 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 626 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 80: k_eff = 0.953900 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 614 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 81: k_eff = 0.959922 res = 8.711E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 602 D.R. = 0.9000\n", + "[ NORMAL ] Iteration 82: k_eff = 0.965827 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 590 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 83: k_eff = 0.971615 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 578 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 84: k_eff = 0.977288 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 567 D.R. = 0.6000\n", + "[ NORMAL ] Iteration 85: k_eff = 0.982848 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 86: k_eff = 0.988297 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 544 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 87: k_eff = 0.993635 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 533 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 88: k_eff = 0.998865 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 523 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 89: k_eff = 1.003988 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 512 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 90: k_eff = 1.009006 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 501 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 91: k_eff = 1.013921 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 491 D.R. = 3.5000\n", + "[ NORMAL ] Iteration 92: k_eff = 1.018732 res = 8.711E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 481 D.R. = 1.2857\n", + "[ NORMAL ] Iteration 93: k_eff = 1.023444 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 471 D.R. = 0.7778\n", + "[ NORMAL ] Iteration 94: k_eff = 1.028057 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 461 D.R. = 0.2857\n", + "[ NORMAL ] Iteration 95: k_eff = 1.032573 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 451 D.R. = 3.5000\n", + "[ NORMAL ] Iteration 96: k_eff = 1.036994 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 442 D.R. = 0.4286\n", + "[ NORMAL ] Iteration 97: k_eff = 1.041321 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 432 D.R. = 2.6667\n", + "[ NORMAL ] Iteration 98: k_eff = 1.045554 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 423 D.R. = 0.7500\n", + "[ NORMAL ] Iteration 99: k_eff = 1.049699 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 414 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 100: k_eff = 1.053753 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 405 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 101: k_eff = 1.057721 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 396 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 102: k_eff = 1.061602 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 388 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 103: k_eff = 1.065399 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 379 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 104: k_eff = 1.069113 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 371 D.R. = 0.8889\n", + "[ NORMAL ] Iteration 105: k_eff = 1.072747 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 363 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 106: k_eff = 1.076301 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 355 D.R. = 4.5000\n", + "[ NORMAL ] Iteration 107: k_eff = 1.079776 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 347 D.R. = 0.1111\n", + "[ NORMAL ] Iteration 108: k_eff = 1.083176 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 339 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 109: k_eff = 1.086499 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 332 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 110: k_eff = 1.089750 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 325 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 111: k_eff = 1.092928 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 317 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 112: k_eff = 1.096035 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 310 D.R. = 2.2000\n", + "[ NORMAL ] Iteration 113: k_eff = 1.099073 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 303 D.R. = 0.2727\n", + "[ NORMAL ] Iteration 114: k_eff = 1.102043 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 297 D.R. = 2.6667\n", + "[ NORMAL ] Iteration 115: k_eff = 1.104946 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 290 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 116: k_eff = 1.107783 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 283 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 117: k_eff = 1.110557 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 277 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 118: k_eff = 1.113268 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 271 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 119: k_eff = 1.115919 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 265 D.R. = inf\n", + "[ NORMAL ] Iteration 120: k_eff = 1.118508 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 258 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 121: k_eff = 1.121039 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 253 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 122: k_eff = 1.123513 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 247 D.R. = inf\n", + "[ NORMAL ] Iteration 123: k_eff = 1.125930 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 241 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 124: k_eff = 1.128291 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 236 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 125: k_eff = 1.130599 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 230 D.R. = 6.0000\n", + "[ NORMAL ] Iteration 126: k_eff = 1.132854 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 225 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 127: k_eff = 1.135057 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 220 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 128: k_eff = 1.137210 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 215 D.R. = 3.3333\n", + "[ NORMAL ] Iteration 129: k_eff = 1.139313 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 210 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 130: k_eff = 1.141367 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 205 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 131: k_eff = 1.143374 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 200 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 132: k_eff = 1.145334 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 195 D.R. = inf\n", + "[ NORMAL ] Iteration 133: k_eff = 1.147250 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 191 D.R. = 0.4545\n", + "[ NORMAL ] Iteration 134: k_eff = 1.149120 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 187 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 135: k_eff = 1.150948 res = 1.549E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 182 D.R. = 1.6000\n", + "[ NORMAL ] Iteration 136: k_eff = 1.152733 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 178 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 137: k_eff = 1.154476 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 174 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 138: k_eff = 1.156179 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 170 D.R. = 1.1250\n", + "[ NORMAL ] Iteration 139: k_eff = 1.157842 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 166 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 140: k_eff = 1.159466 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 162 D.R. = 1.7500\n", + "[ NORMAL ] Iteration 141: k_eff = 1.161053 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 158 D.R. = 1.4286\n", + "[ NORMAL ] Iteration 142: k_eff = 1.162602 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 154 D.R. = 0.1000\n", + "[ NORMAL ] Iteration 143: k_eff = 1.164115 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 151 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 144: k_eff = 1.165592 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 147 D.R. = 1.4000\n", + "[ NORMAL ] Iteration 145: k_eff = 1.167035 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 144 D.R. = 0.7143\n", + "[ NORMAL ] Iteration 146: k_eff = 1.168444 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 140 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 147: k_eff = 1.169819 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 137 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 148: k_eff = 1.171163 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 134 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 149: k_eff = 1.172475 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 131 D.R. = 0.6250\n", + "[ NORMAL ] Iteration 150: k_eff = 1.173756 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 128 D.R. = 1.6000\n", + "[ NORMAL ] Iteration 151: k_eff = 1.175006 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 125 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 152: k_eff = 1.176227 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 122 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 153: k_eff = 1.177420 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 119 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 154: k_eff = 1.178584 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 116 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 155: k_eff = 1.179720 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 113 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 156: k_eff = 1.180830 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 111 D.R. = 0.1250\n", + "[ NORMAL ] Iteration 157: k_eff = 1.181914 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 108 D.R. = 13.0000\n", + "[ NORMAL ] Iteration 158: k_eff = 1.182971 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 105 D.R. = 0.4615\n", + "[ NORMAL ] Iteration 159: k_eff = 1.184004 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 103 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 160: k_eff = 1.185012 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 100 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 161: k_eff = 1.185997 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 98 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 162: k_eff = 1.186957 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 96 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 163: k_eff = 1.187895 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 93 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 164: k_eff = 1.188811 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 91 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 165: k_eff = 1.189705 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 89 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 166: k_eff = 1.190578 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 87 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 