{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markov switching dynamic regression models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides an example of the use of Markov switching models in statsmodels to estimate dynamic regression models with changes in regime. It follows the examples in the Stata Markov switching documentation, which can be found at http://www.stata.com/manuals14/tsmswitch.pdf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Federal funds rate with switching intercept\n",
    "\n",
    "The first example models the federal funds rate as noise around a constant intercept, but where the intercept changes during different regimes. The model is simply:\n",
    "\n",
    "$$r_t = \\mu_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n",
    "\n",
    "where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n",
    "\n",
    "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
    "\\begin{bmatrix}\n",
    "p_{00} & p_{10} \\\\\n",
    "1 - p_{00} & 1 - p_{10}\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\sigma^2$.\n",
    "\n",
    "The data used in this example can be found at https://www.stata-press.com/data/r14/usmacro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+QAAAEnCAYAAAAgvokiAAAAQHRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcrZGZzZzEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvhF0PpwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAjWBJREFUeJzs3Xd4U2X7B/DvSdKku6V7D6CUUUbZe4gsZSgquBiiuH1VXseL4xV8/YlbHLhFUFBQGaIoCsreq8y2FLo3nenMPL8/0gRKBx1Jk7Tfz3XluuzJGU/roc197ue5b0EURRFERERERERE1KYk1h4AERERERERUUfEgJyIiIiIiIjIChiQExEREREREVkBA3IiIiIiIiIiK2BATkRERERERGQFDMiJiIiIiIiIrIABOREREREREZEVMCAnIiIiIiIisgIG5ERERERERERWwICciIjarVWrVkEQhHpfzzzzjFmuMX/+fERERJjlXJa+/ksvvYSwsDDIZDJ4enpadFz1iYiIwPz589v8uvXJzs7GkiVLEBcXZ+2hEBFRByaz9gCIiIgs7ZtvvkH37t1rbQsKCrLSaKzjl19+wf/93//hxRdfxJQpU6BQKKw9JKvKzs7G0qVLERERgX79+ll7OERE1EExICcionYvJiYGAwcOtPYwmqSqqgpOTk5mP+/Zs2cBAP/617/g5+dn9vNbW1VVFRwdHSEIgrWHQkRE1GScsk5ERB3e+vXrMWzYMLi4uMDV1RWTJk3CyZMn6+y3atUqREdHQ6FQoEePHvj222/rPZ9arcZrr72G7t27Q6FQwNfXF/fddx8uX75ca7+IiAhMnToVGzduRGxsLBwdHbF06VIAwIoVKzB69Gj4+fnBxcUFvXv3xltvvQWNRtPs7y8iIgIvvfQSAMDf3x+CIGDJkiUAUOu/rz3m6unlxun/O3fuxCOPPAIfHx94e3tj5syZyM7OrnWsRqPBc889h4CAADg7O2PkyJE4cuRInWtUVlbimWeeQWRkJBwdHeHl5YWBAwfihx9+aPT7MY7lr7/+woIFC+Dr6wtnZ2eoVCpcvHgR9913H6KiouDs7Izg4GBMmzYNZ86cMR2/a9cuDBo0CABw3333mZYxXP1zOHbsGKZPnw4vLy84OjoiNjYWP/74Y6PjIiIiai5myImIqN3T6XTQarW1tslkhj+Br7/+Ol566SXcd999eOmll6BWq/H2229j1KhROHLkCHr27AnAEATed999mDFjBt59912UlpZiyZIlUKlUkEiuPN/W6/WYMWMG9u7di+eeew7Dhw9HWloaXnnlFYwdOxbHjh2rlQE/ceIE4uPj8dJLLyEyMhIuLi4AgEuXLuHuu+9GZGQk5HI5Tp06hf/7v/9DQkICVq5c2azvf9OmTVixYgW+/vprbNu2DR4eHggJCWnRz/KBBx7AzTffjO+//x4ZGRl49tlnce+99+Kff/4x7bNw4UJ8++23eOaZZzBhwgScPXsWM2fORFlZWa1zLVq0CN999x1ee+01xMbGoqKiAmfPnkVhYWGTxrJgwQLcfPPN+O6771BRUQEHBwdkZ2fD29sbb7zxBnx9fVFUVITVq1djyJAhOHnyJKKjo9G/f3988803pv/nN998MwCYfiY7d+7E5MmTMWTIEHz22Wfw8PDAunXrMHv2bFRWVtrMOngiImoHRCIionbqm2++EQHU+9JoNGJ6erook8nEJ554otZxZWVlYkBAgDhr1ixRFEVRp9OJQUFBYv/+/UW9Xm/aLzU1VXRwcBDDw8NN23744QcRgLhhw4Za5zx69KgIQPzkk09M28LDw0WpVComJiY2+n3odDpRo9GI3377rSiVSsWioiLTe/Pmzat1/Ya88sorIgDx8uXLtbYDEF955ZU6+4eHh4vz5s0zfW38WT766KO19nvrrbdEAGJOTo4oiqIYHx8vAhCffvrpWvutXbtWBFDrnDExMeItt9xy3bFfyziWuXPnXndfrVYrqtVqMSoqqtaYjP8/vvnmmzrHdO/eXYyNjRU1Gk2t7VOnThUDAwNFnU7X7DETERHVh1PWiYio3fv2229x9OjRWi+ZTIY///wTWq0Wc+fOhVarNb0cHR0xZswY7Nq1CwCQmJiI7Oxs3H333bXWKIeHh2P48OG1rvXbb7/B09MT06ZNq3XOfv36ISAgwHROoz59+qBbt251xnzy5ElMnz4d3t7ekEqlcHBwwNy5c6HT6XDhwgWz/4yaavr06bW+7tOnDwAgLS0NgCG7DAD33HNPrf1mzZplmpVgNHjwYPzxxx/4z3/+g127dqGqqqpZY7ntttvqbNNqtXj99dfRs2dPyOVyyGQyyOVyJCUlIT4+/rrnvHjxIhISEkzjv/r/4U033YScnBwkJiY2a5xEREQN4ZR1IiJq93r06FFvUbe8vDwAMK0nvpZxKrpxCnVAQECdfQICApCamlrrnCUlJZDL5fWes6CgoNbXgYGBdfZJT0/HqFGjEB0djQ8++AARERFwdHTEkSNH8NhjjzU7cDUnb2/vWl8bq7Ubx9TQz0omk9U59sMPP0RISAjWr1+PN998E46Ojpg0aRLefvttREVFXXcs9f3sFi1ahBUrVuD555/HmDFj0KlTJ0gkEjzwwANN+rkZ74lnnnmmwdZ41/4/JCIiaikG5ERE1GH5+PgAAH7++WeEh4c3uJ8xkMzNza3z3rXbjMXOtm3bVu+53Nzcan1dX1XwzZs3o6KiAhs3bqw1Lkv0zFYoFFCpVHW2N3Ud97Wu/lkFBwebtmu12jrndHFxwdKlS7F06VLk5eWZsuXTpk1DQkLCda9V389uzZo1mDt3Ll5//fVa2wsKCprUe914TyxevBgzZ86sd5/o6OjrnoeIiKgpGJATEVGHNWnSJMhkMly6dKne6c9G0dHRCAwMxA8//IBFixaZAsG0tDQcOHCgVk/zqVOnYt26ddDpdBgyZEiLxmU8/9W9wkVRxJdfftmi8zUmIiICp0+frrXtn3/+QXl5eYvON3bsWADA2rVrMWDAANP2H3/8sU5hvav5+/tj/vz5OHXqFJYvX47Kyko4Ozs3+/qCINTpsb5161ZkZWWha9eupm3XZvaNoqOjERUVhVOnTtUJ6omIiMyNATkREXVYERERePXVV/Hiiy8iOTkZkydPRqdOnZCXl4cjR46YMrgSiQT/+9//8MADD+DWW2/FwoULUVJSgiVLltSZmn3nnXdi7dq1uOmmm/Dkk09i8ODBcHBwQGZmJnbu3IkZM2bg1ltvbXRcEyZMgFwux1133YXnnnsO1dXV+PTTT1FcXGz2n8GcOXPw8ssv47///S/GjBmD8+fP4+OPP4aHh0eLztejRw/ce++9WL58ORwcHHDjjTfi7NmzeOedd+Du7l5r3yFDhmDq1Kno06cPOnXqhPj4eHz33XcYNmxYi4JxwPBAZNWqVejevTv69OmD48eP4+23365TVb5Lly5wcnLC2rVr0aNHD7i6uiIoKAhBQUH4/PPPMWXKFEyaNAnz589HcHAwioqKEB8fjxMnTuCnn35q0diIiIiuxYCciIg6tMWLF6Nnz5744IMP8MMPP0ClUiEgIACDBg3Cww8/bNrv/vvvBwC8+eabmDlzJiIiIvDCCy9g9+7dtQq1SaVSbNmyBR988AG+++47LFu2DDKZDCEhIRgzZgx69+593TF1794dGzZswEsvvYSZM2fC29sbd999NxYtWoQpU6aY9ft/9tlnoVQqsWrVKrzzzjsYPHgwfvzxR8yYMaPF5/z666/h7++PVatW4cMPP0S/fv2wYcMG3HnnnbX2u+GGG7Blyxa8//77qKysRHBwMObOnYsXX3yxxdf+4IMP4ODggGXLlqG8vBz9+/fHxo0bTX3YjZydnbFy5UosXboUEydOhEajwSuvvIIlS5Zg3LhxOHLkCP7v//4PTz31FIqLi+Ht7Y2ePXti1qxZLR4bERHRtQRRFEVrD4KIiIiIiIioo2HbMyIiIiIiIiIrYEBOREREREREZAUMyImIiIiIiIisgAE5ERERERERkRUwICciIiIiIiKyAgbkRERERERERFbQbvqQ6/V6ZGdnw83NDYIgWHs4RERERERE1M6JooiysjIEBQVBIml+vrvdBOTZ2dkIDQ219jCIiIiIiIiog8nIyEBISEizj2s3AbmbmxsAww/C3d3dyqMhIiIiIiKi9k6pVCI0NNQUjzZXuwnIjdPU3d3dGZATERERERFRm2npsmkWdSMiIiIiIiKyAgbkRERERERERFbAgJyIiIiIiIjIChiQExEREREREVkBA3IiIiIiIiIiK2BATkRERERERGQFDMiJiIjswOUyFQrKVdYeBhEREZlRswPyPXv2YNq0aQgKCoIgCNi8eXOt9wVBqPf19ttvN3jOVatW1XtMdXV1s78hIiKi9qZCpcVNH+7F0Nf/xv9+O4/SKo21h0RERERm0OyAvKKiAn379sXHH39c7/s5OTm1XitXroQgCLjtttsaPa+7u3udYx0dHZs7PCIionbnwKVCXC5TQasX8fW+FIx7ZxfWHEqDTi9ae2hERETUCrLmHjBlyhRMmTKlwfcDAgJqff3LL79g3Lhx6Ny5c6PnFQShzrFEREQE7EzMBwCM7OqDXGU1LuaX46XNZ/HjsQz8+NAwODpIrTxCIiIiagmLriHPy8vD1q1bcf/991933/LycoSHhyMkJARTp07FyZMnG91fpVJBqVTWehEREbU3oihid+JlAMD9IyPxx5OjsGRaT8hlEpzOLEVSXrmVR0hEREQtZdGAfPXq1XBzc8PMmTMb3a979+5YtWoVtmzZgh9++AGOjo4YMWIEkpKSGjxm2bJl8PDwML1CQ0PNPXwiIiKrS8ovR1ZJFRQyCYZ29oaDVIL5IyLR1dcVAFBQwUJvRERE9sqiAfnKlStxzz33XHct+NChQ3Hvvfeib9++GDVqFH788Ud069YNH330UYPHLF68GKWlpaZXRkaGuYdPRERkdbtqpqsP7ewNJ/mVqek+bgoAQGG52irjIiIiotZr9hryptq7dy8SExOxfv36Zh8rkUgwaNCgRjPkCoUCCoWiNUMkIiKyeTsTDNPVx0X71tru4yIHABSyFRoREZHdsliG/Ouvv8aAAQPQt2/fZh8riiLi4uIQGBhogZERERHZh7JqDY6lFQEAxkb71XrP29UQkLM3ORERkf1qdoa8vLwcFy9eNH2dkpKCuLg4eHl5ISwsDACgVCrx008/4d133633HHPnzkVwcDCWLVsGAFi6dCmGDh2KqKgoKJVKfPjhh4iLi8OKFSta8j0RERG1C/svFkKjExHp44IIH5da73m7cso6ERGRvWt2QH7s2DGMGzfO9PWiRYsAAPPmzcOqVasAAOvWrYMoirjrrrvqPUd6ejokkivJ+ZKSEjz44IPIzc2Fh4cHYmNjsWfPHgwePLi5wyMiImo3dl8wrB8fe810dQDwqQnICyoYkBMREdkrQRRF0dqDMAelUgkPDw+UlpbC3d3d2sMhIiJqFVEUMWzZP8hVVmP1gsEY0612UL4zMR/3fXMUvYLcsfVfo6w0SiIioo6ttXGoRausExERUcsk5JYhV1kNRwcJhkR61Xnfx6UmQ8415ERERHaLATkREZEN2pVoqK4+vIsPHB2kdd73cTNWWVejnUx2IyIi6nAYkBMREdmgnTX9x69td2bkVdP2TKsXoazSttm4iIiIyHwYkBMREdmY0ioNjqcVA6jb7sxIIZPCzdFQm/Uyp60TERHZJQbkRERENub7w+nQ6UV0D3BDqJdzg/v5mlqfMSAnIiKyRwzIiYiIbEiVWoev9iYDABaO6tzovt6uNevI2fqMiIjILjEgJyIisiHrjqajsEKNUC8nTO8X1Oi+3i7MkBMREdkzBuREREQ2QqXV4fPdhuz4w2O6wEHa+J9pY4b8cjkz5ERERPaIATkREZGN2HgiC7nKavi7K3D7gJDr7u/DNeRERER2jQE5ERGRDdDq9Phk10UAwIOju0Ahq9t7/Fo+rld6kRMREZH9YUBORERkA7acykZGURW8XeS4a3Bok47xrsmQFzBDTkREZJcYkBMREVmZXi9ixU5DdnzByEg4y2VNOs40ZZ1V1omIiOwSA3IiIiIrO5RciEuXK+DmKMOcYeFNPs5Y1I0ZciIiIvvEgJyIiMjKckqrAQCxYZ3g7ujQ5ON8atqelVVrUa3RWWRsREREZDkMyImIiKysrFoDAHBTNG2qupG7kwwOUgEAUMRp60RERHaHATkREZGVlVVrAQBujs0LyAVBgLeLsfUZA3IiIiJ7w4CciIjIyspULQvIgavWkVdwHTkREZG9YUBORERkZVcy5E1fP25kan1WxoCciIjI3jQ7IN+zZw+mTZuGoKAgCIKAzZs313p//vz5EASh1mvo0KHXPe+GDRvQs2dPKBQK9OzZE5s2bWru0IiIiOySaQ15CzLkPjUZcrY+IyIisj/NDsgrKirQt29ffPzxxw3uM3nyZOTk5Jhev//+e6PnPHjwIGbPno05c+bg1KlTmDNnDmbNmoXDhw83d3hERER2x5ghd21mUTfgql7kbH1GRERkd5r9l3/KlCmYMmVKo/soFAoEBAQ0+ZzLly/HhAkTsHjxYgDA4sWLsXv3bixfvhw//PBDc4dIRERkV65kyFswZd3F2IucGXIiIiJ7Y5E15Lt27YKfnx+6deuGhQsXIj8/v9H9Dx48iIkTJ9baNmnSJBw4cMASwyMiIrKonYn5eGztCZRWaZq0vzFD7t6iKes1a8iZISciIrI7zf/Lfx1TpkzBHXfcgfDwcKSkpODll1/GDTfcgOPHj0OhUNR7TG5uLvz9/Wtt8/f3R25uboPXUalUUKmufPhQKpXm+QaIiIha6a1tiYjPUWJoF2/MGRp+3f1bV9StZg05M+RERER2x+wB+ezZs03/HRMTg4EDByI8PBxbt27FzJkzGzxOEIRaX4uiWGfb1ZYtW4alS5e2fsBERERmVFqlQUKu4SFxyuWKJh1T3oq2Z8yQExER2S+Ltz0LDAxEeHg4kpKSGtwnICCgTjY8Pz+/Ttb8aosXL0ZpaanplZGRYbYxExERtdSJ9GKIouG/UwrKr7u/Ti+aAnLXVgTkRRVq6PVis48nIiIi67F4QF5YWIiMjAwEBgY2uM+wYcOwffv2Wtv++usvDB8+vMFjFAoF3N3da72IiIis7WhKkem/Uwsrr7u/MRgHWpYh96op6qbVi1BWN23NOhEREdmGZv/lLy8vx8WLF01fp6SkIC4uDl5eXvDy8sKSJUtw2223ITAwEKmpqXjhhRfg4+ODW2+91XTM3LlzERwcjGXLlgEAnnzySYwePRpvvvkmZsyYgV9++QU7duzAvn37zPAtEhERtZ1jqcWm/04vqoRGp4eDtOHn38YK63KZBAqZtNnXk8skcHeUQVmtRUG5Gp7O8uYPmoiIiKyi2RnyY8eOITY2FrGxsQCARYsWITY2Fv/9738hlUpx5swZzJgxA926dcO8efPQrVs3HDx4EG5ubqZzpKenIycnx/T18OHDsW7dOnzzzTfo06cPVq1ahfXr12PIkCFm+BaJiIjahkqrQ1xmCQBAEAzT0TOKGs+St6bCulFT1pFXqXXYmZCPKrWuxdchIiIi82r2X/+xY8dCFBteo/bnn39e9xy7du2qs+3222/H7bff3tzhEBER2YwzmaVQa/XwcZXD180R8TlKpBZWoLOva4PHXCno1vwK60Y+rgokF1Q0WGk9o6gSC789hoTcMjx9Yzc8eWNUi69FRERE5mPxNeREREQdxZFUw/rxgeFe6OzjAgBIvk6ldeOUdVdFyzPkptZnFXUz5PsvFmDax/uQkFsGALh4+fqF5oiIiKhtmL3tGRERUUdlXD8+MKITSioNgXZKwfUC8pa3PDMyBuQFZVcCclEUsXJ/Kl7/PR46vWhaZ56vrG7xdYiIiMi8mCEnIiIyA71exLGaDPngSC9E1mTIrxeQK80QkJvWkFdcmbL+4d8X8b/fzkOnF3Fb/xB8dHd/AEB+GfuVExER2QpmyImIiMzgQn4ZlNVaOMul6BnoDo3OUG8l9boZckMmvTVryL1rAvLCmqJu2SVVWLHL0BHlhZu6Y+GozqYHA8yQExER2Q5myImIiMzA2H+8f1gnyKQS0xry7NLqRiubl5sjQ17Ti7ygpqjbe9svQK3VY3CkFxaO6gxBEODn7ggAqFDravU+JyIiIuthQE5ERGQGR2vWjw+K8AIAdHKRw9PZkPVOLWw4S25aQ96Kom4+blcy5Am5Smw4kQkAWDylOwRBAGAoGuciN/Q5Z5aciIjINjAgJyIiaiVRFHG0Zv34oIhOpu0R3oYseWPT1s0yZb0mQ15YrsabfyRAFIGbegcgNqxTrf38a7LkXEdORERkGxiQExERtVJWSRVySqshkwjoF+Zp2m5qfdZoQG6OKuuGDHmZSoudiZchkwh4dlL3Ovv51mTS85ghJyIisgkMyImIiFrJmB3vFewBZ/mVwLopldavBOQtz5C7O8ogl175k37X4DDTta9mzJBfZoaciIjIJjAgJyIiaiXj+vHBEbWniEc0ISBXmqastzxDLgiCqRe5i1yKf42Pqnc/P2bIiYiIbAoDciIiolY6kWYIyAfWFHQzMmapG1tDbqx47tqKgBwAAjwM2e+FozubpqZfy8/dsJ1ryImIiGwD+5ATERG1giiKpgx49wC3Wu8ZA/LCCjVKKzXwcK47Ld04Zd29lQH5izf1wP6LhXhoTOcG9zEVdVMyICciIrIFDMiJiIha4XKZCiqtHhIBCPJ0qvWei0IGPzcF8stUSCmsQD9nz1rvi6JoypC3Zg05YMjOX5uhv5apqFsZp6wTERHZAk5ZJyIiaoX0okoAhmDcQVr3z+qVwm7ldd6rVOug04sAWreGvKlMRd2YISciIrIJDMiJiIhawRiQh3k51/t+Z19jQF5Z5z3jdHWpRICTg9RCI7zCWNStTKVFpVpr8esRERFR4xiQExERtcL1AvII74YrrZerrlRYFwTBQiO8wlUhMwX+XEdORERkfQzIiYiIWsEYkIc2EJA3NmVdWZMhd1W0TUkXQRDg787WZ0RERLaCATkREVErZFwnIDdOWU8tqIQoirXeM05Zb21Bt+bwc6uptM7WZ0RERFbHgJyIiKgVMoqqADQ8ZT3UyxkSwdBv/HJ57SC4rPrKlPW2wl7kREREtqPZAfmePXswbdo0BAUFQRAEbN682fSeRqPB888/j969e8PFxQVBQUGYO3cusrOzGz3nqlWrIAhCnVd1NafTERGR7arW6JBbM/W7oYBcIZMiuJOhHVrK5drryM3Vg7w5TBlyTlknIiKyumYH5BUVFejbty8+/vjjOu9VVlbixIkTePnll3HixAls3LgRFy5cwPTp0697Xnd3d+Tk5NR6OTo6Nnd4REREbSaz2JAdd1XI0Mm54WnnkT6uAOoWdiu3xpR1ZsiJiIhsRrMfyU+ZMgVTpkyp9z0PDw9s37691raPPvoIgwcPRnp6OsLCwho8ryAICAgIaO5wiIiIrObq9eONVUmP8HbGHgCphbVbnxmnrLdVUTcALOpGRERkQyy+hry0tBSCIMDT07PR/crLyxEeHo6QkBBMnToVJ0+etPTQiIiIWsVUYb1mSnpDQmrezyqpqrVdacqQW2HKOjPkREREVmfRgLy6uhr/+c9/cPfdd8Pd3b3B/bp3745Vq1Zhy5Yt+OGHH+Do6IgRI0YgKSmpwWNUKhWUSmWtFxERUVu6Xg9yo2BPw/tZxddmyNt+yjoz5ERERLbDYgG5RqPBnXfeCb1ej08++aTRfYcOHYp7770Xffv2xahRo/Djjz+iW7du+Oijjxo8ZtmyZfDw8DC9QkNDzf0tEBERNco4ZT3Mu/GAvKEMuTWqrPvWZMjLqrWo1uja7LpERERUl0UCco1Gg1mzZiElJQXbt29vNDte76AkEgwaNKjRDPnixYtRWlpqemVkZLR22ERERM2Sfp0e5EbGKuv5ZSqotXrT9nJV209Zd3eUwdHB8Oc/X8lp60RERNZk9oDcGIwnJSVhx44d8Pb2bvY5RFFEXFwcAgMDG9xHoVDA3d291ouIiKitiKJ4JUN+nYDc20UORwcJRBHIKb2SJS+zwhpyQRBM68jzyjhtnYiIyJqaHZCXl5cjLi4OcXFxAICUlBTExcUhPT0dWq0Wt99+O44dO4a1a9dCp9MhNzcXubm5UKvVpnPMnTsXixcvNn29dOlS/Pnnn0hOTkZcXBzuv/9+xMXF4eGHH279d0hERACA9MJKLN9xAZVqrbWH0i4UVahRodZBEIBgz8aLugmCgKCafYyt0oCrp6y33RpyAPBzq2l9xgw5ERGRVTX7kfyxY8cwbtw409eLFi0CAMybNw9LlizBli1bAAD9+vWrddzOnTsxduxYAEB6ejokkivPAkpKSvDggw8iNzcXHh4eiI2NxZ49ezB48ODmDo+IiBqw6Mc4HEsrRidnOeYNj7D2cOyecbq6v5sjHB2k190/2NMJyZcrkFVs3Qw5APi712TIWdiNiIjIqpr9CWDs2LEQRbHB9xt7z2jXrl21vn7//ffx/vvvN3coRETUROezlTiWVgwASCmosPJo2oemVlg3Culk2C+zpL6AvG0z5L7GDDlbnxEREVmVxfuQExGR9a05nGb6b+O6Z2od49Tz6xV0MzJVWq85rlqjg1pnKPBmrQx5PjPkREREVsWAnIionVNWa7D5ZJbp66vXMFPLpRc2L0MebFpDbjjOWGEdAFzkbRuQ+zFDTkREZBMYkBMRtXObTmShUq2Dp7NhWnRGcWWTlhdR40xT1r0bL+hmFHxNL3LjdHVXhQxSiWCBETbMz90YkDNDTkREZE0MyImI2jFRFPHdIcN09cfGdgUAVKp1KK7UWHNY7YKpB3mn5k1Zzy2thk4vXlVhvW2z48DVRd2YISciIrImBuRERO3YoeQiXMwvh7NcitmDQ+FfkxnlOvLWUWv1pn7iTZ2y7ufmCJlEgFYvIk9ZbbUK64axGO6D0ioNqjW6Nr8+ERERGTAgJyJqx9bUZMdviQ2Gu6ODKZvLdeStk11SBb0IKGQSU8Xy65FKBAR6GjLTWSVVVutBDgAeTg6QywwfAS5beR15SkEFKtXa6+9IRETUDjEgJyJqp/KV1fjzXC4A4N4h4QCuTJvOKGaGvDWMP78wL2cIQtPXf19d2O3qNeRtTRCEqwq7WW8d+b6kAtzw7i4s3njGamMgIiKyJgbkRETt1LqjGdDqRQwM74SeQe4ArrToymRA3irN7UFuZOxFnlVcZdUp64BtrCP/Ym8yRBE4eKnQamMgIiKyJgbkRETt1O9ncgAAdw8JM20zZciLOGW9NUwF3ZoZkBsz5IYp68aAvO2nrANXtT6zUi/ylIIK7Llw2TCGMhVKWWiQiIg6IAbkRETtkFqrx6XL5QCAwZFepu1X1pAzQ94aGS0NyDsZp6xfWUPubqUMubV7kX93MK3W1xcvl1llHERERNbEgJyIqB26dLkcGp0IN4XMlJUFrkyZziyuYi/yVmjxlHVjhtwGpqz7WXHKeqVai5+OZwC48kAiKa+8zcdBRERkbQzIiYjaoYRcJQCge6BbraJjgZ6OkAiASqvH5XL2oG4JnV5EWmHLAnJjhjyrpAplKkOG3BpF3QAgqKbie1phRZtf+5e4bJRVaxHu7YyZ/UMAAEn5DMiJiKjjYUBORNQOJeQYpv/2CHSvtd1BKkGgx5Vp09R828/noaxaCw8nB0T4NC8gD/RwglDzQCS1wBDUW2sNeUyQBwDgXLYSOn3bzZYQRRHf1kxXv3dIOKID3AAwICcioo6JATkRUTsUn2sIyLsHuNd5L9hU2I3ryFviy73JAIA5Q8OhkEmbdaxcJoG/myEzfbEmALXWlPXOvq5wlktRpdEh+XLbBcPH04oRn6OEQibBHQNDEOXnCgC4xICciIg6IAbkRETtUELOlSnr1wq9ah05Nc/xtGIcTyuGXCrB3OHhLTqH8YGIWqcHYL0MuVQioFdNO7zTmaVtdt3VNdnxGf2C4OksR9eagDyrpArlKm2bjYOIiMgWMCAnIrITR1KKkF54/ax2YbnKVDk72r9uQB5iqvTNDHlzfVWTHb8lNgh+NZnu5rq6yB5gvQw5APQO9gQAnMlqm4A8v6wa284a2vHNHRYBAPB0lsO3puI7s+RERNTRMCAnIrIDaYUVmP3FQcxYsQ9FFepG902sma4e7u0Ml3oKhhlbdTFD3jxphRXYdi4XAPDAqM4tPo/xgYiRNQPyPiGGdeSnM0va5Ho/Hs2ARieif5gnYoI9TNuN09a5jpyIiDoaBuRERHYgMbcMoggUV2rw+u/xje57Zf143ew4cCUg5Bry5lm5LwWiCIyN9kW3emYeNFVwnYDcOlPWAaB3TUB+PkcJbc0UekvR6UX8cMTQ6uyeIbWn+18JyNmLnIiIOhYG5EREdiDjqmz2z8czcSi5sMF9TevH6ynoBlzJkGeVVEHfhtW17VlxhRo/HssEACxsRXYcsK0p65HeLnBVyFCt0eOihQu77U26jKySKrg7ynBzn8Ba73WtecBxkb3IiYiog2l2QL5nzx5MmzYNQUFBEAQBmzdvrvW+KIpYsmQJgoKC4OTkhLFjx+LcuXPXPe+GDRvQs2dPKBQK9OzZE5s2bWru0IiI2i3jem9HB8Ov7Rc3nYFKq6t33/iaHuQ96inoBgAB7o6QSQRodCLyyqotMNr2Z+3hNFRpdOgZ6I7hXbxbda6rp6w7OkjgILXes3FJGxZ2++FIOgBgZv8QODrUrk7PKetERNRRNftTQEVFBfr27YuPP/643vffeustvPfee/j4449x9OhRBAQEYMKECSgra3ga2sGDBzF79mzMmTMHp06dwpw5czBr1iwcPny4ucMjImqXMooMGfInboiCj6scly5X4IvdyXX20+r0uFCTZWwoQy6VCAjyZC/y6xFFEfE5SizfcQFf7k0BACwcHQlBEFp13mDPK73LrTld3ci4jvysBQu75SursSM+HwBw95CwOu8bA/KM4kpUqet/0ERERNQeNTsgnzJlCl577TXMnDmzznuiKGL58uV48cUXMXPmTMTExGD16tWorKzE999/3+A5ly9fjgkTJmDx4sXo3r07Fi9ejPHjx2P58uXNHR4RUbtkzJD3DHLHy1N7AgA+2nkRqQUVtfZLLayAWquHs1yKMC/nOucx4jryhml0ery//QLGvL0LUz7Yi+U7klBapUGUnyum9glq9fmd5FJ4u8gBWHe6ulHvEE8Als2Q/3gsAzq9iIHhnepdf+/tqoCXixyiCFxqw57oRERE1mbWeXIpKSnIzc3FxIkTTdsUCgXGjBmDAwcONHjcwYMHax0DAJMmTWr0GJVKBaVSWetFRNQeiaKIrJpMdmgnJ0zvG4SRXX2g1urx8i9nIYpX1oHH5xhmI0UHuEEiaTiTy17kDXt/+wV88HcS0osqIZdJcGMPP7x1ex9seHS42aaXGwu7udVTBb+t9Qm+UthNY4HCbvqrirnVlx03MvYjv8hp60RE1IGYNSDPzTW0g/H396+13d/f3/ReQ8c195hly5bBw8PD9AoNDW3FyImIbFdplQZlKi0Aw3RnQRDw2i0xkEsl2JtUgP0XrxR4S8htvKCbETPk9TucXIhPd18CAPx3ak+cfHkCvpo3CLMGhsLdjNPLjYXdbGHKeri3M9wcZVBr9UiyQFG1vRcLTMXcbuod2OB+rLROREQdkUUqyVy7vk4UxeuuuWvuMYsXL0ZpaanplZGR0fIBExHZMGMW28dVASe5oRhWhI+LKdv47vZEU5Y8oSZD3lBBNyP2Iq+rtEqDp9fHQRSBOwaEYMHIyHr7uJvDlYDc+hlyQRDQuyZLfiarxOzn/+Fww8XcrmYKyFlpnYiIOhCzBuQBAQEAUCeznZ+fXycDfu1xzT1GoVDA3d291ouIqD0yZrFDrulf/ei4LnB0kOBkegl2XbgMAEgw9SBvWoY8s4QZcsDwEPilzWeRXVqNcG9nvDK9l0WvFxvWCQAQ1Yp+5uZk7Edu7nXk+cpqbI/PA9D4dHXgys+CU9ab5kxmKdYfTYeOrQuJiOyaWQPyyMhIBAQEYPv27aZtarUau3fvxvDhwxs8btiwYbWOAYC//vqr0WOIiDoKYxY79JoibX5ujpg7LAKAYd1zaaUGWSWGfaMDGg/0QmrWkGeXVENrgXXD9mZzXBZ+PZUNqUTA8tn94Grhtd039Q7A7mfH4qnxURa9TlP1CfYEAJwxY6X1KrUOr/52vtFiblczZshTCysabOlHBj8cScetn+zH8xvOYNWBVGsPh4iIWqHZAXl5eTni4uIQFxcHwFDILS4uDunp6RAEAU899RRef/11bNq0CWfPnsX8+fPh7OyMu+++23SOuXPnYvHixaavn3zySfz111948803kZCQgDfffBM7duzAU0891epvkIjI3mUU158hB4CHRneGs1yK05ml+HhnEgDDdGgPp8bXJvu5KSCXSqDTi8hVduxe5NklVfjv5nMAgCfHR5my15YkCALCvV0aLbzXloxT1hNyyqDWtv4BzcX8MtyyYj9+O50DQQAeGdvlusf4uing7iiDXgRSrukeQAZanR6v/noeizeegbYmM75i50WUVWusPDIiImqpZgfkx44dQ2xsLGJjYwEAixYtQmxsLP773/8CAJ577jk89dRTePTRRzFw4EBkZWXhr7/+gpvblSfj6enpyMnJMX09fPhwrFu3Dt988w369OmDVatWYf369RgyZEhrvz8iIrtnypB3qtvGzNtVgfnDIwAAX+0z9Mrufp3sOABIJIKp0rexx3lHtTkuC2UqLfqGeuLRJgSO7VGol+Ehjlqnx4W8phdVyy+rxt/xeTieVoy0wgqUq7T4+Xgmpn20H4l5ZfB1U2DtA0MwvkfDS9CMBEEwTVvnOvK6lNUa3L/6GFbuN/w7f/rGbujs64KiCjW+3JNs5dEREVFLNXtO3tixY2u12LmWIAhYsmQJlixZ0uA+u3btqrPt9ttvx+23397c4RARtXsNrSE3enB0Z3x3MM1Uib37dQq6GYV0ckJKQUVNj3Nvs4zVHh1LLQYATO8bBJmZ2prZG0EQ0CfEA3uTCnA6sxQxNRnzxqi1esz+/FCD2eyRXX3w/ux+8HVTNHkcUX6uOJ5WjCSuI69FFEU8sOoYjqQWwdFBgvdm9cNNvQPRzd8Vj6w9ga/2pWDOsIhm/ayJiMg2dMxPHkREdkIUxQbXkBt5OsuxYGSk6evrFXQzMq4jz+jAldb1ehHHUosAAIMiLD9V3ZZdqbTetHXk649lIKWgAs5yKUI6OcHRwfCRQiYR8O8J3bB6weBmB4hXepGz9dnV4nPKcCS1CHKpBD89NNzUPm5yTAD6hnigUq3DR/8kWXmURETUEtbvt0JERA0qqlCjSqODIABBno4N7nf/qEh8ezAVymot+oV6Nuncxox7dknHDciT8suhrNbCWS5Fz8CO3a2jT02l9QOXCqDV6RudLVCl1uGjvw0B4HOTojF/hOGBUKVaC1FEi9vFGaesx+eUNallakexOS4LADC+h5+pIj5gmNnw/OTuuPurw/j+cDruHxmJcG8Xaw2TiIhagBlyIiIbZsxe+7s5QiFruIezu6MDNjwyHOseHNpgJv1axgC/IwfkR2uy47Fhnh12urrRiK4+8HR2QFphJTacyGx039UHU5FfpkKwpxPuuqqdmbNc1qre7X2CPSCXSZBSUIET6cUtPk97otOL+KUmIL8lNrjO+8O7+mBUlA+0ehHvbb/Q1sMjIqJW6tifPoiIbFxmIxXWr9XZ1xWDIryafO5AD8M5c0o7bpX1K9PVm/5za6/cHB3w+LiuAID3tl9Albr+1mOlVRp8uusSAODpCd0afVDUXJ1c5LilXxAA4OuaIoUd3eHkQuQpVXB3lGFstG+9+zw/uTsA4Je4bJwxcy95IiKyLAbkRGTTqjU6vLUtAct+j8eq/Sn461wuzmaVdpje2cYK6E3NejdHkMeVKeuNFetsz47WFHRjQG4wZ1g4Qjo5IU+pMlXzvtaXe5JRWqVBlJ8rbq0nY9taxnoI287mmh5IdWSbThqy4zf3CWrw4UdMsAem9zU8yHhy/UlU1BR4JCIi28eAnIhs2ie7LuGTXZfw+Z5kLPn1PB787jimfrQPUz/ah6IKtbWHZ3HNyZA3l7+HoeCWSqtHcWXH62OcVVKFrJIqSCVCk9fdt3cKmRTPTIwGAHy261Kdf2OXy64E6v+eGA2pBfqodw9wx4iu3tCLwOoDqWY/vz2p1ujwx9lcALjuw48l03vB312B5MsV+O8v59pieEREZAYMyInIZl0uU+GrvYb+utP7BmFKTAD6hnrCRS5FQm4Z5q08AmV1+w4kMxrpQd5aCpkUPq6GoLwjriM3TlfvFeTeqnXP7c30vkHoFeSOMpW2VuVujU6Pd/9KRKVah74hHpjU6/q9xVvq/pos+bojGSjvwNneHfF5KFdpEezphIHhjXcB8HKR44M7YyERgA0nMrHheON1AIiIyDYwICcim/XxP0mmD/8f3NkPn947AL88NgK/PD4S3i5ynMkqxQOrjjW41rU9sGSGHACCO3BhN2P/8YHhnK5+NYlEwH+mGNYkrzmUhlMZJVix8yJGvbkT645mAACendTdohXQx3bzQ2cfF5SptPj5WIbFrmPrNp/MBgDM6BcESRNmIwzt7I1/jY8CALz8y1lcusx+7kREto4BORHZpPTCSnx/JB2AoWDR1R/+u/q5YvWCwXBTyHAktQiPrD0Otbb9rSnX66/fg7y1OnJht6PsP96gUVG+GBXlA41OxIwV+/H2n4nIVVbDx1WOl6f2xMgoH4teXyIRcN+ICADANwdSodN3vBoHRRVq7ErMB3D96epXe+KGKAzt7IVKtQ6PrT2Bak37fWBJRNQeMCAnIpv03vZEaHQiRkX5YHjXuh/+Y4I9sPK+QXB0kGBX4mU889OpdleYrKBcBbVWD4kABHg03IO8NQKNGfLSjpUhL63SIDGvDAAwgAF5vZ6f3N20Rjwm2B3v3tEX+/9zg2k6uaXdNiAE7o4ypBVW4u/4vDa5pi3ZeiYHWr2IXkHupv7sTSGVCPjgzlh4u8iRkFuGZ3461SEfaBAR2QsG5ERkc85nK/HLKcNUTWM7n/oMivDC53MGwkEqYMupbBy8VNhWQ2wTGTXT1QM9nOBgoR7ZxkrrOSUdK0N+Ir0YoghEeDvDz80yDzvsXUywBzY/OgK/PDYCvz4+ErcNCDFri7PrcZbLTD3OO2ILtM011dVbUsne390Ry+/sBwepgN9O5+C5n09Dz6CciMgmMSAnIpvz1p8JEEVgWt8gxAR7NLrvmG6+uHuw4UP7+zsutKssuXG6uqXWjwNXMuQ5HSxDbizoNpDtzhrVO8QDfUM9LbpevDHzhkVAJhFwOKUIO853nCz59vN5OJ5WDIlg+D3YEqOifPHhnbGQSgRsOJGJl385265+PxIRtRcMyInIphxNLcKuxMuQSQT8e0K3Jh3zyNiukMskOJpajP0X20+W3NLrx4Era8izO1iG3Nh/fDADcpsW5OmE+0cZpsi/suUcKtXtv+J6flk1nt9wGoCh2ry/e8tncEzpHYj3ZvWFIABrD6fjta3xDMqJiGwM+7wQkU3565yh5+6MfsGI8HFp0jEBHo64e3AYVh1Ixfs7LmBEV2+rZfTMKaPIshXWASDY03DuXGU1dHrRIn2lbY1Kq0NcRgkAYCDXj9u8J8dH4bdTOcgqqcIHO5Kw+KYe1h6Sxej1Ip756TSKKtToEeiOZyZFt/qcM/oFQ6XV47mfT+PrfSnYeCITCpkUCgcJFDIJwr1dMDC8EwZGdEJMsEebLksgIiJmyInIxpzNUgIAhkQ2L3P5yNguUMgkOJ5WjL1JBZYYWpvLtGAPciNfNwVkEgE6vYjLZSqLXceWnM0qhVqrh7eLHJFNfOhD1uMsl+HVGb0AAF/tS0F8jrLR/fV6EUUV6rYYmtmtPpiKPRcuQyGT4MM7+5ktOJ41MBT/uyUGMomA4koNcpXVSCusxIW8cmw/n4dlfyTgtk8Povcrf+H5n0+zCBwRURtihpyIbIYoijibXQoA6BXs3qxj/d0dcfeQMHyz35AlHxXlY/dZ8gwL9yAHDBWZ/d0dkVVShezSKotVc7clxuJ/AyM62f090lGM7+GPyb0CsO1cLl7YdAYbHh5eb1/uo6lFWPrrOZzNUmL+8Ai8cFMPyGX2kXtIzC3Dsj8SAAAv3tyjWZXVm2LO0HDcFBOAwgo11Fo9VFodqtR6nM8pxbHUYhxPK0ZhhRrrj2XA3UmGF2/uadbrExFR/ezjrxQRdQjpRZUoq9ZCLpWgWws+jD4yxpAlP5legj12niXX6UVkl1h+DTkABNYE4R2h0rpOL+LHY5kAgHHRflYeDTXHK9N7wkUuxcn0EvxwNL3We1klVXj8+xO447ODplk2qw6k4s4vDtpFwUK1Vo8n152EWqvHuGhfzBkabpHreLsq0M3fDTHBHhgQ7oWRUT54cHQXfDF3II69dCOWz+4HAPhybwp+OpZhkTEQEVFtDMiJyGacyTJkx7sHurWozZefuyPurfkg+/52+664nqeshkYnwkEqtKqoU1MEehoLu9l+4NJauxLzkV5UCQ8nB8zo1/x2UmQ9gR5O+PdEw5rqFzedRe9X/sTwZX9j0vt7cMM7u/Db6RwIAnDX4DAsn90Pbo4ynEgvwdQP9+HAJdt+QPf7mRwk5JbBy0WOt27va5WZG4Ig4JbYYPzrhq4ADD9jYzcCIiKyHAbkRGQzjJmt67U6a8zDY7rA0UGCuIwSnM4sNdfQ2pxx/XiQp5PFC60F1WTIs+0gk9haqw+mAQBmDwqFk5zFq+zNvOERpsr4ZSotskurkZhXBpVWj6GdvbD1iVFYNrM3bokNxm9PjESPQHcUVqhx71eH8WdNwUhbtOpAKgBgwYgI+LoprDqWp27shsm9AqDW6fHwmuPIrFk6Q0RElmH2gDwiIgKCINR5PfbYY/Xuv2vXrnr3T0hIMPfQiMjGna3JkPduRUDu66bA2G6Gqcj/JOSbZVzWYJwt0BZFx4JqMuTtfcr6pcvl2HPhMgQBuHeIZaYEk2VJJQLWPzQUR1+8Ef/8ewx+eWwE1tw/BBsfHY4fFg5Fz6ArtSfCvV2w6dHhmNY3CHoRWLHzohVH3rBTGSWIyyiBXCrBnYPDrD0cSCQC3pvdFz0C3VFQrsaD3x5nkTciIgsye0B+9OhR5OTkmF7bt28HANxxxx2NHpeYmFjruKioKHMPjYhs2NUF3WKCWh6QA8ANPew/IN+XdBkAMKKLj8WvZVpD3s4z5N/VZMfHd/dDmLdl1+WT5QiCAF83BTr7uqJvqCdGRvmgf1j9BfocHaRYMq0nZBIBpzNLkZDbeIV2a1h9MBUAMLVPIHxcrZsdN3KWy/DVvIFwU8hwPkeJU5kl1h4SEVG7ZfaA3NfXFwEBAabXb7/9hi5dumDMmDGNHufn51frOKmUUwmJOpLM4iqUVGrgIBXQLcC1VecaG+0LwJBlzlfaX9ZXpdXhULJh7ebIKMsH5MYMeXap/f2smqpcpcXPxw3F3OYOi7DuYKhNebsqcGMPfwDATzUF/WxFQbkKv53KAQDMHR5h3cFcI9jTyfT7Z7+dF8kkIrJlFl1DrlarsWbNGixYsOC6BUpiY2MRGBiI8ePHY+fOndc9t0qlglKprPUiIvt1riY73s3frdW9d/3cHNE3xJBl35lof1nyE2klqNLo4OOqQPcA87Y+qo8xQ365TAWVVmfx61nDphOZKFdp0dnXBSO7Wv4hB9mWWYNCAACbTmZBrdVbeTRXrD+aAbVOj76hnugX6mnt4dQxoubfyr6LDMiJiCzFogH55s2bUVJSgvnz5ze4T2BgIL744gts2LABGzduRHR0NMaPH489e/Y0eu5ly5bBw8PD9AoNDTXz6ImoLZ0xw/rxq43rbr/T1vfWTFdvq17qXi5yKGp6NeeVqix+vbYmiqKpmNvcoeH19q+m9m10lC/83BQoqlDjn4Q8aw8HAKDV6bHmkOG+nDfMNmsaGB9enUgvRqVaa+XREBG1TxYNyL/++mtMmTIFQUFBDe4THR2NhQsXon///hg2bBg++eQT3HzzzXjnnXcaPffixYtRWlpqemVksF8mkT0zVljvZaaAfHx3wxTVfUkFdpf1NWajRrXBdHXAsCb3yrT19reO/MClQlzML4eLXIrbBoRYezhkBTKpBDP7G/7f/2gj09a3n89DTmk1vF3kuLlPoLWHU69wb2cEezpBoxNxJIUt0IiILMFiAXlaWhp27NiBBx54oNnHDh06FElJSY3uo1Ao4O7uXutFRPZJFEWzVFi/Wq8gd/i6KVCh1tnVB8niCrVptkBbTq1ur4XddHoR722/AAC4bUAI3BwdrDwispY7BhoC8l2J+cizgdoSxlZndw0Oa/UyHUsRBMH0e2g/p60TEVmExQLyb775Bn5+frj55pubfezJkycRGGibT4uJyPxySqtRWKGGVCKYbc20RCLghmj7m7a+/1IBRBGI9neDn7tjm1030KMmQ97OWp99sz8Fx9OK4aqQ4aExXaw9HLKiLr6uGBjeCXoR2Hgiy6pjOZ1ZgsMpRZBKBNwz1PqtzhozIsq4jrzQyiMhImqfLBKQ6/V6fPPNN5g3bx5kMlmt9xYvXoy5c+eavl6+fDk2b96MpKQknDt3DosXL8aGDRvw+OOPW2JoRGSDjNnxKD9XODqYL1N09TpyUbSPPrp7L7TtdHWjIE9D8J9d0n4y5Jcul+PtPxMBAC/e3APBNdPyqeOaNdBQb+anYxlW+51QrdHh2Z9OAwCm9Qk0PQyzVcO7eAMA4nOUKChvfzUmiIiszSIB+Y4dO5Ceno4FCxbUeS8nJwfp6emmr9VqNZ555hn06dMHo0aNwr59+7B161bMnDnTEkMjIhtk7unqRiOjfOAgFZBWWInkggqzntsSRFE0rR9vi3ZnVzMGBTntpPWZTi/i2Z9OQaXVY1SUD+4cxMKfBNzUJxBODlIkF1TgeFqxxa6j14uoUtdfu+LdvxKRmFcGH1c5Xp7a02JjMBcfVwV6BBqWBR64xCw5EZG5ya6/S/NNnDixwSfPq1atqvX1c889h+eee84SwyAiO3E221DQLcbMAbmrQoahnb2xN6kA/8Tno4tv6/qbW1pyQQWySqogl0owJNK7Ta8d2M4y5Cv3peBEeglcFTK8cVufNqlWT7bPVSHDzX0C8fPxTHx/JB0DI7zMcl6NTo/jacU4nlaMY6lFOJFeAmW1BrMHhmLJ9F6mmT8HLhXgq30pAIA3b+sDb1eFWa5vaSO7eiM+R4n9SQWY3rfhQr1ERNR8Fq2yTkTUFMYiZuYOyAFgnB2tI9+XZMiOD4zoBCd52xZ5Mk7nbg8Z8kuXy/HOX4ap6i9xqjpd454hhjXbW+KykVFU2apziaKI38/kYOL7e3DnF4fw9p+J2Jl4GaVVGogisO5oBm5ZsR8X88tRWqXBMz+egigaCrmN7+Fvjm+nTVzdj9xelv8QEdkLi2TIiYiaKl9ZjctlKkgEoGeg+bsljO/hh1d/O4+jqUVQVmvgbsNVtq/0H/dt82sbq6yXVmlQodLCRWGffx5OpBfjyXUnodLqMbqbL2ZzqjpdIzasE0Z09cb+i4X4dPclvH5r7xad52hqEV7/PR4n00sAAJ7ODhjRxQcDwjthYEQnlFRqsOjHU0jILcP0j/ehR6A7skurEe7tjJdu7mHG78jyBkd6wUEqIKukCulFlQj3drH2kIiI2g1myInIqozZ8a5+rhbJCod7u6Czrwu0ehF7Llw2+/nNRaPT42DN+sy2LugGAG6ODnCrCcLtsfWZTi/io7+TcMdnB5FRVIVQLye8eVtvTlWnev3rhigAwM/HMpt9v4uiiBc3ncEdnx3EyfQSODlI8a/xUdj3/A1YcU9/LBgZiT4hnhjdzRe/PzkSw7t4o1Ktw/G0YkgE4L1Zfe3ugZezXIb+YZ0AwFTngoiIzIMBORFZ1amMEgCWma5uNKFmauiO83kWu0ZrnUwvQYVaBy8XuUVmCjTFlXXk9jVtPbukCnd9cQjvbr8AnV7EjH5B2PqvUTZfvZqsZ0hnbwyO9IJap8fnu5ObdewXe5Kx9nA6JIJh6vnuZ8di0YRucK0nyPZzc8R39w/BUzdGwVUhw/OTu2NAuHnWrbc19iMna8hXVmPb2VyczjTUZSBqj+zrES0RtSsVKi2+P2LoujC0s+WKmN3Y0x+f70nGPwn50Oj0cJDa3rNI44fcEV19IJFYJ6sb6OGEC3nldpUhV2v1uOvLQ0grrISLXIrXbo3BrbEh1h4W2YF/3RCFe78+jO+PpOPRsV3g5+543WN2X7iMN7clAACWTO+FucMirnuMVCLgqRu74cnxUXY9Y2NElA/e3X4BBy4VQqcXIbXS7ynqOH6Jy8JLm86iTKU1bfN2kSM6wA1vzOyDMG9nK46OyHxs71MpEXUYX+9LQUG5GuHezrg1Nthi1+kf1gneLnIoq7U4mlJkseu0xqFkw3T1YRZ8MHE9QXaYIf/xWAbSCivh66bA70+OYjBOTTaiqzf6h3lCrdXjiz3Xz5KnFlTgie9PQC8CsweGYs7Q8GZdz56DcQDoE+wBN4UMJZUaTlsniypXabFofRyeXBeHMpUWoV5O8KnpSFBYocaBS4X4dPdFK4+SyHwYkBORVRSWq0wfgv89MdqiWWupRMAN3Q3V1v+ywWnr1RodTtZM3R/a2XrTWYNMvcjtI0Ou0uqwYqfhQ9ljY7uw0BQ1iyAIeGK8YS352sPpKChXQaPT40JeGX47nY0/zuQgPkeJSrUW5SotFn57DMpqLWLDPPHqLb3sPsBuLplUgtsHGh54Lf31HFTa+vusE7XG6cwS3PzhXmw8mQWJADw5Pgo7/z0Wx166EWeWTMQHd/YDYOiSUHFV5pzInnHKOhFZxYqdl1Cu0qJXkDum9g60+PUm9PTHT8czsSM+D69M62lTH6bjMkqg1urh66ZApI/1gsqImmsfSSmCKIr1/oz0ekPLI2tNq7/aj0czkFNajQB3R9w5OMzawyE7NLabL/qEeOB0ZimmfLAXJZVqaHR123q5KmQoV2nh56bAZ/cOgELWtm0JbcXTE7rh11M5SL5cga/2puCxcV2tPSRqR4oq1Ji78ghKKjUI9nTC8jv7YVDElYfUbo4OmN43CMt3JCGloAK/nsrm735qF5ghJ6I2l1lciTWH0gAAz0/u3ibB3agoXyhkEmQWVyEht8zi12sO43T1oZ29rfqgYHwPP7gqZEgtrMTBmjFdrbRSg1Fv7cTtnx2ATm/dXsTVGh1W7LwEAHh0XBc4OnTMAIlaRxAEPFmTJb9cpoJGJ8JVIUNsmCf6hXrC09nQJrFcpYVcJsHncwbAvwlrzdsrd0cHvDzV0LLtw7+TWt3Hnehq7/yViJJKDboHuOH3J0fVCsaNBEEwtbP84WhGWw+RyCKYISeiNvf+9iSodXoM7+LdZi2+nORSjIrywY74fGw/n4ceVqpkXp8rAbl1qy87y2WY3i8I3x9Ox7ojGRjepfb/m9UHU5FVUoWskir8dS4XU9pgZkND1h/NQK6yGoEejuw1Tq0yvoc/Vi8YDJ1ej27+bgj2dKr1YKykUo2Uggp4uci5LALA9L5BWHckAweTC7Fkyzl8NW+gTc04soaCchX2XyzAuO5+cHd0sPZw7NLZrFL8UFPkden0XvBwavjneFv/ELzzZyJOZZQgPkdpU3/PiVqCATkRmY0oinhyXRwOJhci0MMRQR5OCPR0hL+7I9wcZXBzdIBOr8fGk5kADNnxtvwgN6GnP3bE52NHfB7+VZMVs7ZqjQ4n00sAAEMirVfQzeiuQWH4/nA6tp3NRXGFGp1c5ACASrUW3+xPMe332e5LmBwTYJUP4tUaHT7ZZVg7/ui4rh12+jCZz5huvg2+5+ksR2yYvA1HY9sEQcD/bonBlA/24O8EwwPOib0CrD0sq9DpRaw9nIa3/0xEWbVhScOrM3phUi/r/G60V6Io4pUt5yCKwLS+QRhyneKmvm4KTOjpjz/O5mLdkXQsnRHTRiMlsgwG5ERkNskFFdhyKhuAYfrn6czSBvedEhOAvqGebTQygxu6+0MQzuB0ZilyS6sR4GH9qaenMkqg0urh46pAF1/rZ996h3igV5A7zmUrsfFkFu4fGQnAsF67uGZdX0G5CqcyS3EouQjDurT9Q4R1R9KRp1QhyMMRswayqjpRW+vq54oHR3fGip2XsPTX8xgS6Q0P546VGT6RXoyXN5/FuWwlAMDRQYL8MhUeXnMCE3r649UZvRBYUyiTGrc5LgvH04rh5CDFCzd1b9Ixdw0Owx9nc7HxZBb+M6UHnOR8MEv2i2vIichsdibkAwAGhHfCF3MGYMm0nnhodGfMjA3GhJ7+GNrZC72C3NE/zBMv3NSjzcfn66ZA/7BOAIDt8bZRbf1wTRu2IZ29bCajYiySs+5IOkRRhEanx5d7DdnxR8Z2wR01QfDney61+dgM2XHDdR+7gdlxImt5fFwUgj2dkFVShRvf342tp3MgitatLdFWvtqbjJmfHMC5bCXcHWX43y0xOPHyBDxxQ1fIJAK2n8/DhPf24N8/nsIPR9JxIa/MVBCTaitXabHs9wQAwOM3dG3yQ4yRXX0Q0skJZdVa/H4mx5JDJLI4ZsiJyGx2JhoC8pt6B9rsFMYbe/jjeFoxtp/Pa3YfYUu4uqCbrZjRLwivb41HUn45TqQXI7WgElklVfBxVeD2ASHIU1bj+8Pp2JV4uc3X760+kIr8MhWCPZ1wxwCuHSeyFie5FJ/PGYB/rTuJ5MsVeOz7Exjf3Q+v3hKDYM/2mxnOLqnC238mAgBm9g/GCzf1MPXI/vfEaEztE4T/bDyNk+kl2HAiExtOGJZoeTg54NGxXfDQmC5WG7st+ujvJOSXqRDh7YwHRkU2+TiJRMDsgaF4d/sFrDuajtsGcLYU2S9myInILMpVWhypyfYae37bogk9/QEABy8VoKxaY9WxqLQ6nEgvBgAMs3JBt6u5Ozrg5j6Ggm3fH87AZ7sNGen7R0bC0UGKcG8XU0E3Yy/5tqCs1uDTmrE8PaEb5DL+CSOypphgD/zx5Cg8OT4KDlIBfyfkY8J7u3EstcjaQ7OY97dfgEqrx+BIL7x7R19TMG4UHeCGDQ8Px7cLBuPxcV0xtLMXHB0kKK3SYNkfCVix86KVRm57/jqXi6/2GWZf/Xdaz2bPeLpjYCgkAnA0tRgX822rewpRczBDTtQB6PUidl+4jFUHUpGnrMZX8wYipJOzWa+xL+kyNDoRkT4uVu2lfT1d/VzR2ccFyQUV2JtUgJusWCn8dGYpqjV6+LjK0cXX1WrjqM9dg0Px8/Er2R03hQz3DL3S7/Xh0V2w9XQOtpzKxr8ndjP7/VSfr/Yko6RSg65+rrg1Ntji1yOi61PIpHh6QjdM6xuI534+jRPpJXjnr0Sse3CYtYdmdom5ZabfiYunNFyUVCIRMLqbL0bXFAvU6PT4Yk8y3v4zEW//mQhHB6mpPoe5FJSrsPlkFpRVGqi0eqi0emh0ejg6SE1FVd0Uho/9Kp0eaq0eKq0ODhKJ6X1XRxn0oojCcjUKy1UorFDD09kBD43uAqmZ25Puv1iAx78/CZ1exB0DQnBDd/9mnyPAwxE3dPfDjvh8/Hw8C/+Z0rT150S2hgE5UTtWodLip2MZWH0wDSkFFabtK3ZewrKZvc16rX9q1o+PjW64WrGtGN3NF8kFFTiWWtykgLy0UoNHvz+OkV198chY8003PHTJMF19SKR1+4/Xp39YJ3Tzd8WFvHIAwJxh4bXa+fQO8cCIrt7Yf7EQX+1NwZLpvSw6noJylSmT8szEbmb/cEhErdPVzw0f390fo97aiUPJRUjIVaJ7QPtqR/XmtgToReCm3gGIralH0hQOUgkeG9cVGp0ey3ck4X+/nYdCJsG9Zlo29de5XCzeeAaFFWqznO9aXs5yU20RcziRXoyF3x6DWqfH5F4Brfo8Mq1vEHbE52P3hcsMyMluMSAnaqcq1VrcsmI/kvINAZWboww39vDHppNZ2HA8E0/dGAV/d/NUGRdFETsTLwOw7enqRn1CPAAAZ7JKmrT/uqPp2H+xEIeSizC1TyBCvcyTDb66oJutEQQBdw4Kw6u/nYdcJsF9I+pmcx4e0wX7LxZi/dEMPDK2i9nup/qs2HkRlWod+oZ4YJKN1icg6uiCPJ0wqZc/fj+Ti9UHUrFsZh9rD8lsDl4qxD8J+ZBJBDw7qWWB35Pjo1Ct0eOz3Zfw0uazKKlU487BYXWmvTdVWbUGS389j5+PG7L23fxdMbSzN+RSCRQOEsgkElRrdChTaVFWrUVZtQYSQTC9L5dKoNHpa97TQlnzvrerHD6uCpRVa7AjPh8f/p2EW/sHm6WIZnyOEvNXHkGlWodRUT744K5+kElbvvxoZFcf03nzy6rh52b97ilEzWX2gHzJkiVYunRprW3+/v7Izc1t8Jjdu3dj0aJFOHfuHIKCgvDcc8/h4YcfNvfQiDqU1383FOXycZXjyRu7YWZsMFwUMmQUVeJYWjFW7kvBYjNVOj+XrcTlMhWc5VIMjrS94PJaxoD8bJYSOr3YaLZVFEX8eCwDgKHn7Ge7L+H/bm397AK1Vo9jaYaA3JYKul1t9qBQnMoswbDO3vB1q/uBcWRXH8SGeeJkegle/z0eH9wZ2+C5NDo94nOUOJZajPSiSjw4ujOCmlj4KbO4EmsPpQMAnp3Utr3riah55g+PxO9ncrHpZBaem9QdnVzsv4e7KIp44494AIZ2Wy1dliUIAp6fHI1qjQ6rDqTinb8u4L3tFzCiqw+m9Q2Cv7sjzmaV4nRmCc5mKVFcqYa3qxzeLgr4uCrg7iSD5KrffwcvFSKrpAqCADw4ujMWTehm1s4T1Rodxr69C9mlhkKe9T2YbY5tZ3Px4qYzUFZrMSC8Ez6fM6DV4/V2VSAm2B1ns5TYe6GAxd0sSKcXEZdRjCq13rRNhAi11rgEQg+1Tg+pIEAuk0Ahk0DhIEXPQPd6P0PQFRbJkPfq1Qs7duwwfS2VNvyPLSUlBTfddBMWLlyINWvWYP/+/Xj00Ufh6+uL2267zRLDI2r3dibmY01NALN8dixGRvmY3ntkbBfcv/oY1hxKw6Nju5qld6xxuvrIrj520YYq0scVLnIpKtQ6XLpcjm7+bg3ueyK9BJcuV0AmEaDVi/jpWCaeuCGq1T3Mz2SVoFqjh5eLHFF+trV+3MhFIWs0yBYEAa9Oj8H0FfvwS1w27hocVufhwv6LBVix8yJOppegSqOrtX3Do8NrTYNvyId/J0Gt02NYZ2+M6GqbDy+IyGBQRCf0DHTH+Rwl1h/LwMPtoKr41jM5OJVZChe5FP8aH9WqcwmCgFem9URXP1f8eCwDpzNLsTepAHuTCurdv7KoChlFVQ2eL9TLCe/e0c8iD8MdHaR4YnxXvLjpLFbsvITZg0LhLG9+6JBTWoVXfjmHv84b2o32CnLHyvmDWnSu+oyO8jUE5EmXGZBbSLVGh4XfHmvwPm2MQibB3GHheHhMF3i3cDZIe2eRgFwmkyEgoGlTCj/77DOEhYVh+fLlAIAePXrg2LFjeOeddxiQE7VAUYUaz/18GgBw34iIWsE4YJhSHu3vhsS8Mqw5nIbHxnVt9TWNAfk4O5iuDgBSiYBewR44klKE05mljQbkP9Vkx6f3C0JmcRWOpBThy73JeHlqz1aNYUe84Wc2JNJ2+o+3RO8QD9w9OAxrD6fjv7+cxdZ/jYJDzfTDfUkFWLDqKNQ6w9N0d0cZBoR3wvkcJZLyy/HY2hP4Zv6gRqcrns0qNU3HfHZytF3/rIg6AkEQMH9EBJ77+TS+O5iGB0ZGtmpKsrVpdXq8U9PmbOHozmbJ9AmCgHuHhuPeoeFILajAb6ezsfVMLlQaHXoFe6BPsAdigj3g765AcaUaBeVqFJarobymM4irQoZbYoPhqrDcCtRZA0Px+e5kpBdVYvWBtGbVURFFEd8dSsNb2xJRrtJCJhHw8JguePyGrnB0MN/D+9HdfPHJrkvYm1QAvV6EhDVGzKpSrcUDq4/hwKVCODpIEOFde4aIQiaBvOblIJVAp6/Jmuv0KK3UILmgAl/uTcH3h9OxYGQkHhjVGR5OrU8GtScW+ReclJSEoKAgKBQKDBkyBK+//jo6d+5c774HDx7ExIkTa22bNGkSvv76a2g0Gjg48H8YUVOJoogXN53B5TIVuvq54vnJdde5CYKAR8Z2wVPr47ByX4qplVVLFZarcCqzBAAwLto+AnIA6FMTkJ/JLMHtDTxRr1Rr8dvpHACGDyVqrR5zU45g7eE0PDq25U96t5/Pw+c17bvaw3roZydF4/czObiQV47VB1LxwKjOOJ5WjAe/MxTtmdjTH89OikYXX1dIJALOZpXijs8OYm9SAZb8eg7/mxFTb6BdrdHhqfVx0IvA1D6B6N+MIkpEZD3T+wbhjT8SkFVShR3xeZgcY71uFq218UQWUgsr4eUix8JR9X+WbY0IHxc8fkMUHr+hdZl3S3GQSvDk+Cj8+6dT+Gz3JdwzNKxJM5v0ehH/3XLWNFsvNswTb8zsg+iAhh+At1T/sE5wkUtRWKHG+RwlYoI9zH6NjqpcpcWCVUdxJKUILnIpVi0YjEERTZ+NIYqGLj/v/nUBZ7JK8dE/F7E5Lgtb/zWqSfdRR2H2R5ZDhgzBt99+iz///BNffvklcnNzMXz4cBQWFta7f25uLvz9a7c68Pf3h1arRUFBw9MiVCoVlEplrRdRR7fpZBb+OJsLmUTA8tn9Ggy0p/YJREgnJxRWqE0Z4JbafeEyRBHoGeje6mncbal3zTry01mlDe7zx5lclKu0CPd2xpBIL4yK8kGfEA9Ua/T4uqbid3OdzizBv344Cb1oaC02o19Qi85jSzyd5abqtst3JGH3hcu475srRXs+ujsWUf5upqxFTLAHPrizHwQBWHMoHd/sT633vG9uS8DF/HL4uSnwvxkxbfXtEFErOTpIcdfgUADAqgOp1h1MK6i1enz4TxIA4JExXeBiwUy0LbslNhhd/VxRWqXBV3uv/7dPrxfxwqYzWHMoHYIAvHRzD/z88HCLBOMAIJdJMKyLYTnTnqTLFrlGR1RWrcG8lUdwJKUIbgoZvntgSLOCccCQBBob7Yctj4/AZ/cOQKCHIzKKqvD+9gsWGrV9MntAPmXKFNx2223o3bs3brzxRmzduhUAsHr16gaPuTYzIopivduvtmzZMnh4eJheoaGhZhg9kf06nlaMlzefBQA8PaFbo0+IZVIJHhxteNL/+Z5kaHX6Bve9HuN0dXuorn61PiGeAIDz2UpoGvj+jcXc7hgQAkEQIAgCHq+Z4v/twTSUVmrqPa4hGUWVWLDqGKo0Oozu5otXG8gM26M7BoSiX6gnylVazFt55LpFeyb2CsDimiD+ta3n8cORdOj1oun9fUkFpkD9rdv7tIvCUEQdyb1DwyGVCDiUXIT4HPtMmvx0PAOZxVXwdVOYrUWZPZJKBCya0A0A8PXeZOy/WFDr9/XVdHoRz/58GuuOZkAiAO/N6osHRnW2eKtKY8/3PRcYkJuDVqfHQ98dx/G0Yrg7yrB24ZBWzVITBAGTYwLw5m2GzgurD6TiXHbDCZGOxuKLelxcXNC7d28kJSXV+35AQECdCuz5+fmQyWTw9m64eM/ixYtRWlpqemVktC7LR2TPTqQXY97KI6hQ6zCyqw8eGn39aXWzBobC20WOzOIqbD2T06LranR60x+/cd1tv//41cK9nOHmKINKq0dSTa/tq6UWVOBwShEEAbWKxNzYwx/R/m4oV2mx+mBqk69XWqXBfauOoqBchR6B7vjknv6mtdbtgUQi1Ew9N3zdM/D6RXsWjuqMOweFQi8CizeewazPDyI+R4nSSg2e+ekUAGDO0HCMtaOlEERkEOjhhMk1S3JW22GWvFqjw8f/XAQAPDq2C5zktl+w1JIm9wpATLA7KtQ63PPVYYx6ayfe/SsRibllSL5cjlMZJdh/sQBPrY/DhhOZkEoELL8zFrfGtk2RtVFRhs8gx9OKUaHStsk127P3tl/AgUuFcJZL8f3CoaYkRmuN7uaLm/sEQi8CL20+2+CDnY7G4p8GVSoV4uPjERhY//qhYcOGYfv27bW2/fXXXxg4cGCj68cVCgXc3d1rvYg6opPpxZj39RGUq7QY1tkbX84d2KQCOo4OUswbHgGg5R+W1h1Jh7JaCx9XBfqF2tf6XolEQO/ghvuRGwuJjYryRaCHU63jHrvBkCVfdSC1SbMLdHoRj39/AhfzyxHg7oiV8wdatAiPtfQO8cBrt8Rget8grF4w+LpFWwRBwGu3xOA/U7rDWS7FsbRiTP1oH2777AByldXo7OOCxTe1rN8vEVnf/BERAAzLqYor1NYdTDOtO5KOnNJqBHo44q7BYdYejtVJJAK+mjsIdw8Jg5ujDFklVfjon4uYtHwPbnh3N2as2I97vjqMX09lQyYR8PFdsZjet+2WZEV4OyPUywkanYhDyfUvk6Wm2XE+D5/sMtS5efO2PmZfk//fqT3hqpDhZHoJ1rdy2WR7YfaA/JlnnsHu3buRkpKCw4cP4/bbb4dSqcS8efMAGDLbc+fONe3/8MMPIy0tDYsWLUJ8fDxWrlyJr7/+Gs8884y5h0bU7sRllGDu10dQptJiSKQXvp4/sFlP8e8cHAoHqYAT6SU428ha6vooqzV4f4dh5suT47tafDqaJZjWkWfW/t51etEUkM8aWPfp/k0xAfBwckBRhdpU0K4xb/+ZiL1JBXBykGLl/EG1Avz25p4h4fjwrtgmVyKWSSV4eEwX7Fg0Bjf1DoBOL+JifjmkEgHvze5ntrY4RNT2BoZ3Qq8gd6i0eqw7aj8fvKvUOqyoCUgeG2feiuD2LMDDEa/f2htHX7wRH90Vi7HRvpBLJXBTyBDo4Yhu/q4Y2tkLX80biCm927aQnyAIGF2TJW9Jay4ySC+sxNM/xgEA5g+PwDQLPFTxd3fE0zVLIN74IwGF5SqzX8PemD0gz8zMxF133YXo6GjMnDkTcrkchw4dQni4Ye1NTk4O0tPTTftHRkbi999/x65du9CvXz/873//w4cffsiWZ0TXkZCrxJyvD6NMpcXgSC98c1/ze3r6uTliSk312+8OpjXr2BU7L6KoQo2ufq52mz3oE+wJADhzzcOIPRcuI1dZDU9nB0zo6V/nOJlUYlqvtjOh8fVqf5zJwWc1FdXfur0PegZxNk99gjyd8Mk9A/DtgsEY2dUHr98ag36hntYeFhG1giAImF8zE+u7g02bUWQL1hxKw+UyFYI9nTBrIGsUXcvRQYppfYOw6r7BuPB/U3Bm6SQcXDwefz09BuseHGa1ZUbGaetcR94y1RodHl5zHGXVWvQP88QLN/Ww2LXmDQtH9wA3lFZp8Oa2BItdx16YPSBft24dsrOzoVarkZWVhQ0bNqBnzyv9eletWoVdu3bVOmbMmDE4ceIEVCoVUlJS8PDDD5t7WETtSlZJFeatPIKyai0GhnfCN9dZq9uYucMMD8s2x2WhpLJpUwoziirxzb5UAMALN3W32x6zfWoy5PE5Sqi0OgCGopIf7zSsG7ytf0i9BckAYGxNQL7rQn6D50/KKzOthV44KtIiT5rbm9HdfLHmgSGYPcg+H/IQUW3T+gbBy0WO7NJq7IjPs/ZwrqukUo1Pax6i/mt8V8hl9vn3rSMa3tUbUomA5IIKZBRVWns4dmfJlnM4n6OEt4scK+7pb9F7XyaV4P9uNXRP+fFYJtIKKyx2LXvA3zJEdqakUo35K48gT6lClJ8rvp43qFWtWAaEd0KPQMOUwp+OZTbpmDe2JUCt02NkVx+76j1+rZBOTvB0doBGJ+JCrqGw2/6LhTieVgy57Eol+vqMiTYE5GezlMhXVtd5X1mtwUPfHUeFWoehnb3q7QlPRNTeOTpIcXfNLKqGWhzaktd/j0dRhRpRfq6Y2b9tCpKRebg7OqB/mCcATltvrl9PZWPd0QwIAvDhXbFtsrRuQLgXhkQa2qjt6eD/vxiQE9mRao0OC789hqSa4mCrFwyGh3PjhbOuRxAEU5Z8zeG061a8PJ5WjK2ncyAIwAs39bDrtl2CcKWw2+msEoiiiA/+NvTGvHtwGPzdG+6r7uOqMGXYd9UzPe6lTWeRXFCBQA9HfHx3f7udRUBE1FrGFmiHU4pwPtt2W6AduFSAH2seTC+b2btddcLoKIzT1u1hNoatyCiqxAsbzwAAHh/XFSO6+rTZtUfWXOvARQbkRGQHMosr8eS6kziaWgw3hQyrFgxCkKd5nmDO6BcEd0cZ0gorsTup4bVXer2I17aeBwDMGhDaLtZDG4PqM5mlOHipEEdTiyGvKTR2PcZ1crsTa//MkvLKsOVUNgQB+OSe/vBxbVqBMyKi9ijAwxFTYmy7BVq1RocXN50FANwzJAwDI7ysPCJqiZv7GOri7EzM57T1JtDq9Hhy3UmUqQzrxp8cH9Wm1x8RVROQXyqErgO3QGNATmQjckqrsPV0Dv44k4O/4/OwN+kydibk4/Xf4zHx/d0Y+eZO/HkuD3KpBJ/PHYDuAeYLhp3lMtxRU7imoeJu5SotHll7HCfTS+Asl+LfE7uZ7frW1LumsNupzFIsr6kaf+fgUAR4NJwdNxpbM219T9LlWsWKvtybDACY0MMfsWH21Q6OiMgSjMXdNsdlocgGW6B9svMiUgoq4OemwHNcYmS3uvi6YmRXH4gisPZw+vUP6OA++DsJJ9JL4OYowwd3xrb5bL4+wR5wU8hQWqWx6dkzlsZ+MtQonV7EyfRi+Lk5IqSTEyR22NrKll3ML8ef53Lx17lcnMpsvO2YRAD6h3XC4zd0xfAu5p9OdO/QcHy9LwU7E/Nx6XI5uvi6mt5LLagwTZWXSyVYNrM3/BqZzm1Pri7sBgByqQSPjL1+dhwA+oZ4opOzA4orNTiRXoLBkV7IV1Zj88lsAMBDYxpeg05E1JEMCO+EmGB3nM1SYtX+FCyaGG3tIZlcyCszFXJbOr0XPJxatxSMrGvOsHDsu1iA9UfT8dSNUWxb14CDlwpNRWxfv7U3Qr2c23wMMqkEQzp7YUd8PvZdLDC1o+1oGJBTg0RRxHM/n8aGE4b1VE4OUnTzd0V0gBtmDwrDgHBm/lpKFEW8sOkMfjhypS+rIAC9gz3gKJNCpdVBpdVDpxfRO8QD46L9MCrKB57OcouNKdLHBaO7+WLPhcuY+P4eDAjrhDHRvghwd8TSX89BWa2Fv7sCn907oF1lfQM9HOHjKkdBuSFjM3tQaJOLmUglAkZ388UvcdnYmZhvaD93IBVqnR4DwjthQDinPBIRAYaaHQtGRGLRj6fw4T8XcblchZen9mxxhxBz0etFvLDxDDQ6ETf28Mfkmqn1ZL/Gd/dDkIcjskursfV0Dm4bwOJ81yqt0mDRj3EQRWDWwBCrdoEZ0dUHO+LzceBSQZMTIu0NA3Jq0Ce7LmHDiUxIBEAmkaBKo8OpzFKcyizFT8czcd/wSDw7KRpOcj55bK63/0zED0cyIBEMBUgm9QrAjT394Odm3azzyzf3wBPKaiTkluFIahGOpBaZ3usf5onP7h3QbjLjRsbCbjsTL8NBKjT7j8G4aD9DQJ6Qj8fGdcWaQ4Yp/41VaCci6ohu6ReMi/nl+HT3JfxwJAOHU4rw4Z2xiAm2Xlbs74R8HEsrhrNcildn9LLrQqVkIJNKcM/QcLz9ZyK+PZjKgLwer/12Hjml1YjwdsYr03pZdSzGInJHUopQrdF1yBkNXENO9dp6Ogdv/5kIwDB96/yrk7Bj0Rh8ck9/3BobDFEEVu5PweQP9uBwcqGVR2tfvj2Yik92GabGvXFbH6xeMBh3DwmzejAOAFH+btj21GjsfW4c/ndLDMZ394O7owz3Dg3DDw8ObXfBuJHxj8Hdg8OaXShvdDdfCAKQkFuGD3ZcQFm1Fp19XDChh78lhkpEZLckEgHPTe6OtfcPgb+7AsmXK3DrJ/vxvZXW+oqiaJqyO294hNkKpZL13TkoFHKpxJBIyiix9nBsyj8JefjpeCYEAXjnjr6tap1rDlF+rvB1U0Cl1eNEerFVx2ItDMipjriMEiz6MQ6AoQjLnGERkEkl6Ornipt6B+L92f3wzX2DEOjhiLTCSsz+4hA+q1l7RY3bdjYXr2w5BwD494RumFVTSM3WhHo5Y87QcHw9fxBOL5mE127pDYWs/T6xnDc8AuseHIqXp/Zs9rFeLnL0DfEEAHy5NwUAsHB0Z9ZbICJqwPCuPtj25GhM7OkPjU7ES5vP4KQVPogfvFSIUxklUMgkWDAiss2vT5bj7aowVVz/toFitR1RaaUGi2tanC0YEWkT3QQEQcCILt4AgP0dtP0ZA3KqJaukCg+sPgaVVo9x0b4NBijjov3w59OjcecgQ0D53l8XkK+sbsuh2hW9XsTf8Xl4ct1JiCJw95AwPH5DV2sPi2o4SCUY2tm7xdVFx9W0PwMM/clvjQ0219CIiNqlTi5yfD5nAGb0C4JeBP794ylUqXVtOoYVuwzZ8TsHhcLXje0p25s5w8IBAL+ezrbJyv7W8Opv55GnVCHSxwXP2FBhReNMxf0XO+asWwbkZKLV6fHE9ydQUK5C9wA3fHR3f0gbyfK5Ozrgjdv6YEB4J6h1eqyy0d6iliKKIipUWqQXVuJEejG2n8/DPwl5OJVRgsziSlRrdDiTWYrXf4/HyDf/wf01Dzpu7OGPV6dznVp7Ymx/BgDzh4d3yPVPRETNJQgCXp0eY5i+XlCBt/5MaLNrn0wvxv6LhZBJBCxkzY92KTbUEzHB7lBr9Vh/NOP6B7Rz/yTkYcMJw1T1t2/vY1M1oIwB+enMEpRWaaw8mrbHom5k8vmeZEMvQoUMX84dCNcmril5cHRnPPTdcaw5lIZHx3Vt8nH2QBRFJOSWYcupbPwTn4/SKg1UWh3UWj3UOj00OrHJ53JTyDCtXxBevrlnm/d5JMvqHeyB7gFuKK3S4N6h4dYeDhGR3fBwdsCbt/XB/G+O4pv9qZjQ098irT2vZazlMqNfMEI6tX27J7I8QRAwd2gEnttg6BjUUSt4A4ap6v/ZYJiqfr+NTFW/WpCnEyJ9XJBSUIHDyYWY2KtjdTtoP5ETtcq57FIs33EBAPDK9F7N6kU4oYc/Ovu4ILmgAuuOpOOBUfb/pLlcpcXKfSnYciobF/PLG93X0UECH1cFvF3k0ItAYbkKBeVqqHV6KGQS3NjDH9P6BmFstC8zp+2URCJgy+MjodOLNvXEmYjIHoyN9sNdg8Pww5F0PPvTaWx7ahTcHC3XCzwxtwzbz+dBEIBHxtr/ZxZq2KReAXh+42lczC9HvrK63RanvZ6lv51DfpkKnX1c8Mwk25mqfrURXb2RUlCB/RcLGJCT9VRrdDiZbpiqUVatQVm1FhIBuH1gqEWzztUaHRatPwWNTsSkXv64rX/z1r9KJAIeGNUZL2w6g5X7UjBveAQc7DgDXKnWYt7KIzieZigwI5dJMC7aF1P7BCHSxwVymQRyqQRymQSezg719lAVRRFlKi3kUgmD8A5CLrPfe56IyNpevLkH9l28jIyiKvz3l3N46/Y+FvssYSxEO7lXALr6uVnkGmQbPJwd0DPQHeeylTiYXIgZ/TpejZcd5/Ow8UQWJALw9h19bfZz6YguPlhzKB37L3W8deQMyG3EgYsFeG7DaWQWV9V5r6RKg6du7Gaxa7+//QIS88rg4yrH67f2btHa5pn9g/He9kRkl1Zj6+kc3GKnRa1UWh0e+u44jqcVw91Rhpen9sSkmAC4N/NJvSAIzT6GiIioo3JVyPDO7X1x55eHsOlkFuJzlFg2szdiwzqZ7RqiKGL90QxsOZUNAHh0LIurdgTDOnvjXLYShzpgQF5aqcELmwxT1R8Y1RkDws3378nchnXxhiAAF/PLkaeshn8Hms3AlI6Vlau0eHHTGdz91WFkFlfB20WO/mGeGNPN1/SPxpItAI6kFOGLvckAgNdv7Q1v15ZVGXV0kGLesAgAhrXootj0tdW2QqvT48kf4rA3qQDOcilWLRiMOwaGMrAmIiJqA0M6e+Oju2Lh5SJHQm4ZZn56AK/8chZl1U0v8lRSqcbZrNI6x1y6XI47vziE/2w8A51exM29A9E7xMPc3wLZoGE1LbUOdsDM69Jfa6aq+7pg0QTLJffMwdNZjt7Bhn+TuxLzrTyatsUMuRWotXpcyCtDXEYJPt11CVklhqz4PUPCsPimHqbp6WmFFRjz9i7EZZSgSq0z+9rUcpUW//4pDqII3DEgpNXrNe4dGo5Pdl1CfI4S+y4WYFSU7/UPspLSKg2KKtRwkApQyKSQyyT432/nse1cLuRSCb6YMxD9zfhUnoiIiK5vap8gDO/ig//bGo8NJzKx+mAaVh9Mw9VNXxykEgR5OiHQwxFBnk5wVciQXFCBxFwl8pQq036dfVwQE+yBTs4O+OFIBtQ6PZwcpFg0oRvuGxHR9t8cWcWgSC9IBCC1sBI5pVUI9HCy9pDaxI7zedh40jBV/R0bnqp+tRt7+ON0Zin+PJeH2YPCrD2cNsOA3AJ0ehFHUorwx9kc5F3Vm1sUgVxlNRJyyqDW6U3bQ72c8ObMPhjetXZV0TAvZwR6OCKntBon0otNLQHM5bXfziOjqArBnk7477T6+403RycXOWYPCsWqA6n4fHeyTQTkymoNzmaV4kxmKRLzypBaUIHUwsoG+1FKJQI+vjsWI6MsX+GViIiI6vJykePdWX0xs38wXtx0BqmFldBfNfFOpdUjpaACKQUV9R7v6eyAkkoNkgsqkHzVPmO6+eK1W2KaVbiW7J+7owN6B3vgVGYpDl4qxMz+IdYeksUVV6ixuGaq+sJRne0myTQ5JgDvbb+AfUkFKKvWWLS4oy1hQG4moigiLqMEv57KwW+ns5Ffpmp0fw8nwy+HIZFeWDAyEi71FG0TBAFDO3tj08ksHEouNGtA/nd8HtYdzYAgAO/O6mu2G/7+kZH47lAa9l0swKHkQgzt7G2W8zZHWbUG722/gJ0J+UgtrGxwP1eFDBqdoX2ZKBrakv3vlpgOV9mRiIjIFo3o6oN//j0WBeW1P1NVaXTIKa1GdkkVckqrUVqlQaSPC7r5uyHK3xXujg4oLFfhbLYSZzJLkFJQibHRvpjaJ7BFdXLI/g3t4t1hAnJRFPHMT6dwuUyFLr4ueNrGp6pfLcrP1dT+bFfiZUzrG2TtIbUJswfky5Ytw8aNG5GQkAAnJycMHz4cb775JqKjGy6xv2vXLowbN67O9vj4eHTv3t3cQzQbY4/qX09l49fT2cgoulKQzd1RhikxgYgJ8cDVv/o71ayPCPVyatIfhaGdvUwBubkUVajx/FW9CM0ZNId6OeOuwaFYcygdy/5IwOZHh7fpH79DyYX494+nTMsAACCkkxP6hHigR4A7In1dEOHtgggfF9PSAFEUoa159G7P1eGJiIjaG4lEqLdVVbi3S6PHebsqMKabL8Z0s/5sPbK+YZ298fnuZBw04+dpW7Vyfyr+TsiHXCbBh3fF2sVUdSNBEDCpVwA+230J287lMiBvqd27d+Oxxx7DoEGDoNVq8eKLL2LixIk4f/48XFwa/+WZmJgId3d309e+vrb5S1QUReyIz8c7fyYiMa/MtN1ZLsWEnv6Y1icIo7r5QCFr/T8AY7BsrnXkoijixU1nUFCuQpSfq0V6ET45vhs2nsjCqYwS/H4mFzf3CTT7Na5VrdHh3b8S8dW+FIiiIQh/6eaeGBLphU4u8kaPFQQBDlI+MSciIiJqjwZFeEEmEZBZXIWMosp2u2zhdGYJ3vgjHgDw8s090CvI/goXTo4xBOS7EvJRrdHZ1QOFljJ7QL5t27ZaX3/zzTfw8/PD8ePHMXr06EaP9fPzg6enp7mHZFbphZVY8us5/JNgqP5n7FE9rW8QbujuV29P6tYw9zryX+Ky8cfZXMgkAt6f3c8iN7mvmwILR3XGB38n4e0/EzCxl7/FMs/VGh22xGXj8z2XcOmyYZ3Y7IGheHlaT4v2biciIiIi++CikKFPiAdOpJfgYHJhuwzIldUaPP79SWh0IqbEBODeoeHWHlKL9An2QIC7I3KV1dh/sQDje/hbe0gWZ/H5uaWlpQAALy+v6+4bGxuLwMBAjB8/Hjt37mx0X5VKBaVSWetlSaWVGnywIwkT3t+NfxLy4SAV8OjYLjj20o34fM5ATO0TZPZgHLiyjhxAq6etx+co8WJNgYcnx0chJthyT80Wju4MH1c5Ugsr8cORdLOfP7ukCm9tS8CwZX/juQ2ncelyBXxc5fhy7kC8eXsfBuNEREREZGJsf3aoHbY/E0URL2w8g/SiSgR7OuGN2/rYbb0EiUTApF6GIHzb2Vwrj6ZtWDRqEUURixYtwsiRIxETE9PgfoGBgfjiiy8wYMAAqFQqfPfddxg/fjx27drVYFZ92bJlWLp0qaWGDgCoUGmxIz4Pv57Kwe4L+dDoDOuMR3T1xtLpMejq52rR6xuZYx15flk17l91FBVqHYZ19sYjY7uYcYR1uSpkeHJ8FF7+5Rw+2JGEmf1DWhwka3R6bD2dg7NZhkrpiblltYrmBXs6Ye6wcNw5KAwezh2jGiMRERERNd2wzj5YsfMSDiYXQhRFuw1Y6/Pz8Uz8djoHMomAj+6OhYeTfX8enhQTgNUH07AjPg9anR6ydl7jyaIB+eOPP47Tp09j3759je4XHR1dq+jbsGHDkJGRgXfeeafBgHzx4sVYtGiR6WulUonQ0FDzDBzAuiPpWPrreVRpdFfG6e+Gx2/o2uZVOlu7jrxao8PCb48ju7QanX1c8Nm9A9rkxr5zcBhW7k9FSkEFvtiTjEUtrPL42a5LeHf7hTrbB0d6YcGICNzYw7/d/0MlIiIiopYbEN4JDlIBOaXVSCusRIRP47Wt7EVOaRVe/fU8AODpCd3spsVZYwZHeKGTswOKKzU4klqE4V3adztiiwXkTzzxBLZs2YI9e/YgJKT57QWGDh2KNWvWNPi+QqGAQqFozRAbtPvCZbyw6Qz0IhDu7YzpfYMwrW8Quvm7WeR613P1OvLjacXN6pGt14v494+ncCqjBJ7ODlg5f1CbZZEdpBI8Nykaj6w9gS/2XEJsqCfGdfdr1jnKVVp8vT8FAHBrbDCGRHqhW4AbovxcO0xvQiIiIiJqHSe5FLGhnXAktQgHkwvbRUAuiiL+s+EMylRa9Av1xEOjO1t7SGYhk0pwYw9//HQ8E3+ezW33AbnZ04qiKOLxxx/Hxo0b8c8//yAyMrJF5zl58iQCAy1fnftaF/PL8fj3J6AXgTsGhGDXM2Px74nRVgvGgdatI39/xwVsPZMDB6mAz+4d0Oa/fCbHBOCG7n6o1uhx/+qj+O5QWrOOX3soDSWVGnT2ccE7d/TFnYPD0D+sE4NxIiIiImqWoTXryA+2k3XkPx3LxO4LlyGXSfDOHX3b1YzRSb0CAAB/nsuDvqY9cXtl9v9rjz32GNasWYPvv/8ebm5uyM3NRW5uLqqqrvSFXrx4MebOnWv6evny5di8eTOSkpJw7tw5LF68GBs2bMDjjz9u7uE1qqRSjQdWH0VZtRaDIjrhtVtjbGZ9ybAWBOSpBRX4eOdFAMDrt/Y2a7/xphIEw4OAOwaEQC8CL28+i//ber5J/7CqNTp8udeQHX9kbBdIJbbx/4KIiIiI7I/x8/SepMvIV1ZbeTStk11Shf/9Zpiq/u8J3dqstlVbGRnlA2e5FLnKavx8IhO6dhyUmz0g//TTT1FaWoqxY8ciMDDQ9Fq/fr1pn5ycHKSnX6m8rVar8cwzz6BPnz4YNWoU9u3bh61bt2LmzJnmHl6DNDo9Hvv+BFILDdUJP713gFn6iJuLMZg+lVmCSrW2ScesPZwGUQTGdPPFHQPNt76+ueQyCd66vQ+erel5/uXeFDy69gQqVI1/H+uPZqCgXIVgTyfcEhvcFkMlIiIionZqQHgndPF1QUmlBgtWH73uZ1FbJYoi/rPRMFU9NswTD4xqH1PVr+boIMXkGEOW/LmfT2PcO7vw1d5kKKs1Vh6Z+QmiKLaLxw1KpRIeHh4oLS2Fu7t7s49fsuUcVh1IhbNcip8fHo6eQc0/hyWJoogRb/yD7NJqrLl/yHXXkVdrdBi67G+UVGrw9byBNtPD75e4LDz702modXpE+bni03sH1PtET63VY8zbO5FTWo3/3RKDOXbaS5GIiIiIbEd6YSVu/WQ/CivUuKG7H76Y0zbFjs1p/dF0PL/hDOQyCX7/16h2lx03Kldp8dE/SVh3JAOlVYZA3EUuxeoFgzEw4vottdtKa+NQ+7r7LGTjiUysOpAKAHh/dj+bC8aB2uvIDyYXXHf/307noKRSg2BPJ4yNbl4hNUua0S8YPzw4BP7uCiTll2PGx/vw+5mcOvttPJGJnNJq+LkpcMeA5hcFJCIiIiK6Vpi3M76cNxAKmQT/JORjya/nYE/5yaySKvzvt3gAwLMTo9ttMA4Y2igvntIDhxaPx+u39kZnXxdUqHX4cm+ytYdmVhZte2YPzmaVYvHGMwCAf42PMhUQsEUjo3yw8WQWtp7OwTMToxtd324snnbP0DCbW3s9INwLvz0xCk/8cAKHkovw6NoTmD88AhN7+SPSxwU+rgp8uvsSAODB0Z3h6GA7SweIiIiIyL71D+uED+7sh0fWnsCaQ+nQi4CfmwJl1VqUVWug1uohl0kgl0mgkEkR4O6Iu4eEwUVh3dDJUFX9NMpVWgwI74QFI1tWPNveOMmluHtIGLoHumHmJwdwJKUIer0IiY3FOC3VoQPy4go1Hl5zHCqtHuOiffHU+ChrD6lRk3oFwFl+FqmFlTiWVoxBDUzVOJ1ZglMZJZBLJZhlxbXjjfF1U2DN/UPw9p+J+HxPMlYdSDXNUpBLJVDr9Ojk7IC7h4RZd6BERERE1O5MjgnEizf1wGtb4/H94fTr7r85Lgsr5w+Cv7tjG4yufuuOZmBvUgEUMgnevr2PzSXdLC0myANODlIUV2pw8XK5VbtgmVOHDch1ehH/WncSmcVVCPd2xvLZsTb/lMVFIcPNvQPx0/FM/HQso8GAfE1Ndvym3gHwcbVMr3ZzkEklWHxTDwyK8MLaw2lIKahARnEV1Do9AODhMV3gLO+wtygRERERWdD9IyPh5ijD/ouFcHWUwc1RBndHB1NySKXVQ6XV4edjmTiXrcStK/Zj5X2D0D2g7Ze3ZpVU4f+21kxVnxSNzr7td6p6Q+QyCfqHe2L/xUIcTiliQG7PiirUePvPBOxNKoCTgxSf3TsAHs720df69gEh+Ol4JraezsGS6b3qBKyllRr8EpcNAJgzzD4Kod3Y0x839jQUndPo9MgqrkJJlQZ9QzysPDIiIiIiaq8EQcDsQWGYPajxGZn3DA7H/FVHkHy5Ard/ehAr7umPMd1822iUhqnqz/9smKo+MLwT7hvRMaaq12dwhDf2XyzEkZSidlP0uUMVdcsoqsQrv5zF8Df+xg9HMgAAb9zWGz0Cba+IW0MGR3oh3NsZFWodfj+TW+f9n45nQKXVo0egO/qHdbLCCFvHQSpBhI8L+oV62kwPeCIiIiLquMK8nbHxkeEYEumFcpUWC1YdxbLf402Vvy1tzaE07LtomKr+Vgecqn61IZ0NM4SPpBTaVTG+xnSIgFyt1eP5n09jzNs7sfpgGqo1evQO9sAXcwZgRj/76m8tCAJu72+oOv7z8Yxa7+n1ItbWrIGZMzScAS0RERERkRl4Osvx7f2DMTM2GDq9iM/3JGPM2zvx1d5kqLQ6i133cHIhXv3tPADgucndO+RU9av1C/WEXCpBnlKFtMJKaw/HLNr9lHW1Vo9H157Ajvg8AMDobr54eHRnDOvibbcB68wBIXhvxwUcSi5CemElwrydIYoiXv89HikFFXBTyDCjX5C1h0lERERE1G4oZFK8O6svpvYNxBt/JOBCXjle2xqPb/anonvAlfXMgiBgYi9/3DEgpFXxRkZRJR5ZewIanYib+wRiwYgIM3wX9s3RQYq+oR44mlqMIylFiPBxsfaQWq1dB+RqrR6PfW8IxhUyCT6fM8CmenK3VLCnE0Z08cG+iwX4+UQmFk3ohs92J+OrfSkAgFdv6WX1tgxERERERO2NIAi4obs/xnTzw4bjmXh3eyKySqqQVVJVa78d8XnYEpeNZTN7I9TLudnXKVdpsfDbYyiqUCMm2B3v3N7XbpOJ5jY40gtHU4txOKUIswbZZkep5mi3UZtGp8cTP5zA9vN5kMsk+HLuQIxuw+ILlnbHwBDsu1iADcczEeThiDe3JQAAXrq5B26NDbHy6IiIiIiI2i+pRMCsQaGY1jcIO+LzUKW+Mm09q6QKn+2+hH0XCzB5+R7856YeuGdwWJM7Oun1Ip5aF4eE3DL4uinw5dyBcJJLLfWt2J3Bkd5YsfMSjqQWWnsoZiGI7WQ1vFKphIeHBwa8/AvCAryh0Yk4k1VqCsbbshJiW6jW6DDo/3agrFpr2vbwmC74z5TuVhwVERERERGlFFTguZ9P4WhqMQDATSGDu5MD3GraqylkUlyd8NbqxJpWazqUV2uRWlgJuUyC9Q8ORawdFmq2pHKVFn2X/gWdXsSB/9yAIE8nq47HGIeWlpbC3b35xcLbXYY8v0yFAnUJAEAuNUxTb2/BOGBYPzGtbxC+ryniNmtgCJ6fHG3lURERERERUaSPC9Y/OAyrD6birW2JKFNpUabSXv/AGhIBeOu2PgzG6+GqkCEmyB2nMktxJKUIt8TaV5Hua7W7gPz7hUNQpnNAnrIaQ7t4o3uA/bQ0a675wyPwy8ksjOvuh9dv7c11JURERERENkIiEXDfiEjMGhiKnNJqlFVrUK7SoqxaC7VWX2dfuVQChYMECqkEoV7OLVp73lEMjvTCqcxSHGZAbnv6hHi2aKqAPerm74ZTr0yETNohutcREREREdkdF4UMXf06drsycxsc6Y0v96bgSIr9ryNnJGfnGIwTEREREVFHMjjCC4IAXLpcgYJylbWH0yqM5oiIiIiIiMhueDg7INrf0Pv9aEqRlUfTOgzIiYiIiIiIyK4MifQCAPx1Pg/23DiMATkRERERERHZlQk9AwAAm05mYdkfCXYblDMgJyIiIiIiIrsyMsoH/53aEwDwxZ5kvLT5LPR6+wvKLRaQf/LJJ4iMjISjoyMGDBiAvXv3Nrr/7t27MWDAADg6OqJz58747LPPLDU0IiIiIiIisnMLRkbizdt6QxCAtYfT8e+fTkGr01//wCYSRREF5SrkK6sbfF1WVrfqGhZpe7Z+/Xo89dRT+OSTTzBixAh8/vnnmDJlCs6fP4+wsLA6+6ekpOCmm27CwoULsWbNGuzfvx+PPvoofH19cdttt1liiERERERERGTnZg8Kg5NchkXr47DpZBa2nMqGRLjyvlwqgZujA9wcZXBzlMHLRY5ADycEejoi2NMJHk4O0OhEqLV6qLQ6lFRqcCGvDIl5ZUjKK0e5Stvo9fWqylaNXxAtMNl+yJAh6N+/Pz799FPTth49euCWW27BsmXL6uz//PPPY8uWLYiPjzdte/jhh3Hq1CkcPHiwSddUKpXw8PBAaWlph+lDTkRERERERMCO83l4an3cdQPolrg6wL+WXlWJtPdntTgONXuGXK1W4/jx4/jPf/5Ta/vEiRNx4MCBeo85ePAgJk6cWGvbpEmT8PXXX0Oj0cDBwaHOMSqVCirVlZ5zSqXSDKMnIiIiIiIie3NjT38cffFGlFZpTNtEiFBp9Cir1qJMpUFZtRYF5SrklFQju7QK2SVVKK3SQiGTQC6TQCGTwFUhQ5SfK7oFuCHa3w0RPi5wkDa80lupVMLj/ZaP2+wBeUFBAXQ6Hfz9/Wtt9/f3R25ubr3H5Obm1ru/VqtFQUEBAgMD6xyzbNkyLF261HwDJyIiIiIiIrvlJJfCSS619jCaxWJF3QShdl5fFMU62663f33bjRYvXozS0lLTKyMjo5UjJiIiIiIiImo7Zs+Q+/j4QCqV1smG5+fn18mCGwUEBNS7v0wmg7e3d73HKBQKKBQK8wyaiIiIiIiIqI2ZPUMul8sxYMAAbN++vdb27du3Y/jw4fUeM2zYsDr7//XXXxg4cGC968eJiIiIiIiI7J1FpqwvWrQIX331FVauXIn4+Hg8/fTTSE9Px8MPPwzAMN187ty5pv0ffvhhpKWlYdGiRYiPj8fKlSvx9ddf45lnnrHE8IiIiIiIiIisziJ9yGfPno3CwkK8+uqryMnJQUxMDH7//XeEh4cDAHJycpCenm7aPzIyEr///juefvpprFixAkFBQfjwww/Zg5yIiIiIiIjaLYv0IbcG9iEnIiIiIiKittTaONRiVdaJiIiIiIiIqGEWmbJuDcZEv1KptPJIiIiIiIiIqCMwxp8tnXjebgLysrIyAEBoaKiVR0JEREREREQdSVlZGTw8PJp9XLtZQ67X65GdnQ03NzcIglDvPoMGDcLRo0fbeGTtj1KpRGhoKDIyMrhev5V4T5oH70nz4n3ZerwnzYv3pHnwvjQf3pPmwXvSvHhftl5L7klRFFFWVoagoCBIJM1fEd5uMuQSiQQhISGN7iOVSvmP3Yzc3d3582wl3pPmxXvSPHhfmg/vSfPgPWlevC9bj/ekefGeNA/el+bT3HuyJZlxow5V1O2xxx6z9hCIauE9SbaI9yXZGt6TZGt4T5It4n1pn9rNlHVqO2wxR7aG9yTZGt6TZIt4X5Kt4T1JtsYa92SHypCTeSgUCrzyyitQKBTWHgoRAN6TZHt4T5It4n1Jtob3JNkaa9yTzJATERERERERWQEz5ERERERERERWwICciIiIiIiIyAoYkBMRERERERFZAQNyIiIiIiIiIitgQN4B7dmzB9OmTUNQUBAEQcDmzZtrvZ+Xl4f58+cjKCgIzs7OmDx5MpKSkuqc5+DBg7jhhhvg4uICT09PjB07FlVVVab3i4uLMWfOHHh4eMDDwwNz5sxBSUmJhb87sletvS9TU1MhCEK9r59++sm0H+9Laipz/K7Mzc3FnDlzEBAQABcXF/Tv3x8///xzrX14T1JTmeOevHTpEm699Vb4+vrC3d0ds2bNQl5eXq19eE9SUy1btgyDBg2Cm5sb/Pz8cMsttyAxMbHWPqIoYsmSJQgKCoKTkxPGjh2Lc+fO1dpHpVLhiSeegI+PD1xcXDB9+nRkZmbW2of3JTWVue7LL774AmPHjoW7uzsEQaj3fjPHfcmAvAOqqKhA37598fHHH9d5TxRF3HLLLUhOTsYvv/yCkydPIjw8HDfeeCMqKipM+x08eBCTJ0/GxIkTceTIERw9ehSPP/44JJIrt9Tdd9+NuLg4bNu2Ddu2bUNcXBzmzJnTJt8j2Z/W3pehoaHIycmp9Vq6dClcXFwwZcoU07l4X1JTmeN35Zw5c5CYmIgtW7bgzJkzmDlzJmbPno2TJ0+a9uE9SU3V2nuyoqICEydOhCAI+Oeff7B//36o1WpMmzYNer3edC7ek9RUu3fvxmOPPYZDhw5h+/bt0Gq1mDhxYq3fg2+99Rbee+89fPzxxzh69CgCAgIwYcIElJWVmfZ56qmnsGnTJqxbtw779u1DeXk5pk6dCp1OZ9qH9yU1lbnuy8rKSkyePBkvvPBCg9cyy30pUocGQNy0aZPp68TERBGAePbsWdM2rVYrenl5iV9++aVp25AhQ8SXXnqpwfOeP39eBCAeOnTItO3gwYMiADEhIcG83wS1Oy29L6/Vr18/ccGCBaaveV9SS7X0nnRxcRG//fbbWufy8vISv/rqK1EUeU9Sy7Xknvzzzz9FiUQilpaWmvYpKioSAYjbt28XRZH3JLVOfn6+CEDcvXu3KIqiqNfrxYCAAPGNN94w7VNdXS16eHiIn332mSiKolhSUiI6ODiI69atM+2TlZUlSiQScdu2baIo8r6k1mnJfXm1nTt3igDE4uLiWtvNdV8yQ061qFQqAICjo6Npm1QqhVwux759+wAA+fn5OHz4MPz8/DB8+HD4+/tjzJgxpvcBQwbdw8MDQ4YMMW0bOnQoPDw8cODAgTb6bqi9aMp9ea3jx48jLi4O999/v2kb70syl6bekyNHjsT69etRVFQEvV6PdevWQaVSYezYsQB4T5L5NOWeVKlUEAQBCoXCtI+joyMkEolpH96T1BqlpaUAAC8vLwBASkoKcnNzMXHiRNM+CoUCY8aMMd1Px48fh0ajqbVPUFAQYmJiTPvwvqTWaMl92RTmui8ZkFMt3bt3R3h4OBYvXozi4mKo1Wq88cYbyM3NRU5ODgAgOTkZALBkyRIsXLgQ27ZtQ//+/TF+/HjTWrXc3Fz4+fnVOb+fnx9yc3Pb7huidqEp9+W1vv76a/To0QPDhw83beN9SebS1Hty/fr10Gq18Pb2hkKhwEMPPYRNmzahS5cuAHhPkvk05Z4cOnQoXFxc8Pzzz6OyshIVFRV49tlnodfrTfvwnqSWEkURixYtwsiRIxETEwMApnvG39+/1r7+/v6m93JzcyGXy9GpU6dG9+F9SS3R0vuyKcx1XzIgp1ocHBywYcMGXLhwAV5eXnB2dsauXbswZcoUSKVSADCtM3vooYdw3333ITY2Fu+//z6io6OxcuVK07kEQahzflEU691O1Jim3JdXq6qqwvfff18rO27E+5LMoan35EsvvYTi4mLs2LEDx44dw6JFi3DHHXf8f3t3EBJFH8Zx/LfgUom2Im2NMWRCHSpU0CA81KFDdBADbyHUtWC7dIlA8FiHLIQCIcpDhyBDIpEOhZUeFiOaaD25u4e8LFgSW7DRUj7vwdehaRNHm1ret+8H9jL7OKvwZfC/M7ujTCbjz9AkohCmyWQyqdHRUY2Pj6uurk6JRELFYlEdHR2BbmkSG5FKpfTmzRvdvXu34rkf2wnT048zdImNiLrLtfaxkf3UrOsV8Vfo7OzU69evVSwWVS6XlUwmdejQIR08eFCS1NTUJEnav39/4Of27dun+fl5SZLjOBXf2ipJ7969q3g3CghjrS6/d//+fZVKJZ06dSqwnS4RpbWazOfzun79umZnZ3XgwAFJUnt7u6anp3Xjxg0NDw/TJCIV5jh57Ngx5fN5vX//XjU1NWpoaJDjOGppaZHEcRIbc+7cOT18+FBTU1NyXdff7jiOpOUziSv/P0rLH39c6clxHJXLZX348CFwlnxhYcG/yo0usRG/0mUYUXXJGXKsKpFIKJlMKpvN6uXLlzpx4oQkaffu3dq5c2fF7QPm5ubU3NwsSerq6lKxWNSLFy/852dmZlQsFgOXEAPrtVqX37t165Z6enqUTCYD2+kSv8NqTZZKJUkK3H1CWv5c78qVRjSJ3yHMcXLbtm1qaGjQ5OSkFhYW1NPTI4kmsT5mplQqpbGxMU1OTvpv7KxoaWmR4zh6/Pixv61cLuv58+d+T52dnYrH44GZQqGg2dlZf4YusR5RdBlGZF2G/vo3/G98+vTJPM8zz/NMkl29etU8z7O3b9+amdm9e/fs6dOnls/n7cGDB9bc3Gy9vb2BfVy7ds22bt1qo6Ojls1mrb+/3zZv3my5XM6fOX78uLW1tVk6nbZ0Om2tra3W3d39R/9W/HdE0aWZWTabtVgsZo8ePfrp69AlwvrVJsvlsu3Zs8cOHz5sMzMzlsvl7MqVKxaLxWxiYsKfo0mEFcVx8vbt25ZOpy2Xy9mdO3essbHRzp8/H5ihSYR19uxZSyQS9uzZMysUCv6jVCr5M5cvX7ZEImFjY2OWyWTs5MmT1tTUZB8/fvRnzpw5Y67r2pMnT+zVq1d29OhRa29vt69fv/ozdImwouqyUCiY53l28+ZNk2RTU1PmeZ4tLi76M1F0yYL8L7Ty1f0/Pk6fPm1mZkNDQ+a6rsXjcdu1a5f19/fbly9fKvZz6dIlc13Xamtrraury6anpwPPLy4uWl9fn9XX11t9fb319fVV3C4AWBFVlxcvXjTXde3bt28/fR26RFhRNDk3N2e9vb22fft2q62ttba2torboNEkwoqiyQsXLtiOHTssHo/b3r17bXBw0JaWlgIzNImwftajJBsZGfFnlpaWbGBgwBzHsU2bNtmRI0csk8kE9vP582dLpVLW2NhoW7Zsse7ubpufnw/M0CXCiqrLgYGBNfcTRZexf39pAAAAAADwB/EZcgAAAAAAqoAFOQAAAAAAVcCCHAAAAACAKmBBDgAAAABAFbAgBwAAAACgCliQAwAAAABQBSzIAQAAAACoAhbkAAAAAABUAQtyAAAAAACqgAU5AAAAAABVwIIcAAAAAIAqYEEOAAAAAEAV/AOwCwPVs+PxEwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the federal funds rate data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import fedfunds\n",
    "\n",
    "dta_fedfunds = pd.Series(\n",
    "    fedfunds, index=pd.date_range(\"1954-07-01\", \"2010-10-01\", freq=\"QS\")\n",
    ")\n",
    "\n",
    "# Plot the data\n",
    "dta_fedfunds.plot(title=\"Federal funds rate\", figsize=(12, 3))\n",
    "\n",
    "# Fit the model\n",
    "# (a switching mean is the default of the MarkovRegession model)\n",
    "mod_fedfunds = sm.tsa.MarkovRegression(dta_fedfunds, k_regimes=2)\n",
    "res_fedfunds = mod_fedfunds.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>226</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-508.636</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 18 Dec 2025</td> <th>  AIC                </th> <td>1027.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>07:37:30</td>     <th>  BIC                </th> <td>1044.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1954</td>    <th>  HQIC               </th> <td>1034.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    3.7088</td> <td>    0.177</td> <td>   20.988</td> <td> 0.000</td> <td>    3.362</td> <td>    4.055</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    9.5568</td> <td>    0.300</td> <td>   31.857</td> <td> 0.000</td> <td>    8.969</td> <td>   10.145</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    4.4418</td> <td>    0.425</td> <td>   10.447</td> <td> 0.000</td> <td>    3.608</td> <td>    5.275</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.9821</td> <td>    0.010</td> <td>   94.443</td> <td> 0.000</td> <td>    0.962</td> <td>    1.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.0504</td> <td>    0.027</td> <td>    1.876</td> <td> 0.061</td> <td>   -0.002</td> <td>    0.103</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &    226      \\\\\n",
       "\\textbf{Model:}           & MarkovRegression & \\textbf{  Log Likelihood     } &  -508.636   \\\\\n",
       "\\textbf{Date:}            & Thu, 18 Dec 2025 & \\textbf{  AIC                } &  1027.272   \\\\\n",
       "\\textbf{Time:}            &     07:37:30     & \\textbf{  BIC                } &  1044.375   \\\\\n",
       "\\textbf{Sample:}          &    07-01-1954    & \\textbf{  HQIC               } &  1034.174   \\\\\n",
       "\\textbf{}                 &   - 10-01-2010   & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:} &      approx      & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       3.7088  &        0.177     &    20.988  &         0.000        &        3.362    &        4.055     \\\\\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       9.5568  &        0.300     &    31.857  &         0.000        &        8.969    &       10.145     \\\\\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{sigma2} &       4.4418  &        0.425     &    10.447  &         0.000        &        3.608    &        5.275     \\\\\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{p[0-$>$0]} &       0.9821  &        0.010     &    94.443  &         0.000        &        0.962    &        1.002     \\\\\n",
       "\\textbf{p[1-$>$0]} &       0.0504  &        0.027     &     1.876  &         0.061        &       -0.002    &        0.103     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Markov Switching Model Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  226\n",
       "Model:               MarkovRegression   Log Likelihood                -508.636\n",
       "Date:                Thu, 18 Dec 2025   AIC                           1027.272\n",
       "Time:                        07:37:30   BIC                           1044.375\n",
       "Sample:                    07-01-1954   HQIC                          1034.174\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          3.7088      0.177     20.988      0.000       3.362       4.055\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          9.5568      0.300     31.857      0.000       8.969      10.145\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         4.4418      0.425     10.447      0.000       3.608       5.275\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.9821      0.010     94.443      0.000       0.962       1.002\n",
       "p[1->0]        0.0504      0.027      1.876      0.061      -0.002       0.103\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary output, the mean federal funds rate in the first regime (the \"low regime\") is estimated to be $3.7$ whereas in the \"high regime\" it is $9.6$. Below we plot the smoothed probabilities of being in the high regime. The model suggests that the 1980's was a time-period in which a high federal funds rate existed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Probability of being in the high regime'}>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_fedfunds.smoothed_marginal_probabilities[1].plot(\n",
    "    title=\"Probability of being in the high regime\", figsize=(12, 3)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the estimated transition matrix we can calculate the expected duration of a low regime versus a high regime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[55.85400626 19.85506546]\n"
     ]
    }
   ],
   "source": [
    "print(res_fedfunds.expected_durations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A low regime is expected to persist for about fourteen years, whereas the high regime is expected to persist for only about five years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Federal funds rate with switching intercept and lagged dependent variable\n",
    "\n",
    "The second example augments the previous model to include the lagged value of the federal funds rate.\n",
    "\n",
    "$$r_t = \\mu_{S_t} + r_{t-1} \\beta_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n",
    "\n",
    "where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n",
    "\n",
    "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
    "\\begin{bmatrix}\n",
    "p_{00} & p_{10} \\\\\n",
    "1 - p_{00} & 1 - p_{10}\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "# Fit the model\n",
    "mod_fedfunds2 = sm.tsa.MarkovRegression(\n",
    "    dta_fedfunds.iloc[1:], k_regimes=2, exog=dta_fedfunds.iloc[:-1]\n",
    ")\n",
    "res_fedfunds2 = mod_fedfunds2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>225</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-264.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 18 Dec 2025</td> <th>  AIC                </th>  <td>543.421</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>07:37:30</td>     <th>  BIC                </th>  <td>567.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>10-01-1954</td>    <th>  HQIC               </th>  <td>553.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.7245</td> <td>    0.289</td> <td>    2.510</td> <td> 0.012</td> <td>    0.159</td> <td>    1.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.7631</td> <td>    0.034</td> <td>   22.629</td> <td> 0.000</td> <td>    0.697</td> <td>    0.829</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0989</td> <td>    0.118</td> <td>   -0.835</td> <td> 0.404</td> <td>   -0.331</td> <td>    0.133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    1.0612</td> <td>    0.019</td> <td>   57.351</td> <td> 0.000</td> <td>    1.025</td> <td>    1.097</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.4783</td> <td>    0.050</td> <td>    9.642</td> <td> 0.000</td> <td>    0.381</td> <td>    0.576</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.6378</td> <td>    0.120</td> <td>    5.304</td> <td> 0.000</td> <td>    0.402</td> <td>    0.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.1306</td> <td>    0.050</td> <td>    2.634</td> <td> 0.008</td> <td>    0.033</td> <td>    0.228</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &    225      \\\\\n",
       "\\textbf{Model:}           & MarkovRegression & \\textbf{  Log Likelihood     } &  -264.711   \\\\\n",
       "\\textbf{Date:}            & Thu, 18 Dec 2025 & \\textbf{  AIC                } &  543.421    \\\\\n",
       "\\textbf{Time:}            &     07:37:30     & \\textbf{  BIC                } &  567.334    \\\\\n",
       "\\textbf{Sample:}          &    10-01-1954    & \\textbf{  HQIC               } &  553.073    \\\\\n",
       "\\textbf{}                 &   - 10-01-2010   & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:} &      approx      & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       0.7245  &        0.289     &     2.510  &         0.012        &        0.159    &        1.290     \\\\\n",
       "\\textbf{x1}    &       0.7631  &        0.034     &    22.629  &         0.000        &        0.697    &        0.829     \\\\\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &      -0.0989  &        0.118     &    -0.835  &         0.404        &       -0.331    &        0.133     \\\\\n",
       "\\textbf{x1}    &       1.0612  &        0.019     &    57.351  &         0.000        &        1.025    &        1.097     \\\\\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{sigma2} &       0.4783  &        0.050     &     9.642  &         0.000        &        0.381    &        0.576     \\\\\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{p[0-$>$0]} &       0.6378  &        0.120     &     5.304  &         0.000        &        0.402    &        0.874     \\\\\n",
       "\\textbf{p[1-$>$0]} &       0.1306  &        0.050     &     2.634  &         0.008        &        0.033    &        0.228     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Markov Switching Model Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  225\n",
       "Model:               MarkovRegression   Log Likelihood                -264.711\n",
       "Date:                Thu, 18 Dec 2025   AIC                            543.421\n",
       "Time:                        07:37:30   BIC                            567.334\n",
       "Sample:                    10-01-1954   HQIC                           553.073\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7245      0.289      2.510      0.012       0.159       1.290\n",
       "x1             0.7631      0.034     22.629      0.000       0.697       0.829\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0989      0.118     -0.835      0.404      -0.331       0.133\n",
       "x1             1.0612      0.019     57.351      0.000       1.025       1.097\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.4783      0.050      9.642      0.000       0.381       0.576\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.6378      0.120      5.304      0.000       0.402       0.874\n",
       "p[1->0]        0.1306      0.050      2.634      0.008       0.033       0.228\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several things to notice from the summary output:\n",
    "\n",
    "1. The information criteria have decreased substantially, indicating that this model has a better fit than the previous model.\n",
    "2. The interpretation of the regimes, in terms of the intercept, have switched. Now the first regime has the higher intercept and the second regime has a lower intercept.\n",
    "\n",
    "Examining the smoothed probabilities of the high regime state, we now see quite a bit more variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Probability of being in the high regime'}>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_fedfunds2.smoothed_marginal_probabilities[0].plot(\n",
    "    title=\"Probability of being in the high regime\", figsize=(12, 3)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the expected durations of each regime have decreased quite a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.76105188 7.65529154]\n"
     ]
    }
   ],
   "source": [
    "print(res_fedfunds2.expected_durations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taylor rule with 2 or 3 regimes\n",
    "\n",
    "We now include two additional exogenous variables - a measure of the output gap and a measure of inflation - to estimate a switching Taylor-type rule with both 2 and 3 regimes to see which fits the data better.\n",
    "\n",
    "Because the models can be often difficult to estimate, for the 3-regime model we employ a search over starting parameters to improve results, specifying 20 random search repetitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Get the additional data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import ogap, inf\n",
    "\n",
    "dta_ogap = pd.Series(ogap, index=pd.date_range(\"1954-07-01\", \"2010-10-01\", freq=\"QS\"))\n",
    "dta_inf = pd.Series(inf, index=pd.date_range(\"1954-07-01\", \"2010-10-01\", freq=\"QS\"))\n",
    "\n",
    "exog = pd.concat((dta_fedfunds.shift(), dta_ogap, dta_inf), axis=1).iloc[4:]\n",
    "\n",
    "# Fit the 2-regime model\n",
    "mod_fedfunds3 = sm.tsa.MarkovRegression(dta_fedfunds.iloc[4:], k_regimes=2, exog=exog)\n",
    "res_fedfunds3 = mod_fedfunds3.fit()\n",
    "\n",
    "# Fit the 3-regime model\n",
    "np.random.seed(12345)\n",
    "mod_fedfunds4 = sm.tsa.MarkovRegression(dta_fedfunds.iloc[4:], k_regimes=3, exog=exog)\n",
    "res_fedfunds4 = mod_fedfunds4.fit(search_reps=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>222</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-229.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 18 Dec 2025</td> <th>  AIC                </th>  <td>480.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>07:37:30</td>     <th>  BIC                </th>  <td>517.942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1955</td>    <th>  HQIC               </th>  <td>495.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.6555</td> <td>    0.137</td> <td>    4.771</td> <td> 0.000</td> <td>    0.386</td> <td>    0.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.8314</td> <td>    0.033</td> <td>   24.951</td> <td> 0.000</td> <td>    0.766</td> <td>    0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.1355</td> <td>    0.029</td> <td>    4.609</td> <td> 0.000</td> <td>    0.078</td> <td>    0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -0.0274</td> <td>    0.041</td> <td>   -0.671</td> <td> 0.502</td> <td>   -0.107</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0945</td> <td>    0.128</td> <td>   -0.739</td> <td> 0.460</td> <td>   -0.345</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.9293</td> <td>    0.027</td> <td>   34.309</td> <td> 0.000</td> <td>    0.876</td> <td>    0.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0343</td> <td>    0.024</td> <td>    1.429</td> <td> 0.153</td> <td>   -0.013</td> <td>    0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.2125</td> <td>    0.030</td> <td>    7.147</td> <td> 0.000</td> <td>    0.154</td> <td>    0.271</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.3323</td> <td>    0.035</td> <td>    9.526</td> <td> 0.000</td> <td>    0.264</td> <td>    0.401</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7279</td> <td>    0.093</td> <td>    7.828</td> <td> 0.000</td> <td>    0.546</td> <td>    0.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.2115</td> <td>    0.064</td> <td>    3.298</td> <td> 0.001</td> <td>    0.086</td> <td>    0.337</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &    222      \\\\\n",
       "\\textbf{Model:}           & MarkovRegression & \\textbf{  Log Likelihood     } &  -229.256   \\\\\n",
       "\\textbf{Date:}            & Thu, 18 Dec 2025 & \\textbf{  AIC                } &  480.512    \\\\\n",
       "\\textbf{Time:}            &     07:37:30     & \\textbf{  BIC                } &  517.942    \\\\\n",
       "\\textbf{Sample:}          &    07-01-1955    & \\textbf{  HQIC               } &  495.624    \\\\\n",
       "\\textbf{}                 &   - 10-01-2010   & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:} &      approx      & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       0.6555  &        0.137     &     4.771  &         0.000        &        0.386    &        0.925     \\\\\n",
       "\\textbf{x1}    &       0.8314  &        0.033     &    24.951  &         0.000        &        0.766    &        0.897     \\\\\n",
       "\\textbf{x2}    &       0.1355  &        0.029     &     4.609  &         0.000        &        0.078    &        0.193     \\\\\n",
       "\\textbf{x3}    &      -0.0274  &        0.041     &    -0.671  &         0.502        &       -0.107    &        0.053     \\\\\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &      -0.0945  &        0.128     &    -0.739  &         0.460        &       -0.345    &        0.156     \\\\\n",
       "\\textbf{x1}    &       0.9293  &        0.027     &    34.309  &         0.000        &        0.876    &        0.982     \\\\\n",
       "\\textbf{x2}    &       0.0343  &        0.024     &     1.429  &         0.153        &       -0.013    &        0.081     \\\\\n",
       "\\textbf{x3}    &       0.2125  &        0.030     &     7.147  &         0.000        &        0.154    &        0.271     \\\\\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{sigma2} &       0.3323  &        0.035     &     9.526  &         0.000        &        0.264    &        0.401     \\\\\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{p[0-$>$0]} &       0.7279  &        0.093     &     7.828  &         0.000        &        0.546    &        0.910     \\\\\n",
       "\\textbf{p[1-$>$0]} &       0.2115  &        0.064     &     3.298  &         0.001        &        0.086    &        0.337     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Markov Switching Model Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  222\n",
       "Model:               MarkovRegression   Log Likelihood                -229.256\n",
       "Date:                Thu, 18 Dec 2025   AIC                            480.512\n",
       "Time:                        07:37:30   BIC                            517.942\n",
       "Sample:                    07-01-1955   HQIC                           495.624\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.6555      0.137      4.771      0.000       0.386       0.925\n",
       "x1             0.8314      0.033     24.951      0.000       0.766       0.897\n",
       "x2             0.1355      0.029      4.609      0.000       0.078       0.193\n",
       "x3            -0.0274      0.041     -0.671      0.502      -0.107       0.053\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0945      0.128     -0.739      0.460      -0.345       0.156\n",
       "x1             0.9293      0.027     34.309      0.000       0.876       0.982\n",
       "x2             0.0343      0.024      1.429      0.153      -0.013       0.081\n",
       "x3             0.2125      0.030      7.147      0.000       0.154       0.271\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.3323      0.035      9.526      0.000       0.264       0.401\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7279      0.093      7.828      0.000       0.546       0.910\n",
       "p[1->0]        0.2115      0.064      3.298      0.001       0.086       0.337\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>222</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-180.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 18 Dec 2025</td> <th>  AIC                </th>  <td>399.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>07:37:30</td>     <th>  BIC                </th>  <td>464.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1955</td>    <th>  HQIC               </th>  <td>425.713</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -1.0250</td> <td>    0.290</td> <td>   -3.531</td> <td> 0.000</td> <td>   -1.594</td> <td>   -0.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.3277</td> <td>    0.086</td> <td>    3.812</td> <td> 0.000</td> <td>    0.159</td> <td>    0.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.2036</td> <td>    0.049</td> <td>    4.152</td> <td> 0.000</td> <td>    0.107</td> <td>    0.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    1.1381</td> <td>    0.081</td> <td>   13.977</td> <td> 0.000</td> <td>    0.978</td> <td>    1.298</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0259</td> <td>    0.087</td> <td>   -0.298</td> <td> 0.765</td> <td>   -0.196</td> <td>    0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.9737</td> <td>    0.019</td> <td>   50.265</td> <td> 0.000</td> <td>    0.936</td> <td>    1.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0341</td> <td>    0.017</td> <td>    2.030</td> <td> 0.042</td> <td>    0.001</td> <td>    0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.1215</td> <td>    0.022</td> <td>    5.606</td> <td> 0.000</td> <td>    0.079</td> <td>    0.164</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 2 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.7346</td> <td>    0.130</td> <td>    5.632</td> <td> 0.000</td> <td>    0.479</td> <td>    0.990</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.8436</td> <td>    0.024</td> <td>   35.198</td> <td> 0.000</td> <td>    0.797</td> <td>    0.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.1633</td> <td>    0.025</td> <td>    6.515</td> <td> 0.000</td> <td>    0.114</td> <td>    0.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -0.0499</td> <td>    0.027</td> <td>   -1.835</td> <td> 0.067</td> <td>   -0.103</td> <td>    0.003</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.1660</td> <td>    0.018</td> <td>    9.240</td> <td> 0.000</td> <td>    0.131</td> <td>    0.201</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7214</td> <td>    0.117</td> <td>    6.177</td> <td> 0.000</td> <td>    0.493</td> <td>    0.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td> 4.001e-08</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[2->0]</th> <td>    0.0783</td> <td>    0.038</td> <td>    2.079</td> <td> 0.038</td> <td>    0.004</td> <td>    0.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->1]</th> <td>    0.1044</td> <td>    0.095</td> <td>    1.103</td> <td> 0.270</td> <td>   -0.081</td> <td>    0.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->1]</th> <td>    0.8259</td> <td>    0.054</td> <td>   15.208</td> <td> 0.000</td> <td>    0.719</td> <td>    0.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[2->1]</th> <td>    0.2288</td> <td>    0.073</td> <td>    3.150</td> <td> 0.002</td> <td>    0.086</td> <td>    0.371</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &    222      \\\\\n",
       "\\textbf{Model:}           & MarkovRegression & \\textbf{  Log Likelihood     } &  -180.806   \\\\\n",
       "\\textbf{Date:}            & Thu, 18 Dec 2025 & \\textbf{  AIC                } &  399.611    \\\\\n",
       "\\textbf{Time:}            &     07:37:30     & \\textbf{  BIC                } &  464.262    \\\\\n",
       "\\textbf{Sample:}          &    07-01-1955    & \\textbf{  HQIC               } &  425.713    \\\\\n",
       "\\textbf{}                 &   - 10-01-2010   & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:} &      approx      & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &      -1.0250  &        0.290     &    -3.531  &         0.000        &       -1.594    &       -0.456     \\\\\n",
       "\\textbf{x1}    &       0.3277  &        0.086     &     3.812  &         0.000        &        0.159    &        0.496     \\\\\n",
       "\\textbf{x2}    &       0.2036  &        0.049     &     4.152  &         0.000        &        0.107    &        0.300     \\\\\n",
       "\\textbf{x3}    &       1.1381  &        0.081     &    13.977  &         0.000        &        0.978    &        1.298     \\\\\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &      -0.0259  &        0.087     &    -0.298  &         0.765        &       -0.196    &        0.144     \\\\\n",
       "\\textbf{x1}    &       0.9737  &        0.019     &    50.265  &         0.000        &        0.936    &        1.012     \\\\\n",
       "\\textbf{x2}    &       0.0341  &        0.017     &     2.030  &         0.042        &        0.001    &        0.067     \\\\\n",
       "\\textbf{x3}    &       0.1215  &        0.022     &     5.606  &         0.000        &        0.079    &        0.164     \\\\\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       0.7346  &        0.130     &     5.632  &         0.000        &        0.479    &        0.990     \\\\\n",
       "\\textbf{x1}    &       0.8436  &        0.024     &    35.198  &         0.000        &        0.797    &        0.891     \\\\\n",
