gamma-detector.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulating the Signal of a Gamma Detector\n",
    "\n",
    "This IPython Notebook explains how to calculate the signal of a gamma detector using the pulse-height tally. \n",
    "It also shows how to create a realistic image of a detector simulation that takes into account the detector's intrinsic resolution.\n",
    "\n",
    "In particular, this Notebook illustrates the following features:\n",
    "* setting up a simulation of a gamma detector \n",
    "* defining a gamma source from a specific isotopic mixture\n",
    "* reprocessing the simulation output to obtain a realistic pulse-height spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import openmc\n",
    "\n",
    "from scipy.constants import Avogadro"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the Detector Model\n",
    "In the following, we will set up a simplified model of a gamma scintillation detector. \n",
    "In front of this gamma detector, we will place two point sources:\n",
    "* The first source will launch $10^{12}$ gamma particles with an energy of 200 keV per second toward the detector. \n",
    "* The second source simulates the emissions of 1 gram of Cs137.\n",
    "Cs137 is a radioactive isotope that primarily emits gamma particles with an energy of 661.7 keV due to beta decay into Ba137_m1.\n",
    "The Ba137_m1 releases gamma radiation to de-excite. In addition, this source is implemented as an isotropic source.\n",
    "\n",
    "To obtain the emissions from an arbitrary isotopic source, OpenMC provides the `openmc.data.decay_photon_energy` feature, which returns the gamma radiation emitted during radioactive decay.\n",
    "\n",
    "The pulse-height score is determined by the NaI crystal in the detector with the corresponding tally."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "aluminum = openmc.Material()\n",
    "aluminum.add_element('Al', 1)\n",
    "aluminum.set_density('g/cm3', 2.7)\n",
    "\n",
    "sodium_iodide = openmc.Material()\n",
    "sodium_iodide.add_element('Na', 1)\n",
    "sodium_iodide.add_element('I', 1)\n",
    "sodium_iodide.set_density('g/cm3', 3.667)\n",
    "\n",
    "aluminum_oxide = openmc.Material()\n",
    "aluminum_oxide.add_nuclide('O16', 0.4) \n",
    "aluminum_oxide.add_element('Al', 0.6)\n",
    "aluminum_oxide.set_density('g/cm3', 3.97)\n",
    "\n",
    "iron = openmc.Material()\n",
    "iron.add_element('Fe', 1)\n",
    "iron.set_density('g/cm3', 7.874)\n",
    "\n",
    "mats = openmc.Materials([aluminum, sodium_iodide, aluminum_oxide, iron])\n",
    "mats.export_to_xml()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining surfaces\n",
    "c1 = openmc.ZCylinder(r=3.81)\n",
    "c2 = openmc.ZCylinder(r=3.86)\n",
    "c3 = openmc.ZCylinder(r=3.91)\n",
    "\n",
    "z1 = openmc.ZPlane(z0=0.00)\n",
    "z2 = openmc.ZPlane(z0=0.05)\n",
    "z3 = openmc.ZPlane(z0=0.10)\n",
    "z4 = openmc.ZPlane(z0=7.72)\n",
    "z5 = openmc.ZPlane(z0=7.77)\n",
    "z6 = openmc.ZPlane(z0=8.00)\n",
    "\n",
    "s = openmc.Sphere(r=11, boundary_type='vacuum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining cells\n",
    "sodium_iodide_crystal = openmc.Cell()\n",
    "sodium_iodide_crystal.region = -c1 & -z4 & +z3\n",
    "sodium_iodide_crystal.fill = sodium_iodide\n",
    "\n",
    "aluminum_casing = openmc.Cell()\n",
    "aluminum_casing.region = (+c2 & -c3 & -z4 & +z2) | (-c3 & -z2 & +z1)\n",
    "aluminum_casing.fill = aluminum\n",
    "\n",
    "oxide_layer = openmc.Cell()\n",
    "oxide_layer.region = (+c1 & -c2 &-z4 & +z2) | (-c2 & -z3 & +z2)\n",
    "oxide_layer.fill = aluminum_oxide\n",
    "\n",
    "detector_back = openmc.Cell()\n",
    "detector_back.region = -c3 & -z6 & +z4\n",
    "detector_back.fill = iron\n",
    "\n",
    "surrounding = openmc.Cell()\n",
    "surrounding.region = -s & ~(-c3 & -z6 & +z1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "universe = openmc.Universe(cells=[sodium_iodide_crystal, aluminum_casing, oxide_layer, oxide_layer, detector_back, surrounding])\n",
    "geometry = openmc.Geometry(universe)\n",
    "geometry.export_to_xml()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following shows the structure of the cylindrical detector. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa084b36080>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 258.065x259.74 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "universe.plot(width=(10.0, 10.0), origin=(0.0, 0.0, 4.5), basis='xz', color_by='material')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Sources\n",
    "The definition of the first source is straightforward. \n",
    "Keep in mind that the energy in the sources is given in the unit of eV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_1 = openmc.Source()\n",
    "source_1.space = openmc.stats.Point((0.0, 0.0, -10.0))\n",
    "source_1.angle = openmc.stats.Monodirectional([0.0, 0.0, 1.0])\n",
    "source_1.energy = openmc.stats.Discrete([800_000.0], [1.0])\n",
    "source_1.strength = 1e12\n",
    "source_1.particle = 'photon'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The definition of the second source is more extensive and makes use of the `openmc.data.decay_photon_energy` functionality.\n",
    "The `openmc.data.decay_photon_energy` probability distribution for the different gamma energies, per second per atom.\n",
    "Cs137 has a half-life of 30.17 years, while Ba137_m1 has a much shorter half-life of 2.55 minutes with a branching ratio of 0.946.\n",
    "This implies that 94.6% of Cs137 decays into Ba137_m1. \n",
    "\n",
    "In the following, the decay scheme of Cs137 can be seen again (public domain).\n",
    "\n",
    "<img src=\"images/cs137_decay.png\" alt=\"Cs137_spectrum\" width=\"300\" />"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assume an equilibrium between Cs137 and Ba137_m1.\n",
    "To calculate the equilibrium amounts of these isotopes, we use the relationship \n",
    "\n",
    "$\\dfrac{T_{1/2}^{p}}{T_{1/2}^{c} \\cdot b_{p,c}} = \\dfrac{N_{A}^{p}}{N_{A}^{c}}$,\n",
    "\n",
    "where $T_{1/2}^{p}$ is the half-life of the parent, $T_{1/2}^{c}$ is the half-life of the child, $b_{p,c}$ is the branching ratio, and $N_{A}^{p}$ and $N_{A}^{c}$ are the equilibrium amounts of the parent and child, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "half_life_Cs137_seconds = 30.17 * 365 * 24 * 60 * 60\n",
    "half_life_Ba137_m1_seconds = 2.55 * 60\n",
    "branching_ratio = 0.946\n",
    "fraction_atoms_Ba137_m1 = (half_life_Ba137_m1_seconds * branching_ratio) / half_life_Cs137_seconds\n",
    "\n",
    "number_atoms_Ba137_m1 = (1/137) * Avogadro * fraction_atoms_Ba137_m1\n",
    "number_atoms_Cs137 = (1/137) * Avogadro * (1-fraction_atoms_Ba137_m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "openmc.config['chain_file'] = \"./chain_simple.xml\"\n",
    "\n",
    "source_2_Cs137 = openmc.Source()\n",
    "source_2_Cs137.space = openmc.stats.Point((-1.0, 0.0, 0.0))\n",
    "source_2_Cs137.angle = openmc.stats.Isotropic()\n",
    "source_2_Cs137.energy = openmc.data.decay_photon_energy(\"Cs137\")\n",
    "source_2_Cs137.particle = 'photon'\n",
    "source_2_Cs137.strength = np.sum(openmc.data.decay_photon_energy(\"Cs137\").p) * number_atoms_Cs137\n",
    "\n",
    "source_2_Ba137_m1 = openmc.Source()\n",
    "source_2_Ba137_m1.space = openmc.stats.Point((-1.0, 0.0, 0.0))\n",
    "source_2_Ba137_m1.angle = openmc.stats.Isotropic()\n",
    "source_2_Ba137_m1.energy = openmc.data.decay_photon_energy(\"Ba137_m1\")\n",
    "source_2_Ba137_m1.particle = 'photon'\n",
    "source_2_Ba137_m1.strength = np.sum(openmc.data.decay_photon_energy(\"Ba137_m1\").p) * number_atoms_Ba137_m1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "settings = openmc.Settings()\n",
    "settings.particles = 10**4\n",
    "settings.batches = 10\n",
    "settings.photon_transport = True\n",
    "settings.source = [source_1, source_2_Cs137, source_2_Ba137_m1]\n",
    "settings.run_mode = 'fixed source'\n",
