diff --git a/docs/source/io_formats/nuclear_data.rst b/docs/source/io_formats/nuclear_data.rst index 7ad80b2fc..fefdf696d 100644 --- a/docs/source/io_formats/nuclear_data.rst +++ b/docs/source/io_formats/nuclear_data.rst @@ -133,7 +133,7 @@ Reaction Products :Attributes: - **particle** (*char[]*) -- Type of particle - **emission_mode** (*char[]*) -- Emission mode (prompt, delayed, total) - - **decay_rate** (*double*) -- Rate of decay in inverse seconds + - **decay_rate** (*double*) -- Rate of decay in inverse shakes - **n_distribution** (*int*) -- Number of angle/energy distributions :Datasets: diff --git a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb index 5d65a0a20..f2d81b10a 100644 --- a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb @@ -387,6 +387,7 @@ "* `DelayedNuFissionXS`\n", "* `ChiDelayed`\n", "* `Beta`\n", + "* `DecayRate`\n", "\n", "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group chi-prompt, chi-delayed, and prompt-nu-fission cross sections with our 100-energy-group structure and multi-group delayed-nu-fission and beta cross sections with our 100-energy-group and 6-delayed-group structures. " ] @@ -405,19 +406,21 @@ "chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n", "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n", "beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n", + "decay_rate = mgxs.DecayRate(domain=cell, energy_groups=one_group, delayed_groups=delayed_groups, by_nuclide=True)\n", "\n", "chi_prompt.nuclides = ['U235', 'Pu239']\n", "prompt_nu_fission.nuclides = ['U235', 'Pu239']\n", "chi_delayed.nuclides = ['U235', 'Pu239']\n", "delayed_nu_fission.nuclides = ['U235', 'Pu239']\n", - "beta.nuclides = ['U235', 'Pu239']" + "beta.nuclides = ['U235', 'Pu239']\n", + "decay_rate.nuclides = ['U235', 'Pu239']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `Beta` object as follows. " + "Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `Decay Rate` object as follows. " ] }, { @@ -430,74 +433,25 @@ { "data": { "text/plain": [ - "OrderedDict([('nu-fission', Tally\n", + "OrderedDict([('delayed-nu-fission', Tally\n", " \tID =\t10000\n", " \tName =\t\n", " \tFilters =\t\n", " \t\tcell\t[1]\n", - " \t\tenergy\t[ 1.00000000e-09 1.26765187e-09 1.60694125e-09 2.03704208e-09\n", - " 2.58226019e-09 3.27340695e-09 4.14954043e-09 5.26017266e-09\n", - " 6.66806769e-09 8.45278845e-09 1.07151931e-08 1.35831345e-08\n", - " 1.72186857e-08 2.18272991e-08 2.76694165e-08 3.50751874e-08\n", - " 4.44631267e-08 5.63637656e-08 7.14496326e-08 9.05732601e-08\n", - " 1.14815362e-07 1.45545908e-07 1.84501542e-07 2.33883724e-07\n", - " 2.96483139e-07 3.75837404e-07 4.76430987e-07 6.03948629e-07\n", - " 7.65596607e-07 9.70509967e-07 1.23026877e-06 1.55955250e-06\n", - " 1.97696964e-06 2.50610925e-06 3.17687407e-06 4.02717034e-06\n", - " 5.10505000e-06 6.47142616e-06 8.20351544e-06 1.03992017e-05\n", - " 1.31825674e-05 1.67109061e-05 2.11836114e-05 2.68534445e-05\n", - " 3.40408190e-05 4.31519077e-05 5.47015963e-05 6.93425806e-05\n", - " 8.79022517e-05 1.11429453e-04 1.41253754e-04 1.79060585e-04\n", - " 2.26986485e-04 2.87739841e-04 3.64753947e-04 4.62381021e-04\n", - " 5.86138165e-04 7.43019138e-04 9.41889597e-04 1.19398810e-03\n", - " 1.51356125e-03 1.91866874e-03 2.43220401e-03 3.08318795e-03\n", - " 3.90840896e-03 4.95450191e-03 6.28058359e-03 7.96159350e-03\n", - " 1.00925289e-02 1.27938130e-02 1.62181010e-02 2.05589060e-02\n", - " 2.60615355e-02 3.30369541e-02 4.18793565e-02 5.30884444e-02\n", - " 6.72976656e-02 8.53100114e-02 1.08143395e-01 1.37088177e-01\n", - " 1.73780083e-01 2.20292646e-01 2.79254384e-01 3.53997341e-01\n", - " 4.48745390e-01 5.68852931e-01 7.21107479e-01 9.14113241e-01\n", - " 1.15877736e+00 1.46892628e+00 1.86208714e+00 2.36047823e+00\n", - " 2.99226464e+00 3.79314985e+00 4.80839348e+00 6.09536897e+00\n", - " 7.72680585e+00 9.79489985e+00 1.24165231e+01 1.57398286e+01\n", - " 1.99526231e+01]\n", + " \t\tdelayedgroup\t[1 2 3 4 5 6]\n", + " \t\tenergy\t[ 1.00000000e-09 1.99526231e+01]\n", " \tNuclides =\tU235 Pu239 \n", - " \tScores =\t['nu-fission']\n", - " \tEstimator =\ttracklength), ('delayed-nu-fission', Tally\n", + " \tScores =\t['delayed-nu-fission']\n", + " \tEstimator =\tanalog), ('decay-rate', Tally\n", " \tID =\t10001\n", " \tName =\t\n", " \tFilters =\t\n", " \t\tcell\t[1]\n", " \t\tdelayedgroup\t[1 2 3 4 5 6]\n", - " \t\tenergy\t[ 1.00000000e-09 1.26765187e-09 1.60694125e-09 2.03704208e-09\n", - " 2.58226019e-09 3.27340695e-09 4.14954043e-09 5.26017266e-09\n", - " 6.66806769e-09 8.45278845e-09 1.07151931e-08 1.35831345e-08\n", - " 1.72186857e-08 2.18272991e-08 2.76694165e-08 3.50751874e-08\n", - " 4.44631267e-08 5.63637656e-08 7.14496326e-08 9.05732601e-08\n", - " 1.14815362e-07 1.45545908e-07 1.84501542e-07 2.33883724e-07\n", - " 2.96483139e-07 3.75837404e-07 4.76430987e-07 6.03948629e-07\n", - " 7.65596607e-07 9.70509967e-07 1.23026877e-06 1.55955250e-06\n", - " 1.97696964e-06 2.50610925e-06 3.17687407e-06 4.02717034e-06\n", - " 5.10505000e-06 6.47142616e-06 8.20351544e-06 1.03992017e-05\n", - " 1.31825674e-05 1.67109061e-05 2.11836114e-05 2.68534445e-05\n", - " 3.40408190e-05 4.31519077e-05 5.47015963e-05 6.93425806e-05\n", - " 8.79022517e-05 1.11429453e-04 1.41253754e-04 1.79060585e-04\n", - " 2.26986485e-04 2.87739841e-04 3.64753947e-04 4.62381021e-04\n", - " 5.86138165e-04 7.43019138e-04 9.41889597e-04 1.19398810e-03\n", - " 1.51356125e-03 1.91866874e-03 2.43220401e-03 3.08318795e-03\n", - " 3.90840896e-03 4.95450191e-03 6.28058359e-03 7.96159350e-03\n", - " 1.00925289e-02 1.27938130e-02 1.62181010e-02 2.05589060e-02\n", - " 2.60615355e-02 3.30369541e-02 4.18793565e-02 5.30884444e-02\n", - " 6.72976656e-02 8.53100114e-02 1.08143395e-01 1.37088177e-01\n", - " 1.73780083e-01 2.20292646e-01 2.79254384e-01 3.53997341e-01\n", - " 4.48745390e-01 5.68852931e-01 7.21107479e-01 9.14113241e-01\n", - " 1.15877736e+00 1.46892628e+00 1.86208714e+00 2.36047823e+00\n", - " 2.99226464e+00 3.79314985e+00 4.80839348e+00 6.09536897e+00\n", - " 7.72680585e+00 9.79489985e+00 1.24165231e+01 1.57398286e+01\n", - " 1.99526231e+01]\n", + " \t\tenergy\t[ 1.00000000e-09 1.99526231e+01]\n", " \tNuclides =\tU235 Pu239 \n", - " \tScores =\t['delayed-nu-fission']\n", - " \tEstimator =\ttracklength)])" + " \tScores =\t['decay-rate']\n", + " \tEstimator =\tanalog)])" ] }, "execution_count": 13, @@ -506,7 +460,7 @@ } ], "source": [ - "beta.tallies" + "decay_rate.tallies" ] }, { @@ -542,6 +496,9 @@ "# Add beta tallies to the tallies file\n", "tallies_file += beta.tallies.values()\n", "\n", + "# Add decay rate tallies to the tallies file\n", + "tallies_file += decay_rate.tallies.values()\n", + "\n", "# Export to \"tallies.xml\"\n", "tallies_file.export_to_xml()" ] @@ -565,24 +522,37 @@ "output_type": "stream", "text": [ "\n", - " .d88888b. 888b d888 .d8888b.\n", - " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", - " 888 888 88888b.d88888 888 888\n", - " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", - " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", - " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", - " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", - " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", - "__________________888______________________________________________________\n", - " 888\n", - " 888\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", - " Copyright: 2011-2016 Massachusetts Institute of Technology\n", - " License: http://openmc.readthedocs.io/en/latest/license.html\n", - " Version: 0.8.0\n", - " Git SHA1: c21ceb0aea4abc243b84106576c4f9010f608d0b\n", - " Date/Time: 2016-08-11 08:23:44\n", - " MPI Processes: 1\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2016 Massachusetts Institute of Technology\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.8.0\n", + " Git SHA1 | ce7cb67937f2ab26df3d995bdd30e99dbfadda7a\n", + " Date/Time | 2016-08-29 14:29:38\n", + " MPI Processes | 1\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -668,20 +638,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 6.1600E-01 seconds\n", - " Reading cross sections = 3.6500E-01 seconds\n", - " Total time in simulation = 8.3297E+01 seconds\n", - " Time in transport only = 8.3256E+01 seconds\n", - " Time in inactive batches = 4.4890E+00 seconds\n", - " Time in active batches = 7.8808E+01 seconds\n", - " Time synchronizing fission bank = 1.6000E-02 seconds\n", - " Sampling source sites = 1.1000E-02 seconds\n", - " SEND/RECV source sites = 3.0000E-03 seconds\n", - " Time accumulating tallies = 5.0000E-03 seconds\n", - " Total time for finalization = 8.0000E-02 seconds\n", - " Total time elapsed = 8.4019E+01 seconds\n", - " Calculation Rate (inactive) = 11138.3 neutrons/second\n", - " Calculation Rate (active) = 2537.81 neutrons/second\n", + " Total time for initialization = 5.7300E-01 seconds\n", + " Reading cross sections = 3.3600E-01 seconds\n", + " Total time in simulation = 9.3557E+01 seconds\n", + " Time in transport only = 9.3520E+01 seconds\n", + " Time in inactive batches = 4.6600E+00 seconds\n", + " Time in active batches = 8.8897E+01 seconds\n", + " Time synchronizing fission bank = 1.2000E-02 seconds\n", + " Sampling source sites = 6.0000E-03 seconds\n", + " SEND/RECV source sites = 4.0000E-03 seconds\n", + " Time accumulating tallies = 4.0000E-03 seconds\n", + " Total time for finalization = 7.3000E-02 seconds\n", + " Total time elapsed = 9.4232E+01 seconds\n", + " Calculation Rate (inactive) = 10729.6 neutrons/second\n", + " Calculation Rate (active) = 2249.79 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -762,7 +732,8 @@ "prompt_nu_fission.load_from_statepoint(sp)\n", "chi_delayed.load_from_statepoint(sp)\n", "delayed_nu_fission.load_from_statepoint(sp)\n", - "beta.load_from_statepoint(sp)" + "beta.load_from_statepoint(sp)\n", + "decay_rate.load_from_statepoint(sp)" ] }, { @@ -962,6 +933,168 @@ "df.head(10)" ] }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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celldelayedgroupgroup innuclidemeanstd. dev.
0111U2350.0133360.003509
1111Pu2390.0132710.007154
2121U2350.0327390.003864
3121Pu2390.0308810.008109
4131U2350.1207800.017277
5131Pu2390.1133700.031354
6141U2350.3027800.027190
7141Pu2390.2925000.057372
8151U2350.8494900.120338
9151Pu2390.8574900.231893
10161U2352.8530000.659168
11161Pu2392.7297001.342167
\n", + "
" + ], + "text/plain": [ + " cell delayedgroup group in nuclide mean std. dev.\n", + "0 1 1 1 U235 0.013336 0.003509\n", + "1 1 1 1 Pu239 0.013271 0.007154\n", + "2 1 2 1 U235 0.032739 0.003864\n", + "3 1 2 1 Pu239 0.030881 0.008109\n", + "4 1 3 1 U235 0.120780 0.017277\n", + "5 1 3 1 Pu239 0.113370 0.031354\n", + "6 1 4 1 U235 0.302780 0.027190\n", + "7 1 4 1 Pu239 0.292500 0.057372\n", + "8 1 5 1 U235 0.849490 0.120338\n", + "9 1 5 1 Pu239 0.857490 0.231893\n", + "10 1 6 1 U235 2.853000 0.659168\n", + "11 1 6 1 Pu239 2.729700 1.342167" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = decay_rate.get_pandas_dataframe()\n", + "df.head(12)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -971,7 +1104,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -989,7 +1122,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -1010,16 +1143,80 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n", + "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta`, `DelayedNuFissionXS`, and `DecayRate` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n", "\n", "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n", "\n", - "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$" + "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$\n", + "\n", + "First, let's investigate the decay rates for U235 and Pu235. The fraction of the delayed neutron precursors remaining as a function of time after fission for each delayed group and fissioning isotope have been plotted below." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7BTkhBtJCHUkNEmSnFBIb7YxfJ1/MUuLv5UVggNq9Ot9sBiHg5ptL318gNKAuZinJ9PIi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\nQwjBP/7xD8aOHUt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q7RQAGQUZ9P6qN92+7MbSw0vtIu+++1T6wXvuUUNiMMDTT8OCBXYR\np9FoLoPBYOD06dOlyqZNm8aIESMqbF9YWMjo0aMJDw/H29ub9u3b8/PPP9vqR4wYQXBwMN7e3jRv\n3py5c+eWOj86Oho3Nze8vLzw9PSsFaGSV0plun/wwQd06NABV1dXHn300cv2dfToUXr16oWPjw9N\nmzZl2bJltrrY2Fj69++Pn58fwcHBPP3001hK7rCsuSTa+NZUC127duXtt9+2He/fv5958+YRGRlZ\ng1opAh8IJHRcKAYPA/6D/QmfGg6ANFef8Rnm6srWdu2ItKb/k8CmjAzO2yMFYUnat1f/BgSoDXm8\n1UJU4uLUvuuJifaVf4U0rduUjSM34u/uD0CuKZfjF4/TLqid3WTecQcsW6ZSEL7+ukoK07+/3cRp\nNJpKuNSC9EuVm0wmQkND2bRpExkZGUyfPp0hQ4YQFxcHwKuvvkpsbCwZGRksX76ciRMnsmfPnlL9\nfvjhh2RmZpKVlcWRI9WXnvavUpnuDRo0YNKkSTz22GOX7cdsNnPPPfcwcOBA0tLS+OSTT3jooYc4\nad007l//+heBgYEkJSWxd+9efvvtNz788EO7XdeNhDa+NdXGiy++yJdffmnLemI0Gpk0aRKAfTab\nuQoazWxEx0MdaT6/OcIgOPf5OQ4NPVStOoS7ubG5bVvaFOW9A/ZkZ2ORkjyzGVN1eRROnoROneD2\n2yEoqHpkXgFt6rfh91G/08BTpWS0SAsf7/oYKSVrT63ly71f2kWuyaRyf69eXTw3ycsD7eDRaKqP\nq/2NcHd3Z/LkyYSEqNDC/v37ExERwa5duwCIiorCycnJ1rcQglOnTl2zzF69etXYfhYVcSnd7733\nXgYOHIifn99l+zh69CiJiYk8++yzCCHo0aMHXbp04euvvwYgJiaGIUOG4OTkREBAAH379uXQoUv/\nbr755ps0bNgQLy8voqKi2GBd+J+YmMjgwYMJCAigcePGvP/++6XOi4+PZ9CgQQQEBODv788zzzwD\nwJEjR+jRowe+vr60atWKFStW2M6JiIhg1qxZtGnTBl9fX4YPH05hCWfWnj17aN++Pd7e3gwbNoz8\n/Pwr0rWq0Ma3ploZOXIkP/74I25ubjRu3Jj58+eTlZVFv379WLt2bY3q5hrmioO7A7FvxBI3M45G\nb1R/7HOgszO/tW1LN29vevr48G1UFClGIz327uWb6tr5Zds2SE9XWz8eO6bKCgqqR/ZlaF6vOZtG\nbaKRbyNuD72d/w3+H7sTdzN86XCcDPbJVOPoqELki+YhcXHQsCGMGmUXcRpNrWTqVBWtVvYzdeqV\nt79U2+ogKSmJEydO0KJFC1vZ2LFj8fDwICoqiuDgYO66665S57zyyisEBATQtWtXfvvtt0v2nZCQ\nAIBj0cZmVcTdd9+Nr68vfn5+5f4dOHBgpedeqe6VUZEBL6Xk4EG11ubZZ59l4cKF5OXlkZCQwOrV\nq+nXr1+FfR0/fpwPPviAXbt2kZmZyZo1awgPD0dKyd13303btm1JTExk3bp1vPfeezZ7wGKxMGDA\nACIiIoiLiyMhIYFhw4ZhMpkYOHAgffv2JTk5mTlz5vDggw9yokTCgO+++45ffvmFmJgY9u3bx5fW\nVFZGo5H77ruPkSNHkpqayv3338/SpUsvq2uVIqW8Lj5KVc2NwpYtW+SpU6fk+fPnZdu2beWYMWOk\n0WisabWklFKe/+a8zD+XbzsuTC2U+RfyKzmj6sk1mWSm0SjPFxTIxtu2yYmnT0uLxWJ/wfn5UoaG\nSqkivqUMC5Pyww+lvOUWKc1m+8u/QhIyE2R6XrqUUsoHlj4gN8ZsrBa5Bw5I6eKihkYIKVetqhax\nmr8p1t+9WvEbO2VK8f8WSn6mTLny9pdqeyUIIeSpU6dKlU2dOlWOGDHisucajUbZu3dv+eSTT5ar\ns1gscsuWLfL111+XJpPJVr5jxw6ZnZ0tCwsL5fz586Wnp6c8ffp0ufPXrl0rhwwZIh944AH59ddf\nX1aXJUuWXLbNX+VKdJ84caIcNWpUpf0YjUbZuHFj+fbbb0uj0SjXrFkjnZ2dZd++faWUUh45ckS2\nb99eOjo6SoPBUGl/J0+elIGBgfLXX38t9Vv/xx9/yLCwsFJt33jjDfnoo49KKaXcunWrDAgIkOYy\nvz+bNm2SQUFBpcqGDx8up02bJqWUMjw8XC5YsMBWN2HCBNv9/+2332SDBg1Kndu5c2c5adKkSnW9\nWir7+9Web02N0LlzZxo1aoSrqytjxozh448/tnkNanrBRuCDgbgEqfSIeWfy2B6xnd0ddmPKqr5X\nim4ODng6OuLv5MSnzZoxIyLCFtuYazbbT7CLC3z3Hbi7q+PYWHjmGZg9u1bsgFlEsGcw3q4qBuSb\n+76he3h3W90Xe77gXJZ9FqqGhqoPKHNiyBC1ZlWj0dgXBwcHjEZjqTKj0YiTkxMLFizA09MTLy8v\n+pdZmCGl5KGHHsLFxaVcOAOo+OjOnTtz9uxZPvroI1t5hw4d8PDwwMnJiYcffpguXbqwatWqcuf3\n7t0bBwcHXnjhBR566CEAMjIy+P7773njjTdKtc3MzKROnTqcPHmSH374gRkzZrB79+5rHpNLcaW6\nXw5HR0eWLVvGypUrCQoK4j//+Q9Dhw6lYcOGSCnp06cPgwcPJjc3l5SUFFJTU3nppZcq7Ktx48bM\nnj2bqVOnEhAQwAMPPEBiYiKxsbEkJCTg5+dn8+y/8cYbXLDudhYfH09YWFi5TfrOnTtnCykqIiws\nzPYWAiAwMND23d3dnezsbECFuTQos6N0WFhYhboGBgbadK1Kas+vqeZvibe3N0888YTNsNyxYwcd\nOnQgL696821XRGFKITtv2ok5w0xBbAEH7z2IpaB6JwYGIehZYsOd1SkpNPnjD9LL/AhVKR07qlx7\nRVlPTCaVGcVsVp9akCayJEXPjkVaeGHNC8zcPJN8U/5lzro2vLzg99+LDfDsbLjttuLoHI3mRmXq\n1Ir83pWHnVxp2yshNDSUM2fOlCqLiYkhLCyMBx54gKysLDIzM/npp59KtXnsscdISUnh+++/x6Ho\n/2kVYDKZysV8l8S6YUqFdXv37qVdu+LF30XZVcpOFtavX0+PHj1YsWIFDRo04LnnnuOdd965pMy7\n7rrLNqko+yk7yaiMynS/HC1btmTjxo0kJyezevVqTp06xa233kpqaipnz55l7NixODk54evry6hR\no1i9evUl+xo2bBibNm2yLXp9+eWXCQkJoVGjRqSmppKamkpaWhoZGRm2+O2QkBDi4uLKOeWCg4PL\n7RkSFxdXzqiuiKCgoFJGetG5FekaGxtr07Uq0ca3ptawcuVKevTowU033YSbNetHTeLo44j/EH/b\ncfr6dA4NO1StWVBKMv/8ee49dAizlBTae4Fqv35QlHrLwQEefFCtMHzoIWWI10K2xm3l2wPf4mhw\ntGVFsQf168NPP0HRI2o2QwUONY1GU4UMHTqU1157jYSEBKSU/Prrr6xcuZLBgwdf8pwnnniCo0eP\nsnz5cpydnW3lycnJLF68mJycHCwWC2vWrGHRokX06tULUJ7rX375hYKCAsxmM99++y2bNm2iT58+\n5WQcPnyYqKgohBAsX7680msoKCjA2dmZ559/no4dOxIfH09ERMQl269atco2qSj7KTvJKOJyupvN\nZvLz8zGbzZhMJlu7S3HgwAEKCgrIzc3lnXfe4fz584wcOZK6devSqFEjPvroI8xmM+np6cyfP5+b\nb765wn6OHz/Ohg0bKCwsxNnZGTc3NxwdHenYsSNeXl689dZbNr0OHTrEn9ZXih07diQoKIiXX36Z\n3NxcCgoK2Lp1K7feeiseHh689dZbmEwmNm7cyMqVKxk+fHil9wCgU6dOODo68v7772M2m/n+++/Z\nsWNHpbpWNnG7Ji4Vj1LbPuiY7xuavLw82bNnT4nKsicXL14spZTl4rxqgjOvn5Eb2GD7HHrgUPXE\nX5fAYrHINjt2SDZskGzYIDvt2iXzTCaZWyJG0S7MmiXlihVSWixS3neflP37S5mba1+Z14DZYpa3\nfX6bZCqSqciBCwfKjPwM+cmfn9jtXn36qZQGg5QPPyxlQYEqqyXLFjQ3CNSimO+aJi8vT06YMEGG\nh4dLHx8f2b59e7ly5cpLto+NjZVCCOnm5ibr1Kkj69SpIz09PeWCBQtkcnKy7N69u/T19ZXe3t6y\ndevWcu7cubZzk5OTZYcOHaSXl5f09fWVnTp1kuvWratQTmJiohw1apRcuHChTElJsZWfOXPGFn8s\npZTp6enyl19+KXXuzJkzZU5OzrUOSYVcTvepU6dKIYQ0GAy2T0k9+/XrJ9944w3b8fjx46Wvr6/0\n9PSUd911V6m4+3379sno6Gjp6+sr/f395ZAhQ2RycnKFeu3fv1927NhRenl5ybp168q7775bJiYm\nSinVGA4fPlzWr19f+vn5ldP57Nmz8t5775V169aV/v7+8tlnn5VSSnn48GHZvXt36e3tLVu0aCF/\n/PFH2zkRERHlrrvk+oBdu3bJtm3bSi8vLzls2DA5bNgwW8x3ZbpeDZX9/Qppbw9aFSGEkNeLrpqr\np7CwkL59+9rS+Tg7O/PII4/g4uLCnDlzalQ3KSWHhx0m+X/JAASODKT53OYIh4rzy9qLny5eZOCB\nAxS9fGvq5kYnLy++rK7NH379Fbp1UztkggpHqeLV/X+Fr/Z9xchlI23HgR6B3N30bj4e8DEOhir2\nWljZvRvatlWv1P/v/yAtDT7+2C6iNH9DrOEC1fI/Gv0bW7XExsby5ZdfMmXKFAB++OEHBgwYYEtv\nuGLFCqKjozl//jxNmjSpSVU1dqKyv18ddqKpFTg7O7N06VKaNWsGKGN83rx5PPjggzWsmfoDajyr\nMb69fWk4riHN5ynDu7p/qPrXrcu7JTYlOp6XR1CJV6l2p3fvYsP7xAno0AE2bao++Zfh4TYP82Kn\nF23HSTlJ9IzoaTfDG6BdO2V4P/CAGorXXrObKI1Gc52QnZ3NkiVL2LVrly3vdX5+vs3wLlpsOWjQ\nIP73v//VpKqaGkJ7vjW1ipMnT3LbbbfZtqLv16/fNa3StgfSLG3T1cS5iSQvTqb1L60vucOaXXSQ\nkn+dOMHH1m3n7/T15adWrTADLtWVjWTTJrj3XhgzBmbOVAl8awlmi5kBCwfw80m1jXSP8B6se3gd\nf55T8YMdGnSwi9wlS2DAALUbJqg48KoOEdT8/dCe7xuDtLQ0du3aRe/evWtaFU01oj3fmuuGyMhI\nfvjhB5ydnenYsSPz5s0jPT2dESNGsHXr1hrVrSjM5MiIIyS8n0Dk7MhqNbxB/THPiYykt68vTwYH\ns7JVK84XFtJx1y42pqVVjxInT0JhISxeDFlZ1SPzCnEwOLBw0EKa1W3Gw20eZtWDq1h+bDl3LbiL\npBz7bVI0eHCx4X3sGNSrp/Yo0mg0mi1bthAdHV3TamhqEdrzramVbNy4kY4dO3Lo0CEGDRrEwIED\neeutt3Avyj9dg6RvSseroxcGFzV3lVJWuxFeYLHgLAQn8/LouW8fTzdowPiQEPvrcfEiREYWpxsc\nORLuugv2769VMRepean4uvqSXZhNl3ldmDtwrt283iVZt04lijEa1Y6Y+/crQ1yjuRa051ujuX6p\n7O9XG9+aWk18fDwHDx6kb9++Na1KheQcymHfnfuInB1JwP0B1S4/12zmt/R0+tWtW31CFy2Ckumc\nAgNh1SoVAF0LsUgLBlH8ku902mka+Tayi6ytW+GOOyA3Vx0PHaqGS6O5FrTxrdFcv+iwE811S8OG\nDUsZ3nv37mXt2rU1qFExCR8nsLP1TgrPFXJ87HEKLxRWuw7uDg6lDO/1aWn8p8zGA1XOsGEq33cR\nBQUQUP0TjyulyPDOKsjiiZVPcMfXd5BntM8mTp07Q8n1U4sXwz//aRdRGo1Go7lO0ca35rrAbDbz\n73//m+joaNKqK7b5Mjj6OuLgqVbVmZJNHB11tNozoBRRaLHw+NGj3H3gAM7VEQLz3/9C0Xa82dmw\neTPk58Nbb6l48FrImBVj2BS3iUWDFuHmZL9NnPr3V9E4RSxcCEn2CzfXaDQazXWGNr411wVHjx5l\nzpw5ZGRkYKiurB6XIXBoIC0Wt7Adp65KJWZiTI3osik9nYUXLpBrsfBZYiLGMlvxVjne3mpFYVSU\nirVo3x46dYI//6yVxveWuC1sPbuVw8mHeX7N81ikxa4TpVmzwNNTfR86tHg3TI1Go9FoaocVo9Fc\nhg8++IDExEQAnnzySZKSkjh+/HgNawV+ffxo8HwD23HcW3HkHs+tdj1CXF0p2iB4X04Or8fG8mtq\nqn2Fdu0KBw6ofN9r16r4isWLoU4d+8q9BrxcvDiXrdIzbjm7hSkbptBlXhf2nt9rF3l168Ly5SoU\n/rPP1JD88YddRGk0Go3mOkMvuNRcF6Snp9OyZUsSEhIACA4OJiwsjM2bN9e4JzznSA67OuzCkm+h\n0RuNCBkXgjBUf+7rd8+eZdypU7bjDp6ebG7bFueaGJ8TJ1TuvZCQ6pd9CaZsmML036fbjmdEz+DV\nbq+WWoxpD2Jj4dFHVWj8xo21alNQTS1HL7jUaK5f9IJLzXWPj48Pn3/+ue343Llz/Otf/6pxwxvA\nI8qD9n+2p932doSOD0UYBAXnC6pdj2cbNqSTl5ft2GixUCPb3yxapFYe7txZE9IvyatdXyWqXpTt\neEv8FkQ1jNCLL6oMKNrw1mg0Gg1o41tzHdG3b19Gjx5tO54/fz5SSsxmcyVnVQ8ezT3wusULU4aJ\nww8e5siDR6pdBwch+KJ5c5wBAXT38cEkJdkmU/UpceoUTJgAM2bAP/5RfXKvABdHF+YOnGszuOu5\n16PAXMD3R75nfcx6u8ldvBhefrnY8C6o/nmZRqPRaGoR2vjWXFfMmjWLxo0bM23aNFatWsWOHTto\n3bo1p0+frmnVkFKyr88+HL0dabWiVY3o0MzdnU+aNeO3m29mdpMmrEtLo9mOHRzKybG/8JwcePtt\nOHsWpk2DlBRVXoteZXcK6cRrPV/jpwd+4v1+7/P4isd56deX8HDysJvMki9nPvhArVX97Te7idNo\nNBpNLUfHfGuuO/Lz83F1deWrr75i/PjxfPDBBwwePLim1QKgMKUQ53rOtmOL0YLBqWbmuHMTE3k9\nNpYvmjenu4+P/QUmJ0OrVsV59fr3h5tuUqkIP/zQ/vKvkqTsJN7d9i6Tu0/Gw9l+xncRw4cXb7jT\ns6dao1oLoqY0tRgd863R1CyjRo0iJCSE6dOnX75xGXTMt+aGwtXVFYDo6Gj27NlTawxvwGZ4SymJ\nm6h0jBoAACAASURBVBXHZp/NpKxIqRFdBvv7s++WW6rH8Abw94cvvig+/ukn2L4dJk2qHvlXSWCd\nQN68402b4V1gKuDQhUN2kzdiBBSlYF+/vlbORzSaWkt4eDju7u54eXkRFBTEo48+Sm7u1WeWKiws\nZPTo0YSHh+Pt7U379u35+eefbfUjRowgODgYb29vmjdvzty5c0udf/ToUXr16oWPjw9NmzZl2bJl\nf/naqovo6Gjc3Nzw8vLC09OTqCi1BuZyY1KWvzqGGpSRcD18lKoaTcVYLBb5/fffyx07dtS0KlJK\nKff02iM3sEFuYIP8o/Uf0mKy1Kg+hWaznHL6tFxw/rz9hT31lJQq2ETKhg2lzMmxv8y/yJ7EPbLV\nh63k6B9H21XOSy8VD42Tk5Tz5tlVnOY6x/q7p39jpZTh4eFy/fr1Ukopz507J1u2bClfeeWVq+4n\nJydHTps2TcbFxUkppVy5cqX09PSUsbGxUkopDx8+LAsLC6WUUh47dkzWr19f7t69W0oppclkkk2b\nNpWzZ8+WFotFrl+/Xnp4eMgTJ05UxSXanejoaDmvgv/pXG5MrrZ9ZWN4vfHII4/ISZMmXdO5lf39\nas+35rrnyJEjdO7cmRdffBGj0VjT6gCodINuys2Zuz+XxHmJNaZLbH4+LXfu5NPERLqUyIZiN15/\nvXi7+YICOHJExYH/8ov9ZV8DRrORoUuGElUvik/v/tSusqZNgyZNrHKNKga8FqwX1miuC6Q1LCYo\nKIh+/fpx8OBBAAwGQ6l1P6NGjWLy5MkV9uHu7s7kyZMJsaZB7d+/PxEREezatQuAqKgonJycbPKE\nEJyypnA9evQoiYmJPPvsswgh6NGjB126dOHrr7++pM69evXCVJ2L3i9D0RiW5HJjcrXtKxvDinjz\nzTdp2LAhXl5eREVFsWHDBgASExMZPHgwAQEBNG7cmPfff7/UefHx8QwaNIiAgAD8/f155plnAGUT\n9OjRA19fX1q1asWKFStKnRcREcGsWbNo06YNvr6+DB8+nELr5nB79uyhffv2eHt7M2zYMPLz869I\n16tFG9+a654tW/6fvTMPr+laH/9nZ5I5EiQiZKCGFFVUVHSIH70lEjU35qH0auvS6m2rE9Eqty63\n1EUn3yotWgQJbgVFU7NU1CwkMgmSJnIyyHDOWb8/TnIyDzTnnGB9nmc/Z6+911rvu1dyzn732u96\n30NcuHCB9PR0HnnkEVOrA0CTAU3wetdLX457Jw51lml+gJcmJXH5zh1SCwv54dYtwwt0dISFC3Uh\nPs6dg59/hq5d4Y8/DC/7LtEKLR8d/IirGVf56fxPHLh2wKDyGjXSuZuYmUHjxhASIo1vyf1BaKhu\nq6/yXyEpKYldu3bRrVu3v9zXzZs3iY2NpWPH0mzFr732GnZ2dvj6+tKiRQsCAwOBqg1XIYT+IaAi\nJXkpLOo5xmhwcDDOzs64uLhU+hw0aFCNbd99911cXV15+umnOVjNyu+qxqQm7mYMK3L58mVWrFhB\ndHQ0KpWK3bt34+3tjRCC4OBgunbtSmpqKvv27WPZsmXs2bMHAK1WS1BQED4+PiQmJpKSkkJISAhq\ntZpBgwbRv39/0tLS+PzzzxkzZgyxsbHl5G7atInIyEji4+M5ffo0a9asoaioiCFDhjBhwgQyMjIY\nMWIEW7ZsqVXXe0Ea35L7msLCQhYuXEhWVhYqlYr58+ejVqsbxAx4qzdbYdlc9/SvzlQT/6FpUs+7\nWZUuAJ137RoXcnO5ZegU8JMn6wxwBwdITtbF/P7nPw0r8x4wU8w4l3YOjdBZwFPCp7Du9DrGbR1n\nsPTz/frp3OHj43VDotXC7dsGESWRPFAMHjwYFxcXnnnmGfr06cO77777l/pTq9WMHTuWiRMn0q5d\nO/3xFStWkJOTw2+//cbQoUNp1KgRAB06dMDV1ZXFixejVquJjIzk4MGDVfqe7927l1mzZtG8eXO+\n//77WnUpa+TVRkREBJmZmWRkZFT6DA8Pr7bdokWLiIuLIyUlhalTpxIcHEx8fPn7UnVjUh13O4YV\nMTc3p7CwkLNnz6JWq/H09MTHx4cTJ06Qnp7O+++/j7m5Od7e3kyZMoWNxavWjx07RmpqKosWLcLa\n2horKyv8/f05evQoubm5vPPOO1hYWNCnTx+CgoLYsGFDObkzZ87Ezc2Nxo0bExwcTExMDEePHkWt\nVjNjxgzMzc0ZNmwYPXr0qFXXe0Ea35L7GisrK5YuXaovr1y5El9fX8LCwkyolQ5zW3Psu+hSrVs/\nYo1LfxeT6PHPVq3oXpzyvUAIukdHszktzTjCra1h1Soo+wNlaMP/LlkRuAKnRk4AxN2O4609bzGx\ny0QUxXBBJvr31z2XfPcdtG8P69cbTJRE8sCwfft2MjIyiI+PZ/ny5dUadCWsX78eBwcHHB0dGThw\nYLlzQgjGjh1Lo0aNKrkzgC5Shb+/P0lJSaxatQrQzWBv27aNHTt24O7uzmeffcaLL75Iy5YtK7Xv\n168f5ubmzJo1i7FjxwKQlZVFWFgYCxcuLFdXpVJhb2/PlStX2Lp1Kx9//DG///77XY1NXejRowd2\ndnZYWloyfvx4evfuza5du/TnaxuTitzLGFakTZs2LF26lNDQUFxdXRk9ejSpqakkJCSQkpKCi4uL\nfmZ/4cKF3Cp+e5ucnIyXl1elRHvXr1/Xu8OU4OXlpX8LUYKbm5t+39bWlpycHK5fv46Hh0eltlXp\n6ubmptf1XpDGt+S+JygoiN69ewOg0Who2bIlI0eONLFWOh7732P4fu+L3zk/mgQ2MYkOFmZmLG7T\nRl/O12oJMFYElLKkpcHUqTpfiwaEu4M7S/62RF9Oz0vHx/neZjPuhkOH4OuvYcMGePVVg4uTSP4S\nDcHtpLq3Uba2tuVmn2/cuAHA6NGjyc7ORqVSsXPnznJtXnrpJdLT0wkLC8Pc3LxamWq1upy/cqdO\nnThw4ABpaWn873//4+rVq/j5+VXZNiYmppxrTElkkIpvZn/55Rf69OlDREQEHh4evP766yxevLha\nnQIDA/UPFRW3ig8ZNVEcCk9fruuY3G39imNYkZCQEKKiokhMTARg9uzZtGrVitatW5ORkaGf2c/K\nytL7b7dq1YrExES0Wm25vlq0aEFSUlK5Y4mJiZWM6qpwd3cnOTm5UtuqdE1ISNDrei8Y3PhWFKW/\noigXFUW5rCjKO1Wcb6Uoyi+KovyuKEqMoigDDK2T5MFCURQ+/fRTffngwYOV/LtMhaIouI1xw8zK\njKxDWZwJPoM6x/i+3wHOzjzv7AxAYwsLYu/cMa4CeXk6v28h4P/+z7iy68DkrpN52vNpACzNLTme\ncpwiTRHn084bTOYzz0BUFPj7G0yERPJQ0LVrV9avX49Wq+Xnn3+u1pe5hGnTpnHx4kXCw8OxKuOW\nl5aWxo8//khubi5arZbdu3ezceNG+vbtq69z5swZCgoKyMvLY/Hixdy4cYOJEydWknH+/Hl8fX1R\nFKVGVxCAgoICrKyseOONN/Dz8yM5OblGd4Zdu3bpHyoqbhUfMkrIysoiMjKSgoICNBoNP/zwA1FR\nUfTv37/GMamOvzKGZbl8+TL79++nsLAQKysrbGxssLCwwM/PD0dHRxYtWkR+fj4ajYZz585x8uRJ\nAPz8/HB3d2f27Nnk5eVRUFDA4cOH6dmzJ3Z2dixatAi1Ws2BAwfYsWMHIXWY9OnVqxeWlpYsX74c\njUZDWFgYx48fr1HXujykVEl1YVDqY0Nn3F8BvABLIAboUKHOl8Dfi/d9gfhq+rqnUC+Sh4fg4GDR\npUsX8b///U8UFRWJb775RsTFxZlaLSGEEJdnXBaHWx4WqetShVZjmrCD0SqVeO/qVZFZWChy1Grx\nUXy8iPzzT+MI371biA4dhPD1FaKoyDgy75KohCjxyo5XRNLtJPHT2Z/EI58/IsZvHW8U2bm5Qowe\nLcT27UYRJ7lPQIYa1OPj4yP27dtX5bmTJ0+Kjh07CkdHRzF+/HgxevToasPDJSQkCEVRhI2NjbC3\ntxf29vbCwcFBrF+/XqSlpYlnn31WODs7CycnJ/HYY4+J1atXl2v/1ltvCWdnZ+Hg4CACAwPF1atX\nq5STmpoqJk2aJDZs2CDS09P1x69duybmzZunL9++fVtERkaWa7tgwQKRW88hWtPS0kSPHj2Eo6Oj\ncHZ2Fr169dKPZ01jUsKAAQPEwoULa61flzEsyx9//CH8/PyEo6OjaNKkiQgODhapqalCCN0Yjho1\nSjRv3ly4uLiU01kIIZKSksTgwYNFkyZNRLNmzcTMmTOFELpQh88++6xwcnISHTt2FNsr/LBW/F8K\nDQ0V48aNE0Lo/pe6du0qHB0dRUhIiAgJCdH/L9Wka1XU9P01aIZLRVGeBOYKIQYUl2cXK/NpmTqr\ngDghxL8VRekF/FsI8VQVfQlD6iq5/8nMzMTJyYljx47x8ssv4+LiwpdffkmHDh1MrRrZMdnYtrXF\n3O4en5Lrkajbt3nx/HmebdyYBT4++NjYGFZgZiZ4euoyXYLOB/zZZ+HIEd3CzAZGXlEeIzaN4I0n\n36Bf634Gl7dyJbz+ui704PDhsGmTwUVK7hNkhssHj4SEBNasWcPcuXMB2Lp1K0FBQfrQfBEREQQE\nBHDjxg3alsQlldyXmDLDpQdQ1vkmufhYWeYB4xRFSQJ2AP8wsE6SBxRnZ2fMzMywtLRk/vz5HDhw\noEEY3gAOjzvoDe+i7CIuvnzRZKEH29naEt6pExsefdTwhjeAszO8915pedYsePppMLbrSx2xtbRl\n5+idRjG8QRdysMQFNCwMzhvO00UikZiQnJwcNm/eTHR0NOfO6bLp5ufn6w3vksWWw4YN46effjKl\nqhIDY+iZ7+HA34QQLxeXxwI9hBAzy9R5A0AI8VnxTPlqIUSl4JLyqVxyr9y5cwdra2uDRq+oK4mL\nE4l/Px5RKPCY6UHbpaaf2UgrLMTe3Bybe/Vdqwt37kCHDlCyeGX6dKjDanpTU6QpYvWp1aTnpfPB\nMx8YTE7//rB7t24/MBC2b4d6Dg0suQ+RM98PNpmZmURHR9Ovn3Ee9CXGxZQz38mAZ5lyS+B6hTov\nAT8BCCGOAtaKojStqrPQ0FD9duDAAQOoK3mQ0Gq1fPfdd7Rv355jx46ZWh0AMvdkIgp1N7jrK6+T\nd6VyfFhjkafR8Mm1a7Q/fpxDWVmGFWZjo4v7XcJXX5Ua4g0gJntV3Mi5QadVnfjx3I8MeMSw68D/\n9a/S/V274IsvDCpO0kA5cOBAufuc5MHm0KFDBAQEmFoNiQkw9My3OXAJ6AukAseBUUKIC2Xq7AR+\nEkJ8pyiKL7BHCFEpaKZ8KpfcLfPmzePrr79m8uTJfPTRR6ZWB9AtcD7V+xSqIyoAmg5uSqetnUyi\nxxtXrvDdzZs0NjcntmdPLMwM/Cyu1UKvXnD9us4Q79IFFi2CGzegOGtZQ+Jo0lGm/286jo0c+WXC\nLwaX1707lIT2HTcO1q41uEhJA0fOfEsk9y81fX8NanwXC+8PLEM3y75aCPEvRVHmASeEEDuKDe6v\nAXtAC7wlhNhXRT/yh0FSZ44dO8aAAQPIzMzEw8OD2NhYbIzh31wHVCdU/O5XmkCh8/8606S/cWOA\nZ6nVeB4+jKo4RurX7doxpUULwwu+dg1cXUGthscf18X9fvVVcHIyvOy7IDU7Fc+lnqi1Or/8feP3\n0dOjJ5n5mbR0rJxQoz64ehWeew7efx8mTJBuJxJpfEsk9zMmNb7rC/nDILkbcnNzadOmDTdv3gRg\n/vz52NraMmnSJBqbIsFMBY62Pkp+fD4AzSc1p8P/GX9h6IKEBN4vTi3c3NKSEa6uzGrZEm9jPaRo\nNGBIP/O/yJTwKaw+tRqA1o1bk6/JZ4bfDN55qlK6gnqjZEiysuA//4EZM6CJaXIzSRoA0viWSO5f\nTOnzLZGYBDs7O+bMmaMvf/jhh+zbt69cFjRT0n51e5wHONNlXxfar25vEh1mtmyJW/Eq+xtFRURn\nZ2NnTGO4rKzz5+HCherrmoA5z87BylyXPCLudhxv+b9lUMMbdEOyfj20awcJCboXBBKJRCJ5sJDG\nt+SBZerUqbQpTqsuhKBTp060MIZrRR1w7uNMl11dcP5/uqyTBdcLjK6Dnbk5H3p768sX8/KwMrTf\nd0UuX4YhQ6BPHygOvdVQ8HTy5OVuL+vLa0+vRSu0NbSoH7y8YO9eWLMG3NwMLk4ikUgkRkYa35IH\nFktLSz755BN9OSkpqWw2N5OjVWu5ueEm0d2iuTTlkkl0mOrujo+1NR5WVnzaujV2ZmbcKDDig0Bh\nIdjZwY8/6jLMNDDee/o9bCxsaGzdmBGPjiC7IJsFUQvYcn6LwWT27g2dO5eWb982mCiJRCKRmABp\nfEseaEaMGMG0adM4fPgwX3zxBcuWLePxxx8npyTbogkRhYJbP97CZ74PnXd0rr2BAbAyM2NH587E\n9uzJ0GbNmB0XR5tjx7hkDPec3bvhb3+DH37QRT1pgLg7uLP1xa3Ez4ynX+t+PLryUWJuxPCY22MG\nl52eDgMG6Hy+Y2IMLk4ikUgkRkIuuJQ8NDzzzDM0b96cN998k549e5panQbHu3FxFGm1/KNlS7ys\nrQ0vMDYW2reHku/1hg3w00/w6afQANMqZ+VncenPS/h5+BlFnqcnJBXnBw4NheJs1JKHCLngUiK5\nf5HRTiQSoKCggEaNGplajSrJT8nnyowrWDhb0OEb40c+MRlDh8LWrbp9OztYsACmTAFbW9PqVQeK\nNEVYmlsarP8ffoCxY3X7bm66BZgN9N9XYiCk8S2R3L/IaCcSCZQzvO/cucOVK1dMqE0pN9be4Gir\no6SHpXPzh5sUZZg+4+PNwkJ+/vNPwwv65z9L9wsKdH7fDdzwTs9LZ/6v8/Fa6sW5W4ZbJDpyJJSs\nD755Ez7+2GCiJBKJRGJEpPEteai4ffs2oaGheHt78+WXX5paHQCcnnXC2lvn5iHyBddXXTeZLtlq\nNRMuXKDN0aPszsw0vEB/f13WS9DF1Vu3Trd/3XRjUBuhB0I5cO0AO0fvpKNrR4PJsbTUvQQo4auv\nSj10JBKJRFJ/TJo0qVx4YkMjjW/JQ8XFixfZsmULTk5OLFy40NTqAGDjZYP3PG99OWlZEpo7GpPo\n8tOtW4T/+Se5Wi0djTUD/dZb8PTTsG0b9OihW4TZt68u40wDY1/cPg4lHmJf/D4u/Wn4CDWvvlqa\n6bJZM90iTInkYcTMzIy4uLhyx+bNm8e4ceOqrF9YWMiUKVPw9vbGycmJ7t278/PPP+vPjxs3jhYt\nWuDk5ESHDh1YvXp1ufYBAQHY2Njg6OiIg4MDvr6+9X9RBqC2616xYgU9evTA2tqayZMn19pfTfUd\nHBxwdHTUj5GFhQUzZ86s92t6EJHGt+ShoaioiEGDBnH27FliY2MJDw83tUp6XENcsWqpS+iiTlOT\nvCzZJHpkaTTcLs7s8nlKinHCMg4eDL/+CoMGwcqVEBICp083yOyXh5IOEXNTF3rkv8f/i6pAxW+J\nvxlMnpsbLFsGkZFw9qzOAJdIHkYUpWrX9+qOq9VqPD09iYqKIisri48++oiRI0eSmJgIwHvvvUdC\nQgJZWVmEh4fzwQcfcOrUqXL9rly5EpVKRXZ2NhcaWBKw6qjtuj08PPjwww956aWX6tRfTfWzs7NR\nqVSoVCpu3ryJra0tI0eOrNfreVCRxrfkocHS0pKXXy5NmrJ48WI+/fRTLl++bEKtdJhZmmHZpHTx\nXn5ivkn0mNy8OXbFiXbO5OYy9OxZwg093Vpy81QU2LwZJk8GKyvDyrxHXu7+MpZmur/ToaRDeH7m\nyfd/fG9Qma++Cs89Bykp8O67cOCAQcVJJA2Su50IsLW1Zc6cObRq1QqAgQMH4uPjQ3R0NAC+vr5Y\nFmf4FUKgKApXr169Z5l9+/ZF3QBS0tZ23YMHD2bQoEG4uLjUqb+61t+0aROurq707t272jqffvop\nLVu2xNHREV9fX/bv3w9Aamoqw4cPx9XVlTZt2rB8+fJy7ZKTkxk2bBiurq40a9aMGTNmAHDhwgX6\n9OmDs7MznTt3JiIiQt/Gx8eHJUuW0KVLF5ydnRk1ahSFhYX686dOnaJ79+44OTkREhJCfn75e251\nutYX0viWPFS8+uqrWBS/xz9y5Ai//vqrvmxquuzpgvtUd5744wnarzRNyvnGlpZMaN5cXz6fl8cT\nDg7GV0QIOHgQthgumc290Ny+OcMfLU0G1P+R/nwR9IXB5X73HTz2GOTlgY+PwcVJJJUIPRCKMk+p\ntIUeCK1z/erqGoObN28SGxtLx46l6zRee+017Ozs8PX1pUWLFgQGBpZr8+677+Lq6srTTz/NwYMH\nq+07JSUFoN7vJcHBwTg7O+Pi4lLpc9CgQXXqo6rrNgRr165l/Pjx1Z6/fPkyK1asIDo6GpVKxe7d\nu/H29kYIQXBwMF27diU1NZV9+/axbNky9uzZA4BWqyUoKAgfHx8SExNJSUkhJCQEtVrNoEGD6N+/\nP2lpaXz++eeMGTOG2NhYvcxNmzYRGRlJfHw8p0+fZs2aNYDuLfiQIUOYMGECGRkZjBgxgi1l7jXV\n6VqfSONb8lDRokULRowYoS83a9aM1q1bm1CjUqyaWdH+q/bYd7ZHW6Ql91yuSfSY7uGh379y5w4F\nWsOnVC9HYiI89RRMndog/b6n+03X72+/tJ0/8wwfFWbgQIiP17mgeHkZXJxE8kChVqsZO3YsEydO\npF27dvrjK1asICcnh99++42hQ4eWi4i1aNEi4uLiSElJYerUqQQHBxMfH1+p77179zJr1iyaN2/O\n99/X/hZsy11MKERERJCZmUlGRkalz7q4TVZ33fVNYmIiv/76KxMmTKi2jrm5OYWFhZw9e1bvGuPj\n48OJEydIT0/n/fffx9zcHG9vb6ZMmcLGjRsBOHbsGKmpqSxatAhra2usrKzw9/fn6NGj5Obm8s47\n72BhYUGfPn0ICgpiw4YNepkzZ87Ezc2Nxo0bExwcTExxtrIjR46gVquZMWMG5ubmDBs2jB49etSq\na30ijW/JQ0fJghAzMzPUajVCCLTGNjCrQZOvIemzJI61Oca1j6+ZRAdfOzv+5uzMk46OfO/ri0ej\nRmQZ83Vqo0a66d1vvtHF22tg9GrZi67Nu9LWpS3/6vsvsgqymLt/Lnvj9hpMZtOm4ORUWs43jVeS\nRGIyzM3NKSoqH4a1qKgIS0tL1q9fr1/8N3DgwHJ1hBCMHTuWRo0aVXJnAJ1vt7+/P0lJSaxatUp/\nvEePHtjZ2WFpacn48ePp3bs3u3btqtS+X79+mJubM2vWLMYWB+bPysoiLCys0qJ+lUqFvb09V65c\nYevWrXz88cf8/vvv9zwmNVHbddcna9eu5amnnsKrhpmBNm3asHTpUkJDQ3F1dWX06NGkpqaSkJBA\nSkoKLi4u+pn9hQsXcuvWLUDncuLl5YWZWXlz9fr163rXmhK8vLz0byEA3Nzc9Pu2trb6zNapqal4\nlJlkKmlbla5ubm56XesTaXxLHjp69uzJ0qVLiYuLY9GiRbz33nt07ty5QfjrKWYKeRfz6LilIx03\nGvY1YU1s6diRI9260c7WlskXL9L22DGyjTE+mzbBI4+UTzkvRIOKsacoCjtH7+Ti9Is0t29O96+6\ncyv3Ft6NvQ0u+9df4YkndIZ4rmlejEgeUkIDQhFzRaUtNCC0zvWrq1sXPD09uXbtWrlj8fHxeHl5\nMXr0aP3iv507d5ar89JLL5Genk5YWBjmNSziVqvVlXy+y1KcMKXKczExMXTr1k1fLokyUvFh4Zdf\nfqFPnz5ERETg4eHB66+/zuLFi6uVGRgYWC6iSNmt4kNGRep63fXBunXrmDhxYq31QkJCiIqK0i/+\nnD17Nq1ataJ169ZkZGToZ/azsrL0/tutWrUiMTGx0gRZixYtSCpJAVxMYmJiJaO6Ktzd3csZ6SVt\nq9I1ISFBr2t9Io1vyUPJzJkz8fT0JDAwkNzcXMLDwxuE77eZlRntv2yPYw9H/TFTZJ2zLx6LZcnJ\nPG5vz2U/PxyMMT6PPVZqVe7cCatW6VxQ1q83vOy7wN3BHTPFjL6t+3LlH1dYFbSKR1weMajMoiLd\nwsvoaCgshH37DCpOImlQvPjii8yfP5+U4ihMe/fuZceOHQwfPrzaNtOmTePixYuEh4djVWYRd1pa\nGj/++CO5ublotVp2797Nxo0b6du3L6CbuY6MjKSgoACNRsMPP/xAVFQUzz//fCUZ58+fx9fXF0VR\nanUFKSgowMrKijfeeAM/Pz+Sk5NrdGfYtWtXuYgiZbeKDxl1uW4AjUZDfn4+Go0GtVqtv8bqqK3+\n4cOHuX79eo1/B9D5Ue/fv5/CwkKsrKywsbHBwsICPz8/HB0dWbRokV7OuXPnOHnyJAB+fn64u7sz\ne/Zs8vLyKCgo4PDhw/Ts2RM7OzsWLVqEWq3mwIED7Nixg1GjRtWoB0CvXr2wsLBg+fLlaDQawsLC\nOH78eI261vsDjBDivth0qkok9YtarTa1CtWS9XuWONnrpDg94LSpVTEuL7xQMtcthJubEGvXCpGb\na2qt6kR+Ub5B+3/77dKh6dPHoKIkDYDi+568xwoh7ty5I95++23h7e0tGjduLLp37y527NhRbf2E\nhAShKIqwsbER9vb2wt7eXjg4OIj169eLtLQ08eyzzwpnZ2fh5OQkHnvsMbF69Wp927S0NNGjRw/h\n6OgonJ2dRa9evcS+ffuqlJOamiomTZokNmzYINLT0/XHr127JubNm6cv3759W0RGRpZru2DBApFb\nz79tNV23EEKEhoYKRVGEmZmZfiur54ABA8TChQv15drq//3vfxcTJkyoVa8//vhD+Pn5CUdHR9Gk\nSRMRHBwsUlNThRC6MRw1apRo3ry5cHFxqTTeSUlJYvDgwaJJkyaiWbNmYubMmUIIIc6fPy+exj1y\nJgAAIABJREFUffZZ4eTkJDp27Ci2b9+ub+Pj41Ouj9DQUDFu3Dh9OTo6WnTt2lU4OjqKkJAQERIS\nIj788MNadb0bavr+KqIBvc6tCUVRxP2iq+T+JD09nYyMDIMuTKkrqWtSuTSpOImLOTwZ9yTWntYm\n1elibi43CgsJcHY2rKBDh3Sz3aALOXjtGri7G1bmXyT2z1g+P/Y5Wy5s4fI/LmNvZW8QOUlJOnf4\nkomnDRt0YdElDybFrg5VB7Kuf1nyHluPJCQksGbNGubOnQvA1q1bCQoK0oc3jIiIICAggBs3btC2\nbVtTqioxEDV9f6XbieShJyUlhVdeeYW2bdtWuaDGFDQJbIJDj+IQfxpI/sw0SXcAEvPzef70aXqd\nOsXFvDzDC/T3hyef1O0XFuoyXwLU84KX+uSN3W+Qr87n5MsnDWZ4A7RqBf37l5Y//9xgoiQSyT2S\nk5PD5s2biY6O5ty5cwDk5+frDe+SxZbDhg3jp59+MqWqEhMhZ74lDzVCCLZs2cKcOXNo3749W7du\nNbVKev7835+cCTwDgLmjOf6p/pjbGjfroxCCmbGxfJOayh0huOznR1tjpJ3fulXn5z1jBpw5o4t8\noihw8mRpUp4GQIG6gCVHlrDq5CpUBSqS30jGoZFh46Lv2qULPQgwYADs2AFmchrlgUTOfD8YZGZm\nEh0dTb9+/UytisSIyJlviaQarly5wogRI7hw4QLh4eH6lc0NAZf+Llj76FxNNCoNN769YXQdFEUh\nLj+fO8U35RUVVogbjCFDdJFPevfW5VX/9FM4caJBGd4AluaWfHf6O5JVyagKVHz/x/ekqFI4ef2k\nwWQGBsK8eRATozPEpeEtkTRsDh06REBAgKnVkDQg5M+25KGmbdu2+hXuWq2WBQsW8Oabb1YKQ2QK\nFEXBzKb4K2oGBTcKTKLHjJYt9ftfp6Yy5OxZYrKzjSPczAxWrtSF+WiAVqaZYsb0HqVJd2bvm03n\nVZ35NeFXg8qdMwe6dIHTp2HyZGhAz4wSiaQCQUFBDSKalqTh0PDuZhKJkXn99df1+19//TUqlapB\nxPwG6BLZhdaftqZXci9af2yaTJzPOTvjW+xqkqfVYga0sbExviJqtS78YIV4rKZmwuMT9H7eqgIV\n3w3+jlm9Zhlc7ttv62bBH3mkfAIeiUQikTRspPEteegJDAzkkUd0MZqFEHTr1q3GTF3GpJFHIzzf\n9qSReyO0BVoK0wuNroOiKPyjTOKCM7m52Bk4aUMl1q8Hb2/4+GO4edO4smvBsZEj4x8bry+vOb3G\nKHJnztSlnH/vPWjc2CgiJRKJRFIPSONb8tBjZmbGjBkzALCzs0OlUplYo/LkXcnj8vTLHPY4zM3v\nTGN4jnNzw8XCgkFNmvBFu3YoQGGFjGMGxdkZhg2DgwehRw/jya0jr/m9BoB/K39e7PgiF9IuMG3H\nNOIy4wwm08NDF4mxBGP+OSQSiURy70jjWyIBJk6cyJIlS0hKSuLZZ5/llVde4c033zS1WgCoM9RY\nNbOi+4nutHqzlUl0sLewIP7JJ/mpY0fSiooYeOYMfWJijCN80iSdf8Xnn0NxyuGGxqPNHuXS9Esc\nmnyIc7fO8dy653Bs5Egj80YGlVtYCEuXQrt2uhcDEolEImn4yFCDEkkZTp48yejRo5kwYQLjxo3D\n09PT1Co1KHLUakZduECIqyuDmzY1jvvJ3Lnw0Ue6/eefhxEj4MoVWLjQ8LLvgbTcNFxsXDA3M/zY\nlM1HZG0Nt26Bg2EjHUqMiAw1KJHcv9T0/ZXGt0RShpL/MaWBhbQDEFpB2tY0Upal0Cm8E5aNLU2t\nknGIi4M2bUrLzz+vc3geMMB0OjUQhIDOnaE4jwdr1sCECSZVSVKPSONbIrl/kXG+JZI6oihKOcM7\nKyvLhNqUIjSCIy2PcH74ebKisri5vmEsOjydk0ORoZ2NW7eGZ58tLffr1+ANb41Ww56rexi3dRz/\njPynweQoCowbV1r+8kvp+y2RSCQNHWl8SyRVsG3bNgYNGkTr1q0bhAGumCs0GdREXzbVwssS/i81\nlU7HjzPwjz+Iz883vMCJE0v3v/1WN+Wr1eo+GyBHko8we99sOjXrxNu93zaorDFjysg9otskEolE\n0nCRxrdEUoH8/Hy+/fZbkpKS+O2333BqIEGUW3/SGsVKNyuffTyb3HO5JtHjVHY2X12/zqW8PAa4\nuNDOGOnmhw/XuZ68+SYsWwaffKIrN0BL83zaeX744wcSbieQlpeGq52rQeW1bAm+vqVlmXBHIpFI\nGjbS+JZIKvDiiy8SHh5OTEwM4eHhplZHj2UTS5oObqovX/vkmkn0uKPVciw7GzWwKS2NfI3G8ELt\n7SE2FhYvhl9+gZQU2LwZevUyvOy7JOF2Al9Ef8Gfd/5kw9kNaLQaMu5kGFTmokW69aeJiTB6tEFF\nSSQSyUPDpEmTmDNnTr33K41viaQCQ4cO1e+vW7eOM2fOEBdnuHjNd4O5XWkEDVWUClMskOrl6IiP\ntTUAWRoNHycksC8z0/CCS3zxFyyAVauge/fSYw2Ifq370dRW95B0Pfs6Pb7uQceVHSlQFxhMZlAQ\nzJ6ti/198CAcP24wURKJSfD29sbW1hZHR0fc3d2ZPHkyeXl5d91PYWEhU6ZMwdvbGycnJ7p3787P\nP/+sPz9u3DhatGiBk5MTHTp0YPXq1eXaX7x4kb59+9K4cWPatWvHtm3b/vK1GYOarru2MamJ2NhY\nbGxsGD++NNFYQEAANjY2ODo64uDggG/ZV3MSQBrfEkklhg4dik1x+vRz587Rr18/zpw5Y2KtdLT5\ndxtcgl1o91U7epztYZKoLIqiMNbNTV9edf06OcaY/a6KvDy4dMk0sqvB0tySkY+O1Jeb2DQhfmY8\njSwMG/N7/37d2tTp0yE52aCiJBKjoygKO3fuRKVS8fvvv3PixAnmz59/1/2o1Wo8PT2JiooiKyuL\njz76iJEjR5KYmAjAe++9R0JCAllZWYSHh/PBBx9w6tQpADQaDS+88AKDBg0iMzOTL7/8krFjx3Ll\nypV6vVZDUNN11zYmNTF9+nT8/PzKHVMUhZUrV6JSqcjOzubChQuGuqz7Fml8SyQVcHBwYMiQIfry\nmDFjeOGFF0yoUSmWTSx5LPwxWkxtgYWThUlmvgHGlDG+czUanja2X/zNm/Daa9CqFaxYYVzZdWB0\n51Lfj5OpJ1Ew/EOSry9s2wZ//AFlXt5IJA8MJb937u7uDBgwgLNnzwK6LMVl307W5Cpga2vLnDlz\naNVKl7Bs4MCB+Pj4EB0dDYCvry+WlpZ6eYqicPXqVUA3652amsrMmTNRFIU+ffrQu3dv1q1bV63O\nffv2Ra1W/8Ur/+vUdN21jUl1bNy4EWdnZ/r27Vvp3N3cmz799FNatmyJo6Mjvr6+7N+/H4DU1FSG\nDx+Oq6srbdq0Yfny5eXaJScnM2zYMFxdXWnWrJk+U/WFCxfo06cPzs7OdO7cmYgKydl8fHxYsmQJ\nXbp0wdnZmVGjRlFYWAjAqVOn6N69O05OToSEhJBfIaBAdbreLdL4lkiqYOzYsfr9n3/+2WRGblUI\nrSDzl0wujLvAmSDTzMi3t7XlCQcH7M3NGe3mRq6xZ76L30ywb58u82UDw7+VP96NvTFXzOnp0ZNb\nubc4lnyMZJXhpqSbN4fHH2+QnjiSB4DQA6GEHgitt/JfISkpiV27dtGtW7e/3NfNmzeJjY2lY8eO\n+mOvvfYadnZ2+Pr60qJFCwIDA4GqDUohhP4hoCIpKSkAWFhY/GU9yxIcHIyzszMuLi6VPgcNGlSn\nPqq67rqcK0GlUjF37lyWLFlS5bi8++67uLq68vTTT3Pw4MFq+7l8+TIrVqwgOjoalUrF7t278fb2\nRghBcHAwXbt2JTU1lX379rFs2TL27NkDgFarJSgoCB8fHxITE0lJSSEkJAS1Ws2gQYPo378/aWlp\nfP7554wZM4bY2Nhycjdt2kRkZCTx8fGcPn2aNWvWUFRUxJAhQ5gwYQIZGRmMGDGCLVu21KrrvSCN\nb4mkCp577jnGjh3Lpk2bOHHiBHv27GHx4sWmVguAovQirr59FYcnHOjwXQeT6bHe15eb/v581qYN\nP2dkEHLunHEeUrZvh//3/2DlSij+IW5oKIrC+qHrSZmVwpRuU+i3rh/jto7jSobhX0/n5MCHH+qG\nSCJ5kBg8eDAuLi4888wz9OnTh3ffffcv9adWqxk7diwTJ06kXbt2+uMrVqwgJyeH3377jaFDh9Ko\nkc5lrEOHDri6urJ48WLUajWRkZEcPHiwSt/zvXv3MmvWLJo3b873339fqy5ljbzaiIiIIDMzk4yM\njEqfdQkSUN1113auLHPmzGHq1Kl4eHhUOrdo0SLi4uJISUlh6tSpBAcHEx8fX2U/5ubmFBYWcvbs\nWb37i4+PDydOnCA9PZ33338fc3NzvL29mTJlChs3bgTg2LFjpKamsmjRIqytrbGyssLf35+jR4+S\nm5vLO++8g4WFBX369CEoKIgNGzaUkztz5kzc3Nxo3LgxwcHBxMTEcPToUdRqNTNmzMDc3Jxhw4bR\no0ePWnW9F6TxLZFUgYWFBevWreP555+nQ4cOvPfee9jZ2ZlaLQCsXK144uQTtJzZEqumVibTo62t\nLY3MzHj85EkiMzMZ4+aGUd4P3L4NJa9Dv/0WbtzQRUHJyTGG9DrTq1Uv3OzdaO3cmrWD13Jp+iUC\nvAMMKjMlBRo3hvnz4bffQKUyqDiJxKhs376djIwM4uPjWb58ud4oro7169fj4OCAo6MjAwcOLHdO\nCMHYsWNp1KhRJXcG0D1A+/v7k5SUxKpVqwDdfWHbtm3s2LEDd3d3PvvsM1588UVatmxZqX2/fv0w\nNzdn1qxZ+jepWVlZhIWFsXDhwnJ1VSoV9vb2XLlyha1bt/Lxxx/z+++/39XY1JWarru2MSkhJiaG\nvXv38vrrr1d5vkePHtjZ2WFpacn48ePp3bs3u3btqrJumzZtWLp0KaGhobi6ujJ69GhSU1NJSEgg\nJSUFFxcX/cz+woULuXXrFqBzOfHy8sLMrLwZe/36db37TAleXl76txAluJVxnbS1tSUnJ4fr169X\nepjw8vKqUlc3Nze9rveEEOK+2HSqSiTG5+rVq6ZWoVo0+RqRtDJJZJ/ONpkO+RqNcQVmZwthZyeE\nLsWOEA4OQkyeLERqqnH1aIBotUJ06FA6NGvWmFojyV+h+L4n77FCCG9vb7Fv374qz9nZ2YkzZ87o\ny/379xcffvhhjf1NmjRJ9O3bVxQUFNRYb8qUKeL111+v9ry/v7/46quvqjzn6+srtFptuWPXrl0T\n8+bNK3ds69atoqCgQPznP/8Rx44dEyqVSowaNapamQMGDBD29vbCwcGh0hYYGFjj9dR03XUdk6VL\nlwp7e3vh7u4umjdvLuzt7YWNjY3o3r17tfouX768xj6FECI7O1uMGjVKjB8/Xhw5ckS0a9eu2rpH\njhwRbm5uQlPh/hMVFSXc3d3LHRs9enS5Ma/4vxQaGirGjRsnDh48KFq0aFGube/evav8Xyqra3XU\n9P2VM98SSS20bt3a1CpUyZU3rxBlH8WVV6+Q8InpMqs0KjPzIIRAY2jXE3t7GDGitDxyJKxerXN6\nbsAUqAsIuxDGqhOrDCZDUWDSpNLy2rUNNgmoRFJvdO3alfXr16PVavn5559r9DEGmDZtGhcvXiQ8\nPBwrq9K3h2lpafz444/k5uai1WrZvXs3GzduLLeg8MyZMxQUFJCXl8fixYu5ceMGE8tm4C3m/Pnz\n+Pr6oihKra4gBQUFWFlZ8cYbb+Dn50dycnKN7gy7du0iOzsblUpVadu5c+ddX3dt5yry97//natX\nrxITE8Pp06eZNm0aQUFBREZGkpWVRWRkJAUFBWg0Gn744QeioqJ4/vnnq+zr8uXL7N+/n8LCQqys\nrLCxscHCwgI/Pz8cHR1ZtGgR+fn5aDQazp07x8mTJwHw8/PD3d2d2bNnk5eXR0FBAYcPH6Znz57Y\n2dmxaNEi1Go1Bw4cYMeOHYSEhNR4TQC9evXC0tKS5cuXo9FoCAsL43iZuK1V6Wpubl5Dj9UjjW+J\npA5kZ2fz7bffEhAQoA87ZWrMrMwQap1llfFzBpo8E4X7A+Lu3CE0Pp5Hjh3j5wzDJpQByqeb37IF\njJHi/i9w7fY1WvynBcuPL8fZxtmgssom2fnlF10uIonkfqemsKpLly4lPDwcZ2dnNmzYUC5aVUUS\nExP56quviImJwc3NTe+WsmHDBhRFYdWqVbRq1QoXFxfefvttli1bRlBQkL79unXrcHd3p3nz5uzf\nv589e/boo6OUxcXFBScnJzZu3Ejv3r2r1ScrKwsXF5dyx7Zt28b7779f03DcNTVdd03nSggMDORf\n//oXANbW1ri6uuo3e3t7rK2tcXFxoaioiA8++EAfgWTFihVs376dtm3bVqlXQUEBs2fPplmzZrRo\n0YK0tDQ++eQTzMzMiIiIICYmBh8fH1xdXZk6dSqqYl+6kvOxsbF4enrSqlUrfvrpJywtLQkPD2fX\nrl00bdqU6dOns27dunL+69X9L1laWrJlyxa+/fZbXFxc2LRpE8OGDatR1wULFtzT30MR98m0iKIo\n4n7RVfLgMXHiRE6fPs306dP1PnGmRgjB8Q7HuXP5DgAd1nWg+Vjjz/7mqNVMu3yZqKwsAho3Zk2H\nDoaPP67VwiOPgJkZTJgAffrArl3QuTOMGmVY2XeJWqtm79W9fH3qawa2HcjkrpMNLtPHB65d0+0v\nXKhLwCO5/1AUBSGEUeLXyHuscUhISGDNmjXMnTsXgK1btxIUFKQ34CMiIggICODGjRvVGqyS+4Oa\nvr9y5lsiqYUFCxawceNGYmJiUKlUDcLwBt0Xu/mkUmM7aUmSSfQ4rFLxw61bJBYUEJmZidYYQs3M\n4NdfdSnnPT11BrhWC126GEP6XfHtqW8ZsH4AYRfC+Ob3b4wic9486NkT/vtfmDLFKCIlEkkt5OTk\nsHnzZqKjozl37hwA+fn5esO7ZLHlsGHD+Omnn0ypqsTAyJlviaQWVq9ezZRiC6Zr1676GJ9Oxk4s\nUwUZkRn88fwf+nKv671o5G7chwO1VkvLI0e4WVQEQESnTrS3taWtra2RFFCDuXmDDXCdnpeO+xJ3\n1Fpdoo0Pn/mQPXF72B6yHVc7V4PIFKJ0OIqKID0d3N0NIkpiQOTM94NNZmYm0dHR9OvXz9SqSAyA\nnPmWSP4Cw4cP1892nzp1Ch8fn3r3x7tXHHo44PCkAy5BLnT4rgPmDve2+OOvYGFmxqgyYZuGnTvH\nfyuEdTKsAhYN1vAGaGrblOfblC42Cr8UzryAeTSxaWIwmYqiSzE/cya0bKmLxCiRSBoWhw4dIiAg\nwNRqSEyANL4lklpwcnIqlzXsmWeeqTEGqjGxdLak2+FuPBbxGM3HN8fCvn4zqdWVsWWMb3NggbEj\nxBQU6JLvjB0LgwcbV3YdGNN5jH6/UFPIc62fw9zMsA9KWi04O8OhQ7BkiUFFSSSSeyAoKKjes19K\n7g+k8S2R1IFx48bp9/fv39+g0s0rioLmjoa0bWnEh1adRczQdLO3p0Oxm4mtuTkXcnONq0BmJixd\nqnN0XmW4UH73yqD2g7C11I3P1cyrJGYlotFqyC003Dh5ekJoqG5dqkQikUgaDtL4lkjqQP/+/fHy\n8mLMmDF8/fXXFBUVceLECVOrBYDmjoYjrY6Q8nkKVm5WJnkwUBSFhT4+RHTqRKq/Px6NGvHf5GTD\nx/wG3aLLVat0js35+Q3SudnOyo45z8xh9aDVHJtyjJUnVuK11MtoCzBv3YLiKGESiUQiMTFywaVE\nUkc0Gg2KojBp0iQiIiJ49NFHOXDgQIN4bajOVmPhYHo9AMZfuEDEn38yuGlTPmvThsZVxMCtV9as\nKc0s8+STcOQIZGRAhdi5DYV9cfvYE7eHcY+No6NrR4PKys+Hrl3h0iVdgJiUFCjjISRp4MgFlxLJ\n/UtN399ajW9FUWZVcTgLiBZCxNSDfnVC/jBIGgqbNm3C398fDw8PU6tSJZp83UOCWSPTvNiKzs7G\n19YW23vM/HXX/PmnzqLUFCcZ6tkTrl7VBbq2szOODg0UIaB169KY32vW6KIySu4PpPEtkdy//NVo\nJ08A0wCP4u1lIAD4WlGUt+tLSYnkfmHEiBEN0vC+ufEmJ7udJMo+iuvfXDeZHt0dHIxneAM0aQJl\nIwY8/rgu1Md9YHjfKbrD2VtnDda/osArr5SWN240mCiJRCKR1JG6GN9NgG5CiDeFEG+iM8abAc8A\nEw2om0TSoLl16xbffPMN2dnZplYFgISPEsg5lQMayNydaVJdhBCcVKn4IC6OGGOMT5kUwMTGQgNJ\nhFQdN3NuMuynYTRf0pxPD31qUFkjR5bu79kDv/9uUHESiUQiqYW6GN+eQGGZchHgJYS4AxQYRCuJ\npIEzdepU2rVrx+7du7l9+7ap1QGg07ZO+v3MPZmoc9Qm0+WVy5cJOnOGs7m5OBrDJ37w4NJY32lp\nUFgIJ0/qoqA0QOws7fBw8GDziM2sG7LOoLK8vaFpU92+RgO//WZQcRKJRCKphboY3+uBo4qizFUU\nZS5wCNigKIodcN6g2kkkDZDNmzdz+fJl8vLymDRpEq1atTK1SgDYtrPFrpPO1UKbr+XPnX+aRI/d\nGRl8mZrKzaIiUgoLaW1jY3ih7u6wejWcPw+vvgrt28Po0Trf7wZG2IUwWn7WkuXHl7P61GqjyJwy\nRbfgMiAA2rUzikiJRCKRVEOtxrcQ4mN0ft630S20nCaE+EgIkSuEGFNza4nkweP48eP8+uuvFBUV\nsW3bNlOrUw7H3o76/ZTPjZhlsgw9HRywKJ6FPpmdTXJ+vnEET5oEvr7w2GO6hDuXLsETTxhH9l3Q\nrkk7sgqyAF22y/O3zrP29FqDynzzTV24wf37oX9/g4qSSCSSBsekSZOYM2eOqdXQU9dwCKeATUAY\ncEtRFE/DqSSRNGxeeOEF/f6WLVv44IMP+K2BvMtv5KHzdVasFKx9rE2iQ2NLSwIaN9aXh547x/Lk\nZOMp4O+vM8AbaMr5Tq6d6OzaGYA76jv0XN2TX+J/oVBTWEvLe6dpU3BwgN27dQsw1xrW1pdI6h0z\nMzPi4uLKHZs3b165BGhlKSwsZMqUKXh7e+Pk5ET37t35+eef9efHjRtHixYtcHJyokOHDqxeXf4t\nVEBAADY2Njg6OuLg4ICvr2/9X5SBuBvdHRwccHR01Ne1sLBg5syZlerFxsZiY2PD+PHjDan6Q0Ot\nxreiKP8AbgJ7gB3AzuJPieSh5Mknn8TV1RWAjIwMrl27pi+bGq8PvOi0vRO9/+zNo98/ajI9XmjS\nRL+fo9EwrFkz4yuRlwdhYbrp3gbGyI6lqyAD2wayZvAarMytDCrzu+9g3jxd6MFnnjGoKImk3lGq\neZiu7rharcbT05OoqCiysrL46KOPGDlyJImJiQC89957JCQkkJWVRXh4OB988AGnTp0q1+/KlStR\nqVRkZ2dz4cKF+r8oA3E3umdnZ6NSqVCpVNy8eRNbW1tGll2lXcz06dPx8/MzpNoPFXWZ+Z4JtBdC\ndBRCPCaE6CyEeMzQikkkDRVzc3OCg4P1ZQ8PD9o1EEdaRVFoOqgpFvYWaPI0FKSaZk30oJIVfsCV\nO3ewM2boQYCICJ0f+KpVukwzDYxB7Qfp93df2W3QWe8SpkyBw4fhrbd0izAlkvuJu41Bbmtry5w5\nc/RrcgYOHIiPjw/R0dEA+Pr6YlmcAEwIgaIoXK2wRuRuZPbt2xe12nSL3CtyLzHbN23ahKurK717\n9y53fOPGjTg7O9O3b98a23/66ae0bNkSR0dHfH192V888ZGamsrw4cNxdXWlTZs2LF++vFy75ORk\nhg0bhqurK82aNWPGjBkAXLhwgT59+uDs7Eznzp2JiIgo187Hx4clS5bQpUsXnJ2dGTVqFIWFut/S\nU6dO0b17d5ycnAgJCSG/wn2gOl2NRV2M7yR0vt73hKIo/RVFuagoymVFUd6pps5IRVHOKYpyRlGU\n7+9VlkRiLAYPHgyAk5MTZmamSWZTHTl/5HBuxDkOtzjMjf+7YRIdPK2tCWrShGktWrC9UydszcxQ\na7XGES4ENG4Mf/87hIfDgAHGkXsXdHbtTMdmHRnSYQj/ef4/RCVG8c/If3Ir95bBZFacIJT5VCQP\nEzdv3iQ2NpaOHUuzyr722mvY2dnh6+tLixYtCAwMLNfm3XffxdXVlaeffpqDBw9W23dKim59TX1n\nOw4ODsbZ2RkXF5dKn4MGDaqxbV11L8vatWsruZWoVCrmzp3LkiVLajToL1++zIoVK4iOjkalUrF7\n9268vb0RQhAcHEzXrl1JTU1l3759LFu2jD179gCg1WoJCgrCx8eHxMREUlJSCAkJQa1WM2jQIPr3\n709aWhqff/45Y8aMITY2tpzcTZs2ERkZSXx8PKdPn2bNmjUUFRUxZMgQJkyYQEZGBiNGjGDLli21\n6mpUhBA1bsBq4DfgXWBWyVZbu+K2ZsAVwAuwBGKADhXqPAJEA47F5abV9CUkkoZCXl6e2Lt3r7h9\n+7YICwsT48aNE6GhoaZWSwghRO7lXHF99XVRkFZgalXEn4WFYklionjq999F8B9/GEfo3/4mhM62\nFCI83Dgy7wGNViOEEGLQhkGiy6ouYu7+ueJWzi2DykxNFeIf/xCiVSshunY1qChJPVB836v1Xlsf\nW6332LlzS79XZbe5c+tev7q6dUBRFHH16tVyx0JDQ8W4ceNqbVtUVCT69esnXnnllUrntFqtOHTo\nkPjkk0+EWq3WHz9+/LjIyckRhYWF4rvvvhMODg4iLi6uUvs9e/aIkSNHitGjR4t169bVqsvmzZtr\nrfNXqavuZUlISBAWFhbi2rVr5Y7PnDlT/Pvf/xZC1DzeV65cEW5ubmLv3r2iqKhIf/yxyGFeAAAg\nAElEQVTYsWPCy8urXN2FCxeKyZMnCyGEOHz4sHB1dRUajaZcnaioKOHu7l7u2KhRo8S8efP0ZW9v\nb7F+/Xp9+e233xavvPKK+PXXX4WHh0e5tv7+/uLDDz+sUdf6pqbvb12m7BLR+XtbAQ5ltrrgB8QK\nIRKEEEXARuCFCnWmAiuEEKrib396HfuWSEyGjY0Nffv25cSJE/z3v/+lZ8+eTJkyxdRqAWDb1hb3\nye5YNTWsD3FduKPVciEvj3c9PfnpUSP5oHftWrq/bh189hm83fCS8Zopup/fDcM2EDMthtCAUJrZ\nGdY3PiwMli+HpCSdS7xEcr9gbm5OUVFRuWNFRUVYWlqyfv16/cLBgQMHlqsjhGDs2LE0atSokrsD\n6Fz1/P39SUpKYtWqVfrjPXr0wM7ODktLS8aPH0/v3r3ZtWtXpfb9+vXD3NycWbNmMXbsWACysrII\nCwtj4cKF5eqqVCrs7e25cuUKW7du5eOPP+Z3A2S9qqvuZVm7di1PPfUUXl5e+mMxMTHs3buX119/\nvVaZbdq0YenSpYSGhuLq6sro0aNJTU0lISGBlJQUXFxc9DP3Cxcu5NYt3Vu+5ORkvLy8Kr1Bvn79\neqUwvl5eXvq3DCW4ubnp921tbcnJyeH69euVslCXva6yurq5uel1NSZ1CTU4r6qtjv17oHNbKSG5\n+FhZ2gHtFUX5TVGUw4qiPF/HviUSk9OvXz/27dvHa6+91uBSzqtz1KSsTOF0/9MIjWl8DDwaNeLr\n9u0JbNIEa2P5fQ8dWrq/eTP88Qf062cc2feAraWt0WRNnAglYdcvXYLLl40mWiL5S3h6enLt2rVy\nx+Lj4/Hy8mL06NH6hYM7d+4sV+ell14iPT2dsLAwzGv4DVKr1ZV8vsuiKEq1bhcxMTF069ZNXy6J\nrlLxYeGXX36hT58+RERE4OHhweuvv87ixYurlRkYGFguGknZreJDRk3UpHsJ69atY+LEieWOHTx4\nkISEBDw9PXF3d2fx4sVs3ryZJ6oJ4RoSEkJUVJR+Uevs2bNp1aoVrVu3JiMjg4yMDDIzM8nKytL7\nb7dq1YrExES0FdwSW7RoQVJSUrljiYmJdbrPuru7k1whwlaJThV1TUhI0OtqTKo1vhVFWVr8GaEo\nSnjFrY79V7UMueJ/gAU615NngNHAN4qiOFZqJZHcB5Qs9jA1BTcKONT0ELGvxZK5O5PbUabPwimE\nIP7OHcMLeuIJaNmyRCiMHQt/+5vh5f4FMu9k8sMfPzBi0wh2XDZcMClb2/JDsX69wURJHjRCQ6ty\nOtEdr2v96urWgRdffJH58+eTkpKCEIK9e/eyY8cOhg8fXm2badOmcfHiRcLDw7GyKn0TmJaWxo8/\n/khubi5arZbdu3ezceNG/YLCrKwsIiMjKSgoQKPR8MMPPxAVFcXzz1eeGzx//jy+vr4oikJ4eM2m\nUUFBAVZWVrzxxhv4+fmRnJyMj49PtfV37dpVLhpJ2a3iQ0YJd6N7CYcPH+b69euVxvLvf/87V69e\nJSYmhtOnTzNt2jSCgoKIjIys1Mfly5fZv38/hYWFWFlZYWNjg4WFBX5+fjg6OrJo0SLy8/PRaDSc\nO3eOkydPAuDn54e7uzuzZ88mLy+PgoICDh8+TM+ePbGzs2PRokWo1WoOHDjAjh07CAkJqXGMAXr1\n6oWlpSXLly9Ho9EQFhbG8ePHa9S1pgczQ1DT6oCSnMfVP5bVTjK69PQltASuV1HniBBCC1xTFOUS\n0BadH3g5Qst8cQMCAggICPgLqkkk9UN2djarV69m69atFBUVcfjwYVOrhJWrFS7Pu/BnuC7LZfqW\ndJwDnE2iS6FWy1tXrxKWnk4TCwt+f+IJzAwZg9vMDIYM0flXgM7Xom9f3c2/gcb+/uLkFxxOOsxQ\n36H0atnLoLIGDNDlIAL4979h7twGOywPHQcOHODAgQOmVqNBMmfOHObOnctTTz3F7du3adOmDevX\nr+fRatzZEhMT+eqrr7C2tta7JiiKwpdffslzzz3HqlWreOWVV9BqtXh5ebFs2TKCgoIAnTvLBx98\nwKVLlzA3N6dDhw5s376dtm3bVpLj4uKCk5MTGzdu5LnnnqtW/6ysLFxcXMod27ZtG++///69DkmV\n1KZ7YGAgzzzzTLmZ3rVr1zJs2DDs7OzK9WVtbY21dWm+CHt7e6ytrStdB+geLGbPns3FixextLTE\n39+fr776CjMzMyIiIpg1axY+Pj4UFhbSvn175s+fD6A//49//ANPT0/MzMwYPXo0/v7+hIeH88or\nr7BgwQJatmzJunXrykUWqy7MpKWlJVu2bGHq1Kl88MEHBAYGMmzYsFp1NSZKba8i/lLnimIOXAL6\nAqnAcWCUEOLC/2fvzOOqqNc//h5WQdajgIAISC5kuaF4XSoJKlPQvHhzSUtLf7eupWVdr7apWbmk\nN82s225ZLqmoqOS+b6m45C4qssgiCIKALIfz/f0xcABlUznnoH7fr9e8mOU783xmOGfOM995vs9T\nrs0zJeuGK4rSGNXpbi+EyLzpWMKQWiWSOyU2NpYXX3yRZs2a8cMPP1S4WZmSzC2ZHAs5BoCVpxVd\n47uimBnfy/o+KYkZ8fFczM/nQMeOBDgY4cXWjh1qWce//x18feHAATX94OHDYAz7t8HvJ38n4nQE\nGy5s4PTo0zSxa2JQe1euQJMm6rOIuTmkpYGzaZ7LJDVQEi5glC+t/I2tW+Li4liwYAGTJk0CYOXK\nlYSGhurTG65Zs4aePXuSkpJSqVMvufep7vtbmyI73RVF2VSSKvCioiixiqJcrGk/ACFEMfA6sBE4\nCSwRQpxWFGWKoiihJW02AFcVRTkJbAHeudnxlkjqKykpKTz00EPs3r2b5cuX3xLjZ0ocn3DEXKO+\nSiu8XEjW/jvOGHpX7MjK4nx+Pjog8upV4xh94gk4dAjefVd1up2c1LSD9czxBrXXe+nJpVzLv6YP\nOSnWFRvMnqsrTJwIP/4IycnS8ZZI6pqcnByWL19OdHQ0J0+eBCA/P1/veJcOtgwPD+f33383pVSJ\niaix51tRlDPAW6g90vpfBCGEkX5F9TrkU7mkXtKhQweOHj0KwNKlS2nXrh2tWrUysSo1xnpPoz1o\nM9XCD55vetLic+P3sCy/coV/nDoFwKO2tkxt3pw+Gg0W9Sw/uqmYs38Ob214C4DWjVpjZ23H480e\nZ/Yzsw1uu7AQ9uyBnj1l6El9RPZ83x9kZmYSHR1NSD0e+C2pe+6q5xvIEkL8IYS4IoS4WjrVsUaJ\n5J6lX7+y7Jkvv/wyoaGh5NWDPG6KotDq+1a4DnLl4WUP0/zj5ibR8YxGg1WJZ3c8L4/P4uNJM9Ub\ngsJCqEdvJwDCWpZVS43JiGFKzylMD5lucLv/+pcafjJxImTKd40SicHYs2ePHKMmqUBtnO9tiqJ8\npihKV0VROpZOBlcmkdwjlFa7BNXhPXnyJLa2xksfVx0uf3fh4cUP4zrAFfOGRi7xXoK9hQXB5WIb\nnnd1xd3a2rgiVq5UB2G6uMD+/ca1XQN+Gj/auKhV94pFMVqdFktzS4Pb7d8fTpxQL0cl46ckEkkd\nERoaWufVLyX3NrVxvrsAnYBPgdkl091kQJFI7ivatWunT+Cfk5PDzp07TayoItosLamLUjkRfoLr\nR6+bREO/xo0BcLW0pNgUr7bz8uCpp+D8eXjsMePbr4G+rdRS0dbm1sRmxqITOhKzE2vY6+546inw\n8DCoCYlEIpFUgkGzndQlMh5NUp/55JNPSEpKol+/fgQGBrJ9+3aeeOIJnOvBaLaz/3eWgqQCXMJd\ncPm7CxaOxu+BuVpUxLm8PLo4OBCfn8/K9HSe1WhofVNqqzonPR1mzFDTDdrawvHjhrV3h5y7eo5T\naado69aW6bunE3k2ku7NurPi+RUGt52QAP/9L4wZoyaGkdQfZMy3RHLvUt33t0rnW1GUoUKIXxVF\nGVfZdiHEf+tQY43IG4PkXuC9997jyy+/JCAggK+++orWrVubWhJCiCrzoRqbiRcv8kNyMmGNGvGO\nlxf+hna+c3LUUJP8fHX57FmwswNra2jUyLC274Dcwlz+d+h/9Gvdj4c0DxncXufOalIYULOfjBhh\ncJOS20A63xLJvcudOt//FEJ8oyjKpMq230aJ+TpB3hgk9wInTpzA3d2dRvXQsQMovlFMUVoRDZqZ\nJhd5RlERjhYWmBvzYeC558qqyri7q474zz9DWFj1+z0AvPKK6nQDjB4NX35pWj2SikjnWyK5d7kj\n57u+IW8MEsmdk742nQvjLnDj/A1sWtnQ5XQXU0syHr/8Ai+9pM4//DAcOwb3wOCnrPws9iXuo9dD\nvQxmY/t2CApS5z091RCUevKSRIJ0viWSe5m7LbLTQFGU0YqifKUoyo+lU93LlEjuH+Lj45k7d269\nGXxZkFDAjZgbIODGhRsU5xquiEtNFAvBjmvXeOv8eRYkJxveYGioWsoR4PTpep9Xr1hXTJ9FffD6\n3IuvDn6FVqc1mK0ePcqK7Fy+DHPmGMyURCKRSEqoTbaThUAT4BlgB9AUME3KBInkHuDLL7+kQ4cO\nHDhwgIaGjmmuJZ6veWLbpiT9YRFkbjOdAzo3MZGhp09zNi+Pro6Ohjeo0cDf/qbOt20L8fGwd6+a\nZ68ekluUSyf3Tix4bgGRgyOxMDNcL72FBZSvbH3mjMFMSSQSiaSE2jjfDwkhPgByhRA/A32ARw0r\nSyK5N4mMjGTJkiVkZmbSqVMnAgICTC1JT6Nny+LQ0yPTTaLhUHY2b1+4QGJBAUdycmhhY2Mcw/Pn\nq127770HzzwDr76qDr6sZ0TFRNF4ZmM+2vkR8w7MM4rN8ePBykp9QfDss0YxKZFIJA80tXG+S8vB\nXVMU5RHAEfAxmCKJ5B7mypUr7NmzByEEq1atMrWcCti0LHN0035PwxTxnR3s7WlsqRaQSSks5OB1\nI71Ea9dOTWrdowccOQJ//QXh4caxfRt0dO9IkU695e6K28X+xP1M3z2d6wWGu05hYXD1KqxZo45N\nlUgkEolhqY3z/a2iKM7AB0AkcAqYaVBVEsk9SlhYmD6t365duxgzZgw//lg/hkjYtbNDsVIwa2iG\nc4izSeK+zRWF0HKZYIafPs3Lxox1cHcHLy/j2btNmtg1oYunOhi2WBTT+7feJGYnkleUZzCbVlZg\nZqamQn/xRRhXaXJZiUQiuf8YMWIEH374odHt1uh8CyG+F0JkCiF2CCGaCyFchRD/M4Y4ieRew83N\nja5duwJqfu24uDg6depkYlUqDoEOdDrciR6ZPXhk+SNY2Jkm40e/cs53dnExH5uisktGBixaBPXs\n7QSUVbsECGkewpe9v8TNzs2gNk+dUlMOBgaqKQclkvqGj48Ptra2ODg44O7uzssvv0xe3u0/lBYW\nFjJy5Eh8fHxwdHQkICCA9evX67cPGzYMDw8PHB0dad26NT/88EOF/c+cOUNwcDBOTk60bNmy3r3h\nrIklS5bw8MMPY2dnR4sWLdizZ0+l7ebPn0/nzp1p0KABL7/88m1vl1RPbbKdOCmKMkZRlP8qivJF\n6WQMcRLJvchz5d7dm5ub07ZtWxOqqUjDNg0xszRDm6PlRuwNk2h4SqOhgZl660kqLCSv2Mg98Bs3\ngo8PLFlSL/PqlXe+159fT2FxocFtduoEa9fC66+Dn5/BzUkkt42iKKxbt47s7GwOHz7MwYMH+fjj\nj2/7OFqtlmbNmrFr1y6ysrL46KOPeP7554mPjwfg3XffJS4ujqysLCIjI3n//fc5cuQIAMXFxfTr\n14++ffuSmZnJN998w9ChQzl//nydnquh2LRpExMnTuTnn38mJyeHnTt30rx580rbenp68sEHH/DK\nK6/c0XZJ9dQm7CQKNcb7OBBdbpJIJJUQGhoKqL3gnp6eJlZTkZy/cjgacpR97vtI/s4Iaf4qoaG5\nOSPd3Xm7aVN2tm+Pr40NhTqdcYxrtWqKj3/9C376Cfr1M47d26CNSxsC3AMY2nYoX/X+ivXn1zP2\nj7GcSTdeeI6x/h0Sye1QOk7F3d2dZ599lhMlGYvMzMy4ePGivl11oQS2trZ8+OGHeJWEn/Xp0wdf\nX1+io1W3xt/fH8uScSml1YEvXLgAqL3eycnJjB07FkVRCAoKonv37ixcuLBKzcHBwWi1hksXejtM\nnjyZDz/8kM6dOwPqdXR3d6+07XPPPUffvn3RaDR3tL08M2bMoGnTpjg4OODv78+2bdv025KTkxkw\nYACurq74+fkxb17ZQPPExETCw8NxdXXFxcWFMWPG6LedOXOGoKAgnJ2defTRR1mzZo1+m6+vL7Nn\nz6Zdu3Y4OzszePBgCgvVTowjR44QEBCAo6MjgwYNIr+0+nEttNYltXG+GwghxgkhfhJC/Fw6GUSN\nRHIf0Lp1a44dO8aJEyfo2LEjAwcO5O233za1LAAsXS1pOqYpXZO70vzTyns8jMG8Fi2Y6O3NxsxM\n/nb4ME8dO2Ycw717Q3AwzJgBmzYZx+ZtoigKB0cdZGH/hWyK3cSsvbNws3PDwdrBoHYvXoQhQ9Sw\n+PLpByWS+kZCQgJRUVF07Njxro+VmppKTEwMbdq00a8bPXo0DRs2xN/fHw8PD3r37g1Q6SB1IYT+\nIeBmLl++DIBFHRf1CgsLw9nZGY1Gc8vfvn37VrqPTqfj0KFDXLlyhRYtWtCsWTPeeOMNCgoK6lTb\nzZw7d4758+cTHR1NdnY2GzZswMfHB1CvXVhYGB06dCA5OZktW7Ywd+5cNm3ahE6nIzQ0FF9fX+Lj\n47l8+TKDBg0C1LcXYWFh9OrVi7S0NL744gteeOEFYmJi9HaXLVvGxo0biY2N5dixYyxYsICioiL6\n9+/PSy+9REZGBv/4xz9YsWJFrbTWNbXK860oyihFUdwVRdGUTgZRI5HcByiKQtu2bYmNjWX9+vX0\n6tWr3jjf1k2sady3scnivctjqShohWCWnx+b2rUzjtEePcrmf/sNpk2DqVONY/s2KB20u6DfAnaO\n2Mm7j72Lh72HQW0eOwaLF0NKChQWgix2KKnA5MnqVFfLd8Bzzz2HRqPh8ccfJygoiIkTJ97V8bRa\nLUOHDmX48OG0bNlSv37+/Pnk5OSwe/du/v73v2NtbQ2oHSuurq7MmjULrVbLxo0b2bFjR6Wx55s3\nb2bcuHE0adKEX3/9tUYt5Z3AmlizZg2ZmZlkZGTc8jcyMrLSfVJTUykqKmLFihXs2bOHo0ePcuTI\nkTsK3bkdzM3NKSws5MSJE/qQH9+ScT4HDx4kPT2d9957D3Nzc3x8fBg5ciRLlizhwIEDJCcnM3Pm\nTBo0aICVlRXdunUDYP/+/eTm5vKf//wHCwsLgoKCCA0NZfHixXq7Y8eOxc3NDScnJ8LCwjh69Cj7\n9+9Hq9UyZswYzM3NCQ8P178FqElrXVMb57sQ+AzYR1nIySGDqJFI7iM6d+7M0qVLGTFiBB4ehnWc\nbpf8pHzipsVxvN9xk6QcBHCwsGBa8+Y84eSElVltbkV1QK9ypdqjoiApqaJDXs9QjBiTHhoK9vbq\nfGJivUyDLnnAWb16NRkZGcTGxjJv3jy9U1wVixYtwt7eHgcHB/r06VNhmxCCoUOHYm1tXSHUoRRF\nUejWrRsJCQl8/fXXgNqDvWrVKtauXYu7uzuff/45AwcOpGnTprfsHxISgrm5OePGjWPo0KEAZGVl\nERERwbRp0yq0zc7Oxs7OjvPnz7Ny5UqmTp3K4cOHb+va1IRNSU2FMWPG4OrqikajYdy4cURFRdWp\nnZvx8/Njzpw5TJ48GTc3N4YMGUJySWXjuLg4Ll++jEaj0ffeT5s2jdTUVBISEvD29saskt+GpKQk\nfdhQKd7e3vo3DaCGfZZia2tLTk4OSUlJt4SCent710prXVObX7xxqIV2fIQQviWT6d5XSyT3KPUl\n7i/nVA77m+4n9t1YrkZeJe+s4dLY1RadEFy6YYQBoJ06QePGJUZ1MHw4BAUZ3u5dEHctjq8Pfk2/\nJf3YeGGjwexYWqoROaXcRkecRGIUquoosLW1rdD7nJKSAsCQIUO4fv062dnZrFu3rsI+r7zyCunp\n6URERGBubl6lTa1Wq4/5BnjkkUfYvn07aWlp/PHHH1y4cIHAwMBK9z169GiF0JjS7CpFRUUV2m3d\nupWgoCDWrFmDp6cnb775JrNmzapSU+/evfUPFTdPNz9klOLk5FTpQ4IxGDRoELt27SIuLg6ACRMm\nAODl5UXz5s3JyMjQ995nZWWxdu1avLy8iI+PR1fJABQPDw8SEhIqrIuPj69xjJW7uzuJiYm37Fcb\nrXVNbZzvk4Dpf50lknuQ1NRUJk2aRGBgYIUsKKbE1s8WpyAn/XJGVIbJtGRptQw9dQq3vXsZZox8\n32ZmaoXLUv74w/A275IlJ5awL3EfA9sMpJOHYdNWPvZY2fzs2QY1JbnXqAdhJ1XRoUMHFi1ahE6n\nY/369ezYsaPa9q+++ipnzpwhMjISKysr/fq0tDSWLl1Kbm4uOp2ODRs2sGTJEoLLPZUeP36cgoIC\n8vLymDVrFikpKQwfPvwWG6dOncLf3x9FUaoMBSmloKAAKysr3nrrLQIDA0lMTKw23CEqKkr/UHHz\ndPNDRnlGjBjBvHnzSEtLIzMzkzlz5hAWFlZp2+LiYvLz8ykuLkar1VJQUEBxucxUNW0v5dy5c2zb\nto3CwkKsrKywsbHRP+wEBgbi4ODAzJkz9cc6efIkhw4dIjAwEHd3dyZMmEBeXh4FBQXs3bsXgC5d\nutCwYUNmzpyJVqtl+/btrF27lsGDB1d7nbt27YqlpSXz5s2juLiYiIgIDhw4UCutdU1tnO9i4Kii\nKN/IVIMSSe0RQnD+/Hn27NlD586diYiIMLUkAMyszXAd7KpfzvjDdM733IQEdmdlkV5UxK/+/sYx\n2q8fPP00fPYZuLjAP/8JVfQWmRIhBF8e+JLdCbtZe24tYS3D0NgYdrhNSaIeQI37LjR8lkOJpFZU\nF4I1Z84cIiMjcXZ2ZvHixfTv37/KtvHx8Xz77bccPXoUNzc3fQ/y4sWLURSFr7/+Gi8vLzQaDePH\nj2fu3Ln6DFYACxcuxN3dnSZNmrBt2zY2bdqkz45SHo1Gg6OjI0uWLKF79+5V6snKyrolY8iqVat4\n7733qrscd8QHH3xAp06daNmyJW3atCEgIIB3330XUHvTp0+frm/78ccfY2try4wZM/jtt9+wtbXl\nk08+qfX2UgoKCpgwYQIuLi54eHiQlpbGp59+CqhZatasWcPRo0fx9fXF1dWVUaNGkZ2drd8WExND\ns2bN8PLy4vfffwfA0tKSyMhIoqKiaNy4Ma+//joLFy6kRclI8ao+K5aWlqxYsYKffvoJjUbDsmXL\nCC9X6bg6rXWNUlO8p6IoL1W23tgZTxRFEaaKTZVI7oQjR47oXzm6ubmRlJRUafyaKchPzGe/1351\nwRx6XOthkkGYvY4dY0NmJgDftGzJ/xkzNv76dRgwQO0J790bWrc2nu1a0v5/7TmWqmaCiRwUSVir\nMH36M0Px7rsQEKCGoDg51dxeYjgURUEIYZTAf/kbaxzi4uJYsGABkyZNAmDlypWEhobqHfg1a9bQ\ns2dPUlJS9M6k5N6kuu9vbSpc/gz8DuyXqQYlktrTtm1bGpVUc0xNTeXo0aMVBoSYErMGZmXf/mK4\ntu2aSXQ8U67HZ016OltKHHGjYG8PGzao9dTroeMN8IxfWYjMpO2TaPt1W76J/sagNj/9FMLDwdoa\nomVFB4mkzsjJyWH58uVER0dz8uRJAPLz8/WOd+lgy/DwcH0vr+T+pDY932HALMBKCOGrKEp74CMh\nROXJJA2EfCqX3IsMHjyYJUuWAGBnZ0fXrl3ZuNFwg+Zuh0ufXKIwqRDNsxqcg5wxb2iY2LbqOJWb\nS5uDB/XLfTQaIh99FDNTVJ4sKlJHHdYjtlzcQsjCEAA0DTSsHbKWzp6dsTAz3FuKvDzV+d69G7p1\ng/Xr62Uh0AcC2fN9f5OZmUl0dDQhISGmliIxAHfV8w1MBgKBawBCiKOAYRIfSiT3Gc+UG9zXpk2b\neuN4A/i850PL+S1pHNrYJI43gL+tLU3LpQub6O1tXMe7uBjmzlVjwL291QqY9YgezXpgY6GmCMvI\nz6CJXRODOt4AtrbwxhuQkKC+GJCOt0RiGPbs2UPPnj1NLUNiAmrjfGuFEFk3rZOPxxJJLSjvfB8+\nfJicnBwTqrmV/IR8kr5N4uyrpknqrCgKzzg7A9DM2pr0m1JwGRwzMzWp9SuvwJkzaun5eoS1hTU9\nfXoC0NCyIafTT1OsK+Z6wXWD2u3dW8Z7SySGJjQ0tM6rX0ruDWrjfJ9QFGUIYK4oSgtFUeYBew2s\nSyK5L3B3d2fQoEH85z//YePGjRQUFLBr1y5TywJAV6jj6ONHubbjGo49HBE60zxTj2/WjNOdOxPb\npQt+DRrwZWKicQr//PknjBqllnWMjQUHw5Zvv1Pee+w9tr20jagXovj52M+4znI1eNx3KadPq0VA\nJRKJRFJ31Cbm2xZ4D3i6ZNVGYKoQIt/A2m7WIePRJPcseXl59OrViyNHjhASEkJERIRRqxdWhaEz\nZ9yOjkcPHiRXp+NZjYZZfn7YGii/qp5ffoGXSpI5Pf447NgBly6Bj49h7d4hh5IOceLKCZ72e9rg\npeYzMtQonJwcNQw+KwtKCuRJjIiM+ZZI7l2q+/7W6HxXcUBvIUTcXSu7PZvyxiC5p9m+fTuBgYHY\n2tqaWkqlaLO0mDUww8zaNOkQUwoKcLOyMt7DQGoqNGmizisK+Pmp3uaJE1CSpeZBRQhwd1cvEcCe\nPergS4lxkc63RHLvcscDLhVF6aooygBFUVxLltsqirII2G0AnRLJfU3Pnj3rneMthODiuxfZ12wf\nu512k7463WRamlhbG7cX3s0NSks/C6EW27l8+Z5wvNPz0onNjDXY8RWlYsGd9bMbqvUAACAASURB\nVOsNZkoikUgeOKp0vhVF+Qz4EQgH1imKMgnYBPwJyMzvEskdIITg1KlTfPfdd6aWAqhP5mkRaRQk\nFACQsdl01S4BcrRa1qSn80ZMDLmVlCquc559tmz+7Fl1AGY9Znf8bgK/C8TvCz9WnF5hUFu9epXN\n//BDvUsEI5FIJPcs1f3S9AE6CCEGo8Z7TwB6CCHmGjveWyK5H9DpdDz88MP06tWL6OhoCutJ7e5W\n37bSz2euzzTOYMdKEELQ/cgRxp0/j42ZGVpj6Ch1vhVFDXQuLFRjv2/cMLztO8DOyo6nmj9FzBsx\nvNPtHYPaCg4um09KUpPCSCQSieTuqc75vlHqZAshMoGzQogY48iSSO4vCgsLef/997GwsCAzM5Mv\nvvgCKysrU8sCwKGrA+aO6uDGgoQC8k7lmUTH/507x1+5uZzPz8fNygpHY6Tg6tIFliyBtDQ1yLlx\nY3jnHdXbrGcMXzWcDt904NPdn7IzbqfB7Tk7q5fH0REGDACdzuAmJRKJ5IGgOufbT1GUyNIJ8Llp\nWSKR1BJLS0uWLFnCiRMnyMnJYe/e+pOt08zSDNvWZbHoKYtSTKKji729fn5DhpHCXywsYOBANc57\n1Cg128nBg+rgy3qGl4OXfn59zHoOJx9md7xhh99EREB6OixbBs2bG9SURCKRPDBU53z3A2aXm25e\nlkgktURRlAoFd7766ivefPNNCgoKTKiqDOumapVJC40Flo6mKbH+jEajn9+Wmcmzf/3FRWOGf7Rr\nB+U01Deeeajs8/PTsZ8YsmIIh5MPG9SmhwdcuQI//QSDBqmOuEQikUjujirf6wohdhhTiERyv9Or\nVy/+97//ARAVFcUHH3xAYWEh1uXKq5uKVt+2otmEZth3tEcxM03eb68GDfC3teV0Xh5aoKOdHa6W\nJngQuHwZNm2CsLB6lfmki2cX7K3suV54HZ3QsXrQalo1blXzjnfJqFHQsCE88wzUg4+qRCKR3DYj\nRozAy8uLjz76yNRSgNpVuJRIJHVAUFCQvpRwbm4uw4cPx75cqIUpsdRY4tDJAcVMoehqEbpC0wT4\nlu/9ztPpsDN26eUXX4S2bSEqCq5dM67tGrA0tyS4edkoyA0XNhjF7rp18Pvv8MorUE8+rpIHEDMz\nMy5evFhh3ZQpUxg2bFil7QsLCxk5ciQ+Pj44OjoSEBDA+nI5M4cNG4aHhweOjo60bt2aH374ocL+\nPXv2xMbGBgcHB+zt7fH396/7kzIANZ337bY9c+YMwcHBODk50bJlS1atWmWM07jvkc63RGIkHBwc\n6FZSqcTHx4dLly6ZVtBNJH2bxOGuh9nnvY+cv3JMoqG3RkMXe3smeXvzopubcY3n50OfPvDVV6q3\nWQ/jvp9u/jQ+Tj78M+CftGrUiojTEfx89GdTy5JIDE5VNQCqWq/VamnWrBm7du0iKyuLjz76iOef\nf574+HgA3n33XeLi4sjKyiIyMpL333+fI0eOVDjuV199RXZ2NtevX+f06dN1f1IGoKbzvp22xcXF\n9OvXj759+5KZmck333zD0KFDOX/+vLFP675DOt8SiRGZPXs2Z8+eZdOmTRw6dIiwsDASEhJMLQsA\nKw8rfD/xpXt6dxw6OZhEw1MaDfsDAnhKo+HX1FTaHDjA9sxMwxsurWo5aBCMH68W3amH/F/A/3Fx\nzEVGdx7NgGUD+O7wdwgMrzUqCvr2VZPB7NtncHMSyS3cbgpUW1tbPvzwQ7y81IHKffr0wdfXl+jo\naAD8/f2xLAlrE0KgKAoXLly4Y5vBwcFo60Ey/JrO+3banjlzhuTkZMaOHYuiKAQFBdG9e3cWLlxY\nqe0ZM2bQtGlTHBwc8Pf3Z9u2bQAkJyczYMAAXF1d8fPzY968eRX2S0xMJDw8HFdXV1xcXBgzZgwA\np0+fJigoCGdnZx599FHWrFlTYT9fX19mz55Nu3btcHZ2ZvDgwfoUvkeOHCEgIABHR0cGDRpEfn7F\nDNlVaTUWNTrfiqK0VBTlO0VRNiqKsrV0MoY4ieR+o1OnTrRs2ZLJkydz8OBBXnjhBZydnU0tC4DG\noY1xftIZ8wbmppbCgexsnCwsWNC6NY85ORneYKtWYF5y3vHxsHQpTJ4MsYarInknmJuZoygKD7s8\nzJV3rvDHC38wvP1wg9sdPhzWrIGrVyHFNMlwJJK7IjU1lZiYGNq0aaNfN3r0aBo2bIi/vz8eHh70\n7t27wj4TJ07E1dWVxx57jB07qh4Gd/nyZQB9WGFdERYWhrOzMxqN5pa/ffv2rdUxKjvv6tqeO3dO\n37ayhw8hBCdOnLhl/blz55g/fz7R0dFkZ2ezYcMGfHx8EEIQFhZGhw4dSE5OZsuWLcydO5dNmzYB\nav2L0NBQfH19iY+P5/LlywwaNAitVkvfvn3p1asXaWlpfPHFF7zwwgvExFTMeL1s2TI2btxIbGws\nx44dY8GCBRQVFdG/f39eeuklMjIy+Mc//sGKFStq1GpUhBDVTsAx4DUgEAgonWrar64nVapEIjEk\nOp1O5MbkigvvXxBpa9NMLce4hIUJofZ5C9G0qRD//rcQly6ZWlW9YPz4skszerSp1Tw4lPzu1Yvf\n2EkXLwq2bbtlmnTxYq3bV9W2NiiKIi5cuFBh3eTJk8WwYcNq3LeoqEiEhISI11577ZZtOp1O7Nmz\nR3zyySdCq9Xq1x84cEDk5OSIwsJC8fPPPwt7e3txsRL9mzZtEs8//7wYMmSIWLhwYY1ali9fXmOb\nuqK6865N26KiIuHn5yc+++wzUVRUJDZs2CCsrKxEr169btn//Pnzws3NTWzevFkUFRXp1//555/C\n29u7Qttp06aJl19+WQghxN69e4Wrq6soLi6u0GbXrl3C3d29wrrBgweLKVOm6Jd9fHzEokWL9Mvj\nx48Xr732mti5c6fw9PSssG+3bt3EBx98UK3Wuqa6729twk60QoivhRAHhBDRpZMhHgQkEolpOR52\nnAMtDhD/cTxJX9WPQjNFOh0FxqjwEhJSNh8YCDNngre34e3eIUIITl45yey9s5mweYJBbZXLkskf\nf0BxsUHNSSS3YG5uTlFRUYV1RUVFWFpasmjRIuzt7XFwcKBPnz4V2gghGDp0KNbW1reEO4Aa292t\nWzcSEhL4+uuv9es7d+5Mw4YNsbS05MUXX6R79+5ERUXdsn9ISAjm5uaMGzeOoUOHApCVlUVERATT\npk2r0DY7Oxs7OzvOnz/PypUrmTp1KocPGyZdaE3nXZu2FhYWrFq1irVr1+Lu7s7nn3/OwIEDadq0\n6S3H8PPzY86cOUyePBlXV1eGDBlCcnIycXFxXL58GY1Go++5nzZtGleuXAHUkBNvb2/MzCq6o0lJ\nSfpwmFK8vb31bxlKcSs3NsjW1pacnBySkpLw9PS8Zd/KtLq5uem1GpPaON9rFEX5l6Io7oqiaEon\ngyuTSO5zduzYwTvvvEP79u05ePCgqeUA4DrQVT+fcyTHZKXmAaKuXiX8xAlc9+4l6upVwxss73xv\n3VrvPcyk60n0WdSHmIwYejTrYVBb3buXpRm8eBF++cWg5iSSW2jWrNktg9RjY2Px9vZmyJAhXL9+\nnezsbNatW1ehzSuvvEJ6ejoRERGYm1cdUqfVam+J+S6PoihV3g+PHj1Kx44d9culmUNufljYunUr\nQUFBrFmzBk9PT958801mzZpVpc3evXvrHypunm5+yLiZ2p53TW0feeQRtm/fTlpaGn/88QcXLlwg\nMDCw0uMMGjSIXbt26QdsTpgwAS8vL5o3b05GRgYZGRlkZmaSlZWlj9/28vIiPj4e3U0dLB4eHreM\nh4qPj7/Fqa4Md3d3EhMTb9m3Mq1xcXF6rcakNs73S8C/gb1AdMl0yJCiJJL7HSEEERER/PXXX3z2\n2We0b9/e1JIAcBvihoWzGrdYmFxoslLzx3Ny+C4pif3Z2bzcpAn9XVwMb9TfHx5+GPr3h//8R/Uw\nX3oJKhmoZGrOXT3HouOL8HP240nfJwltGWpQe9bW6uUpJTfXoOYk9ZDJvr6Inj1vmSb7+ta6fVVt\na8PAgQP5+OOPuXz5MkIINm/ezNq1axkwYECV+7z66qucOXOGyMhIrKys9OvT0tJYunQpubm56HQ6\nNmzYwJIlSwgOVlN5ZmVlsXHjRgoKCiguLua3335j165dFQqllXLq1Cn8/f1RFIXIyOqLfxcUFGBl\nZcVbb71FYGAgiYmJ+FZzTaKiovQPFTdPNz9k1Oa876Tt8ePHKSgoIC8vj1mzZpGSksLw4cNvaXfu\n3Dm2bdtGYWEhVlZW2NjYYGFhQWBgIA4ODsycOZP8/HyKi4s5efIkhw6pbmRgYCDu7u5MmDCBvLw8\nCgoK2Lt3L126dKFhw4bMnDkTrVbL9u3bWbt2LYMGDar2fAC6du2KpaUl8+bNo7i4mIiICA4cOFCt\n1poeUOqcquJR6tuEjPmW3Ec888wzAhCAWLlypanlVOB4+HGxjW1iG9tE7EexJtGwNDVVHyfa5dAh\n4xnW6dS/b74pRP/+QsyfL0RKivHs15KpO6YKJiOYjBgWUXPMa12weLEQb7whxLp1QuTkGMXkAw/1\nKObb1Ny4cUOMHz9e+Pj4CCcnJxEQECDWrl1bZfu4uDihKIqwsbERdnZ2ws7OTtjb24tFixaJtLQ0\n8cQTTwhnZ2fh6Ogo2rZtK3744Qf9vmlpaaJz587CwcFBODs7i65du4otW7ZUaic5OVmMGDFCLF68\nWKSnp+vXX7p0qUJ88rVr18TGjRsr7Pvpp5+K3NzcO70kt33eQgjx7LPPimnTptWqrRBC/Pvf/xbO\nzs7C3t5e9O7d+5a4+1L++usvERgYKBwcHESjRo1EWFiYSE5OFkKo12jw4MGiSZMmQqPR3HI9ExIS\nxHPPPScaNWokXFxcxNixY4UQQpw6dUo88cQTwtHRUbRp00asXr26gk1fX98Kxyk/BuDQoUOiQ4cO\nwsHBQQwaNEgMGjRIH/Ndnda6pLrvryJqeK2sKIol6oDLx0tWbQe+EUIUVbmTAVAURdSkVSK5V3j3\n3Xf18YD//Oc/mT9/PoDxn74r4djTx8jcpKb3c3zMkQ47OxhdQ0ZRES579qBDfT13JjCQRpaWaExR\n8bIecvDyQQK/V1/9Olo7MqzdMOKz4lk9aLVR7OfkgKKolS8lhqMk1MEoJWflb2zdEhcXx4IFC5g0\naRIAK1euJDQ0VJ/ecM2aNfTs2ZOUlBRatGhhSqkSA1Hd97c2YSdfo2Y4+apkCihZJ5FI7pDyry9/\n/fVXXF1d2bt3rwkVldHsg2Y0bNcQr3974TPFxyQaNJaWdC4pp6gD2h06xI56VnHSlHR074jGRh16\nk1WQhblizgePf2Bwu4sXw+OPQ5Mmali8RCK5lZycHJYvX050dDQnT54EID8/X+94lw62DA8P5/ff\nfzelVImJqE3P9zEhRLua1hka+VQuuZ8oLCzE2dmZvDw1pnrfvn387W9/M7Gq+sWk2Fg+KhkMM9zN\njZ+MXd75r79g9WrYvBkmToRevYxrvwYGLR/E0pNLAZgZMpN/d/+3wW3u2KEWAu3RQ/Z6GwPZ831/\nkJmZSXR0NCHlB3VL7nvutue7WFEUfZ1lRVGaA/U7DYBEUs+xsrLi8ccf1y9XVrTAlAghyD2dS+K8\nRPLOmWbQ5dOasqRKcQUFxhewbRtkZsKECfDYY8a3XwNP+z2tn9+dsBu4/SqAt8sTT6hpB6XjLZHU\nnj179tCzZ09Ty5DUI2rT8x0M/ARcBBTAGxghhDBqLU75VC653/jiiy9YtmwZwcHBPP/887i5uWFj\nY4Otra2ppXFu9Dmurr2K81POeI3zouHDxve2inQ6fklNJdjJCa8GDTiak0NjS0u8GzQwrOGcHFi2\nTO3xzshQE1vXQ5KuJ/H94e95wvsJDicfZuulrcRmxnL8teMoimE7S4WAI0fA1RUqSfkrqSNkz7dE\ncu9S3fe3Rue75ADWQCtU5/uMEMLo3VDyxiC5X/nf//7Hd999R0xMDOvWreOxetDLWpxXjJmNmcGd\nuNrwTVIS7168iJuVFZ/5+dGnUSPDGszKgkaNyvJ8p6WpyzpdWQn6eoRWp2XsH2N53PtxnvR9EpeG\nhk3LOHQoLF8OBQUwZQp8+KFBzT3QSOdbIrl3uSPnW1GUJ4UQWxVF+Xtl24UQEXWosUbkjUFyv7J1\n61asrKwIDAysMSerqRBCmMwRv3TjBpZmZniWVnkxBt26wb596vyTT8L58zB9OgwebDwN9ZSXXior\nsvPWW/Df/5pWz/2MdL4lknuX6r6/FtXs9wSwFQirZJsAjOp8SyT3K08++aSpJVTK9aPXiZ8ez7Ud\n13Dq6USbxW1MosPHxsb4RkNCypzv/HxYvx5atza+jjsguyAbB2sHgx1/4MAy53vLFoOZkUgkkvuW\n2sR8+wohYmtaZ2jkU7nkfken03Hs2DGaNWtGI0OHVtSCEwNOkL4iHYAGDzXgbzGmzcaSUVTEtmvX\neMjGhnZ2doY1tmuXmlMPoHlzqKbsdH0gX5vPxM0T2Ry7mZzCHC6OuWiwNxU5OeDsDFqturx/P3Tp\nYhBTDzyy51siuXe522wnKypZt/zuJEkkkvJMnz4dNzc3Bg8ezNmzZ00tBwD/X/xRrNT7Rv75fAqS\nTJBxpITZCQk027eP2QkJZBQZob5Xly5lKT3i4iApCW7cgOvXDW/7DrA2t8be2p6pPacS80aMQUOE\n7OwqDrKspsq1RCKRSCqhSudbUZTWiqKEA46Kovy93DQcMHC6AYnkwWH79u2cPHkSjUbDd999R7du\n3UwtCQBzW3McuzvqlzO3ZJpEx5bMTKZeukSuToe9uTlBzs6GN2plBbNmQWQkLFwIL76opvZYu9bw\ntm+TqJgo/Of7M3XnVNbFrMPCrLpowrohPFz96+Iis51IJBLJ7VLdXboVEAo4UTHu+zowypCiJJIH\niUWLFvHrr78CsHnz5nqR7aQUuwA7rm1TK0umLEihybAmRtfQwsaGrJLMI7uysijQ6bA2q81Lu7vk\n1VfVv3v2wNixapJrB8PFUt8pjtaOnL2qvi3ZHLsZrU5LzNUY/F0MV5Ro7Fj1eeSRR8AY/wqJRCK5\nn6jS+RZCrAZWK4rSVQixz4iaJJIHiuDgYL777jsA1q9fT/v27enYsSPe3t4mVgZoy2aLrhoh3KMS\nmjVowEM2Npy/cYMbOh1vxsTwvKurcXrAAbp3N46dOyTQMxA7KztyCnO4dO0SjWY2olWjVuwfuR8z\nxTCesZeX2uN94YKaDr1TJ3WSSCQSSc3U5s78qqIoTqULiqI4K4ryY20NKIrSS1GUM4qinFMU5T/V\ntBugKIpOUZSOtT22RHI/UD7byYEDB/jqq69IS0szoaIy/Gb50eLLFnQ+1ZlOR0znXYWUc7QPXr+O\nvanybV+8CJcvm8Z2FViaW/K4d1m11A8e/4ADow4YzPEuZepU9WXA3r1qCnSJRCKR1I7a3J3bCiGu\nlS4IITKBDrU5uKIoZsCXwDNAG2Cwoii35OtSFMUOeAPYX5vjSiT3Ey4uLrRv316/PHbsWDrVk25E\nxVzBc7QnDf0blo7cNomO8s63pZkZnYwd/rFoEfj5qb3ge/ca13YtCPEN0c8fuHzAKDb//W9ITFTT\nDgYGGsWkRCKR1CkjRozgQxNUCquN822mKIr+l09RFA3Vx4qXJxCIEULECSGKgCVAv0raTQVmAKZL\npyCRmJDg4GD9/OHDh02o5FauR1/nwvgLHOp4iJSfUkyiIcjJCRszM4KcnHiucWPjC2jdWu3qTUqC\nf/zD+PZrILi5+vnxb+xP68atSctNY/359Qa1aWMD9aAAquQBwcfHB1tbWxwcHHB3d+fll18mLy/v\nto9TWFjIyJEj8fHxwdHRkYCAANavL/uuDBs2DA8PDxwdHWndujU//PBDhf3PnDlDcHAwTk5OtGzZ\nklWrVt31uRmDms77Zmq6Dvb29jg4OODg4IC9vT0WFhaMHTvW0Kdx/yCEqHYCXgROozrIU4EzwLCa\n9ivZNxz4ttzyUOCLm9q0B5aVzG8DOlZxLCGR3K8cOXJE/PjjjyI2NlYcO3ZMzJ49Wxw7dszUsoQQ\nQqQuTRWxk2PFtd3XRHFhscl05BcXi8zCQhFx5YoYffasWJqaanijublC9OghhIWFEDY2Qty4YXib\nd0CxrlgkZiWKouIi0fGbjsJxmqN4bslzQlusNajd3FwhfvpJiKefFuLHHw1q6oGk5Hevxt/aupjq\n+2+sj4+P2Lp1qxBCiKSkJPHII4+IiRMn3vZxcnNzxZQpU0R8fLwQQoi1a9cKe3t7ERcXJ4QQ4tSp\nU6KwsFAIIcTZs2dFkyZNxOHDh4UQQmi1WtGyZUsxZ84codPpxNatW0XDhg1FTExMXZyiQanpvG+m\nuutQ2bHt7e3F7t27DSPegAwfPlx88MEHBjl2dd/fGnu+hRC/AAOAVOAK8HchxMJa+vaV9Yvo31sr\najLaz4G3a9hHIrmvad++PSNGjGDu3LmEh4cTExODWT1JI+H6vCs+k3xw7O6ImaXpNFmbmbEsLY1v\nkpLwadCAjoYutANgawupqWpFmRs3YMcO2LoVTpwwvO3bwEwxw9PBEwszC37q9xPp49NZOXAl5maG\njY0PDoYRI2DjRjh1yqCmJBJ92Ju7uzvPPvssJ0q+h2ZmZly8eFHfrrpQAltbWz788EO8vLwA6NOn\nD76+vkRHRwPg7++PpaWl3p6iKFwoKbJ15swZkpOTGTt2LIqiEBQURPfu3Vm4sGqXKDg4GK1WW+V2\nY1HTed9MddfhZpYtW4arqyvdqxicPmPGDJo2bYqDgwP+/v5s27ZNvy05OZkBAwbg6uqKn58f8+bN\n029LTEwkPDwcV1dXXFxcGDNmjH7bmTNnCAoKwtnZmUcffZQ1a9bot/n6+jJ79mzatWuHs7MzgwcP\nprCwEIAjR44QEBCAo6MjgwYNIj8/v9Za65JahY8IIU4qipJGSX5vRVGaCSHia7FrItCs3HJTIKnc\nsj1qLPj2Eke8CWqGlb5CiFvevU+ePFk/37NnT3r27Fkb+RLJPcOMGTP4/PPPTS2jUoQQ5J7IRZuh\nxekJp5p3MACjPDwY5eFhXKMhIRATo8737QsdO8J776l59uohbd3aGs3WqFFqhUuA48eNZva+Zfv2\n7Wzfvt3UMuo9CQkJREVFMWDAgLs+VmpqKjExMbRp00a/bvTo0SxYsIAbN27QsWNHevfuDZQ5/+UR\nQugfAm7mcsngbAuLus29HxYWxu7du/XjcMr/7dGjB5GRkTUeo7LzvpmqrsPN/PLLL7z44ouVbjt3\n7hzz588nOjoaNzc34uPjKS5JHSuEICwsjP79+7N06VISEhIICQmhdevWBAcHExoaSkhICL/99htm\nZmYcOnQIAK1WS1hYGCNHjmTTpk3s2rWLfv36ER0dTYsWLQD1gWDjxo1YW1vTrVs3FixYwIgRI+jf\nvz/jxo1j9OjRrFq1isGDBzNhwoQatdY5VXWJi7JXUX2BGCAXiAV0wMma9ivZ1xw4D3gDVsBRwL+a\n9tuADlVsq/NXAhKJpHYkzEsQ2xtsF9vYJvY/tN/UcozLihVCgDp17GhqNbVCW6wVBy8fFF/++aVB\n7cTFlV0aGxsh8vMNau6Bg3oUdjLp4kUx6eLFOlu+XXx8fIS9vb1wdnYWPj4+4vXXXxf5JR84RVHE\nhQsX9G1rG0pQVFQkQkJCxGuvvXbLNp1OJ/bs2SM++eQTodVq9e39/PzEZ599JoqKisSGDRuElZWV\n6NWr1y37b9q0STz//PNiyJAhYuHChTVqWb58eY1t6orqzvtmKrsO5YmLixMWFhbi0qVLle5//vx5\n4ebmJjZv3iyKiooqbPvzzz+Ft7d3hXXTpk0TL7/8sti3b59wdXUVxcW3hjru2rVLuLu7V1g3ePBg\nMWXKFCGE+llZtGiRftv48ePFa6+9Jnbu3Ck8PT0r7NetWzf9Z6U6rXdCdd/f2rxDngr8DTgnhPAF\ngoE9tXTsi4HXgY3ASWCJEOK0oihTFEUJrWwXZNiJ5AEnKyuL1atX88YbbxjsldftYt3UGpGv9vrk\nX8pHe910r1Ev3bjB5wkJ9PnrL+YmJhreYFBQ2cjCo0ch0zSVPmuLTujwmevD8FXDOXf1HEXFhsvP\n3qwZ+Pio8zduwEcfGcyURMLq1avJyMggNjaWefPmYW1tXW37RYsW6QcG9unTp8I2IQRDhw7F2tq6\nQqhDKYqi0K1bNxISEvj6668BtQd71apVrF27Fnd3dz7//HMGDhxI00rKvIaEhGBubs64ceMYOnQo\noN7bIyIimDZtWoW22dnZ2NnZcf78eVauXMnUqVMNNvC+pvO+mcquQ3l++eUXevToUWVdCj8/P+bM\nmcPkyZNxc3NjyJAhJCcnAxAXF8fly5fRaDRoNBqcnZ2ZNm0aqampJCQk4O3tXWn4ZVJSkj58phRv\nb2/9mwYANzc3/bytrS05OTkkJSXh6el5y3610VrX1Mb5LhJCXEXNemImhNiGOkiyVggh1gshWgkh\nWgghppesmySEuKVOsxDiSVFJuIlE8iAxa9YsPv30U5ycnPAp9WxMjMtzLjRs1xAAoRVk7cwyiY7r\nWi3fJCXxfXIyNmZmDC13gzUYzs5qBRkXF3j+eTW4+ccfoR6GB2h1Wv5M/JNhjw7jnW7vMPfZuVia\nWxrUZvmPaG6uQU1JHnBEJWEfoDpX5TOfpKSoWZmGDBnC9evXyc7OZt26dRX2eeWVV0hPTyciIgLz\nauoGaLXaCrHOjzzyCNu3byctLY0//viDCxcuEFhFrs2jR4/SsWNZ6ZLSLCNFRRUfiLdu3UpQUBBr\n1qzB09OTN998k1mzZlWpqXfv3hWyjZSfbn7IuJnanvfN3HwdSlm4cCHDhw+vdt9Bgwaxa9cu4uLi\nAPRhHl5eXjRv3pyMjAwyMjLIzMwkKyuLtWvX4uXlRXx8PLpKigh4eHiQAtP0LAAAIABJREFUkJBQ\nYV18fPwtjvXNuLu7k3hTh018fMUI6qq01jW1cb6vleTh3gn8pijKXCrUvZNIJHXFtGnT+PLLLzlw\n4ACenp74+vqaWpIe55CyXNtpq01TBOhkbi7TExI4lZfHn9evo6njWMoqWbsWUlLUqjL9+8OmTWq0\nRT0j4nQE3X7sxrQ90/ju8HdGsTlmjBoGP348DB5sFJMSEzDZ15fJ5e5Hd7tcl3To0IFFixah0+lY\nv349O3bsqLb9q6++ypkzZ4iMjMTKykq/Pi0tjaVLl5Kbm4tOp2PDhg0sWbKkQirY48ePU1BQQF5e\nHrNmzSIlJaVS5/PUqVP4+/ujKEqNMdgFBQVYWVnx1ltvERgYSGJiYrX3/qioKP1Dxc3TzQ8ZtTnv\nm6nNdQDYu3cvSUlJ1cbenzt3jm3btlFYWIiVlRU2NjZ6pz8wMBAHBwdmzpxJfn4+xcXFnDx5kkOH\nDhEYGIi7uzsTJkwgLy+PgoIC9pbUWOjSpQsNGzZk5syZaLVatm/fztq1axlcww2oa9euWFpaMm/e\nPIqLi4mIiODAgbK6CNVprWtq43z3A/KAt4D1wAUgzCBqJJIHHFtbW65dU2tabd682cRqKmJuV3YT\nyliXYRINneztcSi5GSYWFHDuxg2KjeEEu7qCmZma2iMlBRYvVsNR6hlBPmWa9ifs5+djPzNuw7gq\newzrgv79IToaZsyALl0MZkbygKNUk1R+zpw5REZG4uzszOLFi+nfv3+VbePj4/n22285evQobm5u\n+h7kxYsXoygKX3/9NV5eXmg0GsaPH8/cuXMJDS2Lkl24cCHu7u40adKEbdu2sWnTJn1WkPJoNBoc\nHR1ZsmRJlVlAQA1F0Wg0FdatWrWK9957r7rLcdtUd96g9qZPnz4doFbXAdSQk/DwcBo2bFil3YKC\nAiZMmICLiwseHh6kpaXx6aefAmqWmjVr1nD06FF8fX1xdXVl1KhRZGdn67fFxMTQrFkzvLy8+P33\n3wGwtLQkMjKSqKgoGjduzOuvv87ChQv1gy2r+qxYWlqyYsUKfvrpJzQaDcuWLSM8PLxWWusapbqb\nsqIo5sAGIURIlY2MhKIowpA/IBJJfeDEiRM8+uijANjY2PDkk0/y0ksv8Y96UNgl+2A2R3ocwa69\nHZqnNfhM8UExM/4QjX7HjxN59SoATaysGOXuzkf16A2BqenwTQeOphwFoJNHJ1549AVe6/Qa1hbV\nx8feDSdOqM8jW7aoNYjefrvmfSQ1U5LFwihfMvkbaxzi4uJYsGABkyZNAmDlypWEhobqHfg1a9bQ\ns2dPUlJS9M6k5N6kuu9vtT3fJQMm8xRFcTSIMolEUoE2bdroB4rcuHGDHj163PKqz1Q4dHagR2YP\nAv4MwHeqr0kcb6hYar61rS2TjB0XX1Cg5vv+8ENYtsy4tmtBsG/Z56WHVw/e/NubBnW8AeLj1TGp\n06fD668b1JREcs+Sk5PD8uXLiY6O5uTJkwDk5+frHe/SwZbh4eH6Xl7J/UltAibzgeOKomxCTTcI\ngBBiTNW7SCSSO0FRFH1e09Llm19JmhJzW3OETpB7PBczGzNsW9oaXUNwOef7WE6O8dMj/forfPON\nmv/7oYeMbb1Ggn2Dmb1vNgBbYrcYxWbv3uokkUiqxs7Ojrfffpu3S14NZWZm4uLiot/ev3//akNm\nJPcPtXG+15VMEonECAQHB7N8+XJ69Oihz3YiSgoomJrUJamcH3MeC2cLfKb4mMT59re15XVPT7rY\n2/OkszM6IcjWanGqJO6yThECzp+HoiJ4+GH45JOyFIT1iMe8H+PZh56lp3dPmjo25bM9n7Erfhcr\nnl9h8MwnoF6m/HywsTG4KYnknmbPnj306tXL1DIkJqDKmO/bqGJpFGQ8muRBIS8vD0VROHHiBIsW\nLWLz5s289tpr/Otf/zK1NAqSChBaQYNmDUwthX1ZWUyLj2fntWu87eXFB4YOP9Hp1IGXJfHmHDsG\nbY1XTfJ2EULwzK/P0LJRS0Kah9C7RW+szKvOcHC3LFgAn30G585BeDgsWWIwUw8MMuZbIrl3qe77\nW13P9yqgY8kBVgghwqtpK5FI6ghbW7U3OTk5GVdXV3788ccKuWJNibWHYWOHbwdHCwtecHPjh1at\ncKkmbVadYWYGTz5ZFuc9fTqYm0Pnzmq+vXqGoihsHLbRaPb+/FNNgQ5QQ+0TiUQieaCpruf7iBCi\nw83zpkI+lUsk9YPiG8VcXXeV1IWpmDU0o82iNqaWZDy+/Rb++U913t0dJk+GXr3UUo8POH/+CX/7\nmzrv6QkJCfUyKueeQvZ8SyT3Lnea7URUMS+RSIyMVqslJyfH1DIASJybyKl/nOJq5FWurr6KTntr\nBTJjk1pYyMUbNwxvKKRc1tXsbBg+vN473itPr+Rf6/5Fm6/akFNouM9QQAA4luTFunwZdu82mCmJ\nRCK5p6nO+W6nKEq2oijXgbYl89mKolxXFCXbWAIlkgeZLVu28Nxzz9G4cWN++eUXU8sBwONfHlh5\nqmEeujwdOdGmeyjYmpnJowcP0vLPP1mdnm54g82bQ2lO8dxctbsX6mW1y1I2XdxEM8dmLPr7Imwt\nDTdA1sJCHYdayhdfGMyURCKR3NNU6XwLIcyFEA5CCHshhEXJfOmygzFFSiQPIhkZGRw4cICcnBy+\n//77ejHgEsDSwRLNU2XpDzM3Z5pEx8UbN/g0Lo4LN27QysaGt7y8jGN41Ci1lvpPP8H69dCjB0yc\naBzbt8HZ9LOMihzF+vPrOZx8mHZN2mGm1Kao8Z1T/sWAnZ1BTUkkEsk9S21SDUokEhMwc+ZMZsyY\nAYC/vz8DBgwwsaIynIKdSFmQAsCVJVfwfs/b6BqcLSzYdu0aOiA6J4drRUWGTzcIZY723r1w+jRM\nmgTVlI82FYXFhXx/5HsAsguy0Qkdxbpig6YbfPllcHNTnfCWLQ1mRiKRSO5pDNsNIpFI7piQct2I\nmzdvJjU1laSkJBMqKqMorUg/n3sml+K8YqNrcLa0JMDeHgAd8G1SEvuzsownoFs3mDEDnnoKbI2f\n77wmHnF9BNeGrgBcvXGVx356jMafNeZq3lWD2fTxgdGjVcf71CkoKeInkUgkknJI51siqad0794d\n65KcbWfOnKFly5ZERUWZWJVK07FNafpWU1p+05IuZ7tgbmtuEh3lq13OTEjgRG5uNa0NiBCQl2ca\n21WgKEqFUvN+zn5cGnuJRraNDGp33Tpo2hRCQ2HfPoOakkgkknsS6XxLJPUUGxsbHnvsMf3y7Nmz\nGTlypAkVlaGYKTz034fw+D8PbJqbrpRhSDnnu5GlJSM9PIwr4K+/1IwnXl7w/vvGtV0LyjvfV3Kv\n4GzjXE3ruqFTJ9i5Ey5ehHrycZVIJJJ6hXS+JZJ6TPnQk61bt5pQya1or2tJX5NOzNgYLn992SQa\nujs40MDMDFszM/xsbLhRbOTwFwsLaNcOtm2D2bONa7sWhDQv+/xcL7yOEILL2ZcxZD5nNzfw85M5\nviUSSf1hxIgRfPjhh6aWoUc63xJJPaZ379689NJL/Prrr8yaNYuDBw9y4sQJU8sCIHNLJolzE7Fy\nt8LpCSeTaGhgbs7+jh3J6NGDn1u3ZnV6OivT0oxj/KOP1F7vd95RY77robfp7eTNqoGrSH0nlSd9\nnuThrx6m3f/akZyTbHDb2dkwfz789pvBTUkeAMzMzLh48WKFdVOmTGHYsGGVti8sLGTkyJH4+Pjg\n6OhIQEAA69ev128fNmwYHh4eODo60rp1a3744YcK+/fs2ROb/2fvzOOirPbH/x6GQXZklEFQECQX\nNC8uiSlqkpqmQClqLmSaeq9pqdm3pPSSlWVy9aZ5yZZbeX+aUioa4IaaGmqmopAbikrsKgiyyjIw\nvz8GBpDNlFnQ83695jXP88w5z+czB2bmc875LGZmWFtbY2Vlhbu7e/O/KS2Rk5PD2LFjsbS0xNXV\nlS1btjTYNikpiTFjxiCXy3F0dOSNN96gokJdu6GpMRQ8OML4FggMmJ49e7JhwwZatWpFz549mTFj\nBn/88Ye+1QLA7kU7eh3oRcfAjlh0t9CbHh6Wlvx65w5P/P47m2/dolxXObePHIFTp6CiAg4eVOf9\nTk3Vjey/wAvdXkBhoaC3Q29+GPcDt96+haOVdt1z/u//oHVreP112LxZq6IEjwmSBia3DV1XKpU4\nOzsTHR1Nbm4uH374IRMnTiQ5ORmA9957j6SkJHJzcwkPD2fp0qWcPXu21n2/+OIL8vLyyM/P59Kl\nS83/prTE3LlzMTU1JTMzk02bNvHaa681qP/cuXOxt7fn5s2bxMbGcuTIEb744gug6TEUPDjC+BYI\nWgBDhgzh/PnznD9/nilTpuhbnTqoKlQUpxbrTf4zrVuT5eVFeM+ejFcodCO0ZlLrt99W+1t8841u\nZD8A49zH0cehj9ZzfQMMHlxddygpSeviBI8Bf9VVytzcnKCgIJwq8/+PGTMGV1dXYmJiAHX6Vlll\nalKVSoVEIuHatWsPLHPYsGEolcq/pKM2KCoqIiwsjOXLl2NmZoaXlxd+fn5s3Lix3vaJiYlMnDgR\nmUyGQqFg1KhRXKhMU9TUGN7LypUr6dChA9bW1ri7u3Po0CEAMjIyGD9+PAqFAjc3N9atW1erX2pq\nKv7+/igUCuzs7Jg/fz4Aly5dwtvbG1tbW3r27ElEREStfq6urqxevRoPDw9sbW2ZPHkypaWlAJw9\ne5a+fftiY2PDpEmTKC6u/fvUkK66QhjfAkELQKFQ4ODgoG816lDwRwGn+5zmV9NfOfXkKb3pYWJk\nhMxIx19nI0ZUH5eVwY0b8MEHutXhASgrL+No8lHKysuabvyAjBgBJuoiqFy4AAaSIVPwECQuS+Sw\n5HCdR+KyxPtu31BbXXDz5k0SEhLo0aOH5tq8efOwsLDA3d0dR0dHRo8eXavPu+++i0KhYPDgwRw5\ncqTBe6elqWNejI2bt3SKr68vtra2yOXyOs9+fn719rly5QrGxsa4ublprnl4eGgM6ntZuHAhW7Zs\n4e7du6SlpbFnzx6ef/75etvWN4Y15YaEhBATE0NeXh779u3DxcUFlUqFr68vvXv3JiMjg4MHD7J2\n7Vr2798PQEVFBT4+Pri6upKcnExaWhqTJk1CqVTi5+fHqFGjyMzM5PPPP2fq1KkkJCTUkrt161ai\noqJITEwkLi6ODRs2UFZWxtixY3nllVfIzs5mwoQJbN++vUlddYkwvgWCFkRWVhY//fSTwQRflt0u\no+BcAaoyFeW55dy9fldvupSrVJzOy+PTpCQ+14X7R+/eUJVtJScHWsBW7JzIObT9V1sW7F1Aer72\nLGJzc3Ua9CpawJxE8AijVCoJCAhg+vTpdKlR/SkkJISCggKOHj3KuHHjNKldQV3k7Pr166SlpTF7\n9mx8fX1JTKw7eThw4ACLFi2iXbt2bNq0qUldahqBTREREUFOTg7Z2dl1nsPDw+vtU1BQgI2NTa1r\nNjY25Ofn19t+yJAhXLhwAWtra5ydnenXr1+9hn1DY1iFVCqltLSU8+fPa9xVXF1dOXXqFFlZWSxZ\nsgSpVIqLiwuzZs0iNDQUgN9//52MjAyCg4MxNTXFxMSEgQMHcuLECQoLC1m8eDHGxsZ4e3vj4+NT\nx399wYIF2Nvb07p1a3x9fYmNjeXEiRMolUrmz5+PVCrF39+ffv36NamrLhHGt0DQQggNDaVTp058\n9913FOorn/U92HrbIn9O/6XmASKzsnjx/Hkib9/mbxY68EGXSuHZZ9XH7dtDSgokJEBsrPZlPwAl\nyhJ62fdi3fPriPl7DB1ba7cqqbNz9fHVq1oVJXgMkEqllJXV3q0pKytDJpOxefNmrKyssLa2ZsyY\nMbXaqFQqAgICaNWqVR13B1D7dg8cOJCUlBTWr1+vud6vXz8sLCyQyWRMmzYNLy+veussDB8+HKlU\nyqJFiwgICAAgNzeXsLAwVqxYUattXl4elpaWXL16lR07dvDRRx9x5syZBx6T+rC0tCQvL6+OXKvK\ngmQ1UalUjBw5kvHjx1NUVERWVhbZ2dksXry4TrvGxhDAzc2NNWvWsGzZMhQKBVOmTCEjI4OkpCTS\n0tKQy+WalfsVK1Zw69YtQO1y0rFjR4zu2blMT0/XuLtU0bFjR80uQxX29vaaY3NzcwoKCkhPT6d9\n+/Z1+tanq729vUZXXSKMb4GgBXDo0CH+97//UV5eTq9evfD19dW3Shpsh1fnjs6OytaLDrdKSxl7\n4QJppaWczs/H09paN4KXLVOXmN+8GWbPBm9vdfClgXHu5jnkwXJe2/0aHxzRzTL03LlgZKReATeg\nf1fBA+K6zJWhqqF1Hq7L6l8xrK99Q23vB2dnZ/78889a1xITE+nYsSNTpkwhPz+fvLw8du3aVavN\nzJkzycrKIiwsDKm04WJgSqWyjs93TSQSSYM+4LGxsfTp00dzXpUZ5N7Jwi+//IK3tzcRERG0b9+e\nhQsXsmrVqgZljh49WjOpuPdx7ySjii5dutR5L3FxcfW6imRnZ5Oamsq8efOQyWTY2toyY8YM9uzZ\nU6vd/Y7hpEmTiI6O1gRkBgYG4uTkRKdOncjOztas3Ofm5mr8t52cnEhOTtZkWKnC0dGRlJSUWteS\nk5PrGNX14eDgQOo9u5/3BolW6ZpUGZQSGBjY5H2bE2F8CwQtgNzcXPbu3UtRUZHGV85QMOtcXWTn\nduRtVBU6yjZSA4WJCe6VJd5LVCqidVVm/sknoVs36NkT9u9Xr36/9ZZuZP8FurbtigR1VojrOdf5\nLeU3NsZtJLdYe+P01FOQnQ3HjsHChVoTI3hMeOmll1i+fDlpaeo89QcOHCAyMpLx48c32GfOnDnE\nx8cTHh6OSVUQApCZmcmPP/5IYWEhFRUV7Nu3j9DQUIYNUxelys3NJSoqipKSEsrLy/nhhx+Ijo5m\n5MiRdWRcvHgRd3d3JBJJg64gVZSUlGBiYsKbb76Jp6cnqampjbo77N69WzOpuPdx7ySjCnNzc8aN\nG0dQUBBFRUUcO3aM8PDwelMytmnTBldXV9avX095eTl37tzhf//7Hx4eHk2O4b1cuXKFQ4cOUVpa\niomJCWZmZhgbG+Pp6Ym1tTXBwcEUFxdTXl7OhQsXOH36NACenp44ODgQGBhIUVERJSUlHD9+nP79\n+2NhYUFwcDBKpZLDhw8TGRnJpEmTGh1jgAEDBiCTyVi3bh3l5eWEhYVx8uTJRnVtbFKhDYTxLRC0\nALy9vTVfDmfOnOH9999ns4HkcDN1NcXIwgikYNXfirLb2gvka4wRNapdvnPtGm/q0tfB1ha6djXI\nXN8AJlITvF29NefDNw5n5+Wd5BRrz01IKlUHXe7bp049+MknWhMleAwICgpi4MCBDBo0CLlcTmBg\nIJs3b6Z79+71tk9OTubrr78mNjYWe3t7zQryli1bkEgkrF+/HicnJ+RyOe+88w5r167Fx8cHULuz\nLF26VJN9IyQkhJ9//pnOnTvXkSOXy7GxsSE0NBQvL68G9c/NzUUul9e6tnPnTpYsWfIQo1I/ISEh\nFBUVoVAomDp1Kl9++aUmT/no0aP59NNPNW3DwsLYs2cPdnZ2dOnSBZlMxmeffQY0Pob3UlJSQmBg\nIHZ2djg6OpKZmcnHH3+MkZERERERxMbG4urqikKhYPbs2RrXmKrXExIScHZ2xsnJiZ9++gmZTEZ4\neDi7d++mbdu2vP7662zcuLGWv3lDaSZlMhnbt2/n+++/Ry6Xs3XrVvz9/RvV9RMdf0FJtFnprDmR\nSCSqlqKrQKANvLy8OH78OAAjR45k6dKlDBo0SM9aqSmIK8DsCTOkFrpdPajJrtu38Tl3DgAHExMO\nenjgrgvf75oUFcHRo+o8e/WskumTdb+vY/5edQqvF7q+wM5JO7Uu89gxePdddfaTMWOgxs684D6o\ndHXQyYxO/MY2L0lJSWzYsIH3338fgB07duDj46NJbxgREcHQoUO5ceNGvUa9oOXT2OdXrHwLBC2E\nETVS2zk5ORmM4Q1g6WGJ1EKKqlxFaWapXnR4xsYGWeVKSEZpKbbNnParSQ4fVuf6/ugjg8ytN8Kt\n+v/n0J+HUFZoPyexlxf8+iv885/C8BY8PhQUFLBt2zZiYmI0Kf6Ki4s1hndVsKW/vz8//fSTPlUV\n6Amx8i0QtBCOHTvGoEGDsLCwYMqUKXz99df6VklDwfkC/gz6kzuH7mAfYE/ndfpZyQm4eBFzqZTn\nbG0Z3aYNZkZGDW5NNisqFZw9C1FRMG8e1JNZQN+oVCqeXP8kbrZuPOvyLL3a9SI6OZpXe79Ke+um\ng5gEukesfD8a5OTkEBMTw/CahbkEjzyNfX6F8S0QtBDKysr47bffcHd3Jzo6mgMHDuDi4sI777yj\nb9UoTi0m99dcWj/bmlbtWjXdQYtklZby/Y0bHMjJwdTIiJ979tS+0OeeUwdcAkRGqn0sDJCqSn5j\nfxzL9ZzrjOg0ggX9F+Bk49R05wckORmCg9W+3wqF2hVFcH8I4/vRIDIyklGjRjV7ER6BYdPY51f8\nJwgELQSZTMaQIUM4ePAg33zzDSNGjKhTkU1fmHYwxXSKqb7VANTZTv4sLuY1R0e8awRhahUPj2rj\ne+tWSEtTRxtOn64b+fdJ1S7Aj+N/xETacOaC5mTnTggJUR9bWupEpEBgUFQFcgoEVYiVb4FA0Gwo\nc5VkhWdREFvAE6uf0Lc6umPfPhg1Sn1sbAyTJ8OECSLBNerin23bQkWFOu93VlZ1YVBB44iVb4Gg\n5SLcTgQCgda5c/wOsYNiofJj6pXthcxWpl+lgAKlEkttb/cWFoJcDqWVwabp6eDgoF2ZD8mtwlsc\nvH6Q/df389aAt+ihqFuEo7no3x+q0uyGhsJLL2lN1COFML4FgpaLyHYiEDxipKens3r1ap5//nnm\nzp2rb3UAsHC3wLyrueb8zuE7etPlRkkJ865cocvvv/PC+fPaF2hhoS7lWIUBVrm8l6BDQYReCKWP\nQx/sLOy0Kuvpp6uP335bq6IEAoHA4BHGt0DQArl8+TJ79uzBw8OD5cuX61sdAGS2MtqObas5zzmg\nvQIujVGuUvFNRgb7c3LILCtjly4CLkHtdjJgALz3HiiVsHgx6Lhk8f1QrCxm0x+buKu8S/bdbF73\nfB2FhUKrMp95pvpYpv/NEIFAINArwu1EIGhh7Nq1SxPAM2jQIKKjo/WsUTU5v+QQNywOAJMOJgxM\nGdhED+3gduIE14uLATjcqxfPtG6tO+EXLsCMGWpj3NcX+vXTnez7oERZgjxYTlFZEQBX37iKm9xN\nuzJL4NVXwdsbhg8HFxetintkEG4nAkHLRfh8CwSPELdu3cLe3h4AY2NjsrOzMTExoVUr/ab4A8j9\nLZezA89qzp9OfhpTJ91nQZlz+TJfZWQAEOjkxAwHB7qYmzfR6/FhzOYx7E7YDairXeaV5BH0TBBD\nXYZqXXZFBdy8afAu8QaBML4FgpaL8PkWCB4hFAoFHh4eACiVSrp3786MGTP0rJUay96WtB3XFvsA\ne7pt6IaxjX6ymY6QyzXHwSkpLL5+XS96GCrPdXpOc3z+1nneGvAW/Ry1u0KfmqoOtFQowEDCFAQC\ngUAviDzfAkELZMSIEcTFqd07vLy82LRpk541UiM1lfLk9if1rQbPtm6NBHXiFRXwXdeuulWgsFCd\n73vPHnWuvago3cpvgpql5rOKshj5xEiMjbT7c2Brq/bEWbUKnLRX00cgEAgMHrHyLRC0QEaMqDae\n4uLiMDIyrI9yWU4Zt366RfrX6XqRbyuT0a+yxLu1VEp8UZFuFVAq1Yb3qFGwYYNuZd8H7m3daW+l\nLilfUFrAxcyLqFQqKlQVWpNpYaF2hReGt0AgeNwRK98CQQtk8ODBdO/encGDBzNixAhKS0u5desW\nHTp00LdqFMYXcsbzDDZDbFBM1G4WjcZY2akTpkZGPGVlxY3SUn7JyeFZXVR3+fVX+PlniI+H+fPB\n0VH7Mv8iEomET4Z9gpWJFcZGxvzn5H/Yf30/q0aswr+7v9blZ2XBmTPw3HNNtxUIBAJtMWPGDJyc\nnPjwww91KtewlssEAsF9YWZmxoULF1i0aBHff/89dnZ2LFu2TN9qAWDexZyBNwfyt8i/0W5aO73p\nMdTWlifMzOh5+jQep0+z5dYt3QjeuBH+/W/44w/YtUt9Tdcr7/fBNI9pjHUfS35pPt3tuhM5OZJx\n7uO0KvPoUXUtIjs7eOUVrYoSPGK4uLhgbm6OtbU1Dg4OvPrqqxQ9wOeqtLSUWbNm4eLigo2NDX37\n9mXv3r2a119++WUcHR2xsbGhW7dufPvtt7X6x8fHM2zYMFq3bk2XLl3YuXPnQ783XZGTk8PYsWOx\ntLTE1dWVLVu2NNg2KSmJMWPGIJfLcXR05I033qCionpnLCQkhH79+mFqasqrr76qC/UfKYTxLRC0\nYNq2bcsrr7zCtWvX+O9//6tvdQCQGEmQmkk15xVl2nNlaIo2Mhmb3d3J9PLiG135fY8ZU3387bfq\n8o5/+xsYaCaJKT2nsPDphfRQ9EAi0W5iDWdntQs8QHa2Qc5JBAaKRCJh165d5OXlcebMGU6dOvVA\nNQ6USiXOzs5ER0eTm5vLhx9+yMSJE0lOTgbgvffeIykpidzcXMLDw1m6dClnz6ozOJWXl/PCCy/g\n5+dHTk4OX331FQEBAVy9erVZ36u2mDt3LqampmRmZrJp0yZee+01Ll261GBbe3t7bt68SWxsLEeO\nHOGLL77QvN6+fXv++c9/MnPmTF2p/0ghjG+BoAUjl8uZMGECbdu2bbqxDlGpVPy5/E9O9jhJtHk0\nxSnFetFDIpHQ28oKIy0blbUYNqy6ksytW/DWW3DxIuhShwekWFlMYWmh1u7v7Azdu6uPS0vBgFLU\nC1oAVakQHRwceP755zlfWb3WyMiI6zUyGs2YMYOgoKB672Fubk7WLYCgAAAgAElEQVRQUBBOlcEH\nY8aMwdXVlZiYGADc3d2RVX5+VSoVEomEa9euAepV74yMDBYsWIBEIsHb2xsvLy82btzYoM7Dhg1D\nqVQ+5Dt/eIqKiggLC2P58uWYmZnh5eWFn59fg7onJiYyceJEZDIZCoWCUaNGceHCBc3rL774In5+\nfshrZJZqiJUrV9KhQwesra1xd3fn0KFDmtcyMjIYP348CoUCNzc31q1bp3ktNTUVf39/FAoFdnZ2\nzJ8/X/NafHw83t7e2Nra0rNnTyIiIjSvubq6snr1ajw8PLC1tWXy5MmUlpYCcPbsWfr27YuNjQ2T\nJk2iuLj2b1NjujYnwvgWCB4RUlNTtfZF8Vcpzysn+ZNkii4WoVKqyN6TrVd9yioqOJaby7eVub+1\nipVV7ZKOd+6AiYn25T4EOy7t4LmNz2H3Lzv2XN2jVVk1YoWpsZAmMHASlyWSuCyx2c4fhpSUFHbv\n3k2fPn0e+l43b94kISGBHj16aK7NmzcPCwsL3N3dcXR0ZPTo0UC18V8TlUqlmQTcS1paGqCux9Cc\n+Pr6Ymtri1wur/Ps5+dXb58rV65gbGyMm1t1QS0PD49aBnVNFi5cyJYtW7h79y5paWns2bOH559/\n/i/reuXKFUJCQoiJiSEvL499+/bhUlllS6VS4evrS+/evcnIyODgwYOsXbuW/fv3U1FRgY+PD66u\nriQnJ5OWlsakSZMA9e6Fr68vo0aNIjMzk88//5ypU6eSkJCgkbt161aioqJITEwkLi6ODRs2UFZW\nxtixY3nllVfIzs5mwoQJbN++/b50bW6E8S0QtHAyMjLo3bs3Hh4eBuN/aGxjjOsnrprz27tv602X\nfKWStseOMeXiRVKKi+v9AW12arqexMWpK8vEx2tf7gNiamzKYOfBpL6Zyvju47Uqy9Oz+vi337Qq\nSvCI8eKLLyKXyxkyZAje3t68++67D3U/pVJJQEAA06dPp0uXLprrISEhFBQUcPToUcaNG6cpYNat\nWzcUCgWrVq1CqVQSFRXFkSNH6vU9P3DgAIsWLaJdu3b3lQq2phHYFBEREeTk5JCdnV3nOTw8vN4+\nBQUF2NjY1LpmY2NDfn5+ve2HDBnChQsXsLa2xtnZmX79+jVo2DeGVCqltLSU8+fPa1x+XF3Vvw2n\nTp0iKyuLJUuWIJVKcXFxYdasWYSGhnLy5EkyMjIIDg7G1NQUExMTBg5UV0w+ceIEhYWFLF68GGNj\nY7y9vfHx8anlw75gwQLs7e1p3bo1vr6+xMbGcuLECZRKJfPnz0cqleLv70+/GhWIG9O1uRHGt0DQ\ngsnMzGTDhg1IpVIGDhzI2rVr9a2Shjaj22iOcw7kUFGiH9/vsefPk1deTnJJCaPkcq37NauFjoWQ\nELh+He7eVWc8GTtWXWfdgFCpVPht8ePFH18k6HAQ2Xe1v0Ph6wvGxurNgKeegrIyrYsUPCL8/PPP\nZGdnk5iYyLp165qs6rt582asrKywtrZmTM0JMer//YCAAFq1alXL1aEKiUTCwIEDSUlJYf369YB6\nBXvnzp1ERkbi4ODAZ599xksvvVRvlqnhw4cjlUpZtGgRAQEBAOTm5hIWFsaKFStqtc3Ly8PS0pKr\nV6+yY8cOPvroI86cOfOXxqYpLC0tycvLqyPXqjIla01UKhUjR45k/PjxFBUVkZWVRXZ2NosXL/7L\nct3c3FizZg3Lli3D3t6eKVOmkFG5A5mUlERaWhpyuVyzer9ixQpu3rxJSkoKHTt2rDeNbnp6usZt\nqIqOHTtqdhoATRVoULsaFRQUkJ6eTvv27ev0ux9dmxthfAsELZi7d+/y3nvvERMTw4EDBx4o+l9b\nmLqZImun9p2sKKwg51COXvToUOMHen+OjnTo2FFdxtHVFUaPhuPH4dIlaMJY0DUSiQRlhZLScrU/\n5P7r+0m4nUBiTvO4B9SHlRUcPAi3b8Pu3dXu8QLDxnWZK67LXJvt/EFoaNfK3Ny81nffjRs3AJgy\nZQr5+fnk5eWxqyrzUCUzZ84kKyuLsLAwpFIpDaFUKjU+3wBPPvkkhw8fJjMzkz179nDt2jU8a27n\n1CA2NraWa0xVdpWye2acv/zyC97e3kRERNC+fXsWLlzIqlWrGtRp9OjRmknFvY97JxlVdOnSpc57\niYuLq+VuU0V2djapqanMmzcPmUyGra0tM2bMYM+eB3NJmzRpEtHR0SQlJQEQGBgIgJOTE506dSI7\nO1uzep+bm0tkZCROTk4kJyfXyrBShaOjIykpKbWuJScn1zGs78XBwYHU1NQ6/e5H1+ZGGN8CQQvG\n2dkZd3d3AIqLi1m3bp3BrH5LjCQYW6l9HSWmEkpS9bPqW7PU/KabN5l+6RLKer7Qtcb48dCpk+7k\n/UVGdKp2wn4r6i2e2fAMJ1JPaFXmkCHq5x074O23DTYRjKCF0Lt3bzZv3kxFRQV79+7lyJEjjbaf\nM2cO8fHxhIeHY1IjHiMzM5Mff/yRwsJCKioq2LdvH6GhoQwbNkzT5ty5c5SUlFBUVMSqVau4ceMG\n06dPryPj4sWLuLu7I5FIGnQFqaKkpAQTExPefPNNPD09SU1NbdTdYffu3ZpJxb2PeycZVZibmzNu\n3DiCgoIoKiri2LFjhIeH8/LLL9dp26ZNG1xdXVm/fj3l5eXcuXOH//3vf/Tq1UvTpry8nOLiYsrL\ny1EqlZSUlFBeXl7nXleuXOHQoUOUlpZiYmKCmZmZZrLj6emJtbU1wcHBmntduHCB06dP4+npiYOD\nA4GBgRQVFVFSUsLx48cB6N+/PxYWFgQHB6NUKjl8+DCRkZFMnjy50XEeMGAAMpmMdevWUV5eTlhY\nGCdPnrwvXZsbYXwLBC2cmn54wcHBZGZm1rtaoGskEglP7nySv0X9jcF3BuM4Sz/FZobXKKxz5e5d\n+lpZUfcnQgfk5sLOnWr/bwPiObfalW6SFiYxuWfjP2IPS3k5dO2qDrh0clKfCwSN0Zi72Jo1awgP\nD8fW1pYtW7YwduzYBtsmJyfz9ddfExsbi729vWYFecuWLUgkEtavX4+TkxNyuZx33nmHtWvX4uPj\no+m/ceNGHBwcaNeuHYcOHWL//v2a7Cg1kcvl2NjYEBoaipeXV4P65Obm1skYsnPnTpYsWdLYcDwQ\nISEhFBUVoVAomDp1Kl9++aVm8Wb06NF8+umnmrZhYWHs2bMHOzs7unTpgkwm49///rfm9eXLl2Nu\nbs7KlSv54YcfMDc35+OPP64js6SkhMDAQOzs7HB0dCQzM5NPPvkEUGepiYiIIDY2FldXVxQKBbNn\nzyYvL0/zWkJCAs7Ozjg5OfHTTz8BIJPJCA8PZ/fu3bRt25bXX3+djRs30rlzZ6Dh/xWZTMb27dv5\n/vvvkcvlbN26FX//6qJijena3Eh0EnzUDEgkElVL0VUg0CXHjh1j0KBBALRr1460tDSDKzcPUF5c\njtRUO6sITfF0TAy/VwYWbezWjYB2Oi7+M2EC7N0LXl6waRMYUGpIlUpFh886kJ6fDsCJmSfo36G/\n1uWWlBicF47BIZFIUKlUOslRKX5jdUNSUhIbNmzg/fffB2DHjh34+PhoDPiIiAiGDh3KjRs3NMak\noGXS2OfX8H6hBQLBX+Lpp5/W5PkuLy+v4wunb9K/SeeP0X9wXHGc0luletHBr21bOpma8maHDnhY\nWupWeEUFvPCCuuT83r0GZXiD+gdiRKcRyM3kTOg+AYD91/az/9p+rcoVhrfgcaOgoIBt27YRExOj\nSfFXXFysMbyrgi39/f01q7yCRxOx8i0QPAJs3bqVDh064Obmxv79+9m3bx9ff/01pqam+laNlH+n\n0Mq5FfIRcoxtmjff7f1SVlGBsUTCucJCwrOyiLh9mx/c3XnC3Fy7go8cUa96Z2bCs8+qIw0NkDvF\nd7AyseJYyjH8tvjhbufO3Kfm8rJHXX/Q5iQuDtasgQMH4OxZg5uX6B2x8v1ok5OTQ0xMDMOHD9e3\nKgIt0NjnVxjfAsEjhKenJ46OjowZM4apU6dirm3jsoUx78oVTIyM8G3ThsE2Nsi07Z6Tng5VEfhS\nqTrrSXQ0vPoq1PBFNxQKSwspLCtEYaHQuiyVCszMqrMvHjsGlWl8BZUI4/vRJjIyklGjRjV7ER6B\nYSCMb4HgMaGqHLIhUppVys2NN7EeYI3N0zZNd3hU6NNHvawL6qVdf3/45z+rjfLHmJdfVrvAA7z/\nPixbpld1DA5hfAsELRfh8y0QPCYYquF97sVzHLc7zrVF10hdm9p0Bx1QoVLpJuVgZWlqQF1h5ssv\nDd7wzsjP4JuYb1h5dKVW5fj6Vh+Hh4uUgwKB4PFAGN8CwSPGuXPn+Pjjjxk4cCAXL17UtzoAWA+w\n1hznn8jXTYn3Bjhy5w6z4uNp/9tv7M3WfkXHWqXm9+wxuFSD93I95zo9vujBL3/+whPyJ7Qqa+RI\ntTcOqDcHtm3TqjiBQCAwCITxLRA8YqxZs4b9+/fzzjvv4Obmpm91AHB60wmppdrKKv6zmKLL+qnE\nmXj3LutSU9mfk8NLdnb46CLCz9MTHBxg6FB44w2IiFA/G8jEqCaZhZkcSjzEQKeBTH5yMv7d/Zvu\n9BDY2NSuP3T7tlbFCQQCgUGgdeNbIpGMkkgk8RKJ5IpEIllcz+tvSiSSCxKJJFYikeyXSCRO2tZJ\nIHhUmTJlCt999x1HjhxBqVTSykDyuRmZGGE7ojrA8NaWW3rR42R+PtuzskguKeFobq5uhEqlkJgI\nhw5BQgJ89pna7aR1a93I/wv85+R/mBUxi10Ju9h6catOZC5eDFOmwObNMGmSTkQKBAKBXtGq8S2R\nSIyA/wAjgR7AZIlE0u2eZmeAviqVqhewHfiXNnUSCB5lnnii2k0gIiIClUqFUqnUo0Y1qPFtk71b\nB+4e9TBKLse40i8+pqCAiwUFZJXqIPd41STou+/g8GEIDARH/VT8bIwXur2gOd4Zv5N3D7zLmM1j\ntOomNHMm/PADTJ4MpqZQWQtJIBAIHlm0vfLtCSSoVKoklUpVBoQCL9RsoFKpjqhUquLK0xOAYUci\nCQQGTM1S8z/++CMdOnQgIiJCjxpV02FBB0wcTHCY7UDHoI560cHG2JihNVac+8TEEK5LXwcDDYit\none73jhZqzcfC0oLSLyTyHuD3tO63IMH4cUXwd4edu7UujiBQCDQK9o2vtsDNcvtpdK4cT0T2KNV\njQSCR5g+ffrg4OAAQElJCf/6178YO3asnrVS03pwawakDaDr111p66u/aip+bdpojge3bs2rleOl\nM5KS4Isv1Kk+IiN1K7sJJBIJL3StXh+xM7fDy9lL61l0pFJ1Bsbr19XpBwUCgeBRRtvGd33f2PXu\nX0okkgCgL8LtRCB4YIyMjPCtkb8tNjZWj9rURSKRUHKjhIzvM8g7macXHXxrGN9JxcW6STdYk23b\n4ORJmDoVvLx0K/s+eLHbi5rjQ38eAqBCpd0xGjpUbXTX+NMIBALBI4u2yyqlAs41zjsA6fc2kkgk\nw4F3gSGV7in1sqxGBYahQ4cydOjQ5tJTIHhk8PX15dChQ/j5+TFhwgTy8/MpLS2ljQFYNqmfp/Ln\n+39iO8IW8676qb7pYmbGfzp3xsvamr9ZWHDl7l3uVlTQ28pKu4IrKmD3brh6VZ1X75tvQCbTrswH\nYEjHIbzh+QZjOo8h+242AWEBRF2LIv71eORmcq3LT0iAO3egXz+tizI4Dh8+zOHDh/WthkAg0DJa\nrXApkUikwGVgGJABnAQmq1SqSzXa9Aa2AiNVKtW1Ru4lqm8JBPdBVZXLgwcP8umnn3LixAk+++wz\nZs2apW/VUOYpMTIzwkim/yynR+/cYXp8PCUqFYHOzszTduEblQo6doSUSk+8w4dhyBAoLa0OyDQw\nFu9fjKutKz5dfOhg3UGrsj7/XF34My8PRo1Sp0R/3BEVLgWC5mHGjBk4OTnx4Ycf6kym3ipcqlSq\ncuB1IAq4AISqVKpLEonkA4lE4lPZLBiwALZKJJKzEolEhNsIBA9BlX+ura0tc+fOJT093SAMbwBj\na+Nahrc+f+y7mZuzrUcPkp9+WvuGN6iDLWsW3AkMhM6d1f7fBsrKESuZ89QcrRveoM50klfpiXTj\nhtbFCVoYRkZGXL9+vda1Dz74gJcbCBIoLS1l1qxZuLi4YGNjQ9++fdm7d6/m9ZdffhlHR0dsbGzo\n1q0b3377ba3+Q4cOxczMDGtra6ysrHB3d2/+N6UlcnJyGDt2LJaWlri6urJly5Z62zU1RjVJSEjA\nzMyMadOmaVP1xwZtu52gUqn2Al3vufZ+jeMR2tZBIHgc6dOnD3369NG3GnUozSolaXkSt8NvY2Ru\nhOd5T73o0dbEhLYmJroVOmaMurw8qP0roqKgd2/d6vCA3C66jXUra2RS7bjKTJ6srj1UWgqxseoN\nAidR9UFQSUNBvw1dVyqVODs7Ex0djZOTE7t27WLixImcP38eZ2dn3nvvPb777jtkMhlXrlzhmWee\noU+fPvSu/DxKJBK++OILZsyYobX3pC3mzp2LqakpmZmZnDlzhjFjxtCrV686E4imxqgmr7/+Op6e\n+vmufhTR/96vQCDQOleuXCEuLk7fagCQuTWTtLVpFCcWU3SpiLI7DYZ56ITi8nL23r7NDzdval/Y\ns89Wu5jcvg1yucGnH/zu7HcM+m4QnT7vxIXMC1qTY2WlDrys4pNPtCZK0AL5q7tk5ubmBAUF4VQ5\ngxszZgyurq7ExMQA4O7ujqwy5qLKVe/atdqer39F5rBhwwyipkJRURFhYWEsX74cMzMzvLy88PPz\nY+PGjXXaNjVGVYSGhmJra8uwYcMalb1y5Uo6dOiAtbU17u7uHDqkDtjOyMhg/PjxKBQK3NzcWLdu\nXa1+qamp+Pv7o1AosLOzY/78+QBcunQJb29vbG1t6dmzZ520ua6urqxevRoPDw9sbW2ZPHkypZV1\nG86ePUvfvn2xsbFh0qRJFBcX1+rbkK66QhjfAsEjTHR0NF26dMHb25ujR4/qWx0AHOc4YvVUZXBj\nBeTsz9GbLleKirA7fpzXExLI18UPp7k5eHtXn//+O9y9C5cuNdzHAHjO7Tlu/t9NerXrpVU5fftW\nHx85olVRgr/IsmXLkEgkdR41EyE01b6htrrg5s2bJCQk0KNHD821efPmYWFhgbu7O46OjowePbpW\nn3fffReFQsHgwYM50sg/ZFpaGgDGxs3rTODr64utrS1yubzOc82aDjW5cuUKxsbGuLm5aa55eHhw\n4ULTE+f6xigvL4/333+f1atXNzoZuXLlCiEhIcTExJCXl8e+fftwcXFBpVLh6+tL7969ycjI4ODB\ng6xdu5b9+/cDUFFRgY+PD66uriQnJ5OWlsakSZNQKpX4+fkxatQoMjMz+fzzz5k6dSoJCQm15G7d\nupWoqCgSExOJi4tjw4YNlJWVMXbsWF555RWys7OZMGEC27dvb1JXXSKMb4HgESU9PZ1du3Yhk8kY\nMmQI8+bN07dKgHo7Vz66OmvGrW36KTWvUql46cIFCsrLuVZcjLetrW4EL1gA338P586p83w7OMC/\nDC/Dall5Gc/+71lmR8zmgyMfUFBaoHWZVTv8lpbQp486RlUgeFiUSiUBAQFMnz6dLl26aK6HhIRQ\nUFDA0aNHGTduHK1qBD4HBwdz/fp10tLSmD17Nr6+viQmJta594EDB1i0aBHt2rVj06ZNTepS0whs\nioiICHJycsjOzq7zHB4eXm+fgoICbGxsal2zsbEhv4nSsQ2NUVBQELNnz6Z9E3ExUqmU0tJSzp8/\nr3FncXV15dSpU2RlZbFkyRKkUikuLi7MmjWL0NBQAH7//XcyMjIIDg7G1NQUExMTBg4cyIkTJygs\nLGTx4sUYGxvj7e2Nj49PHf/1BQsWYG9vT+vWrfH19SU2NpYTJ06gVCqZP38+UqkUf39/+tVIn9SQ\nrrpEGN8CwSPK7du3WblyJRcvXmTPnj2a7ThDoM0L1WkPs7ZnoczX/XatRCLB1cxMcx6elaUbwaNG\nwfTp8MQT8PTTEB+vLjtvYMikMkrKS6hQVVChqiDyciSxN2LJyM/QmszOndUr3llZsGmTwXvkCHSI\nVCqlrKy2i1pZWRkymYzNmzdjZWWFtbU1Y2oGNaOeZAcEBNCqVas67g6g/h4YOHAgKSkprF+/XnO9\nX79+WFhYIJPJmDZtGl5eXuzevbtO/+HDhyOVSlm0aBEBAQEA5ObmEhYWxooVK2q1zcvLw9LSkqtX\nr7Jjxw4++ugjzpw588BjUh+Wlpbk5dWuoZCXl4dVI6lUGxqj2NhYDhw4wMKFC5uU6+bmxpo1a1i2\nbBkKhYIpU6aQkZFBUlISaWlpyOVyzcr9ihUruHVLveiSmppKx44dMTKqbY6mp6dr3GGq6Nixo2aX\noQp7e3vNsbm5OQUFBaSnp9eZLHTsWF1Vuaau9vb2Gl11iTC+BYJHlCeffFLzhZObm8tnn33GTz/9\npGet1EjNpBiZq79+ZHYy7ibc1YseNatdfpWezhv3bGlqFVNTmDcP2rXTncy/yItdqwvuzN09l/E/\njdeq3zeosy8WFKiN76AgrYoS/AWWLVuGSqWq82jM7eR+294Pzs7O/Pnnn7WuJSYm0rFjR6ZMmUJ+\nfj55eXns2rWrVpuZM2eSlZVFWFgYUqm0wfsrlco6Pt81qUwbV+9rsbGxtYLbqzKH3DtZ+OWXX/D2\n9iYiIoL27duzcOFCVq1a1aDM0aNHayYV9z7unWRU0aVLlzrvJS4urpYryb00NEZHjhwhKSkJZ2dn\nHBwcWLVqFdu2beOpp56q9z6TJk0iOjqa5ORkAAIDA3FycqJTp05kZ2drVu5zc3M1/ttOTk4kJydT\ncU+xM0dHR1JSUmpdS05ObnIFHsDBwYHU1NQ6fevTNSkpSaOrLhHGt0DwiCKRSGr5Ba5du5acHP35\nV9fEwt2CHtt64PGLBwNTB2LVR8sFbhpgTA3j+1pxMX+zsNBP+sP0dPjqK4Pzs6hZ7bJCVUHcnDiG\ndxquVZm5uepNgW3bQMc7wQID5qWXXmL58uWkpaWhUqk4cOAAkZGRjB8/vsE+c+bMIT4+nvDwcExq\nZDbKzMzkxx9/pLCwkIqKCvbt20doaKgmoDA3N5eoqChKSkooLy/nhx9+IDo6mpEjR9aRcfHiRdzd\n3ZFIJA26glRRUlKCiYkJb775Jp6enqSmpjbq7rB7927NpOLex72TjCrMzc0ZN24cQUFBFBUVcezY\nMcLDwxtMydjQGAH84x//4Nq1a8TGxhIXF8ecOXPw8fEhKiqqzn2uXLnCoUOHKC0txcTEBDMzM4yN\njfH09MTa2prg4GCKi4spLy/nwoULnD59GgBPT08cHBwIDAykqKiIkpISjh8/Tv/+/bGwsCA4OBil\nUsnhw4eJjIxk0qRJjY4xwIABA5DJZKxbt47y8nLCwsI4efJko7o2NjHTBsL4FggeYWqWmjc1NeXv\nf/+7HrWpTZvn22DrbYtEqi45rw/sTEzwsrbWnJsYGTWYukxrjBsHPXrA8ePQhF+mruncpjPubdXp\nyUrKSzhw/YDWZdrYwM2bsHNntQ+4QBAUFMTAgQMZNGgQcrmcwMBANm/eTPfu3ettn5yczNdff01s\nbCz29vaaFeQtW7YgkUhYv349Tk5OyOVy3nnnHdauXYuPj7r8SFlZGUuXLtVk3wgJCeHnn3+mc+fO\ndeTI5XJsbGwIDQ3Fy8urQf1zc3ORy2tXiN25cydLlix5iFGpn5CQEIqKilAoFEydOpUvv/xSk2Zw\n9OjRfPrpp0DjYwTq3wyFQqF5WFpaYmpqWud9gHpiERgYiJ2dHY6OjmRmZvLxxx9jZGREREQEsbGx\nuLq6olAomD17tsY1pur1hIQEnJ2dcXJy4qeffkImkxEeHs7u3btp27Ytr7/+Ohs3bqzlj97Qd7VM\nJmP79u18//33yOVytm7dir+/f6O6fqLj9EparXDZnIjqWwLBX6e0tJS2bduSn59Px44dOXnyJAqF\nQt9qaUj/Op0b/7vB3Wt36X+1P8aWWi89UId/JSez6eZN/Nq2ZapCQTcLC90JLytTF9np2lXtC26A\nvHfwPb47+x1+Xf2Y0WsGNwtvYmduh5dzw4aGoHkQFS5bLklJSWzYsIH331eXNdmxYwc+Pj6a9IYR\nEREMHTqUGzdu1GvUC1o+eqtwKRAI9IuJiQnff/89f/zxBzExMWzfvp2RI0dSUqKfleZ7Kcssw/ld\nZwakDNCL4Q3wlpMTcf36MUouZ8ONG/Q9fVo3aQcPHVJnOlm4ECpXogyRJYOXkP5WOsM7DWfkppH8\n5+R/yC3J1apMlQoOH4bx48HZWV14RyBoKRQUFLBt2zZiYmI0Kf6Ki4s1hndVsKW/v7/BxOEIdItY\n+RYIHgNUKhW9e/emW7duTJs2jZEjR+rcx83Q8fnjD3pYWDC9XTvcdbH6feMGtG8PVYFGP/8Mv/4K\nEyZA//7al/8XuVN8BwkSbExtmm78kNy9q043WDU08fHqzYHHDbHy/WiQk5NDTEwMw4drN15CYFg0\n9vkVxrdA8JhQXl5usAZ3cWoxyZ8mY+piivP/OTfd4VFh1CjYt0993KYNvPYazJoFNdJiPa74+EBV\nTFlQEHzwgX710QfC+H40iIyMZNSoUc1ehEdg2AjjWyAQ1KKiogKlUlknul0fXF96neSP1WmgWjm1\n4umkp3Uf9HgPeUolpRUVtNX2+PzwA1TmBqZrV3WlSwNPbp2Rn8H/i/t/nLt1jk3jmi4q8qBs26be\nBAD1BsHFi1AjNvaxQBjfAkHLRfh8CwQCAK5fv87SpUtxdXXVVBjTN2392iIxVX8/laSUUHBW+5UU\nGyKuoICAixdx+u03dmVna1/giy9ClYvL5cvQzAU3mpuC0gJ6f9WbhOwEXnvqNa3K8vVVZz4BSEsz\nyCKgAoFA8EAI41sgeIwICwtj3759fPjhh0ybNk3f6gBg7Z15yp8AACAASURBVGmNYnx1Bpabm2/q\nRY/M0lL+k5bGnuxsBtnY8Iouit9YWKiXd597Dr79Vm2Av/SSugS9gZGRn8H3Z7/H3c4dN1s3rWc7\nadUKBg2qPtdVAVKBQCDQNsL4FggeE1atWsXbb7/N6dOn2b9/v77VqYViarXxnfHfDFTlut/+vl1W\nxn8zMshWKonKyeGGrjLCfPut2u+7Rw/18fDhBlna8UjSEebvnc/hPw8TeiEUlUpFaXkpygrtZYb5\n5BOYOBH27oX//EdrYgQCgUCnCJ9vgeAx4cyZM/Tt2xdQF0+4evUqmZmZ9OrVS8+aQWF8Iae6n4LK\nj3ivI71oPaS1zvUYcvYs0bnqNHrvODnR18qKiQaUF12f5JXkYb/KnmJlMQATe0zkl8Rf2PHSDgY5\nD2qi98Nz4wacOwcjRmhdlMEgfL4FgpaL8PkWCAT06dNHY2gXFxfTuXNn1q1bp2et1Jh3NafDog60\nf6M9fU70wWaw9tPZ1ccsBwfN8aqUFE7k5emn3DxATo5+5DaAdStrJj1ZXdr5ctZlfp/1u9YN75wc\neOEFcHeH3bu1KkogEAh0gjC+BYLHiJkzZ2qOn3jiCb799ls9alONRCLhiVVP0Pnzzlj3t9ZbtpPx\ndnbYVKZjrAD82rTRrS5lZbBmDQwYAE89VZ3o2kCoGWR5+fZlbE1ttS6zdWu1G3xKCnz2mdbFCQQC\ngdYRxrdA8BgxdepUWrVqBcC5c+e4evWqnjWqTUlaCcmrkonpH0PZnTKdyzeXSplqbw+Ap5UVOjd9\njY0hMRH+8Q91ZRkjw/qK7ufYj97tegPQ3qo913Ouc7voNrE3YrUmUyKBKVPURXcEAoHgUcCwvtkF\nAoFWsbW1Zd68eSxdupRr165hZmZGcHAw+fn5+lYNgEuvXKIovohOKzthbK2fghT/5+RE3FNPcaJP\nH8yNjJhz+TLnCnSQ/jAxEWbMgO++g40bobIUtSEhkUhYOXwlUQFRHJx2kJXHVuL2uRvbL27XifzD\nh2HoUHVSGIFAIGipiIBLgeAxZd68eWzZsgV/f38++ugj2ukitV4TqFQqvRfYqWLR1atE3r7NjHbt\nmOnggELbBXdSU8HZGVQq9XLvn39CcjL07l2dC9yAyCvJY/O5zUx6chKtTbUfHOvhAX/8oT7+6iv4\n+9+1LlLviIBLgaDlIipcCgSCOly+fBlnZ2fMzMz0rUq9FF0uoiy7DJsB+gm+zFMqsZJKdTsZGD4c\nDh5UH9vYQIcOsHWrOtrwMeftt2HVKvXxyJHq9IOPOsL4Fgi0y4wZM3BycuLDDz9s9nuLbCcCgaAO\nXbt2NUjD+9b2Wxx3OM7JbieJnxmvNz2sjY1rGd4VujBMXn65+tjOTp1brwUY3lduX+Gfv/xTk4ZQ\nG7z+unpDACAqSr0pIHh8cHFxwdzcHGtraxwcHHj11VcpKir6y/cpLS1l1qxZuLi4YGNjQ9++fdlb\nYyb38ssv4+joiI2NDd26dasTlB4fH8+wYcNo3bo1Xbp0YefOnQ/93nRFTk4OY8eOxdLSEldXV7Zs\n2VJvu6bGCGDo0KGYmZlhbW2NlZUV7i3ge8qQEMa3QPAYo1KpiImJYcGCBYSEhOhbHQDKc8spvVEK\nwN34u5Sk66jYTT2UVlQQlpmJzx9/EHDpkvYFjhsHVROiq1chLk77Mh+SgLAAhnw/hGJlMXfL7mpN\nTseO8Oyz6mOVCvz9tSZKYIBIJBJ27dpFXl4eZ86c4dSpUyxfvvwv30epVOLs7Ex0dDS5ubl8+OGH\nTJw4keTK2dx7771HUlISubm5hIeHs3TpUs6ePQtAeXk5L7zwAn5+fuTk5PDVV18REBBgcIHrDTF3\n7lxMTU3JzMxk06ZNvPbaa1yq53utqTEC9d/jiy++IC8vj/z8/HrvI2gYYXwLBI8xO3fuxM/Pj1u3\nbjF69Gh9qwOAw6sOtPau9CFWwa3QW3rT5WBODgsSEvg9P5/Vbm7aF2hlBS++CCYmauuyuBg2b1Yv\n9RogNwtuYm9hT9e2Xfl0+KfYmmk39eCwYdXHmZkGl4lRoGWq3GIcHBx4/vnnOX/+PABGRkZcv35d\n027GjBkENVAl1tzcnKCgIJycnAAYM2YMrq6uxMTEAODu7o6sMti5Kgbl2rVrgHrVOyMjgwULFiCR\nSPD29sbLy4uNGzc2qPOwYcNQKrVXBfZ+KSoqIiwsjOXLl2NmZoaXlxd+fn716t7UGFVxv25KK1eu\npEOHDlhbW+Pu7s6hQ4c0r2VkZDB+/HgUCgVubm61ak+kpqbi7++PQqHAzs6O+fPna16Lj4/H29sb\nW1tbevbsSUREhOY1V1dXVq9ejYeHB7a2tkyePJnSUvWCztmzZ+nbty82NjZMmjSJ4uLau3WN6dqc\nCONbIHhMuXbtGkuWLCE9PZ2jR4/i7Oysb5U02E+11xynf5OuFx1UKhX/d+0aqaWlZJWVsev2bd0I\n/vRTdTnHadPU1ua2bWCAVTbLK8rp9VUv/n3i3/ya9CtR17Q/QVi4UL0C/o9/wE8/VbuhCLTPsmXL\nWLZsWbOdPwwpKSns3r2bPn36PPS9bt68SUJCAj169NBcmzdvHhYWFri7u+Po6KhZmKjP2FSpVJpJ\nwL2kpaUBYGzcvJmbfH19sbW1RS6X13n28/Ort8+VK1cwNjbGrcYigoeHBxcuXGhSXn1jBPDuu++i\nUCgYPHgwR44caVBuSEgIMTEx5OXlsW/fPlxcXAD12Pn6+tK7d28yMjI4ePAga9euZf/+/VRUVODj\n44OrqyvJycmkpaUxaZK6yJdSqcTX15dRo0aRmZnJ559/ztSpU0lISNDI3bp1K1FRUSQmJhIXF8eG\nDRsoKytj7NixvPLKK2RnZzNhwgS2b99+X7o2N8L4FggeU5ycnMjKygLUKwz79+/XrA7oG+vB1lBp\nWN2Nv0vR5b/u2/mwSCSSWhUv/5uRQXxhofYrXjo7g60tPPMMnDoFYWFQWZnUkJAaSZnac6rm/KNf\nP+KVna+w+vhqrck0M4Pr1+HLL8HTU50gRvD48OKLLyKXyxkyZAje3t68++67D3U/pVJJQEAA06dP\np0uXLprrISEhFBQUcPToUcaNG6epjdCtWzcUCgWrVq1CqVQSFRXFkSNH6vU9P3DgAIsWLaJdu3Zs\n2rSpSV1qGoFNERERQU5ODtnZ2XWew8PD6+1TUFCAjU3t4HUbG5sm08w2NEbBwcFcv36dtLQ0Zs+e\nja+vL4mJiXX6S6VSSktLOX/+vMadxdXVFYBTp06RlZXFkiVLkEqluLi4MGvWLEJDQzl58iQZGRkE\nBwdjamqKiYkJAwcOBODEiRMUFhayePFijI2N8fb2xsfHp5YP+4IFC7C3t6d169b4+voSGxvLiRMn\nUCqVzJ8/H6lUir+/P/369bsvXZsbYXwLBI8pJiYmvFwjwG/WrFl069aNCgPYyzdzMcOylyUmHUxw\nDnRGainVix4v29sjq1xe/T0/n6Gxsdwq01HxHxsb6N69+vzuXbWzswHxj77/0Bz/lvobzjbOvOzx\nciM9Hh4jI/jhBxgyRF0EVFcbEgL98/PPP5OdnU1iYiLr1q3TGMUNsXnzZqysrLC2tmbMmDG1XlOp\nVAQEBNCqVatarg5VSCQSBg4cSEpKCuvXrwfUK9g7d+4kMjISBwcHPvvsM1566SU6dOhQp//w4cOR\nSqUsWrSIgIAAAHJzcwkLC2PFihW12ubl5WFpacnVq1fZsWMHH330EWfOnPlLY9MUlpaW5OXl1ZFr\nZWXVYJ/Gxqhfv35YWFggk8mYNm0aXl5e7N69u8493NzcWLNmDcuWLcPe3p4pU6aQkZEBQFJSEmlp\nacjlcs3q/YoVK7h58yYpKSl07NgRo3oKjaWnp2tcYqro2LGjZqcBwN6+evfU3NycgoIC0tPTad++\nfZ1+96NrcyOMb4HgMaZmufkbN24QFRVV75edrjEyMcLjoAcDkgbQaUUnWrVv/EdWW7Q1MWFs27aa\n8wl2dthrO9/3vdy5A598Ai4uUBn4ZSh0btOZ4Z2Ga84lSFBYaN9FJjUV3nxTXXK+TRutixNgGG4n\nDe06mZub11p9vnHjBgBTpkwhPz+fvLw8du3aVavPzJkzycrKIiwsDKm04cm9UqnU+HwDPPnkkxw+\nfJjMzEz27NnDtWvX8PT0rLdvbGxsLdeYqswhZfdM4H/55Re8vb2JiIigffv2LFy4kFVVeTXrYfTo\n0ZpJxb2PeycZVXTp0qXOe4mLi6vjSlKT+x0j0KTVq/e1SZMmER0dTVJSEgCBgYGAeve1U6dOZGdn\na1bvc3NziYyMxMnJieTk5HoXgxwdHUlJSal1LTk5uY5hfS8ODg6k3rNdlnxP2qSGdG1u9P8rKxAI\n9Eb37t15+umnAXUkf82gFX0js5UhMZJQlFDExYCLFF4q1IsesytdTyTAbX0ETn36KcTGwi+/QDP4\nuDY3c/rO0Ryfu3UOgDvFd7Qqc/FiGDtWHZcqEPTu3ZvNmzdTUVHB3r17G/Q/rmLOnDnEx8cTHh6O\nSY1/oszMTH788UcKCwupqKhg3759hIaGMqxGpO+5c+coKSmhqKiIVatWcePGDaZPn15HxsWLF3F3\nd0cikTToClJFSUkJJiYmvPnmm3h6epKamtqou8Pu3bs1k4p7H/dOMqowNzdn3LhxBAUFUVRUxLFj\nxwgPD6+1+3k/YwTqFfyoqChKSkooLy/nhx9+IDo6mpEjR9a5z5UrVzh06BClpaWYmJhgZmamMeQ9\nPT2xtrYmODiY4uJiysvLuXDhAqdPn8bT0xMHBwcCAwMpKiqipKSE48ePA9C/f38sLCwIDg5GqVRy\n+PBhIiMjmTx5cqPjPGDAAGQyGevWraO8vJywsDBOnjx5X7o2N8L4Fggec2bOnEn37t1ZvXo1U6dO\n5fLlyxpfcH2T+nkqZweexcLdglYd9LP6/aytLcGdOpH49NNsdHdn7+3b/JabqxvhJ0/C6dNw4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the decay rate data\n", + "dr_tally = decay_rate.xs_tally\n", + "dr_u235 = dr_tally.get_values(nuclides=['U235']).flatten()\n", + "dr_pu239 = dr_tally.get_values(nuclides=['Pu239']).flatten()\n", + "\n", + "# Compute the exponential decay of the precursors\n", + "time = np.logspace(-3,3)\n", + "dr_u235_points = np.exp(-np.outer(dr_u235, time))\n", + "dr_pu239_points = np.exp(-np.outer(dr_pu239, time))\n", + "\n", + "# Create a plot of the fraction of the precursors remaining as a f(time)\n", + "colors = ['b', 'g', 'r', 'c', 'm', 'k']\n", + "legend = []\n", + "fig = plt.figure(figsize=(8,6))\n", + "for g,c in enumerate(colors):\n", + " plt.semilogx(time, dr_u235_points [g,:], color=c, linestyle='--', linewidth=3)\n", + " plt.semilogx(time, dr_pu239_points[g,:], color=c, linestyle=':' , linewidth=3)\n", + " legend.append('U-235 $t_{1/2}$ = ' + '{0:1.2f} seconds'.format(np.log(2) / dr_u235[g]))\n", + " legend.append('Pu-239 $t_{1/2}$ = ' + '{0:1.2f} seconds'.format(np.log(2) / dr_pu239[g]))\n", + "\n", + "plt.title('Delayed Neutron Precursor Decay Rates')\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Fraction Remaining')\n", + "plt.legend(legend, loc=1, bbox_to_anchor=(1.55, 0.95))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compute the initial concentration of the delayed neutron precursors:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, "metadata": { "collapsed": false }, @@ -1045,173 +1242,156 @@ " 0\n", " 1\n", " 1\n", - " (U235 / total)\n", + " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 9.391610e-08\n", - " 2.566220e-10\n", + " 8.779406e-08\n", + " 2.310240e-08\n", " \n", " \n", " 1\n", " 1\n", " 1\n", - " (Pu239 / total)\n", + " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 7.611347e-09\n", - " 2.278727e-11\n", + " 7.150041e-09\n", + " 3.854534e-09\n", " \n", " \n", " 2\n", " 1\n", " 2\n", - " (U235 / total)\n", + " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.102594e-06\n", - " 3.012794e-09\n", + " 9.528171e-07\n", + " 1.124883e-07\n", " \n", " \n", " 3\n", " 1\n", " 2\n", - " (Pu239 / total)\n", + " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.422470e-07\n", - " 4.258670e-10\n", + " 1.303200e-07\n", + " 3.422243e-08\n", " \n", " \n", " 4\n", " 1\n", " 3\n", - " (U235 / total)\n", + " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 6.685886e-07\n", - " 1.826892e-09\n", + " 2.353975e-07\n", + " 3.367779e-08\n", " \n", " \n", " 5\n", " 1\n", " 3\n", - " (Pu239 / total)\n", + " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 5.419872e-08\n", - " 1.622631e-10\n", + " 2.032960e-08\n", + " 5.622830e-09\n", " \n", " \n", " 6\n", " 1\n", " 4\n", - " (U235 / total)\n", + " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 4.886781e-07\n", - " 1.335293e-09\n", + " 4.720335e-07\n", + " 4.240950e-08\n", " \n", " \n", " 7\n", " 1\n", " 4\n", - " (Pu239 / total)\n", + " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.626687e-08\n", - " 7.863920e-11\n", + " 2.626392e-08\n", + " 5.152078e-09\n", " \n", " \n", " 8\n", " 1\n", " 5\n", - " (U235 / total)\n", + " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.469529e-08\n", - " 4.015430e-11\n", + " 2.828001e-08\n", + " 4.006859e-09\n", " \n", " \n", " 9\n", " 1\n", " 5\n", - " (Pu239 / total)\n", + " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.274953e-09\n", - " 3.817026e-12\n", + " 2.430664e-09\n", + " 6.573695e-10\n", " \n", " \n", " 10\n", " 1\n", " 6\n", - " (U235 / total)\n", + " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.185934e-09\n", - " 3.240517e-12\n", + " 1.477575e-09\n", + " 3.414084e-10\n", " \n", " \n", " 11\n", " 1\n", " 6\n", - " (Pu239 / total)\n", + " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 5.371343e-11\n", - " 1.608102e-13\n", + " 6.994534e-11\n", + " 3.439208e-11\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell delayedgroup nuclide \\\n", - "0 1 1 (U235 / total) \n", - "1 1 1 (Pu239 / total) \n", - "2 1 2 (U235 / total) \n", - "3 1 2 (Pu239 / total) \n", - "4 1 3 (U235 / total) \n", - "5 1 3 (Pu239 / total) \n", - "6 1 4 (U235 / total) \n", - "7 1 4 (Pu239 / total) \n", - "8 1 5 (U235 / total) \n", - "9 1 5 (Pu239 / total) \n", - "10 1 6 (U235 / total) \n", - "11 1 6 (Pu239 / total) \n", + " cell delayedgroup nuclide \\\n", + "0 1 1 U235 \n", + "1 1 1 Pu239 \n", + "2 1 2 U235 \n", + "3 1 2 Pu239 \n", + "4 1 3 U235 \n", + "5 1 3 Pu239 \n", + "6 1 4 U235 \n", + "7 1 4 Pu239 \n", + "8 1 5 U235 \n", + "9 1 5 Pu239 \n", + "10 1 6 U235 \n", + "11 1 6 Pu239 \n", "\n", " score mean std. dev. \n", - "0 (((delayed-nu-fission / nu-fission) * (delayed... 