{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "18107c0b",
   "metadata": {},
   "source": [
    "# Test Jupyter Notebook\n",
    "\n",
    "This is a test notebook to demonstrate Jupyter notebook rendering in Jekyll with the Zer0-Mistakes theme.\n",
    "\n",
    "## Purpose\n",
    "\n",
    "This notebook showcases:\n",
    "- Markdown cells with rich formatting\n",
    "- Code cells with Python execution\n",
    "- Mathematical equations using LaTeX\n",
    "- Data visualization with plots\n",
    "- Tables and structured data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6fab8c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting numpy\n",
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      "Collecting pandas\n",
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      "  Downloading pandas-2.3.3-cp314-cp314-macosx_11_0_arm64.whl.metadata (91 kB)\n",
      "Collecting matplotlib\n",
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.10.7-cp314-cp314-macosx_11_0_arm64.whl.metadata (11 kB)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bamr87/github/zer0-mistakes/.venv/lib/python3.14/site-packages (from pandas) (2.9.0.post0)\n",
      "  Downloading matplotlib-3.10.7-cp314-cp314-macosx_11_0_arm64.whl.metadata (11 kB)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bamr87/github/zer0-mistakes/.venv/lib/python3.14/site-packages (from pandas) (2.9.0.post0)\n",
      "Collecting pytz>=2020.1 (from pandas)\n",
      "  Using cached pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)\n",
      "Collecting pytz>=2020.1 (from pandas)\n",
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      "Collecting tzdata>=2022.7 (from pandas)\n",
      "  Using cached tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)\n",
      "Collecting tzdata>=2022.7 (from pandas)\n",
      "  Using cached tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)\n",
      "Collecting contourpy>=1.0.1 (from matplotlib)\n",
      "  Downloading contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl.metadata (5.5 kB)\n",
      "Collecting cycler>=0.10 (from matplotlib)\n",
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      "  Downloading contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl.metadata (5.5 kB)\n",
      "Collecting cycler>=0.10 (from matplotlib)\n",
      "  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
      "  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
      "Collecting fonttools>=4.22.0 (from matplotlib)\n",
      "  Downloading fonttools-4.61.0-cp314-cp314-macosx_10_15_universal2.whl.metadata (113 kB)\n",
      "Collecting fonttools>=4.22.0 (from matplotlib)\n",
      "  Downloading fonttools-4.61.0-cp314-cp314-macosx_10_15_universal2.whl.metadata (113 kB)\n",
      "Collecting kiwisolver>=1.3.1 (from matplotlib)\n",
      "  Downloading kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl.metadata (6.3 kB)\n",
      "Collecting kiwisolver>=1.3.1 (from matplotlib)\n",
      "  Downloading kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl.metadata (6.3 kB)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/bamr87/github/zer0-mistakes/.venv/lib/python3.14/site-packages (from matplotlib) (25.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/bamr87/github/zer0-mistakes/.venv/lib/python3.14/site-packages (from matplotlib) (25.0)\n",
      "Collecting pillow>=8 (from matplotlib)\n",
      "  Using cached pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl.metadata (8.8 kB)\n",
      "Collecting pillow>=8 (from matplotlib)\n",
      "  Using cached pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl.metadata (8.8 kB)\n",
      "Collecting pyparsing>=3 (from matplotlib)\n",
      "  Downloading pyparsing-3.2.5-py3-none-any.whl.metadata (5.0 kB)\n",
      "Collecting pyparsing>=3 (from matplotlib)\n",
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      "\u001b[?25hDownloading kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl (64 kB)\n",
      "Using cached pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl (4.7 MB)\n",
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      "Using cached tzdata-2025.2-py2.py3-none-any.whl (347 kB)\n",
      "Installing collected packages: pytz, tzdata, pyparsing, pillow, numpy, kiwisolver, fonttools, cycler, pandas, contourpy, matplotlib\n",
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      "\u001b[1A\u001b[2KSuccessfully installed contourpy-1.3.3 cycler-0.12.1 fonttools-4.61.0 kiwisolver-1.4.9 matplotlib-3.10.7 numpy-2.3.5 pandas-2.3.3 pillow-12.0.0 pyparsing-3.2.5 pytz-2025.2 tzdata-2025.2\n",
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      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install numpy pandas matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9adc5c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported successfully!\n",
      "NumPy version: 2.3.5\n",
      "Pandas version: 2.3.3\n"
     ]
    }
   ],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "print(\"Libraries imported successfully!\")\n",
    "print(f\"NumPy version: {np.__version__}\")\n",
    "print(f\"Pandas version: {pd.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7566059c",
   "metadata": {},
   "source": [
    "## Mathematical Equations\n",
    "\n",
    "Jupyter notebooks support LaTeX equations via MathJax:\n",
    "\n",
    "Inline equation: $E = mc^2$\n",
    "\n",
    "Display equation:\n",
    "\n",
    "$$\n",
    "\\int_{-\\infty}^{\\infty} e^{-x^2} dx = \\sqrt{\\pi}\n",
    "$$\n",
    "\n",
    "More complex equation:\n",
    "\n",
    "$$\n",
    "f(x) = \\frac{1}{\\sigma\\sqrt{2\\pi}} e^{-\\frac{1}{2}\\left(\\frac{x-\\mu}{\\sigma}\\right)^2}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93098878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8GYr42GOPOb5PfI94/KeffkJbMlUdsZUrV+If//gHNm/eLCuHf/311+eNHV2xYgUeeOAB7Nq1S9ZWEEuV4g+r9corr8jXzcjIQP/+/fHyyy9j2LBhrfzbEBERERG1nhkvr0Z2cWWbH+KYED8suG/MeZ+Xnp6Oa6+9Fs8++ywuv/xyFBcXY9WqVY5B0+neffddeV2/fv16rF27Vl7Tjx49Wg627Cti1113neP5oqaY+B4x0HvppZfwm9/8BnfddRfat2/fYCAmCmAPGDBA/uzx48ejrZhqICZGx2Kg9Itf/AJz5sw57/NFtetp06bJA/7hhx/KzXu333474uPj5chY+PTTT+Uf9LXXXsPw4cPxz3/+U35t7969iI2NbYPfioiIiIio5YlBWEZRhWEPbXp6ugwHFNf1nTp1ko+JQdPZ9OvXD48//rhs9+jRA//+97/l9b19ICYKLCckJDT4HjHoev3113HTTTfJRZdvv/0WW7dulcWftcT3ie9vS6YaiE2ZMkXeXCUGV126dMHzzz8v74sMKmKk/OKLLzoGYmJj3h133CFjQ+3fs2jRIrz11lv44x//CFPZuxiWNS8jNKg90KEfEN0DiOoOhHcCvEz1p3Z7+aVV2HAkD+VVtaipq0dtXZ3t33rU1Najrr4eXWOCMLJrNAJ8XdtsSkTktopOASc3q1t5ARAUAwTHav6NBYJjAL9QMQWud28JQFVNHXJKKpFXWoWCsmrklYl/qxz3g/28MSAxHIM6RSAyyJfHrBVXpoz8c/v37y9XoMTgS1ybT5w4EVdccQUiIiLOOhDTEosrWVlZjvvl5eWOsEStK6+8UkbS/e1vf5N7wcQg7nQiNLKsrAxtya2vzsWSpX2EbCf+yPfff79sV1VVyTDHhx9+uEGVbPE94nvPprKyUt7sioqK5L91dXXypptT22A9uhqBor37U8fD9VYfILILENkN9e0HAwNvVB9c1LZ/noJyLN2diSW7M7HhSL4cdJ2Pr7cVw7tE4pKe0RjbMwZdooPkMntLEuesCAHQ9dwlU+E5Q616zlSVACe3yEGX5ZT4dwssxadc+jn1/mFA0mzUD7wJSBjIQVkbE59raw/l4ovNJ+RnXUW1a58rnaMCMbhTBAbaBmY9YoNhQT0/m5rxf8x+s5t/72jo5WzhhVpWqxVLlizBmjVr5L9ie9D//d//Yd26dY2+jljF0t4X10T29xXxeHR0tNzrdfrPFgMscc0vMinu27ev0b6J7+vatatL/bb/PPv72unvba5eU7n1QEwsP7Zr167BY+K+GDiJEXN+fr6sMdDYc/bs2XPW133mmWfwxBNPnPF4dnY2Kir0W/4NzTqoBmGnsdRVAzn75M2y7zvUr3wW5T1mobT/raiN6KZDTz3H4dxyrDhYgJ8OFGBPVlmzZhRX7c+Rt6cX7UFCqC9Gdg7DhJ4RGNghpEX6KN4sCgsL5ZuJeEMk4jlDLc2V9xlLRT6Ctr+FwNQPYK1p3qy0paIQ2PIuLFveRXVkT5T3uRLlPWai3r9pmdeoaY4XVGDR7lx8l5aLzOLqJh++I7ll8vbllpPyfkSgN64fFIsJnXz52eSi6upq+f9MhPm1dea/ljB8+HB5+9Of/oTu3bvjyy+/dAxw7L+PfeCj/f3szxG/v7imFytsIi/E6cdAbEMSg7YFCxZg5syZmDx5Mi699NIGz9m5c6fcp+bq8RPPEz87NzcXPj4+Db4m9rrB0wdirUWsoIk/qJ0Y2IlEIDExMQgNDdWvY1e+jprSv6Lg4CZE1ObAmn8IyD2gbnmHYKlRg0RLbRUC93wub/XdL0P9yHuBzhdx5rAFHcktxRMLduOnfTmNfr1jZAAuS2qHxIhAeFkt8LZa5L/2m5hV3HQkHyv2ZSO90Dm4P1VUhS93ZMvbZX1i8X/T+qBjZGPDb9eJNxHx5iTOXw7EiOcMtYZzvs9UFMKy9hVg/WuwVJ158VLvG6JWt9oPVlEdoQlAaQ5QlgOUZMFSmg2UZgEl2cCJjbBUq6KsPnn74PPzXxCy7jmg9zS1StblYn7WtZDq2jp8s+0UPt90ApuO5p/x9fAAHwzrEomoIF+EB/ogIsgXkYG2dqCP/GzberwAW44WYNepQlTVOlch8stq8O/Vp/DJVh/8enwgrh6aCB8vThSei1gIEBf/YsXo9L1PRrZ+/Xq5x0uEJIrcDOK+WNjo27evHFCJ9wv77yPeQ8RN+/uJr4ubfSAkBljvvfdeg+eILUeiHplYdRs0aBB+97vf4bbbbsP27dsdIZBHjhzByZMnZT9cPX7ieeJni0yPp4dDNhYe2ehrwI2JtJaZmZkNHhP3xWBJxIGK5Ulxa+w54nvPxs/PT95OZz8ZdBUUhZq4gbDExsKi7YtYIs0/DGx+R90qVTil5cBSeUNcCjDqN0DyXPGL6Nd/k6uorsXrPx3CKysOyNUsrb4JoZiYFIdJye3Qq13IeUMM5w5OlDM/+7NKsGJvFlbszcbGI3motn1YLU3LkgO92y/qgnsu7Y4gv+b/dxZ9McT5S6bBc4Yu+JypKJKDL6z5N1BZ6Hyily+QchXQebQcfFmiejg+l84bmF1ZDOz6GtjyPnBig/qe2kpg11ew7PoK6DYemPVvNZijZtt9qgi/+3w7dqerawk7qwUY2ysWVwzugPF9YuHnfe49ztP7t3d8du46VYQtR/Ox/nAulu/JgogOyymtxmPzd+O/q4/gt5f1wMz+7eVkJZ1J/L+yD1RaegtDa9cKW7VqlSzeLBY2RMIOkdth6tSp+Oyzz+RztL/PuX4/8fj111+PP/zhDzL8sFevXnJQJxL1iTT3gwcPls978sknsXTpUtx9990yaZ897b0YhHXu3Nnlvtv70tj1k6vXU5Z6VwMhDUb84udLXy/+ECIzSmpqquMxkdJSxIAuXrxY3hfLoCJVvYhJtc/adezYEffee6/LyTrEiSNOJBF2oeuKmK3/YtOimFU460kgPvy2vg+sexUoPN7waz0mAZe/BgRGtkl/3cmq/dl4bN4uHM5Rs7FCfJg/bhvTBZP6xiHxAleuhJLKGnybmo7nvt+LLE062nahfvjjlN6YPaB9k9+AXTpniHjO0AVo8D5TXwus/Tfw87+Acs1KitVb7WG++HdAWIcLP95ZaWpAtv1joDzP+bjYRzb1OSDlSq6ONWMV7LUVB/HSD/sdk4KC2NMlBl+XD2yP2FDXVgLOZW9GMZ5fslfuM9Pq2S4YD07shYlJ7Uw12GirFTGRLVwkqXN1Ncad1NtCFsUqlagzJq7NRaZEV4icESJ5x0cffSRT4bfEMXd1bGCqgVhJSQkOHDgg2wMHDpQZD0V8pyjqJgZPImRQLCuKJUlBHJzk5GTcc889MuX9Dz/8IAvBiSVKbfr6m2++Wf6xxIBMpK8XI3CxR+z0vWNuMxCzq60Bdn+jPhBPbXU+HtoemPs/oNPIVu+vO8gqqsBTi9KwYLtzM7mYsRMDsN+M73FBK1XnGpD9+4cDeGv1YVTVOlfeBnUMxxMzk5HSIczl1+JAjJqK5ww1+5wJssD6+S3AsTXOL1q8gAHXAhf/HohQ6atbVE0lkLYAWPIIUJzufDxpFjDtRRlJQue3L7MYD362HaknnauXIrrjL5cny0QbrZFI6scdh/HWpmz8fCC3wddmD0jA3+b2g78PswrbcSBW7xiIievx//znP3JBxZUJZjG2EOGRv/zlL5t0jnrcQEwUZz59Y50gBlIi9lMUdRMxnuJ52u/57W9/i927d8sq2o8++ugZBZ1FDQJ7QWdRzE0UfBMrZa4y7UDMTpwC+5cA3/xKxdzbPxjH/R8w+rcMVTyHzzYdx1MLdqO40rmxc0inCDx9eTJ6x7X+uXAkpxRPL9qNZWnO1K0iauOp2cm4frhrFzS8qKam4jlDzTln8nYuR9SyX8MiUtELFqsKQbzk90BUGySOKssDvn0I2PmF8zGR9n7mS0Av10vjeJqa2jq8ueowXly6zzHxJz5n7h7bDb8e3+O84Yct8T6z7nCejATZcqzA8fV+HcLwxo1DEBfmeas/jeFArN4xEGur1VKPG4gZlekHYnZF6cBXdwBHVjkf6zYOuPwNVZ+FHMR/G/Gh9NIPaoVWEJuPH57SR4ZnWNs4hl3sIXty4W4cynaGRYoPyYcm9jpvX3hRTU3Fc4aafM5s/QiWhb+RyaKkkHjg6g+ADkPa/mCKPWQLH2gYrjjgBmDyM4C/vp/hRnM0txS/+WQbth13DoC6xwbj+Sv7o39ieJu+z4jP3QU70vHHL3egrKrWUavq9RsHY1DHxmtOeRIOxOpNORDjhhByCo0HbpoHXPIH55bogz8Ar40GDq/kkdLMDv7