{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example - FRET histogram fitting\n",
    "\n",
    "*This notebook is part of smFRET burst analysis software [FRETBursts](http://opensmfs.github.io/FRETBursts/).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In this notebook shows how to fit a FRET histogram.\n",
    "> For a complete tutorial on burst analysis see \n",
    "> [FRETBursts - us-ALEX smFRET burst analysis](FRETBursts - us-ALEX smFRET burst analysis.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - Optimized (cython) burst search loaded.\n",
      " - Optimized (cython) photon counting loaded.\n",
      "--------------------------------------------------------------\n",
      " You are running FRETBursts (version 0.7+46.ge31fadb.dirty).\n",
      "\n",
      " If you use this software please cite the following paper:\n",
      "\n",
      "   FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n",
      "   Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n",
      "\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from fretbursts import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lmfit version: 1.0.3\n"
     ]
    }
   ],
   "source": [
    "sns = init_notebook(apionly=True)\n",
    "import lmfit\n",
    "print('lmfit version:', lmfit.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tweak here matplotlib style\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif'].insert(0, 'Arial')\n",
    "mpl.rcParams['font.size'] = 12\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get and process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "URL:  http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\n",
      "File: 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\n",
      " \n",
      "File already on disk: /home/paul/Disk/Python/OpenSMFS/FRETBursts_notebooks/notebooks/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 \n",
      "Delete it to re-download.\n",
      "# Total photons (after ALEX selection):     2,259,522\n",
      "#  D  photons in D+A excitation periods:      721,537\n",
      "#  A  photons in D+A excitation periods:    1,537,985\n",
      "# D+A photons in  D  excitation period:     1,434,842\n",
      "# D+A photons in  A  excitation period:       824,680\n",
      "\n",
      " - Calculating BG rates ... get bg th arrays\n",
      "Channel 0\n",
      "[DONE]\n",
      " - Performing burst search (verbose=False) ...[DONE]\n",
      " - Calculating burst periods ...[DONE]\n",
      " - Counting D and A ph and calculating FRET ... \n",
      "   - Applying background correction.\n",
      "   [DONE Counting D/A]\n"
     ]
    }
   ],
   "source": [
    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
    "download_file(url, save_dir='./data')\n",
    "full_fname = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
    "\n",
    "d = loader.photon_hdf5(full_fname)\n",
    "loader.alex_apply_period(d)\n",
    "d.calc_bg(bg.exp_fit, time_s=1000, tail_min_us=(800, 4000, 1500, 1000, 3000))\n",
    "d.burst_search(L=10, m=10, F=6)\n",
    "ds = d.select_bursts(select_bursts.size, add_naa=True, th1=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting the FRET histogram\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start defining the model. Here we choose a 3-Gaussian model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name             Value      Min      Max     Vary    Expr    \n",
      "p1_amplitude         1     0.01      inf     True    \n",
      "p1_center            0        0        1     True    \n",
      "p1_fwhm            nan     -inf      inf     True    2.3548200*p1_sigma\n",
      "p1_height          nan     -inf      inf     True    0.3989423*p1_amplitude/max(1e-15, p1_sigma)\n",
      "p1_sigma          0.05     0.02      0.2     True    \n",
      "p2_amplitude         1     0.01      inf     True    \n",
      "p2_center          0.5        0        1     True    \n",
      "p2_fwhm            nan     -inf      inf     True    2.3548200*p2_sigma\n",
      "p2_height          nan     -inf      inf     True    0.3989423*p2_amplitude/max(1e-15, p2_sigma)\n",
      "p2_sigma          0.05     0.02      0.2     True    \n",
      "p3_amplitude         1     0.01      inf     True    \n",
      "p3_center            1        0        1     True    \n",
      "p3_fwhm            nan     -inf      inf     True    2.3548200*p3_sigma\n",
      "p3_height          nan     -inf      inf     True    0.3989423*p3_amplitude/max(1e-15, p3_sigma)\n",
      "p3_sigma          0.05     0.02      0.2     True    \n"
     ]
    }
   ],
   "source": [
    "model = mfit.factory_three_gaussians()\n",
    "model.print_param_hints()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previsou cell prints all the model parameters. \n",
    "Each parameters has an initial value and bounds (min, max).\n",
    "The column `vary` tells if a parameter is varied during the fit\n",
    "(if False the parameter is fixed).\n",
    "Parameters with an expression (`Expr` column) are not free but\n",
    "the are computed as a function of other parameters.\n",
    "\n",
    "We can modify the paramenters constrains as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.set_param_hint('p1_center', value=0.1, min=-0.1, max=0.3)\n",
    "model.set_param_hint('p2_center', value=0.4, min=0.3, max=0.7)\n",
    "model.set_param_hint('p2_sigma', value=0.04, min=0.02, max=0.18)\n",
    "model.set_param_hint('p3_center', value=0.85, min=0.7, max=1.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we fit and plot the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4fPhwnm34+/tDJpNh2LBhYtmGDRsgk8my/bRu3Trb+kqlEocPH8bkyZPRpk0blClTBpaWlrCyskK5cuXQpUsX/PLLL0hMTMz359PFiRMnxPHM7ezs0Lt37zzrq3/uwMBA8eaRusTERMk43Orr5KRPnz7i+L6xsbE4deqUtuHnqkWLFuLrp0+fFri9TO7u7uLrhISEHOusX79e/A74+/trbPPp06di/cyL+KKkfv36kuXM+SpyktNnT09Px6ZNm9ChQweUK1cOFhYWKFu2LLp3747du3drFYP6fpXbeydPnhTL2rRpk+M+uX79+ly3oVAo8Mcff6Bv377w9vaGvb09zM3N4e7ujpYtW2LKlCm4cOGCVvHqw4QJE+Dq6goAePjwITZs2GCwbReWrOM+P3782EiRvKV+nBAEAc+ePcuxXmEcJ9Xp+1xX1KjPAZJ5Ds1NhQoVJONZ79mzpzBD09lPP/0kJv/s7Owkiaj8atCgQa7vtW7dWjx+ZY65HRsbix9//BE+Pj5wdXUVvy8jRozArVu38rXtixcvYtiwYfDy8oKVlRVKly6NFi1a4LfffsvXdciqVavw+vVrABCPtXndtMpNhw4d8r0OFZz6+eWDDz4Qzz2GkJGRgUuXLonLnTt31rhOly5dxNe7du3KsY4u11YVK1YU18nr2jExMRErV67EBx98gAoVKsDa2hqWlpYoV64c6tWrh549e2L58uV4+PChVts1hKCgIACAm5sbqlSpInlPn/Mz7N27F927d0fFihVhaWmJ0qVLo3379li/fr1k3pacFObf7F2h7b585coVKJVKAG8fBNF07JXL5ejYsaO4nNN+V1j7MhHljpNBExHlw8uXL9G9e/dsT08/efIET548wY4dOzBy5Eitnxb99ddf8eWXXyIjIyPbe2/evMGbN29w7949rF+/Hm3btsWOHTv0Punt8+fPUb9+/Vyfmn358iVevnyJQ4cOYf78+fjzzz/Rvn17vcaQ1fHjx8XXzZo10zgRV+PGjWFtbY3k5GQoFAoEBQWhXbt2kjrnzp0Tn6axsbHROMGZhYUFmjZtKt5QO3bsGNq2bavLxxGp38jK6W+ui/T0dDx48EBc9vT01Eu7RZ2VlZVkWaFQaL1ueHg4PvroI5w5c0ZS/urVK+zfvx/79+9H586dsX37do2TSRam3bt344svvsDLly+zvRcVFYWoqCicOXMGP/74I1asWCHpMVNYbG1t8c033+Drr78G8PYp+sGDB+t0866ocHJykizHx8cbKZK3st7wzu1YURjHyUz6PtcVNQqFQvIkbU4J+Kxat24tOR+o93orChQKheRmzuDBgw12Y/bs2bPo169ftmNV5vdlw4YNWLFiBUaNGqWxrW+++QaLFi2S3PxTKBSIiIjAuXPnsGzZMq0TPStWrBBf+/n5oW7dulp+IjK2+/fvS5Lo6j2yDCEmJkZy7NXm2kq9zuPHj/H06VODPagRFBSEjz76KMfrhczr+OvXr4sJVqVSCVNTw90KWrFiBR49epStPPMhHgsLC0yaNEny3tmzZ8XXu3btyva0+pgxYzT2FExISMCQIUMkiWUAiIiIQEREBI4cOYLly5dj7969KFOmTH4+EmkpP/tyRESE+NrV1TXXXprq1Pc79QfKMhW3fZmoJGCigYhIS7GxsfDz88O9e/fEsipVqqBx48YwMzPDtWvXcO3aNaxdu1brbvmvXr0SL34qVaqEGjVqwM3NDZaWloiLi8PNmzdx+/ZtAG9vbLRr1w7nz5/PdkOpXbt2sLW1xd27d8WLrOrVq0uewFSPWV1SUpKYZHByckKtWrXg6ekJW1tbpKWl4cmTJzh//jwUCgWio6PRpUsXnDx5slC7MYeEhIiv83qqMpOpqSnq1KkjXsiGhIRku4Gm3madOnW0+gerQYMG4o0l9fV1dfPmTfF1+fLlC9weAHz33Xfi38/a2hqdOnXSS7tFXdYeDOq9OvKiVCrRo0cPXLhwASYmJmjRogWqVKmCxMREnDp1CmFhYQCAQ4cOoXPnzjh27JhON9HHjRsH4O2Tz5mx9ujRA2XLls1Wt0aNGtnKFi9ejK+//hqCIAB4e/O5bt26qFmzJmxtbREbG4ubN2+Kx6P8JFoKaty4cViyZAnCwsIQGhqK1atXS4ayKm4yn3jOpM1QaIVJ/TgB5H6sKIzjJFA457qi5t69e+KNbJlMlq2HVE7Uf8f6OB+oS0lJwf79+3H9+nW8fv0atra2KF26NJo1a4a6deuKwzbl5fz580hJSRGX+/Xrp9cYc3Pr1i1MnToViYmJYk8rFxcXvHz5EseOHUNKSgoyMjLw6aefonbt2nk+mfz1119j0aJF4rKdnR3atGkDd3d3vHz5EsePH8e9e/fQpUsX9OjRI8+4nj17JumdZOhhd6hg1JNmrq6u+OCDD/K1/pUrV7B37168evUKMpkMLi4uqFOnDlq0aAF7e3uN62eeezPl1eMpN7dv3zbIzcnnz5+jY8eOYo9WMzMz+Pj4oHLlyrC2tkZSUhKePn2K69evGy2Rvn37dkkPz6xevHiBxYsX5/r+6tWrs5V17dpVY6Jh2LBhYpKhcePGqFWrFlJTU3H+/Hnx+HDp0iW0bdsW586dg7OzsxafpviLi4vDjh07cOfOHbx58wYODg4oW7Ys3n///RyvSQsiP/uy+n6nyz4XGhqKxMREybVJcdqXiUoKJhqIiLT05ZdfijdeLC0tsXbtWnz88ceSOkeOHMGAAQPw888/azUmctWqVfHbb7+hZ8+eOd6ABIAbN25gxIgRuHz5Mq5du4affvoJ06dPl9QZNGgQBg0ahPXr14uJhiZNmuD333/XGIOVlRU+//xzDBo0CI0aNcrxhkZ8fDzmzZuHRYsWIT09Hf7+/rh7965WNz90oX6DS9sn9CtUqCDeQLt7967e2syUU5v5oVKpsGnTJnE5tyeJNcnIyEBMTAyuXLmC1atXS57SWrRo0TvzT9L//vc/8bWZmRkaNmyo1Xo7d+5EWloa6tevj23btqFq1arieyqVCosWLcKUKVMgCALOnj2L7777TjJmvrYy971bt26JiYbx48dr9eT0wYMHJUmGtm3bYtmyZahevXq2uk+ePEFAQEC2p/ILk5WVFb799lsxubBgwQKMGDEiWy+T4kL9qUkA8PLyMlIkb6kPpVWrVi2ULl06x3qFcZwECudcV9So/+7c3d1haWmpcR3180FsbCyioqLg5uaml3guXryI7t275/iel5cXJk+ejE8++STPGyTqc4uYmJhofUwsqEmTJiE9PR2LFy/GF198IUniP3/+HF26dMGtW7egUqnw7bff4tixYzm2c+LECcmNxoEDB2LFihWSm8KRkZEYPHgwAgMDsXz58jzjyjrXSpMmTXT5eO+E2NhYzJw5U69tVqlSBePHj9dpXUEQsHnzZnF54MCB+T7O9OnTJ8dyS0tLDB06FLNnz8712AoALi4ukMvlYkLy6dOnqFatWp7bzDrMXUhISL4TJLpYvHixmGRo2bIltm3bluPT+enp6Th79izWrFmj083W4iYoKAhpaWnw8vLCtm3b0LhxY8n7GzduxOjRo5Gamop79+5h4sSJBh0O8sKFC5L/C/ShS5cukmF/crNv3z7s27cvx/fq1KmD6dOno2/fvgWOJ7/7svo5NSoqCsnJyRp7Najvd4Ig4O7du2jUqJFYVpz2ZaISw1izUBMRFSd3794VAIg/mzdvzrXu2bNnBblcLqn/5MmTAm0/Li