{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating AnyScript Macros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AnyPyTools can help write the AnyScript macros automatically. Doing so make it easier to create complex macros and do things like parameter studies, Monte Carlo simulation, etc.  \n",
    "\n",
    "Most AnyBody class operations and macros have a correspoding helper class in AnyPyTools. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from anypytools.macro_commands import (\n",
    "MacroCommand, \n",
    "Load,\n",
    "Dump,\n",
    "SetValue,\n",
    "SetValue_random,\n",
    "SaveDesign,\n",
    "LoadDesign, \n",
    "SaveValues,\n",
    "LoadValues,\n",
    "UpdateValues,\n",
    "RunOperation,\n",
    "Export,\n",
    "ExtendOutput,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A quick example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following shows how the to generate a simple macro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[load \"Knee.any\" -def SUBJECT=---\"\\\"S02\\\"\" -def TRIAL=---\"\\\"T04\\\"\",\n",
       " operation Main.MyStudy.InverseDynamics\n",
       " run,\n",
       " print Main.MyStudy.Output.MaxMuscleActivity]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macrolist = [\n",
    "    Load('Knee.any', defs={'SUBJECT':'\"S02\"', 'TRIAL':'\"T04\"'}),\n",
    "    RunOperation('Main.MyStudy.InverseDynamics'),\n",
    "    Export('Main.MyStudy.Output.MaxMuscleActivity'),\n",
    "] \n",
    "macrolist "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each macro object will generate one or more macro commands with the correct syntax.\n",
    "The macro can be launched using the `start_macro()` method of  the `AnyPyProcess`  object.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "77989ba4c6714f43bceb7b24e14e73a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: \u001b[1;36m1\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from anypytools import AnyPyProcess\n",
    "app = AnyPyProcess()\n",
    "app.start_macro(macrolist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of macro commands\n",
    "The `macro_commands` module have classes for generating many of the standard AnyScipt macro commands.\n",
    "\n",
    "- `Load(mainfile, defines, paths)`: Loads a model file in AnyBody\n",
    "- `RunOperation(var)`: Runs an operation\n",
    "- `Export(var, name)`: Exports a variable from AnyBody with a optionally different name. \n",
    "- `ExtendOutput(var, value)`: Add an arbitrary `var=value` to the output.\n",
    "- `LoadDesign(var, filename)`: classoperation \"Load design\" \n",
    "- `SaveDesign(var, filename)`: classoperation \"Save design\" \n",
    "- `LoadValues(filename)`: classoperation Main \"Load Values\" \n",
    "- `SaveValues(filename)`: classoperation Main \"Save Values\"\n",
    "- `UpdateValues()`: classoperation \"Update Values\"\n",
    "- `SetValue(var,value)`: classoperation \"Set Value\" \n",
    "- `MacroCommand(macro_string)`: Add abitrary macro string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating many macros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The macro in the previous example would have been easy to write manually. However, in some cases we want to create many macros. Then it is a big advantage to generate them programmatically. \n",
    "\n",
    "This is easily done in Python with a `for` loop. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[load \"Knee.any\",\n",
       "  operation Main.MyStudy.InverseDynamics\n",
       "  run],\n",
       " [load \"Knee.any\",\n",
       "  operation Main.MyStudy.InverseDynamics\n",
       "  run],\n",
       " [load \"Knee.any\",\n",
       "  operation Main.MyStudy.InverseDynamics\n",
       "  run]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macrolist = []\n",
    "for i in range(3):\n",
    "    macro = [\n",
    "        Load('Knee.any' ),\n",
    "        RunOperation('Main.MyStudy.InverseDynamics'),\n",
    "    ]\n",
    "    macrolist.append(macro)\n",
    "    \n",
    "macrolist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running many macros is only really useful if the macros are different. So let us try to create a macro which sets different values in each run.  \n",
    "\n",
    "Imagine a list of 5 parameters. We want to create five macros that use these values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[load \"Knee.any\",\n",
       "  classoperation Main.MyParameter \"Set Value\" --value=\"2.2\",\n",
       "  operation Main.MyStudy.InverseDynamics\n",
       "  run],\n",
       " [load \"Knee.any\",\n",
       "  classoperation Main.MyParameter \"Set Value\" --value=\"2.5\",\n",
       "  operation Main.MyStudy.InverseDynamics\n",
       "  run],\n",
       " [load \"Knee.any\",\n",
       "  classoperation Main.MyParameter \"Set Value\" --value=\"2.7\",\n",
       "  operation Main.MyStudy.InverseDynamics\n",
       "  run],\n",
       " [load \"Knee.any\",\n",
       "  classoperation Main.MyParameter \"Set Value\" --value=\"2.9\",\n",
       "  operation Main.MyStudy.InverseDynamics\n",
       "  run],\n",
       " [load \"Knee.any\",\n",
       "  classoperation Main.MyParameter \"Set Value\" --value=\"3.1\",\n",
       "  operation Main.MyStudy.InverseDynamics\n",
       "  run]]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameter_list = [2.2, 2.5, 2.7, 2.9, 3.1]\n",
    "\n",
    "macrolist = []\n",
    "for val in parameter_list:\n",
    "    macro = [\n",
    "        Load('Knee.any' ),\n",
    "        SetValue('Main.MyParameter', val),\n",
    "        RunOperation('Main.MyStudy.InverseDynamics'),\n",
    "    ]\n",
    "    macrolist.append(macro)\n",
    "    \n",
    "macrolist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A simple parameter study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us combine the previous to create a parameter study. We will continue with the simplified knee model where we left off in the previous tutorial. The parameter study will vary the patella tendon length from 2.0cm to 8.0cm, and observe the effect on maximum muscle activity.\n",
    "\n",
    "First we create a list of patella length parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08]\n"
     ]
    }
   ],
   "source": [
    "patella_tendon_lengths = [\n",
    "    0.02 + i*0.01 \n",
    "    for i in range(7)\n",
    "]\n",
    "print(patella_tendon_lengths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list of values is added to the macros with the `SetValue` class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_study_macro = []\n",
    "\n",
    "for patella_length in patella_tendon_lengths:\n",
    "    macro = [\n",
    "        Load('Knee.any'),\n",
    "        SetValue('Main.MyModel.PatellaLigament.DriverPos', patella_length),\n",
    "        RunOperation('Main.MyStudy.InverseDynamics'),\n",
    "        Export('Main.MyStudy.Output.Abscissa.t', \"Time steps\"),\n",
    "        Export('Main.MyStudy.Output.MaxMuscleActivity', \"Max muscle activity\"),\n",
    "        ExtendOutput(\"PatellaLength\", patella_length),\n",
    "    ]\n",
    "    parameter_study_macro.append(macro)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, that here we have used two special helper classes `Export(var, name)`, and `ExtendOutput(name, value)`. The `Export` class allows us to rename the output variables, so we don't need to refer to the long AnyBody names in the output. The second `ExtendOutput` is mostly a convinance, as it adds a `PatellaLength` variable to the output, so we don't need to keep track of which length corresponds to different ouptuts.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[load \"Knee.any\",\n",
       "  classoperation Main.MyModel.PatellaLigament.DriverPos \"Set Value\" --value=\"0.02\",\n",
       "  operation Main.MyStudy.InverseDynamics\n",
       "  run,\n",
       "  print \"#### ANYPYTOOLS RENAME OUTPUT: Time steps\"\n",
       "  print Main.MyStudy.Output.Abscissa.t,\n",
       "  print \"#### ANYPYTOOLS RENAME OUTPUT: Max muscle activity\"\n",
       "  print Main.MyStudy.Output.MaxMuscleActivity,\n",
       "  print \"PatellaLength = 0.02;\"],\n",
       " [load \"Knee.any\",\n",
       "  classoperation Main.MyModel.PatellaLigament.DriverPos \"Set Value\" --value=\"0.03\",\n",
       "  operation Main.MyStudy.InverseDynamics\n",
       "  run,\n",
       "  print \"#### ANYPYTOOLS RENAME OUTPUT: Time steps\"\n",
       "  print Main.MyStudy.Output.Abscissa.t,\n",
       "  print \"#### ANYPYTOOLS RENAME OUTPUT: Max muscle activity\"\n",
       "  print Main.MyStudy.Output.MaxMuscleActivity,\n",
       "  print \"PatellaLength = 0.03;\"]]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameter_study_macro[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It means that the macro now starts to look a bit messy. But luckly we don't have think about that.\n",
    "\n",
    "We just pass the `parameter_study_macro` to the `start_macro()` function which gives us back a `results` variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ea3ee2ba69f4e5b8be79e12a904eb3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: \u001b[1;36m7\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = app.start_macro(parameter_study_macro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s9J2FlqS632nTpnHzzTfXWSciIqJRfZ7+/WzTpo2t3Gw2N+nmVyKRtD7SHEJyyTF27FiOHj2Kp6cnPXv2rHVUX4Srt6XnzZt31v4ao4G5+uqrAUWIOp2ff/6Z0tJS2/uNoX///ri6ujJ//vxa0RuagkqlQqvV1thWLi8v5+uvv25yn0OGDGHNmjU1tGdWq5Uff/yxWXM9nYiICPz8/Gp57ycnJ7Np06YG95OQkMDu3btrlH333Xc4OzvTvXt3QDlHer2+hgCckZFRKzoE1P/9GDt2rC20X13fw8YIwSqVCiGETftYzWeffYbFYjln+/o0pg4ODgwbNoxdu3YRHR1d5zwbq8Xs27cvBoOBb7/9tkb5pk2bam3nX3311ezfv7+GGQooJlIqlYphw4Y1auyGEhERQYcOHdi9e3eda+7ZsyfOzs6N6nPw4MEALFmypEb5Tz/9VCsaSFM12BKJ5MIiNcGS886+ffvqDBnVrl27WmYFDeHJJ5/k559/ZvDgwUyZMoXo6GisVivJycmsXLmSp556ij59+jBo0CDuvvtuXn/9dTIzMxk7dix2dnbs2rULBwcHHnvsMQC6dOnC4sWLWbJkCW3btsVgMNClS5c6xx4+fDgjR47kueeeo6ioiAEDBrBnzx5efvllunXrxt13393o9Tg5OfG///2P+++/n2uuuYZJkybh6+tLYmIiu3fv5qOPPmpUf2PGjOH999/nzjvv5IEHHiA3N5f33nuvloDVGF544QWWLl3K1VdfzQsvvIC9vT3z58+3haRTq5t/P61Wq3n11Vd58MEHufXWW5k4cSIFBQW8+uqr+Pv7N3iMgIAArr/+el555RX8/f355ptvWLVqFW+//TYODg6AIsD+8ssvPPzww9x6662kpKTw2muv4e/vz5EjR2r016VLF9auXcvSpUvx9/fH2dmZiIgIZsyYwapVq+jfvz+PP/44ERERGI1Gjh8/zvLly5k/f36DTFwAXFxcGDx4MO+++y5eXl6Ehoaybt06Fi5ciJub2znbOzs7ExISwu+//87VV1+Nh4eHrZ/Zs2czcOBABg0axEMPPURoaCjFxcUkJiaydOnSWna558Ld3Z2nn36a119/nfvvv5/bbruNlJQUXnnllVrmEFOmTOGrr75izJgxzJgxg5CQEP78808+/vhjHnroIcLDwxs1dmNYsGABo0aNYuTIkUyYMIE2bdqQl5fHgQMH2LlzZ6Nv4CIjI7njjjv43//+h0aj4aqrriIhIYH//e9/uLq61vh+VmeE++STT3B2dsZgMBAWFtboGw6JRHKeaV2/PMnlzNmiQwDi008/tdVtTHQIIYQoKSkR//3vf0VERITQ6/W2EFBTpkwRGRkZtnoWi0V88MEHIioqylavX79+YunSpbY6x48fFyNGjBDOzs4COGcUgvLycvHcc8+JkJAQodPphL+/v3jooYdEfn5+jXoNjQ5RzfLly8WQIUOEo6OjcHBwEJ07dxZvv/32OfurDld1OosWLRIRERHCzs5OtG3bVrz11lti4cKFtSI51Hd+z4zwIIQQGzZsEH369BF2dnbCz89PPPPMM+Ltt98WQI0QVvVFhzg9RFk11BHR4JNPPhHt27cXer1ehIeHi0WLFokbbrihVtSGuqhez08//SQiIyOFXq8XoaGh4v33369Vd+bMmSI0NFTY2dmJTp06iU8//bTOcxkfHy8GDBggHBwcakU/yM7OFo8//rgICwsTOp1OeHh4iB49eogXXnhBlJSU1LvOuqIZnDx5Utxyyy3C3d1dODs7i2uvvVbs27dPhISE1Bmd4kxWr14tunXrJuzs7GpFK0hKShITJ04Ubdq0ETqdTnh7e4v+/fuL119/vdacfvzxxxr9Vn9+p4cVs1qt4q233hJBQUFCr9eL6OhosXTp0jq/NydOnBB33nmn8PT0FDqdTkRERIh3333XFp3h9DEa+h05k/qibezevVuMGzdO+Pj4CJ1OJ/z8/MRVV10l5s+fb6tT/T91ZrSZuvo0Go1i6tSpwsfHRxgMBtG3b18RFxcnXF1dxZQpU2q0nzVrlggLCxMajabG+avvv+6ee+5pUAQUiUTScqiEaIH9V4lEckUyYsQIjh8/zuHDh8/bGAUFBYSHh3PjjTfyySefnLVuaGgoUVFRLFu27LzNRyI5nU2bNjFgwAC+/fZb7rzzztaejkQiaQTSHEIikTSIqVOn0q1bN4KCgsjLy+Pbb79l1apVLFy4sMXGyMjI4I033mDYsGF4enpy4sQJPvjgA4qLi3niiSdabByJpCmsWrWKuLg4evTogb29Pbt372bmzJl06NChXgc8iURy8SKFYIlE0iAsFgsvvfQSGRkZqFQqOnfuzNdff81//vOfFhvDzs6O48eP8/DDD5OXl4eDgwN9+/Zl/vz5REZGttg4EklTcHFxYeXKlcyaNYvi4mK8vLwYNWoUb731Vq3QhRKJ5OJHmkNIJBKJRCKRSK44ZIg0iUQikUgkEskVhxSCJRKJRCKRSCRXHFIIlkgkEolEIpFccUjHuDqwWq2kpaXh7OxcI6OURCKRSCSSixchBMXFxQQEBLRIEh/J5Y0UgusgLS2NoKCg1p6GRCKRSCSSJpCSktLgjI2SKxcpBNdBdU75lJQUXFxcWnk2EolEIpFIGkJRURFBQUG267hEcjakEFwH1SYQLi4uUgiWSCQSieQSQ5oyShqCNJiRSCQSiUQikVxxSCFYIpFIJBKJRHLFIYVgiUQikUgkEskVh7QJlkgkEolEImlhLBYLJpOptadxxaHT6dBoNA2qK4VgiUQikUgkkhZCCEFGRgYFBQWtPZUrFjc3N/z8/M7pICmFYIlEIpFIJJIWoloA9vHxwcHBQUaquIAIISgrKyMrKwsAf3//s9aXQrBEIpFIJBJJC2CxWGwCsKenZ2tP54rE3t4egKysLHx8fM5qGiEd4yQSiUQikUhagGobYAcHh1aeyZVN9fk/l022FIIlEolEIpFIWhBpAtG6NPT8SyFYIpFIJBKJRHLFIYVgiUQikUgkEskVhxSCJRKJRCKRSK5g3nrrLXr16oWzszM+Pj7ceOONHDp06Jzt1q1bR48ePTAYDLRt25b58+dfgNm2HFIIlkgucoqMJtILy8ktqaDYaKLCbEEI0drTklzBWKyC8koLhWUmsoqNZBQaMVusrT0tiUTSRNatW8cjjzzC5s2bWbVqFWazmREjRlBaWlpvm6SkJEaPHs2gQYPYtWsX06dP5/HHH+fnn3++gDNvHjJEmkRyEZGUU8qag1kkZpVwLLuEYzmlZBdX1KqnVkGwhwMdfJ3p4ONEuK8znfxd6ODjhFotHTIkLUOx0cSek4UczizmcGYJiVnFHMkqoaCstse1XqMmxNOBtt6OtPV2oksbV67q6INB17DMTRKJpPVYsWJFjdeff/45Pj4+7Nixg8GDB9fZZv78+QQHBzNr1iwAOnXqxPbt23nvvfe45ZZb6h0rISGBZ599lg0bNiCEICYmhi+++IJ27doxYcIECgoK6N27N7Nnz6aiooIpU6bwwgsvMG3aNBYuXIiDgwMzZsxg4sSJzV63FIIlklamsNzEn3vS+XnnSXacyK+zjk6jwmQ5pf21CjieW8bx3DJW7c+0lXs46unb1oN+bT3p186Tdt5O0ktZ0mDKKs1sP55P3LFcNh3NZV9qIRbr2XcdVCpQq1RUWqwcySrhSFYJoHwnne20jIn255YegfQMcZffRckViRCCcpPlgo9rr9M0+TdXWFgIgIeHR7114uLiGDFiRI2ykSNHsnDhQkwmEzqdrlab1NRUBg8ezNChQ1mzZg0uLi7ExsZiNpttddasWUNgYCDr168nNjaW++67j7i4OAYPHsyWLVtYsmQJkydPZvjw4QQFBTVpfdVIIVgiaSUyCo28s+Igy/amU2lWtpLVKhjQ3ouYIDdFo+blRJi3Iy4GHVaroNJipcJspazSTFJ2KUeySjicWcyRzBL2pRWSV1rJ8r0ZLN+bAUCAq4GhHX0YFuHDgPaeOOjlT15Sk+NVuw//Hspiy7E8Ks8wawjysKezvwsdfJzp4OtEBx9n/FwN6LVq7LRqtGoVQkBaYTnHsks5ml3C0ewS/j2YTWpBOYu3pbB4Wwohng48NKQd43oGyd0KyRVFuclC55f+vuDj7p8xskn/+UIIpk6dysCBA4mKiqq3XkZGBr6+vjXKfH19MZvN5OTk1Jmtbe7cubi6urJ48WKbkBweHl6jjoeHB3PmzEGtVhMREcE777xDWVkZ06dPB2DatGnMnDmT2NhYxo8f3+j1nY68IkokFxizxcoXm47zwarDlFYq2oFwXydu6R7Ijd3a4OtiqLOdWq3CoNZg0Glwtdfh72pP//ZetvdNFit7ThawKTGXuGO57DiRT1qhke+2JPPdlmT0WjV923oyuIMXQyO8pZb4CqW80sLmpFzWHcpm3eFsknJq2vy1cbOnb9VOQr92nrRxsz9nnyoVBLo7EOjuwOBwbwCsVsGWpDx+3nmSv/amcyK3jOd/2csP21N4/cYudA5wOS/rk0gkzePRRx9lz549bNy48Zx1z7yGVPur1HdtiY+PZ9CgQXVqiauJjIxErT7lsubr61tDGNdoNHh6etpSIzcHKQRLJBeQHSfy+e9v+ziQXgRA92A3XhzbmZggt2YLpDqNmh4hHvQI8eCxqztgNFmIO5rLv4eyWHMwi5P55aw/nM36w9m8/ucBAlwNDA73pl87T7oFuRPkYS+F4ssQo8nCvtRCdpzIZ2NiDluS8mw7DwBatYreYR4Mi/BhWEcf2nk7tsj3QK1W2QTpGTdE8t2WZD5YdZidyQVc99FGJvQPZcrwcJzs5GVIcnljr9Owf8bIVhm3sTz22GP88ccfrF+/nsDAwLPW9fPzIyMjo0ZZVlYWWq223pTR1SmNz8aZArJKpaqzzGptvjOu/PeRSC4AVqvgnb8PMX/dUQDcHHQ8f23H87o1bNBpGNZREWxevV6QmFXCusOK9m9LUh5phUbbVjWAl5OemCB3uga6EuLlSBs3e4Lc7fFyspPb15cARpOF1IJyTuaXczK/jCOZJexKzmd/elENe3JQtL2Dw70Y3MGbgR28cDbUr5VpCRz0Wu4f1Jax0QHMWJbA8r0ZLNyYxPK96Xxyd0+6BLqe1/ElktZEpVJd9KZoQggee+wxfv31V9auXUtYWNg52/Tr14+lS5fWKFu5ciU9e/asV9MbHR3Nl19+Wa/N8IXm4v5UJJLLAKPJwlM/7ObPvekA3NojkGmjOuLpZHfB5qBSqZRIEr7O3D+orW1LfMPhHHYk57M/rZCckkpWH8hk9YHMGm31WjUhHg6093GyHR18nInwc0YjheMLTkFZJQlpRSRmldiOo9klZNURRaQaLyc93YLd6RPm0aqmMH6uBj6+qwdrD2Xx0u8JJOeVcfsnccy9szvDOvpc8PlIJBKFRx55hO+++47ff/8dZ2dnm4bX1dXVpr2dNm0aqampfPXVVwBMnjyZjz76iKlTpzJp0iTi4uJYuHAh33//fb3jPProo3z44YeMHz+eadOm4erqyubNm+nduzcRERHnf6FnIIVgieQ8kl9ayaSvtrP9RD46jYp3bo3mpm5n32K6ENjrNcr2d4QieBhNFhLSihTNYVoRJwvKSc0vJ72wnErz6V7/p3C209I7zMNmP9rJ30UKxeeBwjITW5IUO+/Nx/I4mFFEfWGiHfWaKttce4I9HYgJcqN7sDuB7heXqcvQCB+WPe7Ow9/sZGNiDvd9uY3Xbozirj4hrT01ieSKZN68eQAMHTq0Rvnnn3/OhAkTAEhPTyc5Odn2XlhYGMuXL2fKlCnMnTuXgIAA5syZc9bwaJ6enqxZs4ZnnnmGIUOGoNFoiImJYcCAAS2+poagEjLqfi2KiopwdXWlsLAQFxfpvCFpGidyS7n3820cyynF2aBlwd096N/O69wNLyJMFivpBUaO5ZzSOCZmlXAwvZjiCnONuh6Oeq7u6MPISD8GdvCS8WGbQUpeGX8nZLByfybbj+dxZpSyEE8HOvg419DOh3o64Gqvu6iE3XNhsliZ9steftpxEoDJQ9rx7MgIaX4jaTKtff02Go0kJSURFhaGwVC3k7Pk/NPQz0FqgiWS80BiVjG3L9hMbmklbdzs+fzeXoT7Orf2tBqNTqMm2NOBYE8Hhkac2q62WAX704rYfEzRUG5NyiOvtJIfd5zkxx0ncdBrGBLuzZhof67u6Iu9XgrE5yIpp5Q/4tNYkZBhc5yspp23o03j3ifME2/nC2dKcz7RadS8e2s0Qe4OfLD6MPPXHSW3pIJ3bo2+pIR5iURyaSKFYImkhckqMnLPom3kllYSGeDC5xN64VNP2LNLFY1aRZdAV7oEujJpcFvMFitbj+exMiGTlQkZpBUa+WtfBn/ty8BRr2FEpB/XxwQwsL0XOo3M1l5NZpGRpbvT+GN3GntOFtrKNWoVvUM9GBHpy/DOvgS6O7TiLM8vKpWKJ67pQKC7Pc/+vIcfd5zE18XA0yMvvH2gRCK5spBCsETSgpRWmJn45TZSC8oJ9XTgq4m9L6gDXGuh1ajp386L/u28ePm6zuxLLeKvfen8sTuNk/nl/LorlV93peLhqOe6aH9u6h5I10DXK1LbV1Jh5q+96fwWn8qmo7k2+16NWsXA9l6Mjfbnmk6+uDvqW3eiF5hbegRisQqe/XkPH/2bSICbPXf2CW7taUkkkssYKQRLJC2E2WLlke92si+1CE9HPV9eIQLwmahUp7TEz4yMYGdyAX/Ep7JsTzq5pZV8GXeCL+NO0NbLkRu7teHGmDYEe16+mk5QbF83Hsnhl12prNqfgdF0Kr5lzxB3ro8JYHQXf7yuwO/L6YzrFURqQTmz/znCi7/vw8/Vjqs6+p67oUQikTQB6RhXB61tWC+59BBCMO2XvSzeloJBp2bxA/2ICXJr7WldVJgtVjYk5vDbrlT+TqgpCHYLduOGrgGMiQ64bOxdrVbBzuR8fotP5c896eSXmWzvtfVy5KZubbixWxuCPC7vG4DGIoTg2Z8Uswh7nYYlD/YlOtCttacluURo7eu3dIy7OJCOcRLJBWTeuqMs3paCWgUf3tFdCsB1oNWobWHZSirM/L0vg193pbLpaA67kgvYlVzAjGX7GdDei1FR/oyI9L3kNKNWqyD+ZAEr9mXw5550UgvKbe95Ouq5rmsAN3VrQ/QVagrSEFQqFW/e3IWMIiMbjuQw8Ytt/P7owAalb5ZIJJLGIDXBddDad5KSS4vtx/MYtyAOq4DXbojk7n6hrT2lS4qsIiPL9qTz++40dqcU2MrVKugZ6sGoKD+u6eR70WpMK81Wtp/I4+99GfydkElGkdH2nqNew8goP26IacOAdp5opVNggyk2mhi3YDMH0ovoHerB9w/0lXGoJeekta/fUhN8cdDQz0EKwXXQ2j8iyaVDkdHEqFkbSC0o5+bubXh/XExrT+mS5nhOKX/uTWfFvgz2phbWeK+tlyODw70ZEu5Nn7YerZqG9ERuKesPZ7PucA5xR3MorbTY3nOy03JVRx+ujfLjqo4+Ml5yM0jOLWP0nA2UVJh5ang4j13dobWnJLnIae3rtxSCLw6kENwMWvtHJLk0EELwxOJ4/tidRrCHA8ufGISTnbQwailO5pexYl8GfydksDO5AMtpGSN0GhWdA1zpFuRG9xB3ugW5nbesaBVmC/tSlWx6itlGPmmFxhp1PB31DOvow6goPwa0l4lCWpJfdp5k6g+70ahV/Di5H92D3Vt7SpKLmNa+fksh+OJACsHNoLV/RJJLg9Mvzj9N7kc3eXE+bxSWm4g7msO6wzmsP5xdw9a2GheDlrbeTrT1dqSdtxOhno54OenxcNTj7qjHzV5XyxxBCEG5yUJeaSX5pSbyyipJKygnKaeUY9klHMsuJTmvDPMZKdu0ahU9QtxtmunO/i4yy9l54vSbzSAPe5Y/Pghng661pyW5SGnt67cUgi8OLhnHuI8//ph3332X9PR0IiMjmTVrFoMGDaqzbnp6Ok899RQ7duzgyJEjPP7448yaNatWvZ9//pkXX3yRo0eP0q5dO9544w1uuumm87wSyZXEidxSXvxtHwBTrukgBeDzjKu9jmuj/Lk2yh8hBCl55exKOaWZTUgroshoJj6lgPjT7IrPRK9Vc7qoahUCk+XcegBPRz3dgt3pFuxG92B3ogNdcZRa/wuCSqXi9Zui2HEin5S8cl7+PYH3b49p7WlJJJcV8+bNY968eRw/fhyAyMhIXnrpJUaNGnXWduvWrWPq1KkkJCQQEBDAs88+y+TJky/AjFuGVv0XX7JkCU8++SQff/wxAwYMYMGCBYwaNYr9+/cTHFw7SHpFRQXe3t688MILfPDBB3X2GRcXx+23385rr73GTTfdxK+//sq4cePYuHEjffr0Od9LklwBmCxWnlgcT2mlhd5hHjw0tH1rT+mKQqVS2VI53xDTBgCjycLx3FKOZZ/S4J7IKyO/tJK8skoKqsKTVZqtdfap16htGmMfZzvCvBxp5+1o0yz7uRhkNIdWxMWgY/b4GMYtiOOXXakMifC2ffYSiaT5BAYGMnPmTNq3V65nX375JTfccAO7du0iMjKyzjZJSUmMHj2aSZMm8c033xAbG8vDDz+Mt7c3t9xyy4WcftMRrUjv3r3F5MmTa5R17NhRPP/88+dsO2TIEPHEE0/UKh83bpy49tpra5SNHDlSjB8/vsHzKiwsFIAoLCxscBvJlcO8tYki5LllosvLK8TJ/LLWno6kAZjMFpFdbBQpeaU1jpP5ZaLEaBJWq7W1pyhpAP9becj228suNrb2dCQXIa19/S4vLxf79+8X5eXlrTJ+S+Lu7i4+++yzet9/9tlnRceOHWuUPfjgg6Jv375n7Xffvn1i9OjRwtnZWTg5OYmBAweKxMREIYQQ99xzj7jhhhvEG2+8IXx8fISrq6t45ZVXhMlkEk8//bRwd3cXbdq0EQsXLjzrGA39HFotXk9lZSU7duxgxIgRNcpHjBjBpk2bmtxvXFxcrT5HjhzZrD4lkmpSC8qZvfoIAC9dFyljl14iaDVqvJzsCHR3qHG0cbPH0U4rtbyXCI9f1Z7O/i4UGc3M/Otga09HImkYQkBl6YU/mujyZbFYWLx4MaWlpfTr16/eevXJW9u3b8dkMtXZJjU1lcGDB2MwGFizZg07duxg4sSJmM1mW501a9aQlpbG+vXref/993nllVcYO3Ys7u7ubNmyhcmTJzN58mRSUlKatL7TaTVziJycHCwWC76+NVNi+vr6kpGR0eR+MzIyGt1nRUUFFRUVttdFRUVNHl9yeTNjaQLlJgu9Qz24pbvcjpVILiRajZrXb4ri5o838dOOk9zeK4heoR6tPS2J5OyYyuDNgAs/7vQ00Ds2uPrevXvp168fRqMRJycnfv31Vzp37lxv/frkLbPZTE5ODv7+/rXazJ07F1dXVxYvXoxOpzi4hoeH16jj4eHBnDlzUKvVRERE8M4771BWVsb06dMBmDZtGjNnziQ2Npbx48c3eH110eqR28/UwAghmq2VaWyfb731Fq6urrYjKCioWeNLLk/+PZjF3wmZaNQqXrsxSmoPJZJWoHuwO3f0Vv6j//vrPkyWuu28JRJJ44iIiCA+Pp7Nmzfz0EMPcc8997B///6ztqlL3qqrvJr4+HgGDRpkE4DrIjIyErX6lHjq6+tLly5dbK81Gg2enp5kZWWdc03notU0wV5eXmg0mloa2qysrFp3Fo3Bz8+v0X1OmzaNqVOn2l4XFRVJQVhSA6PJwst/JABw38AwIvycW3lGEsmVy7MjO7JiXwaHMov5ctNx7h/UtrWnJJHUj85B0cq2xriNQK/X2xzjevbsybZt25g9ezYLFiyos3598pZWq8XT07PONvb25zYhPFNAVqlUdZZZrc2/AW41TbBer6dHjx6sWrWqRvmqVavo379/k/vt169frT5Xrlx51j7t7OxwcXGpcUgkp/Px2qMk55Xh52LgCZm1SiJpVdwd9Uwb1QmAD1YdJr2wdtxoieSiQaVSzBIu9NHM3UohRA1T0TOpT97q2bNnvZre6OhoNmzYUK/N8IWmVc0hpk6dymeffcaiRYs4cOAAU6ZMITk52RZjbtq0afzf//1fjTbx8fHEx8dTUlJCdnY28fHxNdT1TzzxBCtXruTtt9/m4MGDvP3226xevZonn3zyQi5NchmRlFPK/LVHAXj5us4yPqxEchFwa49AeoS4U1pp4fVlB1p7OhLJJc306dPZsGEDx48fZ+/evbzwwgusXbuWu+66y1bnTJls8uTJnDhxgqlTp3LgwAEWLVrEwoULefrpp+sd59FHH6WoqIjx48ezfft2jhw5wtdff82hQ4fO6/rqo1Wv5rfffju5ubnMmDGD9PR0oqKiWL58OSEhIYCSHCM5OblGm27dutme79ixg++++46QkBBbgOf+/fuzePFi/vvf//Liiy/Srl07lixZImMES5rMK38kUGmxMjjcm2uj/Fp7OhKJBFCrVbx2QxRjP9zAn3vTGX8km0EdvFt7WhLJJUlmZiZ333036enpuLq6Eh0dzYoVKxg+fLitzpkyWVhYGMuXL2fKlCnMnTuXgIAA5syZc9YYwZ6enqxZs4ZnnnmGIUOGoNFoiImJYcCAAed1ffUh0ybXQWunXZRcPMQm5nDXZ1vQaVSsnDKEMK+Ge9pKJJLzz6tLE/g89jid/V1Y9thAmb76Cqe1r98ybfLFQUM/h1aPDiGRXKwIIXh7hRKL9K4+IVIAlkguQh6/qgPOdlr2pxexdE8rOB9JJJJLFikESyT18Ne+DPacLMRRr+HRq2RqZInkYsTdUc8Dg5XoEP9bebje1NgSiURyJlIIlkjqwGyx8t7fiqH+/YPa4uVk18ozkkgk9XHfoDC8nOxIzitjybbkczeQSCQSpBAskdTJD9tPciynFA9HPZMGXwIxSK0WJU2muUJ5Lk39Jc1BCLCYwFQOlWUX/ffJQa/liauV3ZrZ/yRSWmE+RwuJRCJp5egQEsnFSHmlhdn/HAbg0WHtcbpYQqIZiyBtJ5zcBum7oSQLynKVo7wAOENQUevAzgkMrqcdbqee27spr7UG0OhBo1UeUUFliTJeRRFUFIOlskq4toCo2m629wBHb3D0Uh5d2oB7qNKPpGUoL4C8o1CSDaVVR1mu8vmoNKBSg1qjPNc7gp0zGFyUR61BEWStZuXzs1RCRQkYC8BYqBzlpz2vPsxG5XM+HY0dOHhWHR7K5xzYCwJ7glcEqFtfn3J7r2A+3ZBEcl4ZizYm8ZiM5y2RSM6BvFpJJGfwxabjZBZVEOhuz119g1t3Mpn7YedXcGwtZB+klqB7NqwmKM9XjguFWgcebcGrA3iFg18XCOimCE0yzXT9VJZB5j5I2wVZByDnCOQchtLmpwVtESwVUJymHABJ62Dnl8pzOxdo0x0ib4KoWxQBvBXQa9U8NSKcJxbHs2D9Me7qG4KHo75V5iKRSC4NpBAskZxGYZmJeWsTAZg6PBw7rebCT6KyDBJ+hR1fwMmtNd9zC1G0b216gGvgado5TyVFprAoGlurWdECVhSfpuUrUDR/FYU1NYHmCkVgtlQdwqoIMqcfWkNNraOwQnlelXYyR9FKF6aAqQxyDinH6RjcICAGArpDUB8I6q1oFK9ErFblhiZlC5zcrgi+2Qdra1+rcfIDF39F2+7gpWje9U6AOKWdt5qV701F8Sntvdmo3JRoqg61rkpT7FrzsHer+VrnAGqt8lmrtYopRHl+1a5DHpTlKIJ69dwripSbtGNr4e8XoMtt0GOC8nlfYK6LDmDBumPsTy9i7r+JvDi28wWfg0QiuXSQQrBEchqfbTxGkdFMhK8zN8S0ubCDmysh7kPYOFsRVEERQiJGQ/Q4RXh08rmwc2oMVisUpSoazJwjimCXHg+ZCYoAXi0oVeMVrgjDQX0huB94trs8tcWVpZC6A5I3K4JvyrZTn+/pOPooWnO/KMXEwKsDeLZXzBtaG4MLuIfULreYIfsAHF2j7FjkJsKOz5UjsBeMelu5YbtAqNUqnr02ggmfb+PruBM8MLgtvi4yVqtEIqkbKQRLJFUUGU18sek4AFOGd0BzIYPuJ62HP59SBEhQzAe63wMxd4Gz74WbR3NQq8EtSDnaX32q3FwJWfur7Jl3KIJgbtV2f85h2PWNUs/BC4L7ntIU+8eA7hIUYApTFbvtlK2Qslmx37ae4ailc1CEw6DeymNAN3D2v/RuAjRaxeTFrwv0fxyOb1R2MA78oZyDT6+GnhPh6hfB3v2CTGlIuDc9Q9zZfiKfT9cf479SGyyRSOpBZoyrg9bOOCNpHeb+m8i7fx8i3NeJFU8MvjCZp0qyYOV/Yc8S5bWjN4x4HbqMuyicjc4bpblVguJmSN6iaEotFTXrqHWKcBXUWzGj8O+qaEfVrWCiUh/lBZCxV9F4n9ymmAgUpdau5xwAIf2qBPw+4Bt1eTsQFmfCqpdgz2LldfX3Ovr2CyLo/3soi3s/34a9TkPs81dJ2+AriNa+fsuMcRcHDf0cLuN/YYmk4ZRVmlm4MQmAh4e2vzAC8PGNsOQ/VY5rKuh1H1z13wumMWtVHD0h4lrlAMUuOX03nNh0SotamqVoj9N2nmqnc1AESL8u4N3xlAOeS8D5Fa5M5ZB79JSpR1aCMt/847XrqtTgGwmBvRUzj+A+4Bp06Wl5m4OzL9y8ALr9p2qH4xD8+iAcWAo3f6JEsjiPDA33JqqNC/tSi/g8NomnRkSc1/EkksuJt956i+nTp/PEE08wa9ass9Zdt24dU6dOJSEhgYCAAJ599lkmT558YSbaAkghWCIBvt+aQl5pJcEeDoyN9j//A8Z/B388rjik+XWB62ZfUNvJiw6tXZV9cG/ltRBQcELRrKZsVQTOjL1gKlWcBc90GNQ7KSYkLgFVRxtw9lMcvexclMPgooSAO10YFVYlbNjpDmWlOYo2tyhNOQpPKk5/9UXmcAsGv2glQkJgb8W0wc7pfJylS4+wQTB5I8R9BGtnwsFl8PkouGOJ4ux3nlCpVDwytD0PfbuTLzYdZ9LgtrgYdOdtPInkcmHbtm188sknREdHn7NuUlISo0ePZtKkSXzzzTfExsby8MMP4+3tzS233HIBZtt8pBAsueKpMFv4ZP1RAB4a2g6t5jyaIVit8O/rsOF/yuvIm+HGj0Fnf/7GvBRRqRSh1j0UutyqlFktkHfslEBcHUYs75gSNzdzn3KcLwyuVQ5r4eAdrphn+EVfuVEuGopWD4OmQsgAWHyn8vl9ehXcuVg5h+eJkZF+tPdxIjGrhK/jTvDIMJn6XCI5GyUlJdx11118+umnvP766+esP3/+fIKDg23a4k6dOrF9+3bee++9swrBCQkJPPvss2zYsAEhBDExMXzxxRe0a9eOCRMmUFBQQO/evZk9ezYVFRVMmTKFF154gWnTprFw4UIcHByYMWMGEydObPaapRAsueL5eUcqmUUV+LkYuLn7eYwIYSpXtoT3/668HvwMDJ1+edv+tiRqTZX5Q4dTgjEojnf5SYq2tlp7W5QKxRk1E34Yi2rbHaNStLZ2zlUaY2dFqHVpU1Or7NFOCU12JZk0tDTBfWDSP/DtOMU8YtEouHUhRIw6L8Op1SoeHtqOqT/sZtHGJCYOCMNefxHZk0uuGIQQlJvLL/i49lp7VI34z3rkkUcYM2YM11xzTYOE4Li4OEaMGFGjbOTIkSxcuBCTyYROV3v3JTU1lcGDBzN06FDWrFmDi4sLsbGxmM2nnIfXrFlDYGAg69evJzY2lvvuu4+4uDgGDx7Mli1bWLJkCZMnT2b48OEEBQU1eH11IYVgyRWN2WJl3jolLvADg9uev7jA5gr47nYlyYBaB9d/CDF3nJ+xrjS0evCOUA7JxY17KNy3En6cAMf+VTTDt34OkTeel+Gu7xrAB6sPk5JXzvdbk5k4MOy8jCORnI1yczl9vutzwcfdcucWHHQODaq7ePFidu7cybZt2xrcf0ZGBr6+NaMX+fr6YjabycnJwd+/tsnT3LlzcXV1ZfHixTYhOTw8vEYdDw8P5syZg1qtJiIignfeeYeysjKmT58OwLRp05g5cyaxsbGMHz++wfOtC6mCklzR/LE7jZS8cjwd9dzR+zxlh7Na4JcHFAFY7wT/97sUgCVXLvZucNePSvg/YYVfJkHShvMylFaj5qEhihnEJ+uPUWGuJyGJRHIFk5KSwhNPPME333zT6IgWZ2qaqwOO1aeBjo+PZ9CgQXVqiauJjIxEfdoOqa+vL126dLG91mg0eHp6kpXV/IyaUhMsuWKxWgUfr1VsgScOPE9bpULAX8/B/t8UDfDt30DogJYfRyK5lNBU7YZUFCkRIxbfCRP+BP9zO+M0llt6tGHOP0fIKDLyy87U83ezK5HUg73Wni13bmmVcRvCjh07yMrKokePU87ZFouF9evX89FHH1FRUYFGU/v66OfnR0ZGRo2yrKwstFotnp6edc/J/txzOlNAVqlUdZZZrdZz9nUupBAsuWJZeziLxKwSnO203N2vjmxYLcGG92Dbp4BKCRnVbtj5GUciudRQa+Dmz+Cbm+FELHx7K0z8Gzxa1mTBTqvh/kFhvP7nAT7bcIzbewZdmBCIEkkVKpWqwWYJrcHVV1/N3r17a5Tde++9dOzYkeeee65OARigX79+LF26tEbZypUr6dmzZ72a3ujoaL788st6bYYvNNIcQnLFUh0X+PZeQecnfNKOL2FNlXPBqLch6tIIGSORXDB0Bhj/nRL7uSRTEYhLslt8mNt7BeFkp+VodinrjrR8/xLJpYyzszNRUVE1DkdHRzw9PYmKirLVmzZtGv/3f/9nez158mROnDjB1KlTOXDgAIsWLWLhwoU8/fTT9Y716KOPUlRUxPjx49m+fTtHjhzh66+/5tChQ+d1jfUhhWDJFcmB9CJiE3NRq+Ce/qEtP0DSelj2pPJ80NPQ58GWH0MiuRywd4P//KzEW847BovvAIupRYdwNui4vZfiRb6o6uZXIpE0jvT0dJKTk22vw8LCWL58OWvXriUmJobXXnuNOXPmnDU8mqenJ2vWrKGkpIQhQ4bQo0cPPv3001bTCsu0yXXQ2mkXJeefZ37czY87TjK6ix8f39XCSSpKc2DeACjJgOjxcNN8GVpLIjkXuUfhk2FQUQgDnoThr7Zo9yl5ZQx591+sAv5+cjARfs4t2r/k4qC1r98ybfLFQUM/B6kJllxxZBdX8Ht8GgD3tXTIJKsVfp2sCMBeETD2fSkASyQNwbMd3PCh8jx2FiT+06LdB3k4MDLSD5DaYIlEoiCFYMkVxzebT1BpsRIT5Eb3YPeW7XzzXEhcBVoD3PY56B1btn+J5HKm8w3Q8z7l+a8PQnFmi3ZffdP7a3wqOSVnJk6RSCRXGlIIllxRGE0Wvtl8AlAuiI3JpnNOTu6A1a8oz699C3wjW67viwyT1USZqYzCikJyynPIKM0gqyyLUlMpVtH8sDUS5RwXGAtIL0knszSTPGMexZXFGM3Gy/scj3xDcZQrzYZfH1B2V1qIHiHudA10pdJs5dvNyeduIJFILmtkiDTJFcUf8WnkllYS4GpgVJRfy3VsLISf7gWrGTrfCD3ubbm+W4miyiIS8xNJLEgkpTiF1JJU0kvSSStNI8+YV287FSocdY446hzxtvfGx8EHX0dffB188Xf0J9glmCDnIFztXC/gai4ezFYzGaUZJBcnc7L4pO0GIrMsk6yyLAoqCig1lVJRK8XzKXRqHX6OfgQ4BRDgGECAUwBhrmF0cO9AsHMwWvUl/Neus4dbF8EnQ+HYWoj9AAY91SJdq1QqJg4M44nF8Xy9+QSTh57HLJESieSi5xL+p5RIGocQgkWxii3gPf1D0WpacCPkz6eh4ITi4X7d7IveDlgIQYmphHxjPpllmWSUZtgeU0tSOZJ/hMyyhm9F69Q6LMKCVVgRKH2XmEqUPnLrbuNq50qwczChLqGEuYbZjmDnYHSa1o8f2VzyjfkkFSadOoqSSC5K5mTJScxWc4P70al1CCEwi1NtTFYTKcUppBSn1KqvV+tp69aWMNcw/B398XXwxc/RDz9HPzwNnngYPC7+8+sdAaPfhd8fgTVvQNhQCGwZB9bRXfyZ+ddB0guN/BGfxm09g1qkX4lEcukhhWDJFUNsYi4HM4px0GsY35JZoxJXw94fQKWGWxYpIZ9akTJTGcnFyZwoOkF2WTbZ5dnklOeQW55LrjGXvPI88ivyMVnPHYbK39GfDu4dCHEJsWkcA5wC8HXwxaA1oFPr0Kg0qFQqhBAYLUZKTaWUmkopriwmqyzLpuXMLM0krTSNlKIUssqzKKwoZG/FXvbm1AzSrlFpCHQOJMzllGAc4hJCsEswngbPljVhaSaVlkpOlpwkuSiZ44XHSSo6JfQWVBTU206v1hPkHESgcyB+jn74Ovji6+iLj4MP7nbuOOudcdQ54qBzQKdWBFarsGK2mjFZTRRWFJJWkkZ6aTppJWmcLDnJsYJjHCk4Qrm5nIN5BzmYd7De8Z11zrgb3PEweOBl74WnvSde9l542XsR4BRAqEsofo5+qFWtaDEXcxccXQP7foalT8ADa0HT/EuWTqPm//qF8vaKgyzcmMStPQIvqu+URCK5cEghWHLF8MWm4wDc1iMQV/sW0oSZyuHPqq3aPpMhqFfL9NtAiiqL2Jm5k20Z2ziYd5DjRcfJKmt4PnV7rb1irlClLfR18MXfyZ/2bu1p79YeZ33Dw0ipVCrstfbYa+3xsvc6a90yUxkpxSkkF1cJj6dpS0tNpZwoOsGJohOsPbm2RjsHrYPNnMLf0R8/Rz/8Hf3xd/THy96rRbWc1dryPGMemaWZpJemk16abtOWpxSnkF6aflb73ADHgJpabpdgQpxD8HX0bbSAqVap0Wv06DV6HHWOBDgF1KpjFVabJv9E0YlTWv7STDLKMsgz5mEVVopNxRSbikkurt8u1k5jR5BzEGGuYXTz6UYvv16Eu4dfOMFYpYJR7yiCcOZe2DIP+j/WIl3f2TuYOf8c4WBGMVuS8ujbtu4UrxKJ5PJGCsGSK4KT+WWsOahs79/dL7TlOl7/LuQfB5c2MGx6y/V7Fvbn7mf5seVszdjKwbyDCGqH+na3cyfYJRg/Rz+bhs/T4ImnvSeeBk/cDe64G9wbnFu+pXHQORDhEUGER0SNciEEWWVZHC86XsOUILk4mfTSdMrMZefUcjrpnPAweOBmcMNB62ATzO219mjVWlSc0voJBEazkXJzue0oriwm35hPXkVeg8wWqgXzusw6LnSqVLVKTZBzEEHOdW/xW4WVoooi8iryyDfm23YHqncKssuzOVl8kuTiZCosFSQWKDbhq06sAsBF70IP3x4MbDOQa8OuxUV/nuOwOnrB8Bnwx2Pw75uKvb1b880XXB103NgtgO+3pvDN5hNSCJZIrlCkECy5Ivh+azJWAf3bedLex6llOs06CLFzlOej3ga78xd8v6iyiOXHlvPLkV84kHegxnuhLqH09OtJV++uhLmGEeoSesk6nalUKsWJztGXPv59arxXbXqQUqTYwmaUZiia2bIMMkoyyDXmYhEWmz3y2bScjcFea29z6rNpnp38CXYOvihNNM6GWqXGzeCGm8ENzvIVMVvNpJekc7zoOIfzD7M9czs7M3dSVFnEvyn/8m/Kv7y77V1GhI7g5g43092n+/k7BzH/gfjvIDkO/noW7vi+Rbr9T98Qvt+awop9GWQVG/FxlokNJJIrDSkESy57Ks1WlmxTHIju7hvSMp1arbBsClhNED4KOo5tmX7PIKssi4/jP2bZsWW2aAE6tY5rgq9haNBQevr1xMfB57yMfbGh1+hp69qWtq5t63zfKqwUVxaTZ1S0nPkV+ac0vCbl8XTnMlAiWdhp7E5pi3X2OOmcFHtZOw/cDe4YtFeecKRVawlyCSLIJYhBgYO4r8t9mK1mDuYdZHP6Zv489ieJBYn8cfQP/jj6B6EuoUyKnsTYtmNb3lxCrYaxH8D8gXBoORxYBp2a/3uLDHCle7AbO5ML+GFbCo9e1aEFJiuRSC4lpBAsuexZkZBBTkklPs52XNPZt2U6jf8WkjeBzgFGv9Pi0SCMZiNfJnzJwn0LKTeXA9DerT23ht/KmLAxiiZPUgO1So2rnSuudq6EubZwJkAJWrWWKK8ooryiuC/qPvbm7OWXI7+wPGk5x4uO88LGF/juwHc81/s5uvl0a9nBfTpB/8dh4/uKNrjtkBbZeflP3xB2Jhfw3ZZkHhraHo360tDoSyQtzSuvvMKrr9ZMVe7r60tGRsZZ261bt46pU6eSkJBAQEAAzz77LJMnTz6fU21RZLIMyWXPN3FKcow7egeja4mwaKW5sOpF5fnQaUpYtBZCCMFfSX9x/W/X81H8R5Sby4n2jubLa7/kl+t/4a5Od0kBWNLqqFQqor2jeaX/K/w77l+e7P4kjjpHEnIT+L+//o9n1j1DWklayw46+BlwC4GiVPj3rRbpcnQXf9wddKQVGllzsOEOpRLJ5UhkZCTp6em2Y+/evWetn5SUxOjRoxk0aBC7du1i+vTpPP744/z8888XaMbNRwrBksuaQxnFbD2eh0at4o6WCou27m0oz1eyWvV9qGX6RLH7ffzfx3l2/bOkl6bj5+jHO4Pf4ZtR39Dd9zzaXEokzcBR58h9Xe5j2U3LuKXDLahQseL4Cm78/UaWH1vecgPpHWDM+8rzrQsg92izuzToNIzrpTjafV2VSVIiuVLRarX4+fnZDm9v77PWnz9/PsHBwcyaNYtOnTpx//33M3HiRN57772ztktISGDMmDG4uLjg7OzMoEGDOHpU+T1PmDCBG2+8kTfffBNfX1/c3Nx49dVXMZvNPPPMM3h4eBAYGMiiRYtaZM1SCJZc1lSnSB7eyRc/1xaw7cw7Bturfnwj34QWCsd1KO8Q45eNZ23KWnRqHQ/HPMwfN/7BqLBRUviVXBJ42XvxSv9X+OG6H+ju051ycznPbXiOmVtnYrKcOyZ1g+hwDXQYoWRm/GdGi3R5V+8QVCpYfzib4zmlLdKnRHI6QgisZWUX/BCiduSgs3HkyBECAgIICwtj/PjxHDt27Kz14+LiGDFiRI2ykSNHsn37dkymun/zqampDB48GIPBwJo1a9ixYwcTJ07EbD7lr7FmzRrS0tJYv34977//Pq+88gpjx47F3d2dLVu2MHnyZCZPnkxKSu1kQY1F2gRLLltKKsz8svMkAHf3ayGHuH9eU5zh2l+j2CW2AEuPLmVG3AyMFiP+jv58MPQDIr0iW6RvieRC09GjI4tGLmJu/Fw+3fsp3x74lv25+3lvyHst48R5zStwZBXs/w1O7mh2JrlgTweGhHuz9lA2321NZvroTs2fo0RyGqK8nEPdWybjYWOI2LkDlUPDwjT26dOHr776ivDwcDIzM3n99dfp378/CQkJeHrWHUIwIyMDX9+afja+vr6YzWZycnLw9/ev1Wbu3Lm4urqyePFidDpFiRQeHl6jjoeHB3PmzEGtVhMREcE777xDWVkZ06crYUinTZvGzJkziY2NZfz48Q1aX31ITbDksuW3XamUVlpo6+1I/3YtEAc0dQck/AKolAtxM7FYLby15S2mb5yO0WKkf0B/loxdIgVgySWPRq3h8e6PM3vYbJx0TuzK2sW4peOIz4pvfue+kdD1DuX5qpegkdquuqiOGvPD9hSMJkuz+5NILjVGjRrFLbfcQpcuXbjmmmv4888/Afjyyy/P2u7Mncpq7XN9O5jx8fEMGjTIJgDXRWRkJGr1KfHU19eXLl262F5rNBo8PT3Jymq+Hb/UBEsuS4QQNlOIu/qENN+kQAhY9bLyPPp28Oty9vrnwCqsvLzpZX4/+jsAD0Y/yENdH0Kj1jRvnhcBwmJBmM1gNqOys0OllX8zDUEIgaioAKtVOWc63SVvCnNV8FUsHruYKWuncCT/CA+seoAFwxc0P3rEsOlKOuUTGxWtcPiIc7c5C0MjfGjjZk9qQTl/7knnlh6BzZufRHIaKnt7InbuaJVxm4qjoyNdunThyJEj9dbx8/OrFT0iKysLrVZbr/bYvgFzOlNAVqlUdZZZrfVn62wo8uokuSzZmVzAwYxiDDo1t3ZvgQta4j9wfANo9HDVC83qyiqsvLLpFX4/+jsalYaZg2Zybdi1zZ/jeUYIgSk1FeO+fVQcPowpMxNzVjbmrCzM2dlYi4sV4fdMzZxOh9pgUA5HRzTu7lWHG1p3d7Te3mh9fE4d3t6oDZd2bF4hBJaCAuXcnH7k5mHJz8eSn4+5IB9rQSHW8nKsRiPCaKx97jQaVHo9Wg+PGudHF9gG+8hI7Dp1RuPk2DqLbCAhLiF8M+obnvj3CTanb2byqsksGL6AGJ+YpnfqFgR9HoRNc2D1y9D+amjGDaRGreLOPsG8+/chvt1yQgrBkhZFpVI12CzhYqGiooIDBw4waNCgeuv069ePpUuX1ihbuXIlPXv2rFfTGx0dzZdffonJZDqrNvhCIYVgyWXJkm1KtrAxXQJwdWjmD81qUS60AL0faFZINKuwMiNuBr8m/opapb6oBWBrRQXlu3ZRumULxt17MCYkYCksbHxHJhNWkwlrcTFkZ8Px4+dsonFzQ+vnh87XF62vL1pfH+W5j4/y2tsbjZsbKvWFt+iyVlRgzs6pEmwzMWdlKTcEmVmYMzKqnmciKiubP5jFgigvx5Saiik1tfb7KhX6sDAMUZE49OiJY98+6IKDLzoNsoPOgTlXzeGxfx5jS8YWJq9WBOGu3l2b3unAKbDzS8jaD7sXQ7e7mjXH23oG8v6qw+xMLuBwZjHhvucvA6REcrHx9NNPc9111xEcHExWVhavv/46RUVF