{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### **Script de análisis en Python para el artículo Supervivencia política de mujeres en Bolivia 2020-2025**"
      ],
      "metadata": {
        "id": "k417ctX5_KHm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "3VLpDbDqfOgU",
        "outputId": "5aecdf62-ed10-460b-ea61-69e7769062f4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x520 with 2 Axes>"
            ],
            "image/png": 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aFQsLC/bu3at0y3v58iXe3t50796dyMhIXrx4wYsXLwgJCaFFixb4+/unmjX0448/1vpy26BBAxITE3nw4ME72/m2NA8fPkxkZCRffPFFqslr0vuKnIyZmZmyHxoaSnh4OA0aNODy5ctKuK2tLZDURUqfSWSSv9SPHTtWK/zN1jwhBL///jvt2rVDCKHU84sXL2jRogXh4eFa9qRH06ZNtVrBatWqBSS1mqWcMCE5/N9//9W5LOnxLhoZNmyY1oQbhw8fJiwsjF69emmV3cDAgFq1anHs2DEg6V4ZGxtz/PhxQkNDs9TGZHx9ffH396d3796EhIQo17569YqmTZty4sQJNBoNGo2G3bt3065duzRbbt6mw+TyJZNsZ4MGDYiOjuaff/4BUFr4Dh48SHR0tM51kIyhoSGffPKJcmxsbMwnn3zCs2fPuHTpEpCkWycnJ60ZN42MjJSZLf/++2+tNLt06YKjo6NO+acsY3h4OC9evKBRo0b8+++/qbqwli1bVumWDuDo6EipUqW0dLt//35q1qyp1TJlaWnJxx9/TEBAQIbdUV++fIkQQqvHQ3qcOHGCWbNm0b17d5o0aaKEv379GgATE5NU15iamirn0+Kff/5h1KhR1KlThwEDBuicZso478Lo0aO5desWK1aswNDwv45cr1+/fuc8586dy5EjR5g/f77yvEzm9OnTfPrpp7Rv357hw4dz6dIlypcvz5dffqmkWa5cOc6cOcOIESNQqVQYGRkxbdo09u/fj4eHh1Z6yffrxYsX71R+iUTy/sguoBJJHmblypWULFmS8PBw1q9fz4kTJ7ReAO7evYsQgmnTpjFt2rQ003j27JnWzG0px6zAfz/W+rywv8nb0kwes5hyFj1d2bdvH19//TW+vr7ExsYq4Slf2Hv06MEPP/zA0KFD+eKLL2jatCmdO3ema9euqcYJpeTBgweo1epU44tKlSqldfz8+XPCwsL4/vvv+f7779NM69mzZ28ty5v1lOwsuLm5pRn+PvckmXfRyJsvdP7+/gBaL9YpSR5bZWJiwoIFC/jss88oVKgQtWvXpm3btvTv319rVsbMsPFN21K+oL9JeHg4cXFxREREvJMGk7l58yZTp07F29ubiIiIVHlAUt1NmDCBxYsXs2XLFho0aED79u3p27fvW7t/Ari4uGBhYaEVVrJkSSCpC3jt2rV58OABJUqUSKXt5K6Bb37MefN+ZsTp06eZMWMGZ8+eTeXAhoeHa5XhTT1D0v9+St0+ePBA+aCRnq1vuydCiAzP//PPP3Tq1Iny5cvzww8/aJ1LdmhTPjuSiYmJ0XJ4U/LkyRPatGmDjY0Nv/32m9YHkbelmRwnMTEx3WUhzMzM0tTDd999x7p165gzZw6tW7dOdc3b8kyLHTt2MHXqVIYMGcKIESPSjJMSY2NjRo8erTiD9evXT/celS5dOlVY8v3S5aOKRCLJGqQDKJHkYWrWrKm0VnTs2JH69evTu3dvbt++jaWlpdLaNXHiRFq0aJFmGsWLF9c6Tvkik5K3vWQBaa5L9b5pZsTJkydp3749DRs2ZNWqVTg7O2NkZMSGDRu0Jo8xMzPjxIkTHDt2jD///JMDBw6wY8cOmjRpwqFDh9K1T1eS67lv377pOhoVK1Z8azrp2aFL/aX3MpWYmJhh+d5FI2++SCansXnz5jQduZStFOPGjaNdu3bs3r2bgwcPMm3aNObNm4e3t7fWtPLva+Ob13733XdUrlw5zTiWlpa8fPkyzXO6EhYWRqNGjbC2tmb27Nl4enpiamrK5cuXmTx5slbL86JFixg4cCB79uzh0KFDjB07lnnz5nHu3DmtSXWyi/Qcgze5d+8eTZs2pXTp0ixevBg3NzeMjY3Zv38/S5YsSdW6nlX/98nY29ujUqky/BASFBRE8+bNsbGxYf/+/amWHnB2dgZIc33Ax48f4+Likio8PDycVq1aERYWxsmTJ1PFeVua9vb2mJiYEBAQkK7zPWDAgFQTNW3cuJHJkyczfPhwpk6dmuoaZ2dnjh07hhBC63mQbEdaZTl8+DD9+/enTZs2aU4mlB7JH6XS+r9Ja4KwlCTfr7TGGkokkuxBOoASST7BwMCAefPm0bhxY1asWMEXX3xBsWLFgKTuX82aNcu0vOzs7AgLC9MKi4uLS3eR5beR3MJ248aNdF/k0+L333/H1NSUgwcParV8btiwIVVctVpN06ZNadq0KYsXL2bu3Ll89dVXHDt2LN26cXd3R6PRcO/ePa1Wv9u3b2vFS54hNDExMVPrWR/SuieQ1IKSrIO0yAyNJN+/ggUL6pSGp6cnn332GZ999hn+/v5UrlyZRYsW8fPPP2e6jcm2WVtbZ3ito6Mj1tbW3LhxI8P00nO0jx8/TkhICDt37qRhw4ZK+P3799OMX6FCBSpUqMDUqVM5c+YM9erVY82aNXz99dcZ5v/o0SNevXql1QqYvBB5cvdhd3d3rl27hkaj0WoFTO6G6u7unmEe6fHHH38QGxvL3r17tVr3krv4vgvu7u6p/p9AN1sNDQ3x9PRMt45DQkJo3rw5sbGxHD16VHHMUlK+fHkMDQ3x8fGhe/fuSnhcXBy+vr5aYZDUmtauXTvu3LnDkSNHKFu2bKo0CxcujKOjIz4+PqnOXbhwQfkQ4eTkxOHDh9O0/U1nbc+ePQwdOpTOnTuzcuXKNK+pXLkyP/zwA35+flp2nT9/XjmfkvPnz9OpUyeqV6/OL7/8ovWh5m0kd+PVtetwSu7fv0+BAgXe6VqJRJI5yDGAEkk+wsvLi5o1a7J06VJiYmIoWLAgXl5erF27Nk3nLL3uR2/D09OTEydOaIV9//336bYAvo3mzZtjZWXFvHnzUk0znlFrgYGBASqVSivfgICAVDPMpfWVOvllKK0uU8m0atUKgOXLl2uFL126NJUdXbp04ffff0/TgXjXetYHT09Pzp07R1xcnBK2b98+goKCMrwuMzTSokULrK2tmTt3LvHx8emmER0dner+enp6YmVlleF9eB8bq1WrhqenJwsXLiQqKirda9VqNR07duSPP/5I88U9WYfJjtebznZya1dKvcbFxbFq1SqteBERESQkJGiFVahQAbVanWEdJJOQkMDatWu18li7di2Ojo5Uq1YNgNatW/PkyROtWXYTEhL43//+h6WlJY0aNXprPmmRVhnDw8PT/OCiK61bt+bChQvKEgUAr1694vvvv6do0aJpOlgpqVOnTpr369WrV7Ru3ZqHDx+yf/9+SpQokeb1NjY2NGvWjJ9//pnIyEglfPPmzURFRSnLfkBSa3qPHj04e/Ysv/76K3Xq1EnXri5duqT6/zt69Ch37txR0jQ1NaVZs2ZpbinLfeLECXr27EnDhg3ZsmVLut3WO3TogJGRkZbmhBCsWbOGwoULay394OfnR5s2bShatCj79u1LtxU4rf+tyMhIli5dSoECBRTN6cOlS5cyrDuJRJL1yBZAiSSfMWnSJLp168bGjRsZPnw4K1eupH79+lSoUIFhw4ZRrFgxnj59ytmzZwkODubq1at65zF06FCGDx9Oly5d+Oijj7h69SoHDx585y491tbWLFmyhKFDh1KjRg169+6NnZ0dV69eJTo6OtX6Zcm0adOGxYsX07JlS3r37s2zZ89YuXIlxYsX59q1a0q82bNnc+LECdq0aYO7uzvPnj1j1apVuLq6ZjgteuXKlenVqxerVq0iPDycunXrcvToUe7evZsq7vz58zl27Bi1atVi2LBhlC1blpcvX3L58mWOHDny3l0M38bQoUP57bffaNmyJd27d+fevXv8/PPPqcYvpsX7asTa2prVq1fTr18/qlatSs+ePXF0dCQwMJA///yTevXqsWLFCu7cuUPTpk3p3r07ZcuWxdDQkF27dvH06VN69uyZJTaq1Wp++OEHWrVqRbly5Rg0aBCFCxfm4cOHHDt2DGtra/744w8gaSKMQ4cO0ahRIz7++GPKlCnD48eP+fXXXzl16hS2trZUrlwZAwMDFixYQHh4OCYmJjRp0oS6detiZ2fHgAEDGDt2LCqVis2bN6f6gOHt7c3o0aPp1q0bJUuWJCEhgc2bNysfEd6Gi4sLCxYsICAggJIlS7Jjxw58fX35/vvvMTIyApImXVq7di0DBw7k0qVLFC1alN9++43Tp0+zdOnSVN0gdaV58+YYGxvTrl07PvnkE6Kioli3bh0FCxZ859b/L774gm3bttGqVSvGjh2Lvb09mzZt4v79+/z+++8ZjtGFJKdn8+bN3LlzRxkLCdCnTx8uXLjA4MGD8fPz01rbztLSko4dOyrH33zzDXXr1lXue3BwMIsWLaJ58+a0bNlSiffZZ5+xd+9e2rVrx8uXL1O1WPft21fZ//LLL/n1119p3Lgxn376KVFRUXz33XdUqFCBQYMG6Vw/Dx48oH379qhUKrp27cqvv/6qdb5ixYpK93JXV1fGjRvHd999R3x8PDVq1GD37t2cPHmSLVu2KA58ZGQkLVq0IDQ0lEmTJqVac9HT01Nx0FauXKlMjlSkSBEeP37M+vXrCQwMZPPmzanWRXwbz54949q1a6mWDJFIJNlMNs86KpFIMoHk6fjTmq4+MTFReHp6Ck9PT2Wq/nv37on+/fsLJycnYWRkJAoXLizatm0rfvvtt7em+ea0+sl5TJ48WRQoUECYm5uLFi1aiLt376a7DIQuaQohxN69e0XdunWFmZmZsLa2FjVr1hTbtm1Tzqe1DMSPP/4oSpQoIUxMTETp0qXFhg0bxIwZM0TKx9vRo0dFhw4dhIuLizA2NhYuLi6iV69e4s6dOxnWsxBCvH79WowdO1Y4ODgICwsL0a5dOxEUFJTmUhhPnz4Vo0aNEm5ubsLIyEg4OTmJpk2biu+///6t+QBi1KhRWmHJ0/S/uWxCcv29uZTAokWLROHChYWJiYmoV6+e8PHx0WkZCCHeTyMp7WrRooWwsbERpqamwtPTUwwcOFD4+PgIIYR48eKFGDVqlChdurSwsLAQNjY2olatWuKXX355a/3oamN62rpy5Yro3LmzcHBwECYmJsLd3V10795da0kAIYR48OCB6N+/v7KkSrFixcSoUaNEbGysEmfdunWiWLFiwsDAQCuv06dPi9q1awszMzPh4uIiPv/8c3Hw4EGtOP/++68YPHiw8PT0FKampsLe3l40btxYHDly5K3lb9SokShXrpzw8fERderUEaampsLd3V2sWLEiVdynT5+KQYMGiQIFCghjY2NRoUKFVPc8PX1lxN69e0XFihWFqampKFq0qFiwYIFYv359msuStGnTJs0ypNSjEEn3tWvXrsLW1laYmpqKmjVrin379ulkT2xsrChQoICYM2eOVnjyMhRpbW8+Q4QQ4uTJk6Ju3brC1NRUODo6ilGjRmktvZJse3pppvU6dePGDdG8eXNhbm4ubG1tRZ8+fcSTJ090KlcyyXpOb3vzGZSYmCjmzp0r3N3dhbGxsShXrpzWciBC/Hff09tSPsMPHTokPvroI+V/ztbWVjRv3jzV/42urF69Wpibm6eqW4lEkr2ohMik0dgSiUQiyfXcu3eP4sWLs3nzZq0WC0nux8vLixcvXrx1nOKHxpw5c9iwYQP+/v7vPaGTJGupUqUKXl5eLFmyJKdNkUg+aOQYQIlEIvmASO6qJ2fgk+QXxo8fT1RUFNu3b89pUyQZcODAAfz9/ZkyZUpOmyKRfPDIMYASiUTygbB+/XrWr1+Pubk5tWvXzmlzJJJMwdLSUqd1NiU5S8uWLdOciEkikWQ/sgVQIpFIPhA+/vhjXr58ya+//oqtrW1OmyORSCQSiSQHkGMAJRKJRCKRSCQSieQDQbYASiR5iBUrVlC9enVMTEy0pjGHpAkiTExMsLS0VLZHjx4p52/dukXTpk2xs7PDycmJjz/+mOjo6GwugUQikUgkEokkJ5EOoESSh3BxcWHq1KkMGzYszfMLFiwgKipK2VxcXJRzvXv3plSpUjx9+pTr169z9epV5syZk12mSyQSiUQikUhyAXISmAxISEjgypUrFCpU6K2L0Uok2UHNmjUBOHHiBK9fvyY4OFg5FxsbS1hYmFZYSu7du8fMmTOVyRIaNWrExYsX040vkUgkEolE8iGh0Wh4+vQpVapUwdAw/7pJcgxgBly8eFF54ZZIJBKJRCKRSCT5nwsXLlCjRo2cNiPLyL+ubSZQqFAhIEkEzs7OOWyNRPIfixcv5ubNm/z4449K2KVLlyhRogTm5uacOnWKkSNHsmjRIlq1agWAr68vn332GXfv3kWj0dCiRQvWrFmTr79wSSQSiUQikejK48ePqVmzpuID5Ffkm18GJHf7dHZ2xtXVNUdtSUxM5N69e3h6emJgYJCjtkhyHmtra8zMzLR0mbyfmJhIzZo1+fjjjzl69CjDhg0jNDSUPn36MHv2bEaMGMGrV68YM2YMkydPZseOHTlVDEkuQD5bJLoitSLRB6kXia7kRq3k96Ff0gHMI6hUKqysrFCpVDltiiSXk6yVlA/Re/fu8fr1a8aOHYtKpcLY2JhPPvlEaR2UfLjIZ4tEV6RWJPog9SLRFamV7Cd/u7f5CLVajbOzc77/IiHJmISEBGJiYkhISECj0RATE0NcXBxhYWHs37+f6OhohBDcunWLtWvX0qVLFwBKly6NpaUlq1atIiEhgcjISNatW0eVKlVyuESSnEY+WyS6IrUi0QepF4muSK1kP7Km8wiJiYn4+fmRmJiY06ZIcpCvv/4aMzMzvvnmG/744w/MzMxo3rw58fHxzJo1CycnJ+zs7Bg5ciQLFy6kW7duAFhaWvLHH3+wbds2ChQoQNGiRQkLC2PTpk05XCJJTiOfLRJdkVqR6IPUi0RXpFayHzkLaAYEBwfj5uZGUFBQjo8B1Gg0vHjxggIFCsgvJJIMkVqR6IPUi0RXpFYk+iD1ItGV3KSV3PTun5XIMYB5BLVaTcGCBXPaDEkeQGpFog9SLxJdkVqR6IPUi0RXpFayH/lJJo+QmJjIjRs3ZPO45K1IrUj0QepFoitSKxJ9kHqR6IrUSvYjHcA8gkqlwtnZWc6QJHkrUisSfZB6keiK1IpEH6ReJLoitZL95EoH8MSJE7Rr1w4XFxdUKhW7d+9OFcfPz4/27dtjY2ODhYUFNWrUIDAwUDkfExPDqFGjcHBwwNLSki5duvD06dNsLEXmolarcXBwyPG+0ZLcj9SKRB+kXiS6IrUi0QepF4muSK1kP7mypl+9ekWlSpVYuXJlmufv3btH/fr1KV26NMePH+fatWtMmzYNU1NTJc748eP5448/+PXXX/n777959OgRnTt3zq4iZDqJiYlcvXpVNo9L3orUikQfpF4kuiK1ItEHqReJrkitZD+5fhZQlUrFrl276NixoxLWs2dPjIyM2Lx5c5rXhIeH4+joyNatW+natSsA//zzD2XKlOHs2bPUrl1bp7xz00xAQgjCw8OxsbGRTeSSDJFakeiD1ItEV6RWJPog9SLRldykldz07p+V5MoWwIzQaDT8+eeflCxZkhYtWlCwYEFq1aql1U300qVLxMfH06xZMyWsdOnSFClShLNnz6abdmxsLBEREcoWGRmp5Jn8N639xMREnfaTfe2U+wkJCan2hRCp9lUqFTY2NsrXESFEmvsajUan/dxQpmTbU+7rU6b169dja2ubr8qUGfdJpVJhZWWVZjnyapny433KLWXSaDTY2tqmipOXy5Qf71NuKBOg/A7llzLlx/uUW8qk0WiwtrZGpVLlmzLlx/uUG8qU3u9QTpXpQyDPOYDPnj0jKiqK+fPn07JlSw4dOkSnTp3o3Lkzf//9NwBPnjzB2NhYcQ6SKVSoEE+ePEk37Xnz5mFjY6NsZcuWBVDGFgYFBREUFATA/fv3efToEZDUJTV5fOGdO3d48eIFkDROMTQ0FIAbN24QHh4OwNWrV4mKigLA5/Jl/vZ7xB7fh2z66yyvY2JJTEzEx8eHxMRE4uLilP2LFy9y+fJlAKKiorh69SqQ1OJ548YNAEJDQ/Hz8wPgxYsX3LlzB4CnT59y7949AHr06IGBgQEqlQoTExPc3d35/PPP+eeffzKlTFeuXOH169dJ5fPxIS4uLs0yAbx+/ZorV668U5m8vLz466+/AHj06BH379/PsvuUXWVKeZ/0KdPDsNfsP3udEzcC+PnsfSZtOMIPx/y4GhTGnhOX+SfwWZ4rU368T7mxTLdv3+by5cs8fvw435QpP96n3FCmmJgYLl26xMWLF/NNmfLjfcotZbp79y5nzpwhMTEx35QpP96n3FCm69evc/nyZUJCQnK8TMn1m9/Jc11AHz16ROHChenVqxdbt25V4rVv3x4LCwu2bdvG1q1bGTRoELGxsVpp1axZk8aNG7NgwYI084qNjdW65uHDh5QtW5YHDx5QpEgR5auBWq3W2k9MTESlUr11X61WK1/C1Go1B28+YebemzyJ+C9PJxtTZrQty0dlHDEwMACSvkwYGBgQGRmJmZkZRkZGyhcTAwMDrX2NRoMQIsP9AQMG8PTpUzZu3EhsbCyXLl1i0KBBfPLJJ8yfP/+9yqRSqUhISFAczOT9lOVI3jc0NFS+9iTvv2uZ0rs3mXGfcnuZHoXH0GzxCWITNOn+H5kYqvGe6IWTlXGeKFN+vE+5tUwajYbXr19jbm4OkC/KlB/vU24ok1qtJioqCjMzMwwNDfNFmfLjfcotZUpISODVq1dYW1uj0WjyRZny433KDWVKTExM83coJ8oUEBCAh4eH7AKa2yhQoACGhoZK61wyZcqUUVrqnJyciIuLIywsTCvO06dPcXJySjdtExMTrK2tlc3KygpAmZVIrVanuW9gYKDTfnK/ZgMDAw7efMKIny9rOX8AT8NjGLnlMkf+eY5KpUKlUmFoaIhKpcLa2hojIyMgyTFO/udKua9Wq9+6r1KpMDU1xcnJCXd3dzp37kyzZs04cuSI8k/07bff4unpiZmZGVWrVmXnzp2K7SdOnEClUnH8+HFq1KiBubk5DRo0UL7UGBoaMmjQIDp27KjYrlKpmDhxIo0bN1bK5OXlxaeffsqXX36Jvb09zs7OzJkzR6tMYWFhjBgxAhcXF0xNTalYsaLS6vfTTz/h4OCglO/+/ft06NABZ2dnrK2tqVGjBt7e3lr3YM2aNZQoUQILCwucnZ3p2rVrhvcpeT+5HCn3U96b5P1k21Puv+t9Sk9vb2ov/HVChs4fQGyChtBXcXmmTPr+P8kyvXuZDA0NsbKywsDAIN+UKT/ep9xQJrVarfwO5Zcy5cf7lFvKZGhoqIzpyi9lyo/3KTeUKb3foZwq04dAnnMAjY2NqVGjBrdv39YKv3PnDu7u7gBUq1YNIyMjjh49qpy/ffs2gYGB1KlTJ1vtTYtEjWDWH7dIq+k1OWzWH7dI1PwXIyEhgYsXL2ZJ3+QbN25w5swZjI2NgaSusD/99BNr1qzh5s2bjB8/nr59+ypdbJP56quvWLRoET4+PhgaGjJ48GC98960aRMWFhacP3+eb7/9ltmzZ3P48GEgqT93q1atOH36ND///DO3bt1i/vz5ykPgTaKiomjdujVHjx7lypUrtGzZknbt2ikfBnx8fBg7diyzZ8/m9u3bHDhwgIYNG+ptc24jpU4yI57kwyIrny2S/IXUikQfpF4kuiK1kv3kSjc3KiqKu3fvKsf379/H19cXe3t7ihQpwqRJk+jRowcNGzakcePGHDhwgD/++IPjx48DSYPUhwwZwoQJE7C3t8fa2poxY8ZQp04dnWcAfRfa/e8UzyNj3xovNiGR0Oj4dM8L4HF4DNW/PoyJ4X/OjhAC1eG/U8V3tDLhjzH19bJ13759WFpakpCQQGxsLGq1mhUrVhAbG8vcuXM5cuSI4iwXK1aMU6dOsXbtWho1aqSk8c033yjHX3zxBW3atCEmJkZrOY63UbFiRWbMmAFAiRIlWLFiBUePHuWjjz7iyJEjXLhwAT8/P0qWLKnYkh6VKlWiUqVKyvGcOXPYtWsXe/fuZfTo0QQGBmJhYUHbtm2xsrLC3d2dKlWq6F5puZSbjyJ0jlfJzTZrjZHkOQwMDChXrly6H1YkkmSkViT6IPUi0RWplewnVzqAPj4+NG7cWDmeMGECAAMGDGDjxo106tSJNWvWMG/ePMaOHUupUqX4/fffqV//PydoyZIlqNVqunTpQmxsLC1atGDVqlVZavfzyFieRMRkWnpJTmL6juL70LhxY1avXs2rV69YsmQJhoaGdOnShZs3bxIdHc1HH32kFT8uLi6Vs1SxYkVl39nZGUiapKdIkSI625EyjeR0nj1LmrDE19cXV1dXxfl7G1FRUcycOZM///yTx48fk5CQwOvXr5UWwI8++gh3d3eKFStGy5YtadmyJZ06dVL6nOdVXr56+0eHN+MlJCQQExODpaVlVpklySOoVKo8/z8gyR6kViT6IPUi0RWplewnVzqAXl5eynSs6TF48OAMuxyampqycuXKdBeTzwocrUx0ive2FsBk7MyNlBZAgSA+Lh4jYyNUaK+Romu+KbGwsKB48eJA0nIKlSpV4scff6R8+fIA/PnnnxQuXFjrGhMT7XySxyMCSt/plINw37yH8fGpy5wyjeR0ktMwMzPTq0wTJ07k8OHDLFy4kOLFi2NmZkbXrl2Ji4sDwMrKisuXL3P8+HEOHTrE9OnTmTlzJhcvXkw1Y2xewt5Ct/v/zacDmBsSQHh4ONHR0QAcPHiQ5s2bZ6V5klxOQkICPj4+VK9e/YMZ+yB5N6RWJPog9SLRFamV7EfWciaiazfMRI2g/gJvnoTHpDkOUEXSbKCnJjfBQJ3kWAkhiIuLw9jYWHG2Mgu1Ws2XX37JhAkTuHPnDiYmJgQGBmp199QXR0fHVFPp+vr6pnL4MqJixYoEBwdz584dnVoBT58+zcCBA+nUqROQ1CIYEBCgFcfQ0JBmzZrRrFkzZsyYga2tLd7e3nTu3Flnu3Ib5VysdYr38vkz7FWC9u3b4+XlhZOTk1ZLu+TDxMDAgCpVqsiuN5K3IrUi0QepF4muSK1kP3luEpj8gIFaxYx2SbOYvunKJR/PaFdWcf6U67LwH6Nbt24YGBiwdu1aJk6cyPjx49m0aRP37t3j8uXL/O9//2PTpk06p9ekSRN8fHz46aef8Pf3Z8aMGXqvrdKoUSMaNmxIly5dOHz4MPfv3+evv/7iwIEDacYvUaIEO3fuxNfXl6tXr9K7d2+lNRGSxj0uX74cX19fHjx4wE8//YRGo6FUqVJ62ZXbeFMnGVGwYEG2b9/O1KlTuXDhgrJOjuTDRv7oSnRFakWiD1IvEl2RWslepAOYQ7Qs78zqvlVxstGeMMXJxpTVfavSsryzVnjKhTWzAkNDQ0aPHs23337LlClTmDZtGvPmzaNMmTK0bNmSP//8Ew8PD53Ta9GiBdOmTePzzz+nRo0aREZG0r9/f73t+v3336lRowa9evWibNmyfP755+nWweLFi7Gzs6Nu3bq0a9eOFi1aULVqVeW8ra0tO3fupEmTJpQpU4Y1a9awbds2ypUrp7dduQk7C2Pe5gOaGKrp0bEtfn5+bNmyhT59+vC///2PokWL0qNHD86cOfPWbteS/ElWP1sk+QepFYk+SL1IdEVqJfvJ9QvB5yTBwcG4ubll6WKQiRrBhfsveRYZQ0ErU2p62KfZopO8UGbKtUokEoCLAS/ptuYsAA4WRoxtUoLdvo+4EhQGQCVXG1b1rUZBC0NatmyJr68vly9fxt7enk2bNrF8+XL8/f2pVq0aY8eOpUePHqnGe0ryL/LZItEVqRWJPki9SHQlN2klO979cwOyBTCHMVCrqOPpQIfKhanj6ZBhdz75ZUTyJkIIFvz1j3L8ecvS9K9blFW9/ptd9eajCApZmWBkZMRvv/1GtWrVCAoKwsrKitGjR/PPP/+wf/9+HB0dGTBgAEWKFGH69Ok8evQoJ4okyQHks0WiK1IrEn2QepHoitRK9iIdwDxCYmIiV65ckf8gEi22nH+Az4NQADwdLehS1ZXExEQe3LlJUYekKZUTNIJdVx4CYGdnx6FDh7SWTFGr1bRq1Yq//vqLf/75h+7du7NkyRLc3d3p06cP58+fz/6CSbIN+WyR6IrUikQfpF4kuiK1kv1IBzCPYGhoSO3ateX0uBKFRI1g/l+3leOh9YthaKBWtNK/jrty7sdT93VKs1SpUvzvf/8jODiYhQsXcv78eWrXrk2tWrXYsmWLsqSGJP8gny0SXZFakeiD1ItEV6RWsh/pAOYRhBBER0fLiTokCjsvBxMVmwCAvYURPWu6Af9ppXetIsrkMLefRBIV8/a1J5OxsbHh008/5fbt2/zxxx/Y2NjQt29f3N3dmTVrlpw9NB8hny0SXZFakeiD1ItEV6RWsh/pAOYREhMTuXnzpmwelwAQm5DI0iP+yvHXHSsoA6eTtWKogkqutgAIYPvFIL3zMTAwoG3bthw6dIibN2/SqVMnvv32W9zc3Ojfvz8+Pj6ZURxJDiKfLRJdkVqR6IPUi0RXpFayH+kA5hEMDQ2pUaOGbB6XAPDzuUAehr0GwKuUI60r/LdsSEqtjGzsqYT/fef5e+VZtmxZVq1aRXBwMPPnz+fkyZPUqFGDunXrsn37duLjdW9hlOQe5LNFoitSKxJ9kHqR6IrUSvYjHcA8ghCCyMhI2TwuITw6jhXe/7X+TWqhvZB9Sq00LV0IVzszAE7ffcGziJj3zt/Ozo4JEyZw9+5ddu/ejZmZGb169aJo0aJ88803PH/+fo6mJHuRzxaJrkitSPRB6kWiK1Ir2Y90APMIGo0Gf39/NBpNTpsiyWE+/+0aodFJrW3tKjpTzsVG63xKrajVKjpVKZwULmCPb+Yt7WBgYECHDh04evQo169fp23btnzzzTe4ubkxaNAgrly5kml5SbIO+WyR6IrUikQfpF4kuiK1kv1IBzCPYGBgQNWqVTEwMMhpUyQ5yKOw1xy69d8ELKMaF08V502tdPx/BxBg45mALLGrfPnyrF27luDgYObMmYO3tzdVq1alQYMG/PbbbyQkJGRJvpL3Rz5bJLoitSLRB6kXia5IrWQ/0gHMIwghCAsLy3PN4xs3bsTW1jZb8xw4cCAdO3Z8rzS8vLwYN26ccly0aFGWLl36XmnqS0BAACqVCl9fXyVs3A5fkhVQy8Oe0s7W3L59GycnJyIjI4HUWvF0tKSQlQkAD8Nec/n/1w3MCq5du8bnn3/OpUuX+P3333n+/DndunXDw8OD+fPn8+LFiyzL+0PnxYsXFCxYkODgYL2uy6vPFkn2I7Ui0QepF4muSK1kP9IBzCNoNBoePHjwXs3jKpUqw23mzJmZZ3AOsmzZMjZu3JipaV68eJGPP/44U9PUl38eR3Dh/ksAVMCSHpUBmDJlCmPGjMHKygpIWysflS2k7K89cS/LbTU0NKRz585cuXIFb29vmjdvzsyZM3Fzc2Po0KFcu3btndM+fvw4KpWKsLCwzDM4E3ny5An9+vXDyckJCwsLqlatyu+///7W6x4+fEjfvn1xcHDAzMyMChUqKLOsxsfHM3nyZCpUqICFhQUuLi7079+fR4/+69JboEAB+vfvz4wZM/SyNzOeLZIPA6kViT5IvUh0RWol+5EOYB7BwMCASpUqvVfz+OPHj5Vt6dKlWFtba4VNnDgxEy3OOWxsbDK91dHR0RFzc/NMTVNfxu3wVfablyuEi60ZgYGB7Nu3j4EDByrn0tLK6KbF+f9VIrgWHI5Gkz1f2czMzGjcuDE//vgjwcHBTJ8+nQMHDlCpUiW8vLzYtWtXvuse2r9/f27fvs3evXu5fv06nTt3pnv37hmOiQwNDaVevXoYGRnx119/cevWLRYtWoSdnR0A0dHRXL58mWnTpnH58mV27tzJ7du3ad++vVY6gwYNYsuWLbx8+VJnezPj2SL5MJBakeiD1ItEV6RWsh/pAOYRNBoNISEh7/V1xMnJSdlsbGxQqVRaYdu3b6dMmTKYmppSunRpVq1apVyb3B1x586dNG7cGHNzcypVqsTZs2e18ti4cSNFihTB3NycTp06ERISksqOPXv2ULVqVUxNTSlWrBizZs3ScgJUKhU//PADnTp1wtzcnBIlSrB3716tNG7evEnbtm2xtrbGysqKBg0acO9eUsvWm11ADxw4QP369bG1tcXBwYG2bdsqcXXlzS6guth448YNWrVqhaWlJYUKFaJfv35aXSD1sevM3Rf88ySpi6eBWsW3XSoC8Msvv1CpUiUKF/5vnN/69euxsbFh586dlChRAlNTUwZ060iNAklO3+PwGM7dD8mU+7B//35KliypOHoBAQFa51N2AS5QoABTpkxh2bJleHh4cOLECTp37kyBAgWYP3++4rRklG9AQACNGzcGkmYjValUivOr0WiYN28eHh4emJmZUalSJX777be0b2gWcubMGcaMGUPNmjUpVqwYU6dOxdbWlkuXLqV7zYIFC3Bzc2PDhg3UrFkTDw8Pmjdvjqdn0jIeNjY2HD58mO7du1OqVClq167NihUruHTpEoGBgUo65cqVw8XFhV27dulsb2Y8WyQfBlIrEn2QepHoitRK9iMdwMwm7lX6W3yMHnFfa0UVsVE8CfoXERulHS+T2LJlC9OnT+ebb77Bz8+PuXPnMm3aNDZt2qQV76uvvmLixIn4+vpSsmRJevXqpTgN58+fZ8iQIYwePRpfX18aN27M119/rXX9yZMn6d+/P59++im3bt1i7dq1bNy4kW+++UYr3qxZs+jevTvXrl2jdevW9OnTR3EQHj58SMOGDTExMcHb25tLly4xePDgdFuSXr16xYQJE/Dx8eHo0aOo1Wo6der03g+ajGwMCwujSZMmVKlSBR8fHw4cOMDTp0/p3r37O9k16bf/ukz2qO6GjbmxUp/Vq1dPFT86Opp58+bx008/cfr0acLCwri+eZZy/n9b/njv+xAUFETnzp1p164dvr6+DB06lC+++CLDOjt58iSDBw9mxowZ3L17l5UrV5KYmMjUqVNxdXXlk08+yTBfNzc3pTvl7du3efz4McuWLQNQyrtmzRpu3rzJ+PHj6du3L3///Xe69jx58oQRI0ZQpkwZypQpw7hx4zh37hwxMTFcv36dNm3aEBOj39IZdevWZceOHbx8+RKNRsP27duJiYnBy8sr3Wv27t1L9erV6datGwULFqRKlSqsW7cuw3zCw8NRqVSpWrtr1qzJyZMndbZXCMHjx4/l2AvJW5FakeiD1ItEV6RWcgAhSZegoCABiKCgIN0vmmGd/vZzV+24XzulH3d9a+24CzzSjveObNiwQdjY2CjHnp6eYuvWrVpx5syZI+rUqSOEEOL+/fsCED/88INy/ubNmwIQfn5+QgghevXqJVq31ra7R48eWvk0bdpUzJ07VyvO5s2bhbOzs3IMiKlTpyrHUVFRAhB//fWXEEKIKVOmCA8PDxEXF5dm2QYMGCA6dOiQbtmfP38uAHH9+vV04zRq1Eh8+umnyrG7u7tYsmSJzjbOmTNHNG/eXCvNZD3dvn1bJ7sGDBggAAEI54HLhfvkfaLEV/tF02bNRIUKFUSlSpWEhYWFGDFihFY6GzZsEIDYuXOnaNSokbC2thalS5cWgPAYsky4T94nzDyri0JOTqJSpUqiUqVKwsHBQVSvXl3v+1C2bFmtvCdPniwAERoaqtiiy/0vVKiQmDNnjnB2dhaAKFq0qNizZ49ISEhIle+xY8e08hBCiJiYGGFubi7OnDmjlfaQIUNEr1690qxvIYTo37+/GD9+vDh69KjYvn276NWrl7CwsBCAsLe3F3PnzhUajSbd69MiNDRUNG/eXADC0NBQWFtbi4MHD2Z4jYmJiTAxMRFTpkwRly9fFmvXrhWmpqZi48aNacZ//fq1qFq1qujdu3eqc+PHjxdeXl562SyRSCQSieQd3/3zIIbZ6GtK3gNB0sQfWcGrV6+4d+8eQ4YMYdiwYUp4QkICNjbaa8xVrFhR2Xd2dgbg2bNnlC5dGj8/Pzp16qQVv06dOhw4cEA5vnr1KqdPn9ZqaUpMTCQmJobo6GhlnF3KfCwsLLC2tubZs2cA+Pr60qBBA4yMjHQqn7+/P9OnT+f8+fO8ePFCaWELDAykfPnyOqWRFhnZePXqVY4dO4alpWWq6+7du0fJkiXfalerVq3YtGkTRtaOyrXDGxZj8MRflVYfFxcXdu/erdVdV6PRYGhoSL169XB0dCQ8PJyvvvoKW1tbCpsLgoCCXWfyfMskou7eBZJaDCMiIoiPj9f5Pvj5+VGrVi2tstWpUyfDOsvo/k+YMIHPP/8cExMTjI2N6dChAx4eHowePRorKysl37S4e/cu0dHRfPTRR1rhcXFxVKlSJd3rli5dip2dHf/++y9ly5alR48exMXFERISgpOTE6GhoQghUKlS//fNnTuXuXPnKse3bt2iSJEiTJs2jbCwMI4cOUKBAgXYvXs33bt35+TJk1SoUCFNOzQaDdWrV1fSq1KlCjdu3GDNmjUMGDBAK258fDzdu3dHCMHq1atTpWVmZkZ0dHS6ZU4r7xcvXlCgQAHUatkpRJI+UisSfZB6keiK1Er2Ix3AzObLDBbaVr0xuHXS3Qziav8DaMb44n/3LiWKF8/0QbJRUVEArFu3LtUL/Zt5pXS6kl+K9elKGRUVxaxZs+jcuXOqc6ampmnmk5xXcj5mZmY65wfQrl073N3dWbduHS4uLmg0GsqXL09cXJxe6bxJRjZGRUXRrl07FixYkOq6ZMf5bXbVqlULowLuaOKSugNbmhjyabOSGKj/c0YsLCyIiIhI0z47Ozvq16/P8ePHlbDqzsYEhSftF2kzij8mtsDX15dPPvmEU6dOYWRkpPN9eBfedv+TH/wLFiygcOHCLF++nC+++IL4+Hg2bdpEjRo10k0X4M8//9QaDwlgYmKSrj3//vsvgwcPVmYlrV27NoMHD6Z27dqcOHGCL7/8kps3b2rVSTLDhw/X6tLr4uLCvXv3WLFiBTdu3KBcuXIAVKpUiZMnT7Jy5UrWrFmTph3Ozs6ULVtWK6xMmTKpZg9Ndv4ePHiAt7c31tbWqdJ6+fIljo6OqcLTQwhBSEgIDg4OOl8j+TCRWpHog9SLRFekVrIf6QBmNsYWWRLXwMya0hWqvoNBb6dQoUK4uLjw77//0qdPn3dOp0yZMpw/f14r7Ny5c1rHVatW5fbt2xQvnnoBc12pWLEimzZtIj4+/q2tgCEhIdy+fZt169bRoEEDAE6dOvXOeetK8tT/RYsWxdAw9b+ZLnYlJGqwbdifl0fWAjC5ZSnF+evfvz/Hjh0jLCwslVOkVqtJSEjgypUr1KxZE4DY2FjCwsLo0bgqf/7xkrhEDa9MHbEqWJhDh75l4MCBlClTRq8ylilTJtWkMG/e7zfR5/7XqFGDzZs3891331G0aFF8fHwoW7asUqb4+HglbtmyZTExMSEwMJBGjRrpXIb169czfPhw2rRpw9OnT9mzZw9LlizhwYMHlCtXjrlz56bp/AHY29tjb2+vFZbc8vbmF0wDA4MMHed69epx+/ZtrbA7d+7g7u6uHCc7f/7+/hw7dizdH8obN25kON7wTQwMDPS+95IPE6kViT5IvUh0RWol+5HtrHkEjUbD48ePs2yGpFmzZjFv3jyWL1/OnTt3uH79Ohs2bGDx4sU6pzF27FgOHDjAwoUL8ff3Z8WKFVrdPwGmT5/OTz/9xKxZs7h58yZ+fn5s376dqVOn6pzP6NGjiYiIoGfPnvj4+ODv78/mzZtTvUBDUiuYg4MD33//PXfv3sXb25sJEybonNe7MmrUKF6+fEmvXr24ePEi9+7d4+DBgwwaNIjExESd7Fp3OgjzEkktsrbmRvSt/Z8z8NNPPxEUFMSQIUM4ffo0iYmJyjmNRoORkRFjxozh/Pnz3L59m8DAQGrXrk3t2rWo45nktKhNLeg+fi5bt27Fy8tL7/swfPhw/P39mTRpErdv32br1q1vXX/xXe6/k5MTpqamLFq0iJ9//pnY2FgAypcvzzfffMOjR4+wsrJi4sSJjB8/nk2bNnHv3j0uX77M//73v1QTGaVk2bJljBgxgiJFilCjRg2+/vprbt26xatXr7hw4QI9evTQuT4ASpcuTfHixfnkk0+4cOEC9+7dY9GiRRw+fFhrZtqmTZuyYsUK5Xj8+PGcO3eOuXPncvfuXbZu3cr333/PqFGjgCTnr2vXrvj4+LBlyxYSExN58uQJT5480WrJjo6O5tKlSzRv3lxnm7P62SLJP0itSPRB6kWiK1Ir2Y90APMIQggiIyOzbIakoUOH8sMPP7BhwwYqVKhAo0aN2LhxIx4eHjqnUbt2bdatW8eyZcuoVKkShw4dSvVi36JFC/bt28ehQ4eoUaMGtWvXZsmSJVotHW/DwcEBb29voqKiaNSoEdWqVWPdunVptgaq1Wq2b9/OpUuXKF++POPHj+e7777TOa93xcXFRXHMmjdvToUKFRg3bhy2trao1eq32vU6LoHfboYpx2OaeKY5Dm3hwoXExcWxc+dOrXBTU1MmTZpE7969GTNmDGq1mh07dgDwadOSSryrN24SHR1N37599b4PRYoU4ffff2f37t1UqlSJNWvWaI2JS4v3uf+Ghob06dMHX19fhg0bRkREBFOnTsXd3Z2xY8fSr18/pk2bxrx58yhTpgwtW7bkzz//zFDDabXOvg9GRkbs378fR0dH2rVrR8WKFfnpp5/YtGkTrVu3VuLdu3dPa0mQGjVqsGvXLrZt20b58uWZM2cOS5cuVVrkHz58yN69ewkODqZy5co4Ozsr25kzZ5R09uzZQ5EiRZRWZV3I6meLJP8gtSLRB6kXia5IrWQ/KiFrO12Cg4Nxc3MjKCgIV1fXnDZH8gEx6der/HopGIAn3w/hnPcBKleuTFhYGNHR0bi4uACwe/duBg0aRI0aNTh06BCQtPbeuHHjCAsLA+D48eOMGzcOX19fJf0KMw8SGZPAky2fM2XscGZMGpOt5cssHj16xJo1a1izZg3Pnz+nVatWjB07lubNm3+QA8lr167N2LFj6d27d06bIpFIJBJJnuNDeff/8N6Q8igajYbg4GDZPP4BEP46noM3nxByYAXBKweQEPGCFi1aULx4ccLDw+nYsSMVKlSgUqVKrFixgsOHD9OoUSMiIyMZOnQoly9fRghBVFQUrq6udOvWjVu3buHq6sqUKVMAaFnOifiQYOKe3edf64pvsSj34uLiwuzZswkMDGTTpk08ffqUVq1aUaZMGVasWEFkZGROm5htvHjxgs6dO9OrVy+9rpPPFomuSK1I9EHqRaIrUivZj2wBzIDc9BVAo9Fw//59PDw8PsiWjQ+JBQf+YfXxewB0qVqYRd0r63X9+vXrlRbA9LTyMDSaeguOAWBiqOafOS3T7GKa1xBCcObMGZYvX87vv/+OhYUFgwcPZtSoUe818VB+Rj5bJLoitSLRB6kXia7kJq3kpnf/rET+R+YR1Go1np6eOf6PIclankbEsOH0fQCMDdRMaF5K7zQGDx5MREREhlopbGeOs03S7JaxCRoO33r6bgbnMlQqFfXq1WPHjh0EBAQwevRoNm/eTMmSJWnXrh2HDx+WYwzeQD5bJLoitSLRB6kXia5IrWQ/sqbzCBqNhgcPHsjm8XzO5N+vEROfdI/71XGnsK1+ax6C7lrpXv2/L1ur/76ndz65HVdXV7755huCgoL48ccfCQoKonnz5pQrV47Vq1crawd+6Mhni0RXpFYk+iD1ItEVqZXsRzqAEkku4VpwGMdvPwfAxEDNqMZZ22VxWINiJHf6vBoURmxCYobx8ypmZmYMGjSIK1eu8Pfff1O2bFlGjx6Nq6srEydO5P79+zltokQikUgkEkm2IR3APIJarcbd3V02j+djpu25oex/VK4Q9hbG75SOrlqxNDWilJMVABoBW84FvlN+eQWVSkXDhg357bffuH//PsOHD2fDhg14enrSsWNHvL29P8juofLZItEVqRWJPki9SHRFaiX7kTWdR9BoNNy7d082j+dTrgaFcTUoHABTQzULulR457T00crg+kWV/c3nAt45z7xGkSJFmD9/PkFBQXz//ffcu3ePpk2bUrFiRdatW0d0dHROm5htyGeLRFekViT6IPUi0RWplexHOoB5CBMTk5w2QZJFfHvwH2X/qzZlsDBJvai9Puiqlc5VXDFUJ3UEvf8impevYt8r37yGubk5Q4cO5dq1a3h7e1O8eHGGDx+Oq6srkydP5sGDBzltYrYgny0SXZFakeiD1ItEV6RWshfpAOYR1Go1rq6usnk8H3LS/zmn74YA4O5gTs+aRd4rPX20YmigpqaHvXK89Xz+7gaaHiqVisaNG7Nr1y7u3r3LkCFD+P777ylWrBhdunTh77//zrfdQ+WzRaIrUisSfZB6kehKXtbKiRMnaNeuHS4uLqhUKnbv3q2ci4+PZ/LkyVSoUAELCwtcXFzo378/jx49yjmD/5+8V9P5hIdhr7nxMDzd7WHYa634iYmJ3Llzh8TE/DlRR35j5syZVK5c+a3xNBrBhF98lePPmpfCyOD9/i311crYJiWU/eRJaHRl48aN2NraKse6lvtthISEULBgQQICAt47LX3x8PDgu+++Izg4mFWrVvHPP//g5eVFlSpVWL9+Pa9fa/9v3rp1C1dXV169epXttmYG8tki0RWpFYk+SL1IdCUva+XVq1dUqlSJlStXpjoXHR3N5cuXmTZtGpcvX2bnzp3cvn2b9u3b54Cl2hjmtAEfIg/DXtNk4XFiE9Lv62xiqMZ7opeyDIBKpcLKyuq9Fut+27UzZsxg5syZ75y+RH/WnrjH88g4AApYGtO2gvN7p6mvVmoVs6dEQUv8n0Xh8yCUwJBoijiYv1PeEydOZMyYMe90bUq++eYbOnToQNGiRd87rXfFwsKCTz75hI8//pijR4+yfPlyhg4dyueff87HH3/MiBEjcHNzo2zZstSuXZvFixczbdq0HLP3XcmMZ4vkw0BqRaIPUi8SXcnLWmnVqhWtWrVK85yNjQ2HDx/WCluxYgU1a9YkMDCQIkXer8fX+yBbAHOA0FdxGTp/kLQ4d+irOOVYrVbj7Oz8Xs3jjx8/VralS5dibW2tFTZx4sR3TluiP/GJGpYf9VeOJ7csjVqd/sMvMTFRpwHS+mpFpVLRsUph5Xi370OdrksLS0tLHBwc3vl6SPpi9uOPPzJkyJD3SkfX+nobKpWKZs2asXfvXvz9/enfvz8rV67Ew8OD7t27c+rUKQYOHMjq1atJSEh47/yym8x4tkg+DKRWJPog9SLRldyolcjISCIiIpQtNjZz5kgIDw9HpVJp9Z7KCXJPTUsyJDExET8/v/dqHndyclI2GxsbVCqVcvzq1Sv69OlDoUKFsLS0pEaNGhw5ckTr+lWrVlGiRAlMTU0pVKgQXbt2Vc4dOHCA+vXrY2tri4ODA23btuXevYwXF/fy8mLMmDGMGzcOOzs7ChUqxLp163j16hWDBg3CysqK4sWL89dff2ldd+PGDVq1aoWlpSWFChWiX79+vHjxQivdsWPH8vnnn2Nvb4+Tk5NWy6YQgpkzZ1KkSBFMTExwcXFh7Nixyvk3+3AD2NrasnHjRuV48uTJlCxZEnNzc4oVK8a0adOIj49/2y1QOH78OFYelXn9/4u+R/keYNHobty48d9SEMndK/fu3UvZsmUxMTEhMDCQ0NBQ+vfvj52dHebm5rRq1Qp///8cyfXr12Ntbc2ePXsoVaoU5ubmdO3alejoaDZt2kTRokWxs7Nj7Nixip46VimMSIgnwucPZkybioWFBbVq1eL48eNadm/cuJEiRYpgbm5Op06dCAkJ0Tr/ZhdQjUbD7NmzcXV1xcTEhMqVK3PgwIEM62b//v2YmJhQu3ZtrfpSqVT8+eefVKxYEVNTU2rXrv3e9fX8+XOcnJyYO3euks6ZM2cwNjbm6NGjBAQEoFar8fHxAcDT05PFixczZcoUrK2tuXr1Kg0aNGDatGk8f/481Ze+vEBmPFskHwZSKxJ9kHqR6Epu1ErZsmWxsbFRtnnz5r13mjExMUyePJlevXphbW2dCVa+O9IBzGR+9Qmix9qzGW5f/H5N7/R6/3ABBwcHrebxHmvP8qtPUKbYHRUVRevWrTl69ChXrlyhZcuWtGvXjsDApElBfHx8GDt2LLNnz+b27dscOHCAhg0bKte/evWKCRMm4OPjw9GjR1Gr1XTq1OmtLTCbNm2iQIECXLhwgTFjxjBixAi6detG3bp1uXz5Ms2bN6dfv37KtPxhYWE0adKEKlWq4OPjw4EDB3j69Cndu3dPla6FhQXnz5/n22+/Zfbs2crL+e+//86SJUtYu3Yt/v7+7N69mwoV9Ft2wcrKio0bN3Lr1i2WLVvGunXrWLJkic7Xv45LwM5rIABCk8iC0d1xdHSkXbt2Wo5kdHQ0CxYs4IcffuDmzZsULFiQgQMH4uPjw969ezl79ixCCFq3bq11XUxMDCtWrGD79u0cOHCA48eP06lTJ/bv38/+/fvZvHkza9eu5bfffgOgsK0Zqsc3sK7eDusG/Vi352+6detGy5YtFWfp/PnzDBkyhNGjR+Pr60vjxo35+uuvMyznsmXLWLRoEQsXLuTatWu0aNGC9u3bazmsb3Ly5EmqVauW5rlJkyaxaNEiLl68mCn15ejoyPr165k5cyY+Pj5ERkbSr18/Ro8eTdOmTSlatCjNmjVjw4YNWnZs27aNMWPG4Ofnx4EDB3BxcSEhIYGuXbsybdq0XDHAW1dUKlWqZ4tEkhZSKxJ9kHqR6Epu1MqtW7cIDw9XtilTprxXevHx8XTv3h0hBKtXr84kK98DIUmXoKAgAYigoCCdr1l86LZwn7wvU7brwWFa6b2J++R9YvGh2+9Utg0bNggbG5sM45QrV07873//E0II8fvvvwtra2sRERGhU/rPnz8XgLh+/Xq6cRo1aiTq16+vHCckJAgLCwvRr18/Jezx48cCEGfPnhVCCDFnzhzRvHlzrXSS79Pt27fTTFcIIWrUqCEmT54shBBi0aJFomTJkiIuLi5NuwCxa9curTAbGxuxYcOGdMvy3XffiWrVqinHM2bMEJUqVUo3fte525X72mThMSGEECEhIcLMzEzs2LFDCJF0jwDh6+urXHfnzh0BiNOnTythL168EGZmZuKXX37Ruu7u3btKnE8++USYm5uLyMhIJaxFixbik08+EUII8eDBA2FapIJi08D154UQQjRt2lRMmTJFCCFEr169ROvWrbXK0aNHDy0dvVluFxcX8c0332hdU6NGDTFy5Mh066ZDhw5i8ODBWmHHjh0TgNi+fbsSlln1JYQQI0eOFCVLlhS9e/cWFSpUEDExMcq5HTt2CDs7OyXs0qVLQqVSifv372vZ+NFHH4kyZcoIKysrYWhoKHr27CnOnDkjNBpNumWVSCQSiUSSe3iXd/9k0np/FEKIuLg40bFjR1GxYkXx4sWLTLDy/ZEtgJmMq50ZtTzsM9zKu+je7PtfenbcuHFDq3m8loc9rnZmmWJ3VFQUEydOpEyZMtja2mJpaYmfn5/SAvjRRx/h7u5OsWLF6NevH1u2bNFaLNvf359evXpRrFgxrK2tlck7kq9Pj4oVKyr7BgYGODg4aLXGFSpUCIBnz54BcPXqVY4dO4alpaWylS5dGkCry2nKdAGcnZ2VNLp168br168pVqwYw4YNY9euXXqP3dqxYwf16tXDyckJS0tLpk6d+tayJvM8MoaLIUnr/AmhYXGPygDY29tTqlQp/Pz8lLjGxsZaZfHz88PQ0JBatWopYQ4ODlrXaTQazMzMtCZQKVSoEEWLFsXS0lIrLLlOrl+/TkzgDV4e+Z7gNUP5aXgjLC0t+fvvv5V69fPz08oXoE6dOumWMyIigkePHlGvXj2t8Hr16mmV8U1ev36NqalpmudS5pdZ9QWwcOFCEhIS+PXXX9myZYvWekQdO3bEwMCAXbt2AUldTRs3bpxqghpHR0cqVKhAcHAwixYtwsfHh7p161KrVi1+/vnnTBs/kNkkJiamerZIJGkhtSLRB6kXia7kZ60kt/z5+/tz5MiR954nIbOQs4BmMt2qu9GtuluGcW48DKft/07plZ5GoyE0NFSreXzHJ+m/fOvLxIkTOXz4MAsXLqR48eKYmZnRtWtX4uKSJqKxsrLi8uXLHD9+nEOHDjF9+nRmzpzJxYsXsbW1pV27dri7u7Nu3TpcXFzQaDSUL19euT49jIy0FzxXqVRaYcnlTe5KGhUVRbt27ViwYEGqtJyd/5tBM610k9Nwc3Pj9u3bHDlyhMOHDzNy5Ei+++47/v77b4yMjFCpVKnWfEvZzfDs2bP06dOHWbNm0aJFC2xsbNi+fTuLFi3KsKzJTNhxFQyTHIwyDoZUcrVNN66Zmdk7dYlILkcyb9ZrcljKejUwUNO2xwD+vtsWgHmdKlC3eAEtpzE7KFCgAKGhoe907bvW171793j06BEajYaAgACtjxDGxsb079+fDRs20LlzZ7Zu3cqyZctSpfHy5Us8PT2xtrZm7NixjB49mgMHDrB8+XL69evHxIkTGT58OMOHD8fJyemdypcVqFQqnJ2dc1XXG0nuRGpFog9SLxJdyctaiYqK4u7du8rx/fv38fX1xd7eHmdnZ7p27crly5fZt28fiYmJPHnyBEj6iG1sbJxTZssxgHkFtVqNg4NDls2QdPr0aQYOHEinTp2oUKECTk5OqdZgMzQ0pFmzZnz77bdcu3aNgIAAvL29CQkJ4fbt20ydOpWmTZtSpkyZd36BfxtVq1bl5s2bFC1alOLFi2ttFhYWOqdjZmZGu3btWL58OcePH+fs2bNcv34dSGrJefz4sRLX399fq7XzzJkzuLu789VXX1G9enVKlCjBgwcPdMr33+dRnLybNGFNQsQz2hf4bxKV0NBQ7ty5Q5kyZdK9vkyZMiQkJHD+/HklLLn+y5YtCyRpRaVS6aWVKlWqkJiYSA0nA4zsXDCyc+HCS2OKFy+uOCtlypTRyhfg3Llz6aZpbW2Ni4sLp0+f1go/ffq0Ymt6tty6dSvNcynzy6z6iouLo2/fvvTo0YM5c+YwdOhQpWU0maFDh3LkyBFWrVpFQkICnTt3TpXXjRs3qFKlinKsVqtp3bo1Bw4cwM/Pj65du7Jw4UKKFClC3759uXjxYrp2ZydZ/WyR5B+kViT6IPUi0ZW8rBUfHx+qVKmi/P5PmDCBKlWqMH36dB4+fMjevXsJDg6mcuXKODs7K9uZM2dy1O68V9P5ADsLY0wMM656E0M1dhb/fRlITEzk6tWrWdY8XqJECXbu3Imvry9Xr16ld+/eWhO47Nu3j+XLl+Pr68uDBw/46aef0Gg0lCpVCjs7OxwcHPj++++5e/cu3t7eTJgwIUvsHDVqFC9fvqRXr15cvHiRe/fucfDgQQYNGqRz3WzcuJEff/yRGzdu8O+///Lzzz9jZmaGu7s7AE2aNGHFihVcuXIFHx8fhg8frtV6VqJECQIDA9m+fTv37t1j+fLlSvfAtzFuu6+yH3ZqK6sWz+fo0aPcuHGDgQMHUqBAATp27Jju9SVKlKBDhw4MGzaMU6dOcfXqVfr27UvhwoXp0KEDkNRampiYqJdWSpYsSZ8+fVg2fRyqgPPEhz9l78krTJ/9NX/++ScAY8eO5cCBAyxcuBB/f39WrFjx1hk9J02axIIFC9ixYwe3b9/miy++wNfXl08//TTda1q0aMHNmzfT/Igwe/bsTK+vr776ivDwcJYvX67M7jp48GCtdMqUKUPt2rWV2bvMzLS7XgcEBPDw4UOaNWuWph2lS5dmxYoVBAcHs2DBAs6ePUvNmjWpU6dOqllus5usfrZI8g9SKxJ9kHqR6Epe1oqXlxdCiFTbxo0bKVq0aJrnhBB4eXnlqN250gE8ceIE7dq1w8XFJc0p+VMyfPhwVCoVS5cu1Qp/+fIlffr0wdraGltbW4YMGUJUVFTWGq4jhW3N8J7oxb4x9dPdUi4CD0lfR9zd3bPs68jixYuxs7Ojbt26tGvXjhYtWlC1alXlvK2tLTt37qRJkyaUKVOGNWvWsG3bNsqVK4darWb79u1cunSJ8uXLM378eL777rsssTO5RSkxMZHmzZtToUIFxo0bh62trc51Y2try7p166hXrx4VK1bkyJEj/PHHH0q/7EWLFuHm5kaDBg3o3bs3EydOxNz8v4XR27dvz/jx4xk9ejSVK1fmzJkzOi0AfuVBKNcehicdxMfw6oY38+fP59NPP6VatWo8efKEP/74461dAjZs2EC1atVo27YtderUQQjB/v37FSc1ufVPX61s2LCBAf37E+V/AUMLe9Q2Tuy9eE9ZqLR27dqsW7eOZcuWUalSJQ4dOsTUqVMzTHPs2LFMmDCBzz77jAoVKnDgwAH27t1LiRIl0r2mQoUKVK1alV9++SXVucyur+PHj7N06VI2b96MtbU1arWazZs3c/LkyVSzdA0ZMoS4uLhUziEkzQravHlz5SNCetja2jJ+/Hju3LnD3r17sbCwSLM7c3aS1c8WSf5BakWiD1IvEl2RWsl+VOLNwU65gL/++ovTp09TrVo1OnfuzK5du9L8yr9r1y5mzZrF8+fPmTRpEuPGjVPOtWrVisePH7N27Vri4+MZNGgQNWrUYOvWrTrbERwcjJubG0FBQbi6umZCySQfMl7fHSMgJKkraW3bKHZM6UloaGiOLwb6Jsf+ecqgjUnr3hWyMuH8V2m3amUlf/75J5MmTeLGjRuo1WqOHz9O48aNc7S+5syZw6+//sq1a9rLuMTFxVGiRAm2bt2aasIbiUQikUgkeYcP5d0/V7rarVq14uuvv6ZTp07pxnn48CFjxoxhy5YtqSa3SF6b64cffqBWrVrUr1+f//3vf2zfvj1Prc+VksTERC5fvpwnm8clcPjWE8X5MzJQMbBq1s0C9b5a8SpVEDMjAwCeRsYS8OJVZpqnE23atOHjjz/m4cOH2Z73m0RFRXHjxg1WrFjBmDFjUp0PDAzkyy+/zLPOn3y2SHRFakWiD1IvEl2RWsl+cqUD+DY0Gg39+vVj0qRJlCtXLtX5s2fPYmtrS/Xq1ZWwZs2aoVarU01ikZLY2FgiIiKULTIyUskv+W9a+4mJiTrtJze2ptxPSEhItS+ESLWvVqspXry4kp4QQvlHSbmfPP7rbfu5oUzJtqfcz49lSkhI4MudN0hmUD0PjFOMAc3sMqnVaooVK6akr2+ZEhMTaVzKUbl+2VH/HLlP48aNU2aUTVlXWXWf0ivTqFGjqFatGo0aNWLAgAGpypS8nIi+9ymt/Zz4fxJCKF1y88L/U358RuSVMqlUKuV3KL+UKT/ep9xSJiEEnp6eqNXqfFOm/HifckOZ0vsdyqkyfQjkSQdwwYIFGBoaMnbs2DTPP3nyhIIFC2qFGRoaYm9vr0y/mhbz5s3DxsZG2ZJnCUxe3y0oKIigoCAgaZrX5NbEe/fu8fTpUwDu3LnDixdJszz6+fkpE1ncuHGD8PCk8V9Xr15VxiNeuXKF169fA0kzCcXFxZGYmIiPjw+JiYnExcXh4+ODSqXC0NAQX19fIKlV4urVqwCEh4dz40aSgxEaGqqsb/bixQvu3LkDwNOnT5X13B49esT9+/dzvEyQtObblStX8nWZfvzrPM+jktaAMzFQ8XmLUlSqVInr169ja2ub6WVSqVQEBwcTFhb2zmUa5fWfA3n41hOio6Nz9D55eXlx/fp15QGdndqbNGkSsbGxrFixQilHXtGeLvfJ398fKysrnj17lm/KlNeeEXmlTPHx8Zibm3Pp0qV8U6b8eJ9yS5n+/fdfoqOjUalU+aZM+fE+5YYy3bx5EysrK8LCwnK8TMn1m+8RuRxA7Nq1Szn28fERhQoVEg8fPlTC3N3dxZIlS5Tjb775RpQsWTJVWo6OjmLVqlXp5hUTEyPCw8OV7datWwIQDx48EEIIkZiYKBITE1PtJyQk6LSv0WhS7cfHx6fa12g0qfbj4+PF+fPnRUxMjBBCCI1GIxISElLtJyYm6rSfVjmyu0zJtqfcz29lik9IFM0XHxfuk/cJ98n7xIaT97K8TMlaiY2Nfa8yVZ19SLH77N0XufI+AcLHxydLtdeiRQuxcuXKPKe9jMrUv39/8emnn4rExEQRExMjLly4IGJjY7O8TAEBAcLCwkK8ePEiy58RGzZsEJUqVcrT9ym3Pffi4uKU36H8Uqb8eJ9yS5liYmLE+fPnRXx8fL4pU368T7mhTOn9DuVEme7fvy8AERQUJPIzeW4h+JMnT/Ls2TNlZkJIarr97LPPWLp0KQEBATg5OaVaxyshIYGXL19muACziYkJJiYmynFERASAMitRytmJUu4bGBi8876hoaFO+0IIypcvr8x4qFKplHRS7qdno7772VGmZNtT7ue3Mu29+ojbT5O+KlVys2VAPY8sL5NKpaJ8+fLK2Nh3LVP7yi5sOB0AwP+8/dkyrLaSfnbdp6ZNm3L27Fmtcb6mpqbK17zk+MmLx2aF9tJa6iKntZeQkMD06dPZunUrL168wMrKimrVqrFt2zasrKzeWqbk+lKr1RgbG1OuXDmMjIy0wrOiTO7u7qlmY87KZ0RKG3LrMyIvPfdS/g69+T+XV8uU0b4s0/vtGxsbU758ea1ndF4vU368T7mhTO/6O5TVZcrP5LkuoP369ePatWv4+voqm4uLC5MmTeLgwYMA1KlTh7CwMC5duqRc5+3tjUajoVatWjll+nuhUqkwNzfXeohKcjexCYksPHRbOZ7cslS23L/M0soor+LK/vmAlyRqcmbC4AULFhAVFaVsKZ2/D5X58+dz6NAhjh07pnSlSWtxel2QzxaJrkitSPRB6kWiK1Ir2U+udACjoqIU5w6S+vX6+voSGBiIg4MD5cuX19qMjIxwcnKiVKlSQNKizS1btmTYsGFcuHCB06dPM3r0aHr27ImLi0sOluzdSUhI4Ny5cx/M4NT8wNf7bvEoLAaA+sULUNezQLbkm1laKWBlQlGHpPUPExIFu6/k/Iycb2P79u1UrFgRW1tbatSowZkzZ5RzcXFxTJ8+HU9PT6ysrKhQoQKXL19+6zkvLy+tdUYPHTpElSpVsLGxoWrVqhw5ckQ5N3DgQIYNG0bPnj2xsrKiVKlSHD9+XDkfHx+v5OPg4ED79u2VMQtCCCZPnoyTkxPW1taULFmSffv2pVnOc+fO0aFDBzw8klqUCxYsyODBg5XWv4EDB2otixMWFoZKpSIgIEAJi4iIoGPHjlhaWlK8eHEtO728vJgyZQotWrTAysqKqlWrcv36deX84sWLKVGiBFZWVnh6erJixQot+/z9/Wnfvj2Ojo7Y29srzmlAQAAqlUoZnxofH8+UKVMoUqQIjo6O9OjRg+fPnyvpqFQq1qxZQ/ny5bG2tqZ9+/bKmA2Avn374uLigrW1NdWqVePYsWNp1hck/a6MHj2aIkWKULBgQfr376+kFRsby+DBgylQoAA2NjaUL1+eixcvppvWh4r8HZLog9SLRFekVrKfXOkA+vj4UKVKFapUqQLAhAkTqFKlCtOnT9c5jS1btlC6dGmaNm1K69atqV+/Pt9//31WmZzlGBgYUKVKFa1maknuJeJ1PFsvBCnHwxsVyyB25pKZWulb+7+FzX849e97p5eV7N+/n4kTJ7Jx40ZevnzJlClTaNeuHSEhIQB88cUX7N+/nwMHDhAREcFvv/2Gg4PDW8+l5O7du3To0IFp06YREhLCl19+Sfv27ZUB6QA7duxg+PDhhIWF0a9fPwYOHKic++qrrzh9+jSnTp3i8ePHlCxZkp49ewJw+PBhtm7dyuXLl4mIiODIkSOULFkyzbLWq1ePlStXsnTpUnx8fN7pR3Pr1q0MGTKE0NBQxo0bR+fOnRXHDGDz5s18++23hIaGUr16da0lMNzd3fH29iYiIoIffviBSZMmcfr0aQBevXpFs2bNKF++PAEBATx58iTN5TMgaeKtffv2cerUKe7fv49KpaJPnz5acX755Re8vb0JDAwkODiYJUuWKOeaNm2Kn58fISEh9OzZk65duyqzN7/J4MGDefnyJdeuXeP+/fvEx8czevRoADZt2sTVq1e5e/cuYWFh7Ny5M8PhAh8q8ndIog9SLxJdkVpJGz8/P2bMmEGTJk3w9PTE2dmZihUrMmDAALZu3UpsbOy7J57DYxBzNUFBQblmIGjKQbWS3M+ig/8oE6h8tPh4tuadmVp5HZcgPL5IKkfRyftE5Ou4TLBQdxo1aiRMTU2FjY2NsjVr1kw5D4grV64IIYRo3bq1WLp0qdb1devWFT/99JPQaDTC3Nxc/P3336nyyOhcsg3Jk0x9/fXXomXLllrnP/roI/HNN98IIYQYMGCA6NGjh3IuODhYAMqkJxYWFsLX11c5//r1a6FWq0VgYKDw9vYWBQoUEIcOHRJxcRnXc2Jioli3bp1o0qSJsLCwEDY2NmLy5MnKIPkBAwaITz/9VIkfGhoqAHH//n3lfKtWrZTyx8fHi9KlS4vNmzcrZZ48ebJy/alTp4SlpWW69nTo0EF8/fXXQgghtm/fLjw9PdPUX/Lg+tDQUCGEEMWLFxfbt29Xzj98+FAAyiRfgPjrr7+U819//bVo27ZtunbY2tqKU6dOCSGEMgmMEEI8e/ZMqNVq8fLlSyXunTt3hJGRkUhISBDr168XJUqUEGfOnFEmCJCkRv4OSfRB6kWiK7lJK7nh3f/SpUuiadOmwsTERDRp0kR88cUXYvny5WLdunViwYIFol+/fqJo0aLC3t5ezJ8/X5kgUh9yZQugJDWJKabVleRuXkTF8uOppBYhAxX8r1eVbM0/M7ViamRARVcbAAQo5cpO5s2bR1hYmLIdPnw4zXgBAQF8+eWX2NraKpuvry8PHz7k+fPnREdHK+sMpSSjc28SHBxM0aJFtcKKFStGcHCwcpyy5cjCwgKAyMhIXrx4watXr2jYsKFin5OTE8bGxgQFBdG4cWNmzZrFtGnTKFCgAF26dNFqWUyJWq1m6NChHD16lLCwMLZu3cqaNWv48ccf31qGZNzdk1p3k/VSpEgRHj78r5vvm+VIOXnLli1bqFq1Kvb29tja2rJ//35lbOaDBw/w9PTUaSzHm/Xp4uKCiYlJhvWZcn3Wr776ihIlSmBtbY2trS3h4eFpjhENCAhAo9Hg4eGh1H2NGjVQq9U8efJEaakdPnw4BQoUYODAgXKsaRrI3yGJPki9SHRFakWbLl260LlzZ548ecLRo0eZN28eY8aMYejQoXz++ef89NNP3L9/n3379nHlyhUWLVqkdx7SAcwjGBgYUL16ddk8ngdY4X2XV3FJD7Hetdwp5WSdrflntlaGN/pvMpgdPsEZxMxZ3NzcWLRokZaz+OrVK7744gscHR0xNzfn7t27qa7L6NybuLq6ao2jgyTnwtXV9a3XOjg4YG5uzvnz57VsfP36NXXr1gVg5MiRnDt3jsDAQExMTNJd6zQlhoaGtG7dmqZNmyrj9CwtLYmOjlbiPH78ONV1Dx48AP7TS1BQEIULF35rfoGBgQwYMIBvv/2WZ8+eERYWRuvWrZU1Gt3d3bl3755ynBFv1ueTJ0+IjY3VqT63bt3K1q1b+fPPPwkPDycsLAwbG5s083Vzc0OtVvPo0SOtuo+JiaFw4cIYGhry5ZdfcvXqVfz8/AgMDGTWrFlvteFDQ/4OSfRB6kWiK1Ir2ty5c4eRI0dia2ubYbw6deqwfft2Jk2apHce0gHMQ8gvI7mf248j2XwuAAAzIwPGNC2e8QVZRGZqpXnZQpgYJj0qHoW95lFo9FuuyBlGjRrFd999x6VLlxBCEB0dzZEjRwgODkalUjFs2DA+++wz7t69ixCC27dv8+DBgwzPvUmPHj04fvw4e/bsISEhgZ07d3LixAllHF9GqNVqhg8fzmeffaYsXBsSEsKOHTsAuHjxImfOnCEuLg4zMzMsLCzSnY56yZIlHDlyhKioKIQQnD59muPHjyuOZNWqVTl48CCPHz8mMjIyTWfG29ubP//8k4SEBL7//nseP35MmzZt3lqO5DwLFiyIWq1m//79HDp0SDnfpk0bYmNjmT59Oq9evSIuLi7dyVn69u3L3LlzCQoKIioqigkTJtCsWTOdJuuKiIjA2NiYAgUKEBcXx+zZs9Md/+fk5ETHjh0ZPXq00rL35MkTdu3apdSFr68vCQkJWFhYYGpq+sFMBa4v8ndIog9SLxJdkVr5j5RLX6VFyvH6usRPC+kA5hESExO5cuWK/AfJ5Yz/5QqJmqT9vrWLUNDKNNttyGytqNUqGpT4bwbTrRcCMyVdXZk8eTKWlpZaW/LELilp164d8+fPZ9iwYdjZ2eHh4cGyZcvQaJJuyIIFC2jatCnNmjXD2tqabt268fLly7eeS0nx4sXZuXMnM2bMwN7entmzZ7Nr1y6KFdNtkp958+ZRp04dmjRpoqzdl+w8RUREMHLkSBwcHHBycuLRo0csW7YszXQsLCz48ssvKVy4MLa2tgwbNozp06fTq1cvIMmxatSoEaVLl6Zy5cppOna9e/dm3bp12NnZsWjRInbu3Imdnd1by1C2bFm++uormjRpgoODAzt27KB9+/bKeUtLS44cOcKlS5coUqQIzs7OrFy5Ms20kmcarVOnDkWLFiU+Pp6ff/75rTYADBgwgHLlyuHu7k6xYsUwMzPLsOVw48aNStdPa2trGjRooCwV9PTpU3r16oWtrS0eHh7Y2NgwY8YMnez4kJC/QxJ9kHqR6IrUSvosWLBA+VAM0L17dxwcHChcuDBXr15953RVQpd+Oh8owcHBuLm5ERQUpFOXJMmHzfl/Q+jx/TkgaezfpWkfYWtunMNWZQ7XH4bT7n+nACjnYs2fYxvksEUSiUQikUgkmUtue/f38PBgy5Yt1K1bl8OHD9O9e3d27NjBL7/8QmBgoFYPHH2QLYB5hOQubdJfz71M/PW/LzFdq7nmmPOXFVqpUNhGmQzm5qMI7jxNu6udJO8hny0SXZFakeiD1ItEV6RW0ufJkye4ubkBsG/fPrp3707z5s35/PPP32u9WukA5hESExO5efOmbB7Ppfx57RFBoa8BMDZQM7N9uRyzJau00qnKfxOE7MoDi8JLdEM+WyS6IrUi0QepF4muSK2kj52dnTJvwIEDB2jWrBmQ5DS/T31JBzCPYGhoSI0aNeTEBLkQIQTT9txUjj9uWAwz45y7T1mllXaVXFD//8z+P599gEYjv9TlB+SzRaIrUisSfZB6keiK1Er6dO7cmd69e/PRRx8REhJCq1atALhy5QrFi7/7RIPSAcwjCCGIjIyUzeO5kI1nAnj5Kg4ACxMDxn9UMkftySqtFLA0wdXODIDI2AT2X0+9tIAk7yGfLRJdkVqR6IPUi0RXpFbSZ8mSJYwePZqyZcty+PBhLC0tgaTlnUaOHPnO6UoHMI+g0Wjw9/dXZjSU5A4SEjUsPHhbOZ7UojQG6rcvgJ2VZKVWuld3U/a3X8ye2UC3b99O9+7dsyWv3Mjp06epX79+lqWfkV4CAwOxtLQkPDw8y/JPZunSpXh5eWV5PrkBS0tLZc3GvIT8HZLog9SLRFekVtLHyMiIiRMnsmzZMqpUqaKEjx8/nqFDh75zutIBzCMYGBhQtWpVuUhmLmPJ4TvKou8OFsYMqOOewxZlrVYG1/PA9P/XBLwaFE5MfNb219doNHz55ZdMmzYtS/PJiGfPntGnTx9cXV2xtramSpUq7N27VyvOo0ePaN26NRYWFhQpUoR169Yp5+7cuUOnTp1wcnLC1taWevXqcfr0aeV8XFwcXbt2pWjRoqhUKnbv3q2Vdr169TAyMmLPnj1ZUr6M9FKkSBGioqKwsbHJkrw/VKKioqhQoUJOm6E38ndIog9SLxJdkVrJmM2bN1O/fn1cXFyUNYqXLl36Xu8F0gHMIwghCAsLk83juYjXcQmsO/mvcjynY3lUqpxt/YOs1Yq5iSFtKyUt0h0Zm8ARv6eZnkdK9u/fj729fba9LMfHx6cKi4qKokqVKpw7d46wsDBmz55Nr169uHXrlhKnV69eODk58ezZM3799VcmTZrE33//DSQt2NqqVSuuX79OSEgIAwcOpHXr1sqC5AD169dn8+bN6U45PWDAAFasWKF3eSIiIt6qg/z4bEnrPkren/yoFUnWIfUi0RWplfRZvXo1EyZMoFWrVoSFhSkTv9ja2rJ06dJ3Tlc6gHkEjUbDgwcPZPN4LmLm3pvEJSY9rIrYmdG6gnMOW5REVmtFazbQy1k7G+jevXtp0qSJVphKpWLFihWULVsWCwsL+vXrR2hoKD169FBa6P755x8l/uLFiylRogRWVlZ4enpqOVIBAQGoVCo2bNhA8eLF03TAihUrxsSJE3F1dUWtVtOuXTtKlSrFuXNJaz7eu3ePU6dOMW/ePCwsLKhVqxZ9+vRh/fr1ANSsWZOPP/4YR0dHDAwMGDZsGAYGBly7dg0AY2Njxo0bR4MGDdL9+tm0aVOOHz9OZKR+y2+MHj2aTz/9NMM4arWauXPnUrp0aWxtbenRo4fS5TO5fsLCwoAkbS1fvpzSpUtjZWVFiRIlOHDgAJDkdE2ZMoUiRYrg6OhIjx49eP78ebr53rx5k9q1a2NlZUXjxo159OiR1vnklldnZ2dcXFwYN24csbGxaaZ1/PhxbG1tWb16NUWKFKFu3boAHDlyhJo1a2Jra0u5cuW0Wm4HDhzIsGHD6NmzJ1ZWVpQqVYrjx48r54sWLarVGrt7926KFi2qdf7bb79VytCoUSNlpjZImrq7b9++ODs7Y2trS8OGDXn9OmmmYJVKha+vL5A0kL9+/frY29vj6OhIr169CAkJUdLZsmWLot/ChQszZ86cdOs0q5G/QxJ9kHqR6IrUSvr873//Y926dXz11Vda7wjVq1d/r6EE0gHMIxgYGFCpUiXZPJ5LCI2O49dLwcrxwu6VctAabbJaK7WLOeBkbQrAsdvPeBYRkyX5APj6+lK6dOlU4Xv27OHUqVP4+/tz6NAhGjVqxJgxY3j58iWVK1fm888/V+K6u7vj7e1NREQEP/zwA5MmTdLqgglJjqaPjw/3799/q03Pnj3Dz8+PihUrAnDt2jWcnZ0pVKiQEqdy5cqKg/cm169fJzIykrJly+pUBwBubm6Ymppy48YNna8BWLhwIYcOHWLKlCkZxvP29ubYsWMEBAQQGhrKuHHj0oy3YsUKli5dypYtW4iIiODo0aO4uyd1e543bx779u3j1KlT3L9/H5VKRZ8+fdJMJyEhgfbt29O0aVNCQkKYO3cuP/zwg3JeCEH79u1xcnLi3r17XL9+natXr/L111+nW4bIyEiuXr3KP//8w99//821a9fo1q0b8+fP5+XLl6xdu5Z+/fpx+/Z/Y3Z37NjB8OHDCQsLo1+/fgwcODDDenqTn3/+mW3btvH8+XMsLCyUrsoajYZ27dphaGjIrVu3ePHiBXPnzkWtTv2Tq1armT9/Pk+fPuXGjRs8fPiQL774AoBXr14xcOBAfvzxRyIjI7l58yYtW7bUy8bMRP4OSfRB6kWiK1Ir6XP//n2tsX/JmJiY8OrVq3dOVzqAeQSNRkNISIj8OpJLmPTrVZJXQSjrbE1ND4ecNSgFWa0VA7WKSm5JY8I0Av7nfTdL8gEIDQ3F2to6VfjEiROxt7fHxcWFRo0aUa5cOerXr4+hoSHdunXj8uXLStwuXbrg5uaGSqWicePGtGjRQqulB2DGjBnY2tpibm6eoT1xcXH07NmT7t27U716dSCpi6itra1WPFtb2zRb68LCwujZsydffvklTk5OOtZCEtbW1oSGhqZ5ztXVFZVKlWorVKgQt2/fZv78+fj4+KSb9ogRI5QxinPmzGHr1q1p6mf16tXMnDmTatWqoVKpKFKkCGXKlAGSxihMnTqVIkWKYGlpyeLFizl8+HCqlj2As2fP8uLFC2bOnImxsTF16tShR48eynkfHx/8/f357rvvMDc3x8HBgS+//JKtW7emWwaNRsP8+fMxNzfH3NyctWvXMnDgQJo0aYJaraZ+/fq0bduWX375RbmmdevWeHl5YWBgwKBBg3jw4IFW69vbGDlyJB4eHpiamtKnTx8uXboEwMWLF/Hz82P16tXY2dlhaGhI/fr1MTExSZVGpUqVqF+/PkZGRhQqVIgJEyZo6dPIyAg/Pz8iIiKwtbWlRo0aOtuX2cjfIYk+SL1IdEVqJX08PDyUHiMpOXDggPL7+y5IBzCPIITg8ePHsn90LuBhaDRH/Z4px8t6Vs45Y9IgO7QyrEExZX/ftdQv+JmFnZ0dERERqcJTtraZm5unOo6KilKOt2zZQtWqVbG3t8fW1pb9+/drjb+DpMlO3kbyZC3m5uZak7ykNUtmeHg4VlZWqcJatGhB/fr1mTlz5lvze5OIiAjs7OzSPHf9+nWeP3+eart79y6lSpXi448/VhzWtDAzM1P04u7uTlxcXJrdNx88eECJEiXSTCM4OFiri6SLiwsmJiYEBwenivvo0SNcXFwwMjJSwpJbEiGp62lYWJhyz2xtbenatStPn6Y/5tTKykrLEQ8ICGDNmjXK9ba2tuzZs0fLIU3phFtYWADo1c32zeuTr33w4AGFCxfGzMzsrWncvXuXDh064OLigrW1NX379lX0aWFhwR9//MGePXtwc3Ojfv36HDt2TGf7Mhv5OyTRB6kXia5IraTPhAkTGDVqFDt27EAIwYULF/jmm2+YMmWKVm8nfZErLuYRDAwMKF++fE6bIQGm7r5B8iOqTjEHShSyyjB+dpMdWqle1B4rU0MiYxIIjY7n5qNwyrlk/kyRlStX1hrPpy+BgYEMGDCAAwcO4OXlhaGhIR07dkz1I5NW17yUxMXF0a1bN+Li4tizZw/GxsbKuYoVK/Lo0SOePXtGwYIFgaSuqyknrkl2/sqVK8eaNWv0niwoKCiImJiYdO9reo7hZ599Rq1atVizZk2G6RsaGipdbwIDAzE2NsbR0ZHAQO2lPtzd3bl79y516tRJlYarqysBAQHUqlULSBoDFxsbm+a4ShcXFx49ekR8fLziBKbMy83NjYIFC/L4se5rTb55D93c3Pj000+ZP3++zmmkxNLSkujoaOVYH1vc3d15+PAhMTExmJqaZhh3+PDhlCxZkk2bNmFra8vu3bu1uqI2bdqUpk2bEh8fz6pVq+jYsSOhoaFv1WxWIH+HJPog9SLRFamV9Bk6dChmZmZMnTqV6OhoevfujYuLC8uWLaNnz57vnK5sAcwjaDQanj17JpvHc5j7L15xwj/p67yhWsWSHrln7F8y2aWVFuX+a3VbdtQ/S/Jo167de7V4REVFIYSgYMGCqNVq9u/fz6FDh/RKIz4+nu7du/Pq1St2796dqhufp6cn9erV48svvyQ6OpoLFy6wZcsWhgwZAiS13LVs2ZKSJUvyww8/pOn8xcbGEhMTgxCC+Ph4YmJilJm+IGmMXsOGDVO1Kr6NRYsWsX79+rc6nPPmzSM4OJiwsDCmT59Oz54903QwPvnkE2bNmoWvry9CCAIDA/Hz8wOgb9++zJ07l6CgIKKiopgwYQLNmjXDxcUlVTq1a9fG3t6eOXPmEBcXx/nz59mxY4dyvkaNGri5uTF16lRlceAHDx7w119/6Vz2Tz75hA0bNnDs2DESExOJjY3l7Nmzir1vo2rVqmzbto2YmBj+/fdfVq5cqXPeNWrUoFSpUowcOZKwsDASEhI4depUmpPYREREYGVlhbW1NUFBQXz33XfKuadPn7Jr1y4iIyMxNDTE2toaQ8Oc+24rf4ck+iD1ItEVqZWM6dOnD/7+/kRFRfHkyROCg4OVd4x3RTqAeQQhBCEhIbJ5PIdZeOg2if8/+G9MkxI42by9i1d2k11aGdespLJ/4s7zLMkvebkEfSc/SaZs2bJ89dVXNGnSBAcHB3bs2EH79u31SuPMmTPs2bOH06dPU6BAASwtLbG0tGTu3LlKnG3btvHw4UMcHR3p0qUL3377LY0aNQJg165dnDt3jt9//x1ra2vl+i1btijXlypVCjMzMwIDA+nevTtmZmZs3rxZOf/TTz8xevRovctfoEABnQbVt2rViqZNm+Lu7o6VlRXLli1LM97YsWMZMWIE3bt3x8rKimbNmiktd1OmTKFFixbUqVOHokWLEh8fz88//5xmOkZGRuzdu5eDBw9ib2/PF198weDBg5XzBgYG7Nu3j4cPH1KmTBlsbGxo06YNd+/qPt60SpUqbNu2jalTp+Lo6EjhwoWZNm1aujOJvsnXX39NWFgYjo6O9O7dm/79++uct1qt5o8//iA6OppSpUpRoEABpk6dmubLzeLFi9m3bx/W1tZ06NCBLl26KOc0Gg3Lli3Dzc0NGxsbVq5cyW+//ZYjrX8gf4ck+iH1ItEVqRXdMDc3V3oavS8qIWs7XYKDg3FzcyMoKCjd9bkkHw7Xg8Npt+IUAAUsjfl7UmMsTD7sXtR15h3lcXjSLKDr+lfjo7L6TWyiC9u2bWP37t1aLUQfEmfOnOHzzz/n1KlTWZK+SqXiypUrVK5cOUvSl0gkEokkr5Db3v1DQkKYPn06x44dS7OV9OXLl++U7of99pqH0Gg0PH36lEKFCuXY198PnTHb/ptZckyTErnW+ctOrXSr5sry/58FdPXxe1niAPbq1YtevXplerp5hbp162aZ85fM8+fP0Wg08tkiyRD5OyTRB6kXia5IraRPv379uHv3LkOGDKFQoUJ6zyGQHrnzDVaSCiEEkZGRmdb0K9GPXZcfEhCSNCGEpYkhvWq+fdbInCI7tTKsYTH+530XAfgGhRGbkIiJoVzHJ68RHR0tu95I3or8HZLog9SLRFekVtLn5MmTnDp1ikqVMnfOCekA5hEMDAwoWbLk2yNKMh2NRrA8xSQnHzf0wNgw936hyk6tWJkaUdLJittPItEI2Ho+kEH1PLIlb0nmIB0/ia7I3yGJPki9SHRFaiV9SpcuzevXrzM93dz7FivRQqPREBwcLGdIygH+uvGE+yGvAChsa8boxmmvg5ZbyG6tDK5XVNn/6eyDbMlTknnIZ4tEV6RWJPog9SLRFamV9Fm1ahVfffUVf//9NyEhIURERGht74p0APMQus5eJ8k84hM1LDx0Wzn+plN51OrM6X+dlWSnVrpUdcXw/+vk/otXhL6Ky7a8JZmDfLZIdEVqRaIPUi8SXZFaSRtbW1siIiJo0qQJBQsWxM7ODjs7O2xtbdNdA1gXZBfQPIJarcbT0zOnzfjg2H4xkPsvklr/ahezp1FJxxy26O1kt1YMDdTUKGrP2X9DAFh57C5T25bNtvwl74d8tkh0RWpFog9SLxJdkVpJnz59+mBkZMTWrVvlJDAfIhqNhqCgINzc3OQMSdlEdGwCs/+4pRx/3rJ0pv3jZSU5oZXRjYsrDuBu34fSAcxDyGeLRFekViT6IPUi0RWplfS5ceMGV65coVSpUpmarqxliSQdvtp9g/jEpAkyPAqYU7XIuze153fqFnfAwjhp9s8XUXHcfRqZwxZJJBKJRCKR5G2qV69OUFBQpqcrHcA8glqtxt3dXX4ZySZComLZ4/tQOV7cPXOn381KckIrKpWKpmUKKcc7Lmb+w0qSNchni0RXpFYk+iD1ItEVqZX0GTNmDJ9++ikbN27k0qVLXLt2TWt7V2RN5xE0Gg337t2TMyRlE5/9chXN/8+OX7GwDVWK2OesQXqQU1oZ1+y/2VGP3n4mlxfII8hni0RXpFYk+iD1ItEVqZX06dGjB35+fgwePJgaNWpQuXJlqlSpovx9V+QYwDyEiYlJTpvwQXD/RRTH7zxXjpf1qpxzxrwjOaGVYo6W1PSw58L9l/z7/BXXgsOp5Gab7XZI9Ec+WyS6IrUi0QepF4muSK2kzf3797MkXekA5hHUajWurq45bcYHwbjtvsp+wxIF8ChgmXPGvAM5qZXOVQpz4f5LAHZdeSgdwDyAfLZIdEVqRaIPUi8SXZFaSc306dPp0KED1apVy5L0ZRfQPEJiYiJ37twhMTExp03J1/gGhXI1OBwAtQoW98g7Y/+SyUmttKrgjJFB0kyp2y8EEp8ou3PkduSzRaIrUisSfZB6keiK1EpqgoODadWqFa6urowYMYK//vqLuLjMW2dZOoB5BJVKhZWVVZ5YhiAvM2HHVWW/XSUXClia5qA170ZOasXGzAh3e3MAYhI0/HZJTgaT25HPFomuSK1I9EHqRaIrUiupWb9+PU+ePGHbtm1YWVkxbtw4ChQoQJcuXfjpp594+fLle6UvHcA8glqtxtnZWc6QlIUc/ecp//7/ou+GahXzOlXIYYvejZzWSv86RZX9XVce5YgNEt3Jab1I8g5SKxJ9kHqR6IrUStqo1WoaNGjAt99+y+3btzl//jy1atVi7dq1uLi40LBhQxYuXMjDhw/fntibaWeBvZIsIDExET8/P9k8nkUIIfjy9+vKcf+67pib5M0hsjmtlZ41i2BpkrQm4NWgMCJj4nPEDolu5LReJHkHqRWJPki9SHRFakU3ypQpw+eff87p06cJCgpiwIABnDx5km3btumdlnQA8wgqlQoHBwfZPJ5F/OITxNPIWADMjAyY0qpMDlv07uS0VowN1XSqkjSYOzZBw4EbT3LEDolu5LReJHkHqRWJPki9SHRFakV3IiIi2L17NyEhIQwZMoQ9e/YwceJEvdORDmAeQa1WU7BgQdk8ngVoNIJv/vRTjj9tWgIjg7xbz7lBKx2rFFb2d13Rv2uCJPvIDXqR5A2kViT6IPUi0RWplfTp3r07K1asAOD169dUr16d7t27U6FCBX7//fd3TlfWdB4hMTGRGzduyObxLGDV8btExCQASZOYfNKoWA5b9H7kBq1ULWKLu70ZAGfuhRD48lWO2SLJmNygF0neQGpFog9SLxJdkVpJnxMnTtCgQQMAdu3ahRCCsLAwli9fztdff/3O6UoHMI+gUqlwdnaWzeOZTFyChhXH7irH09uWzfN1nBu0olKpqJhiDcClR/xzzBZJxuQGvUjyBlIrEn2QeskaTp48mS1r5g0fPpzJkyenCn/27BkVKlTg5MmTmZaX1Er6hIeHY29vD8CBAwfo0qUL5ubmtGnTBn//d3+3kg5gHkGtVuPg4CCbxzOZrecfEBOftFads40pXarl/YVIc4tWRnkVV/YP3XyKECIHrZGkR27RiyT3I7Ui0Yf8pBcvLy9MTEywtLTE3t6eRo0a4ePjk+X5BgQEoFKpCAsLU8IaNGhAcHBwlue9Zs0aFixYoBUWExNDnz59WLlypdIq9S4sXLiQihUrYm1tjaurK59//jlWVlZaWpk1axaFChXC2tqaPn36EBUVpZybNGkSpUqVwsrKCg8PD+bNm5dmPq9fv6Z48eLY2tq+s605jZubG2fPnuXVq1ccOHCA5s2bAxAaGoqp6bsvVZb3/ys/EBITE7l69apsHs9EomIT+J/3f61//+tVJQetyTxyi1ZKO1tjb2EEJNX1hfvvt2aNJGvILXqR5H6kViT6kN/0smDBAqKionjy5Am1atWic+fOOW1StmNqasrhw4dp2LDhe6WTmJjIjz/+SEhICOfOneP48eOMGDFC0cqGDRv48ccfOXnyJIGBgYSEhDB27FgtO3bu3ElYWBh//fUXa9eu5fvvv0+Vz/Tp03F3d38vW3OacePG0adPH1xdXXFxccHLywtI6hpaocK7L1cmHcA8glqtxt3dPV98Scst/HjyPiGv4gBoW9GZ6kXtc9iizCE3aaV9JRdlf4W37AaaG8lNepHkbqRWJPqQX/VibGzMgAEDCAoK4vnz50DSUlLLly+ndOnS2Nra4uXlhZ/ff5PLLV68mBIlSmBlZYWnp6cyqUcy/v7+tG/fHkdHR+zt7RXnsmbNmgC4urpiaWnJli1bOH78uNKitWvXLjw9PbXSOn/+PLa2tsTExBAYGMhHH32Eo6MjdnZ2tGnThoCAACWuRqNR7LaysqJEiRIcOHAAgIEDBzJu3Dglro+PD/Xq1cPW1payZctqLT0wc+ZM2rVrx+jRo7G1taVIkSLs2LEj3TqcPHkyNWrUwMjICFdXV/r164efn5+ilfXr1zN27FhKliyJra0tc+bMYdu2bbx+/RqAOXPmUK5cOQwMDChdujSdO3fm1KlTWnlcunSJAwcOpNmNNS8xcuRIzp07x/r16zl16pRSR8WKFZNjAD8EVCoVtra2sn90JvE04jWrjye1/hmqVUxsXiqHLco8cpNWRjcuoeyfu/+SRI3sBprbyE16keRupFYk+pBf9fL69Wt+/PFHChQogJ2dHQCrV6/mxx9/5I8//uDFixd07tyZdu3aEReX9JHZ3d0db29vIiIi+OGHH5g0aRKnT58G4NWrVzRr1ozy5csTEBDAkydPGDNmDAAXLlwAIDg4mKioKPr06aNlS5s2bQgLC1PSAti8eTPdunXD1NQUjUbDhAkTCAoK4sGDB5ibmzNs2DAl7ooVK1i6dClbtmwhIiKCo0ePptliFhYWRsuWLenZsyfPnz9n9erVDBs2TCvfgwcP0rBhQ0JCQvj6668ZOnQokZGROtXpiRMnqFq16v+xd97hUVRdHH53N70XSEgggRACJLSEojRBEAsoVUUQBFFBkSKICihFuqAi7VOKIIiCoDRFQaV3QiChJKEHSCUEUknfne+PJZvEBEjZZNt9n2ee3JmduXPuzi+zc+bee45GK+fOnSMwMFDzeWBgINnZ2Vy+fLnEsZIkcejQIZo3b67Zlp+fz/Dhw/nf//6HhYVFmWzQZ1q1akXfvn2xs7PTbHvxxRfp0KFDhesUDqCBoFQqOXPmjNEMpdA1H246S3a+eu5frxae1Kthq2OLtIc+aaWGvSV1XWwAyFNK7AgTKSH0DX3Si0C/EVoRlAdj08vkyZNxcnLC1taWDRs2sHXrVszMzAD43//+x8yZM/Hz88PMzIyxY8eSlZXFyZMnAXj55Zfx8vJCJpPRpUsXnn/+eQ4cOADAzp07MTc3Z86cOdja2mJhYUGXLl3KZJOFhQWvvfYa69evByAvL49NmzYxZMgQAOrVq0f37t2xsrLCwcGBzz77jMOHD6NSqZ9/vvvuOz7//HNatWqFTCbD29sbf/+SeZD//PNPatasyZgxYzA3N6dz5868/vrrrFu3TrNPy5Yt6d+/PwqFgjfeeIPc3NxSHbb/smrVKo4ePUrPnj01WsnIyCg2b8/c3BwbG5tSHcopU6aQmZnJyJEjNdu+/PJLgoKCKj1UVV+IiYnh22+/ZdKkSXz44YfFloqilw7goUOH6NmzJ56enshkMrZv3675LC8vj4kTJ9KsWTNsbW3x9PRkyJAhxMXFFavj3r17DBo0CAcHB5ycnHj77beLTSA1NORyOX5+fkY3lEIX3Lx7n6PX7gIgA8Y/6/foAwwMfdPKoLbemvL3h6N0aImgNPRNLwL9RWhFUB6MTS/z5s0jJSWF6Ohoateuzblz5zSf3bhxg8GDB+Pk5KRZkpOTNcFafv75Z1q2bImLiwtOTk789ddfJCUlAXDz5k18fX0r3FM6ZMgQNm/eTE5ODn/99Rf29vZ07NgRgDt37vD666/j5eWFg4MDnTp1IicnR+NI3bx5Ez+/xz8DxcTEUK9evWLb6tevXywYTa1atTRlmUyGtbX1Y3sAf/75Z6ZMmcLff/9N+/btNVqxs7MjNTVVs19+fj6ZmZnY29sXO/6LL77gl19+4Z9//sHWVv0i/+rVqyxfvpwvv/zyse3SBo/yWUDdQzlt2jQ8PDywtramW7du5YreuXfvXho1asR3333H119/zf79+/nhhx9Ys2YNYWFhFbZbL/8r79+/T4sWLfjf//5X4rPMzEzOnDnD1KlTOXPmDFu3buXSpUv06tWr2H6DBg0iPDycf//9l507d3Lo0CFGjBhRXU3QOjKZDHt7e6MbSqELFhdJSfCMvxteLsbT+wf6p5Uh7eohf2BKZHwa93PydGuQoBj6pheB/iK0IigPxqqX2rVrs2rVKiZOnKjpfPDy8uLXX38lJSVFs2RmZjJw4EBu3brF0KFDWbBgAYmJiaSkpNCjRw9NZOy6dety7dq1UiNll8V5btu2LTVq1GDnzp2sX7+ewYMHa77zyZMna56b09LSOHToEECxc1+9evWhdRdQp06dYnMHQe30ViYdxc8//8y4cePYvXs3LVq0KKaV5s2bF3NuwsLCsLS0pGHDhpptX3zxBcuXL2ffvn3F7Dhy5Ai3b9+mYcOG1KhRg969e5OWlkaNGjU0PbLa5FE+C8CCBQtYsmQJy5cv5+TJk9ja2vL888+TnZ1dpvonT57MRx99xPnz57GysmLLli1ER0fTuXNnXn311YobLuk5gLRt27ZH7hMcHCwB0s2bNyVJkqSIiAgJkE6dOqXZZ9euXZJMJpNiY2PLfO7o6GgJkKKjoytkuzbJy8uTgoODpby8PF2bYtBExKVK9SbtlOpO3Cm1mPG3lJqVq2uTtI4+aqX30sNS3Ynq733xnsu6NkdQBH3Ui0A/EVoRlAdj0kvnzp2lb775pti2F198URo1apQkSZK0ZMkSqU2bNtLFixclSZKk1NRUafv27VJaWpoUHh4uyeVy6ezZs5JSqZT+/PNPydraWvrggw8kSZKk9PR0qU6dOtKUKVOkjIwMKScnR9q3b58kSZKUmZkpyeVyKSQkRHPe/fv3S46OjsVsmTlzptS5c2fJyspKunLlimb7q6++Kg0cOFDKzc2VkpKSpD59+kiAlJycLEmSJH3zzTeSr6+vFBoaKqlUKunmzZtSRESEJEmSNHToUI2N9+7dk1xcXKT//e9/Ul5ennTo0CHJzs5OOnz4sCRJkjR9+nSpd+/exWxydHSU9u/fX+r3uWHDBsnFxUXznP5fraxevVry9vaWLl++LKWkpEjdu3eXhg0bpjl+/vz5kpeXl3T9+vUSdd+/f1+Kjo7WLJs3b5YcHByk6OhoKScnp1R7ilKZZ///+iwqlUqqVauW9OWXX2q2paSkSJaWltLGjRvLVKednZ109epVSZIkycnJSbpw4YIkSZIUFhYm1a1bt9w2FqCXPYDlJTU1VTPZGOD48eM4OTnRunVrzT7dunVDLpc/0vvPyckhLS1NsxR0XReMlVapVKWWlUplmcrSgzcuRcv5+fklypIklSgrFAoCAgI0+0qSpBkrXbSsUqnKVNaHNhXYXrRc1W1asPsiBS/Z3u/si4OVucG36b/lgqhYBW/S9KFNw5/yoYBNp26Vu02llXXdJmP5fwJo0qQJMpnMaNpkjNdJH9okl8s1v0PG0iZjvE760iYAf39/FAqFwbepgKK2T5o0ie+//57o6GhGjhzJ0KFD6devHw4ODvj7+7NhwwaUSiX+/v589tlndO3aFVdXVzZt2kTPnj017ShIrXD69Gm8vb3x8PDgf//7H/n5+VhbWzNt2jS6d++Ok5MTP//8c6m2Dxo0iEOHDhEUFET9+vU126dPn87Vq1dxdnamQ4cOPP/888XaN3bsWN5991369++Pvb093bp10/T0FdgH4ODgwF9//cVPP/2Eq6srI0aM4LvvvqNt27bFnksfd20Kyp9++ilpaWk8/fTT2NnZ4eTkxNChQzW/Q2+99RZvvvkmHTp0oE6dOjg6OrJw4UKN7RMnTiQhIYFmzZphZ2eHnZ0dL7zwAiqVChsbGzw8PPD09KROnTq4uLggk8moU6cOCoWiTNoDSE9PL+YT5OTkUF6ioqJISEigW7dumm2Ojo48+eSTHD9+vEx12NraaoIJeXh4cO3aNc1nBcOIK4LBO4DZ2dlMnDiRgQMH4uDgAEBCQgJubm7F9jMzM8PFxYWEhISH1jVv3jwcHR01S0BAAAC3bqkfWKOjo4mOjgbUF7Wg6//atWvcvn0bgMuXL2suSGRkJMnJyQBcuHBBM5757NmzmvmIoaGhmrC2ISEh5ObmolQqCQkJQalUkpubS0hICDKZDJlMpukSz8jI4OzZs4DaAb5w4QKgTgxZEHo4KSlJMwH39u3bGtHExcURFRWl8zaBOppWaGhotbRp3d6z7L+kDtlc01ZBdz9bg29TaddJJpNx8+ZNTeJYfWiTr1UGlmbq201sSjZxKZkmpT19btOVK1ewsbEhMTHRaNpkjNdJH9qUl5eHpaUlp0+fNpo2GeN10pc2Xb9+nfT0dGQymcG36aeffmLcuHHFrpOrqyu3bt3Cy8uLixcvMmDAAMLDwzl8+DDh4eFs2rSJ69evk5GRwcyZM9m1axexsbGsW7eO8ePHs2DBAk2bGjRowLZt2/jzzz+5e/cuP/74o6ZNH374Ibt37yYlJYUXX3yRGjVqkJKSUqxNdnZ2hIeHc+zYsWJtcnR0ZOPGjWRkZPD333/TvXt3JEkiOTmZuLg45HI5vXv35uDBg2RkZLBjxw5atWoFqJOtT5s2TXOdGjVqxLFjx9i/fz/BwcEMHjxYc50+//xzJk+eXOw6JSUl0b59+1Kv07lz5zh58iQZGRnExMRw9OhRIiIiSElJ0bRp1KhRHDx4kPT0dBYuXEhiYqLmOl29epXc3FzCw8MJDw8nIyOD//3vf6X+P3l6emqud1m0V6CZgICAYj7Bw5LNP4oCn8Pd3b3Ydnd390f6I0Vp27atJsVFjx49mDBhAnPmzOGtt96ibdu25bapAJlU4PLqKTKZjG3bttGnT58Sn+Xl5fHyyy8TExPDgQMHNA7g3LlzWbduHZcuXSq2v5ubGzNmzCgWKagoOTk5xTz82NhYAgICuHnzJt7e3pq3BnK5vFhZqVQik8keW5bL5Zq3GwXlgt69omVQv40oWgY4deoULVu2xNLSEkmSNL09RcsqlQpJkh5bLq0d1d0mMzMzzRu6gnJVtUkmk9F69h5N3r+pLzZmWAcfg27Tw66NSqXi1KlTtGrVCgsLC71p04j1p9l7Ue2AD2jjxdy+TU1Ce/repry8PEJDQ2nZsiUKhcIo2mSM10kf2iRJEiEhIbRs2VITWt3Q22SM10lf2pSTk8OZM2do06aN5iW2obfJGK+TPrQpNze31N8hXbTpxo0b+Pj4EBERQe3atSnA0tISS0tLHsV/fZZjx47RoUMH4uLi8PDw0OzXv39/ZDLZI3MlFlDwEqF58+bcv3+fCRMmcOzYMfz8/Fi4cGGFE90brAOYl5dH//79uX79Ovv27cPV1VXz2Zo1a5gwYYLGywd1l66VlRW//vorffv2LdO5Y2Ji8PLyIjo6ulITXbVBwT+IhYWF0U2org7WH7/J1B3qtzo2FgrOf/48Crlxfo/6qpVzMcn0WnYMAEdrM85Of17HFglAf/Ui0D+EVgTlQehFUFb0SSuVefb/r89y/fp1fH19CQ0NLZbXsHPnzgQGBrJ48WItWl4+DHIIaIHzd+XKFfbs2VPM+QNo164dKSkpnD59WrNt3759qFQqnnzyyeo2V2sUvE0RlA+lSmL+7kjN+kfPNTRa568AfdRK8zrOOFqbA5Calc/Z6OTHHCGoLvRRLwL9RGhFUB6EXgRlxRi14uPjQ61atdi7d69mW1paGidPnqRdu3Y6tExPHcCMjAzCwsI0892ioqIICwvj1q1b5OXl8corrxASEqKZDJuQkEBCQoJmkqS/vz8vvPACw4cPJzg4mKNHjzJ69GgGDBiAp6enDltWcYqOfReUj8V7LpORo/7eXGzMGdbB5zFHGDb6rJUezQrzBP12WiSF1wf0WS8C/UJoRVAehF4EZcWQtfIon0UmkzFu3Dhmz57N77//zvnz5xkyZAienp6lTm0rwMXFRTNn1tnZGRcXl4cuFUUvh4AeOHCALl26lNg+dOhQPv/8c3x8Sn+A379/P08//TSgTgQ/evRo/vjjD+RyOS+//DJLlizBzs6uzHbo2xDQgjHVuu4eNySycvNpMeNfcpXqseFLBwTSM7D2Y44ybPRZK/GpWXT4Yh8qCWo7WXP4ky7Ijbw3Vt/RZ70I9AuhFUF5EHoRlBV90kp5n/0f5bOsXbsWSZKYPn06K1euJCUlhY4dO/Ltt98Wy2n4X9atW8eAAQOwtLRk7dq1j/xOhg4dWraG/Qe9dAD1BX1zAPVlfLQhMXnrOTYGqyN51XGy5sikrjq2qOrRd628+UMwBx5EY/1lRFva1nd9zBGCqkTf9SLQH4RWBOVB6KXq+fPPP9m5cydLly7FzMxM1+ZUGH3Sij49+1clejkEVFASpVJJaGioQXaP64qUzFw2n4rWrH/5anMdWlN96LtW+gYV9sBuOyOGgeoafdeLQH8QWhGUB6GXqiM+Pp7+/fvz0ksvERsbi1xu2I/zQisP56+//uLvv/8usf2ff/5h165dFa7XsBVjQpiZmdG2bVuDfsNT3Xzy2zmUD/q3G9eyp51vDd0aVE3ou1aeC6iFtbn61rPlTAzZeeKGr0v0XS8C/UFoRVAehF60j0qlYsWKFfj7+3PgwAF++uknduzYYfAOoNDKw5k0aVKpjrFKpWLSpEkVrtewFWNCSJJEZmYmYsRu2YhNzuTfiNua9cWvBerOmGpG37VibaHAp6Z6Lm6+SmLdsRu6NcjE0Xe9CPQHoRVBeRB60S4RERF06tSJ9957j5dffpnIyEgGDRqk8yGT2kBo5eFcuXKFgICAEtsbN27M1atXK1yvcAANBKVSSXh4uOgeLyPjN4VRcBt5wseFRh4OOrWnOjEErQzvWF9T3h2eoENLBIagF4F+ILQiKA9CL9ohOzubadOmERgYyJ07d9i/fz+rV68ukQLNkBFaeTiOjo5cv369xParV69ia2tb4XqFA2ggmJmZ0aZNG9E9XgYi49IIvqHOMScDFplQ7x8YhlZ6BXriaqvOCXg+JpV793N1bJHpYgh6EegHQiuC8iD0UnkOHDhAixYt+OKLL5g0aRJnz57VRLs3JoRWHk7v3r0ZN24c165d02y7evUqEyZMoFevXhWuVziABoIkSaSnp4vu8TIwbnOYpvxcE3c8nax1Z4wOMAStKOQyXm7lBaiHge48F6dji0wXQ9CLQD8QWhGUB6GXinP37l3eeustunTpQs2aNQkLC2PmzJlYWVnp2rQqQWjl4SxYsABbW1saN26Mj48PPj4++Pv74+rqyldffVXheoUDaCCoVCquXLmCSqXStSl6zbGrSVxKSAfUTsaXr7TQsUXVj6FopVg00FARDVRXGIpeBLpHaEVQHoReyo8kSfz888/4+/uzdetWVqxYwaFDh0qdA2ZMCK08HEdHR44dO8aff/7J+++/z4QJE9i7dy/79u3DycmpwvWKPICPwFRygRgTvf93hLPRqQC8/oQXc/uZRuoHQ+X5bw5y6XYGAH+NfYoAT9OZqykQCAQCQQHXr19n5MiR/PPPP/Tv359Fixbh4eGha7NMDlN59heDbQ0ESZJITU3F0dHRKCI+VQXHriZpnD9nG3Om9TTuN2YPw5C00ryOk8YBXLz3MiveaK1ji0wPQ9KLQLcIrQjKg9BL2cjLy2PhwoXMmDGDmjVrsnPnTl588UVdm1WtCK08mr1797J3714SExNL9JKuWbOmQnWKIaAGgkql4ubNm6J7/CFIksT83Rc169N6BmBlbprvNwxJK2Of8dOUD16+I8b/6wBD0otAtwitCMqD0MvjCQ4OpnXr1nz66aeMHDmS8PBwk3P+QGjlUcyYMYPnnnuOvXv3kpSURHJycrGlopjmE7IBolAoaNHC9OazlZU/z8VzNkbd+9e4lj29W9R+zBHGiyFpxcvFhloOliSk5ZCdp2LfxUSe8XfXtVkmhSHpRaBbhFYE5UHo5eGkpaUxZcoUli1bRmBgIMHBwbRq1UrXZukMoZWHs3z5ctauXcsbb7yh1XpFD6CBoFKpuHv3rng7Ugr5ShWfbDmnWZ/4QmPkctMdQmBoWnmltZem/N2Biic1FVQMQ9OLQHcIrQjKg9BL6ezYsYOAgABWr17NV199ZfLOHwitPIrc3Fzat2+v9XqFA2ggSJJEfHy8GCJXCl/9fYnMXHXy0Jp2ljzdqKaOLdIthqaVdzvVp8BdD41OJTdf/ABUJ4amF4HuEFoRlAehl+LExsbSr18/+vTpQ4sWLQgPD+fDDz8Uue8QWnkU77zzDhs2bNB6vUJ1BoJCoaBp06a6NkPvyMpV8uvpaM36nL5NTX4CsaFpxd7KHD93Oy7fzkCpktgYfJOh7X10bZbJYGh6EegOoRVBeRB6UaNUKlm+fDmTJ0/GxsaGTZs28eqrr5r8s0pRhFYeTnZ2NitXrmTPnj00b94cc3PzYp8vXLiwQvWKHkADQaVSlRr9x9RZe+wGd+/nAdDJrwbPNamlY4t0jyFqZViHQofvx+M3dWiJ6WGIehHoBqEVQXkQeoHz58/TsWNHRo8ezcCBA4mMjKR///7C+fsPQisP59y5cwQGBiKXy7lw4QKhoaGaJSwsrML1ih5AA0GSJO7evYurq6uuTdEbUjPzNHPG5DKY+pJppn34L4aolVdb1WHq9gvkqySu3blPcmYuzjYWujbLJDBEvQh0g9CKoDyYsl6ysrKYNWsWX375JX5+fhw+fJiOHTvq2iy9xZS18jj2799fJfWKHkADQaFQ4O/vj0Kh0LUpesO83ZGkZecD8HLLOvi52+vYIv3AELVippDTup6zZv27A9d0aI1pYYh6EegGoRVBeTBVvezZs4dmzZrx9ddfM23aNEJDQ4Xz9xhMVSu6RPQAGggqlYrbt2/j7u6OXC789pt37/NLsHrun5lcxvhnG+rYIv3BULUyuksDTlwPBmDbmVg+7eGvY4tMA0PVi6D6EVoRlAdT08udO3eYMGEC69evp3Pnzvz55580atRI12YZBKamlfLQpUuXRw4Z3rdvX4XqFd+ygSBJEunp6SJC0gPGbwrTlJ+s74Knk7XujNEzDFUrHRrUwMZC/fbvTkYOVxPTdWyRaWCoehFUP0IrgvJgKnqRJIl169bh7+/Pzp07Wb16Nfv37xfOXzkwFa1UhMDAQFq0aKFZAgICyM3N5cyZMzRr1qzC9WqtBzA6OhqZTEadOnUACA4OZsOGDQQEBDBixAhtncZkUSgUNGwoerkAzsWkcOZWCgAyGXzTP1Cn9ugbhqoVmUzGM43d+ONcPACL91xh6estdWyV8WOoehFUP0IrgvJgCnq5cuUK7733Hvv27eP111/nm2++wc3NTddmGRymoJWK8s0335S6/fPPPycjI6PC9WqtB/D111/XTFRMSEjg2WefJTg4mM8++4yZM2dq6zQmi0qlIiYmRkRIonjv30vNPXBzsNKdMXqIIWvlg25+mvLei4nibWA1YMh6EVQvQiuC8mDMesnNzWXOnDk0a9aMqKgodu/ezc8//yycvwpizFqpKgYPHsyaNWsqfLzWHMALFy7wxBNPALB582aaNm3KsWPH+Pnnn1m7dq22TmPS5OTk6NoEnbP/4m2u3bkPqOf+zetb8e5vY8ZQtdLAzZ6adpYAZOYqOXo1SccWmQaGqhdB9SO0IigPxqiXY8eO0bJlS6ZPn84HH3zAhQsXeP7553VtlsFjjFqpSo4fP46VVcU7QLQ2BDQvLw9LS/WD2549e+jVqxcAjRs3Jj4+XlunMVnkcjm+vr66NkOnSJLE5K3nNeuD29bFzsr8EUeYJoaulT5Bnqw6HAXAH2fj6OhXU8cWGTeGrhdB9SG0IigPxqaX1NRUJk+ezPLly2ndujUhISEEBgbq2iyjwNi0ok369etXbF2SJOLj4wkJCWHq1KkVrldrPYBNmjRh+fLlHD58mH///ZcXXngBgLi4OJHXQwuoVCpu3rxp0t3jW87EkpCmfkNkZS7nsxdFlMjSMHStvN+lAeZydcSrvRcTyVcaZjsMBUPXi6D6EFoRlAdj0YskSWzZsgV/f3/Wr1/PokWLOH78uHD+tIixaKUqcHR0LLa4uLjw9NNP89dffzF9+vQK16u1HsD58+fTt29fvvzyS4YOHUqLFi0A+P333zVDQwWCiqJSSczaGaFZH9PFD3OFCGJrjDjbWNAtwJ1dFxJIysjl8NUkujQS8yoEAoFAUL1ER0czatQo/vjjD3r16sWyZcvw8vLStVkCE+KHH36oknq15gA+/fTTJCUlkZaWhrNzYULnESNGYGNjo63TmCxyuZy6devq2gydsfzQNVKz8gBwsDZj5NNiqMDDMAat9A2qza4LCYA6J6BwAKsOY9CLoHoQWhGUB0PWi1KpZNmyZXz22Wc4OjqyZcsW+vbt+8h8bIKKY8haqS5yc3NJTEws0Uvq7e1dofq02oWiUCiKOX8A9erVE1GRtIBKpeLatWsm2T2em69iyd4rmvUpL/ojl4ub8MMwBq083cgNeyv1+6md5+JIz87TsUXGizHoRVA9CK0IyoOh6iUsLIy2bdsyfvx4hg4dSkREBP369RPOXxViqFqpDi5fvsxTTz2FtbU1devWxcfHBx8fH+rVq4ePj0+F69VaDyDAb7/9xubNm7l16xa5ubnFPjtz5ow2T2WSFATZMTW+2BVJdp76puDuYEn/1hV722FKGLpWLMzkNHS34/TNFFQSrDh0nY+eE0l1qwpD14ug+hBaEZQHQ9LL/fv3+fzzz/nmm2/w9/fn6NGjtGvXTtdmmQyGpJXqZNiwYZiZmbFz5048PDy09iJCaz2AS5YsYdiwYbi7uxMaGsoTTzyBq6sr169fp3v37to6jckil8upU6cOcrlpzXvLyM7jx+M3Netf9BNpHx6HsWjl/SLDfA9cStShJcaNsehFUPUIrQjKgyHpZffu3TRt2pSlS5cya9YsTp8+LZy/asSQtFLdhIWFsWLFCrp3705gYCAtWrQotlQUrX3T3377LStXrmTp0qVYWFjwySef8O+//zJ27FhSU1O1dRqTRalUcvnyZZRKpa5NqVY+3XaBfJU6GbhvTVu6NHbXsUX6j7FopWtjd2o7WwMQHpdGQmq2ji0yToxFL4KqR2hFUB4MQS+3b99m4MCBdO/eHV9fXy5cuMDkyZOxsLDQtWkmhSFoRVcEBASQlKT9nMhacwBv3bpF+/btAbC2tiY9PR2AN954g40bN2rrNCaLTCbD3t7epMag383IYee5OM36N/0DdWeMAWEsWpHJZLzSsg4AkgQ7wmJ1bJFxYix6EVQ9QiuC8qDPepEkidWrV9O4cWP+/fdf1q1bx7///kuDBg10bZpJos9a0TXz58/nk08+4cCBA9y9e5e0tLRiS0XRmgNYq1Yt7t27B6gj0pw4cQKAqKgoJEnS1mlMFrlcjoeHh0l1j3974BoPOv9oWdeJ5l5OOrXHUDAmrfQJqq0pbwsVDmBVYEx6EVQtQiuC8qCverl48SJPP/0077zzDr169eLixYsMGTJEOB86RF+1og9069aNEydO8Mwzz+Dm5oazszPOzs44OTmVCLxZHrQWBKZr1678/vvvBAUFMWzYMMaPH89vv/1GSEhIiSz2gvJT0D3esGFDFAqFrs2pcmKSM1n/YO6flbmc7wa10rFFhoMxacWnhi0t6jhyNiaViwnpnLpxjzb1XHRtllFhTHoRVC1CK4LyoG96ycnJ4YsvvmDu3Ll4e3uzZ88ennnmGV2bJUD/tKJP7N+/v0rq1ZoDuHLlSk341lGjRuHq6sqxY8fo1asX7777rrZOY7LIZDJcXV1N5g3VN/9eIVep1tOwDj64O1jp2CLDwdi0EujlxNkY9TzipXuv8OPbT+rYIuPC2PQiqDqEVgTlQZ/0cvjwYUaMGMHVq1f55JNPmDJlCtbW1ro2S/AAfdKKvtG5c+cqqVdrfa1yuRwzs0J/csCAASxZsoQxY8aIybRaQC6X4+bmZhLd42duJrPlTAwAjtbmvNdZJH0vD8amldFd/TTl49fvolSJIeXaxNj0Iqg6hFYED+PatWt0794dZ2dnateuzYIFC5DL5WRnZ+Pg4ICdnZ1mMTMzo1evXtViV3JyMsOHD6dTp044OzsTGhrKnDlzhPOnZ4h7y6M5fPgwgwcPpn379sTGqqfDrF+/niNHjlS4zkp90+fOndP0+p07d+6Ri6ByKJVKLly4YBIRkj7cHKYpD2zjhaO1ue6MMUCMTSs17S2p62IDQJ5S4o+wuMccISgPxqYXQdUhtCIoDaVSSa9evWjZsiWJiYns27ePZcuW8dNPP5GWlkZqaioZGRlkZGRw7949nJycGDBgQJXaJEkSv/zyC/7+/mzevJlvv/2WI0eO0LRp0yo9r6BiiHvLw9myZQvPP/881tbWnDlzhpycHABSU1OZO3duheutlAMYGBioCU0aGBhIUFAQgYGBJZagoKDKnEaAuntcmwkg9ZXgqLvcuJsJgLlCxgfd/B5zhOC/GKNWXn/SW1NedeS6Di0xPoxRL4KqQWhFUBqXLl3i0qVLTJ8+HXNzcxo1asTbb7/N999/X0Iv27dvR6VSVWlsiBs3bvDiiy8ycOBAOnbsSGRkJCNHjhS9S3qMuLc8nNmzZ7N8+XJWrVqFuXlhh0iHDh04c+ZMheut1BzAqKgoatasqSkLqg65XI6rq6uuzahSJEliwe5LmvXhT/lgbaG1aaomgzFqZWj7eszffRGVBBFxaWTm5GNjKbShDYxRL4KqQWhFUBoFI8GKRnxXqVScO3euhF5Wr17NoEGDsLLS/rz+/Px8Fi1axPTp03FxcWHHjh3VNtRUUDnEveXhXLp0iU6dOpXY7ujoSEpKSoXrrdTrkLp162q89bp16z5yEVQOpVLJ2bNnjbp7fG9kIiE3kwF10vcPn22kY4sME2PUipW5gqaejgBIwJqj4oWTtjBGvQiqBqEVQWk0atSIevXqMW3aNHJycggPD2fNmjWkpaUV08vNmzfZs2cP77zzjtZtCAkJ4YknnuCTTz7hnXfeISIiQjh/BoS4tzycWrVqcfXq1RLbjxw5Qv369Stcr9b6w+fNm8eaNWtKbF+zZg3z58/X1mlMFrlcTt26dY12CINSJbHg74ua9Y+fb4yZwjjbWtUYq1be7Vx4o/vlVLQOLTEujFUvAu0jtCIoDXNzc3bs2EFoaCi1a9dm0KBBDBs2DFdX12J6+eGHHwgKCqJFixZaO3dGRgbjx4/nySefRKVSceLECRYvXoy9vb3WziGoesS95eEMHz6cDz74gJMnTyKTyYiLi+Pnn3/mo48+YuTIkRWuV2vf9IoVK2jcuHGJ7U2aNGH58uXaOo3JIpPJcHJyMtrx0cv2XeHy7QwAgrydeL6Ju44tMlyMVSvdm3pgaaa+ZcUkZxGfmqVji4wDY9WLQPsIrQgeRpMmTfjnn39ISkoiLCyMnJwcOnfurNGLSqXihx9+0Grv386dO2nSpAkrVqzgiy++4NSpUzzxxBNaq19QfYh7y8OZNGkSr7/+Os888wwZGRl06tSJd955h3fffZcxY8ZUuF6tOYAJCQl4eHiU2F6zZk3i4+O1dRqTRalUcubMGaPsHs/JV/LtgWua9Xc71Rc3gUpgrFqRy2V08C2cI7B0b8khEYLyY6x6EWgfoRXBwzh37hz3798nNzeXrVu3smbNGiZPnqzRy7///ktSUhIDBw6s9Lni4+Pp378/PXv2xN/fn/DwcD7++ONiATIEhoW4t5SOUqnk8OHDjBo1inv37nHhwgVOnDjBnTt3mDVrVqXq1poD6OXlxdGjR0tsP3r0KJ6ento6jckil8vx8/Mzyu7xWTsjyclXTyL3dLTihaYlXyQIyo4xa2XsM4VRYf88L14saQNj1otAuwitCB7G5s2b8fb2xtnZma+++ort27cTGBio0cvq1at55ZVXcHR0rPA5VCoVy5cvx9/fnwMHDrBhwwZ27dqFj4+PFlsi0AXi3lI6CoWC5557juTkZCwsLAgICOCJJ57Azs6u0nVrLYze8OHDGTduHHl5eXTt2hWAvXv38sknnzBhwgRtncZkkclkRjmmPS0rl43BtzTrC15trkNrjANj1QpAoLczjtZmpGblk5qVx7mYZJrXcda1WQaNMetFoF2EVgQPY/bs2cyePbvE9gK9bN68uVL1h4eHM2LECI4dO8bbb7/NggULcHFxqVSdAv1B3FseTtOmTbl+/brWX3RozdX++OOPefvtt3n//fepX78+9evXZ8yYMYwdO5bJkyeXq65Dhw7Rs2dPPD09kclkbN++vdjnkiQxbdo0PDw8sLa2plu3bly5cqXYPvfu3WPQoEE4ODjg5OTE22+/TUZGRmWbqTPy8/M5deoU+fn5ujZFq0zcch6lSh06uqG7HR0b1NSxRYaPsWqlgO5FeogXi2GglcbY9SLQHkIrgvKgDb1kZ2czdepUgoKCuHv3LgcOHOD7778Xzp+RIe4tD2f27Nl89NFH7Ny5k/j4eNLS0ootFUUmFU3cogUyMjKIjIzE2toaPz8/LC0ty13Hrl27OHr0KK1ataJfv35s27aNPn36aD6fP38+8+bNY926dfj4+DB16lTOnz9PRESEJrdM9+7diY+PZ8WKFeTl5TFs2DDatGnDhg0bymxHTEwMXl5eREdHU6dOnXK3Q5tIkkRWVhbW1tZGMz8uPjWL9vP2USDAXWOfwt/TQac2GQPGqJWixKVk0v6L/QBYmsmJnPkCcrnxtbO6MHa9CLSH0IqgPFRWL/v37+fdd9/lxo0bfPrpp0yePLlCz5QC/Uef7i369OwPFBsWW/S7kSQJmUxW4XmTWs+kbGdnR5s2bSpVR/fu3enevXupn0mSxKJFi5gyZQq9e/cG4Mcff8Td3Z3t27czYMAAIiMj2b17N6dOnaJ169YALF26lB49evDVV18Z5JxEmUyGjY2Nrs3QKuM3hWmcvzb1nIXzpyWMUStF8XSyobaTNbEpWeTkq/g7PIHuzcS80Ypi7HoRaA+hFUF5qKhe7t69y0cffcTatWvp2LEjO3bswN/fvwosFOgL4t7ycPbv318l9WptCOj9+/eZOnUq7du3p0GDBpphoAWLtoiKiiIhIYFu3bpptjk6OvLkk09y/PhxAI4fP46Tk5PG+QPo1q0bcrmckydPPrTunJycYt2q6enpgHriccHf0spKpbJM5YLO1qLl/Pz8EmVJkkqU8/PzOX78ODk5OYDaES7w+ouWVSpVmcq6blN4TDInrt8DQAZ881qgwbepYOjCf8vV3aYCreTm5hpNm/5bfrVVbQrYdSHeKNqkq+uUk5PDiRMnyM3NNZo2GeN10oc25eXlaX6HjKVNxnid9KVNOTk5HD9+nPz8/DK1KT8/n/Xr19O4cWO2bdvGihUrOHjwIH5+fnrTJmO8TvrQpof9DumqTfpE586dH7lUFK05gO+88w6rV6/mqaeeYvTo0XzwwQfFFm2RkJAAgLt78Txx7u7ums8SEhJwc3Mr9rmZmRkuLi6afUpj3rx5ODo6apaAgAAAbt1SBymJjo4mOlqdgDoqKoq4uDgArl27xu3btwG4fPkySUlJAERGRpKcnAzAhQsXSE1NBeDs2bOa+YihoaFkZanzmYWEhGjEHxISglKpJDc3l5CQEBQKBf7+/pw7dw5QD7U9e/YsAKmpqVy4cAGA5ORkIiMjAUhKSuLy5csA3L59m2vX1KkW4uLiiIqK0mmbRv5Y6Ii38rCgjrONwbcpJCQEgKysLEJDQ3V2nRQKBTY2Npqx4cbQpv9ep7bO97EyUw+F2BORwN2UNINvk66u0/Xr1wkKCiIpKclo2mSM10kf2qRUKmnRogWhoaFG0yZjvE760qabN29Su3ZtFApFsTZduHCBiIiIYm26du0aHTt2ZMiQITzzzDNs3LiRwYMHI5fL9apNxnid9KFNFy9eJCgoiLS0NJ23qeD71SUFfkdZiY2NLf9JJC3h6OgoHTlyRFvVaQCkbdu2adaPHj0qAVJcXFyx/V599VWpf//+kiRJ0pw5c6SGDRuWqKtmzZrSt99++9BzZWdnS6mpqZolIiJCAqSbN29KkiRJSqVSUiqVJcr5+fllKqtUqhLlvLy8EmWVSlVqOTc3V8rNzZUkSZJUKpWUn59foqxUKstULq0d1dWm41eTpLoTd0p1J+6U6k/6U7qTmmnwbSooF9hetFzdbVKpVFJ2dramHmNoU2nlcb+EanS082ysUbRJF9cpLy9PysvLk/Lz842mTcZ4nfShTUqlUvM7ZCxtMsbrpC9tysvLk3JycjR1F2wfOXKk5hktKytLmjdvnmRlZSXVrVtX2rlzp163yRivkz606WG/Q7poU1RUlARI0dHRkq5wc3OTRowYIQUHBz90n5SUFGnlypVSkyZNpMWLF5f7HFqbA+js7FwtUZlq1aoFqN8GFE08f/v2bQIDAzX7JCYmFjsuPz+fe/fuaY4vDUtLy2ITjAt6UAomYBadiFm0rFAoKlw2MzMrUzk/P5/Tp09rhrXKZDJNPUXLD7OxvOWqbNPHv53VbHuldR1qOFgbfJuKlmUyWbFydbcpPz+f0NBQjVaMoU2llfu1rM22UPVbr22hsbzY3NPg21RauarbpFKpCAkJoXXr1ppjDb1NFS2LNj26TUV/hwqCERh6mx5VFm2qXBngzJkzxe4tISEhLF++nIULF3Ly5ElGjBjBhQsXGD9+PDNmzMDW1lav22SM10kf2gRU6HeoqtukKyIiIpgzZw7PPvssVlZWtGrVCk9PT6ysrEhOTiYiIoLw8HBatmzJggUL6NGjR7nPobUhoLNmzWLatGlkZmZqq8pS8fHxoVatWuzdu1ezLS0tjZMnT9KuXTsA2rVrR0pKCqdPn9bss2/fPlQqFU8++WSV2ldVKBQKWrduXUykhsjOs3FEJ6uHC1go5MzoFaBji4wPY9HK42jvWwNnG3MA9kQmkpSeo2OLDBNT0Yug8gitCMrDf/WiVCp5//33adKkCVeuXKFdu3aYm5tz6tQpvvrqK43zJzA9xL2lOK6urixcuJD4+HiWLVuGn58fSUlJmpR3gwYN4vTp0xw/frxCzh9oMQro119/zbVr13B3d6devXqYm5sX+/zMmTNlrisjI4OrVwvze0VFRREWFoaLiwve3t6MGzeO2bNn4+fnp0kD4enpqUkV4e/vzwsvvMDw4cNZvnw5eXl5jB49mgEDBhhkBNAClEqlQf9zSJLEtN/DNesjOtXHylz3b1qMEUPXSllQyGU08XTgyNW7ACw7cJXPezbRsVWGiSnoRaAdhFYEjyI2JYvk+7koVRLhcancScuipoM1TTwd2b5tK2cu3sDZUmLdunV8/fXXjB49usTzosA0EfeWklhbW/PKK6/wyiuvaL1urT19F83TV1lCQkLo0qWLZv3DDz8EYOjQoaxdu5ZPPvmE+/fvM2LECFJSUujYsSO7d+/W5AAE+Pnnnxk9ejTPPPMMcrmcl19+mSVLlmjNxupGqVRqhvXpQ/d0RVh37Ab37qsjU9paKBj/bEMdW2ScGINWysqYrn4aB/D41SQdW2OYmJJeBJVDaEXwKGJTsuj61QFy8lUP2cMdzxEryN32GQ1qW7Bw4UI++eQTvvjiCyZMmFCttgr0C3FvqX60ngjemNC3ZJCGTL5SRdevD3Drnnr454xeAQxt76NjqwSGjiRJPLfwEFfuqKN3HfjoaerVEMOIBAKBoLq5EJvKS0uPPHY/nyub8HW2wMPDAw8PD/r16/fI+AwCQXViKs/+WpsDKKhaJEkiMzMTQ/XXfzsdo3H+mtdxZEi7ero1yIgxdK2UB5lMxsutC2/Q28MqEArZxDElvQgqh9CK4FEoVWXTxaJFS/j++++ZNWsW77//vnD+BAZ9b1EqlUydOhUfHx+sra3x9fVl1qxZet8WrTmAcrkchULx0EVQOZRKJeHh4ZpEmIZEdp6SRXuuaNan9wzQRH0SaB9D1kpF6B3oSYGctoXG6v1NV98wNb0IKo7QiuBRhMelaXU/gelgyPeW+fPn891337Fs2TIiIyOZP38+CxYsYOnSpbo27ZFobaDttm3biq3n5eURGhrKunXrmDFjhrZOY7KYmZnRpk0bXZtRIRbvvUJCWjYA3fzdaVW36tOFmDKGrJWK4OFoTZu6zgTfSObm3Uz2X0qka2N3XZtlMJiaXgQVR2hF8Cju3S9bJOay7icwHQz53nLs2DF69+7Niy++CEC9evXYuHEjwcHBOrbs0WjNAezdu3eJba+88gpNmjRh06ZNvP3229o6lUkiSRIZGRnY2dkZVO9Z8v1cVhy8BoAM+OSFRro1yAQwVK1Uhjb1XAi+kQzAt/uvCQewHJiiXgQVQ2hF8ChcbC0fv1M59hOYDvp4b0lPT9fkA4eSucILaN++PStXruTy5cs0bNiQs2fPcuTIERYuXKg1W65du8aiRYuIjIwEICAggA8++ABfX98K11nlcwDbtm1bLGefoGKoVCquXLmCSvWw6Fr6ycSt5yiYFuDv6UBDd3vdGmQCGKpWKsO7netryqHRKeQ+NAqd4L+Yol4EFUNoRfAozBVle3D/JyKBjJz8KrZGYEjo470lICAAR0dHzTJv3rxS95s0aRIDBgygcePGmJubExQUxLhx4xg0aJBW7Pj7778JCAggODiY5s2b07x5c06ePEmTJk34999/K1xvlcZazcrKYsmSJdSuXbsqT2MSKBQKWrZsqWszysXttGwOXkoE1L1/i15roVuDTARD1EplcbC2oKG7HZdvZ6BUSfxy6pYINFRGTFEvgoohtCJ4GNl5Smb8EVGmfQ9cukPPpUdYOjCIprUdq9gygSGgj/eWiIiIYv5Lab1/AJs3b+bnn39mw4YNNGnShLCwMMaNG4enpydDhw6ttB2TJk1i/PjxfPHFFyW2T5w4kWeffbZC9WqtB9DZ2RkXFxfN4uzsjL29PWvWrOHLL7/U1mlMFkmSSElJMagAF4v2XCEnX23vsA71aOjuoGOLTAND1Io2GNahnqa87tgNndlhaJiqXgTlR2hF8DDm/hVZrl69qKT79Pv2GD8cjRJ6EujlvcXe3h4HBwfN8jAH8OOPP9b0AjZr1ow33niD8ePHP7THsLxERkaWOo3urbfeIiKibC9dSkNrPYCLFi0qti6Xy6lZsyZPPvkkzs7O2jqNyaJSqbh58yZNmzY1iKiq1+5ksDkkGgB7SzNGd/XTsUWmg6FpRVu80sqLqdvDyVdJXLtzn+TMXJxtLHRtlt5jqnoRlB+hFUFp7Lt4mx+P3wRAIYPPezahuZcT52OSuXg9hsb166BSyZj+RzgFj/dyGeQqVcz4I4KjV5P48pUWONuK+7WpYsj3lszMTOTy4v1pCoVCa8NZa9asSVhYGH5+xZ+jw8LCcHNzq3C9WnMAtdHNKXg4CoWCFi0MZwjlx7+e1eQEGtGpPi7ixl5tGJpWtIW5Qk6rus6cjLoHwPID15jcw1/HVuk/pqoXQfkRWhH8l8T0bD769Zxm/fNeTXjjwfD7Fl5O0M5H81meJDFrp7rHwkwux8nGnMT0HPZEJtJ98WEWDwjkyfqu1Wm+QE8w5HtLz549mTNnDt7e3jRp0oTQ0FAWLlzIW2+9pZX6hw8fzogRI7h+/Trt27cH4OjRo8yfP58PP/ywwvXKJC32t6akpBAcHExiYmIJz3fIkCHaOk21ERMTg5eXF9HR0dSpU+fxB1QhKpWK5ORknJ2dS7xp0DcOX77DG2vU4W9tLBSETOmGjUWVTjcVFMGQtKJtimqvpr0lpz7rpmOL9B9T1ougfAitCIqiUkn0XHZEk9evm78bq4a01kRx/K9eJEnio1/PseVMDAAeDlb4ezqw76I6VoBcBh8805DRXRugkOtHJEhB9aBP95byPvunp6czdepUtm3bRmJiIp6engwcOJBp06ZhYVH5zg9Jkli0aBFff/01cXFxAHh6evLxxx8zduzYCkdN1dpT+R9//MGgQYPIyMjAwcGhmEEymcwgHUB9QpIk4uPjcXJy0rUpj0SSJD7ZUvg28OWWtYXzV80Yilaqgo5+NbCxUJCZq+ROeg7X7qTjW1NEnn0UpqwXQfkQWhEUZdafERrnz9nGnAWvtCj27PdfvchkMub0bcrVxHTOxqQSn5aNt6s1n/cKYO5fF8nNV7Hy0DX6tayNl4uNLpok0BGGfG+xt7dn0aJFJabCaQuZTMb48eMZP3486enpmnNWFq252RMmTOCtt94iIyODlJQUkpOTNcu9e/e0dRqTRaFQGMTY6G2hscSnqpO+W5rJmfpSgI4tMj0MRStVgUwmo2ujwjHxi/dc1aE1hoEp60VQPoRWBAUER93lh6M3NOsfPtuwxFSP0vRiZa5gxRutqWmvDqhxMiqZG0mZ7BjVAd+atszt10w4fyaIuLeUDXt7e604f6DFHsDY2FjGjh2LjY34x60KVCoVSUlJ1KhRQ+fd4w9DpZKYWSQM9PtdGmBhJv6ZqxtD0EpVMq6bHzvPxwOwN/I2kiTpTWJZfcTU9SIoO0IrAoDM3HzeXhuiWW9b30Uz768oD9NLLUcrlg9uxcCVJ8hVqlh77AYBHg789cFTWP7nmeHUjXu0qOOEhZnQmzEj7i3FadmyJXv37sXZ2ZmgoKBHPsOcOXOmQufQmgP4/PPPExISQv369R+/s6DcSJLE3bt3cXXV3wnSqw5fJyUrDwB7KzPGdGmgY4tME0PQSlXSwN2eGnYWJGXkcj9XybGrd+ngV0PXZuktpq4XQdkRWhEAvL0uhPQHKR9sLRWsffOJUvd7lF5a1XVmVp8mTNxyHoAp2y/g62ZHq7qFUePD41IZtOokjT3sWTIgiHo1bKugNQJ9QNxbitO7d29N2ok+ffpUyTm05gC++OKLfPzxx0RERNCsWTPMzc2Lfd6rVy9tncokUSgU+Pvrb0TDPKWKRXuuaNY/7eGPXEzi1gn6rpXqoE9gbb4/EgXAsv1XhQP4CIReBGVFaEXw04mbHL92V7P+w5ttsLIofaTP4/TyWhtvIuLSWHf8JrlKFe/9dJo/RneklqMVACsOXidXqSI8Lo2kjBzhABox4t5SnOnTp5da1iZacwCHDx8OwMyZM0t8JpPJUCqV2jqVSaJSqbh9+zbu7u562T0+f/dFsvLU19jN3pIBbbx0bJHpou9aqQ5GdWmgcQBP3bhHvlKFmcI0v4vHIfQiKCtCK6ZNdPJ9pv8erll/s31dnvB5eI9NWfQy5aUALt1O58T1e9xJz+Hd9SFsercdVuYKvnq1Be4OljjZWNC6novW2yPQH8S9pfrR2resUqkeugjnr/JIkkR6ejpazNqhNe7n5LH22A3N+py+zcScKx2iz1qpLpxtLfB58LY4XyWx9UHYcUFJhF4EZUVoxXRRqiQGrjipye9b19WG6T2bPPKYsujFXCHn20GtqO1kDcDZmFQ+23YBSZKwMJPz2YsBjPrPdJKjV5OIjE+rZIsE+oS4txTH2dkZFxeXMi0VpUri82dnZ2NlZVUVVZssCoWChg0b6tqMUpmy/QL5SvU/rU8NG54NcNexRaaNPmulOhnSri4zHgQl2n/pDv3beOvYIv1E6EVQVoRWTJcp288Tk5IFgJlcxuYR7R77oresenGxtWDlkFa88t1xsvKUbDkTQ4CnA2939Cmxb3xqFqM3nOF+rpJpLwUw6Elv8cLZCBD3luIUTSlx9+5dZs+ezfPPP0+7du0AOH78OH///TdTp06t8Dm01gOoVCqZNWsWtWvXxs7OjuvXrwMwdepUVq9era3TmCwqlYqYmBhUKpWuTSlGUnoOO8LiNOsL+wfqzhgBoL9aqW5ef9IbR2v1XOT9lxLJeBC0QFAcoRdBWRFaMU2OXEliY3C0Zn1ev2a4Oz7+JX959NLE05EvX22uWZ/7VyRHriSV2O/E9bukZuWRm69iyvYLvP/zGVIfBJ8TGC7i3lKcoUOHapajR48yc+ZMNm7cyNixYxk7diwbN25k5syZHDx4sMLn0JoDOGfOHNauXcuCBQuKZb5v2rQp33//vbZOY9Lk5OTo2oQSTNgcxoMRITSv40iQt/OjDxBUC/qolerG0kzBS809AMjOU/H3hQQdW6S/CL0IyorQimmRnp3HiPWFKR86+dXg1dZln+NfHr281NyTUV18AfWQ09Ebz3DrbmaxffoG1WHj8LbUclA7oLsuJNBj8WFO30wu83kE+om4t5TO33//zQsvvFBi+wsvvMCePXsqXK/WHMAff/yRlStXMmjQoGKJHFu0aMHFixe1dRqTRS6X4+vrq1eTY6OS7nOwyBu6JQOCdGiNoAB91Iqu6BtUW1PeIuYBlorQi6CsCK2YHsN+OEVmrjqOg4OVGd8PbV3mYyuilwnPNqJrYzcAUjLzGP5jCPf/M3rjyfqu7PrgKbr5q/eLTcmi/4rjfHvgKiqVmENmiIh7y8NxdXVlx44dJbbv2LGjUmkztPZNx8bG0qBBybxvKpWKvDzRPV9ZVCoVN2/e1Kvu8XG/hGrKnfxqiBDNeoI+akVXtKrrjJu9ekTCsWt3iUnOfMwRpofQi6CsCK2YFt8fvk7Ig541GfDT209iYVZ6yofSqIhe5HIZiwYEUr+m+nni0u10Jmw+W8Kxc7a1YNWQ1kzvGYCFQo5SJbFg9yWG/hDMnXTRk2RoiHvLw5kxYwYTJ06kZ8+ezJ49m9mzZ9OzZ08mTZrEjBkzKlyv1hzAgIAADh8+XGL7b7/9RlCQ6BmqNBm3sbm+GxIjQal7h/ry7XTOxqQCIJeJuX8C/UQmkxHoVTgsedGeyzq0RiAQCAyD6HuZLPj7kmb93c71ae7lBMBff/1Fy5YtCQwMpGnTpqxbt67UOnbu3EnXrl1p1KgR/fr1Iy2tMHLn/PnzCQgIIDAwkLZt2xIcHKz5zMHKnFVDWmNvqY5TuDs8gWX7r5aoXyaTMayDD1vfb089VxsADl9Jovviwxy+cqfS34FAoA+8+eabHD16FAcHB7Zu3crWrVtxcHDgyJEjvPnmmxWuVyZpKebqjh07GDp0KJMnT2bmzJnMmDGDS5cu8eOPP7Jz506effZZbZymWomJicHLy4vo6Gjq1KmjW2PCt8Gvb6rLcnOo4Qdu/g+WAPBqC7YV7wouL8N/DOHfiNsA9G/txYJXmj/mCIFAN0TEpdFjifrllE8NW/Z/9LRuDRIIBAI9Jl+pYsDKE5rev0a17Pl7XCdAHa7f1dWVAwcO0Lx5c27cuEHjxo25c+cO9vb2mjoyMjLw9fXl4MGDNG7cmNGjR2Ntbc2XX35JWFgYvXv3Jjw8HDs7O3766SeWLFlSzAkE2H8xkbfWnaLgKXXlG614rkmtUm3OyMlnyrbzbH8QlE4mg/c6+/Lhsw0xFzlgBeVAr579qxCt/Vf07t2bP/74gz179mBra8u0adOIjIzkjz/+MEjnT99Qyc3JrtkcycIeVHmQGAEXtsC+2fDL63DrWOHO8Wfh6BK4sgdSY0HLeVVO37yncf7cHSyZ0evRuYAE1YtKpeLatWtiKMUDAjwdaFHHEVDPW72YIPJHFUXoRVBWhFZMg2X7r2qcvzrO1vz6Xrtin8tkMlJSUgBIS0vD1dUVS0vLYvvs2rWLwMBAzM3NUalUvP/++2zcuFFzfF5eHvfv3wcgJSWl1AftLo3d+Pj5Rpr18ZvCuHw7vVSb7SzN+Oa1QL56tQXW5gokCb47cI3+K44TfU8M/dd3xL3l0Vy7do0pU6bw+uuvk5iYCKj/x8LDwytcp1byAObn5zN37lzeeust/v33X21UKfgvjXqQZB+Ip4cHsvQ49VDQxIjCv24Bhfte3QN7ZxauWzoW7y1s0gfs3CpkhiRJzN9VOCxkXLeGWFuUfU6AoHr474+xqdOvZR3NkOVtobFM7u6gY4v0C6EXQVkRWjFuDlxKZPGeKwAo5DIWDwjCwcpc87lMJmPTpk3069cPW1tbkpOT2bp1a7Ho7wC3bt2ibt26Gr3Uq1eP+Ph48vPzadGiBePHj8fHxwcXFxcsLS05dOhQqfaM7OxLRFwaO8/Fcz9XyYgfQ9gxqiOONuYl9pXJZLzSqg5B3k6M3hBKZHwaobdSeHHJYRYNCKRrY5GjWJ8R95bSOXjwIN27d6dDhw4cOnSI2bNn4+bmxtmzZ1m9ejW//fZbherVSg+gmZkZCxYsID9f5NmqKuRyOXXq1EGuUICTFzR8DjqOg34r4L3D4OpbuHONhhDQB2o0ApkCclIh+gSc/gF2fQz3i4yNj9gBuybC6bUQHQzZj+4d2RQSTfCNewDUr2nLq62Mt3vcUNFoRUTT0vBScw/M5OpkwTtC41CKSHEahF4EZUVoxbhJzcrlvfWnKbg7fvCMH63qFk/tlJ+fz+zZs9m6dSs3b95k7969vPHGGyQllczZJ5PJStVLVFQUW7du5erVq8TExDB+/Hhee+21Um2SyWR8+UoLmniqX9rduJvJ6I1nyFc+vKfIt6Yd295vz9B2dQG4n6ss5sQK9A9xb3k4kyZNYvbs2fz777/FXrR07dqVEydOVLherX3TzzzzTKUSEgoejVKp5PLlyyiVysfv7N8T+q+D0cHwWTy8dxReXg1PTYBGPcC1SLTWK//AyeXwxwew+ln4wgu+aQo/vwr/ToPMe4U2qCTm/hmpWR/Sti5mYmy93lEurZgIrnaWtK3vAkBCWjY7z8Xp2CL9QehFUFaEVowXSZJ4a20I2flqx8rF1oJRXUpGdg8LCyMuLo5OndRzAtu0aUOdOnUIDQ0ttp+3tzc3btzQ6OXGjRt4eHhgZmbGli1baNasGZ6engAMGzaMo0ePkpubW6pt1hYKVrzRChdb9cPv4StJxQLUlIaVuYIZvZuy4o1WfNbDn9b1XMr3hQiqFXFveTjnz5+nb9++Jba7ubmV+uKlrGhlCChA9+7dmTRpEufPn6dVq1bY2hZPCdCrVy9tncokkclk2NvbI5PJynegmSXUaqpeSsO/F1g5FQ4nTY+H1Gj1cuUf6PSJZtfITVPpmCvjH1pT1zKDoX7Z6oikCvFmTZ+osFaMnI5+NThy9S4Aqw5dp3dg7cccYRoIvQjKitCK8bL1TKwmmbpMBj8PfxKFvOR19vLyIj4+nsjISPz9/bl69SrXrl2jUaNGxfZ74YUXGDVqFAkJCTRo0IBvv/2WAQMGAFC/fn1++OEHMjIysLOzY+fOnTRs2LDEMNKi1HG24dtBLRn8/UnyVRIrD10nwMOBPkGPvo8/X0rQmH0Xb5OUnsurresILesJ4t7ycJycnIiPj8fHx6fY9tDQUGrXrvhzjNYcwPfffx+AhQsXlvhMJpMJr76SyOVyPDw8tF9xw+fVSwGZ9+DORbVDmBYPlnYA5OQrybu8l28tIkiUnFAiR/btvQcRSRuq5xf2XS6cQT2gyrRi4AxtV48Fuy+hkiA8Lo3MnHxsLLV2CzRYhF4EZUVoxTi5kXSfaTsuaNYX9m+Bf63S50m7u7uzcuVK+vfvj1wuR6VSsWzZMry9vZk2bRqenp6899572Nvb8/333zNixAjy8/OLpYvo27cvp06donXr1lhaWmJra8uGDRsea2fb+q5M7xnA1B3qwBcTt5yjfk1bmtdxKnNb41OzmLD5LMmZeYTFpDC3b7MyHyuoOsS95eEMGDCAiRMn8uuvvyKTyVCpVBw9epSPPvqIIUOGVLheraWBMEb0KRRsQfd4w4YNUSiqP+jKmiNR7P5zC03lN+jkdIenne+qewxzM9Q72NWCj4oMyfhlEKTFqoPOFA1AY++hfr0oqDJ0rRV9pufSw5yPVc9z/fj5RqUOcTI1hF4EZUVoxfjIU6p4+dujnHtwX3y1VR2+fLWFVuquCr1IksSn286zMTgaAA9HK34f3ZGa9mULIBJ6K5kR609zJz2HpQOD6NnCUyt2CSqHPt1b9OnZHyA3N5dRo0axdu1alEolZmZmKJVKXn/9ddauXVvh70u8/jYQZDIZrq6uOukeT8/OY8m+K6RI/gQr/en3ekeo7ahOL5EarXYEc/4Tmjn2tHo4aVzxeQFYOYJ3e3j9l8JtuffBoviQYUHF0aVW9J13O/kyeqNakxuDbwkHEKEXQdkRWjE+PvglVOP8+dSw5XMtpnWqCr3IZDJm9GrK5dsZnL6ZTHxqNiN/Os2G4W2xMHt8TIIgb2d2ffAUf56LF86fHiHuLQ/HwsKCVatWMXXqVC5cuEBGRgZBQUH4+flVql7hABoIcrkcN7eKpW6oLJO2nCclMw+A7s1q0bS2OqcaMhk4eauX/zL0jyJpKh4sd69Cdqp6Kcp37SE3s7CXsOBvzUZgJcL1lxddakXf6dHMA4vNZ8lVqohJziIhLYtaDta6NkunCL0IyorQinGx63w8f51P0Kx/9WpzbLU4LL6q9GJhJue7wS3ptfQoCWnZhNxMZvrv4czrV7bhnDXsLBnavl6xbRdiU1m67wrz+jXXBJsRVB/i3vJ4vL298fYu5Xm7gggH0EBQKpWaSdfV2T2ekJbFX+fjNevvd/Z9xN5FqOGnXgJ6F27Lz4GkK6DMKdyWlwUpt0BSQVQiRP0nkmzD7sV7C+9cAqe6YG5VgdaYBrrSiiEgl8vo0MCV/ZfUqVCW7r3KHBOfAyL0IigrQivGw737OXzwS5hmvWdzD1rV1W6kzKrUi5u9FSuHtOKV5cfJzVexMfgWTTwdGNy2brnrysjJZ/SGM9y4m0lY9CEWvRZEO19XrdoreDTi3vJwJEnit99+Y//+/SQmJqJSFU+BsnXr1grVK2L4GwgymQwPD49q7x7/cNNZTU6glt5ONCvHZOsSFEQkrd2qcJu5NUyKhnf2Qa9l0HYU1O+inisIYFPkJpyfq+4tnOsBS1vBpsGwfy6Eb1M7hsq8ittmROhKK4bC2GcKh038WeTlhqki9CIoK0IrxoEkSQxceZLcB7n0atpZsHhAkNbPU9V6aV7HiS+K9Pp9/ns4J6/fLXc9kiTR5MHIpttpObz+/QkW/nv5kbkGBdpF3Fsezrhx43jjjTeIiorCzs4OR0fHYktFEUFgHoG+TQStbq4kpPPsokMAyICDnzyNt0s1ztXLvKfuNXR44Awm34CVT0NWcun7N3sVXv5eXVYp1Wks3PzB0RtEclFBEZp//jdp2fkA/DG6I83qVPwmKhAIBIbE1/9cYum+qwDIZfDv+M74utnp2KqKM3tnBN8fiQLA1daC38d0pLZT+Yb2S5LEL6ei+fz3cHIe5EJ8wseFxQMC8XA07WkCpoa+Pfu7uLjw008/0aNHD63Wq9Wn4mvXrjFlyhQGDhxIYmIiALt27SI8PFybpzFJlEolZ8+erdZ0GuM2h2nKXRq7Va/zB2DjUuj8ATjXg0+iYMJleGM7PD8Pgt6A2q3B3BZqNi7cN/kGbBwAi1vAvDqwsgtsHwXHlsHVvXC/4skz9R1daMXQ6N60MDfU4r2XdWiJ7hF6EZQVoRXD53xsKsseOH+gjoZcVc5fdellUvfGPOVXA4C793MZ8WMIWbnlO6dMJmPgE978ProjDd3V30dw1D26Lz7MnojbWrdZUBxxb3k4jo6O1K9fX+v1as0BPHjwIM2aNePkyZNs3bqVjAx1eoCzZ88yffp0bZ3GZJHL5dStWxd5NfVkBUfdIzxOHRlMLoOvX21eLed9LDIZ2LuDbxdo9z70XgbD98LkGGg3unC/nDRwbwYKC8i7D3FnIOwn+Ocz+KkfnFxRuO/9uxC8Cm4cVfc6GjjVrRVD5IMiw0APX0lCpTLdgRBCL4KyIrRi2OTkKxmy+qRmWkeLOo6MfLrqIiFXl17MFHKWDgyirqsNoM7zOnHLOSoywK1RLXt2jOrIwCfUwTZSMvN458cQZvwRTk6+cE6qCnFveTiff/45M2bMICsrS6v1ai0IzKRJk5g9ezYffvgh9vb2mu1du3Zl2bJl2jqNySKTyXBycqq28330a5im3K9lbZxty5ZjR2fI5SAvEhjGMwhGHgFlPty7DncKopE+iEzqXiTUdVwo/PVR4bpdLXBrXBiR1KczOJd/YrmuqG6tGCKezjZ4OlkRl5JNTr6KfyJu80KRXkFTQuhFUFaEVgybUT+fIflBRG8rczk/v9O2Ss9XnXpxsrFg1ZDW9P3fUe7nKvn9bBwBng68V9bAdUWwtlAwr18z2vu68unW86Tn5PPD0RsER91j2est8akh0lZpG3FveTj9+/dn48aNuLm5Ua9ePczNzYt9fubMmQrVqzUH8Pz582zYsKHEdjc3N5KSjHe4XXVR0D3eokWLKo+Q9Nf5eG7dU79pMFfImNW7aZWer0pRmEHNhuqlaETSophbQ8MX1M5hyi3ISFAv1w+oP++zvNABTLgA538tdA5rNNS7iKTVqRVDpn9rLxbtuQLA8oPXTNYBFHoRlBWhFcNl25kY9kQmatZXDG6NnVXVBoKvbr00dLdn4WuBvLv+NADzd1+kcS17nm5UsfQCPVt40qKOE2N+CeVsdArhcWm8tOQws/s2pW+Q7ueGGRPi3vJwhg4dyunTpxk8eDDu7u5aC5Sjtf9+Jycn4uPj8fHxKbY9NDSU2rVra+s0JotcLsfPz6/Ku8clSWLqjgua9Xeeqo+1hZFnC6nXQb2AOqH9nUsPegovqv96FBn+evMYHF1UuC6Tg4tvYe7CFgPApfj/QHVTXVoxdIY/5cPiPVeQgHMxKWTnKbEyN70fHqEXQVkRWjFMbqdl8/Fv5zTrL7esQ+dGNav8vLrQy/NNajGumx+L9lxBkmDMxlB2jOpA/ZoVm+fo7WrDr++24+t/LrHi0HXu5yoZv+ksR67cZWbvJlrNm2jKiHvLw/nzzz/5+++/6dixo1br1do3PWDAACZOnEhCQgIymQyVSsXRo0f56KOPGDJkiLZOY7LIZDLs7e2rPETu+hM3uZuRC4CNhYKPnmtUpefTOyztoU5raDkEXpgLQ7YXHy7qHgCt3wbv9mDlpM5fePcKRP4OB7+AjMI3rETuhC3vwOGv4dIudWAaVdWHla4urRg6tpbmNPZQD1dXSfDT8Zs6tkg3CL0IyorQiuGhTvlwgvwH85xrOVjx5SvVM6dfV3oZ29WP55u4A5Cenc/wH0NIz654migLMzmTe/izdlgbXB8kid9yJobXV50w6fnj2kTcWx6Ol5cXDg4OWq9Xaw7g3Llzady4MV5eXmRkZBAQEECnTp1o3749U6ZM0dZpTJb8/HxOnTpFfn5+1Z1DqWLB7kua9QnPNUIhF/+MxajXEV5aCG/tgok34MOLMHgrPDcHAger5w4WcOOIerjo3pnFI5Ku6go7RkFqbJWYWB1aMRbe7lDYW3usAvmjjAGhF0FZEVoxPOb+Fcn1pPsAKGSw+d12yKvpd11XepHLZXzdP1ATzfPanfuM+yWs0s7a043c2PXBU7R/kCT+7afqV9t3aeyIe8vD+frrr/nkk0+4ceOGVuvVeh7AW7duceHCBTIyMggKCsLPz+/xB+kp+pQLRJIksrKysLa2rrI3JEVzA7nYWHB6ajfxNqYyRAfDjcMPgs9EqoeWqoq8hfzoCtg9mJtw6Cu4tu/BUNIHw0lrNlanwign1aEVY0Gpkmg7by930nMwV8gI/rQbzg/e8JoKQi+CsiK0YliE3kqm77fHNOvTewYwrEP1TVHQtV5u3r1Pr2VHSc1S/+6O7tKAj56v/KgmpUriwKVEnvF3L7Y9X6nCTCGGMFYEXWulKPr07A/g7OxMZmYm+fn52NjYlAgCc+9exaLXa33wsre3N97e3tqu1uSRyWTY2NhUWf3ZeUpWHLquWZ/Zp4nO/wkNHq8n1EsByjx1RNLECLh7FWyLzMGIDoabR9VLUexqqR3C/uvA6kGycpXqkYntq1orxoRCLqN3C0++PxJFnlLiz/PxDG5rOBFftYHQi6CsCK0YDtl5SiYWmffXpp5ztTp/oHu91HW15X+vt2TImpOoJFi2/yr+Hg682Nzj8Qc/AoVcVsL5uxCbyugNZ/jy1Ra0qVf+F7emjq61os8sWrSoSuqtlAP44YcflnnfhQsXVuZUxVAqlXz++ef89NNPJCQk4OnpyZtvvsmUKVM0ToskSUyfPp1Vq1aRkpJChw4d+O677wy2RzI/P5+QkBBat26NmZn2Jx3P2hlBbr56flodJ2teau6p9XOYPApzqNlIvfyXZ2dA05cL01QkRkLqg4ikuRlgWWT8929vQlxYYSRSTURSPzCzrHKtGBt9W9bm+yNRAPx2OsbkHEChF0FZEVoxHOb9FcnlRHU+Zh9XG9YNe+IxR2gffdBLR78afNrDn9l/RgLw0a9n8alhS4Cn9uZUZeUqGbMxlBt3Mxn0/UmOfNIFNwf9ig6u7+iDVvSVoUOHVkm9lfqWQ0NDi62fOXOG/Px8GjVSP+BevnwZhUJBq1atKnOaEsyfP5/vvvuOdevW0aRJE0JCQhg2bBiOjo6MHTsWgAULFrBkyRLWrVuHj48PU6dO5fnnnyciIgIrK8P7x1QoFAQFBVVJeNzUrDz+OBunWf9SX5K+mxIFQz+LUhCRNOM2FO2NvR0BKTfVy+VdhdtlCnAPQDHiUKFWMhLB2kWdDkNQKgEeDtRxsiImJZuw6BQuJ6TTsJb94w80Eqry3iIwLoRWDIO9kbdZ9yColaWZnJVDWmOjg2iV+qKXtzv6EBGXxtbQWLLylIxYH8LvozvioqXh/lbmcoZ1qMfsPyMZ181POH8VQF+0oq8olUq2b99OZKT6RUaTJk3o1atXpb6vSt0R9u/frykvXLgQe3t71q1bh7OzMwDJyckMGzaMp556qjKnKcGxY8fo3bs3L774IgD16tVj48aNBAcHA+rev0WLFjFlyhR691bnfvvxxx9xd3dn+/btDBgwQKv2VBdV9Y+x4uA10rLVE2/7BnnSzrdGlZxHUE4KIpL+l7f+LpnYPjECslPVn8tkhVpZ3w+SLqvzIP63x9DRq7hjaaLIZDLa+LgSE6oOyrNoz2W+Hazdl1b6jvjRFZQVoRX9Ji4lk/d/LkwMPeWlAPzcdfdCSx/0IpPJmNuvGdfuZHA2JpWY5CxG/XyGH99+AnMtzNmTyWQMaVePDg1q4ONaPEl8SmYuTjamNa+8ouiDVvSRq1ev0qNHD2JjYzUdbPPmzcPLy4s///wTX1/fCtWrtdmqX3/9NfPmzdM4f6CeuDh79my+/vprbZ0GgPbt27N3714uX74MwNmzZzly5Ajdu3cHICoqioSEBLp166Y5xtHRkSeffJLjx48/tN6cnBzS0tI0S3p6OgCqB6H7VSpVqWWlUlmmckG8naLl/Pz8EmVJkkqUlUolp06dIjdXnaJBkiSUSmWJskqlKlO5wK74lEzWHFUPf7NQyPiwW8Nqa1OB7UXL2miTLq9TtbTJ2hmVd3t4YjjK7l+hevMvmHgT5bhwVL2/02glLzcH0mJAmQMJ5+HcJtjzOWzoD4uaIa3sXLxNMSFI6Qnk5+WZ3HUa27UBBZyNSdFsN+Q2lfU65ebmEhISQl5entG0yRivkz60qSBSX25urtG0yZiuk0ol0X/FCXIeTOdoU8+JQU946axNubm5nDp1CqVSqfPrZKGQ8e3rQdS0twTg+PW7zPkzUqvXycfVBrlcpmlHXEoWXb46wJw/1VNsjFl7lW3Tw36HdNUmfWLs2LH4+voSHR3NmTNnOHPmDLdu3cLHx0cz6rEiaM0BTEtL486dOyW237lzR+NIaYtJkyYxYMAAGjdujLm5OUFBQYwbN45BgwYBkJCQAIC7e/FJuu7u7prPSmPevHk4OjpqloCAAEAd2RQgOjqa6OhoQO1kxsWph01eu3aN27dvA+phr0lJSQBERkaSnJwMwIULF0hNVffQnD17lowM9dj80NBQsrKyAAgJCSE3NxelUklISAhKpVLzT6FQKGjatCnnzqkndWdkZHD27FkAUlNTuXBBnbw9OTlZ00WclJSkcZJv377NtWvXAIiLiyMqSu30jd9wiuw89T9DT38nZFnJ1dYmgKysLM1QYm21SZfXSWdtksm4HJ9OksIdhUKBra0taekZ8PF1Ip/7hYye30PXqdyr3RVljcYgNydZcixs06lT8GMvZF83QlpQH37ogeqP8URvnQ43j5GVHG/U16mGlUSTmurIWrEp2Ww9eMbg21TW63T9+nVat25NUlKS0bTJGK+TPrRJqVTSsmVLQkNDjaZNxnSdVh+5Tkyyum0KuYw3GxZ/yVPdbbp58yZeXl4oFAq9uE7JcVEsH9wSswepG9Yeu8HqfRFVdp0+3Xae5Mw8Vh2O4tUVx9l9OMRotVfZNl28eJHWrVuTlpam8zYVfL/6wsGDB1mwYAEuLoXBhVxdXfniiy84ePBghevVWhqIIUOGcPjwYb7++mueeEI92fjkyZN8/PHHPPXUU6xbt04bpwHgl19+4eOPP+bLL7+kSZMmhIWFMW7cOBYuXMjQoUM5duwYHTp0IC4uDg+PwmhP/fv3RyaTsWnTplLrzcnJIScnR7MeGxtLQEAAN2/exNvbW/PWQC6XFysrlUpkMtljy3K5HJlMVqycn5+PQqEoVgb124j/lnNyclAoFJibmz9426dCoVAUK6tUKiRJemwZ4NLtDLovPgyAuULGsYldcLWzrLY2mZmZad4CFZQr26b/Xpvqvk760CaZTEZWVhaWlpYoFIrS26TKJz8zBYVdDXWb0hJRrOsO964jk0omq5caPo+y/wZ1m1QqpLO/IHdrhFSjESoza6O4TptP3WLiVvWN/40nvZnVt5nBt6ks2it4s1ow8d4Y2mSM10kf2iSXy8nNzUWhUGj0YuhtMpbrdPF2Bn2/PUaeUv1IN69vU15tVVunbcrPzycvLw8rKytUKpXeXKeNwTeZ/OBeb6GQseGdJ2nt46r16xSVdJ8xG0IJj08DwM7SjHn9mtGzhadRaU8b10mpVJb6O6SLNt24cQMfHx+9SQPh4uLCzp07ad++fbHtR48epWfPnhVOA6E1BzAzM5OPPvqINWvWkJenzrliZmbG22+/zZdffomtre1jaig7Xl5eTJo0iVGjRmm2zZ49m59++omLFy9y/fp1fH19CQ0NJTAwULNP586dCQwMZPHixWU6jz7lAqmKCEmvLD9GyA31m5DnA2qxYohpzXsyViqllbws9ZzBYvMLI6HZq9BtunqfjET4qkg0Xed6ULNI/sLaLcG1YmPSdUl6dh6tZ+8hJ1+Fk7UZp6Y8q5X5IfqOiL4mKCtCK/pJZm4+Ly09wvU76oTv73aqz+Qe/o85qurRZ71M23GBHx8EynGzt+SPMR1xr4LgLTn5Sr7YdZEfjt7QbBv4hBfTXmqCtYWY81aAPmlFn579Qd3BdubMGVavXl2sg2348OG0atWKtWvXVqherT3d2NjY8O2333L37l1CQ0MJDQ3l3r17fPvtt1p1/kDtbMr/kwet6FsBHx8fatWqxd69ezWfp6WlcfLkSdq1a6dVW6oLMzMz2rZtq7V/jPMxqRrnz8pcLiJ/GhGV0oq5NXi0gBYD4NmZMOhXGH8Buk4t3CcnHXw6g+2DJPbJN9TRSI8shK3vwKnvC/fNToMD8yHid0i6CiplpdpWldhbmdOxgToAUkpWPpuCo3VsUfWg7XuLwHgRWtFPxmw4o3H+mtZ2YMJzlU92rg30WS9TXwrgSR/1kLrE9BzeXX+a7Dzt/z5ZmimY3rMJq4a0xslGPc1gY3A0vZYd4VKCdqdHGTL6rBVds2TJEnx9fWnXrh1WVlZYWVnRoUMHGjRoUOYOrdLQ+jdta2tL8+ZV60z07NmTOXPm4O3tTZMmTQgNDWXhwoW89dZbgDoi07hx45g9ezZ+fn6aNBCenp706dOnSm2rKiRJIisrC2tra60kaJ+/+6KmPLm7Pw7W5pWuU6AfaFsrQPHE866+MPR3dfl+UmEvYUGPoWfLwn1vh8OBuYXrZlZQo2Fh2osGz0KtptqxUQt0bezG3ouJAKw9foPB7Yw/J2CV6EVglAit6B8/HI1i70V1/AVLhZwlA4KwMNOPkQv6rBdzhZxvB7Wk17KjxKZkERadwmfbLvDVq82rxNZnA9zZ9cFTfLAxjOAb97iSmEGvZUeY3rMJA5/w0rvvp7rRZ63oEkmSSEtL45dffiE2NlYzP9Lf358GDRo85uhHox93iXKydOlSXnnlFd5//338/f356KOPePfdd5k1a5Zmn08++YQxY8YwYsQI2rRpQ0ZGBrt37zbIHICgHkMdHh6OUln5N1RHriRx5Kp6Qqy3iw0Dn/CudJ0C/UGbWnkstjXA5yl4cgT0XARv/w3NXy383NIOAgeBZxCYWUN+NiScK4xIeqtIVN47l+D3MXDiO7i2H9Jvg3ZGqJeZ/m28UDwIEHA1MYPUrNxqPb8uqFa9CAwaoRX94ta9+8zaGaFZH9KuLvVr2unQouLou15c7SxZOaQVVubqR+EtZ2KKDdXUNh6O1mwY/iRjn/FDJoOcfBWfbjvP6A2hpGblVdl5DQF914qukCSJBg0aEBMTQ4MGDejZsyc9e/astPMHWpwDaIzo2zhgbaBSSTw5by930tXBbhYPCKR3YG0dWyUwCVRKdfL6or2F7Uar5wwChP4MO94vfoy1S2HewlZDoVazKjez//JjBD8YHv1e5/pM6q77uTQCgUBQFKVKouP8fcSnZgNQv4Yteyd0Fr0nFeCPs3GM2aiOTqmQy/jxrSfo0KBq8yEfv3aXcZtCuZ2mfhar42zN0oFBBHk7P+ZIQVWjb8/+TZo0YfXq1bRt21ar9RpkD6ApIkkS6enpVNZfX3M0SuP8OduY07O5pzbME+gR2tKK1pErwKU+NH4ROn0Mr6wpdP5A7dx1+gQavwQuvoAMsu7BzSNwahVk3C7c9/Lf6iT3f3+mdhxjz0Dufa2YOaprYYCbrWditVKnPqO3ehHoHUIr+sOkrec0zp+5XMbmd9vpnfNnKHrp2cKT959WBy5TqiRGbTjDrbuZVXrOdr6u/DX2Kbo0qglATHIWry4/zoqD11Cp9Pv7qgoMRSu64IsvvuDjjz/WenoKMdvSQFCpVFy5coUWLVpoQuuWlzylioX/Xtasf/RcI+Ry/frBEFQebWhFJ3g0Vy8F5GYWj0haq8hn0cFwba960SBTRyR184dun0PNigVC6ORXAxtzBZl5ShLTc4hKuo9PDe0GstInDFYvgmpHaEU/OHApkV9DYjTrC15pTo0HCc71CUPSy4TnGhEZn8b+S3dIycxjxPoQtoxsj61l1T0mu9pZsnpoG9YcjWL+7ovkKSUW/H2Jzo1q0riWQ5WdVx8xJK1UN0OGDCEzM5MWLVpgYWGBtbV1sc91kgbi999/L/O+vXr1quhpdIa+dQNXlnl/RbLi0HUAatpZEPxZN717YygQlIk7l9TzB4sOJ71/p/DzsaHq3kaAI4vg7MbCNBUFf53rqXslS2HUz2f483w8AH0CPVk0IKhq2yMQCARlID07jyfm7CXrQcTKZxq7sfrNNjq2yjhIy86jz/+OaiKqdm9ai28HtayW56RzMSmM2RjKa228eP/pys/vElQcfXv2f1we9aFDh1ao3ko5gP9NxfDQkzxIsmho6JMIJEkiNTUVR0fHCt2MMnPyaTHzH02S2BVvtOL5JrW0baZAD6isVgyWjDtw50FE0jbDCyOX/vYWXNhScn8zK3Uv4cBN4OCh3pZ7H8xtuHI7g2cXHQLA1lLBhc+fN9rv0mT1Iig3Qiu6p/f/jnA2OhUAJxtzgj99Bgsz/ewxMUS9XLuTQZ9lR0nPyQdgwrMNGfOM32OO0g4ZOfnYmCuKjcy6fDud+jVsMTPynLT6pBV9evavSiqlKJVKVabFEJ0/fUOlUnHz5k1NrsPyMm1HuMb5q+tqI5w/I6ayWjFY7GqCTyd48t3iaSuemwODtsCzs6DF6+ARWBiR9HYE2LgW7vvXxzDPC7+d/ehgcRWAzJw8ToVfqd62VCMmqxdBuRFa0S3fHbiqcf5kwM9vP6m3zh8Ypl58a9qxZGAQBT7I1/9e5t+I248+SEvYWZoVc/7iU7Pov+I4r608QWxKVrXYoCsMUSvViVKp5LfffmPWrFnMmjWLLVu2kJ+fX6k6RRTQR2AsbwHu3c+h9ew9FMwr3jKyHa3quujWKIFAl6iU6gT2KbfAt0vh9lXPQGwIAMeUAbyeNwWA2WarGex4HsaHg5mFet97UWDtDNZO1Wu7QCAwOa7eTue5RYc0v+NjujbQm4Tvxsi3B66yYPclQO2YbXu/PX7u9tVqw+St59gYHA3A/15vyYvNPar1/KaKvj37h4eH06tXLxISEmjUSP0/f/nyZWrWrMkff/xB06YVy6Ws1dmt9+/f5+DBg9y6dYvc3OL5s8aOHavNU5kcKpWK5ORknJ2dyzz0toCPfj2n+dFo6ukgnD8jpzJaMRnkCnVCe1ff4tuH7YK7V+FOJIExF3A+kE4y9mxTdmSQZRSyAucPYPtI9TxEh9qFie0L5hjWaAQWNtXbpgoi9CIoK0IruiFfqWLg9yc1v+ONatkbhPNnyHoZ2dmXiLg0dp6LJyMnn+E/hrBjVEccbcyrzYbpPZugkMtQqjB658+QtVLVvPPOOzRp0oSQkBCcndVpQpKTk3nzzTcZMWIEx44dq1C9WnMAQ0ND6dGjB5mZmdy/fx8XFxeSkpKwsbHBzc1NOICVRJIk4uPjcXJyKtdxN+/eZ9/FRM36koEimIWxU1GtCFD37rkHgHsANk1fxunCfpKTMjktNeKPFt9RLJRVdpr6b1qserm6p/AzRy8YXyRkc9QhsKkBrg0KexD1BKEXQVkRWtEN4zeHadI3WZjJ+WX4kzq2qGwYsl5kMhkLXmnO9Tv3iYhP48bdTMb8EsoPb7ZBUU3R063MFczu06xEWoiIuDQszOQ0cLOrFjuqA0PWSlUTFhZWzPkDcHZ2Zs6cObRpU/EAUFpzs8ePH0/Pnj1JTk7G2tqaEydOcPPmTVq1asVXX32lrdOYLAqFgqZNm5Y7PO64TWGacscGrtSvaTw3DEHpVFQrgpIMbltPU/42tPioBt4/BhNvwlv/wEuL4Il3od5T6jmF/01BseUd+K4dzPWA/7WFX4fBwQUQ+Yd6KKkOEXoRlBWhlernn/AE/jgbr1lf9Fogzrb6l/KhNAxdLzYWZqwc0goXW/VLu0OX77Bg98Vqt6PovMD07DxG/nyankuP8GtItNHkzTN0rVQlDRs25PbtkvNQExMTadCg4hFjteYAhoWFMWHCBORyOQqFgpycHLy8vFiwYAGffvqptk5jsqhUKhITE8s1QfZcdAqht1IAkMngm9cCq8Y4gV5REa0ISmdwW28KfnsvJaSTkZ1XfAdrJ/B+EloPgx4L4M2d8Ml1eO2nwn3ystQpJywdQJWvjlQavhX2z4FNg+H3McXrPLUarvwLqTFQDT/uQi+CsiK0Ur2kZuXx8W/nNOs9mtaiRzPDGQpoDHqp42zDt4NaYvbgh2DFoevsCIvVmT0HL9/h5t1MsvKUfPzbOT7cfJaMnMoFA9EHjEErVcW8efMYO3Ysv/32GzExMcTExPDbb78xbtw45s+fT1pammYpD1obAmpubq4Zt+vm5satW7fw9/fH0dGR6OhobZ3GZJEkibt37+Lq6vr4nR8wfnOYpvxiMw9q2ltVgWUCfaMiWhGUjqWZghZeToTeSkECVh2+zvhnyzD3xty6ePntf9TOXFps8dyFiRHgGVi4b046/PlhEQMcCucX1vQHryegdkttNQ8QehGUHaGV6kOSJD7bdp7ULPVLJzd7S5a+rt3//arGWPTStr4r03oGMG1HOACf/HaO+jXsaFbHsdpteam5Jw5W5ny4OYykjFy2hcYSeiuZZa+3pGnt6rdHWxiLVqqCl156CYD+/ftrUmQU9Pz27NlTs17elHtacwCDgoI4deoUfn5+dO7cmWnTppGUlMT69esrHKFGUIhCocDf37/M+++7mMi1B8lMzeQy5vdrXlWmCfSM8mpF8GhGdvZlxPrTAJy+mVzximQycKyjXvyeLX2f3PvQpJ/aObx7BXLSIPqkegFoOaTQAczLhj3TC51Dt8ZgVf4HAKEXQVkRWqk+tpyJZec59dBPByszto3qUG1zz7SFMenljbZ1iYhL45dT0eTkqxixPoTfR3ekpn31D8ft1LAmf33wFBM2n+XwlSRu3M2k77dHmdzdn2Ed6uk8j15FMHStxMbGMnHiRHbt2kVmZiYNGjTghx9+oHXr1pWue//+/VqwsCRacwDnzp1Leno6AHPmzGHIkCGMHDkSPz8/Vq9era3TmCwqlYrbt2/j7u7+2AhJkiQxeWvhsJHBbb2xtdJqwFeBHlMerQgez7MB7tRxtiYmOYtj1+6SmJaNm0MV9abb14JXf1CX83PVEUk1vYWR4N2+cN+7V+Dk8uLHO9Qp7DFs1B3qtudxCL0IyorQSvUQGZfG1O3nNetfvNyc2k7WjzhCPzEmvchkMmb0bsLl2+mcuZVCfGo27/98mp/faYuFWfW3zc3einXDnmD5oWt8/c9l8pQSM3dGcOxaEl++0gJnW/0KNvY4DFkrycnJdOjQgS5durBr1y5q1qzJlStXigVtqQydO3fWSj3/RWteQVEv183Njd27d2uragFqpy49PR03N7fH7nvg8h1up6kjhlmZy/m0h+G+VRGUn/JoRfB4ZDIZfYNqs3TfVVQS7AiLY3in+lV/4iIRSUvFwg7ajyl0DtNiIS1GvVz9F2xcCh3Ae1Hwz5Ti6SpcG4DCXOhFUGaEVqqe3HwVg1afICtPPReqf+s6BjXvryjGphdLMwXLB7ei17KjJKRlc+pGMp//Ec7cvs10Yo9cLuP9pxvwpI8rYzeGEpuSxZ7IRLovPsziAYE8Wd9whlMaslbmz5+Pl5cXP/zwg2abj4+PDi0qG1pzs7t27UpKSkqJ7WlpaXTt2lVbpzEZfv/9dwIDA7G1tcXT05NVq1bRsGFDTYSk27dv4+LiQmBgYLHjVCpJk7wUYNIL/liYiahKpoRCoSimFUHl6RtUW1PeHKInc5pdfOC52TB4C3wY8SAi6d/w0jfwxAio26Fw34RzcHEnHPoSfnsLvm0Lc9QRSRVb36GhxR2hF8FjEfeWquejX89y77563p+VuZzpPZvo2KKKY4x6cXOwYsUbrTS9fhtO3uKnEzd1alOrus789cFTdG9aC4CEtGwGrjrB4j1XUKoMI0qoPmolPT29WICVnJycUvf7/fffad26Na+++ipubm4EBQWxatWqara2/GjNATxw4ECJ5O8A2dnZHD58WFunMQl2797N+++/z6JFi0hLSyM8PJxOnToRExOjiZA0evRogoJK5vT7/WwckfHqSEDN6zgytH3darVdoHtUKlUxrQgqT/2advjUsAXgSmIGZyozF7CqsHYC77bQ+i3o8aU6YEwBtZrBC/Oh5VCo8wRY2IMqTxOR9G70xUK9XN0LKzrDtpFwdAlc2QOpsdUSkVSg34h7S9Vy7FoSv5+N06x/N6gVtpaGO33DWPXSwsuJL/oV9vp9/ns4wVH3dGgROFqb8+2glszu0xQLMzkqCb7Zc5nXV50gITVbp7aVBX3USkBAAI6Ojppl3rx5pe53/fp1vvvuO/z8/Pj7778ZOXIkY8eOZd26ddVscfmo9J3l3LnCuWYREREkJCRo1pVKJbt376Z27dqlHSp4CFOnTmXatGk8/fTTgDrho6OjI1FR6nxhO3bs4N69e7zxxhssWrRIc1xuvoov/y7MUTPxhcYGORlYUHke9qZKUHE6+LoSlaQOrLRk7xXWvvXEY47QI1zqQ9v3CtclSZ1mIjES1e0LpDv6o5mtkHAO4sPUS1EsHdXDR5+fA3UeDPlXqcDA5msIKoe4t1QNyfdz+XDTWc36hGcb0qWx4Q2H+y/Gqpd+LesQEZfG90eiyFdJjPzpNL+P6ajTuZoymYzBbevSqq4zYzaGcjUxg5NR9+i++BALXwukSyP91pO+aSUiIqKY/2JpWXrAH5VKRevWrZk7dy6gDop54cIFli9fztChQ6vF1opQaQcwMDAQmUyGTCYrdaintbU1S5curexpTIb79+9z+vRpevToQcOGDUlLS+Opp55iyZIl+Pr6kpqayocffsju3bs5evRosWPn/RVJbIr6TU+7+i50aFBDF00Q6Bi5XI6vr6+uzTA6xjzTgJ9O3gIgIj4NlUoqlqDXoJDJwMkLnLyQN3yOekU/a/4auPgWT1dx9yrkpEL0CVAUCS4QvAIOf104r7Dgb83GYOVQzY0SVDXi3lI1SJLExC1nSUhT/353aODKqC4VT/CsLxi7XiZ1b8yl2+kcvpLE3fu5vLs+hF/fbY+1hW6HMfp7OPD76A7M+D2CTSHRJGfmkZGt37kC9VEr9vb2ODg8/nfMw8ODgIDic/X9/f3ZsmVLVZmmFSrtAEZFRSFJEvXr1yc4OJiaNWtqPrOwsMDNzU2vxvTqO8nJyUiSxPbt2/n3339xdXXlvffeY/DgwaxZs4Y5c+bw5ptv4ufnV8wBTM/OY32RcejvdtavfyRB9aFSqYiOjsbLy8vgomnpM+4O1nRt7Ma+i4kkpudwIuou7X0N/yVLCb04eEJAL/VSQH4OJF2BOxehRsPC7YkRcP8ORN2BqEPFK3b0gsFboeaD/TPvqXMimhteNEOBGnFvqRrm777EPxGJADjbmLOwf6DhvlwqgrHrxUwhZ+nAIHotO8qte5lciE1j0tZzLHotUOejr2wszJj/SnPaN3DlXEwqPQ2ZleUAALMgSURBVFt46tSex2HIWunQoQOXLl0qtu3y5cvUrVvxKVhBQUFl1tCZM2cqdI5KO4AFDdSncbuGjJ2dHQBjx47VfLczZszAz8+PEydOcOzYsVIv9uoHwxAAfGva8rSed/ULBIZI36Da7LuoflDbdibWKBzAMmFmCbWaqpeivPAFtHoTEi8WT1eRHgep0eq0FgUcmAenvgdnn5I9hq6+oDCv1iYJBPrA2ehkVhy8plmf2asJ7lWVZkagdZxsLFg1pDV9vz1KZq6SHWFxBHg46M1L+N6BtekdWHwa1vmYVK4nZZTYLqgY48ePp3379sydO5f+/fsTHBzMypUrWblyZYXr7NOnj/YMfAhanV187do1Fi1aRGRkJKCeQPnBBx/oXbeuPuPk5IS3t3epn506dYrr16/j6al+k5OTk0NWVhaurjVwHbIELB1RyNQTxwWmi1wur9SbJ8HDeTbAHTtLMzJy8vnrfDwzejXBxoCDNEAl9WJhC7VbqZeiZCXD3WvFh4GmRIOkgnvX1MvFnUWMMIePr6oD2YDaoTSzBKe6Yo6hHiHuLdolJ1/JG2tOURBeqWVdJ3oa0UO5qeilUS17FvYP5L2fTgMwf/dFGtWy18sX8enZeYzeeIabdzM5cf0ec/s21XlvJRi2Vtq0acO2bduYPHkyM2fOxMfHh0WLFjFo0KAK1zl9+nQtWlg6Wvtl/fvvvwkICCA4OJjmzZvTvHlzTp48SZMmTfj333+1dRqTYMSIESxdupTY2FiysrKYOXMmXbt2ZfDgwVy8eJGwsDDCwsKYOXMmjRo1YuhXm8gxV/ccDnjCm4a17HXcAoEuUalUXLt2TfTKVwFW5gqe8lP3+t3PVbL22A3dGqQFqkQv1s6FgWIKGLgRJlyGN7bD8/Og5RCo00adz9DaudD5A3XOwiWBMK82rOwC29+HY0vh6h5IixMRSXWEuLdolxE/niYtS53ywcZCwfphT+rYIu1iSnp5oWktPnjGDwCVBGM2hmqChukTN5IySX8wH7Cuq41eOH9g+Fp56aWXOH/+PNnZ2URGRjJ8+HBdm/RYtPbqetKkSYwfP54vvviixPaJEyfy7LPPautURs+kSZO4d+8eLVq0AKBLly78+OOPqFQqPD09NeOjnZ2dkcws+P1KDjK5AmtzheYGJDBtHhatSlB5ujerxa4L6mjHG4Nv8b4RBGuoFr3IZGDvrl58uxRulyS4n1RyX4Ul5GVC3Bn1ojHWESYVybt1+R+wsIGa/mBrOImPDRVxb9EOv4ZEc/DyHc36qiGtsbUy7NEEpWFKevngGT8i49P4J+I26dn5DP8xhG3vt8feSn+Gtzer48hfY59i/YkbjHiqvq7NKYYpaaU8KJVKvvnmGzZv3sytW7dKpNy7d69iKUhkkqSdV6lWVlacP38eP7/iDsjly5dp3rw52dn6n4fkv8TExODl5UV0dDR16tTRtTml0mvpEc7FpgLwZvu6fN6r6WOOEAgElUGlkmg8dTe5SvWbypOfdsXdQQQ20TrKfEiOejC3sMgcQ9uaMOzPwv2WtFQPKQWwdSs+v7BW05LDUwUCHZOQmkXH+fs18/YHPuHFvH7NdWyVQBtk5OTT79ujXL6dAUA3fzdWvtFa74P6xKZk8c2/l5n6YgCONvrjsOoCfXv2nzZtGt9//z0TJkxgypQpfPbZZ9y4cYPt27czbdo0xo4dW6F6tTYEtGbNmoSFhZXYHhYWhpub/o2DNjSUSiWXL19GqVRqtoXcuKdx/uQyGCt6/wSUrhWB9pDLZbTzLexpWrr3qg6tqTx6qxeFGdTwg4De8PRE6L8ORgfD0D8K91Gp1I6ecz31+v1EiDoIJ7+DP8bCzg+L13lsGZzdBPFnIS+r2ppiLOitVgwISZJ4beUJjfNX28mauX2bPeYow8QU9WJnacaqIa1xtFY7UXsiE/lmz2UdW/Vo8pUqxm4M5bfTMfRYcpjTN5Or3QZT1EpZ+fnnn1m1ahUTJkzAzMyMgQMH8v333zNt2jROnDhR4XorPd5g5syZfPTRRwwfPpwRI0Zw/fp12rdvD8DRo0eZP38+H3744WNqETwOmUyGvb19sfHaH/1amDS2T2BtXGxF97mgdK0ItMsHz/hphm/9eT6e2Qb8AGdweikaFEYuhwE/q8s5GZB0qTASaWKEuiewAGUe7PkcVOo5V8jk4FK/sMfQ60lo8Ey1NcMQMTit6CEzfg/n5t1MABRyGZvebWu036ep6qWuqy3LXg9i6JpgVBIs3XcVfw8HejTz0LVppZKVp8TZRp3fNTYli/4rjvPhsw0Z2dm32nouTVUrZSEhIYFmzdTPGHZ2dqSmqjt+XnrpJaZOnVrheivtAM6YMYP33nuPqVOnYm9vz9dff83kyZMB8PT05PPPP69w96SgELlcjodH4c3jn/AEbjz4ETFXyJjTVwz9FKj5r1YE2qdlXWccrMxIy84nOTOPC7GpNK3tqGuzKoTR6MXSrvSIpAXkZT5IWREJieEPIpVeVS+Rf0CTfoUOoEoFO95X90AWDCd19Db5iKRGoxUdcerGXdYeL5y/OrNXE+o42+jQoqrFlPXylF9NPu3hz+w/1VHxJ2w+i08NW/w9Hp9YvLqxtzJn1ZBWrDt2g7l/XSRXqeLLvy9x/NpdFr7WAjf7qk9LYspaeRx16tQhPj4eb29vfH19+eeff2jZsiWnTp2q1LzJSv+aFUwhlMlkjB8/npiYGFJTU0lNTSUmJoYPPvhAePRaQKlUEhkZiVKpRJIkPtt2XvPZsA4+WFsY3+RxQcUoqhVB1fF8k8Icd4v3XtGhJZXDZPRi5QgvfqWeP/hJVPGIpEFvQINuhfum3ISzG2HvTNg4ABa3gHl1HkQkHQUX/9JZM3SJyWilCsjKzWfYD6c06+3quzKorWGGvS8rpq6Xtzv60C9IndYjK0/J8B9DuHc/9zFH6