5OKF26tABA8PDwENLT03OsFxAQIG5z6NChBdpmTj799FOx/YMHD+q9/Uzu7u7idnbv3q3VOp9//rm4Tv/+/bO937dvX/H9L774Qqs2d+3aJa5TunTpfH2GrH777TexLblcLty8eVPrdf38/CTfp6w/Dg4OwtatW/NsI7/fjSdPnoj1PT09c63n6ekp1gsICND6M+UVX17bEwRBuHXrlmBrayvWHzBggNZtAxDKlSsnREdH51p//vz5Yl1LS0shJiYmx3rqbebG19dXrHP8+PE84xQEQVAqlULFihXFdbp27SoolUqN6xUW9d9dkyZNxPLU1FTJ337hwoXZ1g0JCdHqd5ST48ePG2Td9PR0oWnTpmJdc3NzIT4+Pl/b06dr164JJiYmYjzLly/PtW5hHCcNea7Lzz6vb8uXLxe3/d5772m1TkxMjOSz3r17t0AxZP2eavrp3LmzkJCQkGt7I0aMEOt6eXkVKDZN1I9rAIRVq1blWvfmzZuCTCYTAAgymUx49epVjvWaNWsmttehQwchIyMjx3opKSlCvXr1JNufNWtWtnrz5s2TnHNza09X2p4jDaUg+5P6Z9HXj6+vr86f5ciRI5K2rly5otV66uckTT+lSpUSzpw5k2d79evXF+uPHz9e4/YHDx4s2cbUqVOz1dHle6P+uXI6xjZs2FB8/8GDB1q1WRRkXpfb2tpmu87Zs2eP+Jn+/vtvrdvMely1sbERHj58mGv9zZs3S+rfvn07W53C+JsJQvZrU3385HQs1HV7/v7+Bb7+zO++/Pr1a8l1xZ49e/Ksn5GRIblmBiAcPnw4W73C2JeJKHecDJqISAvr1q0TXzdt2jTb053qmjdvnuf7unBwcEDPnj0BAGFhYbhz545e29eW+mSiR44cKbTtqE/2qO1T0ur1cpossjDa1Nbt27cxdepUcXnEiBGoXbu2zu2p69q1K+7fv4/+/fvrpb2iKvOJs4ULF6JFixbi36N69epYunRpvtqaO3dunnOdTJ48WewirVAo9P7EWV527dolThxoY2ODgIAAg46jrC1zc3PJE7ALFy7MdSLyoio6Ohr9+/eXjNU/YsQIo83LkZSUhEGDBonD6dWsWRMjR47MtX5hHNOMfa4zlIL+7rK2oatSpUph/PjxOHDgAJ49e4aUlBSkpKTgwYMHWL16NWrWrCnWPXToEPr375/rxKWxsbHia0dHR43bjo2NxWeffZbnz8GDBzW2U6dOHYwePTrX92vXri3OiSQIQrZ5PwDgzp074qSwAPDLL7/k2mPS0tIyzyFWMqn/Puzt7QutBybp38aNG8XXderU0WpoOOBt78bu3btj3bp1uHHjBuLj46FUKhEZGSnuP5lP8kdERKBr166S3k1ZdevWTXy9bt26HOcYyHTt2jVs375dUmaoc6L6cEj66mVlCJlDKb3//vvZrnMy526Qy+V4//33dd7GV199lefwSh9//DFatGghLq9Zs0bnbRUHXl5emDJlCo4cOYJXr14hNTUViYmJuH37NpYsWSLpubd+/Xp8/vnnBdpefvdlR0dHtGzZUlyePXs20tLScq2/cuXKbJNt57TfFZd9maikKHr/uRIRFUHqk24OHjxYY/0hQ4bk++ZkZGQkzp8/j5CQELx+/RpJSUmScSUvX74svr527Rrq1KmTr/a1oVQqceHCBVy/fh3h4eFISEhAenq6+L76hda1a9f0vv1M6uPNazs+vvq8FerjVBdmm9qIi4tDjx49xJtS3t7eWLJkSb7a6NGjhzhsTlpaGiIiInDp0iWEhYXhn3/+QbVq1TB9+nR8+eWXJaI7/LNnzzR+Drlcjh49emDZsmUoVaqU1m1bWFho7A5uZmaGgQMHYsGCBQDe7v+6DgGRX+pDQg0YMMBgk7nqYujQofjhhx/w4MEDREdH4+eff8aMGTOMHVY2WeePSEtLQ2hoKE6fPo3k5GSxvFq1apg/f76hwwPw9gbssGHDcOvWLQBvj1F//vlnnkMMFMYxzRDnuqKgoL87QPdzQqZGjRohNDQ0x+1XrlwZlStXhr+/P8aNGyfe/Dpw4AC2bNmS499G/fxsY2Ojcfvx8fEaJ/N2dXXVOAzHRx99pHFb9evXx8WLFwEg200hQPq98/HxyXGYOHVt2rRBuXLl8OLFi1zrqP8+iutcIoZSsWLFbOOYG0tSUhJ27dolLudnEugLFy7kOHykm5sbOnXqhE6dOmHIkCHo1asXFAoF4uLiMHbs2BwnkAWAzz//HD///DPevHmDxMREdOjQATt27Mg2LNmpU6fQv3//bDdEC3qM0FaFChXw4MEDAMCyZcswbdo0g2y3IEJCQsSJf3MaUjIzCVG3bl2tEqe5GTJkiMY6Q4cOFYdQVD8WFTZ/f3/4+/sbbHs9evTAkCFDsiVdzc3NUbNmTfHhhoEDB+Kff/4B8PZG/sCBAyU3/7Wl6748ffp08e9//fp1dOvWDQEBAfDw8BDrqFQqrF69GhMmTMi2fk77XXHZl4lKCiYaiIg0EAQBN27cEJe1Gee3cePGkMlkWv3jdufOHXzzzTc4dOiQ+CSrJtHR0VrV01ZKSgoWLFiAlStXat22vmNQZ2lpKd4AzOtJFnWpqani65yeUFUfg1tfbWqiUCjQvXt3PHz4EMDbpyp37tyZ75seOU20q1KpsHfvXnz++ed49eoVJk2ahJCQEKxduzbfcRZH3bp1w7p16/L9D2idOnW0ugmnPmHp1atX8xueztSfrm/Tpo3BtqsLExMTzJ49W3yqffHixfjss88MOl+ENjTdTAWA9u3bY/369Uab42TKlCn466+/xOUVK1agbt26ea6j7+NkYZ/ripKCng8A3c4J6rQ5D5iZmWHlypW4f/++eOPlxx9/zDHRoN4TJykpqUCx5Yc2Dz2o9yB78+ZNtvfVH1zQ5nsnk8nQpEmTPBMN6r8PffQ+IcPYuXOn+P01MTHJV68pbY7fnTt3xu+//y72Fjt27BguX74sGdM9k4uLCzZu3IiePXtCpVLh8ePH8PHxgY+PD2rXrg2VSoXr16+L1whubm7w9vYWz+OG6h3Xr18/MVny7bffIjAwEB9//DHat29fZCewPXHihPg6a6IhPj4e169fBwC0atVK5224urpqnCwakF7v3bp1C0qlsljOPaSJNtfLdnZ22LFjB+rVq4f79+8DeHvO0SXRoOu+3K5dO0yaNAmLFi0CABw+fBiVKlVCq1at4OnpicTERJw9exahoaEA3vauvHv3rtiLLaf9rrjsy0QlBfuQEhFp8ObNG8mNCPVupbmxt7eHg4ODxnqHDx9GgwYN8M8//2idZAD024Xz9evXaN68OebPn5+v5EFhdiNVvwGj7VMk6vVyuoFTGG3mJT09Hf369RO7f1taWmLfvn2oV69evtrJjVwuR69evXD69Gnxn4d169Zh69atemnfmOzs7DBu3Djx55NPPsGHH36IcuXKiXX27t0LX1/ffCe8tNl/AaB8+fLi66ioqHxtoyAyn/ADgEqVKhlsu7rq37+/OAzYmzdv8NNPPxk5Is1MTU3h4uKChg0bYuzYsThz5gwCAwNznDzTEBYuXIiFCxeKyz/88AOGDx+ucT19H9MK81xX1BT0d5e1jcIkl8slw5Tdvn0bz58/z1ZP/SZrXFycxnYzn2LP+uPr65uv+LT5+6vftFMqldneVz/G6nKMzon67yM+Pj7XIaeoaFEfaqVTp055Ttisq2HDhsHT01NcPnToUK51u3Xrhn/++UfsXSgIAi5evIg//vgD69evF29Ment743//+5+k51NBnsTPjxEjRkgmvz558iRGjx4NLy8vlC1bFv3798fq1asNei2jSWaiwc7OLttT5WfOnBH/J8rv8UidLseSjIwMvH79WudtlgRWVlb45ptvxOXjx49nS7RroyD78k8//YTvvvtOPHcoFAoEBgZizZo12Lp1q5hkaNeuHfbs2SNJYOe23xWHfZmopGCPBiIiDbI+CWdtba3VejY2Nnn+sx8VFYV+/fqJF29eXl749NNP8f7778PLywuOjo6wtLQUh5CZPXs25syZAwB6/Yd53Lhx4tOEFhYW8Pf3xwcffIAaNWqgdOnSsLKygomJCYC3Qx54eXnpPYasXFxcEBkZCUB64zUv4eHh4uucnmpTf6JSX23mRqVSwd/fH/v37wfw9sbmX3/9lWP38IKqVKkSvvrqK3HImsWLF2PAgAF6344hOTs74/fff89Wnp6ejo0bN2LcuHFQKBS4ceMGhgwZotU44pnys/9mSklJQUZGhrgfFKbiNtyHXC7H3Llz0atXLwDAr7/+iokTJxapcaKL8tP2q1atkvxDP2XKFMlyXvR9nCysc11RVNDzAZC/c0JBtWzZEmZmZuJN+pCQkGw32tVvnIaGhiI5OVnrv2FB6GO4PvXvni7H6JyoP8mtUqlw79491KhRQ6f4yDBCQ0MlQ9fkZ9ik/JDL5WjTpg3Wr18P4O3+lJfOnTvjyZMnWLt2LQ4ePIhbt24hJiYGdnZ2qFq1Kvr27YtRo0bBxsZGMjSY+sMRhUkul2PHjh3YuHEjlixZIumZ9urVK2zfvh3bt2/HuHHjMHToUCxcuNBgx6/NmzdLempm+vfffwG8vamddfib4OBg8fXu3buzDW01fvx4VKlSReO2dT2WJCQkwN3dXat1Syo/Pz/xdXJyMp49e4aqVatqvb4+9uVp06Zh0KBBWLlyJY4ePYpHjx4hPj4ebm5uqF+/PoYOHYo+ffrg2bNnkof18trvivq+TFRSMNFARKRB1pt9ycnJWg29omnogjVr1ohPYNSvXx+nTp3K88ZiYfQgePnyJbZt2wbgbbfWwMDAPLspG2oyrGrVqon/+D179kyrdTKfbgGQ4/jO1apVE1/rq83cjBkzBlu2bAHw9h/AjRs3omvXrlqvn1/t27cXEw1Xr15FSkpKgYf1KIpPf5qammL48OGwsLDAoEGDALx9EnHDhg1a/xOjPiZ/XtT3X/VkW2Gzs7MTn6YrLsN99OzZEw0bNsSVK1eQlJSE77//Pt/zkLyLNm/ejLFjx4rLY8aMwffff6/1+vo+ThbWua4oUj8fREZGQqFQSIZTyon6787Z2dmgyTQzMzO4uroiLCwMABATE5OtjvrQFhkZGbhy5YpOw10Yg/p3T5djdE6yTiB74cIFJhpyERsbK+k1ow9VqlTJ99xGmzZtEhPDTk5Okglc9U19vPec9qesbG1tMWHChBzHhM8UFRUlORZnToJuCDKZDEOHDsXQoUPx6NEjnDx5EqdOncLp06fx+PFjAG8f1li3bh1OnDiBoKAggxzDjhw5gg0bNuT6fmRkZJ7DG27evDlbWZ8+fbRKNOh6LNHHMDnaXENfuHBB73McdenSReO8OtpQ3z8A7fYRdfralytUqCDOl5abS5cuia+dnZ01DpdV1PdlopKAiQYiIg0cHBxgbm4uDikRGhqq8eI8Pj4+x3GI1ak/oTN9+nSNTy9reyMpP44dOyZeCHbp0kXjWKiFEUNOatSogb179wLQbnz89PR0cRLVzPVzajPTzZs3kZ6eDlPTvE+D6k9VaXuDYuLEiVi9erW4vHLlykLvYaA+Jr5KpcLr16+zJRrUh65Qn+A7N5q+v8b08ccfY8eOHWKPkRkzZqBfv34abxQC0puF2tYz5ITMpUqVEhMNT548QdOmTQ227YKYN2+e+M/tihUr8NVXXxk5oqJt165d8Pf3F29GDB48WKu5JNTp+zhZWOe6oqhatWqQy+VQqVQQBAHXrl3TuK/pcj7QJ/WbYTklgJo2bQpLS0txouvt27cXm0SD+vdM22N0TsNHqatYsSK8vLzw5MkTAMCff/5p0IlXixNtJgbPL19f33wnGtSHWunfv3+2Cdj1SdP+pAv1J7jt7e1Rq1atPOtr29tO2xvmmby9veHt7S0OwffgwQOsXr0aP//8M9LT0/Ho0SPMmTMnx56jJYku13smJibZ5pnK7/UzoN01dEhIiN73O1dXV70kGrImX/K7jxhyX1bf79Tn29BXm9rsy0QkxTkaiIg0kMlkeO+998TlnLoAZ3Xx4kWN/0C8evVKfK3pAiYjIwNnz57VuN38DmGQnxgAiPMNFDb1SXCDgoI0TtZ56dIl8aLY0tIyxwvN5s2bixe6SUlJuHz5cp5tpqamSv7Wbdu21Rj3t99+i59//llcXrp0KUaNGqVxvYLKfMo1U05d4tWf0NLmyaSbN28WPLBCtHDhQrGXwfPnz7Fy5Uqt1rt586ZWT2Cr/+0bNGigW5DI/z6pfrPz2LFjOm/X0Dp37iw+QaxQKDB//nwjR1R0HThwAAMGDBC7+vfq1QsBAQH5/q7o+zhZWOe6osjS0lKyr6lPTJqbzMmYAe3OB/r05MkTxMfHi8tZnzYF3n4m9Z5dmzZtyvdTqMaiPneRNt87QRBw4cIFjfXGjBkjvj5y5EiRP6+9y86fPy9OPgsU3rBJmdSTszntT7pQv7k6cOBAjT0h1ffp3KSkpIiT3OqqSpUq+OmnnzB37lyxLPNBjcK2fv36bPPAZE7EbWVlhdTUVMl7R44cEdfdunVrjvPIaDsMaXR0NB4+fKixnvoxp3bt2tkmgla/fn79+rXGc15oaKhWf9uiLOvDC/nZRwy5L6empmL79u3i8uDBg/XSbn73ZSKSYqKBiEgL6jd0curGm1Ve3YQzyeX/HYI1Pa20d+/ebOND50T9ie6cJlwsSAzJycmSC6/C1Lp1a3GCyfj4eOzevTvP+pnj7AJvhxHK6ckbW1tbyZij6uvkZPfu3eJQUU5OThp7e3z33XeS7r1z587Ns1uuPv3zzz/i64oVK+b4ZH/m3BoAcP36dY3/KO3YsUN/ARaCatWqoX///uLyTz/9pNVkdampqRo/m1KpxJ9//ikuq+//+ZXffbJz587i623btuV7smtjUk8urFu3TnySmP5z7Ngx9OnTR/wudO7cGVu3btXpn9jCOE4WxrmuqOrRo4f4WtP54MWLF5JeiOrrGsIff/whvra3t0f9+vVzrPf111+L36X4+HiMHj3aIPEVlPr37vLly7h7926e9Y8dO4YXL15obPeTTz4RJ/EUBAH+/v5aHYezCgwMzPc6xUluE4MX5Eeb5J069WNJ9erV0aRJEz1/yv/cu3cP586dE5cLMuFwpqCgIMl8UZ988onGdeLi4jT2FD59+rRk/PmCUB/CU9u5aQpD5kNLTZo0gbm5ueS906dPi6/10SNLm6GJ1L97OV3v2dvbiw/wJCcnS26i50Tb62d/f3+973ezZ8/WatuaBAQEiK9r1KiRr2G2DLkvL1myREzEubu7o2fPngVuU5d9mYikmGggItJCZvdj4O2TGpnj7+fk3LlzkpuUualUqZL4et++fbnWi4qKwsSJE7WKU32Cy5cvX+YrhgMHDuTZJfirr74y2D8mZmZmkp4AM2fOREpKSo51b926JblJNG7cuFzbVR8PPSAgALdv386xXnJysmS84k8++STPYZZ++eUXTJ8+XVyePHmyOGeCLvLzFGpwcLCk+3vv3r1zrFejRg3xqaywsLA8b5wcOHAABw4c0DoGY5k+fbqYLHv16hXWrVun1XozZszI83e8cOFCcRI49fkgdJHffbJXr17ipK6JiYkYNmyY1l31jc3X11dM5imVSsnTk/T23NCtWzdxaBtfX1/s2rUr240WbRXGcbIwznVF1dChQ8Vky71797B27dpc606ePFm82desWbMC9XIC3p5jtJ0H5/z581i0aJG43K9fv1zPR97e3vjuu+/E5d27d2PkyJFF/hhSs2ZNSQ+TCRMm5Pr7USgUmDRpklbt2tvbS4YyDA4Oxocffqj1vCJpaWmYN2+eJAFM+pf1qWRdnoDWdk4jhUKBYcOGifuzs7Nzgf++ERERGDJkiPgAx4gRIyS9dPKyatWqXN8TBAE//vijpCynpIO2DySoDxFkyDlm1EVERIg36nNKJGQmGipVqoSyZcsWeHuLFy/Go0ePcn1/y5Ytkh7jmb0tsmrcuLH4Oq/E9IsXL/I115Kh5GfOrz179kjO/fm5BtbHvqytM2fOYN68eeLy4sWLdb6eylSQfZmI1AhERKSVwYMHCwAEAIKlpaWwefPmbHWOHDkiuLq6CgAEMzMzsf6TJ0+y1V25cqX4voWFhbBp06Zsda5cuSLUqFFDACDY2NiI9WfNmpVjjOHh4ZIYnz17ludnio2NFaytrcV1Pv74Y+H169eSOm/evBFGjRqVLQZPT8882y6o6OhowdHRUdxehw4dhOjoaEmda9euCRUrVhTrtGnTRmO7LVu2FOtXrFhRuHHjRrbttm/fXqzj7Oyc7Xeibt26dYJMJhPrjxs3TqfPq65MmTLChAkThCtXruRaJykpSVi2bJng4OAgbtvJyUmIjIzMdZ3hw4eLdcuXLy/cvn1b8r5KpRI2btwoWFtbCxYWFlr9rT09PcV6AQEB+f2oooCAAJ2+W/379xfXq1ChgpCWlpZn2+bm5gIAoUGDBsL9+/cl9TIyMoSFCxdK/p7Tp0/PdduZdfK6nPrhhx/EOh988IGgUqk0fqZ//vlHEkPbtm2FkJCQHOs+efJEmDFjhrBhwwaN7epC/XfXpEkTjfXPnz8v+b1o8zvKyfHjx42ybmEJDg6W7KtNmjQREhISCtxuYRwn9X2uy42u+7w+zZgxQ4zByspK2L59u+T91NRU4ZtvvpF8n06cOJFnm+rHxNzO1cePHxeqV68urFixItdjtkKhEJYtWyY579rb2wvPnz/Pc/sqlUr46KOPJDHXrFlT2Lp1q5CcnJzrek+fPhWmTJkiWFlZaYzf19dXrHP8+PE84xEEQZg1a5bGNo8ePSqJ+eOPPxbevHkjqRMRESF07NhRcizPq81MX331laRtT09PYf369UJSUlKO9WNiYoQ1a9ZI9pusnjx5otX3V72etr8vXRSF/UlXO3bsEGOXy+Uav+M5qVWrljBx4kQhODg41zpBQUFC/fr1JX+PX3/9Nc92ly5dKqxevTrH60CVSiUcPHhQ8PLyklxX5nXNmPX7YGpqKqxbty7btUF8fLwwYsSIbOfRwMBAQRAEIT09XaxrZWUljBo1Sjh+/LikPOtnV/8+jxo1Ksd66vtqYXyP/vrrr2yfJZNSqRT/J/H399epffXzf+YxwsvLS7h48WK2uhs3bpRc6w4aNCjXdjdu3Chpd+fOndnqBAUFCZUqVRJkMpnO58XCEhAQIDRu3FjYtGlTtuNqpvj4eGHu3LmCqamp5H+FxMRErbejj31ZEARh3Lhxwt9//y2kpqZme0+hUAi//vqrYGtrK26rR48eGtvU975MRLnjZNBERFpaunQpgoKC8PDhQygUCgwaNAhz5sxB06ZNYWJiguvXr4tjWk6YMAF79uzJs0u0v78/lixZgvv37