    "settings.export_to_xml()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Tallies\n",
    "We obtain the values of the pulse-height tallies by utilizing an energy filter with 1000 bins of size 1 keV, ranging from 0 MeV to 1 MeV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tallies = openmc.Tallies()\n",
    "\n",
    "energy_bins = np.linspace(0, 1e6, 1001)\n",
    "energy_filter = openmc.EnergyFilter(energy_bins)\n",
    "cell_filter = openmc.CellFilter(sodium_iodide_crystal)\n",
    "\n",
    "tally = openmc.Tally(name='pulse-height')\n",
    "tally.filters = [cell_filter, energy_filter]\n",
    "tally.scores = ['pulse-height']\n",
    "tallies.append(tally)\n",
    "tallies.export_to_xml()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.4-dev\n",
      "        Git SHA1 | 7090c5cc3c80912880f863f768bf27f3ebb5b7a6\n",
      "       Date/Time | 2023-05-03 16:08:41\n",
      "  OpenMP Threads | 16\n",
      "\n",
      " Reading settings XML file...\n",
      " Reading cross sections XML file...\n",
      " Reading materials XML file...\n",
      " Reading geometry XML file...\n",
      " Reading Al27 from /home/cpf/xsections/endfb71/endfb71_hdf5/Al27.h5\n",
      " Reading Al from /home/cpf/xsections/endfb71/endfb71_hdf5/photon/Al.h5 \n",
      " Reading Na23 from /home/cpf/xsections/endfb71/endfb71_hdf5/Na23.h5\n",
      " Reading Na from /home/cpf/xsections/endfb71/endfb71_hdf5/photon/Na.h5 \n",
      " Reading I127 from /home/cpf/xsections/endfb71/endfb71_hdf5/I127.h5\n",
      " Reading I from /home/cpf/xsections/endfb71/endfb71_hdf5/photon/I.h5 \n",
      " Reading O16 from /home/cpf/xsections/endfb71/endfb71_hdf5/O16.h5\n",
      " Reading O from /home/cpf/xsections/endfb71/endfb71_hdf5/photon/O.h5 \n",
      " Reading Fe54 from /home/cpf/xsections/endfb71/endfb71_hdf5/Fe54.h5\n",
      " Reading Fe from /home/cpf/xsections/endfb71/endfb71_hdf5/photon/Fe.h5 \n",
      " Reading Fe56 from /home/cpf/xsections/endfb71/endfb71_hdf5/Fe56.h5\n",
      " Reading Fe57 from /home/cpf/xsections/endfb71/endfb71_hdf5/Fe57.h5\n",
      " Reading Fe58 from /home/cpf/xsections/endfb71/endfb71_hdf5/Fe58.h5\n",
      " Minimum neutron data temperature: 294.0 K\n",
      " Maximum neutron data temperature: 294.0 K\n",
      " Reading tallies XML file...\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000.0 eV for Na23\n",
      "\n",
      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
      "\n",
      " Simulating batch 1\n",
      " Simulating batch 2\n",
      " Simulating batch 3\n",
      " Simulating batch 4\n",
      " Simulating batch 5\n",
      " Simulating batch 6\n",
      " Simulating batch 7\n",
      " Simulating batch 8\n",
      " Simulating batch 9\n",
      " Simulating batch 10\n",
      " Creating state point statepoint.10.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 6.7828e-01 seconds\n",
      "   Reading cross sections          = 6.6674e-01 seconds\n",
      " Total time in simulation          = 1.4275e-01 seconds\n",
      "   Time in transport only          = 1.3758e-01 seconds\n",
      "   Time in active batches          = 1.4275e-01 seconds\n",
      "   Time accumulating tallies       = 1.0433e-03 seconds\n",
      "   Time writing statepoints        = 3.4274e-03 seconds\n",
      " Total time for finalization       = 1.9635e-03 seconds\n",
      " Total time elapsed                = 8.2454e-01 seconds\n",
      " Calculation Rate (active)         = 700508.0 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " Leakage Fraction            = 0.70247 +/- 0.00145\n",
      "\n"
     ]
    }
   ],
   "source": [
    "openmc.run(\"/home/cpf/Desktop/openmc/build/bin/openmc\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting the results\n",
    "In a next step we want to extract and plot the results of the pulse height tally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = openmc.StatePoint('statepoint.10.h5')\n",
    "tally = sp.get_tally(name='pulse-height')\n",
    "pulse_height_values = tally.get_values(scores=['pulse-height']).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAACK0klEQVR4nO3dd3gUdf4H8Pe27CaBRGoA6Z3QpaOUKDWIgHpgQ1BQOOJPlLPAYcNTORuiR8BDBewiqFguAqH3EjqG3kIPPX3r/P5YdjO7O1uzyQyb9+t5eNidnZ357He2fPKtKkEQBBARERHRLU8tdwBEREREFB5M7IiIiIgiBBM7IiIiogjBxI6IiIgoQjCxIyIiIooQTOyIiIiIIgQTOyIiIqIIwcSOiIiIKEIwsSMiIiKKEEzsiMirBQsWQKVSOf9ptVrUrl0bTzzxBM6ePRvSMVUqFd54443wBhrAOZ955hnJxxYvXgyVSoU1a9YEfdw1a9aE/NyTJ09CpVLhgw8+8LtvWlpaQGVmNpuRkJCArl27et3HZrOhbt26aNOmTcCxluR1ElHZYmJHRH7Nnz8fmzdvRnp6Op566il8//336NGjB/Lz8+UOTVZ33HEHNm/ejDvuuKNUz5OWloZp06b53U+n02HkyJHYunUrMjMzJfdZsWIFTp8+jTFjxoQ7TCJSACZ2RORXq1at0LVrVyQlJeH111/HSy+9hBMnTmDJkiVyhyaruLg4dO3aFXFxcXKH4uRI2ObNmyf5+Lx58xAVFYXHHnusLMMiojLCxI6IguZo6jt16hQAoHfv3ujdu7fHfqNHj0b9+vV9HqugoAAvvPACGjRoAIPBgMqVK6Njx474/vvvXfbLyMjAfffdh8qVK8NgMKB9+/b48ccfw/J6pARyPm9NlJ999hmaNm0KvV6PxMREfPfddz7LYsaMGWjQoAEqVKiAbt26YcuWLc7HRo8ejdTUVABwaRY/efKk5LFatGiBbt264euvv4bFYnF57Pr16/j1118xZMgQVKlSBRkZGXjooYdQv359REdHo379+nj44Yed19WXYK65yWTCW2+9hebNm0Ov16NatWp44okncOnSJZf9Vq1ahd69e6NKlSqIjo5G3bp18cADD6CgoMBvPERkp5U7ACK69Rw9ehQAUK1atRIfa9KkSfj666/x1ltvoX379sjPz8f+/ftx5coV5z6rV6/GgAED0KVLF3z66aeIj4/HDz/8gBEjRqCgoACjR4/2ex5BEDwSHcDe58xdSc43d+5cjBs3Dg888AA++ugj3LhxA9OmTYPRaJTcPzU1Fc2bN8fMmTMBAK+++iqSk5Nx4sQJxMfH49VXX0V+fj4WL16MzZs3O59Xs2ZNrzGMGTMGY8eOxf/+9z8MGTLEuf27775DUVGRs1bv5MmTaNasGR566CFUrlwZ58+fx5w5c9CpUydkZmaiatWqXs8RKJvNhiFDhmD9+vV46aWX0L17d5w6dQqvv/46evfujYyMDERHR+PkyZMYNGgQevTogXnz5uG2227D2bNnsXTpUphMJsTExJQ4FqJyQSAi8mL+/PkCAGHLli2C2WwWcnNzhT/++EOoVq2aULFiReHChQuCIAhCr169hF69enk8f9SoUUK9evVctgEQXn/9def9Vq1aCUOHDvUZR/PmzYX27dsLZrPZZfu9994r1KxZU7BarT6fD8Dvv9WrVwd9vtWrV7s812q1CjVq1BC6dOni8rxTp04JOp3OpSxOnDghABBat24tWCwW5/Zt27YJAITvv//euS0lJUUI5us6NzdXqFChgnDfffe5bO/QoYNQp04dr+VlsViEvLw8ITY2Vvj444+d291fpyAEfs2///57AYDw008/uey3fft2AYAwe/ZsQRAEYfHixQIAYffu3QG/TiLyxKZYIvKra9eu0Ol0qFixIu69917UqFEDf/75JxISEkp87M6dO+PPP//E5MmTsWbNGhQWFro8fvToURw8eBCPPvooAMBisTj/JScn4/z58zh06JDf8wwfPhzbt2/3+Pfuu++G7XyHDh3ChQsXMHz4cJftdevWxZ133in5nEGDBkGj0TjvO0arBtIc6k2FChUwfPhwpKWl4eLFiwCA/fv3Y8eOHRg9ejTUavtXf15eHl5++WU0btwYWq0WWq0WFSpUQH5+Pg4cOBDy+cX++OMP3HbbbRg8eLBLWbZr1w41atRwNmO3a9cOUVFRePrpp/Hll1/i+PHjYTk/UXnDxI6I/Prqq6+wfft27Nq1C+fOncPevXu9JirB+uSTT/Dyyy9jyZIlSEpKQuXKlTF06FAcOXIEAJyJyQsvvACdTufyb8KECQCAy5cv+z1PtWrV0LFjR49/DRs2dNmvJOdzNB9LJbzekuAqVaq43Nfr9QDgkeAGa8yYMbBYLPj6668B2AdNqFQqPPHEE859HnnkEcyaNQtjx47FsmXLsG3bNmzfvh3VqlUr8fkdLl68iOvXryMqKsqjPC9cuOAsy0aNGmHFihWoXr06UlJS0KhRIzRq1Agff/xxWOIgKi/Yx46I/GrRogU6duzo9XGDwYAbN254bA8k4YqNjcW0adMwbdo0XLx40Vl7N3jwYBw8eNDZz2vKlCm4//77JY/RrFmzAF+JfyU5nyNJcySHYhcuXAhThIHp3r07WrRogfnz52PixIn45ptvcPfdd6NBgwYAgBs3buCPP/7A66+/jsmTJzufZzQacfXqVb/HD/SaV61aFVWqVMHSpUslj1OxYkXn7R49eqBHjx6wWq3IyMjAf/7zHzz33HNISEjAQw89FNDrJirvmNgRUYnVr18fixYtgtFodNY4XblyBZs2bQpqKpCEhASMHj0ae/bswcyZM1FQUIBmzZqhSZMm2LNnD955553SeglOJTlfs2bNUKNGDfz444+YNGmSc3tWVhY2bdqEWrVqhRSTuBYvOjo64Oc9+eSTePHFF/HKK6/g0qVLePLJJ52PqVQqCILgPLbD559/DqvV6vfYgV7ze++9Fz/88AOsViu6dOkSUNwajQZdunRB8+bN8e2332Lnzp1M7IgCxMSOiEps5MiR+O9//4vHHnsMTz31FK5cuYL33nsvoKSuS5cuuPfee9GmTRtUqlQJBw4cwNdff41u3bo5R0L+97//xcCBA9G/f3+MHj0at99+O65evYoDBw5g586dWLRoUVhfT6jnU6vVmDZtGsaNG4cHH3wQTz75JK5fv45p06ahZs2azr5twWrdujUA4N1338XAgQOh0WjQpk0bREVF+Xze448/jn/+8594//33cdttt7nUQMbFxaFnz554//33UbVqVdSvXx9r167FF198gdtuu81vTIFe84ceegjffvstkpOTMXHiRHTu3Bk6nQ5nzpzB6tWrMWTIEAwbNgyffvopVq1ahUGDBqFu3booKipyzsXXp0+fIEuMqByTe/QGESmXY1Ts9u3b/e775ZdfCi1atBAMBoOQmJgoLFy4MKBRsZMnTxY6duwoVKpUSdDr9ULDhg2F559/Xrh8+bLL8/bs2SMMHz5cqF69uqDT6YQaNWoId999t/Dpp5/6jQ2AkJKSIvnYokWLPEZ8Bno+qdGigiAIc+fOFRo3bixERUUJTZs2FebNmycMGTJEaN++vXMfx6jY999/XzJecRkZjUZh7NixQrVq1QSVSiUAEE6cOOH3dQuCIAwbNkwAIEyYMMHjsTNnzggPPPCAUKlSJaFixYrCgAEDhP379wv16tUTRo0a5fd1BnrNzWaz8MEHHwht27YVDAaDUKFCBaF58+bCuHHjhCNHjgiCIAibN28Whg0bJtSrV0/Q6/VClSpVhF69egm//fZbQK+TiOxUgiAIciWVRETlwfXr19G0aVMMHToUc+fOlTscIopgbIolIgqjCxcu4O2330ZSUhKqVKmCU6dO4aOPPkJubi4mTpwod3hEFOGY2BERhZFer8fJkycxYcIEXL16FTExMejatSs+/fRTtGzZUu7wiCjCsSmWiIiIKEJwgmIiIiKiCMHEjoiIiChCMLEjIiIiihAcPOGFzWbDuXPnULFiRahUKrnDISIionJKEATk5uaiVq1afic6Z2Lnxblz51CnTh25wyAiIiICAJw+fRq1a9f2uQ8TOy8cC1OfPn06qLUuA2U2m7F8+XL069cPOp0u7Men4PB6KAuvh7