167: k_eff = 1.191430 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 85 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 168: k_eff = 1.192261 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 83 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 169: k_eff = 1.193073 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 81 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 170: k_eff = 1.193865 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 79 D.R. = 4.5000\n", + "[ NORMAL ] Iteration 171: k_eff = 1.194638 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 77 D.R. = 0.8889\n", + "[ NORMAL ] Iteration 172: k_eff = 1.195394 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 75 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 173: k_eff = 1.196130 res = 1.355E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 73 D.R. = 7.0000\n", + "[ NORMAL ] Iteration 174: k_eff = 1.196849 res = 1.452E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 71 D.R. = 1.0714\n", + "[ NORMAL ] Iteration 175: k_eff = 1.197551 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 70 D.R. = 0.1333\n", + "[ NORMAL ] Iteration 176: k_eff = 1.198236 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 68 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 177: k_eff = 1.198905 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 66 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 178: k_eff = 1.199558 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 65 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 179: k_eff = 1.200195 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 63 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 180: k_eff = 1.200818 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 62 D.R. = inf\n", + "[ NORMAL ] Iteration 181: k_eff = 1.201424 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 60 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 182: k_eff = 1.202017 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 59 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 183: k_eff = 1.202595 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 57 D.R. = inf\n", + "[ NORMAL ] Iteration 184: k_eff = 1.203159 res = 1.452E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 56 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 185: k_eff = 1.203710 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 55 D.R. = 0.7333\n", + "[ NORMAL ] Iteration 186: k_eff = 1.204247 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 53 D.R. = 0.6364\n", + "[ NORMAL ] Iteration 187: k_eff = 1.204772 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 52 D.R. = 1.5714\n", + "[ NORMAL ] Iteration 188: k_eff = 1.205284 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 51 D.R. = 1.0909\n", + "[ NORMAL ] Iteration 189: k_eff = 1.205784 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 50 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 190: k_eff = 1.206273 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 48 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 191: k_eff = 1.206748 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 47 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 192: k_eff = 1.207212 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 46 D.R. = inf\n", + "[ NORMAL ] Iteration 193: k_eff = 1.207666 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 45 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 194: k_eff = 1.208109 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 44 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 195: k_eff = 1.208540 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 43 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 196: k_eff = 1.208962 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 42 D.R. = 6.5000\n", + "[ NORMAL ] Iteration 197: k_eff = 1.209373 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 41 D.R. = 0.6923\n", + "[ NORMAL ] Iteration 198: k_eff = 1.209775 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 40 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 199: k_eff = 1.210167 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 39 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 200: k_eff = 1.210549 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 38 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 201: k_eff = 1.210922 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 37 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 202: k_eff = 1.211286 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 36 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 203: k_eff = 1.211641 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 35 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 204: k_eff = 1.211988 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 34 D.R. = inf\n", + "[ NORMAL ] Iteration 205: k_eff = 1.212326 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 33 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 206: k_eff = 1.212656 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 33 D.R. = 2.3333\n", + "[ NORMAL ] Iteration 207: k_eff = 1.212979 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 32 D.R. = 1.5714\n", + "[ NORMAL ] Iteration 208: k_eff = 1.213293 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 31 D.R. = 0.4545\n", + "[ NORMAL ] Iteration 209: k_eff = 1.213600 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 30 D.R. = 0.6000\n", + "[ NORMAL ] Iteration 210: k_eff = 1.213899 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 29 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 211: k_eff = 1.214192 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 29 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 212: k_eff = 1.214477 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 28 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 213: k_eff = 1.214755 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 27 D.R. = 2.6667\n", + "[ NORMAL ] Iteration 214: k_eff = 1.215027 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 27 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 215: k_eff = 1.215292 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 26 D.R. = -nan\n", + "[ NORMAL ] Iteration 216: k_eff = 1.215551 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 25 D.R. = inf\n", + "[ NORMAL ] Iteration 217: k_eff = 1.215803 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 25 D.R. = 1.1667\n", + "[ NORMAL ] Iteration 218: k_eff = 1.216049 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 24 D.R. = 0.1429\n", + "[ NORMAL ] Iteration 219: k_eff = 1.216289 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 23 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 220: k_eff = 1.216524 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 23 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 221: k_eff = 1.216753 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 222: k_eff = 1.216976 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 5.0000\n", + "[ NORMAL ] Iteration 223: k_eff = 1.217193 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 0.8000\n", + "[ NORMAL ] Iteration 224: k_eff = 1.217406 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 225: k_eff = 1.217613 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 4.0000\n", + "[ NORMAL ] Iteration 226: k_eff = 1.217816 res = 1.645E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 2.1250\n", + "[ NORMAL ] Iteration 227: k_eff = 1.218013 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 0.3529\n", + "[ NORMAL ] Iteration 228: k_eff = 1.218206 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 229: k_eff = 1.218394 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 230: k_eff = 1.218578 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 2.1667\n", + "[ NORMAL ] Iteration 231: k_eff = 1.218758 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 0.7692\n", + "[ NORMAL ] Iteration 232: k_eff = 1.218932 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 0.3000\n", + "[ NORMAL ] Iteration 233: k_eff = 1.219103 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 234: k_eff = 1.219269 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = inf\n", + "[ NORMAL ] Iteration 235: k_eff = 1.219431 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 0.2500\n", + "[ NORMAL ] Iteration 236: k_eff = 1.219590 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 7.0000\n", + "[ NORMAL ] Iteration 237: k_eff = 1.219745 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 0.2857\n", + "[ NORMAL ] Iteration 238: k_eff = 1.219896 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 239: k_eff = 1.220043 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 3.3333\n", + "[ NORMAL ] Iteration 240: k_eff = 1.220187 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 0.2000\n", + "[ NORMAL ] Iteration 241: k_eff = 1.220327 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 242: k_eff = 1.220464 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 243: k_eff = 1.220597 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 244: k_eff = 1.220727 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 4.5000\n", + "[ NORMAL ] Iteration 245: k_eff = 1.220854 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 246: k_eff = 1.220978 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 247: k_eff = 1.221099 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 1.7500\n", + "[ NORMAL ] Iteration 248: k_eff = 1.221217 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 1.8571\n", + "[ NORMAL ] Iteration 249: k_eff = 1.221333 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 0.9231\n", + "[ NORMAL ] Iteration 250: k_eff = 1.221445 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 251: k_eff = 1.221555 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 3.5000\n", + "[ NORMAL ] Iteration 252: k_eff = 1.221662 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 0.2857\n", + "[ NORMAL ] Iteration 253: k_eff = 1.221766 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 254: k_eff = 1.221868 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 0.8333\n", + "[ NORMAL ] Iteration 255: k_eff = 1.221968 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 0.4000\n", + "[ NORMAL ] Iteration 256: k_eff = 1.222064 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 257: k_eff = 1.222159 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 258: k_eff = 1.222251 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 259: k_eff = 1.222342 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 2.2500\n", + "[ NORMAL ] Iteration 260: k_eff = 1.222429 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 0.5556\n", + "[ NORMAL ] Iteration 261: k_eff = 1.222515 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 1.2000\n", + "[ NORMAL ] Iteration 262: k_eff = 1.222599 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 263: k_eff = 1.222681 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 264: k_eff = 1.222760 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 265: k_eff = 1.222838 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 266: k_eff = 1.222914 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 