yM1NRWDBw/GggULULduXVhaWuLWrVviPAJ169ZFx44dsXDhwjxjLFWqFFq0aIGzZ89CoVCgbt266NSpEzw8PMQnv729vcVxi52cnDBp0iTxyeMtW7bg0KFDaNKkCcqWLYuwsDCcOHECSUlJMDExwfLlywt93NxMLi4u2LZtG7p27Yr09HQEBgbC09MT7dq1g6urKx4+fIhTp06JT52ULVtWq6E+/vzzTzRu3BhhYWF4+vQp6tWrB19fX1SqVAlRUVE4cuSIOIyUqakpduzYIQ69kNXNmzcxatQoMQYbGxsIgoDPPvtMq884d+7cHOdTSEpKws8//4yff/4Zrq6uqFu3Ljw8PGBra4vk5GQ8ffoUV65ckTyVaWtri3379uX5lNqMGTOwfft2JCUl4fnz55LPHh8fj3PnziE0NBQmJiZYtWpVrk925WbmzJmSOSo0Wbt2LRo1apSvbWQ1Y8YM7NixAyqVCqGhodi4cSNGjBiRa/0+ffrg0aNHuHDhAmrUqIGWLVuicuXKSExMxKlTpyTzljRr1kzSU0UXvXr1wtSpUyEIAg4cOID33nsPzZs3l4z5279/f8nv4YMPPsD333+PKVOmAHg7TEjNmjVRt25d1KpVC7a2toiNjcWNGzdw7949AG+PT0VBkyZN0LVrV8lwXgR07NhRMjmkt7e3+PfVZPDgwbkOPVAYx0l9n+uKshkzZuDs2bM4duwYUlJS0K9fP8yfPx8NGjSAQqHAqVOnJHPgzJkzRy/DrADA3bt3MWbMGIwbNw5Vq1ZFzZo1xUlIX716haCgIMTFxYn1LSwssHv3bpQrVy7PdmUyGbZu3Yry5ctjyZIlAIA7d+5gwIABMDc3h4+PD8qWLQsnJycolUrExMQgJCQk23AgpUqVEuddMYS2bdti4sSJ4rFsy5Yt2L9/P9q2bQt3d3e8fPkSx44dg0KhQMWKFdG9e3f88ssvWrW9aNEiuLu7Y+rUqVCpVHj27Bn8/f3xySefoHHjxihbtizs7e0RExOD0NBQBAcHS54ct7W1LZTPTG+pD8vp5+en8Tuek8TERCxduhRLly6Fu7u7eN1kZWWFuLg4BAcH48GDB5J1Ro4cic8//zzPdu/evYtVq1Zh3LhxqFu3LqpVqwYbGxtER0fj4sWLkiG8PD09cfz48VyvGbOys7NDRkYGRowYge+++w7NmjWDjY0NXr16hVOnTiE+Ph6mpqaoVKmSuH+OHDkSPj4+aN26tXitmZKSgjVr1mDNmjWws7NDvXr1UKFCBTHOu3fv4s6dO+J23dzc9DbETn5lDptkYmKSbZ6gK1euiNff+hg2qVmzZnBxccHu3bvRpEkTNG7cGLVq1UJaWhqCgoIkPR2qVKmS5/XrgAEDsHjxYly/fh1paWno06cPGjRogHr16iEjIwM3btwQz4uzZ89GQEBAkTsvXrx4EYMHD4aZmRlq1KiBatWqwdHREenp6QgNDUVQUJBkGF0nJyccPHgwXxNB62NfBt7OqbNs2TLY29ujYcOG8PT0hImJCcLCwnDmzBnJHBgdO3bE1q1bNbZZmPsyEWVh1DQHEVExExoamu1pqKw/w4YNE1JTUyVPNeb2NMu9e/eESpUq5dleixYthBcvXmj1RKAgvO0FYW9vn2t7vr6+kvrp6enCkCFD8ozB0dFR2LNnj9ZP8OnT/v37BTc3tzzjq1+/vnD37l2t2wwJCRHq1auXZ5tubm7CP//8k2c7WZ+czu9Pbt8Ld3f3fLXTunXrXJ94z+rQoUOSXixZf+zt7YVdu3Zp/bdW/57n90f9yc6CPI2p/vSut7d3tif61NseOnSo8OrVK6FZs2Z5xtahQ4dcn/rKpF4/L9OnT89zW7n1BNm2bZtQqlQprX6Xq1evztfvTFv57dEgCIJw9epVSY8MbX5HWZW0Hg0FOU5o01NI38dJfZ/rclJUnsCOi4sT+vbtm+dnNTMzE7777jut2tO2R0N+vgP169cXrl+/nu/PdvjwYaFp06b52la5cuWEWbNmCfHx8bm2Wxg9GgTh7VOlX331VY7Hj8yfKlWqCLdv39a6TXXnzp0T2rRpo/XvwtraWhg7dqwQHh6erS1tz5GPHz+WtHnq1CmtYs2vorI/5VdERITkCeotW7bo1E5+rkXs7e2F33//Xat2P/nkE63a7NevnxAWFqaxvazfmy1btkg+v/qPqampsHr1amHLli3Z3lu6dKnYpvqT3Zp+6tatm+f14syZM8W6lSpV0up3lB9169YVAAiNGjXK9t5PP/0kbvvBgwc6ta9+bPX19RXi4+OFbt265fk7adiwofDixQuNbT9+/DjP/9lkMpnw7bffCiqVSufzYmFRPz5o89O2bVvh6dOn+dqGvvZlQRCEatWqaYzRwsJCmDlzpqBQKLRqU9/7MhHljj0aiIjyoXz58rh48SICAgKwZcsW3Lp1C4mJifDw8EDDhg0xYsSIfI31WrVqVVy9ehXLli3D7t27ce/ePaSlpaF06dKoU6cOBg4ciI8++ijP+QGyatCgAW7cuIHffvsNx44dw+PHj5GYmJjrZHImJibYsGEDPvroI6xevRoXLlzA69ev4eTkhAoVKqB79+4YPnw4ypQpI45bb0gffvghbt++jYCAAOzZswePHz9GXFwcSpUqhVq1amHAgAEYMGAAzMzMtG6zevXquHDhArZt24atW7fi9u3biIiIgKOjIypVqoSePXti+PDhcHV1LcRPlrunT5/ixIkTOH36NK5cuYKHDx8iMjISycnJsLa2hqOjI6pXrw4fHx/07ds3X+OHdurUCXfv3sWiRYtw+PBhPH/+HCYmJqhQoQI+/PBDjBkzBhUqVDDK31pXM2bMwM6dOyEIAh49eoQ///wTgwcPzrW+h4cHTp48ic2bN2PLli0ICQlBdHQ0nJ2d0ahRI/j7++c614Uu5s2bhxYtWuCPP/7A5cuXERERoXHydeDtWOxdu3bFxo0bcejQIVy/fh1RUVHIyMiAk5MTqlWrhvfffx99+vTJdXJYY6hXrx769OmDv/76y9ihvDP0fZzU97muKHNwcMD27dsxatQobNiwAUFBQQgLC4OZmRnKly+Pjh07YsSIEahRo4bettmyZUtcvnwZ586dw7lz53D//n1ER0cjJiYGSqUSDg4O8PT0RNOmTdGzZ0+0bdtWp+106NABHTp0wIULFxAYGIgTJ07g2bNniImJQWJiIuzs7ODk5ISqVavCx8cHvr6+aNOmjdgD0tBkMhkWLVqEjz76CMuXL8eJEycQEREBe3t7eHt7o2/fvhgxYgTs7e11ar9Zs2Y4duwYrly5gkOHDuHYsWN49uwZoqOjkZKSAgcHB5QpUwYNGzZE69at0atXrwL3Zrh586b4umLFipK5KOhtz5XMOUTs7e11nsz19OnT4v4UHByMiIgIxMTEID4+HjY2NnBzc0ODBg3g5+eHgQMHav13nTlzJpo0aYKjR4/ixo0biIiIEK+Ry5YtCz8/P/Tr10/n3pkDBw5E5cqVsWjRIpw9exbR0dFwcXFBq1at8NVXX8HHxwcAcP36daxbtw6CIMDX1xcdOnQQ24iJicGpU6dw8uRJXLp0CQ8ePEBERAQUCgWsra1Rrlw5NGzYEL1790a3bt3y3L/Vv68DBgzQ6TPl5s2bN2L7ec3P4OHhgcqVK+tlm3Z2dti7dy927dqFDRs24Pr164iIiICDgwPq1KmDjz/+GEOHDoWJiYnGtry8vMT/r3bv3i32SC9TpgxatmyJMWPGFOrExwUxYMAAVKlSBUFBQWJvjpiYGMTExEClUsHR0RHe3t5o1qxZtl622tLXvgwAO3fuRGBgII4fP44HDx4gMjISSUlJcHd3h5eXF7p27Yr+/fujQoUKWrdZ2PsyEf1HJgj/35eaiIiIqARav349hg0bBuDtxHR5TeRHRESkLxMnThSHZFm7dm2eQ/tRyff06VN4eXkBeDs8S1F6qEMQBLi6uiI2Nhb29vZ48uRJjsN7EhER5cU4j6sQERERERERlWDHjh0D8PZpaEPNcUWki2vXriE2NhYAMH78eCYZiIhIJ0w0EBEREREREelRTEyMOFTMjBkz8jUMJpGhHT9+HMDb4eQmTpxo5GiIiKi4YqKBiIiIiIiISI9OnDgBQRDg7e2d57xBREVBZqJhwoQJcHJyMnI0RERUXPGxCiIiIiIiIiI96t27NzgdIhUXf//9t7FDICKiEoCJBiIiKrALFy5g06ZNBW7n999/10M0RO+uX375BQ8ePChQG02bNsWgQYP0FFHxtHnzZpw/f75AbVSpUgXjx4/XU0Q0a9YsxMTEFKiNLl26oEuXLnqKiIiIiIiI1DHRQEREBRYSEoJly5YVuB0mGogKZs+ePTh58mSB2khMTHznEw1HjhzBhg0bCtSGr68vEw16tGHDBjx79qxAbbi6ujLRQERERERUSJhoICIiohLN398f/v7+xg6DiIiI3mEVK1bkcFpERFSiyQSe6YiIiIiIiIiIiIiISEdyYwdARERERERERERERETFFxMNRERERERERERERESkMyYaiIiIiIiIiIiIiIhIZ0w0EBERERERERERERGRzphoICIiIiIiIiIiIiIinZkaOwAq/hQKBW7evAkAcHNzg6kpv1ZERERERERERERERU16ejqioqIAAHXq1IGlpaVe2uUdYSqwmzdvonHjxsYOg4iIiIiIiIiIiIi0dPHiRfj4+OilLQ6dREREREREREREREREOmOPBiowNzc38fXFixfh4eFhxGiIDCslJQWnTp0CALRq1QpWVlZGjoiMRRUfh+gvhkvKXH/9A3J7R+MEVMRwXyHSTJ/7CY9JVFLxfEKkmaH2E55rqDjj+YTeZWFhYeLoNOr3dQuKiQYqMPU5GTw8PFCuXDkjRkNkWCkpKXB1dQUAlCtXjhcn7zDVGxuYW1lIytzLloXcwclIERUt3FeINNPnfsJjEpVUPJ8QaWao/YTnGirOeD4hekufc+1y6CQiIiIiIiIiIiIiItIZEw1ERERERERERERERKQzJhqIiIiIiIiIiIiIiEhnTDQQEREREREREREREZHOmGggIiIiIiIiIiIiIiKd6W9aaSIioneYzNIadp9OzFZGRGQMPCYREVFh47mGiIjUMdFARESkBzILC9h80MvYYRARAeAxiYiICh/PNUREpI5DJxERERERERERERERkc6YaCAiIiIiIiIiIiIiIp0x0UBERERERERERERERDpjooGIiIiIiIiIiIiIiHTGRAMREREREREREREREenM1NgBEBERlQSqN3GIGjtIUua2fDPkDo7GCYiI3mk8JhERUWHjuYaIiNQx0UBERKQXAoT4N9nKiIiMg8ckIiIqbDzXEBHRfzh0EhERERERERERERER6YyJBiIiIiIiIiIiIiIi0hkTDUREREREREREREREpDMmGoiIiIiIiIiIiIiISGdMNBARERERERERERERkc6YaCAiIiIiIiIiIiIiIp0x0UBERERERERERERERDpjooGIiIiIiIiIiIiIiHTGRAMREREREREREREREemMiQYiIiIiIiIiIiIiItIZEw1ERERERERERERERKQzJhqIiIiIiIiIiIiIiEhnTDQQEREREREREREREZHOTI0dABERUYlgYQmbAcOylRERGQWPSUREVNh4riEiIjVMNBAREemB3NIKdgOHGzsMIiIAPCYREVHh47mGiIjUMdGQRWxsLC5fvoxLly7h8uXLePbsGaKjoxEVFQWZTAYnJyfUrl0brVu3xpAhQ1C2bNl8tX/06FFs2LAB58+fx8uXL2FhYYFy5cqhY8eOGDFiBKpXr57vmENCQvDHH3/g8OHDePHiBVJTU1G2bFk0a9YMQ4YMgZ+fX77bJCIiIiLDUyqVePr0KZydnWFpadinQuvWrQtzc3ODbpOIiIiIiEoGJhqyGDJkCA4cOJDr+ykpKXj16hUCAwMxe/ZsTJ06FTNnzoRcnvd0F/Hx8Rg9ejS2b98uKU9OTsbr169x8+ZN/PLLL5gzZw6mTp2qdbwLFizA7NmzoVQqJeUPHjzAgwcPsHHjRgwYMACrVq2CnZ2d1u0SERERkeE9ffoURx8n41paqEG3G/PiEeYOA3x8fAy6XSIiIiIiKhmYaMhDqVKlUL16dVSoUAE2NjZITk7GgwcPcOnSJaSnpyMtLQ1z5szB06dPsX79+lzbUSqV6NWrF44ePSqW1a5dGw0bNkRKSgpOnTqF8PBwKJVKTJs2DUqlEjNnztQY38yZMzFv3jxxuUyZMnj//fdhaWmJK1eu4Pbt2wCArVu3IiYmBgcOHICpKf/kREREREWZSzlveFSuZewwiIiIiIiItMa7zlm0bt0a3bt3R7t27eDl5ZVjnfDwcIwfPx47duwAAGzYsAEffvghevfunWP9efPmiUkGS0tLBAQEoH///uL7aWlpmD59On766ScAwKxZs+Dr6wtfX99c4zx69KgkyTB58mTMmzdP0t1969atGD58OBQKBQIDA7FgwQKtEhhERERERERERERERNrKe7yfd9CkSZMwatSoXJMMAFC6dGls27YNrVu3FstWrVqVY93IyEgsWbJEXP75558lSQYAMDc3x8KFC9GvXz+xTNPwSdOmTRNf9+/fHz/++GO2MXUHDBiApUuXisuLFi1CdHR0nu0SEREREREREREREeUHezToSCaTYfjw4Thx4gQAIDg4OMd6GzZsQFJSEgCgatWqGD16dK5tLly4EH/99RdUKhWCgoJw9epV1K9fP1u9S5cu4eLFiwAAExMTLFy4MNc2P/nkEyxZsgQPHjxAQkICNm3ahIkTJ2r7MYmISEuqhHjEfjNOUub84zLI7eyNFBERvcssU5PQ7/DPkrLtHSdAYWFjnICIiKjE4fUvERGpY4+GAnB3dxdfJyQk5Fhn79694mt/f3/IZLJc26tQoQL8/PzE5T179mhs08/PD+XLl8+1TZlMBn9/f41tEhFRAakykP78qeQHqgxjR0VE7yiZoILrm3DJj0xQGTssIiIqSXj9S0REaphoKICQkBDxtaenZ7b3FQoFzp8/Ly6rD7WUG/U6x44dy7HO8ePHdW7z3LlzSE1N1bgOEREREREREREREZE2mGjQ0atXr7Bo0SJxOaeJoO/duweV6u2TYzKZLMdhkLJq0KCB+Fo9kaFOvVy9vjZtZmRk4P79+xrXISIiIiIiIiIiIiLSBhMN+ZCSkoI7d+5g8eLFqF+/Pl6+fAng7dwLU6ZMyVb/3r174mt3d3dYWlpq3EaFChXE17GxsYiKipK8HxkZibi4OHE5p54UWVlaWsLNzU1cvnv3rsZ1iIiIiIiIiIiIiIi0wcmg83DmzBm0bNkyzzqdOnXCli1b4ODgkO29mJgY8XWpUqW02mbp0qUly7GxsZIkgXqb+W03M2kRGxur1TqZXrx4kef7YWFh4uuUlBSkpKTkq32i4kyhUOT4mt49qhz+/ikKBeTmPCYC3FeItGHsfUOhUPA6joo8nk+INDPUfsLrXyrOeD6hd1lhXfMz0aAjR0dHLFu2DAMHDsy1TmJiovjayspKq3az1lNvI6dlXdrN2oYmeU02ndWpU6fg6uqar/aJSopTp04ZOwQyItOUZDTMUnbi+AmkW1kbJZ6ijPsKUdF04cKFfD+QQmRMPJ8QaVaY+wmvf6mk4PmE3jXR0dGF0i4TDXkoU6YMxo0bBwAQBAEJCQm4d+8egoODERcXh48//hhr167FypUrUbVq1Wzrq2dEzc3NtdqmhYWFZDlrhilrllWXdvmkGhERERERERERERHpCxMNeahUqRJ+//33bOWvXr3Ct99+i/Xr1+P48eNo2rQpjh8/jrp160rqqc/JkJaWptU2U1NTJctZeyxknechLS1Nq7kf1NvVthdEpufPn+f5flhYGBo3bgwAaNWqFcqVK5ev9omKM4VCIT790KpVK632RyqZVPFxSNiyXFLWuk1ryO0djRJPUcN9hUgzhUKBBw8eGG37TZo0QaNGjYy2fSJt8HxCpJmh9hNe/1JxxvMJvcs0DZOvKyYadFCmTBkEBATA3t4ev/76K16/fo0BAwbg5s2bMDExEevZ2tqKr7XtRZC1nnobOS2npKRodTBUbzdrG5rkJ3FgZWWV70QGUUlhaWnJ7/87TJWmQEKWMitLS8j5nciG+wpR0cR9k4obfmeJNCvM/YTXv1RS8HxC75rC+r7LC6XVd8T3338Pe3t7AEBISAgOHToked/FxUV8HRERoVWb4eHhkmVnZ+dc29S13axtEhERERERERERERHpiomGArC2tkbz5s3F5bNnz0rer1atmvg6MjJSq1nsQ0NDxdfOzs5wc3OTvO/u7g5HR0dx+dmzZxrbVCgUiIqKEperV6+ucR0iIiIiIiIiIiIiIm0w0VBATk5O4uuYmBjJe9WqVYNc/vZXLAgCrl27prG94OBg8XWNGjVyrKNefvXq1Xy1aWJikuPE1UREREREREREREREumCioYDCwsLE11mHJLK0tETTpk3F5RMnTmhs7+TJk+Lrtm3b5linTZs2OrfZvHlzWFhYaFyHiIiIiIiIiIiIiEgbTDQUQExMDIKCgsTlnHog9OjRQ3y9fv36PNt78eIFjh49muO6ubV55MgRjTOFb9iwQWObRERERERERERERES6MDV2AEVJbGys1hMlC4KAzz77DKmpqQAACwsLdO3aNVu9oUOHYs6cOUhKSsK9e/ewdu1ajBw5Msc2J0+ejIyMDABAs2bN0KBBgxzr+fj4wMfHB5cuXUJGRgamTJmCzZs351h39erVuHfvHgDAzs4OQ4YM0erzERFRPplZwLpLz2xlRETGkG5ihmtVW2YrIyIi0hte/xIRkRomGtRs3LgRW7Zsweeff44ePXrA3t4+x3o3btzA5MmTcfjwYbHs66+/houLS7a67u7u+PLLLzFv3jwAwBdffAF7e3v07dtXrJOWloaZM2di69atYtn333+fZ6zff/892rVrBwDYsmULypcvj7lz58LM7L9/ILdv344JEyaIy5MmTYKrq2ue7RIRkW7k1tawH/OlscMgIgIAKM0scbRJX80ViYiIdMTrXyIiUsdEQxaXL1/G0KFDYWpqiurVq6NatWpwcnKCTCZDTEwMbty4gYcPH0rW6d27N2bNmpVrmzNmzMDZs2dx7NgxpKSkoF+/fpg/fz4aNGgAhUKBU6dOSeZ6mDNnDnx9ffOM08/PD9OnT8f8+fMBAD/88AM2btyIVq1awcLCAleuXMGtW7fE+u3bt8e0adN0+ZUQEREREREREREREeWKiQY16pMkp6en49atW5Kb9VnZ2dlh9uzZGD9+PExMTHKtZ2Zmht27d2P06NHYsWMHAODmzZu4efNmtnqzZ8/WOiEwd+5cWFhYYO7cuVAqlXj16