QaZTMabHXzY+n57fGrYAnDkahI9Fh/mUJFgRVWFqWvlUfTt25e9e/cCMGbMGKZOnYqfnx9DhgzhrbfeqnC9WvEa/uvg2duLNATaRiaT4erqikwm4+eTt7iTob6J2FgomPhCYx1bJ9AnimpFUHWM7+bHr6djADh0+Q6SJBnkd26SenlYRNICLGzhmemFw0mTLkHe/cKIpPbu0LiHet/0BPj1zZLJ7W1cqrVJ1YFJakVLvLX2FBk56odbO0szfhjW+jFHGD6mrheZTMbcfs24eieDczGpxCRnMXrDGX586wnMFPo5mqBpbUf+GNORadsvsDU0lqSMXIasCea9zr5MeK4h5lVkt6lr5VEUza7w2muv4e3tzfHjx/Hz86Nnz54VrrfSUUDlcjmOjo6PvWgVDVOqS/QtEhCAUiUROOMf0nPUeVw+6+HP8E76FcpXIDAV2s3bS3yqOsLxyjda8VyRXkGBEaHMh3vX4c4Dh9CnM9Rtp/7s6l74qV/JY+zc1c7gEyOg8YvVa69Ar/jp+E2m7LigWf/tvXa0rmd8LwgEpROfmkXPpUdJysgBYFiHekzv2UTHVj2eLadjmLrjApm56hcXQd5OLBkQhJeL8Q5bBv189q8KtNIDOGPGDBwdDXP+i6FQ0D3+b7yZxvlztjHnnad8dGyZQN8o0Iq/vz8KhULX5hg1/VvXYfGDKKDfHbxmkA6g0EsZUJhBzYbqJaB38c9qNYN+qwrTVCRGQMotyLitXpq9WrjvzWOw9d0HvYRFegxrNATzqp9nU1mEVspPTHIm038vdP6GdahnMs6f0IsaD0drVrzRkgErT5CnlPjh6A38PRzo39pL16Y9kpdb1SHQ24kxG0KJiE8j9FYKPZYcZv7LzbUe0EZo5eH8+OOPj/x8yJAhFapXKz2ACQkJRpnqQZ/eAqhUKhLu3KXL0hBy8tX5xxa/FkjvB+PLBYICVCoVycnJODs7IzfxoBVVTUZ2Hs0+/wcJdSqWyFkvYGlmWD9eQi9VQE463LmkdgZ9OoPzg7lewavgr49K7i+Tg4svvPAF+D2Yi5ifAzKF2vnUE4RWyodKJfHG6pMcvXYXgHquNuz/6GmTGeYm9FKcjcG3mLxVHb/BQiFn07ttCfJ21rFVjyc7T8kXuy6y9tgNzbbXn/Rm2ksBWJlr5/dOn7SiT8/+AM7OxTWSl5dHZmYmFhYW2NjYVHiEZaV/WUzlRqYLYlOySL6fi1IlER6XxqZTtzTOXw07C1r7mMZbREH5kMvluLq6Pn5HQaWxszKnUS17Liako5LgpxO3eLujYfXKC71UAZb2UKe1eilK8/7qXj9Nb+GDHsPsFLh7BSyKDK06txn+/BBqNHrQW9hY5xFJhVbKx/dHrmucPxdbcza9286knpmEXooz8AlvIuLSWH/iJrlKFe+uP80fYzri7qDfvf9W5go+79WE9r6ufPzbOVKz8thw8hZno1PYPqqDVuYFCq08nOTkknkZr1y5wsiRI/n4448rXG+lHcBKdiAKHkJsShZdvzqgcfj+S1JGLl2/OsC+j56mtpN1NVsn0GeUSiUXLlygadOmYihFNfBWRx8++e0cAD+duGFwDqDQSzVi5Qj1OqiXAiRJHUgmMQJqNS/cnnQZlLlw+7x6KYq5LQzZAV5t1OvptwFJPe+wCh0MoZWycyE2lS//vgSoL8nSgS31/kFf2wi9lGRazwAu3U4nOOoeiek5vLv+NL+MaKu1nrSq5LkmtWhS25Fxv4Ry6kYyLzX31FpQGKGV8uHn58cXX3zB4MGDuXjxYoXqqLQDqFKV7qAIKkfy/dyHOn8F5OSrSL6fKxxAQTHkcjl169bV+TAKU6FfUG0+3XqefJVEVFIm9+7n4GJb8dw81Y3Qi46RycDBQ70UpdsMaPN2YS9h4sXiEUmdiswfCl4Bh78Ga+cikUgfzDGs2VhrEUmFVsrG/Zw8Bn9/kjyl+gX5u5186dCgho6tqn6EXkpirpDz3aCW9Fp2lNiULMKiU5i6/QILXmluEL3DtZ2s2Ti8LVtDY3mlZfHhkZWJhC20Un7MzMyIi4ur+PFatEWgRZSqsvWslnU/gekgk8lwcnLStRkmg5lCzhM+Lhx7MNTruwPX+OzFAB1bVXaEXvQUuRyc66mXRt0LtxdEJLVzL9yWlayeR5iVDDePqpeifHCucB5i/DlQ5kHNRmBpVy6ThFbKxpDVwaRk5QHgW9OWD59tqGOLdIPQS+m42lmy4o1WvLL8GNl5Kn49HUMTTwfe7GAYo0fMFPISAWxiU7J4/6fTzOrTlOZ1nMpdp9DKw/n999+LrUuSRHx8PMuWLaNDhw4POerxCAdQTwmPSyvzfi28nKrWGIFBoVQqOXv2LC1atBBDKaqJMV0baBzAMzdLjtfXZ4ReDIyCiKRFeekbdUL7pMuFPYZ3Lqr/Zt4DxyIPa0cWQvg2ddmpbvHchQV/H/IWXmjl8fx1Pp7Tt1IAkAHfvBaIhZlp9moIvTycprUd+fKVFozZGArArD8jaehuT3sD7CnOV6r4YGMoZ2NSefm7Y+z5sDN1XW3LVYfQysPp06dPsXWZTEbNmjXp2rUrX3/9dYXrFQ6gnnLvfo5W9xOYDnK5HD8/PzGUohppW98VPzc7riRmcPpWCrfuZuLtahi5koRejARzK/Borl6KkpdV3KGzdFD3HmbchpSb6uXyLvVnCgv4NA54sH/kTpCUaqfQ2Qe5XCG08gjiUrKYtOWcZv29p30r1BtiLIh7y6Pp2cKTiPg0vjtwDaVK4v0NZ/hjdEeDy7Mnl8l4vkktwqJTeK2NV7mdPxBaeRRVNdVOOIB6SlnnEBnSXCNB9SCTybC3t9e1GSaFTCajb8vaLNitDvqwPSyWsc/46diqsiH0YuSY/2eOeK8l6r/37xYmti+YYyiTgcK8cN9DCyD+rLqssERWoyH2BfML3ZtCw+eqpw0GgFIlMX5TGGnZ6jy9Lzb34JPnG+nYKt3yqHvL3LlzOX/+PBs3bnxsPQcOHKBPnz6kpKRo2ULd89FzjbgYn8b+S3dIycxj+I8hbBnZHltLw3k8l8tlDO9Un3a+rjRwKz6sPDtPWaYAN+J3qOwolUrOnz9P3bp1S6SIKA/C1dZTmng6aHU/gemQn5/PqVOnyM/P17UpJkXvwMKcnJtO3TKYCMlCLyaKrSvU6whPDFcPIX1rFwz7q/g+ddqAZ0swtwFljjoa6fnNsHcG/D25+L6Hv4bj38K1/eqopAaif20x/fcLnIxS5+Oq7WTN3D7NyhwQ4+mnn8bS0hJ7e3scHR1p2rQpEyZM4M6dO2U+/+eff15iqJiuedS95dNPPy2T81cWPv74Yxo1aoS9vT0+Pj7Mmzev2Od5eXmMHj0aZ2dnXFxcGDNmjMamnJwchg8fjo+PD/b29jRu3Jg1a9YUO77g+tjZ2WmWygTfKIpCLmPxwCDq11D3ml1MSOfj384azO9HUZrWdizm7KVn59F98WEW/nOJfOWje7HE79DDGTduHKtXrwbUzl+nTp1o2bIlXl5eHDhwoML1CgdQT1HIy/bDUdb9BKaDQqGgSZMmYhx9NVPbyZqG7uq3n7Ep2Ry6kqRji8qG0Ivgobz4NYzYD5NjYWwY0oAN5D41CanZq9DwhcL9JAmOLFI7hev7wNcNYYEP/NAD/pwA537VVQuqhb2Rt/npxC2gcN6fo435ow/6D/Pnzyc9PZ2UlBQ2b95MbGwsrVq14vbt21VgcdWTl5dXbfcWKysrtm7dSkpKCrt27WLFihWsXLlS8/ns2bM5cuQIERERhIeHc/jwYebOnQuoHQ8PDw/27NlDWloaa9euZcKECfzzzz/FzjF//nwyMjI0i6enp9bsd7AyZ+WQ1tg/6PX763wC/9t/VWv164oZf0QQlXSfJfuuMnDVCeJSsh66r/gdeji//fYbLVq0AOCPP/7gxo0bXLx4kfHjx/PZZ59VuF7hAOopzrYWWD5m4rilmRxnW4tqskhgKMhkMmxsbAwipLSx0bWxm6b8v31XdGhJ2RF6ETwWuRxcfJA1fhGLZyYje/l7eH5O4efKXGg7Evx7gmuD4hFJT32v7jUsyuah8PdnEPoTxJ6G3PvV2x4tkpqZy6ifz2jWnwtw5wmfiqfdkMlkBAQE8NNPP+Hg4KAJ8pCRkUHv3r1xc3PD0dGRTp06cfasenju9u3bmTt3Ljt37tT0UIHaCZs8eTLe3t7UrFmT11577ZG9ijKZjMWLF9OoUSOcnJx47bXXSE1N1Xw+ePBgPD09cXBwoFWrVuzfv1/z2dq1awkMDGT69OnUqlWLAQMGIJPJ2LFjB4GBgTg4OFC3bl3Wrl0LlOyx/OSTT6hbty729vYEBATw669lf2kwa9YsjfPQuHFj+vXrx5EjRzSfr1mzhilTpuDh4YGHhwefffaZpkfF1taWmTNn4uvri0wmo23btnTp0qXY8dVBAzc7Fg8M1KTx/Oqfy+yJMEznv4ARneprXoqeupFM98WH+Sc8odR9xe/Qw0lKSqJWrVoA/PXXX7z66qs0bNiQt956i/Pnzz/m6IcjHEA9pbaTNfs+epqdYzqyY1QHZvUK4DV/a2b1CmDHqA7sHNNRJIEXlEp+fj4nTpwQQyl0wHudfSnolL+cmEHeY4a96ANCL4Ky8lCtmFlCl0/htZ9gzGl1IJl3D0HfFdBhHAT0Ltw34w5EbIfjy2DHKFjVFeZ6wqLmsGEAnFlfnU2qNIO+P0n2g5y9Lrbm/G9QS63Ua2ZmRp8+fTh48CCgDgTx+uuvExUVxe3btwkKCqJ///5IkkSfPn349NNPeemllzQ9VADz5s1j586dHDlyhKioKGQyGYMGDXrkedevX8/+/fu5ceMGycnJjBs3TvPZM888Q2RkJHfv3mXAgAG88sorpKenaz6/cOECZmZm3Lp1i/Xr17N9+3bee+89vvrqK1JSUjh16pSmJ+O/tGjRglOnTpGSksK0adN44403iIqKKvf3JkkShw4donlzdTCk5ORkYmJiCAwM1OwTGBjIrVu3ijm3BWRnZxMcHKw5voDZs2fj4uJCUFAQP/74Y7ntKgtdG7vz0XOF80bHbQrjamL6I47Qbxq627NjVEcGPuENQGpWHiPWn+bz38PJzlMW21f8Dj0cd3d3IiIiUCqV7N69m2effRaAzMzMSvWYGs4sUxOktpO1xsFrXseR3FaeWFhYiDckgkeiUCgICgoSQyl0gJONBd2befDnuXhSMvM4dPkOz/i7P/5AHSL0IigrZdaKuTV4tFAv/8XMAnouKRJ8JhLuJxZGJHXyLtw3Ow2+f6ZkmgpnH3U6DB2zbN8VLjxI2SQDNr7TFjOF9t6r165dm3v31PMKHRwceO211zSfzZgxgyVLlhAXF0ft2rVLPX79+vXMnj0bb2/1d7pw4UJq165NXFzcQ4cwfvLJJ5rPZs2aRadOnVi9ejVyuZxhw4Zp9vv444+ZO3cu586d0+Qic3R05LPPPkMul2NhYcGKFSv44IMP6NatGzKZDDc3N9zc3Eo9b1HHdMCAAXzxxRccO3YMH5/y5cabMmUKmZmZjBw5EkDjDBfNMVdQTk9Px9HRUbNdkiTeeecd/Pz86Nevn2b7vHnzCAgIwMbGhn379tG/f3/s7e3p27dvuWwrC+8/7UtEfBp/nosnIyef4T+eZvuoDjhal29Isb5gbaFgXr9mdGxQg0lbzpGek8/aYzcIjrrH1Jf8sbdSt0uSJCzcfYlMyNA84zrbWohODmDYsGH0798fDw8PZDIZ3bp1A+DkyZM0bty4wvXq/g5aQWJjY5k4cSK7du0iMzOTBg0a8MMPP9C6dWv4P3v3HR5F8T9w/H2XnpCE0EMLvYRUepMuqChVQAUpilKl2AsKKF8BURBFBEThJwIiShdRKaG3QEInQOidkB5S7+b3x5klgZQLpF3yeT1PnuzdlpvZ/dzezs7sDKZgmjhxIj/88AORkZG0atWK77//ntq1LaNnvozIBZowl8RKwenpV4k/j94AYHXQtUJfAASJF2G+x44Ve1doNCj9e3Fh/xUIT0EFr/vv3zltGtsw7AycXJsmEXamsRCbDoOGL5veMxpNvZjm0w3SkJvRfPXvGe31+CdrU9c9dztlu3btGqVKmZqTxsfH89Zbb7Fx40bCw8O17vLDwsIyLQBevXqVatWqaa8rVqyInZ0dV69ezbQA6OHhkW46KSmJO3fuULZsWT7++GN+++03bt26hV6vJzo6mrCw+886V6pUKV03/pcuXcq2xjHVrFmzWLhwIVevXkWn0xEbG5tu2+aYNm0av/76K9u3b8fJydSpSmpz2KioKMqUKaNNA+l6nVRKMXLkSEJCQti8eXO6fLRo0UKb7tKlC8OGDWPFihV5UgDU6XTMeN6H83fiOHUjmgthcYxZHsRPg5tYdJ8PXX3c8ansyujlQRy5EsnJG9G8+MP+LNexs9ZLSzdMzaW9vLy4cuUKffr0wc7O1Pu/lZUV77///iNv1yKbgEZERNCqVStsbGz466+/OHnyJF999VW67lC/+OILvvnmG+bNm8f+/ftxcnKiS5cuJCQkFGDKH53BYCAwMBCDwZD9wqJYk1gpWG3rlsXtvw4g/jlxk6j4pAJOUdYkXoS58ixWnMpA9Seg2evg0fL++2XrwYA/oPMU8OsPFf3B2sHUI+nNY+mfHbx1DKZWgYWdYN0bsO97OB+QJz2SphiMvPTDfm2zDdxdGNOxTu5+RkoKa9eupV27dgB89dVXHDp0iF27dhEdHc3FixcBtN4iMxo/rXLlytpyADdv3iQxMZHKlStn+rmXLl3Spi9fvoytrS1ly5Zl2bJlLFu2jD///JOoqCgiIyNxdXVN11vlg2moWrUqO3bsyDZedu3axaRJk/j555+JiIggMjISLy+vHPWEOW3aNObNm8fWrVvT5c/NzY3KlSsTHBysvRccHEyVKlW02j+lFKNGjWL//v38888/6WoFM5LXY9U52lqz4OVGlPqvj4ftZ+7wxd+n8/Qz80OVUo78PrwFw9rWMGv5xBQjEXGF+/czvzz//POMHz8+XWwPGjSI7t27Z7FW1iyyBnD69OlUqVKFRYsWae+lbSaglOLrr79mwoQJ2s75+eefKV++PGvWrOGFF17I9zQ/LisrKxo3bix36kW2JFYKlo2VnjZ1yrI2+DpJBsX87ed596lHb6aR1yRehLnyPVbsXaBWJ9NfKqMRIi+aagvLed5///YpSIqBqwdNf2k5lIKnvwCfPqbXSfdMhUiHRxtD643lQdz978LUzlrP0teaPdJ2MnP69Gk+++wzoqKiePPNNwGIjo7G3t4eNzc3YmNj+fDDD9OtU758eS5dukRKSgrW1qZLuwEDBvD555/TsmVL3NzcePPNN+nUqVOWPVjOmDGD1q1b4+joyCeffMILL7yg1fbZ2tpSpkwZkpKStF5LszJs2DBee+01XnrpJdq0aUNYWBjXrl3D398/3XLR0dFYWVlRtmxZjEYjixcv5vjx42bvry+++IK5c+eyffv2dDWYqYYMGcL//vc/ranq559/ztChQ7X5o0ePZvfu3WzduvWhcdUiIyPZs2ePNhREQEAA8+bN44cffjA7fY+iSilHvnupIQN+3I/BaPod8XR3STfckCWysdLzwdP1qejqwMR1Jwo6ORZjy5YtbNmyhdu3bz80MPyDw5aYyyJrANetW0fjxo3p06cP5cqVw9/fP92X8cKFC9y8eVNrJwumtunNmjVj7969mW43MTGR6Oho7S/15Ja6s41GY4bTBoPBrOnUu1lpp1NSUh6aVko9NJ06P3VaKaXdVUs7bTQazZouLHl6cFrylDt5SkpKKnJ5sqTj1MPv/gXWH4euFvo8paajuB0nyVPO85T6O1RgedLrMbh6YKzzNJSqfj8fXr0xDN+DsfdP0OZdVN1nUaVqAjqID0fZu9zPx5m/YXo11Ff1UEt6of7+CMOhn1HXDqMSY7PM08ajN/jr+P2eDL990Q9nO6vHPk7vvfeeNg5gr169KF++PIGBgZQpUwaj0cibb76JXq+nfPnyeHl50azZ/UJnSkoKzz//PC4uLpQtW5aSJUuilOKdd96hc+fOtGjRgmrVqpGcnMySJUsyPU5gKjS2b99e65Fz1qxZGAwGBg0ahKenJx4eHtSoUQN7e3utNiKzPD333HNMmzaN0aNH4+rqSpMmTTh27NhD8dalSxeef/55vL29qVixIidOnKBly5bplkl7PB6Mvffee4+bN2/i7e2t9YL69NNPa/n7+OOPad68OfXr16d+/fq0bNmS9957DzBdL86dO5eQkBA8PDy09YcNG4bRaCQ5OZnJkydToUIF3NzcGD9+PF9++SV9+vTJ83NEEw9XJj53/ybHu78f5djVyEJ/jjDnvNewaknMUVB5KkwmT55M586d2bJlC2FhYURERKT7e1QWWQA8f/689jzf33//zYgRIxgzZgz/93//B5iaOYDpjlha5cuX1+ZlZOrUqbi6ump/np6mL97ly6bxfa5cucKVK1cA00kjdSDQ0NBQbayeM2fOaO3WT506pR2c48ePa+3Ojxw5oj2YHBQURHy8aWyUwMBAkpKS0jWzSUpK0qYPHTpEUFAQYHqwObUL6KioKO1uWUREBKdOnQJMzwacOWN6RuHWrVuEhoYCcP36da13rYLME5iea5A85W6eDAYDu3fv5u7du0UmT5Z2nNrVLYedtel5jVsxiew4fKrQ5ikkJISgoCBu3LhR7I6T5ClneUpISCAoKKhw5snKhlNhioiK7aDDRxz1fJeogVvhw+ucajOf2FLeWp6Sw84DoIu5gS50C7q9c7Ba/wa6H9rD1Mqc+2uulqfje/+FWyeIjQpn54HDjFthyiNAd193GlWwfew8BQQEcOzYMc6cOUNUVBR//PEH77//PuXKldOOU4UKFfj666+5evUqFy9exNvbm+joaPz8/AgKCsLBwYHt27fz119/cevWLQwGA0ePHmXq1KmEhoayYcMGVq5ciYuLS6bHCaBt27bs2bOH3bt3s3LlSlJSUjhz5gxOTk7MnTuXQ4cOce3aNV566SW2bNlCjx49uHLlCh07diQ4ODjdcTp37hy1atUiODiYAwcOcPDgQQYOHMipU6cYM2YMa9as4fjx48TExLBgwQK2bdtGaGgoX331FV999RXDhg0DTEM1pOYpo9iLi4tj9+7dxMbGcuPGDXbs2MFff/2lxZ6NjQ2fffYZO3fuJCIigk8++YTz500xYG9vT0hICAkJCZw+fVqL/w8//JArV65ozV9PnjxJdHQ0v//+O127ds2V75M554iXm3vQy9c0BEBiipHXfj7IrsAjef99ysM8gakHS3OkXn/nZ55yUvucH+bNm8fixYvZv38/a9asYfXq1en+HpVO5aSRdSFha2tL48aN2bNnj/bemDFjOHjwIHv37mXPnj20atWK69ev4+7uri3Tt29fdDodK1asyHC7iYmJJCYmaq+vXbuGp6cnly5domrVqtpdA71en27aYDCg0+myndbr9eh0unTTKSkpWFlZpZsG092ItNPW1tba3azUaaPRiJWVVbppo9GIUirb6YzyIXmSPEmeci9Pw5cEsumE6Qeoh587M/v6WXyeiuJxkjwV0zwlRpNy8yRWYSFw+yTq9kl0d0LQxd0mZcR+rMvXM6V9x1dYbfsMpbfmc8MAfkjsDEADh3DWjWyBzs0DpdMXjjw95nHS6/UEBQXh4+NTeI5TUYy9HOYpITmFF3/YT9DlSACaVHNj6dDmWOux2DwduxrJc3N2k521I1vgW7VUvubp4sWLVK9enStXrmT5vGx+KV26NAcOHKBmzZq5ul2LfAbQ3d1dq51LVb9+ff744w8AbcDEW7dupSsA3rp1K91YMA+ys7PTetcBU7t0uP/Ab9oHf9NOp30eIqfTqW31s5tWShEfH4+Dg6k3JJ1Op20n7XRmaczpdH7kKTXtaaclT4+fJ6UUiYmJWqwUhTyZO12Y8vTmk3W1AuDmU7e1rq0LW56UUty7dw8HB4dsz3VF8ThJnszPU9rfodR4ttg82btiXa0FVDP18Kj1rxgXhvV/zwbqdDqsVArYuXI4vhw/JXUEoKbuGr8bP8LquyQYvlvruVR/I9jUo2m5+uhdK4OFfZ+yS+OjTKfGi3yfHj1P9jbWzB/QiOfm7OJWdCIHL0Ywef0J/tfT22LzlDbesvKoxy+38lQYDB06lGXLlvHxxx/n6nYLVy7N1KpVK0JCQtK9d+bMGe3h3+rVq1OhQgW2bNmiFfiio6PZv3+/NjaMpTEYDJw4cQJ/f/9CF5yicJFYKRzqVHCmtJMtd+OSiE00sP9COM1rlC7oZD1E4kWYq1jEilOZ9K/bvc+xWsPpN2cHBqzQY2BC1RM46D0h/DyUSTO0VOBPELTENG3r/N+4hWnGMKzawjQOYiGV2w3CikW85JNyLvbMf7kxfefvJSnFyNL9l/Gs6EL/Zg93eiOKloSEBBYsWMDmzZvx8fHBxib9mJAzZ858pO1a5Ddy/PjxtGzZks8//5y+ffty4MABFixYwIIFCwDTnYVx48YxZcoUateuTfXq1fn444+pWLEiPXr0KNjEPyJra2uaNGlS0MkQFkBipfDo5leRRbsvAvDtlrOFsgAo8SLMVRxjJSnFyIilh0nBVEPgW6U07UfMNs00GrVaPgCc3U2FvbAz//VIesD0l+qDa/cLgCdWQ3yEafmy9cChZP5kKB8Vx3jJS35VSjK1pzdvrTQ9Azhx7QnqlHemSbVSBZyynHNzssXOWk9iijHTZeys9bg5Fd4bJvnl6NGjWmXWg88nmluTmhGLfAYQYMOGDXzwwQecPXuW6tWr8+abb/Laa69p85UyDQS/YMECIiMjad26NXPnzqVOHfPH6rl69SpVqlQpFO2AlVLExsZSokSJxzrgouiTWCk8wmISafy/zQBYW+kI+ezpQjeYr8SLMFdxjJVpf51m3nZT5xNOdlbs/6AjJextsl4pJQnCQ+H2yfsD3CdEweAN95dZ1BUu7br/2qWSqSCYWmPo91K+DWqfVx6Ml19//ZVVq1bx22+/FUh6li5dynfffZeu/4is6HQ6goKC8PPzY+nSpWzcuJGlS5fmcSqz99mGk/y4y9QJSpkStqwb3ZqKFjhY+rXIeG2cv9RHERwdHbVzi5uTbYEMAl+Yrv3zksUWAPNDYQoCg8HAkSNH8PX1TddWWYgHSawULu1mbOPiXVOPZ1/18aV3o8L1gyLxIsxV3GJl97kwBvxoGvDdxkrHqhGt8K6c9SDhZts+A67sNxUOo6+mn1eiAryd5jGXgOlgSLpfOCxdq1A3JU2VNl50Oh21atVi7dq1eHt7Z79yIZC2AGg0Gqlbty6//fbbQ+MY5rcUg5HBiw6y65ypZ0uvSi78Prwl9jaW+50sTOeWwnTtn9a5c+cIDQ2lTZs2ODg4oJR6rBtxFtkEtDiysrKiYcOGBZ0MYQEkVgqXl5t78Nmfpq66F+46X+gKgBIvwlzFKVZuRycwbkUQqbfI3+lSN/cKfwBt37k/nRAFt0/frzG0tku/7KHFEHP9/mu9NZSuDeXqQeUm0GJU7qUrF6WNlw0bNlCqVCmLKfw9SK/X079/f+bOnZvng8Bnx9pKz7cv+tP9u91cDr/H8WvRvP/HUWb187PYmvnidG7Jqbt379K3b1+2bduGTqfj7Nmz1KhRg1dffRU3Nze++uqrR9quRY4DWBwppYiMjMz1h7RF0SOxUrgMaOFBaqvP0zdiiE1ILtgEPUDiRZiruMSKUop+C/ZxJ8bUPK11rTIMbV0j7z7Q3hWqNoPGQ+CZL6DzZ2kTA63HQaPBUKUZ2LmAMQXunDI9R3hqffptLX8JVg+H3bPh7L8QdRUK6HiljZd169bRoUMHbd7t27fp378/7u7uVKxYkXHjxmnDcAUEBFCyZEkWLlxIlSpVKF26NO+++266bW/evJmmTZtSsmRJGjRowLp167JNz+LFi9P1BK/T6QgODtZef/3117Rr1y7T9Tt27Mj69esznZ+f3Jxs+WFgYxxtTbVla4Kv88PO8wWcqkdXXM4tj2L8+PHY2Nhw+fJlHB0dtff79evHpk2bHnm7UgAsYNWqVaNu3br4+fnh5+eX4RiFe/fuxd/fn2bNmuHl5cWwYcO0E+XevXu1dRs0aJBuniiejEYjly5d0sbEEQXLztoKn8olAVCgPbtRWEi8CHMVl1iZ8U8IF8LiALDS6/iqjw/6gnp2V6eDZsPgudnw6j/w/mUYfwL6/w5PfgoNB95fNikOQjbCkeXw7yew9HmY1QCmVYWFT8LOR+st8FGljZfg4GDq1asHmC72u3XrRoUKFQgNDeXYsWMcOXKEKVOmaOvGxMRw8uRJzp49y65du/juu+8ICAgATJ1i9OnTh2nTphEeHs78+fN5+eWXH+odPrd5enpy69Ytbty4kaefY666FZyZ2ddXez3tr9NsP3OnAFP06IrLueVR/PPPP0yfPv2h5qi1a9fm0qVLj7xdKQAWAitWrCA4OJjg4GD69ev30HxfX18OHjxISEgIx44d4/bt28ydOzfdvODg4IfmieLJysqqULSjF/cNb3t/ANcVgVezWDL/SbwIcxWHWDl7K4Yf09SkTHzWk/KuhaiDDZ0OXCtD7Seh1VhTZzHaPCvo9wu0nwANekHZ+qbmoonRpt5I7567v2xKEnxVDxY/CxvfhcBFcHm/qTlqLkkbLxEREbi4uAAQGBjI2bNnmTFjBo6OjpQuXZoPP/yQZcuWaesqpZgyZQr29vbUr1+fli1bcujQIQDmz5/P4MGD6dChA3q9ntatW/Pss8/meecyqemPiIjI08/Jiae83Bnb0TQUiVHBG8sOc/G/mxeWpDicWx5VXFxcupq/VOHh4enGLs8peQbQAjg6OmI0Grl79y4ODg7Ex8dr7bzTBkVSUlK6eaJ4MhqNRERE4Obmlm5gVFFwOnuW17q8vh4Zz/WIe1R0e/iEXhAkXoS5inqsJCQbGPNrMIkppmZo/RpXYWDLagWbqJywsYf6z5r+UqUkmQp+t0+aehpNdfccxNww/V3cmX47LpWgyVB44k3Ta6MRUhLANmfnrLTx4ubmRnR0NAAXL14kMjKSUqXuD1+glMJgMNxPgotLuusbJycnYmJitPW3bt3KokWL7mczJQUXFxfOnz+Pj49PunRUqlQpV2oHU9Pv5ub22NvKTWM71ubUjWj+OXmL6IQUhv4cyOqRLXHOrrfaQqSon1sexxNPPMHPP//MZ5+ZmobrdDqMRiNffPEF7du3f+TtSgGwEBg4cCBKKZo2bcq0adMoW7bsQ8tcuHCBZ555hmvXrtG1a1dGjhypzbt48SLdu3cnNDT0oXmi+FFKcePGDUqWLFnQSRH/0et1PFG7DJtP3Qbg++3n+ayHVwGnykTiRZirqMfKF5tCOHXDdJFfu1wJJndvUMApygXWtlDe0/SXVuma8HrAf8NUpBmuIvqa6U/dL4wRcQG+bQSlqt8f1D5tj6RWGRc00saLn58fp0+fBqBKlSqUK1fukZtSVqlShbFjxzJt2rQM58fGxpq1HScnJ+7du6e9zi49J0+epHz58ri7u5uf2Hyg1+uY2c+PXnN3c+ZWLOduxzJ+xREWvNyo4Jou51BRP7c8ji+++IKOHTsSGBhIUlIS7777LidOnCA8PJzdu3c/8nalmF3AduzYwdGjRzl8+DBlypRh0KBBGS5Xs2ZNQkJCuHnzJomJiaxatUqbV61aNY4cOZLhPFH8WFlZ4eXlJU0pCpmxnWpr04cvF54mRBIvwlxFOVaW7b/ET7tNz+faWuv55kV/i+5WP1vWdlDR39SEtPMUGPAHvHkS3rsEr/wN3n3uLxt2FlAQfh5Ob4AdM+D3V2Buc/ifO+z7/v6ySXFwNxSMhnTx8txzz7Ft2zYAmjRpQpUqVZgwYQIxMTEopbh06RJ//fWXWUkfNmwYixYtYtu2bRgMBhITE9m7dy+nTp3K0S5o2LAhS5YsISUlheDgYJYsWZLl8lu3bqVr1645+oz8UsLOmgUvN8bF3lSvs/nULb7ecraAU2W+onxueVxeXl6cOXOG1q1b0717d+Li4ujVqxdBQUHUrFkz+w1kQgqABaxq1aoA2NjYMG7cOHbu3Jnhckajkdu3b+Po6MgLL7yQ4WCkJUqUyHSeKD5SY0Uepi5cvCuVxPe/buRPXI/mzK2YAk6RicSLMFdRjZVrEfF8vPaE9npcx1rUd3cpwBQVIIeSULU5uFW7/17dp+DtczBwHTw1HRoOgspNwdYZjMlQotz9ZS/vhW8bwueVUPPbcu+PNzCe3sgzHdsQFhbG8ePHsbKyYsOGDVy7do369evj6upK165dOXfu3IOpyZC/vz/Lly9nwoQJlC1blkqVKvHxxx/nuAO8b7/9lr1791KyZEnee++9TG/Agyn2ly5dyqhRhXPIDYBqZZyY81JDrdfpb7ac5a9jhaPDmuwU1XNLbnF1deWjjz7it99+Y+PGjUyZMuWxa6KlCWgBiouLIzk5WavyXr58eYYDjJ47d47KlStz9+5dnJ2dWb16tdbG/dy5c3h4eGBjY0NSUlK6eaJ4Ukpx9+5dSpcuXdBJEQ/o4V+JI1dNnSysDrrGe0/VK+AUSbwI8xXFWDEN+bAXg9H03F/VUg6MaFergFNVCJUoCyXaQo22999TyjTMhH2a8RFjb4O1PaTEo7sRjOONYDj2M+ht+LxzZT77YDwr1v9LuXLl0j3Dl1a7du2IjIxM996aNWvSve7QoUO6YSXMYTAYsLW11V77+vqmGwbiQWmHJPj1119p0qRJoR+rrk2dsnzwdH3+t9FUG/rWyiNUL+tEvQqF+4ZGUTy35KaEhASOHj2aYSG5W7duj7RNnZJBNzJ19epVqlSpwpUrVx7qfjU3nD9/nt69e2MwGFBKUaNGDWbPnk21atUYOnQo3bp1o1u3bixYsIBvvvkGKysrUlJS6NixI1988QX29vZZzhNCFC5hsYk0+3wLBqOitJMNBz960mKe0RCiKJqw+hi/7L8MgLVex8732uNemHr9tERGA0RchBtH4MIOOL/N9BpgxB4o/9+zlRd3mZqX1uwAbh55nqwxY8Zw584dli9fnuefVZCUUrz52xFWB10DoEopB9aNao2bk202awrI+2v/nNq0aRMDBw4kLCzsoXk6nS5d50k5IQXALBSmIDAajdy6dYvy5ctLD0kiSxIrhdszs3dw8oap+eecF/151rdigaZH4kWYq6jFyt7QMF78Yb/2elovb15oWrUAU1S0pIuXyIumAp//y6ahLAD+eA2O/Td0Q6maULO9qTBY7Qmwz90aq3bt2hEWFsavv/6Kl1fh6IArLyUkG+g7fy9H/2tx0rJmaX5+pSnWVoXze1uYzi2F6dofTOP9de7cmU8++YTy5cvn2nYLZySIhyiltIelhciKxErh1tXnfoFvQZrxxgqKxIswV1GKlfikFF79v0Dt9RO1y0jhL5eli5dSNUyD1qcdpqpSI6jS3DR+YXgoHFwIv74E06vBT0+bhrDIJQEBARw/frxYFP4A7G2smP9yI8qUMI0Ttyf0Lp9vPF3AqcpcUTq35LZbt27x5ptv5mrhD6QAaDGsrKyoU6eO9JAksiWxUrgNaVkNm/+afZ6/HUtC8qM138gtEi/CXEUpVgb+dIB7Sabvnou9NT8MbFTAKSp6so2X5sPh1b/hvQvwwjJo8pqpJlAZICnWNIRFqm2fmwqI4QV/08xSuLs6MG9AQ2ysTL83P+2+wO+HrhZwqjJWlM4tue35558nICAg17crncBYCKPRyPXr16lYsWKBV4+Lwk1ipXBztLOmu38lfj90ldgkA5tP3eJZn4JrBirxIsxVVGJl0e4LHLx4fyiWn19pir2NXA7lNrPjxd4V6nU1/QFEXIK4NM87JcXBrllg+K9GsKSHqalozfZQvQ04mD8w+7Vr11i4cCEDBgx4rC70LUXjaqX4tLsXH6w6BsCHq49Rs6wT/lXzbzD7atWqYWdnh4OD6dnaDz74gH79+qVbxmg0MnPmTObPn4/RaKRDhw7MnTsXGxvTGJM//vgj06ZNy3BeUTdnzhz69OnDzp078fb2fijfY8aMeaTtWu4ZvBjKaRfHoviSWCncevlX0qZXH75WgCkxkXgR5rL0WLl0N47PNpzUXg9rUwO/fLwYLm4eKV7cPKBymhpZQzK0fRc8WoHeGiIvwaFF8NtA+KIG/P2R2ZuOjo5m7ty51KlTh/79+3Ps2LGcp8/CvNi0Ki83N3Wyk5RiZPgvh7gdnZCvaVixYgXBwcEEBwc/VPgDuHDhAjNmzGD79u2cO3eOW7dusWDBAm3exx9/zM6dOx+aVxwsX76cf/75hz/++INvv/2WWbNmaX9ff/31I29XCoAWQq/XU7NmTYu+6yryh8RK4desRmkquJh66g0IuZ3vP8ZpSbwIc1l6rBiNpiEf/hvxgZplnXj/6YIfiqWoyrV4cSgJbd6BIRvhvYvw4gpoNhzK1AFlBNc0HXVEXoHlL8L+BRB2zjRURRr169fn4sWLfPPNN+zatQsfHx+6d+/O/v37Kco+ec6TptVLAXArOpFhvxwiMaVgHz9Ia9WqVfTs2ZOKFSui0+kYPny41lvr77//Trdu3ahQocJD84qDjz76iMmTJxMVFcXFixe5cOGC9nf+/KM3ibbMs3gxZDQauXTpkgySKbIlsVL4Wel1tKlTBgCDgjlbzRsAOS9IvAhzWXqsvPv7EW5GmWqkbKx0/Pp6c3Q6GYYlr+RJvNg5mwamf3o6jD4I40+AT5oapfPbIGQj/PUOzGkEX3vDujfg+Cq4Fw6Ag4MDo0aN4ty5cyxevJiQkBCaN29Ox44d2bJlS5HsiMTGSs/c/g2pVNLUDDPociQfrzmeb3kdOHAg3t7evPrqq9y5c+eh+ZcuXcLFxUWLlWrVqnH5sml4lsuXL+PhcX+YkLTzioOkpCT69euX6zfepAAohBAFoG/jKtr0+qPXCzAlQhR9Aadv83ua5tZfPu9LWWcZL9fiuVYGx1L3X3u0go4TTc8GWtlC1BU4/DP8PsTUXPTcZm1RGxsbBg0axIkTJ/j999+JjIykU6dONG/enLVr11rsjY7MlClhx/yXG2FvY7r0/y3wKj/vvZTnn7tjxw6OHj3K4cOHKVOmDIMGDcrzzyxKBg0axIoVK3J9u/LUs4XQ6/Xp7oAIkRmJFcvQuFopnO2tiUlIIeJeMieuR9Ggomu+p0PiRZjLUmMlJj6Z4UsPaa871S9H9zTP4Yq8USDxUromPPGm6S8pDi7tgdBtELoVwkKgYsP7y+75Fi7sxKpme3q36UCvngf5599/+fzzz+nRowcNGjTQOiyxti4al8telVz54nlfxiwPAuDTDSepXb4ELWuWybPPrFrVNLyKjY0N48aNo06dOg8t4+HhQWhoqFbLdfHiRW29qlWrEhoaqi2bdl5xYDAY+OKLL/j777/x8fF5qBOYmTNnPtJ2pQbQQhiNRkJDQ4vcHSmR+yRWLEeXBvfH9Zm95WyBpEHiRZjLUmPlsw0nSUg2pdnN0YbvB8iQD/mhwOPF1glqPwlPfQ6j9sHbZ9PXFp7eCGf/hk3vw3dN0c1qQJf4dWyf8wY7/1mPh4cHAwYMoE6dOsyfP5+EhIJ7Vjs3dfOtyPC2ph5QDUbFqKWHuRJ+L08+Ky4ujsjISO318uXL8ff3f2i5nj17snr1aq5fv45Sinnz5vHCCy8A0Lt3b9atW8fNmzcfmlccHDt2DH9/f/R6PcePHycoKEj7Cw4OfuTtFo1bGsWEnZ1dQSdBWAiJFcswrlMdfj9kapa248wdlFIF8kySxIswl6XFyp9Hb/Dbf2Of2VvrWTq0GTZWcu87vxSqeHF6oJbrmRkQusVUQ3hpD8Rch+BfIPgXWtu58uf6CwQfPcbUqVMZMWIEkydP5q233mLYsGGUKFGiYPKQS97pUpfTN6MJCLlDxL1kXvs5kFUjW+Jom7vFglu3btG7d28MBgNKKWrUqMHPP/8MwNChQ+nWrRvdunWjRo0avPXWWzzxxBMAtGvXjmHDhgFQo0YNJk+eTKtWrR6aVxxs27YtT7arU0XxaddccvXqVapUqcKVK1eoXLly9isIIUQOtZi6hRtRpjvLPwxsxJOeFQo4RUIUDdci43n66x1EJ6QAMON5H/qkefZWCE1yvKkQeH6bqUDoVg1eWKrNPjPRny+2R/Dz7quUKOHEmDHjeGPMGEqXLl1waX5MUfHJ9PxuN+fD4gDo6u3OnJf8i33HSMXl2l9ug1kIg8HAmTNnMBgKT7e9onCSWLEsfRrd/4H5PiA0iyXzhsSLMJclxYrBqHhh/l6t8PesjzvPNyq6F3OFkSXFCzYOUKsjdJ4CI3ZDn8X354VfoI7uPAvbRRA62oGBdRP4YupneFSuwNuDnuX62aMFluzH4epgw4KBjXG2M9X6/XnsBnML4DcILCxWsjFt2jR0Oh3jxo0r6KRkSQqAFkKn0+Hs7Fzs78yI7EmsWJbX29Qg9UgFX4nM97GZJF6EuSwpVsb+GsSViHgASjvZ8r+e3haR7qLEkuLlIVZpOtpwqwYj90GXz6nSqDNfP+vGpbFOjGuiZ+GKP6nu2ZDhw4ebxmQzGky1iRaiVrkSfP2CH6mH6Mt/Qthy6la+p8OiYyWNgwcPMn/+fHx8fAo6KdmSAqCF0Ov1uLu7W+wAvCL/SKxYlhL2NtSp4AyAUcGy/fk7vpHEizCXpcTK4csR/Hn0hvb6fz29cHWwyWINkRcsJV6ypdNBufrQYhQM+APeu0jZERuY8vH7XJrWgsljBrBq1Spq167NgN7PcHx8Jfi5B+yeDTePQSHvNKlj/fK83bkuAErB2F+DOXc7Jl/TUBRiJTY2lv79+/PDDz/g5uZW0MnJluXu6WLGYDBw6tSpIlE9LvKWxIrlebVVNW06P8ZlSkviRZjLEmIlJiGZsb8Gkdq5wXO+7jzl5V6gaSquLCFeHomNPdRoC09OxnXcHt7/ajEXL15k9uzZ7NhzAO/vIujx+Z/sX/QhzGsNX9WBP4ZC8DJtMPrCZmS7mnT1Nn1PYhNTeO3nQ0TFJ+fb5xfGWImJiSE6Olr7S0xMzHL5UaNG0bVrVzp16pRPKXw8UgC0EDqdjtKlS1t89bjIexIrlqdXw8pY603H60JYHBFxSfn22RIvwlyWECufrD3BlXBTE7xGHm7M6utXsAkqxiwhXnKLo6Mjo0eP5tzlmyz6Ziqn40vT/Md7dFqSwNbjN1BHf4M1IyD8wv2VYm6ZxiosBHQ6HTP6+FDf3QUw/Q6N/TUIgzF/+oksjLHi6emJq6ur9jd16tRMl/311185fPhwlss8rpMnT7Jp0ybWrVuX7u9RSS+gWSguPQEJIQreiwv2sff8XQCGtq7OhGc9CzhFQliW7wPOMX1TCADOdtZsHPsEVUo5FnCqRHFkMBhYs2YN/5syhaDgYJrVq8wHnd157qvd6K3/a4687g048itUaQY120PNDlDBFwqwGeSV8Ht0m7OLiHum2r/hbWvy/tP1Ciw9BSH12v/kyZNUqlRJe9/Ozi7DYU2uXLlC48aN+ffff7Vn/9q1a4efnx9ff/31Y6fn/Pnz9OzZk2PHjqHT6UgttqUWlh+11lRqAC2EwWDg+PHjhap6XBROEiuW6Y0OtbTpNcHX8u1zJV6EuQpzrJy8Hs0X/xX+AKb09JLCXwErzPGS16ysrOjduzeHDh9m06ZN2JWrQY9vDuLj58/SpUtJSUmBsHNgSIKLO2HLp7CgHcyoCSuHwOElpgfy8lmVUo7M7d8Iq/9apMzbHsrafPg9Koyx4uzsjIuLi/aX2ZiWhw4d4vbt2zRs2BBra2usra3Zvn0733zzDdbW1o+dp7Fjx1K9enVu376No6MjJ06cYMeOHTRu3JiAgIBH3q4UAC2ETqfD3d29UFWPi8JJYsUytahZGidbKwDCYpM4dyt/HsKXeBHmKqyxkpxioP/Cfdpzfz6VXenuVynLdUTeK6zxkp90Oh1dunRh+/bt7Ny5Ew8PDwYMGEDdunWZn9SdxNf3wjNfQt2uYOsM8eFwYhXs+RbS7rergZCYP78JLWqW5pM0LVDe++Mox69F5elnWnKsdOzYkWPHjhEcHKz9NW7cmP79+xMcHIyVldVjbX/v3r18+umnlClTBr1ej16vp3Xr1kydOpUxY8Y88nalAGgh9Ho9pUuXtugekkT+kFixTDqdjo71y2uvf9h5Pl8+V+JFmKuwxsrIpYe1Jmv2NnqWDm1WwCkSUHjjpaC0bt2aP//8k8OHD9O4cWNGjBxJ9cad+GpnNLHPLYD3LsArf0Pb96DxK/dXTEmE/3sOpleDn56G7TNMBUJj3tWWDWzhQd/GpkefEpKNDFtyiLDYrDtBeRyWHCvOzs54eXml+3NycqJ06dJ4eXk99vYNBgPOzqaewsuUKcP169cB8PDwICQkJKtVs2R5e7qYMhgMHDlypFBVj4vCSWLFco3tdL8Z6MFLEeTHI9oSL8JchTFW1gZf499Tt7XX8wY0wtlehnwoDApjvBQG/v7+rFixgtOnT/P000/z/vvv4+HhweQpnxNeog60/xCaD7+/QtRVKFEOjClweQ9smwILO8IXNWDFy3Dm71xPo06n47MeXvhXLQnAtch4Ri49TLIhb4a0kFjJnJeXF0eOHAGgWbNmfPHFF+zevZtPP/2UGjVqPPJ2pQBoIfR6PR4eHhZ5d0TkL4kVy1WzrDPNqpcC4PydOI5ezdtmNyDxIsxX2GIlLCaRt1ce0V4/37Ay7eqWK8AUibQKW7wUNnXq1OHHH38kNDSUAQMGMH36dDw8PHjnnXe4ceP+OJaUrgljj8CYIOg6E+o/B3aukBAJp9bB7ZP3l70XDqc2QMLj/3bYWVsxf0AjyruYnn07cCGcT9efzGatR1PUYiUgICBXOoABmDBhAsb/xpL89NNPuXDhAk888QQbN27km2++eeTtSi+gWZBeQIUQ+e3XA5d5f9UxAAa3rMakbg0KOEVCFD5KKTrN3E7oHVM3+hVc7Nj9fket8wohLM2dO3f4+uuvmTNnDgkJCbzyyiu88847GdfyGFLgehCEboUGPaFsHdP7R1fCqqGgs4LKjU09i9ZoD5UagZX1I6Ur+EokfefvJSnFVAj5vKc3LzWr+qjZLPQs4do/PDwcNze3x3pmsmgUtYsBg8HA4cOHpXpcZEtixbI97e2OrbXp1Pz7oSt51uQmlcSLMFdhipVpf53WCn96Hfz6egsp/BUyhSleLEHZsmX53//+x+XLl5k8eTJ//PEHderU4eWXX+bEiRPpF7ayhipNoN179wt/AMoIpWqCMsCV/RAwFX7qDF9Uh1/7m3oezSG/KiX5vKe39nriuuMEXszdAe0lVswXHR3Njh07Huv5P5ACoMXQ6/XUrl27yFSPi7wjsWLZXB1sqF/B9MB3bKKB3w9dydPPk3gR5iossRJ8OYIFO+53kjSha32qlXEqwBSJjBSWeLE0rq6uvP/++1