267: k_eff = 1.222988 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 9.0000\n", + "[ NORMAL ] Iteration 268: k_eff = 1.223060 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.7778\n", + "[ NORMAL ] Iteration 269: k_eff = 1.223130 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.1429\n", + "[ NORMAL ] Iteration 270: k_eff = 1.223199 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 13.0000\n", + "[ NORMAL ] Iteration 271: k_eff = 1.223266 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.6923\n", + "[ NORMAL ] Iteration 272: k_eff = 1.223331 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.2222\n", + "[ NORMAL ] Iteration 273: k_eff = 1.223395 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 274: k_eff = 1.223457 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 275: k_eff = 1.223518 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 276: k_eff = 1.223578 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 277: k_eff = 1.223636 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 278: k_eff = 1.223692 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.5833\n", + "[ NORMAL ] Iteration 279: k_eff = 1.223747 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.1429\n", + "[ NORMAL ] Iteration 280: k_eff = 1.223801 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 13.0000\n", + "[ NORMAL ] Iteration 281: k_eff = 1.223853 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.6923\n", + "[ NORMAL ] Iteration 282: k_eff = 1.223905 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 283: k_eff = 1.223955 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 4.3333\n", + "[ NORMAL ] Iteration 284: k_eff = 1.224003 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.6923\n", + "[ NORMAL ] Iteration 285: k_eff = 1.224050 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.1111\n", + "[ NORMAL ] Iteration 286: k_eff = 1.224097 res = 1.355E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.4000\n", + "[ NORMAL ] Iteration 287: k_eff = 1.224142 res = 1.452E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.0714\n", + "[ NORMAL ] Iteration 288: k_eff = 1.224186 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.8667\n", + "[ NORMAL ] Iteration 289: k_eff = 1.224229 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.6923\n", + "[ NORMAL ] Iteration 290: k_eff = 1.224272 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.4444\n", + "[ NORMAL ] Iteration 291: k_eff = 1.224313 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.2500\n", + "[ NORMAL ] Iteration 292: k_eff = 1.224352 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.8000\n", + "[ NORMAL ] Iteration 293: k_eff = 1.224392 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.1111\n", + "[ NORMAL ] Iteration 294: k_eff = 1.224430 res = 1.355E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.4000\n", + "[ NORMAL ] Iteration 295: k_eff = 1.224467 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.2857\n", + "[ NORMAL ] Iteration 296: k_eff = 1.224503 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 297: k_eff = 1.224538 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 298: k_eff = 1.224573 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 299: k_eff = 1.224607 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 300: k_eff = 1.224640 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.1667\n", + "[ NORMAL ] Iteration 301: k_eff = 1.224672 res = 1.645E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 2.4286\n", + "[ NORMAL ] Iteration 302: k_eff = 1.224703 res = 1.355E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.8235\n", + "[ NORMAL ] Iteration 303: k_eff = 1.224734 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 304: k_eff = 1.224763 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = inf\n", + "[ NORMAL ] Iteration 305: k_eff = 1.224792 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 306: k_eff = 1.224821 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 4.5000\n", + "[ NORMAL ] Iteration 307: k_eff = 1.224849 res = 1.549E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.7778\n", + "[ NORMAL ] Iteration 308: k_eff = 1.224876 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 309: k_eff = 1.224902 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.1250\n", + "[ NORMAL ] Iteration 310: k_eff = 1.224928 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.2222\n", + "[ NORMAL ] Iteration 311: k_eff = 1.224953 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.5455\n", + "[ NORMAL ] Iteration 312: k_eff = 1.224977 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.1667\n", + "[ NORMAL ] Iteration 313: k_eff = 1.225001 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 314: k_eff = 1.225025 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 315: k_eff = 1.225047 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 316: k_eff = 1.225069 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 317: k_eff = 1.225091 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 318: k_eff = 1.225112 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 319: k_eff = 1.225133 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 320: k_eff = 1.225153 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.0000\n", + "[ NORMAL ] Iteration 321: k_eff = 1.225173 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 322: k_eff = 1.225192 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 323: k_eff = 1.225210 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.3333\n", + "[ NORMAL ] Iteration 324: k_eff = 1.225228 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.2500\n", + "[ NORMAL ] Iteration 325: k_eff = 1.225246 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.5556\n", + "[ NORMAL ] Iteration 326: k_eff = 1.225264 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.2000\n", + "[ NORMAL ] Iteration 327: k_eff = 1.225280 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.6667\n", + "[ NORMAL ] Iteration 328: k_eff = 1.225297 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 329: k_eff = 1.225313 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 330: k_eff = 1.225329 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.5556\n", + "[ NORMAL ] Iteration 331: k_eff = 1.225344 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 332: k_eff = 1.225359 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 333: k_eff = 1.225374 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.6667\n", + "[ NORMAL ] Iteration 334: k_eff = 1.225388 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.5000\n", + "[ NORMAL ] Iteration 335: k_eff = 1.225402 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.0000\n", + "[ NORMAL ] Iteration 336: k_eff = 1.225416 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.3333\n", + "[ NORMAL ] Iteration 337: k_eff = 1.225429 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 3.0000\n", + "[ NORMAL ] Iteration 338: k_eff = 1.225442 res = 1.452E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.5000\n", + "[ NORMAL ] Iteration 339: k_eff = 1.225455 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.4667\n", + "[ NORMAL ] Iteration 340: k_eff = 1.225467 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.2857\n", + "[ NORMAL ] Iteration 341: k_eff = 1.225479 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.5000\n", + "[ NORMAL ] Iteration 342: k_eff = 1.225491 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 343: k_eff = 1.225502 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 344: k_eff = 1.225513 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 9.0000\n", + "[ NORMAL ] Iteration 345: k_eff = 1.225524 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.2222\n", + "[ NORMAL ] Iteration 346: k_eff = 1.225535 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.0000\n", + "[ NORMAL ] Iteration 347: k_eff = 1.225545 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 348: k_eff = 1.225555 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.3636\n", + "[ NORMAL ] Iteration 349: k_eff = 1.225565 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 0.2500\n" + ] + } + ], + "source": [ + "# Generate tracks for OpenMOC\n", + "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, azim_spacing=0.1)\n", + "track_generator.generateTracks()\n", + "\n", + "# Run OpenMOC\n", + "solver = openmoc.CPUSolver(track_generator)\n", + "solver.computeEigenvalue()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.222044\n", + "openmoc keff = 1.225565\n", + "bias [pcm]: 352.2\n" + ] + } + ], + "source": [ + "# Print report of keff and bias with OpenMC\n", + "openmoc_keff = solver.getKeff()\n", + "openmc_keff = sp.keff.n\n", + "bias = (openmoc_keff - openmc_keff) * 1e5\n", + "\n", + "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", + "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", + "print('bias [pcm]: {0:1.1f}'.format(bias))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a non-trivial bias in both the 2-group and 8-group cases. In the case of a pin cell, one can show that these biases do not converge to <100 pcm with more particle histories. For heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n", + "\n", + "* Appropriate transport-corrected cross sections\n", + "* Spatial discretization of OpenMOC's mesh\n", + "* Constant-in-angle multi-group cross sections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is often insightful to generate visual depictions of multi-group cross sections. There are many different types of plots which may be useful for multi-group cross section visualization, only a few of which will be shown here for enrichment and inspiration.\n", + "\n", + "One particularly useful visualization is a comparison of the continuous-energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the `openmc.plotter` module to plot continuous-energy cross sections from the openly available cross section library distributed by NNDC.\n", + "\n", + "The MGXS data can also be plotted using the openmc.plot_xs command, however we will do this manually here to show how the openmc.Mgxs.get_xs method can be used to obtain data." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1e-05, 20000000.0)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create a figure of the U-235 continuous-energy fission cross section \n", + "fig = openmc.plot_xs('U235', ['fission'])\n", + "\n", + "# Get the axis to use for plotting the MGXS\n", + "ax = fig.gca()\n", + "\n", + "# Extract energy group bounds and MGXS values to plot\n", + "fission = xs_library[fuel_cell.id]['fission']\n", + "energy_groups = fission.energy_groups\n", + "x = energy_groups.group_edges\n", + "y = fission.get_xs(nuclides=['U235'], order_groups='decreasing', xs_type='micro')\n", + "y = np.squeeze(y)\n", + "\n", + "# Fix low energy bound\n", + "x[0] = 1.e-5\n", + "\n", + "# Extend the mgxs values array for matplotlib's step plot\n", + "y = np.insert(y, 0, y[0])\n", + "\n", + "# Create a step plot for the MGXS\n", + "ax.plot(x, y, drawstyle='steps', color='r', linewidth=3)\n", + "\n", + "ax.set_title('U-235 Fission Cross Section')\n", + "ax.legend(['Continuous', 'Multi-Group'])\n", + "ax.set_xlim((x.min(), x.max()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful type of illustration is scattering matrix sparsity structures. First, we extract Pandas `DataFrames` for the H-1 and O-16 scattering matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct a Pandas DataFrame for the microscopic nu-scattering matrix\n", + "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", + "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", + "\n", + "# Slice DataFrame in two for each nuclide's mean values\n", + "h1 = df[df['nuclide'] == 'H1']['mean']\n", + "o16 = df[df['nuclide'] == 'O16']['mean']\n", + "\n", + "# Cast DataFrames as NumPy arrays\n", + "h1 = h1.values\n", + "o16 = o16.values\n", + "\n", + "# Reshape arrays to 2D matrix for plotting\n", + "h1.shape = (fine_groups.num_groups, fine_groups.num_groups)\n", + "o16.shape = (fine_groups.num_groups, fine_groups.num_groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matplotlib's `imshow` routine can be used to plot the matrices to illustrate their sparsity structures." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create plot of the H-1 scattering matrix\n", + "fig = plt.subplot(121)\n", + "fig.imshow(h1, interpolation='nearest', cmap='jet')\n", + "plt.title('H-1 Scattering Matrix')\n", + "plt.xlabel('Group Out')\n", + "plt.ylabel('Group In')\n", + "\n", + "# Create plot of the O-16 scattering matrix\n", + "fig2 = plt.subplot(122)\n", + "fig2.imshow(o16, interpolation='nearest', cmap='jet')\n", + "plt.title('O-16 Scattering Matrix')\n", + "plt.xlabel('Group Out')\n", + "plt.ylabel('Group In')\n", + "\n", + "# Show the plot on screen\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# close statepoint file to release HDF5 file handles\n", + "sp.close()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/stress-test/multi-group-xs/mgxs-part-iii.ipynb b/stress-test/multi-group-xs/mgxs-part-iii.ipynb new file mode 100644 index 0000000..127ba26 --- /dev/null +++ b/stress-test/multi-group-xs/mgxs-part-iii.ipynb @@ -0,0 +1,1685 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multigroup Cross Section Generation Part III: Libraries\n", + "This IPython Notebook illustrates the use of the **`openmc.mgxs.Library`** class. The `Library` class is designed to automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, and/or nuclides. In particular, this Notebook illustrates the following features:\n", + "\n", + "* Calculation of multi-group cross sections for a **fuel assembly**\n", + "* Automated creation, manipulation and storage of `MGXS` with **`openmc.mgxs.Library`**\n", + "* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n", + "* Steady-state pin-by-pin **fission rates comparison** between OpenMC and [OpenMOC](https://mit-crpg.github.io/OpenMOC/)\n", + "\n", + "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. You must install [OpenMOC](https://mit-crpg.github.io/OpenMOC/) on your system to run this Notebook in its entirety. In addition, this Notebook illustrates the use of [Pandas](https://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import pickle\n", + "\n", + "from IPython.display import Image\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import openmc\n", + "import openmc.mgxs\n", + "from openmc.openmoc_compatible import get_openmoc_geometry\n", + "import openmoc\n", + "import openmoc.process\n", + "from openmoc.materialize import load_openmc_mgxs_lib\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# create a model object to tie together geometry, materials, settings, and tallies\n", + "model = openmc.Model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pins." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# 1.6 enriched fuel\n", + "fuel = openmc.Material(name='1.6% Fuel')\n", + "fuel.set_density('g/cm3', 10.31341)\n", + "fuel.add_nuclide('U235', 3.7503e-4)\n", + "fuel.add_nuclide('U238', 2.2625e-2)\n", + "fuel.add_nuclide('O16', 4.6007e-2)\n", + "\n", + "# borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", + "water.add_nuclide('B10', 8.0042e-6)\n", + "\n", + "# zircaloy\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide('Zr90', 7.2758e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our three materials, we can now create a `Materials` object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a Materials object\n", + "model.materials = openmc.Materials([fuel, water, zircaloy])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. This problem will be a square array of fuel pins and control rod guide tubes for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create cylinders for the fuel and clad\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n", + "\n", + "# Create boundary planes to surround the geometry\n", + "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n", + "max_x = openmc.XPlane(x0=+10.71, boundary_type='reflective')\n", + "min_y = openmc.YPlane(y0=-10.71, boundary_type='reflective')\n", + "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n", + "min_z = openmc.ZPlane(z0=-10., boundary_type='reflective')\n", + "max_z = openmc.ZPlane(z0=+10., boundary_type='reflective')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a fuel pin\n", + "fuel_pin_universe = openmc.Universe(name='1.6% Fuel Pin')\n", + "\n", + "# Create fuel Cell\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", + "fuel_cell.fill = fuel\n", + "fuel_cell.region = -fuel_outer_radius\n", + "fuel_pin_universe.add_cell(fuel_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='1.6% Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "fuel_pin_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius\n", + "fuel_pin_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Likewise, we can construct a control rod guide tube with the same surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a control rod guide tube\n", + "guide_tube_universe = openmc.Universe(name='Guide Tube')\n", + "\n", + "# Create guide tube Cell\n", + "guide_tube_cell = openmc.Cell(name='Guide Tube Water')\n", + "guide_tube_cell.fill = water\n", + "guide_tube_cell.region = -fuel_outer_radius\n", + "guide_tube_universe.add_cell(guide_tube_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='Guide Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "guide_tube_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='Guide Tube Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius\n", + "guide_tube_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Create fuel assembly Lattice\n", + "assembly = openmc.RectLattice(name='1.6% Fuel Assembly')\n", + "assembly.pitch = (1.26, 1.26)\n", + "assembly.lower_left = [-1.26 * 17. / 2.0] * 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we create a NumPy array of fuel pin and guide tube universes for the lattice." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Create array indices for guide tube locations in lattice\n", + "template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\n", + " 11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n", + "template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\n", + " 8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n", + "\n", + "# Initialize an empty 17x17 array of the lattice universes\n", + "universes = np.empty((17, 17), dtype=openmc.Universe)\n", + "\n", + "# Fill the array with the fuel pin and guide tube universes\n", + "universes[:,:] = fuel_pin_universe\n", + "universes[template_x, template_y] = guide_tube_universe\n", + "\n", + "# Store the array of universes in the lattice\n", + "assembly.universes = universes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the assembly and then assign it to the root universe." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Create root Cell\n", + "root_cell = openmc.Cell(name='root cell')\n", + "root_cell.fill = assembly\n", + "\n", + "# Add boundary planes\n", + "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n", + "\n", + "# Create root Universe\n", + "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", + "root_universe.add_cell(root_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry that is assigned a root universe and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "model.geometry = openmc.Geometry(root_universe)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 10000\n", + "\n", + "# Instantiate a Settings object\n", + "settings = openmc.Settings()\n", + "settings.batches = batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': False}\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings.source = openmc.Source(space=uniform_dist)\n", + "\n", + "model.settings = settings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also create a plot to verify that our fuel assembly geometry was created successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Instantiate a Plot\n", + "model.export_to_xml()\n", + "plot = openmc.Plot.from_geometry(model.geometry)\n", + "plot.pixels = (250, 250)\n", + "plot.color_by = 'material'\n", + "plot.to_ipython_image()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the plot, we have a nice array of fuel and guide tube pin cells with fuel, cladding, and water!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an MGXS Library" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to generate multi-group cross sections! First, let's define a 2-group structure using the built-in `EnergyGroups` class." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a 2-group EnergyGroups object\n", + "groups = openmc.mgxs.EnergyGroups()\n", + "groups.group_edges = np.array([0., 0.625, 20.0e6])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will instantiate an `openmc.mgxs.Library` for the energy groups with the fuel assembly geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize a 2-group MGXS Library for OpenMOC\n", + "mgxs_lib = openmc.mgxs.Library(model.geometry)\n", + "mgxs_lib.energy_groups = groups" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we must specify to the `Library` which types of cross sections to compute. In particular, the following are the multi-group cross section `MGXS` subclasses that are mapped to string codes accepted by the `Library` class:\n", + "\n", + "* `TotalXS` (`\"total\"`)\n", + "* `TransportXS` (`\"transport\"` or `\"nu-transport` with `nu` set to `True`)\n", + "* `AbsorptionXS` (`\"absorption\"`)\n", + "* `CaptureXS` (`\"capture\"`)\n", + "* `FissionXS` (`\"fission\"` or `\"nu-fission\"` with `nu` set to `True`)\n", + "* `KappaFissionXS` (`\"kappa-fission\"`)\n", + "* `ScatterXS` (`\"scatter\"` or `\"nu-scatter\"` with `nu` set to `True`)\n", + "* `ScatterMatrixXS` (`\"scatter matrix\"` or `\"nu-scatter matrix\"` with `nu` set to `True`)\n", + "* `Chi` (`\"chi\"`)\n", + "* `ChiPrompt` (`\"chi prompt\"`)\n", + "* `InverseVelocity` (`\"inverse-velocity\"`)\n", + "* `PromptNuFissionXS` (`\"prompt-nu-fission\"`)\n", + "* `DelayedNuFissionXS` (`\"delayed-nu-fission\"`)\n", + "* `ChiDelayed` (`\"chi-delayed\"`)\n", + "* `Beta` (`\"beta\"`)\n", + "\n", + "In this case, let's create the multi-group cross sections needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we will define `\"nu-transport\"`, `\"nu-fission\"`, `'\"fission\"`, `\"nu-scatter matrix\"` and `\"chi\"` cross sections for our `Library`.