p69QGg7ArB3fADw+OxVVDE9t8ECaIzdGLf3Mxbh/TxfHYqysO4v5Pt6GyRn1gERG1udpq4Ls/wjrvbucgLHE4cOdP+gzChL6XA79aB/Sc7Hxs2wfA21NVNkZy7NOa++paxyBMfLT98pKuWHjfmFYfhDVGXFzP7J+AL+8ehQ4RAfKx7OJKXPP6Ony+6bT97kQmwYEYnXZGeAGX/kkNyIJte+RKMoF3ZwKb3vb4oyUyO931wRZ8vMH5pv/Y9CT848r+MjWvnkTx50emJ+GJmX3lB6Ywf/sp3PS/DSgsa3pdFyKiCyIGNe9fDqx/1fFQ/eBbgZsXAiGu7cFuNeLnX/sJMPNlwDdYPZaZCrwzHShumCDCE+08WYhr3liLnBKVFKpLdBA+v2uUjPrQe19Wn/hQzL93DEZ0VUnFRLjkQ1/swBMLdsmJUiIz4UCMGtf1EuCu1UDXsbYH6oGF9wPbPvLYIyYGMzf+bz2WpakPaR8vC166diB+oVmFMoKbR3XG6zcOgb+P+u+9/nAe5r62BsfzmlcglYioybL2AG9c6gh1r7f6oPDip1A/7QXAW99JKwcRvjToJuDOFUCILZ19dhrwzjTAvo/NA205lo9r31yHfNsEntiL9fWvRsmEHEYRGeSL928bjptGOvdCv/3zEdzy9kZOPJKpcCBGZxccC9zwNSAKPtvNuwdI1Wx09hDpheW48vU12HhEpVoO8vXC27cMk2ESRiSKRX9y50hEB6sLngNZJbj8P2uw44Qzzp+IqFXkHwXenw0UHlP3g9uh/uYFKE+6ypgHPLoHcOsiICxR3c/dr8IUCzwv3G39oVzc+N/1KK6ocSSf+uD24QgPNMjgWUMUeH5yVjKemZMiJ0aF1QdycNNb61GqSaBFZGQciNF5zhArMPFpYPjd6n59HfDVncDueR5z5A5kFWPuf9ZgX2aJvC8GN2KQM6ZHNIxsQGI4vrp7NLrGBMn7IsTk6tfXYcNhzQZ1IqKWVJKtwhHtaeLj+6v9YGJfmJFFdgVu/RYIt62w5B8G3pkK5B+Bp1i5Lxs3v70BpbZEGKO6ReG924Yh1N8HRnbtsI746I4RiLJtD9h+ohB3fbAZVTUMUyTj40CMXAvfENmkhvxC3RfFOL/4BbD3O7c/eiKcTwxeThVWyPsdIwPlRuGm1OnSU8eoQHx19ygM7axCSsqra3Hn+5twKFsNKomIWkxFEfDhXCDvoLof3VNFVYhEUGYQ3hG49Tsg0pZKv+CYWhnLtf0+bmzZ7kzc/u4mVFSrwculvWLw1i1DEejb8nUwW8PQzpFyMBbqr/q7an8Ofvf5dtTVMTE4GRsHYuT6YGzq88CA69X9uhrgs5uAA8vd9ggWVVTjF+9sRG6pyvTVNyFUDsI6RakVJrMQISUilv4i2wpeQVk1bhW/l20TNhHRBauuAD65Dkjfru6Htgdu+Mp8xZLD2quVsehe6n7RSTUYy94Ld7V4Z7paQbIlupjcN862z9hcxZJ7xYXgf7cMhZ+31ZGs6qlFu2Xae6KLL74YH33kep6Da665Bs8//3yrHzgOxKgJZ4tVZZhKvkLdF6mIxQevG6a2F5mX7v1oK/ZnqZWjbjFB+Oj2EbJmiRmJD9T/XD8IveNC5P2juWW48/3NMgskEdEFqa0BvrzNWYMyIBK48Wsg3LbnymxC4oBbFgGxfdX9kgzgvdlASRbcTeqJQlknrMa2cjRrQAL+fd1AmYXXjMTK2L+vG+TIHCwSeLz6k/uvaNK5zZ8/H5mZmXJw5apHHnkEf/nLX2QdsNZkzv9ppG96+8tfB/rMVPdrKoCPrgaOb3Crv8pTC3fLeHl7oWYRohEWaOw4+fMJ8Ve/R6xtMLn5aD4eZOgGEV2IeltG3T0L1X2fIOD6L4AY24qSWQXHALcsBOL6qfvFp4DPb1F10dyE2Df8y/c3odK2l2rOwPZ44aoB8PYy96WhSFYlEnjYPbt4Lz7b6HmJV8jppZdewq233ioLg7sqOTkZ3bp1wwcffIDWZO7/baQPL29g7v+AnlPU/eoy4NMb3Kb2yrtrjuDdtUdlW2Rieu2GwaYLRzybhPAAW9y/CjlZtCMdzy3dp3e3iMislj8BbH1fta0+wDUfAB0Gwy0ERqpBZYhtj9vRn4Hv/w/uoLq2Dr/6cItj/7NITf/M3BR42ZeSTO7qoR3x0CTnZMAfv9qBpbvd4xrFXdXV1eHZZ59F9+7d4efnh44dO8oVKSE1NRXjxo1DQEAAoqKicOedd6KkxLnXfcWKFRg+fDjCw8MRERGB0aNH4+hRdR2XnZ2NH374ATNmzGjwfF9fX6xatco5YH/2WcTGxsqVMzvxPZ988kmr/t7m2IVJxiPqwFz1LvDBXBWOIoo+i9CUG79RAzWTWrE3SxaFtPvr5SkY3tVkexzOI7l9mAw9ERuzRTTKaz8dQrh3R9w5PlbvrhGRmax/A1j9ou2OBZjzBtBtHNyKKPx89QfA21NUOP6G14GEAcCA62BmTy/c7cig2y7UD69ePwh+3ubaE3Y+vxrbTa76ifBE8Vl370db8OHtwzGksyoE7TFev0SfsFpRAumXP7n89IcffhhvvvkmXnzxRYwZMwbp6enYs2cPSktLMWnSJIwcORIbN25EVlYWbr/9dtx777145513UFNTg9mzZ8vH3nvvPTmgE8+zWGwlDVavRmBgIPr06eP4WWPHjsX999+PG2+8Edu3b8ehQ4fw6KOP4vPPP0e7ds5i88OGDZODwcrKSjk4bA3mvWIm/Xn7AVe8Dbx+kUpVLAZkK/4KjH8MZrQvsxj3fbRVvmELd13SDVcOMekeh/MY17sd/jyzLx6bpwad//jhGPp0jMUlvZxvQEREZ3VyC/D9n5z3pz0HJM9xzwPWYQgw7Xlg/n3q/oL7gZjeQPtBMCMRpmeP+vD1ssqoj9hQf7gbcSH+6LQk5JZUycQdIgTz7g+34LvfXIToYHPu924WMQgTobUGVlxcjH/961/497//jZtvvlk+JsICxYBMDM4qKirkICsoSEUnieeJ1aq///3v8PHxkfu4pk+fLr/H29sbSUlJjtcWK2NicHV6WOLTTz+NpUuXytW1nTt3yp87c6Zt241NQkICqqqqkJGRgU6dnMXDWxJDE+nC4+ivfAew2GbSVj0P7PvedEdVZBAUGRKLbUUgJya1w+81YQ3u6KaRnXHbmC6yXVsP3PPRVuzNKNa7W0RkhjT1ooRJnW2/1Kj7gKG3w60NuslZwqW2Evj0RlUzzWS2HMvHI9/sdNx/+vJkDOyoypu4I6vVgueu7C9rognZxZWel9ZerEyFJLT9TfxcF6WlpclVp/Hjxzf6tf79+zsGYYIIPRQrX3v37kVkZCRuueUWTJ48Wa6MiQGdWE2zKy8vh7//mRMNIjTxww8/xJdffikHemIl7nQiFFIoKytDa+GKGF24jiOAy54Elthi50XB51+uBCJaZ/agpYmij798fzNO5Jc70tT/85oB8g3c3f1pah9ZK23J7kyUVNbivo+3YP69Y0yXtpiI2jI5x29VwWOh/RBg/OOecfgn/x3I3AUcXw8UnVDJO24S4fjmSOSUVVSBu953pqm/eWQnXOWmUR9aIgPkv64ZiCn/Womckiqs2JuNt34+jNsv6gqP0ITwQL0E2AY8zfX222/jvvvuw7fffovPPvtMhhmK1a4RI0YgOjoa+fn5jX7fmjVr5L95eXnyph3s2R8XYmJi0Fq4IkYtY+Q9QO/pql1RoD6gasxRp+rfP+zHpqP5jlj5/91sniKWF0pszH7xqv7oEa3eBPdllsgMU0REjdr2IbDzC9X2CwWu+J9pBiItszf6PSA4Tt0/uhpYao5Q/MqaWvzyg83IKlafy8O7ROKR6c7wLXcnSs88f9UAx/2/L96DnSdbNy05ua5Hjx5yMLZ8+Zm1acXeLrGPS+wVs/v5559lqGGvXs7IpYEDB+IPf/iD/JrIeGivGSYeF6GFpw/GDh48iN/+9rcy9FEk+hChiWKVTUuELHbo0EEO5loLB2LUMsSmyFmvABEq1A2ntpgiu9TWY/l4ZYWqMeJttcgilnFh7hcrfy4Bvl54YkoXR90YMVO4en+O3t0iIqMRRY2/fch5f8a/gIjO8CiixtjV76sMkcK6/wDbWzerWkv48/zd2HqsQLbbhwfIupI+Jk9T31SX9IzBnRerVbDq2nrc9/FWlNq2I5C+/P395SDq97//vdwLJgZJ69atw//+9z9cf/318utioCQGRj/++KNc/RKJNsTer8OHD8tEH2vXrpX7wZYsWYL9+/c7knOIgZgYSIkBml1tbS1uuOEGmQREpLUXK2o7duw4o4CzyKo4ceLEVv3dPet/IbWugHA1W+hl2wS78U0g1TZzakDlVbV48LPtqLXFiv96fA8MSAyHJ+oaFYDfT+rpuC9i6AvKqnTtExEZSHWF2hcmypUIg2523+Qc55M4TCUnsRPJO3KNWzT4hz2Z+HjDMdn287bi9RsHI8qTklVo/G5iL/TrECbbh3NKHQmrSH+PPvooHnzwQTz22GNyEHX11VfLDIki4+H3338vwwSHDh2KK664Qu4lEwk7BPF1kV1RPN63b1/88pe/xD333CP/Fby8vORgS+wHsxOZEMWg7fXXX5f34+Pj8cYbb8gizmL1TRD7xr755hvccccdrfp7W+rrRcA3XYiioiKEhYXJrC2hoaG6HkyxrCpOXFELoSmF61rU5neBBb92Fve880dDFvf88/xdeGfNEdnunxiOL+8aafpClhdyzkRHx+Dmdzbi5wO58vHp/eLx8rUDHSlgiQz1PkNta9Hv1OSaIDIG3vEj4Bvo2eeMyKK45T3V7jQauHmhyA4BIyksr8bEF39CZpEKSfz73BRZY8sMWuucOZJTimkvrUJpVa28/8+rB2D2wPYwOzFwEKtDXbp0aTQ5hburr6+XqexF1sTTr1tEaKIYpG3ZssXl7Ievvvoqvv76a7nC1pxj7urYwFjvGOQ+2aX622qsVJcCX9wG1NqyaxmECL2zD8LEDOELV/X3yEFYY9mlQv3V/riFO9Ixb5uxU94SURtIW+AchHn7q7IlTRiEua3JfwPCOzmLPW/6H4xYL8w+CBOheZ6QnON8OkcHyWyRdiKL5NFc5/4jcj9xcXEyzPHYMbUy7AqRFv/ll19Ga/PsK09qHWImQtRcibEVz8tMBdaqJWSjzBA+9IVaehYentIb3WKCde2TUcSHBeCvc1Ic9x/9ZidO5Lde2lYiMriC48C8exoOPtp5TpKHc/INAmZqLtSWPg7kq/pcRvDj3ix8vvmEbAf7eeOZOSmMcLC5fGAHzBmkVsFKKmvw64+3ygzK5L5mz56Niy66yOXniwLR2mQgrYUDMWodYrZ09iuAxXaKrfibYWLon5i/C+mFFbI9unuUrKdFTtP7JeByW5iGqKum3UdHRB5E7Fz45m6gwpZdLmk2MPgWvXtlLF0vAQbf6owAEWH5BtjxUVRRjT99leq4/8i0PkgIv7AU4e7myVnJ6BylVna3nyjE80uZMZjaHgdi1HraDwaG36XaNRWq9ozOH1Dfpabjq60nZTvE3xv/uKK/R9QLa6onZvWVmbWE9Yfz8N9Vh/TuEhG1te0fA0dWqXZYR5UlkXtGzyTqaIZ2UO1DK5z7xnT010VpjgnHi3pE4+qhDEk8nVglfPlakT1SXQO8ufIQU9pTm+NAjFrXpf+nPsCFwz8B21RdBz1kFVfgT187ZwifmNmXM4RnEervg+ev6u+45npuyV7sPlXUJn8nIjKAsjxgySPO+zNeVJlx6Uz+oWqQaieOW6Ga8NPDyn3Z+GTjcdkO8vXC3+b2Y0jiWaR0CMP9E1TGYBH4IfaL1Zk8AoQ5+Mx1rDkQo9blFwxMf8F5f8n/ASXZuvxnEWEa+WUqacjkvnGO8Dtq3IiuUbjzImfNlT98uYMhikSeYtnjQJnKoIq+lwPdJ+jdI2PrMQEYcL1qVxYBC+/XJQKkuKIaD2tCEv80rY8juoEad8dFXdE9Vu0T33a8wDGINRuRXEIoK+O+7rZiP9b2Y98cKj0aUWvqcRmQfAWw8wugPB9Y/EfgirbNLrVgRzqWpWXJdnSwL/5yeTJnCF3wwMSeWLE3G3szi5F6shCfbTqOa4eZI/UxETXTsfXO8DrfEGDSMzyUrpj0F+DAcqAkA9i/BNjxKdD/mjY9ds98twcnC8ple1S3KFzH9+vz8vW24qlZybj2zXXy/t8X78HEvu0QbbJaa6JeVnh4uEz5b6+v5UnlZ+rPkb6+NX6WGISJYy2OuTj2zcWBGLUNkWnrwDKgokANyMSHkxigtYGyqhoZL2/3l8tTPLaYZVP5eXvJ/WLXvKE+oJ5dvAdTkuMQHuird9eIqDWIUiNiP6/d+EeB0Hgea1cERADTXwQ+uVbd/+4PQNexQEhcmxy/nw/k4KP1Kj13oK8X/s6QRJeN7BYlo2S+3npSZlZ+5ts9MjzfjGnaBftgzJPU19fL2nOi5lxbDUDFIMx+zJuLAzFqG8ExarbQngZ54QPAr9aq0MVW9p8fDyKjSG1aHtc7FpP6ts2HojuFKM7sn4D520/J0M7nl+zDU7OdNViIyI2sexXI2qXa8f2Bobfr3SNz6T0VSLkSSP1cTTwuehC4+oNWT3JSXlUrw8e1ZVkSI1nrrSn+NLUPlqdloqiiBl9uOYGrhnTA8K5RMBMxAImPj5dFsKurjVW/tbXV1dUhNzcXUVFRbVI0XoQjXshKmB0HYtR2RPy8CNU4vBIoPAb8+Fdg8l9b9UeKIo1vrFQZ/0RmpEens/5Ncz+glqVloqyqFh+uPyozcCW3D2vhvxYR6V4zTJQakSxqdcd64RcaHmfy31X2xNJsYM9CFabYc1Kr/sg3Vx3CiXwVkjiiaySuH24rNE0uiwnxw0OTe8v6mcKj83Zi0a8vgo+X+dIpiAFCSwwSzDYQ8/Hxgb+/f5sMxFqKeXpK5idmBKf/E/D2V/fXvwqc3NyqP/LpRWmoqlVFGn8xpgu6RAe16s9zV3Fh/vj1+B6yLRJKPT5/FzMzEbkbsX9X1MISxEqYKEFCTRcUpcLxtVkURchnK8ksqsCrK1SdTi+rBU/PTmZZlmYSe+r6dVCTjPsyS/C/1Ydb7g9F1AgOxKhtRXUDLvmDatfXAQt+A9TVtloK36W7Mx0zXfeNUwMJap5fjO6CrraB7Oaj+TKWnojcxJ5v1eqNEBQLjNOkrqemS54LJA5X7Zx9wKa3W+0oPrt4L8qr1efojSM6oXtsSKv9LHcnBrJ/mZ0Ce3nRfy3bjxP5zEJIrYcDMWp7o+4D2tn2GGWkqnDFFlZdW4cnFuxqEC8vijfShWWWenxmX8f9v367R6ZKJiKTqyoFvvu98/7kZ1gzrCUiQLTZJlc8o7IGt7DUE4VyP5MQFuCD39giF+jCaouJAa0gBrhPLtjNw0mthgMxantePuqD3u6Hp4FqFdveUt5dcwQHs1WIzcCO4Zg9gDXDWsIlPWMwMamdbOeUVMrZQiIyuZ+eBQpttZNElj+xmkMXrsNgIOUq1S7PA1Y+1+JZ4p5c6JxwFOHjEUHMaNsSHpjYy5G+fsnuTJnEg6g1cCBG+uhyMdDDtnm56CSw7j8t9tLaAYKYlPzzjL6Ml29BIuGJn7d663h7zRHsyyxuyZcnoraUdxhY+4pqe/kCU59v9Qx/HmX8Y5p90a8DuWovV0v4bmcGNh5Rq2wibNy+ikMXTqwuPjq9j+O+2BddYQv/JGpJHIiRfi57ErDYTsFVLwKlOS3ysv9YvBfFlTWyfdXgRPRPDG+R1yVFpES+e2w32a6tq8efmbiDyLxE9tq6amfYeHR3vXvkXsIT1XEVxHFe+liLvKwYFPz127QGmW1F+Di1HFG2RRTFFkRGyg9tNdqIWhL/15J+YnsDA29U7apiFR5zgbYfL8Bnm1WITYifNx6a3OuCX5POdNcl3dAhIkC21xzMxbepGTxMRGYj9uiKeldCQCQw+jd698g9jb4fCFYh3TIhyuFVF/ySb/98xJGufkz3aIzvE3vBr0ln1uT6v2nOVbFXfjzAfdHU4jgQI31d+ifAx1Z0ctP/Lihso06sziwQadXV/d9M6OGI8aaW5e/jhcc0NdnEzGxVjSoTQEQmsewJsdNItS96EPBnbcBW4RcMjHvUef/7P4kPrGa/XHZxpRwUCCK73yPT+8hBA7W8vglhcmVMyCutwpurmM6eWhYHYqSvkDhN2EYNsOzPzX6pBTtOYeuxAtnuHhuMm0d1bqleUiMuS2qHi3pEy/bJgnJ8upFhG0SmcWQ1cGCpaod2UHXDqPUMuA6IS1HtjB3A9o+b/VIvLN2LElv4/TXDOqJ3XGhL9ZIa8eDEnvC25bP/76pDch86UUvhQIz0N+rXqm6NkDYfOLa+yS9RU1uHF5fua5BQwseLp3drEjOwv5voDP18+YcDKK/iZmYiwxNhA0sfPy0ywZZQglqH1QuY9Ffn/eVPApUlTX6ZXacK8clGZ/j9A5f1bMleUiM6RQXh2mEdZbusqhb//kGtRhK1BF6pkjHCNi592Hl/6aPqQqEJvtpyEkdyVdHFEV0jZZp1an0iEYo9nX1WcSXeX3eEh53I6NIWACc3qXZMH6D/NXr3yHOyBfeaptolGcCal5qcrv7phWmOj8d7x3Vn+H0buW98dwT4eMn2h+uP4ngeizxTy+BAjIxh4E1AtG1m7/h6daHgIrE36V/LnfWsHtSs0lDrE8fbvj3h1RUHuZmZyMhqa9RqjDa9ulitobYx8SnA6q3aP78EFJ50+Vt/2peNtYdyZbtjZCBuGc3w+7YSG+KP28Z0ke3q2nq8oInAIboQHIiRMXh5AxPExnEbsVes1pZS+Tw+3XRc7lESLu4Zg6GdI1url9SIXnEhjs3M+WXVeGs1V8WIDGvbh0CubeIqcQTQa4rePfIsUd2AYXeqdk05sPpFl1fD/mmrjyn8fnIv+HlzAN2W7rykK8IDfWT7m20nkZZe1KY/n9yT6QZir7zyCjp37gx/f38MHz4cGzZsOOtzx44dK/exnH6bNs0WGgDglltuOePrkydPbqPfhhoQFwSdRqt23kFg09su1VL59w+a1TDGy+vitxN6wkuzmbmgrEqfjhDR2VWXAyv+5rx/2RMs3qyHi34H+ASp9pZ3XVoVW7EvG9uOq2RUveNCMDU5vrV7SacJ9ffBPWNVnT0RHvqP7/fyGJFnDcQ+/fRTPPDAA3j88cexZcsW9O/fH5MmTUJWVlajz//qq6+Qnp7uuO3cuRNeXl648sorGzxPDLy0z/v44+ZnM6ILIOLbRNiG3U9/O+9mZlFgMbNIZTCa0KcdizfrpHN0EK4a0kG2RTHt1346pFdXiOhs1r8OFJ9S7Z5TgI4jeKz0EBQFDLetitVWnXdV7PTVsN+M7wGrbeKL2taNIzshPkwltvlhTxY2HM7jn4A8ZyD2wgsv4I477sCtt96KpKQkvPbaawgMDMRbb73V6PMjIyMRFxfnuC1dulQ+//SBmJ+fX4PnRUREtNFvRGdoPxjoO0e1y3KBTY3/beWXq2rw6gpn9iJmj9LXfeN6wNeWqfKdNYeRVVyhc4+IyKE8H1j9gu2ORe0NI/2MvM/lVbEVe7OxXbMaNqlvXFv1khqpoSkiQOz+vniPHCgTNZdtx6jxVVVVYfPmzXj4YWd2PavVigkTJmDt2rUuvcb//vc/XHPNNQgKsr352axYsQKxsbFyADZu3Dg8/fTTiIqKOuvrVFZWyptdUZGKE66rq5M3PYmfL94U9O7HBbn497Ds+hoW1KN+zcuoH3Ib4BNwxtPe+fkwckpUCNzU5Dj0jgs29+9t8nMmLtQP1w1PxDtrjqKiug6v/HAAj89wFn0m9+EW7zMexrLqRVgqCmW7vv81qI/pfUFFhZuK58xpAiJgGXo7LGv+JVfF6le/iPopz55x3MT/sxeXORND/HqcCI0T//fc/+LfqOfM7AHxeH3lQRzMLsXmo/lYtjsD4/uo7MGknzqDnS+u9sM0A7GcnBzU1taiXbuGJ7u4v2fPnvN+v9hLJkITxWDs9LDEOXPmoEuXLjh48CD+9Kc/YcqUKXJwJ8IYG/PMM8/giSc0iSVssrOzUVFRofsfvrCwUJ6MYqBqThEI6zoJAYcWw1KaheKVr6As5aYGzyitrMVrPx2UbRGhceOgyLOGqFLbnTNX9g3DJxusqKipw0cbjuHyPqGIC/Xln8DNuMf7jOewluUgZv1rsl1v9UF28h2oa+P3S54zZ7L0uAYxG96EtaYM2PwOcnrdiLrghtc4Px8uxI4TagDdPToAA2IsHvNZZ+Rz5o7h7fDHhSoE/5lvdyMpAo590qSPOoOdL8XFxe41ELtQYgCWkpKCYcOGNXhcrJDZia/369cP3bp1k6tk48ePb/S1xKqc2KumXRFLTExETEwMQkNDdT8RRcIR0RcjnIjNdtn/Aa8vls2QHW8h+JJ7AW9nwdGXlu9HUYUqHiwy9g3v3Um3rppdS54zoiz3raNL8OpPh2SK34925ONvc1JarK9kDG7zPuMhLMv+A0utLYpj6G2I7jawzfvAc6YxsbAMuwNY8y9Y6qoRs/f9Bqti4oLync+d4fcPTuqNuNMmo92Zkc+ZK2Ni8PG2XGw/UYhDuRXYml2HqSlMoKKnOoOdLyKpoFsNxKKjo+UKVWZmZoPHxX2xr+tcSktL8cknn+DJJzW1U86ia9eu8mcdOHDgrAMxsadM3E4n/vBG+OOLE9EofWm2+H6q8OXeRbAUp8Oy/SNg6O3ySyIj3/9sKdLFDNT9E3qa+3c1gJY8Z+66pDs+WH8MxRU1+HLLSdw9tju6RDcMBybzc4v3GU/ZG2bfa+vlC8vo+2HR6W/Gc6YRo38NbPwvUF0Ky5Z3YbnoASBUlQNZnpaJ1JNqNaxPfCgm9Y33uCQdRj5nRA3Nm95Smbv/s+IQpvVLkP0l/VgMdL642gf9e+oiX19fDB48GMuXL28w+hX3R44cec7v/fzzz+WerhtuuOG8P+fEiRPIzc1FfDxnNnR3yUPO9up/AjVqP9ibqw7JzHzCFYM6yIx9ZBxhgT6486Kusl1bV48XWfiSSD8b3gSqbCEyA64HQvnZZihB0YBYFTstg+LpmRLvn8BMiUZzUY9opLQPk+3d6UWy4DZRU5lmICaIcMA333wT7777LtLS0nD33XfL1S6RRVG46aabGiTz0IYlzp49+4wEHCUlJXjooYewbt06HDlyRA7qZs2ahe7du8u0+KSzhIFA98tUu/A4sOMT5JZU4u2f1WqYj5cF941XNT3IWG4d0wWRQWpv2IIdp3A4p1TvLhF5HlH+Y91/VNviBYz+jd49osaM0mRQ3PwOUHRKpka3r4YlxYdiYpLnhCSaafXlnku7Oe7/50e1b53IbQdiV199NZ577jk89thjGDBgALZt24bFixc7EngcO3ZM1gHT2rt3L1avXo3bbrvtjNcToY47duzAzJkz0bNnT/kcseq2atWqRkMPSQeX/N7ZXvU83ly5D2VVam/YNUM7okNEIP8sBhTs543bL+oi2yKz7xsrWVeMqM2Ji3oRmiikXAFEqv+TZOxVsfpVLzSsGzahB0PeDGpiUhy6xahB9IYjeawrRk1mqWcBhAsmknWEhYXJbC1GSNYhMiqJdPxGiJFtEe/OBA7/JJsP19+LjytHyXpVK39/KeJshRXJeOdMUUU1Rj/zgwwjFX+v1X+4FLGh/Hu5A7d8n3E3NZXAP/sBJRnq/q/WAbF9dOsOz5nzKM0B/pkCVJeh1uqLUWUvIBORcjVs0a/HeORAzCznzJebT+DBz7fL9theMXjn1oZJ4cgzz5ciF8cG+veUqAmrYrfXfwkr6jBnUHsOwgwu1N8H143oKNtVtXV4yxZSSkRtYNuHzkFY7+m6DsKoaatiXnVVuMt7gWNvmCcOwsxk5oAEtA8PcBTf3mkLKSVyBQdiZHydx6Cu4yjZ7GZNx1Sv9bjjYpUMgoztttFd5GqY8OG6o3KVjIhaWW2NSnBkJzLxkfGN+jVqvdQF/XVeP+DiuCpcxr1hhufjZcVdlzivSV5dwb1i5DoOxMgUVsarhCzCw0EL0S2Ke8PMQIQizh3cXrZFiOKH647p3SUi97fzS6DgqGp3vRRoP1jvHpErgqKx0H+6bPpZqvFE7EquhpnElUMSER2sElR9uzMdh7JL9O4SmQQHYmR4dXX1eGpXDLbUqQyJ7asOy/piZA53XNQV9siat34+jIpqlWyFiFpBXR2w+gXn/Yt/x8NsEtuPF+DJ3HGorPeR9zsf+RyoYJibGfj7eOG2MV0dCape+4mrYuQaDsTI8JalZeJgThleqrnc+eBPz6p3OzK8rjHBmJKsiq5nF1fiqy0n9e4Skfva+y2QvUe1E4cDnUbr3SNykaiRmYswfFk7Rt63iPpvW97j8TOJG0Z0RIi/t2yLz7mTBeV6d4lMgAMxMrzXbanPV9QNQFFEsnowYwdw8Ad9O0Yuu+sSZ62VN1YelIWeiaiFicmpVc8571/0O1HsiIfZBI7nleHbVFV+50u/2c4vrHsNqOXeWjMI8ffBLaM6y3ZNXT3eZNkWcgEHYmRom47kYfNRVQenZ7sQBE94yPlFe6FSMrx+HcIxursqqH4ktwzf77JlcyOilnPoR+DUVtWOSwF6XMajaxIibNs+P3XJqNFAz8nqTtEJYNc3uvaNXHfr6C4I8PGS7U82HkNOSSUPH50TB2JkaK/95CwE/MuLu8HaZwYQrlKi48AyIMsWgkOmWhUTWaVYwpCoha183tm+6EGuhplEYVk1Pt14XLb9fay4YUQnYNR9ziesfZmh+CYRGeSLa4epa5SK6jq8/fNhvbtEBseBGBnW/sxiuT9MiA/zx4z+CYDVCxh+t/NJXBUzjTHdo9E3QRU1TD1ZiDUHc/XuEpH7OLEJOLpataN6AH1m6t0jctGHG46irEolMbpycKK8mJd7+xIGqiekbweOrOLxNIk7Lu4CHy8VEvzeWpZtoXPjQIwM6w1NfPVtY7rA19t2ug68AfANUe3tnwClOTr1kJpCFCW9e2zDVTEiaiHaSanRv1aTVmR4lTW1eMdW7F5s5xOfdY47I+91PnHNyzr1kJoqPiwAcwd1kO3iihp8ZlvtJGoMB2JkSBmFFfhmm8quF+rvjWtsS/2Sfygw+GbVrq0ENr2lUy+pqaYkx6OTrQbc6gM5SD3B1MxEF6zwJLB7nmoHRgEpV/GgmsT8baeQVaz2EU1KikPn6CDnF5NmA2GJqr1/CUPxTeT2i5wFnt9Zc4QJquisOBAjQxJx1dW1aufyjSM7IdhPpYR1GHYnYLGdvhveBGq4IdYMvKwW3Hmx8wOKtVaIWsDG/wJ1Nao95BeAjz8PqwmIfbIiZb3dHZr3RsnLGxihCcVf++827B1diO6xwbi4Z4xsn8gvd2yzIDodB2JkOIXl1fhw/THZFuGIt4yyhWpoRXRy7oEozQJSv2jjXlJziZCN6GA/2f5uZ7pM20xEzVRVBmx+R7WtPsCQ23goTeKnfdnYl1ki24M7RcjbGQbdBPiFqfaOT4FiXtCbxS9Gq1T2ApN20NlwIEaG89H6YyipVLO7VwzugJgQddF+hpH3ONtrX2FWKZPw9/HCLaM6ybZI1/zBuqN6d4nIvFI/A8rzVDt5DhAar3ePyEUNVsM0oWwN+IUAQ25R7doqYOObPL4mcXGPGHSNUaGm6w7lYdcphuLTmTgQI0Opqa3D+2udG5fP+uEkJA4DOgxV7axdwOGf2qiXdKHEnj9fL/X288nG4yi3ZQwjoiYWcBYFf+2G38XDZxI7Txbi5wMqc2znqEBcltTu7E8e9kvA6u0MQ60qbaNe0oWwWi241VbgWbAnZSHS4kCMDEXEUZ8qrJDt8b1j0UW7cbkxI37VcFWMTEGEJspyBLZQVHtiFiJqgkMrgOw01U4cAbQfxMNnEv/VrIbddlFXuX/2rMLaA8lXqHZ5PrDtozboIbWEOYM6IMRfDaLnbT+FXBZ4ptNwIEaGIrIL2d000jmTdFZin5g2q1T2vlbsHbWkWzQzhe+uOcICz0RNtV6zGjaCq2FmcaqgHAt2pMt2RKAPrrClOj+nUfc2nHSsYxSBGQT5eeOaoeoapaqmTm69INLiQIwMY29GsYyjFkRctSgAfF4iq9TwXzrvs8CzaaR0CMOgjuGyvUfztyciF+QeBPYtVu3QDkDvGTxsJvHuWmc68xtHdkaArws13+JSgK5jVTv/MLBnUSv3klqKmFS2L3i+v+6oHJAR2XEgRob6cLK7aUQnGV/tEpFVyjdYU+BZxd2T8d0yukuDVTEictH6153t4XeqSSkyvIrqWkeBXx8vC24coRIXuWTUfc72hjdaoXfUGhIjAzExKU62Rc24b1PVaiiRwIEYGYLYJ/T1FrVPKMjXC3MHuxCqYecfBgy8UbVryoHNLPBsFlOS4xBry4q5ZHcGThaU690lIuOrKAS2fajaPoFqMopMYdGOdOSXVcv21JT4s2cFbkzXcUBUd9U+sooFnk3k1tNS2YsackQCB2JkCJ9vOo7yahXzLgZhIf4+TXsBEZ7IAs+m4+NlxfXDnans31/LVPZE57X1A6BK1Z9C/2uBgEbqT5Ehvacp13HTyCashglWKzD0duf9Tf9rwZ5RaxrWJRJJ8aGyvf1EIbYcK+ABJ4kDMdJdXV29jJtu9oeTENkF6D1NtUsygd3zWrCH1JquG95RhugIn2w8JkN3iOgsRJIGbZIOpqw3jR0nCrD9uLoAFxflgzo2YwAtBt5iFVTY9jFQWdzCvaTWYLFYzlgVIxI4ECPd/bQvG0dzy2RbJOjoHhvSvBcafrezvZEzhWYhQnOm91Op7AvKqjF/2ym9u0RkXHu/BQpsmde6TwBieurdI3KRdsX/xpGd5MV5kwWEAylXqnZVMbDjMx5/kxAlW6KDfWX7u50ZSC9kKD5xIEZGS9LRnNUwu06jgJg+qn18HZC5qwV6R23hZk0q+7eZyp7o7LQFnEdoJp/I0PJLqzB/u5pkEnWlZg1Qk0/Nog1PFAWeud/IFPx9vHCdLRRfZM18j6H4xIEY6e1wTilW7M2W7fbhARjfp13zX0zMLg75hfP+JibtMIsBieHyJqSlF2HjkXy9u0RkPBmpwNHVqh3dE+g2Xu8ekYs+33wclba05VcOTkSg7wVkuYzvByQOV+2s3cCxtfw7mMQNI5yh+B9vOIbyKobiezqGJpKhQjW8XE1Zfzb9r3bGz2//FKi0bWgn0xV4JqLTaEOuZYKiC3y/pDbbB/3BumMNLsYvmHZVbMObF/561CZiQ/wbhOLP26ayRZPn4kCMdFNaWSOzJQp+3lZcPURVn78gIpW9Nn4+lfHzZiFSOUcHq1TOi3dl4BRT2RM5iaQMqZ+rtqib2O9qHh2T+Gl/No7lqX3QF/WIRtcYW93LC5E0CwiMVu20+UBx5oW/JrX5pONHG5wDdPJMHIiRbr7eehLFlTWyLeLlI4LUJtYLNvQ2Z3vjW4yfNwlfb5HKvqMjfv7D9UxlT+QgBmH2lPVissmvmUmNSN/Ij6YUcD4Xbz9n/bi6GmDLuy3zutTq+nUIQ98Elcp+x4lC7DxZyKPuwTgQI12IYobvNUjS4ZwhumDx/YH2Q1Q7MxU4sbHlXptalRiIedvCUz/ecJyp7IkEkYxh09vOYzHkVh4XkzieV4Yf92a1zD7o04nzwF4/U5wftWpik4xNZMu0188UPlzPVTFPxoEY6WLtoVzsy1Szu0M6RSC5fVjL/gBt0g6msjeN2FB/GaIo5JVW4ftdGXp3iUh/p7YAGTtUu/1gNdlEpvDBuqOOpIaiZuIF74PWCu8I9Jys2sWngH3ftdxrU6uaOSABQb5esj1/20mU2KKDyPNwIEa6eG+NpoCzJl66xSTPAfxVFj7s+hooy2v5n0Gt4gZN6M5HnCkkapgBdjBXw8xCFKf/1LYP2tfLiquHtsA+6HOF4jNph2kE+3lj1sD2sl1aVcukHR6MAzFqc1lFFVialuko5ju5b1zL/xCfAGDA9apdWwls/aDlfwa1iqGdI9AtJki21x/Ow8FsZr4kD1ZeAOz8SrX9QtUkE5nCwh3pMjOeMDUlzpGMqEV1HQdEdlXtwz8B2fta/mdQq7huWMcGk45iywZ5Hg7EqM19vvmETMYgXDm4g0zS0Cq04Ymb3xY5hFvn51CLx89fq/mA+oRZpciT7fgMqFYZ99D/GsBXTVKQ8b2v2Qd9Y0vug9ayWoEhmlWxTZoSB2RoYktG/w5qW8auU0XYfoJJOzwRB2LU5vVUPt2oQjWEVgnVsIvuDnS5RLXzDgGHV7Tez6IWNXdQBxnKI3yx+QQqa1j0kjyQmCEXk0h2DEs0je3HCxwX1iJD3qCOtlD51jDwesA7QLW3fQRUlbbez6IWJfYN2n3ETMEeiQMxavMkHfZ6KmO6R6NTVCvP7jZIZc+ZQrMQpQwmJ6uQ1fyyaizZxRo55IGOrweydqt24gigXZLePSIXactv3DSyk1zpbzUBEUDKXNWuLFKrqGQKM/onIMTPW7YXbE9HUYUKZSXPwYEYtamPNWFm1wxrxdUwu15TgWDbHrS93wFFp1r/Z1KL0IYnas8bIo/BlPWmVFxRLS+qBXGRPbO/SsrQqobe4Wxvfqf1fx61iEBfb8y2Je0or67FN1tP8sh6GA7EqM2IdOT2lY3IIF9cltSC9VTOxsvHWfSyvhbYzKKXZjGiayS6RKsV0zUHc3E4h+E25EFEpleR8VUQGWCTZundI2pCkg5xUS3MGpiAAFua8laVMACIH6Da6duAdFu5AzJZeCKTdngaDsSozXy15QSqalXCjLmD2sPPuw0+nITBNzuLXm55F6jl0r95knY4V00/2chVMfIg2z9WGV8FkQFWZIIlU/hEsw/6mqHOi+xWZ590FLa+33Y/ly5In/hQDLTtIdyTUYwtxwp4RD0IB2LUJkRaVm142dVt+eEU1gHoOUW1i9NViCKZJmmHj5faW/HFphOoqmHmS/KQJB3asMTBt+jZG2qCPRlFMlGHkBQfKjPjtZmUK5xJO3Z8ClSXt93Ppgty/fBOje4vJPfHgRi1iU1H83EwW4WWDesSie6xwW175IdqUtmzpphpRAX7YaKtzlxuaRWW2erPEbm1I6uB3P2q3fkiIKan3j0iF2mzArfJPmgt/zCg72zVrigE0ha07c+nZpveLx6h/ippx6Id6Si01Z8j98eBGLUJ7WqYNtyszXS9FAjtoNoHljJph0mLXjJpB3mEBinruRpmFhXVtfjalmzBz9uKWW2RpONc4Ylb3mv7n0/N4u/jhTmD1DVKZU0dvtxygkfSQ3AgRq1OzOyIGR5BzPhMSY5v+6Nu9VK1VoT6OrX/gkxhZNcodIoKlO1V+3NwLNdW3JbIHZVkA7vnq3ZgFNBnht49Ihct2Z2JAttKxtSUeIQF+rT9ses4EojqrtpHVqkammQK12uTdmxg0g5PYbqB2CuvvILOnTvD398fw4cPx4YNG8763HfeeUdu+NfexPedvnfpscceQ3x8PAICAjBhwgTs328LCaEW8c22k3KGRxAzPmLmRxcDrmsYnij2YZDhWa2WBhvembSD3JqYJKqzhSUNvAHw9tO7R+SiTzUJha4eqkPkhyDqlQ280Xmfofim0aNdCIZ2jpDtA1kl2HgkX+8uURsw1UDs008/xQMPPIDHH38cW7ZsQf/+/TFp0iRkZWWd9XtCQ0ORnp7uuB092nAT5LPPPouXXnoJr732GtavX4+goCD5mhUVFW3wG3leko42j5nXiugMdLlEtcUs4dE1+vWFmuSKwR3gbVVJOz7bdALVtuybRG5FTA5pL5wH3axnb6gJxEr9zwdyZbtzVCCGd4nU7/j1vxawqv1G2PohUFujX1+o2ansOenoGUw1EHvhhRdwxx134NZbb0VSUpIcPAUGBuKtt9466/eIVbC4uDjHrV27dg0GCf/85z/xyCOPYNasWejXrx/ee+89nDp1Ct98800b/VbubfuJQpmOVRDpWXvHherboQYzhUzvaxYxIX6OunM5JZVYzqQd5I5ObgZy9qp2x1FAVDe9e0Qu+mzT8QZZgcW1h25C2gE9J6t2SYbaF02mILZuhNiSdnyXmiGLg5N7s02ZGF9VVRU2b96Mhx9+2PGY1WqVoYRr16496/eVlJSgU6dOqKurw6BBg/DXv/4Vffv2lV87fPgwMjIy5GvYhYWFyZBH8ZrXXHNNo69ZWVkpb3ZFRUXyX/EzxE1P4ueLAabe/bD7WJOG9eohHfTvV6+psPiHwVJRiPpd36B+8t8AP50Hhzoz2jlzNtcM7YDvdmbItlhlndgWBcHJ1OeM2Vi2vAf75XudCKV2o+PrzudMTW0dPt+sBmJeVgvmDEzQ//cccAOsexbKZv2W91DfYxLMxp3PmbPx9bJgRr94fLThuCwKvnD7Kf3CXE2mzmDni6v9MM1ALCcnB7W1tQ1WtARxf8+ePY1+T69eveRqmVjpKiwsxHPPPYdRo0Zh165d6NChgxyE2V/j9Ne0f60xzzzzDJ544okzHs/OztY9pFH84cXvKk5GMVDVU2lVLeZvPyXbgb5WDI/3PmcYaVsJ6TYNQbs+gqWmHEVr30V50tXwZEY6Z86lR2g94kN9kV5UhZX7crBt/3EkhHH/jB7Mcs6YSnU5Ynd+KQdidd6ByI4ZhXoDvF+2FHc+Z1YfKkBmkZqcHd05FPXlhcjSu4RXaApiAmPhVZYF7PseOYd3oi4oFmbizufMuUzoGoSPbOkPPlp3GJd24uecGc+X4mIVDeY2A7HmGDlypLzZiUFYnz598Prrr+Opp55q9uuKVTmxV027IpaYmIiYmBi5J03vE1GERIi+6H0ifrJRzOioGYFZA9qjU3sdsiU2ZtSdwK6PZDP04HyEjL0PnsxI58z5XDe8GM8v3Q+RZuWnYxX4zXjOFOrBTOeMaaR+BmtViWxaki9HTPsucCfufM58/70zLPHGMd0RG2uMAY9l0I3A6udhqa9F9KllwOj7YSbufM6cS0xMPXr+cAL7skqQml6KEksgusa0ce1VE6oz2PlyenJA0w/EoqOj4eXlhczMhgVdxX2x98sVPj4+GDhwIA4cOCDv279PvIbImqh9zQEDBpz1dfz8/OTtdOIPb4Q/vjgRjdCXTzc562BcN6yT7v1xSBgAtEsBMlNhObkJFrEnI7YPPJlRzpnzuWJIIl5Ytl/mNPhq60n8ZnxPmVWR2p5ZzhnT2Paho2kZeCMsbnhc3fGcySqqwA97s2W7XagfLu0Va5zfzzYQE6xiT/SY36qsiibijueMK64ckoi/fJsm219uPYU/TO6td5dMwWKg88XVPujfUxf5+vpi8ODBWL58eYPRr7ivXfU6FxHamJqa6hh0denSRQ7GtK8pVrdE9kRXX5Maty+zGNuPF8h234RQpHQIM86hEh9E4gPKjul9TSM+LABjukfL9vG8cmw4kqd3l4guXP5R4PBK1Y7sBnQcwaNqEl9sOYHaOlUK5crBifD2MtBlVWQXoMvFmkzBP+vdI3LR7IHt5X5D4SvNOUbux0DvGOcnwgHffPNNvPvuu0hLS8Pdd9+N0tJSmUVRuOmmmxok83jyySexZMkSHDp0SKa7v+GGG2T6+ttvv90xcr7//vvx9NNPY/78+XKQJl4jISEBs2fP1u33dAdfbnauhl05WFWLN5SUKwEvX2fdnpoqvXtETUhlb/eF5jwjMi1tgXmRpMNkqxaeSuxF+WyjMyzxqiEGDJXWlkDYwkzBZsoULFZXBbH/cOV+tepK7sdUA7Grr75aJtwQBZhF6OC2bduwePFiR7KNY8eOyVphdvn5+TLdvdgXNnXqVLnatWbNGpn63u73v/897rvvPtx5550YOnSozLIoXtPV2E5qPIPU11tPyraPlwUzB7Q33mEKjAR6T1ftslxg32K9e0QumtQ3zpHe99vUdJRWskYOmZjIrCVqPQkWq6oBRaaw/nAejuSWyfbo7lHoGBUIwxGfc/7hqr37G6BcRaqQ8V05RDPpqNnqQe7FVAMx4d5775WrWiJ9vAghFKnm7VasWIF33nnHcf/FF190PFdkQVy0aJHcI6YlVsXEypn4ush4uGzZMvTs2bNNfyd3s+pADrKKVQapcb1jERlkW3kymoE3ONusKWYa/j5emNE/QbbLqmodKe2JTOnIKqDQVvS+2zggzIATV9SoTzc2rB1mSD7+QD9bZuCaCmDnF3r3iFwkrp+ig9X109LdmcgvZeSOOzLdQIyMTxsudsVgA4Zq2HUdC4TZ+ndgGVCkUu2T2cITnRdDRGZO0oEB1+vZE2qCksoaLLZNAoX6exu7ruGgm5xthieaho+XFbNtEUVVtXWYt01FGpF74UCMWlRhWbWcuRGignwxtleMcY+w1UvtxxDq64BtKqU9Gd/AxHB0jQmS7XWH8nA8T4UHEZlKRSGwe55qB0QAvafp3SNykQiLFgV3BbFCL1bqDSsuGYi3ZYJO3wZkqWx8ZI7siXafc0+0W+JAjFrUgh2nUFXjrB0mZnQMTTsDLbInirzoZHgipFi7KvblFsbPkwnt/EqFi9kTCHmzcKtZiEx2dnONmJDqdNq9h9rkMGRoveJC0M+WdXrXqSLsPlWkd5eohRn8KpnMRntBrL1QNqyITkCXS1Q7/zDT+5rInIEdYC8hJsJh65jel8yGYYmmJFbgxUq80DU6SK7QG17KFYDVVjp2x2dAnVrNI+PTZp7+nKH4bocDMWoxB7JKsPWYysjUJz4USQmh5ji62vh5e/YyMry4MH+M6aFCX0/kl8sMZkSmkb0XOLFRtUWB+fj+eveIXGTPCizMGdRertAbXlA00GOiahenA4dW6N0jctHM/u3h660u1+dtc0YdkXvgQIw8dzXMTuzL8LMNGtPmA1WleveIXMSaYmRa2kLyA69n7TAT1Q6zhyWK8dflg0z0WdcgPPETPXtCTRAW6CPLtgh5pVX4YY/ah0/ugQMxahGi6vvXW9QsobfVglkDVHpxU/AJAPraCnhXlQB7FundI3KRyFRmryn23U7WFCOTqK12XghbfYCUq/TuEbloy7F8R+2wkV2j0D48wDzHruckZ02xtAVABfcbmTI8kTXF3AoHYtQifj6Qg4witel8bC9R+8Jkm877XeNsc6bQNESmspmammIikxmR4YlyGaVZqt1rChAUpXePyEVfbHaGJc4102qYIJLBiL1iQk25M2MnGd7o7tGID/OX7R/3ZiHLdr1F5seBGLVC7TATFiTtOBIItxXkPPQjUMwiwWbB8EQyHW2pDG1heTK0iupaLNyh6k0G+nphcrIKFzMVhieakpfV4hj4i7xUX2n2KZK5cSBGF6yoohrf71IDl4hAH4zrbeDClmdjtTpXxURNsdTP9e4RuWhAYji62WqKiYQdx2xhQ0SGVJ4P7Fus2kGxQLfxeveIXLQsLRPFFTWyLQZhQX62LIRm0n4wENVDtY+uBvKP6N0jasako9inKPYrkvlxIEYXbNGOdFRqaofZs/uYTn+GJ5q3ppiz6CVripGh7foGqK1SbREm5mXCi3kP9aU28sNsYYl2IsOI9rNOpLInU+gcHYTBnSJke19mCXanc4+fOzDpFTMZNSzRdDHzWlHdgA5DVTtzJ5CRqnePyEUihTRripEp7PjU2e53tZ49oSbIKq7Ayv05sp0Q5o8RXU28r0+edxZncWeurJjG5QOdWz/sCdLI3DgQowtyOKcUm4/my3avdiFIbm+S2mFnw1UxU2oX6o+Le6qaYicLyrHucK7eXSI6kwgDO7ZWtWN6s3aYiczbekpmBxYuFxM/9pkfMwpPBLpcpNp5h4DjG/TuEbloWko8fLzUuTdv+ynU1LKmmNlxIEYtFqoxd7BJClueS985Kp20IPaJ1ar9AGR82tXYb7iRmYxoh2bvab+rWDvMRLQhz3PMHPlh1/86Z3u7JnkMGVpEkC8u7RUr29nFlVhzkJOOZseBGDVbnagdZrvgFRl9Zg8wYbbE0wVGqlorQkkmcHiF3j0iF12W1A7Bts3z36VmyAxnRIYhwr92aIrosnaYaew6VYg9GcWa5EDBML0+MwAfleQIO78GqpkO3Uyh+Hb2azAyLw7EqNlEhjoRBiZc3CMasaGqxoXpMb2vaWuKTbGlky6urMHyNFudJiIjOLkFyD2g2p0vUuFhZApfamuHaTLXmZpfMJA0U7UrC4G93+rdI3LRpb1jEeqvJh0X78xAaSUjd8yMAzFqtnnbnB9Ol7tDqIZdj4lAgMpMhLSFQKWaCSWTbWTmTCEZiXY1jEk6TKO6tg7zt6vPOl8vK2b0i4fb4KSjKfl5e2FavwTZLq+uxZLdrHtqZhyIUbOIsK9FqemyHeTrhcv6mLB22Nl4+wLJc1W7phzYPV/vHpGLhneNQpxtZXbF3izkldrShBPpqbYa2Pmlanv7A0mz+PcwiZX7spFTot5HJiTFIjzQF25DrMyG2iZRDywDShhFYMbwxK+YPdHUOBCjZvlxT5ajsOWk5DgE+Hq515FsMFP4sZ49oSYQexVnDVAzhTV19Y7JAiJdiYvcMtum+l5TAX+TZ5f11CQdA90o8kOwWoH+thIK9bUqQRWZwuCOEegQESDbPx/IQVYR9/iZFQdi1CzfaMMSNeFgbqP9YCCym2ofWQUUHNe7R+Si2ZrzkdkTyRC2f9J4iQwytKKKaiyz7TWNCvLFJb1UiQy3nXTcxklHsxDlE+zXXqKqwvztp/TuEjUTB2LUZAVlVfhxT7Zsx4T4YVS3aPc7iiINv/YDKvUzPXtDTdAnPhS940JkW9S4O5ZbxuNH+qkQiRC+U+3AaKDbOP41TEIkQqiqUXWapvcT9Zvc8JIpuoeaeBQyU4GsNL17RM2YdGR4onm54bsKtbZvUzNQZSsiOLN/ggwHc0uizo92RluknybzrYppVm+J2tzueUBtpWqLvadetjqFZKqEVLPcMfKjsVIKOzjpaBaijEL/DmGyvTu9CHttJRbIXDgQoybTXti6Re2ws4noBHQardo5+4BTW/XuEblITBDYa4uL8MR6DqJJL9s/dbbt+3HI8DKLKhzFcjtFBWJgYjjcVvIcwGLb5536hSgSqnePyEXMFGx+HIhRk5zIL8OGw3my3S0mCMnt3XzTuXY/h3afBxlaQngARnSJku1DOaXYcaJQ7y6RJyo4BhxdrdpRPYCEQXr3iFy0YPspRxDELDmx46aRH0JwLNB1rGoXHgNObNC7R+SiGZqoJLGCWyc2jJGpcCBGTaLdECpmYtz6w0kQaaa9/FR711dALQsnmsXsgSp7osCaYqQLbZiXWA1z9/dLN438cOuwxMZC8RmeaBpRwX64pKdKIpNeWIF1h2zZWck0OBAjl4nwrq819SpmuXNYop1/GNBrsmqXZgOHf9K7R+Siycnx8PVWb3ELd5xCjW1fI1GbEMspOz5tfB8OGdqBrBLsPFkk2yntw+ReHLfXexrgrdKhY9fXqvYdmQLDE82NAzFymdgMuj+rRLaHdIpAYmSgZxy9lCudbdZZMY2wAB9M6BMr26Ig6+oDOXp3iTyJ2FMq9pYKHUepPadkviQdtrqEbs8vBOg1RbXL84ADy/XuEbnosqR2CPbzlu3vdmagvKqWx85EOBAjl83bdqrRrHRur/tlgJ/KTIS0BUB1ud49Ihdpk8mwphjpGpZIpon8sH/Wia03IvGPx9CGJ3LS0TT8fbwwJTlOtksqa7A0LVPvLlETcCBGLqmtq3fMEnpbLZiWEu85R87HH0iaodpVJcC+xXr3iFw0tlcswgNVuvDvd2WitJJ7/KgN1NUCO79UbS9fIGk2D7tJbDlWgGN5qvagqJEZG+oPj9FtPBAQodp7vwUqVQQMGd/lgzjpaFYciJFLxAbQzKJKx8VtRJCvZx25BuGJX+jZE2oCsUfMPmlQXl2LJbszePyo9R1eCZRmqXaPiUCAG6c+dzMeGZZo5+0L9L1ctavLgD2L9O4RuUhkCY4PU5MGK/dlI7+0isfOJDgQI5dow7q02eg8RueLgGC19I/9S4DyfL17RM3ayOwMryVqNTu/aHwShwyturYOi3aky7aftxWTbeFenjvpyOLOZmG1WmQqe6Gmrh7f7lTnMRkfB2J0XhXVtXIDqCA2hE7o087zjprVC0ieq9q1VWqvGJnC4E4R6BChsoGt3p+NrOIKvbtE7qy6Athte3/wDQF6TtK7R+QikdAn17aSID7nQvxVWLNHSRwBhCWq9sEfgZJsvXtELtLuZ5yv2dNPxsaBGJ3X8rQsuQFUEBtCxcZQj5RyhbPNOiumIWrd2VfFRK3LBds5U0it6MBSoNJWQLzPdMDHlhKcDG/eVg8OS7SzWp2fdfW1KpU9mULfhFB0jQmS7Q1H8pBeyMRiZsCBGJ2XthiuR2VLPF3CQCCym2ofWQ0UccbJLLQ177RFyYlanHYPqXbyhgytrKoGS3ZnOkpfiL3QHktb847hiaaadLSviokyhgs56WgKHIjROYkNnyv2qk3n7UL9MKJrlOceMYtFEz9fD+z8SucOkau6xwYjKT5UtrcfL8DR3FIePGp5FUXOrKqB0UCXsTzKJrF0dybKbPWXpqbEOYrBe6R2SUC7ZNU+sRHIO6R3j6gZ4Ynztjsn0cm4PPidhlwh9oaJjZ/2/+BeorCKJ2NxZ9OaqQk1WsBVMWoNIuV3jW0Posg+56WKrJK5ElJpV9A9lnY1l5mCTaNrTDD6dVB1T3eeLMLBbJYgMDoOxOic5mtmVGb254cTorurEEUhfRuQs59nkEnYM0oJDE+kVqEtgsuwRNPILanEyv05sp0Q5o9hnSP17pL+kk/bEy1i3cgUmLTDXDgQo7PKLKrA+sN5st0lOgjJ7VVol8djTTFTah8egCGdVLHSfZkl2JNRpHeXyJ2U5qgsc0JYR6DDML17RC5alJqOWlvkx4wBCTIVuMcLTwQ6jVaHIXc/kL7d4w+JWUzvlyB3UtijP+o5iDY0DsTorBbuSHdMgonVBLERlETI0RyxYcy5kZlvcqYMT2R6X2pRIrucyDInpMxV2efIFOZpUn3PZliiE0PxTSkuzB/Du6hV3UM5pTJEkYyLnxR0VtrwrZn943mk7ELjgS4XqbbYxHxqC4+NSUxNiXfsc1ywgzOF1IK0+2i0YV1kaMfzyrD5aL5s92oXgj62pD4EIGkWYPVxnt91tokGMlmmYCbtMDIOxKhRx3LLZHY5QXwwdY8N4ZE6a3pfzQUYGVp0sB9GdVOZP4/nlWOr7RwnuiAFx4Dj61Q7pg/Qri8PqInCEhtbMSeR+TMS6HGZOhQlGcDRNTwsJiFqvvp42SYdt6ejzhZ6S8ZjuoHYK6+8gs6dO8Pf3x/Dhw/Hhg0bzvrcN998ExdddBEiIiLkbcKECWc8/5ZbbpEhd9rb5MmT4enEakFjGz/Jps8MwMtXtXd+yZlCE+FGZmpx4j3AToQlMozbNLQhytP7MfLjDMlzGz/PydDCA31xSc8Y2c4oqpAFnsmYTDUQ+/TTT/HAAw/g8ccfx5YtW9C/f39MmjQJWVmqztXpVqxYgWuvvRY//vgj1q5di8TEREycOBEnTzZcphUDr/T0dMft448/hqfjh9N5BIQDPSaqdkkmcGRVG/xVqCVMSnbWCNJu0idqNoYlmtKBrBLsTlf7Z/onhqNTVJDeXTKeXlMAn0DV3j0PqK3Wu0fUjEzB2n2QZCymGoi98MILuOOOO3DrrbciKSkJr732GgIDA/HWW281+vwPP/wQv/rVrzBgwAD07t0b//3vf1FXV4fly5c3eJ6fnx/i4uIcN7F65sn2ZhRjb2axbA/qGI7ESNubMJ19I/MOTdpqMrRQfx9c2kvNFGYXV2LdoVy9u0RmlpUGZO5U7Q5DgcgueveIXLRQE/kxg6thjfMNAnraooTK84BDP/H8MonLktohwMdLtr/bmY6qmjq9u0SNME21yaqqKmzevBkPP/yw4zGr1SrDDcVqlyvKyspQXV2NyMjIM1bOYmNj5QBs3LhxePrppxEVpfaRNKayslLe7IqK1IyaGOSJm57EzxepSi+kH/O3nWzw4aT372RY3S+DxTcElqpi1KfNR/3U5wBvP5hNS5wzZiPO6+93Zcr2vG0nMbIr6wY1hSeeM2dj2fG5PYcq6vrOFQdH5x4Zk9HOGdEXe2F3EUk6NTnOMH0znL5zYN31lWzWp36O+m7jPPKcMRt/bysm9InFgh3pKCirxk/7sjC+dyzcVZ3BzhdX+2GagVhOTg5qa2vRrl27Bo+L+3v27HHpNf7whz8gISFBDt60YYlz5sxBly5dcPDgQfzpT3/ClClT5ODOy0vNJJzumWeewRNPPHHG49nZ2aioqIDef/jCwkJ5MoqBalOJ7/tm63HZFsnlhsX7nDX0k4CwzuMQsG8eLJVFKNjyFSo7jzfdYbnQc8aMkqMsCPSxoqy6Dt+lpuPeETGOcEU6P088ZxpVX4/oHZ/JD9J6ixU57cagju+Xpjhn9mWV4WB2qWwPSAiGtbIIWVlM892osP6I9Q2BVU46LkDWsD+1yaSj0c4ZM7qkcyAW7FDtz9cfRoobzznWGex8KS5WkWVuMxC7UH/729/wySefyNUvkejD7pprrnG0U1JS0K9fP3Tr1k0+b/z4xi+qxaqc2KumXRET+89iYmIQGhqq+4koEo6IvjTnRNx+ogAnC6tke0TXKPTp4kyBSo0YfB2wb55shp/4AfXDrjXdYbrQc8asJvbNxDfbTqG4shZ7Ci1y5pBc46nnzBlObIK1SE1cocsliO7MbIlmOWfe3rLX0Z4zpJOMiqGzs4gEVds/grW6FLFF24He0z3unDGj6ZHReGrpMRSWV2PVoUIEh0ci0Nc9L/3rDHa+aMca52Kav0Z0dLRcocrMVOFEduK+2Nd1Ls8995wciC1btkwOtM6la9eu8mcdOHDgrAMxsadM3E4n/vBG+OOLE7G5fVm4I8PRnjUgwRC/j6GJEI2ACKA8H5a938FSUwH4mm9P3YWcM2ausyIGYsKCHRmY2JcZ05rCE8+ZM+xyZpGzpFwBiycfCxOdM2LGfOEOlbZe1BUU9QX17pPhiWyg2z+STRmmmDTTo84Zs/L3tcrz++MNx1BeXYsf9ua4dSZsi4HOF1f7oH9PXeTr64vBgwc3SLRhT7wxcuTIs37fs88+i6eeegqLFy/GkCFDzvtzTpw4gdzcXMTHe95FmcgeZ9+8LOpPTOaF6fl5+wJ9bB9I1aXA/u9b949ELWZMj2hEBKpipct2Z6KsqoZHl1wnitvu+lq1RSmLNlghoJax5VgBThaUy/aY7tGICjbf3t421+USINC2d37vYqCyRO8eUbNKtrC4s9GYZiAmiHBAURvs3XffRVpaGu6++26UlpbKLIrCTTfd1CCZx9///nc8+uijMquiqD2WkZEhbyUl6g1E/PvQQw9h3bp1OHLkiBzUzZo1C927d5dp8T3NhsN5yCxSSUgu6RmLMNtFKp0H66yYko+XFVNS1ISLmClcurvhajvROR39WZWuEEQpC1HSgkzBnqTj9BTfdA5ePkDSLNWuKQf2fsfDZRLDu0QiLlSFyf20LxuFZSxBYCSmGohdffXVMszwsccekynpt23bJle67Ak8jh07JuuA2b366qsy2+IVV1whV7jsN/Eaggh13LFjB2bOnImePXvitttuk6tuq1atajT00JOKOM/o73krgs3WeQwQZNtfsG8JUMEN32acKdRenBGd106VRU7qezkPmIkiP0T9QMHXy4qJfRsmAKNzSL7C2WZxZ9Ow2sJvheraeny/27kFhfRnmj1idvfee6+8NUYk2NASq1znEhAQgO+/ZyiZUF2rssfJ4+LjJetPkIusXkDf2cCGN4DaSmDvt0B/ZxIYMq5hndVMYUZRhZwpLCirQnigr97dIqMTRW1FcVtBFLsVRW/JFNYfzpX1A4WxvWJkXUFyUceRQEg8UJwOHFgm90bLPdJkeGJy/a2fDzsmHa8akqh3l8iMK2LUelbvz0G+bbl6QlI7t82q0zbhiZqZcjL8TOH0fs6ZwsU7OVNILjj8kypuK/ScpIrekiloV75nDmBYYpOI5AN956h2XTWQtrCF/zrUWgYkhqNDRIBsrzmYi9wSZy1c0hcHYnTmhxNj5puuwzAgtINqH1wOlNku0sjwtBdj2vBcorPaaUvScfokDBlaVU0dvrNNtgT6emGcGxe3bZtJxy/07Ak1MZvgNNukowjPXbyLk45GwYEYoaK6Ft/b/lOG+nvj4p7RPCpN/p9kBZJt+0TqaoC0BTyGJpHSPgydolTJgbUHnWFLRI2qqXT+//YNAbpfxgNlEj8fyEGBPfKjDyM/mqX9ICCis2ofXgmUZLXgX4ha04x+3BNtRByIEX7ck4XSqlp5JCYnx8HP24tHpTnsIRsCNzKbaqbQHp5YVw8s3ulM+EN0hoM/AJWFqt17KuDjWtFO0h8jP1qAxeJcFauvc+6VJMPrmxCKLtEqjHr94TxkFVXo3SXiQIwEe2FLgal8L0DCQCCii2ofWcWZQhOZrp0p1Px/IDqDdpKFYYmmjfy4iJEfzceSLaaddJxhm3Ssr4cjeyjpiytiHq60sgbL96haOFFBvhjZ1VawkZqOM4Wm1TsuBN1i1EzhxiN5yCjkTCE1olpTP8k/HOh6KQ+TSTDyowXFJgExvVX72Fqg8ERLvjq1oumaHADaSXjSDwdiHm75nixUVNc5whK9vXhKXBDOFJo4PFF9QHGmkM5q/xKgqkS1+8wAvFnqwJx1MpktscUmHQVmCjaNnu1C0LNdsGxvPpqPkwXlenfJ4/Gq28Mt1GRLtGfUoQvQTswU9lFtzhSairaI+UJmT6TzhiVq9oSSoZWIyI80lVQiOpiRHy2Ck45ukbRjET/rdMeBmAcrrqjGin3Zsh0T4ofhXRiW2CK0F2i7NGmuydC6x4bIEEVh67ECnMgv07tLZCSVxcC+JaodGA10vljvHpGLlqdlorJGRX5MSY5n5EdLiOoGxA9Q7fRtQO5Bno8mwfBEY+FAzIMt3Z0p66oIU5Pj4GW16N0l98DsiaZlz54oLGL8PGntXQzU2MJ4kmYBXix6bxYLtqc3+n+cLhAnHU1JZE4UGRSFHScKcSSnVO8ueTQOxDyYdqOmdoaELlB0dyC+v2qf2sqZQpNmT+RGZmpg11fONsMSTaOoohorbZEfsSF+GNo5Uu8uuY++ttqZAqM/TEW7T5LZE/XFgZiHKiyrxqr96sMpLtQfgztG6N0l942f5weUaXSODkJyezVTmHqSM4VkU14AHFim2iHxQMeRPDQmsXRXJqpqbZEfKfGwMvKj5YR3BNoPUe3MnUD2vhZ8cWpN01LiG62vR22PAzEPJeqpVNfWO5J08MOpFWcKWdzZtKtinCkkac8ioLZKtZNmA1YWvTcLbeIdbUIeaiEMTzSlxMhADEgMl+09GcU4kFWsd5c8FgdiHkqbypcx8600U9hhmGpn7Qay97bGT6FWwJlCOndYoma1mwytoKwKq/bnyHZCmD8GJjLyo8WJ/ZKN/T8hU4UnavdRUtviQMwD5ZZUYs3BXNnuEBHgmBWhFsb4eTeZKbTVjSLPVJoLHPxRtcPEBIstFIsMb8muTNTUqcgPhiW2krAOQOII1c7eA2SltdZPolaYdBQl4eyT8/WiiCa1OQ7EPNDiXRmorXOGJYpittQK+s5uWPCSb3KmoV0lZk0xD5c2H6ivVe3ky1UxWzJf5AcTUrVNeCKLO5tGXJg/hnZSyWsOZZciLZ3hiXrgQMwDLdQsQWsL+1ELC01wburP2cuZQhPRFjcX2RM5U+jBtHs8taUpyFSRH/07hOndJffVZyYAizM8kZOOpqHdN8lJR31wIOZhsoorsP6w+nDqHBXoqCVBrUR74cbsiaYRHxaAoZ3VfhIRmrg3kzOFHqk4Ezj6s2pHdnWWpSDD+35XJiM/2kpoPNBptGrnHlAZFMkUJifHw55IlOGJ+uBAzMN8l5oBW1QiwxLbQhJnCt1jIzPT+3puWGKdc1KFYYnmzJbIyI+2D8UnU4gJ8cPIblGyfTyvXJZtobbFgZgHfzhp03RTKwmJ40yhSU3RzBQyPNFDaVextcl3yNCyiyux7hAjP9o8e6LF6vx/w/BEc5Zs2cHsiW2NAzEPklFYgY1H8mW7W0wQeseF6N0lzyA2+NsxPNFUM4UjuqqZwqO5Zdh5skjvLlFbKkoHjq5R7eieQLu+PP4msXhnOiM/2lpwLND5ItXOPwykb2vzLlDzTOobBy/brCMnHdseB2IeRFucVsyAMFtiG25kts8UMnuiaWcKuZHZA8MSUe9cDWNYomks0MzqM/KjDWlXjRmeaBqRQb4YZQtPPFlQjm3HC/TukkfhQMxTY+Y1mXKoLWYKx2hmCrfzkJvE5GTOFHoshiWaUmaRiPzIk21Gfugx6eil2ru+YXiiiWj3UTI8sW1xIOYhTuSXYesxNcshQhK7xzIsUb/sidzIbKaZwtHdox0zhdtPcCOzRyg8CRxbq9oxvYHYPnr3iFz0baooN6Ha0xj50baCooCul6h24THg5OY27gA118S+7eBtC08U/4fq7FndqNVxIOYhxH+sxorVkh4zhdzIbCbTU5z/XxZpVpXJje2e52yzdpipiD0udjP4Wdf2WLLFlMIDfTGmh5p0PFVYga0MT2wzHIh5CO1Ss5glJB1nCguOAae28E9gwplC8f+IxZ09LSxRk5abDO1UQTk2H1UJqXq1C0GPdoz8aHN9pgNWH+f/ozpb+QcyvGkNJh2ZPbGtcCDmAY7nlTlCqkQB5y7RQXp3yTNxI7MpcabQwxQcB05sUO3YvkBML717RM2acGTkhy4CIoBul6p20Unn/yUyvIl94+DjxfDEtsaBmIdlS+SHk456i5lCb9XmRmZT4UyhB9n9jbPN2mGmspCfdcbA8ERTCgvwwcU9YmQ7o6gCm4+p1WVqXRyIeVi2RO0FJbWxwEigq32m8ARwYiP/BCYxMYkzhR6D2RLNG/lh29fSJz4U3WKC9e6S5+o9FfDydU461tXq3SNykXaynuGJbYMDMTd3JKfUUYg2pX0YOkUxLFFX2hl2Fnc2jbBAH1xkmylML6zAFs4Uuqf8o85Mb3EpQHR3vXtEzaqTyQlHXfmHAd0nqHZJhjMDKRnehKR28PWyOpK81TJ7YqvjQMzNMSzRgDOFjo3MYqaQG5nNQruarM3MRm6Eq2HusT+MkR8GC0/UhPuSoYX6++DinmrSMau4EptsNfmo9XAg5ub44WTAjczdx6t28Sng+Hq9e0TNnClknRU3xIGYKR3LLUPqSZWQKrl9KDozIZX+ek0GvPyc5SAYnmgaM/pz0rEtcSDmxg5ll2B3ugpL7N8hDImRgXp3iQSGJ5p3I3PPaOdMoS1NNrmJvENA+jbVjh8ARHbVu0fUnMiPFJZnMQS/EKDHZapdmgUcXaN3j8hF4/u0g6+3Gh58t5Phia2NAzEPKeLMbIkG0muqZqaQG5nNZLqmBh+LO7sZbfgUsyWayqJUJqQyJE46mlKwnzcu7aXCE3NKqrD+cK7eXXJrHIi5Me0+lqmMmTcO/1DNRuZM4Ng6vXtELhrfJ9YxU/jtzgxuZHYnu75ytlnE2ZQJqfp1CEPHKEZ+GEbPyYC3v2qnzQdqa/TuEbloWoNJR+6Jbk0ciLmpg1kl2JNRLNsDEsPRIYIfTobCmUJTCvH3wVjbRubs4kps5EZm95BzAMhIVe2EQUBEZ717RM0KS2S2REPxC9aEJ2YDR3/Wu0fkovG9Y+Hvo4YIi3dmoKaWicVaCwdibkrM1tsxla8BcSOzabHOihva/bWznazJ9kaGp52tZ+SHAXHS0ZSC/LwxrnesbOeWVmHdIWZPbC0ciHnALOEUzhIaDzcym3ojsx83MruXnZqBWNIsPXtCzU1IlRjOhFRG1GMS4B2g2gxPNBVt4hvtPkxqWRyIuaHDueXYl1ki24M6hqN9uO1NkIyFM4Um3sisZgq5kdkNZO8DsnapdvshQHhHvXtEzUhINZ0TjsYNT+w5UbXLcoEjq/TuEbno0t4xCPDxcoQnVjM8sVVwIOaGlu/Pb3TDJRkMNzKbFsMT3YjIXGrHsETTJqSakhKna1/oHDjpaEqBvt4yQZWQX1aNtQeZPbE1cCDmhpbvcw7EpvLDyeAbmW0zhdzIbCoidp4bmd2wiDPDEk3jgCYh1cCOTEhlaOJzzseWMCxtAVBbrXePyEXaHAPMntg6OBBzM/syi3E4r0K2h3SKQHwYwxINjTOFpsSNzG4iaw+QtVu1E4cDYR307hE1p04mwxKNzTcI6DlJtcvzgMMr9e4RuWhsr1gE+trCE3cxPLE1mG4g9sorr6Bz587w9/fH8OHDsWHDhnM+//PPP0fv3r3l81NSUvDtt982+Hp9fT0ee+wxxMfHIyAgABMmTMD+/fthVotSndkSWcTZBMSHEzcymxI3MrtZWCKLOJsKsyWajPb/l/b/HRmav48XJvRpJ9uF5dX4+UCO3l1yO00eiN18881YuVKf2YxPP/0UDzzwAB5//HFs2bIF/fv3x6RJk5CVldXo89esWYNrr70Wt912G7Zu3YrZs2fL286dOx3PefbZZ/HSSy/htddew/r16xEUFCRfs6JCrSqZiRhU2mcJLRZgSjJrqphqppAbmU2FG5ndAMMSTWl/ZjH2ZqqwxMGdIpDAhFTG1/0ywCdItRmeaCrashAMTzTAQKywsFCuGvXo0QN//etfcfLkSbSVF154AXfccQduvfVWJCUlycFTYGAg3nrrrUaf/69//QuTJ0/GQw89hD59+uCpp57CoEGD8O9//9sxcPnnP/+JRx55BLNmzUK/fv3w3nvv4dSpU/jmG/PN2IgPpoPZpY6wxLgwW0V7Mra+sxu/MCTDb2Qex43M5pWVBmTvUe2OI4FQJjYyCxZxNiHfQFU/UyjPBw7/pHePyEVje8UgyBae+P2uDFTVsLhzS/Ju6jeIAUp2djbef/99vPvuu3J1SgzMxKqTGMz4+PigNVRVVWHz5s14+OGHHY9ZrVb5s9euXdvo94jHxQqalljtsg+yDh8+jIyMDPkadmFhYTLkUXzvNddc0+jrVlZWyptdUZGqY1JXVydvelm43VnnYUpyO137Qk3QbQIsPoGwVJehPm0B6qf8A/Bqnf9HjRHniZiU4PnSdFOT4xwzhAt3nMKY7lHwBO5wzlh2fgWLrV2XNFv8Ujr3yL215DmjnZWf3JefdaaRNBvWnV/KZv3Or1HfdZzbv8+4A18vi8yeOH97OooqarBqf5ajhIuR1BnsfHG1H00eiAkxMTFygCNuIkTw7bffxo033ojg4GDccMMN+NWvfiVXzFpSTk4Oamtr0a6dilW1E/f37LHNap5GDLIae7543P51+2Nne05jnnnmGTzxxBNnPC4GqHqGNJ7ILoSXBairB4a08z5ryCYZT1jHSxBw8DtYyvOQv20BqhLHtOmbhVjpFm9gYnKDXNc3AgjwsaK8ug6LU9Px65Gx8Bb/Cd2c6c+Z+npE7/hCfgDWw4KcmJGo4/ulKc6ZQ7nl2J+l6mT2TwiGtbIIWVlqMpQMLrQfYn0CYbVNOmYN/QPg5eu+7zNuZEzHQMzfrtpfbjgiP/uMps5g50txsQqfbpWBmF16ejqWLl0qb15eXpg6dSpSU1Nl2KDYe/Xb3/4W7kisymlX2sSKWGJiohyghoaG6tavF66LxcPFFfgx9SiSurQ3xIlILhp0DXDwO9mMOLUC9YPntOmbl8Vikecvz5mmG98nQ9YzKqqsxf5iKy7pGQN3Z/pzJnMXrAWHVLvjCER3SdG7R26vpc6ZD3c4k2nNHpSI2FjjzczT2Vl6TQV2fgFrZSFiS9KAHpe57/uMG5kREYUnlxxBSWUtVh0uRFhkFPy8VbiiUdQZ7HwRSQJbZSBWXV2N+fPny1WwJUuWyH1V999/P6677jrHIOTrr7/GL37xixYdiEVHR8vBXmZmZoPHxf24uMYLOYrHz/V8+7/iMZE1UfucAQMGnLUvfn5+8nY68YfX+48fE+KPsd0jDNEXamqdlSCguhSWPQthmf5im4YnijcvnjPNM71fgqOw7LepGbi0d8MVdndl6nMmbZ6jaek7BxYz/g4eeM6cnpBqar8Ec55/nkwUTd/5hWxaxf/DXrZkVe74PuNGAvysuCwpDl9vPYniihqsPZSHcQb8rLMY6HxxtQ9N7qkYsIiEGZ06dZKp4zdt2oS77rqrwUrQpZdeivDwcLQkX19fDB48GMuXL28w+hX3R44c2ej3iMe1zxfE6p39+V26dJGDMe1zxOqWyJ54ttckahXcyGxa3MhsMvX1wC57MiYLkDRT5w5RcxJSDe0ciXahTEhlOt3GA74hqp22EKip0rtH5CJtvT775CNduCYPxF588UWZVVDU8zrbqpEYhIlEGC1NhAO++eabMklIWloa7r77bpSWlsosisJNN93UIJnHb37zGyxevBjPP/+83Ef25z//WQ4c7733XsfIWazmPf3003KVT4RVitdISEiQae6J2hSLO5u3zkqSmhkUG5lZZ8XgMncBubbwtk6jgZDGIyrIeLRJOljE2aR8/IHeU1W7shA4+IPePSIXXdQzGiF+KpBu6a5MVNbU8tjpMRATSTlcjXtsaVdffTWee+45WYBZDAK3bdsmB1r2ZBvHjh2T+9bsRo0ahY8++ghvvPGGrDn2xRdfyIyJycnJjuf8/ve/x3333Yc777wTQ4cORUlJiXxNvX5H8mDdJwC+warNmUJT4UyhiWhLRGhLR5ChibBE+0BM1slM4QDatFjc2ZTEnrDL+qrr7eLKGqzax+LOLeGCknXoQaxm2Ve0TrdixYozHrvyyivl7WzEqtiTTz4pb0S68gkAek0BUj8HKgpUnZVzbGQm47i4ZwyC/bxRUlmDJbszUFmTbLiNzGQPS7QNxCxWoA/DEs0iLb0Yh3JUWOLwLpGIDeFkqWl1Gwf4hQKVRcCeRUB1hVopI8Ob3i8eX2056ajnZ48GoebTfzcbETkxPNG04YmX2T6QxEbm1fs5U2hIGalA3kFNWCIvIsxiUaqzTua0fiy+bWrefkDvaaotBmMMTzSNMd1jEOJvC0/cnYmKaoYnXigOxIgMvZHZWTiczBOeqN3LQkYNS7xcz55QM8MSrRZRxJlhiabHSUdT8vW2YpLt/5+IAFm5L1vvLpkeB2JERt7IfOjMcFsywUZmzhQaD8MSTWvXqSIcyS2T7RFdoxATcmb5GDKZrpcC/mGqvfdboLpc7x6Ri6b100w62spJUPNxIEZk5JnCnV/p2RO6kI3MDE80lvTtQL4tm2/nMUCw+xfedhfaVNnai0AyMW9foPcM1a4qAQ40LDVExjW6WzTCAlSd02WcdLxgHIgRGXUjs2OmsELvHlETNjLbLdrh3NNCBsCwRPOGJdr2h3lZLQxLdNvwRE46mis8UU06llbVYsVehideCA7EiIyGG5lNixuZDYphiaaVerIQx/NU2NqoblGICmZYotvoegngH67aexcDVSr8lIxPmzCH4YkXhgMxIiPqO8fZ5kyhKTcyi5nCn7iR2RhObQUKjqp2l4uBoGi9e0QuYhFnN+blA/SxhSdWlwIHlurdI3KRmBQJD1ThicvTMlFexeyJzcWBGJERdR2r2cj8HTcym3UjM7MnGgPDEk0blmjfHybCEu2THORGmD3RlHy8rI4w4bKqWvy4N0vvLpkWB2JEptjIvEzvHlFzNjKnsc6KMcISv1FtixeLOJvI9hOFOFmgwhJHd49GRJCv3l2iliZWqAMiVXvf90CVKtpNxsdJx5bBgRiRUSVf3nj9IzLNRmYxU7iCM4X6OrkZKDzmXGkOtF30keFpE95M19TpIzcLT0yaqdrVZcD+JXr3iFw0smsUIm2TI8v3ZKKsqobHrhk4ECMyqi6XAAERqs2NzKbdyKxNvU060JaASNbsvSTTFHH2tlow0Ta5QW6IJVtMyVuEJyar8MSK6josT2N4YnNwIEZklo3MnCk06UbmLG5k1ktdHbDbFpZo9QF6T9OtK9Q0W48X4FShKt0xpkc0wgMZlui2Oo0BAm0JdMTnXGWJ3j0iF2lXqrknunk4ECMyTfZEhieacSNzeXUtftjDmUJdnNgAFJ101uezrzCT4TFbogfx8naGJ9ZUAPsW690jctHwrlGIDlaTJCJhR0klwxObigMxIiPrfBEQGOXcyMyZQtOY3iA8kcWddQ9L1IY/kaHV1TnDEn28RFgisyW6PWZPNCWRzXRKsloVq6wR4YmZenfJdDgQIzL6TGEf+0xhObD/e717RC4a0TUSUbaNzGJFrJQzhW2rrhbYPU+1vUQW0qlt3AFqri3H8pFRpMISL+4R48hCSm6s02ggKEa19y8FKov17hG5aLqmZMuC7dwT3VQciBEZnTbBAMMTTbWReUpKnGOmUKSypzZ0bC1QkqHa3Sc46/KR4WkT3GhTZJMbs3oBSbNUu7ZS1c8kUxjSORKxIX6yvXJfNgrLq/XukqlwIEZkdJwpNK1pKcyeaIwizsyWaKawxG9T1UDM18uKCUnMlugxuCfatOGJU21JO6pq67BsNycdm4IDMSIzzRSKjcwilT2ZwrAukYixzRT+tDcbRRWcKWwTtTXOsERvf6DX5Lb5uXTBNh3NR1ZxpWxf3DMGof4MS/QYHUcAwbb9gAeWARWFeveImhGeyD3RTcOBGJEZcCOzaWcKp2lmCpfu4kxhmzj6M1Cardo9LgP8Qtrm59IF017EaS/uyNPCE6sYnmgigzpGID7MX7ZX7c9BYRknHV3FgRiRGXQcqZkpXApUFOndI3KR9mJykS3kilrZLm22RIYlmkVNbZ0jLNHPm2GJnj7paNH+PyZDs2omHWvq6vH9Ltv+XDovDsSITDlT+K3ePaImzBTGhdpnCrM5U9gmYYnzVdsnEOg5qdV/JLWM9YfzkFNSJduX9opFsJ83D62nSRwOhNj21h76EZaKAr17RC7SJtZZyElHl3EgRmQWDE8070yh7QOqupYzha3u8E9AeZ5qi0GYb1Dr/0xq+bDE/gxL9EhWq+OzzlJXA//DS/XuEbloQGI42ocHyPbPB3KQV6omVejcOBAjMuNM4YHlQHm+3j2i5tRZYXHnNsyWyCLOZlFdW4fvdqpwpkBfL4zrHat3l0gvyXMdTf+DTGNvFhaLxfFZV8vwRJdxIEZkqpnC2apdVw3sYXiiGWcK1xzMRW6JygpHLaymCkhboNo+QUCPiTzEJrH6QA4KbBv8x/dph0BfhiV6rPaDgPBOsul7cq0z8Q4Z3vR+CY2ucNPZcSBGZCbaxAM7v9SzJ3RBM4XMnthqYYn2PSW9pgA+avBLxrdwuzORDbMlejiLxRmeWF8HpNn2fJLhJbcPRcfIQNleezAXOZx0PC8OxIjMpMMQIKyjah9aAZTm6N0jchFnCtvATk2WtWRmSzSLyppaLNmtwhJD/LxxSc8YvbtEBgpPtGjDjck0k4519XCEG9PZcSBGZLaZQvsFZn0tZwpNNlPYKUrNFK47lItsW9FaaiE1lcCeRartFwp0G89DaxIr9+WguKJGti9Lagd/Hy+9u0R6i0tBfVR31T66Bihi6Q8zZk9cxPDE8+JAjMhstDP92hUAMtlMIS8sWtTBH4DKQtXuNRXwUSUDyPgWbGe2RGpk0jHJFp6IemD3NzxEJpEUH4qu0UGOkhRZRRV6d8nQOBAjMpu4foB9pvDIas4Umsi0FO1GZg7EWpR2zySzJZpGeVUtlqWpPZNhAT4Y051hiaTUc9LRtJOO9lWx+npgEWuKnRMHYkSm3MhsXxUTM4XzdO4QuapPfAi6xqiZwo1H8pDJmcKWUVXmzCLqHw50G8eT0iR+3JuFsqpa2Z7cNw6+3rwsIZuY3qiO7KnaJzYABcd5aExiRn9OOrqK73hEJt/IzOyJZgtPTHDOFHJVrGXs/x6oLlXtpJmAt28LvTC1NhZxpnOp6DbFeYdJO0yjZ7sQ9GoXItubj+bjRH6Z3l0yLA7EiMwotjcQ21czU3hM7x6Ri7SpuVlnpRXCErWTFGRoJZU1WJ6WJdtRQb4Y2TVK7y6RwVR0m+q8w5ItpjKjvzZpB0Pxz4YDMSKzSlYbmSXOFJpqprBnu2DZ3nKsgDOFF6qiCNi3RLWDYoHOF13wS1LbWJ6WicqaOtmenBwHby9eklBDteGdUR/fX91J3wbkHuQhMmHJlgXMnnhWfNcjMisWdzatGZoPKM4UXqC93wK1tlIAfWcDVqY+N4sFmiLO2j0lRFr1tuyJEicdTaNzdBD6dQiT7Z0ni3Aou0TvLhkSB2JEZhXVDUgYqNrp2zlTaCLai875mtTd1AwMSzSlwvJqrNyXLduxIX4Y2jlS7y6RUWmzoHIgZtpJR2YKbhwHYkRusyrGmmJmnCncdaoIBzlT2Dxleap+mBDaAegwrOX+SNSqluzKQFWtCkucmhIPL6uFR5waF94R6DBUtTN3Atl7eaRMWNxZTDrWiyxV1AAHYkTuMlPIjcymnSnUFrSlJkibD9TVOPdMWvmRZhba2XHtpn6iRnHS0ZQSwgMwzLbafSCrBHszi/XukuHwU4vIzMITgcQRqp2dBmTu1rtH5KLpmotPMRDjTGEzMCzRlPJLq/DzgRzZTgjzx8DECL27REYn9n/Ctmq66ytV/4NMQTvRwknHM3EgRmR2yZrwRPEBRaYQH+acKTyYXYrd6UV6d8lcijOAw6tUO7IrED9A7x6RixbvykBNnbqQnt4/AVaGJdL5hCYAnUapds4+IHMXj5lJTEmJh/2/uEjQw0nHhjgQIzK7pNmAxepcIeBMoWnMGKANT2SdlSbZPU/kU3PWDrNwj5FZzN92qtG6ekTnxFB8U4oO9sPo7tGyfSyvDNtPFOrdJUPhQIzI7ELaAZ3HqHbeIVVrhUxhanKcI0kBwxObiGGJppRZVIF1h3Nlu3NUIFLaq6Q1RK5NOtrKU3DS0VS4J9oNBmJ5eXm4/vrrERoaivDwcNx2220oKSk55/Pvu+8+9OrVCwEBAejYsSN+/etfo7Cw4UjcYrGccfvkk0/a4DciakHcyGxKUZqZwpMF5bLAM7mg4BhwfL1qxyYBsX142EyUpMO+aD+zf4L8zCVySXAM0PUS1S44CpzYxANnEpP6xsHHS/1fX7jjFOpsoclkooGYGITt2rULS5cuxcKFC7Fy5UrceeedZ33+qVOn5O25557Dzp078c4772Dx4sVyAHe6t99+G+np6Y7b7NliUyiRifSZCVi9nXVW6lRaaDI+cTFqx43MLtLWEtLukSTDW6DJljhTE5pL5JLkK5zt1M950EwiLNAHl/SMke3MokpsPJKnd5cMwxQDsbS0NDmI+u9//4vhw4djzJgxePnll+XKlRhsNSY5ORlffvklZsyYgW7dumHcuHH4y1/+ggULFqCmxpbu2EassMXFxTlu/v7+bfSbEbWQoCig61jVLjwOnNjIQ2sSE/u2g6+31bFaUMuZwqaFJWpXg8nQjhdUYIdtf0hSfCi6x4bo3SUymz7TAS8/54RMbcPrOTKuGdpJxx0s2WJnm0I3trVr18rB0pAhQxyPTZgwAVarFevXr8fll2tqKZ2DCEsUoY3e3g1/7XvuuQe33347unbtirvuugu33nrrOcMlKisr5c2uqEhlO6urq5M3PYmfLzLS6N0P0kHfObAeWCab9amfo95eAPM8eM7oK9jXC2N7xmDJ7kzklFRizYFsR7iiUel6zuQegDV9u2zWJwxEfUQXrgCbgDhXlu7Na5DSmp9T1OT3Gd8QWHpMhGXPAqA0C3WHVzonIcnQxvWKgb+PFRXVdfh2Rzoem9YH3l4ttx5ktGsZV/thioFYRkYGYmNjGzwmBlORkZHya67IycnBU089dUY445NPPilXywIDA7FkyRL86le/knvPxH6ys3nmmWfwxBNPnPF4dnY2KioqoPcfXgw4xckoBqrkOSxRwxHr5QtLbRXqUr9E9sD7neGK58BzRn8Xdw7EElsJuM83HEaPUGN8kBjxnAna/D7s6yjFnSaiLCurTX8+NU9tbS2+261qhwkjE3yRxb8dNeN9xi9xAiLEQAxAxcb3URScxONoEqM7h2H5/nzklVXjuy2HMLxTaIu9ttGuZYqLi40/EPvjH/+Iv//97+cNS7xQYsVq2rRpSEpKwp///OcGX3v00Ucd7YEDB6K0tBT/+Mc/zjkQe/jhh/HAAw80eP3ExETExMTIFTc9iRNRrOaJvhjhRKS2FAv0nAKkzYNXRR5iS9KA7uPP+108Z/R3eXgU/rrsGMqqavHTwUI8e1W0I1zRiHQ7Z+rrYTn8veNu8LAbEBzWcJKOjGnXyQIcL6yW7cGdItCvewe9u0QGd9b3mYgrUf/TI7BUFSPgyDL4R/4H8LaFK5KhzR1aJwdiwqpjZZgxtHuLvbbRrmVc3eak60DswQcfxC233HLO54hwQbFv6/SZM7HPS2RGFF8734h08uTJCAkJwddffw0fH59zPl/sQRMrZyL00M+v8f/Y4vHGvib+8Eb444sT0Sh9oTbW70o5EBOsorhzz8tc+jaeM/oK8rfisqR2mLftFArLq/HzwVyM79MORqbLOSOKuObsVe3EEbBGdGy7n00XZGFqpqM9a4Ao4szPJ2rm+4xfkNortv1jWCqLYDm4XN0nwxvXpx2C/bxRUlmD73dl4i+X18PP21aSoAUY6VrG1T7o2lMxau3du/c5b76+vhg5ciQKCgqwefNmx/f+8MMPcvQrBk5nI1aqJk6cKF