3HPPPbY606ZNIzU1la+++gqAyZMn89FHHzF16lQmTZpEXFwcCxcu5Pvv68/q+Oijj/Lhhx8yfvx4pk2bhqurK5s3b6Z3795ERESc93WeiRSCJZcdxUYTS3enAzC+d1DzO9z7I2TuAztXGPRUk7uxCiuvbX6Nn4/8jFql5q2Bb11UArClpATjvgTK43dRunkL5Tt31hbkdDoMEREYOnVC1yYArbcPWh9Fk6txdkal04FWi0qnR6VRIyorFS1nlbbTWlyMOT8fS35BlUY0D3N2DqasU1plYTRiKSjAUlBAxcGD9U9Yo0Hj7o7W0xOtpwcaNzfUzi5oXJyVR2cnVHYGVHZ61AYDKr0dKq2mZmITIbBWVCAqKhEVRuV5WRmW4hKsxUVYioqxFBVhycvDnJeLJScXa2nDIwhobBpcb0Xj7eVt04Br3N3RuLqidnBAZW+vzNFgj0qtQpjNymEyISoqMOfkYs7OtmmUK48nUb4vAXN6OpXHjlF57BhFfygaGW2AP459+uLQuzf20V3Qh4W1ys3Cmdhr7fnw6g959J9H2ZqxlcmrJvPJ8E/o4t1E0yIHD+X3uOol+PcNiLoZdE3f/vVxNnB1Rx9W7s9kybYUXhzbucl9SSSXGidPnuSOO+4gJycHb29v+vbty+bNmwkJOeVUnp6eTnLyqXT0YWFhLF++nClTpjB37lwCAgKYM2cOt9xyS73jeHp6smbNGp555hmGDBmCRqMhJiaGAQMGnNf11YdKNDaGxhVAUVERrq6uFBYW4uLi0trTkTSS77cmM+2XvbT1duSfqUOapxWzmODD7lCQDFe/DIOmNrmrd7e9y1f7v0KtUvPGwDcY2/b8pFpuCJbiYioOHsR44CDGhH2U791HZVJSre14rbc3Dv364tC9O4aoLtiFd0Ct15+3eQkhsBYV2bSppowMzBmZmLMyMWVlKdrWrCwseXnnbQ4NQaXTKRppHx9FS+3jg9bXD51ftebaD52PN6rzeK4AzDk5GBMSKN+9h7KtWynbvRvOCE2kdnTEEBmJoUsUhs6dMXTsiD40FJWmdezPy0xlPPLPI2zP3I6zzpmvR39NO7d2TevMVA4f9lASaIx4A/o/2qy5rTmYycQvtuPuoGPz9Kux0176NvpXGq19/TYajSQlJREWFobhEjfrupRp6OcgNcGSy47F25TYgeN7BTV/W3jPEkUAdvSBPk1PBbn06FK+2q9sIb024LULKgCb8/Mx7ttH+d69GPfvp+LAwbq31gFdQACG6GgcevfCsW9fRYt4AbfWVSoVGldXNK6ucEbYnNMRlZWY8wuw5OUqdra5OVgKi7AUF2EtKsZSXIy1uEjR7BorEBUVWCsqwGKu1ZdKb4fKYEBtp0dlZ0Btb0Dt4oLG2QW1izMaZ2c07h5ovTwVza6nJ2pn54vC5EDr5YXTkCE4DVHC9VnLyynbuZOyzZsp27kL4/79WEtLFQF561ZbO5XBgF14OIaOHTFERWLfpQt27dsrmvzzjIPOgblXz2Xy6snsytrFE/8+wXdjvsNF3wSBRWcPQ56DpY8r9sG97gdd0wWPwR288XMxkFFkZNX+TMZGBzS5L4lEcvEjhWDJZcWB9CJ2pxSgVau4ubkOcVYLbPif8rz/Y0rGqiawP3c/r8YpOdkfiH6A69td37x5nYPKlBRKN8VRtmUz5bv31CvwagP8MUR0xBAZiX2XKAxRUWjr8ei92FDp9eh8fdD5tkDc2csItb09TgMG4FS1tSjMZiqOHsO4by/l+/ZRceAgxkOHEOXlGPfswbhnD/ygtFXZ2WHo2BH77t1x7N8Phx49UJ8nZx4HnQOzhs1i/LLxnCg6wbQN0/jwqg9Rq5pgttH1Dlj3DhSdhF1fQ+9JTZ6XVqNmXM9A5qxJZMm2FCkESySXOVIIllxWLKnSAg/v7IuXk13zOkv4VckQZ+8OPSc2qYs8Yx5P/vskFZYKBrUZxCMxjzRvTnUgKisp2biRkn/XUhoXh+nkyVp19KGhGKKilG3xTp0wdIxA4+bW4nORXFyotFoMEeEYIsJxq7LTExYLlcnJijnM/v2U79uHcV8C1uJiynfvpnz3bvI+/xx0OhxiYnAc0B+Xa69FHxraonPzMHgwa9gs/u+v/2P9yfV8HP8xj3ZrgjmDVg8Dn4TlT0PsbOh+j1LWRG7rGcSH/yay4UgOKXllBHlcWl79Eomk4UghWHLZYDRZ+HWXovW8vVczHeKsVlhflf+87yNg59ToLsxWM8+se4b00nRCXEKYOXhm0zRddSCEwLgvgcLff6fozz+x5OefelOnw6FrV8WWt0cPDJ07o5G27ZIqVBoNdmFh2IWF4TJqFADCasWUnEz53r2Ubd1KaewmTGlplG3bRtm2bWTPmo19TAyuN96Ay7XXttgNVGfPzrzc72Wmb5zOgj0L6OTRiatDrm58R93+A+vfhcIUxYSp+91NnlOQhwMD23ux4UgOP25PYeqIC++xLpFILgxSCJZcNvydkEFhuYkAVwODOng3r7NDf0L2AbBzafL26vs73mdrxlYctA7MHja7aTaPZyDMZgr/WEruooVUJh61lWu8vXC5dhROgwYq29iOF3fsWMnFhUqtRh8aij40FNfrrlNiQicnUxoXR/E/ayiNjaU8Pp7y+Hgy33gT55Ej8XpoMnbtmujQdhrXtbuO/bn7+ebAN0zfOJ3vXb+nrVvbxnWis1dMllb+Fza+r5hIaJp+ebu9VxAbjuTww/aTPHFNOBp169t/SySSlkcKwZLLhsVbFVOI23oGNe+iJYSiVQIlLrC9W6O7+Df5X77e/zUAbwx8o+ne79VTslgoWr6cnI/mUnlCyYSnsrPD+ZprcL3xBhz79bvoMrMJIRBWgdUqsFqU5wAanRqNVn1ROJa1FlaLFbPJitUiUKtVqDQq1CrlUaWqP+XohUKlUqEPCUEfEoL7+PGYsrIo+nM5hb/9RsWhQxQtW0bR8uW4jB2D98MPN9tUYmrPqRzKP8S2jG08te4ploxdgl7TSJOGHvfChvcVE6aEXyH6tibPZ3hnX9wddGQUGVl/OJthHaXtuURyOXJxXTUlkiZyPKeUuGO5qFRK0Ptmkbga0neDzhH6Ptzo5gXGAmZsngHAhMgJXBNyTbOmU7RqFdmzZlN5VNH8atzc8Jx0P27jxqFxbp2A/kIIygorycsoJT+9jPyMUkryKzCWmDCWmpTHMhOcJQCjWqtCq1WjM2jRGzTo7bXY2WuVR0cdBkctBkcddg6nnhucTr1Wa1on9q0QAnOltcY6jSUmKsrMSlmpiYpS5XVluZmKcjOVRguV5WbMlRYs5lM3BHWh0alta7WvOlx9HHD3c8DdzxE3Pwd0+gsbukvn44PnvRPwvHcC5fsSyJk/j5LV/1D0x1KK/lyO6w034P3EE012VNSpdbw7+F1u/uNmEgsSmbd7Hk90f6Jxndg5Qb+HYc3rsOE9iLoFmhgf2U6r4ebugSzcmMT3W5OlECyRXKZIIVhyWfDDdkULPKiDN4HuzXBkEULxNAfoNREcGx8t4a2tb5FTnkNb17ZNc/SpwlJQQMaM1yhavhwAtasrnvfei/t//nPBU+UKIchLKyXlQB4nD+WTcbSQirLa4cYag9UsqDRbqDRaaHj6iVPo7DTYOVQJzQ5a9AYtOjuN7dDaadBo1Wi0KjRaNWqNWpGJzkiWYTELLBYrVrMVi1lgNlkxVVgwV1gwVVioNJ4mzJabqSgzY7Wcv/DqFpOV0oIKSgsq6q6gAjcfBwIj3Ans5E6bcHcMjhcu/ah9VCRBH32kCMMffkjJunUU/vILxf/8g99LL+I6ZkyT+vW09+Slvi/x5NonWbRvEVcFXdX4RBq9H4DYDyH7IBxcBp2bHonl9l5BLNyYxD8Hs8gqNuLjLGO+SiSXG1IIllzyWKyCn3cqERFu79lMh7jjG+DkVtDYQb/HGt38n+R/WJ60HLVKzesDXsdO07QIFSUbNpA+/QXM2dmg0eA5cSKeD0y6oJpfU6WFlIQ8jsZnkXIgn/KimtnjVCpw8bbH3c8RD38HnD3tsXfW2bSYBkddlfCpQqVWoVarQIDFrJgCWMxWLFUCZ7WAWWk0U1FqpqLMhLHMrGhYq7SrxjKzTcMKYKoSUsmvR1g8z6g1qtO006c01QYHHXaOWuwcdDbNdrWmW1ctmFeZhKg1qiqzEarMRqyYjBablrm8xERZUSUFmWXkp5eSl1FKRamZgswyCjLL2Lc+FVTgE+xMSBcv2nXzxiPA8YKYU9hHRRK0YD7l8fFkvPY6xoQE0p56mpJ//sH3xRfRurs3us+rQ65mdNholict57+x/+WH635o3G/I4Ap9HoT17ygmTZ2uq3nT0wjCfZ3pFuzGruQCftuVygODm2//LJFILi6kECy55IlNzCGzqAJXex3XdG7mtmXsHOWx+93g7NuopvnGfGbEKWYQ90be26R0sNayMjLfeYeCxUsA0IeFEfD2TOyjoxvdV1MwlppI3p/LsZ3ZnEjIxVxptb2n1asJaO9Gm47uBEa44xHgiFbX+G15jU6NvunZbbFarFRUaWSrNbMVZWZMFWabYGwyWjBVmR5YzVYsFisWk6CuBJkaTZWmWKtWnuvUVdpkLTqDBp1ejb5KoLVz0Nq0zzo7zXkRNu2dwMWr7hMkhKC82ETGsUJOHszn5ME88jPKyDpRTNaJYrYtS8LVx5523XxoG+ONT4gzqvPs1GUfE0Po4u/Jmb+AnPnzKVr+F2XbtuP/xus4DR7c6P6m9Z7GlvQtHCs8xsfxHzOlx5TGddD3IYj7CDL2QNI6aDu00XOo5rYeQexKLuDnHalMGtS21W21JRJJyyKFYMklz087FC3wDTEBzUtzmn0IElcBKujX+Hi+b215izxjHu1c2/FwTONtic05OaQ8OBljQgIA7v93Nz5TpqC2b4bEeA6sVkHW8SKS9+eRsj+XzKSiGpmTnT0MtO3uTVgXL/zauqLRtY4d7umoNWrsnfTYO53flMQXIyqVCgcXPW1jvGkbo0RAKcmvIOVALsfic0jZn0dhVjk7/z7Bzr9PYHDUEdTZg+DOHgR19sDRtZmxs+ubl06H92OP4jR0CGnPPU/lsWOkPPAgPs8/h+eECY3qy83gxkv9XuKJf5/gi4QvuCr4Krp6d214Bw4eEHMXbPsU4j5ulhA8JtqfV5cmcCizmH2pRXQJdG1yXxLJxU5qairPPfccf/31F+Xl5YSHh7Nw4UJ69OhRb5t169YxdepUEhISCAgI4Nlnn2Xy5KZnV73QSCFYcklTWG7i74QMAG7t0UyHuM3zlMeI0eDRuBBNq0+s5q/jf6FRaXh94OuN9myvPH6c5EkPYEpJQePuTpv3/4djv36N6qMhCCEoyCxTtIiH8kk9lF/Lttfdz4G2Md606+6DV5CT1H5d5Di529GpfwCd+gdQaTRzYl8uR3dmk7w/F2OpiSPbMjmyLRMAjwBHgjp6ENjRnYBwN/SGlr0E2HfpQtgvP5P51kwKliwha+bbmDMy8Xn2GVSNcFK7KvgqxrQdw5/H/uTF2Bf5YewPGLSNsMnt+xBs+wyO/A05R8CrQxNWA672OkZG+vHH7jR+2pEihWDJZUt+fj4DBgxg2LBh/PXXX/j4+HD06FHczhITPCkpidGjRzNp0iS++eYbYmNjefjhh/H29uaWquQ8FztSCJZc0vy5J50Ks5VwXye6tGnGBao0F3Z/rzxvpBa4zFTGm1veBGBi1ESivKIa1b58zx5SHpyMJT8fXVAQwZ9+0qLZuSrKzZw8kMeJfbmkHMij5AwbWr29lqBO7gR39iSoswfOHk13ABJCUF5USGFWJkU52RhLiqkoK6WitISKsjJMFUasFgvCasVqVR5VajVqjRaNRoNKo0Gr06HV69HqDejs7NDq9ejsDOgMhrof7eyUenZ2qNUtGzVBCIHFbMZcUYGpwqgcxqrHigpMxnJMFRVUGsur6lRgNlVirqjAXFmB1WLBarFgsViwmpWbDbVGU7VmDWqNBjsHR+wcHLBzcMLO0RFHVzdcvH1x8fFBp2+c5lZv0NKhpy8devpisVjJPFZE8v5cUvbnkZVcTF5aKXlppexek4JKrcI31IWQKA+CIz3xDmoZ0wm1wYDfKy+jDwok673/kffFF5izMvGfORO1vuE3h9VmEUmFSXye8DkPdX2o4ZPwbAcRo+DQctj8MYz9oAkrUbilRyB/7E7j991pTB/TqXm7TRLJRcrbb79NUFAQn3/+ua0s9BzXofnz5xMcHMysWbMA6NSpE9u3b+e99947qxCckJDAs88+y4YNGxBCEBMTwxdffEG7du2YMGECBQUF9O7dm9mzZ1NRUcGUKVN44YUXmDZtGgsXLsTBwYEZM2YwcWLTMrmejhSCJZc0P+1QokLc0j2weRrLHYvAbAT/rhDSv1FNP9v7Gdnl2QQ6BfJg1wcb1bZ47VpSp0xFlJdjiFQcjbReXo3qoy6KcspJ3JHFiX25ZBwtxHpaSC61VoV/W1cCqzSCPiHOTQo3VlleRuaxRNITD5Nx9DB5qScpzM7EXNE6jmqgCJhavR6NTo9Wp0et1aBWa2wCp0qlhtO/JgKsVkuVsGrGarFiNZswV1ZiNpkwmyqhDjviC4WjmzuuPn74hLXFr104fu3D8fBv0yCtqkajJqCDGwEd3Oh7QzvKiys5eUjZATh5II+iHCMZxwrJOFbIlj+SsHfRE9LZg7Cu3gRHeTTJ3rsalUqF5/33o/XxIW36CxQt/wtzTi6BH33Y4OyFrnauPNf7OZ5Z9wyL9i7ipvY34efo1/BJ9H1YEYLjv4erXlTMJJrAwPZe+LrYkVlUwZoDWYzq4t+kfiRXLtVhFS80Wn3D47H/8ccfjBw5kttuu41169bRpk0bHn74YSZNqj9ZVFxcHCNGjKhRNnLkSBYuXIjJZEKnqx21JjU1lcGDBzN06FDWrFmDi4sLsbGxmM2ndiTXrFlDYGAg69evJzY2lvvuu4+4uDgGDx7Mli1bWLJkCZMnT2b48OEEBTXPGV4KwZJLlmPZJexMLkCtgpu6tWl6R+YK2Pqp8rzvI43yJk8pTuHLhC8BeKbXM43yZC9Zv56Tjz4GZjOOgwYROOuDZmV6KyuqJHGHsvWdcayoxntuvg6ERHoSHOmBfwe3JsWZFVYraYcPcmRbHMfjd5CbmlK3gKhS4eTugYu3L/bOLhgcHRVtp6MjOjtDlSZUg1qtRqVWK1phi9mmLbWYzTZtqqlKo2rTwFZpYStt2lhjDaHbarFQWV4O5eWNXt+5UGs0pzTPBgM6O3t0Bjt0BvsqbfQpzbVWb0Cr06HWaqsEcC2aKg2i1WpFVGuIzWYqy8uoKCvFWKpozEvy8yjMysRkLKe0IJ/SgnzSDh8A/gRAb+9AQEQn2vfsQ7uefXFyb5hwZ++st2mJQblRSt6fR3JCLicPKtE/Dm7O4ODmDPT2Wtp28ya8py9tItyaHJPZ9frr0Xh6kvr4E5Rt3UrKpAcI/uLzBtu5jwwZyfc+37Mzayfv73ifdwa/0/DBQweCX7TiILd9EQx+uklr0KhV3Nw9kHlrj/LzzpNSCJY0GnOllU+eWHfBx31g9hB0dg37rz927Bjz5s1j6tSpTJ8+na1bt/L4449jZ2fH//3f/9XZJiMjA1/fmg7kvr6+mM1mcnJy8Pev/VuZO3curq6uLF682CYkh4eH16jj4eHBnDlzUKvVRERE8M4771BWVsb06dMBmDZtGjNnziQ2Npbx48c3aH310epC8Mcff8y7775Leno6kZGRzJo1i0GDBtVbvyFG2LNmzWLevHkkJyfj5eXFrbfeyltvvYXBIOM8Xk5Uh0UbEu6Nj0szPtt9v0BJJjj7Q+RNjWr6/vb3qbRW0te/L8OChjW4XfmePZx84kkwm3EZPZqAt2eiquOu+VwIIUg7UkD86hRO7M05JZOqoE24O+26eRMc6Ymrd9Oc66xWC8l7d3NkyyYSt2+mrLCgxvvOXt74V2kovUPCcPXxxdnLB61Oh6XUhKWwAktRpfJYWIG1xIS11IIwmrEaLYgKM6I6eYTFqjxWK0xUVQcqVGrATo3KXgVVGdbQqFBp1KBRgUogEIjqRwQCKwIB1a+FqHpdE5VKXTWUGlWNZ2pUQrkhUgk1WAVYBcJSNVfLaa+NAlEuTiUHqX6sCg9X/ajSa1DbaVAZtKgNGtT2WtQBerSudmhc9Whc7dC42oFGhbGkmKLsLPLSU8k8eoSMo4fJPHaUyvIyjsfv4Hj8DlYvnId/hwg69OpHRP9BuHg1PDqKi5c9UYPbEDW4DRazlfTEAo7vzeXozixK8is4uCmdg5vScXDREzm4DV2GtMHeufHOiE4DBhDy9VecuHci5bt3kzplKoEffdigDIcqlYrnez/P7ctu56+kvxgfMZ7uvt0bNrCqysH11weVm9z+j4O2ac6Ut1QJwf8eyia7uAJv5/PjYCiRtBZWq5WePXvy5puKaV+3bt1ISEhg3rx59QrBUDu7ZXUEnvo00PHx8QwaNKhOLXE1kZGRqE/b7fL19SUq6pSZoUajwdPTk6ysrHMv7By0qhC8ZMkSnnzyST7++GMGDBjAggULGDVqFPv37yc4OLhW/YYYYX/77bc8//zzLFq0iP79+3P48GEmVHknf/BB0+3CJBcXFqvgl52pANzaoxnbIULA5rnK896TGnWR3JK+hdXJq9GoNDzb69kGbztVJCWR8uBkRHk5jgMGEDDzrUYLwFaLlaO7solflUzWiWJbuU+oC+G9fGnf06dZkQDy0k6SsO4f9q9fQ0lerq3czsGRtt170a5nXwI7ReLo5o6wCkyZZZhOFmPaXkp+5kFMGaVYS0xNHr+lscnTdWKxPRO1SloBtQqtlz06Pwfs/RwJ9Ysi/Jb+aBx1WC0WclJOkBS/g6PbNpOeeIj0wwdJP3yQ9d99QXBUV6KGXE373v3Q2TX8xlCjVVeZx3gw4Jb2pB8t4PDWTBJ3ZlFWVMm2ZUns/PsEHfv5E3N1EG6+jUtIY+jcmaB580i+915K1q4l/eWX8X/99Qb9Zjp5duLmDjfz85Gfmbl1JovHLkataqBmOvJmWPUylGQoqZS73t6oeVfT3seJmCA34lMK+D0+lfsHNc5xVnJlo9WreWD2kFYZt6H4+/vTuXPnGmWdOnXi559/rreNn58fGRkZNcqysrLQarV4etadaMq+AbtAZwrIKpWqzjKrtfkmJq0qBL///vvcd9993H///YCiwf3777+ZN28eb731Vq36DTHCjouLY8CAAdx5552AYth9xx13sHXr1guzKMkFYdPRHNILjbja67i6UzNiAx/fABl7QWsPPe5tcDOz1czMrTMBGBcxjg7uDfM+N2dnk3L/JCz5+RiiogicMxtVI5yFhBAc2ZbJ5t+PUZxrBJS4ux37+dP1qkDc/ZpuTlFakM+RLZvYv/Ff0g8ftJUbnJwJ7zuADr37ExTZBY1WhznPiPFwPrmJB6g4Woi1tG6BV+2kU7SbLlVaTiedTQuqsqt61KpPaUzVqhrOWbb0wtYqbbFNE1v12qxoj23aWWvVe1VaWoRQtONVbetEpYKqTHIqtUp51Jymca5D86yylalt81YkbVXNhHTV2mKrQJitCJO1hhbcWmY+pSmvehQmK+asMsxZZZTvyamaI+gCnLBr54ZLe3d6jb6ZPjfeRnFeDke3beHw5o2k7N9L8t54kvfGo7d3IKLfQCL6DSYosgtqTcPNX1RqFQEd3Ano4M6g28M5tiubXauSyU4uJmF9KgkbUmnXzYf+N7erN55xXTh070abD97n5KOPUfjzL2h9fPB5omGpkR/r9hh/H/+bA3kH+C3xN27ucHPDBtXqlZvbNa8psYOjxzU5ecatPQKJTyngx+0nuW9gmIyaImkwKpWqwWYJrcWAAQM4dOhQjbLDhw8TEhJSb5t+/fqxdOnSGmUrV66kZ8+e9Wp6o6Oj+fLLL+u1Gb7QtJoQXFlZyY4dO3j++edrlI8YMYJNmzbV2aYhRtgDBw7km2++YevWrfTu3Ztjx46xfPly7rnnnnrnUlFRQcVpdoVFRUX11pVcHFTHBr6+awCGZjjwEPex8hhzZ6McZ346/BOJBYm42rnySEzDoklYSkpIfuBBTKmp6IKDCVowv1E2wDknS9iw5DBpRwoAMDjp6DI0sMnb1ABlhQUc2bqJQ3EbObl/H0Iod9YqtZqwmB5EDr2Gtt17K+YNxZWUbc6ibGcmprSaiY5VOjX6IGd0/o7o/JRD6+uAugm2x1cyQggshZWYMkoxZZRiziilMq0Ec1Y5ptQSTKkllKw/iUqnxj7SE4fuvnQdPpqYkWMozMpk//o1JKxbTWFWJnvXrGTvmpXYO7vQoU9/wvsOJKhz4wRijVZNh6qdhbQjBcSvSraZTBzfm0OPa0PoNiK4wU50zlddhd8rL5Px0svkzpuP1tsbjyqFxdnwtPdkctfJvLf9PWbvnM3wkOE46xuYPbHnRFj/nmIbfCJWsRVuAtdFBzBj2X4OZRaTkFZEVHOi0UgkFxlTpkyhf//+vPnmm4wbN46tW7fyySef8Mknn9jqTJs2jdTUVL766isAJk+ezEcffcTUqVOZNGkScXFxLFy4kO+//77ecR599FE+/PBDxo8fz7Rp03B1dWXz5s307t2biIiI877OM2k1ITgnJweLxVKnUfWZ6vVqGmKEPX78eLKzsxk4cKDikWk289BDD9UStk/nrbfe4tVXX23+oiQXhCKjiRX7lO/ILc2JDZx3DA6vUJ73bXj4pcKKQj6K/wiAR2IewdXu3BdDYbWS9tTTVBw4gMbTk+DPPkVbz3bRmVSUmdi6NIm961IRVoFWp6bHqFBirglC2wQhszgvhyNb4jiyNZbUA/ttgi+Af/sIwvsNpOOAITi5eyAsVsr351KwIwvj4bxT9rpqFfpgZ+zauWFo74Y+yFnR6EqahUqlQutmh9bNDvuOp27KLEWVVBwtwJhYQMXRAiwFFZTFZ1MWn43aRY9DjA+OvXzpd+sd9L35dk4eTODgxnUc3rqJ8uIi9qxewZ7VK7B3dqF9r7506DOA4KhoNNqGaWJUKhVtwt1pE+5OzskSNv5wmNTDBWxdmsTBuHQGjgsntItng7Sj7uPGYc7OJufDj8h87XX0ISE4DRhwznZ3dryTnw7/xPGi4yzYvYCnezXQ0c3BA2LuUJzjNs9rshDs6qBjRGdflu1J56cdJ6UQLLms6NWrF7/++ivTpk1jxowZhIWFMWvWLO666y5bnfT0dJKTk22vw8LCWL58OVOmTGHu3LkEBAQwZ86cs4ZH8/T0ZM2aNTzzzDMMGTIEjUZDTEwMAxrwH3A+UIm68oheANLS0mjTpg2bNm2i32lJAd544w2+/vprDh48WKtNeHg49957L9OmTbOVxcbGMnDgQNLT0/Hz82Pt2rWMHz+e119/nT59+pCYmMgTTzzBpEmTePHFF+ucS12a4KCgIAoLC3FpYDgfyYVjybZknvt5L+28HVk9dUjTtyVXvgib5kC7q+HuXxrc7IMdH7Bo3yLau7Xnx+t+RKs+971k7sJFZL37Liq9npBvv8W+S8NiCacdKWDlZ/soLawEoF13bwbc2qHRsXxL8nI5FLeBQ3EbSD9Sc8vLt20HIvoNJLzvQFx9lJtMS0klpVszKN2cjqWo0lZXH+SMQ3cf7KO90Ti2/lbWlYgQgsqUYsp2ZVG+OxvraclO7MLdceofgCHcHZVahdViITlhD4fjNnBkaxzGklP243aOjrTr0YeO/QcT3CUGTQMc1U6fQ+KOLGJ/SqS0QPnvDO/ty9C7OjZo21cIQfp//0vhz7+g8fQk7Ndf0Pmc26xpw8kNPPzPw+jUOpbfvLzhIdOyDsDHfUGlgSn7wCWgYe3O4N9DWdz7+TY8HPVsmX41uiZGzZCcP4qKinB1dW2167fRaCQpKYmwsDDpjN+KNPRzaDVNsJeXFxqNpk6j6jO1vdU0xAj7xRdf5O6777bZGXfp0oXS0lIeeOABXnjhhRoeh9XY2dlhZye9fS8Vft2lOMTd3JzYwCYj7PpGed7rvgY3yynP4fuDylbPE92faJAAXB4fT1aVU6bv9OkNEoCFEMSvTiHu16MIq8DN14HB48MJ6tRwkw1jSQmHt8RyaNM6khP21ghnFhDeiQ59+tOhd3+b4CuEoCK5iNLN6ZTtyQazUl/tpMOxpx8OPXzQeTfOIUrS8qhUKuyCXbALdsFtTFuMh/Io3ZaJ8VAeFYfzqTicj9bTgGO/ABy6+RAa3Y3Q6G5cfd/DnNy/jyNbYzmyNY6ywgL2r1/D/vVrsHd2IbzfIDoOGEyb8E7njEOsUqno0NOXkChPdvx1nF2rUji8NZPslBJGPRh1Ttt0lUqF34svYty7j4rDh0l79jmCF36G6hymGgPbDKSXXy+2ZWxjwZ4FvNzv5YadNJ9OEDJAMYfY8SUMm3buNnUwqL0XXk525JRUsP5wNld3qvtaJZFILg1aTQjW6/X06NGDVatWcdNNp8JSrVq1ihtuuKHONg0xwi4rK6sl6Go0GiU8UisGvZe0DKkF5Ww+lgfAjc2JDbz/NyjPA5dA6DCywc0W7l1IubmcLl5dGBJ4bm9fS2EhqVOfArMZ51HX4nb7uHO2qSg3s+bLAxyLzwaaoGE7cpD4lcs5HLcBy2kByAPCOxHRfzAd+vTD2eNUQg5LqYmynVmUbs/AnFlmK9cFOuE0oA0OXbykqcNFikqrxj7SC/tIL8y55ZTEpSufY66RwmXHKPwrCftITxx7+WHXzo2Q6BhComO4auJk0g4e4NDmDRyK20h5USG7V/7J7pV/4ubrT9fho4gceg32zmfXpOkNWvrd1J6QKC/+/mwf+eml/PjWdobd3dEWj7g+1AYDbWZ9QNKtt1G2eTM58+fj/cjZ7etVKhWPxjzKPSvu4bcjvzExaiJBzg2MDtPrvioh+AslZrCm8TsZWo2a67sGsCg2iV92pUohWCK5xGnV6BBTp07l7rvvpmfPnvTr149PPvmE5ORkW9zfphhhX3fddbz//vt069bNZg7x4osvcv3116NphEOI5OLktyotcJ8wD9q4NS32LQDbFiqPPSaApmE/g8zSTH449AMAj8Y8ek4ttBCCtBdewJSWhi44GP/XXjtnm9y0Ev6at5fC7HLUWhWDxoUTOSjgnO1MRiMHYtcSv3I52ceP2cq9gkPpOGAIHfsPtml8QYlaYEzMp2x7JuX7c8FSdYOoVePQxQvHfv7YBUtToEsJrac9bmPb4jI8hLJdWZRuSceUXkr5nhzK9+SgcbfDsYcvDt190XoYCOwcRWDnKIbd8wDJe+M5uGk9R7ZuoiAznXXfLCJ2yTdE9B9MzIjR+LUPP+vYAR3cuP2F3qxcuI/UQwWs/CyBjKOFDLitA+qzpGK2a9sW/5dfIu2558mZ+zEOPXvh2Kf3Wcfq7tudAQEDiE2LZcHuBbw+8PWGnaCO14GjjxIu7dBy6Fy3suVc3Ny9DYtik1i1P5MiowkXgzQLkkguVVpVCL799tvJzc1lxowZpKenExUVxfLly20hOZpihP3f//4XlUrFf//7X1JTU/H29ua6667jjTfeuODrk7QsQojTTCGaoQVO3wMnt4JaC93rDwJ+Jp/u/ZRKayXdfbrTL6DfOevnf/MtJav/AZ2ONu+/j8bJ6az1s5OL+WN2PMZSE84eBkY+EIVv6NkFUVNlBbtXLmfrbz9SXqxENdHq9EQMGEzM8NrCiymrjLIdmZTuysJ6mq2vro0Tjr18cejqg9q+1XPoSJqB2k6DU19/nPr6U5laQum2DMris7DkV1C0Opmi1cnYtXPFoYcv9lFeqPUaQmN6EBrTg6snPsSB2HXEr/yT7OPHSFi3moR1qwnuEsOAcf8hILxjveM6uOi5/vEYti5NYseKE+z59yTlJSauntAJzVlsZ11vuIHSzVso/PVX0p5+mrDffj2n0+gjMY8QmxbL0mNLua/LfYS5hp37xGj1yu99w3uw7bMmC8GRAS6093EiMauEFXszGNereWlbJRJJ69FqjnEXM61tWC+pm32phYz9cCN6rZrt/72m6RqYpU8oW6KRN8NtnzeoSVpJGmN+HYPZambRyEX08ut11vrGQ4c5fuutCJMJ3+nT8fi/u89aPyOpkKVzdlNZbsYn1IXrHu2Kwan+9VnMJvb+s5LNvy6hNF8xD3H19SNmxBhlG9vpVPgoYbJQtjeH0i0ZVJ44Ff5P7aDFIcYHh56+6APOLqBLLm2EyUL5vlxKd2RSkVhgK1fZaXDo5oNjb78a3wHFrOYQ8Sv/5NCmDVgtillN2+69GHD73fiEnj1ZROKOLFYtSsBqEbSN8WbE/ZFozmJSYy0rI+m2cVQePYrTkCEEzp93zt2Px/55jLUn1zIqbFTD0ykXpMDsaBBWeGQbeJ9dw10fc/9N5N2/D9G3rQeLHzj3DbHkwtHa12/pGHdxcNE7xkkkjaU6Q9zwTr5NF4CNhbBHMWlojEPcgj0LMFvN9PHvc04BWFgspL/0IsJkwmnYMNzv/s9Z66cdKWDZR7sxVVjwb+/K2Ee6oj+LNjZx+xb+/WIBRdlKykhnL2/63XoHkYOvrhED1pRVpkR42JGJKK+yDVaDIdwDx56+GDp6SFvfKwSVThF2Hbr5YM43KrsBO7Ow5Bkp3ZxO6eZ09EHOOPbxxz5a0Q4HhHckILwjA8b9h7ifv2f/ujUc27mNYzu30XHAEIb+3/04urnXOV77Hj5odWr++mQvx+Kz+WvBXq59IKreeMJqBwfafPA+x2+5lZJ16yhavhzXMWPOuqZHuj3C2pNrWZG0gkldJjUsYY1bEIRfq5hDbF8Eo2aeu00d3NitDe/+fYjNx/JILShvnmmWRCJpNeQVUHJJYLZY+WN3GgA3NcchbvcSMJWBd0fFW7wBJBcl83vi74BiC3wu8r/7HuPuPaidnPB75eWzarRSDuax9MN4TBUW2kS4c91jMfUKwGVFhfw5511+f/c1irKzcHRz56qJk5k46xO6DBthE4ArjheS80UCme/voGRjKqLcjMbNDpfhIfg/3xuvCZHYR0lntysVrbsBl2tC8Hu6J173R2HfxQvUKipTisn/6TAZM7dStPoElqosgK4+vlz70JNMeP9jOg4YAioVB2PX8cVTD3Ngw7/1OhyHRnsx9uGuaHVqTuzN5c+5ezBV1J+Q2hAejufkBwHIfPMtLAUFZ11HR4+ODA8ZjkDwcfzHDT8BPatufuO/g8rSs9ethzZu9vQJUyK1/B6f2qQ+JBJJ6yOvgpJLgo2JOeSUVODuoGNwuHfTOhFCsQUE6HV/g9Onzts9D4uwMKjNIGJ8Ys5a15SeTnZVODSfp6aiqyfcHygmEH/O3YO50kpwpCdjH4muMwKEEIKDm9bzxdSHOBi7DpVKTa/rb+G+OZ/SbeRYtDodwioo359L1rzdZM/fg/FgHqjA0MkDzwmR+D3bC5erg9G4yFCAEgWVWoWhvTued3XCf1pvXK4NReNuh7XMTNHqZDJmbqVg6VHMBUp6bo+AQMY8/gz/efMDvEPCMJYUs/yj//HbOzMozsupc4ygzh6MfawrOjsNJw/ms2LBXqwWa511ATwnTULfrh2W3Fwy3333nGt4uOvDqFCxOnk1+3P3N2zh7a4C91CoKIR9PzesTR1U+yX8ujNVRh6SSC5RGi0Eh4aGMmPGjBoOaxLJ+abaIe66rgHom6rBPBELOYdA5wjRtzeoSUpxCsuTlgPK9uvZEEKQ8eoMrGVl2Hfrhtvt9Y9RlFvO8o/3YDFZCYnyZPTkLnVmf6ssL2PZBzP5c/Y7lBcX4RUUwp2vv8fgu+5FZ6fYORkTC8j6cBe5X+1XbH41Khx7++H7VE+87onEvqMHqrN46EskGmc9LkOD8Hu6Fx53dEQX4IgwWSmJTSPjne0U/HHUphn2bdueu978gAG3341Gq+XYzm18MfVhDsVtrLPvNuHuXP9EDFq9muT9eWz84Ui981Dr9fi/NgOAwp9/oXTzlrPOu717e0aFjQLgs72fNWyxarWSShmUm+ImCrCjuvij16o5klVCQlrRuRtIJJKLjkZLE0899RS///47bdu2Zfjw4SxevLhGtjWJpKUpqTDzd4KSJKVZphDbFymP0beBoWEOE18mfIlVWBnQZgCRnpFnrVv899+UrF0LOh3+r82oN+FAZbmZP+fuobzYhGegk+I0pKtdtzArg+9ffIbDW2JRazT0u/VO/jNzli3igymnnJwvE8j5bC+m9FJUdhqchwTi/1xv3G/ugM5L2ilKGodKo8Khqzc+j3XDa2IUdu1cwSoo2ZRGxnvbKd6YirBY0Wi19L35dv4zczZ+7cOVm7VZM4n94RuEtbam16+tK8MnRoIK9q5LZc+/KfXOwaF7d9zuGA9A+ssvYTUazzrnSV0mAbD6xGqOFx5v2EJj/gMaO0jfDWm7GtbmDFwMOoZXxQmuvkmXSCSXFo0Wgh977DF27NjBjh076Ny5M48//jj+/v48+uij7Ny583zMUXKF8/e+DIwmK2FejsQEuTWtk7I8OFCVaKXHvQ1qklOew2+JvwFwX9TZnegshYVkvK6E4fOaNAm79u3rrGe1WPn7swTy0kpxcNUz5uFo9IbaNsAp+/fyzfSp5KScwNHdg/GvvkP/2+5Eo9VhrTBTsOwYmR/swHggD9Tg2M8fv2d74ToqDI2LvkHrk0jqQ6VSYQh3x3tSNF73R6Hzc0CUmylcdozMD3ZSflCJSOIVFMIdr71Lj7FKwqPNPy9m6QczMdUhuLaN8abfje0A2PjDEY7vrduEAsBn6lS0Pj6YTiSTM2/+Wefa3r09QwKHIBB8kfBFwxbo6Amdr1ee7/q6YW3qoPqm/Pf4NMxnMfOQSC52QkNDUalUtY5HzpHAZt26dfTo0QODwUDbtm2ZP//sv9eLjSbbBHft2pXZs2eTmprKyy+/zGeffUavXr3o2rUrixYtkjZSkhbjtyrHkxtj2jQ9TfKeH8BSCX7REBDToCbfHfiOCksF0V7R9PTteda6Wf97H0tODvqwMDwffKDeerE/JZKckItWp2bMw9E4e9QO3bJn9Qp+ev2/GIuL8G3bgbvefB//DhGA4vSWOXsXJRtTwSIwRLjj+2QP3G9oj8ZRBu2XtDyG9u74PN4dt5vbo3bSYc4pJ/eLBPJ/OYK1woJarWHo3fcx8qEn0Wi1HNm6ie9fesYWveR0uo0IplN/f4SAlQsTyE0tqXNMjbMzvi/+F4DchQsxHj581jne10W5Sf3j6B9kl2U3bGHVMcL3/tRkB7khEd54OOrJKalgY2L9Qr1EcrGzbds20tPTbceqVasAuO222+ptk5SUxOjRoxk0aBC7du1i+vTpPP744/z8c9Nt7S80TRaCTSYTP/zwA9dffz1PPfUUPXv25LPPPmPcuHG88MIL3HXXXS05T8kVSlaRkdiqi8uN3QKa1okQsPNL5XkDk2OUmkpZfGgxABOjJp5V+DYeOkTBjz8C4D/jVdR2dTuf7Vufyp5/TwJwzcTO+ITUNMkQQrBx8Ves+vQjrBYLEf0Hc/urM3H28EJYrBSuPE72gj1Y8oxo3OzwujcSr3uj0Pk4NGhNEklTUalVOPX2x+/pnjgNbAMqKN2aQdacnVQkK/awUUOv4baX3sLB1Y3sE0l8+8JUck/W9B1RqVQMuTOCNhFumIwWls3dTXlJZV1D4jJ8OE7XXA1mM1nvnN1JrptPN7r5dMNkNfH1gQZqdkMGgnsYVBTB/t8b1uYMdBo1Y6P9AfgjPq1JfUguf4QQmIzGC340Rhnp7e2Nn5+f7Vi2bBnt2rVjyJAh9baZP38+wcHBzJo1i06dOnH//fczceJE3nvvvbOOlZCQwJgxY3BxccHZ2ZlBgwZx9OhRACZMmMCNN97Im2++ia+vL25ubrz66quYzWaeeeYZPDw8CAwMZNGiRQ1e29lodJzgnTt38vnnn/P999+j0Wi4++67+eCDD+jY8VQmoREjRjB48OAWmaDkymbZnnSsAmKC3AjxdGxaJ6k7IWs/aA3Qpf672tP56fBPFFcWE+oSyrDgYfXWE0KQ9fY7IATO116LQ6+6YwjnppXYHIL63tiWdt18atXZ9ON3bPlViWE8YNx/6HPz7ahUKkzZZeQtOYTppKI1c+jug9v17VDXYUYhkZxP1AYtbmPbYujkQf4PhzDnGsmevxuXq4JxHhZMm4hO3PXm+/z29gyyk4/zw4zp3P7KTDwCAm19aLRqrn2gCz+9vZ3CrHL+/fogoyZ3qfNG0/fZZylZt57SjRsp2bARp0ED653bxKiJPLbmMX489COTukzCWe9cb11lMWrofjf8MwN2fgUxdzbpnNwQE8BXcSf4OyGD8koL9nU4uEqubMwVFcy559YLPu7jX/6ErgkJOyorK/nmm2+YOnXqWRVAcXFxjBgxokbZyJEjWbhwISaTCZ2u9u5kamoqgwcPZujQoaxZswYXFxdiY2Mxm822OmvWrCEwMJD169cTGxvLfffdR1xcHIMHD2bLli0sWbKEyZMnM3z4cIKCmpexsdGa4F69enHkyBHmzZvHyZMnee+992oIwACdO3dm/PjxzZqYRALwe1Vs4BtjmqgFhlNa4M43gL3bOatXWir5KuErAO6Nuhe1qv6fSenGjZRu2gQ6HT5PTa2zjtlkYdXCBCxmJRJE95Ehteps/nkxm3/+HoCh/3c/fW8Zj0qlwng4n6yP4jGdLEFlr8Xjzo54jIuQArCkVTG0c8P3ie7Yd/UGKxStTibni31YjWZcvHy47aU38Q4OpaywgB9nTCc/o6aW1OCoY+SkKNRaFUm7c0jYULcWVR8cjMedinCa9c47CEv9cYYHBw6mnWs7SkwlLDm0pGEL6XonqDSQHAfZZze5qI/uwe4EuttTWmnhn4OZTepDIrmY+O233ygoKGDChAlnrZeRkYHvGWFAfX19MZvN5OTUbR40d+5cXF1dWbx4MT179iQ8PJx7772XiIgIWx0PDw/mzJlDREQEEydOJCIigrKyMqZPn06HDh2YNm0aer2e2NjYZq+10VfSY8eOERJS+yJ+Oo6Ojnz+ecPS0Uok9XE8p5TdKQWoVTAmuolCcEXJqVigDTSF+PPYn2SVZ+Fj78PYtmPrrSfMZrLeUdK1etx1F/p67kjjfj1Kbmop9s46rvq/TrXurLf+/hOxP3wDwOC77qXHmBsBZbs5/7cjYAV9mAse4zuidZVxfiUXB2oHHZ53dKSsowf5vxyh4kgB2fP34HlvJPauLtz64hv88Oo0ck8mKxrhl2fi5utna+8d5Ey/G9sR+1MisT8eoU24G+5+tXd7vB6aTMFvv1Fx5AgFP/+M+7hxdc9HpWZil4n8P3vvHR5Xeebv32e6NOq9WNWWZRWrWO69YBt3Y1MMBAhlswR2ScKSbELy23yXbMqySZaQxiYxoYSOC+4N94qrJEuybKtbvffp8/vjaEYeVOwZMLi893XNpfE573nPOyDNec5zPs/n+fHhH/OPwn/wSOojaJXX+Hvxi4SkBXBxO5x9Cxb8l9v/HSRJYkVWFH/cV8In52pY6ul3leC2RaXV8tybH38t5/WEtWvXsmjRIqKirv27/PnrmUOCMVQG+dy5c8yYMWPQLLGDtLQ0FFe5K4WHh5Oenu78t1KpJDg4mIaGgXUH7uJ2JnjOnDk0NzcP2N7W1kZi4vD95AUCd/ikT2M3bVQIob4eBn8FG8DUBUGJ19Uhzma38fp5WWv0SOojaJRDOy20rVuP8dJllP7+hHz76UHHVBQ0k7dX1gHPfTQF7885N5zasoFD774BwPQ1jzJh+WrsNjvtO8tpXS8HwN7ZYYQ+OVYEwIKbEu/sMEL/OQOFjxpzXTcNfzyHqaYLbz9/7vv/fk5QdAxdzU18+NKPBhTLZc6NISYlEIvZxq6+pyWfRxkQQOgz3wag8dXfY+0auohtUcIiIvQRNBuanV0er4nj5vjce2AZXJ98LVZkyS4R+4sbaOvxbA7B7YskSah1uq/85UkheUVFBXv27OGpp5665tiIiAjq6upctjU0NKBSqQgODh70GC+va1t3fj5AliRp0G22QewY3cXtILi8vBzrII+kjEYj1dXCK1Hw5WC32/kkt98VwmPOyLIGxj16XR3i9lXto7yjHF+1L/eOHlrDZe3qpvHVVwEIefYZlP7+A8b0dJj49M0iAMbOHkH82BCX/cXHDnHg7bUATL3vYSbdcz92i42WD4rp7PNR9Z0XS+D9o0WLY8FNjWaEL2HPZKEK88LWYZK7Fha3oA8I5L7/7+cERkbT2dTIul/+FGNPj/M4SSEx77FUdHo1TVVdnPikdND5Ax98EHVsLNamJlpeXzvkOtQKNY+mykHtGwVvYLUNLZ9wkrQAfCKgp0nOCHvA6HBfxkT4Yrba2X6+7toHCAQ3KX//+98JCwtjyZIl1xw7ZcoUp4uEg127djF+/PghM70ZGRkcOnQIs9n8paz3i3LdV9ZNmzaxadMmAHbu3On896ZNm9iwYQM/+9nPiI+Pv1HrFNxhFNR0UNrYjValYEHa0K2Hh6XhAlz5TNb8ZV5f0cs/CmVZwv3J9+Oj8RlyXPPav2FtbkYdF0vgIPp3u93OvreL6O0wERSlZ+qqka5LKy9lx59eASBnyUqm3PsgdouN5n8U0ZvbCAqJwHuT8J8f57ktnEDwFaIK0hH27Sy0if7YTVaa3iykt6gZn8Ag7vuPn+MTGERLdRXb/vBrl4Ya+gAtcx6R60rO7q6kqs+D+GokjYawf/s3AJpf/zvmuqEDzdVJq/HT+FHVWcXBKwevvXClqr8o7oznnsErnZ7BIhkkuDWx2Wz8/e9/57HHHkOlGqiW/dGPfsSjj/bLCp9++mkqKip4/vnnKSoq4vXXX2ft2rW88MILQ57jX/7lX+jo6GDNmjWcOnWKS5cu8fbbb1NcXHxDPtO1uO4geOXKlaxcuRJJknjsscec/165ciVr1qxh9+7d/OY3v7mRaxXcQTguJHelhOOr89D/1mGCP/pu8L12IH2h5QKn6k+hlJSsGTN0Yae5ro6Wv78BQNgLLyBpBkomik/UUZ7fjEIlMf+JNJeWyD3tbWz8n59hMRmJz8ph5jcex26z0/JhMYYLLUhqBSGPp6EfHzFgXoHgZkbhpSLkiXS8MkLAZqf5nSIMJW34BoWw4oWfoFSrKT39GUc/esfluMSsUNJmyPrDvW8VYTYOzOD6LpiPV04OdoOBxld+N+QavNXerE5aDcA7F94ZcpwL2d+Qf17eA+1Xru+Yz7EsU17/ibIWatt7PZpDIPg62bNnD5WVlTzxxBOD7q+traWyst/2MCEhgW3btrF//36ysrL42c9+xquvvsrq1auHPEdwcDB79+6lq6uLWbNmkZOTw1//+tdhNcI3kusujHNoLxISEjh58iQhISHXOEIg8Ayrzc6mPleIFZ66QlhMkCu7LVxvQdy7Re8CMD9uPhH6oQPQpv/7P+wGA145OfjeddeA/YZuM0c+vgzAxKUJhIzozyhbLWY2/faXdDY1EhgZxZJ//T6SpKB13SV685pAKRH8jRR0SYHX+0kFgpsKSaUg6IFkmi12DIXNNL9ZQMhTY4kYNZoF3/pXtv/xtxxf/wEhsQkkT+m3PJt2bxKVhS10Nhs4ubWMqatcuy5KkkT4v/+A8vsfoH3TJoK/9U9oh6hDWTNmDW8WvsmJ2hNcar1EUmDS8IsOHgnxM6D8EJz9B8z+odufOzrAi4nxQXxW3sLm3Bq+NXPktQ8SCG4iFixYMKy38BtvvDFg26xZs9zuFpyRkcHOnTuv+xz79+8fsK28vNytcw6F20LDsrIyEQALbignypqp7zDip1MxKznUs0ku7oCeZlnrN2pgoPp5WgwtbC3dCsDDKUM3ejHX1ND2sew2Efqd5waVKhzbWIKhy0xQlJ6s+bEu+/a98ReqLxSg8fJmxff/P7R6Pe1bSuk5VQ8SBK1JRpcc5M4nFQhuOiSlguAHx6AdFYDdZKPp9QJMtd2kzpzrbLG848//S0N5vwZYrVUy84HRAOTuqRq0m5xXRgY+c+eCzTZsO+UonyjmxswF4N0L717fop0Fcu+ChwU3K/oa+nwiGmcIBLcE1xUEv/rqqxj6esG/+uqrw74Egi+Ko/PSkoxItCoPjefP9V34Mh+QNX/XYN3FdZhsJtKC08gMzRxyXNNf/gJmM96TJqGfOHHA/tqSdgr7PE9nPZiMUtn/J5b36Q5yd28HSWLxv75AcHQMHXsq6Toijw9cPRrvsR4G/QLBTYakVhD8SCqaWF/sBgtNa/MxN/Yw8+FvEpeRjcVoZOP//Izezg7nMfEZISRmhWKz2TnwXjF228CsVMizzwDQsXUrxtLBC+mg/2Z2S8kW2o3t117wmKWg8YW2Cqg86uanlVmcHolKIVFQ08Hlhk6P5hAIBF8d1xUE/+///i/d3d3O90O9XnnllRu5VsEdgNFiZVt+LQDLMz10hehqgEu75PfXURBntpmdLZIfTnl4yEI0c00NbevWAxD6L88O2G+12jjw7gUAxkyNJCopwLmvpeYK+974KwDTH3iEkTkT6TnbQOensr4qYFki+vEeFgAKBDcpCq1Sbu0dqcfWZab5zUIw2Vn6nX8nICKSzqZG9qz9s8sj2On3J6HSKqm93E7RsdoBc3qlpeEzb56cDf7Tn4c8d054DsmByRisBtZdWnftxWq8IV3OUnPuPbc/K0CgXsOs0fKNrGijLBDc/FxXEFxWVub0fCsrKxvyVTrMXblAcD0cKG6kw2Ahwk/HpAQPZQH5H4HdCtE5EDbmmsM/rfiUhp4GgnXBLIxfOOS4pv/rywJPnjxoe+S8vVdoru5Gq1e5uEFYLRa2/+E3WExGYtMzmbjiXkw1XbIPMOA7OwafaV/ABk4guIlxFMsp/bVYmnpp+aAYrbeeJc/9AIVSycVjh7hw5IBzvG+QjknLEgA4uv4yvV0DfXdDryMbLEmSMxv83oX3sNgsg45zIatPClWwQW604wHL++oYNp6rGVZfKRAIvn7c1gQfOHDg2oMEAg9xFMQtzYhEofDQGswphXjwuob/o0i2RXsg+YEhm2OYq6tpW9+XBe67AF9NZ4uBz7aUATB11Si8fPrnObHhQ+pKLqHV67n7me9hN1hp/kcRdrMN7ehA/BYM34FRILjVUfpqCH4kBVQShqIWOvdWEjEyicmrZReWT9f+mY6mRuf4jDkjCB7hg7HbwtH1JQPm06Wmytlgu33YbPDixMUEagOp665jX9W+ay80ZpLcWMfcDUWb3f+gwPzUcLzUSipbesi7ch0yDIFA8LXhdhA8f/58YmNj+eEPf0h+fv6NWJPgDqXbaGFPUT3Qn01xm9o8qD8PSg2kD23T4uB803lyG3NRKVTcl3zfkOOa/vLXYbPARz66hMVoJXKUPylTIp3b6y5f5Ph6WWox78ln8AkMpvm9C1hbDCiDdASvSUbyNNgXCG4hNCN8CVwpOz50fFpJ74UWJq28n8hRyRh7utn55/91+gcrlApmP5QMwIWjtdRebhswn0s2uGRgoAygVWqdTW8cHuDDIkn9Eqrc6yyo+xzeGhV3pcrSJsdNvUAguDlxOwiuqanhBz/4AYcOHSIzM5OMjAxefvllrlzxzFtRIHCwp6geg9lGfLA3Y6MHdmC7LhxZ4OTF4H1tOYXDFm1R/CJCvAZ3PXHJAg+iBa693EbJ2UYkSS6GcwS1ZqOBbX/8LXabjeQpM0iZNouOXRUYL7U5i4YU3l+PN6JA8HWgHx+BfnIk2KHl/QvYWk3c/ezzqLRaKs/ncXZHf/Y1ItGf1GnyDeWRdZcHSAt0qan43HXtbPADyQ+gklScaThDUXPRtReZ+YD8s+wgtFUOP3YIlvd5Bm/Jq8E6SHGfQCC4OXA7CA4JCeFf/uVfOHLkCCUlJTzwwAO89dZbxMfHM3fu3BuxRsEdwua+rMmyzCjPuqRZTJD/ofw+69oFcc29zWwvl9ukDmeL5tQCT5mM9/jxLvvsdjtH18uewCnTogiO7vcEPvjOG7TWXMEnMIh5Tz1D7/kmOvfL7ZADVyehidS79fEEgtuBgKWJfY4RVpreLiQgJIJZ33gSgIPvvkHzlf7Ac+LyRFQaBfVlHZSebRwwV+gzfdngbduGzAaH68OZHzcfkLXB115gLCTMlN/nvu/OR3Myc3QIfjoV9R1GTpYP7IAnEAhuDtwOgq8mISGBH/7wh/zqV79i7NixQi8s8Ji2HhMHLsoXOUcWxW0u75a9gfVhMHLeNYdvvLwRi83C2JCxpIWkDTrGXN9A24YNAIQ+OzALXHqukbrSDlQaBROXJji3VxXmc27nFgAWPvM9NDatsxDOZ3o03llhbn88geB2QFIpCP5GCgpfNZb6Hjp2lJM5fxHxWTlYzWZ2/Ol/sdnkjnF6f63Ta/vYhhKsVlf/3quzwc1//duQ53wwRa4P2F62nQ5Tx5DjnDgkEefeBQ+K27QqJXenyw13hCRCILh58TgIPnLkCM888wyRkZE89NBDpKWlsWXLli9zbYI7iJ0FdZitdsZE+JIU7uvZJG54A9vsNj66+BEA9yffP+S41rffArMZr5ycAVlgq9XGsQ1y9inrrlj0AVp5u8XCp2vlx7MZd91N3NgsWtdfwtZjQR2lx39RvCefTiC4bVD6aQm6T9b8dh2twVjSzsJ/fg6Nlzd1JZfI/3SXc2z2/Fi8fNW0N/Y6PbivJuRb3wKgfcsWzHV1g54vKzSLUQGjMFgNbC65joK31OWg8YHWMqg87sEn7Ld43J5fi9nqWfMNgUBwY3E7CH7xxRdJSEhg7ty5VFRU8Morr1BXV8c//vEPFi1adCPWKLgD2HSVFMIjupvkLnFwXd7Ax2qOUd1Vja/Gd0hbNGtXF63vfwBA8JNPDthfeKiG9oZevHzVZC/o7wx3Zvsmmq9U4uXnz4wHv0nP6XoMF1pAKRF0fzKS8gs9gBEIbgt0owPRT5Kzpa0fX8Tb259pD3wDgMPvvUlPh+ysoNGpnE9ZTm4tw9TranXmlZEhF6taLLS8+dag55IkyXmz+1HxR9e2LtPoIXWF/N7DArkpI4MJ8dHS2mPm8OUmj+YQCL4qLBYLP/nJT0hISMDLy4vExEReeuklbNfonnjgwAFycnLQ6XQkJiby2mtDd3K8GXH7arx//35eeOEFqqur2bp1Kw899BDe3t43Ym2CO4SGTgPHSpqBLyCFyP8YbBaIzILw1GsO/7BY1g6vGLkCL5XXoGPaPvgQW1cXmpEj8Zk9y2WfyWDh5FbZEm3CkgQ0Ojnz3NncxLGP5IvmzIcfR2VW0bZZ9jH1XxCHOkLogAUCB/6LE1EG6bC2GWnbUkrWgiWExiVg6O7i0LtvOMelTI8iINyb3k4zZ3cPLFYLfkq+SW378EOsHYPLHZYmLsVL5UVJewlnGs5ce3GOuoLzG8DU4/ZnUyokloyVg/zNonGG4Cbnv//7v3nttdf4wx/+QFFRES+//DL/8z//w+9///shjykrK2Px4sXMmDGDs2fP8uKLL/Lcc8+xbt11NKe5SXA7CD569CjPPvssISGDV9ILBO6yLa8Wmx2yYgKICfLwhsqRrbmOgri67joOXJH16/eNHtwWzW4y0fLmmwAEP/EEksL1T+Xsrkp6O834h3mROqM/cN//1t8wGw1EjU4hdfocWj++iN1oRRPri8+MEZ58MoHgtkWhVRJ072iQoOdUPcZL7cx7Ui52O79vN9XFspuDUqlgykq5Ac253ZV0txld5tHPnIk2KQlbdzetH3ww6Ll8Nb4sTlgM9N8ED0vsVAiIA1MnXNjq0edzWD3uKqzHYLZ6NIfg1sdut2MzWb/ylzvNWo4dO8aKFStYsmQJ8fHx3HvvvSxYsIBTp04Necxrr71GbGwsr7zyCikpKTz11FM88cQT/PrXvx72XAUFBSxZsgQ/Pz98fX2ZMWMGJX2Frd/85jdZuXIlv/jFLwgPDycgIID//M//xGKx8P3vf5+goCBGjBjB66+/ft2fbTiGF072sWnTJhYtWoRarWbTpk3Djl2+fPmXsjDBnYNDCuFxFrjhAtTmgkIF6fdec/j6S+ux2q2MDx9PYkDioGPat2zF0tCAKiwMv2VLXfZ1txs5t0fORk25ZyTKPnlDee4ZLh4/jCQpmPfkt+k5WY+xpB1JrSDwfuEHLBAMhjbRH59p0XQdrqZ13UUivptD2uy7KNi/h0/X/olv/PIVFEolCVkhRCT6U1fazonNpcx9JMU5hyRJBD35BLU//BEtb71F0GOPodAMbHxzX/J9rLu0jt0Vu/l3w78TpBvGRlGhgIwH4ODLkPcBZAztIz4U2TGBRAd4Ud3Wy74LDSwaG3ntgwS3HXazjZr/OPqVnzfqpalIGuV1jZ0+fTqvvfYaFy9eZPTo0eTm5nL48GFeeeWVIY85duwYCxYscNm2cOFC1q5di9lsRq0eaAFaXV3NzJkzmT17Nnv37sXPz48jR45gsfTLnPbu3cuIESM4ePAgR44c4cknn+TYsWPMnDmTEydO8MEHH/D0008zf/58YmJiru8/xhBcVyZ45cqVtLa2Ot8P9brnnnu+0GIEdx5VLT2cqWxDkmBJhocXiLy+zM+o+aAPHnaoxWZh3UX5Uc0DyQ8MOsZus9H8+loAgh57dMDF9PSOCiwmGxGJfiRmhcrzms3s/bushcq+eylBvpG0b5PlEv6LElCHDC65EAgE4L8wDlWoF7ZOM+2bS5j58OPo9D40VpRxbtc2QA50p66Wm21cOFZHe6OrRMF/8WJUERFYG5voGCJZkxacRnpwOmabmY2XN157YRl93xEle6Grwe3PpVBILM2Uv9eES4TgZubf//3fefDBBxkzZgxqtZrs7Gy++93v8uCDQ3deraurIzw83GVbeHg4FouFpqbBdfB//OMf8ff35/3332f8+PGMHj2axx9/nOTkZOeYoKAgXn31VZKTk3niiSdITk6mp6eHF198kaSkJH70ox+h0Wg4cuTIF/7c15UJvloYfS2RtEDgDlvyagGYnBBMuJ/O/QlsNsiXXR7IGNrlwcGBKwdo6G0gSBfEvNjBbdS6DhzAdLkEhV5PwP2uc3a3GZ0V6hOXJzr9jE9tXk9rbQ36gECm3v8wbR+Vym2RE/3l5gACgWBIJLWSoPuTafjTOXrONeI9IYLpDz7Knr/9iSMfvE3ylOnoAwKJHOlPbFowlQXNnNpewbxHr8oGazQEPfooDS+/TPPa1/FftWqAjAlkN5jzR8/zUfFHfDPtmyikYXJBIaMgOgeqT8t1B1MGtky/Fsszo/i/A6XsvdBAp8GMr040yLnTkNQKol6a+rWc93r54IMP+Mc//sG7775LWloa586d47vf/S5RUVE89thjQ5/jc57+DgnGUF7/586dY8aMGYNmiR2kpaWhuOpvNzw8nPT0dOe/lUolwcHBNDS4f2P6edzWBL/11lsYjcYB200mE2+9NXhlrkAwFF/YFaLiCLRXgdYPkq/tTvJRsRww3zPqHtTKwf8IW9bKWqOANQ+g9HW1azuzqwKrxUbkSH9GJAcC0N3Wymcb5XlnfeMJbJVGDEUtoJAIWDlKyCAEgutAE+OLfqJcSNa2qYT02fMJT0zC1NvDsY/7HRomLI0HoPh4He2NvS5zBNx/HwpfX0xlZXTt3z/oeRbGL8RX7cuVriscr7kO+7OMNfLPPM8aZ6RG+pEYqsdosbG7sN6jOQS3NpIkodAov/KXO02nvv/97/PDH/6QNWvWMHbsWB555BG+973v8ctf/nLIYyIiIqj7nC1hQ0MDKpWK4ODBn8p6eV37qejnA2RJkgbd9mUkZd0Ogh9//HHa29sHbO/s7OTxxx//wgsS3DlcbuiiqLYDlUJiUZ+xvNs4pBCpK0A9/B9XVWcVR2qOICGxevTqQcf05ubSc+oUqNUEPfqoy77udiMFfVngCUsSnF8wx9a9j9loIGLUaJInzaRtsyzw95kehTpMOKcIBNeL/8J4FN4qLPU99JxoYPajsutD3qc7aam5AkBEgj+xqUHYbXZO7yh3OV7p40PgGjloHap5hrfam2UjlwHw4cXrKJBLXy3XG9TmyvUHbiJJ0lVtlGvdPl4g+Cro6elxyb6CnHEdLtCcMmUKu3fvdtm2a9cuxo8fP2SmNyMjg0OHDmE2m7/4or8E3A6C7Xb7oHcXV65cwd/f/0tZlODOYEueHFBOTwohUD+wiOWamHuh8BP5fcbg+t6rcWiBp0ZPJcZ3cDG9wxHCf+lS1J/TOp3dVYnVLGuBR6TIWeDW2mryP5X9iWc+/Dhdh6qxNhtQ+GrwmxeLQCC4fhTeavzujgegY3cFkSNGk5gzEbvNxuH3+580TujzDS4+VkdHk2s2OPCRbyCp1fSePUtvXt6g53G4wuyv2k9DzzUeqeqD5XoD6L/pdpOlGXIQfPBiI209Jo/mEAhuJMuWLePnP/85W7dupby8nA0bNvDb3/7WpdbrRz/6EY9elRx6+umnqaio4Pnnn6eoqIjXX3+dtWvX8sILLwx5nn/5l3+ho6ODNWvWcOrUKS5dusTbb79NcXHxDf18Q3HdQXB2djbjxo1DkiTmzZvHuHHjnK/MzExmzJjBXXfddSPXKriNsNvtbHZIITI8lEIUbwdjB/jHQNy0YYeabWY+KZED5vuSBq/yNtfV0bFT7lQV9JhrFrinw0TBwWrANQt8+P23sVmtJGSPJzIyic79VQAELElAob0uyb1AILgK/fgI1CN8sButtG8rY8aaR5EkBZdOHKXmopyJjUj0JyYlEJvNzukdFS7Hq8PC8FssS6Na/vGPQc8xKnAU2WHZWO1WNpUM73gEyF0oQa4/8OAR7KgwH1Ii/bDY7OwsGLyrnUDwdfL73/+ee++9l2eeeYaUlBReeOEF/vmf/5mf/exnzjG1tbVUVvb7dCckJLBt2zb2799PVlYWP/vZz3j11VdZvXrwJ60AwcHB7N27l66uLmbNmkVOTg5//etfh9UI30iu+yq9cuVKQBY1L1y4EB8fH+c+jUZDfHz8sB9cILiaC3WdlDR2o1EqmJ8Wfu0DBsORlRl7n2xnNAyHrhyiqbeJIF0QM2NmDjqm9f33wWrFe8IEdGPGuOw7u6sCi9lGeIIfMamyrVLt5WIuHj8MksSMBx+jfYtcDKdJ8McrM9SzzyQQ3OFIConAFaPkIrmzDYROyiB11lwK9u/h4Dt/54H/9yskSWLCkgSqilq5cLSWnEVx+AX3y6ECH3mU9k820bF9B+Hf/z6q0IF/j6uSVnG24SzrL63nifQnhi+QG323XHfQXiXXISTMcPtzLc2IpKi2g825tTwwQTwlEtxc+Pr68sorrwxrifbGG28M2DZr1izOnLmO5jNXkZGRwc6dO6/7HPsH0feXl5e7dc6huO4g+Kc//SkA8fHxrFmzBq1W+6UsQHBn4pBCzE4Oxc+TaunuJri8R35/HVKI9ZfWA3KHOLVi4PlsRiNtH8j6wMBHvuGyr6fDxPkDrllgu93OoXfeACBt5lx8jQE0FVwBBQSuGOlWQYJAIHBFE+OLfnwE3SfraPukhCkPPkTxkYNUXyig7OwpEsdNIHJUACPGBHLlQitndlQw++H+G1ev9DS8srPpPXuW1g8+JPRfnh1wjgVxC/jVZ7+iqrOK0/WnmRAxYegFqb3kuoOzb8s33x4EwcsyovifncUcLWmiqctIiI+4hgoEXzdua4JTU1M5d+7cgO0nTpwYtrOIQOBAlkLIBSJLPXWFOL++r01yJoSNGXZofXc9h6oPAXBP0uBe1h1bt2FtbUUVFYnv3Lku+87trsRithEW50tsmpwFLj93mqrCfJRqNVPufYi2LX3FcFOiRGtkgeBLwO/ueCQvFebabpRlVrIXycVsh959A5tN7r42YYmsDS46Wktni8Hl+KC+m9nW99/HZhqow/VWe7MoQZZNrLt0HW1eM/tcIgo/kesR3CQ22JvMEf7Y7LA9XxTICQQ3A24Hwc8++yxVVVUDtldXV/PsswPvtgWCz5Nf3U5lSw9eaiV3pYR5NonDrshhXzQMm0o2YbPbGBc2jgT/hAH77XY7Lf94G4Cghx5CUvU/IDF0mzn/OS2wzWbl4LtvAJC1cCmqCjuWhl4U3ir87orz7PMIBAIXlHo1fnfJsoGOPZVMWLwand6HpqoKCg/uAyAqKYDo0QHYrHbO7a50Od53/nxUYWFYm5ro3LFj0HOsTpIlfHsq9tBuHOh65ELsVLn+wNgh1yN4gKNAbrNwiRAIbgrcDoILCwsZN27cgO3Z2dkUFhZ+KYsS3N