LweigLr4eylMX1yMnJQZ06dZy5iS9M7LxwNL/GxcWVWmIXExODuLg4fjAVgNdDWXg9lIXXQ1l4PZSlLK9HIF3DmNgREZEyCQJgsdhva7UA+zvby8Rshspisd8mcsPEjoiIlKmgAKhQwX47Lw+IjZU3HiUoKICuQgXcB8B87RoQFSV3RKQwnO6EiIiIKEKwxo6IiJQpJga4dq34NgExMTBnZ9s767NMSAJr7NykpqYiMTERnTp1kjsUIqLyTaUCbrvN/o/96+xulomlQgWWCUliYucmJSUFmZmZ2L59u9yhEBEREQWFiR0RESmTyQS88Yb9n8kkdzTKYDJB/eabaPb99ywTksTEjoiIlMlsBqZNs/8zm+WORhnMZmjeegvNFy5kmZAkDp4gIiJl0mqBCROKbxOg1cI6fjyyTp1CbZYJSeC7goiIlEmvB1JT5Y5CWfR62D75BHvT0lBbr5c7GlIgNsUSERERRQgmdkREREQRgk2xRESkTPn59jnsAOD6dS4pBgD5+dDedhsGCwKsly8Xlw/RTayxk0m+0YLsQuDc9UK5QyEiUi6Lxf6PnFQWC9RWq9xhkEIxsZPJluNX8fZuLZ5duFfuUIiIlCk6Gjhzxv4vOlruaJQhOhrmEyew7IsvWCYkiU2xMhMgyB0CEZEyqdXA7bfLHYWy3CyToipV7LeJ3PBdIRcu8UdERERhxsRObqywIyKSZjIB779v/8fls+xMJqg//BCNf/mFZUKSmNi5SU1NRWJiIjp16lSq51GpWGVHROST2Qy89JL9H5fPsjOboZkyBS2//JJlQpLYx85NSkoKUlJSkJOTg/j4+FI/HyvsiIi80GqBUaOKbxOg1cI2ciTOnDmDmiwTksB3hUxYX0dE5IdeDyxYIHcUyqLXw/rFF9iVloaaXFKMJLApVmYCq+yIiIgoTJjYyYRd7IiIiCjc2BQrM85jR0TkRX5+8Tx2Z89ySTHAvqTY7bcj2Wy2lwmXFCM3TOxkwgo7IqIA3LghdwSKo7pxAzoAHBNLUpjYycQx3Qn72BEReREdDRw+XHyb7EuK/fUX1q5di14sE5LAxE5mTOyIiLxQq4EmTeSOQllulkn+kSNcUowk8V0hEzbFEhERUbgxsZMZK+yIiLwwm4HUVPs/rrJgZzZDPWcOGqSlsUxIEhM7ubDKjojIN5MJeOYZ+z+ui2pnMkEzcSLazJ3LMiFJ7GMnN3ayIyKSptEADz5YfJsAjQa2++/H+QsXUJ1lQhKY2MlExSo7IiLfDAZg0SK5o1AWgwHWH35ARloakg0GuaMhBWJTrMxYX0dEREThwsROJlxSjIiIiMItIhK7YcOGoVKlSnjQ0RcDwOnTp9G7d28kJiaiTZs2WKTQ6nx2sSMi8qKgwL6k2O23228TUFAAbf366PfkkywTkhQRid2zzz6Lr776ymWbVqvFzJkzkZmZiRUrVuD5559Hfn6+TBF6YoUdEZEfggCcO2f/x7+C7QQBqnPnEH31KsuEJEXE4ImkpCSsWbPGZVvNmjVRs2ZNAED16tVRuXJlXL16FbEKW0RaYC87IiJpBgOwa1fxbQIMBpi3bcOGDRtwF8uEJMheY7du3ToMHjwYtWrVgkqlwpIlSzz2mT17Nho0aACDwYAOHTpg/fr1QZ0jIyMDNpsNderUCVPUJcc+dkREfmg0QLt29n+c2sPuZpnkNGzIMiFJsid2+fn5aNu2LWbNmiX5+MKFC/Hcc89h6tSp2LVrF3r06IGBAwciKysroONfuXIFjz/+OObOnRvOsEvMMd0Ja9KJiIgoXGRvih04cCAGDhzo9fEZM2ZgzJgxGDt2LABg5syZWLZsGebMmYPp06f7PLbRaMSwYcMwZcoUdO/e3e++RqPReT8nJwcAYDabYS6FZVssFgsAwCYIpXJ8Co7jGvBaKAOvh7LIdj3MZqi+/x4AIDz8MKDTle35lchshu3rr1Hnr79g7t0biImRO6Jyryw+H8EcW/bEzheTyYQdO3Zg8uTJLtv79euHTZs2+XyuIAgYPXo07r77bowcOdLvuaZPn45p06Z5bF++fDliSuGDc+SGCoAG+fn5SEtLC/vxKTTp6elyh0AivB7KUtbXQ1NUhHtv/lH/R4UKsLJPmb1Mxo/HHQD+uPNOlomClObnoyCIEdCKTuwuX74Mq9WKhIQEl+0JCQm4cOGC837//v2xc+dO5Ofno3bt2vjll19gNBqxcOFCtGnTxtlv7+uvv0br1q0lzzVlyhRMmjTJeT8nJwd16tRBv379EBcXF/bXtvFINpC5G7GxsUhOvivsx6fgmM1mpKeno2/fvtCxVkB2vB7KItv1KCqC7WaLTv/kZA6gAICiIlj798ely5dxd9++0FWsKHdE5V5ZfD4crYiBUHRi56ByG2kgCILLtmXLlkk+z2azBXwOvV4PvV7vsV2n05XKhdJqHUWv4g+XgpTW9abQ8HooS5lfD50OuNmiIXuHcKXQ6WD+/XdsTUtDcsWK/HwoSGl+PoI5rqI/K1WrVoVGo3GpnQOA7Oxsj1q8cElNTUViYiI6depUKsf3xNETREREFB6KTuyioqLQoUMHj3br9PR0v4MhQpWSkoLMzExs3769VI7vwOlOiIiIKNxkb4rNy8vD0aNHnfdPnDiB3bt3o3Llyqhbty4mTZqEkSNHomPHjujWrRvmzp2LrKwsjB8/Xsaow4fTnRAReVFQALRta7+9Zw9HgAL2JcXatsU9+fnAgQNAfLzcEZHCyJ7YZWRkICkpyXnfMYBh1KhRWLBgAUaMGIErV67gzTffxPnz59GqVSukpaWhXr16coUcFiouKkZE5JsgAI4//PlXsJ0gQHX0KCoAMLNMSILsiV3v3r0h+HlzTpgwARMmTCiTeFJTU5Gamgqr1Vom5+PHkojIC4MB2LCh+DYBBgMsa9Zg8+bN6MoyIQmyJ3ZKk5KSgpSUFOTk5CC+FKu42ceOiMgPjQa48065o1AWjQZC9+64ev06lxQjSYoePFEesCadiIiIwoWJnUxYYUdE5IfFAixaZP93cxnGcs9igWrxYtTauJFlQpLYFOumrPrYOSZYFtjLjohImtEIDB9uv52XB2j5kwWjEdpHHkEnAOZ//hOIjpY7IlIYfkrclFUfOyIi8kOtBnr1Kr5NgFoNW8+euHrlCuJZJiSBiZ1MHE2x7GNHRORFdDSwZo3cUShLdDSsK1ZgY1oakllbRxKY7suMeR0RERGFCxM7uXD0BBEREYUZEzs3qampSExMRKdOncrmhGyLJSKSVlgItGtn/1dYKHc0ylBYCG3Hjuj93HMsE5LEPnZuymyC4lI7MhFRhLDZ7GvEOm4TYLNBtXcv4gGYWSYkgYmdzFhfR0TkhcEALF9efJvsS4qlpWHbtm3oxDIhCUzsZKLimmJERL5pNEDfvnJHoSwaDYQ+fXDJZOKSYiSJfexkxi52REQUjCMX83AiV+4oSKlYYycT1tcREflhsQDLltlv9+/PlScAwGLB+89/DAC40Lcn6iRUkjkgUhp+StyU3ZJi9v9ZYUdE5IXRCNx7r/02lxSzMxoxf/E0AMDWV0cxsSMP/JS44ZJiREQKoVbjesu2MFlsqM7ls+zUauyp0cR5m8gdEzuZqG42xgrsZEdEJC06Gu3ufRsA8NsVI9rU5hJaiI7GkFEfAQC+07M8yBPTfSIiUrwr+Sa5Q1AcgZ15SAITO5mwjx0RERGFG5ti5cbMjohIWmEhFn/zIgAg/+E/ZQ5GIURlYnokTeZgSImY2BERkTLZbOh49gAAYC2Xz7ITlckmlglJYGLnpqymO3FghR0RkRd6PZ4eNhUA8EiUXuZgFEJUJo+zTEgCEzs3ZTXdCVcUIyLyQ6vF8qbdAAAPcw47O1GZPM4yIQkcPCEzTndCRESh4M8HSWG6LxMVFxUjIvLNakXXrL3225Y75I1FKcRlYm0vbyykSEzsZMY/uIiIvCgqwg/f/xMAsPaFh2QORiFEZbLhxREyB0NKxMROJuxjR0Tkh0qFw1XqOm8TXMqEFQMkhYmdTBxfUewjQUTkRUwM+o2dDQCYHx0jczAKISqTBSwTksDBE0RERLcgVgyQFCZ2MileUoyfTCIiIgoPNsUSEZEyFRbi6x9eAQDYHloibyxKISoT64ifZA6GlIiJnZuyWnnCMd0Jq9KJiLyw2dDj1G4AXFLMSVQma2z8ASFPTOzclNXKE0RE5Idej4n3/gMAMIzLZ9mJymSoLkrmYEiJmNjJhSP3iYh802rxa8skAMBQLp8FABA0GmeZDGGZkAQOnpAZm2KJiCgU/PkgKUz3ZcIKOyIiP6xWtDl/+OZtLikGAIJFXCZt5Q2GFImJncw43QkRkRdFRfjtq0kAgLUTh8obi1KIymTVM4NlDoaUiImdTFRcHoeIyDeVCmfiqjtvEyCwTMgPJnYyYx87IiIvYmJw19/nAeCSYk6iMvksOlrmYEiJOHhCJvw7i4iISoIVAySFiZ1MipcUIyIiCozAbI78YFMsEREpU1ER5v78FgBA/fBCmYNRCFGZqP72rczBkBIxsZOJs8aOf3wREUmzWtHvyBYAwNpSXubxViGIymSFlcuskScmdkREpExRUZjc/xkAwAAun2UnKpMkrjxBEviucJOamorU1FRYS/mvQ9XN4ROcx46IyAudDj+0GwAA6K/TyRyMMgja4jLpzTIhCRw84SYlJQWZmZnYvn273KEQERF5x3oBksAaO7k45jvhB5OISJrNhiaXTgEABBuXFAMAwWYVlUkbmaMhJWJiJzPmdUREXhQWIn1eCgBgTcoAmYNRCFGZLHuqr8zBkBIxsZMJJygmIvLvSnSc3CEoiiCwTMg3JnYy42STRERexMaiw7PfAQDmxcTKHIxCiMoklUuKkQQOnpAJ124mIqKSYL0ASWFiJzN+LomIpLFFgyh4bIqViYq97IiIfCsqwszf3wcAqB/+UuZgFEJUJqphn8scDCkREzuZcEkxIiLfBIsVQzPXAgDWcEkxAK5lkmbjkmLkiYkdEREpU1QU3rz7KQBADy4pZicqk/Ya/oSTJ74rZML5iYmIfBN0OszrNAQAcBeXzwIACDqts0w+YZmQBA6eICIiIooQrLGTiepmJzuO+iIikiZYrah946L9DvuTAQAEq41lQj4xsSMiImUqLMSGT8cAANY8dUzmYBRCVCZ/PHZQ5mBIiZjYERGRYhXo9HKHoCgCWCbkW0T0sRs2bBgqVaqEBx98MKDtSsKWWCIiaUJsLBIn/YTEST/BxiXF7ERlYomOkTsaUqCISOyeffZZfPXVVwFvVwIuKUZERCXBPtokJSISu6SkJFSsWDHg7UrCjyURkTTmLZ6YzJE/sid269atw+DBg1GrVi2oVCosWbLEY5/Zs2ejQYMGMBgM6NChA9avX1/2gYYZK+yIiPwwGjH9z08w/c9PoDIZ5Y5GGVgm5IfsiV1+fj7atm2LWbNmST6+cOFCPPfcc5g6dSp27dqFHj16YODAgcjKyirjSMOL050QEfkmWMx4eO9yPLx3OVQWi9zhKIJgsRSXCZdZIwmyj4odOHAgBg4c6PXxGTNmYMyYMRg7diwAYObMmVi2bBnmzJmD6dOnhy0Oo9EIo7H4r5+cnBwAgNlshtlsDtt5HCyiY5bG8Sk4jmvAa6EMvB7KItf1MEOFT3qMBAC0U6n5fgBgAfD+zTJpABXLRAHK4vMRzLFlT+x8MZlM2LFjByZPnuyyvV+/fti0aVNYzzV9+nRMmzbNY/vy5csRExP+kUc5JgDQQhAEpKWlhf34FJr09HS5QyARXg9lKevrYbYBqd1HAACe2rMXBVl7yvT8SlRgKS6TkQcPQXeFc9kpRWl+PgoKCgLeV9GJ3eXLl2G1WpGQkOCyPSEhARcuXHDe79+/P3bu3In8/HzUrl0bv/zyCzp16uR1u5QpU6Zg0qRJzvs5OTmoU6cO+vXrh7i4uLC/tnPX8oAdmwCokJycHPbjU3DMZjPS09PRt29f6Lj+oux4PZRFruthNFvxwtaVAICOHTvi7mbVyuzcSpVTaMaU7asBAC1btUJyhzoyR0Rl8flwtCIGQtGJnYPKbW4QQRBcti1btkzyed62S9Hr9dDrPSd91Ol0pXKhom4eU7h5DlKG0rreFBpeD2Up6+thFVSoXHADAKBVq/leAKA1CSwThSrNz0cwx1V0Yle1alVoNBqX2jkAyM7O9qjFC5fU1FSkpqbCyk6pRETyKijAzv88CgBYPYpLigFwKZOfH8yUORhSItlHxfoSFRWFDh06eLRbp6eno3v37qVyzpSUFGRmZmL79u2lcnwHTndCROQbJw3wJHD2U/JD9hq7vLw8HD161Hn/xIkT2L17NypXroy6deti0qRJGDlyJDp27Ihu3bph7ty5yMrKwvjx42WMmoiISl1sLOq//AcA4AsuKWYnKpP3DFxSjDzJnthlZGQgKSnJed8xgGHUqFFYsGABRowYgStXruDNN9/E+fPn0apVK6SlpaFevXpyhRweoj6C7n0GiYiI/GHtHUmRPbHr3bu330l6J0yYgAkTJpRJPOxjR0SkDExcPLF5mvyRPbFTmpSUFKSkpCAnJwfx8fGldh5x/ZwguFTgERERABiNeG3FXACA6uHWMgejEOIyGfSezMGQEjGxUwD+AUZE5EkwW/Dkjt8AAKstH8kcjTIIluIyWWQN3+pLFDmY2MmENXRERH7odJjVbTgAIFHL+doAuJRJNS1/wskT3xVuyqqPnQqugyc4AQoRkSshKgof9HwcAPBFVJTM0SiDoNM5y2S6jmVCnhQ9j50cymoeOyIiopLgQAqSwho7mYibYvnZJCLyJNhsiDYV3bzDb0rA3sJTXCY2eYMhRWJiR0REylRQgAMfPQgAWP0olxQD4FImP9y7X+ZgSInYFCsT9+lOiIjIFb8aPYl/L1g+JIU1dm44QTERkULExKDF84sBALOiuXwWAJcy+ac+WuZgSIlYY+emrAZPuPax499dREQeVCoURhlQGGXgHFEOojIRWCQkgYkdEREpErupeBJXBLB8SAqbYmUjnsdOxjCIiJTKZMIL674CAKgebiNzMAohLpMB02QOhpSIiR0RESmTyYxnNv8IAFht4fJZAFzK5BvzKzIHQ0rExE4m7C5CROSHVot5He4DANTn8ll2ojJRqzUyB0NKxE+KGzlGxbIplojIk6CPwpt9ngYAfK7TyxyNMgh6vbNMXo1imZAnDp5wU2ajYkv16EREFOkE1gqQBCZ2MuF0J0REvjFv8cQJiskfNsUSEZEy5efj5Lv3AgBWPcglxQBAJSqTeb33yBwNKRFr7GSi4nQnREQ+8auRKHissSMiImWKicEd//ctAOA9LikGALDFRDvLZJzeIHM0pERM7GTi2seOiIjcCQCuxsTb73COKDuVylkmAsuEJLAploiI6BbhMniCtQIkgTV2bspqHjvx31kcsk5EJMFkQsqmhQAA1UNcUgyAa5nc/ZLMwZASMbFzk5KSgpSUFOTk5CA+Pl7ucIiIyi3BbMaL678GAKyyvCFvMEohKpP/mifJHAwpERM7uYj6RrC+johIglaL79v0AwBU45JiAABBVCZWNcuEPPFdQUREiiRE6TFl4LMAgM+4fBYAQIiKcpbJC1FRMkdDSsTBEzJx7WMnWxhERLcE9kX2xDIhKUzsZMJR6kREvnG5RU9cUoz8YVOsEvDTSUTkKT8fmTMeAABseiBT5mCUQSUqk/ndN8scDSkRa+xk4tIUG0Jml51ThLs/XIO567h+IhFFKAGIMRsRYzbKHYmiOMqEdQIkhTV2t6gZ6Ydx/FI+3kk7iKd7NpI7HCKi8IuOxl3jvwAAvMLlswAAgqhMHuCAEpLAxM5NmU1QLJ7uJIQ/u/JNpRsfEZHcBLUaZ+IT7HfUbGACAIjKRGCZkAS+K9ykpKQgMzMT27dvlzsUn8wWm9whEBGVITY8Am4VASwSksAaO5m49rELntnKxI6IIpzZjCe3/woAUI1oLXMwCiEukx7PyBwMKRETu1uU2cY/1YgowhlNeG3VZwCA5aaXZQ5GIUxGZ5nM/L9xMgdDSsTETibieexCmWTSwho7IopwgkaDJYm9AADRGvYcAlzLxKrSyBwNKRETu1sUm2KJKNIJBj2eG/wiAGA2R8UCAAS9wVkmf+eSYiSBfwLJxGVUbAjPN1vZFEtEVK7xZ4AkMLG7RbHGjogincvyWUxiALjmciwSksKmWAUI5QuLiR0RRTpVfj52fPIIAGD70J0yR6MM4jKZ32mlzNGQEjGxk5EKAgSXiU8CZ2FTLBFFOAFAlcKcm7f5nefAMiFfmNgpQCgfTrONNXZEFNmE6Gj0fTIVADDRwMETAGAzGJxl0lvHMiFPTOyUIJSmWAv/UiOiyCaoVDhSrd7N2+wSDgBQq51l0otLipEEvituURbW2BERlTsugyc4ooQksMbOTWpqKlJTU2G1Wkv9XCrYP6ShfDRNXCuWiCKd2YyHdi+1336wlbyxKIWoTNRdnpQ5GFIi1ti5SUlJQWZmJrZv3y53KD5xHjsiinhGE/69bBb+vWwW1Baz3NEog7hMzCwT8sQaOzndrLILpTadTbFEFOkEjQbLm3QFANhUoc0gEHFEZWJVc0kx8hRSYrdz507odDq0bt0aAPDrr79i/vz5SExMxBtvvIEoLnNS6lhjR0QRz2DA0/e/AgD4mEuKAbAvs+Yok9E6/taSp5CaYseNG4fDhw8DAI4fP46HHnoIMTExWLRoEV566aWwBhjJHH9/ci4iIiIKFn85SEpIid3hw4fRrl07AMCiRYvQs2dPfPfdd1iwYAF++umncMZHRETlFAd9euIya+RPSE2xgiDAdrOP14oVK3DvvfcCAOrUqYPLly+HL7pygh9OIiJPqoICbJhjH/m5676NMkejDOIymdf+d5mjISUKKbHr2LEj3nrrLfTp0wdr167FnDlzAAAnTpxAQkJCWAMkIqLySRBsqJOTDQDYxYZHO0FA7ZtlAg6iIwkhJXYfffQRHnvsMSxZsgRTp05F48aNAQCLFy9G9+7dwxpgJCvuY0dERO4EgwH3PT4DAPAEB+UBsC8p5iiTtjq9zNGQEoWU2LVt2xb79u3z2P7+++9Dq+UMKoHi4H0iIu8EtQZ7aza139Fwag8AgKa4TFpzuhOSENLgiYYNG+LKlSse24uKitC0adMSB1XecFkYIiLf+DVpJy4HGwuFJIRUvXby5EnJJbeMRiPOnDlT4qDKjRJMUExEFOkEiwVD/loNAFBZWsocjUKIy+SOh2UOhpQoqMTut99+c95etmwZ4uPjnfetVitWrlyJBg0ahC86IiIqt1RGIz7+40MAwJKXn5Y5GoUwFTnL5JURD8ocDClRUInd0KFDAQAqlQqjRo1yeUyn06F+/fr48MMPwxZcpGMfOyIiH9RqrK/XDgAgcEkxO5W4TOQNhZQpqMTOMXddgwYNsH37dlStWrVUgiIiIrIZDBj50FsAgA8M0TJHoww2Q7SzTIZHcZk18hRSH7sTJ06EO45yjX3siIgoWPzpICkhz02ycuVKrFy5EtnZ2c6aPId58+aVOLBgDBs2DGvWrME999yDxYsXO7f/8ccf+Mc//gGbzYaXX34ZY8eOLdO4iIgodOLEhbMH2HFJMfInpOlOpk2bhn79+mHlypW4fPkyrl275vKvrD377LP46quvXLZZLBZMmjQJq1atws6dO/Huu+/i6tWrZR6bL8UTFPPTSUTkTlVQgOWfT8DyzydAU1QodziKoCosLhOtsUDucEiBQqqx+/TTT7FgwQKMHDky3PGEJCkpCWvWrHHZtm3bNrRs2RK33347ACA5ORnLli3Dww9zeDgR0a1AsAloeiULALCP1VN2QnGZqFgkJCGkGjuTyRS2pcPWrVuHwYMHo1atWlCpVFiyZInHPrNnz0aDBg1gMBjQoUMHrF+/3u9xz50750zqAKB27do4e/ZsWGIOm5tVdvy+IiLyJBgMeOjhd/DQw+/AFsXlswDAptc7y8TMZdZIQkiJ3dixY/Hdd9+FJYD8/Hy0bdsWs2bNknx84cKFeO655zB16lTs2rULPXr0wMCBA5GVleXzuFL9MVQcLk9EdOvQqLGlbhtsqdsGApfPstNoWCbkU0hNsUVFRZg7dy5WrFiBNm3aQKfTuTw+Y8aMgI81cOBADBw40OvjM2bMwJgxY5wDH2bOnIlly5Zhzpw5mD59utfn3X777S41dGfOnEGXLl287m80GmE0Gp33c3JyAABmsxlmszng1xMos9ns7GNntpTsHKURX3njKEOWpTLweiiLXNfDbLY4b1usVr4f4FomNpuNZaIAZfH5CObYISV2e/fuRbt27QAA+/fvd3ksnLViJpMJO3bswOTJk1229+vXD5s2bfL53M6dO2P//v04e/Ys4uLikJaWhtdee83r/tOnT8e0adM8ti9fvhwxMTGhvQA/VLD/tbVmzVpUD3qKpuJLl5aWFr6gyrn09HS5QyARXg9lKevrcTHXin6HdwIA9u8yIfbinjI9vxKdzSkukwuVOyItjct4KkVpfj4KCgIfKBNSYrd69epQnha0y5cvw2q1IiEhwWV7QkICLly44Lzfv39/7Ny5E/n5+ahduzZ++eUXdOrUCR9++CGSkpJgs9nw0ksvoUqVKl7PNWXKFEyaNMl5PycnB3Xq1EG/fv0QFxcX9tdmNpsxedsqAECvXr3QoGpsUM+fuHm583ZycnJYYyuPzGYz0tPT0bdvX48aaCp7vB7KItf1OHryIp4e+QAAYOH/7UfyXU3L7NxKdeDoeUx43F4mL9+3HsnJ3luiqGyUxefD0YoYiJDnsStL7rWAgiC4bFu2bJnk8+677z7cd999AZ1Dr9dDr/fsnKvT6Ur9i0yr1ZboHPzhC5+yuN4UOF4PZSnr66HTRSHj9hYAALWG7wUA0IjKBFoNy0RBSvPzEcxxQ0rskpKSfDa5rlq1KpTDeqhatSo0Go1L7RwAZGdne9Ti3cpKOijWaLFCr2UnWiKKLLboaDz42PsAgH8buHwWYF9SzFEmD3BJMZIQ0qjYdu3aoW3bts5/iYmJMJlM2LlzJ1q3bh224KKiotChQwePduv09PSwTbfiLjU1FYmJiejUqVOpHF/MOUFxCTO7Zq8sxZSf95U4HiIipeKsUESBCanG7qOPPpLc/sYbbyAvLy+oY+Xl5eHo0aPO+ydOnMDu3btRuXJl1K1bF5MmTcLIkSPRsWNHdOvWDXPnzkVWVhbGjx8fSuh+paSkICUlBTk5OYiPjy+Vc5SG77dlYfr94UuqiYjkxjk+PXGlIvInrH3sHnvsMXTu3BkffPBBwM/JyMhAUlKS875jAMOoUaOwYMECjBgxAleuXMGbb76J8+fPo1WrVkhLS0O9evXCGbo8nK3Z/KASEblTFRbi1y+fBwAcHPiHzNEog7hMvmv1pczRkBKFNbHbvHkzDEH2g+jdu7ffxZ0nTJiACRMmlCS0gKWmpiI1NRVWq7VMzkdERNIEwYq2F44AAA4KNpmjUQjB5iyTH2ysFCBPISV2999/v8t9QRBw/vx5ZGRk4NVXXw1LYHIpy6bYcPWxIyKKREKUHk88+DoAoC9HfwIAbLriMqnKMiEJISV27gmPWq1Gs2bN8Oabb6Jfv35hCYy881fDSUQUCQSNFqsb2Qey9dHeErNzlT5tcZk8oGGZkKeQ3hXz588PdxzlWrBpGvM6Iipv+L1nx2Igf0qU7u/YsQMHDhyASqVCYmIi2rdvH664yoVQF1+z8RuOiMoBwWLFXSd2AQBU1hYyR6MQVlGZtIuc+VwpfEJK7LKzs/HQQw9hzZo1uO222yAIAm7cuIGkpCT88MMPqFatWrjjLDNyDJ4INk9jf1kiKg9UxiJ886O93/b34x+QORplUBUVOstkyqC7ZY6GlCikCYr/7//+Dzk5Ofjrr79w9epVXLt2Dfv370dOTg6effbZcMdYplJSUpCZmYnt27eX/slCrLJjjR0RlQeCSoXM6g2QWb0BBFVIP1cRR1CrnWViU4fa7kORLKQau6VLl2LFihVo0aK4ajwxMRGpqakcPBGCYCecZF5HROWBEB2NQU/8BwDwLy4pBsC+pFjyzTLhkmIkJaQ/gWw2m+SCtDqdDjYb5xoKFPvYEREFiN97RAEJKbG7++67MXHiRJw7d8657ezZs3j++edxzz33hC248iLY7yt+vRFRecBczhPLhPwJqSl21qxZGDJkCOrXr486depApVIhKysLrVu3xjfffBPuGMtUWQ6eCHWCYtbYEVF5oCosxA/fTQYAHBuwSOZolEFVVFwmv7SaK3M0pEQhJXZ16tTBzp07kZ6ejoMHD0IQBCQmJqJPnz7hjq/MleXKE6HiyjpEVC4INnQ9vR8AcJzdfAAAKltxmfzKKRJIQlCJ3apVq/DMM89gy5YtiIuLQ9++fdG3b18AwI0bN9CyZUt8+umn6NGjR6kEG6mCHTzBGjsiKg+EKD0mDLHXTnXVRskcjTLYoqKcZRLLJcVIQlCJ3cyZM/HUU08hLi7O47H4+HiMGzcOM2bMYGJXypjYEVF5IGi0SGt+FwCgC5cUs9MWlwmXFCMpQQ2e2LNnDwYMGOD18X79+mHHjh0lDqq8CL2PXdhDISJSNK6RbcdiIH+CSvcvXrwoOc2J82BaLS5dulTioOQkx8oTwfLWdGuzCVBzwkoiihCC1YIOZzIBACprM5mjUQirtbhMuKQYSQiqxu7222/Hvn37vD6+d+9e1KxZs8RByelWWHnC219sVv4pR0QRRGUswk/fvoSfvn0JapNR7nAUQVwmOpNJ7nBIgYJK7JKTk/Haa6+hqKjI47HCwkK8/vrruPfee8MWXKQL9wTFVrbRElEEEaDCiUo1caJSTQgqtkYAAFTiMpE7GFKioJpiX3nlFfz8889o2rQpnnnmGTRr1gwqlQoHDhxwNl9OnTq1tGKNWOHqY8dBFUQUSYToGCQ9/RkA4DV9tMzRKIPVUFwmD7BMSEJQiV1CQgI2bdqEv//975gyZYqzM6tKpUL//v0xe/ZsJCSwzb+02bxkdhbW2BEREZVrQY+VrlevHtLS0nDt2jUcPXoUgiCgSZMmqFSpUmnEVy4EO4+dt4o5bwkfEdGtSPByuzzj6GDyJ+RJcCpVqoROnTqFM5ZyJ9TuEd4SQdbYEVEkURUVYd6iNwAAWX2/ljcYhVAZi8tkWav/yBsMKRJnN1SAsPWxY2JHRJHEasHdxzMAAAusFpmDUQaV1eosk3Qus0YSmNi5Kct57EId5OV1VCyr6Ikogth0UXgh+TkAQAsunwXAtUxUXI2DJPBd4SYlJQUpKSnIyclBfHx8mZwz2HTMWx8Li5WJHRFFDkGnw+LWfQAAr2iZ2AGuZfIAy4QkBDWPHZWOYDvDcroTIqLyiV/z5A9r7G5BnKCYiMoDwWJF4sXj9juWpvIGoxTW4jJR2Ti9GHliYicjRxc7AfaBD7lGC+Kj/Vete11SjIkdEUUQtbEIaQueBQDMf8j7cpblicpY6CyTKUkbZY6GlIhNsQrxj0V70Hbachy5mOt3Xw6eIKLyQFABFypUxoUKlWHj8ll2KpWzTLikGElhjZ0CCALwy66zAIA5a45hxoh2fveXwsETRBRJhOgYdE35CgDwTwOXzwIAm6G4TLikGElhjZ2MpP7Yys41+n2etxo7Dp4gokgi/krj1xtRYJjYKULxN1Z2bpHfvb11pWMfOyKiyMZvefKHTbFuynKCYqkquyt5Jr9P46hYIioPVMYipC6ZDgA4d/dcmaNRBnGZrGv1gczRkBIxsXMjywTF4uaGIPcXY2JHRBHFasWgQ/aRn5/byuCP7VuASlQmG7ikGElgYicjR4WdOCHTqP0Pc/I2oTETOyKKJIIuCq/2HQ8AqM1VFgAANm1xmVi4pBhJ4LtCAYosxX91aQJYQNZrHzv2LiaiCGLT6vD1HfcCAF5mYgfAvqSYo0y4pBhJ4eAJGTlSuEJTcRNDIDV23vrYcboTIopUAocNAGA5kH+ssVMAo6U4sbME0GfCW2JntrK/BRFFDsFqRf2r9jk+YWsibzBKYbM5y0RlqyFzMKRETOwUQFxjZw6g1s3rBMXsY0dEEURdVIg1n40DAPx36B6Zo1EGVWFxmUy5i0uKkScmdgpQaBYldhb/tW5M7IioPBAA5Ohj5Q5DcVgm5AsTOxk5xkmIEztTAM2p3vvYsSmWiCKHLSYWbZ5bCAB4MTpG5miUwSoqkwe4zBpJ4OAJBShyaYotSWLHGjsiUqbs3CIczc4L6jkc6E8UPCZ2CiCusbMJ/uej8/ZlZ+ZklSEpMpf9xKfiATNUMtfyTXh58V5knLwqdyjkQ+e3V6LPjLU4d70wpOd7m7+TiFwxsZORY2KTApPrj7y/WjvW2IXPwQs5aPHaUry77HCZnfOvczfQ7JWleCftQJmdM5K9nXYACzNO48FPNwf8nL/O3cDS/edLMSry5q9zOQHvqzIa8cH/PsIH//sIapP/5RbLBWORs0w0ZpYJeWJipwDuiZ2/fnbeKvQ43UlgLFYbrubbvxDfX3oIggB8vuFkmZ3/g2WHAABz1x0vs3NGsiMXc4N+zqBPNmD8Nzux5/R1v/teyzdhzppjGP/1DvZjLWtWMx7cvxIP7l8JlcUidzSKoLJYnGWiKYs1zemWw8ETblJTU5GamgprGX5g8o2uX1j+RsaevyHdlFHSUbGZ53Kw+/R1PNy5DlQBrIAhN4vVhu+3ZaF746poVK1CwM97+LMt2H7yGla/0DugwSrhpoSy3XP6Og5fzMXfOtaROxRZHc3OQ9s6t3l9fMepq3hgTnFN4OpDl9A3MaEMIotcwTSpClod3un9BAAgnstnAbCvxuEoEyvLhCSwxs5NSkoKMjMzsX379jI7Z6HZvSnW9xffluNXJLeXtDYh+ZP1+Ocv+/C/fcpporqab8Jjn2/Fr7vPejy2YNNJvPrrX7jnw7UAgK83n8QT87f57TO3/eQ1AMAvu84GXcv55SbpcyzYeCKgcwPFTfDeWG0CcovMAceUnVOEh+duwf/2Bn7dhqRuxIuL92Ld4Ut+971RaA66f9Pn649j7JcZMAUwfU9JBfvnjLgPq1bjeTXmrDmGp7/KgNlqw887Xd93ZfF6qJigi8LcLg9gbpcHYNVFyR2OIghRojLhkmIkgYmdjBw/KR41dn6SjeOX8iW3BzK5cSD2nb0RluOEw4z0Q9hw9DIm/rDb47Etx107y7/6619YfegSvt+WFdCxTRZb0P0SX//Nfo5FGaddtr/xeyZWH7qEH922S/FXYTd6/ja0fmM5LtwoCiimaX9kYvPxK0j5bqdz29HsPKzIvOj3uf76O+04dRVtpy3HK0v2BxSLw1v/O4AVBy7if/vOBbT/kYu5WHXQf7xSfOWcZqsNv+05h+yc4rLMK3L9vP225xyyc4sff3fpQSzPvIiVBy4iIc7gsq+G35gkN3alJj/4NaUAwfaxyzdJ9zUJZDmyW821fO81V95erzhRzi0y46XFe7DhyGWP/YwWK8whNl/nGqWvwY2CQGrafGd262/G+sfewJIiqUS/z4y1GPtVBrad8D1S1F8N48wVRwAA324NLFl2993WLLzx219+R3r3/WgdnlyQgR2nrnnd52q+CS8s2oOtbjXWvtbO/GbLKTz7/S7c+58Nzm05otrQOWuO4dnvd2FY6iYAgE0UZ6HZ6vFZyzOyT1NJBfOJE2w2JOReRkLuZQjs32gnKhNVBH7nU8kxsZPTzd/3I25zO5mtNhgtVpy/UYjTVwuczT/nrhfCbLW5LEEmdvZaIXq+txqz1xwNOpRAmtoc5w8no8Xqs2bKV+2Wt2RB3IctdfUx/JhxBo99sVXi3DaP/oyBvkaVl+Tsw/TDLomDFHWAr0mv0/iNAwByCr2fb1eW90QJAIr8TLsSExVYDGLi99L2k9ewYNNJl+b9IrPVpQZNzNeUJZ+sPILFO85gxNwtAcfiSOizc43O65orqrE7eME+8OLs9UIMSd2Ii6KaO7VK5VGbHkwTOUkLplVfXVSIrbNHY+vs0dAaA6vBjnSqogJnmehMRrnDIQViYqcA7gmK2SLgkc+2otv0Vejx3mq8uHgPdp++ju7/XoVHP9+K/Ju1BtFuP/xLdp9D1tUCvLf0UNAxGEUJjlTSknHyKrr/exVGz9/m91gnL+cHPJXEff/ZiK7