diff --git a/BEAVRS/beavrs/assemblies.py b/BEAVRS/beavrs/assemblies.py
index 3c2e06f..0195556 100644
--- a/BEAVRS/beavrs/assemblies.py
+++ b/BEAVRS/beavrs/assemblies.py
@@ -49,7 +49,7 @@ class Assemblies(object):
 
         # Rectangular prism around the edge of the pinlattice
         self.lattice_surfs = \
-            openmc.rectangular_prism(17*c.pinPitch, 17*c.pinPitch)
+            openmc.model.rectangular_prism(17*c.pinPitch, 17*c.pinPitch)
 
 
     def _add_assembly_surfs(self,c):
@@ -57,7 +57,7 @@ class Assemblies(object):
 
         # Rectangular prism around the edge of the pinlattice
         self.assem_surfs = \
-            openmc.rectangular_prism(c.latticePitch, c.latticePitch)
+            openmc.model.rectangular_prism(c.latticePitch, c.latticePitch)
 
 
     def _add_bpra_layouts(self):
diff --git a/BEAVRS/beavrs/constants.py b/BEAVRS/beavrs/constants.py
index 6ff3657..e184d8a 100644
--- a/BEAVRS/beavrs/constants.py
+++ b/BEAVRS/beavrs/constants.py
@@ -153,10 +153,11 @@ class Constants(object):
         self.rcca_banks = self.rcca_bank_steps_withdrawn.keys()
 
         ## Keff=1 at approximately S 298
-        self.set_S(S,False)
-        self.set_SS(SS,False)
+        self.set_S(S)
+        self.set_SS(SS)
 
         self.update_dict()
+
     def set_S(self, _S, print_data=True):
         if isinstance(_S,(float,int)):
             if _S < 0:
@@ -173,7 +174,7 @@ class Constants(object):
         else:
             raise ValueError
 
-        if print_data:
+        if print_data and not self.first_thru:
             print("  RCCA Positions")
             print(f"    A: {self._A:3d}  B: {self._B:3d}  C: {self._C:3d}  D: {self._D:3d}")
             print(f"   SA: {self._SA:3d} SB: {self._SB:3d} SC: {self._SC:3d} SD: {self._SD:3d} SE: {self._SE:3d}")
@@ -215,7 +216,7 @@ class Constants(object):
         else:
             raise ValueError
 
-        if print_data:
+        if print_data and not self.first_thru:
             print("  RCCA Positions")
             print(f"    A: {self._A:3d}  B: {self._B:3d}  C: {self._C:3d}  D: {self._D:3d}")
             print(f"   SA: {self._SA:3d} SB: {self._SB:3d} SC: {self._SC:3d} SD: {self._SD:3d} SE: {self._SE:3d}")
diff --git a/BEAVRS/beavrs/pincells.py b/BEAVRS/beavrs/pincells.py
index 92cc978..1284420 100644
--- a/BEAVRS/beavrs/pincells.py
+++ b/BEAVRS/beavrs/pincells.py
@@ -11,7 +11,7 @@ from beavrs.corebuilder import AxialPinCell
 
 class Pincells(object):
 
-    def __init__(self, mats,constants):
+    def __init__(self, mats, constants):
         """ Creates BEAVRS pincell universes """
 
         self.mats = mats
@@ -48,13 +48,13 @@ class Pincells(object):
 
         # Rectangular prisms for grid spacers
         grid_surfs_tb = \
-            openmc.rectangular_prism(c.rodGridSide_tb, c.rodGridSide_tb)
+            openmc.model.rectangular_prism(c.rodGridSide_tb, c.rodGridSide_tb)
         grid_surfs_i = \
-            openmc.rectangular_prism(c.rodGridSide_i, c.rodGridSide_i)
+            openmc.model.rectangular_prism(c.rodGridSide_i, c.rodGridSide_i)
 
         # Rectangular prisms for lattice grid sleeves
         grid_surfs_ass = \
-            openmc.rectangular_prism(c.gridstrapSide, c.gridstrapSide)
+            openmc.model.rectangular_prism(c.gridstrapSide, c.gridstrapSide)
 
         # Grids axial surfaces
 
diff --git a/BEAVRS/extract-assm.ipynb b/BEAVRS/extract-assm.ipynb
index 8154ac8..6fdc4cb 100644
--- a/BEAVRS/extract-assm.ipynb
+++ b/BEAVRS/extract-assm.ipynb
@@ -249,7 +249,7 @@
    "outputs": [],
    "source": [
     "# Create surface objects for our \"root\" cell\"\n",
-    "lattice_sides = openmc.model.rectangular_prism(17*c.pinPitch, 17*c.pinPitch,\n",
+    "lattice_sides = openmc.model.RectangularPrism(17*c.pinPitch, 17*c.pinPitch,\n",
     "                                                   boundary_type='reflective')\n",
     "min_z = openmc.ZPlane(z0=c.struct_LowestExtent, boundary_type='vacuum')\n",
     "max_z = openmc.ZPlane(z0=c.struct_HighestExtent, boundary_type='vacuum')\n",
@@ -383,7 +383,7 @@
     "# Use a bounding box to define the starting source distribution\n",
     "lower_left = [-17*c.pinPitch/2, -17*c.pinPitch/2, c.fuel_ActiveFuel_bot]\n",
     "upper_right = [+17*c.pinPitch/2, +17*c.pinPitch/2, c.fuel_ActiveFuel_top]\n",
-    "settings.source = openmc.source.Source(\n",
+    "settings.source = openmc.source.IndependentSource(\n",
     "    openmc.stats.Box(lower_left, upper_right, only_fissionable=True))\n",
     "\n",
     "# Export the settings to a \"settings.xml\" file\n",
diff --git a/BEAVRS/extract-pin.ipynb b/BEAVRS/extract-pin.ipynb
index ced0ac1..e9f7561 100644
--- a/BEAVRS/extract-pin.ipynb
+++ b/BEAVRS/extract-pin.ipynb
@@ -194,7 +194,7 @@
    "source": [
     "# Create surface as the boundary of clad and cell pitches\n",
     "fuel_clad_OR = openmc.ZCylinder(name='Fuel clad OR', r=c.cladOR)\n",
-    "pin_sides = openmc.model.get_rectangular_prism(c.pinPitch, c.pinPitch,\n",
+    "pin_sides = openmc.model.RectangularPrism(c.pinPitch, c.pinPitch,\n",
     "                                               boundary_type='reflective')\n",
     "# Create a cell filled by the fuel pin\n",
     "fuel_cell = openmc.Cell(name='Fuel cell',\n",
@@ -329,7 +329,7 @@
     "# Use a bounding box to define the starting source distribution\n",
     "lower_left = [c.pinPitch/2, c.pinPitch/2, c.fuel_ActiveFuel_bot]\n",
     "upper_right = [c.pinPitch/2, c.pinPitch/2, c.fuel_ActiveFuel_top]\n",
-    "settings.source = openmc.source.Source(space=openmc.stats.Point((0, 0, 0)))\n",
+    "settings.source = openmc.source.IndependentSource(space=openmc.stats.Point((0, 0, 0)))\n",
     "\n",
     "# Export the settings to a \"settings.xml\" file\n",
     "settings.export_to_xml()"
diff --git a/BWR/BWR.ipynb b/BWR/BWR.ipynb
index 269d925..a41d644 100644
--- a/BWR/BWR.ipynb
+++ b/BWR/BWR.ipynb
@@ -14,17 +14,8 @@
    "metadata": {
     "id": "EChOeJ7qVUB7"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%env OPENMC_CROSS_SECTIONS /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n",
     "%matplotlib inline\n",
     "import openmc"
    ]
@@ -100,7 +91,7 @@
    "outputs": [],
    "source": [
     "pitch = 1.6256\n",
-    "pin_cell_box = openmc.rectangular_prism(width=pitch, height=pitch)"
+    "pin_cell_box = openmc.model.RectangularPrism(width=pitch, height=pitch)"
    ]
   },
   {
@@ -617,15 +608,15 @@
     "                      [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_region = openmc.model.RectangularPrism(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.region = openmc.model.RectangularPrism(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.region = ~assembly_sleave.region & ~assembly_cell.region & openmc.model.RectangularPrism(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])"
@@ -686,7 +677,7 @@
     "# OpenMC simulation parameters\n",
     "\n",
     "point = openmc.stats.Point((0, 0, 0))\n",
-    "src = openmc.Source(space=point)\n",
+    "src = openmc.IndependentSource(space=point)\n",
     "settings = openmc.Settings()\n",
     "settings.source = src\n",
     "settings.batches = 100\n",
@@ -739,7 +730,7 @@
       "         License | https://docs.openmc.org/en/latest/license.html\n",
       "         Version | 0.13.3\n",
       "        Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n",
-      "       Date/Time | 2023-10-31 21:52:13\n",
+      "       Date/Time | 2023-11-06 17:17:14\n",
       "  OpenMP Threads | 4\n",
       "\n",
       " Reading settings XML file...\n",
diff --git a/Depletion/depletion.ipynb b/Depletion/depletion.ipynb
index ef0fe98..1e16a83 100644
--- a/Depletion/depletion.ipynb
+++ b/Depletion/depletion.ipynb
@@ -16,17 +16,8 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%env OPENMC_CROSS_SECTIONS /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n",
     "%matplotlib inline\n",
     "import math\n",
     "import openmc"
@@ -63,8 +54,7 @@
     "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])\n",
-    "# materials.cross_sections = \"/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\""
+    "materials = openmc.Materials([fuel, clad, water])"
    ]
   },
   {
@@ -115,7 +105,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "bound_box = openmc.rectangular_prism(1.24, 1.24, boundary_type=\"reflective\")\n",
+    "bound_box = openmc.model.RectangularPrism(1.24, 1.24, boundary_type=\"reflective\")\n",
     "root_cell = openmc.Cell(fill=pin_univ, region=bound_box)\n",
     "geometry = openmc.Geometry([root_cell])"
    ]
diff --git a/RBMK/RBMK.ipynb b/RBMK/RBMK.ipynb
index 8c3be2d..178e166 100644
--- a/RBMK/RBMK.ipynb
+++ b/RBMK/RBMK.ipynb
@@ -20,17 +20,8 @@
    "execution_count": 1,
    "id": "7b28ed9f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%env OPENMC_CROSS_SECTIONS /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n",
     "%matplotlib inline\n",
     "from math import pi, sin, cos, sqrt\n",
     "import numpy as np\n",
@@ -312,7 +303,7 @@
     "\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",
+    "settings_file.source = openmc.IndependentSource(space=uniform_dist)\n",
     "\n",
     "settings_file.export_to_xml()"
    ]
diff --git a/SFR/SFR.ipynb b/SFR/SFR.ipynb
index 990d152..bb1461c 100644
--- a/SFR/SFR.ipynb
+++ b/SFR/SFR.ipynb
@@ -24,17 +24,8 @@
    "metadata": {
     "id": "EChOeJ7qVUB7"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%env OPENMC_CROSS_SECTIONS /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n",
     "%matplotlib inline\n",
     "import openmc"
    ]
@@ -497,7 +488,7 @@
     "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",
+    "src = openmc.IndependentSource(space=uniform_dist)\n",
     "\n",
     "settings = openmc.Settings()\n",
     "settings.source = src\n",
diff --git a/TRIGA/TRIGA.ipynb b/TRIGA/TRIGA.ipynb
index 1e875ac..d46f9f9 100644
--- a/TRIGA/TRIGA.ipynb
+++ b/TRIGA/TRIGA.ipynb
@@ -24,13 +24,10 @@
     {
      "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "env: OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n"
-     ]
+     "text": []
     }
    ],
    "source": [
-    "%env OPENMC_CROSS_SECTIONS /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n",
     "%matplotlib inline\n",
     "import numpy as np\n",
     "import openmc"
@@ -796,7 +793,7 @@
     "\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",
+    "settings_file.source = openmc.IndependentSource(space=uniform_dist)\n",
     "\n",
     "settings_file.export_to_xml()"
    ]
diff --git a/VHTR/VHTR.ipynb b/VHTR/VHTR.ipynb
index 61900b5..f75e3db 100644
--- a/VHTR/VHTR.ipynb
+++ b/VHTR/VHTR.ipynb
@@ -20,17 +20,8 @@
    "metadata": {
     "id": "ztdDaVCavN_I"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%env OPENMC_CROSS_SECTIONS /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n",
     "%matplotlib inline\n",
     "import openmc"
    ]
@@ -843,7 +834,7 @@
     "\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",
+    "settings_file.source = openmc.IndependentSource(space=uniform_dist)\n",
     "\n",
     "settings_file.export_to_xml()"
    ]
diff --git a/VVER/VVER.ipynb b/VVER/VVER.ipynb
index 9f57442..2ee3dc9 100644
--- a/VVER/VVER.ipynb
+++ b/VVER/VVER.ipynb
@@ -20,17 +20,8 @@
    "metadata": {
     "id": "BpmPdBBHTz2E"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%env OPENMC_CROSS_SECTIONS /opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n",
     "%matplotlib inline\n",
     "import openmc"
    ]
@@ -519,7 +510,7 @@
     "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",
+    "settings.source = openmc.source.IndependentSource(space=uniform_dist)\n",
     "\n",
     "settings.export_to_xml()"
    ]
@@ -570,7 +561,7 @@
       "         License | https://docs.openmc.org/en/latest/license.html\n",
       "         Version | 0.13.3\n",
       "        Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n",
-      "       Date/Time | 2023-10-19 23:09:50\n",
+      "       Date/Time | 2023-10-26 23:55:50\n",
       "  OpenMP Threads | 8\n",
       "\n",
       " Reading settings XML file...\n",
@@ -694,7 +685,7 @@
       "       79/1    0.36796    0.36399 +/- 0.00137\n",
       "       80/1    0.36640    0.36402 +/- 0.00135\n",
       "       81/1    0.35683    0.36392 +/- 0.00133\n",
-      "       82/1    0.38390    0.36420 +/- 0.00135\n",
+      "       82/1    0.38391    0.36420 +/- 0.00135\n",
       "       83/1    0.37263    0.36432 +/- 0.00133\n",
       "       84/1    0.36042    0.36426 +/- 0.00131\n",
       "       85/1    0.36007    0.36421 +/- 0.00130\n",
@@ -717,21 +708,21 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     = 3.5703e+00 seconds\n",
-      "   Reading cross sections          = 3.5247e+00 seconds\n",
-      " Total time in simulation          = 2.2554e+01 seconds\n",
-      "   Time in transport only          = 2.2468e+01 seconds\n",
-      "   Time in inactive batches        = 2.2849e+00 seconds\n",
-      "   Time in active batches          = 2.0269e+01 seconds\n",
-      "   Time synchronizing fission bank = 4.2737e-02 seconds\n",
-      "     Sampling source sites         = 3.7079e-02 seconds\n",
-      "     SEND/RECV source sites        = 5.6022e-03 seconds\n",
-      "   Time accumulating tallies       = 6.3748e-05 seconds\n",
-      "   Time writing statepoints        = 5.4034e-03 seconds\n",
-      " Total time for finalization       = 7.5800e-07 seconds\n",
-      " Total time elapsed                = 2.6181e+01 seconds\n",
-      " Calculation Rate (inactive)       = 21882.8 particles/second\n",
-      " Calculation Rate (active)         = 22201.6 particles/second\n",
+      " Total time for initialization     = 4.5971e+00 seconds\n",
+      "   Reading cross sections          = 4.5592e+00 seconds\n",
+      " Total time in simulation          = 1.3315e+01 seconds\n",
+      "   Time in transport only          = 1.3242e+01 seconds\n",
+      "   Time in inactive batches        = 1.2438e+00 seconds\n",
+      "   Time in active batches          = 1.2071e+01 seconds\n",
+      "   Time synchronizing fission bank = 4.0056e-02 seconds\n",
+      "     Sampling source sites         = 3.5185e-02 seconds\n",
+      "     SEND/RECV source sites        = 4.6822e-03 seconds\n",
+      "   Time accumulating tallies       = 9.8531e-05 seconds\n",
+      "   Time writing statepoints        = 4.2827e-03 seconds\n",
+      " Total time for finalization       = 1.0640e-06 seconds\n",
+      " Total time elapsed                = 1.7949e+01 seconds\n",
+      " Calculation Rate (inactive)       = 40200.5 particles/second\n",
+      " Calculation Rate (active)         = 37278 particles/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
diff --git a/examples/post_process/post-processing.ipynb b/examples/post_process/post-processing.ipynb
index 3d36ff8..fafed71 100644
--- a/examples/post_process/post-processing.ipynb
+++ b/examples/post_process/post-processing.ipynb
@@ -216,7 +216,7 @@
     "# 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",
+    "settings.source = openmc.IndependentSource(space=uniform_dist)\n",
     "\n",
     "# Export to \"settings.xml\"\n",
     "settings.export_to_xml()"
@@ -348,8 +348,8 @@
       "         License | https://docs.openmc.org/en/latest/license.html\n",
       "         Version | 0.13.3\n",
       "        Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n",
-      "       Date/Time | 2023-10-31 23:19:21\n",
-      "  OpenMP Threads | 4\n",
+      "       Date/Time | 2023-10-27 00:37:10\n",
+      "  OpenMP Threads | 8\n",
       "\n",
       " Reading settings XML file...\n",
       " Reading cross sections XML file...\n",
@@ -357,13 +357,7 @@
       " Reading geometry XML file...\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 O16 from /opt/xdata/endfb-vii.1-hdf5/neutron/O16.h5\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      " Reading O16 from /opt/xdata/endfb-vii.1-hdf5/neutron/O16.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 Zr90 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr90.h5\n",
diff --git a/stress-test/lattice-computations/BEAVRS.ipynb b/stress-test/lattice-computations/BEAVRS.ipynb
index f879f72..1f57fed 100644
--- a/stress-test/lattice-computations/BEAVRS.ipynb
+++ b/stress-test/lattice-computations/BEAVRS.ipynb
@@ -10,20 +10,9 @@
   {
    "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"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "%env OPENMC_CROSS_SECTIONS=/opt/xdata/endfb-vii.1-hdf5/cross_sections.xml\n",
     "import openmc\n",
     "from IPython.display import Image"
    ]
@@ -38,9 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -70,9 +57,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "uo2 = openmc.Material(name='uo2')\n",
@@ -101,9 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mf = openmc.Materials((uo2, zirconium, water, pyrex))\n",
@@ -120,9 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "colors = {}\n",
@@ -144,9 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pitch = 1.26\n",
@@ -185,9 +164,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We need a cell to contain the fuel_pin universe.\n",
@@ -205,9 +182,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -240,9 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "clad_ir = openmc.ZCylinder(r=0.56)\n",
@@ -267,9 +240,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "main = openmc.Cell()\n",
@@ -286,9 +257,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -321,9 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -344,9 +311,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Define the cylinders which bound each radial zone.\n",
@@ -384,9 +349,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "main = openmc.Cell()\n",
@@ -403,9 +366,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -442,9 +403,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "moderator = openmc.Cell()\n",
@@ -457,9 +416,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "lattice = openmc.RectLattice()\n",
@@ -492,9 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "main = openmc.Cell()\n",
@@ -511,9 +466,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -549,9 +502,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -583,9 +534,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -606,9 +555,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "lattice = openmc.RectLattice()\n",
@@ -651,9 +598,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "height = 100 # Finite height is not strictly necessary but may avoid floating-point errors\n",
@@ -673,9 +618,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "root = openmc.Universe()\n",
@@ -689,9 +632,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -718,13 +659,11 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "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.source = openmc.IndependentSource(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",
@@ -735,7 +674,6 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -908,9 +846,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "tallies = openmc.Tallies()\n",
@@ -940,9 +876,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dist_filt = openmc.DistribcellFilter(fuel.id)\n",
@@ -959,7 +893,6 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1124,7 +1057,6 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1182,9 +1114,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1216,9 +1146,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "many_uo2_mats = []\n",
@@ -1236,9 +1164,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1279,5 +1205,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/stress-test/mc-performance/build.py b/stress-test/mc-performance/build.py
index 1cb8946..130f257 100755
--- a/stress-test/mc-performance/build.py
+++ b/stress-test/mc-performance/build.py
@@ -484,14 +484,14 @@ materials += [fuel, clad, cold_water, hot_water, rpv_steel,
 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)
+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)
@@ -642,8 +642,8 @@ 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.source = openmc.IndependentSource(space=
+    openmc.stats.Box([-160, -160, -183], [160, 160, 183]))
 settings.export_to_xml()
 
 plot = openmc.Plot()
diff --git a/stress-test/mc-performance/generate_tallies.py b/stress-test/mc-performance/generate_tallies.py
index 44bcfbe..05b65d3 100755
--- a/stress-test/mc-performance/generate_tallies.py
+++ b/stress-test/mc-performance/generate_tallies.py
@@ -25,7 +25,7 @@ assemblies = ([(1,i) for i in range(6,13)] +
 tallies = openmc.Tallies()
 for i, assem in enumerate(assemblies):
     x, y = assem
-    mesh = openmc.Mesh()
+    mesh = openmc.RegularMesh()
     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)
diff --git a/stress-test/mg-criticality/make_model.py b/stress-test/mg-criticality/make_model.py
index 44a249f..af18923 100644
--- a/stress-test/mg-criticality/make_model.py
+++ b/stress-test/mg-criticality/make_model.py
@@ -98,7 +98,7 @@ class Case(object):
         else:
             raise NotImplementedError
 
-        settings_file.source = openmc.source.Source(space=uniform_dist)
+        settings_file.source = openmc.source.IndependentSource(space=uniform_dist)
 
         settings_file.output = {'summary': False}
 
@@ -311,6 +311,6 @@ class Case(object):
         if success:
             spfile = 'statepoint.' + str(self.batches) + '.h5'
             sp = openmc.StatePoint(spfile, autolink=False)
-            self.keff = sp.k_combined
+            self.keff = sp.keff
 
         return success
diff --git a/stress-test/multi-group-xs/mdgxs-part-i.ipynb b/stress-test/multi-group-xs/mdgxs-part-i.ipynb
index b311465..c19410c 100644
--- a/stress-test/multi-group-xs/mdgxs-part-i.ipynb
+++ b/stress-test/multi-group-xs/mdgxs-part-i.ipynb
@@ -32,7 +32,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -260,7 +260,7 @@
     "# 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)"
+    "settings.source = openmc.IndependentSource(space=uniform_dist)"
    ]
   },
   {
@@ -399,15 +399,15 @@
      "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",
+      "/data/home/jupyter-aparler/.conda/envs/nuclear/lib/python3.11/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",
+      "/data/home/jupyter-aparler/.conda/envs/nuclear/lib/python3.11/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",
+      "/data/home/jupyter-aparler/.conda/envs/nuclear/lib/python3.11/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",
+      "/data/home/jupyter-aparler/.conda/envs/nuclear/lib/python3.11/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",
+      "/data/home/jupyter-aparler/.conda/envs/nuclear/lib/python3.11/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=13.\n",
       "  warn(msg, IDWarning)\n"
      ]
     }
@@ -492,23 +492,23 @@
       "                                      %%%%%%%%%%%\n",
       "\n",
       "                 | The OpenMC Monte Carlo Code\n",
-      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
+      "       Copyright | 2011-2023 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",
+      "         Version | 0.13.3\n",
+      "        Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n",
+      "       Date/Time | 2023-11-07 11:17:14\n",
+      "  OpenMP Threads | 32\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",
+      " Reading H1 from /opt/xdata/endfb-vii.1-hdf5/neutron/H1.h5\n",
+      " Reading O16 from /opt/xdata/endfb-vii.1-hdf5/neutron/O16.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 Pu239 from /opt/xdata/endfb-vii.1-hdf5/neutron/Pu239.h5\n",
+      " Reading Zr90 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr90.h5\n",
       " Minimum neutron data temperature: 294 K\n",
       " Maximum neutron data temperature: 294 K\n",
       " Reading tallies XML file...\n",
@@ -1047,16 +1047,7 @@
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "beta.export_xs_data(filename='beta', format='excel')"
    ]
@@ -1115,14 +1106,12 @@
     },
     {
      "data": {
-      "image/png": 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/kAzDMAzD/GPoLRDvBTcABVrLhep1/RqI7774KoqVXDUsrlvAClYwghGMYQwjGKEc5ahDHYQQYuTNfwFoG4gXJiSgurkM1rBGAALgBjeYq/8zhjEAgIAAAP4IH9Sh/KX/FwTjZqJe8kaTEGgyAprV/7eqBngUUPKB5GnSDscP/XU6eEqCBlMgZRhQZQvUmXGvWnPApB6osANu2QNby261Oba6qhrTP/yOW6AUJo2NMGtshGlTE0yammDU1IQPXF3BV6lg3NyMX6qrMRCtgfjNK3/h19kbYdHQAPvqavz7jz9gLa+CdW0trGtrYdbQAAWPB+eqChg3lQJft627bdxeNH8rASVArRC4Yn8GdUKgTgjUGgFCBRBQBjQIgevWPABtA/HraXvx4o0DKDMDrtoCf9oB9UIAmo/TqXXfsnoHcPd+rXZcWYqi0f8DAPwJ4AjAfegKE0BhChg3A4EygPJxXPoAXkZim+MXfbseRRY/AE3mwPkQoCSMe99kATTYAMYygCiBGjeM9PHC+dNWbY4PCQHS0wEeD3By4l4mJoCpKfcqLuaCeVNT4MUXgZdeavv5ffUVcOsW4OwMBAYCAwYA1tbcjQPDMAzDMHe/e+JPNiHkeXDpK3B3d9fpuRW0ueW9XP1fV4ob8zusO9p8CDWQdbo/H3wAgAoqCCDAuJLpWIWFbfb5RrkPFEoMxEAMx3DYN9vDqLmTkwEQtATsrcJSWuPOUee6rDoAIP914zbL8uYmfLEQqLUAZFYESaNNcMvBBBV2XPBe7QKo+Op9AfCN2v643BTy8GtYWMvyl1OmdFk2X6FAE6XgkdZruGlphoc++ACO1dXwLirCtN9/x4CKCriVlsO0qanN8YMrVR3O+chf6Xj6Qtt1zTygwpR7UQADZVxgn+wsA95pu+8AWQUG1gCF1sBNc6BZAEDQxL3afKcKKE1vdCi/yuEEYHuJW/A42+W1A0Bm9f0A2n5BOaFRoBF/QNlgg5Lr41BS+ABQ7QDUOgK1zgAlQLMZoDDFuXMdA/Hly7lAvD0zMy4gr68HKOWW33oLeOaZtvudOAHY2wMBAdw+DMMwDMP0L0MG4kUAtJuIB6rXdUAp/RTApwAwfPhwqstKNBs1Aw2929fOzabj8bxmoGOMCABQQtm6H5ph62zZYZ9Y1TdQQNGybA5z2Kv/c4AD8pAHYxjDBS6YLXq2dxXtQkCEqM2yOc8Gg/NaI7n2gbySACBAk5BrXXf9w7rNdldfb/AUgKoXP0WUkDZBOAA0+/siKSSkZfnduXNb3tvU1MC2pgY3bW1hVVsLcV4ufm13ziALiw7lCFWAcy330rBqAkbc7Phjszk5C/9K595XmAB/ugIlFkChFXDdFhAoAcsm4JILUODA71iWeV3PF65myrPpsK7R8Rxgqb65c73U8SAVD+CpACUfGfUvA9jaZnOt3XmAZwFUDeECdrW6Ou6lUV0NlHTynGnmTKBB/bNvbw94eHCt6ppXcTHg5QXcdx/wwANcCg7DMAzDMLpjyED8KICXCCEHAIwEUN3f+eEAUCa/gbq6OlRVVUGhUEChUKChoQH19fVoaGiARCJBSUkJqqurMXv27A7Hh0eMQk5ODurq6uDs7IyGhgZUV1dDJpOhsbGxzb6TXhjb4XglUXJNt2q16v/y0bb1PR3pWCha2GadSqXCIstFsOHbYIhwCBZNWAQ7pR2UMiWUNUo0VzajLquOu1GgQHDYkDbHe9RboaKbz4ZPueNMG7mX3SDbNttDVf74ZUIOlAKg1pqg8EEjFHsS5HoAOe4qXLFToIpydylGneRLKCdPAkpLOy27ytISVZbcjUutqSlqra077HNk/tMIf20tbORyDLt6FQt++QXeeXnwz82FRUPbuytnV88Ox//LyhOaez+7BmD89a4/i9qApg7r9jiNQvZ1I2S6CqDwcIexqTnkTXLIm+SobqhG9q1sVDdWQ6FS4OFwxw7HWzrIUdXYYXUrnvoOj6+Ep3fHxyRNcyYDRlUAAGHxg+AVjUJTiTdohRdQ4Q3wGwC5K9BsjkEds6Kg/eN56xb3Sk3tvCpffw088UTbdW+8wQXpEyawFnWGYRiGuR16C8QJIbEAHgLgQAgpBPAmACEAUEo/BhAHYAqAHAB1AJ7WV126w+fzYWlpCUvLjq3VAPDggw92e/wvv/zS5bbGxkZUVFSgoKAAubm5iIiI6LDPkCFDUFVVBblcDpVKhebmLvJSANwfcX+b5Vu3bkFaw+WN/4E/ELs/Fh4eHggKCkLwg8EIDAyEVCrFQw89hKDAIAhs2n7dRm5GcJzjiOZbzaDNFMbuxmgqbWp5KW61ttQTIQHPuO0gOy5ZSlwHIFAA1rcorL9vRIDWdmJCQAQ8CF2NYTzcHGh3HzLK3h5j6utRqVCAD8CEx0NJUxOKm5qgoG1bsG06af1ODQtDU0UFbtra4sTw4TgxfHjLNi8eD/bNzahpakJgZSXmEoKZ7U/Qh2Rq84GDO6yb/fFpICuLWwiuB6ZN4/I8ggMAPz8u0jUzAwgBpR1b5DdGrMel0ksolZfC1tQWdc11KKsrQ1ltGUrlpahurG7Zd8JQnw7HE+Oalpu45gG/AQN+a7sdPFCoYEStQUQHoD2IUXMzl4/e2Aiouniio23EiLbLVVXA5s2ty8HBQGgoEBTU+nJ2BvgdHyQwDMMwDKOmz1FT5vawnQJYoq/y7wbGxsZwdXWFq6sr7rvvvk73ycnJaXlPKUVFRQWKi4tRXFyMvLw8/PrrrygoKEBZWVmH/Phz5zomhefl5SEvLw/Hjx9vWbdhwwbweDwUFRXBxcWlZb2ppykCvgvocA4NhVwB+SU56rLqQJs6BpL12fVdXzwA2kBBQdGYXQ/FzY4tyuOvG+P+dBc4znaEwLr1R1FFKcqbmyGtq8MluRzZ9fXwMjXtcHxuQ9c5RVdVKlzl8wFTU2SamuKKuXmHQFz644+w5vHgUlPD5W6UlnL/z88Hrl8Hfv8dKCzk8jz8/TsWUlzc+v6vv7iXhpkZ18uyvh7w9wd57z3goYfaHP6fkf/psv4AIG+So6C6AJnlmRg1aFSbbU3KJhBC2jxNaY+qc6aaSDUGOAvbbBMKAdG2EAQ4BWDy4OkYZTcT5TeMUFzMXdbVq8APPwDl5Vz6yuB29yG//952uf3la9jacp1Sjx0DOrmXYhiGYZh/NNJZS93dbPjw4TQ5OdnQ1bgrpKenY926dbh27RrKy8tRVlbWZYu6QCBAU5M6eFNLSEjA0qVL8eCDDyIqKqrTFvvuUErRWNQI2R8yNJc3gzZR1EnqUJtZizpJHZrLWutiFmiG+9Lb3oz8NeUvVPzEJccIXYRwmOoA6wetYTfRDkaORj2Wn1tfj9+qqpAql6NOpUJ5czOy6upwpa5OKzuf86SzM/b5+bVZ53X+PK41NMCSz8e/XV2xaMAAiExN23xGALjmY6WSa0LWNmBA58nXndm0CVizpu26U6cAX9/bHteQUoryunJIy6W4XnUdORU5yKnMQU5FDjLLMlHTVNOyb83qGlgYtUbCl0ovYegnQ1uWJ3lPQoRnBB70eBBhrmEQ8ARQURX4vM6btI8f5zp/VlRwH013eDygqalt63hhIfcaORJo/3EzDNMWISSFUjq85z0ZhrnXsED8b6SpqQlXrlzBX3/9hfT0dJw+fRppaWmoq6uDr68vsjRpFGrz58/HN998AwAQCoWYOnUqpk2bhsjISDg4ONxxfeSX5aiIq4DsDxlsxtpg4NKBbbb/7vY7moo7tpSDAJbDLWERagHbCbZwnOUIwu99tNagVCKzrg4/3rqFs9XVyKyrw3teXpjj5NRmP+Hp0x1SYJyEQkTY2GC6gwMGGBtjmIUFLLtLYamrAzIzAYkEyMjgXpcucVGmtsuXubQVjeZmrsVcqeR6SZ46xQ1GriOUUhTICnCh6ALSb6RjQ8SGNtvfOPkGNp/Z3OmxZkIzBDkF4a8bfyHCMwJPD30as/079o/QuHmT+wjS01tfqamtOeju7kBeXttjliwBdu/msoPuv58b1SU8nA29yDCd+ScH4ikpKU4CgeAzAIHQ7ySEDKMPKgCXFQrFs2FhYTc724EF4v8AlZWVqKmp6ZDa4u3tjatXr3bYn8fjYfTo0Rg0aBCMjY0xf/58jB49GkKhsMO+dyJ5WDJq02tBFT38DPIAjzUeGLyhY5727SppbITbuXPdZXYA4IaG9DUzww8BAfAxN+99AUVFXP7GTz8BaWnAxYtc07DGt98CUVGty0IhMHYsMGUK9xKJgBs39DYL0Om801j761qklqSitrm2232tja1Ruaqy45OCbqhUwPnzQGws4O0NLGs7/D4CA7l7Fm22tkBkJJdqP2ECYGPT6+IY5m/tnxyIp6WlHXVxcfFzdHSU8Xi8eytgYf7xVCoVKSsrsy4tLZWEhIRM62wfFoj/g+3YsQP79+9Heno66uq6H4rv2WefxZ49e/RSj9rMWjQWNkJ2XoaK+ArIzsk6DAnp960fnB9zbrOOqigI7/bzGhpVKpyrrsbRW7dwpa4OZ2UyVCkUne4rve8++OhyaJBly4AdO7re7u4OFBRwzcRLlrQN2nWIUoqcihz8lvcbfsv/DadzTyOvum3z9XDX4bj4/MU2677880tIb0mxIHgBApy67mfQlYAA7iFCV3g8wMqKS6t/6622DxMY5p/mHx6IXwsKCqpkQThzr1KpVCQ9Pd02JCRkSGfbWSDOgFKKrKwsHD16FEePHsW5c+c6jPLx4YcfYvHixW3W1dTUdDnazJ1ormzGzQM3kb8lH42FjQABHqx/EDyj1hbl5opm/O76O8yDzOH+mjscH3XsU4ttZ1SU4i+5HMdu3UJMaSly1J1BbQUCVIwe3WbfP2tq8ERmJlYOGoQoJyeY9HV4EIUCOHcOOHKEazXPzOx63yFDuN6T/SSvKg+nck/hm/RvcCr3FHZM2oEXR7zYZh+37W4oruE6q258aCPWjl3b53Lkci495do1rjOndt9XbfPnc7OIMsw/1T88EM8NCQkpN3Q9GOZOpKWlOYSEhHh2to0F4kwHN27cwJEjR/DFF18gJSUFKpUKVVVVsLJqnaK9rq4ODg4OCAkJwfLly/Hoo4+Cr4ex6pQNStRcqIHNgzZt1mcvy0bRjtb5n0y8TeC60BXOC5xh4t6uU+Vtym9owL6SEpjy+XilXVrPhEuX8EtVFQDAx9QUx4OCILqTFvPCQi4gj4sDEhKAWq10kVmzgEOH2u7/88+Am5vem4rrm+vBIzwYC1pn86moq4D9e/Zt9nvE5xEsGbEED3s9DB7pexonpVxe+dGjXNaOVNq6raAAGNi2ewFycriUF4b5J2CBOAvEmXsbC8SZ21ZXV4dLly5h1Ki2w+e9/vrr2LJlCwBumMZdu3Zh7ty5MO9LHvUdOOd5Do15ncyGQwDHRx3hscYDFiH6GS9PqVLBJCmpQ0fP8TY2WOTmhmn29hDy7qBPUX098P33XHPx2bPAn39yg3RrUAo4OnIz8EydCnzwAddq3k/+uvEXHvzywTbjnGt423njhbAXcCLnBJ4IegKPBTwGc6O+/0xcugRs28bNCnr0aNttR48C06dz9yFLlwKvvspGXmH+3lggzgJx5t7GAnFG58RiMaTazZYArK2tER0djeXLl+slZUWbsl6J/PfyUbqnFM1VzVDJO85KYzLEBN47vOEQeecjwGhrVKmw/vp1fFxS0mlOuauREbxNTPCooyMWubnB6E6C8qIiLuLU9ssvXG9GgItAd+wAXnyxX4ccUaqUOJFzAjsv7ET81fgu9xtkNQjXll2DgKe7ug0dygXqGsOHA2+/zX0kLCBn/o5YIG7YQFwqlRo98sgjouzs7JYu5tHR0QMsLCyUGzduvKFZl5OTI3ziiScGl5eXCwkheOqpp8rWrl17EwDq6urIyJEjxU1NTUSpVJKpU6dWvv/++8UA4ObmFmRubq7k8XgQCAT08uXLneYqXr16Vfjrr79aPPfcc5X6vmZtXdVvzpw5nidPnrS2t7dXaH827W3atMkpJibGkVKKJ598smzdunU3AWDDhg1OX331lSMhBGKxuO7bb7/NNTMzu+uC0s6+677qLhBnQwExt+Wzzz7D9OnT26SjVFdX480334SXlxemTJmCxYsXo7irxN87xDflY/C6wXig4AGE3wiH3zd+sJ1g22afhmsNuPzIZVzf2M3c9bfBmMfDFi8v3AoPR1xQEKbZ27f5h1TS1IQkmQwvX70Kt3PnUN7UyRCNvdU+CAe41BUNSoH//IebcOirr7jc86Ym7v96xOfxEekTiRPzT+DKS1ew/P7lsDa27rCfrFGm87LbD92enAxMnAhERHAPEBiGYQxBKBRi27ZthVevXs24ePFi5ueff+6UkpJiAgAmJib0zJkzUqlUKsnIyJCcPHnS6uTJky2PC0+fPn0lKytL0lUQDgBxcXFWqampOhw1oPc6q98zzzxTfvTo0ezujrt48aJJTEyMY2pqamZmZmbGiRMnbC5fvmx8/fp14aeffup86dIlSXZ2doZSqSSfffaZnf6v5O7DAnHmtowePRpHjhxBaWkptm7dCm+thN2ysjL89NNP+Oijj+Dp6YmLFy92c6Y7xzfjw3meM0J+DkHYn2GwGKqVksID3BZ3EszqAI8QTLa3xw9BQbh+//1Y6+EBF6O2ExFVNTeDp+tm2tdeA9avbztVZXY28OSTgJ8f8Oyz3ERBMTFcoK5nInsRtk/cjqLoInzyyCfwc2idOGnL+C0dWsOl5VIoVT3MAtSN0lIuc2fECMC4NXUdp08Do0cDXl7AJ5/c9ukZhmFui4eHR/Po0aPrAMDW1lbl5eVVn5+fbwRwwwJbW1urAKCpqYkoFArSlwEG4uPjLdauXTvo+PHjtmKx2F8ikfQ8652eTZ48We7o6Nhtq096errp0KFD5ZaWliqhUIjw8PCaAwcO2ACAUqkktbW1vObmZtTX1/MGDhzYZkZCmUzGe+ihh7x9fX39RSJRwJ49e1pa23bv3m0XFBTkJxaL/efNm+ehUDc+7dq1y97Hx8ff19fXf8aMGYMBYP369c4ikShAJBIFbNy40QngnnIMGTIkICoqysPb2zsgPDxcJJfLW76QVatWuXh6egaGhYX5ZmdnG/dUnzvBAnHmjjg4OGDFihXIysrCl19+2WGs8ubmZhgZ9d/vC8tQS4SlhEG0WwSBvQD2U+1h5NC2/MbSRsj+1G1LrbuJCTYOHoy8++/H+15esFCnoyweMAB27cZfv9Xc3GFUmj6xtQXefJPrxbhhQ9sBt3NyuJbxa9eAp57iEq37ibmROZ4Pex4ZizPw28LfsChsEZ4Pe77NPvImOUZ+NhIhH4fgcObh2/4cZswALlzgBpNpn5Vz7Rq3zsUFKGeZpQzDGIBUKjWSSCRmY8eOlWvWKRQKiMVif2dn55CxY8fKxo0b19Irf/z48aKAgAC/rVu3dppLOXHiRHlQUFDt4cOHc7KysiT+/v63/ag1LCzMVywW+7d/HTlypMuc0p7q15XQ0ND6CxcuWJaWlvJramp4CQkJ1gUFBUaDBw9uXrJkSengwYODnZycQiwtLZWzZs1q84f58OHDVi4uLs1SqVSSnZ2dodmemppqcvDgQbvk5OSsrKwsCY/Hox9//LF9cnKyydatW11Pnz59RSqVSj755JP8pKQks/3799unpKRkJicnZ8bExDiePXvWFADy8/NNli5dejMnJyfD2tpaGRMTYwsASUlJZt9//71denq6JCEhITstLc28u/rcKRaIMzrB5/OxcOFCXLlyBR988EHLCCvh4eEICQlps69CobizQLQHhBC4LXJDeFk4Ag51HFUkfXI6UoelQvKkBKrmjrnld8KIx8PLgwahZNQobB48GFu8vNpsp5RiREoKhiYnQ9rD2O09srEB1q0DcnOBjRs7nwFHa6Sb/kIIwRiPMdj9yG7weW1H0ll2YhmqG6uRUZaBF46/gEZFJx1u+8DNDfjoIyArixvmUFtjI2D3j3zQyTB/X9HRGEAIwnrzmjsXHu2PnzsXHtr7REdjQE9ldtVy3dX66upq3qxZs7zeeeedAjs7u5Y/MgKBAFlZWZL8/Py/UlNTzS9evGgCAGfOnMmSSCSZP//8c/aePXucfvrpp05HGrh27ZpJaGhoAwBIJBKjxx57zGPSpEktPfV37dplv2bNGueoqCiPyMjIIYcPH+70D0BKSoo0KytL0v41Y8aMms727239OjNs2LCGZcuWlY4fP94nIiJCFBAQUMfn81FWVsb/8ccfbXJyctJLS0v/qqur4+3evduu3bH1SUlJVosWLXI7ceKEhb29vRIATpw4YXn58mWzkJAQP7FY7H/mzBmra9euGcfHx1tNnTq10tXVVQEAzs7OylOnTllMmTKlysrKSmVtba2KjIysTExMtAQANze3xlGjRtUDwNChQ+tyc3ONASAxMdFiypQpVZaWlio7OzvVww8/XNVdfe4UC8QZnTI2NsayZctQVFSEDRs24Msvv+ywz/PPP48ZM2agtLRUr3UhhIDHb/sjXvx5MeSXuAaKm1/dxAXfC5Bd0H0es4VAgNUeHjBrN6Tjx8XFuN7QgLTaWgRdvIi8+vo7L8zaGli7lgvIN21qDcgHDuRaxbWpVP2SrtIZFVUhPqe1Y2d5XTkmfjMRORU5d3xuLy/uQcDPPwMe6j+9773XdjJThmGY2+Hs7Kyorq5u88u8oqKC7+DgoNiyZYujpkU5NzdX2NjYSCIjI73mzJlT8dRTT1V1dj4HBwflmDFjao4dO2YNAIMHD24GADc3N0VkZGTVuXPnOgw1VVJSIrC0tFRqZrj29/dv+u6779rMvpacnGy2cePGGwcOHMjbu3dv3oEDBzpNnehri3hv6ted5cuXl2dkZGQmJydLbW1tlT4+Pg3Hjh2zcnd3bxwwYIDC2NiYzpgxo+r3339vE+AHBwc3pqamSoKCgurXrl3r9sorr7gCAKWUzJkz55bmBiI3N/fy9u3b+9whzcjIqOWPIZ/PpwqFottcoa7qc6fYnylGLywsLLBu3TqIRKI26xMSEvDll1/i6NGj8PX1RU7OnQdhfUGbKIig9d9aw/UGpN6fiuyXs6Go0W8HRwD4SKvzajOleFwiQfadtoxrWFsDa9ZwAfmiRcDBg22TqAFg61ZuyMPruu3A2htKlRIv3fcSLIxaf9f+lvcbhn4yFPsu7QOlFA2KhjsqY8IE7vJPnwb+/e+22ygFHnvMIJfOMMw9zNraWuXk5NR89OhRSwC4ceMG/9SpU9bjxo2Tr169ukwTELq7uzdHRUV5+Pj4NKxfv77NCBvFxcWC8vJyPgDI5XKSmJho5efn1yCTyXiVlZU8gMtBTkxMtAoODu7QQpOdnW3k7OzcZTpKY2MjEQgElKdufXj99dddly5dWtbZvn1pEe9t/bpTVFQk0FzDjz/+aPPss89WeHp6NqWmplrU1NTwVCoVfv31V0s/P782fwByc3OFlpaWqsWLF1dER0eXXrp0yQwAJk2aJDt+/Lit5rw3btzgX7lyxWjixImyY8eO2ZaWlvI16yMiIuRxcXE2NTU1PJlMxouLi7ONiIjotOVfY9y4cfK4uDgbuVxOKisreQkJCTbd1edO9d94ZwwDYNWqVS3va2pqcODAAbz22msQ9NPQe26L3ODwqAMuT7sM+SU5aCMFKFD03yKUHy6Hz0c+sI+07/lEtynW3x/zJRJcUk/Y80dNDUKTk7Hd2xvPu7re8eygALiAfPfujuvz8oDXXweUSuDXX4HERGDkyDsvr5eEfCFeG/0a/j3033jrt7ewO3k3FCoF5E1yLPxhIQ5nHkZycTKiH4jGy/e/3CGtpS8efLDjupdeAv73P+DwYS6T5/XX7+BiGIYxiO3bUbx9O257OK7YWOTFxiKv5z3b2rdv3/XFixe7r1y5chAArFq1qjggIKBNbl1CQoLFkSNH7EUiUb1YLPYHgA0bNhQ9/vjj1QUFBcKFCxcOViqVoJSS6dOnV8ydO7daIpEYzZw50xvgOi8++uijt2bPnt3hMW1ISEhDRUWFUCQSBezevTt3woQJtdrb4+PjLcaMGSNXqVRYsmSJW2RkZLWm4+idKCwsFHRVv6lTpw4+f/68ZWVlpcDZ2Tn4tddeK16+fHn52LFjvfft25fn6enZDADTpk3zqqqqEggEAvrBBx/kOzg4KMeNG1c7derUyuDgYD+BQICAgIC66OjoNjcOKSkppqtXrx6oGTZx9+7deQAQFhbWsGbNmqLx48f7qFQqCIVCumPHjvzx48fXrlixomTMmDFiHo9HAwMD6w4dOpQ7b968W8OGDfMDgAULFpSFh4fXS6XSLjuvjR49um7mzJkVgYGBAfb29s3BwcG13dXnTrFxxJl+QynFRx99hOXLl6NJa0i/kSNHIiYmBj4+Pv1an/rr9bjy4hVU/tx2SFaLUAuEJIZAaCPs4sg7o6QU7+XnY11uLpq1/v1NsrWFm7Ex3vDwwGBTU90X/PTTwN693HtTU+DMGWDYMN2X00spxSmYd3gerty60mHbvKB5+GbWNzor6/p1Ln1F+9fdypVcX1cT3UzEyjB6w8YRZxP6tFdaWsqPjo52S0pKspo/f365TCbjb968uWTnzp0OsbGx9iEhIbWhoaH1K1eu7LRVnOlfbEIf5q6SnZ2NBQsW4I8//mhZZ2pqivfeew/19fV49tlnYdNZx0M9oJTi5v6byHk5B83lrSMn8a34CEsOg5lIf0O2/llTg/mZmZC0S00x4fHwgS5byAEuAh0/nmsF1zAyAj7/vGMvx35U21SL5fHLsSd1T5v1y+9fju0Tt+usHEq5GTi3b28bjAcEcKM8GvB+hGF6xAJxFoj35Mknn3SPiYnJN3Q9mM6xCX2Yu4pIJMKZM2ewefNmaDqe1NfX46WXXsKrr76K+++/H9f7KZGXEALnJ5wxInMEjAZqPakigJGrfoddHGppiZSwMCwfOLDN+gaVCp/oeiIkQoD4eOCttwDNcIpNTcCCBVxeuUrFpa70M3Mjc3w69VMceuwQbIxtAAB8wsdzw57TaTmEcOnxf/0F3H9/6/qMDC4759NPdVocwzBMv2JB+L2LBeKMQQgEAqxevRoXLlxAYGBgm21SqRRLlizp1/oYORjh/mv3w3aiLYiQYNj5YRBY6D9v3YTPx3Zvb/wSEgInrfHGd4pEumsN1xAKgTfeANLSuJk4NTTzw/v6Aq+8ovdZOTszy28W0henI8IzArum7IKfo1+b7ZX1lbhVd+uOywkMBH7/HfjwQ8BM/bBDoQBeeAEICeGGPGQYhmGY/sICccagQkNDkZycjFdffbXN+qCgoH6vC0/IQ8iJEDxQ/ADMxW1HZ6KUQqXQ7Zjj2sbb2kJ6332IcnTEa4MGIdy643TxOuPnx0Wjkya1rvv1Vy4K3bYNWLZMf2V3Y6DVQPzy5C94IeyFNusbFY2YGjsVo74YhWuV1+64HEKAxYu5+xHtlJS//gIGDwbKWEYlwzAM009YIM4YnLGxMd59913ExMSAz+dj8uTJ2LJli8Hq034mTgDIWpiFP7z+QFPZbU9m1iMboRCxAQF4e8iQDts+LipCmlzeyVG3ydoaOHYMWLq07XpCgClTdFdOH/EIr8OTgDn/m4OzBWdx5dYVjPp8FKoaqnRSlrc3N8yhp2fruhs3DJKhwzAMw/xDsUCcuWssWLAASUlJOHz4MHjtZmJJTU1tM9JKfyrcWYgbMTfQmN+I857nIbuo+wmAtPHaBaKfFhdjUXY2RqWm4ueKCt0VJBAA//0vl6eh+bxtbYHQUN2VcYcKqgtwtuBsy7LYQQxrY909LbCwAK5cAcLCuOWvvwaG/yO7xDEMwzCGwAJx5q7ywAMPwKTdeHKpqakYPXo0Hn74YVToMhDtpRtft87LoKpT4dK4S6j8tbKbI3SnuKEBS7KzAQB1KhWic3Kg85GOFi8GTpwAXF25qSnd3NpuV+kvJacnVsZWCHRq7UNwOu80nj/2PJQqncwsDIBLnb94kRvNce5cnZ2WYRiGYXrEAnHmrnbr1i2MHTsW9fX1OH36NKKiovq9DsN+Hwb7aa2T/KjkKqRPS0f1+Wq9l12tVMKG3zqxTUZdHXbrekQVgOusee1aa9Owxs2b3DAjP/+s+zJ7wdrEGieeOIFpvtNa1n3252d4/ODjaFQ0QqlSQkXv/EaBECA8vOP6r7/m+rcyDMMwjD6wQJy5qx0/fhxyrdxogUDQ7ykqhE8Q9EMQQk6GwMiNyx9X1aqQPjkd8jQd5m13ws/cHBfDwuCl9ZTgpexsvHHtmu5bxtvPbFNSAjzwANdcPH0616HTAEyFpjj02CE8GfJky7pDmYcQuT8SLx5/EQuPLNRJMN5eUhLw1FPA5s3c/xmGYRhG11ggztzVHnvsMcyYMaNl+aeffsIzzzyj+yC0F2zH2SLklxAIHblhBhVVClwafwk1aTV6LdfT1BTnhw3DSEvLlnWb8/Pxck4OqpubuznyDp05w01JCXAjqpSW6q+sHgh4Anw5/Uu8PPLllnUnr5/EZ39+hq/++gorE1bqvMzZs1uzcmJigG+/1XkRDMMwzD8cC8SZu5qpqSkOHjyIxYsXt6z75ptvsHbtWoPUx1xsjuCfg8G35tJFFLcU+PP+P1GXU9fDkXfGwcgIJ0NDMcXOrmXdjqIiuJ8/jy9KSvRT6N69baehNDbWTzm9xCM8bJ+4HW9FvNVh24WiC2hU6HYQ8F9+AUxNW5effx5IT9dpEQzDMMw/HAvEmbsen8/Hrl278Pzzz7ese/vtt/Hf//4X77zzDlT93JnQMtQS/rH+gHpwE1WDCilhKVA16rce5nw+fggMxBxHx5Z1MqUSz0mliLt155PddBAb2zrQNqXAvHlcdGpAhBC88eAb2D1ld5v1owaNgrFAtzcKQUHAhQuA5uOWybiRHQsLdVoMwzD3AD6fHyYWi/1FIlHA5MmTh9TU1PQqfsrJyRGOHDnSx8vLK8Db2ztg06ZNTpptdXV1JCgoyM/X19ff29s7YPny5QM02zZt2uQkEokCvL29AzZu3OjU+dmBq1evCvfs2WN7Z1fXd25ubkE+Pj7+YrHYPzAw0A/o/lo1utqnu8/ibhMdHT1g3bp1zro6HwvEmXsCIQQffvghpmiNcf3yyy9j9erViI6O7vdUFfvJ9nCa2/o7xuUZF/CM9f/PScDjIUYsxn1aaSoqAIX6mBLSygr46SfAx4dbbmoCZszgolOFArh6Vfdl9tKiEYvwxbQvAAALQxZiY8RGvZQTGAgkJACaj7uwkAvGq/XfT5dhmLuIsbGxKisrS5KdnZ0hFArptm3bHHs+ChAKhdi2bVvh1atXMy5evJj5+eefO6WkpJgAgImJCT1z5oxUKpVKMjIyJCdPnrQ6efKk+cWLF01iYmIcU1NTMzMzMzNOnDhhc/ny5U5bGuLi4qxSU1PNdHmtvXX69OkrWVlZksuXL2cC3V+rRlf7dPVZGOK6+hsLxJl7hkAgwLfffoth2tMhAvjvf/+LP/74o9/r4/e1H+xn2MNzgydE74v6rVwTPh8/BgXBU925coSlJR5z7NXfhL5zcuIi0YEDueXaWmDyZGDaNGDkSG46SgN5eujTOL3wNL6Y/gWM+G0nYVKoFDorJyQEOHyYG3Yd4NJTAgIMmjLPMIwBjR49Wp6Tk2MslUqNRCJRgGb9unXrnKOjo9u05Hp4eDSPHj26DgBsbW1VXl5e9fn5+UYAwOPxYG1trQKApqYmolAoCCEE6enppkOHDpVbWlqqhEIhwsPDaw4cOGDTvh7x8fEWa9euHXT8+HFbsVjsL5FIOs5G14+6u9ae9unqs2hPJpPxHnroIW9fX19/kUgUoHkasHv3brugoCA/sVjsP2/ePA+FgvsbsGvXLnsfHx9/X19f/xkzZgzWnGf9+vXOIpEoQCQStTxxkEqlRkOGDAmIiory8Pb2DggPDxfJ5XICAKtWrXLx9PQMDAsL883OzjbuqT59wQJx5p5iYWGBH3/8Ee7u7i3rHB0dEdZ+2L1+QAhB0PdB8Fzn2e9lOxgZ4efgYCwZMABnhg6FjVCov8Lc3bnhC+3VQzhWVHAt5bduAZGRQHm5/sruwYMeD3aYifP4leMI+igIRbIinZXzr38BX3zRulxUBAQHA/X1OiuCYZh7QHNzM+Lj462CgoL6/K9fKpUaSSQSs7Fjx7YMt6VQKCAWi/2dnZ1Dxo4dKxs3blxtaGho/YULFyxLS0v5NTU1vISEBOuCgoIOQfbEiRPlQUFBtYcPH87JysqS+Pv73/aQYmFhYb5isdi//evIkSOWXR0zfvx4UUBAgN/WrVsdenOtPe3T2WfR/pjDhw9bubi4NEulUkl2dnbGrFmzZKmpqSYHDx60S05OzsrKypLweDz68ccf2ycnJ5ts3brV9fTp01ekUqnkk08+yQeApKQks/3799unpKRkJicnZ8bExDiePXvWFADy8/NNli5dejMnJyfD2tpaGRMTY5uUlGT2/fff26Wnp0sSEhKy09LSzLurT98+eRaIM/cgFxcX/PTTT7C2toaNjQ1+/PFHCPUZiPZR441GZD6Vqfd0GZGZGXb5+MCo3SykcoUCcoXuWoQBAH5+XPBt3u5J4f33t+3RaGCfp36OabHTkFWehSn7p0DWqLtZUBcs4NLkNcrKgGXLdHZ6hmF6IToaAwhBGCEIi45Ghzzi557DQM32N99EhzzeuXPhodm+dSs6BJBdaWxs5InFYv+goCD/gQMHNi1btqxPLRDV1dW8WbNmeb3zzjsFdnZ2LR2KBAIBsrKyJPn5+X+lpqaaX7x40WTYsGENy5YtKx0/frxPRESEKCAgoI6vNZ+EtmvXrpmEhoY2AIBEIjF67LHHPCZNmjREs33Xrl32a9ascY6KivKIjIwccvjwYavOzpOSkiLNysqStH/NmDGj02HBzpw5kyWRSDJ//vnn7D179jj99NNPFj1da0+fR2efRfvjhg0bVp+UlGS1aNEitxMnTljY29srT5w4YXn58mWzkJAQP7FY7H/mzBmra9euGcfHx1tNnTq10tXVVQEAzs7OSgA4deqUxZQpU6qsrKxU1tbWqsjIyMrExERLAHBzc2scNWpUPQAMHTq0Ljc31zgxMdFiypQpVZaWlio7OzvVww8/XNVdfTr9orrBAnHmnuTv74+4uDikpKRgxIgRhq5Oi/rr9bgguoAbMTdwdWX/51DfbGrCiJQUzMnIQLOuO7GOGAEcOQIYaTXMuLh0DM4NpFHRiP2X94OCuwGqbqhGeZ1uW+u//pq79wC4j2PnTp2enmGYu5QmRzwrK0uyb9++AhMTEyoQCKj2YAENDQ08ANiyZYujpkU5NzdX2NjYSCIjI73mzJlT8dRTT1V1dn4HBwflmDFjao4dO2YNAMuXLy/PyMjITE5Oltra2ip9fHwa2h9TUlIisLS0VGoaovz9/Zu+++67PO19kpOTzTZu3HjjwIEDeXv37s07cOBAp6kTfW0RHzx4cDMAuLm5KSIjI6vOnTtnDgC9udae9mn/WWgLDg5uTE1NlQQFBdWvXbvW7ZVXXnGllJI5c+bc0nw/ubm5l7dv335bM98ZGRm1tKDx+XyqUCg65sf0UJ++lskCceaeNWrUKAwZMqTNOkop9uzZg3IDpUtkzM6Asoa7IS7cWoiq36r6rewahQJ+Fy4gq74eJyor8eKVK7ov5F//Avbv5+aFf/NN4P33dV/GbcquyMb5wvMty66WrhhoNVCnZRAC/PYbsHEjcP68wUd0ZBjGgAYOHKioqKgQlJaW8uvr60l8fLw1AKxevbpMExS6u7s3R0VFefj4+DSsX7/+hvbxxcXFgvLycj4AyOVykpiYaOXn59cAAEVFRQIAyM7ONvrxxx9tnn322Yr25WdnZxs5Ozt3mY7S2NhIBAIB5amfmr7++uuuS5cuLets3760iMtkMl5lZSVP8z4xMdEqODi4XqVSoatr1ehqn+4+C225ublCS0tL1eLFiyuio6NLL126ZDZp0iTZ8ePHbTWf2Y0bN/hXrlwxmjhxouzYsWO2paWlfM16AIiIiJDHxcXZ1NTU8GQyGS8uLs42IiKiywlBxo0bJ4+Li7ORy+WksrKSl5CQYNNdfbo6T1cEfT2AYe5WN2/exOOPP45Tp07h+PHjOHLkSIf8YX3z+8oPF4MuckOZAMh6JgvD/xwOgaX+/6l9X16OCq2UlBtNTVBRCp6uP4NHHwVyc4EBnYwupVIBPMPc3wc6BeLrmV9j1nezAADnC8/jxeMv4vNpn+v050AoBAw0jD3D/ONt347i7dvRZWvnnj0o3LMHXQ4yGhuLvNhY5HW1vS+MjY3pihUrSkaMGOHn7Ozc7O3t3SFwTEhIsDhy5Ii9SCSqF4vF/gCwYcOGoscff7y6oKBAuHDhwsFKpRKUUjJ9+vSKuXPnVgPAtGnTvKqqqgQCgYB+8MEH+Q4ODh1SHkJCQhoqKiqEIpEoYPfu3bkTJkxok1MdHx9vMWbMGLlKpcKSJUvcIiMjqzUdJe9EYWGhYObMmd4AoFQqyaOPPnpr9uzZsvj4+C6vdezYsd779u3Lk0qlxp3t4+np2dTVZ6EtJSXFdPXq1QN5PB4EAgHdvXt3XlhYWMOaNWuKxo8f76NSqSAUCumOHTvyx48fX7tixYqSMWPGiHk8Hg0MDKw7dOhQ7ujRo+vmzZt3a9iwYX4AsGDBgrLw8PB6qVTaaWfX0aNH182cObMiMDAwwN7evjk4OLi2u/r09fMkhpih8E4MHz6cJicnG7oazF3ovffew8qVrTMsfvPNN5inndTbTwp3FeLaq9egauCicad5TvD72k/vNwXNKhUe+esv/FxVBQAwJgQpw4cjoD9SRyjl8jS+/57r2GnAnP3/O/N/eO3ka63L//o/rAzX/cyb2igFXn8dePVVQGvOJYbRCUJICqV0uKHrYQhpaWm5ISEhhusRfg8pLS3lR0dHuyUlJVnNnz+/XCaT8Tdv3lyyc+dOh9jYWPuQkJDa0NDQ+pUrV3baKs7oT1pamkNISIhnZ9tYIM78bUyePBknTpwAAFhbW+Pq1auw14z00c9ufHMDmfMzW5Z9P/eF6zN9Th3rM5lCgdF//on0Wu6GfZiFBc4PGwahPlupKQWeeIKbAAgAoqOBbdv0V16P1aF45ugz2HtpLwCAgOCrmV/ht7zfsHPKzg5DHd6pq1eBceOA/HwgNBT480+dnp5hWCDOAvHb8uSTT7rHxMTkG7oeTPeBOMsRZ/42PvnkE1iqZ16prq7GWgPmDzg/4QyXZ1xalrNfyoY8o8tRnHTGSiBArL8/jNWt76lyOTbn6/n3cEkJN8mPRlIS0NDhCW2/IYTgk0c+wYMeDwIAKCgWfL8An6Z+ik2nN+m8vLff5oJwALh0Cdi+XedFMAzD9BkLwu8NLBBn/jbc3d2xa9euluWPPvoI8fHxBquPaIcIZn5cvw1VvQqXxl6Csr7PIxv1WYC5Od4a3DJvATbl5uLprCz8WdNlX5Q7s3Nn6yybPB7w0UeASYdRp/qVEd8IsY/GwsbEBgBaRlJ57/f3UCrX7Uw8e/YA2vMpffwxUHfHWZgMwzDMPwELxJm/lQULFmDmzJkty08//TQqKipQW9thXgC945vz4fuFL6BODVfcUuDyjMv9UvbyQYMw2pob+UkJYG9pKZ7IzES9Ug83Am++yU0/CXCdNZ988q6IRAdYDsCOSTvarNsxeQdcLFy6OOL28PlcWrzm3iM7G1i1SqdFMAzDMH9TLBBn/lYIIfjkk0/g5OQEACgpKcGDDz4IsViMsrL+759iNdIKFiEt8xygJqWmX1rF+YRgr1gME60Oopl1dfhSH/Oym5hw+eGaiX0kEmDFCt2XcxvmB8/HIz6PAABcLFzgZeull3JCQwGthzHYtYsLzhmGYRimOywQZ/52HB0dsWfPnpbljIwMFBYW4tlnn9X7bJftEUIQ/EsweOY8WI+1xgOFD4Bv2vkMabrmZWqK7d7eLcsCQjBdX51X/fyA//63dfnjj4HDh4H0dOD33/VTZi9o8sUXD1+MzCWZGD9kvN7KeuYZYNq01uWnn+Zm32QYhmGYrrBAnPlbmjZtGp555pk268rKyiCX67/DZHtG9kYYdWMUhp4aCr5J/wThGi8OGIAJNjYQm5oiNSwMbvrM3X72WWD27NblefOAoUO5VBUDdt4cYDkAH0Z+2JIvrlHXXIeK+g5zZNw2QtrmixcXA/7+QFOX020wDMMw/3QsEGf+tt5//314enq2LM+ePbtlVJX+JjA3zNxZhBDEBgQgbcQIBFlY9HzAnRUGfPop4O7OLTc2Akol15Hz//5Pv2X3UXJxMoZ9MgwLjyzU6VMSJyfuI9AoLwfmztXZ6RmGYZi/GRaIM39bVlZW2Lt3L5ycnHDo0CFER0cbukotKKXIfy8fjTca9V6WvVAIo07GES/QRyu1rS3wzTdtZ9cUi4HHHtN9WbcpryoPD3z+AKS3pDh25Rg+S/1Mp+efMQMYMaJ1OS6OuydhGIZhmPZYIM78rY0dOxbXr1/HrFmzDF2VFpWnKvG76++4tvIaMqMyez5Ax6oVCsy6fBn+Fy8iXx/B+OjRwGutM1uCx+OC8btAo6IRG09vhEKlAACYCkxhItB9uo5mFBUvLyAzEzA21nkRDMMwzN8AC8SZvz0zM7MO6y5evIgq9VTw/a34o2I032gGAFSdqkJlUmW/lU0pxQOpqfi+vBxypRKrr13TT0Gvvw54egLLlgFnznBpK3cBI74RSuQlLctO5k541P9RnZdjYwNcucK9tLKjGIZhGKYNFogz/yiFhYWYP38+7rvvPrz11lsGqYPvHl8Q49bA9OrLV0FV/TOaS1J1NTK1xvi+Ul+PZpVK9wWZm3PDGH7wAZeucpcghODTqZ/CytgKAJBXnYc1v67RS1mDBrXN0GEY5t4klUqNRCJRgPa66OjoAevWrXPWXpeTkyMcOXKkj5eXV4C3t3fApk2bnDTb6urqSFBQkJ+vr6+/t7d3wPLlywdotrm5uQX5+Pj4i8Vi/8DAQL+u6nH16lXhnj17+vUXanfXNGfOHE87O7uQ9p9Ne53tl5aWZiwWi/01LwsLi6EbN2506u48htLZd61L7M8E849BKcX777+Pb775BgCwY8cOZGdn93s9BFYCeL/vDaj7b8pT5bjx9Y1+KftBGxvMd279fdKkUoGnr9Zqzbji2uLi2g5zaAADrQbi/Ynvtyx/cP4DnM0/2y9l5+b2SzEMwxiAUCjEtm3bCq9evZpx8eLFzM8//9wpJSXFBABMTEzomTNnpFKpVJKRkSE5efKk1cmTJ801x54+ffpKVlaW5PLly13mK8bFxVmlpqZ2fMSrR91d0zPPPFN+9OjRHv+IdrZfSEhIY1ZWlkR9zRITExNVVFRUlZ4u467GAnHmH0Mul2Pfvn0tyz4+PjAyMjJIXdwWucH9FfeW5WurrkEhU/RL2e8NGQJzdVPtX7W1+OZGP9wEyGTAzJlAZCTw6qtAVpb+y+zG06FPY6LXRAAABcXy+OWoaazBnyV/6qW8q1eB4cOBwYOB+Hi9FMEwjIF5eHg0jx49ug4AbG1tVV5eXvX5+flGAMDj8WBtba0CgKamJqJQKAjpQyNIfHy8xdq1awcdP37cViwW+0skkn7549XdNU2ePFnu6OjY4x+unvY7evSolbu7e6OPj0+bwV5lMhnvoYce8vb19fUXiUQB2k8Ddu/ebRcUFOQnFov9582b56FQcKfftWuXvY+Pj7+vr6//jBkzBgPA+vXrnUUiUYBIJArQtLpLpVKjIUOGBERFRXl4e3sHhIeHi+RyecsXsmrVKhdPT8/AsLAw3+zsbOOe6nMn9BqIE0ImEUKkhJAcQshrnWx3J4QkEkL+JIT8RQiZos/6MP9slpaWePPNN1uWi4qKDDacIQC4v+EOowHc79Km0ibkvZXXL+W6GBtjxaBBLctrrl9Hg1LPs33KZMBZdatzczPwxhv6La8Hmol+jPlcL8qLxRfh/oE7Hol9BHXNdT0c3Xfh4UBKCvf+6aeBfp5XimGYfiaVSo0kEonZ2LFjWyavUCgUEIvF/s7OziFjx46VjRs3rlazbfz48aKAgAC/rVu3OnR2vokTJ8qDgoJqDx8+nJOVlSXx9/e/7RkKwsLCfLXTQjSvI0eOdPsHsbNr0oXY2Fi72bNn32q//vDhw1YuLi7NUqlUkp2dnTFr1iwZAKSmppocPHjQLjk5OSsrK0vC4/Hoxx9/bJ+cnGyydetW19OnT1+RSqWSTz75JD8pKcls//799ikpKZnJycmZMTExjmfPnjUFgPz8fJOlS5fezMnJybC2tlbGxMTYAkBSUpLZ999/b5eeni5JSEjITktLM++uPndKb4E4IYQP4EMAkwH4A5hLCPFvt9saAN9RSocCiAKwW1/1YRgAeOGFF+Ctnm2yqqoKb7/9tsHqIrAQwOu91inXC7YXQH65fyYcemXQIDgJhVy5jY3YUVionxFUAG7svhEjWqeZvP9+buZNA/Ow8cBL973UslzVUIXimmJsP7dd52Vpd0coKQEuXtR5EQzzjxAdHz2AbCBhvXnNPTjXo/3xcw/O9dDeJzo+ekBn5WjrquW6q/XV1dW8WbNmeb3zzjsFdnZ2LZ1wBAIBsrKyJPn5+X+lpqaaX7x40QQAzpw5kyWRSDJ//vnn7D179jj99NNPnU76cO3aNZPQ0NAGAJBIJEaPPfaYx6RJk4Zotu/atct+zZo1zlFRUR6RkZFDDh8+bNXZeVJSUqSatBDt14wZM2q6+gy6uqY71dDQQH755RfrBQsWdBi1YNiwYfVJSUlWixYtcjtx4oSFvb29EgBOnDhhefnyZbOQkBA/sVjsf+bMGatr164Zx8fHW02dOrXS1dVVAQDOzs7KU6dOWUyZMqXKyspKZW1trYqMjKxMTEy0BAA3N7fGUaNG1QPA0KFD63Jzc40BIDEx0WLKlClVlpaWKjs7O9XDDz9c1V197pQ+W8TvA5BDKb1GKW0CcADA9Hb7UACaHxRrAMV6rA/DwMjICO+8807L8q5du3D9+nWD1cdprhPMQ9VpgkogY06GTieY6YqlQIA3tYbzeCM3FyHJyahobtZ9YcbGwNKlrctXrgD8/p1htCurR69u6bgJADYmNhhiO6SbI27Ps88CIlHr8oYNOi+CYRg9cXZ2VlRXV7f5pVVRUcF3cHBQbNmyxVHTopybmytsbGwkkZGRXnPmzKl46qmnqjo7n4ODg3LMmDE1x44dswaAwYMHNwOAm5ubIjIysurcuXPm7Y8pKSkRWFpaKoXqBhR/f/+m7777rs1j1OTkZLONGzfeOHDgQN7evXvzDhw40GnqRF9bxHtzTbfr4MGD1v7+/nWDBg3qkLoSHBzcmJqaKgkKCqpfu3at2yuvvOIKAJRSMmfOnFuaG4jc3NzL27dv73P8aGRk1PLHls/nU4VC0W2uUFf1uVP6DMTdABRoLReq12lbD2A+IaQQQByA/+ixPgwDAJg1axYeeOABAEBTUxNWr16NQ4cOQSbTyVOmPiGEwOZBm5bl+qx63PimfzpuPufqCm/1lPcKSlGlUOCtPD2lxyxfziVIA0BFxV0Tidqb2ePVUa+2LFsZW+HxgMf1UtbBg62jOMbFAb/8opdiGIbRMWtra5WTk1Pz0aNHLQHgxo0b/FOnTlmPGzdOvnr16jJNQOju7t4cFRXl4ePj07B+/fo2v8iLi4sF5eXlfACQy+UkMTHRys/Pr0Emk/EqKyt5AJeDnJiYaBUcHFzfvg7Z2dlGzs7OXaajNDY2EoFAQHnq/j+vv/6669KlS8s627cvLeIqlQpdXZMuHDhwwO6xxx6r6Gxbbm6u0NLSUrV48eKK6Ojo0kuXLpkBwKRJk2THjx+3LSoqEgDc93HlyhWjiRMnyo4dO2ZbWlrK16yPiIiQx8XF2dTU1PBkMhkvLi7ONiIiosuWfwAYN26cPC4uzkYul5PKykpeQkKCTXf1uVOGmXe71VwAeyml2wghDwD4ihASSClt89iDEPI8gOcBwN3dvZPTMEzvEUKwdetWhIeHAwC+/fZbfPvtt1i1alWb1vL+4vWuF0r3lkIpU8I8yBzWo6z7pVwhj4ctQ4ZgjkTSsq6yuRmU0i4fud42ExNg61bgUfWY3R9+CLzwAuDfPlut/718/8v48OKHeNDjQWyK2AQ+Tz+t9cHBwMKFwJdfcsuvvsrljbMhDhmm97ZP3F68fWLfWz81YmfH5sXOju1zi8O+ffuuL1682H3lypWDAGDVqlXFAQEBbebMTUhIsDhy5Ii9SCSqF4vF/gCwYcOGoscff7y6oKBAuHDhwsFKpRKUUjJ9+vSKuXPnVkskEqOZM2d6A4BSqSSPPvrordmzZ3doFQoJCWmoqKgQikSigN27d+dOmDChVnt7fHy8xZgxY+QqlQpLlixxi4yMrNZ0srwT3V3T1KlTB58/f96ysrJS4OzsHPzaa68VL1++vHzs2LHe+/bty/P09GwGgK72k8lkvDNnzljt27ev0+8jJSXFdPXq1QN5PB4EAgHdvXt3HgCEhYU1rFmzpmj8+PE+KpUKQqGQ7tixI3/8+PG1K1asKBkzZoyYx+PRwMDAukOHDuXOmzfv1rBhw/wAYMGCBWXh4eH1Uqm0y86uo0ePrps5c2ZFYGBggL29fXNwcHBtd/W5U0Rfj8HVgfV6SulE9fJqAKCUbtHaJwPAJEppgXr5GoD7KaU3uzrv8OHDaXJysl7qzPyzPProozh8+HDLspGRETIzMzFkiO5TE3pS/mM5VPUqOM3u32FUKaW4PyUFF+RyOAuF+C4gAA/a2OirMGDcOODUKW75X/8C5s0DLl8Gtm3TT5m9VNVQBRsTG72XU1TEpajUq9u73nsPeOUVvRfL3OMIISmU0uGGrochpKWl5YaEhJQbuh53m9LSUn50dLRbUlKS1fz588tlMhl/8+bNJTt37nSIjY21DwkJqQ0NDa1fuXJlp63iTP9KS0tzCAkJ8exsmz4DcQGAKwDGAygCcBHAPEpphtY+PwH4llK6lxDiB+AkADfaTaVYIM7oSnZ2Nvz8/KBUjxgyffp07N27Fzb6CkTvUmerq3GmuhpL3dxgqu/c7bQ0YNgwoP0kQunpQGCgfsvuo0JZIW7V3UKIS4hOz7tuHbBpU+vylStt88cZpj0WiLNAvCdPPvmke0xMTL6h68F0rrtAXG8PRSmlCgAvAYgHkAludJQMQshGQsg09W4rADxHCEkDEAtgYXdBOMPokkgkwqJFiwAAJiYmmDx58j8uCAeAcGtrrHJ3138QDgAhIcBzz3Vcv133I5XcrprGGqz5dQ1EO0V48siTUKp0O7TjK68AAq2kwMf1k5LOMMw/CAvC7116zU6klMZRSn0opV6U0rfV69ZRSo+q30sopeGU0hBKaSil9Gd91odh2lu3bh2WLFmCa9eu4YUXXjB0dVo0ljYiIyoDDcV6GlKwB80qFa7U6X48bQBcc7C1Vh78I48A77/f9f79rKSmBO+efRcNigb8deMvxKTF6PT8VlbAf7S6pRcWAor+mcuJYRiGucuwbkLMP5qjoyN27doFV1edjEKkE5IFEpwbcA5l35YhZ1lOv5ZNKcWx8nJ4//EHJqSloV4fE/04OnL5Gdqs+6eDak/yq/MR/mU4mlXcMI6BjoEQ2es+b+TddwFPT2DNGi5vXGDobvMMwzCMQbBAnGHaUSgUOHr0aL+M591p+ZUKboR9AOWHy9FcqYexvbtwUSbD7IwM5Dc2Ir+xEbuKivRT0IsvAlOnAj/+CBw9qp8ybsMgq0EQO4hblkNdQjHafbTOyxEIgGvXuIcD6mGBGYZhmH8gFogzjJZPP/0Unp6emD59Ok6ePGmQOoh2abXAqoDCHYX9VvbRW7fQpL4BIQBq9dEiDgBmZlwAPmVK6+DadwFCCLaMbxnYCd+kf4P0G+l6Kksvp2UYhmHuISwQZxi1srIy7Nq1C0XqVuB3333XIPUw9TTFwBUDW5aLdxVDWaengLidle7usFfnSVAANv2ZM1FdDbz9NpBv2D5Ho91H4xGfRwAAFBRv/PpGv5RLKVBV1S9FMQzDMHcJFogzjNrZs2eRns61fhJCcN9990HVfpi9fjJkyxAYexgDAJrLm1HyWUm/lGslEOBNT8+W5fcLC6Hoj89g3z7Aw4NLmjbQDZC2t8e9DQKuyfrYlWM4m38W1yuvQ6HSfa/Kpibg9dcBGxtg6FCdn55hGIa5i7FAnGHUpk6dCm9vbwBcp0UXFxfwDDTtIU/Ig/urrbPIFrxXAFVT/9wUPOfqCkd14nJ+YyMOl+t5CF+FAsjM5FrEAeCzz4Di2544TyeCnYPxRPATLcszv50J753eOCQ5pPOy/vc/YMsWQCYDcnOB8+d1XgTDMAxzl2KBOMOo8fl8vPzyyy3LH3zwQctkP4bg8owLhE5cQNxY2IjiT/onODXh87F4wICW5e0FBfptFV+5Evi//+Pem5sDn3zCjaxiYBsf2ggBj0vNKasrg4qqsPXcVp134p03D7C0bF3+7391enqGYRjmLsYCcYbRsnDhQtja2gIArl69imPHjhmsLnxTPlxfaB1W8fq666DK/hnJZZGbG4zUvQn/qKnBgHPnkFpTo5/Cnn229X1tLXDffXfFUCKDbQdjjv+cNutsTWxR06Tbz4GQtuOK//Ybl67CMAzD/P2xQJxhtJibm+PFF19sWd62bRtqampQo68gtAeWQ1ubSpVVSpTuK+2Xcp2NjPCEs3PLcllzM7YVFOinMH9/bihDjffe0085t2HZyGUt7wU8Ab6a+RWsjK10Xs66dYCLC/e+uBj49ludF8EwzB3g8/lhYrHYXyQSBUyePHlITU1Nr+KnnJwc4ciRI328vLwCvL29AzZt2uSk2VZXV0eCgoL8fH19/b29vQOWL1/e8ihy06ZNTiKRKMDb2ztg48aNTp2fHbh69apwz549tnd2dX3T1TV1d62dUSgU8PPz84+IiPDWrHNzcwvy8fHxF4vF/oGBgX76vpbbFR0dPWDdunXOPe/ZMxaIM0w7L730EoTqFtkzZ87Azc0NH3zwgUHq4jDDAUIXri5EQNBU3n9NpcsHDmyznFhVpZ8JfgDgtdda33/9NTfd5F1g5MCRGOk2Eu7W7tg8bjNMhaZ6KcfYGHjppdblbdu4UVQYhrk7GBsbq7KysiTZ2dkZQqGQbtu2rVf5c0KhENu2bSu8evVqxsWLFzM///xzp5SUFBMAMDExoWfOnJFKpVJJRkaG5OTJk1YnT540v3jxoklMTIxjampqZmZmZsaJEydsLl++bNzZ+ePi4qxSU1PNdHmtt3tN3V1rZ9566y1nb2/v+vbrT58+fSUrK0ty+fLlTP1eyd2BBeIM086AAQMQFRXVslxTU4MdO3agvr7D7wu9I4RAtEOEAYsHIFwWDo+VHv1WdpCFBf5l29rQsmLQIJjy+fopbNQoYLR64pzmZm7K+7w84Phx/ZTXBwcfO4irS6/i1fBX9dIarvHii4CpOs5PSwN+/VVvRTEMcwdGjx4tz8nJMZZKpUYikShAs37dunXO0dHRA7T39fDwaB49enQdANja2qq8vLzq8/PzjQCAx+PB2tpaBQBNTU1EoVAQQgjS09NNhw4dKre0tFQJhUKEh4fXHDhwwKZ9PeLj4y3Wrl076Pjx47ZisdhfIpEY6fXCe7im7q61vatXrwrj4+Otn3vuuT6PBiCTyXgPPfSQt6+vr79IJArQPBHYvXu3XVBQkJ9YLPafN2+eh0LBjXK1a9cuex8fH39fX1//GTNmDAaA9evXO4tEogCRSNTyxEEqlRoNGTIkICoqysPb2zsgPDxcJJfLW2Z8WLVqlYunp2dgWFiYb3Z2tnF3dekLFogzTCeio6PbLFtYWODatWsGqYvTHCf4fOgDgWn/z4P+yqBBmOvkhAvDhmHFoEH6LWzVqtb3O3YA3t7A/Pmto6kYyECrgS2dNrUpVbp9OmBvDzz9dOtyVBRgoNEzGYbpQnNzM+Lj462CgoL63DIjlUqNJBKJ2dixY+WadQqFAmKx2N/Z2Tlk7NixsnHjxtWGhobWX7hwwbK0tJRfU1PDS0hIsC4oKOgQ0E6cOFEeFBRUe/jw4ZysrCyJv7//bT8yDQsL8xWLxf7tX0eOHLHs7rjOrqm79RpLliwZ9O677xZ2NjLZ+PHjRQEBAX5bt2516OzYw4cPW7m4uDRLpVJJdnZ2xqxZs2SpqakmBw8etEtOTs7KysqS8Hg8+vHHH9snJyebbN261fX06dNXpFKp5JNPPslPSkoy279/v31KSkpmcnJyZkxMjOPZs2dNASA/P99k6dKlN3NycjKsra2VMTExtgCQlJRk9v3339ulp6dLEhISstPS0sy7qkuvPnAtLBBnmE6EhoZi3LhxLctPPfUUAgICujni72minR32+/tjhJX+WoJbTJkCaD5jhYJ7VVcDu3bpv+xeUlEVjl85jof2PoRNv23S+fmXLm19X14O7N6t8yIY5p4WHR89gGwgYWQDCYuOb9v6DADPHXtuoGb7m4lvdsjhnXtwrodm+9bfOw/0OtPY2MgTi8X+QUFB/gMHDmxatmxZn1pyq6urebNmzfJ65513Cuzs7FpusQUCAbKysiT5+fl/paamml+8eNFk2LBhDcuWLSsdP368T0REhCggIKCO38XTyGvXrpmEhoY2AIBEIjF67LHHPCZNmjREs33Xrl32a9ascY6KivKIjIwccvjw4U5/maekpEizsrIk7V8zZszosoNUV9fU1XqN2NhYawcHB8WYMWPq2m87c+ZMlkQiyfz555+z9+zZ4/TTTz9ZtN9n2LBh9UlJSVaLFi1yO3HihIW9vb3yxIkTlpcvXzYLCQnxE4vF/mfOnLG6du2acXx8vNXUqVMrXV1dFQDg7OysPHXqlMWUKVOqrKysVNbW1qrIyMjKxMRESwBwc3NrHDVqVD0ADB06tC43N9cYABITEy2mTJlSZWlpqbKzs1M9/PDDVV3VpavPqyssEGeYLkRHR2PUqFE4dOgQ1q5da+jqtCFP67SR4d7G47VtFQeA8HAgMNAw9enEUelRTI2ditN5p/HhxQ9R36zbdCVfX0Akal2+i/qtMsw/miZHPCsrS7Jv374CExMTKhAIqPakbw0NDTwA2LJli6OmRTk3N1fY2NhIIiMjvebMmVPx1FNPVXV2fgcHB+WYMWNqjh07Zg0Ay5cvL8/IyMhMTk6W2traKn18fBraH1NSUiKwtLRUavo0+fv7N3333Xd52vskJyebbdy48caBAwfy9u7dm3fgwIFOUyf62iLe1TX15lrPnDljkZCQYOPm5ha0cOHCIefPn7ecPn36YAAYPHhwMwC4ubkpIiMjq86dO2fe/vjg4ODG1NRUSVBQUP3atWvdXnnlFVdKKZkzZ84tzXeUm5t7efv27X0e89fIyKildw6fz6cKhYJ0t39ndelrmSwQZ5guTJkyBWfPnsWsWbPQVWtEfyv5ogS/u/6O5NBk1PzZ/yO5FDY04NWrV/G/mzf1U0BUFODeOpER5s0Dpk/XT1l9lFKcgvfPvd+yXFlfibMFZ3VezjvvcP8PCwO++krnp2cYRkcGDhyoqKioEJSWlvLr6+tJfHy8NQCsXr26TBMQuru7N0dFRXn4+Pg0rF+//ob28cXFxYLy8nI+AMjlcpKYmGjl5+fXAABFRUUCAMjOzjb68ccfbZ599tmK9uVnZ2cbOTs7d5mO0tjYSAQCAdWkf7z++uuuS5cuLets3760iKtUKnR2TV2tb+/DDz8sunHjxl9FRUXpe/fuvXb//ffX/PDDD9dlMhmvsrKSB3C514mJiVbBwcEdWjtyc3OFlpaWqsWLF1dER0eXXrp0yWzSpEmy48eP22o+txs3bvCvXLliNHHiRNmxY8dsS0tL+Zr1ERER8ri4OJuamhqeTCbjxcXF2UZERHT7B3XcuHHyuLg4G7lcTiorK3kJCQk2XdWlu/N0pv+TThnmHkFI5zfClNIut+mTqkmFK4uvgDZyN+w5S3MwNKn/5kT/9sYNzMvMhApAkLk5Zjs66v5zEAqB6Ghg9WrgueeAyEjdnv8OCPlC/Jb/GwCAgODsM2cxcuBInZczcyaQnw/oOyWfYe5F2yduL94+seuWzj1T9xTumbqny2GXYmfH5sXOjs3rantfGBsb0xUrVpSMGDHCz9nZudnb27tDq3VCQoLFkSNH7EUiUb1YLPYHgA0bNhQ9/vjj1QUFBcKFCxcOViqVoJSS6dOnV8ydO7caAKZNm+ZVVVUlEAgE9IMPPsh3cHDokPIQEhLSUFFRIRSJRAG7d+/OnTBhQq329vj4eIsxY8bIVSoVlixZ4hYZGVmt6Ux5J7q6JhsbG2VX1zp27Fjvffv25Xl6ejZ3dd7CwkLBzJkzvQFAqVSSRx999Nbs2bM75FynpKSYrl69eiCPx4NAIKC7d+/OCwsLa1izZk3R+PHjfVQqFYRCId2xY0f++PHja1esWFEyZswYMY/Ho4GBgXWHDh3KnTdv3q1hw4b5AcCCBQvKwsPD66VSaZedXUePHl03c+bMisDAwAB7e/vm4ODg2q7q0tfPk+h6ljh9Gz58OE1OTjZ0NZh/oKysLGzduhW3bt3C999/b5g6PJeF0s/UY4nzgPCycAjt9D/5Tb1SiYHnzqFC3QsdAH4NCUGErR6Gr62r414OvU7f7DcR+yJwKvcUAGD16NXYPH6zYSvE/CMQQlIopcMNXQ9DSEtLyw0JCenzyBr/RKWlpfzo6Gi3pKQkq/nz55fLZDL+5s2bS3bu3OkQGxtrHxISUhsaGlq/cuXKTlvFGf1JS0tzCAkJ8exsGwvEGaYXioqK4O7uDk0+YFpaGoKDg/u9Hk3lTbjgewGKCi4g9t7hjYH/GdjDUbrxZGYmvrrBPW180NoaiaGh4PXnk4HaWsC8Q7pgv/o+83vM+m4WAMDe1B4Fywv0NrY4w2iwQJwF4rfjySefdI+Jick3dD2Y7gNxliPOMD1QKBSYOHEitDvlxMTEGKQuRg5GGLK5pUM8SvaUoL9uprUn+PldJkNRY6P+C1UqgSNHgMmTuZ6MzV0+1ewX03ynwdPGEwBwq/4Wvkn/Ru9l5uQA06YBFR0yRBmGYbrGgvB7AwvEGaYHAoEAI0e25gI/+OCDePfddw1WH6e5TuCZcf90a9NrUXOxfzptDrW0xFhrawCAglLsKirSf6EqFfDCC8CJE0BREXDsmP7L7Aafx8dLI1qnwHz//Pv44s8v8MbJN/RS3oQJ3Cgqx45xqfMMwzDM3wsLxBmmF5YvX97y/syZM8jNzTVYXQRWAjg95tSyXPJZSb+VvVyrB+GnJSWQa+WM61xFBfDEE9yA2gBACPDnn/orr5f+PezfMBdyKTKSMgn+ffTf+L+z/4dCWZf9w26bQKs7fWwsN7Q6wzAM8/fBAnGG6YXAwEA8/PDDALghmnYZeJIZ1+dahyot3VcKhbx/IrRH7O3hrZ6HvUqhwLv5+Tivr5kvra2B8+dbp5fctQvYpPtJdPrKxsQGT4U81WadkirxVZruxxrU/jFragIkEp0XwTAMwxgQC8QZppe0W8W/+uorNDXd9mzCd8wizALEmOsoSZsoij7shzQRAHxCsMzNrWV5U34+/pOTo6fC+MDCha3Lx4/rp5zb8J+R/2mzvOKBFVgQskDn5Xh5AY880rocG6vzIhiGYRgDYoE4w/TShAkT4KYOQsvLy/HBBx8YbBhDvjEfpoNbR+so2tk/gTgALHRxgQWv9VdHck0NLsv1NNPn00+3vo+PBwp1n/5xO8QOYkz0mtiy7G7tjoFW+hm95rnnWt9/9RXXf5VhGIb5e2CBOMP0Ep/Px4IFra2eq1atwpIlS6AwUOLuoFe5fG1TH1MMfmtwv5VrIRDgcafWHHVPExOoutn/jgweDIwbx71XqYB9+/RVUp+teGAFFoYuxJ8v/ImlI5fqrZzJkwFHR+59URFw8qTeimIYhmH6GQvEGaYPnnqqbW5wSUkJfv75Z4PUxXmBM0aVjcJI6Ui4LnTt+QAdetLFBQBgLxDgSWdnBFtY6K+wf/+79f3nnwNffw2sWqW/8nppgtcEfDn9S4S6hOq1HKGQ67Oq8fHHei2OYRiG6UcsEGeYPhCLxW2GMnzggQfg4eFhkLrwhDwYOXQ5I69ejba2xk9BQSgZNQobBuu5NX7mTMDGhnt//TqwYAHw7rvA1av6Lfc21DbV4nrldZ2fVztV/vvvgQsXdF4EwzAMYwAsEGeYPtK0igsEAjz00EMICAgwcI36H48QTLK3h5DXD79CTE3bNglr7Nmj/7J76XrldTzzwzNw2eaCF398UefnDwnhBpHRePNNnRfBMAzDGAALxBmmj6KiorBz506UlpZi8+bNhq5Oi6aKJhR/VmzQOuhtls9nnmm7/MYbwFL95WX3VVVDFfZe2gt5kxwJVxP0Mqb4nDmt7++C4dQZ5h9DKpUaiUSiNi0u0dHRA9atW+esvS4nJ0c4cuRIHy8vrwBvb++ATZs2tXSmqaurI0FBQX6+vr7+3t7eAcuXLx+g2ebm5hbk4+PjLxaL/QMDA/26qsfVq1eFe/bssdXltfVGd/VLS0szFovF/pqXhYXF0I0bNzpp76NQKODn5+cfERHh3b81773Ovs/+Iuh5F4ZhtNna2uKll17qecd+0lzdjLRxaZD/KQcoYBNhAzMvs34rv6q5Gd+VleHL0lLMc3LCfwbqYfSQYcOA0FDg0iVu2cMDGDCguyP6zc9Xf0bk/khQcDchvg6+KKgu0PkoKuvXA7/+Crz4IvdiGObuIhQKsW3btsLRo0fXVVZW8oYOHeo/ZcoUWVhYWIOJiQk9c+aM1NraWtXY2EhGjBjhe/Lkyerx48fXAsDp06evuLq6dtvzPy4uzkoikZgAqOyXC9LSVf1CQkIas7KyJAAXcLu4uIRERUVVae/z1ltvOXt7e9fL5XJ+P1X3nsJaxBlGByilqK2tNUjZhE8gT+OCcADI25jXb2U3qVRYee0aXrhyBedlMnxZWqq/wl54AZg2DThypG3StIHdP/B+CHitbRrfPvotHhj0gM7LcXPj0uJffRWwtNT56RmGuUMeHh7No0ePrgMAW1tblZeXV31+fr4RAPB4PFhbW6sAoKmpiSgUCkII6fW54+PjLdauXTvo+PHjtmKx2F8ikRimg1A3jh49auXu7t7o4+PTMsnG1atXhfHx8dbPPfdceWfHyGQy3kMPPeTt6+vrLxKJArRb/Hfv3m0XFBTkJxaL/efNm+ehGaFs165d9j4+Pv6+vr7+M2bMGAwA69evdxaJRAEikShA0yIvlUqNhgwZEhAVFeXh7e0dEB4eLpLL5S0f+qpVq1w8PT0Dw8LCfLOzs417qo++sECcYe7AjRs3sGXLFvj6+mLlypUGqYPAQgDbca2/K8qPlkOl0NuAgm3UKZXYV1LSspxeW4u8hgb9FPbii8APPwDTp3NDidwlrIytMN13esvyN+nfGLA2DMPcDaRSqZFEIjEbO3ZsyyQLCoUCYrHY39nZOWTs2LGycePGtbTejB8/XhQQEOC3detWh87ON3HiRHlQUFDt4cOHc7KysiT+/v63PaNcWFiYr3Y6ieZ15MiRLm/xe6ofAMTGxtrNnj37lva6JUuWDHr33XcLeV30Jzp8+LCVi4tLs1QqlWRnZ2fMmjVLBgCpqakmBw8etEtOTs7KysqS8Hg8+vHHH9snJyebbN261fX06dNXpFKp5JNPPslPSkoy279/v31KSkpmcnJyZkxMjOPZs2dNASA/P99k6dKlN3NycjKsra2VMTExtgCQlJRk9v3339ulp6dLEhISstPS0sy7q48+sUCcYW4TpRTff/89Xn/9dWRnZ2P//v1o0FcQ2oMh7wwB35J76qesUqLiREW/lGsjFGKGZpBrAC+4usLDxKRfygYAyOXAxYv9V14XFgS3ji//Tfo3UKrYrDsMoxfR0QNASFivXnPndhzSau5cjzb7REf3mOPWVct1V+urq6t5s2bN8nrnnXcK7OzsWlpFBAIBsrKyJPn5+X+lpqaaX7x40QQAzpw5kyWRSDJ//vnn7D179jj99NNPnY4He+3aNZPQ0NAGAJBIJEaPPfaYx6RJk4Zotu/atct+zZo1zlFRUR6RkZFDDh8+bNXZeVJSUqRZWVmS9q8ZM2bUdLZ/b+rX0NBAfvnlF+sFCxa0pM3ExsZaOzg4KMaMGVPX6QcFYNiwYfVJSUlWixYtcjtx4oSFvb29EgBOnDhhefnyZbOQkBA/sVjsf+bMGatr164Zx8fHW02dOrVSkybj7OysPHXqlMWUKVOqrKysVNbW1qrIyMjKxMRESwBwc3NrHDVqVD0ADB06tC43N9cYABITEy2mTJlSZWlpqbKzs1M9/PDDVd3VR59YIM4wtykzMxOLFi1qWVapVEhPTzdIXSyHWWLAota/JyWflXSzt249pR5THAAOl5dDoeqH1niZDFi8mMsTj4wEmpv1X2Y3HvZ6GI5m3A1JUU0RTued1mt5tbXAunXAhAl6LYZhGADOzs6K6urqNvnNFRUVfAcHB8WWLVscNS3Kubm5wsbGRhIZGek1Z86ciqeeeqqqs/M5ODgox4wZU3Ps2DFrABg8eHAzALi5uSkiIyOrzp07Z97+mJKSEoGlpaVSqH4a6O/v3/Tdd9+1yUNMTk4227hx440DBw7k7d27N+/AgQOdplX0tUW8N/U7ePCgtb+/f92gQYNa8sjPnDljkZCQYOPm5ha0cOHCIefPn7ecPn16m/Fug4ODG1NTUyVBQUH1a9eudXvllVdcAYBSSubMmXNLc5OQm5t7efv27X0ejcDIyKhlBAE+n08VCkW3+UBd1UefWCDOMLfJ398foaGhLctvv/02RowYYbD6uP679ffFreO30FjS2C/lPmxrC2f1H4eSpiacrKrSf6FCIfDtt0BNDVBWZvDpJoV8IaICo1qW3z37LhYeWYjDmYd1XlZBAWBlBWzaBPzyC5CcrPMiGIbRYm1trXJycmo+evSoJQDcuHGDf+rUKetx48bJV69eXaYJFt3d3ZujoqI8fHx8GtavX39D+xzFxcWC8vJyPgDI5XKSmJho5efn1yCTyXiVlZU8gMtPTkxMtAoODq5vX4fs7GwjZ2fnLtNRGhsbiUAgoJoUkNdff9116dKlZZ3t25cW8d7W78CBA3aPPfZYm0exH374YdGNGzf+KioqSt+7d++1+++/v+aHH35oM9FCbm6u0NLSUrV48eKK6Ojo0kuXLpkBwKRJk2THjx+3LSoqEmg+8ytXrhhNnDhRduzYMdvS0lK+Zn1ERIQ8Li7OpqamhieTyXhxcXG2ERERnbbua4wbN04eFxdnI5fLSWVlJS8hIcGmu/roExs1hWHuwFNPPYVL6pE8Dhw4YNDRVMx8zGA91hrVp6sBJVC6rxQer+l/siEBj4cnnJ2xvZAbsi+mtBQT7ez0V+DPPwPz5wMV6t/5Q4ZwTcQGtiB4AXZe2AkAiL8aDwAolZdilt8snZYzcCBgYcE9FAC4McV//FGnRTDM3Wv79mLcRstoi9jYPMTG9rlH+759+64vXrzYfeXKlYMAYNWqVcUBAQFtWjsSEhIsjhw5Yi8SierFYrE/AGzYsKHo8ccfry4oKBAuXLhwsFKpBKWUTJ8+vWLu3LnVEonEaObMmd4AoFQqyaOPPnpr9uzZHfKSQ0JCGioqKoQikShg9+7duRMmTGjzSy8+Pt5izJgxcpVKhSVLlrhFRkZWazqO3onCwkJBV/UbO3as9759+/Ls7OyUZ86csdq3b1+fP9eUlBTT1atXD+TxeBAIBHT37t15ABAWFtawZs2aovHjx/uoVCoIhUK6Y8eO/PHjx9euWLGiZMyYMWIej0cDAwPrDh06lDtv3rxbw4YN8wOABQsWlIWHh9dLpdIuO7SOHj26bubMmRWBgYEB9vb2zcHBwbXd1UefiN7G/dWT4cOH02TWBMTcJW7evAk3NzdoenNnZ2fD29twQ6WWfl2KrAVZAACBnQDh5eFd5jHqUppcjlD1v0sTQvD2kCGY7+wMJyM9dOzPzwc8PQHN767r17llA6OUQvyhGFduXWlZR0BQFF0EV0vdPt185hngyy+59/fdB/zxh05Pz9xlCCEplNLhhq6HIaSlpeWGhIR0OuLGP1lpaSk/OjraLSkpyWr+/PnlMpmMv3nz5pKdO3c6xMbG2oeEhNSGhobWr1y5stNWcaZ/paWlOYSEhHh2tq3H1BRCyLBOXl6EENaazvzjOTk5YfLkyS3LMTExBqwNYOpl2vJeUaFA9e/V/VJuiIUFQsy5tMEGSrHi6lXsv3Gjh6Nuk7t72+RoA3/mGoSQNp02Paw9cOG5C3CxcOnmqNvz1luAZhCCCxe4YQ0ZhvnncHFxUe7fvz+/oKDg8pYtW0pramr41tbWqjVr1tzMyMjI3L9/fz4Lwu8NvckR3w3gPIBPAewBcA7A/wBICSEP67FuDHNP0Ex5DwBffPEFNmzYgPPnzxukLlYjrcAzb/1nXfi+7md47MqTLm0Dzi9LS/tnps0vvgD6o4NoLzwR9ETL+0ZlIwIcA/TyRGLAAEDr/u9uuRdhGMZAYmJi8g1dB+b29CYQLwYwlFI6nFIaBmAogGsAJgB4V5+VY5h7wSOPPAJbW65zelFREdavX4/du3cbpC6ER+AwzQFGLkYYsGgARP8V9VvZ85yc2vxCmeXgAL2FxzNmAOrPHHl5XLPwXWCw7WCsfXAtjkYdRf7L+TAVmvZ80G3SntMoJuauuRdhGIZh+qA3gbgPpTRDs0AplQAQU0qv6a9aDHPvMDY2xty5c9usO3ToEOrq7rifzG3x3++PUSWj4LPbB8Zuxv1WrouxMSbZ2cEIwBxHR0Q5O4Ovr/x0Y2Ng5szW5e3bgUWLgBMn9FNeH2yM2IipvlMh5Ot30qGpU1vvRXJzgdhYvRbHMAzD6EFvAvEMQshHhJCx6tduABJCiDEAww7eyzB3Ce30FD6fj23btvVLJ8m7zQ6RCKXh4fguIAC+Znoe9Wn27Nb3//sf8PHHrT0Y7yIqqsLJaydR39xhxK87YmzMBeMay5fr9PQMwzBMP+hNIL4QQA6Al9Wva+p1zQAi9FMthrm3jBgxAmKxGAAwfPhwjBs3Dqam+ktLuFt5mZrCtr+mnx8/HrCxabvu+PG7YihDjQ8vfIjB/x2Mf331L/wg/UHn53+iNSUdZWVAaqrOi2AYhmH0qMdAnFJaTyndRimdqX5tpZTWUUpVlFJ5f1SSYe52hBB8/PHHyMzMxPnz5+Hj42PoKgEAVM0q3PzuJuQSw/1TVemrw6aRETB9euvy0KFcaspdcgPUqGhEUn4S8qu5PlSHMg/pvIwJE7gxxTWOH9d5EQzDMIwe9TgEISEkHMB6AB7a+1NKh+ivWgxz7xk7dqyhq9CG9AUpSr8oBVVQOM52RMD/AvqtbIVKhcPl5fhfWRkuymTIGTkSAp4eJvKdPRvYt497L5cDY8bovozbQClFwO4AXK3kxhW0MraCi7nuhzEkBHjtNaC8HFiyBDDgEPYMwzDMbejNWOCfA1gOIAWAUr/VYZi/l7q6OpjpO1e6C003mkAVXGv0rRO3QCntt7z1S3I5nsnKQq16KI/fqqsxTtOzUJcmTAAeeQSIjGzbedPACCGYMGQCrqZwgfgc/znYOWWnXsp64w29nJZhGIbpB71poqqmlP5EKb1JKb2leem9Zgxzj2pubsaHH36IcePGwcPDA83NhunTPHD5wJb3KrkKNck1/Vb2wbKyliAcAE5VVemnIGNj4Ngx4MUXAWdn/ZRxmxaEtE7uc1ByEA2KBgPWhmEYhrkb9SYQTySEvEcIeUB7dk2914xh7lFXr17FG2+8gcTERJSXlyMxMdEg9bAZYwPLByxblsv+13+TrM1ydGx5b83nY627e7+VDUqBK1d63k/PHhj4AIbYchl81Y3V+PHKjwauEcMwDHO36U0gPhLAcACbAWxTv7bqs1IMcy974oknUF3NTS1PCMGff/5pkHoQHoHnG54ty2X/K9PfTJftDLe0hJuREQCgWqnEGZlM/4UqlcCqVcCQIUBgIFBZqf8yu0EIaTPT5uGsw3otT6EAvvoKGD0ayGdz7DGMTvH5/DCxWOwvEokCJk+ePKSmpqZXnV5ycnKEI0eO9PHy8grw9vYO2LRpk5NmW11dHQkKCvLz9fX19/b2Dli+fPkAzbZNmzY5iUSiAG9v74CNGzc6dX524OrVq8I9e/boIe+ve+Xl5fxJkyYNGTx4cMCQIUMCfvnlF/P2+8yZM8fTzs4uRCQSdeig1N22u0V0dPSAdevW6f1Ra29GTYno5DVO3xVjmHvVTK1c5SlTpmDVqlUGq4vtBFvwrfkAgIbcBtSk9E96Co8QzHBwaFk+XF7eD4XyuDSV3FyguRk4ckT/ZfbgUb9HW94fzTqKNb+uweakzXopy80NePJJ4OxZYNs2vRTBMP9YxsbGqqysLEl2dnaGUCik27Ztc+z5KEAoFGLbtm2FV69ezbh48WLm559/7pSSkmICACYmJvTMmTNSqVQqycjIkJw8edLq5MmT5hcvXjSJiYlxTE1NzczMzMw4ceKEzeXLlzudnS0uLs4qNTW13zsiPf/884Mefvhh2fXr1zMkEokkNDS0Q+7dM888U3706NHszo7vbts/TZeBOCFkvvr/0Z29+q+KDHNvmTVrVsv7xMRE1NfrdiKXvuAZ8eAwvTUgLvuu/9JTZmqlpxwpL9dva/zly1xLeGYmt2xjA6ifShhSsHMwPG08AQDyZjneTnob//3jv1CqdN/v3d+/9f0h3Y+UyDCM2ujRo+U5OTnGUqnUSLtFd926dc7R0dEDtPf18PBoHj16dB0A2Nraqry8vOrz8/ONAIDH48Ha2loFAE1NTUShUBBCCNLT002HDh0qt7S0VAmFQoSHh9ccOHDApn094uPjLdauXTvo+PHjtmKx2F8ikRjp9cLVbt26xf/jjz8sX3755XKAu6FwcHDo8Ett8uTJckdHR0Vn5+huGwDIZDLeQw895O3r6+svEokCNK3+u3fvtgsKCvITi8X+8+bN81AoWk+xa9cuex8fH39fX1//GTNmDAaA9evXO4tEogCRSNTyZEEqlRoNGTIkICoqysPb2zsgPDxcJJfLCQCsWrXKxdPTMzAsLMw3OzvbuKf66EJ3LeKaxwyWXbx6RAiZRAiREkJyCCGvdbHPY4QQCSEkgxCyvw91Z5i7kp+fH3x9fQFwo6b8/PPPBq2P45zWgLjk85J+S0950NoadgJuYKbCxkbsKirCNX3dlAwZAty82bqcmAi8/LJ+yuoDQghm+M5os+5m7U2cLTir87Jeeqn1fV0dl6nDMIxuNTc3Iz4+3iooKKjPv8ykUqmRRCIxGzt2bMvEDgqFAmKx2N/Z2Tlk7NixsnHjxtWGhobWX7hwwbK0tJRfU1PDS0hIsC4oKOgQZE+cOFEeFBRUe/jw4ZysrCyJv79/0+1eV1hYmK9YLPZv/zpy5EiHeE8qlRrZ2dkp5syZ4+nn5+f/+OOPe8hkMp2OT3v48GErFxeXZqlUKsnOzs6YNWuWLDU11eTgwYN2ycnJWVlZWRIej0c//vhjewBITk422bp1q+vp06evSKVSySeffJKflJRktn//fvuUlJTM5OTkzJiYGMezZ8+aAkB+fr7J0qVLb+bk5GRYW1srY2JibJOSksy+//57u/T0dElCQkJ2WlqaeXf10dW1dvnBUUo/Uf9/Q2evnk5MCOED+BDAZAD+AOYSQvzb7SMCsBpAOKU0ANzMnQxzTyOEtElP+f777w1YG8B4QOsTTUWFArIL/ZCvDUDI42GqvX3L8tKcHHxRUqKfwszMgClTWpePHtVPObdhhnhGy3tTgSm+nf0twlzDdF/ODEDzEKKyEjh/XudFMIzhRUcPACFhICQM7VqfAQDPPTewZfubb3bM750716Nl+9atDh22d6GxsZEnFov9g4KC/AcOHNi0bNmyPuXbVVdX82bNmuX1zjvvFNjZ2bUMKSUQCJCVlSXJz8//KzU11fzixYsmw4YNa1i2bFnp+PHjfSIiIkQBAQF1fD6/0/Neu3bNRJMWIpFIjB577DGPSZMmtczzsmvXLvs1a9Y4R0VFeURGRg45fPiwVWfnSUlJkWZlZUnav2bMmNEhn1GhUJDMzEyzJUuWlGVmZkrMzMxUa9eu1elECcOGDatPSkqyWrRokduJEycs7O3tlSdOnLC8fPmyWUhIiJ9YLPY/c+aM1bVr14wBID4+3mrq1KmVrq6uCgBwdnZWnjp1ymLKlClVVlZWKmtra1VkZGRlYmKiJQC4ubk1jho1qh4Ahg4dWpebm2ucmJhoMWXKlCpLS0uVnZ2d6uGHH67qrj66utYe72AIIY6EkNcJIZ8SQr7QvHpx7vsA5FBKr1FKmwAcADC93T7PAfiQUloJAJTSm2CYvwHt9JQffvgBe/fuRUZGhkHqYhFiAZ5p6z/1wvcL+61s7fQUADikz1zxOXNa3x88qL9y+ijcPRz2ptwNSaOyEYFOgTA36tCv6Y7x+W2HUj+s376hDPOPoskRz8rKkuzbt6/AxMSECgQCqtIaprWhoYEHAFu2bHHUtCjn5uYKGxsbSWRkpNecOXMqnnrqqarOzu/g4KAcM2ZMzbFjx6wBYPny5eUZGRmZycnJUltbW6WPj0+HHOySkhKBpaWlUigUAgD8/f2bvvvuuzztfZKTk802btx448CBA3l79+7NO3DgQKcpFX1pEff09GxydnZuGjduXC0APP7445VpaWk6zVMPDg5uTE1NlQQFBdWvXbvW7ZVXXnGllJI5c+bc0nwPubm5l7dv3158O+c3MjJqeTTM5/OpQqHodpKNzupzO+V2pjePEn4AYA3gFwA/ar164gagQGu5UL1Omw8AH0LIWULIeULIpM5ORAh5nhCSTAhJLivrvxxXhrldw4cPx8CB3DjeVVVVePrpp/H5558bpC6ET2A73hZ8Cz7sH7GH+6v9N5Tgw7a2MNWaROg+S0s0af3h0qkpUwATE+59ejogleqnnD4S8AR4c+yb+HL6l7jxyg34O/r3fNBt0rr/w+HD3EiODMPox8CBAxUVFRWC0tJSfn19PYmPj7cGgNWrV5dpgkV3d/fmqKgoDx8fn4b169ff0D6+uLhYUF5ezgcAuVxOEhMTrfz8/BoAoKioSAAA2dnZRj/++KPNs88+W9G+/OzsbCNnZ+cu01EaGxuJQCCgPPWsxq+//rrr0qVLOw2i+tIi7u7urnBxcWlKS0szBoCff/7ZytfXV6cTJeTm5gotLS1VixcvroiOji69dOmS2aRJk2THjx+31Xw2N27c4F+5csUIACZOnCg7duyYbWlpKV+zLSIiQh4XF2dTU1PDk8lkvLi4ONuIiIguRywYN26cPC4uzkYul5PKykpeQkKCTXf10dW19mZmTTNKqb6GfRAAEAF4CMBAAL8RQoIopVXaO1FKPwXwKQAMHz6c/Wlh7nqa9JSdO1tnUzx06BC2bdvWb7Nbagv4PgA8gR6mmO+BKZ+PKfb2OFRejvssLPDigAEw0sdU9wBgYQFMngxoUoE++ojrtOnvDzz2mH7K7KX/jPxPv5QTEQFYW3P9VHNzgd9/B8LD+6Vohukf27cXo7tW0D17CrFnT9eP/WJj8xAbm9fl9j4wNjamK1asKBkxYoSfs7Nzs7e3d4dgNCEhweLIkSP2IpGoXiwW+wPAhg0bih5//PHqgoIC4cKFCwcrlUpQSsn06dMr5s6dWw0A06ZN86qqqhIIBAL6wQcf5HfWGTIkJKShoqJCKBKJAnbv3p07YcKEWu3t8fHxFmPGjJGrVCosWbLELTIyslrTcfRO7dy5M/+JJ54Y0tTURNzd3RtjY2NzAWDs2LHe+/bty/P09GyeOnXq4PPnz1tWVlYKnJ2dg1977bXi5cuXlwNAd9sAICUlxXT16tUDeTweBAIB3b17d15YWFjDmjVrisaPH++jUqkgFArpjh078n18fJqGDx/esGLFipIxY8aIeTweDQwMrDt06FDuvHnzbg0bNswPABYsWFAWHh5eL5VKO+3UOnr06LqZM2dWBAYGBtjb2zcHBwfXdlcfXXyOAEB66rhFCHkLwO+U0rg+nZiQBwCsp5ROVC+vBgBK6RatfT4G8Ael9Ev18kkAr1FKL3Z13uHDh9Pk5OS+VIVhDOLUqVOIiIgAwA1htX79eqxYsQLGxp2OQvW3daWuDuZ8Ptz647r37weeeKLtugcfBE6f1n/ZfVRQXYCBVgN1fmM2c2bryI3+/oCBMqIYHSKEpFBKhxu6HoaQlpaWGxIS0g/jn977SktL+dHR0W5JSUlW8+fPL5fJZPzNmzeX7Ny50yE2NtY+JCSkNjQ0tH7lypUstaCfpaWlOYSEhHh2tq03gXgNuBFUGgE0AyAAKKW004R/reMEAK4AGA+gCMBFAPMopRla+0wCMJdS+hQhxAHAnwBCKaW3ujovC8SZe4VCoYCrqyvKy8vh6uqK33//HZ6enoau1t+bTAY4OQGNja3rCAEKC4EBHft1GcJ/z/8XX/31FVJKUpC+KB2BToE6Pf8HHwDLl3PveTygtrY1Y4e5N7FAnAXit+PJJ590j4mJYdN73QW6C8R7M6GPJaWURyk1pZRaqZe7DcLVxykAvAQgHkAmgO8opRmEkI2EkGnq3eIB3CKESAAkAni1uyCcYe4lAoEAu3fvxu+//47CwsK7JginlEJ2QYb6XMONb643VlbAxImty8HBQEwMt/4uoKIqHLtyDCklKQCAw5m671H53HPcvQcAqFRAXJ+eZTIM83fBgvB7Q5c54oQQMaU0ixAyrLPtlNLUnk6uTmeJa7dundZ7CiBa/WKYv5052iN53AWurbmGoh1FUNYo4fSEE/y/1l/HwfaUlOJsdTUOlZWhuKkJ/wvQ08zGs2e3Dl9oZATMn6+fcvqoUdEI753eKJRx6atCnhC36nTf7mBuzqXKGxsDS5dymTkMwzDM3am7zprRAJ4H0NlkyRQAm+aeYW4DpdQgHTYBoCalBsoars/PrWO3+rUuSVVVGJeWBk0yXFFjo37yxqdOBUJDgenTuaD8LmEsMIbITtQSiL834T0su3+ZXsr6sTfjWjEMwzAG192EPs+r/x/RyYsF4QzTB01NTTh06BDmzZuH+++/32D1GLh0YMt7pUwJ+Z/ybvbWrTeuX4d2j5Qf9DWmuI0N8OefwPr1QKBu86/vlPbkPvFX4w1XEYZhGOau0KtxxAghgeqp6J/UvPRdMYb5OyktLcW8efMQGxuLCxcuQCKRGKQethNsYR7cOplM+dH+6wM1S2tyn5GWlnjOVWfzIfROveFz4qf7ts5pdvL6Scga+2eWU4ZhGObu1JuZNd8EsFP9igDwLoBp3R7EMEyLxsZG+Pv7o6mpdd6F+HjDtIbyBDx4vOHRslzxY4c5IvRmpkPrbNKX5HI06GtiH211dcDmzcADDwAiEdd70YA8bDww1GUoAKBJ2YSfsn/Se5nnzwP//rfBL51hGIbpRG9axGeDG4KwlFL6NIAQcDNtMgzTC8bGxhg3rjWba/ny5Xj55ZcNVh/bh20BPve+JrkGjaWN3R+gI0NMTRFszrXGN1KKExX9cBNgbAxs3cpFo0VFXMqKgWmnp8RejsVHFz/CMekxnZejUAB2dtw9yBdfsNFTGIZh7ka9CcTrKaUqAApCiBWAmwAG6bdaDPP3Mktr7vHz588brLMmAAhthLAZY9OyXPGTYVrFD+srR1zjwgVg5EigspJb5vHuukD8B+kPWBy3GB/88YHOyxEIuPsQjR07dF4EwzAMc4d6E4gnE0JsAOwBkAIgFcA5fVaKYf5upk6dCj6fa4Y+d+4ciou7nqG5P9hF2rW8L91X2m/lztTKEz9eXo5kmQw1CoV+CnN0BFK48bohFAJ5ecCzz+qnrD4IcgrCYJvBbdadzj2N8jrd35hMb01Jxy02QwPDMMxdpzcT+iymlFZRSj8GMAHAU+oUFYZhesne3h5jx45tWf7hhx8MWBuAb8FveV99phqqpv5JIA42N8cQ9TSPcpUKI1JTcVRfEeLgwfj/9u48Lqp6/x/46zMLILKDrAoqDAzDKui1BEIlBSVTMQu9lq23q3bril1NU3O5pfd71dti3tIWtX5qXTVzIZEMStNKIE1BEEQEUZB932bm/P4YZhhkEXTODML7+XjMo7O/P+c0Du/5zGeBt7dquaUFuHCBnzi9xBhrVyvubO6Md6PehZHQSOexli5tW754Eais1HkIQggh96Gno6b4t86GGQTAgzEWc7dzCCHtaTdP2b9/P06fPo26ujqDlMV6knXbigKo+L5CL3EZY+2apwDAMT6raqOj25b7UCNp7USc4zgsHLMQFsa6n/1z5EhgdOvE6C0twNGjOg9BSL+XlZVlJJFI2s1AFhcX57x69WoH7W05OTnisWPHerq7u/t4eHj4rF+/3l69r76+nvn5+Xl7eXnJPDw8fBYvXuys3ufi4uLn6ekpk0qlMl9fX++uynH16lXxjh07rLvaz4fu7qknx6xfv95eIpH4eHh4+Kxbt67DuX1FZ/8/9aUno6Z8BuAzALMATGt9PcZzuQjpd2bMmKFZ/uGHHxAWFoaTJ08apCym7qYQWYvAxAxmo8wgsu1ubi/d0m6ewgAYC3pUH3Bvpk5tWz52DOC4ro/Vo5BhIYh0j8SGiA34Yf4PEDD+noHW9z8c0X2fUEJIK7FYjM2bN9+4evVq+rlz5y5/+umn9qmpqSYAYGJiwp0+fTorKysrIz09PePkyZMWJ0+e1Iwl++OPP17JzMzMuHTp0uWurh8fH2+RlpZmqo97Uevunu52zLlz50x27949JC0t7fLly5fTjx8/bnXp0iUeZnF7sPXk0/8hjuNGcxw3n+O451pfz/NeMkL6GRcXF4wdO7bdtngD1tKOyRiDR5oewei00bAcq7+BkB62sIC9SJX4DzMywltubnc54z6EhgIWrTXN168Dp04BO3cafExxoUCI4/OO443QNyC1k/Iaa9q0tuUTJ1SjqRBCdM/Nza0lNDS0HgCsra2V7u7uDfn5+UYAIBAIYGlpqQSA5uZmJpfLWW867SckJJitWrVq2NGjR62lUqksIyND923ZOtHdPd3tmIsXLw4aNWpUrbm5uVIsFiMkJKRm3759VtrnVldXC8aPH+/h5eUlk0gkPto1/tu2bbPx8/Pzlkqlsrlz57rJWz+8tm7dauvp6Snz8vKSzZgxYwQArFmzxkEikfhIJBJNzXtWVpbRyJEjfWJjY908PDx8QkJCJLW1tZqHvmzZMsfhw4f7BgcHe2VnZxvfrTx86Uk12FnGmIzjOMPMQEJIPxIdHY1ff/0VAGBqaoohWrXD+mbsaJiKCQFj2CGVYpixMQLNzPgdQUYsBiZPBvbvV62PH6+qFXdwAKZM4S/uPeI4TufPw8cHcHQEiopUbcTXrVO9CCH8ycrKMsrIyDANDw/XTF8sl8vh6+sry8/PN54/f/7tiRMnatomRkRESBhjeO6550pef/31Dj23IyMja/38/Oq2bNlSMGbMmMb7KVtwcLBXXV2d8M7tGzduLJgxY0ZNb+6pu2Py8vKM1q1b51JUVCQcPHgwl5iYaBkQENCuPebBgwctHB0dW5KTk3MAoKysTAgAaWlpJvv377dJSUnJNDY25ubNm+f60Ucf2T700EN1mzZtcjp79mymk5OTvLi4WHjq1CnTPXv22Kampl7mOA7BwcHeERERNXZ2dor8/HyTL7/8MnfcuHHXp06dOnL37t3WCxcuLD916pTpN998Y3Px4sWMlpYWBAYGykaNGlXfVXn41JNEfDdUyXgRgCaofk3mOI7z57VkhPRDU6ZMwerVqwEAVlZWWDdAM6LH72gnzqupU9sScXXTlCNH+kwiXt9Sj13ndyE+Jx6F1YVIezlNp9dnDHB1VSXigOpRDNC3HSH3pKsvx11tr6qqEsTExLhv3LixwMbGRtMTXiQSITMzM6O0tFQYHR3tfu7cOZMxY8Y0nj59OnPEiBEthYWFookTJ3r6+Pg0TpkypUOym5ubaxIYGNgIABkZGUZr1qxxqq6uFh4/fjwXUNUUFxUViXJyckxqamqEL7zwQmlMTEyH6XtTU1OzevsMurqn7o6xsbFpfO2114oiIiI8Bw0apPTx8alXjx6mFhQU1PDmm28OW7Bggcv06dOroqKiagHg+PHj5pcuXTINCAjwBoDGxkaBvb29vKqqSjht2rQKJycnOQA4ODgotm/fbjZ16tRKCwsLJQBER0dXJCUlmc+ePbvSxcWlady4cQ0AMGrUqPq8vDxjAEhKSjKbOnVqpbm5uRIAJk+eXNldefjUk0T8UwBPA7gIgOZmI+Q+BAUF4e9//zvCw8MRERFh0PHE76SoU0A4mPcv//p3Z8L98MNAUJBhytIJARNgyYklaJCrmstcLb8Kdxt3ncaYO1c1rDoAXLkCKBSAsB/+ryb9X1xOjvN/btxw6smxsfb2pXtlsuva2+ZkZLjtu31bUxOweOjQW1s8PLodT9bBwUFeVVXV7l9MeXm5cMSIEU0bNmwYsmvXriEAcPz48WwnJyd5dHS0++zZs8vnz59f2dn17OzsFGFhYTVHjhyxHDNmTOOIESNaAMDFxUUeHR1defbs2cF3JuK3bt0SmZubK8RiMQBAJpM1f/3119ejoqJGqo9JSUkx/eyzzwoEAgFKSkqEixYtGtpZIt7bGvGmpiZ2t3vq6pjFixeXLl68uBQAXnnlFZehQ4c2a5/n7+/flJaWlnHgwAHLVatWuXz//ffVmzZtusVxHJs9e3bZhx9+WKh9/Ntvv92rDp9GRkaajkFCoZBraGjotkl2V+XpTcze6kkb8RKO4w5zHHeN47jr6hefhSKkvxIIBPjPf/6DGTNmwNzc3NDFgaJZgayXsnDG5QxOWZ6CvEa/DYg5jkN6XR025ecjga+ZNh0dgeDgtvW//71PjCcOAH8U/4Gxn4zVJOEA8NP1n3Qe57nnAFNT4KGHgA8+UM1tRAjpGUtLS6W9vX3L4cOHzQGguLhYmJycbDlx4sTa5cuXl2RmZmZkZmZmuLq6tsTGxrp5eno2rlmzplj7Gjdv3hSVlpYKAaC2tpYlJSVZeHt7N1ZXVwsqKioEgKp9clJSkoW/v3+HTizZ2dlGDg4OzXduV2tqamIikYgTtP7jXrFihdOrr75a0tmxqampWeoya786S8KVSiW6uqeeHFNYWChSl//YsWNWL774YrsP+ry8PLG5ubly4cKF5XFxcUXnz583BYCoqKjqo0ePWqvPLy4uFl65csUoMjKy+siRI9ZFRUVC9fYJEybUxsfHW9XU1Aiqq6sF8fHx1hMmTOiyiQ0ATJw4sTY+Pt6qtraWVVRUCBITE626Kw+felIj/jtjbA+AI1A1TQEAcBx3kLdSEUL0QlGhwK1P2r7sl35TCsdnHPUWf/3163grLw8AMHvIEETa2HR/wr2Kjla1zZg6VTWmXx/hbO6Mi8UXAQAMDL+99BtGO4/WeRwLC6C2VtVMhRDSe7t27bq2cOFC16VLlw4DgGXLlt308fFp0j4mMTHR7NChQ7YSiaRBKpXKAGDt2rWFTz31VFVBQYH42WefHaFQKMBxHJs+fXr5nDlzqjIyMoxmzpzpAQAKhYLNmjWr7IknnuhQix0QENBYXl4ulkgkPtu2bcubNGlSu7bWCQkJZmFhYbVKpRKLFi1yiY6OrlJ3oLwf3d1TeHi4x65du65nZWUZd3XM448/7l5ZWSkSiUTcu+++m29nZ6fQvn5qauqg5cuXDxUIBBCJRNy2bduuA0BwcHDjypUrCyMiIjyVSiXEYjH3/vvv50dERNQtWbLkVlhYmFQgEHC+vr71Bw4cyJs7d25ZUFCQNwA8/fTTJSEhIQ1ZWVlddmgNDQ2tnzlzZrmvr6+Pra1ti7+/f1135eET4+4ynBdj7PNONnOGGjll9OjRXEpKiiFCE6JTLS0tOHPmDOLj4xEREYHJkycbpBxnnM+g+ZaqosVyvCVGJY3SS9yrDQ3waO24CgCWQiFKQ0Ig4qO6trFRNd97H8xEx34yFr8VqtqNfPXEV3jS50kDl4j0NYyxVI7jdP8N7QFw4cKFvICAAN1PO/uAKyoqEsbFxbmcOnXKYt68eaXV1dXCd95559YHH3xgt3fvXtuAgIC6wMDAhqVLl3ZaK07068KFC3YBAQHDO9t31xpxmkWTEH7885//1HTWLC4uNlgibv9ne9zYdAMAUH+5HpySAxPwn7CONDGBm7ExrjepKpX+4uQEBXr2M12vmZjc/RgDiXKP0iTix3OOUyJOCLkrR0dHxZ49e/LV688884yrpaWlcuXKlbdXrlx525BlI73Tkwl9PBljJxljl1rX/RljK/kvGiH91759+/Cvf/1Ls/7dd99BqTRMX2j3je4Q26k6ALUUt6AmrdumdTrDGMNUW9t267xO7qN2+zbw+efArFnAvn38x7uLKZK2zqTHc47jbr9S6kJJiWrKe0JI/7B79+78ux9F+qKe/NXbAWA5gBYA4DjuDwCxfBaKkP5OIpGgqbUm2MTEBDt27NBLAtYZJmSwiWprm11+jKdOk52I0moT/h1fnTXvtGsX8PzzwMGDwP/+p5+Y3RjjPAY2g1TP4VbtLfxW+BtO5p7k5f3wySfAkCGAvT3wxBM6vzwhhJBe6kkibspx3G93bKO52Qi5D6NGjYK9vWoUpsbGRjg5OeHO8VX1yfaxtprpsmNleos70coKRq3tti/W1eFG433NU3F3n34KaM9meuIE0NTU9fF6IBQIMdm9rVlS6OehePSLR5Fdnq3zWA0NQGlra9ucHKClRechCCGE9EJPEvFSxpg7AA4AGGNPAOB1TEVC+juBQICoqCjN+nfffWfA0gDWkdZA6/eAmnM1aC7ucpQsnTITiRBmaalZP15eDiWfvwzs3g0kJ6uWn3wSOHcOMNLLTNHdinJvey/Ilap6ju+ydf+emD+/bVmpBH7/XechCCGE9EJPEvFFAD4GIGWMFQL4O4C/8lkoQgaCKVoTzRg6ERdZimDk1JaQFu/pdLhYXkzRaif+Rm4uYjMy+AsWHd22rFQCUmmfGEkl0iOy3bqXrRcGGw3WeRwLC2C01tgbf/yh8xCEEEJ64a6JOMdxuRzHPQpgCAApgHAAoXwXjJD+bvLkyVBPvvDrr7+itLRU025c3xhjEBi3fRwUf6nHRFyrnXiZXI7vysvRzFfH1alT25ZPnOgzbTMczRwxyrFt2MiNj27Ei0H8TDoUq9XDx8Df/wghZMDrMhFnjFkwxpYzxrYyxiYBqAcwH0AOABpfi5D7ZGNjg4ceegiAaobJgIAAzNduO6BnQ2KGaJaZkf5qib1NTTFMq3lInUKB87W13ZxxH3x8AFdX1XJ1NfDzz/zEuQdRHlHwsPHAK2NewQirEbzF0f4ukpgINOunFRIhhJBOdFcj/gUALwAXAbwEIAnAbAAzOY6broeyEdLvaTdPuXnzJhISEiCXG6YvtMurLvB4zwNhdWEIPht89xN05M5hDF9xccGfLCz4Cta+ecqxY6rOmtd5nzztrtaOX4vsv2Xjg6kfIMAxgLc4Uing5qZarqkBzpzhLRQhhJC76C4RH8lx3LMcx30MYA4AGYBIjuPO66VkhAwA2ok4oBpBJSsryyBlMRlqgqGvDoXQVP+jtzxpb49XXFxwzM8PG/megl47Ef/4Y8DODnj2WX5j9oBYKNZLHMba14qvX6+XsIQQQjrRXSKuaTzJcZwCwA2O43geW4yQgUV7GEMAOHHiBHx8fAxYIsOYaG2NDyQSTLW1hSnfwzhOmNA202ZNDVBbq2qiwldzmHtQ11yHI1lHsODoAlwpu6Lz6wcFtS0nJ1PzFEIIMZTuEvEAxlh166sGgL96mTFWra8CEtKfCQQCzJo1C1OnTsUHH3wALy8vQxep/zM1VSXj2lxcgGvXDFOeTjz77bN4fN/j+Cj1Ixy9clTn19fusKlUAt98o/MQhPQrQqEwWCqVyiQSic+UKVNG1tTU9Gga4JycHPHYsWM93d3dfTw8PHzWr1+vqXmpr69nfn5+3l5eXjIPDw+fxYsXO6v3rV+/3l4ikfh4eHj4rFu3zr7zqwNXr14V79ixw/r+7q53ursnte7u7cKFC8ZSqVSmfpmZmY3q7h4NJS4uznn16tUOfMcRdbWD4zjDzS5CyACybds2QxehnebSZtz69BZu770N+6fs4bbcTe9lKGtpwaW6OoRbWfETIDq6bciQ8HAgKalPDGOo5JR4/9f3kVHSNoTjdznfIe7hOJ3GMTMDhg0DCgqAESNUwxoSQrpmbGyszMzMzACAxx9/fMTmzZuHrFmz5q7DS4nFYmzevPlGaGhofUVFhWDUqFGyqVOnVgcHBzeamJhwp0+fzrK0tFQ2NTWxMWPGeJ08ebLKwsJCsXv37iFpaWmXTUxMlOHh4Z4xMTFVvr6+HYbVio+Pt8jIyDABUMHDbff6ntTHdHVvERERdQEBAU3qZymXy+Ho6BgQGxtbqa/y9zU9+kZHCBk40mem49ob11B3oQ5Fu4r0GrtGLsfDaWmw//lnTP3jDzQqFPwEUjeSNjFRzfneRwiYAJ+kfaJJxGd5z8LqR1bzEuuHH4CqKiA3F7ijqwIhpBuhoaG1OTk5xllZWUYSiUTTlnD16tUOcXFxztrHurm5tYSGhtYDgLW1tdLd3b0hPz/fCFD9ImppaakEgObmZiaXyxljDBcvXhw0atSoWnNzc6VYLEZISEjNvn37rO4sR0JCgtmqVauGHT161FoqlcoyMjL0MjtZd/ek1tW93enw4cMWrq6uTZ6enu0ayFVXVwvGjx/v4eXlJZNIJD7qWv9t27bZ+Pn5eUulUtncuXPdtAc32Lp1q62np6fMy8tLNmPGjBEAsGbNGgeJROIjkUg0vyxkZWUZjRw50ic2NtbNw8PDJyQkRFJbW8sAYNmyZY7Dhw/3DQ4O9srOzja+W3l0ocsacUKIYWRnZyMzMxPTpk0zSHybqTaoOl0FAGjIboCiUQGhiX5+IHs1Jwe/VleDA1CvVOJUVRUmaY0zrjMjRgAnTwIPPwwMGqT769+HKI8opJekAwCczZ0R5hbGSxwPD14uS0i/1tLSgoSEBIvJkyf3uoluVlaWUUZGhml4eLimQ4pcLoevr68sPz/feP78+bcnTpxYl5aWpli3bp1LUVGRcPDgwVxiYqJlQEBA3Z3Xi4yMrPXz86vbsmVLwZgxY+6rD19wcLBXXV1dhw/6jRs3FsyYMaOmN/fU3b3deczevXttnnjiibI7tx88eNDC0dGxJTk5OQcAysrKhGlpaSb79++3SUlJyTQ2NubmzZvn+tFHH9m+8sorZSkpKSabNm1yOnv2bKaTk5O8uLhYeOrUKdM9e/bYpqamXuY4DsHBwd4RERE1dnZ2ivz8fJMvv/wyd9y4cdenTp06cvfu3dZ+fn6N33zzjc3FixczWlpaEBgYKBs1alR9V+Xp3RPuGiXihPQR5eXleOihh5CdnQ0zMzOUlpbC2Nj47ifqmMOfHXBt5TVACUAJVJ2ugs2jPCTDnTBiDOoJ7l2MjMDjZPfAxIl8Xv2eRXlEYfPZzQBUzVIIIYbX1NQkkEqlMgAYO3ZszWuvvVZ6/fr1Hg91VFVVJYiJiXHfuHFjgY2NjWbGMpFIhMzMzIzS0lJhdHS0+7lz50zGjBnT+NprrxVFRER4Dho0SOnj41Mv7KITe25urklgYGAjAGRkZBitWbPGqbq6Wnj8+PFcQFVLXFRUJMrJyTGpqakRvvDCC6UxMTEdvkSkpqb2eriuru7pbvem3t/Y2Mi+//57yy1btty489ygoKCGN998c9iCBQtcpk+fXhUVFVX78ccf21y6dMk0ICDAu/V8gb29vRwAEhISLKZNm1bh5OQkBwAHBwfF9u3bzaZOnVppYWGhBIDo6OiKpKQk89mzZ1e6uLg0jRs3rgEARo0aVZ+Xl2dcWloqmjp1aqW5ubkSACZPnlzZXXl6+7y6Qk1TCOkDampqsHz5clxr7TBYW1uL06dPG6QsJq4mcF7Q9utqxQm9NT1sN8vmECMjTOajNrwzDQ2qmTZ37tRPvG6EuYbBVGwKAMgpz0FOeY5e4nK8fushRDficnKcWXJyMEtODo7LyXG+c/9LWVlD1fvfunatQ0e7ORkZbur9m/Lz7XoaV91GPDMzM2PXrl0FJiYmnEgk4pRaswA3NjYKAGDDhg1D1B0R8/LyxE1NTSw6Otp99uzZ5fPnz6/s7Pp2dnaKsLCwmiNHjlgCwOLFi0vT09Mvp6SkZFlbWys8PT071HjfunVLZG5urhCLVd8HZDJZ89dff91uUoSUlBTTdevWFe/bt+/6zp07r+/bt6/TJhXBwcFe2h0o1a9Dhw6Zd3Z8T+6pq3tT279/v6VMJqsfNmxYh8kz/P39m9LS0jL8/PwaVq1a5fL66687cRzHZs+eXab+/5CXl3dpy5YtN7uL3RUjIyPNJ55QKOTkcnm3nYQ6K8+9xO3MXRNxxlgMYyybMVZFo6YQwo/Bgwfj22+/1UzmExoaCiMjvTT365RNVFsCXH6iXG9xJ1pbQ9TajvB8bS1uNXXom6R7+fmAjQ0QGQn8/e+AgSZUUjMWGWPiiLba+oScBHAch2aF7scYbG4Gli5VTfIzeLBqbiNCSM8MHTpUXl5eLioqKhI2NDSwhIQESwBYvnx5iTpZdHV1bYmNjXXz9PRsvLNz582bN0WlpaVCAKitrWVJSUkW3t7ejQBQWFgoAoDs7GyjY8eOWb344osdPoizs7ONHBwcuvxgaGpqYiKRiBMIVKneihUrnF599dWSzo5NTU3NUpdZ+9VZsxSlUomu7qkn96a2b98+myeffLLTPzB5eXlic3Nz5cKFC8vj4uKKzp8/bxoVFVV99OhRa/WzKS4uFl65csUIACIjI6uPHDliXVRUJFTvmzBhQm18fLxVTU2NoLq6WhAfH289YcKELpvZTJw4sTY+Pt6qtraWVVRUCBITE626K09X1+mtntSI/x+AxzmOs+Q4zoLjOHOO46iPPSE6JBAIEBUVpVl/9NFHERbGT9vgnrAabwUmViXEdRfq0Fysn4GmLUQihFq2VZocL+f5S0B1NfD774D6Z9+qKuC33/iN2QNR7m3vhX/9/C8M+88wvPfLezqPI5cD//43kJWl+lHgwAGdhyCk3zI2NuaWLFlya8yYMd5hYWGeHh4eHWqtExMTzQ4dOmR7+vRpc3Ut81dffWUJAAUFBeKwsDAvT09P2ahRo2QTJkyonjNnThUAPP744+7u7u4+jz32mMe7776bb2dn16HnekBAQGN5eblYIpH4JCYmDr5zf0JCgllYWFitUqnEggULXKKjo6vUnSzvR3f3FB4e7pGXlyfu7t4AVefH06dPW8ybN6+ysxipqamDAgMDvaVSqeztt992Xr169a3g4ODGlStXFkZERHh6enrKJk6c6FlQUCAGgNGjRzcuWbLkVlhYmNTLy0u2cOHCYaGhofVz584tCwoK8g4ODvZ++umnS0JCQhq6uq/Q0ND6mTNnlvv6+vo8+uijEn9//7ruynO/z1GNcXf5PZIx9jPHcSG6Cni/Ro8ezaWkpBi6GITo3FdffYXY1gGex44di19++cWg5fl9/O+o+lH1uen9pTcc/sz7cKoAgP/Lz8ey3FwAwOwhQ/A1nxMc/ec/QFzr0IDm5sBzzwELFqiqiA0otyIX7u+7t9s2YfgE/DD/B53HcnNT/SgAqAaTOXZM5yHIfWKMpXIcN9rQ5TCECxcu5AUEBJQauhwPgqKiImFcXJzLqVOnLObNm1daXV0tfOedd2598MEHdnv37rUNCAioCwwMbFi6dGmnteKEPxcuXLALCAgY3tm+ntSIpzDGvmKMzWltphLDGIvRbREJIZMnT4b6J8TffvsNpaWG/dtjNspMs3zz43tqhndPtNuJHysrw/zLl9Gk7NAPSDcmT25bFgqBLVsMnoQDwEjrkZDYSNpt+6P4D16ap8RofZrzPakpIYQ/jo6Oij179uQXFBRc2rBhQ1FNTY3Q0tJSuXLlytvp6emX9+zZk09JeN/Tk0TcAkA9gMkAprW+HuOzUIQMRNbW1nj44YcBABzHISEhwaDl4Zrbfi2rOVeDu/16piu+gwfDpbV9fL1Sid3FxThTVXWXs+6RTAY4t/b3qqwE+tCvbVEebc1TYn1jUfR6EYyEuu83sGBB23JSEtDSovMQhBAD2L17d76hy0Du7q6JOMdxz3Xyel4fhSNkoJmiNbPKu+++i9jYWL0lwHdyeLqtKYqyUYn6nPtuWtgjjDFMsbVtt+1EBU8jtzAGTJqkFegEP3HugToRNzMyg5OZE0QCfkablUhUzVMAoLYWMHCLKEIIGVB6MmrKUMbYN4yx262vA4yxofooHCEDzWStphIpKSn46quvcPHiRYOUxWKMBSxCLeDyNxeMTh+NwZIOfYF4o908xdnICH+2t+cvmHbzlMRE/uL00oThE5A8PxllS8uwJXILb3EY67OPgBBC+r2eNE35HMBhAM6tryOt2wghOhYUFARr6/bDvJ4wUC0tEzIEnQqC5H0JzGRmdz9BhyKsrWErEmGmnR1WDx8On8E8fgl49NG25Z9/Bv7yF9VkPwYeWHuQeBDCh4fz0hzlTto/Chw4APDVJJ8QQkh7PUnEh3Ac9znHcfLW104AQ3guFyEDklAoREREhGZ99uzZmpFUBhJLkQi3Q0Jw0NcXLzs7g7Fu51q4P/b2wKhRqmWlEtixQ9VY+soV/mLeAyWnxPmi8/ij+A+dX1t7ktGMDODUKZ2HIIQQ0omeJOJljLF5jDFh62segDK+C0bIQKVunuLv74+oqCgMHTowW4IJ+Ey+76TdNkPNwJ1ltR29chSOmxwx6uNReOfUOzq/vq2tak4jtR07dB6CEEJIJ3qSiD8P4EkARQBuAXgCwHN8FoqQgezJJ5/ErVu3cOHCBTz/fN/oF61sUqL0SCmK93Q6iZpeKDiOv46r2om4tTXw/feqJip9QFl9GTJKMlBSrxp17Pvc76HkdN92ZNy4tuVbOpuqghBCSHfu2g2f47jrAB7XQ1kIIQAsLS1hqTW7pKEVvFeAq3FXASUgthPDYa5+JvZR+3/FxThUUoKTlZU4GxQEL1OdzSzcJiQEGDRINb1kRQXg6QmYmOg+zj1Y9v0yfPr7pwAAU7EpJrlPQlVjFawHWd/lzF7GWQYEBwNz56pGUiGEEMK/LhNxxthSjuP+jzH2AYAO1VAcx73Ka8kIIRoNDQ0YNGiQQWIbuxgDrRWwLaUtaLrVBGMnY73E/qmyEityc5Hf1AQAOFFezk8ibmwMrF6tai8+aRIwbJjuY9yjye6TNYm4v70/9s7ay0uc0FDVixBCiP50VyN+ufW/fWeGC0IGmH//+984duwYzp49i/z8fDg46Lc2GgBsomwABs3X8bJjZXB+0VkvsS/W1WmScAD4raaGv2BvvMHfte9DxIgIMDBw4HDu5jlUNVbB0qTv/GJCCCHk3nXZRpzjuCOti/Ucx+3SfkE10yYhhEfnzp3Dtm3b8OOPP6K5uRnff/+9QcohMhPB8TlHzXplcqXeYk/WGsrRVCDAp56eeosNjgOuXdNfvC7YmtoiyCkIAKDgFEjKSzJwiQgZWLKysowkEomP9ra4uDjn1atXt6sZycnJEY8dO9bT3d3dx8PDw2f9+vWaCRDq6+uZn5+ft5eXl8zDw8Nn8eLFmtoMFxcXP09PT5lUKpX5+vp6d1WOq1evinfs2KHbNmk9sH//fovhw4f7urq6+q5YscKxs2PWrl1r7+Hh4SORSHymTZs2or6+nnX3PPqazv5/6ktPOmsu7+E2QogOvfbaa8jLy9Osp6enG6wsLgtdNMsViRXglPoZY9tj0CC4GauawdQrlfzWiKtxnGre9+HDgZEjgdu3+Y95F5NGtg30nXiV3xl36uuBTz9VtdC5cYPXUIT0K2KxGJs3b75x9erV9HPnzl3+9NNP7VNTU00AwMTEhDt9+nRWVlZWRnp6esbJkyctTp48qZkg4ccff7ySmZmZcenSpctdXT8+Pt4iLS2Nh7Z5XZPL5Vi8eLFrfHz8lStXrqQfOHDARn1PateuXRNv377d4fz58xnZ2dnpCoWCffLJJzbdPQ/SpstEnDE2pbV9uAtj7H2t104A8p5cnDEWxRjLYozlMMa6/N2XMTaLMcYxxkb3+g4I6ae0Z9mMjY3FO+/ofti6njIbZQaRraolW8vtFtT+UauXuIwxTNIaVy+Rr6nutXEc8OuvQH5+a1DDTzU5yb0tEf8261usTV6Lbee28RLL2Rl48UXVwDE0jCEhPefm5tYSGhpaDwDW1tZKd3f3hvz8fCMAEAgEsLS0VAJAc3Mzk8vlrDfzIyQkJJitWrVq2NGjR62lUqksIyOD/5m+ACQnJw92c3NrkslkzSYmJlxMTEz5/v37re48TqFQsLq6OkFLSwsaGhoEQ4cObenueWirrq4WjB8/3sPLy0smkUh81LX+27Zts/Hz8/OWSqWyuXPnusnlqtRz69attp6enjIvLy/ZjBkzRqivs2bNGgeJROIjkUh81q1bZw+ofs0YOXKkT2xsrJuHh4dPSEiIpLa2lgHAsmXLHIcPH+4bHBzslZ2dbXy38vCluxrxm1C1D28EkKr1Ogwg8m4XZowJAXwIYAoAGYA5jDFZJ8eZA3gNwK+9LTwh/dkkrekOf/75Z/6G7usBJmCwmdSWEFec0ENC3GqSVvMU3hPxM2cAJyfg999V6xYWQGkpvzF7IGRYCAaJVJ11C2sKsebHNfhvyn95ieXu3rb87be8hCCk38vKyjLKyMgwDQ8P19RayOVySKVSmYODQ0B4eHj1xIkT69T7IiIiJD4+Pt6bNm2y6+x6kZGRtX5+fnUHDx7MyczMzJDJZM33Wrbg4GAvqVQqu/N16NAh8zuPLSgoMHJxcdHEGjp0aHNhYWG7ZHrEiBEtixYtKhoxYoS/vb19gLm5uSImJqb6bs9D7eDBgxaOjo4tWVlZGdnZ2ekxMTHVaWlpJvv377dJSUnJzMzMzBAIBNxHH31km5KSYrJp0yanH3/88UpWVlbGxx9/nA8Ap06dMt2zZ49tamrq5ZSUlMu7d+8e8vPPPw8CgPz8fJNXX331dk5OTrqlpaVi9+7d1qdOnTL95ptvbC5evJiRmJiYfeHChcHdleden3VPdNdG/EJre3A/AF9qtQ//FkBTV+dp+ROAHI7jcjmOawawD8D0To5bD+BfUCX8hJBWf/rTn2BhYQEAKCgowBUDz/RoPbktIdbneOIR1tZQ1xv9Wl2NY6WlqJb36Ee53vPwaGuKIhQCubnAa6/xE6sXjEXGeMTtkXbbLt2+hJs1N3UeKyambVmh0PnlCXngdFVz3dX2qqoqQUxMjPvGjRsLbGxsNIP+i0QiZGZmZuTn5/+RlpY2+Ny5cyYAcPr06cyMjIzLJ06cyN6xY4f9d999Z9bZdXNzc00CAwMbASAjI8PoySefdIuKihqp3r9161bblStXOsTGxrpFR0ePPHjwoEVn10lNTc3KzMzMuPM1Y8aMe2r7V1JSIjx27JhVTk7OxaKioj/q6+sF27Zt09TcdPU81IKCghpOnTplsWDBApfjx4+b2draKo4fP25+6dIl04CAAG+pVCo7ffq0RW5urnFCQoLFtGnTKpycnOQA4ODgoACA5ORks6lTp1ZaWFgoLS0tldHR0RVJSUnmAODi4tI0bty4BgAYNWpUfV5ennFSUpLZ1KlTK83NzZU2NjbKyZMnV3ZXnnt5Lj3VkzbiJwBoj5s2CEBPeo25ACjQWr/Ruk2DMRYEYBjHccd6cD1CBhSxWIwJEyZo1k+cOGHA0gCcvK1Gvu6POijq9ZOl2YrFCDZXVdQoATx26RJ/NePa090rFMDPP/MT5x5Mdm9rqjTcajj2ztoLC+NO/87eF+15jC5fBqp5rQsipHdy4nKck1lycE9eGXMy3O48P2NOhpv2MTlxOXcdAsrBwUFeVVUl1N5WXl4utLOzk2/YsGGIukY5Ly9P3NTUxKKjo91nz55dPn/+/MrOrmdnZ6cICwurOXLkiCWgqlEGABcXF3l0dHTl2bNnB995zq1bt0Tm5uYKsVgMAJDJZM1ff/31de1jUlJSTNetW1e8b9++6zt37ry+b9++TptU9KZGfNiwYe1qwG/cuNGuhhwAjhw5YuHq6trk7OwsNzY25mbMmFF55swZMwDoyfPw9/dvSktLy/Dz82tYtWqVy+uvv+7EcRybPXt2mfpLQl5e3qUtW7bcU82DkZGR5o+XUCjk5HJ5t22COivPvcTtqZ4k4iYcx2l+Smhdvu/OAowxAYAtAJb04Ni/MMZSGGMpJSUl9xuakAeGdjvxY8eO4cCBA2hsNMyPRzZTtOZA54DyE+V6i63dPAVQjSfOX7C2JkEw8JcfbdodNqubqvGkz5MwM+q04uy+DBnS/rtIcrLOQxDyQLG0tFTa29u3HD582BwAiouLhcnJyZYTJ06sXb58eYk6WXR1dW2JjY118/T0bFyzZk27nw1v3rwpKi0tFQJAbW0tS0pKsvD29m6srq4WVFRUCABV2+SkpCQLf3//hjvLkJ2dbeTg4NBlc5SmpiYmEok4gUCV1q1YscLp1Vdf7TRh6k2NeHh4eF1eXp5JZmamUWNjIzt48KDNrFmzKrWPGT58eHNaWppZTU2NQKlU4ocffjD39vZuVCqV6Op5aMvLyxObm5srFy5cWB4XF1d0/vx506ioqOqjR49aFxYWitTP/MqVK0aRkZHVR44csS4qKhKqtwPAhAkTauPj461qamoE1dXVgvj4eOsJEyZ0WcM/ceLE2vj4eKva2lpWUVEhSExMtOquPF1dRxd6kojXtdZcAwAYY8EAOrxJOlEIQHtWjKGt29TMAfgCSGaM5QF4CMDhzjpschy3neO40RzHjR4yZEgPQhPSP2i3E09ISMATTzyBM2fOGKQsJkNNILYXQ2Qtgs0UG5h66q/zvnYiLmIMTkY89lPSnu6+D3TUVPO198WiMYvw5cwvkbEwAwLWk4/ve9NHv4sQYjC7du269vbbbztJpVJZeHi417Jly276+Pi0a6abmJhodujQIdvTp0+bq2uZv/rqK0sAKCgoEIeFhXl5enrKRo0aJZswYUL1nDlzqm7cuCF66KGHpF5eXrKgoCDvyZMnVz7xxBMdfocKCAhoLC8vF0skEp/ExMQONeYJCQlmYWFhtUqlEgsWLHCJjo6uUneUvB+tI5/kR0VFeUokEp8ZM2aUjx49uhEAwsPDPfLy8sQTJ06smzZtWoW/v7+3l5eXj1KpZHFxcSXdPQ9tqampgwIDA72lUqns7bffdl69evWt4ODgxpUrVxZGRER4enp6yiZOnOhZUFAgHj16dOOSJUtuhYWFSb28vGQLFy4cBgChoaH1c+fOLQsKCvIODg72fvrpp0tCQkK6zFVDQ0PrZ86cWe7r6+vz6KOPSvz9/eu6K8/9PsfusLt1AGOMjYGqffdNqKb1cATwFMdxqXc5TwTgCoAIqBLwcwDmchzX6RhsjLFkAK9zHNftBEKjR4/mUlJojiEyMHAch5EjR7YbxnDZsmXYuHGjQcqjlCshEPGXAHalSamE9alTaOA4BJmZITkwEOai7uYjuw+NjYCNjWq6ewA4dgxITwcWLQL4mNWzDzp5Enj0UdWypyeQmQn0YoAHomOMsVSO4wbkqGIXLlzICwgIMHyP6T6mqKhIGBcX53Lq1CmLefPmlVZXVwvfeeedWx988IHd3r17bQMCAuoCAwMbli5dSs0I+oALFy7YBQQEDO9s313/knEcd44xJgXg1bopi+O4lh6cJ2eMvQIgAYAQwGccx6UzxtYBSOE47nCP74CQAYoxhkmTJmFH6zhyzs7OGDFixF3O4o8hknAAMBYI8F1AAHxMTWHHZ204AJiYAOHhwPHjqvXoaNV/AwLa15b3EXKlHCKBbr+UhIQARkZAczNw5Qqwdy8wd65OQxBC7oOjo6Niz549+er1Z555xtXS0lK5cuXK2ytXrjT85Aekx3r6V9ULqiEIg6AahvCZnpzEcVw8x3GeHMe5cxz3duu21Z0l4RzHjb9bbTghA5F28xSZTIaXX37ZgKUxnHArK/6TcDXtthlqfaiZSmF1IZZ/vxzB24Mxbe80nV/fxARw0Jpj7osvdB6CEKJDu3fvzr/7UaQvums1CmPsLQDjoUrE46EaF/w0gN28lowQAgCIiIjA22+/jUmTJiEoKOjuJ+hRc2kzjOz0lBzrk3bNN2PA7NlAaKjhynOHZkUzNv6sap5kIjJBo7wRJiLdTlj36KPA55+rln/7TaeXJoQQ0qonNeJPQNXOu4jjuOcABADo0NieEMIPGxsbrFixAmPGjIFQKLz7CTxruN6A8xPO4yezn/DLsF/0Nt29Wr1CgYTycizJycF5vqa89/FRTewDqGbaXLIEmN7ZNAj6t/P8TgRtb/tC1qJowYWiCzqP89JLqtEcn3wS+PprnV+eEEIIelAjDqCB4zglY0zOGLMAcBvtR0MhhAwgLSUtqEyu1KzX/F4Di2Ddj2fdaWylEk+lp+No6/CFFiIRAs07DH17/xgDpkxRDaQ9aVL7dhoG5jDYAZWNlQCAYRbDcHHBRVia6L5u5OGHgWL9zdtECCEDUk9qxFMYY1YAdkA1xX0agLN8FooQ0rmioiJ8+eWXeOaZZ5CVlWWQMpgHmYMZtw2hUbxbf9na1yUlmiQcABL5HE/8k09UU96vXQu4dZgXxGAecXsERkJVc6CC6gLUtdTd5QxCCCF9VbeJOFPN37qB47hKjuM+AjAJwPzWJiqEED174YUX8PTTT+OLL77AcfWoHnrGBAy202w167UXa7s5WrcirKw0ywIArw/j8ce5Pjpe32CjwQgZFqJZ/z63JxMdE0II6Yu6TcQ51SDj8VrreRzH/cF7qQghHSxatAjx8Zp/jkhISDBYWSTvSjTL1T9X6226e0djY/gPVs1loQQgFuhpOMXUVGDdOiAsTDWmuIFpz7KZmMv/aC4cp3oEhYV3P5YQQkjP9eSvWFrrpD6EEAPy9PTULMtkMvzzn/80WFmMXYxh6qOa3IZr5lD5U6XeYmvPsslr0xRtGzcCb70FnD7dJ6aanOSulYhfTcSNqhs4nX+al1jz5qmGMxw9WvVdhBBCiO70JBEfC+AXxthVxtgfjLGLjDGqFSdEz7THE7958yb8/f0NWBrAZrKNZrniRIXe4k62aYt7ooLnuJWVwIoV7cfv6wPjiY9yHAXbQarmQcV1xRj27jA8tf8p3G2m5HtRVKSa2AfoE7dOCCH9SpeJOGPMtXUxEsBIABMBTAPwWOt/CSF65O3tDWdnZwBAZWUlUlIMO/+V9aS2munSo/qbgTrM0hLGre23L9fX40ZjI5Q8JKAAVFXB774L5LfOlfHee8DOnfzE6gWhQIiIkRHttt2suYmMkgydx9KeUVNfP0AQQshA0V2N+CEA4DjuOoAtHMdd137ppXSEEA31dPdqiQaunhw0chDQ2p+xMbsRTTeb9BNXKESoZdtwfcGpqdhx6xY/wdTT3WuCD1INrt0HaLcTZ2B4dOSjaJA36DxObCwgah3otqqK2okTQogudZeIaw8ZMJLvghBC7m6y1oyPJ06cgEKhQLO63YCeGQ81brd+++vbeos9Sat5yu2WFpzgs6pWe5bNPtA+XE07ETcSGuHInCMY7Txa53FMTYFHHmlb70OPgBC9EQqFwVKpVCaRSHymTJkysqampkc9xXNycsRjx471dHd39/Hw8PBZv3695pt8fX098/Pz8/by8pJ5eHj4LF682Fm9b/369fYSicTHw8PDZ926dV1++7969ap4x44d1l3t58v+/fsthg8f7uvq6uq7YsUKx86OWbt2rb2Hh4ePRCLxmTZt2oj6+noGALNnzx5uY2MTIJFIfPRb6t6Ji4tzXr16Ne+TSHT3RuK6WCaEGMijjz6qWf75559hZ2eH/fv3G6QswsFCmIxQTavOxAzyCrneYk+2bv9355fqal7aR6uCaSXi338PKPQzQszduFm5wdfeF+OHj8fq8NVoVvD3hSwysm2ZEnEyEBkbGyszMzMzsrOz08ViMbd58+YhPTlPLBZj8+bNN65evZp+7ty5y59++ql9amqqCQCYmJhwp0+fzsrKyspIT0/POHnypMXJkycHnzt3zmT37t1D0tLSLl++fDn9+PHjVpcuXTLu7Prx8fEWaWlpprq817uRy+VYvHixa3x8/JUrV66kHzhwwEZ9T2rXrl0Tb9++3eH8+fMZ2dnZ6QqFgn3yySc2APD888+XHj58OFufZe7LukvEAxhj1YyxGgD+rcvVjLEaxli1vgpICGljb2+PUaNGAQA4jkNlZSVOGDAz8tzuicAfA/FI0yMYsXaE3uIGmJnBTtQ2MfBBHx8wvsb9lsmA1rb5qKwEUlKA0lKgvp6feL1w/uXzSJqfhBVhK2BhzN/sptqJeGJin/kuQohBhIaG1ubk5BhnZWUZadfqrl692iEuLs5Z+1g3N7eW0NDQegCwtrZWuru7N+Tn5xsBgEAggKWlpRIAmpubmVwuZ4wxXLx4cdCoUaNqzc3NlWKxGCEhITX79u2zurMcCQkJZqtWrRp29OhRa6lUKsvIyDDi9cZbJScnD3Zzc2uSyWTNJiYmXExMTPn+/fs7lE+hULC6ujpBS0sLGhoaBEOHDm0BgClTptQOGTKk25qb6upqwfjx4z28vLxkEonER13rv23bNhs/Pz9vqVQqmzt3rptcrrrM1q1bbT09PWVeXl6yGTNmaP4YrVmzxkEikfhIJBLNLwtZWVlGI0eO9ImNjXXz8PDwCQkJkdTW1jIAWLZsmePw4cN9g4ODvbKzs43vVh5d6HKKe47jhLoKQgjRncjISPz++++a9YsXLxqsLDYRNnc/iAcCxrBk2DAIGEOkjY1mbHFeMKaqFVd30oyJAW7dAvbuBZ56ir+4PSAU6Odj2s9P1TT+9m2grAzYsQP461/1EpqQPqWlpQUJCQkWkydP7nWFZFZWllFGRoZpeHi4ZhY0uVwOX19fWX5+vvH8+fNvT5w4sS4tLU2xbt06l6KiIuHgwYO5xMREy4CAgA5T6EZGRtb6+fnVbdmypWDMmDGN93NfwcHBXnV1dR0+UDZu3FgwY8aMGu1tBQUFRi4uLpqf4IYOHdr866+/mmkfM2LEiJZFixYVjRgxwt/Y2FgZFhZWHRMT0+NndvDgQQtHR8eW5OTkHAAoKysTpqWlmezfv98mJSUl09jYmJs3b57rRx99ZPvQQw/Vbdq0yens2bOZTk5O8uLiYiEAnDp1ynTPnj22qamplzmOQ3BwsHdERESNnZ2dIj8/3+TLL7/MHTdu3PWpU6eO3L17t7Wfn1/jN998Y3Px4sWMlpYWBAYGykaNGlXfVXl683y7o6fZMAghuhKpVT3p4uKCc+fOGbA0hvOGmxuWuroiwMyMv9pwNa1Osrh5UzXDTR8byy+3Ihf/Pfdf/P3433V+bYEAGDq0bX37dp2HIKRHcuJynJNZcnAySw7OictxvnN/1ktZQ9X7r711rUP73ow5GW7q/fmb8u16GrepqUkglUplfn5+sqFDhza/9tprvRoqqqqqShATE+O+cePGAhsbG6V6u0gkQmZmZkZ+fv4faWlpg8+dO2cSFBTU+NprrxVFRER4TpgwQeLj41MvFHae9+Xm5poEBgY2AkBGRobRk08+6RYVFaXp17d161bblStXOsTGxrpFR0ePPHjwYKc/naWmpmZlZmZm3Pm6MwnvqZKSEuGxY8escnJyLhYVFf1RX18v2LZtW49rboKCghpOnTplsWDBApfjx4+b2draKo4fP25+6dIl04CAAG+pVCo7ffq0RW5urnFCQoLFtGnTKpycnOQA4ODgoACA5ORks6lTp1ZaWFgoLS0tldHR0RVJSUnmAODi4tI0bty4BgAYNWpUfV5ennFSUpLZ1KlTK83NzZU2NjbKyZMnV3ZXnnt5Lp3pskacENI3jRs3Dr6+vhg3bly7pJzwSKttPgBAKFQNIdJH1DXXQbpVihZlCwBgWcgyOJk76TTGtGlAWppqOT1d9V2E7+8/hPQV6jbi2ttEIhGnVGpyajQ2NgoAYMOGDUN27do1BACOHz+e7eTkJI+OjnafPXt2+fz58ys7u76dnZ0iLCys5siRI5ZjxoxpXLx4cenixYtLAeCVV15xGTp0aIdOILdu3RKZm5srxGIxAEAmkzV//fXX17UT8ZSUFNPPPvusQCAQoKSkRLho0aKhndVM96ZGfNiwYc2FhYWaZjA3btxoV0MOAEeOHLFwdXVtcnZ2lgPAjBkzKs+cOWO2cOHCHvWs9/f3b0pLS8s4cOCA5apVq1y+//77amtra8Xs2bPLPvzww3ZjN7399tu9HsrKyMhI06lIKBRyDQ0N3VZMd1aeTZs26WS4LqoRJ+QBY2RkhIsXL+Ljjz9GTEwMBPqa5r0bVWeqkPlCJn7z/k1v091r4zgOGXV1yOar3ba9PdDaNh8A8MUXwP/+x0+sXsopz8FbyW9BLBRrtp24qvt+Ay+/rPqvkZFqls2WFp2HIOSBMnToUHl5ebmoqKhI2NDQwBISEiwBYPny5SXqGmVXV9eW2NhYN09Pz8Y1a9YUa59/8+ZNUWlpqRAAamtrWVJSkoW3t3cjABQWFooAIDs72+jYsWNWL774YocENjs728jBwaHLXtpNTU1MJBJx6r8RK1ascHr11VdLOju2NzXi4eHhdXl5eSaZmZlGjY2N7ODBgzazZs2q1D5m+PDhzWlpaWY1NTUCpVKJH374wVx9bz2Rl5cnNjc3Vy5cuLA8Li6u6Pz586ZRUVHVR48etVY/m+LiYuGVK1eMIiMjq48cOWJdVFQkVG8HgAkTJtTGx8db1dTUCKqrqwXx8fHWEyZM6LKGf+LEibXx8fFWtbW1rKKiQpCYmGjVXXl6ei93QzXihJD70lLegt9Df9eMrVTxfQXsHu/xL7737VBJCRZmZ+NWczNecnLCdi8vfgJNngz8/jvg4tKneiuW1Zdh89nNAABjoTH+9ei/MGHEBJ3HcXICLl5U9V3tA9/9yADlscXjpscWj5td7ffa4XXDa4fXja72y/bKrsv2ynQyF4qxsTG3ZMmSW2PGjPF2cHBo8fDw6JBoJiYmmh06dMhWIpE0SKVSGQCsXbu28KmnnqoqKCgQP/vssyMUCgU4jmPTp08vnzNnThUAPP744+6VlZUikUjEvfvuu/l2dnYdPnQCAgIay8vLxRKJxGfbtm15kyZNateOPCEhwSwsLKxWqVRi0aJFLtHR0VXqjqP3o3UkmPyoqChPhUKBuXPnlo4ePboRAMLDwz127dp1feLEiXXTpk2r8Pf39xaJRPDx8amPi4srAYBp06aN+OWXX8wrKipEDg4O/m+88cZNde2/Wmpq6qDly5cPFQgEEIlE3LZt264HBwc3rly5sjAiIsJTqVRCLBZz77//fn5ERETdkiVLboWFhUkFAgHn6+tbf+DAgbzQ0ND6uXPnlgUFBXkDwNNPP10SEhLSkJWV1Wmn1tDQ0PqZM2eW+/r6+tja2rb4+/vXdVee+32Oaoy3Ib94Mnr0aM7QMwoS0pc0Nzfjl19+gZOTEyQSiUHKcNr2NOTlqt7rdjF28D3gq5e4OfX1iLhwAflNqsmE3IyNce2hh/hpM379umqkFKm0T7XJUCgVsN9kj/IGVYXZhb9egL+Dv4FLRXSJMZbKcZzuB4l/AFy4cCEvICBAf1P3PsCKioqEcXFxLqdOnbKYN29eaXV1tfCdd9659cEHH9jt3bvXNiAgoC4wMLBh6dKlndaKE/5cuHDBLiAgYHhn+6heg5AH2I4dO2Bra4vw8HDs2LHDYOWwDGub6bLuYofO/bwZZmKCEq0JjaSmpqjjq7bazQ3w9u5TSTigGjlFe3Kf4znHDVgaQoihODo6Kvbs2ZNfUFBwacOGDUU1NTVCS0tL5cqVK2+np6df3rNnTz4l4X0PJeKEPKCSkpKwfft21NaqRsJKSEgwWFmkO6VgYlWC2pDdgKZb+pnu3lggwAStyX2m29nBTKSnFneFhaohDTMy7noo36I8ojTLCVf19z54wH5QJWRA2b17d76hy0DujhJxQh5QlZWVUDfTMjIywsMPPwyFgdoui63EsAxpqxWvSKzQW+xIrenuE/ic6l7bqlWq8fyee041nriBTXZvm/3z1PVTqG2uhVzJz0ynv/2mGkp9yBDVSCqEEELuHSXihDygJk6cCPXYss3NzVi/fj26GmtWH6wnt9VMGyoR/6GyEi1aw4nxIjcXKCpqW+8D44k7mztr2oW3KFsQvjMcdv9nh9rm2ruc2Xuffw58841qctGfftL55QkhZEChRJyQB5SlpSUefvhhzXqigRNCm8ltCXF5Yjk4pX7aLXgOGgQ3Y9VMxDUKBc5W93rCu57jOOCRR4BPPlGtBwcDs2b1iTYake5tY8qn3UpDVVMVkvOSdR5HPYwhANTUqFroEEIIuTeUiBPyANOe0MeQbcQBYLD/YAjMVR8pLcUtqL2g+9rYzrDWae7V3rp2DZ/e0sk8C50FUw1jqPb448A//tEnOnBqtxNXS7mp+xGmAgIAK6u29dxcnYcghJABgxJxQh5gk7WSwhMnTsCQw5EyIQPX3Ba/aHdRN0frlnYinlxVhW18VtNqJ+IndD9xzr0KGRYCU3HbHBNJ85OwZvwancdhDJg9u23dwN//CCHkgUaJOCEPsODgYNi0JqFFRUV49tlnsXv3boOUhQkYzALMNOsVJ/TXTjzC2rrdh1labS1uN3c54dx9BotoW/7llz4z1b2xyBgThqsm8hlpPRIM/NXSa/0Q05e+ixBCyAOHEnFCHmBCoRCTJrWNIb17926DJeIA4LzAGRYhFvB4zwNBvwbpLa6lSISHLSw06y87OWEQX9M/DhkCBLXem0IBJCXxE+cevBPxDrL/lo2rr15F+PBw3uJERLTNrpmSouq4SQghpPcoESfkAafdThwATp06hfr6+57F+J44PeuEoNNBGPrqUIjM9DSed6tIGxt4DhqEV1xc8LKzM8z5HE9cu3nKf/4DTJ/e1oHTgPwd/OFh48F7HCsrYOxY1TLHAbt28R6SEEL6JUrECXnAadeICwQC7N27FyJ9TWrThyx3dUXW2LH4QCLBKHNzfoNpJ+I//QQcPgzs389vzHtQ21yLY1eO8TKmeEBA2/K//63zyxNCyIBAiTghD7ihQ4fCx8cHgwYNQmRkJIKDg2FkZGToYumdiK+mKJ0ZNw4wNW2/LTkZMNAvEZ3588E/w+ZfNnhs72P4rfA3nV//scfalouL+0xTeUJ0Lisry0gikfhob4uLi3NevXq1g/a2nJwc8dixYz3d3d19PDw8fNavX2+v3ldfX8/8/Py8vby8ZB4eHj6LFy92Vu9zcXHx8/T0lEmlUpmvr693V+W4evWqeMeOHdZd7efL/v37LYYPH+7r6urqu2LFCsc793d33wAgl8vh7e0tmzBhAv8/192jzv5/6gsl4oT0A4cOHUJFRQXi4+Ph5uZm6OIAABpvNSL/P/loLuGp02QPKPgaRcbYGBg/vm199mzg4sWOybkBcByHzNJMXC2/ihZlCwDgxFXd96iMjAS05486d07nIQh5oIjFYmzevPnG1atX08+dO3f5008/tU9NTTUBABMTE+706dNZWVlZGenp6RknT560OHny5GD1uT/++OOVzMzMjEuXLl3u6vrx8fEWaWlpev2QkcvlWLx4sWt8fPyVK1eupB84cMBGfU9q3d03APzzn/908PDwaNBnuR8klIgT0g94eHjAuHVSG0PjOA6/+fyGX5x/QW5cLoq/KNZr/KKmJqy5dg0Pp6XhsYsX+Quk3TyF4wCJhL9YvVDXUgf///rj18JfAQDedt6wH2x/l7N6TyQCVq4EvvwSqKwEHn1U5yEIeaC4ubm1hIaG1gOAtbW10t3dvSE/P98IUDUbtLS0VAJAc3Mzk8vljPVi/oGEhASzVatWDTt69Ki1VCqVZWRk6OVnz+Tk5MFubm5NMpms2cTEhIuJiSnfv3+/lfYx3d331atXxQkJCZYvvfRSl126q6urBePHj/fw8vKSSSQSH3Wt/7Zt22z8/Py8pVKpbO7cuW5yuaqJ3datW209PT1lXl5eshkzZowAgDVr1jhIJBIfiUTis27dOs0HXlZWltHIkSN9YmNj3Tw8PHxCQkIktbW1DACWLVvmOHz4cN/g4GCv7Oxs4+7KwqeB15CUkAGiqanJIMk5YwzKprZp5ov/XzGGxQ3TS2yO4/DhzZv45/XrAABjxtCgUGCQdtWtrkRHA9evqxLyRx7R/fXvkZmRGUJdQ5GUpxrNZWnIUjwb+Cwvsdas4eWyhDzwsrKyjDIyMkzDw8M1M5vJ5XL4+vrK8vPzjefPn3974sSJdep9EREREsYYnnvuuZLXX3+9Q9IaGRlZ6+fnV7dly5aCMWPGNN5P2YKDg73q6uo6fChu3LixYMaMGTXa2woKCoxcXFw0P2sOHTq0+ddffzW781y1O+970aJFw/7v//7vRlVVVZcfwgcPHrRwdHRsSU5OzgGAsrIyYVpamsn+/fttUlJSMo2Njbl58+a5fvTRR7YPPfRQ3aZNm5zOnj2b6eTkJC8uLhaeOnXKdM+ePbapqamXOY5DcHCwd0RERE1ISEgDAOTn55t8+eWXuePGjbs+derUkbt377b28/Nr/Oabb2wuXryY0dLSgsDAQNmoUaPqOytLb59vb1GNOCH9SHZ2Nv72t7/B09MTzz77rMHKMWTWEM1y3aW6dok5nxhj+KakRLPezHFIq+Vphk8PD2DLFiAqqk80SdGmPd19wlWacYf0H3Fxcc6MseCevObMmdOhnd6cOXPctI+Ji4tz7iyOtq5qrrvaXlVVJYiJiXHfuHFjgY2NjebDTyQSITMzMyM/P/+PtLS0wefOnTMBgNOnT2dmZGRcPnHiRPaOHTvsv/vuu04T3dzcXJPAwMBGAMjIyDB68skn3aKiokaq92/dutV25cqVDrGxsW7R0dEjDx48aNHZdVJTU7MyMzMz7nzdmYT31p33vXfvXks7Ozt5WFhYt51ngoKCGk6dOmWxYMECl+PHj5vZ2toqjh8/bn7p0iXTgIAAb6lUKjt9+rRFbm6ucUJCgsW0adMqnJyc5ADg4OCgSE5ONps6dWqlhYWF0tLSUhkdHV2RlJSk6bHv4uLSNG7cuAYAGDVqVH1eXp5xUlKS2dSpUyvNzc2VNjY2ysmTJ1d2VZb7eSY9QYk4If1EXV0djh49iq1btyI7OxuJiYlQKvWTAN/J5VUXiJ3FAACumUPlT5V6i609y+YLjo4IsbTUW2w0NAB5efqL1wXt6e5PXD0BhZL3vyWE9FsODg7yO2t0y8vLhXZ2dvINGzYMkUqlMqlUKsvLyxM3NTWx6Oho99mzZ5fPnz+/srPr2dnZKcLCwmqOHDliCQAjRoxoAQAXFxd5dHR05dmzZwffec6tW7dE5ubmCrFY9bkqk8mav/766+vax6SkpJiuW7eueN++fdd37tx5fd++fZ02qwgODvZSl1n7dejQoQ7DTQ0bNqy5sLBQ0wzmxo0b7WrI1Tq779OnT5slJiZaubi4+D377LMjf/nlF/Pp06ePuPNcf3//prS0tAw/P7+GVatWubz++utOHMex2bNnl6m/JOTl5V3asmXLzc7u526MjIw0nYWEQiEnl8u7bBPUWVnuJWZvUCJOSD9x7do1xMXFadabm5tx9epVg5TFxMUE9rPb2iWXHS3TW2ztRPwnfQ3lceWKahgRW1vgmWf0E7Mb/g7+cDRTDW5Q3lCO1FupvMbLywOWLQNeeonXMIQYhKWlpdLe3r7l8OHD5gBQXFwsTE5Otpw4cWLt8uXLS9TJoqura0tsbKybp6dn45o1a9p1jrl586aotLRUCAC1tbUsKSnJwtvbu7G6ulpQUVEhAFTtk5OSkiz8/f07dGzMzs42cnBw6LLne1NTExOJRJygdfSoFStWOL366qslnR3bmxrx8PDwury8PJPMzEyjxsZGdvDgQZtZs2ZVah+jVCrR2X1/+OGHhcXFxX8UFhZe3LlzZ+5DDz1U8+233167M0ZeXp7Y3NxcuXDhwvK4uLii8+fPm0ZFRVUfPXrUurCwUKR+5leuXDGKjIysPnLkiHVRUZFQvX3ChAm18fHxVjU1NYLq6mpBfHy89YQJE7qt3Z84cWJtfHy8VW1tLauoqBAkJiZadVWW7q6jC9RGnJB+wsfHBy4uLigsLAQAHDt2DBIDdiC0fcwWhe+pylJ2pAwe73p0+VOuLoVZWsJEIECjUokrDQ3Ia2jA8EGD+A0qFgPHjqmWz5wBKioAa72PMqbBGEOkeyR2XVDNtLPl7BZYGFtgachSnU/4c+gQMHOmalkgALZtUz0OQviwZcuWm/daMwoAe/fuvb53797rdz+yvV27dl1buHCh69KlS4cBwLJly276+Pg0aR+TmJhodujQIVuJRNIglUplALB27drCp556qqqgoED87LPPjlAoFOA4jk2fPr18zpw5VRkZGUYzZ870AACFQsFmzZpV9sQTT1TfGT8gIKCxvLxcLJFIfLZt25Y3adKkOu39CQkJZmFhYbVKpRKLFi1yiY6OrlJ3oLwfrSOi5EdFRXkqFArMnTu3dPTo0Y0AEB4e7rFr167rWVlZxl3dd09ipKamDlq+fPlQgUAAkUjEbdu27XpwcHDjypUrCyMiIjyVSiXEYjH3/vvv50dERNQtWbLkVlhYmFQgEHC+vr71Bw4cyJs7d25ZUFCQNwA8/fTTJer24V0JDQ2tnzlzZrmvr6+Pra1ti7+/f11XZbm/J3h3jONreC+ejB49mktJSTF0MQjpk55//nl8/vnnAIA1a9bgrbfeMlhZlM1K/Gz7MxS1qmYRYzLGYLB3h19ceRF14QISKioAAB95euJl57s2A713a9cCGzYATa1/k6VSYO9eIDCQv5g9sPfiXsw9OLfdtvei3sOrY1/VaZzaWkB7/qSDB9sSc6IbjLFUjuNGG7ochnDhwoW8gICALkfcGKiKioqEcXFxLqdOnbKYN29eaXV1tfCdd9659cEHH9jt3bvXNiAgoC4wMLBh6dKlndaKE/26cOGCXUBAwPDO9lHTFEL6kclaQ+olJBi2k57ASACryVaa9Vs7b+kttnbzlG9LS5FQXs5fMCentiT84YeBy5cNnoQDwCT3SWBo/wvE0StHdR7HzAxwcWlbv9bhh2dCiK45Ojoq9uzZk19QUHBpw4YNRTU1NUJLS0vlypUrb6enp1/es2dPPiXhDwZKxAnpRyZNmqRp/vHrr7+inM8EtAfk5W1Tq5f8T39/E7QT8e/KyzHj0iXUKXjqsBgd3bZ87lyfmWLSztQOY1zGaNZjvGOwIWIDL7H+9re25Z9+4iUEIaQbu3fvzjd0Gci9oUSckH7E1tYWY8eOBaDqQPPxxx9jz549BiuP9jCGTdea0FLRope43qamGKo1hnqjUonvW5uq6JyLCxAUpFqWywED/xKhTXsYwyGmQxDsHMxLHO2mKCdOAPX33TKVEEIGBkrECelnpk+frllesWIF5s+fjyoD1dLax9qDiRlMRpjAZbELBIP085HDGMMUrVrxYcbGGCTgMfa0aW3LR47wF6eXYrxjsCxkGZLmJ+H9Ke/zFsfTU9U0HlCN4Pj997yFIoSQfoUScUL6mRkzZrRbl8vlBmsvbmRnhEcaHsFDuQ9BskUCoQnvk5RpTLezAwCYCwSYY2+PyVqJuc5pJ+LHjgHffgu88w5/8Xoo0DEQGx/diPHDx8NIyO+M2Frf//DNN7yGIoSQfoMScUL6GalUCk9PT816SEgInJx4n5OgS0zI/5CFnYmwskKCvz9KQ0PxL3d3foMFBQHqkVkqKoAZM4A33wSKi7s9zRAUSgWqmzqMjnbftL//7doFFBXpPAQhhPQ7lIgT0g9p14pPmTIFYWFhhiuMgZgIhZhsYwMjPpukqDGmmtDnTt99x3/sHvqj+A88/+3zcNrshDdPvqnz6//pT23jh3Mc8D5/LWEIIaTfoESckH7o6aefxscff4ybN2/izTd1n3TdK0WjAnXZdXc/8EGk3TzFyAj4xz+AYH46R96LaxXX8Pn5z1FSX4LDVw5D13NICARtfVaBPvUdhBBC+iyaWZOQfsjX1xe+vr6GLoZG1dkqZL2QhfqseoisRQgtDdVr/NvNzThaVobDpaX4P3d3eJryMGvxxImAiQnQ2Ag0NwN/+QvgodtZLO/VssRl2Hx2s2a9WdGMmzU34WLh0s1ZvbdgAVBeDsTGqm6fEEJI9ygRJ4TwruV2C+ovq8a0k5fJUZ9bD9ORPCTDnaiVyzHt4kX8VlMDAAixtMQ/XF11H8jUVDWOX1OTqpnKkCF3P0dPnMydoOBU46g/NPQh/Pz8zxAw3f8gOn++6kUIIaRnqGkKIf1cZmYm/vWvfyEkJASZmZkGKYNNlA2gNWBK8Zf668T4RXGxJgkHgMNlZfwF27MHOHAAeO45wNKSvzi9NFPaNtB36s1UXjprEkII6T1eE3HGWBRjLIsxlsMYe6OT/XGMsQzG2B+MsZOMMTc+y0PIQMNxHBYuXIg33ngDZ86cwREDjXEtMBbAbrqdZr36jP4Swcft2uIKAPyH7xFU7qTjttj3ws3KDcFOqvbqLcoWHLtyzMAlIuTBIhQKg6VSqUwikfhMmTJlZE1NTY/yp5ycHPHYsWM93d3dfTw8PHzWr19vr95XX1/P/Pz8vL28vGQeHh4+ixcvdlbvW79+vb1EIvHx8PDwWbdunX3nVweuXr0q3rFjh/X93V3v7d+/32L48OG+rq6uvitWrHC8c3939+3i4uLn6ekpk0qlMl9fX2/9lrzn4uLinFevXu3AdxzeEnHGmBDAhwCmAJABmMMYk91x2O8ARnMc5w9gP4D/46s8hAxEL7/8MpKSkjTrhkrEAcBjS1t76cqkSshr5XqJ62JsjD+ZmwMAlAAy9TXt43ffAS+/DLi6Atev6ydmN2K8YzTLBzMP8h6voQHYuhU4d473UITwztjYWJmZmZmRnZ2dLhaLuc2bN/eo7ZlYLMbmzZtvXL16Nf3cuXOXP/30U/vU1FQTADAxMeFOnz6dlZWVlZGenp5x8uRJi5MnTw4+d+6cye7du4ekpaVdvnz5cvrx48etLl26ZNzZ9ePj4y3S0tL0086vlVwux+LFi13j4+OvXLlyJf3AgQM26ntS6+6+AeDHH3+8kpmZmXHp0qXL+ix7X8RnjfifAORwHJfLcVwzgH0ApmsfwHFcEsdx6r+KvwAYymN5CBlwHnnkEc3yyJEjceDAAYOVxcTNBIP9BgMAuGYOFd/zNOV8J2Zo1YofKi3VT9D33gO2bwdu3ADi4/UTsxvaiXj8lXgkXUvCJ2mf8BJrzhxg8GDgb38DVq3iJQQhBhMaGlqbk5NjnJWVZSSRSHzU21evXu0QFxfnrH2sm5tbS2hoaD0AWFtbK93d3Rvy8/ONAEAgEMDS0lIJAM3NzUwulzPGGC5evDho1KhRtebm5kqxWIyQkJCaffv2Wd1ZjoSEBLNVq1YNO3r0qLVUKpVlZGTwO2tXq+Tk5MFubm5NMpms2cTEhIuJiSnfv39/u/J1d989UV1dLRg/fryHl5eXTCKR+Khr/bdt22bj5+fnLZVKZXPnznWTy1UVOlu3brX19PSUeXl5yWbMmDFCfZ01a9Y4SCQSH4lEovllISsry2jkyJE+sbGxbh4eHj4hISGS2tpaBgDLli1zHD58uG9wcLBXdna2cXdl0RU+E3EXAAVa6zdat3XlBQA04BUhOjR16lQIharG2bm5uWhpaTFoeWwfs9Uslx3lsa32HbQT8ePl5WhQKPgLVlMDvPACcOZM27YffuAvXg9J7aSQ2qnmoW9UNGLi7olYeGwhqhqrdB5r8OC2FjmnT+v88oQYTEtLCxISEiz8/PwaentuVlaWUUZGhml4eHiteptcLodUKpU5ODgEhIeHV0+cOLEuMDCw4bfffjMvKioS1tTUCBITEy0LCgo6JLGRkZG1fn5+dQcPHszJzMzMkMlkzfd6X8HBwV5SqVR25+vQoUPmdx5bUFBg5OLiook1dOjQ5sLCwi6T7M7uOyIiQuLj4+O9adMmu87OOXjwoIWjo2NLVlZWRnZ2dnpMTEx1Wlqayf79+21SUlIyMzMzMwQCAffRRx/ZpqSkmGzatMnpxx9/vJKVlZXx8ccf5wPAqVOnTPfs2WObmpp6OSUl5fLu3buH/Pzzz4MAID8/3+TVV1+9nZOTk25paanYvXu39alTp0y/+eYbm4sXL2YkJiZmX7hwYXBXZbnX59yZPtFZkzE2D8BoAP/uYv9fGGMpjLGUkpIS/RaOkAeYjY0NwsPDNeuGbJoCtE/ES78pBafUT/tpqakpPAcNAgDUKZX4rqwMJc33/Dere2ZmwPHjqoQcAD74ANi7l59YvRQjjWm33qJswXc5uq//iItrW25sVA1pSIguxMXFOTPGghljwXfWPgPASy+9NFS9/6233urQvnfOnDlu6v1dJYGdaWpqEkilUpmfn59s6NChza+99lqvflqrqqoSxMTEuG/cuLHAxsZGqd4uEomQmZmZkZ+f/0daWtrgc+fOmQQFBTW+9tprRREREZ4TJkyQ+Pj41KsrVO6Um5trEhgY2AgAGRkZRk8++aRbVFTUSPX+rVu32q5cudIhNjbWLTo6euTBgwctOrtOampqVmZmZsadrxkzZtR0dvz93Pfp06czMzIyLp84cSJ7x44d9t99953ZnecFBQU1nDp1ymLBggUux48fN7O1tVUcP37c/NKlS6YBAQHeUqlUdvr0aYvc3FzjhIQEi2nTplU4OTnJAcDBwUEBAMnJyWZTp06ttLCwUFpaWiqjo6MrkpKSzAHAxcWlady4cQ0AMGrUqPq8vDzjpKQks6lTp1aam5srbWxslJMnT67sqiz380zuxGciXghgmNb60NZt7TDGHgXwJoDHOY5r6uxCHMdt5zhuNMdxo4f0oSHBCHkQTJ/e1iLs0KFDhisIgEHugzSDpsrL5aj+TT+dNhlj7WrFn7p8GW9eu8ZXsPazbObnA6K+MVKsdvMUBobnAp+Dh43uxzqXyQAvL9WyQgEkJ+s8BCF6pW4jnpmZmbFr164CExMTTiQScUqlJqdGY2OjAAA2bNgwRF2jnJeXJ25qamLR0dHus2fPLp8/f35lZ9e3s7NThIWF1Rw5csQSABYvXlyanp5+OSUlJcva2lrh6enZeOc5t27dEpmbmyvErVPaymSy5q+//rpdh5SUlBTTdevWFe/bt+/6zp07r+/bt6/TZhW9qREfNmxYuxrwGzdutKshV+vqvkeMGNECAC4uLvLo6OjKs2fPDr7zXH9//6a0tLQMPz+/hlWrVrm8/vrrThzHsdmzZ5ep/z/k5eVd2rJly83O7udujIyMNLVAQqGQk8vlrKtjOyvLvcTsCp+J+DkAEsbYCMaYEYBYAIe1D2CMjQLwMVRJ+G0ey0LIgKWdiP/www+4du0aCgs7fCfWC7G9GAKjto+dmx/f02foPZmplYjLOQ5Hysqg5GtEE+1ZNo8e5SfGPQhyCoKrpWoMdQ4cYn1jMdp5NC+xnniibfnbb3kJQYhBDR06VF5eXi4qKioSNjQ0sISEBEsAWL58eYk6WXR1dW2JjY118/T0bFyzZk27cVtv3rwpKi0tFQJAbW0tS0pKsvD29m4EgMLCQhEAZGdnGx07dszqxRdf7PC7UnZ2tpGDg0OXP+01NTUxkUjECQSqz9wVK1Y4vfrqq502K+hNjXh4eHhdXl6eSWZmplFjYyM7ePCgzaxZsyq1j1EqlejsvqurqwUVFRUC9XJSUpKFv79/h2Y+eXl5YnNzc+XChQvL4+Liis6fP28aFRVVffToUWv1sykuLhZeuXLFKDIysvrIkSPWRUVFQvV2AJgwYUJtfHy8VU1NjaC6uloQHx9vPWHChC5r+CdOnFgbHx9vVVtbyyoqKgSJiYlWXZWlq2vcC96qaTiOkzPGXgGQANUIwp9xHJfOGFsHIIXjuMNQNUUxA/A/xhgA5HMc9zhfZSJkIHJzc0NgYCDOnz+P5uZmuLu7Y+HChdi6davey8IYg+U4S1R8XwGRtQiDhg/SW+w/WVjAQSxGcWs7eTFjuNXcDBfjTgcjuD/as2xevgxcvQroe9jETjDG8Iz/M8guz0aMdwzGDRvHW6zp04G331YtHz0KyOV95ocB8gDbsmXLze5qQXfs2HFjx44dN7rav3fv3ut79+7VyTBGxsbG3JIlS26NGTPG28HBocXDw6NDrXViYqLZoUOHbCUSSYNUKpUBwNq1awufeuqpqoKCAvGzzz47QqFQgOM4Nn369PI5c+ZUAcDjjz/uXllZKRKJRNy7776bb2dn16E5REBAQGN5eblYIpH4bNu2LW/SpEl12vsTEhLMwsLCapVKJRYtWuQSHR1dpe5AeT9aR0TJj4qK8lQoFJg7d27p6NGjGwEgPDzcY9euXdezsrKMO7tvPz+/hpkzZ3oAgEKhYLNmzSp74oknOvw0mpqaOmj58uVDBQIBRCIRt23btuvBwcGNK1euLIyIiPBUKpUQi8Xc+++/nx8REVG3ZMmSW2FhYVKBQMD5+vrWHzhwIC80NLR+7ty5ZUFBQd4A8PTTT5eEhIQ0ZGVlddqePTQ0tH7mzJnlvr6+Pra2ti3+/v51XZXlfp+hNsb1gTFue2P06NFcSkqKoYtByANl7dq1WLNmjWbd1dUVeXl5aP0CrFf1OfUQGAlg4mpy94N17K9ZWfj41i0IALwvkWCRi26neG9n2rS22vDNm4GxY1Wzb44axV/MPkSpBIYNA262pkxJScD48QYt0gOLMZbKcRw/P130cRcuXMgLCAjQ01BHD7aioiJhXFycy6lTpyzmzZtXWl1dLXznnXduffDBB3Z79+61DQgIqAsMDGxYunQpdbbTswsXLtgFBAQM72wfJeKEDAAXLlxAYGCgZn3cuHE4cuQIbGxsDFcoA0itqcGlujo8ZmsL29Z2lbzZvl01jjjQVjs+ezbw9df8xu1DXn5Z9RgAwM8P+OMPw5bnQUWJOCXi9+KZZ55x3b17d76hy0G6T8T7xKgphBB++fv7w82tbeLaf/7znwMuCQeAYHNzzHd05D8JB9p32Gxs/cX6yBGguu9ML596MxUrTq6A7zZf/H7rd51ff+zYtuVLl4C6uq6PJYToFiXhDwZKxAkZABhjeO2117B27VqcP38e4/tQGwFlixKKOh7H9TYUZ2cgOLhtffBg4Omn+1Q2uunMJmw4vQHpJen4Nkv3PSr//GegtZ8YOA749FOdhyCEkAcaJeKEDBCLFy/G6tWrERAQYJC24XeqTqnG+cnncWrwKeSuyNV7/EaFAt+VlWFzQcHdD75Xj2v1PQ8PV7XTcNLpyFf37LXvXsORK23jyh/POa7zGMbGgL8/YGsLzJvXfjAZQgghPI6aQggh3cl5LQfVZ1TNNIq/LIbHux56+4JQ2dKCoWfPok6phBDAs3w1V/nzn4G33gIeegiYOVP3178P5Y3lqGtR1c7P9p6Nz2d8zkucX35RJeSEEEI6ohpxQgagmpoafPrpp3jxxRcNVoZhcW3zfcnL5ahJua8J3Hrlr1euoK51Ig4FgPiyMn4CubsDeXnA2bOAAZ91Z7Rn2UwvTcdgow5zaugEJeGEENI1SsQJGWCamprg5uaGF198EZ9++ikuXLhgkHLYPm4LsUNbLXTx/yvu5mjdmmDdNrmcm7ExHrGy4i+YVidZjT4wWlWkRyQGiVTjuGeUZCCrNMvAJSKkU0qlUmn4tnSE3KPW96+yq/2UiBMygHAch0mTJqGiokKzbefOnQYpi0AsgPeX3pr123tvQynv8rNKpx63tdUs32hqgplQqJe4yMwEVq9Wzf9+XadzQvSaqdgUUR5RmvVvMr/hPaZSCXzxhWpyH0J66FJJSYklJePkQaRUKllJSYklgEtdHUNtxAkZQBhjsLS01KxHRkZi+fLlBiuP9QRrGDkboflmM1put6DiRAVsp9re/cT75GRsjIcsLPBLdTUUAI6WlWG+oyPvcbF4MXC8tVPkvn3AsmX8x+xGjHeMJgE/ePkgpnlOw43qG4j0iNR5rHnzgP/9D2huBurr24ZYJ6Q7crn8xaKiok+Kiop8QZWH5MGjBHBJLpd32TaRJvQhZID56quvEBsbC0A1w2Zubi6E+qoR7sTVpVdR8G/VyCVDnhoCn30+eon7r/x8vJGrGq3lUWtrJAYE8BesoQH4z3+ADz4AiopU28aNA37+mb+YPVDZWIkh/x4CubKtitrV0hW5r+ZCKNDte0IiAXJyVMseHkB2tk4v368N5Al9COnv6NslIQPMjBkzYGdnBwDIz8/HiRMnDFoeh6cdNMulB0shr9JPu4U59vaaD8DvKyqQXluL283N/AQzMgK2bWtLwt94A/j+e35i9YKViRUiRkS025ZflY/vc3Vfttdfb1u+eVNVM04IIQMdJeKEDDDGxsaYP3++Zn27eg5yAxnsMxgia1UrOa6Fw80dN/US19XEBFO0Zhcdk5aGxeoqW10TClVtM9SuXAEGDeInVi/FeLeNnsLAMFs2G0MGD9F5nBdeANSPu74eOHZM5yEIIeSBQ4k4IQPQSy+9pFk+cuQIsrKycPHiRYOUhQkYxEPaRk+5uU0/iTgAvOzsrFluUCpxoKQEFS0t/ATT+vKDI0cAvoZM7KXpXtMhZKpmKBw4vBX+FoKcgnQeRyQCFixoW9+xQ+chCCHkgUOJOCEDkJeXFx555BEAgEKhgK+vL+bMmQND9RlxecUFACAwEcAixEJv5ZhiY4OhWgNdm4tEyGlo4CeYtzfwpz+plltaVJ01+wAHMwdMl07HGOcx2Dl9J0Zaj+Qt1gsvtC0fPw7k5/MWihBCHgiUiBMyQP3lL3/RLMvlcqSnpyM1NdUgZXF63gl+8X4Iqw+D7AuZ3mbYFAkEeFFryvkXHB0xxsKCv4DateI7dwJXrwIJCfzF66EvZn6B3176DfMD52OQmL8mMyNGAI8+qlrmOOCzz3gLRQghDwQaNYWQAaqxsRHOzs6aMcVHjhyJTz75BBMmTDBwyfTrRmMjtt28iZecnDCC73bb5eWAk1P7nooODsCNG6q2G31Mo7wRJiITnV7z66+Bp55SLQuFqo6b9vY6DdHv0KgphPRfVCNOyABlYmKCZ555RrMeHR094JJwABhqYoJ3Ro7kPwkHVL0VH3+8/bbiYiA5mf/YPVTVWIVt57Yh6OMgLDi24O4n9NL06W3fORQK1fxGhBAyUFEiTsgA9pe//AXz5s3DTz/9hPfee8/QxdHgOA5Nt5oMXQx+aDdPAYDHHgPMzAxTlk5klmZiUfwi/F70O75O/xpVjVU6vb6xMTB+fNv6wYM6vTwhhDxQKBEnZACTyWT44osvEBYWprd22d2R18qR+UImTlufxi8jf9HblPdqHMchtaYGC69cwcGSEn6CREa2b4uxaBHw0EP8xOql5LxkPPNN268kSk6JlJu6bwr4zjuAWAxMnQrEx+v88oQQ8sDoe40SCSEDVtP1JhR9VqRZLzlQAoenHLo5Q7c25OfjzWvXAACZ9fWIGaL78bQhFgN//jNw4ADwzDOATKb7GPdoiOkQXCm/AkA1pvi5l87B195X53HGjAFqalS144QQMpBRjTghBIBqGMOEhATExsZi8+bNBinDYJ/BMHI00qwXvluot9iVLS1Y25qEA0BSZSVy+RrKcP164No11X9dXfmJcQ987H0Q7hYOQDWm+NfpX/MWi5JwQgihRJwQ0urLL79EVFQUvvrqK3z00UcGG1Pc+ZW2SXZqfq+BvFo/U95bicV4zM5Osz7Lzg4jTHQ7YojG4MGA4I6PX4UCaB3BxpAWjVmkWd6euh3NCpqLnhBC+EKJOCEEt27dwqJFbQlYTk4O0tLSDFKW4W8Ox2D/wQAArolDyQGe2mp3QnumzZOVlWhU6qGNulwOfPEF4OsL/PWv/Me7ixnSGXA2Vz2H4rpiHLzMf2/KM2cArWHtCSFkwKBEnBACJycn+Pj4aNaXLFmC4OBgg5XH4em2duE3t93UW+38o9bWGNlaC14pl+N/fHXY1HbpkqqteGYm8L//AZcv8x+zG2KhGC8Hv6xZf/eXd/Gfs//Bd9nf6TxWXZ1qGPWQENWU92fO6DwEIYT0aZSIE0IAtJ9p8+jRowZrmgIADvMcwIxVo7jUpNSg7HCZXuIKGMNLWjNtfnzzJr8Bf/gBiItrWzc3B9LT+Y3ZAy8FvQSRQNWX/9fCXxF3Ig7/PvNvnccZPBhoaWlbf/NNnYcghJA+jRJxQggA4KmnnoK5uTkAICsrC6dOnTJYWYwdjeGy0EWzfmXBFXAK/XwxeM7JCaLWoRzPVFfjy6IilDbz1E66shJISlItGxsD588DTzzBT6xecDJ3wizvWe22JeUlIac8R+exnn66bTkvD3jAJnsmhJD7Qok4IQQAYGZmhrlz52rWt2/fbsDSAA7POACtQ5s332rGze08106r4xoZYaZWp82nMzOxs6iomzPuw7RpgKenarmpCfjwQ37i3APtTpsCJsB7Ue/B0cxR53HWrgXUk5rm5QG//abzEIQQ0mdRIk4I0dBunvK///0PmzZtwq5duwxSFjM/M4iHiDXreevy9BZbu9MmAHx48yaa+Oi4KRarZrdR++AD4Pp13ce5B6GuofCz99Osj7AaATMj3c8AamUFxMa2re/YofMQhBDSZ1EiTgjRCAoK0nTSbG5uxj/+8Q+8/vrrqKrS7TTnPcGEDB7vegAMsAyzRPBv+us8OsHKStNpEwACBw/mr818TAwwdqxqubkZWL1atSzXz7CNXWGM4Y3QN/Bm2JvIey0P07ym8RbrpZfalv/f/1PVjBNCyEBAiTghpJ2/3DGOXGlpKT744AODlMU+1h4P33wYo34aBZNhPI3p3QkBY/hra634CBMTxDo4wEQo5CcYY8C//tW2vns38NprwPDhQEEBPzF7aK7fXPxz4j8xzHIYr3EeeggYPVq13NgIzJ7dvhMnIYT0V8yQIyPci9GjR3MpKSmGLgYh/VZjYyN8fHyQm5sLABg9ejR++uknDFI35B0gylpacLG2FuFWVmCtnTd59dhjwLFj7be98oqquUof8vut35GYm4ilIUt1et2ffwZCQ9vWFywAtm3TaYgHFmMsleO40YYuByFE96hGnBDSjomJCd5//30AwJAhQ/D3v/+9zyXh+qhAsBWLMd7aWj9JOABs2KCqHdd24kSfqRqWK+X469G/Inh7MJZ9vwxnC87q9PohIUBQUNv6xx8DN27oNAQhhPQ5lIgTQjqIjo7Gjh07kJ2djT//+c+GLo5GzYUanAs4h+xXsg0Sv1oux9YbN/j5IuDnB8yf37Y+fDjwxx+qDp19QGVjJY5lHwMH1b0vObFE5zG++abtu4ira5/5DkIIIbyhRJwQ0qkXX3wRlpaWhi6GRv6mfKQGpqLujzrc2n4LLRX6zdJ2FRVh2Nmz+FtODvbevs1PkLVrVeOJA6pG001N/MTppWZFM0ZvH40b1aoqag8bD3w2/TOdx3F1BVauBLZuBXJzgREjdB6CEEL6FErECSE9UllZiaVLl+Lo0aMGiW871VazzMk5ZP9Nf7Xi2fX1WH71KqoVCgDAP65eRV3rsk65uqqy0F9+UU13b2Gh+xj3wEhohFfHvqpZv155HUqOh+EcAaxbByxa1LGVDiGE9EeUiBNC7urHH3+Em5sb/v3vf2PJkiVoMUCbgcGywbCaYKVZL/mmBM23eZrx8g4WIlG7xHusuTlMBTx9fL74Yttwhn3Ia2Nfw1gXVblalC14/tvnoVDy8GWEEEIGEErECSHdam5uxvvvv4/q6moAwJUrV7B//36DlMVnvw9MfUwBAFw9h/wN+XqJ62BkhI3u7pr1Y+XlyGlo0EtsFBSoRk/RnvjHAIQCIT59/FOIBao2678W/or3fn2P97hlZcD06UBlJe+hCCFE7ygRJ4R0y8jICEqtWSXd3Nwwe/Zsg5RFbCPGyLdHatYL/1uIxoJGvcT+i7MzxpibAwCaOQ4Ls7P5H73l3DnA3R348EPg7beB33/nN95d+Nj7YNUjqzTrb558Ex+lfISXDr/Ey7NYuxZwcAAOHwaefFLnlyeEEIOjRJwQclf/+c9/YNI60+T169fx8ccfG6wsto/bwvxPqoSYa+JwfZ1+poQXMoaPPD01H5rfV1TgK746bQKqmTV/+QVQfwlqbATOn+cvXg+9EfoG/B38AQCNikYsOLYAn/z+Cb7N+lbnsS5eBNQtghITgdRUnYcghBCDokScEHJXw4cPx5tvvqlZf/PNN3GbzyS0G4wxjHi7bTiNW5/cQv6/9dNEJcjcHK+4uGjWX83Jwd+zs6Hgo2acMdVg2upMdOpU4LnndB+nl8RCMT57/DMIWfuZRv/50z91Xiu+e3fbIDIAsGOHTi9PCCEGR4k4IaRHXn/9dXh4eAAAqqqq8MYbb6CwsBByuVzvZbGOsMYgr7ZJhnKX5uLWzlt6ib1+xAg4GRkBAEpaWvBeYSE+u8VDbKFQNcmP2tGjfWaqyWDnYLw+7nXNuligSs51PfmRqWn7iUW3bwfS0nQaghBCDIoScUJIj5iYmOADrazo888/R1BQEObNm6f3ZJwxBvd/uwNaeV9zsf5GUHm39QuJ2oeFhfwEe+wxICqqbX3RIuAz3Y/ffS/eCn8LnraeAIBIj0gMGTyElzgvvtj2CDhO1W/VAN/9CCGEF5SIE0J6LCoqCjNnztSs3759G1999RVeeOEFvZfFbpodpJ9LwcQMku0SuC1z01vs2UOG4FErK836EL5mv2QM+Oqr9sMZvvgi8PnnwLPPAocO8RO3BwaJB+Hz6Z9jT8weHI49DCdzJ17iMAa8917bBKNnzwKTJwNXr/ISjhBC9Irx3utfx0aPHs2lpKQYuhiEDFjXr1+Ht7c3GrSG79u6dSsWLVpkkPLIa+UQmYn0HvdqQwN8f/sNcxwc8J6HB8xFPJahshKIiOjYLmPwYOD0aSAwkL/Y92DDqQ0Y4zIGj458VGfXXLNGNYqKmrEx8OOPfXLIdZ1jjKVyHDfa0OUghOge1YgTQnrFzc0N33zzDUxNVeN5Dxs2DHPmzDFYeTpLwhuuNaDxBr/DGroPGoQb48bhM6m0QxKu81k3rayAEycAP7/22+vqVDNw9iHvnHoHK35Ygan/byq+Tv9aZ9d96y1g6dK29aYmICwMqKrSWQhCCNE7SsQJIb0WGRmJn376CYGBgfjpp59gY2Nj6CJpVKdV4zfv35Din4KWCn5nALXtpEnKxZoauJ09i91FRToOZqsaw08qbdsmFAIGaBbUlarGKqz7cR0A1eybO8/v1NlIKowB//oX8PLLbdvmzAEsLXVyeUIIMQhKxAkh9yQ4OBhpaWkYPnx4u+319fVYvXo1mpqa9F6mpptN+H3s7+CaOMgr5Djndw6KRv1Nw17Q2IiHfv8dZXI55mdm4r+67sTp4ACcPKma5AcANm8GRo7s/hw92nZuG5oUbf/f65rrUFJfotMYH30ErFsHTJkC7Nyp00sTQojeUSJOCLlndw5XV19fjz/96U9Yv349pk+fjsZG/cx6qWbkZASLhy00682FzUiflY7GfP2U44uiItRrzUJ6qLQUTVrrOuHsDPzwg2qQ7ddea78vPV012LaBhhV5efTLmCqZqln/Kf8nBH0chLMFZ3UaZ9UqID5eVUuuzQDf/Qgh5L5QIk4I0Zk1a9YgPT0dAJCQkID/9//+n17jM8YQmBQIs0Azzbby+HL86v4rMp/LRF1mHa/xF7q44BGLti8CJyoq8OiFCyht1vHQiq6uwNNPd9w+dy7wl78A/v7AmTO6jdkDNoNscGTOEawdvxasdWzJwppChO8Mx4e/fYif839GVSM/jbqrqgBHR9W8R7p+3IQQwhdKxAkhOmOsPQ0igKVLl+Lll19GcnIylEqlzmde7AwTMoz+fTSGLR2m2cbJORTtLMI573P4VforyhPKeYltJRbj+8BAvOjoqNl2uqoKQampWHPtGhZmZSGhrIyfmTgPHwb++EO1fPmyKhE3QM24gAmwOnw14v8cD2sTawCq9uKvfPcKHtn5CBw3O+KN79/QaczmZsDHRzW4zHffAUOGqJqv0BCHhJC+joYvJITo1LvvvovFixd32O7i4gInJyfY2NhgxYoVeOSRR3Q+E+Odyk+U4/o711H1Y8da2GHLhsF9ozsvcTmOw5YbN/CPq1fR2SeslUiEvLFjYanL8cfXrVMNLaLN1RX4299UY4+bmgKtM4LqS15lHmZ9PQtpt9oPu/gn5z/h15d+1VmcmzcBb2+gurrjvocfBubNA6ZPB1xcdBZSr2j4QkL6L15rxBljUYyxzK1I3wAADd9JREFULMZYDmOsQxUIY8yYMfZV6/5fGWPD+SwPIYR/f//733Ho0CG4ubWfYKewsBApKSk4ceIExo8fj//+97/t9iuVSih13J7aZrINRiWPwqifR7VrrgIALovaZ2WVP1fi2pprKEsog7z2/mqSGWNYMmwYDvr4wKqT8cWblEpY3LE9o64OV+rr0XCvQx8+/riquYr2dfPzgX/8Axg6VFVlbG4OBAQABw/eW4xeGm41HD8//zOeD3y+3fbZstkdjn35yMtY9+M6nLp+Ckqud+8DZ2fg2jVg9GhAcMdftbNnVROSDh2qqin/299UM3QSQkhfwFuNOGNMCOAKgEkAbgA4B2AOx3EZWscsBODPcdxfGWOxAGZyHPdUd9elGnFCHgxKpRJnzpzB3r178b///Q8lJe1Hz7hx4wZctKood+7cieeeew4mJiZwcXHBjBkzYGdnB7FYDJFIhJKSEly5cgVGRkbw8/PDsmXL2l3vu+++w4kTJwAAzs7OGDFiBIC2DqX5+fnIPJyJ6l+q4e/mj+WZy9udv2XcFvx6VlVLaw97ONk5wcjJCEYORhAYC3At/xpKKkvABAwR0RF45sNn2p2/6elNuHzpMhhj8PD0gL2zPcCAFk6J3MZGpKReQlllBQBg+OzJOPTP9sMOPjT+VTQUFQMABrmPgLWdNQYLBDAVCmEqEOBq2iU0NzZAAGD+shfw7NMT250fE7oItVXVgEIBLxsLmN4qBJqbwVo/4y82A/LW5ZXRfgjbuaXd+VOHTYNCLgcY4O85EiJjI1VWKxBACYbzl7I0x36a8B6GSodr1otyb2L+xAUAAIFAgMAAWbtrNzQ04WJGJuTKFnCMwxfntsPNse38H789iTcXblSdLxTA1EUEkaDtpWwESm9UgDEGY2MjnLh6pN31//vPbfj64yMAA4yNjGHj5ISaaqC+AYASgLIWTfWlAAAjI3OcvP51u46eC2etxcWff1Gdb2IKGwcHzX7GgNqKSlRXqf7fWdnZ4fAfX7SL/8KUJci+oPrTNniwGVZ8tBxhEUHQFaoRJ6Qf4ziOlxeAhwEkaK0vB7D8jmMSADzcuiwCUIrWLwddvYKDgzlCyIOlpaWFO378OPfYY49xYrGY8/Ly6nDMkiVLOAA9ejk4OHQ4Pzo6usfn+/r6djhfOkja4/PHWo/tcL6D0KHH50d7zOhwvikG9/j8mNBFHc4XQtTj89+YubzD+T09FwAXv+NIu3PPfPNTr84vzM5vd/7z0S/0+FwG1qHs032e7PH5JjDpcP44+0d7fL41s+lwvq9ZcLtj1iz6T4dj7geAFI6nv9X0ohe9DPvic15oFwAFWus3ANw5GbHmGI7j5IyxKgC2UCXkGoyxvwD4CwC4urryVV5CCE9EIhEiIyMRGRmJhoYG3L59u8Mx+fn5Pb6eUCjUZfEAAAITAdDQs2OZ4P7athuz+2sVqB6R5J5ZW7dfLyjo/Lie4u7vl1ULE/P7i99pS3x9nU0IIfeOz0RcZziO2w5gO6BqmmLg4hBC7sOgQYM6tB8HgE8++QRxcXG4ePEiqqqqoFAoUF5eDrlcDrlcjvz8fGRnZ0MulyMoqOPP/gEBAbhw4QIAwNHREW5ubuC0EsSCggIUtk6w09n5D8c8jMrvKgEArsNcYW9hD0WtAsoGJaAArt24hrK6MoAD/AP8O5wvs5dBUaxq3+1q7gobs9bZRluLcLX0KmrkNapjvWUdzvc0l+BG3Q0AgJvFSAwyMYOS46AAByUH3CjPQZNC9U1B4u3R4Xxn8TDUylW9FV2sJDAStR/BJr8sEwpO1fbd1VfS/mShEEMEDpC37nezkECgbmzNqX45zau+ojnczmVIu9MtbK1gzWwBqEZNcbNoXz65ogUFtdcAACImgvEgk3b7/WV+sD6oOl/MjOBiPlxrL4cmeSNu1au+LAwSmna4d8dhdrDOUJ0/SGAKezPndvsbmutxu/EmAMDGaEiH84fYD4F1ier8wSJz2A5yaLe/rqkGZc2qZkPOph3fu3aWjrCuU51vKbaG83CnDscQQkhn+Gwj/jCANRzHRbauLwcAjuM2aB2T0HrMWcaYCEARgCFcN4WiNuKEEEIGEmojTkj/xeeoKecASBhjIxhjRgBiARy+45jDAOa3Lj8B4IfuknBCCCGEEEL6C96aprS2+X4Fqg6ZQgCfcRyXzhhbB1XHk8MAPgXwBWMsB0A5VMk6IYQQQggh/R6vbcQ5josHEH/HttVay40AOg4oSwghhBBCSD9HU9wTQgghhBBiAJSIE0IIIYQQYgCUiBNCCCGEEGIAlIgTQgghhBBiAJSIE0IIIYQQYgCUiBNCCCGEEGIAlIgTQgghhBBiAJSIE0IIIYQQYgCUiBNCCCGEEGIAjOM4Q5ehVxhjJQCu37HZEkDVXbZpr2sv2wEo1XExOyvP/Rzf3f6e3Pud27p7Nrp+Hrp+Ft0d09PtvVnv689D1++NO9cH2r+V/vTe6O4YXfxbuXNfX38eD/K/FTeO44bo8HqEkL6C47gH/gVg+922aa/fsZyij/Lcz/Hd7e/JvXd3/3w/D10/i+6O6en23qz39eeh6/dGd++VgfBvpT+9N7o7Rhf/VjrZ16efx4P+b4Ve9KJX/3z1l6YpR3qw7Ug3+3Stt9e/2/Hd7e/Jvd+5rbtno2u6fhbdHdPT7b1d16W+/t64c32g/VvpT++N7o7Rxb+Vgfbe6GybPp8HIaQfeuCapugaYyyF47jRhi5HX0HPoz16Hm3oWbRHz6M9eh5t6FkQQnqqv9SI34/thi5AH0PPoz16Hm3oWbRHz6M9eh5t6FkQQnpkwNeIE0IIIYQQYghUI04IIYQQQogBUCJOCCGEEEKIAVAiTgghhBBCiAFQIt4Nxpg3Y+wjxth+xtgCQ5fH0BhjMxhjOxhjXzHGJhu6PIbEGBvJGPuUMbbf0GUxFMbYYMbYrtb3xJ8NXR5Do/dEG/qsaI/+lhBCutJvE3HG2GeMsduMsUt3bI9ijGUxxnIYY290dw2O4y5zHPdXAE8CCOGzvHzT0fM4xHHcSwD+CuApPsvLJx09i1yO417gt6T618tnEwNgf+t74nG9F1YPevM8+ut7Qq2Xz6JffFZ0p5fPo9/8LSGE6Fa/TcQB7AQQpb2BMSYE8CGAKQBkAOYwxmSMMT/G2NE7Xvat5zwO4BiAeP0WX+d2QgfPo9XK1vMeVDuhu2fR3+xED58NgKEACloPU+ixjPq0Ez1/Hv3dTvT+WTzonxXd2YlePI9+9LeEEKJDIkMXgC8cx/3EGBt+x+Y/AcjhOC4XABhj+wBM5zhuA4DHurjOYQCHGWPHAOzhsci80sXzYIwxABsBfMdxXBrPReaNrt4b/VFvng2AG1Al4+fRT7/U9/J5ZOi5eHrVm2fBGLuMfvBZ0Z3evjf6y98SQohu9cs/nt1wQVsNHqBKJFy6OpgxNp4x9j5j7GP0z1qMXj0PAH8D8CiAJxhjf+WzYAbQ2/eGLWPsIwCjGGPL+S6cgXX1bA4CmMUY+y8G1vTenT6PAfaeUOvqvdGfPyu609V7o7//LSGE3KN+WyOuCxzHJQNINnAx+gyO494H8L6hy9EXcBxXBlX71wGL47g6AM8Zuhx9Bb0n2tBnRXv0t4QQ0pWBViNeCGCY1vrQ1m0DFT2PNvQsukbPpj16Hm3oWbRHz4MQ0isDLRE/B0DCGBvBGDMCEAvgsIHLZEj0PNrQs+gaPZv26Hm0oWfRHj0PQkiv9NtEnDG2F8BZAF6MsRuMsRc4jpMDeAVAAoDLAL7mOC7dkOXUF3oebehZdI2eTXv0PNrQs2iPngchRBcYx3GGLgMhhBBCCCEDTr+tESeEEEIIIaQvo0ScEEIIIYQQA6BEnBBCCCGEEAOgRJwQQgghhBADoEScEEIIIYQQA6BEnBBCCCGEEAOgRJwQQgghhBADoESckH6KMWbLGDvf+ipijBW2LtcyxrbxFPPvjLFnutn/GGNsHR+xCSGEkAcNTehDyADAGFsDoJbjuE08xhABSAMQ1DrDYGfHsNZjQjiOq+erLIQQQsiDgGrECRlgGGPjGWNHW5fXMMZ2McZOMcauM8ZiGGP/xxi7yBg7zhgTtx4XzBj7kTGWyhhLYIw5dXLpiQDS1Ek4Y+xVxlgGY+wPxtg+AOBU3/yTATyml5slhBBC+jBKxAkh7lAl0Y8D+BJAEsdxfgAaAES3JuMfAHiC47hgAJ8BeLuT64QASNVafwPAKI7j/AH8VWt7CoAwnd8FIYQQ8oARGboAhBCD+47juBbG2EUAQgDHW7dfBDAcgBcAXwCJqpYlEAK41cl1nABc1lr/A8D/Y4wdAnBIa/ttAM66Kz4hhBDyYKJEnBDSBAAcxykZYy1cW8cRJVSfEQxAOsdxD9/lOg0ATLTWowE8AmAagDcZY36tzVZMWo8lhBBCBjRqmkIIuZssAEMYYw8DAGNMzBjz6eS4ywA8Wo8RABjGcVwSgGUALAGYtR7nCeAS76UmhBBC+jhKxAkh3eI4rhnAEwD+xRi7AOA8gHGdHPodVDXggKr5ypetzV1+B/A+x3GVrfsmADjGZ5kJIYSQBwENX0gI0RnG2DcAlnIcl93FfgcAeziOi9BvyQghhJC+hxJxQojOMMa8ADhwHPdTF/vHAGjhOO68XgtGCCGE9EGUiBNCCCGEEGIA1EacEEIIIYQQA6BEnBBCCCGEEAOgRJwQQgghhBADoEScEEIIIYQQA6BEnBBCCCGEEAP4/7+NVMvnDI5bAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
        "<Figure size 576x432 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1384,14 +1373,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1447,14 +1434,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
diff --git a/stress-test/multi-group-xs/mdgxs-part-ii.ipynb b/stress-test/multi-group-xs/mdgxs-part-ii.ipynb
index 91d0285..d4736ce 100644
--- a/stress-test/multi-group-xs/mdgxs-part-ii.ipynb
+++ b/stress-test/multi-group-xs/mdgxs-part-ii.ipynb
@@ -297,7 +297,7 @@
     "# 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)"
+    "settings.source = openmc.IndependentSource(space=uniform_dist)"
    ]
   },
   {
@@ -331,7 +331,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -397,7 +397,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Instantiate a tally mesh                                                                                                                                                                  \n",
+    "# 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",
@@ -464,17 +464,17 @@
      "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",
+      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/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",
+      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/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",
+      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/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",
+      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/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",
+      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/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",
+      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=23.\n",
       "  warn(msg, IDWarning)\n"
      ]
     },
@@ -507,23 +507,23 @@
       "                                      %%%%%%%%%%%\n",
       "\n",
       "                 | The OpenMC Monte Carlo Code\n",
-      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
+      "       Copyright | 2011-2023 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",
+      "         Version | 0.13.3\n",
+      "        Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n",
+      "       Date/Time | 2023-11-06 20:56:04\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 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",
+      " 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 O16 from /opt/xdata/endfb-vii.1-hdf5/neutron/O16.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 Zr90 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr90.h5\n",
       " Minimum neutron data temperature: 294 K\n",
       " Maximum neutron data temperature: 294 K\n",
       " Reading tallies XML file...\n",
@@ -537,82 +537,82 @@
       "\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",
+      "        1/1    0.99606\n",
+      "        2/1    1.04749\n",
+      "        3/1    1.02998\n",
+      "        4/1    1.01909\n",
+      "        5/1    1.01068\n",
+      "        6/1    1.04796\n",
+      "        7/1    1.02180\n",
+      "        8/1    1.03041\n",
+      "        9/1    0.99557\n",
+      "       10/1    1.02019\n",
+      "       11/1    1.00722\n",
+      "       12/1    1.02203    1.01462 +/- 0.00741\n",
+      "       13/1    1.01177    1.01367 +/- 0.00438\n",
+      "       14/1    1.03241    1.01836 +/- 0.00562\n",
+      "       15/1    1.03434    1.02155 +/- 0.00540\n",
+      "       16/1    1.02384    1.02193 +/- 0.00442\n",
+      "       17/1    1.02815    1.02282 +/- 0.00384\n",
+      "       18/1    1.04619    1.02574 +/- 0.00443\n",
+      "       19/1    1.03496    1.02677 +/- 0.00404\n",
+      "       20/1    1.01787    1.02588 +/- 0.00372\n",
+      "       21/1    1.04886    1.02797 +/- 0.00396\n",
+      "       22/1    1.00158    1.02577 +/- 0.00423\n",
+      "       23/1    1.01636    1.02504 +/- 0.00396\n",
+      "       24/1    1.01508    1.02433 +/- 0.00373\n",
+      "       25/1    0.95954    1.02001 +/- 0.00554\n",
+      "       26/1    1.03097    1.02070 +/- 0.00523\n",
+      "       27/1    1.03576    1.02158 +/- 0.00499\n",
+      "       28/1    1.01183    1.02104 +/- 0.00474\n",
+      "       29/1    1.03387    1.02172 +/- 0.00453\n",
+      "       30/1    1.01176    1.02122 +/- 0.00433\n",
+      "       31/1    1.03633    1.02194 +/- 0.00418\n",
+      "       32/1    1.04652    1.02306 +/- 0.00414\n",
+      "       33/1    0.98985    1.02161 +/- 0.00421\n",
+      "       34/1    1.01121    1.02118 +/- 0.00405\n",
+      "       35/1    1.03004    1.02153 +/- 0.00391\n",
+      "       36/1    1.03398    1.02201 +/- 0.00378\n",
+      "       37/1    1.04675    1.02293 +/- 0.00375\n",
+      "       38/1    0.99813    1.02204 +/- 0.00372\n",
+      "       39/1    1.05411    1.02315 +/- 0.00376\n",
+      "       40/1    1.05007    1.02405 +/- 0.00374\n",
+      "       41/1    0.99881    1.02323 +/- 0.00371\n",
+      "       42/1    1.04085    1.02378 +/- 0.00363\n",
+      "       43/1    1.01012    1.02337 +/- 0.00355\n",
+      "       44/1    1.01510    1.02313 +/- 0.00345\n",
+      "       45/1    1.03833    1.02356 +/- 0.00338\n",
+      "       46/1    1.00385    1.02301 +/- 0.00333\n",
+      "       47/1    1.02360    1.02303 +/- 0.00324\n",
+      "       48/1    1.01066    1.02270 +/- 0.00317\n",
+      "       49/1    0.98707    1.02179 +/- 0.00322\n",
+      "       50/1    1.01133    1.02153 +/- 0.00315\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",
+      " Total time for initialization     = 3.6394e-01 seconds\n",
+      "   Reading cross sections          = 3.4823e-01 seconds\n",
+      " Total time in simulation          = 8.2881e+00 seconds\n",
+      "   Time in transport only          = 8.1978e+00 seconds\n",
+      "   Time in inactive batches        = 7.4927e-01 seconds\n",
+      "   Time in active batches          = 7.5389e+00 seconds\n",
+      "   Time synchronizing fission bank = 1.0994e-02 seconds\n",
+      "     Sampling source sites         = 9.4419e-03 seconds\n",
+      "     SEND/RECV source sites        = 1.5279e-03 seconds\n",
+      "   Time accumulating tallies       = 3.5476e-02 seconds\n",
+      "   Time writing statepoints        = 3.2617e-02 seconds\n",
+      " Total time for finalization       = 1.6240e-06 seconds\n",
+      " Total time elapsed                = 8.6614e+00 seconds\n",
+      " Calculation Rate (inactive)       = 33365.8 particles/second\n",
+      " Calculation Rate (active)         = 13264.6 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",
+      " k-effective (Collision)     = 1.02733 +/- 0.00284\n",
+      " k-effective (Track-length)  = 1.02153 +/- 0.00315\n",
+      " k-effective (Absorption)    = 1.02638 +/- 0.00281\n",
+      " Combined k-effective        = 1.02616 +/- 0.00228\n",
       " Leakage Fraction            = 0.00000 +/- 0.00000\n",
       "\n"
      ]
@@ -742,8 +742,8 @@
        "      <td>1</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000101</td>\n",
-       "      <td>2.537729e-05</td>\n",
+       "      <td>0.000122</td>\n",
+       "      <td>3.807030e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -753,8 +753,8 @@
        "      <td>2</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.001282</td>\n",
-       "      <td>3.179682e-04</td>\n",
+       "      <td>0.001547</td>\n",
+       "      <td>4.775303e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -764,8 +764,8 @@
        "      <td>3</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000813</td>\n",
-       "      <td>2.001150e-04</td>\n",
+       "      <td>0.000980</td>\n",
+       "      <td>3.006970e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -775,8 +775,8 @@
        "      <td>4</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000641</td>\n",
-       "      <td>1.556332e-04</td>\n",
+       "      <td>0.000770</td>\n",
+       "      <td>2.340328e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -786,8 +786,8 @@
        "      <td>5</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000023</td>\n",
-       "      <td>5.409817e-06</td>\n",
+       "      <td>0.000027</td>\n",
+       "      <td>8.143610e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -798,7 +798,7 @@
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>0.000002</td>\n",
-       "      <td>4.317338e-07</td>\n",
+       "      <td>6.498748e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -808,8 +808,8 @@
        "      <td>1</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000105</td>\n",
-       "      <td>2.667910e-05</td>\n",
+       "      <td>0.000101</td>\n",
+       "      <td>2.343214e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -819,8 +819,8 @@
        "      <td>2</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.001332</td>\n",
-       "      <td>3.344146e-04</td>\n",
+       "      <td>0.001282</td>\n",
+       "      <td>2.937441e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -830,8 +830,8 @@
        "      <td>3</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000845</td>\n",
-       "      <td>2.105053e-04</td>\n",
+       "      <td>0.000813</td>\n",
+       "      <td>1.849319e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -841,8 +841,8 @@
        "      <td>4</td>\n",
        "      <td>total</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000665</td>\n",
-       "      <td>1.637544e-04</td>\n",
+       "      <td>0.000640</td>\n",
+       "      <td>1.439161e-04</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -864,16 +864,16 @@
        "\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  "
+       "0  (((delayed-nu-fission / nu-fission) * (delayed...  0.000122  3.807030e-05  \n",
+       "1  (((delayed-nu-fission / nu-fission) * (delayed...  0.001547  4.775303e-04  \n",
+       "2  (((delayed-nu-fission / nu-fission) * (delayed...  0.000980  3.006970e-04  \n",
+       "3  (((delayed-nu-fission / nu-fission) * (delayed...  0.000770  2.340328e-04  \n",
+       "4  (((delayed-nu-fission / nu-fission) * (delayed...  0.000027  8.143610e-06  \n",
+       "5  (((delayed-nu-fission / nu-fission) * (delayed...  0.000002  6.498748e-07  \n",
+       "6  (((delayed-nu-fission / nu-fission) * (delayed...  0.000101  2.343214e-05  \n",
+       "7  (((delayed-nu-fission / nu-fission) * (delayed...  0.001282  2.937441e-04  \n",
+       "8  (((delayed-nu-fission / nu-fission) * (delayed...  0.000813  1.849319e-04  \n",
+       "9  (((delayed-nu-fission / nu-fission) * (delayed...  0.000640  1.439161e-04  "
       ]
      },
      "execution_count": 20,
@@ -907,7 +907,7 @@
     "# 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",
+    "\n",
     "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n",
     "precursor_conc.get_pandas_dataframe().head(10)"
    ]
@@ -994,8 +994,8 @@
        "      <td>x-max out</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.03118</td>\n",
-       "      <td>0.000831</td>\n",
+       "      <td>0.03147</td>\n",
+       "      <td>0.000703</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1005,8 +1005,8 @@
        "      <td>x-max in</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.03188</td>\n",
-       "      <td>0.000783</td>\n",
+       "      <td>0.03109</td>\n",
+       "      <td>0.000648</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1038,8 +1038,8 @@
        "      <td>y-max out</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.03172</td>\n",
-       "      <td>0.000610</td>\n",
+       "      <td>0.03183</td>\n",
+       "      <td>0.000776</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -1049,8 +1049,8 @@
        "      <td>y-max in</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.03070</td>\n",
-       "      <td>0.000660</td>\n",
+       "      <td>0.03220</td>\n",
+       "      <td>0.000812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -1083,12 +1083,12 @@
        "       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",
+       "2      1  1  1  x-max out   total  current  0.03147  0.000703\n",
+       "3      1  1  1   x-max in   total  current  0.03109  0.000648\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",
+       "6      1  1  1  y-max out   total  current  0.03183  0.000776\n",
+       "7      1  1  1   y-max in   total  current  0.03220  0.000812\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"
       ]
@@ -1133,14 +1133,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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jRvDDDz/w8ssv07p163IfMyvRrFkz5syZw0svvcRTTz3FE088wblz53j55Zdxc3Nj7ty5lrYRERG88cYbPPnkk0yePJlz587xxz/+0WYYw/W54AYOHEh0dDTFxcUsWbKE3NzcMv8dbpSWlsaJEycAyM3NxWw28/777wPXH6ur6l0GIiJ1ifJaed0Q8nrfvn089thjeHt789JLL1kGgUr4+/uXeUwRkfpAea28bgh5/cYbb7B3715CQkJo06YNeXl57N27l9dee42goCCGDx9e4fclcluoxQUzpQy2VpH28PAwd+vWzbxs2TLz1atXrdoXFBSYY2NjzV27djW7ubmZ77jjDvO9995rfuaZZ8zffPONpd2JEyfMISEh5qZNm5oBq5WCFy9ebG7Xrp3Z1dXV3KlTJ/Obb75Z4erM9tq/f7+5d+/eZldXV7O3t7f5d7/7nXnlypVWq0iX+OCDD8wDBgwwu7u7m11dXc1t27Y1h4WFmXft2mVpY6ufBw4cMAcGBpobN25sbtmypXnSpEnm9PR0M2BevXp1qT7l5+ebXV1dzYMHDy6z33v27DEPHTrU7OnpaXZ2djb/4he/MA8dOtS8YcMGq3Zbtmwx33///WYXFxezr6+vefHixZU+l8XFxeY//OEP5rvuusvs4uJivv/++81bt241d+3a1TxixAhLu5JVpG8+dom33nrL0gcPDw/z8OHDzV988UWpdu+88465U6dOZjc3N7O/v785MTGxzFWklyxZYn755Zctfevevbt5x44dFX5PZvP1lalv/pkuedn69xARqQ+U1/+lvK7/eV3yvZf12r17d4U1RETqIuX1fymv639e79+/3/zII4+YfXx8zC4uLubGjRubu3btal6wYIE5Ly+vwv1Fbhcms7mcFRBEbhMffvghjz76KB999BFDhgyp7e5YycjI4N5772Xu3Lm89NJLNX78EydO4Ofnx9KlS3nxxRdr/PgiIiIllNdlU16LiEhdobwum/JapGZoShO5rX355Zd8//33vPDCC3Tr1o3Q0NBa7c/Ro0dZt24dQUFBuLu7c+zYMctjUBMnTqzVvomIiNQW5bWIiEjdp7wWkbpCA95yW4uKimL//v088MADvPPOO6VWZa5pTZo0IS0tjVWrVvHTTz/h4eFBcHAwCxcutCxCIiIicrtRXouIiNR9ymsRqSs0pYmIiIiIiIiIiIiINAgVL0srIiIiIiIiIiIiIlIPaMBbRERERERERERERBoEDXiLiIiIiIiIiIiISINQ5xatLC4uJisri6ZNm9b6AgciIreK2Wzm0qVL+Pj44OBg3GePV69e5dq1a3bv7+Ligpubm2H9kYZNmS0it4NbkdnKa6lJymsRuR3UxWts5XXtqXMD3llZWbRp06a2uyEiUiNOnjzJXXfdZUitq1ev0rJRIy5Xo4a3tzcZGRkKZakUZbaI3E6MymzltdQ05bWI3E7q0jW28rr21LkB76ZNmwJwcj24N65erVn9ZxnQo+u2EWpYre/8uhhSp2tGqiF1AOawwJA6QfkHDKkDsNb1SUPqBLHfkDoAl2lqSJ3BSZ8YUgeg38jthtRpT4YhdQBeyY8xpM5W12GG1AHozmFD6ngXnTakzqVcM/7tiiy/84xw7do1LgPTAVc79s8H/pydzbVr1xTIUiklP7+hJ/8PZ/dG1ar13otPG9ElABa9aVgpppw15k64Ja4zDKkDxmXRqsPPGVIH4GKRhzGFPjOmDEDKb3sbUueX8z81pA5A6u+7GlLnF/xgSB0AVwoMqdPqnVxD6gCMHrfGkDp/Oz3ekDq5l6FNDwzLbOW11LSSn92gk6txquZF9oczwo3oEgCLEgwrRYxB+bHof6YaUwjIprUhddZ8+ltD6gBcdDEor78ypgzA9l/3M6TO4GnGXWMfiutkSJ3WZBtSB+Bnqve3dokOG7IMqQMwYtRfDamzLm+sIXUuXYK7OxqX11C9zFZe1646N+Bd8oiVe2Nwb1K9Wq7uxv1AOXKHYbVwcDekjGN1T9ANmhj0o+Ceb9wjco1cXQypcweOhtQBMBtVq7ExPwNAtf9oLeGCcf+/GPVz0MjV2ZA6AHcYtGSBe5Gxj4HeisdKm4Bd/5p1LhCkziv5+XV2b1TtAW93Y37lA/YNIJXF3d2Y/0ddXY37HXvNqN/XdxiXRe6FBhUy8FqgibtBf9sY+APVxN2YvyOaGrgMjyvG/IxX81eAFWeD/rZxr84t1DYYndnKa6kpJT+7Tu6Nq33tYOTvREPz2qDLdTcDv0HDrq+aGJjXRn17Bv7Ob2xUXhv4t+QdhuW1cbnhaNT1rDERCxg3FmHQ6baoK9fYyuvapfMvItLAOP/nVVVFRndEREREyqS8FhERqR/syWzlde3SgLeISAPjhH2/3BUIIiIiNUd5LSIiUj/Yk9nK69pl3POQN4mPj8fPzw83NzcCAgLYu3fvrTqUiIjcwIn/fgJdlZcC+fakvBYRqR3Ka6kK5bWISO2xJ7OV17Xrlgx4JyYmMm3aNGbNmsXhw4fp27cvoaGhZGZm3orDiYjIDZyq8ZLbi/JaRKT2KK+lspTXIiK1S3ld/9ySAe9ly5YxceJEJk2aRKdOnYiLi6NNmzasWLHiVhxORERE7KC8FhERqfuU1yIiIlVj+ID3tWvXOHToECEhIVbbQ0JCOHDggNGHExGRm9jzeLS9C2dJ/aW8FhGpXcprqQzltYhI7VNe1z+G32Gfk5NDUVERrVq1streqlUrsrOzS7XPz88nPz/f8nVubq7RXRIRua1oESypjKrmNSizRUSMpLyWylBei4jUPi1aWf/cskUrTSaT1ddms7nUNoBFixbh4eFhebVp0+ZWdUlE5LagRbCkKiqb16DMFhExkvJaqkJ5LSJSe7RoZf1j+IC3l5cXjo6OpT5tPnv2bKlPpQFiYmK4ePGi5XXy5EmjuyQiclvRIlhSGVXNa1Bmi4gYSXktlaG8FhGpfcrr+sfwAW8XFxcCAgJITk622p6cnExQUFCp9q6urri7u1u9RERE5Naqal6DMltERKSmKa9FRESq7pZ84BAdHU1ERAQ9evQgMDCQlStXkpmZSWRk5K04nIiI3MDeBTK0qMbtR3ktIlJ7lNdSWcprEZHaZU9mK69r1y0Z8A4PD+fcuXPMnz+f06dP07lzZ7Zt20bbtm1vxeFEROQGuoCWylJei4jUHuW1VJbyWkSkdmnAu/65ZVPKREVFERUVdavKi4hIGeydL0xzjN2elNciIrVDeS1VobwWEak99mS28rp26fyLiDQwJStI27OfiIiI1AzltYiISP1gT2Yrr2uX4YtWioiIiIiIiIiIiIjUhjr7gUNW/+ZccjdVq0ZHjhnUG2hFL8NqfRNnTJ0BfGxMIWDwW3sMqZM86SFD6gB04XND6jTlkiF1ALqmfGNInYnhyw2pA/DW/zxvSJ1nvo4zpA5Ad9d0Q+psYJQhdQA6TThhSB3z/Or9XrIw7seyFD0iLTVti0cvoGm1amw2hxjTGeDZ5QcMq/U7XjakTg/SDKkD8CpTDKlzT8BRQ+oAdOGfhtTJ6uFjSB2AcBINqTPQc58hdQBOY8z391CEMTkLMP69eEPqvPPGbw2pA1D8uDFZu9En1JA6V3ILgF2G1LqR8lpq2ice91LdvN5oNub/K4DoPxn3/9XLjtMNqdOOE4bUAVjNBEPq3NOn7uX1yS5tDKkDMIwPDanzyD0phtQB2EtfQ+pET1hhSB2A51fPMKTO8lf/15A6AOYuxuT1O51HG1Ln56ICYJMhtW6mKU3qH51/EZEGRotgiYiI1H3KaxERkfpBi1bWPxrwFhFpYHTHmIiISN2nvBYREakfdId3/aPzLyLSwGgRLBERkbpPeS0iIlI/aNHK+kfnX0SkgdEdYyIiInWf8lpERKR+0B3e9Y9DbXdARERERERERERERMQI+sBBRKSB0SJYIiIidZ/yWkREpH7QopX1jwa8RUQaGD0iLSIiUvcpr0VEROoHTWlS/+j8i4g0MFoES0REpO5TXouIiNQPWrSy/tH5FxFpYPSItIiISN2nvBYREakfNKVJ/aNFK0VERERERERERESkQdAd3iIiDYzmBBUREan7lNciIiL1g+bwrn90/kVEGhgnR3A22bGfGSgyvDsiIiJig/JaRESkfrAns5XXtUtTmoiINDBOTva/REREpGYor0VEROqHmszr+Ph4/Pz8cHNzIyAggL1795bbfs+ePQQEBODm5kb79u1JSEgo1SYpKQl/f39cXV3x9/dn06ZNVu9/8sknDBs2DB8fH0wmEx988EGpGhs3bmTQoEF4eXlhMpk4cuRIqTbHjx9nxIgRtGzZEnd3d0aPHs2ZM2cs73/88ceYTCabr88++8zSztb7tr6v8mjAW0SkgXF2tP8lIiIiNUN5LSIiUj/UVF4nJiYybdo0Zs2axeHDh+nbty+hoaFkZmbabJ+RkcGQIUPo27cvhw8f5qWXXmLKlCkkJSVZ2qSmphIeHk5ERARHjx4lIiKC0aNHc/DgQUubvLw8unbtyvLly8vsW15eHn369GHx4sVlvh8SEoLJZCIlJYX9+/dz7do1hg0bRnFxMQBBQUGcPn3a6jVp0iTatWtHjx49rOqtXr3aqt24ceMqfR5BU5qIiIiIiIiIiIiI1Kply5YxceJEJk2aBEBcXBw7duxgxYoVLFq0qFT7hIQEfH19iYuLA6BTp06kpaURGxvLyJEjLTUGDhxITEwMADExMezZs4e4uDjWrVsHQGhoKKGhoeX2LSIiAoATJ07YfH///v2cOHGCw4cP4+7uDlwftPb09CQlJYWHH34YFxcXvL29LfsUFBSwZcsWnnvuOUwm6zljmjVrZtW2qnSHt4hIA1OTj0jXxuNWFR23oKCAGTNm0KVLF5o0aYKPjw9PPfUUWVlZljbnz5/n+eefp2PHjjRu3BhfX1+mTJnCxYsXSx3ro48+olevXjRq1AgvLy8ef/xxq/c/++wzfvWrX9GsWTOaN29OSEiIzce7REREbqQpTUREROqHmsjra9eucejQIUJCQqy2h4SEcODAAZv7pKamlmo/aNAg0tLSKCgoKLdNWTXtlZ+fj8lkwtXV1bLNzc0NBwcH9u3bZ3OfLVu2kJOTw/jx40u999xzz+Hl5cWDDz5IQkKC5S7xytKAt4hIA+PsCM5Odryq+MhVbT1uVdFxr1y5Qnp6OnPmzCE9PZ2NGzfy9ddf8+ijj1pqZGVlkZWVRWxsLJ9//jlr1qxh+/btTJw40arPSUlJREREMGHCBI4ePcr+/fsZO3as5f1Lly4xaNAgfH19OXjwIPv27cPd3Z1BgwZZ/sAQERGxpabyWkRERKrHrsz+T17n5uZavfLz820eIycnh6KiIlq1amW1vVWrVmRnZ9vcJzs722b7wsJCcnJyym1TVk179e7dmyZNmjBjxgyuXLlCXl4ev/vd7yguLub06dM291m1ahWDBg2iTZs2VtsXLFjAhg0b2LVrF2PGjOGFF17glVdeqVJ/dH+AiEhD44h9H2dWcdXp2nrcqqLjenh4kJycbHXs1157jZ49e5KZmYmvry+dO3e2Gmjv0KEDCxcu5Mknn6SwsBAnJycKCwuZOnUqS5cutRoI79ixo+W/jx07xoULF5g/f74lpOfOncv9999PZmYmHTp0qNpJFRGR20cN5bWIiIhUkz2Z/Z+8vnkwd+7cucybN6/s3W6a2sNsNpfaVlH7m7dXtaY9WrZsyYYNG/jtb3/Lq6++ioODA0888QQPPPAAjo6lP63/4Ycf2LFjB3/7299KvTd79mzLf3fr1g2A+fPnW22vSJ0d8O606wQ0ca9WjacH/Z8xnQHCSTSs1kdjhhpSJ40eFTeqrElmQ8qcZGzFjSopg3aG1FlA1T4FKleCMb8QVnV4xpA6AG99/ZwhdVb+1pAyAJjnGHOeXvRZYEgdgJGr1xpS5/942pA6V3OvAWsMqVWKE/ZdQP/nCaHc3Fyrza6urlaPJcF/H7eaOXOm1XZ7HrdatWoVBQUFODs7k5qayvTp00u1KRkkt+e4ABcvXsRkMtGsWbNy27i7u+P0n2fP0tPTOXXqFA4ODnTv3p3s7Gy6detGbGws9913H3B98NvLy4tVq1bx0ksvUVRUxKpVq7jvvvto27ZtmcdqaC526Yp7Ne84TKeTMZ0BYnnRsFpD+MiQOsM++ochdQDMxvwZgcu53IobVVJBdvX+Zithvs+QMgCYTNGG1Fkx3pg6AMb8tQWmIwYVAjDNM6SMQ3aeIXUATAaVOuHZzpA6V7lmSJ1SqpnXIlV1sW1X3Kv5jPdR7jGmM8BCx1mG1epBmiF1Rn/0oSF1wLi8bnTxvDGFgKs5zQ2pY+5g3ECZyRRuSJ21sycbUgcMzOur8QZVwrC8dvvJuJ8nDCp1jhaG1LlleQ32ZfZ/8vrkyZOWOa2BUtfWJby8vHB0dCx15/XZs2dL3aFdwtvb22Z7JycnWrRoUW6bsmpWR0hICMePHycnJwcnJyfLPNx+fn6l2q5evZoWLVpYPYldlt69e5Obm8uZM2cq3W9NaSIi0tA4VePF9U+gPTw8LC9bd2vX1uNW9hz36tWrzJw5k7Fjx1r9oXGjc+fOsWDBAp555r8fRn333XcAzJs3j9mzZ7N161aaN29O//79OX/++l93TZs25eOPP2bt2rU0atSIO+64gx07drBt2zbLwLmIiIhN1cxrERERqSHVyGt3d3erV1kD3i4uLgQEBJR6Wjk5OZmgoCCb+wQGBpZqv3PnTnr06IGzs3O5bcqqaQQvLy+aNWtGSkoKZ8+eLTWobTabWb16NU899ZSln+U5fPgwbm5u5d7AdjMNeIuIiJWTJ09y8eJFy6tkehFbautxq8oet6CggDFjxlBcXEx8vO07LHJzcxk6dCj+/v7MnTvXsr1kUYxZs2YxcuRIAgICWL16NSaTiQ0bNgDw888/85vf/IY+ffrw6aefsn//fu677z6GDBnCzz//XOZ5EBERqWm1sdD0J598wrBhw/Dx8cFkMvHBBx9YvW/UQtMnTpxg4sSJ+Pn50ahRIzp06MDcuXO5du0W3u0nIiJisOjoaN566y3efvttvvrqK6ZPn05mZiaRkZHA9Sk/n3rqKUv7yMhIvv/+e6Kjo/nqq694++23WbVqFS+++N8nXqdOncrOnTtZsmQJ//73v1myZAm7du1i2rRpljaXL1/myJEjHDlyBLi+/taRI0es1uc6f/48R44c4csvvwSuT+955MgRqxvPVq9ezaeffsrx48dZu3Yto0aNYvr06VbTggKkpKSQkZFRag0tgA8//JA333yTf/3rXxw/fpy33nqLWbNm8fTTT5f5YYEtuj9ARKShqeYj0iWfPJenth63qspxCwoKGD16NBkZGaSkpNj8ni5dusTgwYO544472LRpk9Wny61btwbA39/fss3V1ZX27dtbgv+vf/0rJ06cIDU1FQcHB8u25s2bs3nzZsaMGWPzXIiIiNTklCYlCz7Hx8fTp08f3njjDUJDQ/nyyy/x9fUt1b5koenJkyezdu1a9u/fT1RUFC1btrSsu1Gy0PSCBQsYMWIEmzZtYvTo0ezbt49evXoBkJeXR9euXZkwYYJlvxvduNB0165duXDhAtOmTePRRx8lLe36FBU3LjTt7+/P999/T2RkJFlZWbz//vsA/Pvf/6a4uJg33niDu+++m3/9619MnjyZvLw8YmNjq37CREREblSNKU2qIjw8nHPnzjF//nxOnz5N586d2bZtm2W6zNOnT1sNQvv5+bFt2zamT5/O66+/jo+PD6+++qpV5gYFBbF+/Xpmz57NnDlz6NChA4mJiZasBkhLS2PAgAGWr6Ojr0/pN27cONasWQPAli1bmDBhgqVNybXujXOSHzt2jJiYGM6fP0+7du2YNWtWqSlL4fpilUFBQXTqVHpaS2dnZ+Lj44mOjqa4uJj27dszf/58nn322SqdSw14i4g0NA5cX1TjFrrxcasRI0ZYticnJzN8+HCb+wQGBvLhh9bzIpb1uNWNoXjj41aVPW7JYPc333zD7t27LQPqN8rNzWXQoEG4urqyZcsW3NzcrN4PCAjA1dWVY8eO8dBDD1nqnjhxwvIHx5UrV3BwcLC6u7zk65I7xEVERGyqgbwuUVsLTYeGhhIaGlpmv4xaaHrw4MEMHjzY0qZ9+/YcO3aMFStWaMBbRESqrwYzOyoqiqioKJvvlQw+36h///6kp6eXWzMsLIywsLAy3w8ODrY8fV2W8ePHM378+HLbLF68mMWLF5fbBq7fJFaWmzPdXoZPabJo0SIefPBBmjZtyp133sljjz3GsWPHjD6MiIiUpYbmBK2tx60qOm5hYSFhYWGkpaXxl7/8haKiIrKzs8nOzrY82nzp0iVCQkLIy8tj1apV5ObmWtoUFRUB1+90j4yMZO7cuezcuZNjx47x299eX9111KhRAAwcOJALFy7w7LPP8tVXX/HFF18wYcIEnJycrD4hr4uU1yIitayaeZ2bm2v1ys/Pt3mYkgWfb1442p6FptPS0igoKCi3TXmLSFeGPQtNl9XG09OzWn2pK5TZIiK1TGtu1DuGn/49e/bw7LPP8uCDD1JYWMisWbMICQnhyy+/pEmTJkYfTkREbuaEfZ8+V3Gx9dp63Kqi4/7www9s2bIFgG7duln1effu3QQHB3Po0CEOHjwIwN13323VJiMjg3bt2gGwdOlSnJyciIiI4Oeff6ZXr16kpKTQvPn1Fe7vvfdePvzwQ15++WUCAwNxcHCge/fubN++3TIlSl2lvBYRqWXVzOs2bdpYbb7xkeIb3YqFplu3bl3hQtP2sHeh6ZsdP36c1157jT/96U9296UuUWaLiNQyezK7itfXYizDB7y3b99u9fXq1au58847OXToEP369TP6cCIiUotq43Grio7brl27Ch/HqswjW3B9/rDY2NhyH4ceOHAgAwcOrLBWXaO8FhGp306ePGk1KFzRQk61tdB0ZVVnoekbZWVlMXjwYEaNGmWZwqW+U2aLiIhUzS2/wb5k9eyG8jiZiEid50iNzS8mDYfyWkSkhlUzryuzyDTU3kLTVVHdhaZLZGVlMWDAAAIDA1m5cmWV+1FfKLNFRGqYrrHrHcPn8L6R2WwmOjqahx56iM6dO9tsk5+fX2r+ORERqYYamsNbGo7K5DUos0VEDFVDeX3jgs83Sk5OtiwKfbOSRaRvVNZC0ze3KatmWW5caHrXrl1lLjQdEhKCi4uLzYWmAU6dOkVwcDAPPPAAq1evxsHhll7q1hpdY4uI1AJdX9c7t/T0P/fcc/y///f/2LdvX5ltFi1axMsvv3wruyEicntxROEqVVKZvAZltoiIoWowr6Ojo4mIiKBHjx6Wu59vXmj61KlTvPvuu8D1haaXL19OdHQ0kydPJjU1lVWrVrFu3TpLzalTp9KvXz+WLFnC8OHD2bx5M7t27bLKksuXL/Ptt99avs7IyODIkSN4enri6+trWWg6PT2drVu3Whaahut3L7u4uFgWmr5y5Qpr1661GsBt2bIljo6OZGVlERwcjK+vL7Gxsfz444+WY3p7e9+6E1sLdI0tIlILdI1d79yyf67nn3+eLVu28Mknn3DXXXeV2S4mJobo6GjL17m5uaUWYBERkSqw93Griqe0lgaosnkNymwREUPVYF7X1kLTaWlpDBgwwPJ1SYaMGzeONWvWGLbQ9M6dO/n222/59ttvS2VZZdbsqC90jS0iUkvsyeyGEz/1kuED3mazmeeff55Nmzbx8ccf4+fnV257V1fXChdYERGRKtDjU1IJVc1rUGaLiBiqhvO6NhaarmiRaKMWmh4/fjzjx48vt019pmtsEZFapmvsesfwf65nn32Wv/71r2zevJmmTZtaHknz8PCgUaNGRh9ORERE7KC8FhERqR+U2SIiIlVj+ID3ihUrgOufxN9o9erVDfpTdxGROkOfPkslKK9FRGqZ8loqSZktIlLLlNn1zi2Z0kRERGqRwlgqQXktIlLLlNdSScpsEZFapsyud/TPJSLS0Dhg3yJYxUZ3RERERMqkvBYREakf7Mls5XWt0oC3iEhDY++nz7p5SEREpOYor0VEROoHezJbeV2rHGq7AyIiIiIiIiIiIiIiRqi7d3g3A5pUr8Rf88Ya0RMAnmnyhmG13B2vGVJnG38wpA5AKzoYUuc3vb8zpA7AqU89jSl0dpExdQB8jCnzXtsnjCkE/IF2htQ5seJdQ+pcZ8xHmZ+z2ZA6AF/ib0idpINPGlKHvFxgjTG1bqY7xqSGnd/nSqG7qVo1GnPFoN6AD1mG1XqEfxhTaL0xZQDeHBphSJ2Cre8ZUgeA8QWGlDFtdzakDgBjjCkzcfVyYwoBpoPPGVMozJgyAObP5xlSx/SgIWUA+PdnbQ2pM/38CkPq5ObCLEMq3UR5LTXM/DGY3atXw4tzhvQFoA0nDas1ig+NKbTVmDIAy4dOMqTO1eVvGVIHgNkG5fX7Bub1w9X8ofyPiQsMzOtDBuX1vcaUATCb5xlSxxRqSBkAvv97S0PqRH9jUF5fvkV5DbrDux6quwPeIiJiH0c0J6iIiEhdp7wWERGpH+zJbOV1rdKAt4hIQ6M7xkREROo+5bWIiEj9oDu86x0NeIuINDSO2PfbXZ9Ai4iI1BzltYiISP1gT2Yrr2uVFq0UERERERERERERkQZBd3iLiDQ09s4Jas8+IiIiYh/ltYiISP1gT2Yrr2uVBrxFRBoae+cE1SNXIiIiNUd5LSIiUj/Yk9nK61qlAW8RkYZGF9AiIiJ1n/JaRESkftCAd72jAW8RkYZGF9AiIiJ1n/JaRESkftCAd72jAW8RkYbGAfvmC9MyxiIiIjVHeS0iIlI/2JPZyutapdMvIiIiIiIiIiIiIg2CBrxFRBoap2q8REREpGYor0VEROqHGszr+Ph4/Pz8cHNzIyAggL1795bbfs+ePQQEBODm5kb79u1JSEgo1SYpKQl/f39cXV3x9/dn06ZNVu9/8sknDBs2DB8fH0wmEx988EGpGhs3bmTQoEF4eXlhMpk4cuRIqTbHjx9nxIgRtGzZEnd3d0aPHs2ZM2es2rRr1w6TyWT1mjlzplWbzMxMhg0bRpMmTfDy8mLKlClcu3at3PNwMw14i4g0NLqAFhERqfuU1yIiIvVDDeV1YmIi06ZNY9asWRw+fJi+ffsSGhpKZmamzfYZGRkMGTKEvn37cvjwYV566SWmTJlCUlKSpU1qairh4eFERERw9OhRIiIiGD16NAcPHrS0ycvLo2vXrixfvrzMvuXl5dGnTx8WL15c5vshISGYTCZSUlLYv38/165dY9iwYRQXW09oPn/+fE6fPm15zZ492/JeUVERQ4cOJS8vj3379rF+/XqSkpJ44YUXKnUOS+jPJRGRhsYR++YEtWcfERERsY/yWkREpH6wJ7PtyOtly5YxceJEJk2aBEBcXBw7duxgxYoVLFq0qFT7hIQEfH19iYuLA6BTp06kpaURGxvLyJEjLTUGDhxITEwMADExMezZs4e4uDjWrVsHQGhoKKGhoeX2LSIiAoATJ07YfH///v2cOHGCw4cP4+7uDsDq1avx9PQkJSWFhx9+2NK2adOmeHt726yzc+dOvvzyS06ePImPjw8Af/rTnxg/fjwLFy601K6I7vAWEWlodMeYiIhI3ae8FhERqR+qkde5ublWr/z8fJuHuHbtGocOHSIkJMRqe0hICAcOHLC5T2pqaqn2gwYNIi0tjYKCgnLblFXTXvn5+ZhMJlxdXS3b3NzccHBwYN++fVZtlyxZQosWLejWrRsLFy60mq4kNTWVzp07Wwa7S/qbn5/PoUOHKt0fDXiLiIiIiIiIiIiIGKxNmzZ4eHhYXrbu1AbIycmhqKiIVq1aWW1v1aoV2dnZNvfJzs622b6wsJCcnJxy25RV0169e/emSZMmzJgxgytXrpCXl8fvfvc7iouLOX36tKXd1KlTWb9+Pbt37+a5554jLi6OqKiocr+n5s2b4+LiUqU+6/4AEZGGxhH7frsXGt0RERERKZPyWkREpH6wJ7P/k9cnT560mobjxjugbTGZTFZfm83mUtsqan/z9qrWtEfLli3ZsGEDv/3tb3n11VdxcHDgiSee4IEHHsDR8b/zu0yfPt3y3/fffz/NmzcnLCzMcte3rf7a02cNeIuINDT2Pu6sRBAREak5ymsREZH6wZ7M/k97d3f3Ss077eXlhaOjY6m7mM+ePVvqjucS3t7eNts7OTlZBo/LalNWzeoICQnh+PHj5OTk4OTkRLNmzfD29sbPz6/MfXr37g3At99+S4sWLfD29rZaUBPgwoULFBQUVKnPdfbPpQd77MHJvUm1akxgtUG9gd0MMKzWi/zBkDq/M80xpA7Av809DamT/KlPxY0qKeTQXkPqnAtoZEgdAE+/q4bU6cgxQ+oAPHk2qeJGlfGWMWUATOFmQ+o80uFaxY0q6Zu8/zGkTqO7fjakDpeM+VmySYtgSQ3bQzCNca5WjWd53aDewLkzLQyrNe1jY+rEvfeMMYWAp2PeM6TO3xYNM6QOwOjYD40p1MyYMgAn1t1pSJ25vGxIHQAGG1TnfYPqACbTOWMKdTPu/7vXiaq4USXM9FxiSJ1LTmbggiG1rCivpYbt8OxHY/fqDQFM5k2DegOX85oaVuv5j42p89yKPxpTCHg+xpgLrL8uesyQOgBjP/3AmEK215qzyz+TuxhS51WmGFIHgGCD6hh5jW3UzbgPV9ykshbwe0PqLGsfbUid3Fwzt+wxqBpYtNLFxYWAgACSk5MZMWKEZXtycjLDhw+3uU9gYCAffmj9d/jOnTvp0aMHzs7OljbJyclWd1bv3LmToKCgqnWwCry8vABISUnh7NmzPProo2W2PXz4MACtW7e29HfhwoWcPn3asm3nzp24uroSEBBQ6T5oDm8RkYamBhfBio+Px8/PDzc3NwICAti7t/wPqvbs2UNAQABubm60b9+ehISEUm2SkpLw9/fH1dUVf39/Nm3aVKXjFhQUMGPGDLp06UKTJk3w8fHhqaeeIisry9Lm/PnzPP/883Ts2JHGjRvj6+vLlClTuHjxYqljffTRR/Tq1YtGjRrh5eXF448/bnlvzZo1mEwmm6+zZ89W6hyKiMhtSotWioiI1A81lNfR0dG89dZbvP3223z11VdMnz6dzMxMIiMjAYiJieGpp56ytI+MjOT7778nOjqar776irfffptVq1bx4osvWtpMnTqVnTt3smTJEv7973+zZMkSdu3axbRp0yxtLl++zJEjRzhy5AgAGRkZHDlyhMzMTEub8+fPc+TIEb788ksAjh07xpEjR6zuHl+9ejWffvopx48fZ+3atYwaNYrp06fTsWNH4PqClH/+8585cuQIGRkZ/O1vf+OZZ57h0UcfxdfXF7h+l7i/vz8REREcPnyYf/zjH7z44otMnjy5UnfKl9CAt4iI2CUxMZFp06Yxa9YsDh8+TN++fQkNDbUKxRtlZGQwZMgQ+vbty+HDh3nppZeYMmUKSUn/fUohNTWV8PBwIiIiOHr0KBEREYwePdrqkaaKjnvlyhXS09OZM2cO6enpbNy4ka+//trqU+WsrCyysrKIjY3l888/Z82aNWzfvp2JEyda9TkpKYmIiAgmTJjA0aNH2b9/P2PHjrW8Hx4ezunTp61egwYNon///tx5pzF3l4qIiIiIiEjDFx4eTlxcHPPnz6dbt2588sknbNu2jbZt2wJw+vRpq+ttPz8/tm3bxscff0y3bt1YsGABr776KiNHjrS0CQoKYv369axevZr777+fNWvWkJiYSK9evSxt0tLS6N69O927dweuD7x3796d3//+v3fxb9myhe7duzN06FAAxowZQ/fu3a1uYjt27BiPPfYYnTp1Yv78+cyaNYvY2FjL+66uriQmJhIcHIy/vz+///3vmTx5MuvWrbO0cXR05KOPPsLNzY0+ffowevRoHnvsMas6laH7A0REGpoamhN02bJlTJw4kUmTJgEQFxfHjh07WLFihc2VpxMSEvD19SUuLg6ATp06kZaWRmxsrCWQ4+LiGDhwIDExMcD1T7D37NlDXFycJQQrOq6HhwfJyclWx37ttdfo2bMnmZmZ+Pr60rlzZ6uB9g4dOrBw4UKefPJJCgsLcXJyorCwkKlTp7J06VKrgfCST6cBGjVqRKNG/5026ccffyQlJYVVq1ZV7WSKiMjtR3N4i4iI1A/VmMO7qqKiooiKsj2925o1a0pt69+/P+np6eXWDAsLIywsrMz3g4ODLYtdlmX8+PGMHz++3DaLFy9m8eLFZb7/wAMP8Omnn5ZbA8DX15etW7dW2K48usNbRKShceC/c4xV5fWfRMjNzbV65efnlzrEtWvXOHToECEhIVbbQ0JCOHDggM1upaamlmo/aNAg0tLSKCgoKLdNSU17jgtw8eJFTCYTzZo1K7eNu7s7Tk7X/zJJT0/n1KlTODg40L17d1q3bk1oaChffPFFmTXeffddGjduXO4fEyIiIkC181pERERqiD2ZrbyuVbf89C9atAiTyWQ1N4yIiNxC1ZwTtE2bNnh4eFhetu7WzsnJoaioqNQqya1atSq1AnSJ7Oxsm+0LCwvJyckpt01JTXuOe/XqVWbOnMnYsWPLnPPr3LlzLFiwgGee+e/Cgt999x0A8+bNY/bs2WzdupXmzZvTv39/zp8/b7PO22+/zdixY63u+q4vlNciIjVMc3iLHZTXIiK1QHld79zS0//ZZ5+xcuVK7r///lt5GBERuVE1H5E+efKk1cCwq6trmbuYblou3Gw2l9pWUfubt1emZmWPW1BQwJgxYyguLiY+Pt5mn3Jzcxk6dCj+/v7MnTvXsr24uBiAWbNmWaZcWb16NXfddRcbNmywGhyH63enf/nll7z77rs2j1OXKa9FRGqBpjSRKlJei4jUkhqc0kSMccvu8L58+TK//vWvefPNN2nevPmtOoyIiNzMnsejS16Au7u71cvWgLeXlxeOjo6l7qo+e/ZsqbuvS3h7e9ts7+TkRIsWLcptU1KzKsctKChg9OjRZGRkkJycbPPu7kuXLjF48GDuuOMONm3ahLOzs+W91q1bA+Dv72/Z5urqSvv27W0uzPnWW2/RrVs3AgICbH7/dZXyWkSkllQzr+X2orwWEalFyut655YNeD/77LMMHTqUhx9++FYdQkREaomLiwsBAQGlFodMTk4mKCjI5j6BgYGl2u/cuZMePXpYBprLalNSs7LHLRns/uabb9i1a5dlQP1Gubm5hISE4OLiwpYtW3Bzc7N6PyAgAFdXV44dO2ZV98SJE5ZVsktcvnyZv/3tb1aLW9YXymsREZG6T3ktIiJSebfkBvv169eTnp7OZ599VmHb/Px8qwXRcnNzb0WXRERuHzX0iHR0dDQRERH06NGDwMBAVq5cSWZmJpGRkQDExMRw6tQpyxQfkZGRLF++nOjoaCZPnkxqaiqrVq1i3bp1lppTp06lX79+LFmyhOHDh7N582Z27drFvn37Kn3cwsJCwsLCSE9PZ+vWrRQVFVnuCPf09MTFxYVLly4REhLClStXWLt2rWWBToCWLVvi6OiIu7s7kZGRzJ07lzZt2tC2bVuWLl0KwKhRo6zORWJiIoWFhfz617+u2kmsZVXJa1Bmi4gYSlOaSCUpr0VEapmmNKl3DL/D++TJk0ydOpW1a9eWulvOlkWLFlktjtamTRujuyQicntxxL4FNar4yFV4eDhxcXHMnz+fbt268cknn7Bt2zbL3c+nT5+2mvrDz8+Pbdu28fHHH9OtWzcWLFjAq6++apkfGyAoKIj169ezevVq7r//ftasWUNiYiK9evWq9HF/+OEHtmzZwg8//EC3bt1o3bq15XXgwAEADh06xMGDB/n888+5++67rdqcPHnScqylS5cyZswYIiIiePDBB/n+++9JSUkp9SjxqlWrePzxx+vVI8ZVzWtQZouIGKqG8rpEfHw8fn5+uLm5ERAQwN69e8ttv2fPHgICAnBzc6N9+/YkJCSUapOUlIS/vz+urq74+/uzadMmq/c/+eQThg0bho+PDyaTiQ8++MDq/YKCAmbMmEGXLl1o0qQJPj4+PPXUU2RlZVnanD9/nueff56OHTvSuHFjfH19mTJlChcvXrSqdeHCBSIiIiwZFRERwU8//VS1k1QHKa9FROoAezJbU5rUKsM/bzh06BBnz561msO0qKiITz75hOXLl5Ofn4+j43//1WNiYoiOjrZ8nZubq0AWEamOGrxjLCoqiqioKJvvrVmzptS2/v37k56eXm7NsLAwwsLC7D5uu3btLIthliU4OLjCNgDOzs7ExsYSGxtbbruSgfT6pKp5DcpsERFD1WBeJyYmMm3aNOLj4+nTpw9vvPEGoaGhfPnll/j6+pZqn5GRwZAhQ5g8eTJr165l//79REVF0bJlS8sH1ampqYSHh7NgwQJGjBjBpk2bGD16NPv27bN8UJ2Xl0fXrl2ZMGGC1QfcJa5cuUJ6ejpz5syha9euXLhwgWnTpvHoo4+SlpYGQFZWFllZWcTGxuLv78/3339PZGQkWVlZvP/++5ZaY8eO5YcffmD79u0APP3000RERPDhhx9W/YTVIcprEZE6QHd41zuGn/5f/epXfP7551bbJkyYwL333suMGTNKhbGrq6vNBdFERMRO9i6QoU+gbytVzWtQZouIGKoG83rZsmVMnDiRSZMmARAXF8eOHTtYsWIFixYtKtU+ISEBX19f4uLiAOjUqRNpaWnExsZaBq7j4uIYOHAgMTExwPVB1j179hAXF2eZriw0NJTQ0NAy++Xh4VFqXY7XXnuNnj17kpmZia+vL507dyYpKcnyfocOHVi4cCFPPvkkhYWFODk58dVXX7F9+3Y+/fRTy2D7m2++SWBgIMeOHaNjx45VP2l1hPJaRKQOsCezdX1dqwwf8G7atCmdO3e22takSRNatGhRaruIiIjUDuW1iEj9dvO8zGUNcl67do1Dhw4xc+ZMq+0hISFlPqGUmppKSEiI1bZBgwaxatUqCgoKcHZ2JjU1lenTp5dqUzJIbq+LFy9iMplo1qxZuW3c3d1xcnKy9NfDw8NqCrTevXvj4eHBgQMH6vWAt/JaRESk6gyfw1tERGqZPfOB2vtYtYiIiNinmnndpk0bq3mabd2pDZCTk0NRURGtWrWy2t6qVSvLos43y87Ottm+sLCQnJycctuUVbMyrl69ysyZMxk7dizu7u4225w7d44FCxbwzDPPWPX3zjvvLNX2zjvvrFZ/REREAF1f10M1cvo//vjjmjiMiIhAjc4JKg2L8lpEpAZVM69PnjxpNShc0RQWJpPJ6muz2VxqW0Xtb95e1ZrlKSgoYMyYMRQXFxMfH2+zTW5uLkOHDsXf35+5c+eW29/q9qcuU16LiNQwzeFd7+j0i4g0NA7YN1+YnvkRERGpOdXMa3d39zLvgr6Rl5cXjo6Ope50Pnv2bKk7tEt4e3vbbO/k5ESLFi3KbVNWzfIUFBQwevRoMjIySElJsfl9Xbp0icGDB3PHHXewadMmnJ2drfp75syZUvv8+OOPdvVHRETEij2ZrevrWqXTLyLS0GhKExERkbqvhvLaxcWFgICAUotDJicnExQUZHOfwMDAUu137txJjx49LAPNZbUpq2ZZSga7v/nmG3bt2mUZUL9Rbm4uISEhuLi4sGXLFtzc3Er19+LFi/zzn/+0bDt48CAXL16scn9ERERK0fV1vaPTLyIiIiIi0oBFR0cTERFBjx49CAwMZOXKlWRmZhIZGQlATEwMp06d4t133wUgMjKS5cuXEx0dzeTJk0lNTWXVqlWsW7fOUnPq1Kn069ePJUuWMHz4cDZv3syuXbvYt2+fpc3ly5f59ttvLV9nZGRw5MgRPD098fX1pbCwkLCwMNLT09m6dStFRUWWu8Y9PT1xcXHh0qVLhISEcOXKFdauXUtubq5lwc6WLVvi6OhIp06dGDx4MJMnT+aNN94A4Omnn+aRRx6p1wtWioiIiH3q7ID3fXyJC24VNyzH5Ky1BvUGJvu8Z1itx1hXcaNKeNP8giF1AGYTa0idGbxsSB2AJQHPG1LHk58NqQPAncbMAXiSNobUAXjwzs8NqbPxpSGG1AH4F3cbUuc+jhtSB8AUZzakzkOzkituVAmFuXl8akglGzSHt9Swx87vwL2wejW2eD5qTGeA91pNNqzWoPDNhtSZ5veGIXUAzBnG1InAuHPO9quGlDH/onp/+93IdOisMYUM/N1ovmBMHdMIY+oA8GTpu2ntYTbuT2W6MMCQOq5cM6ROPleBhYbUslKDeR0eHs65c+eYP38+p0+fpnPnzmzbto22bdsCcPr0aTIzMy3t/fz82LZtG9OnT+f111/Hx8eHV199lZEjR1raBAUFsX79embPns2cOXPo0KEDiYmJ9OrVy9ImLS2NAQP+++8ZHR0NwLhx41izZg0//PADW7ZsAaBbt25Wfd69ezfBwcEcOnSIgwcPAnD33dZ/Y2ZkZNCuXTsA/vKXvzBlyhRCQkIAePTRR1m+fHnVT1YDNuj8J9XO6z6eB4zpDPB+kycNq/XYUGOusZd3+V9D6gCYjblMYzyDjCkEOK/JNaTOtRYVT6dUWaZ/GHSivIwpA2C+ZEwd0yhj6gBg0P8uRub1IIPGNZY6vmhInXzHfGCZIbVK0Rze9Y5Ov4hIQ+OIfXOC2rOPiIiI2KeG8zoqKoqoqCib761Zs6bUtv79+5Oenl5uzbCwMMLCwsp8Pzg42LLYpS3t2rUr9/3K1Cjh6enJ2rXG3fAkIiJiYU9m6/q6VmnAW0SkodEd3iIiInWf8lpERKR+0B3e9Y5Ov4hIQ+OIfb/d9Qm0iIhIzVFei4iI1A/2ZLbyulZpwFtEpKHRHWMiIiJ1n/JaRESkftAd3vWOQ213QERERERERERERETECPq8QUSkodGilSIiInWf8lpERKR+0KKV9Y4GvEVEGho9Ii0iIlL3Ka9FRETqB01pUu/o9IuINDRaBEtERKTuU16LiIjUD1q0st7RgLeISEOjR6RFRETqPuW1iIhI/aApTeodLVopIiIiIiIiIiIiUsvi4+Px8/PDzc2NgIAA9u7dW277PXv2EBAQgJubG+3btychIaFUm6SkJPz9/XF1dcXf359NmzZZvf/JJ58wbNgwfHx8MJlMfPDBB6VqbNy4kUGDBuHl5YXJZOLIkSOl2hw/fpwRI0bQsmVL3N3dGT16NGfOnLG8f+LECSZOnIifnx+NGjWiQ4cOzJ07l2vXrlnVMZlMpV62vq/yaMBbRKShcarGS0RERGqG8lpERKR+qKG8TkxMZNq0acyaNYvDhw/Tt29fQkNDyczMtNk+IyODIUOG0LdvXw4fPsxLL73ElClTSEpKsrRJTU0lPDyciIgIjh49SkREBKNHj+bgwYOWNnl5eXTt2pXly5eX2be8vDz69OnD4sWLy3w/JCQEk8lESkoK+/fv59q1awwbNozi4mIA/v3vf1NcXMwbb7zBF198wZ///GcSEhJ46aWXStVbvXo1p0+ftrzGjRtXqXNYQn8uiYg0NFoES0REpO5TXouIiNQPNbRo5bJly5g4cSKTJk0CIC4ujh07drBixQoWLVpUqn1CQgK+vr7ExcUB0KlTJ9LS0oiNjWXkyJGWGgMHDiQmJgaAmJgY9uzZQ1xcHOvWrQMgNDSU0NDQcvsWEREBXL9L25b9+/dz4sQJDh8+jLu7O3B90NrT05OUlBQefvhhBg8ezODBgy37tG/fnmPHjrFixQpiY2Ot6jVr1gxvb+9y+1Qe3eEtItLQ6I4xERGRuk95LSIiUj9UI69zc3OtXvn5+TYPce3aNQ4dOkRISIjV9pCQEA4cOGBzn9TU1FLtBw0aRFpaGgUFBeW2KaumvfLz8zGZTLi6ulq2ubm54eDgwL59+8rc7+LFi3h6epba/txzz+Hl5cWDDz5IQkKC5S7xytKAt4hIA2N2ALOjHS8lgoiISI1RXouIiNQPdmX2f/K6TZs2eHh4WF627tQGyMnJoaioiFatWlltb9WqFdnZ2Tb3yc7Ottm+sLCQnJycctuUVdNevXv3pkmTJsyYMYMrV66Ql5fH7373O4qLizl9+rTNfY4fP85rr71GZGSk1fYFCxawYcMGdu3axZgxY3jhhRd45ZVXqtQf3R8gIiIiIiIiIiIiYrCTJ09apvgArO6AtsVkMll9bTabS22rqP3N26ta0x4tW7Zkw4YN/Pa3v+XVV1/FwcGBJ554ggceeABHR8dS7bOyshg8eDCjRo2yTOFSYvbs2Zb/7tatGwDz58+32l4RDXiLiDQwRU7XX/bsJyIiIjVDeS0iIlI/2JPZJe3d3d2tBrzL4uXlhaOjY6k7r8+ePVvqDu0S3t7eNts7OTnRokWLctuUVbM6QkJCOH78ODk5OTg5OVnm4fbz87Nql5WVxYABAwgMDGTlypUV1u3duze5ubmcOXOm0v3WA3EiIg1MSRjb8xIREZGaobwWERGpH2oir11cXAgICCA5Odlqe3JyMkFBQTb3CQwMLNV+586d9OjRA2dn53LblFXTCF5eXjRr1oyUlBTOnj3Lo48+annv1KlTBAcH88ADD7B69WocHCoemj58+DBubm40a9as0n2os38uXcQDZxpVq8avfD40qDewqcjFsFqbnT4yps7i2IobVZLLjOcNqbPkueWG1AF4efn/GlKnF3sMqQNwMM+YOudoYUwhgG+MeQxl2z2vGVIHYOQ72wypM3rcO4bUAfjnLGP+f/mJZobUyaOQEYZUKq3Q0UShY9V/LgodzYDZ+A5Jg/eNZxvucK/eZ+jhrDeoN8CRpw0rtfOUMf9PrMkIN6QOwAz8Danza3YbUgdg7UOTDaljumxImeveMqbMtOG251m0h+lQjDGF2hlTxsha63jMmEJAN4YaUudPSyr/2Gu5ruYCC42pdQPltdS07zx9qp3Xz/CGQb0BTkYYVmpb44uG1Pn758GG1AF4iX6G1BnAMUPqALwT/FtD6hg6EcKLxpSZ9isD83qPQXl9rzFlAOhhTJmNDDGmENCCMEPq/GFJ1eZmLtPVXGCZMbVuYk9m25PX0dHRRERE0KNHD8vdz5mZmZY5rmNiYjh16hTvvvsuAJGRkSxfvpzo6GgmT55Mamoqq1atYt26dZaaU6dOpV+/fixZsoThw4ezefNmdu3aZbWQ5OXLl/n2228tX2dkZHDkyBE8PT3x9fUF4Pz582RmZpKVlQXAsWPXfzd5e3vj7e0NwOrVq+nUqRMtW7YkNTWVqVOnMn36dDp27Ahcv7M7ODgYX19fYmNj+fHHHy3HLKnx4Ycfkp2dTWBgII0aNWL37t3MmjWLp59+usLpYG5UZwe8RUTEPkVOThQ5Vf3P0CInM1BgfIdERESkFOW1iIhI/WBPZtuT1+Hh4Zw7d4758+dz+vRpOnfuzLZt22jbti0Ap0+fJjMz09Lez8+Pbdu2MX36dF5//XV8fHx49dVXGTlypKVNUFAQ69evZ/bs2cyZM4cOHTqQmJhIr169LG3S0tIYMGCA5evo6GgAxo0bx5o1awDYsmULEyZMsLQZM2YMAHPnzmXevHnA9UHwmJgYzp8/T7t27Zg1axbTp0+37LNz506+/fZbvv32W+666y6r771k7nFnZ2fi4+OJjo6muLiY9u3bM3/+fJ599tkqnUsNeIuINDBFjo4U2XHHWJGjLqBFRERqivJaRESkfrAns+3N66ioKKKiomy+VzL4fKP+/fuTnp5ebs2wsDDCwsq+Iz84ONgy4FyW8ePHM378+HLbLF68mMWLF1erxuDBgxk8eHC5bSpDc3iLiIiIiIiIiIiISIOgAW8RkQamGEeK7HgV41jlY8XHx+Pn54ebmxsBAQHs3bu33PZ79uwhICAANzc32rdvT0JCQqk2SUlJ+Pv74+rqir+/P5s2barScQsKCpgxYwZdunShSZMm+Pj48NRTT1nmGoPr8489//zzdOzYkcaNG+Pr68uUKVO4eLH0/I8fffQRvXr1olGjRnh5efH444+XarNmzRruv/9+3Nzc8Pb25rnnniv3PIiIiNRkXouIiIj97Mls5XXtuiUD3qdOneLJJ5+kRYsWNG7cmG7dunHo0KFbcSgREblJIY52v6oiMTGRadOmMWvWLA4fPkzfvn0JDQ21mlPsRhkZGQwZMoS+ffty+PBhXnrpJaZMmUJSUpKlTWpqKuHh4URERHD06FEiIiIYPXo0Bw8erPRxr1y5Qnp6OnPmzCE9PZ2NGzfy9ddfW60MnZWVRVZWFrGxsXz++eesWbOG7du3M3HiRKs+JyUlERERwYQJEzh69Cj79+9n7NixVm2WLVvGrFmzmDlzJl988QX/+Mc/GDRoUJXOZW1RXouI1J6aymtpGJTZIiK1R3ld/xg+h/eFCxfo06cPAwYM4O9//zt33nknx48fp1mzZkYfSkREbLj+iXLVP88sorhK7ZctW8bEiROZNGkSAHFxcezYsYMVK1awaFHpVdITEhLw9fUlLi4OgE6dOpGWlkZsbKxlUY24uDgGDhxITMz1ldFjYmLYs2cPcXFxlpWmKzquh4cHycnJVsd+7bXX6NmzJ5mZmfj6+tK5c2ergfYOHTqwcOFCnnzySQoLC3FycqKwsJCpU6eydOlSq4HwkhWm4XrmzZ49mw8//JBf/epXlu333Xdflc5lbVBei4jUrprKa6n/lNkiIrXLnsxWXtcuwwe8lyxZQps2bVi9erVlW7t27Yw+jIiIlMH+C+jKL8Jx7do1Dh06xMyZM622h4SEcODAAZv7pKamEhISYrVt0KBBrFq1ioKCApydnUlNTbVaxbmkTckguT3HBbh48SImk6ncC8OLFy/i7u6Ok9P1aExPT+fUqVM4ODjQvXt3srOz6datG7GxsZYB7eTkZIqLizl16hSdOnXi0qVLBAUF8ac//Yk2bdqUeay6QHktIlK7aiKvpWFQZouI1C77BryV17XJ8ClNtmzZQo8ePRg1ahR33nkn3bt358033zT6MCIicovk5uZavfLz80u1ycnJoaioiFatWlltb9WqFdnZ2TbrZmdn22xfWFhITk5OuW1Katpz3KtXrzJz5kzGjh2Lu7u7zTbnzp1jwYIFPPPMM5Zt3333HQDz5s1j9uzZbN26lebNm9O/f3/Onz9vaVNcXMwrr7xCXFwc77//PufPn2fgwIFcu3bN5rHqCuW1iIhI/aDMFhERqRrDB7y/++47VqxYwT333MOOHTuIjIxkypQpvPvuuzbb5+fnlxpcERER+9mzAFbJC6BNmzZ4eHhYXramJylhMll/am02m0ttq6j9zdsrU7Oyxy0oKGDMmDEUFxcTHx9vs0+5ubkMHToUf39/5s6da9leXHz9EbRZs2YxcuRIAgICWL16NSaTiQ0bNljaFBQU8OqrrzJo0CB69+7NunXr+Oabb9i9e3eZ56EuqGpegzJbRMRI1c1ruX3oGltEpHYpr+sfw6c0KS4upkePHrzyyisAdO/enS+++IIVK1bw1FNPlWq/aNEiXn75ZaO7ISJy26ruI9InT560uhPa1dW1VFsvLy8cHR1L3VV99uzZUndfl/D29rbZ3snJiRYtWpTbpqRmVY5bUFDA6NGjycjIICUlxebd3ZcuXWLw4MHccccdbNq0CWdnZ8t7rVu3BsDf39/qXLRv396yQKatNi1btsTLy6vMxTvriqrmNSizRUSMpClNpLJ0jS0iUrs0pUn9Y/gd3q1bt7a68IfrC5OVdeEfExPDxYsXLa+TJ08a3SURkdtKkZ0rSJd8Au3u7m71sjXg7eLiQkBAQKnFIZOTkwkKCrLZr8DAwFLtd+7cSY8ePSwDzWW1KalZ2eOWDHZ/88037Nq1yzKgfqPc3FxCQkJwcXFhy5YtuLm5Wb0fEBCAq6srx44ds6p74sQJ2rZtC0CfPn0ArNqcP3+enJwcS5u6qqp5DcpsEREjVTev5faha2wRkdplT2Yrr2uX4Xd49+nTx+rCH+Drr78u88Lf1dXV5mCKiIjYpwgnO+8Yq9oq0tHR0URERNCjRw8CAwNZuXIlmZmZREZGAtcvtk6dOmV53DYyMpLly5cTHR3N5MmTSU1NZdWqVaxbt85Sc+rUqfTr148lS5YwfPhwNm/ezK5du9i3b1+lj1tYWEhYWBjp6els3bqVoqIiyx3hnp6euLi4cOnSJUJCQrhy5Qpr1661ety3ZcuWODo64u7uTmRkJHPnzqVNmza0bduWpUuXAjBq1CgA/ud//ofhw4czdepUVq5cibu7OzExMdx7770MGDCgyv8GNamqeQ3KbBERI9VUXkv9p2tsEZHaZU9mK69rl+ED3tOnTycoKIhXXnmF0aNH889//pOVK1eycuVKow8lIiK1KDw8nHPnzjF//nxOnz5N586d2bZtm+Xi6/Tp01Z3Hvn5+bFt2zamT5/O66+/jo+PD6+++iojR460tAkKCmL9+vXMnj2bOXPm0KFDBxITE+nVq1elj/vDDz+wZcsWALp162bV5927dxMcHMyhQ4c4ePAgAHfffbdVm4yMDNq1awfA0qVLcXJyIiIigp9//plevXqRkpJC8+bNLe3fffddpk+fztChQ3FwcKB///5s377danqUukh5LSIiUj8os0VERKrG8AHvBx98kE2bNhETE8P8+fPx8/MjLi6OX//610YfSkREbCjCwa7Hp4rsOFZUVBRRUVE231uzZk2pbf379yc9Pb3cmmFhYYSFhdl93Hbt2lkWwyxLcHBwhW0AnJ2diY2NJTY2tsw27u7urFq1ilWrVlVYry5RXouI1K6azGup35TZIiK1y57MVl7XLsMHvAEeeeQRHnnkkVtRWkREKmDvitAK5NuP8lpEpPYor6UqlNkiIrXHnsxWXteuWzLgLSIitadkkYyq7yciIiI1RXktIiJSP9iT2crr2qUBbxGRBqYYJ7vuGCvGdAt6IyIiIrYor0VEROoHezJbeV27qr4suIiI1Gklj1vZ8xIREZGaUdN5HR8fj5+fH25ubgQEBLB3795y2+/Zs4eAgADc3Nxo3749CQkJpdokJSXh7++Pq6sr/v7+bNq0yer9Tz75hGHDhuHj44PJZOKDDz6wer+goIAZM2bQpUsXmjRpgo+PD0899RRZWVlW7VauXElwcDDu7u6YTCZ++umnUn35+uuvGT58OF5eXri7u9OnTx92795duZMjIiJSDl1f1z919g7vNcd/i/sd1auRck+gMZ0B3A8XGFaLhwYaUmb2jJcMqQPwv2eXG1Jn3fLhhtQBmGdqZEgdc59gQ+oA7Nv3gCF1ztDKkDoApk8qXnivMibeY8zPAEDLcZmG1JnOnw2pA9Dz6OeG1AnsmmJInULygEcNqSVS2/7nxZO4u1Svxq+XrzWmM8Dpbj6G1TL/0Zg7M/yHHjKkDkBrsipuVAkj2FRxo8paY0wZh3vzjCkEFF9ubEiduOMzDakDQA9jytxl/saYQsBR7jekznC2GFIHoA0nDanjMN6YnyfzpTzM8wwpVWsSExOZNm0a8fHx9OnThzfeeIPQ0FC+/PJLfH19S7XPyMhgyJAhTJ48mbVr17J//36ioqJo2bIlI0eOBCA1NZXw8HAWLFjAiBEj2LRpE6NHj2bfvn306tULgLy8PLp27cqECRMs+93oypUrpKenM2fOHLp27cqFCxeYNm0ajz76KGlpaVbtBg8ezODBg4mJibH5PQ4dOpT/+Z//ISUlhUaNGhEXF8cjjzzC8ePH8fb2NuI01nsdFmfh7la9GqN+v8GYzgBPtXnXsFrXgj0MqdPt41RD6gA05oohdYbwkSF1ADyPnDKkzvm0XxhSBwAvY65n475/0ZA6AIw3psw9GUeNKQT8v7xuhtT5Jf8wpA5AN44YUsdjWrYhdcy5l8idZ0gpaQDq7IC3iIjYR4tgiYiI1H01mdfLli1j4sSJTJo0CYC4uDh27NjBihUrWLRoUan2CQkJ+Pr6EhcXB0CnTp1IS0sjNjbWMnAdFxfHwIEDLQPQMTEx7Nmzh7i4ONatWwdAaGgooaGhZfbLw8OD5ORkq22vvfYaPXv2JDMz0zIYP23aNAA+/vhjm3VycnL49ttvefvtt7n//usf4ixevJj4+Hi++OILDXiLiEi1aNHK+kdTmoiINDBFONj5yJUiQUREpKZUN69zc3OtXvn5+TaPc+3aNQ4dOkRISIjV9pCQEA4cOGBzn9TU1FLtBw0aRFpaGgUFBeW2KatmZV28eBGTyUSzZs0qvU+LFi3o1KkT7777Lnl5eRQWFvLGG2/QqlUrAgICqtUfERER+zJb19e1SXd4i4g0MPasIH19P2MeJxQREZGKVTev27RpY7V97ty5zJs3r1T7nJwcioqKaNXKekq9Vq1akZ1t+zHy7Oxsm+0LCwvJycmhdevWZbYpq2ZlXL16lZkzZzJ27Fjc3d0rvZ/JZCI5OZnhw4fTtGlTHBwcaNWqFdu3b6/SwLmIiIgt9mS2rq9rlwa8RUQamCKcKLLj17seuRIREak51c3rkydPWg0Ku7q6lrufyWS9JoHZbC61raL2N2+vas3yFBQUMGbMGIqLi4mPj6/SvmazmaioKO6880727t1Lo0aNeOutt3jkkUf47LPPaN26tV19EhERAfsyW9fXtUsD3iIiIiIiIvWMu7t7pe6C9vLywtHRsdSd12fPni11h3YJb29vm+2dnJxo0aJFuW3KqlmegoICRo8eTUZGBikpKVW6uxsgJSWFrVu3cuHCBcu+8fHxJCcn88477zBzpoGLz4qIiEidpwllREQamGK75gN1pNiOx6pFRETEPjWV1y4uLgQEBJRaHDI5OZmgoCCb+wQGBpZqv3PnTnr06IGzs3O5bcqqWZaSwe5vvvmGXbt2WQbUq+LKlSsAODhYX946ODhQXFxc5XoiIiI3siezdX1du3SHt4hIA2PPCtLX99McYyIiIjWlJvM6OjqaiIgIevToQWBgICtXriQzM5PIyEgAYmJiOHXqFO+++y4AkZGRLF++nOjoaCZPnkxqaiqrVq1i3bp1lppTp06lX79+LFmyhOHDh7N582Z27drFvn37LG0uX77Mt99+a/k6IyODI0eO4Onpia+vL4WFhYSFhZGens7WrVspKiqy3DXu6emJi4sLcH1O8ezsbEutzz//nKZNm+Lr64unpyeBgYE0b96ccePG8fvf/55GjRrx5ptvkpGRwdChQ6t8vkRERG5kT2br+rp2acBbRKSBKcTBzkWwdAeUiIhITanJvA4PD+fcuXPMnz+f06dP07lzZ7Zt20bbtm0BOH36NJmZmZb2fn5+bNu2jenTp/P666/j4+PDq6++ysiRIy1tgoKCWL9+PbNnz2bOnDl06NCBxMREevXqZWmTlpbGgAEDLF9HR0cDMG7cONasWcMPP/zAli1bAOjWrZtVn3fv3k1wcDAACQkJvPzyy5b3+vXrB8Dq1asZP348Xl5ebN++nVmzZvHLX/6SgoIC7rvvPjZv3kzXrl2rfL5ERERuZE9m6/q6dmlKExGRBqZkQQ17XiIiIlIzajqvo6KiOHHiBPn5+Rw6dMgyaAywZs0aPv74Y6v2/fv3Jz09nfz8fDIyMix3g98oLCyMf//731y7do2vvvqKxx9/3Or94OBgzGZzqdeaNWsAaNeunc33zWazZbAbYN68eTbbjB8/3tKmR48e7Nixg3PnzpGbm0tqaiqhoaF2nSsREZEb1WRex8fH4+fnh5ubGwEBAezdu7fc9nv27CEgIAA3Nzfat29PQkJCqTZJSUn4+/vj6uqKv78/mzZtsnr/k08+YdiwYfj4+GAymfjggw9K1di4cSODBg3Cy8sLk8nEkSNHSrU5fvw4I0aMoGXLlri7uzN69GjOnDlj1ebChQtERETg4eGBh4cHERER/PTTT1ZtMjMzGTZsGE2aNMHLy4spU6Zw7dq1cs/DzTTgLSIiIiIiIiIiIlKLEhMTmTZtGrNmzeLw4cP07duX0NBQq6ewbpSRkcGQIUPo27cvhw8f5qWXXmLKlCkkJSVZ2qSmphIeHk5ERARHjx4lIiKC0aNHc/DgQUubvLw8unbtyvLly8vsW15eHn369GHx4sVlvh8SEoLJZCIlJYX9+/dz7do1hg0bZrWextixYzly5Ajbt29n+/btHDlyhIiICMv7RUVFDB06lLy8PPbt28f69etJSkrihRdeqPR5BE1pIiLS4Ng/J6geuRIREakpymsREZH6wb45vKue18uWLWPixIlMmjQJgLi4OHbs2MGKFStYtGhRqfYJCQn4+voSFxcHQKdOnUhLSyM2NtYyDVlcXBwDBw4kJiYGuL5ux549e4iLi7OszREaGlrhU1Elg9InTpyw+f7+/fs5ceIEhw8fxt3dHbg+9ZinpycpKSk8/PDDfPXVV2zfvp1PP/3UMgXam2++SWBgIMeOHaNjx47s3LmTL7/8kpMnT+Lj4wPAn/70J8aPH8/ChQsttSuiO7xFRBqYqq4efeNLREREaobyWkREpH6oTl7n5uZavfLz820e49q1axw6dIiQkBCr7SEhIRw4cMDmPqmpqaXaDxo0iLS0NAoKCsptU1ZNe+Xn52MymXB1dbVsc3Nzw8HBwbKgdWpqKh4eHlbrffTu3RsPDw9Lf1JTU+ncubNlsLukvyVTslWWBrxFRBqYIhwptOOlC2gREZGao7wWERGpH+zJ7JK8btOmjWW+ag8PD5t3agPk5ORQVFREq1atrLa3atWK7Oxsm/tkZ2fbbF9YWEhOTk65bcqqaa/evXvTpEkTZsyYwZUrV8jLy+N3v/sdxcXFnD592tKXO++8s9S+d955p6U/tvrbvHlzXFxcqtRnTWkiItLA2LtARhHmW9AbERERsUV5LSIiUj/Yk9kleX3y5EmraThuvAPaFpPJZPW12Wwuta2i9jdvr2pNe7Rs2ZINGzbw29/+lldffRUHBweeeOIJHnjgARwd//thva3j3tyfyrSpiAa8RUQamCIc7JwTtOgW9EZERERsUV6LiIjUD/Zkdkleu7u7V2reaS8vLxwdHUvdxXz27NlSdzyX8Pb2ttneycmJFi1alNumrJrVERISwvHjx8nJycHJyYlmzZrh7e2Nn5+fpS9nzpwptd+PP/5o6Y+3t7fVgpoAFy5coKCgoEp91pQmIiIiIiIiIiIiIrXExcWFgIAAkpOTrbYnJycTFBRkc5/AwMBS7Xfu3EmPHj1wdnYut01ZNY3g5eVFs2bNSElJ4ezZszz66KOWvly8eJF//vOflrYHDx7k4sWLlv4EBgbyr3/9yzINSkl/XV1dCQgIqHQfdIe3iEgDY++CVpoTVEREpOYor0VEROoHezLbnryOjo4mIiKCHj16EBgYyMqVK8nMzCQyMhKAmJgYTp06xbvvvgtAZGQky5cvJzo6msmTJ5OamsqqVatYt26dpebUqVPp168fS5YsYfjw4WzevJldu3ZZFpIEuHz5Mt9++63l64yMDI4cOYKnpye+vr4AnD9/nszMTLKysgA4duwYcP2ObG9vbwBWr15Np06daNmyJampqUydOpXp06fTsWNHADp16sTgwYOZPHkyb7zxBgBPP/00jzzyiKVNSEgI/v7+REREsHTpUs6fP8+LL77I5MmTK3WnfIk6O+Cd2aEFTd2rdwP6hwwzqDfwyx7GrV761d52htS59/++N6QOAFONmQuwF60NqQPwg3mvQZWMm+fwJI8ZUqcFOYbUATBPNKZOAN2MKQT8eLyNIXV6dzhiSB0A8xCD5qfqbEyZ3ELwMKZUKbqAlppWMAcKKv+3h02uXDOmM8Bk3jOslmmcMbUeYF/FjSrpHzxiSJ0hBp5z/mBMmaLkJsYUAky7DCpk3D8dZoP+JDE1useYQkCLtJ8NqWO+z5AyAJgOGVMnMCDFkDqFjfL4zJBK1pTXUtNyf+cE7tX7m7gZPxnTGSCaeMNqmWKMqdWWfxtSB+AIvQ2p8yteNqQOwPnBvzCkjjm54jaVZUo06DrtU2dj6gDmDGPqmNp0NaYQ4H7koiF1rjWp5h/tNzAl/dKQOm1HGvP/XbHrZXINqVRaTQ14h4eHc+7cOebPn8/p06fp3Lkz27Zto23btgCcPn2azMxMS3s/Pz+2bdvG9OnTef311/Hx8eHVV19l5MiRljZBQUGsX7+e2bNnM2fOHDp06EBiYiK9evWytElLS2PAgAGWr6OjowEYN24ca9asAWDLli1MmDDB0mbMmDEAzJ07l3nz5gHXB8FjYmI4f/487dq1Y9asWUyfPt3qe/zLX/7ClClTCAkJAeDRRx9l+fLllvcdHR356KOPiIqKok+fPjRq1IixY8cSGxtbpXNZZwe8RUTEPiUrSNuzn4iIiNQM5bWIiEj9YE9m25vXUVFRREVF2XyvZPD5Rv379yc9Pb3cmmFhYYSFhZX5fnBwsGWxy7KMHz+e8ePHl9tm8eLFLF68uNw2np6erF27ttw2vr6+bN26tdw2FdGAt4hIA2PPCtLX9yu+Bb0RERERW5TXIiIi9YM9ma28rl1atFJEREREREREREREGgTd4S0i0sBoTlAREZG6T3ktIiJSP9TUHN5iHMPv8C4sLGT27Nn4+fnRqFEj2rdvz/z58yku1q38IiI1oQgHSyBX7aWHfm4nymsRkdqlvJbKUmaLiNQu+zJbeV2bDL/De8mSJSQkJPDOO+9w3333kZaWxoQJE/Dw8GDq1KlGH05ERG5SaOciWPbsI/WX8lpEpHYpr6WylNkiIrXLnsxWXtcuwwe8U1NTGT58OEOHDgWgXbt2rFu3jrS0NKMPJSIiNti/CFbRLeiN1FXKaxGR2qW8lspSZouI1C77Fq1UXtcmw++vf+ihh/jHP/7B119/DcDRo0fZt28fQ4YMMfpQIiJSy+Lj4/Hz88PNzY2AgAD27t1bbvs9e/YQEBCAm5sb7du3JyEhoVSbpKQk/P39cXV1xd/fn02bNlXpuAUFBcyYMYMuXbrQpEkTfHx8eOqpp8jKyrK0OX/+PM8//zwdO3akcePG+Pr6MmXKFC5evFjqWB999BG9evWiUaNGeHl58fjjj1u9bzKZSr1sfV91jfJaRESkflBmi4iIVI3hd3jPmDGDixcvcu+99+Lo6EhRURELFy7kiSeesNk+Pz+f/Px8y9e5ublGd0lE5LZSbOciWMVV3CcxMZFp06YRHx9Pnz59eOONNwgNDeXLL7/E19e3VPuMjAyGDBnC5MmTWbt2Lfv37ycqKoqWLVsycuRI4PodTOHh4SxYsIARI0awadMmRo8ezb59++jVq1eljnvlyhXS09OZM2cOXbt25cKFC0ybNo1HH33UcidUVlYWWVlZxMbG4u/vz/fff09kZCRZWVm8//77lj4nJSUxefJkXnnlFX75y19iNpv5/PPPS31vq1evZvDgwZavPTw8qnQua0NV8xqU2SIiRqqpvJb6T9fYIiK1y57MVl7XLsMHvBMTE1m7di1//etfue+++zhy5AjTpk3Dx8eHcePGlWq/aNEiXn75ZaO7ISJy27JnBemS/api2bJlTJw4kUmTJgEQFxfHjh07WLFiBYsWLSrVPiEhAV9fX+Li4gDo1KkTaWlpxMbGWga84+LiGDhwIDExMQDExMSwZ88e4uLiWLduXaWO6+HhQXJystWxX3vtNXr27ElmZia+vr507tyZpKQky/sdOnRg4cKFPPnkkxQWFuLk5ERhYSFTp05l6dKlTJw40dK2Y8eOpb63Zs2a4e3tXaXzV9uqmtegzBYRMVJN5bXUf7rGFhGpXfZktvK6dhk+pcnvfvc7Zs6cyZgxY+jSpQsRERFMnz7d5uAHXB/MuHjxouV18uRJo7skInJbsW8F6aqtIn3t2jUOHTpESEiI1faQkBAOHDhgc5/U1NRS7QcNGkRaWhoFBQXltimpac9xAS5evIjJZKJZs2bltnF3d8fJ6fpnwenp6Zw6dQoHBwe6d+9O69atCQ0N5Ysvvii173PPPYeXlxcPPvggCQkJFBcXl3mcuqKqeQ3KbBERI9VEXkvDoGtsEZHaZV9mK69rk+F3eF+5cgUHB+t/VEdHxzIv/l1dXXF1dTW6GyIit61CHHG049PkklWkb37s1dbv6ZycHIqKimjVqpXV9latWpGdnW2zfnZ2ts32hYWF5OTk0Lp16zLblNS057hXr15l5syZjB07Fnd3d5ttzp07x4IFC3jmmWcs27777jsA5s2bx7Jly2jXrh1/+tOf6N+/P19//TWenp4ALFiwgF/96lc0atSIf/zjH7zwwgvk5OQwe/Zsm8eqK6qa16DMFhExUnXzWm4fusYWEald9mS28rp2GT7gPWzYMBYuXIivry/33Xcfhw8fZtmyZfzmN78x+lAiImKDPStIl+wH0KZNG6vtc+fOZd68eTb3MZlMVl+bzeZS2ypqf/P2ytSs7HELCgoYM2YMxcXFxMfH2+xTbm4uQ4cOxd/fn7lz51q2l1xEzpo1yzLlyurVq7nrrrvYsGGDZXD8xoHtbt26ATB//vw6P+CtvBYRqV3VzWu5fSizRURqlz2ZrbyuXYaf/ddee405c+YQFRXF2bNn8fHx4ZlnnuH3v/+90YcSEZFb4OTJk1Z3Qtu6Q8jLywtHR8dSd1WfPXu21N3XJby9vW22d3JyokWLFuW2KalZleMWFBQwevRoMjIySElJsXl396VLlxg8eDB33HEHmzZtwtnZ2fJe69atAfD397c6F+3btyczM9Pm9wjQu3dvcnNzOXPmTJnnoi5QXouIiNQPymwREZGqMXxCmaZNmxIXF8f333/Pzz//zPHjx/nDH/6Ai4uL0YcSEREbiqs8t9j1V8kq0u7u7lYvWwPeLi4uBAQElFocMjk5maCgIJv9CgwMLNV+586d9OjRwzLQXFabkpqVPW7JYPc333zDrl27LAPqN8rNzSUkJAQXFxe2bNmCm5ub1fsBAQG4urpy7Ngxq7onTpygbdu2Nr9HgMOHD+Pm5lbufOF1gfJaRKR2VTev5fahzBYRqV32ZLbyunbp/noRkQbGnhWkS/ariujoaCIiIujRoweBgYGsXLmSzMxMIiMjgesLJp06dYp3330XgMjISJYvX050dDSTJ08mNTWVVatWsW7dOkvNqVOn0q9fP5YsWcLw4cPZvHkzu3btYt++fZU+bmFhIWFhYaSnp7N161aKioosd4R7enri4uLCpUuXCAkJ4cqVK6xdu5bc3FzL3OUtW7bE0dERd3d3IiMjmTt3Lm3atKFt27YsXboUgFGjRgHw4Ycfkp2dTWBgII0aNWL37t3MmjWLp59+WnNniohIuWoqr0VERKR67Mls5XXt0oC3iEgDU4gjDjWwCFZ4eDjnzp1j/vz5nD59ms6dO7Nt2zbL3c+nT5+2mvrDz8+Pbdu2MX36dF5//XV8fHx49dVXLfNjAwQFBbF+/Xpmz57NnDlz6NChA4mJifTq1avSx/3hhx/YsmUL8N85tUvs3r2b4OBgDh06xMGDBwG4++67rdpkZGTQrl07AJYuXYqTkxMRERH8/PPP9OrVi5SUFJo3bw6As7Mz8fHxREdHU1xcTPv27Zk/fz7PPvtslc6liIjcfmoqr0VERKR67Mls5XXt0oC3iEgDc/3TZ3sWwap6IEdFRREVFWXzvTVr1pTa1r9/f9LT08utGRYWRlhYmN3HbdeunWUxzLIEBwdX2AauD2jHxsYSGxtr8/3BgwczePDgCuuIiIjcrCbzWkREROxnT2Yrr2tXnR3w/iMzcKFRtWq8NeN5g3oDG5YcMKxWT64YU+hPxpQB4F8mQ8pcevMeQ+oAdDB9YEidnuY9htQBaMYEQ+qkFfUwpA7Ab5cb82934rkfDKkDcFeHbw2ps5VIQ+oA9DjlYUidA9ien7qqruQWgMcuQ2qJ1LZvPdpxh3v1lgXJx7h5SE1fGFYKCo0pc6LIz5hCgOlfxtTp3LWNMYWA95LL/5Cqskx/ed+QOgArfx1hSJ1Pft3PkDoApmOTDanzz5+7GFIHYD7GLHr3IscqblRJzu2mGFLnwIZfGVIn9woY81eESO3KdmzNZcfq5fUJ2hnTGcBk3K8NuGpMme+PdzSmEGAy5pKIToN8jCkE/DPZmPww7fjckDoAa8LDDalzMLxXxY0qybQ/2pA6X51sZ0gdMC6vp3PGkDoA9J5uSJkTWzoZUkd5LTeqswPeIiJiH80JKiIiUvcpr0VEROoHzeFd/2jAW0SkgdEFtIiISN2nvBYREakfNOBd/2jAW0SkgSm28wK6WIEsIiJSY5TXIiIi9YM9ma28rl0a8BYRaWAKccRkR7hqFWkREZGao7wWERGpH+zJbOV17areihUiIiIiIiIiIiIiInWE7vAWEWlginDEwY5f75pjTEREpOYor0VEROoHezJbeV27dIe3iEgDU7Kghj0vERERqRk1ndfx8fH4+fnh5uZGQEAAe/fuLbf9nj17CAgIwM3Njfbt25OQkFCqTVJSEv7+/ri6uuLv78+mTZus3v/kk08YNmwYPj4+mEwmPvjgA6v3CwoKmDFjBl26dKFJkyb4+Pjw1FNPkZWVZdVu5cqVBAcH4+7ujslk4qeffrLZ548++ohevXrRqFEjvLy8ePzxxys+MSIiIhW43fMaYOPGjQwaNAgvLy9MJhNHjhwp1SY7O5uIiAi8vb1p0qQJDzzwAO+//77l/Y8//hiTyWTz9dlnn1na2Xrf1vdVHg14i4g0MBrwFhERqftqMq8TExOZNm0as2bN4vDhw/Tt25fQ0FAyMzNtts/IyGDIkCH07duXw4cP89JLLzFlyhSSkpIsbVJTUwkPDyciIoKjR48SERHB6NGjOXjwoKVNXl4eXbt2Zfny5TaPc+XKFdLT05kzZw7p6els3LiRr7/+mkcffbRUu8GDB/PSSy+V+T0mJSURERHBhAkTOHr0KPv372fs2LFVOU0iIiI23e55XdKmT58+LF68uMw2ERERHDt2jC1btvD555/z+OOPEx4ezuHDhwEICgri9OnTVq9JkybRrl07evToYVVr9erVVu3GjRtXqXNYQlOaiIg0MFoES0REpO6rybxetmwZEydOZNKkSQDExcWxY8cOVqxYwaJFi0q1T0hIwNfXl7i4OAA6depEWloasbGxjBw50lJj4MCBxMTEABATE8OePXuIi4tj3bp1AISGhhIaGlpmvzw8PEhOTrba9tprr9GzZ08yMzPx9fUFYNq0acD1O8NsKSwsZOrUqSxdupSJEydatnfs2LGCMyMiIlKxmlq0sq7mNVwfzAY4ceJEmW1SU1NZsWIFPXv2BGD27Nn8+c9/Jj09ne7du+Pi4oK3t7elfUFBAVu2bOG5557DZDJZ1WrWrJlV26rSHd4iIg1MMU4U2fEq1megIiIiNaa6eZ2bm2v1ys/Pt3mca9eucejQIUJCQqy2h4SEcODAAZv7pKamlmo/aNAg0tLSKCgoKLdNWTUr6+LFi5hMJpo1a1bpfdLT0zl16hQODg50796d1q1bExoayhdffFGtvoiIiIB9mV3V6+v6lte2PPTQQyQmJnL+/HmKi4tZv349+fn5BAcH22y/ZcsWcnJyGD9+fKn3nnvuOby8vHjwwQdJSEiguLi4Sn3RgLeIiIiIiEg906ZNGzw8PCwvW3d+AeTk5FBUVESrVq2strdq1Yrs7Gyb+2RnZ9tsX1hYSE5OTrltyqpZGVevXmXmzJmMHTsWd3f3Su/33XffATBv3jxmz57N1q1bad68Of379+f8+fN290dERKS6KvsBdX3K67IkJiZSWFhIixYtcHV15ZlnnmHTpk106NDBZvtVq1YxaNAg2rRpY7V9wYIFbNiwgV27djFmzBheeOEFXnnllSr1RbfziYg0MEV2PiKtObxFRERqTnXz+uTJk1aDwq6uruXud/OjwmazudS2itrfvL2qNctTUFDAmDFjKC4uJj4+vkr7ltz1NWvWLMsj3KtXr+auu+5iw4YNPPPMM3b1SUREBOzL7JK8vnkwd+7cucybN6/M/ep6Xpdn9uzZXLhwgV27duHl5cUHH3zAqFGj2Lt3L126dLFq+8MPP7Bjxw7+9re/2axTolu3bgDMnz/fantFNOAtItLAFOFg5wW0HvoRERGpKdXNa3d390rdBe3l5YWjo2OpO7nOnj1b6o6vEt7e3jbbOzk50aJFi3LblFWzPAUFBYwePZqMjAxSUlKqdHc3QOvWrQHw9/e3bHN1daV9+/ZlLvQlIiJSWfZkdkleV/YD6vqQ1+U5fvw4y5cv51//+hf33XcfAF27dmXv3r28/vrrJCQkWLVfvXo1LVq0KLVQtS29e/cmNzeXM2fOVLrfGt0QEWlgCnG0+yUiIiI1o6by2sXFhYCAgFKLQyYnJxMUFGRzn8DAwFLtd+7cSY8ePXB2di63TVk1y1Iy2P3NN9+wa9cuywV6VQQEBODq6sqxY8es6p44cYK2bdtWuZ6IiMiNqpPXJR9Ql7zKGvCu63ldkStXrgDg4GA91Ozo6Fhq/m2z2czq1at56qmnLP0sz+HDh3Fzc6vS+h66w1tEpIEpwgmTHb/eixQJIiIiNaYm8zo6OpqIiAh69OhBYGAgK1euJDMzk8jISABiYmI4deoU7777LgCRkZEsX76c6OhoJk+eTGpqKqtWrWLdunWWmlOnTqVfv34sWbKE4cOHs3nzZnbt2sW+ffssbS5fvsy3335r+TojI4MjR47g6emJr68vhYWFhIWFkZ6eztatWykqKrLchebp6YmLiwtwff7R7OxsS63PP/+cpk2b4uvri6enJ+7u7kRGRjJ37lzatGlD27ZtWbp0KQCjRo2q8vkSERG5kT2Z3ZDyGuD8+fNkZmaSlZUFYPmQ2dvbG29vb+69917uvvtunnnmGWJjY2nRogUffPABycnJbN261er7TElJISMjg4kTJ5Y6Bx9++CHZ2dkEBgbSqFEjdu/ezaxZs3j66acrnL7tRhrdEBERERERacDCw8M5d+4c8+fP5/Tp03Tu3Jlt27ZZ7n4+ffq01dQffn5+bNu2jenTp/P666/j4+PDq6++apkfGyAoKIj169cze/Zs5syZQ4cOHUhMTKRXr16WNmlpaQwYMMDydXR0NADjxo1jzZo1/PDDD2zZsgX47xydJXbv3k1wcDAACQkJvPzyy5b3+vXrB1x/HHr8+PEALF26FCcnJyIiIvj555/p1asXKSkpNG/evJpnT0REpGbU1bwG2LJlCxMmTLC0GTNmDPDfOcmdnZ3Ztm0bM2fOZNiwYVy+fJm7776bd955hyFDhlh9n6tWrSIoKIhOnTqVOgfOzs7Ex8cTHR1NcXEx7du3Z/78+Tz77LNVOpca8BYRaWCKcbRrAcpiTWkiIiJSY2o6r6OiooiKirL5XsnF7I369+9Penp6uTXDwsIICwsr8/3g4GDL4lm2tGvXrtz3S8ybN6/cBb7g+gVybGwssbGxFdYTERGpCnsyuyHlNcD48eMtHzKX5Z577iEpKancNgB//etfy3xv8ODBDB48uMIaFamzA97zeJmmVG/F0L5LdhrUG0i7GGBYrWYePxlS5/S2DobUAQz7SWjKJWMKAR+YpxtS5zjGnafFzDSkzvnYXxhSB2D6jFcMqXO+jXF92niyV8WNKuEkbSpuVEmN3S4aUqfF1XOG1HGjyJA6tlwPYnsWwdKAt9in966j0KRqC4zdLGbQ7w3qDfS675+G1Uq8N9yQOkWFBv7/NcaYMv96/0FjCgF/vW+sMYXcjCkDsJQXDanzzfSuhtQBcJ6da0idgxiTs2Dc7/4/DZhtSB2Ae3YfNaROt1GphtQpys2D8Q8bUsuqrvJaatiDO/4FjauX1/OGzjCoN3ClY2PDar3fseyBnKo4ec6464+CyOqd6xJfffCAIXUA5nZ9ueJGlVFoTBmAeGwP7FXVP1/vb0gdAI9J2RU3qoRvDRyLOEfV1zewZV3obwypA+C2/rwhdbo8aszf7kW5l4FfGlKrVG07Mlt5Xbvq7IC3iIjYRxfQIiIidZ/yWkREpH7QgHf9owFvEZEGphAHzHZdQDtU3EhEREQMobwWERGpH+zJbOV17dLZFxEREREREREREZEGQXd4i4g0MEU4Yc+v9yJFgoiISI1RXouIiNQP9mS28rp2VfkO708++YRhw4bh4+ODyWTigw8+sHrfbDYzb948fHx8aNSoEcHBwXzxxRdG9VdERCpQ9J8VpO15ScOhvBYRqduU1wLKaxGR+kB5Xf9UecA7Ly+Prl27snz5cpvv//GPf2TZsmUsX76czz77DG9vbwYOHMilS5eq3VkREalYsZ1hXKxAblCU1yIidZvyWkB5LSJSH9iT2crr2lXl++tDQ0MJDQ21+Z7ZbCYuLo5Zs2bx+OOPA/DOO+/QqlUr/vrXv/LMM89Ur7ciIlKhQhxxsCNcFcgNi/JaRKRuU14LKK9FROoDezJbeV27DF20MiMjg+zsbEJCQizbXF1d6d+/PwcOHDDyUCIiUobrnyg72fFSIN8ulNciIrVPeS0VUV6LiNQN9mW28ro2GTqDenZ2NgCtWrWy2t6qVSu+//57m/vk5+eTn59v+To3N9fILomIiMhN7MlrUGaLiIjUJOW1iIiIfQy9w7uEyWSy+tpsNpfaVmLRokV4eHhYXm3atLkVXRIRuW1oESyprKrkNSizRUSMpLyWylJei4jULuV1/WPogLe3tzfw30+iS5w9e7bUp9IlYmJiuHjxouV18uRJI7skInLb0QW0VMSevAZltoiIkZTXUhHltYhI3aC8rn8MHfD28/PD29ub5ORky7Zr166xZ88egoKCbO7j6uqKu7u71UtEROxXVOxo96uq4uPj8fPzw83NjYCAAPbu3Vtu+z179hAQEICbmxvt27cnISGhVJukpCT8/f1xdXXF39+fTZs2Vem4BQUFzJgxgy5dutCkSRN8fHx46qmnyMrKsrQ5f/48zz//PB07dqRx48b4+voyZcoULl68WOpYH330Eb169aJRo0Z4eXlZFo262blz57jrrrswmUz89NNP5Z6H2mZPXoMyW0TESDWZ11I/Ka9FROoG5XX9U+U5vC9fvsy3335r+TojI4MjR47g6emJr68v06ZN45VXXuGee+7hnnvu4ZVXXqFx48aMHTvW0I6LiIhtRYWOFBdWPVzNVdwnMTGRadOmER8fT58+fXjjjTcIDQ3lyy+/xNfXt1T7jIwMhgwZwuTJk1m7di379+8nKiqKli1bMnLkSABSU1MJDw9nwYIFjBgxgk2bNjF69Gj27dtHr169KnXcK1eukJ6ezpw5c+jatSsXLlxg2rRpPProo6SlpQGQlZVFVlYWsbGx+Pv78/333xMZGUlWVhbvv/++pc9JSUlMnjyZV155hV/+8peYzWY+//xzm+dj4sSJ3H///Zw6dapK5/FWUV6LiNRtNZXXUrcpr0VE6j57Mlt5XbuqPOCdlpbGgAEDLF9HR0cDMG7cONasWcP//u//8vPPPxMVFcWFCxfo1asXO3fupGnTpsb1WkREat2yZcuYOHEikyZNAiAuLo4dO3awYsUKFi1aVKp9QkICvr6+xMXFAdCpUyfS0tKIjY21DHjHxcUxcOBAYmJigOuP5O7Zs4e4uDjWrVtXqeN6eHhY3QkF8Nprr9GzZ08yMzPx9fWlc+fOJCUlWd7v0KEDCxcu5Mknn6SwsBAnJycKCwuZOnUqS5cuZeLEiZa2HTt2LPW9rVixgp9++onf//73/P3vf7f3lBpKeS0iIlL3Ka9FRESMV+UB7+DgYMxmc5nvm0wm5s2bx7x586rTLxERsVNRoROmwir/esf8n31yc3Ottru6uuLq6mq17dq1axw6dIiZM2dabQ8JCeHAgQM266emphISEmK1bdCgQaxatYqCggKcnZ1JTU1l+vTppdqUDJLbc1yAixcvYjKZaNasWblt3N3dcXK6fh7S09M5deoUDg4OdO/enezsbLp160ZsbCz33XefZb8vv/yS+fPnc/DgQb777rsy69c05bWISN1W3byWhkF5LSJS99mT2crr2mXoHN4iIlL7igodKCp0tON1PRLatGmDh4eH5WXrbu2cnByKiopKLZjUqlWrUgsrlcjOzrbZvrCwkJycnHLblNS057hXr15l5syZjB07tsw5LM+dO8eCBQt45plnLNtKBq/nzZvH7Nmz2bp1K82bN6d///6cP38egPz8fJ544gmWLl1qcxoXERGRslQ3r0VERKRm2JfZyuvapI8bREQamKJCR0zVmBP05MmTVgPDN9/dfSOTyWRdw2wuta2i9jdvr0zNyh63oKCAMWPGUFxcTHx8vM0+5ebmMnToUPz9/Zk7d65le3FxMQCzZs2yTLmyevVq7rrrLjZs2MAzzzxDTEwMnTp14sknnyzzexYREbGlunktIiIiNcOezFZe1y4NeIuINDCFhY6YCuy/gHZ3dy/zTugSXl5eODo6lrqr+uzZs6Xuvi7h7e1ts72TkxMtWrQot01Jzaoct6CggNGjR5ORkUFKSorN7+nSpUsMHjyYO+64g02bNuHs7Gx5r3Xr1gD4+/tbtrm6utK+fXsyMzMBSElJ4fPPP7csdFkygO/l5cWsWbN4+eWXbZ4LERGR6ua1iIiI1Ax7Mlt5Xbvq7ID3HXl5NK3mz8ZHbkON6Qzg7nHNsFrP85YhdZZ3nlhxo0p6zqA+PcdGQ+oAvEisIXWisX1Xpz2aYcxq6ItnzDCkDsCfiTGkTs+TjxlSB6AdGYbU6c9BQ+oAmEYYU+c0zQypc4my52qsD1xcXAgICCA5OZkRI/57cpOTkxk+fLjNfQIDA/nwww+ttu3cuZMePXpYBpoDAwNJTk62msd7586dBAUFVem4JYPd33zzDbt377YMqN8oNzeXQYMG4erqypYtW3Bzc7N6PyAgAFdXV44dO8ZDDz1kqXvixAnatm0LQFJSEj///LNln88++4zf/OY37N27lw4dOpRzBhuWNg9/jYP7HdWqsejgfIN6A+ZehpXioOM+Q+qkP/yQIXUAzF8ZU8dkMuZ3NcDfxzxuSB3zOkPKAGA62tWQOk/8+W1D6gD8ld8YUsfxzP8ZUgdgUitj/gY07zakDAC/4qQhdXpwyJA6+VzlX4ZUEqldLQadxMG9eotdztuzxKDegLm/YaXYS4ohdb4bf1/FjSrJbFDMmkxXjCkE/P1Jg/L6PUPKAGD6wpgfhInPLjekDsBbPGdIHcczWwypAzC21V8NqWP+uyFlABhPoiF1mnLJkDrXuIpBfyZLA1BnB7xFRMQ+5iInzEV2/Hqv4j7R0dFERETQo0cPAgMDWblyJZmZmURGRgIQExPDqVOnePfddwGIjIxk+fLlREdHM3nyZFJTU1m1ahXr1v13hGvq1Kn069ePJUuWMHz4cDZv3syuXbvYt29fpY9bWFhIWFgY6enpbN26laKiIssd4Z6enri4uHDp0iVCQkK4cuUKa9euJTc317JYZ8uWLXF0dMTd3Z3IyEjmzp1LmzZtaNu2LUuXLgVg1KhR8P/Zu/e4qOr8f+CvkcugJKOAcklAtFYlLxikoqJYiaCpmQhmO2kpyZJ5wdrES5quq66s8VNE0kwyS9nCCymrwldFTTS5uWaul8JAhQw1UEwY4Pz+cDk5zgyX4TDMjK/n4zGP75cz7/M+nzmxvpjPfOYcQGNSu/Za5D169KjzBplERESGymsiIiJqGr0ym3ndonj2iYjMTZXFg4c++zVCWFgYbt68iaVLl6KoqAg9e/ZEamqquPq5qKhIvPQHAHh6eiI1NRVz5szB+vXr4erqirVr14rXxwaAgQMHYseOHVi4cCEWLVqErl27IikpCf3792/wca9evYqUlAerKby9vdXGfPjwYQQEBCA7OxunTj34BsFTTz2lVpOfn4/OnTsDAFavXg1LS0solUr8/vvv6N+/Pw4dOoT27ds36lwRERFpMFBeExERURPpk9nM6xbFCW8iInNjwDfQkZGRiIyM1PpcYmKixrahQ4ciJyenzp4hISEICQnR+7idO3cWr6WtS0BAQL01AGBlZYWYmBjExDTsEksN7UtERMQJbyIiIhPBCW+TwwlvIiJzUy0DqmT67UdERESGwbwmIiIyDfpkNvO6RXHCm4jI3FT976HPfkRERGQYzGsiIiLToE9mM69bVKuWHgARERERERERERHR4y4+Ph6enp6wsbGBj48Pjh07Vmd9RkYGfHx8YGNjgy5duiAhIUGjJjk5GV5eXpDL5fDy8sKuXbvUnj969ChGjx4NV1dXyGQy7N69W6PHzp07MWLECDg6OkImkyEvL0+jpri4GEqlEs7OzrC1tcWzzz6Lr7/+Wq2mc+fOkMlkao958+ap1RQUFGD06NGwtbWFo6MjZs6cicrKyjrPw6M44U1EZG6qmvAgIiIiw2BeExERmQYD5XVSUhJmz56NBQsWIDc3F/7+/ggODkZBQYHW+vz8fIwcORL+/v7Izc3F/PnzMXPmTCQnJ4s1mZmZCAsLg1KpxJkzZ6BUKhEaGopTp06JNeXl5ejTpw/i4uJ0jq28vByDBg3CypUrddYolUpcuHABKSkpOHv2LF555RWEhYUhNzdXrW7p0qUoKioSHwsXLhSfq66uxqhRo1BeXo7jx49jx44dSE5Oxty5c+s9fw/jJU2IiMwNvyJNRERk/JjXREREpsFAlzRZs2YNpk6dimnTpgEAYmNjceDAAWzYsAErVqzQqE9ISIC7uztiY2MBAD169EBWVhZiYmIwfvx4scfw4cMRHR0NAIiOjkZGRgZiY2Oxfft2AEBwcDCCg4PrHJtSqQQAXLlyRWdNZmYmNmzYgH79+gEAFi5ciI8++gg5OTno27evWNe2bVs4Oztr7XHw4EH88MMPKCwshKurKwDgn//8J6ZMmYLly5fDzs6uznHW4gpvIiJzUwVApceDb6CJiIgMh3lNRERkGvTJ7EbmdWVlJbKzsxEYGKi2PTAwECdOnNC6T2Zmpkb9iBEjkJWVBZVKVWeNrp5NMXjwYCQlJeHWrVuoqanBjh07UFFRgYCAALW6VatWwcHBAd7e3li+fLna5UoyMzPRs2dPcbK7drwVFRXIzs5u8Fi4wpuIyNxU/++hz35ERERkGMxrIiIi06BPZv+vvqysTG2zXC6HXC7XKC8pKUF1dTWcnJzUtjs5OaG4uFjrIYqLi7XWV1VVoaSkBC4uLjprdPVsiqSkJISFhcHBwQGWlpZo06YNdu3aha5du4o1s2bNwrPPPov27dvju+++Q3R0NPLz8/HJJ5/ofE3t27eHtbV1o8bMCW8iIiIiIiIiIiIiibm5uan9vHjxYixZskRnvUwmU/tZEASNbfXVP7q9sT31tXDhQty+fRvp6elwdHTE7t27MWHCBBw7dgy9evUCAMyZM0es7927N9q3b4+QkBBx1be28eozZk54ExGZG14TlIiIyPgxr4mIiExDE67hXVhYqHbdaW2ruwHA0dERFhYWGquYb9y4obHiuZazs7PWektLS3HyWFeNrp76+vHHHxEXF4fvv/8ezzzzDACgT58+OHbsGNavX4+EhASt+w0YMAAAcPnyZTg4OMDZ2VnthpoAcPv2bahUqkaNmdfwJiIyN/reQZpvoImIiAyHeU1ERGQampDXdnZ2ag9dE97W1tbw8fFBWlqa2va0tDQMHDhQ6z5+fn4a9QcPHoSvry+srKzqrNHVU1/37t0DALRqpT7VbGFhgZqaGp375ebmAgBcXFwAPBjv999/j6KiIrXxyuVy+Pj4NHg8XOFNRGRuuGKMiIjI+DGviYiITEMTVng3RlRUFJRKJXx9feHn54eNGzeioKAAERERAIDo6Ghcu3YNW7duBQBEREQgLi4OUVFRCA8PR2ZmJjZv3ozt27eLPWfNmoUhQ4Zg1apVGDt2LPbs2YP09HQcP35crLl79y4uX74s/pyfn4+8vDzY29vD3d0dAHDr1i0UFBTg+vXrAIALFy4AeLCC3NnZGd27d8dTTz2F6dOnIyYmBg4ODti9ezfS0tKwd+9eAA9uSHny5EkMGzYMCoUCp0+fxpw5czBmzBjxOIGBgfDy8oJSqcTq1atx69YtvPvuuwgPD1dbKV8frvAmIjI31dDv02feBIuIiMhwDJzX8fHx8PT0hI2NDXx8fHDs2LE66zMyMuDj4wMbGxt06dJF61eRk5OT4eXlBblcDi8vL+zatUvt+aNHj2L06NFwdXWFTCbD7t271Z5XqVR4//330atXL9ja2sLV1RWvv/66+Ga61saNGxEQEAA7OzvIZDL89ttvOsddUVEBb29vyGQy5OXl1fkaiYiIGkSfzNYjr8PCwhAbG4ulS5fC29sbR48eRWpqKjw8PAAARUVFKCgoEOs9PT2RmpqKI0eOwNvbG8uWLcPatWsxfvx4sWbgwIHYsWMHtmzZgt69eyMxMRFJSUno37+/WJOVlYW+ffuib9++AB5MvPft2xcffPCBWJOSkoK+ffti1KhRAICJEyeib9++4t8HVlZWSE1NRYcOHTB69Gj07t0bW7duxWeffYaRI0cCeHA5l6SkJAQEBMDLywsffPABwsPD1SboLSwssG/fPtjY2GDQoEEIDQ3Fyy+/jJiYmEadS67wJiIiIiIiMmNJSUmYPXs24uPjMWjQIHz88ccIDg7GDz/8IK6oelh+fj5GjhyJ8PBwbNu2Dd9++y0iIyPRoUMH8U10ZmYmwsLCsGzZMowbNw67du1CaGgojh8/Lr6JLi8vR58+ffDGG2+ovfmude/ePeTk5GDRokXo06cPbt++jdmzZ2PMmDHIyspSqwsKCkJQUBCio6PrfK1//etf4erqijNnzjTllBEREbWIyMhIREZGan0uMTFRY9vQoUORk5NTZ8+QkBCEhITofD4gIEC82aUuU6ZMwZQpU+qsefrpp5GcnKzz+WeffRYnT56sswcAuLu7i6vC9cUJbyIic8OvSBMRERk/A+b1mjVrMHXqVEybNg0AEBsbiwMHDmDDhg1YsWKFRn1CQgLc3d0RGxsLAOjRoweysrIQExMjTlzHxsZi+PDh4gR0dHQ0MjIyEBsbK67UCg4ORnBwsM5xKRQKjeuKrlu3Dv369UNBQYE4GT979mwAwJEjR+p8nf/+979x8OBBJCcn49///nfdJ4WIiKihDHRJE5KO0U5437OVw9JW1qQe9tfvSzQa4B3Xf0jWa+2t9yXp84n9nyXpAwAbMEWSPv8+lyhJHwBI/VJzFYheLjXt9+hhU53q/sSroYRc6caELdK0cX26nzSNAHh8/Ks0jZ6W7jwJ/0+aPrI90vwO4F4ZAIU0vR7FCW8ysMLkPwFtGn49NW36hNX/SX9DyT4cIFkvZA2Wpk8nadoAgKzuxY0Nt9JTokYAJqokaSNbZCVJHwCIXzZFkj5L8UH9RQ0kWyRNn+BlB6RpBGBjr1nS9NG82oXeanqMlqTPVvtQSfr8Dml+vzU0Ma/LysrUNsvlcq03wqqsrER2djbmzZuntj0wMBAnTpzQeojMzEwEBgaqbRsxYgQ2b94MlUoFKysrZGZmYs6cORo1tZPk+iotLYVMJkO7du0atd8vv/yC8PBw7N69G23atGnSGMzVzSS3Juf1069Jt3Je9s8+kvVC+vPS9JEyryX6Nx/bJPx97i5NG1m4NH0AYPemEZL0efnaHkn6AMDmWGn6BK5Ol6YRgG2e0pz0bXGStAEA3BwVJUmfE5Dm5on3UIWNknTSghPeJofX8CYiMjeqJjyIiIjIMJqY125ublAoFOJD20ptACgpKUF1dTWcnJzUtjs5OaG4uFjrPsXFxVrrq6qqUFJSUmeNrp4Ncf/+fcybNw+TJk1q1I2pBEHAlClTEBERAV9fX72PT0REpBXfX5sco13hTUREeqqGfje04k0riYiIDKeJeV1YWKg2KaxtdffDZDL1b84JgqCxrb76R7c3tmddVCoVJk6ciJqaGsTHxzdq33Xr1qGsrKze63sTERHpRZ/M5vvrFsUJbyIic1N7B2l99iMiIiLDaGJe29nZNWgVtKOjIywsLDRWXt+4cUNjhXYtZ2dnrfWWlpZwcHCos0ZXz7qoVCqEhoYiPz8fhw4datTqbgA4dOgQTp48qTHp7+vri9deew2fffZZo8dEREQk0iez+f66RfGSJkRERERERGbK2toaPj4+GjeHTEtLw8CB2q+b6ufnp1F/8OBB+Pr6wsrKqs4aXT11qZ3svnTpEtLT08UJ9cZYu3Ytzpw5g7y8POTl5SE1NRUAkJSUhOXLlze6HxEREZm2Rk94Hz16FKNHj4arqytkMhl2794tPqdSqfD++++jV69esLW1haurK15//XVcv35dyjETEVFdqprwILPBvCYiMnIGzOuoqCh88skn+PTTT3H+/HnMmTMHBQUFiIiIAABER0fj9ddfF+sjIiLw888/IyoqCufPn8enn36KzZs349133xVrZs2ahYMHD2LVqlX473//i1WrViE9PR2zZ88Wa+7evStOQgNAfn4+8vLyUFBQ8OAUVFUhJCQEWVlZ+OKLL1BdXY3i4mIUFxejsrJS7FNcXIy8vDxcvnwZAHD27Fnk5eXh1q1bAAB3d3f07NlTfPzpT38CAHTt2hWdOkl4F8JmwLwmIjIBfH9tcho94V1eXo4+ffogLk7z1q737t1DTk4OFi1ahJycHOzcuRMXL17EmDFjJBksERE1ACe8CcxrIiKjZ8C8DgsLQ2xsLJYuXQpvb28cPXoUqamp8PDwAAAUFRWJk9AA4OnpidTUVBw5cgTe3t5YtmwZ1q5di/Hjx4s1AwcOxI4dO7Blyxb07t0biYmJSEpKQv/+/cWarKws9O3bF3379gXwYOK9b9+++OCDDwAAV69eRUpKCq5evQpvb2+4uLiIjxMnToh9EhIS0LdvX4SHhwMAhgwZgr59+yIlJaXxJ8PIMK+JiEwA31+bnEZfwzs4OBjBwcFan1MoFBpfa1u3bh369euHgoICuLu76zdKIiJqOH3DlYFsVpjXRERGzsB5HRkZicjISK3PJSYmamwbOnQocnJy6uwZEhKCkJAQnc8HBASIN7vUpnPnznU+X2vJkiVYsmRJvXWN7WsMmNdERCZAn8zm++sW1ew3rSwtLYVMJkO7du2a+1BERATwppWkF+Y1EZGBMa9JD8xrIqIWwJtWmpxmnfC+f/8+5s2bh0mTJum803ZFRQUqKirEn8vKyppzSERERPSIhuQ1wMwmIiJqScxrIiKihmn0NbwbSqVSYeLEiaipqUF8fLzOuhUrVkChUIgPNze35hoSEdHjgdfwpkZoaF4DzGwiIkkxr6kRmNdERC2IeW1ymmXCW6VSITQ0FPn5+UhLS6vz0+fo6GiUlpaKj8LCwuYYEhHR40PVhAc9VhqT1wAzm4hIUsxraiDmNRFRC2NemxzJL2lSG8aXLl3C4cOH4eDgUGe9XC6HXC6XehhERI+vauh3vTBeY+yx0ti8BpjZRESSYl5TAzCviYiMgD6ZzbxuUY2e8L579y4uX74s/pyfn4+8vDzY29vD1dUVISEhyMnJwd69e1FdXY3i4mIAgL29PaytraUbORERaafv16f4lSuzwrwmIjJyzGsC85qIyCTok9nM6xbV6AnvrKwsDBs2TPw5KioKADB58mQsWbIEKSkpAABvb2+1/Q4fPoyAgAD9R0pEREQNxrwmIiIyfsxrIiIi6TV6wjsgIACCIOh8vq7niIjIAKqh36fJ/MqVWWFeExEZOeY1gXlNRGQS9Mls5nWLapabVhIRUQvS9w7Serzpjo+Ph6enJ2xsbODj44Njx47VWZ+RkQEfHx/Y2NigS5cuSEhI0KhJTk6Gl5cX5HI5vLy8sGvXrkYdV6VS4f3330evXr1ga2sLV1dXvP7667h+/bpYc+vWLbzzzjvo1q0b2rRpA3d3d8ycOROlpaUax9q3bx/69++P1q1bw9HREa+88or43M2bNxEUFARXV1fI5XK4ublhxowZKCsra9D5IyKix5gB85qIiIiagHltcjjhTURkbvS9g3Qj7yKdlJSE2bNnY8GCBcjNzYW/vz+Cg4NRUFCgtT4/Px8jR46Ev78/cnNzMX/+fMycORPJycliTWZmJsLCwqBUKnHmzBkolUqEhobi1KlTDT7uvXv3kJOTg0WLFiEnJwc7d+7ExYsXMWbMGLHH9evXcf36dcTExODs2bNITEzE/v37MXXqVLUxJycnQ6lU4o033sCZM2fw7bffYtKkSeLzrVq1wtixY5GSkoKLFy8iMTER6enpiIiIaNzJJCKix4+B8pqIiIiaiHltchp9SRMiIjJy+txBuna/RlizZg2mTp2KadOmAQBiY2Nx4MABbNiwAStWrNCoT0hIgLu7O2JjYwEAPXr0QFZWFmJiYjB+/Hixx/DhwxEdHQ0AiI6ORkZGBmJjY7F9+/YGHVehUCAtLU3t2OvWrUO/fv1QUFAAd3d39OzZU22ivWvXrli+fDn+/Oc/o6qqCpaWlqiqqsKsWbOwevVqtYnwbt26if9/+/bt8Ze//EX82cPDA5GRkVi9enXjTiYRET1+DJTXRERE1ET6ZDbzukUZ7YS3Z68bQCu7pjU5It3HKbGYIVkvmb0012EL/5NMkj4AsOSSNH2EuM+kaQRg+/KxkvTxwg+S9AEA4bo053y0678k6QMA3wwIlaTP0DHfSdIHADBfomsNDpbud3z/8aGS9Ok59rQkfarL7uK8JJ20NUeTrgn66OU45HI55HK52rbKykpkZ2dj3rx5atsDAwNx4sQJre0zMzMRGBiotm3EiBHYvHkzVCoVrKyskJmZiTlz5mjU1E6S63NcACgtLYVMJkO7du3qrLGzs4Ol5YNozMnJwbVr19CqVSv07dsXxcXF8Pb2RkxMDJ555hmtPa5fv46dO3di6FBpft9MRekxBeysm9YjLmxq/UUN9E7AAMl6CYul6SM7IE0fAMAOadqsWvGONI0AvL9onTSNBkvTBgAizyVK0mfsM9sl6QMA/zevrSR9/j36lfqLGkg4K00f2T5p+gCATKIvybz4r3RJ+txBM11DmdfwJgMr/UoBO6um9fjsNWneewDAlAFJkvUS5krTR/axNH0AAJ9I0+avyz6UphGAf3wo0R82IdK0AYCXT0nzR9Kr/T+VpA8AbJ/2piR9DvpLM6cBAEK+NH2k/B23D78vSZ/+m6SZi2i2vAZ4DW8TxEuaEBGRGjc3NygUCvGhbbV2SUkJqqur4eTkpLbdyckJxcXFWvsWFxdrra+qqkJJSUmdNbU99Tnu/fv3MW/ePEyaNAl2dto/SL158yaWLVuG6dOni9t++uknAMCSJUuwcOFC7N27F+3bt8fQoUNx69Yttf1fffVVtGnTBk8++STs7OzwyScSvcMhIiIiIiIiokbhhDcRkblp4k2wCgsLUVpaKj5qLy+ijUymvgpfEASNbfXVP7q9IT0belyVSoWJEyeipqYG8fHxWsdUVlaGUaNGwcvLC4sX/7HqpaamBgCwYMECjB8/Hj4+PtiyZQtkMhm++uortR4fffQRcnJysHv3bvz444+IiorSeiwiIiIRb1pJRERkGpjXJsdoL2lCRER6UgHQ52ow/7sKlJ2dnc6V0LUcHR1hYWGhsar6xo0bGquvazk7O2utt7S0hIODQ501tT0bc1yVSoXQ0FDk5+fj0KFDWl/TnTt3EBQUhCeeeAK7du2CldUf3/N1cXEBAHh5eYnb5HI5unTponFjTmdnZzg7O6N79+5wcHCAv78/Fi1aJPYgIiLS0MS8JiIiIgPRJ7OZ1y2KK7yJiMxNdRMeDWRtbQ0fHx+Nm0OmpaVh4MCBWvfx8/PTqD948CB8fX3FiWZdNbU9G3rc2snuS5cuIT09XZxQf1hZWRkCAwNhbW2NlJQU2NjYqD3v4+MDuVyOCxcuqPW9cuUKPDw8tL5G4I9V6xUVFTpriIiIDJHXREREJAHmtcnhhDcRkbkx0Feko6Ki8Mknn+DTTz/F+fPnMWfOHBQUFCAi4sHdxqKjo/H666+L9REREfj5558RFRWF8+fP49NPP8XmzZvx7rvvijWzZs3CwYMHsWrVKvz3v//FqlWrkJ6ejtmzZzf4uFVVVQgJCUFWVha++OILVFdXo7i4GMXFxaisrATwYGV3YGAgysvLsXnzZpSVlYk11dUP/jKxs7NDREQEFi9ejIMHD+LChQv4y1/+AgCYMGECACA1NRVbtmzB999/jytXriA1NRV/+ctfMGjQIHTu3LlxJ5SIiB4vvKQJERGRaTBgXsfHx8PT0xM2Njbw8fHBsWPH6qzPyMiAj48PbGxs0KVLFyQkJGjUJCcnw8vLC3K5HF5eXti1a5fa80ePHsXo0aPh6uoKmUyG3bt3a/TYuXMnRowYAUdHR8hkMuTl5WnUFBcXQ6lUwtnZGba2tnj22Wfx9ddfi89fuXIFU6dOhaenJ1q3bo2uXbti8eLF4vv0WjKZTOOh7XXVhZc0ISIivYSFheHmzZtYunQpioqK0LNnT6Smpoqrn4uKitQu/eHp6YnU1FTMmTMH69evh6urK9auXYvx48eLNQMHDsSOHTuwcOFCLFq0CF27dkVSUhL69+/f4ONevXoVKSkpAABvb2+1MR8+fBgBAQHIzs7GqVOnAABPPfWUWk1+fr44Wb169WpYWlpCqVTi999/R//+/XHo0CG0b98eANC6dWts2rQJc+bMQUVFBdzc3PDKK69g3rx5EpxhIiIiIiIielwkJSVh9uzZiI+Px6BBg/Dxxx8jODgYP/zwA9zd3TXq8/PzMXLkSISHh2Pbtm349ttvERkZiQ4dOojvszMzMxEWFoZly5Zh3Lhx2LVrF0JDQ3H8+HHxfXZ5eTn69OmDN954Q+39+cPKy8sxaNAgTJgwAeHh4VprlEolSktLkZKSAkdHR3z55ZcICwtDVlYW+vbti//+97+oqanBxx9/jKeeegrff/89wsPDUV5ejpiYGLVeW7ZsQVBQkPizQqFo1LnkhDcRkbmphn6fJuvxlavIyEhERkZqfS4xMVFj29ChQ5GTk1Nnz5CQEISEhOh93M6dO4uXFdElICCg3hoAsLKyQkxMjEb41ho2bBhOnDhRbx8iIiINBsxrIiIiagJ9MluPvF6zZg2mTp2KadOmAQBiY2Nx4MABbNiwAStWrNCoT0hIgLu7O2JjYwEAPXr0QFZWFmJiYsSJ69jYWAwfPhzR0dEAHnwTOyMjA7Gxsdi+fTsAIDg4GMHBwXWOTalUAniwSluXzMxMbNiwAf369QMALFy4EB999BFycnLQt29fBAUFqU1id+nSBRcuXMCGDRs03nO3a9cOzs7OdY6pLrykCRGRuVE14UFERESGwbwmIiIyDU3I67KyMrWHrns9VVZWIjs7G4GBgWrbAwMDdS6yyszM1KgfMWIEsrKyoFKp6qxpjoVbgwcPRlJSEm7duoWamhrs2LEDFRUVCAgI0LlPaWkp7O3tNbbPmDEDjo6OeO6555CQkICamppGjYUT3kRE5oY3wSIiIjJ+zGsiIiLT0IS8dnNzg0KhEB/aVmoDQElJCaqrq+Hk5KS23cnJCcXFxVr3KS4u1lpfVVWFkpKSOmt09WyKpKQkVFVVwcHBAXK5HNOnT8euXbvQtWtXrfU//vgj1q1bJ96Pq9ayZcvw1VdfIT09HRMnTsTcuXPx97//vVFj4SVNiIjMTRUAmZ77ERERkWEwr4mIiEyDPpn9v7wuLCyEnZ2duFkul9e5m0ymfiBBEDS21Vf/6PbG9tTXwoULcfv2baSnp8PR0RG7d+/GhAkTcOzYMfTq1Uut9vr16wgKCsKECRPES7g83KdW7X25li5dqra9PpzwJiIiIiIiIiIiIpKYnZ2d2oS3Lo6OjrCwsNBYeX3jxg2NFdq1nJ2dtdZbWlrCwcGhzhpdPfX1448/Ii4uDt9//z2eeeYZAECfPn1w7NgxrF+/HgkJCWLt9evXMWzYMPj5+WHjxo319h4wYADKysrwyy+/NHjcvKQJEZG5qWrCg4iIiAyDeU1ERGQaDJDX1tbW8PHxQVpamtr2tLQ0DBw4UOs+fn5+GvUHDx6Er68vrKys6qzR1VNf9+7dAwC0aqU+1WxhYaF2/e1r164hICAAzz77LLZs2aJRr01ubi5sbGzQrl27Bo+HK7yJiMyNvm+E+QaaiIjIcJjXREREpkGf7NVjn6ioKCiVSvj6+oqrnwsKCsRrXEdHR+PatWvYunUrACAiIgJxcXGIiopCeHg4MjMzsXnzZmzfvl3sOWvWLAwZMgSrVq3C2LFjsWfPHqSnp+P48eNizd27d3H58mXx5/z8fOTl5cHe3h7u7u4AgFu3bqGgoADXr18HAFy4cAHAgxXkzs7O6N69O5566ilMnz4dMTExcHBwwO7du5GWloa9e/cCeLCyOyAgAO7u7oiJicGvv/4qHtPZ2RkA8M0336C4uBh+fn5o3bo1Dh8+jAULFuCtt96q93IwD+OENxGRuamGftcE5U2wiIiIDId5TUREZBr0yWw98josLAw3b97E0qVLUVRUhJ49eyI1NRUeHh4AgKKiIhQUFIj1np6eSE1NxZw5c7B+/Xq4urpi7dq1GD9+vFgzcOBA7NixAwsXLsSiRYvQtWtXJCUloX///mJNVlYWhg0bJv4cFRUFAJg8eTISExMBACkpKXjjjTfEmokTJwIAFi9ejCVLlsDKygqpqamYN28eRo8ejbt37+Kpp57CZ599hpEjRwJ4sLL88uXLuHz5Mjp16qT22muvPW5lZYX4+HhERUWhpqYGXbp0wdKlS/H222836lxywpuIyNxwxRgREZHxY14TERGZBgOt8AaAyMhIREZGan2udvL5YUOHDkVOTk6dPUNCQhASEqLz+YCAAHHCWZcpU6ZgypQpddY8/fTTSE5OblKPoKAgBAUF1VnTEJzwJiIyN3wDTUREZPyY10RERKbBgBPeJA3etJKIiIiIiIiIiIiIzILxrvCOBtC6aS2yPfpIMhQA8Hn6B8l6zTylz8X6NOVckqQNAGBJPV9daLBUaV4bALxauEeaRm4SvTYAssZdMkinV3d9Kk0jADgpzesrq7aWpA8AdMNPkvR57/hfJOkDAGFIkqSPP45J0qcS93Fekk5aVAHQ59eC1wQlfX2DJn+EPiN0syRDAYDRz6dI1ku2/IY0jZr+rTyRsEWaPi6YI00jAHCWpo0wQpo+ACCT6FdqT8mr0jQCIAyVpo+sU/01DfUO/iFJn5ujFkvSBwDeH7VEkj79cUqSPvegApAuSS81zGsysLIM6Hfd+IdMzvqXJGMBgNd7SNdL9qFE7/m6S9MGAITT0vRpW6790gZ6eUKaNpLm9bfS9Nl+5k1pGgEQJJpKkkn4+zQfH0jS58b0jyTpAwBzpv9dkj4BOCJJn3uoAnBIkl4a9Mls5nWLMt4JbyIi0o++wcpAJiIiMhzmNRERkWnQJ3uZ1y2KE95EROaGK8aIiIiMH/OaiIjINHCFt8nhhDcRkbnhG2giIiLjx7wmIiIyDZzwNjm8aSURERERERERERERmQVOeBMRmZsqACo9HlUtMVgiIqLHlIHzOj4+Hp6enrCxsYGPjw+OHav7RtwZGRnw8fGBjY0NunTpgoSEBI2a5ORkeHl5QS6Xw8vLC7t27VJ7/ujRoxg9ejRcXV0hk8mwe/dutedVKhXef/999OrVC7a2tnB1dcXrr7+O69evq9Vt3LgRAQEBsLOzg0wmw2+//ab2/JUrVzB16lR4enqidevW6Nq1KxYvXozKysqGnyAiIiJd9Mlsvr9uUY2e8K7vj5aHTZ8+HTKZDLGxsU0YIhERNUp1Ex5kNpjXRERGzoB5nZSUhNmzZ2PBggXIzc2Fv78/goODUVBQoLU+Pz8fI0eOhL+/P3JzczF//nzMnDkTycnJYk1mZibCwsKgVCpx5swZKJVKhIaG4tSpU2JNeXk5+vTpg7i4OK3HuXfvHnJycrBo0SLk5ORg586duHjxIsaMGaNRFxQUhPnz52vt89///hc1NTX4+OOPce7cOXz00UdISEjQWW9MmNdERCaA769NTqOv4V37R8sbb7yB8ePH66zbvXs3Tp06BVdX1yYNkIiIGqkK+n1/p0bqgVBLYl4TERk5A+b1mjVrMHXqVEybNg0AEBsbiwMHDmDDhg1YsWKFRn1CQgLc3d3FidUePXogKysLMTExYqbExsZi+PDhiI6OBgBER0cjIyMDsbGx2L59OwAgODgYwcHBOselUCiQlpamtm3dunXo168fCgoK4O7uDgCYPXs2AODIkSNa+wQFBSEoKEj8uUuXLrhw4QI2bNiAmJiYes5Oy2JeExGZAH0ym++vW1SjJ7zr+6MFAK5du4YZM2bgwIEDGDVqlN6DIyIiPXDCm8C8JiIyek3M67KyMrXNcrkccrlco7yyshLZ2dmYN2+e2vbAwECcOHFC6yEyMzMRGBiotm3EiBHYvHkzVCoVrKyskJmZiTlz5mjUNHX1cWlpKWQyGdq1a9fkPvb29k3qYQjMayIiE8AJb5Mj+TW8a2pqoFQq8d577+GZZ56pt76iogJlZWVqDyIiImpejc1rgJlNRGRM3NzcoFAoxIe2ldoAUFJSgurqajg5Oaltd3JyQnFxsdZ9iouLtdZXVVWhpKSkzhpdPRvi/v37mDdvHiZNmgQ7Ozu9+/z4449Yt24dIiIi9O5hLJjXREREjdfoFd71WbVqFSwtLTFz5swG1a9YsQIffvih1MMgInp8qcAV3lSvxuY1wMwmIpJUE/O6sLBQbVJY2+ruh8lkMrWfBUHQ2FZf/aPbG9uzLiqVChMnTkRNTQ3i4+P16gEA169fR1BQECZMmCBewsWUMa+JiIyAPpnN99ctStIV3tnZ2fh//+//ITExscF/6ERHR6O0tFR8FBYWSjkkIqLHTw30u6EGA/mxoU9eA8xsIiJJNTGv7ezs1B66JrwdHR1hYWGhsfL6xo0bGiu0azk7O2utt7S0hIODQ501unrWRaVSITQ0FPn5+UhLS9N7dff169cxbNgw+Pn5YePGjXr1MCbMayIiI6FPZvP9dYuSdML72LFjuHHjBtzd3WFpaQlLS0v8/PPPmDt3Ljp37qx1H7lcrvHHGhERNUFVEx70WNAnrwFmNhGRpAyU19bW1vDx8dG4OWRaWhoGDhyodR8/Pz+N+oMHD8LX1xdWVlZ11ujqqUvtZPelS5eQnp4uTqg31rVr1xAQEIBnn30WW7ZsQatWkl+90+CY10RERoLvr02OpJc0USqVePHFF9W2jRgxAkqlEm+88YaUhyIiIl2qAOjzbWJB6oGQsWJeExEZAQPmdVRUFJRKJXx9fcXVzwUFBeI1rqOjo3Ht2jVs3boVABAREYG4uDhERUUhPDwcmZmZ2Lx5M7Zv3y72nDVrFoYMGYJVq1Zh7Nix2LNnD9LT03H8+HGx5u7du7h8+bL4c35+PvLy8mBvbw93d3dUVVUhJCQEOTk52Lt3L6qrq8VV4/b29rC2tgbw4HrhxcXFYq+zZ8+ibdu2cHd3h729Pa5fv46AgAC4u7sjJiYGv/76q3hMZ2fnxp8wI8G8JiIyEvpkNt9ft6hGT3jX90fLo5/IW1lZwdnZGd26dWv6aImIqH4qcMKbmNdERMbOgHkdFhaGmzdvYunSpSgqKkLPnj2RmpoKDw8PAEBRUREKCgrEek9PT6SmpmLOnDlYv349XF1dsXbtWowfP16sGThwIHbs2IGFCxdi0aJF6Nq1K5KSktC/f3+xJisrC8OGDRN/joqKAgBMnjwZiYmJuHr1KlJSUgAA3t7eamM+fPgwAgICAAAJCQlq16QeMmQIAGDLli2YMmUKDh48iMuXL+Py5cvo1KmT+ukSjPsPHOY1EZEJ0CezjTt+zF6jJ7zr+6OFiIiIWh7zmoiIHhYZGYnIyEitz2nLhaFDhyInJ6fOniEhIQgJCdH5fEBAQJ0Tzp07d27QhPSSJUuwZMkSnc9PmTIFU6ZMqbePMWJeExERSa/RE971/dHyqCtXrjT2EERE1BTV4ApvYl4TERk75jWBeU1EZBL0yWzmdYuS9BreRERkJBiuRERExo95TUREZBqY2SbF9G9dTUREREREREREREQEI17hXdpfAbsnmtZDZiXdxy/fqXpJ1qtVtjTj+qeg/Rp8+rBFZ0n69Ogq3Tm/4dZWmj7oKkkfAEDsfyVp8+WIqZL0AQAkSNPL7ltJ2gAAinwlOufXpWkDAJ2fPy9Jn3b4TZI+1SiXpE9Li4+Px+rVq1FUVIRnnnkGsbGx8Pf311mfkZGBqKgonDt3Dq6urvjrX/+KiIgItZrk5GQsWrQIP/74I7p27Yrly5dj3LhxDT6uSqXCwoULkZqaip9++gkKhQIvvvgiVq5cCVdXVwDArVu3sHjxYhw8eBCFhYVwdHTEyy+/jGXLlkGhUKgda9++fVi6dCn+85//wNbWFkOGDMHOnTsBAGfOnMHKlStx/PhxlJSUoHPnzoiIiMCsWbOafG5NiSoHUNk1rYfVP6QZCwBcfF66G3n9Pluf6w1ommG7TpI+ACDbPEOSPkKwdPm49+3nJekjO/N/kvQBgGVT35Wkz9fQfX3ixpIdGCBJn883SDemGEhznuyX3pekDwCsyl8iSZ+0LYMl6dMKVZL0IWpplb/aosKuibk26a40gwGQ8mWgZL2EN6XJ6+lusZL0AQDZx9L8PSgM7ShJHwD4au5LkvSRZX8jSR8AWDdomiR9lmO+JH0AQJbcRZI+sZumS9IHANZDmvmfv3+yTJI+APDRIWnOecaX/STpY4VqSfqQeeAKbyIi0ktSUhJmz56NBQsWIDc3F/7+/ggODkZBQYHW+vz8fIwcORL+/v7Izc3F/PnzMXPmTCQnJ4s1mZmZCAsLg1KpxJkzZ6BUKhEaGopTp041+Lj37t1DTk4OFi1ahJycHOzcuRMXL17EmDFjxB7Xr1/H9evXERMTg7NnzyIxMRH79+/H1KnqHyAlJydDqVTijTfewJkzZ/Dtt99i0qRJ4vPZ2dno0KEDtm3bhnPnzmHBggWIjo5GXFycJOeYiIiIiIiIiBrHaFd4ExGRcVuzZg2mTp2KadMerMqIjY3FgQMHsGHDBqxYsUKjPiEhAe7u7oiNjQUA9OjRA1lZWYiJicH48ePFHsOHD0d0dDQAIDo6GhkZGYiNjcX27dsbdFyFQoG0tDS1Y69btw79+vVDQUEB3N3d0bNnT7WJ9tqV5H/+859RVVUFS0tLVFVVYdasWVi9erXaRHi3bn+sHn7zzTfVjtOlSxdkZmZi586dmDFDmlW4RERERERERNRwXOFNRGR2VE14NExlZSWys7MRGKj+VdTAwECcOHFC6z6ZmZka9SNGjEBWVhZUKlWdNbU99TkuAJSWlkImk6Fdu3Z11tjZ2cHS8sFnwTk5Obh27RpatWqFvn37wsXFBcHBwTh37pzOHrV97O3t66whIiIyRF4TERGRFJjXpoYT3kREZqeqCQ+grKxM7VFRUaFxhJKSElRXV8PJyUltu5OTE4qLi7WOqri4WGt9VVUVSkpK6qyp7anPce/fv4958+Zh0qRJsLPTfqHpmzdvYtmyZZg+/Y/r7P30008AgCVLlmDhwoXYu3cv2rdvj6FDh+LWrVta+2RmZuJf//qXWh8iIiLtmpbXREREZCiGy+v4+Hh4enrCxsYGPj4+OHbsWJ31GRkZ8PHxgY2NDbp06YKEhASNmuTkZHh5eUEul8PLywu7du1Se/7o0aMYPXo0XF1dIZPJsHv3bo0eO3fuxIgRI+Do6AiZTIa8vDyNmuLiYiiVSjg7O8PW1hbPPvssvv76a7Wa27dvQ6lUQqFQQKFQQKlU4rffflOrKSgowOjRo2FrawtHR0fMnDkTlZWVdZ6HR3HCm4jI7DRtxZibm5sYPgqFQuvlSWrJZOo3CBIEQWNbffWPbm9Iz4YeV6VSYeLEiaipqUF8fLzWMZWVlWHUqFHw8vLC4sWLxe01NTUAgAULFmD8+PHw8fHBli1bIJPJ8NVXX2n0OXfuHMaOHYsPPvgAw4cP13osIiKiP3CFNxERkWkwTF631H2yysvL0adPnzrvRVVeXo5BgwZh5cqVOmuUSiUuXLiAlJQUnD17Fq+88grCwsKQm5sr1kyaNAl5eXnYv38/9u/fj7y8PCiVSvH56upqjBo1CuXl5Th+/Dh27NiB5ORkzJ07t0HnsBav4U1ERGoKCwvVVkLL5XKNGkdHR1hYWGisqr5x44bG6utazs7OWustLS3h4OBQZ01tz8YcV6VSITQ0FPn5+Th06JDW1d137txBUFAQnnjiCezatQtWVlbicy4uLgAALy8vtXPRpUsXjT84fvjhBzz//PMIDw/HwoULtb5+IiIiIiIiIl1a6j5ZwcHBCA4OrnNstZPSV65c0VmTmZmJDRs2oF+/fgCAhQsX4qOPPkJOTg769u2L8+fPY//+/Th58iT69+8PANi0aRP8/Pxw4cIFdOvWDQcPHsQPP/yAwsJCuLq6AgD++c9/YsqUKVi+fLnOb20/iiu8iYjMTtO+Im1nZ6f20DbhbW1tDR8fH42bQ6alpWHgwIFaR+Xn56dRf/DgQfj6+ooTzbpqans29Li1k92XLl1Cenq6OKH+sLKyMgQGBsLa2hopKSmwsbFRe97HxwdyuRwXLlxQ63vlyhV4eHiI286dO4dhw4Zh8uTJWL58udbXTkREpImXNCEiIjINzXvJUKDl7pMlpcGDByMpKQm3bt1CTU0NduzYgYqKCgQEBIhjUSgU4mQ3AAwYMAAKhUIcT2ZmJnr27ClOdteOt6KiAtnZ2Q0eC1d4ExGZnSro93Xnxr2BjoqKglKphK+vL/z8/LBx40YUFBQgIiICwINPjq9du4atW7cCACIiIhAXF4eoqCiEh4cjMzMTmzdvFj9VBoBZs2ZhyJAhWLVqFcaOHYs9e/YgPT0dx48fb/Bxq6qqEBISgpycHOzduxfV1dXiinB7e3tYW1vjzp07CAwMxL1797Bt2zbxjw8A6NChAywsLGBnZ4eIiAgsXrwYbm5u8PDwwOrVqwEAEyZMAPDHZHdgYCCioqLE41hYWKBDhw6N/i9ARESPE8PkNRERETWVPpn9IK/d3NzUti5evBhLlizRqG6O+2S5uLjUe58sKSUlJSEsLAwODg6wtLREmzZtsGvXLnTt2lUcb8eOHTX269ixozgebeNt3749rK2tGzVmTngTEZkdfa/v2bh9wsLCcPPmTSxduhRFRUXo2bMnUlNTxdXPRUVFapf+8PT0RGpqKubMmYP169fD1dUVa9euFb9qBQADBw7Ejh07sHDhQixatAhdu3ZFUlKS2ifA9R336tWrSElJAQB4e3urjfnw4cMICAhAdna2eM2yp556Sq0mPz8fnTt3BgCsXr0alpaWUCqV+P3339G/f38cOnQI7du3BwB89dVX+PXXX/HFF1/giy++EHt4eHjU+VUvIiIiQ+U1ERERNZU+mf2gviGXDH1YS90nSwoLFy7E7du3kZ6eDkdHR+zevRsTJkzAsWPH0KtXL61j0TaehtTUhxPeRERmR9+vOzd+n8jISERGRmp9LjExUWPb0KFDkZOTU2fPkJAQhISE6H3czp07iyGvS0BAQL01AGBlZYWYmBjExMRofX7JkiVaP50nIiKqn+HymoiIiJpCn8xWv2RofVrqPllS+fHHHxEXF4fvv/8ezzzzDACgT58+OHbsGNavX4+EhAQ4Ozvjl19+0dj3119/Fcfj7OysdkNNALh9+zZUKlWjxsxreBMRmZ3ar1s19sE30ERERIbDvCYiIjIN+mR24/K6pe6TJZV79+4BAFq1Up9qtrCwQE1NjTiW0tJSfPfdd+Lzp06dQmlpqTgePz8/fP/99ygqKlIbr1wuh4+PT4PHwxXeRERERERERERERC2ope6TdffuXVy+fFn8OT8/H3l5ebC3t4e7uzsA4NatWygoKMD169cBABcuXADwYEW2s7MzunfvjqeeegrTp09HTEwMHBwcsHv3bqSlpWHv3r0AgB49eiAoKAjh4eH4+OOPAQBvvfUWXnrpJXTr1g3Ag5t0enl5QalUYvXq1bh16xbeffddhIeHN2ilfC2u8CYiMjv63kGaK8aIiIgMh3lNRERkGgyT12FhYYiNjcXSpUvh7e2No0ePNug+WUeOHIG3tzeWLVum8z5ZW7ZsQe/evZGYmKhxn6ysrCz07dsXffv2BfBg4r1v37744IMPxJqUlBT07dsXo0aNAgBMnDgRffv2RUJCAoAHlwNNTU1Fhw4dMHr0aPTu3Rtbt27FZ599hpEjR4p9vvjiC/Tq1QuBgYEIDAxE79698fnnn4vPW1hYYN++fbCxscGgQYMQGhqKl19+WedlRnXhCm8iIrPDm2AREREZP+Y1ERGRadD/ppWN1RL3yWrIPa6mTJmCKVOm1Fnz9NNPIzk5uc4ae3t7bNu2rc4ad3d3cVW4vjjhTURkdngTLCIiIuPHvCYiIjIN+t+0kloGJ7yJiMxO7Q019NmPiIiIDIN5TUREZBr0yWzmdUviNbyJiIiIiIiIiIiIyCwY7wrvCgBWTWsheMokGQoADMT/SdbrLZ//J0mfe2gjSR8AmAVpxjS22/b6ixooCmsk6bOtbbgkfQDgpTtfSdJnyoF4SfoAQOIN7dd2arSG3+y2fkuladP/yyPSNAIwCV9K0uc3tJOkTyXu43tJOmnDr0iTYVnlAVa2TWxyXYqRPOCFH6RrJpGz6CVZr4NT/SXpMwXSZdGmUmmy6GAfaV4bAAx/5Xj9RQ2w0PufkvQBANnYuq+N2FCzqqX5uw0AbuZ2kqTPVx+8JEkfAJhwsGnXbKx1WSbN78DvknTRhnlNhuX4n3LYPdHEJt5SjOSBgTghWa/79tL0kTKvj0zvX39RA0RCuhxaf2uuJH0u+rhJ0gcAnn7lqiR9ZvhulqQPAMhekCav37u5WpI+AFAhU0jSZ++05yXpAwAvuR+SpE++7DtJ+jRfXgO8pInpMd4JbyIi0hNvgkVERGT8mNdERESmwXA3rSRpcMKbiMjscMUYERGR8WNeExERmQau8DY1nPAmIjI7vAkWERGR8WNeExERmQbetNLU8KaVRERERERERERERGQWuMKbiMjs8CvSRERExo95TUREZBp4SRNT0+gV3kePHsXo0aPh6uoKmUyG3bt3a9ScP38eY8aMgUKhQNu2bTFgwAAUFBRIMV4iIqqXqgkPMhfMayIiY8e8JuY1EZFpYF6bmkZPeJeXl6NPnz6Ii4vT+vyPP/6IwYMHo3v37jhy5AjOnDmDRYsWwcbGpsmDJSKihuAbaGJeExEZP+Y1Ma+JiEwD89rUNPqSJsHBwQgODtb5/IIFCzBy5Ej84x//ELd16dJFv9EREZEe+BVpYl4TERk/5jUxr4mITAMvaWJqJL1pZU1NDfbt24c//elPGDFiBDp27Ij+/ftr/VpWrYqKCpSVlak9iIioKWrvIN3YBwP5caFPXgPMbCIiaTGvqW7MayIiY6FPZjOvW5KkE943btzA3bt3sXLlSgQFBeHgwYMYN24cXnnlFWRkZGjdZ8WKFVAoFOLDzc1NyiERERHRI/TJa4CZTUREZEjMayIiIv00+pImdampqQEAjB07FnPmzAEAeHt748SJE0hISMDQoUM19omOjkZUVJT4c1lZGQOZiKhJ+BVpqps+eQ0ws4mIpMW8proxr4mIjAUvaWJqJJ3wdnR0hKWlJby8vNS29+jRA8ePH9e6j1wuh1wul3IYRESPORX0++edN9V4XOiT1wAzm4hIWsxrqhvzmojIWOiT2czrliTpJU2sra3x3HPP4cKFC2rbL168CA8PDykPRUREOlU14UGPA+Y1EZExMGxex8fHw9PTEzY2NvDx8cGxY8fqrM/IyICPjw9sbGzQpUsXJCQkaNQkJyfDy8sLcrkcXl5e2LVrl9rzR48exejRo+Hq6gqZTKZx7WmVSoX3338fvXr1gq2tLVxdXfH666/j+vXranUbN25EQEAA7OzsIJPJ8Ntvv2mM5fbt21AqleJlPJRKpdY6U8K8JiIyFnx/bWoaPeF99+5d5OXlIS8vDwCQn5+PvLw8FBQUAADee+89JCUlYdOmTbh8+TLi4uLwzTffIDIyUtKBExGRLrwJFjGviYiMn+HyOikpCbNnz8aCBQuQm5sLf39/BAcHi5nwqPz8fIwcORL+/v7Izc3F/PnzMXPmTCQnJ4s1mZmZCAsLg1KpxJkzZ6BUKhEaGopTp06JNeXl5ejTpw/i4uK0HufevXvIycnBokWLkJOTg507d+LixYsYM2aMRl1QUBDmz5+v8zVOmjQJeXl52L9/P/bv34+8vDwolcrGnKYWwbwmIjIFvGmlqWn0d+iysrIwbNgw8efaa4NNnjwZiYmJGDduHBISErBixQrMnDkT3bp1Q3JyMgYPHizdqImIiKhOzGsiIqq1Zs0aTJ06FdOmTQMAxMbG4sCBA9iwYQNWrFihUZ+QkAB3d3fExsYCeHAJjaysLMTExGD8+PFij+HDhyM6OhrAg+tGZ2RkIDY2Ftu3bwcABAcHIzg4WOe4FAoF0tLS1LatW7cO/fr1Q0FBAdzd3QEAs2fPBgAcOXJEa5/z589j//79OHnyJPr37w8A2LRpE/z8/HDhwgV069atAWepZTCviYiIpNfoCe+AgAAIglBnzZtvvok333xT70EREVFT8CZYxLwmIjJ+TcvrsrIyta26rttcWVmJ7OxszJs3T217YGAgTpw4ofUImZmZCAwMVNs2YsQIbN68GSqVClZWVsjMzBRvpPhwTe0kub5KS0shk8nQrl27Bu+TmZkJhUIhTnYDwIABA6BQKHDixAmjnvBmXhMRmQLetNLUSHoNbyIiMgb6fD269kFERESG0bS8dnNzE69XrVAotK7UBoCSkhJUV1fDyclJbbuTkxOKi4u17lNcXKy1vqqqCiUlJXXW6OrZEPfv38e8efMwadIk2NnZNXi/4uJidOzYUWN7x44dmzQeIiKiB/j+2tToc1vwZlX76XZZuQTNqiXo8T9VkgzogUrcl6TPfVRI0gcAqnBPkj4qifoAQCV+l6aRUFZ/TQOpyqR5fZK9NgBldyRqJN1/Osn+Xa+W8H93FRL970Wq//1Wlj3oU9+KHv2UQ79Pk6X7N4UeD2JmS/HvR6UEPf7nTpl0/7uykuifoepq6f49K5dotYikWSRR1JbLpFsJUybVewxp/tl/4K40J0qQLPyBsrvS9Lkn2QmX6H0AINlveO2vgPSZ3bS8LiwsVJsU1ra6+2EymUztZ0EQNLbVV//o9sb2rItKpcLEiRNRU1OD+Pj4Ru+v7bhNGY85kfQ9toT/JkqZ14JE/25USZrX0kxISPX+A5Aur+9a1kjTCEaa1+US5fUd6eYipOp0z1LCv7WMLK9r+xjPe2y+v25JRjfhfefOgz/g3V5s4YE8SjGm/poGOi1ZJylpv5GMeZglWacDCslaSWZ7Sw+gOX09SrJW2ZJ1ktadO3egUEjzi2VtbQ1nZ2cUF3+kdw9nZ2dYW1tLMh4yf2JmT2zhgTzqy9stPQItpPv3bJxknU5K1km6LJJuTJLZK2GvFdL8ey/lb7h0f9ockKzTFMk6SUuqzJYqrx0dHWFjY1NvraOjIywsLDRWOt+4cUNjhfbD/bXVW1pawsHBoc4aXT3rolKpEBoaivz8fBw6dKhRq7trx/LLL79obP/111/1Go+5EfM6qIUH8qjF0k0uS0e69/3G+E5ms2SdrkvWSTJS5vUyadJRygtatJOs01HJOhkrY3qPzffXLcfoJrxdXV1RWFiItm3b1vlpfFlZGdzc3DRWNhg7jtvwTHXsHLfhGXLsgiDgzp07cHV1laynjY0N8vPzUVmp/1JZa2vrBr15JgIaltmm+m+CqY4bMN2xc9yGZ6pjN/S4pc5sQ+e1tbU1fHx8kJaWhnHj/vjILC0tDWPHjtW6j5+fH7755hu1bQcPHoSvry+srKzEmrS0NLXreB88eBADBw5s1Gupney+dOkSDh8+LE6oN4afnx9KS0vx3XffoV+/fgCAU6dOobS0tNHjMUfmnNeA6Y6d4zY8Ux07x90wxvgem++vW47RTXi3atUKnTp1anC9nZ2dSf0PvhbHbXimOnaO2/AMNXapPnV+mI2NDQOVDKYxmW2q/yaY6rgB0x07x214pjp2Q45b6sw2dF5HRUVBqVTC19cXfn5+2LhxIwoKChAREQEAiI6OxrVr17B161YAQEREBOLi4hAVFYXw8HBkZmZi8+bN2L79j+9zzJo1C0OGDMGqVaswduxY7NmzB+np6Th+/LhYc/fuXVy+fFn8OT8/H3l5ebC3t4e7uzuqqqoQEhKCnJwc7N27F9XV1eKqcXt7e3FVXHFxMYqLi8VeZ8+eRdu2beHu7g57e3v06NEDQUFBCA8Px8cffwwAeOutt/DSSy8Z9Q0rDeVxyGvAdMfOcRueqY6d464f32NTLaOb8CYiIiIiIiLphIWF4ebNm1i6dCmKiorQs2dPpKamwsPDAwBQVFSEgoICsd7T0xOpqamYM2cO1q9fD1dXV6xduxbjx48XawYOHIgdO3Zg4cKFWLRoEbp27YqkpCT0799frMnKysKwYcPEn6OiogAAkydPRmJiIq5evYqUlBQAgLe3t9qYDx8+jICAAABAQkICPvzwQ/G5IUOGAAC2bNmCKVOmAAC++OILzJw5E4GBgQCAMWPGIC7OnC/bSERERLpwwpuIiIiIiMjMRUZGIjIyUutziYmJGtuGDh2KnJycOnuGhIQgJCRE5/MBAQF13jysc+fODbq52JIlS7BkyZI6a+zt7bFt27Z6exEREZH5a9XSA9CXXC7H4sWL670bubHhuA3PVMfOcRueKY+dyFiZ6v+uTHXcgOmOneM2PFMdu6mOm8iYmfL/rkx17By34Znq2DluosaTCQ35SJ2IiIiIiIiIiIiIyMiZ7ApvIiIiIiIiIiIiIqKHccKbiIiIiIiIiIiIiMwCJ7yJiIiIiIiIiIiIyCxwwpuIiIiIiIiIiIiIzIJRT3jHx8fD09MTNjY28PHxwbFjx+qsz8jIgI+PD2xsbNClSxckJCQYaKQPrFixAs899xzatm2Ljh074uWXX8aFCxfq3OfIkSOQyWQaj//+978GGjWwZMkSjeM7OzvXuU9Ln+tanTt31nr+3n77ba31LXW+jx49itGjR8PV1RUymQy7d+9We14QBCxZsgSurq5o3bo1AgICcO7cuXr7Jicnw8vLC3K5HF5eXti1a5fBxq1SqfD++++jV69esLW1haurK15//XVcv369zp6JiYla/xvcv3/fYGMHgClTpmiMYcCAAfX2be5zTmSKmNeGY6qZzbxu/uww1cxmXhMZjqnlNWC6mc28bv5zbaqZzbxWx7ym5mK0E95JSUmYPXs2FixYgNzcXPj7+yM4OBgFBQVa6/Pz8zFy5Ej4+/sjNzcX8+fPx8yZM5GcnGywMWdkZODtt9/GyZMnkZaWhqqqKgQGBqK8vLzefS9cuICioiLx8fTTTxtgxH945pln1I5/9uxZnbXGcK5rnT59Wm3caWlpAIAJEybUuZ+hz3d5eTn69OmDuLg4rc//4x//wJo1axAXF4fTp0/D2dkZw4cPx507d3T2zMzMRFhYGJRKJc6cOQOlUonQ0FCcOnXKIOO+d+8ecnJysGjRIuTk5GDnzp24ePEixowZU29fOzs7tfNfVFQEGxsbycZd39hrBQUFqY0hNTW1zp6GOOdEpoZ5bdi8Bkwzs5nXzZ8dpprZzGsiwzDFvAZMO7OZ183LVDObef0H5jU1K8FI9evXT4iIiFDb1r17d2HevHla6//6178K3bt3V9s2ffp0YcCAAc02xvrcuHFDACBkZGTorDl8+LAAQLh9+7bhBvaIxYsXC3369GlwvTGe61qzZs0SunbtKtTU1Gh93hjONwBh165d4s81NTWCs7OzsHLlSnHb/fv3BYVCISQkJOjsExoaKgQFBaltGzFihDBx4kTJxywImuPW5rvvvhMACD///LPOmi1btggKhULawdVD29gnT54sjB07tlF9DH3OiUwB89qwzCWzmdfNmx2mmtnMa6LmYw55LQimk9nMa8My1cxmXjOvqfkY5QrvyspKZGdnIzAwUG17YGAgTpw4oXWfzMxMjfoRI0YgKysLKpWq2cZal9LSUgCAvb19vbV9+/aFi4sLXnjhBRw+fLi5h6bh0qVLcHV1haenJyZOnIiffvpJZ60xnmvgwe/Ntm3b8Oabb0Imk9VZ29Ln+2H5+fkoLi5WO6dyuRxDhw7V+fsO6P7vUNc+za20tBQymQzt2rWrs+7u3bvw8PBAp06d8NJLLyE3N9cwA3zEkSNH0LFjR/zpT39CeHg4bty4UWe9MZ5zopbEvG6Z/DD1zGZeG0d2mFJmM6+JmsZc8howrcxmXrccc8ps5jWRfoxywrukpATV1dVwcnJS2+7k5ITi4mKt+xQXF2utr6qqQklJSbONVRdBEBAVFYXBgwejZ8+eOutcXFywceNGJCcnY+fOnejWrRteeOEFHD161GBj7d+/P7Zu3YoDBw5g06ZNKC4uxsCBA3Hz5k2t9cZ2rmvt3r0bv/32G6ZMmaKzxhjO96Nqf6cb8/teu19j92lO9+/fx7x58zBp0iTY2dnprOvevTsSExORkpKC7du3w8bGBoMGDcKlS5cMOFogODgYX3zxBQ4dOoR//vOfOH36NJ5//nlUVFTo3MfYzjlRS2NeGz4/zCGzmdctnx2mlNnMa6KmM4e8Bkwrs5nXLZfXgPlkNvOaSH+WLT2Aujz6KaIgCHV+sqitXtt2Q5gxYwb+85//4Pjx43XWdevWDd26dRN/9vPzQ2FhIWJiYjBkyJDmHiaAB/8w1erVqxf8/PzQtWtXfPbZZ4iKitK6jzGd61qbN29GcHAwXF1dddYYw/nWpbG/7/ru0xxUKhUmTpyImpoaxMfH11k7YMAAtZtXDBo0CM8++yzWrVuHtWvXNvdQRWFhYeL/37NnT/j6+sLDwwP79u3DK6+8onM/YznnRMaEeW24/DCHzGZet2x2mFpmM6+JpGPKeQ2YVmYzr1s+rwHTzmzmNVHTGOUKb0dHR1hYWGh8qnPjxg2NT39qOTs7a623tLSEg4NDs41Vm3feeQcpKSk4fPgwOnXq1Oj9BwwYYPDVrg+ztbVFr169dI7BmM51rZ9//hnp6emYNm1ao/dt6fNde7fuxvy+1+7X2H2ag0qlQmhoKPLz85GWllbnJ8/atGrVCs8991yL/jcAHqxO8PDwqHMcxnLOiYwF87pl8wMwvcxmXjdsn+ZiDpnNvCZqPFPPa8D0M5t5bVimntnMa+Y1NZ1RTnhbW1vDx8dHvCNwrbS0NAwcOFDrPn5+fhr1Bw8ehK+vL6ysrJptrA8TBAEzZszAzp07cejQIXh6eurVJzc3Fy4uLhKPruEqKipw/vx5nWMwhnP9qC1btqBjx44YNWpUo/dt6fPt6ekJZ2dntXNaWVmJjIwMnb/vgO7/DnXtI7XaIL506RLS09P1+mNMEATk5eW16H8DALh58yYKCwvrHIcxnHMiY8K8btn8AEwvs5nXD7REdphLZjOviRrPVPMaMJ/MZl4blilnNvOaeU0SMdDNMRttx44dgpWVlbB582bhhx9+EGbPni3Y2toKV65cEQRBEObNmycolUqx/qeffhLatGkjzJkzR/jhhx+EzZs3C1ZWVsLXX39tsDH/5S9/ERQKhXDkyBGhqKhIfNy7d0+seXTcH330kbBr1y7h4sWLwvfffy/MmzdPACAkJycbbNxz584Vjhw5Ivz000/CyZMnhZdeeklo27atUZ/rh1VXVwvu7u7C+++/r/GcsZzvO3fuCLm5uUJubq4AQFizZo2Qm5sr3ml55cqVgkKhEHbu3CmcPXtWePXVVwUXFxehrKxM7KFUKtXuov7tt98KFhYWwsqVK4Xz588LK1euFCwtLYWTJ08aZNwqlUoYM2aM0KlTJyEvL0/td76iokLnuJcsWSLs379f+PHHH4Xc3FzhjTfeECwtLYVTp05JNu76xn7nzh1h7ty5wokTJ4T8/Hzh8OHDgp+fn/Dkk0+2+DknMjXMa8PltSCYdmYzr5s3O0w1s5nXRIZhinktCKab2czr5j/XpprZzGvmNRmG0U54C4IgrF+/XvDw8BCsra2FZ599VsjIyBCfmzx5sjB06FC1+iNHjgh9+/YVrK2thc6dOwsbNmww6HgBaH1s2bJF57hXrVoldO3aVbCxsRHat28vDB48WNi3b59Bxx0WFia4uLgIVlZWgqurq/DKK68I586d0zlmQWj5c/2wAwcOCACECxcuaDxnLOf78OHDWn83Jk+eLAiCINTU1AiLFy8WnJ2dBblcLgwZMkQ4e/asWo+hQ4eK9bW++uoroVu3boKVlZXQvXt3yf+wqGvc+fn5On/nDx8+rHPcs2fPFtzd3QVra2uhQ4cOQmBgoHDixAlJx13f2O/duycEBgYKHTp0EKysrAR3d3dh8uTJQkFBgVqPljjnRKaIeW04ppzZzOvmzQ5TzWzmNZHhmFpeC4LpZjbzuvmZamYzryerbWNeU3ORCcL/7oRARERERERERERERGTCjPIa3kREREREREREREREjcUJbyIiIiIiIiIiIiIyC5zwJiIiIiIiIiIiIiKzwAlvIiIiIiIiIiIiIjILnPAmIiIiIiIiIiIiIrPACW8iIiIiIiIiIiIiMguc8CYiIiIiIiIiIiIis8AJbyIiIiIiIiIiIiIyC5zwJiIiIiIiIiIiIiKzwAlvIiIiIiIiIiIiIjILnPAmIiIiIiIiIiIiIrPACW8iIiIiIiIiIiIiMguc8CYiIiIiIiIiIiIis8AJbyIiIiIiIiIiIiIyC5zwJiIiIiIiIiIiIiKzwAlvIiIiIiIiIiIiIjILnPAmIiIiIiIiIiIiIrPACW8iIiIiIiIiIiIiMguc8CYiIiIiIiIiIiIis8AJbyIiIiIiIiIiIiIyC5zwJiIiIiIiIiIiIiKzwAlvI5SYmAiZTKb26NChAwICArB37169+8bHxyMxMVG6gUqo9jVfuXKl0fsuWbIEMplM+kFJyBTGaCg//PAD5HI5ZDIZsrKyWno4RER6Y143jilkoSmMsTk9+vtc+1i5cmVLD42ISG/M68YxhSw0hTE2t59//hlvvvkmXF1dIZfL8eSTT2LcuHEtPSwio2HZ0gMg3bZs2YLu3btDEAQUFxcjLi4Oo0ePRkpKCkaPHt3ofvHx8XB0dMSUKVOkHyxRA1RXV+PNN9+Eo6Mjrl+/3tLDISKSBPOazElISAjmzp2rts3d3b2FRkNEJB3mNZmL77//HgEBAejSpQtiYmLQqVMnFBUV4cCBAy09NCKjwQlvI9azZ0/4+vqKPwcFBaF9+/bYvn27XoFM5k2lUkEmk8HS0nj/Z/3RRx/h6tWreP/99zFr1qyWHg4RkSSY19QYxp7XTk5OGDBgQEsPg4hIcsxragxjzWtBEKBUKuHm5oZjx45BLpeLz4WFhbXgyIiMCy9pYkJsbGxgbW0NKysrte2VlZX429/+hu7du0Mul6NDhw5444038Ouvv4o1nTt3xrlz55CRkSF+jatz584AgPv372Pu3Lnw9vaGQqGAvb09/Pz8sGfPnmZ5HSdPnsSgQYNgY2MDV1dXREdHQ6VSaa1NSkqCn58fbG1t8cQTT2DEiBHIzc2t9xhJSUkIDAyEi4sLWrdujR49emDevHkoLy8Xaz7//HPIZDJkZmZq7L906VJYWVmprUJOT0/HCy+8ADs7O7Rp0waDBg3C//3f/2nsu2/fPnh7e0Mul8PT0xMxMTENOS0AHoTX3//+d3h4eMDGxga+vr5IS0tDQEAAAgICxLojR45AJpPh888/x9y5c/Hkk09CLpfj8uXLAIBPP/0Uffr0gY2NDezt7TFu3DicP39e7ViP9qw1ZcoU8XcDAK5cuQKZTIZ//OMfWL58Odzd3cWxaXv9uly6dAkffPAB4uPjYWdn1+D9iIhMDfOaeV3LFPOaiOhxwbxmXtcypbw+evQo8vLyMHv2bLXJbiJ6hEBGZ8uWLQIA4eTJk4JKpRIqKyuFwsJCYebMmUKrVq2E/fv3i7XV1dVCUFCQYGtrK3z44YdCWlqa8MknnwhPPvmk4OXlJdy7d08QBEHIyckRunTpIvTt21fIzMwUMjMzhZycHEEQBOG3334TpkyZInz++efCoUOHhP379wvvvvuu0KpVK+Gzzz6T9LWdO3dOaNOmjeDl5SVs375d2LNnjzBixAjB3d1dACDk5+eLtcuXLxdkMpnw5ptvCnv37hV27twp+Pn5Cba2tsK5c+fEusWLFwuP/iovW7ZM+Oijj4R9+/YJR44cERISEgRPT09h2LBhYk1FRYXg7OwsvPbaa2r7qlQqwdXVVZgwYYK47fPPPxdkMpnw8ssvCzt37hS++eYb4aWXXhIsLCyE9PR0sS49PV2wsLAQBg8eLOzcuVP46quvhOeee058ffWJjo4WAAhvvfWWsH//fmHTpk2Cu7u74OLiIgwdOlSsO3z4sABAePLJJ4WQkBAhJSVF2Lt3r3Dz5k3h73//uwBAePXVV4V9+/YJW7duFbp06SIoFArh4sWLYo+hQ4eq9aw1efJkwcPDQ/w5Pz9fACC4ubkJgwcPFpKTk8XXZWVlJZw4caLe11VTUyMMGTJEPKe1v+OnT5+ud18iImPFvH6AeW0+eQ1AaN++vWBjYyNYW1sLzz77rPDpp5/Wux8RkTFjXj/AvDaPvF66dKkAQEhOThaCg4MFuVwu2NraCqNGjRLOnz9f7zkhelxwwtsI1Qbyow+5XC7Ex8er1W7fvl38x+5hp0+fFgCo1T/zzDNa/wF+VFVVlaBSqYSpU6cKffv2leQ11QoLCxNat24tFBcXqx2ve/fuaoFcUFAgWFpaCu+8847a/nfu3BGcnZ2F0NBQcZu2QH5YTU2NoFKphIyMDAGAcObMGbV9ra2thV9++UXclpSUJAAQMjIyBEEQhPLycsHe3l4YPXq0Wt/q6mqhT58+Qr9+/cRt/fv3F1xdXYXff/9d3FZWVibY29vXG8i3bt0S5HK5EBYWprY9MzNTAKA1kIcMGaJWe/v2baF169bCyJEj1bYXFBQIcrlcmDRpkritsYGs63W9+OKLdb4uQRCEdevWCe3btxf/u3PCm4jMAfOaef0wc8jrSZMmCV988YVw9OhR4euvvxaCg4MFAMLChQvr3ZeIyFgxr5nXDzP1vJ4+fboAQLCzsxOmTp0qpKenC59//rng4eEhODo6CtevX69zf6LHBS9pYsS2bt2K06dP4/Tp0/j3v/+NyZMn4+2330ZcXJxYs3fvXrRr1w6jR49GVVWV+PD29oazszOOHDnSoGN99dVXGDRoEJ544glYWlrCysoKmzdv1viazqMEQVA7blVVVZ31hw8fxgsvvAAnJydxm4WFhca1pg4cOICqqiq8/vrrar1tbGwwdOjQel/XTz/9hEmTJsHZ2RkWFhawsrLC0KFDAUDtNf3lL38BAGzatEncFhcXh169emHIkCEAgBMnTuDWrVuYPHmy2lhqamoQFBSE06dPo7y8HOXl5Th9+jReeeUV2NjYiP3atm3boGvCnTx5EhUVFQgNDVXbPmDAALWvQD1s/Pjxaj9nZmbi999/17hxipubG55//vkmfaVZ1+s6evQoqqurde73888/Izo6GqtXr1b7705EZC6Y18xrwPTzGgC++OILTJo0Cf7+/hg/fjxSU1Px0ksvYeXKlWpf5SciMkXMa+Y1YPp5XVNTAwDw8/PDJ598ghdeeAF//vOfsXv3bpSUlGD9+vV6j4nInBjX1fdJTY8ePTRuqvHzzz/jr3/9K/785z+jXbt2+OWXX/Dbb7/B2tpaa4+SkpJ6j7Nz506EhoZiwoQJeO+99+Ds7AxLS0ts2LABn376aZ37fvbZZ3jjjTfUtgmCoLP+5s2bcHZ21tj+6LZffvkFAPDcc89p7dOqle7Pau7evQt/f3/Y2Njgb3/7G/70pz+hTZs2KCwsxCuvvILff/9drHVyckJYWBg+/vhjzJs3D+fOncOxY8fw8ccfa4wlJCRE5zFv3boFmUyGmpqaBr0+bW7evCmO6VG6JopdXFy09nh0OwC4uroiLS2t3nHoout1VVZW4u7du1AoFFr3e/vtt9GzZ0+MHz8ev/32GwDg3r17AB78tyotLdW5LxGRKWBeM68fHqc2ppDXuvz5z3/G3r17kZWVheDgYL3HRUTU0pjXzOuHx6mNKeS1g4MDAGDEiBFq2729veHi4oKcnBy9x0RkTjjhbWJ69+6NAwcO4OLFi+jXrx8cHR3h4OCA/fv3a61v27ZtvT23bdsGT09PJCUlQSaTidsrKirq3Xf06NE4ffp0g8fv4OCA4uJije2PbnN0dAQAfP311/Dw8GhwfwA4dOgQrl+/jiNHjoifOgMQJ1sfNWvWLHz++efYs2cP9u/fj3bt2uG1117TGMu6deswYMAArT2cnJzEuzg35PVpUxtctX8APLq/tk+hH/7v9XCPoqIijdrr16+LrwV4cJOW0tJSjTpdf8Tpel3W1tZ44okntO4DAN9//z1+/vlntG/fXuO5YcOGQaFQ6PxvQ0RkqpjX9WNeG1de61I70VLXZAgRkaliXtePeW1ced27d2+dzwmCwLwm+h9OeJuYvLw8AECHDh0AAC+99BJ27NiB6upq9O/fv8595XK52qevtWQyGaytrdX+cS8uLm7QXaQdHBzEEGiIYcOGISUlBb/88ov4qWp1dTWSkpLU6kaMGAFLS0v8+OOPGl8rqk/t63j0jsUPf6r8MB8fHwwcOBCrVq3C999/j7feegu2trbi84MGDUK7du3www8/YMaMGTqPa21tjX79+mHnzp1YvXq1+PWkO3fu4Jtvvql33P3794dcLkdSUhJeeeUVcfvJkyfx888/6/za1cP8/PzQunVrbNu2DRMmTBC3X716FYcOHVL7FL1z58746quvUFFRIZ6rmzdv4sSJE7Czs9Poret1+fv7w8LCQueYduzYgfv376tt279/P1atWoWEhAQ888wz9b4uIiJTw7yuH/PauPJal88//xxWVlbw8fFp9L5ERMaOeV0/5rVx5XVwcDDatGmDf//735gzZ464PScnB8XFxTo/RCB63HDC24h9//334jW7bt68iZ07dyItLQ3jxo2Dp6cnAGDixIn44osvMHLkSMyaNQv9+vWDlZUVrl69isOHD2Ps2LEYN24cAKBXr17YsWMHkpKS0KVLF9jY2KBXr1546aWXsHPnTkRGRiIkJASFhYVYtmwZXFxccOnSJUlf08KFC5GSkoLnn38eH3zwAdq0aYP169ejvLxcra5z585YunQpFixYgJ9++glBQUFo3749fvnlF3z33XewtbXFhx9+qPUYAwcORPv27REREYHFixfDysoKX3zxBc6cOaNzXLNmzUJYWBhkMhkiIyPVnnviiSewbt06TJ48Gbdu3UJISAg6duyIX3/9FWfOnMGvv/6KDRs2AACWLVuGoKAgDB8+HHPnzkV1dTVWrVoFW1tb3Lp1q85zY29vj6ioKKxYsQLt27fHuHHjcPXqVXz44YdwcXFp0Ce17dq1w6JFizB//ny8/vrrePXVV3Hz5k18+OGHsLGxweLFi8VapVKJjz/+GH/+858RHh6Omzdv4h//+IfWMAYeXAtu+PDhiIqKQk1NDVatWoWysjKd/x1qaQvcK1euAHjwx9DDXyskIjJFzGvmtTnk9erVq/HDDz/ghRdeQKdOnXDjxg1s3rwZBw8exJIlS9RWsRERmSLmNfPaHPK6Xbt2WLp0Kd59911MmTIFr776KoqLi7Fo0SK4u7trnG+ix1bL3S+TdNF2F2mFQiF4e3sLa9asEe7fv69Wr1KphJiYGKFPnz6CjY2N8MQTTwjdu3cXpk+fLly6dEmsu3LlihAYGCi0bdtWAKB2p+CVK1cKnTt3FuRyudCjRw9h06ZN9d6dWV/ffvutMGDAAEEulwvOzs7Ce++9J2zcuFHtLtK1du/eLQwbNkyws7MT5HK54OHhIYSEhAjp6elijbZxnjhxQvDz8xPatGkjdOjQQZg2bZqQk5MjABC2bNmiMaaKigpBLpcLQUFBOsedkZEhjBo1SrC3txesrKyEJ598Uhg1apTw1VdfqdWlpKQIvXv3FqytrQV3d3dh5cqVDT6XNTU1wt/+9jehU6dOgrW1tdC7d29h7969Qp8+fYRx48aJdbV3kX702LU++eQTcQwKhUIYO3ascO7cOY26zz77TOjRo4dgY2MjeHl5CUlJSTrvIr1q1Srhww8/FMfWt29f4cCBA/W+Jm1qf8dPnz6t1/5ERMaAef0H5rXp53VKSoowePBgoUOHDoKlpaXQtm1bwd/fX9i+fXu9+xIRGTPm9R+Y16af17U2bdok9OzZU7C2thYcHByE1157TSgsLGzw/kTmTiYIddwBgegx8c0332DMmDHYt28fRo4c2dLDUZOfn4/u3btj8eLFmD9/vsGPf+XKFXh6emL16tV49913DX58IiKiWsxr3ZjXRERkLJjXujGviQyDlzShx9oPP/yAn3/+GXPnzoW3tzeCg4NbdDxnzpzB9u3bMXDgQNjZ2eHChQvi16CmTp3aomMjIiJqKcxrIiIi48e8JiJjwQlveqxFRkbi22+/xbPPPovPPvtM467MhmZra4usrCxs3rwZv/32GxQKBQICArB8+XLxJiRERESPG+Y1ERGR8WNeE5Gx4CVNiIiIiIiIiIiIiMgs1H9bWiIiIiIiIiIiIiIiE8AJbyIiIiIiIiIiIiIyC5zwJiIiIiIiIiIiIiKzYHQ3raypqcH169fRtm3bFr/BARFRcxEEAXfu3IGrqytatZLus8f79++jsrJS7/2tra1hY2Mj2XjIvDGziehx0ByZzbwmQ2JeE9HjwBjfYzOvW47RTXhfv34dbm5uLT0MIiKDKCwsRKdOnSTpdf/+fXRo3Rp3m9DD2dkZ+fn5DGVqEGY2ET1OpMps5jUZGvOaiB4nxvQem3ndcoxuwrtt27YAgGcLd8DCrk2TeqUvGCPFkAAAK+Ika4XofdL0+efgv0jTCEA+PCXp8/nx6ZL0AYBSO4U0jS5I0wYA9k14XpI+o8IPSdIHAPI2/UmSPh3xqyR9AOAm2kvSp9eunyTpAwCjxyVJ0uczvC5JnztlAnq7VYr/5kmhsrISdwHMASDXY/8KAB8VF6OyspKBTA1S+/vbszAZFna2Tep1dEGQFEMCIHFmb5emT9zIqdI0AvBfdJOkz+dHJMzsjhJl9o/StAGAlNEvStJnzOR0SfoAwNnPukjSpyNKJOkDAFfxpCR9fL45L0kfABg5+mtJ+mzBm5L0uVsm4Fm3O5JlNvOaDK32d9e78Ksmv8c+tGCUFEMCYJx5HTvyLWkaAbiMpyTpw7xuGOZ1wzCvG6cpmc28bllGN+Fd+xUrC7s2sGzim2c7ff6C1EHCVmjiyxLZSPgCrdFamka2dtL0AWD3hESNmvY3nXorO2n+J2NnLUkbAMATdhaS9GkL6b7eWAFpxtTEv8fVWErUzE7C8wSgWb5WagtAnzg1ukAgo/dHZts2ecLbaDNbon+HbCT8h5+Z3cBWdlaS9JGoDQDjzOwnjDKzpfljWcrzBEif2cxrMpSH32Mzr+vGvG4g5nWDMK8bxtjzGtAvs5nXLYvnn4jIzFj979FY1VIPhIiIiHRiXhMREZkGfTKbed2yOOFNRGRmLKHfP+4MBCIiIsNhXhMREZkGfTKbed2ypLtt6SPi4+Ph6ekJGxsb+Pj44NixY811KCIieogl/vgEujEPBvLjiXlNRNQymNfUGMxrIqKWo09mM69bVrNMeCclJWH27NlYsGABcnNz4e/vj+DgYBQUFDTH4YiI6CGWTXjQ44V5TUTUcpjX1FDMayKilsW8Nj3NMuG9Zs0aTJ06FdOmTUOPHj0QGxsLNzc3bNiwoTkOR0RERHpgXhMRERk/5jUREVHjSD7hXVlZiezsbAQGBqptDwwMxIkTJ6Q+HBERPUKfr0fre+MsMl3MayKilsW8poZgXhMRtTzmtemRfIV9SUkJqqur4eTkpLbdyckJxcXFGvUVFRWoqKgQfy4rK5N6SEREjxXeBIsaorF5DTCziYikxLymhmBeExG1PN600vQ0200rZTKZ2s+CIGhsA4AVK1ZAoVCIDzc3t+YaEhHRY8GQN8Fq7A2UMjIy4OPjAxsbG3Tp0gUJCQkaNcnJyfDy8oJcLoeXlxd27dql9vyKFSvw3HPPoW3btujYsSNefvllXLhwQa3m7t27mDFjBjp16oTWrVujR48eGl/73bhxIwICAmBnZweZTIbffvtNjzNg+hqa1wAzm4hISrxpJTUG85qIqOXwppWmR/IJb0dHR1hYWGh82nzjxg2NT6UBIDo6GqWlpeKjsLBQ6iERET1WDHUTrMbeQCk/Px8jR46Ev78/cnNzMX/+fMycORPJycliTWZmJsLCwqBUKnHmzBkolUqEhobi1KlTYk1GRgbefvttnDx5EmlpaaiqqkJgYCDKy8vFmjlz5mD//v3Ytm0bzp8/jzlz5uCdd97Bnj17xJp79+4hKCgI8+fPb+QrNw+NzWuAmU1EJCVD37SyJT6kPnr0KEaPHg1XV1fIZDLs3r1b7XmVSoX3338fvXr1gq2tLVxdXfH666/j+vXranXFxcVQKpVwdnaGra0tnn32WXz99df6nQgTw7wmImp5vGml6ZF8wtva2ho+Pj5IS0tT256WloaBAwdq1MvlctjZ2ak9iIjI+DX2BkoJCQlwd3dHbGwsevTogWnTpuHNN99ETEyMWBMbG4vhw4cjOjoa3bt3R3R0NF544QXExsaKNfv378eUKVPwzDPPoE+fPtiyZQsKCgqQnZ0t1mRmZmLy5MkICAhA586d8dZbb6FPnz7IysoSa2bPno158+ZhwIAB0p8cE9DYvAaY2UREpqqlPqQuLy9Hnz59EBcXp/U49+7dQ05ODhYtWoScnBzs3LkTFy9exJgxY9TqlEolLly4gJSUFJw9exavvPIKwsLCkJubK8HZMW7MayIiosZrlkuaREVF4ZNPPsGnn34qrqwrKChAREREcxyOiIgeYoibYOlzA6XMzEyN+hEjRiArKwsqlarOmrpuylRaWgoAsLe3F7cNHjwYKSkpuHbtGgRBwOHDh3Hx4kWMGDGi4S/yMcC8JiJqOYa8aWVLfUgdHByMv/3tb3jllVe0HkehUCAtLQ2hoaHo1q0bBgwYgHXr1iE7O1ttMj4zMxPvvPMO+vXrhy5dumDhwoVo164dcnJy9Dgbpod5TUTUsnjTStPTLCvsw8LCcPPmTSxduhRFRUXo2bMnUlNT4eHh0RyHIyKih+gbrrX7PHpjI7lcDrlcrrZNnxsoFRcXa62vqqpCSUkJXFxcdNbo6ikIAqKiojB48GD07NlT3L527VqEh4ejU6dOsLS0RKtWrfDJJ59g8ODBOl//44h5TUTUcgyR18AfH1LPmzdPbbs+H1Jv3rwZKpUKVlZWyMzMxJw5czRqHp7w1kdpaSlkMhnatWsnbhs8eDCSkpIwatQotGvXDv/6179QUVGBgICAJh3LVDCviYhalj6ZzQnvltVsl5SJjIxEZGRkc7UnIiId9L1eWO0+j97YaPHixViyZInWfRpzAyVd9Y9ub0zPGTNm4D//+Q+OHz+utn3t2rU4efIkUlJS4OHhgaNHjyIyMhIuLi548cUXdY7vccS8JiJqGYbKa2P5kLoh7t+/j3nz5mHSpElql+FISkpCWFgYHBwcYGlpiTZt2mDXrl3o2rWr3scyNcxrIqKWo09m8xreLYvnn4jIzNTeQVqf/QCgsLBQ7U2mttVi+txAydnZWWu9paUlHBwc6qzR1vOdd95BSkoKjh49ik6dOonbf//9d8yfPx+7du3CqFGjAAC9e/dGXl4eYmJiOOFNRERGwRB5/bCW/pC6PiqVChMnTkRNTQ3i4+PVnlu4cCFu376N9PR0ODo6Yvfu3ZgwYQKOHTuGXr166XU8IiKihtInsznh2rKa5RreRERkuh69yZG2N9D63EDJz89Po/7gwYPw9fWFlZVVnTUP9xQEATNmzMDOnTtx6NAheHp6qtWrVCqoVCq0aqUecRYWFqipqann1RMREZmGhuQ1YBwfUtdHpVIhNDQU+fn5SEtLU5vI//HHHxEXF4dPP/0UL7zwAvr06YPFixfD19cX69evb/SxiIiIyPwZ7QcOpxUeANo2qcdOIViawQB4b/kByXqtkU+XpI8rrkvSBwA+wpz6ixrg6aFnJOkDAF7IlqRP4dNu9Rc10AhI83sw1vugJH0A4GuESNLn7+HLJOkDAB9tkub3yXvNQkn6AIDgqt9qo0dtGDRZkj6/oxLAdkl6PaqpX5FuqKioKCiVSvj6+sLPzw8bN25Uu4FSdHQ0rl27hq1btwIAIiIiEBcXh6ioKISHhyMzMxObN2/G9u1/nIdZs2ZhyJAhWLVqFcaOHYs9e/YgPT1d7ZIlb7/9Nr788kvs2bMHbdu2Fd9sKxQKtG7dGnZ2dhg6dCjee+89tG7dGh4eHsjIyMDWrVuxZs0asU9xcTGKi4tx+fJlAMDZs2fRtm1buLu7q90Ak+p3RuGOpmb2V8JL0gwGwLwP90rWa4OtNP+b74gbkvQBgKUVH0jS5+kXjC+z8906S9IHAF7E/0nS55X+/5akDwB8DGluMBcfPleSPgCwdtNMSfr0+8dZSfoAgCCXJrP/38i3JOlzH5UAEiXp9TBD5fXDH1KPGzdO3J6WloaxY8dq3cfPzw/ffPON2jZdH1I/fB3vRz+kbojaye5Lly7h8OHD4oR6rXv37gEAP8iWQLbCE8zrurmiSJI+APD3igWS9GFeN4yUef0JpknSZ134+5L0AZjXDdFceQ3wkiamiOefiMjMNPUmWA1V3w2UioqKUFBQINZ7enoiNTUVc+bMwfr16+Hq6oq1a9di/PjxYs3AgQOxY8cOLFy4EIsWLULXrl2RlJSE/v37izUbNmwAAI0bVW3ZsgVTpkwBAOzYsQPR0dF47bXXcOvWLXh4eGD58uXiZDwAJCQk4MMPPxR/HjJkiEYfIiKi5mKovAZa7kPqu3fvih8sA0B+fj7y8vJgb28Pd3d3VFVVISQkBDk5Odi7dy+qq6vFD7Lt7e1hbW2N7t2746mnnsL06dMRExMDBwcH7N69G2lpadi7V7oJUyIiIl1400rTwwlvIiIzY6gVY0DdN1BKTEzU2DZ06FDk5OTU2TMkJAQhIbq/uVB7DdG6ODs7Y8uWLXXWLFmyROfNOImIiJqbIfO6pT6kzsrKwrBhw8Sfo6KiAACTJ09GYmIirl69ipSUFACAt7e32pgPHz6MgIAAWFlZITU1FfPmzcPo0aNx9+5dPPXUU/jss88wcuRIPc4GERFR43CFt+nh+SciMjNNvQkWERERNT9D53VLfEgdEBBQ5wfVnTt3btAH2U8//TSSk5PrrSMiImoOvGml6eH5JyIyM4ZcMUZERET6YV4TERGZBq7wNj2t6i8hIiIiIiIiIiIiIjJ+/MCBiMjMGPImWERERKQf5jUREZFp4E0rTQ8nvImIzAy/Ik1ERGT8mNdERESmgZc0MT08/0REZoY3rSQiIjJ+zGsiIiLTwJtWmh6efyIiM8OvSBMRERk/5jUREZFp4CVNTA9vWklEREREREREREREZoErvImIzAyvCUpERGT8mNdERESmgdfwNj08/0REZsbSArCS6bGfAKBa8uEQERGRFsxrIiIi06BPZjOvWxYvaUJEZGYsLfV/EBERkWEwr4mIiEyDIfM6Pj4enp6esLGxgY+PD