9j/vz5Lo1Ot23bhoiIiLMOwogMrftlgJ9tVTZtAVBdrnePqBnZE+dv50bmRmmzpIkizmQKIlRIJKIRRN28qSks4kwXiNkTTcnfx0smqBKKK2rw095seDr9h4wu6NOnj1zVuuOOO7Bhwwb8/PPPuPfee3HNNdcgIUFdvJw8eVIO3MTXtYMwEWr4v//9T94X+8nETcSqCyKDosjEKNLbHzhwAK+++ir++te/yvpjRKbk469S2QtVxcA+ZxgXGdtFPWIQFqBW7JfuzkR5lXqfIhux8TnVli1RFHXtyzIjZrHlWL6skyeM7haF6GBOdNIFEvXEAqNUe99ioNK1/ThkrOyJ8zjpaI6BmPDhhx/Kgdb48eMxdepUmcL+jTfecHy9uroae/fuRVlZmby/ZcsWmVExNTUV3bt3R3x8vON2/Phx+RwRpvjKK6/IFbcBAwbg9ddfxwsvvIDHH39ct9+T6IKlsM6KGYk9YVOSVai12Cu2fI8zlItE7vP1QOExdShElrRg7g0zi/nbTjXIlkh0wbx8gCTbZExNBbDnWx5UkxjTPRqRQb6yvWx3pgxT9GSmyJooiAyJH3300Vm/3rlzZxn+YDd27NgG9xsjVtnEjcitdLkYCIqVqX2xfwlQXgAEhOvdK3JxpvCTjccdxZ2n92PB20bDEvtdxfPJJGpq67AoVYUl+npZMDHJ2HsfyURSrgQ2/c/5/tD/ar17RC7w8bJiWko83l93FJU1dfh+ZwbmDvbc5D2mWREjIhdZvZz7Z2qr1F4xMoURXZ1hWz/uzUZRhcoy5/Fqq1XxVsE7AOg9zeMPiVmsOZiLnJIq2R7dJQwh/udOmEXkssThQKjtAv7Qj0BpLg+eScweyPBEOw7EiNx1ptBu5xd69oSaQCQymN5PhW5V1dRh8U7X6iS6vYM/AOW2YsC9pgB+9kpiZHTaxDOX9YrUtS/kZkRWuuQ5ql1XA+z+Ru8ekYsGdYxAh4gA2V69PxvZxZUee+w4ECNyR+0HARFdVPvwSqCYF/RmIVJ7283bdlLXvhgyLFE7yUCGVlFdK8OOhGA/L4zqEqZ3l8jdaN8PUjnpaBYizbz9s66uHli4w3MzBXMgRuSOLBbnB1R9nTOsiwxvQGI4OkUFOsK6Mgor4NEqS4A9i1TbPxzoPkHvHpGLVuzNRrFtI/7EpDj4G7hIOZlUXAoQ3VO1j60BCk/o3SNy0ewB7R1tUUPTU/FdkchdMXuiiWcK1QeUyDckknZ4tL3fAdUqG65MWe+tsm2R8WnP3ZnMlkitNemorSm201biggyvR7sQ9IlXdU+3HS/AkZxSeCIOxIjcVUwvNVsonNwM5B7Uu0fkotma8MRvPD08MfUzZzuF2RLNoriiGsvSVAmGqCBfjOpmq/lE1KqTjgxPNOtn3TwPXRXjQIzIY5J2cKbQLLrGBKN/B7WfZtepIuzP9NBipaU5wIHlqi2yo3UcqXePyEWiKLlITS1MTYmHtxcvN6iVRHUDEgaqdsYOIHsfD7VJzByQIBc1hXnbT5637JQ74jsjkTuzp7G3JzzwwDc5s7KHJ3r0qpjY21hfq9opc1WWNDIF7ey2uNgialUNwhO5KmYW8WEBGNZZZVM9lF2KnSeL4Gn4qUbkzsI6AJ1Gq3bOPiAjVe8ekYum94+H1T5TuO2UR84UNggzYrZE08gqrsCq/dmy3T48AIM7RujdJXJ3Mo297Q1zx2ecdDSR2QO1STs8b9KRAzEid8ekHaYUG+KP0d2jZftEfjk2H82HR8k/Chxfp9oxfYB2yXr3iFy0YHu6TEktXD6wPaz2GQWi1hKaAHS5SLXzDwMnNvFYm8SU5Dj4eFkcdQdr7W8eHoIDMSJ3lzQbsHo794nVqX0bZHziItZjwxO14UViMsG+kYAM75utznN19kCGJVIb6Xe1s73jEx52kwgP9MXYXrGynVVciXWHcuFJOBAjcneBkUC38apddFLVWiFTmNg3Dv4+6m160Y50VNmSH3heWKJm/wcZ2oGsYqSeLJTtlPZh6B4boneXyFP0mQl4+6v2zq+Amiq9e0TNqil20qOOGwdiRJ5Au79GxM+TKQT7eeOypDjZzi+rxsp9at+N28vYCWTtVu3E4UBEZ717RC76WrMapl3RJWp1/qFAr6mqXZ4HHFjGg24S4/vEys874bvUDFRU25I0eQAOxIg8Qe+pgE+Qau/6Bqiu0LtH5CKPrCkmMnzaMUmHadTV1eObrSpbopfVghn9GZZIbaz/Nc72jk95+E3C38cLk/qqScfiyhr8uCcLnoIDMSJP4BsEJM1U7cpCYN9ivXtELrq4ZwwiAn1kWxTILamsce9jJ/Yw2sMSLV5A38v17hG5aOORPJwsKJftMd2jERPix2NHbavbOCBQJTnC3u+AChUmS8Y3y0OLO3MgRuSJG5m3cyOzWfh4WTG9n/qAqqiuw/c7M+DWRKbEohOq3X08EGS7qCLD067YzhnEsETSgZePs35mbSWwex7/DCYxqlsUooPV5M0Pe7JQWF4NT8CBGJGn6HIxEGKbcTqwFCjN0btH5CJt5jm3D0/UhhMxLNE0xJ6OhTvSZTvQ1wuXJbXTu0vkqRpkT+SeaLPw9rJiRv942a6qrcPiner9xN1xIEbkKaxeQD9b0o66GpVVikxhUMcIJEYGyPbPB3JkwVy3JPYu7vxatcWext7T9O4RuUjs6SiuUGGzk/vGIdDXVjKDqK21HwREdVftI6uAguP8G5jELE32RG3iH3fGgRiRJ+mn2ci8/WM9e0JNYLFYMKu/+oAStS5FwVy3tO87tYdRSJql9jaS+bIlMiyR9CRqDmpXxbTJf8jQ+ncIQ9cY9b6/7lAejueVwd1xIEbkSdolAXEpqn1qC5CzX+8eUTPCE922zsq2jxvPfkaGVlBWhR/3qixnIkHHqG7c10dGKtnyKVBfr2dvqAmTjnMHdWi0OLy74kCMyNP0v9bZZtIO0xCFcZPbh8r2jhOFOJhdArdSkuWs+xPaAeh8kd49IheJvWHVtepCd1b/BJm6nkhXkV2AxBGqnb0HyNjBP4hJzNbUH/xq60nUu/kgmgMxIk+TfAVgsTo3MtfX6d0jctFsbfz8FjebKRQp6+ttRTz7XQVY+fFkFtpZa4YlkmH012YKZk0xs2gfHoCRXaNk+3BOKbYeL4A74ycdkacJaadqrQiFx4Cja/XuEblo5gDnasNXW07IArpuQ7tnkWGJpnEstwybjubLds92wUiKV6u2RLpLmg14+ar2zi+AWjevwehG5mj2mX652VbOxE1xIEbk4Uk7LKmcKTSL2BB/XNxD7b85VViBtYdy4RYydzlDhxIGATG99O4RuUhbTkGEFIk9HkSGEBgJ9Jio2iWZwOGf9O4RuWhKSjwCfLxke8H2U6issUVLuCEOxIg8kUgL7hus2ru/AWrcNB26G7picKL7zRRq9ypq9zCSoYm9G9qwRG3oLJHxaopx0tEsgv28MTk5TraLKmrwQ5pKBuSOOBAj8kS+gSo9uFgRqyyG/5Ef9O4RuWh8n1iE+qsaTd/tzEBJpcnDbepqnUVXrT5A8ly9e0QuEkljDuWUyvaIrpFICFe17ogMo+ckwD9MtdMWAJVuluTIU8ITt7jZnmgNDsSIPJVmptB/3zxdu0Ku8/fxwoz+KpV9eXUtvks1eU2xQyuAkgznRVOQ2qRNJqsdpsl0RmQY3n5A38tVu7oM2LNI7x6Ri0Z1i0a7UD/ZXrE3C7kllW557DgQI/JUIj14qLp48ju+CijN1rtH5KK5g511Vr7ccsKNwhJZO8wsqmrqHPXsfL2tck8HkdH3RGOH5v2GDM3LanGksq+pq8f87afgjjgQI/JUIj24reilRaQN3/mV3j0iFw1MDEfX6CDZXncoD8fzysx57CqLVbiQEBDh3FhPhvfDnkzkl1XL9qS+cQj199G7S0SNSxwOhHdS7YM/AoXuG+bmbuZqijt/5abhiRyIEXkyzQqEhTOFpiEy02lXxUz7AbV7PlBTrtpib5gIIyJT+EKTKOZKzblIZMhJxwHX2e7UNyyVQYbWs10IUtqrPX6pJwuxL7MY7oYDMSJPFtsH9fH9ZdOSvg3I3qt3j8hFYk+OPVP4V1tPyAx25q4dxmyJZpFVXIEf96pQ5vgwf4zurkoqEBmWNux524ci5aeevaFmJ+044XbHjgMxIg9Xn6JJ78uZQtMQGepGd1MXwEc1RXVNo+AYcGSVakd1B9oP1rtH5KJ5W0+h1lZMXFwk2YuMExlWRGe1L1rIOwQcX693j8hFM/onwNv2HiPKZdjfe9wFB2JEni55LuotXs7ECbUmT4fuQeYObm/emmL2lPX22WoWAjYFsfL6+ebjjda1IzK0gTc421s/0LMn1ATRwX4Y2ytGtjOLKrHmYI5bHT8OxIg8XXAsKjuNVe3idOAga4qZhUiSEOSrBtGLdqSjoroWpiDCgrTZErVFV8nwtcP2ZapaTEM6RaCLLWkMkeH1mQn4hqj2rq+BKlUDj4xvrhsn7eBAjIhQ3ltTRHfr+zwiJhHo642ptrThxZU1+H6XrR6X0Z3cDOTuV20RLhTeUe8eUXOSdAxhkg4yEd9AINlWU6yqRCULIlMY1ycWof7esr14ZwZKKt0ncocDMSJCZeLFqA9SS//Y+x1QmsujYhLa7Inai2RD2/aRs83aYaYhVlzttcMCfLwwrZ8qLE5kGgNuaJi0g0zBz9tL7hUTyqtr8V1qOtwFB2JEBHj5OMPD6qqBVM3+HTK0YZ0j0SEiQLZ/PpCDjMIKGFp1OZD6hWr7BKpwITKFZWmZKKpQM9FTkuMQ7KdmqIlMI3GYSg4kiGRBeYf17hG5aI4mPPFzs0w6uoADMSKS6gdc7zwSW95nel+TsFotjvh5kUzq660Gj58X4UCVhard93LAP1TvHpGLPt/kvPi5gmGJZEYiKZCjphgzBZvJoI7h6Bqj9qRuOJyHwznuscePAzEiUmJ6Ax2GqnbWLkDUFSPTbWQWdVYMXVNMuwdx4I169oSaQKy0rtqvaoeJFdgRXaJ4/MicRM1Ci+3yd9vHQF2d3j0iF1gsFlw1xJml9bNNzuytZsaBGBE5Mb2vKXWMCpQhisKBrBKZ2c6QRP0eR+2wHkDHEXr3iFwkiobby/eIgb9YiSUypdAEoNs41S7U1DMkw5szqL2jppgo2VJTa/5BNAdiROTUdw7grfYbIfVztZ+HTFdTzLAzhdraPWLQz9phpiBWWL/QhiVqEsQQmZI2FJ9JO0wjNsQf43rHynZWcSVW7FWr9GbGgRgROYn9On1nq3ZFIbBnEY+OSYgMdoG2mmLztp1CWZXB0vuKQuH2bImigLgIDyJT2HKsAIds+zFGdo1CYmSg3l0iujC9pgL+4c59q+Lzjkzh6qHO8MRPNhp00tEdB2J5eXm4/vrrERoaivDwcNx2220oKVFFJc9m7NixMqZUe7vrrrsaPOfYsWOYNm0aAgMDERsbi4ceegg1NQa7gCHSLTyRNcXMQmSwm2FLJy5qrIgCz4ZycLkqGC70nAyEtNO7R+SiLzY7L3a4GkZuwccfSLlCtWvKVYFnMoVLesagXaifbP+4NwtZRQbPFOwuAzExCNu1axeWLl2KhQsXYuXKlbjzzjvP+3133HEH0tPTHbdnn33W8bXa2lo5CKuqqsKaNWvw7rvv4p133sFjjz3Wyr8NkYF1Gg1EdFHtQz8BBcf07hG56OphBp4p3PKesz2ISTrMoryqFgu2qwF0kK8XpqTE6d0lopYPT9zKmmJm4e1ldUwI1dbV48stBs8U7A4DsbS0NCxevBj//e9/MXz4cIwZMwYvv/wyPvnkE5w6deqc3ytWuuLi4hw3saJmt2TJEuzevRsffPABBgwYgClTpuCpp57CK6+8IgdnRB5J7NsZaP+AqldZpcgUBiaGo1e7ENnefDQf+zOLYQglWcC+xaodHAd0v0zvHpGLvt+VIVdYhWn94hHoy9ph5CYSBgKxSap9YgOQs1/vHpGLTs+eaOhMwedhinfUtWvXynDEIUOGOB6bMGECrFYr1q9fj8svv/ys3/vhhx/KgZYYhM2YMQOPPvqoHJzZXzclJQXt2jlDZCZNmoS7775brr4NHDiw0desrKyUN7uioiL5b11dnbzpSfx8cULq3Q8yj0bPmZSrYfnhL7CgHvXbPkD9RQ860/2SoV01pAOeWpQm2x9vOIZHpvXR/31m20ew1qmL+fr+16BenEt8jzIFbeKXuYPaN/uzhZ9NZMhzpv91sC59RDbrt36A+vGPt97PohaTGBGAkV0jsfaQqie2/lAuhnQKN9T1r6v9MMVALCMjQ+7f0vL29kZkZKT82tlcd9116NSpExISErBjxw784Q9/wN69e/HVV185Xlc7CBPs98/1us888wyeeOKJMx7Pzs5GRUWF7n/4wsJCeTKKgSpR884ZX0Qkjobf8dWwFBxD/rYFqGo/kgfTBMYk+sLXy4Kq2np8ufk4bhkYAV9vq37vM/X1iN70jiP8IidxCmqzslq0P9Q6jhdUYM3BXNlODPdDx4BqZDXzb8fPJjLiOWNNGIcYqzcsdTWo2/ohsvveAVhNcWns8Sb1DJUDMeG91fvRMaCToa5/i4tdi0jR9Wz74x//iL///e/nDUtsLu0eMrHyFR8fj/Hjx+PgwYPo1q1bs1/34YcfxgMPPNBgRSwxMRExMTENQh/1euMSSUlEX4xwIpLxnfWcGfYL4Phq2Yw4sgj1A2fp10lymZiymtQ3Cwt2pKOwohbbcuoxvV/Diaw2fZ85tg7WgsOyWd9xFKJ6DmvRvlDreXvLXkf7+hGdz5i4bAp+NpExz5lYoMckYO8ieJVlI7ZgK9B7Wiv9LGpJV0VE4fkVJ1BcUYMfDhTgL3MHGOr619/f3/gDsQcffBC33HLLOZ/TtWtXGVZ4+iycyGwoMimKr7lK7C8TDhw4IAdi4ns3bNjQ4DmZmZny33O9rp+fn7ydTvzhjfDHFyeiUfpC5tDoOdNnOhAQAZTnw5K2AJapRUCALd0vGdq1wzvKgZjw2aYTmDnAWWOszd9nNDV6LINugoXvS6ZQVVOHL7eo2mE+XhZcMSTxgj9T+NlEhjxnhtwqB2KCdcu7QNKM1vtZ1GIC/ay4fGB7vLf2KCqq67AoNRMTuvgb5vrX1T7o2lMxau3du/c5b76+vhg5ciQKCgqwefNmx/f+8MMPcrbEPrhyxbZt2+S/YmVMEK+bmpraYJAnsjKKVa2kJNsGTiJP5e0HpFyl2jUVwM4v9e4RuUjUeuocpfbCrj6Qg+N5Zfocu8piZ1pov1AgiauqZrF0dyZySlTSqol94xAdfObkI5Fb6DYOCOuo2geWAflH9e4RNSNpx6eaovNmov+Q0QV9+vTB5MmTZSp6sYL1888/495778U111wj938JJ0+elAM3+wqXCD8UGRDF4O3IkSOYP38+brrpJlx88cXo16+ffM7EiRPlgOvGG2/E9u3b8f333+ORRx7BPffc0+iKF5FH1xTb/I7c70PmmEW+SlP08lO9Utnv/AqoVoWAkTwX8GUhYLP4aIPzYvT6YbaLVCJ3ZPUCBt9ku1PfsNQGGVpy+zD0TVBbglJPFmJ/tk6Tju4+ELNnPxQDLbHHa+rUqTKF/RtvvOH4enV1tUzEUVam/ghiJW3ZsmVysCW+T4RBzp07FwsWLHB8j5eXl6xJJv4Vq2M33HCDHKw9+eSTuvyORIYT3w+IH6DaGTuAU1v07hG5SNRZ8bJaZPvzzcdRU6tDJiltQXDWDjONIzml+PmAStIhVlZHdI3Su0tErWvADYDFS7W3fgDUVvOIm8TVmknHBbvU+5aZmCY1jMiQ+NFHH5316507d25QR0Akz/jpp5/O+7oiq+K3337bYv0kcjtDbwPm36fam94C2g/Wu0fkgtgQf4zvHYsluzORWVSJFXuzMSGp+ckWmixrD3Bio60zfYGEQW33s+mCaIuBXzusI6y2AT2R2wqNB3pNAfYsBEoyVN3DPtwrZgaz+rfHXxalobKmDov35OKJ6loE+Jlmnck8K2JEpBMRUib29wipXwLlBfxT/H979wEeVbH2Afy/6b0BSQgJJbTQWwADiDSRooiiiBdBkCvqBRRBveD9FLui96JiQUQEQbCAogKKShOlE+kloYUWSiC9l93vmTOb7AYCBLLZc87u//c8+2TO7pKdzA579j0z845OiC/RFX25VmU0TGwUTrpI0rE0/lRZko4hHSLVrhKRfYikHaV2zGOr60Sgjzv6t5QJ9jLzS5SLj3rCQIyIrs3DF2gzTJaL84A937DFdKJ7k1qoHShT6K5LuIDzmXba57Aoz5It0dUDaP2AfV6XqoxJOshpRfcCgswXr46uBdKS1K4RVZL1muhv4/WVtIOBGBFdXwerK4Xb5zJph06INWL3m7NKlRhNWGqvE5TIlJiXJsvNBwM+IfZ5XaoyJukgpyXSjbd/2HzApB16ckuDGqgXIpNB7UhKQ1qOzPiqBwzEiOj6wpoDdeNk+WICcGITW00nhsZGls0K/Hr7SRiNdsh8KYL1Uh3/Wf2vRzbBJB3k9NqNAFzM6ROYtEM3XFwMGNezISZ2j8TGf/dEsK8H9IKBGBFVTuwYS1kk7SBdiAz2wa2NaynlU6l52HysmrNKJe8EzuyQ5bBWQFSn6n09spmvtp8sKzNJBzkl/zCg6QBZzj4PJDCZm54yBQ9rH4YQHQVhAgMxIqqc5oMAH3Ma6wM/AtkpbDmdGGY1f/7LLSfsOBo2hkk69JSkw7whKpN0kFOzTtoh9s8kqkYMxIioctw8gbbDZdlYZEnGQJrXp1kYavnLTepFRqmzGXnV80Iio+bepbLs4Q+0ur96Xods7rcD53DJvK6ib4tw1PST/YXI6TToAQTXtyTtSD2udo3IgTEQI6LK6zDKUo6fBxhV2CSYbpiHm0tZKnuRtOOrrZYpaDa1+yuZWVNo+yDg6Vc9r0M299U2S58YbrXtAZFzJ+0A8PcXataGHBwDMSKqvBoNgeiesixS+x5by9bTieGd68LNvDHv4m0nUVBcYtsXMJnKT0u0XlNImsYkHUSXafdQ+aQdxfrJwkf6wkCMiG6MWPdTipte6kZYgBfuaCE3vbyYXYhV+87Z9gWObwAuHZbl+rcCoTG2/f1UbZikg+gyfqFAzJ2ynJPCpB1UbRiIEdGNadIP8K8tywm/ABln2II6MTKuXll5wWYbJ+3Y/pmlHPuIbX83VZv8ohIm6SCqzFR8omrAQIyIboyrO9B+pCybSoCdC9mCOtGpQQhiwv2VcvyJNOw7k2GbX5yZDBxaKct+YZYryaR5K/acLUvSIUZMmaSDyKzBbUBwA1k+th64aB7xJ7IhBmJEdONEIGYwf3zEfwGUFLMVdcBgMGCE1ajYQluNiv29QAblgljk7qavfVyclclkwryNloxwo7uav3QSkUzaYT0Vf+tstgrZHAMxIrpxgZFyiqKQlQwkrmIr6sTgtnXg7yUXof+w6wzSc6u4CL2kyLLXjsG1/HQe0rQdJ9KwPzlTKbeODET7ukFqV4lIe0k73H1keddiIN9GswiIzBiIEdHNsc6KZ70+iDTN19MN93WIVMoFxUZ8u+NU1X5hws9A1llZbtofCKxjg1qSPczfmFRWHtWlvjJiSkRWvIOBNg/KclGOzKBIZEMMxIjo5jTsBQSZp7kdWwdcOMSW1IkRt1imJ3655aSyt9hNs05Zbz2NhzQtOT0Pq/bLzJliXdjA1uYEPERUXufHyk9PNNp46w9yagzEiOgmPz1cLjtBzWJL6kR0LT90b1JLKZ9MzcUfiRdu7hddTASO/yHLIQ2BBj1sWEuqTl9uOVEWgIs95jzdXNngRBWp1VReeBTSTwCJv7KdyGYYiBHRzWs3AvCQWfiw+2sgN5WtqRMjrUbFvth0c0k7DNYpncVomAjOSRcp67/adlIpu7salECMiK6h8xOWMi86kg3xrElEN88rQC5mForzgR2fszV1omdMKCKDvZXyH4kpSLqYc0P/3lCYDexaJA/cvCzrKEjzftx1Bmm5RUp5YKvaCA3wUrtKRNrWqI8c9S/dvP78frVrRA6CgRgRVY0yPdFgSdpRXMUsfGQXri4GPGQ1KrZwy42NinkfXApDQZY8aP0A4BNi6ypStaWst0rSwZT1RDcxFZ+p7Mk2GIgRUdWENABiBsqyyJ534Ae2qE48EBsFTzd5GhDZE3MLK7kfnLEYvnsXWI7jxlVTDcnWth5PxaFzMoBuVzcIbaOYsp6oUtr+wzIVf883nIpPNsFAjIiq7pZ/WcpbPhaX3dmqOhDs64FBbSKUclZ+MX7YmVy5f3hwOVyzz8hy475yMTvpgvUGziJlPRFVkqd/+an4f3/BpqMqYyBGRFVXrwsQ3lqWk3cCJ7ewVXViZFz9cl/SjddLZW8ywbD5I8tx3PhqrB3Z0qnUXPx+4LxSDvX3RP+WTFlPdEM6j7VMxd/2GVBSyVkERFfBQIyIqk5sBGs9PU2MipEutIoMRMf6wUr58IVsrEu4Tir7U1thSI5XiqawlkCD7vaoJtkoZX1pnC3WB3qYp6USUSWFRANN+sly5mng0HI2HVUJP4WJyDZa3Av4hcnyoRVAmiUhAGnb2O7mbGAAZm84du0nb/qgrGgSwbcIwknzxPq/0pT1Hq4u+AdT1hPdHCbtIBtiIEZEtuHmAXR8VJZNRmDbHLasTvSOCUXDWr5KedvxVOw6lV7xE1OPAYdWKsUSn1AZfJMuLNt5Bpn5chrVXW0iUNPPU+0qEelTdA+gVowsn9wMJO9Su0akYwzEiMh2YkcDruYveH8vAErTm5OmubgY8Oit0WXHn244WvETt8wSUbZSzG01AnD1sFcVqYop6+dbp6xnkg6imydmAZQbFfuErUk3jYEYEdmOb02gzQOyXJAJ7DRv+EuaN7hdnbJRklX7zuHEpcs2eM5LA3Z+qRRN7j7IbW5+n0nz1iekKOv/hNh6wcq6QCKqgtbDAC/z1g/7vgOyZBIcohvFQIyIbKvzE5by1lmAsYQtrANe7q4Y3VVmUBQJHT7705LmXLFjHlCUK8tth8PkyS/zevHx+iNl5Ue7W0Y+iegmefgAHR6W5ZJCYItVJlmiG8BAjIhsK6w5EN1TlkXCjsRVbGGdeKhzPfh4uJZt8Hwpu0A+UFwIbPvU/CwDTNbBNmna9qRUbE9KU8qNQv1wezNzQh0iqhrxOVg6PXv750DeVdbWEl0DAzEiqt4Nnq33nCJNC/Rxx7COdZVyQbERCzafkA/s/x7IOivLMQOBkAYq1pJuxKz1lvV+j9/WUFkPSEQ2EFAbaPsPWS7MArYzQRXdOAZiRGR7jfoANRrL8omNwMmtbGWdeKRbfbiav6wv2JyEvIJiYPOHlid0maBe5eiGHDybibWH5L5wEYFeuLttBFuQyJa6PgUYXCzJjArN07eJKomBGBHZnosL0G2i5fjP/7KVdSIy2Ad3tq6tlNNyi/DX798B5/bKB+t0AKI6q1tBuqnRMLE2zN2Vp3wim2/wXLqNR+4lYOdCNjDdEH4qE1H1aP0AEBgly4d/414rOjLWKqGD387ZlgfixnMDZ504eSkXK/YkK+UQX4+yKadEZGPdnraUN86Ua2qJKomBGBFVD1d3OW2jFEfFdKNFRCC6NaqJFoYkxJXEyzsD6wLNBqldNaqk2RuOKtkvhdFd6sPbnISFiGwsvCXQ+A5ZzjwN7F3CJqZKYyBGRNWn3QjAz5yl7eBy4MJBtraORsUmuC0rOzaJtWGubqrWiSrnQlY+lsSfVsq+Hq4YGSe3JSCianLrJEt543uA0cimpkphIEZE1cfdC+jypOX4zxlsbZ24NeAc+rluV8rnTMHYFnKn2lWiSvr8ryQUFssvgsNvqadkwySialT3FqBuF1m+mAgcWsHmpkphIEZE1St2NOAdIsv7lgKXLAkESLsMG94pK39SfBdm/SVHWEjbMvKK8OUWue2Ah6sLxnTjVgNEdnHrZEv5rxmAyTw3mOgaGIgRUfXy8AXizPuKmYxy2gZp2/kDwIEfleJFBOOrkl5Yn5CCnSflxsCkXSIIyxZbDgAY0qEOwgK81K4SkXNo1BsIby3LyTuBY+vVrhHpAAMxIqp+ncYCnoGyvOsrIP0UW13LrEbDTsT8EwXwUMrvrj6sYqXoevKLSjBv43GlLLaCe6x7QzYakb0YDOUzKIpRMaLrYCBGRNXPKxDoPFaWjUXApplsda1KSQD2m5N0+NZCq7snIjLYWznckJiCHUmp6taPrurbHadwMVumzu7fqjbq1/RlaxHZU/O7gRDzBZDjG4DTO9j+5BiBWGpqKoYPH46AgAAEBQVhzJgxyM7Ovurzk5KSYDAYKrwtWWJJLVrR419//bWd/ioiJ9L5CcDdR5b/XgBknVe7RnTV0TDz2oYuE+Dh7YcnezUue/jd1YlsNw0qKjHi0w3Hyo6fuI2jYUR25+J62bYtHBUjBwnERBC2f/9+/P7771ixYgU2bNiAsWPNV9grEBUVhbNnz5a7vfzyy/Dz80P//v3LPXfevHnlnjd48GA7/EVETsa3BhD7iCwX5wObP1S7RnS5i4eBfd/Jso94v8YoxXva10G9GjKI3njkErYeu8S205jv4k/jdFqeUr6tSS20rGOeCkxE9tVmGOBfW5YTVgLn9/MdIH0HYgcPHsSqVavw2WefoXPnzujWrRs++OADZeQqOTm5wn/j6uqK8PDwcrdly5Zh6NChSjBmTYywWT/Py4uLm4mqhbIXlacsb58L5HKam6b8+T+ZUEWIGw94ys9Kd1eXcqNi7605olYN6Sprw95fY1m/92TvRmwnIrW4ecpzXam1r/O9oKvSxe6cmzdvVoKl2NjYsvv69OkDFxcXbN26Fffcc891f0d8fDx27dqFjz766IrHxo0bh3/+85+Ijo7G448/jtGjRytTFK+moKBAuZXKzMxUfhqNRuWmJvH6JpNJ9XqQfti1z/iGwtDuIRh2zAWKcmDaMgumHlOr/3Xp+lKPwbDnW4hPPpN3MExiNMyqT9zVOhwfrjuC4xdzsPV4KnacrIF+NWuyZTVgweYknM3IV8q9YmqhXVSQ5s4BPDeRU/WZ9qNg2PQhDFnJyqiY8cQWIKqT2rVyaEaN9ZfK1kMXgdi5c+cQGhpa7j43NzeEhIQoj1XG3Llz0axZM3TpYt5wz+yVV15Br1694OPjg99++w3/+te/lLVnTz5ptQntZd58801lmuPlUlJSkJ8vT4ZqvvEZGRlKZxSBKpHW+oxL0+Go9fcXMBiLlUAsJXoITF5BfKNUFrDudfiYSpRydsuHkZMhprnJqW6lRsWGYtoqmZVv1l+n0K6OrzL7gNSTU1CCj9bK0TARRI/uUBMXLlzQ3FvCcxM5W5/xbvc4Aje8qJSLfn0BaXctkJkVySn6S1ZWlvYDsSlTpmD69OnXnZZYVXl5eVi8eDFeeOGFKx6zvq9du3bIycnBO++8c81AbOrUqZg0aVK5ETGxJq1WrVpKMhG1O6IYzRN10UJHJO2ze58RF1XaPAjsXAiXwiyEJiyE6fZXq/916erSkmBIlPuGmbwC4dtzInxFpsvL/KNmLSyMv4AjKTnYfyEfh7Nc0b1J+YtkZF9iSmJGvgyg72pTG11b1NfkW8BzEzldn6nxOEz7voAh9Sg8k7chNHs/0LCX2rVyWEaN9ZfKLnNSNRCbPHkyRo0adc3niOmCYt3W5Vf4iouLlUyK4rHrWbp0KXJzczFy5MjrPlesQXv11VeVqYeenua1LJcR91f0mHjjtfDmi46olbqQPti9z/SYAuxdoiTtMGybA0Pnx4GgKPu8Nl1p47uAeTTMcMs4GHyCK2wl0T0m3t4E4xfvVI7fX3MUtzUNu+ZUbqo+qTmFmPtXklJ2czFgct+mmv7c57mJnKrPuHgCvf4DLJVJqlzWviIDMT3+LTph0FB/qWwdVK2piFpjYmKuefPw8EBcXBzS09OVdV6l1q5dq0S/InCqzLTEQYMGKa93PWIdWXBw8FWDMCKygcBIoPNjslxSAKzjYmbVXDwC7Fosy54BlvflKga0rI0mYTKJx85T6VifmGKPWlIFPl53BNkFxUr5gY5RqFeD+4YRaUrze4DwVrJ8djdwUM48ICqlfshYCWJtV79+/fDoo49i27Zt2LhxI8aPH49hw4YhIiJCec6ZM2eUwE08bu3IkSNKqnuRjONyy5cvVzIx7tu3T3nerFmz8MYbb2DCBKtsN0RUPbo9DZSuDdv9NXBuL1taDaunAUb5ZR63/AvwvvZ6PRcXAyb2ttpX7PdEZU4+2dfZjDws2HJCKXu6uWCCVVZLItIIMSrS+yXL8drXgBLz5y2RXgIxYdGiRUqg1bt3bwwYMEBJYf/pp5+WPV5UVISEhARlCqK1zz//HJGRkejbt+8Vv9Pd3V3JoihG3Nq2bYvZs2djxowZmDZtml3+JiKn5h0M3DrZfGACVl+ZAIeqWdJG4NAKWfYLK59y+Rr6Ng9Dk1reSnnP6QysOai95BCObuaawygsllm5RnWpj/BAbrtCpEmNegP1usryJTEDYZHaNSINMZh4KbPKRLKOwMBAJVuLFpJ1iPV0IsukFubIkvap2meK8oEPY4GMU/L44eVAg+72rYOzEql1P+sFJMv1Xhj0AdB+ZCX/qRFLNyfiueVHleNmtQOwYkI3uLpwrZg9HEvJxu3vbkCJ0QR/TzdseK4ngn09oGU8N5FT95mTW4HPzQMCAXWACfGAu7yYRY7ZXyobG6hfUyJyXu5eQM//WI5/f7Hc3lVUjfZ9ZwnCQlsAbYff0D+/NToQrerIzIoHz2ZiyQ5zME3V7t3Vh5UgTBjbPVrzQRiR06vbGWjSXzZD5hlg+2dO3yQkMRAjInW1HgqEtZRlERgcWMZ3pLoV5QFrrKaC9n0VcHG94exU/xkQU3b8zq8JyMwvsmUtqQL7kzOwfHeyUq7h64HR3RqwnYj0oLfYLsk8a+DPGUB+hto1Ig1gIEZE6hIBQB+roGDNK0BxoZo1cnxbZlmmgzbqI9cw3IRODUIwsFVtpXwppxAfrj1iy1pSBf77a0JZeVzPRvDzVHUXGiKqrLAWQKv7ZTkvFdj0IduOGIgRkQaIQKB0bVhaEhA/T+0aOa6ci/JqrGBwAaq4mfaU/jFK1j5h3sbjOH4xxxa1pApsPnoJ6xLkdgF1grwx/Ja6bCciPen5POBivniy+SMgm9t/ODuOiBGR+sSGwLe/Yjn+YzqQn6lmjRzX+jeBwixZbjcCCGtepV8XFeKDx7pHK+WiEhNeX3nAFrWkyxSVGDHtp31lx0/1aQxPtxubTkpEKgtpAHQYJctFOcAaq9T25JQYiBGRNkS0A1oOkeXcS8CmmWrXyPGkJAA7zKON7r7lE6VUweM9GiI8QKZPX33wAv7gJs82N39jEhLPZyvlNlFBuK99pO1fhIiq323/BjzNWfR2fikzKpLTYiBGRNrR6/8AF3fLtI10ZuKzKZGV0lQiy90mAv5hNvm1Ph5uyhTFUq+uOKCM4JBtnMvIx3urE8sGj1+9u4WysTYR6ZBfqDzXlVo5mZs8OzEGYkSkHSHRQMcxslyUC/zyb7Vr5DiO/QEkrpJl/wggbrxNf/3dbSPQrm6QUj5yIRuLtpyw6e93Zq+tPICcQhlAD+9cF60jZTsTkU7FjgHCW8ny+b3A9jlq14hUwkCMiLSlx1TAN1SWE1YCB5erXSP9M5YAv/1f+TTKHj42fQmRzn7aXS3K7XWVlsPsl1W18chFrNhzVimH+Hrgmb5Nq/w7iUhlrm7AQHPSJGHt60DWOTVrRCphIEZE2uIdBPR/y3L883NM3FFV2+YA5/bIsrgK23oYqkPbqCAMMa9dysgrwrvm6XR0cwqLjXjxR0uCjin9YhDkw82biRxCVCeg/UhZFgmUrC+WkdNgIEZE2tPiXrm/lZCVDKx7Xe0a6VfaCbk3W6l+0wGX6vvof65fU/h4yGx+X245gUPnmP3yZs396ziOpsjtANrXDcJ9HZigg8ih9H4J8A6W5b1LgOMb1K6Rfh3+DUGrxgHZ56EnDMSISHtERoKB/wPcvOXx1tnAmXi1a6U/JhOwYqJMkyzEPgLU71qtLxkW4KVsNCwYTcAryw/AJOpBNyQ5PQ8z1xxWyiIvxyt3t2SCDiJH41sD6PNS+cQdxZzSfcPy0mBYMRFeSathmBUHpB6HXjAQIyJtCq4P9JhiPjAByycys9SN2v0VcHStLAfUAfq8DHsY060BokJkEL3p6CV8s53ZL28mQUdekUzQMeKWemhZJ9Dm7xMRaUC7kUCdWFm+mAhs+UjtGunPqqkwZMm1tIhoL78/6AQDMSLSrrhxQFhLWRZrnLZ+onaN9CP7gnJyKiMWhnuZ966pZl7urnhlUEuroOIgTqfl2uW1HcGGxBT8vFcu3K/h64FJTNBB5LjEVPE7ZwAG81fyP97m1i03IuEXedFRzMLw8IfprvflrBqdYCBGRNrl6g7c+Z6YqyiPxVqx9JNq10offn4WyE+X5Zb3AU372fXle8aEYmisXNOUXVCM55bugVHMVaRrKiguwbSf9pcdTx3QDIHe5r31iMgx1W4DdPynZeuWX60uotHV5aYCy58qO8zqMlXO/tARBmJEpG1RHcvvLSYCDK45uraDK4ADP8iydwjQfzrU8H93NkdEoFfZFMVFW7m32PW8t/owjl+Ua/pi6wXj3nb6+lJBRDep538sW7eIbVvESA9d26opZck5TI1uR17Te6E3DMSISPt6vwj4hcmy2JSYe4tdXV66XPBdSgRhvjWhhgAvd7x9X5uy4zd+PoQTl8yJQ+gKm49ewid/HFXKbi4GvDqYCTqInGrrlr6vWY5/miCnmFPFDq0E9nwjy56BMInZMzqakliKgRgRaZ9XYPlRnV+ekwEHXen3F4Bs88agjfsCre5XtZW6Na6J4Z3rKmWRfOLZJZyiWJH03EJM+nZX2WDv5L5N0ay2fdb0EZFGtB4KNDFPI89JAX54AjAa1a6VRqckTrQci+8HARHQIwZiRKQPzQcDje+QZZEd6afxnKJ4uWPrgb8XyLKHH3Dnu5q4Qvj8gGZlWRS3JaVi3qYktaukKSK9//PL9uJsRr5yHBddA491j1a7WkRkb+Lz+u6PLDNAjqxmkqqKiCUKOebRQhG4thkGvWIgRkT62lvMK0gei+mJ2+aoXSvtKMwpt2hZ2ZsmUBsbAPt6uuEdqymKb686hKMp2arWSUuW7DhdliVRJOaY8UAb7hlG5KzEVPLBsyzHq6cBZ/eoWSNtOfATsG+pLIvvAzqdkliKgRgR6UdQFHCPVQr73/4DnPlbzRppg5jP9tOTQJp5pKluHBBrTnCiEbdE18CoLnJvl4JiI55ZshslzKKoJOZ4abklS+Jb97ZC7UDzRuZE5Jwa9Qa6TJDlkkLguzFAIbcAQc4lYOUkSzsNeAcIqA09YyBGRPrStD8QN95yglo6GsjPgFPbOttyhdDDX05tEXvTaMy/+8WgQU1fpbzzZDo+3XAMzqyoxIiJX+9EbqHcuPmB2Cj0b6XvLxVEZCO9XpRp7Us3enb2lPYmE/DzZLl2Tmg6UPU10LagvTM1EdH19J4G1ImVZTEKJLJLOWtK+xOb5chgqXtmATUaQou8PVzx3/tbl80ieff3RMSfSIOzEn//7tPyIoIIUF+8q7naVSIirXDzAIbMBdx95HH8fDktz1lt+RjYv0yWvYM1swa6qhiIEZE+T1D3z5PZFIUDPwLbP4PTyToHLHkYMBbL464TgWZ3Qcs61AvB2FtlIorCEiMeWxiP5PQ8OJstxy5hllWq+veHtVXW0hERlanZuHzGYHHRMeOM8zXQkTXAb/9nORbrwvzNCU10joEYEelTUF1gsNV6sV+fB5J3wmmUFAFLRpdtZokG3YFeL0APnrmjqZIZULiYXYBHF+xAbqE5mHQCGblFmPSNJVX9pL5N0DrSnISGiMhauxEya7CQnw4sewwwyunMTuHSUbkEwWRO43/rZKCFuT0cAAMxItKvmAHALeMs68WWjHKe9WKrXwJObpLlgDrAkM8BV32MqLi7uuDj4e1Rr4accrM/OVPZX0ykcXd0hcVGPLEoHsnmVPW3RIfgse7anEpKRBogpt/d9R4QYM6Cm/QnsO4NOIX8TOCrBy3n9Sb9gZ5WI2MOgIEYEembSNNep4PVerEnHX+92L7vgc0fyrKLOzB0AeBXC3oS7OuBz0bGws88HW/l3rOYueYIHJnRaMJzS3dj09FLynGwjztmDG0LVxf9r3Mgomok1kQNmQMYzF/b//wvsONzx25yYwnw/aPAxQR5XCsGuPdTTSaiqgrH+muIyDnXi91nvV7sB2D9W3BYFw4BP5qzRgpi/UCkOXGJzjQO81fWRpUl71idiF/2noWjevvXBPywK1kpe7q54LOHOyIiiKnqiagS6nUB7rAaCVs5GTi4wnGbbu1rQOIqy35hwxYDXgFwNAzEiEj/guuV3wDzj7eATeYRI0eSlw588xBQlCOP2zwIxD4CPevdLAzP3RFTdjzp293Yn+x400sXbE7CJ+bkHGIAbOaD7dChXrDa1SIiPbnlCaDLk7Is1kyJ/cVOboHD2bsU+GuGLItRQJGcS6PZgKuKgRgROYaYgcAdb1qORUr3+C/gMMQc+S/vBS4dlsdhrYCBMxwife/jt0XjnnZ1lHJeUQnGLohXkng4il/3n8O0nyybNr88qAXuaBGuap2ISKf6vAy0GirLxfnA4gfkTAlHkbyr/KyPvq8DDXvBUTEQIyLHEfcvoMfzluPlTwH7voNDBGEL7wHOxMtjnxrAAwsAD/P+MjpnMBjw5r2t0CZKZg48k56HxxfGI8+80bGexZ9IxZNf7SxbtvhEj4YYEVdf7WoRkV6JNVJ3fwRE97RkUvxyCJAppz3rPkPi1/8Ais1bmrQdLkcBHRgDMSJyLLc9B8SVXk0zAd+PBRJ/hb6DsHvLB2EPLwdC5F5cjsLL3RVzRnRAWICncrzjRBpGzN2KjLwi6NXRlGyM+WIHCopl2mUx6vfcHU3VrhYROcLa6AcWArXbyOPM08CX98np63p1/gAwrz+Qad4nLbKjw2zafC0MxIjIsYgP7b6vAe0flsdis+NvRgDHN0CXqXvFlc4zO8oHYWEt4IhCA7zw2ciO8DdnUhTB2IOfbkFKlv6mKV7IyseoeduQnisDya6NamD6kNbK6B8RUZV5+gP/WAIE1TN/6OyXa4iL9fd5CXGhcf4Ay76Yoc1lcg43eWHOkTEQIyLHI77siitpLe6VxyUFci+S0+aARjdB2L3A6e2WIGzkTw4bhJVqFRmIr8beghq+HsrxgbOZGDp7szJdUS8SzmXh3o834VSqrHNMuD9mPdQBHm485RKRDfmHASOWyfND6R5jXw3T18hY0kbgi7uBvDR5HNEeGLUS8AuFM+BZgYgck4ur3HOkST95XJgtR5dOboXugjDvEBmEhbeEM2hZJxDfPh6HiEAv5fj4xRzcN2sTjlzIhtatT7iAIbM24XSaDMLqBHlj/uhOCPByV7tqROSIRDZBMTLmbl4zfHQt8Fkfud5K6w7/Ls91hVnyuF434OGfAJ8QOAsGYkTkuFzdgfvnA/VvtSxqFtMfts7W7qbP4qqgCBitgzAxHdFJgrBSDWv5YckTXRBd01c5PpuRr4yM7Tuj3dT2X2xKwiPztyO7oFg5blknAN890QXh5oCSiKhaRHYAHvpOni8EkV13Ti9tT8nfv0zOVBGZH4VGtwPDl8gpl06EgRgROTZ3b+DBryzBmFgz9stzcv+VAo2NsCT9BczqCpzeZhWEOc9I2OXEaJIYGWteW27imZpTqKwZ23rsErSkuMSIF3/cp6SoN5rj+34twvHtY3EMwojIfhs+j10H1GpmufAosu3u+Fx778DOL4GljwBGczKm5oPlmjAHyQR8IxiIEZHjE1fYRvwAdJlguU+ktRdXDFMSobqSImD1y8D8Oy0Zo5TEHCIIawVnVtPPU1kz1rG+3Pw4q6AYIz7fhtl/HFUCILVl5hfhkS92YMHmE2X3iRT1Hw9vDx8PmXSEiMgugusDY34DGt9hufC44mng5+eAEjlSr6rcVOD7x4Afx8kNqYW2DwH3fS4zQTohBmJE5Bxc3WQ2xaELAQ/z1IeLCcCcnsC+79Wrl5jHP7cv8NcMmW5fEKN3j/3p9EFYqUBvdyx4pDN6NK2lHBcWG/HmL4dwz8ebsD9ZvamKYs3akI83YUNiinLs7mrAO/e1xr/7xcDFhdkRiUgFXgFyFkjZNi4Ats0GFt9vSYihhgM/Ah91BvZ8bbmv8+PAoA/kmm4nxUCMiJxL80HA2PUyPW5pEo+lo4FVU4Ei81x1exBr1MT0jE9uBZL/lve5uAF9XgJG/ggE