y8eJGvv/6a7du34+XlRc+ePTlw4EDWK/v2gzGHYexReG42ePYA+5KQGA2nN4BdifvLhm6Fk2shPiLbND3fqDKvtKoOQLJBMfyXw1yPjH/0TD5AYiVzffv2Zc6cOQDEx8fTuHFj+vbti7e3N3/88ccjb1f2tIXQ6XQ4OztbZBe5In9JrFi+3o3uNzv5ee+lPP0siRdhrsIQK4nJBgb+dEAb8qFJNTdeaf3oHSGIvFMY4sWSOTo6Mnr0aM6dO8eiRYs4deoUzZo1o1OnTmzdujXrTsLcPKDRYOj7f/DueXhtK3T9Cpwr3F9m9zfw20BTZzI/dISt/4NLe8CQnOEmP3ymHq1qlQYgLDaRYUsOkZCcOzV2EiuZ27FjB0888QQAq1evRilFZGQk33zzDVOmTHnk7UoB0EKkpKRw8OBB0+ChQmRBYsXyvdCkKvY2ptNz6O1YYhIy/kHODRIvwlyFIVZeWxJIdILp8x1trVg8pEmBpUVkrTDES1Fga2vL4MGDOXHiBCtXriQ8PJyOHTvSsmVL1q1bp3UQkim9lekZwCZD07/v7gtl6pqajV4LhB1fwKKnYXp102D0DxQwra30zHmxIVVKOQBw7FoUH6w6liu9VUusZC4qKopSpUydw23atInevXvj6OhI165dOXv27CNvVwqAFsLKyooGDRo89oCSouiTWLF8ttZ6+jSqAkCSQbHp+M08+yyJF2Gugo6V3w5eYceZMO31woGNcbKTIR8Kq4KOl6LGysqK559/nkOHDrFx40ZsbGzo3r07vr6+LFu2LOeFpycnw+gDMP4EdP8OvHqDY2lIijH1Ipq2Nm7rFDi+CjddLD8MbIyjremYrg66xsKdF3IlbxIrGatSpQp79+4lLi6OTZs20blzZwAiIiKwt7d/5O1KAdBC6HQ6HB0dpXpcZEtipWjo2bCSNr066FqefY7EizBXQcbKzagEPlx9THvdv1lVWtYqk+/pEOaTc0ve0Ol0PP300+zYsYMdO3ZQpUoV+vfvT926dVmwYAGJiTkcsN21MvgPgOd/grfPwevbof1H9+dH34AdM+D3IfBFDeqt68b6+ltorj+JDSlM/esUO87ceew8SaxkbNy4cfTv35/KlStTsWJF2rVrB5iahnp7e2e9chakAGghUlJS2Ldvn1SPi2xJrBQN/lVKUq20IwB7Qu9yOTwuTz5H4kWYq6BiRSnF60sCSTGamppVcXPgs+5e+ZoGkXNybsl7qePBHT58mEaNGjF8+HBq1KjBzJkziY2NzfkG9Xqo6AeVG91/Txmh+Sgo5wkouB5EzZAF/Go7hSN2rzFEv5HRyw5zMcy836g33niDKlWq4OLiQqVKlRg3bhz37t1j3759tG3bFjs7O0qUKKH9Xb9+Pef5KEJGjhzJvn37+Omnn9i1a5fWUU6NGjUe6xlAGQcwC4VpLBClFElJSdja2sodEpEliZWiY8KaY/yy7zIAvRpWYmZfv1z/DIkXYa6CipVf9l1iwprjANhY6dj+djsqujnm2+eLRyPnlvwXEhLC9OnTWbJkCS4uLowdO5bRo0drz5A9tugbcD4AQreizm9DF3eHMUmjWWdsSe1yJVjbpySOh+b/N/5gO3B6uJb+1KlTVK1aFScnJ8LCwujTpw8dOnTg3XffpUuXLvTo0YNx48blTnofQWG69s9LjzYqpCgQ0jZamEtipWh4ubmHVgD858QtlFJ5ciEl8SLMld+xcvZWDJ9tOKm9njegkRT+LIicW/JX3bp1+emnn5g0aRJfffUV06ZNY8aMGQwfPpw333wTd3f3x/sAF3fwexH8XkRnNBJ35QiXfr8BdxRnb8eyadVaekUsheClpuUr+JgKgzXbQ5XmYGNP/fr1tc0ppdDr9Zw7d05iJQtXr15l3bp1XL58maSkpHTzZs6c+UjblCagFsJgMBAYGCiDZIpsSawUHXUruFDKydTJRWxiCgcu5O7guyDxIsyX37ESn5TCyKWHSUwx9XI4qIUHHeuXz5fPFo9Pzi0Fp2rVqsyePZuLFy8yZswYFixYQPXq1RkxYgQXLjx+py0A6PU4efgze1BbXOxN9UmLblYnsNLLUP6/Z9NuHoXdX8PP3WF6NbhpqsmfNm0aJUqUoFy5chw5coQRI0YQGBiIUoopU6ZQqlQp/P39+fnnn3MnrRZsy5Yt1K1bl++//56vvvqKbdu2sWjRIn766SeCg4MfebvSBDQLhakaWCmFwWDAyspKmlKILEmsFC2T1p1g8Z6LADxRqzRLhjbP1e1LvAhz5Xes9Ju/l/3/3fSoW96ZtaNbYW8jtQSWQs4thUdUVBRz585l1qxZhIeH8+KLL/L+++/ToEGDXNn+9jN3GLLoAP89psu8AQ15qpqV1lyU0G2QGAPvXQRrW9NCAdM4dfQwS4+lMOytCbjX8uHAgQM0aNAAR0dHtm7dSt++fVm8eDE9e/bMlXSaozBd+wM0bdqUp59+msmTJ+Ps7MyRI0coV64c/fv356mnnmLEiBGPtN0iUQM4bdo0dDpdujbDCQkJjBo1itKlS1OiRAl69+7NrVu3Ci6RuUDuoglzSawUHaPb19Km910Ix2DM/Xt2Ei/CXPkVK8sPXNYKfwAz+/lK4c8CybmlcHB1deWDDz7g4sWLzJo1i+3bt+Pl5UXPnj05ePDgY2+/bZ2yvP90Pe31m78d4XSsPfj0hZ7z4K3TMGr//cIfwLGV1A//G9/ITQx5pinq6EpatGiBq6srNjY2dOnShWHDhrFixYrHTp8lO3XqFAMHDgTA2tqa+Ph4SpQowaeffsr06dMfebsWXwA8ePAg8+fPx8fHJ93748ePZ/369axcuZLt27dz/fp1evXqVUCpfHwGg4GgoCA5mYpsSawULWWc7fD4rzfQZINibXDuDgkh8SLMlV+xcicmkS//Pq29frVVdRpUdM3TzxS5T84thY+joyNvvPEG586d46effuLkyZM0bdqUJ598km3btj3WoO6vPVGDHn4VAbiXZOC1nwOJiPvveTWdDkpWub+wUvDsLGg9nmQXD86GGzkeaf9QrKT2eFmcOTk5ac/9ubu7Exoaqs0LCwvLbLVsWfSejY2NpX///vzwww+4ublp70dFRfHjjz8yc+ZMOnToQKNGjVi0aBF79uxh3759BZjiR2dtbU3z5s2xtpZ+e0TWJFaKnv7NqmrTuTHobloSL8Jc+RErRqPi7ZVHuBuXDEDrWmWY8Gz9bNYShZGcWwovW1tbhgwZwsmTJ/ntt98ICwujQ4cOtGzZkvXr12M0GnO8TZ1Ox7TePnhXMt2suRIez+jlh0kx3N9WbGwsixYtIjIqClXtCY6V782UvTq69HqZ6g3b888//3Dv3j0MBgNbtmxh3rx59O7dO9fybUk+/fRT4uLiaN68Obt27QLgmWee4a233uJ///sfr7zyCs2bP/ojIRZdABw1ahRdu3alU6dO6d4/dOgQycnJ6d6vV68eVatWZe/evZluLzExkejoaO0vJiYGQPsiGI3GDKcNBoNZ06l3VtJOp6SkPDStlMpwOi4ujuRk049iatv6B6eNRqNZ04UhT6lpTzsteXr8PCmliImJ0bZTFPJUFI9TTvI0sLkH+v8eoTl1I5rYhKRcy1NKSor2gyvHSfKUVZ6MRqP2O5RXeVq0+wLb/xtUuqyzHbP6+qDT6eQ4WWCeUlJSiI2N1bZdFPJU1I6TlZUVvXr1IjAwkL/++gsrKyu6deuGn58fv/zyi1bzZG6e7Kz1zBvQkNJOpqaeu8/d5X9/nkyX9mXLllGzZk2cnZ3p3r07zzzzDF9+/S1RUVFMmjSJChUq4Obmxvjx4/nyyy/p06dPvh+nwmDy5MnExcUxc+ZMmjVrpr3XsWNHVqxYQbVq1fjxxx8fefsWWwD89ddfOXz4MFOnTn1o3s2bN7G1taVkyZLp3i9fvjw3b97MdJtTp07F1dVV+/P09ATg8mVTN+xXrlzhypUrAFy4cEEbnDI0NFR7vvDMmTNaleypU6eIiIgA4Pjx40RFRQFw5MgRbYDOoKAg4uPjAQgMDCQpKQmD4X7PWUlJSdr00aNHCQoKAkx3UY4cOQKYajyPHzf1rBQREcGpU6cAU9XwmTNnALh165ZWbXz9+nWtF6iCzBNAfHy85CmX82QwGNi/fz93794tMnkqiscpJ3kypiThU9l0V1UBC3ecz7U8hYSEcOLECW7cuCHHSfKUZZ4SEhI4fvx4nuVpx5k7TPnTNB/gvbYVuBdxW46Thebp3LlzHD58GIPBUGTyVBSPU2hoKLdv3+app57ihx9+YO3atVSuXJmXX36ZunXrsmDBAg4fPmx2nso72/JGQ3tsrEx3LRftucQfh64SHx/PmTNn+Pfff7l48SI7duzg/PnzTJgwQUvDhg0b2LNnD9HR0WzevJlWrVrl+3FK3b8FLbVAWqNGDe0xNycnJ+bNm8fRo0f5448/8PDweOTtW2QvoFeuXKFx48b8+++/2k5p164dfn5+fP311yxbtowhQ4aQmJiYbr2mTZvSvn37TB+aTExMTLfOtWvX8PT05NKlS1StWlW7a6DX69NNGwwGdDpdttN6vR6dTpduOiUlReshK3UaTHcj0k5bW1trd09Sp1Pv3qSdTq0Bym46o3xIniRPkqfCm6dNx28yYulhACqVdGD7220sPk9F8ThJnh4tT7EJyTSbuo34ZNNd/heaVOHznl4WnaeieJwkT8UnT4GBgUyfPp0//vgDd3d33nzzTYYNG4aDg4NZeVpx6BofrTYVpmyt9ax4vTneFZ0L/XG6ePEi1atXL/BeQPV6Pbdu3aJs2bJ5sn2LLACuWbOGnj17phs0Mu1B/fvvv+nUqRMRERHpagE9PDwYN24c48ePN+tzClNXsEopYmNjKVGihHSnLLIksVI0GY2K+p9s0sZE2/N+eyqWfPwBsSVehLnyMlZ6fLeb4CuRAJR0sGH/Rx2xs5ZePy2ZnFuKhpCQEKZPn86SJUtwcXFh7NixjB49mlKlSmW77oQ1x/hln6kVXXkXO9aPbk05F/uHlitMsVJYrv31ej2urq7Z7o/w8PBH2r5FPpnbsWNHjh07lu69IUOGUK9ePd577z2qVKmCjY0NW7Zs0R4eDQkJ4fLly7Ro0aIgkvzYjEYjZ8+exdfXN13BV4gHSawUTXq9jta1SrPltOn5qG+2nGNab5/H3q7EizBXXsXKgh2hWuFPByx5tZkU/ooAObcUDXXr1uWnn35i0qRJfPnll0ydOpUZM2YwYsQIxo8fj7u7e6brfvJsA87cjOXAxXBuRScy/JdDLH+9+UPfb4mVjE2ePBlX17zpAdkiawAzkrYJKMCIESPYuHEjixcvxsXFhTfeeAOAPXv2mL3NwnIXQAghAI5ejaDbHNM5zNXBmiMTuxRwioR4POfvxNJp5nZtAOlR7WvyTpd6Wa8khCgwt2/f5uuvv+a7774jMTGRV155hXfeeYfq1atnuHxYbCLdvt3F9agEAPo1rsK03t4FXtOXmcJy7a/X67l58yblypXLm+3nyVYLgVmzZvHss8/Su3dv2rRpQ4UKFVi1alVBJ+uRKaWIjIykiJTXRR6SWCm6fCq74epgA0BUfArBlyMee5sSL8JcuR0rRqPihQX7tMJfnfIleLtz3VzZtih4cm4pmsqVK8fnn3/OpUuX+OSTT1i5ciW1a9dm4MCBnDx58qHly5SwY8HAxtjbmIocKwKvsGTfpXTLSKw8LK8LyEWmABgQEKDV/gHY29vz3XffER4eTlxcHKtWraJChQoFl8DHZDQauXTpkvaQqxCZkVgp2p7xvn8e+789Fx97exIvwly5HSvjfwvmdoyp4zVbKz2/vt680NYKiJyTc0vRVrJkST788EMuXbrEzJkz2bZtGw0aNKBnz54cPHgw3bJelVwZ18QZQ7xpeLXJ60+yN/SuNl9i5WF5XRguMgXAos7KykraRguzSKwUbWM61ib1GvnAxQiMxsf7kZB4EebKzVjZfPIma4Ova69n9vOllJPdY29XFB5ybikeHB0dGTNmDKGhofz444+cPHmSpk2b8uSTT7Jt2zatIPPnD9NJWTcRY1ICBqNi1LLDXAm/B0isZMRoNOZZ80+QAqDFMBqN3L17V+6OiGxJrBRt7q4OtK1j6hb6WmQ8By4+Wg9gqSRehLlyK1ai7iUxalmQ9vpprwo861PxcZMnChk5txQvtra2vPLKK5w8eZLffvuNsLAwOnToQMuWLVm/fj1Tp04l5s41rPf+gFKK8LgkXl9yiHtJKRIrBUAKgBZCKcWNGzekfbTIlsRK0dfTv5I2vfrwtcfalsSLMFduxcpLC/drw5mUdrLlmxf9cyN5opCRc0vxZGVlRZ8+fTh8+DAbN27EysqKbt268dJLL/HKK69wYd/f2J75F4BTN6J5e+UR9pwL49c959gbGobhMVu1CPMUmV5A80Jh6QlICCHSik8y0HjKv8QlGbCz1nNkYmfsbaTpjCj8Zm85w6x/zwKmIR/+GvsE9dxdCjZRQog8tXPnTj7//HM2bdqEq6srMTExVBkwFdwbPLSsu6s9E5/z5CmvzIeXyEvF5dpfagAthNFo5Pbt21I9LrIlsVL0OdhaaRfNiSnGx+oMRuJFmOtxY+X0zWi+/q/wB/Dmk3Wk8FeEyblFAAwcOJChQ4cSEhJC+fLliY+Px2g0cm35JxkufzMqgRG/HGbT8Rv5nNLixSIHgi+OlFLcvXuX0qVLF3RSRCEnsVI8DGjmwaFLpmEgVh66yrC2NR9pOxIvwlyPEyspBiMv/bCP1CZHDSq6MLpDrdxNoChU5NwiANq0aUP58uWxsbHB2toaGxsbwsMjWHk0LMPlFabWAZPXn+RJzwpY6aVn4LwgBUALYWVlRf369Qs6GcICSKwUD938KvLJuuPEJKRwMSyO8LgkSjnZ5ng7Ei/iQYmJiYwePZrNmzcTFhZGpUqVePfdd3nllVdwcnLC1dU13fIJCQk888wzrFu3LtNtfrftHOFxyQDYWetZOrSZDPlQxMm5RQAMHTr0off2ht5l9Q/7Ml1HATeiEjhwIZwWNeUGQl6QJqAWwmg0cuPGDWlKIbIlsVI8WOl1vNS0KgApRsWGo9ezWSNjEi/iQSkpKbi7u7N582aio6NZvHgxb731Fps2bcLGxobo6GhiY2OJjY0lPDyckiVL8sILL2S6vUOXIvhm6znAdGf/u5f8KemY85sVwrLIuUVk5nZMQq4uJ3JOCoAWQilFTEyM9KYlsiWxUnz0SNsbaNCj9QYq8SIe5OTkxKeffkrNmjXR6XQ0b96c9u3bs2vXrodiZc2aNRiNRnr16pXhtqITkhn7a5DWs9/YTrXp5FkhX/IhCpacW0Rmyjnb5+pyIuekAGghrKysqFOnjgySKbIlsVJ81Hd3oV4FZwCCLkdy8np0jrch8SKyk5CQwIEDB/Dz83soVn788Uf69++PvX3GF2oDf9zP1Yh4ABp7uDG6vTz3V1zIuUVkpmn1Uri72pNZI3Adpt5Am1YvlZ/JKlakAGghjEYjV69elaYUIlsSK8VLq1r3n4+YveVMjteXeBFZUUoxdOhQateuTY8ePdLFyqVLl9i8eXOGz/gAfLHpNMFXogBwsNHz9Qt+WFvJZUdxIecWkRkrvY6Jz3kCPFQITH098TlP6QAmD8mZ2IIkJiYWdBKEhZBYKT4Gt6yuTW8/c+eRmltJvIiMKKUYOXIkISEhrFmzBr1eny5WFi1ahL+/P76+vg+te+luHAt3XtBej3+yLpXdHPMl3aLwkHOLyMxTXu58P6AhFVzTtx6o4GrP9wMaFtg4gMWF9AJqIfR6PTVrPlo376J4kVgpXqqUcqSCix03oxNJSDay9fRtOtYvb/b6Ei8iI0opRo0axf79+9myZYvW82dqrBiNRhYtWsQHH3zw0LrJBiNjfw0myWCq+Wns4cbrbWrkX+JFoSDnFpGdp7zcedKzAgcuhHM7JoFyzqZmn1Lzl/ekBtBCGI1GLl26JE0pRLYkVoqf5xtX0abnBpzL0boSLyIjo0ePZvfu3fz777+4ubkB6WPl33//JSwsjBdffPGhdb/ZcpbgK5EAeJR2ZPErTfMz6aKQkHOLMIeVXkez6m74uaXQrLqbFP7yiRQAhRDCwg1rU0N7biL4ciRJKXLBJR7dpUuXmDt3LiEhIXh4eFCiRAlKlCjBiBEjtGV+/PFHnn/++YfGBFwXfI1v/xvywVqvY/YL/pSwk8ZGQghRmMhZ2ULo9Xo8PDwKOhnCAkisFD/O9jbULl+CM7diMShYtv8Sg1tVz35FJF7Ewzw8PLJ9lvS333576L070Ym8+dsR7fWbnevgV6VkbidPWAg5twhzSazkP6kBtBBGo5HQ0FBpSiGyJbFSPA1JU+D7ee8ls9eTeBHmyipWlFL0mb+HlP/G+6tU0oFhbeT5r+JMzi3CXBIr+U8KgBbEzs6uoJMgLITESvHTp1FlrP97duJ8WBwR95LMXlfiRZgrs1j5bMNJLt69B4CVTsevrzeTZ3mEnFuE2SRW8pcUAC2EXq+ncuXK6PVyyETWJFaKJ2srPY2ruWmv524zrzMYiRdhrsxi5eDFcH7afVF7PbFbfaqUcsrn1InCRs4twlwSK/lP9rSFMBgMnDlzBoPBUNBJEYWcxErxNbp9bW16TdB1s9aReBHmyihWEpMNDFl0UHvdvEYpBrYw7/lTUbTJuUWYS2Il/0kB0ELodDqcnZ3R6aRJjciaxErx1apWaRxtrQC4E5vIudsx2a4j8SLMlVGsDFl8kNjEFABK2Fnx46DGBZU8UcjIuUWYS2Il/0kB0ELo9Xrc3d2lelxkS2Kl+NLpdHRKMwj87M1ns11H4kWY68FYWbb/MntC72rzfxzUBCc7m4JKnihk5NwizCWxkv9kT1sIg8HAqVOnpHpcZEtipXgb07GWNr3l9O1su/OXeBHmShsrVyPu8fHa49q8QS08aFajdAGmThQ2cm4R5pJYyX9SALQQOp2O0qVLS/W4yJbESvFWq5wzZUuYelO7l2Rg97mwLJeXeBHmSo0VgBfm78Pw35APVUs5MvG5BgWZNFEIyblFmEtiJf/JQPAWQq/XU65cuYJOhrAAEiuih39Ffth5AYBlBy7TunbZTJeVeBFZuRYZT0RcEgaj4sT1aMLjEtl+5hxXI+MBsNLBr8OaoZchH8QD5NwizCWxkv+kAGghUqvH69evj5WVVUEnRxRiEitiZPtaLNp9kRSj4sCFcFIMRqytMm7wIfEiMnMtMp4OXwaQmJL54Mw6nQ6lpPAnHibnFmEuiZX8J01ALYROp8Pd3V2qx0W2JFaEm6MtnRuYOoMJi01iZxbNQCVeRGYi4pKyLPwBpBgVEXFJ+ZQiYUnk3CLMJbGS/6QAaCH0ej2lS5eWHpJEtiRWBEAPv0ra9OrD1zJdTuJFZCb1Gb/cWk4UL3JuEeaSWMl/sqcthMFg4MiRI9JDksiWxIoAaFe3HK4Oplb+fx67zpezZtO4cWPs7Ozo0aOHtpzBYGD58uW0atUKFxcXatSowc8//1xAqRYF5V5SCseuRrE66Cpf/h1Cuxnb6P39brPWPXE9Oo9TJyyR/BYJc0ms5D95BtBC6PV6PDw85O6IyJbEigCwtdZT392FfefDMRjhZLwrEyZMYPPmzVy9elVbLjo6mrFjxzJ58mRef/11AgMD6dy5MzVq1KB169YFmAOR25RSXIuMZ9fZMA5fjiDkZgzXIuOJvJdMymPU4oXHJeZiKkVRIb9FwlwSK/lPCoAWQqfTUbJkyYJOhrAAEisi1dAnarDvfDgAF2w96NGjPcHBwekKgHv37sXe3p4RI0YA0KxZM3r16sXChQulAGihjEbFyRvR7D4XxpGrkZy7HcvNqARiE1PIi9aapZzscn+jwuLJb5Ewl8RK/pMCoIVIrR739fWVHpJEliRWRKqO9cpRpoQtYbFJXLx7j5tRCQ8tk5ycTFJSEgaDQYsXo9HIsWPH8ju5IocSUwxcCIvj3O1YNp+8TdCVCO7EJHIvKWfNqKz0OjxKO9Koqhu1ypWgVrkS1Cxbgoh7SfScuyfb9RtUdHnULIgiTH6LhLkkVvKfFAAthF6vp3bt2lI9LrIlsSJS6XQ6+jfzYPaWsygFa4Mf7gymVatWJCYmMnfuXIYPH86BAwdYvXq1jMlUiETFJxN6J5aQmzGsPnyVy+HxpBiNhMcl5ahGz95aT1lnO6qXdcKroitNq5fCs6ILZUvYZdj7Xuy1FLO2ayVjAIoMyG+RMJfESv6TAqCF0Ol0ODs7F3QyhAWQWBFp9fSvxOwtZwFYHXSN5g/ML1OmDBs2bOCdd95h0qRJeHp6MmTIEPbt25f/iS3GlFJcDLvHrnNhBF2O4MztGK5HJKDXm4byyIkSdta4u9pTq1wJ/KqUpEm1UtQuXwJne5scbcfNyRY7a32WQ0HYWetxc7LN0XZF8SC/RcJclhwrU6dOZdWqVZw+fRoHBwdatmzJ9OnTqVu3bkEnLUtSALQQKSkpBAUF4e/vj7W1HDaROYkVkVa1Mk74Vy1J0OVITt+MoWpy+lqdlJQUbG1t2bFjhxYv/fr1o23btgWR3CIvxWDk6NUo9oaGceRaFOfvxHIrKpHYxBRy8nielV5HfXdnapU1NdmsXtqJ2uVLUK1MCWytc+cueqWSDmx9ux0RcUkYjIqjVyI4GXoJz5oe+FRxw0qvw83JlkolHXLl80TRIr9FwlyWHCvbt29n1KhRNGnShJSUFD788EM6d+7MyZMncXJyKujkZcqy9nIxZmVlRYMGDaRttMiWxIp4UKuapTl8KQKdTsfhKEcqGo0kJCSg1+uxsbEhOTmZlJQUkpOT+eWXXwgICCAoKKigk23R7iWlcP5OHP+cuMnhy5FcuhvHndhEEpKzHlj9QS721tRzd9Gey/Mo7Uh9dxfcXezR50PTy0olHbQCnk9lV+L9yuPg4CADNotsyW+RMJclx8qmTZvSvV68eDHlypXj0KFDtGnTpoBSlT0pAFoInU6Ho6NjQSdDWACJFfGgQS2r883fx9DZOnL1np5D69fj4OBA27ZtCQgI4IcffmD16tWkpKTQsmVLtm7dSsWKFQs62Rbhbmwix65G8e+pW5y+EQM6uBmVwLXI+Bxtx8HWinLOdtQo44RPZVeaVS9NfXeXQtW8Us4tIickXoS5CmOsxMTEEB19f4xTOzs77Oyy7/E4KioKgFKlSuVZ2nKDFAAtREpKCoGBgTRu3NjiqsdF/pJYEQ8q62xHtQpluBx+D/uq3qw6dJWeDSsBpngZNmwYP/zwg8RLJoxGxbnbsewODSP4ciSXw+9hbaXj3O1YIu4lm70dHeBsb03Fkg7ULl8Cv8olaVK9FLXLOeNgW/jvfMu5ReSExIswV2GMFU9Pz3SvJ06cyKRJk7Jcx2g0Mm7cOFq1aoWXl1cepu7x6ZRSeTAqUNFw9epVqlSpwpUrV6hcuXKBpkUpRVJSEra2ttL0RmRJYkVkZP72UKb+dRowddv/55gnAImXtBJTDARfjmTv+bscvxbF+Ttx3IpJIC4xZ8MqONtba002K7jYU620I4083KhSyhFrK8vt5U5iReSExIswV2GKldRr/5MnT1KpUiXtfXNqAEeMGMFff/3Frl27CrzckJ3CUcwWZrHEttGiYEisiAcNbOHB9E2nMSo4eT2ae4kpONqZfgKKW7xEJySzNzSMAxciOHkjist37xEWm5Rlb5cZqeBiT81yTtQqW4ISdtb4VSmJb5WSlHXOeFiFoqC4xYp4PBIvwlyFLVacnZ1xcTF/jNPRo0ezYcMGduzYUegLfyAFQIthMBgKXfW4KJwkVkRGHGyt8a7kypGrUSjgp90XGN2hdpGNF6UU1yPj2X3uLocuRaDTweXwe5y7HcvtmMQcbcvJ1oryrvbULFsizfN5zjkeVsHSFdVYEXlD4kWYy5JjRSnFG2+8werVqwkICKB69eoFnSSzSBPQLBS2JqAGgwErK6sie2dZ5A6JFZGZP49eZ9QyU++eldwc2P1eB4uPlxSDkZCbMewODePS3XskJBs5dyeW87djiUk0byBzAL0OXBxsqFjSgTrlStCwqhtNq5eiRtncG1bB0ll6rIj8JfEizFWYYiWn1/4jR45k2bJlrF27Nt3Yf66urjg4FN4hciyrmF3MpX45hMiOxIrIyNNe7thZHyExxci1iHhuRMVTwcXeIuIlPsnA4csR7Et9Pi8sjjsxidxLytnzeaWcbKlVtoTWGUsjDzcaeZSkUknHfBlWwdJZQqyIwkPiRZjLUmPl+++/B6Bdu3bp3l+0aBGDBw/O/wSZSQqAFsJgMBAUFGSR1eMif0msiMzo9Tpa1SzN1pA7AHyz5SyfdfMsVPESFpvIvvN3OXghglM3o7l89x7hcUkkGXL2fF5lNwdqlHVCKfCq5EqLmqXxquhKqUI0rIKlkXOLyAmJF2EuS44VS21IabFNQKdOncqqVas4ffo0Dg4OtGzZkunTp6erfk1ISOCtt97i119/JTExkS5dujB37lzKly9v1mcUpiagQgiRG4IvR9Bj7h4AXB1sODKxc76nwWhUXLwbx4EL4cQmphB6J5Zzt2M5ezuWyBwOq1DCzpoKrvbUKmd6Pq91rTLUspBhFYQQQhQuxeXa37KK2Wls376dUaNG0aRJE1JSUvjwww/p3LkzJ0+exMnJCYDx48fz559/snLlSlxdXRk9ejS9evVi9+7dBZz6nFNKER8fj4ODQ4G3jxaFm8SKyIpfVTdcHayJik8hKj6ZI1ciqF3aLk/iJSnFyMkbUYTcjOFWdKJW0Au5GUOK0fx7j3odlHS0pVJJB+pWcNaez6tW2rKHVbA0cm4ROSHxIswlsZL/LLYG8EF37tyhXLlybN++nTZt2hAVFUXZsmVZtmwZzz//PACnT5+mfv367N27l+bNm2e7zcJ0FyAlJYWgoCD8/f0trnpc5C+JFZGdN5YdZv3RGwDUKV+CRmUUnjU98KnihpVeh5uTqbBlruj4JA5diuDAxQiOX4viYlgcd2ITSUjOWbPN8i52ONhYUd7FHs+KLjTxcKORhxvlXOzloqAQkHOLyAmJF2GuwhQrhenaPy8VmW9kVFQUAKVKlQLg0KFDJCcn06lTJ22ZevXqUbVq1UwLgImJiSQm3u8ePCYmBgCj0Zjuv16vTzdtMBjQ6XTZTuv1enQ6XbrplJQUrdej1GlI/zCswWDA2tqaxo0bYzCYOjxQSmE0GrGysko3bTQaUUplO51RPgoiT6k9P6VOS54eP0/W1tY0bNhQu2AuCnkqisepoPJ0JTyOv0/c0s5zZ27FcuYWcOKk9p6dtZ7Nb7ahSiknLR86nY4bkffYfyGCQ5cjOHU9misR8UTcSyLZkLPaPHdXe2ys9FQvU4I2tUrhW7UUNcs54WSjl+NUyPOU+juUeu+4KOSpKB6nwpAnnU5Ho0aNilSeiuJxKgx50ul0NGnSBKPRqK1bkHkqDopE2xmj0ci4ceNo1aoVXl5eANy8eRNbW1tKliyZbtny5ctz8+bNDLczdepUXF1dtT9PT08ALl++DMCVK1e4cuUKABcuXOD69esAhIaGcuuW6YLqzJkzhIWFAXDq1CkiIiIAOH78uFZIPXLkCLGxsQAEBQURHx8PQGBgIElJSdp4KAaDgaSkJAIDA1FKERYWRlCQqQv32NhYjhw5ApgKv8ePHwcgIiKCU6dOARAWFsaZM2cAuHXrFqGhoQBcv36dCxcuFHieAOLj4yVPuZwnpRRHjhwhPDy8yOSpKB6ngsrTpZt3s+1QJTHFyLIdJ5i3PZSR/7eXjjO2UvPDjbScHsD4347wy77LHLocye2YxEwLfzrAyUZH7bKO9PCryNstSrL69Uac+uwpvu7owsZRzVg0pAn1be7QoIIjTjZ6OU6FPE+JiYlERUUVqTwVxeNUmPJ08eJFlFJFKk9F8TgVhjzFxMQQHh5e4HlK3b9FXZFoAjpixAj++usvdu3apVXXLlu2jCFDhqSr0QNo2rQp7du3Z/r06Q9t58EawGvXruHp6cmlS5eoWrVqgd490el0BAcH4+3tja2tbbG4IyR5erQ8KaUIDg7Gx8cHGxubIpGnonicCipPwZfD6TF370Pnv0dlrddR0tGGym4O1Kvggn8VV5rVKEXVUiVQyijHyQLyFBcXR//+/Tl58iQODg6UK1eO77//nmrVqqXL07Fjx3j11VeJi4vDxsaGJk2a8M033+Ds7IxSCr1ej5eXl5bfb7/9ltatW8txKqZ5SkpK4tixY/j5+QEUiTwVxeNUGPKUnJzM8ePH8fb2Rq/XF2ieLl68SPXq1Yt8E1CLLwCOHj2atWvXsmPHDqpXr669v3XrVjp27EhERES6WkAPDw/GjRvH+PHjs912cWkHLIQoPpbtv8yHq4/leD07az1lStjhUdqRBhVdaFKtFI083Chdwi4PUinyU0JCAlu3buXpp59Gp9MxZ84cfv/9dwICAtItd/bsWeLj4/Hx8cFgMPDSSy9Rv359Jk2aBJgu8B/8zRVCCEtSXK79LfYZQKUUb7zxBqtXryYgICBd4Q+gUaNG2NjYsGXLFnr37g1ASEgIly9fpkWLFgWR5MeilCIqKgpXV1d0OukMQWROYkVkJTwuMfuFgCbV3BjQ3IOaZUtQs2wJGVahCLO3t+eZZ57RXjdv3pwvv/zyoeVq1apFVFSUdie+SZMmxaa5lMg5+S0S5pJYyX8W+wzgqFGj+OWXX1i2bBnOzs7cvHmTmzdvam2RXV1defXVV3nzzTfZtm0bhw4dYsiQIbRo0cKsHkALG6PRyKVLl7QqbiEyI7EislLKybwau57+lenuVwmvSq5S+CtmZs+eTffu3R96P+25JS4ujoULFz60XMeOHfH19eXNN98kLi4uv5IsCiH5LRLmkljJfxbbBDSzOwSLFi1i8ODBwP2B4JcvX55uIPgKFSqY9RnFpRpYCFF8HLkSSffvsh8Lde2oVvhWKZn3CRKFyueff8769evZsmULjo6OGS6TlJREr169qFGjBt988432/uXLl6latSpxcXEMHz4cZ2dn5s6dm19JF0KIx1Zcrv0tugloduzt7fnuu+/47rvv8iFFectoNBIREYGbmxt6vcVW3Ip8ILEismKlN695jbnLiaLjyy+/ZNWqVWzevDnDwp/RaOT27dsMHz4cd3d3Zs+enW5+1apVAXBycmLkyJG8/vrr+ZJuUTjJb5Ewl8RK/pO9bCGUUty4ccOsgq8o3iRWRFbcnGyxs8761G9nrcfNyTafUiQKg5kzZ7J8+XL+/fffTDtxSU5O5uWXX6ZUqVIsWLAgXUuciIgI7t27B5gu5lasWIG/v39+JF0UUvJbJMwlsZL/LLYJaH4oLtXAQoji5VpkPBFxSRiMihPXowmPS6SUkx0NKrpgpdfh5mRLpZIOBZ1MkU9Sf+tq1KiBs7MzAHZ2duzfv59PPvmEihUrMnz4cJYuXcqAAQPw8fHRCn+tWrXiu+++Y+/evQwbNkzrDr5hw4bMnj2bUqVKFWTWhBAiR4rLtb8UALNQmILAaDQSFhZGmTJlpHpcZEliReSExIswl8SKyAmJF2GuwhQrhenaPy/JN9JCKKW4e/euVI+LbEmsiJyQeBHmklgROSHxIswlsZL/pAYwC8XlLoAQQgghhBDFXXG59pcaQAthNBq5ceOGjJEisiWxInJC4kWYS2JF5ITEizCXxEr+kwKghVBKERMTI9XjIlsSKyInJF6EuSRWRE5IvAhzSazkP2kCmoXiUg0shBBCCCFEcVdcrv2lBtBCGI1Grl69KtXjIlsSKyInJF6EuSRWRE5IvAhzSazkPykAWpDExMSCToKwEBIrIickXoS5JFZETki8CHNJrOQv64JOgDCPXq+nZs2aBZ0MYQEkVkROSLwIc0msiJyQeBHmkljJf1IDaCGMRiOXLl2S6nGRLYkVkRMSL8JcEisiJyRehLkkVvKfFACFEEIIIYQQopiQJqAWQq/X4+HhUdDJEBZAYkXkhMSLMJfEisgJiRdhLomV/CcFwCykVkXfuHGjgFNiSsvly5epWrUqer1U3IrMSayInJB4EeaSWBE5IfEizFWYYiX1mr+oN0eVAmAWbt26BUDTpk0LOCVCCCGEEEKI/HDr1i2qVq1a0MnIMzIQfBZSUlIICgqifPnyBX5HIiYmBk9PT06ePImzs3OBpkUUbhIr4pNPPuHff//l6tWrbNq0iQYNGjy0zN69e3n55ZepVq0aISEh1K1bl/Xr1+Pg4IDRaOSzzz5j+/btWFlZ4ebmxvTp06levXoB5EYUFnJuETkh8SLMVZhixWg0cuvWLfz9/bG2Lrr1ZFIAtBDR0dG4uroSFRWFi4tLQSdHFGISK2LHjh3UqFGD1q1bs2bNGvz8/B5aJiAggHHjxrFjx46H4mXNmjVMnTqVXbt2YWNjw5QpUzh69Ci//fZbPudEFCZybhE5IfEizCWxkv+KbtFWCCGKqTZt2jzW+jqdjsTERBISErC2tiY6OprKlSvnUuqEEEIIUZDkqVwhhCimQkNDeeKJJwD44YcftPefe+452rVrR4UKFXB3d2fLli18+umnBZVMIYQQQuQiKQBaCDs7OyZOnIidnV1BJ0UUchIrwhwNGzbk6tWrHDhwgPHjx7N48WKtiWdgYCDHjx/n2rVrXL9+nY4dOzJ8+PACTrEoaHJuETkh8SLMJbGS/+QZQCGEKKKqVauW6TOAD5o6dSrXr1/n22+/ZfTo0VSsWJEPP/wQgBMnTtC5c2euXbuWxykWQgghRF6TGkAhhCiGbty4oY1zFBMTw4YNG/D39wegRo0abN26laSkJAA2bNiAl5dXgaVVCCGEELlHCoBCCFHEDBs2jMqVK3P16lW6dOlCrVq1ABg6dCjr1q0D4I8//sDb2xtfX1+aN2/Ok08+yZAhQwAYNWoU1atXx9fXFx8fH7Zs2cL3339fYPkRQgghRO6RJqBCCCGEEEIIUUxIDaAQQgghhBBCFBNSAMwnU6dOpUmTJjg7O1OuXDl69OhBSEhIumUSEhIYNWoUpUuXpkSJEvTu3Ztbt25p848cOcKLL75IlSpVcHBwoH79+syePfuhzwoICKBhw4bY2dlRq1YtFi9enNfZE7ksv+Llxo0bvPTSS9SpUwe9Xs+4cePyI3siF+VXrKxatYonn3ySsmXL4uLiQosWLfj777/zJY8i9+RXvOzatYtWrVpRunRpHBwcqFevHrNmzcqXPIrckZ/XLal2796NtbW1WR1XicIlv+IlICAAnU730N/NmzfzJZ9FhRQA88n27dsZNWoU+/bt499//yU5OZnOnTsTFxenLTN+/HjWr1/PypUr2b59O9evX6dXr17a/EOHDlGuXDl++eUXTpw4wUcffcQHH3zAnDlztGUuXLhA165dad++PcHBwYwbN46hQ4fKhZqFya94SUxMpGzZskyYMAFfX998zaPIHfkVKzt27ODJJ59k48aNHDp0iPbt2/Pcc88RFBSUr/kVjye/4sXJyYnRo0ezY8cOTp06xYQJE5gwYQILFizI1/yKR5dfsZIqMjKSgQMH0rFjx3zJn8hd+R0vISEh3LhxQ/srV65cvuSzyFCiQNy+fVsBavv27UoppSIjI5WNjY1auXKltsypU6cUoPbu3ZvpdkaOHKnat2+vvX733XdVgwYN0i3Tr18/1aVLl1zOgchPeRUvabVt21aNHTs2V9Mt8l9+xEoqT09PNXny5NxJuCgQ+RkvPXv2VAMGDMidhIt8l9ex0q9fPzVhwgQ1ceJE5evrm+vpF/krr+Jl27ZtClARERF5lvbiQGoAC0hUVBQApUqVAkx3PZKTk+nUqZO2TL169ahatSp79+7Ncjup2wDYu3dvum0AdOnSJcttiMIvr+JFFD35FStGo5GYmBiJJwuXX/ESFBTEnj17aNu2bS6lXOS3vIyVRYsWcf78eSZOnJgHKRcFIa/PLX5+fri7u/Pkk0+ye/fuXE590Wdd0AkojoxGI+PGjaNVq1ba2Fo3b97E1taWkiVLplu2fPnymbZr3rNnDytWrODPP//U3rt58ybly5d/aBvR0dHEx8fj4OCQu5kReS4v40UULfkZK19++SWxsbH07ds319Iv8ld+xEvlypW5c+cOKSkpTJo0iaFDh+Z6PkTey8tYOXv2LO+//z47d+7E2louS4uCvIwXd3d35s2bR+PGjUlMTGThwoW0a9eO/fv307BhwzzLU1Ej37QCMGrUKI4fP86uXbseeRvHjx+ne/fuTJw4kc6dO+di6kRhI/EizJVfsbJs2TImT57M2rVr5bkLC5Yf8bJz505iY2PZt28f77//PrVq1eLFF198nGSLApBXsWIwGHjppZeYPHkyderUya3kigKWl+eWunXrUrduXe11y5YtCQ0NZdasWSxZsuSx0l2cSAEwn40ePZoNGzawY8cOKleurL1foUIFkpKSiIyMTHd35NatW1SoUCHdNk6ePEnHjh15/fXXmTBhQrp5FSpUSNejUuo2XFxcpPbPAuV1vIiiI79i5ddff2Xo0KGsXLnyoebmwnLkV7xUr14dAG9vb27dusWkSZOkAGhh8jJWYmJiCAwMJCgoiNGjRwOm2iOlFNbW1vzzzz906NAhbzMoclVBXLc0bdr0sQqbxVJBP4RYXBiNRjVq1ChVsWJFdebMmYfmpz4c+/vvv2vvnT59+qGHY48fP67KlSun3nnnnQw/591331VeXl7p3nvxxRelExgLk1/xkpZ0AmOZ8jNWli1bpuzt7dWaNWtyNxMi3xTEuSXV5MmTlYeHx2OlX+Sf/IgVg8Ggjh07lu5vxIgRqm7duurYsWMqNjY2bzIncl1Bnls6deqkevbs+XgZKGakAJhPRowYoVxdXVVAQIC6ceOG9nfv3j1tmeHDh6uqVauqrVu3qsDAQNWiRQvVokULbf6xY8dU2bJl1YABA9Jt4/bt29oy58+fV46Ojuqdd95Rp06dUt99952ysrJSmzZtytf8iseTX/GilFJBQUEqKChINWrUSL300ksqKChInThxIt/yKh5PfsXK0qVLlbW1tfruu+/SLRMZGZmv+RWPJ7/iZc6cOWrdunXqzJkz6syZM2rhwoXK2dlZffTRR/maX/Ho8vN3KC3pBdQy5Ve8zJo1S61Zs0adPXtWHTt2TI0dO1bp9Xq1efPmfM2vpZMCYD4BMvxbtGiRtkx8fLwaOXKkcnNzU46Ojqpnz57qxo0b2vyJEydmuI0H76hu27ZN+fn5KVtbW1WjRo10nyEsQ37GiznLiMIrv2Klbdu2GS4zaNCg/MuseGz5FS/ffPONatCggXJ0dFQuLi7K399fzZ07VxkMhnzMrXgc+fk7lJYUAC1TfsXL9OnTVc2aNZW9vb0qVaqUateundq6dWs+5rRo0CmlVE6bjQohhBBCCCGEsDwyDqAQQgghhBBCFBNSABRCCCGEEEKIYkIKgEIIIYQQQghRTEgBUAghhBBCCCGKCSkACiGEEEIIIUQxIQVAIYQQQgghhCgmpAAohBBCCCGEEMWEFACFEEIIIYQQopiQAqAQQggBKKXo1KkTtWvX5ujRo3Tq1IkLFy4UdLKEEEKIXCUFQCGEEMXG3r17sbKyomvXrg/Nu3jxIlZWVsyZM4eXX36ZkiVLUr169QJIpRBCCJF3dEopVdCJEEIIIfLD0KFDKVGiBD/++CMhISFUrFixoJMkhBBC5CupARRCCFEsxMbGsmLFCkaMGEHXrl1ZvHixNi8gIACdTseWLVto3Lgxjo6OtGzZkpCQkHTb+P7776lZsya2trbUrVuXJUuW5HMuhBBCiMcjBUAhhBDFwm+//Ua9evWoW7cuAwYM4KeffuLBRjAfffQRX331FYGBgVhbW/PKK69o81avXs3YsWN56623OH78OMOGDWPIkCFs27Ytv7MihBBCPDJpAiqEEKJYaNWqFX379mXs2LGkpKTg7u7OypUradeuHQEBAbRv357NmzfTsWNHADZu3EjXrl2Jj4/H3t6eVq1a0aBBAxYsWKBts2/fvsTFxfHnn38WVLaEEEKIHJEaQCGEEEVeSEgIBw4c4MUXXwTA2tqafv368eOPP6ZbzsfHR5t2d3cH4Pbt2wCcOnWKVq1apVu+VatWnDp1Ki+TLoQQQuQq64JOgBBCCJHXfvzxR1JSUtJ1+qKUws7Ojjlz5mjv2djYaNM6nQ4Ao9GYfwkVQggh8pjUAAohhCjSUlJS+Pnnn/nqq68IDg7W/o4cOULFihVZvny5WdupX78+u3fvTvfe7t278fT0zItkCyGEEHlCagCFEEIUaRs2bCAiIoJXX30VV1fXdPN69+7Njz/+yIwZM7LdzjvvvEPfvn3x9/enU6dOrF+/nlWrVrF58+a8SroQQgiR66QGUAghRJH2448/0qlTp4cKf2AqAAYGBnL06NFst9OjRw9mz57Nl19+SYMGDZg/fz6LFi2iXbt2eZBqIYQQIm9IL6BCCCGEEEIIUUxIDaAQQgghhBBCFBNSABRCCCGEEEKIYkIKgEIIIYQQQghRTEgBUAghhBBCCCGKCSkACiGEEEIIIUQxIQVAIYQQQgghhCgmpAAohBBCCCGEEMWEFAAt1PHjx/n1118LOhlCCJErIiMjmTlzJkajsaCTIoQQQhRpOSoAVqtWjbp16+Ln54efnx9Dhw7Nk0RFRkYybdq0XNnW4MGDqVSpkpZmPz8/Fi1aBEC7du1Ys2ZNttswd7m8sG7dOsaPH5/uvaNHj/LZZ5/x1FNPFUiazDF06FC2bdtW0MlIpyCPY264du0aL7zwAjVq1KB27dq0bduWffv2afMnTZrEuHHjCi6BmVi8eDGnT59+5PX9/PyIiYl5rDS8/fbbTJo0Kcfr6XQ6vL298fX1xdvbm5UrV+Z4Gxl9h1MdP36catWq5XibWa03adIkypYti7+/P3Xq1KFJkybMnj0bg8GQ48/JT8OGDcPT0xO9/v7P0qRJk0hISNBef/LJJyxdujTf0vTMM89ovxupseDn58cTTzwBwLx58/Dx8cHPz4969erRv39/bd0Hfy/9/Pw4duxYtp/Zrl07qlevjp+fH7Vq1eLJJ5/kzz//NCu9AQEBbNq06dEym4GLFy9SsmTJXNtecHDwI9+4NPfYL168mB49egAQGBhIv379Hunz0mrcuDEBAQE5Wmfr1q00bdoUT09PGjRowLvvvpvu5saGDRuoV68etWvXplevXkRHRwNw7Ngx2rRpQ7169fDy8uKVV14hPj5eWy9tHPr5+bFz587Hzp8QohhSOeDh4aGCgoJyssojuXDhgnJ1dc2VbQ0aNEjNmjUrw3lt27ZVq1evznYb5i6XleTk5MdavyBYYpqzkhvHsaDExsaqWrVqqWnTpmnvbd68WZUpU0YdO3ZMKaXUxIkT1dixY/Pk8x8nFh51v+dm/L311ltq4sSJOV4PUBEREUoppQ4ePKgcHBzUnTt3zF4/uzwcO3ZMeXh45DhdWa33YByEhoaqFi1aqDfeeCPHn5OXDAaDMhgMSimlLl26pNauXfvQMmn3f0F7MC0HDx5U1atXV3fv3lVKKWU0GtWhQ4e0+Y/6e/ng92Xbtm2qfPny6o8//sh23dw+B+Tmb7FSSi1atEh1794917aXX5/RqFEjtW3bthytc/jwYRUaGqqUUio+Pl61atVKLVq0SCmlVExMjCpXrpw6deqUUkqpUaNGqbffflsppdSZM2fUkSNHlFJKpaSkqL59+6Y7dxWm74QQwnI9dhPQgIAA/Pz8tNcP3pn++++/ad26NY0aNaJp06ZarVBAQABeXl6MHDkSX19fGjRoQGBgIADDhw8nJiYGPz8/GjduDMDNmzfp27cvTZs2xdvbmwkTJjxu0tNZtmwZzZo1w9/fH19fX9avX59u/pYtW2jSpAm1atXirbfeQikFwLlz5+jUqZN2FzhtDZNOp2PixIk0adKEDz74gGPHjtG6dWsaNmyIp6cnU6ZM0ZZNSkrinXfewcvLC19fX612L+3dTIAZM2bQoEEDvL296d+/P1FRUYDpTnm/fv147rnn8PT0pEOHDoSHh2eY12rVqvHOO+/QqFEjatWqxYwZM9LNe++992jatCmDBg0iNjaWV155BS8vL7y8vJg8ebK2bLt27XjjjTcy3C9pa9uy27dpLVq0CD8/P3x9fWncuDEXL14EYMmSJfj4+ODj40PXrl25du2atn86derEiy++iLe3N40bN+b8+fOZbj+z4zhz5kyaNGmCn58fTZo0Ye/evQAYjUZGjx5N/fr18fX1pVGjRlqNRGax/aCsYrdatWp88skntGjRgurVq6eLibSWL1+Om5sb7733nvZex44dGTJkCF988YX23pUrV+jQoQP16tXjueee4+7duwCsX79ei1EvLy/Wrl1rVtrSxsKTTz7J77//rs0PCAjA398fyPwYL1y4kMDAQMaPH4+fnx8bN27EYDBose7l5cUbb7xBUlISYKqxf+WVV2jTpg1eXl6A6XsUGRkJmO7ot2zZEh8fH5o2bcru3bsz3F83btygS5cueHp60qlTJ65evarNS05O5v3336dp06b4+fnRt29fIiIiMtxOWo0bN6ZEiRJcvHiRt99+W4uXNm3aEBISoi334Pf+we/wpEmTqF27No0aNUpXG5KSkkKXLl1o3LgxDRo04KWXXiIuLi7b9bJTo0YNfvrpJ77//nvtfJHdeXnEiBH4+Pjg7e3N0aNHGTx4MN7e3jRr1kz77mV1HKOiohg6dKh2PnvllVe0PPTu3ZsuXbrg5eXFjRs3ePvtt+nduzeffPJJun05fPhwAJ544gn8/Py4ffs2gwcP5uuvvwYyP2dmdZ7NTVevXsXZ2RlnZ2fAdNwbNmyY65/Trl07Jk2axNSpU4HM8xccHMy8efNYunQpfn5+fPrpp0Dm5859+/bRqFEj7Zzw/fffZ5qGiRMnar8XGzdu1N7v378/jRs31rZ98+ZNAO7cuUPnzp3x9vbGx8eHIUOGcPv2bT755BO2bduGn5+fdnwz28aD0h77B1s7zJkzh8GDBz+0zoPXJ5ntiwft2bNH2y9DhgwhJSVFm2futYi/vz81atQAwN7eHj8/P+337K+//sLf35969eoBMHLkSJYvXw5A7dq18fHxAcDKyoomTZpo6wkhRK7JSWnRw8ND1alTR/n6+ipfX1+1atUqtW3bNuXr66stk/bOdGhoqGrevLmKiopSSil19uxZVaFCBZWQkKC2bdumrKys1L59+5RSSn3//feqc+fOSqmM7zp27txZBQQEKKVMd9W7dOmifvvtN6WUUh9//LH6/vvvM0zzoEGDVMWKFbU0+/r6qh07diil0t9pDQsLU0ajUfv88uXLq4SEBG25Dh06qKSkJBUXF6caNWqkli5dqpRSqmnTpmrevHlKKdOdu1KlSqmLFy8qZSpZqMmTJ2tpiY6O1rZ579495efnp/bu3auUUmrSpEmqW7du2vzbt28rpdLfzdy4caOqV6+edvfvtddeU8OHD1dKme78enh4qLCwMKWUUv369VOff/55hvvEw8NDvfzyy8poNKo7d+6oKlWqqN27d2vzXn31VW1fvPvuu+qll15SBoNBxcbGKj8/P/Xrr79mu1/M3bdpbdu2TVWrVk1dv35dKaVUXFyciouLU8eOHVPly5dXV69eVUopNWXKFPXUU09p+8fFxUWdP39eKaXUe++9p15//fUM851VelP3t1JK7d27V9WtW1cpZbqLW69ePa2mIjIyUhkMhixj+0FZxa6Hh4dWM3Pnzh3l4uKi5TOtESNGqDFjxjz0/qpVq5Snp6dSyhQDZcuWVTdu3NDWee2115RSSvn4+Kg9e/YopUw1L6kxlF3a0sbC0qVLVdeuXbXPHjhwoPrmm2+UUtl/f9LWaMydO1e1bdtWJSQkqOTkZPX0009rNZuDBg1SPj4+Kjo6Wlue/+54JyYmqipVqqhNmzYppZTauXOnKl++vIqJiXlovzz//PNqwoQJSimlrl69qsqUKaPdRf/f//6nPv30U23ZTz/9VI0cOfKhbaT9bKWU+vfff5WLi4uKjIxMFy/Lly9XXbp0SbdO2u992u/whg0blKenp4qKilJGo1H1799fO18ajUbt+2s0GtXw4cPV1KlTs13vQZnVArm4uKj9+/ebdV4ODAxUSik1YcIEVbp0aa2mYuTIkVpNRVbHcfDgwWrEiBHa9yZ1f02cOFG5u7urmzdvaulKuy+XLVv20L5MW9uRtkVHZufMrM6zj+PBtMTFxalWrVqpChUqqL59+6pvv/1WhYeHa/Mf/L309fVV9+7dy/ZzMqoxP3z4sHJwcMg2fw8e+6zOnd26dVPLli3Tlk2b9lQXLlxQgPr999+VUkr99ddfqk6dOtr8tMdu6tSpatiwYUoppWbOnJnuPJxaS5pR7Vxm23hQ2mP/YD6//fZbNWjQoIc+I+31SVb7Iq3ExERVuXJl9e+//yqllPr7778VoNUAZnXOzMyNGzdU+fLl1cGDB5VSSn355Zfp9k9cXJzS6/UPtRiIjY1VdevWVatWrdLeA1TDhg2Vj4+PGj9+vIqNjc3ys4UQIiPWOS0wrlixIt0dtazaxW/atIlz587Rpk0b7T29Xs/ly5cBqFWrFs2aNQOgRYsWfPnllxluJy4uji1btnDr1i3tvdjYWO1Oceqdzsy888472T4bdeHCBfr378/Vq1extrYmPDycCxcuaHfoBg4ciI2NDTY2NgwYMIDNmzfz3HPPcfjwYa0Wonbt2rRu3ZqdO3fi4eEBoN35BoiPj2fkyJEEBwej1+u5cuUKwcHBNG/enA0bNjB9+nTs7OwAKFu27ENp3Lx5M/369dOeyRgxYgR9+vTR5j/11FOULl0aMO3PrJ43efXVV9HpdJQpU4ZevXqxefNmWrZsCZjutOp0Ou0zv/rqK/R6PU5OTgwcOJB///1Xe64io/3y0ksv5Wjfpvrzzz95+eWXcXd3B8DR0RGAbdu28dRTT1GpUiXAdLf0008/1Z5pSq09S53+9ttvM813ZukNCgrif//7H3fv3sXa2pqQkBDi4+OpUaMGKSkpvPLKK7Rv356uXbui1+uzjO3atWtr72UXu4C2v8qUKUONGjW4cOGCltec6tq1KxUqVADg9ddfp1evXoCptnDs2LE8//zzdO7cGT8/P7PSljYWevbsyZgxY7hx4wbOzs5s2LCBmTNnAuYfYzDF1ODBg7VYf+211/juu++02s0+ffpotSpphYSEoNfr6dKlCwCtW7emfPnyBAcH07p163TLbtmyRTufVKpUiW7dumnz1qxZQ1RUFH/88QdgqknK6jm8J554AisrK9zc3Fi7di2urq4sW7aMb7/9lpiYGIxG40O17Wm/9w+mq2/fvri4uACm59527doFgFKKWbNm8eeff5KSkkJUVJT2ncxqPXOp/2q7zTkvN2rUCDDVetaqVUs7jk2bNmX16tVA1sdxw4YN7N+/X3ueL+357JlnnqF8+fLa623btjFv3jySkpJISUkxu7Yjs3NmVufZ3OTo6MjOnTsJDg5m586drFq1iunTp3PkyBFKlSoFPPx7+ahSjx3kLH9ZnTvbt2/PZ599xtmzZ+nQocND36FU9vb22nmkRYsWhIaGavOWLVvGkiVLSEhIICEhgTJlygDQvHlzZs2axVtvvUWbNm2yfF49s23ktqz2hZWVlbbc6dOnsba2plOnTgB07txZq8kz55z5oOjoaJ577jneffddrUWTOZKSkujXrx+dO3emZ8+e2vuXLl2iatWqxMXFMXz4cN555x3mzp1r9naFEAIgxwXAhzZgbZ2uc4G0D+wrpXjyySdZtmzZQ+tdu3YNe3t77bWVlVW6ZhZppf747du3L906uemFF15g2rRpPP/88wCUKlUqXV4elHpRnN37JUqU0KY//PBDypQpQ1BQENbW1vTq1SvLz8jOg59l7v7Mbltp05zdZ5ozP6f7Nju5ne+kpCR69erFtm3baNKkCdHR0bi6upKYmEjJkiU5fvw427dvZ9u2bXzwwQfs2LEjy9hOy5zYNSf9DRs2ZMGCBQ+9v3fv3iybnaXuq5kzZ3LixAm2bdvGoEGD6N+/PyNHjsw2bWljwcHBgT59+rBkyRLKli1Lhw4dtBsOj3OMs/rO5HRdc5ZTSvHtt9/SuXNns9bduXNnuo4wLl++zOjRozl48CA1a9bk6NGj6QpTYH4e0qZr2bJlbN26le3bt+Pi4sI333zD1q1bs13PHKk3NOrVq8fBgwdzdF429/tlbprS7pvLly8zYsQIgoODqVKlCkeOHKFt27bmZitD5p5nf/75Z+0GRqpDhw6lKwxkR6fT4e/vj7+/P2+88Qaenp4EBARoBabccvDgQa1J9OP8jqQ9RuPGjaN79+5s3ryZDz/8EC8vrwwLEnZ2dtp6VlZW2u/9rl27+Oabb9i7dy/lypVj3bp1fPLJJ4CpoBgcHMzmzZtZtWoVH3/8MUFBQQ9tO6ttZCWr6w5z5eQ7lLpsTq9FYmJieOqpp+jevTtvvvmm9n7VqlX5999/tdcXL17E3d0da2vTJVlycjL9+vXD3d2d2bNnp9tm1apVAXBycmLkyJG8/vrrZudDCCFSPfYzgDVq1ODSpUvcuXMHMLWxT9WlSxc2b97M0aNHtfcOHDiQ7TZdXFyIj4/XnicpUaIE7du3T9cz6PXr19M91/O4IiIitFqkX3755aFngn755ReSk5OJj49n2bJldOrUCWdnZxo2bKj1Knru3Dl27dr10MVg2s+oXLmyVsOU9gegW7duzJ49m8TERABtf6bVqVMnfvvtN623sPnz55t9EfugxYsXAxAeHs7q1avp2LFjhst16tSJH3/8EaUUcXFxLFmyJN1nZrRfMsp3Vvs21XPPPccvv/zCjRs3ALh37x737t2jffv2bNq0ievXrwOmnvc6duyYowu1rNKbkJBAUlKS9sOatgbxzp07xMXF0blzZz7//HOqVavGyZMnzY7t3IrdF198kbt37zJ9+nTtva1bt/LTTz/xzjvvaO9t3LhRuzu9cOFC7XicPn2aBg0aMHr0aEaMGMG+ffseKW1Dhgxh0aJFLF68OF0tV1bH2MXFRXv2DEwx9fPPP2s1PgsXLjQrjuvWrYvRaNS+N3v27OHmzZsZ1rB06tSJn376CTA9D7hu3TptXo8ePZg1axb37t0DTHF24sSJbD8/VVRUFDY2Nri7u6OUYs6cOWav26lTJ1auXElMTAxKqXSF+oiICMqUKYOLiwsxMTHadzS79bJz8eJFXn31VUaMGIGLi8sjn5czyktmx7Fbt258+eWXWq+HGZ3PwNTjs7W1NaVLl0YpxXfffZduvrOzc7rYSSuzc2ZW59m0Bg4cSHBwcLq/nJxTTp8+nW4fXrlyhTt37mi1Rbll586dTJo0Sashzyp/D37Xsjp3hoSEUL16dV577TU+/PDDdD0KmyMiIgJnZ2dKly5NUlIS8+fP1+ZduHCBEiVK0LdvX7799lvOnDlDbGzsQ+nLahtZqVWrFoGBgRgMBu7du6fV5mfF3N+RevXqkZKSoj0Xu3nzZq3WMyfnzNjYWJ566imeeuqph54TfOqppzh8+LDWO/LcuXN54YUXANOzwC+88AKlSpViwYIF6QqqERER2nnLaDSyYsUK7TlsIYTIiceuAaxYsSLvvvsuTZs2pXz58jz99NPavFq1arFs2TKGDRvGvXv3SEpKwt/fP9tak1KlSjFw4EB8fHwoUaIEgYGBLF26lDfffBMvLy90Oh1OTk7Mnz+fypUr88knn1CxYkXtofIHzZgxI93FVP/+/dNdNAPMnj2b559/npIlS9KhQwetMJCqfv36tGrVivDwcLp3766drJcuXcrw4cOZM2cOOp2OhQsXPrRuqgkTJvDyyy/zf//3f9SsWZMOHTpo89577z0++ugjGjZsiI2NDRUrVkz3sD3A008/zfHjx2nRogV6vR4fH59HbvpRtmxZGjVqRFRUFKNHj9aamj3o448/ZsyYMXh7ewOm5nl9+/bNdr+kld2+TdWmTRsmTpxIly5d0Ol02Nra8vvvv+Pl5cWMGTO0ZkRVqlThhx9+eKR8Z5RenU7HlClTaNq0KWXKlEmXhytXrvDaa6+RnJyMwWCgVatWPP3009jY2Jgd21nFrrmcnJwICAjgrbfeonr16lhbW+Pu7s66deu0DgPA1FzxpZde4tq1a9SuXVuL+w8//JCQkBBsbW1xdHTUOnzIadqaNm2KlZUV586dS1doy+oYv/7667z11lvMmjWLzz//nNdff53Q0FCt5rJdu3ZmDV9ha2vLqlWrGDNmDG+99Rb29vb8/vvvGda2zZ49m8GDB+Pp6UmlSpUe+q4lJibSrFkz7eLqvffeo0GDBtmmAcDb25sXXniBBg0aULp06XQdvGTnmWee4cCBAzRs2BAXF5d058uBAweydu1a6tatS9myZXniiSe4dOlStutlZOnSpWzfvp179+7h4uJC//79eeONN4BHPy8/KKvjOGvWLMaPH4+3tzc2NjY0adIkw++sj48Pffr0wdPTkzJlyjy0L9966y2efPJJHB0d+eeff9LNy+ycmdV5Njfdu3eP8ePHc/PmTRwcHFBKMW3atHQ3JPr164eDg4P2etasWbRv356hQ4fSrVu3dE2T0xo/fjyTJk0iLi4ODw8PfvjhB5599lkg69+Rnj17smTJEvz8/OjVqxeffPJJpufOOXPmsHXrVmxtbbGysuKrr77KUf6feuopfvnlF+rWrUvp0qXp1KmT1qlKQEAAM2fO1GqMZ8yYgaurKx07duTLL7/Ex8eHli1b8u2332a6jYykfl979erFypUrqV+/PpUrV8bf318rGGXG3N8RW1tbVqxYwciRIzEYDDRp0gRfX19tvrnnzNmzZ3PgwAHi4uJYtWoVYPr9/Oijj3B2dmbhwoX06NGDlJQUvLy8+L//+z/A1Gx41apV+Pj4aIW7Vq1a8d1333H69GmGDRuGTqcjJSWFhg0bpqshfOaZZ/j0009z1NRUCFE86VTahwtEsVCtWjXWrFnz2M+mpF7w5eQCWAghhMipZ555hv79+6cba1EIIcSjeewaQCGEEEKIvNKpUycSExMzrTEVQgiRM1IDKIQQQgghhBDFRI46galWrZr2gHSqxo0bZzkURKqvv/460wFec+LBQZUfRbt27bSuxhcvXqw9iJ0X0n5Wfsqt/Z2qTJkyMhhtLlBKpXu2qzDz8/MjJibmsbYxevRoVqxYkUspytwTTzzBhQsXzFo2N84huf39KkweHGT7QW+//bY2EP3GjRu1geD//vtvbZmffvqJzz//PN16ffr0Yc+ePXmSZnPNmzePGTNm5Oln5OQ3Zc2aNWZ3vpKTuH3++efTPfeenwIDA7VhggqTatWqERwc/Mjr5/W1Qn5ZsmQJzZs3p1u3bmb1uCqEKJpy3AtoYmIiP/74Y44/qLBeMBWVk/qDstrfRqNR651P3JeT4SMe1cqVK6lTp442TmR+f35OBAcHZzgen7mUUvz1118888wzuZiqjL311ltMnDgxzz8nVWE9n+W1a9eusXHjRu0C/5NPPmHjxo1s3LiRjz76CIBbt26xZMkS3n333XTrfvTRR7z//vtmfU5efRdSx03LiZyeL/OqAGgpGjdu/Eg3fQrb+e9BhfFa4VF+y19++WX27dvHunXrsh1DWQhRdOW4ADhp0iQ+++yzDHvcun37Nr169cLb2xsvLy+tS+dPP/2U69ev069fP/z8/AgODmbLli20aNECf39/GjRo8EiFyrRu3rxJ+/btadSokdbdfWYnxlKlSmFlZcXChQsJDAxk/Pjx+Pn5sXHjRo4dO0br1q1p2LAhnp6eTJkyRVtv/fr1+Pj44Ofnh5eXF2vXrgVMY6w1adIEPz8/mjRpwt69ex/6rAcFBATg5eXFiBEj8PHxwdvbm6NHjzJ48GC8vb1p1qyZ1huawWDgnXfe0e60v/HGG9oQGQsXLsTT0xM/Pz+8vb3Zv39/hvt70qRJ9O7dmy5duuDl5cWNGzd4++23tXS3adMm3WC269ato379+vj4+Dx0IZfVemklJyfz/vvv07RpU/z8/Ojbt682PEBG6X7Qg3e8N2zYQLt27dLtv5EjR+Lr60uDBg0IDAzMMB2DBw/mlVdeoWXLltSpU4dBgwYRHx+fbl6bNm20cbZmzJhBgwYN8Pb2pn///kRFRXHnzh2qVaumXaz9/vvv+Pr6Eh8fz7PPPpuuB8V//vmHZs2aZZiW+fPnawO/g6mGeMyYMbRo0ULrVfPLL7+kadOmNGzYkKeeekqrLZw0aRL9+vXjueeew9PTkw4dOmgDkD/Ovvrzzz+1nu78/Py0Y6HT6YiMjAQyP+bx8fH069cPT09PfH190/UMGhgYSN26dXF2dmbSpEn07duX5557jjp16vDss89y/PhxunTpQp06dXjxxRe172tMTAyvvfYaTZs2xcfHh9dff12L9ylTplC/fn38/Pzw8/PT9k3Xrl3566+/Mh0ywBzmnkNyej7LLNbN/R5lte3BgwczbNgwOnbsSJ06dejVq5e2rx6syZszZw6DBw8GyPI8l5WffvqJ3r17a70x2tjYcO/ePeLi4rC1tQVMPVhOnz5dG9MslZ+fH3fu3OHUqVMZblun0zFx4kSaNGnCBx98kGUcnD59mpYtW9KgQQN69OhB586dtRqvwYMH8/XXX2vbffvtt5k0aVKG+ySr79qD58u///6b1q1b06hRI5o2baoNE5BWRr8pmZ2/N27cyLp165gxYwZ+fn4sXLgwR79jaT24P1KHCoKsv09p5eQ3KbvzTdoOxjLbbwEBATRo0IBXX30VPz8/Vq9ezdmzZ+natStNmjTBx8dHG2Ilq/NMWjdv3qRv3740bdoUb2/vh4ZfMGe5U6dO0aVLF3x8fPDx8WHevHm5dq2Q0f4eOHAgXl5eNGrUKF0NZUa/Q5BxbKaVVbqy+g3J6jpDCFEEqRzw8PBQQUFBasCAAWrKlClKKaUaNWqktm3bppRSqm/fvur9999XSil169YtVblyZbV3795066YKDw9XKSkpSiml7t69q6pWraquXLmilFJq7dq16tVXX80wDYsWLVLdu3d/6P34+HgVExOjlFIqJSVFde3aVS1fvjzbPLVt21atXr1aex0dHa0SEhKUUkrdu3dP+fn5aXnw8fFRe/bsUUopZTAYVEREhFJKqdu3b2vr7927V9WtWzfbz922bZuysrJSgYGBSimlJkyYoEqXLq1OnTqllFJq5MiR6u2331ZKKTV37lzVtm1blZCQoJKTk9XTTz+tpk2bppRSysXFRV2/fl0ppVRSUpK2Dx7c3xMnTlTu7u7q5s2b2ntp0718+XLVpUsXpZTp2JUqVUqdOHFCKaXU/PnzFaAuXLiQ5XoP+t///qc+/fRT7fWnn36qRo4cmWW603rwWK9fv161bds23f7bt2+fUkqp77//XnXu3DnDdAwaNEh5enqq6OholZKSop599ln1v//9T5vn4+OjoqOjlVJKbdy4UdWrV087tq+99poaPny4UkqpHTt2qBo1aqj9+/erSpUqqZCQEKWUUv/8849q0aKF9nndunVTP//880PpSEpKUnZ2duny2rZtW9WlSxeVlJSklFJq6dKlaujQodp34+eff1bPPPOMUsp0DD08PFRYWJhSSql+/fqpzz///LH2VUhIiCpbtqwWd0lJSSoyMlIppRSQYYynPearVq1Kt9/v3r2rTX/wwQdq/vz5WtqrV6+uwsPDldFoVG3atFHNmjVT0dHRKjk5Wfn6+qoNGzZo+/z//u//lFJKGY1G9eqrr6ovvvhChYeHK1dXV3Xv3j2llFJxcXEqPj5e+7z27dur9evXP7TfH5Qb55CcnM8yi3Vzv0dZbXvQoEGqadOmKi4uTqWkpKiWLVuqZcuWKaVM+3zs2LHadr799ls1aNAgpVTW57kH10urQ4cO6fbxjh07VNOmTVXTpk3V7t271fr169W4ceMyXFcppYYMGaK+/fbbDOcBavLkydrrzOJAKaUaN26sFi5cqJRS/9/evQdFWf1/AH/vsiCzNWCyyCUyBpbIuEMSmOQYV+kyxGx2EUaEhKCiHW1aAcs/NMcag0bKYHTESGVCSGSaRoY1mEaLSwaCaCTMOECkIjLInWV5vn/scIZd9nl2Wer7+33l8/oLdp/Lec6e8znnPLfDtbW1cTY2NlxJSQnLk4KCAradXbt2cXv37l1wbKbq2vx42d3dzYWFhXHDw8Mcx3HcjRs3OGdnZ5aH8xm2KULx2zCtQmWQr9yayg+hfJxvMW2SqXgTEBBgMt/q6uo4kUjE1dfXs+MNCQlh+xsbG+P8/Py4pqYmwTgzX0xMDNueRqPhYmNjufLyco7j9Oss33IajYbz8vJidYjjOG5gYIDjuH+mr2CY3wA4tVrNcRzHfffdd5y3tzc3Ozsr2A4Za8vnM1W3+doQoXJKCHnwWPQW0H379iE0NHTBvHtqtRqXL18GAKxevRqJiYlQq9UICwtbsI3BwUGkpaXhzz//hEQiweDgIK5evQo3NzfB+ZH4zM7OQqVS4eLFi+A4Dnfu3IGvr6/ReemETExMICsrC62trRCLxejt7UVrayvCwsIQGRmJ999/HwqFAjExMewsZ0tLCz755BMMDg6yyXknJib05n8yRi6XIyQkBIDuthm5XI4nn3wSgG6+tbNnzwLQ5WtKSgpWrFgBANixYwe++uorqFQqREZGIjk5GS+99BI2b96MJ554gnd/8fHxcHJyYv/X1taisLAQIyMjmJ2dZWcCGxoa4O/vj6eeegoAkJaWxuYQE1rPUFVVFYaHh9kkvdPT03B3dweARaVbKP/mrrSFh4fj0KFDvMtu2bKF3c6YlpaGw4cPIzc3F4Du2aS579RqNV577TWsXLkSAJCZmYlXX30VgO45s7S0NKxfvx6lpaUszdHR0VAqlWhpacGqVavQ1NSE8vLyBWm4e/curKysFsxbl5SUBGtra5Znzc3NrFxotVq9ZePi4uDg4MCOub29fUl5VVtbi7i4OFburK2tYW9vv2B9vt88ICAA169fR1ZWFjZu3Kh3u2d1dTUuXLjA/o+JicEjjzwCAAgODsaKFStYvgcFBeHGjRssD3799Vfk5+cD0NVJKysr2NnZwcvLC0lJSYiJicELL7ygN/eWs7Oz4CT2piwlhgjFM76ybm49Eto2oJv7TSqVAtDFjblJq4UIxTkhfX19ejEkIiKCXdEcGRlBfHw8zp8/jy+++AKXLl2Ck5MT8vPz2dVBU79Ramoq+5uvHNy/fx+tra3saqafnx82bNhg8pgNmapr8+Pl+fPn0dXVheeee459LxaL0dPTAy8vL8H9CMVvQ5aUQVP5wZePxpjbJplLKN8AwMPDAxs3bgQAdHZ2oqOjQ+9YR0ZGcO3aNURERPDGmTljY2O4cOECbt++zT4bHR1dcGVdaLnOzk5MTk7ijTfeYN/JZDKjx2ZJX8GQu7s7IiMjAejaqPT0dPT29gq2Q8DCttzcdAH8bchiyikh5H+fRQNAd3d3vPnmmyZvG5q7TciYt99+G/Hx8aisrIRIJEJwcDAmJyctSQ4A3W2Yd+7cQWNjI2xtbbFz506LtpebmwuZTIaWlhZIJBIkJiay7eTn56OjowN1dXXYtm0btm7dCqVSicTERNTV1WHdunW4f/8+7O3tMTU1ZXIAaGtry/62srJa8D/fMxHz87WyshKXL19GfX094uPjsX//ft7OwvyBR09PD9599100NzfD09MTbW1teo003/4Wsx7HcSgsLDR6u4456ZZIJHqdMsPf09z8MnVMxiYSN7YcoBvsOzo6ore3V+/z7OxsFBYWwsnJCampqawRnU8qlWJqagocx/Hun+M45OTkID093Wh6+I7538wrod/cw8MD165dw08//QS1Wo0PP/wQra2tGBgYwMqVK/U6KeaWd47jUFlZafSkQENDA3755RfU19cjLCwMZWVliIiIYMdsqs4JWUoMEYpnxsr6+vXrza5HpmKlJWVCKM4JkUqlvMvl5uYiLy8Pt27dwrlz51BXV4e9e/fi1KlT2L59O0uDsRMMcwzrgrFyMP/2xjnz65Ox4zZWx03VNcO0REdH693qbSmhdvGfasfm70OoPhkyt46aijfz982Xb3/99deCPF61ahXvi1qMxZm5E0pz6wO6GDE/3cbSxLdcR0cH73qGFttXMHyUwhiRSGS0fBh+JtRmmarblvQzCCEPnkU/Azhnz549OHnyJPr7+9lnUVFROHr0KABgYGAA33//PaKjowEAdnZ2es/nDA0N4fHHH4dIJMLPP/+MK1euWJoUtj1nZ2fY2tri1q1bOHPmjFnrGUuXm5sbu5JXW1vLvvvjjz/YcxmZmZloaGjA5OQkpqensWbNGgBAYWHhko7DmKioKJSWlmJ6ehozMzM4duwYYmJiMDMzg+7ubjz99NP44IMPoFAo0NTUZPS4DA0PD8Pa2houLi7gOI49awHozgq2tbWxB96PHz/OngUQWs9QQkICCgoK2POi4+Pj6OjoEEz3fHK5HG1tbZiYmMDMzMySOl8VFRUYHR2FVqtFSUkJoqKijC4XFRWF8vJy1sksLi5mA9gvv/wSQ0NDuHLlCoqLi3Hp0iW2XnJyMmpqalBSUrLgyvgce3t7PProo4JXaBISElBUVMSuBmk0GrS0tJg8PkvzKjY2FjU1Ney31mg0C8qN0G/e19cHkUiEl19+GYcOHQLHcejt7cXZs2ctftNmQkICPv30U9YxGRoaQldXF0ZGRnD79m1ERETgo48+woYNG/Ty5vr16wgICLBon3P7MTeGmBvP+Mr6YuqRpbFSLpfjt99+g1arxfj4OLsSP7dNvjgnxN/f3+izig0NDRgeHkZcXBzGxsZY51EsFmN0dJQtt5jfiK8c2NnZISgoCKWlpQB0nfaLFy/qHfdcPBkcHMSPP/7Iu31z61psbCzUajXa2trYZ8ZiFrCwbPDFb2PLWtKOmcoPvnxcCnPjzWLyzdvbG3Z2digpKWGfdXV14d69e7xxZr6HH34YmzZtwsGDB9ln/f39C644Cy3n7e0NqVSKsrIy9t3du3cBLL2vYMzNmzfZM5EVFRVwcnKCm5ubYDtkiqV1W6icEkIePBZPBC+TyZCdna33GuHDhw8jMzMTfn5+4DgOeXl57Laz7Oxs7NixA1KpFCdOnMDBgweRlZWFffv2ITAwUO+lGdXV1aiursaxY8eM7rumpkbv1q8tW7ZAqVRCoVDAx8cHrq6uvB18Q+np6di1axcKCgpw4MAB7NmzB8nJyfjmm2/g6emJ559/ni2bm5uLzs5O2NjYQCqV4uuvv4adnR3279+P0NBQyGSyRd9yam4au7u7ERwcDED34hClUgmtVovU1FTcu3cPEokEjo6OrPE0zG9Dfn5+eP311+Hj4wMHBwe9zrqjoyOOHz+OV155BTY2Nnq3jAitZ0ilUmFqagrPPPMM6xCqVCrI5XLedM8XFhaG+Ph4+Pr6wsXFBc8++6zRl8WYY926dYiNjcXAwADCw8N5X3O/efNmXL16FeHh4RCLxfD398eRI0fw+++/49ChQ2hsbMTq1atx8uRJJCUlobm5GQ4ODpBKpUhMTER/fz8ee+wx3nQoFArU1NRALpcb/X7r1q0YHBzEpk2bAOgGEKmpqQgKChI8PkvzSi6Xo6SkBElJSdBoNLCyskJRURFCQ0PZMkK/eXt7O3JycsBxHGZmZpCcnAx/f39kZGTg22+/Nbl/YwoKCrB7924EBgZCLBZDIpHgs88+g62tLRQKBRtgeHl5Ydu2bQB0HSmtVssGF/92DDE3nvHVUTc3N7PrkVCsFJKYmIgzZ85g7dq1cHNzQ1BQEDsZIxTnhCgUCpSWluKtt95in2k0GqhUKlRUVADQDRI9PDzg6+sLmUzGbhscGxtDe3u72bGZrxzI5XKUlpZi+/bt+Pzzz+Hl5aV39TQ9PR0KhQJr166Fh4cH722ti6lrcrkcp0+fRkZGBsbHxzE9PY2goCCjAx/DNoUvfgO6E0cpKSmoqqrCO++8w24bXGw7JpQfQvloKVPxZi7eLybfJBIJfvjhByiVShQUFECr1UImk+H06dO8ccbQqVOnsHPnTvj6+kIkEuGhhx5CcXGxXl03tdy5c+fw3nvv4cCBAxCLxcjKykJGRsaS+wrG+Pj44MSJE8jOzoaNjQ3KysogEol42yFzWFq3hcrpxx9/DFdXV96Tm4SQ/z00ETx54KWkpCAwMFBwbrOl0mq1CAkJQWFhIbsl0Zienh4oFAo0NjY+sLfY/P3334iNjdU76/9v2717N+Ryud7AhPzzZmdnERoaiqqqqgWdalOKiorQ19dn9htHF0OhUODFF19kz8GR/zvl5eU4evSo2Veelqv6+noolcolzU1ICCGWsvgWUEKITnV1NTw9PREeHi44+AOANWvWQKVSsdepP4hcXFz+q4M/AHB1ddV7gQj5d4jFYhQXF+PmzZsWrZuTk/PPJ4r8v3HkyBHk5eUteq5FQggh/110BZAQQgghhBBClgm6AkgIIYQQQgghywQNAAkhhBBCCCFkmaABICGEEEIIIYQsEzQAJIQQQgghhJBlggaAhBBCCCGEELJM0ACQEEIIIYQQQpYJGgASQgghhBBCyDJBA0BCCCGEEEIIWSZoAEgIIYQQQgghy8R/ACYhB+2tFdLUAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_csv(\"renuncias_2020_2025julio_calculado.csv\", encoding=\"utf-8\")\n",
        "\n",
        "# Recalcular 'tasa_mensual_prom' y 'var_interanual_%' :\n",
        "if \"tasa_mensual_prom\" not in df.columns:\n",
        "    df[\"tasa_mensual_prom\"] = (df[\"renuncias\"] / df[\"meses_cubiertos\"]).round(2)\n",
        "if \"var_interanual_%\":\n",
        "    df[\"var_interanual_%\"] = (df[\"renuncias\"].pct_change() * 100).round(1)\n",
        "\n",
        "# Tendencia lineal (2025 como año parcial)\n",
        "x = df[\"anio\"].astype(float).values.copy()\n",
        "x = np.where(df[\"anio\"] == 2025, 2025 + 7/12, x)\n",
        "y = df[\"renuncias\"].astype(float).values\n",
        "X = np.vstack([x, np.ones_like(x)]).T\n",
        "slope, intercept = np.linalg.lstsq(X, y, rcond=None)[0]\n",
        "y_trend = slope * x + intercept\n",
        "\n",
        "# === Gráfica===\n",
        "fig, ax = plt.subplots(figsize=(9, 5.2))\n",
        "ax.plot(df[\"anio\"], df[\"renuncias\"], marker=\"o\", linewidth=2, label=\"Renuncias\")\n",
        "ax.plot(df[\"anio\"], y_trend, linestyle=\"--\", linewidth=1.5,\n",
        "        label=f\"Tendencia lineal (pendiente ≈ {slope:.2f})\")\n",
        "\n",
        "ax2 = ax.twinx()\n",
        "ax2.plot(df[\"anio\"], df[\"tasa_mensual_prom\"], marker=\"s\", linestyle=\"-.\",\n",
        "         linewidth=1.8, label=\"Tasa mensual promedio (proxy)\")\n",
        "\n",
        "ax.set_title(\"Renuncias de mujeres electas por año (2020–2025*)\")\n",
        "ax.set_xlabel(\"Año\")\n",
        "ax.set_ylabel(\"Total de renuncias\")\n",
        "ax2.set_ylabel(\"Tasa mensual promedio (renuncias/mes)\")\n",
        "ax.grid(True, linestyle=\":\", linewidth=0.8, alpha=0.7)\n",
        "\n",
        "ax.annotate(\"Elecciones Subnacionales\\n(mar 2021) → pico de renuncias\",\n",
        "            xy=(2021, 158), xytext=(2021.35, 140),\n",
        "            arrowprops=dict(arrowstyle=\"->\", lw=1),\n",
        "            fontsize=9, ha=\"left\", va=\"top\")\n",
        "\n",
        "ax.annotate(\"Reactivación 2024\",\n",
        "            xy=(2024, 97), xytext=(2024.35, 120),\n",
        "            arrowprops=dict(arrowstyle=\"->\", lw=1),\n",
        "            fontsize=9, ha=\"left\")\n",
        "\n",
        "ax.annotate(\"Dato parcial 2025\\n(ene–jul)\",\n",
        "            xy=(2025, 35), xytext=(2024.4, 55),\n",
        "            arrowprops=dict(arrowstyle=\"->\", lw=1),\n",
        "            fontsize=9, ha=\"right\")\n",
        "\n",
        "for _, r in df.iterrows():\n",
        "    ax.annotate(f\"{int(r['renuncias'])}\",\n",
        "                xy=(r[\"anio\"], r[\"renuncias\"]),\n",
        "                xytext=(0, 8), textcoords=\"offset points\",\n",
        "                ha=\"center\", va=\"bottom\", fontsize=9)\n",
        "\n",
        "for _, r in df.iterrows():\n",
        "    ax2.annotate(f\"{r['tasa_mensual_prom']:.2f}\",\n",
        "                 xy=(r[\"anio\"], r[\"tasa_mensual_prom\"]),\n",
        "                 xytext=(0, -14), textcoords=\"offset points\",\n",
        "                 ha=\"center\", va=\"top\", fontsize=8)\n",
        "\n",
        "lines1, labels1 = ax.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax.legend(lines1 + lines2, labels1 + labels2, loc=\"upper left\", frameon=False)\n",
        "\n",
        "fig.text(0.01, -0.02,\n",
        "         \"Fuente: Elaboración propia con base en Observatorio de Paridad Democrática – TSE. \"\n",
        "         \"Datos hasta julio de 2025.\\n\"\n",
        "         \"Nota: La 'tasa' mostrada es un proxy (renuncias/mes). \"\n",
        "         \"La tasa anual (%) requiere total de mujeres electas por año.\",\n",
        "         fontsize=8)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"renuncias_linea_etiquetas.png\", dpi=220, bbox_inches=\"tight\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"\n",
        "Barras 100% apiladas (porcentual) por motivo de renuncia, 2016–2025*,\n",
        "con etiquetas internas, totales arriba de cada barra, eje X = \"Año\", eje Y = \"Frecuencia relativa (%)\".\n",
        "\"\"\"\n",
        "\n",
        "from pathlib import Path\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import MaxNLocator, FuncFormatter\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# 1) Ruta del archivo (ajusta si lo tienes en otra carpeta)\n",
        "# ---------------------------------------------------------------------\n",
        "XLSX_PATH = Path(\"Cantidad de renuncias que se registran en los TED y el TSE \"\n",
        "                 \"agrupada por motivo de la renuncia  Gestiones 2016 hasta julio 2025.xlsx\")\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# 2) Carga y normalización (soporta formato “largo” o “ancho”)\n",
        "# ---------------------------------------------------------------------\n",
        "xls = pd.ExcelFile(XLSX_PATH)\n",
        "# toma la primera hoja no vacía\n",
        "for sh in xls.sheet_names:\n",
        "    df0 = pd.read_excel(XLSX_PATH, sheet_name=sh)\n",
        "    if df0.dropna(how=\"all\").shape[0] and df0.dropna(how=\"all\").shape[1]:\n",
        "        df_raw = df0.copy()\n",
        "        break\n",
        "\n",
        "lower_cols = {str(c).lower().strip(): c for c in df_raw.columns}\n",
        "\n",
        "def find_col(pats):\n",
        "    for key, orig in lower_cols.items():\n",
        "        for p in pats:\n",
        "            if p in key:\n",
        "                return orig\n",
        "    return None\n",
        "\n",
        "col_year = find_col([\"año\", \"anio\", \"gest\", \"year\"]) or df_raw.columns[0]\n",
        "col_mot  = find_col([\"motivo\", \"causa\"])\n",
        "col_cnt  = find_col([\"cantidad\", \"total\", \"renunc\"])\n",
        "\n",
        "# Detecta si está en formato “largo” (anio/motivo/cantidad) o “ancho” (anio + columnas por motivo)\n",
        "if col_mot is not None and col_cnt is not None:\n",
        "    data_long = df_raw[[col_year, col_mot, col_cnt]].copy()\n",
        "    data_long.columns = [\"anio\", \"motivo\", \"cantidad\"]\n",
        "else:\n",
        "    motive_cols = [c for c in df_raw.columns if c != col_year]\n",