9.39e-08 2.57e-10 \n", - "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.61e-09 2.28e-11 \n", - "2 (((delayed-nu-fission / nu-fission) * (delayed... 1.10e-06 3.01e-09 \n", - "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.42e-07 4.26e-10 \n", - "4 (((delayed-nu-fission / nu-fission) * (delayed... 6.69e-07 1.83e-09 \n", - "5 (((delayed-nu-fission / nu-fission) * (delayed... 5.42e-08 1.62e-10 \n", - "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.89e-07 1.34e-09 \n", - "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 7.86e-11 \n", - "8 (((delayed-nu-fission / nu-fission) * (delayed... 1.47e-08 4.02e-11 \n", - "9 (((delayed-nu-fission / nu-fission) * (delayed... 1.27e-09 3.82e-12 \n", - "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.19e-09 3.24e-12 \n", - "11 (((delayed-nu-fission / nu-fission) * (delayed... 5.37e-11 1.61e-13 " + "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08 2.31e-08 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.85e-09 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07 1.12e-07 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 3.42e-08 \n", + "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.35e-07 3.37e-08 \n", + "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08 5.62e-09 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 4.24e-08 \n", + "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 5.15e-09 \n", + "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08 4.01e-09 \n", + "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09 6.57e-10 \n", + "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 3.41e-10 \n", + "11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.44e-11 " ] }, - "execution_count": 22, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Set the time constants for the delayed precursors (in seconds^-1) using some ficticious time constant data.\n", - "precursor_halflife = np.array([55.6, 24.5, 16.3, 2.37, 0.424, 0.195])\n", - "precursor_lambda = -np.log(0.5) / precursor_halflife\n", - "\n", - "# Create a tally object with only the delayed group filter for the time constants\n", - "beta_filters = [f for f in beta.xs_tally.filters if f.type != 'delayedgroup']\n", - "lambda_tally = beta.get_condensed_xs(one_group).xs_tally.summation(nuclides=beta.xs_tally.nuclides)\n", - "for f in beta_filters:\n", - " lambda_tally = lambda_tally.summation(filter_type=f.type, remove_filter=True) * 0. + 1.\n", - "\n", - "# Set the mean of the lambda tally and reshape to account for nuclides and scores\n", - "lambda_tally._mean = precursor_lambda\n", - "lambda_tally._mean.shape = lambda_tally.std_dev.shape\n", - "\n", - "# Set a total nuclide and lambda score\n", - "lambda_tally.nuclides = [openmc.Nuclide(name='total')]\n", - "lambda_tally.scores = ['lambda']\n", - "\n", "# Use tally arithmetic to compute the precursor concentrations\n", "precursor_conc = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True) * \\\n", - " delayed_nu_fission.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True) / lambda_tally\n", - " \n", - "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n", + " delayed_nu_fission.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True) / \\\n", + " decay_rate.xs_tally.summation(filter_type='energy', remove_filter=True)\n", + "\n", + "# Get the Pandas DataFrames for inspection\n", "precursor_conc.get_pandas_dataframe()" ] }, @@ -1224,7 +1404,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -1243,7 +1423,7 @@ "(0, 7)" ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, @@ -1251,7 +1431,7 @@ 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1295,7 +1475,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -1306,7 +1486,7 @@ "(0.001, 20)" ] }, - "execution_count": 24, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, @@ -1314,7 +1494,7 @@ "data": { "image/png": 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oXeI190z1eA2RhpoMtUO1Jp9TEdNjMMRDpr4n7mUoNbk9KT1YgJYk5vn46/Nw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\n3182EO86hkpVfaSWZTIY0ppYK3/TzVe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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/docs/source/usersguide/input.rst b/docs/source/usersguide/input.rst index 2ab823e6e..425724bb6 100644 --- a/docs/source/usersguide/input.rst +++ b/docs/source/usersguide/input.rst @@ -1859,6 +1859,11 @@ The ```` element accepts the following sub-elements: | |deposited locally. Units are MeV per source | | |paticle. | +----------------------+---------------------------------------------------+ + |decay-rate |The delayed-nu-fission-weighted decay rate where | + | |the decay rate is in units of inverse seconds. | + | |This score type is not used in the | + | |multi-group :ref:`energy_mode`. | + +----------------------+---------------------------------------------------+ .. note:: The ``analog`` estimator is actually identical to the ``collision`` diff --git a/openmc/mgxs/mdgxs.py b/openmc/mgxs/mdgxs.py index 8941d4c6f..9fa286ff3 100644 --- a/openmc/mgxs/mdgxs.py +++ b/openmc/mgxs/mdgxs.py @@ -20,7 +20,8 @@ if sys.version_info[0] >= 3: # Supported cross section types MDGXS_TYPES = ['delayed-nu-fission', 'chi-delayed', - 'beta'] + 'beta', + 'decay-rate'] # Maximum number of delayed groups, from src/constants.F90 MAX_DELAYED_GROUPS = 8 @@ -213,7 +214,7 @@ class MDGXS(MGXS): Parameters ---------- - mdgxs_type : {'delayed-nu-fission', 'chi-delayed', 'beta'} + mdgxs_type : {'delayed-nu-fission', 'chi-delayed', 'beta', 'decay-rate'} The type of multi-delayed-group cross section object to return domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh @@ -249,6 +250,8 @@ class MDGXS(MGXS): delayed_groups) elif mdgxs_type == 'beta': mdgxs = Beta(domain, domain_type, energy_groups, delayed_groups) + elif mdgxs_type == 'decay-rate': + mdgxs = DecayRate(domain, domain_type, energy_groups, delayed_groups) mdgxs.by_nuclide = by_nuclide mdgxs.name = name @@ -1574,3 +1577,155 @@ class Beta(MDGXS): super(Beta, self)._compute_xs() return self._xs_tally + + +class DecayRate(MDGXS): + r"""The decay rate for delayed neutron precursors. + + This class can be used for both OpenMC input generation and tally data + post-processing to compute spatially-homogenized and energy-integrated + multi-group and multi-delayed group cross sections for multi-group + neutronics calculations. At a minimum, one needs to set the + :attr:`DecayRate.energy_groups` and :attr:`DecayRate.domain` properties. + Tallies for the flux and appropriate reaction rates over the specified + domain are generated automatically via the :attr:`DecayRate.tallies` + property, which can then be appended to a :class:`openmc.Tallies` instance. + + For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the + necessary data to compute multi-group cross sections from a + :class:`openmc.StatePoint` instance. The derived multi-group cross section + can then be obtained from the :attr:`DecayRate.xs_tally` property. + + For a spatial domain :math:`V`, energy group :math:`[E_g,E_{g-1}]`, and + delayed group :math:`d`, the decay rate is calculated as: + + .. math:: + + \langle \lambda_d \nu^d \sigma_f \phi \rangle &= \int_{r \in V} dr + \int_{4\pi} d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu^d + \sigma_f (r, E') \psi(r, E', \Omega') \\ + \langle \nu \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi} + d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu^d + \sigma_f (r, E') \psi(r, E', \Omega') \\ + \lambda_d &= \frac{\langle \lambda_d \nu^d \sigma_f \phi \rangle} + {\langle \nu^d \sigma_f \phi \rangle} + + Parameters + ---------- + domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh + The domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + The domain type for spatial homogenization + groups : openmc.mgxs.EnergyGroups + The energy group structure for energy condensation + by_nuclide : bool + If true, computes cross sections for each nuclide in domain + name : str, optional + Name of the multi-group cross section. Used as a label to identify + tallies in OpenMC 'tallies.xml' file. + delayed_groups : list of int + Delayed groups to filter out the xs + + Attributes + ---------- + name : str, optional + Name of the multi-group cross section + rxn_type : str + Reaction type (e.g., 'total', 'nu-fission', etc.) + by_nuclide : bool + If true, computes cross sections for each nuclide in domain + domain : Material or Cell or Universe or Mesh + Domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + Domain type for spatial homogenization + energy_groups : openmc.mgxs.EnergyGroups + Energy group structure for energy condensation + delayed_groups : list of int + Delayed groups to filter out the xs + tally_trigger : openmc.Trigger + An (optional) tally precision trigger given to each tally used to + compute the cross section + scores : list of str + The scores in each tally used to compute the multi-group cross section + filters : list of openmc.Filter + The filters in each tally used to compute the multi-group cross section + tally_keys : list of str + The keys into the tallies dictionary for each tally used to compute + the multi-group cross section + estimator : {'tracklength', 'analog'} + The tally estimator used to compute the multi-group cross section + tallies : collections.OrderedDict + OpenMC tallies needed to compute the multi-group cross section. The keys + are strings listed in the :attr:`DecayRate.tally_keys` property and + values are instances of :class:`openmc.Tally`. + rxn_rate_tally : openmc.Tally + Derived tally for the reaction rate tally used in the numerator to + compute the multi-group cross section. This attribute is None + unless the multi-group cross section has been computed. + xs_tally : openmc.Tally + Derived tally for the multi-group cross section. This attribute + is None unless the multi-group cross section has been computed. + num_subdomains : int + The number of subdomains is unity for 'material', 'cell' and 'universe' + domain types. When the This is equal to the number of cell instances + for 'distribcell' domain types (it is equal to unity prior to loading + tally data from a statepoint file). + num_nuclides : int + The number of nuclides for which the multi-group cross section is + being tracked. This is unity if the by_nuclide attribute is False. + nuclides : Iterable of str or 'sum' + The optional user-specified nuclides for which to compute cross + sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides + are not specified by the user, all nuclides in the spatial domain + are included. This attribute is 'sum' if by_nuclide is false. + sparse : bool + Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format + for compressed data storage + loaded_sp : bool + Whether or not a statepoint file has been loaded with tally data + derived : bool + Whether or not the MGXS is merged from one or more other MGXS + hdf5_key : str + The key used to index multi-group cross sections in an HDF5 data store + + """ + + def __init__(self, domain=None, domain_type=None, energy_groups=None, + delayed_groups=None, by_nuclide=False, name=''): + super(DecayRate, self).__init__(domain, domain_type, energy_groups, + delayed_groups, by_nuclide, name) + self._rxn_type = 'decay-rate' + self._estimator = 'analog' + + @property + def scores(self): + return ['delayed-nu-fission', 'decay-rate'] + + @property + def tally_keys(self): + return ['delayed-nu-fission', 'decay-rate'] + + @property + def filters(self): + + # Create the non-domain specific Filters for the Tallies + group_edges = self.energy_groups.group_edges + energy_filter = openmc.Filter('energy', group_edges) + + if self.delayed_groups != None: + delayed_filter = openmc.Filter('delayedgroup', self.delayed_groups) + return [[delayed_filter, energy_filter], [delayed_filter, energy_filter]] + else: + return [[energy_filter], [energy_filter]] + + @property + def xs_tally(self): + + if self._xs_tally is None: + delayed_nu_fission = self.tallies['delayed-nu-fission'] + + # Compute the decay rate + self._xs_tally = self.rxn_rate_tally / delayed_nu_fission + super(DecayRate, self)._compute_xs() + + return self._xs_tally diff --git a/src/constants.F90 b/src/constants.F90 index 481ca8e03..6cbe4edb8 100644 --- a/src/constants.F90 +++ b/src/constants.F90 @@ -290,7 +290,7 @@ module constants EVENT_ABSORB = 2 ! Tally score type - integer, parameter :: N_SCORE_TYPES = 23 + integer, parameter :: N_SCORE_TYPES = 24 integer, parameter :: & SCORE_FLUX = -1, & ! flux SCORE_TOTAL = -2, & ! total reaction rate @@ -314,7 +314,8 @@ module constants SCORE_PROMPT_NU_FISSION = -20, & ! prompt neutron production rate SCORE_INVERSE_VELOCITY = -21, & ! flux-weighted inverse velocity SCORE_FISS_Q_PROMPT = -22, & ! prompt fission Q-value - SCORE_FISS_Q_RECOV = -23 ! recoverable fission Q-value + SCORE_FISS_Q_RECOV = -23, & ! recoverable fission Q-value + SCORE_DECAY_RATE = -24 ! delayed neutron precursor decay rate ! Maximum scattering order supported integer, parameter :: MAX_ANG_ORDER = 10 diff --git a/src/endf.F90 b/src/endf.F90 index 07094d5d9..0ba2db388 100644 --- a/src/endf.F90 +++ b/src/endf.F90 @@ -40,6 +40,8 @@ contains string = "fission" case (SCORE_NU_FISSION) string = "nu-fission" + case (SCORE_DECAY_RATE) + string = "decay-rate" case (SCORE_DELAYED_NU_FISSION) string = "delayed-nu-fission" case (SCORE_PROMPT_NU_FISSION) diff --git a/src/input_xml.F90 b/src/input_xml.F90 index bcdf310e8..64fd30ea6 100644 --- a/src/input_xml.F90 +++ b/src/input_xml.F90 @@ -3297,22 +3297,6 @@ contains ! Check if total material was specified if (trim(sarray(j)) == 'total') then - - ! Check if a delayedgroup filter is present for this tally - do l = 1, size(t % filters) - select type(filt => t % filters(l) % obj) - type is (DelayedGroupFilter) - call warning("A delayedgroup filter was used on a total & - &nuclide tally. Cross section libraries are not & - &guaranteed to have the same delayed group structure & - &across all isotopes. In particular, ENDF/B-VII.1 does & - ¬ have a consistent delayed group structure across & - &all isotopes while the JEFF 3.1.1 library has the same & - &delayed group structure across all isotopes. Use with & - &caution!") - end select - end do - t % nuclide_bins(j) = -1 cycle end if @@ -3357,20 +3341,6 @@ contains allocate(t % nuclide_bins(1)) t % nuclide_bins(1) = -1 t % n_nuclide_bins = 1 - - ! Check if a delayedgroup filter is present for this tally - do l = 1, size(t % filters) - select type(filt => t % filters(l) % obj) - type is (DelayedGroupFilter) - call warning("A delayedgroup filter was used on a total nuclide & - &tally. Cross section libraries are not guaranteed to have the& - & same delayed group structure across all isotopes. In & - &particular, ENDF/B-VII.1 does not have a consistent delayed & - &group structure across all isotopes while the JEFF 3.1.1 & - &library has the same delayed group structure across all & - &isotopes. Use with caution!") - end select - end do end if ! ======================================================================= @@ -3484,8 +3454,9 @@ contains end if ! Check if delayed group filter is used with any score besides - ! delayed-nu-fission - if (score_name /= 'delayed-nu-fission' .and. & + ! delayed-nu-fission or decay-rate + if ((score_name /= 'delayed-nu-fission' .and. & + score_name /= 'decay-rate') .and. & t % find_filter(FILTER_DELAYEDGROUP) > 0) then call fatal_error("Cannot tally " // trim(score_name) // " with a & &delayedgroup filter.") @@ -3639,6 +3610,9 @@ contains ! Set tally estimator to analog t % estimator = ESTIMATOR_ANALOG end if + case ('decay-rate') + t % score_bins(j) = SCORE_DECAY_RATE + t % estimator = ESTIMATOR_ANALOG case ('delayed-nu-fission') t % score_bins(j) = SCORE_DELAYED_NU_FISSION if (t % find_filter(FILTER_ENERGYOUT) > 0) then diff --git a/src/output.F90 b/src/output.F90 index 9244e2d95..a43735da2 100644 --- a/src/output.F90 +++ b/src/output.F90 @@ -786,6 +786,7 @@ contains score_names(abs(SCORE_NU_SCATTER_N)) = "Scattering Prod. Rate Moment" score_names(abs(SCORE_NU_SCATTER_PN)) = "Scattering Prod. Rate Moment" score_names(abs(SCORE_NU_SCATTER_YN)) = "Scattering Prod. Rate Moment" + score_names(abs(SCORE_DECAY_RATE)) = "Decay Rate" score_names(abs(SCORE_DELAYED_NU_FISSION)) = "Delayed-Nu-Fission Rate" score_names(abs(SCORE_PROMPT_NU_FISSION)) = "Prompt-Nu-Fission Rate" score_names(abs(SCORE_INVERSE_VELOCITY)) = "Flux-Weighted Inverse Velocity" diff --git a/src/tally.F90 b/src/tally.F90 index d5af4ddc1..f9e233d73 100644 --- a/src/tally.F90 +++ b/src/tally.F90 @@ -93,6 +93,8 @@ contains integer :: score_bin ! scoring bin, e.g. SCORE_FLUX integer :: score_index ! scoring bin index integer :: d ! delayed neutron index + integer :: g ! delayed neutron index + integer :: k ! loop index for bank sites integer :: d_bin ! delayed group bin index integer :: dg_filter ! index of delayed group filter real(8) :: yield ! delayed neutron yield @@ -670,6 +672,143 @@ contains end if + case (SCORE_DECAY_RATE) + + ! Set the delayedgroup filter index + dg_filter = t % find_filter(FILTER_DELAYEDGROUP) + + if (survival_biasing) then + ! No fission events occur if survival biasing is on -- need to + ! calculate fraction of absorptions that would have resulted in + ! delayed-nu-fission + if (micro_xs(p % event_nuclide) % absorption > ZERO .and. & + nuclides(p % event_nuclide) % fissionable) then + + ! Check if the delayed group filter is present + if (dg_filter > 0) then + select type(filt => t % filters(dg_filter) % obj) + type is (DelayedGroupFilter) + + ! Loop over all delayed group bins and tally to them + ! individually + do d_bin = 1, filt % n_bins + + ! Get the delayed group for this bin + d = filt % groups(d_bin) + + ! Compute the yield for this delayed group + yield = nuclides(p % event_nuclide) & + % nu(E, EMISSION_DELAYED, d) + + associate (rxn => nuclides(p % event_nuclide) % & + reactions(nuclides(p % event_nuclide) % index_fission(1))) + + ! Compute the score + score = p % absorb_wgt * yield * & + micro_xs(p % event_nuclide) % fission & + / micro_xs(p % event_nuclide) % absorption & + * rxn % products(1 + d) % decay_rate * 1.e8 + end associate + + ! Tally to bin + call score_fission_delayed_dg(t, d_bin, score, score_index) + end do + cycle SCORE_LOOP + end select + else + + ! If the delayed group filter is not present, compute the score + ! by accumulating the absorbed weight times the decay rate times + ! the fraction of the delayed-nu-fission xs to the absorption xs + ! for all delayed groups. + score = ZERO + + associate (rxn => nuclides(p % event_nuclide) % & + reactions(nuclides(p % event_nuclide) % index_fission(1))) + + ! We need to be careful not to overshoot the number of delayed + ! groups since this could cause the range of the rxn % products + ! array to be exceeded. Hence, we use the size of this array + ! and not the MAX_DELAYED_GROUPS constant for this loop. + do d = 1, size(rxn % products) - 2 + + score = score + rxn % products(1 + d) % decay_rate * 1.e8 * & + p % absorb_wgt * micro_xs(p % event_nuclide) % fission *& + nuclides(p % event_nuclide) % nu(E, EMISSION_DELAYED, d)& + / micro_xs(p % event_nuclide) % absorption + end do + end associate + end if + end if + else + + ! Skip any non-fission events + if (.not. p % fission) cycle SCORE_LOOP + ! If there is no outgoing energy filter, than we only need to + ! score to one bin. For the score to be 'analog', we need to + ! score the number of particles that were banked in the fission + ! bank. Since this was weighted by 1/keff, we multiply by keff + ! to get the proper score. Loop over the neutrons produced from + ! fission and check which ones are delayed. If a delayed neutron is + ! encountered, add its contribution to the fission bank to the + ! score. + + score = ZERO + + ! loop over number of particles banked + do k = 1, p % n_bank + + ! get the delayed group + g = fission_bank(n_bank - p % n_bank + k) % delayed_group + + ! Case for tallying delayed emissions + if (g /= 0) then + + ! Accumulate the decay rate times delayed nu fission score + associate (rxn => nuclides(p % event_nuclide) % & + reactions(nuclides(p % event_nuclide) % index_fission(1))) + + ! determine score based on bank site weight and keff. Note that + ! the units of the decay rate have been converted from inverse + ! shakes to inverse seconds (1 shake = 1.e-8 seconds) + score = score + keff * fission_bank(n_bank - p % n_bank + k) & + % wgt * rxn % products(1 + g) % decay_rate * 1.e8 + end associate + + ! if the delayed group filter is present, tally to corresponding + ! delayed group bin if it exists + if (dg_filter > 0) then + + ! declare the delayed group filter type + select type(filt => t % filters(dg_filter) % obj) + type is (DelayedGroupFilter) + + ! loop over delayed group bins until the corresponding bin is + ! found + do d_bin = 1, filt % n_bins + d = filt % groups(d_bin) + + ! check whether the delayed group of the particle is equal to + ! the delayed group of this bin + if (d == g) then + call score_fission_delayed_dg(t, d_bin, score, score_index) + end if + end do + end select + + ! Reset the score to zero + score = ZERO + end if + end if + end do + + ! If the delayed group filter is present, cycle because the + ! score_fission_delayed_dg(...) has already tallied the score + if (dg_filter > 0) then + cycle SCORE_LOOP + end if + end if + case (SCORE_KAPPA_FISSION) ! Determine kappa-fission cross section on the fly. The ENDF standard ! (ENDF-102) states that MT 18 stores the fission energy as the Q_value diff --git a/tests/test_mgxs_library_condense/inputs_true.dat b/tests/test_mgxs_library_condense/inputs_true.dat index e58015868..b0165324b 100644 --- a/tests/test_mgxs_library_condense/inputs_true.dat +++ b/tests/test_mgxs_library_condense/inputs_true.dat @@ -1 +1 @@ -08c5f1c783dd88c5fed51c054718ca09fc4e99aa4560a6f928b3902991948f3a878d055ac46c07548904285c2c5f22dc2a3d8c1bb82b8e73d76dd790820117df \ No newline at end of file +f541a29b2b9c4d8d82f2cfc2ea96b9ee6c9b8e96936fe8a8df3984f6c839583b2b04e79e1535e2f4cc54b9d59c36a9b2a4dd604f2c86ed8bb8f2f929ec625259 \ No newline at end of file diff --git a/tests/test_mgxs_library_condense/results_true.dat b/tests/test_mgxs_library_condense/results_true.dat index d1a964a86..cd84d0a3a 100644 --- a/tests/test_mgxs_library_condense/results_true.dat +++ b/tests/test_mgxs_library_condense/results_true.dat @@ -61,6 +61,13 @@ 3 10000 4 1 total 0.002752 0.000240 4 10000 5 1 total 0.001231 0.000105 5 10000 6 1 total 0.000512 0.000044 + material delayedgroup group in nuclide mean std. dev. +0 10000 1 1 total 0.000000 0.000000 +1 10000 2 1 total 0.032739 0.028454 +2 10000 3 1 total 0.120780 0.170809 +3 10000 4 1 total 0.302780 0.109110 +4 10000 5 1 total 0.000000 0.000000 +5 10000 6 1 total 0.000000 0.000000 material group in nuclide mean std. dev. 0 10001 1 total 0.311594 0.013793 material group in nuclide mean std. dev. @@ -123,6 +130,13 @@ 2 10001 3 1 total 0.0 0.0 3 10001 4 1 total 0.0 0.0 4 10001 5 1 total 0.0 0.0 +5 10001 6 1 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +0 10001 1 1 total 0.0 0.0 +1 10001 2 1 total 0.0 0.0 +2 10001 3 1 total 0.0 0.0 +3 10001 4 1 total 0.0 0.0 +4 10001 5 1 total 0.0 0.0 5 10001 6 1 total 0.0 0.0 material group in nuclide mean std. dev. 0 10002 1 total 0.904999 0.043964 @@ -186,4 +200,11 @@ 2 10002 3 1 total 0.0 0.0 3 10002 4 1 total 0.0 0.0 4 10002 5 1 total 0.0 0.0 +5 10002 6 1 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +0 10002 1 1 total 0.0 0.0 +1 10002 2 1 total 0.0 0.0 +2 10002 3 1 total 0.0 0.0 +3 10002 4 1 total 0.0 0.0 +4 10002 5 1 total 0.0 0.0 5 10002 6 1 total 0.0 0.0 diff --git a/tests/test_mgxs_library_distribcell/inputs_true.dat b/tests/test_mgxs_library_distribcell/inputs_true.dat index 924c53838..90cfedb91 100644 --- a/tests/test_mgxs_library_distribcell/inputs_true.dat +++ b/tests/test_mgxs_library_distribcell/inputs_true.dat @@ -1 +1 @@ -9ce3d6987d67e92b0924916bb54288429d2bd6dfd12a69f86c5dbefb407f7eb72adb0e44d558c09e9a39610ffeb651aee4aedc629cf3a28a181d62ca4cfbcd5a \ No newline at end of file +6dc13fba93a14c554db49aebe02fc3c6eb3243b84db930657d3e6e9a26bba563b919f9f65dc066dc87773e581951881f632b799859b723f942497c7938358ec1 \ No newline at end of file diff --git a/tests/test_mgxs_library_distribcell/results_true.dat b/tests/test_mgxs_library_distribcell/results_true.dat index 5a996c8fa..87f18db1c 100644 --- a/tests/test_mgxs_library_distribcell/results_true.dat +++ b/tests/test_mgxs_library_distribcell/results_true.dat @@ -61,3 +61,10 @@ 3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4 1 total 0.002727 0.000135 4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 5 1 total 0.001210 0.000058 5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 6 1 total 0.000504 0.000024 + avg(distribcell) delayedgroup group in nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.000000 0.000000 +1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 2 1 total 0.032739 0.046300 +2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 3 1 total 0.120780 0.170809 +3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4 1 total 0.000000 0.000000 +4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 5 1 total 0.000000 0.000000 +5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 6 1 total 2.853000 4.034751 diff --git a/tests/test_mgxs_library_hdf5/inputs_true.dat b/tests/test_mgxs_library_hdf5/inputs_true.dat index e58015868..b0165324b 100644 --- a/tests/test_mgxs_library_hdf5/inputs_true.dat +++ b/tests/test_mgxs_library_hdf5/inputs_true.dat @@ -1 +1 @@ -08c5f1c783dd88c5fed51c054718ca09fc4e99aa4560a6f928b3902991948f3a878d055ac46c07548904285c2c5f22dc2a3d8c1bb82b8e73d76dd790820117df \ No newline at end of file +f541a29b2b9c4d8d82f2cfc2ea96b9ee6c9b8e96936fe8a8df3984f6c839583b2b04e79e1535e2f4cc54b9d59c36a9b2a4dd604f2c86ed8bb8f2f929ec625259 \ No newline at end of file diff --git a/tests/test_mgxs_library_hdf5/results_true.dat b/tests/test_mgxs_library_hdf5/results_true.dat index 3108573b3..8cec5c629 100644 --- a/tests/test_mgxs_library_hdf5/results_true.dat +++ b/tests/test_mgxs_library_hdf5/results_true.dat @@ -111,6 +111,19 @@ domain=10000 type=beta [ 3.21434855e-04 1.09939816e-03] [ 1.82980497e-04 4.50738567e-04] [ 7.48899920e-05 1.88812772e-04]] +domain=10000 type=decay-rate +[[ 0. 0. ] + [ 0. 0.032739] + [ 0. 0.12078 ] + [ 0. 0.30278 ] + [ 0. 0. ] + [ 0. 0. ]] +[[ 0. 0. ] + [ 0. 0.02845437] + [ 0. 0.17080871] + [ 0. 0.10910951] + [ 0. 0. ] + [ 0. 0. ]] domain=10001 type=total [ 0.31373767 0.3008214 ] [ 0.0155819 0.02805245] @@ -212,6 +225,19 @@ domain=10001 type=chi-delayed [ 0. 0.] [ 0. 0.]] domain=10001 type=beta +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +domain=10001 type=decay-rate [[ 0. 0.] [ 0. 0.] [ 0. 0.] @@ -337,3 +363,16 @@ domain=10002 type=beta [ 0. 0.] [ 0. 0.] [ 0. 0.]] +domain=10002 type=decay-rate +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] diff --git a/tests/test_mgxs_library_mesh/inputs_true.dat b/tests/test_mgxs_library_mesh/inputs_true.dat index f62e0aa05..435b5cc1d 100644 --- a/tests/test_mgxs_library_mesh/inputs_true.dat +++ b/tests/test_mgxs_library_mesh/inputs_true.dat @@ -1 +1 @@ -5f167bdd4d6ae5873d48483e85aceaec8a934239ed5a50ef6f6500ce204f5851ae330621a5007f3b3d6bdab49f2cd627d011c1f6e6983fec958a6984eb9cb7ca \ No newline at end of file +9b394184e2329d0a0f56c1534ee8723bdbe6b5188959c7f2fb4900c1a184b10a211bbe0827b2a220b6a4eaa84865cc9a4368a787269f3d8ee3e61f6fe5798fff \ No newline at end of file diff --git a/tests/test_mgxs_library_mesh/results_true.dat b/tests/test_mgxs_library_mesh/results_true.dat index b6656d67b..8ad0f2eee 100644 --- a/tests/test_mgxs_library_mesh/results_true.dat +++ b/tests/test_mgxs_library_mesh/results_true.dat @@ -208,3 +208,29 @@ 21 2 2 1 4 1 total 0.002143 0.001028 22 2 2 1 5 1 total 0.001026 0.000492 23 2 2 1 6 1 total 0.000408 0.000196 + mesh 1 delayedgroup group in nuclide mean std. dev. + x y z +0 1 1 1 1 1 total 0.00000 0.000000 +1 1 1 1 2 1 total 0.00000 0.000000 +2 1 1 1 3 1 total 0.00000 0.000000 +3 1 1 1 4 1 total 0.30278 0.428196 +4 1 1 1 5 1 total 0.00000 0.000000 +5 1 1 1 6 1 total 0.00000 0.000000 +6 1 2 1 1 1 total 0.00000 0.000000 +7 1 2 1 2 1 total 0.00000 0.000000 +8 1 2 1 3 1 total 0.00000 0.000000 +9 1 2 1 4 1 total 0.00000 0.000000 +10 1 2 1 5 1 total 0.00000 0.000000 +11 1 2 1 6 1 total 0.00000 0.000000 +12 2 1 1 1 1 total 0.00000 0.000000 +13 2 1 1 2 1 total 0.00000 0.000000 +14 2 1 1 3 1 total 0.00000 0.000000 +15 2 1 1 4 1 total 0.00000 0.000000 +16 2 1 1 5 1 total 0.00000 0.000000 +17 2 1 1 6 1 total 0.00000 0.000000 +18 2 2 1 1 1 total 0.00000 0.000000 +19 2 2 1 2 1 total 0.00000 0.000000 +20 2 2 1 3 1 total 0.00000 0.000000 +21 2 2 1 4 1 total 0.00000 0.000000 +22 2 2 1 5 1 total 0.00000 0.000000 +23 2 2 1 6 1 total 0.00000 0.000000 diff --git a/tests/test_mgxs_library_no_nuclides/inputs_true.dat b/tests/test_mgxs_library_no_nuclides/inputs_true.dat index e58015868..b0165324b 100644 --- a/tests/test_mgxs_library_no_nuclides/inputs_true.dat +++ b/tests/test_mgxs_library_no_nuclides/inputs_true.dat @@ -1 +1 @@ -08c5f1c783dd88c5fed51c054718ca09fc4e99aa4560a6f928b3902991948f3a878d055ac46c07548904285c2c5f22dc2a3d8c1bb82b8e73d76dd790820117df \ No newline at end of file +f541a29b2b9c4d8d82f2cfc2ea96b9ee6c9b8e96936fe8a8df3984f6c839583b2b04e79e1535e2f4cc54b9d59c36a9b2a4dd604f2c86ed8bb8f2f929ec625259 \ No newline at end of file diff --git a/tests/test_mgxs_library_no_nuclides/results_true.dat b/tests/test_mgxs_library_no_nuclides/results_true.dat index edd99b44c..961ae9ba9 100644 --- a/tests/test_mgxs_library_no_nuclides/results_true.dat +++ b/tests/test_mgxs_library_no_nuclides/results_true.dat @@ -123,6 +123,19 @@ 6 10000 4 2 total 0.011426 0.001099 8 10000 5 2 total 0.004684 0.000451 10 10000 6 2 total 0.001962 0.000189 + material delayedgroup group in nuclide mean std. dev. +1 10000 1 1 total 0.000000 0.000000 +3 10000 2 1 total 0.000000 0.000000 +5 10000 3 1 total 0.000000 0.000000 +7 10000 4 1 total 0.000000 0.000000 +9 10000 5 1 total 0.000000 0.000000 +11 10000 6 1 total 0.000000 0.000000 +0 10000 1 2 total 0.000000 0.000000 +2 10000 2 2 total 0.032739 0.028454 +4 10000 3 2 total 0.120780 0.170809 +6 10000 4 2 total 0.302780 0.109110 +8 10000 5 2 total 0.000000 0.000000 +10 10000 6 2 total 0.000000 0.000000 material group in nuclide mean std. dev. 1 10001 1 total 0.313738 0.015582 0 10001 2 total 0.300821 0.028052 @@ -247,6 +260,19 @@ 4 10001 3 2 total 0.0 0.0 6 10001 4 2 total 0.0 0.0 8 10001 5 2 total 0.0 0.0 +10 10001 6 2 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +1 10001 1 1 total 0.0 0.0 +3 10001 2 1 total 0.0 0.0 +5 10001 3 1 total 0.0 0.0 +7 10001 4 1 total 0.0 0.0 +9 10001 5 1 total 0.0 0.0 +11 10001 6 1 total 0.0 0.0 +0 10001 1 2 total 0.0 0.0 +2 10001 2 2 total 0.0 0.0 +4 10001 3 2 total 0.0 0.0 +6 10001 4 2 total 0.0 0.0 +8 10001 5 2 total 0.0 0.0 10 10001 6 2 total 0.0 0.0 material group in nuclide mean std. dev. 1 10002 1 total 0.664572 0.031215 @@ -372,4 +398,17 @@ 4 10002 3 2 total 0.0 0.0 6 10002 4 2 total 0.0 0.0 8 10002 5 2 total 0.0 0.0 +10 10002 6 2 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +1 10002 1 1 total 0.0 0.0 +3 10002 2 1 total 0.0 0.0 +5 10002 3 1 total 0.0 0.0 +7 10002 4 1 total 0.0 0.0 +9 10002 5 1 total 0.0 0.0 +11 10002 6 1 total 0.0 0.0 +0 10002 1 2 total 0.0 0.0 +2 10002 2 2 total 0.0 0.0 +4 10002 3 2 total 0.0 0.0 +6 10002 4 2 total 0.0 0.0 +8 10002 5 2 total 0.0 0.0 10 10002 6 2 total 0.0 0.0