{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nlMFvhQXj0Fi",
        "outputId": "c2f56fc1-cd7f-4aa1-cdbc-dd3c336dfd0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyod\n",
            "  Downloading pyod-3.5.2-py3-none-any.whl.metadata (58 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/58.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.0/58.0 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: openpyxl in /usr/local/lib/python3.12/dist-packages (3.1.5)\n",
            "Requirement already satisfied: joblib>=1.5 in /usr/local/lib/python3.12/dist-packages (from pyod) (1.5.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from pyod) (3.10.0)\n",
            "Requirement already satisfied: numpy>=1.19 in /usr/local/lib/python3.12/dist-packages (from pyod) (2.0.2)\n",
            "Requirement already satisfied: numba>=0.51 in /usr/local/lib/python3.12/dist-packages (from pyod) (0.60.0)\n",
            "Requirement already satisfied: scipy>=1.5.1 in /usr/local/lib/python3.12/dist-packages (from pyod) (1.16.3)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from pyod) (1.6.1)\n",
            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.12/dist-packages (from openpyxl) (2.0.0)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba>=0.51->pyod) (0.43.0)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=0.22.0->pyod) (3.6.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->pyod) (1.3.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib->pyod) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->pyod) (4.63.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->pyod) (1.5.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->pyod) (26.2)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib->pyod) (11.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->pyod) (3.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib->pyod) (2.9.0.post0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib->pyod) (1.17.0)\n",
            "Downloading pyod-3.5.2-py3-none-any.whl (396 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m396.0/396.0 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pyod\n",
            "Successfully installed pyod-3.5.2\n",
            "Experiment over outlier percentages: [1, 2, 3, 4, 5]\n",
            "Number of runs per percentage: 30\n",
            "================================================================================\n",
            "\n",
            "========== PERCENTAGE = 1% ==========\n",
            "  Run 5/30 completed for 1%\n",
            "  Run 10/30 completed for 1%\n",
            "  Run 15/30 completed for 1%\n",
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            "  Run 30/30 completed for 1%\n",
            "\n",
            "========== PERCENTAGE = 2% ==========\n",
            "  Run 5/30 completed for 2%\n",
            "  Run 10/30 completed for 2%\n",
            "  Run 15/30 completed for 2%\n",
            "  Run 20/30 completed for 2%\n",
            "  Run 25/30 completed for 2%\n",
            "  Run 30/30 completed for 2%\n",
            "\n",
            "========== PERCENTAGE = 3% ==========\n",
            "  Run 5/30 completed for 3%\n",
            "  Run 10/30 completed for 3%\n",
            "  Run 15/30 completed for 3%\n",
            "  Run 20/30 completed for 3%\n",
            "  Run 25/30 completed for 3%\n",
            "  Run 30/30 completed for 3%\n",
            "\n",
            "========== PERCENTAGE = 4% ==========\n",
            "  Run 5/30 completed for 4%\n",
            "  Run 10/30 completed for 4%\n",
            "  Run 15/30 completed for 4%\n",
            "  Run 20/30 completed for 4%\n",
            "  Run 25/30 completed for 4%\n",
            "  Run 30/30 completed for 4%\n",
            "\n",
            "========== PERCENTAGE = 5% ==========\n",
            "  Run 5/30 completed for 5%\n",
            "  Run 10/30 completed for 5%\n",
            "  Run 15/30 completed for 5%\n",
            "  Run 20/30 completed for 5%\n",
            "  Run 25/30 completed for 5%\n",
            "  Run 30/30 completed for 5%\n",
            "\n",
            "Summary table with 95% confidence intervals (first 20 rows)\n",
            "    Outlier %                        Method   F1_mean  F1_lower  F1_upper  \\\n",
            "0           1                   ABOD (pyod)  0.000000  0.000000  0.000000   \n",
            "1           1                   HBOS (pyod)  0.000000  0.000000  0.000000   \n",
            "2           1        IsolationForest (pyod)  0.000000  0.000000  0.000000   \n",
            "3           1                    kNN (pyod)  0.000000  0.000000  0.000000   \n",
            "4           1                    LOF (pyod)  0.000000  0.000000  0.000000   \n",
            "5           1                  OCSVM (pyod)  0.000000  0.000000  0.000000   \n",
            "6           1                    PCA (pyod)  0.000000  0.000000  0.000000   \n",
            "7           1                  COPOD (pyod)  0.000000  0.000000  0.000000   \n",
            "8           1         Random Forest (error)  0.466667  0.433654  0.499679   \n",
            "9           1        Neural Network (error)  0.606667  0.533328  0.680005   \n",
            "10          1  Autoencoder (reconstruction)  0.010000 -0.001394  0.021394   \n",
            "11          1              Combined (RF+AE)  0.026667  0.002780  0.050553   \n",
            "12          1        One-Class SVM (with y)  0.003333 -0.003484  0.010151   \n",
            "13          1     Isolation Forest (with y)  0.000000  0.000000  0.000000   \n",
            "14          1                  LOF (with y)  0.000000  0.000000  0.000000   \n",
            "15          1                    NNFA (ξ=0)  0.693333  0.662427  0.724239   \n",
            "16          2                   ABOD (pyod)  0.227273  0.227273  0.227273   \n",
            "17          2                   HBOS (pyod)  0.000000  0.000000  0.000000   \n",
            "18          2        IsolationForest (pyod)  0.000000  0.000000  0.000000   \n",
            "19          2                    kNN (pyod)  0.000000  0.000000  0.000000   \n",
            "\n",
            "    AUC_mean  AUC_lower  AUC_upper  \n",
            "0   0.494118   0.494118   0.494118  \n",
            "1   0.581373   0.581373   0.581373  \n",
            "2   0.739882   0.731547   0.748218  \n",
            "3   0.704167   0.704167   0.704167  \n",
            "4   0.672941   0.672941   0.672941  \n",
            "5   0.758889   0.758833   0.758945  \n",
            "6   0.754118   0.754118   0.754118  \n",
            "7   0.709412   0.709412   0.709412  \n",
            "8   0.968977   0.963951   0.974003  \n",
            "9   0.921895   0.904957   0.938834  \n",
            "10  0.569144   0.542343   0.595944  \n",
            "11  0.874484   0.859958   0.889009  \n",
            "12  0.452559   0.412453   0.492664  \n",
            "13  0.720595   0.707344   0.733846  \n",
            "14  0.574020   0.574020   0.574020  \n",
            "15  0.988196   0.984432   0.991960  \n",
            "16  0.531318   0.531318   0.531318  \n",
            "17  0.438515   0.438515   0.438515  \n",
            "18  0.547834   0.538999   0.556669  \n",
            "19  0.528116   0.528116   0.528116  \n",
            "\n",
            "Full results saved to 'outlier_percentage_study_with_CI.xlsx'\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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jYmKYIAjFan/16lUWEBDAAHOel9jYWJaTk8MYM0+rOnfuHJsxYwZbv369VbulS5dKYxg1apQkYGRnZ7PFixdLN+gBAwbY7NNyLC0PABMnTmR37txhjJmnN1he3P79918pEfjIkSNZXFycNIUiKSmJjRkzRhp7cZMcW15iJk6c6LBO586dbR6Oc3JypPwVQ4YMYZcuXZK2ZWdnsxMnTrD33nuP7dq1y2lbjEajJIr16dPH6XY//fSTdA4KTq96UAGpOO0Ze3AByRLmX716dRYTEyO9zObl5bEff/yRVa9enQH2p/hYHoI9PDyYh4cHW7NmjTQ9MzU11SpnSWE4IyCdO3dOqpPfV8rC/uXLl0v77tmzJzt16pTUR05ODtu/fz8bMGCAjRBQErb5+voyb29vtnbtWsm25ORk9txzz0kvDv/9959Ne2f95uWXX2Zr1661mhqg1+vZwYMHWYsWLRgA1rRpU7ttR48eLdkwb948SXBNSUmRhCHLC5G9FyHLFJWgoCC2bNky6XgbDAZ2+PBhaSpX06ZNi3093blzp/SCo9Pp7NbJLyBZch/Zw5LEt2HDhjbbLL/dKlWqsKioKKZSqZinpyerX78+Gz9+vN1zYyE6OpoBYC+99BLLyclhJpOJLVmyRPJJi7/cunWL+fr6svDwcCsB/WG4efOmNPZ33333ofoqawHJYDCw5ORktnbtWla1alUGgPn5+bGUlBSreidOnJDGuHv3bof9LVu2TKrn7DXLgmXsHh4eLCQkhG3btk16aU9OTpbu4SUlIPn6+jKlUskWLVok+UdqaiobMWKENIaff/7Zqu2RI0esfqf5x5iamsr27dvHhg4dyq5fv16ssRdFfgGpdevWzMvLi6lUKlalShXWu3dvtn37dofC5ebNm6W2cXFxDvfx3nvvScJNfhYsWCC1LyxlQL9+/RgA1qBBg2KPrySu797e3kylUrGVK1dKuf2uXr1qJUz9+OOPNu3feecdSYj58MMPJQE8LS2NffDBB3bvlRZOnjwpJeyvV68e2717t+SzJpOJnThxgr3zzjs2Uzotvu7r68tCQ0PZ9u3bpXYXLlxgrVq1kn4LGRkZVm0tApCfnx/bsmWLdE0WBIFdv36dffvtt1Yfhgqyf/9+6RnT2Q97BEEQRNE8lgJSYSt7FBbRYqEkBaRffvnF6mtKSEgI69+/P/v000/ZoUOHinzwtzyM+Pv7O726Tm5uLvPz85O+Ntnj888/l2w6ceKE1bb8x7KwL9uWnECTJ092WMfywvj88887ZbuFOXPmSC9g9l4Sr127Jn1Vy/9Ac/z4cQaYcw8VlXPKWeLj46XjYS+fhiOSk5OldlOnTrXa5soC0m+//Sa9wDvyueTkZElUyy+cMGa9qmFhuaKKwhkByfLVHbj/ZbQs7E9LS5OEyoEDBzodqVGSth06dMimrU6nY5UrV3boqyWRryIrK4sFBwczAOzIkSNW25KSkqTf5ccff2y3vcWv7QlICQkJTCaTMTc3N3b69Gm77bVaLatSpYrVOXcWS7Lp+vXrO6yzYcMGyT5H+X/yCy0BAQE22/OP0RKBlT+CSqlUsuXLl9vt+9ixY1J0acFV2D777DOp3sCBAxkAtnPnzmIdg8I4ePCgtK/CVsFyhrISkGrVqiXZnP9P3bp12T///GNTf8eOHVIde9stbN++Xap35syZYtlkGbtMJmMnT550WK+kBKTCftPNmjVjANiIESOsyi1CadeuXZ0ZUomRX0ACzGJyfh8HwLp3725XEMj/3GIv95iFzz77TKqXvx9LRLWvr2+hNk6YMEF67ioOJXl9txexIwiCFN1Zr149q23Xrl2TnjUdPZNZxq9QKNiNGzestlmE66ioKBuhpzAsvq5Sqezme7xz5450fgt+6LREis2ePdvp/eUnJSWl0PshQRAE8WA8ljmQbt++7fCP0WgsU1vat2+PvXv3olatWgCAW7duYePGjXj//ffRqVMn+Pr6okePHvjtt99s2ubk5GDDhg0AgEmTJiEsLMypfR44cABpaWkA4DDHyJgxY1CpUiUAwPfff2+3Ds/zmDhxot1tiYmJOHToEORyOd59912Htrz88ssAgIMHDzrMuWSPF198ETzP49q1azh8+LDN9piYGIiiiCpVqqBjx45SuSWngcFgwN27d53eX2Hk78ff39/pdgEBAXb7cHUs+QRefPFFhz6X/7jv27fPbp169eoVmivqQTEYDDh//jzGjRuHFStWADDnh3n22WcBlI39mzdvRlZWFhQKBRYtWuR07qmSsu3JJ5+08nsLKpUK3bp1AwD8+++/TtlUXDw8PKS8OL///rvVNkvOJ41Gg7feestue3s5wSysXbsWgiDg6aefRqNGjezW8fT0RK9evQA4Pj6OuHHjBgAgMDDQYZ1OnTpBpVIBAGbNmmW3Tv5yrVZrs71p06b44osvkJiYCL1ej7S0NGi1WmzZsgWRkZEwGAwYM2aM3fwerVu3xm+//YauXbtCo9EAAJo1a4YffvgB48ePBwDs3bsXsbGx6NevH3r06AHGGJYuXYq6detCpVIhNDQU48ePR1ZWlpNHxkz+65Sfn1+x2pYXgYGBCA4OtsoZ1bBhQyxduhQNGza0qZ//mFiOrz3ybyvucbTw9NNPo0mTJg/UtjiEhYVh6NChdrf17NkTgO31wHKvTElJKda9+WHx8fHBu+++i+PHjyMnJwfp6enIzc3F6dOnMWDAAADAnj17MGjQIJu2D3vuLH8vrG3+7cU97yV1fQ8LC8Pw4cNtynmex9SpUwEA586dw5kzZ6RtW7ZsgclkglqtxqRJk+z2O3XqVKhUKhiNRikfHQDEx8dL1/LZs2fD29u7qKHa0LdvX9SuXdumPDAwUMoz5sgHb968Wez9AeZrFM+bX3Ms13aCIAji4Sk8a/MjCmOsvE2wonPnzoiLi8ORI0ewb98+HD9+HKdPn0ZaWhqMRiN2796N3bt348MPP8RHH30ktTtx4oQkeBXnRfzEiRMAzA8hNWvWtFtHJpOhU6dOiImJkeoXpEaNGggKCrK77ejRowAAURRRt25dh7ZYHkxzcnJw9+5dh/0VJCwsDB06dMChQ4fw3XffoXPnzlbbLUmzLUKThcjISNSuXRsXLlxAy5Yt8frrr6Nbt25o0KBBqSZLf5SwnNuvv/7aobgIQEqmaUnMXJAnn3yyxGwaPny43QdqAKhWrRp27NghJakvC/uPHTsGwPxibxFinaGkbGvZsqXDtpUrVwYASUR+UHbu3InvvvsOf//9N27fvo3c3FybOgUTFJ88eRIA0Lx5c7i7u9vtNzIyEmFhYUhOTrbZZjk++/fvR0hIiEPbLEm0HR0fR1gSUBcmjgQEBGDcuHGYP38+Dhw4gCFDhmDq1KmIjIzEzZs3sXz5cixbtgwKhQJGo9Hq+mNh3LhxNmUajQa9e/dG+/bt8cQTTyAhIQHvvPMOevfubSNAtmjRwuHLZW5uLsaMGQNvb28sWbIEgDmp94IFCxAaGopBgwbh9OnT+Pzzz3HixAn8+uuvRS7gUJE5cuSI9Pe0tDRs374d06ZNQ+fOnfHKK69g1apV5Tb+krwGFsYTTzzhUMR2dD3o3Lkz1Go1Tp06hbZt2+LVV19Fp06d7CamLkkaN26Mxo0bW5VxHIdGjRohNjYWQUFBWLp0qbRwRadOnUrVnpKkpK7vlmTa9mjbti3kcjlMJhNOnDiBBg0aALj/3PfEE0/YJOC34Ovri+bNm+Po0aNWz32W+5lMJkP37t0LG6JDHuSe9Oyzz+KHH37AF198gZSUFAwYMADR0dFWH98Kg+d5eHt7Iz093WpxAYIgCOLheHSfGisYPM+jffv2VivaXLhwAT/88AMWLlyInJwcfPzxx2jRooUUSXHr1i2pbnh4uNP7unPnDgAgNDS00HpVqlSxql+QwsQey9ceURQdrgBXEHsvoIXx8ssv49ChQ9iyZQuWL18ufRU8ffo0zp49K9XJj0wmQ2xsLF544QUkJCRg0qRJmDRpEjQaDdq0aYPevXtj6NChRX6BzE/+qKPiRBLlX6mqOJFL5Y3l3Gq1WrvRFQVxdF6dFQudwcvLS1qlUCaTwcvLCzVr1sTTTz+Nl19+2UqsKAv7Lb/N4vwuS9I2T09Ph20sL8sPGm0piiKGDBmCH374wapPX19fKJVKAOYXIJ1Oh5ycHKu2lod4ywuDI0JDQ+0KSJbjk5OTY9O3PYp7TbGsKGWJMHLE7NmzkZycjNjYWMTExNis5tSqVSs0btwYK1eulFb7chZ/f3988MEHGDlyJJKSknDq1KkiV5zMz8yZM5GQkICVK1eiUqVKuHjxIhYuXIjg4GCcPHkSQUFBMJlM6NKlC3755ResWbMGI0eOdNo2Cw8rQJYHfn5+eOWVV9C5c2fUr18f33zzDZo0aYI333xTqpP/t1OY/+TfVtjvrTBK8hpYGA9yPYiMjMRXX32F1157DX/88Qf++OMPAOaIkY4dO2Lw4MHo2bNnsVb2LAlmz56NL7/8EjqdDtu3b7cSkAqeO0dCiaNzZ/l7UdcNy/binveSur4X9uymVqvh7++P27dvWz27Pcxzn+V+FhAQ4FD4L4oH8cHBgwfjr7/+wtKlSxEbG4vY2FgA5o+XXbt2xSuvvIJmzZoVul83Nzekp6e73GqBBEEQFZnHcgpbRaF27dqYOXMmduzYIT2kffXVV9L2sn5wK0hhETuWyKLg4GAwc66tIv/YW4a4MPr06QONRoPs7Gyr5Wct0UdNmza1G/3UqFEjXLhwAVu2bMGoUaNQv3595OXl4eDBgxgzZgxq165tFfpdFOHh4dJDlSXCwhlOnTol/b1evXpOtytvLOd2xYoVTp3XtWvX2u2nJCO+lixZglu3buHWrVu4fv06zp8/jx9//BGvv/66zQNvWdj/oL/NkrKtNPn666/xww8/QCaTYdq0aYiPj5emYVnOQd++fQE4jvZ82OMzceJEp47PL7/8Uqz+LQJJenp6ofXkcjl++OEH7Nq1CwMGDEDt2rURHh6Otm3b4vPPP8dvv/0mvfw5ivIsjPxLx1+5csXpdv/++y8WLVqEJ598EqNGjQIA/Pjjj2CMYciQIZJgIZfLMWHCBAAo1lLk+a+n+a9fFY3w8HD07t0bALB69WqrbfnFzevXrzvsI/+2ogRRR7h61OuLL76IpKQkrFy5EgMGDEBYWBhSUlKwceNG9OrVC+3bt3dKCClJPDw8UL9+fQC2v43injsvLy94eHjYtE9PT0deXl6R7Yt73ivC9d0e5fms+dlnn+HixYuYPXs2unfvDh8fH1y6dAnLly9H8+bNpeuYIyxCd0X6SEcQBOHqkIBUAejUqRNq1KgBALh48aJUnn8KR3GmalheIgpOLymIZfuDfCW12JaamupUpMCD4OHhgRdeeAHAfdFIEAQpNLxg9FF+lEolevfujVWrVuHMmTNISUnBypUr4efnh+TkZIf5IuyhUCjQtm1bAOapNc7mRdi6dSsAc/RZhw4drLZZvsgV9tXMEuZe1ljObXGnB7kKZWH/g+6jIhxby1fgESNGYObMmahRo4bNNK380ZH5seQWKiofhaOXv9I+Phb7nI2ueeaZZxAbG4vz588jMTERv/32G8aOHQuFQiFNAWnTpk2p2FoQURQxatQocByHVatWSS99lpfsyMhIq/pRUVFW252hUqVKkohkEaYqKpZIjEuXLlmV16lTR/JnSySrPSzbQkJCSi0flCvcB/z8/DB69GjExsbi6tWruHTpEiZNmgSO43DkyBGHeRTLA4uwBDh37gp+YCpu++J++Cmp61dh4pher5ciofM/uz3Mc19ZPM8VRo0aNTB58mTs3r0bd+/exR9//CHluVuyZAl27Nhht11eXp702yksrx1BEARRPEhAqiBYvpLln1rRvHlzacrITz/95HRfzZs3B2B+UPjvv//s1hEEQUpO/cQTTxTbXktuB0EQsGfPnmK3d5b8Sbhv3bol/V8ul2Pw4MFO9+Pv74/Ro0dj3rx5AMxf14szHe31118HYM69smjRoiLrx8fHSy/iL7zwgk0+F8u0F3vTeADzy6Kj3FQApBeg0njBs5zbnTt3lnjfZUFZ2G8RDU6cOFGsBKDlfWyd8RuLTzpK/pudnY3jx4/b3WaZinXixAmHLyJXrlxx6PeW43Pw4MFSmZJgeaFMSEh4qH5OnTqFuLg4AIUL2Y74888/pb87m3Nm+fLlOH78ON5//327L7YFIyoKi7AojDfeeAOA+Rq2fv16p9uJovhA+ystLMJZwak1Go1G8rO9e/fabcsYk3JQde3atdRsLOo+AMDhb620iIyMxJw5c6T764EDB8p0/9nZ2ZKAU/C3UbNmTVStWhWA43OXk5Mj5cUqeO6io6OlqdCO2iclJeH8+fN22xdFSV3ff/31V4fX6CNHjsBkMgG4/6yX/+8nTpxwKDpmZGRY5UqyYLmflfbznDPwPI9WrVph8+bN0rl25IP5r+N16tQpE/sIgiAeB0hAKmf2799f5Ev+P//8g3/++QcArHJhaDQaDBw4EAAwd+7cQh8y89OlSxcpnNfR18NVq1ZJUQL2VjspiqioKCmqZsqUKUV+JX3QfBpPPfUUKleuDEEQEBMTI0UiPf3003a/OOn1+kL7szw8ArCb/NYRzz33nDTeWbNmFfqAePfuXfTr1w86nQ4ajQYff/yxTR3LClPbtm2z6x/r1q0r9EuiJfdDRkaG02NwFsvUmLNnz0qrnDkiJycHBoOhxG14GMrC/n79+sHLywsmkwlvvfWW00JeeR9bZ/zGsgKP5ZpUkI8//thhFF7v3r3B8zxycnKkBM8FcbSyGQC88sorkMvlSE1NxfTp0x3WA8yr8VmSaTtLu3btAJjzfyQmJharrYXc3FxJULa38lBRvpCWlobZs2cDMC8W4MwqXdevX8eUKVNQo0YNaRUmC5aX7L/++suq3CJSVa9evcj+8/Pqq69KiXnfeOMNuyuE5kcQBHz00UfYtWtXsfbzMFheoB0RFxeHH3/8EQBsoj8BSBGohw8ftivQbNq0SRKgHkQgdBbLfeDvv/+2e38/f/68FMla0jh7ryzOfdIZivp9TJ06VRKPn3/+eattHMdJ5yM2Ntbub3jZsmXIzs6GTCbDiy++aLXN3d0dffr0AWCeZmbvucXykSn/ao/OUlLX96tXr2LdunU25aIoSteOunXrSr9TwDzlXy6XQ6fTSWMoyOzZs6HX66FQKKTjAJgjgCzXxg8++KDMpi0W5oMymUz6gOrIBy2/3eDgYGmlY4IgCKIEYI8J06dPZwDYgwxZp9OxlJQU6c/SpUulvvKXp6SkMEEQitW3v78/q1mzJvvoo4/YX3/9xfR6vbTt5s2bbNGiRSwgIIABYHK5nJ0+fdqqfXJysrQ9LCyMbdiwgeXm5jLGGBNFkZ05c4a9++677Ntvv7Vql38Mo0ePZrdu3WKMMZaTk8OWLFnCFAoFA8AGDBhgY7PlWLZv377QsZ05c4Z5eHgwAKx27dps+/btLC8vT9p+7do19u2337JOnTqxESNGFOu45efdd99lAFidOnWYu7s7A8A2bNhgt+7atWtZmzZt2MqVK9nly5elcpPJxPbu3cuqVKnCALDWrVsX246bN2+y6tWrS+dq3LhxLC4uTtqekZHB1q5dy6pWrcoAMJlMxr7//nu7fR08eFA6PyNGjGCpqamMMcYyMzPZokWLmFKpZH5+fgwAGzp0qE37F198kQFgbdq0YWlpaQ5ttuzj8OHDxdo2fPhwBoBxHMcmTJhgdSx1Oh37448/2Hvvvcf8/f1ZcnKyVdv27dszAGz69OkO7XKGhIQEycY1a9YUq21Z2L9y5UrJvueff56dOnVK2paTk8N27tzJevbsyTIzM8vUtsJ+v874zdSpUyUfX7VqlXTNunnzJpswYQIDwPz9/R365siRIyX/nz9/PsvKymKMMZaamsreeustBoD5+Pg4HMfMmTOl4/rSSy+xM2fOSNuMRiM7deoUmzlzJgsLC2NHjhxxeBwcERQUxACw2NhYh3X+/PNPNmvWLHbu3Dlp/Hq9nu3Zs4c1adJEuh7fvn3bpu23337LXnjhBbZ582ar7bm5uWzbtm2sZs2a0vgKsyE/L7zwAgPADh48aLMtLi6OAWAKhYJt2bKFiaLIzp49y0JDQxkAtmrVKqf2kZ9Lly6xSpUqSf2OGTOG/fXXX8xkMkl1EhIS2LJly1iNGjUYALZt2zarPsLDwx36CGP3rz+OthdG9erV2cKFC9n58+et7sm3b99my5cvl+6ZKpXKyn8sGI1G1qBBAwaAhYaGSsdVEAS2ceNG5uXlxQCw7t27F9s2xu6PvajrVkZGhnQPbdWqFbtw4QJjjDGDwcC2b9/OKleuLN0HwsPDbdoPHTq0yGO4Zs0au+1HjBjB+vXrZ+OnWVlZbMWKFUypVDIAbPLkyTZ9Psy5q127Nlu0aBGLi4uTzp0oiuzff/+Vrk+FHfuMjAwWEhLCALC6deuyEydOMMbMv8/ly5dLdr/++ut221+5ckV6jmjbti3777//GGOMZWdns5kzZzKO4xgANm/evGKPjbGSub57e3sztVrNVq9eLT1TXb16lfXv3186Plu3brXZ9zvvvCPte9q0aSw9PZ0xxlh6erp0XQfAJk6caNP21KlTTK1WMwCsfv36bM+ePcxgMDDGzM9Pf/31Fxs9ejQ7cOCAVTtnfN2RnzZq1IiNHTuWHT58mGVnZ0vl169fZ2+++aZk7969e+32O3r0aAaA9e/f3+G+CYIgiOJDApITWB6wnPmTkJBQrL4tDzqWPzzPM19fX6ZSqazKPT092aZNm+z28b///U96GbC8mPn7+0s3ewBs8eLFNu0sL2uWBwpfX18ml8ulso4dOzKtVmvTzlkBiTHGfv/9d6sxWmxzc3OzGt/DCEj//vuvVV8+Pj5WQlV+Cp5LlUrF/P39Gc/zUlnlypXZ+fPnH8iWO3fusG7dulntQ61WSy/Elj+VKlVie/bsKbSvl156yWZcFjvHjh1b6MvBr7/+Kj3oymQyVqlSJRYeHm7zkvCgApJer2cjRoywss/Dw4P5+vpaHUsA7Nq1a1ZtXUFAKiv7Z8+ebdWfm5sb8/PzsyqzPMSXlW2F/X6d8Zv09HRWu3Ztq2uWj4+P1G706NGF+mZWVhaLjo62uib4+vpK7adOncratWvHALA5c+bYtBdFkX344YdSfctx9ff3ZzKZzOr4/P7774WdHruMHz+eAWCDBw92WGfbtm1W104/Pz+rfdevX9/hvaDgNcjd3d3GdpVKxZYtW+aUvdu3b2cA2Msvv+ywzrhx46yOleXvLVu2lF4Ci8v169dZ586dbe5ffn5+0kt6/v3kf1FmrHQFpPz7lsvlzN/fn3l6elqVBwYGsn379jnsIyEhgUVEREj1NRqN1T21SZMmhYrzheGsgMQYY1999ZWVr3t6ekrHt1WrVuyLL75gQMkLSJa2+a9BBe9j0dHRVi/2Fkrq3CkUCrvPC88884zdZxMLJ06ckERsyzGzfBQDwLp27cp0Op3D9rt27WIajUaq7+3tbfX7HD58OBNFsdhjY6xkru+TJ0+WrqEKhYL5+vpatZs6darDfecXmSzPm/n3O2jQIIfXhH379jFvb2+b85P/2DoSih9EQLK0tVxnfXx8JHHP8uett96y26cgCNIHwe3btzvcN0EQBFF8SEBygtIUkLRaLdu0aRMbM2YMa9WqFQsMDGRyuZwplUoWHBzMOnTowGbNmiVFCBXWz9y5c1mrVq2Yj48PUyqVLCwsjHXo0IEtWrTI7pdwxhg7dOgQ69OnDwsJCZEeRDp27Mi++eYbq6/J+SmOgGSxbcGCBaxdu3bSi5KHhwerU6cOGzJkCIuJibH7EFocGjduLJ2DkSNHOqx39+5d9u2337Lhw4ezRo0asaCgICaXy5m3tzdr0aIF+/jjj21e6B+EQ4cOsREjRrBatWoxLy8vplKpWGhoKOvevTtbtmwZy8nJKbIPQRDYkiVLWOPGjZmbmxvz8vJibdu2ZRs3bmSMFf1ysHv3bvbUU0/ZCGT5sZQVV0CycOzYMTZs2DAWGRnJ3NzcmEKhYCEhIaxDhw5s2rRp7N9//7Vp4woCUlnaf+bMGTZy5EhWo0YN5ubmxjw8PFitWrXYoEGD2NatWx1GLZaWbUX9fp3xm7S0NDZhwgQWERHBFAoFCwgIYB07dmQ//PADY6xo39Tr9Wz+/PmsYcOGzM3Njfn4+LAOHTpIX80bNmzIALAVK1Y4HMeZM2fYmDFjWJ06dZiHhweTy+UsICCAtWnThr333nvs2LFjDtsWhiVix93d3eF16erVq+y9995jLVu2ZMHBwUyhULCgoCD21FNPsdWrVzOj0eiw/8TERDZr1iz27LPPssjISObj48Pkcjnz9fVlTzzxBJs4cSK7cuWKU7ZmZWWxKlWqMH9/f5aSkuKwniiKbNGiRaxWrVqSH7355pssIyPDqf0Uxm+//cbeeOMN1rBhQ+bn5yddTxs1asRee+019ssvv9htV5iAdO3aNcnvCvMBR/z000/snXfeYa1bt2ZhYWFMrVYzlUrFKleuzLp27co+++wzp67zWq2WTZs2jdWvX5+5u7szT09P1qxZM7ZgwQKraOHiUhwBiTHzb7JTp07My8uLubm5sfr167O5c+cyvV7vUABi7OEEpEuXLrHPP/+cvfDCC6x27dqSnwYFBbEuXbo4fEZ42HO3evVqNnz4cNawYUPpt6XRaFhkZCQbNGgQ2717t1P93Lp1i7311lssKipK+ogTHR3NvvzyS6cixS9dusRGjhzJIiIimEqlYgEBAaxLly5s8+bNxR6TPR72+q7X69ns2bNZ/fr1mUajYd7e3qxz585s165dRe578+bNrHv37tLzZmBgIOvevbvdqKWC3Llzh02ZMoU1adKEeXl5MbVazSIiIli3bt3YqlWrbCJqH0ZA+uOPP9jMmTNZ586dWfXq1ZlGo2FKpZKFh4ezAQMGsJ9//tlhn4cOHWKAOYLQ0bMsQRAE8WBwjFXgZVQIgiCIR4rs7Gz4+/vDYDDgt99+k1Y4LEs6deqEw4cPY926daWa44awz/r16/HSSy+hevXquHDhAhQKRXmbRDgJnbvSo0OHDvj1118xffp0l1r9zhV55ZVXsGbNGsycORPTpk0rb3MIgiAeKSiJNkEQBOEyLFq0CAaDAX5+fg+0AmRJYEnk/emnn1bopeorKocOHQJgXuSBBIiKBZ07orxJTk5GTEwMAgMDMWHChPI2hyAI4pGDBCSCIAiizMjKysLAgQOxd+9eq9XekpKS8N5770lf1idMmAC1Wl0uNrZu3Rp9+/bFuXPnsGnTpnKx4XHm8OHDqFevns0qWYTrQ+eOKG9mz54Ng8GAGTNmSCuLEgRBECWHvLwNIAiCIB4fBEHAhg0bsGHDBgDm5bABs7BkoU+fPpg8eXK52Gdh/vz5qFevHoxGY7na8TiSkJBQ3iYQDwidO6I8EUURVatWxSeffIJRo0aVtzkEQRCPJCQgEQRBEGWGh4cHvvjiCxw4cABnz55FSkoK8vLyUKlSJTRv3hwvv/wy+vTpA47jytXOiIgIyjNCEARRgeB5vtw/PhAEQTzqUBJtgiAIgiAIgiAIgiAIolAoBxJBEARBEARBEARBEARRKDSFrRiIoogbN27A09Oz3KdXEARBEARBEMSjCGMMWVlZqFy5MnievncTBEG4CiQgFYMbN24gLCysvM0gCIIgCIIgiEee5ORkVKlSpbzNIAiCIO5BAlIxsKwWlJycTEuDEgRBEARBEEQpoNVqERYWJj17EwRBEK4BCUjFwDJtzcvLq1wFJEEQEBcXh7p160Imk5WbHQSRH/JLwlUh3yRcEfJLwlVxJd+klBEEQRCuBU0qrqAIglDeJhCEDeSXhKtCvkm4IuSXhKtCvkkQBEHYgwQkgiAIgiAIgiAIgiAIolBIQCIIgiAIgiAIgiAIgiAKxaUFpL///hvPPPMMfHx84O7ujlatWmHjxo3F6uPGjRsYP3486tatC3d3dwQHByM6OhrfffddhQ3P5XketWrVomVNCZeC/JJwVcg3CVeE/JJwVcg3CYIgCEe4bBLtw4cPo1u3blCr1Rg4cCA8PT2xZcsWDBgwAMnJyXjnnXeK7OPKlSto2bIl7t69i27duuG5556DVqvF9u3b8fLLL+PQoUNYs2ZNGYym5FEqleVtAkHYQH5JuCrkm4QrQn5JuCrkmwRBEIQ9OMYYK28jCmIymVC7dm1cu3YNf/75Jxo3bgwAyMzMRIsWLZCYmIj//vsP4eHhhfYzZswYrFixAp999hnGjx8vlWdkZKBRo0a4evUqEhMTi+zHglarhbe3NzIzM8t9FbYzZ86gQYMG5b46BkFYIL8kXBXyTcIVIb8kXBVX8E1XeeZ+UARBgNFoLG8zCIIgikShUBTrWu+SEUiHDh3C5cuXMXz4cEk8AgBvb2988MEHGDZsGNatW4dp06YV2s+VK1cAAM8884xVuY+PD6Kjo/H9998jNTXVaQGJIAiCIAiCIAjCHowx3Lp1CxkZGeVtCkEQhNP4+PggJCQEHMcVWdclBaRffvkFANC1a1ebbd26dQMA/Prrr0X2U79+fezbtw+7d++2iUA6evQoQkJCULdu3ZIxmiAIgiAIgiCIxxaLeBQUFASNRuPUyxhBEER5wRhDbm4u7ty5AwCoVKlSkW1cUkCKj48HAERFRdlsCwkJgYeHh1SnMN577z389NNPeOutt7B37140bNhQyoGk0Wiwbds2uLm5OWyv1+uh1+ulf2u1WgDmsFRLAm6O48DzPERRRP7ZgJbygom6HZXzPA+O4+yWA4AoilKZIAhgjIExZlNfJpOBMWZV31Je0EZH5eUxpsLKaUwVY0wAHPpkRR3To3ieHscxWa6Zlv08CmN6FM/T4zYmi1+KoljoWCvSmAraSGOqmGOy9FmeY6qIC90IgiCJR/7+/uVtDkEQhFNY9JA7d+4gKCioyOlsLikgZWZmAjBPWbOHl5eXVKcwgoOD8ccff2DIkCHYs2cP9u7dC8B8kF577TU0atSo0PZz5szBzJkzbcrPnTsHDw8PAICfnx+qVq2Ka9euIS0tTaoTEhKCkJAQJCYmIisrSyoPCwuDv78/4uPjodPppPLq1avDy8sLcXFxVjfNWrVqQalU4syZM1Y21KtXD0ajERcvXpTKZDIZGjRogKysLGn6HgCo1WrUrl0b6enpSE5Olso9PT0RGRmJO3fu4NatW1J5eY2pQYMGMBgMNKYKOqb69eujWrVqiIuLe2TG9Ciep8d5THFxcY/cmIBH7zw9bmO6efPmIzemR/E8PU5jCg4ORoMGDcp1TNnZ2ahoWHIeaTSacraEIAiieFiuW0ajsUgBySWTaHft2hUHDhxAfHw8atSoYbM9NDQU2dnZRYpIly5dwnPPPQcPDw8sXrwYjRs3RkZGBtavX4+pU6eiRYsWOHLkiMODZC8CKSwsDGlpaVJCv/L4IsUYg8FggFqttvuFib6y0ZjKY0w8zyMvLw9KpdIqZLsij+lRPE+P45hEUYROp4NarQbHcY/EmB7F8/S4jYkxBp1OBzc3N4pAojG51JgAwGAwQKFQWN3Py3JMWq0Wfn5+FSqJtk6nQ0JCAqpVqwa1Wl3e5hAEQThNca5fLhmBZIk8ciQQabVa+Pr6FtnPsGHDkJSUhCtXriAkJAQA4OHhgUmTJuH27dv47LPPEBsbixdffNFue5VKBZVKZVMuk8lsRCfLzdBe3ZIuFwQB//33n8PVMSwvSAVxZGNxy0tjTEWV05hcf0yF+WVFHRPw6J0n4PEbE2MMly5dsvLNij4me9CYKtaYBEGQ/LKw+hVpTM6W05hce0yCIODixYsOnzPLYkyO2hAEQRDli/07STljyX1kL8/RrVu3kJ2dbTc/Un6ysrJw9OhR1KlTRxKP8tOxY0cAwKlTp0rAYoIgCIIgCIIgCKKsGDZsGCIiIsrbjMeOv/76C0qlEklJSeVtitOsXLkSVatWtZpdRDwYLikgtW/fHgCwf/9+m2379u2zquMIg8EAAEhNTbW7PSUlBQDsRhgRBEEQBEEQBEEQwNq1a8FxnPRHLpcjNDQUw4YNw/Xr18vbPJeh4HHK/2fSpEnlbZ5dZs+eje3btxerzZQpUzBo0CCEh4eXjlGlwLBhw2AwGLBq1aryNqXC45JT2Dp37ozq1avj+++/x7hx49C4cWMA5ilts2fPhlKpxMsvvyzVv3nzJjIzM1GpUiVp+pu/vz9q1aqFixcv4quvvsKIESOk+hkZGViwYAGA+5FIFQ0K7SVcEfJLwlUh3yRcEfJLwlUh3yTs8dFHH6FatWrQ6XT4888/sXbtWvz+++84e/Ys5X3Kh+U45ad+/frlZE3hzJ49G3379kWvXr2cqn/69GkcPHgQx44dK13DShi1Wo2hQ4di0aJFGDt2rFV+N6J4uKSAJJfL8dVXX6Fbt25o164dBg4cCE9PT2zZsgVJSUlYsGCBVbji5MmTsW7dOqxZswbDhg2TyhcvXoyePXti5MiRiI2NRZMmTZCeno4dO3YgJSUFffr0wVNPPVX2A3xIZDKZlDOBIFwF8kvCVSHfJFwR8kvCVSHfdB10RgG7z9zE/nO3kZFrgI9Gia71gvFMg0pQK8pe5OvevTuaN28OABgxYgQCAgIwb9487NixA/379y9ze1yV/MepJMnJyYG7u3uJ91sc1qxZg6pVq6JVq1Yl1qejcYmiKC0cVRJ99+/fH59++ikOHz6MTp06PXCfjzsuOYUNMEcG/f7773jyySexYcMGrFixAsHBwYiNjcU777zjVB/du3fHsWPH0K9fP8TFxeGzzz7Dhg0bEBERgaVLl2LDhg2lPIrSgTEGrVZrs2IGQZQn5JeEq0K+Sbgi5JeEq0K+6RociLuNFrMP4u2N/2B/3C38mZCG/XG38PbGf9Bi9kEcjLtd3iaibdu2AIDLly9LZQaDAdOmTUOzZs3g7e0Nd3d3tG3bFocPH7Zqm5iYCI7jsGDBAqxevRqRkZFQqVR44okn8Pfff9vsa/v27ahfvz7UajXq16+Pbdu22bUpJycH77zzDsLCwqBSqVCrVi0sWLDAxp85jsObb76JTZs2oW7dunBzc0Pr1q1x5swZAMCqVatQo0YNqNVqdOjQAYmJiQ9zqKw4dOgQ2rZtC3d3d/j4+OD555/H+fPnrerMmDEDHMchLi4OgwcPhq+vL6Kjo6Xt69evR7NmzeDm5gY/Pz8MHDgQycnJVn3Ex8ejT58+CAkJgVqtRpUqVTBw4EBpoSqO45CTk4N169ZJU+3yB2PYY/v27ejUqZPdCJ49e/ZI4/L09ESPHj1w7tw5qzrDhg2Dh4cHLl++jGeeeQaenp7SglaWcxITE4N69epBpVJh7969AMx5i7t37w4vLy94eHigc+fO+PPPP636tkwh/PXXXzFmzBgEBQWhSpUq0vZmzZrBz88PP/74Y6FjJArHJSOQLLRo0QJ79uwpst7atWuxdu1au9ueeOIJbNy4sYQtK19EUcSVK1ccro5BEOUB+SXhqpBvEq4I+SXhqpBvlj8H4m5j1HcngHuah1jg/1l5Joz87gRWv9QcXeoGl4+RgCSq5F8dW6vV4quvvsKgQYMwcuRIZGVl4euvv0a3bt3w119/SalJLHz//ffIysrC6NGjwXEcPv30U/Tu3RtXrlyBQqEAYM6L26dPH9StWxdz5szB3bt3MXz4cCtxADCLnz179sThw4fx6quvonHjxti3bx/ee+89XL9+HYsXL7aqf+TIEezYsQNvvPEGAGDOnDl49tln8f7772P58uUYM2YM0tPT8emnn+KVV17BoUOHnDoumZmZNnl4AwICAAAHDx5E9+7dUb16dcyYMQN5eXlYunQpnnzySZw8edImKXi/fv0QFRWF2bNnSyLYrFmz8OGHH6J///4YMWIEUlJSsHTpUrRr1w6nTp2Cj48PDAYDunXrBr1ej7FjxyIkJATXr1/Hzp07kZGRAW9vb3z33XcYMWIEWrRogVGjRgEAIiMjHY7r+vXruHr1Kpo2bWqz7bvvvsPQoUPRrVs3zJs3D7m5uVixYgWio6Nx6tQpq3GZTCZ069YN0dHRWLBgATQajbTt0KFD2LhxI958800EBAQgIiIC586dQ9u2beHl5YX3338fCoUCq1atQocOHfDrr7+iZcuWVraMGTMGgYGBmDZtGnJycqy2NW3aFEePHnU4RsIJGOE0mZmZDADLzMwsVztMJhM7deoUM5lM5WoHQeSH/JJwVcg3CVeE/JJwVVzBN13lmbs45OXlsbi4OJaXl/dw/RhMrMGMvSxi4k4WXsifiIk7WcMZe1meofTP05o1axgAdvDgQZaSksKSk5PZ5s2bWWBgIFOpVCw5OVmqazKZmF6vt2qfnp7OgoOD2SuvvCKVJSQkMADM39+fpaWlSeU//vgjA8B++uknqaxx48asUqVKLCMjQyrbv38/A8DCw8Olsu3btzMA7JNPPrHaf9++fRnHcezSpUtSGQCmUqlYQkKCVLZq1SoGgIWEhDCtViuVT548mQGwqlvYcbL3J/9YgoKC2N27d6Wyf/75h/E8z15++WWpbPr06QwAGzRokNU+EhMTmUwmY7NmzbIqP3PmDJPL5VL5qVOnGAC2adOmQm12d3dnQ4cOLbSOhYMHD9qcG8YYy8rKYj4+PmzkyJFW5bdu3WLe3t5W5UOHDmUA2KRJk2z6B8B4nmfnzp2zKu/VqxdTKpXs8uXLUtmNGzeYp6cna9eunVRmOf7R0dEOr1+jRo1ibm5uTo33caI41y+XncJGEARBEARBEATxOLH7zE1o80woagIhA5CZZ8KeszfLwiwAwFNPPYXAwECEhYWhb9++cHd3x44dO6wigWQyGZRKJQBzNFtaWhpMJhOaN2+OkydP2vQ5YMAAqwgmy7S4K1euADAvlnT69GkMHTpUWiwJALp06YK6deta9bV7927IZDKMGzfOqvydd94BY8xmZkvnzp2tImMskSx9+vSBp6enTbnFpqJYtmwZDhw4YPUn/1iGDRsGPz8/qX7Dhg3RpUsX7N6926av1157zerfW7duhSiK6N+/P1JTU6U/ISEhiIqKkqYKWo7Vvn37kJub65TdRXH37l0A1hFnAHDgwAFkZGRg0KBBVjbJZDK0bNnSZvoiALz++ut299G+fXur8yoIAvbv349evXqhevXqUnmlSpUwePBg/P7779BqtVZ9jBw50mH0pK+vL/Ly8krsmDyOuPQUNsIxtNIB4YqQXxKuCvkm4YqQXxKuCvlmyfLc0t+RkqV3qm56rqFYfU/acgbz9lx0un6gpwo/jY0uuqIdli1bhpo1ayIzMxPffPMNfvvtN6hUKpt669atw8KFC3HhwgUYjUapvODKZABQtWpVq39bxIn09HQAQFJSEgAgKirKpm2tWrWsRKmkpCRUrlzZSvwBgDp16lj15WjfFtElLCzMbrnFpqJo0aKF3STalv3XqlXLZludOnWwb98+m4TSBY9ZfHw8GGN2jwcAadpftWrV8Pbbb2PRokWIiYlB27Zt0bNnTwwZMsRKiHsQWIF8UvHx8QDgMDG1l5eX1b/lcrnN9EMLBcebkpKC3Nxch8dMFEUkJyejXr16DvuwZzutwvbgkIBUAZHJZKhdu3Z5m0EQVpBfEq4K+SbhipBfEq4K+WbJk5Klxy2trlT61pvEUuu7IPmFkV69eiE6OhqDBw/GxYsX4eHhAcCc3HnYsGHo1asX3nvvPQQFBUEmk2HOnDlWybYtOIoUKShSlAaO9l2eNhXEzc3N6t+iKILjOOzZs8eunZbzAAALFy7EsGHD8OOPP2L//v0YN24c5syZgz///NOhgFMY/v7+AGyFNFEUAZjzIIWEhNi0k8utJQeVSgWetz8RquB4H4TC+khPT4dGoymR/TyukIBUARFFEenp6fD19XX44yOIsob8knBVyDcJV4T8knBVyDdLnkBP2ygdR6TnGqA3iU7XV8l5+GqUpWJLYVhEoY4dO+KLL77ApEmTAACbN29G9erVsXXrVqsoj+nTpz/QfsLDwwHcj3LJz8WLF23qHjx4EFlZWVZRSBcuXLDqq7yw7L+g3YDZxoCAALvL2ecnMjISjDFUq1YNNWvWLHKfDRo0QIMGDTB16lQcO3YMTz75JFauXIlPPvkEQPEicSzCckJCgo1NABAUFISnnnrK6f6cITAwEBqNxuEx43neJmKsMBISEqSINOLBIAGpAsIYQ3JyMnx8fMrbFIKQIL8kXBXyTcIVIb8kXBXyzZKnOFPGtp68hrc3/uN0/bl9GuCFJsWPJikJOnTogBYtWuCzzz7DhAkToFarpagYxpgkThw/fhx//PGHzZQxZ6hUqRIaN26MdevWYdKkSdL0qwMHDiAuLs5KFHrmmWewevVqfPHFF5g8ebJUvnjxYnAch+7duz/McB+a/GOZPHmy9Bs7e/Ys9u/fjyFDhhTZR+/evTF58mTMnDkT69evtxKAGGNIS0uDv78/tFotNBqNVfRPgwYNwPM89Pr70ynd3d2RkZHhlP2hoaEICwvDiRMnrMq7desGLy8vzJ49Gx07dpSm0VlISUlBYGCgU/soiEwmQ9euXfHjjz8iMTFRyll1+/ZtfP/994iOjraZIlcYJ0+exIsvvvhAthBmSEAiCIIgCIIgCIJwAZ5pUAkzfjqHrCISaXMAvNzk6F6/UlmZZpf33nsP/fr1w9q1a/Haa6/h2WefxdatW/HCCy+gR48eSEhIwMqVK1G3bl1kZ2c/0D7mzJmDHj16IDo6Gq+88grS0tKwdOlS1KtXz6rP5557Dh07dsSUKVOQmJiIRo0aYf/+/fjxxx8xYcKEQpeoLyvmz5+P7t27o3Xr1nj11VeRl5eHpUuXwtvbGzNmzCiyfWRkJD755BNMnjwZiYmJ6NWrFzw9PZGQkIBt27Zh1KhRePfdd3Ho0CG8+eab6NevH2rWrAmTyYTvvvsOMpkMffr0kfpr1qwZDh48iEWLFqFy5cqoVq2alDTcHs8//zy2bdtmJRB6eXlhxYoVeOmll9C0aVMMHDgQgYGBuHr1Knbt2oUnn3wSX3zxxQMfs08++QQHDhxAdHQ0xowZA7lcjlWrVkGv1+PTTz91up///e9/SEtLw/PPP//AthAAxaUSBEEQBEEQBEG4AGqFDIv6NQY4s0hkD+7efxb2awy1wn6+nrKid+/eiIyMxIIFCyAIAoYNG4bZs2fjn3/+wbhx47Bv3z6sX7/eblJpZ3n66aexadMmCIKAyZMnY+vWrVizZo1NnzzPY8eOHZgwYQJ27tyJCRMmIC4uDvPnz8eiRYsedqglwlNPPYW9e/fC398f06ZNw4IFC9CqVSscPXq00OTP+Zk0aRK2bNkCnucxc+ZMvPvuu9ixYwe6du2Knj17AgAaNWqEbt264aeffsLbb7+NGTNmwMPDA3v27EGrVq2kvhYtWoRmzZph6tSpGDRoEFasWFHovl955RVcv34dR48etSofPHgwfv75Z4SGhmL+/PkYP348YmNj0bhxYwwfPryYR8maevXq4ciRI6hfvz7mzJmDmTNnIjw8HIcPHy5U7CrIpk2bULVqVYfJvgnn4Fh5ZAOroGi1Wnh7eyMzM7NYoXIljSAIUgifoyRvBFHWkF8Srgr5JuGKkF8Sroor+KarPHMXB51Oh4SEBFSrVq1EVrE7EHcb7246jcw8E3gOEBmk/3u7ybGwX2M8VTe4BCwniOLRuXNnVK5cGd999115m+I0er0eERERmDRpEsaPH1/e5rgcxbl+kYBUDCrizYwgCIIgCIIgKhIV8Zm7pAUkANAZBew5exP7zt5GRp4BPm5KdKsfjO71K5V75BHx+HL8+HG0bdsW8fHx5Z6Y3FlWrlyJ2bNnIz4+HipVySSSf5QgAamUcJWbmSiKuHPnDoKCgmh1DKJcYSIDMwhgegEmnREp6XcRXDkYcncVOJnzqzoQRGlC10zCFSG/JFwVV/BNV3nmLg6lISARBEGUBcW5ftETSwWEMYZbt26BtD+irGEig5hngilTD+OdXBhv5sCUmgchywBRb8Kdu3dgytDBeMtcLuYawUTyU6J8oWsm4YqQXxKuCvkmQRAE4QhahY0gCIcwgYHpTRANIpjeBGYUnWzIIOpMEHUmgOfAq+Xg3eXgVXTJIQiCIAiCIAiCqIjQ2xxBEBJMEMH0AsR709KcFowKQ2QQc40Qc42AjAevkUOmkYOjufsEQRAEQRAEQRAVBhKQKiAcx8HPzw8cRzlmiIeDmUQwgwBRf08wMj24YMRxHHw9vAv3S0GEmGWAmGUAp+DBaxTg3eTg5DSblig96JpJuCLkl4SrQr5JEARBOIIEpAoIz/OoWrVqeZtBVECYSbwvFhkeTjAqCM/xCPWv5LwtRhFCph5Cph6cSnZfTOLpgZUoWeiaSbgi5JeEq0K+SRAEQTiCPvtXQERRxNWrVyGKJffyTzyaMKMIIccIU5o5sbXxVg6EdJ05uXUJikcAIDIR1+/ehMiK3y/TCxDSdeak3HfzIOaZKHknUWLQNZNwRcgvCVeFfJMgCIJwBAlIFRDGGNLS0ugFm7CBGQUI2QaY7ubBcDMHxtulJxjZ7JsxpGdnPpxfsnurvN3NM4tJGTqIeqHkjCQeS+iaSbgi5JeEq0K+SRAEQTiCprARRAVGNNybiqY35zGC+Ag97IkMYrYRYrYRnNycfJt3U4BTkO5NEARBEARBEARR1pCARBAVBMYYmNG8SpplpbRHSjAqBGYSIWgNELQGcErZPTFJDk5GYhJBEARBEARBEERZQAJSBYTjOISEhNDqGI84+QUj8V7Sa1cWjDiOQ6B3QKn7JTMIEAwChEwDOJUMMo0cnJqSbxOOoWsm4YqQXxKuCvkmQRAE4Qj6fF8B4XkeISEh4Hk6fY8SjDGIegGC1gBjSi6MN3JgupMLIVMPpjO5tHgEmFdhC/YJAM+VkV8yBqYzmROE38yBKU0HUUfJtwlb6JpJuCLkl4SrQr5JPKokJiaC4zisXbu2vE1xKWbMmAGO45CamvrAfXz66aeoXbt2hUq+P3DgQPTv37+8zahw0J2hAiIIAi5fvgxBoOTCFRkmMoh6EwSt/r5glJILQasH0wtABRFCmElE3oU0ZOy+gpsbziJj9xXkXUgr9aTd1kYwiLlGmFLzYLyVC1OG3jzFjyBA10zCNSG/JFwV8k2iIGvXrgXHcVCr1bh+/brN9g4dOqB+/frlYFnp8Msvv4DjOHAch//9738224cNGwYPD48H6nv37t2YMWPGQ1roWmi1WsybNw8TJ06sUMLzxIkTsWXLFvzzzz/lbUqFouKcYcKKrKys8jaBKCZMZBB1JgiZ9wSjmzkwpeRB0BoqlGCUH31CJlLXnEPWwaswXNFClmKC4YoWWQevInXNOegTMsveKEGEmG2A6U6ueRU6raFsxSzCJaFrJuGKkF8Srgr5potg1AH/xAIbhgBrepj//0+subwc0Ov1mDt3brnsu7woabFn9+7dmDlzZon2Wd588803MJlMGDRoUHmbUiyaNGmC5s2bY+HCheVtSoWCBCSCKCUsgpEpUw/jnXuCUWoehKyKKxjlR5+QicxdCeax2IHpBWTuSigfEclig1E0R3jdyoExJRdCtgFMqNjHnSAIgiCIx4ALu4GFtYBto4ELu4Ck383/3zbaXH5xT5mb1LhxY3z55Ze4ceNGme/bHjqdrlSnTDVu3Bg7d+7EyZMnS20f5UlOTk6J9LNmzRr07NkTarW6RPpjjCEvL8/utpI45/nH3b9/f2zduhXZ2dkP1efjBAlIBFFCMIFBzDPBlHFPMLqRDVNqHsQsgzkBdgUXjPLDTCK0B686VVd78KpLRAAxvQAhwywmme7mQcw1grl4XimCIAiCIB5DLuwGYgcDunsf4Zho/X9dJvDDIHO9MuSDDz6AIAhORyGtX78ezZo1g5ubG/z8/DBw4EAkJydb1YmIiMCwYcNs2nbo0AEdOnSQ/m2ZVhYbG4upU6ciNDQUGo0GWq0WaWlpePfdd9GgQQN4eHjAy8sL3bt3f+ipSWPHjoWvr6/TUUh79uxB27Zt4e7uDk9PT/To0QPnzp2Ttg8bNgzLli0DAGmKnCVZfdOmTdG7d2+r/ho0aACO4/Dvv/9KZRs2bADHcTh//rxUdurUKXTv3h1eXl7w8PBA586d8eeff1r1ZZmG+Ouvv2LMmDEICgpClSpVHI4lKSkJNWrUQP369XH79m2H9RISEvDvv//iqaeestkmiiI+++wz1KtXD2q1GsHBwRg9ejTS09Ot6kVERODZZ5/Fvn370Lx5c7i5uWHVqlWFnnMA2LRpk+RfAQEBGDJkiM0US8t0w8uXL+OZZ56Bp6cnXnzxRWl7ly5dkJOTgwMHDjgcI2ENrcJWAeE4DmFhYbQ6RjnDBAamN0E0CGB6AcxY/iJJWaG7lOEw8qggTC9AfzkD6lp+pWyVkzCz0CfmmQCeA+8mB6+Rg1fR5fBRha6ZhCtCfkm4KuSb5YxRB2x//d4/HH3oYgA4c713LgKKkon8KIpq1arh5ZdfxpdffolJkyahcuXKDuvOmjULH374Ifr3748RI0YgJSUFS5cuRbt27XDq1Cn4+Pg8kA0ff/wxlEol3n33Xej1eiiVSsTFxWH79u3o168fqlWrhtu3b2PVqlVo37494uLiCrWzMLy8vPDWW29h2rRpOHnyJJo2beqw7nfffYehQ4eiW7dumDdvHnJzc7FixQpER0fj1KlTiIiIwOjRo3Hjxg0cOHAA3333nVX7tm3b4ocffpD+nZaWhnPnzoHneRw5cgQNGzYEABw5cgSBgYGoU6cOAODcuXNo27YtvLy88P7770OhUGDVqlXo0KEDfv31V7Rs2dJqP2PGjEFgYCCmTZvmMALp8uXL6NSpE/z8/HDgwAEEBAQ4HPexY8cAwO6xGT16NNauXYvhw4dj3LhxSEhIwBdffIFTp07h6NGjUCgUUt2LFy9i0KBBGD16NEaOHIlatWpJ2+ydc0u/TzzxBObMmYPbt29jyZIlOHr0qI1/mUwmdOvWDdHR0ViwYAE0Go20rW7dunBzc8PRo0fxwgsvOBwncR96Y6qA8DwPf3//8jbjsYMJIphegKgXwAyPl2BUEMOVYkxL4wD95UzXEZDyIzKIOUaIOUZwcl4SkziFrLwtI0oQumYSrgj5JeGqkG+WM3HbAV2GExWZuV7cj0CjAaVrUz6mTJmCb7/9FvPmzcOSJUvs1klKSsL06dPxySef4IMPPpDKe/fujSZNmmD58uVW5cVBp9PhxIkTcHNzk8oaNGiA//77zyqB80svvYTatWvj66+/xocffvhA+wKAcePGYfHixZg5cyZ+/PFHu3Wys7Mxbtw4jBgxAqtXr5bKhw4dilq1amH27NlYvXo1WrdujZo1a+LAgQMYMmSIVR9t27bF559/jvPnz6NOnTo4evQolEolunXrhiNHjuCNN94AYBaQoqOjpXZTp06F0WjE77//jurVqwMAXn75ZdSqVQvvv/8+fv31V6v9+Pn54eeff4ZMZv9Z98KFC+jcuTNCQ0Oxb98++Pr6Fnp8Lly4AMAsLubn999/x1dffYWYmBgMHjxYKu/YsSOefvppbNq0yar80qVL2Lt3L7p16yaV/fLLLwBsz7nRaMTEiRNRv359/Pbbb9LUuejoaDz77LPS+bKg1+vRr18/zJkzx8Z+uVyOsLAwxMXFFTpO4j4kIFVABEFAfHw8oqKiHP74iYeHmcT7YpFecIlpWOWNmGeCITkLhpvFmDPNACHPWHpGlRDMJELIMkDIMoBT8OA1CrOYJKOZvhUdumYSrgj5JeGqkG+WAqvaA9l3nKubl1a8vn8aBxyc4Xx9jyBg9K9F13NA9erV8dJLL2H16tWYNGkSKlWqZFNn69atEEUR/fv3t1oaPiQkBFFRUTh8+PADC0hDhw61Eo8AQKVSSX8XBAEZGRnw8PBArVq1Hjp/kbe3NyZMmIDp06fj1KlTaNKkiU2dAwcOICMjA4MGDbIar0wmQ8uWLXH48OEi99O2bVsAwG+//YY6dergyJEjeOKJJ9ClSxdJ+MjIyMDZs2elKX+CIGD//v3o1auXJB4BQKVKlTB48GB8+eWX0Gq18PLykraNHDnS4e/67NmzGDBgAGrUqIE9e/ZYtXPE3bt3IZfLbVal27RpE7y9vdGlSxerY9KsWTN4eHjg8OHDVgJStWrVrMSj/BQ85ydOnMCdO3cwY8YMq7xLPXr0QO3atbFr1y6bROWvv/46HOHr62tlI1E4JCBVUHS68ll94VFGEowsohEJRmAig+lOLgxXs6BP0sJ0J9dxNHUhmG7mIuOny1BF+UJVzRu8yrUfSJlRhJCph5CpB6eSmcUkNzk4nsL5Kyp0zSRcEfJLwlUh3yxhsu8AWaWUeNqkK72+HTB16lR89913mDt3rt0opPj4eDDGEBUVZbd9/qlLxaVgpAtgzrWzZMkSLF++HAkJCRCE+2kWSiKabvz48Vi8eDFmzJhhNwopPj4eANCpUye77Z0RYoKDgxEVFYUjR45g9OjROHLkCDp27Ih27dph7NixuHLlCs6fPw9RFCWxKSUlBbm5uVbTvSzUqVMHoigiOTkZ9erVk8rtHT8Lzz33HIKDg7Fv3z4bQai4xMfHIzMzE0FBQXa337ljLagWZlfBbUlJSQBgd9y1a9fG77//blUml8sLzffEGKMpu8WABCTisYUZRYh6E5jBLBxBIMEIAMRcI/RXs2BI0sKQnAWmcy7XUVEYkrJgSMpCFs9BGe4JdZQvlBFe4JUuLibpBQh6AUIGB15tFpM4tYxuNARBEARBOIeH/Zdou+SlmUUhZ5GrAbdipAkoji0OqF69OoYMGSJFIRVEFEVwHIc9e/bYjXbJL044ep4SBMFu24LRRwAwe/ZsfPjhh3jllVfw8ccfw8/PDzzPY8KECSWySpslCmnGjBk4deqUzXbLPr777juEhITYbJfLnXvljo6Oxs8//4y8vDz873//w7Rp01C/fn34+PjgyJEjOH/+PDw8POxGQTmLveNnoU+fPli3bh1iYmIwevRop/rz9/eHyWRCVlYWPD09pXJRFBEUFISYmBi77QIDA522q7BtzqBSqaymNxYkPT3dodhJ2EICEvHYwIyCFGEk6gWAVuACYI4yMt7KgeGeaGRKsb9sJgDI/NRQVvWEsooHtAeuOpdIW8aBU8vAckzmf4sMhgQtDAlaQM5BFe5ljkwK9wKncOHpYgWTb2vk4N0ULh9NRRAEQRBEOVOcKWP/xALbnHt5BwA893mZ5kCyMHXqVKxfvx7z5s2z2RYZGQnGGKpVq4aaNWsW2o+vry8yMjJsypOSkqymZRXG5s2b0bFjR3z99ddW5RkZGYUmgC4OEyZMwGeffYaZM2faJACPjIwEAAQFBdldjSw/hX2AbNu2LdasWYPY2FgIgoA2bdqA53lER0dLAlKbNm0kYS0wMBAajQYXL1606evChQvgeR5hYWFOj3H+/PmQy+UYM2YMPD09raaYOaJ27doAzKuxWRJ9A+ZjcvDgQTz55JMPLQAVJDw8HIA58XbBqK+LFy9K253BZDIhOTkZPXv2LFEbH2Vc+G2NcATP86hevXqhSioBiAYBQpYBprt5MNzIhvF2LoQMvVkAeMzFIyHbgLy4u8jck4DUr84gY+sl5J64bSMecQoequre8OwYBv+hdeE/uDY8o0OhivCG11NVndqX99MRCBhWD759ouDWMAC8Jp9ubWLQX86Edm8iUr85i8x9idBfyQRz9WgwkUHMNsKUkgvjrRwIWv1jnVTd1aFrJuGKkF8Srgr5ZjlTtxeg9gFQVKQzZ65X9/lSN8kekZGRGDJkCFatWoVbt25ZbevduzdkMhlmzpwJxqyfuRljuHv3rlU/f/75JwwGg1S2c+dOJCcnO22LTCaz2c+mTZtslnR/GCxRSD/++CNOnz5tta1bt27w8vLC7NmzYTTa5v1MSUmR/u7u7g4AdkUzy9S0efPmoWHDhvD29pbKf/75Z5w4cUKqA5jH3bVrV/z4449ITEyUym/fvo3vv/8e0dHRTk2fs8BxHFavXo2+ffti6NCh2LFjR5FtWrduDcCclyg//fv3hyAI+Pjjj23amEwmu+N3lubNmyMoKAgrV66EXq+Xyvfs2YPz58+jR48eTvcVFxcHnU6HNm3aPLA9jxsUgVQB4TiuWBeDxwHGGJjRvEoa0wsQDRRhlB8miDDezJFyGQl3HYdGywPczFFG4V5QhLiDk9l/gFFV84Z3j2rQHrwXicRBWlUWDOBUMng9VRWqauabn6KSOxSV3OERHQrjjWzoL2VAdylDmiLHjCL08RnQx2eAU/JQVfOGKsoXyjAPl05kzUwiBK0BgtYATimTIpMcHTei7KFrJuGKkF8Srgr5ZjmjUAMvrAR+GIT7D1cFufeM8cJKc/1yYsqUKfjuu+9w8eJFqzw7kZGR+OSTTzB58mQkJiaiV69e8PT0REJCArZt24ZRo0bh3XffBQCMGDECmzdvxtNPP43+/fvj8uXLWL9+vRTV4wzPPvssPvroIwwfPhxt2rTBmTNnEBMT43QEk7NYciH9888/khAEmHMcrVixAi+99BKaNm2KgQMHIjAwEFevXsWuXbvw5JNP4osvvgBgTiINmFd369atG2QyGQYOHAgAqFGjBkJCQnDx4kWMHTtW6r9du3aYOHEiAFgJSADwySef4MCBA4iOjsaYMWMgl8uxatUq6PV6fPrpp8UeI8/zWL9+PXr16oX+/ftj9+7dDnM7AebpjPXr18fBgwfxyiuvSOXt27fH6NGjMWfOHJw+fRpdu3aFQqFAfHw8Nm3ahCVLlqBv377Ftg8w59CaN28ehg8fjvbt22PQoEG4ffs2lixZgoiICLz11ltO93XgwAFoNBp06dLlgWx5HCEBqQIiCALi4uJQt27dx3Z1DMYYmEEEM9yflgZGglF+BK1eEoyM17IdRshwKplZMKrqCWVVL8jcnU9sqKrmjYDh9aC/nAHd5QxkZ2bDw9sD6kgfqCJ9wMlthR+O56Cs4gllFU94tKsC47Vs6C6lQ385U5oSxwwidBfTobuYDk4lgyrSG+oavlBU8XDpRNbMIEAwCBAyDeBVZjGJU1Py7fKGrpmEK0J+Sbgq5JsuQK3uwMDvge2vA7oMgOMBJt7/v9rbLB7V6l6uZtaoUQNDhgzBunXrbLZNmjQJNWvWtFpSPSwsDF27drWaLtStWzcsXLgQixYtwoQJE9C8eXPs3LkT77zzjtN2fPDBB8jJycH333+PDRs2oGnTpti1a5fd/EwPg4+PDyZMmGCzwhcADB48GJUrV8bcuXMxf/586PV6hIaGom3bthg+fLhUr3fv3hg7dixiY2Oxfv16MMYkAQkwC0SbNm1CdHS0VNasWTNoNBqYTCa0bNnSar/16tXDkSNHMHnyZMyZMweiKKJly5ZYv369TV1nUSgU2Lx5M7p3747nn38eBw8eLLSvV155BdOmTUNeXp7VdLWVK1eiWbNmWLVqFT744API5XJERERgyJAhePLJJx/INgvDhg2DRqPB3LlzMXHiRLi7u+OFF17AvHnzbKYYFsamTZvQu3dvq/xNROFwrGC8H+EQrVYLb29vZGZmluuXGUEQcObMGTRo0OCxubEzkYEZBTD9/cTXJBhZw0wiDDeyzcmqr2ohpOsd1pUHaaAM94SqqhfkwZoSETgEUcD55HjUCYuCjC++XzJBhCE5G/r4dOgTMs3nuACcmxzqSG+oavhAUdm1xSQJjgPvJjeLSSpKvl0ePI7XTML1Ib8kXBVX8E1XeeYuDjqdDgkJCahWrZrV0uIPhVEHxP0IXPgJyEsH3HyB2s+Zp62VY+QRQeQnMzMT1atXx6effopXX321vM1xmtOnT6Np06Y4efIkGjduXN7mlCvFuX5RBBLhkjCRgRmE+xFGJBjZxZShN6+WdjULhutZgMn+MeLc5FBW9YTqXpQR7+Z6P31OxkMV4QVVhJdZDLuaBV18OgyJWil6iuWZkHf2LvLO3gWvkUNVwwfqKF/IQzSuK8wwBjHXCDHXCMj4e1Pc5C6/+hxBEARBEOWMQm1OkF0OSbIJwlm8vb3x/vvvY/78+Rg+fHiFyZ82d+5c9O3b97EXj4qL671FEo8lkmB0b4U0ZiTByB7MKMBwPV+UUabBfkUOUAS7QxluFozkQW6uK7DYgZObk3erqnubcyMlac2RSYlaQDD7hZhrQt6/qcj7NxW8h+K+mOTKYxVEiFkGiFkGcArevIqbRm53qh9BEARBEARBVAQmTpwo5WmqKMTGxpa3CRUSEpAqIDzPo1atWhVG3bUHE5mU7JqRYOQQxhiEdHOUkf6qFsbrOQ6Tg/MaOZThXuY/VTzAq8v2581zPGpUqgaeK1m/5BQ81DV8oK7hA9EgwJCoNUcmJWVJx0LMNiLvdAryTqeA91JCXcMHqigfyANcV0xiRhGCUQ9Bqwenkt2b5qaoGNPyKhiPwjWTePQgvyRcFfJNgiAIwhEkIFVQlEpleZtQLJjAwPQmiAYRTG+iJc8LQTQIMCZnQX/VHGUkZtkuBwoA4AFFJQ/z1LRwL8j81eUulijkpXtJ4ZUyqGv6Ql3TF6LeBP0VLfSX0mFIzgLuuZSoNSD35B3knrwDmY8Kqiiz+CT3dyu883KE6QUI+nvJt9VmMYlzk5f7+XyUqGjXTOLxgPyScFXINwmCIAh7kIBUARFFsdyTGxYFE0TbCCPCLowxmO7qzLmMkrQw3sqRxJCC8J4KKKt6QRXuCUUVT5fKoyMy8X4Sba707eJVcrjV8YNbHT+IeSbor2RCdykdxmvZ0oq3QoYeuX/fRu7ftyHzU0Md5QNVDR/IfV008SRjEPNMEPNMAH8/+Tavokv1w1ARrpnE4wf5JeGqkG8SBEEQjqC3EqJEYCbxfsJrvQBmIsGoMESdCYbkLHPy6yQtxFyT/Yo8B0Wohzn5dbgXZL4qikqxA+8mh1s9f7jV84eYa4Tucib08ekw3siR6ghpOuQcv4Wc47cgD3AzRyZF+UDmpSpHywtBZBBzjBBzjODk/P0pbgqaUkAQBEEQBEEQRNlDAhLxQDCTeF8sMpBgVBSMMZju5MFwVQtDUhaMt3OkKJmCyLyVUFb1MifADvUAp6Cvf8WB1yigaRAATYMACNlG6C9nQB+fYY7suocpNQ+m1Dzk/HET8iCNFJkk83TNkH1mEiFkGSBkGcApZVJkEicjMYkgCIIgCIIgiLKBBCTCKZhRvD8djQQjpxDzTDBc1d7LZZQFlucgykjOQRnqeX/FNB8XjYipgMg8FNA0CoSmUSCELAP0lzKgi8+A6U6uVMd0JxfZd3KRffQGFJXcoapxT0xyV5Sj5Y5hBgGCQYCQqQenlpvFJDc5Jd8mCIIgCIIgCKJU4Rijpa+cRavVwtvbG5mZmfDy8io3OxhjEEURPM+X2nQmZrw/HU00iIBAglFRMJHBdDsX+ntRRvlFioLIfFX3o4wqezwSy7gzxiAyETxXen5ZUgiZeugumSOTTKl5dusoQj3MkUmRPuDdXFxr5+7nS+JUMpc//mVNWVwzCaK4kF8Sroor+KarPHMXB51Oh4SEBFSrVg1qtYvmWiQIgrBDca5fLv5WRDjCYDCU6M1JNNyLLNKbhSNHS8UT1gg5RmlamiE5C0wv2K3HKXgowjzNuYyqerpu3p2HxGgyQaVwzWlg+ZF5q+DeLBjuzYJhStdBH58B3aUMCGk6qY7xejaM17OR9es1KKp4msWk6t7g1S542WQMYq4RYq7RnHxbY86X5EpJ1subkr5mEkRJQH5JuCrkmwRBEIQ9XPBNiCgKURRx8eLFB14dgzEGZjSvkmZZKY0EI+dgAoPxVo55xbSrWQ6jVwBA7q+GMtwLyqqeUFRyf7Tz1ch4QAVcvpSEOlWiwFWggDW5rxryFiFwbxEC09086OLNkUlCpt5cgQHG5CwYk7OQ9cs1KMM8oYrygaqaN3iVCwo0IoOYbYSYfS/59j0x6VGIcntQHvaaSRClAfkl4aqQbxIEQRCOIAHpMSC/YCTey2FEgpHzCFkGabU0Q3IWmNG+OsIpefO0NEuUkYfrR+I8DJycB+cmB6+Wg1fJIAgCOKUMimB38CIg5gkQ84wOj5crIvd3g4e/G9xbhsCUmmeOTIrPgJhlMFcQmdkPkrTIknFQVvWEOsoXyggvl4z2YSYRgtYAQXsv+bZGDt5NAU5G02UIgiAIgiDKC47jMH36dMyYMaNM9pecnIyoqCj8/PPPePLJJ8tkn4RjIiIi0KFDB6xdu7bU9jFw4ECIooiNGzeWaL8kID2CMMbADBbByARmEAFKdeU0TBBhvJED/b0oo/zTmgoiD3QzRxmFe0IR7P7IJzLmFObl5Dm1vFDBhFPIIFPIIPNSmlfsyzNBzDOZxcsKAMdxUARqoAjUwL11JZju5Jojky5lQMw2misJDIYELQwJWkDOQRXuBVWUL1QRXi4Z7XM/+bYBvMosJnFqSr5NEARBEITzLF++HG+88QZatGiB48ePP1Rfu3fvxl9//VVmIsrjzEcffYSWLVs+NuLRsWPHsH//fkyYMAE+Pj6PpQ0TJ05E8+bN8c8//6BRo0Yl1i8JSBWU/CHFTGRgRiFfhBEJRsVFyNRLgpHhWjbgYJU5Ti2TooxUVT3Ba1xzpa6SRFo2Xi0HpyhcGLEX6s7Jecg8lZB5KsGE/GJSxfBTjuOgCHaHItgdHk9WhvFWDvQWMSn33sp6Jgb95UzoL2eCU/BQRnhBXdMXyqqerjd1kTGIOhNEncmcL0ltTr7tkrmdShCahkG4IuSXhKtCvuka6AU99ifux6Grh5Chz4CPygedqnZC14iuUMnKJ59mTEwMIiIi8Ndff+HSpUuoUaPGA/e1e/duLFu2jASkUiYlJQXr1q3DunXrytuUMuPYsWOYOXMmhg0bVq4CkiMbLl68CJ4v3XeEJk2aoHnz5li4cCG+/fbbEuv30X5jeEThOR71ouqAZZtgNOgrzIu4K8GMIgw3su9NR8q6n++mIBwgD9JAdS/KSB6oefQjNjgOnJK/Lxo5GU0jk8nQoEGDwruW8ZB5KCHzUIIJZiGD5ZnMidsrgA9zHAdlJQ8oK3nAIzoUxhvZ0F8yJ+BmOnN0FTOKZoEpPgOckoequjdUUb5QVvF0valjYr7k27J7+ZLcCo8uq4g445sEUdaQXxKuCvmma3D46mFMPToVWoMWPHiIEMGDx8GrBzH3r7mYFT0LHcI6lKlNCQkJOHbsGLZu3YrRo0cjJiYG06dPL1MbCFt0Oh2USqVDQWL9+vWQy+V47rnniuwrNzcXGo2mpE10aURRLPOFA1SqshGA+/fvj+nTp2P58uXw8PAokT5d7NM44QxCjgHpySkwafXmVb8qwIt3ecMYgyldh9zTd5Cx4zJSvjqDzJ+uIO/fVBvxiNfIoa7tC69u4Qh4pT78+tWEe4uQR3uKGmeORJH5qqEIcYciUAOZh7JYU7EYY9BqtWBO+iMn4yBzV0Ae4AZFJXfI/dTg3eRABVnOmuM5KKt4wrNDGAJeqQ+fnpFQ1/EDly+xNjOI0F1IR+ZPV5D6zVloD10159FyxRxkgggxywDTnVwYb+dAyDKAOYjEq2gU1zcJoiwgvyRcFfLN8ufw1cMYf3g8sgxZAAARotX/swxZGHdoHA5fPVymdsXExMDX1xc9evRA3759ERMTY1Pnl19+Acdx+OWXX6zKExMTwXGclPNl2LBhWLZsGQDzBzrLHws5OTl45513EBYWBpVKhVq1amHBggV2/XL9+vVo1qwZ3Nzc4Ofnh4EDByI5OdmqTocOHVC/fn3ExcWhY8eO0Gg0CA0NxaeffmrTn06nw4wZM1CzZk2o1WpUqlQJvXv3xuXLl4ttn16vx1tvvYXAwEB4enqiZ8+euHbtmt3je/36dbzyyisIDg6GSqVCvXr18M0339g9vrGxsZg6dSpCQ0Oh0Wig1Wrt9gkA27dvR8uWLW0EBMsx+d///od27dpBo9Hggw8+kOyePn06atSoAZVKhbCwMLz//vvQ620/uq9fvx4tWrSARqOBr68v2rVrh/3791vVWb58OerVqweVSoXKlSvjjTfeQEZGhl17nDlHS5cuRb169aR9Nm/eHN9//z0AYMaMGXjvvfcAANWqVZN8KzExEYDZ3958803ExMRINu3du9dp37Vw4cIF9O/fH4GBgXBzc0OtWrUwZcoUp2yIiIjAsGHDrPq7cuUK+vXrBz8/P2g0GrRq1Qq7du2yqmOxcePGjZg1axaqVKkCtVqNzp0749KlSzbHqUuXLsjJycGBAwdstj0oFIFUARFFEUl3rqFOWBRk3KMVKVCSiAYBxuvmKCN9Utb9RMgF4QBFJXfz1LRwT8gD3KxuYI8sHAdeLZNyGj2sOCaKIq5cufJAq7ZwPAdOowCvUZinZOoF81Q3nalCJHzneE5Kns46iDAkZ5mjkK5kSknEmV6ALi4Nurg0cG5yqCPNkUmKSq4nTDKjCCFTDyFTD04lA69RmP3Exex0lofxTYIoLcgvCVeFfLN80Qt6TD06FQDAYP8ZiIGBA4epR6fiUOihMpvOFhMTg969e0OpVGLQoEFYsWIF/v77bzzxxBPF7mv06NG4ceMGDhw4gO+++85qG2MMPXv2xOHDh/Hqq6+icePG2LdvH9577z1cv34dixcvlurOmjULH374Ifr3748RI0YgJSUFS5cuRbt27XDq1CmrqUPp6el4+umn0bt3b/Tv3x+bN2/GxIkT0aBBA3Tv3h0AIAgCnn32Wfz8888YOHAgxo8fj6ysLBw4cABnz55FZGRksewbMWIE1q9fj8GDB6NNmzY4dOgQevToYXM8bt++jVatWkniRmBgIPbs2YNXX30VWq0WEyZMsKr/8ccfQ6lU4t1334Ver4dSaX/xHqPRiL///huvv/663e13795F9+7dMXDgQAwZMgTBwcEQRRE9e/bE77//jlGjRqFOnTo4c+YMFi9ejP/++w/bt2+X2s+cORMzZsxAmzZt8NFHH0GpVOL48eM4dOgQunbtCsAspsycORNPPfUUXn/9dVy8eFHynaNHj0KhuJ8SxJlz9OWXX2LcuHHo27cvxo8fD51Oh3///RfHjx/H4MGD0bt3b/z333/44YcfsHjxYgQEBAAAAgMDpf0cOnQIGzduxJtvvomAgABERETYCFqF8e+//6Jt27ZQKBQYNWoUIiIicPnyZfz000+YNWuWUzbk5/bt22jTpg1yc3Mxbtw4+Pv7Y926dejZsyc2b96MF154war+3LlzwfM83n33XWRmZuLTTz/Fiy++aJOXrG7dunBzc8PRo0dt+nhQSEAiHhkYYxDu6qC/mgXDVS2MN3Icig+8h8L8wh/uBWUVT9dcjr00sOS8cZODU8lcUhDgeM68upub3JwQXi+Yc/bkCYDg+hExnIyHKsIbqghvMJMIw1UtdPEZMCRq74tJeSbknb2LvLN3wWvkUNXwgTrKF/IQjcuJl0wvQNALEDLuCY4aBTi1zOXsJAiCIIhHgf2J+6E1OI4mscDAoDVosT9xP56LLHpq0sPyv//9DxcuXMDSpUsBANHR0ahSpQpiYmIeSEBq3bo1atasiQMHDmDIkCFW23bs2IFDhw7hk08+kSI63njjDfTr1w9LlizBm2++icjISCQlJWH69On45JNPpMgZAOjduzeaNGmC5cuXW5XfuHED3377LV566SUAwKuvvorw8HB8/fXXkjjx7bff4ueff8aiRYvw1ltvSW0nTZokRRc5a98///yD9evXY8yYMVK01RtvvIEXX3wR//77r9WYp0yZAkEQcObMGfj7+wMAXnvtNQwaNAgzZszA6NGj4ebmJtXX6XQ4ceKEVZk9rl69iry8PFSrVs3u9lu3bmHlypUYPXq0VLZ+/XocPHgQv/76K6Kjo6Xy+vXr47XXXsOxY8fQpk0bXLp0CR999BFeeOEFbN682WoKneVYpaSkYM6cOejatSv27Nkj1alduzbefPNNrF+/HsOHDy/WOdq1axfq1auHTZs22R1Tw4YN0bRpU/zwww/o1asXIiIibOpcvHgRZ86cQd26daWygpFHhTF27FgwxnDy5ElUrVpVKp87d67TNuRn7ty5uH37No4cOSId85EjR6Jhw4Z4++238fzzz1sdX51Oh9OnT0vCoa+vL8aPH4+zZ8+ifv36Uj25XI6wsDDExcU5PbaiIAGJqNCIehMMydkwXDXnMhJzjPYr8hwUld2huhdlJPNTPz4vwDIevJvMnM9IVbFe/DmOM6/4ppYDPoB4T0xieaYKMb2Kk/NQVfeBqrqPOTdSkhb6+HToE7WAYL6xirkm5P2birx/U8F7KO6LSUEuFgnHmJQAHTxnzpWkUTw+4itBEARBPCADdg5Aal6qU3Uz9ZnF6nvmHzPx2cnPnK4f4BaADc9uKNY+AHP0UXBwMDp27AjA/Iw2YMAArF+/HgsXLizRaLXdu3dDJpNh3LhxVuXvvPMONm/ejD179uDNN9/E1q1bIYoi+vfvj9TU+8c3JCQEUVFROHz4sJWA5OHhYSVWKZVKtGjRAleuXJHKtmzZgoCAAIwdO9bGLstzmbP27d69GwBs6k2YMEGabgWYxZYtW7agf//+YIxZjaVbt26IjY3FyZMnrVZQGzp0aJHiEWCOMALMAoM9VCqVlYADAJs2bUKdOnVQu3ZtK1s6deoEADh8+DDatGmD7du3QxRFTJs2zSb/kuVYHTx4EAaDARMmTLCqM3LkSHzwwQfYtWuX1f6dOUc+Pj64du3aA0e/AUD79u2txKPikJKSgt9++w3jx4+3Eo8APPCz++7du9GiRQsrwc7DwwOjRo3C5MmTERcXZyUMDR8+3CrqrG3btgDM0+Dy1wPM5z7/eXxYSECqiHCASqE0/+UxgzEGU0qeebW0JC2Mt3LgILoXvJfSnPy6qicUoR6PXGLgwuDkvDmKRy0v0xf80k4+x6tk5vF4qyAaBHMCbp1JiuxxZTgFD3UNH6hr+EA0CDAkaKG7lA5DUpYUKSdmG5F3OgV5p1PAeymhruEDVZSP602rFBnEHCPEHCM4OS+JSUWt0leelGViRIJwFvJLwlUh3yxZUvNScSf3Tqn0rRf0pda3BUEQEBsbi44dOyIhIUEqb9myJRYuXIiff/5Zmq5UEiQlJaFy5crw9PS0Kq9Tp460HQDi4+PBGENUVJTdfvJPjQKAKlWq2DxP+fr6WkUDXb58GbVq1YJc7vg12Vn7kpKSwPM8IiMjrerVqlXL6t8pKSnIyMjA6tWrsXr1arv7vHPH+hw7iihyhKOcZqGhoTbT3+Lj43H+/HmH060stly+fBk8zxcqxFiORcExK5VKVK9eXdpuwZlzNHHiRBw8eBAtWrRAjRo10LVrVwwePNhKYCuK4h6//FjErIJCzcOQlJSEli1b2pTn96n8+ysoXFkEwvT0dJs+GGMl+h5BAlIFRMbLEFW5enmbUWaIeSYYks2CkSE56/7S6QWRcVCGepinpVX1hMxH5Vov3aUMp+ClfEblIZbJZDLUrl27zPbHK2WAUgaZtwrMKEo5k5hBKDMbHhReKYO6li/UtXwh6k3QX9FCfykdhuQs3MuPCVFrQO7JO8g9eQcyHxVUUfcik/xc66GemUQIWQYIWQZwStk9MUkOTuY6YlJZ+yZBOAP5JeGqkG+WPAFuAU7XzdRnQi84WB3YDiqZCt4q71KxxcKhQ4dw8+ZNxMbGIjY21mZ7TEyMJCA5evYWhJJ/PhNFERzHYc+ePXYjoAomjXYUJVXeCeNF0fzwN2TIEAwdOtRunYYNG1r925noIwDSdDh7woKjfkRRRIMGDbBo0SK7bcLCwpza94PgzDmqU6cOLl68iJ07d2Lv3r3YsmULli9fjmnTpmHmzJlO7cfeuMvSdx+W4vhyenq6Q5H1QSABqQIiiiLSsjPg4+4FnnOdl7SSgokMpju5MFzNgj5JC9PtXId1ZT4qKMM9zQmwQz2KtWrYo4D0wq6Wl3v0hyiKSE9Ph6+vr8NlREsLTsFDplBC5qUEM+UTk/Sud8EvCK+Sw62OH9zq+EHMM0F/JRO6S+kwXsuWouuEDD1y/76N3L9vQ+anhjrKB6ooX8h9yiZpprMwgwDBIEDQGszJt+/lsirvXFvl6ZsE4QjyS8JVId8seYozZeynyz/hg98/KLriPaa3nl7qOZBiYmIQFBQk5fHJz9atW7Ft2zasXLkSbm5uUiREwYTEBSNNAMcv7OHh4Th48CCysrKsonwuXLggbQcgJbSuVq0aatas+UBjK0hkZCSOHz8Oo9FoE8FUXPvCw8MhiqIU1WTh4sWLVv1ZVmgTBAFPPfVUiYzDQtWqVeHm5mYVOVYUlvxNnTt3LvRjfGRkJERRRFxcHBo3bmy3juVYXLx4EdWr3w+AMBgMSEhIeODxuru7Y8CAARgwYAAMBgN69+6NWbNmYfLkyVCrHyxVibO+axnH2bNnC+2vODaEh4fb+AVg61PFxWQyITk5GT179nyg9vagu0IFhDGGG3dvlbtaXpKIuUbkXUhD5r5EpH5zFumb45Hz1y0b8YhT8FBGeMGjfRX4v1QH/kPqwLNtFajCvR4b8YhTySDzUUER4g5FkAYyT2W5i0eA2S+Tk5PL3S85OQ+ZpxKKQA0UldzNkWhqOVABotF4Nznc6vnD9/kaCBheDx7tq0BR2d2qjpCmQ87xW0hbfx5pGy4i53+3IWid/1JZJjAGpjNBSNfBeDMHpjSdWdArJ99wFd8kiPyQXxKuCvlm+dI1oiu8lF7gikhVwYGDl9ILXSNKbuqYPfLy8rB161Y8++yz6Nu3r82fN998E1lZWdixYwcA84uuTCbDb7/9ZtXP8uXLbfp2dzc/4xR8YX/mmWcgCAK++OILq/LFixeD4zgpmXLv3r0hk8kwc+ZMG39ljEn5f4pDnz59kJqaarNvS5/Fsc/y/88//9yq3meffWb1b5lMhj59+mDLli12RYmUlJRij8OCQqFA8+bNceLECafb9O/fH9evX8eXX35psy0vLw85OTkAgF69eoHneXz00UdSFJUFy7F66qmnoFQq8fnnn1udo6+//hqZmZl2V6QrioLnValUom7dumCMwWg058N15FuF4azvBgYGol27dvjmm29w9epVq235x1gcG5555hn89ddf+OOPP6SynJwcrF69GhEREQ+crykuLg46nQ5t2rR5oPb2oAgkolxgIoPxVg4MSeYV00wpeQ7ryvzUUIWbV0xTVHJ3qakxZQLH3Y/mUMvByVxfCHEVOBkPmYcSMg+zz4l5JjCdCaJOAFz8wZjXKKBpEABNgwAI2UboL2dAF58O0637oqopJQ+mlDzk/HET8mCNlDNJ5mF/KddygTGIuUaIuUZz8m2NArymfKZZEgRBEISro5KpMCt6FsYdGgcOHJidZJ8WcWlW9CyoZKUbjbxjxw5kZWU5jGBo1aoVAgMDERMTgwEDBsDb2xv9+vXD0qVLwXEcIiMjsXPnTpscPgDQrFkzAOYk0926dYNMJsPAgQPx3HPPoWPHjpgyZQoSExPRqFEj7N+/Hz/++CMmTJgg5RSKjIzEJ598gsmTJyMxMRG9evWCp6cnEhISsG3bNowaNQrvvvtuscb78ssv49tvv8Xbb7+Nv/76C23btkVOTg4OHjyIMWPG4Pnnn3favsaNG2PQoEFYvnw5MjMz0aZNG/z888+4dOmSzX7nzp2Lw4cPo2XLlhg5ciTq1q2LtLQ0nDx5EgcPHkRaWlqxxpGf559/HlOmTIFWq4WXl1eR9V966SVs3LgRr732Gg4fPownn3wSgiDgwoUL2LhxI/bt24fmzZujRo0amDJlCj7++GO0bdsWvXv3hkqlwt9//43KlStjzpw5CAwMxOTJkzFz5kw8/fTT6NmzJy5evIjly5fjiSeesFmBzxm6du2KkJAQPPnkkwgODsb58+fxxRdfoEePHlJEmMW3pkyZgoEDB0KhUOC5556TRB17FMd3P//8c0RHR6Np06YYNWoUqlWrhsTEROzatQunT58utg2TJk3CDz/8gO7du2PcuHHw8/PDunXrkJCQgC1btjxwNOiBAweg0WjQpUuXB2pvDxKQiDJDyDZIya8NyVlgBvuJjzklD2UVz/u5jDxd6GW4rODuLZl+L6dReU8BehTgeA4ydwXgrgATmSQkiTqTlMTaVZF5KKBpFAhNo0AIWQbo4zOgu5QO0537wqvpdi6yb+ci++gNKCq5Q1XDB6oaPuYxuwoig5htgJhtuJezyywmPS7RgwRBEAThDB3COmBJxyWYenQqtAYtePAQIUr/91R6Ylb0LHQI61DqtsTExECtVjt8AeV5Hj169EBMTAzu3r0Lf39/LF26FEajEStXroRKpUL//v0xf/58m6TDvXv3xtixYxEbG4v169eDMYaBAweC53ns2LED06ZNw4YNG7BmzRpERERg/vz5eOedd6z6mDRpEmrWrInFixdL+W/CwsLQtWvXB5q2I5PJsHv3bsyaNQvff/89tmzZAn9/f0RHR6NBgwbSmJ2175tvvpEEtu3bt6NTp07YtWuXTR6h4OBg/PXXX/joo4+wdetWLF++HP7+/qhXrx7mzZtX7HHk56WXXsKkSZOwY8cOpwQbnuexfft2LF68GN9++y22bdsGjUaD6tWrY/z48VbTBT/66CNUq1YNS5cuxZQpU6DRaNCwYUO89NJLUp0ZM2YgMDAQX3zxBd566y34+flh1KhRmD17tsNpgoUxevRoxMTEYNGiRcjOzkaVKlUwbtw4TJ06VarzxBNP4OOPP8bKlSuxd+9eiKKIhISEQgUkAE77bqNGjfDnn3/iww8/xIoVK6DT6RAeHo7+/fs/kA3BwcE4duwYJk6ciKVLl0Kn06Fhw4b46aefHihKy8KmTZvQu3dvm4TvDwPHKD7VabRaLby9vZGZmemUeltaGLR5SIxPQFhgKGQuPDedCSKMN81RRvqrWgh3dQ7rygPdoKx6L8oo2P3xjLLhOfOqaW5ycCpZhRONBEFAYmIiIiIiSnQp19KGMQamFyDmmiqEmJQfU6Ye+vgM6C+lw5Rq//elCPUw50yK9AHv5prfDDilzByV5KYold9+RfVN4tGG/JJwVVzBN13lmbs46HQ6JCQkoFq1aiW2ip1e0GN/4n4cunoIGfoM+Kh80KlqJ3SN6FrqkUfEo8Wrr76K//77D0eOHClvU4gy4vTp02jatClOnjzpMEeVheJcv0hAKgaucjMTsg0QMlws58k9BK1eSn5tvJbtcHl1TiUzC0ZV70UZuVKURFki48G7ycxT01Syx2rVOFeEMQZmEMxJuPMEQLDvv66IKV13LzIpA0KaHTGJA5RVPKGK8oGqujd4tQuKSRwHXmUWkyjyjiAI4vHFVZ65i0NpCEgEUVJcvXoVNWvWxM8//1ys5e6JisvAgQMhiiI2btxYZN3iXL9c8A2CKApRFHE7IxWB3n7lvgobM4kwXM82T027qoWQ7ljYkgdroKzqCVW4F+RBmsf25ZCT8+Du5TPiVY/OV2dRFHHnzh0EBQVV2FVbOI4Dp5KDV8kBH0C8JyaxPBOYybXFJLmvGvIWIXBvEQLT3Tzo4jOgj8+AkHnvN8kAQ3IWDMlZyPrlGpRh+cQkV8lHxBhE3b1IMEtEnvu98/EQPAq+STx6kF8Srgr5JkE8elStWhU6nePZIMSjR2xsbKn069IC0t9//43p06fj2LFjMBqNaNCgAd5++22ruYWFERERYXfJyPz89ttvaNu2bUmYW2YwxpCSmYoAL18UsUBDqexbyDSY8xhd1cJwPRsw2Q9i49zkUOWLMnLV6TNlgTnfizmqwmVe1ksYxhhu3bqFwMDA8jalxOCVMvP58laBGS2RSSaHkXWugtzfDR7+bnBvGQJTap45Mik+A2KWwVxBZObfcJIWWTIOynAvcwLual7gFC7in2K+5NsyHrxGDplG/kD2PYq+SVR8yC8JV4V8kyAIgnCEy77RHz58GN26dYNarcbAgQPh6emJLVu2YMCAAUhOTrZJUGaPCRMm2F02LzU1FcuWLYOvry+eeOKJUrD+0YIZBRiuZUtT00StwX5FDlCEuEMZbhaN5IFuj/WULE6Zb+U0BX3Bq+hwChlkChlkXiowo2iOlMkzgRmE8jbNIRzHQRGogSJQA/fWlWC6nWuOTLqUATHHvMwpBAbDlUwYrmQCcg6qCG9zAu4IL9dJbi2IELMMELPuJd/WKMyCrKvYRxAEQRAEQRCPAS4pIJlMJowcORI8z+O3336Tkj5NmzYNLVq0wAcffIC+ffsiPDy80H4mTJhgt3zhwoUAgCFDhtAcZTswxiCk62FI0kJ/VQvj9RyHiYV5d4WU/FpZxcM186qUIZwqn2hEL7ePLJyCh0yhhMxTCWbKLyaJgIumleM4DooQdyhC3OERXRnGmznmBNyXMyDmmsyVTAz6S2aBiVPwUFbzhjrKB8qqnuBkruHPzChCyNRDyNSbf28WMekxnRJLEARBEARBEGWFS77tHzp0CJcvX8bw4cOtMoZ7e3vjgw8+wLBhw7Bu3TpMmzbtgfr/+uuvAZiz0VdEOI6Dr4d3iUb3iAYBxuQs6O/lMhKzjPYr8hwUlcxRRqqqXpD5qx/rKCNwnLVo9DiuHncPjuPg5+f32PkDJ+ch81BC5qEEE9h9MUkvuLSYpKzsAWVlD3i0DYXxRrY5MulyBpjOHFHFjCL0/6VD/186OCUPVXVvqKJ8oazi6TJ+zvQCBL0AIYMDrzaLSZzaNhn94+qbhGtDfkm4KuSbBEEQhCNcUkD65ZdfAABdu3a12datWzcAwK+//vpAfR87dgznz59H8+bN0ahRowe2sTzheR6h/pUeqg/GGEypeebk10laGG/lAA7SuvCeCqjCvaCs6gVFFY9HNoeP03D3XlbdaKWo/PA8j6pVq5a3GeUKJ+Mgc1dA5q4AExnYPTFJ1LmwmMRzUFbxhLKKJ1j7KjBcyzJHJl3JNItgAJhBhO5COnQX0sGpZFBF+kAd5QNFqIdr+D9jUn4q8Bx4jRy8m0JKUk++Sbgi5JeEq0K+SRAEQTjCJQWk+Ph4AEBUVJTNtpCQEHh4eEh1iosl+mjEiBFF1tXr9dDr768qptVqAQCCIEAQzC9WHMeB53mIogiW7wXRUm6pV1Q5z/PgOM5uOWBeEYMZReSdTUXev6nQpedA7ekGZTUvKCO9pelSMl4GxhhEll8N4iDjeZjyDDAkZ8F41ZzPiFmmrRREZo5OUFQ1/+F9VObVqTgOPMdDEEUABcZqp5zn7o1JLDCmeyvHWdvouLywMYnM+rg7KrfY6Ki8yDHdWxFKrlGCV8vN/QBgTAQE6/NkNSYH5TLZvTHZKS/oS47Ky9L3nCnnOA7JycmoXLmy1aotFXlMD32eVDx4tQoycBDyjBDyjOYVxkTmkr8nxjHIq7hDXsUdmnaVYEzOgfFypllMupc4nOkF6OLuQhd3F5ybHMpIL6hq+EBeSQOe58t/TCIgaE2AVg+5Ug6oeTAlhxt3biE0NBQ8zz8evkdjcvkxiaKI69evo0qVKpDL5Y/EmAraSGOqmGNijOHGjRuoVKmS1f28LMdUsC+CIAjCNXBJASkzMxOAecqaPby8vKQ6xSE7OxsbN26ERqPBoEGDiqw/Z84czJw506b83Llz8PDwAAD4+fmhatWquHbtGtLS0qQ6ISEhCAkJQWJiIrKysqTysLAw+Pv7Iz4+3mopxerVq8PLywtxcXFWN81atWpBqVTi4t5T8PgzD7zR/PrFAzCkGGG4ooX42zVoGytgqqxA3bCayNblIOnONYAxyDMZNKkcPNJlMN3Ozf/uZoXJnYMhiIchkId7uC+Cgivj+t2bSM9OBrLNdQK9AxDsE4DklOvI1uVIbSv7h8DPwwdXbiVCb7yfYDs8qAo83Txw8fplq4eEGpWqQSGX43yytQhYJywKRpMJl24mSGU8z1uP6R4qhRJRlasjI0eLG3dvSeUeandEBIchJTMNKZmpUrmvhzdC/SvhZtptpGff951Cx+Tth4SUJOhFozRlp3r16vDivRB3xv55OnPmjNWYGjRoAIPBgIsXL0plMpkMDRo0QFZWFq5cuSKVq9Vq1K5dG+np6UhOTpbKPT09ERkZiTt37uDWrftjLSvfc3ZMdevWxe3bt5GWliaFvVf0MZXkebpx95Y0JiYwBHn7I1Djh+TbLvx7cndHRJdw3L6bgsz4O1DfEKC6LYK7d0hZngn6s2nQn02DoAb4CA/41auE69xdZOtzXWJMabfScD31JtJzMnH3Rgo83T1RPSwCdzJScDstxTwNlQf8/P0fWd+jMbnmmBhj0vUyPDz8kRjTo3ieHscxBQUFIS0tDXq9Hjk59+9PZTmm7OxsEARBEK4Hxwp+onABunbtigMHDiA+Ph41atSw2R4aGors7Oxii0hff/01RowYgaFDh2Lt2rVF1rcXgRQWFoa0tDR4eXkBKJsvUnlxd5G2/rxD8ceCZ/dwqCp7Qp+khT5JC2NylpTPxAY5B0WoB1ThXlCEeYD3Vlrb+DDROhbbK2AEEifnwanlkLkpIHdTVMgvh+X1NRQA/v33X9SrVw8y2f1pjhV5TGVxnox5BnPOJJ0JzCS6/O+JGUUYk7JguJQJfZIWEGwvTLyHAsoa3lDV8IYs0M1hZFJZjckkCLh47RJqVakBGS9zeI2QKeQQOQbIOLNoLOPBK3jIFHIIEK3ygTwKvvco/p4q0pgEQcC5c+dQv359KBSKR2JMBW2kMVXMMYmiiHPnzqFu3bpW9/OyHJNWq4Wfnx8yMzOlZ25XR6fTISEhAdWqVaNFegiCqFAU5/rlkhFIlsgjRwKRVquFr69vsfstzvQ1AFCpVFCpVDblMpnM6oYK3L8Z2qv7MOXMKCJ9c3yR4hEAZO1NQlYh9WR+aiirmpNfKyq7F7lKGM/xgJ30JjJHY3VY7mCsnPPlHMfZLXdkY3HKOQUPpVoJzk1uk9/pYc9fYeUcx9ktd+RLxS0vTdvtlQuCII3J2d+Hq48JKP3zpHBTAm5mAVc0CGB5Jog6E2QO8tiX++9JJYO8ph/cavpBNAgwJGRCF58Bw9UsabVGMdsI3elU6E6nQuathKqGD1RRvpD7q8FxtsehtMck481TBmU8L12n7I2VmURzkdFyIRUgAhChtxgKTs6ZV6STcxBkPDgZZ76Wyjgrgaki+N6j+HuqaGOyvJAXVr+ijcmZchpTxRmTvf7LYkyO2hAEQRDli2usy1wAS+4je3mObt26hezsbLv5kQojLi4Of/zxB2rXro3o6OgSsbMsyD2TApbnIFdRQQqIR5yCh7K6Nzw7VIH/y3XhP7g2PKNDzUtyP+ZLzHNKGWReKiiC3aEIdofMW0XJwR8SjuMQEhJCq7Y8BLxSBpm3xS81kHmpwClc97fKK2VQ1/KDz7PVEfBqPXh2DoOyqqfVnUXINCD3f3eQHnsRaTEXkH38JkxpOsedlgIcxyHQO+DhfVMQwfQCxFwjBK0BQroOptQ8GG/lwHg9G4abOTCm5MKUpoOQqYeQY855xUy20QAEQddMwlUh3yQqCsOGDZPSihQFx3GYMWNG6RrkgA4dOqBDhw5O1c3OzkZQUBBiYmJK1ygXo1WrVnj//ffL2wzCCVzyzaR9+/YAgP3799ts27dvn1UdZ7FEH7366qsPaV3Zojt3125EgCM4JQ9N0yD4vFADASPqw+eZanCrHwCZl7Loxo84nEoGmY8KihB3KII0kHkpXfrlvKLB8zxCQkIcfskkigenkEHmpTSLSSFmkZNzYZGTV8nhVscfPj0jETC8Pjw7hkFRxcPq+iVk6JH7922kfX8Bd7+/gJwTt2DK0DvutKRs43gE+wRIU+BKjfwCU5YDgemOHYHJSALT4whdMwlXhXyTcMTly5cxevRoVK9eHWr1/9k78/go6vv/P2dmr9wnEO6EACISlKLgASheaC3eIApSbK221VZbtSq2Ra39etdW61GtP0E5vBCPWm9Ag4jWG0HucAoEcl+b3Z2Z3x+zu8lmN8kGkuwmeT8fjw+b/cxnZz6TfNidee37/Xq7SE1N5aSTTuIf//gHdXV1sZ5et+Af//gHKSkpzJgxI9h3++23BwsaKYqC3W4nNzeX3/72t5SXl4ftIzc3N2R849bYv+xw+cMf/oCiKFxyySURt2/fvr3ZeSiKwj333BMce/PNN/Poo4+G+LQJ8UlcprCddtppDBkyhMWLF/Pb3/6WY445BrBS2v7v//4Ph8PB7Nmzg+P37t1LRUUFffv2jWi87fV6ee6557Db7SGv6wrotb6o0tcC2Holknxiv46bUFdCUVCcGmqCDdVlCxphCx2Druts376d3NxcCT1vZxSbipbiQEtxYOpGsGS96TEgDoUHNcFGwlFZJByVhVHrxb21gvrNZXh/aDBj1Uvd1KzZR82afdh6JeAclo5raDpaanja8OGiGwa7DuxhYK/+zabadgq6gamD6WnGm65RSpzlwRT42Z8ip8p7WHdC3jOFeEXWphCJN998k2nTpuF0Opk9ezajRo3C4/GwatUqbrrpJtatW8eTTz4Z62k2S11dHTZbXN76BvF6vfzjH//gd7/7XcT/e48//jjJycnU1NTwwQcf8Mgjj/Dll1+yatWqsLHHHHMMN9xwQ1i/w9E+QQWmabJkyRJyc3N54403qKqqIiUlJeLYSy+9lB//+Mdh/WPGjAn+fN5555Gamspjjz3GnXfe2S5zFDqGuPxfZLPZ+Pe//82UKVOYNGkSM2bMICUlhaVLl7Jjxw4eeOABcnNzg+NvvfVWFixYwDPPPMOcOXPC9vf6669z4MABLrzwQnr37t15J9IOaIk26xv8aO4RFVCdPfyDXlFQXZZopLhscsPVyTSuQCN0DIqmoiU70JIdmLppRbDU+TDq9fgUkxLtJBZkk1iQjV7toX5rBe7NZfj2NVRp8x2ow3egjprVe7H1ScQ1LB3n0HS05PaKnDT9le7i7/cTgghMPQ55zxTiFVmb8YFRX0/V229T9f4H6BXlaGnppJx+GilnnYUawae1oygqKmLGjBkMHjyY5cuX07dv3+C2a665hi1btvDmm2922nwOha5gbP6f//yHAwcOMH369IjbL774YrKzswG4+uqrmTFjBi+88AKfffYZ48aNCxnbv39/Zs2a1abjn3LKKeTm5kZVbGrlypXs3r2b5cuXM2XKFF555RV++tOfRhz7ox/9qNW5qKrKxRdfzLPPPssdd9whKbRxTNzGpk6ePJlVq1Zx0kkn8cILL/D444/Tp08fnn/++Yhqaku01Tw7nnAdlRX9PY8JzvzwCKxuj6qgJtqxZSVg75uELSsBNdEuN1NCt0fRFLQkO7Zs/9rPdKEm2CBOP3S1ZAeJR/ci8+LhZM0eSfKJ/bD1SggZ49tfS/WqHyiZv56ypZup/fYAek0zjuI9Dd3A9DRKkSuvt1Lk9tfg/aFRilxJnZUiV+1pSJEz4lw8EwRBEEKoWr6czRMn8cPNt1D1wQfUfvY/qj74gB9uvoXNEydRtXxFp83lvvvuo7q6mqeffjpEPAowdOhQrrvuuuBzn8/HX/7yF/Lz83E6neTm5jJ37tyQ6tZgpVr95Cc/YeXKlRx77LEkJCRQUFDAypUrAXjllVcoKCjA5XIxduxYvvrqq4jz27ZtG1OmTCEpKYl+/fpx5513hqWGN/VACqSFbdmyhTlz5pCenk5aWhpXXHEFtbW1NGXhwoWMHTuWhIQEMjMzmTFjBrt27Qob9+STT5Kfn09CQgLjxo2jsLCw2d9rU1599VVyc3PJz8+PavzEiRMBK7Wws1m0aBEjR45k8uTJnH766e3i2XTGGWewY8cOvv7668OfoNBhxGUEUoBx48bx1ltvtTpu/vz5LSql//3vf9txVp1LYkEvyt/YFpWRtuLUcOand/yk4gFVsVLTEmwoTk1UaqHHo6gKSqIdNdGOaZiWF4+/ohtxKB5oqQ4Sf9SbxB/1xldeT/2Wcuo3l+EracjN9+6twbu3hurCPdj7JVuRSfnplkgmhBOIYGpuu9oQvRSsHCcRTIIgCHFH1fLl7L7m2oYOwwh5NKqq2H3NNQx49J+knHpqh8/njTfeYMiQIZx44olRjb/yyitZsGABF198MTfccAOffvopd999N99//z3Lli0LGbtlyxYuu+wyrr76ambNmsUDDzzA1KlTeeKJJ5g7dy6//vWvAbj77ruZPn06GzduDPHn0nWds846i+OPP5777ruPt99+m3nz5uHz+aJKhZo+fTp5eXncfffdfPnll/z73/+md+/e3HvvvcExf/3rX/nTn/7E9OnTufLKKzlw4ACPPPIIkyZN4quvviI9PR2wghauvvpqTjzxRK6//nq2bdvGueeeS2ZmJgMHDmx1LqtXr+ZHP/pRNL9iwPIYAiJWJ/d6vRw8eDCkLzExkcTExKj33xz19fUsXbo0GNRx6aWXcsUVV7Bv3z5ycnLCxtfW1obNBSA9PT0krXDs2LEAfPzxxyHpbUJ8IVfhcY5iV8mcNpyS59a3GomUevqgbl1dTbGpKC5LNOrxqXpxiKIoDBw4UMS8OEBRFRS/wGqafjHJ7cOo00E3Yj29MGzpTmzH9iHp2D74ytzUby7HvbkMvcz/TaUJ3j3VePdUU/XhbhwDUnAOS8c5JA3V1fzHmOkzcG8pp35bOb1rTarW7cA5xPJa6s7vlc1imJgePSqBCVvTdDlVBKZ2Rt4zhXhF1mZsMerr+eGWW60nzaWmmyYoCj/ccivDCj/q0HS2yspK9uzZw3nnnRfV+G+++YYFCxZw5ZVX8tRTTwHw61//mt69e/PAAw+wYsUKJk+eHBy/ceNGVq9ezQknnADAyJEjmTJlCr/4xS/YsGEDgwYNAiyR5Oqrr+ajjz4KqWjmdrs566yzePjhh4PHmjp1Kvfeey+//e1vgylfzTFmzJhgtgpASUkJTz/9dFBA2rFjB/PmzeOuu+5i7ty5wXEXXnghY8aM4bHHHmPu3Ll4vV7mzp3LMcccw4oVK4JeQyNHjuSqq65qVUDy+Xxs3bq1xd9zaWkpADU1NSxfvpxHH32UXr16MWnSpLCx7777Lr169QrpmzdvXrtUovvPf/5DeXl50Oj7/PPP56qrruL555/n+uuvDxs/b9485s2bF9b/ySefcPzxxwef9+/fH4fDwfr16w97jkLHIQJSFyBhZBZZl4+k9KVNViRSwBPJ/6g4NVJPH4Qzr/ulryl21TLATrChxnEFKsHKXc7Kyor1NIQmKIpiCa8uG6SD4ReTzDqrvHy8YctwYRuXQ+JxfdBL3JYAtLkMvcJjDTDBs6sKz64qqlbuxjEwBdfwdBx5aSHvEfVFFVS+vxOzXg++V3qUejzbKqku3NNt3zMPCxGYOhV5zxTiFVmbsaXq7bcxKitbH2iaGJWVVL3zDmnnntth86n0z6U5g+SmBDI/fv/734f033DDDTzwwAO8+eabIQLSyJEjg+IRwPjx4wE49dRTg+JR4/5t27aFCEgA117bEK2lKArXXnstb775Ju+//35INbNI/PKXvwx5PnHiRJYtW0ZlZSWpqam88sorGIbB9OnTQ6JocnJyGDZsGCtWrGDu3Ll8/vnnFBcXc+edd4YYVc+ZM4ebbrqpxTmAJQ6ZphkxmijAEUccEfK8oKCAZ555JmJU0fjx47nrrrtC+oYMGRL82ev1UlFREbLd6/VSX18fFi2UmZkZEvW1aNEijj32WIYOHQpYa+Occ85h0aJFEQWkq666imnTpoX1jxw5MqwvIyMjYrSSED+IgNRFSBiZRb+546n97iC1X++npqSKxNQkXPlWSkd3+jZdcWio/kgjxd59zqu7o+s6mzdvZtiwYVK1JY5RnZoVwZfmxPDomG5/RTdvfIlJiqJgy04gOTuBpPE5+A7UUb/FikwyqvyeSIaJZ0clnh2VoCk4B6fiHJYOQOU7Oxp2ZoY+mvU6FW8WkXZOnohIbUEEpnZF3jOFeEXWZvtTdNHF+KK8KdYjlGVvib1/+jPFD/4t6vG27Gzylr4c9fjU1FQgemP1HTt2oKpqUFwIkJOTQ3p6Ojt27AjpbywSAcGK2k0jdgL9ZWVlIf2qqoYIIwDDhw8HGlK8WqLp8QMCTllZGampqWzevBnTNBk2bFjE19vtdoDgeTUdZ7fbw+bXEk29mxqzdOlSUlNTOXDgAA8//DBFRUUkJCREHJudnc3pp5/e7L4+/vjjECEvwOrVq3n++edD+oqKioIFrMrLy/nvf//Ltddey5YtW4JjTjrpJJYuXcqmTZuCv/8Aw4YNa3EujTFNU6If4xwRkLoQil0laUxv7ENS2PXdd/QZOBhN7R4f7IqzkWjUjcSwnobb7W59kBA3qA4NHBpaqhPTa/jT3HzNVwGLEYqiYO+diL13Ikkn9MW3vxb35nLqt5RjBAy2dZP6bRXUb6toeWeNqHx/J9lXHCXvOe2FCExtRt4zhXhF1mb74jt4EN/+/R2yb7O+vsP2DZaA1K9fP7777rs2vS5aEaA5kbK5/pYElkOhteMYhoGiKLz11lsRxyYnJ7fLPDIzM1EUJUwga8ykSZOCKXlTp06loKCAmTNn8sUXX4RECEXD0UcfzXvvvRfSd8MNN5CTkxMWMdXY1+ill16ivr6eBx98kAcffDBsv4sWLeKOO+5o01waU15e3mraoRBbREASYoOi+EUjzRKNNLmBE4RYothVNLsDLcWB6WskJtXHoZiUk4Q9J4nkCf3w7q2hfnM59VvLMWpbLzbQGLNep35rOa4jMjtotkIIhyIwaSqKrecKTIIgdA9sbbgh1svLMZtUK2sJxelE85s4t/dcAvzkJz/hySef5JNPPglJN4vE4MGDMQyDzZs3c+SRRwb79+/fT3l5OYMHD27z8VvCMAy2bdsWEvWyadMmgGDUzOGQn5+PaZrk5eWFRdY0JnBemzdv5tRGxuZer5eioiKOPvroFo9js9nIz8+nqKgoqnklJyczb948rrjiCl588cVWU/WakpGRERYVlJGRQd++fVuMFlq0aBGjRo2K6Gn0r3/9i8WLFx+ygLRnzx48Hk/IuhHiDxGQhM5DUVBdWtAIW24GBCE+UWwqWrIDLdmBqRv+am66JSa18zd/h4OiKDj6JePol0zyxP54f6im8oNdGFWeqPdRuXI37g1lqEl2qyXb0ZJsqEkO1CQbaqJd3qs6i7YITH5hSQQmQRC6Am1JGat47TV+uPmWqMf3/cudHeqBBPCHP/yBRYsWceWVV7J8+XL69OkTsn3r1q385z//4brrruPHP/4xc+fO5e9//zv/+te/gmP+9jcrze6cc85p9/n985//DJpom6bJP//5T+x2O6eddtph7/vCCy/k1ltv5Y477mDhwoUhkVWmaVJaWkpWVhbHHnssvXr14oknnuCKK64I+iDNnz+f8ijTEk844QRWrlwZ9dxmzpzJn/70J+699942C0iHwq5du/joo4+44447uPjii8O2ezweZs6cyaeffhr0rGoLX3zxBUDU1f6E2CACUhdEBQb3HoCqdIGoHVWxUtP8wpFc4HdfAjnobQ2hFeIbRQuISWAaphWV5LYEpbgSk1QFx4AUtBRHmwQkvAaeXS34OihYlR/9ApOWbG8Qm5LsaP5HxaVJzn5HE43A5E+LaywwEUiVi7PPH3nPFOIVWZuxJeWss1D/+n8YVVUtf84qCmpKCilTpnT4nPLz81m8eDGXXHIJRx55JLNnz2bUqFF4PB5Wr17NSy+9xJw5cwArNeqnP/0pTz75JOXl5Zx88sl89tlnLFiwgPPPPz+i787h4HK5ePvtt/npT3/K+PHjeeutt3jzzTeZO3duWBWyQyE/P5+77rqLW2+9le3bt3P++eeTkpJCUVERy5Yt46qrruLGG2/Ebrdz1113cfXVV3PqqadyySWXUFRUxDPPPBO1B9J5553Hc889F9FHKBJ2u53rrruOm266ibfffpuzzjrrcE+3RRYvXoxpmpzbjGD54x//GJvNxqJFi0IEpC+//JKFCxeGjc/Pzw+JaHvvvfcYNGgQY8aMaf/JC+2GCEhdEMVdTkrtfvAlgSMJ7IkQT15IqmLdcCXYUJxyU9VTUBQlaLQodE8UVUFLskOSHdMwg0KS4faBER9ikurSGipVtgcmGLU+Kz3uQF0LB1YaBKVkuxW91EhgCjapJtlxGKa1LpszhG8qMAV/jo3AJO+ZQrwiazO2qE4n/e65h93XXAOKEllE8l9b97vnHlSns1Pmde655/Ltt99y//3389prr/H444/jdDoZPXo0Dz74IL/4xS+CY//9738zZMgQ5s+fz7Jly8jJyeHWW2+NmPZ0uGiaxttvv82vfvUrbrrpJlJSUpg3bx5//vOf2+0Yt9xyC8OHD+ehhx4KpmcNHDiQM888M0RMueqqq9B1nfvvv5+bbrqJgoICXn/9df70pz9FdZypU6eSnZ3Niy++yB//+MeoXnPVVVdx1113cc8993S4gLRo0SIGDRrUbDpeeno6EyZM4IUXXghGnAEsWbKEJUuWhI3/6U9/GhSQDMNg6dKl/PznP5d7xzhHMdvbiawbU1lZSVpaGhUVFTH9YPXs3cP677/niIw0tMB/MFuCJSQ5k8Hm6vQ5KTbVn5qmoTpFl+yJ6LrO+vXrGTlypFRt6WGYpolZr1upbnWxFZPqNpRS9f7OqMennD4IZ24qRo0XvcaLUe3FqPVi1PjQqz0YNT6MGquvPUQpxa4GU+WaCkwNP4svXEwICEyaGmLyTcCLSWvfC1p5zxTilXhYm/Fyzd0W3G43RUVF5OXl4XId/rV41fLl/HDLrRiVlaCqYBjBRzU1lX733EPKqe0bzSPEnr/85S8888wzbN68uUd9Nrz66qtcdtllbN26lb59+8Z6Oj2Otrx/yZ1+F8Voqvv56qxWVwJo4Ej0RyclgdYxf2bFrqK6bCgJNvlWXQCsi06h56EoiiUgu2yY6VaakVFnCUrozUSDdBCuoelUF+6JyvxbcWq4hqaj2Kz3MltW5FK40JC+1yAwedGrrceg+FTjxXS3fFzTa6CX16OXt2yQqiTY/F5MkQQmf0u0ybd07Um0EUyNBaag4fehCUzyninEK7I2Y0/KqacyrPAjqt55h6r33kevqEBLSyPljNNJmTKl0yKPhM7ld7/7HY888gjPP/88M2fOjPV0Oo17772Xa6+9VsSjLoAISN0SHTxVVgPQnJaQFBCVDgPFoVmeRgk2FLt8Qy4IQiiKoqA4bVYkYroTw2MJSWadD9PX8WKSYlNJPX0QFW+2XsUk9fRBVoRJNPv1p+9pSfYWx5k+A6M2ICj5MPxRTAGBKdCaFSkC+6nz4avzwcEWSmmroCZGFpc0fySTmmxHcUgqcbvQJoGpUdTSYQhMgiD0bFSnk7Rzz+1wk2whfkhOTqa4uDjW0+h0Pvnkk1hPQYgSEZB6Anq91dylgAr2BHAkWylvNkerL1ecjUSjKG+2BEEQAFSHZkUopjkxvQ1pbq0JKIeDMy+NtHPyqHx/Z8RIJMWpkXr6IJx5ae1+bMWmoqU60VJb/mbY8OgN0UvVoeJSg9jUSjqggRURVe3F19LBbEozAlNoKp28vx8mrQlMitJg7G1TMDAtwbFeR3UoVqqcCH2CIAiCIMQxIiB1QVRFYWhaKod2qW+At8ZqAKq9IdXNkQiKal3kOjVUl2aJRuLFIUSBqqocccQRUrVFaBbFrqHZNbRUp3XjHBCTPO2fKuHMSyP7iqOo31qOe2sFep0XLcGOKz8NZ356zMWSoLCW0XyeuWmamG491IspRGDyt9oW5SPwmegVHvSKlqvTKU4toh9TSOW5RHvcVTPrMpgmprdBYDJNk/zMQRglbrwB4UhrlBoX4WcRmYTOQD7PBUEQhOYQAamLYm8vUcfwgrsc3OWoDhUlJQk1JRUlIQ0crUcnCUJjHLJmhChRbCpaigMtxWGJSe6AmGS0XLa4jcdwHZGJc3gGhmmgKmqXuvlWFMXymEuwQQuViE3dDPoyNRWYGqfSmZ5W0ubqdfR6Hb3UjbeFcWpiBG+mJqbgikvS5qLBbmtyGaYbmHorfu1qo6pxgYgmtVF0k6qIyCccNvJ5LgiCIERCBKQuiGGafF9azpGZ6RyWdbUCqkNBdSooTvwX+7VQUws1+0C1gTMFnKlW6yAzbqF7YBgGa9eupaCgoEdVjRAOH8WmoiU70JIdlhjitjyTjHq9XcQkwzT4ftdmjhw4DE3pfmtT0ZSgGNcSpldHbxTJFEyfa2IKjt7y79yo9VlRTwfqmh+kKmFeTBGNwHtwAYZDXpeGZVTfqsgU8GMKeWz0s4hMQjPI57kgCILQHKII9DQUUJ1+0chBy98QGz6oK7MaWJ5JzhSrOZJBvl0WBKGdUTTLr4cku+Un449MMtztIyb1ZBS7hi1dg/Tm/ZlM08Ss1yOkyvmNwAN+TbXelsNkDBOjyoNR1UranF1tEJOSQwWmoPiUZJdU6rbSmh8TBEWmFtPmRGQSBEEQBKERIiD1ABQVlIBoZG9FNGoJb63VqveDooEz2R+dlAI2KSUqCEL7oqgKSqLlu2MafmGjzofhbsVcWjhkFEVBcdlQXTZsWQnNjjMN0/pbNE2Xqw59brpb9rcyvQZ6eT16eX3L83JpIalyEaOZEmwieLQFv8iE12heC/Qbf1tik9rws01t6JPqcoIgCILQYxABqZuiaPhT0xRUewdc3Jk6uCusBmBzNYpOSgExXhQEoR1R1AY/oGCUTCAySe+4im5CZBTVihTTkuwtjjN9BkZtIy8mv8jU1Ai8tap8plvH59ahxN3CpIhcaS7JhpbkaIhmcsbWn8n0Gbi3lFO/rZz0inqq0nbgHJKOa2jszd3D8Bt/A5g0IwYqSoPBd2Oz76Zpc4IgCIIgdHlEQOqCqIrCkZnpYVXYFJuC6vQLR7ZOvjj2ua1WcwBQrBQ3lz86yd78t9hC90FVVQoKCqRqi9DhNI6SATD8YpLp9mH6woUIVVE5cuAwVEXWZmej2FS0VCdaastRqoZHD/dmCjMFbyXyzMSKgKpuyQIcsClh0Ushz5PtaIl2FHv7r5f6ogoq39+JWW+JMQ7AU1KJZ1sl1YV7SD19EM68tHY/bodimpg+E3xRikyqAjZ/qlzjn0Vkihvk81wQBEFoDhGQuihe3cCpqSh2v2jkiIFo1CwmeKqsBqDaLSHJ5TfjVsWQsbvi8XhwuZovSy4IHYHq1FCdGuDE8OiWAbfbFxLV4vX5cNqlqlC8ojo0y1A7o/n3D9M0Md16xFS5xuKTUetr+WA+E73Cg17Rij+TU2skMNlQ/VFMIal0CfaoU7jqiyqoeLOo+fOr16l4s4i0c/K6nojUGiEiUzMERKYWvJnQFKnu10nI57kgCIIQCRGQuiCmBltrqygYnIGtA74hbXcML9SVWg2lwYzblQqOpFjPTmgnDMNg48aNUrVFiCmqQwOHhpbmxPQaGHU+vLX1bNlVxJEDh6MhN59dFUVpSGMkuwV/Jt3EqGsQmfQQn6YG36ZAFFCz+6nX0et19FI3LcU0qYm2yNFMjSrPYVOofH9nVOdZ+f5Osq84Kv7S2TqagMhEy/7sLZp+i8jULsjnuSAIgtAcIiB1QVSHhmJTuqhxpQneGqtV7wPVZqW7OVMtQUlr2U9DEAQhWhS7imZ3QJKGus+Oo18yKmqDeXDgUW/yPPCzbkrlty6IoiloyQ605JYjzkyvERa9FPa82gt6y2vAqPVZUU8H6lqYFK2oIo3mVa9Tv7Uc1xGZ0b2gp6EbmHorv06/0beVIudPj1OtSG1FVRtS6QRB6HGsXLmSyZMns2LFCk455ZRYT6dL8uKLL/LLX/6SnTt3kpycfMj7acvfIjD2pZde4uKLLz7kY7bGLbfcwooVK/j000877BhdnR729ZYQdxg+cJdDxU7Y/x0Ub4CKPeCulBs3QRDaHUVTrNLxTg01wUpH0lId2NKd2DJd2LMTsPdOxJ6ThKN/MvZ+ydhzkrD1TsSWnYCW4UJLc6KlOFAT7aguG4pDC1alEroOil3Flu7E0T8Z1/AMEsf0JmVCf9Km5JJx4TCyLh9Jr1+OJvvKUWReegRp5w4h5bSBJB3fl4SCbBxD0rD1SURNstNqYFsbP86qPv6ByhW7qPnfPurWl+DZWYWv1I3haTlqSvBjmJgeHcPts0zbK+rRy9z4DtTh3V+D94dqPD9U491fg+9gHb4yN3qlxxIQ/emvplR6FIQw1q1bx6xZs+jfvz9Op5N+/foxc+ZM1q1bF+upAbBs2TLOPvtssrOzcTgc9OvXj+nTp7N8+fJYT61VDMPg2WefZfz48WRmZpKSksLw4cOZPXs2a9asAeC3v/0tiqKwZcuWZvdz2223oSgK3377LQC5ubkoisLpp58ecfxTTz1lRfgqCp9//nmr89R1nXnz5vGb3/zmsMSj5li8eDF///vf232/0XL99dfzzTff8Prrr8dsDvGORCB1Ubpk8FE0+OqsVlMMitoQneRMAbvk4sc7EuouxCuHujYV1e/JEuV402wUvdQkqiks2ikwRohbGhu227JaSJszTKsqYDPRTJ491eCL/m9t1vpwryuJPCeHiprssNLjkv2m30l21GRH8HmsK811Cfz/J1usANiCH1Pw524qHMvneXzg8+ps/aKYbd8cxF3jxZVkZ8jR2eSP7Y3N3rl/o1deeYVLL72UzMxMfv7zn5OXl8f27dt5+umnefnll3n++ee54IILOnVOAUzT5Gc/+xnz589nzJgx/P73vycnJ4e9e/eybNkyTjvtND7++GNOPPHEmMwvGn7729/y6KOPct555zFz5kxsNhsbN27krbfeYsiQIRx//PHMnDmTRx55hMWLF/PnP/854n6WLFlCQUEBo0ePDva5XC5WrFjBvn37yMnJCRm/aNEiXC4XbncLVU4b8cYbb7Bx40auuuqqQz9ZP5MmTaKurg6HoyFiePHixXz33Xdcf/31h73/QyEnJ4fzzjuPBx54gHPPPTcmc4h3REDqgmiaSsHgHhDabhpQX2k1AM1pCUmBJmbccYWmaRQUFMR6GoIQRmeuTUUJlDCP/jUBIckSmPyRDwZNhCcD08ASnCQ6M+5QVKuym5YUOQ274r9F1BdVtDkSKRKmx0AvdbfszRSoNJfsaCQw2dECz5PtKAk2EZlaI/D/z2u0bP5tC5h/N/Jh8kclBvu6EPJ5Hh8UfXOADxZ8T32tryENVoFtXx2g8MXNnDZnJHmjsztlLlu3buXyyy9nyJAhfPTRR/Tq1Su47brrrmPixIlcfvnlfPvttwwZMqRT5tSYBx98kPnz53P99dfzt7/9LeS97bbbbuO5557DZovf2979+/fz2GOP8Ytf/IInn3wyZNvf//53Dhw4AMD48eMZOnQoS5YsiSggffLJJxQVFXHPPfeE9J900kn873//44UXXuC6664L9u/evZvCwkIuuOACli5dGtVcn3nmGU466ST69+/f1tMMQ1XVuDTrnz59OtOmTWPbtm0xWc/xjqSwdUFM06SyzmN9092T0Ouh9iCUFcG+tXBwM1TtB09trGcm4F+XlZU9b10KcU+8r82QtLpE6yY/LK2uTxKOvi2n1amR0upEIIgLHEPS2iQeJU/qT8b04aT9OI/kkweQOLY3riMysPdPRktzQmtVV/2V5rx7qqnfWEbtl8VUf7SHiv8WUfbiJg7+v3UcePxbDi5YR9nSzVS8s52qVXuo/boY95ZyvPtq0Ks8ksYVDaYVxWTW6xi1XvQqD3p5vZUaV1yLd281nj3VePfV4D1Qi6/UbaXUVXusqDWPjqm3EAUVA+L9PbMnUPTNAf77xFpLPIKG9w//Y32tj/8+/i1F3xzolPncf//91NbW8uSTT4aIRwDZ2dn861//oqamhvvuuy/Yf/vttwfTrebMmUN6ejppaWlcccUV1NaGX7svXLiQsWPHkpCQQGZmJjNmzGDXrl2tzq2uro67776bESNG8MADD0QUxi+//HLGjRvX7D4KCwuZNm0agwYNwul0MnDgQH73u99RVxfqbbdv3z6uuOIKBgwYgNPppG/fvpx33nls3749OObzzz9nypQpZGdnk5CQQF5eHj/72c9aPIeioiJM0+Skk04K26YoCr179w4+nzlzJhs2bODLL78MG7t48WIUReHSSy8N6Xe5XFx44YUsXrw4pH/JkiVkZGQwZcqUFucXwO128/bbb4elw1144YX86Ec/CumbOnUqiqKEpIJ9+umnKIrCW2+9BVi+RoqisHLlSgBOOeUU3nzzTXbs2BFMq8vNzQ3Zr2EY/PWvf2XAgAG4XC5OO+20FlP6AFasWIGiKCxbtixsW+B39sknnwT7Auf32muvtfwL6aHErxQrNIthmGzbX03BoIzum8rWKiZ4qq1WhWXG7Uzxp7ulgiZLu7MxDINt27ZJ1RYh7uhua7PNaXWRDMKbMQ+XtLqOwTU0nerCPa1WfgNQnBoJI7MsAbB35DGmaVqCRbU/Va7aawkS1d5GfR5MTwvChGFiVHkxqlqoMadgiZKNopiCqXKNUugUTb6PbJFAhTkfmDSzBhSlIXpJVcDmT5UL/Kx2XoW57vae2dXweXU+WPB966KzCR8s+J4592Z2eDrbG2+8QW5uLhMnToy4fdKkSeTm5vLmm2+GbZs+fTp5eXncfffdfPnll/z73/+md+/e3HvvvcExf/3rX/nTn/7E9OnTufLKKzlw4ACPPPIIkyZN4quvviI9Pb3Zua1atYrS0lKuv/76Q16vL730ErW1tfzqV78iKyuLzz77jEceeYTdu3fz0ksvBcdddNFFrFu3jt/85jfk5uZSXFzMe++9x86dO4PPzzzzTHr16sUtt9xCeno627dv55VXXmnx+IMHDw7OY9q0aSQmJjY7dubMmdxxxx0sXrw4RLTRdZ0XX3yRiRMnMmjQoLDXXXbZZZx55pls3bqV/Px8wBJPLr74Yuz26IoYffHFF3g8njCxaOLEibz22mtUVlaSmpqKaZp8/PHHqKpKYWFhMBWssLAQVVUjCmVgRYtVVFSwe/duHnroIYAwn6V77rkHVVW58cYbqaio4L777mPmzJktml6fcsopDBw4kEWLFoWlWS5atIj8/HxOOOGEYF9aWhr5+fl8/PHH/O53v4vqd9OTkLtsoXtg+KCuzGoA9sQGQcmRJN/CC4LQYzk0HyfAMJoITTRJszMlrS5KFJtK6umDqHizqNWxqacPssSjlvbX2Jspu3lvJsNjiUxWRTmPX2jy+vs86NVeTHcLopZJ0MfJ19J8EmxNRKUmHk3JdpRO9mvpcoSITM2gKEFfpua8mTpLZBI6jq1fFDdEHrVCfa2PrV8e4IjxOa0PPkQqKir44YcfOO+881ocN3r0aF5//XWqqqpISUkJ9o8ZM4ann346+LykpISnn346KCDt2LGDefPmcddddzF37tzguAsvvJAxY8bw2GOPhfQ35fvvvwc4rLTLe++9l4SEhvfSq666iqFDhzJ37lx27tzJoEGDKC8vZ/Xq1dx///3ceOONwbG33npr8OfVq1dTVlbGu+++y7HHHhvsv+uuu1o8ft++fZk9ezbPPvssAwYM4JRTTuGkk07inHPOYcSIESFjhw0bxnHHHccLL7zAfffdh6panxfvv/8+xcXF/OUvf4l4jFNPPZWcnByWLFnCH//4R77//nu+/vpr/vGPf7Bt27aofk8bNmwAIC8vL6R/4sSJGIbBxx9/zNlnn813331HWVkZ06ZNo7CwMDiusLCQo48+mtTU1Ij7P+OMM+jfvz9lZWXMmjUr4hi3283XX38d9E3KyMjguuuu47vvvmPUqFERX6MoCrNmzeJvf/sbFRUVpKWlAXDgwAHeffddbrvttrDXDBkyhPXr17fyG+mZiIAkdE+8tVar3g+KBs7khugkW8ulnQVBEHoylo8ToGltj3LSzRaNw4NjeqDg5MxLI+2cPCrf3xkxEklxaqSePghnXlq7HVN1aKiZGmQ27zFh+oxgFJNR7QkKTIEoJqPai9HKzaxZ58NX54MDdc2OUZxaiKhk/dzgyaQm263USxE/msf0/3/SWwlOadb02/JkEpGp83nx//5HbaUnqrHumhaiAiOwYuEGPlm2NerxiakOps89LurxVVVVACGiUCQC2ysrK0PG/vKXvwwZN3HiRJYtWxaMVnnllVcwDIPp06dz8ODB4LicnByGDRvGihUrWhSQKisro5pfSzQWj2pqaqirq+PEE0/ENE2++uorBg0aREJCAg6Hg5UrV/Lzn/+cjIyMsP0EIqX+85//cPTRR0cd2QOWt9C4ceP4f//v/7Fs2TKWLVvGjTfeyKmnnsqzzz4b4jk0a9YsrrvuOj766CNOOeUUwIomcjgcTJs2LeL+NU1j+vTpQQFp0aJFDBw4kIkTJ0YtIJWUWMUdmp77mDFjSE5O5qOPPuLss8+msLCQAQMGMHv2bC688EJqa2tJSEhg1apVzQpD0XLFFVeEmG4HouK2bdvWrIAEMHv2bO6++25efvllfv7znwPwwgsv4PP5Is4pIyODr7766rDm2l0RAakrooDLLuHiUWPq4K6wGoDN1WDE7UgBVX6X7UU8GuEJAsja7GiCUU5RXlU0mzrXkvjUDXDmpZF9xVHUby3HvbWc2soaElOTcOWn48xPbzXyqCNQbCq2NCekOZsdY+omRm1oqpzeJLLJqPG2qGqY9Tp6vY5e2nylH8WuoiaFVpZrLDBpyQ4Ul4hMraIbrYtMqmX0baXIKSiqX1iyKZiKKe+Z7UxtpYea8voO2bfuNTps39AgzASEpOZoTmhqmk4VEB/KyspITU1l8+bNmKbJsGHDIu43IMJUV1dTXV0d7Nc0jV69egWjWVqbX0vs3LmTP//5z7z++uuUlZWFbKuosO4fnE4n9957LzfccAN9+vTh+OOP5yc/+QmzZ88OVjY7+eSTueiii7jjjjt46KGHOOWUUzj//PO57LLLcDqbf48Fy1D6mmuu4ZprrqGkpISPP/6YJ554grfeeosZM2aERPLMmDGD3//+9yxevJhTTjkFt9vNsmXLOPvssyMKWwEuu+wyHn74Yb755hsWL17MjBkzDun9tKk/mqZpnHDCCcE5FhYWMnHiRCZMmICu66xZs4Y+ffpQWlrabBpktLS0nlpixIgRHHfccSxatCgoIC1atIjjjz+eoUOHho03TVM+a5pBBKQuiKaqjOifHutpdF18bqvVHABFBUdyg6Bkbz4VQGgZTdPCwmwFIR6QtRl/HFpaXQv+TXoTMcokbqOcFJuK64hMXEdkkh7ryUSJoiloKQ60FAd2kiKOMQ3TMoVuHMlUE0iZ8wR/Rm/+72J6DfTyevTy+uYrzKlKg6jUKIqpoc+Bmmiz1pjQPIaJ6dEjikxqkl3eM9uZxNToo9/dNV50b/TG6ppdxdVMBcjDnQtYfjB9+/bl22+/bXHct99+S//+/cPSk5rzJQqIEIZhBI2VI40NeOA88MAD3HHHHcH+wYMHs3379uBaXbt2Leeff37U5xVA13XOOOMMSktLufnmmxkxYgRJSUns2bOHOXPmYBgNf4vrr7+eqVOn8uqrr/LOO+/wpz/9ibvvvpvly5czZswYFEXh5ZdfZs2aNbzxxhu88847/OxnP+PBBx9kzZo1YX4+zZGVlcW5557LueeeyymnnMKHH37Ijh07gl5JvXv35owzzmDp0qU8+uijvPHGG1RVVTFz5swW9zt+/Hjy8/O5/vrrKSoq4rLLLmvT7yorKwuwxJoBAwaEbJswYQJ//etfcbvdFBYWctttt5Gens6oUaMoLCykT58+AIctILW2nlpi9uzZXHfddezevZv6+nrWrFnDP//5z4hjy8rKyM7unCqHXQ0RkLoghmFSVuUmI9mJKsro4WEaUF9pNQDN0SAmOVNBFc+GaDEMg7KyMjIyMoL52IIQD8ja7PpYaXVWal3UolOkdLoIaXaxSqszTIPymkrSk1JRla6/LhVVQUuyIofoE9kA1jRNTLceErUUEJga0ui8mC3dPBsmRqUHo6V0IMUSQRp7MgVT55LtaEkO1CSbmH83g2EalJSUyHtmO9KWlLGNa/by/vzvox4/edaIDvVAAvjJT37CU089xapVq5gwYULY9sLCQrZv387VV1/d5n3n5+djmiZ5eXkMHz682XGzZ88OOXYg7WzChAlkZGSwZMkS5s6d22Yj7bVr17Jp0yYWLFjA7Nmzg/3vvfdes/O94YYbuOGGG9i8eTPHHHMMDz74IAsXLgyOOf744zn++OP561//yuLFi5k5cybPP/88V155ZZvmBnDsscfy4Ycfsnfv3qCABJaZ9ttvv81bb73F4sWLSU1NZerUqa3u79JLL+Wuu+7iyCOP5JhjjmnTXAJiXVFRUZjn1MSJE/F4PCxZsoQ9e/YEhaJJkyYFBaThw4cHhaTm6Mion0Dk1pIlS6irq8Nut3PJJZdEHFtUVMTRRx/dYXPpyoiA1AUxTZNdJbWkJzmjv5IWokP3QG2J1VAsM25Xqj/dLfK3roKFaZrs2rWrxUoZghALZG32TIKeL1GObzGtrhkx6nAwTZMfSvaRlpjSYz7LFUVBSbChJtigV+QxpmlieoyQKKaGlLkGj6YWK9qZBMUo3/7mh6mJtoaUucbpckkNolMs0gpjjbxnxpb8sb0pfHFzVEbazkQb+T9q5j9TO3LTTTexcOFCrr76aj766KNgJApAaWkpv/zlL0lMTOSmm25q874vvPBCbr31Vu644w4WLlwYIiCYpklpaSlZWVkMGTKEIUOGhL0+MTGRm2++mVtuuYWbb76Z+++/P0yEWLhwIcOHD2fcuHFhrw8ITo0jWEzT5B//+EfIuNraWlRVDUnvzM/PJyUlhfp6K4WwrKyM9PT0kOMHRJrAmEjs27eP0tJSRo4cGdLv8Xj44IMPUFU1LM3q/PPPJzExkccee4yVK1dy6aWXRpV6euWVV6JpGuPHj291bFPGjh2Lw+Hg888/D1ZWCzB+/Hjsdjv33nsvmZmZHHXUUYAlLD3zzDOkp6dz1llntXqMpKSkYNpge5Odnc3ZZ5/NwoULcbvdnHXWWRGjjCoqKti6dSu/+tWvOmQeXR0RkAShWUzw1litai+oNivdzZVmCUpa9OHCgiAIQnxzWGl1rZiHB8UooVUURbEMt50J2LKaTys3vXqTNLnwanNmXcs34EatzzIIb8X8O1hZrpnUOdUh0cpC+2Gza5w2ZyT/ffzblo2sFDhtzkhsnVDhcNiwYSxYsICZM2dSUFDAz3/+c/Ly8ti+fTtPP/00Bw8eZMmSJcHy8G0hPz+fu+66i1tvvZXt27dz/vnnk5KSQlFREcuWLeOqq64KqXoWiZtuuol169bx4IMPsmLFCi6++GJycnLYt28fr776Kp999hmrV6+O+NoRI0aQn5/PjTfeyJ49e0hNTWXp0qVhnjqbNm3itNNOY/r06YwcORKbzcayZcvYv38/M2bMAGDBggU89thjXHDBBeTn51NVVcVTTz1FamoqP/7xj5ud/+7duxk3bhynnnoqp512Gjk5ORQXF7NkyRK++eYbrr/++jChIzk5mfPPP5/FixcDtJq+FmDw4MHcfvvtUY1tisvl4swzz+T999/nzjvvDNmWmJjI2LFjWbNmDVOnTg2KaJMmTaKmpoaampqo0tfGjh3LCy+8wO9//3uOO+44kpOTo4qsipbZs2dz8cUXAzRbse7999/HNM1WKw/2VERAEoRoMXzgLrcagC3BEpJcqZawJOmEgiAIPYaQtLoov09oLDQpXh/Kfs0SIlAxdcPa5uuZVeraimLXsGVokNFChTndCBWYqr3oNY1MwKu9GLWtm3/76nUoacX8u0mqnJZkb+hLsov5t9Am8kZn8+NfFvDBgu+tSCQFa536H52JNk6bM5K80Z3n0TJt2jRGjBjB3XffHRSNsrKymDx5MnPnzm2xAlZr3HLLLQwfPpyHHnoo6HM0cOBAzjzzzLBIl0ioqsqzzz7Leeedx5NPPskDDzxAZWUlvXr1YtKkSdx3332ccMIJEV9rt9t54403+O1vf8vdd9+Ny+Xiggsu4Nprrw1JYRo4cCCXXnopH3zwAc899xw2m40RI0bw4osvctFFFwGWifZnn33G888/z/79+0lLS2PcuHEsWrSIvLy8Zud/xBFH8Pe//53//ve/PPbYY+zfvx+Xy8WoUaN46qmngqbPTZk5cyaLFy+mb9++nHrqqa3+ntqDn/3sZ1x00UXs2rWLgQMHhmybOHEia9asCUk1zMnJYejQoWzZsiUqAenXv/41X3/9Nc888wwPPfQQgwcPblcBaerUqWRkZGAYRrNr66WXXmLChAmHJIj2BBQzGscpAbDKRKalpVFRURFmENeZ6JX72b5lA7m9U9DEJDI+CJpxp1qCkq3lSgvdEV3X2b59O7m5uW3OPxeEjkTWphCPtLQurQgmA1P3Ry/pJqb/eaBfIpraB9NoXGEukDLXkCoX6Dvs37emhEYyJTUx/k62W+bfMRSZTJ+Be0s59TsqcVfXkpiZTMJR2SQW9ELp5Oq/8XLN3RbcbjdFRUXk5eW1WxU7n1dn65cH2Pb1Adw1XlxJdoYc04v8H/XqlMgjQWiKruuMHDmS6dOnNxvBE8/4fD769evH1KlTefrpp8O279u3j7y8PJ5//vkeFYHUlvcvEZDaQNx8mFUfgMrdsTu+0Dqas4kZd8/zUBAEQRA6joY0OcOKWjIskQm/6BQQm4TDxzRNzDpfeGW5RpFMeo0HfId5Sa0Smh7X2AQ8ENGUZO+QCnP1RRVUvr/T8pZqEu2iJNjInDachJFZreyl/Yiba+420BECkiDEIy+88AK/+tWv2LlzZ9SV5eKFl19+mWnTprFy5UpOPvnksO233HILy5cv57PPPovB7GKHCEgdRLx8mBmVxRTv3kzvtASpwtYlUPzRSX5ByRG5Ok1XxzAMiouL6d27t1RtEeIKWZtCPNIZ69I0zaCgFIxoMkzwGQ0CVAwq0HVHTNPErNf9YlIgesnTKHXOEp1Mz2GKegqoiU3T5PyV5Rp5NLXF/Lu+qIKKN4taPW7W5SM7TUSKl2vutiACkiDEL59++inffvstf/nLX8jOzubLL7+M9ZTiira8f4kHUhfENE32lbvplZrQYyq3dG1M8FRZrQpQ7aHRSVr3+G9omib79u2jV6+OrwYiCG1B1qYQj3TGulQUBWwKig2g+XSXhvS4plFMhohMUaIoCorLhuqyYctu3vzb8OiNKst5mqTOWX2mu5UKczXW+JYswpUEW2ianD9VruFny/zb9BlUvr+z9RM0ofSlTfSbO77T09kEQRAOl8cff5yFCxdyzDHHMH/+/FhPp0vTPe5cBaErYXihrtRqAPZES0hypoAjScy4BUEQhE5F0VSUVuxULN8lIyQ9runP4svUOqpDs6q2tWT+7TOCUUxBP6aaRp5M1R6relwLmHU+fHU+ONhChTmHCnbNSluLArPOR+13B0ka0zuq8YIgCPHC/PnzRThqJ0RAEoRY4621WvU+UDRwJoMzzRKUbI5Yz04QBEEQUDQFNK3FinOt+jL5DBGZokCxqdjSnJDWfEEOU29s/u2JXG2uxgstZMyZHgPaklKngFsEJEEQhB6NCEhdEEWBzGSHZK91R0wd3BVWA7C5GlLdHMlxbcatKAqZmZlSpliIO2RtCvFId1yXiqqAqqCgQjPaR0RfpiZRTJIy1zqKpqClONBSHNhJijjGNEyMOp8lKtU0qSwXEJ0qPNEf1ASjruXIJ0EQBKF7IwJSF0RVVQZldy3He+EQ8bmtVnMAFLWRGXcq2OPLoFFVVQYNGhTraQhCGLI2hXikp65L8WXqPBRVQUuyzLYhcgGP8v8W4dlWEeUOQU2QWwdBEISejHwKdEEMw2D3wWoGZCahdkApVyFOMQ2or7Qae0BzhJpxq60YWHQwhmGwe/duBgwYIJWuhLhC1qYQj8i6bBnxZeocnEPSoheQTHCNyu7YCQmCIAhxjQhIXRDThNJqD/0zI4csCz0E3QO1JVZDsQy4A4KSo/PXhmmalJaW0r9//04/tiC0hKxNIR6RdXn4HJIvk/9nfCamIb5MrqHpVBfuicpIW0mwkSgCkiAIQo9GBCRB6BaY4Km2WtVeUG0NkUnOFNBauLoWBEEQhG6K+DK1jGJTST19EBVvFrUyEDKnDUexS7ScIAhCT0YEJEHojhg+qCuzGoAtAVx+McmRbDmxC4IgCIIQlS+TaTYSkrqZL5MzL420c/KofH+nFYmkACbBRyXBRua04SSMzIrxTAVBEIRYI18jdEEURSEn3SUagBA9vjqo3g8lW2Dft1CyFaoPgK++3Q6hKAo5OTndqqKQ0D2QtSnEI7IuuxaKoqBoKqpDQ02woSU70NKc2DJd2HslYs9JwtE/GXvfZOx9ErFlJ6ClO9FSHaiJdhSXzYreiVPvSmdeGtlXHEXqGYNwDkuHAQm4RmaRcckR9Js7XsQjIS7ZtWsXLpeLjz/++LD3pSgKt99+e9Rjr7322sM+Zku8/fbbJCcnc+DAgQ49jiC0FRGQuiCqqpCTnogqF53CoRAw467cDcXrYf96KN8FdeVgGIe8W1VVycnJETNYIe6QtSnEI7IuuyeKpqDYNVSXX2RK9YtM2QnY+yTh6JeMvV8y9j5JlsiU4UJLdaIm21FdNhSHFjORSbGpuI7IJOOCYQy49liyLx9J0pjekrYmMH/+fBRF4fPPP29xnGmaPPfcc0yaNIn09HQSExMpKCjgzjvvpKamJmz8KaecYomzEdqGDRtandedd97J+PHjOemkkw753Jpj9erV3H777ZSXl7f7vqPhrLPOYujQodx9990xOb4gNIeksHVBdMNg+75KcnunoMXpN1lCF0Kvh9p6qD2IZcad3MiMO3LZ34i70XW2b99Obm4umhbbinCC0BhZm0I8Iuuy5xL0ZWpBmGnNlylYYa4DUuZ0w2DH1q2yNuMAn8fDpjWr2PK/NdRVV5KQnMrQ445n+PETsDkcsZ5eCLquc9lll/Hiiy8yceJEbr/9dhITEyksLOSOO+7gpZde4v3336dPnz4hrxswYEBEkaRfv34tHu/AgQMsWLCABQsWtMv86+rqsNkabo1Xr17NHXfcwZw5c0hPT2+XY7SVq6++mhtvvJE77riDlJSUmMxBEJoiAlJXxIQqty/WsxC6JSZ4qqxWBaj2BjHJmQpay28ZVVVVnTNNQWgjsjaFeETWpdAcbfZl8hlWVbkIHk1tF5lMWZtxwJbPP+Xtxx6ivqYaRVEwTRNFUdj82WqWz3+Ss6/5Hfljx8d6mkHuu+8+XnzxRW688Ubuv//+YP9VV13F9OnTOf/885kzZw5vvfVWyOvS0tKYNWtWm4+3cOFCbDYbU6dOPey5A7hcrnbZT3ty0UUX8Zvf/IaXXnqJn/3sZ7GejiAAksImCEJLGF6oK4XyHbB/LRzYCJV7ob66SxqFCoIgCEJ3IcSXKdGOluzA1owvk6131/Nl6sls+fxTXnvgLur9aV+m/5or8FhfU8Or99/Fls8/jdkcG1NXV8f999/P8OHDI0YTTZ06lZ/+9Ke8/fbbrFmzpl2O+eqrrzJ+/HiSk5ODfQ8//DCapoWknT344IMoisLvf//7YJ+u66SkpHDzzTcH+xp7IN1+++3cdNNNAOTl5QXT6rZv3x42h1GjRuF0OjnqqKN4++23W5xzdXU1SUlJXHfddWHbdu/ejaZpIb+/3r17M3r0aF577bVWfx+C0FmIgCQIQvR4a6F6H5Rshn1roXQb1JSAzxPrmQmCIAiCEAFFUyyRKUpfJjXRHusp92h8Hg9vP/aQVQmP5r6sM8GEtx97CJ8n9tdgq1atoqysjMsuuywkDawxs2fPBuA///lPSL+u6xw8eDCkVVdXt3g8r9fL//73P370ox+F9E+cOBHDMFi1alWwr7CwEFVVKSwsDPZ99dVXVFdXM2nSpIj7v/DCC7n00ksBeOihh3juued47rnn6NWrV8g5//rXv2bGjBncd999uN1uLrroIkpKSpqdd3JyMhdccAEvvPACuq6HbFuyZAmmaTJz5syQ/rFjx7J69eoWfx+C0JmIgNQFURSFgVmJUoVNiC2mDu4KqNgJxetQDm5kYGYCircu1jMThBAURWHgwIFS7UqIK2RdCvGE4vdkUl02NKdN1mYM2bRmFfU11TQvHgUwqa+pZtOnh1+B7HBZv349AEcffXSzYwLbvv/++5D+DRs20KtXr5DWWoWznTt3UldXR15eXtgxUlNTg2KRaZqsWrWKiy66KCgaQYOo1Jz59ujRo4Pi1Pnnn8+sWbOYNWsWSUlJwTHff/89hYWF/PGPf+S6665j2bJl1NbWsmTJkhbnPnv2bPbv3897770X0r9w4UImTZrEoEGDQvqHDBnCwYMHKS4ubnG/gtBZiAdSF0RVFbJS4i9PV+jZqHo9WTagdDNoTkjIgIR0sCfEempCD0dVVbKypAS1EF/IuhTiFVmb7c/CW6+nprwsqrF1bfSfevdfj1C4eH7U45PSM5h199/bdIzWCHhmtWT0HNhWWVkZ0p+bm8tTTz0V0teagXYgyicjIyOkX1VVTjzxRD766CPAEnlKSkq45ZZbWLp0KZ988glnnHEGhYWFjBo16rDMsU8//XTy8/ODz0ePHk1qairbtm1r9XX9+vVj0aJFnHXWWQB89913fPvtt2G/h8bnePDgQXr37n3I8xWE9kIEpC6Ibhhs3lPOsL5pUoVNiBt0w2Tz3gprXVJvpbpV7wObyxKTXOlgF+FT6Hx0XWfz5s0MGzZMKgoJcYOsSyFekbXZ/tSUl1Fd2nxq0+Ggez0dtu9oCYhDLZmvNycyJSUlcfrppx/Scc0IfpyBCnB1dXUUFhbSt29ffvSjH3H00UdTWFjIGWecwapVq5g+ffohHTNA00ghsMSesrKWhUJVVZk5cyaPP/44tbW1JCYmsmjRIlwuF9OmTQsbHzhHiQgU4gURkLoiJri9RqxnIQhhRFyXPjdU7bWaLaEhMsnm7PT5CT0Xt9sd6ykIQhiyLoV4RdZm+5KUntH6ID91VVXo3uh9jTS7g4Q2lHhvy1yi5cgjjwTg22+/5fzzz4845ttvvwVg5MiRh328QIRcJLFmwoQJeL1ePvnkEwoLC5k4cSJgCUuFhYVs2LCBAwcOBPsPlebE1UiiVlNmz57N/fffz6uvvsqll17K4sWL+clPfkJaWlrY2MA5ZmdnH9Z8BaG9EAFJEITOw1cHVXVQ9QPYExsik2yOWM9MEARBEAShQ2hLytj6j5bz1qN/i3r8mVf/hpETJx/CrNqPCRMmkJ6ezuLFi7ntttsiiivPPvssAD/5yU8O+3iDBg0iISGBoqKisG3jxo3D4XBQWFhIYWFhsJrapEmTeOqpp/jggw+Cz1uiIyN+Ro0axZgxY1i0aBEDBgxg586dPPLIIxHHFhUVkZ2dHWLgLQixREy0BUGIDd5aqNwDxevgwCaoPgC6N9azEgRBEARBiBnDj5+AMykZaE3AUHAmJTN8fGQj6M4kMTGRG2+8kY0bN3LbbbeFbX/zzTeZP38+U6ZM4fjjjz/s49ntdo499lg+//zzsG0ul4vjjjuOJUuWsHPnzpAIpLq6Oh5++GHy8/Pp27dvi8cIGGaXl5cf9nwjcfnll/Puu+/y97//naysLM4+++yI47744gtOOOGEDpmDIBwKEoHUlfC6Yf2rqN8tZVTlPtRNaZA7EYacLOlAQsxRFRjSJ5lDsuXy1litcjc4khsikzR5ixIOH1VVGTJkCKoq35kI8YOsSyFekbUZW2wOB2df8ztevf8uMBUiV2NTQIGzr/kdNkfnRXH/v//3/3j77bfD+q+77jpuueUWvvrqK+69914++eQTLrroIhISEli1ahULFy7kyCOPZMGCBe02l/POO4/bbruNyspKUlNTQ7ZNnDiRe+65h7S0NAoKCgDo3bs3RxxxBBs3bmTOnDmt7n/s2LEA3HbbbcyYMQO73c7UqVNDKrEdDpdddhl/+MMfWLZsGb/61a+w2+1hY4qLi/n222+55ppr2uWYgtAeyN1ZV2HDf+HVX4G7HEVRsZkGoMD2Qlj9MEyeC4NPjPUshR6MoiikJrTDRYyn2moVjcWkNBGThENGUZSwi0tBiDWyLoV4RdZm7MkfO57zbvwjbz/2EPU11SiKgmmawUdnUhJnX/M78seO79R5Pf744xH758yZQ0pKCi+++CLPPvss//73v/nTn/6Ex+MhPz+fefPmccMNN7Sb+AJWBM8tt9zC66+/zqxZs0K2BQSkE088MUQInThxIhs3bozK/+i4447jL3/5C0888QRvv/02hmFQVFTUbufQp08fzjzzTP773/9y+eWXRxzzyiuv4HQ6D9vwWxDaE8WMxulLAKyyk2lpaVRUVHTuB+uG/8Lzl/mfNPMtBMCZd0Fu7MNYhZ6Jbpis31XGyIEZHVAdUAFnSoOYpEpVGCF6dF1n/fr1jBw5UioKCXGDrEshXomHtRmza+7DwO12U1RURF5eHi5X+1Sd9Xk8bPr0Y7Z89gl11VUkJKcwdNwJDB9/UqdGHsUrP//5z9m0aROFhYWxnsohccEFF7B27Vq2bNkScfuYMWM45ZRTeOihhzp5ZkJPoy3vX/KVfrzjdVuRR0Bk8SjQr8DKu2HWUklnE2KG3mFytAn1lVZDAVeqleLmSgcJsReiQNf1WE9BEMKQdSnEK7I24wObw8HIiZNjbpIdr8ybN4/hw4fz8ccfc9JJXetL9L179/Lmm29G9IwCePvtt9m8eTPvvPNOJ89MEFomru+8/ve///HjH/+Y9PR0kpKSOP7443nxxRfbvJ/i4mJ+97vfMWzYMFwuF1lZWZxwwgnNhmHGFetfBXc5zYtHAUwr7afow46fkyDEFBPcFVC+A/avhdIiqCsDw4j1xARBEARBEIROYtCgQbjd7i4lHhUVFbFw4UIuvfRS7HY7V199dcRxZ511FtXV1fTu3buTZygILRO3EUgrVqxgypQpuFwuZsyYQUpKCkuXLuWSSy5h165d3HDDDVHt5+uvv+bMM8+krKyMc845h4svvpjq6mq+//573njjDX71q1+1vpNYsuE/oKhgRnNzrMDm92HoGdCBpScFIW4wDUtgdZdb/09caf7IpDT5PyAIgiAIgiDEFR9++CFXXHEFgwYNYsGCBeTk5MR6SoLQJuLSA8nn8zFixAh2797NmjVrOOaYYwCoqKhg3LhxbN++nU2bNjF48OAW91NZWUlBQQF1dXW8//77jB49Ouw4Nlv0GlpM8rGfOQd2rGrbaxKzIWcU5BRAn1GQlQ9q3GqFQjfBNE3cXh2XXUOJtXijaJaIlJAOzlQRk3o4pmnidrtxuVyxX5uC4EfWpRCvxMPaFA8kQRCEzqPLeyAtX76crVu3csUVVwTFI4C0tDTmzp3LnDlzWLBgAX/+859b3M9jjz3Gzp07efrpp8PEI6BN4lHMSMxoQwSSn9qDsG2l1QBsLugzEvoUWKJS75HgSOyI2Qo9HIctToxgTR3qSq2maJaQ5Eq3jLjlRq1H4hCzUSEOkXUpxCuyNgVBEIRIxKWCsnLlSgDOPPPMsG1TpkwBrPC/1njhhRdQFIWLLrqIjRs38u6771JXV8eIESM466yzusaH44ifwPdvRD8+Iw+q94G3rqHP54Y9X1oNLEEqM98Sk3IKrGilpF7tO2+hx2GYsHZnGQWDMtDiSaMxdagtsZpqs4SkhHRLTBJ6BIZhsHbtWgoKCqTalRA3yLoU4hVZm4IgCEJzxKWAtHnzZgCGDRsWti0nJ4fk5OTgmObweDysXbuWXr168cgjjzBv3jyMRia7Q4YM4dVXX6WgoKB9J9/ejDwf3rrZMg1u0UhbAUcSXPCEVeK8tAj2rbXa/rVQc7BhqGlAyWarrXvF6kvJsdLdAqJSRq4lNAlCd8LwWRF6tQdBtTeKTEqO9cwEQRAEQRAEQRDimrgUkCoqKgArZS0SqampwTHNUVpaiq7rlJSUcOedd3Lfffdx+eWX4/V6+de//sVdd93F1KlT2bBhQ7N5fvX19dTX1wefV1ZWAlZp00B5U0VRUFUVwzBobCcV6G9aBrW5flVVURQlvN/mhPMfh+cvAxSUCCKSiRXuYZx8K5rNiWmaGJlDIXMojLwATBOtthhj37ew7zuUfd9BWVHovqr2WW3L+9ZzRzJm76Mwc0Zh9hkFvY5EsTtRFQXdCJ2DohCxX1Ws843UD1bESjT9mqpY5xSh3zBNmrp4RepXAFVVMAwz5DfY3NzlnNp+TmD5JjTdT9yek+GBqmKoKka1OcCVjuFMtYTYwBw1zfo7Nanwpmla2P/55vo7/D1CtYTepnNsrr8nnpOu69ba9B+nO5xTd/w79bRzCqxLwzBaPNeudE5N5yjn1DXPKbDPWJ5T030JgiAI8UFcCkjtQeMPv2uvvTakatudd97Jxo0befHFF3n55ZeZNWtWxH3cfffd3HHHHWH969atIznZiljIzMxk0KBB7N69m9LS0uCYnJwccnJy2L59O1VVVcH+gQMHkpWVxebNm3G73cH+IUOGkJqayvr160M+NI844ggcw6awY/xfGfTl/2HzVmP6haTAo25LZOeo31KjHkkBUOX2sm1/dXAfLrvKiP45lJHOLvuxMBA0bw3ZtVvo69mKZ/c32Eo2ohqehpP0VKPs/hRl96fW71Ox4cvIxzHgaIqdQzmYNAzdYQl8A7MSyUpxsXlvBW5voyivPsmkJjhYv6sMvdF1zBH9UnHYNNbuLAv5vRYMysDj09n4Q2WwT1OgYHBmM+eUTll1PbtKaoP9KS4b+TmpFFfUsa+84febmexgUHYyu0trKK1uOM+cdBc56YlsL66iyu1r+DvJObX5nEYOzMCrG6zbVRY03exS51S+l40/bLQi72xONLuLgmN+RFVVFdu2bWv4O7lcjBgxgrKyMnbt2tVwTikp5OfnU1xczL59+xrOqTPeIxwO1q5dG3pOBQV4PB42btzY8HfSNAoKCnrcOe3cuZPS0lLWrVtHampqtzin7vh36mnnZJompaWl/PDDDwwePLhbnFN3/Dv1xHMKlA3fsWMHNTU1MTmn6upqBEEQhPgjLquwTZs2jZdffpnPP/+csWPHhm1PSUkhIyODnTt3NruPmpqaoMjzwQcfcOqpp4ZsX7RoEbNmzeK6667j73//e8R9RIpAGjhwIKWlpcGKEJ36jZTPjbL+dSvtrHo/iisVc/BEzLyTwWb5OR1yZIvPCyWbUfZbEUrK/rVWafQWMNMGYvY5CnIKUHMK0FMGhBgUS7ROzzonVSG4j8ZVW7ryOWl2F2YgMsme0NAv31p3qXMKtMDxusM5dce/U087p8bRRxKBJOcUT+cUwDTNkM/zzjynyspKMjMzpQqbIAhCJ9Dlq7AFvI82b94cJiDt27eP6upqxo0b1+I+kpKS6N+/P3v27CE9PT1se6Cvrq4ubFsAp9OJ0+kM6w9c7DUm8GEYaWy79WtJMOZSzKGn4S7ZgcuuoUaoKKUoSkQTY1VRoLl+uwNyjrLa0ZeAaULlngYfpX1roWJXyOuUil0oFbtg09vWHF3p4BeUyCmA7OGg2dHUCAeFZo2WI/Uf0jlF6m9uLu3V34PPyTRNvLqBy66Flf3tqueEXo9Ssx+tZr9VzTAhw/JM0rRm/8+3tb9d3yOi7A+IKNHOsaufk6IoeL1e7HZ7cG129XOKhJxT1zon0zSD67Kl8V3pnKLtl3OK73MyTRO3243L5Qr7PG/rHNvaHzgnMe8WBEGIT+LSJfnkk08G4N133w3b9s4774SMaYlA1NH69evDtgX6cnNzD3WaMcMwTDb+UBnRg6bdUBRIGwBHnA0n/wEueQ4ufxXOvAtGz7CEIrWJ/uguhx0fw6dPwGvXwPxz4I3r4LOnYOcaqK+KdCShm2CYdPy6jCU+N1TthQPfQ/EGqNoPvvrWXyfEHMMw2LhxY9i33YIQS2RdCvGKrE2hO2MYBqNGjeKvf/1rsG/OnDkoihLWTjnllE6d2/HHH88f/vCHTj2mILSVuBSQTjvtNIYMGcLixYv5+uuvg/0VFRX83//9Hw6Hg9mzZwf79+7dy4YNG8KMtX/5y18CcM8991BeXh7s37dvH//4xz9QVZWLLrqoQ8+lW5GQDrkT4PhfwnmPwpw3YerDMO4XMOj48LLougf2fgNfL4K3b4EFU+GlK6DwQdj8LlTuJSyvSRC6Ar46qPoBitfDgY1QXQw+T+uvEwRBEARB6GLMnz8fRVFwuVzs2bMnbPspp5zCqFGjQvpyc3NRFIXf/OY3YeNXrlyJoii8/PLLYceI1G655ZaQ1z/22GMoisL48ePbfC5Llixh165dXHvttcG+q6++mmuuuQZFUejTpw+/+MUv+Pvf/x523EPFMAzuu+++YHrQ6NGjWbJkSdi4m2++mUcffTTEq0wQ4o24TGGz2Wz8+9//ZsqUKUyaNIkZM2aQkpLC0qVL2bFjBw888EBI5NCtt97KggULeOaZZ5gzZ06w/8QTT+T3v/89f/vb3xg9ejRTp07F6/Xy2muvUVxczP/93/8xfPjwzj/B7oLNCX1HWw3ANKBsh5Xutv8767Fqb+hryoqs9v0b1vPEbMgZZaW89RkFWfnhkU2CEM94a61WuQfsSVaaW0I6aPZYz0wQBEEQBKHdqK+v55577uGRRx6J+jVPPfUUt956K/369Ytq/J133kleXl5IX1NxatGiReTm5vLZZ5+xZcsWhg4dGvV87r//fmbMmBFS7btXr14888wzzJgxg6effpqEhIQW9tB2brvtNu655x5+8YtfcNxxx/Haa69x2WWXoSgKM2bMCI4777zzSE1N5bHHHuPOO+9s1zkIQnsRt3fqkydPZtWqVcybN48XXngBr9dLQUEB9957L5dccknU+3nwwQcpKCjg0UcfDSrbY8aM4YknnuCCCy7owDPoWJrzcIkpigqZeVYbea7VV3OwQUzatxZKtlhCU4Dag7BtpdXA8pnpc5QlJuUUQO+R4Ejs7DMRDpG4XJedibfGapW7wZHcyDMpbt9qewzipyHEI7IuhXhF1mZ8YHoNatcewL2uBL3Wh5Zow3VUFokFvVDsnZ9Icswxx7RJEDrqqKPYuHEj99xzDw8//HBUxzj77LM59thjm91eVFTE6tWreeWVV7j66qtZtGgR8+bNi2rfX331Fd988w0PPvhgSP9zzz0XDGBob/Foz549PPjgg1xzzTX885//BODKK6/k5JNP5qabbmLatGnB/2+qqnLxxRfz7LPPcscdd0T0IBOEWBPXdzXjxo3jrbfeanXc/PnzmT9/frPb58yZExKZ1NXRNJWCwZmxnkZ0JGXDkFOsBlakRvH3sM8vKhWvA28jI3OfG/Z8YTWwRKmsoQ2CUs4oSOrV2WchRIGmKl1nXXYGnmqrVTQWk9JETIoBgfLQghBPyLoU4hVZm/FB3foSSl/ahFnnswqOmIACdetKKH9jG5nThpMwMqtT5zR37lwuu+yyqAWh3Nxcxo8fz1NPPcUtt9wSdRRSSyxatIiMjAzOOeccLr744jYJSK+++ioOh4NJkyaF9O/bt4+RI0eSmNj+X1q/9tpreL1efv3rXwf7FEXhV7/6FZdddhmffPIJEyZMCG4744wz+Oc//8nXX3/NmDFj2n0+gnC4xKUHktAypmlSWeeJWHI17rEnQv+xMPancM4D8NM34MKn4MTfwpDJluDUGNOAg5tg3SvwwR2waBosmQHL74L1r0HpttCIJiFmdOl12aGY4KmCip1WNF7JVqgtBUNv/aVCu2CaJpWVlbI2hbhC1qUQr8jajD1160soeW69JR6BJR41ejTrfJQ8t5669SWdOq+8vDxmz57NU089xQ8//BDVa2677TZ8Ph/33HNPVOMrKio4ePBgSGvMokWLuPDCC3E4HFx66aVs3ryZ//3vf1Hte/Xq1YwaNSpY/TLAgAED2Lp1K9XV1S2+vum8mmv19Q1FVr766iuSkpI48sgjQ/YVqCj+1VdfhfQHKpB//PHHUZ2TIHQ2IiB1QQzDZNv+6u5R7Uq1QfYwGHUhnD4PLnsJLn0eJt8GI8+DzCGE1Xmv2gdb3odVD8HLP4MF58JbN8NXCy3TbqmMFRMMk+6zLjsME+orodzvFSZiUqdgGAbbtm2TikJCXCHrUohXZG3GFtNrUPrSpgbRqNmBWBFK3s79OwUEoXvvvTeq8UOGDOHyyy/nqaeeYu/eva2OP/300+nVq1dIC/DFF1+wYcOGoG/QhAkTGDBgAIsWLYpqLhs2bAjzVwKYNWsWdXV1nHTSSSxYsID169dTXFwcNq7pvJprjQ2y9+7dS58+fcLS0fr27QsQJsT1798fh8MRsYq4IMQDkkshxBeKAik5Vht2htVXXwX718N+v49S8QbQG4lEnmrY9anVwC9KDW9kzl1gmRoLQlzhF5PqK61UTWeKlebmTANVtH1BEARB6InUrj3QEHnUCmadj9rvDpI0pncHz6qBgCD05JNPcssttwSFkJb44x//yHPPPcc999zDP/7xjxbHPvroo80WOVq0aBF9+vRh8uTJgJUKdskll7Bw4UIefPDBVr27SkpKyMjICOvPy8tj5cqVTJ8+PWh7kpSUFBaR9N5777W4/wBHHXVU8Oe6ujqcTmfYGJfLFdzelIyMjLDIK0GIF0RAEuIfZwoMGm81AN0LJZsbfJT2fwd1ZQ3jDZ9VXr14PXz7otWXNjDURyltoCVWCUI8YBrgrrCaooIz1S8mpYqYJAiCIAhdnP2PfIVR5YlqrF7rbdO+y5ZuovKtoqjHqykO+vzm8Lx12iIIQbjo1BLjxo2LaKKt6zrPP/88kydPpqio4XzHjx/Pgw8+yAcffMCZZ57Z6lwipWauWbOGiy++GLvdzty5cxk1ahTZ2dlh404//fRW99+UhISEkJS2AG63O7g90hzFQFuIV0RA6ooo4IpB5YW4QbNb1dl6j4TR08E0rRLqgUpv+9ZCxa7Q11Tsstomvym7K92q9pZTYLXs4VJ2vR3o0euyvTANcJdbTdHA1UhMkouJQybwTZ8gxBOyLoV4RdZm+2JUedAroxOQ2ozP7Lh9N8OQIUOYNWtWVIJQgNtuu43nnnuOe++9l/PPP7/Nx1y+fDl79+7l+eef5/nnnw/bvmjRolYFpKysLMrKysL6L7vsMvLy8njvvfdaXPv79u2Laq5paWlBYahv376sWLEiTBQKpPNFMhYvLy+PKGAJQjwgAlIXRFNVRvRPj/U04gdFgbQBVjvibKuvrtyKTNr3nZX6dmCjFZkUwF0OOz62GoDmgN5HNkQp9TnKinwSokZTFVmX7Y2pW9F1dWV+MSnNLyaliJjUBjRNY8SIEbGehiCEIOtSiFdkbbY/aooj6rF6rRd8bTCUtCloidF/CdqWubTEH//4RxYuXBi1F1J+fj6zZs3iX//6F+PHj2/z8RYtWkTv3r159NFHw7a98sorLFu2jCeeeCJiRE+AESNGhEQvARQXF1NUVMQNN9zQqnAaTboewDPPPBNMhTvmmGP497//zffff8/IkSODYz799NPg9sbs2bMHj8cTZrotCPGCCEhdEMMwKatyk5HsRJWbyMgkpEPuBKuBZax9YGODj9L+dZa3UgDdYxlw7/2moS8jL9RHKSVHbtpbwDBNyqrrZV12FKYOdaVWU22hYpLQIoZhUFZWRkZGBqqkBApxgqxLIV6Rtdn+tCVlrObL/ZS9uCnq8RkXDe9UD6QAjQWhwYMHY7O1flsZSH2777772nSsuro6XnnlFaZNm8bFF18ctr1fv34sWbKE119/nUsuuaTZ/Zxwwgncc8891NfXB32JUlNTcblcLF++nF//+tctpo4digfSeeedx+9+9zsee+wx/vnPfwJWitoTTzxB//79OfHEE0Ne+8UXXwCE9QtCvCACUhfENE12ldSSnuQMK1AmNIPNCX1HWw2sNKGyHf4oJb+oVNWkMkRZkdW+f8N6npjdICjlFFgV4lT5LxTANJF12VkYPqgtsZpqbyQmJcd6ZnGJaZrs2rWL9PT0WE9FEILIuhTiFVmbsSWxoBflb2yLykhbSbCROCp2qU6BtLSNGzeGiCbNERCdFixY0KbjvP7661RVVXHuuedG3H788cfTq1cvFi1a1KKAdN555/GXv/yFDz/8MJju5nK5uOGGG/jrX//K8ccfz9SpU+nbty9HHHEEEyZMCHn9oXggDRgwgOuvv577778fr9fLcccdx6uvvkphYSGLFi0KM/5+7733GDRoEGPGHJ5PlSB0FHL3K/RMFBUy86x25FSrr+ZgqKBUssUSmgLUHoRtK60GYHNZqW6BtLfeI8GR2NlnIvR0DK+1NmsPWqmYrnQrAs+RFOuZCYIgCILQRhS7Sua04ZQ8tx5aymRTIHPacJQY+k8OHTq0zYJQIPVN1/WoX7No0SJcLhdnnHFGxO2qqnLOOeewaNEiSkpKyMrKijhu7NixjB49mhdffDHEL+muu+5i1KhRzJ8/n0ceeYTS0lJ8Ph9XXnklTz31VNTzbI577rmHjIwM/vWvfzF//nyGDRvGwoULueyyy0LGGYbB0qVL+fnPfy4m2kLcopiRrOiFiFRWVpKWlkZFRQWpqakxm4desZ+1676jYFAGmipvLh2GtxaKv2+o9la8DrzhpTaDKCpkDQ2t9pbUq/PmG2N0w2TtzjJZl/GC5rCiklzpPV7Y1HWdtWvXUlBQ0GqJX0HoLGRdCvFKPKzNeLnmbgtut5uioiLy8vLaxYS8bn0JpS9tsiKRFCwxyf+oJNjInDachJGRhRKheZ577jmuueYadu7c2WKU3eOPP86vf/1rdu7cycCBAztlbq+++iqXXXYZW7dujdpvSRDag7a8f0kEUldEgRSX/Ok6HHsi9B9rNbDShkqLGiKU9q+1opYCmAYc3GS1da9YfSk5ln9SIPUtI9cSmropsi7jCN0D1futpjktMSkhHezNm0t2Z1JSxCtKiD9kXQrxiqzN2JMwMot+c8dT+91B3N8dxKjzoSbYcI3KJnFUdkwjj7oyM2fO5N577+XRRx/ltttua3ZcIH1t//79nSYg3XvvvVx77bUiHglxjUQgtYG4+Tak+gBU7o7d8QUL07RuzoNpb99B6TZajDd2JPsjlPyCUq8Rlj+TIHQWNldDZJJdyjQLgiAI8UfcXHO3gfaOQBJix9atW7n00kvZtGkTu3fvJjlZPCaF7o1EIHVzDMOkuLyW3mkJUu0qliiKFWGUkgND/aZ69VWwf31DtbfiDaDXN7zGUw271lgNLBPu7OGh1d4S0jv9VNoDwzQprqiTdRnv+NyWYXzVXrAlNEQmdWMh0zAMiouL6d27t1QUEuIGWZdCvCJrU+jp7Nixg/LychYvXizikSA0QQSkLohpmuwrd9MrNUGqXcUbzhQYNN5qALoXSjY3+Cjt/w7qyhrGGz4oXm+1b1+0+tIGNvJRKoC0AZZYFeeYJrIuuxq+Oqiqg6ofrJTNQGSSzRHrmbUrpmmyb98+evXqOZ5kQvwj61KIV2RtCj2dU089lU2bNsV6GoIQl4iAJAgdiWa3qrP1Hgmjp1sqS+WeRj5K30H5ztDXVOyy2qa3rOeudCtCKSAqZQ+39isI7Ym31mqVe8Ce1BCZJGtNEARBEARBEAREQBKEzkVRrIiitAFwxNlWX12530fpOyv17cBGKzIpgLsctq+yGljVtXof2SAo9TnKinwShPbCW2O1yt2Wb1dCBrjSREwSBEEQBEEQhB6MCEhdEEWBzGSHZAl1FxLSIXeC1QB89ZaIFPBR2r/O8lYKoHtg7zdWC5CR1+CjlFMAyTmdnvamIOuyW+KptlpFUzGp63x8KIpCZmYmShdIBRV6DrIuhXhF1qYgCILQHF3nDkAIoqoqg7LF0K3bYnNC39FWAzANKNsRWu2t6ofQ15QVWe37N6znidl+MckvKmUOsQy7OxBVVWRddmtM8FRZrUKxot4CYpKqxXpyLaKqKoMGDYr1NAQhBFmXQrwia/PwkALXgiB0NdryviUCUhfEMAx2H6xmQGYSqirfDnV7FBUy86x25FSrr7Yk1Efp4GZLaApQexC2rbAagD3B8mEKpL31HgmOxHadpmGY7C6tkXXZIzChvtJqxL+YZBgGu3fvZsCAAVJRSIgbZF0K8YqszUPDbrfSvGtra0lISIjxbARBEKKntrYWaHgfawkRkLogpgml1R76ZybFeipCrEjMgiGnWA0s8+PiDQ2iUvE68NY1jPfWwZ4vrAaWKJU1tFG1t1GQdIjVVnz1sO1DlO2FZFSUoqRlQu5EGHJyty4NLwRoJCYpaoOY5EyDOLnxME2T0tJS+vfvH+upCEIQWZdCvCJr89DQNI309HSKi4sBSExMlDRAQRDiGtM0qa2tpbi4mPT0dDSt9S+CRUAShO6APRH6/8hqYJlwlxY1ilJaCzUHG8abBhzcZLV1r1h9KTnQp1HaW0auJQi0xPaPYeXdlkcOCimYmGUKbC+E1Q/D5Lkw+MSOOGMhHjENcFdYTVHBmeoXk1LjRkwSBEEQhI4iJycHICgiCYIgdAXS09OD71+tIQKSIHRHVBtkD7PaqAutsLXq/aE+SqXbgEb5rlX7rLblPeu5I9kfoeQXlHqNCI0o2v4xvPvH4FPFv6/AI54aeOc2OPMuyD2pg09YiDtMw6og6C4HRQNXIzFJvpEVBEEQuiGKotC3b1969+6N1+uN9XQEQRBaxW63RxV5FEAEpC6IoijkpLvkHkyIHkWxIoxScmDo6VZffRXsX99Q7a14A+j1Da/xVMOuNVYDvyg13BKUeo2Awgf9A5szXTMBxYpQmrVU0tl6MqYOdWVWUzTLKykhw0p364Q3MkVRyMnJkVQCIa6QdSnEK7I2Dx9N09p0QyYIgtBVUEwpFRA1lZWVpKWlUVFRQWpqauwmUn0AKnfH7vhC90T3QslmKzopYM5dV9Y++548F4ad2T77EroPqi1UTBIEQRAE4uiaWxAEQQhBIpC6ILphsH1fJbm9U9Ck2pXQXmh2qzpb75EwerqV9la5J7TaW/nOQ9ixAkWrREASwjF8VkXB2hJQ7Y3EpOR2PYyu62zfvp3c3Fz5RliIG2RdCvGKrE1BEAShOURA6oqYUOX2xXoWQndHUSBtgNWOONvqc5fDvnXw8d+h5kCUO/JX6RKEljC8UHvQaqodEtItMcnRPtUmq6qq2mU/gtCeyLoU4hVZm4IgCEIkpCyOIAjR40q3DLF7HQm0IfrtwAb47N9QstWKbBKEljC8lkB5cBPsXwcVe8BTG+tZCYIgCIIgCEKPRiKQBEFoO7kTYPtH0Y/3ueHrhVZLHwz5k62WPrjj5ih0D3QP1BRbTXM2RCbZE2I9M0EQBEEQBEHoUUgEUhdEURQGZiVKFTYhdgw5GRzJRBWFpGih48p3wBfz4cWfwtKfw9eLoHJvB01U6Fbo9VC934poK/7eWjded6svUxSFgQMHSkUhIa6QdSnEK7I2BUEQhOaQKmxtIG4qQkgVNiEe2LEa3rnN/yTS24j/wnPKX6HXCCj6CLYutwy5I43vdaQVlTTkFEju3TFzFrontgQrKikhHWzOWM9GEARBOEzi5ppbEARBCEEEpDYQLx9meuV+Nm9cz7C+aVKFTYgt2z+GlXeDpxoTBQUz+IgjGSbPhcEnhr6muhi2fQjblltRJJHIKYD8UyHvZEjM7PjzELoP9kRLTHKlg80BWBWFNm/ezLBhw6SikBA3yLoU4pV4WJvxcs0tCIIghCIeSF0RE9xeI9azEATLUHvWUij6EIoKqaooJTktE/ImWuJPpGiQ5N4weprVKvfCthVWZFLJloYx+9ZabfUj0O8YGHKqtU9XWqedmtBF8dZarXIP2JP8fknJuN2tp7oJQmcj61KIV2RtCoIgCJEQAUkQhMPD5oRhZ2Lkn8HWnWUUDMqIPjIutS8cc5nVynfCVr+YVL7D2m4asOdLq616CAYca0Um5Z7k92AShBbw1ljNMKG2DPZ9C5rN8uVSNf+jCorapE/z96lN+vz9qtgHCoIgCIIgCD0PEZAEQYgP0gfB2J/Cj2ZDWZElJG1dDpU/WNtNHXZ9ajXNDgPHw5DJVoqcVOQSosE0wfABPtAPZ0dKE0FJCxWbwoSnZsQoEaIEQRAEQRCELoR4ILWBeMnHNquKqSouIsVllwoZQtxgmiZVbm/7rkvThIObLCFp20qrAldTbC4YdIIVmTRwnJgoC2F0yNpsF5QoxSa1UbRU0z5/E7ocpmlSVVVFSkpKnK1LoacTD2szXq65BUEQhFBEQGoDcfNhJlXYhJ6IacD+9X7PpBVQVxo+xp4IuROsam79j7UilQShJxAxDU+J0Ofvj5ia598mCIIQY+LmmlsQBEEIQQSkNhAvH2Z6xX7Wr/+OkQPb4DUjCB2Mbpis31XWOevS0C0/m63LrYpu9ZXhY5wpkDvRikzqdwyokrHbU+nUtdnVCQhRIVFQkfrEJ+pw0XWd9evXM3LkSKnCJsQV8bA24+WaWxAEQQhF7qi6KLrIfkIc0mnrUtWg3xirnXSdZbK9dQVs/wg8NdaY+irY+F+rJWRYVeHyJ0NOgXWDK/Qo5D0zSkzDatB+PlGtpuY18ZLqQT5Run5Yv2RB6DBkbQqCIAiREAFJEISujWqzvI8GjgP9d7D7cysyafsq8PnLENeVwfpXrZaUDXmnwNBTodeRkrIjCB1COxqWH7JPVCNhSnyiBEEQBEEQDhsRkARB6D5oDqsq2+ATLfFo5xorMmnnJ6B7rDE1B+G7l62WkmNVcss/FbKGipgkCHGHaVVgbI9oiDal4YlPlCAIgiAIQlPEA6kNxEs+tllVjLtkBy67JpVbhLjBNE3cXj0+16WnFnauhi3LYfdn/siIJqQNtFLc8k+FjNxOn6LQccT12hS6Hi35REWdmqdhAm63G5fLJetSiCtM04z52oyXa25BEAQhFBGQ2kC8fJiZVcUYFbtRFeSiU4gbTNPEMIn/dVlfZaW3bV0Oe75o8HtpTEaeJSTlT4a0AZ0/R6Fd6TJrU+hRmCYYioaq2VA0uz/VztakBQQn/3NNAseFjsc0TQzDQFVVEZAEQRCEEERAagPx8mGmV+xn7brvKBgkFYWE+EE3TNbuLOta67KuHIo+ssSkvd8AEd4Os4c3iEnJfTp7hkI70CXXptDtOeR1GRCTAhFNkUSnpj+LcCq0AV3XWbt2LQUFBVKFTRAEQQhBvsoSBKHnkpAOI8+1Ws1BKPrQEpP2r2sYc3CT1T59AvqMsoSkIadAYlasZi0IQk/G8EVOw20Jpamo1FR4iiBEiegkCIIgCEITREDqithdoNmtC7zDK28jCEKApGwYdZHVqvbBtpWWmHRwU8OY/d9ZbfU/oe/RVmTSkEngSo/VrAVBEFonYESu10f/GkWNLDIFxahI0U9qx52DIAiCIAgxRwSkrogzGZyp0OcowARvrVVxylsLXre/dLlkJgrCIZOSA0fPsFrFbquS29blUFbkH2DC3q+t9vHfof9YS0zKnQDOlNjNWxAEob0wDat6ZaCCZTQEzcKbi2xqJvpJEARBEIQugXggtYF4ycdu1dzQNMFbZzVfXcPPpkQrCR1HjzAqLi2CbSssQaliV/h21Q4DjoOhp8KgE8GR2PlzFMLoEWtT6HLIugygNBGbxEw81oiJtiAIgtAcIiC1gXj5MDvk8qo+T3i0UlvC2QWhBXpUqXTThJItVlTS1uVQvT98jOaAQSdYnkmDTgCbs/PnKQA9bG0KXQZZl4dJiF9T03Q7MRM/HA75OrMdiZdrbkEQBCEUEZDaQLx8mLVrdQxDjxytJClwQhvpsZWuTBMOfA9bllu+SbUHw8fYE2DwSVaa24BjLXFJ6DR67NoU4hpZlzEgYnpdSyl3PVN0kipsgiAIQnNIDHBPR9X8nkrJDX2m6Y9SCghL/oiltlZ9EYSegKJA75FWO+HXsG+tleK2bSW4y60x3jrY8r7VHEmQO8mKTOr/I78ZviAIgtDhtKeZeNBQXMzEBUEQhJ6D3LkI4SiKFTFhTwjt170NqW+BVDifOzZzFIR4RFGt6mx9j4YTr4UfvrZS3LYXQn2VNcZTA5vesporDfImWZFJOaPFTFYQBCHeEDNxQRAEQQgiAlIXJSYhxZodtDTrpjeAYYSmvgVS4Uyj8+cnxByt50X6N49qs9LVBhwLE34Hez63IpO2r7IEWAB3BXz/htUSMq2opCGToc9I6wZEaDdkbQrxiKzLboppWM3wtuFFSisCU2MvJ63DzcRjlbomCIIgxDfigdQGJB+7DfjqQ6OVvHVtvJAShG6Krx52fWZFJu1YHTmVIrmPJSTlT4bs4T3Sg0MQBEGIgjaZiQcq2sX/Z4pccwuCIMQnIiC1gXj5MDNNk6qqKlJSUrpW5RbdFyFayY0YdncPTNOkyu0lxWXvWusylnhrYccnsG0F7Pw0ssia2t8SkvJPhcwhnT/HboCsTSEekXUpxIxWzJ68QZsAAO4qSURBVMRNm4uqeiOm15nxcs0tCIIghCICUhuIlw+zeKiO0W6YZqhRdyBiydRjPTOhjUhFocPEUw3bP7Yik3Z/Hvn/QEauPzLpVEgf2OlT7KrI2hTiEVmXQryiuzJYu7NcqrAJgiAIYYgHkhBbFAUciVYjs6Hf52kw6g4IS22pmiIIXQ1HMgyfYjV3BRQVwrbllhF3wFOsbDt88YzVsoZaQlL+ZEjpG8uZC4IgCIIgCILQAxABSYhPbA6rNcbQQ426A5FLYtgtdDdcaXDkT6xWWwJFH1mRSfvWNowp2WK1z56E3kfCkFMh/xRI6hWzaQuCIAiCIAiC0H0RAamL4nK5Yj2FzkfVwJlstQCm2ciwu1EqnOGL3Tx7MC67VA5rdxKz4KgLrFZdDNtWWmLSgQ0NY4q/t9qaxyCnwIpMGnIyJGTEbNrxhqxNIR6RdSnEKz3yOlMQBEFoFfFAagOSj92F0L2Ro5UEobtQ+QNsXWGluZVsDd+uqNBvjJXiljsJXPKeJQiCIERBYhakD4rpFOSaWxAEIT4RAakNxMuHmWEYlJWVkZGRgarKt5dRYxh+Mckd6q8kKXDtgmGalFXXk5HsRJWKQp1L+Q5LTNq6HMp3hm9XNBhwnBWZlHsSOJI6f44xRNamEI/IuhTiFSMhkzIjOabXmfFyzS0IgiCEIilsXRDTNNm1axfp6emxnkrXQlWtG2dHEpDV0B9MgWskLOmemE2zq2KasKuklvQkJ8i9UOeSPhjGzoEf/RRKt1lC0tYVUPWDtd3UYdcaq2l2GHi8FZk06ASwJ8R06p2BrE0h3vAaJjX1Prbur2a4pmGzKWgoqKrVrJ9jPUuhpyLXmYIgCEJziIAkCDan1RrfR+u+htQ3b+MUOAnYE+IYRYGsfKsddyUc2GiluG1dATUHrDG6F7YXWs3mgsEnWmLSgHHW/wNBENoV3QC3z0e9z6Dea+D26vgME8M0qfPq7K9yR4xAUgBV8QtKCiiKgqYqqAr+PsXqUxQUFWz+sapiNU0EKEEQBEEQ2hkRkAQhEpoNtBRwpjT0mWaoUbfXbYlMYtgtxCOKAr1HWG38L2H/OisyadtKqCuzxvjc/mil5WBPgtwJVprbgLGgyseDILQVw4B6n47bZ1iPXgOvfmhp0iagmya6buI9xPk0FZtsqoKigNZEbFIV/OKUFfmkBoQpidgTBEEQBKERcofQRUlJSWl9kNC+KAo4Eq1GZkO/z9MoWskvLOn1MZtmLElxyVtKXKKoVnW2nAI44VrY+40VlVT0IdRXWmO8NbD5Has5UyFvkhWZ1PcYqwJiF0fWptDemCZWVJHPwO21Iow8PqNNcapOe8f+3zJMMHQT3yFGzyr4hSW/uKQ1EpiCYhP+yKiQfknD6+rIdaYgCIIQibg20f7f//7HvHnzWL16NV6vl4KCAn7/+98zffr0qF4/f/58rrjiima3r1ixglNOOSXq+YihnxA1hh45WkkMu4V4wvDBni+sCKSiVZaI1JSEDMg7GYaeCn1GWWKUIPRAAgKR22tFFtX79EOSZTy6yYc7vaze46Oy3iTVqXBifxsnD7Lj0LpXyE8gDU/TFNSAx5MSGhml+lPzFJUGHyhJw4stUoVNEARBaIa4/Up2xYoVTJkyBZfLxYwZM0hJSWHp0qVccskl7Nq1ixtuuCHqfZ133nkcc8wxYf25ubntN+FOxDAMiouL6d27t1Rhi1dUDZzJVgtgmg2G3T53g7eScajJCfGFYZoUV9TROy1BKgp1FVQbDBxvtQn1sPt/lpi0Y7Xf8wsr3W39q1ZL6gVDJluRSb1G0FXyW2RtCm3Fq5u4fXrQs6jep2O0w9dtq/d4uX9NHdVeS1wxsR5X7fbx2Jdu/nB8Aif0tx/+geKEYBqe79AjoCL5QAXS8KyfG3ygtEaRUZqiigB1iBimSfG+fXKdKQiCIIQRlxFIPp+PESNGsHv3btasWRMUfyoqKhg3bhzbt29n06ZNDB48uMX9BCKQnnnmGebMmXPY84qXb0N0XWft2rUUFBSgaV0/taTHo3sbGXUHHuvpaobdumGydmcZBYMy0FS5Se/SeOtg5xorzW3XJ9YabUpKX0tIyj8VMvPjWkyStSm0hM8wqQ+KRVZkka891KImrN7j5fbCOiDyu3tgZd4+MYETu5GIFGsimY6ramQfqKD/U6OUvTh+a+swdFcGa3eWx/Q6M16uuQVBEIRQ4jICafny5WzdupUrrrgiJHIoLS2NuXPnMmfOHBYsWMCf//zn2E1SENoLzW41V6MLJMPwi0nu0IglU4/dPIWegz3BLw5NBk8N7PjYikza/XmDaXzVXvh6sdXSBlpCUv6pkNGysC8IsaSxybUVWXToJtdtwaOb3L+mefEo0K8A96+p44Xzbd0unS1WtKsPVKM0vBDTcSKk4YkPlCAIgtANiUsBaeXKlQCceeaZYdumTJkCwIcffhj1/r766itKSkrw+Xzk5uZy+umnk5WV1S5zFYQOQVXBkWQ1Gq1VX31DtFJAWNI9MZum0ANwJMGwM63mroTthVZk0g9fNnh6VeyCLxdYLTO/QXxK7R/buQs9moDJtdur4/bpePyG17Hgw51eqqPIVjaBai/87bM6juljI8muWM2hkGQn+FzEpc7DxIpSO9QcxubS8CJFRikq2BTxgRIEQRDil7gUkDZv3gzAsGHDwrbl5OSQnJwcHBMNDz/8cMjzhIQE5s2bx80333x4E40RiqKQmZmJ0hPjqns6NqfVEtIb+gy9wai7cbRSJ6fAKUBmsgNZld0YVyqMOMdqdWVQ9JEVmbT3W4LrrXSr1f73b+h1hBWVNGQyJPeO2bRlbfYM6n2GlYbmT0c7VJPr9kI3THZWGnxforN4Xdsqc36ww8cHO3zNbndoDWJSY2HJEpqa9Pv7koNjrH6bpHN2CkEfKN3kUB0PI5qOKxHS8Jr4QB1qGp6CXGcKgiAIkYlLAamiogKwUtYikZqaGhzTEnl5eTzyyCNMmTKFAQMGUFpayvLly7n11lu55ZZbSExM5De/+U2zr6+vr6e+vuGir7LSKnet6zq6bqUSKYqCqqoYhkFjO6lAf2Bca/2qqqIoSsR+sIyzGzNw4MDgXBqjaRqmaYaN1zQtbI7N9cfqnJrrl3Nq5ZxQMG2JYEuEBOuCT1UU9PraEG8lRXejmjp6k29RVf83opH6IfxL1+b6NVVhYFYShknIvjRVwTBNmrqtRepXsC6SDcMMufFTFOtCuOkcm+tvz3MyTTNif48/p4QMjBHnYo44F2oOoBR9iLJtBUrx+oaBBzZabc3j0GcUxpBTMfNOhsTMzj0noH9mknUjZ5g96+/UTc+p3mdQ59GD3kUeXcfEGh9yTlhzb0s/hMvvzfWrinVOJlBRb/B9icGGEp0NJTobS3Rqm9eADguPbqXFlbkPXSJz+kWoRLtCsh2SHIGfLfEp0d5IjHJAil0lMaS/IY0r8DsIEPj9Ntcfy7/TofbH8px0kzBfrrbO3a6p/v+bDT5QqqpgU1VQTFTUoA+U3W4waFBeyPUudO61UdN9CYIgCPFBXJpon3nmmbz33nts3ryZoUOHhm3v378/1dXVUYlIkVi3bh3HHnssiYmJ7N+/H5stso52++23c8cdd4T1FxYWkpxsVdfKzMxk0KBB7Ny5k9LS0uCYnJwccnJy2Lp1K1VVVcH+gQMHkpWVxYYNG3C73cH+IUOGkJqaytq1a0M+NI844ggcDgdr164N9tV7dVRXEhmZvTiwZ3vw2ye7TeOYo0dTWVnJtm3bguNdLhcjRoygpKSEXbt2BftTUlLIz89n37597Nu3L9gfi3MCKCgowOPxsHHjxmCfpmkUFBTIObXXOaWlsGHDetx1dZaXjeFjSO9EUhMcrN1Rit7o3eCIfqk4bBprd5aFntOgDDw+nY0/VDackwJHDcxg874Kauv14LeWLrvKiP7plFS52VVS23BOLhv5OansK69lX3nD7zcz2cGg7GR2HqymtLohNS8n3UVOeiJb91VS5W64IxuYlUhWiosNe8pxexsuRIf0SW6XcyoYnEllnYdt+6sb/k5yTi2fk1ZByddvk7D7IxKrimiKiUp15lGU55xE+lGnkZLRq8PPaefBGmrqfSQ5baQm2OXv1MXOaW9ZHT7DxDBMEpw2khMclFS5qa1vmHtKgp2URAcllW7qvQ3vzelJDhJddorLa/E1mnxmihOXw8a+0poQQatXWgKaprCvtGEuADmZiei6yYEKy8fIZ5jsqYFir4Pvir18f1Bnfx3tggLkpsKZuTZ8isbBKi9VHpM6H9T6TLymSp0PKusN6nxQ54tdyQWnBskOhQQNnJpJog0SbAopToWsRBuqqWPDCPZnJ9nITrHjrfdgQyfBZgkeHfV3AkvQzMlMwu3xUVrV8KWgTVPonZ5IrdtLeU3D/w+nXSMr1UVVrYequoaYoUSnjfRkJ+XV9TFde51xTtnZ2aRk9Mbj8VBd3fD/tTOvjaqrq5k4caKYaAuCIMQZcSkgTZs2jZdffpnPP/+csWPHhm1PSUkhIyODnTt3HvIxzjjjDN5//32+/fZbCgoKIo6JFIE0cOBASktLgx9msYhsOVBRy7r168kaODQsvFjTNGyqgl0Dh03Foao47CoJDjs2JbxQUo+L1pFzCp87oPjqrIglnz8VzleH6r8liTa6AODbHaUcNTC00pVETPTgc6rYZUUlbV2OUr6DppiKhjJgLEbeZMzcCeBI7pBz8ukm63aVBdem/J3i95x0v8l1vU/Hq5vUeX14G918xyoKpLTOSkWzmsHmUp36VgIkshMUjszSGJGlUeMxWPx99AlMfxjv4vRce1RzN0yTOi/Uek1qfVDlMajxQo3XpMZjUuM1qfVCtTfws0mNF6o9Dc87KlIqGhJsDSl4iXZLkGqIjGrobxwN1dBvbVP9FzfdNQKps88pKS2bg5UeRo4cGVKFrTOvjSorK8nMzBQBSRAEIc6IyxS2gPfR5s2bwwSkffv2UV1dzbhx4w7rGNnZ2QDU1NQ0O8bpdOJ0OsP6NU0LK2sa+DCMNLa9+1VNQ1EU/wd56HjTBK9u4tWh1qMDgQ9zSwizaQp2TcVpUy2BSdOtR5uKvYlTY2eeU2v9iqJE7G9ujm3t7/HnpCWjOZND+/yeSlrAU8lbB0bDDVBTD1fdMIPmoE1LpauK0nAVHE1/M94czZVgb3N/M7YOkfotc9MIc5Rzav2cMgbB2J9arXSbZb69dTlU7rHmYeqw6zPUXZ/Bqr/BwHGWZ9LgE8Ce2G7npKmErU35O8X+nEwT3D4dt9vwi0YGnggm1wFxoOl+Ih020ti29nt0ky1lVhra+oOWaFRc2/J3bXYVhmVqjMzSODJb48gsjV6JDe/NHt3k9S1earwtRwspQJIdTh5kD35B1NrcVUUhxQkpwcuVtpdd1w0rwqnGLzIFxKUav9hUE/Lc3zyEPK87RBHKiqIyOVh36N9nJtqI7AHV6HlTD6jGLcEe/ntubo115NqDiP+Fm+1v6xxb6/foJh/u9PJp8X5Kqn30X/stU47K4ccFfXHZG9ZVZ1wbNfcaQRAEIbbEpYB08sknc/fdd/Puu+8yY8aMkG3vvPNOcMyhous6n3/+OQCDBw8+9Il2QXy6iU/XqfOEf3WqKOD0C0kBUckSmSzBScwUexh2l9Uao/saGXX7PZZ89cQugULoMmQOsdqxP4OSzbBlOWxbAdX7re2GF3Z8bDXNaYlI+afCwPGWcbzQpTFN8OgGbr+5db3XEoxi/c5hmibFtaYVWXRQZ32JztYyHW+4jhVCTpIVXTQy28aRWRpD0lXsLVRGc2gKfzg+gXmFdTjx8GP1U87UPiedaspJ5l39WP5rjMeDgz8cn9DpVdY0VSHZYUX/HCq6YTZEPoUITU36m4pTjfrdh2h7U+tP7ztwiCKUAqFRThEq30UyKE9u1Jdgo0tfJ63e4+X+NXVUe63IZANYf3A/76zbz+1vrONv047h9JF9Yj1NQRAEIcbEZQqbz+fjiCOOYM+ePaxZs4ZjjjkGsMy1x40bx/bt29m4cSO5ubkA7N27l4qKCvr27RtivP3FF1+ERTDpus4tt9zCAw88wOTJk1m+fHnU86qsrCQtLS3m4bQHKuvYuvMHEtMyUZTOq+9qtyk4NDU0gskvMNmkzmzPxTDA58aor6V4/x56J4JKbEplC10M04Ti9VZU0raVUFsSPsaeAIMnQP5kGHAcaPY2H8YwTYor6uidltDst/5C++LRGyqiub0G9d7YVkQLUOcz2VyqN6SjHdQpbcWI2mWDIzKtqKJAdFGG69A+83Z99zFjNz9MmlKDbipoihl8rDCT+GL4dQw86sRD2nd3wGc0pNg1Fpuqm4l6iiRQtZZa2FGoSnORUOFRTw3CU2i/K0Yi1Oo9Xm4vtDyWIv1vUPz/PHn5sZzRSSJSvFxzC4IgCKHEpYAEsGLFCqZMmYLL5WLGjBmkpKSwdOlSduzYwQMPPMANN9wQHDtnzhwWLFjAM888w5w5c4L9iqIwevRoRo8eTf/+/SktLeXDDz9k06ZNDBgwgA8//JAhQ4ZEPad4+TA7WF3P3kZGqfGAqlrRSw5NCwpNjQWmrvytnNBGDAPqK6CuHOorwRQxSYgCQ4d9ay0xqehDcFeEj3EkQ94kKzKp3zGgthJE66uHbR/C9lXWWnSmQu4EGHKyRDW1I17DxO3V8fiMoFikx8GlhWma7Kk2+P5gg2C0rdyI6N/WmIEpKiP8YtHILI3cNLXZ9L22kLT3U/p+ehcASoTbdNOfXLR3/B+p6Tv+sI/XU/HqJrU+S2yqDhOawsWpSNFQEYK0OwVVIVxYcjQf9ZTYeKzDStNzam0ToTy6ySWvVkWVXpmaYOPTuaeHpLN1FPFyzS0IgiCEEpcpbACTJ09m1apVzJs3jxdeeAGv10tBQQH33nsvl1xySVT7uOGGG1izZg3vvfcepaWlOBwOhg4dyh//+Ed+//vfk5GR0cFn0TEYhkHF/t2k9uqH0oz/TWdjGFDnMaiLEHmiKISkxdk1BaemBZ+3x4W5EHt0XWf79u3k5uaiJWRAQoYlCrgroK4M6quQVDehWVTNEoX6HQMn/RZ++MryTCr6CDz+KkCeatj4X6u50i0haMhkyCmwXt+Y7R/DyrvBU42JgoJpPW7/CFY/DJPnwuCeG+lxqOgGuH0+6n3+CCOvHlZePFbUeE02loRGF1V6Wp5bkh1LLPK3EVk2Up3t/5mk6B76fPmQ9XMz74OBNdrny4coOutZTM3R7vPoCdg1hTRNIe0wNGKP3ooHVNO0vAhjW0uDjIRhQpXHpMoDh/p5qTUWocIiocKjnjaW6lRH4e9uAhV1Pt76bi8XjBlwSHMTBEEQuj5xG4EUj8TLtyHFFbV89913ZA0cGmai3RXRVAWHrbGxt4rd/+iwxYdAJrSOruusXbuWgoKCyOaXhm5FJdWV+QUBeesRokD3wu7/WZFJOz62fLeakphtiUn5p0LvkbBjNbz7R//GZhMy4My7IPekjpp5l8fwV0Rz+yzfIrfXwKvHR0ShYZrsrNDZcNDLphIvW0o87Kn0omGVjbehoym69eh/bkdnYLJJfhrkpZrkpkLvBAPVNFBMHcXQwdQtc3fDemz8c2Bb5HEGGL5GPwde40MxDOy1+3FWFkV9fuX551HVfwK6IxXdkYZhT4ROTFkXDp+gCNVIbKpuxQOqaX8ET/mYoipw5sgcnrg8vEJyexMv19yCIAhCKHEbgST0HHTDpM7TvLG3o5GY1DgtzqGpzVYXEuIQVYOkLKvpXktMcpc3RJcIQiQ0uxUpNPhEKyVt5xpLTNr5Cegea0ztQfhuqdWSeoO7jJYFShNQrAilWUu7TzqbX8TAL3Bg6OHPg/2hz03dh9fnxevz4fV68Pp86F6vtd00UAydRNNnCSeG0UhM8VnbAwJLY7HFaE10Cfzs359fgAnur9E409AxdOtRMX2opsERis4Zjc/f1dwvphFe4KC/xTHpW18jfetrweemooIzFcOVhulMw3CkYjpT0Z2pGI5UfA7r0etIRben4HWkoasuDESqjxUOTcGhKWREsy4jYJpWKl3rHlDN+0JVe81WUzbbgmFCeZ2n/XYoCIIgdDlEQBLiGtOEeq+VKhEJm6aEVIprSJOzmhCnaHZI7mU1n8cSkurKrMpugtAcNqc/be1k8NTCztVWmtuuTy2hBKCmOMqdmZZ4ufZlGDguVFgJ+7mpKOPvMyOMifj6VvZlNn1tpONFEoGajDsMqUABHP7WJehh3x0opgHucjR3efQvUm3gSsP0i06mK9V6dKai+0Uo3ZGK4bQEKJ89BdPmwjBAN01M00Q3TQyjfUUIIToURcFpA6dNITPh0PZhmlZlu6YeULVek2r/87e3edhVFd0fWFUgPaHLvEsIgiAIHYAISF0QRVFIyeojxtSATzfx6Tq1RI5ealotToy9Ow5FURg4cGDbf682ByT3tpqv3hKS6srBFyFVSRACOBJh6OlWq6+yjLK3LrfS3drC/56ymhBzdDR8aHhNDR8qPjR0VLzY0M2G5zoapqrhsNlw2jVcdg2Xw46qalakjqphKhqoNkxFtX5WrNc0/hnFet7458jjVEzV1tAXPIa1f0XVsNvtOB0OHA47TrvDSuNVrTmgaPDR/Vb0XFQinwLpg2DAsZaHXNPmi7KIhuGD2hKU2pIwva3ZWoaaE1yp4EoLa4YzDcOZ2vAYEKBUB4ZpWs0w/eKTFV1smiYGWP2GKdFQnYyiKCTYIMGmkN2MCJXhUrjv0+jWlGHClFGdU4VNEARBiE9EQOqCKKqKKzk91tOIe0wT3F6rKlBTFMUfvdRIVBJj78NDVVWysrIObyc2J6TkWM1b1+CZpNe3yxyFboozBY4422qvXQv7v4v1jA4fRbXEB7+40fDo72v63D/GVFQMRfM7/6joqBiKCopfAPGPMf3CRlBg8QsilljSjNASHNdEaFEbbVc0TLXheEEBxj9Xj6GyvUphQ5nJ+hJYXwp7alUMFJoLK7KrMDTDMrkemW099kpUMBUFN9DZNUltqmKJVnaNBLuG06bSqm4+ZLKVdhkVJoyZCcPOjLzZVx8uKtVXRhab3BVWhKcehUsyWO+1NQes1gTV38KwJ4SKTc5w8SkgSpnONCvtTtEwDMvLSjdN8AtOBmaw3xKkwqOh/MOFduLkQXYe+9IddRW2s0f17aypCYIgCHGICEhdEMMwKP2hiIycwXFTha2rYZrg9Zl4fTo19eHRS6rqj15qJCoFDb41RaKXIqDrOps3b2bYsGGRTbTbij3Baql9rXSlujL/jZD4LwgtkJCJdasT5S1mch8YOD5UqGkcORLSbwsfF/ZzhDFh+2oqAjX5WdFoXZEINbl2e3XqffFjcg1W+syBWjOkKtrmMr3V6lR9EhWO9AtFR2Zr5KdrOLTYvec6bWpQLHLZNeyHMpchJ1vV/zw1tHqb7kiCvJObH2JzNkRtRoNpWlFLzQpMzTQzylr23jqrVe1rdaiC/8LTkRQxyiliS0gDR0pIpUXDAJ1Ael2D2GSYjcSnRul3hmlFQRn+10g0VAMOTeEPxycwr7Cu2XdOxf/Pg9OOwWXv+sVbBEEQhENHBKSuiGmiez3+gr9CR2AYUOcxqCNy9JJdC0+Nc9rE2Nvt7qBYAEei1dL6Q321X0yqACPKb9WFnkPuBNj+UfTjj/t585EecYRpQr1fKHL7dDw+g/o4K9FU7zPZXNYgFq0v0Smpa/k23anB8MyG6KIRWRpZCbH7YkQBSyxyaLjsKi6bjXax07M5YfJceOc2mhc4/Z8dk+e2r7G7ojQI8ik50b3GNMFb4xeTWopuahIFZUa5Jj01Vqv8IdqTsCIN/aKS6m8tCk+OpBYr15lmE8HJMNExMY3I0VCN0/L0gCBldg8h6oT+dm6fCPevqaPaa0WaGVieR4ZpRR49OO0YTh8p6WuCIAg9HRGQBKGNmCZ4fAaeZm7eNFVpEJSaeC+JsXc74Ey2mjnA8r5xl/vFJF+sZybEA+0Z6RFD6n1W8QC3T7cKCfj0uLpRNU2TvdWB6CIf35fobC0z0FuZZP8U1Yos8re8dBVbDEX3xulolmCkRRP8dWgMPhHOvMuq/uepxkRB8X8VpGBa63HyXGtcrFEUcCRbLbV/dK8xDUvgb1VoaiRK1VdGOSHTGltfCRW7ojwHtcXUOsWVhuZvwX57QlTRf41pHA1lmg1peWYzZuQh0VBxZFJ+Yn87L5xv46NdXtbsVymp9tG/dwZnjcrh7FF9JfJIEARBAERAEoR2RzdM6jw6dZ7Ixt5hht6NqshJalwbUBS/r0aqPzyj0h+ZVBl96oXQ/YhlpMch4tXNYGRRQCyKhxvKxtR6TTaWWpFF35fobCjRKa9veZKJNhiR1ZCKNiJLI80ZWxHdYVNx2VQS7DZcDuu9t1PJPQlmLYWiD6GokKqKUpLTMiFvoiVmxsF6PGQUteE9mYHRvcbwRSc6NW7emuj2bRr+ogxl0Z+Dv3JdW5pq+//s/Xt4W1edL/6/99662/I1cWwnTpz7pXXvza2lpS1tCgwDAy0tPWf4wpnpHIaZzmGmB+ZHOQzDeYaW8i1fDjOcYS48QIehQ2kLDJTe6YW2SdqkbdqQNIlzceLEdhzfJNuyLnuv9ftjS7JkSbbsSNaS/H499ZN6a0teO/5Ekt9e67M80KHZ03XOgyWyz4bKmCVVxNlQLkPD+9pd+P2LF+FcIIKOjo7CLEknIqKKoUkpFXubqq5gMIja2loEAgHU1NSUbBznRsM41TcEp8fHwKHCOB2avTzO0DN2kHMoPntJSonR0VH4/f7S1qUQ9m+4J0Zmt6SCKkvXq9PM9Kgu2UwPU8jJoChmL0mzFHsZFlKiOyiSQdG7gxa6AmLGUGtFjT7Zu6jRwPKa0m5IoAFwJ3oXuXR4C7UcrUCklBgNx+D3OPlaPhtWbIam4eexc91cGO48gqYpO9sZrqIMRUp71tNkT6jss6ESM6WmNimXsQh8p19B7eCbcFphGP7F0Db8HrDpI4DTU5Qx56LKe24iIkrHAGkWVHkxGxiLoHdkvvedoVJLNPZO9l+a0oeJP4BkISz7h4eJYXu5m1KLgKjozEh8pscr9g+c7hpg5dXzNtPDSja5tpLL0cyZ1niVwGhU2kHRgJkMjcZmaC/mdwEbGh3YFJ9dtL7BQLWrtM9BhqbFexfNw3I0Ki/Zdq7LurQu5SPfnevmIq+d66YEUHqRFw2khu6aDk0Ke1aZFICnDviDf7J3upwnqrznJiKidAyQZkGVF7P+QAgHDh5Ew9JV0HVOLab0xt5OI96DKWUHufmYBWBZFg4ePIhNmzapOeVdWPaspPAIw6QFxhISB7uHsamtvmj/FqQEwqaFcHwJWmSaPmmlZAmJk0GBg/GlaO8OWugOTj9OXQNW1urYlDK7aKm/9KF1yZejnaf5qEuaIykBc2JKyJTHrKdiLp92VU8/qymjiXj6znXT6noVeOZ/xT+ZZtnv7Q8BGz5QiKuZkSrvuYmIKB17IJUpKdT7wYRKZzaNvbPtIFcolqVw7yHdAKoa7Q8rNhkmRcdKPTKaB4Wc+CMlELXEZFgUswMjFSPJ4bDAoUELBwfsmUWHhyxMzNBvvs6t2WFRPDBaV2/A6yxtwJG6HM3t1OF1GiVtvl0oCk5IIyD+Wxmf/eFvye8+aTvX5fkRGZ3FznVj9kfwTL4XkbZzXc4Ppw944WuJi8h1cfbj/eJPgbsPz/tyNiIiUgcDpDISjll4Yn8vfvV2D3oGRtBQexhbVy3C1WsWFTQEoMozm8beiYDJHT+mV8APaRkMJ1C92P4wo3aQNDEMxEKlHhkpKGpN7ogWjglEYmrtiJYQsySOB0S80bWJdwcs9I5PP1KHDqyp07FhUXw5WqOBJVVayWcXGVp8dzSXDq/DXpbG5WiktKLvXJcSOuX34LPfuW6mxwuPAAf/E7j4tgI8HhERlSMGSGXi2YNncfcj+xCcMKFr9q4c2uAQdh0fwr+8fBx/9b612LyysdTDpDIkJZINfbNxxJfEJRp7p85gcpbZkpGsHC6gusn+MCPxXXtG7OULtODEhL0jWtQUybBItSbXCQMhu9F1Yjla57CFLBlxmsU+LbkMbdMiA2vqDbiM0iczTkOHxxlfjua0n2uIKt6cd64bzXOWU3B2O9flM95Dv2KARES0gLEH0iyUaj32swfP4k9+tBeQ065Mx5c+uBFbGCLRPNI0JHeLcxoahBmF0+WGrunJ24HJGk3MatDSbks/abr7IMv9ps5KyHX7jI839YFiE3aQNDEMWJGcfwekPintUMiexTL5fbYEEDZNRMzJHdHMmbYaK5GoJdE5bMVnF9l/npuYfqwuA1hXb6TtjLbIV/pgRgPgdtizizzxZWmVsBxttnLVJVHBTbdz3cFfAqGB/B+r/WrgU78u3ljj2AOJiEhNnIGkuHDMwt2P7MsZHgHJlen4P8914sFP13M5G80bKYFwzJ6pIaWElALaRKTsfxiaHL4PgA+6NQFHOAAjOgJdxHfmmSmomny0bH/kDLIyxzD9/WYax9TbZ3q8jG9drmAP+Y1j6vVkBIY5Hk+b8nXPl0M3MBG1ELGkvTNaTCBmqdlLTkqJvnGZbHJ9aMDE0RGBmXpyt1Zr2NjowIb4zmir63QlghldQ7LRtddhwO0woPNlCgDgcii44QBVHsMJ+Brtj6kGjgJdLyOvjSU0HfDWF3x4RERUPhggKe6J/b0IztTxFPbL/ljExKvHBnDd+qbiD4xoCikFBruPorFtDTStvH8omjov09K9sHxewNcMPTYOIzICIxIEZAzczW1+ZORauQKrLMctS+L0wBiWNPigKxhuTsQkjgxN7or27qCF4fD0deV1AOsbJmcXbWg0UO9RI5XhcrT8CAnsPzWMjuX1UGAVIS1U7VcDXb/N71wpgA0fKu54iIhIaQyQFPfMgbPJnkf5+L8vHMXP3jyNKrcDXqeBKrcDPpcBn8uBKpcBX/zzqsQxtwFv4jaXg1sJE81AOKsgnFWIVbVCj43BiARgRIOAnDnopbmb+hQ4GfLJ7CekHBBSKhPzSSlxelRMhkUDFk4ExIzP8W01erLJ9cZFBlbU6Eo8XyeXozn1eNNrA04FxkVEeVp1LbDz74HoOKb/hYhm79q26cPzNTIiIlIQAyTFjYSieYdHABAxBboG576TlNuho8rlgM9tZAZPaYFUIoCa/DxxP5ehl/0SJqIZaRqEyw/h8iMmJfTYqD0zKToKyBk6GdOCMR6VaTOLDg2aGI1Of59qJ7Ch0cCmRfZytA2NBvwuNZ5TdQ3wOB3wJgIjLkcjKm8ON3DdPcDTX4IdCU/TbfMP/glweuZxcEREpBoGSIqr87lmNQPJqWuAZm/nPBcRUyBiRjF0HruZG7o2GSglwqWUmVBTP08NqHxu+35el6HkUhOirDQNwlUD4apBTAro0WB8ZtIoADX77FDhWULiVFAkZxa9O2jhVFBM+zt9XQPaa/Vkk+uNiwws8+vKPP85DA3eeFDkdRlcjkZUiVZsB276O+DF+4DoGKSmQ5Mi+Sc8tXZ4tP79pR4pERGVGHdhm4VS7AjxszdP469++nbe5//Vjetw3fomxCyBUNTCeMREKGohFDUxHrUQith/TqR8HopZGI/Y5yTPjViYiJV2FoW9BC99iV2V24gHTfYxb8bSvPSAqiK2mS8TySbaGmegJQkLRtSemaTHRsF+SaUh40vY7N3/ClebgYjAoUELB+Nh0eFBC6EZVjLWuTW7yXWjgU2LDKxrMOBzqvHvxV6OFp9ZxOVoRSelhJB2iMjnTFKCGQFOvAR56jXADAO+BmgbPmQvW5vnmUfchY2ISE0MkGahFC9m4ZiFzfc+h9EJc6aV6ahyO/DgpzcXbBc2S9hbDI9HTYQiFkKxyQAqETalBlTJz2MWQpH4/aIWrBJui+00tOSMpvyW5k0etwMqe4tpvrmfmZQSViwKw+ni31c2woIRDcCIBKDHxsAwqfiilsRLp2LYecZEICxQ69GxfakD1y53wjXLrsWmkDgxYs8uSgRGPWPTzy4zNGB1fWJ2kQObFhlortKU+fehAfC67OVobqcOr8PB5WjzSEr7NdbD1xhSjPQ2IOxpgsfjKVltMkAiIlITA6RZKNWL2XMHz+LOH+0F5LQr0/G/PrgRm1dm2aK1hKSUiFoiLVBKBE0T0dRjU2dIpQZXJsKx0i0D0jXAmxouuVLCJnfKsryUmVBTz61yV26D8qgp8MrRAew+PoChwCgaav3YumoRrl6zqGBhZsURpr3ELTIC3Rwv9Wgq0s4zMfy/uycwFpvs6pH4s9oJfGGrF9uWOnPef3AifSnakSELkRkmZTZ6NbvRdXxntLX1BtwOdf7dOwwNHoc9u8jrtJejMbcoHUvIyV3YKvT1gcqT5anH/lMj6OjogGGUZldVBkhERGpigDQLpXwxe/bgWfzPR/YhMGEmeyIlfxhyO/CX71urXHhUSJaQGUvskrOeEkvxUgKp8YiJiVjqDCn7/BJOhoLboecInlIak7szgydvyjn2D3zq/KDx2olBfOu5ToxHzIwf0qvcDvxVhddlIWhWNBkmadZEqYdTEXaeieFvX7b/LqcL3f/2PV5sX+pE1JI4OmylLUfrD03/ZOHUgbUNxmTvokYDTVVqBaaJ5Wje+JI0J/eKVwoDJFIVAyQiIsqFAdIslPrFLByz8OTvevHLfT3oGRhBQ60f21YvwlWrOdMjH/ZyAZESPE0uzbODpvQZUlNnRiWW5kWt0s2GMnQto9n45NK89F5R3hxL87xOoyA/rLx2YhBf+/W7AKb/If1LH9yILQyR8qJZEXsnt0gAmhUu9XDKUtSSuO0XoxiPzbxI0KkDq+p0HB8RmGmSY3OVlmxyvbHRgdV1ulKBjAbYYZHLgMepw+NwgC3g1MYAiVTFAImIiHLhLmxlxOM08AeXLsNVqxpw4OBBNCxdBV0vzQt7OdI0ze5p5DJwPnFGokF5stl4dLIv1PiUXlFpzcoTs6Wi9vK9uSS3lpAYjZgYjZgAInO+Bq9zcsaTL96sfMaleSmBlEPX8a3nOgHk/iE9MRvp/zzXiQc/Xc+QMw/ScMP0LYHpWwLNDE+GSWL673WiSXTqrwMSn8u08+LHU05Kfi5T7pd8LJnfY6XcJlMfJ+X/MeUxZPxOEtPfN9+vn/jjtZ4YxmJT/4ayiwng8FBmcuQxgPUpM4s2NBpo8KpVvw5dSza75nK08qVQBkmUplTBERERqY0zkGZBld+GDIxF0DvC2QnlTMSbp6YvxYvPiJo682lKr6jUGVJmKdfkzcLyBh8aqlxIPN2khg5px5ASFKQcnwwW5JRzUo5leczUc2XKgYyAQsosIYbMEWjItK+fPDfl66c9Zq7QJC0gkekBiUz/+tnGWB7f+fKwzK+nzC4ysLJWV25GSHJ3NIcBj0uHi9OLiKhYfI1A3fKSDkGV99xERJSOM5DKkJQS0YlxOD0+pfrhUP50TYvP8HEAcM/pMaSUiFkyJXjKDJty9YpKBFQTUQsTsRk6AxfAqaEQTg2Fiv51iGbrgkU6/s/7qks9jDSJ5Wgepx0WebkcrSJJKTEajsHvcfK1nJQipcRoMAi/38/aJCKiNAyQypAUAoH+02hsWwNN4xTjhUrTNLgcGlwOF+p9c38cS8jMHfGmBFITKQHUeNTEgZ4gQtHiB0/ZJN7KJt7TapqG1Le3mgZo0JInaqnH0s6xb0wcn/wcyTfMaceyPCagTY5j6tdPHWPKY6Z//cmvo6Wcn3ZdWspjxz9JnA8poEsTmrSynIscY5vy95fzNm3mx0j7+5k8mPV6pvzdTDeeaa9l2vFoeK03ht6x/OZnaQDqPaVPZoz48lo7NNLhcRhcjrYACAkcPztm90Di95sUIqTE8ePHS9oDiYiI1MQAiWiBM3QN1R4Hqj35Px3c+8S72H1iEPksgNUAXNnegLtvWgctJXABJgOFzKBh+gCGppASemzU7pkUHQVkacI9Faw7oeMbr+W3xFcCuGrZ/L8Muhw6PA4dXqeDy9GIiIiIqGwwQCKiWdu6qhG7jg/mda4EcPXaRfHlelQUmgbhqoFw1SAmBfRoEEYkYIdJKN2ugaVw7XIn/vHN8Iy7sGkAqpzANW3Ooo5HA+CON7r2uOzZRQ7F+isREREREeWDv/YsR5oGw+lC+sIdovlz9ZpFqHI7ZqxADUC124GrVi+aj2ERAGg6hLsOsZoVCDduQqx6OYSzBlggzxcuQ8MXtnoB5L7ixPEvbPXCVeC1Q4amodrtwKJqN5bVebF6cTXa6r1YVO1CtcvB8IjSeJx8G0Zq8ng8pR4CEREpiO9cypCu62hoXQlN57ePSsPl0PFX71sLYOYf0v/yfWvhcrBWS0LTYXnqEK1tR7hhE2LVyyCcflR6mLRtqRN/+x4vquKTi9J6NsGeefTV93ixben5zz5yOXT4PQ40+T1Y3uDDqsVVaKn1oN7nhNfFXkaUm6Fr2LC0Trkd/4gMXceGDRvY/4iIiDJoUubTxYQAdbYUPRecQFdPP9xVNdA0/mBOpfPaiUH8n+c6MRYxocFeMpT4s9rtwF++by02r2ws7SApkzDtJW6REejmeKlHUzRRS+K33TG8etrESFigzqPjqmUOXNPmnNPMIw2A2xFfihZflsYZRTRXQkoMj0VQX+2GzqSRFCK8DRgW1aivr4deol9WqvKem4iI0rEpSRmSUmJ08CxcPj9/u00ltWVlIx78dD1ePTaAXccGMBQYRUOtH9tWL8JVqxdx5pGqdAcsbyMsbyM0K5oMkzRrotQjKyiXoeF97S5cv8KJvqEQmhu8s/pB3dA0e2c0lw6vw4DbYYATP6lQpAS6B0Ooq3JX+qRAKjNSSnR3d6Ourq7UQyEiIsUUJEA6evQozp07h8bGRqxbt64QD0lEZcLl0HHd+iZcu7YRg91H0di2BrrOae/lQhoumL7FMH2LoVkReye3SACald9OZpXEaejwOOO7ozl1uBmAEhERERElzfndsWVZ+Lu/+zs0Nzdj/fr1uPrqq/H1r389efuPf/xjbN++HQcOHCjIQImIqLik4YbpW4JI/TpE6tbB9DZB6u5SD6soNAAeh4E6nxPNtR60L6pCe6MPzTUe1HodDI+IiIiIiKaY0wwky7Lwe7/3e3jmmWfgcDiwceNGHDx4MO2cq666Cn/4h3+In/3sZ7jgggsKMliK0zS4PFXchY2UooF1WUmkwwPT0QyzqhmaOTE5M0lESz20OfG4DFQ5HfC5DXtZGpejkQL8HnYSIDX5/f5SD4GIiBQ0p3cu//RP/4Snn34a119/Pf7t3/4Nra2tGU322tvbsXr1ajzzzDP48pe/XJDBkk3XddQuWVbqYRCl0ViXFSXRKkjTALi8kC4frJpW6OY4HJEgjOgINGE3T7dP1RL/xe9nR4mpLYeSj5ly++RdNWiJ+yfP09LOn/z/lPulPmbiHlrKfePnuJbwhyFSi6FrWN3M5sCkHkPXsXp1e6mHQURECppTgPTggw+ioaEBjzzyCOrr63Oet3HjRrz99ttzHhxlJ4XA+MgAfLUN3IWNlCGlQCgwVHF1mRakICXoSAk97OOJc1KCEKQHKamhRuLP1CAl9TGnBimJY9nOzwhSZvqaGeNOOX/GJtM1AFrsDsDRMWBiGAgHAGHm89dZEkJK9I2E0FQ7uybaRHOm6YDuAHQnoBuA4cz4XEBH/7lBNDXWQYcAhGX/O8r1IUWpr4oWCCEl+vv60NTUVLJd2IiISE1zCpAOHTqEq6++etrwCABqa2vR398/p4FRblJKhAKD8NbUcxc2mnepQYSuaclAQkqJ4dEh1Dc2wjB0zBSkTJ2ZUsggJe/w5ryClAVO0wC33/6QEogE42FSEJBWqUeXRkqgbySMxTVe7nZFc6elBkHxj5yfz7yRgLQs9A0MYXHLUsDIY+MBIbIES4nQKTblc1PpUJfUJqVEX18fFi9eXOqhEBGRYubcA8ntnrmxam9vb17nEVW6bKHF1JBk6uyVXIFLatiSLWjJFrJkC1imfXxo0Kd+nRkCFcuyED7rxJomP4x8fhiiyqFpgKfW/hACiASAiRE7VOKsCVKWNkMINOXzUofKug7oLgCu/O9jzTJw4r9XIiIimsacAqQVK1bgnXfemfacWCyG3/3ud1i7du2cBkYEqB+8JJbDZI6JM1logdJ1wFtvfwgBhEfsmUmRUQCy1KOjSqfp8WViDsBwpCwby/F5pTNmeZ05ZznFsoRQnOVERES00Mzp3dPNN9+Mb3/72/iXf/kX/Mmf/EnWc/7hH/4B586dw2c+85nzGiBloWnwVNdOG0yoFrzoM8x2YfBS/jRNQ0NDA79vNEnXAV+D/SEse1ZSeGTewyQNQEO1i6vXytVMIVDq52XUr0XJ58yCzHLKNeuJs5zKhQYFa5OIiJSgSSln/S6+t7cXF1xwAYLBIP78z/8cH//4x3H11Vfj1ltvxV//9V/jpz/9Kf6//+//Q319PQ4cOIBFixYVY+zzLhgMora2FoFAADU1pds5xRISphAMXoioPFnm5Myk6FipR0PzLWeD6RzLyPiaVlmyznKaIXSi+eVrBOqWl3QIqrznJiKidHMKkADgt7/9LT760Y9iaGgoI7CQUqKurg6//OUvcfXVVxdkoCpQ5cVMCIHTp09j2bJl3B2DlMG6pDkxo5NhUixUlC8hhMTpoXEsa6iCrjOMKIoCN5heCPicmScpZ+7dxFlOBSU8DTg9ppW0NlV5z01EROnm3ADgmmuuwYEDB/Ctb30LTzzxBI4fPw4hBNra2vD+978fn//857F06dJCjpXipJQYGhri3y8phXVJc+JwAdVN9ocZsYOkiRHAnCjYl5AAhsaiWNpQVbDHrHxl1mC6DPE5M0+altLLyZPffZKznHIFTpzlNB0JiaGhYdYmERFlmFOAdOrUKWiahra2Nnz961/H17/+9UKPi4iIFhqHG/A32x+xCTtImhgGrEipR1YZ2GCaForZ9nJKznKapll44nMrfg43BSAiogVoTu8Q29vbsW3bNrz66quFHg8RERHg9NofNS1ANGQHSeERwIqWemRqqdAG00TzKm2WU55mWkaXOJYInKRVvPETERHNkzkFSDU1NVi5cmWhx0J50jQNzc3NbJZNSmFdUtG4fPZH7VIgMhbvmTRizxbIg6YBzXWe8lhllWwwnU8vIS4dK2d8zixzuhHv5+XO73wpZ15Glxo4lXCWE2uTiIhymVOAtGnTJnR3dxd6LJQnXdfR3Nxc6mEQpWFd0rxwV9sfNUvtHdwmhoFwYNoeJrqmobnON4+DnIINpikLPmcuMJpm/zs3nPnfZ6ZZTlYs/ViBZjnp8QCJiIhoqjkFSHfeeSfuvPNO7NmzB1deeWWhx0QzsCwLXV1daG9vh2Hwhw1SA+uS5pWmAW6//SElEAnGw6Rgxg9RlpDo6h9Fe5MfRkF2YZupwbSRsozMyVlClBWfM2lGhZ7lNDVwyjHLyRICXceOsTaJiCjDnAKkT3/603jrrbdw00034fOf/zw+9rGPob29HW53ni9wdN5GR0dLPQSiDKxLKglNAzy19ocQQCRgL3GLBJPbeY+GZ9hliQ2mqQT4nEkFVahZTlLD6OjJ4o2TiIjK1pzeBaf+NuLLX/4yvvzlL+c8V9M0mCa3RyUionmg64C33v4Qwu6XFBoGjHHAtwhwutlgmogoIdssJ4sNv4mIKLs5BUhS5t/UbzbnEhERFYyuA74GwF0LuEftJtxcjkFERERENCdzCpCEEIUeB82Cpmloa2vj7hikFNYlqYq1SSpiXZKqWJtERJQLGzmUIV3X0djYWOphEKVhXZKqWJukItYlqYq1SUREubDpQxmyLAuHDh2CxTXqpBDWJamKtUkqYl2SqlibRESUy3nNQDJNE48++iheeOEFnDlzBgCwdOlSXHfddbjlllvgcHCCU7GEw+FSD4EoA+uSVMXaJBWxLklVrE0iIspmzgnPvn37cMstt+DEiRMZjbK/973v4ctf/jIeeeQRXHLJJec7RiIiIiIiIiIiKqE5BUg9PT246aabMDAwgCVLluD222/H6tWrAQDHjx/HT37yExw7dgw7duzAvn370NLSUtBBExERERERERHR/JlTgHT//fdjYGAAf/zHf4xvf/vb8Hq9abffe++9+Iu/+At873vfwze+8Q1861vfKshgyabrOlatWgVdZwsrUgfrklTF2iQVsS5JVaxNIiLKRZNT15/lYd26dYhGozh27BgMw8h6jmmaWLNmDVwuF44cOXLeA1VBMBhEbW0tAoEAampqSj0cIiIiIqKKw/fcRERqmtOvFrq7u7F9+/ac4REAOBwObNu2Dd3d3XMeHGVnWRb279/P3TFIKaxLUhVrk1TEuiRVsTaJiCiXOQVIbrcbwWBwxvNGR0fhdrvn8iUAAHv27MEHPvAB1NXVoaqqClu3bsVPf/rTOT/e8PAwli5dCk3TcPPNN8/5cVTAF3VSEeuSVMXaJBWxLklVrE0iIspmTj2QNm3ahBdeeAHd3d1oa2vLes6pU6fwwgsvzHkXthdeeAE7duyAx+PB7bffDr/fj8ceewy33XYburu7cffdd8/6Mf/8z/8cgUBgTuMhIiIiIiIiIlqo5jQD6ZOf/CQmJibwvve9D0888UTG7Y8//jhuvPFGhMNhfPKTn5z145umiTvvvBO6ruO3v/0t/uVf/gXf/OY38fbbb2PdunW45557cPLkyVk95mOPPYaHHnoI999//6zHQ0RERERERES0kM0pQLrzzjtxww03oLOzEx/60IewePFibN68GZs3b8bixYvx4Q9/GJ2dnbjhhhtw5513zvrxn3/+eRw7dgx33HFH2gym2tpa3HPPPYhGo3jwwQfzfrxz587hT//0T/GHf/iH+OAHPzjr8ahG13WsX7+eu2OQUliXpCrWJqmIdUmqYm0SEVEuc3plMAwDv/71r/GFL3wBVVVVGBwcxN69e7F3714MDg6iqqoKf/3Xf43HH398Ti8+L774IgDgpptuyrhtx44dAICXXnop78f7zGc+A8Mw8O1vf3vWY1GVy+Uq9RCIMrAuSVWsTVIR65JUxdokIqJs5vyrBZfLha9//es4d+4cXn75ZfzkJz/BT37yE7z88ss4d+4c7rvvvjm/+HR2dgIA1q5dm3Fbc3Mzqqurk+fM5N///d/xs5/9DP/0T/+E+vr6OY1HNUII7N+/H0KIUg+FKIl1SapibZKKWJekKtYmERHlMqcm2qncbjeuuuqqQowlKdHoura2NuvtNTU1eTXD7unpwV/8xV/gE5/4BD784Q/PehyRSASRSCT5eWLnOcuykrtTaJoGXdchhICUMnlu4vjUXSxyHdd1HZqmZT0OIO1F3LIsSCkhpcw43zAMSCkzXvQNw8gYY67jpbim6Y7zmsrjmgDkrMlyvaZK/D4txGtKPGcmvk4lXFMlfp8W2jUl6lIIMe21ltM1TR0jr6k8rynxmKW8Ju4CR0SkpvMOkFT2x3/8x3A6nfj7v//7Od3/vvvuw1e/+tWM4wcOHEB1dTUAoKGhAcuXL8fp06cxNDSUPKe5uRnNzc3o6urC6Oho8nhbWxsaGxvR2dmJcDicPL5q1SrU1NTg4MGDaS+a69evh8vlwv79+5PHEi/ykUgkbSaWYRjo6OjA6Ogojh8/njzu8XiwYcMGDA8Po7u7O3nc7/dj9erV6O/vR19fX/J4Ka4JADo6OhCNRnH48GFeUxle06ZNmxCLxXDgwAFomlYR11SJ36eFeE2nTp3C0NAQDhw4gJqamoq4pkr8Pi20a5JSYmhoCD09PVixYkVFXFMlfp8W4jU1NTUBAE6ePInx8fGSXNPY2BiIiEg9mpz6K4o8fOc738H/+B//A7/4xS/woQ99KOs5v/rVr/CRj3wE//iP/4j//t//+6we/9Zbb8Wjjz6KvXv34vLLL8+43e/3o76+HqdOncr5GA8++CA+9alP4ZFHHsEtt9ySPN7V1YWVK1dix44deOqpp6YdR7YZSG1tbRgaGkJNTQ2A0s1AOnDgADo6OpI/qCfwt2y8plLOQHrnnXdwwQUXwDCMirimSvw+LcRrMk0TBw4cSNZmJVxTJX6fFto1JV7LL7zwQjidzoq4pqlj5DWV5zUJIXDgwAFs2rQp7fV8Pq8pGAyioaEBgUAg+Z6biIhKb04B0o033oj9+/ejp6cn+SIwlWVZaG1txaWXXjpjUDPVPffcg/vuuw//8R//gdtvvz3ttr6+PrS0tOD666/Hb37zm5yP8bnPfS6vptkXX3wx9u3bl9e4gsEgamtrS/5ilnhxTbxgE6mAdUmqYm2SiliXpCoValOV99xERJRuTkvYDh06hAsvvDBneARMTkV99913Z/341157Le677z4888wzGQHS008/nTxnOtu2bcs6/XVsbAwPP/wwli1bhh07dmD58uWzHp8KotEoPB5PqYdBlIZ1SapibZKKWJekKtYmERFlM6cZSB6PB7fccgv+/d//fdrz/ut//a947LHHMDExMavHN00T69evx5kzZ7B7925ccsklAOzm2ps3b0ZXVxcOHz6M9vZ2AEBvby8CgQBaWlpyNt5OmM0StqlU+W2IZVnYv38/Ojo60qYWE5US65JUxdokFbEuSVUq1KYq77mJiChd7ilE0/D7/ejp6ZnxvJ6eHvh8vlk/vsPhwPe+9z0IIXDNNdfgT/7kT3D33Xfj4osvxpEjR3DvvfcmwyMA+OIXv4iNGzfi5z//+ay/FhERERERERERTW9OAdLFF1+MnTt3pu3wMFV3dzd27tyJjo6OOQ3suuuuwyuvvIKrrroKDz/8ML773e9iyZIl+MlPfoK77757To9JRERERERERESzN6ceSHfccQeef/55fPSjH8WvfvUrNDc3p93e19eHj33sY4jFYrjjjjvmPLjNmzfjySefnPG8H/7wh/jhD3+Y12O2t7dn7ExRjjjdnVTEuiRVsTZJRaxLUhVrk4iIsplTDyTLsvDe974Xr776KrxeLz74wQ9iw4YNAOwG20888QRCoRC2bduGl156CQ7HnHIq5XA9NhERERFRcfE9NxGRmuYUIAH2E/unP/3pZN+hxDafiYf78Ic/jB/84Aeoq6srzEgVoMqLmZQSo6Oj8Pv93PqXlMG6JFWxNklFrEtSlQq1qcp7biIiSjfnqUE1NTV47LHH8M477+Cpp57CyZMnAQDLly/HzTffjIsvvrhgg6R0QggcP36cO7eQUliXpCrWJqmIdUmqYm0SEVEu57227KKLLsJFF11UiLEQEREREREREZGC5rQLGxERERERERERLRwFCZBM08Q3v/lNvOc978HGjRtx44034vvf/34hHppy8Hg8pR4CUQbWJamKtUkqYl2SqlibRESUTV5NtH/2s5/hM5/5DO6880587WtfS7tNCIH3v//9eO6555D6UJqm4Q//8A/xwx/+sOCDLhU29CMiIiIiKi6+5yYiUlNeM5BeeOEFDA4O4pZbbsm47V//9V/x7LPPQkqJ3//938d3vvMdfOELX4DX68WPfvQjPPPMMwUf9EInhMDg4CCEEKUeClES65JUxdokFbEuSVWsTSIiyiWvJtqvvfYaWlpacOmll2bc9s///M/QNA233347fvzjHyePb968Gbfccgt+9KMf4aabbirciAlSSnR3d6Ourq7UQyFKYl2SqlibpCLWJamKtUlERLnkNQOpt7cXl1xyScbxgYEB7Nu3DwDw+c9/Pu22j370o2hvb8drr7123oMkIiIiIiIiIqLSyStAGhgYQH19fcbxPXv2AAAWL16cNWDatGkTenp6zm+ERERERERERERUUnkFSIZh4Ny5cxnH33zzTQDAZZddlvV+dXV1ME3zPIZHufj9/lIPgSgD65JUxdokFbEuSVWsTSIiyiavHkgrVqzAm2++iWg0CpfLlTz+m9/8BpqmYcuWLVnvNzAwgCVLlhRmpJRkGAZWr15d6mEQpWFdkqpYm6Qi1iWpirVJRES55DUD6brrrsPg4CC+/OUvJ4+98MILeOmllwAAH/zgB7Pe76233kJra2sBhkmphBDo6+vj7hikFNYlqYq1SSpiXZKqWJtERJRLXgHS5z73ObhcLjzwwANoa2vDZZddhh07dgAAtmzZgiuuuCLjPrt27cK5c+dyzk6iuZNSoq+vD1LKUg+FKIl1SapibZKKWJekKtYmERHlkleAtGbNGvz4xz9GVVUVzpw5g3379sE0TbS2tuLBBx/Mep9//ud/BgDccMMNhRstERERERERERHNu7x6IAHARz/6UVx99dV4/PHHcfbsWSxfvhwf+chHUFVVlfX8zZs349JLL8X1119fsMESEREREREREdH8yztAAoCmpib8t//23/I697Of/eycBkQz0zQNDQ0N0DSt1EMhSmJdkqpYm6Qi1iWpirVJRES5aJILnPMWDAZRW1uLQCCAmpqaUg+HiIiIiKji8D03EZGa8uqBRGoRQuDUqVPcHYOUwrokVbE2SUWsS1IVa5OIiHJhgFSGpJQYGhri7hikFNYlqYq1SSpiXZKqWJtERJQLAyQiIiIiIiIiIpoWAyQiIiIiIiIiIpoWA6QypGkampubuTsGKYV1SapibZKKWJekKtYmERHlwl3YZoE7QhARERERFRffcxMRqYkzkMqQZVk4duwYLMsq9VCIkliXpCrWJqmIdUmqYm0SEVEuDJDK1OjoaKmHQJSBdUmqYm2SiliXpCrWJhERZVPUAOmjH/0oVq9eXcwvQURERERERERERVbUAKm3txddXV3F/BJERERERERERFRkXMJWhjRNQ1tbG3fHIKWwLklVrE1SEeuSVMXaJCKiXBz5nLRz5845PTjXTxeHrutobGws9TCI0rAuSVWsTVIR65JUxdokIqJc8gqQrr766jn9FkJKyd9eFIFlWejs7MTatWthGEaph0MEgHVJ6mJtkopYl6Qq1iYREeWSV4CUsHz58lk9eG9vL2Kx2KzuQ/kJh8OlHgJRBtYlqYq1SSpiXZKqWJtERJRNXgHSihUrcOrUKezcuRMtLS15P/i2bdvw+uuvz3lwRERERERERERUenk10b788ssBAG+99VZRB0NEREREREREROrJO0CSUuKNN94o9ngoD7quY9WqVdB1bqJH6mBdkqpYm6Qi1iWpirVJRES55N1E++KLL8bY2NisHvyP//iPcfPNN89pYJSbpmmoqakp9TCI0rAuSVWsTVIR65JUxdokIqJcNCmlLPUgykUwGERtbS0CgUBJX1gty8LBgwexadMm7o5BymBdkqpYm6Qi1iWpSoXaVOU9NxERpePc1DJlWVaph0CUgXVJqmJtkopYl6Qq1iYREWXDAImIiIiIiIiIiKaVV4D093//93juueeKPRYiIiIiIiIiIlJQXgHS5z73OTz00ENZb7v++uvxjW98o6CDounpuo7169dzdwxSCuuSVMXaJBWxLklVrE0iIsolr13YpvPiiy+ivb29AEOh2XC5XKUeAlEG1iWpirVJKmJdkqpYm0RElA1/tVCGhBDYv38/hBClHgpREuuSVMXaJBWxLklVrE0iIsqFARIREREREREREU2LARIREREREREREU2LARIREREREREREU0r7ybaR48exb/927/N+jYA+OQnPzn7kVFOuq6jo6ODu2OQUliXpCrWJqmIdUmqYm0SEVEumpRSznSSruvQNG1uX0DTYJrmnO6rmmAwiNraWgQCAdTU1JRsHFJKhMNheDyeOX9fiAqNdUmqYm2SiliXpCoValOV99xERJQurxlIy5cv55sbhQghcPjwYXR0dMAwjFIPhwgA65LUxdokFbEuSVWsTSIiyiWvAKmrq6vIwyAiIiIiIiIiIlVxcTMREREREREREU2LAVKZ4pRiUhHrklTF2iQVsS5JVaxNIiLKJq8m2mRjQz8iIiIiouLie24iIjVxBlIZklIiGAyC2R+phHVJqmJtkopYl6Qq1iYREeXCAKkMCSFw/PhxCCFKPRSiJNYlqYq1SSpiXZKqWJtERJQLAyQiIiIiIiIiIpoWAyQiIiIiIiIiIpoWA6Qy5fF4Sj0EogysS1IVa5NUxLokVbE2iYgoG+7CNgvcEYKIiIiIqLj4npuISE2cgVSGhBAYHBxkc0NSCuuSVMXaJBWxLklVrE0iIsqFAVIZklKiu7ub26uSUliXpCrWJqmIdUmqYm0SEVEuDJCIiIiIiIiIiGhaDJCIiIiIiIiIiGhaSgdIe/bswQc+8AHU1dWhqqoKW7duxU9/+tO87//kk0/i9ttvx4YNG1BXVwefz4cNGzbgj/7oj3DkyJEijrz4/H5/qYdAlIF1SapibZKKWJekKtYmERFlo+wubC+88AJ27NgBj8eD22+/HX6/H4899hhOnjyJBx54AHffffeMj3HXXXfhV7/6FbZs2YLW1lY4nU68++67ePLJJ+FwOPDEE0/g+uuvz3tM3BGCiIiIiKi4+J6biEhNSgZIpmliw4YNOH36NHbv3o1LLrkEABAIBLB582Z0dXXhyJEjWLFixbSPEw6H4fF4Mo7/5je/wfve9z5cccUV2LNnT97jUuXFTAiB/v5+NDU1QdeVnkRGCwjrklTF2iQVsS5JVSrUpirvuYmIKJ2S71ief/55HDt2DHfccUcyPAKA2tpa3HPPPYhGo3jwwQdnfJxs4REA3HDDDaivr8fRo0cLNeR5JaVEX18fd8cgpbAuSVWsTVIR65JUxdokIqJclAyQXnzxRQDATTfdlHHbjh07AAAvvfTSnB9/165dGB4exoUXXjjnxyAiIiIiIiIiWigcpR5ANp2dnQCAtWvXZtzW3NyM6urq5Dn5eOaZZ7Bz505EIhF0dnbi8ccfx6JFi/Ctb32rYGMmIiIiIiIiIqpUSgZIgUAAgL1kLZuamprkOfl45pln8M1vfjP5+Zo1a/CTn/wEl19++bT3i0QiiEQiyc+DwSAAwLIsWJYFANA0DbquQwiRNtU3cTxx3kzHdV2HpmlZjwP2evQEIQTq6+uTY0llGAaklGnnJ45PHWOu46W4pumO85rK45o0TUN9fX1FXVMlfp8W4jUJIVBXV5e8vRKuqRK/TwvtmhJ1mTinEq5p6hh5TeV5TVJKNDQ0ZH2dn69rmvpYRESkBiUDpEJ74IEH8MADD2BsbAwHDx7E//7f/xtXXXUVvv/97+OOO+7Ieb/77rsPX/3qVzOOHzhwANXV1QCAhoYGLF++HKdPn8bQ0FDynObmZjQ3N6Orqwujo6PJ421tbWhsbERnZyfC4XDy+KpVq1BTU4ODBw+mvWiuX78eLpcL+/fvTxtDR0cHotEoDh8+nDxmGAY6OjowOjqK48ePJ497PB5s2LABw8PD6O7uTh73+/1YvXo1+vv70dfXlzzOa+I1zfWa6uvrceDAgYq6pkr8Pi3UaxoZGam4awIq7/u00K5J1/WKu6ZK/D4ttGtavnw5jh07VrJrGhsbAxERqUfJXdhuvfVWPProo9i7d2/WWUJ+vx/19fU4derUnB7fNE1cccUVOHr0KE6cOIHFixdnPS/bDKS2tjYMDQ0ld4Qo1Qyknp4eLFu2LGPM/C0br6mUM5C6u7vR2tqatmtLOV9TJX6fFuI1WZaFM2fOYOnSpdB1vSKuqRK/TwvtmoQQOHPmDJYtWwaHw1ER1zR1jLym8rwmKSV6enrQ0tKS9no+n9cUDAbR0NDAXdiIiBSj5AykRO+jzs7OjACpr68PY2Nj2Lx585wf3+Fw4LrrrsPbb7+NvXv34v3vf3/W89xuN9xud8ZxwzBgGEbasdQX2KnnFuP48PAwli1blvV8TdOyHs81xtkeL9Y1TXec16T+NVmWlbMuy/WagMr7PgEL75qklBgZGUFbW1vynHK/pmx4TeV3TYm6nO78crumfI7zmtS+JsuyMDQ0hKVLl2Z9/Pm4plz3ISKi0lJyF7Zrr70WgN27aKqnn3467Zy56unpAQA4nc7zehwiIiIiIiIiokqnZIB0ww03YNWqVXjooYewb9++5PFAIIB7770XLpcLn/zkJ5PHe3t7cejQoYzG2nv37s36+E8//TR+/vOfo66uDtu2bSvKNRARERERERERVQoll7A5HA5873vfw44dO3DNNdfg9ttvh9/vx2OPPYaTJ0/igQceQHt7e/L8L37xi3jwwQfxgx/8AJ/61KeSx6+88kpceOGFuOiii7Bs2TKMj4/jnXfewcsvvwyn04nvf//7qKqqmv8LPE+apqG5uRmappV6KERJrEtSFWuTVMS6JFWxNomIKBclAyQAuO666/DKK6/gK1/5Ch5++GHEYjF0dHTg/vvvx2233ZbXY9x777144YUX8NJLL+HcuXPJnU7+5E/+BJ/73OewcePGIl9Fcei6jubm5lIPgygN65JUxdokFbEuSVWsTSIiykXJXdhUFQwGUVtbW/IdISzLQldXF9rb29lkkJTBuiRVsTZJRaxLUpUKtanKe24iIkqnZA8kmtno6Giph0CUgXVJqmJtkopYl6Qq1iYREWXDAImIiIiIiIiIiKbFAImIiIiIiIiIiKbFAKkMaZqGtrY27o5BSmFdkqpYm6Qi1iWpirVJRES5KLsLG+Wm6zoaGxtLPQyiNKxLUhVrk1TEuiRVsTaJiCgXzkAqQ5Zl4dChQ7Asq9RDIUpiXZKqWJukItYlqYq1SUREuTBAKlPhcLjUQyDKwLokVbE2SUWsS1IVa5OIiLJhgERERERERERERNNigERERERERERERNNigFSGdF3HqlWroOv89pE6WJekKtYmqYh1SapibRIRUS7cha0MaZqGmpqaUg+DKA3rklTF2iQVsS5JVaxNIiLKhb9aKEOWZWH//v3cHYOUwrokVbE2SUWsS1IVa5OIiHJhgFSm+KJOKmJdkqpYm6Qi1iWpirVJRETZMEAiIiIiIiIiIqJpMUAiIiIiIiIiIqJpaVJKWepBlItgMIja2loEAoGSNheUUiIcDsPj8UDTtJKNgygV65JUxdokFbEuSVUq1KYq77mJiCgdZyCVKZfLVeohEGVgXZKqWJukItYlqYq1SURE2TBAKkNCCOzfvx9CiFIPhSiJdUmqYm2SiliXpCrWJhER5cIAiYiIiIiIiIiIpsUAiYiIiIiIiIiIpsUAiYiIiIiIiIiIpsVd2GZBlR0hpJQQQkDXde7cQspgXZKqWJukItYlqUqF2lTlPTcREaXjDKQyFY1GSz0EogysS1IVa5NUxLokVbE2iYgoGwZIZUgIgcOHD3N3DFIK65JUxdokFbEuSVWsTSIiyoUBEhERERERERERTYsBEhERERERERERTYsBUpkyDKPUQyDKwLokVbE2SUWsS1IVa5OIiLLhLmyzwB0hiIiIiIiKi++5iYjUxBlIZUhKiWAwCGZ/pBLWJamKtUkqYl2SqlibRESUCwOkMiSEwPHjx7k7BimFdUmqYm2SiliXpCrWJhER5cIAiYiIiIiIiIiIpsUAiYiIiIiIiIiIpsUAqUx5PJ5SD4EoA+uSVMXaJBWxLklVrE0iIsqGu7DNAneEICIiIiIqLr7nJiJSE2cglSEhBAYHB9nckJTCuiRVsTZJRaxLUhVrk4iIcmGAVIaklOju7ub2qqQU1iWpirVJKmJdkqpYm0RElAsDJCIiIiIiIiIimhYDJCIiIiIiIiIimhYDpDLl9/tLPQSiDKxLUhVrk1TEuiRVsTaJiCgb7sI2C9wRgoiIiIiouPiem4hITZyBVIaEEOjr6+PuGKQU1iWpirVJKmJdkqpYm0RElAsDpDIkpURfXx93xyClsC5JVaxNUhHrklTF2iQiolwYIBERERERERER0bQYIBERERERERER0bQYIJUhTdPQ0NAATdNKPRSiJNYlqYq1SSpiXZKqWJtERJQLd2GbBe4IQURERERUXHzPTUSkJs5AKkNCCJw6dYq7Y5BSWJekKtYmqYh1SapibRIRUS4MkMqQlBJDQ0PcHYOUwrokVbE2SUWsS1IVa5OIiHJhgERERERERERERNNigERERERERERERNNigFSGNE1Dc3Mzd8cgpbAuSVWsTVIR65JUxdokIqJcuAvbLHBHCCIiIiKi4uJ7biIiNXEGUhmyLAvHjh2DZVmlHgpREuuSVMXaJBWxLklVrE0iIsqFAVKZGh0dLfUQiDKwLklVrE1SEeuSVMXaJCKibBggERERERERERHRtBggERERERERERHRtBgglSFN09DW1sbdMUgprEtSFWuTVMS6JFWxNomIKBdHqQdAs6frOhobG0s9DKI0rEtSFWuTVMS6JFWxNomIKBfOQCpDlmXh0KFD3B2DlMK6JFWxNklFrEtSFWuTiIhyYYBUpsLhcKmHQJSBdUmqYm2SiliXpCrWJhERZcMAiYiIiIiIiIiIpsUAiYiIiIiIiIiIpsUAqQzpuo5Vq1ZB1/ntI3WwLklVrE1SEeuSVMXaJCKiXLgLWxnSNA01NTWlHgZRGtYlqYq1SSpiXZKqWJtERJSL0r9a2LNnDz7wgQ+grq4OVVVV2Lp1K37605/mdV8pJZ588kn86Z/+KS666CLU1tbC5/Ph4osvxr333lvWzQEty8L+/fu5OwYphXVJqmJtkopYl6Qq1iYREeWi7AykF154ATt27IDH48Htt98Ov9+Pxx57DLfddhu6u7tx9913T3v/SCSCD3zgA3C73Xjve9+LHTt2IBwO4+mnn8aXvvQl/OIXv8CLL74In883T1dUWHxRJxWxLklVrE1SEeuSVMXaJCKibJQMkEzTxJ133gld1/Hb3/4Wl1xyCQDgb/7mb7B582bcc889uOWWW7BixYqcj2EYBv7u7/4On/3sZ1FfX588HovF8LGPfQy/+tWv8H//7//F5z//+WJfDhERERERERFRWVNyCdvzzz+PY8eO4Y477kiGRwBQW1uLe+65B9FoFA8++OC0j+F0OvGlL30pLTxKHP/iF78IAHjppZcKPnYiIiIiIiIiokqjZID04osvAgBuuummjNt27NgB4PzCH6fTCQBwOJScgDUjXdexfv167o5BSmFdkqpYm6Qi1iWpirVJRES5KJmgdHZ2AgDWrl2bcVtzczOqq6uT58zF97//fQDZA6pUkUgEkUgk+XkwGARgrwtPrA3XNA26rkMIASll8tzE8alryHMd13UdmqZlPQ4AQojkMSklnE4npJRpxwF76V6u41PHmOt4Ka5puuO8pvK4Jl3X4XQ6YVkWNE2riGuqxO/TQrwmIQQMw0jWZiVcUyV+nxbaNUkpk59Pd63ldE1Tx8hrKs9rAgCXy5Xxej6f18QeTEREalIyQAoEAgDsJWvZ1NTUJM+ZrSeffBL//M//jI0bN+KP/uiPpj33vvvuw1e/+tWM4wcOHEB1dTUAoKGhAcuXL8fp06cxNDSUPKe5uRnNzc3o6urC6Oho8nhbWxsaGxvR2dmZthPcqlWrUFNTg4MHD6a9aK5fvx4ulwv79+9PHku8yK9bty4tSDMMAx0dHRgdHcXx48eTxz0eDzZs2IDh4WF0d3cnj/v9fqxevRr9/f3o6+tLHi/FNQFAR0cHotEoDh8+zGsqw2vatGkT3nzzTTidzuQbznK/pkr8Pi3Eazp16hSGhobQ0NCAmpqairimSvw+LbRrklJiaGgIa9aswYoVKyrimirx+7QQr6mpqQn9/f2oqqrC+Ph4Sa5pbGwMRESkHk1O/ZWDAm666SY8++yz6OzsxJo1azJuX7p0KcbGxmYdIu3Zswc33HADHA4HXn75ZVxwwQXTnp9tBlJbWxuGhoZQU1MDoDS/kbIsCwcOHEBHR0fab4YA/paN11S6awKAd955BxdccAEMw6iIa6rE79NCvCbTNHHgwIFkbVbCNVXi92mhXVPitfzCCy+E0+msiGuaOkZeU3lekxACBw4cwKZNm9Jez+fzmoLBIBoaGhAIBJLvuYmIqPSUnIGUmHmUKyAKBoMZzbFnsnfvXtx0003QdR1PP/30jOERALjdbrjd7ozjiR9AUiVeDLOdW4zjmqYll2JMlet4rjHO9nixrmm647wm9a8pdXlQvv8+VL8moPK+T8DCuybDMDJqs9yvKRteU/ldU+IH8unOL7dryuc4r6l8rinb48/HNeW6DxERlZaS3fESvY+y9Tnq6+vD2NhY1v5Iuezduxc33ngjhBB4+umnceWVVxZsrERERERERERElU7JAOnaa68FADzzzDMZtz399NNp58wkER5ZloWnnnoKW7ZsKdxAS0TXdXR0dOT8jRFRKbAuSVWsTVIR65JUxdokIqJclHxluOGGG7Bq1So89NBD2LdvX/J4IBDAvffeC5fLhU9+8pPJ4729vTh06FDGkrc33ngDN954I0zTxJNPPolt27bN1yUUXTQaLfUQiDKwLklVrE1SEeuSVMXaJCKibJTsgeRwOPC9730PO3bswDXXXIPbb78dfr8fjz32GE6ePIkHHngA7e3tyfO/+MUv4sEHH8QPfvADfOpTnwIADA0N4cYbb8TIyAhuvvlmPPvss3j22WfTvk5dXR0+97nPzd+FFYgQAocPH0ZHRwfXiJMyWJekKtYmqYh1SapibRIRUS5KBkgAcN111+GVV17BV77yFTz88MOIxWLo6OjA/fffj9tuu23G+weDQQwPDwMAnnrqKTz11FMZ56xYsaIsAyQiIiIiIiIiovmkbIAEAJs3b8aTTz4543k//OEP8cMf/jDtWHt7e8YWpkRERERERERENHtK9kCimXFKMamIdUmqYm2SiliXpCrWJhERZaNJTtPJWzAYRG1tLQKBAGpqako9HCIiIiKiisP33EREauIMpDIkpUQwGOQSPVIK65JUxdokFbEuSVWsTSIiyoUBUhkSQuD48eMQQpR6KERJrEtSFWuTVMS6JFWxNomIKBcGSERERERERERENC2ld2EjIiIiIqLikrEYRCQKGY2A846IiCgXBkhlyuPxlHoIRBlYl6Qq1iapiHVJ80lKCRmNQkYikJFIMjCSkQikNRkbabU1rE0iIsqKu7DNAneEICIiIiKVSSGyhkQiEgHyeNfvqK+Dc+nS4g90GnzPTUSkJs