1rFfXXTA28MVn46IxZhuDcq2ldl7JgODPtyI6asOIb/Ifps/X8ouUKYi9ntvAw6bE4gE+bhj4ZjOuD82ym71ICKqkAhs7ngdGPQh4OJuSeLxQQdgyydAcaH9Gi77AvDtSHnLuSDv8wyUdev3ltyg2ok5919PRM6pZiPgn6uBVkMt9235GHi/NbBxZvWvHUs/CSwZJadnFOXI+0Iayikl3Z526quD1yLSv79wZ3N8/0QXNA2To5olRhNmrT+K/u//iS3VvHZMBHsfrTuC295Zr0xFLDYvCBMJRZb9qytuiTankCYi0oL2I+SFvdL09rmXgFX/Bj6MBfYuBYzG6r3YuOdb4KNOcjSsVJP+wLitsm4GzhwwmExaTR2mH5mZmQgMDERGRgYCAuSicrUYjUZcuHABoaGhcHHyqwxUOU7dZ8TH3465wC9TLIuGBe9goPMTQOexsmwL4oR3bB2w/TMgcZVlfrzQboS8MujpBz3QQp8R0xM/+eMoPlx7BIVWa8UeiI3Cw13qo1ltf5ttnmw0mrBs5xn877cEJGdYRk19PFwxtnu0cuN6MO33GdIX9hkbSj8FrH4J2Le0/P3hrYHbXwYa9rLtmmcx+rbtU+DIasv9IvnUgHeAlkOqJQAzauwzprKxgW4Csddffx0rV67Erl274OHhgfT0629WJ/60adOmYc6cOcrzu3btilmzZqFx48Zlz0lNTcWECROwfPly5Y0bMmQI3n//ffj5Vf4LEQMx0jOtfXip4tw+4I/pwMHllnVaglhL1nEMEDfu5jeXFBtr7v5KBmCXjpR/zCsIGDQTaH439ERLfebIhSxM+W4vdpwov/ahYS1fDGpTB4PaRqCBOQX+jRDnj6RLudh89BIWbT2B/cmZZY+J5V8PdIzC032aIDSAqen11mdIH9hnqkHyThmQHVtf/v7oHkCnsUDduJvbw0uEEmfigT3fyERYYuTNWot7gf5vA35yna8z9JdMRwvEREAVFBSE06dPY+7cuZUKxKZPn44333wTX3zxBRo0aIAXXngBe/fuxYEDB+DlJU+e/fv3x9mzZzF79mwUFRVh9OjR6NixIxYvXlzpujEQIz3T2oeXqi4cAv56F9i7BDBZrTly9QRqtwbCWgJhLSw/xaJoa4U5QMZpefUx46Q86YnpH0W55Z/nHwHEPgJ0GFWtJyZn6TNixEoES2/9cgg5hVeuFRP7eQ1qE4Hbm4ejhp8HfNxd4eZ6Zb1PpeZi87FL2HL0kvJT7F12uZ5Na2HqgGZoYp4aSfrsM6R97DPVSIxY/T4NOLfnysdCW8hU+KU3//Dyj4uwQZzT8jOBnBTg0EoZgKUdv/J3+dcGBvwXaHYnnK2/ZDpaIFZq/vz5mDhx4nUDMfFnRUREYPLkyXjmmWeU+0RjhIWFKb9j2LBhOHjwIJo3b47t27cjNjZWec6qVaswYMAAJeAT/74yGIiRnmntw0sTUo8DG98Hdi0CSq6xqDmoLlCjkbz6JwKwy68CXk5kQxRXHZsOkFkcdUqrfUbsM7Zy71ks35WMbUmp13yup5uLMrVQTCn09XRFTkEJzqTnXfX5Yi+z/wxshq6NalZDzR2fVvsMaRf7TLU3MLD/e2DNK0C6ZfuNK4REAx6+MvAqELcsmRb/asSFy6b9gdYPAI362C0tvVFjnzGVjQ30+03gOo4fP45z586hT58+ZfeJBuncuTM2b96sBGLipxhlKw3CBPF88QZu3boV99xzT4W/u6CgQLlZN3ZpJxA3NYnXF0Go2vUg/WCfqUBQPWDgDODWZ2AQSTwOrYChohOVSLohbtdg8vBTTkim2DFAaDPrhodeabXPBHm7YXinKOWWnJ4ng7I9Z7HvjGVaYamCYqNyS8u1WhtoxcvdBbH1gpUEHHHRIWgTGaSkpNfa36wXWu0zpF3sM3Ygpgw2HQgcXQND0kbg5CZllMxgvYY59dh1f40JBuVCo6n1UCDmTsAr0PKgnf7PGzX2GVPZejhsICaCMEGMgFkTx6WPiZ8icrbm5uaGkJCQsudUREx3fPnll6+4PyUlBfn5dkx/fZU3XkTfojNq4YoAaR/7zLW4AW2fVG6Gwmy4XUqAW2oC3MVPc9mlKBcmgwuMvuEo8auNEv8IlPhFlP0sCm8vgzHhgjl1r87poc+Ik9vdTf1wd9PGOJmWj9WJaUhMyUVeoRG5RSXIKzIir+ynOIEDzcN90CHSHx2i/NE8zFfJ0igV4eJFuVcYOW6fIW1hn7Gj4Fh5a/eUcq5zP/c3PM7ugMfZ7XC/sFcJtcR5zOgRAJOHP4wefspPcSuq0QT5DQfC6Gf+vp1ZAGTa/1xn1NhnTFZWlvYDsSlTpijruK5FTB+MiYmBlkydOhWTJk0qNyIWFRWFWrVqaSJrosgUJuqihY5I2sc+U1mhQGS0WFlquctkhFFMR/QOhsHFTflAddirWzruM+J6W2zTumpXw6nprc+Q+thn1D7X3accmZTRMYOS6bD0f671BiueALSQ79eosc+Y0lwU16PqdwaxfmvUqFHXfE50tOgMNy48XC4uPH/+PGrXrl12vzhu27Zt2XPEfFJrxcXFSibF0n9fEU9PT+V2OfHGa+HNFx1RK3UhfWCfuVkugH/5UXdnwT5D7DPEzxlnoJ/vkgYNff+tbB1UDcRE1Cpu1UFkSRTB1Jo1a8oCLzFyJdZ+PfHEE8pxXFyckvQjPj4eHTp0UO5bu3atElWLtWRERERERETVQf2QsZJOnjyp7CEmfpaUlChlccvOzi57jpjCuGzZsrKoWGRXfO211/DTTz8paetHjhypZEIcPHiw8pxmzZqhX79+ePTRR7Ft2zZs3LgR48ePVxJ5VDZjIhERERER0Y3SzXKGF198UdkPrFS7du2Un+vWrUOPHj2UckJCgrJQr9Rzzz2HnJwcjB07Vhn56tatm5Ke3nre5qJFi5Tgq3fv3mUbOs+cOdOufxsRERERETkX3e0jpkXcR4z0TGt7b5D2sc8Q+wzxc4a0xKix7zKVjQ3UrykREREREZGTYSBGRERERERkZwzEiIiIiIiI7IyBGBERERERkZ0xECMiIiIiIrIzBmJERERERER2xkCMiIiIiIjIzhiIERERERER2RkDMSIiIiIiIjtjIEZERERERGRnDMSIiIiIiIjsjIEYERERERGRnTEQIyIiIiIisjM3e7+gIzKZTMrPzMxMtasCo9GIrKwseHl5wcWFcTaxzxA/Z0h9PDcR+ww502dMpjkmKI0RroaBmA2IN16Iioqyxa8jIiIiIiIHiBECAwOv+rjBdL1QjSoVhScnJ8Pf3x8Gg0H1CFwEhKdOnUJAQICqdSF9YJ8h9hni5wxpDc9NpOf+IsIrEYRFRERcc4SOI2I2IBo4MjISWiI6oRY6IukH+wyxzxA/Z0hreG4ivfaXa42ElVJ/EiUREREREZGTYSBGRERERERkZwzEHIynpyemTZum/CRinyF+zpAW8NxE7DPEz5grMVkHERERERGRnXFEjIiIiIiIyM4YiBEREREREdkZAzEiIiIiIiI7YyBGRERERERkZwzEHMhHH32E+vXrw8vLC507d8a2bdvUrhJp1JtvvomOHTvC398foaGhGDx4MBISEtSuFunIW2+9BYPBgIkTJ6pdFdKwM2fO4KGHHkKNGjXg7e2NVq1aYceOHWpXizSqpKQEL7zwAho0aKD0l4YNG+LVV1+FyWRSu2qkERs2bMBdd92FiIgI5Rz0ww8/lHtc9JUXX3wRtWvXVvpQnz59cPjwYWgVAzEH8c0332DSpElK6vq///4bbdq0wR133IELFy6oXTXSoD/++APjxo3Dli1b8Pvvv6OoqAh9+/ZFTk6O2lUjHdi+fTtmz56N1q1bq10V0rC0tDR07doV7u7u+OWXX3DgwAH873//Q3BwsNpVI42aPn06Zs2ahQ8//BAHDx5Ujt9++2188MEHaleNNCInJ0f5jisGHyoi+svMmTPxySefYOvWrfD19VW+D+fn50OLmL7eQYgRMDHCIT68BKPRiKioKEyYMAFTpkxRu3qkcSkpKcrImAjQunfvrnZ1SMOys7PRvn17fPzxx3jttdfQtm1bvPfee2pXizRInHs2btyIP//8U+2qkE7ceeedCAsLw9y5c8vuGzJkiDKy8eWXX6paN9Ieg8GAZcuWKbN6SkfDxEjZ5MmT8cwzzyj3ZWRkKH1q/vz5GDZsGLSGI2IOoLCwEPHx8crwaykXFxflePPmzarWjfRBfFAJISEhaleFNE6MpA4cOLDc5w1RRX766SfExsbi/vvvVy70tGvXDnPmzGFj0VV16dIFa9asQWJionK8e/du/PXXX+jfvz9bja7r+PHjOHfuXLnzU2BgoDJYodXvw25qV4Cq7uLFi8q8ahHxWxPHhw4dYhPTNYnRU7HOR0whatmyJVuLrurrr79Wpj6LqYlE13Ps2DFlmpmYNv/8888r/ebJJ5+Eh4cHHn74YTYgVTiKmpmZiZiYGLi6uirfbV5//XUMHz6crUXXJYIwoaLvw6WPaQ0DMSInJ0Y49u3bp1x1JLqaU6dO4amnnlLWFIqEQESVucgjRsTeeOMN5ViMiInPGrF2g4EYVeTbb7/FokWLsHjxYrRo0QK7du1SLhSK6WbsM+SIODXRAdSsWVO5cnT+/Ply94vj8PBw1epF2jd+/HisWLEC69atQ2RkpNrVIQ0T059F8h+xPszNzU25iTWFYlG0KIsr10TWRNay5s2bl7uvWbNmOHnyJBuKKvTss88qo2JiLY/IsDlixAg8/fTTSqZfousp/c6rp+/DDMQcgJjm0aFDB2VetfWVSHEcFxenat1Im8SCVhGEiUWua9euVVIFE11L7969sXfvXuUKdelNjHaIKUOiLC4GEVkT050v3xZDrP2pV68eG4oqlJubq6xxtyY+W8R3GqLrEd9lRMBl/X1YTHUV2RO1+n2YUxMdhJiDL4btxRejTp06KVnMRIrP0aNHq1010uh0RDH148cff1T2EiudOy0WtYrsVESXE/3k8jWEIi2w2B+KawupImIkQyRfEFMThw4dquxt+emnnyo3ooqI/aHEmrC6desqUxN37tyJGTNm4JFHHmGDUVnm3iNHjpRL0CEuBopkY6LfiKmsIqNv48aNlcBM7EsnpraWZlbUGqavdyAidf0777yjfKkWKaXFlCGRKYaoopSvFZk3bx5GjRrFBqNK6dGjB9PX0zWJqc9Tp05VNlQVX4rERcNHH32UrUYVysrKUr44i9kaYiq0+AL94IMPKhv0itk/ROvXr0fPnj2vaAgxGCFS1IsZP2JPXXHBJz09Hd26dVO2W2nSpIkmG4+BGBERERERkZ1xjRgREREREZGdMRAjIiIiIiKyMwZiREREREREdsZAjIiIiIiIyM4YiBEREREREdkZAzEiIiIiIiI7YyBGRERERERkZwzEiIiIiIiI7IyBGBERERERkZ0xECMiIiIiIrIzBmJERERERER2xkCMiIjoJqWkpCA8PBxvvPFG2X2bNm2Ch4cH1qxZw3YlIqKrMphMJtPVHyYiIqJr+fnnnzF48GAlAGvatCnatm2Lu+++GzNmzGDDERHRVTEQIyIiqqJx48Zh9erViI2Nxd69e7F9+3Z4enqyXYmI6KoYiBEREVVRXl4eWrZsiVOnTiE+Ph6tWrVimxIR0TVxjRgREVEVHT16FMnJyTAajUhKSmJ7EhHRdXFEjIiIqAoKCwvRqVMnZW2YWCP23nvvKdMTQ0ND2a5ERHRVDMSIiIiq4Nlnn8XSpUuxe/du+Pn54bbbbkNgYCBWrFjBdiUioqvi1EQiIqKbtH79emUEbOHChQgICICLi4tS/vPPPzFr1iy2KxERXRVHxIiIiIiIiOyMI2JERERERER2xkCMiIiIiIjIzhiIERERERER2RkDMSIiIiIiIjtjIEZERERERGRnDMSIiIiIiIjsjIEYERERERGRnTEQIyIiIiIisjMGYkRERERERHbGQIyIiIiIiMjOGIgRERERERHBvv4f5DIYy7KPEb0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot generated successfully!\n"
     ]
    }
   ],
   "source": [
    "# Generate sample data\n",
    "x = np.linspace(0, 10, 100)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "\n",
    "# Create a simple plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y1, label='sin(x)', linewidth=2)\n",
    "plt.plot(x, y2, label='cos(x)', linewidth=2)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Trigonometric Functions')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n",
    "\n",
    "print(\"Plot generated successfully!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a52402a7",
   "metadata": {},
   "source": [
    "## Data Tables\n",
    "\n",
    "Pandas DataFrames render as nice HTML tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70de151f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (5, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>New York</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>28</td>\n",
       "      <td>Boston</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eve</td>\n",
       "      <td>32</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age           City  Score\n",
       "0    Alice   25       New York     95\n",
       "1      Bob   30  San Francisco     87\n",
       "2  Charlie   35        Chicago     92\n",
       "3    David   28         Boston     88\n",
       "4      Eve   32        Seattle     91"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a sample DataFrame\n",
    "data = {\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],\n",
    "    'Age': [25, 30, 35, 28, 32],\n",
    "    'City': ['New York', 'San Francisco', 'Chicago', 'Boston', 'Seattle'],\n",
    "    'Score': [95, 87, 92, 88, 91]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(f\"DataFrame shape: {df.shape}\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e86bdde",
   "metadata": {},
   "source": [
    "## Code Formatting\n",
    "\n",
    "Jupyter notebooks display code with proper syntax highlighting:\n",
    "\n",
    "### Lists and Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "99bf6859",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Fibonacci numbers:\n",
      "F(1) = 0\n",
      "F(2) = 1\n",
      "F(3) = 1\n",
      "F(4) = 2\n",
      "F(5) = 3\n",
      "F(6) = 5\n",
      "F(7) = 8\n",
      "F(8) = 13\n",
      "F(9) = 21\n",
      "F(10) = 34\n"
     ]
    }
   ],
   "source": [
    "# Fibonacci sequence generator\n",
    "def fibonacci(n):\n",
    "    \"\"\"Generate Fibonacci sequence up to n terms.\"\"\"\n",
    "    fib = [0, 1]\n",
    "    while len(fib) < n:\n",
    "        fib.append(fib[-1] + fib[-2])\n",
    "    return fib\n",
    "\n",
    "# Generate and display first 10 Fibonacci numbers\n",
    "fib_sequence = fibonacci(10)\n",
    "print(\"First 10 Fibonacci numbers:\")\n",
    "for i, num in enumerate(fib_sequence, 1):\n",
    "    print(f\"F({i}) = {num}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb317f42",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This test notebook demonstrates the key features of Jupyter notebook rendering in Jekyll:\n",
    "\n",
    "✅ **Markdown formatting** with headers, lists, and emphasis  \n",
    "✅ **LaTeX equations** for mathematical notation  \n",
    "✅ **Code cells** with syntax highlighting  \n",
    "✅ **Data visualization** with matplotlib plots  \n",
    "✅ **Data tables** with pandas DataFrames  \n",
    "✅ **Rich output** from code execution  \n",
    "\n",
    "The notebook conversion system:\n",
    "1. Converts `.ipynb` files to Jekyll-compatible Markdown\n",
    "2. Extracts images to `assets/images/notebooks/`\n",
    "3. Adds proper front matter with metadata\n",
    "4. Maintains code cell formatting and outputs\n",
    "5. Preserves mathematical equations for MathJax rendering\n",
    "\n",
    "**Next Steps:**\n",
    "- Add more complex visualizations\n",
    "- Include interactive widgets (note: will be static in Jekyll)\n",
    "- Test with larger datasets\n",
    "- Verify GitHub Pages compatibility"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}