TV3pMXHricj6W7r8AjY8syFsCJk4GT18tcN7+bc85nBXNoWWy2FyOcSwH6PXhejz9VYbfuMVhuSfFv+z07A/oLT531wqKpy/Q+coARXwldo4BNRarDb/uPuuRRBvNvpPYmKjiztmBNO0CQJHEMU9eLq5VvOfDtej8zkqXa+GQ56UmFADOXJMeNCQu/pUHXGOsEV/clPr+zdHI3pKzPaevY9n+C877RWar87PmcK3AjF93n3VpuqXSIwiAWa2BWa3xWTNb3jjKhEgKEzsZefvZNlltLk1Sv+4+5+y7te3EVedgC19Jj9jGo5fx7tKDPgdXiH9QpebJczTFbTzqOXBjw5HLeE90/N4frMH4b3YiPYBE4NDNqSoG/2eDS7Ng0gdrMP6bHVhzyHvnfm81dmpR5iQuo2e/34Uhs4oXzTa6JXYbLtg/DqsPXcI7aQew49Q1rDmUjQ+WHYLVJricz3GKApMF037PdDn/xyuPOJv0CkwWvJN2ADv91Jw5XMkrTuzcm+ilCILgtVkYKC6jr7ecwsQfduP+2RtdElF/EyWLa+zGfpWBHafsNWorD1zEjPTDkjW9Ul0FxMmnI6HbcMTz2koldumZF/FR+mFUiS3uPC9u8haHMObLDMxIP4xZq47AYrWhesXixM4xvUxukffyEtee5xRaPGrsPll5BBN/2I3Hv/D/Bw6VnC0mFk1e/BVNXvwVFi4pBgCwRheXiYlLipEEjpVWIKkfN/faOW/7OXy3NQtL/7qA1Efa49HP7c2Qt98Wjce61pM+p+jHzv3HDPBdy+Ro5qxTOQYPd67r3L7qYLbPqSG+3nLKebvQbMU/f9mHGcPbuexzo1D8Ay5ApVI5/xcPFhEnGNcKTBj5xVb8rWMdaN2S38t5xU0XJovV5RjiVse56467zDPXJKEC+rQofi1fbzmFrcevok7lGCzYdNLlHFfzTfh97zkMaXc7UlcfdR7r5L8HAfDejAsAV0Txia/v/I0nsPHoZcx65A4Ybr4XsnOK0PejdS7Pt9kEl2vlSLb/3GeviTp3o8jlNUvVrokZ3N53zy3cjeY14pxJe9va8binhes1/s/KIx7HcTRPfyYqU/djA65Jl+OSPnWzBrVqheLEbtfpa+jdrLp9P7djfHLz/LNWH/V4fRduFGGsjxrZ/aKBQ7lFZmeS2rh6BZdaZUcTLoUiiOlOIP6Ml0YsRJGHNXYy8vbznnXFszO8VF8n8Q+du3/+sg/rDl9C6upjzm0nLkuPpgVckwjH7dyi4mkuNAHMvebenHo1vzhJsdkE11pBm4BX3UZauk8t4a7He6ux98x1dJ2+EnPWHHOpQRMPOJm77jjWH7mMZ7/f5bNWs9Bsc6mx0vp4iccv5TsnNQaA01cLsfJgtkdS53AsOw9/nbvhUv6OMhDXaP3rj0zc8+EanL1eCKtNcGmeFDcZTvs9EysOZLuU0ew1x1wSXwDYf+4GOr290nnf2RQr6mQtrqX0V2Pn3tx9+mqhS03sBbe+cq//uh9fbj4Fdz9mnMGNAjPeFq22MfGH3ej89gqXcnX8gfH675l4Z7fGpabvsqg2c/T87c5k0VtNtFTS2nX6Sok9i+0VJXY5RRbnYIkabqNjiYiUijV2ClIz3oDzN4qczZMOKhUQLZHYfTGqE4akbvTYLiae/02q6bLAZEF2jtGlpiSvyIJDF3Ix6JP1GN6pDt4Z1tqledMb9zm+xM2KKd/txJ/7L+DrMZ3Ro0k1/N/3u/wez92Za4W472ZT6rtLD6J5jYrOx3IKPWt6AOl5yhy2nbji8uNv8VEj8PHKI/hYoibKmyKLDYM+2eCy7emvM7DiQLbLti82nAAAvLx4L+KitUjbV9zHS6pGVtwHT6pm9bkfdrvUSjquuUsSLLpO7n3IjmbnoW7lGOg0Khy8kItMP9OhuK/7KZXUObT/13KPbdm5Rny8ong5N0ei+N22MwBUmLLkL6/H23b8KvokJkiWQ6jE752cIrPz2B3rV8KGo54jqyl4QdW8GY14c/kcAMC1Hv8unYBuNaIy2d/6VZmDISVijZ1C/DiuG1rWigMAbHWbn00Q4FEzAwBVfNTYOZwXdZaX6jt336yN6P3BGizPLE4oco0WzFxxGBabgO9u9q1Ti94pjv5jgiC4TD3hntgdv5yP7Nwi7Dh1DX/e7JQ+8ottOHA+x+ckyIFOhiuuTRoxt3h1gIqG4r9XfDU1uj+2JTt8Hwep/oXuSZ3YpVyjS1IH2GuMth6/4jLKVvya9TrPeI+71co6ml3Fq5JkiQaU7Dt7A+dvFGJX1jUs3X8efWasRbNX/8TsNccw8OP1yPAx/QhgH3V87FKez30cvM148vOu4j8+dp++jjTRe2N5pmeZxUfbJ2X9esspHM3Ow7kA5/sTC2Txjyt5Jly8eex7midgaLtaHvucv1GIvWeuB31+CozKYsHju/6Hx3f9DyorlxQDXMuES4qRFNbYyUj841LRoMVtMfZEzX36EwD471rPdUUrGoKbddxRa3P6agEOX8zFPS0SnM2n8zeedO5XaLLCqHdNesQ1doezc3HueiEMWg0e+bx4GhGT1ebSLHY134TOb3s2ff2yy3uTa6HJKjla0tfrAVzncjPoNM4aSF/Nz6Up2PO619ICwP/2nsf/9p5H3crFncaPX8rHpmOX0b1RVVzOLa69q1ZRj0u5nlMfOBJBce3f37/Z4bx9Nd+EbtNXuTxHEIpHkAbi111nMalfs4D3d+c+mGHCtzu97Gn3TFJjvJ12AGsPX8LaGWtDOmftStE4fdX3+2ztzVU5qlfUo0lCBcx8qD0u5Rmx8egVtKkdDwDOslsxqRcaVw98WTsKjKDVYeadD9vvaDgKFLAvKeYoEy4pRlJYY6cQ0ToNKscGt2RORX1wH+pfdp1F1pUCDPpkPcZ8mSG5TBdgb54V9736bmuWSy3PgJnr8eSCDLzr9uNvsthQFMCSS9t9zFV2Jd+IPgH+WHtbaaNAlMTsDmCRd6XLcpuy5ZHP7EusOZpcZz96ByrHSL93Fm4/jeycIpcm01BquHzZcPQyvthwwusaxuE08Z4m6NygcomP80T3BogT1ey+dm+i131HdKrjHOjxVI+GADy7NbDWzjdxLXwwLbG2qCjMvOtRzLzrUdi4pBgA+5JijjLhkmIkhYmdjPSi0o+J0jibmAKlDnC6E4cCkxX3pW5Azs0akk8lagEB4NilfJdpTf75yz7naghimedclx4zWrxPniy2K+u683YFt+T0Kx99tNx5q9nLDyAGAGFJEOTy4fLDyL5ZQ1etoh6xeunaDJPVhs7vrAzbcnNSdmZdx7/+yPSo+SsNFQ1atLo9PizH+fLJzs77tW4zeJ04ukeTas7buptrilmsgkuyEkjTrhIYLVb8tONMmc/DJ+5dEeroVg6KtQt23WYqf5jYyUirLv6AGqI0Lk1Sg1rX9Pncn/7ePaRzXhfN/3XgvO+O8f64JwtGi9XvElVir92biK4Nq7hsE08xUtq6N6rif6dS1rl+aMllbpHZWUNWM96ACgE0y98KIzsHt/XsxyYWZ9BBo1bhmzFdgjquzm0Qze2VolFdVB4xUVqvNead6ldy3nZMn3PoYi6W/VXcJ9LXFDZKMnPFEfxj0R50fnulyyCb0hZqKiLYbIgrykNcUR7nO3EQBJYJ+cTETkbi35ponQa1KxVPNjnzoXYY6WXOOQDoUK+S18fkklNkCSqx69qwisc8c2VpfK9GePaeJnhlUAvZYqhd2XWC0V5Nq+GlAc1cJuOVcq3ADLNVgEatQo04Ayp4qbETa1Q9tkSxBks8p2Gg3nugjc/HK9xsPr2rSVW8NEC6X98bgz2bVSf1bYZn726Mqckt8M/k5ujWsAqqVdA7Hy8yWxEnkRz/392NXZao02qKvzLHf1PcF/BWqbETr6zxt083+9gzvEKtZVIXFmDvxw9h78cPQVtU+k39twJ1YaGzTKK4zBpJiOjE7oMPPkDLli3RqlUrfPPNN3KH40Gc0+g0agzvWAfPJDXGovHdoNOo8UIAHdIXPt01pPU8pXzwt7Ylev6BczmSo3e9iY7SQONjOpLSZtBpMKlvU4y92W/KoX/LBDzXpwnG3NWg1GNw7xvXo0lVTOjdOKARz4C9r5dWo/Zo0paS3Lom3hzS0nm/YbXYkBPrQJqxpyQ3x4TejTBvdMeAjys1rY+Y+HWO69lIcp/RdzbAV092RqzoWJVidJjUrxme6tkQT/dsBJVKhSht8ddfrF4ree5Yt3J1r/lz8Lc0m1KI06uyHFgk7pIYzNJgrI8iCl7EJnb79u3Dd999hx07diAjIwNz5szB9evX5Q7LhftvRJRWjRf6N0Onm81zUnOwadUqfDu2uBmqS8Mq2PlqX/QTrfDwXJ8mGNWtHkZ3rx9UPP1aeq4S8XDnOgE/32S1YcnNARkNq/qvHTLo1GVSY/d/dzfG493qYf7oTrj/jtvRvEZFLHy6q8s+nz7Sznm7b2INPNenKSb1beqz1tSXx7rWdTnHs/c0wf3tb/cYOVnPrZwq3Uz0xEnH/mn98VhX37Vf7gmIlI71KuNvHYqvp8liw20xvptwpQYV1K8Sg88e74jHuxWXzT3Nq3vsF2fQ4aUBzZHUzPMxX3y9JcQTTmvUKsx6pD3G9WqIpgmu5dqzaTU8e08T532dRvqrLvWRO/D33o3QvVEVybkaDVrX52nV0scpkFhGTexqvkkRfaMCicFbrPnm0GveQl3n1RYdg8YvLEHjF5bApFd+V4KyYDFEs0zIp4hN7A4cOIDu3bvDYDDAYDCgXbt2WLp0qdxhufCX00j9GE1JboE7G1d12WbQafBvURNWbJQW04a0wuC2rv30Zj96h8/zVdRrPZqUhra73XeQbr7ZkuWM6eOH2vncV2qZNG/02tDfqpViovDmkFZIal4dM4a3w9LneqKLW9++e1pUx6ONrbivTU3cd7OfV6xei38NbYX3HmiDIRJzmPny1tDWLueoWiEKM0a0w4pJvVz2e6hTHbz3oP3aDWpT09nHTK8tLpsKei3eGtoaHSWa398a2sq5jz8VDa61Utm5Rq8Jj8MDHWp7NPtfzjMhPlqHN4e0wod/a4uRXevh05EdUK2iXvIYqiDbKaMkrnWjarF44I7a6OJWU3hvm1qYMrCFZMIVIyoTnZf3z6A2NfHygOb2peokko/b3GpUo7wsT1Loo8Zu9aFs3PGvdPz7z4Ne9ykr/tKrNY5Yl7rGuvJgNv6ZocW7yw57eaaf84Y8eEIFi0YLi0Z767R3lzYVy4R8U2xit27dOgwePBi1atWCSqXCkiVLPPaZPXs2GjRoAIPBgA4dOmD9+vXOx1q1aoXVq1fj+vXruH79OlatWoWzZ30vWVXW/Cd2njsUeqkZECdJ1pvfnJVEP0pNqldAsp8BGSqVCjq3H8h4PzU63kRHaTCk3e0+aw2l1gr15ok7g28WrRVvQPu6t+FvHWsHtH/nagI+/Ftrj8RieKc6+Pih9kGfHwBe7N8MHetVwoMdPGN4Y3Ciswn+5L8HIfWRO5znlmpelyqvga1qAIDPpdMcxBM3A/YaO18rcwBAbJTG430qnhPvgQ618a+hraDTqBHlI0k0iCZT7tbQ96AVqeM83q0+Phze1qWPm5jU6xCPMo8Kssn/maTG6NqwMga2ruF6Hi81du8uPYjrolVBxN74zb56xn/LcGCQN/6Sqjf/yATgOW/mO3/apzb6YmPgo9a9xhDq8+Sv8CS6JSg2scvPz0fbtm0xa9YsyccXLlyI5557DlOnTsWuXbvQo0cPDBw4EFlZ9hqjxMREPPvss7j77rsxbNgwdOrUCVqFTebo77dGqqajwMtUHuIaLcccW+LEzvHDP7p7fZf5u/wRdyj3NwHrcFEC5fghl1odAbD/oanXqgP6sn6xv/fBBA93ruN1mpi/JzXGLxPuDHoi53BKSWqMxX/vjpgozzK3eFuKAcDUQS0QrdO4NCe+cm8Lj1pORxNs1pUCuKsVb0BfURN97M0YxDVr/mrstBp1wCM+vfU/A1xH5EqtgCKml0hg/U3t886w1ojWaVwGVIj7L3pLyMTEr/OF/s3ww9PdXGpOAd9L1M1adVRyezD9TgHgwo0iPP1VBjZKLGH25aaTeGnxHufqL77M33gCLy/e67KvvyZRX8l5SYSclJmMmLJ6Hqasnge1WTpxLm9U5uIy0ViCe29R+aCsTEdk4MCBGDhwoNfHZ8yYgTFjxmDs2LEAgJkzZ2LZsmWYM2cOpk+fDgAYN24cxo0bBwAYO3YsGjdu7PV4RqMRRmPx8P+cHPtUIGazGWZz+D88ZrMZnaoJ2HYJaFI9NuBz9G5axe++JrMFZrMZ0aKra7XZYDabMXVgU0zp3wTNXk+XjMn9N1yrKm5iala9AmrF67HuyBVIqX1b8Y+3XqOG2WxGlJffiWidBhaLBTbR4vQT726Ej1cd84zLYkWsrjiwuxpXwYajV5DUrCreHNwCryY3w4jPtmHfWdfpW4ymwK+dYz9f+zvW8vWmbuVoZF0tRJvacT6PU9GgRW6RBV3r3+Z1v0ZVorFjahJ0N8tRvO3B/25F5nn7ShVqwQqz2YZ7Wyfg511n0bHebcg4dR2AvdbUIlpyyGq1wGoFXu7XBC/8tB8DWybg2CXfHejNZjMe7nQ7tokmlR7UqoZk3O61huJ9nundEC/8tB99W1R3We+2RpweF3KMLs8R167FRGlQYLKiS714n2XarHqMR3lV1BcfRw2b3/eCuP+Y131t3kd9f77hBEZ2qY1at0WjyGzF4p1n0btpNZfELnXVYSRU1Pts2p/2234sz7yI5ZkXceRf/Zzbd2Vdx+s3a/8GtkzAXY1913xO+91e+5bUrIqzD6R7Qmg2m7Hj1DWcvV6E+9rWdOnz+vOOLAxuY6/lD6hsfDCKWhqsFkvAx7AVGTFu288AgA9MU0rlu/hWIy6Tl03Ps0wUIJDfj3CdIxCKTex8MZlM2LFjByZPnuyyvV+/fti0aZPzfnZ2NqpXr45Dhw5h27Zt+PTTT70ec/r06Zg2bZrH9uXLlyMmJkbiGSXXNB54uY0FVQw3kJaW5mWv4kvUJM6G8/s24fw+b0e073vw0BGkFR5y2aYx5rqdw749Si3AZLN/maelpUGwaSDO7tasXOHc98KFc7CvGS+drV05dQiAvYbj2uWLSEtLw7EzKuc2sQKTFWlpaTh3Tu083rkTh5372ns83Zwz7NAh3KaH87Hatmy82EZANcMF52t6tBbwRa4aR3JEgw7+ykTaNe+LyEtJT/dMeB2ebQrkmIDpe6Q/Nk0M+XiojQ3VDFd9XE/gn63txzm2cz0801j/8nKKr9Gff/4JwF4j8mIboJrhMnZlaWAVVOhQMQf7L+bCUb6OmLQC8EJrICH6LPaddL3e7tLS0qC6eezqBuBiIZAQfQZpaWc89i3Mdz2WuAyKz3kOy3OKr/mzTfMxbZcGRmvxe9BcVHycV9oYkWcBDmxbiwNBltN1I+B47+7I2I7cI76rja7fKD6vt+uXZy4+ppSnPl+LlEQbfjulxspzarynOQBBKC6T95cfAQDcOL4bVb30e99/UjqOiZuLz7txyzbkHPZXDWbff+WmHTAet+9bUOB6jZb8noYXt9n3u3Rkl8t7a9KifTi6fzea3Sa4PM/Xe9ubImtxPLt274b6zK6AnnfgkgVHO98PADhyKgtpacrqTiOHvRctyLpZJqcvZod0Pah0+Pr9KKmCAs9WGW9uycTu8uXLsFqtSEhwHcWZkJCACxeK52kaOnQorl+/jtjYWMyfP99nU+yUKVMwadIk5/2cnBzUqVMH/fr1Q1xcXNhfg9lsRnp6OkYN7QudzntT4cTNy523B3RojOS7vdc6OvZt0KgRkvs2cdnWo3VDJPdv6rHv33s3Rqxei+4NK6NZjYp4Z/9aXBStOTo4eQBe2rYCAFC3Tm3kFVmw/5rrwuyVY3X4anRHXMw14ssj9rm9GtStjeTkVjiQfgQ4fUIy3uTkZCzP2wtcsV+zrh3aYdEJe9YapdXAeHN5snatWqBaRT2+P2Z/rOMd7TCkrWd/wUN/HsKRTcV9gBo3bY7knoH1zXNcj759fV8PAJi+Z7nk9qZNGuGpPk0kHwunBWe2IivfvupHcnKyx+MN29/A0ew83N++FsZ8tRO4fsXrvt+e34oz+Tc8tjtIPcebz7O24FxBca2pt+cOMFvx7bbTSGpaDQ2rxWJl3i6sPHjJ+ZzZxzchu8i+hvHQZP/Xwxuj2YrXd9rXKm57R0ckNavmc/+fr+zEmcOXfcaeU2jG1IzVXo9x+IYayckD8GnqZgC5KLRKJ82JHbpJDoYBgC/PbsPp/OsecYi/C7p16YSeTaq6P9WFY/96jYs/B//OXAeYimudZx+LA2D/wWjVoSsqXDoM5BVfw9kHNNjyci8YDmwFbs6ZFsx7wiG3yIKXt9lXJmnXrh2S2/ju7+tgOHQJ447aM+Cnm9VHcr+mfp4R+dR/XcT/HbeXybB6NZCc7HvuRyp9wfx+hMrRihiIWzKxc3DvgyYIgss2ce2dP3q9Hnq956g+nU5Xahcq2OM/3atxQPsKKpVzv2n3tUTavvN45p6mLs99c0hL/LnvAp7q1dhlRGWl2CiXxC7aUFwmeq0GRRrP0X+Pda2PVnUqo1DUXBert7+uIov3WgWdTge1qO9TxejiPlFRWjUmD2yO9MyLGNm9Af46V/ymrmCQLrMJSU3w17nc4mZDUTkEKpDr8cqgFvgx4zT0Wg2evKs+nl+452bM2lJ9rzhjFC2GLnW+jg2qomMD+4++IKqdkdo3SnSs5/o0wcwVR1zPFcTrce+v5+25Op0O43sXJ8D/GtoaOT/swhN3NoBOp3PpY1eSz5/4eYUWwe9x3hraGs8t3I2nejTwum+M4LsPWr0qMdDpdH5HAj/x5Q7sf6O/5GAQcb8+tUaLueuO48jFXJd9rILv97a46TTPZHPu697X7YSob6Y+SofrEv0Bt2W5/qCEcj20ohZsjUYT8DG0ovenWh348yKZxqVM1CwTBSnNfCGY4yp28IQvVatWhUajcamdA+xNr+61eJFiVLd6fgcBNK9REQAwuE1x/51R3etj4bhuHgMMHu9WH98/3dVjmgxvyyoB9g7s4k7szRLs53OMzIwVDRBwTKvhvnaseHkmd+KpOPRaNZ64swG+e6orYqK0aHbztQFAkZepJapV1OPH8d2cc+j1b1lDcr+SGtujIZY/3wu//99dGNa+eMBIWa2iEcA4ACfHJMtS88wBwNM97ZMz92mRgP+727W20dtzvBnXS3rCYH9q3RaNReO7O0dtT+htP07byiWf9Ncx6KZTAEu31akcg5/+3h0DWnmvTZIaPLFp8t34/Zm7AAD5RiuW7j+PTD/L9RWZbXjpp724lu86IODE5XzsOHXNef/A+Ry8u/Qgft7l2gRZKLHCi80mYPXBbOQZLS7L/eUWmSEIAtYcysb1Qu8DEPKMFmTneC4zZjRbSzwiNeT1YW0CtFYLtFYLBOHWmAS61AnFZcKhwiTllqyxi4qKQocOHZCeno5hw4Y5t6enp2PIkCElOnZqaipSU1NhtQa+NFZZCGQusCUpd+JynhG1K4XeJ7BjvUrYdEx6cIRWrUIT0cjYX59xPZ94IXrHxK51q7jG0rF+ZWw/eQ1SxCM+3UfniUfn+pvTLm1iD1zJN+H226J97hdudSqXTl9Md4k147Hl+FX/OwJIal4da1/sjVpeyqJPYoLzcfHgh9Hd6we91Fr/ljWQEKfHRYnkIBgDWtXEiufvwt7Na0p0HABY/3ISbhSaUTM+PO8FcfI+vlcjPN6tHmrdFu0ciX45z+iy1JgvP+88i7PXCrFwXDfntqQP1rjssyjjtORzpUbH/7HvPJ79fhcaVYvFrzcTTcDeDPpR+mF84mXUrsOhC7kwWT2TpyKLzWUsrcVq8zrtjDclWVLs6AdDAQAfdM8I6RiRRl1Q6CyTKV9ulDcYUiTFJnZ5eXk4erT4i+jEiRPYvXs3KleujLp162LSpEkYOXIkOnbsiG7dumHu3LnIysrC+PHjS3TelJQUpKSkICcnB/Hx8SV9GWEjNSu+O4NOU6KkDgAmJDXG5XwT9py+jlfdVh3QqFUY36sRcgot6NcyweN84ik9HM1pT97ZAO8vO+TcPvGeJig0WZ21fO7xO0hNKDt/dCdsOX7FZQoPKQadpkyTui9GdcT2k9f8LmAfLpP6NYUAAfe2Cex89ar4XgVE6vG6lWOC/vEGgHqVY0uc2NmPE4O/wlABGhOllZxqJlTiP7AqxeicCXOo8z1uPXEV7y09iN2nr2PBE509Hj+SnSf5PHFi997SgziSnedcQu3YpXyXOfV+23MuoFi8TaD86pL9Lvcf/Xwrvnyyc1DzUIY6QbHg9Q4ReaPYxC4jIwNJSUnO+46BDaNGjcKCBQswYsQIXLlyBW+++SbOnz+PVq1aIS0tDfXqhbYElFL1bFoN6w5fCmppr5Iw6DR4Z1hryce0ahUMOg1ek1hkHXCtsXOIjtKgT4sErDhw0Xn8N+5r6bGfY18Hqfm0kppXR1KQzYNl4Z4WCbinRdl1Aaig1+L1wdJlGC6c0N4/cZ/CinotNGqVs+YuGLPX2MdGrzp40eOxUxLzE9q35yPzXA6qVoxyPl/8x9Lu09eDjiNQW09cxUuL9+LfD7QOOGkWXG4HXka26Bi0mfgDAOBhfdnWwCuVJdrgLJO+XFKMJCg2sevdu7ff6vsJEyZgwoQJYT2v0ppiF4zuhNwiS8g1AuHkrznLIOr0LZ4vq2G1WHibq0J8jV2aYkuwhBjJp16VGJc57yKZuFZZpVKhUowOl/NCn0RXqnn17PVCyX2/2nwKX20+5TKBtbjf3cHzuVJPC5vf9pxDTpFZspZRSuhrzAI5Bnv3D4F/bdipVM4y4V9gJEWxiZ1clNYUq1arZE/q5jx6B1YfysajfhaiFw+sEFdcPHtPE1zOM/ptqhQ37YTSDEjhE+rPxT+TW8BqE/BggMu43crclyl74I7aJVo2zDG9TzDEtXxrDl1y3j54IfCpEUIlPp8/LjV2bFIlKlVM7Mivga1rYqCfdWbdiZeNqqDXYsbwdn6f47JcFr/9ZdW+rvfRy75Uio3CjBHtwhuMQrWr41pGLw9oHnBiVzk2ClfdRsQWSYx09SffKP2c435WFClr4u+DoFqrzSY8t+FbAEBh15fCHNUtylRcJufaPCdvLKRIrBahUuFvPVAH8V7i0a7h7PBOgdvwchJ+eLor2ta5Te5QFGvdi0lYNL6byxQ8gL3G+s+JPXw+9/bbojF/dCf8mnKnx2PepvHx5cRl6QROan1aWVvtRB/0QL8bAEBttuC5jd/juY3fQ2O1+H/CLWbKz/vwyGdbguqbqbaIysQSeWVCJcdfTwqrAS1rYOlfFzCiU/CDPcRNubF6vjXlULtSTIlHVke6ulViPKbxcXDM7ejN8I51kNS8usearQCQUxS+dSav5Hv29asR53utY4cH7qiNn3bal4zr0aQq1h+5XOJ4Qh3datVo8FX7Qfbb6sj7Tvh+WxYAYGfWtYDmWgQAm7hMNIGPTKbyI/I+KSWktMETt5o5j92BApM14MTMWyVCRQPfmnTrUXuZpPrNIS3RNzHBOQBJaj/xFCWlwX1lEG8GtqqBaUPso66zrhRg/ZH1AIBKUQJ+nHAX+s4Mfu40IcQaOyFKj9f6/R0AMFbnffL0W53FGsRIYVGZPBDBZUKhY1Osm5SUFGRmZmL79u1yh3JLUqlUQdW2efs6c18Rg+hW8cAdngNHonUav6PKr+X7r7GrFV/601tUNGhRQW//57IajMb/yHhvxFOchDAjzM1jRK5gpoAJdYQxlR9M7EhWMV4mOa1awXPdXqJbwXsPei7KLjWZb9OECi73rwVQY1e7BKub1AgwKRQvXSge0KTX+F/1xRtxMhdMEhPZ6RxR6WBiR7J6sX8zJNaMw1tDWznvt6kdjyfuqi9vYEQh0kg0s0olRHNHdnS5LzXgwZ1aJX38QLw9tBWaVK+AhDjPP5ocx+zcoDKaiBJOcWKnu/kSujSw9wWr4mNdaXdCiKNiNQUFOPL+EBx5fwi0hdKTNZc3msLiMokqkp7nkMo3tneRrKrHGZAmGkmYktQYKUmNZYyIKPykauzqV41F/SoxOHlzdQn3GrsKei3yjK6jHm2CPbkLpQdwk4SKSJ/Uy37uyf9zeezYO8nScUeJJmG+WXv29rBW6DNjXXB95VyWFAvueTob+zu7Y5mQL6yxc5OamorExER06tRJ7lCI6Bb1kNuocG/rqooHUVwvcK2xa1TNcw3f26J1Aa0b7c+DHQKbQFq8tF8FnWOb/bWEMqEyENwUlVaDAV0mLECXCQtgiorg7hlBlIklqrhMzJFcJhQyJnZuOHiCiErq3w+0waNdildqMeikv2o1oiTNPVH6bFRH992RWCsu5KZYsQ/+1laySdadShTf7bH27EN/87UEk9i5TlAcRI2dWo2LFaviYsWqgDqyfq7ENZdB9SQUlYkQYWVC4cF3BRFRKbinRXUA9nWPvc0N+FyfppLbh7arheoVDagRZ4BaBdS9OWjiwQ61XZLBktAGmBS0uj0OANCp6s3E7mZ/QatNgMUaWHLnOt1JEEF6OUYkiLTXQ8rBPnZERKUgqVl1rJjUE3EGHSp7GWgwqE1NnL3eHO+kHXRue7pnQ0we0BwAsPIfvZBvtCBWr0W+0YLqcQaPFSS2Tb0H1wvM6PfROpftk/o2xeIdZ5B1VXrQgVYTWIK48OluuJ5fhIz1KwEAem1xs7LJaoNVEPDb7nPo1awaqleUHnnrulZsEBmNyYSnt/4EALB2ejbw590Cgqm5dGE2OsvkepvxYYyIIgUTOyKiUqBSqdC4uu+VKAAgsWa8y/0BrWo4+97F6rXOeSEd/7s3xVavaEDVWM9m1e6NquDMtQJkXS2AVqL5tlP9yjh1RfoxsVi9FlHq4uNHiUb4Gs02fLzyCBZsOonO9Svjx/HdJI/h0uwYRD6jNlvwzzXzAQBv//3vgT/xFuCa7Ab+PLWluEymPDE2vEFRRGBiR0Qko/honcv9On6WdJPqYycehDGiYx00rVERHetXRtMaFVEjzoD72tXyeM5rgxNRK176MX/n16pVsNgEGC02LNh0EgCw7eRVr88R5y3B1FTZtGosbnWP/bYmsn6uQq2xs6k1zjLhkmIkJbI+KWHAJcWIqCzFRRd/DatV8NpsW7yP7xq2nk2rYVCbmvZjG3SY1K+Z9Hl9POaPXquGxWTFr7vP+t33vaUH8cfe8877wfSxs0Xp8cKg5wEAo7Q6P3vfWlymgAli+IS4TLikGEnh4Ak3HBVLRGVJXGMXF63zO+pVnNiJR97WrmRf7qtrw8AWky8JR4xrDl3yOL+72WuOufTzC27lCXLHQRfkDxM7IiIZiZfwCmTJLnHi51ixBbAPtNjzWj9UKYPl+OaNts/zefhirnNboAvZB5OYuNZqRZaQB08Q+cHEjohIRuJETZzkeSOepUQ8z5xeq0F8TNk0V7asFQ+1CriSX7xaxoWcIny+/rjLflIjYG1BtMVqCguwd+YI7J05ArqiyFpSzHU1jsCfpxaVCZcUIylM7IiIZPZw57rQa9UYc1cDv/uGax67koiO0kj2BXzrfwdc7ktNYhxMPZUAIM6YjzhjfsQ1QZbk5TjKhEgKB08QEcls+v2tMf3+1gHtG44lxcJBPJ+dNyaJCYyDaYK06g3o/dR/AQA9o6TnyLtV2UJcecISpXeWSUcuKUYSmNgREd1CwrGkWDjovSyTJmaSqLELZlSsoFLhZOXbAQB3KeR1h4trU2wwbbFqZ5l04JJiJIHvCjepqalITExEp06d5A6FiMjDB39riwp6LV4fnChrHIYAauwk15ONtDbVEAWVzBEFgTV2blJSUpCSkoKcnBzEx8f7fwIRURlqW+c27Hm9n+w1d4YyqLFTmc0YufMP+50OTwX+xFtAyHmdpbhMito8Eb6AKGKwxo6I6BYjd1IHAAZdIDV2nhO9i/uW3Sgw492lB3E0O9djPwBQW8z4V/qn+Ff6p9BYzKEHq0Ch5nVqc3GZaC2WsMZEkYE1dkREFLRAEjupGjtxQjN1yT78sfc8Fmw8iQP/GuCxr02lxv+a3Wm/rY6s5bNCHTwhuJQJ62bIExM7IiIKWiCTKUs3xRanMZuPXQEAFJqll3C06Q14dugUAMAjETYC1KUpNphl1vQGpNwskwcirEwoPJjuExFR0AJripWosRMlMUVeEjrnvqKMJ9LGGohfWzBTwHBJNvKHiR0REQXN2+CJRz/fgnWH7WvISjbF3kxiZq06gnyT78QukolzuWAGlBD5w8SOiIiC5m2C4o1Hr+DxedsASNfYOZKYD5Yf9nsOTWEhtqQ+ji2pj0NnjOQlxQLP7NQuZcIlxcgT+9gREVHQ8oz+R2T6GxXrjyAIqJF31XEn4OfdCsTlEFSNnahMVJFVJBQmTOzcpKamIjU1FVZr+W0iICLy51q+ye8+0k2xgZ/DGqVH8uhPAABttZE1UMBl7EQwy6yJyiQxynO9XiI2xbpJSUlBZmYmtm/fLncoRESKdcVPYnfmWoGXwRMCth6/EtA5BLUGmQkNkZnQEIImsqY7EUKssRM0ojKJsClgKDyY2BERUdA6N6js8/FHPtsKs1W6j92IuVuCPl+EtcS6DZ6IsBdHsmJiR0REQZt4TxOfj2ddLYBVoipKarqOCnovvYIsZjy4bwUe3LcC6khbeUI8eCKYJ5ojt0woPNjHjoiIghar10Kt8t2MKJXYSe0f5WWyY7XZjA/SZgIApj48IpQwFct1jr4gRsWKymTKiL+FOyyKAEzsiIgoJGqVymczolXiMakkxizRFw8AbCoVVjXsaH9ehC2fZQuxKdamLi4TLilGUpjYERFRSNQqFXw1JH67Jctj27rDlz22GSX64gHAutN5+PVvbwAA/qaLnFGxaw5lY9uJq877NumXL8mmN+DJm2XCJcVIChM7IiIKiUrl+/Gz1z0n0JXaZrLYIAgCVG4H/HX3uRLFp1Sj57vOuhDc3H7hjoYiDetxiYgoJGp/mV0QLH7m/IjkfCaSXxuVPSZ2REQUEnX48jrJyYwN5iKsnvsUVs99KqKXzwpq8ERRYbkoEwodm2KJiCgk6jBmdiaLDbFuXcZUAtDg2nn7nSD6oSmZVBIXzATFKkFwlgmXFCMpTOyIiCgk4UjrNGoVrDYBJokBFEatDg88+h4AoEGELJ8lPQVMEEuK6aIirkwovJjYERFRSMJRY6fT2BO777dlwWIV8I9+TZ2DKGxqDXbUTgQA1IuQqT2k+hIGu6SYo0zqc0kxksDEzk1qaipSU1NhtVrlDoWISNE0YRg8EaVRo8hsw8wVRwAAd7eojjvqVirxcZVKcjUODnWlMIqMP4HCKCUlBZmZmdi+fbv/nYmIyjH36UlCodW4/gxdyzcBsCc7GpsVyQc3IPngBqgtlhKfSwmkJ20O4gAWS3GZWCOjTCi8WGNHREQhCcfYCa3bQRw1WoIARFnMmP3rvwEAk4fdV/KTKYDVWrI+dhqzyVkmU+4fHLa4KHIwsSMiopCEYx4798Qut8heC2UVBNhUKmyp0wqAfSmtSCBVYxdMHzubSh1xZULhxcSOiIhCEo68QqNxPcj1QjMAey2WUafHQ4/Ya6eGRRlKfjIFKGkfO6ve4CyTByKkTCi82MeOiIhCEpZRsW6jXW8UOPrYue4XKQMMpEfFBrOkWGSUA5UeJnZERBSScKwopnFLDlccyMYfe88FlexI2X7yKv6393yJjlEabJI1djIEQhGLiR0REYXkrsZVAQAVDaH36nFP7DLP5+CZ73bh4IVc6M1GpM3/P6TN/z9oTUVBHfdvn25Gync7cTQ7L+TYSkNJ57FTG4tCLhMqH9jHjoiIQjJ1UCLqVYlFcqua2HLiCl5avBcA8FSPBjh9tRBL/7rg9xg6jXT9wrnrhVALAhKzTwAAVMFkPyLnbxSicfUKIT23NFhtnitsBFM7qbLZnGWiDrFMKLIxsSMiopBU0GsxvlcjAIBBV5yg9WhSDQIQUGLnXmPnoFWrYNTq8NjwfwEAqoa4fFY4JlEOJ4mV04LqN2fR6Z1lUoNLipEEJnZERFRi4po3lSrwhMpbYqdWqWBTa7ChQXsAwJAgls8SjzwNxwCPcLJI1tgF/nxBU1wmD3BJMZLAPnZERFRiOm3xz4kgeM5P54232iqtJvSEzCyqFvOWOMpFIq/j4AkKK9bYERFRielEiZhNEAJOzLzlNGqVChqbFT1P7LTfbz0o4FjEiV04JlEOJ+kauyAyO4sFScfsS16q2wZeJlR+MLEjIqISE89HJwiARh1Yg5C3ZkitWo0oixnzF08DALw4qF/AsZhFy3YprcaupBMUa8wmZ5lMGRx4mVD5wcSOiIhKTNyXzSYIATfFemuHVKkAm0qFPTWa2HcLIkET19iVdD68cJNK7IJbUqy4TLikGElhYkdERGElCIH3kfOW1Px17gaMOj2GjPoIADAkygBBEHDgfC4qx0ahRrznclqCIOD45XwUmqzFxxedwPF4/Sqxzpq8nCIzCk1WJMT5X54rmH29kU7sgltSzFEmXFKMpET04ImPPvoILVu2RGJiIp599lkuxUJEVAYMOk3ggye89LJ7J+2gx7b0zItI/mQ97nx3FXKKzB6Pf7M1C/d8uBb3/meDc5s4kfpum/3xf/y427mtzRvL0eWdlbiSZ/Qbq2Pfq/kmv/t6Y5X4HeIvE4VTxCZ2ly5dwqxZs7Bjxw7s27cPO3bswJYtW+QOi4goYr08oDmGtquF7o2qQBtAH7tvxnQJakTo6WuFAOzJ2qVcz0Rs1qojHtvEidTs1ccAAEt2n/PYL/N8TsBxHAhiX3clXSuWyJ+Iboq1WCwoKrIvuWI2m1G9enWZIyIiilx/793IeTuQQQt3NakKW5r3x/VmI75d+AoA4IfEL1wek2rSlB6YUHxb56N5OJjcqiR5mNXqO0Z/1EVFWPzNiwCARa2+8LM3lUeKrbFbt24dBg8ejFq1akGlUmHJkiUe+8yePRsNGjSAwWBAhw4dsH79eudj1apVwwsvvIC6deuiVq1a6NOnDxo1auRxDCIiCr+ApzvxkdWoBQEdzx5Ax7MHPJYUs0gkSFK1YeJkz9vyZWVJqinWFsToCZXN5iwTLilGUuR/l3uRn5+Ptm3bYtasWZKPL1y4EM899xymTp2KXbt2oUePHhg4cCCysrIAANeuXcMff/yBkydP4uzZs9i0aRPWrVtXli+BiKjcCqQp1h+TVoenh03F08OmwqLTuTwm1XwpWYsneE/sxEmlv+nuxMlXSabGK+moWKsuymuZEAEKboodOHAgBg4c6PXxGTNmYMyYMRg7diwAYObMmVi2bBnmzJmD6dOnY8WKFWjcuDEqV64MABg0aBC2bNmCnj17Sh7PaDTCaCzus5GTY+9DYTabYTZ7dtItKccxS+PYFDxeD2Xh9VCWUK6HYLMEdFyr1FIMN1nVGixv2g0AMFilhtVaPNq1yOT53SyVNJnMFud+WtEKXGazGRbRtCgWi9Xn6zO77GsJ+b1pNHk+z2L1fW6XOFQqZ5kMU6n5GVGAsvi+CubYik3sfDGZTNixYwcmT57ssr1fv37YtGkTAKBOnTrYtGkTioqKoNPpsGbNGjz99NNejzl9+nRMmzbNY/vy5csRExMT3hcgkp6eXmrHpuDxeigLr4eyBHM9Ci2Av5+YtLQ05OZqAPivAjt37hy0OWcB2LOzDRs34mxF133MZs9jbd+egaJj9oQv/0bx42lpabDYimPctm0bcg57rzozi/bdum0brh8KrRl05yWV8zU4nDx5EmlpxwN6/sFzxc8/d/Yc0tLOhBQHhV9pfl8VFBQEvO8tmdhdvnwZVqsVCQkJLtsTEhJw4cIFAEDXrl2RnJyM9u3bQ61W45577sF9993n9ZhTpkzBpEmTnPdzcnJQp04d9OvXD3FxcWF/DWazGenp6ejbty90rE6XHa+HsvB6KEso16PQZMXk7St97pOcnIxPjm4ECvMlH1fbrOh85i8AQPVW96BFncr45eQhAEDnLt3QqX4ll/3/sTXdYyRCu/Z3oH9L+2/FwosZOJZ71XnuApMF2LrKfrzOnXFX4ypeY803Fu/bpXNndG/kfV9finadBY7+5bKtbr16SE5uEdDzz609iovrlwAAarW5B8nJ7UOKg8KnLL6vHK2IgbglEzsHlVtHB0EQXLa9/fbbePvttwM6ll6vh16v99iu0+lK9YeltI9PweH1UBZeD2UJ5noIKo3ffXQ6nc853PQWM374/p8AgBf7bYRGIzqmWu0Ri9TgCZVa49wvSlf8fJ1OB1Vxyy60Wo3P16ayiPfVhvy+VEmVi0oV8PGiLBZnmUwZsJGfDwUpze+rYI57SyZ2VatWhUajcdbOOWRnZ3vU4hERUdkLeIJiH5mdoAIOV6nrvC3mo2ueC/HgiSi3wRPBjEYNZl9fpOexC/z5gkrltUyIgFs0sYuKikKHDh2Qnp6OYcOGObenp6djyJAhJTp2amoqUlNTXTrpEhFRcNQBJ3bes5oinQH9xs4GAAzRR7s8ZgkwsxO8jIq12gSXJMtfcuW6b+hJntRgkWBWRbIYop1l8oBbmRABCk7s8vLycPToUef9EydOYPfu3ahcuTLq1q2LSZMmYeTIkejYsSO6deuGuXPnIisrC+PHjy/ReVNSUpCSkoKcnBzEx8eX9GUQEZEPoaZIUiNg/e0nnqC4yGx1ecxfjZx4X6lat0D5m0TZHy5SQf4oNrHLyMhAUlKS875jYMOoUaOwYMECjBgxAleuXMGbb76J8+fPo1WrVkhLS0O9evXkCpmIiIIUTO1Xdm6R83awiV12ThG0ohq7U1cKEB9T3G/par4JRWYrcgrN0Gs10OvUMJptzn3ETbrX8k3IzilCtYp6qFQqFJmtLvtmXSlAkcWKJtUrePQFD3RJsSKzFUaLDfHROudtvVYtuUYukZhiE7vevXv7rZ6eMGECJkyYUEYRERFRuFWO1eP01ULJx/RmIz7/6V8AgLHmV2HUFQ9wCzSxswkCft55BpN+3OOyPfmT9ejdrJrz/j8W7cE/Fu1xfzr2vN4P8dE6l6XAHMd6vFs9vDmkFTq/vQI5RRbseb0ftp24iqe+ygAAvNi/GVKSGnvE484q0arc6a0VyDXaj9n57RUw2udmgd5sxNc3y+SPVrMDKQIqZxS78oRcUlNTkZiYiE6dOskdChHRLe2ZpMbo06I6VkzqhfZ1b0OLmp5TR80Y3hbt696GeaM7IiXJddlHtSCgx6nd6HFqN9RuCZHU0lxSrDbgvaWHJB9bc+iS3+dnnsvxer6vNp8CAOQU2YfM/nX2Bg5fzHU+fuxSnsdzAq2gzDUWH9OR1AFuZcIlxUiCYmvs5MI+dkRE4fFC/2bO279MuBMA0H36Spy7Udyk2qhaBedjdzdPwNebTzkTJZNWh4n3/sN5WyzgptgSdkqL0qpunk96sIZLy5JKeg3bkjC7vU5xmURxqhOSwMSOiIgUyarW4NeWSZKPBZpAlXSaEseat94GTLgkmILr0mPhYLa4Hk9cJg9o+BNOntgUS0REt5xAa+JKMjUJUDxFircaQrNbghn2xC7Mx6PIx3TfDeexIyJSBrXNilYXjwEA9ic0gk1dvGpDKNOdhKK4KdZLYiduolV5Jnol5d4UKy4TlY0T8pMn1ti5SUlJQWZmJrZv3y53KERE5ZreYsZvX03Cb19Ngt7iOs1HMKNiS8LRFOs1sRM3lZZBU6y4THQmU1jPRZGBNXZERKRIggo4E1fdeVss8Bq7ksWgVvmpsRPV0JltQqk3xfoqEyKAiR0RESlUkc6Au/4+T/KxQFd/KGmNnb/ziRMvs8UGUyknduIy4ZJiJIVNsUREdMsJdLRrSfvY+TufOPGy2Gzhn+4kzMejyMfEzg0nKCYiUr5Aa+zCldh5r7Er3m6yln5TLJE/TOzccPAEEVHpcV871Re9xYS5P7+FuT+/Bb3FdaCAtwmD3YWrKdZ7HzvXptjSTuzEZaI1GcN6LooM7GNHRESKpLbZ0O/IFudtsUDzp9JO7MR96sxWG0xhbjp1P564TFYHmNxS+cLEjoiIFMms0WJy/2ect8UCrbELVwWat6ZYi9uoWEuYa+zcjycuE6uWP+Hkie8KIiJSJItGix/aDZB8rKxWnvB3nLJuihWXyQNarhVLntjHjoiIbjlKGTxR2k2xHBVLwWKNnRsuKUZEpAwqwYbGl08DAI5WrQNBVVwXYQ0w4QlXYuet6Ve8MoTZavNYKaKk3OfFE5eJylYjrOeiyMAaOzccFUtEpAwGswnp81KQPi8FBrPbqNgybor11sIqrskzWwVYwjygwb2PnbhMdBwVSxJYY0dERIp1JTpOcnvgS4qVco2dW1NsuJtOpY7nrUyIACZ2RESkUIVRBnR49jvJxwJN2MKU13mtsTO5NcWaSrkpVlwmDxi4pBh5YlMsERHdcgJO7Eq5xs69KTbso2LDnChS5GNiR0REZSaIhSd8CnhUbJj62HlfUsy1xi7QuEp6XiJv2BRLRESKpLeY8O6fHwMAXh44EUZtlPOxxTvOYPGOMwCABlVjcTlPeiCBY59QJX+yHt0bVUGn+pUlH3/t17+ct7/dmuXy2M87z+LnnWfRsFosojRq5BSace5Gkccxftp5Bj/tPIP+LRPw6WMdXJZdW3Uw22VfcZlsbvXvkF8XRS7W2LlJTU1FYmIiOnXqJHcoREQR58X+zQAAj3Sp63dftc2GoZlrMTRzrceSYmInLucjt8gSthjF8owWLM+8iKyrBSEf4/ilfBy8kCuZ1Ikt++sidp2+7nOfQMuEyi/W2LlJSUlBSkoKcnJyEB8fL3c4REQRZUi729GtYRVUq6j3u++CcXdis/kVpGde9FhSLFxa1orD/NGd0PmdlT73E1A2TaKOPnUGnRpFZs/EzazR4s27nwIAWLikGEngu4KIiMpU9ThDQPtVq1wBq0c8iXl/ZJZaLE0TKqJ6nAGxURrkm5Q/Mb1Fo8W8TkMAcEkxksamWCIiIqIIwcSOiIiUyWZDhfNnUPvGRagE9icD7EuK1b5x0V4m7GNHEpjYERGRIqkKCzFieE9s+HSMx5Ji5ZXBbMKGT8dgw6djuKQYSWIfOyIiUiyzITrsk/7e6gp0/geeUPnFxI6IiBRJiI3FV+l/4V+lOHjiVlMYZUDipJ8AcEkxksamWCIiIqIIwcTODScoJiIiolsVEzs3KSkpyMzMxPbt2+UOhYiofDMacde7UzD9z08QZTHLHY0iRFnMmP7nJ5j+5yfQcEAJSWBiR0REiqSyWNDsj4V4eO9yaGzKnzy4LGhsVjy8d7m9TKwsE/LEwRNERKRIgk6HjKf+gdUHs2HRaOQORxEsGg3e7zESAGDlkmIkge8KIiJSpqgo7Hk8BakcFetk1uiQ2n0EAC4pRtLYFEtEREQUIZjYERGRMgkCDNeuoHLBDUAQ5I5GGQQBlQtusEzIKyZ2RESkSKqCAjx6Xyfs/M+jiDZz+SwAiDYbsfM/j2Lnfx5FlLFI7nBIgdjHzgvh5l9COTk5pXJ8s9mMgoIC5OTkQKdjPwm58XooC6+HspTl9bAa82Ez2kd75ubmIse5vQA2IfxLixkL8pCTk2M/r8n78Y0FebAZC8J+fnf5ubnIydHZX6/ZMx6rqchZJkUF+aX2G0WBK4vPh+M6CwHU0qqEQPYqh86cOYM6derIHQYRERERAOD06dOoXbu2z32Y2Hlhs9lw7tw5VKxYESqVKuzHz8nJQZ06dXD69GnExcWF/fgUHF4PZeH1UBZeD2Xh9VCWsrgegiAgNzcXtWrVglrtuxcdm2K9UKvVfrPicIiLi+MHU0F4PZSF10NZeD2UhddDWUr7esTHxwe0HwdPEBEREUUIJnZEREREEYKJnUz0ej1ef/116PV6uUMh8HooDa+HsvB6KAuvh7Io7Xpw8AQRERFRhGCNHREREVGEYGJHREREFCGY2BERERFFCCZ2pWj27Nlo0KABDAYDOnTogPXr1/vcf+3atejQoQMMBgMaNmyITz/9tIwiLR+CuR4///wz+vbti2rVqiEuLg7dunXDsmXLyjDayBfs58Nh48aN0Gq1aNeuXekGWM4Eez2MRiOmTp2KevXqQa/Xo1GjRpg3b14ZRRv5gr0e3377Ldq2bYuYmBjUrFkTTzzxBK5cuVJG0UaudevWYfDgwahVqxZUKhWWLFni9zmy/5YLVCp++OEHQafTCZ999pmQmZkpTJw4UYiNjRVOnToluf/x48eFmJgYYeLEiUJmZqbw2WefCTqdTli8eHEZRx6Zgr0eEydOFN59911h27ZtwuHDh4UpU6YIOp1O2LlzZxlHHpmCvR4O169fFxo2bCj069dPaNu2bdkEWw6Ecj3uu+8+oUuXLkJ6erpw4sQJYevWrcLGjRvLMOrIFez1WL9+vaBWq4WPP/5YOH78uLB+/XqhZcuWwtChQ8s48siTlpYmTJ06Vfjpp58EAMIvv/zic38l/JYzsSslnTt3FsaPH++yrXnz5sLkyZMl93/ppZeE5s2bu2wbN26c0LVr11KLsTwJ9npISUxMFKZNmxbu0MqlUK/HiBEjhFdeeUV4/fXXmdiFUbDX488//xTi4+OFK1eulEV45U6w1+P9998XGjZs6LLtk08+EWrXrl1qMZZHgSR2SvgtZ1NsKTCZTNixYwf69evnsr1fv37YtGmT5HM2b97ssX///v2RkZEBs9lcarGWB6FcD3c2mw25ubmoXLlyaYRYroR6PebPn49jx47h9ddfL+0Qy5VQrsdvv/2Gjh074r333sPtt9+Opk2b4oUXXkBhYWFZhBzRQrke3bt3x5kzZ5CWlgZBEHDx4kUsXrwYgwYNKouQSUQJv+VcK7YUXL58GVarFQkJCS7bExIScOHCBcnnXLhwQXJ/i8WCy5cvo2bNmqUWb6QL5Xq4+/DDD5Gfn4/hw4eXRojlSijX48iRI5g8eTLWr18PrZZfW+EUyvU4fvw4NmzYAIPBgF9++QWXL1/GhAkTcPXqVfazK6FQrkf37t3x7bffYsSIESgqKoLFYsF9992H//znP2URMoko4becNXalSKVSudwXBMFjm7/9pbZTaIK9Hg7ff/893njjDSxcuBDVq1cvrfDKnUCvh9VqxSOPPIJp06ahadOmZRVeuRPM58Nms0GlUuHbb79F586dkZycjBkzZmDBggWstQuTYK5HZmYmnn32Wbz22mvYsWMHli5dihMnTmD8+PFlESq5kfu3nH/6loKqVatCo9F4/HWVnZ3tkck71KhRQ3J/rVaLKlWqlFqs5UEo18Nh4cKFGDNmDBYtWoQ+ffqUZpjlRrDXIzc3FxkZGdi1axeeeeYZAPbEQhAEaLVaLF++HHfffXeZxB6JQvl81KxZE7fffjvi4+Od21q0aAFBEHDmzBk0adKkVGOOZKFcj+nTp+POO+/Eiy++CABo06YNYmNj0aNHD7z11lts8SlDSvgtZ41dKYiKikKHDh2Qnp7usj09PR3du3eXfE63bt089l++fDk6duwInU5XarGWB6FcD8BeUzd69Gh899137KsSRsFej7i4OOzbtw+7d+92/hs/fjyaNWuG3bt3o0uXLmUVekQK5fNx55134ty5c8jLy3NuO3z4MNRqNWrXrl2q8Ua6UK5HQUEB1GrXn3ONRgOguLaIyoYifsvLbJhGOeMYrv7FF18ImZmZwnPPPSfExsYKJ0+eFARBECZPniyMHDnSub9jiPTzzz8vZGZmCl988QWnOwmjYK/Hd999J2i1WiE1NVU4f/6889/169flegkRJdjr4Y6jYsMr2OuRm5sr1K5dW3jwwQeFv/76S1i7dq3QpEkTYezYsXK9hIgS7PWYP3++oNVqhdmzZwvHjh0TNmzYIHTs2FHo3LmzXC8hYuTm5gq7du0Sdu3aJQAQZsyYIezatcs59YwSf8uZ2JWi1NRUoV69ekJUVJRwxx13CGvXrnU+NmrUKKFXr14u+69Zs0Zo3769EBUVJdSvX1+YM2dOGUcc2YK5Hr169RIAePwbNWpU2QceoYL9fIgxsQu/YK/HgQMHhD59+gjR0dFC7dq1hUmTJgkFBQVlHHXkCvZ6fPLJJ0JiYqIQHR0t1KxZU3j00UeFM2fOlHHUkWf16tU+fwuU+FuuEgTW0xIRERFFAvaxIyIiIooQTOyIiIiIIgQTOyIiIqIIwcSOiIiIKEIwsSMiIiKKEEzsiIiIiCIEEzsiIiKiCMHEjoiIiChCMLEjIlKY3r17Q6VSQaVSYffu3WE99smTJ53HbteuXViPTVRerVu3DoMHD0atWrWgUqmwZMmSoI8hCAI++OADNG3aFHq9HnXq1ME777wT9HGY2BHRLW306NHOREX8b8CAAXKHViJPPfUUzp8/j1atWgW0/+DBg9GnTx/JxzZv3gyVSoWdO3eiTp06OH/+PP7xj3+EM1yici0/Px9t27bFrFmzQj7GxIkT8fnnn+ODDz7AwYMH8fvvv6Nz585BH0cbcgRERAoxYMAAzJ8/32WbXq8v1XOaTCZERUWV2vFjYmJQo0aNgPcfM2YM7r//fpw6dQr16tVzeWzevHlo164d7rjjDgBAjRo1UKFChbDGS1SeDRw4EAMHDvT6uMlkwiuvvIJvv/0W169fR6tWrfDuu++id+/eAIADBw5gzpw52L9/P5o1a1aiWFhjR0S3PL1ejxo1arj8q1SpkvNxlUqFzz//HMOGDUNMTAyaNGmC3377zeUYmZmZSE5ORoUKFZCQkICRI0fi8uXLzsd79+6NZ555BpMmTULVqlXRt29fAMBvv/2GJk2aIDo6GklJSfjyyy+hUqlw/fp15OfnIy4uDosXL3Y51++//47Y2Fjk5uYG9Tp9xXjvvfeievXqWLBggctzCgoKsHDhQowZMyaocxFR+DzxxBPYuHEjfvjhB+zduxd/+9vfMGDAABw5cgSA/TuhYcOG+OOPP9CgQQPUr18fY8eOxdWrV4M+FxM7IioXpk2bhuHDh2Pv3r1ITk7Go48+6vzSPH/+PHr16oV27dohIyMDS5cuxcWLFzF8+HCXY3z55ZfQarXYuHEj/vvf/+LkyZN48MEHMXToUOzevRvjxo3D1KlTnfvHxsbioYce8qhNnD9/Ph588EFUrFgx4Pj9xajVavH4449jwYIFEATB+bxFixbBZDLh0UcfDbrMiKjkjh07hu+//x6LFi1Cjx490KhRI7zwwgu46667nN8Nx48fx6lTp7Bo0SJ89dVXWLBgAXbs2IEHH3ww+BMKRES3sFGjRgkajUaIjY11+ffmm2869wEgvPLKK877eXl5gkqlEv78809BEATh1VdfFfr16+dy3NOnTwsAhEOHDgmCIAi9evUS2rVr57LPyy+/LLRq1cpl29SpUwUAwrVr1wRBEIStW7cKGo1GOHv2rCAIgnDp0iVBp9MJa9as8fqaevXqJUycONFlWyAxHjhwQAAgrFq1yrlPz549hYcfftjjHK+//rrQtm1brzEQUWgACL/88ovz/o8//igA8PiO0mq1wvDhwwVBEISnnnrK5bMsCIKwY8cOAYBw8ODBoM7PPnZEdMtLSkrCnDlzXLZVrlzZ5X6bNm2ct2NjY1GxYkVkZ2cDAHbs2IHVq1dL9js7duwYmjZtCgDo2LGjy2OHDh1Cp06dXLa5d3bu3LkzWrZsia+++gqTJ0/G119/jbp166Jnz55BvcZAYmzevDm6d++OefPmISkpCceOHcP69euxfPnyoM5FROFjs9mg0WiwY8cOaDQal8ccn+eaNWtCq9U6v2sAoEWLFgCArKysoPrdMbEjoltebGwsGjdu7HMfnU7ncl+lUsFmswGwf/EOHjwY7777rsfzatas6XIeMUEQoFKpPLa5Gzt2LGbNmoXJkydj/vz5eOKJJzye50+gMY4ZMwbPPPMMUlNTMX/+fNSrVw/33HNPUOciovBp3749rFYrsrOz0aNHD8l97rzzTlgsFhw7dgyNGjUCABw+fBgAPAZD+cPEjojKvTvuuAM//fQT6tevD6028K/F5s2bIy0tzWVbRkaGx36PPfYYXnrpJXzyySf466+/MGrUqFKLcfjw4Zg4cSK+++47fPnll3jqqaeCTiKJKDh5eXk4evSo8/6JEyewe/duVK5cGU2bNsWjjz6Kxx9/HB9++CHat2+Py5cvY9WqVWjdujWSk5PRp08f3HHHHXjyyScxc+ZM2Gw2pKSkoG/fvi61eIHg4AkiuuUZjUZcuHDB5Z94RKs/KSkpuHr1Kh5++GFs27YNx48fx/Lly/Hkk0/CarV6fd64ceNw8OBBvPzyyzh8+DB+/PFH56hUcTJVqVIl3H///XjxxRfRr18/1K5dO+jXGGiMFSpUwIgRI/DPf/4T586dw+jRo4M+FxEFJyMjA+3bt0f79u0BAJMmTUL79u3x2muvAbAPmHr88cfxj3/8A82aNcN9992HrVu3ok6dOgAAtVqN33//HVWrVkXPnj0xaNAgtGjRAj/88EPQsTCxI6Jb3tKlS1GzZk2Xf3fddVfAz69VqxY2btwIq9WK/v37o1WrVpg4cSLi4+OhVnv/mmzQoAEWL16Mn3/+GW3atMGcOXOco2Ld59EbM2YMTCYTnnzyyZBeYzAxjhkzBteuXUOfPn1Qt27dkM5HRIHr3bs3BEHw+Of4Q0+n02HatGk4ceIETCYTzp8/j59//hmtW7d2HqNWrVr46aefkJubiwsXLmD+/PkefYUDoRKkOoQQEVFI3n77bXz66ac4ffq0y/Zvv/0WEydOxLlz5/xObNy7d2+0a9cOM2fOLLU433jjDSxZsiTsS5YRkbxYY0dEVAKzZ8/G9u3bcfz4cXz99dd4//33XfrQFRQU4K+//sL06dMxbty4gFermD17NipUqIB9+/aFNd6srCxUqFAhpDUoiUj5WGNHRFQCzz//PBYuXIirV6+ibt26GDlyJKZMmeIc4PDGG2/g7bffRs+ePfHrr78GtJTX2bNnUVhYCACoW7duWJcus1gsOHnyJAA4FxonosjBxI6IiIgoQrAploiIiChCMLEjIiIiihBM7IiIiIgiBBM7IiIiogjBxI6IiIgoQjCxIyIiIooQTOyIiIiIIgQTOyIiIqIIwcSOiIiIKEL8P6a11zYp/r10AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we want to display the pulse-height value in the center of the bin\n",
    "energy_bin_centers = energy_bins[1:] + 0.5 * (energy_bins[1] - energy_bins[0])\n",
    "\n",
    "plt.figure()\n",
    "plt.semilogy(energy_bin_centers, pulse_height_values)\n",
    "\n",
    "# plot the strongest sources as vertical lines\n",
    "plt.axvline(x=800_000, color=\"red\", ls=\":\")     # source_1\n",
    "plt.axvline(x=661_700, color=\"red\", ls=\":\")     # source_2\n",
    "\n",
    "plt.xlabel('Energy [eV]')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Pulse Height Values')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this moment, a few points can make things easier to understand:\n",
    "* We have plotted red vertical lines at for the energies of the strongest sources in the spectrum - the 661.7 keV Cs137 line and the artificial 800 keV line. But also other characteristic peaks of a gamma spectrum can be seen.\n",
    "* The prominent peak at the first pulse-height value, corresponding to 0 keV, indicates that particles have deposited no energy in the sodium-iodide crystal. This peak does not have a counterpart in real-life detector measurements, as no interaction implies no possibility of measurement. Consequently, we will exclude this peak in subsequent analyses.\n",
    "* The current simulation statistics are not optimal and could benefit from simulating a substantially larger number of particles.\n",
    "* In the illustrated example (despite the suboptimal statistics), peaks are displayed as narrow lines. However, in real-world experiments, these peaks are broadened due to the inherent detector resolution. In the next section, we present a method to post-process the pulse-height data to simulate realistic detector signals."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post processing the results\n",
    "The intrinsic detector resolution is the result of several physical effects such as charge collection statistics or electronic noise.\n",
    "Further it depends on the energy of the incoming particle. \n",
    "The resolution of a detector R(E) is given as the quotient of the full width at half maximum (FWHM) and the energy (E) of the incoming particle:\n",
    "\n",
    "$R(E) = \\frac{\\mathrm{FWHM}(E)} {E}$\n",
    "\n",
    "Usually, this is determined for a detector in several measurements with known sources, and then a function is fitted to these measured values. In this example we will use\n",
    "\n",
    "$R(E) = \\frac{a + b\\sqrt{E+cE^{2}}}{E}$.\n",
    "\n",
    "Further, we assume \n",
    "\n",
    "$a = 1000$ \n",
    "\n",
    "$b = 4$ \n",
    "\n",
    "$c = 0.0002$ \n",
    "\n",
    "In a next step, we can broaden the results of the pulse-height tallies according to this function.\n",
    "The idea is to go through all the energy bins, determine the respective Gaussian function, and sample according to that.\n",
    "Here we make use of the fact that for a Gaussian \n",
    "\n",
    "$\\sigma(E) = \\dfrac{\\mathrm{FWHM}(E)}{2 \\sqrt{2 \\ln{2}}}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b, c = 1000, 4, 0.0002\n",
    "number_broadening_samples = 1e6\n",
    "\n",
    "def gauss(E, a=a, b=b, c=c):\n",
    "    sigma = (a + b * (E + c * E**2)**0.5) / (2 * (2 * np.log(2))**0.5)\n",
    "    return np.random.normal(loc=E, scale=sigma)\n",
    "\n",
    "samples = np.random.choice(energy_bin_centers[1:], size=int(number_broadening_samples), p=pulse_height_values[1:]/np.sum(pulse_height_values[1:]))\n",
    "broaded_pulse_height_values = gauss(samples)\n",
    "\n",
    "broaded_spectrum, _ = np.histogram(broaded_pulse_height_values, bins=energy_bins)\n",
    "renormalized_broaded_spectrum = broaded_spectrum / np.sum(broaded_spectrum) * np.sum(pulse_height_values[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "plt.semilogy(energy_bin_centers[1:], pulse_height_values[1:], label=\"original simulation result\")\n",
    "plt.semilogy(energy_bin_centers[1:], renormalized_broaded_spectrum[1:], label=\"broaded detector response\")\n",
    "\n",
    "# plot the strongest sources as vertical lines\n",
    "plt.axvline(x=800_000, color=\"red\", ls=\":\", label=\"gamma source\")     # source_1\n",
    "plt.axvline(x=661_700, color=\"red\", ls=\":\")                           # source_2\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Energy [eV]')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Pulse Height Values')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Current Limitations\n",
    "There are currently still some limitations in the simulation. These will be addressed in the future:\n",
    "* Some isotopes perform a beta+ decay before emitting a gamma. The resulting positron then annhilates with an electron, producing two 511 keV gamma particles that generate an annhilation peak in the detector. These 511 keV gamma particles have to be inserted manually as source.\n",
    "* No summation peaks can be simulated yet.\n",
    "* A comprehensive validation of this method will be published soon."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}