5AKkNCCAwPD6O+vh66zjZWpAbWJamKtUkqYl3S+ZKmaQdD0ShkODwZGMVi5/W4QkoMDg6yNomIKAMDpDIkpUR3dzfq6upKPRSiJNYlqYq1SSpiXVK+RMqys7RZRaZVlK/H2iQiolwYIBERERERlZCUMiUgiti9isJh+0/BbhNERKQGBkhERERERPNAWtZkSJQaGMViefUnIiIiKiUGSGXK7/eXeghEGViXpCrWJqmIdVm5ZDSatvQsOasoZpZ6aHlhbRIRUTbchW0WuCMEEREREQHxZWdpIVEUMmI3sy7nZWfchY2IiHLhDKQyJIRAf38/mpqauDsGKYN1SapibZKKWJflQwphB0SJnkSJwCgarchlZ0JK9PX1sTaJiCgDA6QyJOMv7IsXLy71UIiSWJekKtYmqYh1qR4Zi03ucJY6qygWK/XQ5hVrk4iIcmGAREREREQLgpQSMhabDIjCkcnAyBKlHl5JiWgUoV27EHrrLTiHBtHTuhQ1N74P/ptvhu52l3p4RESkAAZIRERERFRREsvOkrOIopPNrCtx2dn5Cu3Zg4HvfAdifBzQNOhSYuzQYYw99xz0r92L1q9/Hf7rryv1MImIqMQYIJUhTdPQ0NAATdNKPRSiJNYlqYq1SSpiXRaGNM1kP6L0Hc8W1rKz8xHaswf93/jG5IHE/jrCnpElRkdx+s/+DMv+73fgv/76EoyQiIhUwV3YZoE7QhARERHNPxmNToZE4fDkrCLTKvXQypqIRnH6zjshQqHJ4CgbTYPu92Pty7+dl+VsfM9NRKQmzkAqQ0IInD59GsuWLePuGKQM1iWpirVJKmJdZpJSTplFNDmrSAr+vrMYQrt22cvWZiIlRDCI0aefRu3v/37xB0ZEREpigFSGpJQYGhrC0qVLSz0UoiTWJamKtUkqWsh1KS0rIyQS4bC92xlzoqKQUsIaGYHZ24tYTw9i8T/Dv/td/g+i6xh99jkGSERECxgDJCIiIiIqOBmL2UvNIuH0wChmlnpoFUuEQmkBUWpgJCcmpr2vpWnoq6tCX00VYoYBp2WhOTiO5pFxGFICQsAKBObpSoiISEUMkIiIiIhoTqSUaUvNUnc8k5Yo9fAqkozFEDt7FmZPT1pYFOvthRgZmd2DaRogJc7W+PB2WxNMh2H3Qkocr6vGgVYLF3f3Y8lYGEZtbVGuiYiIygMDpDKkaRqam5u5cwsphXVJqmJtkorKrS6lEJkhUTgMEY1y2VkRSCFgDQ7awVA8HDITf547l9whLS+6DkdTE5wtLXC0tsLZ0gJn/M+JAwdw8N++jzfamyfPT9Rk/E/T0PFGezMu7+pD643vK+BVEhFRuWGAVIZ0XUdzc/PMJxLNI9YlqYq1SSpStS6laUKEI5OziBKBUSxW6qFVHBlvTJ1tuZnZ1wcZjc7q8Yy6uoyAyNHaCueSJdCczqz3cW++Eu+8+Gv7k1xhZnw20jvLl2D7ddfNakxERFRZGCCVIcuy0NXVhfb2dhiGUerhEAFgXZK6WJukolLXpUjd4Sx1VpFpzftYKp0Ih+1QKBEQpcwoymsHtBSa15sMh5ytrZOBUUsLdJ9v1mM7cfQwYkYeuwBqGmKGhqP79mLTexgiEREtVAyQytTo6Giph0CUgXVJqmJtkoqKXZdSiMldzlLCIhmNQgquOyskaZow+/uzNrC2hoZm92AOB5zNzXZAlDKbyNnaCr22tqDLHk8cPpCcYTQTTdNw9PVdDJCIiBYwBkhEREREZUxaVrIf0eSMoghklMvOCklKCWtoyA6IzpxJX3J29uzs+hJpGoxFizKXm7W2wrFoEbR5mpU2FgzkFR4B9vVPjDGMJyJayBggEREREZUBGY1mhkQRLjsrNGtsLNmwemoDaxmJzOqx9Jqa9IAosfRsyRLobneRrmBmwZEhvPHK8+jv6c77PpqmwVvtL+KoiIhIdQyQypCmaWhrayubnVtoYWBdkqpYm6SiXHUppZxcdhYOJ/9fRiJcdlZAIhKB2deXtYG1CAZn9Via250eEC1dan/e0gKjurpIVzA3oyPDeOPV53Fo316I2cyYgl2bazZvK9LIiIioHDBAKkO6rqOxsbHUwyBKw7okVbE2SUWapqHe64UIBGClziqKRgHmRAUhLQvmwIAdEKU0r4719MAaHMx76RYAwDDgWLIkc8lZSwuMhgblA+rRwDDeeOWFeHA0OWPN6XJBCAHLNGd4BA3uqiqs23JVcQdKRERKY4BUhizLQmdnJ9auXcsdhUgZrEtSFWuTVCJCIViBAGLDwzg2MIBV9Q3QFQ8fVCalhDUykjaDKDmjqK8PmDEYSWc0Nk4uM0uERa2tcCxeDM1Rfm+bRwMjePPVF/DuW3umBEduXLzlaly05Wr0dXfhiYf/DbmTSw3QgPf/2V/C4XLNy7iJiEhN5fdKSACAcDhc6iEQZWBdkqpYm1RKIhKBNRKAFRhJNrYWQiBqWZBS2rtg0bREKJR1h7NYby/kxMSsHkuvqkrrR5QMiZqboXs8RbqC+TUWHMGbr76Ig2+9DmGlzzi6aPPVuHjre+Dx+gAA7es24f0f/ySe/+VPEQlPJHdl0zQNUkq4q6rw/j/7S6y+fEupLoeIiBTBAImIiIiowGQsBisQgBUIQEwwwMyHjMUQO3s2o4F1rKcHYmRkVo+luVxwNDdnbWCt+/3KLzmbq/HRIN589QUcePO1jOCo48qrcMnW98Djq8q438r1m/D//OWXcOzgfpw4fhhDgQAWLWnG2i3bsW7LVZx5REREABggERERERWEtCxYwSCskRGI8VCph6MkKQSswUE7HJqyw5l57hwwm8bOug5HU1NGQORsaYHR2AhN14t3IYoZHw3izZ0v4uAbr8GyJpftOZwudFy5HZdsuwbeLMFRKofDifUXXYYN73kvDg0OoqOjg8t+iYgoDQOkMqTrOlatWgV9Ab0xIvWxLklVrE0qJiklxOioPdNodDTvndJ0TcPy2tqK7H+U+DtJhESpy83Mvj7IaHRWj2fU1WUERI7WVjiXLIHmdBbpKspDaGw0OeMotRH2bIKjqXRN43MmERFlxQCpDGmahpqamlIPgygN65JUxdqkYrDGxiECI7CCQUhrdtuhA3ZdVrvcRRjZ/BHhsB0KJQKiREjU2wsxNjarx9K83mQ45GxtnQyMWlqg+3xFuoLyFRobxVs7X8Tv3tg9JThy4sIrtuPSbdfAW1U9p8fmcyYREeXCAKkMWZaFgwcPYtOmTZxaTMpgXZKqWJtUKGJiIt7XKAgZi53XY1lCoHNoEGsbGmEoPNNDmibM/v6sDaytoaHZPZjDAWe8L5EjZTaRs7UVem1txfYlKqTQ+Bje2vkiDuzdDdOcrEGHw4kLrtiKS7e/F75ZBEeaw4DmctkfThc0lxPS7cb+/fv5nElERBkYIJUpK6UxIpEqWJekKtYmzZWMRu3QaGQEIjK7pVczETK/5W7FJqWENTSU1o8oduaM/fnZs7PrS6RpMBYtylxu1toKx6JF0BhIzElofAz7dr2E3+3dBTMlvDQcDlx4xTZcuu1a+Kr9We+rOZ3QXE7oiaAo9SPL98OyLD5nEhFRVgyQiIiIiFJI04w3ww5AhCqnGbY1Npaxw1nicxmJzOqx9JqarDucOZYsge4u76V5KpkIjWPfzpewf+/OjODogsu34rLt74XP74fmTAmI3O6UWUXOBdVMnIiIiosBEhERES14UgiIYNCebTQ2BhRpcpCIRhHatQvjr70G9/AwBurrUbVlC3zbtkEvwFbpIhKB2deXsdws1tsLEQzO6rE0tzs9IFq61P68pQVG9dz661B+JkLj2Lfrt9i/ZyfM2OTMN8PhwIXbr8WVH/gwqhc3TYZEXP5HRETzQJNSkfnTZSAYDKK2thaBQKCkzQWllAiHw/B4PHzDQMpgXZKqWJuUi5QSYnzcXp4WDOa9g9pchfbswcB3vgMxPg5oGiBl8k+9qgqL7roLviuumHnclgVzYMAOiKbMKLIGBuzHzZdhwLFkSeaSs5YWGA0N/DczjzRDR9iMYd+u3+KdV15ALGVWmOF04qIbbsbmD9+C6obGoo5DhedMVd5zExFROs5AKlOuAvyWkqjQWJekKtYmpRKhULwZdgDSnJ9eL6E9e9D/jW9MHkiEPPE/RSiE/vvvR9MXvgDflVfafYlGRtJmECVnFPX1ASk7b+XDaGycXGaWCItaW+FYvBiag28H54vmdMSbVbvS+hKFY1G88eSv8NZTv0R0YiJ5vuFwoOOGHdj8kVvhb1g0b+PkcyYREWXDdwxlSAiB/fv3o6Ojg7tjkDJYl6Qq1iYB9tIuayQAKzACGT2/HdRm/bWjUQx85zv2J7lmB8WP93/zm3AuWwbz7FnIlCAhH3pVVVo/omRI1NwM3eM5n0ugfGlI70c09WNKP6Lw2BjeePxnePOJXyI6Mdlvy3A4cOH1O7DlI7fC3zh/wRHA50wiIsqNARIRERFVJBmLJWcaiYlwycYR2rXLXraWD9NErKsr582aywVHc3PWBta6388lZ/NBQ/aAyO3Oux9ReHwMb/z6P/HmE/+ZFhzphgMd19+IzR/5OGoWLS7mVRAREc0aAyQiIiKqGNKyYAWDEIEArLE8Q5siG9+1a7LnUZ4czc0ZAZGzpQVGYyN31ZoHmqFnBkROF3SXE9p5LO+KhMaTwVEkNFmfumHgwvfeiC1/8HHULG4qxCUQEREVHAMkIiIiKmtSSojRUXum0eho0Zth50OEQgjt3YvxnTsxsXfvrO7r3rQJLf/7fxdpZJSgOYz0cMidEhYVuC9UJBTCm0/+J9749S8QGU8Pji547/uw5SMfR23TkoJ+TSIiokJjgFSGdF1HR0cHdP4GkhTCuiRVsTYrlzU2DhEYgRUMQlqi1MOBmJiYDI327QNic+i1pGkw/P6Cj22h0pzOtGbVaR/z0N8nEgrhrSd/iTd+/QuEx8cmx6XruODa92HrRz+O2qbmoo9jNvicSUREuTBAKlPRaBQeNsQkxbAuSVWszcohwmG7r9FIAHIuAU2hxzMxgdAbbyC0cydCb72VNTTSqqog8+2BJCV8W7YUeJQVLLVptdudGRKVqCdUdCKEt556HHsf/znCY6OTw9V1bLrmemz96O2oW6JWcJSKz5lERJQNA6QyJITA4cOHuTsGKYV1SapibZY/GY1ONsMOR0o9HIiJCUy8+aY90+ittyCj0YxzjLo6+LZtQ9X27XCtXInT//2/Q4RC0/dB0jToPh98W7cWcfTlR9O17DuauVx5N62eL9HwxGRwNBpMHtd0HZvecz22fPTjqG9uLeEIZ8bnTCIiyoUBEhERESlHmiasYBDWSMAOXkpMhMOTodGbb+YOjbZuRdX27XCvX5+2RGrRXXeh//77czfTjocgi+66C/p5NGkuV5qhT84gcrrSl505naUe3oyi4Qnse/rX2POrn6UHR5qOje95L7Z+7HblgyMiIqKZMEAiIiIiJUgh7GbYIyOwxsaAEvfCFpHIZGj0xhtZQyO9thZVidBow4acfXV8V1yBpi98AQPf+Q7E+PhkkBT/U/f5sOiuu+C74opiX1bJaE5HPByaEhC53fPSj6gYYuEw9j3za+z55WOYmBIcbbj6Wmz96O1oaF1awhESEREVDgOkMsUpxaQi1iWpirWpLiklxPi4PdMoGCj5DmoiEsHEW29NhkaRzCVzek0NqrZuhW/7dng2bsw7/PBdeSWW/eu/IrR7N8Z378bYyDCq6+rtx9q6tfxnHqX2I8r2UUFNmWORMPY984QdHAUDkzdoGjZedS22fux2NLQuK90AzxOfM4mIKBtNyukW41OqYDCI2tpaBAIB1NTUlHo4REREZUuEQnZfo2AQMmaWdiyRCCb27bMbYb/xBmQ4nHGOXlMD35YtqNq+HZ5Nm8p2xsx505AZECWXnqnVj6gYYpEw3n72Sez55WMIBUYmb9A0bNh+DbZ+7HY0Lm0r2fgqBd9zExGpiTOQypCUEqOjo/D7/RX/Ro3KB+uSVMXaVIeIRmENj8AKjEBGS7uDmohGEd63D+M7dyK0d2/20MjvnwyNLrigoKGRlBLjsSiqnKXbKSwXzdCzNKt2QXc5oZX7LKk5ikUjeOfZp7Dnl49ifGR48gZNw/pt78G2j92OxmXLSzfAAuJzJhER5cIAqQwJIXD8+HHujkFKYV2SqlibpSVjMbsZdiAAEZoo6VhENIrw229PhkYTmePRq6vt0GjbNnguvBCaozhvlYSUOBUIYH3jIhgl+CFdcxhpAVHarKIiXXM5ikUj2P/cU3j9P6cERwDWxYOjRW0rSjS64uBzJhER5cJ3CERERFRQ0rJgBYMQgQCs8fGSNsOWsRgmUkOjLDu66dXV8G3ebM80KmJoNN80pzO9WXXqB4OBaZnRKN75zdN4/T8fwfjwUNpt67ZchW23fAKLlreXZnBEREQlUhnvkIiIiKikpJT2DmqBAMToaEmbYctYDBPvvGOHRnv2ZA+Nqqrg27wZvm3b4O3oKIut4jMkmlYnehBN/eDyo1kzYzHsf/5pvP6LRzA2NJh229ot27HtY5/A4hUrSzQ6IiKi0mKAVKY8Hk+ph0CUgXVJqmJtFo+9g9oIrNFRSNMq2ThkLIaJ/fsR2rULoddfhxgfzzhH8/mSM41KHRppmgaXYcwY8mi6lj0cWiBNq+eLGYvhdy88i9d+8VOMDQ6k3bbmym3Ydssn0NS+qkSjm398ziQiomy4C9sscEcIIiIiQITD9g5qIwHIWOmaYUvTtEOjnTunD42uuMIOjS6+WMmZRmn9iJyu9GVnCo63klhmPDj6+SMYHTyXdtvqK7Zi2y2fwJKVq0s0uoWL77mJiNTEGUhlSAiB4eFh1NfXQ9f1Ug+HCADrktTF2iwMGY3aoVEgABGOlG4cponw736H8V27EHrtNYixsYxzNK8XviuvVDY00n0+aP5qBKJRNDQ1wVBsfAuBZcZw4MXfYPfPH8bowNTgaAu2fewTWLJqTYlGV1p8ziQiolwYIJUhKSW6u7tRV1dX6qEQJbEuSVWszbmTpmnvoDYSgMjSR2jexmFZdmi0c2fu0MjjSc408lxyCXSVtpvXAKO6GnpNDYzqamhOJyzLwpn9+9HQ3Fzq0S0olmniwEu/wWs/fxjBc/1pt6267Epsv/W/LNjgKIHPmURElAsDJCIiIkqSQiSbYVujoyXbQU1aFsIHDkzONAoGM85JhEa++Ewj3e0uwUiz0wwdenU1DL8fut/PXc9KzDJNHPzt89j9s4cRPHc27baVl16B7bfcgeY160o0OiIiovKgdIC0Z88efOUrX8HOnTsRi8XQ0dGBv/qrv8LHP/7xvO5/7Ngx/OhHP8Kbb76JN954Az09PVixYgW6urqKO3AiIqIyIqWMN8MOQIwGIS1RmnFYFsIHD07ONMoWGrnd8F5+ub087dJL1QqNHIYdGNXUQK+uZoNrBQjLsoOjnz+MwNm+tNtWXnI5tt1yB1rWri/R6IiIiMqLsgHSCy+8gB07dsDj8eD222+H3+/HY489httuuw3d3d24++67Z3yMl19+GV/96ldhGAY2btyIvr6+Ge9TLvx+f6mHQJSBdUmqYm1mJ0Ihe6ZRMAgZM0syBmlZCL/7LkK7dmF8926IQCDjHKVDI5cTRk2NHRxVVc3qvqzL4hGWhXdfeRG7H/sJRs72pt3WfvFl2HbLHWhdt6FEo1Mfa5OIiLJRchc20zSxYcMGnD59Grt378Yll1wCAAgEAti8eTO6urpw5MgRrFixYtrHOX78OPr7+3HxxRfD6/XC4/Ggubl5zjOQuCMEERGVOxGNwhoZgQgEICLRkoxBWhYihw5hfOdOjL/2GsTISMY5mssF72WX2aHRZZdBV2hbcd3jhu6vgVFbo9S4KCU4+tlPMNKXHhytuOhSbL/1DrSu21ii0VG++J6biEhNSs5Aev7553Hs2DF8+tOfToZHAFBbW4t77rkHn/rUp/Dggw/ib/7mb6Z9nFWrVmHVqlVFHu38E0Kgv78fTU1N3B2DlMG6JFWxNgEZi9nNsAMBiNBEacYghB0a7dqF0K5dsGYKjS69FLrXO/8DzUH3+WDUxJenFaBBN+uysISwcOjV32L3Y/+B4d6etNuWd1yC7bfcgaUbNpVodOWFtUlERLkoGSC9+OKLAICbbrop47YdO3YAAF566aX5HJJSpJTo6+vD4sWLSz0UoiTWJalqodamtCxYwSBEIABrfLwkzbClEIgcPjwZGg0PZ57kdMJ36aXwbd8O3+WXqxMaZdk5rZAWal0WmhAWDr/6W+x67CcY7j2TdtvyCy/CtlvuwLKNF5ZodOWJtUlERLkoGSB1dnYCANauXZtxW3NzM6qrq5PnFFMkEkEkEkl+How387QsC5ZlAQA0TYOu6xBCIHU1YOJ44ryZjuu6Dk3Tsh4H7N8GJViWBSklpJQZ5xuGYTdDFSLj+NQx5jpeimua7jivqTyuCUDOmizXa6rE79NCvKbEc2bi61TCNeX6PgkhIMbGYAUCkGNj0CQg4q8XaWPXtLyP65pmX9PUsccbRIspY9GkRKSzE+M7d2Ji925YQ0PI4HTCe8kl8G3fjqorrgA8Hvt1DYAlxKzHWKhrMhyG3fy6utr+M7Fzmq7n/H7M9d9Toi6FENPWZLnU3nz/exLCwqGdL+O1nz2M4Z7Tabcv29SBrR/7BJZtvCB5rByuCVDj+5R4zFJe09THIiIiNSgZIAXiDTRra2uz3l5TU5M8p5juu+8+fPWrX804fuDAAVRXVwMAGhoasHz5cpw+fRpDKW+Sm5ubk/2WRkdHk8fb2trQ2NiIzs5OhMPh5PFVq1ahpqYGBw8eTHvRXL9+PVwuF/bv3588lniRj0QiaUGaYRjo6OjA6Ogojh8/njzu8XiwYcMGDA8Po7u7O3nc7/dj9erV6O/vT2swXoprAoCOjg5Eo1EcPnyY11SG17Rp0ybEYjEcOHAgufNQuV9TJX6fFuI1nTp1CkNDQzhw4ABqamoq4pqmfp9WNjej5+RJnO3vB+KvEXUeD1r9NegbG8VIyhgX+arQVFWF7kAA47HJHkgt1X7Ue704PjyEaMrYl9fWotrlRufQYFpYtKq+AU5dx+HBAUAI6F1dMN58E559b8MaHEQGhwOuiy/C6EUXweroQMjrxahhYI3Xi+GJCfSOTf79VjldWFFXh4FQCAOh8eTxolwTAM3phOZyYcPaC+GYp39PUkoMDQ0ld4gt19qb739PUgoYgSG88ctHMXQmPThauvFCXPH7t2BMd2DYFBjev78srkm171NTUxMA4OTJkxgfn/z3N5/XNDY2BiIiUo+STbRvuukmPPvss+js7MSaNWsybl+6dCnGxsZmHSLNtol2thlIbW1tGBoaSjb0K8VvpIQQ6OnpwbJlyzLGXC6/OZx6TdMd5zWVxzVpmobu7m60tram9Uwo52uqxO/TQrwmy7Jw5swZLF26FLquV8Q1SSkhwmF7idpIAJplzftsHQ1A9OhRjO3ciYldu7KHRoYBzyWXwLdtG6qvvBKaz5cWQhV7plGu44bbBUdtLeDzpe2cNp//noQQOHPmDJYtWwaHw1FWtTfT8WL8e5JCoPP1ndj92E8wdKY77bylGzZh68c+gRUdl5TVNU09nm3spfg+SSnR09ODlpaWtNfz+bymYDCIhoYGNtEmIlKMkgHSrbfeikcffRR79+7F5ZdfnnG73+9HfX09Tp06NavH5S5sRERUzmQ0CisQsJthhyMz36HQX19KRI8exfiuXRjfuRPWwEDmSQ4HvBddZDfCvvJKGLPc2r5YdI/b7mdUw53TykkiONr16H9goPtk2m2t6zdh+613YPmFFydnvlJl4HtuIiI1KbmELdH7qLOzMyNA6uvrw9jYGDZv3lyKoSlBCIHTp09j2bJlab8ZIiol1iWpqtxrM9EM2xoZgRgPzf/XlxLRY8eSjbDN/v7MkwwD3osushthX3kljPgy71Ir9M5phVTudVlsUggc3bMbux59COdOdaXd1rpuI7bf+l+wvIPBUTGwNomIKBclA6Rrr70W9913H5555hncfvvtabc9/fTTyXMWqkTfhKVLl5Z6KERJrEtSVTnWphQCYnTUnm00OjrvO6hJKRE9ftwOjXbuzB0adXRMhkZ+//wOMpvUndP8fmgOJd/mACjPupwPUkoc3bMLux79D5w7eSLttpa167H91v+CFRddyuCoiFibRESUi5LvrG644QasWrUKDz30EP7iL/4Cl1xyCQC7ufa9994Ll8uFT37yk8nze3t7EQgE0NLSkrPxNhERkeqssTFYIwGI0SCklbnTYTFJKRE9cQKh+PI08+zZzJN0HZ6ODlRt3w7f5s1KhEaaoUP3+2H4/ek7p1FZkVLi2N7XsPPRh3Cu63jabc1r1mH7rf8F7RdfxuCIiIiohJQMkBwOB773ve9hx44duOaaa3D77bfD7/fjsccew8mTJ/HAAw+gvb09ef4Xv/hFPPjgg/jBD36AT33qU8njAwMD+J//838mP4/FYhgYGEg754EHHsCiRYvm4aqIiIgyiYmJZF8jGTPn9WtLKRHt6poMjVJ2aErSdXguvHAyNFKgH4nmdCRnGunV1QwVypiUEsfffB07H3kI/SeOpd3WvHqtHRxdcjm/x0RERApQMkACgOuuuw6vvPIKvvKVr+Dhhx9GLBZDR0cH7r//ftx22215PcbY2BgefPDBtGPj4+Npx/72b/+27AIkTdPQ3NzMN1OkFNYlqUrF2hTRqN3TKBCAiERnvkMBSSkRO3ky2Qjb7O3NPEnX4bnggsnQSIHZvZrLCSO+NE1XpDH3+VCxLueTHRztwa5HH8LZ40fTbluyai2233oHVl56xYL9+ymlhV6bRESUm5K7sKmKO0IQEdFcSdOc3EEtNDG/X1tKxE6dmgyNenoyT9J1eDZtskOjLVuUCI10r8densad0yqGlBIn9u3FrkceQt+xzrTbmlauxvZb78CqyzYzvFjg+J6biEhNys5Aotwsy0JXVxfa29thsNcDKYJ1SaoqZW1KISDiO6hZ4+Pz3gw72t2N8Z07Edq5E7EzZzJP0DR4Nm2Cb/t2VG3ZAqOubn4HmIXKO6cV0kJ7zpRSouvtN7HrkYfQe/Rw2m1N7aux7dY7sPpyBkcqWGi1SURE+WOAVKZGR0dLPQSiDKxLUtV81qaUEmJszJ5pFAxCivlNjaLd3cmeRrHTpzNP0DS4N25E1bZt8G3dCkd9/byOL3M85bNzWqEthOdMKSVOvv0mdj76EHo704OjxStWYtutd2DNFVsZHClmIdQmERHN3sJ5l0ZERFREYnzcXqIWDEKa1rx+7eiZMwjt3GmHRt3dmSdoGtwbNtjL0xQIjbhzWuWTUuLk/n3Y+ciP0XvkUNpti5e3TwZHul6iES4sQgpY0rL/FFby82zH3Jq71MMlIiJFMUAiIiKaIxEO26HRSAAyFpvXrx3r6cF4IjQ6dSrrOe4NGyZnGjU2zuv4pkrunFZbC72qijNOKpSUEqf2v42djz6EnsMH025b1LYC2269A2uv3MbgaBaklJNBT+qfIv3zbMcSf8pZrJ+tdZS+/xkREamJAVIZ0jQNbW1tfPNNSmFdkqoKXZsyGoUV72skwpGCPGa+Yr29dmi0axdiXV1Zz3GvXz8506jUoVGF7ZxWSJX2nCmlRPeBd7DzkYdw5tCBtNsaly3HtlvuwLot2xdkcJQMeESWEGjK7J/UcxOfC4h5HW+l1SYRERUOA6QypOs6Gkv8QwHRVKxLUlUhalNa1mRoNB4q0MjyE+vtxfiuXQjt3IlortBo3Tr4tm1D1bZtcCxaNK/jm4o7p+Wnkp4zE8HR6Xd/l3bcDo4+gXVbrirb4CjX7J+0gCfHjKC5zP4ppagVxe7e3Xi7/21ErAjq3HW4fvn1uKn9JrgNLmsjIiJAk1KWx6uaAlTZUtSyLHR2dmLt2rXcHYOUIGMxxCbCOHriONa0t8MwHNB0DdA0QNft32LG/x+aVrY/SFB5mutzppTS3kEtEIA1OjqvO6jF+vrs0GjXLkSPH896jmvtWlRt346qrVvhWLx4/gaXhe7zwaiNN8Gu4J3TCqkSXstPH/wddj7yY3Qf3J92vKF1mR0cbbsaul7aa8s562fK7B9TmlmDoPme/VMqe/v24rtvfxfjsXFo0CAhoUOHgECNqwZfu/preG/be+dtPKq85yYionScgVSmwuFwqYdAC4yMRiGiMchYFDIahYxE7D+jUUghYQmBiaEhRDUdRj4BkQY7SNJ1AJodOMUDpmTIlAidZrw95bE0zQ6sUm7XAPvz1NtpQZnNc6Y1Nm7PNBoNQlrz98Nj7OzZ5O5pOUOjNWvsnkbbtsHZ1DRvY8uwgHdOK6RyfS0//e7vsOvRh3Dqd++kHa9vXYZtH7sd67e/pyDBUersH1OYWQOgbKFPIgwSUpTN7J9S2tu3F9/c+83k31Xiz0R4NhodxV88/xf49nXfxnXLryvZOImIqPT4jo+IktJCotSAKB4SFfaLwf7hPP4D+ry+xdeQEjLp2cOslNs1DZMBVMbtKYFVltvTZl8lPifliIkJe6ZRIAAZM+ft68b6++3QaNcuRI8ezXqOa/XqZE8j55Il8za2qdJ2TvP7OZNwATpz6CB2PvJjnPrd22nH61uW2sHRVdekBUe5Zv/kCoMW6uyfUopaUXz37e9OG7RJSGjQ8L9e/V94funzXM5GRLSAMUAiWmCSIVF0SkBUjJBIVdL+zTaEBcBKHJo3OQOoLLOrpi7/m+n2jNlXKbfzB/50Ihq1ZxoFAhCR6Lx9XfPcOYwnZhrlCo1WrbJnGm3fXtrQyOmwA6OaGu6ctsBIKZMzec4cOog9P/spTv8ufamav6kJmz70QSy78jJIHTgR7EoLg6h0TGEibIYRMkOYMCfSP2KT/390+CjGY+MzPp6ERDAaxDNdz+BDqz80D1dAREQqYoBUhnRdx6pVq6Dzh0HKwQ6JpoRDkQhkLFa0kEjXNCyvrYXOHzBnZH8PZOlmXyXDqByzr1Juzzr7KjXMyjb7KjXMUmD2VepzpjTN5EwjEZqYtzGYAwPJnkaRI0eynuNauRK+7dtRtW0bnM3N8za2qbhz2vwo9mt56uyfmbZ4nzr7J9H4eeTESXQ+/iwG3+1Me2zf4kasfv8NaLnyEuiGgXERAicLnT8hBSJWJGvQM91HKBZC2Aonzw+ZIcRErODj06Hj+VPPM0AiIlrAGCCVIU3T2FCQMkOi+JIzEY3OcyJh0zQN1S5Oa1deYukgALVmX+np4VbG7KqZb0+GXVNv1zRUCYHYqVOwxsbm7YLNwcHk7mm5QiNnezuqtm1D1fbtcLa0zM/AsuDOafNvutfyfLZ4n7Yx9HnO/hnp6sbRx5/BwMH0uvUuasDq99+A1s2XQi/Txt/FELNimaFOfOZP2AxPfh6bQNgKIxSbnBWUOksobIaV7tkkIDASGSn1MIiIqIQYIJUhy7Jw8OBBbNq0qWx3bqGZSSntGUPRzKbVpQqJshHRqN3D5bXXMDYyjOq6elRt2QLftm3QuRsTTVGK2VeWEOgcGsTahsb8GryfB3NwEKHduzG+axcihw5lPce5YoW9e9q2bXC2thZ1PDlp8Z3Tarhz2nwTUiAUC2EsMobjR46jeWUzoEOJbd8DXd04+uvncO5Aeu16Gxuw+v3Xo3XLZRUTHAkpMsKbRMiTz6yf1A9TzF/ftFx0TYfP4YPX4U3/cHrhNeJ/Tr3N4cV/Hv1PHBo6lFfN6dBR564r/sUQEZGyGCCVKctib4FKUC4hUS6hPXsw8J3vQIyPA5oGQ0pMaBomXn8d+ve/j0V33QXfFVeUephEELJ4/5jMoSE7NNq5E5HDh4EsX8u5fHmyp5Fr6dKijWU6mq5Br6rizmnzLCZiyRkniaVGEhJCCETMCMatceiytEvSA6dO4+jjz+Lc76YGR/VYffP1aN16uRLBkZTS/vucTdATm8CElb4cLGSGELEipb4cAIDH8OQV9HgdXvgcPngc9vk+px0WeRwe+Bw+OHXnnJYJB6NBvDv0bl7nCghcv/z6WX8NIiKqHHz3SFRkaSFRakAU70mkekiUS2jPHvR/4xuTBxI/NMf/FKEQ+u+/H01f+AJ8V15ZghESFY85PDw50+jdd7OHRm1t9u5p27bBtWxZCUbJndNKIWJFEIqFEDJDCMVCiIr0Bu1RK4rdvbuxp3cPhoPDqB+ux5UtV2Jry1a4jPmdCRY4dQZHf/0szu1PDxA8DXVYffP1WLr1cugFCBoTDZ2nLvFKPZbaxye18fPURtBClr7ZkkN3ZA11Ep8ngp1sQU9qIORxeKBrpf03ubVlKx488OCMjbQ1aPC7/Lip/aZ5GhkREamIARJRASRDopSASEYido+iMg6JchHRKAa+8x37k1wzO6QENA0D3/kOlv3rv3I5G5U9a2QE44mZRrlCo6VL7UbY27fD1dZWglFy57T5JKVE2ApjPDaeDD5MmXs5096+vfju29/FeGwcGjR7e/QJDXvO7sGDBx7EZy/5LC5fcnnRxx3sPoOjv34O/e8cTDvuqa/Fqpuvx7JtV0AzDDsMCwcz+vjMaolXbCIjRCsFDdpkqJMj5JnxIz5LyGk4S305BeMyXPjsJZ/FA3seyLmMTYP9HPK1q78Gt8Feh0REC5kmZRHn9VeYYDCI2tpaBAKBkjaxllIiHA7D4/HwB4N5JKXM3Pa+gkOi6Yy99BIG/uEf8j7fc/nl8KxeDc3lmvZDT/y/05l+mwJLJ6h8SSkRsSy4DWPWz5lWIIDx3bsR2rUL4YMHAZE5+8HR2mr3NNq+Hc62tpI8Lyd3Tqupge7zzfvXXyiEFMnZMuOxcXtGTJ7bj+3t24tv7v3mtL1mNGi4+4q7cUXz3Jf+msLMGfSEevoRe/kI9KNDafeJ+jR0X6Cja3kU43JyZpAKDZ1duis9wMkz6Elb7uXwwWW4Sj7bR2XZwk0dOgQEalw1+NrVX8N72947b+NR5T03ERGlY4A0C6q8mElp907QS7gtdqXKCIlSdjZbaCFRKikEzL4+RLu6ED1xAqPPPw8RCMzfAByOtGBJzxZAJUInt3v6MCpXaDX1sRhalb1Eg/fQ66/DGh2F4ffDt3nzjA3erUAAoddew/jOnblDo5aWydBo+fKSPBdz57TiS4QxiSVpc90lK2pF8afP/emMy4QAwOvw4vNXfh6mMNOWd2Xs6JXj82zbt9cHnbi4sxbtZ6vSjo97TLyzOoDOZWMQBXzK0zU9r6AndXnX1JlBiQDIoXOy/HyJWlG81vsa9vXvQ8SMoNZTixuW34Cb2m+a95lHqrznJiKidAyQZkGVFzPLsrB//350dHRwF7Y5SAuJpjStXsghUYKIRhHr7kb0xIlkYBQ9eRIyHC710OZXIrQqRBg10zmJD/anKZipDd4TSyohJfSqqowG71YwaIdGu3Yh/LvfZQ+NmpsnQ6MVK+Y/NOLOaUUXtaJp/YsiojCNln97+rf4x33/WJDHmo26oBOXHK1Fe9+U4MhtYv/qIDrbRmGlvI1wG+7M5V0OH7xOr93s2enNvtPXlNk/Lt3FX3CVGQ0adE2HrunwG36cPX62pO8zVXnPTURE6fhrHapIyZAotSdRNAoRiYdEBACwxsYmQ6J4YBQ7cwYo5C5/mgb3hg2o/YM/yFwCmPiIxewAb+r3LHWHukTIl3IbzCJunWyakKYJGQrluUClALKFVnMNo7LN2IrPzkp7rAoMrfJt8L7orrsgo1F7ptF0oVFi97T29nn/oZg7pxVPon9RamA0Xf+iuTx+z3gPDg8dxs86f1awx53K0IxkyJMIfupHXVj6Tgz+ExPpJ1e74d2+Hs1XbkSHrzoj/OESr/KgQYOh2UtyE3/q0DOPxQMhXZtyWzwsmno+YNetGTNxFmdLfJVERKQivhOlsiWFyAgiRCQCGY0xJJpCSglrYGAyLOrqQuTECVjnzuV1f0dTE1zt7XCtXAnXypWI9fVh+Ic/zPeLw3/jjfBddtncLyDXQ1vW9GHUNB/5nJPtsQoark1VwtBq1mFUvr2sUkOrxLEihlZ5N3gHMPD3f5/1ZseSJfBt22Y3wl65cv5DI+6cVhSp/YsSS74sWbh/z1EriuOB4zg8dBhHho/gyNARjMZG5/RYDe4GXLv82mm3dU/MBEpt6DzWexZHn3gOfW/uT6t/d40fK296L9qu3gLDVTkNoMuBDj0tyEkENjp06LqednvyttTzocHQjYzzCsWMWYhETcQiFsyoBTMq4PBy9hgREWXHAImUlhESJZpWMyTKSVoWYj09k7OK4oGRGBub+c66DueyZXZQlAiM2tthVFennSaiUQQeeQQiFMr9QzoAaBp0nw++rVvP86pyPLxhQPN6Aa+3KI+fTbbQaurMqGxLJOcUbMUfaz5CKysUKt7XmCrf0Gqm5YNud0awNfHOO/aytdkOqalpMjRatWr+QyPunFZwhepflEswEsTh4cM4PHQYh4cP40TgBExx/jOYNGhYU78Gt62/Le/7jPWexbEnn0fvG2+nPSe7aqqx6sb3ou09Wxkc5SE1pMk5iwfpx3KeDx2GrlabAcsU8aBIwIxaiEUtSJHt3wSff4iIKDsGSGVI13V0dHRAr5DfSqeFRKlNqxkSzUiEw4ieOpXWryh26pQdOsxA83jskCglKHK2tU3bXDhBd7mw6K670H///ZP9ZTK+gP0GdNFdd+X1mOVCydBqFmFUvrOxsi3nKphShFY5OFpasPh//A+4Vq+e/9DI5YRRW2sHR9w57bwVq38REF+ONtaTFhj1jfdNe59qZzXW1a/D+ob1mDAn8Iujv8jva0HiyuYr8zp37Ow5HHviOfTunRIc+aux8sZrsfyarTAq6Pk3IRHiJJZypc7eyTmLZ8pt2c6vJJYlYMbDoljEghmzIKz8AlRdq6z3mUREVDgMkMpUNBqFp4x23UmGRFObVkejkLEi9rGpIFYgkN7YuqsLsZ6e6WcAxRl1dWlL0FwrV8KxZMl5LY3xXXEFmr7whdyNin2+jEbFNDclCa1MMy10Eqn/fmcZRk29T65zihpa5WA0NMC9Zs28fT3unFY4YTOM8dh4UfoXRa0ojo0cw5HhI8klaWOx6WdxNlc1Y339eqxvWI/19evRUt2SDCWiVhTPnnw2r13YqpxV2NKyZdpzxs+ew7Enf4OePfvSg6PqKqy88Vq0XbMNDrcawVHajJ0sy7ayfuRxHk0SlrCDoqi9DC0WERDW+T2fltv7TCIimh8MkMqQEAKHDx9Wbhe2rCFRJAoZY0g0G1IImP39Gc2traGhvO7vaGnJWILmqK8vylh9V16JZf/6rwjt3o3x3bsxNjyM6vp6VG3dCt/WrRU182ih0RwOu2HzfIdWU5f8TWmEP1MYFdq7N+/eXtC0jOWZBced0wpCSIGwGUbIDGE8Nl7w/kWBSCA5s+jI0BEcDxyf9vENzcCqulXJwGhd/TrUumtznu8yXPjsJZ/FA3semHYZnQYNn73ks3AZ2etkvH8Ax574DXr2vJUWHDmrq7Dyfddg+bXbzys4yrc5c+pyrXybM1NhCCHjIdHkUjTLLGz4LqTA4cNHlXufSUREpccAiWZFCpEZEEUZEs2VjMUQPX06fWbRyZOQ+SztcTjgWr48PSxasQL6PP7AD9jL2aqvuQbeq6/G4OAA2hsXweC0d5qDZGh1Hsu53GvWYOAf/iG/k6WEb8v0Mz3mQtM16NXVyUbY3Dlt9ixhpfUvmjAnCta/SEhhL0dLCYz6QtMvR/M7/VjXsC65JG1V7aqcIU8uly+5HHdfcTe++/Z3MR4bhwYNEjL5Z5WzCp+95LO4fMnlGfcNnRvE0Sd/g97X34JMmannrPJhzU3XY/V7r4HL6805eyfbsq602T5FaM5M508KGZ9VNNmzyIoVb6amZQqcfncYvcdHEBgeRc/uA1h9yWKsvrwJDieDJCIiYoBEWUztt8KQqDDE+DiiJ0+mh0WnT+e1Fb1eVZU2o8i1ciWcS5fyB1OiKXzbtkH//vfnvcE7d047PzErlt7w2goX7LHnshytpaolObNofcN6tFa1FiRcuaL5Cnx38XfxWu9reKPvDQwFh7CodhHes+w9uHbZtfA6vWlLvsbOncO+//wFDr/y27TgyFPtxxW/9we49Obfg8vL/lmVQEo5GRTFZxdZpoDMY5l6IfR0juD1x08gFrbsHtoSCPYN4MS+Abz8007c8KlNWHnRonkZCxERqYs/fZap851SnBoSiUjEblgdjUJGI5BmEXd8WgCklLCGhtJ6FUVPnIB59mxe9zcWLUrvV9TeDsfixWXxm2G9DMZIlW0+G7xz57S5SyxHSwRGMVG4DRNGIiM4MnQk7+VoDt2BVbWTy9HW1q+ddjnaXBiaAa/DC5/DB6cw4BwZRMv+QQz2e9DY1Iy1nla0rFkCR7weA/192P2zh3Hgpd+kB0dV1bj89/4Al978IbjZeL1sSSlhxqaERbH5C4um6ukcwauPHk0ZYPqfkZCJJ777Dj7wmQ6svHjxvI+PiIjUoclSvVqVoWAwiNraWgQCAdTU1JR6ODNKhkSRyOSuZtH48jOGRAUhLQuxvr7JXkXxwEgEgzPfWdfhXLo0Y2aR4fcXf+AFpukaNK8XmtMFSAEIYf/QI6XdGFlKe6vg5G182qHiCu3Zk7vBe1XVnBu8c+e02ZNSYsKcSAuMCtW/6HyXo21o2ICVtStnvRxtJm7dDa/TDoy8Di88DrsZ8dG9r+Gpf/wWIuNj0DQNUsrkn+6qalzzXz6NvqOHceCl30BYk39H7qoqXPHBP8Cl7/99BkdlyIxZMCOTTa7NaOnCoqksU+BX//C2PfNoBm6fA5+6/6p5Wc5Wbu+5iYgWCgZIs6DKi5mUEqOjo/D7/fYP4/GeRAyJiktEIoh1d2f2K4rMvFW05nbDtWJF+hK0tjbobvc8jLzwNIcB3euF7vPZMy+8XlimwGhwFDW1fhiOmd9cSmGHSRDxN9LxsCk9aBIpx6cLpLKfZ///PPyFkJJENIrQ7t0IvfYaosEgXDU18G3ZMusG78md02pry/bf7Hya2r8obIYhUJi+LYnlaIeHDyeXo820s1mxlqMl6NDhdXjtGUZOOzBy6JkTvI/ufQ3/+cDfxZ+T8nticvuqcPkHP4LLPvD7cPuqCjZmKh4rlrobmgUzpvYvTU7uH8Trj5/I+/z3fXoT1m9pLuKIbKq85yYionRcwlaGYoEAOvfvx4bGRugKvykpZ9boaHpQ1NWF2JkzeW0zrtfUpDe2XrkSzuZmaGW8k4nmdEKv8tmBkc8HabgQi1qIRgXMUQvm0BjMmIkTZ45i5dI1MAwHdENL+dAz/t8wNOjxHk7FXviTEVZN/X8pc4dV2e7DsKospDZ4Pzw4gPX5NnjnzmmzEhMxOywqQv+i1OVoh4cO40TgxKyWo62rX4cad2F/+HTqzuTMIp/TB4/hmTGQMqNRPPWP38o7PHJ6vLji9z6Cyz7wYXiqirxTIM2ZZSaWoU02uVY5LJoqGjZx4p08d60EAA04vu/cvARIRESkJgZIZUjGYoBp2jOM2Kj1vEgpYZ47NxkUxcMia2Agr/s7lixJ61XkWrkSRn192fdC0T3uZFgknB5YMBCNT7uPnYtBiui095dSwjIlrBn6g2uall/QZJxfnWu6nvy3UszvTGoglTOsmia4yjgvdZZVRnBVxAtZgLhzWv6K1b9ISIEzY2eSM4sODx3G2dD0veP8Lr89sygeGBV6OZoGDR6HJy0wcurOWT/Okd2vIDI+fePuVNf+1/+Gi298/6y/DhWPZQm7yXXESu6MJqzi7YhWSJYpMDoYRqB/AoGBCQT6QwgMhDERnP61PIMEwuOF61dGRETlh++QacGQponYmTMZM4vE+PTLHwAADgdcy5alzyxasQJ6VQUsKdAA3euFFg+LhOFGVGh2n4aggJSzfIM5C6oFTedL0zQgZabZvIRVUwKpaYOrlEAKUuYOqzKCqyJeSAkld06rqYFeXc2d07KY2r9owpyAKQuzG2fEitjL0eKBUT7L0VqrWrGuYTIwaqlqKWhg79AcdliU0r/ofB4/Egqh9+hhvPaLR/K+j6ZpOPnOWwyQSkjEw6LUnkWWqX5YJIXE2HAEgYEJBM9NIBD/GBsKT7spZd40wFM1+wCViIgqBwOkMuUyjLKf5VJMYmIC0ZMn08Oi7m4gNvNvzjSvN6OxtWvZMmjOynjTpBk6NI8HltML4fBAGC6YpoQVFZARCWCuv13U4HK4UIzYpNKCpkJIhlWGUfwlgIUMpBKzp7LeVpykStO05HMmd06bniWsjMCoUP2LRsIjyaVoh4cPoyvQNe1yNKfuxKraVcnAaF3DOtS4CrccTYMGj+FJzizyOXxwGnN/npdSYuRsL3oOv4vezkPoOfwuznWfzL4T4AyPMzE2Oudx0OwIIZMhkb0jmqV8WCSlRHgslgyIEh/BgQkIM796c7oN1Cz2Qjc0nDuZZ71JYNUl3IWNiGghY4BUhgxdx5qGxlIPQxnm8HDajKLoiRMw+/ryetNuNDSk9SpytbfD0dRUUTMRpGFAurwQDjcshxuW5oQwJaQlAQsACjObQNd1tLWsLMhjzRWDpuKY17Aq10yqHE3Ts+7wN+U8HcCGpsXcOS2LRP+iCXMCoZjdv0gWYMqZkAJnRs+kBUb9of5p7+N3+dN6F62qXXVegc5UDs2R1uja6/BC1+b+bzwWjeDssU70HDkU/3gXE8HAeY9T0zR4q8tvN85yIIWEGZsMimJRC1ZM7bAoOmEiODCRERbls2saAOiGhppFHtQu9qFmsf1n7WIvvH4nNE2b9S5sqy9jgEREtJAxQCpDQkoMT0yg1uOBvoB+ey6FgHn27GSvokS/opGRme+saXC2tmbMLDJqa4s+7vkkhISlOSCcXginG5bhhtRSmnebAAo0m2AqKQVGx4PwV9VAO48fyuYDgyZ1JcPbAoZVQggMDw+j3uMp0COWr4gVSfYuCsVCiM7Qz2w2j3t0+Giyd1HnSOfMy9GqW9MCo0IvR0vOLoovSXMb57eD3ujgAHqOvIuew++ip/MQ+k8cg7By/9CtaToWr1iJ1vUbIC2Bt597Mq+vI6XEms3bzmusFO9xGEvpWRSxl6GpuvmwFRMIDqbMJor/OTGa/6zg6no3apu8qF3kRW2TFzWLvaiu90DXc/+7Mhw6Nv/eSrz66NHpH1wDbvjUJjic5bshCBERnT8GSGVISonesVHUuN1AhQZIMhZDtLs7LSiKdnVBhmfe3UdzueBcvjx9Cdry5dAr7IdHYUmYloQwXBAOjx0WOV0la/4rpcS54bOo9vkrpiwZNFUGKSW6u7tRV1dX6qHMq9T+RRMx+89C9S8aDg8nZxYdGTqCrmAey9Hq0ndH87sKN8vG0IzkrKJE7yJDn/sPupZp4lzXcfQceRdnjhxC75FDGB2cfrcqT1U1WtZtQOu6jWhdtwHNa9bB5fECsHdhO7TrZUTGxzF9UzEN7qoqrNty1ZzHvhBJKWHFRLK5dSxizyxSMSxK9ik6N4HAuRAC58IInAthbDiSd785r9+JmnhIlAyLGr0wnHN7jWldW4erblmD1x8/Yc9E0mCPJf6n2+fADZ/ahJUXLZrT4xMRUeVggEQlZ42PZzS2jp0+DUzzm90Evbo6PShauRLO1lZoRmX9hsyypN2nyAIshxvC8ABuNzS3O225XYXkNmWHQROpQEiRNruoUP2LhBQ4PXo6uRztyPCRGZej1bhqkn2L1jesx8qalQVdjubSXcmlaD6HDx7H+f2CIBQMJJeh9Rx+F2ePH4UZjUx7n4albXZYtH4DWtduREPr0pzLnx0uF97/Z3+JX/y/fwfIxE/nU2mABrz/z/4SDlfhdpKrRGYsvWeRGVUvLJJSYmI0ljabKNmnyMqzT5HHsAOixfZsokRg5PIW/u1769o6fOiui3H60DB6j40gMDKKxqY6rL60CasvW8yZR0REBIABEs0jKSWswcHJoCgeFpn90/8gkuBoaspcgtbYWHFNcE3TDiNMU8KSOoTDDbjc0Ks9gNsDTQMYL5QnBk1USMXqXxQ2w/buaPHAqHO4EyEzNO19llYvnQyM6tejuaq5YM/NOnR4HJ603dEc+tzfvghhYbD71GRgdORdjPT1Tnsfp9uDlrXr4rOLNqJ57fpZ9ylaffkWfPh//i889Y/fQmR8DJqmQUqZ/NNdVYX3/9lfYvXlW+Z8bZVocmaRldwZrVgN9+cqOmGmN7M+N4HAwCz6FDk0e0ZRyqyimpQ+RfPFcOhYcWEj1mxejJNnjqKj4wIYFfYLOSIiOj8MkMpUldOldHAiLQuxnp6MmUViNI+dPnQdzmXL0mcWtbfDqK4u/sDnkZT2jCIzJpOhkQUD8HigezzQaz3QXC6Uz1s3DT6PD5wHdf4KFTQZKf+/0Pn95d+UuFj9i4bCQzgydCQZGJ0MnpxxOdrqutXJwKjQy9GcujNtKZrX4T2v17tIaBy9Rw6hp9Nudt3beQjRiYlp71PbtCQZFrWu34hFbSugF+AH6TVXbMFn/unfcOS1V9H52k6MDJxD3aLFWLtlO9ZtuWrBzzyyLLtnUXJ2UczKe7bOfDBjFkYHwhkNrcNjefYp0gB/vceeTZTyUV3vhjZNn6L5pkGriOdMIiIqPE2qNudXYcFgELW1tQgEAqipKdxWwrNlDg4i1ttXsq8/lYhEED15Mi0sip06BRmd+YcbzeOxQ6KUoMjZ1ga9wt5E24GAPbso8WGZEprTBS0eGGkeDzQnM10qPAZN5UdKibAVxnhsvKD9i4QU6B7tTi5FOzx0GOcmpu/tU+uqTc4sWt+wHitrV57XDKBUGrTJ2UXxHdKc+tyXukkpMdzbg97OQ3az6yPvYuD0qWl35TQcDixZtRat6+3eRa3rNqKqrn7OY6D8CEsgFhX2bmjx0EhYauyIJoTE2HAYgf705Wdjw9Mva0zl9TuTAZEdGPlQ0+iZc5+i+eSpdqKm0VvSMajynpuIiNLxp9UyJKRE//g4Fvl8874LmxUIpM0oip44gVhvr72d9gz0ujq4U3oVuVauhGPJkpw9I8qVvfOLhJkSGFmmBDRAd3ugee3AyOnxVNS1SykwHBxCfU2D8ruwLTQLfUaTEAL9/f1oamqCrui/OSFFcilaYoZRIfoXhc0wjo4cTQZGR4aPYMKcfvbNsuplaYHREt+Sgs14dWiOtKVoHocH+nk8X8QiYfQd60wuR+s9cggTo8Fp71NV35AMilrXbUTTytVwOAvXnylf5VCXhSKETAuKzKgFyyx9WCSlxEQwagdEAxN2YDQwgeBAeHZ9iqbMKKpZ7IXLU75vsYUU6OvrWxC1SUREs1O+r24LmJQSA6FxNHq9RduFTUoJs79/sldRPDCyhobyur+jpSWjX5GjvvJ+oyvEZL8i07SXo1nxN52arkPzuKHVeOydeDxupZcdzpVlCpx+dxinjwwhODKGmrphLFvXgGUb62E4+MaznFRq0CSlRF9fHxYvXlzqoSSZwkwGRSEzhLBZmP5FQxNDac2uu4JdEDL3D+pO3Yk1dWuSgdG6+nWodhVmubAGDR7Dk5xZ5HV44TLmPrtUSonRwXP2zKLOQ+g5fAjnTh6HmGbDBU3X0dS+Ci1rN6B1/UYsXbcR/kWLlXguVrEuC0EKmdwNLREaqRAWRUImgvGQyA6LQggMhGFG8u9TlOhNlBoWearnt09RsWm6Bl3XKrI2iYjo/DFAKiMiEsHoU09h5Ikn4O7pxUB9Paq2bIFv27bzWvIlYzFET59On1nU1QUZmr5pKgDA4YBr+fL0fkUrVkD3lnbqczEIEQ+Kkj2LkAyLAEBzGNC8XjjiS9LgclfMdva59HSOZGz7O3YugJ4jAbz13Cls/r2VaF1bV+phUoFVatBUTFErmja7KCLyXwqTS+pytMNDh3F4+DAGJgamvU+tuzY5s2hd/bqCLkczNCNtKZrX4T2v2UWWGUP/iePxRtf2DKOxocFp7+Op9qfMLtqA5tXr4PSc3w5tlJuUMi0oMqMCllnaHdHMmIVgvE9R8NxkYJRvnyJNA6obPBkziqrr1OpTdD40XYPh0OFw6jAc9ofu0OBw6tANHVYeu+ASEdHCxACpTIw+/zx6/n9fhAgGAV2HIQQmNA0Tr78O/fvfx6K77oLviitmfBwRCiUDouTMotOnAXPm3hp6VVVmv6Jly6A5Kq+MLEum7YZmxmTGKj3d5YRR5Yn3MPIuuP5FPZ0jePXRo5MHZPqfsbCFVx89iqtuWcMQaYFaqEFTon9RamBUiP5FYTOMzpHOZMPrzuHOmZej+ZelBUaFXI7m1t3JoMjn9MFtuM/r8cZHhuMzi+zA6OzxTlix6X/ob1y23O5dFJ9hVN+ytKJmg6jEXp49JSyKlS4sEpbA2HDEXn7WP7kEbXxkFn2KalwZy8/8jZ6KmD2bLSQynDoMR/k8lxIRkXoW1k+8ZWr0+edx+s/+fPJAIsmIv2kToRD6778fTV/4AnxXXhm/ScIaHk4Pirq6YPbl13zbWLQocwnaYjWm/ReaZU3OKkp8ZKz40ADdHW947fXaDa8X8Na2linw+uMn8jr39cdP4EN3XVwRb8ipOIodNGmahoaGhqI9f03tXzRhTky7i1m+BicGcXj4cDIwOhk8Oe1yNJfusndHa1iP9fXrsbZ+bcGWo+nQ02YW+Rw+GPrcnwOFsDBw6mRyZlHPkXcRODv965PT40XL2vXJ2UUta9bDU8a7cxa7Ls+XGV+GZi9Hs/+/FGGRlBKhYDStmXXg3ARGB/PvU+TyGKhtijeyToRFizxwlnGfIqB4IZHqtUlERKXDXdhmoRQ7QohIBJ3vuQZidHTaXWQAQHO7UX3TTYidOoVoVxdEIDDzF9B1OFtb05egtbfDqNAdL6bOKjJNmfWvVdM1aG43NI8Xuje+QxrfSEFKiUjIxLE3+3Hwld6877d4eTXqlvjgcBownPabXYdLh+E07P932m96HS7DfjPsmjzGv3earWxBk6Zp0HSk/6klPk/9/8ljuRSjf5GQAqeCp9L6F81mOdr6+vVor20v2HI0l+5KC4w8xvk9B4bHx9DbedgOiw6/i96jRxALTz97qm5Ji70cbf1GtKzdgEXLV0A/j9CKcrNik0FRYnZRKd4eRkKxtJAoERqZ0fx6KBkOHTWLPPGwaHL5maeqfPsUabqWHhAtkJlE3IWNiEhN5f2rlwVg9Kmn7GVreZCRCEZ/9auct2suV+YStOXLobvPb9mBiqSUsCyk9CvKHRYBgGbo8aVo8f5Fbk/F9y/KRgh7R5pQMIrxQAShYBShgP0xHrQ/F+bsf6g4d2oM506NzWlMiTfLqaGSIx48GVPCp9RjDqcBw6WnB1RTAqxKfvO9kE2d0SSkwOBwPxrrm2bVkycRJJkyhogVRliEEbbCiMkoEA+boNk9U+z/t4OoxDGkhFKT59lPLGEzjM7hzmRgdHTkaMmWo+nQ4XGkN7t26nPflUxKiaGe0+hNzi46hMHTp6a9j8PpwpLVa5I7o7Wu2wBfbd2cx1AOhBA4ffo0li1bNq87XVmmSNsNLRa1IMX8hkVm1LIbWp8LI3AulAyMIuP5LfXUNMDf6ElvaL3Ii6r68tysIiMkSv5/aUKiUtUmERGpjwGS4kaf+w2g68howDMDvaZmMiiKfzibmyty2ZWU8R3QTAkrZSnadDSHA5rXXo6mezzQSrCFcymYMSslEIoiFIykfT4xGkUBNoIqKMu0m7JGp//Zek6Sb9qzzIqaDKJSZkq5sgRYLj0+ayoz1CrHH2QqkpQIjgfQWLfYbvY+g6gVRdgKY8KcQNgM51iONrt/KEPRIRwbP4LOsSM4Nn4E3ROnpp215NJdWOVfjbW167Cudj3W1q1Blas6LZgSJqBpMiWgyq/eHJoDPqcv2fDa6/CeV63GwmH0HTuSshztEMJjo9Pep7qhcTIsWr8BTe2rYDgWxvNwgpQSQ0NDWLp0adG+hmUJOyiKWMkZRvku+yoEYQmMDkXSZhMFzs2uT5Gv1oXaRV7UNnlRE//T31B+fYp0Q8syi0jNmUTzUZtERFSeGCApzgqMzCo8ci5fjiVf+hKMCl27bjfxnAyMErOLZqK7XMkZRprHU5ENr6WUiE5YyVBoPGDPJEr9PDox90a+hlOHr8aFqloXgoNhhALRvO/btMKPC65ZCitmxZuw2s1XzfjnVuKYGf9hJ2bFbxfx2+O/LY+fWyhSSMQi9pKNYkibIZU6K8qhp8yOmmlZnw7DMTXA4uypfFimwOl3h3H6yBCCIyH01R3HsnUNWLaxPvnDp5QSESuCsBVG2LRnGE3XZygfQgqcnujGsfEjODreiaNjRzAUm373sFpHLVZXr8OaqrVYU7Uebb7lcGgpz1NjQBh51Gl8NpSmaXbvNl2D2+GG1+mF1+GBz+WDU3dCE4AW06BZQCRqpi/ji8+USl3ilyClRPBcf0qz63dx7uQJyGlep3TDQFP7KrQkd0fbiJpF3B680EQ8LJpciiYgrMI9X05HSolQIJqx/Cw4GM57dpPL67CXni1KX37mdJfPL77SQqK0ZWfqhURERERzUXk/RVcYo7Yu/xlImgZnSwscjY1FH9d8ECKlX5FpL0ez8vnNqQbobo89wygRGFXAFGwpJCbGYpkBUXyp2Xggel7hisvrgK/WhaoaF3y1Lvhq3Kiqnfx/l9dIhpIn9w/m3UQbANovWoRFywrT7FZKCWHKjPApI3TKGkTFQ6tkgJV+jhUTBf3tvFXgwCuVpmsFWtaXOcPKcJT/7KmezhG8/vgJxMKWPetIAmPnAug5EsCbz57Eph2L4V9hIGJFzrt/UdiawPHxozg63olj4504Pn4UYRHOeb4GDa2epVhdtQ5rqtdiTdU6LHIVZpMCAwY8mgduhxsewwO3EV/SIwHEgFhMIIb8Z39YZgzDZ7rQf/IIBk51or+rExPB4Wnv46muQfOa9WhevR4tazdgycq1cHk9yUAKuv18VilbopeCEHKyyXXEDowsc37CovB4LKOhdXBgFn2KnDpqF3nTl581eeH2OcrieSdnSOTUobOmiYiowjFAUpz/fTdg9Nln8ztZSvi2bCnugIpEWOm7oJkxmffEK03XoXnc0D32cjR4yrMHgmWKyZ5DwUhKQBSfRRSMzb1PhQZ4q53w1bonA6LU/69xweHK/7e8yzbW463nTtk/nM/A6TGwbEP93MadhaZpMJwaDKeOYnTvEpbII3QSydlUkwHW5LHUAGvyMYoweyps5fU9mIvZL+tLmU2VXNY3ZYaVU4fhMoryQ5aQAgICUgI9R4bx+s9PTt4o///t3Xl8VNXdBvDn3lmTyUY21pCQQFg0iBIQBQwICqW0UG3FpQjoW6xLaxGQ2qIRRSuI1FptERdA0FpRCqIiagGpIJuIImtCSNglbNkz2z3vHzNzM5NZkkCSuSHP10+czLnLnEkOycyTc37X99ZhVfD9hz8i4ydRiOtibPBjnbOdRX7FQeSX59VvOZpkRBdLBjIsrrAow9IVkXpLgx834LllI8x6M0w6M8x6MwyXWES7svQ8zhTlodj9cfZYAZRQl8eTJMS17YSk1EwkpXZDUmo3RCe28/kZXFHiREVJRZDDvYuaByh07jUTyjOjyvtzuGdLye5gyrs4utZJkoR27drV2VehCPesopqaRU0VTHtz2Jx+xaxLiqtgraxnnSJZQnS8yWc2UWxSJCxxRs1/f1p7SFTfsUlERK0Pr8LWAJq+CpskQY6MRKfXXoNsbPgboubkdIdF3vWKGlLiSdLrfApeSy2kCLi92uGqO+RdlNorJKqusF/0uWWdpAZBllgTIt3BkOfziGhDo0+fP5F3AZvez69zv4G/7IoO3eIa9bFbKldx5yChk2cJX5DwyWELPJuqZrvS7IVwL5ascwWArg9XHSpZ72qTDZJruYdBgqyH+1aCTg9IBkDSA7JegqQXkPSu+5JBqMcIIbD7rRI4rXV/LXQmCb0nxEHWB3+T5BROHK86qi5FO1SRV4/laHHqzKIMS6b/crSLJEuyKyjSmWHWmWDSmxpUFLw2xenEhVNHUVx0EKcLXTOMys8VhzzGYI5AYueuSOrcDUlpmUhM6QpjRORF96GpeIdQQYOnukKrgMv6mvYNtWuZtlfNIqvr50JTvlRT6xSdrkTJmSqUnHbNKKq4UP9lypGxxprZRO6P6ASzppdttfaQSOt4FTYiIm1igNQA4fplVrZuPY49+KDrTtDLiEmwPPIgTH2vhuz6MyxkSXJ9DteLX1mSIUF2vRCG5H7j0bQvkpxOT82imo+GlheRjQbXMjSzGbI5QpP1i4QQsFY4fK5cpn7uvn8pdXYMZp0rFPIERGpY5JpJFK6p/4GWCXluDWYd+o/uwvCoGSlOJeisqFA1pdSwyqbA4fDUqHL67O+0X56/KmI6G2BJ1kHSuYIqp2zHGcdp/Og4gRP2YzhuO4oqqQJO2QGHbIdTssMp212fy3Y4ZSc6RHT0Coy6NdpyNINshNlrKZpRd2l/HLBWluNMUR5OF+WhuOggzh49BIct9HK26MR2PrOLYtu27qsyeUIo35lTNeGSz+e1g6la+ylCQVFRITq06wTFIcFudQXCTfWyTAiBigs219XPTleqV0ArO2etd/hsitR7zSZyzyxK1G6dooAhkfuWIVFwTqcThYWFSEtLgy5MF19hgEREpE0MkBognL/Mytatw4k/PgaltNR1/VohICQJkhAQkREQ994G9OnV4PNKnv/cf4mVUVP7xPW57BU+ufeDpIZUEqAGUYoCCIcMpwNwOgDFCUiiJsByHVvHGw/JXfDac3U0s1kTV45TnAqqyuyuUKjEptYcqvSaRXQptXPMUYaaWUMxrlDIOyDS6otzwF2oeP95HDtwDqUXyhETF4VO3ePRqUdNoWJqGkIAAp6lWwKKqLn1LOdShBMCwqtdQAilZl8oUISA8D5HgOVYrtpTgGJ31aBSHAKKHVDcswld7TXbnXbffWrafe8rdrgK4TdP+ZYmIcmeWVGuN6yyHmogJbvbarZJkHWA5L6V9RIk95tco8EAk9EAs8kEs8kIg0EPnc49I0Lnmq3leTMs60LPhhGKgtLikzhddNC9HO0gSk+fCPk8dHoDElIykJTmDow6d4M5im/cGptvcXf3z8xaxd0vVXWF3bXkzD2bqOR0FUrOVNV76ZvOIPvNKIpJioDZor0r5TEkanxOpxO7d+9GVlYWAyQiIvLBAKkBwv3LTLFaUbZ2LYo/XInyUz/CEBMBXHMlkJ0FNONl6IVwhUOukEhSb+tbh1Zy/fnWHT7p3LOLTDXL0mRdzdIC93+ekCrgTCp36CVJtfZDzX51cdicNaGQV1Fqz+dV5faLvry9JEu+4ZAnIHLfj4gxXhZBi6I4cfh4Prp07ApZ1m7gFQ7NGfS0VIrTK4TyCZxChFCeNq8gq7LYAeXiV4O2KK6lfZ43yg4ojpNw2I7DUXUctqpjUJzBC3kDgDEyDjGJXRDbNh1x7TMQm5wCg9HgOp/eHVjpfC833hzLuC5njT1r0251ugKiWlc/a1CdogSzX1gUGautOkUMiZoXAyQiIgpGe2uBKCi7Hth4hYRP9DJOlchoY9Chn0XCAB3QVFWPhAAUB+B0AopDUm8vmixDmE2A2QhhMkExGuCT8CjVjT4TQQgBZ7UER7kCW7kCW5mAtcwJW5kT1jIF1jInHNUX/6B6o4wINSByX7nMHRJZYo0wWwy82lALoQY97nBGEe7QRtQKf1px0NNUZJ0EWVf3vxMhBBzCAZtig02xwqbYYBc2WBUbFMWK0i8kRJ5Mdi/freNcUHAyugA7O30GnWKAXjEgSdcW7QwdkKRviwRdMswiEsLpCriEJ9xyuoIu4b513Reu/RwCihMQF7Fct76EEBBKCZzWE7A6T0JxnIBwnkHolFuGpEuGrO/g/mgPSY5GeRlQXgYcz3cAqMeVFSX4XJq85nPZJ9DS1Qqg/LbVOt7/2Jrt8mUSEvjVjatV3N1e7cSm9/MD1o1TnApKz1b7Xf2ssqT+dYoscbULWkcgOt6kmTpFDImIiIi0jwFSC7H+yHrM3DQTpbZSSJAgICDZJGyv2IslZz7CA21/ib6Wnpf0GELxCorcs4qUS73Ak14HmE2AyQSYjICx8WdKCUXAXuEKh6xlCmw+H07YyhUo9ftjbED6CAnGKBnGaBnGaB2MUTJM0Z77MnQm77/IOwFUwYlqlEsSKoQEqUKdJ+WeKSVDAtQZU5L3fz5LBgPPsvLZDzVLC8NNkiQktWnbLH1pSNCjKO5bCNcsH1Er/GHQ0ygUocAu7LArNne4UxPw2BQbbMJ1367Y1Xar577w2i/AsXbhez/U96mbJRvDML5efZYg43D7b9GnR09kWLoh3dIVkbpLLwitk3Qw680wSkYYhAl6YYDidBdQdyhQHAqcjpr7TrsCp1O4bgNst9usqLxwHJUlRaguPYLq8qNQHIGvalbz5CIh69urgZGkS4YkNcLPXwFXP5vhKmDeJFkKHTjpfcMHtVC7zjeMUmdXBTu+1nLBxvp55nQo2PZRPQI6AFs/LED2qFSUnbOqgVGD6hRZ9P7LzxIjGnSlzaYi63y/1gyJtEmSJKSkpGjitQUREWkLA6QWYP2R9Xh4/cPqfc+bJ89thVKNeSffxtT2dyG7niGSonhmFkmuW4fUOH8tNxpcQZHZ5PpohKnPikO4Zw4p6qwhW5lS01auXPTyMkiA0VITBqkfUTJM7rBINjT8BZRnhor7TrOQILsuZQ3fmlVqUXWvAEuG135qKCWpoZVa/wq1gi51WWHN0kCb04ovi9dj05mNKLWXIsYQg4GJNyAnaSgMsqlRgx7PLB8GPfWnCMUVwniFOMGCGtd2r5k9tfcRvsd5h0E2Uf+ZEE2pIGEXBh6+FSanOWTNNQEFVl017J3OY0z7+y768SRIMMpGmPVmmNwFr/Xypf1qrSw5j2Kv2kXnjh+G4gye5kuShLh2nZGU2hVJqZlISOmKiJgkNbRSvMOqWoGV4lTgtNfa7ggQdtkVOJ2hw66mJBQBh03AYWve4MozKyZUYKUGUt6BlSfAcm8/f6rCtWytHhw2BVtW1h026Y2yX0Hr2KQImCLDW6eIIVHLJ8syEhISwt0NIiLSINZAaoBwrMe2Oq248b0bUWYrq/NNs0U2459pf4RR9n3xqIZFDsldu6iRwiJJcgVGZlNNaNTAq/MIIeC0egdErg9ruVP93FF18UNU0gHGaM+MIZ3XTCL3h0Xm8rKLJEHCdyU78UbRq6h0VtTMjHPfRuoicU/qfbgq9ppwd1WTnMLpH+B4ghnhFcwoVti8gxp1yZY1cJgjfGfyOETLLQgkQ4ZRNsIom2CUjTDIRhhlI0yyEUbJpN6v2ceAXSU7YTzRBiMP/B8ABAyRhHud7NrubyC+ixn3pz/st08wOkkHk87kCox0Zph1pkv6K73idOD8ySPusMgVGFWcPxPyGIM5Ur0qWlJqNySkZMBovvSZU5dCCBE8sPIKoeodWAXa7n2803P/8n8JI8kSYhLNiE2MQGyyazZRbHIEImPCV6eIIdHlzel0Ii8vD926dWMNJCIi8sEZSBr3WeFnKLWV1mvfCqUaW8r24DpzH5/i1o1Wh0OWasIiz5K0Ol68CiFgrxQ+y8nUkMjddikFb3UmSQ2DTLWWmRmjZegjtLG863L0bck3+EfBi34z4jy3lc5KvFLwIh5I/wP6tJAQSQjhDnZqBzFe4Y3iqrfjWXblN0un1kyfgEu6hA1OcanrQ8NHL+ld4Y3kG954gh6jbIRBMvrc9w16ah9r9AqIau7rpYb/impv7og3q1/Fp93fwND8u2B2RkKBAhmyemvVVWN912Uoit+DYXG/DXk+o2yESW+GWWeGWW+CQb602R3WijI1KCouyseZo4fgtFtDHhOT1B5JqZnuj26ITe4AqYFhfVOTpJolZs3JdXXAes6gamBgpagztmoCK8/2+i4nu1iRMUb0vrETYpMiENUmPHWKgoVEej3/8NIaVFeHLsJPREStEwMkjVt3ZJ36xqc+/lm8HK9jpXrlMtldP8dTKydUm6S2u28lGZKsh6zTQdbpIcs6yHYZUrnsPkaGTuhhqrLAWG2BqSoKpmoLDFWRMFZFwlAZCUO1GZJycX+9EhBAhAMi0g5YHJDcH7A4IUcpkC0KdEYJgAS7JMMJGVWe5+fun1wlq/WE1DapdptnWVfNfdfSrpo28mVXbFhU9Gqds+IEBBYVvYp5V/4dBvniS727Cifb3XVzPDNuai+5qj1rx1M7x+ZbbydI0OMJe+r7b02LDJIrxDF4hTkmNcwJFOiY1CDHUDvg8XxIvvcNshE6Kfy1VILJjuuPd48tRVH8D1ia/TjSz/ZBl3O9YXJEwqqvxOH471GQsAtO2YFIXST6xvVTj5UlWZ1VZNa5lqRdyr9/oSgoOX3CHRa5lqSVFp8MeYzOYEJiSgaS0rq5AqPOXWGyRF90Hy53kiS56hUZmjm4UkTdgZXXkr+87T+i5HRV/U4uAW3aRyKlZ3zTPgl4hUQGr+V5DImIiIgoCAZIGnfBekF9Q6tT9CHfDAGucjs2uKf0BHpv38A/mhqcJkRZ4xFtbYMoaywirfGIsrZBtLst0h4Tss5IKE7JgXLjeZSZzqPcdM7r1vV5hfECFDnALA0bgHPuj2YiewVQkuQdRkk1be5C194hlfc23wBL8gm1fI6rfYzX5546R4Ha/EIyz/Y6wjEJkt9xgR5f8jp+T+luVDor6/W1q3RW4p1jbyElIjV4/R0ReiaPXbG36LpHvmFNsNk6QQIe93aTV5vf0i3JAINsbH1hp/v9rTrJUAKMOiPuSf8tXsn7K5yyA3lJO5CXtCPAoRJ+0+VBxEckwCS7AiOj7tKuZ2mrrsTZo4dQXOieYXQkH/bq0P9OLG0S1ZlFSamZaNO+M+QwLRmh+pNkCXqjrt6FqWVJqncRbQigY2abS+hdrccOEBLp3Z8zJCIiIqKGYICkcXGmOMiQkXKuV8DlGOnnrsLAw7eqyzEiYEa8Lq6mMLH6n6jV5rpvtEfCYo1DpDUOUdY4WKxxakAUZW0Ds9Ny0X236apRZjyHctN5lJnOqSGR6/PzqDSUAVLLCAUUdwFnAM1WFPty8tXZL8PdBT8SJJh8wpjgS6u8wxrPLJ+AS7ekmmVYnsBHLxkabxml12kkyfc+JMl1V/LdX/L63PtW8v1fzb6Bjq/dJtXewatPgfpZqw8+X48GHl+fr+Wgdv1hjp2Kf373T1TY/etzRRui8fTApzEsdZjPca7C7QCEO64Urg/h+p/r6n/usoFCEbhw+hRO5e/Hybz9OJV/AGePF7l2CkLW65GYko626Zlo26U7krtkIjI23nVOT819z/k9j+19H0Jt996XtK9Tzzb49osj9SqkbTDr0KlHwwIkWecKhbyLfTMkooslyzLS09Mha2ypLBERhZ+mi2hv374dubm52Lx5M+x2O7KysvDII4/gtttuq/c5rFYr5syZg6VLl+Lo0aOIj4/H6NGjMXv2bCQnJzeoP+Eo6Lf60Gq8+uE7GHngXgjhgLDlw2nPhxDVkCQzdIaukIxdIUl6fNr9DQzv1AsDTFcDAIQC2KwSbFUSbNUybDY9bFYdbFUyrJWuwtXiEi5vL0cI6KMAnUVA515SJkUpkC1O11Izo6IGL4p61a2a8MqzzbvNc6UtBbWO8Wnz3bfm6l5e56x9Hu+rfqH2vsKrH0HOU+s4Eeh8tbbVnMfrqmOXYfqkcwKppyzofCoCJrsOVoMTR9pVoahdBZwXMZFCJ+l8llb5hDeyASadyT/g0fmGPSbZBKOuZsmVSWeCSWdU24yyGTpZp161zjtcCRq4SLV3CBaO+Le52qUAbXUff7nX8PJeJuo9I85zxT+fKwCqM/4kv/18trk/r6wux5pPl6Bo5zcQVXbIEUakZ/fH2J9OhiWiYcvC7DYrfjyUhxMH97s/9qGqtCTkMZGxceiQ2RMduvdEh8yeaNslA3rjpc1yqk0onrDLP/SqHULBk1MJr21e4Zhrkycgcx3g0+71eaAgSw3Y1HavcwgGXifyLmDT+/l17jfwl13RoVucX3vtkEjvNaOIIRFdblhEm4hImzQbIK1fvx4jRoyA2WzG7bffjujoaHzwwQcoKirCvHnzMHXq1DrPoSgKRo0ahbVr12LAgAHIyclBXl4e/vOf/6BLly7YsmULkpKS6t2ncPwyq6iuwqvT1kFXeQyOys8AYYXrnaaouZVM0EeOAEypSGgn4KzWuQKjagDiIl9Uyl6Xt3cXpFavZOZuk/V8wdpQtQMsz33/kKx+4VjdAVbN8bVDNfXctR8f7n3957C52oSCXee/RXH1aaT8GIFB3yXA5NBBgYAMSb216p3431VncaxtFdKi0zAq7acw6ozuIMcEk74m/DHpXSGPUWeETtZd9qGJVvgFNKHCG89Sx1phT6htdZ2rKb/P+Tu24tN//BXWinJIkgQhhHprskThJw9OQUbfa4MeX3b2DE4c3KeGRacPH4LiDD57RJJkJKV2QfvMHuiY2QMduvdETFJbjuVaPAFTwNDL637t0EstWh0s9BK1Aqtg7QGCMqF2qOlDrxN5F7Dto8OumUi1fpUbzDpc+/N0pF6RwJCIwsrpdGLv3r3o1asXr8JGREQ+NBkgORwO9OjRA8eOHcOWLVvQp08fAEBJSQn69++PwsJCHDx4EKmpqSHPs2jRItxzzz2444478Pbbb6sv5BcsWID7778fkydPxquvvlrvfoXjl9mBLSexduFHsFesqnNfg2UMdMaMep1X1sPvimVqSBQlw8DL22ufZymRLKnLmSRPmyRBkr3aZHeb936yVHMO77Z62HhsI1avXYQbv0lyd8X/OM9sq3V9i/HzEfdgcKfBjfCkLy/es2kCzaDx3EKCT70sdX+vwMb72NqBTaiw53KUv2MrVs2bjZp0ojbX2B8zbSa6Zl8Lp8OB4sICr8BoP8rOFod8DLMlCu0ze6BDN1dY1K5rJozmiKZ4OhRmwUIv3/ZaoZeoCbJqh14Ou4LC74tR9MNZnD9bgvjEWKRfnYxu2ckwmFhZgMLP6XRi9+7dyMrKYoBEREQ+NBkgffbZZxgxYgQmTZqEN99802fbkiVLMHHiRMyaNQtPPPFEyPNcf/31+Prrr1FYWOgTNgkh0LVrV/z4448oLi5GRET9XvSH45fZx//Yif0bn3HPPKqDZIIp9j5Ikh46s6SGQbWDIlO0DJ2Zl7dvVu4QR5Ik9fOawKd2m2/4E6gt3OFeVXUFPp/xJAx2KWB45CEgYDcI3DTnSUSYL76eVlNwxzD1njVT75k6XgXR1fN6B0FqcXP++2sKDpsNC357N6wVFairYJnOYEC79G748fAhOGyhf8bGd0xxLUfL7IEOmT0R36EjJNYHoUughTfpRIFoYWwyQCIi0iZN/qlrw4YNAICbb77Zb9uIESMAAF9+Gboob3V1NbZu3Yru3bv7zVSSJAk33XQTXn31VezYsQODB2t3ZsTZo7vqFx4BgLBC1u1C5tirQi4vs1UAqGiU7l1+vGfweAU4CNDmvZ8aAKHWrB7Zq+SN95KLuuuoatrp7/fCaK/7zbMECUa7hJNfbkNy715+2yS/UEZyF4P2hDK++3lqFsnuNM0V7KhRkLrN0+Z3LsnrMUMEX/4EgODfNKUBZ6KmdWjHVlgryuu1r9Nux/EDe/3aDSYz2nfLRIfMnmif2QPtu/VARFTDaiYREREREV1uNBkg5eXlAQC6devmt61du3aIiopS9wnm0KFDUBQl4Dm8z52Xlxc0QLJarbBaa8Kb0tJSAK6/zDjdtTAkSYIsy1AUxadWgqfdWatmRrB2WXbNSKjdXl12ADWFEupWdXYjvntjY732JWouB1d9ioOrPg13N4gCiklqiw6ZPdCxey+065qJhJRUyO6/ujfWz3LP1YwURalXu06nc9U4C9Be+/dNsPam/v3E53Rpz8kzG9rjcnhOtfvI59QynxMAdO/eHUIIn34253OqfS4iItIGTQZIJSWuK9vExsYG3B4TE6Pucynn8N4vkL/85S+YNWuWX/uePXsQFRUFAIiPj0fnzp1x7NgxnDt3Tt2nXbt2aNeuHQoLC1FWVqa2p6SkICEhAXl5eaiurlbb09PTERMTg7179/r80tQbbOB144mImkZMxxT0u/s+9Wf5kSNHsGdvzaykxvpZ3r17dxiNRuzevdvn8bOysmCz2XDgwAG1TafTISsrC2VlZSgoKFDbzWYzevTogfPnz+Po0aNqe3R0NDIyMnD69GmcOnVKbW/q3098Tpf+nIQQSEhIuKyeE3D5fZ9a23Nq27YtkpOTw/qcysvrN5OUiIialyZrIN188834/PPPkZeX5/PXOY+OHTuivLw8ZPizefNmDBw4EHfddReWLVvmt/21117D5MmTMX/+fEyZMiXgOQLNQEpJScG5c+fUAKqp/yK1ev5fcGjHVtQ3RIqMTUByRg91b8n9f0+9Fd92+ExuUq9S7l7mAwi1rWb5luR7Dsm9xEiWIDz7w7u2j+T3Vy21L43R7iloWke75OlUkHa/cwdrb8y+t+DndOSH71B2JnSRYW/RiUlIuaK3pp9Tg9tbwPepNT6no3u+r/fYlCQJGdkDMHrKH1vV7AI+J208J6fTiT179uDKK6+EwWC4LJ5T7T7yObXM56QoCvbs2eN3FbbmfE6lpaWIj49nDSQiIo3R5Awkz6yhYAFRaWkp2rRpc8nn8N4vEJPJBJPJ5Neu0+n8igp6fhkG2vdS2jOvvR6HdmwJ2sfa+o+9E92uvcH9S95Tj8frc/cVuyTZXU9G9tpOVE97N67Dmlfm13v/QbffjV6DhzZhj4hcGjI2hRDodu31Pj93m+pn+cW0S5IUsD1YHxvazucU/ufkeUMeav+W9pzq087n1HKeU6DzN8dzYmF5IiJt0uQlZLzrE9V26tQplJeXB61t5JGeng5ZloPWSgpVZ0lLMgcMgskSVa99TZYoXDV8KGISIhAdb4YlzgRLrAmRMUaYowwwWwwwRehhNOthMOqgM8jQ6WSGR9RgNeOyrrEjwWSJQua1A5ujW0Qcm0RERERETUSTAVJOTg4A4LPPPvPbtnbtWp99gomIiED//v1x4MABFBUV+WwTQuDzzz+HxWJBdnZ2I/W6aeiNRvzkwSmu5SJB3xC5lor95MEp0BuNzdk9aqVqxiUQelyC45KaFccmEREREVHT0GSANGzYMKSnp+Odd97Brl271PaSkhI8++yzMBqNuPvuu9X2kydPYv/+/X7L1SZPngwAeOyxx3zWdr/66qsoKCjAXXfdhYiIiKZ9Mo0go++1GDNtJkwWC4Caeh+eW5PFgrHTZyKj77Vh6yO1PhyXpFUcm9QSyLKMrKysoMuHiMKFY5OIiILRZBFtAFi/fj1GjBgBs9mM22+/HdHR0fjggw9QVFSEefPmYerUqeq+EydOxJIlS7Bo0SJMnDhRbVcUBaNGjcLatWsxYMAA5OTkID8/HytWrEBaWhq2bt2KpKSkeveptLQUsbGxYSvo57DZcHDrJuRt24zK0hJExsSiW//rkXntQP4VncKG45K0imOTtEwIgerqapjNZjXcJNICLYzNcL/mJiKiwDQbIAHAtm3bkJubi82bN8NutyMrKwuPPPIIxo0b57NfsAAJcF1J7bnnnsPSpUtx9OhRxMfHY/To0Zg9ezbatm3boP5o5ZeZ0+nE7t27kZWVxSKDpBkcl6RVHJukRRyXpFVaGJtaec1NRES+NHkVNo/+/ftjzZo1de63ePFiLF68OOA2k8mE3Nxc5ObmNnLviIiIiIiIiIhaBy5uJiIiIiIiIiKikBggtVCc7k5axHFJWsWxSVrEcUlaxbFJRESBaLoGktZwPTYRERERUdPia24iIm3iDKQWSAiB0tJSMPsjLeG4JK3i2CQt4rgkreLYJCKiYBggtUCKoqCgoACKooS7K0QqjkvSKo5N0iKOS9Iqjk0iIgqGARIREREREREREYXEAImIiIiIiIiIiEJigNRCmc3mcHeByA/HJWkVxyZpEcclaRXHJhERBcKrsDUArwhBRERERNS0+JqbiEibOAOpBVIUBWfPnmVxQ9IUjkvSKo5N0iKOS9Iqjk0iIgqGAVILJITA0aNHeXlV0hSOS9Iqjk3SIo5L0iqOTSIiCoYBEhERERERERERhcQAiYiIiIiIiIiIQmKA1EJFR0eHuwtEfjguSas4NkmLOC5Jqzg2iYgoEF6FrQF4RQgiIiIioqbF19xERNrEGUgtkKIoOHXqFK+OQZrCcUlaxbFJWsRxSVrFsUlERMEwQGqBhBA4deoUr45BmsJxSVrFsUlaxHFJWsWxSUREwTBAIiIiIiIiIiKikBggERERERERERFRSAyQWiBJkhAfHw9JksLdFSIVxyVpFccmaRHHJWkVxyYREQXDq7A1AK8IQURERETUtPiam4hImzgDqQVSFAVHjhzh1TFIUzguSas4NkmLOC5Jqzg2iYgoGAZILZAQAufOnePVMUhTOC5Jqzg2SYs4LkmrODaJiCgYBkhERERERERERBSSPtwdaEk8f4kpLS0Naz+cTifKy8tRWloKnU4X1r4QeXBcklZxbJIWcVySVmlhbHpea3MWFBGRtjBAaoCysjIAQEpKSph7QkRERER0eSsrK0NsbGy4u0FERG68ClsDKIqCEydOIDo6OqyXNi0tLUVKSgqOHj3KK1OQZnBcklZxbJIWcVySVmlhbAohUFZWhg4dOkCWWXGDiEgrOAOpAWRZRqdOncLdDVVMTAxfdJLmcFySVnFskhZxXJJWhXtscuYREZH2MNInIiIiIiIiIqKQGCAREREREREREVFIDJBaIJPJhNzcXJhMpnB3hUjFcUlaxbFJWsRxSVrFsUlERMGwiDYREREREREREYXEGUhERERERERERBQSAyQiIiIiIiIiIgqJARIREREREREREYXEAKmFWLZsGe677z5kZ2fDZDJBkiQsXrw43N2iVuz48eN48cUXcfPNN6Nz584wGo1o164dbr31VmzdujXc3aNWrLq6Go888ghuuOEGdOjQAWazGe3atcPAgQOxaNEi2O32cHeRSDVnzhxIkgRJkrBly5Zwd4daqbS0NHUc1v4YMmRIuLtHREQawSLaLURaWhqKioqQmJgIi8WCoqIiLFq0CBMnTgx316iV+uMf/4g5c+YgIyMDQ4YMQVJSEvLy8rBy5UoIIfDOO+9g3Lhx4e4mtUJnzpxBSkoK+vfvj8zMTCQlJeH8+fNYs2YNioqKcPPNN2PNmjWQZf4NhcLrhx9+QHZ2NvR6PSoqKvD1119jwIAB4e4WtUJpaWm4cOEC/vCHPwTcxtebREQEMEBqMb744gt069YNqampeO655/DYY48xQKKwWrFiBRISEpCTk+PT/r///Q/Dhg1DVFQUTp48ycsAU7NTFAUOhwNGo9Gn3eFw4KabbsKGDRvw0Ucf4ac//WmYekgE2O12DBgwAAaDAd26dcOyZcsYIFHYpKWlAQAKCwvD2g8iItI2/vm1hRg+fDhSU1PD3Q0i1S233OIXHgHA4MGDMXToUJw/fx67d+8OQ8+otZNl2S88AgC9Xo9f/OIXAID8/Pzm7haRj2eeeQZ79uzBm2++CZ1OF+7uEBEREdVJH+4OENHlx2AwAHC9YSfSCkVR8OmnnwIArrzyyjD3hlqznTt34plnnsFTTz2FXr16hbs7RAAAq9WKxYsX48SJE4iJiUG/fv1w7bXXhrtbRESkIXx3R0SN6siRI/jiiy/Qvn17ZGVlhbs71IrZbDY8++yzEELg7Nmz+O9//4v9+/dj0qRJGDZsWLi7R62U1WrF3XffjT59+uDRRx8Nd3eIVKdOncKkSZN82vr164d//etfyMjICFOviIhISxggEVGjsdvtGD9+PKxWK+bMmcNlGRRWNpsNs2bNUu9LkoRp06bhL3/5Sxh7Ra3dE088gby8PHzzzTf8GUmaMWnSJAwePBhXXnkloqKicPDgQcyfPx9Lly7FsGHDsHv3bkRHR4e7m0REFGasgUREjUJRFEycOBEbN27Eb37zG4wfPz7cXaJWLioqCkIIOJ1OHD16FK+88gpef/11DBkyBKWlpeHuHrVCX3/9NebNm4eZM2dyGSVpSm5uLm688UYkJycjMjISffr0wVtvvYXx48ejqKgIr732Wri7SEREGsAAiYgumaIouOeee/DOO+/g17/+NRYsWBDuLhGpZFlGp06dcP/992PhwoXYtGkTnnnmmXB3i1oZh8OBCRMmoHfv3vjjH/8Y7u4Q1ct9990HANi0aVOYe0JERFrAJWxEdEkURcGkSZPw1ltv4Y477sDixYshy8ymSZtuvvlmAMCGDRvC2xFqdcrLy5GXlwcAAa8SCADXXXcdAOA///kPxo4d21xdIwoqMTERAFBRURHmnhARkRYwQCKii+YdHo0bNw5Lly5lTQ/StBMnTgCouVIgUXMxmUy49957A27buHEj8vLy8POf/xxJSUlIS0tr3s4RBbF161YA4JgkIiIADJCI6CJ5lq299dZb+NWvfoVly5YxPCJN2Lt3L9LS0hAZGenTXllZiUceeQQAMGrUqHB0jVqxiIgIvP766wG3TZw4EXl5eXjssccwYMCAZu4ZtXb79+9H586d/X5m7t+/HzNmzAAA3HnnneHoGhERaQwDpBbi9ddfx1dffQUA2L17t9rmWYYxaNAg/N///V+4uket0FNPPYUlS5YgKioKmZmZmD17tt8+Y8eORZ8+fZq/c9Sqvffee5g/fz4GDRqEtLQ0xMTE4Pjx41izZg3Onj2LwYMHY8qUKeHuJhGRJrz77ruYP38+brjhBqSmpsJiseDgwYP45JNPYLfb8dhjj+GGG24IdzeJiEgDGCC1EF999RWWLFni07Zp0yafooYMkKg5FRYWAnDV9QhWkDgtLY0BEjW70aNH48SJE9i8eTO+/vprlJeXIzY2Fr1798btt9+Oe+65B3o9f/0REQHA0KFDsW/fPnz77bf43//+h8rKSiQmJmLUqFF44IEH1NpxREREkhBChLsTRERERERERESkXbxUEhERERERERERhcQAiYiIiIiIiIiIQmKAREREREREREREITFAIiIiIiIiIiKikBggERERERERERFRSAyQiIiIiIiIiIgoJAZIREREREREREQUEgMkIiIiIiIiIiIKiQESERERERERERGFxACJiFolIQT+/e9/45ZbbkFKSgrMZjPatGmDPn364NFHH8WRI0fC0q8nn3wSkiThySef9GnfsGEDJEnCkCFDwtIvooaaMmUKZFnGjh07fNorKirw4IMPom3btjCbzbj22muxbt26oOf56KOPIEkSnn322aD7zJ49G5Ik4ZNPPmm0/hMRERGRLwZIRNTqnDhxAgMGDMDtt9+OlStXol27dhg7diwGDx6M48eP4/nnn0dmZiZeeeWVRn1chkCtW7Bw8HK0b98+vPzyy7j11luRnZ3ts23ChAn4xz/+geTkZIwYMQK7d+/GiBEjsGXLFr/zlJeX44EHHkBWVhamT58e9PGmTJmCtm3bYsqUKbDb7Y3+fIiIiIiIARIRtTLnz5/H4MGDsW3bNlx99dXYvXs3tm/fjnfffRcffvghTp48iXnz5sHhcOChhx7CSy+9FO4uAwD69++Pffv24a233gp3V4jqNH36dDgcDr+wbNeuXfjggw8wbNgw7Nq1C6tWrcLq1avhcDgwe/Zsv/P8+c9/xvHjx7Fw4UIYDIagj2exWDB9+nQcPHgQ//znPxv76RARERERGCARUSvz0EMPoaCgAF26dMG6detwxRVX+GzX6/WYOnUq/va3vwEApk2bhn379oWjqz4iIyPRo0cPdO7cOdxdIQrp4MGD+OSTTzBgwAC/f1/ffPMNAOCee+6BTqcDAAwbNgxdu3bF5s2bffbdvn07Xn75ZTzwwAMYMGBAnY979913w2Aw4KWXXoIQopGeDRERERF5MEAiolajoKAA7777LgBg3rx5iIuLC7rvAw88gKuuugp2ux1z58712TZx4kRIkoTFixcHPHbx4sWQJAkTJ05U24YMGYKhQ4cCAL788ktIkqR+pKWl1dn3upa/nT9/Hrm5uejTpw+io6MRGRmJrKwszJ49G5WVlX77ey+nOnLkCO69916kpKTAYDD49DuYwsJCte8OhwNz587FFVdcgYiICCQmJuK2227D/v37gx5fVVWFF154AQMGDEBcXBzMZjO6d++ORx99FGfPnvXb3/treu7cOfzhD39ARkYGTCaT39dk3bp1+NWvfoVOnTrBZDIhKSkJ/fr1Q25ubsBzHzx4EPfddx8yMjJgNpsRGxuLG264AcuWLQvY9yFDhkCSJGzYsAG7du3CLbfcgsTERJhMJvTq1QsvvPCCX4AhSRJmzZoFAJg1a5bP99/76713717k5uZi4MCB6NixI4xGIxISEjB8+HC89957Qb+eALBq1SoMHjwY0dHRiI2NRU5ODj7++GOf71UgDR07dXnllVcghAg4jjxf//j4eJ/2hIQElJeXq/cdDgcmT56MDh06hKx95C0pKQmjRo3CoUOH8Omnnza430REREQUmj7cHSAiai6rV6+GoiiIi4vDz3/+85D7SpKE8ePH47vvvsPq1ashhIAkSRf92CNHjoTZbMbatWvRtm1bjBw5Ut2WmJh40ecFXKHDyJEjcfToUbRv3x6DBg2CwWDAtm3b8Pjjj+ODDz7Ahg0bEBsb63dsXl4err76ahiNRgwcOBBCiAb3Z9y4cVi9ejVycnLQu3dvbNu2DcuXL8eaNWvw2Wef4brrrvPZ/8SJExg5ciR2796N+Ph49OvXD9HR0di5cyeef/55LF++HBs2bEBqaqrfY505cwbZ2dm4cOECBg8ejL59+8JoNKrbf//73+Pvf/87AKBPnz4YPHgwSkpKcODAATz11FMYOnSoT+C0fPly3H333aiurkaPHj0watQolJSUYOvWrRg/fjzWrVuHN998M+DzXrt2LebPn4+MjAzcdNNNOHnyJL766itMmzYNR48exYsvvqjuO2HCBOzatQvfffcdrrrqKvTp00fdNmjQIPXz+fPn44033kCPHj2QlZWFuLg4HDlyBOvXr8d///tfbNmyBfPnz/fry9y5czFjxgwAwLXXXov09HTk5+dj9OjRePTRR4N+7y5l7ASzcuVKAMDw4cP9tnlCrH379qn/Bux2O/Lz89GxY0efr4NniVt0dHS9H/umm27CqlWrsHLlSvzkJz+p93FEREREVA+CiKiVGD9+vAAghg4dWq/9v/zySwFAABAFBQVq+4QJEwQAsWjRooDHLVq0SAAQEyZM8Glfv369ACBycnKCPmZubq4AIHJzc+t1bGVlpcjIyBAAxMyZM4XValW3VVRUiDvuuEMAEJMmTQr4OADEr3/9a1FdXR20T4EcPnxYPT4xMVF899136jaHwyF+97vfCQAiNTXV59yKooiBAwcKAOLee+8VpaWl6ja73S6mTp0a8Hvk+ZoCEMOGDRMlJSV+fXrppZcEAJGQkCDWrVvnt33r1q3iyJEj6v3vv/9emEwmYTabxQcffOCzb2FhocjKyhIAxJIlS3y25eTkqH1ZsGCBz7b//ve/QpIkodPpxNGjR322BfveetuwYYM4dOiQX/v+/ftFp06dBACxdetWn207d+4UOp1O6HQ6sWLFCp9t7733npBlWf1eeLvYsRNKfn6+ACCSkpICbi8uLhbR0dGiY8eOYtu2beL8+fPikUceEQDE73//eyGEEAUFBSIyMlL88pe/rPfjeuzcuVMAEBkZGQ0+loiIiIhC4xI2Imo1iouLAQBt27at1/7e+3mO1ZolS5bg0KFDGD16NJ5++mmf2TiRkZFYuHAhkpOTsXTpUpw/f97v+Pj4eLz88sswmUwX3YeZM2eid+/e6n2dTofnn38eHTt2RFFRET744AN129q1a7Fp0yb06dMHCxYs8JldotfrMXfuXFx55ZVYv349fvjhB7/HMhgMWLhwIWJiYnzaHQ4Hnn76aQDAwoUL1eWC3vr374+UlBT1/jPPPAOr1YrZs2fjlltu8dk3NTUVb7zxBgAELaR+yy234L777vNpu/HGGzFixAg4nU6sX78+4HGh5OTkID093a+9e/fuePzxxwEA77//vs+2l19+GU6nE7fddht+8Ytf+Gz71a9+5ffcPC517ATy7bffAgB69uwZcHtiYiLmzJmD48ePo3///mjTpg3mz5+Pnj17qgW377//frWWkbf6LKfz1Fw6dOgQSktL69VnIiIiIqofBkhEREGIFlCI9+OPPwbgWkYWSFRUFLKzs+FwOLB9+3a/7cOHD2/Q8qRAJkyY4NdmMpnUPm3YsMGvv7feeiv0ev9V1LIs44YbbgAAv6LKAHD11VcHDFi++eYbFBcXIzEx0S9ECURRFKxZswZA8K9ddnY2oqKi8O2336K6utpv+89+9rOAx3nCk+PHj9fZj0DKy8uxfPly/OlPf8LkyZMxceJETJw4UQ3iDhw44LP/l19+CQC46667Ap4vWPuljp1AfvzxRwCumkbB3H///fj6668xbdo0TJ48Ga+88gp27NiBNm3a4O2338batWsxd+5ctG/fHpWVlXjwwQcRGxsLi8WCpKQkPPHEE3A6nQHPbTQaERUV5dMXIiIiImocrIFERK2Gp7ZPfd9Ynj59Wv08KSmpSfp0qQoKCgAA48ePx/jx40PuG2gWVX0KeIcSFxcXtBh5ly5dAADHjh1T2zz9ffzxx9UZNcE0pL9FRUUAXDN16lOr6uzZs+oMFe9ZSaH2967RAyDoFfE8s6MChU51Wb16NSZNmhSw2LdH7Zk1nq9vsK9NsPZLHTuBlJSUAIDfDLHaBgwY4HdltXPnzmHKlCkYPHgwfvOb3wAAfv3rX2PVqlWYNm0aBg0ahJUrV+Lpp59GRUUFXnjhhYDnjomJQXl5eb1nTRERERFR/TBAIqJWo2/fvli2bBl27twJh8MRcAaMt23btgFwzaZoSNCiKMqldLNBPI81cuTIOpfmBSpKHRER0ST98uY9k8vT30GDBiEjIyPkcbUvAQ80Xn+9v0eBZlDVFmiJnyw37iTe48ePY9y4caiqqsKjjz6Ku+66C2lpaYiKioIsy/jss88wYsSIoDPjggVnwdovdewE4gkTL2b52NSpU1FaWoqFCxdCkiQcOHAA//nPf3DnnXdizpw5AFyzvvbs2YOXX34Zs2bNUmcbefOEWG3atGlwH4iIiIgoOAZIRNRq/OxnP8PUqVNRUlKCVatW4dZbbw26rxACS5cuBQCMHj3a5024p1ZMWVlZwGM9s2GaQ0pKCvbv3497770Xv/zlL5vtcT0uXLiACxcuBJyFVFhYCADo1KmT2uaZ7TNmzBhMmzat0frhmQ108ODBel0xLzExEREREaiqqsK8efMu+Up4jWH16tWoqqrCL37xCzUw8ZaXlxfwuI4dO6KgoACFhYXo1auX33bP96G2phg7ycnJABByBlUg69evx+LFi/Hkk0+iR48eAIBdu3YBAK6//nqffQcNGoStW7di79696N+/v882q9WKiooKAPWvdUZERERE9cMaSETUamRkZOC2224DAEyfPh0XLlwIuu8//vEPfP/999Dr9Zg+fbrPNs9Spn379vkdJ4RQa+vU5gmeHA7HxXQ/IM+lyt97771GO2dDeYI2bzabDf/+978BAEOGDFHbPf1dvnx5o9aYys7ORmJiIoqLi9XLyIei0+lw0003AWi+r11d3/9z584BCDzbRwiBd955J+BxnppRwbYHa2+KsXPNNdcACPxvIxir1Yrf/va36NmzJx577DG13RMCegIhD8/9QCGhp/B6165d61xGR0REREQNwwCJiFqVV155BWlpaTh8+DBuvPFG7Nmzx2e7w+HA/Pnz8fDDDwMA5syZ47eUavjw4QBcwcnevXvVdrvdjhkzZgQtOOyZiZOXlwe73d4oz2fy5MlITU3F8uXLMWPGjICzok6dOoXXXnutUR4vkKefftrnimmKomDGjBk4duwYUlJSfGZ6jRkzBv369cO2bdswadKkgLV1zp8/jwULFjQoaNPr9fjzn/8MwPU12bhxo98+27dv96nHlJubC6PRiOnTp2PJkiUBlx7+8MMPWLFiRb37EYrn+197zHl4im+///77OHnypNrudDrxxBNPBCwqDgAPPfQQZFnGu+++i1WrVvlsW7Fihc9V8Lw1xdhJT09H586dUVxcjPz8/Hod8/TTTyMvLw+vvfaaz5XgPGHU22+/jaqqKgDAmTNnsGLFCpjN5oCzrTxfoxtvvLHefSYiIiKi+uESNiJqVeLj4/HVV19h7Nix2LFjB7KyspCdnY2MjAxUVlbi66+/RnFxMYxGI1544QU1SPI2cOBAjBkzBqtWrUJ2djYGDRqEiIgI7Ny5E6WlpXj44Yfxt7/9ze+4zp07Izs72+dxzWYzEhMT8dxzz13U87FYLPj4448xevRozJ07FwsXLkTv3r3RqVMnVFZW4uDBg9i3bx+Sk5PVwsSNqXPnzujbty+uueYaDBkyBAkJCdi+fTsOHToEi8WCd955B2azWd1flmWsXLkSP/3pT7FkyRK8//77uOqqq9C5c2fYbDYUFBRg9+7dcDqdmDhxYp11qrw9/PDDOHDgABYsWICcnBxcffXV6N69O0pLS7F//34UFBRg/fr1apBzzTXXYNmyZepVzmbOnIlevXohKSkJ586dw+7du3Hs2DGMGzcOt9xyyyV/rUaMGAGLxYKVK1di0KBB6NatG3Q6HQYOHIhJkybhZz/7Gfr27YtvvvkGmZmZyMnJgcViwdatW3HixAnMmDEj4NK2vn37Yvbs2fjTn/6EsWPHYsCAAUhPT0d+fj62bduGqVOn4oUXXvAJZ4CmGztjx47FSy+9hM8//xxdu3YNue+ePXswd+5c3HfffRg4cKDPtq5du+KOO+7Av/71L1xxxRW45pprsGnTJpw+fRqPPfYYLBaL3/m++OILtQ9ERERE1MgEEVEr5HQ6xb/+9S8xZswY0aFDB2E0GkVMTIzIysoSU6dOFYcPHw55fHV1tZg5c6ZIT08XBoNBJCcnizvuuEPk5+eLRYsWCQBiwoQJfscVFRWJO++8U7Rv317o9XoBQKSmpqrbc3NzBQCRm5vrc9z69esFAJGTkxOwP6WlpWLu3LniuuuuE3FxccJgMIj27duLfv36ienTp4vNmzf77B/scerr8OHDat/tdrt45plnRI8ePYTJZBLx8fHi1ltvFXv27Al6fHV1tViwYIEYOnSoSEhIEHq9XiQnJ4s+ffqIBx98UKxdu9Zn/1Bf09rWrFkjxowZI9q2bSsMBoNISkoS/fv3F7NmzRJnz54N+FymTJkirrzySmGxWITZbBapqaliyJAh4rnnnhP5+fk+++fk5AgAYv369QEfP9TXduPGjWL48OGiTZs2QpZlv+dUVlYm/vSnP4nu3bsLs9kskpOTxdixY8WOHTvqHAMrVqwQAwcOFBaLRURHR4tBgwaJlStXio0bNwoA4rrrrgt4XEPHTl0OHDggJEkS/fv3D7mfoiji+uuvFx06dBAXLlwIuI/VahUzZswQnTp1EgaDQaSnp4u5c+cKRVH89j19+rQwGAwiIyMj4HYiIiIiujSSEI1YhIKIiFqFwsJCdOnSBampqUGLNJM2PPXUU8jNzcXvfvc7vPTSS83ymKNHj8bHH3+M77//HllZWc3ymC+88AKmTZuGv/3tb/j973/fLI9JRERE1JowQCIiogZjgKQteXl5SExM9Lt0/Ycffohx48bBarVi+/bt6Nu3b7P0Z+/evbjqqqswZswYvP/++03+eBUVFUhPT0dcXBx++OEHGAyGJn9MIiIiotaGNZCIiIhauLfffhvPPvssrr76aqSkpMBut+PAgQM4cOAAAODJJ59stvAIAHr16oWHHnoIL774Inbs2IHs7Owmfby//vWvOH36NBYtWsTwiIiIiKiJcAYSERE1GGcgacuWLVvw97//HVu2bEFxcTGqq6uRkJCAfv364YEHHsDIkSPD3UUiIiIiauEYIBERERERERERUUhyuDtARERERERERETaxgCJiIiIiIiIiIhCYoBEREREREREREQhMUAiIiIiIiIiIqKQGCAREREREREREVFIDJCIiIiIiIiIiCgkBkhERERERERERBQSAyQiIiIiIiIiIgqJARIREREREREREYX0/0VorNRyNMSJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================================================================================\n",
            "Statistical significance (Wilcoxon) between NNFA (ξ=0) and best standard method per percentage\n",
            "================================================================================\n",
            "1%: NNFA(0) vs Neural Network (error): p = 0.062236 -> NOT significant\n",
            "2%: NNFA(0) vs Neural Network (error): p = 0.000016 -> significant (p<0.05)\n",
            "3%: NNFA(0) vs Neural Network (error): p = 0.000005 -> significant (p<0.05)\n",
            "4%: NNFA(0) vs Random Forest (error): p = 0.000002 -> significant (p<0.05)\n",
            "5%: NNFA(0) vs Random Forest (error): p = 0.000002 -> significant (p<0.05)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "All plots and tables generated. Results saved to Excel and PNG files (with confidence intervals).\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "!{sys.executable} -m pip install pyod openpyxl\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import math\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from scipy.stats import wilcoxon, t\n",