44dO1ZnfUZGBnx8fGBjY4MuXbogISFBoyY5ORleXl6Qy+Xw8vLCrl27Gn3cKVOmQCaTqT0GDBigVjN9+nR07doVrVu3RocOHTB27Fj897//Vau5ffs2lEolFAoFFAoFlEolfvvttwaenYbjhDcRkZmxstD/QURERIbBvCYiIjINhsrrpKQkzJ49GwsWLEBubi78/f0RHByMgoICrfX5+fkYOXIk/P39kZubi/nz52PmzJlITk4WazIzMxEWFgalUokzZ85AqVQiNDQUp06davRxg4KCUFRUJD5SU1PVnvfx8cGWLVtw/vx5HDhwAIIgIDAwENXVfyx1nzRpEvLy8rB//37s378feXl5UCqVjT9Z9eCENxEREREREREREVELWrNmDaZOnYpp06ahR48eiI2NhZubGzZs2KC1PiEhAe7u7oiNjUWPHj0wbdo0vPnmm4iJiRFrYmNjMXz4cERHR6N79+6Ijo7GCy+8gNjY2EYfVy6Xw9nZWXzY29urPf/WW29hyJAh6Ny5M5599ln87W9/Q2FhIa5cuQIAOH/+PPbv349PPvkEfn5+8PPzw6ZNm7B3715cuHBBmpP4P5zwJiIyM/yKNBERkfFjXhMREZkGQ+R1ZWUlsrOzERgYqLY9MDAQJ06c0LpPZmamRv2IESOQlZUFlUpVZ01tz8Yc98iRI+jYsSP+9Kc/ITw8HDdu3ND5esrLy7FlyxZ4enrCzc1NHItCoUD//v3FugEDBkChUOh8jfrin0tERGbGygKw0uPjTKsa6cdCRERE2jGviYiITIM+mV2b12VlZWrb5XI55HK5Rn1JSQmqq6vh5OSktt3JyQnFxcVaj1FcXKy1vqqqCiUlJXBxcdFZU9uzoccNDg7GhAkT4OHhgfz8fCxatAjPP/88srOz1V5PfHw8/vrXv6K8vBzdu3dHWloarK2txfF27NhR43V07NhR52vUF1d4ExGZG4smPIiIiMgwmNdERESmoQl57ebmJt6gUaFQYMWKFXUeSiaTqf0sCILGtvrqH93ekJ711YSFhWHUqFHo2bMnRo8ejX//+9+4ePEi9u3bp7bfa6+9htzcXGRkZODpp59GaGgo7t+/r/M4DXmN+jDaFd6lzn1g18Tp+DN4WprBAFgq/0CyXl74QZI+U/YlSdIHAIRR0vRpXfqENI0A3P+trSR9BA8rSfoAgEwWIE2fGEGSPgAgVSfZE0sl6gRAtkSSNk/c/VWSPgCA69K0+R1tJOlzvzn/+bWEfh9ncsUY6anU0avJmX0OXaQZDIDFtksk69UN0lxLTrnva0n6ANJldtty6WbN7kqV2U/aSNIHAGSyZ6Tpk2CEmd05SqJOkCyzFfclXJWj/b5MjVYt0cywVH00MK/JwJjX9WNeNwzzumFknf8qUScwrw3YRyt9Mvt/eV1YWAg7Oztxs7bV3QDg6OgICwsLjZXON27c0Fh9XcvZ2VlrvaWlJRwcHOqsqe2pz3EBwMXFBR4eHrh06ZLa9tqJ/aeffhoDBgxA+/btsWvXLrz66qtwdnbGL7/8otHr119/rfNY+uAKbyIic2PZhAcREREZBvOaiIjINDQhr+3s7NQeuia8ra2t4ePjg7S0NLXtaWlpGDhwoNZ9/Pz8NOoPHjwIX19fWFlZ1VlT21Of4wLAzZs3UVhYCBcXF501wIPV2xUVFeJYSktL8d1334nPnzp1CqWlpXUeSx/8c4mIiIiIiIiIiIioBUVFRUGpVMLX1xd+fn7YuHEjCgoKEBERAQCIjo7GtWvXsHXrVgBAREQE4uLiEBUVhfDwcGRmZmLz5s3Yvn272HPWrFkYMmQIVq1ahbFjx2LPnj1IT0/H8ePHG3zcu3fvYsmSJRg/fjxcXFxw5coVzJ8/H46Ojhg3bhwA4KeffkJSUhICAwPRoUMHXLt2DatWrULr1q0xcuRIAECPHj0QFBSE8PBwfPzxxwCAt956Cy+99BK6desm6bnkhDcRkbnhV6SJiIiMH/OaiIjINDThkiaNERYWhps3b2Lp0qUoKipCz549kZqaCg8PDwBAUVERCgr+uJaMp6cnUlNTMWfOHKxfvx6urq5Yu3Ytxo8fL9YMHDgQO3bswMKFC7Fo0SJ07doVSUlJ6N+/f4OPa2FhgbNnz2Lr1q347bff4OLigmHDhiEpKQlt2z64VJKNjQ2OHTuG2NhY3L59G05OThgyZAhOnDihdqPKL774AjNnzkRgYCAAYMyYMYiLi2v8yaoHJ7yJiMxNK/CGVkRERMaOeU1ERGQaDJjZkZGRiIyM1PpcYmKixrahQ4ciJyenzp4hISEICQnR+7itW7fGgQMH6tzf1dUVqampddYAgL29PbZt21ZvXVNJfg3vFStW4LnnnkPbtm3RsWNHvPzyy7hwQZobSBARUQPwmqDUAMxrIqIWxrymBmJmExG1MOa1yZF8wjsjIwNvv/02Tp48ibS0NFRVVSEwMBDl5eVSH4qIiLThG2hqAOY1EVELY15TAzGziYhaGPPa5Eh++vfv36/285YtW9CxY0dkZ2djyJAhUh+OiIiI9MC8JiIiMg3MbCIiosZp9s8bSktLATy4RgsRERmABXhNUGo05jURkYExr0lPzGwiIgNjZpucZp3wFgQBUVFRGDx4MHr27Km1pqKiAhUVFeLPZWVlzTkkIiLzZwn9wlgm9UDIVDQkrwFmNhGRpJjXpAe+xyYiagH6ZDbzukVJfg3vh82YaUnZxAAAi5tJREFUMQP/+c9/sH37dp01K1asgEKhEB9ubm7NOSQiIvNnAf2uL8ZPrB9bDclrgJlNRCQp5jXpge+xiYhagD6ZzbxuUc024f3OO+8gJSUFhw8fRqdOnXTWRUdHo7S0VHwUFhY215CIiB4PFk14NFJ8fDw8PT1hY2MDHx8fHDt2rM76jIwM+Pj4wMbGBl26dEFCQoJGTXJyMry8vCCXy+Hl5YVdu3apPb9ixQo899xzaNu2LTp27IiXX34ZFy5cUKu5e/cuZsyYgU6dOqF169bo0aMHNmzYoFZTUVGBd955B46OjrC1tcWYMWNw9erVxp8EE9fQvAaY2UREkjJgXpN54HtsIqIWwrw2OZJPeAuCgBkzZmDnzp04dOgQPD0966yXy+Wws7NTexARURPoewfpRl7kKikpCbNnz8aCBQuQm5sLf39/BAcHo6CgQGt9fn4+Ro4cCX9/f+Tm5mL+/PmYOXMmkpOTxZrMzEyEhYVBqVTizJkzUCqVCA0NxalTp8SajIwMvP322zh58iTS0tJQVVWFwMBAlJeXizVz5szB/v37sW3bNpw/fx5z5szBO++8gz179og1s2fPxq5du7Bjxw4cP34cd+/exUsvvYTq6urGnQgT1di8BpjZRESSMlBek+nje2wiohbGvDY5kp/+t99+G19++SX27NmDtm3bori4GACgUCjQunVrqQ9HREQtZM2aNZg6dSqmTZsGAIiNjcWBAwewYcMGrFixQqM+ISEB7u7uiI2NBQD06NEDWVlZiImJwfjx48Uew4cPR3R0NIAHK5QyMjIQGxsrfnV3//79an23bNmCjh07Ijs7G0OGDAHwYOJ88uTJCAgIAAC89dZb+Pjjj5GVlYWxY8eitLQUmzdvxueff44XX3wRALBt2za4ubkhPT0dI0aMkPZkGSHmNRERkWlgZhMRETWO5Cu8N2zYgNLSUgQEBMDFxUV8JCUlSX0oIiLSxgArxiorK5GdnY3AwEC17YGBgThx4oTWfTIzMzXqR4wYgaysLKhUqjprdPUEgNLSUgCAvb29uG3w4MFISUnBtWvXIAgCDh8+jIsXL4oT2dnZ2VCpVGrHcnV1Rc+ePes8ljlhXhMRtTCu8KYGYmYTEbUw5rXJkfz0C4IgdUsiImqMJoZrWVmZ2s9yuRxyuVxtW0lJCaqrq+Hk5KS23cnJSVx19Kji4mKt9VVVVSgpKYGLi4vOGl09BUFAVFQUBg8ejJ49e4rb165di/DwcHTq1AmWlpZo1aoVPvnkEwwePFgci7W1Ndq3b9/gY5kb5jURUQvjm2FqIGY2EVELY2abHP7nIiIyN62g3w0yah78Hzc3N7XNixcvxpIlS7TuIpPJ1H4WBEFjW331j25vTM8ZM2bgP//5D44fP662fe3atTh58iRSUlLg4eGBo0ePIjIyEi4uLuIlTLSpb/xERESSaWJeExERkYHok9nM6xbFCW8iInOj76fP/1s8VFhYqHZzo0dXdwOAo6MjLCwsNFZD37hxQ2OFdi1nZ2et9ZaWlnBwcKizRlvPd955BykpKTh69Cg6deokbv/9998xf/587Nq1C6NGjQIA9O7dG3l5eYiJicGLL74IZ2dnVFZW4vbt22qrvG/cuIGBAwdqHT8REZGkmpjXREREZCD6ZDbzukVJfg1vIiIybXZ2dmoPbRPe1tbW8PHxQVpamtr2tLQ0nRPGfn5+GvUHDx6Er68vrKys6qx5uKcgCJgxYwZ27tyJQ4cOwdPTU61epVJBpVKhVSv1iLOwsEBNzYOP2X18fGBlZaV2rKKiInz//fec8CYiIiIiIiIyYca7wvsIgLZNa+GKIilGAgDojHzJek2GNDcXmZIuSRsAwJpRkZL0uf+3eEn6AABipPk4TLZDkjYPDOhUf00DTJ67QZI+ACA79xdpGj0hTRsAEIQlkvSRTZCkDQCg+CuFJH2ivpfmv13ZXWCBJJ20MNCKsaioKCiVSvj6+sLPzw8bN25EQUEBIiIiAADR0dG4du0atm7dCgCIiIhAXFwcoqKiEB4ejszMTGzevBnbt28Xe86aNQtDhgzBqlWrMHbsWOzZswfp6elqlyx5++238eWXX2LPnj1o27atuCJcoVCgdevWsLOzw9ChQ/Hee++hdevW8PDwQEZGBrZu3Yo1a9aItVOnTsXcuXPh4OAAe3t7vPvuu+jVq1edlzwhHQ6iyf+GuFUXSjIUAOhsIV1mv4kvJekz9YgkbQAA/xj1jiR97s5eJ0kfAMAnEmX2NknaPODtJUmbydONMLPvS9MGkDCz35CkDQDg1hYbSfpEZRl5Zht4hXd8fDxWr16NoqIiPPPMM4iNjYW/v7/O+oyMDERFReHcuXNwdXXFX//6VzHjayUnJ2PRokX48ccf0bVrVyxfvhzjxo0Tnz969ChWr16N7OxsFBUVYdeuXXj55ZfF51UqFRYuXIjU1FT89NNPUCgUePHFF7Fy5Uq4uroCAK5cuaLx4Xatf/3rX5gwQcI/GM0d87peU49I0gYA87rBmNcNIlleKyVpAwC49fljktcAV3ibIK7wJiIyNxZNeDRCWFgYYmNjsXTpUnh7e+Po0aNITU2Fh4cHgAcrpgsKCsR6T09PpKam4siRI/D29sayZcuwdu1ajB8/XqwZOHAgduzYgS1btqB3795ITExEUlIS+vfvL9Zs2LABpaWlCAgIgIuLi/hISvrjw8QdO3bgueeew2uvvQYvLy+sXLkSy5cvV3uj/tFHH+Hll19GaGgoBg0ahDZt2uCbb76BhYU+F1QlIiJqJAPlNQAkJSVh9uzZWLBgAXJzc+Hv74/g4GC1nH5Yfn4+Ro4cCX9/f+Tm5mL+/PmYOXMmkpOTxZrMzEyEhYVBqVTizJkzUCqVCA0NxalTp8Sa8vJy9OnTB3FxcVqPc+/ePeTk5GDRokXIycnBzp07cfHiRYwZM0ascXNzQ1FRkdrjww8/hK2tLYKDgxt/MoiIiBrLQHlN0jHeFd5ERKQfA64Yi4yMRGSk9m+IJCYmamwbOnQocnJy6uwZEhKCkJAQnc/X3uiyLs7OztiyZUudNTY2Nli3bh3WrZNw1QwREVFDGTCv16xZg6lTp2LatGkAgNjYWBw4cAAbNmzAihUrNOoTEhLg7u6O2NhYAECPHj2QlZWFmJgY8YPq2NhYDB8+HNHR0QAefLMrIyMDsbGx4re3goOD65yUVigUGpcyW7duHfr164eCggK4u7vDwsICzs7OajW7du1CWFgYnnhCwq8oEhER6cIV3iaHK7yJiMyNBf4I5MY8+Ak0ERGR4RgorysrK5GdnY3AwEC17YGBgThx4oTWfTIzMzXqR4wYgaysLKhUqjprdPVsqNLSUshkMrRr107r89nZ2cjLy8PUqVObdBwiIqIG0yez+f66RXGFNxERERERkYkpKytT+1kul2u90XRJSQmqq6vh5OSktt3JyUm8D8ajiouLtdZXVVWhpKQELi4uOmt09WyI+/fvY968eZg0aRLs7Oy01mzevBk9evTgTaaJiIhIJ67wJiIyNwa8JigRERHpqYl57ebmBoVCIT60XZrkYTKZTO1nQRA0ttVX/+j2xvasi0qlwsSJE1FTU4P4+HitNb///ju+/PJLru4mIiLD4vtrk8MV3kRE5kbfa4LWSD0QIiIi0qmJeV1YWKi2Clrb6m4AcHR0hIWFhcbK6xs3bmis0K7l7Oystd7S0hIODg511ujqWReVSoXQ0FDk5+fj0KFDOld3f/3117h37x5ef/31Rh+DiIhIb/pkNt9ftyiu8CYiMjf6XA9U3zfdREREpJ8m5rWdnZ3aQ9eEt7W1NXx8fDRuDpmWlqbzsiB+fn4a9QcPHoSvry+srKzqrGnspUZqJ7svXbqE9PR0cUJdm82bN2PMmDHo0KFDo45BRETUJHx/bXJ4+omIzA1XeBMRERk/A+Z1VFQUlEolfH194efnh40bN6KgoAAREREAgOjoaFy7dg1bt24FAERERCAuLg5RUVEIDw9HZmYmNm/ejO3bt4s9Z82ahSFDhmDVqlUYO3Ys9uzZg/T0dBw/flysuXv3Li5fviz+nJ+fj7y8PNjb28Pd3R1VVVUICQlBTk4O9u7di+rqanHVuL29PaytrcV9L1++jKNHjyI1NbXxJ4CIiKgpuMLb5HDCm4jI3LSCftcL43d+iIiIDMeAeR0WFoabN29i6dKlKCoqQs+ePZGamgoPDw8AQFFREQoKCsR6T09PpKamYs6cOVi/fj1cXV2xdu1ajB8/XqwZOHAgduzYgYULF2LRokXo2rUrkpKS0L9/f7EmKysLw4YNE3+OiooCAEyePBmJiYm4evUqUlJSAADe3t5qYz58+DACAgLEnz/99FM8+eSTCAwMbPwJICIiagp9Mpvvr1sUJ7yJiIiIiIjMXGRkJCIjI7U+l5iYqLFt6NChyMnJqbNnSEgIQkJCdD4fEBAg3uxSm86dO9f5/MP+/ve/4+9//3uDaomIiOjxxglvIiJzo+9XpKulHggRERHpxLwmIiIyDfpkNvO6RXHCm4jI3PANNBERkfFjXhMREZkGTnibHE54ExGZGwvod01QffYhIiIi/TCviYiITIM+mc28blGc8CYiMjdcMUZERGT8mNdERESmgSu8TQ7vGUpEREREREREREREZoErvImIzI0F9PvXvUrqgRAREZFOzGsiIiLToE9mM69bFCe8iYjMjb5fkWYiEBERGQ7zmoiIyDTok9nM6xZltKf/kMsA2No1bXiv4zOJRgNUVssl6xWZLk2fVz/6VJpGAOYuipekT+LqMEn6AMCUkiRpGjlL0wYA/i9zoCR9PsE0SfoAAAZI1CdWoj4AZB0kajRYoj4A3sNqSfps8nxLkj73yyRpox1vgkUGltH1uSZn9mQJM7tKwl/myP+Tpk/w6p3SNALw/qJ1kvRJ3CRhZj9hfJn9Te4LkvT5HK9L0gcA4CtRnxiJ+gCQPS1RI6leG4AZWC9Jny+6TJWkj9Bcmc28JgNjXtePed1AzOuGYV43iNHnNcCbVpogo53wJiIiPXHFGBERkfFjXhMREZkGrvA2ObxpJRERERERERERERGZBX7eQERkbrhijIiIyPgxr4mIiEwDV3ibHJ5+IiJz0wr6XS+M3/khIiIyHOY1ERGRadAns5nXLarZT/+KFSsgk8kwe/bs5j4UEREBf3z6rM+DHlvMayIiA2Nekx6Y10RELYB5bXKa9fSfPn0aGzduRO/evZvzMERE9DB+RZoaiXlNRNQCmNfUSMxrIqIWwkuamJxmW+F99+5dvPbaa9i0aRPat2/fXIchIqJHWTThQY8d5jURUQthXlMjMK+JiFoQ89rkNNuE99tvv41Ro0bhxRdfbK5DEBERURMxr4mIiIwf85qIiKjhmmWB/Y4dO5CTk4PTp0/XW1tRUYGKigrx57KysuYYEhHR44NfkaYGakxeA8xsIiJJMa+pgZjXREQtjJc0MTmSr/AuLCzErFmzsG3bNtjY2NRbv2LFCigUCvHh5uYm9ZD+f3t3HlZVubcP/N4xmgHmwPQGiFYGYQ5gCjlgvW7FciawOqilKC+aAtVRNF89eoo4mYfXDMkTgh5L+RWilqRiKmpuTSbzlDkUBSXEwQocEhnW7w9jH7d7gL143JP357rWdbXX/q7velgat/vZayAiurPYQd4DNXjJ1R3F2LwGmNlEREIxr6kdmNdERBZATmYzr81K+IR3cXExampqEBwcDHt7e9jb26OwsBBr1qyBvb09mpubNeqTk5NRV1enXiorK0UPiYjoziL3CdL8BvqOYmxeA8xsIiKhmNfUDsxrIiILwLy2OsInvJ944gmcOnUKZWVl6iUkJATPPfccysrKYGen+RWHk5MTXF1dNRYiIuoAEz4EKz09Hf7+/nB2dkZwcDAOHz5ssL6wsBDBwcFwdnZGr169kJGRoVWTm5uLwMBAODk5ITAwEHl5eRrvp6SkYNCgQXBxcYG7uzsmTpyIM2fOaNQoFAqdy5tvvqmu+fbbbzFp0iT06NEDrq6uiIqKws8//2z8QbBSxuY1wMwmIhKKD62kdmBeExFZAOa11RE+4e3i4oKgoCCNpXPnzujWrRuCgoJE746IiMwkJycHCQkJWLJkCUpLSzFs2DBERESgoqJCZ315eTnGjh2LYcOGobS0FIsXL8b8+fORm5urrlGpVIiOjkZMTAxOnjyJmJgYREVF4fjx4+qawsJCzJ07F8eOHUNBQQGampqgVCpx5coVdU1VVZXGsmHDBigUCkyZMgUAcOXKFSiVSigUCuzfvx+ff/45rl+/jnHjxqGlpeU2HTHLwrwmIiKyfMxrIiIi4wmf8CYiIjMz0SXSq1evxsyZMzFr1iwEBAQgLS0NPj4+WLdunc76jIwM+Pr6Ii0tDQEBAZg1axZeeOEFrFq1Sl2TlpaGUaNGITk5GQ899BCSk5PxxBNPIC0tTV2ze/duzJgxAw8//DD69euHrKwsVFRUoLi4WF3j6empsezYsQMjR45Er169AACff/45vv/+e2RnZ6Nv377o27cvsrKycOLECezfv9+4A0FERCQHb2lCRERkHUyY1+a4iro9+50xY4bWFdRDhgxRv//LL7/gxRdfRJ8+fXD33XfD19cX8+fPR11dnUafnj17avVZtGiRMYeoXUzyz6WDBw+aYjdERATID9c/tqmvr9dY7eTkBCcnJ411169fR3FxsVYwKZVKHD16VGd7lUoFpVKpsW706NHIzMxEY2MjHBwcoFKpkJiYqFVz84T3rVoDtGvXrjrf//nnn7Fr1y5s3LhRva6hoQEKhULj53J2dsZdd92FI0eO4L//+7/17s+WMa+JiEyog3lNdy7mNRGRicnJbBl53XoVdXp6Oh577DG8++67iIiIwNdffw1fX1+t+tarqGNjY7F582Z8/vnniI+PR48ePdRXN7deRb1y5UpMmjQJeXl5iIqKwpEjRzB48GCj9jtmzBhkZWWpXzs6Oqr/+8KFC7hw4QJWrVqFwMBA/PDDD4iLi8OFCxfw0UcfaYx7xYoViI2NVb++5557jD9YbeAZ3kREtuYuyLu/2B+J4OPjAzc3N/WSkpKitYva2lo0NzfDw8NDY72Hhweqq6t1Dqu6ulpnfVNTE2praw3W6OspSRKSkpIwdOhQvZf1bty4ES4uLpg8ebJ63ZAhQ9C5c2csXLgQV69exZUrV/DKK6+gpaUFVVVVOvsQEREJ1cG8JiIiIhORk9ky8tpcV1G3d79OTk4aV1LffNJZUFAQcnNzMW7cOPTu3RuPP/44XnvtNXz88cdoamrS6OPi4qLRhxPeRETUtg5eIl1ZWYm6ujr1kpycrHdXCoVC47UkSVrr2qq/db0xPefNm4cvv/wSW7Zs0bvPDRs24LnnnoOzs7N6XY8ePfDhhx/i448/xj333AM3NzfU1dVh4MCBOh/+REREJBxvaUJERGQdOpDX9fX1GktDQ4POXbReRX3rVdFyrqIuKipCY2OjwZrWnsbs9+DBg3B3d8eDDz6I2NhY1NTU6BxXq7q6Ori6usLeXvMfL6mpqejWrRv69++P1157DdevXzfYRw7+c4mIiDS4urrC1dXVYE337t1hZ2endeZ1TU2N1hnarTw9PXXW29vbo1u3bgZrdPV88cUXsXPnThw6dAj33Xefzn0ePnwYZ86cQU5OjtZ7SqUS3377LWpra2Fvb48uXbrA09MT/v7++n9wIiIiIiIionby8fHReL1s2TIsX75cq+52XEXt5eXV5lXU7d1vREQEnn76afj5+aG8vBxLly7F448/juLiYq1boALAxYsXsXLlSsyZM0dj/YIFCzBw4EDce++9+OKLL5CcnIzy8nK89957On9GuSx2wvvxfx+D67WO9QhxL267qJ0+tntaWK/o0RvbLmqHLYNeENIHAKQTYvrMwjAxjQA4p/0ipM/vbrrv6yuHYofub9WM1lNMGwCQLonpo3hGTB8AwBgxbaR/iukDANFwbLuoHVZ1fklIn2vNDQDWCumlxQT3BHV0dERwcDAKCgowadIk9fqCggJMmDBB5zahoaH4+OOPNdbt3bsXISEhcHBwUNcUFBRo3Md77969CAsLU7+WJAkvvvgi8vLycPDgQYMT1JmZmQgODka/fv301nTv3h0AsH//ftTU1GD8+PEGfnLSZcS/T3Q4swPdvxYzGAB7oPvvoBzPPrFBSJ8toQIzWyWmj9DMXm6BmZ37mZhG94tpAwDS72L6CM3sIW2XtIfIzJ6Fq0L6vNl1npA+1+yvA1gvpJcG3sObTIx53Tbmdfswr9vH1vN6BnSfqWwsi89roEP38K6srNQ4oUzX5PDNzHUVdVs10dHR6v8OCgpCSEgI/Pz8sGvXLo3bhwI3zmp/8sknERgYiGXLlmm8d/Nn/UceeQT33nsvIiMj1Wd9i8J/LhER2ZrWe4bJ2c4ISUlJiImJQUhICEJDQ7F+/XpUVFQgLi4OAJCcnIyffvoJmzZtAgDExcVh7dq1SEpKQmxsLFQqFTIzMzVuR7JgwQIMHz4cqampmDBhAnbs2IF9+/bhyJEj6pq5c+figw8+wI4dO+Di4qL+1tnNzQ2dOnVS19XX1+PDDz/EW2+9pXP8WVlZCAgIQI8ePaBSqbBgwQIkJiaiT58+xh0IIiIiOUyU10RERNRBcjL7j/r2XEENmO8qajn7BQAvLy/4+fnh3LlzGusvXbqEMWPG4J577kFeXp765DZ9hgy58W3O+fPnhU548x7eRES2xkT3BI2OjkZaWhpWrFiB/v3749ChQ8jPz4efnx8AoKqqChUVFep6f39/5Ofn4+DBg+jfvz9WrlyJNWvWqJ8eDQBhYWHYunUrsrKy8MgjjyA7Oxs5OTnqp0cDwLp161BXV4fw8HB4eXmpl1tvW7J161ZIkoRnntF9asWZM2cwceJEBAQEYMWKFViyZInGwz2IiIhuK97Dm4iIyDqYIK9vvor6ZgUFBRpXPN+s9Qrpm+m7ivrWmtaecvYL3LhlSWVlJby8vNTr6uvroVQq4ejoiJ07d2o8R0uf0tJSANDoIwL/uUREZGvsIO+3u4wzxuLj4xEfH6/zvezsbK11I0aMQElJicGekZGRiIyM1Pt+6yVabZk9ezZmz56t9/033ngDb7zxRrt6ERERCWfCvCYiIqIOkJPZMvLaXFdRt7Xfy5cvY/ny5ZgyZQq8vLzw/fffY/Hixejevbv6FqeXLl2CUqnE1atXsXnzZvVDOgGgR48esLOzg0qlwrFjxzBy5Ei4ubnhxIkTSExMxPjx4+Hr62v8ATOAE95ERLaG9wQlIiKyfMxrIiIi69CBe3gbIzo6GhcvXsSKFStQVVWFoKCgdl1FnZiYiHfeeQfe3t56r6J+9dVXsXTpUvTu3VvrKuq29mtnZ4dTp05h06ZN+O233+Dl5YWRI0ciJycHLi4uAIDi4mIcP34cAHD//Zo31S8vL0fPnj3h5OSEnJwc/OUvf0FDQwP8/PwQGxuLP//5z8YfrDbwn0tEREREREREREREZmaOq6jb2m+nTp2wZ88eg9uHh4e3eTX2wIEDcezYMYM1onDCm4jI1vAhWERERJaPeU1ERGQdOvDQSjIPTngTEdkaXiJNRERk+ZjXRERE1sFEtzQhcXj4iYhsDR+CRUREZPmY10RERNbBRA+tJHHuMvcAiIhIMLsOLERERGQaJs7r9PR0+Pv7w9nZGcHBwTh8+LDB+sLCQgQHB8PZ2Rm9evVCRkaGVk1ubi4CAwPh5OSEwMBA5OXlabx/6NAhjBs3Dt7e3lAoFNi+fbvG+42NjVi4cCH69u2Lzp07w9vbG9OmTcOFCxe09qVSqfD444+jc+fO6NKlC8LDw/H7778bfyCIiIiMxc/XVocT3kRERERERDYsJycHCQkJWLJkCUpLSzFs2DBERESgoqJCZ315eTnGjh2LYcOGobS0FIsXL8b8+fORm5urrlGpVIiOjkZMTAxOnjyJmJgYREVF4fjx4+qaK1euoF+/fli7dq3O/Vy9ehUlJSVYunQpSkpKsG3bNpw9exbjx4/XqFOpVBgzZgyUSiW++OILnDhxAvPmzcNdd/HjLBEREWnjLU2IiGwN7wlKRERk+UyY16tXr8bMmTMxa9YsAEBaWhr27NmDdevWISUlRas+IyMDvr6+SEtLAwAEBASgqKgIq1atwpQpU9Q9Ro0aheTkZABAcnIyCgsLkZaWhi1btgAAIiIiEBERoXdcbm5uKCgo0Fj39ttv49FHH0VFRQV8fX0BAImJiZg/fz4WLVqkrnvggQeMPxBERERy8B7eVodfiRMR2Rr7DixERERkGh3M6/r6eo2loaFB526uX7+O4uJiKJVKjfVKpRJHjx7VuY1KpdKqHz16NIqKitDY2GiwRl/P9qqrq4NCoUCXLl0AADU1NTh+/Djc3d0RFhYGDw8PjBgxAkeOHOnQfoiIiNqNn6+tDie8iYhsDSe8iYiILF8H89rHxwdubm7qRdeZ2gBQW1uL5uZmeHh4aKz38PBAdXW1zm2qq6t11jc1NaG2ttZgjb6e7XHt2jUsWrQIzz77LFxdXQEA3333HQBg+fLliI2Nxe7duzFw4EA88cQTOHfunOx9ERERtRs/X1sdHn4iIhsj3QVIMh6QIfErUCIiIpPpaF5XVlaqJ4UBwMnJyeB2CoVCs48kaa1rq/7W9cb2NKSxsRFTp05FS0sL0tPT1etbWloAAHPmzMHzzz8PABgwYAA+++wzbNiwQe9EPxERkShyMpufr82LE95ERERERERWxtXVVWPCW5/u3bvDzs5O68zrmpoarTO0W3l6euqst7e3R7du3QzW6OtpSGNjI6KiolBeXo79+/dr/FxeXl4AgMDAQI1tAgIC9D50k4iIiO5s/L6BiMjGNNvLX4iIiMg0TJXXjo6OCA4O1no4ZEFBAcLCwnRuExoaqlW/d+9ehISEwMHBwWCNvp76tE52nzt3Dvv27VNPqLfq2bMnvL29cebMGY31Z8+ehZ+fn1H7IiIikoOfr60PDz8RkY2RG64MZCIiItMxZV4nJSUhJiYGISEhCA0Nxfr161FRUYG4uDgAQHJyMn766Sds2rQJABAXF4e1a9ciKSkJsbGxUKlUyMzMxJYtW9Q9FyxYgOHDhyM1NRUTJkzAjh07sG/fPo2HSV6+fBnnz59Xvy4vL0dZWRm6du0KX19fNDU1ITIyEiUlJfjkk0/Q3NysPmu8a9eucHR0hEKhwCuvvIJly5ahX79+6N+/PzZu3IhvvvkGH330kfEHg4iIyEhyMpufr83LYg9/eQ93uLh27AT0RPxd0GgA1EQJa1Xk/i8hfVQn+gvpAwCLMV5In774WUgfALj2312F9FE4C2lzw1QxbRImiLvXoGJPsphG/cW0AQCEi2mzA6PFNAIAPCuky9K3Vgnpg2v1ANaK6XWLJjsFmuyMv39mk50EQBI/ILJ5IjJ7MV4XNBqgsW6isF7fu30mpE+Z6kEhfQBgsaAw6o1LQvoAwLWnLDCzx4hpkzBFYGbvEpTZIWLaABB2nERm9m+IEdJn4VtvC+lzI7PXi+l1E1PmdXR0NC5evIgVK1agqqoKQUFByM/PV58hXVVVpXF7EH9/f+Tn5yMxMRHvvPMOvL29sWbNGkyZMkVdExYWhq1bt+LVV1/F0qVL0bt3b+Tk5GDw4MHqmqKiIowcOVL9OikpCQAwffp0ZGdn48cff8TOnTsBAP3799cY84EDBxAeHg4ASEhIwLVr15CYmIhffvkF/fr1Q0FBAXr37m3UcbjTMa/bZvN5PUZQXt8jpM0NzOv2scC8vnyH5DUgL7P5+dq8LHbCm4iI5Gm2t0ezvfEfoJvtJQCN4gdEREREWkyd1/Hx8YiPj9f5XnZ2tta6ESNGoKSkxGDPyMhIREZG6n0/PDxc/bBLXXr27Gnw/ZstWrQIixYtalctERGRSHIym5+vzYsT3kRENqbZzg7NMs4Ya7ZjIBMREZkK85qIiMg6yMls5rV58aGVRERERERERERERGQTeIY3EZGNaYEdmmH8GWMtvL8YERGRyTCviYiIrIOczGZem9dtOcP7p59+wp/+9Cd069YNd999N/r374/i4uLbsSsiIrpFE+xkL3RnYV4TEZkP85qMwcwmIjIf5rX1EX6G96+//orHHnsMI0eOxKeffgp3d3d8++236NKli+hdERGRDs2wQ7OM7zOb0XIbRkOWinlNRGRezGtqL2Y2EZF5ycls5rV5CZ/wTk1NhY+PD7KystTrevbsKXo3RESkh/wP0MZfVk3Wi3lNRGRezGtqL2Y2EZF5yZvwZl6bk/BbmuzcuRMhISF4+umn4e7ujgEDBuAf//iH6N0QERFRBzCviYiIrAMzm4iIyDjCJ7y/++47rFu3Dg888AD27NmDuLg4zJ8/H5s2bdJZ39DQgPr6eo2FiIjku/Hts7yF7hzG5jXAzCYiEol5Te3Fz9hERObFvLY+wm9p0tLSgpCQELz++usAgAEDBuCrr77CunXrMG3aNK36lJQU/OUvfxE9DCKiOxYvkab2MDavAWY2EZFIzGtqL37GJiIyL97SxPoIP8Pby8sLgYGBGusCAgJQUVGhsz45ORl1dXXqpbKyUvSQiIjuKM0ynyDNb6DvLMbmNcDMJiISiXlN7cXP2ERE5iUns5nX5iX8DO/HHnsMZ86c0Vh39uxZ+Pn56ax3cnKCk5OT6GEQEd2xmmEv84wxPkX6TmJsXgPMbCIikZjX1F78jE1EZF5yMpt5bV7Cz/BOTEzEsWPH8Prrr+P8+fP44IMPsH79esydO1f0roiIyMzS09Ph7+8PZ2dnBAcH4/DhwwbrCwsLERwcDGdnZ/Tq1QsZGRlaNbm5uQgMDISTkxMCAwORl5en8X5KSgoGDRoEFxcXuLu7Y+LEiVofAhUKhc7lzTffVNdUV1cjJiYGnp6e6Ny5MwYOHIiPPvqoA0fDujCviYiIrAMzm4iIyDjCJ7wHDRqEvLw8bNmyBUFBQVi5ciXS0tLw3HPPid4VERHp0Iy7ZD5Uw7hIyMnJQUJCApYsWYLS0lIMGzYMERERei+vLS8vx9ixYzFs2DCUlpZi8eLFmD9/PnJzc9U1KpUK0dHRiImJwcmTJxETE4OoqCgcP35cXVNYWIi5c+fi2LFjKCgoQFNTE5RKJa5cuaKuqaqq0lg2bNgAhUKBKVOmqGtiYmJw5swZ7Ny5E6dOncLkyZMRHR2N0tJSo46DtWJeExGZl6nymqwfM5uIyLzkZTbz2pyE39IEAJ566ik89dRTt6M1ERG1Qe4ToZuNrF+9ejVmzpyJWbNmAQDS0tKwZ88erFu3DikpKVr1GRkZ8PX1RVpaGoAb954sKirCqlWr1BPRaWlpGDVqFJKTkwHcuAdlYWEh0tLSsGXLFgDA7t27NfpmZWXB3d0dxcXFGD58OADA09NTo2bHjh0YOXIkevXqpV6nUqmwbt06PProowCAV199FX//+99RUlKCAQMGGHk0rBPzmojIfEyV12QbmNlEROYjJ7OZ1+bFrxuIiGyMnAdgtS4AUF9fr7E0NDRo7eP69esoLi6GUqnUWK9UKnH06FGd41KpVFr1o0ePRlFRERobGw3W6OsJAHV1dQCArl276nz/559/xq5duzBz5kyN9UOHDkVOTg5++eUXtLS0YOvWrWhoaEB4eLjefREREYnS0bwmIiIi02BeWx9OeBMR2ZgW2P/xUA3jlpY/Lvrx8fGBm5ubetF1tnZtbS2am5vh4eGhsd7DwwPV1dU6x1VdXa2zvqmpCbW1tQZr9PWUJAlJSUkYOnQogoKCdNZs3LgRLi4umDx5ssb6nJwcNDU1oVu3bnBycsKcOXOQl5eH3r176+xDREQkUkfzmoiIiExDTmYzr82LR5+IyMZ09BLpyspKuLq6qtc7OTnp3UahUGi8liRJa11b9beuN6bnvHnz8OWXX+LIkSN697lhwwY899xzcHZ21lj/6quv4tdff8W+ffvQvXt3bN++HU8//TQOHz6Mvn376u1HREQkAm9pQkREZB14SxPrY7ET3v7rauDq3HadIZMX5IkZDIBI94+E9fq2r+6zEI0VfOqwkD4AYC/of8W/4c9C+gDAwBP6J7CMUbJnqJA+AID7JSFt0n5KFNIHALBcTJsAVYmYRgC++iVYSJ8hOCikDwCEC+rV4yXdD2Q0Vkv9JVx8VUgr4VxdXTUmvHXp3r077OzstM68rqmp0TpDu5Wnp6fOent7e3Tr1s1gja6eL774Inbu3IlDhw7hvvvu07nPw4cP48yZM8jJydFY/+2332Lt2rX417/+hYcffhgA0K9fPxw+fBjvvPMOMjIyDPz0dCv/jTVw7dSxHhNm7RQzGAAT3cTl/9FeTwjpE/yduMxuFvTPt//DAiF9ACDi8DYhfT7Nmdx2UXuFiMnsNT/PF9IHALBWTJugT0+IaQTgVM2jQvoMFpjZY5EvpM/xl84J6dNSfxkXLDSziYzBvG6bzee1SlBe5wrM6/7M6/awxLxWYo+QPvcxr+k24C1NiIhsjPFPj7Yz+htrR0dHBAcHo6CgQGN9QUEBwsLCdG4TGhqqVb93716EhITAwcHBYM3NPSVJwrx587Bt2zbs378f/v7+eseZmZmJ4OBg9OvXT2P91atXAQB33aUZg3Z2dmhpadHbj4iISBRT5DURERF1HPPa+ljsGd5ERCRPM+6SeYm0cWdXJCUlISYmBiEhIQgNDcX69etRUVGBuLg4AEBycjJ++uknbNq0CQAQFxeHtWvXIikpCbGxsVCpVMjMzMSWLVvUPRcsWIDhw4cjNTUVEyZMwI4dO7Bv3z6NW5bMnTsXH3zwAXbs2AEXFxf1GeFubm7o1Ok/py3V19fjww8/xFtvvaU19oceegj3338/5syZg1WrVqFbt27Yvn07CgoK8Mknnxh1HIiIiOQwVV4TERFRx8jJbOa1eXHCm4jIxsh9InSTkYEcHR2NixcvYsWKFaiqqkJQUBDy8/Ph5+cHAKiqqkJFxX9uAePv74/8/HwkJibinXfegbe3N9asWYMpU6aoa8LCwrB161a8+uqrWLp0KXr37o2cnBwMHjxYXbNu3ToAQHh4uMZ4srKyMGPGDPXrrVu3QpIkPPPMM1pjd3BwQH5+PhYtWoRx48bh8uXLuP/++7Fx40aMHTvWqONAREQkh6nymoiIiDpGTmYzr82LE95ERDam9anQxm9nvPj4eMTHx+t8Lzs7W2vdiBEjUFJi+H7xkZGRiIyM1Pt+64Mu2zJ79mzMnj1b7/sPPPAAcnNz29WLiIhINFPmNREREcknJ7OZ1+bFe3gTERERERERERERkU3gGd5ERDamReYDMlp4yRUREZHJMK+JiIisg5zMZl6bFye8iYhsjNwnQvOhGkRERKbDvCYiIrIOcjKbeW1enPAmIrIxTbhL5kOwWm7DaIiIiEgX5jUREZF1kJPZzGvz4oQ3EZGNkf8QLH4DTUREZCrMayIiIusg76GVzGtz4kMriYiIiIiIiIiIiMgm8AxvIiIbI/+eoLzkioiIyFSY10RERNZB3j28mdfmxAlvIiIbww/QRERElo95TUREZB044W19OOFNRGRjmmEn6yFYDGQiIiLTYV4TERFZBzmZzbw2L97Dm4jIxrQ+UEPOQkRERKbBvCYiIrIOpszr9PR0+Pv7w9nZGcHBwTh8+LDB+sLCQgQHB8PZ2Rm9evVCRkaGVk1ubi4CAwPh5OSEwMBA5OXlGb3fGTNmQKFQaCxDhgxRv//LL7/gxRdfRJ8+fXD33XfD19cX8+fPR11dnUafX3/9FTExMXBzc4ObmxtiYmLw22+/GXGE2ocT3kRENqYZd6kvuTJuYSQQERGZCvOaiIjIOsjLbOPzOicnBwkJCViyZAlKS0sxbNgwREREoKKiQmd9eXk5xo4di2HDhqG0tBSLFy/G/PnzkZubq65RqVSIjo5GTEwMTp48iZiYGERFReH48eNG73fMmDGoqqpSL/n5+er3Lly4gAsXLmDVqlU4deoUsrOzsXv3bsycOVOjx7PPPouysjLs3r0bu3fvRllZGWJiYow+Vm3h6QFEREREREREREREZrR69WrMnDkTs2bNAgCkpaVhz549WLduHVJSUrTqMzIy4Ovri7S0NABAQEAAioqKsGrVKkyZMkXdY9SoUUhOTgYAJCcno7CwEGlpadiyZYtR+3VycoKnp6fOsQcFBWlMtPfu3RuvvfYa/vSnP6GpqQn29vY4ffo0du/ejWPHjmHw4MEAgH/84x8IDQ3FmTNn0KdPn44cPg08PYCIyMbIO1tM3oOziIiISB5T57U5LpE+dOgQxo0bB29vbygUCmzfvl3j/cbGRixcuBB9+/ZF586d4e3tjWnTpuHChQsadeHh4VqXUU+dOlXWcSAiIjKWKfL6+vXrKC4uhlKp1FivVCpx9OhRnduoVCqt+tGjR6OoqAiNjY0Ga1p7GrPfgwcPwt3dHQ8++CBiY2NRU1Nj8Geqq6uDq6sr7O3t1WNxc3NTT3YDwJAhQ+Dm5qb3Z5TLYs/wrv2fzmhwVXSoRzfUChoN8CpWCeuleENMrx7QfUmDHDXwFdInHH8R0gcASkYNFdJHKhDSBgCgyOnY30m1ImcxfQBIKjF9FH0HimkEoHvZj0L6XMR/CekDAIp3Rwjp03XWT0L6SM23b3JZ7odhTniTXBem34tLHcxsb1xou6idVuJ1Yb0Ub4vp5dZQLaQPAPzmpPusCmMNw1+F9AGAI6NGCekjNLNzxWR2S1FnIX0AQPpUTB9F6CAxjQD4qM4K6VOJB4T0AQBFqpjMdphVL6SPdKmTkD63MmVet16qnJ6ejsceewzvvvsuIiIi8PXXX8PXV/tzQOsl0rGxsdi8eTM+//xzxMfHo0ePHuozxlovkV65ciUmTZqEvLw8REVF4ciRI+oPsleuXEG/fv3w/PPPq7e72dWrV1FSUoKlS5eiX79++PXXX5GQkIDx48ejqKhIozY2NhYrVqxQv+7U6fb8udgy5nXbmNftw7xuH5vP67cE5fUMy85rQF5mt9bX12v+fE5OTnByctKqr62tRXNzMzw8PDTWe3h4oLpa9++m6upqnfVNTU2ora2Fl5eX3prWnu3db0REBJ5++mn4+fmhvLwcS5cuxeOPP47i4mKdP8/FixexcuVKzJkzR2O87u7uWrXu7u56f0a5LHbCm4iI5JHzBOnW7YiIiMg0TJnX5rpEOiIiAhEREXrH5ebmhoICzZmzt99+G48++igqKio0JuPvvvtuvZdRExER3U5yMrs1r318fDTWL1u2DMuXL9e7nUKh+SWQJEla69qqv3V9e3q2VRMdHa3+76CgIISEhMDPzw+7du3C5MmTNbatr6/Hk08+icDAQCxbtszgftrzM8rBW5oQEdkYuU+QlvsUaSIiIjJeR/O6vr5eY2loaNC5H3NdIi1XXV0dFAoFunTporH+/fffR/fu3fHwww/j5ZdfxqVLlzq0HyIiovbqSF5XVlairq5OvbR+UXyr7t27w87OTutM55qaGq2zr1t5enrqrLe3t0e3bt0M1rT2lLNfAPDy8oKfnx/OnTunsf7SpUsYM2YM7rnnHuTl5cHBwUFjvD///LNWr3//+98G9yUHJ7yJiIiIiIisjI+PD9zc3NSLrjO1gdtzibShmo5cknzt2jUsWrQIzz77LFxdXdXrn3vuOWzZsgUHDx7E0qVLkZubq3U2GRERkSVydXXVWHTd/gMAHB0dERwcrHXlU0FBAcLCwnRuExoaqlW/d+9ehISEqCea9dW09pSzX+DGLUsqKyvh5eWlXldfXw+lUglHR0fs3LkTzs6at/MNDQ1FXV0dvvjiC/W648ePo66uzuC+5ODpfERENob38CYiIrJ8Hc3ryspKjUlhfR+gW5nrEun2amxsxNSpU9HS0oL09HSN92JjY9X/HRQUhAceeAAhISEoKSnBwIHinkNDRESkS0fu4W2MpKQkxMTEICQkBKGhoVi/fj0qKioQFxcH4Mbtw3766Sds2rQJABAXF4e1a9ciKSkJsbGxUKlUyMzMVN9aDAAWLFiA4cOHIzU1FRMmTMCOHTuwb98+HDlypN37vXz5MpYvX44pU6bAy8sL33//PRYvXozu3btj0qRJAG6c2a1UKnH16lVs3rxZfQUaAPTo0QN2dnYICAjAmDFjEBsbi3fffRcAMHv2bDz11FPo06eP0cfLEOFneDc1NeHVV1+Fv78/OnXqhF69emHFihVoaWkRvSsiItKhGXfJfIo0L/q5kzCviYjMq6N53d4zxsx1ibQxGhsbERUVhfLychQUFGhM5OsycOBAODg4aF1GbauY2URE5iUvs43/fB0dHY20tDSsWLEC/fv3x6FDh5Cfnw8/Pz8AQFVVFSoqKtT1/v7+yM/Px8GDB9G/f3+sXLkSa9as0XhQdFhYGLZu3YqsrCw88sgjyM7ORk5OjvoB0+3Zr52dHU6dOoUJEybgwQcfxPTp0/Hggw9CpVLBxcUFAFBcXIzjx4/j1KlTuP/+++Hl5aVeKisr1ft6//330bdvXyiVSiiVSjzyyCP45z//afSxaovwM7xTU1ORkZGBjRs34uGHH0ZRURGef/55uLm5YcGCBaJ3R0REt2iS+RAsOduQ9WJeExGZl6ny+uZLlVvPwgJuXKo8YcIEnduEhobi448/1lin7xLpxMREjRpjL0lunew+d+4cDhw4oJ5QN+Srr75CY2OjxmXUtoyZTURkXnIyW+7n6/j4eMTHx+t8Lzs7W2vdiBEjUFJSYrBnZGQkIiMjZe+3U6dO2LNnj8Htw8PD1VeDGdK1a1ds3ry5zbqOEj7hrVKpMGHCBDz55JMAgJ49e2LLli0oKioSvSsiItJB7gMom9F8G0ZDlop5TURkXqbMa3NdIn358mWcP39e/bq8vBxlZWXo2rUrfH190dTUhMjISJSUlOCTTz5Bc3Oz+qzxrl27wtHREd9++y3ef/99jB07Ft27d8fXX3+Nl156CQMGDMBjjz1m9LGwRsxsIiLzkpPZ/HxtXsKvXx86dCg+++wznD17FgBw8uRJHDlyBGPHjhW9KyIiIpKJeU1EdOcw1yXSRUVFGDBgAAYMGADgxsT7gAED8L//+78AgB9//BE7d+7Ejz/+iP79+2tc/nz06FEAN85Q/+yzzzB69Gj06dMH8+fPh1KpxL59+2Bnd2dcncbMJiIiMo7wM7wXLlyIuro6PPTQQ7Czs0NzczNee+01PPPMMzrrGxoa0NDQoH7dekNzIiKSp0XmQ7BaeEuTO4qxeQ0ws4mIRDJ1XpvjEum2Lm/u2bNnm5c/+/j4oLCw0GCNreNnbCIi85KT2fx8bV7CJ7xzcnKwefNmfPDBB3j44YdRVlaGhIQEeHt7Y/r06Vr1KSkp+Mtf/iJ6GEREdyw5T5Bu3Y7uHMbmNcDMJiISiXlN7cXP2ERE5iUns5nX5iV8wvuVV17BokWLMHXqVABA37598cMPPyAlJUVnGCcnJyMpKUn9ur6+Hj4+PqKHRUR0x2h9grSc7ejOYWxeA8xsIiKRmNfUXvyMTURkXnIym3ltXsInvK9evYq77tL8Q7Wzs0NLS4vOeicnJzg5OYkeBhHRHasJdrCT8QFa7lOkyToZm9cAM5uISCTmNbUXP2MTEZmXnMxmXpuX8AnvcePG4bXXXoOvry8efvhhlJaWYvXq1XjhhRdE74qIiHSQ8wTp1u3ozsG8JiIyL+Y1tRczm4jIvORkNvPavISfX//2228jMjIS8fHxCAgIwMsvv4w5c+Zg5cqVondFRERmlp6eDn9/fzg7OyM4OBiHDx82WF9YWIjg4GA4OzujV69eyMjI0KrJzc1FYGAgnJycEBgYiLy8PI33U1JSMGjQILi4uMDd3R0TJ07EmTNnNGoUCoXO5c033wQAfP/993prPvzwww4eFevAvCYiIrIOzGwiIiLjCP+6wcXFBWlpaUhLSxPdmoiI2kHOE6RbtzNGTk4OEhISkJ6ejsceewzvvvsuIiIi8PXXX8PX11ervry8HGPHjkVsbCw2b96Mzz//HPHx8ejRowemTJkCAFCpVIiOjsbKlSsxadIk5OXlISoqCkeOHMHgwYMB3Jg0nzt3LgYNGoSmpiYsWbIESqUSX3/9NTp37gwAqKqq0tj3p59+ipkzZ6r34+Pjo1Wzfv16/O1vf0NERIRRx8FaMa+JiMzLVHlN1o+ZTURkXnIym3ltXjy/nojIxsh5gnTrdsZYvXo1Zs6ciVmzZgEA0tLSsGfPHqxbtw4pKSla9RkZGfD19VV/WAsICEBRURFWrVqlnohOS0vDqFGjkJycDODGQ5cKCwuRlpaGLVu2AAB2796t0TcrKwvu7u4oLi7G8OHDAQCenp4aNTt27MDIkSPRq1cvADfue3lrTV5eHqKjo3HPPfcYdRyIiIjkMFVeExERUcfIyWzmtXnxkaFERDamCXayFwCor6/XWBoaGrT2cf36dRQXF0OpVGqsVyqVOHr0qM5xqVQqrfrRo0ejqKgIjY2NBmv09QSAuro6AEDXrl11vv/zzz9j165dmDlzpt4excXFKCsrM1hDREQkUkfzmoiIiEyDeW19OOFNRGRjbnz7bC9juRHIPj4+cHNzUy+6ztaura1Fc3MzPDw8NNZ7eHigurpa57iqq6t11jc1NaG2ttZgjb6ekiQhKSkJQ4cORVBQkM6ajRs3wsXFBZMnT9b5PgBkZmYiICAAYWFhemuIiIhE6mheExERkWnIy2zmtTlZ7C1N6uCG5g7Ox5c1DxA0GkBxXlgr4JqYNv8+rn2PXLkU34vp80C0l5hGAL4vcBfSR/F5jZA+APDP6Eghfcqi+wvpAwCKPa8K6fPtKXF/dq9jiZA+L+KqkD4AgDGJQtpc/Ow+IX3qrwBuQjqJV1lZCVdXV/VrJycnvbUKhULjtSRJWuvaqr91vTE9582bhy+//BJHjhzRu88NGzbgueeeg7Ozs873f//9d3zwwQdYunSp3h5k2O/oBLsOZvahumGCRgMoaiVhvXBN/99nY9Qd9Gy7qJ0UP4rp02umt5hGAC5/IuYf9IriZiF9AOD/TRknpE/pFIH/ntyxQkifH1XdhPQBgNeEZXaTkD4AgKliMvv6STFJa8mZTWQM5nXbbD2v63Y7CumjKL4upA8AfDBlopA+p6Y8IqQPIC6vv1eJmdMAgFQsEtJHaF5HMq/JclnshDcREcnT0XuCurq6akx469K9e3fY2dlpnXldU1OjdYZ2K09PT5319vb26Natm8EaXT1ffPFF7Ny5E4cOHcJ99+n+IuLw4cM4c+YMcnJy9P4sH330Ea5evYpp06bprSEiIhKN9/AmIiKyDryHt/XhLU2IiGxMaxjLWdrL0dERwcHBKCgo0FhfUFCg97YgoaGhWvV79+5FSEgIHBwcDNbc3FOSJMybNw/btm3D/v374e/vr3ecmZmZCA4ORr9+/QzWjB8/Hj169NBbQ0REJJop8pqIiIg6jnltfXiGNxGRjWmRGa4tRm6TlJSEmJgYhISEIDQ0FOvXr0dFRQXi4uIAAMnJyfjpp5+wadMmAEBcXBzWrl2LpKQkxMbGQqVSITMzE1u2bFH3XLBgAYYPH47U1FRMmDABO3bswL59+zRuWTJ37lx88MEH2LFjB1xcXNRnhLu5uaFTp07quvr6enz44Yd466239P4M58+fx6FDh5Cfn2/Uz05ERNRRpsprIiIi6hg5mc28Ni9OeBMR2Zgm2EEhI1yNfYp0dHQ0Ll68iBUrVqCqqgpBQUHIz8+Hn58fAKCqqgoVFRXqen9/f+Tn5yMxMRHvvPMOvL29sWbNGkyZMkVdExYWhq1bt+LVV1/F0qVL0bt3b+Tk5GDw4MHqmnXr1gEAwsPDNcaTlZWFGTNmqF9v3boVkiThmWee0fszbNiwAf/1X/8FpVJp1M9ORETUUabKayIiIuoYOZnNvDYvTngTEZFs8fHxiI+P1/ledna21roRI0agpKTEYM/IyEhERup/QGzrgy7bMnv2bMyePdtgzeuvv47XX3+9Xf2IiIiIiIiIyPJxwpuIyMY0ww53yfj1znuMERERmQ7zmoiIyDrIyWzmtXlxwpuIyMbcCGPjw5WBTEREZDrMayIiIusgJ7OZ1+bFCW8iIhvDD9BERESWj3lNRERkHTjhbX044U1EZGP4ECwiIiLLx7wmIiKyDnxopfXhhDcRkY1pgT2aZfx6b2EkEBERmQzzmoiIyDrIyWzmtXndZe4BEBERERERERERERGJwK8biIhsTLPMS6R5jzEiIiLTYV4TERFZBzmZzbw2L054ExHZmGbcJfMDNC/6ISIiMhXmNRERkXWQk9nMa/PihDcRkY258XAMPgSLiIjIkjGviYiIrIOczGZemxcnvImIbEwz7KGQ8etdzoOziIiISB7mNRERkXWQk9nMa/Pi+fVEREREREREREREZBP4dQMRkY1pgZ2sB2S08JIrIiIik2FeExERWQc5mc28Ni+LnfAeuOs0cLdrh3q8PiFR0GiA630chfX6qE+kkD7/+qmvkD4AgEhnIW3O3d9PSB8A+J/gDDGNLotpAwDvYZaQPoW5Y4T0AYCuE38S0qcGHkL6AEA5egrps3/cU0L6AABWiWnTV/mFkD7N9ZcBPC6kl1ZvmfcE5VOkSa6BOzqe2W9NiRc0GqDBzUlYr+29Jwrp88W3w4X0AQDMUAhp813/h4X0AYCngz8U0+g3MW0AIAvPC+nz6WeThfQBgB4TKoT0uQQXIX0A4AweFNJn/ySBmb1ITJvAx4uF9LmR2SOE9NLoy7wmE2Net83W83pq8FYxjWrFtAGAf2KakD6WmNe/424hfQDmdXvcrrwG5GU289q8LHbCm4iI5OEHaCIiIsvHvCYiIrIOnPC2PpzwJiKyMU24C5KsD9B8rAMREZGpMK+JiIisg5zMZl6bF48+EREREREREREREdkEnuFNRGRjmmEPOb/emxkJREREJsO8JiIisg5yMpt5bV5Gn+F96NAhjBs3Dt7e3lAoFNi+fbvG+5IkYfny5fD29kanTp0QHh6Or776StR4iYioDc1/PEFazkK2g3lNRGTZmNcEMK+JiKwB89r6GD3hfeXKFfTr1w9r167V+f7f/vY3rF69GmvXrsWJEyfg6emJUaNG4dKlSx0eLBERta1FZhi3MJBtCvOaiMiyMa8JYF4TEVkDOZnNvDYvoye8IyIi8Ne//hWTJ0/Wek+SJKSlpWHJkiWYPHkygoKCsHHjRly9ehUffPCBkAETEZFhTbCTvZDtYF4TEVk2U+d1eno6/P394ezsjODgYBw+fNhgfWFhIYKDg+Hs7IxevXohIyNDqyY3NxeBgYFwcnJCYGAg8vLyNN5v6+zlxsZGLFy4EH379kXnzp3h7e2NadOm4cKFCzrHJEkSIiIidPayVsxrIiLLx8/X1kfoQyvLy8tRXV0NpVKpXufk5IQRI0bg6NGjIndFRER63PhG2V7GwkC+UzCviYjMz5R5nZOTg4SEBCxZsgSlpaUYNmwYIiIiUFFRobO+vLwcY8eOxbBhw1BaWorFixdj/vz5yM3NVdeoVCpER0cjJiYGJ0+eRExMDKKionD8+HF1TVtnL1+9ehUlJSVYunQpSkpKsG3bNpw9exbjx4/XWZ+WlgaFQmH0z2+tmNdERJZBXmbz87U5Cb2DenV1NQDAw8NDY72Hhwd++OEHnds0NDSgoaFB/bq+vl7kkIiIiOgWcvIaYGYTEVmr1atXY+bMmZg1axaAGxPHe/bswbp165CSkqJVn5GRAV9fX6SlpQEAAgICUFRUhFWrVmHKlCnqHqNGjUJycjIAIDk5GYWFhUhLS8OWLVsA3Dh7OSIiQu+43NzcUFBQoLHu7bffxqOPPoqKigr4+vqq1588eRKrV6/GiRMn4OXlJf9gWBHmNRERkTxCz/Budeu37pIk6f0mPiUlBW5uburFx8fndgyJiOiOwYdgUXsZk9cAM5uISCRT5fX169dRXFyscZYwACiVSr1nCatUKq360aNHo6ioCI2NjQZrOnrmcV1dHRQKBbp06aJed/XqVTzzzDNYu3YtPD09O9TfGjGviYjMi5+vrY/QCe/Wf3y0fhPdqqamRutb6VbJycmoq6tTL5WVlSKHRER0x+GEN7VFTl4DzGwiIpE6mtf19fUay81n9N6strYWzc3NOs8SvjUHWlVXV+usb2pqQm1trcEafT3b49q1a1i0aBGeffZZuLq6qtcnJiYiLCwMEyZMkN3bGjGviYgsAz9fWx+hE97+/v7w9PTUuCzt+vXrKCwsRFhYmM5tnJyc4OrqqrEQEZF8zS12she6M8jJa4CZTUQkUkfz2sfHR+MsXl23JrmZsWcJ66q/db2xPQ1pbGzE1KlT0dLSgvT0dPX6nTt3Yv/+/erbq9xJmNdERJaBn6+tj9H38L58+TLOnz+vfl1eXo6ysjJ07doVvr6+SEhIwOuvv44HHngADzzwAF5//XXcfffdePbZZ4UOnIiIdGtuskNLk/HhKsnYhiwX85qIyLJ1NK8rKys1JjKdnJx01nfv3h12dnZGnSXs6emps97e3h7dunUzWGPozGN9GhsbERUVhfLycuzfv1/j59q/fz++/fZbjVucAMCUKVMwbNgwHDx40Oj9WRLmNRGR5ZOT2fx8bV5GT3gXFRVh5MiR6tdJSUkAgOnTpyM7Oxt//vOf8fvvvyM+Ph6//vorBg8ejL1798LFxUXcqImIiMgg5jURkW1r75m7jo6OCA4ORkFBASZNmqReX1BQoPcWIaGhofj444811u3duxchISFwcHBQ1xQUFCAxMVGjxtCZx7q0TnafO3cOBw4cUE+ot1q0aJH6YZut+vbti7///e8YN26cUfuyRMxrIiIi8Yy+pUl4eDgkSdJasrOzAdy4rG358uWoqqrCtWvXUFhYiKCgINHjJiIiPZqb7GUvxkpPT4e/vz+cnZ0RHByMw4cPG6wvLCxEcHAwnJ2d0atXL2RkZGjV5ObmIjAwEE5OTggMDEReXp7G+ykpKRg0aBBcXFzg7u6OiRMn4syZMxo1CoVC5/Lmm29q1KlUKjz++OPo3LkzunTpgvDwcPz+++9GHwdLxLwmIrJspszrpKQkvPfee9iwYQNOnz6NxMREVFRUIC4uDsCNez5PmzZNXR8XF4cffvgBSUlJOH36NDZs2IDMzEy8/PLL6poFCxZg7969SE1NxTfffIPU1FTs27cPCQkJ6prLly+jrKwMZWVlAP5z9nJFRQUAoKmpCZGRkSgqKsL777+P5uZmVFdXo7q6GtevXwdw40zyoKAgjQUAfH194e/vb/SxsDTMayIiy2eqvCZxhN7Dm4iIzK+56S40N9nJWIyLhJycHCQkJGDJkiUoLS3FsGHDEBERof4Qe6vy8nKMHTsWw4YNQ2lpKRYvXoz58+cjNzdXXaNSqRAdHY2YmBicPHkSMTExiIqKwvHjx9U1hYWFmDt3Lo4dO4aCggI0NTVBqVTiypUr6pqqqiqNZcOGDVAoFJgyZYrGvsaMGQOlUokvvvgCJ06cwLx583DXXYxGIiK6/UyV1wAQHR2NtLQ0rFixAv3798ehQ4eQn58PPz8/ADdy8+b89vf3R35+Pg4ePIj+/ftj5cqVWLNmjUaOhoWFYevWrcjKysIjjzyC7Oxs5OTkYPDgweqaoqIiDBgwAAMGDABwY+J9wIAB+N///V8AwI8//oidO3fixx9/RP/+/eHl5aVejh49Kuu4EhERiSYvs+V9rjTHSWXt2e+MGTO0TigbMmSIRs369esRHh4OV1dXKBQK/Pbbb1r76dmzp1afRYsWtePIGIdfNxAR2ZjmJjsoTHAP79WrV2PmzJnqy4zT0tKwZ88erFu3TueDszIyMuDr66t+6FRAQACKioqwatUq9QfotLQ0jBo1CsnJyQBunHFWWFiItLQ0bNmyBQCwe/dujb5ZWVlwd3dHcXExhg8fDuDG2WA327FjB0aOHIlevXqp1yUmJmL+/Pka4frAAw8YdQyIiIjkMlVet4qPj0d8fLzO91rPJr7ZiBEjUFJSYrBnZGQkIiMj9b7fevayPj179jT4vj5ytiEiIpJLTmbLyevWk8rS09Px2GOP4d1330VERAS+/vpr+Pr6atW3nlQWGxuLzZs34/PPP0d8fDx69Oih/ozdelLZypUrMWnSJOTl5SEqKgpHjhxRf0nd3v2OGTMGWVlZ6teOjo4a47l69SrGjBmDMWPGqD/T67JixQrExsaqX99zzz1GH6u28DQ2IiIb09Rkh6ZGGcsfgVxfX6+xNDQ0aO3j+vXrKC4uhlKp1FivVCr1npGlUqm06kePHo2ioiI0NjYarDF0llddXR0AoGvXrjrf//nnn7Fr1y7MnDlTva6mpgbHjx+Hu7s7wsLC4OHhgREjRuDIkSN690NERCRSR/OaiIiITENWZsvI65tPKgsICEBaWhp8fHywbt06nfU3n1QWEBCAWbNm4YUXXsCqVavUNTefVPbQQw8hOTkZTzzxhPpENGP26+TkBE9PT/Vy62fwhIQELFq0SOvM71u5uLho9LkdE94We4b3vWMuQOF6qUM9Fn/2d0GjAaQnhLXCAexuu6g95jmL6QNAqhTTR6EQ0wcAPv3TZCF9pH8KaQMAUJwZI6TPvCl/E9IHAN7Gn4X0cbx4SEgfAIjs9pGQPtLHbde018v4q5A+V3G3kD7XcQ2nhXQSz8fHR+P1smXLsHz5co11tbW1aG5uhoeHh8Z6Dw8PVFdX6+xbXV2ts76pqQm1tbXw8vLSW6OvpyRJSEpKwtChQ/Xez3Ljxo1wcXHB5Mn/+Z3y3XffAQCWL1+OVatWoX///ti0aROeeOIJ/Otf/+KZ3kZyHfMzFK5XO9TjpT3pgkYDSKOFtcIeUZm9SFxASlVi+gjN7BmCMjur7Zr2UpwRM6aXnhCTHwCwCq8K6eN48UshfQBgUjftS1rlkMS0AQD8BQuF9LkAbyF9ruMazgrpRGReQvL6M4F5LfAzNvO6fZjX7cO8bh/mdfvU19drvHZycoKTk5NWXetJZbfe3kPOSWWZmZlobGyEg4MDVCqVxgOmW2taJ7yN2e/Bgwfh7u6OLl26YMSIEXjttdfg7u6u/4fXIzU1FStXroSPjw+efvppvPLKK1pni3eUxU54ExGRPFKzPaRmGb/e/9imsrISrq6u6tW6wriV4pZ/gUuSpLWurfpb1xvTc968efjyyy8Nnpm9YcMGPPfcc3B2/s+XhC0tLQCAOXPm4PnnnwcADBgwAJ999hk2bNig85YsREREInU0r4mIiMg0ZGX2H/XtOaEMMN9JZe3db0REBJ5++mn4+fmhvLwcS5cuxeOPP47i4mKDcwa3WrBgAQYOHIh7770XX3zxBZKTk1FeXo733nuv3T3ag/9aIiKyNU12NxY52wFwdXXVmPDWpXv37rCzs9MK3pqaGq2gbOXp6amz3t7eHt26dTNYo6vniy++iJ07d+LQoUO47777dO7z8OHDOHPmDHJycjTWe3l5AQACAwM11gcEBOh96CYREZFQHcxrIiIiMhE5mf1HvTEnlAHmO6msrZro6Gj1fwcFBSEkJAR+fn7YtWuXxtXUbbn5bPNHHnkE9957LyIjI5GamqqeFxCB9/AmIrI1rWEsZ2knR0dHBAcHo6CgQGN9QUEBwsLCdG4TGhqqVb93716EhITAwcHBYM3NPSVJwrx587Bt2zbs378f/v7+eseZmZmJ4OBg9OvXT2N9z5494e3tjTNnzmisP3v2LPz8/PT2IyIiEsYEeU1EREQCdCCvW08oa130TXib66QyOfsFbpxE5ufnh3PnzumtaY/W+32fP3++Q31uxQlvIiJb06wAmmQszcbdIDApKQnvvfceNmzYgNOnTyMxMREVFRWIi4sDACQnJ2PatGnq+ri4OPzwww9ISkrC6dOnsWHDBmRmZuLll19W1yxYsAB79+5FamoqvvnmG6SmpmLfvn1ISEhQ18ydOxebN2/GBx98ABcXF1RXV6O6uhq///67xvjq6+vx4YcfYtasWVpjVygUeOWVV7BmzRp89NFHOH/+PJYuXYpvvvlG4+GWREREt42J8pqIiIg6SE5mG5nX5jqpTM5+AeDixYuorKxUXz0tV2lpKQB0uM+teEsTIiJb0/THImc7I0RHR+PixYtYsWIFqqqqEBQUhPz8fPUZ0lVVVRq3B/H390d+fj4SExPxzjvvwNvbG2vWrMGUKVPUNWFhYdi6dSteffVVLF26FL1790ZOTg4GDx6srml9UnR4eLjGeLKysjBjxgz1661bt0KSJDzzzDM6x5+QkIBr164hMTERv/zyC/r164eCggL07t3buANBREQkh4nymoiIiDpITmbLyOukpCTExMQgJCQEoaGhWL9+vdZJZT/99BM2bdoE4MZJZWvXrkVSUhJiY2OhUqmQmZmJLVu2qHsuWLAAw4cPR2pqKiZMmIAdO3Zg3759Gs/Bamu/ly9fxvLlyzFlyhR4eXnh+++/x+LFi9G9e3dMmjRJ3af1ZLTWs7VPnToFFxcX+Pr6omvXrlCpVDh27BhGjhwJNzc3nDhxAomJiRg/fjx8fX2NP2AGcMKbiIhki4+PR3x8vM73srOztdaNGDECJSUlBntGRkYiMjJS7/ut9yRry+zZszF79myDNYsWLdJ6GjURERERERGRqZnrpLK29mtnZ4dTp05h06ZN+O233+Dl5YWRI0ciJycHLi4u6j4ZGRn4y1/+on49fPhwAP85Oc3JyQk5OTn4y1/+goaGBvj5+SE2NhZ//vOfhR9LTngTEdkanjFGRERk+ZjXRERE1sFEZ3gD5jmprK39durUCXv27DG4PQAsX74cy5cv1/v+wIEDcezYsTb7iMAJbyIiW8MP0ERERJaPeU1ERGQdTDjhTWJwwpuIyNY0AWiUuR0RERGZBvOaiIjIOsjJbOa1WXHCm4jI1jT/scjZjoiIiEyDeU1ERGQd5GQ289qs7jL3AIiIiIiIiIiIiIiIROAZ3kREtob3BCUiIrJ8zGsiIiLrwHt4Wx1OeBMR2Rp+gCYiIrJ8zGsiIiLrwAlvq8MJbyIiW8MP0ERERJaPeU1ERGQdOOFtdTjhTURka5ohL1z5UA0iIiLTYV4TERFZBzmZzbw2Kz60koiIiIiIiIiIiIhsAs/wJiKyNbxEmoiIyPIxr4mIiKwDb2lidSx2wvvXHG+gk2uHevhN/0bQaADFOw8J64XtY8T0uV9MGwBQvCao0SeC+gDAPWLaKJ4R0wcADm4ZLKRP+MnjQvoAwNr3xPR5/O1DYhoB2OL1gpg+a4W0AQBUTVklpM8Z9BHS5wqakSmkkw78AE0mVp/j0eHMvm/mOUGjARTvPiCsFz4SlNkC/xkhLLOPCOoDAL+JaaMYJ6YPABR/HCikT/DnXwvpAwBvbRbT5/F14jL7/907XUwfgZn97XObhPS5iO5C+lxGM7KFdLoF85pMjHndDpaY1wcF9QGAy2LaiMzrLz7uK6TPo5+fEtIHsPG8zhDSBgDwfXSWkD418BDS57blNcAJbytksRPeREQkU+Mfi5ztiIiIyDSY10RERNZBTmYzr82KE95ERLamGfIekMGHahAREZkO85qIiMg6yMls5rVZccKbiMjWyHmCdOt2REREZBrMayIiIusgJ7OZ12Z1l7kHQEREREREREREREQkgtET3ocOHcK4cePg7e0NhUKB7du3q99rbGzEwoUL0bdvX3Tu3Bne3t6YNm0aLly4IHLMRERkSFMHFrIZzGsiIgvHvCYwr4mIrALz2uoYPeF95coV9OvXD2vXaj+K/erVqygpKcHSpUtRUlKCbdu24ezZsxg/fryQwRIRUTvwAzSBeU1EZPGY1wTmNRGRVWBeWx2j7+EdERGBiIgIne+5ubmhoKBAY93bb7+NRx99FBUVFfD19ZU3SiIiaj+54cpAtinMayIiC8e8JjCviYisgpzMZl6b1W1/aGVdXR0UCgW6dOlyu3dFREQAH4JFsjCviYhMjHlNMjCviYjMgA+ttDq3dcL72rVrWLRoEZ599lm4urrqrGloaEBDQ4P6dX19/e0cEhEREd2iPXkNMLOJiIjMiXlNRETUPkbfw7u9GhsbMXXqVLS0tCA9PV1vXUpKCtzc3NSLj4/P7RoSEdGdgfcEJSO0N68BZjYRkVDMazIC85qIyIyY11bntkx4NzY2IioqCuXl5SgoKDD47XNycjLq6urUS2Vl5e0YEhHRnaOxAwvdUYzJa4CZTUQkFPOa2ol5TURkZsxrqyP8liatYXzu3DkcOHAA3bp1M1jv5OQEJycn0cMgIrpzNUPe/cJ4j7E7irF5DTCziYiEYl5TOzCviYgsgJzMZl6bldET3pcvX8b58+fVr8vLy1FWVoauXbvC29sbkZGRKCkpwSeffILm5mZUV1cDALp27QpHR0dxIyciIt3kXj7FS65sCvOaiMjCMa8JzGsiIqsgJ7OZ12Zl9IR3UVERRo4cqX6dlJQEAJg+fTqWL1+OnTt3AgD69++vsd2BAwcQHh4uf6RERETUbsxrIiIiy8e8JiIiEs/oe3iHh4dDkiStJTs7Gz179tT5niRJDGMiIlNphrwHavCSK5vCvCYisnAmzuv09HT4+/vD2dkZwcHBOHz4sMH6wsJCBAcHw9nZGb169UJGRoZWTW5uLgIDA+Hk5ITAwEDk5eVpvH/o0CGMGzcO3t7eUCgU2L59u8b7jY2NWLhwIfr27YvOnTvD29sb06ZNw4ULFzTq5syZg969e6NTp07o0aMHJkyYgG+++UbegbAwzGsiIisgJ7P5+dqsbstDK4mIyIzkPkFaxiVX5vjwnJKSgkGDBsHFxQXu7u6YOHEizpw5o1GjUCh0Lm+++aa6Jjw8XOv9qVOnGn8QiIiI5DBhXufk5CAhIQFLlixBaWkphg0bhoiICFRUVOisLy8vx9ixYzFs2DCUlpZi8eLFmD9/PnJzc9U1KpUK0dHRiImJwcmTJxETE4OoqCgcP35cXXPlyhX069cPa9eu1bmfq1evoqSkBEuXLkVJSQm2bduGs2fPYvz48Rp1wcHByMrKwunTp7Fnzx5IkgSlUonmZs4mEBGRCZgor0kc4Q+tJCIiM2uEvK8zjXyKdOuH5/T0dDz22GN49913ERERga+//hq+vr5a9a0fnmNjY7F582Z8/vnniI+PR48ePTBlyhQA//nwvHLlSkyaNAl5eXmIiorCkSNHMHjwYAA3Js3nzp2LQYMGoampCUuWLIFSqcTXX3+Nzp07AwCqqqo09v3pp59i5syZ6v20io2NxYoVK9SvO3XqZNxBICIikstEeQ0Aq1evxsyZMzFr1iwAQFpaGvbs2YN169YhJSVFqz4jIwO+vr5IS0sDAAQEBKCoqAirVq1SZ2laWhpGjRqF5ORkAEBycjIKCwuRlpaGLVu2AAAiIiIQERGhd1xubm4oKCjQWPf222/j0UcfRUVFhfrfE7Nnz1a/37NnT/z1r39Fv3798P3336N3797GHxAiIiJjyMlsGXlN4vAMbyIiW9PcgcUIN394DggIQFpaGnx8fLBu3Tqd9Td/eA4ICMCsWbPwwgsvYNWqVeqamz88P/TQQ0hOTsYTTzyh/sANALt378aMGTPw8MMPo1+/fsjKykJFRQWKi4vVNZ6enhrLjh07MHLkSPTq1UtjTHfffbdGnZubm3EHgYiISC4T5fX169dRXFwMpVKpsV6pVOLo0aM6t1GpVFr1o0ePRlFRERobGw3W6OvZXnV1dVAoFOjSpYvO969cuYKsrCz4+/vDx8enQ/siIiJqFxPkNYllsWd4173nBtcOju7D6U+JGQyAqJCPhfWS5orpo3hNTB8AgPZdBWT5n8rVYhoBWPdakphGM8S0AYDwz4+3XdQOf3rsH0L6AMDmqbFC+uwfIO7/F6mq7Zr2UKSK6QMAnvPqhPS5+/9KhfSpb5aE9NGp9f5icrYDUF9fr7HayckJTk5OGutaPzwvWrRIY72cD8+ZmZlobGyEg4MDVCoVEhMTtWpunvC+VV3djT/brl276nz/559/xq5du7Bx40at995//31s3rwZHh4eiIiIwLJly+Di4qJ3X6RbXbobXO061mPHTGXbRe00MWSPsF7SHDF9FMli+gAA/iqmzcwlum8xIEfma/PENIoT0wYAggu/FtJn+gjdX+LJsfHy/wjpsz9AYGb/KqaPYqmYPgDQK6FaSB+P1BohfeobblNmmyCvAaC2thbNzc3w8PDQWO/h4YHqat3Hurq6Wmd9U1MTamtr4eXlpbdGX8/2uHbtGhYtWoRnn30Wrq6uGu+lp6fjz3/+M65cuYKHHnoIBQUFcHR0lL2vOxHzum0Wmde/23ZeP1p4Skgf5nX7iMxrP9W/hfTpnnpRSJ/blteAvMzmhLdZ8QxvIiLS4OPjAzc3N/Wi61Ln2/Hh2VCNvp6SJCEpKQlDhw5FUFCQzpqNGzfCxcUFkydP1lj/3HPPYcuWLTh48CCWLl2K3NxcrRoiIiJL1Z68vplCodB4LUmS1rq26m9db2xPQxobGzF16lS0tLQgPT1d6/3nnnsOpaWlKCwsxAMPPICoqChcu3ZN1r6IiIjItlnsGd5ERCRTE+R9nfnHN9aVlZUaZ1XpOluslbk/PM+bNw9ffvkljhw5onefGzZswHPPPQdnZ2eN9bGx/7k6IigoCA888ABCQkJQUlKCgQMH6u1HREQkhInyunv37rCzs9P68rimpkbrS+ZWnp6eOuvt7e3RrVs3gzX6ehrS2NiIqKgolJeXY//+/VpndwNQT+w/8MADGDJkCO69917k5eXhmWeeMXp/RERERpGT2XxopVnxDG8iIlvT2IEFgKurq8ai6wO0JXx4fvHFF7Fz504cOHAA9913n859Hj58GGfOnFE/pMuQgQMHwsHBAefOnWuzloiIqMNMkNcA4OjoiODgYK2HQxYUFCAsLEznNqGhoVr1e/fuRUhICBwcHAzW6OupT+tk97lz57Bv3z71vwnaIkkSGhoajNoXERGRLB3IazIPTngTEdkaEzwEy5wfniVJwrx587Bt2zbs378f/v7+eseZmZmJ4OBg9OvXr82f6auvvkJjYyO8vLzarCUiIuowEz20EgCSkpLw3nvvYcOGDTh9+jQSExNRUVGBuLgbN+JNTk7GtGnT1PVxcXH44YcfkJSUhNOnT2PDhg3IzMzEyy+/rK5ZsGAB9u7di9TUVHzzzTdITU3Fvn37kJCQoK65fPkyysrKUFZWBgAoLy9HWVkZKioqAABNTU2IjIxEUVER3n//fTQ3N6O6uhrV1dW4fv06AOC7775DSkoKiouLUVFRAZVKhaioKHTq1Aljx441/mAQEREZiw+ttDq8pQkRka3p4CXS7ZWUlISYmBiEhIQgNDQU69ev1/rw/NNPP2HTpk0Abnx4Xrt2LZKSkhAbGwuVSoXMzExs2bJF3XPBggUYPnw4UlNTMWHCBOzYsQP79u3TuGXJ3Llz8cEHH2DHjh1wcXFRnxHu5uaGTp06qevq6+vx4Ycf4q233tIa+7fffov3338fY8eORffu3fH111/jpZdewoABA/DYY48ZdyCIiIjkMFFeA0B0dDQuXryIFStWoKqqCkFBQcjPz4efnx8AoKqqSj0JDQD+/v7Iz89HYmIi3nnnHXh7e2PNmjWYMmWKuiYsLAxbt27Fq6++iqVLl6J3797IycnB4MGD1TVFRUUYOXKk+nVS0o2H0k+fPh3Z2dn48ccfsXPnTgBA//79NcZ84MABhIeHw9nZGYcPH0ZaWhp+/fVXeHh4YPjw4Th69Cjc3d2NPxhERETG4i1NrA4nvImISBZzfXhet+7GE9jDw8M1xpOVlYUZM2aoX2/duhWSJOm8t6ejoyM+++wz/N///R8uX74MHx8fPPnkk1i2bBns7OxEHB4iIiKLEh8fj/j4eJ3vZWdna60bMWIESkpKDPaMjIxEZGSk3vfDw8PVz+vQpWfPngbfBwBvb2/k5+cbrCEiIiK6GSe8iYhsTTPkfZss45Irc3x4buuDcavZs2dj9uzZOt/z8fFBYWFhu/oQERHdFibMayIiIuoAOZnNvDYrTngTEdkauQ/H4EM1iIiITId5TUREZB3kZC/z2qw44U1EZGuaIe+eoPwGmoiIyHSY10RERNZBTmYzr82KE95ERLamCYBC5nZERERkGsxrIiIi6yAns5nXZiXnnAIiIiIiIiIiIiIiIovDM7yJiGwNzxgjIiKyfMxrIiIi68AzvK0OJ7yJiGyN3GBlIBMREZkO85qIiMg6yMle5rVZccKbiMjWNEPeGWN8qAYREZHpMK+JiIisg5zMZl6bFSe8iYhsDc8YIyIisnzMayIiIuvAM7ytDh9aSURka5o6sBAREZFpMK+JiIisgwnzOj09Hf7+/nB2dkZwcDAOHz5ssL6wsBDBwcFwdnZGr169kJGRoVWTm5uLwMBAODk5ITAwEHl5eUbvd8aMGVAoFBrLkCFDNGrWr1+P8PBwuLq6QqFQ4LffftPaz6+//oqYmBi4ubnBzc0NMTExOus6ihPeRERERERERERERGaUk5ODhIQELFmyBKWlpRg2bBgiIiJQUVGhs768vBxjx47FsGHDUFpaisWLF2P+/PnIzc1V16hUKkRHRyMmJgYnT55ETEwMoqKicPz4caP3O2bMGFRVVamX/Px8jfevXr2KMWPGYPHixXp/xmeffRZlZWXYvXs3du/ejbKyMsTExMg5XAYpJEmShHftgPr6eri5ueEcAJcO9vI4JWJEfxB48xfF+4IOeXcxbQBAWiCmj+PFejGNADS+5yqkj7RQSBsAgOJbQX92TXJu2Kib1EdMH0WsmD4AsPIfLwvpMwfvCukDAMvwFyF9xmKXkD5X65sQ7XYIdXV1cHUV83e99fcnBtQBdjJ6NtcDpW5Cx0S2rfXv3El0PLP9T4sY0R/qxLVSbBf0e99ZTBsAkJaJ6aP46ZqYRgCQLeYHlJYIaQMA6FT3i5A+12rvFdIHAKTeYvJf8byQNgCA1KwXhfSZhn8K6QOIy+xx2Cmkj+jMZl6TqTGvjcC8bhfmdfswr9vHUvMa6GBmy8jrwYMHY+DAgVi3bp16XUBAACZOnIiUlBSt+oULF2Lnzp04ffo/v5zj4uJw8uRJqFQqAEB0dDTq6+vx6aefqmvGjBmDe++9F1u2bGn3fmfMmIHffvsN27dvb/PnOHjwIEaOHIlff/0VXbp0Ua8/ffo0AgMDcezYMQwePBgAcOzYMYSGhuKbb75Bnz6CJrjAM7yJiGxPcwcWIiIiMg3mNRERkXXoQF7X19drLA0NDTp3cf36dRQXF0OpVGqsVyqVOHr0qM5tVCqVVv3o0aNRVFSExsZGgzWtPY3Z78GDB+Hu7o4HH3wQsbGxqKmp0TkufVQqFdzc3NST3QAwZMgQuLm56f0Z5eKENxGRreE9QYmIiCwf85qIiMg6dCCvfXx81PerdnNz03mmNgDU1taiubkZHh4eGus9PDxQXV2tc5vq6mqd9U1NTaitrTVY09qzvfuNiIjA+++/j/379+Ott97CiRMn8Pjjj+udwNc3Xnd3d6317u7uen9GuQTeqIOIiCxCEwA5V3XyjDEiIiLTYV4TERFZBzmZ/UdeV1ZWatzSxMnJyeBmCoXmLXUkSdJa11b9revb07OtmujoaPV/BwUFISQkBH5+fti1axcmT55s6EcyuB994+koTngTERERERERERERCebq6tque3h3794ddnZ2Wmc619TUaJ193crT01Nnvb29Pbp162awprWnnP0CgJeXF/z8/HDu3Lk2f7abx/vzzz9rrf/3v/9tcF9y8JYmRES2pglAo4yFl0gTERGZDvOaiIjIOsjJbCPz2tHREcHBwSgoKNBYX1BQgLCwMJ3bhIaGatXv3bsXISEhcHBwMFjT2lPOfgHg4sWLqKyshJeXV/t+wD/GUldXhy+++EK97vjx46irqzO4LzmMnvA+dOgQxo0bB29vbygUCoNP55wzZw4UCgXS0tI6MEQiIjIKH4JFYF4TEVk85jWBeU1EZBVMlNdJSUl47733sGHDBpw+fRqJiYmoqKhAXFwcACA5ORnTpk1T18fFxeGHH35AUlISTp8+jQ0bNiAzMxMvv/yyumbBggXYu3cvUlNT8c033yA1NRX79u1DQkJCu/d7+fJlvPzyy1CpVPj+++9x8OBBjBs3Dt27d8ekSZPUfaqrq1FWVobz588DAE6dOoWysjL88ssvAICAgACMGTMGsbGxOHbsGI4dO4bY2Fg89dRT6NOnj/EHzACjJ7yvXLmCfv36Ye3atQbrtm/fjuPHj8Pb21v24IiISAY+BIvAvCYisnjMawLzmojIKpgor6Ojo5GWloYVK1agf//+OHToEPLz8+Hn5wcAqKqqQkVFhbre398f+fn5OHjwIPr374+VK1dizZo1mDJliromLCwMW7duRVZWFh555BFkZ2cjJycHgwcPbvd+7ezscOrUKUyYMAEPPvggpk+fjgcffBAqlQouLi7qPhkZGRgwYABiY2MBAMOHD8eAAQOwc+dOdc3777+Pvn37QqlUQqlU4pFHHsE///lP4w9WG4y+h3dERAQiIiIM1vz000+YN28e9uzZgyeffFL24IiISIYmyLthVYvogZA5Ma+JiCwc85rAvCYisgpyMltmXsfHxyM+Pl7ne9nZ2VrrRowYgZKSEoM9IyMjERkZKXu/nTp1wp49ewxuDwDLly/H8uXLDdZ07doVmzdvbrNXRwl/aGVLSwtiYmLwyiuv4OGHH26zvqGhAQ0NDerX9fX1oodEREREtzA2rwFmNhERkakxr4mIiIwn/KGVqampsLe3x/z589tVn5KSAjc3N/Xi4+MjekhERHcWOQ/Aal3ojmFsXgPMbCIioZjX1A7MayIiC8C8tjpCJ7yLi4vxf//3f8jOzoZCoWjXNsnJyairq1MvlZWVIodERHTnaYG8B2rwEuk7hpy8BpjZRERCMa+pDcxrIiILISezmddmJXTC+/Dhw6ipqYGvry/s7e1hb2+PH374AS+99BJ69uypcxsnJye4urpqLERE1AF8CBa1QU5eA8xsIiKhmNfUBuY1EZGFYF5bHaH38I6JicF///d/a6wbPXo0YmJi8Pzzz4vcFRER6dMEoP0nAf2HJHogZKmY10REFoB5TW1gXhMRWQg5mc28NiujJ7wvX76M8+fPq1+Xl5ejrKwMXbt2ha+vL7p166ZR7+DgAE9PT/Tp06fjoyUiorY1gh+giXlNRGTpmNcE5jURkVWQk9nMa7MyesK7qKgII0eOVL9OSkoCAEyfPh3Z2dnCBkZERETyMa+JiIgsH/OaiIhIPKMnvMPDwyFJ7f+a4vvvvzd2F0RE1BHN4BljxLwmIrJ0zGsC85qIyCrIyWzmtVkJfWglERFZCEnGIkN6ejr8/f3h7OyM4OBgHD582GB9YWEhgoOD4ezsjF69eiEjI0OrJjc3F4GBgXByckJgYCDy8vI03k9JScGgQYPg4uICd3d3TJw4EWfOnNGoUSgUOpc333xTa3+SJCEiIgIKhQLbt283/iAQERHJZaK8JiIiog5iXlsVTngTEZEsOTk5SEhIwJIlS1BaWophw4YhIiICFRUVOuvLy8sxduxYDBs2DKWlpVi8eDHmz5+P3NxcdY1KpUJ0dDRiYmJw8uRJxMTEICoqCsePH1fXFBYWYu7cuTh27BgKCgrQ1NQEpVKJK1euqGuqqqo0lg0bNkChUGDKlCla40pLS4NCIecUOyIiIiIiIiKyNArJmOunTKC+vh5ubm4oq3OHi2vH5uN7ja4WNCpg254IYb0mf/OpkD5zHkoT0gcA1r+7QEgfabC4SaPN/bUnpuSIKf5ISB8AyAx+VkifBVf+T0gfALj8SQ8hfV6PThTSBwDexRwhfb7/MEBIHwDAh2LaHPt//YT0uVLfjP92+xfq6urg6uoqpGfr70+gDoCcnvUA3No9psGDB2PgwIFYt26del1AQAAmTpyIlJQUrfqFCxdi586dOH36tHpdXFwcTp48CZVKBQCIjo5GfX09Pv30P78nx4wZg3vvvRdbtmzROY5///vfcHd3R2FhIYYPH66zZuLEibh06RI+++wzjfUnT57EU089hRMnTsDLywt5eXmYOHFimz873dD6d+5EnQ/u6WBmPzTkB0GjAj489pSwXk+XfSKkz5z+aUL6AAIzu7+4zN44OEpInxnHc4T0AYB/Do4U0ifmp81C+gAADjoLafPqc4uF9AGAbDwvpE9l/oNC+gAA3hPTpmSbmH9HXK5vxgi3s8Iy29R5TcS8bj/mdfswr9uHed0+J7YFCelzub4Zj7udtqDP2Mxrc+IZ3kREpKG+vl5jaWho0Kq5fv06iouLoVQqNdYrlUocPXpUZ1+VSqVVP3r0aBQVFaGxsdFgjb6eAFBXVwcA6Nq1q873f/75Z+zatQszZ87UWH/16lU888wzWLt2LTw9PfX2JyIiIiIiIiLrwQlvIiLS4OPjAzc3N/Wi62zt2tpaNDc3w8PDQ2O9h4cHqqt1X11TXV2ts76pqQm1tbUGa/T1lCQJSUlJGDp0KIKCdJ8ZsHHjRri4uGDy5Mka6xMTExEWFoYJEybo3I6IiIiIiIiIrI+9uQdARESiNf6xyNkOqKys1LjkysnJSe8Wt977WpIkg/fD1lV/63pjes6bNw9ffvkljhw5onefGzZswHPPPQdn5/9cmrhz507s378fpaWlercjIiK6vTqW10RERGQqcjKbeW1OnPAmIrI5TX8scrYDXF1d27zHWPfu3WFnZ6d15nVNTY3WGdqtPD09ddbb29ujW7duBmt09XzxxRexc+dOHDp0CPfdd5/OfR4+fBhnzpxBTo7mfQb379+Pb7/9Fl26dNFYP2XKFAwbNgwHDx7U2Y+IiEicjuU1ERERmYqczGZemxNvaUJEZHMaO7C0j6OjI4KDg1FQUKCxvqCgAGFhYTq3CQ0N1arfu3cvQkJC4ODgYLDm5p6SJGHevHnYtm0b9u/fD39/f73jzMzMRHBwMPr103zY6KJFi/Dll1+irKxMvQDA3//+d2RlZRn+4YmIiIS4/Xl9s/T0dPj7+8PZ2RnBwcE4fPiwwfrCwkIEBwfD2dkZvXr1QkZGhlZNbm4uAgMD4eTkhMDAQOTl5Wm8f+jQIYwbNw7e3t5QKBTYvn27xvuNjY1YuHAh+vbti86dO8Pb2xvTpk3DhQsX1DW//PILXnzxRfTp0wd33303fH19MX/+fPUzPIiIiG4/0+U1icEJbyIikiUpKQnvvfceNmzYgNOnTyMxMREVFRWIi4sDACQnJ2PatGnq+ri4OPzwww9ISkrC6dOnsWHDBmRmZuLll19W1yxYsAB79+5FamoqvvnmG6SmpmLfvn1ISEhQ18ydOxebN2/GBx98ABcXF1RXV6O6uhq///67xvjq6+vx4YcfYtasWVpj9/T0RFBQkMYCAL6+vgYn0ImIiKxRTk4OEhISsGTJEpSWlmLYsGGIiIhARUWFzvry8nKMHTsWw4YNQ2lpKRYvXoz58+cjNzdXXaNSqRAdHY2YmBicPHkSMTExiIqKwvHjx9U1V65cQb9+/bB27Vqd+7l69SpKSkqwdOlSlJSUYNu2bTh79izGjx+vrrlw4QIuXLiAVatW4dSpU8jOzsbu3bu1HkZNRERE1Iq3NCEisjmmuUQ6OjoaFy9exIoVK1BVVYWgoCDk5+fDz88PAFBVVaXxQdrf3x/5+flITEzEO++8A29vb6xZswZTpkxR14SFhWHr1q149dVXsXTpUvTu3Rs5OTkYPHiwumbdunUAgPDwcI3xZGVlYcaMGerXW7duhSRJeOaZZ4z6uYiIiEzDdLc0Wb16NWbOnKn+EjgtLQ179uzBunXrdD6cOiMjA76+vkhLSwMABAQEoKioCKtWrVLndlpaGkaNGoXk5GQAN77oLiwsRFpaGrZs2QIAiIiIQEREhN5xubm5aV3Z9fbbb+PRRx9FRUUFfH19ERQUpDHR3rt3b7z22mv405/+hKamJtjb8yMtERHdbrylibXhvw6IiGxOE+RdPmV8IMfHxyM+Pl7ne9nZ2VrrRowYgZKSEoM9IyMjERkZqff91gddtmX27NmYPXt2u2qN6UtERCRGx/K6vr5eY62Tk5POB01fv34dxcXFWLRokcZ6pVKJo0eP6tyDSqWCUqnUWDd69GhkZmaisbERDg4OUKlUSExM1KppnSSXq66uDgqFQus5G7fWuLq6crKbiIhMRE5mc8LbnHhLEyIim2Pae4ISERGRHB3Lax8fH7i5uakXXWdqA0BtbS2am5u1HgDt4eGh9aDoVtXV1Trrm5qaUFtba7BGX8/2uHbtGhYtWoRnn31W7wO0L168iJUrV2LOnDmy90NERGQcfr62NvxKnIjI5pjuEmkiIiKSq2N5XVlZqTEprOvs7pspFAqN15Ikaa1rq/7W9cb2NKSxsRFTp05FS0sL0tPTddbU19fjySefRGBgIJYtWyZrP0RERMbjLU2sDSe8iYhsjuluaUJERERydSyvXV1d9Z4FfbPu3bvDzs5O68zrmpoarTO0W3l6euqst7e3R7du3QzW6OtpSGNjI6KiolBeXo79+/fr/LkuXbqEMWPG4J577kFeXh4cHByM3g8REZE8vKWJteEtTYiIiIiIiGyUo6MjgoODtR4OWVBQgLCwMJ3bhIaGatXv3bsXISEh6olmfTX6eurTOtl97tw57Nu3Tz2hfrP6+noolUo4Ojpi586dcHZ2NmofREREdGfhGd5ERDaHtzQhIiKyfKbL66SkJMTExCAkJAShoaFYv349KioqEBcXBwBITk7GTz/9hE2bNgEA4uLisHbtWiQlJSE2NhYqlQqZmZnYsmWLuueCBQswfPhwpKamYsKECdixYwf27duHI0eOqGsuX76M8+fPq1+Xl5ejrKwMXbt2ha+vL5qamhAZGYmSkhJ88sknaG5uVp813rVrVzg6OuLSpUtQKpW4evUqNm/ejPr6evUDO3v06AE7OzvjDyEREZFReEsTa8MJbyIimyP3ARl8qAYREZHpmC6vo6OjcfHiRaxYsQJVVVUICgpCfn4+/Pz8AABVVVWoqKhQ1/v7+yM/Px+JiYl455134O3tjTVr1mDKlCnqmrCwMGzduhWvvvoqli5dit69eyMnJweDBw9W1xQVFWHkyJHq10lJSQCA6dOnIzs7Gz/++CN27twJAOjfv7/GmA8cOIDw8HAUFxfj+PHjAID7779fo6a8vBw9e/Y0+ngQEREZR05m8/O1OXHCm4jI5vAMbyIiIstn2ryOj49HfHy8zveys7O11o0YMQIlJSUGe0ZGRiIyMlLv++Hh4eqHXerSs2dPg++3pwcREdHtxzO8rQ0nvImIbA4fWklERGT5mNdERETWgQ+ttDZ8aCURERERERERERER2QSFZGHXh9XX18PNzQ11JwFXlw42+1zIkAAAv/xJ3JPA775yTUifUZ33CukDAG/iFSF93sezQvoAwJpfFgrp82tXcX92XSeL+bNDiJg2AKB4TND/wvcL+tkA1PxXDyF9ytBfSB8AGLX/SNtF7bD5CSFt8DuA2QDq6urg6uoqpGfr709gD4DOMjpcATBa6JjItqkz+7yAzC4SMiQAQP1oB2G9HK+JufeeyMxejSQhfT4QmNl/r1kspE+jk5A2AACH5wU1EpnZgwVldk9x/3z/tre3kD7fw19IHwB4fL9KSJ8cQZl9FcALEJfZzGsyNeZ1+zGv24d53U7M63ax1LwGOprZzGtz4i1NiIhsDh9aSUREZPmY10RERNaBD620NpzwJiKyOXxoJRERkeVjXhMREVkHPrTS2nDCm4jI5vAhWERERJaPeU1ERGQd+NBKa8OHVhIRERERERERERGRTeAZ3kRENoeXSBMREVk+5jUREZF14C1NrI3RZ3gfOnQI48aNg7e3NxQKBbZv365Vc/r0aYwfPx5ubm5wcXHBkCFDUFFRIWK8RETUpsYOLGQrmNdERJaOeU3MayIi68C8tjZGT3hfuXIF/fr1w9q1a3W+/+2332Lo0KF46KGHcPDgQZw8eRJLly6Fs7NzhwdLRETtwQ/QxLwmIrJ8zGtiXhMRWQfmtbUx+pYmERERiIiI0Pv+kiVLMHbsWPztb39Tr+vVq5e80RERkQy8RJqY10RElo95TcxrIiLrwFuaWBuhD61saWnBrl278OCDD2L06NFwd3fH4MGDdV6W1aqhoQH19fUaCxERdUTrE6SNXRjIdwo5eQ0ws4mIxGJek2HMayIiSyEns5nX5iR0wrumpgaXL1/GG2+8gTFjxmDv3r2YNGkSJk+ejMLCQp3bpKSkwM3NTb34+PiIHBIRERHdQk5eA8xsIiIiU2JeExERyWP0LU0MaWlpAQBMmDABiYmJAID+/fvj6NGjyMjIwIgRI7S2SU5ORlJSkvp1fX09A5mIqEN4iTQZJievAWY2EZFYzGsyjHlNRGQpeEsTayN0wrt79+6wt7dHYGCgxvqAgAAcOXJE5zZOTk5wcnISOQwiojtcI+T9eudDNe4UcvIaYGYTEYnFvCbDmNdERJZCTmYzr81J6IS3o6MjBg0ahDNnzmisP3v2LPz8/ETuioiI9OIZY2QY85qIyBIwr8kw5jURkaXgGd7WxugJ78uXL+P8+fPq1+Xl5SgrK0PXrl3h6+uLV155BdHR0Rg+fDhGjhyJ3bt34+OPP8bBgwdFjpuIiPRqfaCGnO3IVjCviYgsHfOamNdERNZBTmYzr83J6AnvoqIijBw5Uv269d5g06dPR3Z2NiZNmoSMjAykpKRg/vz56NOnD3JzczF06FBxoyYiIiKDmNdERESWj3lNREQkntET3uHh4ZAkyWDNCy+8gBdeeEH2oIiIqCN4iTQxr4mILB/zmpjXRETWgbc0sTZC7+FNRESWoBGAncztiIiIyDSY10RERNZBTmYzr83J4ia8W7/drr8soNnvAnr8ob7e8Lfuxmi6IqhPs6BGAK6gWUif67gmpA8A1NcL6mMv7s/OXtTvK3GHCbgi6EBdEjeoS4L+f7ki8BvRekH/u4j6tdLap60zeuS5AnnfJjeIHgjZOHVmXxLQ7KqAHn8QmdmOgn41iszsy4Iyu0FkZov4OwCgUeCvIQebzmxxf8cv1bcI6WOJmS3q18rty2zmNZkG87r9mNftw7xuJ+Z1u1h+XgPyMpt5bU4K6fb8TZDtxx9/hI+Pj7mHQURkEpWVlbjvvvuE9Lp27Rr8/f1RXV0tu4enpyfKy8vh7OwsZExk25jZRHQnEZXZzGsyNeY1Ed1JLOkzNvPafCxuwrulpQUXLlyAi4sLFAqF3rr6+nr4+PigsrISrq6uJhxhx3DcpmetY+e4Tc+UY5ckCZcuXYK3tzfuuusuYX2vXbuG69evy97e0dGRYUzt1p7MttbfCdY6bsB6x85xm561jt3U474dmc28JlOy5bwGrHfsHLfpWevYOe72scTP2Mxr87G4W5rcddddRn0T4+rqalX/w7fiuE3PWsfOcZueqcbu5uYmvKezszMDlUzGmMy21t8J1jpuwHrHznGbnrWO3ZTjFp3ZzGsypTshrwHrHTvHbXrWOnaOu238jE2txH3lQURERERERERERERkRpzwJiIiIiIiIiIiIiKbYLUT3k5OTli2bBmcnJzMPRSjcNymZ61j57hNz5rHTmSprPX/K2sdN2C9Y+e4Tc9ax26t4yayZNb8/5W1jp3jNj1rHTvHTWQ8i3toJRERERERERERERGRHFZ7hjcRERERERERERER0c044U1ERERERERERERENoET3kRERERERERERERkEzjhTUREREREREREREQ2waInvNPT0+Hv7w9nZ2cEBwfj8OHDBusLCwsRHBwMZ2dn9OrVCxkZGSYa6Q0pKSkYNGgQXFxc4O7ujokTJ+LMmTMGtzl48CAUCoXW8s0335ho1MDy5cu19u/p6WlwG3Mf61Y9e/bUefzmzp2rs95cx/vQoUMYN24cvL29oVAosH37do33JUnC8uXL4e3tjU6dOiE8PBxfffVVm31zc3MRGBgIJycnBAYGIi8vz2TjbmxsxMKFC9G3b1907twZ3t7emDZtGi5cuGCwZ3Z2ts4/g2vXrpls7AAwY8YMrTEMGTKkzb63+5gTWSPmtelYa2Yzr29/dlhrZjOviUzH2vIasN7MZl7f/mNtrZnNvNbEvKbbxWInvHNycpCQkIAlS5agtLQUw4YNQ0REBCoqKnTWl5eXY+zYsRg2bBhKS0uxePFizJ8/H7m5uSYbc2FhIebOnYtjx46hoKAATU1NUCqVuHLlSpvbnjlzBlVVVerlgQceMMGI/+Phhx/W2P+pU6f01lrCsW514sQJjXEXFBQAAJ5++mmD25n6eF+5cgX9+vXD2rVrdb7/t7/9DatXr8batWtx4sQJeHp6YtSoUbh06ZLeniqVCtHR0YiJicHJkycRExODqKgoHD9+3CTjvnr1KkpKSrB06VKUlJRg27ZtOHv2LMaPH99mX1dXV43jX1VVBWdnZ2HjbmvsrcaMGaMxhvz8fIM9TXHMiawN89q0eQ1YZ2Yzr29/dlhrZjOviUzDGvMasO7MZl7fXtaa2czr/2Be020lWahHH31UiouL01j30EMPSYsWLdJZ/+c//1l66KGHNNbNmTNHGjJkyG0bY1tqamokAFJhYaHemgMHDkgApF9//dV0A7vFsmXLpH79+rW73hKPdasFCxZIvXv3llpaWnS+bwnHG4CUl5enft3S0iJ5enpKb7zxhnrdtWvXJDc3NykjI0Nvn6ioKGnMmDEa60aPHi1NnTpV+JglSXvcunzxxRcSAOmHH37QW5OVlSW5ubmJHVwbdI19+vTp0oQJE4zqY+pjTmQNmNemZSuZzby+vdlhrZnNvCa6fWwhryXJejKbeW1a1prZzGvmNd0+FnmG9/Xr11FcXAylUqmxXqlU4ujRozq3UalUWvWjR49GUVERGhsbb9tYDamrqwMAdO3atc3aAQMGwMvLC0888QQOHDhwu4em5dy5c/D29oa/vz+mTp2K7777Tm+tJR5r4Mbfm82bN+OFF16AQqEwWGvu432z8vJyVFdXaxxTJycnjBgxQu/fd0D/n4OhbW63uro6KBQKdOnSxWDd5cuX4efnh/vuuw9PPfUUSktLTTPAWxw8eBDu7u548MEHERsbi5qaGoP1lnjMicyJeW2e/LD2zGZeW0Z2WFNmM6+JOsZW8hqwrsxmXpuPLWU285pIHouc8K6trUVzczM8PDw01nt4eKC6ulrnNtXV1Trrm5qaUFtbe9vGqo8kSUhKSsLQoUMRFBSkt87Lywvr169Hbm4utm3bhj59+uCJJ57AoUOHTDbWwYMHY9OmTdizZw/+8Y9/oLq6GmFhYbh48aLOeks71q22b9+O3377DTNmzNBbYwnH+1atf6eN+fveup2x29xO165dw6JFi/Dss8/C1dVVb91DDz2E7Oxs7Ny5E1u2bIGzszMee+wxnDt3zoSjBSIiIvD+++9j//79eOutt3DixAk8/vjjaGho0LuNpR1zInNjXps+P2whs5nX5s8Oa8ps5jVRx9lCXgPWldnMa/PlNWA7mc28JpLP3twDMOTWbxElSTL4zaKuel3rTWHevHn48ssvceTIEYN1ffr0QZ8+fdSvQ0NDUVlZiVWrVmH48OG3e5gAbvxiatW3b1+Ehoaid+/e2LhxI5KSknRuY0nHulVmZiYiIiLg7e2tt8YSjrc+xv59l7vN7dDY2IipU6eipaUF6enpBmuHDBmi8fCKxx57DAMHDsTbb7+NNWvW3O6hqkVHR6v/OygoCCEhIfDz88OuXbswefJkvdtZyjEnsiTMa9Plhy1kNvPavNlhbZnNvCYSx5rzGrCuzGZemz+vAevObOY1UcdY5Bne3bt3h52dnda3OjU1NVrf/rTy9PTUWW9vb49u3brdtrHq8uKLL2Lnzp04cOAA7rvvPqO3HzJkiMnPdr1Z586d0bdvX71jsKRj3eqHH37Avn37MGvWLKO3Nffxbn1atzF/31u3M3ab26GxsRFRUVEoLy9HQUGBwW+edbnrrrswaNAgs/4ZADfOTvDz8zM4Dks55kSWgnlt3vwArC+zmdft2+Z2sYXMZl4TGc/a8xqw/sxmXpuWtWc285p5TR1nkRPejo6OCA4OVj8RuFVBQQHCwsJ0bhMaGqpVv3fvXoSEhMDBweG2jfVmkiRh3rx52LZtG/bv3w9/f39ZfUpLS+Hl5SV4dO3X0NCA06dP6x2DJRzrW2VlZcHd3R1PPvmk0dua+3j7+/vD09NT45hev34dhYWFev++A/r/HAxtI1prEJ87dw779u2T9Y8xSZJQVlZm1j8DALh48SIqKysNjsMSjjmRJWFemzc/AOvLbOb1DebIDlvJbOY1kfGsNa8B28ls5rVpWXNmM6+Z1ySIiR6OabStW7dKDg4OUmZmpvT1119LCQkJUufOnaXvv/9ekiRJWrRokRQTE6Ou/+6776S7775bSkxMlL7++mspMzNTcnBwkD766COTjfl//ud/JDc3N+ngwYNSVVWVerl69aq65tZx//3vf5fy8vKks2fPSv/617+kRYsWSQCk3Nxck437pZdekg4ePCh999130rFjx6SnnnpKcnFxsehjfbPm5mbJ19dXWrhwodZ7lnK8L126JJWWlkqlpaUSAGn16tVSaWmp+knLb7zxhuTm5iZt27ZNOnXqlPTMM89IXl5eUn19vbpHTEyMxlPUP//8c8nOzk564403pNOnT0tvvPGGZG9vLx07dswk425sbJTGjx8v3XfffVJZWZnG3/mGhga9416+fLm0e/du6dtvv5VKS0ul559/XrK3t5eOHz8ubNxtjf3SpUvSSy+9JB09elQqLy+XDhw4IIWGhkr/9V//ZfZjTmRtmNemy2tJsu7MZl7f3uyw1sxmXhOZhjXmtSRZb2Yzr2//sbbWzGZeM6/JNCx2wluSJOmdd96R/Pz8JEdHR2ngwIFSYWGh+r3p06dLI0aM0Kg/ePCgNGDAAMnR0VHq2bOntG7dOpOOF4DOJSsrS++4U1NTpd69e0vOzs7SvffeKw0dOlTatWuXSccdHR0teXl5SQ4ODpK3t7c0efJk6auvvtI7Zkky/7G+2Z49eyQA0pkzZ7Tes5TjfeDAAZ1/N6ZPny5JkiS1tLRIy5Ytkzw9PSUnJydp+PDh0qlTpzR6jBgxQl3f6sMPP5T69OkjOTg4SA899JDwf1gYGnd5ebnev/MHDhzQO+6EhATJ19dXcnR0lHr06CEplUrp6NGjQsfd1tivXr0qKZVKqUePHpKDg4Pk6+srTZ8+XaqoqNDoYY5jTmSNmNemY82Zzby+vdlhrZnNvCYyHWvLa0my3sxmXt9+1prZzOvpGuuY13S7KCTpjychEBERERERERERERFZMYu8hzcRERERERERERERkbE44U1ERERERERERERENoET3kRERERERERERERkEzjhTUREREREREREREQ2gRPeRERERERERERERGQTOOFNRERERERERERERDaBE95EREREREREREREZBM44U1ERERERERERERENoET3kRERERERERERERkEzjhTUREREREREREREQ2gRPeRERERERERERERGQTOOFNRERERERERERERDbh/wNUJyR1VyvfeAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 1296x648 with 12 Axes>"
+       "<Figure size 1800x900 with 12 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1200,7 +1198,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,
diff --git a/stress-test/multi-group-xs/mgxs-part-i.ipynb b/stress-test/multi-group-xs/mgxs-part-i.ipynb
index 1257424..b4ae26b 100644
--- a/stress-test/multi-group-xs/mgxs-part-i.ipynb
+++ b/stress-test/multi-group-xs/mgxs-part-i.ipynb
@@ -33,7 +33,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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w3bt3BwC9WSjEcnNzw8svv4wDBw5gxYoVaNGiBTp16mRxP5rzdvXqVdHbGHuATPP3df36dYPrNcsrPqzWrFkzpKam4s6dOzh27BgWL16MsrIyTJ06FRs2bNDJ+Ndff5nNpun7yZlVTGXQuHnzpsFtNH0Z2qYia87lk+rVq4cGDRqguLgYhw4dEr1dSUkJ4uPjUadOHZw+fRopKSlYsmQJ5s6dizlz5pgs8mw97oo0H2pLSkr01mk+bFhCs+/09HS972nNV2lpqaiHNTX279+Pjz/+GK1atcKSJUvw2Wefwc3NDePHjzc4g4wxarVaO3vK48ePcfjwYYuPjxBeUDFNCKeaNWsGV1dX/PHHH0Z/yGl+DWzpU/aMMUycOBErVqxAWFgYdu/eDXd3d6tyaoo8Hx8f7TLNHUVDv1YHgNu3b+u0s8b48eNRVlaGGzduYNy4cVb1oZmB5Ouvv7Y6h0a7du0AlE+B9qT79+/j5MmT8PDwQHBwsN56FxcXtGvXDnFxcUhJSQEA7UtwPD090aJFC9y4cQOnTp0ymcHb2xtBQUG4evUqzp07p7c+IyNDJ2tFWVlZyMvL01uuOZ62bdua3Le9zuWrr74KAJg/fz4YYybbFhUVASi/znJzc9GlSxe9O7x5eXnIysoy+iHIkuN2cXEBUD57jCG+vr4AYHBqPEunsQP+OaeWfLAw5e7du4iJiYGbmxs+//xzVK1aFc2bN8d//vMf3LhxAxMnTrS4T29vb7tkI0TRZB6zrThHjhxhgiCw+fPnyx2FELNGjx6t98CYxpUrV5i/vz9TqVQGX8JiTFlZGRs/fjwTBIGFh4eLmjXhp59+Mrj81q1brGXLlkwQBJaSkqJdXlpayurUqWPwBSv37t1jzZo1Y4IgsFWrVonK/OQDiBp79+5lO3fu1JlxwZIHEIuKilhgYCATBIFt2bJFb79XrlzR+XPDhg2NPux58eJF5urqyqpXr87OnTuns27KlClMEAT26quvapcdP37c4AtJ0tLSmCAILCoqSrtszZo1TBAEFhISojebR0lJic5sHgsXLtQ+CFhxNo/bt29rZ9uoODNExVktZsyYodP3Tz/9xKpUqcJ8fX3Zw4cPtcvtcS6NKSgoYG3atGGCILCRI0caPEcPHz5kc+fOZQsWLGCMlV9vnp6erGHDhjoPExYVFbGxY8dqH7Sr+GCgNce9e/duJggCi4+PN5h9y5Yt2pfNVPTzzz8zLy8vow8gqlQqg/0VFxezxo0bM7Vazfbs2WOwzZEjR/RmljFG85Dx+++/r7O8rKyMPf/883rfx4wxtnTpUnb69GmD/f3f//0fc3d3Z66urjrXICGVDY2ZrqCsrAzTp09HSEgIl6+DJc5n2bJl+PHHH7F+/Xr88MMPeOGFF+Dj44NLly5h586dyM/Px9tvv23wJSzGzJs3D0lJSfDw8EDr1q2xcOFCvTZt27bFgAEDtH/W/Aq4U6dOqF+/PlxcXHDx4kXs2bMHhYWFeOWVVzB8+HBte5VKhRUrViAqKgoTJkxAamoq2rRpg3v37iE9PR05OTkICQmx+o6yRp8+fWzavmrVqkhLS0NYWBiioqLwySefoGPHjtoHBTMyMlBcXCyqr4YNGyIxMRGTJ09Gu3btMGzYMNSqVQvff/89/ve//yE4OFj70BcAbNq0CatXr0bXrl3RqFEj+Pr64s8//8SuXbvg7u6uM8Z9/Pjx+L//+z989tlnaNy4MSIiIuDn54e//voLBw8exLhx4zBnzhwAwNtvv429e/di586daN26Nfr27YuCggKkpaUhJycHcXFxBuf3fv7557F27VocPXoUXbp0wfXr17Uvefn000/h5eXlkHPp4eGBffv2YciQIUhOTsauXbvQu3dvNGrUCIwxnDt3Dt9++y3y8vKwYsUKAOXX27Rp07B48WK0bNkSERERKCoqQkZGBu7fv48ePXpo78rbctxdunSBWq1GYmIi7ty5ox0+Mm3aNPj4+GDAgAH417/+hZSUFFy9ehWdOnXC5cuXkZ6ejgEDBmDLli0GMzAjd+CrVKmCbdu24cUXX0R4eDi6dOmC1q1bQ61W48qVK/jpp59w4cIF3LhxAx4eHibP62effYatW7ciNDRU52U3QPnQpY0bN6JVq1aYPHkyQkNDtWP8v/jiC8ycORPNmjVD586dUadOHe1Dsd999x0EQcAHH3wg2avlCVEEuat5JVm5ciV788032ZgxY+jONOHGw4cP2YIFC1iHDh2Yj48Pq1q1Kqtduzbr378/++qrryzub8yYMUylUjGVSqUz1ZXmS6VSsZiYGJ1tPv/8czZkyBDWqFEj5uXlxVxdXVm9evXYwIEDWXp6utF9HT16lA0bNozVrVuXVa1alXl7e7MOHTqwJUuWWDSPcHx8PFOpVHp3pg0xdmd6w4YNTKVSGZzW7PLly2zSpEksMDCQubq6slq1arFnn32WLVq0SKddQECA2WkI9+/fz8LCwpivry9zc3NjTZo0YTNnzmS5ubk67Y4ePcomTpzIWrduzWrUqME8PDxYkyZN2NixY43eCUxOTmahoaGsWrVqzN3dnTVq1IiNHDmSnThxQqddYWEhW7hwIWvRogXz8PBgPj4+rFu3bgbnf9acr5iYGPb777+zAQMGMF9fX+bp6cm6du3K9u/fr7eNPc6lOWVlZSwtLY0NGjSI1atXj7m7uzO1Ws2Cg4PZhAkT2A8//KDTvqSkhC1btow988wzzMPDg9WpU4eNHj2aXb58WXvNG7ozbclxM8bYvn37WEhIiPZO85P9/vXXX2z48OHav9NOnTqx7du3s4MHDxq8M929e3ejd6Y1bt26xd555x3WokULplarmZeXF2vatCkbOnQoS05O1pnyz5BLly6x6tWrs+rVq7PLly8bbaeZOrFfv37aZSdOnGDz589nPXv2ZIGBgczDw4O5u7uzxo0bs5EjR+rNf01IZSQwZmbQmZO4c+cOunbtiqNHj+KNN95A48aN8e6778odixBCZHXx4kU0atQIY8aMcaopzpz1uAkhlqMHEP82a9YsvPXWW9qHpGiYByGEEEIIMYeKaZRPv3XixAnt+ExW/mZImVMRQgghhBCl47KYzsvLQ1xcHMLCwuDn5weVSoWEhASjbWNjY+Hv7w8PDw+0bdtW+/CIRmZmJn777Tc89dRT8PPzw+bNm7Fo0SKMGTPGAUdDCCGEEEJ4xeVsHjk5OVizZg3atGmDyMhIrF271uiwjEGDBuHYsWNYsmQJmjZtiuTkZERHR6OsrAzR0dEAyp+EHzp0KIDyu9JvvvkmAgMDMXPmTIcdEyGEKFFAQIDJt2xWVs563IQQy3FZTAcEBODevXsAyh8cXLt2rcF2e/bswYEDB5CSkoKoqCgAQGhoKC5duoQZM2YgKioKKpUKnp6e8PT01G6nVqvh4+OjnWCfEEIIIYQQQ7gspisyNbZ5+/bt8Pb21t511oiJicGIESNw9OhR7RukKlq/fr2ofV+/ft3oq4EJIYQQQoj86tSpo50bXQrcF9Om/PrrrwgODoZKpTs0vGXLlgCA06dPGyymxbh+/ToaN26MgoICm3MSQgghhBBpVK9eHb/99ptkBXWlLqbv3LmDxo0b6y2vUaOGdr21rl+/joKCAnz++ecIDg62uh9H6tq1KzIzM+WOIRpveQH+MlNeafGWF+AvM+WVHm+ZKa+0eMt75swZjBw5EtevX6diWqmCg4PRrl07uWOIolKpuMkK8JcX4C8z5ZUWb3kB/jJTXunxlpnySou3vI7A5dR4YtWsWdPg3ee7d+9q1zuTf/3rX3JHsAhveQH+MlNeafGWF+AvM+WVHm+ZKa+0eMvrCJW6mG7VqhXOnDmjN73RL7/8AgBo0aKFHLFk4+/vL3cEi/CWF+AvM+WVFm95Af4yU17p8ZaZ8kqLt7yOUKmL6cjISOTl5WHr1q06yzds2AB/f3907tzZ5n3ExsYiIiICKSkpNvdFCCGEEEJsl5KSgoiICMTGxkq+L27HTO/duxf5+fl4+PAhgPKZOTRFc3h4ODw8PNCnTx/07t0bEydOxIMHDxAUFISUlBTs378fycnJRl/0YonExERuxg699NJLckewCG95Af4yU15p8ZYX4C8z5ZUeb5kpr7R4yRsdHY3o6GhkZWWhffv2ku6L2zvTkyZNwrBhwzBu3DgIgoC0tDQMGzYMUVFRuH37trbdtm3bMGrUKMyZMwd9+/bFTz/9hNTUVO3bD53JV199JXcEi/CWF+AvM+WVFm95Af4yU17p8ZaZ8kqLt7yOIDBTbz0hRmk+6Rw/fpybO9NZWVncZAX4ywvwl5nySktpeXfvBjw8gJ49jbdRWmZzKK/0eMtMeaXFY16p6zUqpq3EYzFNCHFuvXsDvr7Ali327Tc9HQgKApo3t2+/xD7Onj2rHRJJSGXi7e2NJk2amGzjiHqN2zHThBBCLKNSAVLcPpk6FRg1Cpg/3/59E9ucPXsWTZs2lTsGIZL5448/zBbUUqNimhBCnIRKBTwxU6hd2OFZbiIRzR1pnt7WS4gYmjcbKuG3LlRMO5GkpCSMGzdO7hii8ZYX4C8z5ZWW0vKKKaaVltkcyisOT2/rJYQ33M7moRQ8zTOdlZUldwSL8JYX4C8z5ZWW0vK6uAClpabbWJtZrqdvlHaOzeEtLyG8cuQ80/QAopXoAURCCG8GDCgvetPT7dtvYCAwYgSwYIF9+yW2o59VpLISe2074nuA7kwTQoiTkPLWidi+P/wQePBAuhyEEOJoVEwTQogTkeJhQUv6jI0Fzp2zfwZCCJELFdOEEEIIIYRYiYppJxIRESF3BIvwlhfgLzPllRZveQHrM8v19A1v55i3vEQ+Bw8ehEqlQkJCgtxRiBlUTDuRKVOmyB3BIrzlBfjLTHmlpbS8YgpeazJbOnTEnoW30s6xObzlrWyys7MxdepUtGjRAtWqVYObmxv8/f3x0ksvYd26dSgqKnJYlosXL0KlUiEmJsZkO4Emclc8mmfaiYSFhckdwSK85QX4y0x5paXEvOZ+Ljsisz2LaSWeY1N4y1uZzJs3DwkJCWCMoUuXLnjhhRfg7e2NGzdu4NChQxg/fjxWrVqFn376ySF5NEWysWK5c+fOyM7ORq1atRySh1iPimlCCHEiUg3HsKRfmpCVONqCBQsQHx+PBg0aIC0tDR07dtRr8/XXX+O///2vwzJpZiY2NkOxh4cHvQqeEzTMgxBCnIRUvy2m30ITJbtw4QISEhLg6uqKPXv2GCykAeDFF1/Enj17dJZt3rwZ3bp1Q7Vq1aBWq9GyZUssWrQIjx8/1ts+ICAAgYGBKCgowIwZM9CgQQO4u7ujSZMmWLJkiU7b+Ph4NGrUCACwceNGqFQq7dfGjRsBGB8z3b17d6hUKpSWlmLhwoVo0qQJ3N3d0aBBA8TFxekNVTE3nETT35PKysqwcuVKdOzYEd7e3vDy8kLHjh2xatUqvQ8A1u5j3bp1CAkJgZ+fHzw8PODv74/evXtj8+bNBvtRKiqmbcTTGxB37NghdwSL8JYX4C8z5ZWW0vKKuSNsbWa57jYr7Rybw1veymDDhg0oKSnB4MGD8cwzz5hs6+rqqv3/mTNnIjo6GmfPnsWoUaMwdepUMMbw7rvvIiwsDMXFxTrbCoKA4uJihIWFYdu2bQgPD8eECRPw6NEjzJo1C/Hx8dq2PXr0wBtvvAEAaNOmDeLj47Vfbdu21evXkOjoaKxYsQKhoaGYNGkSPDw88P777+PVV1812N7U2GtD60aMGIEpU6YgJycHEyZMwGuvvYacnBxMnjwZI0aMsHkfM2fOxPjx43H79m0MHz4cb731Fl588UXcuHEDX375pdF+xHLkGxDBiFWOHz/OALDjx4/LHUW0YcOGyR3BIrzlZYy/zJRXWkrL+9JLjA0YYLqNNZkbN2YsLk5cW4Cxo0ct3oVRSjvH5jg6L48/q+ytR48eTBAElpSUJHqbzMxMJggCCwwMZLdv39YuLykpYeHh4UwQBLZgwQKdbRo2bMgEQWDh4eFFg54qAAAgAElEQVSssLBQu/zWrVusevXqrFq1aqy4uFi7/OLFi0wQBBYTE2MwQ0ZGBhMEgSUkJOgsDw0NZYIgsA4dOrB79+5pl+fn57PGjRszFxcXdv36de3yCxcumNxPaGgoU6lUOsuSk5OZIAisU6dOrKCgQGcf7du3Z4IgsOTkZJv24evry+rVq8cePXqk1z4nJ8dgPxWJvbYd8T1Ad6adCG+/NuEtL8BfZsorLd7yAvxlprzEnBs3bgAA6tWrJ3qb9evXAwDee+89nQcAXVxcsGzZMqhUKiQlJeltJwgCli9fDjc3N+0yPz8/RERE4MGDB/jjjz+0y5mNv85ZunQpqlevrv2zWq3Gyy+/jLKyMmRlZdnU97p16wAAixYtgoeHh84+NENWDB2/JVQqFVxdXQ0O/6hZs6ZNfTsaPYBICCHEZvQAYuUzcSLw11+O25+/P7BqleP2Z8qJEycgCAJ69Oiht65p06bw9/fHxYsX8eDBA/j4+GjXVa9eHYGBgXrb1K9fHwBw7949u+QTBAEdOnTQW675wGDrfk6cOAEXFxeEhobqrQsNDYVKpcKJEyds2sfLL7+M5cuXo3nz5hg2bBief/55PPvss6hWrZpN/cqBimlCCHESUhWxgkDFdGWklMLWVnXq1EF2djauXr0qepvc3FwAQO3atY32efXqVeTm5uoU08YKwSpVysut0tJS0RnM8fb2lmw/ubm5qFmzJlxcXAzuo1atWsjJybFpH//v//0/NGrUCOvXr8eiRYuwaNEiVKlSBeHh4Vi2bJnBDyVKRcM8CCHEiUgx8wbN5kGUrFu3bgCAb7/9VvQ2mqL4+vXrBtdrlvNwF1UzjKKkpMTg+vv37+stq1atGu7evWuwKC8pKUFOTo7Ohwhr9qFSqfDGG2/g5MmTuHnzJr788ktERkZi586d6NOnj94DnkpGxbQTMfeWJaXhLS/AX2bKKy3e8gLWZ7bkbrM9i2/ezjFveSuDmJgYVK1aFV9++SXOnDljsq1mWrl27dqBMYaDBw/qtTl37hyuXr2KwMBAnYLSUpq7vva8W22Ir68vAODKlSt6654cx63Rrl07lJaW4vvvv9dbd+jQIZSVlaFdu3Y27aMiPz8/REZGYvPmzejRowfOnj2L06dPmz4wBaFi2onw9uYt3vIC/GWmvNLiLS9gXWY5h3nwdo55y1sZNGzYEPHx8SgqKkJ4eDiOHz9usN3evXvRp08fAMDYsWMBAPPnz9cZzlBaWoq3334bjDGMGzfOplyaAvTy5cs29WOOt7c3goODkZmZqfNhorS0FG+++SYKCwv1ttEc/6xZs/Do0SPt8oKCArzzzjsAoHP8lu6jqKgIhw8f1ttvcXEx7t69C0EQ4O7ubuUROx6NmXYi0dHRckewCG95Af4yU15pKS2vmCLW2sxyDfVQ2jk2h7e8lcWsWbNQUlKChIQEdOzYEV26dEH79u3h5eWFmzdv4tChQzh37pz2hS4hISGIi4vD0qVL0aJFCwwZMgRqtRp79+7F6dOn0a1bN8yYMcOmTF5eXnj22Wdx6NAhjBo1Co0bN4aLiwsGDBiAli1bmtzW0plAZs6ciTFjxuC5557DkCFD4O7ujoyMDJSWlqJ169Y4deqUTvvo6Gjs3LkTW7ZsQfPmzTFgwAAIgoAdO3bg4sWLGD58uN61bMk+CgoK0K1bNzRu3Bjt2rVDw4YNUVhYiG+++QbZ2dno378/mjVrZtExyomKaUIIIQ5FDyASOcyePRtDhw7FypUrkZGRgQ0bNqCwsBC1atVCmzZtMGvWLIwcOVLbfvHixWjbti1WrFiBTZs2obi4GI0bN8aCBQvw1ltvaR/20zD3whJD6z/77DNMnz4de/fu1c7A0aBBA5PFtLG+TK0bPXo0ysrK8P7772PTpk2oUaMGBgwYgAULFmDw4MEGt0lJSUFoaCjWrVuH1atXQxAEBAcHY8aMGZg4caJN+/Dy8sKSJUuQkZGBH374ATt37oSPjw+CgoLwySefaO+M80Jgtk506KSysrLQvn17HD9+XGfcECGEKNVLLwFVqwLbt9u332bNyvt+/33zbQUBOHIECAmxbwZiGP2sIpWV2GvbEd8DNGbaiWRmZsodwSK85QX4y0x5pcVbXsD6zHI9gMjbOeYtLyHEPCqmncjSpUvljmAR3vIC/GWmvNJSWl4xBa8jMtvz96FKO8fm8JaXEGIeFdNOJDU1Ve4IFuEtL8BfZsorLSXmNXdX2NrMcj2AqMRzbApveQkh5lEx7UTUarXcESzCW16Av8yUV1q85QWszyzX0ze8nWPe8hJCzKNimhBCiE3oDYiEEGdGxTQhhDgJ3uZuKiwEHjyQOwUhhJhGxbQTsXWCeUfjLS/AX2bKKy0l5jV3F9kRmcUW9bNnA+Hhptso8RybwlteQoh5VEw7kQYNGsgdwSK85QX4y0x5pcVbXkBZmQsKgLw8022UlFcM3vISQsyjNyDaKDY2FtWrV0d0dLTiXxM7depUuSNYhLe8AH+ZKa+0lJjX3F1hazNLNYREqrxy4S0vIbxKSUlBSkoK7t+/L/m+qJi2UWJiIr1VihBCCCFEQTQ3OTVvQJQSDfMghBAnItXMG7z1Swgh9kLFtBPJzs6WO4JFeMsL8JeZ8kqLt7yA9ZnlmimEt3PMW15CiHlUTDuRuLg4uSNYhLe8AH+ZKa+0eMsLWJdZzrvHvJ1j3vISQsyjYtqJrFixQu4IFuEtL8BfZsorLaXlFXP3WGmZzaG8hBC5UTHtRHibkom3vAB/mSmvtJSY19xdZCVmNuXwYb7y8nZ+K4OtW7di6tSp6NatG3x8fKBSqTBq1CiT25SWlmLt2rV4/vnn4evrC7VajaCgIAwfPhxnz561KkdRURHWrVuH/v37w9/fHx4eHvDy8kLjxo0RFRWF5ORkFBUVWdU3kRfN5kEIIcSh7Dm+esQIQOGzkhKZzZ8/Hz///DO8vb1Rr149ZGdnQzDxqTIvLw8DBgxARkYG2rZti5iYGLi7u+Pq1avIzMzE2bNn0aRJE4sy/Pbbbxg0aBD++OMP1KpVC7169ULDhg0hCAIuXbqEgwcPIi0tDUuWLMHPP/9s6yETB6NiuoLhw4fj4MGDKCgoQJ06dfD2229jwoQJcscihBDFk2ueaULMSUxMRP369REUFITvv/8ePXr0MNn+1VdfRUZGBj799FODNUBJSYlF+7927RpeeOEF3LhxA3FxcUhISICbm5tOG8YYdu7ciWXLllnUN1EGGuZRwdy5c3H16lU8ePAAn3/+OaZNm4YLFy7IHctulixZIncEi/CWF+AvM+WVltLyiilMlZbZPL7y8nd++de9e3cEBQUBKC9aTcnKykJqaiqGDx9u9GZalSqW3Yf897//jRs3bmDMmDFYvHixXiENAIIgYODAgcjIyNBZfvDgQahUKiQkJOB///sf+vTpA19fX6hUKly+fBkAUFhYiEWLFqFly5bw9PREtWrV8Pzzz2Pz5s16+6nYnyEBAQEIDAzUWbZhwwaoVCps3LgRX331Fbp06QIvLy/UqFEDQ4cOxblz5yw6H5UR3ZmuIDg4WPv/Li4u8PHxgbe3t4yJ7KugoEDuCBbhLS/AX2bKKy3e8gLWZ5Zvnmm+zjGP14Qz+eKLLwCUv/AjNzcXu3btwpUrV1CzZk306tVLW5SLVVBQgJSUFAiCgNmzZ5tt7+LiYnD5kSNHsHDhQjz//POYMGECbt26BVdXVxQVFSEsLAyZmZlo3rw5pkyZgvz8fKSlpSE6OhonTpzA4sWL9fozNczF2Lpt27Zh7969GDRoEHr27IkTJ07gyy+/REZGBo4cOYKmTZuaPb7KiorpJ7z88svYtm0bACA1NRW1atWSOZH9GPskqlS85QX4y0x5paW0vGIKXmszWzIcw75tlXWOzVHaNUF0/fTTTwCAS5cuISgoCHfv3tWuEwQBEydOxEcffQSVStwv9o8dO4bi4mI0aNBA746vJb755huDw04WLlyIzMxM9O/fH9u3b9fmmjNnDjp16oSlS5eif//+eO6556zet8auXbvw1VdfoV+/ftplH330EWJjYzFp0iQcOHDA5n3wiorpJyQnJ6OsrAzp6emIiYnByZMn6elrQggxgd5SWAl16ADcuOH4/dauDRw75vj9/u3WrVsAgOnTpyMyMhLz589HvXr1cOTIEbz++utYuXIl/Pz8MHfuXFH93fj7HNatW9fg+k8++UTbBigv2EePHq1XeLdt29bgsJN169ZBpVLhgw8+0Cnwn3rqKcyePRsTJkzAunXr7FJM9+rVS6eQBoApU6bgo48+wnfffYfLly87bb1ExbQBKpUKAwcORFJSEtLT0zFlyhS5IxFCiM14fJiPCnWZ3LgB/PWX3CkcrqysDED5sM/Nmzdrhzy88MIL2Lp1Kzp06IBly5bh3//+N6pWrYqDBw/i4MGDOn0EBgbilVdeEbW/Tz/9FKdOndJZ1q1bN71iulOnTnrbPnz4EOfPn0f9+vXRuHFjvfW9evUCAJw4cUJUFnNCQ0P1lqlUKnTt2hXnz5936puP3BbTeXl5mDdvHk6ePIkTJ07gzp07mDt3rsFPi3l5eXjvvfeQlpaGu3fvolmzZnjnnXcQFRVlch8lJSXw8vKS6hAcLicnh6thK7zlBfjLTHmlpcS85opTR2S27zCPHADKOsemKPGaMKh2befa79+qV68OAOjfv7/e2OE2bdqgYcOGuHjxIrKzs9GyZUt8//33mDdvnk677t27a4vp2n8fz7Vr1wzur2KhGxMTg40bNxpsV9vAecnNzTW6ruJyTTtbPf300w7ZD4+4nc0jJycHa9asQXFxMSIjIwEYHzQ/aNAgbNq0CfHx8di3bx86duyI6OhopKSkaNvcvHkTW7duRX5+PkpKSrBlyxYcPXoUvXv3dsjxOMLYsWPljmAR3vIC/GWmvNJSYl5zxakjMtv3DrnyzrEpSrwmDDp2DLh61fFfMg7xAIBmzZoB+KeofpKvry8YY3j06BGA8lnAysrKdL6+++47bfuOHTuiatWquHLlCv7880+T+zY104ih+qZatWoAoDNMpKLr16/rtAOgHQpibHq/+/fvG81w8+ZNg8s1+6+4H2fDbTEdEBCAe/fuISMjA4sWLTLabs+ePThw4ABWrVqFCRMmIDQ0FKtXr0bv3r0xY8YM7a90gPKB9P7+/njqqaewYsUKpKenw9/f3xGH4xDx8fFyR7AIb3kB/jJTXmkpLa+YIRPWZpbqAUTz4u3ZmeSUdk0QXS+88AIA4JdfftFb9/jxY5w9exaCICAgIEBUfx4eHhgxYgQYY5g/f749o8Lb2xtBQUG4evWqwenpNNPstWvXTrvM19cXALTT6lV07tw5PHjwwOj+nhzOApS/KTIzMxOCIKBt27aWHkKlwW0xXZGpT3Pbt2+Ht7c3hg4dqrM8JiYG165dw9GjRwGU//ri0KFDuH//Pu7evYtDhw6ha9eukuZ2tIrfUDzgLS/AX2bKKy2l5RVTxFqTWb5p8QBAWefYHKVdE0TX4MGDUbduXWzevFk7s4dGfHw8Hj58iB49euCpp54S3eeCBQtQu3ZtbNy4ETNnzkRhYaFem7KyMpOFrDFjx44FY0zv5mBOTg7+85//QBAEnd+GBAcHw8fHBzt37sTt27e1yx89eoRp06aZ3Nd3332H3bt36yxbsWIFzp8/jx49eqB+/foW568sKkUxbcqvv/6K4OBgvWlsWrZsCQA4ffq0Tf3369cPEREROl8hISHYsWOHTrv9+/cjIiJCb/vJkycjKSlJZ1lWVhYiIiKQk5Ojs3zu3Ll6E/5fvnwZERERyM7O1lm+fPlyzJgxQ2dZQUEBIiIikJmZqbM8JSUFMTExetmioqLoOOg46Dgq0XH88kuMXoFqj+O4dCkCjx6JOw4gApcuiTuO3bsjkJdXef8+HHkczmzHjh0YM2aM9qUpQPm8zZplFf/O1Go1NmzYAEEQ0K1bN4wYMQJvv/02unbtiiVLluDpp5/Gp59+atH+69atiwMHDqBp06b473//i/r162P48OGYOXMm4uLiMHr0aAQEBGDHjh0ICAhAw4YNRfetybZz5060bt0acXFxmDJlCpo3b47Lly8jLi4OXbp00bavUqUK3nzzTeTm5qJt27aYMmUKXn/9dbRs2RL5+fmoW7eu0RuUERERiIyMRFRUFP7973+jX79+mD59OmrWrImVK1dadE7s6YcfftB+f6SkpGhrscDAQLRp0waxsbHSh2CVwO3bt5kgCCwhIUFvXZMmTVjfvn31ll+7do0JgsAWL15s1T6PHz/OALDjx49btT0hhDjaiy8yNniw/ft95hnG3nhDXFuAse++E9d20iTGWrc23x9jjO3fz9gXX4jr15nQzyrG4uPjmSAITKVS6XwJgsAEQWCBgYF625w6dYoNGTKE+fn5MVdXV9awYUM2adIkdv36datzPH78mCUlJbHw8HBWt25d5ubmxtRqNQsKCmJDhw5lycnJrKioSGebjIwMo/WNRmFhIVu4cCFr0aIF8/DwYD4+Pqxbt24sNTXV6DZLly5lQUFB2mObOXMmKygoYAEBAXrnY/369UwQBLZx40a2e/duFhISwjw9PZmvry8bMmQIO3v2rNXnxBZir21HfA9U+jvT5B9P3sFQOt7yAvxlprzSUlpeMcMmrMls6TAPsWOmxfVbnjc1FfjoI8tyyEFp14Qz0DwkWFpaqvOleWDw/Pnzetu0atUKaWlpuHXrFh4/foyLFy/i448/Njpzhhiurq4YO3YsvvrqK/z1118oLCxEfn4+zp07hy1btmDEiBGoWrWqzjbdu3dHWVkZ5syZY7RfNzc3zJo1C7/88gsKCgqQm5uLQ4cOmZyxbMaMGTh37pz22BYvXgwPDw9cuHDB4PnQ6NevH44cOYK8vDzcvXsXaWlpBqflczaVvpiuWbMm7ty5o7dc81ajmjVrOjqSbLKysuSOYBHe8gL8Zaa80lJaXjFFrCMyiy2mxbVT1jk2R2nXBCHEdpW+mG7VqhXOnDmjMzAf+OdJ3RYtWsgRSxYff/yx3BEswltegL/MlFdaSsxr7m6vEjObxlde/s4vIcScSl9MR0ZGIi8vD1u3btVZvmHDBvj7+6Nz58429R8bG4uIiAidOasJIYQYZ99hHv+05fENj4QonSAIRt/joWSahxEd8QAit29ABIC9e/ciPz8fDx8+BFA+M4emaA4PD4eHhwf69OmD3r17Y+LEiXjw4AGCgoKQkpKC/fv3Izk52eYLJDExkaY6IoRwQ6qCU755pqXrkxACvPLKK6Jfj64k0dHRiI6ORlZWFtq3by/pvrgupidNmoRLly4BKP/klJaWhrS0NAiCgAsXLmjfEb9t2za8++67mDNnDu7evYvg4GCkpqZi2LBhcsYnhJBKQaoHEKXOQQgh9sD1MI8LFy5on8at+GRuaWmptpAGAE9PTyQmJuLatWsoLCzEiRMnnLKQNjRPqZLxlhfgLzPllZYS85orOK3JLO9d4fK8vAzzUOI1QQixDdfFNLHMlClT5I5gEd7yAvxlprzS4i0v4JjM9i16+TrHPF4ThBDTqJh2ImFhYXJHsAhveQH+MlNeaSkxr7lC1prM8g7zCJOgT+ko8ZoghNiGimlCCCEOJefDin8/ZkMIIXZDxTQhhDiRyvqQntjjCgiQNAYhxAlxPZuHEsTGxqJ69eraKViUbMeOHRg4cKDcMUTjLS/AX2bKKy3e8gKOyWzJ3WbzRfIOAPycY7muiTNnzjh8n4RIydw1nZKSgpSUFNy/f1/yLFRM24ineaZTUlK4+sHOW16Av8yUV1q85QWszyzV0A3zbVPAUzHt6GvC29sbADBy5EiH7ZMQR9Jc40+ieaaJJDZv3ix3BIvwlhfgLzPllZbS8oqZPs6azPI+gCg+rxIeUnT0NdGkSRP88ccf2pebEVKZeHt7o0mTJnLHoGKaEEKchSAAZWX279fSItW+wzyIOUooNgipzOgBREIIcRK8vNikIiny/vgjMGCA/fslhDgnKqYJIcRJSFVMK+F14mKOTbP+zz+B9HT7ZyCEOCcqpp1ITEyM3BEswltegL/MlFdaSssrpuB0RGb7FtMxEvQpHaVdE2LwlpnySou3vI5AxbQT4e3NW7zlBfjLTHmlpbS8Yu4gOyKz2MJX3B3vf/LyML5aadeEGLxlprzS4i2vI1Ax7USUPg/2k3jLC/CXmfJKi7e8gLIyiyu6y/PyUEgDyjq/YvGWmfJKi7e8jkDFNCGEEJvJ+Ypwsf3yMhSEEMIXmhrPRjy9AZEQQqQg1QOIvNxtJoQojyPfgEh3pm2UmJiI9PR0LgrpzMxMuSNYhLe8AH+ZKa+0eMsLWJdZqnmmxbX7Jy8PxbezXBNyorzS4iVvdHQ00tPTkZiYKPm+qJh2IkuXLpU7gkV4ywvwl5nySou3vIBjMtt3uMU/eXkY5kHXhPQor7R4y+sIVEw7kdTUVLkjWIS3vAB/mSmvtHjLC1iXWao7wuL6te0cT5oElJTY1IVFnOWakBPllRZveR2Bimknolar5Y5gEd7yAvxlprzS4i0v4JjM9r1DbFveVauA4mI7RRGBrgnpUV5p8ZbXEaiYJoQQ4lBKGG5BCCH2QsU0IYQQh5KrmKYinhAiBSqmnciMGTPkjmAR3vIC/GWmvNLiLS9gfWb5ClW+zrEzXRNyobzS4i2vI1Ax7UQaNGggdwSL8JYX4C8z5ZUWb3kB6zJLNc+0OA2syiAXZ7km5ER5pcVbXkcQGKNffFkjKysL7du3x/Hjx9GuXTu54xBCiFkREeVF586d9u23dWugWzdgxQrzbQUB+OILQMzU/FOnAocOAadOme6PMWDCBODnn4GjR423LSoC3NzK9z9iRPl2ggAUFAAeHsDNm8DTT5vPRQjhhyPqNbozTQghxKHkvoVj7C527dqOzUEIqRyomCaEEGITJQyxsCSD3MU8IaRyoWLaiWRnZ8sdwSK85QX4y0x5pcVbXsAxmS0pZiu2zc0FCgufbJFtsK09LF4MzJ5t3z7pmpAe5ZUWb3kdgYppJxIXFyd3BIvwlhfgLzPllRZveQHHZLak6K14x7lXL2DBgidbiM9rabF9/Djw44+WbWMOXRPSo7zS4i2vI1Ax7URWiHk6SEF4ywvwl5nySou3vIBjMlt7Z/rhQ0N3pv/Jq4ThJubQNSE9yist3vI6AhXTToS36Wx4ywvwl5nySou3vID1ma0tkG3fj/RT+dmTM10TcqG80uItryNQMU0IIcQmjipOze3HXJGuWf9kOx7uaBNClIuKaUIIIQ4l9s60o4pcmt2DEGILKqadyJIlS+SOYBHe8gL8Zaa80uItL+CYzPYtXv/Ja4/iu1Wrf/5fiiKbrgnpUV5p8ZbXEarIHYB3sbGxqF69OqKjoxEt5pVeMiooKJA7gkV4ywvwl5nySou3vIBjMtu3SP0nr9hhHqb88ouNccyga0J6lFdavORNSUlBSkoK7t+/L/m+6HXiVqLXiRNCeNO/P6BS2f914m3bAl26AB9/bL6tIADr1gExMebbTpsGZGT8U+A2awaEhwMffKDbH2PAa68BJ06Ynsru0SNArQZSUspfZ/7k68Q1d7Y1PxWHDgUePAC+/tp8VkKIMtHrxAkhhHDBUbN5EEKI0lAxTQghTkIps1ZIVUyL7Zdm8yCE2BMV004kJydH7ggW4S0vwF9myist3vIC1me2pCC1djYPw/vIMbPe/H4deafcma4JuVBeafGW1xGomHYiY8eOlTuCRXjLC/CXmfJKi7e8gPWZ5Xtpi3TnWIoi25muCblQXmnxltcRqJh2IvHx8XJHsAhveQH+MlNeaSktr5ji0JrM8g6TiNf+nxTzV9v72JR2TYjBW2bKKy3e8joCFdNOhLdZR3jLC/CXmfJKS2l5xRSGjshsy11s/WMQn9eaO832vjuttGtCDN4yU15p8ZbXEaiY/ltRURFiYmLQoEEDVKtWDSEhIfjhhx/kjkUIIYpnScGpmcrOEfvSOHwYKC21vA96MJEQIgYV038rKSlBo0aNcOTIEeTm5mLixImIiIjAo0eP5I5mF2fOAAcOyJ2CEFJZiS08bSmmTe3D1LquXYFr16zblhBCzKFi+m9qtRqzZ89GvXr1AACjR49GWVkZzp07J3My+6hXD3j//SS8/DJw9arcacRJSkqSO4LFeMtMeaXFW17A+szyjVcWn9eWO+KZmUBQkO6yPn0ASyc2cKZrQi6UV1q85XUEKqaNyM7OxqNHjxD05L+enPL2BoKCsvDvfwMTJgDJyXInMi8rK0vuCBbjLTPllRZveQHrMlt6Z9e+45Btzysmz+3bwPnzusu+/hrw87Ns385yTciJ8kqLt7yOQMW0AQUFBRg1ahRmz54NtVotdxy7+fjjj9G8ObBrF/DHH+Wv883NlTuVcR+LeTexwvCWmfJKi7e8gPWZLbkzbd9iWrpzLDZnWRkg9pksZ7om5EJ5pcVbXkegYvoJxcXFGDp0KFq0aIFZs2bJHUcSVaoACQnA+PFAZCSwf7/ciQghPLOkQLa0mH6yraltzfVrTREv5q47Y8CJE5b3TQipHLgtpvPy8hAXF4ewsDD4+flBpVIhISHBaNvY2Fj4+/vDw8MDbdu2xebNm/XalZWVYdSoUXB1dXWKMUHPPVd+l/rrr8vvUtNLjQgh1rC0mJablC+Y+eUXy9oTQvjHbTGdk5ODNWvWoLi4GJGRkQAAwci/0oMGDcKmTZsQHx+Pffv2oWPHjoiOjkZKSopOu9deew03b95EamoqVCpuT41FPD2BDz4AJk0Chg8Htm+XOxEhhDfyjpm2LoOlfYrN3KqV/XMQQpSN24oxICAA9+7dQ0ZGBhYtWmS03Z49e3DgwAGsWrUKEyZMQGhoKFavXo3evXtjxowZKCsrAwBcunQJSUlJ+PHHH1GrVnldjv8AACAASURBVC14e3vD29sbhw8fdtQhSS4iIsLouo4dgd27gR9/BMaOBe7fd2AwI0zlVSreMlNeafGWF7A+sxTDPJ5sa7hgFp9XiiIeAEaMEN/Wma4JuVBeafGW1xG4LaYrYib+hdy+fTu8vb0xdOhQneUxMTG4du0ajh49CgBo2LAhysrKkJ+fj4cPH2q/nnvuOUmzO9KUKVNMrndzAxYtKh9LPWgQ8M03DgpmhLm8SsRbZsorLd7yAtZllnLM9JP0t7Uurz23SUsT34+zXBNyorzS4i2vI1SKYtqUX3/9FcHBwXrDNlq2bAkAOH36tE399+vXDxERETpfISEh2LFjh067/fv3G/w0N3nyZL3x2VlZWYiIiEDOE4OY586diyVLlugsu3z5MiIiIpCdna2zfPny5ZgxY4bOsq5duyIiIgKZmZk6y1NSUhATE6P9c5cu5WOpJ02KQr9+O5CfL89xhIWFGTyOgoICUcehERUV5bC/j2bNmon++1DCcYSFhdl8XTnyOMLCwiT7/pDiOMLCwgweByDd97mp4zh50vxxhIWFWXxdnT0bgUePxB1HUVEEbtwQdxzp6REoKNA9jt9/f/Lvo/wcf/NNFO7dE3ddrVs3GRXnp2ZMM91XBIAcneXnzhn/+wD0jwMw/fehuSaU8O+V2OsqLCxMEf9eiT0OzTmW+98rscehySv3v1dij0OT98nj0JDzOFJSUrS1WGBgINq0aYPY2Fi9fuyOVQK3b99mgiCwhIQEvXVNmjRhffv21Vt+7do1JggCW7x4sVX7PH78OAPAjh8/btX2vPjmG8Z69GDs8GG5kxBCbFFaylhEBGP9+9u/786dGRs3TlxbDw/GEhPFtZ0+nbHg4H/+/MwzjMXG6rbR/BR7/XXG2rUz3A/A2KVLjN2/X/7/X3zxz3YAY/n5//x/xZ+Kgwcz9uKL5f//5Ze66yq2FwTd/gghyuGIeq3S35kmtnnhBWDbNiApCZg5E3j8WO5EhBBrlJYCLi7SzaYh3zAP+Wky3b4tbw5CiDwqfTFds2ZN3LlzR2/53bt3teudxZO/GhGrevXyYrpLFyA8HPjpJzsHM8LavHLiLTPllZaS8mqKaXOsyeyoMdOGPwiIy2vthwhLsj7zjPk2SromxOItM+WVFm95HaHSF9OtWrXCmTNntLN2aPzy92SgLVq0kCOWLJ6cCtBSAwYAmzcDK1YAs2ZJf5fa1rxy4C0z5ZWWkvKKLaatyezIl7boS9H2K7YvsYW1pe0KCsr/27698bZKuibE4i0z5ZUWb3kdodIX05GRkcjLy8PWrVt1lm/YsAH+/v7o3LmzTf3HxsYiIiKCi4vL0ItqLFWzJrBxY/lUeuHhwMmTdghmhD3yOhpvmSmvtJSUV2wxbU1mS+762n+YyWaHDP3Q5DY1K5gmR1aW8TZKuibE4i0z5ZUWL3k1DyM64gHEKpLvQUJ79+7VTmUHlM/MoSmaw8PD4eHhgT59+qB3796YOHEiHjx4gKCgIKSkpGD//v1ITk42+qIXsRITE9GuXTubj4U3gwYBXbsC06YBzZsD77wDVK0qdypCiDFii2lrSflWQXtta8u+NP+/a5fj9k8IsV50dDSio6ORlZWF9qZ+XWQHXBfTkyZNwqVLlwCUv/0wLS0NaWlpEAQBFy5cQIMGDQAA27Ztw7vvvos5c+bg7t27CA4ORmpqKoYNGyZnfO499RSQklL+FR4OLFsGONGoGUK4oimmpXr7oFQvbTH1Zw0x/Wnm3jDU3tT29riTXlAAqNW290MIUSaui+kLFy6Iaufp6YnExEQkJiZKnMj5CEL527969ACmTgU6dQLeekvaO2CEEMtJeWfaUQ8gmttOrpk+xBTjSpyFhBBiH5V+zDT5h6EJ0O2lTp3yt4DVqgX07w/8+aftfUqZVyq8Zaa80lJS3opT45kq7KzJLO+Y6RhRhWrF/Uo1PaAYSromxOItM+WVFm95HYGKaSdS8a1FUhAEYOxYYOVKIDa2/L9PTKJiEanzSoG3zJRXWkrKqymmXVxMf19am1mqMdMV2xougsNMrLN+v7ZsY8iKFcDQocq6JsTiLTPllRZveR2BimknEh0d7ZD9BAQAO3eW/8COjAT+HtZuMUfltSfeMlNeaSkpb8ViuqTEeDtrMjtqmAdgaNtoyYZQVCzQS0vNt6+Y48kPLMuXA1u3KuuaEIu3zJRXWrzldQQqpokkVCpgyhTggw+A118H1q+nMYOEyKliMS2mMLSEI4tpaxmamcOSbSx9Xv3Jd4X98Ydl2xNC+EHFtI14mmdaDo0bA199Bdy6Vf4rzmvX5E5EiHPSFNNVqpi+M20NS8dMWzubh7FlxmbpMNZO7HJj+7Ol7c2b4vsjhFjPkfNMUzFto8TERKSnp3Pxa4/MzExZ9uviAsycCSQklI+p3rhR3A9TufLagrfMlFdaSsor9s60tZltKZBt24/leeV8ANHfXznXhFhKuo7FoLzS4iVvdHQ00tPTHTKTGxXTTmTp0qWy7r958/K71DduAFFR5f81Re681uAtM+WVlpLyir0zbU1m+78i3Ph+9C0VPZuH1MNLxPRfWrrU7L99SqOk61gMyist3vI6AhXTTiQ1NVXuCKhSpfwu9ezZwKhR5dPpGaOEvJbiLTPllZaS8oq9M21NZinHTJtvazxvfr74/dib8dypqFPHkUlsp6TrWAzKKy3e8joCFdNORK2gV3C1bAns3g38/DMwejRw965+GyXlFYu3zJRXWkrKW1pa/mHWXDFtTWapxkyLowZj+hkuXAC8vGzv3ZKs4s5D+fktLLRt6lBHUtJ1LAbllRZveR2BimkiG1dX4D//ASZPLn848euv5U5ESOUl5QOIgOPGTIvNUFSkv87SIt5YVnsM02jeHNi82fZ+CCHyo2KayK5zZ2DXLmDPHmDSJCAvT+5EhFQ+SpkaD7Ct8Da0rVRjoY31a2yYhiU5Ll8Gzp0z3UbOByUJIeJRMe1EZsyYIXcEo9Rq4MMPgcGDgYgI4LvvlJ3XGN4yU15pKSmv2Je2WJNZ3nmm/8lrbfFpr6nxxCnPW1ICzJkDPHpk7/7tT0nXsRiUV1q85XUEKqadSIMGDeSOYFavXuVvT9yxAzhypAEePJA7kWV4OMcVUV5pKSlvxWEepu5MW5NZ3jHT/+Q1VxQ78mUxxvele355GH6qpOtYDMorLd7yOgIV005k6tSpckcQxdsb+OgjYPHiqRg4EPj2W7kTicfLOdagvNJSUl6xd6atzeyIO9OGi/apinm7qrgPFcq5JsRS0nUsBuWVFm95HYGKaRvRGxCl060bkJ4ObNsGTJ0q7zRXhPBO7J1pa8j1inBLMjgin9zngBDyD0e+AbGK5Huo5BITE9GuXTu5Y1RaXl7Axx8D33wD9O8PLFgAhITInYoQ/ijlAUQpHlYU2x8Vu4Q4j+joaERHRyMrKwvt27eXdF90Z9qJZGdnyx3BIhXz9u5dfof644/LC2p7FwP2wvM55gHltV5xcfl0lOaGeViTWapiWtywiX/yKmn2C+NZDJ/fixeVW+wr6ToWg/JKi7e8jkDFtBOJi4uTO4JFnsxbvTrw2Wfl01JFRABnz8oUzATez7HSUV7rFRWVF9PmhnlYk9mRDyDqbxunXVZxnalM9ii6L1823a/xYzR8fgMDDfepBEq6jsWgvNLiLa8jUDHtRFasWCF3BIsYyisIwNixwMqVwIwZwOLF5XfclKIynGMlo7zW0xTT5u5MW5NZEMS/zc/SQtZ8gWw475PFrKki3priPihIf5m4ae6Mn99LlyzP4QhKuo7FoLzS4i2vI1Ax7UR4m87GVN6GDYHt24H69YF+/YAzZxwYzITKdI6ViPJaT+ydaWunxpPipS3iNLDruGqxrH8VuPHzu3QpUFBgbb/SUdJ1LAbllRZveR2BimnCLUEAXn4Z2LSp/C71Z5/JnYgQ5RJ7Z9oa8s4zrUyHDwMdO1q2ze7dgKenNHkIIdKhYppwr06d8pe8/PknMGwYcOWK3IkIUZ6KxbScD/Da/wHE8v7EtHVkEf/XX8CxY+LaXrsmbRZCiLSomHYiS5YskTuCRSzJW6UKEB8PzJsHvP56+UtfrP81rPUq8zlWAsprPbHDPKzJbEmRauvDf/r7WmJwnT0eMpSm+NY/v8nJun8eN06K/VpPSdexGJRXWrzldQQqpp1IgRIH45lgTd5mzYBdu8qLhsGDgdu3JQhmgjOcYzlRXus9fixumIc1mW15qNB2BQb7M/dqcfnon98vv9T987p1QEKCg+KIoKTrWAzKKy3e8joCFdNOJEFJ/zqLYG1elar87nRCAhAVBXz1lZ2DmeAs51gulNd6Fe9Mmyqmpc5s/4cVxeW1Zqy2NEW3ft6jR/VbxcdLsW/rKOk6FoPySou3vI5AxTSptFq1Ki+k//c/YPjw8jGMhDiroiLAze3/s3fm4VGVZ///TEgCCWFfBIIoIFRQqQRc+6pdEBBwFBRp3MFd1KZLqCuLSgtobVS0WkCtFQc3QFSwuLRWXvtaSfRX2UQtomxKAIEQAlnO748nh8xMzsycM5kzZ57M/bmuuWZy5syZ73nyzMk399zPfUNWVuIXINrFMNQ/u/FGpiOZW6fVPMKPkw4LIgVBcA8x00KzJjcX7r8f7rkHrr0WnnvOa0WC4A1mZDo7Wz32gro6lWbiRo51+H7RXpcM8ywGXRDSBzHTaUR5ebnXEhyRSL0nnKByqT/7DK6/Hg4cSNihQ0jnMU4Gojd+7JppNzXbrboRvH9syi07IMZ/PLdJnTlhl1Sax3YQve6im95kIGY6jZg0aZLXEhyRaL2ZmXDffap8nt8Pf/tbQg8PyBi7jeiNH7tm2k3N8aR5xDbf1noTsQCxKeY78nukzpywSyrNYzuIXnfRTW8yEDOdRkxPpRUtNnBL77BhsGwZvPUWXHVVYit+yBi7i+iNH7tm2qnmujr75rSuzpmZtlelY7qrEefEL0KcnugDuk4qzWM7iF530U1vMhAznUYUFBR4LcERbupt3RoefBB+8Qu49FJYvDgxx5UxdhfRGz92zbRTzbW19vOga2vVAsh4I9PWxtZar9W+5vsmPtXECc7nhHmN+uyzRGuxRyrNYzuIXnfRTW8yEDMtpDUFBariR2mpilLv3u21IkFwB7cWIJpmOtH7QmMjG8nYOjW8do/blKh0Ik34q6/C55+rOvqCIKQeYqaFtKdlS5g5E26+GcaPh+XLvVYkCInHbTNtx3jW1qq1C251TEyUgV29Gr79tmnHSKSZrqyE7dsTdzxBEBKLmOkmUlRUhN/vJxAIeC0lJgsWLPBagiOSrfe001SU+p13VMWPffucH0PG2F1Eb/zU1CjTG8tMO9XsNDLtxEyH72dtrBdYPh8t3zqWQR8xAp591pbEOHA2vpddpu4ffljde1EjPJXmsR1Er7voojcQCOD3+ykqKnL9vcRMN5GSkhKWLVtGYWGh11JiUlZW5rUER3ihNycH/vAHuPxyuPBCZaydIGPsLqK3afh8sc20U81ummmI3mBFPS5rcppHcnE2vs8/H/rzSSclUIpNUm0ex0L0uosuegsLC1m2bBklJSWuv5eY6TTiscce81qCI7zUe/bZquLHK6/AbbfZr0stY+wuorfpxDLTTjUnMzJt/by13kRU4XCnNF7T5sSGDU16eVyk4jyOhuh1F930JgMx04IQgbw8ePxxGDMGzj8fPvzQa0WCED+mMfR6AWJWliqRZ5donQ3tNmsJ39+J0Y7XlLsZ/d60yb1jC4LgHDHTghCD4cNVhPrRR+Hee73JWRSEROGlma6pUQt+a2vt7W+vzrS7pEbXxFD69PFagSAIwYiZFgQbdOgAf/2r+iM2Zow3X7UKQlMwI6xeR6azs+2baYheZzreyLTd7eb7bdli7/iCIKQnYqbTCL/f77UER6SaXp9PLUycPx/uvBN+9zuorg7dJ9U0x0L0uksq6o1lpp1qdtNM28uZbtCbqG6FhhH63kcfnZjjKhIzJxLfmTEyqTiPoyF63UU3vclAzHQaccstt3gtwRGpqrdnT5X20bs3jBoFn3zS8Fyqao6E6HWXVNSblRXdTDvVnMzIdDjK8N7SyPymNqk3J2KRivM4GqLXXXTTmwzETAfxpz/9iYKCArKzs5kxY4bXchLO8OHDvZbgiFTW6/NBYSEsXAizZsH996t80FTWbIXodZdU1JuREd14OtXs1Ey3bGl/3YE9g2xfr900D59P3dwx6ImbE8mKTqfiPI6G6HUX3fQmAzHTQfTo0YN7772XCy+8EF8yv0MTtKVrVwgE4NhjJZdaSE+SHZluXGfa3ai0Dn8Kqqpg82avVQhC+pLptYBU4oILLgDg1VdfxdDnO0PBY8xc6h//GG65BX7yE7j1VhUBFISUYOhQFqzdAT3Vj0/t4shjS7p1Uz21beC2mY5GtMu0F23I3T6mFTt3wtKlqmur/NkSBG+QP/dpxNKlS72W4Ajd9PbsCVddtZTsbLjgAvjqK68VxUa3MRa9cbJ1K52rtsLWrY0eh9+Wbt0KO3bYPrT3CxCX2i6hF4/ZTLxBTeyc6NpVGWo3SZl5bBPR6y666U0GYqbTiEAg4LUER+imF2DRogA33QR//CPcfDMsWJDa0SLdxlj0xofRvoN60KUL5OdT3iof8q1vgZwcFZm2iRel8b79Vi2iVJ8t98a4KSkekT/3idd7110JP2QIqTKP7SJ63UU3vclAzHQa8cILL3gtwRG66YUGzccdB6+9Brt2wcUXq6BfKqLbGIve+Kj883PqwZtvwpYtTDp3iyqebHF7obLSdooHeBOZPu44ePZZ86cXHP/DGi0P2/zZvX+CU2NOOCFV5rFdRK+76KY3GWhrpisqKpgyZQrDhw+nS5cuZGRkRKzAUVFRQVFREfn5+eTk5DB48OCYk0EWIApNpUULmDJFVfq45hp4+unUjlILzZdDh9w7drCZjjW/nRhvE6tLcWWlOpbTz1OimrwIgiAEo62ZLi8vZ968eVRXVzN27FggsgEeN24czz77LNOnT+fNN9/klFNOobCwsNFXFbW1tVRVVVFTU0N1dTVVVVXU1dW5fi5C82bAAHjjDSgvh/HjYft2rxUJ6UZVlXvHrq5WtaszMiDW5dKpmbaXM20dSW5KPMQsjdeU43gRj/n22+S/pyAIGlfzOPbYY9mzZw8Au3btYv78+Zb7LV++nLfffptAIMCECRMAOOecc9i8eTPFxcVMmDCBjPqyC/fddx/33nvvkdfOnDmTZ555hiuvvNLlsxGaOy1aQHExrFkDV12lItX101EQXMdNM11To8x0ixaxzXI8ZtpOaTyT4H2dVPpwIwL9y18m/pix6NZNoumC4AXaRqaDiVbGbsmSJbRp04bx48eHbJ84cSLbtm3jww8/PLJt+vTp1NXVhdyak5GeOHGi1xIcoZteiK35xBNVlHrdOrjiCti9O0nCIqDbGIve+KhykObhVHN1NWRmNpjpaNTWqn3tRm3DzbT5ODRdY6JmaRvuzokDBxJ/zFSZx3YRve6im95k0CzMdDTWrFnDgAEDjkSfTU466SQA1q5d26Tjjxo1Cr/fH3I744wzGpWOWblypWU/+8mTJ7NgwYKQbWVlZfj9fsrLy0O2T5s2jdmzZ4ds+/rrr/H7/WwI6xby6KOPUlxcHLLtnHPOwe/3s2rVqpDtgUDA8sMxYcIET89j+PDhludRWVmZsudRUFAQ8/eRlQUzZsD111fygx/4efBB785j+PDhTZ5Xyfx9DB8+3LXPhxvnYXYKS+bn3Oo8qg6qGhITp08HQk1lyHls387wfftYGQjYnlfr15cxb56fmprykDQPq/PYtu1rnnzSz7599s5j+XI/Bw6E/j6++CKAYQT/PtQYv/nmBPbuDS/ZZT2v5s2bDDSch2Go3wf4gdDfx+efTwNCzwO+pq7OD4R3aXoUKA7bVll/XPM8zO5xAayN9QQal89bWX+McELPA6CwMPHzavjw4Sl93Q0/D/Nz5/X1yu55mHq9vl7ZPY/gDoipdt0N1F+7/H4/vXv35uSTT6aoqKjRcRKO0QzYuXOn4fP5jBkzZjR6rl+/fsZ5553XaPu2bdsMn89nzJo1K673LC0tNQCjtLQ0rtcLgmEYRmWlYRQVGcaNNxrG/v1eqxGaK6v+vNbY1W2gYaxdaxiGYYwZE2HH0lKVfuzgurZ0qWHMn28Yl11mGN9/H33fV181jD//2TDOP9/esW+80TAGD274eehQw7j+esPIyDCMJ55Q7weG8c03hnHLLYYxaFDDvhs3qucMQ91/9ZVhfPGFevzyy6HPffttw2MwjI4dDWPOHMO46CLDGDGiYXvwzeez3p4Kt5077Y2vIKQDyfBrzT4yLQipTE6Oqkk9fjz4/bBypdeKhObIzi4DWTpzLQwcCNhbLGgXM80jK0s9jkaiqnmYOEnb8PmcVfGIVR4vlQs+/eQnXisQhPSi2ZvpTp06sWvXrkbbd9cnq3bq1CnZkgShET/9KSxbBitWwLXXwvffe61IaE5UVkJubsPPOTlw8GBijm0uQHTDTMfqYhit1J0To62raY7EmjXwzjteqxCE9KHZm+lBgwaxfv36RiXuPv30UwBOPPFEL2R5QnhOUqqjm15omua8PBWlnjgRxo1LTpRatzEWvfFx8GComc7NVQbbCqeK44lMOzGosfe1pzjRiw/jj+wnZ04MGwZPPpmYY6XKPLaL6HUX3fQmg2ZvpseOHUtFRQUvv/xyyPZnnnmG/Px8TjvttCYdv6ioCL/fr0V7zTlz5ngtwRG66YXEaP7Rj1T3xNdfVy3JKyoSICwCuo2x6I2PAwcam+lIkWmnis0605mZKkodDXfqTM+x3NfKhEc6ntVr3YtIJ29O3Hhj6M/hv/P//tfecVJlHttF9LqLLnrNxYjJWICobZ1pgBUrVnDgwAH2798PqMocpmkePXo0OTk5jBw5knPPPZebbrqJffv20bdvXwKBACtXrmThwoVN7nRYUlJCQUFBk88lGSxatMhrCY7QTS8kTnPr1vDII/DuuyqX+v774cwzE3LoEHQbY9EbH/v3Q9u2DT9Hi0w7Vew0zaNlS+e5zuGPzZJ56jiLbB0vNcrigfMRThy5uaHj0LevvXFJlXlsF9HrLrroLSwspLCwkLKyMoYMGeLqe2ltpm+++WY2b94MqO6HL730Ei+99BI+n49NmzbRq1cvABYvXsxdd93F1KlT2b17NwMGDGDRokVccsklXspPOrnBoSkN0E0vJF7zT38KQ4bArbeqHMg773S+gCsauo2x6I2PvXtDzXROTmQz7VSxmwsQ7UWmEzPGdqPWTSc15oST80qVeWwX0esuuulNBlqneWzatOlIc5Xa2tqQx6aRBmjdujUlJSVs27aNqqoqPv7447Qz0oK+tGsHf/kLHHMMjB6tGr4IghP27bMZmW7VSlX8aNXK9rHNNI9kVvOwbt4SvQNitGoehqEW7QX/bNV9UUeGDm28bedOd7tiCkK6oXVkWhDSBZ8PrrxSRap//Wvo319FqXNyvFYm6ICVmbbMmR44EBw2sqqpcW8BYrToqdOIcaz9Bw1q+nukIqWl8M03KnUM4Lvv4KijYMsWb3UJQnNC68i04IzwzkOpjm56wX3NPXvCCy+o1I/Ro+GTT5p2PN3GWPTGR8cd62h92glHvtaIlubhVLObCxCtaFwar9jS9EYz7ImtJuKU5M+JXr1g2jT12Gz5/sAD9l+fKvPYLqLXXXTTmwzETKcRwakvOqCbXkie5gsvhBdfhHvvhccei79Ml25jLHrjI6u2Ct+6dUe+24+2ANGp5njSPOxGfMNTM6wXINrXG6kudXIj0N7MifDf9yuvqPvPP4/92lSZx3YRve6im95kIGY6jbj11lu9luAI3fRCcjV37gwvv6yM9OjRoTmfdtFtjEVvYohWGs+p5mnTVIq1GznT4Wba2gTfauufScOw/0+nu6XxvJkTTz0V+rOZ5tG/f+zXpuo8joTodRfd9CYDyZluIkVFRbRv3/5ICRZBSCYZGarSx7hx8JvfwHHHwd13q/JjgmASbkJzcxObM9uihT0zbeZXO8mZtrOv1QJEJ/s1h9xoQRBCCQQCBAIBvk9CS2GJTDeRkpISli1bJkZa8JT8fAgE4OSTVZT644+9ViSkMtFypuPBrDXtRmk8qzrTwc+DvYhzcJQ7Ua3GdeTNNxtvmzkz+ToEwW0KCwtZtmwZJSUlrr+XmOk0YsOGDV5LcIRuesF7zRddpEz1nDkwY0Zsc+O1XqeIXudYmcFoaR5ONXfpAgMGuNcB0cpAh5bG22C7aUuk/cI7BUbbt+l4OycmTWq87e674cMPI78mFeaxE0Svu+imNxmImU4jpkyZ4rUER+imF1JDc5cu8PzzKhdy9Ojolc5SQa8TRK9zqqoal42OtACxvBwKC51pPv10+wsQq6shO9u+UY2U5hEaYZ5iOxfa3C/8mK+/Hvk1ic+d9n5OWDFuXOTnUmEeO0H0uotuepOBmOk0Yu7cuV5LcIRueiF1NPt8UFiomr1Mnw6zZzeUxAomVfTaRfQ6Z98+yMsL3RYpzeOzJet44JMNcXUGsmOmDx9W+9nFykw3bswy1/YCRKfR5miNXuLH+znhlLlz59Kundcq7JMKnzsniF79ETOdRuhWzkY3vZB6mrt3VyX0jjoKxoyBjRtDn081vbEQvc7Ztw9qu3ZXZTe6dwciR6azaqsYxudxtcdzEplO1AJEZaJ72Y5Mp0b+s/dzIhaDB6v7w4fVmPXq1Yt9+7zV5IRU+Nw5QfTqj5hpQWjm+Hxw9dXw5z/DlClQUhJ/XWpBP/buBV+P7uoriiAzbZUzHU9Kg2lQkxWZDsb8tsVNM50aBjy5mM2gevaEt97yVosg6IDt0nilpaX44rjSDhgwgBzpeSwInnP00bBkCTz5JFxwAcydC8cc6ZddggAAIABJREFU47UqwW3CW4lD5DSPpuQH21mAGByZrqtTpR2jYS8yre5jmd546ky7V2taD3buhP37vVYhCKmP7cj0KaecwtChQx3dTjnlFNavX++mfsEBs2fP9lqCI3TTC6mv2edTlQv++Ee46SYYP362VpG3VB/fcFJBr5WZbtHCOofeMMCpYtNwOolMR3r/cOrqIlf/MA05NMzhaGX0IFWizN7PiWh06KDug6t+BM/jHTuSLCgOUuFz5wTRqz+Omrbcfffd9OnTx9a+dXV1XHvttXGJEtyhMpGFZZOAbnpBH83HHQevvQYjRlQyfjw88gj06OG1qtjoMr4mqaDXykxD5KhrvIqd5ExnZiozHSvlIzx6Hd4NUZnpyoSnebhbGs/7ORENs7/FkiXq/qOP4O9/b9DcvXuq/FMSmVT43DlB9OqPIzM9ZswYTj31VFv71tTUpIWZ1qkD4owZM7yW4Ajd9IJemlu0gLffnsHatTBxIlxyiYpGpfJX2zqNL6SG3n37oFs3+/vHq9iumTYj07FSQkAZ7mipIMpEz3Ctmoc7eD8nrNi2LfRn01T/5S+wY0dqao5EKnzunCB63SGZHRBtm+nFixfTv39/+wfOzGTx4sX07ds3LmG6UFJSQkFBgdcyBCFuTjgBli+Hhx+GCy+ERx8FWazdfNi3D8uyZlbGcsDvLlcPRo5UIWQbPLUL6AlDq+HEKuCx+ie6dYPVq0P2PXxYHTY7O7bxhsZpHuH/6AXnTNvB6cJbd0rj6YUOaR2CYIUZ5CwrK2PIkCGuvpdtM33hhRc6Png8rxEEIfm0aAG/+pUqn3fDDSpKffXVqR2lFuyxd691mocVmRV71IOdO20fvzPAVshG3TBLqG3frspBBPH4Lmh5HPxxD7RdifWqnSATHmmRojkv6+qCc6dj49QYf/cdvPees9ekA/ffr7omCoKgcJTmIWjM0KGUb91KZye9fD2mvLZWK72gn+Zwvf2B5UDFB7B7MrRvDy3iKaBpEZVMBOXl5XTu3Dnhx3WLVNC7axd0yj0Ia/8LffqoUh5Y/6N0uHM+G3fDoHz7c7h8F3TuBNU1cOAAtK/crtxtXR1s3Rqyr2m8O4Ct1OFoFT/MnOmMjHLq6mKPcTxpHps3O9vfHuXUj0TK0bp1pGdCNUdrPZ4KpMLnzgmiV3/iMtPvvPMOu3fvZvz48QB8++23XH311Xz88cece+65zJs3j1bh/WsFb9mxg0k7drDMax0OmARa6QX9NFvp9QFtzB8sahHbwqVC1pMmTWLZMn1GOBX07toFHb9dD6cNgdJSqE9LM81lsKle/afV/Oxnfowt9jVP8sOyZbBlk6oS88gHQyPmBpjG+/u9qitjppVnD0rwjrYA0XzeMCZhGI31NqWah7tl8VL3KhF5XZnSbKbKpnrqRyp87pwgevUnLjM9bdo0hg0bdsRMT5kyhVWrVjFs2DBeeeUV+vXrx9SpUxMqVGgi3box3UxY1ATd9IJ+mmPpNVBpAoYB7ds5MBhOVrw5YPr06a4c1y1SQW9traqeEY7ZuCU3N/yZ6XG9T8uWcOgQUb+RMI33zGK49lr4wQ+iH9NM44j2fMuW0xNeZ9pdpnstIA6mA/DKK+onF750Siip8LlzgujVn7jM9MaNG/ntb38LQHV1NUuWLGHWrFlMnjyZBx98kKeeekrMdKqxejW6LZPUTS/opzmWXh/QHnjjDXjoIdWR+uyzkyAsArot9k1lvW3bqsWJwWZaGVL7moPN7hEzHQVz3+xstRjRDlZmOjhnumXLgqgmeffuhsdOS+O5E51O3TkRGaX50089lmGTVP7cWSF69SeuduL79u2jQ31l99LSUioqKrjgggsA1dxlszuJZoIgeMTo0Soq9eKLcN11KnVA0Ju2bZve3S74iw07ZtrEThm9mhpVC90Ks8pGXZ2Kukcz08GtEdK9MocgCO4Ql5nu2rUrn332GaDyp4855hh61q/a3r9/P1mxKvELgqAd7durFuTXXgsTJjQ0dRBSl2h1ms3IdDBOI7GHDikTDc7MtJ3SeHv3xj5OuJm20m8eJzjNQ6rUCIKQSOIy0yNHjuTOO+/k17/+NX/4wx9CSuB99tlnHHvssYnSJySQBQsWeC3BEbrpBf00x6P3tNNU2sfq1XDVVaFfo7tNOoxvIikvh0iL7tu0aWymVeTWvuaqKjDXmmdm2mvEAioyHSvNI5LhDd5eVwdVVQssI9PRFiA6WYiYePSawwq9NHv9uXOK6NWfuMz0zJkzGTx4MPPmzaOgoIC7gwpOPv/885x55pkJEygkjrKyMq8lOEI3vaCf5nj1tmwJM2fC5Mkwfnzkr+MTTbqMb6LYsSPyWlCryLTCvubgyLQd42maWMvI9Lp1qoPQunW237+uDmpqyhzlQnuPXnNY0Vjz44+ra4C5GHHHjlQZX+8/d04RvfoT1wLELl268Oabb1o+9+6775JTX8dUSC0ee+yx2DulELrpBf00N1XvqaeqKPXUqSrt46GHVDqIW6Tb+DaV+My0fc3BZtoJlpHpqiplpKuqYr4+OGf6qKMes4xMhxu74DrT3qZ56DWHFY01T56s7nfsgKFDoXt3+Pe/4ZRTkizNAq8/d04RvfrT5KYtO3fu5ODB0GK0e/fupZf0IxaEtKBVK5gzBz74AMaNg+JiOO88r1UJEGSmBwyANWtCVuO1bQtfftm04zs106aJtbMA0Q52FiCG7y+4h900H0FobsRdzeOaa64hNzeXo446imOPPTbk1rt370TrFAQhxTnzTHjjgXUMvvwEpo1fZ2sBmeAu335bb6ZzclQKRdC3homo5hGcM+0EOwsQ7USPg810rBSDAQOclcYT7LF3Lzz6qHos4yakK3FFpouKiggEAlxzzTWcdNJJtIznez5BEJodOb4qcnav44IRVYwdC3fcAeee67Wq9GXr1shpHpEXINrHiZkOTrOwswAx2nFMws10pMol4a/9/nt77yVVP2Lz17+qG0hkWkhf4opML1++nN///vfMnTuXG264gauvvrrRTUg9/H6/1xIcoZte0E+zW3oLCtSixNdeg5tuanoE1ETG1xlbt0J91dJGRM6Ztq+5okKZcjvU1CgTDfYi09Ewc6Zra+Grr/xHzHQs82ua6UmT7L9P4tFrDivsaT7nHJdl2MTrz51TRK/+xGWmq6qqGDRoUKK1CC5zyy23eC3BEbrpBf00u6m3dWt45BFVk/qCC+Ddd5t+TBlfZxw6FDlyHNlM29dcUQF5efb2DY5iJ6I0nrkAsVu3Wyw7FlpF2e3mTLubrqDXHFbopdnrz51TRK/+xJXmcd555/H+++/z05/+NNF6BBcZPny41xIcoZte0E9zMvT++Mdqtf9vfwuLF8OsWfYNWDgyvhEYOlStNgzj6V1AcGS6W7cjtczatGn8jYEykfY1V1QoU26HcDPdKDJ9+eXqfuRIyM6mbR18A7R6q+EcXvsOstfB1MPQ/m5o0QL+uR8yi7ux4qerbUem7eKOqdZrDiv00izXCXfRTW8yiMtM33PPPVx00UXk5eXh9/vp1KlTo306duzYZHGCIDQP8vLgscfg7bfh/PPhnntA/hdPIFu3WprpzgBbrV+SmanSJIJxWu1i/37o0aPh52jms6qqYf2jZZrHnj3qfudOQH1t2hOgiiPncBRANXQAqM97zgEO7q6jrs5+mofJtm3R9xcEQbBDXGb6xBNPBKC4uJji4uJGz/t8PmrDr9LNlKKiItq3b09hYSGFhYVeyxGElGbYMNVB8Y474KWXVEk9uzm3QhQ6dIAdO9jbqgu1Gdl07AA1tcrsdgiu+x22GtGqFrMTwnOmzVxmK1N78GBDZDo726KcdH6+CjXXU1cH27ar13Suj9d8+5167YEDqp55plFNq73fcbh1e0c50ybbt0ffXxYgCoK+BAIBAoEA39tdcdwE4jLTU6dOjfq8L42uQCUlJRQUFHgtwxZLly4Naf2e6uimF/TTnHC9YV/VW9EGmIvK5933NGS3sV+reOnBg1zYu3dD27UUJ2nz4bnnYMgQZv7oTT7JKGDlSvjX+/C//wu3327/MMpsLgXsaQ7PmW7ZMnKednCaR6tWar8Qwn6ne3bB0Z3h/HNh2TK17fxT4eST4dln4cH7YGBVGfuKh9Dypucwvohtfp3mTLvzp8z++KYO9jXPmwfXXeeumlik/XXYZXTRawY5y8rKGDJkiKvvFZeZnj59eoJlCMkgEAho8QEw0U0v6Kc54XrDvqqPRkugC0C4qYpCALhQow6ryZ4PGRkN5ck2b4Zjj3X2emU2A8RrpnNzQyPQwYSb6ViNDq2i5OHb6uqU2qtsLkB0Gnn/5htn+9vD/vimDvY1X3+9Sp+ZNs1dRdFI++uwy+imNxk4NtOVlZX069ePJ554gvPPP98NTYJLvPDCC15LcIRuekE/zQnXG/ZVvV2qqmB/BbRrGzGgDcALELlwcgqS7PnQogXU1v9zsmkT2FknFGxCldm0r9nKTFdWqqyTcILNdE5OfGY6/PlTHrmcYcDBOSM5vTZb/SNRv1jx2Bq1gDGYTteGbus6qvE+AL790HYmXBq7s3lc7GAop6DHtysKZ/N4+nRvzXTaX4ddRje9ycCxmc7NzeXgwYO0bt3aDT2CIOhMnOkXrYCDe+C6IlUXeepUZ22qBUVGRkMqw6ZNId3DLWndWplf83IezwLEYDOdk6OOZ0VwxNpOZDoaR9qSH1DfhOTs38mR7yvqFytmEVrIBIDdYdu+s9gHwAD2qZQkN8ig+fc1X71aFZkRhHQgrjSPn/zkJ7zzzjtSGk8QhITRoQP85S/wyiswerRanKjJcoSUIdhM79wJnTtH379LF7Wfaaab2gHRjExH2tfM0Ik3zSOcgx3yOXioxZEc7Joa6NpFPVddo9qpB9OxI+ze3fBz167w3XeNj+vzQds2sNeyDnf8ZFJDJ3axm+Zf7WrFCjHTQvoQl5m+++67GTduHNnZ2Vx00UV079690aJDKY0nCEI8XHQRnHWWqkvdpg3ce6+q3CDEpkULVe7OqomJFZ07KzNt5lbHU1c5+D3MnGkrEpEzDfDVVw3vueLe1axYocosvveeeu6f/6zf73Po3z/0tS/8STURMlm93Nrw5bWGe+5Sc1CIj/vuUyUwBSEdiKsD4pAhQ9i8eTMzZsxg0KBBdOnShc6dOx+5denSJdE6hQQwceJEryU4Qje9oJ/mVNXbtSs8/TRcfLEy10uXAuvWMbF9e1i3zmt5tkna+LZqBQMHUpfditpaFZG1k1repQuUlzf8rKLa8WuOluaRKDP91lsNz9fVwYcfTjwSja+rgxkzIr/eyT8L7nVBTM3PXHSca25Ku/imkqrXtUiIXv2R0nj17Ny5k6uvvpr33nuP/Px8HnvsMYYNG+a1rISiW9ci3fSCfppTXe/ZZ8Py5SqH+pOnqhi+d2/Tkm2TTNLGd+BAWLuW726G2i9g7Vo44YTYLzPTPEycdkAMJ1aah5l2kplp32xFq11dVwc9ew4/YqZra6MvfnOSE+7en7HU/sxZo5fmVL+uhSN69UdK49UzefJkevToQXl5OW+99RaXXHIJX3zxRbNKV9GtqYxuekE/zTrobdkSZs+G//c0/PA1+L//g9M1yaVO9viaaR5r1tgz0507Q1lZw8/KTNvXHG44o6V5HDignrd6nRVmZNjMA7cqElNXB/37F9qOIid6v/hI/c9cY/TSrMN1LRjRqz9xpXk0NyoqKnj11VeZMWMGrVq14vzzz+eHP/whr776qtfSBEGo54c/VPf//CdMnqzKsgmhZGQoI+gkMh2c5mGayKoqy+7kjQg3ndHSPPbvd9btMtxMB2Oa8bq6hrboZgTbid5E7SsIQnoTV2QaYOPGjTz55JNs2LCBg0GhCMMw8Pl8vPvuuwkRmAw+//xz8vLy6NGjx5FtJ510EmvXrvVQlSAIVkyZAm/vBr9fLU78n//xWlHqUF2tzOW2bRB0OYuIdZoHPPkk3H9/9N47dXXWkelIr4nHTN9wA+zaZZ2eYeZMZ2crM233mHbQMFNREAQPiSsyvWbNGgYPHszrr7/OihUr2LNnDxs3buQf//gHX375JYZm/9JXVFTQtm3bkG1t27alopmFvlatWuW1BEfophf006yd3vr7YcNgyRLVRds0XKlIsse3uhqystRjO4awY8fQsVOmdRW1tbFT0ysrG9I2TKLlTMdjpn0+68i0iVpsuYqamsaRaas/Q07bibuDXp85hV6atbuuiV7tictM33nnnYwYMYI1a9YAMH/+fLZs2cJrr73GoUOHmDlzZkJFuk1eXh779oUWFN27dy9tnFz5NWDOnDleS3CEbnpBP83a6Q163K4dPPEETJwIhYWwYEHqfTWf7PGtrlbpGfn59vbPympoPw7m+M2xlTKxezd06hS6LS8vcvpNPGY6IyO2mS4tnXMkMu00zcObCLRenzmFXpq1u66JXu2Jy0yXlZVx9dVXk5GhXm5GokePHs1vfvMb7rjjjsQptKCiooIpU6YwfPhwunTpQkZGBjPMekgW+xYVFZGfn09OTg6DBw9u1AqzX79+VFRUsG3btiPbPv30U06wk3SoEYsWLfJagiN00wv6adZK7+WXswhg5EjVJrH+dvrFPfnbup5c8uue7G7dk9oePUOet3VzqbtEsse3uhq+/hrOPDO+16tL+SJbZrq8vLGZbtMG9kVodHLgQENzGDuYaSQtWoQafmgwwTU18POfL2r0fCTCzynSOdo5/3gYwDo+4nMGoE95R4VG1wk0u64hepsDceVM79mzhw4dOtCiRQuysrLYs2fPkeeGDBkS0dgmivLycubNm8fJJ5/M2LFjmT9/fsRyfOPGjWP16tXMnj2b/v37s3DhQgoLC6mrqzuyIjUvL48LLriAadOm8eijj/LWW2/xn//8B7/f7+p5JJvc8O9kUxzd9IJ+mrXSu2cPuWCZlOsjqPVzhGoSXpDs8TXLzf3kJ/G9XhlIe5p37WrcYbFtWxWBjnTsDAfhGzPNw1xgaPV8bS20bZvLwYP2mtR4Xc2jFVUMZQOt0Ke8o0Kj6wSaXdcQvc2BuMx0fn4+39b3ae3bty/vvfce5557LqAiunl5eYlTaMGxxx57xMDv2rWL+fPnW+63fPly3n77bQKBABPq216dc845bN68meLiYiZMmHAkuv74449z1VVX0alTJ3r27MmLL77YrMriCYL25Odb10cLwzBgfwUcPqxSQbLsXOXsdDhJZdatg/Hj6XrUS1x33UB69bL/0qyshlxr00TajUyHm+k2bSKbaacEm+lINalralS0e/9+e2baSc60LEIUBMEucZnpH/3oR/zf//0fF198MZdffjlTp05l+/btZGdn88wzz3D55ZcnWmdEoi12XLJkCW3atGH8+PEh2ydOnMill17Khx9+yBlnnAFA586deeONN1zVKghCE1i92tZuPqAtsHkz3Ho79O6t2hrn5LiqzluqqmDdOjI7VTH3z85e2qmTijJ36xZajs6OmT7++NBtrVsnrmShaWiD87rDNd1+u8qbr6lpXF3ETgfEaIY51fLvBUFIXeLKmb7rrruOpEBMmTKFm2++mSVLlvDSSy8xYcIEHnzwwYSKjJc1a9YwYMCAI9Fnk5NOOgkgIaXvRo0ahd/vD7mdccYZLF26NGS/lStXWqaNTJ48mQULFoRsKysrw+/3Ux5cABaYNm0as2fPDtn29ddf4/f72bBhQ8j2Rx99lOLi4pBtRUVF+P3+RitxA4GAZXvQCRMmeHoexcXFludRWVmZsudxww032P59pMJ5FBcXN3leJfM8iouLbf8+jjkGZs/+mnfe8fPjH28guFpnss7DfI9kfs6dnseqVRN44QV1HipyW8yGDSs5dCj678PMmQ4+j2BzGus8MjMbTHKk83jtNT/ffbcqLCc6wKFDDeexZEkxNTXw/vsT2LMn9PcBK4GG82gwyJOBBXzwQfC+ZfX7hv4+YBowO2zb1/X7bgjb/ihQHLatsn5f9ftoeDaAdZvuCUD082hAnUcobpxHMeHn0UDk8/Dq74c5l7y+Xtk9D1Njql53w88jWEuq/f0IBAJHvFjv3r05+eSTKSoqanSchGNozs6dOw2fz2fMmDGj0XP9+vUzzjvvvEbbt23bZvh8PmPWrFlxv29paakBGKWlpXEfI9k88sgjXktwhG56DUM/zemid98+w7jtNsO45hrDKC8PemLtWsMYOFDdu0DSxre01DDA+MVZzq9Hf/iDYbz7rnr89NOGAY8Yjz1mGFlZ0V83ebJhbN7cePuYMdb7n39+6M+XXmoY+/dHPv7nnxtGUZG6ffGF2jZkiGGAYbRpYxgPPaQeX3HFI8avfmUYP/+5YZx5ptpmGIaxfr16HHx74onQn1u0aLwPGEZurmH8/vfWzzXlNphS4xEwBlOa8GO7e3skrtd5Rbpc17xCN73J8GvSATGNuPXWW72W4Ajd9IJ+mtNFb5s28PDDcO21MGECBAL1Ucr69IiYRZXjRIfxDW7coiK3t0YtR2dilTMdCasGL61aqWGPlE4RK2f6s8/U/Zgxt7JvH3z7bew8Z7vNXcz3d4PUnxFW6KVah89dMKJXf+LugFhTU8OLL77IP/7xD3bt2kWnTp348Y9/zCWXXEJmZtyHTSidOnVil0U3h927dx95XhCE9OH002HFCnjgAbjoIpg7CWw0CtSGeNZRdukCX36pHjtZgHjgQOOmLQCvv67MeZcuDdsqKlQN6mBMM52VBZ9+CgMGhD5vlTMdbJaffFLdZ2WBuQY9VjfMSG3JrTh0KPqx4uE51HqiFYykmuzEv4GL7KAbp2Bv3YIgpBtxud7y8nJGjBjBxx9/TGZmJh07djxSVePBBx9k5cqVdLYbsnCRQYMGEQgEqKurC8mb/vTTTwE48cQTvZImCIJHZGXBnXfC55/DnGugBFX5Qy9rY01BgfPXdO8O77+vHgcvQLRb+cKKPXtCzfT+/apsXjCmma6ttV60GByZjrQAERo6PkLsBYjhkelo/zD82eFCTju0R1WhOooofdpTlAyaMCEEoZkTl5n+5S9/ycaNG1m4cCHjx48nMzPzSKT6hhtuoKioiOeeey7RWh0zduxY5s2bx8svv8wll1xyZPszzzxDfn4+p512WpPfo6ioiPbt21NYWHikbnWqsmHDBo4PX36fwuimF/TTnM56+/WDP/4RGAq33gqj74Hzz09sSbRkj+8ppzh/Tc+esHWreqwM9AZ8vtiaI43TL38JB8NqfVt1PzTNdCSipXkEv/eOHRuA46NqMnHyD4LdRjBO2EY+G6njOLJi75wiZFHNHr6jjvZeS7FNOl/XkoEuegOBAIFAgO+//97194rLTL/22mvcd999IeYxMzOTSy+9lO+++45p06YlTGAkVqxYwYEDB9hfX9R07dq1vPzyy4DqxJiTk8PIkSM599xzuemmm9i3bx99+/YlEAiwcuVKFi5cGLHRixNKSkooiCcc5AFTpkxh2bJlXsuwjW56QT/N6a7XvASUlMCslfCXv8CsWcpoJ4Jkje/ult15e8A0LhnY3fFrO3ZUpfHAjNROISMjuuZoEd1u3Rp3QYxkpsNNd/h7hEemrXjqqSmA0hurKYyTnGk3UGkSfky9OjCYMnoyhC14HyCzS7pf19xGF71mkLOsrIwhQ4a4+l5xmWnDMCKmSJxwwglRaz8niptvvpnNmzcD4PP5eOmll3jppZfw+Xxs2rSJXvVdCxYvXsxdd93F1KlT2b17NwMGDGDRokUhkep0Ye7cuV5LcIRuekE/zaJXkZMDM2bAf/+rahcfdxzccUdjA+iUZI3v83/vTvf7poNzL90oNcLnmxvTlFZWWudLg3UXRCsz3bq1yruOhFXOtBW//vVcLr5YPU7UAkR3/4Tp9ZmrohW30Y/baOW1FNvIdc1ddNObDOIy0z/72c94++23GTZsWKPn3n77bX4Sby9bB2zatMnWfq1bt6akpISSkhKXFaU+vZy0RUsBdNML+mlOe71mg6mRIyE7mz7Ai0DVu7D/IfDlKNMX0aN16xa1mUyyxvf11+HVV5t+HMOAjIxeMc10tEoebdrYi0wHm+lI+c0tWsSOTB93XMMYJ7Kah3vo9Zlbz0DOZaPXMhyR9tc1l9FNbzKwbabNChgAU6dOZezYsdTU1HDZZZfRrVs3tm/fzsKFC1myZAmLFy92RawgCEJC2aMWhB2pDVdPq/obh4Bo6XZNWaWXID79FPr2hZYt4z+GaYDNaHAsU7prV2Qz3bYt7NgRum33bpVOEkxeXsPCQyszXV0N2dnR24mfcUbjLowmkQz6FVfAX/9q/ZpYrxcEQbDCtpm2qs7x0EMP8dBDDzXaPmTIEGpTIwQgCIIQmfx8Ff6MQp2hIqs11dC2HWRlotzdd99Be+8XZf3udzBzZtOO0bMnbNmi/jfIyIhtpnfuVN0PrWjbFjaGBTLLy1XqTDCtWyuTHYnDh1WKR7TIdOvW6nm71NaG7p/IxaaCIKQvti9DU6dOtX3QRCzsExLP7Nmz+e1vf+u1DNvophf005z2eqOkaJhkAO1Q+dST71T+e7q/jDY/HgIxqha5Pb5lZcrP9+nTtOP06aPOzzCgtnY2hhFd8zffwNFHWz9nleZhlRaSlwdffx35PczItFXOdHDU+IEHZgP2xjjcTHsTfbavN3XQS3PaX9dcRje9ycC2mZ4+fbqLMoRkUFlZ6bUER+imF/TTLHrt06cPLFoE774LxcXwBLHrU7utd+ZM1dmxqfTvD598YtZsroyZvbJ5Mwwdav2c1QJEKzMdawGincg0wMGDDWMcyxyHm+louGe09frMKfTSLNc1d9FNbzKQduJNpKioCL/fTyAQ8FpKTGbMmOG1BEfophf00yx6nfPTn4K5mP222+CRRyKXeHNT7+LFygT37Nn0Y/Xvr1IzDAOys2dmkQzcAAAgAElEQVTENJJffQXHHGP9nFVkevdu6NAhdFvr1tbNWkyscqbDdRmGszF2YqYtmucmCO/nsHP00pwK1wkniF53CAQC+P1+ioqKXH+v1Oj7rTE61ZkWBCExZF6tqoD86auRHJyeTcUUqG1VX/kjPMstRsWPeFi3DhYsgKVL6zccPKjyNPr0UXX+HNKjh2rcMmCA0h8rMv3995HTxa0i0zU1jU1sXp69yHRwmodppiOZ/URFpmXxoSDoTzLrTNuOTA8aNIg1a9bYPnBtbS2DBg1i/fr1cQkTBEFIWeqrgPh27iR3z1a6HNpK3t6t+LZtVa40+LZtW0Lfeu9e1bFxwYKgVtrr18OJJ6r7OMjIUAbSNL12zGSkpTFWzVis9m3duiFFJVY1j2jtxK247rrI1TyybDQfFDMtCIITbJvpNWvWOM6TWbNmDQejtbgSkkp5ebnXEhyhm17QT7PojZP8fMubkZ9PVed8duXks7dlV8oBI4EVP77/Hq66Cu6/XwW8E0lenvofITOzPGpkuro6enTXNM5vvNGwqDHS+5lY7XP4cGMzbeoKjlBbzYn5863f06xdHQt319CnyBy2yQDW8T4/YADrHL/2jTe8+cckZa4TNhG9+uMozWPs2LFkZ0dbbqPw+XxJ6YIoOGPSpElatAA10U0v6KdZ9MZJhLQNHw01qqv7DeSiL77jL1+Uk9m+J7m50KIJq1QOHoTqA/BCW2g5PuxJM6n48stVDkgc9OsHa9fCgQOTMIxlEQ3lli2x87QNQ0m54w646SbrTpKxslGqq1WXRauc6eA/L5MmTcJue+7USPOwrzcVaEUVc9hIK6ocv/bf/4YnnlDlG086yQVxEUiZ64RNRK/+2DbTV155peOD+3w+OkUqRiokHd0qsuimF/TTLHrdI6tiD9OBDtU7YS/q1gRy6m/sjLLT99E6zESnf3948UXo0GH6kXrTVvz3v9C7d/RjZWWpyDKoTJcePRrvE6vLolVpPKsFiNOnT+e116yfD6euzlldaneY7rUAx0wHro3jdTNmqCynKVPUNynTpln/Y5VodLpOgOhtDti+rDzzzDMuyhCSgW4LJXXTC/ppFr0ukp9PQVhOQZ0Bhw5BVVVDfnJmpjKWGb763GWUkaw+rMyfLwPatrGX69uU3A+zokf//gX1bcWt9/vsM/jBD6Ifq0cPqKxU5nbTptjm2zAad0m0Ko1nlX7iZE7YjUy7m+ah0RyupymKe/RQ5dhXroQLLoDJk2HcOHfHWKvrBKK3OeD5/+iCIAjNEotUkAwaIsym0fzmG2Uk9+xR9xkZcPLJ6ta5Y6NDuIbZlrtbt3oTH8HsfPYZDB8e/Vj5+ereMBqKjETDMFRHxeDIcvACxF/+Ek4/vXGaR7RItNVzNTWh/5REOkfJUkw8w4fD2WfDnDmwcCE88AD07eu1KkFIDGKmBUEQPMDnUyazqd0LE0W7dnDnnfDpp0SNTH/1FRx7bPRjmTnVdXXqH4Zhw5zrCY5MA3z+ufUCxGCclsYT05xcWrWCqVPhiy/gN7+BggK4/Xab37oIQgojTVvSiAULFngtwRG66QX9NIted9FN78yZ8PXXC6JGpq1qRodzwgkN+27aFLnBi0m00njBRstqPydj7KRpi3voNScg8YqPO041HTruOBgzBr78MrHH1+1zJ3r1R8x0E9GpA2JZWZnXEhyhm17QT7PodRfd9ALs3VtGTY11ZLq8XKVjxGLgQHVfW6vyw1u1ir5/pNJ4wZFpw7COSDsZY7sLEN2NWOs1J57jcsqAFYzkG3o6utEz8s13dE8Ki3vyxn960u6EnhzoGH1/J7eyX/wicr/7FES364QuepPZAdFnSA27uDA76pSWlkoyviAIzYarroJevaCkpHEnw+XLVQ70LbfEPo7Pp5qn1NTAU09F3gfgvffgnHNCTeztt8OVV6ptJ54If/mLqq/9+efQsqVayHn22eq15nHOPhv++U/1+NNPG5dju+QSlXJy/fXq5xYtlOEXIrOV7vRgh9cynJOfr+o4CmlPMvya5194CYIgCKlDhw6wc6d1Huu//w2jRtk7zqmnwiefwDXXxN7XKqRz8KCqRW2aXcNoMM12cqYjLUD0Ps1DL7aRTx02Ot1Y0DPf2f6HDsO+fapVfVZTf0+J7mokCFFI2GXl73//OwMGDKCbTGBBEARt6dBBmWAr0/nJJ6oRix0CAVWt4c03I+8zaVLkqLVppsMbtjSF8Hbi8r1sbE7BukGRHQyHgeGWQM12GH0VPPSQ+kZCEHQgYTnTixcv5tNPPwWUsRYEQRD04wc/gP/3/xpHpmtqVB5zy5b2jtOnjzLCHaOU97v8cnUfLTJt5ltbRaadVoEwFzU6QQxdcuneXdWlvu02VS5SEHQgYWa6W7duPPnkk8yaNYs333yT75vQiUtwB7/f77UER+imF/TTLHrdRTe9APPn+9m0qXFk+oMP4IwznB0rVkqFaYajmWnTvBuGynEO3j87O3SMY0Waq6vt1Zl2F/3mRLI1d+2q6lBPnNjQsMcJun3uRK/+JMxM33XXXcyZM4cePXrw4YcfMm7cOAoKCpgwYQIPP/wwmzdvTtRbCXFyi51VQymEbnpBP82i11100wtQXKw054flu776qupgl0iimW3T+AZHws0KI8Fm2ukYt4gv/TeB6DcnvNA8ZAj8/OeqHrUjtm/nlnbtYPt2V3S5gW7XCd30JoOELsXo06cPffr0oXfv3px11lkYhsHnn3/ORx99RElJCccddxyTJ09O5FsKDhgeq21ZiqGbXtBPs+h1F930AowYMZzNm1XbZxPDgP/8p3F1jKYSK3Lt8zWkZQRHpk2ysiKPsVWU2jBSoUGIfnPCK80//zmsXQt//nNDBZaYbN/O8OeeU20zu3d3VV+i0O06oZveZOBKnemzzjoLAJ/PR//+/bnsssswDIOKigo33k4QBEFIIL16hf782mvwk58kPi0iuIZ0OOa24Dxp00yb28KNcaw0D8NwnjMteMuMGfD3v6ubIKQqcZvp+fPn46RE9VVXXcWIESPifTtBEAQhifTuDR9/rJquPPoo3Hpr4t8jN1fd2/lTEtziPN4FiD5ffK8RvCMjA+bPh9//XrUhF4RUJG4zff3117PFQUH0wYMHc/LJJ8f7dkICWLp0qdcSHKGbXtBPs+h1F930QoPm3/4WfvUr8PtV3mqbNol/L7PSR7iZtiqXZ7UAMSvL2RjHE5n+6itn+8dGvznhtebWrdWcuP56sFPbQLcR1u06oZveZNCkNI+dO3fy6quvsnjxYr7++utEaRJcQoeW58Hophf00yx63UU3vdCgOT8fXn4Z/vpXcOtLxQ4d1H1dXej2a65pHBGOZKYDgQDHHKN+ttOQxWlkOrwLZNPRb06kguaePWHWLHsVPrxX6wzdrhO66U0GTVqAeMYZZzBo0CCqq6tZv349I0aM4LHHHuPoo49OlD4hgbzwwgteS3CEbnpBP82i11100wuhmjt1cve9THN81VWx9w1O8zDJzlZ6DxxQz51/fuj+VnifM63fnEgVzaeeqlrCFxfDH/8Yeb/UUGsf3a4TuulNBk2KTM+bN4+PPvqITz75hF27djFq1CjGjBkjUWpBEATBNjt2qPtoudORItOg0gBycmJHpsOreZitygV9KCyEvDxV4UMQUoW4zXTbtm0ZPHjwkZ/z8vK48cYbee2115g5c2ZCxAmCIAjpQ3i6RzCRSuMFYyfNw/vItNBUolb4aNUKBg5saJ0pCEkgbjN98cUX8+yzzzba3qtXL4466qgmiRIEQRDSgyeeaHgcbKbDTa9Vmke4mbaTDy1mWn/MCh+/+51FhY+BA1Vx6oEDPdEmpCdxm+kHHniAv/3tb1x77bVs2rTpyPba2lq2bduWEHFCYpk4caLXEhyhm17QT7PodRfd9ELyNQcb5OA0j5yc0P2sItOtWoXqdZrm4Q36zYlU1Ny6NTz9NNxwA+zdG/qcbp870as/cZvpDh06sGrVKrKysjj++OPp3bs3P/rRj+jXrx+jRo1KpEYhQejWtUg3vaCfZtHrLrrpheRrDjbTwZHp4DbiYG2mc3JC9Qab6dRdgKjfnEhVzT17qvrTl14KwT3hdPvciV798RlOOq9EYNeuXfzv//4vhw8f5qyzzkqLNI+ysjKGDBlCaWkpBQUFXssRBEHQkqefhkmT1OMDB1QjF58PbrlFNYsB9fNDD8G778Lrrze89vHH4aabGn6+4gp47jn1+OOPIWhZD6C6OC5b5k7NbKExTXcX9vjnP2HOHHjhBRWxFoRgkuHXmlQaz6RTp074/f5EHEo7ioqKaN++PYWFhRQWFnotRxAEQSuC0y6CzVdeXuh+hw41rj0dvsYsODJdWdn4vQxD1qU1R84+W91PmCCGWmggEAgQCAT43k6nnyaSEDOdzpSUlEhkWhAEIU46d254HJzmERw9njkTevWCDz4IfW14XnWwmZ4yxfr97FT8EPTDNNQ//zksWiSGWuBIkNOMTLtJk+pMC3qxatUqryU4Qje9oJ9m0esuuumF5Gvu2rXhcXBkOthM9+1rnTKQmxuqNzjKfehQ5PeM1UHPXfSbE7poPvts+M1vYPjwVRw44LUa++h2ndBNbzIQM51GzJkzx2sJjtBNL+inWfS6i256Ifmag5fYRIpMZ2RY16A+5ZRQvbEWIJrbwhcyJhf95oROms85B2AO48fD7t1eq7GHbtcJ3fQmAzHTacSiRYu8luAI3fSCfppFr7vopheSr7lLl4bHTs109+6hemOZaW9NtIl+c0IrzevW8dbuz3lw0jrGjwcdGjLrdp3QTW8yEDOdRuTm5notwRG66QX9NIted9FNLyRfc3Y2jBmjHkdK84hkpiFUbywz7X2NaQD95oRWmquqyN2wgYF9qpg/X1WKefddr0VFR7frhG56k4GYaUEQBMFTzIix08h0OJEqg5jI4sP0ondvePVVeP55mDYNamu9ViQ0V8RMC4IgCJ5imuBgA3zccQ2P7Zrp4DSO1I1MC8mkdWvVerxvX/D7LdqPC0ICEDNdz5/+9CcKCgrIzs5mxowZXstxheLiYq8lOEI3vaCfZtHrLrrpBW80d+qk7k3DfP75obnUGRkNUcVf/CL0tcF6Y6V5pEaNaf3mhG6ardReeSXMnQt33gm/+hXs2pV0WRHR7Tqhm95kIGa6nh49enDvvfdy4YUX4gvvDNBM6NWrl9cSHKGbXtBPs+h1F930gjea+/ZV96aZDjfCeXmwb59q2hJ+eQ7WGyvNI7xFuTfoNyd00xxJbe/e8OKLMHYsXHYZPPAAVFUlVZolul0ndNObDMRM13PBBRcwZswY2rVrRwI6rKckt956q9cSHKGbXtBPs+h1F930gjeazZSOSJfeDh1g505llsPNdLDeWJHp1DDT+s0J3TTHUnvWWbB8ORx9NIwaBc8+610+dV0dXH21ZuOr4XXNbWQ5hiAIguApppmuq7M2wR06wHffKbMc7YtDPdI8BFe5/HJ1P3KkKhUTgQzg58AE4MAvYPf1avfWrSEzwSUUDVSjoKyg+XnoMOzfXz9PDcg7sRu+0tWJfWMhaYiZFgRBEDylTx91bxiq0UaHDqHPd+wI33yj9muKmU6NyLTgKnv2qPudO23t7gPy6m8cAvYnXpIPCF/72rL+ZlLx33oNgpakpZleuHAhN954IwBnn302b7zxhseKksOGDRs4/vjjvZZhG930gn6aRa+76KYXvNHcurVadFhXB59/Dv37hz6fmws7dsAPf9hgptu1a6xXj5zpDYBec0Irzfn5bKir4/gmlG6pM6CyUuVT+1DzpmWr+m9GbB6jtg727lXztV07yPBBdY2KULdoAdmmvLo61u/dS8uaThzerf5xTHV0vK65jRY50xUVFUyZMoXhw4fTpUsXMjIyIlbcqKiooKioiPz8fHJychg8eDAvvPBCyD6XXXYZ+/fvZ//+/ZZGurkuQJwyZYrXEhyhm17QT7PodRfd9IJ3mvv0UWZ648bGZtrnUwsQ27ZVPx9/PHz1lXocrFePNA/95oRWmlevZsppp8GWLXHfMrZuIW/PFjof3ELLnVt4b+EWfjNhC8MHbOHOK7fw/97YgvFN49ftX7+Ffz6/hTm3bWH0oC189f4WOlSo47FlC1k7tpBTvoXsb4Ne9/rr/Laykp1znuaPf/R68Oyh43XNbbSITJeXlzNv3jxOPvlkxo4dy/z58yMa3nHjxrF69Wpmz55N//79WbhwIYWFhdTV1VFYWBjxPWpra6murqampobq6mqqqqrIzs4mI0OL/zdsMXfuXK8lOEI3vaCfZtHrLrrpBe80d+yo8qI3boSLL278/PffKzN98KAy3e3bq+3BevWITOs3J3TTnMg5nJcHo0erm2HARx+pBYtr1kCPHmoe7tqlbnl5UFAAJ5wAv/mNKuloSy9w9Kkw4x6V5pTq0Wkdr2tuo4WZPvbYY9lTnwe1a9cu5s+fb7nf8uXLefvttwkEAkyYMAGAc845h82bN1NcXMyECRMimuP77ruPe++998jPM2fO5JlnnuHKK69M8Nl4h27lbHTTC/ppFr3uopte8E7zj34E778PGzZAv36Nnz90qKGaR7BRDtYbHHlOXTOt35zQTbNbc9jng1NPVTfDUGnZ+/erf/KC66I7pReAT9VQf+wxuOeeRCl2Bx2va26jXdg1Wtm6JUuW0KZNG8aPHx+yfeLEiWzbto0PP/ww4munT59OXV1dyK05GWlBEIRU5n/+p6FEWevWjZ+/6y444wxlaCJ1Q8zJaXicumkeQnPA54OuXVWN9KYY6WCGD4f33lN51YJeaGemo7FmzRoGDBjQKPp80kknAbB27dqEv+eoUaPw+/0htzPOOIOlS5eG7Ldy5Ur8fn+j10+ePJkFCxaEbCsrK8Pv91NeXh6yfdq0acyePTtk29dff43f72fDhg0h2x999NFGXYoqKyvx+/2sWrUqZHsgEGDixImNtE2YMEHOQ85DzkPOIynncffdxdxxB5inE34e06fDKafA+vUBvv3W+jxWr244D2WmVwIN52FGpidPngyEngeU1e9bHrZ9GjA7bNvX9ftuCNv+KI3771XW77sqbHsAaHweqljb0rBtoefRQGqfRyrMK50+H9988zU7dvh5/HG9z8PL30cgEDjixXr37s3JJ59MUVFRo+MkHEMzdu7cafh8PmPGjBmNnuvXr59x3nnnNdq+bds2w+fzGbNmzUqYjtLSUgMwSktLE3ZMt0nk+ScD3fQahn6aRa+76KbXMFJf8x13GMaxxzb8HKz33XcNQ9low+jbt+GxeXvhhYbXtWjR+Pnk3GZ59L7J1+wVqT6HQygtNWaBYdR7iT17DGPkSMOoq/NYVxS0Gl8jOX6tWUWmhehUVlZ6LcERuukF/TSLXnfRTS+kvuZ27VTJMZNgveecA0OHqsexcqa9W1ue2uNrjV6aU30OhxOstn17NY8DAc/kxES38U0GzcpMd+rUiV27djXavnv37iPPpzORygmmKrrpBf00i1530U0vpL7mTp0a+nJAqN6MDFVhAVLZTKf2+Fqjl+ZUn8MhtGrFjIEDQxL6f/1reOopVdkjFdFqfJNEszLTgwYNYv369dSFrU759NNPATjxxBO9kCUIgiAkiJNOgjvuiL1frAWIzbSdgKAbAwfC2rXqvp6sLLj/frXoVtCDZmWmx44dS0VFBS+//HLI9meeeYb8/HxOO+20hL9nUVERfr+fQCp/JyMIgtBMOO00+N3vYu9nZaaDq4Q0oxYCQjPk9NOhogI++8xrJfpiLkZMxgJELepMA6xYsYIDBw6wf/9+QFXmME3z6NGjycnJYeTIkZx77rncdNNN7Nu3j759+xIIBFi5ciULFy50pbNhSUkJBQUFCT+uG5SXl9O5c2evZdhGN72gn2bR6y666QX9NEfSa1U+b9CghsdN6DbdRMoBfcZXoZfm5jKHp02DqVPh+ec9EBUFXca3sLCQwsJCysrKGDJkiKvvpc3/5jfffDOXXHIJ11xzDT6fj5deeolLLrmECRMmsHPnziP7LV68mCuuuIKpU6dy3nnn8dFHH7Fo0aKo3Q/ThUmTJnktwRG66QX9NIted9FNL+inOZLeWDnTV1zhkqCY6DW+Cr00N5c5fNxxasFtaWmSBcVAt/FNCq7VCWnm6FgaTyethqGfXsPQT7PodRfd9BqGfprD9fr9qixb9+7RS7WtW+dVmbnSFCh1lxzNXqH7HA7mm28MY9y4JIqxgY7j67Zf0yYyLTQdXdJRTHTTC/ppFr3uopte0E9zuF4zIh2pS6KJdwsQ9RpfhV6adZ/DwfTsCUcfDf/6VxIFxUC38U0GYqYFQRCEZodVmocg6Mjtt8OsWV6rEKIhZloQBEFodtTWRn9eSuMJutCtGxx/PLz7rtdKhEiImU4jFixY4LUER+imF/TTLHrdRTe9oJ/mcL2mSU7dNA+9xlehl2at5vC6dSzo0QPWrYu62+23w+9/H/ufxGSg1fgmCTHTTUSnOtNlZWVeS3CEbnpBP82i11100wv6aQ7Xa6Z3hJuOAwdCf/bOTOs1vgq9NGs1h6uqKNu+Haqqou7WoQNcdJHqjOg1uoxvMutM+wxDMsviwaxbWFpaKsn4giAIKcK4cbBkiWrQEmygw//SffEF9OuXXG3phrgLG5SVwZAhqv5dDC9RWwvDh8Mbb4R28xSikwy/JpFpQRAEodlgdjmsqfFWhyAkmhYt4Oqr4emnvVYihCNmWhAEQWg25OWpe7sLEAcPdlePICSSwkJ46SWorvZaiRCMmGlBEASh2WCa6ViR6RYt1P2UKe7qEYREkpkJF14Iy5Z5rUQIRsx0GuH3+72W4Ajd9IJ+mkWvu+imF/TTHK7XTPOIhblf8hci6jW+Cr00azeHHe5/xRXw17+6IsUWuo1vMsj0WoCQPG655RavJThCN72gn2bR6y666QX9NIfrNSPTsTD3S76Z1mt8FXpp1moOX365Gt2RIyE729ZLOgBP7YGa7pDZwk1x1txSVaVaM8ZDt26wenViBaUAUs0jTqSahyAIQurxxBNw002Nt4f/pTMMyMiAV15RJceExCPuwgbdu8OOHV6rSB75+bBlS1LfMhl+TSLTgiAIQrOhXTt7+5kR6eC0kIyM2M1eBCGh5Oc3JPA7wADKy6FL58RLcpVu3bxW4ApipgVBEIRmQ6dOzvYPTgvp3Ru+/DKxegQhKnGmPPiAGbfA5MkwYEBiJQnOkQWITUSnDohLly71WoIjdNML+mkWve6im17QT3O43o4dnb2+R4+Gx8lJS9BrfBV6adZ9Dttl/Hh4+eUEi7GBLuObzA6IYqabSElJCcuWLaOwsNBrKTHRwfAHo5te0E+z6HUX3fSCfprD9VpFpiOVvxsxQn3LbnLddXDNNfHpsFNFRBkfvcZXoZdm3eewXX70I/jXvxIsxga6jG9hYSHLli2jpKTE9feSBYhxIgsQBUEQUo99+xrnTa9erTo2R8LMnz58GJ59Fq691vn75uVBRUX0fb79Fo46yvmxdUXchftceik88gh01i13OolIO3FBEARBcECbNo232S1/l5Xlbqm85JfhE5o7I0fC3/7mtQpBzLQgCILQbLAyrBkO/tKJmRZ0YsQIWLHCaxWCmGlBEAShWZMMM20npUHMtJBojjpKlcirrfVaSXojZjqNmDhxotcSHKGbXtBPs+h1F930gn6a7ei122Ic4je8dupTq2PrNb4KvTQ3xzkcjdNOg3//O0FibKDb+CYDMdNpxPDhw72W4Ajd9IJ+mkWvu+imF/TTbEevEzOdGWf3BftmWq/xVeiluTnO4WiMGpXcVA/dxjcZSDWPOJFqHoIgCKnJWWfBqlUNP+/fH9qcJRwzGm0YsGgRxFPpNDtbVQOJxp490KGD82PririL5FBbC2PGSO50JKSahyAIgiA45IEH1L1Z2SMZkWnJmRa8okUL6NoVtm3zWkn6ImZaEARBaFZkZ6v7115T905MbIsW8b2n/TQPQUg8Y8fC4sVeq0hfxEynEauCv/fUAN30gn6aRa+76KYX9NNspffEE+F//gfOOcd5qoH7OdN6ja9CL83NYQ47ZcQIWLkyAWJsoNv4JgMx02nEnDlzvJbgCN30gn6aRa+76KYX9NNspTc7G95/P77juZ/modf4KpxrvvFGF2TYpDnMYafk5EBuriqT5za6jW8ykAWIcaLjAsTKykpyc3O9lmEb3fSCfppFr7vophf005wIvcELEFeuVFE+N6iqglatKgF9xlfhXPOtt6o2116QjnMY4Pnn4dAhcLtynW7jKwsQhYSi0+QH/fSCfppFr7vophf005xovdEi02YudtOOrdf4KvTSnK5zeNQoWL48IYeKim7jmwzETAuCIAhCPdHMdLTvcTt1in1sJ50YBcEp7dtDRYV0Q/QC+WgLgiAIQj3RqnlEW2S4eXPsY0s1D8FtCgrg44+9VpF+iJlOI4qLi72W4Ajd9IJ+mkWvu+imF/TTnGi98Uam7dey1mt8FXppTuc5/LOfwTvvJOxwlug2vslAzHQa0atXL68lOEI3vaCfZtHrLrrpBf00J0LvzJkNj4Mj03/6U+h+dsrfxUav8VU41+xlFD4d57DJmWfCv/6VsMNZotv4JgOp5hEn5urQs846i/bt21NYWEhhPD1oBUEQBE/55BMYPFhFns3HAE88Yb/Em2HENpB29jHp2xe+/NLevqnIbbfBww97rSI9GTUK3nhD0ooCgQCBQIDvv/+e999/39VqHnFW1BRMSkpKtCmNJwiCIDRm0CC4+mr1ODjNI93NiKAnvXvDV1+p+3TGDHKawU83kTQPQRAEIa3JyICnn1aPg9M8fD7n5fDkG3CF/CPiHaeeCv/+t9cq0gsx02nEhg0bvJbgCN30gn6aRa+76KYX9NOcaL3hkWmnZjo8z7oxSgzr8jMAACAASURBVG/nzs6O6y3Ox9jLBNJ0n8Num2ndxjcZiJlOI6ZMmeK1BEfophf00yx63UU3vaCf5kTrDS+N59RMx64lrfT26RN9r9SK7Kb3nHCbROv9wQ9g7dqEHjIE3cY3GYiZTiPmzp3rtQRH6KYX9NMset1FN72gn+ZE6w2PTGdlOXt9LDM9dGiD3rPPjrxfapUGSO854TaJ1puRAV27wo4dCT3sEXQb32QgZjqN0K2cjW56QT/NotdddNML+mlOtN5gM92ihXMzbRVR7toV7r9fPR40qNeR/VLLMEdDSuO5iRt6hw2Dt99O+GEB/cY3GYiZFgRBEIR6gs1zy5ZupHko9DLTgm64aaaFxoiZrufw4cNMnDiRXr160a5dO8444wz+5Xblc0EQBCGlCO5kGI+ZbtnSenu4cY5lplMrZ9o5uuvXnR49VJqH/MOWHMRM11NTU0OfPn344IMP2Lt3LzfddBN+v5+DBw96LS1hzJ4922sJjtBNL+inWfS6i256QT/Nidabk9PwOB4zHfx6k8xMqK1Vj//znwa9+hgd52N8zDEuyLBJus9hk759YdOmxB9Xt/FNBmKm68nNzeWee+6hZ8+eAFx55ZXU1dXxxRdfeKwscVRWVnotwRG66QX9NIted9FNL+inOdF6gyOq8Zjp3NzG2zIzoaZGPa6pqTzyPvqYaedjbDfdxQ3SfQ6bnH46fPhh4o+r2/gmA2knHoENGzZQUFBAeXk5uRZXR7OjjpvtKQVBEITkYxrq996D22+HaBl/ublQWamM8ZYtkJ/f2Ej26QPjx8OsWXDNNfDUU3DmmSpaHcns6N5O/OGHVUtxwTs2boTHH4eSEq+VeEsy/JpEpi2orKzkiiuu4J577rE00oIgCELzJytL1ewFuO46632CF3n17GmdK9yiBdTVqcfBRrs5h7IGDfJagdCvnzLUgvukrZleuHAhbdq0oU2bNowePfrI9urq6v/f3p3HN1Xn+x9/J6xdUlqWCpRV1ipl0UGmFEZUqEDHCswtWBm4lGW8IEi5XnDQEaiMV+HO405BvY4ggmAnMDBYyyCI3NGr/FB0KCiyKMoqKNKWTpsi0OX7+yOmNE3TJmm+Ofm07+fjkUfbk5PkdQ4hfHs4C1JSUtCvXz8sXrzYwEIiIjJSs2bAwoW1z+PJlQzN5pv7TP/hD/avJpP9jAvuSD+A7847jS4gx3nSb9wwuqThEzOYttlsWLRoERITE9GuXTuYzWZkZGS4nTc9PR0xMTEICQnBoEGDsGXLFqd5Jk+ejOLiYhQXF2Pnzp0AgIqKCkyZMgXNmzfHunXrtC9ToOXl5Rmd4BVpvYC8ZvbqJa0XkNeso/ezz+xfQ0NvblGuD7P55vOUltp7TSbgqaeAESPq//z68T2hk87e224D/H31b2nrNxDEDKbz8vKwdu1alJaWYvz48QAAk5tf3SdMmICNGzdi2bJl2L17NwYPHozU1FRYrdZaX+ORRx7BpUuXsHnzZpiNPHpCk+nTpxud4BVpvYC8ZvbqJa0XkNeso9dx4ZawsLoH055s9as6mHb0mkz2wfqgQTU/JriOfed7QiedvQMH3vzl0F+krd+AUALl5eUpk8mkMjIyXO7buXOnMplMavPmzU7TExMTVUxMjCovL6/xOc+cOaNMJpMKDQ1V4eHhlbd9+/bVOP/BgwcVAHXw4MH6L1CASGpVSl6vUvKa2auXtF6l5DXr6P3qK6UApa5dU+rIEfv3M2bYv1a/ffqp/WtV1eeJi1Nq7tybvYBSw4fbf05Pr/l5g+t20OvHFBX5/Y/FY3wP33T8uFL//u/+fU6J61f3eE3k5ldVy1Ebb775JiwWC1JSUpymp6Wl4eLFizjg5tDprl27oqKiAiUlJZW7fxQXFyMhIcGv7UaSdtYRab2AvGb26iWtF5DXrKPXsWW6RQvg9tvtB9M5Tm1XXXQ0MGZM7c9Xdcu0o9fxH6s1bfneuNGHaK34ntBJZ2+vXsDJk/59TmnrNxBEDqZr88UXXyA2NtZlN424uDgAwNGjR/36emPHjkVycrLTLT4+HtnZ2U7z7dmzB8nJyS6Pf/TRR132z87NzUVycrLLfklLly51OVn6uXPnkJycjBPVdop64YUXsLDakTNXr15FcnIy9u3b5zTdarUiLS3NpW3SpElcDi4Hl4PL0eiWo+olxZctWwqbbQVKS+3n7f1pSQAkAziBLl2At992Xo6HHnJaEpw+nYzvvnNejvJy+3I4Dkx0iIiYBIslG1eu3Jz27/++56fXuyk2FgAeBVD9+J7cn+atvl/rUrhefOXmcjh7AUD1Iy+v/jTvvmrTrQBc/zymTeP7KhiWo0kT+8Gv0pfDoa7lsFqtlWOx7t27Y+DAgUhPT3d5Hr/Tts1bo8uXL7vdzaNXr15qzJgxLtMvXryoTCaTev755/3SIHE3DyIiqltxsfOuGyNHKjVpkv376rsz1OSNN5zneeQRpV599eb9f/2rUrm59u/nznWet1+/m/M5pn3xhevr9u9v9K4fwbubBzn7zW+UOn/e6ArjcDcP8itpZyiR1gvIa2avXtJ6AXnNOnrDw4Fz527+7O5qhdHRNT+++rx/+pP9Yi2AvXfChJsHHs6c6Vtjkya+Pc433q9jI0/tx/ewswED/HsQorT1GwgNbjDdpk0b5Ofnu0wvKCiovL+xys3NNTrBK9J6AXnN7NVLWi8gr1lXb+fON793N5h+9dWaH1t1P2jH/tcO1XtDQm5+780VAwM7mOZ7QifdvQMHAocP++/5pK3fQGhwg+n+/fvj+PHjqKh2VMeRI0cAAP369TMiKyi89NJLRid4RVovIK+ZvXpJ6wXkNQei191g2t3W16r//LRq5Xxf9V5fr4IY2LO38j2hk+7euDjg88/993zS1m8gNLjB9Pjx42Gz2bBt2zan6Rs2bEBMTAyGDBni19dLT09HcnJyneewJiIimcrLnQevju/dDWirDpCff97z1+nTBxg8uO757rgDePZZz5/XCA35UunSWCyAzWZ0ReA5DkYMxAGITeueJXjs2rWr8tR1gP3MHI5Bc1JSEkJCQjB69GiMGjUKs2fPRlFREXr06AGr1Yo9e/YgKyvL7YVefJWZmcnTxBARNWDXr9tPk1edJ1um69onuuqgc86cuucBgG7dar4UuVLyL0NOeoSH2wfU4eFGlwROamoqUlNTkZubizs1X99e1GB6zpw5OHv2LAD71Q+3bt2KrVu3wmQy4fTp0+jSpQsAYPv27XjqqaewZMkSFBQUIDY2Fps3b8bEiRONzCciIoFatHDdXQNwv2Xam0uQcwsuBUJcHHDkCBAfb3RJwyRqN4/Tp0+joqICFRUVKC8vd/reMZAGgLCwMGRmZuLixYu4du0aDh06xIE0UOP5JIOZtF5AXjN79ZLWC8hrDkTv9u3AypU3f3YcVFj9HNEOffu6f6769L75pv1r4Afg3jcbuYWc72FX/jwIUdr6DQRRg2mqn7lz5xqd4BVpvYC8ZvbqJa0XkNcciF6LxfmsG8OH27+6GzA67l+71vW++vRW/y/6AQNqnq/aBYD9gO8JnQLRO2CA/wbT0tZvIHAw3YgkJiYaneAVab2AvGb26iWtF5DXHOje4cOBjh2BU6eAUaNqn7dTJ9dp1Xu92crcrp39q2MQ/8ADNc/Xo4fnz+kZvid0CkRvp07At9/657mkrd9A4GCaiIjIQ9262W/du7ueQ1q3/v2dfx49OrCvT3KZTPZ9/L3Zn588J+oARCIiIiO9/rp/n8+bLdMmEzBs2M2fExKAK1eAqCj/NlHD1LUrcPas/RdB8i9uma4nSeeZzs7ONjrBK9J6AXnN7NVLWi8grznQvSZT/Q6uq957yy11P6bqgDsiwvm+wFwJke8JnQLVe/vtwNGj9X8eKes3kOeZ5mC6njIzM5GTk4PU1FSjU+okYcBflbReQF4ze/WS1gvIa5be27Zt3Y8pLa17ngcf9DHII96vYyPP5iH9PaGLvwbTUtZvamoqcnJykJmZqf21TErxLJe+cJwE/ODBg7xoCxEROTGZgF27PNuv2THwrP6vcVQUUFgIfPwx8POf2+//4Qf7/I6DEYuL7Vur58wBXnoJmDDBfiVFb668qIPNBoSFGdtAzgoKgPnzgU2bjC4JrECM17hlmoiIKAj17Gn/WnUrb3T0zYF09fsA+zmxgwE30wWf1q3tA2ryPw6miYiIglBWlv3r4MH2LdKeGjFCSw41AE2berbbEHmHg2kiIqIg5NjqXHW3jupq2gJ8//36mki2Pn2Ar74yuqLh4WC6EUlLSzM6wSvSegF5zezVS1ovIK+5ofR26ODb8zkG09Wvjlhdy5a+Pb9dw1jHwSqQvXFxwJEj9XsOaes3EDiYbkSkXbVIWi8gr5m9eknrBeQ1N5Tems5+4c0FNpYvr/3+Dz7w/LlcNYx1HKwC2Xv77cCxY/V7DmnrNxB4Ng8f8WweRETkjskEvPsuMHKkZ/PGxLhe7vnECSA2tvaD+YqKgFatXOepPjj/5BPgrrs8a/cHns0jOP3zn/Yzvzj2x28MeDYPIiIioby53HhNW6Y92dTFzWHkjVat7ANq8i9eTrye0tPTERkZidTUVBEXbiEiosCoaz/mqsw1bNriYJrId1arFVarFYWFhdpfi1um60nSFRD37dtndIJXpPUC8prZq5e0XkBec7D2FhUBP/uZ6/Saejt3rnnLdLNmdb9OYAbTwbmO3QnW94Q7ge6NiKjf1mkp6zeQV0DkYLoRWblypdEJXpHWC8hrZq9e0noBec3B2mux1Dy9pt4pU2oeTPfqZb/CYW0CM5gOznXsTrC+J9wJdG/PnsA33/j+eGnrNxB4AKKPJB6AePXqVYSGhhqd4TFpvYC8ZvbqJa0XkNfcEHoXLwa2bAFOnfL++QoKgDZt6j4A8fp1oEUL75/f7iqAm83DhwMffuh+7ieeAH7/e+/2GfenhvCe0GnTJqB5c2DSJN8eL2398gBE8itJb35AXi8gr5m9eknrBeQ1N4RepWreMu0JT3YFAeyDJ4eNG93PV/Plpp2b6zoryPPPGzeQBhrGe0Knvn3tZ4rxlbT1GwgcTBMRERmorMzzQXF1Fov3u3r06+f8c2Tkze+9OWiSZOrTB/jyS6MrGhYOpomIiAx044bzluNAePXVm99PmOCf5+RlzGWIiLAfIEv+w8F0I7Jw4UKjE7wirReQ18xevaT1AvKaG0Lv9et6B9O+7EIyfrz9q33QbW8eNsw+7a67gMOHXR+zdKnzVm6jNIT3hG5NmwKlpb49Vtr6DQQOphuRLl26GJ3gFWm9gLxm9uolrReQ19wQem/cqM/BgXV7+umaOoB77nH/mI4d7V9nzAAAe7PjoMOmTYEBA1wfEx8P5OXVK9UvGsJ7QrfevYGvvvLtsdLWbyDwbB4+kng2DyIiCj7TpgGXLgG7dvnvOatujV6yBMjIuDnt0CFg4MCb802fDrz2mv1nxy4njz4KvPSS88GRju//+lf7riHVt3hzNCHHpk32/fQfesjoEv0CMV7jFRCJiIgMtGqV3oFo587OP3t7MoZ27YDLl2/+7OuZRyh4xMUB27YZXdFwcDBNRERkoFat9D13SAgwc6Zn89a06wYA/PAD8Oab/msi49X39HjkjPtMNyInhP3NkdYLyGtmr17SegF5zeyt3S9+Ufv9v/71ze/dbbE+ceJE5QGJEvA9UbeWLe0HvvpC2voNBA6mG5FFixYZneAVab2AvGb26iWtF5DXzN7a7d7tOq3qbhqbNgELF7qee7oqd82HDtUzThO+Jzzny+5F0tZvIHAw3Yi8+OKLRid4RVovIK+ZvXpJ6wXkNbO3Zg8+CMyf7zzt7bdrnrdvX2DwYPfP5a7ZcRBjsOF7wjPV94X3lLT1GwjcZ7oRkXY6G2m9gLxm9uolrReQ18zemmVnu04bM8b9/NW3UFb92ZPmvn09DAsAvic806MH8M03QHS0d4+Ttn4DgVumiYiICCaT/RzSABATU/t81R0/rqeJ9Ln1VvtgmuqPg2kiIqJGpLZT2znua9uWp8Br6Pr1Az77zOiKhoGD6UZkxYoVRid4RVovIK+ZvXpJ6wXkNbNXH8euHpKaAfZ66vbbgSNHvH+ctPUbCNxnup7S09MRGRmJ1NRUpKamGp1Tq6tXrxqd4BVpvYC8ZvbqJa0XkNfMXj2qbpWW0uzAXs+YzUCnTsC5c/bLy3tKyvq1Wq2wWq0oLCzU/lq8nLiPeDlxIiKSxmQCTp4EevZ0np6WBnz1FfD//p99njVrgN/8xvXARJPJfgGXceNu/gzwUuJSZWcDFy8Cc+YYXaJPIMZr3M2DiIioEfH3vtBVL/xCsowcCbz7rtEV8nE3DyIiokZu5UrPr4hXdTBuNgPDhulpIv3Cw+1/nsXFgMVidI1c3DLdiOTl5Rmd4BVpvYC8ZvbqJa0XkNfMXv9o186+/2xNgrXZHfZ6Z8yYmq+U6Y7RvcGIg+lGZPr06UYneEVaLyCvmb16SesF5DWz1zuJiUBUlHePMbrZW+z1zgMP1HyRH3eM7g1GHEw3IsuWLTM6wSvSegF5zezVS1ovIK+Zvd555x2gdWvvHmN0s7fY65327YEbN4AffvBsfqN7gxEH042ItLOOSOsF5DWzVy9pvYC8Zvb63+TJzj9LaK6Kvd6bORNYt86zeYOhN9hwMP2Thx56CO3bt0dERAT69OmDtWvXGp1EREQUcG+84f6+++4DOne++fP27cDYsfqbSK9Ro4C9ez0/CJWccTD9k6VLl+Lbb79FUVER3njjDTz22GM4ffq00VlERERBY+9eoOqGyQcfdB5ck0xmMzBjBvDCC0aXyMTB9E9iY2PRtKn9TIFNmjRBREQELA3sPDHrPP0/nCAhrReQ18xevaT1AvKa2auftGb2+iY11f7LUl0n6wiW3mDCwXQVkydPRkhICBISErBmzRq0bdvW6CS/ys3NNTrBK9J6AXnN7NVLWi8gr5m9+klrZq9vTCZgyRIgI6P2+T75JBf79gHl5YHpkoCXE6+moqICOTk5mD59Og4fPowubi5Yz8uJExFRQ7V2bc2XE6eGb+pUYNEioF8/1/uuXwfuvx8YOhT49FPg2WeBu+4KfKM3eDlxTbKysmCxWGCxWJCUlOR0n9lsxrhx45CQkICcnByDComIiIgC79lngSefrPkXqVdesZ/54z//E9i6FVi8GCgpCXxjsBExmLbZbFi0aBESExPRrl07mM1mZLj5fwibzYb09HTExMQgJCQEgwYNwpYtW5zmmTx5MoqLi1FcXIydO3fW+DxlZWUIDw/3+7IQERERBavOnYF77wVeftl5elER8Le/AQ8/bP85MhJ4/HHguecC3xhsRAym8/LysHbtWpSWlmL8+PEAAJPJVOO8EyZMwMaNG7Fs2TLs3r0bgwcPRmpqKqxWq9vnv3TpErZt24aSkhKUlZXhL3/5Cw4cOIBRo0ZpWR4iIiKiYPXYY8C77wIHDtyctnKlffBsrjJyHDsWOHYMOH8+8I3BRMRgulu3brhy5Qree+89PFfLr0Bvv/029u7di5dffhmzZs3C3XffjTVr1mDUqFFYuHAhKioq3D529erViImJQXR0NF588UXk5OQgJiZGx+IYJjk52egEr0jrBeQ1s1cvab2AvGb26vGznwG//a39eynNDuytP7MZeP11+8GIK1cCK1YAly/bL0dfvXfxYuC//sug0CAhYjBdVW3HS7755puwWCxISUlxmp6WloaLFy/iQNVfsaq45ZZb8MEHH6CwsBAFBQX44IMPMGzYML92B4O5c+caneAVab2AvGb26iWtF5DXzF49Bg26+d/3Upod2OsfERFATo79QMSYGOBPf7Kf8aN67+DBwNmzQEGBQaFBQNxgujZffPEFYmNjYTY7L1ZcXBwA4OjRo35/zbFjxyI5OdnpFh8fj+zsbKf59uzZU+Nvn48++qjLORtzc3ORnJyMvGone1y6dClWrFjhNO3cuXNITk7GiRMnnKa/8MILWLhwodO0YcOGITk5Gfv27XOabrVakZaW5tI2adIkQ5cjMTGxxuW4evVq0C5H3759Pf7zCIblSExMrPf7KpDLkZiYqO3vh47lSExMrHE5AH1/z+u7HImJiUHxeeXpcjjWsdGfV54uh6M3GD6vPF2OxMTEoPi88nQ5HOvY6M8rT5fD0Wv051VNy9G0qX1Xjttuy8WDD9qXw9FbdTlSU+1XwzR6OaxWa+VYrHv37hg4cCDS09NdnsffxJ0aLy8vD9HR0Vi2bBmWLFnidF/v3r3Rs2dPvP32207Tv/vuO8TExOC5557DE0884ZcOnhqPiIiIyH5Gj4cfBt56y+gSVzw1HhEREREFtbAwIDwc+P57o0uM0aAG023atEF+fr7L9IKfduRp06ZNoJOCSvX/Ggl20noBec3s1UtaLyCvmb36SWtmr17uepOTATdnG27wGtRgun///jh+/LjLWTuOHDkCAOhX0+V8GpHaTg8YjKT1AvKa2auXtF5AXjN79ZPWzF693PWOHAns3RvgmCDRoPaZ3r17N8aOHYvNmzdj4sSJldNHjx6No0eP4ty5c27PT+0txz44w4cPR2RkJFJTU5GamuqX5yYiIiKSZswY+4VdmjQxusQ+6LdarSgsLMSHH36odZ/pplqeVYNdu3ahpKQExcXFAOxn5ti2bRsAICkpCSEhIRg9ejRGjRqF2bNno6ioCD169IDVasWePXuQlZXlt4F0VZmZmTwAkYiIiBq9O+4ADh8G7rzT6BJUbuR0bPzUScxges6cOTh79iwA+9UPt27diq1bt8JkMuH06dPo0qULAGD79u146qmnsGTJEhQUFCA2NtZlSzURERER+VdCArB/f3AMpgNJzGD69OnTHs0XFhaGzMxMZGZmai4iIiIiIoef/xx44w1g3jyjSwKrQR2ASLWr6QTowUxaLyCvmb16SesF5DWzVz9pzezVq7be1q2BK1cCGBMkOJhuRKpetUgCab2AvGb26iWtF5DXzF79pDWzV6+6ejt1As6fD1BMkBB3No9gwSsgEhERETlbv95+EZdgOVSNV0AkIiIiIjGGDrUfhNiYiDkAMVilp6fzPNNEREREAHr3Br76yugK5/NM68Yt0/WUmZmJnJwcEQPpffv2GZ3gFWm9gLxm9uolrReQ18xe/aQ1s1evunpNJqBlS6CkJEBBbqSmpiInJycgZ3fjYLoRWblypdEJXpHWC8hrZq9e0noBec3s1U9aM3v18qT3rruATz8NQEyQ4AGIPpJ4AOLVq1cRGhpqdIbHpPUC8prZq5e0XkBeM3v1k9bMXr086X3/feDAAeCJJwLTVBsegEh+JekvKyCvF5DXzF69pPUC8prZq5+0Zvbq5UnvnXcCBw8GICZIcDBNRERERH5jsQA2m9EVgcPBNBERERH5Vfv2wHffGV0RGBxMNyILFy40OsEr0noBec3s1UtaLyCvmb36SWtmr16e9jamgxA5mG5EunTpYnSCV6T1AvKa2auXtF5AXjN79ZPWzF69PO296y7gk080xwQJns3DRxLP5kFEREQUCKWlwLhxwM6dxnYEYrzGKyDWE6+ASEREROSsWTMgLAy4cgWIigr86wfyCojcMu0jbpkmIiIicu/VV4GICGDiROMaeJ5p8qsTJ04YneAVab2AvGb26iWtF5DXzF79pDWzVy9veseOBXbs0BgTJDiYbkQWLVpkdIJXpPUC8prZq5e0XkBeM3v1k9bMXr286e3YESgoAK5d0xgUBLibh48k7uZx7tw5UUcNS+sF5DWzVy9pvYC8ZvbqJ62ZvXp527tqFdC9O5CcrDGqFtzNg/xK0l9WQF4vIK+ZvXpJ6wXkNbNXP2nN7NXL295f/QrYvl1TTJDgYJqIiIiItOjUCbh0CSgvN7pEHw6miYiIiEibiROBy5eNrtCHg+lGZMWKFUYneEVaLyCvmb16SesF5DWzVz9pzezVy5fetDSgfXsNMUGCg+lG5OrVq0YneEVaLyCvmb16SesF5DWzVz9pzezVS1pvIPBsHj6SeDYPIiIiosaEZ/MgIiIiIgpiHEwTEREREfmIg+lGJC8vz+gEr0jrBeQ1s1cvab2AvGb26ietmb16SesNBA6mG5Hp06cbneAVab2AvGb26iWtF5DXzF79pDWzVy9pvYHQZNmyZcuMjpDou+++w5o1a/DII4+gQ4cORud4pE+fPmJaAXm9gLxm9uolrReQ18xe/aQ1s1cvab2BGK/xbB4+4tk8iIiIiIIbz+ZBRERERBTEOJgmIiIiIvIRB9P1lJ6ejuTkZFitVqNT6rRu3TqjE7wirReQ18xevaT1AvKa2auftGb26iWl12q1Ijk5Genp6dpfi4PpesrMzEROTg5SU1ONTqlTbm6u0QlekdYLyGtmr17SegF5zezVT1oze/WS0puamoqcnBxkZmZqfy0egOgjHoBIREREFNx4ACIRERERURDjYJqIiIiIyEccTBMRERER+YiD6UYkOTnZ6ASvSOsF5DWzVy9pvYC8ZvbqJ62ZvXpJ6w0EXk7cRxIvJ96mTRv06NHD6AyPSesF5DWzVy9pvYC8ZvbqJ62ZvXpJ6+XlxA3w0UcfISEhAcuXL8dTTz3ldj6ezYOIiIgouPFsHgFWUVGBBQsWID4+HiaTyegcIiIiIgpyTY0OCCavvPIKEhISUFBQAG6wJyIiIqK6cMv0T/Lz87F69WosXbrU6BRtsrOzjU7wirReQF4ze/WS1gvIa2avftKa2auXtN5A4GD6J4sXL8bjjz+OiIgIAGiQu3msWLHC6ASvSOsF5DWzVy9pvYC8ZvbqJ62ZvXpJ6w2ERjmYzsrKgsVigcViQVJSEg4ePIhDhw5hxowZAAClVIPczaNdu3ZGJ3hFWi8gr5m9eknrBeQ1s1c/ac3s1UtabyCIGEzbbDYsWrQIiYmJnr6b4gAAFHxJREFUaNeuHcxmMzIyMtzOm56ejpiYGISEhGDQoEHYsmWL0zyTJ09GcXExiouLsXPnTuzbtw/Hjh1DdHQ02rVrhy1btuC5557DtGnTArB0RERERCSViMF0Xl4e1q5di9LSUowfPx6A+90wJkyYgI0bN2LZsmXYvXs3Bg8ejNTUVFitVrfPP3PmTJw8eRKfffYZDh8+jOTkZMydOxd//OMftSyPUS5cuGB0glek9QLymtmrl7ReQF4ze/WT1sxevaT1BoKIs3l069YNV65cAWA/UPDVV1+tcb63334be/fuhdVqxaRJkwAAd999N86ePYuFCxdi0qRJMJtdf38ICwtDWFhY5c+hoaGIiIhAVFSUhqUxjrS/ANJ6AXnN7NVLWi8gr5m9+klrZq9e0noDQcRguqra9mV+8803YbFYkJKS4jQ9LS0NDz/8MA4cOID4+Pg6X2P9+vUe9xw/ftzjeY125coV5ObmGp3hMWm9gLxm9uolrReQ18xe/aQ1s1cvab0BGacpYS5fvqxMJpPKyMhwue/nP/+5GjJkiMv0L774QplMJrV27Vq/dVy8eFFFRkYqALzxxhtvvPHGG2+8BektMjJSXbx40W9jwOrEbZmuTX5+Pnr27OkyvXXr1pX3+0uHDh1w7NgxfPfdd357TiIiIiLyrw4dOqBDhw7anr9BDaYDTfcfDhEREREFNxFn8/BUmzZtatz6XFBQUHk/EREREZG/NKjBdP/+/XH8+HFUVFQ4TT9y5AgAoF+/fkZkEREREVED1aAG0+PHj4fNZsO2bducpm/YsAExMTEYMmSIQWVERERE1BCJ2Wd6165dKCkpQXFxMQDg6NGjlYPmpKQkhISEYPTo0Rg1ahRmz56NoqIi9OjRA1arFXv27EFWVpbbC70QEREREflCzJbpOXPmYOLEiZgxYwZMJhO2bt2KiRMnYtKkSbh8+XLlfNu3b8eUKVOwZMkSjBkzBp9++ik2b96M1NRUA+uB1157Db169YLFYsFtt92GU6dOGdpTmxEjRiAkJAQWiwUWiwUjR440OskjH330EcxmM5599lmjU+r00EMPoX379oiIiECfPn2wdu1ao5PcunHjBtLS0tClSxe0atUK8fHx+Oijj4zOqtXLL7+MO+64A82bN0dGRobRObW6fPkykpKSEB4ejj59+mDv3r1GJ9VK0rqV+N6V9NlQnZTPYIn/xkkaQwBAeHh45fq1WCxo0qRJUF9V+ujRo/jFL36ByMhI9OjRA+vWrfPuCbSddI8q5eTkqAEDBqjjx48rpZT65ptv1JUrVwyucm/EiBEqKyvL6AyvlJeXqyFDhqihQ4eqZ5991uicOh07dkyVlpYqpZT65JNPVMuWLdWpU6cMrqpZSUmJeuaZZ9T58+eVUkq9/vrrqm3bturq1asGl7mXnZ2tduzYoVJSUmo8J30wSUlJUTNnzlQ//vijysnJUVFRUSo/P9/oLLckrVuJ711Jnw1VSfoMlvZvnLQxRHUXL15UTZs2VWfOnDE6xa0777xTLV++XCmlVG5urrJYLJXr2xNitkxLtnz5cvzxj39E3759AQC33norIiMjDa6qnarlSpPB6JVXXkFCQgJ69+4toj02NhZNm9r3smrSpAkiIiJgsVgMrqpZaGgonn76aXTq1AkAMHXqVFRUVODrr782uMy9Bx98EL/85S/RqlWroH4/2Gw2vPXWW8jIyEDLli3xwAMPYMCAAXjrrbeMTnNLyroFZL53JX02VCXtM1hCo4PEMURVWVlZGDp0KLp27Wp0ilvHjx+v3INh0KBBiI2NxZdffunx4zmY1qy8vByHDx/G/v370blzZ9x666145plnjM6q04IFCxAdHY2RI0fis88+MzqnVvn5+Vi9ejWWLl1qdIpXJk+ejJCQECQkJGDNmjVo27at0UkeOXHiBH788Uf06NHD6BTxTp48ifDwcHTs2LFyWlxcHI4ePWpgVcMl5b0r7bNB4mewlH/jpI4hqtq0aROmTp1qdEatEhMTsWnTJpSVleHAgQM4f/484uPjPX48B9OaXbp0CWVlZfjoo49w9OhRvPfee8jKysLGjRuNTnNr5cqVOHPmDM6fP4+kpCSMGTMGRUVFRme5tXjxYjz++OOIiIgAADEHmmZlZaGkpARWqxVpaWk4d+6c0Ul1unr1KqZMmYKnn34aoaGhRueIZ7PZKt+3DhEREbDZbAYVNVyS3rvSPhukfQZL+jdO4hiiqs8//xwnT55ESkqK0Sm1WrlyJdavX4+QkBAMGzYMzzzzDKKjoz1+PAfTfpaVlVW5w31SUlLlh/YTTzyBiIgIdO3aFY888gh2795tcKld9V4AGDx4MEJDQ9GiRQssWLAAbdu2xf79+w0utavee/DgQRw6dAgzZswAYP+vu2D777ua1rGD2WzGuHHjkJCQgJycHIMKnbnrLS0tRUpKCvr164fFixcbWOistvUb7MLDw13+Ef/nP/8p4r/1JQnW925tgvGzoSYSPoOrC+Z/46oLCQkBELxjiLps2rQJycnJLhsNgklJSQnuu+8+/OEPf8CNGzfw1VdfITMzE3/72988fo5GP5i22WxYtGgREhMT0a5dO5jNZrdHqNtsNqSnpyMmJgYhISEYNGgQtmzZ4jTP5MmTUVxcjOLiYuzcuRORkZFO/4Xr4Otv7rp7/U137759+3Ds2DFER0ejXbt22LJlC5577jlMmzYtaJtrUlZWhvDw8KDtraiowJQpU9C8eXPvj3I2oLcqf24l83d7r169YLPZcPHixcppR44cwe233x6UvdX5ewukjl5/vncD0VtdfT4bAtGs4zNYZ69u/u6Niory6xgiEM0OFRUVsFqtmDJlit9adfQeO3YMZWVlSElJgclkQvfu3fHAAw/gnXfe8TzKv8dDynP69GkVGRmpRowYoWbNmqVMJpPbI9RHjRqloqKi1Jo1a9T7779fOf+f//znWl/jqaeeUr/85S9VcXGxOn/+vOrbt6/PRxLr7i0sLFR79uxR165dU9evX1erVq1St9xyiyosLAzKXpvNpi5cuKAuXLigvv32WzVx4kT1xBNPqIKCAp96A9H8/fffq61btyqbzaZKS0vVli1bVFRUlPr222+DslcppWbOnKlGjBihrl275lNjoHvLysrUjz/+qKZNm6Z+97vfqR9//FGVl5cHZbvOs3no6NW1bnX1+vO9q7vX358NgWjW8Rmss9ff/8bp7lXKv2OIQDUrpdSePXtUdHS03z4fdPXm5+ersLAw9de//lVVVFSoM2fOqNjYWLVmzRqPmxr9YLqqvLw8t38oO3fuVCaTSW3evNlpemJiooqJian1zXLjxg01a9Ys1apVK9WpU6fK068EY+/ly5fVz372M2WxWFTr1q3Vvffeqw4ePBi0vdVNmzbNr6dl0tH8/fffq+HDh6tWrVqpqKgoNXz4cPXhhx8Gbe+ZM2eUyWRSoaGhKjw8vPK2b9++oOxVSqmlS5cqk8nkdHv99dfr3auj/fLly2rs2LEqNDRU9e7dW7377rt+7fR3byDWrb96db53dfTq/GzQ1Vydvz+D/d2r8984Hb1K6RtD6GxWSqmpU6eq+fPna2v1Z++OHTvUgAEDlMViUR07dlT/8R//oSoqKjzu4GC6isuXL7v9Q5k5c6aKiIhwebNYrVZlMpnU/v37A5VZib36SWtmb+BIa2evXtJ6lZLXzF79pDUHS2+j32faU1988QViY2NhNjuvsri4OAAIulNZsVc/ac3sDRxp7ezVS1ovIK+ZvfpJaw5kLwfTHsrPz0fr1q1dpjum5efnBzqpVuzVT1ozewNHWjt79ZLWC8hrZq9+0poD2cvBNBERERGRjziY9lCbNm1q/C2moKCg8v5gwl79pDWzN3CktbNXL2m9gLxm9uonrTmQvRxMe6h///44fvw4KioqnKYfOXIEANCvXz8jstxir37SmtkbONLa2auXtF5AXjN79ZPWHMheDqY9NH78eNhsNmzbts1p+oYNGxATE4MhQ4YYVFYz9uonrZm9gSOtnb16SesF5DWzVz9pzYHsbeq3ZxJs165dKCkpQXFxMQD7EZ6OlZ+UlISQkBCMHj0ao0aNwuzZs1FUVIQePXrAarViz549yMrK8vuVwNhrXK/EZvYGjrR29rJXejN72Rz0vX47yZ5g3bp1q7z4gNlsdvr+7NmzlfPZbDY1f/581aFDB9WiRQs1cOBAtWXLFvY2sF6JzewNHGnt7GWv9Gb2sjnYe01KKeW/oTkRERERUePBfaaJiIiIiHzEwTQRERERkY84mCYiIiIi8hEH00REREREPuJgmoiIiIjIRxxMExERERH5iINpIiIiIiIfcTBNREREROQjDqaJiIiIiHzEwTQRkR9t2LABZrPZ7e2DDz4wOlGbM2fOOC3r9u3bvXr86tWrYTab8c4777idZ+3atTCbzcjOzgYAjBs3rvL14uLi6tVPROQLXk6ciMiPNmzYgOnTp2PDhg3o27evy/2xsbGwWCwGlOl35swZ3HrrrXj66aeRlJSEXr16ISoqyuPHX7lyBR07dkRycjK2bNlS4zxDhw7FqVOncOHCBTRp0gQnT55EQUEB5syZg9LSUnz++ef+WhwiIo80NTqAiKgh6tevH+644w6jM1BaWgqz2YwmTZoE7DV79OiBu+66y+vHRUVFYdy4ccjOzsaVK1dcBuInTpzAxx9/jMcff7xyeXr16gUAsFgsKCgoqH88EZGXuJsHEZFBzGYz5s2bh02bNiE2NhZhYWEYOHAgdu7c6TLvyZMn8fDDD+OWW25By5Ytcdttt+F//ud/nOZ5//33YTab8cYbb+Dxxx9HTEwMWrZsiW+++QaAfReJ3r17o2XLlrj99tthtVoxbdo0dO/eHQCglEKvXr0wevRol9e32Wxo1aoV5s6d6/PyerIMM2bMwPXr15GVleXy+PXr11fOQ0QULLhlmohIg7KyMpSVlTlNM5lMLluId+7ciX/84x/4/e9/j7CwMKxcuRLjx4/Hl19+WTnIPXbsGIYOHYpu3brhv//7v9G+fXvs3r0bjz32GPLy8rBkyRKn51y8eDGGDh2KNWvWwGw2o127dlizZg3+7d/+Df/yL/+CVatWobCwEBkZGbh+/TpMJlNl37x587BgwQJ8/fXX6NmzZ+Vzbty4EcXFxT4Ppj1dhvvuuw9du3bFa6+95vRa5eXl2LRpE+Lj42vcfYaIyDCKiIj8Zv369cpkMtV4a9asmdO8JpNJdejQQdlstspply5dUk2aNFHPP/985bT7779fdenSRRUXFzs9ft68eSokJEQVFhYqpZR67733lMlkUiNGjHCar7y8XLVv317Fx8c7TT937pxq3ry56t69e+W0oqIiFRERodLT053mve2229R9991X67KfPn1amUwm9frrr7vcV9cyXLlypXJaRkaGMplM6tChQ5XTduzYoUwmk3r11VdrfO27775bxcXF1dpHRKQDd/MgItJg06ZN+Mc//uF0O3DggMt899xzD8LCwip/jo6ORnR0NM6dOwcAuHbtGv73f/8X48ePR8uWLSu3eJeVlWHMmDG4du0aPv74Y6fn/NWvfuX085dffolLly5h4sSJTtM7d+6MhIQEp2kWiwXTpk3Dhg0bcPXqVQDA3//+dxw/ftznrdLeLkNaWhrMZjNee+21ymnr169HeHg4HnroIZ8aiIh04WCaiEiD2NhY3HHHHU63QYMGuczXpk0bl2ktWrTAjz/+CADIz89HeXk5Vq9ejebNmzvdkpKSYDKZkJeX5/T4Dh06OP2cn58PALjllltcXis6Otpl2rx581BUVFS53/KLL76ILl264MEHH/Rw6Z15sgyORsA+yB85ciT+/Oc/o7S0FHl5edixYwdSUlKcfvEgIgoG3GeaiCiIRUVFoUmTJpg6dSoeffTRGufp1q2b08+OfaAdHAP277//3uWxNU3r2bMnxowZg5deegmjR49GTk4Oli9f7vK8OpdhxowZ2LNnD7Kzs3HhwgWUlZVh+vTpPr0+EZFOHEwTEQWx0NBQ3HPPPcjNzUVcXByaNWvm9XP07dsX7du3x1/+8hcsWLCgcvq5c+ewf/9+dOrUyeUx8+fPx/33349//dd/RfPmzTFr1qyALsO4cePQpk0bvPbaa7h48SL69OnjsksKEVEw4GCaiEiDI0eO4MaNGy7Te/bsibZt29b6WFXtWlqrVq3CsGHDMHz4cMyePRtdu3ZFcXExvv76a+zYsQN///vfa30+k8mEjIwMPPLII0hJSUFaWhoKCwuxfPlydOzYEWaz6x5/o0aNQmxsLN5//31MmTKlzua6eLsMzZo1w69//WusWrUKALBixYp6vT4RkS4cTBMR+ZFjV4i0tLQa71u7dm2duytU350iNjYWubm5WL58OX73u9/hhx9+QGRkJHr37o2xY8fW+liHWbNmwWQyYeXKlZgwYQK6d++O3/72t8jOzsb58+drfMzEiRORkZFRr3NL+7IMDjNmzMCqVavQtGlTTJ06td4NREQ68HLiRESNVGFhIXr37o0JEybgT3/6k8v9d955J5o1a+ZythB3HJcTX7duHaZMmYKmTfVvr1FKoby8HPfddx8KCgpw5MgR7a9JRFQVz+ZBRNQIXLp0CfPmzcP27dvxf//3f9i4cSPuuecelJSUYP78+ZXzFRcXY//+/XjyySdx6NAhPPnkk16/1owZM9C8eXNs377dn4tQo/Hjx6N58+b48MMPfT5AkoioPrhlmoioESgsLMTUqVPx6aefoqCgAKGhoYiPj0dGRgYGDx5cOd/777+Pe++9F23btsXcuXNdrq5Ym9LSUqctw7feeisiIyP9uhzVnTp1CoWFhQCAkJAQxMbGan09IqLqOJgmIiIiIvIRd/MgIiIiIvIRB9NERERERD7iYJqIiIiIyEccTBMRERER+YiDaSIiIiIiH3EwTURERETkIw6miYiIiIh8xME0EREREZGPOJgmIiIiIvLR/wcwWUbzha5yjwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -299,7 +299,7 @@
     "# 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",
+    "settings.source = openmc.IndependentSource(space=uniform_dist)\n",
     "\n",
     "model.settings = settings"
    ]
@@ -450,9 +450,9 @@
      "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",
+      "/data/home/jupyter-aparler/.conda/envs/nuclear/lib/python3.11/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",
+      "/data/home/jupyter-aparler/.conda/envs/nuclear/lib/python3.11/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=4.\n",
       "  warn(msg, IDWarning)\n"
      ]
     },
@@ -485,22 +485,22 @@
       "                                      %%%%%%%%%%%\n",
       "\n",
       "                 | The OpenMC Monte Carlo Code\n",
-      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
+      "       Copyright | 2011-2023 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",
+      "         Version | 0.13.3\n",
+      "        Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n",
+      "       Date/Time | 2023-11-07 11:19:46\n",
+      "  OpenMP Threads | 32\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",
+      " Reading H1 from /opt/xdata/endfb-vii.1-hdf5/neutron/H1.h5\n",
+      " Reading O16 from /opt/xdata/endfb-vii.1-hdf5/neutron/O16.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 Zr90 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr90.h5\n",
       " Minimum neutron data temperature: 294 K\n",
       " Maximum neutron data temperature: 294 K\n",
       " Reading tallies XML file...\n",
@@ -514,82 +514,82 @@
       "\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",
+      "        1/1    1.17150\n",
+      "        2/1    1.15245\n",
+      "        3/1    1.13200\n",
+      "        4/1    1.12676\n",
+      "        5/1    1.18447\n",
+      "        6/1    1.13531\n",
+      "        7/1    1.10874\n",
+      "        8/1    1.15356\n",
+      "        9/1    1.15175\n",
+      "       10/1    1.18493\n",
+      "       11/1    1.14031\n",
+      "       12/1    1.13002    1.13516 +/- 0.00514\n",
+      "       13/1    1.15465    1.14166 +/- 0.00714\n",
+      "       14/1    1.14178    1.14169 +/- 0.00505\n",
+      "       15/1    1.17987    1.14933 +/- 0.00858\n",
+      "       16/1    1.13472    1.14689 +/- 0.00742\n",
+      "       17/1    1.17411    1.15078 +/- 0.00738\n",
+      "       18/1    1.16947    1.15312 +/- 0.00680\n",
+      "       19/1    1.15485    1.15331 +/- 0.00600\n",
+      "       20/1    1.15172    1.15315 +/- 0.00537\n",
+      "       21/1    1.19138    1.15663 +/- 0.00597\n",
+      "       22/1    1.19872    1.16013 +/- 0.00648\n",
+      "       23/1    1.19084    1.16250 +/- 0.00641\n",
+      "       24/1    1.13795    1.16074 +/- 0.00619\n",
+      "       25/1    1.16798    1.16122 +/- 0.00578\n",