\n", + "\n", + "**Note**: A variety of different approximate transport-corrected total multi-group cross sections (and corresponding scattering matrices) can be found in the literature. At the present time, the `openmc.mgxs` module only supports the `\"P0\"` transport correction. This correction can be turned on and off through the boolean `Library.correction` property which may take values of `\"P0\"` (default) or `None`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Specify multi-group cross section types to compute\n", + "mgxs_lib.mgxs_types = ['nu-transport', 'nu-fission', 'fission', 'nu-scatter matrix', 'chi']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports `\"material\"`, `\"cell\"`, `\"universe\"`, and `\"mesh\"` domain types. We will use a `\"cell\"` domain type here to compute cross sections in each of the cells in the fuel assembly geometry.\n", + "\n", + "**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell or universe) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property. In our case, we wish to compute multi-group cross sections in each and every cell since they will be needed in our downstream OpenMOC calculation on the identical combinatorial geometry mesh." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Specify a \"cell\" domain type for the cross section tally filters\n", + "mgxs_lib.domain_type = 'cell'\n", + "\n", + "# Specify the cell domains over which to compute multi-group cross sections\n", + "mgxs_lib.domains = model.geometry.get_all_material_cells().values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can easily instruct the `Library` to compute multi-group cross sections on a nuclide-by-nuclide basis with the boolean `Library.by_nuclide` property. By default, `by_nuclide` is set to `False`, but we will set it to `True` here." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute cross sections on a nuclide-by-nuclide basis\n", + "mgxs_lib.by_nuclide = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we use the `Library` to construct the tallies needed to compute all of the requested multi-group cross sections in each domain and nuclide." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct all tallies needed for the multi-group cross section library\n", + "mgxs_lib.build_library()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tallies can now be export to a \"tallies.xml\" input file for OpenMC. \n", + "\n", + "**NOTE**: At this point the `Library` has constructed nearly 100 distinct `Tally` objects. The overhead to tally in OpenMC scales as $O(N)$ for $N$ tallies, which can become a bottleneck for large tally datasets. To compensate for this, the Python API's `Tally`, `Filter` and `Tallies` classes allow for the smart *merging* of tallies when possible. The `Library` class supports this runtime optimization with the use of the optional `merge` paramter (`False` by default) for the `Library.add_to_tallies_file(...)` method, as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a \"tallies.xml\" file for the MGXS Library\n", + "tallies = openmc.Tallies()\n", + "mgxs_lib.add_to_tallies_file(tallies, merge=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, we instantiate a fission rate mesh tally to compare with OpenMOC." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a tally Mesh\n", + "mesh = openmc.RegularMesh(mesh_id=1)\n", + "mesh.dimension = [17, 17]\n", + "mesh.lower_left = [-10.71, -10.71]\n", + "mesh.upper_right = [+10.71, +10.71]\n", + "\n", + "# Instantiate tally Filter\n", + "mesh_filter = openmc.MeshFilter(mesh)\n", + "\n", + "# Instantiate the Tally\n", + "tally = openmc.Tally(name='mesh tally')\n", + "tally.filters = [mesh_filter]\n", + "tally.scores = ['fission', 'nu-fission']\n", + "\n", + "# Add tally to collection\n", + "tallies.append(tally)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "model.tallies = tallies" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=126.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=21.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=2.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=3.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=4.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=96.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=15.\n", + " warn(msg, IDWarning)\n", + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=114.\n", + " warn(msg, IDWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.1\n", + " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", + " Date/Time | 2022-10-05 23:45:30\n", + " MPI Processes | 1\n", + " OpenMP Threads | 2\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U235 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/pshriwise/data/xs/openmc/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294 K\n", + " Maximum neutron data temperature: 294 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000 eV for U235\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.04638\n", + " 2/1 1.00498\n", + " 3/1 1.00535\n", + " 4/1 1.02695\n", + " 5/1 1.00781\n", + " 6/1 1.02035\n", + " 7/1 1.03808\n", + " 8/1 1.02532\n", + " 9/1 1.02783\n", + " 10/1 1.01371\n", + " 11/1 1.03205\n", + " 12/1 1.01947 1.02576 +/- 0.00629\n", + " 13/1 1.01843 1.02332 +/- 0.00438\n", + " 14/1 1.03269 1.02566 +/- 0.00388\n", + " 15/1 1.03879 1.02829 +/- 0.00399\n", + " 16/1 1.02251 1.02732 +/- 0.00340\n", + " 17/1 1.01274 1.02524 +/- 0.00355\n", + " 18/1 1.02454 1.02515 +/- 0.00307\n", + " 19/1 1.01993 1.02457 +/- 0.00277\n", + " 20/1 1.00665 1.02278 +/- 0.00306\n", + " 21/1 1.02200 1.02271 +/- 0.00277\n", + " 22/1 1.03748 1.02394 +/- 0.00281\n", + " 23/1 1.02803 1.02425 +/- 0.00260\n", + " 24/1 1.03052 1.02470 +/- 0.00245\n", + " 25/1 1.02027 1.02441 +/- 0.00230\n", + " 26/1 1.02597 1.02450 +/- 0.00216\n", + " 27/1 1.01776 1.02411 +/- 0.00206\n", + " 28/1 1.02652 1.02424 +/- 0.00195\n", + " 29/1 1.01063 1.02353 +/- 0.00198\n", + " 30/1 1.03300 1.02400 +/- 0.00194\n", + " 31/1 1.02020 1.02382 +/- 0.00185\n", + " 32/1 1.03720 1.02443 +/- 0.00187\n", + " 33/1 1.02797 1.02458 +/- 0.00179\n", + " 34/1 1.01994 1.02439 +/- 0.00172\n", + " 35/1 1.02626 1.02446 +/- 0.00166\n", + " 36/1 1.03183 1.02475 +/- 0.00162\n", + " 37/1 1.03410 1.02509 +/- 0.00159\n", + " 38/1 1.00919 1.02452 +/- 0.00164\n", + " 39/1 1.04388 1.02519 +/- 0.00171\n", + " 40/1 1.01254 1.02477 +/- 0.00171\n", + " 41/1 1.01584 1.02448 +/- 0.00168\n", + " 42/1 1.01002 1.02403 +/- 0.00169\n", + " 43/1 1.00277 1.02339 +/- 0.00176\n", + " 44/1 1.00672 1.02289 +/- 0.00177\n", + " 45/1 1.03974 1.02338 +/- 0.00179\n", + " 46/1 1.00460 1.02285 +/- 0.00181\n", + " 47/1 1.03954 1.02331 +/- 0.00182\n", + " 48/1 1.01414 1.02306 +/- 0.00179\n", + " 49/1 1.02016 1.02299 +/- 0.00174\n", + " 50/1 0.99791 1.02236 +/- 0.00181\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 1.2070e-01 seconds\n", + " Reading cross sections = 1.1526e-01 seconds\n", + " Total time in simulation = 2.9052e+01 seconds\n", + " Time in transport only = 2.9007e+01 seconds\n", + " Time in inactive batches = 2.2160e+00 seconds\n", + " Time in active batches = 2.6836e+01 seconds\n", + " Time synchronizing fission bank = 3.0553e-02 seconds\n", + " Sampling source sites = 1.9077e-02 seconds\n", + " SEND/RECV source sites = 1.1409e-02 seconds\n", + " Time accumulating tallies = 1.0753e-03 seconds\n", + " Time writing statepoints = 6.1566e-03 seconds\n", + " Total time for finalization = 1.0271e-05 seconds\n", + " Total time elapsed = 2.9182e+01 seconds\n", + " Calculation Rate (inactive) = 45126.1 particles/second\n", + " Calculation Rate (active) = 14905.4 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.02393 +/- 0.00174\n", + " k-effective (Track-length) = 1.02236 +/- 0.00181\n", + " k-effective (Absorption) = 1.02412 +/- 0.00167\n", + " Combined k-effective = 1.02362 +/- 0.00128\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "# Run OpenMC\n", + "statepoint_filename = model.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the last statepoint file\n", + "sp = openmc.StatePoint(statepoint_filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint is now ready to be analyzed by the `Library`. We simply have to load the tallies from the statepoint into the `Library` and our `MGXS` objects will compute the cross sections for us under-the-hood." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize MGXS Library with OpenMC statepoint data\n", + "mgxs_lib.load_from_statepoint(sp)\n", + "# Retrieve OpenMC's k-effective value\n", + "openmc_keff = sp.keff.nominal_value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voila! Our multi-group cross sections are now ready to rock 'n roll!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extracting and Storing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Library` supports a rich API to automate a variety of tasks, including multi-group cross section data retrieval and storage. We will highlight a few of these features here. First, the `Library.get_mgxs(...)` method allows one to extract an `MGXS` object from the `Library` for a particular domain and cross section type. The following cell illustrates how one may extract the `NuFissionXS` object for the fuel cell.\n", + "\n", + "**Note:** The `MGXS.get_mgxs(...)` method will accept either the domain *or* the integer domain ID of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve the NuFissionXS object for the fuel cell from the library\n", + "fuel_mgxs = mgxs_lib.get_mgxs(fuel_cell, 'nu-fission')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `NuFissionXS` object supports all of the methods described previously in the `openmc.mgxs` tutorials, such as [Pandas](https://pandas.pydata.org/) `DataFrames`:\n", + "Note that since so few histories were simulated, we should expect a few division-by-error errors as some tallies have not yet scored any results." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellgroup innuclidemeanstd. dev.