       "\\textbf{x2}    &       0.1633  &        0.025     &     6.515  &         0.000        &        0.114    &        0.212     \\\\\n",
       "\\textbf{x3}    &      -0.0499  &        0.027     &    -1.835  &         0.067        &       -0.103    &        0.003     \\\\\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{sigma2} &       0.1660  &        0.018     &     9.240  &         0.000        &        0.131    &        0.201     \\\\\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{p[0-$>$0]} &       0.7214  &        0.117     &     6.177  &         0.000        &        0.493    &        0.950     \\\\\n",
       "\\textbf{p[1-$>$0]} &    4.001e-08  &          nan     &       nan  &           nan        &          nan    &          nan     \\\\\n",
       "\\textbf{p[2-$>$0]} &       0.0783  &        0.038     &     2.079  &         0.038        &        0.004    &        0.152     \\\\\n",
       "\\textbf{p[0-$>$1]} &       0.1044  &        0.095     &     1.103  &         0.270        &       -0.081    &        0.290     \\\\\n",
       "\\textbf{p[1-$>$1]} &       0.8259  &        0.054     &    15.208  &         0.000        &        0.719    &        0.932     \\\\\n",
       "\\textbf{p[2-$>$1]} &       0.2288  &        0.073     &     3.150  &         0.002        &        0.086    &        0.371     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Markov Switching Model Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  222\n",
       "Model:               MarkovRegression   Log Likelihood                -180.806\n",
       "Date:                Thu, 18 Dec 2025   AIC                            399.611\n",
       "Time:                        07:37:30   BIC                            464.262\n",
       "Sample:                    07-01-1955   HQIC                           425.713\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -1.0250      0.290     -3.531      0.000      -1.594      -0.456\n",
       "x1             0.3277      0.086      3.812      0.000       0.159       0.496\n",
       "x2             0.2036      0.049      4.152      0.000       0.107       0.300\n",
       "x3             1.1381      0.081     13.977      0.000       0.978       1.298\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0259      0.087     -0.298      0.765      -0.196       0.144\n",
       "x1             0.9737      0.019     50.265      0.000       0.936       1.012\n",
       "x2             0.0341      0.017      2.030      0.042       0.001       0.067\n",
       "x3             0.1215      0.022      5.606      0.000       0.079       0.164\n",
       "                             Regime 2 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7346      0.130      5.632      0.000       0.479       0.990\n",
       "x1             0.8436      0.024     35.198      0.000       0.797       0.891\n",
       "x2             0.1633      0.025      6.515      0.000       0.114       0.212\n",
       "x3            -0.0499      0.027     -1.835      0.067      -0.103       0.003\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.1660      0.018      9.240      0.000       0.131       0.201\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7214      0.117      6.177      0.000       0.493       0.950\n",
       "p[1->0]     4.001e-08        nan        nan        nan         nan         nan\n",
       "p[2->0]        0.0783      0.038      2.079      0.038       0.004       0.152\n",
       "p[0->1]        0.1044      0.095      1.103      0.270      -0.081       0.290\n",
       "p[1->1]        0.8259      0.054     15.208      0.000       0.719       0.932\n",
       "p[2->1]        0.2288      0.073      3.150      0.002       0.086       0.371\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds4.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to lower information criteria, we might prefer the 3-state model, with an interpretation of low-, medium-, and high-interest rate regimes. The smoothed probabilities of each regime are plotted below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, figsize=(10, 7))\n",
    "\n",
    "ax = axes[0]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[0])\n",
    "ax.set(title=\"Smoothed probability of a low-interest rate regime\")\n",
    "\n",
    "ax = axes[1]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[1])\n",
    "ax.set(title=\"Smoothed probability of a medium-interest rate regime\")\n",
    "\n",
    "ax = axes[2]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[2])\n",
    "ax.set(title=\"Smoothed probability of a high-interest rate regime\")\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Switching variances\n",
    "\n",
    "We can also accommodate switching variances. In particular, we consider the model\n",
    "\n",
    "$$\n",
    "y_t = \\mu_{S_t} + y_{t-1} \\beta_{S_t} + \\varepsilon_t \\quad \\varepsilon_t \\sim N(0, \\sigma_{S_t}^2)\n",
    "$$\n",
    "\n",
    "We use maximum likelihood to estimate the parameters of this model: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma_0^2, \\sigma_1^2$.\n",
    "\n",
    "The application is to absolute returns on stocks, where the data can be found at https://www.stata-press.com/data/r14/snp500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9EAAAEnCAYAAACuZMBeAAAAQHRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcrZGZzZzEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvhF0PpwAAAAlwSFlzAAAPYQAAD2EBqD+naQAA7upJREFUeJzsnXeYZVWV9t+bb+Xq6pxpMnTTBMkZAQGRER1xEBUQM4ZR59MRHRVGBDENjDAiIzmJMKAokmmSNNABGrqbbugcq6urK4ebz/fHuWuftfc554aKt6j1ex4eum7dOvfcE/bZa7/vWitgWZYFQRAEQRAEQRAEQRCKEhztHRAEQRAEQRAEQRCEsYIE0YIgCIIgCIIgCIJQIhJEC4IgCIIgCIIgCEKJSBAtCIIgCIIgCIIgCCUiQbQgCIIgCIIgCIIglIgE0YIgCIIgCIIgCIJQIhJEC4IgCIIgCIIgCEKJSBAtCIIgCIIgCIIgCCUiQbQgCIIgCIIgCIIglIgE0YIgCELZ/Pd//zcCgQAWLFjg+ftNmzYhEAjgV7/61Yju15VXXolAIDCgv129ejWuvPJKbNq0aWh3agh45ZVXcOWVV6Kjo2O0d2VISafT+P3vf4+jjjoKTU1NqK6uxty5c/HRj34UjzzyiOv977zzDs4991w0NTWhsbERxx13HB588EHPbT///PMIBALqv1AohKlTp+KCCy7AO++8o72Xv4//9/Of/9y13ZaWFlx66aWYNGkSqqurcdxxx+HZZ5/13IdnnnkGxx13HKqrqzFp0iRceumlaGlpGcCREgRBECoJCaIFQRCEsrntttsAAKtWrcJrr702ynszNKxevRpXXXVVxQbRV1111fsuiP7sZz+Lb3zjGzjttNNwzz334K9//Sv+4z/+A+FwGE8++aT23q6uLpx55pnYsGEDbrnlFtx333045ZRTsHjx4oKfcc0112Dx4sVYtGgR/v3f/x1PP/00TjjhBGzfvl173yc+8QksXrxY++/iiy/W3pNMJnH66afj2WefxQ033IC//OUvmDp1Ks4++2y88MIL2ntfeOEFnHPOOZg6dSr+8pe/4IYbbsAzzzyD008/HclkchBHTRAEQRhtwqO9A4IgCMLYYunSpVixYgXOPfdcPPbYY7j11ltxzDHHjPZujSnS6TQCgQDC4dF9DPf39yMejw9YvR8MGzduxAMPPIAf//jHuOqqq9Trp59+Or74xS8il8tp7//HP/6B7du34/HHH8fZZ58NAPjwhz9c9HP2228/HHvssQCAk08+GY2Njfj85z+PO+64Az/84Q/V+6ZOnare58ett96KlStX4pVXXsFxxx0HADjttNNw6KGH4nvf+562oPTd734X+++/Px566CF1nufNm4cTTjgBt912G7761a8W3XdBEAShMhElWhAEQSiLW2+9FQDw85//HMcffzz++Mc/oq+vz/O9uVwOP/vZzzBnzhzE43EceeSRLuvr7t278aUvfQmzZ89GLBbD5MmTccIJJ+CZZ57R3nfbbbfh0EMPRTweR1NTEz72sY+5bLleBAIBXHnlla7X99prL1x66aUAgDvuuAMXXHABADsoIjvvHXfcod5PKmJ9fT2qq6txwgkn+Np4OWQrvvvuu/Fv//ZvmDlzJmKxGNatW1fSdq+88kp897vfBWAHYbRvzz//fMnfj75jIBDAU089hcsuuwyTJ09GdXU1kskkTj31VCxYsABLlizBSSedhOrqauy99974+c9/rgWzuVwOV199NQ444ABUVVWhsbERCxcuxA033FD0OJjs2bMHADB9+nTP3weD+hQlFAoBANauXVv2Z3EoUN68eXPZf/vII4/ggAMOUAE0AITDYXzmM5/B66+/rtTt7du3Y8mSJfjsZz+rLZQcf/zx2H///T2t6oIgCMLYQYJoQRAEoWT6+/tx//3346ijjsKCBQtw2WWXobu72zcv9cYbb8QTTzyB66+/Hvfccw+CwSDOOecczYL72c9+Fn/+85/x4x//GE899RT+8Ic/4IwzzlBBFgBce+21+PznP4/58+fj4Ycfxg033IC33noLxx13HN57771Bf69zzz0X11xzDQDgpptuUnbec889FwBwzz334EMf+hDq6+tx55134k9/+hOamppw1llnlRRIA8AVV1yBLVu24Oabb8Zf//pXTJkypaTtfuELX8A3vvENAMDDDz+s9u2II44Y0He97LLLEIlEcPfdd+Ohhx5CJBIBADQ3N+PTn/40PvOZz+DRRx/FOeecgyuuuAL33HOP+ttf/OIXuPLKK/GpT30Kjz32GB544AF8/vOfH5DN/KCDDkJjYyOuuuoq3HLLLUVt9Keeeir2339//PCHP8Srr75a9ucRtHgxefJk7fX77rsPVVVViMVi+MAHPoDbb7/d9bcrV67EwoULXa/Ta6tWrVLv46+b76XfC4IgCGMUSxAEQRBK5K677rIAWDfffLNlWZbV3d1t1dbWWieddJL2vo0bN1oArBkzZlj9/f3q9a6uLqupqck644wz1Gu1tbXWt771Ld/PbG9vt6qqqqwPf/jD2utbtmyxYrGYddFFF6nXfvKTn1jmow2A9ZOf/MS13blz51qXXHKJ+vnBBx+0AFiLFi3S3tfb22s1NTVZ5513nvZ6Npu1Dj30UOvoo4/23XfLsqxFixZZAKyTTz55wNv95S9/aQGwNm7c6Np+qd/v9ttvtwBYF198seu9p5xyigXAeu2117TXDz74YOuss85SP3/kIx+xDjvssEJftywee+wxa9KkSRYAC4A1ceJE64ILLrAeffRR13sXL15szZo1y9p3332thoYG6/XXXy+4bTruDzzwgJVOp62+vj7rxRdftPbdd18rFApZK1asUO+96KKLrHvvvdd68cUXrYceesg655xzLADWf/zHf2jbjEQi1pe//GXXZ73yyisWAOu+++6zLMuy7r33XguAtXjxYtd7v/SlL1nRaLSk4yMIgiBUJqJEC4IgCCVz6623oqqqChdeeCEAoLa2FhdccAFeeuklT0X44x//OOLxuPq5rq4O5513Hl588UVks1kAwNFHH4077rgDV199NV599VWk02ltG4sXL0Z/f79mTQaA2bNn44Mf/GDJSvBAeeWVV9DW1oZLLrkEmUxG/ZfL5XD22WdjyZIl6O3tLbqdf/7nfx6W7ZaLuR/EtGnTcPTRR2uvLVy4ULM9H3300VixYgUuv/xyPPnkk+jq6hrUvnz4wx/Gli1b8Mgjj+D//b//h/nz5+PPf/4z/umf/glf//rX1fvWr1+Ps88+G9/+9rexZMkS7L///vjQhz6EZcuWqfdcffXViEajrqJd//Iv/4JIJILq6mqcfPLJyGazeOihhzSV+N5778VFF12Ek046Cf/8z/+Mv//97/jIRz6Cn//859i9e7e2vUL54+bv/N47GjnogiAIwtAhQbQgCIJQEuvWrcOLL76Ic889F5ZloaOjAx0dHfjEJz4BwKnYzZk2bZrna6lUCj09PQCABx54AJdccgn+8Ic/4LjjjkNTUxMuvvhiNDc3AyicOztjxgzN9j0c7Nq1C4BdvTkSiWj/XXfddbAsC21tbUW3Y+7/UG23XPxykCdOnOh6LRaLob+/X/18xRVX4Fe/+hVeffVVnHPOOZg4cSJOP/10LF26dMD7U1VVhfPPPx+//OUv8cILL2DdunU4+OCDcdNNNyl79G9+8xsEAgF885vfRGNjI55++mnsv//+OPPMM/HGG28AsHPPzzjjDMRiMW371113HZYsWYLly5djy5Yt2LBhA84///yi+/WZz3wGmUxG+24TJ070vN7oPDU1Nan3AfB9L71PEARBGJtIdW5BEAShJG677TZYloWHHnoIDz30kOv3d955J66++mpVAAqACoQ5zc3NiEajqK2tBQBMmjQJ119/Pa6//nps2bIFjz76KL7//e+jpaUFTzzxhApIdu7c6drWjh07MGnSpIL7HYvFPFsKlRp80/Z/+9vf+lZvnjp1atHtmOrjUG233O83GBU0HA7jO9/5Dr7zne+go6MDzzzzDH7wgx/grLPOwtatW1FdXT3gbRNz5szBl770JXzrW9/CqlWrMH/+fKxfvx7V1dWqSFdDQwOefvppnHXWWTjjjDPw4x//GM899xxeeukl1/b23ntvHHnkkWXvh2VZAPQCZ4cccgjefvtt13vpNeqbTv9/++23XRXE3377bd/+6oIgCMLYQJRoQRAEoSjZbBZ33nkn9tlnHyxatMj137/9279h586dePzxx7W/e/jhh5FIJNTP3d3d+Otf/4qTTjpJC7aJOXPm4Otf/zrOPPNMLF++HABw3HHHoaqqSitwBQDbtm3Dc889h9NPP73gvu+111546623tNeee+45pYQTpGBy5RUATjjhBDQ2NmL16tU48sgjPf+LRqMF98GLcrbrt2/lfL+hprGxEZ/4xCfwta99DW1tbWX31+7u7vbdR6q6PmPGDAB2ULpjxw7Nul9fX48nn3wS8+bNw7e+9S1cfPHFOOGEEwb2ZTy4++67EYlE8IEPfEC99rGPfQxr1qzRWlllMhncc889OOaYY9T+zpw5E0cffTTuuecelbYAAK+++irWrl2Lj3/840O2n4IgCMLII0q0IAiCUJTHH38cO3bswHXXXYdTTz3V9fsFCxbgxhtvxK233oqPfOQj6vVQKIQzzzwT3/nOd5DL5XDdddehq6tL9QXu7OzEaaedhosuuggHHngg6urqsGTJEjzxxBMq0GhsbMSPfvQj/OAHP8DFF1+MT33qU9izZw+uuuoqxONx/OQnPym475/97Gfxox/9CD/+8Y9xyimnYPXq1bjxxhvR0NDg+g4AcMstt6Curg7xeBzz5s3DxIkT8dvf/haXXHIJ2tra8IlPfAJTpkzB7t27sWLFCuzevRu/+93vyj6mtbW1JW/3kEMOAQDccMMNuOSSSxCJRHDAAQegrq6u5O83FJx33nlYsGABjjzySEyePBmbN2/G9ddfj7lz52K//fZT7wsEAjjllFNUGy4v1q5di7POOgsXXnghTjnlFEyfPh3t7e147LHHcMstt+DUU0/F8ccfDwD43ve+h4ceegjnn38+vv3tb+Okk05CT08PFi1ahJUrV2L27Nl48MEHcdlll+Hkk08u6zv98pe/xOrVq3H66adj1qxZaGlpwa233oqnnnoKV155peZ0uOyyy3DTTTfhggsuwM9//nNMmTIF//M//4O1a9e6WrJdd911OPPMM3HBBRfg8ssvR0tLC77//e9jwYIF+NznPlfWPgqCIAgVxqiWNRMEQRDGBOeff74VjUatlpYW3/dceOGFVjgctpqbm1V17uuuu8666qqrrFmzZlnRaNQ6/PDDrSeffFL9TSKRsL7yla9YCxcutOrr662qqirrgAMOsH7yk59Yvb292vb/8Ic/WAsXLrSi0ajV0NBgffSjH7VWrVqlvcerOncymbS+973vWbNnz7aqqqqsU045xXrzzTdd1asty7Kuv/56a968eVYoFLIAWLfffrv63QsvvGCde+65VlNTkxWJRKyZM2da5557rvXggw8WPHZUJdrvfaVu94orrrBmzJhhBYNBrYp4qd+PqnMvWbLEtQ+nnHKKNX/+fNfrl1xyiTV37lz1869//Wvr+OOPtyZNmmRFo1Frzpw51uc//3lr06ZN6j3d3d0WAOvCCy8seFza29utq6++2vrgBz9ozZw504pGo1ZNTY112GGHWVdffbXV19envb+lpcX6xje+Yc2dO9cKh8NWU1OT9eEPf9h6/PHHrd7eXuuYY46xamtrrX/84x8lHXfi0UcftU488URr8uTJVjgcturq6qyTTjrJuv/++z3f39zcbF188cVWU1OTFY/HrWOPPdZ6+umnPd/71FNPWccee6wVj8etpqYm6+KLL7Z27dpVcH8EQRCEyidgWfmkH0EQBEEQhEFCla1XrFihFHRBEARBeD8hOdGCIAiCIAwZixYtwoUXXigBtCAIgvC+RZRoQRAEQRAEQRAEQSgRUaIFQRAEQRAEQRAEoUQkiBYEQRAEQRAEQRCEEpEgWhAEQRAEQRAEQRBKRIJoQRAEQRAEQRAEQSiR8GjvgEkul8OOHTtQV1eHQCAw2rsjCIIgCIIgCIIgvM+xLAvd3d2YMWMGgsHCWnPFBdE7duzA7NmzR3s3BEEQBEEQBEEQhHHG1q1bMWvWrILvqbgguq6uDoC98/X19aO8N4IgCIIgCIIgCML7na6uLsyePVvFo4WouCCaLNz19fUSRAuCIAiCIAiCIAgjRikpxVJYTBAEQRAEQRAEQRBKRIJoQRAEQRAEQRAEQSgRCaIFQRAEQRAEQRAEoUQkiBYEQRAEQRAEQRCEEpEgWhAEQRAEQRAEQRBKRIJoQRAEQRAEQRAEQSgRCaIFQRCEccn2jn70JjOjvRuCIAiCIIwxJIgWBEEQxh3NnQmc8otF+OCvn0c2Z4327giCIAiCMIaQIFoQBEEYd2za04tMzsKuriT+/Mb20d4dQRAEQRDGEBJEC4IgCOOa/3rmXaQyudHeDUEQBEEQxghlB9EvvvgizjvvPMyYMQOBQAB//vOftd9bloUrr7wSM2bMQFVVFU499VSsWrVqqPZXEARBEAZNjlm4t7X348FlW0dxbwRBEARBGEuUHUT39vbi0EMPxY033uj5+1/84hf4zW9+gxtvvBFLlizBtGnTcOaZZ6K7u3vQOysIgiAIQ0HGyIN+bUPbKO2JIAiCIAhjjXC5f3DOOefgnHPO8fydZVm4/vrr8cMf/hAf//jHAQB33nknpk6divvuuw9f/vKXB7e3giAIgjAEZC2r4M+VgGVZ+MEjK9FUE8F3zzpwtHdHEARBEIQ8Q5oTvXHjRjQ3N+NDH/qQei0Wi+GUU07BK6+84vk3yWQSXV1d2n+CIAiCMJzkDCXaqsAgurUnhftf34L/eX59Re6fIAiCIIxXhjSIbm5uBgBMnTpVe33q1KnqdybXXnstGhoa1H+zZ88eyl0SBEEQBBdmW6tcBdYVy+R3yrLc+ysIgiAIwugxLNW5A4GA9rNlWa7XiCuuuAKdnZ3qv61bpbiLIAiCMLzkDGXX/LkS4IFzJdrNBUEQBGG8UnZOdCGmTZsGwFakp0+frl5vaWlxqdNELBZDLBYbyt0QBEEQhIJkDeW54oNoUaIFQRAEoWIYUiV63rx5mDZtGp5++mn1WiqVwgsvvIDjjz9+KD9KEARBEAaMqexWYozKA2ezmrggCIIgCKNH2Up0T08P1q1bp37euHEj3nzzTTQ1NWHOnDn41re+hWuuuQb77bcf9ttvP1xzzTWorq7GRRddNKQ7LgiCIAgDxSwsVolKNN+nbLby9k8QBEEQxitlB9FLly7Faaedpn7+zne+AwC45JJLcMcdd+B73/se+vv7cfnll6O9vR3HHHMMnnrqKdTV1Q3dXguCIAjCIHAVFqvAGJVbzkWJFgRBEITKoewg+tRTTy3YaiMQCODKK6/ElVdeOZj9EgRBEIRhwwyiK7GFVIaVDJecaEEQBEGoHIalOrcgCIIgVDJmTnQlBqk5TYmuwB5cgiAIgjBOkSBaEARBGHe47dyVF0TzQF9iaEEQBEGoHCSIFgRBEMYd7j7Ro7QjBciyyFmUaEEQBEGoHCSIFgRBEMYdpERHQgEAlZkTzQuLVaLdXBAEQRDGKxJEC4IgCOMOCkrDwaD2cyUhfaIFQRAEoTKRIFoQBEEYd5CdO5xXoisxRtX6RFfiDgqCIAjCOEWCaEEQBGHcQVbpaMh+DFainTsjSrQgCIIgVCQSRAuCIAjjDlJ5I/kguhJj1FxOlGhBEARBqEQkiBYEQRDGHZmsbueuxCA1K0G0IAiCIFQkEkQLgiAI446sS4muvCCV94mWFleCIAiCUDlIEC0IgiCMO3KuFlejuTfeiBItCKNHdyKNh5dvQ1ciPdq7IghCBSJBtCAIgjDuIJWXWlxVpBIthcUEYdS4+9XN+M6fVuDOf2wa7V0RBKECkSBaEARBGHeYSnS2AoNorcVVtvL2TxDez7T1pOz/96VGeU8EQahEJIgWBEEQxh2k8oZVi6vR3BtvMllRogVhtKB7Lif3niAIHkgQLQiCIIw7nMJithJdkXZutk+VuH+C8H6GivlVoktFEITRR4JoQRAEYdzh2LkrNyc6JznRgjBqkFtFivoJguCFBNGCIAjCuMPV4qoCO0hxBSxbiTsoCO9j0lkJogVB8EeCaEEQBGHcoXKigxVs5+ZKtBQWE4QRxVGiR3lHBEGoSCSIFgRBEMYdNEGOhCvXzi19ogVh9Ejno2dxgQiC4IUE0YIgCMK4g9SliFKiR3FnfJA+0YIweiglWm49QRA8kCBaEARBGHeQ8hxWOdGVN1PW+kRX4P4JwvsZaXElCEIhJIgWBEEQxh3Kzl3BLa4yYucWhFEjo+zccu8JguBGgmhBEARh3OGqzl2B8+ScBNGCMGrQIpakUgiC4IUE0YIgCMK4I6eqc1dyYTHn3zKRF4SRhSriV+LYIAjC6CNBtCAIgjDucNm5KzBI5VWBpUKwIIwsTouryhsbBEEYfSSIFgRBEMYduTFg585aUp1bEEaLdE5yogVB8EeCaEEQBGHckcmZQXTlTZS5nVsm8oIwsogSLQhCISSIFgRBEMYdNDEO5+3cFRhDS4srQRhFKCc6W4mDgyAIo44E0YIgCMK4w7Fz20F0JU6UaRIPSBAtCCNNJm/nrsR6CYIgjD4SRAuCIAjjjuwYqM6dk5xoQRg1pMWVIAiFGPIgOpPJ4D/+4z8wb948VFVVYe+998Z//ud/IieVRQVBEIQKgR5JkbD9GLQswKqwQDorfaIFYdSQFleCIBQiPNQbvO6663DzzTfjzjvvxPz587F06VJ87nOfQ0NDA/71X/91qD9OEARBEMqG7NuRYEC9ZllAIOD3FyOPXp1bFqIFYSSRwmKCIBRiyIPoxYsX46Mf/SjOPfdcAMBee+2F+++/H0uXLh3qjxIEQRCEAeEUFnMMWVnLQhCVE0VnJSdaEEaNdFZaXAmC4M+Q27lPPPFEPPvss3j33XcBACtWrMDLL7+MD3/4w57vTyaT6Orq0v4TBEEQhOHELCzGX6sUNCU6W1n7Jgjvd0SJFgShEEOuRP/7v/87Ojs7ceCBByIUCiGbzeJnP/sZPvWpT3m+/9prr8VVV1011LshCIIgCL5QUBphSnSFxdBaVeBKrB4uCO9nlBIt954gCB4MuRL9wAMP4J577sF9992H5cuX484778SvfvUr3HnnnZ7vv+KKK9DZ2an+27p161DvkiAIgiBoOEp00PVapZCRwmKCMGrQPSctrgRB8GLIlejvfve7+P73v48LL7wQAHDIIYdg8+bNuPbaa3HJJZe43h+LxRCLxYZ6NwRBEATBFycnmtu5R2tvvMlKiytBGDWkxZUgCIUYciW6r68PwaC+2VAoJC2uBEEQhIrBqc7NCotV2GRZs3NLTrQgjCgZUaIFQSjAkCvR5513Hn72s59hzpw5mD9/Pt544w385je/wWWXXTbUHyUIgiAIA4ImxrywWCX3iRY1TBBGDsuynMJiFTYuCIJQGQx5EP3b3/4WP/rRj3D55ZejpaUFM2bMwJe//GX8+Mc/HuqPEgRBEIQBQRPjsJYTPVp7401Wy4kWN5cgjBR6PYJR3BFBECqWIQ+i6+rqcP311+P6668f6k0LgiAIwpBAMWk46N3iakdHP8LBAKbUx0d61xSSEy0Io4MsYAmCUIwhz4kWBEEQhEqHJsmhYAAUR5PFO5nJ4uzrX8RHfvsyMqMoQ/GJfKVVDheE9zNpdt9XWq0EQRAqAwmiBUEQhHEHqbzBQADBgB1F01y5sy+NrkQGLd1JbGvvH61d1ALnjBQWE4QRQ1/AGsUdEQShYpEgWhAEQRh38BZXwSAF0aREOyrUxtbekd+5PDxwFjVMEEaOdJanUoidWxAENxJEC4IgCOMOCkptJdp+zSuIXr+7Z8T3jchJTrQgjAqaEi0xtCAIHkgQLQiCIIw7clpOdF6Jzk+WUyyI3jCKSrRe3EiCaEEYKbj6LC2uBEHwQoJoQRAEYdxBE+OQlhPtFBYjNoyiEi19ogVhdDBTKSqth7wgCKOPBNGCIAjCuEPZuYNw2blTFZITzRWwnATRgjBimItWcvsJgmAiQbQgCIIw7qCAORTkhcXs3/Gc6F1dSfQkMyO+fwDAu2tJcSNBGDnM+03SKQRBMJEgWhAEQRh3qD7RnnZufQK9cffoqNE5yYkWhFHBbCknfdoFQTCRIFoQBEEYV1iWpVTnYNBdnTtlBNEbWkcnL5qrYZITLQgjh3m/yf0nCIKJBNGCIAjCuILPh0OBAAJGdW5eWAwANoyWEs32U5RoQRg5smLnFgShCBJEC4IgCOMKrvCGQgGEDDu3W4kenSBaqnMLwujgsnPL/ScIgoEE0YIgCMK4gotMdk50/nUjJzoWth+RW/aMfhAtSpggjBzmopX0ihYEwUSCaEEQBGFcwSfEoSCzc6vq3Lade2JNFADQm9Lt3SOFBNGCMDq4gmi5/wRBMJAgWhAEQRhX8AlxMBBAMP8kNO3c9VURAEAiPUpBtCVBtCCMBpms5EQLglAYCaIFQRCEcQXPbwwFnZxoy7BzO0H06PRozmk50dInWhBGClGiBUEohgTRgiAIwriCK7zBAFSfaBKflBIdt4Po5Cgp0RmxcwvCqCB9ogVBKIYE0YIgCMK4ghTeYAAIBAII+BQWayAlOjM6QXROqnMLwqhgOj/k/hMEwUSCaEEQBGFcQUp0KF+WO2i0uHLs3GEAQDprjYoSzBVzy5I2O4IwUkiLK0EQiiFBtCAIgjCuyCol2g6eKZimmJWqc5MSDYxOcTEzcBc1TBBGBvPekxZXgiCYSBAtCIIgjCtoghzOB88BlRNtKNHxygqiJS9TEEYGc8HKVKYFQRAkiBYEQRDGFUqJVnZu+3WzxVU8EkI0bD8mE5mRr45tql+iRAvCyGDmRMsCliAIJhJEC4IgCOOKnE9OtGPntifQ0XAQcQqiR1iJtiwL5rw9K2qYIIwIpvIs1fEFQTCRIFoQBEEYV1ArK+oPTYq0o0TbAXMsHEQ8EgIw8kG016RdekULwsggSrQgCMWQIFoQBEEYV/jZuc2c6OgoBtFe1m1RwwRhZJCcaEEQiiFBtCAIgjCuUHbugNniyv59Mm0H0bYSTXbukVWBvZQvyYkWhJHBZecWJVoQBAMJogVBEIRxBSm6IUOJtsjOnaUgOlQRdm5TKRcEYXgxF6wkk0IQBBMJogVBEIRxBalKwfwT0KVE53Oi7cJiFESP7CyaB8xUIVyCaEEYGTJZ/X6XegSCIJhIEC0IgiCMK3I5bzt31mhxFQsHEYuMTnVuLYgO2fsgdm5BGBmkR7sgCMUYliB6+/bt+MxnPoOJEyeiuroahx12GJYtWzYcHyUIgiAIZZEx7dz5JyHZuZMZnhOdV6IzIxxE5/clEBAlWhBGGnPBKitCtCAIBuGh3mB7eztOOOEEnHbaaXj88ccxZcoUrF+/Ho2NjUP9UYIgCIJQNjlXTrTZ4sorJ3qEC4uxNly0n2IpFYSRwbRzywKWIAgmQx5EX3fddZg9ezZuv/129dpee+011B8jCIIgCANC5USb1bnz82alREeCiIdH1s69cnsnHly6FRccOdvet2AA4aAo0YIwkriVaLn3BEHQGXI796OPPoojjzwSF1xwAaZMmYLDDz8c//u//zvUHyMIgiAIA8KvOnfWspDJ5tTvoyHHzp0coSD6v599D3cu3oy/vrXD3sdAQNnNJSdaEEYGaXElCEIxhjyI3rBhA373u99hv/32w5NPPomvfOUr+OY3v4m77rrL8/3JZBJdXV3af4IgCIIwXKg+0Yad27Is1d4KsJXoqijlRI+MlXrTnl4AQHciAwAIMyU6J0G0IIwI7hZXcu8JgqAz5HbuXC6HI488Etdccw0A4PDDD8eqVavwu9/9DhdffLHr/ddeey2uuuqqod4NQRAEQfCE4mQKngOsxVWS5T5HQyNr57YsC1va+gAA/Sn784JBnhMtE3lBGAnM+gNi5xYEwWTIlejp06fj4IMP1l476KCDsGXLFs/3X3HFFejs7FT/bd26dah3SRAEQRAUpp0730EKOaZEh4IBhENBxFRhseEPond3J1UBs95kxtmP/H7KRF4QRgbJiRYEoRhDrkSfcMIJWLt2rfbau+++i7lz53q+PxaLIRaLDfVuCIIgCIInys5tFhZjSjT1Zh7J6tyb8yo0APTng/ZgQJRoQRhpspITLQhCEYZcif72t7+NV199Fddccw3WrVuH++67D7fccgu+9rWvDfVHCYIgCELZkKpEBbuc6twWkvl+0LEIBdEjZ+fesscJovvydu6wpkRLiytBGAnEzi0IQjGGPIg+6qij8Mgjj+D+++/HggUL8NOf/hTXX389Pv3pTw/1RwmCIAhC2dCEmAp25WNo5CzLaW+Vz4WOh0eusNiWNncQHQoGECQlOisTeUEYCcTOLQhCMYbczg0AH/nIR/CRj3xkODYtCIIgCIPCUaIpJ5rZufPBcjRs2rlHQInmdu5UJr+PkJxoQRhhXC2u5N4TBMFgyJVoQRAEQahksion2v5Za3GllGg7eB5RO7eHEh0OBlWQL3mZgjAykJ07EqIFNrn3BEHQkSBaEARBGFfkjOrcZOfOspxod2GxkVaiqbCYYzsXNUwQRgZSomkxTe49QRBMJIgWBEEQxhWk6AY9qnMrJdpVWGx4c6L7Uhns7k6qn3tTTourkOREC8KIQjnRlNYhlfEFQTCRIFoQBEEYV5hKdCjgWDZVTnReiSYlariV6K1t/fo+5ufswYD0iRaEkYbs3FRgMCf3niAIBhJEC4IgCOMKs7AYtbqyeHXuCOVEj0wQza3cnHBI+kQLwkjj2LnzqRSSEy0IgoEE0YIgCMK4glzRpEAHAqT0Mjt32LBzD3OLKwqiG6oi2uuhgBNES59oQRgZaKFNcqIFQfBDgmhBEARhXGHauYNan+h8YTGjxVUqkxtWS+fODtvOvffkGu31YJAH0SM/kV+1oxNPrGwe8c8VhNHEzImWIFoQBBMJogVBEIRxRcYnJ1pvcaUH0YDTQ3o42NmVAADMbarWXg+xnOjRsHP/6x/fxFfuWYbNe3pH/LMFYbQwc6LFzi0IgokE0YIgCMK4Iqf6ROt27pzlBMoqiA47j8mB5EW39abQm8wUfV9zZz6Inqgr0XZ17tFTw9p6UwCA1p5kkXcKwvsHlRMdkcJigiB4I0G0IAiCMK5wFRajnGhm56ZcyHAoqJTgRKa8ILqlO4FTfrEIn7tjSdH3UhC91yRDiQ6OrhKdzi8q9KWGv0+2IFQKys4dkhZXgiB4I0G0IAjCOKOlKzGulZWssnPbP/OcaLJzR5kC7VToLs/O/eaWDnQnM3h9Yxva84qu3/7s6iqgRIdGLyc6nbe19ksQXRa5nIUrH12Fv7y5fbR3RRgAmSw5Uux7fzyPl4IgeCNBtCAIwjji1Q17cPQ1z+K6J9eM9q6MGqadm3KjLQ87N8AqdJdp516/28kjfnNbh+/79vQkkclZCAUDmDWhSvtdKBhQ+zkqSnTe1to/zC2+3m+s3tmFO17ZhF89tXa0d0UYAHSvkZ1bcqIFQTCRIFoQBGEc8d6ubgDAu83do7wno4dp51Y50Tl3YTH73wPrFb2upUf9+40tHb7v25m3ck+pi6GKFTIDRrfFVS5nqWMlSnR50LXSn5K2ZGMRV59oUaIFQTCQIFoQBGEcQcoi/X88kjWUaLJz2znRXnZuUqLLC4jW73aC6De3dvi+j4LoaQ1xFbATQZYTnR3heCzNgnbJiS6PVP5kpUf6pAlDQlZaXAmCUAQJogVBEMYRNKlPDWO7pkrH3SfasXM7SrQTzKqc6DIKi1mWpQXRK7Z2wPKxhDZ32j2ipzfEEQkFkN8dex8DPCd6ZM9Zhi20iJ27PDJqsWr83mdjGafFlX3vy2kUBMFEgmhBEIRxBE3qk+N4VkhfXVXnDlKLK6c6t1dhsWQZgeTuniS6ExkEA7YltLM/jY2t3r2WlRJdX4VAIKBZyUOh0avOzQNAsXOXR1qU6DFLLmeBbjW6F3OSEy0IgoEE0YIgCOMIsnGPZyWaFN1wULdz55idmweylKdcjhpL+dCzm6qxYGYDAP+8aAqipzfE85/tqOB2TvToWEpTWbFzDxSeNuHnQBAqE75YRfeitLgSBMFEgmhBEIRxhGPnHr9BEeVEBwNGn+gchiwnmipz7zO5FofNbgTgnxfdzHKizc8ezT7RYuceOFyBHs/1B8YiGZY2QfeitLgSBMFEgmhBEIRxhGMzHb+TQopvQoYSbWlKtKMGxyLlV+den1ei951Si32n1AIAdnT0e753Z5eTE21/th5Eq+rcI3zOdDt3ZkQ/e6zDA7FSLd2tPUk8/vZOsYCPMroSLYXFBEHwRoJoQRCEcYTYuT0Ki7GcaK8WV3HV4qocJdoOoveZXINoyN5W2mMinstZ2NWZBOAo0VoQzVpcjXxOtPN5Yucuj3TGOXalBsXXPb4GX713OZ5evWu4dksoAe7AkD7RgiD4IUG0IAjCOELZucex2uVn585Z8CksRnbu8pXofSbXIpLfVtpj4aKtL4VUNodAAJhS586J5i2uRrq4kaZEi527LHh7sFLvteYu29bf2pMcln0SSoNcBIEAEBmlegSCIFQ+EkQLgiCMI6TFFVei7Z9VYbGco0RHQu7q3OW0uNrVbQdCMydUIZpvUeWlSFI+9KTamArcSf2ifRwtJVrLiRYluiz4gkmp91oyLakWlQAFzJFg0EmlGMC9l8nm8Mq6VvQmy0+F6E1mxvUYLQhjAQmiBUEQxhFi5y6kRFsqcIyG3Ep0skQ7dzZnqUl3LBxSAblXEN3ZnwYATKiOqNf4Z4eDQaVEj3Sf6JQo0QOGB8KlBsW0SCM50aMLjQG8HsFAXCCPvb0TF/3hNfz6qXfL+rv+VBYn/WIRPvn7xWV/piAII0d4tHdAEARBGDm4nduyLATyAeR4ImvmRDM7N1k5wyHnuNTE7EdlVyJd0vZ5EBQJBVQQnfIIpsg+Tmo34BQyo32jFleZUS0sJkF0OaQHUFiMjnFGguhRhRwf4WBA1UsYyL1Hreu2tfeV9Xe7uhJo602hu8TxRhCE0UGUaEEQhHGEtN5xVCWzOnfOstQxibAgelJNDADQ1psqaft6EB0sqERTsTJeTEyvzu3Yzkc6L1NaXA0cXlisVNeHo0SPz/uyUqBFjHAogBC1vxuAEk3OlXLvHd5BQVprCULlIkG0IAjCOIJP0MdrcTFu1wT06tw0gSb1FwAm1kYBAHt6Sg2inWMcDQVVQO6lMJISrbXUYkF0MMiU6BGvzu3sr1TnLo+BtLjqT1HwND7vy0oho5wqTk40D2atEgNqurdLcXE8umIHzr/pH9je0S9jtCCMESSIFgRBGEfwCfp4zYtWSnReZSJLey7ntKGiPGQAmFhrK9F7SqyaTMeV7KCOEu1h586rVfEIV6KdgDpcKdW5JYgui9QAHB/JvGI50oslgk6GuVFUYbH8vdeXyuD0X7+Abz/wZtHt0DhQygLUw8u34c2tHfjHe63aAkypdRgEQRh5JCdaEARhHCFBtGOLJgU6xAqLqcq8rLjXxBpbiW7tTZWUR07HmLbh5ER7KdFk53YC56hfn+gRz4nWFbFMNodwSNbeSyGjFRbzvs8sy8KL77Vi3ym1mNlYpWy/4/W+rBQoiLULi9mv0biwakcXNrT2Yk8JqR10b5di56ZznsrmtPsumc0CiPj8lSAIo4k8DQVBEMYRWmA0TifrdAhCAXdONE2WeWExsnOnMjn0lNCuJqWCaHsb0bB/iyvHzu2dE837RI94TrRRDVzyoktHW6zyCaJX7+zCJbe9jn/705tIZ3NKgTaPuzCyZNhCWsjoE72jox9AaZb7cuzctOiSzua0bYsSLQiViwTRgiAIg+DBpVvx9Opdo70bJVPK5P79Ts6nOneSLSpEWE50dTSM6qitFJdSXIyOMSnKys7tsWihCotxO3eEt7hylOj0SLe4ykgQPVC0Flc+i1Ut+V7iOzsTSLBjy4uSCSOP1uIqoC9g7eiwK26XsgCZVHbu0hfeMllLczGM1zFaEMYCwx5EX3vttQgEAvjWt7413B8lCIIworT2JPHdh97Cd0rIj6sURIl227nJnc2DaK5EA44a3VpCcTEKgkw7t2dOtGdhMdbiiuVUj7Sd28zNlbzo0imlCj4F173JrFpMAUZ+sUTQyWotrvTXSInO5IpXzk6VYefmrQfTkhMtCGOCYQ2ilyxZgltuuQULFy4czo8RBEEYFboTtsLQncyUXLF1tBEl2ikSRCoTKb2pAkF0U03pxcVSPko09ebmJL2UaCMnmn6mgLsQW9v6cN0Ta9DSlSj63mKYllWp0F06GS2I9r7P6DrpT2U0JXqkF0sEnTTrFW+2uKIgmr/PD1qUS2etovbvtI8SXco9LwjC6DBsQXRPTw8+/elP43//938xYcKE4foYQRCEUYMHXWOloq4UFuN2bvtnsnOnfOzcADApX1yslIJCtB0KnqOsGJd5nXgVFtP7RAdUgF3K+bpr8Sb87vn1eGDJ1qLvLYbYuQdOKW2K6F7sS2e1YystrkaXbJaU6KBaTKMxYzsLor3ux+fXtuCpVc0A9AC42L3jlxM9XsdoQRgLDFsQ/bWvfQ3nnnsuzjjjjILvSyaT6Orq0v4TBEEYC/AJzliZ+GbEzq1UpWBAt3NTsBMMOFZvwukVXVyJdlXnDgdcvyOKFRYLBQOIhkL59xY/X9wdMVjEzj1w0iUo0WT7tyygnS3OlNoSSxgeqLBbOBhQY4SnEm2cp0w2h6/cswyX37scPcmMZsUudu/Q2JPOGYXFxukYLQhjgWFpcfXHP/4Ry5cvx5IlS4q+99prr8VVV101HLshCIIwrHClYaxMfFOanXt8BkVmBW6nsFg2/7p7fZl6RZeUE0127vz2ebusdMYCos57VWExLYh2VGmuRJcyoaaFkeQQqMZmQSwJoktHC6J9zhu/F9u0IFoCp9GE7rNoOOj0ic5a6Elm0JVwFqfMRUie296dSGvnt9i942/nlmtBECqVIVeit27din/913/FPffcg3g8XvT9V1xxBTo7O9V/W7cO3oImCIIwEoxFJVq3Co6NwH+oUYXFfHKiI4YKDTi9okuxc5tKdJhtz7T2UuAejzA7N8uPDgYCyg5eTkXgoZh8pw0luk/s3CXDVfxidm5Av66kxdXo4qRYBDUleidToQH3mN/DqnAn0jlNiS5WT0Ds3IIw9hjyIHrZsmVoaWnBBz7wAYTDYYTDYbzwwgv47//+b4TDYWQN5SMWi6G+vl77TxAEYSyQZJOdsVIMiKti47awmKvFlf06TVi9lOhJteUUFrO3T4XFAoGA6hnttnN7KdHeOdGlFBkqFESnszk8sXJnSW26vPY1MQaV6CdW7sQJP38Oy7e0j+jn6gts3mMDf0+b2LkrBl6ngNwq2ZyeDw24x89elkLRn8oaOdGF0yuUnTtraYtXUlhMECqXIQ+iTz/9dLz99tt488031X9HHnkkPv3pT+PNN99EKBQqvhFBEIQxAFcaxowSnZOc6JxRnTtgFBaLhDyUaJUTXX5hMf5vc7HFqc7tPBujrpxop0VW0bY6WQqi3ZPvx1c24yv3LMcvn1xb9DvY+2pW5x58nvVI88w7Ldje0Y+X3m0d0c/NlHCfpcXOXZFQKkQsElRjRM6yVI9owjyvPSyITmSy2kJWf6q06tzpbE6778TOLQiVy5DnRNfV1WHBggXaazU1NZg4caLrdUEQhLEMVyLGwsTXsiyxCsLdJ1pV585SQSGPnGhqcTUAO7fz76yvnds3JzoQ0ALsVDaHeNB/MTqV355Xf1lqe1Vq+ytTER2Ldm6n/+7I7nsphcVS7Phqdm5RokcVzc6dHyMy2ZxWVAzwaAGXdK6xRCqrja/FFqDoXstkc1L8URDGCMPaJ1oQBOH9TCmWzUoim7PA2xSnxqlVkERCsnNTrEvn0OwRDQCT8kp0W2+yqBqsCouxqtwRpSYbFul8sKvlRJt2bvZzMWWqkJ3bUalLm5ibAf9YtHOrIHqEgxE+HvhW59aU6KTn68LIo9m5g6REwxVEl6VEF1iAyuUstbCXzlrafSd2bkGoXIalOrfJ888/PxIfIwiCMKLo1bkrf+JrtiwaC4H/cGAWFiM7NxHxyImekC8slrOAjv40mmqirvcQXnbuqG9OdBElOhhAOBhAIGC3QrLfHyn62V6Tb/pdokRF2W3nHnsTeiqeN9K2WF2J9r7PeH0CniYwFsaSsU5zZwI3PPseLjl+Lg6cptfi4fekKiyWs8rMic5p92Cheyed49eKKNGCMFYQJVoQBGGAjJXq3Ns7+rGupds16ZPCYrqdmwh7VOeOhIJoqLKD12LFxSho0uzcYW8l2rOwWERXogMBR432smlzUoWUaAqiS1S36HvUxez19kJqWqUyWko0X4Dwu8/8W1yVt7i1uzuJL921FM+vbSlzL8cvf35zO+5/fQvuWrzZ9TunTgFrcZWzsKPTtHPr56mXWbb7Uhnt94VaXJmuBV6dXXKiBaFykSBaEARhgIwVO/cnb16Mj/z2ZbQb+bzjdYKmguiAXp2b8KrODTjFxYr1inbs3O7CYmZbMW4dJaLs8ynAV22uiix8KDu3R7BN+1UoEN/U2osv3LkUyza3q/fX5xcPxmKfaApIRtXOXUJhsfY+nhNd3r4+v7YFT63ehTte2VTeTo5j+vKqcV/SnavM70kVRFsWdnXZi2dUqd+rTzTR2Z/WfldoAYpfH5mcpV0743WMFoSxgATRgiAIA4RPosqd+I4UZENMpHNo7ixcXXa8kLUKK9Fe1bkBYFK+uFhzV7/n7wk6rlGP6twuJZpVAiZMJdp+LZR/f6lKdAE7dwEl+q8rduCZd3bhgSVb1L7WxW0leiTs3JZl4eHl27B+d8+QbC89gnbu+17bggtufgUdfanSCotluALJ/l0k596E7Pk9ibFXPX20oOvBa1GK27m5Ek33z+Q6exwwzyu3c3cZQXShwmLczp3KSJ9oQRgrSBAtCIIwQPjEvFKt0TyY6jYm2ZU8QStWvGsotl2OnRsAFsxsAADctGh9wYI/TnVuZzt+OdGJjFdhMefftC+lKtGFiofR7xIFAnEqjtSXyqrcTLKxj4Sde9nmdnznTyvwg4ffHpLtlVtMbTDc//oWLNnUjtc2tpWWE11CwbFSoO/W46GqCt6oINrjuuApFiFjbAgGgAnV9v1gnid+/DtMJbpAiyt+fWRyltHiauy5PwRhvCBBtCAIwgDhAU2ltqXhAVN3Up/YVWoed0t3Akf97Blc+eiqYdm+o0TbP5dq5/7m6ftiUm0U61p68Lvn1/tuP6WC6MJKtGU56pZeWIzZuZUSTTnRhSfV9HuvoNGxevtvgwKBRNppxzWSdu6debfE7u7CeeeloizsIxCM0CJDIp3Vi0P5KtHer5c7ltB5HYuF30YLOicpj2PNe7cHjcGhviri1Cdw2bmdINpt5y6gRBuOJu5EKOY8EQRh9JAgWhAEYYCMhcJivBLzWFGi39raiT29Kbz43u5BbWdnZz/+5feL8fe3d2qvm9W5zYmyn527sTqKn5w3HwBw06J1aOn27rfs3ydan7TzSbhfEB0qMydaKa8egbJj5/bfBgVi/SwQrI+PnBLdlbCDj94ifXVLZSQLi9EiA1+A4Pvgt28m5bpa6DrqFSW6ZCg49WrzR8c/Fg66XCn18YiqdeDqE53yz4kuWJ1bK0JnaUF1skKfK4IgSBAtCIIwYLQWV8NoPx4MBYPoCp2gkWLelxxc0PbSu614bWMb7n1Nr8DrtnPrfxcO+j8aP7JwOg6aXo901sJrG9o830N5uLywGPWe1ibIWhDNLNyhoNon2pVScqJzrCiRlxLNA0o/uzwp0f2pLCssRjnRwx+k0TU62HNPZAocj6HGUaJzWju5coPlcusrpMTOXTapAosrqk4BKyxGNFRFWJFAfzu3S4kusTp3JqtfO6JEC0LlIkG0IAjCAOETc78KvKMNVw9J5SMqVYnu6qe83MEFBVRAq61X/95k51ZKdImFxQC7p/Qx85oA2Pm7Xqjq3FqfaLd6RYswwYD7MymopoCe1OlCCx9aeoGRWwno59tvO3TM+9NOgSNSogvlUpfDptZeXHjLYrzwrttp0M2UaMsa/MJUoWBpqKFAqS+VVW4HwFlUMfELrnMWtL8vBl1HyUyuYgscVhqkQHsXFnOUaHNsqK8Ke97LQDE7d2lKdDqb8xwjBEGoPCSIFgRBGCBjw87NcqINJXqg6txwT9QpkOpLZQcVSJGKY7b2omK4/oXFCj8aPzB3AgD/IJosmDwwVjnRHipTLBxCwNgHyoGmONzJw/SfVJvns1Bf8ITPpL4n6ViSSSGjnOihUqKfeWcXXt3Qhj8t2er6HV2jOWto1GOlvg/zNWtZlgqUeozaA36W3EJt8coZT/g41Ct50SVBx6xgYTHWJ5rQ7dz6+espWJ271CDa0vPpK3ShUxAECaIFQRAGjKZEV6idOznEdu6tbX04/KdP45q/vzPoffOjK7+fmZw1qOCHAs62vpQWjBdrcRUuoEQDThC9emeXZ2BJroQI7xNNE++MW2XiLa0ICppp31QQXUANNifc5nv57/1U5V5WWIwm9w0qiB6aAI2CDa+8Zx58DIU92bG3D29wyccCs9WUn0ulUICUKWM84dsZCcv9+4HC1bkdO7eZ6sHt3K7CYuzYd/SVbuc28+d1JVqCaEGoVCSIFgRBGCCpMWDnTmgtrozq3APY51U7OtGdyGDx+j2D3jc/+H4OpiI0V5soALQsy1VYzIihtYJgXsxorML0hjiyOQtvbu1w/d67sJi7xVVCKdHuzztm3kRMrIli70m1AJz86lLt3ICHMu0RwJv0UU50OqsCuaaaiNpeqcHoi+/uxtnXv4i3tnW4fqdszx55z3yhZyjyotMFgqWhhF+n5mLVQFpZlXNv8vMsxcVKg64HLzdAkt2XgUBAU6PrWRDtKizGrldzEaSQnTtjtLjiC7KiRAtC5SJBtCAIwgDhE5xMrjInO4Xs3ANReVWbpGFU9ignGhicPZUHF215Szef29Lk2LRs+vWJ5pAavdzD0k0T8+I50fa/eY9o4oYLD8OrPzgdDfmetJQjXUiJNitym+eIVwb3U6J5YTG6vidUR9VCg5nr6cff396JNc3deGrVLtfvSLHzUqL5NToUFbrTObpehzmI1moPFA6iyRVR6P5LlzGe8HGoZ4gKsr3fKdQ/nNu5AWi9ohuqHDt3ocJiRF2seFE+zc5t5LVLTrQgVC4SRAuCIAwQrTp3hfaJ1hWywRcWK2SDHCp4AbS+QShrXkE0n7BGDcs04dcnmkNB9FKPIJom6Lw6t3eLK7KNuj8vEAhoSnYpLa6KK9HOteCVE21ZllLsk5mc9j2ouJiZ6+lHQuUHu89fHyvAZaKd+6EIovPHe9iV6HQBxwc75z97bDVO/uUidPSlCivRZYwnfBwazP0ynnBcKu5rkNu5AadCPgDUx8OIerhKMtmcZ0DeWFO8x7oWROdymjItdu6hZ01zFz5582K8tmH43FTC+ECC6HFGLmf5FpQRhEqn0qyKpfSCHW10O7d9/ChmHEhgkcoMv7LHlbzB5OHy4KKtzw6iuZLr5B3rf1eoOjdxxBw7iF5Rsp3bQ4lmhcWKQaqYV/9nomhOdJFcy2RGb69DwWA0FERjXhEvVYmm7XsFwhRQeN3PmhI9SFU1m3Os+8OuRLPr1Fw44Mf9ryt2YmtbP1bt6PKt2g2UV7yPb1/aXJWGWgwsUp0b0AsN1ld594n2u1YnVEcBFLZz84W1dFavAyF27qHn8beb8fqmNvz5zR2jvSvCGEeC6HHGxbe9jpN+sUiKjwhjjhufew8Lr3qqolaPx2p17uq8fXggdu5CVW2HCq7kDcbSywMnqtDN20qRbdusjF2sOjcATK2PA3BbdwHn2GjVucNefaL9lWgTVViswDkzA0WXnVsrLOae1JsLFnTthENBVVzMLJjkB23fK7joHSElmt+TPKAeDgr1Y6f9yOUstPYk1fsL2rnLUaLZPT5Uxd/e7/iNY5Zlqde8FtnqfQqL+Y1TdN+ks5bvMyJjXKdSWGx4oXv1/W6VX7a5XY03wvAgQfQ4Y9nmduzuTmJrW/9o74oglMWSTe3I5iys3NE12rui4BOcTIXaufnknlSqmnye3mDs3MOqRLOc6MEUl/Kyczsqk9NWysyJLkWJjueVYXPSC5TTJ1rPvSxEdCDVuQsUFvMKov2cHpFQQAUD5SrRXspoP8uJ5lXTczlLe/9g83vN8zKcCz/9HveZ2o/857b1pZTSn0jnPIuHUQ5tWS2uRIkuG7o+zZ7c/J6J5RcbtcJicV5YzPk7v3unMa9EA/5qtHmu+fve74HeaDASz7DRZk1zF/75d6/gX//4xmjvyvsaCaLHEZZlKWtnIWuRIFQiHfnJeyWlI/BJ+XD3oR0oXsdLBdEl7vNNi9bhs7e+hmQmy+zcw3ceuBLdN4jzzQPO9j5dieaBq2nnLtbiCtCLgZnHmCbXkWI50fn9i5di56bCYgUmfu4g2nu//Lbjp6ZFmBJdahDtKNH+OdGWpT+L7KCav2+wSrS+sDWc16xX7QEyONB+7O52VKF+HyW6Nm7fm36Lct2JNJZvadcWH/h1PhwpL32pTMU6bQYKz4XWq9a70z1CzJnCC4vxRRBavDDHktpYWAXhfnnRKeNc8/elMjntXAuDh8aBQguSY53t7bZQtqm1b5T35P2NBNFDRC5n4ct3L8W3H3hztHfFl3TWUhOUwbSNEYTRoFPltFbOtZsaY0o0URPL27lLXIn/w0sb8NJ7rVi1owuprL29dNZCbhjssclMVpvIDq6wGMuJ7qVFGHfgahYWC5Vg5+YWbLPStWPndt4T9mpxVaBPtIlfRWCOGSQW7hNdjhJdvp27kBLNLcfc7m1a4webE23mFY+UEk1BM0+bsCwLLSyI5n24iWg4yBZbvPf1h4+sxMf/5xW8uqFNvcbfO5hq9l70p7I46bpF+OTvFw/pdkcbPsZ4tX7j6R68zmB9VdizyB9dq001jvIM2I6Vqvx14Ge1Nx0J/H05q7ye4UJxaFx8P6v89EzqSpQ2XgsDQ4LoIWJDaw+eXLULj7yxvWID1ESRyqyCUMmQEl1JFqyxlhNN1ERLt3Mn0lm05wOnZDqnBWbDob6b+aSDCQr492s37dwscHX1iS6hxVUgEFCBrVuJLtHOXU5hMbJzF5j4uXOinZ8tSy9YlPA493726fAA7NxKifZQk7nCzP9tVrUerBJdrFr5UOL13K/OOz4AOxBq6Uqon3uTGdVujSzcVZGQSiXIZHNo6025Fja2tdvK0ppmJ62FX+dDrUTv6OzHnt4UVmztGHOK6NOrd+Hs61/EOzv1FCDzXkhmmX2a3ZMq3SNg2LnD7gUxWiyaWBPTPisaDqIqat/ffnNDsz2i6RSk6/a5NbvwnQfeFMv+IBkPdm7eHWE4FrtHm65EWhsDRwsJooeIVSxP05wIVAp8oid2bmEskctZavJeSQtAmppRsUH04OzczZ3OxD+Z0S2oXna4V9a1Ym1z90B2FYC7hVL/EBUWUznRaXcxL1ef6BJaXAFA3CewdVpDscJi+W1mSmxxZcKV6NaeJJ5a1Vw055fvlyug9Cos5jM5jzIl2qvF1a6uBNa19BifbX+el5rsp0S7FlAGnRNt2rlHRokmaqLO4kg6m9OUaK4QUS/weMRRorsSGZz2q+fx0Zv+oW2TvkNzl35fEgNZeOhPZfGdP72Jx9/e6fodjR85y7+3eKXy2Fs7sKa5G8+tadFez+QsLW3Ay86tpXvkx4dYOIh4JIRoyJ1aQcd9Yq2uRMfCIVRTEJ32PjfmdWrex/Tz755fj4ff2I6X39vtuR2hNJzCYmPrei4HEs0sC+h5HxYS/sZ9b+Ds61/Ce7sGPtcYCiSIHiJWs5XOSrVP8AlvparlguBFd8LJlayUiZypZgy3nduyLPxp6VbXQ+Oqv67CP//uFV+F0ktxpEldKRWLd7IgOpHO6RPOrP6Zu7oS+Mytr+Hzdy4p/GUKMJRKtBZE97kLixGmnbuUwmKAkxdtXpNkz/RqceXVZornV/vBc6KvfHQVvnT3Mnzi5sXYvKdXvaeQ8lqs6BjgX5QqUqTF1b/8fjHO/e+X0Mms3nRMzG3yXtTA8CrRo1VYjKiKOkp0OmNpOdG8eB4dW1uJtq+THR396OxPY11Lj3aPUgCwS1vc4opo+ffLaxv34OHl2/HzJ9a4fqdV909W5tzGDzon5pzML0gFvBe2yNZNC0kRj9QMcgA0VEXU+2k7xezcxa5L2icaG80xUigP1d7s/RxEs/vWa+FzrLOlzXbkmIu3I824CqI7+9L43O2v468rhr433GqmRHf2V+YAxyfZY0WJtiwLdy3ehGWb24q/WXjf0tGfUv+ulDwmM2AZbjv34vV78L2H3sIPHnlbe/3/lm3Dss3tWLPTe0XWa8GsltlMi+13c5dTyd/MVzaV6K1tfchZtno9UOunOeHl6ug7O7vw9OpdJW+LXytmiys+STbt3OES7NyAE/z6FfDSg2j3xNvsR1sIrkRvyxeNWbG1A5+4ebE6x+b54D+balcpLa4A+9iEgo6du8OYkFmWhc1tfUhmctjV7VZHU5mc9p1T2ZwWFPJFki7j2TlY22qxQmtDScLj2MUjQacnezaHFnZ8+IJBfZyU6JDKnecTX71as5cSPbgaAqT4b97T51rI4I6FwToDCvHqhj2ePdcHAwUSZtBpLiB5FdyLht1KdH3+HvDqE02LF9XRsAqagXwQXcTOXWwMpuuYrgNpYzY4VGGxCplLDAd8fDfH1fcDdC/R4vhoMa6C6JfXtWLR2t2445VNQ7pdy7K0ILpSlejEGFSiV+/swo//sgo/eHjlaO/KqDDWctCGi3YPhWu0KTQRGw7W5C3SZns6Upr9+kF6TRSqmUJWzNK201C8ClUkb+2xH2iZnDVgq5w54eUTxsvvXY4v3rVUU18LoeVE96WQY/vF1V9TiS7Zzh2hnGjv3GMeRHtNvBMe1nI/eE40f8bs7k5iaz5P1q1Ee1cgtn9XmhJN36HeJyc6mckplwjPx/WrGG0+e3jQ51aiB1lYLFfYJjuUeC1MR0J6oTBNic5f59FQUKVXxJkSrfXLZseIrrVdXfa2eF9jYGALD3zfzVQMXkulZ5gU0O5EGhff+jouuf31IX3m0f1ljikFlWiPOgWUE12fr5yuCot55KLXxkKqNZa9nSCzc/vlRBf+znSv0r3jV0VfKA2VE10hc4nhgC9+VWpMMhhoXKLF8dFiXAXR9IAe6lW8lu4k9rATWalWm7GoRLfnK+q2j/Jq02jw4NKtOPSqp7B0k6jwHez8Jypk9diciA23Er2x1Q4c9/Qm1UQzm3Mm0Ht6vO8RL8WxmuVqFgsstJzodFYbR8xJSBsbBwc6zprWM9pOOpvDpnzwzHNLC8EDxZxlTyacSTLLiR60ndv5rnxCHPVocZXOuFsTxUqyc1MQnXMpC/T5hQJl83feSrRHEG1YWc0gWrdm5/Nnc5Zv72LTnq8p0flnJ12fgy2SZd6TI50THQ0FnYJyGSMnOn8cI6GA+r52TrR9vPlx5seI7j9ye5gLJwMJsPi+rzaKcPEFouEqaNXem0Yqm0NHX3pIa0vQ+TYXZ1xBdNZ9fL1qJig7t1oQ444K+9jUxMKoijp/GwuHUBWxg+9y7dxkiKFxgoLosSKCeFEJRa6c6tzv3yCap3G9H+3cSonuHd3vNq6CaHoADHVhIq5CA5V7wWpK9BgJomk/K6mY1Ejx0nut6Epk8NpGCaL5hLJSVo/NiY9ZYXWooQAynXWKrPGAdndPEol0Fr96cq1mi/RS7qPhoGMPLjJpNZVozc5tLGjsYWr4QAMgd060/fPu7qSn4lkIs3hWW2/Ks62US4kuocUV4LTJSmi2aeff0aI50QMrLEbKAm3fqTbrXdXX/lz9d94trjzU1PznNlbbBZM6+9OaWsjPBf3bnJzy7ZqF4vScaPvf0xri+d8NsrBYAcVxqOlPubcdDgVUUJzK5tDS5S4sFgkHlTOkKhJS1x5fKPFS+PvTWXQlMq7v1DcAyzW3opvzGX6dDFcQ3Zd2K+1DgapQ7LJz+98nXikWIdPOXUCJromFtfZ5sUhQtRT0KzrrtwDrFIDM2rUEVO/1sTkfemVdKxZe9RQeWrZtVPdj3Nm5K1TYGyiW5TjKRltgG5dB9FCv4pkrt5VqndCqc4+RlUzaZ6/iSO8HmjsTeG7NLs/VWdUiRtpZaL1pK0WJdtm5M8O7wr5ht2NhJts0v4/39KTwzDu7cOOidbiOFQjyCpbCoQBiHhNBL5q1wmJZ3c5t/C135AzUckjj56R8lVsKpHgwX+okks4RBTLtfSlPu2bAeBKGS1SiY8rO7W2b5op2wZzoMgqLdSXS6jMm18W0z3cp0R65tH4/A95BEgV1pMKlMjnfBVk65+bklG/XDIz57+jcT6uPa9sbKCPZ4srrPuN27vbelHasaMEgEgqqKt6+dm4fhb+lK+H6ToO1c5vtoBI+tvyhhF8TXlXjB4qjRBfOifaszs3t3K7CYv59omtjYZUDbW8niNkTqgHYOede+AXRVLsimc4hnXWKQA624N5o8eJ7rehJZvDKutZR3Y/EIJToR97Yhje2tA/1Lg05/VqtifJikr5URnOVVRpeXTdGi3EVRNMDYKgHIFq5jaiCIJU5wPELb6wou/1sclisivBY5N//7y1cdsdSfO2+5a5zQgsHUkTECKIrVIlOD6MSnUhnsaPTyYWm/Ge+uNTak1QVK3d0OO/1s5lGPHJ0vSisRJs50UOnRE+tJzXS/pkH86UEV9zqStva05PyVH/d1blLezTSRJsv7NBnUkEuwrNPdBmFxeg9ZNsPBJx2OnRP0DUZZMWsCL/CYpZl4Qt3LsGX716qzhm3oUfzz7WaaEh9H+4M8WpXZd6julpt5kS7A0ulRA9xiytTjR9KvHOiA+pa2t6h1zKgiW005OTM8j7RPIh2Fif049rclXAr0QN4XvB9X9PcjYxH3j4AdA9TEM0n/MOhRLvs3K6ikDzFwt+pQgXgvAuLOakIXImOhoPYe3INAH0hlOPX2YGU6GQmpx2jsTonoMJ6o73/vOhhOTn4m1p78e0HVuDbD7w5THs2dGh27jKFvX/+3WKc8otFA3aebG3rw+L1ewb0t6XA7wVRokcQx849tJPdtfmWMwtnNQIYI0r0GAmiE5qSMjb2uRy25QsCPb6yGZfdsUQb0MlixweyXM7CrS9vxJtDXMW00tGqc1fItTvQnOiWrgRufXmjZ6sgP7a09Wl9TSlY1ZTo3iR2dtiTlF1dTt6013gXYbmahZRo6klMmIXFzEk9XxUeqOWQgovpRiDFqxGXUoE4nXV6wdK22vtSnoXFzJzo0qtzuwuL8crcAbZdrzzKgRQWo5zruphTBdixJ9r7UZef7PPUB3dOtP3z7p4knnmnBU+u2qXy7ifXxtT7qMhaIBBAo0deNF+U7vNRorXCYkav3F6PFldKiR7inGi7snkftrZ5K4J7epJY09zl+btieLm7IiEnbWKHEURTnnM0HMRB0+sBAPtPq3OUaLYYT/eAeVybO91BdG8qU3Zxrn4t5zqnrgNAXyAaCSV6KJ1GAyos5rGwZba48ho76dqvjYUR15ToEOZNygfRrd7tePxSarQgOs2D6OEXaizL8s1fvvvVzfjk7xejpSuBvlQG37z/Ddy1eFPRbVJhvdEujFZoIbgQe3rt/S+1JsdoMtDq3JZlYU1zF7qTGW3hupy//9wdS3DRH17F+t3D036KjxGiRI8g1IIglc1pK62D3m5+gN5roj1QVm5hMWbBGyMrmXw/x8o+lwPPVXll/R4tUKCBgj8wl29px0//tho/+cv4qlbO+89WSjEQc0Jbap/oW17cgJ/+bTXufW1zyZ9lKhit+Yc4f1C2dqeUatyfzqrFF69Fh3AooCb3hY7nri79IZpIZ7UJn8vO3cOD6IHauU0lmgopOUFIKb2j+fmZ1lBl719vynOSbLa4KlWJ9mpxRXm4UWMbNBEvZh31I2oE2nXxiKtPdUoF0c7km/Br98QrRr+bXxAmhRvQLemqzRVb/e8vQYkuZOf2UqJp0WMgASHHDKJ7k1mcf9MrOOeGl9DS5Z4gfv7OpfjwDS/5BtmF8FqYDgedxSpSot3XWgDnHDIdr/3gdHz55L2dFlceSrR5XHcxOzep2ZZVvtJnuqB4ippWWGyY5jb8GTeUz3nVqzyV0QJCl507m3X9Tusjr3Ki7fsqEnanZtC1WxsPI87u1Vg4iL0n1QKwFze9xkXfnOioM77wYzQSOdFfu285Tv/NC56uxVteXI/XN7bh9y9uwB9f34pHV+zAjc+tK7pNqgkw2nM5ryC6lHGG7qu+VLbinZF6TnTpC/Z9qaxafB6IY3X97l6sa+mBZTm9nIcaTYmWIHrk6EnwvMqhm4jTw3NKvb16X7mFxcaiEs1sZRUSPA0l5rXCF2CcnGiuNqa0/48XuGWnUlIR3BVeS7s+d+eV3c2tpT9gNhktnSgnmgdve3qT2MkCTWqB46XscIWskBLdbAQayXROr85tFhbrZXbugVbn9smL1XOii0/m+URpYk1U/Z2X+utucTUYJTofRBtBb8TTzp3VtlMIM9Cur4qo70DfKZk1lGjNZm6ki5ASzYJomhdOYko0X1DwanOlV+curkSbFm2uStF4SAsoOWtwi2amnXt3TxKtPUn0JDO47R+bXO/fvKcXOQtYtaN8NdprXIqGAyrgot7edF0TdHyn1scRCARU0M2fDeS8cCnRzM7dUBVRAXq5Sh/NB8iurwfRzmcOV2Ex3c7tpBkMBsuy1NhnWfoxKaxEu8eHffJ27AOn2Y4BJzXDUvtJKUeNVVEjJzqEhuqIGoO4yk94tUcMBwNaRX6v+2w4eW5NCza29rryuLsTadVm8U9LtuLWlzcCKC1QqwQ7d8boU5/K5PC9h1bgQ//1YtG5hbZgWOF56dyFVE5M0ltgwbMUnl/bMqDPLQetTkMq6xoXR5IhD6KvvfZaHHXUUairq8OUKVNw/vnnY+3atUP9MQNCrxA6dAedHkBT80VeKtfOPQaV6LT74fp+IZHOqgnihGp7cspzt+i7e63Sj7diYx39PCe6Mq6DpCuvrrTJPo1DOz2UMD825pVomlQ5dm69wAbPu2zpTiCTzXlO0HjrnULB/07DzpXM+BcWy+Usw849yJxoVqHZsiw9J7qMIDoadqrj9iaznm2lTPd2udW5udrv9IjWN+qVR+lV5MwP0/JdHw8zJVxXoutLUKLpPtrtYU2kCb/9PZzP9WpzxZ8lPX450QUCAD5RM/PhgcGNdeY9yb/rPa9u1r6HZVkqSKQ0m3LwmnCGg0FXTvTspmrtPabrgRZwuNBFx888rs2dSS3oq8lX+S5XqaRzSAF+W4/3ouVwBdH8+khkcnjh3d047D+fxt/f3jngbaayOS0Fhi9Ql9Qnmi1s/fSjC/DK9z+IQ2c3AnBSM+hzAGeht7E64sqJBuDkRXsG0e4xOBwKqHHBLuY3cjnR2ZylrjXzfiW3CmDnyNN1nUjnXN8jm7Nw6e2v40t3LUUyk0V7H7WZHdx19Pe3d+LQq57CC+/uLvtvTTEmmcni8beb8V5LD9a1FLYf8/nocLkyhgq+eM5jkmzOKqii92ipNwMJop1zUk7KWjmY+8Vr5ow0Qx5Ev/DCC/ja176GV199FU8//TQymQw+9KEPobfXu6DCSMIvjqGaiPM+rVPyD6BKVaLHYp/osVhRvFRoYAsEHLuprkTb1xWfENHDc6Aq31ilIu3caV1xzGQtvL6xDR/6rxcKFtWgoGCnkSPJ+ce6Vnz21tewJa8CbMwr0TSJU4XF2P2Rs/SHSUtXUpswkMUX0O3cZhsgDrdQ25/nnxPd0Z82Jv7+k4xVOzo9gzfAGT9pQp/NVyTmqngp1z/tZ4y1EOpLZQr2gSUG0yeaPtcMjuhnbvt3qnOX3uKKqK+KMCW8hJzo/OeatvJWj/7iEzUl2jkWjdUDy4nW7Nz5ffXqBU2LiI3VEZXvPZiAwZzU8/z+nmQG97zqpFQkM86C02Ds3NxVwKtzb88r0XONINpt+3dfC37HdRdTomPhEFssGpgSTTb+Pp/F6+FavOVtzxLpLF5Z14rO/jQWrWlxvTeRzuLG594rmrtuLjhoQbThyvBuceUEwuFQEDMaq9TP/Jyls5a+IF5jKtH2e1VetEeeqFcQzd1CbiV6eJ///QUC9nd2duf3zz1GdicyWLm9E9/505vY2dmPJZva8Pza3Xhq9S6s3O6cr8Hu/3/+dTU6+9O45LbXy/5bM70pkc6x1mGFr299wbDCg2iPnOhcflHj2Guf9W23VqgdYTF6kxm8zlqydg5TcGuewz09Kdz96uZRqZo+5EH0E088gUsvvRTz58/HoYceittvvx1btmzBsmXLhvqjymawKyxe8At1at7OXak50aX2if7avcvxL79fXBE5H3rBk6F/cPBFkJGGBra6WFgpR152bq9Jaipjr/q29abw5KrmIc3xr0S4Ep3JWRXxfUmBoDYk6WwOT61qxru7evDkqmbfv6PgslDRjvte34KX3mvF397eAcCxAB69VxMA1uKqwH3c0p3QxicKgACjsFgJSjQpk8lM1phwMjt5jx4U+01Itrb14bzfvowv3rXU8/equFSDo0b2JDJafnYpSoYTLIdUbqFt/fLKiTbt3KXmRPsXFjODI94vmBhIYTGiPh5RE32aVKTy39lRot3BPVmyCynRk1hOdLiIEs0XNEqpzk12bmrPRRPqtt6U2lZdPOIEhINQrcyx3fyuvF8tnx9sbfdf4PKDCkFOqNbzyc1e3vtOqdX+zlwcMX8G/I9rM8uJjmpKdHnHjK6Fpvx9zvtGaznRI1FYLJ1VP+/ucV+bz77Tgl899S6ue3yN63ccc6LNg4akcRy16twl9G7nC2SpTE6p0OFgADXRkLYoRv/ee7J93r0qdHu5hSKhoNqHlFFYbLitxPqcQz+OtHhx4VFzMLkuhtpYWI1t3Yk0bn15Ix5evh2/fHItnljpPAdf2+gsLA9WEJk1wVnQ8FqUKIRXSzindVgRO7dHi7pKhd+3JNg8tHwbXnqvFbu7k5qjgDOYOOmV9Xu059tAXbl/e2sHvnDnEl8l29yvJ1Y140d/Xomv3LPMtxjecDHsOdGdnZ0AgKamJs/fJ5NJdHV1af8NF4Px+n/3wRW4/N5lrjwdfjIn1+aV6Iq1c7MHlc/3T2VyeOztnXhtY5srH3I04JaU4WhtdOEti/HBXz8/KjkVdJ00VEeUcuSZE+1TXK0vmcW1f38HX757GZ4oELSNdXI5SytkBFRGfjxN0ElRS2ctda4KTTbpd93JjO9qMF0Hu7vtQjQ0+f/AXhMAeCvRJru6klqQVsXsy9ESc6IpcJ0z0VbPzOrc/N+moulnKV23uwc5y1vty+Us1UansTqi9nFre7820ewpwa7qWKWDqM4vdPTzINrozczF6FKrc3u1uEorO7cRHBVocRUvoU90OBTU9rG+KqyCeJeduyqivc5/R44Euoe8AhWeEx0tw85dkhKdfz99Rm8qg18+uQZHXv20/T2DAdTFw8o9MBj1M2NMqOi+2S8fyG5s7VX3ILdnDkaJbqzWrfCmYmcG0ebvva4987jSAkRrT1IFVLFwUFVzHmhONAXRZlBLjHQQ3