+      "       26/1    1.15718    1.16097 +/- 0.00542\n",
+      "       27/1    1.16126    1.16099 +/- 0.00509\n",
+      "       28/1    1.18699    1.16243 +/- 0.00501\n",
+      "       29/1    1.15404    1.16199 +/- 0.00476\n",
+      "       30/1    1.17851    1.16282 +/- 0.00459\n",
+      "       31/1    1.14133    1.16179 +/- 0.00448\n",
+      "       32/1    1.13015    1.16036 +/- 0.00451\n",
+      "       33/1    1.22717    1.16326 +/- 0.00520\n",
+      "       34/1    1.22948    1.16602 +/- 0.00569\n",
+      "       35/1    1.17805    1.16650 +/- 0.00548\n",
+      "       36/1    1.11704    1.16460 +/- 0.00560\n",
+      "       37/1    1.13863    1.16364 +/- 0.00547\n",
+      "       38/1    1.21268    1.16539 +/- 0.00556\n",
+      "       39/1    1.15605    1.16507 +/- 0.00537\n",
+      "       40/1    1.18146    1.16561 +/- 0.00522\n",
+      "       41/1    1.19373    1.16652 +/- 0.00513\n",
+      "       42/1    1.12231    1.16514 +/- 0.00515\n",
+      "       43/1    1.17583    1.16546 +/- 0.00500\n",
+      "       44/1    1.13660    1.16461 +/- 0.00493\n",
+      "       45/1    1.14348    1.16401 +/- 0.00482\n",
+      "       46/1    1.18429    1.16457 +/- 0.00472\n",
+      "       47/1    1.13033    1.16365 +/- 0.00468\n",
+      "       48/1    1.17366    1.16391 +/- 0.00457\n",
+      "       49/1    1.16592    1.16396 +/- 0.00445\n",
+      "       50/1    1.14117    1.16339 +/- 0.00437\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",
+      " Total time for initialization     = 3.4052e-01 seconds\n",
+      "   Reading cross sections          = 2.8779e-01 seconds\n",
+      " Total time in simulation          = 7.4353e+00 seconds\n",
+      "   Time in transport only          = 2.2516e+00 seconds\n",
+      "   Time in inactive batches        = 3.1451e-01 seconds\n",
+      "   Time in active batches          = 7.1208e+00 seconds\n",
+      "   Time synchronizing fission bank = 1.6041e-02 seconds\n",
+      "     Sampling source sites         = 1.1222e-02 seconds\n",
+      "     SEND/RECV source sites        = 4.7934e-03 seconds\n",
+      "   Time accumulating tallies       = 5.0984e+00 seconds\n",
+      "   Time writing statepoints        = 1.6719e-02 seconds\n",
+      " Total time for finalization       = 1.4286e-03 seconds\n",
+      " Total time elapsed                = 7.8041e+00 seconds\n",
+      " Calculation Rate (inactive)       = 79489.2 particles/second\n",
+      " Calculation Rate (active)         = 14043.4 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",
+      " k-effective (Collision)     = 1.16469 +/- 0.00432\n",
+      " k-effective (Track-length)  = 1.16339 +/- 0.00437\n",
+      " k-effective (Absorption)    = 1.16069 +/- 0.00333\n",
+      " Combined k-effective        = 1.16167 +/- 0.00303\n",
       " Leakage Fraction            = 0.00000 +/- 0.00000\n",
       "\n"
      ]
@@ -687,8 +687,8 @@
       "\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",
+      "            Group 1 [0.625      - 20000000.0eV]:\t6.82e-01 +/- 2.28e-01%\n",
+      "            Group 2 [0.0        - 0.625     eV]:\t1.40e+00 +/- 4.97e-01%\n",
       "\n",
       "\n",
       "\n"
@@ -745,16 +745,16 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>total</td>\n",
-       "      <td>0.668083</td>\n",
-       "      <td>0.001798</td>\n",
+       "      <td>0.668538</td>\n",
+       "      <td>0.001531</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>total</td>\n",
-       "      <td>1.292060</td>\n",
-       "      <td>0.007737</td>\n",
+       "      <td>1.291603</td>\n",
+       "      <td>0.006380</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -762,8 +762,8 @@
       ],
       "text/plain": [
        "   cell  group in nuclide      mean  std. dev.\n",
-       "1     1         1   total  0.668083   0.001798\n",
-       "0     1         2   total  1.292060   0.007737"
+       "1     1         1   total  0.668538   0.001531\n",
+       "0     1         2   total  1.291603   0.006380"
       ]
      },
      "execution_count": 19,
@@ -787,16 +787,7 @@
    "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/mgxs.py:2007: 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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "absorption.export_xs_data(filename='absorption-xs', format='excel')"
    ]
@@ -876,8 +867,8 @@
        "      <td>6.250000e-01</td>\n",
        "      <td>total</td>\n",
        "      <td>(((total / flux) - (absorption / flux)) - (sca...</td>\n",
-       "      <td>1.110223e-15</td>\n",
-       "      <td>0.011435</td>\n",
+       "      <td>2.220446e-16</td>\n",
+       "      <td>0.009455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -886,8 +877,8 @@
        "      <td>2.000000e+07</td>\n",
        "      <td>total</td>\n",
        "      <td>(((total / flux) - (absorption / flux)) - (sca...</td>\n",
-       "      <td>-2.997602e-15</td>\n",
-       "      <td>0.002567</td>\n",
+       "      <td>-9.992007e-16</td>\n",
+       "      <td>0.002183</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -899,8 +890,8 @@
        "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  "
+       "0  (((total / flux) - (absorption / flux)) - (sca...  2.22e-16   9.45e-03  \n",
+       "1  (((total / flux) - (absorption / flux)) - (sca... -9.99e-16   2.18e-03  "
       ]
      },
      "execution_count": 22,
@@ -966,8 +957,8 @@
        "      <td>6.250000e-01</td>\n",
        "      <td>total</td>\n",
        "      <td>((absorption / flux) / (total / flux))</td>\n",
-       "      <td>0.076103</td>\n",
-       "      <td>0.000658</td>\n",
+       "      <td>0.076044</td>\n",
+       "      <td>0.000561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -976,8 +967,8 @@
        "      <td>2.000000e+07</td>\n",
        "      <td>total</td>\n",
        "      <td>((absorption / flux) / (total / flux))</td>\n",
-       "      <td>0.019441</td>\n",
-       "      <td>0.000093</td>\n",
+       "      <td>0.019368</td>\n",
+       "      <td>0.000091</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -989,8 +980,8 @@
        "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  "
+       "0  ((absorption / flux) / (total / flux)) 7.60e-02   5.61e-04  \n",
+       "1  ((absorption / flux) / (total / flux)) 1.94e-02   9.11e-05  "
       ]
      },
      "execution_count": 23,
@@ -1049,8 +1040,8 @@
        "      <td>6.250000e-01</td>\n",
        "      <td>total</td>\n",
        "      <td>((scatter / flux) / (total / flux))</td>\n",
-       "      <td>0.923897</td>\n",
-       "      <td>0.007833</td>\n",
+       "      <td>0.923956</td>\n",
+       "      <td>0.006477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1059,8 +1050,8 @@
        "      <td>2.000000e+07</td>\n",
        "      <td>total</td>\n",
        "      <td>((scatter / flux) / (total / flux))</td>\n",
-       "      <td>0.980559</td>\n",
-       "      <td>0.003729</td>\n",
+       "      <td>0.980632</td>\n",
+       "      <td>0.003169</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1072,8 +1063,8 @@
        "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  "
+       "0  ((scatter / flux) / (total / flux)) 9.24e-01   6.48e-03  \n",
+       "1  ((scatter / flux) / (total / flux)) 9.81e-01   3.17e-03  "
       ]
      },
      "execution_count": 24,
@@ -1140,7 +1131,7 @@
        "      <td>total</td>\n",
        "      <td>(((absorption / flux) / (total / flux)) + ((sc...</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.007861</td>\n",
+       "      <td>0.006501</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1150,7 +1141,7 @@
        "      <td>total</td>\n",
        "      <td>(((absorption / flux) / (total / flux)) + ((sc...</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.003731</td>\n",
+       "      <td>0.003171</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1162,8 +1153,8 @@
        "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  "
+       "0  (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00   6.50e-03  \n",
+       "1  (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00   3.17e-03  "
       ]
      },
      "execution_count": 25,
@@ -1196,7 +1187,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/stress-test/multi-group-xs/mgxs-part-ii.ipynb b/stress-test/multi-group-xs/mgxs-part-ii.ipynb
index a32106c..45a3b87 100644
--- a/stress-test/multi-group-xs/mgxs-part-ii.ipynb
+++ b/stress-test/multi-group-xs/mgxs-part-ii.ipynb
@@ -32,7 +32,7 @@
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "plt.style.use('seaborn-dark')\n",
+    "# plt.style.use('seaborn-dark')\n",
     "\n",
     "import openmoc\n",
     "\n",
@@ -121,7 +121,7 @@
     "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')"
+    "box = openmc.model.RectangularPrism(1.26, 1.26, boundary_type='reflective')"
    ]
   },
   {
@@ -204,7 +204,7 @@
     "# 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",
+    "settings.source = openmc.IndependentSource(space=uniform_dist)\n",
     "\n",
     "# Activate tally precision triggers\n",
     "settings.trigger_active = True\n",
@@ -307,14 +307,14 @@
     "\n",
     "        # Set the cross sections domain to the cell\n",
     "        xs_library[cell.id][rxn_type].domain = cell\n",
-    "        \n",
+    "\n",
     "        # Tally cross sections by nuclide\n",
     "        xs_library[cell.id][rxn_type].by_nuclide = True\n",
-    "                \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",
+    "\n",
     "model.tallies = tallies"
    ]
   },
@@ -379,13 +379,12 @@
       "                                      %%%%%%%%%%%\n",
       "\n",
       "                 | The OpenMC Monte Carlo Code\n",
-      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
+      "       Copyright | 2011-2023 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",
+      "         Version | 0.13.3\n",
+      "        Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n",
+      "       Date/Time | 2023-11-07 12:05:29\n",
+      "  OpenMP Threads | 32\n",
       "\n",
       " Reading settings XML file...\n",
       " Reading cross sections XML file...\n",
@@ -409,180 +408,535 @@
       "\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",
+      "        1/1    1.22394\n",
+      "        2/1    1.22784\n",
+      "        3/1    1.25162\n",
+      "        4/1    1.18933\n",
+      "        5/1    1.24249\n",
+      "        6/1    1.22774\n",
+      "        7/1    1.22083\n",
+      "        8/1    1.21236\n",
+      "        9/1    1.24216\n",
+      "       10/1    1.20525\n",
+      "       11/1    1.22801\n",
+      "       12/1    1.24750    1.23775 +/- 0.00975\n",
+      "       13/1    1.23962    1.23838 +/- 0.00566\n",
+      "       14/1    1.24706    1.24055 +/- 0.00455\n",
+      "       15/1    1.22279    1.23700 +/- 0.00500\n",
+      "       16/1    1.18058    1.22759 +/- 0.01025\n",
+      "       17/1    1.21435    1.22570 +/- 0.00887\n",
+      "       18/1    1.23701    1.22712 +/- 0.00781\n",
+      "       19/1    1.23145    1.22760 +/- 0.00690\n",
+      "       20/1    1.21727    1.22656 +/- 0.00626\n",
+      "       21/1    1.22156    1.22611 +/- 0.00568\n",
+      "       22/1    1.25529    1.22854 +/- 0.00573\n",
+      "       23/1    1.23090    1.22872 +/- 0.00527\n",
+      "       24/1    1.23231    1.22898 +/- 0.00489\n",
+      "       25/1    1.23425    1.22933 +/- 0.00456\n",
+      "       26/1    1.22500    1.22906 +/- 0.00428\n",
+      "       27/1    1.21126    1.22801 +/- 0.00415\n",
+      "       28/1    1.21880    1.22750 +/- 0.00395\n",
+      "       29/1    1.25221    1.22880 +/- 0.00395\n",
+      "       30/1    1.23143    1.22893 +/- 0.00375\n",
+      "       31/1    1.24257    1.22958 +/- 0.00363\n",
+      "       32/1    1.23452    1.22981 +/- 0.00347\n",
+      "       33/1    1.20175    1.22859 +/- 0.00353\n",
+      "       34/1    1.22931    1.22862 +/- 0.00338\n",
+      "       35/1    1.23860    1.22902 +/- 0.00327\n",
+      "       36/1    1.18381    1.22728 +/- 0.00359\n",
+      "       37/1    1.20017    1.22627 +/- 0.00360\n",
+      "       38/1    1.23447    1.22657 +/- 0.00348\n",
+      "       39/1    1.20672    1.22588 +/- 0.00342\n",
+      "       40/1    1.20439    1.22516 +/- 0.00339\n",
+      "       41/1    1.21524    1.22484 +/- 0.00329\n",
+      "       42/1    1.21467    1.22453 +/- 0.00320\n",
+      "       43/1    1.21988    1.22439 +/- 0.00311\n",
+      "       44/1    1.21610    1.22414 +/- 0.00302\n",
+      "       45/1    1.20887    1.22371 +/- 0.00297\n",
+      "       46/1    1.23855    1.22412 +/- 0.00291\n",
+      "       47/1    1.21390    1.22384 +/- 0.00285\n",
+      "       48/1    1.21694    1.22366 +/- 0.00278\n",
+      "       49/1    1.24311    1.22416 +/- 0.00275\n",
+      "       50/1    1.22343    1.22414 +/- 0.00268\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\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",
+      "       51/1    1.22495    1.22416 +/- 0.00261\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       52/1    1.21607    1.22397 +/- 0.00256\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       53/1    1.21531    1.22377 +/- 0.00251\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       54/1    1.22888    1.22388 +/- 0.00245\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       55/1    1.20765    1.22352 +/- 0.00242\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       56/1    1.22969    1.22366 +/- 0.00237\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       57/1    1.24581    1.22413 +/- 0.00237\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       58/1    1.20849    1.22380 +/- 0.00234\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       59/1    1.23903    1.22411 +/- 0.00232\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       60/1    1.20733    1.22378 +/- 0.00229\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       61/1    1.23517    1.22400 +/- 0.00226\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       62/1    1.22390    1.22400 +/- 0.00222\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       63/1    1.22695    1.22405 +/- 0.00217\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       64/1    1.20634    1.22373 +/- 0.00216\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       65/1    1.22490    1.22375 +/- 0.00212\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       66/1    1.21709    1.22363 +/- 0.00208\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       67/1    1.22494    1.22365 +/- 0.00205\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       68/1    1.21584    1.22352 +/- 0.00202\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       69/1    1.21189    1.22332 +/- 0.00199\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       70/1    1.21044    1.22310 +/- 0.00197\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       71/1    1.21279    1.22294 +/- 0.00194\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       72/1    1.24979    1.22337 +/- 0.00196\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       73/1    1.21196    1.22319 +/- 0.00194\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       74/1    1.23868    1.22343 +/- 0.00192\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       75/1    1.22632    1.22347 +/- 0.00189\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       76/1    1.20763    1.22323 +/- 0.00188\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       77/1    1.20654    1.22299 +/- 0.00187\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       78/1    1.21582    1.22288 +/- 0.00184\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       79/1    1.22156    1.22286 +/- 0.00182\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       80/1    1.26424    1.22345 +/- 0.00189\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       81/1    1.23856    1.22366 +/- 0.00187\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       82/1    1.22635    1.22370 +/- 0.00185\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       83/1    1.23682    1.22388 +/- 0.00183\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       84/1    1.21823    1.22381 +/- 0.00181\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       85/1    1.24048    1.22403 +/- 0.00180\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       86/1    1.22127    1.22399 +/- 0.00177\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       87/1    1.22862    1.22405 +/- 0.00175\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       88/1    1.24011    1.22426 +/- 0.00174\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       89/1    1.22234    1.22423 +/- 0.00172\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       90/1    1.23789    1.22440 +/- 0.00170\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       91/1    1.21690    1.22431 +/- 0.00169\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       92/1    1.23422    1.22443 +/- 0.00167\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       93/1    1.21595    1.22433 +/- 0.00165\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       94/1    1.23509    1.22446 +/- 0.00164\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       95/1    1.19459    1.22411 +/- 0.00166\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       96/1    1.22999    1.22417 +/- 0.00164\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       97/1    1.22043    1.22413 +/- 0.00162\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       98/1    1.22169    1.22410 +/- 0.00160\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "       99/1    1.22003    1.22406 +/- 0.00158\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      100/1    1.20257    1.22382 +/- 0.00158\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      101/1    1.21000    1.22367 +/- 0.00157\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      102/1    1.23888    1.22383 +/- 0.00157\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      103/1    1.21211    1.22371 +/- 0.00155\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      104/1    1.20659    1.22352 +/- 0.00155\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      105/1    1.21947    1.22348 +/- 0.00153\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      106/1    1.23843    1.22364 +/- 0.00152\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      107/1    1.22764    1.22368 +/- 0.00151\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      108/1    1.23291    1.22377 +/- 0.00150\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      109/1    1.22325    1.22377 +/- 0.00148\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      110/1    1.21052    1.22364 +/- 0.00147\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      111/1    1.23348    1.22373 +/- 0.00146\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      112/1    1.22456    1.22374 +/- 0.00145\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      113/1    1.20578    1.22357 +/- 0.00144\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      114/1    1.22042    1.22354 +/- 0.00143\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      115/1    1.22143    1.22352 +/- 0.00142\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      116/1    1.23161    1.22359 +/- 0.00140\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      117/1    1.22951    1.22365 +/- 0.00139\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      118/1    1.21103    1.22353 +/- 0.00138\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      119/1    1.24296    1.22371 +/- 0.00138\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      120/1    1.22464    1.22372 +/- 0.00137\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      121/1    1.24168    1.22388 +/- 0.00137\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      122/1    1.22327    1.22387 +/- 0.00136\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      123/1    1.22344    1.22387 +/- 0.00134\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      124/1    1.21236    1.22377 +/- 0.00134\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      125/1    1.25017    1.22400 +/- 0.00134\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      126/1    1.22034    1.22397 +/- 0.00133\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      127/1    1.20737    1.22383 +/- 0.00133\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      128/1    1.22039    1.22380 +/- 0.00132\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      129/1    1.19783    1.22358 +/- 0.00132\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      130/1    1.20356    1.22341 +/- 0.00132\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      131/1    1.24384    1.22358 +/- 0.00132\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      132/1    1.22925    1.22363 +/- 0.00131\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      133/1    1.22562    1.22364 +/- 0.00130\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      134/1    1.21676    1.22359 +/- 0.00129\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      135/1    1.20402    1.22343 +/- 0.00129\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      136/1    1.21821    1.22339 +/- 0.00128\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      137/1    1.25992    1.22368 +/- 0.00131\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      138/1    1.21551    1.22361 +/- 0.00130\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      139/1    1.21593    1.22355 +/- 0.00129\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      140/1    1.20545    1.22341 +/- 0.00129\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      141/1    1.23441    1.22350 +/- 0.00128\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      142/1    1.21756    1.22345 +/- 0.00127\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      143/1    1.24155    1.22359 +/- 0.00127\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      144/1    1.25159    1.22380 +/- 0.00127\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      145/1    1.21877    1.22376 +/- 0.00127\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      146/1    1.21616    1.22371 +/- 0.00126\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      147/1    1.23678    1.22380 +/- 0.00125\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      148/1    1.20358    1.22365 +/- 0.00125\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      149/1    1.21250    1.22357 +/- 0.00125\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      150/1    1.20860    1.22347 +/- 0.00124\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      151/1    1.22502    1.22348 +/- 0.00123\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      152/1    1.21802    1.22344 +/- 0.00122\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      153/1    1.24871    1.22362 +/- 0.00123\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      154/1    1.22950    1.22366 +/- 0.00122\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      155/1    1.19887    1.22349 +/- 0.00122\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      156/1    1.21147    1.22340 +/- 0.00122\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      157/1    1.20278    1.22326 +/- 0.00122\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      158/1    1.21427    1.22320 +/- 0.00121\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      159/1    1.21654    1.22316 +/- 0.00120\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      160/1    1.24186    1.22328 +/- 0.00120\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      161/1    1.21032    1.22320 +/- 0.00120\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      162/1    1.19739    1.22303 +/- 0.00120\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      163/1    1.22975    1.22307 +/- 0.00119\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      164/1    1.21494    1.22302 +/- 0.00119\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      165/1    1.20907    1.22293 +/- 0.00118\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      166/1    1.20143    1.22279 +/- 0.00118\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      167/1    1.22043    1.22278 +/- 0.00118\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      168/1    1.22880    1.22281 +/- 0.00117\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      169/1    1.24968    1.22298 +/- 0.00117\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      170/1    1.22392    1.22299 +/- 0.00117\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      171/1    1.23658    1.22307 +/- 0.00116\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      172/1    1.22377    1.22308 +/- 0.00116\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      173/1    1.21977    1.22306 +/- 0.00115\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      174/1    1.21514    1.22301 +/- 0.00114\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      175/1    1.20258    1.22288 +/- 0.00114\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      176/1    1.21552    1.22284 +/- 0.00114\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      177/1    1.20676    1.22274 +/- 0.00113\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      178/1    1.23262    1.22280 +/- 0.00113\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      179/1    1.20232    1.22268 +/- 0.00113\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      180/1    1.22200    1.22268 +/- 0.00112\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      181/1    1.21723    1.22265 +/- 0.00112\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      182/1    1.22808    1.22268 +/- 0.00111\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      183/1    1.22492    1.22269 +/- 0.00110\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      184/1    1.22204    1.22269 +/- 0.00110\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      185/1    1.22431    1.22270 +/- 0.00109\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      186/1    1.22800    1.22273 +/- 0.00109\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      187/1    1.22258    1.22273 +/- 0.00108\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      188/1    1.24411    1.22285 +/- 0.00108\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      189/1    1.23613    1.22292 +/- 0.00108\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      190/1    1.22197    1.22291 +/- 0.00107\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      191/1    1.21940    1.22289 +/- 0.00106\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      192/1    1.21190    1.22283 +/- 0.00106\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      193/1    1.21577    1.22280 +/- 0.00106\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      194/1    1.22491    1.22281 +/- 0.00105\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      195/1    1.21509    1.22277 +/- 0.00104\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      196/1    1.22257    1.22276 +/- 0.00104\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      197/1    1.21065    1.22270 +/- 0.00104\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      198/1    1.23695    1.22278 +/- 0.00103\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      199/1    1.21822    1.22275 +/- 0.00103\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      "      200/1    1.21757    1.22272 +/- 0.00102\n",
+      " Triggers unsatisfied, no result tallied for score total in tally 23\n",
+      " The estimated number of batches is -2147483637\n",
+      " Creating state point statepoint.200.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",
+      " Total time for initialization     = 5.0144e-01 seconds\n",
+      "   Reading cross sections          = 4.5672e-01 seconds\n",
+      " Total time in simulation          = 8.5119e+01 seconds\n",
+      "   Time in transport only          = 3.2873e+01 seconds\n",
+      "   Time in inactive batches        = 7.6939e-01 seconds\n",
+      "   Time in active batches          = 8.4350e+01 seconds\n",
+      "   Time synchronizing fission bank = 1.9959e-01 seconds\n",
+      "     Sampling source sites         = 1.7881e-01 seconds\n",
+      "     SEND/RECV source sites        = 2.0610e-02 seconds\n",
+      "   Time accumulating tallies       = 5.1680e+01 seconds\n",
+      "   Time writing statepoints        = 5.8805e-02 seconds\n",
+      " Total time for finalization       = 4.1537e-03 seconds\n",
+      " Total time elapsed                = 8.5672e+01 seconds\n",
+      " Calculation Rate (inactive)       = 129973 particles/second\n",
+      " Calculation Rate (active)         = 22525.3 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",
+      " k-effective (Collision)     = 1.22328 +/- 0.00091\n",
+      " k-effective (Track-length)  = 1.22272 +/- 0.00102\n",
+      " k-effective (Absorption)    = 1.22396 +/- 0.00079\n",
+      " Combined k-effective        = 1.22359 +/- 0.00070\n",
       " Leakage Fraction            = 0.00000 +/- 0.00000\n",
       "\n"
      ]
@@ -672,25 +1026,25 @@
       "\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",
+      "            Group 1 [821000.0   - 20000000.0eV]:\t3.30e+00 +/- 1.30e-01%\n",
+      "            Group 2 [5530.0     - 821000.0  eV]:\t3.97e+00 +/- 8.86e-02%\n",
+      "            Group 3 [4.0        - 5530.0    eV]:\t5.50e+01 +/- 1.26e-01%\n",
+      "            Group 4 [0.625      - 4.0       eV]:\t8.84e+01 +/- 1.94e-01%\n",
+      "            Group 5 [0.28       - 0.625     eV]:\t2.90e+02 +/- 2.66e-01%\n",
+      "            Group 6 [0.14       - 0.28      eV]:\t4.49e+02 +/- 2.50e-01%\n",
+      "            Group 7 [0.058      - 0.14      eV]:\t6.87e+02 +/- 1.76e-01%\n",
+      "            Group 8 [0.0        - 0.058     eV]:\t1.44e+03 +/- 1.62e-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",
+      "            Group 1 [821000.0   - 20000000.0eV]:\t1.06e+00 +/- 1.57e-01%\n",
+      "            Group 2 [5530.0     - 821000.0  eV]:\t1.21e-03 +/- 1.56e-01%\n",
+      "            Group 3 [4.0        - 5530.0    eV]:\t5.81e-04 +/- 2.04e+00%\n",
+      "            Group 4 [0.625      - 4.0       eV]:\t6.54e-06 +/- 1.72e-01%\n",
+      "            Group 5 [0.28       - 0.625     eV]:\t1.07e-05 +/- 2.64e-01%\n",
+      "            Group 6 [0.14       - 0.28      eV]:\t1.55e-05 +/- 2.51e-01%\n",
+      "            Group 7 [0.058      - 0.14      eV]:\t2.30e-05 +/- 1.76e-01%\n",
+      "            Group 8 [0.0        - 0.058     eV]:\t4.24e-05 +/- 1.61e-01%\n",
       "\n",
       "\n",
       "\n"
@@ -723,14 +1077,14 @@
       "\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",
+      "            Group 1 [821000.0   - 20000000.0eV]:\t2.52e-02 +/- 1.50e-01%\n",
+      "            Group 2 [5530.0     - 821000.0  eV]:\t1.51e-03 +/- 8.70e-02%\n",
+      "            Group 3 [4.0        - 5530.0    eV]:\t2.07e-02 +/- 1.26e-01%\n",
+      "            Group 4 [0.625      - 4.0       eV]:\t3.32e-02 +/- 1.94e-01%\n",
+      "            Group 5 [0.28       - 0.625     eV]:\t1.09e-01 +/- 2.66e-01%\n",
+      "            Group 6 [0.14       - 0.28      eV]:\t1.69e-01 +/- 2.50e-01%\n",
+      "            Group 7 [0.058      - 0.14      eV]:\t2.58e-01 +/- 1.76e-01%\n",
+      "            Group 8 [0.0        - 0.058     eV]:\t5.40e-01 +/- 1.62e-01%\n",
       "\n",
       "\n",
       "\n"
@@ -790,8 +1144,8 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>H1</td>\n",
-       "      <td>0.233979</td>\n",
-       "      <td>0.003921</td>\n",
+       "      <td>0.233843</td>\n",
+       "      <td>0.002374</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>127</th>\n",
@@ -799,8 +1153,8 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>O16</td>\n",
-       "      <td>1.566955</td>\n",
-       "      <td>0.006599</td>\n",
+       "      <td>1.563140</td>\n",
+       "      <td>0.003891</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>124</th>\n",
@@ -808,8 +1162,8 @@
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>H1</td>\n",
-       "      <td>1.589223</td>\n",
-       "      <td>0.003224</td>\n",
+       "      <td>1.590210</td>\n",
+       "      <td>0.001807</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>125</th>\n",
@@ -817,8 +1171,8 @@
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>O16</td>\n",
-       "      <td>0.286382</td>\n",
-       "      <td>0.001478</td>\n",
+       "      <td>0.285848</td>\n",
+       "      <td>0.001017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>122</th>\n",
@@ -826,8 +1180,8 @@
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "      <td>H1</td>\n",
-       "      <td>0.011405</td>\n",
-       "      <td>0.000244</td>\n",
+       "      <td>0.010769</td>\n",
+       "      <td>0.000138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>123</th>\n",
@@ -835,8 +1189,8 @@
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "      <td>O16</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.000003</td>\n",
+       "      <td>0.000003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>120</th>\n",
@@ -844,8 +1198,8 @@
        "      <td>1</td>\n",
        "      <td>4</td>\n",
        "      <td>H1</td>\n",
-       "      <td>0.000015</td>\n",
-       "      <td>0.000009</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>0.000004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>121</th>\n",
@@ -862,8 +1216,8 @@
        "      <td>1</td>\n",
        "      <td>5</td>\n",
        "      <td>H1</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>0.000005</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.000002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>119</th>\n",
@@ -880,15 +1234,15 @@
       ],
       "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",
+       "126     3         1          1      H1  0.233843   0.002374\n",
+       "127     3         1          1     O16  1.563140   0.003891\n",
+       "124     3         1          2      H1  1.590210   0.001807\n",
+       "125     3         1          2     O16  0.285848   0.001017\n",
+       "122     3         1          3      H1  0.010769   0.000138\n",
+       "123     3         1          3     O16  0.000003   0.000003\n",
+       "120     3         1          4      H1  0.000012   0.000004\n",
        "121     3         1          4     O16  0.000000   0.000000\n",
-       "118     3         1          5      H1  0.000005   0.000005\n",
+       "118     3         1          5      H1  0.000002   0.000002\n",
        "119     3         1          5     O16  0.000000   0.000000"
       ]
      },
@@ -945,18 +1299,18 @@
       "\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",
+      "            Group 1 [0.625      - 20000000.0eV]:\t7.79e-03 +/- 1.22e-01%\n",
+      "            Group 2 [0.0        - 0.625     eV]:\t1.82e-01 +/- 1.12e-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",
+      "            Group 1 [0.625      - 20000000.0eV]:\t2.17e-01 +/- 7.27e-02%\n",
+      "            Group 2 [0.0        - 0.625     eV]:\t2.53e-01 +/- 1.11e-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",
+      "            Group 1 [0.625      - 20000000.0eV]:\t1.45e-01 +/- 6.93e-02%\n",
+      "            Group 2 [0.0        - 0.625     eV]:\t1.74e-01 +/- 1.22e-01%\n",
       "\n",
       "\n",
       "\n"
@@ -1006,48 +1360,48 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>20.715441</td>\n",
-       "      <td>0.045146</td>\n",
+       "      <td>20.780474</td>\n",
+       "      <td>0.025302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>U238</td>\n",
-       "      <td>9.579757</td>\n",
-       "      <td>0.012606</td>\n",
+       "      <td>9.584864</td>\n",
+       "      <td>0.006964</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>O16</td>\n",
-       "      <td>3.155966</td>\n",
-       "      <td>0.003977</td>\n",
+       "      <td>3.159245</td>\n",
+       "      <td>0.002191</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>U235</td>\n",
-       "      <td>485.656482</td>\n",
-       "      <td>0.899766</td>\n",
+       "      <td>485.324537</td>\n",
+       "      <td>0.544794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>U238</td>\n",
-       "      <td>11.191961</td>\n",
-       "      <td>0.021372</td>\n",
+       "      <td>11.195219</td>\n",
+       "      <td>0.012455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>O16</td>\n",
-       "      <td>3.790699</td>\n",
-       "      <td>0.007656</td>\n",
+       "      <td>3.788412</td>\n",
+       "      <td>0.004606</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1055,12 +1409,12 @@
       ],
       "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"
+       "3     1         1    U235   20.780474   0.025302\n",
+       "4     1         1    U238    9.584864   0.006964\n",
+       "5     1         1     O16    3.159245   0.002191\n",
+       "0     1         2    U235  485.324537   0.544794\n",
+       "1     1         2    U238   11.195219   0.012455\n",
+       "2     1         2     O16    3.788412   0.004606"
       ]
      },
      "execution_count": 21,
@@ -1115,23 +1469,23 @@
     "\n",
     "# Inject multi-group cross sections into OpenMOC Materials\n",
     "for cell_id, cell in openmoc_cells.items():\n",
-    "    \n",
+    "\n",
     "    # Ignore the root cell\n",
     "    if cell.getName() == 'root cell':\n",
     "        continue\n",
-    "    \n",
+    "\n",
     "    # Get a reference to the Material filling this Cell\n",
     "    openmoc_material = cell.getFillMaterial()\n",
-    "    \n",
+    "\n",
     "    # Set the number of energy groups for the Material\n",
     "    openmoc_material.setNumEnergyGroups(fine_groups.num_groups)\n",
-    "    \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",
+    "\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",
@@ -1188,480 +1542,480 @@
       "[  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 ]  Iteration 0:  k_eff = 0.422947   res = 4.915E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -57705 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 1:  k_eff = 0.475805   res = 2.518E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  5285 D.R. = 5.1231\n",
+      "[  NORMAL ]  Iteration 2:  k_eff = 0.491328   res = 4.930E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1552 D.R. = 1.9580\n",
+      "[  NORMAL ]  Iteration 3:  k_eff = 0.487304   res = 8.137E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -402 D.R. = 1.6503\n",
+      "[  NORMAL ]  Iteration 4:  k_eff = 0.483787   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -351 D.R. = 0.3569\n",
+      "[  NORMAL ]  Iteration 5:  k_eff = 0.477137   res = 2.435E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -664 D.R. = 0.8385\n",
+      "[  NORMAL ]  Iteration 6:  k_eff = 0.468798   res = 3.811E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -833 D.R. = 1.5652\n",
+      "[  NORMAL ]  Iteration 7:  k_eff = 0.460184   res = 1.231E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -861 D.R. = 3.2302\n",
+      "[  NORMAL ]  Iteration 8:  k_eff = 0.450461   res = 4.870E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -972 D.R. = 0.3956\n",
+      "[  NORMAL ]  Iteration 9:  k_eff = 0.441253   res = 2.027E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -920 D.R. = 0.4161\n",
+      "[  NORMAL ]  Iteration 10:  k_eff = 0.431872   res = 4.507E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -938 D.R. = 2.2239\n",
+      "[  NORMAL ]  Iteration 11:  k_eff = 0.422820   res = 3.781E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -905 D.R. = 0.8389\n",
+      "[  NORMAL ]  Iteration 12:  k_eff = 0.414380   res = 2.450E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -843 D.R. = 0.6480\n",
+      "[  NORMAL ]  Iteration 13:  k_eff = 0.406607   res = 3.085E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -777 D.R. = 1.2593\n",
+      "[  NORMAL ]  Iteration 14:  k_eff = 0.399282   res = 4.537E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -732 D.R. = 1.4706\n",
+      "[  NORMAL ]  Iteration 15:  k_eff = 0.392976   res = 1.815E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -630 D.R. = 0.0400\n",
+      "[  NORMAL ]  Iteration 16:  k_eff = 0.387339   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -563 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 17:  k_eff = 0.382584   res = 4.235E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -475 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 18:  k_eff = 0.378660   res = 1.149E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -392 D.R. = 2.7143\n",
+      "[  NORMAL ]  Iteration 19:  k_eff = 0.375564   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -309 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 20:  k_eff = 0.373412   res = 1.875E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -215 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 21:  k_eff = 0.372281   res = 1.875E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -113 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 22:  k_eff = 0.371898   res = 1.633E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -38 D.R. = 0.8710\n",
+      "[  NORMAL ]  Iteration 23:  k_eff = 0.372505   res = 1.028E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  60 D.R. = 6.2963\n",
+      "[  NORMAL ]  Iteration 24:  k_eff = 0.373979   res = 6.170E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  147 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 25:  k_eff = 0.376305   res = 2.783E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  232 D.R. = 0.4510\n",
+      "[  NORMAL ]  Iteration 26:  k_eff = 0.379483   res = 2.178E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  317 D.R. = 0.7826\n",
+      "[  NORMAL ]  Iteration 27:  k_eff = 0.383500   res = 2.662E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  401 D.R. = 1.2222\n",
+      "[  NORMAL ]  Iteration 28:  k_eff = 0.388295   res = 2.541E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  479 D.R. = 0.9545\n",
+      "[  NORMAL ]  Iteration 29:  k_eff = 0.393849   res = 3.630E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  555 D.R. = 1.4286\n",
+      "[  NORMAL ]  Iteration 30:  k_eff = 0.400141   res = 3.630E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  629 D.R. = 0.1000\n",
+      "[  NORMAL ]  Iteration 31:  k_eff = 0.407137   res = 4.719E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  699 D.R. = 13.0000\n",
+      "[  NORMAL ]  Iteration 32:  k_eff = 0.414782   res = 5.686E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  764 D.R. = 1.2051\n",
+      "[  NORMAL ]  Iteration 33:  k_eff = 0.423064   res = 3.267E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  828 D.R. = 0.5745\n",
+      "[  NORMAL ]  Iteration 34:  k_eff = 0.431936   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  887 D.R. = 0.5926\n",
+      "[  NORMAL ]  Iteration 35:  k_eff = 0.441350   res = 1.452E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  941 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 36:  k_eff = 0.451302   res = 1.210E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  995 D.R. = 0.0833\n",
+      "[  NORMAL ]  Iteration 37:  k_eff = 0.461718   res = 1.694E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1041 D.R. = 14.0000\n",
+      "[  NORMAL ]  Iteration 38:  k_eff = 0.472586   res = 1.089E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1086 D.R. = 0.6429\n",
+      "[  NORMAL ]  Iteration 39:  k_eff = 0.483854   res = 3.751E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1126 D.R. = 3.4444\n",
+      "[  NORMAL ]  Iteration 40:  k_eff = 0.495492   res = 7.259E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1163 D.R. = 0.1935\n",
+      "[  NORMAL ]  Iteration 41:  k_eff = 0.507463   res = 3.025E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1197 D.R. = 4.1667\n",
+      "[  NORMAL ]  Iteration 42:  k_eff = 0.519734   res = 3.751E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1227 D.R. = 1.2400\n",
+      "[  NORMAL ]  Iteration 43:  k_eff = 0.532267   res = 3.025E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1253 D.R. = 0.8065\n",
+      "[  NORMAL ]  Iteration 44:  k_eff = 0.545033   res = 5.445E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1276 D.R. = 1.8000\n",
+      "[  NORMAL ]  Iteration 45:  k_eff = 0.557997   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1296 D.R. = 0.5333\n",
+      "[  NORMAL ]  Iteration 46:  k_eff = 0.571128   res = 2.178E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1313 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 47:  k_eff = 0.584397   res = 7.259E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1326 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 48:  k_eff = 0.597776   res = 9.074E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1337 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 49:  k_eff = 0.611237   res = 4.235E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1346 D.R. = 0.4667\n",
+      "[  NORMAL ]  Iteration 50:  k_eff = 0.624752   res = 5.263E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1351 D.R. = 12.4286\n",
+      "[  NORMAL ]  Iteration 51:  k_eff = 0.638299   res = 4.779E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1354 D.R. = 0.9080\n",
+      "[  NORMAL ]  Iteration 52:  k_eff = 0.651853   res = 3.993E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1355 D.R. = 0.8354\n",
+      "[  NORMAL ]  Iteration 53:  k_eff = 0.665390   res = 4.053E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1353 D.R. = 1.0152\n",
+      "[  NORMAL ]  Iteration 54:  k_eff = 0.678893   res = 3.025E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1350 D.R. = 0.0746\n",
+      "[  NORMAL ]  Iteration 55:  k_eff = 0.692338   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1344 D.R. = 9.6000\n",
+      "[  NORMAL ]  Iteration 56:  k_eff = 0.705709   res = 3.448E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1337 D.R. = 1.1875\n",
+      "[  NORMAL ]  Iteration 57:  k_eff = 0.718987   res = 4.840E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1327 D.R. = 0.1404\n",
+      "[  NORMAL ]  Iteration 58:  k_eff = 0.732158   res = 3.327E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1317 D.R. = 6.8750\n",
+      "[  NORMAL ]  Iteration 59:  k_eff = 0.745205   res = 1.754E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1304 D.R. = 0.5273\n",
+      "[  NORMAL ]  Iteration 60:  k_eff = 0.758114   res = 1.512E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1290 D.R. = 0.8621\n",
+      "[  NORMAL ]  Iteration 61:  k_eff = 0.770875   res = 3.267E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1276 D.R. = 2.1600\n",
+      "[  NORMAL ]  Iteration 62:  k_eff = 0.783473   res = 2.541E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1259 D.R. = 0.7778\n",
+      "[  NORMAL ]  Iteration 63:  k_eff = 0.795900   res = 1.815E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1242 D.R. = 0.7143\n",
+      "[  NORMAL ]  Iteration 64:  k_eff = 0.808146   res = 1.089E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1224 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 65:  k_eff = 0.820201   res = 1.391E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1205 D.R. = 1.2778\n",
+      "[  NORMAL ]  Iteration 66:  k_eff = 0.832058   res = 2.057E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1185 D.R. = 1.4783\n",
+      "[  NORMAL ]  Iteration 67:  k_eff = 0.843711   res = 2.601E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1165 D.R. = 1.2647\n",
+      "[  NORMAL ]  Iteration 68:  k_eff = 0.855153   res = 2.783E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1144 D.R. = 1.0698\n",
+      "[  NORMAL ]  Iteration 69:  k_eff = 0.866379   res = 3.327E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1122 D.R. = 1.1957\n",
+      "[  NORMAL ]  Iteration 70:  k_eff = 0.877384   res = 6.049E-10  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1100 D.R. = 0.0182\n",
+      "[  NORMAL ]  Iteration 71:  k_eff = 0.888166   res = 4.477E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1078 D.R. = 74.0000\n",
+      "[  NORMAL ]  Iteration 72:  k_eff = 0.898721   res = 1.452E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1055 D.R. = 0.3243\n",
+      "[  NORMAL ]  Iteration 73:  k_eff = 0.909046   res = 4.174E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1032 D.R. = 2.8750\n",
+      "[  NORMAL ]  Iteration 74:  k_eff = 0.919140   res = 1.754E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1009 D.R. = 0.4203\n",
+      "[  NORMAL ]  Iteration 75:  k_eff = 0.929002   res = 2.117E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  986 D.R. = 1.2069\n",
+      "[  NORMAL ]  Iteration 76:  k_eff = 0.938631   res = 2.783E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  962 D.R. = 1.3143\n",
+      "[  NORMAL ]  Iteration 77:  k_eff = 0.948026   res = 5.263E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  939 D.R. = 1.8913\n",
+      "[  NORMAL ]  Iteration 78:  k_eff = 0.957187   res = 2.722E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  916 D.R. = 0.5172\n",
+      "[  NORMAL ]  Iteration 79:  k_eff = 0.966117   res = 7.864E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  892 D.R. = 0.2889\n",
+      "[  NORMAL ]  Iteration 80:  k_eff = 0.974815   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  869 D.R. = 3.6923\n",
+      "[  NORMAL ]  Iteration 81:  k_eff = 0.983284   res = 3.085E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  846 D.R. = 1.0625\n",
+      "[  NORMAL ]  Iteration 82:  k_eff = 0.991523   res = 2.420E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  823 D.R. = 0.0784\n",
+      "[  NORMAL ]  Iteration 83:  k_eff = 0.999538   res = 6.049E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  801 D.R. = 2.5000\n",
+      "[  NORMAL ]  Iteration 84:  k_eff = 1.007328   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  779 D.R. = 4.8000\n",
+      "[  NORMAL ]  Iteration 85:  k_eff = 1.014897   res = 1.633E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  756 D.R. = 0.5625\n",
+      "[  NORMAL ]  Iteration 86:  k_eff = 1.022248   res = 2.662E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  735 D.R. = 1.6296\n",
+      "[  NORMAL ]  Iteration 87:  k_eff = 1.029384   res = 1.573E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  713 D.R. = 0.5909\n",
+      "[  NORMAL ]  Iteration 88:  k_eff = 1.036308   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  692 D.R. = 1.2308\n",
+      "[  NORMAL ]  Iteration 89:  k_eff = 1.043024   res = 1.512E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  671 D.R. = 0.7812\n",
+      "[  NORMAL ]  Iteration 90:  k_eff = 1.049535   res = 2.964E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  651 D.R. = 1.9600\n",
+      "[  NORMAL ]  Iteration 91:  k_eff = 1.055844   res = 5.384E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  630 D.R. = 1.8163\n",
+      "[  NORMAL ]  Iteration 92:  k_eff = 1.061956   res = 3.267E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  611 D.R. = 0.6067\n",
+      "[  NORMAL ]  Iteration 93:  k_eff = 1.067875   res = 2.299E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  591 D.R. = 0.7037\n",
+      "[  NORMAL ]  Iteration 94:  k_eff = 1.073603   res = 3.630E-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",
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-      "[  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",
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-      "[  NORMAL ]  Iteration 142:  k_eff = 1.200317   res = 3.993E-08  delta-k (pcm)\n",
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-      "[  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",
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-      "[  NORMAL ]  Iteration 146:  k_eff = 1.203470   res = 6.654E-09  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 147:  k_eff = 1.204175   res = 3.448E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 156:  k_eff = 1.209282   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 47 D.R. = 1.4545\n",
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-      "[  NORMAL ]  Iteration 162:  k_eff = 1.211697   res = 5.263E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  ...  = 32 D.R. = 0.7129\n",
-      "[  NORMAL ]  Iteration 165:  k_eff = 1.212676   res = 1.936E-08  delta-k (pcm)\n",
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-      "[  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"
+      "[  NORMAL ]  Iteration 95:  k_eff = 1.079147   res = 4.114E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  554 D.R. = 11.3333\n",
+      "[  NORMAL ]  Iteration 96:  k_eff = 1.084509   res = 7.804E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  536 D.R. = 1.8971\n",
+      "[  NORMAL ]  Iteration 97:  k_eff = 1.089693   res = 3.146E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  518 D.R. = 0.4031\n",
+      "[  NORMAL ]  Iteration 98:  k_eff = 1.094704   res = 6.836E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  501 D.R. = 2.1731\n",
+      "[  NORMAL ]  Iteration 99:  k_eff = 1.099547   res = 4.598E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  484 D.R. = 0.6726\n",
+      "[  NORMAL ]  Iteration 100:  k_eff = 1.104225   res = 2.480E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 467 D.R. = 0.5395\n",
+      "[  NORMAL ]  Iteration 101:  k_eff = 1.108742   res = 5.505E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 451 D.R. = 2.2195\n",
+      "[  NORMAL ]  Iteration 102:  k_eff = 1.113102   res = 5.082E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 436 D.R. = 0.9231\n",
+      "[  NORMAL ]  Iteration 103:  k_eff = 1.117310   res = 4.779E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 420 D.R. = 0.9405\n",
+      "[  NORMAL ]  Iteration 104:  k_eff = 1.121371   res = 1.089E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 406 D.R. = 0.2278\n",
+      "[  NORMAL ]  Iteration 105:  k_eff = 1.125287   res = 3.569E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 391 D.R. = 3.2778\n",
+      "[  NORMAL ]  Iteration 106:  k_eff = 1.129062   res = 3.630E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 377 D.R. = 0.1017\n",
+      "[  NORMAL ]  Iteration 107:  k_eff = 1.132703   res = 6.654E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 364 D.R. = 1.8333\n",
+      "[  NORMAL ]  Iteration 108:  k_eff = 1.136210   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 350 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 109:  k_eff = 1.139590   res = 1.149E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 337 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 110:  k_eff = 1.142845   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 325 D.R. = 2.5263\n",
+      "[  NORMAL ]  Iteration 111:  k_eff = 1.145979   res = 2.178E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 313 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 112:  k_eff = 1.148997   res = 1.210E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 301 D.R. = 0.5556\n",
+      "[  NORMAL ]  Iteration 113:  k_eff = 1.151901   res = 4.840E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 290 D.R. = 0.4000\n",
+      "[  NORMAL ]  Iteration 114:  k_eff = 1.154696   res = 2.117E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 279 D.R. = 4.3750\n",
+      "[  NORMAL ]  Iteration 115:  k_eff = 1.157385   res = 1.210E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 268 D.R. = 0.5714\n",
+      "[  NORMAL ]  Iteration 116:  k_eff = 1.159971   res = 7.864E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 258 D.R. = 0.6500\n",
+      "[  NORMAL ]  Iteration 117:  k_eff = 1.162458   res = 4.658E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 248 D.R. = 5.9231\n",
+      "[  NORMAL ]  Iteration 118:  k_eff = 1.164849   res = 4.235E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 239 D.R. = 0.9091\n",
+      "[  NORMAL ]  Iteration 119:  k_eff = 1.167147   res = 3.509E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 229 D.R. = 0.8286\n",
+      "[  NORMAL ]  Iteration 120:  k_eff = 1.169355   res = 9.074E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 220 D.R. = 0.2586\n",
+      "[  NORMAL ]  Iteration 121:  k_eff = 1.171476   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 212 D.R. = 2.1333\n",
+      "[  NORMAL ]  Iteration 122:  k_eff = 1.173514   res = 3.206E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 203 D.R. = 1.6563\n",
+      "[  NORMAL ]  Iteration 123:  k_eff = 1.175471   res = 1.996E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 195 D.R. = 0.6226\n",
+      "[  NORMAL ]  Iteration 124:  k_eff = 1.177351   res = 1.149E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 187 D.R. = 0.5758\n",
+      "[  NORMAL ]  Iteration 125:  k_eff = 1.179155   res = 1.331E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 180 D.R. = 1.1579\n",
+      "[  NORMAL ]  Iteration 126:  k_eff = 1.180887   res = 3.146E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 173 D.R. = 2.3636\n",
+      "[  NORMAL ]  Iteration 127:  k_eff = 1.182549   res = 5.021E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 166 D.R. = 1.5962\n",
+      "[  NORMAL ]  Iteration 128:  k_eff = 1.184143   res = 3.932E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 159 D.R. = 0.7831\n",
+      "[  NORMAL ]  Iteration 129:  k_eff = 1.185673   res = 4.840E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 152 D.R. = 0.1231\n",
+      "[  NORMAL ]  Iteration 130:  k_eff = 1.187140   res = 3.025E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 146 D.R. = 0.6250\n",
+      "[  NORMAL ]  Iteration 131:  k_eff = 1.188547   res = 6.231E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 140 D.R. = 20.6000\n",
+      "[  NORMAL ]  Iteration 132:  k_eff = 1.189896   res = 4.840E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 134 D.R. = 0.0777\n",
+      "[  NORMAL ]  Iteration 133:  k_eff = 1.191189   res = 5.082E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 129 D.R. = 10.5000\n",
+      "[  NORMAL ]  Iteration 134:  k_eff = 1.192429   res = 1.815E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 123 D.R. = 0.3571\n",
+      "[  NORMAL ]  Iteration 135:  k_eff = 1.193617   res = 6.231E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 118 D.R. = 3.4333\n",
+      "[  NORMAL ]  Iteration 136:  k_eff = 1.194755   res = 5.686E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 113 D.R. = 0.9126\n",
+      "[  NORMAL ]  Iteration 137:  k_eff = 1.195846   res = 5.021E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 109 D.R. = 0.8830\n",
+      "[  NORMAL ]  Iteration 138:  k_eff = 1.196890   res = 6.473E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 104 D.R. = 1.2892\n",
+      "[  NORMAL ]  Iteration 139:  k_eff = 1.197891   res = 8.409E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 100 D.R. = 1.2991\n",
+      "[  NORMAL ]  Iteration 140:  k_eff = 1.198849   res = 8.106E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 95 D.R. = 0.9640\n",
+      "[  NORMAL ]  Iteration 141:  k_eff = 1.199767   res = 5.445E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 91 D.R. = 0.6716\n",
+      "[  NORMAL ]  Iteration 142:  k_eff = 1.200645   res = 5.868E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 87 D.R. = 1.0778\n",
+      "[  NORMAL ]  Iteration 143:  k_eff = 1.201486   res = 4.416E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 84 D.R. = 0.7526\n",
+      "[  NORMAL ]  Iteration 144:  k_eff = 1.202291   res = 1.149E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 80 D.R. = 0.2603\n",
+      "[  NORMAL ]  Iteration 145:  k_eff = 1.203060   res = 1.996E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 76 D.R. = 1.7368\n",
+      "[  NORMAL ]  Iteration 146:  k_eff = 1.203797   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 73 D.R. = 3.8788\n",
+      "[  NORMAL ]  Iteration 147:  k_eff = 1.204502   res = 4.598E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 70 D.R. = 0.5937\n",