44CuLmpYThrfGgeKzpsmxeLylh7PBtkm12GxsubwBkDeBg9J45g7GwCEmnI+Be1/kKDlVjNloJjvYhbqzse1h89BBNleVovfVMXHovHbvlwhzf2TEovEQxnEDwZeEzKRJloBZbpwnzuXYm3iM3rzn64TtY+iyWcvrcJAqP1GDo6rddktRqAh+Su121vPW208T/atKC00gKTMJoNbKtbNvwi1Eo+hvyeOgS4eiKebK8hbp2wzVGCwSCG43bQbBWq6W+fqDhd21tLSqVCAAEw2Oz2dnalwVZ6qkrhKND3Mg54DN8JvlU3SmqOqvQq/XMj5s/6JiWt+UKcv9ly1AGBDi3W8xWcvfK3qTZC2KdWeDj6+Qs9Phlq7DmdWLtMKH01+IzxcPPIxAIBkVSKfBbEA9A54EqMmfcjT4wiM7mRgoPfArAiJRAQmJ8sJhsnD94xeX4gPvvR9JoMJw/jyE3d+D8kuTMBjvqBobFUX9weQ/0tLj9eaICvBgfF4jdDluFJOK2ZrCbLsFXx/X+93c7CJ4/fz4/+tGPXBpmtLW18eKLLzJ//uBBhkDg4ExlKzXtBny0KmYne+CgYLdDfp/B/dhrt0l2aP0WJyzGWz2wcYW5tpbOPrPvzxfEXTxRT2+HCZ9ALaPGy8H2xeNHaKm5glavJ3P2Ijr6LNH85se61aJSIBBcH96Zoagj9NgNVnqO1jFxuRy0ntj4IVaLBUmSyJ4vyyby9l3BYrI6j1UFBeG3dCnQf7P7eZYkLEGtUHOh5QKFzdd4mhmaDBEZcj1CwQaPPo+jO6bjiZjg9sJh9dXT0/M1r+TOxvHf/1rWa26nbn/zm98wc+ZM4uLiyM7OBmTbtPDwcN5++20Pliq4k3B0TFqQGo5OrXR/guoz0FIKKi8Ys2TYoe3GdvZUyA4SQ0khWt99T7ZFmzwZ3ejRzu12m52zfRrDzHkxKJUK7DabMwucs3glps+asfdaUIV74z3OQ5s3gUAwLJJCwu/ueJrfKKDraA2p35nDiY0f0dHYQOHBvYydu4CROWEc31hKZ4uBC8frSJ8Z7Tw+6BsP075+PR07dxL2g+8PaIIToAvgrti72F6+nfWX1pManDr8gsbeB3V58hOpCU+6/XkWjY3gPzcXcK6qjaqWHmKCRFfJ2wmlUklAQAANDXITFm9vb+EW9BVit9vp6emhoaGBgIAAlMrh4wy3g+Do6Gjy8vJ45513yM3NxcvLi8cff5wHH3zwazM7FtwaWG125yPAZZ66QjiywGMWg9Zn2KFbS7displICkwiLThtwH6b0UjbR7K0IuhzWeCyvCba6nvQeKlInS6v9dLJYzRfqUTrrSdj+t20/VHOGvkvjEdSiC85geBGoUsORJPgh6msg54DdUxYvpoDb6/lxIYPSJ05F6VKRea8GA5/dIlzeypJnR6Fou9vUpeaitf4HHpPnab1/fcJ+853Bsx/T9I9bC/fzrbSbfzb+H/DSzVMC/ax98Lu/4DKY9BWCQHuef6G+eqYnBjM0ZJmtuTV8u3ZI906XnDzExERAeAMhAVfPQEBAc7/D8PhkYhXr9fzrW99y5NDBXcwJ8qaaew04u+lZtqoEPcnsFrgfJ+V0TWkEHa73anxW520etA78Y7t27G2taGKisRn9myXfWd3yVng9FnRaHQqOQv88XsAZC9ajulok9wYI84PXUqQ+59FIBBcN5Ik4b8ogcY/5dJzup60b8/mM7+PaW+o58KRA6TNmkfKtEhObi2jvaGXstxGRmb31wsEPfww1adO0/bxx4Q+8wzS5xI2kyInEe0TTXVXNXsq9rBs5LKhF+MXBfHTofyQnA2e8W9uf56lGVEcLWlmc26NCIJvQyRJIjIykrCwMMxm87UPEHypqNXqa2aAHXhcyVZYWEhlZSWmz/kvLl++3NMpBbc5Dm/gu9Mi0Kg80M+WHYDuRvAKglHzhh1a1FJEcWsxaoWaJQmDyyba3pcrvAPvfwDpqj+Y2stt1JW2o1BJZMwZAcDl0ydorCxH4+VF5qQFtP2f3L7Vf1G8eNQlEHwFaGP90KUGYyhspnt/LeOX3sOhd9/gxIYPSJk+G41ORfqsaE5vr+DsrkoSs0Kdf5u+8+ahDAnB2thE56d78bt7ocvcCknBylEr+eO5P7Lh8obhg2CQXSLKD0H+xx4FwYvSI/iPT85TWNtBaWMXiaHDP9US3JoolcrrDsYEXw9uRyKlpaVkZmaSnp7OkiVLWLlyJStXruSee+7hnnsGb0QgEJitNnac/6JSiD5XiLR7QDm89OaTy58AMDd2LgG6gAH7DRcu0HvuHKhUBNzr2u7boQVOnhSB3l+L3W7n+MeyFjj77mWYjreADXRjgtDG+3v2WQQCgdv43x0PEhgKm0nLmIPO14/W2houHD0IQMacGJQqBfVlHdRe7i/eljQa59956/vvDzr3ipErkJA4WXeSK51XBh3jJGU5KDXQUAh1593+HIF6jfNp2BbhGSwQfG24HQR/5zvfISEhgfr6ery9vSkoKODgwYOMHz9+0P7OAgHA0ZJmWnvMBOs1TE70QD5g6oGizfL7jOGlECaria1lWwFYOWrloGNa35MvhL7z70IV0i/NaK3rpiyvCcBZcV565iQN5SWotTqypiyi55ys83IY+QsEgq8GdZg3Xhmyq4zhcD3jl6wE4Pj6D7DZrHj7aUieIusAz36ueUbgffeBJNFz/DjG0rIBc0f6RDIpchIgN9gZFq8ASFogv3fUKbjJ0j7PYEcLeYFA8NXjdhB87NgxXnrpJUJDQ1EoFCgUCqZPn84vf/lLnnvuuRuxRsFtwJY+O6BFYyNQKT2QQlzcDqYuuQglZtKwQw9cOUC7sZ0wrzCmRE4ZsN/a1U3HZjmgDlzzoMu+3L1XwA7xGSEERujlLPB6OWDOunspps9awd5XqDPC1/3PIRAIvhB+c2NAgt6CZtKz5qHT+9Bac4WLxw4DkH1XLEhQ3lfc6kAdHY3PrFkAtH0weLMLx02zo8vksDhuxvPXge0aYwdhQVoEGqWCi/VdXKzvdPt4gUDwxXE7GrFarfj4yPqlkJAQamrk4CYuLo7i4uIvd3WC2wKTxcbOgjrgizTI+Fj+mX4vXEOD65BCLBu5DKVioB6rY/MmbD09aBIT8Z44wbnd0G2m+Lj8aDJrXgwAVwrzqbt8EZVaQ/a0xfSclbPAvvNEFlgg+DpQh+vxSpef3hiONTBu8QoATm5aj91uJyDcm/h0ubtj3j5XWUPgg3Kb9baNG7EZBnZsmxs7Fx+1D9Vd1ZyqOzX8QpIWgtYfOq7IThFu4u+lZuboPkmE8AwWCL4W3A6C09PTycvLA2DSpEm8/PLLHDlyhJdeeonExMQvfYGCW59DlxrpMFgI89UyId4DKURPC1ySG1pcSwrR1NvE4Wo5I7Ri1IoB++12u1MKEbjmAZeitqIjtVhMNoKjfYgaHQDAyU2yG0XanPlYTreDzY42KQBtrJ/7n0MgEHwp+M6Rb1J785tIz5mHSqulobyEyvNyV7iMufL+C8dqMfZanMfpp09HHRWFrb2dju07BszrpfLi7oS7Afik5JPhF6HWQWpfAZ3Hkgg5KbAlr1Z0GBMIvgbcDoJ/8pOfYOt79PNf//VfVFRUMGPGDLZt28arr776pS9QcOvjKPxYPDYSpSd+uoUbwWaG8LEQljL8uUq2YLVbyQzNJME/YcD+3rNnMV68iKTT4b9ypXO7zWojf7+cNcqYOwJJkmisLKfs3GkkScG4GUvoPi23C/cTWWCB4GtFE+WDLjUY7GA+0UL6bLlbqeOmdcSYQAIj9ZiNVi4c7S88k5RKAh6QWx+3vv/eoHOvGCnfPO+u2E23uXv4hYy9T/5ZsBEspmGHDsZdqeFoVQpKm7oprO1w+3iBQPDFcDsIXrhwIatWyd23EhMTKSwspKmpiYaGBubOnfulL1Bwa2MwW9ldKAePyzIjPZvEIYXIuG/YYXa73Zm9GSwLDP2V4X5LFqP068/mluc109liQKdXM3qC3FHq1GbZZzhp0lSkIhNY7WgT/YUjhEBwE+C4Ge3JbWTctMVIkoKKvLM0lJciSf32hnn7qrDZ+rOsAatXgVqNITcPQ+HANsmZoZnE+8XTa+llV/mu4RcRPwN8IsDQBpd3u/0ZfLQq5iTLfsbCJUIg+OrxoEJpIEFBQcIrVTAo+4sb6TJaiPLXkR0T6P4E7Veg4gggQfrqYYcWNBdwue0yWqWWu+PvHrDf0tpKZ98j0MA1a1z25e6tAiBtRhQqjZLO5iYuHDkAwPi5K+j+TNY0Cy2wQHBzoIn2QTcmCOxgzzMwevI0oP/mNXlSBFpvFR1NBirONzuPU4WE4Df/LgBa3x9YICdJkvMmeuPljcMvQqGUO8hB/826myzN7HeJEJIIgeCr5UsJggWCoXDY/yzJiHS2MXULR4e4uKngP2LYoY4L1rzYefhqBjo3tK/fgN1sRpeWhtfYsc7tjVWd1FxqQ1JIpM+KBuD0tk+wWa2MSE3Hq1IDVjuaeD+0iSILLBDcLPj2aX97ztYzfrYcuF44epCOpgbUWiWp02TNbV7fTa6DgL6b4PYtW7B2dQ2Yd1niMhSSgjMNZ6jsqByw3wXHzXnxdjAOnOtazB0ThpdaSVVLL3lX2q99gEAg+NIQQbDghtFjsvBpkeym8IVdIRzZliEwWo1sL9sODO4NbLfbaftIbrYR8IBrcV1+XwX5yHGh+ATqMHR3kf+pnDGesHCVMwvsNzdWPPEQCG4itLF+aEcHgg20ZUpi0zOw22yc2SbLotJnRyNJcOVCK801/QGq94QJaBITsff00LFl64B5w/XhTImS7RWvWSAXlQ1BI8HSC8Xb3P4M3hoV81IckgjhEiEQfJV87UHwn/70JxISEtDpdOTk5HDo0KFhxx84cICcnBx0Oh2JiYm89tprA8a0tbXx7LPPEhkZiU6nIyUlhW3b3P9yEnwx9l5ooNdsJTbIm4wRHmRQG4uhLg8UKkhdOezQfVX76DB1EKGPYGLExAH7e06exFRejsLbG/8l/W2UeztNXPxM1ixn9mWV8vbswNTbS/CIWII7w7GbbaijfdAmBbj/GQQCwQ3Fd5b8hKj7dAPj58v1Knl7dmLo6sIv2IvELLm5xtV2aZIkEXCfXGPguDn+PCtHrgRkz2CrzTr0AiSpv0Auf/C5roUjSbA1r9ZFvywQCG4sX2sQ/MEHH/Dd736XH//4x5w9e5YZM2awaNEiKisHf/xUVlbG4sWLmTFjBmfPnuXFF1/kueeeY926dc4xJpOJ+fPnU15ezscff0xxcTF//etfiY6O/qo+lqCPLblyoceSjEjPMqiOLPCou8B7eGs1pzdw4uDewG0fyXP5LVmCQq93bi84XIPVYiMszpfwBD8sZjNntsvdoiYsWU33cTkz4zszWmSBBYKbEG2iP+oRPmCxEdQRQkhsPGajgdzdcuIjY64cJF88Xoeh2+w8zn/lCiS1GkNBwaAFcnNi5+Cr8aWuu47P6j4bfhGOJ1WXP4XuJrc/w+zkUHy0KmraDZytanX7eIFA4BkeBcFvv/0206ZNIyoqioqKCgBeeeUVPvnkGo+NPsdvf/tbnnzySZ566ilSUlJ45ZVXiImJ4c9//vOg41977TViY2N55ZVXSElJ4amnnuKJJ57g17/+tXPM66+/TktLCxs3bmTatGnExcUxffp0MjMzPfmoAg/pMlrYV+yQQnjgCmG392dVxg7vCtHU28TRmqPA4K4Q1rY2OnfuBCDg/v65rFYb5w9UA7KvqCRJXDi8n+7WFnyCgonRjsbWbUEZqMUrPdT9zyAQCG44kiThO7MvG3y8lgmL5Wzwme2bsJjNRI4KICTGB4vZRuHhfrmBKjAQX0eB3CDZYK1Sy6L4RQBsLtk8/CJCkiAyC+xW2dLRTXRqJfNTZVeazbnCJUIg+KpwOwj+85//zPPPP8/ixYtpa2vDapUfEwUEBPDKK69c9zwmk4nTp0+zYMECl+0LFizg6NGjgx5z7NixAeMXLlzIqVOnMJvlO/xNmzYxZcoUnn32WcLDw0lPT+cXv/iFc52DYTQa6ejocHkJvhh7CusxWmwkhuhJjfSgsUTNGWgtA7U3JC8adujW0q3Y7DayQrOI84sbsL9902bsJhPaMWPQpac7t5fnNtHdZsTLV82ocWHY7XanljB74TK6j/Y5QkyPRlKKLLBAcLPilR6CMkiHrcfCCPVofIKC6Wlvo/joQSRJYuxsOUguOFTtapfWJ4no2LwFW0/PgHmXj1oOwJ7KPdfhGexwiVg3/LghcCQLtuXXYhWSCIHgK8HtIPj3v/89f/3rX/nxj3+MUtn/2Hn8+PHk5+df9zxNTU1YrVbCw8NdtoeHh1NXVzfoMXV1dYOOt1gsNDXJj6BKS0v5+OOPsVqtbNu2jZ/85Cf85je/4ec///mQa/nlL3+Jv7+/8xUTE3Pdn0MwOI4Cj6VfVAqRvBg0+iGHXe0NvGzkskH3Owvi7rvXZS35fVng1OlRKNUKrhTm01hZjkqjZXT0JKzNBhTeKrwnRLi/foFA8JUhKSR8Z8iSt+6jtWTNl3X/Z7Zvwm63kzQh3GmXVlnQb5fmPWkS6pgYbF1ddOzYOWDejJAM4vzi6LX0sqdiz/CLSFsFSFB5FNqqhh87CDOSQvHTqWjoNHKyvMXt4wUCgfu4HQSXlZWRnZ09YLtWq6W7+xp3yoPw+QDJbrcPGzQNNv7q7TabjbCwMP7yl7+Qk5PDmjVr+PGPfzykxALgRz/6Ee3t7c5XVZX7X2CCftp7zRy42AjA0kwPXCFs1n5rtGtIIYpbi7nUegmNQsPC+IUD9htyczFeuiR3iFvWHyS31nVTXdyKJEFa38XToQVOmzkX42fyhVI/ORKFZqDGWCAQ3Fx454Sj8FZhbTEwOmoSKrWGhrISqosLUWuUjJkqZ1rPH6x2HiMpFATcK2dwByuQkySJZYny98amkk3DL8A/GuKny+/Pu58N1qgULEyTb7iFS4RA8NXgdhCckJDAuXPnBmzfvn07qamp1z1PSEgISqVyQNa3oaFhQLbXQURExKDjVSoVwcHBAERGRjJ69GiXLHVKSgp1dXWYTIO3tdRqtfj5+bm8BJ6zq6AOs9XO6HAfRocP9Ou9JuWHoKsevAJh5PBdCB0FcbNjZuOvHehA4dD6+S1c6NIhznEhjM8IwTdIR3tDHZdPnQAgI2MB5qpOUCnwmeqhtZtAIPhKUWiU6KfIf6+mUy2kTJ8NwNltcvCa3nezW3G+mY6mXudx/vesBKVSbql+6dKAeR1PmD6r+4yarmsEp1+4cYa8/u35dVisNo/mEAgE14/bQfD3v/99nn32WT744APsdjufffYZP//5z3nxxRf5/ve/f93zaDQacnJy2L3btdXk7t27mTp16qDHTJkyZcD4Xbt2MX78eNRqNQDTpk3j8uXL2Gz9XyAXL14kMjISjUZz3esTeI6j/ecX9gZOXQGqof+fmW1mtpXJFeDLRy4fsN/a1UXHNtk7+OqCOLPRyoVj8s2UoznG2R1bwG4nLiMbRZF8s6TPCUPpI35nBIJbBZ8pkaBSYL7SRWamXD9y6bNjdDQ2EBDuTUyq3GGu4FB/NlgdFobPnNkAtH08MHiN8oliQsQEALaUbhl+ASnLQaGG+nxouOD2+qeODCbQW01zt4njpUISIRDcaNwOgh9//HF++tOf8oMf/ICenh4eeughXnvtNX73u9+x5nOtaK/F888/z9/+9jdef/11ioqK+N73vkdlZSVPP/00IMsUHn30Uef4p59+moqKCp5//nmKiop4/fXXWbt2LS+88IJzzLe//W2am5v5zne+w8WLF9m6dSu/+MUvePbZZ939qAIPaOk2cfiyrM/2yBXCYoTCvseO15BCHK0+SouhhSBdEFOjB944dWzZir23F01iIl7jxjm3XzpZj6nXgl+oFzFjgjD19pC/dxcA46YtxXChBSTwmTF8hzqBQHBzofTRoB8vP0lUXrDIzTPsNs7ulIPX9JnyTW/hkVqs5v5ESWBfgVz7xk+wGY0D5nXcZG8u2Tx8a2PvINnSEeC8+9lgtVLB3eny9+bmXCGJEAhuNB5ZpP3TP/0TFRUVNDQ0UFdXR1VVFU8++aTb8zzwwAO88sorvPTSS2RlZXHw4EG2bdtGXJxc4V9bW+viGZyQkMC2bdvYv38/WVlZ/OxnP+PVV19l9erVzjExMTHs2rWLkydPkpGRwXPPPcd3vvMdfvjDH3ryUQVusuN8HVabndRIPxJDfdyf4NJuMLaDbxTEDv5EwIFDo7c4YTFqhXrAfmdB3L39BXF2u538A7JpfvrMaCSFRMGBTzH19hAYGU1AUyAAutRg1CFe7q9fIBB8rfhOjwYJDMWtjJsmB6/5e3diNhiIHxuMT6AWQ5eZy2canMfop09HFRGBtb2dzt0DC+Dmx83HS+VFeUc5eU15wy/AKYn4SLZ6dJNlfcmDHQV1mCxCEiEQ3Ei+ULOMkJAQwsLCvtACnnnmGcrLyzEajZw+fZqZM2c6973xxhvs37/fZfysWbM4c+YMRqORsrIyZ9b4aqZMmcLx48cxGAyUlJTw4osvumiEBTcOpytEpgdZYOj3Bk5fBYqhfz3bje3sq9oHDO4NbCgqwlBQAGo1/iv799eXddBU1YVSrSBlSiR2m42zO2QP0HFzl9FzTi7oc1SaCwSCWwtViBe6FLlGJLAtCP/wCIzd3RQe2otCqSBthizTcniEA0hKJQGrZH/hwSQRerWeebHzgOvwDE5eBGo9tJZD9Wm31z8pMZgQHy3tvWaOXHa/8YZAILh+risIzs7OZty4cdf1Ety5NHYaOV4quyosHeuBHtjYCRd3yO+vIYXYWb4Ts81MUmASyYHJA/a3fSxXZ/vOm4cqqL/bnKMgLml8GDofNWW5p2mtrUHj5U2cLgUscotkTZwojhQIblV8psnfP71nGxk3V84Gn9kuSxlSpkWhUEjUlbbTWNXpPCZg9SqQJHqOH8c0iEOQo0Bue9l2TNbBi6wB2dJxzGL5vQcFckqFxOKxskvEZuESIRDcUFTXM2jlypU3eBmC24Ht52ux2SFzhD+xwd7uT3BhG1gMEDwKIofv8OeQQqwYuWKAbZ7NaKR9i6wBDLhKKtPbZeLyKfkRaPosWe97pq9yfOycBRhOyVkXn2lRokWyQHALo030Rx2px1zbTYI+nSNeXrRUV1GRd5b4zHEkjgvl8qkGzh+sZs7DYwBQR0ejnzKF7qNHad+wgdDnnnOZc1LEJMK8w2joaeDAlQPMj5s/9ALS75WfahWsh4U/h0FauQ/H0owo3jpWwe6CegxmKzq1eJIpENwIrisI/ulPf3qj1yG4DdiS+0VdIa5qkzxMEFrRUUFuYy4KScHihMUD9nfu2YOtvR1VZCT6qVOc24uO1mK12AiL8yU83o+WmitU5J0FSSI9YRaGojoUPmq8M0SLZIHgVkaSJHymRdH68SUMp5pIm3UXZ3ds5uyOzcRnjmPsrGgun2rg4ok6pq4ahdZLvhQG3Lua7qNHaVu/gZBnn0W6SkanVChZmriU18+/zqbLm4YPgkfOlS0eu+ply8fE2W6tf3xcIBF+Ouo6DBy82MiCNNGwRyC4EbitCT558iQnTpwYsP3EiROcOnXqS1mU4Najrt3AyQrZ0meJJ64Q3U1Qsld+n37vsEMdmrwpUVMI9R4YsLavk6UQAfesdF7E7DY7hYfkR4tpfRXi53ZtBSBx3ARs+XKjF5/JkUiqLySVFwgENwHemWEo9Gqs7UbS+4LQ0rOnaG+oI3JUAIGReiwmGxdP9HvP+8ybh8LfH0tdHd1Hjw6Yc8VIub7gcPVhWgzDWJipNLLFI3gkiVAoJBaPlb9HHZaTAoHgy8ftq/2zzz47aEe16upqYUN2B7M1vxa7HXLiAokK8MBVoXAj2K0QmQUho4YcZrPbnF6dyxMHegObrlTTfew4AP59hS4AVy620t7Yi0anJGl8OCZDLwX7PwUge9zdmKo6QSmhn+RhQZ9AILipkNQK9JP6MqiFBuIyssFuJ3f3diRJchbIFRyqcdqeKbRaZ2fJtnXrB8yZGJBIanAqFruF7WXbh1+Ao66hcJNs/egmjuLiPUX19Jqsbh8vEAiujdtBcGFh4aAFcNnZ2RQWFn4pixLcejg8LT3yBgbId7RJHj4LfLbhLNVd1ejVeubEzhmwv33DBrDb8Z48Gc2Ifp/fgoPy+kZPikCtVVJ0aD+m3h4CIiLxbZC72nlnhqL0Fc0xBILbBZ/JUaCUMFV0kD1hEQD5+3ZjMZlInhSBUq2gubqL+rIO5zEB98p1BJ2ffoqltXXAnA7P4C0l12icETtVtno0tsPlgbZr1yI7JoARgV70mKzsvdBw7QMEAoHbuB0Ea7Va6uvrB2yvra1FpbouibHgNqOqpYdzVW0oJA+lEG1VUHkUkCBt1bBDHVIIh2/n1ditVto2yNmbqwviejpMlPVZn6XNiMZutzulEONmLaP3vOxo4TNN2KIJBLcTSj+NU+Mf0BKEb0gohs4Oio8dQqdXMypHtvi8uoOcbswYdKmpYDbTsWnTgDnvjr8bpaTkfPN5SttKhz65QiFbPUJ/vYMbSJLkrK/YIlwiBIIbgttB8Pz58/nRj35Ee3u7c1tbWxsvvvgi8+cPUygguG1xaNYmJwYT5qtzf4KCvseOcdPAf+hA1GAxsLN8JzB4m+TuY8ex1NSi8PPDd/5dzu0XjtVis9kJT/AjZIQP1cWFNFWWo9JoiVWNAZsdTYIfmmgPmnsIBIKbGqddWn4T2TOXAP31AGl9fuCXTzVg7DE7j/Hvywa3fbxuQIe4YK9gpkdPB2Bz6TU8gx1Ptop3yBaQbrKsTxKx90IDnQbzNUYLBAJ3cTsI/s1vfkNVVRVxcXHMmTOHOXPmkJCQQF1dHb/5zW9uxBoFNzn9Uogv6goxvBRi/5X9dJm7iNRHkhOeM2B/+3pZUuG/dAkKnRyM2212Z5bHoQE8t1O+AKZMmY3xnJwF9hVZYIHgtkQzwlf2/bbaSfAei1Klou7yRepKLhGR6EdQlB6L2Ubxif4nnP5LlyJptRgvXcJw/vyAOR2ewVtKt2CzD9PVLTJLtny09MoWkG4id97UY7TY2FM08AmsQCD4YrgdBEdHR5OXl8fLL79MamoqOTk5/O53vyM/P5+YmJgbsUbBTUxJYxeFtR2oFBJ3p3tg49NYDHX5oFD1V1MPgUMKsTRxKQrJ9VfX0trqbHfqf5UU4sqFVjqaDGi8VIwaH053WyuXTshV32MTZ2PrsaAM0Do7TAkEgtsPRzbYlNtK8qQZgHwz7FogV+3M+ir9/PBdsADob7xzNbNjZuOr9qWuu45TdcO4IklSv9vNefddIq6WRGzOFS4RAsGXjUdeUHq9nm9961v88Y9/5Ne//jWPPvooarX6y16b4BbA4Q08PSmEIL0HRWUO+6BRd4F30JDDmnubOVJ9BIClI5cO2N+xeQt2sxmtQ8/XhyMLnDwpArVGSd6nO7BZLUQmjUFxyQKAfnIkklI0xxAIble80oJR+GqwdZnJSJLbHxcfPUhvZwfJkyJQqRW01HRTV3pVgdxqWc/bsXUrtt5el/m0Si0L4uUg+bolESV7obvZ7bUv66uzOHSpkbaeYTrVCQQCt3E7CH7zzTfZunWr898/+MEPCAgIYOrUqVRUVHypixPc3NjtdjblykHmMk+kEHZ7f3bkGt7A28u2Y7VbGRsylkT/xAH729b3F8Q5ur11txspy5W7wKXNiMJmtZK3R27LnDNxCebqLtkWbXy4+2sXCAS3DJJSgU+fXZq6XCIsfiQWs4nz+3aj9VYzarxcIFd4VYGc98SJqEeMwNbVReeuXQPmdNQl7CrfRa+ld8B+JyFJcgdMmwUKN7i99qRwX8ZE+GK22tlZUHftAwQCwXXjdhD8i1/8Ai8vuSr/2LFj/OEPf+Dll18mJCSE733ve1/6AgU3LxfqOilp7EajVDA/zYNAsuYMtJSC2huSFw071NEmeWniwCywobAQ44ULSGo1fkuX9K+vryAuItGP4GgfLp86TldLM15+/gR3yxdE78xQlD7CFk0guN3RT4wEhWyXljNF/h7J3b0Nm83qLJC7dLoBQ7dcgCYpFPivugeAtvUDg9fssGyifaLpsfSwt3Lv8Cd3eAbnD5RWXA/LMoUkQiC4EbgdBFdVVTFqlNzMYOPGjdx7771861vf4pe//CWHDh360hcouHlx2PbMTg7FT+eBHMYhhUheDNqhnRkut16mqKUIlaRiUcLAYNlhau8zbx6qwECgr0Pc4b4OcX0XuNxdcmFK1sxFGBy2aFM8LOYTCAS3FEo/DV7psvY/3DQCrV5Pe0M9FblnCU/wIzhaj9Vso/iqDnIBK1eCJNFz4gSmzzWJkiTJWSB3TUlE2ipAkq0g2wY2m7oWjidtR0uaaOx0v/GGQCAYHLeDYB8fH5qb5QBi165d3HWXbEWl0+no7R3mkZDgtsJutzuzEo4shVvYrHDe0SDjvmGHOi4wM0bMIFAX6DqN0Uj7Ftm03qHhA6i60OIsiBuZE0ZLTTWV53NBkkgKzAGrHfUIHzQxvu6vXSAQ3JI4bnoN+S2MnS5renP3ODrIyTfLhYf7O8ipo6LQT5kCQPuGjQPmW5YoB8HHao7R2NM49In9oyFetlVzfu+5QWywN5kxAdjssP28yAYLBF8WHvkEP/XUUzz11FNcvHiRJUvkx88FBQXEx8d/2esT3KTkXWmnsqUHL7WSeSlh7k9Qfgi66sErEEbOHXKY1WZla6msQXdkXa6ma+9ebO3tqMLD0U+d6tzuyAJfXRAHkJg1AUu+7NcpssACwZ2FJt4PdYQeu9lGctgkAEpPn6SjqZHRE8OdBXJXd5BztF9v27gBu83VDi3WL5bM0Exsdhvbyq5hgeYokMt33yUC+gvktghJhEDwpeF2EPzHP/6RKVOm0NjYyLp16wgOlh8vnT59mgcffPBLX6Dg5sQhhZiXEoa3xoNOgQ5v4NSVoBpak3uy/iT1PfX4anyZNWLWgP0OrZ7/ypVISiXQ1yGuryAudXoUZpORgv2yfVpW6nys7UYU3ipnJymBQHBnIEkS+ilyMGkv6iUmNQO73Ub+3p1ovdWMdHSQO9zfoc33rnko/Pyw1NTSc/z4gDkdBXIOC8chSVkOCjXU50NDkdtrd3Tj/Ky8hdp28dRVIPgycDsIDggI4A9/+AOffPIJd999t3P7f/7nf/LjH//4S12c4ObEZrM7u8R5JIUwG6Cw74JxLSlE34Xl7vi70Shdg2VzXR3dhw8DEHDPSuf2C8drsVnthMXLHeIuHT+CoasT35BQ9A16APQTIpDUHjkECgSCWxjv7DAknRJrs4FxmXKNQf7eXVgtFlKny99nl0/VY+qVLRQVOh1+SxYD/fUHV7MwfiFqhZri1mKKW4qHOXEQJPV1VfUgGxzp78XEeNlGUmSDBYIvB7ejgIMHDw77Etz+nKpopbbdgK9WxazRHmRTL+8GYzv4RUPslCGH9Zh72F2xGxi8TXL7xk/AbsdrfA6aPimO3W6n6Ih8gUjru6Dl7t4OwLipSzFdbgcJ9JMi3V+3QCC45VFolOhzZDcb/9YAvP0D6G5tofT0Z0SO9CcwwhuLycbFk/0d2gL6JBGdu3djbW93mc9f6+98SrWldMvwJ3dKIj6SLSLdxNFGeXNezTVGCgSC68HtIHj27NkDXo72yXPmzLkRaxTcZDi8gRemR6BTK92fwCGFSF8NiqF/BT+t/JReSy8xvjFkhma67LPb7bRt6PMGXtXfIa7mUhtt9T2otUpGjQ+jsaKMmotFKJRKYjVjANCNCUIVpHN/3QKB4LZA31cPYLzYRvY0OcvrKJBzZIMLr5JE6NLT0SYlYTeZ6Ng2UPt7dRtli80y9IlHLwK1Htoq4MowneaGYNHYSJQKibwr7ZQ1dbt9vEAgcMXtILi1tdXl1dDQwI4dO5gwYQK7BjEUF9xemK02tuXLFkLLPZFCGDqgWC5Su14pxLLEZc4GGA56T5/GXFGJ5O2N38IFzu2OC1fShHA0OpUzCzw6Zyrm83IGRxTECQR3NuoQL7RJAWCHRJ8MkCQq8s7SWltN8uQIFCqJxspOGivlIlpJkvoL5AbxDJ4RPYMAbQBNvU2cqD0x9Ik13pDS53XuSAa4QYiPlmmjQgDYnCuywQLBF8XtINjf39/lFRISwvz583n55Zf5wQ9+cCPWKLiJOHy5iZZuEyE+GqaODHZ/ggtbwGqEkGSIGDvksPruek7UyReTwdokOy5EfovuRqGXdb6GbjMlZ2SbotTpUZh6eyg8tA+AsYlzsButKIN1aEcFuL9ugUBwW+HTJ4myFHaSmDkegLxPd+LloyExS5Z5XZ0N9l++DFQqDPn5GC5edJlLrVRzd7xcI3PtNsp9N/8F68E6TNZ4CBzJh0/OVTut3AQCgWd8aZVBoaGhFBcPUxQguC3YfE6+KCweG4lK6cGvjyP7MfZe+Fx292q2lW3DZreRHZZNjG+Myz5bdzcdO+RsskOrB1B8og6rxUbwCB/C4ny5cOQgZkMvgZHRaKtkBwufiZFIiqHPKxAI7gx0KcEo/TTYui1kjZEL1s7v34PFZHJKIi5+VofZaAVAFRyMz2xZ+9s+SDbYIYn4tOJTus3DSBUSZ4NXEHQ3QtkBt9e9MC0cjUpBSWM3hbUd1z5AIBAMidtRTF5enssrNzeXHTt28O1vf5vMzMxrTyC4ZTGYrc7e9SuyPJAUdDVA6X75ffrqIYfZ7XZnm+TBvIE7duzE3tODJi4Or3HjnMc4O8T1XcDO7Za1ezmTlmKu6QaVhPd4D9o7CwSC2w5JKaGfKLdP19d54xsciqGzg4snjjBidCB+ITpMBiuXTzc4j3HUH7Rv2oTdbHaZb2zIWOL94jFYDc6C3kFRqiFNbsfsiUuEr07N3GTZym2TkEQIBF8It4PgrKwssrOzycrKcr5fvHgxJpOJtWvX3og1Cm4SPi1qoNtkJTrAi3Gxgdc+4PMUbAC7DaJzIHjkkMOKW4u53HYZjULDgrgFA/a3rZcL4vxXrXJqhevLOmip6UalVjB6Yjh1ly/SWF6KUq0myp4AgPfYUJR6D9o7CwSC2xL9xAhQgKmik3FT5MZPubu3IykGL5DzmTkDZUgI1pYWug64ZnGvbqO8peRaLhF9koiizWB23/PXkYTYkluLzSYkEQKBp7gdBJeVlVFaWkpZWRllZWVUVFTQ09PD0aNHGTNmzI1Yo+AmweEKsSwzakCh2nWR96H88xoFcY4s8KyYWfhr/V32GcvK6D19GhQK/FeucG53XKhG5oSh9VaTu0cuiEuZOAtjoVwQp58sbNEEAkE/Sj8tXqlybUOsegySQkFNcSFNVRWMmSJLp+pK22mu6QJAUqnwXyHbNQ5WILckUQ6kP6v7jLruuqFPHDMJ/GPB1AkXd7i97jljwvDRqqhu6+VMZavbxwsEAhm3g+C4uDiXV0xMDDqdsJu63ekwmNlXLBedeSSFaC6B6lMgKYeVQlhsFraVyjKGQb2BN2wEQD99GupwWdpg6rVw6ZTs6Zk6LQpjTzfFRw8BkBY9Ayw21JF6NLG+7q9bIBDc1jhujs2FHSRly63X8z7dgd5fS/xYOUAuOtzfnMJRh9B14ACWxkaXuaJ9ohkfPh479uE9gxWKfs/gPPddInRqJQvS5O+/T84JSYRA4CkeFcZ99tlnvPzyy7zwwgs8//zzLi/B7cnO83WYLDaSwnwYE+FBMOnQviXOBp+wIYcdqzlGs6GZQG0g06KnueyzW620b9wIuBbEXTpVj8VkIzDCm8hR/hQe2ofFZCR4RCyqMvlRoX5ypGfZa4FAcFujHRmAKsQLu8lKRuJsAAoP7sVsMjolERdO1GI12/rGj8QrMxOsVto3DXSCcEgiNpVsGt69IeN++eelXdDT4va6HS4R2/JrsVhtbh8vEAg8CIJ/8YtfMHnyZP7+979z6tQpzp4963ydO3fuBixRcDPgKMBY7okUwm6HvA/k944v/qHO0yeFWJSwCLXCVb/bfeQIloYGlP7++Myd69zukEKkTJMvCnl75MeLOTlLsTT1ImmVeGcNHXgLBII7F0mSnB0ktVfU+IeFY+zu5uKxw8SmBeMTqMXYbaH0XH/Wt98zeP2AQHd+3Hy0Si1l7WUUNBcMfeKwFAgfCzYzFG50e93TRoUQpNfQ3G3iSEmz28cLBAIPguDf/e53vP766xQVFbF//3727dvnfO3du/dGrFHwNdPYaeTI5SZA1gO7Tc0ZaCkBlReMWTLksA5TB3sr5d+h5aMGSiGc3sDLlqHQaOS1VXXSUNGJQikxZnIEtZcu0FRZjkqtIdwkW6t5Z4eh0HrQ2U4gENwR6HPCQKXAUtdDzgTZlzx3z3YUCokxU+UAufBIv+zAb/EiJJ0OU0kJhrw8l7l8Nb7MjZVv0h039UOS0Vcf4YEkQq1UsHis7G6xSUgiBAKPcDsIVigUTJs27doDBbcN2/JrsdkhMyaA+BC9+xM4vuDHLAbt0FKK3eW7MdlMjPQfSWpQqss+S2srXZ9+CkDA6n4pRFFfFjghMxQvX40zC5w2cS6mi20A+IiCOIFAMAwKbzXemXKDjGhpJAqlktqLF2isLCdlaiRIcOVCK+2NPQAofX3xXSB7Cw9WIOeoZ9heth2z1Txgv5P0ewEJKo9CW6Xb616RFQ3AzoI6DGar28cLBHc6bgfB3/ve9/jjH/94I9YiuEnZeE52hfCoTbLVAufXye8zHhh2qCNrsnzU8gGSi44tW7GbzWhTUtClpABgMVkp/qyvIG56JIauLmdBXErEFLCBJt4PdYQHgbtAILijcNwsm4o7GD1OTvTk7dmOX7AXsSlBABQdubpATi7w7di6FVuvq83Z5MjJhHqF0mZs42D1waFP6h8N8dPl9x54BufEBhLlr6PLaGHvhYZrHyAQCFxwOwh+4YUXKC4uZuTIkSxbtoxVq1a5vAS3FxXN3ZytbEMhwbIMDzKqZQeguwG8g2Hk3CGHVXVUcabhDApJwZKEgZKJtg2yN3DAPfc4t5WcacDUa8E3SEfMmCAKD+3FYjYRGpuAokRuRyqywAKB4HpQj/BBHe0DFjvpMXJnuMKD+zAbDM4CuaJjtdj6itC8J05AHR2NrauLzj17XOZSKVROu7RNl68lieirk8j7UK6fcAOFQmJ5XzZ449lqt44VCAQeBMH/+q//yr59+xg9ejTBwcH4+/u7vAS3FxvPynKDaaNCCPPzwArP0SY57R65U9IQbC6Vq6wnR04mXO/a1c1QVISxsAhJrcZv2VLn9sK+rEzKNPlxpbMgLmsJ1nYTCr0Kr/QQ99csEAjuOOQCOVljq6lU4B8egam3hwvHDhKfEYKXr5qedhMV5+UiNEmhwH+VfFPetm79gPkckoiD1QdpNQzj5ZuyHJQaaCyC+vNur/uebDkI3lfcQFuPye3jBYI7GbeD4Lfeeot169axfft23njjDf7+97+7vAS3D3a7nU/6pBAr+7INbmHqkTsiAYwd2hXiWm2SHZo7n7lzUQXKnepa67qpudSGJEHK1EiqiwtpvlKJSqsltFfO/nrnRCCpPHIBFAgEdyDemWFIWiXWZgPjx8tBbN6eHShVCsb0PVW6uoNcwMqVIEn0HD+O6YprJjYpMImUoBQsNgvby7YPfVKvABi9UH7vaCjkBskRvoyJ8MVstbMtf5gGHQKBYABuRwhBQUGMHDl0y1vB7UPelXZKm7rRqRUsTI9wf4KL28HUBQFxEDNxyGFnGs5Q3VWNXq1nXuw8l302k4mOTXKAHLCqXwrh0ObFpgfjE6hzZoHHTrgLU0kHAD4TPVizQCC4Y1FolXhny3aKkbY4FEoVdZcv0lBeKj9xAirON9PVagRAHR2NfspkANo3DF0gt7lkoJ+wC44kwfl1YHPf89eRDRaSCIHAPdwOgv/f//t//PSnP6Wnp+dGrEdwE7Gh7wt1QWoEPlqV+xNc3SZ5GG9hxwViQdwCvFReLvu69u7F2t6OKjwc/XS5gMRqsXHhuBwEp06Lorezg4vHDwOQHDIR7KBNkg3wBQKBwB0cnsHmi52MyZkJyAVygRF6opICsNvhwrH+bLB/X4Fc24b12D8XwC5KWIRKUnG++TylbaVDnzRpAWj9oaMaKo64veblWVFIEnxW3sKVVnFtFgiuF7eD4FdffZXt27cTHh7O2LFjGTdunMtLcHtgsdrYkid/0a/M9sAVorsZLvcViwzTIMNgMbCzfCcwhBSiT2vnv3IlklL2+i3LbaK304y3n4a4scEUHtyL1WwmLH4kUolsR+QzSRTECQQC99FE6tHE+YHNTlqUfONddHg/JkMvqdMcnsG12G1yEZvvXfNQ+Plhqaml5/hxl7mCvYKZHi3PMaxnsFoHaSvk947GQm4Q6e/F5AS5xbNooywQXD9up/dWrlx5A5YhuNk4fLmJpi4TQXoNM5JC3Z+gYD3YLBCZBaHJQw7bV7WPLnMX0T7R5ITnuOwz19bSfVjO8F4thXCY1o+ZGolCITmlEOPHLsF23ozCV4Ouz9JIIBAI3EU/KQJTRQfqCgiIiKKtroYLRw6SOuMuDn14ic5mA1UXWohNDUah0+G/dAmt775H27r16KdOdZlr+ajl7L+yn82lm/nX7H9FqRiicU/GA3DmLSj8BBb/D6jde5J1T3Y0x0qb2Xi2mmdmjxRt4gWC68DtIPinP/3pjViH4CbDkU1YlhGJWulBcVnue/LPzDXDDnNkR5YmLkUhuZ6nfeNGsNvxnjABTVwcAB1NvVQVtQCQOi2S6qICWmquoNbqCOoKw0wn+gnhSJ6sWSAQCADvsaG0bynF2mZkQvYydm//P/L27CBj3kJGT4wgf/8VCg/XEJsqZ1/9V6+m9d336Ny9G2t7O8qrnJJmjZiFn8aPhp4GTtSdYGrU1MFPGjsV/GOgvQqKt0H6arfWfPfYCH7yyXkuNXRRWNtBWpRwaxIIroWIFAQD6DZa2HFerjJeke2BK0TTJag+DZKyryPS4DT0NHC05ijQX0DiwG6zOV0h/K/uEHe0FuwwYkwg/qHe5O6Rq64zJszHXN4JEugnCimEQCDwHEmtwDtHtmoMt8SgVKmoL71Efellp2dwWW4TPR2yJZkuNRXtmDHYTSbat2xxmUuj1LAoYRFwDUmEQtEvHct1XxLhp1NzV4pc1CcK5ASC60MEwYIB7C6sp9dsJS7Ym+yYAPcncGjaRs0Dn6GlFFtKt2Cz2xgXNo5Yv1iXfT0nT2GuqkKh1+O3YAEANquNoj4pROr0KHo62rl0Qi4iSQocD4BuTBCqAK37axYIBIKr0Pe5y5hLOkkZNxuQ7dJCRvgQFu+HzWp3FuhKkkRAX7Oo9kE8g1eMlPW+n1Z8Sqepc+iTZvQ9Obu8B7oa3V6zw8ryk3M1WG3uNd4QCO5ERBAsGICjTfKKrGj3dWU2W38QPEybZLvdzsbLG+XzjFoxYH/7ernVst+SJSi8vQGoKGihu92ETq8mMTOUwgOfYrVYiEhIghI5I6MXBXECgeBLQB3qjXZUANghNVyWMBQdOYCpt4c0Rwe5I7XY+7q8+S1biqRWYygsxFBU5DJXekg6if6JGKwGdpXvGvqkoaMhKhvs1v52824wOzkMfy81DZ1Gjpc2u328QHCnIYJggQtNXUYOXWoCYGWWB64QlcegrRI0vjBmYPtjB/lN+ZS1l6FT6lgQt8Bln7Wzk46d8oUi4CophMOkPnlKBAqVRN6nsqtETuoS7L0WlIFadKMD3V+zQCAQDIKjg5yy3EZQVAxmQy9Fhw8wanwYaq2Stvoeai61AaAKDMRnnuxz/vkOcpIkOW/2Pyn5ZPiTOrLBee+7vV6NSsGSvvb2688ISYRAcC3cDoJ7e3uH3FdbW/uFFiP4+nE8RsuMCSAx1Mf9CRxf3Gkrhq1u/uSyfCG4K+4ufDSu5+nYug27wYBm1Eh0GRkAdLUaqciXg/PUaVFUFeTTWluNWudFQIfsBKGfGImkEBXRAoHgy8ErNRiFrxpbl5nxmXLL9rw9O1BrlSRNkDXDLh3kVsvFbO2bN2MzGl3mchT/nm04S3l7+dAnTV8NChXUnIXGYrfX7GicseN8LT0mi9vHCwR3Em4HwdnZ2Zw5c2bA9o8//piMvoBFcOvy8ekrANw7zoOCOLMBCvqyHBlDu0IYrUZnG9GVo1YO2N+2Xs6iBKy+1ynHuHCsBrsdIkf5ExSpJ6+vIC5rwkIsV7pBKaEfH+7+mgUCgWAIJKUC/QQ5GxxqiEKpUtFQXkJ9ySVngVzJmUYM3bI/uX7qFFQREdja2+nau9dlrjDvMKZFTQOuUSDnEwqj7pLf57qfDR4fF0hcsDfdJquzwFkgEAyO20Hw/PnzmTp1Kr/61a+w2+10dXXxzW9+k8cee4z/+I//uBFrFHxFFNZ0UFTbgUapYFmmB1KIi9vB2C7b/MRNG3LY3sq9dJo7idRHMiFigss+w8WLGPLyQKXCf7ncPMNus1PY1ybZWRD32TEARvlmA+CVFozSV+P+mgUCgWAY9BMjQAJLeRfpObLcIXfPDsLifAke4YPVYqP4hBxsSkol/vesBKDt44GaXockYlPJJqw269AnddRT5H/kdhtlSZJYlT0CgHVnrrh1rEBwp+F2EPz73/+ejRs38rvf/Y6ZM2eSmZlJbm4uJ0+e5F//9V9vxBoFXxGOL8x5KWEEeHsQUDqyFmPvk+1+hsAhhVg+cvlAb+B18oXDd85sVMGyB2fVhRY6mw1ovFSMHBfG+X27sVktRCWkYC+RHzmKgjiBQHAjUAXo0CXLkqsxIZMAuHDUtUCu8HCNs0DO4RLRffQo5mpXXe7smNn4afyo76nnRN2JoU+avAi0frJnsAdtlFf1Pck7WtJMTdvQEkaB4E7Ho8K4BQsWsGrVKo4cOUJVVRW/+tWvSE1N/bLXJvgKMVttfNLnCrF63Aj3J+hu6m+TPEyDjPrueo7Vyllch22QA5vJRPtGOUAOuLffX9hZEDcxHJVKIu9TuUPcuNS7sZusqEK90CYKY3iBQHBj0E+Wb7KlMiuhIxKwGI0UHtpH0oRwlGoFLTXd1Jd1AKCJicF78mSw251e5w60Sq3TM9jhjjMoai9IdbRRdl8SERPkzaSEIOx22CA8gwWCIXE7CC4pKWHKlCls2bKFnTt38oMf/IAVK1bwgx/8ALPZfCPWKPgKOHixkaYuE8F6DbOSPWiTfH7ddbVJ3ly62ekNHOMX47Kva88erO3tqCIi0E+fDkBPh4myc30FcTOiqMg/R3t9HVpvPf7NcuCrnxQpWoQKBIIbhm50IMoALfZeC+PTZdebvN3b0XqrGJUjN6gouLpA7j75Jr5t/XrsVlfZg6MOYm/lXjpMHUOf1JFMKPgETD1ur3l1jpzM+Pj0FWeWWiAQuOJ2EJyVlUVCQgK5ubnMnz+f//qv/2Lv3r2sX7+eiRMn3og1Cr4CHFKIFVnRnrVJPveu/HOYLLDdbndKIQYtiPv4Y0B+nCgplQBcOFaLzWYnPMGPkBG+5O2Rs8DZ4xdjqe9FUivQjwtzf70CgUBwnUgKyWmXFtQdhkqjpamqgpqLF5ySiMsn6zH2ym4MvnfdhdLfH0ttLd1Hj7rMlRacxkj/kRitRnaW7xz6pLFTwT8WTJ1yG2U3WTw2Ei+1krKmbs5Utrl9vEBwJ+B2tPOnP/2J999/n4CAAOe2qVOncvbsWcaNG/dlrk3wFdHWY2JPYQMA9+Z4IIWoL4Dac6BQw9j7hxyW25hLeUc5XiovFsS7egObrlyh++gxkCT8+zR1dpudgkPyo7y0GVF0tTRz+dRxABK80wHwyghF4a12f80CgUDgBvrxEaCQsFzpJnO8/P2Vt3sbESP9CYrSYzHbuNhXIKfQavFbIbeCb/voY5d5JElyJgGGlUQoFJD1oPz+3Dtur9dHq2JRuhy4iwI5gWBw3A6CH3nkkUG3+/r6snbt2i+8IMFXz+bcGkxWGymRfqRG+bk/gSMLPHoh6IOHHOb4wr8r9i70ar3Lvra+gjj91KloRshFHVcutNLRZECjUzIqJ5z8fbuw22zEJWdiK5GLPXwmi4I4gUBw41H6avBKl7/fkvxzACg+fhhDVydpM+RscMGh6v4CudWyJKJz714sTU0ucy0duRSlpCSvMY/SttKhT+p4slayD9rdD2QdkogtuTUYzMO4UQgEdyged4wrLCxkx44dbNq0yfnavHnzl7k2wVfEx2ccBXEeeANbzZD3ofw+6+Ehh/WYe5zewPck3eOyz26x0N5XQOLQ0gEUHJbXlTwpAqUKZ4e4rJHzwWJHHaVHPcKDhh4CgUDgAU4XmlITkfHJWM1mCg58yuiJESjVCpqr+wvkdMmj0WVmgMVC+yeuXeJCvEKYET0DgA2XXYvnXAhK7LObtHvkGTwlMZgofx0dBgt7iurdPl4guN1xOwguLS0lMzOT9PR0lixZwsqVK1m5ciX33HMPK1euvAFLFNxILjd0kVvVhlIhsSLLgyD48qfQ3QDeIZA0f8hhO8t30mPpIdY3lvHh4132dR0+jKW+HmVgID5z5wLQ3W50FsSlzYym7Nwpupqb0Pn6oa/3BsBncpQoiBMIBF8Z2kR/VKFe2E1WcsbILg95e+QCuSRHgdyhfjcGh8tN20cfDyhOcyQDNpVswmwbpqg86yH557l3wc0CN4VC4p6+5IajEZJAIOjH7SD4O9/5DgkJCdTX1+Pt7U1BQQEHDx5k/Pjx7N+//wYsUXAjcWjFZo8OJdRX6/4EDq1axgOgHFqb65BCrBy1ckDg6iiI81+xAoVG9ie+uiAuONqH3N1yFnlCznKsLUYknRKvLA9cLAQCgcBDJElySrD8WvzReHnRWltDVUEeaTPlYPPyqQaMPXJQ67doMZK3N6bycnpPn3aZa8aIGQTrgmkxtHDwysGhT5q6EtR6aCmBqs/cXvOqPsvLgxcbaegwuH28QHA743YQfOzYMV566SVCQ0NRKBQoFAqmT5/OL3/5S5577rkbsUbBDcJitbG+Lwhe7UlBXE8LFMvBqTNbMQhl7WWcaTiDQlKwfORy1zU0NtK1bz8AAfeuBvo6xPXZDaXNiKa9oZ6yc/IFJEaZBIB+XDgKjdL9NQsEAsEXwDsnHEmtwNpoYFyObJeWu3s74Ql+zgK54hOy9EDpo8dvsZwx/nyBnFqhdn4fbrg0jCRC69PvGexBgdzIUB/GxQZgs8O6M8IzWCC4GreDYKvVio+PrMMMCQmhpkYOVuLi4iguLnZ7AX/6059ISEhAp9ORk5PDoUOHhh1/4MABcnJy0Ol0JCYm8tprrw059v3335crcYVMY1D2FzdS32EkSK/hrpRw9yfI/xhsZojIgIj0IYc5NG/To6cTrnc9T9vGjWC14pWdjXbUKEDuENfRJHeIGzU+jPy9O8FuZ1TaZKxlsl+mXhTECQSCrwGFToV3tix9SPCSv/cunzxGT3sbaTPkbPDVBXKBfZKIjp07sXa4+gKvTFoJwKHqQzT0NAx9UkeSoWCDR57BD0yQPdk/PFUlPIMFgqtwOwhOT08nLy8PgEmTJvHyyy9z5MgRXnrpJRITE92a64MPPuC73/0uP/7xjzl79iwzZsxg0aJFVFZWDjq+rKyMxYsXM2PGDM6ePcuLL77Ic889x7p1A3u0V1RU8MILLzBjxgx3P+Idw/snqwC5IE6j8sQbuC8rMUxBnNlmZtPlTQCsGrXKZZ/dZuv3Br7vPuf2gkN9HeImRaBQ2MjfuwuAjBGzwA7akf6ow7zdX69AIBB8CThuwm1lBmKTMrFZreTv3UXypHBUfR3k6kr7CuQyM9EmJWE3GGjfssVlnkT/RLLDsrHZbWwq2TT0CeOmQUAsGDvgwla317s0Iwq9RvYMPlHW4vbxAsHtituRz09+8hNsNhsA//Vf/0VFRQUzZsxg27ZtvPrqq27N9dvf/pYnn3ySp556ipSUFF555RViYmL485//POj41157jdjYWF555RVSUlJ46qmneOKJJ/j1r3/tMs5qtfLwww/zn//5n24H5ncK9R0G9hXLmQdHlsC9Ca72Br5vyGGHrhyi2dBMkC6ImTEzXfb1nDiBuaIShY8PfncvBOSCuPLcvoK4GVFcOnGUnvY2fAND0FbLmmORBRYIBF8nmigfNLG+YLWTnSgXBOft2YFaq2DUeNcCOUmS+jvIffDhwAK5UXKB3IZLG4bO0ioUkOkokHNfEqHXqliWKdu4vf/Z4EkmgeBOxO0geOHChazqa2aQmJhIYWEhTU1NNDQ0MLevsv96MJlMnD59mgULXJsmLFiwgKOf67Dj4NixYwPGL1y4kFOnTrm0bHZolp988snrWovRaKSjo8Pldbvz8ekrWG12JsQHMirM1/0JrtMb2CGFWJa4DLXCtXCu9QPZWs1/+TIU3nJmt+iIa0HcuV1yp6SJ2SuwdZtR+GrwSh36fAKBQPBVoJ8iB5Xe9V54+frT2dxI6ZmTTknE5dMNGLrl65L/ihVIWi3G4mIMfU9SHSyMX4i3ypvKzkpO17sWz7ng8Awu3e+RZ/CaibEAbDtfR3vPMG4UAsEdhMc+wVcTFBTktlVVU1MTVquV8HBXjWh4eDh1dXWDHlNXVzfoeIvFQlOfGfmRI0dYu3Ytf/3rX697Lb/85S/x9/d3vmJiPMiM3kLYbHY+6JNCPDAh1v0JrvYGzv7GkMMaexo5dEXWeK9KcpVCWJqa6NyzB4CABx5wrsuRPUmfFU1jZTnVFwqQFArCzfI69RMjkDxp6ywQCARfIt7pISi8VdjaTUzMkQvXzu3aKt/Aj/DBarZx4VgtAEp/f/zuvhvov/l3zqP25u4Eed/wnsEJEDcd2TP4PbfXmznCnzERvpgsNjaeEwVyAgG4EQQ/8cQT1/Vyl88Hz3a7fdiAerDxju2dnZ184xvf4K9//SshISHXvYYf/ehHtLe3O19VVVVufIJbj2OlzVS29OCrVbF4bIT7E1zaJXsD60Nh1F1DDttUsgmr3UpmaCaJAa6ylLYNG8BiwSszE11yMgAV+U10tRrR6lWMygkjty8LPDZ7HtYrPaAAn4kerFcgEAi+ZCS1Au8J8vdRNCNBkqjIO0tbXQ3pfXZp5w9WY7f1dZDru9nv2LZtQIGcQxKxq3wXnabOoU+a3Vd/cfYd6JMlXvd6JckpfXvvs0pRICcQ4EYQ/MYbb7Bv3z7a2tpobW0d8nW9hISEoFQqB2R9GxoaBmR7HURERAw6XqVSERwcTElJCeXl5SxbtgyVSoVKpeKtt95i06ZNqFQqSkpKBp1Xq9Xi5+fn8rqdcRTErciOwlujcn+CM2/JPzMfHNIb2G63O72BP58FtttstH34EQAB99/v3H7+oJydSJkSic1ipPDQPgDGhEwGwCslGKW/B17GAoFAcAPwmRgBElgrekjJkIuwc3dvZ/TEcNQ6Je0NvVwplq+LXtlZaJNGyQVym1y7q2aGZpLon4jBanB21hyU1BWg8YXWMqg47PZ678mWi6Av1HWSd6Xd7eMFgtuN6w6Cn376adrb2yktLWXOnDmsXbuWDRs2DHhdLxqNhpycHHbv3u2yfffu3UydOnXQY6ZMmTJg/K5duxg/fjxqtZoxY8aQn5/PuXPnnK/ly5czZ84czp07d9vLHK6H1m4TO8/LNxJrPJFCdNTImWCAcY8OOexU/SnKO8rxUnmxMH6hy77uY8cwV1Wh8PV1emi2N/ZSWShXLafNjKbw0H7Mhl5Co+JRlMs970VBnEAguJlQBXuhGx0IQFrEdAAK9u9BkiyMmSRniR0395IkEXC/nA1u+9C1QE6SJGeyYN2lgW5HTjR6GNvXWt6RjHCDAG8Ni9LldTmSIQLBncx1B8F/+tOfqK2t5d///d/ZvHkzMTEx3H///ezcudPjxyrPP/88f/vb33j99dcpKirie9/7HpWVlTz99NOALFN49NH+QOvpp5+moqKC559/nqKiIl5//XXWrl3LCy+8AIBOpyM9Pd3lFRAQgK+vL+np6Wj6upHdyaw/W43JaiM92o/0aH/3Jzj3DthtsmVPSNKQwz6+KFufLU5YjF6td9nX5iyIW47Cywvoq6S2Q0xqEP6hXuTukm2AJqQtw26yogr1QjsqwP31CgQCwQ3EUSCnqoSgsGgM3V0UHz1E2ixZElGWK8u8APxXLEfS6TBevEjvuXMu8ywfuRy1Qk1hcyEFzQVDn9CRfCjcJDcschNH8mPTuWq6jRa3jxcIbifcqjDSarU8+OCD7N69m8LCQtLS0njmmWeIi4ujq6vL7ZM/8MADvPLKK7z00ktkZWVx8OBBtm3bRlxcHAC1tbUunsEJCQls27aN/fv3k5WVxc9+9jNeffVVVq9e7fa570TsdjsfnJT/e3pUEGezwZm35ffDZIFbDa3srpAz9veNdrVPszQ20rl3LwABD8hSCKvZRtFRuYAkfWY01RcKaKqqQKXVEtAqZ1l8pka5XXwpEAgENxrd6ECUwTrsBisTxsod4M7t2kZwlA+Ro/zlDphHZO9zpZ8ffov6Osh9rkAuUBfIXXFyjYUjiTAoUdkQPhasRsj/yO31Tk4MIj7Ym26Tla15tW4fLxDcTnhcZi9JEpIkYbfbnb7BnvDMM89QXl6O0Wjk9OnTzJzZ7yX7xhtvsH//fpfxs2bN4syZMxiNRsrKypxZ46F444032Lhxo8fru504U9nGxfoudGoFK7Ki3J+g/CC0VYDWH1KWDzlsU8kmzDYzKUEppIWkuexrW7deLojLzkY3ejQAl880YOgy4xOoJX5ssNMWbcK4ZVibjUhaJd7jwtxfr0AgENxgJIWEz2T5+zS4MwylWk196SXqLl8kvS8bXHi4BptVvk4G9t38d2zfjrXdVZfrSBpsK91Gt7l7iBNK/UmI02+Cm09iJUni/r4CuXeFZ7DgDsetINhoNPLee+8xf/58kpOTyc/P5w9/+AOVlZXOVsqCm5d/HK8A5O5BfrrBC9qGxaFBy7gPNIN3bLPb7c4sxr2j73XdZ7PR9tHAgriCPs1c6vQoejvbuXRC9omO08kBtPe4MBRaDwr4BAKB4CtAnxOGpFZgbTSQnS1nes/t2srIrDC8fNV0txkpz2sG+jrIjR6N3WgcUCA3Pnw88X7x9Fh6hi+Qy7gPlFpoKICaM26v996cEaiVEueq2jhfLQrkBHcu1x0EP/PMM0RGRvLf//3fLF26lCtXrvDRRx+xePFiFArh23qz09hpdD76enRKnPsTdDdDUd8X9jBSiNP1p50FcUsSl7hOceQo5upqFH5++C2SfTGbrnRRW9KOpJBInRZF3qc7sFktxCdlY68wAOAzxYOstUAgEHxFKLzVeGfLT6tG6rMAuHD0IMbeTlKmyt9f5w/KDS4kSXJKwdo+/GBAgZwjeTCsJMIrUHaKAI8K5MJ8ddydLhcav3Ws3O3jBYLbheuOXl977TX8/PxISEjgwIED/NM//ROrVq0a8BLcnHxwshKT1UZWTAAZIwLcnyDvA7CaIDJTfg3BRxflTO9gBXGt78kG7/4rVqDQ6YD+yunEzBB0Pgpyd8vZj6y4+WAHbVIA6rDBs84CgUBws+DTF+xSaSQ2YSxWs5n8T3eSNiMKJKgqaqWtvgeQi4IlLy+Mly7Te+qUyzyOArmC5gIKmwuHPqEjGZH/MRjdr8l5rC8Z8sm5Glq7TW4fLxDcDlx3EPzoo48yZ84cAgICXLqrff4luPmwWG28c0LWfj021YMssN3en20YJgvcZmgbsiDOXF1NV5++O/DBBwEw9Vq4eEK2a0ubFc2lE0fpbm3BNyAEXY0s1xBZYIFAcCugjtCjSfAHG2SPlG0hc3dvxydQQ1y63OrdcdOv9PXFf9kyoD854OC6C+Tip0NQIpi6oHCj2+vNiQskNdIPo8XGR6eFXZrgzuS6hZZvvPHGDVyG4Eayp6ie2nYDwXoNi8d64LV75RQ0FoHKC9LvHXLYcAVxrR98CDYb3lMmo01MAKDoWC1mo5XACG9GJAfy/n9sAWBS1j3YKywoA7ToxgS5v16BQCD4GvCZGklLWTvedVr0vgF0Njdy+dRxxs4aQ0V+M0VHa5m4LAGNTkXgQw/S9uGHdOzaTVhDA+qw/uLf+0bfx/ay7Wwt3coL41/AWz3I0zBJguxH4NP/lJMUw7SwHwxJknhsahz/vi6ft49X8OT0RJQK4cAjuLMQYt47gDePygVxaybGoFUp3Z/gzBvyz7SV4BUw6BC73e6UQny+IM5mMtH2sZzRCHzoIXm8zc75A3JWZOzsETSUlVBzsQiFUkWYQc7++kyJRBJfygKB4BbBKzUYpZ8GW7eFSePkVshnd2wmts//3NRr4eJn9QDoxozBa9w4sFicBcMOri6Q21a2begTZj0EkhKqTkBDkdvrXZ4Zjb+XmqqWXvYXN7h9vEBwqyOC4NucS/WdHCttRiHBw5M8kEL0tkJ+XwejcY8NOWy4grjOnTuxtrSgiojAd84cAKqKWmir70GjU5I8OYKzO+Siu6xxC7HWG0ClwHt8hPvrFQgEgq8JSalAP0l+2hZhjkVSKLhSeJ6mqnLGzh4BQP7+K85iOIc0rO2DD7Gbzf3zXG+BnG8EJMtuFJx63e31emmU3D9eXtdbxyrcPl4guNURQfBtjuOLbX5qOFEBXu5PkPs+WHohLA1iJw857MOLsvH7oAVx77wLyP6YkkpW4OTtlyulx0yJxGLq5sLRgwAk+eYA4J0VilLvgY2bQCAQfI3oJ0WAUsJaayAzaz4AZ3duYczUSFRaJS013VRfbAPAd+EClMHBWBoa6Ny7z2WeqwvkCpqG6SA34Un5Z+77HhXIfWNyHJIEBy42UtY0hDexQHCbIoLg25hOg5n1Z+Rg87Ep8e5PYLfDybXy+wlPyBq0QWjqbXIWxD2Q/IDLPkNhodweVK0m4F45s9He2EPFedkzc+zsEeR/uhOr2UxcYgZUyO1FfaZFu79egUAg+JpR+mjwzgwFIDlgAgBFh/Zjs/YyZpL8dCu/Lwmg0Gic34uDFcgtiF8AwPvF7w99woTZcoGcsQPOD5M1HoK4YD1zkmU98tsiGyy4wxBB8G3M+jPVdJusjArzYcrIYPcnKDsIzZdA4wMZDww5bN3FdVhsFjJDM0kJTnHZ5/hi95s/H1WofGHIP1ANdohNC8IvRMu53bLmLTthAdhAm+iPJtI1mywQCAS3Cs6b+AoLI+JSsZiMnN+3m/TZ8vayc410tsg+6IEP3A8KBT3Hj2MsKXGZZ03yGgC2l22nzdA2+MkUChj/hPz+5N/c7iAH/d7xH52uosdkcft4geBWRQTBtyk2m503+0zQH50ShzREFndYTvVlgTMeAK3voEMsNotTCrFmzBqXfdaODto3y44PgQ/J2jez0cqFo3LTjrGzR3D55DG6mpvw9Q9BV6sBwGe6yAILBIJbF020T59dmp1xI+XGQOd2biUwwovo5EDsdpyFweqoKHz6aiVa33PN+GaGZpISlILRamTD5Q1DnzDrYVDpoC5fdvNxk5lJocQHe9NpsLDuTLXbxwsEtyoiCL5N2VNUT2ljN746FavGjXB/go5auLBVfu/QnA3Cvqp9NPQ0EKQLYkHcApd97Rs2YDcY0I4ejVeOrPW9+Fkdxh4LfqFexKUFc2a7XBA3KXMl9l4rymCdsEUTCAS3PL7TZZcb7zodep9AOhrrKT19kow58vdx4eEaLCYr0J8kaN+4EVt3vy5XkiRncuGD4g+w2qyDn8w7CNL6mlWd/Jvba1UoJB6bGg/A2kOlWG3uZ5MFglsREQTfhtjtdl47ID9We2RyHD7a67aD7ufMW2CzQMxkCE8bctj7F+TMxeqk1WiUmv412Gy0vitLIQIfehBJkrDb7eTtk7VwY2dF01BeQvWFAhRKJaHdckW1z9QoYYsmEAhueXQpwSiDdNh7rUzKWgnAmW2fED82GJ8gLYZuM5dOybZk+ilT0MTFYevqon3zZpd5FiUswk/jR3VXNUdqjgx9QkeyomAD9LS4vd4HJsQQ4K2mvLmHXQV1bh8vENyKiCD4NuRURStnKtvQKBV8c1q8+xNYLXD6Dfn9hKeGHFbSVsJndZ+hkBQDOsR1Hz6MqaIChY+PszNSzcU2Wmq6UWkUpEyN5PTWjQDkZC/F1mJC0irRjw93f70CgUBwkyEpJGfHy3DDCBRKJVWF+TRWljF2lqtdmqRQOLPBLW//w2mhBuCl8mLlqJUAvHfBtXjOhegcuaW91Qhn/+H2er01Kh6dLGuDXztQ4rIGgeB2RQTBtyH/15cFXp0TTZivzv0JLm6HzhrwDoHU5UMOc2SBZ4+YTaSPaye6ljflNssBq1ej0MtFbrl75dacyZMjMRnaKT52CICRXpkA6MeHo/Akay0QCAQ3IfoJ4UgaJbZmE+OyZP/0M1s3kjotCqVaQWNlJ7WX2wHwX7UKhbc3ppISuo8cdZnH4bpzpPoIVR1DtDiWJBjflw0+9TrYbG6v99Gp8WhVCnKvtHOizP1sskBwqyGC4NuMS/Wd7ClqQJLgn2YkejaJwxYt+xug0g46pNvczeZS+bHd5wvijCUldB85ApJE4DceBqCtoYeyvCYAMueO4NzOrdisVpKSJ2O/YgRJlkIIBALB7YJCp3I+3RqlzwLgwtGDWEwdJE+W7dJyP5WDWqWvL/6rZF1vy9tvucwT6xfLtOhp2LHzQfEHQ59w7L2g9YfWMijd6/Z6Q3y03NfXPMORTBEIbmdEEHyb8ZeDpQAsTI0gMdTH/QmaLkPpPkCC8Y8POWxzyWa6zd3E+8UzOdK1iUbL228D4DNvLpqYGADy9l4BO8SlB+MToCRv93YAMqJnA7J+ThXsQTMPgUAguInRT40CCexVRkaOnoDNauXcrq1kzpW/G0tzG2lv7AEg6BsPgyTRfeAgxrIyl3keTJblEusvr6fX0jv4yTR6yJLHOZMZbvLU9EQUEuwrbuRCXYdHcwgEtwoiCL6NqGs3sPGcbG/zz7M8zAJ/9n/yz6QFEBg/6BC7vT8bsWbMGhf7NWtbG+0bPwEg6JFHATD2mCk6JtuiZd4VQ8HBvRi6uwgJj0N9RT7WZ5rIAgsEgtsPdYiX0/Ema8Q8AHJ3b8c3SElsWjDY+5IEgCY+Hp9ZswBofdtV1zs9ejrRPtF0mjrZXrZ96BM6JBHF26Gl1O31xofoWZQuy9v+csD94wWCWwkRBN9GvH6kDLPVzsSEILJjA92foLcNzr4jv5/yzJDDTtSd4HLbZbxUXiwf6aoZbvv4Y9kWbcwYvCfK3ZIKDtdgMVoJjvYhOsmfM9s2AjApZQV2sw11tA/aRH/31ysQCAS3AA7vc1WVREhYLIauTgoP7iVrnpwNLjpai7HHDEDQY3LyoG3jRjq7ptMAAEooSURBVKwd/ZlYpULp1Aa/U/TO0IVroaNh1HzADif+4tF6vzVTTqJsyq2hum2IrLNAcBsgguDbhPZeM++eqATgaU+zwGfeAnM3hKVCwqwhh71dKMsdVoxcga+mv4mG3WKh5Z13AQh65BEkScJqtZHfZ4uWOS+GsnOnaa2twcvbD99G+VjfmdGeNfMQCASCWwBtoj/qET5gsTEpdQUAp7d+QnSyP0FResxGK4WH5adl3pMno01Kwt7TQ9u69S7zrEpahZfKi4utF/ms7rOhTzj52/LPs2+Dod3t9WbGBDAlMRiLzc7rh8uufYBAcIsiguDbhLeOltNltDA63IfZo8Pcn8Bqgc/6sgaTvy1XGg9CWXsZB68cRELiG6nfcNnXuWcPltpalEFB+C2VK6FLzzTS1WrEy0/D6AnhzizwlOxV2HssKAO0eKWHur9egUAguEWQJAnfGXLBmW+jH15efrTWVlOee4bMvmxw3r4qbFYbkiQR+Ij83dr6j39gt/Y3yPDX+jufvjmSEYMyci6EjgFTl0d2adAvqXvvs0qauowezSEQ3OyIIPg2oL3HzF8OydqtZ+eMQuFJs4kLW6C9CryDYez9Qw57p0iWS8waMYs4vziXfS1vyV/KgWseQKHVYrfbObdHzk6PnRVNc3U5lefzkBQKwnvlC4LP9GgkpcgCCwSC2xuv9BCUgVrsPRYmZ8suEKe3bmD0xHC8fNV0tRopOdsIgP/y5SgDAjBXV9O519Xl4RspcoB84MoBytvLBz+ZJPVng0+8BkN1mhuGWaNDGRvtT4/Jyp/3C6cIwe2JCIJvA/7vYAmdBgtjInxZluFhgdnxP8s/xz8J6sG9hduN7Wwq2QTAI6mPuOzrzT9P75kzoFYTsEa2TKsraaehohOlSkH6zGhObZYf7U3IXIat1YykU6GfEOHZegUCgeAWQlJKTm1wRO8IFAollefzaK4qI72veYbDLk2h0xFwv5yMaH3LNeMb7x/PrBGyXO0fRcNkeTMeAK8gaKuE4m3ur1eSeGFhMgBvH6+gtl1ogwW3HyIIvsVp7DTy9yPlADw/f7RnWeDq01B1HBTq/tabg/DRxY/otfSSHJjMhIgJLvta3nwTAL+770YdJssxHF/oyZPCMRvauHD0IACJ2rEA+EyORKFVur9egUAguAXRj49A8lJhazMzPkvupHly0zrSZ0ajVCmoL+ugrlTW8AY+/BCoVPScPEnv+QKXeRxJiE0lm2g3DqH5VXv121we+5NH652ZFMLE+CBMFhu/33vZozkEgpsZEQTf4vxp/2V6zVYyYwKYn+phy2FHFjh9NfgOnpk128zOlp2PpD7iUshmrq6mY7ts2RP8+DcBuTlG6Tn50V7GvBhObd2A3WYjNXUW9nozKCXRHEMgENxRKLRKfCbL9mOJGjkZcPH4EUw9zYyeJH9/n90lS8jU4eH4LVoEQMvrr7vMMzFiIqMDR9Nr6WXdpXVDn3DCU6BQQeVRqDnr9nqvzgZ/eLKKiuZut+cQCG5mRBB8C1Pd1ss7x+UvzO8vSPbMYaGjBgo2yO8dGrJB2FW+i4aeBoJ1wSxKWOSyr/nNN8FqRT91CrrUVADO7a7E3tccw9vXRv7eXQCkh0wHwDs7DKWfxv31CgQCwS2Mz9QoUErY602kp8/FbrdxassGsu6KBeTmGa11crAZ/OQTAHTs2IHpyhXnHJIkObXB7xa9i9lmHvxkflGQJuuPOf6aR+udmBDErNGhWGx2XtlzyaM5BIKbFREE38L8/tNLmKw2JicGMW1UsGeTnPwb2CwQNw2isgYdYrfbnZXIa8asQaPsD16tbW20fSxnIoKekKUU3e1GLhyrA2DcwjjO7tiCxWgkLj4D6YoFAN8Z0Z6tVyAQCG5hlL4a9OPkrG9q0BQACvbvQedtJiEzBOxwdrec3NCNGYN++nSw2Wj5+xsu8yxOXEyQLoj6nnr2VOwZ+oSO5Mb5ddBZ59GaX1ggZ4M3nqvmYn2nR3MIBDcjIgi+RSlr6uaj03Jm4PsLPcwCGzvlIBiGzQKfbThLQXMBGoWG+5NdnSNa338fe08P2jFj0E+bCsjdj6wWGxGJfoSM0HJ25xYAcuLvBjvoxgShDte7v16BQCC4DfDpSwJIVywkJo7DYjZxdsdmxi2UHXeKj9fR1SrbkjmywW3r1mFpbXXOoVVqWZMsFyG/Xfj20M0zosdBzGSwmfulb24ydoQ/d6dFYLfDb3dd9GgOgeBmRATBtyj/u/siVpudOcmh5MQFeTbJqddlI/XgJEheMuSwNwvkordlI5cRpOs/l81opKWvtWfwk08gSRKmXgvnD8qtm7MXxHF+/x4MnR2ERSSiviL/uvnOHuHZegUCgeA2QB3mjS5VbpmcPWI+AOd2biUoSkPkKH9sVju5e+XCYu/Jk9GlpmI3GGh9912Xee5Pvh+NQkN+Uz5nGs4MfcLp35V/nlwrdwb1gOcXjEaSYEdBHXlXPJtDILjZEEHwLcjpilY25dYA8G99j6ncxmyAY3+U30//HigG/1UobStlb9VeJCQeTX3UZV/7xk+wNjejiorE7+67ATh/qBpTr4XACG9iUwM4vVXWG08evQKsdjQJfmjjRYtkgUBwZ+M3R26SoaqGyKgkDN1d5H+605kNLjhYjaHbjCRJBPVlg1v/8Q42g8E5R7BXMMtGyi4Tr59/nSFJWghhaWDqhJN/9Wi9o8N9uSdLzmC/tLkQm22IzLNAcAshguBbDIvVxk82ngfg/vEjSI/2MKA89w501YPfCBh735DD1p5fC8Dc2LkkBvS3Y7ZbrbT8/e8ABD/2GJJajdVsc9qiZS+I49JnR+hobCAgIAKvOtl72G9OrGfrFQgEgtsITYwv2qQAsMHEvkD21NaNjBjjT3C03ErZ8VTNb+FC1NHRWFtbad+wwWWeJ9KfQCEpOHjlIMUtxYOfTKGQkx0gSyJMPR6t+YWFyXhrlJyqaGXdmSvXPkAguMkRQfAtxlvHKiiq7SDAW80PF6V4NonVAkd+J7+f9hyoBndpqO2qZVupbLL+ZLqrf3Dn3r2YystR+PsTcO+9ABSfqKOn3YRPoJakCWGc3CQXzE1JWwVmG+poH/lLXyAQCATObLCuTkNQYBRdzU0UHz1I9gI5G5y3twqLyYqkUhH0uOz52/z3N1xaKcf6xTI/TpZUOJIWg5J2DwTGQ08znHnLo/VGBXjx3buSAPjl9gu09Zg8mkcguFkQQfAtRH2Hgd/ulosSfrBwDEF6Dy3GCtZDWwV4h0D2I0MOe7PwTSx2C5MiJjE2dKxzu91up+Vv8pdt4Jo1KPR6bDY7Z3ZVAJA5L4aKvFM0VpTh5eWPf7OcrfabE+NZAZ9AIBDchmgS/NHE+YHFzpQU2crsxMaPSBwXjE+Qlt5OMxeO1QIQsOoelP7+mCsr6dzt6gbhSFLsLN9JVWfV4CdTqmDad+T3R18Fi2cB7OPTEhgd7kNLt4mXdw6ReRYIbhFEEHwL8V9bi+gyWsiMCWDNhBjPJrHZ4PD/yu8nfxs03oMOazG0sO6inMl9cqxrFrjns5P05uYiaTQEfeNhAMrONdLe0IvWW0XKtEiOffw+ANOz7sNutKEK85ILQQQCgUAAyH6/vn3ZYN8mX3x9Q2ituULJZ0fJni9Lx87ursRmtaHw9ibwYfn7tukv/+fiBpESnMK06GnY7DbeOP/G0CfMfAh8IqCjGvI/9GjNaqWCn61IB+C9zyo5W9l6jSMEgpsXEQTfIhy53MTm3BoUEvx8Zbpn7ZEBLu6AhkLQ+MrdhIbgnaJ3MFgNpAanMjlyssu+pj/LNjsB965GFRqK3W7n1PZyAMbOHkHNhVzqSy+h1ekJ7pBbKPvOjkHydM0CgUBwm6JLDkQdqcdutjEtQ67POL7+A8b8/+3dd3gVVfrA8e/clpub3gPp9FBD7x2CoAiKnVWUorgqKj+7u9ZdFSsKIgqiKyigAiorvXcQCE1qIBBKQgrp7bb5/TFwMUtCMw3zfp4nz3Mzc+acM5NzkzfnntIpFLOnkdyMYo5sTwPA7/6/oVgslOw/QP7ataXyudAb/FPiT2QUZZRdmNEMnR/TXm/4CJyOstNdQcd6AdzeJgxVhX/8tA+HTJITNygJgm8AJXYH//xZmwz3QOfo658Mp6qw4UPtdYfR4O5bZrICW4Fri+TRLUaXGsJQuDOBwi1bwGgkYLQWRB/fk0HGyXyMbnpa9g5n8zzt2q6t70ItdKD3c8PSKuj66iyEEH9hf+wN9j3nh4eHL5mnkjm+Zxtx/bTj2xcdx+lUMfj54XevtjZwxmefleoNbhfSjlZBrbA6ra7NjcrU7iEw+0JmIhxYeN31fnFgLN5mA7+fyWXWlhPXnY8Q1UmC4BvAp6sSOZZeQKCnG+PjG11/Rknr4NRvYDBDp7+Xm+yHQz+QZ80j2juaPhF9Sp1z9QIPHYKxbl1UVeW3X48DWi9wWtJ+Uo4cwmR0J7hQW07Hq2cEil6amhBClMW9eSCGQHfUYgdd47QNibbMm0PznmG4eRjIPltI4vazAAQ89BCK2Uzx7j0UbNrkykNRFFdv8NxDc8m15pZdmJsXdHxEe73+fW2I3HUI8nLj2ZuaAPD+0kOcPHd9K04IUZ0kMqnhNiZmMGl1IgCvDm6Kt9l4fRmpKqx6U3vd5gHwDC4zmdVh5Zv92szhkc1HotfpXeeK9uyhYP160OsJePhhAE7szSQ9OQ+Dm564/hFsma+NBe7a9k7UXBs6bxMebUOur85CCFELKDoFr15ar69/diDuFm/STyRx8vedxPXVxga7eoMDA/G9Sxs2caFT4oKeET1p4NuAAlsBcw/OLb/AjmO1IXGpe+HAL9dd7/s6RNI60pe8EjuPz07Aar++gFqI6iJBcA2WllfMk3N2oapwT/sIBreqe/2ZHV56vhfYHbo/U26yeUfmkV6UTrAlmFvq3VLqXMZnUwHwGTwYU0TE+V7gJABa9AgjI/kQpw7sw2hwI6RA+4Xu3TsCxSjNTAghLsfSOhhDgBm10EG3Vhd6g2fTvFcYbhYDWamFHN2pjQ0OGDUKxWikaPsOCrZtc+WhU3SMbK5trPHN/m8osBWUU5j/xbHBq/993WOD9TqFT+5pjbfZwO6T2by75OB15SNEdZHopIZyOFWenL2LjPwSmoR68dqtza4/M6fzYi9wx0fAq+ye2WJ7MdP3TAdgTIsxGPUXe52LDxwgf/Vq0OkIeETrBU7+/RxpJ/IwmHTE9Y9ky4WxwG3uQs23o/cx4dE+9PrrLYQQtYSiV/Dqp60PHJATjLvZm7PHEjlzaBet+l4cG6w6VYwhIfjcMQy4tDd4YMxAoryjyC7J5rsDpbdZLqXzY+DuBxmHYc9leo2vIMLfwvt3tgJg+oYklv2eet15CVHVJAiuoSatOsLmY5lYTHom39cGs1F/5YvK8/t8OLsP3LwvrhNZhh8P/0haURqhHqHc3vD2Uucu9AJ7DxyIW0xMqV7g5j3CyEo5SvK+PRgMZkILzy/50ycSxSBNTAghroalVRCGIHfUIgddW51fKeLHObToFYbJ3cC5MwUcTUgHIHD0aDAYKNy8hcKdCa48DDoDY1uNBeDr378mz5pXdmFm74u7yK15+7rXDQaIbxbKqG4xADzzw25OZcn4YHFjkAilBtqUmMHHK48A8O/bmtMg2PP6M3PYYfVb2usuT2gfg5WhyF7E9L1aL/AjLR/BpL+4EUfx4cPkLVsGQOBYbULFyQPnOJuUi96o9QJv+n4WAF1b3YFa4EDv6yZjgYUQ4hooOgXvftoY4MCcYNzdvElJPETK4T207BMOwPZFSVpvcFgYvrcNBcroDY4eSD2feuRac5m1f1b5BbYfA54hkJ0MO//zp+r+/E1NaBXhS26xnce/k/HB4sYgQXANk5RRwLg5Cagq3N0ugttah/+5DHd/B+eOgiVA2xyjHHMPziWzOJMwzzCGNBhS6tyFX7BeAwbg1rCh1gv83+MANO8eRkbyAZL37cZkNFOnWPsF7t1XeoGFEOJaubcIwhBiQS1xusYGb5gzk5a9wjCZ9WSeLuDYbq03OODhh0Gvp2D9eop273blodfpeTRO+33/zf5vyCnJKbswkwV6PKu9XvceWK+/B9dk0DH5Xm188K6T2bz6y75SS7gJURNJlFKDJGUUcM8Xm8nItxJbx/vPjQMGsJfAmgna627jtaVxylBoK2TGvhmA1gts1P1hLPD+/eQtXgKKQuDftV+qyfvPkXosB71BR1x8BBvmaKtJdGt9j7YusL8ZS5uyV58QQghRvj/2BgfkBuFh8SXt+FGS922jZR9tqNm2hUk4nSqmiAh8hmidFmkTJ5bKJz4qnoZ+Dcm35btW/ClTmxHgGwn5Z+G3aX+q7hfGBysKzN52kld+/l0CYVGjSRBcQxzPKODeL7ZwNreEhsGezBzVAXfTnxgHDLD9K8g9BV51of2ocpN9d/A7skqyiPSKZHD9waXOXfjF6n3zzZgbN0Z1qmz56SgALXqFkXpkF6mJhzGbPQnO11av8O4TKesCCyHEdXJvFogx1AO1xEn3uHsB2Dh3Fi161cXNoo0NPrJNm4AW9NjfUYxGCjdvoWDzZlceOkXHY620FSBm7Z9FVnE52xsbTNDrRe31ho+guJxe46sU3yyU9+7QAuGZW07w2i8SCIuaSyKVGuBEZgH3TttCam4xDYI9+W5MJwI93f5cpkXZ2sdbAD2fA6N7mcnyrfl8te8rAMa2GotBZ3CdK9y+nYJ168FgIGjcEwAk7kgj42Q+JrOe1vERbJyr7UzUo/W9qEUODAFmLK2lF1gIIa6XolPw7q/1Bvtm+uLjHUxWymmO7lhHmwHaChJbFybhsDm1scH3aLvIpX00sVTA2SeyD7H+sRTaC/n696/LL7Dl3RDYGIqyYP2Hf7r+d7QNZ8KwligK/GfzCV5fuF8CYVEjSRBczS70AKfkFFM/yIPvxnQkyOtPBsAAa96BwgwIbASt/1ZuspkHZpJrzSXGJ4ZBMYNcx1VVJe3DjwDwvWMYpshIHA4nW345BkDr+EhO7N5MxskTeHkG4J8VCIB3vygUvXJpQUIIIa6auWkAxjBPVKuT7s21IHfzD7OJ7RaCxcdEXmYxv284DUDgIw+jWCwU79lD3ooVrjwUReHvcdruoLMPziajKKPswnR66Pea9nrLFMg8+qfrf1e7CN65vQUAX286zusL9+NwSiAsahYJgquJ06kyc8sJBn2ynjM5xdQL8mD2mE4Ee5n/fOZpB2DbF9rrgRNAX/Yuc5lFmfznd21G8KOtHi21O1z+2rUU7dyJ4uZG4KPaWOADG1PITS/C3ctIsx512PjDtwD0aHEvaokTYx0P3FsF/fn6CyFELacoCj4DowHwSLMQHBhNXmY6B9Yto/3N2nJk2xcdx1psxxAYiP8D9wOQ/vHHqI6Lm1/0DO9J84DmFNmLmLp7avkFNh4I9fuCwwpLX6qQe7i7fSRv/yEQvuvzzRxLz6+QvIWoCBIEV4OT5wr525db+edP+yi0Omgf7cecMZ0I9q6AAFhVYfHzoDqgyS1Qv0+5SafsmkKBrYBmAc0YED3gYhZOJ+kTPwbA72/DMYaEYLM6XOsCtxsUzeHNq8k5m0qgXwSeadoSbj4DY1B00gsshBAVwdzAD7dGfuBQ6dJA2xxj64Lvqd/WF+8gd4rybOxZdRKAgJEj0fn4YE08Ss7Cha48FEVhfLvxgLYW/LHsY2UXpihap4nOAIeXwOFlFXIP93aIZOLdcXi6GdhxIouBH69n+vpj0issagQJgqvQhd7fARPXseloJmajjlduacrchztXTAAMcGAhJK0FvRsM+He5yY5mH2XekXkAPNPuGXTKxaaQu3gxJQcPovP0JGD0aAD2rj5FYY4VrwAzjdoHXtwdrtEd4FRxa+CLuZFfxdyDEEIIAHxuigYF3M4aiKzTnMKcbHYvWUinW+sBkLAsmaJ8K3pvbwLHaL+vMyZNRrVe3PyifWh7ekX0wqE6+GjHR+UXFtjw4lKaS174Uxto/NHQ1mEsfboH3RsGUmJ38q9fD3C39AqLGkCC4Cr0/faTrt7fDtH+LHmyByO7xaCrqN5TWxEsfVl73fVJ8IsuN+mHOz7EoTroE9GHdqHtXMdVm430Tz4BIGDUSAx+fhQX2Ni59AQAHQfHsGvpz+RnnSM8JBZzmraphs/AmIq5ByGEEC6mup5Y4rTJxu3DtHkb236ZR536RgIjPLEWO9i5RPv97Dd8OIagIGynT5P1/Q+l8hnfdjwGxcCaU2vYlrKt/AJ7PAcewdr68ls/Kz/dNQrzdeebkR1467YWeJj0bD+RxV2fb6bY5rjyxUJUEgmCq9BtbcJoFeHLK7c0Zc7DnYgO9KjYAjZ+DDnJ4B1+cTvMMmxJ2cK6U+swKAaebls6XdacudhOJKP398f/gQcASFh2gpJCO/51PajT0MS2n38EoGOktpyae1wQprA/saudEEKIcnnHR4FewZCp0LReN2zFRWz68Vs6Da0PwN41p8k7V4zO3Z3Ax7SJcBmffoojN9eVR4xPDHc21rZifn/7+zjVcnZ0M3tD/9e112vfhdyUCrsPRVG4r2MkS5/uQbcGgTzVrxFm459cClSIP0GC4CrkZtCz4NEuFdv7e0F2srbGI0D8m9pOQGVwOB28/9v7ANzV+C6ifaJd5+xZWaRPngxA0Lgn0Hl4kJNexK6V2pizTkPrs/mHWdhKimlavzuGDAX0Cj7x0f9bjBBCiApi8DPj2UVbh72Fbw8UFPatWo67Rw5hjXxx2J1snp8IgO+wYZjq18eRlUXGlNI9uWNbjcXT6MmBcwdYeHThJeW4tLwHwtuDNR9WvFrh9xPuZ2HmqA4M7xhZ4XkLcS0kCK5iFR78gjYZ7pdxYC+G6O7Q7LZyky48tpBDWYfwMnoxttXYUucyPp2CMycHt0aN8L3jDgA2zU/EaVcJb+KHxSuHfWtWoKDQwrc7AJ6d6mDwr6DxzEIIIcrk3TsCxayHLAcdWw5FVZ2snTWDLsMagAJHtqeRkpiNYjQS8sLzAJybNYuSpCRXHv5mf8a0HAPAJwmfUGQvKrswnQ4GvgsosGcuHFlRdro/QVEUFEUmUovqJUHwX8H2GXBsNRjMcMtH2izfMhTaCpm0cxIAD7d8GD/zxYlsJYmJZM3WJruFvPQiisHA6UNZHEtIR1Gg6x0NWDfrS1BVOrW6Hc45UNz0ePWR/+SFEKKy6SxGvHtr2yZHO2NxM7qTvHcX+ZmHaNqlDgDrvz+C6lTx7N4dj549wG4n7d33SuUzPHY4dT3qklaYxje/X2Y75bA20PF8R8kvj2sbaQjxFyNB8I3uXBIs+6f2uu+r2uzeckzbO420ojTCPMO4L/Y+13FVVTn7zgRwOPDs1xePTp1wOlXW/3AEgGY9wsg5u5/kfXswmzyJsjUGtI0x9B5lr0EshBCiYnl2CUPvb0YtcNAzTtsEae3ML2l3cxRGs5705DwObtG2Uw55/nkwGMhfvZr8jRtdebjp3Xiq7VMAfLnvS87knym/wL6vgH99yEuBxS9U2n0JUV2qPQieMmUKMTExmM1m2rZty/r16y+bfu3atbRt2xaz2Uy9evWYOrX04t/Tpk2je/fu+Pn54efnR79+/di27TIzYW9kTif8/DjYCiCq68X/2stwJOsIX+/7GoDn2j+HSW9ynctfu5aCDRvAaCTk2WcBOLDxDJmn8nGzGGg7MIK1M2cA0KvVcG175GB3PM/3PgghhKh8ilGH7y3a0mh+WQEE+kRw7swpEn9bRbtB0QBs+eko1mI7bvXq4XffvQCkvfMOqt3uyuem6JtoG9KWInsRb219q/wtjU0WuG0qKDrYMwcO/lqp9ydEVavWIHju3Lk89dRTvPzyyyQkJNC9e3cGDhxIcnJymemTkpIYNGgQ3bt3JyEhgZdeeolx48Yxb948V5o1a9Zw7733snr1ajZv3kxkZCTx8fGcPn26qm6r6mz7HE5sAKMHDPlUG8dVBqfq5M0tb2JX7fSO6E2fyIsbaKhWK2nvTADA/4H7MUVFUVJkZ+v57ZHb3xzD4c0ryEo5TbB/DD6ZvgD4Dq6Poq/2/6GEEKJWMcf6uzbQ6N7wLgA2/fAdjTv44R3kTmGulR3nl0wLeuwx9L6+lBxJJGvuXFceiqLwSqdXMOgMrD21lpXJK8svMKIDdBmnvV74JBRkVtq9CVHVqjWK+fDDDxk1ahSjR48mNjaWiRMnEhERwWeflb024dSpU4mMjGTixInExsYyevRoRo4cyfvvv+9K8+233/L3v/+duLg4mjRpwrRp03A6naxceZk3+Y0o4wiseE17Hf8m+Je/Tu+CIwtISEvA3eDOSx1Lb4d57rvvsB4/jj4gwLU98vZfkyjKs+EXaiGmlTsb584CoFvMMFC1Pe3NDWVjDCGEqGqKomi9wToFU6aBRhEdKc7LZfOPM+k6rAEAu1ecJDejCL2PD4HjngAg45NJOLKzXfnU863HQ80eAuDtbW9TYCsov9DeL0FQLBSkw6/jK+3ehKhq1RYEW61WduzYQXx8fKnj8fHxbNq0qcxrNm/efEn6AQMGsH37dmw2W5nXFBYWYrPZ8Pf3r5iK1wR2KywYq60GUa83tBtZbtLMokw+3PEhAI/FPUaoR6jrnC01lYxJ55dEe+pJ9J6eZKUWsGf1KQC63tGQ9d99hbWokGYxPTGe04NBcX0cJ4QQouoZgy14dtOWTIvz64MOHbtXLMHscY7wJn447E42nJ/T4XfXXbg1bIgjJ4e0Dz4olc/DLR8mwiuCtMI0JidMLr9Agxvc9hkoetj/E+yeW35aIW4g1RYEZ2Rk4HA4CAkJKXU8JCSE1NTUMq9JTU0tM73dbicjI6PMa1544QXCwsLo169fuXUpKSkhNze31FeNtvhZOL0d3HxgyORyV4MA+GD7B+Rac2ni34ThscNdx1VVJfWNN3EWFOAeF4fvsGGoTpU13x7C6VCJahEAzpMc3LgWg85Ec8+uAHj1CJcl0YQQopp594lE52lEyXPSLe5uUFVWfjmFLsPqo9MpJO3O4NiudBSDgdBXXwEg+4cfKfjDHBmzwcw/Ov4DgO8Ofsf+zP3lF1i3NfR8Tnu98ElI2V1p9yZEVan2QZ3/u06gqqqXXTuwrPRlHQd49913mT17NvPnz8dsLj9we/vtt/Hx8XF9RUREXMstVK3fvoQdXwMKDJsOPuHlJt2aspWFxxaicHH81wV5y5eTv2oVGAyEvvE6ik7Hgc0pnDmSjcGko+vtMaycoQ1L6dl6OOQ70Pu44dWrBj8bIYSoJXRmg2u7+jpF0fh4BZOWdJST+9YS119bunL93MNYi+1Y2rXD9y5t/HDqq6/hLClx5dMlrAsDowfiVJ28sfkNHM7LbGPc41lo0A/sRTBnOBSU3fkkxI2i2oLgwMBA9Hr9Jb2+aWlpl/T2XhAaGlpmeoPBQEBAQKnj77//Pm+99RbLli2jZcuWl63Liy++SE5Ojuvr5MmT13FHVeDEJlh8/j/xvq9Ao/hykxbaCnlj8xuAtjNci6AWrnOOvDzOvvkvAAJGj8LcqBGFuVY2zdN2HOowuB4HN/1KVsppQvxjCMzRfh4+N8egM8kWl0IIURNYWgdjivQCq5M+sdqSaRvnzqRpV2+8A83kZ5Ww9WdtknPwM/+HPigQa1ISmZ9/USqf5zo8h5fRi98zf2fWgVnlF6jTa50v/vUg5yR8PwIcZQ9FFOJGUG1BsMlkom3btixfvrzU8eXLl9OlS5cyr+ncufMl6ZctW0a7du0wGi+uV/vee+/x5ptvsmTJEtq1a3fFuri5ueHt7V3qq8bJOQXfPwBOOzS7Hbo9fdnk7/72Lsl5yYRYQhjXZlypc2kffog9PR1TVJRrMtyGH45QUmgnMMKTqGZ6ti74HoBu0XeCQ8XcxB/3FoGVc29CCCGumaJT8Lu9oTZJLt1I83o9sRYVsn72V/S6rwkAe9ac4uzxXPTe3oS+/DIAGdOmUZKY6Mon0D2Q8e20CW8f7/yYQ+cOlV+oux/cMxtMntrqREtfKj+tEDVctQ6HGD9+PNOnT2fGjBkcOHCAp59+muTkZMaO1da7ffHFF3nggQdc6ceOHcuJEycYP348Bw4cYMaMGXz55Zc888wzrjTvvvsu//jHP5gxYwbR0dGkpqaSmppKfn5+ld9fhbEVwZz7tJm5IS2uOA54ZfJK5h2Zh4LCW93ewtt0Magv3LmT7NlzAAh94w10bm4k/57Jkd/OoijQa3hjVv/nCxw2G+0b34IhS0Ex6fEdWl+2uBRCiBrGGOqBVy9tWFwzcxdMencObVqHw36CRh1CQIXVsw7icDjxGjAAz969wWYj5ZVXUZ1OVz7DGg6jV0QvbE4bL6x/gWJ7cfmFBjeB28/3Jm/7AnbOrMxbFKLSVGsQfPfddzNx4kTeeOMN4uLiWLduHYsWLSIqKgqAlJSUUmsGx8TEsGjRItasWUNcXBxvvvkmn3zyCcOGDXOlmTJlClarlTvuuIM6deq4vv64jNoNxVasfeSUshssAXDPt2DyKDd5emE6r216DYAHmz1IhzodXOdUq5WUV7QJEj7DbsejYwdsVgdrZ2v/9bfoHc65Uwkc37UDTzdf6qnNtbQDojD4ymQ4IYSoibx7R2IIcociJ71b3A/Aiumf0mFwBG4eBjJP5bN75UkURSH0lX+is1go2rmT7O+/d+WhKAqvd3mdAHMAidmJTNw58fKFNrkZep3vBf7v07KRhrghKWq5W8XUXrm5ufj4+JCTk1O9QyNsxTB3OCSuAIMZ/jYPoruVm1xVVR5d8Sgbz2ykiX8Tvh30bamd4dI++JDMadPQ+/tTf9Gv6H192TQvkYTlyXj6uTF4XENmvzyO4oJ8bm09Dvdsd0yRXgSNbYWik15gIYSoqUqO5ZD+xR4AthQs4kTaXuIG3EydxkNYPfMgBqOOe17piE+QO+e+mcnZt95CZ7EQ89MCTJGRrnzWnVrHYysfA2Bqv6l0DetafqFOJ8wfA/t+BJ0B7vwaYgdX5m1eUY35+y1uCNW+OoQoh7UQZt+jBcBGC9z3/WUDYNCWuNl4ZiNuejfe6f5OqQC4YOs2MqdPByD0tVfR+/pyJjGbhBVaT3v3uxuy9pvPKC7Ip3lMT9yz3UGv4DesoQTAQghRw7nV88Gjo7YOfMfQm9EpenYt/RUP7zTCGvlitzlZ+fV+nE4Vv+H34d6uLc7CQs48+1ypLZV7hPfgnsb3APCPjf/gXPG58gvV6eC2z6H5Hdp8le9HwO8LKvU+hahIEgTXRNYCmH03HFutbYk8/Eeo1/OylxzJOsJHOz4CYHzb8dT3re8658jJ4czzz4Oq4nPHMLzj47EW2Vnx1X5QoUmnUAqz93Bs52+YTZ40c9cmJnr1isAYUv7QCyGEEDWHz8AYdF4mlDyVPnHafJpln39CtzujMJr1pBzNIWHZCRS9nrAJE9B5elK0ezcZn00tlc//tfs/6vnUI6Mog1c3vcplPzDWG7TxwS3vAdUBP46CvT9W5m0KUWEkCK5p8s7CrDsgaZ02+/b++RB9mY+jgOzibMatGkeJo4RuYd24t8m9rnOqqpLy2mvYU1MxRkUS+uKLAKz//jB5mcV4BZiJ6+fH6q+1SQ7xLUZBkRNDkDvevWVNYCGEuFHozAb8hmodIAE5wUSGtCA3PY2di7+j+12NANj2SxLpyXkYw8IIfe01ADI++4zCnQmufMwGMxN6TMCgM7Dm5Bqm7pn6v0X9T8F6GDoF4v6mBcLzx8C2aSCjLUUNJ0FwTZK4AqZ2heRNYPKC+xdAZKfLXmJz2hi/djyn8k8R5hnGW93eKrWKQ87PP5O3eAkYDIS9/z46Dw+OJqRxcHMqKNB3RCyr/zMFa1EhcfX64X7ODDoF/7sboxikeQghxI3EvVkgljbBoEKnoJsxKCb2rlyK2XKGeq2DcDpVls/4HbvVgc8tN+N962BwOjnz3HM4/rCKUhP/Jq7d5KbsmsLyE8vLK1Kj08Otk6DNA6A6YdEz2rKeRVmVebtC/CkS5dQEdiss+wfMGqYtgxbcFEYvh4gOV7x0wrYJ/Jb6GxaDhcl9JuNn9nOdsyYnc/aNNwEIevxx3Fu0oCCnhDWztNUg2sRHkZm8lRN7EvAxB9HYoK2p7N0/ClO4VyXcqBBCiMrme2t99H5uKAUq/Vo+BGjDIjrfFo7F20RWaiGbFhwFIPSf/8QYFobt1CnXJkoXDGs0jOGxwwF4ecPLl18/GLQxwrd8DPH/Ap0RDvwCn3WDE5sr/iaFqAASBFe31L0wIx42TdK+bz8axqyC4NgrXjr34FzmHpqLgsKEHhNo4NfAdc5ZUsLpZ57FWViIe7u2BIwZjaqqrPrmAMUFNgIjPKnfWs/q/0xDQaFPo/vBpmKK9sarZ/lbMQshhKjZdGYD/nc3BgV8cn1pXLcT+ecyWTfzM3rfr22isXf1KZL3Z6L38qLue++CTkfOzz+T/dNPpfJ6pt0zdK7TmSJ7EU+seoLMoswrFK6DLk/AqGXaznK5p+DrQbD6LSi5gdfrF39JEgRXB1WF4xu1sb9Tu8GZBDD7wt3fws0fgNH9illsS9nGO9veAWBcm3H0iuj1h+xVUl97neI9e9D5+BA2YQKKXs+uFSdJ/v0ceoOOnvfV49dPJmC3ltC58W2Y8owobnr872osq0EIIcQNzi3aB6/z8zrivHvhYfLlyLZNnDu1meY9wwBY8fUBCrJLsLRpQ+Df/w5A6iuvUrR3rysfg87Aez3fI8o7ipSCFMavGY/tarZKDmsDj6yDVvdqwyPWToCJzWH121BwhUBaiCoiQXBVcjq1BcW/7K/9Z5y4HBSdtg3yoxsh9parymZ/5n6eXvM0dtXOoJhBjGo+qtT5rJkzyVmwAHQ6wj78AGNYGKcOZbF5vrZNZpdh9UlY9A3nTp8kLLAxEbaGAPgObYDBXzbFEEKIvwLvvpEYwz2hRCW+2UgA1s78kvqtVfzrelCUa2XJF/tw2J0E/v1RPHv3RrVaOfXY49jS0lz5+Lj58EmfT/AyerEzbScvbngRu9NeXrEXuXnBbVNh2Jdar3BRFqx9RwuGFz8P2clXzkOISiRBcFXaPVvb/vjUb6B3g3Yj4YkdcOdX4HN1QxAOZB5gzLIx5FpziQuK4/Uur5eaCFewaRNnJ7wLQPBzz+LZtSt554pZNn0fqgqNO4aicJD961bhprfQtc7toIJ7qyAscUGVcttCCCGqnqLXaZOcjTpM2Ua6Nbsbp8PO4snv0ef+epjcDaQey2Hjj4koOh1133sXU/362NPSOD3uSZxWqyuvej71eL/n+xh0BpYeX8pL61+6ukAYoMUd8Ph2bTONOq3AVghbp8K0PnA1vcpCVBIJgqtSs9u0/4a7jYen9sItH2nfX6VD5w4xZrkWALcMasln/T7DbLjYc2tNTubU0+PB4cBnyBD8R4zAYXOy5It9FOVp44Cb9zCzasZUFBRuajEGJd+J3t+M35D6pYJpIYQQNz5jkAXfIdp8kbDCaOrXaUNu+lm2zPuCfg9qc0/2rjnFoS0p6D09ifh0Mjpvb4p27SL1jTdKrRHcJawLH/X6CIPOwOLji3lpwzUEwjq99jfw4bVw/08Q01PrCNIbK/qWhbhqEgRXJZNF+2+436vgFXJNlx46d4jRy0aTU5JDy8CWTO03FU+Tp+u8I7+AU489hjMnB3OLFoS+ofUQr/v+MGnHc3GzGOg3ogGLJ7+L3VpCl8bDMOeYUYw6Av4Wi84iv4iEEOKvyKNdiGs3uXbe8Xi5BZD422ayTm+i3c3RAKz+9hDpyXmYoqMJ++ADbaLcj/PImvVtqbx6RfTig54fYFAMLE5azMsbXsbhdFx9ZRQF6veGEb9Az+cr6haFuC4SBFc1nf6aL7kwBCK7JJsWgS2Y2n8qXqaLS5g5i4s59dhjlBxJRB8USPjkSejc3Ni/8Qz7158BBfo/FMv62Z9y7swp6ge1JtyqLajud3tDTHU9yytaCCHEX4Dv4PqYIr3AqtK/4UPoFQNrZn5JSEQ2Uc0DcNicLP58L8X5Njy7dyP4//4PgLNvv03uokWl8uoT2UcbGqEYWJS0iJc2vHR1k+X+13X8PRSiIkkQXMMtPLqQ+xffT1ZJFs0Dml8SAKtWK6effIrCrVvRWSxETJmCMSSE5P2ZrP1WW9Ox4+B6JO36haPbt+LjHkw7vwEAeHapi6V1cLXclxBCiKqjGHQEDI9F52nEWKCnX+wIVKeThRPfoU28F96BZvIyi1n02R5sVgf+Ix/C9847wenk9HPPk7dqdan8+kb15b2e77kC4ZFLR5JWmFZO6ULUTBIE11A2p40J2ybw0oaXXNshfx7/Od4mb1ca1W7n9LPPkb92LYrZTMTnU3Fv0YKzx3NZ/Pk+nE6Vhu1DMBoPsOO/CzAoJvo3fNC1HrDPzTHVd4NCCCGqlN7HjYD7moAOfIsCad/gZqxFhSya9G963x+Fm8VAytEclk3/HdWpEvraq3jfcgvY7Zx+6ikKNm0qlV+/qH5M6jsJL5MXu9J3cfd/7yYhLaGc0oWoeSQIroEyizJ5eNnDzDowC4CHWz7M5D6TSwfATicpL/+DvKVLwWgkfNIkLO3bk322kP9O3o29xEFErB8N25awcsZn6NAxqOWj6PMUdF4mAobHoujlxy+EELWJWz1ffAZpE7LrOZvTsG4HctLOsm7WRwwY0wS9UcfxPRms+fYQ6HTUffstPPv1RbVaOfnY4xTu3Fkqv25h3Zhz8xwa+DYgoyiDkUtHMvfg3FIT6oSoqSQKqkFUVeW/x/7LnQvvZPvZ7XgYPZjYeyJPtH4C/R/GTqkOB6mvv0HOzz+DXk/Yhx/g2b0bBdkl/PLxLorzbQRFetFhsC+/fjwB1emkX7OHcM/VJsIFPtAUvZepGu9UCCFEdfHsWhdLuxBQoY2lD3V86pNy+CB7lv+H+FFNURQ4sCmFrT8fQzEaCfvwQzy6dUMtKuLkw49QtGtXqfwivSP5dtC3DIgegN1p519b/8VTq5/iVN6p6rlBIa6SBME1xO+Zv/PA4gd4cf2LpBelE+0dzXeDvqNvZN9S6ZyFhZwa9yTZc+eColD3nbfx7t+fkkIbCyftJu9cMT5B7vS8tw6/fPAGJYUFdGlwO36FgaAD/7/FYorwKqcWQggh/uoURcHvtoaYm/iDQ6V76J34uAVxcONakvf8TM/7GgOwY8kJdq88ic5kInzSJ1jatcOZn8+JEQ+Su3x5qTwtRgvv9XiP8W3Ho1f0rDq5iiE/DWFSwiQKbYXVcZtCXJGiymcWl8jNzcXHx4ecnBy8vb2vfMGfcCb/DF/s+YL5R+ajouJucOfhlg9zf9P7cdO7lUprT0/n5KN/p3jfPhSTSQuABw2iMNfKwkm7yDiZj8XbxIAxUSz65DVy0s7SMrw3scYOAPjd0QiPdte2NJsQQoi/JqfVQfq0vdhO5uE0qyw8PIViRz7th9yBu09Pti1MAqDLsAa07h+Js6CAU+PHU7B2HSgKwc8/h/+IEZesMX8k6wgTfpvA1pStAIRYQniyzZMMiB6ASV+5n0JW5d9vceOTILgMlf0mKrAVsOz4MhYeW8hvqb+5jt9S7xaeavMUIR6XBqoliYmcfPgRbGfOoPf1JXzKp1jatCHvXDG/fLyL7LOFuHsZ6f9QFMs+f5Ps1BQa1+1InFsvALzjo/DuE1nh9yKEEOLG5ci3kj51D/aMIuyeDn7Z9wk21UrH2+9Gb+rMzqXa1sbtBkXTYXAMOByk/vvfZM+eA4DfffcR8tKLKAZDqXxVVWVV8ire2/4ep/NPA9r2yzdF38St9W+lRWCLStmgSYJgcS0kCC5DZb2JDmcdZsa+Gaw8sZJiRzEACgod6nTg8bjHiQuOK/O6vFWrOfP88zjz8jBFRRHxxeeYoqLIPlvIzx8nkH+uBE9/N/o/FMOSKW+QdeYUjep0oLWlNzjBo2MovkMbyI5wQgghLmHPLCLts904823YvOws3DcZm7OELncNx+TRmS0/HQOgRe9wut/ZEBQ499XXpL33HqgqHj26U/eddzD4+1+Sd4mjhJn7ZzL7wGzSii4uoRbtHc2t9W9lVItR6JSKG5kpQbC4FhIEl6Gy3kQ7zu7gwSUPAtovgCENhnBzzM3U8axTZnpHXh5n33qbnAULAHBv25bwyZMw+PmRcSqfXz5OoCjPhm+IhX4PRbN40htknkqmcWhH4iy9QAX3VkHa3vE6CYCFEEKUzXomn4zpe3EW2rF52Pjv/s+wOovods8DuPt2Zv3cIwA07hRKn/uboNPryF26jDPPPYdaUoLe35/Q11/Du3//MvN3OB1sTd3KL0d/cXUENQ9ozuxbZlfofUgQLK6FBMFlqKw3kaqqTEqYRO+I3jQPbH7ZntmCTZs48/I/sKekgKLgP/IhgsaNQ+fmxtGENFb+5wC2YgeBEZ50HRbA4slvkZt+liahnWnl3gMAS9sQ/IY1lABYCCHEFdlSC0ifvlfrEXa3sejQ5xQ7CogbcAthsbeyauYhVKdKWGM/4kc1w+JtovjgQc489zwlhw8D4H3rYEJffhm9j0+55VwYEujj5kOfyD4Veg8SBItrIUFwGarzTWQ7m0bGp5+S/f33ABgjI6n7zttY2rTB4XCyZcFRdq04CUDdhr607Kmy+NN3KSksoEV4T5oaOwHnh0AMaSABsBBCiKtmSyvUAuFcK3Z3O4sOf0GRPY96bdrTvM9IVs08it3qxMPXjQFjmlOnvg9Oq5WMyZ+SOX06OJ0YgoMJfv45vG+6CUVftVsjSxAsroUEwWWojjeRLTWVzGnTyf7hB1SrFQC/++4l+Jln0FksFGSXsHT6PlIScwCI6x+Jt38SK6ZNxulw0KnBUKIc2rI2nl3r4nNLPRkDLIQQ4prZM4tIn7YXR3YJTgusPPYN54pSCI6uT+8Hn2Hd3NNkpRai0yl0GdaAln3CURSFol27OPPCi1iPHwfAVK8egY+OxXvQoCoLhiUIFtdCguAyVNWbSFVVSg4fJmvOHHJ+nIdqswHa2N+gJ8fh0UFb2ixpTwarZx2kKNeK0ayn9/2NOHtkBVsXzEWvGOnXZAS+xQEAePUKx3tAtATAQgghrps9q5iM6XuxZxaDAbZlLSEpYzdeAUEMeuJ5ft9gJ3GHNtGtfusgut/TCA8fN5xFRWR+9RXn/vMNzhyt08YUHU3AmNF49e+PvpIDUwmCxbWQILgMlfkmUh0OinbuJG/FSvJWrsR26uKOOpZ27Qh8/DEsHTuiKAq5GUWsn3uY43szAQgI86DrsBA2zP6UM4cPYDF4M6DhSEzFbqBX8BvaAI/2oRVaXyGEELWTo8DGue8OUHJUC2YT7bvZcXIJOr2Brnf/DZNHezbPP4bTqWIy6+k4pB7Ne4Sh0+tw5OeTNWsW5776Gsf5YBiDAY8OHfDs1xevPn0whlb83ysJgsW1kCC4DJX1Jspfv4Ezzz2HIyvLdUxxc8Oja1f8Hxzh6vm12xwkLEtmx5ITOGxOdDqFVv3C8fJLZs1/pmItKqKOdwO61bkdnVVB52kk4G+xuEWXPxFBCCGEuFaqQyXn12PkbzoDQJYpnVWHZ2JXbUQ0bUH7oQ+zfVEGaSfyAAiM8KTHPY2pU1/7e+TILyBr9nfk/Pwz1sSjpfK2tG9P5Df/qdBPLiUIFtdCguAyVNabyJqczNH4Aeh8fPDq1QvPfn3x7NoVncWinS+yc2BTCrtXnSQvU1tHOKyxLx0G12Xnr99waNM6dIqeTvWGEqE2ABWMYZ4E3N8Ug6/b5YoWQgghrlvBb6lk/ZQIDhWHu5MNJ+eRmncMNw8P+jz0KA5nA7b+fIySQjsAMa0CiesXQZ0Gvq4gtyQpifxVq8hbsZKiXbvw7NOHiE8nV2g9JQgW10KC4DJU5puoMCEB9xYtSu2uk5NeyJ7VpziwKQVbsQMAi4+JzkOjyUvfytYF31NSWECAuS49Y+7BWGzU0rQJxndoA3Smqp19K4QQovYpOZ7DudmHcOSUAHBKSWRL0i84VBthTZrS6fYHOLZHz4GNKa5rAiM8adU3goZtQ9AbL26KYc/IwJGXh1tMTIXWUYJgcS0kCC5DZb+JnA4naSfySN5/jpP7M0lNyoXzPwW/UAst+4SBmsjmH2aSm56GXjHQLmoQ0bpYUEHnZcTvtoa4Nw2o8LoJIYQQ5XEW28n5NYmC31IBsLnZ2HhqAWfzkwBo3KUHLfrcybE9JRzakorD5gTA7GEkoqk/kU39iYj1x6OSPr2UIFhcCwmCy1BZb6L05Dx2LDnOqYNZro+MLohs5k+zbkHkn9vL7mWLSTt+FAUdTUI60dyvG7oS7eMk97ggfAfXR+9hrLB6CSGEENei+HAWWfMO48jRlvTMdc9mU+J8cqzp6A0GYrv3pkmXfqSftrBvzWkKzqe7ICDMg6jmgXQaWrHLeUoQLK6FBMFlqKw30dmkXH6csB0AN4uB8CZ+RDYNwN0rh6O/rebAhtVYi4oAiPFtRZvQfhhKtGETeh83fAfXw715YIXVRwghhLhezmI7OYvO9wqfjyTSDWfYmrSQAns2AEHR9WjRZwD+dVuTmlRM8u+ZpCXngQp1Gvhw+zNtK7ROEgSLayFBcBkq603kdKrsXHoCvxAnRXlJnNq/l1P795KTdhYAs96DJnU6Ud87DkORFvzqPI149Y7As2MdFIPuctkLIYQQVc6WVkju8hMU7c3QDiiQbc5k38m1nMlLREVFbzBQp2ETwpu2IKReLHZ7MG4WMzEtK7ZjR4JgcS0kCC5DZb2JkvftYfkXk8g+e3HSgF4xUsejPs3Du+Nt9Uc5/9NQzHq8eobj2SUMnZtMfBNCCFGzWU/nk7vsOMWHLi4D6jA5SS48wMHULeTaMlzH9UYjUS3iuO35Vyu0DhIEi2thuHISUVE8fH0pTM8mzKMRUSHNCTJH4FZs1gJfbbItpkgvLG1DsLQKQmeWH48QQogbgynMk8CHmmM9nU/hjrMU7kqDQjsxhmbEhDfDaVTJ1WdxOusQZ84dxumQPjhRvSTKqkLuORZuixqnfaMC2vBf9D5uuMcF4dE2BGOwpdrqJ4QQQvxZpjBPTGGe+AyKoejAOQp3nKU4MQudDXxt/vi6d6ZZWGdQ5FNOUb0kCK5CpjAvAAzBFtxivDFF++AW7Y3e161CZ8cKIYQQ1U0x6LC0CMTSIhDV7sR6Op+SpBysx3MpOZ6LOdK3uqsoajkJgquQwdeNuq90QmeR5c2EEELUHopBh1uUN25R2jhd1amiljiquVaitpMguIpJACyEEKK2U3QKiruEIKJ6yZpbQgghhBCi1pEgWAghhBBC1DoSBAshhBBCiFpHgmAhhBBCCFHrSBAshBBCCCFqHQmChRBCCCFErSNBsBBCCCGEqHUkCBZCCCGEELWOBMFCCCGEEKLWkSBYCCGEEELUOhIECyGEEEKIWkeCYCGEEEIIUetIECyEEEIIIWodQ3VXoCZSVRWA3Nzcaq6JEEIIIa7Whb/bF/6OC3E5EgSXIS8vD4CIiIhqrokQQgghrlVeXh4+Pj7VXQ1Rwymq/Lt0CafTyZkzZ/Dy8kJRlArNOzc3l4iICE6ePIm3t3eF5i3KJ8+9eshzrx7y3KuHPPfq8cfn7uXlRV5eHnXr1kWnkxGf4vKkJ7gMOp2O8PDwSi3D29tbfklWA3nu1UOee/WQ51495LlXjwvPXXqAxdWSf5OEEEIIIUStI0GwEEIIIYSodSQIrmJubm68+uqruLm5VXdVahV57tVDnnv1kOdePeS5Vw957uJ6ycQ4IYQQQghR60hPsBBCCCGEqHUkCBZCCCGEELWOBMFCCCGEEKLWkSBYCCGEEELUOhIEV4IpU6YQExOD2Wymbdu2rF+//rLp165dS9u2bTGbzdSrV4+pU6dWUU3/Wq7lua9ZswZFUS75OnjwYBXW+Ma2bt06Bg8eTN26dVEUhZ9++umK10hb//Ou9blLW68Yb7/9Nu3bt8fLy4vg4GCGDh3KoUOHrnidtPnrdz3PXNq7uBYSBFewuXPn8tRTT/Hyyy+TkJBA9+7dGThwIMnJyWWmT0pKYtCgQXTv3p2EhAReeuklxo0bx7x586q45je2a33uFxw6dIiUlBTXV8OGDauoxje+goICWrVqxeTJk68qvbT1inGtz/0Caet/ztq1a3nsscfYsmULy5cvx263Ex8fT0FBQbnXSJv/c67nmV8g7V1cFVVUqA4dOqhjx44tdaxJkybqCy+8UGb65557Tm3SpEmpY4888ojaqVOnSqvjX9G1PvfVq1ergJqVlVUFtfvrA9QFCxZcNo209Yp3Nc9d2nrlSEtLUwF17dq15aaRNl+xruaZS3sX10J6giuQ1Wplx44dxMfHlzoeHx/Ppk2byrxm8+bNl6QfMGAA27dvx2azVVpd/0qu57lf0Lp1a+rUqUPfvn1ZvXp1ZVaz1pO2Xr2krVesnJwcAPz9/ctNI22+Yl3NM79A2ru4GhIEV6CMjAwcDgchISGljoeEhJCamlrmNampqWWmt9vtZGRkVFpd/0qu57nXqVOHL774gnnz5jF//nwaN25M3759WbduXVVUuVaStl49pK1XPFVVGT9+PN26daN58+blppM2X3Gu9plLexfXwlDdFfgrUhSl1Peqql5y7ErpyzouLu9annvjxo1p3Lix6/vOnTtz8uRJ3n//fXr06FGp9azNpK1XPWnrFe/xxx9nz549bNiw4Ypppc1XjKt95tLexbWQnuAKFBgYiF6vv6T3MS0t7ZLegAtCQ0PLTG8wGAgICKi0uv6VXM9zL0unTp04cuRIRVdPnCdtveaQtn79nnjiCX755RdWr15NeHj4ZdNKm68Y1/LMyyLtXZRHguAKZDKZaNu2LcuXLy91fPny5XTp0qXMazp37nxJ+mXLltGuXTuMRmOl1fWv5Hqee1kSEhKoU6dORVdPnCdtveaQtn7tVFXl8ccfZ/78+axatYqYmJgrXiNt/s+5nmdeFmnvolzVNiXvL2rOnDmq0WhUv/zyS3X//v3qU089pXp4eKjHjx9XVVVVX3jhBfX+++93pT927JhqsVjUp59+Wt2/f7/65ZdfqkajUf3xxx+r6xZuSNf63D/66CN1wYIF6uHDh9V9+/apL7zwggqo8+bNq65buOHk5eWpCQkJakJCggqoH374oZqQkKCeOHFCVVVp65XlWp+7tPWK8eijj6o+Pj7qmjVr1JSUFNdXYWGhK420+Yp1Pc9c2ru4FhIEV4JPP/1UjYqKUk0mk9qmTZtSy7mMGDFC7dmzZ6n0a9asUVu3bq2aTCY1Ojpa/eyzz6q4xn8N1/LcJ0yYoNavX181m82qn5+f2q1bN/XXX3+thlrfuC4sRfS/XyNGjFBVVdp6ZbnW5y5tvWKU9cwB9auvvnKlkTZfsa7nmUt7F9dCUdXzo/SFEEIIIYSoJWRMsBBCCCGEqHUkCBZCCCGEELWOBMFCCCGEEKLWkSBYCCGEEELUOhIECyGEEEKIWkeCYCGEEEIIUetIECyEEEIIIWodCYKFENXqtddeIy4urrqrIYQQopaRIFgIUWkURbns14MPPsgzzzzDypUrq7uqpRw/fhxFUdi1a1d1V0UIIUQlMVR3BYQQf10pKSmu13PnzuWVV17h0KFDrmPu7u54enri6elZHdUTQghRi0lPsBCi0oSGhrq+fHx8UBTlkmP/OxziwQcfZOjQobz11luEhITg6+vL66+/jt1u59lnn8Xf35/w8HBmzJhRqqzTp09z99134+fnR0BAAEOGDOH48ePl1i0rK4vhw4cTFBSEu7s7DRs25KuvvgIgJiYGgNatW6MoCr169XJd99VXXxEbG4vZbKZJkyZMmTLFde5CD/KcOXPo0qULZrOZZs2asWbNmqsqVwghRNWRnmAhRI2zatUqwsPDWbduHRs3bmTUqFFs3ryZHj16sHXrVubOncvYsWPp378/ERERFBYW0rt3b7p37866deswGAz861//4qabbmLPnj2YTKZLyvjnP//J/v37Wbx4MYGBgSQmJlJUVATAtm3b6NChAytWrKBZs2au66dNm8arr77K5MmTad26NQkJCYwZMwYPDw9GjBjhyvvZZ59l4sSJNG3alA8//JBbb72VpKQkAgICLluuEEKIKqQKIUQV+Oqrr1QfH59Ljr/66qtqq1atXN+PGDFCjYqKUh0Oh+tY48aN1e7du7u+t9vtqoeHhzp79mxVVVX1yy+/VBs3bqw6nU5XmpKSEtXd3V1dunRpmfUZPHiw+tBDD5V5LikpSQXUhISEUscjIiLU7777rtSxN998U+3cuXOp69555x3XeZvNpoaHh6sTJky4YrlCCCGqjvQECyFqnGbNmqHTXRytFRISQvPmzV3f6/V6AgICSEtLA2DHjh0kJibi5eVVKp/i4mKOHj1aZhmPPvoow4YNY+fOncTHxzN06FC6dOlSbp3S09M5efIko0aNYsyYMa7jdrsdHx+fUmk7d+7sem0wGGjXrh0HDhy4rnKFEEJUDgmChRA1jtFoLPW9oihlHnM6nQA4nU7atm3Lt99+e0leQUFBZZYxcOBATpw4wa+//sqKFSvo27cvjz32GO+//36Z6S+UNW3aNDp27FjqnF6vv+I9KYpyXeUKIYSoHDIxTghxw2vTpg1HjhwhODiYBg0alPr6317aPwoKCuLBBx9k1qxZTJw4kS+++ALANQbY4XC40oaEhBAWFsaxY8cuKePCRLoLtmzZ4nptt9vZsWMHTZo0uWK5Qgghqo70BAshbnjDhw/nvffeY8iQIbzxxhuEh4eTnJzM/PnzefbZZwkPD7/kmldeeYW2bdvSrFkzSkpK+O9//0tsbCwAwcHBuLu7s2TJEsLDwzGbza6VLMaNG4e3tzcDBw6kpKSE7du3k5WVxfjx4115f/rppzRs2JDY2Fg++ugjsrKyGDly5BXLFUIIUXWkJ1gIccOzWCysW7eOyMhIbr/9dmJjYxk5ciRFRUV4e3uXeY3JZOLFF1+kZcuW9OjRA71ez5w5cwBtHO8nn3zC559/Tt26dRkyZAgAo0ePZvr06Xz99de0aNGCnj178vXXX1/SE/zOO+8wYcIEWrVqxfr16/n5558JDAy8YrlCCCGqjqKqqlrdlRBCiL+C48ePExMTQ0JCgmwFLYQQNZz0BAshhBBCiFpHgmAhhBBCCFHryHAIIYQQQghR60hPsBBCCCGEqHUkCBZCCCGEELWOBMFCCCGEEKLWkSBYCCGEEELUOhIECyGEEEKIWkeCYCGEEEIIUetIECyEEEIIIWodCYKFEEIIIUStI0GwEEIIIYSodf4fEFZk8xOnjroAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for data in results:\n",
    "    plt.plot(\n",
    "        data['Time steps'],\n",
    "        data['Max muscle activity'],\n",
    "        label='{:.1f} cm'.format(100* data['PatellaLength'])\n",
    "    )\n",
    "\n",
    "plt.title('Effect of changing patella tendon length')    \n",
    "plt.xlabel('Time steps')\n",
    "plt.ylabel('Max muscle activity')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
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      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_view_name": "ErrorWidgetView",
       "error": {},
       "msg": "Failed to load model class 'LayoutModel' from module '@jupyter-widgets/base'"
      }
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     "0d24beb4bb6c448c8ad08daee160b953": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
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       "error": {},
       "msg": "Failed to load model class 'LayoutModel' from module '@jupyter-widgets/base'"
      }
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     "0ea3ee2ba69f4e5b8be79e12a904eb3c": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_c13b73fff27646f99711ad9858f586a8",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Processing tasks <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> 7/7 <span style=\"color: #808000; text-decoration-color: #808000\">0:00:15</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "Processing tasks \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 7/7 \u001b[33m0:00:15\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "11400b7db356447ea8aac81532c4d29b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_view_name": "ErrorWidgetView",
       "error": {},
       "msg": "Failed to load model class 'LayoutModel' from module '@jupyter-widgets/base'"
      }
     },
     "129faf74be434ae5a6a86e415ab146be": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_view_name": "ErrorWidgetView",
       "error": {},
       "layout": "IPY_MODEL_9bf5fdef05354997a6b1dd190ec64406",
       "msg": "Failed to load model class 'HTMLModel' from module '@jupyter-widgets/controls'",
       "style": "IPY_MODEL_4a01d95b762645e19efde9885e2e15a4",
       "value": " 7/7 [00:05&lt;00:00,  1.51it/s]"
      }
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     "17e49e989fbc434aab2750a95cdde67d": {
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      "model_module_version": "2.0.0",
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      "state": {
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       "error": {},
       "layout": "IPY_MODEL_ef217a21fbc14bc4ba20dfd22df8dc87",
       "msg": "Failed to load model class 'HTMLModel' from module '@jupyter-widgets/controls'",
       "style": "IPY_MODEL_f3595b4e49284744819a231e9c51e8c4",
       "value": "100%"
      }
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     "1c66b897ee8d41febdcecef7385de4a4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_view_name": "ErrorWidgetView",
       "children": [
        "IPY_MODEL_faea7a5f646b4b868292362cee8de848",
        "IPY_MODEL_617aad94c3694b65ba34ebd3526f985a",
        "IPY_MODEL_129faf74be434ae5a6a86e415ab146be"
       ],
       "error": {},
       "layout": "IPY_MODEL_66200b9858c44bb8bb641b8a8fc76854",
       "msg": "Failed to load model class 'HBoxModel' from module '@jupyter-widgets/controls'"
      }
     },
     "20391c25a3b445f7a557ab1e1fce540f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_view_name": "ErrorWidgetView",
       "description_width": "",
       "error": {},
       "msg": "Failed to load model class 'DescriptionStyleModel' from module '@jupyter-widgets/controls'"
      }
     },
     "3312a61e86cd4882885876566c4f4e47": {
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