epxbVLdBTPVxKSwEl0oJ7p4ikUoGFDulXQ2h/ZeclBEEQgEtLGWtkNKtFdOdMajjkI4GFABeDKTHdEWV173NLEmr0R/YO4EPPbNE/Hkt0/GxBqqB+T0F/7bWzvxGLPjc4WyL50dVM47XecA8Kel2wq8040ZRPN6K8XuG20hY0wp0Wl0J9L4xRNOam2XzyKA1kkhVd4866X3bCt32KMVYjnc+vJGPPNOi5ZfzTHvbfrcXV1JLB+kGl2uQDOsQbRlWfjOd76DE088EQsWLPB8z7XXXouGhgb13+zZs4dtf7q1i6P0QaizP40Hl23D399udpW255PUhmpa4c+NSlBWDK06t8/km99AlZDzMZzVubM5C0s2tWNbe79qDTSSkG21Ph5RE9uepP0az2Xt87BCAvaAT/lI7+4anlYClUBPKgNzcbES2lzRPc6VaJpwFFJK+T3mp0ZTEcTd3Uk1WayLhbFPvspra4/dwqpwEJ1QD5t4JKRN7EotLNbabU8wZk2ozn+vrBaYJDM53P3qZpz6y0Wuh5ffJKO1QJuT7mRG5THWxyNKQTbVhlJaXHHFmVuH/ZQmXlys9Orcup0acCbofoXFchaU8lGK6sXhE/t6rTq3aef2yonOqr8DnHuo1SsnWlOi3dW5fftE++TuerW4IrW7N5nBPa9uQc4CDpxWh2s+dggiIRYQDqISsZmqQNXj506sxoy802F1vogYDxC3tfeXNclPZ3PqvpjAHB9h1iea2G9Knfazn+2fYx7XGQ1xhIMBWJZjEzdz/8uBziE5TjQ7NzuGwzUnMPtE0zVC4w+HguFixTXNgop8/ucuCskcbx450V7wNlfUgpHcPnxRjMaBfSY7dm7z2qJUi+qoMUaHnJoH/D4jJ1ohHlq2DX9aurXge/zwSiEDqP2RHUQfOL0OU+rimNlYpcab7kRaucZSmZzm/Fi6yXk+ZHPWoNKy+PP1/5ZvKyvwMefmfIH+/WTn1lTzZAa3vLhBW5TySzvt9XA+lgq5LObPbAAw8CCa/o6quZuY8563tnWqfz++cuCC0prmLhxy5VP4n0XFK80TwxpEf/3rX8dbb72F+++/3/c9V1xxBTo7O9V/W7e6b3rLsvCbp9/F06t3DXhfUoaCkkhn8cTKnfj8HUuKlkjn/R3Nxt50MquiITWZBirT6sEDD78gmg8MfirZSKIVFhvihYmRtEd5QSuBPIim64ZPXHpTWWVR0dvJZNT5KpRfO9ahvJqqSEhNXCpJiabJfiZrqQlHIaWUT1D81BQ6r7u7k6oVz5T6GCbV5e2W6Rx6U1lPdwZNxFq6HSU6HglqymuY20wLTEBa86rPzHw+oOmySWZy+NuKHdi0pw+35au0En5qyW6loudc1it6yNfF7KJZpEaaFshSJjB0frjNtY8dM1NpCjIFsNTq3DGjsBfgBG6mwsiLEaWzOViWVZLqxeGBuV6dW1eiVRDtEdxTmx5TiSZlLRwMqGDZ/h5uJZrn3vO+zzQJdVfndlu+HTU1pSZNj1x+Aj55lL2QTgsogylCZAYaFOjWxyNqokeVuPkzuz+d9cwV94M/SyYYRdn4ea+KhDCj0ajO7Vps8VCijeMaj4SU4k0L+7HB2LlTpETHtJ+BEVKijRxsGjv29CZdASftQ3tvqqB105xoa3buMqtze8HTM+h+oAUUXYm2tzOnqQbBgP08N9MK6Dqt1hY6HSXatHMDhdXoRDqL7//fW/j+/701IMWU39P8c7a196MnmUEkFMA+kx1HBY03XYkMOo05Mj1nzGtnMKIIf77u7k5i6ebS1UfTyk8uAqD44pMWRFfgHJ/Dv6dl2c4Ajp8SPZjaUdQd5KBp9kLhQINoSnWkau4m5r3AU0+fWNk8YJfDs++0oD+d1VwTxRi2IPob3/gGHn30USxatAizZs3yfV8sFkN9fb32n8nb2zvx38++h6v+umrA+2MOJP3pLG57eROeXdOC59/1tgwQPIg2q8CR3aEqEkIoGEBdfkJdicXFTGu01wOIB5PdFbDSxgeCYnbu9t4Ublq0TmvzUwiuZo1E30UTpURXhd1BtDFIeFXq7k1l1fuLWdvGMrzqacxD+RstzCA6xZVon3vHsiztHvO7Vul63N2dVGkV0xriqI6GVZDc2p30XAw7JB8ctHQlWBAd0tSRUpVoUu5mTnAXvqO/pe9DyhCps34Tbj6BNPeffkcBFn3X9XklmizApdgZ1WQ4EnKUaF5YLGIq0c6/I6VW5/YoLEYOErfC6HxAKptDMuNUDy6lsBigT+zrtOrcWbVdgKnNPLjP71ddzP5dNmchnXXUogX566YmFjYUsdKVaLruaaymYMLLzk3pTzQBmtlYpRVkqh4CJZoUPtMhXV8VwfwZ9lxj5Y5O1z4CwNYyKnRT0BkKBtSxB/ScaMDO5QwbFm937nxxJToeCanFEDp/Uc3OXd4xc+zc9r73sSAqORJBtDZxd1TXdNZyTcQpGM7krIKpc4Xs3IWD6NIWtni1fZoXNlTZCxs0BgYDjrU1Gg5ir4m2Gr18S4e2LQqi+fUfZjnRtp279CC0J5lBJmfZxSaLzEXT2Rx+89RaLN3E7NaaA9D5XFKh951S59n6riuRVueLjD0fP2Km5+f2DeIZ3sdSGABo9TKKYc4d+PEpqkSPwL3gx/8t24Yv3rW0JHGLinFyVF2VeXZqra8S7ZMT3ZVI4+9v7/Sde1mWpeahB6ogemDHiO5r0/lL+O1DMGB3QeBF7MqBWvuVky4x5EG0ZVn4+te/jocffhjPPfcc5s2bN+ht0olp6XavSpaK1yoYnSi+EuWFHkTrq2x0kdFkgQaTylSizdL+7skzX12rhO/Ab+JiRQ7ue30LfvnkWtzy4oaStq0VxBkFJbqT2blrY/p1Yw4SvcomrCvR9P4d72Ml2pmgOPZVupbbelP46E3/wF2LNw3JZz25qhmn//p5rNzeWfS9NPGqjTkTHzoffpNYu12T87Pf4kc3s3NTv+epdXbQQYFka0/S82FyWL6Ct61EO8FMnAVgpeREpzI5dY3ObPTuPJDMZF1Bzpwmsn772Ll7/HPQKCCYREF0TFeiyRJZWmExd3Xu/lRWy5XmcDt3yX2iDSUYcKyhLjs3C8zTmZya+MUjQbX4WgxNiY5HlBJOn0/frc4jiDat3oB9vdI5PjY/uaqJhrQAwkuJ7ipi56agfiKzbBPKzl3nKLaAkzNKDKUSXWMc37p4GAtm2IsGjp1bv7bLyYvm8wB3dW7nWqKCSHGjPgHHq6idmRMdCwdVsE7ujWg4hNr8uTWL/JW6/6REm/ZqojeZGXT/Zi+0oC2d1c65qdryOUohS3chJdpt5/aomF9EiVY1DjKWWuidYNi5Y+GQVrTwgwdOAQA8sVJXBTPKzs06KASdDgrJdM41sS80Z+G92IsFXU+v3oX/fm4drnvCKdSmOUfY567JBxmkNBI03nT1O0H0dR9fiG+evh++euo+np/rVfl5857ekhbIaTyh+6mYm5Rjzns1O3cZhcVGMohOZ3P46WOr8fTqXSW1feNOoBq2MFMbC6tFdr8gusfHyn/z8+tx+b3L8U83vuxpn+9KZNT7D8j3Ux+ImJhIO200S7VzA/b9eubBUwEAT6waWGu8d/LPAr6IWIwhD6K/9rWv4Z577sF9992Huro6NDc3o7m5Gf39A5/k00MilXEPJKXiCqLTjopXbKVuO8uXbTeVaKb0ANzWUtlKNOAdlPLjVAl2Fb6PxfJgqbdnqTa8Xk2JHg07NynRES2nCHAPEvRQNO3c9P4dneXl8I0l6P5srGb21fy1/Mr6VqzY2oEHlgws98vkz29sx/rdvfif54vnxNDDuIZNfGgC4Xc9ma975eLzfrXdyQw259tbUd9kyiW1g2h7H3hf34WzGgHYD0AqwhOLhAyVw1HMKJ/PhArEhIIBTMkH8OYEJJnJucZWCqL9FMTdzKJF1zNdu6YSTROAdXklmooylTKB4QFHNWv941fMa0BBtJcSnfFWooPBgFKl0llL5bLOaKxyVQf3I6bZucOu+4HUh7oC1blrWRBN+xAJBXDE3An5v41o1wr/HmQhLlZYzLwuez1cP1SMiKA+uoQqLDYI2ydN9mqNINq2c9sTvfdaepBIZ13Pu21lVOju93F8hNliFeD0iNZcIeHi7dX6jOrc8UhILWjsZnbuhfkJ8j8KtNgzybGewFRYTAui2TWUs4anPaafnRtwF77j9/6eAs96dxDtzokOGe3eAGeeUcwdQjb8VDarAjGy8tP5NRfSzjlkOgDgmXdaPOsomPed6hOd9bBzF0oZ4o7CIvM4yidt90nR4NfCjk690CRB483OzoSqX/JPh83Ad87cH7MmVBesOE+8va0Tp/zyefz7/71VcH8B5xqYma/VYc7LC+EuLObdrs+L0cqJfnldqxITeP6vH3zhi56lAHDwjHo0MteAF72aK8T5vm9u7QBg19/56d9Wu/6OXHWN1RHVqWggdm5+vfrZuRNp94LwflNrccy8iQCATa3lF4bsS2VUK9FC95bJkAfRv/vd79DZ2YlTTz0V06dPV/898MADA94mLy7RVsaKE8dl505lVQBi5nCYlJITTavPytYyQBvDcGIq0V4DhhZEJ0d/IUBfEXdf2J19aXUeadWqp8QFDFPVHWnoGmmo4oXF9Eko4aVEd/Sn1QMhkc6NasP54YTuz8aqqCtoofFgqM4fLdg9806L70NmXUs3bnjmPRV8cpWLHhp+D2PzwbvTw4bWn85qhdTIbjo1/zB0lOiUmljNZC0/9plSo5TNzfk+0/FwEHFDXaQH3W6f1V46FhONvqecZCbnOvYUKPgpJVxZ6ktl8dCybTjsP5/Gkk1tasI8uVa3c5Pll/pkJ9K5oi34eJ9oPSfaO+dxMHZurYCXqs7tDoYoOE9nc9iWf65Q0bZS8CsslvTJiU5nLXWceD9hp+q5fX1Mqo3hlP0n46OHzcDlp+1jFKFz27k7+tNq4YOf53TWyrdBIyXaPo9a2xTVxiqsfc7ephI9wJ7HHFIYTSW6viqMafVxNNVEkc1ZWNvc7UpfKkuJzn+nqqh+n0WNwmKknJlF/jjedm4PJZqC6B7Hzn1aXul8a1uHZ2VrL/i128QKi9H5NZ+7A1lcf2tbh5qEe8FVSbMStbkozifZbb3+39Gsm8H3m9witEjHra8l27lVYTFLc0sBzvk1x5jDZzdiekMcPckMXn6vVb2uHBNGGkWMKdGmfbtQwMfnCcXO16r880UrYOozN6L7mKcsAM54Q/dMPBJUY1MoGHD1Rjc/AwDea7Gt4sWCxCxb9KH7yXSIFqJQYbFigfFotbj62wpHWS0tiLb3MxIKaN0C5s+oLxqj+HUx4uPSnYs3421jP8hVN72hSt0HPclM2dWuuXPCz85N52FGgzPv2X9qnSrsN5DgfU1zt3IJjqoSbVmW53+XXnppWdvh+br8YWAGsaViPiD7Ull1wxRTonkQbTYP54XFAKc/51hQor2CUn4DVYKdmw945kpsIp3F6b95Hmdf/xJyOQu78qtWpe43n/yNSk40KdHx4jnRNJjxFfsWIwDbUWIu+FiDgq6m2qirpQ8pEaW0PCrns1KZHJ5427vK42+efhf/9cy7eHj5dgDOZB9w7Et+D2PzOmv2OGfm367N56FR3+RJrL0NqSZU+AuwH2KT8wGyCqINm2k0FMSU/Ht2+az2qiC6NuZrbUyksq7J0NwiSjSfFPelMnh+bQs6+9N4bk2LOv60b9zeWBcL4+wF09TPxVIw+GSYlOhMzlH5h6SwmLJzMyVaVed2T8JpIpLK5pQKzM9dMbii01AVcezkpEQbLa74a9xmTn9H7p3JdTHEIyHccOHh+OhhM1358/wzAXsiSwqxa3KfdCzzFJT1ppygjCYoVdGQdu/MY4WKAKZED6Y6t4+duz4eQSAQUHnRq3Z0qWCDvmM5OdF+du5wMGjkRJMSrd+LHM8WVx5KNM01KICLhYOYWh/H/Bn1sCzg+bW7y9p3wDlflgVtgZZTrgKXyuRw0f++hk/d8qpv4Ke3uNIdh2b1eP75hVxn5jOUF1Iy0x5SbJ5Rqp2b38vtqrCYffxo8c/ldgkG1BjGbblUjFCzczMXQzKTLauwGD/OheailmWpwnrc8cGVuD4tcLS3xRVAwAmqt+SD6MYq/1QNWrztNwIVp75LYVcdH/eVnbscJdosLMZiimIKpOkEHAmSmSyeWu3MRdY0dxXt/qPEvXBIexbMn9Ggain4XRdeC55e799iLDKSq256Q1z7zHLjiC4jpdQr95++33RWpPHAaXWeNTtK5Z2dTh71qCrRQ8XtrziVXnkQPVRK9J7epFJ7iil4hZRotQJNOdGq3+/oBdGZfOVXE/Nh6NUDrkfLpRn9INovNwuwb+LWnhS2d/Rjd0/SUaJLHNz6kiM/IHKcwmK8T7T9mvnApP3jK/ZmH+8dPm26tuzpw5+Wbi17RbBSIAvZzMYqFfgkDSV6qFwTXCX985vbPd9DK640iYmHQ8oWSGOKn1JK1yape152blM5oIJQU+rzQXSNY+em62S/KbUIBmxFsT4expR8oL2lzbYnxSNBxA07N+VY7/JRommBYlJt1FeV4eMhTf4pGPK6p5KZrKsoFR2TLW19jp3bUKIB4EPzp6EuFlafU+xBR5OlaDioVb2l8zY0La48qnMXUKJ5r2iyC8+aUHoQrdm5WU50Ml8o0rRz2/tmf1+ymcfCjkpE+zC5VrdWh1g+Zpip8vGIExQ6rgt3rqZSovPXKm9pQ+etOhrSggZTia4dAiWazoWZc06TvPkzqEJ3p7oOD5pu53tubSt9UTKhBdHcqh3UrqXZ+SC6kBLtaX1NZfLV3J1rl1dQ53932gG2Gr3I6LHan8ri7lc3u9rq0RhiV7EPae/n340oN4hu70uhJ5lBfzrre0xNxxn/uZCdu9B80Ez/0nKiDZv/gKpzU2GxTA6dRourhbMace4h0/Glk/d2/d25eUv30+/sUmJROv//Kj8l2iOV0VxEfGtbB75233JsbevTFp4KzeOauxLqGPLAWy8s5la13UG0/TONJ43V+rVJQXQg4J/uw114hebkNH6Eg84zrBxxzRSTtOrcZfSJLvc+uGvxJnzhziV4clVzwaryJi++24ruRAZT62NoqokinbXwTr5Xtx+qAwVbbAPySnS8cKDpV1iM3q+KJxsxDgkC0xviiISctpLlBrRmHrWXpVu1+mML0AdMq9cK3JULD6LLOD2VG0TzFcahUKLNiSmfLBdSojPZnBasFMuJHm07d08ygw/914v4xM2LXYG0uXrlmRM9gMJiu7oS+MlfVuK9XYVv7HKxLEtvcWXsL7fbbW3rU9dJqfvNHxo9o9HiyrNPtLedWynR7IHW3KlPLvwqPV/111X43kNv4cX3SlMmKo2dbHB2+vI6hcXo58EuEvSlMtpq/OINezyvaTMPLxoOegZMXg9kekBRldZultdO+D2cp+WD6Ia82tHVn1H3xKwJ1bjrsmNwx+eOQiAQwNT8e0mJtguL6RN3CspNR4P6nnmr5KTamKaccWg8DAUDuP7Cw/CDDx+IBXl1rz+ddS0kmMpRX8rJRd3a1qfsW5THxSey5x06HYFAQKu0XQg+GTZzU4HC1bm91EAvPPtEU2stj0A8wooRbe+wz81AlejaeFhTorkltSoSUt8hqZRoJ7in705jKM+b49sA9JzdQCCAhmpqc0WTb7dCRvcnb/dE1zW9v4ZVmo+Gg9qECHACnMGMzY4SrS8C0URybj63s7kzoa7Dg6bb1++Ojv6SxxS1oGYWFgt627l5uznzugx7pBLk8spwkivRRhAdy38OWbpffHe31uLrkTe240d/XonfPL1W+zsuBIRDziIJWbrp+qGiWQMJooltPuo+v5d7khntWnYp0bywWAHLupnn7ZUTTc9dzc7NApBCxNiCGI2DFDxGw0Hc9Okj8Jlj57r+jqrgdycy6hirFld8oTPoLJS096U97Nz6z7e9vBGPvbUTf3lzuza3KTQfWsWqGFMqBqDbWb3ciXUuO7d+bZgLPBRET6yJqfva/D488CnkqqPPqImFMaGGjk8Zdm5jbjXgPtFliEy5nIXrHl+DZ95pwZfvXoZLbn+95L/9xzrb9n/W/GmqKNhb2zoK/g0tFMQjTtpHNBzEvlNqWYziF0R7L6BQTON06tD/nsQOGscHqgqb16uXpTvh4cA7YOrglGgqMFkuFRtE72EHjk+82opU0vbDHPj5iSmUE93cldBWJUw7tyuIHmU79yNvbMeG1l4s29zu+s70UKH5oVcQrT/MSvsO/7d8G+5cvBm/fW7dAPfam1Q2px17c3954Ze3tnWq95bqAujzsS+NFKpPdFVEPVi6fOzcPUqJdl432zr4KdE0uI2FNljtvSlXrs2ODifXhgJBetjvYTlxg7XkU+2FeCSIU/afDMsCLrntddfihDlxi4WDnjm0XtcU3V9T6mNqAmcqQ34PZwp0+Bijxp9oCCfuN0lN0Kbnc4VonDMLHnE7d5ePZYrG3Yk1/ko0HYuaaAgfWTgDXzp5H806a9o3zQlxXyrjrUTnv+sq9mA7Yd9JAJzgqtiEJ2XYMmuMvG6XnZsp0aGSg2gnR58WLVM+La4AJyBN53LYnnc4zSxLic4XsIyFEQoGtJxoHgjEwiEtnxLQg3u6j2gMnVTrDqIpGDSvbXOiQu4YOnx28TZHbaZgvDeZQS7nLIxWRUPqXM6bWOM65nQdlTJZbelK4Nl3drkWjlMZ/5xo/r1be5Iq5WufybWIR4LI5KySi4s5OdHuVnJ0zmuiIRVk+dnl7Z/5ooXzel8qC6/q3AQFfYfNbkRTTRTdiQxWsDxksl9u2qMHslxFp+9gf6eM5rCgY1VuTjRX+rzyzHlhM8AdEJm53Tw1r7WQEp0/VuSy6GbzGbPIHrk07L8r0c4d5n2iqTp3tNCfqO3SAldPIoNszlK5mNyZEQkF1THfw1xHapHDGP/o/HYl9MXgQvOhVUbgQM8s/uzS84DtbZmF+kxl2gyiD5llP5f2n1qrvqP5bODBk/lM1PYx/3f2/WQf72IddjhmYTHNxl5Gde5yWsBuaetDbyqr5t4vvddacptOeibOm1SDQ2dREF04L5q3tqRx4sBpdlsy1WGhhD7RZosrwElJMYVCmiepxf4BBrRm7ORVoZu+36TaKL55+n74xgf3xbSGuPquXaxmRynkcpZq31YulRtEs8GRT7zKKWXPMSfYpSrRZmDS3pdCLmfhrW0dSGacFXd6APFS/yONZVm499XN6mczyKILjwYer4lzd4lK9MrtnaogBZ0T+nmoMNVYcwWRP5DfYJOFnhLbcOg50cUHxCdXNWNDvkrwUKD3iabcrJx3/hMF0ex1t53be8JHn1OJxe5MvvnHN3DejS+rPGDLstT3mtlYxfpE60o0MHg3we4e+3hOrovhN588FHtPqsGOzgS+eNdSdT31p7KuasHRcEhZ+zheig29Vh0Nq1VUM7fI6+E8qTaqJtv84aRyJI3PX5h/2BLxSAhVXCEL2z3tadzyskzxnOhIKACvAtL0IOaTqlg4qAIic1JitqvRalP0pdVnUhD92eNsJee8Q2eo72/2D161oxMX3/a6a/FFTYbz35FPUO1j4m3ntr9rqS2u7G2TUrimuUvZSD2D6JCjXJOVfyB2bpq0xlSNgKw2Ptpqs77gRMcjGg657dyFlOiQdxBNExXKmaQAopflRMfDIScYzlt6iepoSJ1LszI3/46lKJ8/eGQlPn/nUizesEd7PZPzrs5N4y1Vut/dnVTBYX1VRDlFqFprMfzs3GHWJ3rWhGp1XWn3ouFi4ce7KqIvQnj1iSboc0LBAA7Oq+mbWcBM957pPOGLGoCjhvanctpEnwK6cttBdvZzJdr9jDKfdWZAxEWUZCarWa/bCuZE2++jVmo8+Kd7ga6LpFZYrEQ7d8ixWncYSnQhAoGACt57kmmVrgPA1Z+djnlbb0rNUVTbOGNs3Zo/tnYeaYnzOGPO5lXAlP+bnk0uO7cRNJvHYf6MBjx8+fH47acOV9/R7BPNz8+OAkE0V6Kb8mPOYAqLeW3bj4HmRFP/4YNn1KtAutQYgT+HqfNGUSWajUe0SPuBfPeFYoEmv7/p3uRtp+h5ZQa7qrBYPk+5fsBKdHE7Nxcvv3Pm/vi3Dx0AwHk25azyHDPbO/rRl8oiGg6WdA9zKjaIpgsnkc5qk8q2gdq5k7qfn5+Yzv60b44CTeC5reaxt3fin278B3791LvuwmJVeoGokWTZ5nZtNcXMdaQHB5W4L1ZYzO8i7ElmcMHNi3Hh719FOptT33VDa++genqamPtn/swfyG9saVf/zlmlNUvXcqKL7PfK7Z348t3L8J0/rSi63VKwLEsNQg1MiQbsh4mZz0WFefiEw1yh97NzqyC6AovdmVA/YFqsaO9zKpBPbYipoMWszg0MPq+d5+NOrI3hrs8fDQBYub1Lfc4ej2qwfnZur3uB9rE2Flaq8eub2jzfwyF7NgDNjmWOP8QRcyZoP8dYtVTAtgoGAgGlRrd0J7Fye6emXPGc6EAgoNnBTbjSxy3X5hhi5jf2syCaCAac9kfnLZyBv379RPzXJw91fRYdp3tf24IX392NB5fpbc6Shq262jhGpt2aJjdedlo/uCX8d8+vx9nXv4Q/vGzX8/DKbaXP3Nbej0zOQpi1Dyvp88J6FwjnfnCU6GjYPrc8nxKA9nv6HY0nXkE0XS9mkTW+iJPM5JSKNpkFWWQnjEWCLLfZKUIXCOQD7Pw5MXtEA0BtLG8RLeFZSguqWwyVtVCfaMD53q09KaVU1sXCan827i4tiO73CaKjoaC6N2ezCsXae8x+4uy6rGbF1zQlOuLOiebXIhUh5AutdO/t6kpqk+d+ZkWn72B/nrPoEQ4GnIq75SrRzL3nVazNfFabec78OWd+ttd4TNDYqJTohLO47rJze/RTL2bnpnu5oy+FTH7+WIoSDTjBe3cig3TO+WzNzh0KKit6Jmepc0lBNH++JNJZ9fzqTqQ10ahQMGFaWOnveNEvWsDPsaKMbju3fn81ehyHI+ZMwMTamBNEG8IWD552+ggC/O9qYmFn4S6VdfX+9sNUorVtD1OfaDrOC2Y0aB0OSoEExUk1UbU4/l5LT0G13llsC+KCD8zCzZ/5AL595v4AnBglk7O83agehcVoDhkMADPyQTJfBLAsixUW0+3c5c47TbGnkJ3bdJPFI85czE9p94LGxil1Mde1XIyKD6LNQGGgSjQVzKLKtlzltCz/oJcsd1TFs7M/pVo1bNjd43oAqVWeIQhYeE7W1rY+PLdmV8H33/vaFu1nfpPlcpYaZGilpdgN5PewfGtbh91nO5lBZ39aHTvLcioJDwWmUm7uL38gmyvcpSxiFKvOnUg7/R9pRd9P7S2XRDqnVqDr4xGEggE1oexOZFwqfF/Kfo0vHNK/qYiUl507m7PUItRouCPKhVYtaeJFx3tSbcy2qEacoCGXs7QJmnnO23pTuPbv7+ChZdtK+uzdKmi0j+esCdVKqaIFKa++pLY9r3gfTMAZh2piIRy3t93T8FWjpys9nLlSqgXRcceOxW1bnGkNccxocP4mHjZtpvaDhgqzbGrtxb/8fjEuvOVVdd/R2EvHg0/STWXPDFLo9+ZEybRz9ybd/Xkn1saUkh0MBnDIrAaEWWBRY+REU966qXKbilK1oZabajP9XGplbmc79r/puUB54FGP7VCAtKnVDs6mN8ZLto4Dzjmga4DbyU37uhNE28chnXGCaPN68crLpuDPXGygRdiOvrT2vOCTe5VXGg6pHP5dXQkt/zYYDOCU/SejoSqC0w+a6vr82jKUaGo3Zy6yk02XX6/V0ZA6D3Rtp7I5NQmsjYexFwXRrSUG0Smnzy93hYRDQZw1fxo+dfRsfP2D+6rXq4xAm8Ovv3gkxPplMyU6HHLZufl2pufvfb6wSvdHvyFMOAsAQfUdAFsp5Mq3cz7KS5vRc6I9lGif5zzdV609TtBvXguFCovRQg4tlGSYbTzpKixmv9eyLNd95Ae5j8huGvO4r/yoZe6MNAvqzMJi0XDQVYWdFhh5wMdzzXuSGSMn2vu539mfVvNbmhN6KtHprPo/zTnMQMMMqs0FHk4VazfI4ee2UIBI+1gTC6EuHlaLn6Wq0Qnj+vLbBxNTxChnMYkr0Y1KPS8xiGZK9JT6OD4wdwIsC/jRX1b6Oi75vCAeCeHsBdPUeMHrZXipxF527k7lmoyw4snO+7r6nQU3GnsGa+em8czLzt2fdsZbTiDgLPaZqbeFoLiyqSaq2mGWSsUG0Z39GaQyOVchGr9Bsy+VwZfuWooHlmzx/D1dGDQpNuno997uDiOITmcttarELxxVnZtNMAbDaxv24PifP4dr/r4GAPClu5fhsjuW+vZatCxLVeOkKqe8dQ1ffaPVO69VN/5w9VvJWbHVsQB19ae1v1m9s8vrT3x5ZvUurNzubQMv1pKrUB/PUvK5+4qs1n7rj2/i6Gue1YqWDZWaSwNLKOiodrw4h7s6d9ZX5T9gWh0Ae5La3JnQ7Er8AVrOytxokM7mWNs5+350rNz2wBxn9tXO/rRWuIpP6J9Y2YwP/vp5/P7FDbjy0VUl2fvNfFwASiWke8lPifasqOtxTdFrNbEwjtvHDqLf3t6pXVf0cCJLKWAq0fYgz+3cVR6Tt8OZGm3nauZzXJldmZToF97djd6UnUZAE+89xqICn1QWy4krVYluZyoOYVaKNuGtjyzLwru7bNeCueDqKErunGivCTIFs6VW5gagKb5m0SRvO7f9GZvyNuFyiooBzsSCrgH67EzOUuODE0QbPaRJiQ4FtAWao/aa4LL/A9zOrc82uU2PVzqna6A3mdWU6MPzvb2XbmpTE2C6Pi48eg7e/PGZymrI4YFGMWg8NZ+7ZhVmQO9xG4+ElDuNFJ9apkRvKtHO7adEkyX32o8vxGH540Cf67zHPyfarmDuKHdciXYVFvNSolkwwheZuKXbdLPQ5yVYT/V4JMjOR3nPQH5OvJ7Zfj1Zp+fHvHTWUueXxkYKAtp6U74uQl7cjgIteh6S04vcDnSd8HlSsSCaCouRUlaqCg2wVIVERi2mh4MBbSGEahGY9QroZz5/4VXPu42caL/nPl0bE6ojKo/VKyea7nE6drxqOFFfJCeaQ+OwX4sroHBhMVqcromGEQw6fZBLdanSOTYr9gO2IyHtU0yQu24A+5op1mqKoJjh4On1jhJdwv5mWNE6WqT82ccWIBwM4OnVu/DESu82nAm2iGkSCAR8CyBnsjlNwKFz77RjjXhWwKbzNaE64hIUB1pYjMZgLzs33b9mGhuAAVXopriysTrqEgSKUbFBNGBPWE3lwq8K3+sb2/DU6l34zdPvev6eJq9etjXAP+ilSfzek2vVRPntfNCnFfbJP8BoRa9Uq4YfK/I5Dw+/sQ2bWntV+fU1PkHqnt4UOvrSCASAU/MtLnZ18iDaudmpumpxO7f3d+CWz66EXl34nTKC6K1tffjCXUvx5buXef7eXKHmN3dnf7pgUFhKwMiVaDNA7Uqk8fQ7u5DK5PDWtk6WXuAeOLM5C5/+w6v4+n3Li34m3z5gP3wooKllBaPcfaIzvlajvSfVIBiwJ9LHXvssPnbTK+p3fACrdCWa7x/djyrPJm8RijH7qvnQpMl2TzKDbz/wptpGTzJTUj64VxBNPS1p0umlREdZoRhObyqD/3r6XXz7gTdVsK/s3NEwZjRWYe7EauQsYMlGx9JN38OrtybgTFBSmZxabPCqnn34nEb17zhTSbhqTosEL7zrVG4nuyctGNDDm0/8zXxMc/VWKdHGfUXHmL6DWbcB8B+jnW07dtPd3Ul1jbuVaH0iwa2SXlZNx85dujLMt2+qbF558hQwbWylytzVrvcU/CxjgsLPCU0+aCJOQRWNGymmRK9kVXl/ct58zxxwpzq3f2Exev7ZtmPnnDtKdBBHz2sCALy2sU0FHLWG/d8LnhNdaBEswRRT06mW9gqijWvXvN5qY2G1EL2B2bkXrW3B1+5b7p2jl7/OvQqLeWG2weJoOdGsgjlXomPhkLvFVcjZ5oz8eEnjZzZnoY0tAPJUL7NNJ+1bHwuiY+EQK+hXphLNzklXIuOaVPs912zVy/5Mev7S2Eh5njnLf67F80LNwp1qccWwc+tBdGFVOaKCaPsYl5NLqezcyQxriae3QyNHgjuIzufx9yTx0Rtfxn/8+W3NldeTyGhFx/xcefx5Z3Y84AsbqYzd+YK2UxsLu+7ZmmhYU3YLHQvldDDOO9/PQkVQ+UI0/6xSi4vR2NTgs49+RWa9agiVci/s6UmiuSuBQAA4cHp9WTECBdCBgLNIc+C0enz11H0AwDfeSRhxiYmf1dr8PqmM3aqT5k8NTInmc6pmY56mfYbxPde1dOOmRet8C6vR+/edUgvA/Vzn389UooGBBe8UVzZVR1xpX8Wo6CB6d3dSDZ60UuZXnZtWEnZ1JT0POlkvvKqQAvYFbVdJ1B/WzfmHzfSGuKvFQ1d/GgnjAdTICh2UUx3OtT99zsr6Dc++p173yikCgPfyasysCVXYa5I9MeMPSnr4hoMBdZF5DwoZ7W+8VuVWsKIGXczODZRXJp6scnZSv3ugN9VYftOR6mMqYkQpVhs+iJOq9fJ7rehNZvDq+j0q8NnVldBULjMg29rWh3+s24O/vbXT85h6wXtEEzRp5HZux7rqLjZGNFRHcXDeKQHYbgAK9Pm+eq3MZXMW1jR3ldW3cLjgDxWaeNEiFrVNUC2FMlmXK4Xuy2dW70J/Oot5k2rUPbuzy72y/cNH3sZX71mm7lPvIFrvpUxq1UTWuidmTH74/ty0aB0eeWO7KgTSY0wAyNK9mFm66drdiwXR05gSXRN1LGyFVpy5Es2rc3Oli4Jzfg/v6kqgq99RSCgvj6sPpn3PXL0ltbgnmUF7rzMW0n1ErYW88p2KBdG8sBip0Pa29euB94nm+wR4Ty6cwmLlPRYdd4Q+Vnpth/ZlY6u93+VU5gaciRS1J+PnhMYUCrSpHystbDrVuUM442B7ofXwOY0qN9/k40fMwqGzGnDSvpO11/kkkMbQ6mhYLaT0MCU6HgnhqL3sIHrtrm7lFDs2f90Xgq6prFG92YRP0sxF9kzWXZ3btEGbc4I6Zufe0dmvnju/f2E9HntrJ2545j2Y0IStoSqit7jyDaKd103bP1/kqooE9cUJpkSb7g/uhjGV6D29Sa3TBV+8MjuMVHvauZ39KLfei9kW1HRsUNBiPsuroyGVfrc73zmBxsbG6qi6Dv3aXPGxsU5ZUO19MXOiaayj4xsIePd551B1bhrDygqiyXWWcILocCigWfnp2plouCfp55fXtWLFtk7c99oWTbyw7dzebjQOL6Rp1plwta1LZ33bWwF22g0/f41V/qo8dxJxtJzozoTv/Nl5htrXaanFxdp6U+hKpNU5NscBwq8+Du+nTvP9UuaZ1NN5r4k1qI2FVTpMKXZjWshuqo5qaT+fOnoOAHsObbaRBKCNv16oDh9GoOlVnDWRzjI7d5jVfXIr0dNZClkDc8xxfvHEWvzyybV49M0dnvtG19k++SDaa45gjlkcv+C9EDRGTXg/2bkBe8JFk679ptoHtN0nOOUTarPiIODceH5WwW3tfTjpuudcqii1v2qqibrsOl0Jp4AKrYjQpD2dtUoqbuUHf/A88sZ29e8tbd42l3X5Qkz7Tq51WVAB5+HAJ9OefaIN65w5SOzqSmirhHZOtLOva5q7PW9qL7jVzCvXmCYvTsDAg2hyCNRoAQ1RUk60UUTt6dW78JlbX8M37n8DL+d78wH2caSHOOBflRBwW0r94PYYoo49WOncNFGeYdJfia6Ph3HbpUfh/i8eq44VDdDFlOjfPb8OZ1//kqso02jA95UCaqf3INm5HYuqqQrT+Xx0hT04n3foDLUyurMzgb5UBs+t2ZVXrrK497UteHxls7qWzBxgwAlWdikl2n4PqWuAPaH1mnBtzxePAhz3Bi8sBkBZuv/61g585g+v4b7Xtqj3TKiOqACW27mDwYBrIuO1IrtgZr1SJfl9zyfbU+rd4+GurgRa8w/vunhY/R0P1M0Jb63Rh5cmZX94aSMO/+nT+NtbOwE4CxUU4HmpesWVaCdH9F3Wx9vMBTRzonmvYG+bm/3/cnKUAf+JildONKncNMEvpzI3AFx0zBz89KPz8cWT5gGwrwU6x12GEk3B65LNdtFFXljsmx/cD9d87BDc/8VjfT/r3IXT8Zevn4g5E3W1nE9S+pgCW00OgaSuRE+ui2HvyTWwLODvb9v2w/MOnVH0u1ZHQuqcdBewEGuLb/lxj+YIXn2iTRs0VW8mamJhTKyJoi4ehmU51fPp2n1w2TbXdduhBdF6dW4vqgqo1VHWl7uaK9FJFtSG7bzuap8UBZrM7ulNaUWnCE2JNlLSKMhJpPTFkNq4HmiVSqeRKrfVmMPQNdRkPMero2E1X6PjrQpbxcLq/Xt8UvycOU/QVcxJtbhSOdH2z0s22vfKjIaqohX6Sfnf3VW+nVvLic5SDQV9MZaeKWYQrfL48/ucs4DH8uMrYM8t9CC6iBJdG1OBA9nATTW2P5VV8zy/wkt8LlPIzl3tYee2LEube6YyuYKpm4DjflJtrgoEpW29KXzov17Aeb99Wc0j/YJov7S5PiaYcddgMcVz9U47HqGK+Upo80kh5dAcx7w3ptbHEQ4GkMlZ2NWVwLb2Ptz8wno1r+SLX174WZ7p3ubnry+V1Yrg1rGaLASJeHzhn5R+8/jQfPmdZm/BjT6LlOi23pSraBwfB/2+W1lKNOVEV0fVc6xUKjqItpVo+8vtP9XO+8zmLE+rLl+BXuWRY6uCaJ8J2qI1u7GjM4Hn1rRo6isNuo1VUddKY08yo4JMXtmSJqnlNIA38VtV88sDXt+SD6Kn1KpVaG7nTrBJTVUZQbQ5AK8wcrK7EroS3ZfKYnOJeWQ8+NzqUXCE9pluaL6/dBxmT6jW1BwKqEvKidbaODgT8ufWtKhADLALG+hKtL7tZqZy+j3QTbg9hqiLOSt8lPNBRUR4TrRpN62NhTGlLo7j9pmoVZEH9EGy08PS/E6+ENyKIn0H/dja1ofL711WdqP6TDaHb/3xDdyWr2QM6CuzdP1TlU7Hzl1AiU5k0NGXwot5a/I/HTpdBd87OxL43fPrcdkdS3HPq5u1SSWd20J2brOw2CGzGtS+REMhT8WJt5d5UwXRVFhMV6J3dSXx8rpW/Ncz76oaA5Q3XRcPu9RC05LqFcjFwiEclrd0T6yNYt6kGjRURVSbDMApLMZp7kq48qHtz+BKdLHCYvb+UOoLnRM6xkqJ7nLbe4vnRFNQkcF7LXohw1a22JVy2bn1wmImFDyXU1gM8H6QA97Vuc3rxKsqdSEaqiL47HF7YSI7RmTbpnGJPvfIvWwnwvLN7ciywpLRcBBT6uO46Jg5JRdCMvcByNu5U5SfGNIm4mbBu2PmOcrzpNpYSUp0MBhArY9qxek0HCy9yQxO//UL+PeH3lILB/z6NK/dycY1HgnZRefmGZZuGttTmRxuZeMW4DjHGqoi2vVgFg1zPse/OreuRDvH1VSiAT0Q4NvhiniLhzuPK9HKTRd127mT7Dw6ueOlLRQTKqcz/2w2lWh6rk9wBdEhVRdiff4cdLNFSNqeV4oNoBdXIkWb0gPNFld0bzzyhl2E8qOHFV/kISWa9qkcJZqnKuh2bmfsoWKKNAcA7DHKK0Dlc+KeZEabx/mlQ2h2brYABrgDyV62TT/3H7+vCh2Lag87d28qq5wSdJ35Wbp7jGcoCVeF5tv3vLoZrT0pbN7Tp7brtxjgVziPpwfQPt743DocetVTeGLlTs+/AYA1eSX6oOl2DKOcPCUo0U57K/3eCAUDyp23rb0f1z/zHn7++Bo8tNS+fp2cYT8l2lsN5+eY4oREOqveVx93Uix6khlV+JicEAdNdxyRfoXFSIhY19IDL+g5NqepWo2ffLziBd7iUff4Sgp4OUo0zSXfd0r07u6kKkQzs7FKWVu9KnRzmzfP9yL8cqIpHlm62c5JzOScvrTJjNOSo6E64mlRoYGILrhAIKBVLy2VXM7CJbe9jo/e9A+ksznX39IEjwfRz63ZhSN++jQeWLJFXZD7Talz8ji7k8qmyx8oVfkLjx6eW/b04eq/rca29j4VuNLFayoAK4z+dO29KXWMSF0qtbgYDz63F6jaSSu83NJH6uGspiqtOA/1Gy23Onc6a2E7q27Nj/+uroQ2cTAXcTQl2sN64gW3xxD8wUoKAE0UelMZdTxM+2EtexhMUKuyKe1z7P12X4+0v17Hv7Mvjfte2+LqK8q5+YX1+Pvbzbhp0ToA9mDK82v9eHNrB/785g7cmP87QF+ZNatzu5XorJbjB9hWpMdXNiOTs3DQ9HrsO6WO2Rr7VUC3rqVHswi19tjuFhpr+KSagkxSQlrzY8+UujguPX4vfGDuBOw3tdYz8OJFiWiRwrSiTamP48cfORgfO3wmAD2FpTYWxo2fOhxLfniGa9wyJ1JeBTYA4JefWIhfX3Aojt9nEhqqInj1itPxh4uPVL+fUu8Oonn6gmZbZw/konZu4+dNe3qxpyeJ3lQWgQAwb5K9ykyT2b0n16gxp2QlOpnR7NyAXrgsaVTZ9VPtCGXnLqPFFeC/2h/0ULJ4/uthsxtxpEdBrXKhe0JVNc1/xoHT6lEXC6MnmcGa5i4VUBazqRaDT476WACm2471Y38Mc26ce8i0ktX+WlaAyQ8+Vrf3pfD29k5saO3F31fuVAFKNBR0irIVsHNToSkAWnEx85l876tbtECFJmyN1VGtwJffgkwhyze/RrjCbyrRgD4O8Gs6EAgwF05/wSDarUQ7i+xc0aLCaMs2t6uqziaJdBY3v7Beq2pOC6KH5IvXmbUD+tRzXj8v1dGQciC+m1/sJTW0Nh5WwaWf+4vvO+URkyijlGjKic7msKcniefX2s+ujx8x03ObnJhx3rzaOvnhLJhnnPsyHNCV6CDlRDvbrWbV2v2wLD2PNOvTyogH0WoBLH9NmxXS+1KF7dyAoUQXDKLdnRvo/g4HA5iXn8P5dULpM4oT0uKLX+eeRDqLuxZvUj+Ts8R0pKjt+zgteD91um6eXG07a+54ZZPn3wBOh585+QWhRsMVUQhaIJrosbBMLqZt7X2qKw7dW4VyhgGuRLsXS4B8EM3GAeWcZEo04CzQUBB9YL7ILcAdS7rjgOZQfkE0XWcNVRHMbrK/IxckeNqU1yJwvYdSXgyaL094vynRrT0pNcmfVBdTN4tXFb52Hzt3bzKDlq6EWi00AxB60PAHJJ0wWn0JBuxBb0KN+6ajyTg/mRPKLGEP2EVLXnh3N1Zs7cDmPX3qpNKD/4yD7By2PfmV9p2d/fj2AyvQ1pvC71/coC7IfabUYnJtDIF8oSlaPeeTGlOJvvXlDfjDyxtx8wvr1f6QzdOcvFBlbprI8sDz0PxDdkuBqtkcbuf2bH2R3z9auUtksmpFlVazZ02o1oJomviUEkSbliVTQeerYFzhcinRA7FzUxDNBiTeO5ICZqdtjLOgY9oP+aBmrnLyIDqVybmKOdD1YaoDj7+9E6f/5gX84JG3cc3f3/H9Hv/I297f3NqBXM7Cxbe9js/d/rpa7Ono866eStdIe19K2f9NJTqbs7Arf3+ZOdHJTM6l+vckMnj2HbsN3EcWTgcAzc5NilJzVwK7u/Vz1p3MqImVd060bueeWBPFFR8+CP/31eMRj3gr0bxn7cbWXnT0pdTCDV/Nv+zEefjNJw9Vr5E1qi5fdK7QgwKwg6Kwj+I1d2IN/vkDs1TQUhW1WwsR3M5NL+/qSqrvycdLPkk3q7G67d36zxtb+9QYNbOxyrWy3lAVUQtgvJ+uF9Us95bcIzQp5ZPHJFNe+d/Z38W7ailQvhLNi5TRdwC83TXvMfv5jz5ycFHLaEmfHyYlWq/OHQoGcEQ+SF+6qb3k1j3F4GNMv5YT7eQJ0rGna5enP5Ri5SacAkwF7NxsPtDRn1YLgt0JJwWG98YuVFiMq1Okgm7c3auUilAwgCl1MXthgi0WK8dadQSxsG0fjoaCvgFHwerc7P6sjhpKNBX6iri/i6loUx2FnZ0JtbhE90kpOdH9KdZCLxzCXpNqcPw+E5GzgAde3+L5vR5cuhU/f3wNfvXkWgD2pJmeRYfMpCDaGRf72XOtLh7RXFZV0bDqPPFu3nFC85HaWFg9B69+bDW+dt9yl82c7zstjNLzmQJXPo49vHw7MjkLC2c1YN8pdSiGed64CleMWlX/JK3y9iNBvVUijel8DK5ifcO9oOHEXDTxmg+pRWOmRFO/ZVUPIz+X7U8Xt3PT66FgwLPyNf8OgF68jG+bUhEWb9ijjZeEmRKlCov5zLf//MZ2rV4GD9I4XGH1gtu56Z4kgf+1jW2eRTIB516bmh9naLGlnJzoSR4pi04Q3Y8N+XTO3awALlAgJ9pHrVXHNh5m/eKzWs0Hu0ViXmRLZLCzM4GuRAbhYEAtetF7AX0O2sPmWTs7E658fd6StS4eVovtVEPE/m7OHNarKwn/3Ptf34LvPbSiaA9xJyc68v5Top08xajKC/BacTJ7EXb0pbBoTQtO+eUiHP/z59RBnGSoHF45aZspAGAXDi+lz6GcR34yG0uwl5jc8uIG9W9ecfabp++LBTPr8a0z9lfb3bynD99+4E31ng27e9Gcv1H3nVKLcCioBl66gVWVTY+caApoVuUtudFQUClQ5uBLk9aj8lZBWmWLhYOYk181KtTjj8MVXK+VbdpnWpSwLGdiTHlVsydUqQDLziF1F0rywywgQYsndC7PWjBNfRZfyS2UE12ynZut7BE814QGwSZl52aBdY1+DfOAZYJRZMMcJM19p2Bpe0e/WqD4v2Xb8NV7l6t77zVWOZqzrb0Pm/LHbHtHP154bzd2d9sFbNbv7sHK7Z044qdP40d/Wen6W7rmLMvZV74y29GXxq6uBLI5C+FggLVZcixGNLEl50VvMqMWdaglHU0kN7f1qcmbWXywtdv5uS4W1h48tO3d3UlkcxZbGdbHAj6hooC123gYv7m1w1VZlAgEAmoscixV/qv5ZpuegVLHbFtH5AuRNXcmVPXoGWyBin9OnPWaBLwKi+n71NqTVC6WfafUotrY59pYGL/+5KH4xT8vxKEe7ZY4NInc2NqD7kQGoWAAH8iPR62aEu2fE+1dWMz+v9+ChB/8uEyrj6sJ5OGspRFBx3hOU7VnW6eBQJ9PEyi+WEDj9Oub2rTCYoOB59PRtWorpvbn8uceHfsZjVX46qn74JLj5qpjUAp0XRVSovkkzbLsAmYEPQciIacNXWEl2jl2tCCysbVXjQ9NNVGVWrYhr7bmcpYawxqqIggEArjrsqNx52VH+1pfNTu3q0+0bueu5jZ5Y3GCfxdzYcjpFZ1Q+08FKPXq3HrPVa/q3PTaRcfYBY0eWLpVWTk5Sza15z8zv5CRzKg5EgXR9Oy+adE6LLjySTyfb81ZY1Q2r46GcED+WG9q7UUinVXXW308jE8fMxfzZ9QjnbXw2Fs78Ux+AZVw2qyF1Dlu7Ukik82phVs+xlJdEHIFFYM7BmpjYZzp0evcDz0n2rFz83x4p7CYHkTz+3tmY5VyADbVRJ3ODca58Sou5uREx1XaRB9zvNmfnXfCsdRF35zo/GfzjiNeOIs0LG+bBW/UseD2f2zCmf/1omrdSlBqB403xQqL3fPaZgDO+K721xwH6tz9tzn9zHVTaxwDu96D29JtWZa618gV16AKM5aeE+2tRNvHadnmdpXLTq5Bmq/6LZg6aq1p53as8lXsPNECLS008ArYpELvM7lWG4P4Z9D9ZqZemGo0X8Cwg2j7O9J8BHAWCELBgKd4wYPoa//+Dv60dBtefHc3LMvC39/eqYkbBO8T/T6szm1/ucm1MRUgeBUcMAPWy+9djs/dsQStPXofUrNPNF2InM35hyOtoFLwbNqNOFXMm1+ohP0TK3fidSMoWbG1QwtUWnuS6vv806Ez8bdvnISDptdjdn5f731tM17d0IbqaEibcE6ui6kLyMnl1IPoeCSo3RyAEwSSJcRuYq9XIqdtkPJOD8Tt+cCkLh5Rqt8Opk53JdL4wSNvq5xIy7LUvjR3cSXafWEnlBLtnLNkOgfLstT7ZzdVq4fItIYqZokuvMpnWe7Cb1Rh8JcXLMR/nHsQfvSRgwC4H0hmdW6/XpyFaPUoGOFl556kKdHufBVAX01vMFZlzXwUvu9p1oMwkbaV3WWb23HFw28DsCdMwYB9fez06Nv4yro92s83P+84GXZ0JPD6xjbkLOCl91rNP9WKy9D9zPc1k7PUgs3U+rgKTFV17rRTeITOf08yoxRmUpdoIvnm1g6Vc7WrK6Gdpz29Kc98aMB+eAUDdvGW1p6k+kzzocYtsl6F7gDbxVEor8xc0PObgAP6KvpgguhAIKDGitPzk8CW7oQqhnLgdEeR4Q9kruzZ++oOik2eeceeDO07udZlSayNhTF/RgM+edTsouosrRRTnuTcidUq2OfnVSmvRsEk+7u4j5nqE112iyvnOEyqjeHZ/3cK7rzsaJx6wGTXe795+n74/jkH4m/fPLGszygE3RM0Fk9mTpUjqbjYxjatsNhgoGvPshwnVnUkpJRo/nzm1+a/n30grvroAs0JUYy6IuoQ4B7jvOozhIMBJ4h2FRbzDqJpbrC9o1+ryk/BNTlbelIZNbbQsTl0dqMqGuiFVlgsrB8PPpZw5bGXqTh0zfnZuQFoqSx0X1BthZZup/pxopCd2+i1/qGDp2FSbRS7upJ4do0e3ADA8i12EE3HqyOfZhePBNVxo+fsy++1Ipuz1PPBbA9WHQ2pOQ0tzColOh7GQdPr8dg3T8KFR80G4FRBJjQ7dx3ZuZPa85wHQ5QacvqBpQXDfPHj3EOm+1pnveB9ormdmyvRXoXF7EUV53MOnlGPY/e27/HZE6pc4y6NaV7WVj0n2qk9QApxJOTkX/ensmobxZToYrZ2x1nhLn5WF4vgwqNn48R9J6nn4fXPvKfldDturpD2eV6iVTKTVTnJx+8zSfudy5GSf6b7Vef2yokGnPP0V1ZHR32vpFMklgr+kp27lJZcrT6L9oAzX3h1gzMPc5ToItW5ffpEOyp/iDlWMy7Rhy+kKis3mysAts0+ELCfE/RMMOspvGcE0ST6xCNBxMKhgkq0Xwob7duG3T3qmn1t4x48tXoXLr93Of79/97S3p/LWazF1fusT/Q7zV3o7E8jGACmshZTXjcL5URTf8dX8i1jPnfCXvjuWQcgEABmNMQRC4e0SQR57jmbmRUVcB5UPCfalZPoZec2gv1t7X34yj3L8YU7lyiLq2VZWgsr+3396gHQyCzktK8PLbOLB/zzEbNwyfF7qd/vO9mxUkwzWvNwOzd/SAKOCkyDWm08zKzFzo3O20qR5ZIC5npmw+G5zr97fj3ue22L6md3zd/fwcIrn8IbW9o1u7tnTnSabF5h9TBIZLJo70urlbeZjVU4af9J+Myxc/Dds/bX2kQVIplxVqMpkKVx+sBp9fjCSXtjSl3cc1XKr9ogULoSTYE3b13ELV60wNHEcqJ7PfIP+d8BHkq0cRz4yqPp6Ni8pw/fuG85Utkczpo/FVd/dAEOnGYrF8s3d7i+A1Uwp3PDF4J2dPQri/6Wtj5XoRKe20/HzLQ3vZ3PI+b3KOUCJjNZtapJ12Jnf1ptix5Y0/PBFbfztPWmtPSB3T2OEm06VULBgAqs17f0qAmPGSjzFVEzEKcgddmWdqd1mWcQrS/oFQqi+QTALye3VD5/4jycuO8k/Et+MprOWli+pQOAU1EUgJbrGQsHtXHUr8UVX+hbusm+PvadUuvKOzJX9gth/u3J+0122TWBIjnRHsdsoHZuPvZPqo1hSl0cp+w/2XMxYEZjFb5yyj6+lWEHAi0I0DjOnSqHzW5EJBRAS95JAQw+iI6FQ+qao8W1aqaQ0YQpGCi/57YJz3/3wxVEe9Tk0OzcxrHn9yu/DmmS2tyVUEU6J9fFVMoQTexo3IpHgiUvaBVSovlY4nVc+d/zBQHzvNLYpynR+fs5nbXUAqpvTrSHEh0NB/GJD9jjxL2v6Zbulu6EGlfbeshd5OQa0iJ7dyKD7kRaBdP8u/KxrCoaQiAQUGr0u7u6WWEx53vTwsA7xnnn+67yp7v1Sr/xcFC7RqOhYMlt53gXklJyqDm0/z3JDNIZnz7RlBPN7ucqZu8HbMfVOYfYqUtH7dXkSh+gcfH1jW045ZeLlFqayjgL6HZOtLNQQ0pvVSSEKhbwFnNI0dyrUGVu+g60TZoH81z3/afW4Z4vHINHLj8BsXAQK7Z2aC0glZsrqhcW80qf3LC7F5mchbp4GIfONopz+ijRfmNNn48SffFxeyEQAJZv6XCJQaQM18cdZVfZuUvIiaa6L6b7EIBKY+Q5wnSfm44VExJeaB/WNnfj0ttfx+v5Z3RNlCvROVbDxz5mfJ79jiqcpqczREJBtd+qpkwRJbpL2frtz3HGWifVkueme0HX3yamOL+2sQ1PrrLz11fu6NQWZboSabUI2lj9PlOiKRA6fp9JqI9HnJxoYwXHzruxT86ZB9uriJPrYrjrsqPxk/Pm42un7YunvnUyHvzq8QD0VeDZHko0yf08z4n/PxBw7KIEv1idEvb6flLBs65ERk147n99K55b04JIKIAT97VXyqjirJlbQsEC3TQfmj8VZxw8VT2EqSQ84G7Nwx8oys6db1tgBpw10bAT0LEBRVmom6pdFbPr4mG18r0zH1h39qdx92LbSkOTrZfX7UEqm8Pdr+oWm5bupNrH217eiEtue90p2hYNqVWn/lRWBWBT6mKIR0KIhUO4+vxD8MEDp5akXAD6KqhZDdgrJ5bDA9FUJqdN3EstLEbtx3hOaj0bmOgc8wCfgt5qIy9Kt3P750QDur17t5G//cLaFuzoTKA6GsJvPnkYgsGAspySwkDkcpbKh/4njxzHHR39Wk/z94wCUDxvvt1DiQaAt/KFwOawHFlSFbkSPbfJHmg37+mDZdn3DR23aR7nD3BSFwD7nNF94lXUiq4BmpzXGpZvoHAQfUZe4X19ozMR8MprcynRBQJLPgHwygsqh88etxfu+cIxaKqJqsWBVCaHUDCgjSlcvY2Fg9rPZhC9V7769ukHTcV++UkwPaj2nVLrelAVWjAw4e+NhoP4yin7sH6yLIhmfT2BUgqL2f8vu0902P/cjwSqCnO3u5JrPBJSriFisIXFAGdBmRYQq6LOwist3MUjoUHnfDs50e7xnJ4X5uTZy6kWCQXV/WTWNuELYvx5O7k2hkgogGzOUve+rUTb9wQp0TRuFeqNa1KoxVXEsHPTWOFlk+cuJPOanl5Pi9pOTvSMxir1fWnMowXbuKed291K5lNH20H0S+/t1hZD+UJrd9KuJE6BWkNVBDWxsJo3bO/od1Vfro7q4yqlfOw/LV9cbJeuRBNU9XhNcxeSmSy+99AK3PfaFtVmLW7YuSmIDgZs6zw/3nMmVpdc9I7SRQCnnVyp1LJnPTklI0Hv6tz1VWEVUFdHQ1rwMH9GA047YAqe/3+n4v+ddYArF3lqfk52+z82YvOePlVVnhTBcNAuhKv6RDM7d03MqXPQl8qUkBOtz5P94OMwOe7ovPLreXJdTLkMeAFSs8OFKizmIa6Rm+2AqXVqYZ3gC1ChYEDNnfw6AfDFJv4MOnvBNNXx4k2je01zp32c+TySlGhu5fdDOWC8lGiP2iHdiYxq4Qn4L7A3MCUZsN2tz6/drVql1TC3o1ZYLB7R/t/Vn1atqrxqAqgix0Z3E8LMee82rgMKore296tjpVJTi1Qe56zc3oln80647kRGC+ZpjKqNhRENB8uaiwAVHkQT5x1qr7RR7oOpoPG8m699cF/8/rMfwFPfOhkn7+/Y6fabWqdWbvhNzCeuC2baF8Hmtl5YlqVWmOmipwnSzMYq12SplJxosksDdqC8rqUH//m3VQCA7511oLJ/0epMYz6/iuABf10sjGPmTUR9PILTDrS/50Jm7Z5mBtEZ52HIbw6vNgJ18bCnortFtZWqclni6uIRzMivNFNvynte3ayC2d3ddi5Scz6Yfnq1nb80d2KN2p+dnQn8+Y3t+M+/rcYL7+7G4yvtlSN7RTQfPGWyarXbqwARrZIWq8xHq43xSFB7KERDQW0gn+IxKeaBqFlMolD7j11dTiGFXR5KNO8TzfPB6RKgYJ2vRgcC0FamTWuTK4hmx8Uc0KiX78JZDeoBdcTcRgB23g3n3ZZu7OlNoSoSwudO2Mv1Xbd19GsTLN7PN5HOalZ+Zf0zg+h8Dq0WRFOLK5YTPWeifd2pIKImqhXS8nqo89ZIrT1JlQ8/1+Oaoocv2UTNno2AHpiYizKnHjAFdfGwmpBGQgHPB4CpRBcqIFM/RHZuE/6w33tSjbZtrt5Gw0HtZ/PBc9w+E/G3b5yIaz9+iKuNk50TXbgQWSG4Lfuio+dgWkO8JCWaB/pex5+qaZernupKdOmB1FBBAQ4trpvFM80J/mCVaMCZhJGjpiZvveUMtoAZ4F+de21zNxZe9RSu+fs7JSk6kVAA3z/7IHz11H1wtHE84pGQegbw4CwYdCpcUz7/pNqYcrtt3tOHbM7S2luVSty4lzihYECN+VVRFnjmn3s8F7CQEj3NIyd6cl3MtcDu1ye632cyPndiDU7abxIsC7ifFRh7w1hobetNKXGDHFLkVnt7W6er2E9VRFeiaT8oB/3d5m6tTzRxwDQnz/tPS7biT0u34eePv6NcQ/GwY+du60tpxebM47bXRPf478dnjp2LMw+eijs+d1RZKQqAT060UZ2bxJFAIKCCqKqIPdGfUB1BKBhQc7698mM1v35DwYAqSEWOxBX5uhzKeVUbQzAYUM8anjZWxQJ2vTq391i9YIa9L+ainQmfK9O58Kv8/aVT9kE4GMAr6/dgfb54FtmtaTzn6ZPcHQAAa/Jz7gOm1bnmcvx+rWbuPj8lmtu56b2RUACHzGzA/lP0hTVCFRVjz1V+zxYbu1ROtMecY2pdzPNZtbs7WbzFlRFEm8WAecpgfyqjYqEGw87d0p3EprxKfBCrzK320acwK42h63Z727npOphaH0NVJIRszlLzyVKVaE7O0o/1eva5Tnsr+++KVb83qdggmoo2REIBnD3fDqJJbWjp1gMXrtDVxyM4a/40V89BDr+J+WrOqftPQSgYQCKdQ0t3UlmRKDA5dFYjvnXGfrj6/AWu1Q7dzu1tL1m7y1G/3t3Vgzte2YhEOocT952Ez584T03AVBBtTP55MHHagVPU4P/zjy/EDRceho8fMUv93syJ5lU9VUCaznq2EaiJhdVDik9eVG9mpkQTdfGwqkwK2NZv3kszZ9mrz7TqQ4PmtPq4sk89vnInvsfyFeg9VXm12d7nnFI4vYrCOblGhQcnGrxromFtcj25LqYtXPDBjx7wPBClYNAJdL3t3G29KZz6y+dxwc2L0Z1w7Oja4Bp3Fl9ooKiOhtSKPG27OhJWD73aaFh7gJvVubvzAwcFfnwBwKwkToVyDpvtFP6hIkCrdnRqVRHJOnfIrAbMn9GgFqZoAr+9vV+zTPMg2qzEbuZE04OBHvx8sYTusz29KTVJmmMEvlzdB7zVaP6sbe1JqRQOqsjLoXtJqVEegVK4gBI9rT6Ok/dzFvT8cm749Rw1lF6TocqJNpnKjt2BxsqyrkSHNBuqV6G0BTMbUBsLa8d0Um0MjdVR1wOwHDs3X6S4/NR97Nfyk2RS3CzLUtcH7Tcf9z37RCs7d7mFxVhO9Cgo0aY13bw+jzSD6DK/nxd0/ZHDqCoawrSGuBaEDMV1WceCDc4/1rUilcnhmXd2qcU3v8k9YKu7J+43Cf9+9oGe55fuWXMxhxbeybkysTaGGY1ViIaDSGVz2N7e71SuLaNPcKHq3Py1qkhIjW/07NSq5PMg2tgOBay7u5PqWTq5Lqbu8TfyKRsJ9qzh/+9PZZXaap7Li462C4z9aek2PL+2BVv29LncSnt6UmpuRhNUql1gLsrS5/JJP40RFESv3eUE0Xy8qI2FVd/5m1+wC7TyZ3Q8EkJTtZOfSc9sGhd4ED3XY/z3o7E6iv+9+EicesCUkv+G4I65JLNzhzUl2vk3PVfpmNx66VG4/dKjXE45fg9UR0OueyKTs7Bsc7urBojT8cCpaG+nEuRt3szO7XefnbjfJCz54Rn4zpn7F/zuwWDAqfycV327fWqFzGysUuLSojUtsCyL2bntbUxg59ac07zLg2hjXmAeK97n/o0t7a4xhy8ukNBy8IwGxCMh5U5Zv7sH2ZyFax9/B8+s3qUch/w8hYIB9feFOvjwQnpehcXCoSCmN7rnNi3dCbVg79viSinJ9va3GkF0TSysnXs6P5RGRvu/dJNd92ZiTdTHxUdxSF6Jzo8Hx+Tz+Le192upfkqJriLXb8Bl6U6yWgee383Idfdy6vHFDlVULB/nFRIvvKjYIJpW0E7Zf4p6OJESa66aqJWEEvv00QOhJhrSVnjmz6hX/Wg37+lzrTAHgwF864z9ceoBU1ytJbgFqKHKu1rgGq5E7+rB0nwly88cOxdBlntJg5hZoIEHEx+a7xS/mFATxUcPm6ntAz0Mlm/pQCKddVqOhEPqYmnrTblWzoB8EJ2/yXhONAWvc5qqPZRouyIjPSSfXt2Ctt4UmtjNRb1yOdMb4ip4+OWTa5HK5FwDqZ1r5ti5VVExDys+bxNVCFrNrI7p1hxTSeKrlxQM8ECUlHy60dv7Up5VS9/a1oH+dBZrmrvVMa+L6QE8LSa0dCeZUu5UvaUHH181NR9ofn2i6RhzKzqtcpqOy8NYVeE5TdWYVBtFOmthFWsdR8rt3pNqEAoGcGjezkS5YTs6+7WH0Fpm597a7n3/0v1mLo7M0YJo+zqgFedJtVGX8mYqwbzC9FwPpaGzP411+SB/jsfv6QFIVX+98pOiBYLoSXVRnMKKTPm1UDCdJoXQc6KHLogm9QrQez7an1NAiS6wervXJOd77TulRv09V+/LUaIbqiN44EvH4m/fOFGpapNr7f+3dts9v9NZSymzUQ8l2uuY0X1Qrt2Zb8u89kYCU20w7wfeizoaCg5JWy16JlMrHJqEn7CvU7xnKJRov+rc1IN9y54+pW6YjgdOMYs+HTNzMYfGZNXdo9Z2udBiwYbWHmexvSwlmgfRHv3EmX135oQqbYzmf0uT4VDQ3ebOriTupGNEw7bL6vh8UHLDs+/hv55+19XiStm50xmtOBfnjIOnYnJdDK09SVx6+xKc/MtFWJoPjOle3tObUoE/zWVofrXUK4g20mRoMktB9Lb2frVoYz73aKzy6vIRj4QQDgXVvIeEA6VEs+O2V4FraCihY5TNWXoFeZ4Tzf5NQRQtqB8xZ4LmtDS3C1AQ7b4mF2/Y4wqiVbGvZJYF0WH1en8qU7RPNG2vlPGFAjQqYlbIKn5K/ns+v3Y3EumcWgB31OCgqgfE5ygAU6KL2Lmro07Q+Ow7u/Cx/3kFn731NU3Z5lXsTz1gCo7aawK+dNLeAIB9WLHBRWta8PsXNuCHf35b2ZinGgG8kxftX0OHAs5IKACzpSQxq9F5tpK6u7s7qWzyfoEmzXM7++0OC2Y7xppYSKV3tHYn1bOUxhu6Bqga/8Ez6j3POx3zXSon2j4e+02pQ1NNFJal50U7tnHn+5pBtOmcMTGvT3IyA87CoqZE9+li6ftGiaaAhbcboMn01vZ+rfesapTt0cfZC1qdqY3bgzZNWA+cXq/yKzft6XXlRHMK5SR6KdGJdFbZHgDb+kSTcrLMmpMfsxr4zMYqTKmLoakmqgYWP47aqwkzG6vQ2Z/GX1fsYC2ugphcF8PkuhhyFvDsml2uv62LOTnRPBDaonKiq1w3NV24pPo9lW9Cv3BWgwpi3syvfHOmNcTVar9l2YPwf350vvaeuGHnptxsLyW63qOquBe0AlrDBk/AqyiUM/Dukx+oeSBK9vSDp9cjSJUIC+TmAFDV2ac26IP6hOqIWl2lyWmcVb2lgYjbuc1JH7c2WZal9pUCNF6NkQa0/Y2emIfPaVT/DgQCODyvRvOcNwqiKej88XkH4xsf3BffOmM/hIIBWLqrSst9MVc923rtwIcWJ0w1QLdz6/fakXObXAGY+bDkgeGx8yZqv6OFpx35xRAvJZoWUug7eVn+uK3KVeG7JoZT2f3qFzDWVzkOkGIVIvXxZ+iGcX7sDi6gREeNCV+h1Vt+THmONX9YFVIRvThm74mqoBDg9E7vT2fRyxQ0e7/zQXTRnGiyc5erRPuPHyNBzHj+mNa/CTVR7Jc/7kNh5QbcljkaQ09kQfRQLO54PYcAZ0KVyVnK2eJ17wJ27muxPNd5+b+llCSCL8ABzjN670mOfXMgdu66eBixcDBvYXYfJwqgqqK2C2s6ew7pSnTY9RoRCARw52VHq1zF6Q1xBAIBfOHEvZWD44Zn33O1dnSU6JyTL+1RA+Kajx2C4/eZiIPYs29iTVQVcNrTk2R27kh+H+zjSRNnzb5tHIuqfMpHU01UFZekoN4cQ/16NEdCAXXu6dypIDp/jGMDtHMPhupoSC2M0DGKhPSFEN4lgESlYhXAeQBREw17jquL17MgOn9MVAX4VEYpgzz/mtu5y80Z9YJvF3BEDy9H0mkH2kr/6xvbtJoXfN5NFvK3mFDTnUirRZX9p9a5xmZ+bHieM6WFvbGlA39c4qQr8OBtdlM1HvzK8Th3oR2gOXUSepQjY1dXUqWkmY4B0zHoBS0OTqzxX5igOXBTTVT1aN7N6gv5udkaq6MqkH4l7+rhY2RfKquOr+PccAon0rhD4/ICHws/fe8WZed2crxp4YuLizQ/5dexGUQXqzxu1pL6zLFzEQjY99dnj5sLAKqvNuDcf+TWfN8o0T867yD8z6ePwIcPmaZem95ot7pJZXLqQgecQmOlKtF0cdCJ+sUnFuLK8w7GvEk1SqnasqfPyYn2CqI1O6V+GMlKznM817X0IGc5xWs2tPbCsuwAgSau5k3eYBQqiYaDePTrJ+Jv3zix4GogYF9Inz7Wtlzd89oWR4nOF3uhirmvbrADuhks0KiJhV2KrmVZ2NbmKMA10bDWd48GJLKXUIGFhTMbVJN5yivjTGuIa7mgV5+/wNU/tYrZvJLprFIyPXOi8/vRl8p6KsIEf1DUaEq0fg64BYhWG+lGtyxLKdEzJ1Spm7C12yuIdm5aaklgrk4GAgHXd4pHgmp1nAZcbrMyH2i8Ond/OquCcS8lmuzhvGrl9Ia4a8AnSze34JESRBPXg6bX498+dACqo2HNPk2T952dCaWKU+G+iTWOI6I/nVX2W64o1URDWg6yea8dudcEV8Bp3kc0AZ1aH1NtVgB70JxUq2/bKwd+OptIH7f3RHw1PwHl8J6hXI1srI4gGg5iSn1cFSP0G6QDgYBKLyk2URmunGhNiTZaVvDJZizi2M1j4WBBC3RNLKyudd5BgC9eFeqJXQq2amJvb3d3UqtYqqpz85xorz7R+ZfKrc7Nj4tXvvxwwz8/GPBuMUOW7qEoKgboquvcidU4dm97cYq3dSolV7kYPHeUQ+MPx09FLKVQ3PfOPgA3XXQEPnzIdO31WUYQTVb5eaQ8tfY4hcXKtHPffulRuO3So4rauQHvlBbAKWbmNwZMb6jCg185Dpefug+uPM9enA4GA/je2Qeqns8EBTZOEO20WvTKrTzz4Km474vH4vF/PQkvfu80/ODDB+KmTx+hnqFtTImeYCjRxKn7O1Zoszo3Hx8+f8I87e/qjPGCukiY8P2mhTYKrGIqZZAH0SOjRAcCAXVttynFUXfnRDwU8mnGwruJZlGO6Uo0tdx7e3snNubvH6VEx5x5E7dz83ZUxQqLlYPZK7qngMq996QazG6qQiqbw9P5XuA10ZCWxnZIfj77NguiqX3SlLoYJtREEY+EtIWumqhesK3a45n7iyfWKrGhn80ZTeY02QXpelNZVQUaAN7Iz4PNuR7tR+Eg2r+oGEFz570n1ahzaRfpLVydG7DVYAB4Lt+qbnpDHFeedzAOmFqHjx8+U31PSgnlcw4znZXy4U3cdm4nF5/u2TWsNR1VI+fnaS9XEK2naXlB+zqhOoKFsxpx46eOwC2fPRKH51MVNzBB04wf3zdK9JymGnz4kOnaCkwkFFSqJbd0mysJxYirINo+WOccMh2X5gdpCqJtJZpsWu7tFlKiG9UNklKKOa22fGDuBG2g5AGjaRP16ks9rSHuWh3345NHzkYkFMCKrR1Yki9dT5VkD5nZCMCxxR6ztzP5qY25C4t19KVVXsSsCdUIBgPagEcPBLJLkGq3YGaDGvhX5qst81WiafVx1efwgg/Mwlnzp2HWhGpNMeGr9X2prCqwUsjODfhXWaTtAPkKlCyoKaRE782U6LsXb8LhP30aT62yB/Xp9XF1/miguO3ljTjpF89hw+4eTYmlNgJelb/N/N54JKSs0oRu59avkUZms6T2Y+FgQH1WW08K331wBW55cb16OBzCts+t3ASv0E2tAVQhLo+V+5ns+jx4Rr26Jtbli3nRvXto/rP29KbUwyQSCmgTrdlN1doYYD4UjtqryQ7i2APVzH2iCei+U2q1SciUuph2z81tqvEsEHPc3hPx2WPn4urzF+DeLxzjmZ+kqQbsGuKLMqflc+cKqcy02FEsR1jLiS7wMCkXeug1VkdcueRmWx4KREuZVB05177Hj5rn5OdyVaXc1V8v6N5t7XGC6GjYsS/rOdHuzyMlOlKmEk1KcFNNtOzK3kMBPy9NrKge56i97Ht4qJTo8w+fieP3mYifnHcwnv72Keq+4gG8V9HKcvHqtpDK5DxbIs5jaQNcjS8lB3xibQznLpzuOj5muyNHiXYmdp2GZblUjt93km8v6fMPm4FDZzXggLxa41VcEbBzPc8/bAa+eop7YY+ojYXxvbMPVIoe8TnWHhNw7g+Vr8oKi3ktOnFmTajGl07eB8fuPdFZTO5JMTcfFRbTj+cxezepeU59VcTVJ5q48Og52hhvjhfcNXMEc1JxlwadOyoQSWMst3WXOrcaCmge1N7Hg2hu53a+7+dPnIebP3MELs4raX7odu6w9hw5a/40zG6qQjZn4Zl8YVd6VtKxzrKeudWszVFnf1qNqeYCxkCgQIXS1rqTbhsvEQgE1LPzsbfsXsxmwEsF1qijB+AU8j2ApSXRIjk5FOheqoqGVN9pe/9COGh6vdZhxkx74ETDQVWUdD1LkaR58BSXEq0LbZlszrXoSIr29AILJ8fvOxGRUAAfPGiKEuO4El2o/SWlelC/9zlN1bj0hHl48tsnY0p9XH1PUqL5nMN85lNRZhN3YTGmRLOq+gSJTfswsYNElfdaemBZVtHCYnxfyT167sLpOO3AKWq7W9v6lFtN5USrwmLvEyXaD3qYbN7DVxLKy4mu8lHxAKjm3uu5TctTifbPr6MbJGc5Qeja/IUyf0aDsoIBwBE8Xy0c1Fazy1nZ9mJSbUytrFMREXqo8EreAHA0m9zyIJpubFJ/J9fF1PHjx4AWFcyH5CGzGtSNRIPw6Qc5D/NpDXEcPmcCVvzkQ/jFJxYCsFX0vZmqwNtybWvvRzKTQzAAz6IKvB9oV4HiYr0lKtF6EG3vUyqTwx+XbEVHn2MXmtZQpVa6KTi959XN2NrWjweWbtWaytM14RVEc8UhnK/CehibFAB2xVZVWMwYzKoiTh90ukcaqiJqUHn+3RY8uGwbfvHEWmWrntkYVw8XryB64awGhIN2r9ntHf3oSqSd9lIeK/c8CJ41ocopDNNsHwMKoumz2ntTTnGeqqg2GTUXFcwghXJx+HEw81LPmj8N3zx9P1xxzkGaXXlyXUwLeL0WBAD7mvrp+QtU7QIv+H7VxcLq4cX35cKjZ+PwOY345JGzPbcBOEF00ZxovohX5qBfiCP3asLCWQ343PHzXBYyPnmPhh07dzHrOQD85l8OxUvfOw3z2Yo1zw0fiokZnds1zd1OIUV2XkLBgDovBe3c5eZE57c1GpW5AX2i5JWvDwAn7z8ZTTVRV5GxgbJgZgPu++Kx+NwJ81yBZ6Hc5HJR/XSNLhFGEV7EI0FtPD2YtaCMDGLhwAyqKEDkba5osd2sEzIY/uMjB+MvXz9RPfe82vwB9jV9/YWH44sn7132Z+w3tU5bxFdBdH48sSygs9+pzVEqjhLt2LlJWJhpHM+ZjVW49uOH4Cun7IODp9e7+kQTcaMLhOl8sZ8ztZhWH8clbHFAK/pXq9dmoYU9un5nN1WV3N5qKKBnVhvLfeULBREtXSaMsxdML6qS8eCmhhXAAmy18KyDbWcnFV6j5xPfLq+9QgEFL+ZbThFIP5QSnTbs3D7PEgqil+fnseb7Dp7egGDA3ncK2GixhPqMA86igXJRsRQGfgxO2HcSLsq3ciPxp5+KdfncC9zlZuKyc+fvh878/XHt42vwgZ8+rQLn/pTd3QaAVjDY5Ki9mvD2lWfh8lP3VYvIu7oSmvPUD2o9SefbFKX+f3tnHh5Vlef97629KqmqpBIqe0JIWAJhTdgCsoPQTYNiqwNOUBt0UHFApxe723lB3+bR1rfVbhwVbYdRp6ehbe3RUV5oaBBbWRsSQWhQWU1IIBvZIKksZ/6onJt7q25VqpKqmyr4fZ6H5yGVm1v3/OrUOee3i57orjZd0rlk81CoPc9pHC5v3lqOpzomxBhl4dyMMTDGRIVamp4xItUGo06DqsZWfH2lqdtA4Gdd57pJjiTyDXCf+axGHToZcOZKMyrqr4vPxCOI9VpNUHtG1CnRXMmQ5lXWXQtOiebFGZT6iXHrzJmqpu7Kkgr39XeINeg0Ymgh32ClpfZzJcU+CjLlocuJslDQvh/KVk3PkRUl4YfHkR5KNPdUAO4FO7MrN/xS/XU0t7aLecjSL4tUBmI4t8RqlhhrQLLN5OXRGpcVj5FpdjhiDOIkt3u088qR5E6aJK0vuDKaYjf79PpYA8iLluZESxdkb0+0u42AVuOuEsj3OF6pmZOVYOn2RDe5W3vwkJH3jpTJ+lJzlKpGZyqE7Sl5onkYj+fCJwjd/Q65t9hm1ouLHg+Fae9k4vMlxBgxNjMOggDFYiUmvVY8lB69eFUMx06MNShuetKDZ0a8RVwsj5dflbUq4J7oWokn2m7WyUJFfS3OgPvgxOeAVCHz9ESbDVo8PneILCoC6FKiJYpPX4rKSBddi7G7IItUSU+Pt+BPD0/B9xT6anN4iJWn5doTk7479K8nL1Ew2Ex6fLh6KtbMGez1O1k4t04rHkB8FUqT/63WK1VBum6G4mA2d7i72OKmvWfw7pEyAN7hj/xZlQ4Xve0TzQ2HUuOomki96r5C/xJjjTj4s9l4eenYsD/PG8sLkBZnxi9uy+/zvbixUNonWlpbhGM362WRaDIlug8h7NL9zGrqLnyVK0lT4etsMIXFgkVa8NDf4TFY7irsPqDzdUSqJHAlOJhe9FJP9Lkurxz36CfZTLLzSGqcGfPzU/DEgmHQaARZVI2nwvjY3CGYNcyJVQped41GwAePTMXOx6eJPXsBz/Zz8n1h6mB3FAA3BqoVys3heyf3mCbGGmXtzXoT1SLzREucITqNgCHJsfjR/KFYJNl/+F6plVTM5kqVWaJY8nDcGIM2JIYGfnb884nL6OxkfsO5AbfHVXoW8PQWmg1ace88VlaPqsZWvH/UvQdIHUTc0Mr3MjHVx+McOHOoU+xOwTuR+AvnBuQKm6cR3DNNTFq7BnC3fG3vZPjvknIAwB+PlqHuWhsyHGbcOiIZ/uBznL/HBYlu5E+JHpIkT9fyLKrK5wNPs5PqQdKz/wgfRcUA99lSI7gdil9dbgRj7gKe8RY9Bjut0Aju819VUyuqGltRd60NGkFeO8Wk14qRsntPV3W3GvPjPOBncel9APf5mBs77n59PyY/sxuff1PtNb6YIM5UfT+5qIyYsyyZKLUe7vie4MJXCkNMj7fAqNOgtb0TPLNVaXOUWmKUNpg4iwHNLndLp8aWejGfdFiyFVcaWvExKhBj0MpCTQC3ZdBXi6vekJdiw21j0vCnri8nP/gmxhqRFmdG+dXrEAR3+LzTasSVxlbEmnTufpJdP5+qbJD1iBZlIFOivT3R+Wl2CILgZYVLtpnw/sNFcLV3+vRiSRckaTg3D4tWKirW/Sw6VDe14nRlIzo6mWLRA2l1bulm7bnRWgw6vHD3GHR2MlhNelhNetRfbwNj7s33P+4fj7Kr15GXYhP/tqqpVcwJB7pzj/m84vQUzs0NB44YA7ISLLICMHeMS0eyzSQLj+XEWwy43NAqfmY2iSdaiUSrEb9ZOhZXGloV88wBd170sbJ6HL1QJyobvtqByJRohwWJsUZs+vQsDpytxYlL9Wh2dcBq1Ilhd66OTtGjH2cxyNrTKVXL5kijKawyT7RvBVSamzTAagQkHi1fnuhAkHoQeB/JmmZX0N7JJePSoNMKYv6aLwRBgM2kF3t1q4EsnFviie5toRnpYSQU4dz/OCkLr//1LMrqruPVT84AgFe7FbNBCzT3VFgsuENiUU4C/uP+8T6Lq4QbmSfaT3VwtULNc51WfP7ErJDci0coSHu38nzoDIdZNO7GmQ2yQ5A0vDfYQnFSTHp3/+uqxlbZ3mA368X35wbyUOzXvvDlie4rt49Nx8fHKxEnCaXWdRUNdHV0imerYDzRPJT+eHk9GlvbodcK4n5u0GmQGGsUFTVPT7/0YOyprFgMOvz7feN9vq/7b91nBZ1GQHsnk33PpWuxXitgYleRSe6JVqsyNydWbGnpVqS4MqLXuGUfbEQM4FlYTCsaBgqy4kVj20t3j0Feig1nq5pkBvoYoxbX2zrESDpp0VX+eYXC2AkAy4uysOvvl/Hx8QqkxplEz7iv1CCjTotf3jEKS984AKC75ZyUkel2nL7ciONlV/HJ6StodnVgdLpdNK4C3YomnxcGSTi3dL7NGDpAHOul+hbUX2vrDuf2obxJPdG3jU3D7w5eQCdzzzvPtVeaE93Q0iae1f76dTU6Ohn+vas97Iop2QEbLbgDiHd/SbaZZMU0PRnsoWB6nqk9lVRp6onUK+2vL7i2q+vQ5YZW0fEUbzFAp9VAp3V/585WNcvyorO7ep5LmT5kAD79qgqffl0lOmX8rUmrpucgPkaP7xd4e/FzBsTii7J6