        "    data_long = df_raw.melt(id_vars=[col_year], value_vars=motive_cols,\n",
        "                            var_name=\"motivo\", value_name=\"cantidad\")\n",
        "    data_long.columns = [\"anio\", \"motivo\", \"cantidad\"]\n",
        "\n",
        "# Limpieza\n",
        "data_long[\"anio\"] = pd.to_numeric(data_long[\"anio\"], errors=\"coerce\").astype(\"Int64\")\n",
        "data_long[\"motivo\"] = data_long[\"motivo\"].astype(str).str.strip()\n",
        "data_long[\"cantidad\"] = pd.to_numeric(data_long[\"cantidad\"], errors=\"coerce\").fillna(0.0)\n",
        "\n",
        "# Filtra rango y remueve filas “TOTAL”\n",
        "data_long = data_long[(data_long[\"anio\"] >= 2016) & (data_long[\"anio\"] <= 2025)].copy()\n",
        "data_long = data_long[~data_long[\"motivo\"].str.lower().str.fullmatch(\"total\")]\n",
        "\n",
        "# Agregado\n",
        "agg = data_long.groupby([\"anio\", \"motivo\"], as_index=False)[\"cantidad\"].sum()\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# 3) Cálculo porcentual por año\n",
        "# ---------------------------------------------------------------------\n",
        "pivot_cnt = agg.pivot(index=\"anio\", columns=\"motivo\", values=\"cantidad\").fillna(0.0)\n",
        "years = sorted(pivot_cnt.index.astype(int).tolist())\n",
        "xlabels = [str(y) for y in years]\n",
        "totals = pivot_cnt.sum(axis=1)\n",
        "pivot_pct = (pivot_cnt.div(totals.replace(0, np.nan), axis=0) * 100).fillna(0.0)\n",
        "\n",
        "# Ordena motivos por peso total descendente (para apilar de mayor a menor)\n",
        "motive_order = pivot_cnt.sum(axis=0).sort_values(ascending=False).index.tolist()\n",
        "\n",
        "# Paleta fija para consistencia\n",
        "palette = [\"#4E79A7\", \"#F28E2B\", \"#E15759\", \"#76B7B2\", \"#59A14F\",\n",
        "           \"#EDC948\", \"#B07AA1\", \"#FF9DA7\", \"#9C755F\", \"#BAB0AB\"]\n",
        "color_map = {mot: palette[i % len(palette)] for i, mot in enumerate(motive_order)}\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# 4) Gráfica apilada - con los porcentajes visibles\n",
        "# ---------------------------------------------------------------------\n",
        "fig, ax = plt.subplots(figsize=(12.8, 9.2), dpi=170)\n",
        "\n",
        "bottom = np.zeros(len(years))\n",
        "for mot in motive_order:\n",
        "    vals = pivot_pct[mot].reindex(years).values\n",
        "    ax.bar(xlabels, vals, bottom=bottom, label=mot, color=color_map[mot])\n",
        "    bottom += vals\n",
        "\n",
        "# Totales en la parte superior de cada barra (negro)\n",
        "tvals = totals.reindex(years).values\n",
        "for xi, (x, t) in enumerate(zip(xlabels, tvals)):\n",
        "    if t > 0:\n",
        "        ax.annotate(f\"{int(t)}\", xy=(x, 100), xytext=(0, 8),\n",
        "                    textcoords=\"offset points\", ha=\"center\", va=\"bottom\",\n",
        "                    fontsize=12, color=\"black\")\n",
        "\n",
        "# Etiquetas internas de % (blanco, negrita, sin bordes)\n",
        "cum = np.zeros(len(years))\n",
        "for mot in motive_order:\n",
        "    vals = pivot_pct[mot].reindex(years).values\n",
        "    for xi, (x, v) in enumerate(zip(xlabels, vals)):\n",
        "        if v > 0:\n",
        "            ax.annotate(f\"{int(round(v))}%\",\n",
        "                        xy=(x, cum[xi] + v/2),\n",
        "                        ha=\"center\", va=\"center\",\n",
        "                        fontsize=10, color=\"white\", fontweight=\"bold\")\n",
        "    cum += vals\n",
        "\n",
        "# Diseño general\n",
        "ax.set_title(\"Composición porcentual de renuncias por motivo (2016–2025*)\",\n",
        "             fontsize=20, fontweight=\"bold\", pad=12)\n",
        "ax.set_xlabel(\"Año\", fontsize=14, labelpad=8)\n",
        "ax.set_ylabel(\"Frecuencia relativa (%)\", fontsize=14, labelpad=8)\n",
        "ax.tick_params(axis=\"both\", labelsize=12)\n",
        "ax.yaxis.set_major_locator(MaxNLocator(nbins=6, integer=True))\n",
        "ax.yaxis.set_major_formatter(FuncFormatter(lambda v, pos: f\"{int(v)}%\"))\n",
        "ax.set_ylim(0, 108)\n",
        "ax.grid(True, axis=\"y\", linestyle=\":\", linewidth=0.9, alpha=0.7)\n",
        "ax.spines[\"top\"].set_visible(False)\n",
        "ax.spines[\"right\"].set_visible(False)\n",
        "\n",
        "# Más espacio abajo; leyenda y fuente separadas\n",
        "plt.subplots_adjust(bottom=0.38)\n",
        "ncols = min(4, max(1, len(motive_order)))\n",
        "ax.legend(title=\"Motivo\",\n",
        "          loc=\"lower center\", bbox_to_anchor=(0.5, -0.20),\n",
        "          ncol=ncols, frameon=False, fontsize=11, title_fontsize=12)\n",
        "\n",
        "# Fuente/caption\n",
        "fig.text(0.5, 0.04,\n",
        "         \"Fuente: Elaboración propia con base en Observatorio de Paridad Democrática – TSE. 2025 parcial (hasta julio).\",\n",
        "         ha=\"center\", va=\"center\", fontsize=10)\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "# Guardar\n",
        "OUT_PNG = Path(\"renuncias_motivos_100pct_2016_2025_final.png\")\n",
        "plt.savefig(OUT_PNG, dpi=170, bbox_inches=\"tight\")\n",
        "plt.show()\n",
        "\n",
        "print(f\"Figura exportada en: {OUT_PNG.resolve()}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "r7aW8PDBtokn",
        "outputId": "df69fdb9-1cc1-4bc8-c7f1-e0b1f22dde4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2176x1564 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Figura exportada en: /content/renuncias_motivos_100pct_2016_2025_final.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"\n",
        "Supervivencia por departamento con origen en (0,0):\n",
        "- Eje X = días desde una fecha base por dpto.\n",
        "  * base = min(FechaInicio) si es >= 2021-05-01\n",
        "  * si hay inicios anteriores, base = 2021-05-01 y se recorta a 0\n",
        "- Tick de origen: \"2021 (may)\" cuando la base es mayo-2021\n",
        "\"\"\"\n",
        "import os, re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.lines import Line2D\n",
        "\n",
        "CSV_PATH = \"RENUNCIAS_en_ejercicio_EG2020_ES2021_2025-09-26.csv\"\n",
        "OUT_PREF = \"supervivencia_lineas_csv_bottom_v5_\"\n",
        "\n",
        "assert os.path.exists(CSV_PATH), f\"No existe el CSV: {CSV_PATH}\"\n",
        "df = pd.read_csv(CSV_PATH)\n",
        "\n",
        "# Parseo de fechas y filtro\n",
        "df[\"FechaInicio\"]   = pd.to_datetime(df[\"FechaInicio\"], errors=\"coerce\")\n",
        "df[\"FechaRenuncia\"] = pd.to_datetime(df[\"FechaRenuncia\"], errors=\"coerce\")\n",
        "df = df[df[\"FechaInicio\"].notna() & df[\"FechaRenuncia\"].notna() & (df[\"FechaRenuncia\"] >= df[\"FechaInicio\"])].copy()\n",
        "\n",
        "# Limpieza de textos\n",
        "for col in [\"Departamento\",\"Municipio\",\"Cargo\",\"Organización Política\"]:\n",
        "    if col in df.columns:\n",
        "        df[col] = df[col].fillna(\"\").astype(str).str.strip()\n",
        "df[\"Organización Política\"] = df[\"Organización Política\"].replace(\"\", \"(sin organización)\")\n",
        "\n",
        "# Colores: MAS-IPSP azul; resto desde con asignación estable\n",
        "cmap = plt.get_cmap(\"tab20\")\n",
        "orgs_all = sorted(df[\"Organización Política\"].unique().tolist())\n",
        "color_map = {org: (\"tab:blue\" if org.upper()==\"MAS-IPSP\" else cmap((hash(org) % (cmap.N - 1)) + 1))\n",
        "             for org in orgs_all}\n",
        "\n",
        "def safe_name(s: str) -> str:\n",
        "    s = (s or \"\").lower()\n",
        "    s = re.sub(r\"[^a-z0-9]+\", \"_\", s).strip(\"_\")\n",
        "    return s or \"sin_nombre\"\n",
        "\n",
        "def rel_days(dt, base):\n",
        "    # días desde base, recortado a 0\n",
        "    return max(0, (dt - base).days)\n",
        "\n",
        "df = df.sort_values([\"Departamento\",\"FechaRenuncia\",\"Municipio\",\"Cargo\"]).reset_index(drop=True)\n",
        "\n",
        "for dep, ddep in df.groupby(\"Departamento\", sort=False):\n",
        "    # Fecha base por depto\n",
        "    min_inicio = ddep[\"FechaInicio\"].min()\n",
        "    may2021 = pd.Timestamp(\"2021-05-01\")\n",
        "    base = min_inicio if min_inicio >= may2021 else may2021\n",
        "\n",
        "    # Coordenadas relativas (x = días desde base)\n",
        "    starts = np.array([rel_days(d, base) for d in ddep[\"FechaInicio\"]])\n",
        "    ends   = np.array([rel_days(d, base) for d in ddep[\"FechaRenuncia\"]])\n",
        "    y = np.arange(len(ddep))\n",
        "\n",
        "    # Etiquetas Y\n",
        "    y_labels = (ddep[\"Municipio\"].replace(\"\", \"(sin municipio)\") + \" — \" + ddep[\"Cargo\"]).str.replace(r\"\\s+\", \" \", regex=True)\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(11, max(4.2, len(ddep)*0.48)))\n",
        "\n",
        "    # Trazo + puntos\n",
        "    for i, org in enumerate(ddep[\"Organización Política\"]):\n",
        "        col = color_map[org]\n",
        "        ax.plot([starts[i], ends[i]], [y[i], y[i]], lw=2.0, alpha=0.95, color=col)\n",
        "        ax.plot(starts[i], y[i], marker=\"o\", ms=4.0, color=col)\n",
        "        ax.plot(ends[i],   y[i], marker=\"o\", ms=4.0, color=col)\n",
        "\n",
        "    # Eje Y\n",
        "    ax.set_yticks(y)\n",
        "    ax.set_yticklabels(y_labels, fontsize=9)\n",
        "    ax.invert_yaxis()\n",
        "\n",
        "    # Eje X: 0 en el origen; ticks anuales desde la base\n",
        "    max_x = max(ends.max(), 30)\n",
        "    ticks = [0]\n",
        "    labels = [\"2021 (may)\" if (base.year==2021 and base.month==5) else f\"{base.year}\"]\n",
        "    last_year = int((base + pd.Timedelta(days=int(max_x))).year) + 1\n",
        "    for year in range(base.year + 1, last_year + 1):\n",
        "        t = (pd.Timestamp(f\"{year}-01-01\") - base).days\n",
        "        if t <= max_x + 30:\n",
        "            ticks.append(t)\n",
        "            labels.append(str(year))\n",
        "\n",
        "    ax.set_xlim(0, max_x)\n",
        "    ax.set_xticks(ticks)\n",
        "    ax.set_xticklabels(labels, fontsize=10)\n",
        "\n",
        "    # Leyenda abajo (Potosí más arriba)\n",
        "    used = ddep[\"Organización Política\"].unique().tolist()\n",
        "    handles = [Line2D([0],[0], color=color_map[o], lw=3) for o in used]\n",
        "    ncol = min(4, len(used)) if len(used) > 0 else 1\n",
        "    y_anchor = -0.24 if dep != \"Potosí\" else -0.12\n",
        "    ax.legend(handles, used, title=\"Organización\",\n",
        "              loc=\"upper center\", bbox_to_anchor=(0.5, y_anchor),\n",
        "              ncol=ncol, frameon=False, fontsize=9, title_fontsize=10)\n",
        "\n",
        "    # Estilo + espina izquierda en x=0\n",
        "    for sp in [\"top\",\"right\"]:\n",
        "        ax.spines[sp].set_visible(False)\n",
        "    ax.spines[\"left\"].set_position((\"data\", 0))\n",
        "    ax.spines[\"left\"].set_visible(True)\n",
        "    ax.set_xlabel(\"Inicio de ejercicio → Fecha de renuncia\", fontsize=10)\n",
        "    ax.set_title(f\"Supervivencia — {dep}\", fontsize=16, pad=8)\n",
        "    plt.subplots_adjust(bottom=0.26)\n",
        "\n",
        "    out = f\"{OUT_PREF}{safe_name(dep)}.png\"\n",
        "    plt.savefig(out, dpi=220, bbox_inches=\"tight\")\n",
        "    plt.close(fig)\n",
        "    print(f\"Guardada: {out}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBVRWuipOK3u",
        "outputId": "1536768d-dc29-4725-ca5a-926324acca99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Guardada: supervivencia_lineas_csv_bottom_v5_beni.png\n",
            "Guardada: supervivencia_lineas_csv_bottom_v5_chuquisaca.png\n",
            "Guardada: supervivencia_lineas_csv_bottom_v5_cochabamba.png\n",
            "Guardada: supervivencia_lineas_csv_bottom_v5_la_paz.png\n",
            "Guardada: supervivencia_lineas_csv_bottom_v5_oruro.png\n",
            "Guardada: supervivencia_lineas_csv_bottom_v5_pando.png\n",
            "Guardada: supervivencia_lineas_csv_bottom_v5_potos.png\n",
            "Guardada: supervivencia_lineas_csv_bottom_v5_santa_cruz.png\n",
            "Guardada: supervivencia_lineas_csv_bottom_v5_tarija.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ===================================================\n",
        "# C) MODELO DE COX UNIVARIANTE + FOREST PLOT\n",
        "# ===================================================\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from pathlib import Path\n",
        "\n",
        "# 1. Cargar base corregida\n",
        "RUTA_ARCHIVO = \"supervivencia_procesada_CORREGIDO.xlsx\"  # <-- ajusta esta ruta\n",
        "df = pd.read_excel(RUTA_ARCHIVO, sheet_name=\"procesado\")\n",
        "\n",
        "df[\"_duration_days\"] = pd.to_numeric(df[\"_duration_days\"], errors=\"coerce\")\n",
        "df[\"_event\"] = pd.to_numeric(df[\"_event\"], errors=\"coerce\").fillna(0).astype(int)\n",
        "\n",
        "# 2. Limpiar covariables\n",
        "def clean_str(s):\n",
        "    if pd.isna(s):\n",
        "        return np.nan\n",
        "    return str(s).strip()\n",
        "\n",
        "# Cargo\n",
        "df[\"Cargo_std\"] = df[\"Cargo\"].apply(clean_str) if \"Cargo\" in df.columns else np.nan\n",
        "\n",
        "# Departamento\n",
        "dept_col = None\n",
        "for c in [\"Depto\", \"Departamento\", \"departamento\", \"Dept.\", \"Dept\"]:\n",
        "    if c in df.columns:\n",
        "        dept_col = c\n",
        "        break\n",
        "\n",
        "def norm_depto(x):\n",
        "    if pd.isna(x):\n",
        "        return np.nan\n",
        "    t = str(x).strip().lower()\n",
        "    mapping = {\n",
        "        \"santa cruz\": \"Santa Cruz\",\n",
        "        \"la paz\": \"La Paz\",\n",
        "        \"cochabamba\": \"Cochabamba\",\n",
        "        \"tarija\": \"Tarija\",\n",
        "        \"potosi\": \"Potosí\",\n",
        "        \"chuquisaca\": \"Chuquisaca\",\n",
        "        \"oruro\": \"Oruro\",\n",
        "        \"beni\": \"Beni\",\n",
        "        \"pando\": \"Pando\",\n",
        "    }\n",
        "    return mapping.get(t, str(x).strip())\n",
        "\n",
        "df[\"Departamento_std\"] = df[dept_col].apply(norm_depto) if dept_col else np.nan\n",
        "\n",
        "# Titularidad\n",
        "tit_col = None\n",
        "for c in [\"Titularidad\", \"titularidad\"]:\n",
        "    if c in df.columns:\n",
        "        tit_col = c\n",
        "        break\n",
        "df[\"Titularidad_std\"] = df[tit_col].apply(clean_str) if tit_col else np.nan\n",
        "\n",
        "# 3. Subconjunto con duración válida\n",
        "data = df[[\"_duration_days\", \"_event\", \"Cargo_std\", \"Departamento_std\", \"Titularidad_std\"]].copy()\n",
        "data = data.dropna(subset=[\"_duration_days\"])\n",
        "data[\"_duration_days\"] = data[\"_duration_days\"].astype(float)\n",
        "data[\"_event\"] = data[\"_event\"].astype(int)\n",
        "\n",
        "# 4. Selección de niveles con suficientes eventos\n",
        "def keep_levels(s, events, min_events=5, top_k=8):\n",
        "    counts = events.groupby(s).sum().sort_values(ascending=False)\n",
        "    keep = counts[counts >= min_events].index.tolist()\n",
        "    if len(keep) == 0:\n",
        "        keep = counts.head(top_k).index.tolist()\n",
        "    return set(keep)\n",
        "\n",
        "keep_cargo = keep_levels(data[\"Cargo_std\"], data[\"_event\"], min_events=5, top_k=8)\n",
        "keep_dept  = keep_levels(data[\"Departamento_std\"], data[\"_event\"], min_events=3, top_k=8)\n",
        "keep_tit   = keep_levels(data[\"Titularidad_std\"], data[\"_event\"], min_events=3, top_k=4)\n",
        "\n",
        "data.loc[~data[\"Cargo_std\"].isin(keep_cargo), \"Cargo_std\"] = np.nan\n",
        "data.loc[~data[\"Departamento_std\"].isin(keep_dept), \"Departamento_std\"] = np.nan\n",
        "data.loc[~data[\"Titularidad_std\"].isin(keep_tit), \"Titularidad_std\"] = np.nan\n",
        "\n",
        "# 5. Construir matriz de dummies (0/1) con referencia implícita\n",
        "X = pd.get_dummies(\n",
        "    data[[\"Cargo_std\", \"Departamento_std\", \"Titularidad_std\"]],\n",
        "    drop_first=True,\n",
        "    dtype=float\n",
        ")\n",
        "X[\"duration\"] = data[\"_duration_days\"].values\n",
        "X[\"event\"] = data[\"_event\"].values\n",
        "\n",
        "# 6. Implementación básica de Cox univariante (un beta por dummy)\n",
        "def cox_univariate(duration, event, x, max_iter=50, tol=1e-8):\n",
        "    # Ordenar por tiempo\n",
        "    order = np.argsort(duration)\n",
        "    t = duration[order]\n",
        "    d = event[order]\n",
        "    xv = x[order]\n",
        "\n",
        "    beta = 0.0\n",
        "    for _ in range(max_iter):\n",
        "        ebx = np.exp(beta * xv)\n",
        "        # sumatorias al revés: riesgo acumulado R(t_i) = sum_{j>=i} exp(beta*x_j)\n",
        "        rev = ebx[::-1].cumsum()[::-1]\n",
        "        rev_x = (xv * ebx)[::-1].cumsum()[::-1]\n",
        "        rev_x2 = ((xv**2) * ebx)[::-1].cumsum()[::-1]\n",
        "\n",
        "        S0 = rev\n",
        "        S1 = rev_x\n",
        "        S2 = rev_x2\n",
        "\n",
        "        xbar = S1 / S0\n",
        "        # Score y Hessiano\n",
        "        U = np.sum(d * (xv - xbar))\n",
        "        I = np.sum(d * (S2 / S0 - (S1 / S0) ** 2))\n",
        "        if I <= 0:\n",
        "            break\n",
        "\n",
        "        paso = U / I\n",
        "        beta_new = beta + paso\n",
        "        if abs(paso) < tol:\n",
        "            beta = beta_new\n",
        "            break\n",
        "        beta = beta_new\n",
        "\n",
        "    # Varianza aproximada\n",
        "    var = 1.0 / I if I > 0 else np.nan\n",
        "    se = np.sqrt(var) if var == var else np.nan\n",
        "    HR = float(np.exp(beta))\n",
        "\n",
        "    # z, p-valor e IC95%\n",
        "    if se == se and se > 0:\n",
        "        from math import erf, sqrt\n",
        "        def Phi(z): return 0.5 * (1 + erf(z / np.sqrt(2)))\n",
        "        z = beta / se\n",
        "        p = 2 * (1 - Phi(abs(z)))\n",
        "        lcl = np.exp(beta - 1.96 * se)\n",
        "        ucl = np.exp(beta + 1.96 * se)\n",
        "    else:\n",
        "        p = np.nan\n",
        "        lcl = np.nan\n",
        "        ucl = np.nan\n",
        "\n",
        "    return {\n",
        "        \"beta\": beta,\n",
        "        \"HR\": HR,\n",
        "        \"LCI95\": lcl,\n",
        "        \"UCI95\": ucl,\n",
        "        \"p\": p\n",
        "    }\n",
        "\n",
        "# 7. Ajustar un Cox univariante por cada dummy\n",
        "resultados = []\n",
        "for col in X.columns:\n",
        "    if col in [\"duration\", \"event\"]:\n",
        "        continue\n",
        "    res = cox_univariate(\n",
        "        X[\"duration\"].values,\n",
        "        X[\"event\"].values,\n",
        "        X[col].values.astype(float)\n",
        "    )\n",
        "    res[\"Covariable\"] = col\n",
        "    resultados.append(res)\n",
        "\n",
        "out = pd.DataFrame(resultados)[[\"Covariable\", \"HR\", \"LCI95\", \"UCI95\", \"p\"]].sort_values(\"p\")\n",
        "\n",
        "# 8. Forest plot (HR con IC95%)\n",
        "def pretty_label(s):\n",
        "    s = s.replace(\"Cargo_std_\", \"Cargo: \")\n",
        "    s = s.replace(\"Departamento_std_\", \"Departamento: \")\n",
        "    s = s.replace(\"Titularidad_std_\", \"Titularidad: \")\n",
        "    return s\n",
        "\n",
        "df_plot = out.head(10).copy()  # por ejemplo, top 10 covariables más significativas\n",
        "df_plot[\"Etiqueta\"] = df_plot[\"Covariable\"].apply(pretty_label)\n",
        "\n",
        "ylabels = df_plot[\"Etiqueta\"].tolist()\n",
        "HR = df_plot[\"HR\"].values\n",
        "lcl = df_plot[\"LCI95\"].values\n",
        "ucl = df_plot[\"UCI95\"].values\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(9, 6))\n",
        "ypos = np.arange(len(ylabels))\n",
        "\n",
        "# Longitudes de barras (error) izquierda/derecha\n",
        "xerr = np.vstack([HR - lcl, ucl - HR])\n",
        "\n",
        "ax.errorbar(HR, ypos, xerr=xerr, fmt='o', capsize=3)\n",
        "ax.axvline(1.0, linestyle='--')  # HR=1, sin diferencia\n",
        "\n",
        "ax.set_yticks(ypos)\n",
        "ax.set_yticklabels(ylabels)\n",
        "\n",
        "ax.set_xscale('log')  # escala log para leer mejor HR muy grandes/pequeños\n",
        "ax.set_xlabel(\"Riesgo de renuncia — Hazard Ratio (escala log)\\nHR = 1 sin diferencia; HR > 1 mayor riesgo; HR < 1 menor riesgo\")\n",
        "ax.set_title(\"Modelo de Cox univariante — Forest plot (HR con IC95%)\")\n",
        "ax.invert_yaxis()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# ver la tabla:\n",
        "print(out)\n"
      ],
      "metadata": {
        "id": "WWgUeJd5ERJM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}