        "from sklearn.metrics import roc_auc_score\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from pyod.models import abod, hbos, iforest, knn, lof, ocsvm, pca, copod\n",
        "from sklearn.ensemble import IsolationForest as SklearnIForest\n",
        "from sklearn.svm import OneClassSVM\n",
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "\n",
        "# ------------------------------------------------------------\n",
        "# 1. Dataset creation (Concrete Strength) with given outlier percentage\n",
        "# ------------------------------------------------------------\n",
        "def create_concrete_dataset(seed_shuffle, outlier_percent):\n",
        "    \"\"\"\n",
        "    outlier_percent: процент выбросов от общего числа наблюдений (1..10)\n",
        "    Возвращает X, y, true_outliers.\n",
        "    Выбросы создаются заменой (n//2) наибольших и (n//2) наименьших значений прочности на (max+min)/2.\n",
        "    \"\"\"\n",
        "    url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/concrete/compressive/Concrete_Data.xls\"\n",
        "    df = pd.read_excel(url)\n",
        "    df.columns = [\n",
        "        'Cement', 'BlastFurnaceSlag', 'FlyAsh', 'Water',\n",
        "        'Superplasticizer', 'CoarseAggregate', 'FineAggregate',\n",
        "        'Age', 'CompressiveStrength'\n",
        "    ]\n",
        "    total_rows = len(df)\n",
        "    n_outliers_total = int(np.round(total_rows * outlier_percent / 100.0))\n",
        "    if n_outliers_total % 2 != 0:\n",
        "        n_outliers_total += 1\n",
        "    n_outliers_per_side = n_outliers_total // 2\n",
        "\n",
        "    Smax = df['CompressiveStrength'].max()\n",
        "    Smin = df['CompressiveStrength'].min()\n",
        "    Savg = (Smax + Smin) / 2\n",
        "\n",
        "    df_sorted = df.sort_values('CompressiveStrength', ascending=False).reset_index(drop=True)\n",
        "    outlier_indices = []\n",
        "\n",
        "    for i in range(n_outliers_per_side):\n",
        "        df_sorted.loc[i, 'CompressiveStrength'] = Savg\n",
        "        outlier_indices.append(i)\n",
        "    last_start = total_rows - n_outliers_per_side\n",
        "    for i in range(last_start, total_rows):\n",
        "        df_sorted.loc[i, 'CompressiveStrength'] = Savg\n",
        "        outlier_indices.append(i)\n",
        "\n",
        "    df_sorted['is_outlier'] = 0\n",
        "    df_sorted.loc[outlier_indices, 'is_outlier'] = 1\n",
        "\n",
        "    df_shuffled = df_sorted.sample(frac=1, random_state=seed_shuffle).reset_index(drop=True)\n",
        "    X = df_shuffled.drop(['CompressiveStrength', 'is_outlier'], axis=1).values.astype(np.float32)\n",
        "    y = df_shuffled['CompressiveStrength'].values.astype(np.float32).reshape(-1, 1)\n",
        "    true_outliers = df_shuffled['is_outlier'].values.astype(int)\n",
        "    return X, y, true_outliers, n_outliers_total\n",
        "\n",
        "# ------------------------------------------------------------\n",
        "# 2. Helper: top‑K outliers\n",
        "# ------------------------------------------------------------\n",
        "def get_top_k_outliers(scores, k):\n",
        "    idx = np.argsort(scores)[-k:]\n",
        "    pred = np.zeros(len(scores), dtype=int)\n",
        "    pred[idx] = 1\n",
        "    return pred\n",
        "\n",
        "# ------------------------------------------------------------\n",
        "# 3. NNFA algorithm – возвращает (pred, scores) with ksi = 0, 500 epochs\n",
        "# ------------------------------------------------------------\n",
        "def run_nnfa_original(X, y, n_outliers_desired, random_state):\n",
        "    ksi = 0\n",
        "    torch.manual_seed(random_state)\n",
        "    np.random.seed(random_state)\n",
        "    random.seed(random_state)\n",
        "\n",
        "    Q, N_x = X.shape[0], X.shape[1]\n",
        "    N_y = 1\n",
        "\n",
        "    def scale_to_minus1_1(data):\n",
        "        min_val = data.min(axis=0)\n",
        "        max_val = data.max(axis=0)\n",
        "        range_val = max_val - min_val\n",
        "        range_val[range_val == 0] = 1.0\n",
        "        scaled = 2.0 * (data - min_val) / range_val - 1.0\n",
        "        return scaled, min_val, range_val\n",
        "\n",
        "    X_scaled, _, _ = scale_to_minus1_1(X)\n",
        "    y_scaled, _, _ = scale_to_minus1_1(y)\n",
        "    X_tensor = torch.tensor(X_scaled, dtype=torch.float32)\n",
        "    y_tensor = torch.tensor(y_scaled, dtype=torch.float32)\n",
        "\n",
        "    log2q = math.log2(Q)\n",
        "    N_min = (N_y * Q) / ((1 + log2q) * (N_x + N_y)) + 1\n",
        "    N_max = (N_y / (N_x + N_y)) * ((Q / N_x + 1) * (N_x + N_y + 1) + 1) - 1\n",
        "    if N_max > Q:\n",
        "        N_max = min(Q // 2, 20)\n",
        "    N_lim = N_min + ksi * (N_max - N_min)\n",
        "    N_start = max(1, int(np.ceil(N_min)))\n",
        "    N_end = max(N_start, int(np.ceil(N_lim)))\n",
        "\n",
        "    error_matrix = []\n",
        "    for N in range(N_start, N_end + 1):\n",
        "        model = nn.Sequential(\n",
        "            nn.Linear(N_x, N),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(N, N_y),\n",
        "        )\n",
        "        criterion = nn.MSELoss()\n",
        "        optimizer = optim.Adam(model.parameters(), lr=0.01)\n",
        "        model.train()\n",
        "        for epoch in range(500):\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(X_tensor)\n",
        "            loss = criterion(outputs, y_tensor)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "        model.eval()\n",
        "        with torch.no_grad():\n",
        "            predictions = model(X_tensor).numpy().flatten()\n",
        "            errors = (predictions - y_scaled.flatten()) ** 2\n",
        "        error_matrix.append(errors)\n",
        "\n",
        "    error_matrix = np.array(error_matrix)\n",
        "    scores = np.mean(error_matrix, axis=0)\n",
        "    pred = get_top_k_outliers(scores, n_outliers_desired)\n",
        "    return pred, scores\n",
        "\n",
        "# ------------------------------------------------------------\n",
        "# 4. Wrappers for standard methods – возвращают (pred, scores)\n",
        "# ------------------------------------------------------------\n",
        "def run_pyod_detector(model, X_scaled, n_outliers):\n",
        "    model.fit(X_scaled)\n",
        "    scores = model.decision_scores_\n",
        "    pred = get_top_k_outliers(scores, n_outliers)\n",
        "    return pred, scores\n",
        "\n",
        "def run_random_forest(X, y, n_outliers, rs):\n",
        "    rf = RandomForestRegressor(n_estimators=100, random_state=rs)\n",
        "    rf.fit(X, y.ravel())\n",
        "    scores = (rf.predict(X) - y.ravel()) ** 2\n",
        "    pred = get_top_k_outliers(scores, n_outliers)\n",
        "    return pred, scores\n",
        "\n",
        "def run_neural_network(X, y, n_outliers, rs):\n",
        "    mlp = MLPRegressor(hidden_layer_sizes=(20, 10), random_state=rs, max_iter=500)\n",
        "    mlp.fit(X, y.ravel())\n",
        "    scores = (mlp.predict(X) - y.ravel()) ** 2\n",
        "    pred = get_top_k_outliers(scores, n_outliers)\n",
        "    return pred, scores\n",
        "\n",
        "def run_autoencoder(X_scaled, n_outliers, rs):\n",
        "    torch.manual_seed(rs)\n",
        "    np.random.seed(rs)\n",
        "    random.seed(rs)\n",
        "    input_dim = X_scaled.shape[1]\n",
        "    class AE(nn.Module):\n",
        "        def __init__(self):\n",
        "            super().__init__()\n",
        "            self.encoder = nn.Sequential(nn.Linear(input_dim, 16), nn.ReLU(), nn.Linear(16, 4))\n",
        "            self.decoder = nn.Sequential(nn.Linear(4, 16), nn.ReLU(), nn.Linear(16, input_dim))\n",
        "        def forward(self, x):\n",
        "            return self.decoder(self.encoder(x))\n",
        "    ae = AE()\n",
        "    optimizer = optim.Adam(ae.parameters(), lr=0.01)\n",
        "    criterion = nn.MSELoss()\n",
        "    X_tensor = torch.tensor(X_scaled, dtype=torch.float32)\n",
        "    ae.train()\n",
        "    for _ in range(300):\n",
        "        optimizer.zero_grad()\n",
        "        loss = criterion(ae(X_tensor), X_tensor)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    ae.eval()\n",
        "    with torch.no_grad():\n",
        "        recon = ae(X_tensor).numpy()\n",
        "        scores = np.mean((recon - X_scaled) ** 2, axis=1)\n",
        "    pred = get_top_k_outliers(scores, n_outliers)\n",
        "    return pred, scores\n",
        "\n",
        "def run_combined_rf_ae(X, y, X_scaled, n_outliers, rs):\n",
        "    rf = RandomForestRegressor(n_estimators=100, random_state=rs)\n",
        "    rf.fit(X, y.ravel())\n",
        "    rf_err = (rf.predict(X) - y.ravel()) ** 2\n",
        "    torch.manual_seed(rs)\n",
        "    np.random.seed(rs)\n",
        "    random.seed(rs)\n",
        "    input_dim = X_scaled.shape[1]\n",
        "    class AE(nn.Module):\n",
        "        def __init__(self):\n",
        "            super().__init__()\n",
        "            self.encoder = nn.Sequential(nn.Linear(input_dim, 16), nn.ReLU(), nn.Linear(16, 4))\n",
        "            self.decoder = nn.Sequential(nn.Linear(4, 16), nn.ReLU(), nn.Linear(16, input_dim))\n",
        "        def forward(self, x):\n",
        "            return self.decoder(self.encoder(x))\n",
        "    ae = AE()\n",
        "    optimizer = optim.Adam(ae.parameters(), lr=0.01)\n",
        "    criterion = nn.MSELoss()\n",
        "    X_tensor = torch.tensor(X_scaled, dtype=torch.float32)\n",
        "    for _ in range(300):\n",
        "        optimizer.zero_grad()\n",
        "        loss = criterion(ae(X_tensor), X_tensor)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    ae.eval()\n",
        "    with torch.no_grad():\n",
        "        ae_err = np.mean((ae(X_tensor).numpy() - X_scaled) ** 2, axis=1)\n",
        "    scores = (rf_err / np.max(rf_err) + ae_err / np.max(ae_err)) / 2\n",
        "    pred = get_top_k_outliers(scores, n_outliers)\n",
        "    return pred, scores\n",
        "\n",
        "def run_oneclass_svm_with_y(X, y, n_outliers):\n",
        "    Xy = np.column_stack((X, y.ravel()))\n",
        "    model = OneClassSVM(kernel='rbf', gamma='auto')\n",
        "    model.fit(Xy)\n",
        "    scores = -model.decision_function(Xy)\n",
        "    scores = np.nan_to_num(scores, nan=0.0, posinf=1.0, neginf=0.0)\n",
        "    pred = get_top_k_outliers(scores, n_outliers)\n",
        "    return pred, scores\n",
        "\n",
        "def run_iforest_with_y(X, y, n_outliers, rs):\n",
        "    Xy = np.column_stack((X, y.ravel()))\n",
        "    model = SklearnIForest(random_state=rs)\n",
        "    model.fit(Xy)\n",
        "    scores = -model.decision_function(Xy)\n",
        "    scores = np.nan_to_num(scores, nan=0.0, posinf=1.0, neginf=0.0)\n",
        "    pred = get_top_k_outliers(scores, n_outliers)\n",
        "    return pred, scores\n",
        "\n",
        "def run_lof_with_y(X, y, n_outliers):\n",
        "    Xy = np.column_stack((X, y.ravel()))\n",
        "    model = LocalOutlierFactor(novelty=True)\n",
        "    model.fit(Xy)\n",
        "    scores = -model.score_samples(Xy)\n",
        "    scores = np.nan_to_num(scores, nan=0.0, posinf=1.0, neginf=0.0)\n",
        "    pred = get_top_k_outliers(scores, n_outliers)\n",
        "    return pred, scores\n",
        "\n",
        "# ------------------------------------------------------------\n",
        "# 5. Main experiment over outlier percentages (1% to 5%)\n",
        "# ------------------------------------------------------------\n",
        "percentages = [1, 2, 3, 4, 5]\n",
        "n_runs = 30\n",
        "\n",
        "standard_methods = [\n",
        "    'ABOD (pyod)', 'HBOS (pyod)', 'IsolationForest (pyod)', 'kNN (pyod)',\n",
        "    'LOF (pyod)', 'OCSVM (pyod)', 'PCA (pyod)', 'COPOD (pyod)',\n",
        "    'Random Forest (error)', 'Neural Network (error)', 'Autoencoder (reconstruction)',\n",
        "    'Combined (RF+AE)', 'One-Class SVM (with y)', 'Isolation Forest (with y)',\n",
        "    'LOF (with y)'\n",
        "]\n",
        "nnfa_methods = ['NNFA (ξ=0)']\n",
        "all_methods = standard_methods + nnfa_methods\n",
        "\n",
        "results = {p: {m: {'f1': [], 'auc': []} for m in all_methods} for p in percentages}\n",
        "\n",
        "print(\"Experiment over outlier percentages:\", percentages)\n",
        "print(f\"Number of runs per percentage: {n_runs}\")\n",
        "print(\"=\"*80)\n",
        "\n",
        "for pct in percentages:\n",
        "    print(f\"\\n========== PERCENTAGE = {pct}% ==========\")\n",
        "    for run_idx in range(n_runs):\n",
        "        seed = run_idx\n",
        "        X, y, true_outliers, n_outl = create_concrete_dataset(seed_shuffle=seed, outlier_percent=pct)\n",
        "        scaler = StandardScaler()\n",
        "        X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "        pred_abod, scores_abod = run_pyod_detector(abod.ABOD(), X_scaled, n_outl)\n",
        "        pred_hbos, scores_hbos = run_pyod_detector(hbos.HBOS(), X_scaled, n_outl)\n",
        "        pred_knn, scores_knn = run_pyod_detector(knn.KNN(), X_scaled, n_outl)\n",
        "        pred_lof, scores_lof = run_pyod_detector(lof.LOF(), X_scaled, n_outl)\n",
        "        pred_ocsvm, scores_ocsvm = run_pyod_detector(ocsvm.OCSVM(), X_scaled, n_outl)\n",
        "        pred_pca, scores_pca = run_pyod_detector(pca.PCA(), X_scaled, n_outl)\n",
        "        pred_copod, scores_copod = run_pyod_detector(copod.COPOD(), X_scaled, n_outl)\n",
        "        iforest_model = iforest.IForest(random_state=seed)\n",
        "        pred_iforest, scores_iforest = run_pyod_detector(iforest_model, X_scaled, n_outl)\n",
        "\n",
        "        pred_rf, scores_rf = run_random_forest(X, y, n_outl, rs=seed)\n",
        "        pred_nn, scores_nn = run_neural_network(X, y, n_outl, rs=seed)\n",
        "        pred_ae, scores_ae = run_autoencoder(X_scaled, n_outl, rs=seed)\n",
        "        pred_combined, scores_combined = run_combined_rf_ae(X, y, X_scaled, n_outl, rs=seed)\n",
        "        pred_ocsvm_y, scores_ocsvm_y = run_oneclass_svm_with_y(X, y, n_outl)\n",
        "        pred_iforest_y, scores_iforest_y = run_iforest_with_y(X, y, n_outl, rs=seed)\n",
        "        pred_lof_y, scores_lof_y = run_lof_with_y(X, y, n_outl)\n",
        "\n",
        "        pred_nnfa, scores_nnfa = run_nnfa_original(X, y, n_outl, random_state=seed)\n",
        "\n",
        "        all_data = {\n",
        "            'ABOD (pyod)': (pred_abod, scores_abod),\n",
        "            'HBOS (pyod)': (pred_hbos, scores_hbos),\n",
        "            'IsolationForest (pyod)': (pred_iforest, scores_iforest),\n",
        "            'kNN (pyod)': (pred_knn, scores_knn),\n",
        "            'LOF (pyod)': (pred_lof, scores_lof),\n",
        "            'OCSVM (pyod)': (pred_ocsvm, scores_ocsvm),\n",
        "            'PCA (pyod)': (pred_pca, scores_pca),\n",
        "            'COPOD (pyod)': (pred_copod, scores_copod),\n",
        "            'Random Forest (error)': (pred_rf, scores_rf),\n",
        "            'Neural Network (error)': (pred_nn, scores_nn),\n",
        "            'Autoencoder (reconstruction)': (pred_ae, scores_ae),\n",
        "            'Combined (RF+AE)': (pred_combined, scores_combined),\n",
        "            'One-Class SVM (with y)': (pred_ocsvm_y, scores_ocsvm_y),\n",
        "            'Isolation Forest (with y)': (pred_iforest_y, scores_iforest_y),\n",
        "            'LOF (with y)': (pred_lof_y, scores_lof_y),\n",
        "            'NNFA (ξ=0)': (pred_nnfa, scores_nnfa)\n",
        "        }\n",
        "\n",
        "        for method, (pred, scores) in all_data.items():\n",
        "            scores = np.nan_to_num(scores, nan=0.0, posinf=1.0, neginf=0.0)\n",
        "            if len(np.unique(scores)) == 1:\n",
        "                auc = 0.5\n",
        "            else:\n",
        "                auc = roc_auc_score(true_outliers, scores)\n",
        "            tp = np.sum((pred == 1) & (true_outliers == 1))\n",
        "            fp = np.sum((pred == 1) & (true_outliers == 0))\n",
        "            fn = np.sum((pred == 0) & (true_outliers == 1))\n",
        "            precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0\n",
        "            recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0\n",
        "            f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0\n",
        "            results[pct][method]['f1'].append(f1)\n",
        "            results[pct][method]['auc'].append(auc)\n",
        "\n",
        "        if (run_idx+1) % 5 == 0:\n",
        "            print(f\"  Run {run_idx+1}/{n_runs} completed for {pct}%\")\n",
        "\n",
        "# ------------------------------------------------------------\n",
        "# 6. Compute mean, std and 95% confidence intervals (t-distribution)\n",
        "# ------------------------------------------------------------\n",
        "def mean_ci(data, confidence=0.95):\n",
        "    \"\"\"Return (mean, lower, upper) of 95% CI using t-distribution\"\"\"\n",
        "    n = len(data)\n",
        "    mean = np.mean(data)\n",
        "    if n < 2:\n",
        "        return mean, mean, mean\n",
        "    se = np.std(data, ddof=1) / np.sqrt(n)\n",
        "    t_crit = t.ppf((1 + confidence) / 2, df=n-1)\n",
        "    margin = t_crit * se\n",
        "    return mean, mean - margin, mean + margin\n",
        "\n",
        "summary_data = []\n",
        "for pct in percentages:\n",
        "    for method in all_methods:\n",
        "        f1_mean, f1_low, f1_high = mean_ci(results[pct][method]['f1'])\n",
        "        auc_mean, auc_low, auc_high = mean_ci(results[pct][method]['auc'])\n",
        "        summary_data.append({\n",
        "            'Outlier %': pct,\n",
        "            'Method': method,\n",
        "            'F1_mean': f1_mean,\n",
        "            'F1_lower': f1_low,\n",
        "            'F1_upper': f1_high,\n",
        "            'AUC_mean': auc_mean,\n",
        "            'AUC_lower': auc_low,\n",
        "            'AUC_upper': auc_high\n",
        "        })\n",
        "df_summary = pd.DataFrame(summary_data)\n",
        "print(\"\\nSummary table with 95% confidence intervals (first 20 rows)\")\n",
        "print(df_summary.head(20))\n",
        "\n",
        "with pd.ExcelWriter('outlier_percentage_study_with_CI.xlsx', engine='openpyxl') as writer:\n",
        "    df_summary.to_excel(writer, sheet_name='summary', index=False)\n",
        "print(\"\\nFull results saved to 'outlier_percentage_study_with_CI.xlsx'\")\n",
        "\n",
        "# ------------------------------------------------------------\n",
        "# 7. Plotting with confidence intervals (fill between)\n",
        "# ------------------------------------------------------------\n",
        "methods_to_plot = [\n",
        "    'Random Forest (error)', 'Neural Network (error)', 'Autoencoder (reconstruction)',\n",
        "    'Combined (RF+AE)', 'One-Class SVM (with y)', 'LOF (with y)'\n",
        "] + nnfa_methods\n",
        "\n",
        "plt.rcParams.update({'font.size': 14})\n",
        "\n",
        "# F1 plot with confidence bands\n",
        "plt.figure(figsize=(12, 8))\n",
        "for method in methods_to_plot:\n",
        "    means = []\n",
        "    lows = []\n",
        "    highs = []\n",
        "    for pct in percentages:\n",
        "        row = df_summary[(df_summary['Outlier %'] == pct) & (df_summary['Method'] == method)]\n",
        "        if not row.empty:\n",
        "            means.append(row['F1_mean'].iloc[0])\n",
        "            lows.append(row['F1_lower'].iloc[0])\n",
        "            highs.append(row['F1_upper'].iloc[0])\n",
        "        else:\n",
        "            means.append(np.nan); lows.append(np.nan); highs.append(np.nan)\n",
        "    plt.plot(percentages, means, marker='o', linewidth=2, markersize=8, label=method)\n",
        "    plt.fill_between(percentages, lows, highs, alpha=0.2)\n",
        "\n",
        "plt.xlabel('Outlier percentage (%)', fontsize=16)\n",
        "plt.ylabel('F1 Score', fontsize=16)\n",
        "plt.title('F1 Score vs Outlier Percentage (95% CI, 30 runs, 500 epochs)', fontsize=18)\n",
        "plt.xticks(percentages, fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=12)\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "plt.tight_layout()\n",
        "plt.savefig('f1_vs_percentage_with_CI.png', dpi=150)\n",
        "plt.show()\n",
        "\n",
        "# AUC plot with confidence bands\n",
        "plt.figure(figsize=(12, 8))\n",
        "for method in methods_to_plot:\n",
        "    means = []\n",
        "    lows = []\n",
        "    highs = []\n",
        "    for pct in percentages:\n",
        "        row = df_summary[(df_summary['Outlier %'] == pct) & (df_summary['Method'] == method)]\n",
        "        if not row.empty:\n",
        "            means.append(row['AUC_mean'].iloc[0])\n",
        "            lows.append(row['AUC_lower'].iloc[0])\n",
        "            highs.append(row['AUC_upper'].iloc[0])\n",
        "        else:\n",
        "            means.append(np.nan); lows.append(np.nan); highs.append(np.nan)\n",
        "    plt.plot(percentages, means, marker='s', linewidth=2, markersize=8, label=method)\n",
        "    plt.fill_between(percentages, lows, highs, alpha=0.2)\n",
        "\n",
        "plt.xlabel('Outlier percentage (%)', fontsize=16)\n",
        "plt.ylabel('ROC-AUC', fontsize=16)\n",
        "plt.title('ROC-AUC vs Outlier Percentage (95% CI, 30 runs, 500 epochs)', fontsize=18)\n",
        "plt.xticks(percentages, fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=12)\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "plt.tight_layout()\n",
        "plt.savefig('auc_vs_percentage_with_CI.png', dpi=150)\n",
        "plt.show()\n",
        "\n",
        "# ------------------------------------------------------------\n",
        "# 8. Wilcoxon test (unchanged)\n",
        "# ------------------------------------------------------------\n",
        "print(\"\\n\" + \"=\"*80)\n",
        "print(\"Statistical significance (Wilcoxon) between NNFA (ξ=0) and best standard method per percentage\")\n",
        "print(\"=\"*80)\n",
        "for pct in percentages:\n",
        "    std_methods_only = [m for m in all_methods if m not in nnfa_methods]\n",
        "    best_std = max(std_methods_only, key=lambda m: np.mean(results[pct][m]['f1']))\n",
        "    f1_nnfa0 = results[pct]['NNFA (ξ=0)']['f1']\n",
        "    f1_best_std = results[pct][best_std]['f1']\n",
        "    if np.all(np.array(f1_nnfa0) == np.array(f1_best_std)):\n",
        "        p_val = 1.0\n",
        "    else:\n",
        "        _, p_val = wilcoxon(f1_nnfa0, f1_best_std)\n",
        "    print(f\"{pct}%: NNFA(0) vs {best_std}: p = {p_val:.6f} -> {'significant (p<0.05)' if p_val < 0.05 else 'NOT significant'}\")\n",
        "\n",
        "# ------------------------------------------------------------\n",
        "# 9. Boxplot for a specific percentage (e.g., 2%)\n",
        "# ------------------------------------------------------------\n",
        "pct_show = 2\n",
        "plt.figure(figsize=(14, 7))\n",
        "f1_data_show = [results[pct_show][m]['f1'] for m in all_methods]\n",
        "plt.boxplot(f1_data_show, labels=all_methods, patch_artist=True)\n",
        "plt.xticks(rotation=90, fontsize=10)\n",
        "plt.ylabel('F1 Score', fontsize=14)\n",
        "plt.title(f'Distribution of F1 for {pct_show}% outliers (n_runs={n_runs}, 500 epochs)', fontsize=16)\n",
        "plt.grid(axis='y', linestyle=':', alpha=0.7)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"\\nAll plots and tables generated. Results saved to Excel and PNG files (with confidence intervals).\")"
      ]
    }
  ]
}