MfqBO7ik+4c5iLzoqPNE883kYu01tLR14EpjC+p4YniAOdF8sikVadBqBC/rhVKYlnQSKX2YfEP92/laFL95ENdcHZiY7cDo9DjR63vL4AFeXxCp5ypQz3pPSA+SbRIljk/+AbFGGHQa8RDIFWVeCOnEpQYxNFiqZEnDuZU80aO67pds9yj01OVF9hcGKv0MzAatqChwA0O6Qj6057Os3VqKxf/2uSz0nyNt4xDrJycaABaNTsVtXVXipWMenBSLotxEMTyXe4GqG10yJZojrVwLeBebACBWIQXk80q62VkM7pDtmcOcigpMnOiJdo/bZtJ59c6TkhBjUPQUShknyYv2lw8NyEP2MuItmDDIAY3gzh/8Y5eHcFJOAqwmvfi5nqt2f652s15WC8DfM0mL0nj2+vaFRVKx1Gk1yQ7GffFE8M3YpHe3u+MbcrDVmk16Le4qzPCqMK4E34hDWVjMH3JPdHdOdG8VYO4V7qkndqCYDVqx6jDg3njn58ut+Pw9lQuL9a5PtEYjYMZQp5cBTi2kn79nZe5oR1ooktfv4AVmZkn689rNepkSm+uMFY08fc0D5+uZp2w9q9cr1U4JFTLjagg90QadBm//YAJ+4xGhwA/Q3WGhgb8n3we54pXrtMo+A17E1KTXeNV94XNZEHrfIk2v1YjyknmiJWvx2MzugpQ2j/xJtfD0Vg5PcZ+XeOREsLUZAM/exzoMGhCLHWunYVNxgfi6RiPgoRk5eP7O0bK1jjsTqpq8w7m77x8aQ1FRTqKYvvfGX8+Jirs/g+zknATxDLUg39s7y43qWw5/iy2HvwUA/Ow7eTIvKd+P+bwQPdF6LbISYjA1NxFLJ2QiNc4Mm0kvfvdPVTaIRdB8Ga2l82fSoATR06u0l0tzok9KDALnqpux+fNzOFfdDLtZjzv9pH554vk+90zM9NtuLD5G3h7UM+rPc5wLJEUXpTpRT8Zj7izi45Suo3m8uFhlgxjKrVQkcPoQ9+d+8Fyt6JTytw6OTLfjF7eNVNQJeUrThGwH7pbIVxrJFMyZJuo80VIl+t5/P4SSi1fFBTpQpXP55Cwk20yYK+m1LGWwM1a0dNlMOkVLkNVPYTGgW5F55v+fQkcnw+h0O357byE0GgFFuYl476HJiov2AA9rdyjIcFjw5Hfz8M6BC7hVsviMTLdj+4lK0Rr8qztH42x1k5irOjzVhj2nq3CivAEHzrkrSku/MEqFBuIsepj0GrS0dYrXenmie6gwCcgLC0j7RLd3HaSkyqYn0o2ko5Nh19+vYMVUeXVP7tnwzon2P4fc3ne398Pzy84/u5rmVnFT4N58wN066lh5vXi4UJKD1DggPQCMyYjDh190FdXowUrGD3Nc2bWb9bKogRS7SSz6Y/XozekL7jU+ealB9EDzdnCe8PnE89b1WreB5lhZPX5/yN0y4pbB7kXREWNA+dXr4sE4zqyXVaVXCud+9Z5xOFZeL7MyioXWjLoec4STbSY0tjRhgNWIjs5uo1LfPNHdYWFA92EgnIqVtev7p5YnWuoBM0pqDwSSE62Ev9oUvWXphEy8te88appceGrRCK9DxB0FafjvkktixXkp3eHc6uVFhgJf3rYbAelhpqm1HXazXvRE56fZRS+x3aKHUafFiFQbLje0YFBiLBwxBlQ2tPT580yLM6P026te3+URqXbsONHdIjJU+7USjhgDYgxaNLs6QuqJ9oXFoJUVOgrmO+qZl5/nEW2XGmfGF2X1SI0z+6y7YOnqItJbshNjcLa6Wab8S89Wt0gM2mtm5yJ3QCxuG+s7zSYcSGVqNenEM41bse3opSdaqkS754lntKMv+DquFM6t9Mx9Zcm4dJyrbsbG3d+IrymlWEr57b2F+MPfvsW0wd6RWuO7lCN+3po3PElWMBfoTpMyiOHc3TnRWo2A/1w5UXZ9XooV5Vfd0SbBKNGjM+zIT7PjVGWjorOEr9MXapq9vOq/3H4KgFsJDmZvTYg1QOhqNafXCviHCZk9/s2QpFjxrOqlREvOUaMz4mTOEXk4t38l2q3c14th8dKUo2HJVnx8vELmiVaarzkDYpFqN+FSfYsYVZvux8Hij6UTMjBpkEM8x9rMOlTUt8g888FU6I4+JbrroFvd5EJ1k7vKsVLMvj+sJj3uUHDzcwZLcgV85SUbuno8Xm/r8OGJdv9dRyeDzeQOQ5Iq3gVZygVeeHEqIHDPeiCsvGUQVt4iLz6yID8Z/3nggpijGR9jQEFM93PxL8dfTl1BdVMr9FoBUySbj1I4tyAImDc8GX87XyuGS1lNevEAoNcKAXlLcgbEwqDToKMrjNpTxkq5u5zZw5JwqqIReSk2fPZNNT457a1Ei9W5DTrxC2PSa3rcJKQHpTyPFkDpXZvg4XO1YmjoQzNy8NT/nATgzkHJTohBVWMrBEFZwTLptaLiLT0AjJYU/OpJSeT90nkxOGlONAAsHJWCv5y6grNVzX7bJ0hJizMj2WZCZUMLdnzpbuEwMFF5ERvsjMXCUSnISrCIYUyTByXgWFm92HKLW5S5Es1DkGxdnmin1YhOxhTD9heMTJFZRYFuq/4AhQ3Lk7sKM/DHI2WYPChBtH4atBqvonjBwA/q/OCyZFw6ml0dipt9qOBenGCrSfYW6Xw06DTiQaSv4dyhPJiZ9Fr8z6NT4WrvVAxtfnBaDh6c5p1TCfS+T3R/I/NE95M3PFwYde6oG1d7Z7cSXe1e17ITYzAo0b2e8pSr9x8uQnsHg9mgRbyoRPft8+TrumdUiacnWqkAaajg7Q9PVTaG1BPtC6mikDMgJqhicQ6PPcWzhzNfZz2LjAHdc1mp3VAw8NBsk496AVMHd59jcp1WrJkTmKIZSqSh0cNTuvNKpw5OxBffXu1VZJR0LQ3WuMlDf3ndFLtZj2S7SQyNB5TruPSFNbMH46NjFaIRvScPoEmvxfLJAxV/l5diw389MBHldddhNmgxXeGMWJgVD6tJJ4Ymd1fnVpbVsGQbdv39itsT3dbd+kuJ+BgDnvxuXte5xYJbBifij0fKFD21hQMdMGg1uFBzDf/T5RyxmXRoaGlHWweDXivgPo/q+T2h12rgsBhQ0+zCd0amBBQFNyTJin1namDxaCUKyNeA73h4/s0GLe4qTMc1V4esCLAS3IjAw7ml514x77yyEdxkNExBiRYEAdOHDsDvD7kjDOaPSMbtXdGhweLOi+5WmH/+3eFe1wRS54UTNiX6lVdewfPPP4+KigqMGDECL730Em655ZY+39dmcodtKfVXC1WPTqlFwl+ek82sw/W2DpgN3puaNEzpn2cPDvhwIyssFkbLNuCuMLr/p7N9/p6Hc3NLVWGWQ7ZIe1bo4/z6H8aAMcgqGSfZTThb1Qyn1eSzwrEUk16LTcUFaHF1INaoExdxAFg5NVvRk8T5wdRs3D9lIM5WN2P2r/bi4NlaNLe2yzYV0RNt1HblYGgwKj2uR+u31HDg6YmelJ2AMRlxYii3QafB0gmZeG3vGdQ2u5CfZsfARAsOna9FYqzR5+Eu02HBlcZW2SI2ItUGp9VdeKQnpYMbcLjCOjTJ6u6r2FXgYUpuIq63dXQp0YHNS0EQsHbOYDzx/nHRaOUrJ1qjEfDysnGy1yYNSsCmT88CcB+e+KGMG4pET7RFD51Wg+1rp4ExFnCYL998PStzK/HAtEFiNVsGd6hhQWZ8nwqm8M+Sb7DLJmZ69WENNf80LQeOGAPm5ClH1IQa6Wdh1GlFJWJEqnf4VSB4eu1DRW9DDntbnbu/kVfnvrE80YDbQFbT7kJTSzta2jpwqd4dCTQwMQaDBsTi4LlacR0x6rTg04nXSOmrEr1sQiaqG11e7YVGSNq6aISeK+r3lcwuJTqUhQR9ITXUFk/KCsorbDXqxJxqwLvfPP9Z6bAsKtF9NAzy0F5pBw+n1SQao/3lcaqFdN2TFsJ7eelYdDL0aj+KVfBEB4rUcKHTCJiSk4j4GAM++uepKL14FQ0tbViQn+LnDsGj02rw1v0TsOTVfUiNMwWdSuNJUU6i39+nxplR8q9zxffh3yVfsuJz9ciFOrFlqr/IPamjatHoVIxItSsaoGKNbkV+71dV4nnx3qKBold+8Zi0HouLKjEy3Y7Pv6nGDzz6qvticFeR40yPVqKAPK1J6XN/7vujA3oPHonKc5ClSjdvjfX3igaxoJ6vnu+3j03H1sPf4jsjU/Di3WPCWuNDSafzRVhW/a1bt2Lt2rV45ZVXMGXKFGzatAkLFizAyZMnkZnZ94NlpsOCq9fqodUIePHuMVi7pQQWg070wPUVadU6fyFaNpMelxtaFfu0OroUmYEJFp+WMyW49cig1ajmYfJFRrwFVqNOrIw63aPYEc8LN2g1soVFEAR47rlJVrcS7a/nnSczJTlv2RKF7Ye3Du3xbwXB3SaLF3/Zd6YGc4cnoaWtAwfP1eJ0V4GyGIMOjhgD9j0xO6A8CGlusefhQKMR8Ivb8vG9lz8DY0B+qg0mvRb/9cAk1F9vQ2qcWbSQ+7PoZjos+NuFOplMTXotdj42HUDPm6vUgJMWZ8bd4zMgCAK+MzIFF2quYdKgBJj1WvzXwYsoHOjbGOHJ3eMz8LcLdWJec1YQ4TTjsx3QagR0dDJMzU0UF2x+6Oe5d5O7wq+CNYjxVjjBbjxOqwn7n5jV54IpfEEPpiBFX5mck+Czz2w48GxxtXhMGopyEoPO++aInugQFavpK9y4F33h3DeuJxqAWKSvqbUdb+8/D9ZVrCchxoDlk7Nw9ZpLsYAMj0wLpE+0P7ISYvCru7wPjMk2E+ItetRda4PNrA/IONwXcpyxwMnLYQ0b50hDTJf4idpTQhAEMZQe8PZELxmbhvR4s2I7RZNChfDesHBUKuItBoyVtIfUagR88MgUMMb6FCoeKqSOB2lIrCAI6O0SJI3cCMabBkBWhGpKbqJomBqWbPOp2ISCzAQLPv3xjD5/TwNFqhxyp5VSyDXQrdDx/sUDEywB9xIXBO/6SlLmDE/C3q+qxJ/vKszA+0fLcbmhBQ94RI0GysalY1Hb7PLp4PB6hrwkvL3vgmLbzWHJVoxItWGwM9ZvgdeekMo21xkra8eXYjfjoRk5ePWTM2DMPQd9FQ2ekO3Al0/dGlSodW/p93DuF154AStWrMDKlSsBAC+99BJ27NiBV199Fc8880yf75+dGINjZfVYPDoVi0anIiPeDI0ghKQ4DeDOIeaVlP21meKeWKXw2u8XZOCbqib807ScoAqbpHd9QZ02Y78v9BqNgLxUGw6dc4fNe1YM5uP3VVFRCs//TQpCiZayaEwq2jsZ5uQ5Ay6kJAgCZg514u39F/D6p2fw9v7zOHSuVlYhmy8OgSpt3BOdGGtUDMfOT7Nj+aQsvLX/ghiyLM2VmdhVTXt0hm9LOM/18JzPgYYLSgvc/GTBMFFeUu/wxEEJOPzzOUEdyARBwP9dnI/GljY4YgxBVaONNepQkBWPQ+dqMXNYt3FEKveJ2Q6vHKZAyewKufSsOBkIoVA8CgfGI9cZi8V+2ldFOwOsRmQnxiDeohcNOb1VoIFuJTrcHrxASRILTvY+rL8/kPfCvfE80dxj98W3V/H//vwVAOBHtw6FIAjIS7Hh1X8sUPw7vraEK7JAEAQMT7Xh829qwh41BgA/mJINi16Lu8YHXmyot3CDZ3q8ucc8VSW4Eq20T+q0Gp8eQ96y0V9ByUDQagSfKV/9fa7iyDzRKaFTUnnkhiXIgo/SSL2Fo0Lrce4JNRQjJX72nTzMGubEbB/RXAMTLLKuKs/eMSpkvcTn5Dnxr//t/r/drEd6vBlbHpyEhpa2gPPYPeEdZAIlyWbCjsemKf7OpNfi43/ue/QwT0uwm/X47fJCr+f74byh+KqyEX85dQV5KTa/xki15kkwDsyQP5HL5cKRI0fwxBNPyF6fN28e9u3bF5L3eGRmLuLMeqye5e5lOtZPaG9v0GrcLRlOVjT43Ry5J1ZJqctMsOCVe5Q3d38MTrJiw+35/dZz1JMRXUp0ss0ka1oPdCuUgSjRPKSlp/wJX5j02l6Fx3Il+vD5OvG1AVajO6diXJrfsHAluOHAMx9ayv/53gjMHZ6s6OUtyHLgwE9n+1U+puQk4N/2fINxWXFBPRuHe8gnDHTge342w94oj2aDFpuKC3v1XC/cNRrHyupxq6Sgn1SJXjtniNKfBcSyiVkYkmwN+vMMFUk2E3Y9Pr1f3lst9FoN/vzYNGhDdAgdkxEHg1aj2KatP3hs7hDMynOKtRyihRvdE80jFTZs+zs6OhmmDxmg6DnxhK8t4Qz7G5Fqx+ff1MAeok4a/hhgNeLR2d7928PBK/eMw9v7z+OXd4zq1d/z/GN/+6QSo9Lt+NPDRRFz/gknsZJIPn8ey2CxmtyRG8F6osvrrov/n9dDb+IbhSSbCYvH+M6t1Wk1GJZiwxffXkXxpCxM6qWRX4kUuxn5aTZ8Wd4gtjfrq/EoEpmQ7cALd43GqPQ4xTZyWo2AXy8dizc+PYvZeU6FO6hPMJ2RQq5EV1dXo6OjA0lJcstOUlISKisrva5vbW1Fa2ur+HNDg3f/N0+GJFnx1OL8vj+s3/dwK9GeLRik8LAOz0bqfeWeiVk9X6QSc4cnYfPn53FnYbqXBXdEmg0Wg1YsGe+P+6YMRLLN1GPj+FBTlJuAWwYnov66O59nTp4Tuc7YXlujpw0ZgC2HLuKOcb5D3LQaQVa4xJOeqpNPHJSAL9ff2mMBMV+MSo/D9rW3IMsREzFWd8BdedyzNRk3rkwa5OhTaLJB59u7QYSOUCokhQMdOLZ+nmotunoixqiLyjnEreYmvcZvX9BoJcthwaFztaJn9JklIwNa17hBIZj2TMFS0NX6L8NHCGK0cuuI5D7t1dz77BnK3ROCIITcKRKp8NS2ken2PrdhkzI6Iw6X6lvEfNdAkaZDqpEyEC2s/95w7P2qCg9O612ItT8Wj07Dl+UNKFIxLUttBEHAEj/nZcAdlfHY3N47UULNo7MH48kArxUYY6znywLn0qVLSEtLw759+zB58mTx9Q0bNuCdd97BqVOnZNevX78eTz31lNd96uvrYbOFLw+jJ46VXcW/7fkGP5k/TFbJTUp1Uys++7oa8/OTI+YgGA7qml2wmfWKYSwtbR0w6jQRpawR0UNHJ8POk5cxOSeBNm6C6AWdnQw/+uMxDE6KxarpypXHo5mr11w4eK4W6fFmDPboOeyPmqZWbPj47/iHCZmYEKZoh85Ohr+cuoIxGXF9Sm240Th0rhYbd3+Ndd8bEVIv640EYww7TlRieIq9TzmnnnR2MjS52oMOwy+ru4YXd36NR2bm+DzzEqGls5Oh5Ns6jEyLC6khhegbDQ0NsNvtAemhIVeiXS4XLBYL3n33Xdx+++3i62vWrEFpaSn27t0ru17JE52RkdHvSjRBEARBEARBEARxcxCMEh1y04fBYEBBQQF27twpe33nzp0oKiryut5oNMJms8n+EQRBEARBEARBEEQkEpZSZ48//jiKi4tRWFiIyZMn4/XXX8fFixexatWqcLwdQRAEQRAEQRAEQahCWJTou+++GzU1NXj66adRUVGB/Px8bNu2DVlZkVMwiyAIgiAIgiAIgiCCJeQ50X0lmFh0giAIgiAIgiAIgugr/ZoTTRAEQRAEQRAEQRA3KqREEwRBEARBEARBEESAkBJNEARBEARBEARBEAESlsJifYGnaDc0NPTzkxAEQRAEQRAEQRA3A1z/DKRkWMQp0TU1NQCAjIyMfn4SgiAIgiAIgiAI4maipqYGdrvd7zURp0Q7HA4AwMWLF3t8eCXGjx+Pw4cPh/qxwnrvhoYGZGRk4Ntvvw1bRfJolEs4700y7597A+GVfTTLJZzvEe75Hm7ZROPnSjJX/94kc/XvTTJX/94k8/57j2iWfbR+rmrLvL6+HpmZmaI+6o+IU6I1Gneatt1u75WwtFpt2JSicN4bAGw2W1Q+e7TeGyCZq31vKeGQfbTLJVrne7ifO5o/V5K5uvcGSOZq3xsgmat9b4Bk3p/vEY2yj/bPVW2Zc33UHzdcYbFHHnkkKu8dbqJVLiTzG+ve4Sba5RKtsg/3c0f75xoOSObqQzJXH5K5+kSzzNV8j3AQzfPxZpS5wALJnFaRYJpc3yjcjGPub0jm/QfJXn1I5upDMlcfkrn6kMzVh2Tef5Ds1UdtmQfzfhHniTYajVi3bh2MRmN/P4pq3Ixj7m9I5v0HyV59SObqQzJXH5K5+pDM1Ydk3n+Q7NVHbZkH834R54kmCIIgCIIgCIIgiEgl4jzRBEEQBEEQBEEQBBGpkBJNEARBEARBEARBEAFCSjRBEARBEARBEARBBAgp0QRBEARBEARBEAQRIKREh4hnnnkG48ePh9VqhdPpxG233YbTp0/LrmGMYf369UhNTYXZbMaMGTNw4sQJ2TWtra149NFHkZiYiJiYGCxatAhlZWWyawYOHAhBEGT/nnjiibCPMdJQU+YA8PHHH2PixIkwm81ITEzEkiVLwjq+SEUtuX/yySde85z/O3z4sCpjjRTUnOtfffUVFi9ejMTERNhsNkyZMgV79uwJ+xgjDTVlfvToUcydOxdxcXFISEjAgw8+iKamprCPMdIIlcxff/11zJgxAzabDYIg4OrVq17vVVdXh+LiYtjtdtjtdhQXFyted6Ojpsw3bNiAoqIiWCwWxMXFhXFUkY1aMj9//jxWrFiB7OxsmM1m5OTkYN26dXC5XOEeYsSi5nxftGgRMjMzYTKZkJKSguLiYly6dCmcw4tI1JQ5p7W1FWPGjIEgCCgtLQ3DqNyQEh0i9u7di0ceeQQHDhzAzp070d7ejnnz5qG5uVm85rnnnsMLL7yAl19+GYcPH0ZycjLmzp2LxsZG8Zq1a9fiT3/6E7Zs2YLPPvsMTU1NWLhwITo6OmTv9/TTT6OiokL89+STT6o21khBTZm/9957KC4uxv33348vvvgCn3/+OZYtW6bqeCMFteReVFQkm+MVFRVYuXIlBg4ciMLCQtXH3Z+oOde/+93vor29Hbt378aRI0cwZswYLFy4EJWVlaqOub9RS+aXLl3CnDlzkJubi4MHD2L79u04ceIE7rvvPrWH3O+ESubXrl3D/Pnz8bOf/czney1btgylpaXYvn07tm/fjtLSUhQXF4d1fJGImjJ3uVy488478dBDD4V1TJGOWjI/deoUOjs7sWnTJpw4cQIvvvgiXnvtNb+f0Y2OmvN95syZ+MMf/oDTp0/jvffew5kzZ/D9738/rOOLRNSUOefHP/4xUlNTwzIeGYwIC1euXGEA2N69exljjHV2drLk5GT27LPPite0tLQwu93OXnvtNcYYY1evXmV6vZ5t2bJFvKa8vJxpNBq2fft28bWsrCz24osvqjOQKCJcMm9ra2NpaWnst7/9rYqjiR7COdeluFwu5nQ62dNPPx3G0UQH4ZJ5VVUVA8A+/fRT8ZqGhgYGgO3atUuNoUUs4ZL5pk2bmNPpZB0dHeI1JSUlDAD7+uuv1RhaxNIbmUvZs2cPA8Dq6upkr588eZIBYAcOHBBf279/PwPATp06FZ7BRAnhkrmUzZs3M7vdHupHj1rUkDnnueeeY9nZ2SF79mhHTdl/8MEHTBAE5nK5Qvb80Ui4Zb5t2zY2bNgwduLECQaAlZSUhGMYjDHGyBMdJurr6wEADocDAHDu3DlUVlZi3rx54jVGoxHTp0/Hvn37AABHjhxBW1ub7JrU1FTk5+eL13B++ctfIiEhAWPGjMGGDRtu6vAcTrhkfvToUZSXl0Oj0WDs2LFISUnBggULvEJNblbCPdc5H374Iaqrq29KD50n4ZJ5QkIC8vLy8Pbbb6O5uRnt7e3YtGkTkpKSUFBQoNbwIpJwyby1tRUGgwEaTfd2bDabAQCfffZZeAcV4fRG5oGwf/9+2O12TJw4UXxt0qRJsNvtQd3nRiRcMid8o6bM6+vrxfch1JN9bW0tfve736GoqAh6vb5vDx3lhFPmly9fxgMPPIB33nkHFosldA/tA1KiwwBjDI8//jimTp2K/Px8ABBDIZOSkmTXJiUlib+rrKyEwWBAfHy8z2sAYM2aNdiyZQv27NmD1atX46WXXsLDDz8cziFFPOGU+dmzZwEA69evx5NPPomPPvoI8fHxmD59Ompra8M6rkgn3HNdyptvvolbb70VGRkZoR5GVBFOmQuCgJ07d6KkpARWqxUmkwkvvvgitm/fflPnMIZT5rNmzUJlZSWef/55uFwu1NXVieFqFRUVYR1XJNNbmQdCZWUlnE6n1+tOp/OmS1uQEk6ZE8qoKfMzZ85g48aNWLVqVe8f+AZCDdn/5Cc/QUxMDBISEnDx4kV88MEHfX/wKCacMmeM4b777sOqVatUS/kjJToMrF69GseOHcPvf/97r98JgiD7mTHm9Zonntc89thjmD59OkaNGoWVK1fitddew5tvvomamprQDCAKCafMOzs7AQA///nPcccdd6CgoACbN2+GIAh49913QzSC6CTcc51TVlaGHTt2YMWKFX174BuAcMqcMYaHH34YTqcTf/3rX3Ho0CEsXrwYCxcuvKkVunDKfMSIEXjrrbfwq1/9ChaLBcnJyRg0aBCSkpKg1WpDN4goI9Qy7+kevb3PjUS4ZU54o5bML126hPnz5+POO+/EypUre3WPGw01ZP+jH/0IJSUl+POf/wytVovly5eDMdbrZ452winzjRs3oqGhAT/96U/7/JyBQkp0iHn00Ufx4YcfYs+ePUhPTxdfT05OBgAvq8qVK1dE60tycrLoifB1jRKTJk0CAHzzzTchGUO0EW6Zp6SkAACGDx8u/t5oNGLQoEG4ePFi6AcUJag51zdv3oyEhAQsWrQo1MOIKsIt8927d+Ojjz7Cli1bMGXKFIwbNw6vvPIKzGYz3nrrrXAOLWJRY54vW7YMlZWVKC8vR01NDdavX4+qqipkZ2eHa1gRTV9kHgjJycm4fPmy1+tVVVVB3edGItwyJ7xRS+aXLl3CzJkzMXnyZLz++ut9e+gbBLVkn5iYiCFDhmDu3LnYsmULtm3bhgMHDvTt4aOUcMt89+7dOHDgAIxGI3Q6HXJzcwEAhYWFuPfee0MwAm9IiQ4RjDGsXr0a77//Pnbv3u11+MnOzkZycjJ27twpvuZyubB3714UFRUBAAoKCqDX62XXVFRU4MsvvxSvUaKkpARAt7J3s6CWzAsKCmA0GmUl+dva2nD+/HlkZWWFc4gRidpznTGGzZs3Y/ny5TdtLpFaMr927RoAyPJz+c88IuNmoT/W9KSkJMTGxmLr1q0wmUyYO3dumEYXmYRC5oEwefJk1NfX49ChQ+JrBw8eRH19fVD3uRFQS+ZEN2rKvLy8HDNmzMC4ceOwefNmr7X9ZqM/5zv3QLe2tvbpPtGGWjL/zW9+gy+++AKlpaUoLS3Ftm3bAABbt27Fhg0bQjMYT8JWsuwm46GHHmJ2u5198sknrKKiQvx37do18Zpnn32W2e129v7777Pjx4+zpUuXspSUFNbQ0CBes2rVKpaens527drFjh49ymbNmsVGjx7N2tvbGWOM7du3j73wwguspKSEnT17lm3dupWlpqayRYsWqT7m/kYtmTPG2Jo1a1haWhrbsWMHO3XqFFuxYgVzOp2strZW1TFHAmrKnTHGdu3axQCwkydPqjbGSEMtmVdVVbGEhAS2ZMkSVlpayk6fPs1++MMfMr1ez0pLS1Ufd3+i5jzfuHEjO3LkCDt9+jR7+eWXmdlsZr/+9a9VHW8kECqZV1RUsJKSEvbGG2+I1eZLSkpYTU2NeM38+fPZqFGj2P79+9n+/fvZyJEj2cKFC1UdbySgpswvXLjASkpK2FNPPcViY2NZSUkJKykpYY2NjaqOub9RS+bl5eUsNzeXzZo1i5WVlcne62ZFLdkfPHiQbdy4kZWUlLDz58+z3bt3s6lTp7KcnBzW0tKi+rj7EzXXGCnnzp0Le3VuUqJDBADFf5s3bxav6ezsZOvWrWPJycnMaDSyadOmsePHj8vuc/36dbZ69WrmcDiY2WxmCxcuZBcvXhR/f+TIETZx4kRmt9uZyWRiQ4cOZevWrWPNzc1qDTViUEvmjLnbK/3Lv/wLczqdzGq1sjlz5rAvv/xSjWFGHGrKnTHGli5dyoqKisI9rIhGTZkfPnyYzZs3jzkcDma1WtmkSZPYtm3b1BhmRKGmzIuLi5nD4WAGg4GNGjWKvf3222oMMeIIlczXrVvX431qamrYPffcw6xWK7Nareyee+4JqE3NjYaaMr/33nsVr9mzZ486g40Q1JL55s2bfb7XzYpasj927BibOXMmczgczGg0soEDB7JVq1axsrIyFUcbGai5xkhRQ4kWGLuJM9wJgiAIgiAIgiAIIghu7uQIgiAIgiAIgiAIgggCUqIJgiAIgiAIgiAIIkBIiSYIgiAIgiAIgiCIACElmiAIgiAIgiAIgiAChJRogiAIgiAIgiAIgggQUqIJgiAIgiAIgiAIIkBIiSYIgiAIgiAIgiCIACElmiAIgiAIgiAIgiAChJRogiAIgiAIgiAIgggQUqIJgiAIgiAIgiAIIkBIiSYIgiAIgiAIgiCIACElmiAIgiAIgiAIgiAC5H8BbP95FUtAq5UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the federal funds rate data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import areturns\n",
    "\n",
    "dta_areturns = pd.Series(\n",
    "    areturns, index=pd.date_range(\"2004-05-04\", \"2014-5-03\", freq=\"W\")\n",
    ")\n",
    "\n",
    "# Plot the data\n",
    "dta_areturns.plot(title=\"Absolute returns, S&P500\", figsize=(12, 3))\n",
    "\n",
    "# Fit the model\n",
    "mod_areturns = sm.tsa.MarkovRegression(\n",
    "    dta_areturns.iloc[1:],\n",
    "    k_regimes=2,\n",
    "    exog=dta_areturns.iloc[:-1],\n",
    "    switching_variance=True,\n",
    ")\n",
    "res_areturns = mod_areturns.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>520</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-745.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 18 Dec 2025</td> <th>  AIC                </th> <td>1507.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>07:37:30</td>     <th>  BIC                </th> <td>1541.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>05-16-2004</td>    <th>  HQIC               </th> <td>1520.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 04-27-2014</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    0.7641</td> <td>    0.078</td> <td>    9.761</td> <td> 0.000</td> <td>    0.611</td> <td>    0.918</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>     <td>    0.0791</td> <td>    0.030</td> <td>    2.620</td> <td> 0.009</td> <td>    0.020</td> <td>    0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.3476</td> <td>    0.061</td> <td>    5.694</td> <td> 0.000</td> <td>    0.228</td> <td>    0.467</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    1.9728</td> <td>    0.278</td> <td>    7.086</td> <td> 0.000</td> <td>    1.427</td> <td>    2.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>     <td>    0.5280</td> <td>    0.086</td> <td>    6.155</td> <td> 0.000</td> <td>    0.360</td> <td>    0.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    2.5771</td> <td>    0.405</td> <td>    6.357</td> <td> 0.000</td> <td>    1.783</td> <td>    3.372</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7531</td> <td>    0.063</td> <td>   11.871</td> <td> 0.000</td> <td>    0.629</td> <td>    0.877</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.6825</td> <td>    0.066</td> <td>   10.301</td> <td> 0.000</td> <td>    0.553</td> <td>    0.812</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &    520      \\\\\n",
       "\\textbf{Model:}           & MarkovRegression & \\textbf{  Log Likelihood     } &  -745.798   \\\\\n",
       "\\textbf{Date:}            & Thu, 18 Dec 2025 & \\textbf{  AIC                } &  1507.595   \\\\\n",
       "\\textbf{Time:}            &     07:37:30     & \\textbf{  BIC                } &  1541.626   \\\\\n",
       "\\textbf{Sample:}          &    05-16-2004    & \\textbf{  HQIC               } &  1520.926   \\\\\n",
       "\\textbf{}                 &   - 04-27-2014   & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:} &      approx      & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &       0.7641  &        0.078     &     9.761  &         0.000        &        0.611    &        0.918     \\\\\n",
       "\\textbf{x1}     &       0.0791  &        0.030     &     2.620  &         0.009        &        0.020    &        0.138     \\\\\n",
       "\\textbf{sigma2} &       0.3476  &        0.061     &     5.694  &         0.000        &        0.228    &        0.467     \\\\\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &       1.9728  &        0.278     &     7.086  &         0.000        &        1.427    &        2.518     \\\\\n",
       "\\textbf{x1}     &       0.5280  &        0.086     &     6.155  &         0.000        &        0.360    &        0.696     \\\\\n",
       "\\textbf{sigma2} &       2.5771  &        0.405     &     6.357  &         0.000        &        1.783    &        3.372     \\\\\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{p[0-$>$0]} &       0.7531  &        0.063     &    11.871  &         0.000        &        0.629    &        0.877     \\\\\n",
       "\\textbf{p[1-$>$0]} &       0.6825  &        0.066     &    10.301  &         0.000        &        0.553    &        0.812     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Markov Switching Model Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  520\n",
       "Model:               MarkovRegression   Log Likelihood                -745.798\n",
       "Date:                Thu, 18 Dec 2025   AIC                           1507.595\n",
       "Time:                        07:37:30   BIC                           1541.626\n",
       "Sample:                    05-16-2004   HQIC                          1520.926\n",
       "                         - 04-27-2014                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7641      0.078      9.761      0.000       0.611       0.918\n",
       "x1             0.0791      0.030      2.620      0.009       0.020       0.138\n",
       "sigma2         0.3476      0.061      5.694      0.000       0.228       0.467\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          1.9728      0.278      7.086      0.000       1.427       2.518\n",
       "x1             0.5280      0.086      6.155      0.000       0.360       0.696\n",
       "sigma2         2.5771      0.405      6.357      0.000       1.783       3.372\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7531      0.063     11.871      0.000       0.629       0.877\n",
       "p[1->0]        0.6825      0.066     10.301      0.000       0.553       0.812\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_areturns.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first regime is a low-variance regime and the second regime is a high-variance regime. Below we plot the probabilities of being in the low-variance regime. Between 2008 and 2012 there does not appear to be a clear indication of one regime guiding the economy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Probability of being in a low-variance regime'}>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9UAAAEnCAYAAACnj2AkAAAAQHRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcrZGZzZzEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvhF0PpwAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsfXeYHMXx9ju7ezlIQjmLJJAIQkQjgshBJhhjgwMZnPgcMPCzTTAYbIMJNhgDxpgcTTA5CBBCgAJCWQIFEMq6U9blsGHm+2O2eqp7umdn907SCc/7PPfc3YaJPd1V9b5VZTmO4yBChAgRIkSIECFChAgRIkSIkDdiO/oAIkSIECFChAgRIkSIECFChJ0VkVMdIUKECBEiRIgQIUKECBEiFIjIqY4QIUKECBEiRIgQIUKECBEKRORUR4gQIUKECBEiRIgQIUKECAUicqojRIgQIUKECBEiRIgQIUKEAhE51REiRIgQIUKECBEiRIgQIUKBiJzqCBEiRIgQIUKECBEiRIgQoUBETnWECBEiRIgQIUKECBEiRIhQICKnOkKECBEiRIgQIUKECBEiRCgQkVMdIUKE/2k89thjsCxL/CQSCQwaNAgXX3wx1q5d26n7siwLP//5zztteytWrIBlWbjzzjtzfpbOc8WKFeK1iy66CMOGDZM+N2zYMFx00UXi/5qaGvzhD3/A3LlzO+egC8D777+Pgw8+GBUVFbAsC6+88or2c5MmTYJlWXjxxRc7bd+0zUmTJnXaNneGfevQ1Y5ne4Cesccee2xHH8pOhWOOOQbHHHPMjj6MCBEiRNhuSOzoA4gQIUKEroBHH30Ue++9N1pbW/HRRx/h1ltvxYcffogFCxagoqJiRx9eh/HNb34T06ZNQ//+/QM/9/LLL6O6ulr8X1NTg5tuugnDhg3DAQccsI2P0g/HcXDOOedg+PDheO2111BRUYG99tpru+3/wAMPxLRp0zBy5Mjtts+usO8ILvr3749p06Zh991339GHslPh/vvv39GHECFChAjbFZFTHSFChAgA9t13Xxx88MEAgGOPPRaZTAZ//OMf8corr+CHP/yh9jstLS0oLy/fnodZMHr37o3evXvn/Nzo0aO3w9GER01NDbZs2YKzzjoLxx9//Hbff3V1Nb7xjW9s9/3u6H3/ryOTySCdTqOkpORrew/4OXY2okBQhAgR/tcQyb8jRIgQQQMypFeuXAnAlUpXVlZiwYIFOOmkk1BVVSWcvC1btuDyyy/HwIEDUVxcjN122w3XXXcd2tvbtdv+17/+heHDh6OkpAQjR47Ef/7zH+n9jRs34vLLL8fIkSNRWVmJPn364LjjjsPHH3+s3Z5t2/jzn/+MIUOGoLS0FAcffDDef/996TM6+bcOXP49adIkHHLIIQCAiy++WEjk//CHP+DJJ5+EZVmYNm2abxs333wzioqKUFNTE7ivyZMn4/jjj0dVVRXKy8sxZswYvPnmm+L9P/zhDxg0aBAA4Le//S0sy/LJ1XVoa2vDlVdeiX79+qGsrAxjx47FnDlzfJ+bOXMmzjjjDOyyyy4oLS3F6NGj8fzzz0uf0UmeaSwsXboU48aNQ2VlJQYPHoyrrrrKd8/XrFmD73znO6iqqkL37t3xwx/+EDNmzAglKe7ovnV47rnncNJJJ6F///4oKyvDiBEj8Lvf/Q7Nzc05v2vCa6+9hsMPPxzl5eWoqqrCiSeeKI2Lzz//HJZl4YUXXhCvzZo1C5ZlYZ999pG2dcYZZ+Cggw4y7mvjxo0oLi7G73//e997ixcvhmVZuOeee8RnwzxHJPG+/fbb8ac//Qm77rorSkpK8MEHH2jl30uXLsXFF1+MPffcE+Xl5Rg4cCBOP/10LFiwQNou3b9nn30W1113HQYMGIDq6mqccMIJWLJkie/4x48fj+OPPx7dunVDeXk5RowYgVtvvVX6TJgxq0PQOeaz3cmTJ+Pwww9HaWkpBg4ciN///vd46KGHfHOLKv+m/d9xxx247bbbMGzYMJSVleGYY47BF198gVQqhd/97ncYMGAAunXrhrPOOgsbNmzw7f+5557D4YcfjoqKClRWVuLkk0/WPtsRIkSIsL0ROdURIkSIoMHSpUsBQGJ3k8kkzjjjDBx33HF49dVXcdNNN6GtrQ3HHnssnnjiCVx55ZV48803cd555+H222/Ht7/9bd92X3vtNdxzzz24+eab8eKLL2Lo0KH4/ve/L+UBb9myBQBw44034s0338Sjjz6K3XbbDcccc4w2n/Xee+/F+PHjcffdd+Opp55CLBbDqaeeqnV488GBBx6IRx99FABw/fXXY9q0aZg2bRouu+wynHvuuejXrx/uu+8+6TvpdBr/+te/cNZZZ2HAgAHGbX/44Yc47rjjUF9fj4cffhjPPvssqqqqcPrpp+O5554DAFx22WV46aWXAAC/+MUvMG3aNLz88ss5j/vaa6/FsmXL8NBDD+Ghhx5CTU0NjjnmGCxbtkx85oMPPsARRxyBuro6PPDAA3j11VdxwAEH4Nxzzw2VP5tKpXDGGWfg+OOPx6uvvopLLrkEd911F2677TbxmebmZhx77LH44IMPcNttt+H5559H3759ce655+bcfkf3bcKXX36JcePG4eGHH8b48eNxxRVX4Pnnn8fpp59e0LE888wzOPPMM1FdXY1nn30WDz/8MLZu3YpjjjkGkydPBgDss88+6N+/PyZMmCC+N2HCBJSVlWHhwoUi+JJOp/Hhhx/ihBNOMO6vd+/eOO200/D444/Dtm3pvUcffRTFxcVCWZLvc3TPPfdg4sSJuPPOO/H2229j77331h5DTU0Nevbsib/85S8YP3487rvvPiQSCRx22GFaZ/naa6/FypUr8dBDD+HBBx/El19+idNPPx2ZTEZ85uGHH8a4ceNg2zYeeOABvP766/jlL3+JNWvWiM90dMyazjHsdufPn48TTzwRLS0tePzxx/HAAw9g9uzZ+POf/xxq3wBw3333YcqUKbjvvvvw0EMPYfHixTj99NNx6aWXYuPGjXjkkUdw++23Y8KECbjsssuk795yyy34/ve/j5EjR+L555/Hk08+icbGRhx11FFYuHBh6GOIECFChG0CJ0KECBH+h/Hoo486AJxPPvnESaVSTmNjo/PGG284vXv3dqqqqpx169Y5juM4F154oQPAeeSRR6TvP/DAAw4A5/nnn5dev+222xwAzrvvviteA+CUlZWJbTqO46TTaWfvvfd29thjD+MxptNpJ5VKOccff7xz1llnideXL1/uAHAGDBjgtLa2itcbGhqcXXbZxTnhhBN857l8+XLx2oUXXugMHTpU2tfQoUOdCy+8UPw/Y8YMB4Dz6KOP+o7rxhtvdIqLi53169eL15577jkHgPPhhx8az8dxHOcb3/iG06dPH6exsVE6z3333dcZNGiQY9u2dI533HFH4PYcx3E++OADB4Bz4IEHiu87juOsWLHCKSoqci677DLx2t577+2MHj3aSaVS0jZOO+00p3///k4mk5G2+cEHH4jP0FhQ7/m4ceOcvfbaS/x/3333OQCct99+W/rcT37yE+M11Z1PIfsOA9u2nVQq5Xz44YcOAGfevHl5HU8mk3EGDBjg7LfffuJ6OY7jNDY2On369HHGjBkjXjvvvPOc3XbbTfx/wgknOD/60Y+cHj16OI8//rjjOI4zZcoU3zOjw2uvveb7XDqddgYMGOCcffbZxu/leo523313J5lMSt+h94LuVTqddpLJpLPnnns6v/71r8XrdL3GjRsnff755593ADjTpk1zHMe9XtXV1c6RRx4pjVsVYcesDkHnGHa73/3ud52Kigpn48aN4jOZTMYZOXKkb24ZO3asM3bsWN/+R40aJR3n3Xff7QBwzjjjDGnfV1xxhQPAqa+vdxzHcVatWuUkEgnnF7/4hfS5xsZGp1+/fs4555xjPPcIESJE2B6ImOoIESJEgCv3LioqQlVVFU477TT069cPb7/9Nvr27St97uyzz5b+nzhxIioqKvCd73xHep0k1KoM+/jjj5e2GY/Hce6552Lp0qUSK/XAAw/gwAMPRGlpKRKJBIqKivD+++9j0aJFvmP/9re/jdLSUvE/Mb4fffSRxIZ1Nn72s58BAP7973+L1+69917st99+OProo43fa25uxvTp0/Gd73wHlZWV4vV4PI7zzz8fa9as0TJ+YfGDH/wAlmWJ/4cOHYoxY8YIqevSpUuxePFiwWim02nxM27cONTW1ubcv2VZPnZ3//33F+kCgMvGV1VV4ZRTTpE+9/3vf7/gcwu7bxOWLVuGH/zgB+jXrx/i8TiKioowduxYANCOrSAsWbIENTU1OP/88xGLeeZEZWUlzj77bHzyySdoaWkB4I77ZcuWYfny5Whra8PkyZNxyimn4Nhjj8V7770HwGWvS0pKcOSRRwJw0xr4vaGxfOqpp6Jfv35CRQEA77zzDmpqanDJJZdIx5jPc3TGGWegqKgo53mn02nccsstGDlyJIqLi5FIJFBcXIwvv/zSuF2O/fffH4CXWjJ16lQ0NDTg8ssvl8YtR2eMWd055rNdUpf06tVLfD8Wi+Gcc87JuV/CuHHjpLEyYsQIAG4hRQ56fdWqVQDc+5tOp3HBBRdIx1haWoqxY8f+T1WkjxAhQtdE5FRHiBAhAoAnnngCM2bMwJw5c1BTU4P58+fjiCOOkD5TXl4uVcYGgM2bN6Nfv34+Y7hPnz5IJBLYvHmz9Hq/fv18+6bX6LN/+9vf8LOf/QyHHXYY/vvf/+KTTz7BjBkzcMopp6C1tdX4ffW1ZDKJpqamEGdfGEjK/K9//QuZTAbz58/Hxx9/nLNt2NatW+E4jrYSOUnG1euWD0zXg7a5fv16AMDVV1+NoqIi6efyyy8HAGzatClwH+Xl5VIgAwBKSkrQ1tYm/t+8ebMvKANA+1o+CLNvHZqamnDUUUdh+vTp+NOf/oRJkyZhxowZQmKvG1tBoOtpuo+2bWPr1q0AICTdEyZMwOTJk5FKpXDcccfhhBNOEIGnCRMm4IgjjkBZWRkALzeffqgCdyKRwPnnn4+XX34ZdXV1ANyaAf3798fJJ58sjiHf5yhXZXzClVdeid///vf41re+hddffx3Tp0/HjBkzMGrUKO12e/bsKf1PhcHosxs3bgQAUT9Ah84Ys4D/HPPZbmeM51122UX6v7i4OPB1GtN0nIcccojvOJ977rlQ5x4hQoQI2xJR9e8IESJEgMuMUPVvE3QsUs+ePTF9+nQ4jiO9v2HDBqTTaYnVAYB169b5tkGvkfH91FNP4ZhjjsE///lP6XONjY3a4zJts7i4WGKCtwV+9atf4cknn8Srr76K8ePHi2JcQejRowdisRhqa2t971F+rXrd8oHpetD1pW1fc8012rx3AJ3Stqtnz5749NNPQx3f9sDEiRNRU1ODSZMmCXYagHBM8wVdT9N9jMVi6NGjBwDXYRw+fDgmTJiAYcOG4eCDD0b37t1x/PHH4/LLL8f06dPxySef4KabbhLb+PGPf4zTTjtN/M+rVF988cW444478J///AfnnnsuXnvtNVxxxRWIx+PiM/k+RyaWWMVTTz2FCy64ALfccov0+qZNm9C9e/dQ2+Cgug1cqaKis8aseo75bLdnz57CueXYHuOZjpPqUESIECFCV0PkVEeIECFCB3D88cfj+eefxyuvvIKzzjpLvP7EE0+I9znef/99rF+/XrA7mUwGzz33HHbffXfBVFmW5WtzM3/+fEybNg2DBw/2HcNLL72EO+64Q7CXjY2NeP3113HUUUdJTkYhUFk1FQcddBDGjBmD2267DZ999hl+/OMf5+zrXVFRgcMOOwwvvfQS7rzzTsFM2raNp556SjhgheLZZ5/FlVdeKRyIlStXYurUqbjgggsAuE7CnnvuiXnz5vkco87E2LFj8fzzz+Ptt9/GqaeeKl5Xq71vL9D1UMfWv/71r4K2t9dee2HgwIF45plncPXVV4vtNzc347///a+oCE444YQT8Pzzz2Pw4MFC7jt8+HAMGTIEN9xwA1KplFSkbMCAAcZidyNGjMBhhx2GRx99FJlMBu3t7bj44ot955vPcxQWuu2++eabWLt2LfbYY4+8tzdmzBh069YNDzzwAL73ve9pnfttNWbz2e7YsWPx1ltvYdOmTcLJtW1bquq+rXDyyScjkUjgq6++8qXgRIgQIUJXQORUR4gQIUIHcMEFF+C+++7DhRdeiBUrVmC//fbD5MmTccstt2DcuHG+Ssa9evXCcccdh9///veoqKjA/fffj8WLF0uO1mmnnYY//vGPuPHGGzF27FgsWbIEN998M3bddVek02nfMcTjcZx44om48sorYds2brvtNjQ0NEisX6HYfffdUVZWhqeffhojRoxAZWWlz9n51a9+hXPPPReWZQnJaC7ceuutOPHEE3Hsscfi6quvRnFxMe6//3589tlnePbZZ0Ozhjps2LABZ511Fn70ox+hvr4eN954I0pLS3HNNdeIz/zrX//CqaeeipNPPhkXXXQRBg4ciC1btmDRokWYPXt2pzgKF154Ie666y6cd955+NOf/oQ99tgDb7/9Nt555x0AkHJLtwfGjBmDHj164Kc//SluvPFGFBUV4emnn8a8efMK2l4sFsPtt9+OH/7whzjttNPwk5/8BO3t7bjjjjtQV1eHv/zlL9Lnjz/+eNx///3YtGkT7r77bun1Rx99FD169Ahsp6XikksuwU9+8hPU1NRgzJgxPqY23+coLE477TQ89thj2HvvvbH//vtj1qxZuOOOOwLl20GorKzEX//6V1x22WU44YQT8KMf/Qh9+/bF0qVLMW/ePNx7770Att2YDbvd6667Dq+//jqOP/54XHfddSgrK8MDDzwg2rFty/E8bNgw3HzzzbjuuuuwbNkynHLKKejRowfWr1+PTz/9FBUVFZ0y30WIECFCoYhyqiNEiBChAygtLcUHH3yAH/7wh7jjjjtw6qmn4rHHHsPVV18tclU5zjjjDPz85z/H9ddfj7PPPhsrVqzA008/LbVZuu6663DVVVfh4Ycfxje/+U089NBDeOCBB0QBJxU///nPceKJJ+KXv/wlfvCDHyCdTuPNN9/05YQXgvLycjzyyCPYvHkzTjrpJBxyyCF48MEHpc9861vfQklJCU4++WTsueeeobY7duxYUeTtoosuwve+9z3U19fjtdde63DLqVtuuQVDhw7FxRdfjEsuuQT9+/fHBx98IHJyAeDYY4/Fp59+iu7du+OKK67ACSecgJ/97GeYMGFCYEunfFBRUYGJEyfimGOOwW9+8xucffbZWLVqFe6//34AKEgq3BH07NkTb775JsrLy3HeeefhkksuQWVlpWhhVgh+8IMf4JVXXsHmzZtx7rnn4uKLL0Z1dTU++OAD33g97rjjEIvFUFFRgcMPP1y8Ttf72GOPzcsx+973voeysjKsWbPGx1ID+T9HYfH3v/8d5513Hm699VacfvrpeO211/DSSy9J4ytfXHrppXjrrbeQyWRw2WWX4bTTTsPdd9+NIUOGiM9sqzEbdrujRo3Ce++9h7KyMlxwwQX48Y9/jH322UcE0rp161bwMYTBNddcgxdffBFffPEFLrzwQpx88sn4zW9+g5UrVwYWRowQIUKE7QHLcRxnRx9EhAgRIkTYefH666/jjDPOwJtvvolx48bt6MPp8rjllltw/fXXY9WqVQWzmxEidBWcdNJJWLFiBb744osdfSgRIkSIsMMQyb8jRIgQIUJBWLhwIVauXImrrroKBxxwgJQ3HMEFSXf33ntvpFIpTJw4Effccw/OO++8yKGOsNPhyiuvxOjRozF48GBs2bIFTz/9NN577z08/PDDO/rQIkSIEGGHInKqI0SIECFCQbj88ssxZcoUHHjggXj88cc7lAf9dUV5eTnuuusurFixAu3t7RgyZAh++9vf4vrrr9/RhxYhQt7IZDK44YYbsG7dOliWhZEjR+LJJ5/Eeeedt6MPLUKECBF2KCL5d4QIESJEiBAhQoQIESJEiFAgokJlESJEiBAhQoQIESJEiBAhQoGInOoIESJEiBAhQoQIESJEiBChQEROdYQIESJEiBAhQoQIESJEiFAgdopCZbZto6amBlVVVVEhnAgRIkSIECFChAgRIkSIsM3hOA4aGxsxYMAAxGJmPnqncKpramowePDgHX0YESJEiBAhQoQIESJEiBDhfwyrV68ObIW5UzjVVVVVANyTqa6u3sFHEyFChAgRIkSIECFChAgRvu5oaGjA4MGDhT9qwk7hVJPku7q6OnKqI0SIECFChAgRIkSIECHCdkOuFOSoUFmECBEiRIgQIUKECBEiRIhQICKnOkKECBEiRIgQIUKECBEiRCgQkVMdIUKECBEiRIgQIUKECBEiFIi8neqPPvoIp59+OgYMGADLsvDKK6/k/M6HH36Igw46CKWlpdhtt93wwAMPFHKsESJEiBAhQoQIESJEiBAhQpdC3k51c3MzRo0ahXvvvTfU55cvX45x48bhqKOOwpw5c3Dttdfil7/8Jf773//mfbARIkSIECFChAgRIkSIECFCV0Le1b9PPfVUnHrqqaE//8ADD2DIkCG4++67AQAjRozAzJkzceedd+Lss8/Od/cRIkSIECFChAgRIkSIECFCl8E2z6meNm0aTjrpJOm1k08+GTNnzkQqldrWu48QIUKECBHyxsrNzWhNZnb0YUSIECFChAgRdgJsc6d63bp16Nu3r/Ra3759kU6nsWnTJu132tvb0dDQIP1EiBAhQoQI2wPLNjZh7B2T8ItnZ+/oQ4kQIUKECBEi7ATYLtW/1WbZjuNoXyfceuut6Natm/gZPHjwNj/GCBEiRIgQAQDWbG0FAKze0rqDjyRChAhdDesb2jBr5dYdfRgRIkToYtjmTnW/fv2wbt066bUNGzYgkUigZ8+e2u9cc801qK+vFz+rV6/e1ocZIUKECBEiAADsbOCXfm8rNLalkM7Y23QfESJE6Fz8/JnZOPufU7FiU/OOPpQIESJ0IWxzp/rwww/He++9J7327rvv4uCDD0ZRUZH2OyUlJaiurpZ+IkSIECFChO0BcqYz29Cprm9JYdRN7+K0f0zeZvuIsPPDcRw8Mnk5pn6lT5eLsP2xobEdALCxqV28VteSxH0fLMXaukjdEiHC/yrydqqbmpowd+5czJ07F4DbMmvu3LlYtWoVAJdlvuCCC8Tnf/rTn2LlypW48sorsWjRIjzyyCN4+OGHcfXVV3fOGUSIECFChAidCDtLHm9LonrGii2wHWDxukbY9rZlxCPsvFi6oQk3v7EQ17/y2Y4+lAhZZLLPa4Y9tze/vhB3vLME375/Sqfvz9nGipkIESJ0DvJ2qmfOnInRo0dj9OjRAIArr7wSo0ePxg033AAAqK2tFQ42AOy666546623MGnSJBxwwAH44x//iHvuuSdqpxUhQoSvLdY3tKGpPb2jDyNCgdge8u9eVSXi7y0tyW22nwh67KhAxtSlm3DNSwtCzw/N2Qr0Le1RJfrtgVkrt+KBD78KHB80LfD5YcbKLQCA9Q3tuq8UjB89MROn/WNylCYSIcJOgLz7VB9zzDGBUbPHHnvM99rYsWMxe3ZURTVChAhff9S3pjD2jg+wR59KvPGLo3b04UQoALbGaO5sxFmhznX1behVWRLw6Y7BcRzMWLEVu/Wu2Kb72VnQ2JbCyXd9hKP27I3bvrP/dt33Pz/8Ch9/uQlH79kLp+7XP+fnt0cqQgQPZ/9zKgCgoiSB878xVPsZuif8lvSuLNkmhQ0nLt6AjO1gQ2M7BnQv6/TtR4gQofOwXap/R+i6aEtl8NiU5Vi1uWVHH0qECAUh08Wksxsb29CWsrEyeqZ2WlDg2C6AHJq1civeWlCb83PcSaqtb8t/R3DZ1jDS0Dmr63DOv6bhmDsmad//bG39/9QasHRDE2rq2/DRlxu3+77b07b0Oxcc4cB1rXnu644Pl5jHhk7J0qeqdJscx/YqmhghQoSOI3Kq/8fx7Ker8IfXF+LoOz7Y0YcSIQQcx4nyLxlueWsRDrjpXazc3HWqsJJKr6s5+9saO0qemMrYqOnk4kB06wpxZM7+51Rc/vRsfF5Tn2Mf3rbX1ed//Lbt4Kx/TsV3HpiW8zgX1jQAAJra01inOPAbGttw2j8mb5M1oDXZNSXLdH93xDNK83c65L7pseJjsqauNXKytzGC1hRPyeK91pulc7SlOmfcO44j2PCg271sYxO+XN/YKfuMECFC4YicagXJkNHjrwuWs5YQQee+sbE9WsS3I9IZG3e+swQfK0zKb16cjyNum4i6KAcTAPDgR8vQ2J7GtS8v2NGHIkDOUlij+euArc1JHHbL+/jti/N97zmOg8sen4H/98zsbTKH/O6/CzDmLxM7tTpyphMkt+TImsCvxbqG/JnqpmQa81bXYdbKrWhLBa9bXPL97kK5xWVtnbfv9nRhzsCd7yzBH177XDqnR6csx8gbx+O9hesL2ua2hLMd2b+M7eDlOWuwekuLtM8Mk0E4joNZK7dq86y9z7u/73l/Kcb8ZSKe/TRqNbotEaQ0osAID3BXlnrZlBs6Ka+aD0/TWLVtB8f99UOceNdHqG9Ndcp+tzdem1eDw299H3NX13XK9tY3tHVakCHMmmXbDp6YtgLzOun4dwTe/Xwd/vbukq8tabOlObldgqg7rVN994QvcOLfPsSmpo5NXq/OXYtb31oEx3GwfFMzRt/8Lm5+fWHO781fU4cLH/kUi9cFG05dATV1rbh9/GIfQwEAe/SpFH/PW1On/f6HX2zEIX+egBtf+3xbHWLeyCcSvDMW+Hjn8/W494OlOP/hT6XXX5i1BrX1bXhk8vIddGRdE/NWB7OCnYnxn9Vi6lKzA6cawYDLpnY1zFtdhwmKw/PVxiY8PnWFMM4ythPq+flyQxM2NycxdZn/utS1pDBh0Qa8Ob8Wa7Z2fs7hf2evAQBc/7JXHdlxHCxYU18wY+Q5XYUfVy4Dl2+7EPm3w25LLueQG4bjP5Od6ooSzxnY0px/sC6ZtnHvB0vx2NQV+GqjF6S96fWFcBy30FJXg65687bC1K824dfPzcNNr7vrJ+2SB92mLN2Ms/85FTe/7l9jVfnvXRO+AIC8A4mO4+DxqSvw2Vp5rpy3ug5vzK+JguYKkgHznk6SzS/f+sbC0jlU8KBexnaweksLxv39Yzw3wysGnGLBmcW1wfZoMm3jV/+Zg5eyc2YhWL2lBZs7aHeruOr5uaitb8O37uucyukXPvIpTrzrI8w32LRh4DgOLnlsBr5135Sc68jHSzfhhlc/x5mddPw7Aj9+chbumbgUz3y6KveHDchFSranM6gtQJXVUSyqbcAhf56A3/7XH/TvbOxUTjWPoNw94Ut8uaEJt7y5qEPbvO3txfjXR8vw5YYmPDdjNZqTGTwyxXVY6ltTmL1qq/Z7L89Ziw+/2Ij/zip8cuoI1tW3YewdH+Du7AIbhEenLMf9k77CL5+d44tC8YVg6tLN2u/f8/6XAIAnpq0s/IBDYuaKLTjzvimY9pX+WABXsj7ihvF4c37uvMXfvjgfh93yvmAJcsFxHExcvN744H+wZMN2iUY2tXsGuc6p+XJDk/j7s7X1OOv+Kfhkmfma5YPGthSWsu0Xgu1toIWtpLu5qR1H3jYRN7xaWHuadfVt+NnTs3Hho59ig4FdpFPPZPNdX527Fvvc+A7e+Xyd9vM7Cj97ahZ+9ORMbGhsQ0syjauen4cT//Yhbnztczw2ZQUA4Jx/TcNxf/0wp1Eh2PmM/77zVxava0TGdvD+ovVo7uTq6MuY6uajLzfh9Hsn4zca5jwMvEJEhY/jhrbg8+MOnS7gmQvcoM/FqPNpf/ryLUbneXNT/k41f/a4w1aS2LHmhW07mPrVJtS3+IMb21P+XZfdP/12NEG3mux6s07DcIaR/4bB1K8248bXPsdFj34q3bPLn56Nnz8zB49mn/mwmLJ0E06+6yOM/yz3OtzZeGXOWkxasmGbbLuKMc6meU8n/5aUJwXWSPDvx2F/A+98vg4Laxvw2/8uwFcbm7L79T6/OkfQ8v1F6/Hq3Bpc+fy8glQpDW0pHHX7BzjoTxNyzo11LUlc89J8zFyxJed2u5cXi78745lcvM5lqe96L7d9zDFh4XocdftEzFq5BRnbwcTFGzBvTT3+k8PR5AHUQtQCjuPgtvGL8dQnHbOx21IZ/PiJmXjgw68K3sZrc2uM7z08eTn+74V5WpLg9vGLccDN72LBGjPBcc1LC3D4rRMx+Ut/8H1LcxIvzV4TSi28YE09Xp27NvT6vCRrd7z7+bptzsTvVE71Z5octckBjNHDk5fjksdmBLLZVCykJZnBgO5eoYn6lhSue3kBvn3/VJ8El39vQ2PnRuzCYtKSDVi5uQX3T/pKMhoa21K+KCIt1J+u2IIXlQgln7RN8snde1eIv7e1EfLCzDWYt7oOv/rPHOPkdHOWAfl/z/grytu2y1DRgjFt2WZsbk7i2ZDRt0lfbMQlj83EmL9M9L1XU9eKSx+bgUsfD8e8rNzcXLCckss1dSzWF0za9O7C9ZizymUbwmLJukbjYnf1C/Nw4l0fYorm2Xpi2gqMvGE8PggwaB786Cvsdf14fLrcv/3m9jQenrw8L0bs/16Yh+8+MFVr4AzexauGyg0ZEzM85avNWLO1FS/MXFOQgmHZpiY4DpDKOMYgE39GMraDX/1nLpJpGz95chYA9/r86Y2FOzznuq41BccBGtvS+GDxRvx39hphJC7f1IT61hRmrdyKVVtaQucHpzRONT/PxbUNeGzqClz6+Ez8PRus6yh0Y4CcuzcX1BoNXMdxsHRDk/Y+2EoeayFoyMlUd8wI5460k2Moq4zXe0wCzg2TzQHP5UdfbJTmHQIPjnC10/6Dumk/s70w6YsN+MG/p+P3mgCazrHlmLJ0k2/dn7RkA857aLqW/frwi4046vaJeFcTOFNZTcFUs2fFK4xnfn7o98CACtAtyTSemb5KG/BblnXCNjUl8S9mdK/N1iO4+Y2FxnH40MfLcOOrn0lj5b+z12DJ+kb89KnZeHnOGjz76Sr8d9aa0OqQ2vpW/OLZOVr7KggTFq7HFc/NxY+ecAOChFfnrsW5/5qGrzY2oa4lie/8cyqufH6u1vBuS2WwbGOTdp3oVlYk/l5hyKvWMdV8LK0vIJ1DB37ojuOgpCgu/r/mpQWwbUc6hlzkQVHcM/mnLN2E5vY0Pv5yY+i1kNeuyFWIc8KiDXj209V48KNlObd74JDu4u85CpG1eF0DvnnPx3h9nmvftKUymL+mDusb2nI6VR8s2ZjX3DNxyQas3tKKD7/YJM2Zz80MJs+qWSDm87X1yNhOqOMjrNzcgn9O+gp/eO3zDuXjT1qyEe8uXI+/T/iyYIXmpyu2GI/7j28sxAuz1uCZ6X57eupXm9GSzOChyeb7/dLstQCAK56b43vv7xO+wJXPz8PfcgRCFtU24Jx/TcOv/jMXH2mccx2ak+4YaGhL44sNjfjvrDX4wb8/EXNfZ6pZdyqn+qMv/BdwQ2O7cWF84MOvMHHxBlz+9GyjkU0TUjJtozThTVgLaxvwcfaGTdWwpqmsU70xhFM9d3UdHpuyXHucH32xEZc8NiNv9pNYmWTaxitz3YHalsrgzPum4Jg7Jkk5t1uavWO89a1F2MoMJ35Ic1bVaR/ogd3Lxd9hC0KlMjb+O2sNTvvHx7js8RnGiaKxLYVT//4xfp51kFdvdSfqDY3t+NMbC7VGxt79q8Tf6oT5zufrcPq9k3HH+CUAvEjzS7PXhnJiKMrmOH6WalFtA2wH2NTUnrMAz7SvNmPsHZNCpRLowHetW9i5zJJy81Lp8B7ARY9+iu88MM1nBDqOg+nLt8BxgHsnLpXeq2tJ4o7xS9CSzOAPr32ujSi6EsOVSGZsPDx5GWzbwa1vLRJqh7+//yX++MZC/OnNcNfFcRy8NGctZqzYqmXiy5iRQaqS2au2Yt8b38F9Hyz1fZ7yrFpTGSzd2IS3F9Ri3N8/FrUFnpm+Ci8GqE+4fPmp6Su144AbOWnbQTzmtU5qTWZw69uL8dDk5XgzRIXo9nRmm7H+nI1Vn8+1da1Yy871c01+cENbCouyckNyQtOactn8+Beta8AHi92AzKyVsvHUlsoUtLhVlXhGMFVzJqciYzt4boY+93TCog044W8f4q/vLvG91xkVd3MxFnzTtfXhDTCCial+f9F6fO/BaViz1TN61W2/MqdG+12+VnDU1rfigkc+xc+emuV7r5Ex8pyl4NWQFyiS4+b2dDYXfNsVMluxyT1/XXBPMNWaa97YlsIPH5qO8x/+VDq+J6atxOSlm/BDxbGub0nh6hfmYfWWVjw/0z/WvJ7G2X1qpOdBLdzUsTi0p7ceq9fvlTk1uPblBdq5bw1ziP798TKsq2/zBX2vM0jK//TmIjw+bSUmfeE5wCXMXvr1c/NwzUsLcNUL83DEXybiZ0/NwpXPzQ20a96YV4vX59Xg/Ic/1TJXBE4StCYz+ENWIp/KOHiePdu3j1+C6cu34Pi/fojfvDgfM1duxUuz1+IFzXx+5r1TcNxfP8SI34/HH5S0Nn4Llm80ONW2X8nCzYvOIltUppof3KfLt2D85+ukcbRmayvW1bfh/z0zWxs05+P9jfm1uOjRT3H+w5/i6hfmhWLweBvAGTkYaBqbbSGYRwvedicskgP2r8ypwec1DfjFs3Pw1oJaHHPHJJxx7xQcdsv7+O4D03zHrdr6rwYwryp4cItf10W1DXh7QS1ueWsRlm7wBxb5fZq/th63v7MYh93yPr734CdGtSsH1dRI2w6+XF+4SpDuSWsqgy83NKEtlcGMACeZg5kpUq0lHV6b57+mjW3uevf2gnWSj6HDpqakz3YidcHT01calYdbm5P48ZMz0ZodW89MD8fst7R7+5q+bAv+Mn4xpn61Gbe9vRhrtrbgmDsn4dv3TwlQcLXjsayCORd2KqeaRzSlyJCGRWlPZ4TD++nyLbjq+XnawgX04KQytpRD8/ZntcIoUnOQ6PNAbqd66YZG/PDfn+APry/EE9NWSO/VtSTxq//MwcTFbgT8nc/X4dEpy40y0UenLMcV/5mDVMbGMjbZP/vpKjiOg6enr8Kyjc1obE9jIcutIUlfcTyGrS0pyZjnD1syYwtDt6k9LSZFPmHQwM+Fnz01C1e9MA+frW3AhEUbcNXz83wTFeDK6BfVNuCN+bVoTWawikVaX5i1Bvvc+A5+9MRMaaLcrZeXB65GuSnYQNshRcG6hrZQhYwG9fBYgI2N7Zi1cguuf2UBmtvTkiR6s2J82raDp6evxEMfu1G6adl9vTa3pqB8Wp5vZ4oIk0FEm0+F7P+TztiC/f7Fs7IiYEtzUsgUpy3bLBmPD328HI3ZyW7l5hYt+794XaOI/k1cvAEvzlqDf320DH977wssrGkQ0qL3Fq4PJfNpak+LMfOxxvDi42l2duw+Mnk52tM27njH7yxxpm3+6nrc+8FSLKxtwMuz16C2vhXXvrwAV78wz7iorGHjs64lJfJ5OfgQz9gO+nfznIuVW5qFXXT3hC+0gR66LkvWNeIbt7yPq16Ypz2WjoJLGOkZ717uOqhrt7aK+wgAn6/1O9W/enYOTv37x/hyfaPHVGvuKTfk5q2ux8yV7sL/xbpGMf+0pzM47s5Jokdsfufhbf/DrNG/kRni/5mxSuusr1DmCt02O6ImyIepbk1l0NCaH5trKmJ06eMz8cmyLfjls3N87+/RpxKW5T7bdP582jDJv7c2u+eyVSOlJhYAcIMvdK35tSPnqj2dwTUvLcAhf56As/85Vev85cLzM1Zj4uLcxc/qstd/XUObb53OCOPZ/z2uDOI2Ab3e2JbG+Q9/KgzHW95aJLY/XyN9pOugOsd8jlc/w6HKv+kZBfwBV1Ll6QI6FCSzLKAtZePxaSukgAgAvL94Q2Dg/HNmCzH/CvGYhUOG9cCgHmXY3JzE25+tw0tz1uI3L843GvT8GH/0xEwRoOMY/1ktDvrTBDycrSFy/6SlWLO1FUVxd+fPTF8lrl0xSzd4l9WK+PObi3z3/8usY5S2HTz1yUopOM/vwTLDOuAFSPhrHVOeuNuTu3uoqid1Olq6oUl6bfXWFjzz6Sq8Ob8Wj05d4ds+t0VeyQarAeCVuTW4+Q050J2xHcxdXSet03wuV4OiunNxf+de59WgIAcj13H507OxrqEN5cVxWBYwc+VWzFKcVrVV3cOTlwmHL1fQlg414/ht1Z89PRsPfrQM173sV77wU5y9cqsI5E5fvgXfvn8qbnlrEZrb09jSnNQ+DzwIs7C28PowPIA4f00dbh+/BN99YJoxsMzBn59JAe3kAPfeq9eS5pJkxtbaRQAwvK9nu6sFM0ll0diWNh7vvR8sxeotreiTrbQ/YdEGYxoeB1+jHpq8TMwHr82rwY+emIU1W1sxe1UdzntourYIcG19G+58N1wqwU7lVH9e0yDkPnzA6yTg6+vdi0bRl9fm1eDEuz7y5VLS+E5mbGnyeDkrUwBcp1p9EGixDYpINrSl8OMnZqE5G5G5e8KXWLm5GX95ezEenbIcf35zEba2pGBZQGN7Gj95chZuen0hfvLkLLwxvwa27bgGa/Zc73rvC7wytwbTl23Bsk2eg7d4XSPeW7heMlC4DIiiL8fs1RuALPNWJ44Ji9ajLZXBMXdMwvF//RCpjO2Tb4bBzOyE+/1DB6MobuHNBbUYeeN47HHdW/h3Vg7kOA6e/sRzzJZuaBKGyyVH7IriRAytqQzeW7he6hnJ78V7C+WoJkW4KDrKI/lBDCQhxiyFpRub8Oc3F+GpT1bh+ZmrZae6KYnlm5rxq//Mwd/eXYLLnpiJ617+DH96cxGWb2oWTHJjexozV2xFTV0r3l5Qi/qWFJrb03jn83WBecv8mnOjnweTvtrQnP2se666fNbNTe340RMzhXQKAJpZ1K49beP6Vz4TY+wrJTJ/7csLcNGjn+Kyx2fi0Wyk7oQRfQC4ufa0WBH4gpjKOLj+Fe95+91L80VEtrEtjWnLNmdbhpgdlzpmxOtkgvyrc7KG++BdPCZHleHxXPSJizcIBnbxukbJqDOlCxBTPSDrKOvy+vn5pG0HA7p5gRoeDFu2sRmvzl0rfff/XpiHg/70HqYv24wz75uMrS0pIZkKwpfrG/GDf3+iZeVMsIXR40kIB/dwr926hjbJuNal3tRkq0bX1Ld5TrVOSs1eWlvXKipVN7anxfO+qSmJmvo2zFtTL1I4zv7n1FD5eNyYnfzlJmRsR6q+W1vfhg80RgIFofTsoPu7IyKBfAqVAUBtQ34FXGQWy3+g85iDR0bfwO5lGDvcXQeIVeXfNcm/g5j7JuaYtaYyYg7hn6WqvvNW1+PZT1ehJbsmqtV+19W3BRZB+rymHr/573xc/vRsLctNdQwAoJ4ZR+r4FUETzfnwdDHu4NBcUloUE6kRC2sa8Fz2OlqWaw+oDpV67byaC3L1b3d//nNWjzWISW0VgXD/dihIdtCQHu7/W1uFIVxZksBRe/YCEJxTydcsujZXnzQcS/54Cl746RhMuvoY/PuCg3HzmfugvDiOJesbjRJNzpK3pjJCzcSxZJ27vy+ywXyaC285az/0KC9CTX0bJmaVLz1YsAEA/u/kvTCyfzXqW1O4kwVYHcdzTsuL40jbjsS68nFrCq7qC5X5x0o++HJ9I0b8fjzuYMoZfh9tjaOnOuFrt7aKAJbOOeA2An2N5oPHpq6Q7u8z01fiW/dNwT8neakCfP8zczjVFDTSpQSp4Ofw5YYm6TjU4PuI/tX4+DfH4uwDBwFwiRkOPi90Ly/CVxubcdGjM3DJYzOw1+/HBxZ5FV0fbEf7LAIuG6wysVJQYPEG1LWksEtFsTjGBz9ahn1ufAcH/vE9XPOSXw3Cgz6mrhFfrG/Eza8vxMtz9HZsU3taIhjnrvZSAt8N0YGBjw1Teh93vNX7z9llIvpU8PHzX2bXtKczqGXPzCOTl2sDIBQY/Plxe+DgoT2QsR2tOkhFC2PFV29xt0EKwkW1DSgriqNXZTEW1jbglLvdVAN+/GqgJgg7lVMNeBEUHkGe/OUm3w2kwh9De1bg0YsPwQkj+sCyXAnXdCYjpYchmbalKF4jGyBbW1KoURbKZFZqW9+aMubNPvTRMizb1IwB3UqxZ59K1LemcPxfP8QDH36Fm15fKGRJj1x4CI7YoyeKEzERybnqeTev9cS7PsI/Ji5FY1tKFL6ZvWorVmXZy0OH7QLArdzHpQvEbjqOg63ZyfWMAwYAcOXsoiVE9rL1qnQLRbw6twYvzV6LTU3tWFvnLrqcAV1Yq2eq566uw7F3TsI7n6+D4zhisb7ihOG47ez9RXTccYA/v7UIr85di1krt2IJYw6nfOUaxMWJGK7/5ggsvOlkXHD4UACQIl98IZu4eL308JGB15ZyZbP8YRj/2To0tAUbunwMLK5tFE7X3NV1WLpRZqofn7oCr86twT0Tl4qFHXANv6/YZ9/5fB1++NB0/Ozp2Tj4z+/hwD++h588OQvj/v4x/v3RMq3sistouXPDP0qsKx0yfWf8Z7W474OlyNgOHvx4Gd5buB7XvrxAGPmNrAhazAJen1eDG177TOSYAsCQrGP62doGTFqyERMWrUdzMoN9BlTj/h8ehN16VWBzcxL//mgZ6ltT+O2L8/H41BWifc7e/VyJfjJji8AWsTgUt3h1zlqc//CnGHvHJKPshjvVX6xvMhqsgCsxTaZtFLPQNi94157OSKz/OyxSumR9Ixaxsf3CzNXa55qc6jF7uAZoncZxUtmF3tVefjw5vbQ4PcwW+Izt4M0FtWhsS+PCRz/1tUlavaUFD328TCuN+sFD0zH1q8047+HpvvdM4IYhHXKfqhIUx2OwHUiytS/WN/qMG/n7WfZNsxAGyQqXZI1l/r22dAZvfVaLWSu34o35sqrmlrcW+fK5+PWub01hxeZmEewcnc3Ve3GWf+ElIyLIkclX/s3XoZxOtXJd8q0ALsmHNecgjcPsccUs4NyDBwNwg4zpjC05aVsMTLWQMGvuZaMyHimvWudUq2OIpxi0pTI48a4Pcfo/JhsDbROyQdS2lC1Jyp+ctgKH3/o+hl//Nk6++yO0pzPSs/m5ojbjOdXqvvhcRNewLZURrx+6a08AbtB1VlZ1cfTw3tirrzvnqTnX3rXL/tYw1cHyb/k9fl9VJpUcCl2wgK71yAHVANzxSWqK6tIEzjxgIADgFaUAEN8fXwPpeGIxC4nsnJuIx3DiyL644PBhOCc7zki9pYLIhm/u1x+Au04uXteAnz8zW0iyM8q1oqDBqMHdxfbp2abPnD5qAP7v5L3ws7G748bTRwJwSRVio/mlGbO7ey+nMZuQD3GTU+1o7hf/XiFO9dSvNiOZsfGfT1dp5eWO4x8fjiPnVNfWt4pnjdbOjO0Iu0etaL5brwo8eMFB2HegOyZWsPOlYD5XhfB5ZumGpkCZLwWNwqh91PHKC/DSOnjGqAH43al749kfHYaelSU4a7Q7Xt+cXyut1WTzFSdiePKSw1BVmsCslVsxcfEGZGwHf3pzoTGPnz9j3Aa7+9wD8MQlh2LvflWwHUj2HgCtuuDkffrhr+eMwoPnH4SeFV4httfn1fjWSV4bYGFtA2av2oqz7p+C+z5Yivlr6nDZ4zNx0l0f4ZEsGafD7JVbpTH4xrxasQ5OX7Y5UDHpOI40H3385SZc+fxc/PbF+Tj69g/EGGDiFLz7uTcu0hlbOK7xmIWvNjZrSSOJDP1yowgArd3aCsdx0/l2qSjG2rpWjNcodumeFMdj+P6hQwAAz366Omfqgi6v/sbTRwrH+tpxe+Ppy76BIbuUY11DG37x7By8wPLo86mNtNM51VOXboJtO1L0a+pXm7H/H97FtS8vEBePqjf371aKY/fqg4cuPAQ/yN6Em173igRJ8u+AaIQqAecDdJPBECEj6QeHDcEN2ck9bTvYtVcFdssW//ruQYNw7N598NSlh2HRzafg7V8djWP36o32tC0i/nNXbxWsEAC8taAWadtBaVEM9/5gNE4Y0UfIocjJXpmdEBva0uJaHbNXH1SWJFDXkhLycJpExg7vg95VJdjSnMStb3kPbTpjI8Ou9aLaBlz38gIc/9dJkvHwxrwaLN/UjLcX1KIlmRHXtao0gW8fOAiTrj4GE68ai0uO2BWAGzT46VNyoTGq5jm4R5lYqL93iHvP3l+0QUReeeBza0sKjzGZEzGn7amM5FAP7F6G9rSNt3JUDOcTy1sLasU25q6ukyaJTY1JUbRjZP9qnDSyr1igP69pkIydJ6atwPJNzYjHLKQyrqPfs6IYyYyNP7+1CHdrIvR88uGOoE6KrxaJ+ulTs3HHO0twxztLhIymsS0tmGZiqntWFOOv54yCZQFPfbIK905cKoIBJ4zoi5vP3AffO2Qw/vitffHHM/fBz4/dA//4/mgUJ2L4zSl7AQD+/fFy/L+nZ+O5matx42ufC3bszu+OEs70ZUftJpx0ALg0OwZemrMWk5duwqotLXhsynKsb2jDz5+ZLbG3da3ys6UuhnwuTaZtbG5ulyLV3KletrEZGdtBIntgfB1ftaUFc1bVif+3tqR8rYcAL+efggYtSf9kzY9JzTEmVc3RWVaIR6iXbmgSCxN3qPtVu6z4vROX4k9vLsIbmnwm2k4YSb16nNxYS8Qt9M8WbOSsdyrj+IpUecyZA17YSzWidI4CzVcUVOPzaWsyg5Z2T0pGWLO1FQ9+tAy3vi0bFermV29pEUbKz8buDsA1glQDkPYZxpEJC9XBB4IqCMvbXp+nU22Sf/M6A95n3ffjMQvHj+iLnhXF2NDYjqlfbZYMWjWthSDk0prLoRoslFfNP1tb34b1DW1iO1RheW1dqzi2hrYUGtvSqKlvMxri3MAndnH1lhb88c1FqM1+74v1TVi5uUUKyKk53fyxVHfFn0k6Xs5SE9O7dEOTGL/7DKgWhdlUCbh37Rzpfzmn2hzEoc85jsyyAn6nj+YNNVDQns4IA3tkf8+ppuB3VWkRTt6nL4oTMXy1sVlKH+PPJl8D6WWu7uK49MhdEbNcA/2Rycvx4qw1OO+h6bjiP3PgOI7IqTxgcHccPbw3bAc45e6P8cb8Wjw2dQVSGZupaUiN5f6OxywcNNS9D3ReFCT77kGD8P+O3QOxmIVDd90FQ3uWC8UbIDtwR2SDo5+wdYJfu1xMNb/MfLvrG9rzrpFAKq6tLSmhrFCZanWTbuqO/D/NOzT+L396Fg798wSsb2gT12jU4O743iGDcd8PD0RJIi7WmHUsGEABjAVr6z35tLKeBUnARbA/RPobjfEfH70bLMvN957LUkYAYK9+Vfjp2N1FpfBv7NYT/apLUd+awgeLPbuA5tvSRAz7DeqGJy89DHv3q8JZowfi9FEDYDtu2tv8NXWoqWvFFf+ZI2wjT0XiSIHIb40eiKOH98ZJ+/QD4Jcu64JY4/ZzP3vSPv3wybXHY87vT0T38iI0JzP4TGGj+ZyzqLYRf3l7MeasqsMd7yzBGfdOwQSmANzaktKOLZoPD9vV9QF4sLM5mfHNgRzcnzr/G0NhWa4q5LmZq7FqS4vIc+fz03uLvGvAA/2meRDwrtOgHmWwHeBX/5mD5va0COAM7VmO874xFIBrW6rnSceZiMfwzf37o7IkgbV1rcZ2wIQWJX+7Z0UxfnDoENx97gG4btwI/PCwodirXxXe/fXROCV7j/kcmI9dtdM51fWtKcnQOmrPXkjELDS2u1UvT//HZKzY1Cyc0P5MennlicNRXZrAwtoG0aeP7pnKVBNIGhPkVJvyqmmyS8RjOGrP3rjhtJH41fF74s1fHokJvx6Ld399NP5y9v4AAMuyEI+5P/d8fzQuGjNMSEdWbWnB2jrPsSJnatdelehTXYqHLjwEM687Ee9fNRaXHuU6LMRkU3S9vDiOypIEvrGb+8CRcU+LVnHCwrezkT/+MKZsOYK1tq4VT09fha82NuMH/54uHmRyIhva0mKhjscsYeAN7VmB3XpX4vpvjsCZBwxA2naEzI4GMU3QXL47ckA1RvavRjJjCwkzPdh9swzgn95cJCQ9Qv6dstHOHJPvH+qxM0Hg58rlLSs3t0j5Z5ua24WhdcUJe+LBCw7GKfu650H5wsXxGBIxS4yDm8/cBxOvcu/7zOtPEI6pKgEG/PJvXbXaJdke6fSaung98OFXqGtJCVb0kcnL0dCWEu26KksTOGv0INx0xj4AXMkOGU2796nABYcPw1/O3h/nf2Mozj98GK4+eS/s1ttVUpy8Tz8cOKQ7WlMZTF66CUVxSzjRowZ3x74Du+HiI3bFqEHd8JOjdxNRxR7lRbjqpL0kGTsAPD5tJa74z1y8Mb8Wv35urgiw1Ck5nGpetWp8t6Vs2almDARJv0cN7o5dWOQYcOeBj7L5uB67KY+VZNoWRsdeWadaFwFVWZ60xijdPdsfXpd3OrJ/NfZk/ePJkCG2IUhtUaVcVxPUyDr9H7MsUV2YgoUUhFBlaZ4BIi+26jyqc5BO3ddlp0jWyRf11lRGGHT82lGAq6k9LV/j7N8k/1xU2yici6P27I2R/auRyjh4QykMR/vU57Gancgg8M/Xt6bw2dp67HvjO7ht/OLAzwL5M9WmysMDWV2ItAgcuP9bloXirLEJuA5JZ8m/S4vceWZ+dq1U7/vyTc3i+wO7lyFmufeU8t+5va7rEbyhsU2StM/M5oPe8tYiJNM2xuzeUxTx2tTULikFPlNqAqjV0OX9tPveI4VM/25l2CP7bC7d0IQvsvLkvfpWYb9B3aXzJ5jk37qcap1SVi2GFSRPFnVQlMtXW+cFBYb1cgP6Da0p4SxVlSZQVVokUnt4cSd+nG0pW6zbDnM6dBi8SznGZVnom99YiKtfmIfJSzfhlbk1qMumQQFAWXEcP8raLRySAkZR1iVilqhkTY4izZMJdkCWZeFbWQaeZML8+pFTzR1HPh62NCe16QhqoIRfDyBbIyFHSz0VXIVF65yqNlGdt4zCVHPQ+J+9qg5tKRvLNzWLuXlQ9zL85ez9MSIbYOmbdao5w06OiO14z5q6ryAJuAiEhJhEabv7DKjGt0e7du9fssFTmstLlWBhPGbhzKz68rV5TEqc/TxVSj9gcHeMv+Jo3HXuAbjjO/tj1ODuqGtJ4ex/TsWYv0zEK3NrRCcKTrbR37zQ6Ekj+wJwiyZzO0OdQ7qVFeEbu/UU/xfFY+hRUSxIL7XoKvchmtrT+HT5FliWV+n/lH364Y1fHCn2pTqJgBcEP/OAgVL3GLLBg9rU8mDJteNG4MWfHo6TRvYV5B9PFSPUMqKvka0BFHTUdUkggu7Wb++HAd1KsWJzC/705iKRrjp4l3JccPhQFCdimLe6Do9NXYGz7p+C+yctlY6zKG6htCiOY/d25ysdAcJBfgFdl9P2749EPIbTRw3Aj47eDbHsPS4tiuPAod0ByOkTX2v5d3valk7w4QsPwcKbT8ETlxyK/t1KsWxTM/723heCqeZtsnpWluAHh7lREHLgaJJyC5XJD8aAbqU4PrvIBDnVpkR5W1l0LjlyV/z6xOEoL04gFrMwvG+V9MASqkqL8Icz9sEVJ+wJwO09uEbTf3C3Xl6rq27lRdi9d6UwKijyQ041ORG0iFC7JHpGYpaF7x48yLePdMbWVvTtXVWCpvY0fvzETCTTtmA469lCXV2agKVEsWMxC3efewDev2os7jp3FB69+BAxMZKRS3mdhLMPco/rJVoUswf982P3wM+P3QMAcOvbi9DU7jn0bekM2tKeHOW7Bw9GLFvYIqiyYdjqw5ubksLB6pfNr6UFihyn3XpX4OBh7gSzR59KnHvwYOzWuxLD+1bBsixccPgwJGIWVm5u8bXC4JNXSzIjDE++pn2RrRKpSuQSypi64oQ9sWefSjS0pfH8jNVSHh0AnHPwYJQWxVBT3yYm+z16VyIIlmXhmnEjxP9/PHNfPHDeQdi7XxV+NnY3AMDvTxuJV39+JHpWluC8bwzBWaMH4qYz90VZcRznHDwYiZiFO76zP3btVYH61pRwgCmSvHJzs5BwUnqCmjOsRjJbkxmJ5XUDUu6zQ4UKh/etFNHU0qIYDsneIzLkz8vOEaqEr6bOlSiVFsUE806s//jPanHJYzOwpTkpM9UZfx4cAOyevb5SEa/sInTUnr3wzhVHY8KVR7vbUHLTgnLUgtrtuMfjZ2e5/DtmWRigbONwocAwOwsyO+8on5OPoVdlMb65v2tsE9PH87naUhm0pvx1AhzmlPB7TNd3aE93PqS5vaokgbLiOL59oGtUv6QUT1EdTumYNfLLMODXtT1t44ZXP0PadqS8RN1ngfyLG/FxxTfVW9OOT12LqIqvrcifTYXKHI0TQSCDZc8+bqBpU9ZAVD/L91WciAl2jGTJUmBG08lgUpaNooDczBVbMGXpJrz92TrELODG0/dB32zF8S3NScmpXlvXKikVHGX8c2zUOdXZuaBvdYlwqr9iTPXwvlUYJRiaOm1V6CAZN31cN95UtlIqpLVRllia5N80Bw7sXiZaRslMtXtNTxjhOg1zmWono8w3SxSFlImpBoDbv7M/rjpxOA7ddRfs3c+zd5ra0yJwVlESx5F79MIBg7vL5237nVeyReIxC4ms2oXmG7H+xWWz9ltZsmDy0k3Y2NguPSsDu5dhaM9y2I7H9KnzgRokcRhjHJSCEaaAEgd//inAK8u//Q60Oh44mtrTSGVsMe5TGY84omtHEEw1OwbuNJJdoJpHQa0WxX0JkVNN1zFmWcLu/WTZFrQmM4Kp1vW9PyTrpHL7mOw+CvJxlBbF8cQlh+LkffpK6ygF73kQR+dU7zOgGgO6laI1lZEC/Oo9OGPUAKl9GYEcbdWpphog/FE6as/e+ODqYzD1d8fhgfMPwj4DqsWxqAUGa+tbRU2ZQ3fdRcxFAETAKqhQL59vE3ELBw3dBQ9ecDC+k7W9KU1GXedpDeWKFwrYqs8NwAPgxbjzu6MAuOvylyztsFdliSD4bnp9IeasqsPDHy93j5OY6ph7bYmQG59NOzWBFIVXnTQct5+9P3576t7Gz5ISghfl/Foz1e1pWaZdFHcj70cP743bv+OyvgvW1muZasBbPNRqm8mM47two4f0wD4D3AGiyjW4A76xqR3Xv7IAP35ipnaxDlp0gtC/WyniMQvJtC1JUwm7sf7RBDL261tTqG9JCae6p+JUf7p8C9rTGekY9+hThTG795Qiz+6DIw/WfQdWY+JVY1FdmsDWlhQWrK0XTmFDa0owaVWlcuEQgmVZ2L13Jc4aPQjH7tVHsJ/qORAOz05EtA+ey3XVScNRXhxHKuNgc1M7Y6ozImJZmoihb3Upjs6qDp6cthKO46AlmfYZJboFQGU1AdfhIuOLFiRiLwm796nET8bujr36VuHWb+/nW+wrSxKCFVUZWNUxIQk4N5ZIOklGDx07/2ZpUQzfP2QITs0yBis2N4trRE51aVEcR+zujgsKWO3eJ9ipBtwF7S/f3g9/+ta++N6hQ3DSPv0w/oqjcUqWheSoKi3CXecegDNGuQGU6745AnNuOBHfPXgwfnTUbuJz147bG6OHdEdjm6s8oWJDdG3VPFXVAOLBFAIpAUi+vGefKmHAHbZrT+w3sLv4bLeyIsHktCqyXVq4B/UoF9euNeWmOjw6ZQUmLt6AqV9t8rFguki9cKo5U511qkcN7u6mP8RkJka0TlMsG86Wqw4xxxvzazDihvF4fV6NTzLInynVMSfJmzoHZpjjKbURU46P3isriuOMUQNw7bgRQj7/5YYmpDO2VLehNWkLaShnLPkxc9k97ZoCipQLTrnsZxwwADHLbRnIcwaD5N80DeRb/Vvd1hcB7VHUPLBNAQW69Pvy/pZ6VrMZgIKrXIkAQAQ73eJH3nZMtQ3oM7rcbZpPqssSYpv8Nz9eLhkmRp2eK36tdUz1+1np94VjhqG8OI6GtrSocE7yvZ7Z4NvmpqRgGijIyIuVSdcuBFNdy5jqYb3KEcsWF61vTSEes7Bb7wrs1a8KRXELdS0pychXAxJe/QG/rRDUUov+5oe7tSUlBQtozlINTHKqByhOtbpWV2TnNR5IV7tKkFKODj/IvikvTuAXx++J539yOMZfcbRQkzQn0x5TXeQG35++7DB8+H/HiO9mHE9B482BnlFNDgbNN/QZlajYtVcFDhjcHRnbwduf1Ur3O2ZZwr4gJo+uN63Nalswfmn5374gWb5ONfv87FVb0dSe9s3TPvm3rQ/aEtZsbWVBWVs4JarD17ebX/7N2VByAlWCJai1qFDQhaj+zYO6fP1pTWWMTDUAFCXkNRJgTHXC/3nAXeMfOO8g3P6d/fG9QwaL1wC5doRwqtn4tixLrIe8YBid4lF79sLTlx2G677pEQ4c5FTPWL5FVrtm5/5RWbULAJxz8CAUJ2JiTbcsSwQUVbXarW8tRjJt45BhPbB77wqMyto3owZ3F7WUZq4wtzDkzzgnZejcdZXnAc9eFIqXkoQ4h4U1DUbVWjxm4fDde6JPVQna07Yo+Eq2/2WKcsUL0MhBoWP26o2SRAwrN7dINXFUEPnRq7IE5xwyGOXFZkVfj6xTzZnqr7lT7UWuihMxiQmlXKHlm7wk+f6MqQa8CdedoLyII5d/H7NXb4zoX42LjhiGkf2rEbPc6DWPPPLWMV+ub8JTn6zCuwvXS21oMoohky8S8ZiYYGjC55vatZffqS4vTgiJw8otzaLvKDmGe/apRFHcQnvaxuampI/BeOD8gzDhyrHCCU9nvPz14/fug5NG9sU93xstRaTenF8rHjh3oZaj37kwtGe5dF6Dd5GNesq/9KQ5yB6zBcuy0D07Ida1pCT5NzlXJAOiqNsjU5bjmDsnYfTN7+G4v34opCWAvi3VudmJF4Ao5b9kXSNsxx1PPbPXu7q0SGrJtXvvShy7Vx+88+ujRURVxZF7uI7+FKWCvbpQCqdaeZ3n/qRtW1oM/nzWvnj84kPRo6JYXKP61rSQa/L7QzIawK2Y2VMTSNDhe4cOETkw+cCyLGHIffvAgThhRB+cc/AgXHbkbqJwzdq6VhFB7lftXtfWVEaWLitWRlsyIxYOklDf8c4SPD19pciRGd63CheNGYYLDh+K6785Anv18wIIe/erQnmxO15ak/JYoL6/g3uUCeMTcA1EGvPpjOJgaoye6tKEYN55IaTF2UWBWHRhNCoyOjXwww14flwqfv7MHKQyDm4bv9gnW/TmKpntri5N4IDsIqmqKczybz+b4h5bHPd8fzS+feAgDO5RjrKiOJJpGyu3tEjzaVs6g9aUdz0JqnpDfZ2YanIM6VntU1WK4dkiUrwFUUrMJ35roTPk3wCM/TZ1n81HYgaY2VY+hQmnmozW7Jii+V6VE/NWivKxmp0+mk+qs8+z+KxyOpxVi1lucArwHD6+aZ1TPWtlHQC3Ngg5PJubk+hTVYKrT3ZTaWid28zk3wdm5Yjvs/63knReOSe+zqvy737dSlGSiEuB32E9y1FaFEdJIo69+7k2CM8nFAF8W74uumMIKppH76vBGD6mvTaY8jbWioCg51SnbUeocSggErMs3/fV+WaxUo9Fp7Yzgeanpra0eIYrSuLiPU6C8ACC56B5+xTyb+U5Lor7j4fGyzrWqQAAYjFg34HufLt8ExV3zX5nsDtuVKfaVHVfHUfrWQeCtlQGN7++0MgWOo4jxlhZkUsSTF+2Wbn3/rVE52hzcNIgmfaYRfUa9dPIv7nDTPJ4dXwGzVmqgiAItriv7hxFKWttKW891zHV5ADy6xLEVBMsy8I5Bw/GT7I1N1R5s4mpBiAK5L23cL0IhGbYs3DEHr20AQDAtS+6lWXzqrNMbjJt+zr0dC8vwolZqTkH2Uy868qny7fgtXk1sLJqHcuy8P1Dh+DUffvhd6fsjd17V6JXpeu8kg305fpGiaBIZ7xnh/tUdO66FmMAd6o9m3Joz3JUlSbQnrb9dViYmtKyLFGHiNKOaF7do08VHjz/IFx6pOtck4Q9rTzjFSUJQZbpCpsRKAhfUay/LxwU+NvyPyP/TnlMtfqQ9awsEZMDGRMDFKbaMybkyAuXxhwybBe8/aujcMiwXVBWHBdyL57ozyMwvBIgH5A02eWz6KigQUYRxEOGes6ZyvASuAR8s5B/l4jjizGWgiZtOu7q0iLs1rtSRILcllruuR6+e088eMHBYr/E8L0+38u/omIztK0wKC2KS4b8IEX+bSkLvVjM6ZhZ5J1X/+YFKwBg3L798cvj9kB5cRwrN7eIB+X28UtEM3vVgOhXXSryaADgsGykkeTufapKpPtLEnAA2F2jJFBxZLZgFVU+J6gT2KrNzfoWBSyan8o4UlDgtP0HiOPtJgIPSR9TDQDHMad6996VPtn+tkRpURwPXXgIbv/OKMRilsjv2tDQLuTfPI2DM9F0TWgu4JHtHx+9G3542BA4DnDdy5+JVgrD+1aie3kxbj5zX+zZtwp79fPu2Yj+1cKpVp0LKlI2qEc5ShIeU9LSnhGLnFtMRja61DE1rFeFt1hl793nNQ1I2w56VRaLZ0HNGaQ5Rw38cGfXVAWT5wW6FUzlsUb/cgYRAAb2KEdJkWy88u/ROapzqe5zfEy56S/uPLJkXaO07dZkRhh0nOXgxyw51dnXh/WU540+Vd6YoWvJHyEyMHVGqSlXMhfyKWymfjafaLj7fe9vXY45wJ1qOcDLjSX1OHRsdaBTnTVYaI7JaJxGsS8WaKZxTsEqWf7tvxY0rrqVFeFgtg7efOY+Yt8UDFy5pUVcn0uOHAYAeHzaCpE+Is21yvMptdRyFKc6OzftwZQ8XKFEcz6dk7sN9bcXBCU4ynscfqZa/gx/FmjuU9cPLv8uL44LZ4ReJ2OdCMygGgkkec+VU60DrTlN7Z5TzVkjvi3uRKZtJ5s+gOxxWuIc0oqCR2dvCcbNkVslxSxLGOhq3RJyxOevqYdOzq/+rd46tbbHI1OW4+8T/EVJAaCh1ZPDkzM1b3WdUsxO80zZ/vFQVZoQz9ZyRZmTEk61bDtTCptJ/m07buqcGjgImrO8Wi+558SMMj+VsvWc7DSdo8rnMUIuppqD+wPqb5NTPXJANUYN6oZUxhEpRSIokMNuisUsUUjsk2XuXETFIRMxC+d9YygO360nrhs3Qnv8FPxqaPWCtfdmW+l+/9AhIkDUu6oE/zzvIBy+e09YliXmxdZkBis2NePEuz7Cz5+ZLbYh0gJi8riISUy1zql2xwjZlFWlRbAsSxADC5RiZWnlmlIXFQKvp3TSPv1ESqqwfRT5N+DluU82VHQHPKa6PIB0IPTIXqu6Zi7//hpX/25Lew+ZLnJF7SIIKlPNB4kkN2OycjWKty9JwFmxE77QrDIYtZz9KRSDFSk0FcMC9Ew1AAzNfmfl5hbRIoVkcQBj621vUVAnDiE9tR3B6Ki5uvtlH2Ceg9aWskVOXVimWj2XIYpx7B2vPPHR/CUcxtaUKLLWnraFgUFMdSxm4cqT9sKU3x6He74/Gm/98ihRjfzqF+Zhc1O7z3HYf1A3jBxQLdo0UaE3+hg5gATZqc4toR41qBuqshXZeX6SahTVGiriqkw1X8D4OKZr1MDy6CrZ/RnQvUwce5hgwLYEMYwbGtsEU82vM4+g0yWpYHJsMgbKiuP445n74pIjdkXf6hLs3a8Klx+zO/oo94ycO8B1OCkq2pKUC2KtYWyPZVki6tnUnhaLitrfUpdTPayn36kmNmTUoO7C+fSYakdik/1Mtb46PAdn6fp3KzPKv3lxFDpXeu5V41rKqWYbVI9PGMKKwUGG3ObmpFz9m+VUc9abn5os/3bfGOpzqr3c4phyvfm2g6p/q3/ngimo0b3cH2BU95tP2w5AcQzZreHbpYKVnrySfnuOhHr6WqdaOMr+IINgqoWEUn9deU5eLGYJVQ+xqNww1hUNtZlBdsq+/VAUt3DW6IFSugmphijoWVoUwyn79sc5Bw+C4wC/fm5u9rn2tsv3m87YUrE2Wg9qlfoZfG4nFYT7vntOvOicSf4t5+MGjEXl3urUSgSPqVac6uw1Hpidu2g9oDmN1mqeFkBQ10Syd3TBslygebq5PSOe4XLGHnE7hLc7U9No4tr0GPe3LpeVnn9HcUzjCsnAf+87sBuK4hY2NyclNZCJqdY5vIQ26upgcEKJNOleXoTe2XkrmZGfTTV46Z6PNx7KiuL41gED8NtT9kaPCvf+LlOdamHLKfLv7JrY0JYW6ysFPcj22dyU9M1vOkWJev5hUmj4sw24azdANVLyY6rbQzDVBFWZQb/TzK6Kx/zb+V628Op/Zqx27wEFBUIY+yTNphovlE/du6oEvSpL8OyPv4HvHjxY+92qkqwdx5jq2mxg7LT9/Gl3BDou23FE1xrut5hy7ePsexnl+QO8AAaRChQ02z+rbpundkJQnerde0rvc6Un4I1TOj5V/g1A2HNqC1KO/Jhq11dqzNYkAP6nmGr/BRrJnJrKkoSPLeWDRI3GJg1RPIr+8JwsU7EgXZuMMA+aCWp+8Sn79sM3dtsFp48aIBZG33eIqd7cIgwkGigAtEy1eojkkPGWWmpOMDnVKtTodxiQkdKtrMh/z1iUGfA/mGSwbmxslyKn1INTnYx7VBTjjFEDMHJANa775gj0qSpBMm1j9dZW8dAeNLQH9uhTiQvHDENJIo7fnz4Slxyxq8ToAh5zQRjZn7MWuZ3qRDyGw7KOOm9PoWMFdWsTDw6lMzIryhfObuWMzRdMtXydqUK6eo7bG4KpbmwXeS27VBSLRVIn/fXYZVsYLqWJOGIxCzecPhLTrz0B4684Gr85xV+gorw4gZH9q2FZbh2F0uy2bEc2GniFSsBbQJpZgTzH8VcWVnPKhvUs943pLzd4rXkIPChiO96co+Ysr97qTzlR8Q6TRqlzHzcK4pYlHAfAXeRoPgx0qvlcqpyvKbjoMfFyMIirTPg++XVt1YyB6tIiyXntw/qDq4wE37bumgUZy0Ew2Y/qPAH4ma185d9G454dhCmnmgcZ1PPX5XbL40V+j3JjKd/P1H6LpwnELPhyqrmzrjPW+XdH9K/G/BtPxt/OGSV9huTfy7PtKLuXuf/fcPo+6F1VgrV1rZixYqvxem1qSsoOd/a9daxFJyDXnNiLO9XZMbdeIyH3HGf3dSm1wXDN+Hm729Dk1bIX2kROtfwZj6l25y6/U51lqgXp4H1XnW8yGflY81Hi8TlTyL8ZU21ZlgiW24685vNxmmCFylKKkkd3PBKZwi6OZbH3si/T26VFMRFo5i17zE61vE+VZXa3rZ8gRNHT6lKjjaqqoNxj9uyCkqIY7v7eaJz3jaFi3NNzAGSdaiKOEvI1qi5NiGAyHQsx5xR8t1lgIyGcqs7JqVadUjqW9nRG2HQ6pjomAs/ePvJiqhWm20tdcViOvv97p48agPLiOJZtbMbnNQ2hmWrAC1rXZOcUIqV6syCwCYKpZoXKaP4NSv2iR4Lbkbw7Dt3XYuVkpXWCjb1ycX9s6XgoOLd/1jdQK4CrtvugHuUiGN6vutR3j4sMxQi5jxbXBAJVNCfDM9XdyorEHESkTj4qsvBUYhdBe9pzfos1kStulPbv5jdkdA4l4C7iKcN2yan+nMm/TRG6jGbCLTSnGpCdaqqY+p8fHx7qO6u2tIhz4Tmy4gFT5HgcNOhTGc8pUJnqwbu4+Vlq8ShizmgCCAMquqbmUwNgi6x7rFwCBngGwlqlQjpJh035LbSNipIE0NiOVMYWD+1BQ3vgWlbd+vxs3rD6cPVTxtiBQ3qgJBHD8L5VItqaCxTw4IWxMqpjopH9AaQ28IwKPi51TDWXyKtKgvO/MRRnHjDQGKzZXiBnqCWZEcZg9/IilBcn0JZKStdJ5OsWM6Y6SZHqcNcfAP51/kGoqWvFXv2qJEeuLWmjJBFHKuP1jadoKk3Qm5vbJSk0N3zStu1nqpn823HcBTyZrb7JF0ZuHLppGNn7rGxPkn9r1pWm9jQ+Zjn7KrOccWT5d2lRHL2rSrCxsR0Du5d5VXZ9udLZ37ZcHM//OX1wsZg5674+1Ul/TrVR/m172x/coxx1Le48zeXfuoVXFPbTOTKK4ijsUDIt7Dr2jDNMralMIOujg875A8LJv0051UCw/Jv+jsG7l16hMpJ/O9Jv/j1+HDynWq0sqzNiVImobn4lRRYZURRkqSxxJbEUeOX74tdLbY+Zsd08VLUoJZd/D+8XzFSrLJiuPY36HoefrdQ/X4C3hqgsOHVDoUAG3Su61xQQ4QoGgqlGgmc7+A7ZCJrfGtpS4hlW72PMssR6R4+EatS7OdVk8MvBsSINsyin/XnHbVkW6OOqoiBmWRg1qDvmr6nHvNV1OG3/Adn32bXQkCi6/4PSJwAvaNOvW6ln79iqU617pvR2Jq3hXP6dzHhOsXqNrGwwdfmmZqyrb8OuvSpE4LKyJIEtzUkpz7isKI7G9nTgnKXWAgkCbYbmaVq7W5N2OKaaB2XzYKrjynjnqRn0t8rqA+41GdSjDF+sb0JDW8ooFddBldpTYcQ+IZxqXU51s1KbQAfvufbmO66MovlWZao9JYA81stL3PtP2+DVvwFvflS7Fumu05jde2Hl5lU+AhEwp8BxX0QXMOdIZTwiNgxTHY9ZqC51/ZqtLUn0zhZTC4udj6lOe1Wd1agKIMu/+2sq4Zoi9LL8W94ubbOmvk3kJurkaYA6ybq/w0SvTOADbWD3slCsN0V+Vm5uxlbG9BE8KZQsx+Oga8BlVypTbVmWxFbTaarR7zA4bu8+2KNPpSgCwcHl6nRMtH+AR93lQkrEcuomYw4ub9U9tBzFiZjkdKry7z7VpZj0f8fg2R9/I3CfHKq8HdAz1TpGjUu+07YcAOGyvO6aiq/qBMNlgTsS5cUJVGWNLzJOu5cVi+g1ZylpHi3PLihtyUxeiyph8C7lIv+8KNtfHPCM1I++2Ij61hR6VhQL9oIMxHX1rFqw4hxkbLnQ3/6DuuHo4b1lmaPjCMOQv87nIXc7WUm0MsGvNkgTCTNXbJGcFJ724X6HGcjZXVK7vt17V0qpILriWDrVD4cpuOgx4I4UKGhNeTnVnPXmz0ezpvp33LKkoJwk/9YUYAqq/s0/lwdRbZR/B7Hh5FTkm1MtVfyWxpz3N3WBEPN8CPm3rq1WUEEmtYYGva2yarxKfDxmiaB3SzKDrS0p6RrplGCmtYqjZ4VsmPL5TGIAlaAJYUOjXLE5YzvY2NQO23HnVJKXD+9bhYriOHpVFot0K8AL5K+TnGpH+5vP8UEty9RAvXr9efyVJJB8O5/V1COVcVBaFEPf7DOhzvMUYKXnPyOtRf4AL99HPvLvyuw8vYmNMdUZ4AEwR1wrW3KcEjFLyHKFwU0Gu6ZQGV9jeQCR/xbBIHFenlR33mqPUDGx0+qzz++vJ/n3HRoAbw3pV10q53/7Airy9/gzxR8LUqatU4rrkhOsOk+0b8BTWQimuoQ65niOJqm5gpyNfHKqVfk3OdVtIXOq+bUuJKfaC4p7/6eVNdH/XW/chJmbCANY4M1xHDHn9K7yk4AqaI6lnGrHccIx1TF+rORU+5lqNYDAn0U+J1AdBNpGU3tW/l3qdZQBNPVVHPk+A8DZBw5EImbhmL17+46bxmkyY8NxHFZQzTtOXWoXBw/AB1X95qBiZdRZIZ+A907KVFNVZ/9oH5xtd9PUnsYADVMdZ8aVamDRQq4665UlCezWqwLLNjXjs5oGjB3eW1tIBfBLPwG5Yne+4PnFA7rnfugAt3JezHKDAMTW7sJzqqVroI82C2kVc9p0jua+A7th8tJNsCzXAF+6oUk41dV55FQP6lGOCVeO1b6nFqMQhplF8m/33HjldcCTf+diLLlxnzZI3Tl6VhYLdr5fN390UW3jlgvepOC9pi7QjmM2uISRJh2/Iu8qo8q83kJbmUfQY3ujT3UJGjd6jlP38iLhfOhYSompTuXPVKugSDwZFi/OcguSfGv0QDFeKCjBDReV+ePR/UuO3FW0tOORZr6A88WG/81zs9WAS66cairSIfbn+GWFKgv4x2/ti+nLNuPo4b2lY03bjq8av604Zn6n2n8+gCdBTKZtSWLKi83JTLX33VZNobKYZUk97rn8W1W7AKz6t9ap9hvEYWAiZXTboJeoKE9H5N+ygy3va21dq4apNsu/NwfkVPPjJlCAg5RJqnMijtH2tmNlFRF9qkqwobEda7e2SsarLmjtKPO+Dmr7Q54OQMNPbQEnO9UyU207jgjs9WXS3MqSBF7/xZFuAI6tFcRCbWxqRzpjIxGP+SrJc8NdPQbd+FHvs/oZfp118u9X5rhFOE8c2U8cq+pUk7Gu5hcDvDiQ5RYMU84jH9KAHDRyJCzLTdPhoM3xsamm0fBCZSkfU+0/Hl7s1EtHkZ8FcuJ5kI6KH65nwRbHUL8gKKfaU9npJ4h1DR5TzRU0apqCT/5tezU8eNCyuyY4nsp486xOOUNjl5w9Vf7N16nyEE61F+zPQ/5NKhQKoOes/u2RPwRv/Q+RUx3zxgUgq0jobx1TDTC71NYHNkygdak9bWNrSyov+TcFv2hNTjJ1ZZDDyNlcK3s7eBDXpNLVyb8ty7sXXk61nAKkSxnjAS1+TQ8etgsW3nyKVnnMFRV8DuD2LWfhdSDVW3E8pt2HDj0qirFic4voVf21bqnlOEBTu1xAgSMWs0Retc65EdUtbTlanUp7fap1F17kVWcl4PnkVHek+ne3siKxAKr9Y4O+Q314yQHh8m+LR9iUBYbAi4CYihgAXvufwT3K0TsbxSeHM2z171xQDWJ1AqtW8sMInvw7eJjz/HF6aHULM6EXY0Oo1VNHoObXAv4cF7UAFoEbHkHVPd2WL+5rVKiiMkR+yY5CHyVq68q/aaH1nG0aC7xit9fXsvDprZQVSqlrSYpCX2cfOEh8hiLD6zkrpTgp0kJgcJg508tf559P297iyReq+hav8BztT4WP2VKYOptH2rNjcXjfKpx/+DDXeGVjiTu5tFk1QGmqEq7a3nzh5dttavNkhdKizM5DL/8GBjHWkEf+1cJw7rkQqwcfVIYoLEyf1bPh7msU/MmXqTa11FLHQCpj+5QIwfLv4JxqNR8/n5ZaXu6++5qXV90ifV4r/xZrle8tgR7lRdI4486jF0iQ7y+/Xqr8O51xxPPdt1o2enfrXekrJNqr0u0GkbEdwcaq1dC5xJTgOaq6ceL97Ti6gCtdb0c4OaJ4ZcYWnS3OGj1AfMfPVFP1b7+ig64PrR9qUTUTk6cDzZl0ncuK4j52T1dIVW1xZFmWpKbjBbtyVv9Wjltus+p9J2ZZkrqBYFKIqPOIrIag3wanmlWX50WlCpV/6wojyn2q/deoL2Oq29O2ODdSjVEFdsBzepNp2xgo8MZg7vnTz1TnV/1bYqoDai6p4NeMB9vSnKk2zDe6Il5hAkylRXFhj9fWt+Yl/yZbl3KYecA8SNrMGWf+TKWVdVYlzng3AJqu4pYliv/65d/kVJP6k6tNvHukXieTs8t9jrTtQFf9m46RxltjW8ptR5fdHzH55QHyeBVqr+p8iojudE414DGQphvx7QMHoldliej5xmFpJlfAjfiYCpUBwL4DXUf98xq3vQJ9Vp281EqdQMdyqgFPAj4gpFMNAGOHy8WmeARfelCyl0CVcEmFykTEzn8eJ4zoix8eNgTXjhthlJR1FHEWidJJ1nm+MEe9KFQWlqn2jHudhIzAK6mrOdWFwDP4WVEmcqqzY1wdr9zZzrBJMqWRxxDoOm0ooDr79gY3YIvjMZQVxaU8KwJdEl317zCLqgk8Uv76vBokMzZG9K+W0ksoKCEz1TJry41BXXSVPuMVRfFet5hRx6X93EhZraQ86OwbveqBve8Et8fhz31KM0bDyr/VhZT3meXyqq2sP6Qa6SbwvHratctUu3NkSSImKWV00Wzats4oNLXQyQWT0ax7mYyAQp1q2dnSG/30vxo0iWmMLEJO+bdiwJKx1K1Mcao1wRxHWRP7ZgMfm5rafeux6XyD5MaJeExi6brnKNCpHqdP/u04XhGpEHN9PGYJ45i+55N/MzaM4CjvcTjKtfczou5vzhrSZ6Z8tRmbmtqxS0UxjtrTs4eM8m9FDgt4wSdai/zB7QKY6mzFYx27FtfcJ0nNo3RHSGUcieTQqcwklYLC7IoAk3JtY7wyuIGd1ilEitm8pn7HFHSrZX3QpaJSyn5pkwkReNAHZHVpXClGkGiZ6uyau66+TVICiUJlzHnkefDG+kK250zlakvoyYIhbb+5PS2uY+g+1SmzklUFX+/4s8UDzyammgfp8qn+DXhdidbVt3WIqSaHsbQoFqiu5OlPUtprRrYp1G3oCvzFYpa4F0RgNCk51UXsXum6bATZ1xx8nMpKC78tRef15zcX4dwHP8GkL1wihAIPFSGl34Dn1xFT/bXOqQa8cvKmXNnvHToEM647XuTDcHDpc0ZZxFOaG0bgbbX4ZHnYrrvID6Yk13F/d6T6N+CxwXQMYTCWBRSK4zGJleQLhcngFaXsbcfYhgFwF9o/n7UfTtm3n68wWT451UHgizZnZul1ndQJ8Cr35WIseRVRUyEPDsmp1lT1zRd8ciYIppqxA3xs0THbrIBErpxwWmhpM12aqWbXtVu52/uwXMi/GTOrMNWtSS+yHbZQnA58ex9+4Rb5+tYBA7SfWVdvln9nGMPMW3Pw+8MNI3+kmDnVGjmdKtXV592rRrhG/m2bjYIiA1PNnQRJ8q4WNtLIE93tuv+r8u8tmpZG7v6875IxAch5WvsO7Iby4jgOGNwdal9sOncCLfa5qn/n06fa9NEgBrKsWI76h4WsiGDb1TCZPvm3lGMqf76JXVv1WNW/+Wc945s+Jxv7uiBunKUSBAZmOMuRYz2lvGcgIKfa0V87cva89xxhOIY1yrxCRHJVc1/1b+n+ye+px0DQBUHoXOQCju7vV+asBQCctn9/6Tk2O9V+ZpbWfwpSUhDO9FwHoUKRf5dr5mgh/3Zk+bfKRHM7jTt2urVPl1NKNg9vIyZVBo9By1Sb/hby87jmOwH3F/DymPt1K9Wm5wHyc5yI654pb3vdyuQ0CEAmDXQOmBi3DW1oSXlqUB4kUNdbwBwM1I1vE1SbjlICyIYDcjHV3jFQTZV8qn8D2eud3Uw6w3Oq9eNbkn/nwVQDnsKxljnVoZhqkVOddapFq6jguYnXSuDzfbtoXZkNnBlaaqWVcxTy7+y1blBaahUrzjBtQ2w35HWSnWrmi/CcaiVgTsHMmjr3d7OmdV8uEFO9VTDVX9Oc6uJEDGl4SfpB+nhTNFtXsAKQC5XptkuVSjc1yW2b/nbOAWhP2/jWfVOwakuLbOgEsD/54PenjcT3Dx0iVTbPhf0GdkOP8iJsbUlhl4pi2cDURIJNOdUuU22Wf3P48rTyqP4dBHXiE7l1Mf1+CWGqfwMyUx0kdSf0yhpt1aWJDjluhIRmEVYXaLlSu7yYcAZT13KAoKoqKrswU80XGAqacIk3ga4ZGWtcrdCRnOpSxlTTAqZKPbVMtS0bvTwX2iT/loqiqI5nzEISlJpAwRPZaefQOm+KQaM6FZL6QzNvunJLd9Hizq8X1VerBRsKlRmKIfKq+wCwtZnlcOdT/duy0KuyBNN+d7ymorB/G16hMt8pS45WLoOQwyj/DmAgKehnZ69vEONg2pcqE+VQW1nx32pw2XQOpiADMRQVxXFffiNn1Yh18xwj9z2TA2EaQ/zYTeCqLD7n8TQiuSikty81p9p15sKtfwQKtBL7qLbL4uwrQX2PQ1Yk6J9nQJkXs5+ZvWorAODkffpJ3+FrpmV5Rrm2aKYm39JxuH2Tv1NNDJDO0OXHoGOqaR7lcyg/d919kpi67DbpsD1puBwUi1uWlN9NcJT7QRDjPR4DkAl9f9tSGXE9+leXSQEvNfjjBV9jAGxkHL9DCgTJv/XOEyDLv1uTXl4yvx+8+jfB5FTzwnJp20GQj+uTf2fHBV/PtUy1Ut8D8BzFfPpUA/K6yAMspqK1svw7u72Qxj7VR1qyrlHUAtJVv1ZRLZhqkn/nLlIGqDa/d4zkLKYMwRYxPytBLQpY0Pf98m+/U83vUdiUWNX2EEy1xpbiATjAG5ct7eHbaRFoDRGFyr6uTDUVdCHpQ3EB8k5dEQxAnXD8l4WkJO1puf1LaVEcu1QUayOaYQqrhEFpURz7DuyWV5XNeMwScq8eSvEWEbXi0WZlkCeE08aKX+VInlJzqDuLqVbzT71F0X3d5FSb+lSr4GX7wxUqcx2+zpB+A375Cv+bjs01Or3Py5Vs3dfdSuBmtYWPndhJmGoyEMjRJYeKF5Uh44yznKUhi1LowOXfTYaFS+fIqwa7qQiZZVmSzE8nEeff0eU/ud+Vj1tntKlMNR8ztI0g+TfgKTdSBkOR70KtN2EKLnKnmjNNW5j8m7+u61PNHTzafrfyIl9gNK5EswHeUsvs8Lrn53vbCHNOteY1nYGaR5VRyaAPYIW4sSgYYlb8UT02XQsc3dwEQHo2pHmaMRsi99Xxy795sIPvVjVi+Hu5DFdeP6R7mV/+reZU82ehoU1OIbId/fMbBM748WOne0D3Tav6yDEWOdOqMqK6YCPtQ031qWZrQVVJQlxTfZV89x++jvJ7GTIGJPbFoXMGeI4zz3EVRn2cmGpvx5JTrbFTeC0d77hJteG+pxawk9dZb1s6Bpq/znO91fd095dY6tKiGKrLEnLwR9mvjqlWzwcIkH+LwISZqd7Q2C7m1/LihLQGcceK7GQTi8fnEd2couuUoxYqo5zW4nhM+9zrcqrb8sipjhuc6rTtiKCAkamWqn/n9yzQtX534ToArkPNFTYmeDnVJP+m+xR8rlIAgDPVWabZ1DaX9/Hm67jwh1KUU+0eD6/NQJtKdsCpBjzbo521QuT2uVpziU6P9uux+eH9RVX+/bV1qkuEU52W/s8HuuICgFrEQeNUJ7yJkmRWnDHU5SJ5BXo6SFUXiJP26QvAa49D4O1UTHlRCe5oGgx+Fd3K9ZKyjsKX98IKJgD6qCzgTci5mWp3O8mQhcr2zvYlpYJ4HYUotKBZhHTy71jMYhO652ykMg5rmeEfw9VlOw9T3Zcz1VkpTrlS/Zuv08S0kFwsEbNCM346ENPZlvScalUur1vI1Nx37gybpN1c5mhqO8Wd83TGP8cQtKyrykTaimHoBMu/AVm5ou5ble6q1V51Rh/A+1Q70jlJ8m+2P+5ckDwx7ELNa2kQ1KrBHIXKv+l73cuLcNMZ++D/Hbt7wD7c3yVsfiKWJZ998W2prwOyeiKuOE+Oo68orELHyAGeU11ZmpDmaW6gej3ZNbndUnDQ23DS0BuZf9cEnp4TSv7Njczs9RfGuqQ0CTefqG21VIeK9qd3uvzb8z2rjnw89Ijo5N+m9Z2ryHjwO6Z5Tuh55oEqPmfkY9+oTrRe/s2cFVbgS1X88PmUnGrL0s8DuhoCukr4/Ppbll4Or1MjAt4zonZH4N/XFRulgna9q0rcOhqGuj9yTjWzC8R98Laps4mSGVt0rSnS2M4UgHKL7GULyRXHpbxl/kzTeDAy1bz+hvI817emcMRtE3HNSwvEuQJM/p112upyECN0HRzHc9Lb86j+bUlzFkR9Gm5vhWGqTWmUJtAcsT6bbnKAJlVVhyoDU50rlU+kONjyeuYx1XqVLmequZrAk3+7hepoHeA2P++qA8jPQz7qXXqeTGoUldAUhXuJqWYBorDoSKGyrmtVa1BcFANSXpQmbHl0DrkHovd6Mm2LAabbLnfMSPLGnW+dbEo1ZLY3vrlff5RcEBc52QTukHkLjPxdUaiMsZ+myYXgZ6o7y6nWRxPpdRMjXh+SqfYCCLZRBsNx8NAeGH/FUaHkOmHAI54EWpDk6t/exM3l32rhICA4pxpw73dZB+TR2xoSU509bjpemly5wUHGWl1ruEBKLuiYanXh0i1kjiMbxiamGqDx68iBK7VPZPY7PB+OFwsL4xCpctEgY83ksHi93CkaLBuTDpgRrjLV2cNVjW+vloGcU13Xwp1q/kx4323J3hNu1AYxmMHyb7PDq/6dC/w6XjhmGOavqcN9H3yldcwFsxWzRMXofJhqndGuvk7HpMq/ZTYsxBgyBBlEgRrGdgIys+E5GUyqqjj3PDgI+Hux5yf/5gE5f0stv6zWP3+WZ1vqcaY6rMyZZLTCqbZlh9ljw3SpFLnHIn0kEbeAFGeq2fZYwEt37Hwt4Os0Z20J9AxKTrWNnHOGDmpPaq38W5PjzNOcaE6U5d/BNooXLPCTHaYiTlKhMo3zTN/x/ibbwR+YUCumc5ADSz3WeY6ob55Vnik+d3OHrrsupzodTBqUFrn508mMLVQWvDp7xuYFNbPtidoDCpWxU1UDrV+sb0RtfRs++mKjex7KGknrLzGFJYb1XGo7aTsojll5MdVqvR5eqTpXRW+6LjzwFjbApHYmGj2ke6jvkY3dkswglbHRnHUYc8m/yQfNOF5LLYDnVOsDCHE2lnntktIiT/7dksyIMcjnkuJ4zFX2UkcCFhTLJxBH6RQ8aMhrHqnqGjpOwVQLNVUhTPXXXP5ND4mo/l0AE8WjoHKhsuDKiHxfje1p32u6CK8pX3l7wbIsnDiyr1jkvdfd3zoHlSAKlbHKmrmYP75QlyRiHaq+zCHLCtl1jXnv6xx4eshyOVicMQuqdE6wLAt796vOK/IVBC6xIagVL6VWIBYP4sgLdVt2kg2q/g24DuGOUlCEgZRTnZ3gyrLX22OqvfMm42xryOJ0uVCqc6qVMabL0fGztmamS1RwdXgUWN4eZ6pFNVXuaCpGms55U6V3qszRVsaWDmRQk3HklyV6/6v5sJ5xoj+3VMbWysoBOYCgy6nmpxtk3OtzRSlA4P+8KV85F/y5y/KCr/0sk1Lms3jzw5LXHdq3957aOlFSumR3qWPYxPYN7JyQ1pUklPY0HisnWDXNODMGuQ1jiJ+DCb0MTDW/FyZmnxgJUqqYqvcHgQxmn/zblvsga3Nuc1x7zhCL1CCbnGq//FuMBWVe4deFB8N1AV5dWpwsO/YdshF+tU+Q/NthwQH45lHL8npV07mbCAxdQCXO7AfAr9rgElZTAEuV5gN6+XdGc88JVG2fxi1Xb6i1HdSxaDNHmz8X5CBzuGk28jFyWJYl1GwUEDIy1Za/V7EKzlSraxA5Wqp6gw6L1t96oTY0MdWyU+weT3immo8Xvi7ya20SqHBHlTucYdBfSR0My1RzG6SpLR3aYZTVqd7rQv6dI6daCogqhcqINY/HLImoITWEV6jM/Z1v4WZal3hFel0nFTo+eh6SncBUf+2rf/vk3wUYzrp8HcB9yEWhMkP/a3qd9s8lNLo+qNxo6krQyVaMOdU5WmpxmCRlHYUUTXT0UlmTBBwwRzkJgoWzwxUq62wkNAa/mo9o27JEVypUxpysFuFU+4+/u8ROdN792RaoKEkIA8wo/2bzHL2XzCNKHYSyYve617emxDb9TLV/Hz5pKa/+rdwTiQEQTrWJqXaEgynnVMvGCpeW0oLrL2ykOBVMEmaKyvO+9ep+VeZbzakOI/9WmUndtnLKvwOcraBcUTUwAcjXLJ9CZercpAu2in0I59diNTvCy8xMxr3HmHFn1n1PsHOSJFuea3T5jyZ2jtbCypKEdP25AU5zqb4KeXabSjDKz1R7f+dyqk2Fyvi9kAJfbIzRs07ziczOhVsT+jGmmp+zGjhIa+Z73VBT7624rkq+r96p1j/XJqba0jwnIsDL1lHuSHS+/Du7D5s7Od48yqfIhJCGEgOrtwl1NUhoPMj7877Dg9e52Gn+mWKNUy0qwGvu72YfU82PR733kM6Ty5T5bbAsy5eO56bZBNs3ZMMJp1phqrkNIuTfGf2cxZ8rdU5R82zVeZOc6lzyb5mpdreZH1Pt/e2eH/9bDuKY9q1Ko8OAE13F8ZjUqjMIRdn2ooCr2BU1LXJW/2Z+D7sXPvm36lRL5+i9JgqVpWyWTy0TNWrOvRdkzc+2puvfKtVN8LahBsxFobIO5FTTGlLXkoRtO/8DTDW11CqAqZabmbPFlBcqMzzA9GB78m/NjZUMavd3R/tUdzbkllr0mvwZkUNpe/1x86n+3VmVv9Vj44aJqTCHylrnLFRGEbW0WYa7LaFr96NW8VYluqb8QJp4dKoCvsh25XZahD7ZvpndQsi/1fPpKFNNUc1NrBqwOinrFjJ10eJMlyq5i2uMFXXBof+TadsrcGRgTQBv4br08Rk4/Nb3Ud+a8hldOmMtl4HMpdrqfh3fOSsso2HbRQlvmzpHjo7VEYa193prUif/1m4i+56Zgcsluc2DqJZ6ZgMyC+bfBxmSfgMkDOR0Ef/rRdwgUpggrlbyJKUy86k7VvV9ntNnKfO06qxzp0UXdNDdG90+cxmuZBDFY5Y0L8iMpP/cHMdh7fi83ryqE5sLNG+1p23UtaTEvmxHHq9hC1n5A2CQjoe2Kcm/HXkfatC8khWW4+ulVtGR/VtiqrmRnYd9o87TuQqVkV9mUvyoBrep/62upZa+Z7t33haTf5uYap2DHdTNQ3d/qS1irypv3AKUSqTMs0qAUpJ/K/dYbTUqt43VT5a01q5v9DPVvKVWIpa7UJlkzyjPc1KRBJvk3/U5OrjIrSnd3/nlVFtsLvTmaYm8yZlS4ORt65cWxUVBxZEDqvMiAci2bmxLi/aiuat/u7/VtZ+CuCYySTpH9tyIPtXpjFDuqs82X98Bzy7It3AzbYeYalU+rqZ20ZzRkerfFIy1Hfc6b3Om+v7778euu+6K0tJSHHTQQfj4448DP//0009j1KhRKC8vR//+/XHxxRdj8+bNee9XZaoLyamWimCw+a09nWGOjP6mE5vQ1J7Kfk4j/2bXvrOqf3c2pEVEw/oChkJleVT/7kwmlFdKlgp2sUPmTnVvpYpizkJlEutLi872u2e8MAmBJvRiIf+WF1SdLAfwHM5c1b+7cpEywrCeboE9aj9RJpjqLAPLrpfaQqmjrc5ozGykgi1FcV+gQreQqS21uMOsGj3cKDYt4IKJYQymrvo3l5IDwGdr69HQlsZq1uaviMlwZRZFZk11UFlM1WDUMcAEYfSpTjWTPKtOFAdtj++Tqp467GtBBo0up5qedV3xoM6Tf9Pr/s9yBzxX0R/9vgzHS/ebihw6Gmc/QP6trf5tuB48NcKfpuP+zRnVIHk8v8zBhcp8hydhUHe31kW/6lLJ+OJBJTkg4WT36V17CqC565+cy5sLpUVxYZRtbGrX7gvQ92DXO9VKAIzW4zgVaXL/bw1gqtVnw7Is0Z6nukyTd655TrgyUL6X4dfKkkRMcoR0dT24feIF1PSKAY+pJoNbb6PoWmrx9DFxTrY8Pi3N9dApyvhneDcR7z3/dggbFaaa26hq8MdW7n1GOmbFqS6Xg9G8Lkcup5oz1VKOO7sHvCOODkHVv1WVkL9QmXvMdP75MNXtearVdIV7gwLd6r51QcswoArgYaXfhCrWq5rWwVzybx5Q1BVmNLVi1bVTk+59ymbttGSbXy1UJq5RnrZ1kRI4C3L86RwBby31Ar/hbcKSRJylFCa3LVP93HPP4YorrsB1112HOXPm4KijjsKpp56KVatWaT8/efJkXHDBBbj00kvx+eef44UXXsCMGTNw2WWX5btrFGcjFiT/LETiyfti8gWOohnufkxMtbs/4dRrCpXpFtAupv6WW2ppHFRAdTSDJxdCtZSn1blOm47RkOTfrDBHL9WpDlmoLBmyUFlnI5AdYNW/uREjFSrjTHXSbFyoOdVdHTedsQ/u/O4oHJ1tDceLhwGyU6E6uKUdlX+TU51lqnVBCK1TrbAdfDJW7wlnQUwLOMnB25nBLBlrwsiSnWouq/Ny/bix6u0jTHscr1CZKafab4Srx6gOSSEptx1fcTMOL4+bjXOSf7PXwsi/+XlTHndOdjAPp1oNjnAD2fRZy5KrqYaFJAvWGPe8b7S5OJOeUVYhXw/vb1oLK1T5t8YB0AVxdUYbYG6pZVm55cZDepbj7987APd8/wDpdVPqF40hfu0pKCc5sXks5Nyxkp4Ng4Mt5N+6AI9yb1VGlLajlX+TIay5ZrQeSIXKNAFeUaiMTQ4y4+s/ZhMsy5LmTZ0zwFk1UUfC1isGaIx7TrXBAdI46kFt3dRiaLnYacBjyHgLOfVz+pzqrFNNOdXseNS8ezVQJadUyNul+0vOG29daFIdCqc6Ww+gvDgu1iDOVMes3HUgZKZadaqzTHVGHveCqS6WFwsTMaK2pgTyY6rdc8l+n13vjHKuOnCb35RmEYQR2e4xR+3ZK/R3AM+2bmA51bnyhU1kIo0HURXe19LT/c19BbVPNZd/c3jpXbLjnrf8m3KqiTBS7SglDYbuIe2X5N/51kCivOotLcm8Cojm7Tn87W9/w6WXXorLLrsMI0aMwN13343Bgwfjn//8p/bzn3zyCYYNG4Zf/vKX2HXXXXHkkUfiJz/5CWbOnJnvrn1OdEHVvzXRJcCLuAPmKB5FZxp11b8t3eQrG1hdBXFNZM7PkHlRprDy79KiuLgnaiXwjoI/OCrrAsgOPcmoCLlyqrkBVIgB1VHQeegMLsFU244UpJEKlfHgkGCqczjVOwFTPXiXcnznoEFiLFLksFXkVHvn7XOqO1z9290nVWbVBSF0BqGar8kNdTVCywvAmBbwIiVnEFCYaoNDJBYYZigI5tL2z1Oq06UiobAvPmOP/e+T7hrOrZjJwwKZ6rTf+SW1glTAKuCZ1TkLQfLvwvtUy/vTGeXeZ71ARnFCrgmQz77Uv9Wq23yep7mfF0Oic+VzjQpdzjYQVv7NHXhI++dMoK25N+o+w7KiZx4wEAcN3UV6zVSsh8Yuv/Y0f/CAj1rvIAg6Rw2A1FqoIPk3Cwh4yhP3PcmptuXv6i6b51QzploxUAGvWGAi5jkwjuZehgWfS8tyFCrj8mBdFwVRqCwdbKNYmvsRDwgw6SqDE0wOtq8ytyZooovPeYXKSqTjUp8JvrbwgIrpPgzv67b+3HdgNwDZ9DZNgISDxkRdiye7FvvKyLnDuVtq8WCSIv8mp1phquneqkHxoBQ+HpwF8supBvRqGV7nJBdTrasoHwY3n7kPXr58DI7bu0/o7wCMqW5LCYcxF0kiFWlk90VU/xbnqg/8c9s4ZkEqVMY7QHAUMbIK8OfNh4VI8UjqmWre9ovvh/bbkgzH5qsgpUddS1IiNXIhL680mUxi1qxZOOmkk6TXTzrpJEydOlX7nTFjxmDNmjV466234DgO1q9fjxdffBHf/OY389k1AP+DVZj82/0dZPyaJhx6SMkBJ60/wNhf2z/hdrWcai9qZc775oXKPKY69/XWRb87A/zB0clpucOYL1NdxPJFuQGxvaDPx5cXP84uulVJGXOvYar18m8v2KBOgDsDynx9qplTrci9O5pTTfuiHqJap1pjEDqObGi1GYprANAWgPEtGGQ0cqZaE3wpElJQ93W+wKi5iKqhzxkQs/xbZqpVllQywn0ttUwBAyb/DvBc6ZnkdllbypbyrXM9rr68K9t7nnS7lnMZw3vV6vHwtBX/Pugzhcm/TZJsURCGtUPx9qUcF3uP50769qVxEABI/Ul9aTqKUcodQvqc3JPX25/ZqfYdWmhwZYjUMooYLmGMexJleb0Jvy/JMeIBJ967V2Mr5JJ/cycqoThvOqY6qCoxFYDUVf/WVclPxGPMkTCnjuUCN251xYN4zRc6dTe44V+b6Rq052KqNXOtJZ4FFqD2Pb/e9wim1BCf/Dvk/aWcao+p9jt5gFJUUJPKo96HK08cjglXHo0zRg0AEK4Qq0qGlBXHJVuDBzY4W6lDEFNN8xxtz1eozLeemx0iVSWaP1PtJ9oydu7iY3HN9/Jpn1tenMDoIT3y7sJCBFJjWxpNQv4drqWWGqjxqn/rc+11lfjVllqe/Ft1qrM2g6alVj4gMqDNUC9IXdvp9FT5d75MNe8Jvs2Y6k2bNiGTyaBv377S63379sW6deu03xkzZgyefvppnHvuuSguLka/fv3QvXt3/OMf/zDup729HQ0NDdIP4I885SpApYOUH6IxHhIxyxh59Rcq88u/dZPsdlQShwIfhCajVC5Upjf4dSBpSmc71THNpMDnIl7lNe+cas7K70D5ty51gLM8fOHxItW2xH6YJh5g55N/qwiSf6v3OJc6IRdoe3TNdVHOsqK4jwFSFTASU+2Tdnv33ZS3WaTIGwHZ4VDZQDXyn854QZdizVhSj9m03ql9qlUjU5a42mhLZTBr5dZsGyH9+fOWWqpknCOt5N8RWlOZ0K1M1BQL6RpqnUj2dx5OtSr/DmqpxWWotLbks3ib2GPVuHeNc/NxqYGZjEaDbGLF1eqzOoeAy9BVQ9VUlEkNLhTKcnDw/HZdkICM8ZJELFRl/sB9aaShgFoR2a844fdKHJ/BseKyekAtVOaIzwP663bRmGE4bu8+OGGEx5QF5VQXxb0CQboASVjwtUdn6JoCxvTMcvuMxmzQuse3yceZTuKtVgbn5yYqeEt1c7y/dS3kCLwrA0c6Y4teuEQGmMYOLw7nBVTMtXsS8Rj26FMlAnZcEZQrp5pQXhTXKqriIQKBfHz7c6opUCoHndRCZYRgploOBhJTHVatJikj2H3SKSM4Ymy/prop2wJkWze0ptDSHq6ydczg96jVv1UyhgeV0mwO5u3UPPl3cE61V70/T6c6+3nR2cZATtDc6fWpdqTv5aqQroLmqca2tI8oCEJBnoMaWXEcxxhtWbhwIX75y1/ihhtuwKxZszB+/HgsX74cP/3pT43bv/XWW9GtWzfxM3jwYAD+Flodl3/73zdNNgBzqtv9TrUuolmIJGR7QCeBN/Wp5i1ewkSYaFLeVvJvKX/FwFT3VJzqXK3XeGXjHVKoTJyb9xotSJL8WxjGfLzJRr+Xd+I//uKE145hZ5B/qyADrFVhqkmOxIdwZ+VUEypL/OM5FrNQrnyOMziAnAutGj1STpahkq5gqpnhomupxR0X/ptHmOXCVYpTnMMoEPJvbZ9q+f90xsFf312Cs/85FW99VqstLAjIi27QoqVjxwFXAh7W2VJbBekCExwyEx+4aQkq489l1io4Y8bldOH3pXeqhfybt91R2DndGuD1Is9xPdjfwqnOGiBinmbGjdxSy/2e185I70CowQVH+V4h0FVMpmMFGFNdZK54HBamnthpA1PNr686VNRUBK/woBxIkwuVyfvQxQOO3bsPHrnoEPRh7X1UAxWQpaEyOyd/JywqJKfaP0+bOlvQ/dG102nNwVTHNHOtvqWW7KDy+ZDPqepr/HVdv3e6luq93dKShOO4x0A5nDyw4c+p9gdUdC21ODzG0JtnTa3HVKe6rDiuXDtvncg1Z+lsYQLPs9XVxVAd4kCmmhEMvAVsWNLNSw3w5nq+judkqp38q393BGRbu0x1yOrfzMbkY9BzquW5msBrtfAANr/3DW1esUoOHswBvOBlR3Oq/Uy1tz33Hmad6uy4FDnVecq/6ZrWZYNeYZGXV9qrVy/E43EfK71hwwYfe0249dZbccQRR+D//u//sP/+++Pkk0/G/fffj0ceeQS1tbXa71xzzTWor68XP6tXrwbgd6JNMu0giEVcmbAJQc4UPdjU0qs4h1MtGJou5lRbmgU/TC5nGPZ2m8m/NQYRv66034riuG+hzuVg0X1MZ2xjbsm2hC4f35N/U8EcmXWQosfM/vTk38GL5s7IVPv6VLNJ3rIsyRHuLPk3wTSe1cXMUYxoWrRilt/45NJXE1PtyRtZ1F+Tl+nlz7qv07XhShNdayP3s94xmwKAojJ0hoxDxSlXHKLVW1oBuFVkTYE7b5u5qn/7HXnAHethnS3eSpGfh267/HPq37lglH9rtsHn3lxFf3SQ22h5+xcMsSaXOa44EjytJGxLLYnpSBErFMueC8Q2/A6Atx3Pufe2z3erjofOYIIs5hA6mvOhZ7U4HoOp4nFY8PVKty9A7VPtfVcdK6pzot5f+l/OqXa0QYxc4J+j7fLexjqHL19HojJkoTKVxaSxpsuppvdMajqd0s3fUss/Pi3FYKdteK/pnGp/bQIR7FTuLeVT71JebFBveJ/VpWrYIe6xpAjKDjSeushRrXGqdbZGmJxqqfq3oaUWbZdAZlehTHWbpi5CLujSQjIZJ6cKSk/0hNplhyCY6rZU6Hxh6bnlgaqU3FLLJ//mQU8eUGHyb54CxKHmVOdi/k3wq1H0dhSdH90LtaVWvkw12XZbmlN5fS+vIVBcXIyDDjoI7733nvT6e++9hzFjxmi/09LSgpjioMTjVC5fb6yUlJSgurpa+gGAknjHJZ6mAk+E4gAHzM9U+yOmOuOjq+VU6wph+CrzxuXiAEC4CNN3Dx6MUYO749g8iy/kgk6uyBc86slYWZrwOVQ5mWqSttrODmGquYyIoFb/lhY0yzJKxExtBwg7s1NdyuTf3GCkcVAqOdUdY6rVwIxp0VKvo3o/BLuiWW3jBmOFwyvEI+dUCymiwZDzftvM8aa860Lk33LUWcrnVYzfdMYR7AVfjI051awYog5q3h2hJZkJ7WzxvDlAzm0NyncG8utTrRac490mVDjMaKM5Kr9CZX6Dnl+jBBsTqgxY6oKhMGz6nGp+3P5j8G/Xez6L+DhXmEApn5Ht19+WTXZ2CgENETWgrhphJUVe7rCU/pQXU53dl61KmJl9wP9W2GgOeSw6vueZ/ldbavH7FDawr1Zw58fMU+P4mMqXNOCByLIijfw75h+bANAucqq9uVQY3Nn5xiTRD7J5dAH7mOLg0vt0XN5r/r91TrXOIQc8p5ryqQG1pZY8TkWgSqgU5HlEBzqe9jTrbmK4TtQDmVBWJDPVGRb8LcnhVOu6mRB4yzyuShGFyhSbLUxOdTrjSMqwsKSbrt0UL/BpGt+iMrbtD9RsS1Dgw22pFY6pNhVoJvvE1GpNV/dF6lOdygTIv7N2tcJUF+pUC8LIV/3b+5vPtzTWverf+dmEZNtRekbYW5u3ZX3llVfi/PPPx8EHH4zDDz8cDz74IFatWiXk3Ndccw3Wrl2LJ554AgBw+umn40c/+hH++c9/4uSTT0ZtbS2uuOIKHHrooRgwYEBe+/bJvwsIC0kRaI21VBzgTJETr8up1vWpNjmsOxp0PLqoLUFnzIcxKsbt1x/j9uvfSUfqgUcTdfLvEf2r0bOiGGN27+ULtuRiqkkWm0rbBUfTOgLB1iiODuCNR2nijlkSy8kX71xMdZ/qEixZ3+gr5rYzgE+KbSnb11OUR7d1/U/zgbqI6+TfgF9SpDJu5Fzqnp24xlgxtdRqU6pPpm0HRXHLYy6kgnbeMWRY8R2ed20y1syGmexwmQxL9zO2ULikGSselFNdqPybgk65Hlc1tzmVkc9fRaFMtcoaBbXU4o5iMTN8w0LXUovvppgxmapTqivOE9xSS3891HGjk4py+bdQFtDnNSwhYG6p1ZF5mbfUklJtsmOBntWSRFxp9aVXkQQhpgkuAOY8U5O8Xv2fXye1N327lFMtO6RhJdoWWzbUQE0iHtPK2vP1I3Ix1TI75r1OY4LfByH/TmaD4Ybz1OeC0xhE9j0di+1tQ81TV//2z7O678rpkpub5R7V/PzVscOPXRcgzSX/bgvhcPrk30Vxz/FiDovrWAUXKpNJAvkzXImSYt9X+1QTwlT/zjCmupjVRcgFOdjmHbtwqg0+ga4+0/awG3tnAzDrG9uFw5iLheXj32L3Rci/TfaH5rmR5d9eoTK1jS4vRApwpjo/h4iOqcVQ/VtV19BQS6ZdQoFskVyBBxV0TamQYFBqsHS8ee0FwLnnnovNmzfj5ptvRm1tLfbdd1+89dZbGDp0KACgtrZW6ll90UUXobGxEffeey+uuuoqdO/eHccddxxuu+22fHfdudW/bUfLQBQFbJP2L/pUs8+q8kL+d1djqqUF35ALkhCLFcsJ3Y6Opgoe+VedKQDoUVGMT649HomYhU+WbZG+m7NQmaZfbtgHqDMQj/mNWVWyy/NeYxbkQmXse20BThwAXP/Nkfj4y404enjvTj6LbQ9+H1uSaW+SFwtxjH22g/LvopDyb2Uxsx3klCwSOENiCubo0jAA1xEoivM0AW9bfF7L2LZ/LNl+p5jmAWNLLXpGlKgz7ZMjlXHEONTl8xJ4H8swhcpUX68lmUG3Mi/QFAS1pRbfn45F1uUoh4EaSOWHpdYe4Q54cSJ/p1oX2ODHLYxNxxEGvppHyscL3Q/d+TqG66GqsXROMj8OWxkLUlEqdkJqTnVnrKW6QALfNj2rcqEy1l81D/WSpIhj+9KlbrjHAPa3fP3Va0//ikBa9suq/JtvJ+zSLUkps7dASIZjlraoU0fk30GFyvw51f61TcyPgqk2sYp+R903ZjWMIz83VQVEx0igP3UF/2zlXtNQ2tjoOtW9qjynWufke/+7f+taapnOnZ5rcsD491Xocqob26l9oa2Xfxvm7qDq39yR5t+n6001Uuj0g1SpXFXCiw2GhVxtPrvWMIWKianWqQW3h60/eJdyAMDyjU2hHUZOwliOd4z0TKUMdq9WTacEVJra9W291D7VXuAh1GkKCKbakFMt1T1gz0wyY4vWm0D+TDUF/LZmneoSQ8qEioI0oJdffjkuv/xy7XuPPfaY77Vf/OIX+MUvflHIriSoTHVB1b/5oqCxpoLYb8+p9udU66t/y+91FeiKXPmcaiGr8iJYO7Lgmi7PyCQn9cm/c7XUYv1yw/bk7kzEFYMfYM5SwnOq+b2S7qGOqTac8179qrBXv6pOPoPtA4qQtqdttKYyvnHA86A73KdalX8bJmR1ITHJq4KY6rTS/1P3GbVPYsq2UYY4YwO9vFU+jtIsCCVVkjcw1Ub5t5ByOeI7/Psc6YwtHBS+yPkCBlKfarPjqi7KBFf+7f6dS4Kq5janDNJbcU6S3DkPp1oxxGT5qGdMA9wh9Tpb5JdT7b8Hsvzb75yoeZsZFgTibdls25ECFRnD9VDXOJ2U1jsO71p718fbJr/M27Kllj/P2f3dzlguL4/U9gUO8tmXWrhQDpzq80pVs0R9VlW5Pn1elX/zMRzWBpGL/sjPistUe+/lkh2bkKtQmU5JB5hyqrPBqFSwQou+wiW+9Jrcwxfa9+h9/htQ6xrIz1FQ0CQOd7uinVaFX/6tqjd4igRXXeRqbeblpNq+11TomGrPPvECBfFY7joQunQ2QlJiqr336Lwsy0JpIi7GdJicas5K5rP+6ypjpzXX2rhf9r18i/YVgqE9KwBAFAgDwudUu8oG73V6pkxpj7rCk7GY54u1pzKspZY8drxCZfLalC9TrSotVDUKH/b8HibTNprbvWBbvv6il1PtPqPF8XBjqosJk4OhOrwdc6r1xlQQQ0kParNGYhtU/buL+dTaIirqMYqBbJBcbG/o2myYJjA+oRbHc8uAaGFOhsg52hbQyb/9OdVyMEGSJ7Jx3GpoO/B1ARlhrSyfliZVLvPvaEstX/VvQzX7ctWpViR7tBDoFhIuHzMz1XLOIEH0flQlh7bjm4NUY8/HgLBAgMk59Xqv+h1cdXsqU21OMfEW3eBCZX5HHpDHQK5nPKills5p1gVHw4A+S4YxD0SqQQGek1oIU62TnvK5oJgxmf4Cav4ACw/EqUaw6Xqo7ANtnzNTvMUQbSZn9e9t2FJL7YnttWBhLbXYeuPlVOfBfhkcQ+5Ic4dIUkb4rr33N2+pFWcKAEDtUy1vM3ShMp6fmP0+N7i5nVOofVNZEpymo+spDXjPhuRUK0qenEWlNMEAHTMe0wXFsscSVv5tKj7Hz2lzU5apZjnVEquuPHdiH6yyf66Aky7AbqoZU1mSkLZTXpxQVHHeMeZT/dvUUgvwnjv1HMIGyT2m2mYpHPkw1e5vde2mY8w1pmyFxd3WqCxJSEGYBAtwmKCrhQDoqn8rLLCm5oW/TzXlVIeUf+d5iRI+plohHiz5GeVMNX3HbX9aWPBPONUhx9RO5VSrD1Yh8m8uI9YZU2Hk395nvZuk6zXcdeXf7u+gthhkQHhy4h07VGj3YQqk8HGSq0gZIBfy8F7bfvdMlaYC3gLOmWo+qXHJE3fGTRKZrwvKlGJlgPfsSYtwAXMDhz+n2sRU+1tq6RYtHVPNJaYmNle01FLl32SM53CYuWFazNhslW00qT8Iap9qla1RjRGZqdZvmxsC5BDojAOvjZc8XzezFIBchr3aUsskwyVIzFIeXrV6PLpCR+o+LCt3JV39vvzH6LCvc4ZYlfdz5oK2w+e8QAmyNnAsOyjcefScDL8jxtsJ6noSe/uHtP1CwNVOurnWk3/HtfNrXtW/JUmx97qqyKDj4JdbtUv4/+kMq9auqONa2Ryh9nguqPp3dnNeRwzPqXYcefzmAzJWeREs3TGY6lPoqn8LFstY/dtzdDPqs8BsC/X55YdH76msM0HcF00ahe5zAC9U5pd/O4ragM/tusr+Zqbav6aY7pllWVIF8LLimBRI4cHXXIFAqX6Ar/q34/s7Zsljia/hQU4yt70LYqo56x9y7Qa8ZzzNr8t2MruG9CwXf1eUJHI+g1KhX61TTYEz+QTo2UhLc7ecUy1aapXonWqve0f+wUl3O+6xtxjqBakpGvQ8JNO2mBcKUS6SbRdGLSEdT9572oFQneiSAnrR8vwZHTlSEij/lvfHb66uLVIuWc6OAjcwTQxAQpFc7GimOi4W89wRci7/DjNG6KGVKp1v15xqf0BG3wYpe94xpVAZZ6q7yP3aVihjbbVUY6Izq3+rfa9NhcrUnGrHgcKumHP9vFYg5gJ5uiIzgL8KN2+HpI4j3WdMxpppqhJ9qjUttRxHDuykbV7926zY4UFJWjB5tJsk96ac6tZkJifDTvDJvxXGUIVO4hwGqgOmM8rV/6WWWhk966OD5Nw69Nt7jVf/VtN8dMwFD2iozJLJQVCDQbR97hRLzr2iMtI59+739ddqW7TUonP1+lR78m+eW5lX9W8DM6QGcPR5uvK2+LOqlfeLoIA3dtRnMuyh66p/ey21YloWtVD5t0myyh1gnXqB34eEouQJ01NYnTN0gQK6f5ZleXV4hFOtv1eqw8ufIflZ9f7elGXBeOFQk5PHg2OS9DgHeaMGKnMRBlwCXlqkttTynt9cgUA+boOZaj0jXBqSqebXuzCmmo1pdpx0XCYVlK6i9vay9Yfu4jnVYTq56NJyAM8+EXUTAlhgXoyNX1+6/9Vq9W+W3uXuo7D5QhB8pJpVvs/vD/dpUhnuVOdvz6u23deSqVYdpEKYanlw+a0lU/8+ILj6OGedCDR4u1pONXfITPJvGsitya7CVPPFXH5NBZcBh3mY1EIIQP4N6jsCfm4EH7uosDxUkTKdsaXIN0VqC6mMvzOACtvI8m/3WpR1olNtWRbK2TYqDYXKThzZF7v1rsAJI9wWcv5Fy5yjz3Oq6R6qz5mobqtW/xZ5SvQ9ZojxOYiNG85u+PI0cyx4xcJwkZ1575y9/zlTHZR7yYOSOqeacrSSSqSbf6dg+bdiCKrsoMwshfeqVfZOl5Op7iNm8Ry1fJhq/zFKTpcmzceTafsNd34/Mj6n1vubXw6TXDalkX9Lc7dyfVSFh0n+3RGbVbTAURlA4VQz+Tdn0OnZyCNQqWsBCfgZeK+avj5oQcerfh6Q6ygAOvm3t43QOdXscyK/VBQxsmRZe47gtglVWSdAV6SMb08tXufJv71xqhZUNQXDZYkvzRnIbs9vE/LnljvddFwEnaxb1+/dxG5vyhYq4y21dMwwbc9z3L36B2Fbaon/c9hy3KkuL05o2yolYmGqf3OmWh7T/PmmZ0J1SPl6HsxUe9e7EKaat9Ti90kXxOHQFVncXrb+kGxeNRCuABddWl/vd1X+rYwN/r8IfrBCZRyqjeQVKnPEvoH8r1GuQmV8m/wZdpnq/McDQS3+Ftam3qksb5VFLkz+7T0IOllfUE61T/6dg6mmybeLEdViMeGTiE/+LapqBk8s2wu6nHVTVDBfxpLOdUc51QmNU00LkiT/ZhF2Gm+mXNQdfb+2Fbj8W5VcdWb1b0CWk5vk34ft1hMTrzoGR+zRC4DfECInKahQmdSn05B37Jd/y44mpa2oygUu2+LVndVcQPrXKP/2LZDeezr5dxvrU63KLb1z40x1ti0HM+jIwU4ri7L3Hb9awQSf/NvAGIr/Hf53Pk617GjoCj+J/6koUowz1fnkVPv3y/eva81D14HLk0VghjmN6jmbckLpux4D7v4v51Qzo9XH5OudT3/1b0jfKwQyI+lfp3n1b16orBCm2tQWSXUuMkL54b2mjnNTTnaRsm7w9UvNR85Hos3VA4An/47HeEut3LJjEw4Y0h2HDtsFPzhsiPZ9iQHkYyKIqQ6YZwE1KC87zpyJ1pEMccX2MLU/o0Pl9S3Ee4a/61vdfNTubN4zt9TSy79zBZx8TnUOu5k71WUFMtVq2gOvhA7oW2r5mOqQdpyn+CqMqRYOpzIv0HmZxjdPXdmRTHWYVlG61liATv6tssDe3ynG3BfFLWm8VRTHjUHzZAeZ6iLFPtcpLaSgGQUDbUfYFYXYg75q5iHHVEHVv3cUiovki1lIoTKvuIBe1hcUjVAf7CItU+1nD7oaU62LhPoraWcN/gCmbXtCOJHs+poknyUhc3EIdM/bdlD7MG1ARuMIcSNGOGSGKPHXNqeayb9VR7Az+1QD8vNukn8TeL6mnFNtVnroAiMqG0YGg6/6t1JRs4g7Lso48jMoKnPijT2z/DtruGhYY7Vyczsr+CfLROVtWpa7OKcy3tjmTDU52F4AQf6+1FYtF1Ot1C3wV5eWP1+w/Fs5Hkn+bXCWeI5afjnVfueW75/LSNU0H4sZIXSubocHdwyrfWVNLdRM8m/+fc6oquNMllB6+zNX/+64U83l8PQ/4Bl/JYm4wkIFFyzS7kuTpwv4gzl65Ye8LUe6LixYwXoVA/7AW6Hy7HjMgp1xfPLvorilNc7zrXhcXpzA8z893Pi+rhc2wFJp2BxJ817QPMu36TgsmKUEmBzHHyQC5GcFUJVA3t90H/TVv/X3l46bB3B1rDp9TzjuAeoPFaoTkis4xGW8pUWe5J+nEvGcat2cpQblgqp/tzMGlCM0U82e6/YOVP82FUs05lSzuWt7Vv8G1Jzq3OfKg2GwvWMku8LUSpbPHSl2n6zsmkVzjk7JJ3KqqahqAfMo4C9GqHvG3fNzfHM7VSYvLSBVWA1WhE033qksb35SllUYGyc1bNcwEIUXKnN/BxVx6SrQsQMm+bf3/449B5qseMELy3CreBQ1HFPtfraFRcK2Z/uwQPl3wnOEvImbs5x6i397FlrbnhBMdTLtyyULm4OV774As/ybYBkMdl3FWgLdd26UqIYFGZCqxE5tM8XzZ9UWPiq7wesSALJRYJQQsl7ugN+x4v83t6e17+nmQXURr8oGLyzLW9TUAAJBkn/nGO4+9s2Qs6v7X5cmZIJJDu2+p37WO7ZcUkoddDJUvuYIGa0UPIV4n46BO0eclfrPp6tw5n1TsLGx3Sj/Vqt/i+I97PoK5k7DbnqOvzxuVXm+2t+6EMgttfzbFjnVCTl3OFe/2lz7MrH8/P+g8SY55ZJT7V1Xx3F8KSIpg7OSCyZVRyLm1ZnQjanOgq7TB6AvHKUWcjQ7QPBtU6cmSTM2Tt0HOdCme6VKs6XAjWYM2LYj5iFO5sgttbxzkCu/+4O4prmbgpeEIDUm4AUzqWKyrhq7K/+mYlX+OhDqOPf1qdYw1apDKivPQvapLoCp5vV6+GHnyqmWVa/ytrY1JKbakEbBYQrUJBWmWiXP+LNBY5WuN/fH1HZagDfve7YKpO+Hhc8X0TLVLGjG7iFVJldbpIaBGqwISg2WjiXvPe1AlEgFqGIFOT4xNrnq5N/5FCorziH/LlQeta3Bc0hUto+gDtwdzXzScyixegHXlSpHhplcVbnW9s4fD1WoTHFO6NxNzFauhXNnhWipxeTfdLvyzaXPBT4Rl+dw0k2SvSClh2ChuVOtSqiE/FvPVHv5sCxQphhwQWOJtmGaB8Sx+hZIbjDKC1kT658ZpIbhx0QgprqsKC7yuGmfam4zL1SWa45VA1dqNVqTNFu33yCoVbb5GqXuw2HHXkj1b51jyFk43Tyv6yed0XwnnXHw39lrMG91HWas2GJ0DNVinHS6ElMd8xhVf5Eob5uSsafcH7W/dSEQrQsdPXvczqq8ah2JPAKVUo4fOxVVAeAFqbzXVIeE/yvLvz0FgC4YQ45MvpeMpwa42/HmMF3Bo852JORx63cA+LPuyb+DC3RabH42tdQCvPvBp+GYYtupKh/1b5q3uH3Jn1XaPx/jvAWkSf7Ox62uhWLQbeDzbNhCZbT+6Z4FSf6tSVnxKzLk//k8ZyxUFrKLC2/5VUgOLZf/58NU82KGYn7aTmZX76oSEfQPU6gsd0stUqOYmeqkEvzgtrXaTgvQ9akuLJVUlV3ragJIBRTZWKPK5IUUtS4rikvzwNey+ncJa75daCEmHtHQtVIpNKdaNdoKqby5vaCPNitOdBdjqnUFcIIMaZpUw0yu6j3f3ueqa6nlMdVssmDGgI7l5NjRQZBtBVn+LY9d7gQXMomqKGWLVi5ZlxcJVlrgBDDAunuojj2PiVELlcnOLR/DUrsoxkp7+flqPi5nWPXnJ/pUaypxq05Ko8JU6ySV6nYJnCWhOciTnLufqRBjIO0xBDnvj8y+pZS5388ic8cxcNMSPOeP75u242g/a1korKWWZHAr+49ZTD3BZdey8ysrCeTgEM21ats+XT6y6qyndIwqm8OE/NvAyqUysvGXK+gTBl71b//4BTwDs1hyqjkbH35OjWmuPaBpqaWxF9QYjlRZX3Nd3QJNfrYwXaDckrNa7nY8R05uTSWPqc6CrjAWoGeqyUHMlfcel44b0nHz20rXTC5U5v7WVf+W0kSUuThteGboZV6UkNuWJlYxY3tzkZdSES63Xb5m4XKqyWnjqTM8lYcIKF1xRT9TrSqt+DOhz12WCo8GrOc8EFgQUy05nN7ryQCVGcDGaQ411raAZVkYkmWry/OSf0NRf1D1bzlYQ+ABM0/54v4vp8eZ5d/tadlWyVcirz7TusAZD4zw82vI1iwohGSxLEtSAYQlqnYqy5s/KMUFGs18kKhRYyC/6t+6QmW6iber5VTHNQ+YT/7tY6p37DnocvWCbBy6V2EepjAP7baEF/H0XqNJrpgFksigilmeTNNUqKyoi425zoIn/874nLXOrP7Nt5dvzpLOCdMZe/Qaj/SrC45oqaU4WyrDxYNgfExI8m9NDhgdc662LOp4Uw1Lk/ybR8Z1a1Kx8qxRxLu0KM7Y8WykO7udsuxC15aycx43QXUUfPLiAMltPoXK1EAPwFlL+bOC6cshpTRBYjeVdYc7yDyw4pN/2/IxcymlyGW31YJJ/G/5fNWWWpalz5FVP686EO42/E5JR3IWTUVKRU61kH/z4ky2JHkNvy/vuGXHyPQc6x0w9X+5/7fnWKn51Hzb+Rr6KkHAKwNr1+FOXmq4ekF2qv051aptZa7+7R9nevm3P1BA18Nx/PdKl4LBU3HE52z/d+h8YpY8tmRnzTsHfuy8sr+q/tCBs325Au7VZVm1UDZ4mdCw4jHLEjaWjqn2OdW2+mxz+bfeBg3NVLPxWlCfakse74R2A4NOoGFYaC/7joLyqkMVKuPqB3aaFBAx5VTz+dYLMvhVoGo7Lb4ttaVWvqSVOl51zi0/Pz59NrSRU12YPciv7dey+jd3pAspUgbIg0SNGgOyE6PCX6hMYzjZsgEIdH4kt6PQ5Zb5jPmA0vo7AkL+nXbYa+brSpHNcH2q1ZyNHS//VtlFQJameYxQJP+msSE71R0/f9pXPvIqU60G3WKrSvj1FcL9BhrAnFuKMLNgIDdg0hnHt2D6CuDYudkOUf1bw6y5kivvs7JcVTbETNslUG5WWXFcLGJqpXNyxCWGIMftVufnoEJc6jl0pKUW/9tUUZvLvwvNqRYGP2MDaDjp0nziGsM9ZlmSQ5BmuezGPFIKbClSWvpu3LJk2bVifJpyqgHZWA+bOx8Ek/zbc3KyTnVRjDnghVWtNRdgk8/Ry6n2Xgtyqvn3OZsu5M/sGL0gbOjDzn5eDgJx+TcvwKYeR2eBFw7jl0LXSSGsyowftyr/5sfv3Wv/8XjFAL33dH8L+bdGaQF4z2g7C+JITrwm4OVuQ+dU82dee+oA5OukBjJVqEw1dzp5fj3Zyjp1jT/NQf6fP9sm+TdXnoViqu1C+1S7v9VnMxdTLRUq64SgX744aGgPAMAevStzflZSJmnUH3QPtAVVFVuThhIPdATLv+X1O990kTCF9kQtD2UNaWh1A/yFFq7lhEpQYEc6voL2tIOg5lQXAn4/VEkKkIOpVvbJHR61TzWfRLsaUx2TFny9wetjqruK/JtNzoE51UL+HSanWs3Z2L7nqsq/eSEpyalmi0/u6t9da8x1FoilbNHk05aELGwSel8k/9ZEYVVwdkVX2EpbsVK5h7oF2ZT/llbYWx4E42OCO55B8m/BVhoeFyGzVKTY9LcFveMpOewhzq+a5VSrTDUds2gfxhmCHAu1WnzJX6hMOW6NXDMM1MJd/NhUp5G37ymspZbf0eW5x0FthLjDzdl+nQGty8EX56sYSuo8zbcprzeQfvMqxoRU2gZK6Djp84XPa7rjACDaWnGD3HFIslgoU+0P/LjbMzjVBqmw+7/3N3eUeYElKlJWXhwXeYS5Ci2ZwPOmAbm+h9exYNuRBpyp5YEjz/lirGtIlRknElTZOj98nRSZ52MDfkeX4Dm8fvm3rhK4GG+KjcLvq/w9pkzihSlDPBvcxslFGhy2a0/s3a8K3xo9UDofab6NITAQmIupDtOnujQRbj3nx0dMdUk+TLVwyhXHXygJTIEab64MSnHaVvjxUbvh5H36YRirBG4CVy1Z7FYkMzZs25Eq/Pu+G7OQsR1RFFdXqExHPKg1UQpvqZWb9LIseV8Ej6kuzF/k51UUQLhKx1fQnnYQZPl3YRdJF5XkCKL4VdYzqE81n1S6mE8t5QiZGABfdKiryL95TnXAhaWHKBxTrZ7rjmGqRbEhNiz5sSWZNE3X45jj68pUl2Xva2sq42uNInLAYlannH+pYKpzjyG5Gqv5fQ6vUJmfZQr6HqBhqnlOtWTMeU6BKGamYR7VnFv/scpFR1Sm22RLuEXMzIyZep9GD+mO8uI4Dtt1F1GNXy2OxlvWhM3TirM5j29TnEOAI6MLkpigk397Dqz8WW7ckxHY0ZZa/HpoK3wLZ9YzCLlzz6WUaTbGTEoE3pGAb58XT9Idh+iXzZ4blclX0xjovAqFqVWTj6lOxKWK82kDk1bIvtRxp6ow6DscUk41C1bwPMI24VQnhFMtAjz5yr8Zq8WPmefpcweks0kDU4BSx1T7a1Do532egkGX0yuW521DlyeudnYxqjaE/NsfRNPd3zbWF52D31d1nHrzPQsSGIgRDm5H5AoO9a4qwfgrjhb/61pq5WoDqFb7Dqr+bWKES4vDKVM7zlT7bUuAs7f66yUVJy3wWesIYjELu/aqCPdZpv5QTcZkxjbKvwGwQJoc/JALlZnl32S3ZhSnPCzU6693/JE9Rvke0lzYGfLvr2WhMovJ5Apnqr0bopd/BznVZm2/r1AZm0S7WvVvvuCbGAB1cdrx8m/1wQ7+PD1EYSQbqhO9vQMItHtyhrjBwsejMO4Y+2NkqrtaJKeTUJ5lqqXKz9lzJblYZ/So5tsJI//mLbV0TphuIaDjTmXMC7IpwEMy7IxiyAGyAofLr7n8W80FFO0ujPJv2Zj2O+Xar0kOvG7banB0996VmHvDSbj+tJFCMZJWHPmiPFkawJ8XqRpQJhbZ3W/gpuXtaI5HV4hQ/Sw953nJv9lH6XhpF3FLrtScUY5LcvqYhJsbqDyXXQ2i8N+0P9oGILdfkYO4EK8D8nOjyux5wDBXIb0w4MchOTwkx806OXKhstzt5nQwVXDucEstEQS3JEeRxg2XzHp57fk61bItIwzuGOtZLBUMzWvzOcGrf/Ngjk4m7Mu3NMm/NY46vSbV2dFI5rlD4m7De08XbKJnWZdvTccAyEEcDjkA4H1PbqkVE8fkhHg2JPl3nrazrh9zIh7csUAdw/70JYf9bZB/Z9ff4ngsMJjG6x8UklNtkg4HKcj46xkebOiiHpWp+jfgjkMRQDAw1YD/PuWq/u3rU+3kP4/y7RDMfar96mOvUFmUU20E3chCmWrZqdbJvwOcasVB48fgK1TGNt3VnGo530sf5dzRxbtUcGOP/28CLVRhmr6rD8uOqv4t8vHZ5K7LqY7HvEJlRqa6wOejq4O3dlKNo3wk/2HgOdXh5d9cXsgRJqc6rnnGTGNRrf7Nx7BU4Ikt+HJLLbDP6BlWDk/+7R+jqvHHIbd88W9bUvtkmTAa82JRZgWz3HP15oKweVq88jPfJj8H+X/ZmA0LXXsbnjfMwQu4FdZSy2+oc+WRyKPjbDQxyjFvG5x90hUl4mOInwd/TW1PlGasBg9oqA6A3IpOb9TyfXaspRZz1jQSdipM5GupVYAxKOWKa5xi9X8d+0/g/3p9lOV1Q1WjuJ/1xlc+UJ1IkUcb9wIk3HbqbPuGV62XKhVn2XjZqVbIgBDybzUYK8m/qWJ6QFAsV6EykZ4iFdrz9kHPgIlV5fdVri7ubYe31ApTxE8qVFZgOoBaH4O31FKDkv5xbsNxPCaZF4r0WqXJ+xXESA5bpqNMNd1q1SHL1VLLs/k7Z37alpDsE59TnfG13dR9189U8z7VAU61YqvkX6gsty/iFVCUz63DTjULUn4t+1QD3sXpFPm3zqkOWIFUB00uVOb+9iS8foOjq0AqWmCIsO3oNlMqRN5LyOh7qaj+nfth8uePb9/HQuQEaVIH+DiXKuoq0UMVul5+Xwdw9k1VWQzsXoaYBQzqkTvHKAxGDe6OmAUcOLR77uNihqjOBwsqAFKI/Ftlb/kYTilMNX22WDDV/lzAXEygJ/92t83PMYip5g58kAQeMKdhqG28BOMuyb/1+yeoQU9e8JDOQTpum/8d3qn2mCRulEMcr/xZ97eVQ0qZa198W7xgjq4VjspUZ5R77xVl8qp/q61mxL7Y+dB8pLI+MYs7Cd7xiXZGUjBKPr+UxinZFi21aL9en+q4ZDB7xmA+klLatvxsqAoJHVOtzh+6QmU8WMEDeW4Oe3ZfGgcxDNTCmbQd3lKLG6+d7VTrxi2gb6nlk4bmkn/b/vxXi10zXVFF2mRQTjUvBiiYU03Qy92O+5ueddWWNT0T/D7TWOSfCZtTnW9qlI6p5h0LAE1fedWpzji44dXPcdAfJ2BtXasIYLnf1QetyoTaMNiGE4HAjMPY//zl32qbxVyFyrjMf0dU/84Huv7yhLYkTxEz2ynJtHyOpTkLlck2aqEpPL6aR1rVn/tbHYeNQv5dmD1ciPx7p8qpBrwTK7QPLb+f6kMEBDvrPqY6QP7NJ9Su9pxJLbUME/KOdjRVeNGycBHBo4f3xsdfbsKhu/bIue0wORvbEmpFVb4gFcX9TnWcGcz/a4XKuPxQnaQHdC/D2786Gr0qiztlXyeO7IvPbjpZSM6DYBkMQfW4da8lM35DjmAaiylbif5KOdVyLqou10818nIteGo/WLXwjuU/ZbEvUZ1Ws2k+36oGcVFcXpTVgmtp2/Y5iybQvoX82w42AguVf6uBHv63iQ3n+YmFttSibXHjjjOOav0BucKw957H+thM/q048LYjtqueozBumHRSduDd91W5uBroAeTgUNgq70GQK55zh8eWjrmkiDPVdmHVvzX5lnxfBDU4pv4NyNeZO8rcEeRsesyyYDuefD9f+TcPuvNjlFpqSUx1XpvPCVN7HN19UO0SY09hnu6gCSD6rxnbpqIkk587+F7j6Sne5/wOtskB1BUYBNxx66hzueOvZq4DX0fydaoTYk6QlUHFilPNCQxfTrXtYM6qLWhqT+PztfXanGpfoTJRFyc8U+09w3nIv+l6q0x1jloKPE1iR1T/zgc8oKgWFW1iLTDzk39zpjqopZZsM+RL0Pmrf5vzvk158WEUqzpU/i851WH17Sosyy3w4Tj+XqXuds03XN1nmD7VltUFW2rF/BOyOqH5WmrtaPm3iJCHy6k+5+DB+O5Bg0Jde8uyUBS3vH6cO6pQmTD4vYkhEWPjlfepjssOmYrtHRjYXtDJRflY2KtfVafuL4xDDaiRYP89CexTHchU68eiJ8P2tkXjJJmWDThdTrXqkOmcQflYiTXW5FTbgGXwquW2V7qgAZMlGhZPtTiaXPk2nMNj6r1LCGIH8ylUlmHzvrpvU55hzJIrs6czdqg5SGe0c6c1uPq35jmKWeAspST/1uxLkn8r2+XOH52KJP8mGbrl3x+B55cHtWULCx5Y0RV5o/0Vx2PMkPQzkKH2xYIFEtNs68cA97VNedf8b8uSg+NSMCVmAUyhki97pq5HZEwn4pZ0XcS5drr82/2tpmgQJKZamTNM6x6vKK5zQumaZWy/g6cGxXRzA3+NnmV+31RlD2DOqebBMJkV98u/eUA06DbITHV+94tfOx7Y4PZwe8oGSr3v+JlqW1Sob2pPS9fDlFMdNp2LV/8W7H8e5yjk38ox5yJw+FypBi27GkzVvwGgOek51Tqlh7+eUdapzsFUe4XK3O8J9VK+8m9DsF13jDr1MdBZOdXhtrHTaURpAipU/g2YCxMAwVG8RDxm7JHoq+DchR8yXWsR9bTV3KQdLf/2SuaHfzDzCWZIxv12Pld1PHKJlStN809quQqVfV2rf8uOQva1LvCMcYmvbl7XGbahWmoZxqKu96Pq0AAmp7oA+bfS3kplUAJzqgPY5CAGhXKYvCrU7uu8T3XYViaqYZxP9e/8+lT7DTFVWupt1/0dj/lZnzDQttRiBjaXkapML5e0cueeG4pCtueEkX975wJ4QR83iO13nOl+yL2j5fPj9yhX0CcMuAMvF5hSnBzGVHPH3pSvq98XvH2xi6cafaGqf3OnnK1/ujZRvDBc2AC0/9jlZ4XnW1qasdzZ7JyJdRLvSy21VKZav+7xgArdAkvzjKY0gQhfEVqNlJvfo9zy7+x4SwW31NIFP0Vgkbe5CvFsFEvBy8KY6owic+b1L9Q5y9+n2qtQX5/NcyWYZNb7DuyGXpUlOHp478Dj4woUmjPysX90gSLd+yq4AqfQSvvbC/L4l8+zqY051aGYavd1ztzqirl2Vk51mJZaIh3SICvrDPl3WJ9z52OqQ0pCgkBRSV0+aq4LV5KIIZ3MZD/rj2ZmlEm2Kz5kYsFnD5jqgPoLle1YJ80rma+XCnUUpmDJ9oBXyMed9FSZW9yykIEjFZ5RWU4VO/p+bSt01eIgckV9/8SulVVRYCSgdYe5pZbfiaJxouZUk3HH56u0Yhjmkq/RM+E5Ad57LvOn/x5nwXX3KSjXj6LmXjEnOo+sQZnJHQwgcOcN8Ds3/srcZicnCDrnj+fb6/ZhWX7WpzxEBgM/LjodztzrjHNd9W9+zFJLLRbk07Jz7ABoX2rwMx4zSW/V4/AHZrZtSy2/w+MVOYqzPELmVOcxz+SSMKv7DiqMJ32fd4BgjDI36mPKPcj3mvGAADfCE0zJkCrQYQ8DLzCTP1NtUtTxWgH6wJcaDAJ7z/1N90VydLP/8HtE85jjuO/HYpZ2PjHJv0XgwlGfO6bWYdcgzDrYGTnVOplzSTyGZNr2BffVZzmdMTvVJpuuV2UJPr32+Jzjl89Z7YY89SDkYjlNa7D+uoTe7XaFFBhSzofLv7XBf4XAoXGWS/5Naxp9zwv85XeR/PJvsy1luoeFdoTh7VS/li21AE8b3xGmWkRyNZGpXBMOlxEEMdVhJDk7Crqeuj75dxcrVKYyIJ1dEEKq5L6dz5VfWykaTIZn9tB4j1K1dY0KE8O5s0NXub4rPGOmar+EwJzqgIIopvlIlWG7sk/3PbX6N5EGnNXhiw//jLn6N8m//U6A6qRwyNW/zdsFzAZykjGm0rEozEkQuPMG+CPaqiMT1AYpCLrCaTzfXtoHCwgkmOQ4LFOd0Thi3GkVY9L2O7P6DhByfmJe8m8RAET2+95cpWOjfQXTbP8Y0uZUd+BZ1xU55OfBnRyvZkXG9/0w4A68ibEEuDrJey2IqU6LZ0mfU80L1KULDEDzZ4XPJYl4zOd8bou1kp4XU2oTd7J8wX/D8VjsfujGkj+9zO9w66p/6+TffE7TVgy31fFmbqklVRdnrDQPmodpM5qQFEH53TPurFOBR7rORHKptSB0OdVt2fOta1GY6oDc5TABIa+IbWFMtVApGOZ5s1Pt/uZ1E7pqoTJTQBEAmrNOdVHc0qo71VRDEVDJ1VJLLVRWIAnia5unubciZcSwduaTY8/BmeqwHXV2OqeaHuLOkH/rcnZy5WrzgRTUp5pL+7oadD111YHuynu8/3d0TrXlW/Q6d/vc4dj+hcqYU82Y6oQwVMnA44aT+3nTJPK1ZaqZ0d8V5d+uget/P6j6d5BTbWSqFYaLF6biBk3a9qo4y0XvZMMwV69TIf9WCqTR3yanmjt0+qCB2UD2HHl3n7QLXb/tXGPA11LLx6zA+H8h8u+g6sHis0ogQ43s50JQSy0+HnhfZrrEXBrOWWCd7Nnfgs2RflsWr+YtB/t4qopsfEIcJ52Len34dQhSO4SFxc+ZX7uMO/6T3KnWBDjyyqnm114j3xb7FvaC2fHmx8rVSjw4zplqOs80ey0feAEXWcabkOYY9/VtUS+GxkY4pjq3NNTdpvcdXW6nes34rfbnVHvv0d9ycVE5SK5+J7+WWnxf/qKTAOutHTA+izvCVLN9JZVAjWnOUsdwKuOx2Q0GprrQZ5v3qabjK4SpVosIEsIUKutKqjkd+HyvzrNNwqk2PDtiTleYavLF4jFtzjIvVOY4jmgxl68vEaa9by4J//aUf+90lndHq38DcmQLkG9aTvm3ialWoplhjb0dAcmgCmD7igJyl7Y3fI5lJ19X3oNue58rn4ht25vcaTFT22fFLCCewwD/2hYq4wxbF4oOyy21/BN7kLTbc6r94y5sn+qYZfkWP3qf/uXyb5V5zDVfCSl2Rg4aun/naqll3nag/NvQG1tiqkMGVtT52STDJRQq/9Ydjzc29PugW2xifYz70rCbElMtGVLZ48q+KDN33jHTmCSpprsfpV8uORI6Ga0i3ZX7KcuSd/d3dlu2fwxxplJtxVUIdEYwbZsHEYq5U83l33nMM/I85b3uz6n2M5n+ccI/7zk12uB4zPLdg3wvmTc2VKaa9anWOJ+dhaCaN4B8H1RFlmm+5C/rit6pknPde2owCdBLwjmjpdY64H+3p7wcfulYNQwo/a/LS01p2HUVQYqgXODPtxoAFjnVaf24JjQzibFf/m22QUMdHwu0E5OeTyFjXTBat33T61KtiC5gi+jAVTq+nOrsvTE+O6r9KZhq1xeq1LDUgNoONnwAPGg77nFqcqpzyL8LLVRWKRUq+9o61Z0h/5YHiUnSrd+/9z6/yKY+1V3xGdPm2mkONBHAIm1v8FYvwDZwqmOFLzodBZ+0dUy1tqKukuei4mtbqEzDAHWFuFXOlloB0VVP/ubfrsq8qIwKGexcWaI61WpxG0BW6bjSW/dvY/VvpVCZ2pLLxFSnubQ8h7zdn3KSjXQrc6ooVJbJR/7t/iYjOFehMpUhCgtd+zAucdbtU2V92kMy1TpHLMOutV7i7b7Gc0y5SoGuucpUSzJUxZHQBRDSzHGJ6dYb5ThUBwKQ1QSmgpr5gMtqVeadM9I8p7qdORH5OPSmXHF/ZW9/jQLVl+ROtqwAoM97gTOeS19IKzD389ljUwzwoljM53xuC2bOUuwzFdwWUc8tl2PAt6t7RnV56Pw689/8b36PuC1B90AXlKIxZ6r+7ZN/O968JKfy5LY1OWmQb+ccfo1Vp5qOXZ2zTI4bANSFLFQWFjynuhCmOmehMsMY19nRXZWp5kotdTlrDstUK/eJ2F+d9BuQx1kqY2sDQmEQpuWtCCQaAnGF5lSXF3vf+9oy1dVl7g2sNtzIMFAfIu5UhylURijSFiqTo5ldMXLFc+2CZJ9SP8gd7KQJZ2Ib5XJ1pJBHR8GN0kxGruTKf/O+o3SI7SEMj68TZEfBfa0rqEGkllqaWxJUXKM9gKn2tRnJGjGi+AdzlnTGQdp2hAHMxzWPyutaHanw+lTLudxALvk3j1D73+fzrSmnmhZzVf7Njf5c86y/pZZ8k9TDL5yp1hjlbGzIn82+T061gfUxQZIwKwZ/3JLZaEfsS94nl13zPOx2xlTzoAvfh7fGee95ef2soBZzSlTn2NR6CpCl116wovBnXRyHwrxnHEewhpbljvUwqRlh9uWTfyuDSdQosP33kiDnVHsKAG7UC3UTc/51lazDHbs3x6aZAxqL+Wt5bIu5V602bHof0FUGNjjV7Dh1cmnO+rufZ99V5g5+C3Wv8WPQ9bYW8u+U3gGU7AGFFaexoJOYB82BcneTDjjVSv5zWKa6sc3MVOfqB537+DzlEq0V+dhwXqDalEpncKp1THUXsEV04Eotf8DDnetN10x0KVFajFFAxeRUFylOdaFBvlDVv7ObNNUj6Qym+mvbp/qyI3dDt7IinDV6YMHbUJk/rrfPJZs1yr8NE29XjFzpmINclXl3tJOmSls7+7LyiXN7y4lVplptz6BKW+IxSywkYQyPrxM4i6JjBXcUPCbUbxQDwQ4zGSS6Z0ydj8qK42hNZcTcpcuhTUtMtS2e8UD5tzLmVCQU+bePrbH13+NMqG7bUtV95RpRpFs9V5JXpiUGVrt7AV+bIENuq3ROWeSXUy3vjx+bysSqgQxatMMy1TKbLu/DZSzd16TggyK7dtkw75jpfrQrLLFW/q0ZM7oqyjrHWc3B5mwrQS5UJm+/EOhaUNF58PxWy/I71fmuf7rgH6CpOq+Rf5ty793ve/eRB014azk61ELznvmzQkEAMmRV22lbmDdqUVIVkoLO0Nvet03uVGvUbiKPO88+1Y7yLLjH4J9npfFGTrUhp1oqDJaRx2lGuR/uZ3Kr9+SWWnmOZc126bViMWfJKSvqfMp7IZtaahX6bHeUqfbGtH68mY5rp6r+LewTBzSdxGMWMrYjmGpz8MD9rcq/B/UoAwAM7Vlh+J47F9mO6+wWmq7n60UfQFCY5ozOyKkOW6hsp3Oqh/WqwP+dvHeHtkH3RCf/zqdQmU6GpObQbItCHh0Fj6IHHWdQ64rtDZ43w//vLCTi4QMrnQ1+KryVDUmGvUmNGczZ13T2fnE81iXHXWcgzpy7oAJY2xuc6cg7pzpASqmeG8mYVOc2HmO5T5IB589FBvwOC33FNG7UStwy8wLY0C9mtsah45AUIgn5fXom1d7YgqlmTnWu4KXaUktlDFVHhvs++VT/1jn5qlEutqs4mMWKCiHsvgDPmebb9Fr1aeTfZBAqKgUhe5aYar8TyvclS2Vlp0sqmMZTAYRT7W1TfW74dej8llre61IrHp/zGG58+fbFAgn8vFTD3cup9l4z5d6rx6Mz6nkBs5T4bF6HLm1XqFxINcXuF/9sZ0KksYRwcsLKv/ntS2X891RXYE/sgwVjAH1/eK74o8rstmMKmri/TdW/LWaC8uJZsvybBwlyBxb5PJuv/DvGnCOCJ//WBwKDeiGbWmoVzlTTvfMKleVjw6kpDSpMgZoYm0O7Un0XHeSWWu5r5UVxNLanhVNtGhf+QmXu6wcN7YGXLx+D3ftUGvdbFI+hPW0jlXGkNSEf5NOn2phTXWANLimnOmTEpIvGVbYt/PJvNuHklH9nc7oVx4UbDkDn5IBtK6iFNwD9hNyVmGo1z6qzZTbFUv749r1pnBmRi5G4x6H2M7as4PPf0QGQbQldHlNXCCBI8m+NLRjULoQMpTAttWiuUnObdQyV+76tZTd4RNd2cjO+aiVutdBTUEstwTJqNi7Jv2Pq4ik7aIJxj3tzLc8hDoKlGMa5qn/rmNkw0KX9qO28vM/K7+cr/9ZJsnX9iuV7IO9TdbjVlATv+5ydc6Rz5eNWXVt5Sy2+L9oPr2Dtb6nld146sgzJLbXkIIGo/J0NWqnrna4mQhB4sIA7GCamWm6dJG9LcqqlNcB9zVGMelWxkn9LLW+7dA9Upnqbyr81ihsOuVNHboMbkMcol7QTfNdMkoa7v71nzPuex17L++FBK/5dwHtuTH2qZVZdP0/zNT6MTdSRQmWAf23KLf82y8HVz3aYqaaAb9oWa2lJPLwTpaYWqjCZg8IWseGb17oadAHF0my+cGMOplotVEb33rIsjB7SA9WaHtUEctRTaVuq+5APVAWb7jgFUapZrIsTsYKDsSWJGHapKEZxPIZu5ebzlI6voD3t5FAnUB7FyFmoLGvUqpEw3oYCAFRmoCtBOKhsAOoGuiyz2rHRAZ8EpZMva2IHFioD3Mk4AyfLDsjGqqWM17hlBTr+OzoAsi1Bpy1JrrrA6XKJr87B1N0Tn+EewGYTSFWjk3/rqn+nWdXNRMwtZuY4cqGyMH02EzG2KNv+qrSA/nty9W//+3weVedUWkypoqvKVLvnF24+4BWoAb/R58939jt0YaCVf7OUBd0+RI4aVfQP2ada10ubB2i4IeUo61GcPUfcuSfnkVf/Vqu7C/m3xpBU8xNVmbI6Frjjr9q027Kllnp/VQcnLANqgi74B5irzqs53hz8K1ytxJkybx6I+dtD5XnsvD6M147PkralSkE7E/lUYw4zh/JtAp7dY2meUR3r60/t888N+rQG1utdEwAjNYiv+jd3qqXaF5ys8X8m0KlmKqBCasbEY5YU5BJOtaG4Ip13cTyWcz7znLW8D0s6llY2Z6mqp8Dv55B/G1MK2LgIChx3BUhKreztoCJcQv6dI3VCV8QvF4oSMaCdCpUVxlT75d/mVDpdIK60A0WtLcvCU5cehpZk2pg7ruJ/06lWJtBCqn+r+npfoTKNcdVVQGOUD0Ad2xe0eG1vqDkTnV6ozNB/fHshFgOQUas1y1FvXlFXPUS+eH1dK38DerapK0iupEiwZnHWBaV0veFVqAuIUf5t+QsUAf4iKjHL8hUrse3c85WUw8fytIEc8m87WKYfpqVWSjDVWUNNatURbqFWJZxqpVf1ngXJcYMQJP9Wt+OIa+7+zrulloZNl3tOZ9/TBKCkfFzmcOtyqrm8ke9DV9Xdb4ApjrPi0JiqZLvbkI+Bf68QeEawkuNqO56Dk5AZWfW7YWEZzsuUyx9e/u0FK3igyFMoyHJY97N5Hbo0l6nrraeG2XYBTa8wUu7CUaojYJL98uNMa+YMXg3f/TxzuMXzi+xvP+usPveJmIV28GfF//yY5N/8lHjwT3LemMQ8jKxW7lOd/01T1yq6JqTsMBUqK0mEcao7to7TnNWaZE51IS21jH2qw3+vqzLVfD6iIyRbQlT/NjifPpVCHudYxNLGaOrL9xqZCphyqAoajkKLlBFGDqgGADQ0NIT6/NfX+g6A2o4oH/k33SA1/0CNZnrGTScccCdD7dMNGFgkqU/1jp0sVHa981tq7dgAAl/U08o5+iSVMa9QGaFkBwcFthf4dbLtjhvanQVd8T+OoJzqwM8oCwjNP2SoyE6UbEyrfycYm82f/TDtybghls74c2x1gQTadlBOdUKSJeqlnGqfav659pASV6EkEE51sPy7o9W/dfmaqs3my6nO0XvetC+AtdRScjvpc2r1bD5eeT6o2koKoOrf/n3pAgi6lktxaV/y53hPXtouzWVy3j8Z3iEuzP9v793jLSmqe/HVvfc+r3mceTFnZmBmGB7KKAIyIC8NoDCKEo0m6hUFH+ANIl6RmESu+V2QTwyJN0GMuQhGRL1JlORGf5qEoOMPRBRfFwckoGhUHB4zAgPMIDDnsXf//uhdXatWraqufu7e59T38zmfs/fu7qrq6qrqWmt911oGYOUGllu7kS7guAbAMgEL8JxAJTBH9gsAzFjE9HG05inRv8WzCFXaP25L1rb3elHyDBJXJE1gL3/tTZT+RiEHvasNjEGKIAg0mi+n+OJyJtuifyf0bxorwGrdjv+bApUpVvUuP3a4bA+2x1w0u4mR/m1g14i2Uis8h+mC9G+xF3qmL1QHQbY9nMxYwC/0XJDR+HdVjgBophENAO+b5Pgb71uqRbozLgAYgP7sM1mq0TtNWKqzMkGpYYFNqcWw9ASKCtVZkesVdfXVV8OmTZtgbGwMtmzZArfddpv1/OnpafjgBz8IGzduhNHRUTj44IPh05/+dK4GlwGa02w0R6AyujDRQDi2iLeDRkInJos0RbPo3+pmrXShesD+49gHiy4+ib8I3qiSx4FfXvPapxptVnDU4kEDCwccXdiF2s3mijfRv0nAMNWnWtY/gxVnIZ/LWvF1NdK/VX9sSoE0CZ5pVnAcy4C+1JM0Xv22iirxNYIanipUk00utRjq/s5mQcgGSgON29Yvx2CBFMczp9TCwdSonzMSuroRtooHyXFxvrg9LKhh+jeNYE2t4ir9W323BAGfUkv8JgVDWZ4Y40oE8hKUqaqPOX6+PRmorCRLtSn692yPCh89zTKtR6KXn6XlWFUU4Wch53i+PsM0efEcxVzUU0+Vv/ZySn8MJWMApYZa9ikJi4KhxVO3hRYzfynVm/uNxgpI/LAZpdSMwafalFILKxmw0kxGYjc/i45FeemCNJ9qkR6Mtpta4TkUDVQmxoNYszoZA7VSJRSFMU81I4w30YgGoCoUxX0K+vfefWnRv8mzz9C3iU91V3drdAU2GADwCk5u7yOQN/J3XmSmf99www1w0UUXwdVXXw0nnXQSXHvttXDGGWfAvffeCxs2bGCvecMb3gC//vWv4brrroNDDjkEHnnkEZibm2PPrQM0EIbqU21/4EmgMhP9m1qqG7Dhp2iRxRjA5FPdHEs1TbNR9uI1aAUC3oTRxYej89FxNZohLsAwAwvVUnE1yBZBvw1SOHC1VFMh2sXvWrwIqYVLjf4t5/UMohK3QjVisABus2m9wutiTP/Gm0RJ/26HgaKsw1ZsbljaAuiIl6dQDBShf1MKJxVudJ9q+TkL/RunNhKQwbjUcqjVNntKLd36hX37xBoZYUtmIIQjeT62aIpnQOnfnM8vF/2bbjTDQB5XqOahLqSJto91QtjzLJ9SqwgrRRXu5e/dni7g0HmX9f0XKHXZLdU2lgT9jpXKSqoy9Azp/ibrHkSN/t2f1/0HqwummYp2gs3qhNsXf+YZg+ZyI7ZftP0FPkZcR7rsvFMt3GK8JNHdGQXYNAmOJ9spPyvxMbARhGEd2e4dr60jeejfhn4W82Wmy6fUcrFUY2NBHlCf6tGM+5/U8WYUNnVGTRNc0TjgWDTClDq1dAwA5NpnzFNdQMHYack+KhLMrR0GiKmmX8+x9ATGa7ZUZxaqr7zySjj33HPhvPPOAwCAq666Cr761a/CJz7xCbjiiiu082+66Sa49dZb4Re/+AWsWLECAAAOPPDAYq0uCEr1EZqMMEgXqKSlmh9olCLUxMAF0vIpF2lunCuU6AELaon2ndEkl4EieRzLgGqplgIKAPLdQfRvqq0zpXqbb8CxC5oUDFARDhipmlVaOVmq1ecso3+rfsbYxxJra7Fw1EbUUBykEG/8TV0pItSLjbYqlETSL7gdwhzybVOp5XrhNlqiyKtNg7LhPnGNHFsX/Vv6+cvf5Njg65AptbIJ1baUWnr0b/X54iBXPP1btVSrggSxiqNxS4OAqgHT9JRaikWXWLfKpn9L6yFRKvVQnur+BqwI5REA0S2JUE0ppjQ6OICeJhF/x+5P0q0Ap9RC60BPPtcswAooMffkuyg+pyrGGAAScE2WQ8VqpdbPBTESEHFLEiGUcdFIfNaZ+Suag5+HdIUQdajvbC4QnaR/99kRZFALqnoUqWu5YhHFzCSH56zsb3JoQrSAcAERqo0+1e6W6rx75cSnWliqMwamogoV7bjJUu3I+GwC8Boc9Jt7wPIJ5RyTUF2I/t1/h890e4XS4YrUXKZ2cvsaAaq0qhqZRt/MzAzccccdsHXrVuX3rVu3wu23385e85WvfAWOOeYY+MhHPgL7778/POc5z4H3v//98Oyzz+ZvdUHQCJZrJ8dg48oJeNGmFanXyujfRHNHtJllpACpCpQKCcAvBk0OVFa2H21boaHWr0BQo+Cqiw/VpMZWCvX60QzB9oYZOMq+2HM3wadaTamlL+y24BrJOSxbRP1NC1TGWKhMPtWmtFu9XqTRgzm00bqpWrqlkE0ZPD1E0+c2JzahWmz+xL1iy65UDLoFY8JCJC4TtxMDP8Js0b/19YkK9PRccRxr9d3q0suSKb3UjVQP9R2AfBY4sjdOqbUP0Tm7SBiOr+n/ztC/qUCn0L97qgCPr8XzhqaNU+6rwFzHPubU8k6jf7uwSGww0b9pdNq5nr5e2L53kQCFXc4w2yAgcyMz3RIpBMQ8EWOTUrOrcG9L5rZBuaTSv8k+zKIQlxtvQZfWj3H7CxwpH4C4hmj7PaosUs/D10tFjv6+poHT4s8q/Zvm5nVNqZVV6ATgBKv4v0kRKNpKqe0cuLzhedomfKqz5uGm1H9T+RTcNrGxlmqGSXfA8nHlHNMap1mq89C/53qFcnljIybL+iPKXIy6faozWaofe+wx6Ha7MDU1pfw+NTUFu3btYq/5xS9+Ad/61rdgbGwMvvSlL8Fjjz0GF1xwATz++ONGv+rp6WmYnp5OvrtGXXOFmHNYm/b/XXyy0wtbUMXpYk59jZpkRaPgBmDahnfQfrpUsCw9+ndTLNU9dYOE/0tNn/681EBlzRtzZQFb9MuwXpUFMRyjiBfCuGAnRVJqiXmAab1yMy3rn6GWakYrr6RKSdmYTc/FGmfV8iI/x5aJ2eS7Ygll6d/ml6WYh5xVvh3GUWVnHNcDGixM3PNoO74ne0ota9EKbPRv3W9bPRcLYi7gfDux2xHe2NOUWlLQR/0aygCI2FKt+yCrVnEuJzemdLL0b0JD70aY/q1HFE5zT3CBaGaXCLq9noz+PWKgf+eP/s0Lxfg7XS64cwSwYjXZc0QR+9xdUi1xwEqgJFBZEt/DLJiWBZvVCUBdS+lzMQVbAtAV80qEb+G2YFEUUas0gJyrWqyA5J3d068R9O9Zs+ApqOrYTWVOs1SLuZb+nDuW2BUu0INqhkrbbdG/XZHXlkEDhmVJpwWgM1dN5bv83sQYSgCqAi7oP6r9l40njAgAW/Rv+3cbpKI4KmSptgU0xWWy0b8LpNTKg1y1UctQFEVGa1Gv14MgCODv//7v4UUvehG88pWvhCuvvBI+85nPGK3VV1xxBUxOTiZ/69evz9NMIzTLXxhAu+WWIFxoFU3Rv8Xi2WifaibAAtfMdop2qE6EIX2BlFt+0UAeRYEt1dJHSrUOzCKqKxX8Ffp3E6TMiqBQWhs0x3C7OMOmS/TvNMUWABaqhRAly+L8irAFAUcGxpu1NMZKcg8ocJi6SZTrHrW60JRetvujL3VqucWRrcUGLKF/pywIekotdUNro39n8anm6N8hqZvWQYVqE+2VQqV/q79ha35PEbr6bWKUeGEgnzEOPMTldY7vNf6Ox22isDak1KLCMX5vinKln6ZqLcfX5YH0bSd+yr0oqcuUUit7nur4f6xUkr/TZ4vzyAvQ4aZaupECBCmKePp3MZ/qXg8FKiPRv6tKbanW4WKpdld+2Cz4cozqzBdbJG/x0aggIwwSXI4ppVZcZ/wfC9KUZkwFd5tQ2im4P9As1f2vpuCK4n6zWAnzR/9Wr8tqqebijCjHU+jfGE109wSgsSvEswlhv8WjyTnO0b+zWKpRJgfOfcsVHcucB7CzDUSU87qQ6e5WrVoFrVZLs0o/8sgjmvVaYO3atbD//vvD5ORk8tvmzZshiiJ48MEH2WsuueQS2LNnT/L3wAMPZGlmKjQf1QzzQCyAVBtGKUKYqtg00JduEPAUWjz480yEMkGDq5Uf/XuwCgSxmcUb4CRPdb85M8hKoQUqQy+vQStAqgS+N0wxHTRCtImim2QAx0BlLEVc/T6eBCpTrbfcZhpA30BykU6xgB1Ypnk7CcxC8lwjoYtuaDhBCkMRqqnVqSUFXkyjbYUBaosb/VtULdopNuwj/fUc3w+mRNNjaeAYStRKrp3b7wIxRrjxw9aFyuOiEKvxB+LzaH5o2mbx+z7NUi3rovRvhUZLN/qIUh4xrAXsFyw3e2af6iJLG+djLr5Lq2E/pVZBS7VJMKTfuz07SwJAVepgBYjqq64rU/Lm/8VUUWqppoJpFQrNLJZDLQ2fZZ9ClVaK4Gyhf+P+iP/rCiZNQZaMNf0a8dGUUguXowQqU1x5VOYFvoZD4TzVigJCTwNI6d95LNX5o3/zylhX0DGttcuU+9ygaGgi5Bos3y9BEMC6ZZICblK2lBGobAYJ1XlECUUpxBRg84sfc/DrLxOZbm9kZAS2bNkC27ZtU37ftm0bnHjiiew1J510Ejz88MPwm9/8Jvntpz/9KYRhCAcccAB7zejoKCxdulT5KxNUw5dlkJxw8ErYvHYpvObI/ZXf6WaVWgaaBOr3ZtLEKYLmwOnf8f+8lLY0DDrSOX4Jm3yqMX3GFqgsLdf6MAO/yKTAOKjWSGCroKtPtRaojBnTQRAo85D6VOMNlWQ0IIG5S8eS+jsuC8BOXxNRY+dIGiAspNCxp0b/1sseaeM1hmyQacRxhuLq6o8nrRGiPHXTFzGbXoFM9G8ivMbtFceo8CSO95+NaKNBmNDrirTPOM4AFlgF6DgAwMopOSaxpZrmqab0b87ihympUqGh+2Erqej6VXKU0iL+eLRtNPp3z8mnOutGnd/kUUrzXC+CiOzlTanX4utRADjFpzr+jNeBuZwUbWyZ1TJRkHdRFQpNGvOGAq+bJpcRttzkmejsFrku6sqCVjJ+dUs1FbRN9G/FN7qvXJLRv918qufIPA6YuWZC4TzVAe5zef0ok/4Ot3sQluosgjyAPqZNx9N+b6IBTYALFtkKAtgfCdWmceESUNWEDnLhKmKptrFTAOT9CcMTPqfulFqZa7v44ovhU5/6FHz605+GH//4x/C+970PduzYAeeffz4AxFbmc845Jzn/rLPOgpUrV8Lb3/52uPfee+Gb3/wm/OEf/iG84x3vgPHxcVM1lYJuLLO8GPZfNg7//t6XwBuOVSnpGt2H8TdrCnRLPd9GVdAcrOQi+tHVhzIrir50ikLZyGgbT/UFi+mvAmMLxFKNn/tcL30zUReUlFrMXpDzqXalmOJ7ToI4CUs1Z6FiGkDzp3Zz0b+RpZoIoT0ipApgyz1XtC2WAQ4YiHNjY+HPlf5N/SIT+ndHp3+nWQ5tkJZic90C1M85q6WaT6klxgPvZ0bp3wBq0ClxjZKnGllCcR2coEvTOWEGhc1ijv2tR1lLtXpdHkghVFcS0KBRmmtGTks1FaI5f2mTsoW7RpyKWQXdnpzPOMJ/7rywSIgUQmESqIwIe1W8KhMFutGn2ixU2+5VdyFD45ZYsW3uGwqLhfwWkHVWLLP4Ece+6tItgKN/B0wf4M84p/ycw55I8akuSP/GrzJpqVZTatVqqc6Qq5yDLciVrV26pXrw+xATcEotzKpYt2wsOcfEYKD3mcVvPHHhmushJazz5Vo5WJmktJE8Q2ydbnSgMgCAN77xjbB79264/PLLYefOnXD44YfDjTfeCBs3bgQAgJ07d8KOHTuS8xcvXgzbtm2D97znPXDMMcfAypUr4Q1veAP86Z/+aXl3kRFFotmllTkMgcqk35tdk61ohAdsqaZ0urK7tTPge8Wa6a54SbZU64BAHFBI/U1NCdYA021FwH1RJQUxK7DPEueD6+RTbVBcxQqt/suCWqoR3ZcG4uHqohtuAFV4sXWl2ac6AoD4GN0g4pc4t85iZR2ljiuWalRnbKVTlWxpYwALCtjvXdTJ5Z4VyONTrVjBEoWrei5lM3HWKRuUZyDeO0jQlRtz/HzVcQCgWu7EOFHyVPeoZZe2H91rotiR9yZ9mfGGTr1nXJ5gYzwzowr28X1xPeEGJdI48XFNrIb9CgrTvw0bdZfo36Z85hgixZ04HweNo0JpZp9q1E9CAE1SahErchVrb9o8UJQ45LnYBKqQzAc89iizQA1iJpUXAJTVou73NDcbg3Ubxwtg6d/E0h23TXV14eaaCUUDsXKKMwCphKI+1S55qjutQA2SmVOo1vZCGS3Vsq/TxxtG0TWiTmDlZYBccBT6t2H/UcQiP4Lo3zRWUBaIMWuOUB7/F89wbKQFT03PxZ+bLlQDAFxwwQVwwQUXsMc+85nPaL8ddthhGmV8kDAFXSijTGkx6P/egA0/RUIbSnnp2mhWdUNuiuP/pVuqU3w2qgZO+SMWhsSnmtGIarS3UOYRzhqoY5iAH01VrgB5wNFpMVwie5seW1uhMpHo38hPifPDo3Ul/mN4s8bQgzkIy/FcTxdKBOdJbGjGOiHsm+0l/tBxG/WyFfo3M6YFZrtqYDqc3iut3bjubi9S+kdYRU3RzOPv1qIVSD9jXdCkQkJC/07muexfF7DRv5Eyl/Mz4+jfnAA8rUTeJoHKiOJYtWJR5Weg3D+1bnPC/cH7LQYAgJ/9+jcwPdeF0Xar9JRa1No4k1BxW2w92aN/x/+pgos+W+rfDaDS9UV7KVqB6laQBCwMAm0dyNpnOHK5GLPSUt2/j4pSWwKks07wswj6a4FLrmY9Lad+zDZ/JUNDXiceFU0VJ9ZswSCgKbWmEROEE6o5BSlVYnFzzQQsaObZH3BzHJc1oymPhKXaLNCMdVow252T5eYcS1qu8syW6vi/KU+1aUy5BBptChQmbU+uvVioNkVNLxKoDEf/tinXXctJy6Ut1nFM+a5bqJ6/u28L6KAog6JN6d9cEJemgFIljJq4Blk/tWdWcscOWoEg6uz19JRaHP2GG8OijEGzCqoEVni4BqmqA3gOcRaWYpZqed44if6tBCrrX24TqjlBSw2AY+5MMa5mumpwpSiS7RCbt0UjUl9r8/mzRd3H/uRzKKBTEOgv0XRLdZC0dZaxEnECqoArHTu+Nv6vRsQWdfPCUkL/bsk1wKkuhZKt/obpwWrUYEiOC2BBghunWBjG7ebulQZ1agWBIqQlioREMJD1iHFy0H6LYPlEB2a6Pbjn4b1KeWVE/2YDlRGfapuCJ0td1BWDE7Kptwbd23PDAT/fnqKs4Ny7MjVdCbpKA5XRgKFVvGrSnjFVeou2dVo8NTQp12JlT6JtM9G/cTR0AMpqUedCMq4RNR8fF9eI8TbSCtk2U39pAKT8IgopFwNOUUu1aX+U5KmeVQcxjeTPgQo7ebeYWdgKHLgAn7hs05iqQo6oClgxFKE9g+JTbdh/aPvPDPcpBPWZfirOrNfTtpnGLmUbqPTvhvtUzwcU8REwlmmgfzeREkLzdJtuf9ARsTGKaMtcMNIu9tIpCiXFTUQod4F+Lu2PdhgkL5NB+79XCXzbM4i2OmjgF69NqMXQ6WN82fh5Tojo39RSbRCiBOhmb5axgMTH+TYASKF3ristYwCq5U9sonAaC26jKoA3QJwFRdz7HNF0t7QNsrnd4hqAPhME3btob5exPgnkon+j9gTk3SDPBeVc2/PjwNK/EZOH8xWk4wBAzSnNjdM0+rdi8aPrNPLt5vzrFUs18gt+4YblAADww189EddVgjuVaBpWAgH0LYckEnORjSQ+Xxei06N/m5QvGDg9Xi8Clf5NBMS80b8jhTWl9otLcKy8SJOJNCGKpPsyQfcH15VBXMT0gAivVKEYRZE2F7ACBwDM7gYpuYG7Fvo3N9dMKOpTjecDrldYojVLdU9kVwiV9WFyvJN8HqdCdW5LtXo/mQOVEYGMO2a7TqAB2xAj8NjHqSnV6N/8Dej0b/d6cVrMJFBZHqVOQv82CP79IsUcwfsPb6muAfSZliGDUG1mk32qbf5DGEpKrYFH/652AcP3OohAZTivaZe82DWf6oCzcgaKxn6+AtNJm0T/5izVakCd9EBlJku1Gqisb6nuRUrqJyUi9pwu1FMFDd6szaINoc3SI+IOzBFLdRf5qIpNFmep5jYoaRaUjmId798rUioJS3XahlrcVi+KEuthGCCGiI3+zcevYcH7GaubctO5bfIOSUMa/Vt0CQ6WRTf9AOo84vrRlKeapX8zjKLEStJTrSS2dhy9YRkAAGx/4EkA4JUVWYE3lpT+LSxtWLmqzN+Ma6q0NFILXvw/YWB0Gfq3i1CN3gE0DaNmqc4qVCcCodykivbaImiXhXRLNVk3xXsvZSNHg8fhauh94XUQK+QA+OwAppzzptzWNDAeBRf7glrDaTdZ6d9K6sIcPq2GOW7KU43HI752+YRZqM47lnRLdda5Gv9nY5HYmA/kUBMNaAKYqYXZTMsnOoklN41aTctywQgjVOfZr7UT+rdd8Bd7DW+prhlVUInpwis2Yk2wolFoFC5DG9uKpXrA9O+CloM0tAdslcdKGUqT4e6d97O2L47zBVmtlHUAt0Fs2vALwMmn2rAOqekhpKUab+6wZXImY6CyOYYOyUHMkdleBDQFlVj3xCZrYlS+1Gy+nWraPn3cdpB1HG9ck+jfjr6dOFhYEnypFSpWueR+iBCdLfq3Wh+AVJiZhKXEamuxmNjqAsDvHbF2YGupvKEkowDqLmxlM9K/OQGetfip1yo05cguGCTKjjCAozcuBwCA7cJSbfHLdwXOJU8th8LShn1A1YBYGa1fKawDMa67HP1bG39M+aEMRNdFLkO4v2ctDBEbEsUbDlQm6N/CxcTBhzkvbOtQEOhjQLz3TPmE8bUAvNtboqhl7ksyHHQBWXwXP1FFuEifZVLimHyO6TPESDI5UKWspd/w2mrynbVBVRDLz8IqrEX/RgodvHdcvmgk+Tw2UpalWr0ua6AyKZCZldEcsIIfoBnKfRNU15f4tzAIlFzVxujfmvLf/T6xTzVWtGSFSOdpUm4mhsKenqaOKm+qxvzefRtQRSh8HLIeABSKRdNAqU6mNg7azxhDt9aW255BR8/G1E+az4/eO478KtAK5TMaNKugaujjd/D3ywVcUtO0pQvVRvqVYqkWPsDqhquFLfjMRkysTxzVTb5k2erRPQgBt6f5GYuvYpM1MeIqVGMLin48EeQVTTceA26+nUlE414kn08YKFY5gSKBymh06/hzoBwToBvxNtr4ONWFzhNlYeEqCPRnLYVZNF6RcpUTHruR2geU/o2fK8egwUEY6TVK9G/0PjrygGUQBgAP79kHu/bsK8enGj0Hzac6EXJC7XyAPD7V8X9Tmp5kLvXyWarV6N9qznDK5MkqrIjzY/q3oOSLQGWqoqYa+re5TO5eOinUUHotN5boushH/46U6wXweNJTakWMZTud/p1Q+BkFaaKEy7AnWjTagiWjbVg20bEGDzPBFKiMyykPYLZUr5hAQjW59/Is1fl8qrl1N0vQvCZbqjFTizKMhF+1af7QfUkmS7UYH+j9naefEoNRCptP7AlHWmGyDo4OQ/TvYUcVSdtpoLKE5taADT8FHZcmK087hZpZJzQLSIWW6kHQp/H4oZZqzqLJpdmS9O/5rStrhwFMAxLWGvAywy8a8W7Gihq36N/8feDnibWuOEIz9l3lNmLipUQ1uqZ74NBG5ZvkPilUy1eLtFro56flh098qtHmNAzkWHelf4vDOE1Qpx0qvwuk+bjawLn9UJ9M07mUMupaF76Go8jj8YAfcSsMNMsFt3T0qKXaEjeEvkuCAPU947uPr8XZKBaNtuGwNUvh3p174Yc7nkB1MR3hCPmsdSXBPoaOa6K8uoALNIUhxjoV8AH0lE3c8MMWaex+gRkrs44sDlPbsQVcuo+oY7kKQcLW17Z1NE3xYXMho3EPOJeGxKeaiY2gpcdDQjqngBSWXZNV1cZ0SAKVUeOQZW6MtlvwxQtOZOOxuIALvAhgpn/LPUyo7K1WYEu1Fqgs31iie9OsluqEwcD0tduYao5y3wRFPuk/KtHcMw5fCz/euRdetGkFe20pluq5gkJ1yt5WFIn3hCPtOAPJWA4lUhEsSKGaLj5lvBdoSoZuMnCbN9Fc/ZOV3M1No3+X3K2dFAGoanCRacWCTocQpnYKqIHKmjfmyoQexXWQrQFjGxR/YWb+aL6BhrUCP2scgANHXMVRSjkamyhCvnyYzVoq/bv/guz1jH6/+y0ZBQCANUvHkt+SDVYq/Vs/LjZIs8iPG1tUZxkfSA4q/Vta3zj6N90EZ4r+zbj9iGGQRv+2Bcxh67L5OTPMhSDQUwVh0mYY8DRnLFzhOsQwUyx+dK1CCsBUizmxfh69cRncu3Mv3PXAk6Wm1OKUFs/OiEBlcn6FyvsvW73UxYpCKJ84n2pb0DxZvsqAUOjfybskX6CyljJX+u8iklJLoJKUWpYiueeQvPdSNgU2hqJ+X/IzNZjY6N9UQUYVUgB9dwOSwk1rq6VfZbBB92sAAA6dWmI9boNikUX1JNG/qaUa7WHwmoKFas2nOudQouM7a8ow2/yYL5ZqrBgSrRTtPeu4DfCmF603zmXandmEarknoQF4syBtjsv0cvL912n1heqafaoXplBdAZUYb9oihWJRuOjS4erL2SRLddX076LRMYsCC9U0wAx9Xiz9O5A0q/luqaYWuCZoiNkgXMh3jfP3c40TgJUreMOA/dhCJLy4pNTikPauswVXEnjNUfvDfktG4cWHrIK/+96vFCubKXVMpxXAbDcyWKrRS5nx/XVN04HHiNjQdlqBRu0E4AMRucJK/1bqUC3E+B5cfbhxuzT6N2Ox5NxIhJVFfOc2PFhowO3j3nGc4MI9dxpNWFn3+of2WxwrZp6aniuH/m0ZI8/M6JZDZcOc8f1nY40AyLlEg6YB2FkTAjgAHLaUxoHKQKk76x5WZU2pgcrqiHjMMXhsVi53SzX9bhaIVKZJ/J8yNASwIpymu+oyTAMX+rdtmHMR/LnvZQLv/3BfCSVUnBoukuspCk6J91bYp3p8hArV+dpPFdZZLdW2etPG1LAI1eIWu70IhNMvx6biUGTvzdG/8zALExcPUzA1osQMQ4BlEx14at+cosipAwtSqHalXWYqk9A/mxz921Xb3CSfao1dUHJ7VKti/feK/f26KLUMPobP1TYBYZAaIXG+IBEec1piqgA3h9LGlKsbSvJcw1A5B1sHYt/V+DMf2Eilf3NIm1NtZB02uIrCWCeE1xy1f1xnEMAc2lGalQYhzHa7bB/hNF4KTZmZEzbg46Lf2i01iJaAi0+rCdy6jxWuAkqQuUCd53ks1Vwsj7RNNzf+uDGg53VW6+cinSffGVaNaB/+3AU1UBmA3Mh3u1EpMUps1z47q6bUAlDbnfWdYKOUAmCfantKLaNQHarvAJxeUAYszOceg4VImlKLrh9VuLfR9o60Qni2Fz8fblMtY4m4Rf/mvtP7wqdqQWgZpRuNS4GVfpQZwaVwS2sr1zZdKWu8pDBMCggswM50e9COAphTxo06ThWf6pLo34V9qq197S5UN3Crn0CJXZGR9VMoT3X/WexDrLo8+2u5B+Kv5eLs/OXvHQm/2v0MbFy5KHN9RbAgheoqLdUAIqJnfq1M1dBeIIY1SImIPWDrpytlPS8GnVILR/5Ni/4dhvwGOU2bN19A6d9NeZlhiwpAeT7V2MqMLbsK/TsIrOuYaIpN3+Ia/Tv2b+Y3+1Rzr1J++XI7rQCenY19nLljcZ09JR2Tzt6wNh0CVHRiqQ5NPtXqtdl8quP/HLWUo1Djc0X/FkmphWN5pPWRbrkzRP+OImLJF/Rv/R3HWfy4564L4pHGPMF0+Cgq/j61XfvMzBwAkOjfFktmGqSl2h6ojFLrAczKF4wwoNR5RPsn7Ius+xs1p7tUQAHwrKmyQdsr1geu/vi4m9sT5+8vYLPGSReR+LvuUx1pCiYb/bsXARsYz1S/6ZircaQMGFNqYebUbA/e9Jnvw84n98GmVbEgE4aBsp9ahlJqUVpuWUJ1dku1+VgaQ1NZI5qyEWHAjSfX7s6qwMYQz16srwA5LdVE0UrBCf7HHbQSjjtoZea6isIL1VCO4Isndi+KoMtsrpoC10nSHrD1FqMKdgHGCKLqDoLqjqMTU59q7oVPn0ecuiJQrpuvwAGzAJozx4TFTQC/3FlLNfnN9NykskQ839iyuw/Tv8MUodpAGaTtt0FE3qR5qtUy7AIWB9FPXGRP8VKemVOtlXSOZqF/JxHDw0BhiAjQTXOWPNU2+ndkENyFwC+FSLcKe4rvrS7o6kKz/b0XMoJ4XA9P/04ouYwCAZfJbfa5QFA0nZGMht6TvuoF5rrt2n1c9O8ClmqaN5VCjPm5nk4Pxj78pqB19FnhMU37M+tmHwuRYo2VearVc6vQ39L2jrRbABBvym3raLoAxF8HYKeG69G/dSG5R8YuvkZLmabQv7P7VCdK1hpYA0l78FwgQV2DQAZf277jSeUcbKnutAJYMmbJU52b/k2VMOXRv9P61KZQbBI4Gce1vVkC4lGILCC/mZZCdSFLdUqgMvl9cM9ifpu0DKDPpYy5QOkV0mJQvOyy4Wqp7xTYVJQN2saytbLYUj2IoGwt5GOXWB1EGhMmUAQnkImFJ6umdtig0RsbMsfomEwLfsdFcOcgxgFNmSasHS7+0gnrwcECYoJMbyWVhhRW64+hfVs2LocVi0Zg46oJvU4kaOJgVVkjkvL075CNzK35TGYKVGamRJvqSKxbgVwDnOpihHRhGOWo3GlKjiDgN5FdEqFaPHsu+jf3XLhno4wTQzu5CMqFfKodrjX6VGd8J9ii7ANICx+l1gM4+lQTpcmsTZmScYHEAqEol6bUSs6tYPNKixxpmccXAPb3tj8jW9t1BZP+WcYSUMvtofgI4rnj9Hjc802jf9tuRTS7Vp9qwxwPgiAZy3v3zSa/73km/txCyv6xdktJtUiF6tIs1VnjHzgoMFyubYpynwN3G6576CIGLfG8n9onheo8zzmNjVJ1zKUs8JZqKEfDhxdBLh9nk6Cn1OLPa6cIBXWiimeG0RSf6i4KOiOaxL08gz6tEqc2cc3XOewQY9GW/3gQoGMyLfgdDX5ktFQnwnRfadISEVe7Sr22bpCuBOZz0jbfNj/QpB7LRtU0Z695yxaY6fZYq43Mja36VNO+Stsg4MMy+rdMIVU2/ZsTNLsGYUmcioMVutWll4ct5ZzQjKFbHQM2IFevp7Zd0r9FOeaNZRDwGzouEBQ9hgUTSf3Xy3KFyzJhslRnrVcG++KPY1cK+rxVVgB/PX2+s3NoTGvviywtV4XIJKd7YqmufvOq+chixg8zPmUOW3tb6HqEm25jcVA2iyYkI8GZMoK6kZ5Sq9dDedENkYlt+5uE/u24jysDtoBcI+0Qpud68OQzUqh+alowC2RKrdFOSwlORgOV5d3T6cyGrAowS9lpQvWQWKpd3M+M1xbYey8ajUXMvc/KsZHnOae5NtYZXyAN83v3bYC+uBafDEqgMhTRs4k+1a5WHkwnHnRqMJ3eUW75aal9qgb2wcI5HvExAeq3JX4Tmt/xkfk9rZsY/RtAH5NZLdVpPtXUr0hYXEUxLhr3QvRvHP3bQkuldaaVHwSBkQaJhQ/st6hpz1PGgBLUaU6nyuL74eidrqBpsuJ69HJxmXQ+uwvV8jOlpnLB3NLWfU5ZIcp2jf7NPRduzNn8lZPxjHKUy8jv+ee6yyYSpzgqYqlO29R1FEu1eqzbD2T1wOPPmGMXkH7FCsaiwcSwEDnbTXkXVbC/0S2P9nXU5O9NoSmRFGUQPRcdS97N8XcuO4CM/i3aGf/nLdWQSv+2RmM2rOVVCnXKXCD1invAQjW+ToydsU6oWKdpOrG8Y4myM7LSv/VAZ+6CMl4WmrjXFyjkU11gzovnXdRSnSjOTD7V3lI9WLhuZjOVicpQUiw0cJ65+jomfjEN4LBX/TLHC/FAApVhzTaKnImPyXPxNZKC+fsnHwz7LRmDlz53qp5GDwji/mdzpoypCnRMKlYvTrBwpFUlm0bCRJieU+nfTn54ls5Kp3+L6N/mlFqu1h9XKH61aOOq9529HE4AwcpCNTiUmY6bBo6hlCa4a0K1a0otJXiYWn8c1E49n3Y/F7iJG6eUwpwI8EyaI63OkBeqbeMk8ZFFSoaEAVBgs8QK96EaXNA0Z7NH/7afL32qe+x4+4N/vAv+9Uc74R/eeZyxfLNPtXpuXvp3ryefvViD0sZUGbApJ9kMAULpmDH6d1qAPfpZzEs9sBwXqAwpTYgHQC+KEtclc6Ay831IpVN9ggRmv9F6xT08+SwvVAtl8Fgnjf5drH2iT4um1Oq04pglcZvsfWpTzDQJvFDt1uCsCmwMYanG8TLyKEbbKSxMzaV3gA9jYQrVjhaULAgCGbChG0XshqMpSNtcCbQTX87BWz65oDplQol0PoBnhjXbNCenSfhqhwFM938LwwCOPXAFHHvgilraO0hQ+ndT5hj3co5/5xd510Blkt6o+szvm1Xp3y6pQawptVLmlNgczfX4lFoBeWHqsSuyPyf5rCMlWJVu3bSXjU+fwYHKHOjfWSzVohhV0GQEd9R/4lTxnE25jSm4lFrYzzlNacNZ7ri5RINtJf7biVXeLpzo7xv1GpOVgYs2X+RVxE2PNhKqw4D4jlqErjSkbTwTn+ouT/++f/fTAABw/2PP8OUT4VlalIsLW1iIpIHK6hDkaJmqn7s+AISyL91SbW67LaWW+Gykf0dybRLlWC3VvQime0VSavFr+SBSagHI5/PkMzPadThQ2VgnhIkRKXKUFagsaVM/bmdWowitNr7eUai2WPCbBO42XPtbD1SWQagmFP+8ssTU0jEAANhvySh7vGr30CxYkEI1p00vAyIvK/ZBGzRtmoPJ8klBow4PElVTnQSFKQwGI6Rh6uecZqlWzxVjSonI2RDBsg4ktNAkpVYz7t1kYTG9SDSLklG5pW5oxXdhqQ4N44Qtw3JO2vsOW6o5Wmqa71WeF10bW3yQNYiO97SysdJzhstTbaF/Z/OpdqN/dxlLdYg24m516fVimnRacEc2qwAzQGaJBkX0R6I4xhtu5t2SZunQ50H/PMRSwG3MCzwG4u+qYnKkHSp91C6wvqa9QzqKW4N6rIeE2Zm5Lr0UAKTFR9yPjf6d9dWAhcjEUl1joDIb/dtuqc4qVKM6LfeFXbMAdPp3hOjfogmYQs/Rv8XzohRoAbvyU29/2jVFgdcFEyNrj8lS3X9+Y+0WjHVCOGzNEnh6Zg5WLBrRzs3dPnRtVku1Tv+2M8xM1zaZ/l2qT3WG+6R+83mVoq8+ch0sG+/Aiw7ijUauMk0dWJBCdVWpCMIwAOhHKi2DrlYVnH2qU4SCOkH7sexuXbV4BN583AZYuWhkIEIaFqrFJjIRlgwWp2HRkpYN8c6bbZhPtenlbJpfgsKZ0P0Nm8I2CdIhyp2eVbXptpedm0+1vR9l9O8eS1FOo3sHOZYR1qc61C1WLi/6MAigi6iXrTCUQjXjn2z6bgMXpTpNcBenJgoEV/o3G6gs/h7nK1bPT9uExwwA/SFRy7noD9FOziqPy+R+wzBZP7HlPlE8FJzrQvEtysJjlPq3FrFUpzUT+1RTpQ1WrM4Y8lzj/uhGEXJpCLXnnNnKjoRIPaVW9ZtXjfHTtj8Hd0u1+l2NSULOtTBNtOwAKOYApX/PpabUyh+orM6Ix7hsqtiQlmpdqMaW6vGRFgRBAP/ynhdDtxfBvTv3KucW2cPgAIsjGS3VGjMii0+1RaHYJHB7Wte5S7szy31iZgJAfllipB3Cac8zuzXa5m/dWJhCdUUUJiXXMMlb2CS4UieTwEgNuAktrVTJC1gQBPDh176g1DKzAGu2qaXapAQyURXnO8T9S+FokK2RoPNIbDZs86cVBNAFu3KAptTqkEBlLgKHC/07bU6pear14/TyIr5YtAxcZyz82etmy+r3tbBUd0JkqVaiW6vXZfGpdqV/45gbHGXUBaqQrv7WCtOtitp7IOSf0SyRCmj6LmtwOqTwkTm01fJN7cSKxsiQCSErcBwKakWnAk6Vlmqcp1qLDh3JMWDKcx2id0MXZEAxLt5AViUxHq96Si313CpYXfRdlhaojGZHMMG277NZ94WM1U0UV2afajqXcWRwgQil1DJZVa1ZGgxreZVCnSmlFoCLpTronxcrrTqtEDotzlCSv/1FLNW6Esc+3jDUYIbN3oPhNTgI3PtbV5Znq3OsE8K+2Wpd9crYa5SFhmxH64W2gJbUC3gTIK0qzZtodLyZmpjQTRtI/26KdbIs4NygaT7V4tbVzWwNjWwIqojeXwa01B4kwBh7jbJx5x8idcPQLdXxebY5ISPkGk9JFUwTSzVjXYvLtr/Y8sxZsVnCFOQWY1F1eVmL6rmgTqZ0V/F39/ZyASplujxdcFfnsLReutWl14st5Wn9rwlIAe9TrdNdqf+2uUyOqpq2SaNuDtinuug6h6sOAtWNgKY3skUoT0OqgsoS/TtCtGuhOKMQzdHGdMDkcM847bAQKVxsTBG2q1h79dSEdiGHjhcTuMB8pnK56N9JKjkm5kKXzHvlfc5YtpOUWsZAZeZ7oRHGufspGyprQz02YglU1m6pPtUYroE6XYCvzepTTatth+7zfljo3wD2NdiGogIrtlZXJVTrMp0XqmtFGQF0OOANGkcDbApcnfrXLRuHIADYf9l4Hc2ywha5cz4g2Xj3cPRv3o+N20Q0cZxVhTppb1lAh2SaTzWAm6abPm9R7r45uZHm6lfbFij/beeYICxBc90eK/il0b/zKC9FnTNzql9tVp9qANmPik91smGW5xXJU82lfhJNxeVwUcLxGuACal2PUOaAkBGuXCzELgrUxGKXWJ7NG0uZQ928Vplce1oJ9b/H9mseUJcZ3BZKG8X9lTWlVpbo3xydWPpU80K16DMuaGMaYyQNWIiU9G+TT3Wmot3qJ2ViyyPrU+3opmbzDaXF4lNDMi/pehAhdz/puqArhARwSi2jpdqBUZS23pYJm6Ap5s0eJlBZvKb0faq1aN/0eRRpn7y4KP07TYljurYBticrQuad5IKiyg8c8b0uS/Ug94QLk/6dQoPLXW6/HEz/biIlxHUArl8xAV+76Ldg9ZKxOpplRZMCEVQB1adatcqYrPRFfP6GGWW+jMsE3UjLzZ5NkJWfTc+QCudC8BHRvzHF1oTEp9rhHBOS6N+GlFraZr5ES/UMMg8FOSmuon6ce9clUFmu6N+MpQuzqPmAZnIjnqUu3E5MP08VohkGjMszEveR+FRbBGZMU5bnpLSLCibdCNqGtTAr6MZSpX+bI9VWFais29NzvvciOQZokDiBgAhWiVAX6gyFrIoILEQK6r9p/aiF/p1Cx02YPGkCkGU+2O6L9jG3PkgmhTree1GkZUrAAeBMVlVbv3KBSnGdVUBV/qptTvJUc5bqMEyeC7VU0+dVOPp3HyPtYnN1JCUVpunapij3TeDifGS9LgiyryeLsKW6oj7SZbpKqnHCghSqi0bHNKIU0XkAAHvDSURBVCERjJDmsonzjLbJ1sZDp5ZU2xhHcFTF+QQ8duZ6ctMf/6fnqtcANMPvvS40SSuJQS0fpjQ0GG0HrTh1wxhJ6N/qhtfqLy02xZZz0l6WSfRvJvgOV3aZPtXYYsfRlLPQv2dQnmrMLhKgQk6WQGU0Xy3+zNG/lTnc0gV8GzhaaRdZj9Mswpxw67KO9KilmrG24zJpXWkpWqjbC3anKrpZohRI3HZK/642pVYsiHDRvzHjwCRUc/0K0B/TBQVfLETqgcr4c8uEHjjKrtxwdVOzuZDZjmH/6Pi/Wi5WjIjLQmbsCuAAcKb5ZmcdmdpsvqYoVIaHeswWqKwVBolyaswSCJDWkRWKT3WLj6huAh3CWSzVnPK0qaBrnysUlkKO+T5eg6WaNmuQe8KG2HjqRVWbcqzhTayNDdnwYzSVPmvDvKd/I5aDpH/zGmkayAf/thBQh19fHpg27LbNni3Ik4AYBzr9W81TbRM4knMcNmsmJGmAujplNa7fvknK85ikpVqmFmLp3w5vMiqgt5DgWRb9m7NAi6Zy0bq5jb2rpZoLmIR9O9P6n1OCuGx6NP9tvA4ZIo6HlrWKVikOc+nUis51NaoztVRbApVl5HamNVNEtI4iNWUYQNy/Qpg20r+F5Z55N+jP2bHRpOxY+FNTalWdhYOrw2YlBcBMnqxCtblOfEwGbjMHKhNEGsqyYIVqJgAchT2lFr+W10X/NjEJ9u7jfarFtTS9Uh4XHhMUn+qClmqcmi2V/o1dRBqyDzEhr5LQ5t7jgkWj8rlXFZ+pSYYWL1Qz34uW2+vpdKAmIc2i1ETM+0BlyJqlBSoz3HuRQDrDDNeYAHWDCg40vzQHLAyaNoWS9m2nf1v98DKcYwJOccSn1CLfieU+j0CUWMfnEP074KjL6WWLe098qpFVjxN45Xf39grDIucrzwnuuNnt5P2RXmEURWyUcpw7Ok2I5oQMN6E6/p/cq8Xix4279Hao8wanJSo61+maqQQqo5a0AutruiuFnPgzNGVZJNkRJqE6YN4Bot6iSvMAvYsS4a+/5tQRcZoLXCfXUv38/RaPAgDAqv5/E6zByDTFDnr2SX/E3/Vo3nK/R5VIXUNKLaFIMQWxtKbUMqzlVe6JcNn0PSWUUZzusRUGcPDqRQAAcMjqxcYyAYopBYoFKqNCtbulGitFmr4HozRuV7Qsz94FEzXQv5sk0yxQ+rf6vXRLNbIYNMWKhsH50jUd+qQZUEMqgqQ6gmapNimBhinyZJnQN0cDaggBfWm1HHz9XF7KbVJOQv9OUmrp9VO4UMTT6d/x8dmeKaUW3XDLz3nXWGmpRvTvMF+gMk2obiH6t9Wn2l2qjhgLdCKkoDrYQGVIiEwDd0ovQhZwB+GKCi80Gra5bpX+jddiE4tEZXGo5ZmUZJy1r+i7mgZMw+smDRrlovAywdWnGgBgdk63VCeBygz0b9N8xuwLgax9Jv2B5TgVgkYdjDGu/a0wgLlexFp233L8Rli/Yhxecuh+1nJpN+B7sSkLRJUy6r1aDk6pRWMIdCM+pVZCqzdYqu0ptfg2V/kebFust7YUVq0ggHe+5CB41RHrtIC3ZRq3WpZ5nAY6LjIFKhuiPRin6M18XY71d1EN9G+dsVNJNU5YkEK1ttko2VKN02Q0xYqGkbapaSKaRO+oAnLs9JIgJqFh4yS+Fl3shhVNpX+brGC2yMGhRSARSOjfJkt1IrikC9U2ZVTaMijp37zQZ42um3ON5XyqxSbbVJcJ4pRZRGmlQYgA+ABgruBTaqnH4jqY89D7w7Ue+lsXvXfSLEEc08XJUt2z0L+p4MKsVWlWBd1S3UNCS2rzrMDjPwhUxYNO/5bfs1t77cdHkFWc+k3jlFomS3WY9Cv9PdCfQca5J26714vQXOHXjyrew9zcbocBTDPHAGJa8SsOX5teruE9ytWJxxl26wPQ5x6OoUPHriml1hxhAFDY+lXWQX6vUJKwMU2sQnUYQBAEbAaZMgOVqT7V2RYJPYWbu/CJH1/zo3/jz+6NLao4GK8hpZYm0wxQqp5n9j43VGXpSujfiIbXROVVVfT3KlHHy3yQkC9uxlJt2CQt2EBlDaL6YATkpSWE6aKW6v2XxxuSdcviKPwdYql2E5gdBO80S3W/rbMGn2qbsi7vIxL3ii12YWDfIJuQWKpR+iFJzdatyAKugcPwtdxGRKWYQ9IGgcQy62AZZ4VqFDApDNO191zaryz07yR4mI3azSgG096/YkoIgUMJwFZw3adjEveRRv/Gzybjjjk9T7U8Tq3RcXT9+HN6oDK1Hi5QWdYuk4omPaVWHdZRm1tCkfecTZljnxvq/OXytnfJfg8L1VxKrTmirEhrK9fuOunftpRadN4o19niiVRkqc5M/yb1KpbqlD4dprg2eaN/F3UzrMNSre0JB/gsFqSluiq/IDFeFLpaQzb8GHVGjSwLdVKdBoHEn1KJ/q0LzwByvHr6d4ym3Dqlf7v4VKsWTf68Vx6+Fr787gk4bG0ciT8Rqkn0b9sYMAW9M7WfLSPJGxyx7ACOipp8zrnGUkt1EPQtjC1zXa5ltUPpd1we/Tv+z9G/+ZRa+hwWG3EbA4NrkkL/RrRrakVL6kNfA2ZNMUGnf5s3a5zvL70tUyBGNdcvX35WULo93n/T6N+qIJHR+pXSj3jjTq3RWMg20b9NQjVH/85sqUaWWWExF3O/jiCnnNUpC5PCWK7FMmo7hmPlANhTalFLNZtSqxclaf3aBgHQdp+DT6lFhWq7pdqEMgOVJYH0Qrdgixia5R1Hm88QUb7pe7C8Sm6OtZEFE6PVW6p1+WBwz2JhWqotWslC5SaLr1xIm6i9ok1qCn3WhqZaJ8uCGthEjQxqCsq0YAOVlUgbKxNUuBDfO5YXs4ufUxgGcOT6ZYlFQJQnon/bNtj0s22YpHWjEATmuj3WemtjwBT2qU78x1WBK6nLoXxxyiyyVLvRv7NH/+aUJT3GGo7Pw/eUZhznKOJqjntG8Ua6iDtmikaMIenf8XebFboV6OPONVBZEv27W2JKLbJBxMIypY3agjOlIe2dGo+9+PMscafA1uk0+rcWbT1k6N8Z5x4WCBPhT7yLDEyEMsG5AySsnwIcWxvbzRb8UjQniXrPMFlwnvC4rr5CqMtF/9YzfFDYHlny7LV9nPmaorApSG10a9t6oqfWy9k4kO3LSv0G0PsRj7G099YwueDl9qlW1vfs9U7UYanWxpIXqmtFVfRnHKgsKmkTUAWaRJVwhckCMl8g1vEuZ6k2CEu2NDXzGXWmEskCRZAKXaN/Z48earJU2zY7JsogRqqlul/GLLNRjK83l1fUpzoRhBNLkLqwuqwHiaUaUS9FeZiiyUX3dUWXWKziz3q5nOUV99Eclwgcgev/CAVFEvfFWcIFuAjHmejfSaRxecxkDXcRvGU71fPmeuUF/qQWc9x2aqm2BWBLrSfl/Hh96Ls2UEv1HLZUG+IXmN4NAUf/ztZ28QyxsC+ZLvTcTEU7IQjUyPWtwG0tTYONqaGvXfp1Jvq3yhCRbRbXUAVkLwKNAZDWVgyZHtGunCoTVkt1x7zJzWSpLvBsRT/aFNgm2KJ/p6Zpw3uwhuxDTAiYMe0CJUhdjjGG6d9VuSlSOWuQ/u25RL6rr74aNm3aBGNjY7Blyxa47bbbnK779re/De12G4466qg81ZaGqqjEXHCKJgo7aVFgmwguzcZ8QjJ2urq1ybQZsPk5zWdQgaopt04DNkk/QNumQ352feFIn2o1TzVdanAAGReh2pWyOtfrGVJqmTdJecdnm/hUiypMcQZsSISFOTG/Qkn/Zvyd5fcslup+XcxmC5fLRbNWLNV2mdoY/VusHRybxW6NyyJUqxY71Q1FPZda7uK2qedodHBhqUY+1WWl1KLWSdwu6huK15nM0b9T2onXB5yDHYAI1f3PVFiQUdX1d3lx+nf8fxq1QzyLuvx4qbLJpETIW6ZWh0VpL40l8XfO8kxdLBTmGbOeJIHKDO8G232KttncbcqGzdWMWofHO26WyTLHUmKptvh3p10rgN+baYJyUStunbCt0zYUVY7jlFpVrReaTDdMluobbrgBLrroIvjgBz8I27dvh5e85CVwxhlnwI4dO6zX7dmzB8455xx42ctelruxZaEqqoASqKwkH7AqUFSTPQg01Y+2LCQvYcZSrdOk9I3qggpU1tCgddSP9rA1S6EdBvD8dUuN1+SzVMfn7ZsVUeL567HG3SVQmTv9W8+RzF1fRqAyE/27RQaBW0qt+H9iqW7x9G9qdcoS/ZuL6k2jByt1GKyhqZZqVBYOcCaeC0f316xxSiRs/hy2biFUM3FDTNYzhcWRIgxYo38XnOuKBTRU/Y+pb2gRS3VaM4MgSARV3adaPlscqV5tG/9uiCPaq3VlnXsy9oAU9k0pteoIPBQGyKe7gAXARl23WbHFPcqUWoxQTQLp4XgA3PlzCa2e7z+XlFp17olwO2mbafTv1UtHjeeaygQoNrdlusnsZdBqs1iq25a1r2nIHaisoHIc07+LuG/YUCdrIw2ZV6grr7wSzj33XDjvvPNg8+bNcNVVV8H69evhE5/4hPW63//934ezzjoLTjjhhNyNLQtV0Z9xBOcmR/8G4Dd9TUadkS4HgSRQWU/3tzIJkXkXyWGHaRM+aFAB5gUHTMIP/8fp8IEzDjNeo6TkyGupNlih8QbDNJbU9qdsIESe6m6P9enVN9zuZZtAg4tJ6z9VDKaXJdqAA5VhRagAjWngkuJKILHeptC/OaUrviatTlyWEsuDuB1hwcEWzAVH6U7bSIqmJS5OgbkOrh1pllTJxAmT+oSisXhKLdxW9bsmVCtWqGzjN+38MAhkZHuN/t1Fn/ko0eIrrcYllVoaxPWKpZpR0gBU58erKJsC6aZRrk81PmYekwmbJUmppV6P6d8JkwatK1xKrdlufvo3p6gKgmqNI7a9BmV4TC0ZSz7bXZ/KU9BIS3X2BUJXRuvrognDRP+2KTbt1xVTHOBAZXUwWwAGy9zMNAJnZmbgjjvugK1btyq/b926FW6//Xbjdddffz38/Oc/h0svvTRfK0uGzaJSBCFafGVglWZONDXAwgAb4oj5LlRjf/w0+neywSioQRxW2KwKgwT38lk61rFudvJogQUlWvg8UsqhQIehsRWif4eyXt6nWr2+SJ7fpE5hzdPo39lfolRYwMGiej1d4KUWKheIazkfYlwMZ3nF95AuVMvPOHMATT1l2xCZjqVtDin9O7SMYa4ddCgE5BgXiVwIIUXXfTv9m1qDs7NIkvNT2tkK5dietkX/FmO1AP07a5+J54mF/bR3Udkw0r8LLPa2jbeuLNDHNI0lINZhzExskediSqkl5o4p/ZNOR9fbXadSXfGrTbFU74cs1Wk0duW+Clmq4zZkTacFoPddFku1zb2mabCtwTbYqP8uqMOnuo5YD67IlFLrscceg263C1NTU8rvU1NTsGvXLvaan/3sZ/CBD3wAbrvtNmi33aqbnp6G6enp5PvevXuzNDMVOv273HLx5qap1Oq4Xc31+6aoi3Y2KCj++MlLm99IhIz1p+n+PGWiSZEeMSj92wV5hGpKceOovvF5jE+1pY606mVKrV4iIAYBoM/m55J3vrZIMCfT5tpJqO6fM4sClSXprphAZe1WADDrTv/Gm2fViiTndlJH8n5QzwsDddOdVhemMPci0GioVn9npo0Acb/MoHM0n1AS/Vux6BoEOqtVMNDPF+0QoPT/vKAuCXafavnZJSo6RlozY0aAmm9eQPj8A1jo3wahGiuK6LmuENcL4b7TCsxCfEV7ByqsmBgqWZAtpZZ+Xi+xVPcF4v48iVBg2pC0s9cDNqUWdfGi0KynYagpFmkMjyphUzBp9O8lo0mb0t7N7TCQyuEyon/nsFTTJmbJU13GO64u5KVxqwa47Pc4rkT/rmaj2iSjW647pJsnU07NbrcLZ511FnzoQx+C5zznOc7lX3HFFTA5OZn8rV+/Pk8zjaiKPoq1k9Ta0TTYKHtNRF20s0EBK2SEP6Vp45RsmIdIS1ommrSAYuQJBJLnRUe18dx4AFA3GCb6JkbaOiDp31Lx01E2W+r5WFjK+4gSn2piqaS0SZfyxb0nkcRboWaFij+rViTXQGVYEOasYBz9mz6zhHKeUqcMhCk3dtx7hxMOuO8moZcKmrjtiQDP+GbTOmxWNZPFnLNUl55SC9WnR//Ob6kWChJZF2lHEEgWBglGxuWp1ujfhjgK1PrO1Z0G0SeSem7uh6qWXtUya1YwZwH3DLjP9Lv4mORnJ3Os28MBAvvX94/heAACOGVax/CioP2qrifpc7ts2FwhKMNjdZ/+7aKIMjF1skLMjzIs1aoy2l5eGS5OdYELvueCooHKFo3gPNWZL3dCk4xumW5x1apV0Gq1NKv0I488olmvAQCeeuop+L//9//ChRdeCO12G9rtNlx++eVw1113Qbvdhptvvpmt55JLLoE9e/Ykfw888ECWZqZC8/sqaTKogcqGx6d6GKycdDI3VVmRF2Lhiqm18W9plLuW4jM7BA+xJJj8CweNIMcmR7XMuT3DtkGottHYTOdkCbSCN4A41zOtI/leiqW6Py8SSyVfl8saTv2z2yFP/6bBvlwt1fg8Ll2VmlKLZwklwbkMqZRoXVh4iyI964SNPWHyc8bPirP8aPRvy8aYe16acBPiNsrf8dgU1qwyU2qFgZp+yhaoLI+F1Da3w0CPjcCl2ErGaov2qy5YiXYWjYQrrp9m6rYJe2WCritJnuoC9eksCv4zrV9c102EajP9myo4uxFoKbUwM4HS+k1t5Vy96lSq26yVeI0YaYWwfKITt8nhdVZWoK/EUl2CUN0KJS09rbhhiv6dVwmjKv6z1zsxiunf899SnYn+PTIyAlu2bIFt27bBa1/72uT3bdu2wWte8xrt/KVLl8Ldd9+t/Hb11VfDzTffDP/n//wf2LRpE1vP6OgojI6OssfKAJ28ZfU/pvCWFa20KgxbjuMmRferAonwgLTYYgEyWQdUP8iKG9ggNDdQWfYNQluZh2710PQ6XDAoALeUWu1W4BwECm+ulc3+bPybTVlZlk91YqnOoWQT7eOol1jgpYECXS3VvRT6t2oNF8fUMvA7xFoXCoTJ0b85mryW3x19x+eJ+w4Cdax1WoHiT9+N9H40KVZsfnmmdnDPtKgCPCBrJi6PKhCKBCqLyw+SB91uBTCDMme1giARyqZJMDLOUm1ip3DK5qJBe+gYtMXuqIz+TcaUzJOdvz5roD7LnlDUKaZ3j/QLlx9eCMs9JqXWjPKO5+9Hi+rOKDZUyyNbTGmwzV8syI51Qlg8FosVTpZqS7lZIPqnjEBlYRA/x7koSrVU2zIfNA15mHQAZB3McY/YUl2Vq55NUVs3MgnVAAAXX3wxnH322XDMMcfACSecAJ/85Cdhx44dcP755wNAbGV+6KGH4HOf+xyEYQiHH364cv3q1athbGxM+71OqJSN8ujPmP7NafGbhDz+n4NEkzRRVYBa0QDki9k0XhdsoLKGshZwO1yHJxZ+XOch1cabfKpHHYTqThjCPhC+evb68cZ+ek7f7NusPXkfkdjU0KBsuqIpvQIqVHZaQZL/mKN/Y+aRC7iI3KZyqEWZnptG/06s6cja2u1FCi2clm8TtlS6bfylE4aatXW225V9xkU6N2Yq0H/j2kHfS60wUBQMRdd9zQKKBoU1T3WOaJ4tpe/08UoDlbUIHRxAZ03gtuP/cZmiz4Ccm63dmvWbYbzkLdsVVGDkgnNmhS0Wh901QlUyiOEo3UP0VHqivVxKLfx8TXRlOsy5Z1A0gFQWcEo3gTHkNjEx0oYlYx3tGhPKslSLPqEKZxfQakVgvLlelMlS3ZTYLiZw7CkX4DUyzz3ivOVVBSrT43QMkVD9xje+EXbv3g2XX3457Ny5Ew4//HC48cYbYePGjQAAsHPnztSc1YNGWX4cFHjzZHoZNgU26lMT0aToflVALAJ8GhN+vA6TlrRMVMU0KYo89CqOypcGE/1bC27DBCrT/J5b7mNICRzF0L81NkkJbBgT1Z8KOU6ByjRhLgToKxRUarZaN6VvmoBP4+jOaoRxnh3gaqnGQnlSPs6Xm1j25DVU8WDyscPpi7jNdI/QYO3psri61Huh0b/V61Whuuhcp3NU8am25anOUTGuS5+zmP7NByPD0JkZervEZ5tF1qnddB1xYCKUDcoSkOtXfkqWbc9js8Dj+YX/q+5+8Tk0cn0vRag2dZ+N/i0VZnx7q4DdUi2FpvGRFixJLNXpbSpLMZDkqc4VqEx/9q7jLcv7c9BQY5xkEKoLWqrDMIDxTguene1WJg/VpexzQWahGgDgggsugAsuuIA99pnPfMZ67WWXXQaXXXZZnmpLQ1VWWqnR1ANXNA15IwEOCnlS6AwTxP08tS/m0oaB3OSZxuswaUnLhC2K6yBhswqawNF006DTv/nNdId54dNzstDPuTbaNtxF6bO0fQBIWLTUZQLnTx5FqqCIPwtByDWjFi5DoY8Gog75G5fjGSAD/RvlxcU+26bUPvFntQyTsk4yZFQqsVBkSPq3Whd3P1x6rCzjBEci545nBe0PW1C2IoHKAIjSkxnHYmxPz8a8cJuVjVo0g2Q+6/UVZXXpLiJmNkot9O8wSMZeWdG/0wKuKQoVolyjSjeVIaKO9y6Jr9OL+KjqWlstzzBhHdW4h7NZlHGAv/FOCzatXARjnRAO3m9xarll7UPFXM0TqMwW7C/N8D1Me7C8xkQluGPOe1w02heqK1ov6lL2uSCXUD3ssFlXipUb/8eBypoq/OWNBDgoNGnSVAFxP3ufnQOA2A+F25AqG1AlUNn86g8bdOtlM+49T5RMk4+wDXTjkPjxkX4YaeNUFvyGO0suaUFZnUVBtPCGm7My4mvzwBSgiVr1XIrnrMLdntgwy997ZNPsTP/G0b+ZsaCm1BLt5pUcrim1whCnBZPXcQwInSInP+Nm4JyviqWa0F256N+aa0Yi3KNzLFaFNH/gonOdKr7wdz36t/ycJ8AObnuHuQ8xjznWh60sADTnGSaBxkbJ+G7QrKSWHMVVbZKpUiqP8pHCGrTPgcWRRL0ne7uIo39jgbs/19utEGbmemxUda2tytrJr6U2hVnZsNG/sTvSxEgLli8agds/8DKYGNEzB2jlFrSCJm0q4FNNq8XKtlaKkG5TFjYNeVOwlfGMRFotU2C+otDe7QN8FgsovJFEVfn9VDpQs32q1ZRaA2yII5pqnSwL4nk827daLBpFwR0ML89h0pKWCW1j15BVTE3X43ZNngA8WtCiRGCm5+mbYdpX7Yz+UnQjaNtwq5rx1KJZUPqdaXOdh/7dRpbYLmupVjfTaUilf7M+1Xwb59Lo3/2NegsJHNhiJqrnLOZ8G/X2tkOe/g0QCxJc3BDT/disJLb3sSmVVF5oPtWoPBqrQGlzjs2gauXn6N/x8elZPhgZhqkfONq+LeiWC+j5OOq/7kKQrWxXUEWyYG3lEZpwOQJ0nNnp3+oa0SVMltjton8uUV7ioLVCsUID03Gg73kuPgU+p+q4OFb6N3omQnhasWgExjrpQrUaqKx4+/JE/+bclhIFVUq/lvGOqws25aUNZbAJRLCyqoRd/RlWUo0TFqSlWhlcJQoj6kKq/tY0mISzJgP71w1Lm11BFyuchsDoR10R46LpsFkVBok89G/xPLNZqolAKTbTpAzsI2qihmaNPo6jfQOoG26TPy13zBV6gCdRNt8HNnCCuPgpYoRqIdCnBQ2j1+F24rbhYkwptYQg707/Vu8hIlY0W/A8k5+zUJzglGO4baJ+jo2lC8yifHM7bPPGFD8gLxQlQ6gqbailGo+94im1dKHNFP2bg56XXZ/z4rMWhT+rpTrkxyR3rDL6Nxkv7zhpEywabcNpm/XUra7g5qSArgxCn/vDQswvMY87SOlGjSiYcUIj8idR1S2KGpo5gWs7p1CpCuocV4/h98y4gyCNob5/8t/D4r4RQvhzZwGnbHRlkA2TpTpvW8uI0C5YC5X5VDfI6LYgheqq8vuplB/eEtEUlJXKoE6EAUAXfZ5PoIsNTkNgEk6GzS++LOjRbZtx73noeOVaqgPjeVzQOwB7ZF+Xum05bEsJVGagf6f5RHLQqdYhhGEs0KjUbD1ljgtwmis1+Fb/vaAI7vF/E13aVagOA9knCv2bpYi6KT1E37YJ/RsrULpRxCo4Tdbw0KJgsV2vBegqONdpf+A+0H2qi62vCn2cGcfCqjbTz1OdRv8OAjXqe1yO3t6i6Sez+FRX9d4JyH0dd9BKOO6glYXKtAkVVh9mNCfxUoCZiZR5gveCNFq4vN6d/s1R12ulf6covsIgXn/GHSjfGGUFW339MQfAvtkuvPHY9Zmv5dxRXN/Lw7QHy+tTrdK/89U9ISzVVQnVBRWJZWJBCtVVBXjAAWOoxaBpsFkOmoq4zXrE2fkAKiguQpbqwLAYDpOWtEzkCVJVB/JEMk0EmAJCdRIJmIyhEUZgptW0M7aZttOW1i1PDu60+rjUOlSINUG795a0SHA5pKVPtVtbTewkUS8XDM0kpKT7VMvzsc82FdattGvDRjkJCtUKjNdHEaKwOwgrrvTvNFp/0XVOo3+j8vTo3yhQWY56VUs1VYTJfubS03FlhUGgB8RinqFudc3WdtoMWzDCqpbeKoQVW8wLW8wWHLMAz2GcHSAicx9TxiVdXK3DFphO9/3Xj+V53+SF6gaij9ORdgj7ZntOftQYWd8/JqxeMgZ/sPW5ua7lYm2ItqRRuvMKqoNA3j1/GXNRjIvKUmoVVCSWiYZ7AVSDqjR8cnNjzkPaFAwjdXg+C5H0fhRLtWHzawseMp9hogQPGgGz8UmDyfJqg4n+TQXLESZPtU1ocWmCbqm20L9L2HBQa44oMs+LnktTIy7l6N8d5DPpAlMcDRzQSJ4L1nNTU2ohf2ZxD3M9nKqnv7lXUmqpZZj8GTH1kQtUFrefTxupKwnUMrlz8HfaRm2uF9yxKMIJ2jwDmFNqBUG+DT+nqMDHhHCS5Km21BEQBUAYyt+TOkJ+Hcg692zX10X/5iyzhcu0jlN6X/qxHlJaKb9j+nfC8sACt2C+mFk+WlsD9TPnu8sJ2lXBRv8GkCyP8U42O12e91/Z4NzJZNC/tEBl6nVNBjem3a4rvu8WMYLqYLYAeKG6dpTh68eWiyzVNHBF05BHABg0hikoRFboPtVt9piyMFo07/MZNkvXIGELhGOCfHlnEar59DpUaOTo3zQncFZFFRXobdZoztqTFaZI71w03DRwwkKiCGXyVHPRfW1IBF2yNiX0b4ZibhKqsYDMAafUEmXMoajsXER4kwWdHhN93g4J/Rs9e9XFySx0cRkMbEJbGo2vXPq3SqM2+W/nVVimCaMjbTpf7UIW987mUj/ZBEQX2NaRumiWVfgL24QK26YcM02wgg0r3aSSTL1+DvlU62unnZmQfA4D4ASbqgLuckjzfRYK3PGRbBszoVgYpGGHmy+JpTqlXXX6tRdFXqOUjY3mCsG8pGtsWcgTuLQqLEz6d0WUDZWG13Cf6oqs9VVCtWgMSaMdQRe5xYj+bRqvoWETNd9h8kMdNPJYV/I8Q53+LQQDtYyRlEBlrYDfrNlAX4q4LZqwVMKGg1pzOLq8a9la5PMwlPRvJMMmdE1URy9K9yejFFDaZo7+bbLMplnHsaVb1DfbRZZqxpJpo8hxawzNo9si/cHFDQkCB99fSzt0F4JyA5VpPtX9h0r9qQHQhj/n+oov62j3wVguU4Qsrp9U4Uuvl57jAt1n1rxXqOrVk5VB41SmZX22+VRjKjdH/1bSyxElUq8XJfnc6dppjf5NBKCQsYja6OxlQ2WsMEJ1/94mRrKJFDLIZoHGFQSdHi3FUm3vV1XgLL1ppSJvGt0y4i+97ugD4P7HnoFXvmBNruvTYFMY140FL1SXaqnuTyosVDdlw09he8E0FWUFtWgi6ItqYsRkqUYvN0dt6nxDWuTWQQFvDJwFPYOV2QaToEk3JiOMpZqydLJSCDWf6pZ5TpaRLsVkdctD/+aEXWyFEuCiWveiCFpgr8dE/07qwJZqhjoNIPsMW51tdbVCSdHGabg4qyV9BibFKr4WvxuwYBgZon+Lcqn7k7Khs7RD2+Bq/QOFQIU1MWZp5G/crjw5qgHU8a/Rv8NA+81OBzakVWLeiTb/YLd2q9/x/VOlSXX0b9yecuqwGRJs1n1Rf0zzxteE6HdVwZRE/44w/Ztft/m2qp+5tlcVcJdDmpVTzB+XNFpcuQPNK0zqDpACK+3dMkwuiXmYdMm1YZx5J+9zOnrDcvi7847Lda0LtHfKADeFDdetVAPbRqNQucLqgSKjNlVgxc0aFktnnS+RukEXq0Uj2FItf1cEN4MwNd9BF8ymzDHOapSGVg5rmBb927CZ7rT0+ULp31kZK5pPtWUtLWPDoQd40vvLtWg6btqtQFmzBaJIHhdw8atOyz2tBkMzpNRytVQn7xdZn2KpZizENuscZwVtt0KjX7yJ/g1A3VL03/R28G2K22Bucx4EpC5xT9SfGkCOvdzxACwW+DAItJy6aXmLA+ZZctbKokwe2zpCy6tMqK5AgW4rk1bB0Xp7Pd5SjY0oNNXZHJondO20BaajayfHGFH2cDUK1dx8kJbqfEL1IIUgTkkvfIAXpdxPVUGPqwC3d3RFovxv6D3a3kF1w1uqS+x8TP/mgrg0CVXQq6pGGZavpoK+VBYhn2rTZqCoJWVYkUXjXyfyrCtJNOsMuSroZlxG/1bLGGnruc7pRj8rhVCzrhErltKuEtZZl5RaRSzVOLKvgEyphS2z6eUnuacZASqug/Op5tuIrc4cMP1b3AO2bnOKFr0u9BlbVfufO61A8QOP+yvuC2yds/mnSv9P+Rvd8NiCZpbtU02FFdG+EUaoFvMqr0817ntdEcbMJYfo30nbEoYLul4IKJbn7AKNgs8q8fj4AWWBU8wUhU3BZHeNiP/3oggi5CYiFKLYp5quszi4WRZmQkDW5RbT9jyKxbxIC2wrlFJ5hepBvsM5dswHzjgMbvvZY3DsphXWa8sIxlkX8tK/AfpzsNtcY1aTon8vTKE6B03TBUk+QyZvYdOQx/9z0LBZNIYddBwqgcoMi6FpEzXfUTQQT1XIEzwrD4Vfo38zAlQQ8AFGaJRnW/5gDtQ31JX+nXfDoQcqE79nT3dEm9AJQwP9u19HRku1MaVWaK5Dy53drzM9pZZ8v3DBzTifW5vAivtGbnRDAJBlCmaDoLSaKOycwsMm3FP2BEbZkf4pm0RaqnVhwJUCaqyLUVTgsm2sD70snvnBBrCyzEOndlNLNW17f4PNnVsWqjB8WAPH0XEW6sd6kRrQUPQLDmQoihHlYz9sWzpCra1kvnDzpyrjENuelLV8/YoJuOvBPbBhxUSucgdpWYzp3ur6feyBK+DYA+0CNQAx9DR8T1oksK20VJfapNKgKbIH2M4FKVS3LC/xQuWKxRcHKmvKjp9gmGgrAsOkFcwKej+YdsQFJ8Of51tfpIHzgWoCVDqe2zV5nqG2QWeUK60gSBVuhJCUlOPQBD3XKo4MrJ6LT807RDnaLID60nR9/ppQ2QqUNVuA2wS75Ko20aFt9G9d0A2Vsox1Ifq4aOaMYqkW5eHnbZ43bKCyMIAoUn9vBQF0IVYap90vbodNAOAiWHPfg6D4XKfKZCv9u2CgMnxfnE+1LT0dhRb/QAQlS5nj3Pc00Nu1UfCro3/Lz2W93wLLWmd1YUCfVcWVjP5N3f1Em6NIMkiyBaZT28Ypx5T3TcV7gLS911/87hHwrlMOhuetXZqpXLmeD/YdHgaBMXikDcPkkqiOqYzXNnyvqe09BtjOhuodqkVVAiXOU52k1GroRCsywQaFOlNI1A1NqDbQv/FpedIxzQfYNt6DRBH6d5Z7CALVH5MrgwZC4qzZdLPmFKjMEHmcu74Mn2o9anJcjmKpdnyLaVbh0ED/TmjNsuA0IRdfZ9qw4zIkVVw9VzyyLPTvhDLe96nGwie+ZZswqzJg4gPtFg2OFSTl9SKpiLAxR6TvLzpuUJRwbczDSLDBpHjihOrClmpFEOGifxMFlc1SHQSsEoRjGOjKrWzt19dXMudrECSqCABls9TZhGx8nRCQY+tx/BsOYJaMGUYQzxqYDn9W5jHz3q/6FWhS7AssGm3D89dNZlZ6NSXYal6ZQFVcl9qk0lEkToFJYdcUmJTvg0DDh0E1qErTquSpHqLo302dKBR10p3qhm6pRkK1YWPMpUlaCNAtMQNqCEEe+neeQGUAJM0NMw4oZZClA4cBy3ywgW78cRAjLQBVCRvjMKQbyvh/mo8fB7rpiX3K489c9G8s9LjkqTZFRObriP/rOZv71q9UoVoK5VKo1t85to23yZ9efG6HIZhoqHGqIF6JwD0bm0LUxpYoW7gyRv/mUmoJ5UIJQrVGoQ50S3VaNGjunc1SwmldGXd5dEzSQGXcfCwb1dC/+fK57/grpoJjdgaOlSAVcf26UJ/N9JVd2ej+eD7qjA3a5qrZWpjVkXc+cGiCTzWAeY+Vel0Fyp+qUGS8DFugMi9U14yqBEocqEzSgUorvlQMo0/1MKUvyAp6PxMoT7Vpk5wEuWroQlcVOP/EJkC1Crpdk/dlhTdoJv9Zzqda3VhmZ6y0FSGaCunquWW5a7SZeZ9HgaFbQU307/5xxac6vfyuwXKLqaD0XJP1OM1SHSGlrSh/tm8RM2309IBivDAr+rvdCrSykv5ypn8zY9NmqQ71ZySPQWFQa6+ojw9UVmwjia+jVskg0IXVLPRvcSq+H3OgsowbaO0ZuLNTykJWtxQX2OjftjGJ1zERYT8eO/FvPRSYlrNUz84ZfKqtz1ttS9Y5VQVMKdsKldlv96Bf4dQtyhXcO7apUN/HGa9tuAHHxpKrGwvSp7oKfx0AOWi7liAuTYEarG1w7cgCjuo2X0DvZ7GR/s1souZZX6ShSVQfjDxCpBoUyh0djtpNguuolLb4vxoUqhj9O40+jusv8ohaYQCzXVWAy7OZ0a3C2Nokf+d9qt3p3/ReOfq3iSreRn6aNojsWSJvMABvqbYpT03nSYtUCK1ATdMlTrNF/+ayNNhSatksiAojo4R5jpsaBDKqMkf/PuKASdiycTlsfd5U4bo4CjUVqqiQrZQVBtrcFeXgc+J6i20w6em0XXUIc2mBsXKVaVmr6NAyKQ7mkLEEu45Qn2r8uAX9O4ul2uR2ge8jq0K0KFphANAtec/cEEu18u7OYqm2KASbhqxBSTGa8pxM0BWJg2kHwEIVqivQggLIzYESqKwhG36KYVoMBIaxza7Qon+P8JZqH6iMoTc25PZz+VTnpNQpAcLEOCCbXVXwDPVziFDsMo4wjZUGQ+N8lrOUba4zhH39KNTcy91dgaF+b4chEhL1PNXCOogFSBtM0b9tdZgicuP0WHxd2LdTXNNLfhOwbbxNx9poTKq+lLIubKm2+VSHjJBB22GzIJbtU019IF+4YTksHm3DiQev1M5dNNqGf37XibnrUujfjGCamf5N5p74Xf/NLjBmaTcAH2QNt6sKKGOopEpsQoV1DKPrhKVaBO0DUOnf4jo8bsU1WVKo0Yj4nCW1buZeFYIV914aBIKc4y2PG9KgUISdWjS+RNVokqFlQQrVaUEX8gLTv6mPTdMw/PTvATakAlBDpeJTbbjvFmMFWghoUk5CDNUK5nZNXloVF3Ubz2Mt+rdBuMnaZrwRjK018hjnsyzryv+MWi19/OfJ4MAJC5jOLICtTmEQR4VFQX+NMFluuTqkVZs/1z2lFqZ/6xknbM/AxIBJfKpbxJ89QD7oKP+uJqAwiho6NpXzLW3kfEmLgCq+jly/DO66dGslm0WF/s0E3KOCNhWy6fm8skLva9vzcIHuJqEHWROoau9QhU+1qwsCgDkyvupTHf8WRTr9Gxc/M8evC7bAdFT5yfrOV9BHNqxeOgrP7u7CikUjpZUppsCgjSR595Y2lk3TYHPVSkPT6d+CsZUoqwc4nhakUF3VYqTkqe7xm6amoEgkwEHB5Oc0H0A3Lkr0byy4KAtjfM2gX0h1gyogmnL/eYKWrF4yFv9fOpqpLiWyN2NBCBkrI0Bx+neH1Gulf5e0zrZDtc0AkAQwiyL3tYATNmz0b2kFjtws1YY4GlwdaUG+uo707zCUm/vZOdWaj+sW53Ltop9Ff3daoVZWYklHN2Pz1U6sahYlro2W2w7N7c8DTplc1QZM6VPqUx1yKZbM7dDmqkipxTxfbX3MOPc04a9lfl5VvYereNfbGIo2RofqU63Tv7uRzkzEuY+FpZpmMkhjJuB2q8p0XaFSR17ez779RbD76RlYtTjbu8qGpliq8xqZuDnZVBRRcnMxWZqGMAiUVJODwgIVquXnMiczDnpjovc1BbaXSFORJzftsADfW6cVKIFzTIvhQg1URiMSNwV5fA1Pf94U/N25x8ER6ycz1TVCfJvj//I4tW60Ofp3yFuzbcCCQBjwFhRcvjyWWrRTnbiN7TD2tXbta7qpxgKpif4dMMdNMFluMWWaqwMjUcy6Rv8OZBlC0DW6i9gECSbOCKa4ivPFuovp6TbLM+//SYVw/lpTWUVQ53vPlqanxViqrdGgA15pp87xQDnGtcOp3eR0SlM2WXHLRNkMBb1Ms4JJVxLJz3z0bz5GQjsMYabby0X/pvEoOBcJ25yqAutXTMD6FROllim6YNCKcRuTxoZhon8HBcYLxxBrGlpBAF3gU1XWiQbrHapDFQs2gByo3cgc3bUpGMaUWmVFE24i8L1NjKi6LpM2tOmUnKpQdvCispBnXWmFAbz40FWwdKyTqa606N9hQARgzrIVkNQxDm1ut8zX2yzVReYrZ3HHvzunL0PXir5J1mwuiBhSOjjI1MY0ipImKmnbPYaqDSCp7u5CtdzczyZ5qnlB2q70kJ+X9Mfi0rGOFsMhsYojPnxAdhEsVVURMM3n00fZtqRsy4MqAmAZ60LFu6XUMm/HWiFP/+bmX9GYMTj4HYAu7Fe1f8LImzfYBpvyxmapFFZnADVuAVbIdRklmXics3lSapH9GT41YJ9zc96DWdAUC2heGvcw7UmL7Pk5dkTToMzvAc6HBSlUV7Fg47KUQGUN7WFl49XgiYJRR4CUQQGPQxz5mx7jPjd9MS8bNkrrIFFEE5wVbTb6t9oveKPA+1TnoX+rAdJsG3ibZSgLTIKVTEfmVg4n5IvfsNDMWYHLiP4dn6P+16yC/R/SU2rJsmlwM+65c3WZLI7nnLARPvjKzfCW4zdoGzHxXVDNaR1xufIzn0PdLNDYBP8yNt51WFm58nVrLyNk2aJ/07nKxNNIYmyUkN2EcweQx4qXn15/+XXY5kIaqyZRXKHgfFjhFjH7vWSudE0ptWzPW20b994vS2k5SCR7mAErBVSlSobryDu3ybApNlOvDZrxnGyoO3CfCQuU/l3NYsQFKmuqBpHmqx0GNFWYKgM40AKO/A1g1jAetN8iCAOAQ1cvrq2dTUBTA9YVtRBlgUv0b2z5opZZABFVFpRr0uvlhSzcDlke/zkrTBS7rBsyPqevLjRjGre4xCVPtZH+jdrfiyJoQWB8P0jLuT0yWhdZukURwnps8l+2Rc3Hn1ctHoV3/tZB2jUxM0EX+jlqMy2Xs7Rx7aJlUXeDosDjsOpXs+2+wiDQhCpbSq0goOuLWbAqQ3GAqZRUIZAndkSe+mUd5ZRpa3eaQjQMA4BelFiq4/d1f+0w7PeSAIIJ/ZumUHOjf+N5J77j//TzMIFTCA8CeWWChUL/FmvVoJ+TDU2RDxakUG0LmlJGuUORp3oIqUPDSFnPAuEjOkEs1abF8OD9FsP3P3gaLJ8oLxrnMKCpY7dOKpgaMCz+rwjIQUCCPOntaoVBZvYHFXJUYUk9t6zn1FHqRG3p33ge+ncr2STE3xX6d0+3VKfRsfF1tvQ83V4EnZYtT7WoL6Uuxpqe5PI2WCRc6d/qOer5lNLKXcsKfxYriU04KNv6UOfagdcDLdhXqPtU2+jfYRCwVlBOUCzjHvFl9jzVuYpPRRWCuzMjghOq+z/NKWtD/FsXZQewCdXcGHBpKw1SxwXYG9YYM40JVJaX/l0R67UK2BTgrtc2+R7rYNC4YEEK1erLqbxycZ5qGf27vPLLBA2EMQzA7Rz0IlwF4sU8gsWjqqUaRxKla0WZkTiHBU3VDuOmVL3JUVNqMRvskM9BrQow/GbNXq8qhNuyCJQlENGo5rTMPIHKqOWeo39jX8ooA/2bNgfXK4pJT6lll6p5+ncv+Y2rm7bLxQWIKqDFdyVQGVUipFBVNYHGMgY5xVAR1EmZxe3l0lJRS6XNUm2KtK8omQyMlTzgghwmxyoySmDQdarsMu0+1fq1dNzjtTOKIMVSXSylFhbgcR15g2s1CU0JVJaXZZYnteOgUIRJx7HhmoamMBgb6vFbLaqyeIqyFGpcQwfhMPrj1BEgZZAQ90cDleFjw/KsqkS7oeOgzkU9LVBZKwjYtFt6Si3QzrGhTep1FZaKrLOmwHSSwu1WDn9t/N9E/5bH08sX55gEZQCZKsvEZJJCtb0unDpEVMdt3m0WPxf6KGUyyHdcT/ndVK6oUv3NLNBUpZjhyqh67bC9Y4MgYFJqmbdjQUDazvhUh8kcV6/LA9UfnLadP69MVGEBtO0flHti6qPjHrN0ekpKLXmNeJ7Tc4ZAZda85Gq7ed/5chVOg0BTLNV5rbjDZKkukka3KTR9G5rCYBzSqVgMVXHvE4tBz0yNawqq0ARXjWFawPJALFyLiE81AE/tW6hoanCQOhf1DhOojG4MOGGFbs6yKgLaRMixCWZFAqOY65S/Z41+z1ruBf1bEaplwC9xDy6ByroG+jduXi8RqtPo33apGl/f0jb88jzbOu+i9KDnUAE+DOxKBHbcUaHaIhy0C2wEOeSN8puvLqqQkJ8BdMt0WjRoZewzm1z2t5zrI75Mj1Je7jPh6y9/jS+iABRtkC4W6tqQKOJQW0c7cb/tm+3GdRgUaGxbFQVAAJyf/DDu4SikpXqw7cirKLIpBJsGm2Iz9drEUl1mi8pFVQGoM7djYDUPEFVFkU7oPnNyA9akTT9GU3P92lBn5NZBQIyVRaNmS/V8vO+saKpvfR0BfAS4QGU4tVErJAIJM35Csllzo3+rabpsCo6yrIxKnmrmntzp36jMltofOIJvpFiq488uQnVkEJTxeI36srL02+YFzLTo35w1fY6xVNvWeRfqP32G1E+UFUCY+WlK80W/6wJ6iI6xTcwEWzvKBhUM6XjNFv2b71dOUVTG+ojn8kBSalXwnGzMgTSFqDi9q1iqhVDN07/H2rFy/OnpubjOQE1VZqP70/vn5khTLHNF0GICaA6mHfnGm4kV1EQUcQsZNp9qb6keAKqg04oHOWuhxjUFwyigtpRJM7h2VAUxFjmhOqv/6HxGU1InUNS5qCv0b85SjQQgbE3EFgFK/3ZpM6Viuwb/KbLhMAXkoRTuNKT5+gq5WcbDQBvnFDo2gJn+jeug9G+TD3GaEI9T+Egrmi7oWqN/OzAJqKKICvA8VVa/3jYWbJZPzoWhCOq0LJkUWOI/FaJtdOBWQIIKMsEJQzTfuTZkgRpkjaYDq16QqMIargajosfsQhX1j1YUbr1ICW4oMNa3VD87003qUAUbN/o3XWcTRsIQGkYomiKs5VVQmNIXNhFFjImS2dXce2yKoXDBCtWi00uN/k02N/i3pqFOGlxZkH6hwxvt0gbpU83Rv+P/8/C2M6Op+crr3OQoAcOSDba6MVgy1oYwAJgc77BtbAU6HTy13lAV5tXNn3puWS+5dosvJ+uLnvOpxm3sMtRszufaBHG9LW9zGv07sVR37fV1e/rmfo6xftsUUC7CF32GYt3l0nfROk1jg9Zlsx5Td4OiaBGlUpWgaSvFrYj/I9RSbRnHJgWYGlBMFdpxXVmhKK9o1Go8ByvqQ1xsWc/JZsFPmwtJKrkkpZb8DdO/cVtHO31L9cxcUgcu22qppsosRiGg/NakF2EGJP7hA97Y5FUUDVNsoiIuFcMQqEwyOAYrH+QSqq+++mrYtGkTjI2NwZYtW+C2224znvvFL34RTj/9dNhvv/1g6dKlcMIJJ8BXv/rV3A0uC8lLqcTOp1p8/FvToE6wATYkA4IKnlmTIH2qfaAyG8reaJeFIikrsoKP/o3aEgawbGIErnnLFrjmLVu0c8XnrIwVvMEOyCbRxbc2D0x+tZLu6laOmh6qn44L/dZj6N+iukz0b7KeYoFIWLREcXpe5vh7N6U+jv4tlLkmKqMuSKQ/e2rdF33IUc0F0lL+WJUvFit2GRulOimzdG5QyraW/zkIjEpTEyuEm79lxB6hqdRoW+TnXMWn11+BsGJb69LuScYt0KN/q/Rvec1YX6jGQQltEeGN7QkNKbVqHMtVIaF/D3hfQxVg7tep78MmowiDZb9+lpkmZ5tpiotkZnHqhhtugIsuugg++MEPwvbt2+ElL3kJnHHGGbBjxw72/G9+85tw+umnw4033gh33HEHnHrqqfDbv/3bsH379sKNL4IqhBRR1owlh2dTQANhDAM42tN8go3+zfnLLVQ0VTscFHhpZQXnZ8xRBLc+fw0cd9BKdK7aRhe/WqVeIsw7+ykWon9jv1q9Pmf6N2Op5ujfyQY5lBR6t+jfvJ80/q1H6jD5EKflxcb+2xo11UC1tlNe+T6k6ZOoAG8TQLhnRT/H31GbSDvUuABsEzOhTjYJDSJG3TSoBZhaMukxTlmiWI1ZhkBOoVqxqBLh3zC+ykQV9G/7GLTfE2WDtAKpYOpFEaskG2vrtHluDWLbSsYpN27rjA9QFZpiqc7rFjJMwXOLzKkPvmozXPfWY+DUw1aX3azS0BSKeubX1JVXXgnnnnsunHfeebB582a46qqrYP369fCJT3yCPf+qq66CP/qjP4Jjjz0WDj30UPizP/szOPTQQ+Ff/uVfCje+CKSmt8wy4/+qpbq88suEQqMcEkGNRuydbxD3tWjUHP17WBQgVaKp2uFaU2q19RekavHjr1MDk2VfBzpkE2Gi+Irj8lj+DuECruHfc9G/W3qfCUGWo3+75KkWulRWqE6E80ipS7faqm0xAQvworo5RtC1WWZtzy5pD7HgJDRYQ6RzADn2TOXTNcxqxWYYGUWgtKniSUrno/gq/lP6dxgExjkYhtgnG8dI0OdG4LAOpIGLXSDbqba5CijrVEnve3UM0vrweeb5i+nfcm3gFWqC/i3QIs/XFpiOtkdZpxmDUNMFOhOSlFqDFoQyKpcF6oiEXxbouz8LViwagZdtnhr4c7JB3NOgn0Om5WpmZgbuuOMO2Lp1q/L71q1b4fbbb3cqo9frwVNPPQUrVqzIUnXpCCt4ADRgDMDgFwsThpE6JJo5LO3NCvFMuDzVktpXa5MaiaaO3TqppSNM9G+XlBKUVpk5pVZLtRrbBGcbhTQL0nyqXddwfK30P5W/9UgQMZX+nV6+yU8a/yYFd1kHhhAi04TqRIAP5UYdW9GS8ixsARcmgUb/7n+dnUuP/m2ap7oiwTxuy/eprm+OUuGTzlMuqjZuUkcLCtg/z9ivjLCV11KNyrCl1Kpqe1NFACjb+pwW/yEkcwy7znR7EQpuKK+hlupYKYXqtFmqSR+npdQaVmX7QfstAgCATasWDbQdLgpGDnW6fBUFx2qZT2gx82IQ0HfvFjz22GPQ7XZhampK+X1qagp27drlVMZf/dVfwdNPPw1veMMbjOdMT0/D9PR08n3v3r1ZmumEKvj34qHOIkt1Uxe7YaQOzXcKtPDBwoGlBJriL9IEUN/hpiDI+WLOA1OaqTCIBTYzjVTdnGVNqaVGY3b3UywybE2CVVbmSlr074Sa3dMt1WlCLgCmZJsFTWHwFudqNFSyeTdBsaYTZa6J8k3HpMvzMdK/RR8xA92Uvo0eT45ZgoeVLcDVmlKLjFvqvkT7AVs/AeKUTLNdGeSKj/osP0uXBmCPZ2o7ukynqVcvSFShOLUpHdMUouL8OZynun+akqcaPz9iqcaKFQBdWWFrD8fOyktZbhJOfe5q+NYfnwrrJscH2o68LmVcnvimos6Un4NAomwaNOshz0X0xRdFkdOG7POf/zxcdtllcMMNN8Dq1WZu/hVXXAGTk5PJ3/r16/M004oqHgCN/t2kDT/FMOXXE2g1ZNJUhfdvfS6c++JNcPSGZdqxhFI5T+89C9qKj+0AG0JQ55zC9G9W0DQJ1YoAw1t+rfWGKh3XaoEsacOBfarTBGMbVAFEpx32CP0bBxjLRP/mBE0hnEe0DvW8JKWWs081E6jMIPTY6N+m50OF2sRix9RF6zEJ97aUY7TrTNT/vKjTUk0FQ5pfOAgClXFChKfRTshfT/LRJ+dwyoycfabmqdZp6tznMlEGhZ1CVRCpx9L8xMX5s0yeapzjXvGp7pB+Cwn922qpRtcFPP27qVkwsuKA5RMD39fkjTE0TJbqOpRhgwSXvnEg7chy8qpVq6DVamlW6UceeUSzXlPccMMNcO6558I//uM/wmmnnWY995JLLoE9e/Ykfw888ECWZjqBy+lYuEyy4WjyuC1Dm103cMj8+YjTnjcF/8+Zz2PzlTaF2tIEhGTD0RQMiv7NRUg2vVioAJ6d/k0tKKjsjNYfV7QNbeQEYxt45YM8ztG/aYAxG2z0b1G19KnW24TblWapxgK8qA9b0QRs1mgXAZNaw0TZNsUxR0+1jTObxa386N/oc8WLKRV6xFeTFZjSv0fb0tIZKNfzfVJm/mJcBk39ZGM/lIUqfFXTlEjc86Hnz+E81UIBFkXW6N+4fFuqMgyqVGBp/oZx4JEdece0i8tVU1DGutBkcOvjQNqR5eSRkRHYsmULbNu2Tfl927ZtcOKJJxqv+/znPw9ve9vb4B/+4R/gVa96VWo9o6OjsHTpUuWvbFSRdF6LwtrghW4YJ1gSKXJYGlwihiFPYF0w+YwOGnXOKWOaKWZjjWGjf7usVx3i32y7vizLPRXkaZmu6yxWAHA5fWlk7hbazGZKqWWhj4pzIoMA3grd6uOCqcmI3PxGL09qJPoMNd9SZgchLjEJxFkE57bCjODbmAV1zlEq/IbM3Gwr964ew5ZOrAAzKSHE+C5DYa4G1LL5VFfTiVXUkUZ/5Z4PPdZVXEPiY70I+1Tj50cClYUBWYPc6N9BQN10xDlq2R75kTfoG1Zu25gHTYALM2mY0RQXyUw+1QAAF198MZx99tlwzDHHwAknnACf/OQnYceOHXD++ecDQGxlfuihh+Bzn/scAMQC9TnnnAMf+9jH4Pjjj0+s3OPj4zA5OVnirWRDFVoNMb8wRaipqCMtRtngopsuFDTFX6QJaGr0bxvFtWx0UBAc1TIZ/ze569Fzs1ruqC+3NfhPSfREk081lxbLBluKp25PWptwnmpxuy5CddfiZyw35Wp5Rkt1NwP9m1i3TfOD1hU4jNeQlCXZWHpQNHoPpv62tcOW6qgMwaFONgkN1MexI0bwPA4I/RtZqjFrQp2z+LM+H/LeI76MCgtZFXFF6y/rnacq5JjjYQDQ410ZtbgF6Hl0e+qaITBaUkotnL4LHxvGuDhNRd45Mz7SggtPPQSCQFeiNA3z3adasvQG247MQvUb3/hG2L17N1x++eWwc+dOOPzww+HGG2+EjRs3AgDAzp07lZzV1157LczNzcG73/1uePe73538/ta3vhU+85nPFL+DnMhq5XAqs18Wl7OwaShDm103kkkzJO0tE/M9R3cWxJr7eJ41qT/yBjvJg44pUJlgNBjq1wLgZNyYYatxQDZ7Vlpvgf5oET9uWqbrS1SltKoW0C7o6a7wJthFqDZF9BZl4XKM0b9DsVFPs1TL6zX6t8GSabKK08/KOaQsW1A0WY++TqsKJ3MdtLhqo38XLs4KahVPLIyoYl1JJa8f66hjlLueVTKVoIjgUtclx2pY56pwd0oLfmZTSHJzTPwWOdK/W1SotixcqjKMKgT0Nd4r24uhSLqp97/8uSW3phrMd59qsTUZ9J4ws1ANAHDBBRfABRdcwB6jgvI3vvGNPFVUjqwbsixlCjRov68h62a6CVjIfsVNCcLQFLSCAOaiqFH9UeeGnctTHddrV77Qc7Nqr0eIMOpqqS5iue+QiOMCWS3VphRfcdsijf4dCzJ9YVhmSTTC5lPdIhZv07mJUJ0ixHeRpTrJOsEwpEz+t/p5fD2mgEk2FycuXonJao3P58prGaj/eVGFBdQE+hy4uakGHDTTh3H6urSUWqKsXpT/Hm3CH8eMKRutCt53ARnLWp2WtTNx7evpeardo3+rfWfzqabMDs4qPYyGkaZCSTc1T/vSpgCfD2gK/XvBZr1N23zmgS2fY9NQB4WrbHBRLxcKxNAakkdVOTgq5aAR1LjJwWsNJ7A4WarD7G1ut6hlzaz9Li/6Ny8Ycvm5bTBRL0XbRMTtiLECu1mqzZToZANO6d8GK2CelFqSIYXqtTwD/NVI/yYWHOlTbY7+LTaoJj9qOs5sY8gUpC4v6qV/q/fFrVmdUJ3HJksnnms2N4akvoIKaDX1k/l5VeXmQiOll4E0CzvXv7Q9XeRiIYVqfj7T6N9Z6N+6P77elvkS/bsJqHNdGBTqZNINAmlBWuvCAhaq+/9LfAA0v3CTJ2edqUXKQkJ/G5L2lokksN4CvHcOTfSvLztSsQ22PNXxf75+aq1xsVaq9arrhu36unyqXeeEaVNBBeceYwV2i/4d/2d9MpFVC0AK11pe5pYq4JuQCP5EsQFg7nfqR+pC46VCm6jLif6tCJW4HVRI4z9z9RdFvfRv9TP3/lKifweqO4aR/m3oL9YNJOc6hMs1pdSqcvNaBZUzLRYH17+0PWr07/g3Y/TvNhOoTFm/LIHKSNq0tGCUfl9QDDR2xHxEHcqwQaIpTNYFK1RXYelaPjGifG+y8GfbbDUVVURsHxY0RQvXFDRxLKibnGrrGimJ/p01z6bqixyQDT4R7jKWbUJLUSDg37Ot4SZhBluc4v96ZG2XPNU2+re4BZqnmm6GhaIgPaWWzHNN6zNZMjX6t4OASZWv4msSqMwU8ImU6a58of3BMzLygrP4VQVKa0/mpmIF5nNRA9CUWgG75tno3/S3LMDjklqq62AJVeHuhLvCFmDPxjSZK0j/xkXTfuXqE585FwmfUqs8YEr9fO3L+a6EEa+KQTNZh0ScKh/cwlQUY52QRPMsrejSMYyRAEWbh6S5paKJltlBooo880WBH03VC7uyGWd8BY3Rv8nGW2mzw9hqk5RatiwCZeUTN6XUSjTTjn1t8qkW1wtBFW+QRXVOlmom+jZtd6QFKiNtTGimdiduLJTblBk2Nx8XyiMdL1K4sFHd+/8dhL+0Y3S8FUWdG0vdp1p8lue0FTcOdc6olmreJ9tEJ5YCfLG2x7mz1X4SX6t8FxWlr3NIU/LZFJJJhH1kqcbxFrho/pT+jV0AAOwKAzqHcZNk/6Pzm/QiHEJU4Q7aNMx3n+oqZLpc7Rho7QOEzX8mL4IggOUTkgLe5IVuGH1ImjJpBgFP/1bRRCVDnZYDE/071ae6IP2bRh23WiAVi2l62SaouXyRENHKNieUa5n8xxGlf6M81WmBw+Lr9HoEKI3clNPaNfq3mvaLLwPXSz/H18rPLj7VWCiw0b+5gDG5heqS6d80pkCVoMFAuY17h9wfvgZbqlshtsSAck1SB/Pc877bRR2c328d76Iq4qfQ+ADacYvSXjRjFuWpbqG1IWKUZLqlWl0vaQweU1vx2AHg1/gGbzWHAlVkA2oauHgk8wlNUYwsWKG6iuiSACoFfNAP1waTX1aTId5BTe7XqpBsqBberbOwUfUGhToDx5jo3+Kjk+WRWE6yWqppAB27b2/+DlGFRP33ooHKTPTvIJB+k0Xp36JqISx3o0j5XcA9pRamqKvHTIoO23kmJiqN/i0uEUI1N/+41Ic2K4mqFDIfK0NRVWcsEcri4BT5ujsFEqodLNWKtZL5Pe/+RtTBCX5VWJH1OuL/Za7vtqB9uE5uXMj88b3kuzgfz1Xl+ZE81ZRVYns2AVGcpLn4NEm5PIwoyuwYBqSllBt2NCWQ8TweQnaEBV86JuBgZU2eoMNsqR70pBkEmrJgNAWtBr4E66SWKpbWDJZq3XoG6Hs2oRrnLRblYWDaYpE1RqW6633sWrZC/8a0Ykr/7jOvsSDjFP3bgf7tnKc6LaWWsJgRxQiAqnBRA/BYlB4mZoPBYsblxE7KTYRH+Zudhs6fB0AYGSXMKVObqgBVaIhbUdLCEXo7bhL1qU6jf3PPM+892taRrFH386AKJmFcrlo+hi1OB3V7iNc29TdabqGUWuQZB2Qs4f+mNnu4oyk5jqsEDX4331CHss+pHYOtfnCoyj93eCzVQyxUD0dzS0VoeeEvRDQlJyFGVgG1CLCgmSVQWUDaqFLCHOpVhPn0daQoDRXAbF1ct2y8/3/MqRyV/q0rA2j0b2xZcstTrbdRQNyDKEdYvul8FkJkt5tmqZZ1UcFjvyWyP2z0fBcBk/a9Tv82X2OkoZOGWPNUl8z+KCvNW9a6wiBg58IIURjhd9woic+SJkxxCqe8ARPFeOCCadXBGJNjqJpyOYWAuGfutqQySeSCl89B/AagWphHU1JqdSwLLmUjcFZpVWlpLMrDAU2hDleJ+c5sqMpQmhXtgdY+QFTlF7QM+1Q3eOCa0p00GVVR9ocBC1mhwCGsYWOXFWkpW8qEyRqZ9mIRG7Eo0q2cLn0Z9mmPvSid/p20rReV5lON63jHizfBMQeugCMPmHQqRxXsVGotgPRTxv6RVOC2oWegdMe/Bco5pnNFs9Kif0eI/k3fM2uWSqHaFJwtvjZdYKUCr7hktmu2ynPsBPyO0e5ZeS7qMdOzz4usMQTKqytQhCEBGoRP9Gc7DPXI64wiEXcJF3sg77tSNItL+1QP/bua/VncLxHbdts7JRGge5KhkcRbQHNVTYmmp9RSxrot+rcW+0ItBx/rRlGj3oPDiKCGMT1o1MmkGwSaYmgZEnGqfFQVmGAZslQ3edzWaVUrC6LN81HLloamLBhNQVNyEmLUyf4w56lO7xfcd3nWARGxmObVZa2WjCCRFSZrZacVwpaNy5UIyjak+VQLynUX+1QTYdiGLrJw6/cASjndHk+fFs81rT7F75tUt2ZyNPlss064WG3p85U0WGGxY+6VGYM2+rfN18+Fop4FdVpr6HrAWZrbLdUajYVhU05403PjFAZ571GUxVGU62BNFaWvm2AzptjeKZKhgXLYh2IuGOjfbYb+jdcvR0t1S1N+cm1u0ItwCJGwL5q0oSgZdcaTGASaYnhauEK1eMGV/ASWDUv07yGcYEnuyuFobqlYCPSkLGiikgE3peq530HUUI5aa1vX8FhScxo71h3Kl5dNWMLtKaIZN/lUZ4U5anL8n1qR8f25pNRKLNxMG919quP/6Xmq5X3QMtYsHdfqjT/TNuntozBRmOcslmrO59ZG/8ZVaz7VpafU4j9XASU4G1JI4L4Y0QKVxZ/bLSpU84IyF08Bf84770S5tkBlVSolbP7NRWDb99mUBeJeRaq7MJSsDYX+jYXmFmHyhHlTapnXWckKMRbl4YCFsL8KlLE4uHZUhSpy2+dqx0BrHyDkprzccnFKrSZPUBM1r8mo6kU7DBB7m4V47xyaGLitTvp3p8XPXy7yMgWmOeax3AnrmqCCJ+VaaJNF1piyNOxplmpBqcbCcSb6d8+B/q35VKvnCVp6ekotFP2bzIE1kyafavU8E43YdE5MQY4/z/bM0b+5DaqtLlN0d+1YCXO97PJs0PtO7xdlHIaY/h1AiwrcyaaRr4P7PX+ear19tJ5q6d/V1GFNm2WxjlNlUqzk6P9msFQHQaBQwKkSk/NXl9eidpF1OktgSg83yLk54IZUiDqZdIOAXDO8UD0QVEVhmhwfjkBlwxhevw4NeVOxYtFo//9IypkLA02h+mDU+dISm7UwoFZA8d8iVCOhx7RBt0FsBjEF0nR9IoAX6A8uqFgeKMIME+hNGJykpVren4ul2kr/DtRzTDmtxb26p9RSFRsj7VBR7Lr7VPMdS59vEvFYCBesxa9/rUFxaxPuaXnYzaGMdT+NWVEmTPRvXK2J/t1uhUoOa2zpNrEPeAtmvntMhHuLpbpKv8yqIozb2m4T5KkyCQu6cyioIJ1jWKjG7jKtUA8wiEGDkJkYFgt5T1QmmiKQVYk6FYqDQFNcIRZsoLKq6B6KpbrBKguqCR0GSPr3cLS3TLx/63PgJYeugtM2Tw26KY1AE+nfdQrVS8c68AenPwfGR1pkY57eL1ghkSd4iRB0WkF69PAy1llFAC6wGVBSGTGsAo7+Ta3YNkQGSjf+LdLqIAKmo1DdRUI57pOppaPKM1E/820CMPerKpTLMTKLLHb6NWKj71aXTeAuO1o3rrrqpYP6wXJzYYSmqOt/bYe6woqjf+NzuDgLeeeduI6zplbl78zVX/a73tYvtrWKCtABEnRVS7V63VgbK4XkWHdZx1pBAHP9IGSmd8tCEAbrwEKgf9fp+jIINIX+vWCF6qo25cuRJbHJwl+dqUXKQpJLsMHKiqqwcvEovPIFawfdjMagifRv3JQ68kC+52WHMm1If7Fgv0Kb0GWC8HMNw3RqdhluC1xU4zwwCSOavzPKU50I3A6maiyMU8g61PLouVkt1a1Ava+1yJ86Pm5+PraI3Nw1OCiaSKlly+lrGhv0EpsyqiyWgkCdwXqUuCVISFYE4ZZqiceWTC0yeCJogvK7rEP/Pe+8s12fMBEqXHurStuF1z79mPmdQqnerUDS8ed62KdavZbSvxNlhUPfxef2hWrTXGJcAjyyo4nMt7KRx9VrmGCb27W2Y6C1DxBTS2M67eoloylnZsOycWmpbnL4b2WCDckoqCpiu8fwoemW6kG9tFw2vHgetZgNWho6iNJtop8KyOjfTkWzKEsBiK/FwhrNIa1Sq/tCrgv9u2ehRIfqOaZAZZyfJofEpzpUn+HUpJqz2xScjdadnf5tjv7NWX1sbgaq4G+uv4w55WKdLwt0bnB5kGkgNmkhDhXLM77e9NxocCtaVxaIvuEiVNeRecEm4JZSLqf4sgjySaTvrk7/7lpYG6OY/h3Ka1wyFqixL/A9oHYlY6o578FhRFXjrUnIw0obJlQVJysrFqyl+pJXbobfPmIdHH/QylLLnUT076en50otu0yYqHlNxkKg6Hi4oYkptZqQsoKj3mrnoL7jNmhpwD7VNgskAJTislGFTzUnrHE5pMVpLvRvk6CMf9Mo5uSG8L32epFxkycUADSl1pqlqpLY1UJsEjCpYCy+zwqLHXMdl57G5sts96kuVwjm/I6rAlUWcFTdEeLaIA7pKbX4jb9JaWJLHeUCcRmXUosT7svG5rVLYXK8A8dtWlFqubY9hO2dIk5P8lSH8rw5Q3o8AICxjuozj+n97m01K1JcAlN6pKMqZkST0IT9SZVoylxYsEL10rEOnHjIqtLLHUW5Cfc+O1t6+WXBlPewyfCRLj0EmshaUBVVg2mDiy8i3hQHQfZ1QFjQtM0eU0C7hDlLKbJ5YbMQAvDprqgwbENkoX/LgGe0DnIeatdcL4IRQ7/hoGi4T6aWEku1hT2h0oXZajTlQ5Lyq2uO/h0wwokrDd3mU13GnLLVVTYoZZezktJAbIklMyQ+1WEAXNRtE9OkKP1blMtZVOtwvZlaOgZ3/MlpzjnoXWFru01ZQKN/B5i1gdJsUeBc1Up0d4e8oImQj5QttH2S8ppanIcFC4H+rYyheThe8HwZJOZh1zYHe/c1V6h2of41DYetWQLtMIDD1y0ddFM8BoymRHrEoJvgQcBlM40tiXnotYmlOgxShZT/8qINcPxBK+Dw/SedyubbW461Ui1HD+rUS1JqSStsmBxLL99kfQbQU2r1DNYtLCTZBHkswOP7WjtJfKot1gkXdwU9WJbaH9y9cq4ZJt9fvQ71WBBIi20Zc6rOrBdU4cRZSTtt9d6x0NVppdO/8XPjgu/lVULZfH+lxT1X0c4oW6AGsOd1dglilgjQgSzDFrRPtVTLZ9h2kGpkW6mCxa3NHu5I5tY8lqrnu6W6rnUpDQvWUl0H9s320k8aEIZRqH7hhuVw56VbYfGoH7YLHU3xn8Fowpxy2WSptEL92jS0FZ9quyXx/JMPhvNPPtipXGN9FdC/O4oAQgReZEWmVmwbuijAmV63Wg6mmGPgZ2Dzq1at6fL3NZMq/RuXT63KLgIm9cOjQhqfPowT/vgytToMQvpcLyplTqnB6goXZ0WLMAGkVRGPQxP9OzRGDzcpmbh+zHuPoo2cRbWJLCFXJP1imaPcGiPuWQjQLSLoxtfpF2LmIk6p5WSpRu84o4KqgcrlYcRCoH/XGU9iEGhK9G9vqV6gwONumOaXF6g9AJq5sWsC/Vtuyl2E6nShmAOmdNehSKBRkPPClLOZCrwimBim47pE/7bSvwmN3JRSC7era4mOJtuojjtK/+aCVwmYAomZ2hNbUkkZzHWcNbCIxbxtEYSyouzAZzZQhQRL/yZjG1uI8TFMvTe5bHB9nHe+iKJseaqbtPa6wtZ2G/2bY1DYxrEAtlQHgZwv2XyqzQEhbcHVPNyxEOjfed71w4SmrEteqF6gmO+J4D3mN8qkhJaFOqmlxjY49AvOlYqb6U7/DvvnU+VcRUI1joJc4HkrVFmSHxiAz1Mtg5ill28SlHHdohxh+KZrLx5DXYt1vIco6ntQ7I7VS2j074D9DKBmfTDSv4nA6yJIcLEvbAK8IhhayitjqtcxXrnycT5xhTHRUoUu3Hdq9G9kqTbcA6coynuPMvo38zwSqmyuogcKji0gYLsvziqtK6n068ZM0b+d6N9B/z9RpLCp01KL87CgKQJZlahz7RsEmuIK4adiBRhtN79bXfzpPDyaiiZqlptA/3YJVBaizVoe5ZoQSLH1DaC6TXZZwapaigCi+joC6AIvthA50b8t0b/FppcT3DFi39v4M85/S4Hb+Mje6eT3EfLusVknnOjfxLqv0b+ZB7J0LM6AsWSsjc6Tx21lcM+3nShxis+pOq019L6kBR8L1apyR1qIA21usim1DIqLohG6E+GPkdYSNswQ7hts7w2b1VdXJjG/MYWa8lS70b/F/8A4V31KrXKwEITq+e9TrStzBwHPpa0AS8c78OhT0+knDhCSPjvghnh45EATfaDCkoS/Ili9ZFT5zwH7vOajf0uf6jpe1EpapQJ14PZxQZ2owNsK5f1ls1Sb6xY08m4kKeYUrTCAXjcCi0yt+GSf8tzVcMW//wTWrxjXzrNZgV2evUb/drBUv/IFa+HpmTl42eap5DdbSi18zNQftC15YRJCqwBVsiUb9xCPQ3Mgqw6hhouvil+4YUwX9qlO2mF+HsMoyLkEI3MSqkM31sZoKSm1zAEhOUWNR3ZIBsNg21El5r1PdUPmgheqK8DSsXbzheqGDEAPjzxoYno1hV41oHZd8srNcOYR6+CEg1caz5EbsXz56nH07zzXZwX1Lc0LfClnoRd+ymqe6viYS55qHDWcglrDe5agZq0wgNluZLVUd1H08OeuWQK3/uEpsB+jSLH5ECtWaEO/6vRvc/kC4yMtOOeEA9W6DIIgLqfbi9jyElePEoZXvfRv9DnE9G/5e6et0r+x0KU+O0CBePg6yqR/Y4u5fmx4FfK2KM9WK3agn2tzYxCgKbVkALh06Q23J82P3tO/iyGZW/N4PxyS9WS+oSnBa/1UrAAffu0LAADgvS87dMAtMYPTmnt4DAswhbkpaELu98nxDrz40FUpKbWkBSSPH7iM/l0PnZb6luYF7hM1orgQnOPvOLI2tWLbgAOcUYh1VpyTCOCcgBmo53KgPtkbVy6CiRFdR257Pi4uQPj9gFOM4d9cYAuYhtvGFVdmXmQ1r3Ph4ux1udC/0UYQ+13rPtUBPH/dJIy0Q3jhhuXyd1wHo0DJ22dJai+mk5oSZTcPuFzfAuI3G1sCn6spqZjrjPRvF0s1CkyH33cc62MYWQNNwkLoR5MCbr5APLtB35u3VFeA4w9aCT++/BUwPtJKP3lAGGZts4eHtF41ZwAPS5wClVaYXSjuoL6vg/5dVlBFhf5NAkQBIPo3sgKLa7oO2RF7Fp/qkNYRqXVjUMs5AMAPdzwB/+WT34VLzjgM3n7SJivVXK3X/Hyc6N/kHJ26ba+fO89sJeTTZpU512009LJBqeacIrCD0tPh9nVaoRpMLwxgy8blcPdlW5U0TSYGgPic1/J25Ppl0A4DeOGGZfp9WYTPpsMWc8LGfuIEaBf6N47+nTmlFhISuDR1AADLJuL4BcsnRlLL8zDDpmyZL2hCzJcqMd5XYGFF1iDgheqK0GSBGsDTvz2GG020lij04gbPKxnEKB8dVqxto+3QSKkuE52S6N/Y6Mb5n1L6N+6fbPRv/ZgYD6KYriVSOCdU/8tdD8PMXA/+3+0PKUJ1Wn8ovpiMtY3WqV9PBUPSVsfnoShGWKWD2bJqyy2cFXUG66E+jKJqxQ9auFIQoSm2VOtKDyxQ07LUZ61elxWvfMFaeOlhq9kNagutH8MGm0UysNwXR/XWmR/6dbj/glA+F5fo3yGzTtM6/vx1R8A9D++FIw+YTC3Pw4ymBLmqEvNdqH7d0fvDo09Nw1uO3zDQdniheoGizI2Kh0fdkFFPB9wQhCak1HIBjirLBb1Jw1tPPBAAAF5z1P65rs+Ksmj1pkAtJvq3kqe6IP1b/CaE6SSnNZu+J1TOBQC4+8E9AADw451PwcxcL4k0nrZ+U0szbVMQxPdtTKllsLZyx21Is4rb/OGEAFLG+FKVSMXLs4FS3m0ptZI0d/1Duk8139hAURTpEe2LUOZNFp8mKjRdcepzV8PDTz4LRzBCqMkazP3GpZfjnhG1VItrOg6WatHP8Tzl23bgqkVw4KpFqWV52LHQ6N9DOHVTMbV0DP7Hbz9v0M3wQvVCBc6B6OExbGhKTkKMOgTMMoA3EKZgODY8Z2pJEjfiiadntHLLBhYWSqN/M4J6L4oUizT2IXaL/q3Xw9WRdq5o21xfcp7r9uCeh/cCAMBMtwc//fVTVqEcI43uHAYBdKPIjf4dctZuV6EafWbptebyypzrtUb/JhGbpZ+z/J3SvxOf21ag5LA2tZWjfHPllYkqy64a7z3tUPhvLzvEEPcg/s8eY5RJ2lxg5uJYm/epdlnHMJvQMwurhS2mw3xBWW5UHnb4QGULFGUGf/HwqBtNjNZZ54a9CBSf6oIb5DooZWVRdtU81XqZ3ShShGdMu0yzVEdRBA888QwAACweZSizQjjXKOZmIVJYvn/+6NPw7Gw3Of6jB/c407/T+s5mnQPQBUP6ujhoPzcrWdrcsAnOSV70Et5VdVIgaV2cLzKlf8tgO2EqZT4+D52jpODSj5eFozcsg6mlo3Dqc1eXXnYdMAblS/refEx+1wUw7hkp9G90TZbo362wOZGN5yuGmX3hirTUhR7lIJdQffXVV8OmTZtgbGwMtmzZArfddpv1/FtvvRW2bNkCY2NjcNBBB8E111yTq7Ee5YHz7/LwGBaIoBSj7WbpBavczJYF2cbieU45P86ywUXqzoM0qmwvUv2YFfp3iqn6nof3wi8efRpG2iGcdMgqvW5UBy7PRoUW9O+7H9qjHL/7oT1J4LS0/lAtxPrxIGW8Uusntfaf8hw3wSqV/h2YhYYyBYpBRv/mgod1kpzv4pr+760gOQZgZr6MdVrwphdtgDcccwAsHpXEQy4oWlk4aL/F8N1LXgbvePGm8gsfIGwKRioDc/Rv7jqcpxpbt7PmqS4azd3DjmFmX7jCW6rrQWb69w033AAXXXQRXH311XDSSSfBtddeC2eccQbce++9sGGD7iD+y1/+El75ylfCO9/5Tvi7v/s7+Pa3vw0XXHAB7LfffvC7v/u7pdyER3YsBB8Sj/mLs47bANNzXXjdlgMG3RQFYRBAz0KnbQLw3JeMlWJl0c9lIuwHeepFxQQhxVLd0oWrKIoUizSmxKfRv79850MAAHDa5tWwZKyjHc9C/6aW6rsffBIAANavGIcHHn8W7n7oSWtKLrVeNwuxqRgqDONN/bEHroDJCf1eOajCrF6ZLR1KIniWMKnwfdZlqQ4C6hcrz+m0VWEJ04PVMWpu6xWve4Fed8VMnvm4b+CYBAJc9G96GtclNFJ7Qu93CVSmpNSSZXiUj4WwHx5phTDSN0K4+PR75EPmLcqVV14J5557Lpx33nmwefNmuOqqq2D9+vXwiU98gj3/mmuugQ0bNsBVV10FmzdvhvPOOw/e8Y53wF/+5V8WbrxHfuy/bBzGOy04ZLUPcuExfDh4v8Xwp7/zAth/2figm6IAB5dpKvAGfhjo3wByE1qkDiVAFEf/7kUQRer5LvTvbi+Cr9z1MADEwds4FKF//6hvqT7rRRsBAOC+XU8ldPA0OTON/p32/G3Rv0973pS9coSA0Mj1dvbPy0gNz4p6o3+r9XAb9/2XjcPkeAcOW7NEOdYO+ejfeev2SIdcF/VjXJ5qF0s1DlQWBrIcF6EGszdw7AuP8pEE75zH/TvSDuGatxwNn3jz0VoWAY/ykMlSPTMzA3fccQd84AMfUH7funUr3H777ew13/nOd2Dr1q3Kby9/+cvhuuuug9nZWeh03DTdHuVi+aIR+PYHXgqLGP8/Dw+PfBiGgCe4jUU339jgUqUrSbsVwEy3oFCN28oIV/9+9y7Yb8moco64p4effBa+9bPHkmMRRPDoU9Nw/2NPwwNPPAu/3jsNS8facMpz9+Pr7pfzq8efgW/97LEkCBlnsBLC1F0PPAmz3R7c2w9S9vLnT8Hf3vYLePzpGfjV7th/OzWlVgrt2oX+3woD6PYiaIUB/GbfXPL7aZvdfWptUchx/dwx6XfsXJ0Raf1RJmiGDW6uLRnrwLc/8FIYa6s08HYrVNO+ZRz3ZUT/XmhIWAKs8kn9jtcG/BuF8KmWbAXzuRQBak8TA3POJ6xfPgEAAAf0/89XvPQwd0WoRz5kEqofe+wx6Ha7MDWlPpipqSnYtWsXe82uXbvY8+fm5uCxxx6DtWvXatdMT0/D9PR08n3v3r1ZmunhiBWLRgbdBA+PeYVOGMI+6CmRe5sG0bZOK0wC5rRzqug7YQhrlo7BTLcHExUq6CZG2vDMTDfJkZ0HrSC2/nWjCEZRAKFjN62Ar9z1MHz//sfh+/c/nvweBJImfst9j8It9z1qLf/MI9cZLQBCQPrynQ/Dl+98GP2ujxPxfK74958kvy0Za8OBKxfBC/afhFt/+qjyuw04yBcngHccnv9oO4RnZrrQaYXwm2kpVG9c6c5ymhhpwStfsAaCIICJEb3NU0vHYOeefYpSQ9bfUv4XQUzpj9OIuQSLKgLRXmGVbLd46iX2hcZzU+S27kXZfSA7hro8zBB91mL6jM7TVhhoc4Z7RhP99Uq4MIj/Iw6xQDpo7oqy867THnaceMgq+PrFJ8OGFfNbqPaoHrlSatGXcxRFVo05dz73u8AVV1wBH/rQh/I0zcPDw2NgOP+Ug+GXjz0NByxvFi0d4x0v3gST4x14yaGrYOlYB173wv3hOX36aVaEYQD/9t9eDN0oqlSR8GevPRx2PP5MIbp/uxXCn/7O4bBvtqsIMmcfvxGO2bgcPnv7/XDnA08CAMDJz90PRtsteMXz18A37nsU9j47q5W3bKIDm1Ythv0Wj8DyRSPwuhea/ftfc9T+8MMdT8AzMzKK91Hr4yjKFG8/6UD41G2/VCjnbzx2PYRhAL9/8kHw7GwX1k6OwXGbVsLz1y213vPKRSPwluM3wIoJXoH6rpMPhh/v2gubLALyH738ubBzzz5YMzkGbz5uI9z90B74L8fq8VNsCIIArn7zFuPxa8/eAjv37GMtReeffFAcbfqw4tGmgyCA95x6CDzxzGzlSuU1k2PwrlMOhrWTYwAA8NtHroV7d+6F3z5ynfGa33nh/vCLx56GV71gLQRBAO8+9RB4/OkZWLlYHyc2nHXcBtg324XTNnvLlCtef8wB8OhT++CMw3VDz+nPm4Kv3bsLnnxmFpZNdOC0zVOwZKwD7z71YPjWf+6Gbq8Hbztxk3bdmqVjcNZxG2B1X1l05pFr4UcP7YHXvpB3E8E454QDYXK8A8cdtBIWj7bh1UeugyPXLyt8nx48Dlm9eNBN8JgHCKIoJVcIwszMDExMTMA//dM/wWtf+9rk9/e+971w5513wq233qpd81u/9Vvwwhe+ED72sY8lv33pS1+CN7zhDfDMM8+w9G/OUr1+/XrYs2cPLF1q30R4eHh4eHh4eHh4eHh4eBTF3r17YXJyMlUOzWRaGBkZgS1btsC2bduU37dt2wYnnngie80JJ5ygnf+1r30NjjnmGKM/9ejoKCxdulT58/Dw8PDw8PDw8PDw8PBoGjLz9S6++GL41Kc+BZ/+9Kfhxz/+Mbzvfe+DHTt2wPnnnw8AAJdccgmcc845yfnnn38+/OpXv4KLL74YfvzjH8OnP/1puO666+D9739/eXfh4eHh4eHh4eHh4eHh4TEAZPapfuMb3wi7d++Gyy+/HHbu3AmHH3443HjjjbBxY5zuY+fOnbBjx47k/E2bNsGNN94I73vf++B//a//BevWrYO//uu/9jmqPTw8PDw8PDw8PDw8PIYemXyqBwVXLruHh4eHh4eHh4eHh4eHRxmoxKfaw8PDw8PDw8PDw8PDw8NDwgvVHh4eHh4eHh4eHh4eHh454YVqDw8PDw8PDw8PDw8PD4+cyByobBAQbt979+4dcEs8PDw8PDw8PDw8PDw8FgKE/JkWhmwohOrdu3cDAMD69esH3BIPDw8PDw8PDw8PDw+PhYTdu3fD5OSk8fhQCNUrVqwAAIAdO3ZYbwbj2GOPhR/84AeVtamq8vfu3Qvr16+HBx54oJJI58PaL1WW7ft8MGUDVNv3w9wvVdZR9XgHGN6+930+v8oe5rV9WN8bvs/rL3+Y+7yO8qusw/d9/WUPos/37NkDGzZsSORRE4ZCqA7D2PV7cnLSuQNbrVal6beqLn/p0qWVlD/M/eL7vP7yq267QBV9P+z9MqzjHWB4+973+fwqW2AY1/Zhfm8A+D4fRPnD2Od1lF9HHb7v6y0bYDB9LuRRE+ZtoLJ3v/vdQ11+VRjmfvF9Xn/5w9rnAMPfL77v51fZVWNY+8X3ef1l11F+VfB9Xj/mQ7/4vq+//IXY50GU5nXdALgm3Z4PWEj32hT4Ph8cfN/XD9/n9cP3+WDg+71++D6vH77PBwff9/VjEH3uWudQWKpHR0fh0ksvhdHR0UE3pXIspHttCnyfDw6+7+uH7/P64ft8MPD9Xj98n9cP3+eDg+/7+jGIPnetcygs1R4eHh4eHh4eHh4eHh4eTcRQWKo9PDw8PDw8PDw8PDw8PJoIL1R7eHh4eHh4eHh4eHh4eOSEF6o9PDw8PDw8PDw8PDw8PHLCC9UeHh4eHh4eHh4eHh4eHjnhheqSccUVV8Cxxx4LS5YsgdWrV8Pv/M7vwH333aecE0URXHbZZbBu3ToYHx+HU045Be655x7lnOnpaXjPe94Dq1atgkWLFsGrX/1qePDBB5VzDjzwQAiCQPn7wAc+UPk9Ng119jkAwL/927/BcccdB+Pj47Bq1Sp43eteV+n9NRl19f03vvENbayLvx/84Ae13GuTUOeY/+lPfwqvec1rYNWqVbB06VI46aST4JZbbqn8HpuGOvv8hz/8IZx++umwbNkyWLlyJfzX//pf4Te/+U3l99g0lNXnn/zkJ+GUU06BpUuXQhAE8OSTT2p1PfHEE3D22WfD5OQkTE5Owtlnn82eN99RZ59/+MMfhhNPPBEmJiZg2bJlFd5Vs1FXn99///1w7rnnwqZNm2B8fBwOPvhguPTSS2FmZqbqW2ws6hzvr371q2HDhg0wNjYGa9euhbPPPhsefvjhKm+vkaizzwWmp6fhqKOOgiAI4M4776zgrmJ4obpk3HrrrfDud78bvvvd78K2bdtgbm4Otm7dCk8//XRyzkc+8hG48sor4W/+5m/gBz/4AaxZswZOP/10eOqpp5JzLrroIvjSl74EX/jCF+Bb3/oW/OY3v4EzzzwTut2uUt/ll18OO3fuTP7+5E/+pLZ7bQrq7PN//ud/hrPPPhve/va3w1133QXf/va34ayzzqr1fpuEuvr+xBNPVMb5zp074bzzzoMDDzwQjjnmmNrve9Coc8y/6lWvgrm5Obj55pvhjjvugKOOOgrOPPNM2LVrV633PGjU1ecPP/wwnHbaaXDIIYfA9773Pbjpppvgnnvugbe97W113/LAUVafP/PMM/CKV7wC/vt//+/Gus466yy488474aabboKbbroJ7rzzTjj77LMrvb8mos4+n5mZgde//vXwrne9q9J7ajrq6vOf/OQn0Ov14Nprr4V77rkHPvrRj8I111xjfUbzHXWO91NPPRX+8R//Ee677z7453/+Z/j5z38Ov/d7v1fp/TURdfa5wB/90R/BunXrKrkfBZFHpXjkkUciAIhuvfXWKIqiqNfrRWvWrIn+/M//PDln37590eTkZHTNNddEURRFTz75ZNTpdKIvfOELyTkPPfRQFIZhdNNNNyW/bdy4MfroRz9az40MEarq89nZ2Wj//fePPvWpT9V4N8OFKsc7xszMTLR69ero8ssvr/BuhgdV9fujjz4aAUD0zW9+Mzln7969EQBEX//61+u4tcaiqj6/9tpro9WrV0fdbjc5Z/v27REARD/72c/quLXGIk+fY9xyyy0RAERPPPGE8vu9994bAUD03e9+N/ntO9/5TgQA0U9+8pNqbmZIUFWfY1x//fXR5ORk2U0fWtTR5wIf+chHok2bNpXW9mFHnX3/5S9/OQqCIJqZmSmt/cOIqvv8xhtvjA477LDonnvuiQAg2r59exW3EUVRFHlLdcXYs2cPAACsWLECAAB++ctfwq5du2Dr1q3JOaOjo3DyySfD7bffDgAAd9xxB8zOzirnrFu3Dg4//PDkHIG/+Iu/gJUrV8JRRx0FH/7whxc0jUegqj7/4Q9/CA899BCEYQgvfOELYe3atXDGGWdolJSFjKrHu8BXvvIVeOyxxxak9Y5DVf2+cuVK2Lx5M3zuc5+Dp59+Gubm5uDaa6+Fqakp2LJlS12310hU1efT09MwMjICYShfz+Pj4wAA8K1vfavam2o48vS5C77zne/A5OQkHHfccclvxx9/PExOTmYqZz6iqj73MKPOPt+zZ09Sj0d9ff/444/D3//938OJJ54InU6nWKOHHFX2+a9//Wt45zvfCf/7f/9vmJiYKK/RBnihukJEUQQXX3wxvPjFL4bDDz8cACChTE5NTSnnTk1NJcd27doFIyMjsHz5cuM5AADvfe974Qtf+ALccsstcOGFF8JVV10FF1xwQZW31HhU2ee/+MUvAADgsssugz/5kz+Bf/3Xf4Xly5fDySefDI8//nil9zUMqHq8Y1x33XXw8pe/HNavX1/2bQwdquz3IAhg27ZtsH37dliyZAmMjY3BRz/6UbjpppsWtA9klX3+0pe+FHbt2gX/83/+T5iZmYEnnngiobft3Lmz0vtqMvL2uQt27doFq1ev1n5fvXr1gnNzwKiyzz141NnnP//5z+HjH/84nH/++fkbPI9QR9//8R//MSxatAhWrlwJO3bsgC9/+cvFGz7EqLLPoyiCt73tbXD++efX5iboheoKceGFF8KPfvQj+PznP68dC4JA+R5FkfYbBT3nfe97H5x88slwxBFHwHnnnQfXXHMNXHfddbB79+5ybmAIUWWf93o9AAD44Ac/CL/7u78LW7Zsgeuvvx6CIIB/+qd/KukOhhdVj3eBBx98EL761a/CueeeW6zB8wRV9nsURXDBBRfA6tWr4bbbboPvf//78JrXvAbOPPPMBS3gVdnnz3/+8+Gzn/0s/NVf/RVMTEzAmjVr4KCDDoKpqSlotVrl3cSQoew+TysjbznzCVX3uYeOuvr84Ycfhle84hXw+te/Hs4777xcZcw31NH3f/iHfwjbt2+Hr33ta9BqteCcc86BKIpyt3nYUWWff/zjH4e9e/fCJZdcUridrvBCdUV4z3veA1/5ylfglltugQMOOCD5fc2aNQAAmrblkUceSbQya9asSSwUpnM4HH/88QAA8J//+Z+l3MOwoeo+X7t2LQAAPO95z0uOj46OwkEHHQQ7duwo/4aGCHWO9+uvvx5WrlwJr371q8u+jaFD1f1+8803w7/+67/CF77wBTjppJPg6KOPhquvvhrGx8fhs5/9bJW31ljUMdbPOuss2LVrFzz00EOwe/duuOyyy+DRRx+FTZs2VXVbjUaRPnfBmjVr4Ne//rX2+6OPPpqpnPmEqvvcQ0ddff7www/DqaeeCieccAJ88pOfLNboeYK6+n7VqlXwnOc8B04//XT4whe+ADfeeCN897vfLdb4IUXVfX7zzTfDd7/7XRgdHYV2uw2HHHIIAAAcc8wx8Na3vrWEO9DhheqSEUURXHjhhfDFL34Rbr75Zm0TtGnTJlizZg1s27Yt+W1mZgZuvfVWOPHEEwEAYMuWLdDpdJRzdu7cCf/xH/+RnMNh+/btACCFv4WCuvp8y5YtMDo6qoT+n52dhfvvvx82btxY5S02FnWP9yiK4Prrr4dzzjlnQfsh1dXvzzzzDACA4t8rvgvmxkLBINb2qakpWLx4Mdxwww0wNjYGp59+ekV310yU0ecuOOGEE2DPnj3w/e9/P/nte9/7HuzZsydTOfMBdfW5h0Sdff7QQw/BKaecAkcffTRcf/312tq+0DDI8S4s1NPT04XKGTbU1ed//dd/DXfddRfceeedcOedd8KNN94IAAA33HADfPjDHy7nZigqC4G2QPGud70rmpycjL7xjW9EO3fuTP6eeeaZ5Jw///M/jyYnJ6MvfvGL0d133x296U1vitauXRvt3bs3Oef888+PDjjggOjrX/969MMf/jB66UtfGh155JHR3NxcFEVRdPvtt0dXXnlltH379ugXv/hFdMMNN0Tr1q2LXv3qV9d+z4NGXX0eRVH03ve+N9p///2jr371q9FPfvKT6Nxzz41Wr14dPf7447Xec1NQZ99HURR9/etfjwAguvfee2u7xyairn5/9NFHo5UrV0ave93rojvvvDO67777ove///1Rp9OJ7rzzztrve5Coc6x//OMfj+64447ovvvui/7mb/4mGh8fjz72sY/Ver9NQFl9vnPnzmj79u3R3/7t3ybR7Ldv3x7t3r07OecVr3hFdMQRR0Tf+c53ou985zvRC17wgujMM8+s9X6bgDr7/Fe/+lW0ffv26EMf+lC0ePHiaPv27dH27dujp556qtZ7HjTq6vOHHnooOuSQQ6KXvvSl0YMPPqjUtVBRV99/73vfiz7+8Y9H27dvj+6///7o5ptvjl784hdHBx98cLRv377a73uQqHONwfjlL39ZefRvL1SXDABg/66//vrknF6vF1166aXRmjVrotHR0ei3fuu3orvvvlsp59lnn40uvPDCaMWKFdH4+Hh05plnRjt27EiO33HHHdFxxx0XTU5ORmNjY9Fzn/vc6NJLL42efvrpum61Mairz6MoTuX0B3/wB9Hq1aujJUuWRKeddlr0H//xH3XcZiNRZ99HURS96U1vik488cSqb6vxqLPff/CDH0Rbt26NVqxYES1ZsiQ6/vjjoxtvvLGO22wU6uzzs88+O1qxYkU0MjISHXHEEdHnPve5Om6xcSirzy+99NLUcnbv3h29+c1vjpYsWRItWbIkevOb3+yUFme+oc4+f+tb38qec8stt9Rzsw1BXX1+/fXXG+taqKir73/0ox9Fp556arRixYpodHQ0OvDAA6Pzzz8/evDBB2u822agzjUGow6hOoiiBewh7+Hh4eHh4eHh4eHh4eFRAAvbmcLDw8PDw8PDw8PDw8PDowC8UO3h4eHh4eHh4eHh4eHhkRNeqPbw8PDw8PDw8PDw8PDwyAkvVHt4eHh4eHh4eHh4eHh45IQXqj08PDw8PDw8PDw8PDw8csIL1R4eHh4eHh4eHh4eHh4eOeGFag8PDw8PDw8PDw8PDw+PnPBCtYeHh4eHh4eHh4eHh4dHTnih2sPDw8PDw8PDw8PDw8MjJ7xQ7eHh4eHh4eHh4eHh4eGRE16o9vDw8PDw8PDw8PDw8PDICS9Ue3h4eHh4eHh4eHh4eHjkxP8PO+c+SkoGsbkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_areturns.smoothed_marginal_probabilities[0].plot(\n",
    "    title=\"Probability of being in a low-variance regime\", figsize=(12, 3)\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