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" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "3 1 1 U235 8.099261e-03 1.626934e-05\n", + "4 1 1 U238 7.326723e-03 2.168273e-05\n", + "5 1 1 O16 0.000000e+00 0.000000e+00\n", + "0 1 2 U235 3.613773e-01 1.025247e-03\n", + "1 1 2 U238 6.739270e-07 1.911057e-09\n", + "2 1 2 O16 0.000000e+00 0.000000e+00" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = fuel_mgxs.get_pandas_dataframe()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can use the `MGXS.print_xs(...)` method to view a string representation of the multi-group cross section data." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t1\n", + "\tNuclide =\tU235\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [0.625 - 20000000.0eV]:\t8.10e-03 +/- 2.01e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t3.61e-01 +/- 2.84e-01%\n", + "\n", + "\tNuclide =\tU238\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [0.625 - 20000000.0eV]:\t7.33e-03 +/- 2.96e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t6.74e-07 +/- 2.84e-01%\n", + "\n", + "\tNuclide =\tO16\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [0.625 - 20000000.0eV]:\t0.00e+00 +/- 0.00e+00%\n", + " Group 2 [0.0 - 0.625 eV]:\t0.00e+00 +/- 0.00e+00%\n", + "\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/tallies.py:1255: RuntimeWarning: invalid value encountered in true_divide\n", + " data = self.std_dev[indices] / self.mean[indices]\n" + ] + } + ], + "source": [ + "fuel_mgxs.print_xs()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can export the entire `Library` to HDF5 with the `Library.build_hdf5_store(...)` method as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Store the cross section data in an \"mgxs/mgxs.h5\" HDF5 binary file\n", + "mgxs_lib.build_hdf5_store(filename='mgxs.h5', directory='mgxs')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The HDF5 store will contain the numerical multi-group cross section data indexed by domain, nuclide and cross section type. Some data workflows may be optimized by storing and retrieving binary representations of the `MGXS` objects in the `Library`. This feature is supported through the `Library.dump_to_file(...)` and `Library.load_from_file(...)` routines which use Python's [`pickle`](https://docs.python.org/3/library/pickle.html) module. This is illustrated as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Store a Library and its MGXS objects in a pickled binary file \"mgxs/mgxs.pkl\"\n", + "mgxs_lib.dump_to_file(filename='mgxs', directory='mgxs')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate a new MGXS Library from the pickled binary file \"mgxs/mgxs.pkl\"\n", + "mgxs_lib = openmc.mgxs.Library.load_from_file(filename='mgxs', directory='mgxs')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Library` class may be used to leverage the energy condensation features supported by the `MGXS` class. In particular, one can use the `Library.get_condensed_library(...)` with a coarse group structure which is a subset of the original \"fine\" group structure as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a 1-group structure\n", + "coarse_groups = openmc.mgxs.EnergyGroups(group_edges=[0., 20.0e6])\n", + "\n", + "# Create a new MGXS Library on the coarse 1-group structure\n", + "coarse_mgxs_lib = mgxs_lib.get_condensed_library(coarse_groups)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellgroup innuclidemeanstd. dev.
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" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "0 1 1 U235 0.074479 0.000151\n", + "1 1 1 U238 0.005950 0.000017\n", + "2 1 1 O16 0.000000 0.000000" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Retrieve the NuFissionXS object for the fuel cell from the 1-group library\n", + "coarse_fuel_mgxs = coarse_mgxs_lib.get_mgxs(fuel_cell, 'nu-fission')\n", + "\n", + "# Show the Pandas DataFrame for the 1-group MGXS\n", + "coarse_fuel_mgxs.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verification with OpenMOC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course it is always a good idea to verify that one's cross sections are accurate. We can easily do so here with the deterministic transport code [OpenMOC](https://mit-crpg.github.io/OpenMOC/). We first construct an equivalent OpenMOC geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an OpenMOC Geometry from the OpenMC Geometry\n", + "openmoc_geometry = get_openmoc_geometry(mgxs_lib.geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can inject the multi-group cross sections into the equivalent fuel assembly OpenMOC geometry. The `openmoc.materialize` module supports the loading of `Library` objects from OpenMC as illustrated below." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ WARNING ] Group cross sections by nuclides are not currently supported.\n", + "[ WARNING ] ... Contributions from all nuclides will be summed.\n" + ] + } + ], + "source": [ + "# Load the library into the OpenMOC geometry\n", + "materials = load_openmc_mgxs_lib(mgxs_lib, openmoc_geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to run OpenMOC to verify our cross-sections from OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Initializing a default angular quadrature...\n", + "[ NORMAL ] Initializing 2D tracks...\n", + "[ NORMAL ] Initializing 2D tracks reflections...\n", + "[ NORMAL ] Initializing 2D tracks array...\n", + "[ NORMAL ] Ray tracing for 2D track segmentation...\n", + "[ WARNING ] The Geometry was set with non-infinite z-boundaries and supplied\n", + "[ WARNING ] ... to a 2D TrackGenerator. The min-z boundary was set to -10.00 \n", + "[ WARNING ] ... and the max-z boundary was set to 10.00. Z-boundaries are \n", + "[ WARNING ] ... assumed to be infinite in 2D TrackGenerators.\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 0.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 10.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 20.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 30.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 40.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 50.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 60.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 70.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 80.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 90.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n", + "[ NORMAL ] Initializing FSR lookup vectors\n", + "[ NORMAL ] Total number of FSRs 867\n", + "[ NORMAL ] Initializing MOC eigenvalue solver...\n", + "[ NORMAL ] Initializing solver arrays...\n", + "[ NORMAL ] Centering segments around FSR centroid...\n", + "[ NORMAL ] Max boundary angular flux storage per domain = 0.42 MB\n", + "[ NORMAL ] Max scalar flux storage per domain = 0.01 MB\n", + "[ NORMAL ] Max source storage per domain = 0.01 MB\n", + "[ NORMAL ] Number of azimuthal angles = 32\n", + "[ NORMAL ] Azimuthal ray spacing = 0.100000\n", + "[ NORMAL ] Number of polar angles = 6\n", + "[ NORMAL ] Source type = Flat\n", + "[ NORMAL ] MOC transport undamped\n", + "[ NORMAL ] CMFD acceleration: OFF\n", + "[ NORMAL ] Using 1 threads\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0: k_eff = 0.823342 res = 9.831E-02 delta-k (pcm) =\n", + "[ NORMAL ] ... -17665 D.R. = 0.0983\n", + "[ NORMAL ] Iteration 1: k_eff = 0.780160 res = 4.646E-02 delta-k (pcm) =\n", + "[ NORMAL ] ... -4318 D.R. = 0.4725\n", + "[ NORMAL ] Iteration 2: k_eff = 0.739336 res = 9.631E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -4082 D.R. = 0.2073\n", + "[ NORMAL ] Iteration 3: k_eff = 0.710750 res = 8.566E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -2858 D.R. = 0.8894\n", + "[ NORMAL ] Iteration 4: k_eff = 0.689598 res = 5.205E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -2115 D.R. = 0.6076\n", + "[ NORMAL ] Iteration 5: k_eff = 0.675031 res = 3.605E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -1456 D.R. = 0.6926\n", + "[ NORMAL ] Iteration 6: k_eff = 0.665895 res = 2.538E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -913 D.R. = 0.7040\n", + "[ NORMAL ] Iteration 7: k_eff = 0.661318 res = 1.889E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -457 D.R. = 0.7443\n", + "[ NORMAL ] Iteration 8: k_eff = 0.660529 res = 1.493E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -78 D.R. = 0.7904\n", + "[ NORMAL ] Iteration 9: k_eff = 0.662872 res = 1.268E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 234 D.R. = 0.8490\n", + "[ NORMAL ] Iteration 10: k_eff = 0.667780 res = 1.140E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 490 D.R. = 0.8995\n", + "[ NORMAL ] Iteration 11: k_eff = 0.674766 res = 1.065E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 698 D.R. = 0.9337\n", + "[ NORMAL ] Iteration 12: k_eff = 0.683410 res = 1.014E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 864 D.R. = 0.9527\n", + "[ NORMAL ] Iteration 13: k_eff = 0.693354 res = 9.759E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 994 D.R. = 0.9623\n", + "[ NORMAL ] Iteration 14: k_eff = 0.704290 res = 9.438E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1093 D.R. = 0.9671\n", + "[ NORMAL ] Iteration 15: k_eff = 0.715957 res = 9.154E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1166 D.R. = 0.9698\n", + "[ NORMAL ] Iteration 16: k_eff = 0.728134 res = 8.892E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1217 D.R. = 0.9714\n", + "[ NORMAL ] Iteration 17: k_eff = 0.740633 res = 8.644E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1249 D.R. = 0.9721\n", + "[ NORMAL ] Iteration 18: k_eff = 0.753297 res = 8.404E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1266 D.R. = 0.9722\n", + "[ NORMAL ] Iteration 19: k_eff = 0.765995 res = 8.167E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1269 D.R. = 0.9719\n", + "[ NORMAL ] Iteration 20: k_eff = 0.778619 res = 7.930E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1262 D.R. = 0.9710\n", + "[ NORMAL ] Iteration 21: k_eff = 0.791079 res = 7.691E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1246 D.R. = 0.9698\n", + "[ NORMAL ] Iteration 22: k_eff = 0.803304 res = 7.447E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1222 D.R. = 0.9683\n", + "[ NORMAL ] Iteration 23: k_eff = 0.815235 res = 7.199E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1193 D.R. = 0.9667\n", + "[ NORMAL ] Iteration 24: k_eff = 0.826828 res = 6.947E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1159 D.R. = 0.9650\n", + "[ NORMAL ] Iteration 25: k_eff = 0.838047 res = 6.693E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1121 D.R. = 0.9634\n", + "[ NORMAL ] Iteration 26: k_eff = 0.848868 res = 6.436E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1082 D.R. = 0.9617\n", + "[ NORMAL ] Iteration 27: k_eff = 0.859271 res = 6.179E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1040 D.R. = 0.9600\n", + "[ NORMAL ] Iteration 28: k_eff = 0.869247 res = 5.922E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 997 D.R. = 0.9585\n", + "[ NORMAL ] Iteration 29: k_eff = 0.878788 res = 5.667E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 954 D.R. = 0.9570\n", + "[ NORMAL ] Iteration 30: k_eff = 0.887894 res = 5.415E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 910 D.R. = 0.9556\n", + "[ NORMAL ] Iteration 31: k_eff = 0.896566 res = 5.168E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 867 D.R. = 0.9543\n", + "[ NORMAL ] Iteration 32: k_eff = 0.904810 res = 4.925E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 824 D.R. = 0.9530\n", + "[ NORMAL ] Iteration 33: k_eff = 0.912635 res = 4.688E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 782 D.R. = 0.9519\n", + "[ NORMAL ] Iteration 34: k_eff = 0.920049 res = 4.457E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 741 D.R. = 0.9508\n", + "[ NORMAL ] Iteration 35: k_eff = 0.927066 res = 4.233E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 701 D.R. = 0.9498\n", + "[ NORMAL ] Iteration 36: k_eff = 0.933696 res = 4.016E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 663 D.R. = 0.9488\n", + "[ NORMAL ] Iteration 37: k_eff = 0.939954 res = 3.807E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 625 D.R. = 0.9479\n", + "[ NORMAL ] Iteration 38: k_eff = 0.945855 res = 3.606E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 590 D.R. = 0.9471\n", + "[ NORMAL ] Iteration 39: k_eff = 0.951412 res = 3.413E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 0.9464\n", + "[ NORMAL ] Iteration 40: k_eff = 0.956641 res = 3.227E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 522 D.R. = 0.9456\n", + "[ NORMAL ] Iteration 41: k_eff = 0.961556 res = 3.049E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 491 D.R. = 0.9448\n", + "[ NORMAL ] Iteration 42: k_eff = 0.966174 res = 2.879E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 461 D.R. = 0.9443\n", + "[ NORMAL ] Iteration 43: k_eff = 0.970507 res = 2.716E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 433 D.R. = 0.9435\n", + "[ NORMAL ] Iteration 44: k_eff = 0.974571 res = 2.561E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 406 D.R. = 0.9430\n", + "[ NORMAL ] Iteration 45: k_eff = 0.978380 res = 2.414E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 380 D.R. = 0.9423\n", + "[ NORMAL ] Iteration 46: k_eff = 0.981948 res = 2.273E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 356 D.R. = 0.9417\n", + "[ NORMAL ] Iteration 47: k_eff = 0.985287 res = 2.140E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 333 D.R. = 0.9413\n", + "[ NORMAL ] Iteration 48: k_eff = 0.988410 res = 2.013E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 312 D.R. = 0.9409\n", + "[ NORMAL ] Iteration 49: k_eff = 0.991331 res = 1.893E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 292 D.R. = 0.9402\n", + "[ NORMAL ] Iteration 50: k_eff = 0.994060 res = 1.779E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 272 D.R. = 0.9399\n", + "[ NORMAL ] Iteration 51: k_eff = 0.996609 res = 1.671E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 254 D.R. = 0.9393\n", + "[ NORMAL ] Iteration 52: k_eff = 0.998988 res = 1.569E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 237 D.R. = 0.9390\n", + "[ NORMAL ] Iteration 53: k_eff = 1.001209 res = 1.473E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 222 D.R. = 0.9386\n", + "[ NORMAL ] Iteration 54: k_eff = 1.003280 res = 1.382E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 207 D.R. = 0.9380\n", + "[ NORMAL ] Iteration 55: k_eff = 1.005211 res = 1.296E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 193 D.R. = 0.9378\n", + "[ NORMAL ] Iteration 56: k_eff = 1.007012 res = 1.215E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 180 D.R. = 0.9374\n", + "[ NORMAL ] Iteration 57: k_eff = 1.008689 res = 1.138E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 167 D.R. = 0.9369\n", + "[ NORMAL ] Iteration 58: k_eff = 1.010251 res = 1.066E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 156 D.R. = 0.9369\n", + "[ NORMAL ] Iteration 59: k_eff = 1.011706 res = 9.981E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 145 D.R. = 0.9362\n", + "[ NORMAL ] Iteration 60: k_eff = 1.013060 res = 9.344E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 135 D.R. = 0.9361\n", + "[ NORMAL ] Iteration 61: k_eff = 1.014320 res = 8.743E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 125 D.R. = 0.9357\n", + "[ NORMAL ] Iteration 62: k_eff = 1.015492 res = 8.177E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 117 D.R. = 0.9353\n", + "[ NORMAL ] Iteration 63: k_eff = 1.016582 res = 7.648E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 109 D.R. = 0.9353\n", + "[ NORMAL ] Iteration 64: k_eff = 1.017596 res = 7.147E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 101 D.R. = 0.9345\n", + "[ NORMAL ] Iteration 65: k_eff = 1.018538 res = 6.680E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 94 D.R. = 0.9346\n", + "[ NORMAL ] Iteration 66: k_eff = 1.019414 res = 6.237E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 87 D.R. = 0.9338\n", + "[ NORMAL ] Iteration 67: k_eff = 1.020227 res = 5.828E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 81 D.R. = 0.9343\n", + "[ NORMAL ] Iteration 68: k_eff = 1.020983 res = 5.442E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 75 D.R. = 0.9338\n", + "[ NORMAL ] Iteration 69: k_eff = 1.021685 res = 5.080E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 70 D.R. = 0.9336\n", + "[ NORMAL ] Iteration 70: k_eff = 1.022337 res = 4.739E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 65 D.R. = 0.9328\n", + "[ NORMAL ] Iteration 71: k_eff = 1.022942 res = 4.422E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 60 D.R. = 0.9331\n", + "[ NORMAL ] Iteration 72: k_eff = 1.023504 res = 4.124E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 56 D.R. = 0.9327\n", + "[ NORMAL ] Iteration 73: k_eff = 1.024026 res = 3.843E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 52 D.R. = 0.9317\n", + "[ NORMAL ] Iteration 74: k_eff = 1.024510 res = 3.586E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 48 D.R. = 0.9331\n", + "[ NORMAL ] Iteration 75: k_eff = 1.024959 res = 3.341E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 44 D.R. = 0.9317\n", + "[ NORMAL ] Iteration 76: k_eff = 1.025375 res = 3.115E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 41 D.R. = 0.9325\n", + "[ NORMAL ] Iteration 77: k_eff = 1.025762 res = 2.898E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 38 D.R. = 0.9303\n", + "[ NORMAL ] Iteration 78: k_eff = 1.026120 res = 2.703E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 35 D.R. = 0.9327\n", + "[ NORMAL ] Iteration 79: k_eff = 1.026452 res = 2.519E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 33 D.R. = 0.9318\n", + "[ NORMAL ] Iteration 80: k_eff = 1.026760 res = 2.341E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 30 D.R. = 0.9295\n", + "[ NORMAL ] Iteration 81: k_eff = 1.027046 res = 2.180E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 28 D.R. = 0.9312\n", + "[ NORMAL ] Iteration 82: k_eff = 1.027311 res = 2.028E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 26 D.R. = 0.9301\n", + "[ NORMAL ] Iteration 83: k_eff = 1.027556 res = 1.889E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 24 D.R. = 0.9315\n", + "[ NORMAL ] Iteration 84: k_eff = 1.027783 res = 1.757E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 22 D.R. = 0.9305\n", + "[ NORMAL ] Iteration 85: k_eff = 1.027994 res = 1.632E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 21 D.R. = 0.9284\n", + "[ NORMAL ] Iteration 86: k_eff = 1.028189 res = 1.521E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 19 D.R. = 0.9322\n", + "[ NORMAL ] Iteration 87: k_eff = 1.028370 res = 1.412E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 18 D.R. = 0.9285\n", + "[ NORMAL ] Iteration 88: k_eff = 1.028538 res = 1.311E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 16 D.R. = 0.9287\n", + "[ NORMAL ] Iteration 89: k_eff = 1.028693 res = 1.222E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 15 D.R. = 0.9316\n", + "[ NORMAL ] Iteration 90: k_eff = 1.028837 res = 1.134E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 14 D.R. = 0.9279\n", + "[ NORMAL ] Iteration 91: k_eff = 1.028970 res = 1.056E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 13 D.R. = 0.9314\n", + "[ NORMAL ] Iteration 92: k_eff = 1.029093 res = 9.786E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 12 D.R. = 0.9268\n", + "[ NORMAL ] Iteration 93: k_eff = 1.029207 res = 9.093E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 11 D.R. = 0.9292\n", + "[ NORMAL ] Iteration 94: k_eff = 1.029313 res = 8.445E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 10 D.R. = 0.9287\n", + "[ NORMAL ] Iteration 95: k_eff = 1.029411 res = 7.848E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 9 D.R. = 0.9294\n", + "[ NORMAL ] Iteration 96: k_eff = 1.029502 res = 7.272E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 9 D.R. = 0.9265\n", + "[ NORMAL ] Iteration 97: k_eff = 1.029586 res = 6.769E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 8 D.R. = 0.9309\n", + "[ NORMAL ] Iteration 98: k_eff = 1.029663 res = 6.294E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 7 D.R. = 0.9298\n", + "[ NORMAL ] Iteration 99: k_eff = 1.029735 res = 5.827E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 7 D.R. = 0.9259\n", + "[ NORMAL ] Iteration 100: k_eff = 1.029802 res = 5.413E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.9290\n", + "[ NORMAL ] Iteration 101: k_eff = 1.029863 res = 5.032E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.9295\n", + "[ NORMAL ] Iteration 102: k_eff = 1.029920 res = 4.648E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.9237\n", + "[ NORMAL ] Iteration 103: k_eff = 1.029973 res = 4.328E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.9313\n", + "[ NORMAL ] Iteration 104: k_eff = 1.030022 res = 4.004E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9249\n", + "[ NORMAL ] Iteration 105: k_eff = 1.030067 res = 3.734E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9326\n", + "[ NORMAL ] Iteration 106: k_eff = 1.030109 res = 3.452E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9246\n", + "[ NORMAL ] Iteration 107: k_eff = 1.030148 res = 3.195E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9253\n", + "[ NORMAL ] Iteration 108: k_eff = 1.030184 res = 2.979E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9325\n", + "[ NORMAL ] Iteration 109: k_eff = 1.030217 res = 2.750E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9230\n", + "[ NORMAL ] Iteration 110: k_eff = 1.030247 res = 2.541E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9240\n", + "[ NORMAL ] Iteration 111: k_eff = 1.030276 res = 2.364E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9302\n", + "[ NORMAL ] Iteration 112: k_eff = 1.030302 res = 2.178E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9215\n", + "[ NORMAL ] Iteration 113: k_eff = 1.030326 res = 2.022E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9285\n", + "[ NORMAL ] Iteration 114: k_eff = 1.030349 res = 1.896E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9374\n", + "[ NORMAL ] Iteration 115: k_eff = 1.030369 res = 1.753E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9246\n", + "[ NORMAL ] Iteration 116: k_eff = 1.030389 res = 1.619E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9236\n", + "[ NORMAL ] Iteration 117: k_eff = 1.030406 res = 1.500E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9266\n", + "[ NORMAL ] Iteration 118: k_eff = 1.030423 res = 1.406E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9375\n", + "[ NORMAL ] Iteration 119: k_eff = 1.030438 res = 1.287E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9154\n", + "[ NORMAL ] Iteration 120: k_eff = 1.030452 res = 1.193E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9269\n", + "[ NORMAL ] Iteration 121: k_eff = 1.030465 res = 1.095E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9174\n", + "[ NORMAL ] Iteration 122: k_eff = 1.030477 res = 1.033E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9437\n", + "[ NORMAL ] Iteration 123: k_eff = 1.030488 res = 9.328E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9029\n", + "[ NORMAL ] Iteration 124: k_eff = 1.030498 res = 8.716E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9344\n", + "[ NORMAL ] Iteration 125: k_eff = 1.030508 res = 8.527E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 0.9783\n" + ] + } + ], + "source": [ + "# Generate tracks for OpenMOC\n", + "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=32, azim_spacing=0.1)\n", + "track_generator.generateTracks()\n", + "\n", + "# Run OpenMOC\n", + "solver = openmoc.CPUSolver(track_generator)\n", + "solver.computeEigenvalue()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.023625\n", + "openmoc keff = 1.030508\n", + "bias [pcm]: 688.3\n" + ] + } + ], + "source": [ + "# Print report of keff and bias with OpenMC\n", + "openmoc_keff = solver.getKeff()\n", + "bias = (openmoc_keff - openmc_keff) * 1e5\n", + "\n", + "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", + "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", + "print('bias [pcm]: {0:1.1f}'.format(bias))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a non-trivial bias between the eigenvalues computed by OpenMC and OpenMOC. One can show that these biases do not converge to <100 pcm with more particle histories. For heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n", + "\n", + "* Appropriate transport-corrected cross sections\n", + "* Spatial discretization of OpenMOC's mesh\n", + "* Constant-in-angle multi-group cross sections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flux and Pin Power Visualizations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will conclude this tutorial by illustrating how to visualize the fission rates computed by OpenMOC and OpenMC. First, we extract volume-integrated fission rates from OpenMC's mesh fission rate tally for each pin cell in the fuel assembly." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the OpenMC fission rate mesh tally data\n", + "mesh_tally = sp.get_tally(name='mesh tally')\n", + "openmc_fission_rates = mesh_tally.get_values(scores=['nu-fission'])\n", + "\n", + "# Close the statepoint file now that we're done getting information from it\n", + "sp.close()\n", + "\n", + "# Reshape array to 2D for plotting\n", + "openmc_fission_rates.shape = (17,17)\n", + "\n", + "# Normalize to the average pin power\n", + "openmc_fission_rates /= np.mean(openmc_fission_rates[openmc_fission_rates > 0.])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we extract OpenMOC's volume-averaged fission rates into a 2D 17x17 NumPy array." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# Create OpenMOC Mesh on which to tally fission rates\n", + "openmoc_mesh = openmoc.process.Mesh()\n", + "openmoc_mesh.dimension = np.array(mesh.dimension)\n", + "openmoc_mesh.lower_left = np.array(mesh.lower_left)\n", + "openmoc_mesh.upper_right = np.array(mesh.upper_right)\n", + "openmoc_mesh.width = openmoc_mesh.upper_right - openmoc_mesh.lower_left\n", + "openmoc_mesh.width /= openmoc_mesh.dimension\n", + "\n", + "# Tally OpenMOC fission rates on the Mesh\n", + "openmoc_fission_rates = openmoc_mesh.tally_fission_rates(solver)\n", + "openmoc_fission_rates = np.squeeze(openmoc_fission_rates)\n", + "openmoc_fission_rates = np.fliplr(openmoc_fission_rates)\n", + "\n", + "# Normalize to the average pin fission rate\n", + "openmoc_fission_rates /= np.mean(openmoc_fission_rates[openmoc_fission_rates > 0.])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can easily use Matplotlib to visualize the fission rates from OpenMC and OpenMOC side-by-side." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'OpenMOC Fission Rates')" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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6l+o+xJbZz7T8xCNd64xRzjwLGQZDZRgzhJ2WYfOyzHUx3s/ruRnmPhg9+3m/rroM76HZ/nbNrRvut4d/+nVNybAPM8wBo4cybNd9fog7l9C8OmxT8R0UR/QhhJBz0dGHEELORUcfQgg5Fx19CCHkXHT0IYSQc5VMDn6wpD9KekbSAkmfLhIzVtJ6SXPSny8VKyuEtiRyO+RNJbcp3glcbmZPSeoGPCnpETN7plHcn83szArqCaGlRW6HXGnyEb2ZLTezp9LHG4F/sOcEyiHUnMjtkDcVTTxST9JQ4CgoOpLnzZLmkgzPuMLMFpQoYyIwEaDPkM7c8MGzytZ5Jd9y2/XdqV9wYzgjw2CGk/2BE7bGj3l8+tFuzFvW/81vz6l+XRseb+/XxV/cmDV0dmN6ZRg0kmmQ12J/uw55/7NuzD/mOvt5ywF+W1KV5nZhXtN3CFxdvr4sM0Mtf+IwN4bj/Lze0Mnf391H+THbZ410Y1bU9XBjBsz368pSznY6ujEck+E9NM8vpvtWfz8bfl0Dr/Fnn1txvzNT1eWlV1X8ZaykrsC9wGfMbEOj1U8Bh5jZaOAHlBn/ZWa3mFmdmdV16+uPkAyhuVUjtwvzmu7+9JchNIeKOnpJ7UneCD8zs183Xm9mG8xsU/p4KtBeUp9K6gyhJURuhzyp5KobkUyQ/A8z+06JmAFpHJKOTet7tal1htASIrdD3lRyjv6twIeBeZLmpMu+AAwBMLObgXOASyXtBLYA51tbvItaCA1FbodcaXJHb2aPQflvGczsRuDGptYRQmuI3A55EyNjQwgh56KjDyGEnIuOPoQQcq4qA6aqrR276cbGsjFzeaNf0M0ZKlufYRaZ4/0QZZkZKIP2GSbiKTcwol73p3f4MZOX+AVdlaE9j2WIuS3Dfr7SD8kyI1j7gxpf8t7QzvZ+Gc2hU8/NDBs3q2zMwSz2C8owM1SWwVBbtvnldPPHb3H4rufcmO73+PnIT/2QAR9a78YccN5rboxl2K4s+4csg87u8os5ijluzOJx5S/qWnjt5pLr4og+hBByLjr6EELIuejoQwgh56KjDyGEnIuOPoQQci46+hBCyLno6EMIIeeiow8hhJxrkwOmNtGVv/DWsjFPcJxbzrUPXOfGPMtQN2bo5pfdmE5d/BsXnjDFH1wxY/yxbsyJdRlma3pHhgFKXfwQDva3a9In/LomfShDXf38uh6lnxtzcu8/ll2/ZP/tGRpTfdt2dWTh+vKzQz3a42S/oPH+fsoyM1SWwVB6NUNdV2TItdl+CNMz3PxzbIYBSk9mGJyVYbv69fbrkjPpE5Dp9bqDbm7MkPXl+6Ftu0rPrFWNGaYWSZonaY6kPV5OJW6QtFDS3yX58+mF0Moir0OeVOuI/iQzW11i3enA8PTnOOBH6e8Q2rrI65ALLXGOfhzwE0vMBHpKGtgC9YbQnCKvQ82oRkdvwDRJT6Yz3jc2GBrcqWlJuiyEtizyOuRGNU7dnGBmSyX1Ax6R9KyZ/WlvC0nfTBMBug/pUYVmhVCRquc1Bx9U5SaGkE3FR/RmtjT9vQqYAjS+bGQpcHDB3welyxqXc4uZ1ZlZ3QF9D6i0WSFUpDnyWr17N1dzQyiroo5eUhdJ3eofA6cA8xuFPQB8JL1K4XhgvZktr6TeEJpT5HXIm0pP3fQHpkiqL+vnZvaQpEsAzOxmYCpwBrAQeA34aIV1htDcIq9DrlTU0ZvZi8DoIstvLnhswCf3ptz27KA/K8vGDMowzc7Wzf6Ahze85LdnxsgMg5gu8es65+Y73Zhfzf6w36AMAzl4rx/CkAwxGWaGmpTlosIMs2LxTb+uRVf6r4WXG6soP6CmufLatu/P1kW9ysb8avT73HLewuvdmO2zRroxmWaGyjAY6rHr/SEE+2eYGez4DIO8Zs7b42XZw07auTEnZNiujavauzHPtTvcjemQ4fX6C/7r7uUO20t353ELhBBCyLno6EMIIeeiow8hhJyLjj6EEHIuOvoQQsi56OhDCCHnoqMPIYSci44+hBByrk3OMLWeHjzMqWVj3sMDbjmdVmWozJ+wiBE84wd18kOGssiNub3uAjfmYzPucmOe+vQRbszRM/7hxvB9P6TBPRxLuSlDzBl+yJmbf+PGjOoyr+x6I8OAs2bQqfNmho2eVTZmY4aZhkbPft6NWVHn3xiw+z0ZZmLKMDNUpsFQH57rxkxqfJOJYjEZypl5pz+oKst2Zdk/B1/gJ/+A2evdmIfryvd3ACOd3FnYeXPJdXFEH0IIORcdfQgh5Fx09CGEkHPR0YcQQs5FRx9CCDnX5I5e0uGS5hT8bJD0mUYxYyWtL4j5UsUtDqGZRW6HvGny5ZVm9hwwBkBSO5Jp1KYUCf2zmZ3Z1HpCaGmR2yFvqnXq5h3AC2b2cpXKC6GtiNwONa9aA6bOB0qN4nmzpLnAMuAKM1tQLEjSRGAiQIch/VhHz7IVXvr4HX6r3mpuSJZZqKYz1o0593sPujFvl1/Xe8e4IfC0v11HX5dhUJA/7gam+3XxQIa6NmSoa7xf13/hnyHxcmchr2VozL9VlNuFec3AIcxfcEzZysYfebbfojp/Pw2Yn+E1+akfkuX1zzIzVKbBUObXNSnDe2jSHH9QFfMy5PW7/boGjPIHQ2V5vcY3mGe+uKsW/KB8wJYuJVdVfEQvqQPJxHW/LLL6KeAQMxsN/AC4r1Q5ZnaLmdWZWV37vj0rbVYIFatGbhfmNQf2bba2hlBONU7dnA48ZWZ7TPJqZhvMbFP6eCrQXlKfKtQZQkuI3A65UI2O/gJKfLSVNEBKPmtJOjat79Uq1BlCS4jcDrlQ0Tl6SV2AdwEXFyy7BMDMbgbOAS6VtBPYApxvluFEXAitLHI75ElFHb2ZbQZ6N1p2c8HjG4EbK6kjhNYQuR3yJEbGhhBCzkVHH0IIORcdfQgh5FybnGFqGx1ZyLCyMTPeeqxbzolT/AEPD473R7Cf+7g/qxE3+3UdYQf55VyxxI/5gF/X//35592Ya1d9za/rKr+uV77R1Y15hhFuzImz/br61010Y7bToez6/djtltEcOnXezLAjy88SNIWz3HKuzLCfsswwNeBDGQb7jPXrmjnPn9Epy8xQmQZDfcgNyTTD1PEZtus/X8OXtmJkhv2c4fWaUneZGzPSyZ2YYSqEEPZh0dGHEELORUcfQgg5Fx19CCHkXHT0IYSQc9HRhxBCzkVHH0IIORcdfQgh5FybHDDVj1Vcyg1lYy7LcD+pW8d/wo05lifcmJUnuCH0/6QfM/w2fzDU/def4saM+/40N+baaRkGQ33DD2GVH/LCNw5zY57hCDembtRsN2Y2dW7MnGS615LW8zO3jOawdUsX5s8tP8NUt9E3ueXMrRvuxmynoxtzwHn+TFvdn9zhxuyknRuTZRBTlpmhspSTpT0Z0ogN57V3YxZnmBlqZV0/N6YbG90YL3cqnmFK0u2SVkmaX7Csl6RHJD2f/j6wxHMnpDHPS5qQpb4QWkLkddhXZD11Mxk4rdGyq4E/mNlw4A/p3w1I6gVcAxwHHAtcU+qNE0IrmEzkddgHZOrozexPwJpGi8cB9TN03wFFb9JxKvCIma0xs7XAI+z5xgqhVUReh31FJV/G9jez5enjFUD/IjGDgcUFfy9Jl4XQVkVeh9ypylU36RRqFU2jJmmipNmSZm9+xf+SKITmVu28Zu0rVWpZCHunko5+paSBAOnvYtdnLIUGX0sflC7bg5ndYmZ1ZlbXpe8BFTQrhIo0W15zYN+qNzaELCrp6B8A6q82mADcXyTmYeAUSQemX1adki4Loa2KvA65k/XyyruAvwKHS1oi6SLg68C7JD0PvDP9G0l1km4DMLM1wLXArPTnK+myEFpd5HXYVyg5Ddm2HFnX0e6ZXX42prX0dMs5gSf9yr6fYaYZf0wV/Nzfj7bGr0v/yFDXWzO8Zldk2K6ZGep6LENd12Woa2p16sqyD9/eq/yAsjl1n2Tj7H9maHR17XfUGOs4/dGyMf/qcYhbTt8Mg2s4xt88e9EvRq9WKdf8sXAwPUNdWWaGyjAYiusz5FrvDO/XQzPUNcuv6xW6uTFD1r9cdv22sSez++k5RRsdt0AIIYSci44+hBByLjr6EELIuejoQwgh56KjDyGEnIuOPoQQci46+hBCyLno6EMIIefa5AxTyxjMNXy5bEwHtrvlnHCqP+DhO/5kTXz2RD/md/LrOv3HfjnPX1h+oBjA8Gl+XTdef5Ebc9kX/9dv0Al+Xb957GQ3pv8X/Kmqjpnt13VS3e/cmA5sK7teld2nrMk6ttvGsB4vlI2Z8O87JJc2dYq/nzbM89uzpfxuAqBfhkFDG1f5MzF1v8efqYp3ZxgMdbEfkmVmqG4ZtmtVhrHOnTf7Md0zvF4Txt/rxni5s7Bd6Rc0juhDCCHnoqMPIYSci44+hBByLjr6EELIuejoQwgh59yOXtLtklZJml+w7FuSnpX0d0lTJPUs8dxFkuZJmiMpy41KQ2gxkdthX5HliH4ye85w/wgw0szeCPwT+HyZ559kZmPMLMtdokNoSZOJ3A77ALejN7M/AWsaLZtmZjvTP2eSzJkZQk2J3A77imoMmPoYcHeJdQZMk2TA/5jZLaUKkTQRmAiw/5ABPMOIspW+nuf8ln3YD/nswxkGz0zNMBjqPX4xvOqHDJ+/xA/KMBhm4yn+jDVLv9rLjRl8tj9q5Mxp5WdNAuAUfz8/WzfUjZlxd+MD8CK8mbMWT/LLSFSc24V5Td8hzL//mLIVrh7X22/VID+k+9YMed2pOjMoPdfucDfm4AsWuzEDRq13Y1aM7OHGLG4wb3txxxw6343JNBgqy35+wt/PTzPGjVlxv/NirOtSclVFHb2kLwI7gZ+VCDnBzJZK6gc8IunZ9ChqD+kb5RaAznUj2t78hmGfUq3cLsxrDauLvA6toslX3Ui6EDgT+KCVmHjWzJamv1cBU4Bjm1pfCC0lcjvkTZM6ekmnAVcC7zWz10rEdJHUrf4xcArgf14KoRVFboc8ynJ55V3AX4HDJS2RdBFwI9CN5CPrHEk3p7GDJE1Nn9ofeEzSXOBvwG/N7KFm2YoQmiByO+wr3HP0ZnZBkcVFb3toZsuAM9LHLwKjK2pdCM0ocjvsK2JkbAgh5Fx09CGEkHPR0YcQQs6pxNVjrUqD6oyLyt8+ZNy1d7nlHIw/SOMH86/yG9TRD2G4vx9/nZziLevsx/0ZlHirX9cr+AOmpjPWjTmXB/32fN8fELLi0/5Al+Gbn3djNq3o47dn2EIn4GzM5mWYzqi61LXOGOXcFmdrhoLO8kPstAybtyxDXeP9XJvL692Y0bP915YswwwyzEI2t2643x7+6deVYWaoLIPX9FCG7brPD6GTs35eHbap+A6KI/oQQsi56OhDCCHnoqMPIYSci44+hBByLjr6EELIuejoQwgh56KjDyGEnIuOPoQQcq4aM0xV34HA+eVD7n+u2P2oGrro8BvdmF+PPN2Ned/cqW7Mg7zDjcky8sqO8Et5gFPdmHUZBmf1ZJ1f2V3+oJEbP32RG/Nfn7vNryvLjX4fmpQhyIvJMgKuGWxeBjMnOUHvzFDQCW7EwGtecGOOYo4bc0eGgXd/4X1uzMN1fs6OzzAz1JS6y9yYbmx0YwZl2K4J4+91Y7LMDMUlfghzHssQ9HtnfekRcFluU3y7pFWS5hcsmyRpaXob1zmSivYqkk6T9JykhZKu9uoKoSVFbod9RZZTN5OBYhN1ftfMxqQ/exzySmoH3AScDowALpBUfiLYEFrWZCK3wz7A7ejTeTD9GaL3dCyw0MxeNLPtwC+AcU0oJ4RmEbkd9hWVfBl7maS/px9/DyyyfjA0uKvYknRZUZImSpotaTZrX6mgWSFUrGq53SCvKTozYQjNrqkd/Y+Aw4AxwHLg25U2xMxuMbM6M6vjwL6VFhdCU1U1txvkNQdUoXkh7L0mdfRmttLMdpnZbuBWko+yjS2FBl+jH5QuC6HNitwOedSkjl7SwII/x1P8wrhZwHBJr5PUgeSCyQeaUl8ILSVyO+SRex29pLuAsUAfSUuAa4CxksYABiwCLk5jBwG3mdkZZrZT0mXAw0A74HYzW9AcGxFCU0Ruh31F25xhqmed8TZnJp7f7PAL6tPeDRn9ykw35jiecGOWZZhq5vfr/UFV5/TwB2l8ihvcmGv4shuzkn5uzOEZZuK5kU+6MQNeXenG7Jje3Y3JNPhkmLO+zEw8zUl6o4E3+C7DwLLjJ/kxGa7sHzlulhuzcP1hbszWRb38ukb7dc1fcIxfzpEZypnrl9NpqH+x1bAe/qCz+ff7dfF1P8QfSAfwcWf9GZj9PWaYCiGEfVF09CGEkHPR0YcQQs5FRx9CCDkXHX0IIeRcdPQhhJBz0dGHEELORUcfQgg51zYHTEmvAC8XLOoDrG6l5jRVtLn5NbW9h5hZi985r0hew76zz1vTvtLmknndJjv6xiTNTu7+Vzuizc2v1tpbTK1tQ621F6LNEKduQggh96KjDyGEnKuVjv6W1m5AE0Sbm1+ttbeYWtuGWmsvRJtr4xx9CCGEpquVI/oQQghN1OY7ekmnSXpO0kJJGe6y3bokLZI0T9KcZELotied9HqVpPkFy3pJekTS8+nvYpNit5oSbZ4kaWm6r+dIOqM127g3ai2vIXK7ubREbrfpjl5SO+Am4HRgBHCBpBGt26pMTjKzMW34kq7JwGmNll0N/MHMhgN/INPUFS1qMnu2GeC76b4eY2berB5tQg3nNURuN4fJNHNut+mOnmRi5oVm9qKZbQd+AYxr5TbVPDP7E9B4ip1xwB3p4zuAs1qyTZ4Sba5VkdfNJHK7uLbe0Q8GFhf8vSRd1pYZME3Sk5ImtnZj9kJ/M1uePl4B9G/NxuyFyyT9Pf3426Y+kpdRi3kNkdstrWq53dY7+lp0gpkdTfKx/JOS3t7aDdpbllyKVQuXY/0IOAwYAywHvt2qrcm/yO2WU9Xcbusd/VLg4IK/D0qXtVlmtjT9vQqYQvIxvRaslDQQIP29qpXb4zKzlWa2y8x2A7dSO/u65vIaIrdbUrVzu6139LOA4ZJeJ6kDcD7wQCu3qSRJXSR1q38MnALML/+sNuMBYEL6eAJwfyu2JZP6N29qPLWzr2sqryFyu6VVO7f3r6w5zcvMdkq6DHgYaAfcbmYLWrlZ5fQHpkiCZN/+3Mweat0m7UnSXcBYoI+kJcA1wNeBeyRdRHKHxfNar4V7KtHmsZLGkHwUXwRc3Frt2xs1mNcQud1sWiK3Y2RsCCHkXFs/dRNCCKFC0dGHEELORUcfQgg5Fx19CCHkXHT0IYSQc9HRhxBCzkVHH0IIORcdfQgh5Nz/B9mtw6FLaFC8AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Ignore zero fission rates in guide tubes with Matplotlib color scheme\n", + "openmc_fission_rates[openmc_fission_rates == 0] = np.nan\n", + "openmoc_fission_rates[openmoc_fission_rates == 0] = np.nan\n", + "\n", + "# Plot OpenMC's fission rates in the left subplot\n", + "fig = plt.subplot(121)\n", + "plt.imshow(openmc_fission_rates, interpolation='none', cmap='jet')\n", + "plt.title('OpenMC Fission Rates')\n", + "\n", + "# Plot OpenMOC's fission rates in the right subplot\n", + "fig2 = plt.subplot(122)\n", + "plt.imshow(openmoc_fission_rates, interpolation='none', cmap='jet')\n", + "plt.title('OpenMOC Fission Rates')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# close statepoint file to release HDF5 file handles\n", + "sp.close()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}