+      "[  NORMAL ]  Iteration 148:  k_eff = 1.205177   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 67 D.R. = 0.4211\n",
+      "[  NORMAL ]  Iteration 149:  k_eff = 1.205822   res = 5.928E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 64 D.R. = 3.0625\n",
+      "[  NORMAL ]  Iteration 150:  k_eff = 1.206439   res = 4.053E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 61 D.R. = 0.6837\n",
+      "[  NORMAL ]  Iteration 151:  k_eff = 1.207028   res = 6.049E-10  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 58 D.R. = 0.0149\n",
+      "[  NORMAL ]  Iteration 152:  k_eff = 1.207592   res = 2.843E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 56 D.R. = 47.0000\n",
+      "[  NORMAL ]  Iteration 153:  k_eff = 1.208132   res = 3.085E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 53 D.R. = 1.0851\n",
+      "[  NORMAL ]  Iteration 154:  k_eff = 1.208647   res = 4.235E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 51 D.R. = 0.1373\n",
+      "[  NORMAL ]  Iteration 155:  k_eff = 1.209139   res = 1.512E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 49 D.R. = 3.5714\n",
+      "[  NORMAL ]  Iteration 156:  k_eff = 1.209611   res = 2.117E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 47 D.R. = 1.4000\n",
+      "[  NORMAL ]  Iteration 157:  k_eff = 1.210060   res = 5.989E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 44 D.R. = 2.8286\n",
+      "[  NORMAL ]  Iteration 158:  k_eff = 1.210490   res = 4.053E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 42 D.R. = 0.6768\n",
+      "[  NORMAL ]  Iteration 159:  k_eff = 1.210901   res = 1.089E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 41 D.R. = 0.2687\n",
+      "[  NORMAL ]  Iteration 160:  k_eff = 1.211293   res = 6.049E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 39 D.R. = 0.5556\n",
+      "[  NORMAL ]  Iteration 161:  k_eff = 1.211668   res = 2.541E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 37 D.R. = 4.2000\n",
+      "[  NORMAL ]  Iteration 162:  k_eff = 1.212026   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 35 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 163:  k_eff = 1.212367   res = 7.864E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 34 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 164:  k_eff = 1.212693   res = 2.783E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 32 D.R. = 3.5385\n",
+      "[  NORMAL ]  Iteration 165:  k_eff = 1.213005   res = 1.815E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 31 D.R. = 0.0652\n",
+      "[  NORMAL ]  Iteration 166:  k_eff = 1.213302   res = 4.840E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 29 D.R. = 2.6667\n",
+      "[  NORMAL ]  Iteration 167:  k_eff = 1.213587   res = 1.149E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 28 D.R. = 2.3750\n",
+      "[  NORMAL ]  Iteration 168:  k_eff = 1.213857   res = 7.864E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 27 D.R. = 0.6842\n",
+      "[  NORMAL ]  Iteration 169:  k_eff = 1.214116   res = 3.932E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 25 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 170:  k_eff = 1.214363   res = 2.299E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 24 D.R. = 0.5846\n",
+      "[  NORMAL ]  Iteration 171:  k_eff = 1.214598   res = 5.445E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 23 D.R. = 2.3684\n",
+      "[  NORMAL ]  Iteration 172:  k_eff = 1.214823   res = 5.142E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 22 D.R. = 0.9444\n",
+      "[  NORMAL ]  Iteration 173:  k_eff = 1.215038   res = 3.146E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 21 D.R. = 0.6118\n",
+      "[  NORMAL ]  Iteration 174:  k_eff = 1.215242   res = 1.633E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 20 D.R. = 0.5192\n",
+      "[  NORMAL ]  Iteration 175:  k_eff = 1.215438   res = 3.025E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 19 D.R. = 1.8519\n",
+      "[  NORMAL ]  Iteration 176:  k_eff = 1.215624   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 18 D.R. = 0.3200\n",
+      "[  NORMAL ]  Iteration 177:  k_eff = 1.215801   res = 8.469E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 17 D.R. = 8.7500\n",
+      "[  NORMAL ]  Iteration 178:  k_eff = 1.215971   res = 6.352E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 16 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 179:  k_eff = 1.216132   res = 5.021E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 16 D.R. = 0.7905\n",
+      "[  NORMAL ]  Iteration 180:  k_eff = 1.216286   res = 6.231E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 15 D.R. = 1.2410\n",
+      "[  NORMAL ]  Iteration 181:  k_eff = 1.216433   res = 6.594E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 14 D.R. = 1.0583\n",
+      "[  NORMAL ]  Iteration 182:  k_eff = 1.216573   res = 3.630E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 14 D.R. = 0.5505\n",
+      "[  NORMAL ]  Iteration 183:  k_eff = 1.216706   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 13 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 184:  k_eff = 1.216834   res = 2.541E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 12 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 185:  k_eff = 1.216955   res = 2.359E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 12 D.R. = 0.9286\n",
+      "[  NORMAL ]  Iteration 186:  k_eff = 1.217071   res = 2.541E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 11 D.R. = 1.0769\n",
+      "[  NORMAL ]  Iteration 187:  k_eff = 1.217181   res = 4.416E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 1.7381\n",
+      "[  NORMAL ]  Iteration 188:  k_eff = 1.217285   res = 5.505E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 1.2466\n",
+      "[  NORMAL ]  Iteration 189:  k_eff = 1.217385   res = 2.541E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 0.4615\n",
+      "[  NORMAL ]  Iteration 190:  k_eff = 1.217481   res = 2.117E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 0.8333\n",
+      "[  NORMAL ]  Iteration 191:  k_eff = 1.217572   res = 3.630E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 0.1714\n",
+      "[  NORMAL ]  Iteration 192:  k_eff = 1.217658   res = 4.900E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 13.5000\n",
+      "[  NORMAL ]  Iteration 193:  k_eff = 1.217741   res = 2.420E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 0.4938\n",
+      "[  NORMAL ]  Iteration 194:  k_eff = 1.217819   res = 1.815E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 0.0750\n",
+      "[  NORMAL ]  Iteration 195:  k_eff = 1.217894   res = 9.074E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 196:  k_eff = 1.217965   res = 1.815E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 197:  k_eff = 1.218033   res = 8.469E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 0.4667\n",
+      "[  NORMAL ]  Iteration 198:  k_eff = 1.218098   res = 1.210E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 0.1429\n",
+      "[  NORMAL ]  Iteration 199:  k_eff = 1.218159   res = 3.025E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 2.5000\n",
+      "[  NORMAL ]  Iteration 200:  k_eff = 1.218218   res = 6.654E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 2.2000\n",
+      "[  NORMAL ]  Iteration 201:  k_eff = 1.218274   res = 2.662E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 202:  k_eff = 1.218327   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.3636\n",
+      "[  NORMAL ]  Iteration 203:  k_eff = 1.218377   res = 1.331E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 1.3750\n",
+      "[  NORMAL ]  Iteration 204:  k_eff = 1.218425   res = 2.541E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 1.9091\n",
+      "[  NORMAL ]  Iteration 205:  k_eff = 1.218471   res = 1.512E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.5952\n",
+      "[  NORMAL ]  Iteration 206:  k_eff = 1.218515   res = 3.630E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.2400\n",
+      "[  NORMAL ]  Iteration 207:  k_eff = 1.218556   res = 3.085E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 8.5000\n",
+      "[  NORMAL ]  Iteration 208:  k_eff = 1.218596   res = 5.445E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.7647\n",
+      "[  NORMAL ]  Iteration 209:  k_eff = 1.218634   res = 6.049E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.1111\n",
+      "[  NORMAL ]  Iteration 210:  k_eff = 1.218669   res = 7.864E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.1300\n",
+      "[  NORMAL ]  Iteration 211:  k_eff = 1.218704   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 4.9231\n",
+      "[  NORMAL ]  Iteration 212:  k_eff = 1.218736   res = 6.654E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.1719\n",
+      "[  NORMAL ]  Iteration 213:  k_eff = 1.218766   res = 2.722E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 4.0909\n",
+      "[  NORMAL ]  Iteration 214:  k_eff = 1.218796   res = 1.210E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.0444\n",
+      "[  NORMAL ]  Iteration 215:  k_eff = 1.218824   res = 8.046E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 66.5000\n",
+      "[  NORMAL ]  Iteration 216:  k_eff = 1.218850   res = 9.982E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.2406\n",
+      "[  NORMAL ]  Iteration 217:  k_eff = 1.218875   res = 3.630E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.3636\n",
+      "[  NORMAL ]  Iteration 218:  k_eff = 1.218900   res = 1.633E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.4500\n",
+      "[  NORMAL ]  Iteration 219:  k_eff = 1.218923   res = 2.420E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.1481\n",
+      "[  NORMAL ]  Iteration 220:  k_eff = 1.218944   res = 3.085E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 12.7500\n",
+      "[  NORMAL ]  Iteration 221:  k_eff = 1.218965   res = 1.452E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.4706\n",
+      "[  NORMAL ]  Iteration 222:  k_eff = 1.218985   res = 2.057E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.4167\n",
+      "[  NORMAL ]  Iteration 223:  k_eff = 1.219003   res = 1.089E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.5294\n",
+      "[  NORMAL ]  Iteration 224:  k_eff = 1.219021   res = 5.686E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 5.2222\n",
+      "[  NORMAL ]  Iteration 225:  k_eff = 1.219038   res = 1.089E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.1915\n",
+      "[  NORMAL ]  Iteration 226:  k_eff = 1.219054   res = 2.117E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.9444\n",
+      "[  NORMAL ]  Iteration 227:  k_eff = 1.219070   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.3714\n",
+      "[  NORMAL ]  Iteration 228:  k_eff = 1.219084   res = 3.388E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.1667\n",
+      "[  NORMAL ]  Iteration 229:  k_eff = 1.219098   res = 2.117E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.6250\n",
+      "[  NORMAL ]  Iteration 230:  k_eff = 1.219111   res = 1.210E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.5714\n",
+      "[  NORMAL ]  Iteration 231:  k_eff = 1.219123   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 2.4000\n",
+      "[  NORMAL ]  Iteration 232:  k_eff = 1.219135   res = 1.633E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.5625\n",
+      "[  NORMAL ]  Iteration 233:  k_eff = 1.219146   res = 6.654E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.4074\n",
+      "[  NORMAL ]  Iteration 234:  k_eff = 1.219157   res = 1.028E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.5455\n",
+      "[  NORMAL ]  Iteration 235:  k_eff = 1.219167   res = 6.654E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.6471\n",
+      "[  NORMAL ]  Iteration 236:  k_eff = 1.219177   res = 5.445E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 0 D.R. = 8.1818\n"
      ]
     }
    ],
@@ -1691,9 +2045,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "openmc keff = 1.222044\n",
-      "openmoc keff = 1.218846\n",
-      "bias [pcm]: -319.8\n"
+      "openmc keff = 1.223594\n",
+      "openmoc keff = 1.219177\n",
+      "bias [pcm]: -441.7\n"
      ]
     }
    ],
@@ -1733,26 +2087,26 @@
     "\n",
     "# Inject multi-group cross sections into OpenMOC Materials\n",
     "for cell_id, cell in openmoc_cells.items():\n",
-    "    \n",
+    "\n",
     "    # Ignore the root cell\n",
     "    if cell.getName() == 'root cell':\n",
     "        continue\n",
-    "    \n",
+    "\n",
     "    openmoc_material = cell.getFillMaterial()\n",
     "    openmoc_material.setNumEnergyGroups(coarse_groups.num_groups)\n",
-    "    \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",
+    "\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",
+    "\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",
@@ -1801,706 +2155,700 @@
       "[  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 ]  Iteration 0:  k_eff = 0.366745   res = 6.049E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -63325 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 1:  k_eff = 0.391062   res = 5.324E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  2431 D.R. = 0.8800\n",
+      "[  NORMAL ]  Iteration 2:  k_eff = 0.392876   res = 4.840E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  181 D.R. = 0.0909\n",
+      "[  NORMAL ]  Iteration 3:  k_eff = 0.380986   res = 5.324E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -1189 D.R. = 11.0000\n",
+      "[  NORMAL ]  Iteration 4:  k_eff = 0.374905   res = 2.420E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -608 D.R. = 0.4545\n",
+      "[  NORMAL ]  Iteration 5:  k_eff = 0.369482   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -542 D.R. = 0.8000\n",
+      "[  NORMAL ]  Iteration 6:  k_eff = 0.365432   res = 4.840E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -404 D.R. = 2.5000\n",
+      "[  NORMAL ]  Iteration 7:  k_eff = 0.362942   res = 6.775E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -248 D.R. = 1.4000\n",
+      "[  NORMAL ]  Iteration 8:  k_eff = 0.361361   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -158 D.R. = 0.8571\n",
+      "[  NORMAL ]  Iteration 9:  k_eff = 0.361166   res = 6.775E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  -19 D.R. = 1.1667\n",
+      "[  NORMAL ]  Iteration 10:  k_eff = 0.361886   res = 1.549E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  72 D.R. = 2.2857\n",
+      "[  NORMAL ]  Iteration 11:  k_eff = 0.363597   res = 9.679E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  171 D.R. = 0.0625\n",
+      "[  NORMAL ]  Iteration 12:  k_eff = 0.366212   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  261 D.R. = 10.0000\n",
+      "[  NORMAL ]  Iteration 13:  k_eff = 0.369672   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  346 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 14:  k_eff = 0.373850   res = 1.355E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  417 D.R. = 1.4000\n",
+      "[  NORMAL ]  Iteration 15:  k_eff = 0.378775   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  492 D.R. = 0.5714\n",
+      "[  NORMAL ]  Iteration 16:  k_eff = 0.384321   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  554 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 17:  k_eff = 0.390468   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  614 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 18:  k_eff = 0.397157   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  668 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 19:  k_eff = 0.404338   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  718 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 20:  k_eff = 0.411974   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  763 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 21:  k_eff = 0.420027   res = 1.936E-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",
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-      "[  NORMAL ]  Iteration 134:  k_eff = 1.149120   res = 9.679E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 187 D.R. = 2.0000\n",
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-      "[  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 ]  Iteration 22:  k_eff = 0.428443   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  841 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 23:  k_eff = 0.437200   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  875 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 24:  k_eff = 0.446255   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  905 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 25:  k_eff = 0.455580   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  932 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 26:  k_eff = 0.465142   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  956 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 27:  k_eff = 0.474915   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  977 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 28:  k_eff = 0.484870   res = 1.742E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  995 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 29:  k_eff = 0.494984   res = 1.549E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1011 D.R. = 0.8889\n",
+      "[  NORMAL ]  Iteration 30:  k_eff = 0.505232   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1024 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 31:  k_eff = 0.515593   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1036 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 32:  k_eff = 0.526047   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1045 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 33:  k_eff = 0.536574   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1052 D.R. = 0.2000\n",
+      "[  NORMAL ]  Iteration 34:  k_eff = 0.547157   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1058 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 35:  k_eff = 0.557779   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1062 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 36:  k_eff = 0.568425   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1064 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 37:  k_eff = 0.579080   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1065 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 38:  k_eff = 0.589731   res = 1.161E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1065 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 39:  k_eff = 0.600366   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1063 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 40:  k_eff = 0.610974   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1060 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 41:  k_eff = 0.621542   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1056 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 42:  k_eff = 0.632063   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1052 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 43:  k_eff = 0.642526   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1046 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 44:  k_eff = 0.652925   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1039 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 45:  k_eff = 0.663250   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1032 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 46:  k_eff = 0.673494   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1024 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 47:  k_eff = 0.683653   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1015 D.R. = -nan\n",
+      "[  NORMAL ]  Iteration 48:  k_eff = 0.693719   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  1006 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 49:  k_eff = 0.703687   res = 1.355E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  996 D.R. = 1.7500\n",
+      "[  NORMAL ]  Iteration 50:  k_eff = 0.713552   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  986 D.R. = 0.7143\n",
+      "[  NORMAL ]  Iteration 51:  k_eff = 0.723311   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  975 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 52:  k_eff = 0.732959   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  964 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 53:  k_eff = 0.742493   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  953 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 54:  k_eff = 0.751909   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  941 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 55:  k_eff = 0.761205   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  929 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 56:  k_eff = 0.770378   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  917 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 57:  k_eff = 0.779427   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  904 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 58:  k_eff = 0.788350   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  892 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 59:  k_eff = 0.797144   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  879 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 60:  k_eff = 0.805809   res = 1.355E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  866 D.R. = 3.5000\n",
+      "[  NORMAL ]  Iteration 61:  k_eff = 0.814344   res = 1.161E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  853 D.R. = 0.8571\n",
+      "[  NORMAL ]  Iteration 62:  k_eff = 0.822748   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  840 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 63:  k_eff = 0.831020   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  827 D.R. = 2.5000\n",
+      "[  NORMAL ]  Iteration 64:  k_eff = 0.839161   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  814 D.R. = 0.2000\n",
+      "[  NORMAL ]  Iteration 65:  k_eff = 0.847169   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  800 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 66:  k_eff = 0.855045   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  787 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 67:  k_eff = 0.862789   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  774 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 68:  k_eff = 0.870401   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  761 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 69:  k_eff = 0.877882   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  748 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 70:  k_eff = 0.885232   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  735 D.R. = 0.2000\n",
+      "[  NORMAL ]  Iteration 71:  k_eff = 0.892452   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  722 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 72:  k_eff = 0.899542   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  709 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 73:  k_eff = 0.906504   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  696 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 74:  k_eff = 0.913339   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  683 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 75:  k_eff = 0.920047   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  670 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 76:  k_eff = 0.926629   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  658 D.R. = -nan\n",
+      "[  NORMAL ]  Iteration 77:  k_eff = 0.933088   res = 9.679E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  645 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 78:  k_eff = 0.939423   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  633 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 79:  k_eff = 0.945637   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  621 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 80:  k_eff = 0.951730   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  609 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 81:  k_eff = 0.957705   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  597 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 82:  k_eff = 0.963562   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  585 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 83:  k_eff = 0.969304   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  574 D.R. = 2.6667\n",
+      "[  NORMAL ]  Iteration 84:  k_eff = 0.974931   res = 9.679E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  562 D.R. = 0.1250\n",
+      "[  NORMAL ]  Iteration 85:  k_eff = 0.980444   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  551 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 86:  k_eff = 0.985847   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  540 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 87:  k_eff = 0.991140   res = 9.679E-09  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  529 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 88:  k_eff = 0.996324   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  518 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 89:  k_eff = 1.001402   res = 1.161E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  507 D.R. = 6.0000\n",
+      "[  NORMAL ]  Iteration 90:  k_eff = 1.006375   res = 1.258E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  497 D.R. = 1.0833\n",
+      "[  NORMAL ]  Iteration 91:  k_eff = 1.011244   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  486 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 92:  k_eff = 1.016012   res = 0.000E+00  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  476 D.R. = -nan\n",
+      "[  NORMAL ]  Iteration 93:  k_eff = 1.020679   res = 1.258E-07  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  466 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 94:  k_eff = 1.025249   res = 2.904E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  456 D.R. = 0.2308\n",
+      "[  NORMAL ]  Iteration 95:  k_eff = 1.029722   res = 3.872E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  447 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 96:  k_eff = 1.034099   res = 1.936E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  437 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 97:  k_eff = 1.038383   res = 5.807E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  428 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 98:  k_eff = 1.042575   res = 7.743E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  419 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 99:  k_eff = 1.046677   res = 4.840E-08  delta-k (pcm) =\n",
+      "[  NORMAL ]  ...  410 D.R. = 0.6250\n",
+      "[  NORMAL ]  Iteration 100:  k_eff = 1.050690   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 401 D.R. = 1.6000\n",
+      "[  NORMAL ]  Iteration 101:  k_eff = 1.054616   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 392 D.R. = 1.3750\n",
+      "[  NORMAL ]  Iteration 102:  k_eff = 1.058457   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 384 D.R. = 0.4545\n",
+      "[  NORMAL ]  Iteration 103:  k_eff = 1.062214   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 375 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 104:  k_eff = 1.065888   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 367 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 105:  k_eff = 1.069481   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 359 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 106:  k_eff = 1.072995   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 351 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 107:  k_eff = 1.076432   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 343 D.R. = 1.2000\n",
+      "[  NORMAL ]  Iteration 108:  k_eff = 1.079792   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 335 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 109:  k_eff = 1.083078   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 328 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 110:  k_eff = 1.086291   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 321 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 111:  k_eff = 1.089431   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 314 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 112:  k_eff = 1.092501   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 307 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 113:  k_eff = 1.095502   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 300 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 114:  k_eff = 1.098435   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 293 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 115:  k_eff = 1.101302   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 286 D.R. = 1.6000\n",
+      "[  NORMAL ]  Iteration 116:  k_eff = 1.104105   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 280 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 117:  k_eff = 1.106844   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 273 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 118:  k_eff = 1.109520   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 267 D.R. = 1.2000\n",
+      "[  NORMAL ]  Iteration 119:  k_eff = 1.112135   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 261 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 120:  k_eff = 1.114691   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 255 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 121:  k_eff = 1.117188   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 249 D.R. = 0.3750\n",
+      "[  NORMAL ]  Iteration 122:  k_eff = 1.119629   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 244 D.R. = 3.3333\n",
+      "[  NORMAL ]  Iteration 123:  k_eff = 1.122013   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 238 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 124:  k_eff = 1.124342   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 232 D.R. = 0.4000\n",
+      "[  NORMAL ]  Iteration 125:  k_eff = 1.126617   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 227 D.R. = 2.7500\n",
+      "[  NORMAL ]  Iteration 126:  k_eff = 1.128841   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 222 D.R. = 0.6364\n",
+      "[  NORMAL ]  Iteration 127:  k_eff = 1.131013   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 217 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 128:  k_eff = 1.133134   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 212 D.R. = 1.7143\n",
+      "[  NORMAL ]  Iteration 129:  k_eff = 1.135206   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 207 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 130:  k_eff = 1.137231   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 202 D.R. = 1.4444\n",
+      "[  NORMAL ]  Iteration 131:  k_eff = 1.139208   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 197 D.R. = 0.8462\n",
+      "[  NORMAL ]  Iteration 132:  k_eff = 1.141139   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 193 D.R. = 0.5455\n",
+      "[  NORMAL ]  Iteration 133:  k_eff = 1.143025   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 188 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 134:  k_eff = 1.144867   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 184 D.R. = 2.2500\n",
+      "[  NORMAL ]  Iteration 135:  k_eff = 1.146666   res = 1.549E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 179 D.R. = 1.7778\n",
+      "[  NORMAL ]  Iteration 136:  k_eff = 1.148423   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 175 D.R. = 0.3750\n",
+      "[  NORMAL ]  Iteration 137:  k_eff = 1.150139   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 171 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 138:  k_eff = 1.151815   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 167 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 139:  k_eff = 1.153451   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 163 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 140:  k_eff = 1.155049   res = 1.355E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 159 D.R. = 4.6667\n",
+      "[  NORMAL ]  Iteration 141:  k_eff = 1.156609   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 156 D.R. = 0.0714\n",
+      "[  NORMAL ]  Iteration 142:  k_eff = 1.158132   res = 1.452E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 152 D.R. = 15.0000\n",
+      "[  NORMAL ]  Iteration 143:  k_eff = 1.159620   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 148 D.R. = 0.2667\n",
+      "[  NORMAL ]  Iteration 144:  k_eff = 1.161073   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 145 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 145:  k_eff = 1.162491   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 141 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 146:  k_eff = 1.163875   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 138 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 147:  k_eff = 1.165227   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 135 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 148:  k_eff = 1.166546   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 131 D.R. = 0.9000\n",
+      "[  NORMAL ]  Iteration 149:  k_eff = 1.167835   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 128 D.R. = 1.1111\n",
+      "[  NORMAL ]  Iteration 150:  k_eff = 1.169094   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 125 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 151:  k_eff = 1.170322   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 122 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 152:  k_eff = 1.171521   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 119 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 153:  k_eff = 1.172691   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 117 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 154:  k_eff = 1.173833   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 114 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 155:  k_eff = 1.174948   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 111 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 156:  k_eff = 1.176037   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 108 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 157:  k_eff = 1.177100   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 106 D.R. = 2.2000\n",
+      "[  NORMAL ]  Iteration 158:  k_eff = 1.178137   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 103 D.R. = 0.4545\n",
+      "[  NORMAL ]  Iteration 159:  k_eff = 1.179149   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 101 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 160:  k_eff = 1.180137   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 98 D.R. = 0.4000\n",
+      "[  NORMAL ]  Iteration 161:  k_eff = 1.181102   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 96 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 162:  k_eff = 1.182044   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 94 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 163:  k_eff = 1.182963   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 91 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 164:  k_eff = 1.183859   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 89 D.R. = 2.6667\n",
+      "[  NORMAL ]  Iteration 165:  k_eff = 1.184735   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 87 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 166:  k_eff = 1.185589   res = 1.355E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 85 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 167:  k_eff = 1.186424   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 83 D.R. = 0.0714\n",
+      "[  NORMAL ]  Iteration 168:  k_eff = 1.187237   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 81 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 169:  k_eff = 1.188031   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 79 D.R. = 0.2000\n",
+      "[  NORMAL ]  Iteration 170:  k_eff = 1.188807   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 77 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 171:  k_eff = 1.189563   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 75 D.R. = 0.4000\n",
+      "[  NORMAL ]  Iteration 172:  k_eff = 1.190302   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 73 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 173:  k_eff = 1.191023   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 72 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 174:  k_eff = 1.191726   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 70 D.R. = 0.8000\n",
+      "[  NORMAL ]  Iteration 175:  k_eff = 1.192413   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 68 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 176:  k_eff = 1.193083   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 67 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 177:  k_eff = 1.193736   res = 5.807E-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 ]  Iteration 178:  k_eff = 1.194374   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 ]  Iteration 179:  k_eff = 1.194996   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 ]  Iteration 180:  k_eff = 1.195603   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 60 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 181:  k_eff = 1.196197   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 59 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 182:  k_eff = 1.196775   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 57 D.R. = 2.5000\n",
+      "[  NORMAL ]  Iteration 183:  k_eff = 1.197340   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 56 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 184:  k_eff = 1.197890   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 55 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 185:  k_eff = 1.198428   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 53 D.R. = 2.3333\n",
+      "[  NORMAL ]  Iteration 186:  k_eff = 1.198953   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 52 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 187:  k_eff = 1.199465   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 51 D.R. = 1.1429\n",
+      "[  NORMAL ]  Iteration 188:  k_eff = 1.199964   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 49 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 189:  k_eff = 1.200452   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 48 D.R. = 0.3750\n",
+      "[  NORMAL ]  Iteration 190:  k_eff = 1.200927   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 47 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 191:  k_eff = 1.201391   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 46 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 192:  k_eff = 1.201843   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 45 D.R. = 2.2500\n",
+      "[  NORMAL ]  Iteration 193:  k_eff = 1.202285   res = 8.711E-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 ]  Iteration 194:  k_eff = 1.202717   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 43 D.R. = 0.4444\n",
+      "[  NORMAL ]  Iteration 195:  k_eff = 1.203138   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 42 D.R. = 2.7500\n",
+      "[  NORMAL ]  Iteration 196:  k_eff = 1.203549   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 41 D.R. = 0.7273\n",
+      "[  NORMAL ]  Iteration 197:  k_eff = 1.203949   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 40 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 198:  k_eff = 1.204339   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 39 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 199:  k_eff = 1.204721   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 38 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 200:  k_eff = 1.205093   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 37 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 201:  k_eff = 1.205456   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 36 D.R. = 0.0909\n",
+      "[  NORMAL ]  Iteration 202:  k_eff = 1.205810   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 35 D.R. = 12.0000\n",
+      "[  NORMAL ]  Iteration 203:  k_eff = 1.206156   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 34 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 204:  k_eff = 1.206492   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 33 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 205:  k_eff = 1.206822   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 32 D.R. = 1.1667\n",
+      "[  NORMAL ]  Iteration 206:  k_eff = 1.207143   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 32 D.R. = 0.1429\n",
+      "[  NORMAL ]  Iteration 207:  k_eff = 1.207456   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 31 D.R. = 9.0000\n",
+      "[  NORMAL ]  Iteration 208:  k_eff = 1.207762   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 30 D.R. = 0.1111\n",
+      "[  NORMAL ]  Iteration 209:  k_eff = 1.208060   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 29 D.R. = 7.0000\n",
+      "[  NORMAL ]  Iteration 210:  k_eff = 1.208351   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 29 D.R. = 0.5714\n",
+      "[  NORMAL ]  Iteration 211:  k_eff = 1.208634   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 28 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 212:  k_eff = 1.208911   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 27 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 213:  k_eff = 1.209182   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 27 D.R. = 2.0000\n",
+      "[  NORMAL ]  Iteration 214:  k_eff = 1.209445   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 26 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 215:  k_eff = 1.209701   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 25 D.R. = 1.6667\n",
+      "[  NORMAL ]  Iteration 216:  k_eff = 1.209953   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 25 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 217:  k_eff = 1.210198   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 24 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 218:  k_eff = 1.210436   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 23 D.R. = 6.0000\n",
+      "[  NORMAL ]  Iteration 219:  k_eff = 1.210669   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 23 D.R. = 0.8333\n",
+      "[  NORMAL ]  Iteration 220:  k_eff = 1.210897   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 ]  Iteration 221:  k_eff = 1.211119   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 22 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 222:  k_eff = 1.211335   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 21 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 223:  k_eff = 1.211546   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 21 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 224:  k_eff = 1.211752   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 20 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 225:  k_eff = 1.211953   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 20 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 226:  k_eff = 1.212149   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 19 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 227:  k_eff = 1.212341   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 19 D.R. = 0.4444\n",
+      "[  NORMAL ]  Iteration 228:  k_eff = 1.212527   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 18 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 229:  k_eff = 1.212709   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 18 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 230:  k_eff = 1.212886   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 17 D.R. = 1.2000\n",
+      "[  NORMAL ]  Iteration 231:  k_eff = 1.213060   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 17 D.R. = 0.8333\n",
+      "[  NORMAL ]  Iteration 232:  k_eff = 1.213229   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 16 D.R. = 1.4000\n",
+      "[  NORMAL ]  Iteration 233:  k_eff = 1.213394   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 16 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 234:  k_eff = 1.213555   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 16 D.R. = 0.8571\n",
+      "[  NORMAL ]  Iteration 235:  k_eff = 1.213711   res = 8.711E-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 ]  Iteration 236:  k_eff = 1.213864   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 15 D.R. = 0.4444\n",
+      "[  NORMAL ]  Iteration 237:  k_eff = 1.214014   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 14 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 238:  k_eff = 1.214160   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 14 D.R. = 5.5000\n",
+      "[  NORMAL ]  Iteration 239:  k_eff = 1.214302   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 14 D.R. = 0.5455\n",
+      "[  NORMAL ]  Iteration 240:  k_eff = 1.214441   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 13 D.R. = 0.8333\n",
+      "[  NORMAL ]  Iteration 241:  k_eff = 1.214576   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 13 D.R. = 0.4000\n",
+      "[  NORMAL ]  Iteration 242:  k_eff = 1.214708   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 13 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 243:  k_eff = 1.214837   res = 1.936E-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 ]  Iteration 244:  k_eff = 1.214962   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 12 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 245:  k_eff = 1.215085   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 12 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 246:  k_eff = 1.215204   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 11 D.R. = 1.6667\n",
+      "[  NORMAL ]  Iteration 247:  k_eff = 1.215321   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 11 D.R. = 0.8000\n",
+      "[  NORMAL ]  Iteration 248:  k_eff = 1.215434   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 11 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 249:  k_eff = 1.215545   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 11 D.R. = 2.4000\n",
+      "[  NORMAL ]  Iteration 250:  k_eff = 1.215654   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 251:  k_eff = 1.215759   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 0.8889\n",
+      "[  NORMAL ]  Iteration 252:  k_eff = 1.215862   res = 1.355E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 1.7500\n",
+      "[  NORMAL ]  Iteration 253:  k_eff = 1.215963   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 10 D.R. = 0.2143\n",
+      "[  NORMAL ]  Iteration 254:  k_eff = 1.216061   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 255:  k_eff = 1.216157   res = 8.711E-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 ]  Iteration 256:  k_eff = 1.216250   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 257:  k_eff = 1.216341   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 9 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 258:  k_eff = 1.216430   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 1.5000\n",
+      "[  NORMAL ]  Iteration 259:  k_eff = 1.216516   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 2.6667\n",
+      "[  NORMAL ]  Iteration 260:  k_eff = 1.216601   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 261:  k_eff = 1.216683   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 262:  k_eff = 1.216764   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 8 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 263:  k_eff = 1.216842   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 2.4000\n",
+      "[  NORMAL ]  Iteration 264:  k_eff = 1.216919   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 0.5833\n",
+      "[  NORMAL ]  Iteration 265:  k_eff = 1.216993   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 1.5714\n",
+      "[  NORMAL ]  Iteration 266:  k_eff = 1.217066   res = 1.355E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 1.2727\n",
+      "[  NORMAL ]  Iteration 267:  k_eff = 1.217138   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 7 D.R. = 0.3571\n",
+      "[  NORMAL ]  Iteration 268:  k_eff = 1.217207   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 269:  k_eff = 1.217275   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 1.2000\n",
+      "[  NORMAL ]  Iteration 270:  k_eff = 1.217341   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 271:  k_eff = 1.217405   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 272:  k_eff = 1.217468   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 0.6000\n",
+      "[  NORMAL ]  Iteration 273:  k_eff = 1.217529   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 6 D.R. = 2.6667\n",
+      "[  NORMAL ]  Iteration 274:  k_eff = 1.217589   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 275:  k_eff = 1.217647   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 276:  k_eff = 1.217704   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 9.0000\n",
+      "[  NORMAL ]  Iteration 277:  k_eff = 1.217760   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.8889\n",
+      "[  NORMAL ]  Iteration 278:  k_eff = 1.217814   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 279:  k_eff = 1.217866   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 ]  Iteration 280:  k_eff = 1.217918   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 281:  k_eff = 1.217968   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 5 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 282:  k_eff = 1.218017   res = 2.904E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 283:  k_eff = 1.218065   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 1.3333\n",
+      "[  NORMAL ]  Iteration 284:  k_eff = 1.218112   res = 1.452E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 3.7500\n",
+      "[  NORMAL ]  Iteration 285:  k_eff = 1.218158   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.8000\n",
+      "[  NORMAL ]  Iteration 286:  k_eff = 1.218202   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.3333\n",
+      "[  NORMAL ]  Iteration 287:  k_eff = 1.218246   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 1.7500\n",
+      "[  NORMAL ]  Iteration 288:  k_eff = 1.218288   res = 0.000E+00  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.0000\n",
+      "[  NORMAL ]  Iteration 289:  k_eff = 1.218329   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 290:  k_eff = 1.218370   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 4 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 291:  k_eff = 1.218409   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 9.0000\n",
+      "[  NORMAL ]  Iteration 292:  k_eff = 1.218448   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 ]  Iteration 293:  k_eff = 1.218485   res = 1.936E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = inf\n",
+      "[  NORMAL ]  Iteration 294:  k_eff = 1.218522   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.5000\n",
+      "[  NORMAL ]  Iteration 295:  k_eff = 1.218557   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 7.0000\n",
+      "[  NORMAL ]  Iteration 296:  k_eff = 1.218592   res = 1.742E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 2.5714\n",
+      "[  NORMAL ]  Iteration 297:  k_eff = 1.218626   res = 2.033E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.1667\n",
+      "[  NORMAL ]  Iteration 298:  k_eff = 1.218658   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.3810\n",
+      "[  NORMAL ]  Iteration 299:  k_eff = 1.218691   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 0.1250\n",
+      "[  NORMAL ]  Iteration 300:  k_eff = 1.218722   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 301:  k_eff = 1.218753   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 3 D.R. = 8.0000\n",
+      "[  NORMAL ]  Iteration 302:  k_eff = 1.218783   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.1250\n",
+      "[  NORMAL ]  Iteration 303:  k_eff = 1.218812   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 7.0000\n",
+      "[  NORMAL ]  Iteration 304:  k_eff = 1.218840   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.7143\n",
+      "[  NORMAL ]  Iteration 305:  k_eff = 1.218868   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.8000\n",
+      "[  NORMAL ]  Iteration 306:  k_eff = 1.218895   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.7500\n",
+      "[  NORMAL ]  Iteration 307:  k_eff = 1.218922   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.5714\n",
+      "[  NORMAL ]  Iteration 308:  k_eff = 1.218948   res = 1.936E-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",
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-      "[  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 ]  Iteration 309:  k_eff = 1.218973   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 4.0000\n",
+      "[  NORMAL ]  Iteration 310:  k_eff = 1.218997   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.7500\n",
+      "[  NORMAL ]  Iteration 311:  k_eff = 1.219021   res = 3.872E-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",
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-      "[  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 ]  Iteration 312:  k_eff = 1.219045   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.2500\n",
+      "[  NORMAL ]  Iteration 313:  k_eff = 1.219067   res = 9.679E-09  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",
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-      "[  NORMAL ]  ...  = 1 D.R. = inf\n",
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-      "[  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",
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-      "[  NORMAL ]  ...  = 1 D.R. = 1.6667\n",
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-      "[  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",
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-      "[  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 ]  Iteration 314:  k_eff = 1.219090   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 5.0000\n",
+      "[  NORMAL ]  Iteration 315:  k_eff = 1.219111   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.4000\n",
+      "[  NORMAL ]  Iteration 316:  k_eff = 1.219133   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 0.1429\n",
+      "[  NORMAL ]  Iteration 317:  k_eff = 1.219153   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 318:  k_eff = 1.219173   res = 7.743E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 2 D.R. = 8.0000\n",
+      "[  NORMAL ]  Iteration 319:  k_eff = 1.219193   res = 9.679E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.2500\n",
+      "[  NORMAL ]  Iteration 320:  k_eff = 1.219212   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.1000\n",
+      "[  NORMAL ]  Iteration 321:  k_eff = 1.219231   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 7.0000\n",
+      "[  NORMAL ]  Iteration 322:  k_eff = 1.219249   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.7143\n",
+      "[  NORMAL ]  Iteration 323:  k_eff = 1.219267   res = 1.452E-07  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 ]  Iteration 324:  k_eff = 1.219284   res = 1.549E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.0667\n",
+      "[  NORMAL ]  Iteration 325:  k_eff = 1.219301   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.8125\n",
+      "[  NORMAL ]  Iteration 326:  k_eff = 1.219318   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.5385\n",
+      "[  NORMAL ]  Iteration 327:  k_eff = 1.219334   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.7143\n",
+      "[  NORMAL ]  Iteration 328:  k_eff = 1.219349   res = 1.645E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 3.4000\n",
+      "[  NORMAL ]  Iteration 329:  k_eff = 1.219365   res = 1.258E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.7647\n",
+      "[  NORMAL ]  Iteration 330:  k_eff = 1.219380   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.0769\n",
+      "[  NORMAL ]  Iteration 331:  k_eff = 1.219394   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 6.0000\n",
+      "[  NORMAL ]  Iteration 332:  k_eff = 1.219409   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.6667\n",
+      "[  NORMAL ]  Iteration 333:  k_eff = 1.219422   res = 1.161E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 3.0000\n",
+      "[  NORMAL ]  Iteration 334:  k_eff = 1.219436   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.9167\n",
+      "[  NORMAL ]  Iteration 335:  k_eff = 1.219449   res = 1.065E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.0000\n",
+      "[  NORMAL ]  Iteration 336:  k_eff = 1.219462   res = 1.452E-07  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 1.3636\n",
+      "[  NORMAL ]  Iteration 337:  k_eff = 1.219475   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 ]  Iteration 338:  k_eff = 1.219487   res = 5.807E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.8571\n",
+      "[  NORMAL ]  Iteration 339:  k_eff = 1.219499   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.1667\n",
+      "[  NORMAL ]  Iteration 340:  k_eff = 1.219510   res = 6.775E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 7.0000\n",
+      "[  NORMAL ]  Iteration 341:  k_eff = 1.219522   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"
+      "[  NORMAL ]  Iteration 342:  k_eff = 1.219533   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 2.5000\n",
+      "[  NORMAL ]  Iteration 343:  k_eff = 1.219543   res = 3.872E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.8000\n",
+      "[  NORMAL ]  Iteration 344:  k_eff = 1.219554   res = 8.711E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 2.2500\n",
+      "[  NORMAL ]  Iteration 345:  k_eff = 1.219564   res = 4.840E-08  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 1 D.R. = 0.5556\n",
+      "[  NORMAL ]  Iteration 346:  k_eff = 1.219574   res = 9.679E-09  delta-k (pcm)\n",
+      "[  NORMAL ]  ...  = 0 D.R. = 0.2000\n"
      ]
     }
    ],
@@ -2523,9 +2871,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "openmc keff = 1.222044\n",
-      "openmoc keff = 1.225565\n",
-      "bias [pcm]: 352.2\n"
+      "openmc keff = 1.223594\n",
+      "openmoc keff = 1.219574\n",
+      "bias [pcm]: -402.0\n"
      ]
     }
    ],
@@ -2593,19 +2941,17 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 3200x2400 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "# Create a figure of the U-235 continuous-energy fission cross section \n",
+    "# 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",
@@ -2676,14 +3022,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 3200x2400 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -2734,7 +3078,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,
diff --git a/stress-test/multi-group-xs/mgxs-part-iii.ipynb b/stress-test/multi-group-xs/mgxs-part-iii.ipynb
index 127ba26..3767c73 100644
--- a/stress-test/multi-group-xs/mgxs-part-iii.ipynb
+++ b/stress-test/multi-group-xs/mgxs-part-iii.ipynb
@@ -320,7 +320,7 @@
     "# 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",
+    "settings.source = openmc.IndependentSource(space=uniform_dist)\n",
     "\n",
     "model.settings = settings"
    ]
@@ -339,7 +339,7 @@
    "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",
+      "image/png": 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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1622,7 +1622,7 @@
     },
     {
      "data": {
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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1677,7 +1677,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,