hW2bduWrV7//v2xatUqmJryz01ERERERERERERE+sU7z2rGjBkDPz8/HDlyBBcuXMDt27cRGhqKuLg4AIC9vT08PDxQr149tGvXDr1794atra1WbTs4OGD79u0YNWoUNmzYgKCgIISFhcHMzAzly5dHx44dMWLEiBwnlM6NTCbD9OnT0bt3b6xduxaBgYF4/vw5lEolPDw80KxZMwwdOlQcYomIiIiIiIiIiIiISN+YaMiiatWqqFq1KsaOHVso7bdr107vN/5r1KiBxYsX67VNIiIiIiIiIiIiIiJtyI0dABERERERERERERERFV/s0UBERKQHqsQEvP5OOseO07cLILe1M1JERPQus0hLRvcTayRl+1qPQqq5tZEiIiKikobXv0REpI6JBiIiIn3ISIfy1rVsZURExiBXZaB8xMNsZURERHrD618iIlLDoZOIiIiIiIiIiIiIiEhnTDQQEREREREREREREZHOmGggIiIiIiIiIiIiIiKdMdFAREREREREREREREQ6Y6KBiIiIiIiIiIiIiIh0xkQDERERERERERERERHpjIkGIiIiIiIiIiIiIiLSGRMNRERERERERERERESkMyYaiIiIiIiIiIiIiIhIZ0w0EBERERERERERERGRzphoICIiIiIiIiIiIiIinTHRQEREREREREREREREOmOigYiIiIiIiIiIiIiIdGZq7ACIiIhKBFMzWLRona2MiMgYMuSmuFehXrYyIiIiveH1LxERqeF/G0RERHogt7GF05R5xg6DiAgAkGZuhX98Rxg7DCIiKsF4/UtEROo4dBIREREREREREREREemMiQYiIiIiIiIiIiIiItIZEw1ERERERERERERERKQzJhqIiIiIiIiIiIiIiEhnTDQQEREREREREREREZHOTI0dABERUUmgSkrEm99+lJQ5fP4N5Da2RoqIiN5l5mkp6BD0p6QssNlApJlbGSkiIiIqaXj9S0RE6phoICIi0od0JVLPnpCWjfnSKKEQEZmo0lEt9Jqk7GiTvsYJhoiISiZe/xIRkRoOnURERERERERERERERDpjooGIiIiIiIiIiIiIiHTGRAMREREREREREREREemMiQYiIiIiIiIiIiIiItIZEw1ERERERERERERERKQzJhqIiIiIiIiIiIiIiEhnTDQQEREREREREREREZHOmGggIiIiIiIiIiIiIiKdMdFAREREREREREREREQ6Y6KBiIiIiIiIiIiIiIh0xkQDERERERERERERERHpjIkGIiIiIiIiIiIiIiLSGRMNRERERERERERERESkM1NjB0BERFQimJjCrHa9bGVERMagkpvgeanK2cqIiIj0hte/RESkhmcAIiIiPZDb2sHl+9+MHQYREQAg1dwaOzqMN3YYRERUgvH6l4iI1HHoJCIiIiIiIiIiIiIi0hkTDUREREREREREREREpDMmGoiIiIiIiIiIiIiISGdMNBARERERERERERERkc44GTQRERERkZq0tDRcv37d4NtVKBR4/vw5UMrD4NsmIiIiIiIqCCYaiIiI9ECVnIzEDSslZbZDP4Xc2tpIERGRrq5fv46ZAYfgUs7b4Nt+/FKBSqUK3o6ZUoFWwfslZacadIPSzLLgjRMREYHXv0REJMVEAxERkT4oU5F8cI+kyHbgMAD8R4uoOHIp5w2PyrUMvt2YF490XldQqZCcEIfEmAgg6gXq3T8teX95ZBKSzW1gaWMHCxtbWDu4wN61NKzsnAoaNhERvYt4/UtERGqYaCAiIiIiKobSFCkIf3gLUaEPEPPiEWJePkF6mgIA4GQiA6o5S+o/uHAErzOEbO2YmVvCwtYBpWMbIikpCQ0bNoSdnZ1BPgMREREREZUMTDQQERERERUTyfGxeHr9PF6EXEHks/tQZaQjQ5mGjHQlMpRpUGWkQ5WRAbkJsiUa3kSFIT5DgNzUFHITU5iaWcDcyhrKNAWSX8XhwIFwnDhxAiYmJqhbty5atWqFDh06wN3d3TgfloiIiIiIig0mGoiIiIiIijBVRjpCb13Cg0vHEfH4DgRBBQBIT1MgPiocgpC9l0KGWfbL/AylEkplOpD6dtnM0grmVtmHt8jIyEBwcDCCg4Pxyy+/wMfHB127dkW7du1gbm6u3w9HREREREQlAhMNRERERERFUGpyIu5fOIJ75/9FSvzrbO+bmJpDQPYkg0wmg4mZWbZyC2tbmKcpIahUUKkyYG5pk+u2U1JSYGJiAnNzc1y8eBEXL17Ezz//jN69e6NPnz5wcXEp2IcjIiIiIqIShYkGIiIiIqIiRKlIxrXAHQg5fRCJb6JhZmENE9Psl+0yuRxm5pYAZDC1sICpuQVMzSwgNzGBg4ksW30bR2ekqc3R0HrQl3Au64XX4aF4dOUUypvGIywsDGlpaYiIiEBKSgpsbGzg5OQEW1tbxMbGYs2aNdiwYQP69OkDf39/ODs7Z9sOERERERG9e5hoICIiIiIqAjLSlXh57zouH9iMtOREKJLeQJWhgpV9BqztncR6ZuaWKF+7MSrU8kHE4zsIOXsItk5ucCxdHvYuHrB2cIablSVwZ7ek/cbd/RGVkoqk19FIiI2AS/lKsLZ3ho2jC0xMzTG+fVXUrl0bhw4dwueff47U1FQkJSUhKSkJZmZmcHFxgaOjI9LS0vDnn39i165dGDhwIIYNGwZr6+xDMBERERER0buDiQYiIiIiIiN7fucKLv29Aa/u30R6mkLyXmpSAqzsHOFesRqqNe2A8jUbwtTcAgDgWqEy6rTtCQtrW8k6VoqEbIkGzzpN4W5pl2ccVlZWePPmDTw8PFC6dGkkJCQgLi4OSUlJCA8PR0xMjJhwSE1NRUBAAPbv349x48aha9eukMvlevhtEBERERFRccNEAxERERGRkaQkxOHC3gA8vHQMyfGvka5Mg0wmg0z23w17MwsrNOzyMWq16pptfStbB73HZGJiAkdHR8TFxcHe3h729vZITU1FbGws4uLiEB4ejtevX6NUqVKwsbFBTEwM5s6di3379uHbb79FpUqV9B4TEREREREVbXzkiIiIiIjIwARBwKMrp7D7hy9w6+Q+JL6OhiojQ1LHwtoGjqXKoWrTdijlVcNgsQ0ZMgQHDx7EnDlzULNmzbexWFjAw8MDlStXFnszhIaG4sWLF0hPTwcAXL9+HQMHDsTq1avFMiIiIiIiejewRwMRERERkQGlJiciaPca3AsKREpCHCBI3zcxM4edcyl4N2yF2q27wblMRYPHaG5ujg8++ABdunTBpUuXsHr1aly7dg1mZmbw8PCAi4sLIiIikJCQgKSkJJQqVQoODg5IT0/H6tWrcebMGcyfPx8VKlQweOxERERERGR4TDQQERERERlI5NN7OLZhEaJD7yM9LU3ynlwuh7mlNcrWqI+W/T8zSoIhK5lMhsaNG8PHxwcXL17E0qVL8fDhQ5ibm6N8+fJITExEeHg4wsLCkJCQAA8PD5iamuLOnTsYOHAgvv76a3Tr1g0ymczYH4WIiIiIiAoRh04iIiIiIipkgiAg5Oz/sG/J14h4dDtbksHC2hZlq9dHHb+eaNxtWJFIMqiTyWRo0qQJtmzZgmnTpsHB4e3cELa2tvD29oaLiwsSExORkpIirqNQKDBv3jzMmzcPaVk+LxERERERlSxMNBARERERFaJ0ZRrO7liOM9t+R9LrKAjCf2MlyeRy2LuWRtNeo9B90hI4lS7aQw2ZmJigV69e2LlzJzp16gTgbRLC3d0dI0aMQO3atbOts3//fowYMQJhYWGGDpeIiIiIiAyEQycRERHpg9wEpuUrZisjonebIjEexzcuQvTzh7CwtoUiKR4ZSiUAwMzCEmWq1oXvoAl678EgyOSIdiidrUxfnJycMH/+fHTq1Alz586Fra0tfvjhB8jlcvz888/YuXOnpH5ISAgGDRqE77//Ho0bN9ZbHEREZES8/iUiIjVMNBAREemB3M4erss3GTsMIipC4qPDcCxgIRJiIwC87b1g6+yO+MhXsLR1QIPOA9Cg8wCYmJrpfdsKCxts6Pat3tvN6v3338f27dsRFxcHa2trAMCUKVPg4+ODOXPmIDk5Waz75s0bfPbZZ5gyZQp69epV6LEREVHh4vUvERGpY6KBiIiIiEjPIp/ew/GNi5GWkigpt7KxR+2PPkRp79ooX7OhkaLTLycnJzg5OUnK/Pz84OXlhUmTJuH+/ftQqVSwtraGSqXCggULEB4ejjFjxnCSaCIiIiKiEoKJBiIiIiIiPXp28wLObPsdKlWGpNzG0Q1t/b+GY6lyRorMsCpVqoRFixbBz88PUVFR8PDwECeR/uOPPxAREYHp06fDzEz/PTqIiIiIiMiwmGggIiIiItKTJ1fP4n+r5kDIyICti7v4xL5L2UpoM3QSrOwcjRugASUmJmLq1KlwcXGBSqXCq1evoFQq4eLiAplMhgMHDiA6Oho//fSTOOwSEREREREVT/qbEY6IiIiI6B324NIJ/G/lLKQmJSBNkYzkN7EAgHLVG6D9qG/fqSQDAGzbtg2PHj0CALi5ucHDwwNRUVGIioqCIAgAgAsXLuCzzz5DUlKSMUMlIiIiIqICYqKBiIiIiKiA7gX9i8DV85Ca/N8Nc0ViPBxLlYfv4Ikws7AyYnTGMXz4cPTr109cdnR0RPny5REbG4vIyEgx2XDjxg2MHTsWCQkJxgqViIiIiIgKiEMnERER6YFKkYKk3VslZTa9BkBu+e7dXCR619w5cwjH1y+EMlUhKbe2d0L9TgMgl5sYPCZTZSp87hyVlF2q6Yd0MwuDxSCXyzFp0iSULVsWS5cuhSAIsLW1haenJ54/fw5BEFCqVCnIZDLcvn0bY8aMwfLly2Fvb2+wGImISHe8/iUiInVMNBAREelDqgJJWwMkRTYf9AT4jxZRifbw8skckww2ji74cMKPKO1dyyhxmWWkofmNQ5Kya9VaGjTRAAAymQwDBw6Evb095syZA0EQYGVlBU9PTzx79gyCIKB06dKQyWS4e/cuPv30UyxfvhyOjo4GjZOIiHTA618iIlLDoZOIiIiIiHTw/PZlBK6emy3JYOvsju6TlhgtyVAUde3aFXPnzoVc/vbfDwsLC1SsWBGJiYmIiIgQh1G6f/8+PvvsMyQmJhozXCIiIiIiyicmGoiIiIiI8ins0R0c+G0a0lKSJeX2rqXR65tf4O5Z1UiRFV2dO3fGd999JyYbzM3N4enpiYSEBERHR4v17t69iy+//BKpqanGCpWIiIiIiPKJiQYiIiIionyIefEEfy+ZBEWSdPJiGydX9Pj6ZziXqWicwIqB9u3b48cff4SJydt5K8zNzVGxYkW8efMGsbGxYr3g4GBMnjwZSqXSWKESEREREVE+MNFARERERKSlpLho7F00EcnxryXlVnYO6PHVYjiX8TRSZMVHmzZtMG/ePMhkMgCAmZkZKlSogOjoaLx580asd/bsWcyaNQsqlcpYoRIRERERkZaYaCAiIiIi0kKaIgV7F32JhJgISbmFtS0+nLgQbhwuSWsdOnTAN998Iy6bm5ujQoUKiI+PF+drAIDAwEAsWrRIUkZEREREREUPEw1ERERERBqoVBk4tGw6okIfSMrNLK3Q5bP5KFPlPSNFVnz16dMHY8aMEZcbNWqEPXv2wMrKSlJvx44d2Lp1q6HDIyIiIiKifGCigYiIiIgoD4Ig4NLfGxEVel9SbmJmhnbDp8KzThMjRVb8DR8+HAMHDkSTJk2wfPlyNG/eHIsXL4aZmZmk3tKlS3Hs2DEjRUlERERERJqYGjsAIiIiIqKi7O65/+H++X9hYW0HudwUCbGRkAFo0mMEqjVrb+zw9CIjPR137twxyrbHjh0LExMTMbnQpEkTzJkzB9OmTRPrCIKA6dOnY/Xq1ahdu7ZR4iQiIiIiotwx0UBERERElIuwh7dw5cAWcdnM0gpOpcujYt3m8PlwiBEj06+48GfYFg64vDTsvwcxLx5h7jDAx8dHUt6hQwe8fPkSy5YtE8vS0tIwceJErF+/HmXLljVonERERERElDcmGoiIiIiIcpD4Ogqnt/4GQVBJylsO+AJe9ZobKarC41LOGx6Vaxl0m3n1pKhVqxYaNmyIwMBAWFpaAgCSkpIwdOhQzJo1SywriLp168Lc3LzA7RARERERveuYaCAiIiIiyiIjPR2ntvyC1OQESfl7fr1LZJLBWPLqSZGuTMOtu2EIi4qBuaUtzCzeJhYi4x/jk2k/oH7H/pDJdZ9yLrfeFERERERElH9MNBARERERqREEAZf/3oDYsKfizW0AKFejAd5r29OIkZVMOfWkiI8Ox8nNSxEX8RwObmWREBsBCxsbmJi+ncch9tUTRIXeR912vY0RMhERERERZaH7I0BERERERCXQvn378PRGEOKjw6BIjIcgCLB38UCLvmML9AQ9ae/V/euIi3gOAJDJZbB1dEVSXCwE1X/DWN04uguhty8bK0QiIiIiIlLD/5SIiIj0QgaZvYPkB5AZOygiyqfr169j2bJlb+dlEICkuBikJMSh5cDPYW5pbezw8kGGZAtbyU9xOiZVa9YBFd9rJi7LTU1hZeuApLgYCIIglp/bsRxxES+MESIREfH6l4iI1HDopBw8ffoU//77L06ePImbN28iNDQUiYmJsLOzQ7ly5dCsWTMMHDgQvr6+WrXl5eWVr+37+fnhyJEjWtcPCQnBH3/8gcOHD+PFixdITU1F2bJl0axZMwwZMgR+fn752j4REeWf3MERpbb8Y+wwiKgAkpKSMGDAAKSmpkrKy1SuAycPTyNFpZsUS1us6Pu9scPQmUwmQ9NeI/E6/DneRL5NJJhZWiIjPQ2pSfGwtHUAACjTFDi5aSk6fzYf5pZWxgyZiOidw+tfIiJSxx4Naq5evYomTZrAy8sLo0ePxpYtW3Djxg3ExcUhPT0dr1+/xs2bN7F69Wq0bt0abdq0QWhoqFFjXrBgAerWrYtFixbh5s2beP36NZKTk/HgwQNs3LgR7dq1w8CBA5GQkKC5MSIiIqJ3lCAIGDVqFJ49eyYpd/KogE5j50Am4xOahmZmYYXWgyfCzOK/BIKlrT0y0tORnvZfMig+Jgznd6+W9HQgIiIiIiLDYo8GNffu3cPFixclZVWrVkXt2rXh6uqKuLg4nDt3Di9evH2q6sSJE2jWrBlOnz6NSpUqaWzfzs4OQ4YM0VivevXqWsU7c+ZMzJs3T1wuU6YM3n//fVhaWuLKlSu4ffs2AGDr1q2IiYnBgQMHYGrKPzkRERFRVgEBAfj7778lZeaWVuj25U+SG91kWPaub+fGOLFpsVhm7eCMlPjXMDE1E+fMeHbzAty9/kX1Zh2MFSoRERER0TuNd51zULlyZYwcORKDBg1C2bJlJe+pVCoEBATgiy++QHJyMl69eoWPP/4Y586d0/ikm7OzM37//Xe9xHj06FFJkmHy5MmYN28ezM3NxbKtW7di+PDhUCgUCAwMxIIFCzBz5ky9bJ+IiIiopLh37x6mTJkCldpEwzKZHL6DJsKpdAUjRkYAUL5mQ9Rq1RW3T70dnkMmk8HSxh6KpARY2TmI9a78sxmu5b3hWs7bWKESEREREb2zOHSSGg8PDwQEBODu3bv45ptvsiUZAEAul2PEiBHYvHmzWHb+/HkEBgYaMlRMmzZNfN2/f3/8+OOPkiQDAAwYMABLly4VlxctWoTo6GiDxUhERERU1KWlpaFfv35ISkqSlJev2RC1fD80UlSUVb0OfeGilkCQm5rCxNQMqcmJYplKlY5TW36VlBERERERkWEw0aDG19cX/v7+MDEx0Vi3Z8+eaNy4sbh84MCBwgxN4tKlS+IQTyYmJli4cGGudT/55BNUqVIFAJCQkIBNmzYZJEYiIiKi4mDSpEl48OCBpKxMmTJo3H24kSKinMhNTNFywOeSYazMrawhqFTISFeKZUlxUTi3cyXnayAiIiIiMjAmGgqgRYsW4uunT58abLt79+4VX/v5+aF8+fK51pXJZPD39xeX9+zZU4iRERG9u4TUVCQd2C35EVJTNa9IREZz4sQJbNiwQVJmZWWF7777DqbmFkaKSj9M09NQ794pyY9pepqxwyoQO2d3NO05UlJmaWOP9FSFJLHwIiQY987/a+jwiIjeObz+JSIidZyjoQDU52TIyMgw2HaPHz8uvm7durXG+up1zp07h9TUVFhYFO9/nomIihpBkYyElUslZVbvt4GMx1uiIikhIQEjRoxAenq6WCaTyTBnzhx4e3sDj+8bMbqCM0tPhd/FvyRl9zzrI93UPJc1ioeKdZvh1YMbeHTl5NsCGWBmaY30VAXMLP/r7RB8cAtKe9WAY+ncH8ghIqKC4fUvERGpY6KhAG7evCm+zqtXQab09HQEBgbiypUriI6OhpWVFdzc3NC4cWM0atQIZmZmWm03JCREfN2gQQON9dXrZGRk4P79+6hTp45W2yIiIiIqiT755BOEh4dLylq0aIGxY8fi8uXLRoqKtOHTbSiiQh8gPuoVAMDetTQq+7TBtcAdYp2MdCVOb/sdncfNg6lZ8U6uEBEREREVB0w06Oj58+c4duyYuNyuXTuN67x8+RIdO3bM8T13d3dMmDABX331VbZJndVFRkYiLi5OXPb09NS4XUtLS7i5uSEqKgoAcPfu3XwlGl68eJHn+2FhYeLrlJQUpKSkaN02UXGnUChyfE3vHlUOf/8UhQJycx4TAe4rVLTs378f+/fvlwy34+joiDVr1kChUPA7WsSZmVuiRd8x+N+K2ajc0BcNP/gYZhZWSE1KQMjZQ2K9uIjnCD74Jxp398+1LYVCwWvXYobnEyLNDLWf8PqXijOeT+hdVljXv0w06GjixInicEkVKlTAhx9+WKD2IiMjMW3aNOzZswf79u2Dh4dHjvViYmIky6VKldKq/dKlS4uJhtjY2HzFpk1vjUynTp2Cq6trvtonKilOnTpl7BDIiExTktEwS9mJ4yeQbmVtlHiKMu4rZEwJCQlYuHAhbG1t8ebNGwiCAJlMhkGDBuHq1asA8P+TQ+d8LUZFg2s5b3T/chHsXP67Fq7fqT/CH9/B67BnYtm984EoU/U9lKuRcy/gCxcu5PvamIoOnk+INCvM/YTXv1RS8HxC75ro6OhCaZeTQetgw4YN2LVrl7j8/fff5znngYODA0aNGoVdu3bh0aNHSEpKQmpqKp49e4YtW7agadOmYt1Lly6ha9euSE5OzrGtxMREybKVlVWO9bJSr5e1DSIiIqJ3xe7du5GcnAxbW1u4u7vD3NwcPj4+aNGihbFDo3xSTzIAgImpGd7vNw4mWeahOLdzFVIS3xgyNCIiIiKidw57NOTT5cuX8emnn4rL/fr1w8CBA3Ot7+HhgZcvX8LGxibbexUqVMDAgQMxYMAAzJ07F7NnzwYABAcHY/HixZgxY0a2dbJ258prmCV16omQ/HaPef78eZ7vh4WFoXHjxgCAVq1aoVy5cvlqn6g4UygU4tMPrVq1gqWlpZEjImNRxcchYctySVnrNq0ht3c0SjxFDfcVKgpOnDiBp0+fwt7eXizr0aMHZs6cCWvr/56+dHZ2xrXTocYIkQrIsVQ5NOo6GBf2rhPLUpMTcGHPOvgOmgiZTCap36RJEzRq1MjQYVIB8HxCpJmh9hNe/1JxxvMJvcs0DZOvKyYa8uHJkyf48MMPxZv9derUwapVq/Jcx8LCIs/eDgAgk8kwa9YsPHz4EJs3bwYALFmyBFOnToWpqfRPlPXAl5aWptXBMDU1VXytbS+ITPlJHFhZWeW7faKSwtLSkt//d5gqTYGELGVWlpaQ8zuRDfcVMob4+HgsXboUcvl/HXodHBwwa9YsODs7S+ryH83irbJPGzy6chLRzx+KZc/vXMaTa2dQqX5LSV0ej4o3/v2INCvM/YTXv1RS8HxC75rC+r5z6CQthYWFoX379ggPDwcAVKpUCYcPH4aDg4PetjFnzhzxdVxcHIKCgrLVsbW1lSxr2ztBvV7WNoiIiIhKuiVLlmQbi3/SpEnZkgxUvCXGRuLY+oWIenYfcrn0gZ2L+zcg6Q3nYyAiIiIiKgxMNGghJiYG7du3x6NHjwC8HQ7pyJEjuU7YrKtKlSqhYsWK4nJISEi2Oi4uLpLliIgIrdrOTJAA4D/URERE9E75559/sGPHDknZ+++/j06dOhkpIioM9y8cxd+/TEHYgxuQyeUwMTWDIAji+0pFMs7vWi0pIyIiIiIi/WCiQYP4+Hh06tQJt2/fBvD2Rv+///4LLy+vQtmeevIiJiYm2/vu7u5wdHQUl589e6axTYVCgaioKHG5evXqBQuSiIiIqJhISkrC559/jmfPniEiIgIqlQo2NjaYNm1atvH6qXhTpqYgPe2/+cyUaSmwdpA+YPPqwQ08vHTc0KEREREREZV4TDTkISkpCV26dMHly5cBAPb29jh8+DBq1apVqNvMlNME0gBQo0YN8fXVq1c1thkcHCy+NjExQdWqVQsQIREREVHxMX78eERGRgIAYmNj8fjxY/Tt2xfu7u5Gjoz0rcb7neFStpKkLCX+NUwtpHNuXDmwGYmxkYYMjYiIiIioxGOiIRcKhQLdunXD2bNnAQDW1tY4ePAgGjZsWGjbTElJwf3798Xl3IZmatOmjfj6xIkTGts9efKk+Lp58+YaJ6cmIiIiKgkuX76Mv/76S1JmZ2eHQYMGGSkiKkxyuQma9R4FmdxEUm5mbiUdQilNgXM7V0FQqQwdIhERERFRicVEQw6USiV69+6NY8eOAQAsLCywb98+tGjRolC3++eff0KheNvdWyaToVWrVjnW69Gjh/j6yJEjePHiRZ7tbtiwIcd1iYiIiEoqlUqFUaNGIT09XSyTyWRYvHgxHBwcjBgZFSYnD0/U9v1QUpaS8BqOpcpLyiKehODZrYuGDI2IiIiIqERjoiGLjIwMDBw4EAcPHgQAmJqaYseOHWjXrl2+20pLS0NaWppWdR89eoQpU6aIy+3atUOpUqVyrOvj4wMfHx8xXvX1slq9ejXu3bsH4O0TfEOGDNE2fCIiIqJia9GiRZKeogDQokUL9O3b10gRkaHUadsTDm5lJWWJsZGwtLGXlN0/H4jo6GhDhkZEREREVGIx0aBGEASMHDkSO3fuBADI5XJs2rQJ3bp106m9V69ewdvbG4sWLcLz589zrKNSqbBt2zY0a9ZM/EfHzMwM33//fZ5tq7+/ZcsWTJ06FUqlUlJn+/btmDBhgrg8adIkuLq66vRZiIiIiIqLsLAw/PTTT5IyKysrrF271kgRkSGZmJqhSY/hkrKM9DTYOkvn5chQKrFhwwbJsEpERERERKQbU2MHUJSsWLEC69evF5e9vb1x5swZnDlzRuO6Li4umDNnTrbyFy9e4Ouvv8bkyZPh5eWF2rVrw8XFBWZmZggPD8f58+fFCQqBt8mNtWvXapwLws/PD9OnT8f8+fMBAD/88AM2btyIVq1awcLCAleuXMGtW7fE+u3bt8e0adM0fg4iItKN3MEJpf8+bewwiAjAqFGjkJycLCmbMGECypcvn8saJU+KpR0WD/7N2GEYTalKNeDdwBePgv+bqyz6+UOUrlQT4Y/viGXXr19HYGAgOnbsaIwwiYiKNV7/EhGROiYa1Kjf8AeABw8e4MGDB1qt6+npmWOiIZMgCHj8+DEeP36ca51KlSrhjz/+gK+vr1bbnDt3LiwsLDB37lwolUq8evUK27Zty1avf//+WLVqFUxN+ecmIiKikm3//v04ceKEpKxSpUqYOnWqcQIio2nQeQCeh1xBWkqiWJYQGwlLa3sokuPFsp9++glNmjSBo6OjEaIkIiIiIioZOHRSIfL09MTNmzexatUq+Pv7w8fHBxUrVoSdnR3MzMzg4uKCOnXqYOTIkdi3bx8ePHigdZIBeDuh4fTp03H9+nV8+eWXqF27NhwcHGBtbQ1vb28MGjQI//77L7Zu3Qp7e3vNDRIREREVY0qlEl999ZVkKBwTExOsXLkSJiYmRoyMjMHS1h4NOvWXlCXFRcPVs7KkLC4uDkuXLjVkaEREREREJQ4fcVcze/ZszJ49W2/tyWQy1K5dG7Vr18bo0aP11m5WNWrUwOLFiwutfSIiIqLiYN68eXj16pWkrHv37mjRooWRIiJjq9yoNR5dOYWo0P8mBn91/wZKVaqJF3eCxbIDBw6gc+fOaNq0qTHCJCIiIiIq9tijgYiIiIiKvbCwMCxfvlxSZm9vj99+e3fnKSBAJpejcfdhkMn++7dHlZEOSxt7mJpbSOouWLAAKSkphg6RiIiIiKhEYKKBiIiIiIq9cePGZbtJPHXqVI67T3Au44nqzd9O9mxiao73/HqjeZ9PUK25dALoV69eYeXKlcYIkYiIiIio2OPQSURERHogKNOQeuGspMyiSQvIzMyNFBHRuyM4OBhHjx6VlFWpUgWfffaZkSIyPpMMJbxf3JKUPSpXGxkmZkaKyLjea9cbylQF3vPrCRtHVwBA+RoN4ZL8DM+fPxfrbd26FV26dEG1atWMFSoRUbHB618iIlLHRAMREZEeCMlJiPtxpqTMffN+yBz4jxZRYVKpVPj5559RsWJFREVF4c2bN5DL5fjtt98gl7+7nXfNlQp8eOoPSdnyjxYg5R1NNJhbWqNZ71GSMplcjmHDhuGHH35AWloagLffp++//x5//PHHO/39ISLSBq9/iYhIHRMNREREheTq1WtQ2dgabHt169aFuTn/saN3yz///IM7d+7AzMwMZcqUgaOjI95//320bNnS2KFRMeDh4YHhw4dLhky6desW9u3bh549exoxMiIiIiKi4oWJBiIiokKy+uQjJJlZGWRbMS8eYe4wwMfHxyDbIyoKEhISsk32XLNmTSxbtsxIEVFxNGTIEBw8eBChoaFi2W+//YbWrVvDycnJiJERERERERUfTDQQEREVklJe1ZBiaWfsMIhKrDVr1uD169eSsq+//hpmZu/m8ECkm/DwcAwePBjfffedWBYfH4/ffvsNM2fOzGNNIiIiIiLKxIFHiYiIiKjYCQ0Nxfbt2yVlrVu3RpMmTYwUERU36WkKbN++HX379sXBgwfRvn17yfv79+/H9evXjRQdEREREVHxwkQDERERERU7U6dOhVKpFJfNzc0xceJEI0ZExUnsq2c4ueVnHDhwAOnp6bh69Srq168Pa2trSb3vv/8eGRkZRoqSiIiIiKj4YKKBiIiIiIqVI0eOYN++fXj48CFev34NQRAwcOBAlC1b1tihUTFh7+YBE1PpEFubNm3CyJEjJWUPHz7M1nOGiIiIiIiyY6KBiIiIiIoNlUqFr776CoIgICMjA+Hh4Xj58iUGDRpk7NCoGDE1M0f1Fp0lZWFhYQCAqlWrSspXrlyJyMhIg8VGRERERFQcMdFARERkAOlpqYgKfYjQWxcReusiIp/eQ7oyzdhhERU7AQEBePjwoaSsbdu2cHR0NE5AVGyV8qqB6tWrS8oCAgLw2WefScqSk5Px888/GzAyIiIiIqLix9TYARAREZVkEU/uIuTMQby6fx0Z6UrJe3ITU3hUro1qzTqgTNW6kMlkRoqSqHhITU3FvHnzJGVWVla8CUw6kclk6NevHxYtWiSWJSQk4OLFi+jRowf27t0rlgcGBuKjjz5C/fr1jRApEREREVHRxx4NREREheTM9uUIXD0Xz+9czpZkAABVRjpe3ruGY+sX4t818xEX8cIIURIVH/Pnz0dUVJSkbMSIEXB3dzdSRFTceXt7o0OHDpKy7du3o3fv3rC3t5eU//TTT1CpVIYMj4iIiIio2GCigYiIqJCEPbihdd2IJyE4+Pu3eHj5ROEFRFSMxcTEYNWqVZIyFxcXzJ492zgBUYkxbtw4mJr+19FbqVRi8+bN+PTTTyX17t+/L+nlQERERERE/2GigYiIyEBkMjlsndxg6+wOmSz7KTgjXYmgXasR/L9tEATBCBESFV1fffUVkpKSJGXffPMNrKysjBQRlRRly5ZF3759JWWHDx9GjRo14O3tLSlftmwZ4uPjDRkeEREREVGxwDkaiIiICpmlrQNqt+6GSvXfh4W1HQAgTZGCZzfP4+axvUiKkw4Fc/vkfmQo09Co62DO20AE4N69e9izZ4+kzMvLC2PGjDFSRFTSjBgxAvv370diYqJY9vvvv2PSpEmS79mbN2+wcuVKTJ482RhhEhEREREVWezRQEREpAc3b97Msbx8zUboNnEharToLCYZAMDc0gpVfNqg28SFqNa0Q7b17p77H24d31tY4RIVK+PHj0d6erq4LJPJ8OOPP0Iu56Us6YeDgwOGDx8uKbty5QoUCgX8/Pwk5Tt37sTDhw8NGR4RERERUZHHHg1EREQFFBkZiclz50EZl4LkpGREJ6bBzMICpRt3QMOugyHL42aoqbkFGnf3h3MZT5zfsw6C8N9Eo9f+/Qu2zqXgVa+5IT4GUZF08eJFnD17VlLWsGFDfPDBB0aKqHhIsbDB8o8WZCuj3PXv3x87duxAeHi4WPbrr79i6dKlOH36NNLS0gAAKpUKixYtwooVK9jrjIjeaTI7B7hv3p+tjIiI3k1MNBARERWASqXC9OnTERP7GgCQpBLwOl2FWi380PDDIVrfhKrs0wYmZhY4s30ZgP/mZwjatRqOpcrCycOzMMInKtLS0tIwevRoSW8GuVyOkSNH4tKlS4W23Tt37qDYXybL5EixtNNcj0Tm5uYYO3YsZs6cKZY9efIEV69ehb+/P1avXi2WX758GceOHcvW24GI6F0ik8shc3AydhhERFREFPP/oIiIiIxr8+bNCA4OlpS5eVZB427++X7S1atec6QmJ+DS3xvEsoz0NJz681d88PkCmJpb6CVmouJi9erVuHX7DqC2L5WuWAOHn8tx+Pn9Qtvu4+BzqNSgVaG1T0VXp06dsGXLFty7dw8A0LZtW9SqVQvt27fH/v37Jb0dli5divfffx8WFjw2ExEREREx0UBERKSj0NBQrFixQlLm6uqKMm365DlcUl6qN++IuIgXeHDxqFgWHx2GKwe3oEmP4XmsSVSyCIKA33//HZDJIJO93Z/kpqboNGYOnEqXL9Rtx7x4VKjtU9Ell8sxbtw4/Pnnnxg7dixq1qwpvjdx4kR888034nJ4eDj+/PNPDBs2zBihEhEREREVKZxBj4iISAeCIOCHH36AUqkUy2QyGUaPHg0zS+sCte3z4RC4lK0kKbt/4QgintwtULtExcnp06ehUChgYmomllWq37LQkwxEzZs3x++//y5JMgBvezc0bNhQUhYQEIDY2FhDhkdEREREVCQx0UBERKSDo0eP4uLFi5KyAQMGoHr16gVu28TUDC36jYWpmXQ4jgt7/4AqIz2XtYhKDpVKhWXLlsHKygo2jm6wdXGHpa09Wg+eaOzQ6B0mk8kwceJEybB4ycnJWLlypRGjIiIiIiIqGphoICIiyqe0tDT89ttvkjIPdzeMbNkMFk8fwvvNS5QLfwB5AZICDm5lUL9jP0nZm8gXCDlzSOc2iYqL//3vf3j06O3wRTKZDBZWNmg/ajpsndyMHFnxIc9IR7nwB5KfghyT6K3q1auja9eukrK9e/fi4cOHRoqIiMh4BKUSqTevSn4Etd6+RET0buEcDURERPm0c+dOvHz5UlL21ZgxSJ47Ge4AxgDAHWD5RwuQYmKn83aqNmuPR1dOITbsqVh24+huVKzbDDaOrjq3S1SUKZXKbE+IO5WugEr1WhgpouLJQpmCfv/+Kikr6DGJ3g6bN2bMGPz7779QKBQA3vbAWbp0KX7//XdJbwciopJOSE7E62lfSMrcN++HzMHJSBEREZExsUcDERFRPigUCgQEBEjK6tati5YtW+p9W3K5CRr3GA7gvxtX6cpUXP5ns963RVRU7NmzB69evZKU1evQV+cJ1on0QaVS4cCBAxgwYABMTU0xZMgQyfsXLlxAUFCQkaIjIiIiIjI+/sdGRESUD/v27cPr168lZRMmTEBhPcTqVqEyqjRuKykLvX0R0S8eFc4GiYxIoVBg3bp1kjLH0uVRtnp9I0VEBBw/fhz9+vXDrFmz8PDhQ6xfvx6DBw+Gm5t0KK+lS5ciIyPDSFESERERERkXEw1ERERaUiqV2LBhg6SsefPmqFOnTqFut37HfjC3spWUXTu8o1C3SWQMCxcuRHBwMJKTk8Wyak3bczgaMqqTJ0/iyZMn4vLOnTuRkJCAcePGSeo9efIEe/bsMXR4RERERERFAhMNREREWjp06BAiIyMlZcOHDy/07VpY26K274eSsrCHNxH+6Hahb5vIUJKTk7Fy5UokJyfj2bNnCA0NRYUKFeBc1svYodE7bvTo0TA1/W9qu7S0NKxduxZdunRB9erVJXVXrlyJxMREQ4dIRERERGR0TDQQERFpQaVSYf369ZKy+vXro169egbZfrVmHWBlJ51Y71rgXxAEwSDbJypsP/zwA968eSMuJyUloXbt2kaMiOitMmXKoFevXpKyffv2ISIiAhMnTpSUx8XF4Y8//jBkeERERERERQITDURERFo4cuQIQkNDJWXDhg0z2PZNzS3wnl9PSVlU6H28CAk2WAxEhSUpKQlr1qyRlJUqVQp9+vQxUkREUsOHD4e5ubm4nJGRgYCAADRs2BC+vr6Sulu3bs02oTkRERERUUnHRAMREZEWtm/fLlmuXr06mjVrZtAYvBv6wtbZXVJ268Q+9mqgYm/BggWIj4+XlE2cOFEyXA2RMbm6umZLfO3btw9hYWEYP348TExMxHKlUonffvvN0CESERERERkVEw1EREQaPHz4ENevX5eUDR061OAT1JqYmqFuu96SsujnDxH55K5B4yDSp8TExGxDzZQuXRpjxowxUkREORsyZEiOvRoqVKiAvn37Sur++++/uHPnjqFDJCIiIiIyGiYaiIiINNi9e7dk2cXFBW3atDFKLBXrNoeNo5uk7Papv40SC5E+zJ8/P1tvhkmTJrE3AxU5efVqGDVqFOzs7CTv/fLLL+xxRkRERETvDCYaiIiI8pCcnIx//vlHUta9e3ej3QSVy01Qs2UXSdnLe9eQEBNulHiICiI+Pj7bJOtlypTBqFGjjBMQkQa59Wqwt7fPNm/PlStXEBQUZOgQiYiIiIiMgokGIiKiPAQGBiI5OVlclslk6NmzZx5rFD7vRr6wsJY+Ofvk6lkjRUOku7lz5yIhIUFSxt4MVJTl1auhf//+KFWqlOS9X3/9FSqVypAhEhEREREZBRMNREREedi1a5dkuUWLFvDw8DBSNG+ZmVuiWrMOkrJXD24gJibGSBER5d+bN2+wadMmSVnZsmUxcuRII0VEpJ3cejWYm5vj008/ldR9+PAhDh48aOgQiYiIiIgMjokGIiKiXISEhCAkJERS1rt371xqG1a1pu1hYvrfjS5BpcLRo0eNGBFR/syZMweJiYmSsm+++QYmJiZGiohIO3n1avjggw9QuXJlyXsrVqxAWlqaIUMkIiIiIjI4JhqIiIhykfUp1NKlS6NFixZGikbK0tYe3o18JWUnT57kzSwqFuLj47FlyxZJWbly5eDv72+cgIjyKadeDX/99Rfkcjk+//xzSd2IiAhs377d0CESERERERkUB8AlIiLKQUZGBg4fPiwp69q1K+TynHP0Mls7uPy+Ebdu3cLmoKdwq1AZCnPrQo2xWtP2uH/+X3E5ISEBR44cQZcuXfJYi8j4fvzxx2y9GSZPnszeDHqkMLfG+g+nZSsj/cjs1fDnn3+iTJkyGDlypHjsbd68ORo2bIgrV66I9QMCAtC9e3fY29sbK2QiIr3LvP7NWkZERO8mJhqIiIhycPHiRcTGxkrKOnfunGt9mYkpzDy9kB4ZjQjreMgdC38eB8dS5VDauxbCH90Wy7Zv385EAxVpycnJ2LBhg6SsTJky7M2gZ4LcBDEGOA69y4YMGYLKlSujS5cukgnMZTIZvvjiCwwdOlQsi4+Px/r16/HFF18YI1QiokKRef1LREQEcOgkIiKiHB06dEiyXLNmTXh6ehopmtxVa9pesnz79m3cuXPHSNEQabZz5064ubnB1dVV7CE0fvx49magYsfV1RXdunWTJBky1apVC+3atZOUbdu2DREREYYKj4iIiIjIoJhoICIiykKhUODEiROSsk6dOhknGA3K1WwIa3tnSdlff/1lpGiI8qZQKLB582aYmJjAzc0NlStXRqtWrTB69Ghjh0akd+PGjZMk0NLS0rBy5UojRkREREREVHiYaCAiIsri1KlTSE5OFpflcjk6duxoxIhyJ5eboGpT6VOzhw8fRnx8vJEiIsrdnj17JEOSmZiYYPHixZJJdYlKivLly6N3796Ssn/++QcPHz40UkRERERERIWHiQYiIqIssg6b5OPjAxcXFyNFo1llnzaQmfx3Sk9LS8v2GYiMLS0tDRs3SieMbN68OWrWrGmkiIgKT1JSEgBg5MiRsLb+bxJuQRDw+++/GyssIiIiIqJCw0QDERGRmvj4eJw7d05Sltck0JmEjHQonz2BaWQ4SiXHwiUuDDJVRmGFKWFl64BSXtKbtfv27TPItom0tX//fkRFRUnKRo4caaRoSj6ZKgMucWGSH0Mdk95lDx48wLRp09CtWzckJSXB2dkZgwYNktQ5c+YMgoODjRQhEZH+ZF7/qv8IGenGDouIiIwk+8xlRERE77DTp08jI+O/m3Hm5uZo06aNxvWExATEfDYEHgC+BoDrwPKPFiDF0q7QYlVXvkZDPIt8IC7fv38fd+/eRfXq1Q2yfaK8KJVKrFu3TlLm4+OD9957z0gRlXyWacnw/3uBpMyQx6R3TUZGBiZPnoyTJ0+KZX/99Rf8/f0xaNAg7Ny5UzJs2C+//IL169dDJpMZI1wiIr3IvP5V5755P2QOTkaKiIiIjIk9GoiIiNQcP35csty0aVPY2NgYKRrtuZSrlG14J/ZqoKJiw4YNOHPmDCIjI5Ge/vZJxxEjRhg5KiL9MTExkUz8DABbtmyBQqGAtbU1Ro0aJXnv9u3bOHHihAEjJCIiIiIqXOzRQERE9P8UCgWCgoIkZdr0ZigKZHI5WrVqhcDAQLHs0KFDmDBhAiwsLIwYGb3rMjIysHjxYqhUKsTExCA2NhZ16tRBw4YNjR0aveMy0tNx584dvbXXqFEj/P333+JyUlISFi9ejA4dOqBcuXKws7NDeHg4AMDa2horVqyAr68v5HI++0VERERExR8TDURERP8vKCgIqamp4rL8/2/eFxfvv/8+/v33XwiCAABITEzEsWPHtJpjgqiwbNu2Dc+fPxeXBUFA06ZNOWQMGV1c+DNsCwdcXurvXyLBqQKinv03jN3yjX/hVkY5mJiaQl6xCSLv70C6MhVlADx+/BiHDh3CBx98oLftExEREREZCxMNRERE/y/rsEkNGzaEg4ODkaLJPzc3N/j4/B979x0XxZn/AfwzCwtL700pUkTFgr2LvUYTY4wxxqhJLskld8mlX6qXdmmXdrlLLybWxCSnsfeKXVFsCAhI771v/f3Bz4GhCOjuzgKf9+vFK/t855mZLwZm2fnO8zzDcPLkSTH2xx9/sNBAstHr9XjvvffE4hcAODg44JVXXpExK6J6Hv6h8Avra7TjjZj7IHZ+/YbY1mnVqC4rRM/hk+Ab0gdZibHIS4kXt3/99deYNm0alEql0XIgIiIiIpIDCw1ERNTpqNVqxMbGtmsfrVaLbdu2obKyUowFBATg1KlTbdpfUVmB7u06o2nMnTtXUmg4c+YM8vLy4O3tLWNW1FX98ccfSElJkcTmzZsHT09PmTIiMi3vHr3gE9wHuSlxYuzyoa0IGzoBgkKBgdMWYNfXb4nbsrKysGHDBixYsECOdImIiIiIjIaFBiIi6nRiY2OxfMV2ePiHtnmfgvSrSMkuksRiKlxxeXdCm/Z30FTjjda7mdyECRPg4OAgFkwMBgN27NiBJUuWyJwZdTUGgwHvvvuuZDSDnZ0d/vGPf8iYFZHp9Z84V1JoKCvMRvrl0wjsNxzdew2Em18gUJkrbv/uu+8wZ84c2NnZyZEuEREREZFRsNBARESdUnunw0i7eAJKlap+/+4hCB44us3729WUA6fblaJJ2NjYYPLkydi0aZMY2759OwsNZHaHDx9GXFycJHbbbbfBz89PpoyIzMM3rB/c/XqgKPuaGLt4YBMC+g6DIAgIHzEFKfvWiNuKiorwyy+/YNmyZeZPloiIiIjISBRyJ0BERCQ3g8GAjLgYSSyg7zCZsrl1s2bNkrQTExNx9epVmbKhrurNN9+EXq8X2zY2NhzNQF2CIAjoO36OJFaYmYzc5MsAAPfuwRgwYIBk+08//YSysjKz5UhEREREZGwsNBARUZdXkpuOqjLptEkBEUNkyubWDR48uMmaDNu3b5cpG+qKYmNjm6xvEhUVhZCQEJkyIjKvwP7D4ejmJYldOrhZfD1//nzJtvLycqxevdosuRERERERmQILDURE1OVlxUsXjnZw9YKLtyUs7XxzFAoFZsyYIYnt2LFD8nQ5kSm9+eab0Gq1Ytva2hrLly+XMSMi81IorBAxbrYklpV4HkVZqQCAHj16YMqUKZLta9euRWFhodlyJCIiIiIyJhYaiIioy8uMPydpd+8VCUEQ5EnGSGbOnClp5+bm4ty5c/IkQ11KcnIy9u/fL4kNGjQIQ4Z03FFCRDcjdOh4qBycJbFLh+pHNfz5z3+GQlH/caympgY//PCD2fIjIiIiIjImFhqIiKhLU9dUIe9avCTWvddAeZIxop49eyIsLEwS27Ztm0zZUFfyz3/+E7W1tWJboVDg5ZdfljEjInlYK23Qe/R0SSz90imoa6oA1I1qmD1bOurh999/R1ZWltlyJCIiIiIyFhYaiIioS8tOvACDoX5KIYWVNXxCI2TMyHgaj2rYs2cP1Gq1TNlQV5CXl4fNmzdLYj179mwyRQxRVxE+ciqsbVSwsrZBr1HTcfvT/4KNyl7c/sgjj0CpVIptrVaLb7/9Vo5UiYiIiIhuCQsNRETUpTWeNsknJAJKG5U8yRhZ43UaKioqEB0dLVM21BV88sknqKysFNuCIOD555+XTA9D1JXY2jti/H1/w7wX/4Phty+Fo7u3ZLuvr2+ThaG3bt2K5ORkc6ZJRERERHTLrOVOgIiISC4Gg6HJQtDde0Xe1LFqlXb4ZeqTSDl3BC5e3eDePRi1SjtjpHnTfHx8MHjwYMTExIix7du3Y9KkSTJmRZ1VaWkpoqOjERQUhKKiIpSXl8Pf37/JTVQyj+vXpMYxMr9u4Td+X3nggQewceNGVFdXAwD0ej2++uorfPDBB+ZIj4jopgn2jnB757MmMSIi6ppYaCAioi6rODsV1RUlktjNrs+gt7JGhm9PxKvi4OHSHVW+PW89QSOYNWuWpNAQHR2NiooKODryQyAZ1/r161FdXQ17e3vY29tDo9Hg7bfflkwLQ+Zz/ZpEls/d3R2LFi3C999/L8b27duHy5cvIyKic0zlR0Sdk6BUwrb/ILnTICIiC8Fx7ERE1GU1njbJyd0Hzp5+8iRjIpMnT4a1df1zBRqNBocOHZIxI+qMqqur8fPPP0tit99+O+bMmSNTRkQdy+LFi+Hs7CyJffHFFzJlQ0RERETUfiw0EBFRl5Vz9aKk3dr0Fh2Rk5MTRo0aJYnt2bNHpmyos9q4cSNKS0slsaVLl8qUDVHHoFarodPpANRdqxv/zhw/fhxnzpyRIzUiIiIionZjoYGIiLokrboWeakJkphfz/4yZWNaU6dOlbSPHTuGiooKmbKhzkaj0WD16tWS2Lhx4xAWFiZTRkSWTVNbjc2bN2P27NnYtWuXGL/nnnvg4eEh6fv555/DYDCYO0UiIiIionZjoYGIiLqkvGvx0Ou0YlsQFPAJ6ZxzYUdFRUnmyef0SWRM27ZtQ05OjiS2bNkyeZIhsnAJJ/Zi/8oP8euvv6KoqAirV68WCwkqlQp/+tOfJP3Pnz+PI0eOyJEqEREREVG7sNBARERdUk6SdNokz4Aw2Kjsbv6ABj3sasrhpFPDQVMNu5pywKC/xSyNw9HRscn0Sbt375YpG+pM9Ho93nnnHVy7dg2lpaUwGAwYOHAgIiM73zRkHc7/X5MaflnKNakrUzm6QKdWi+34+HjJ9Ehz585Ft27dJPt88cUX0Ov5/46ILI9Br4e+tFjyZeD1ioioy7JuvQsREVHnk331kqTtG9b3lo5nV1uJx399ua6RdRA4DXxx9zuoVjnd0nGNZcqUKZJRDMeOHUN5eTmcnCwjP+qYDhw4gPj4eGg0GmRlZSEvL4+jGSyE5Jr0/yzpmtRVBfQZDHsXd8BQKcbWrFmDoUOHAgCUSiUeeeQRvP766+L2hIQE7N27t8k0eEREcjOUlyJv8e2SmPfqTRBc3GTKiIiI5MQRDURE1OXUVJajKOuaJOYX2k+eZMxk/PjxsLGxEdtarRYHDx6UMSPqDD744ANoNBqxbWVlhXHjxsmYEZFlExQK9IiUjjA7fPgwrl27JrZnzZqF4OBgSZ+vvvpKXDiaiIiIiMgSsdBARERdTm7SJQD1i2taK23hGdi5F651cHBoMn3Snj17ZMqGOoPY2FicOnVKEhsyZAinTSJqRffeg+Hg4CCJrV27VnytUCjw5z//WbI9NTUVW7duNUt+REREREQ3g4UGIiLqcrKTpNMmeQf3hpW1soXenUfjaTeOHz+OsrIymbKhju6TTz5BTU2N2La2tsazzz4LQRBkzIrI8lkrbTBp0iRJbMuWLSguLhbbEydORK9evSR9vv32W6gbrO9ARERERGRJWGggIqIuJ+eqdCFov7DOPW3SdVFRUZw+iYwiPT0dO3bskMRCQ0M5hzxRG02ZMgXW1vXL5anVavz2229iW6FQ4C9/+Ytkn+zsbGzcuNFcKRIRERERtQsLDURE1KVUFOWhvChXEvPt5OszXGdvb4/Ro0dLYrt375YpG+rIvvzyS5SXl4tthUKBJ554AlZWVjJmRdRxuLm5YcaMGZLYr7/+KhmxMGrUqCZTkX3//feSkURERERERJaChQYiIupScpIvS9q29k5w8w2QKRvza/zE+YkTJzh9ErVLSUmJZD55APDx8cGCBQtkyoioY1q0aJGkXVRUhG3btoltQRCajGooLCzE+vXrzZIfEREREVF7sNBARERdSm7KFUnbJyQCgqLrvB2OGzdOMn2STqdDdHS0jBlRR7Ny5UoUFBRIYkuXLm2yuC0R3Vh4eDiGDx8uia1ZswYGg0FsDx48GCNHjpT0+fHHH1FRUWGWHImIiIiI2qrr3FkhIiICkJcSJ2n7hPSRKRN52NvbN7lptX//fpmyoY6mtrYWX3/9teRGqKurKx5++GEZsyLquBYvXixpp6Sk4NixY5LY448/LmmXlZVhzZo1Js+NiIiIiKg9WGggIqIuo7KkABXF+ZKYT3BvmbKRz6RJkyTto0ePorq6WqZsqCP5448/kJGRIYnNmTMHvr6+MmVE1LGNGjUKISEhktjq1asl7YiICEycOFESW7NmDUpKSkydHhERERFRm7HQQEREXUZuo9EMNnaOcPX2lykb+URFRUHRYLqo2traJk/QEjWm1+vx6aefQqvVijEHBwc8+eSTMmZF1LEJgoD77rtPEjt58iSSkpIkscceewyCIIjtqqoq/PTTT2bJkYiIiIioLVhoICKiLiMnSboQtE9w7y61PsN1zs7OGDp0qCTG6ZOoNYcOHcKVK9I1TkaNGoWIiAiZMiLqHGbOnAl3d3cAwNChQ/Hpp58iODhY0ickJAQzZsyQxH755Rfk50tH6RERERERyaXr3V0hIqIuK6/xQtDBXWt9hoYaT8Nx6NAhaDQambKhjuCTTz5BbW2t2FapVHj66adlzIioc7CxscErr7yCNWvW4KuvvsLYsWMlo86ue+SRR2BlZSW21Wo1vv/+e3OmSkRERETUIhYamnHt2jV8++23WLx4MSIjI+Hm5galUgl3d3cMGDAAjz76KA4ePHhTx967dy+WLFmC8PBwODg4iMd8/vnnmzwl2FZxcXF4/vnnMWDAALi7u8PBwQHh4eFYunQp9u7de1PHJCLqbCpLi1BelCuJdbWFoBuaMGGCpF1ZWYlTp07JkwxZvAsXLiA9PR2+vr6wsbEBAPTq1QtRUVEyZ0bUOYwfPx69evW6YZ+AgADccccdktiGDRuQmZlpytSIiIiIiNqEhYYGzp49ixEjRiA4OBiPPPII1qxZg/Pnz6OkpARarRbFxcW4cOECvvnmG0yYMAETJ05EWlpam45dVlaGhQsXYsqUKVi1ahUSExNRVVUlHvPDDz/EgAED8O6777Yr53feeQeRkZH48MMPceHCBRQXF6OqqgqJiYlYuXIlpkyZgkWLFqG8vPxm/kmIiDqNvEbrMyhV9nDzDZQpG/l5eXlhwIABkhinT6KWrFq1CgqFAm5ubggJCUH//v3xz3/+s9mnronIdP70pz+JxT4A0Ol0+Oabb2TMiIiIiIiojrXcCViS+Ph4nDx5UhILDw9Hv3794OnpiZKSEhw9ehQZGRkAgAMHDmDUqFE4fPgwQkJCWjyuRqPBvHnzJKML+vXrhyFDhqC6uhqHDh1CTk4ONBoNXn75ZWg0GixfvrzVfJcvX4633npLbHfr1g1jx46FSqXCmTNncOnSJQDAunXrUFhYiK1bt8Lamv/Liahrym08bVIP467PoFaqsDnqQaRdOg0nDx+4+QZArVQZ7fimMHHiRJw/f15sHzhwAC+99BJvHpNERkaGpAglCAL+8pe/NJl+iyzL9WtS4xh1bN7e3pg/fz7Wrl0rxrZv345ly5Y1WdeBiMjUBHsHuP79zSYxIiLqmngnoRlhYWF47733kJGRgfj4ePz+++/4+uuv8csvvyA1NRXfffcd7O3tAQBZWVm47777YDAYWjzeW2+9JRYZVCoV1q1bhwsXLuDHH38Uj/n888+L/f/xj3+0OjXT3r17JUWGF154ASkpKfjll1/w008/4eLFi1i7di1UqroPlLt27cI777xz0/8mREQdXW6ydCFo7+DeRj2+zkqJhKBBOG3vg/MeoUgIGgSdldKo5zC2xjeKi4uLce7cOXmSIYu1du1ayd85Dg4OmDdvnowZUVtcvyY1/LL0axK1zbJly2BnZye29Xo9vvrqKxkzIqKuSlDaQDV2ouRLUNq0viMREXVKfLy9AT8/P6xYsQL333+/ZKG1hhQKBR566CG4u7uLH7KPHz+OXbt2Yfr06U365+Xl4eOPPxbbn376KRYuXCjpY2Njgw8++ABpaWn45ZdfAAAvvfQSjh492mKuL7/8svh64cKFeP/995v0uffee1FaWorHHnsMAPDhhx/i8ccfh6enZ4vHJSLqjKorSlFWkC2JdeX1Ga7z9/dHeHg4EhISxNj+/fsxePBgGbMiS1JaWoo//vgDBoMBVVVVAOrmkr98+XIrexpH3Xn45yp1PXq9HkeOHMG6devw4IMPYujQoeI2d3d33Hvvvfjhhx/E2N69e3HlyhX07m3cIjoRERERUVvxk1sD48ePx/jx49vU984778Tw4cPFqZa2bt3abKHhp59+QmVlJYC6aZgeeeSRFo/5wQcf4Ndff4Ver8exY8dw9uxZDBo0qEm/U6dOiee1srLCBx980OIxH330UXz88cdITExEeXk5Vq1ahaeffrpN3yMRUWeRn5ooaVvbqODerYc8yViYiRMnSgoN+/btwzPPPANBEGTMiizFr7/+itraWlRVVSGrsAxKWztcsw3Fv3cntL6zESTHHEXIYC44TV3Lzp078dVXXyE9PR0AYGdnJyk0AMD999+PX3/9VbIO2xdffIHPPvvMrLkSEREREV3HQsMtGDNmjHjD/9q1a8322bhxo/h62bJlN7xxExgYiMmTJ2P37t0AgA0bNjRbaGh4zMmTJyMgIKDFYwqCgGXLluGVV14Rj8lCAxF1NfnX4iVtz4BQKBTNj1zraiZNmoSvv/5abOfm5iIuLg4REREyZkWWQK1W46uvvkJWVhbs7OxgrbRFzxGTEDJwtNlyKMxIMtu5iCxFaWmpWGQAgEOHDiEjIwP+/v5izMnJCUuWLMHnn38uxo4ePYpz585h4MCB5kyXiIiIiAgA12i4JQ2LBjqdrsn2mpoaHD9+XGxPmDCh1WM27LNv375m+zRckLG9xzx69Chqa2tb3YeIqDPJS5U+fe0d1EumTCxPSEhIk4J1w/cZ6rq2bt2K1NRUlJaWIisrC5UlBfAO5O8OkanNnj0bDg71i6kaDAasX7++Sb+FCxfC3d1dEvviiy9uuHYcEREREZGpsNBwCy5cuCC+bm5UQXx8PPR6PYC6okRzoxMaazgvdlxcXLN9GsbbMo92wz46nU4yRQYRUWen1ahRlJkiiXkFhcuUjeURBKHJotB79+7ljaou7vristfXZQAACICVDRd4JDI1e3t7zJ07VxL7448/pL+PqJtS6cEHH5TEYmJicOLECVOnSERERETUBAsNNyk9PV0y4mDKlClN+sTH10/V4e3tDZVK1epxAwMDxddFRUXIz8+XbM/Ly0NJSYnYDgoKavWYKpUKXl5eYvvKlSut7kNE1FkUZiRDr9eKbUFQwDOwp9HPY1dTjmdXPYEV6bvx4bGv8OyqJ2BXU976jhZg0qRJknZaWhpSUlJa6E1dQXR0tOSBCgBwdPNCjwEjZcqI2uv6NanhV0e5JhFwzz33QKGo/6hWWVmJzZs3N+k3b948+Pj4SGIc1UBE5qIvLUbOnHGSL31psdxpERGRTLhGw016+umnxemSAgMDMWfOnCZ9CgsLxdeNPwC0xNfXV9IuKiqSFAkaHrO9x71etCgqKmrTPtdlZGTccHt2drb4urq6GtXV1e06PlFHVlNT0+xrklfD/xf5jaZNcvUNgI3KztwpmUVNTc1NXYODg4Ph4eEhKW7v3LkTy5YtM2puzb0my/TVV1+hrKxMvFmpVCoROmQ8rKyVMmdG1Dm0dr12c3PD6NGjcejQITG2Zs0azJ49W1KAAIAlS5bg/fffF9sXL17Ezp07MX78eOMnbgH4fkLUOnP9nuibOXZ1TQ0UNrwnQJaP7yfUlZnq3i0LDTfhp59+wu+//y623333Xdja2jbpV1FRIb62s2vbTa3G/Roeo7n2zRy38TFac6PFphs7dOgQPD0923V8os6i4c0AkldiYiIAPwBAXqOFoE21PoNB3/TpUU1tDaByMsn5mnPixIl2F5Ov8/X1RVJS/cK7v/zyC7p162as1CT4u2LZUlNTsWfPHmi19SOBVCoVAvsNlzEros6lLdfrgIAAlJWVie3Lly/j3//+N/r27Svpp1QqYWtrKykWv/XWW6iurm5SlOhs+H5C1DpT/p5YV1dhSKPYgf0HoLWzN9k5iUyB7yfU1RQUFJjkuJ37L08TOH36NP785z+L7XvuuQeLFi1qtm/DiqhNG+c0blywaFxhalxlvZnjcsQBEXUVBr0e+WmJkpix12eoKivCmW1rsfnTF5ps++PDZ7B3xfvIjD9n8dNY9O/fX9LOyMi46aIFdWz79++XPJRgZWWF/v37w9befEUzIgJCQ0ObFHwPHz7cpJ+VlRWmT58uieXm5uLs2bMmzY+IiIiIqCGOaGiHlJQUzJkzR7zZ379/f3z99dct9m+4JoNarW7TOWprayXtxiMWGq/zoFar27T2Q8PjtnUUxHXp6ek33J6dnY3hw+uecoyKioK/v3+7jk/UkdXU1IhPP0RFRbXp95FMz93dHecOp6E0PwvqaukoLu8exik0GPR6XDm6E7G7f4VGXQM3KwGAu7SPQY+shFhkJcTCv88QTFj8NAQTPl06YsQIDB069Kb2nTx5MrZs2SJ5elahUGDatGlGyY2/Kx1DZmYmLl68CADik9BeXl5YtmwZdt74zwEiaoe2Xq81Gg3effddsZ2bm4uePXsiODhY0m/KlCm4dOmSZGRaTEwMnn/+eVhbd66PfHw/IWqduX5P9GUlKF/zhSQ2YeIEKJxdTXI+ImPi+wl1Za1Nk3+zOtdfnSaUnZ2NqVOnIicnBwAQEhKCnTt3wsXFpcV9HB0dxddtHUXQuF/DYzTXrq6ubtPFsOFxGx+jNe0pHNjZ2bW7kEHUWahUKv78W4jr18XG6zPYO7vDwfXWp3fT1FbjyK9fIf3SqTbv4+TubdIiA3DrP4MTJkzAli1bxPaRI0ewdOlSY6Qmwd8Vy/W///0PxcXFEAQBQF2xISoqCr1798bO9IRW9iaitmrrdfCOO+7AN998g+Li+sVVN27ciJdffrlJ3yeeeALPPPOM2M7JycHu3bsxb9484yRtgfh+QtQ6U/6e6NU1KG8Us1OpoODvJXUwfD+hrsZUP++cOqkNCgsLMXXqVPEJIT8/P+zZswd+fn433M/Dw0N8nZub26ZzXS9kXOfuLn06tuExb/a4jY9JRNRZ5TUqNHj3uPX1GdQ11dj7w/vtKjI4uHoicur8Wz63qU2cOFHSPnfunOTmFnVupaWlWLduneThBFdXV5MUm4iobWxsbJoUCrZs2YLS0tImfceNG9dk/YbvvvuuzSOriYiIiIhuBQsNrSgrK8OMGTNw6dIlAHU3+nfv3t1kuHJzevWqv6GVl5fXplXs09LSxNfu7u7w8vKSbPf29oarq6vYTk1NbfWYNTU1ksXhevfu3eo+RESdQUFqo/UZbrHQoNNqsO/H95Gf1vjJbgE9Ikc36T9k1mKoHF0w4o4HobS1/CdkRo4cKRklp9frm50PnDqn33//vckDD71798aECRPkSYiIAADz58+HlZWV2Far1diwYUOTfoIg4C9/+YsklpeXh99++83kORIRERERsdBwA5WVlZg1axZOnz4NAHB2dsbOnTubPCnUkl69eonzGxsMBpw7d67VfWJiYsTXffr0abZPw3hbFnlreEwrKyuEhxt3IVQiIkukqalCWWG2JOYVEHZLxzy3c32T6Zhs7Bwx5cEXMWzOkib9QwaPxdznP0H33gNv6bzmYmtri9GjpQWTffv2yZQNmZNarcbKlSsla3S4uLjg/vvvl9zgJCLz8/LyarJezvr166HVapv0HTZsWJO1H1asWIGqqiqT5khERERExEJDC2pqanD77bfjyJEjAAB7e3ts27YNQ4YMafMxVCoVRo4cKbYPHDjQ6j4HDx4UX0+aNKnZPg2ntmjvMUePHg1bW9tW9yEi6uhKcqWLG1lZK+HmF3hLx+w74XZ496gfFaZydMH0R5fDr2f/FvdR2nSsRcUaP71+4sQJ3qDqArZt24bk5GRJzNfXF3PnzpUnISKSuPfee8XX3t7euPvuu6HT6Zr0EwQBjz/+uCRWXFyMn3/+2eQ5EhEREVHXxkJDMzQaDe666y7xKU5bW1v88ccfGDNmTLuP1fAD+o8//njDvhkZGdi7d2+z+7Z0zD179rS6UvhPP/3U6jGJiDqbxoUG9+7BUFhZ39IxVQ5OmPLQSwgdMh5KGxWmPPQSXH38b+pYBr0ecUe2I2b72lvKydjGjh0reYJdo9GIRXfqnPR6PVauXImSkhIx5uDggPnz58PZ2Vm+xIhIFBERgfnz5+Odd97Bpk2b8MADD7T48NCAAQMwduxYSazxiCUiIiIiImNjoaERnU6HRYsWYdu2bQAAa2trrF+/HlOmTLmp4y1duhQODg4AgPj4eHz33Xct9n3hhRfEJ5NGjRqFwYMHN9tv2LBhGDZsmJjviy++2OIxv/nmG8THxwMAnJycsGRJ06k9iIg6o9I8aaHB8xanTbrOylqJUXc9gtv+9h7cfG9uhERlaRH2/PAeTm9ZhUuHtiD76kWj5GYMzs7O4nvMdfv375cpGzKHI0eOIDk5GU5OThAEAUDdmlQLFy6UOTMiaujFF1/EtGnTYG3detG88aiGiooKrFq1ylSpERERERGx0NCQwWDAn/70J3HBNIVCgVWrVuH222+/6WN6e3vjmWeeEdtPPvkk1q9fL+mjVqvx4osvYt26dWLs3XffveFxG25fs2YNXnrpJWg0GkmfX375BU899ZTYfu655+Dp6Xkz3wYRUYdiMBiajGi41fUZGhIEAU7u3je1r06rwY4vliMnqb64cGLD99Bq1MZK75Y1nKIPAKKjo6FWW05+ZFyrVq2CtbU1/Pz80LNnT/Tv3x+LFi1CSEiI3KkR0U0KDw/H1KlTJbF169ahqKhIpoyIiIiIqLO7tTkkOpkvv/xSMr1RaGgooqOjER0d3eq+Hh4eeOONN5rd9tprr+HIkSPYt28fqqurcc899+Dtt9/G4MGDUVNTg0OHDiE7u37B0jfeeAPjx4+/4fkmT56MV199FW+//TYA4L333sPKlSsRFRUFW1tbnDlzBhcv1t/Emjp1Kl5++eVWvw8ios4gNzcXmppqKFX16yN4Bhqv0HArrKyV6Dvhdpza9KMYKy/KxaUDmxA5db58iTUwfvx4vPfeezAYDACAqqoqnD59uslC0dTxXbp0CTExMWLbysoKr776KmbPni1jVkRkDI8++ij27t0LvV4PoG4Nuh9++AHPPfeczJkRERERUWfEQkMDeXl5knZiYiISExPbtG9QUFCLhQalUon//e9/eOSRR8TRDBcuXMCFCxea9Hv99dfbXBB48803YWtrizfffBMajQZZWVnNLvS2cOFCfP31120aZk1E1BlcvXpV0rZzcoO9i0e7j5OfmgDPwJ7idDLG0mvEFFw7dxT5aQli7NKhzQgbNhEOru3P09g8PT3Rv39/nD9/Xozt37+fhYZOaPXq1ZK2l5cXpk+fLlM2RGRMPXr0wOzZs7Fp0yYx9vvvv2Px4sXw9fWVMTMiIiIi6ow4dZKZuLi44JdffsHu3buxePFihIaGwt7eHi4uLujXrx+effZZxMbGtmvUgSAIePXVVxEbG4tnnnkG/fr1g4uLC+zt7REaGorFixdj9+7dWLduHRdzJKIuJSkpSdL2DAhtd7EgPy0RO756HTu/egP5aa0XnTXWttg7/G6scu2N/wWPxd7hd0Nj3fxCnYJCgRFzH4Ag1L8N67QanNu1vtn+cpgwYYKkffDgQfGpWOocsrKysHfvXkns3nvvhVKplCkjMqbr16SGXy1dk6jjyszMxLffftvi9flPf/qT5GEjjUZzwzXjiIjaQ1DZw+nPT0u+BJW93GkREZFM+Ih7A6+//jpef/11k55jypQpN72wdEv69OmDjz76yKjHJCLqyJoUGto5bZLBYMDZHXUjxPLTErDjy38gfMQUjJj7YIv7aK1tcK5XFC5ml8DDNxR+YX1veA43vyCEj5iC+OO7xFjy2Wj0GTsL7t2C2pWvKUycOBGfffaZ2C4qKsL58+cxcOBA+ZIio1qzZo3k5qS9vT3uvPNOGTMiY7p+TaLOx2Aw4Pjx41i/fj2io6NhMBjQq1cvREU1/f/drVs3zJs3T7JG3KZNm7BkyRIEBgaaM20i6oQEW1s43DZP7jSIiMhCcEQDERF1KrW1tUhPT5fEPNu5EHRO0mXkpsRJYs6efrecW2P9J98JpY2qQcSAC/s2GP08NyMgIAChoaGS2P79+2XKhoytrKwMGzduRHFxMXQ6HQBg7ty5cHJykjkzImqLzz//HIcPHxbX0mlYSGjswQcfhK1t/WgWvV6Pr7/+2uQ5EhEREVHXwkIDERF1KleuXBFvnAKAICjg0T24zfsbDAbE7vlVErN38UD4SOOORgMAO0cXRERJF91Nu3QSxdmpRj/XzZg4caKkvX//fvGmFnVsv/32G/Lz85GTk4OrV68iJycHo0aNkjstImoDQRCwYMECSez48eNITW3+vcPT0xP33HOPJLZr1642r0VHRERERNQWFjl1UnZ2Nnbu3Inc3FxxUcLu3bvLnRYREXUAFy5ckLRdffyhtLVr8/4FaYnIT02QxPpPnAsra9PMW997zAzEHdkBdXWFGLuw/w9ELXqyXcfRabW4fPmyUXPz9PREZWWl2E5MTMTvv/+OoCDp1E6RkZGwsbEx6rnJdNRqNX7++WcUFxcDqHu6WafT4dtvv2WxgaiDmD59Ov7973+jtLRUjK1fvx7PP/98s/2XLl2K33//XbymGwwGfPnll/j444/Nki8RERERdX5mKzTU1NTgnXfeAQAEBwfjgQceaLbfp59+ipdeeglqtVqMWVlZ4aWXXsIbb7xhllyJiKjjunjxoqTtGdizXftfPrxN0nZw9UToENPNc26jskfE2Jk4t7t+FEXaxZOoLCmAg6tnm49TkpOKn3MAj0zjvbUbDAaUG+xQXV4ixv61ejt6Dp8ktgszkvDmA8CwYcOMdl4yrW3btiErKwtVVVVizMPDo8kTz0RkuWxtbTF37lz89NNPYmzz5s34y1/+Anv7pguxuri4YPHixZIpkw4dOoSLFy+iX79+ZsmZiIiIiDo3sxUaDh06hLfffhuCIEgWl2xo48aNeOaZZ5rEtVot3n77bdjY2OCVV14xdapERNSBNR7R4BkQ2kLPpsoLc5F26ZQk1mfMTJONZriu16hpuHhwM7TqGgCAwaBH/LFdGDxzUbuO4+Hf+iLU7dVz+ETEHdkutsvyM41+DjIfvV6PVatWiaMZAMDBwQHdu3fHpEmTbrAnEVma+fPnY9WqVeKi7lVVVdi6dSvuvvvuZvsvWrQIP//8s2QUxBdffIEvvvjCLPkSERERUedmtjUadu7cKb6eP39+s31efPFFAHXzjvr6+mLevHkYOnQogLqnKt9+++0mC3wSERFdl5+fj9zcXEmsPQtBXzm6E0D9GgRKGxVCh05o0752NRV4bP1L+CzzAF4/9SMeW/8S7GoqWt8RgI2dA0IHS0dNJJzcB01tdVtTN5mAvtKRCsU5aSgvypMpG7pV0dHRSE5OltxodHd3x1133QWl0rQFNTKv69ekhl9tvSZRx+Dn54eoKOl7x/r161tcS8fBwaHJqPKTJ0/i9OnTJsuRiDo3fWkJcu+bLfnSl5bInRYREcnEbIWGkydPAgB69eoFHx+fJtsPHz6MhIQECIKAyMhIXL58Gb/99htOnjwpzjWqVquxcuVKc6VMREQdTONpk5S2dnDx6tamfdXVlbh6+oAkFjZ8EmxUbV3fwQD72go46TVw1NbAvrYCDYsWrek9ZjoAQWxraqqQFHO4zfubindQOFQOzpJYeqNRH9RxrFy5EiUlJeKNSFtbW7i4uODOO++UOTMyvrprUsOv9lyTqGNovCh0SkoKTp1q+Rp99913w8vLSxL74osvWixOEBHdmAGGslLJF99riIi6LrMVGvLy8iAIAiIiIprdvnXrVvH18uXL4erqKrZfe+01ODg4AAD27dtn0jyJiKjjalxo8PAPhaBo21td4sl94tRFACAICvQePd2o+d2Is6cf/PsMAgCoHF0wYPJdCOo/wmznb4mgUMC/zxBJLO0iCw0d0YULF3D27FnJtEkeHh6YPHlykxuPRNQxDBs2DMHBwZLY+vXrW+xva2uLhx56SBI7f/48oqOjTZIfEREREXUdZis0FBQUAKj7QNucQ4cOAQDs7Owwc+ZMyTZHR0eMHj0aBoMBcXFxpk2UiIg6rMbrM3gFtm3aJINej4QTeySxwH7D4ehm3puv/SbMxai7HsG8v3+GyCl3wc7Rxaznb0lgo+mT8tMSJQtEU8ewatUqVFZWQqPRAACsra3h7Ozc5IloIuo4BEFo8jt86NAhZGdnt7jPHXfcgW7dpKP9vvzyS3GtByIiIiKim2G2QkN5eTmAuj+GG6upqUFMTAwEQcDIkSNha2vbpM/1P4ZLSkpMmicREXVMer0eV65ckcTauj5DTnIcKorzJbE+Y2YYLbe28goMQ9jQCSZffLq9fMP6QmmjahAxIP3yGdnyofZLT0/H/v37UVRUJMbc3d3Rq1cvREZGypgZEd2q2267Dfb29mJbr9fjt99+a7G/UqnEo48+KoklJCRg7969JsuRiIiIiDo/sxUaHB0dAdSPbGjo+PHjUKvVAIBx48Y1u//1BQr5pA0RETUnNTUVVVVVkpiHf0ib9k06c0DSdvUJgGdgT2Ol1uFZWSvRvfcgSYzrNHQsa9asQW1tLSorKwEACoUCbm5uWLBgQbMPgRBRx2Fvb485c+ZIYhs3bkRtbW2L+8ycObPJlEtffvkldDqdSXIkIiIios7PbIWG4OBgGAwGcVHohjZv3iy+joqKanb/6wUKZ2fnZrcTEVHX1nhqPZWjM+ycXFvdT6OuQfql05JY2NAJvPnaSEDEUEk7J+ky1DVVLfQmS1JcXIxNmzZJ1mZwc3ODq6srZsww/8gdIjK+xtMnlZaWYteuXS32VygUeOyxxySxtLQ0ybp5RERERETtYbZCw+jRowEAmZmZWL16tRjPzc3FTz/9BABwcHAQ+zV28eJFCIKAHj16mDxXIiLqeC5duiRpu3h3b9N+ShsV5jz1PgZMmgd7Fw8oFNYIHjTWFCl2aN16DYTCylps6/VaZF45K2NG1Fa//vorampqJNNPuru744477oBKpWp5RyLqMIKCgjBy5EixbWVlhczMzBvuM3HiRPTu3VsS+/bbb8WR5kRERERE7WG2QsPSpUvF1w8++CCWLFmCZ599FiNGjEBRUREEQcA999zT7PoMOTk5SEpKAgD079/fXCkTEVEH0nhEg4tX2woNAODo7o3IqfMx74V/Y+Zf3oLKwcnY6d20qrIiXDywCUVZqbLmYaOyg1+Y9D04rdFIELI8NTU1WL9+PUpLS8XpJ11cXKBUKjF//nyZsyMiY1qwYAE8PDzwyCOPYMuWLfjzn/98w/6CIODxxx+XxLKzs7Fx40YTZklEREREnZV1612MY+jQoXj44Yfx7bffQqfTYc2aNZLtzs7OeO2115rdd8OGDTAYDBAEocURD0RE1HXpdLomC0G3dURDQ4JCAfduQcZK65ZkJcTiytGdyEo4D4NBj8qSAoyY+6CsOQX2HYrM+PpRDFkJsQgdMl7GjKg1W7ZsQUlJCaqrq8WYh4cHxo4di+7d2/87QkSWa+zYsdiyZYu4tl1bjBo1CgMHDsS5c+fE2Pfff4/bb7+dI56IiIiIqF3MVmgAgC+++AJ2dnb44osvoNVqxXhQUBBWr16NwMDAZvf75ptvxNdTp041eZ5ERNSxJCcnN5nqwdm7m0zZGEd+2lVkxp8T2ymxRzHktsWwVtrIlpN/nyEQhO9hMNQ9Ga9V16AgPQlAX9lyopbp9XrxwY5u3brB3d0dDg4OsLKyajKfOxF1fAqFAgpFywPW1Wo1YmNjm8THjx+PI0eOiO3Kykq89957uO2224yaX2RkJGxs5HsPIyIiIiLTMmuhwcrKCp9++ilee+01HD9+HGVlZQgMDMSIESNgbd18KkVFRZg7dy7mzp0LJyenFosRRETUdV2+fFnS9vLygo3KXqZsjCN0SBTO7/0fAAMAQFNThaz4cwjsN1y2nFSOzvDu0Qu5KfXTVOWlxAG4XbacqGUHDhxAenq62FapVPjPf/6DXr16wcnJcqYHIyLziI2NxfIV2+HhH9poiwJ6lwAUpF8VI1+t+hVxhgAobY0zqqEwIwlvPgAMGzbMKMcjIiIiIstj1kLDdR4eHm1+Qsbd3R3/+Mc/TJwRERF1ZI0LDSEhIdC20LejcHTzgk9wH+Sm1H9vqRdOyFpoAICAiKGSQkNuShx0Op2MGVFzDAYDVq1aJYmFh4dj2LBhEARBpqyISG4e/qHwC2s6Cm30gkex/fOG09gaUJyVgsipXMuFiIiIiNrGbItBExERmUrjhaB79OjR6j6XD2/FxQObUFGUZ6Ksbl2PyJGSdkZcDDTqGpmyqRMQMUTS1tRUIyEhQaZsqCWxsbG4cOGCJLZkyRIWGYi6ILVajby8G7/XefqHIqCvdLRBXPQ21FSWmzI1IiIiIupEzFZoUCgUsLKywpNPPnlT+z///POwsrJqcYolIiLqmtRqdZMb3cHBwTfcR6/X4dKhLTi782ds+NdT2Pb5a8hPu3rDfeQQ2Hc4BKH+rVqrqUXWlXPyJQTA0d0b7n49JLFTp07Jkwy1qPFoBl9fX0yZMkWmbIhIDllZWfjvf/+LWbNm4c0332y1/8CpdwOoL0Zq1DW4dGCTCTMkIiIios6kQ921NxgMcqdAREQWJikpCVqtdKKkHj16AJkZLe6TmxyHmopSsV2YkQRbe4dbykNjZYOjA2Yi71o87J3d4OjuDY3VrS16qXJ0hk9IBHKSLoqxa+ePI2jAyBvsZXqB/YejKPua2D516hT0ev0NFyEl80lNTcWhQ4eg0+mgUCggCAIWLVrEhzW6mOvXpMYx6hpOnDiBv/71r+Lnp+PHj2P69Ok33MfVxx/Bg8Yg5Wy0GIs/vgt9xs2EvbO7SfMlog7KVgWHex9oEiMioq6JnziJiKhDa7w+Q2BgIOztb7wQdOr545K2e7dgOHv63VIeWqUtjkXOwsVibYtzYN+MHpGjJIWGzPiz0NRWQ2lrZ5Tj34yg/iNwbtd6sV1aWopz585h8ODBsuVE9VavXg2DwYDMzExotVr4+vpi6tSpcqdFZnb9mkSWR6fVNnnvMjatVguDwYDKykoxtnbtWlj1n33D/SIn34Vrscdg0Ov+P1cNLuzbiBFzHzRpvkTUMSlUdnBaxOsDERHV6TCFBo1GAwBQKpUyZ0JERJak8c2avn1vfINfr9Mi9eJJSayHzCMEbiSw71Cc2PiD5KZPRtxZBA8cLVtOzp5+cPMNRHFOmhjbs2cPCw0WID8/H1u2bEFtba14g7GiogLz5s3DF198gf79+8ucIRGV5KTi5xzAI9PEH8V8+yDv7BGxuW3vQczqPe2Guzh5+CBs6AQkntwrxhJP7UdE1Gw4uXubLFUiIiIi6vg6TKHh6tW6ubNdXV3lTYSIiCxK44Wg+/Tpc8P+OUmXoK6ukMSC+o8wel7GYmvvBL+wfshKiBVj184fk7XQANT9mzUsNOzbtw/PPfccp0+S2bp166DRaFBSUgIAEAQB7u7usLGxQa9eveRNjohExhz51hJHN09kxJ0R21pNLbKvXoB/n0E33K//pDuRHHMIOm3dg14GvQ7n9/yOMQseM2m+RERERNSxWfzdAJ1Oh99++w27d++GIAit3kAiIqKuo7a2VixEXxcREXHDfdIvn5G0PfxD4WjhT2k2HnGRnXgeGnWNTNnUCewnLc4UFBQgNja2hd5kDmVlZfjtt9+g1+vFQoOLiwusra1xxx13wMaG8/MTdSVOHj7oFh4piaVeONHquncOLu4IHyFdPD7l3BGU5La89hERERERkUlGNISEhLS4beXKldiyZUubjqPRaJCXlyfOMSoIAubMmWOsNImIqIOLj4+HXq8X2wqFAuHh4bh06VKz/Q0GAzLiYiSxwL7DTJqjMfj3GQJBYSWZPik74QIC+8mXu4t3N7j6BCA/NVGM7dmzB4MG3fhJWTKd3377DVVVVSgrKxN/Lzw8PCAIAubNmydzdkQkh/ARUyQj4srys1GYkQzPgNAb7td3wu1IPLUf2v8vahsMesTu/g3jFz9lynSJiIiIqAMzSaHh2rVrEAShSdxgMKC8vBzl5eVtPlbDJ24iIiLw2GMcsktERHUaT5vUo0ePGy4EXZiZjKqyIknMP2KISXIzJlt7R/gE90ZOUn0BJSMuRtZCA1A3fVLDQsO+ffvw7LPPcvokGdTU1GDt2rUAgOLiYgCAs7MzbGxsMGrUKPj7+8uZHhHJxL/3IDi4eqKypECMxR/f3Wqhwc7RBX3GzMCF/RvFWNqlkyhIT2p1XyIiIiLqmky2RkNLQ3JbG6rb2PWnU++66y688MILsLOzM0Z6RETUCbR3IeiMRtMmOXv4wcWrm1FyUdVW4p6dn6K2shxWBadhfdkWv0x/CjW2DkY5fkCfIdJCw5UYGPR6CDLe1A/sPwKnt6wW2/n5+Th//jwGDhwoW05d1aZNm1BSUoLq6mrU1NQ9gezh4QEAmD9/vpypkUyuX5MaMuY1iToGQaFAz+GTcW7XL2Is9fwxDL3tPtjaO91w34hxtyH+2G6oayrFWMz2tZj68KvNPlRGRF2PvrwMRX//iyTm/v7nUDg5y5QRERHJySSFhpSUFEnbYDAgJCQEgiBgyZIleP3111s9hiAIUKlUcHV15ZzCRETUrPYuBN14fQb/iCFGu1kiGPTwLM2pa2grgeq6mLH49xmMU1tWwsXbHwERQ+HfZ7DRjn2zXL27w9HdC9CWibE9e/aw0GBmWq0WK1euBFA/msHR0REqlQq+vr4YO3asnOmRTCTXpAYx6nrChk3A+T2/i22dVoOrpw+ib9TsG+5nY+eAfhNuR8yOdWIsNyUOGXExCOgAowGJyAz0OmjTrzWJERFR12SSQkNQUFCzcYPBACcnpxa3ExERtVV1dXWTwvaNCg3lRXkoyU2XxDrSjRJHd2/M+/tncHD1lDsVCd/QfiiLPyq29+7di2eeeYbTJ5nRrl27kJOTA51Oh7KyuqLP9dEM8+bN4/8Loi7OztEFQf1H4HL0NjGWcGIPIsbOanVUXO8xMxB/fLdk6qWYHevQvfdAKBRWJsuZiIiIiDoek02d1NiKFSsAtP60KRERUVskJiZKpuO7PtVeSxpPm6RycIZXYE+T5WcKllZkAADfMGmhIT8/HxcuXEBkZKSMWXVOarUasbGxkpher8fHH3+MyspKlJaWQqfTwdbWFgaDAbW1tQgMDMSpU6du+dx105SZ7c9GIjKy8JFTJIWGiqI8ZCWeR/deA2+4n5W1EgOn3Y0j678UY2X5Wbh66gDCR0w2VbpERERE1AGZ7RPj0qVLzXUqIiLqAq5cuSJp9+jRA7a2ti32T798WtLu3nuQrOsbdBZO7t7o3r07SkpKxNjOnTtZaDCB2NhYLF+xHR7+9Qux5qbE4WxcMgwGAyqKS6HX6aFU2iOvrBZ+PcPx48lcALm3fO7kmKMIGRx1y8chInl4BYXDwcUDtVUVYizhxJ5WCw0AEBw5BnHRO1CUVT+KMHbPbwgeOBpKW66fR0RERER1LPbRNI1Gg6KiIri7u0OpVMqdDhERWZjGhYbevXu32FddXYm8a/GSWEDEUJPk1RUNHz4cu3btEtt79uzBs88+CysrTqthbB7+ofALq1v03GAw4NyuX6BUqaCuqYZBr4e1jS3snVwBARg88174BLf8e9EehRlJRjkOEclDEAT4hEYg7cJJMZYRdxaVJQWtjpYTFAoMnrkIe77/pxirqSjF5UNbETmVi80TERERUR2zPsqZnJyM5ORkZGZmttjn6tWruP322+Hk5IRu3brB3t4eM2fOxKVLl8yYKRERWbr4eGnh4EaFBqXKHrP++k8MnHYPvHv0htJGBb+wfqZOscsYNWqUpF1UVITTp0+30JuMJTclDgXpVwEAtZV1azPY/X+RwdUnAN49esmYHRFZGq+AMFjb2EIQFAjsOxxTHnoZ9i4ebdrXL6xvk9EPlw9vRVVZkQkyJSIiIqKOyGwjGi5duoT+/ftDEAQ89thj+O9//9ukT1paGkaNGoWioiJx3m2dToedO3fi8OHD2Lt3L0aMGGGulImIyEJpNBokJUmfsL5RoUEQBLh3C4J7tyD0n3gHtBo1rJU2pk7T5CpLCpGVEIugASNlzcPX1xd9+vRBXFycGNuxYwffs03s0oFNAACdVgt1TRWsrK1ha+cIoG4+dkEQ5EyPiCyMldIGkdMWIGzoeDi4uLd7/8EzFyEr4TwMBj0AQKupRezu3zHqroeNnSoRERERdUBmG9Gwc+dO8fUDDzzQbJ9nnnkGhYWFzW6rqqrC4sWLodFoTJIfERF1HMnJydBqtZLYjRaCbqyjFxnO7lyPTZ+8gP+9/wSOb/gOOVflH/U3Y8YMSXvv3r2ora2VKZvOrzAzBVmJ5wEACisFHF094RUUDgiA0kaFkEFjZc6QiCyRd1D4TRUZAMDVxx+hQ8ZLYklnDqIkN8MYqRERERFRB2e2QsO+ffsAAJ6enhgyZEiT7RkZGdiwYQMEQYCDgwPWrl2LsrIyXLhwAYMHDwZQd2Np/fr15kqZiIgsVOP1Gfz9/eHo6ChTNuZXkH4VpXn1N3ayEmJlzKbO1KlTJU/QV1VV4ciRIzJm1Lld/P/RDAAgCAq4eHfH/Fe+xKy/vI3hcx/kAq1EZBKRU+fDWmkrtg0GPWK2r5UxIyIiIiKyFGYrNKSnp0MQBERGRja7/ffffxenS3rxxRexcOFCODo6om/fvli1apXYb9OmTc3uT0REXUd71mfojLr3kr6XZibEiu+hcvH29m7yIMGOHTtkyqZzK8lJR9rFE5JY7zEzYK20gYd/CEczEJHJ2Du7IWLcbZJYZvw5ZFvAyDoiIiIikpfZCg0FBQUAgO7duze7/cCBA+LrZcuWSbb16dMHQ4YMgcFgwLlz50yUIRERdRSNRzT06tW1Fr3tFi4tNFSVFqK6vFimbOpNnz5d0o6OjkZFRYVM2XReF/ZvlLSVKnv0GjVNnmSIqMuJiLoNKkcXSSxm+1oY9HqZMiIiIiIiS2C2QsP1tRfs7e2b3X7kyBEIgoCIiAh069atyfbQ0FAAQE5OjumSJCIii6fX65GQkCCJtTSiQV1TjUuHtqAkN0P2J/6NycW7O+ydpXNsl+TIP0f25MmTYW1tLbbVajX2798vY0adT0VxPq6dPy6J9RkzEzaq5v++IiK6EZ1Wg5RzR7D7u3egrqlq0z5KWztETpkviRVlpSAl9qgpUiQiIiKiDsJshQalUgkAzT7ZGB8fL454GDduXLP7u7m5Aaib85mIiLqu1NRU1NTUSGItjWjIuXoRMdvXYvOnL+B/7z+Jk3/82CkKDoIgoHuvgZJYSU6aPMk04OzsjNGjR0tinD7JuJLOHARQ/zOstFGh9+jpLe9ARNQMg8GAc7t/xf/eewLRv3yOnKSLSI453Ob9w4ZNgIuXdKT6uV3rodNqjJ0qEREREXUQZis0eHt7AwDi4uKabNu5c6f4uqVCQ1lZGYCWR0QQEVHX0HjaJG9vb7i7uzfbt+EiyVWlhSjNz5IsWNyRdQsfIGmXFmRDq1HLlE29GTNmSNqnTp0SRzXSrcnNzUV2wnkAgEGvR0VRPvzCB8LGzkHmzIiooxEEAaV5WaipLBNj8cd3t7kYr1BYYdCMhZJYZUkB4qK3GzVPIiIiIuo4rFvvYhwDBw5ESkoKzp49i6tXryIsLEzc9tNPP4mvJ0yY0Oz+ycnJANDstEpERNR1NF4IuqXRDAaDAVmJFySxxosoG5PWSolz4eNQmJUClYMLHFzcobVSmux8vmH9IAgKGAx1c2Ib9HoUZaYgoM8gk52zLaKiomBnZ4fq6moAdVNd7dq1C3PnzpU1r85g8+bN4k3A2uoKqGsqcS32KErzMtB79DSEj5gic4ZkSa5fkxrHiK7rNXKKZGH5svws5CZfhm9o3zbt799nMHyC+yA3pf5Bsov7NyJk8DjYO7sZPV8iskBKW9jPurNJjIiIuiazjWi48866Nx+9Xo8777wT+/fvx4ULF/DYY4/h7NmzEAQBo0ePhp+fX5N9NRoNzp8/D0EQutyCn0REJNV4RENL6zNUlxWhsiRfEvPr2d9keWmUKuwdsQCr3fpgQ8g47B2xABqlymTns1HZwysoXBIrzEgy2fnaSqVSNXloYNu2bfIk04lkZWUhOjoaQF0RrbaiHCoHZyisFCjNy0DmlXPyJkgW5/o1qeGXKa9J1PH4hETA2Uv6EFf8sd1t3l8QBAyZdR+A+pGCGnUNzu1ab6wUicjCKezt4fzYM5IvBWehICLqssxWaFi4cKF4M+jy5cuYMmUKBg4ciG+++Ubs8+KLLza77549e8QnI4cPH276ZImIyCIZDIY2j2goyEiWtFWOLnD1CTBZbnLwC+snaRc2+p7lMnPmTEk7Li4OSUnyF0E6shUrVkCvrxu9olXXQqfVQOXoIm4PH8nRDETUPoIgoNfIqZJY+uXTqCwtavMxPPxDEDokShJLOnMIhZkpRsmRiIiIiDoOsxUarK2tsXHjRnTv3h0Gg0HyBQBPP/00brvttmb3XbVqlfh64sSJZsmXiIgsT3Z2NsrLyyWxlkY0NH663y+0X6dZn+G6xoWG8sJcVFeUypRNvZEjR8LLy0sS46iGm5eTk4PNmzeL7drKctg6OkNhZQUAcHTzQreeA1ranYioRSGDx8G6wTQnBoMeV0/tb9cxBk5bAKVNw9EyBpzevLLN6z0QERERUedgtkIDAISHh+Py5cv45JNPcNddd2Hq1Kl46KGHsGvXLnz44YfN7lNYWIjTp08jKCgIffv2xciRI82ZMhERWZDG0yY5OzvDx8enST+9Xt/k6X7fsLbNOd2RePiHNLq5A+RcvSRTNvUUCkWThwd27doFnU4nU0Yd208//QStVgsA0Ot1UNdUwa7haIYRUyAozPonHRF1EjYqewQPGiuJJZ7aB71O2+Zj2Du7oe+EOySxvNR4pF440cIeRERERNQZmW0x6OscHR3xt7/9DX/729/a1N/DwwMJCQkmzoqIiDqC5tZnaG6UQlpaGjQ11VCq6m/CN376vzNQWFnDO7gPMuPPirGcpIsIHjhaxqzqzJkzBz/++KPYLioqwpUrV9C3b+cr+JhSbm4uNm7cKLY1NVWwtXcURzNYWSsROnSCPMkRUacQPmIKEk/uFdvVZcXIiItBYL+2T1kbMW4Wrp7ah4ri+rWRYravg3+fwbBW2hg1X7oxtVqN2NhYWc4dGRkJGxv+/yYiIuqqzFZo2LRpk/h65syZUCqV5jo1ERF1Em1dCPrSJelT/U4evnBw9TRZXnLyC+snKTRkX70Eg8Eg+zRRQUFBGDBgAM6fPy/GTp48yUJDO33//ffQaDQA6tYo0dRUwcXHX9we2H8EVA5OcqVHRJ2Ae7cgeAWGIz+t/uGu+ON72lVosLJWYsis+3BwzadirLIkH3HR29B/4lwjZkutiY2NxfIV2+HhH2rW8xZmJOHNB4Bhw4aZ9bxERERkOcxWaJg7dy4EQUDv3r1x++23m+u0RETUiTReCLqlQsPly5clbb9Q09/ctlVX4Y4D3+K2kkIoSy/CJnkP/pjwMGpt7E163oZTQrl4d0ePyFHQ67Swspa/oD9nzhxJoeHy5cuoqKiQMaOOJTMzE3/88YfYrq6uhpWNrTiaAQB6jeAi0NS869ekhsxxTaKOKXzkZEmhISfpIkrzsuDi3a3NxwjoOww+wX2QmxInxi4e2NRksWgyPQ//UPh1wikjyfLoK8pR/M+XJTG3V96BwpEPQRARdUVmKzSoVCrU1tZi8ODB5jolERF1IgUFBSgsLJTEevXq1aSfWq1uUpDw69nfpLkBgEKvQ0Du1bpGLYCyupipufoEYML9z6AwIxk+IX0t6sbC1KlT8eGHH6K2thZA3doZZ86cwbx582TO7NaYa1qKb775BmVlZWK7tLQUtvb1azO4+QXBM7CnyfOgjklyTWoQI2pOUP+ROL1lNWqrysVYwok9GDZnSZuPIQgChs6+H1v/8wqAuoWgteoanN25HiGDxhk7ZSKyBDotNBfPNYkREVHXZLZCg5+fH65duwaVStV6ZyIiokYaFw/s7e0REBDQpN+FCxegVqsbRAT4hESYODv5CIKAgIihKM3LkjuVJhwdHTFp0iRs375djJ0+fRoGg0HGrG6dOaalqCjOR/T2feK/lV6nRbVaC0eH+tEM4SOmyD5FFhF1DlbWSoQNHY9Lh7aIsaSYQxg4fQGUNm3//OberQfChk7A1dP7xVhyzCF4dA8BEG7MlImIiIjIwpit0NC/f3+kpKTg6tWrrXcmIiJqpPH6DD179oRCoWjS79SpU5K2R/dg2No7mjS3rkyn1TaZqqqh0NBQVFZWwmAwoLa2FikpKdiwYQPCw41zw0muhSdNPS3F4XX/gbWtrdiuqSyHrX39NARKWzuLWPSbiDqPniOm4NKhrbg+GkFTU4XU2GMIGzaxXccZOH0BUs8fg0ZdI8YuH94Kw6IJRsyWiIiIiCyN2QoN9957LzZt2oSjR48iPT292adQiYiIWtLW9RliYmIkbd/QzjuawRKU5KTi5xzAI7P5PykMeiuUGVSoKS8FULduxEertyBi3OxbPndnXXiyODsV184fE9sGgwFWVtYw6PViLGRwFJS2dnKkR0SdlJO7N7qFD0BWQt3UcM4efrBux2iG6+wcXdB/0p2I2bFOjJXkpOPo0aMYPrztC0wTERERUcditkLD3Xffjc8//xzR0dFYtmwZtm3bBtsGT+oRERHdSOMRDc2tzwAAo0ePRkFBAQrOXAQAeAf3MXluXV1rT/dHjJuFC/s2iO2CtKvwCgyDtQ3/DmjOud2/Sdp6nRbWNrbQauqnBAsfMdncaRFRF9B71HRYK20RPnIKfEP73vT0bL3HzEDiyX0oL8oVYz///DMefvhhODg4GCtdIiIiIrIgTeecMNWJFAr88ssvGDJkCA4cOICRI0di165d5jo9ERF1YGVlZcjKkq5B0NKIhmXLluGll17ClIdewfRH/wGf4Ob7dXYNp6yQW9iQ8QDqb1apayolT+xTvfy0q8iIOyOJdQ8fCFsHZ7HtExwBVx9/c6dGRF1A994DMX7xU/AL63dLa8BYWSsx5LbFklhpaSm+/vrrW02RiIiIiCyU2UY0PPjggwDq5tSOjY3F+fPnMXPmTLi5uWHgwIHw8vKCnV3rUwAIgoDvv//e1OkSEZEFaTxtklKpREhIyA33sbK2hneP5kc9dEZajRrZiReQduE4cq/Fo7aqHPNf/sIiFgt2bDQdBwAknNiLsKET5EvKAhkMBpxtMNUIANjaO2H8/c9AYWWFQ2v+jeKcNISPnCJThkREbeffZzC69xqIzPhzYuznn3/G3LlzW30PJyIiIqKOx2yFhh9//LHJzQ6DwYCioiLs37+/XcdioYGIqGtpXGgIDQ2FtbXZ3sI6BG1tNc7v3QClqn4+7bL8bLh4d5Mxq3rhIyZLCg2FGUkozEyBR/dgGbOyLJlXziI3JU4S6zfhdtio6h7E8O7RC33GzuK6I0TUIQiCgKGzlyD76kUxptfr8f777+Orr76yiEI4ERERERmP2aZOAuoKCw2/Worf6IuIiLqehIQESbul9Rm6MlsHZ9g6OElijW9ay6l770Gwd3aXxBJP7pMpG8tj0OsRs+NnSczB1RO9Rk1r0pc354ioo3D29EXfqNmS2JkzZ7B7926ZMiIiIiIiUzHb46ArVqww16mIiKiTYaGhdYIgwL1bDxSkJ4qxvJQrFrNosEJhhbBhE3F+7+9iLOXcEQyeuUh8Yr8rS4o5hNK8DEls4LS7YWWtlCkjIqJ6xdmpgCDAzTew3fv2m3AH4qJ3AKgWY5988gnGjh0Le3t7I2ZJRERERHIyW6Fh6dKl5joVERF1Imq1GikpKZJYz549m/SrqqqCnZ1dl37a2717sKTQkJsSB4PBYDH/JmHDJuLCvg0wGPQAAK26BslnD6N3M0/tdyVadS1id/8mibn5BSE4coxMGRERATqtBmkXTyL++G7kpybAv88QTFzybLuPY21ji95jZyLzyP/EWH5+Pr777js8+eSTxkyZiIiIiGRk1qmTiIiI2islJQU6nU4Sa67Q8Pe//x233XYbXnvtNezfvx81lWXmStFiuPsFSdpVZUWoKM6XKZumHFzc4R8xRBKLP7ITBr1epowsw5WjO1FVViSJ9R49AzVV5TJlREQEJJ05hOhfPkd+at2owswrZ1FZUnBTx/IJ7oP+/ftLYmvWrMG1a9duNU0iIiIishAsNBARkUVrvBB0t27d4OjoKInpdDrExsYiLy8P27dvx4oVK1CSk2bONC2Cg5sXVA7Oklhu8mWZsmler5HS0QtlhdnIbLBIdFdTU1mOiwc3SWK+of2QffUC/vfuEzi87j/ITY7jOlVEZHbBA0dDaaMS2waD/qbX1hEEAffffz+sresH1Ot0Orz//vu8vhERERF1Eiw0EBGRRUtMTJS0m1uf4cqVK6iqqpLE3Px6mDItiyQIArx79JbE8lLiW+gtD9/QCLj6BEhicdHbZcpGfrG7f4WmRvqz2zdqNtIunIRer8W188ew69u3cPX0AXkSJKIuS2lrh+DB4ySxxFP7odNqbup4vr6+WLx4sSR26tQpbN/edd8DiIiIiDoTFhqIiMiiNR7R0Ny0SWfOnJG0u3XrBlt7xyb9TEmnsEZ84ECcsvNGrHsI4gMHQqcw21JIIp+QPpJ2bopljWgQBAF9xs6UxHKSLqIkJ12mjORTnJ3a5Ong4MgxKMxMgV6vFWNW1koE9h1m7vSog7t+TWr4Jcc1iTq2XiOmSNo1FaVIv3T6po/30EMPwdfXVxL7+OOPUVpaetPHJCIZWSthO2aC5AvWSrmzIiIimZjt08aDDz5olOMIgoDvv//eKMciIiLLZjAY2jSiISYmpkkfc6/QoLaxw5bxD+HigU3w8A+FX1hfM2dQxydYOqKhojgflSWFcHD1kCWf5gQPHIOY7etQ22ANgrgjOzDqrodlzMq8DAYDTm1eJS6MDQBW1jYYOG0Bdn37lqRvjwGjzV44o47v+jWJ6Fa4+gbAu0dv5F27IsYSTuxBj8hRN3U8Ozs7/P3vf8fTTz8txkpKSvDZZ5/htddeu+V8ici8FA6OcHvxrdY7EhFRl2C2QsOPP/4IQRCMciwWGoiIuoacnByUl0sXxG08okGv1+PcuXOSWO/evXGywtTZWSZXnwDY2DlCXV3/D5CbEoeQQWNlzErKylqJ8JFTcGHfBjGWci4aA6cvgJ2ji4yZmU/6pVNNRpv0m3A7inPSmiy2Gj5S+kQxEZE59Ro5VVJoyE2JQ0luBlx9/G/qeOPGjcOkSZOwb1/9iK4//vgDs2fPxqBBg245XyIiIiKSh1mnTjIYDO36am4/IiLqOhqPZnB0dISfn1+TPhUV0qpC797Sp/q7EkGhgHePcEksL+VKC73l02vkVCgaTOOi02pw5chOGTMyH61GjdNb10hiDq6eiBh3GxJO7JHEPbqHwMM/xJzpERFJBPQdClWjInDC8T0t9G6b5557Dvb29pLYO++8A43m5tZ/ICIiIiL5mW1Ew4oVK9rUT6/Xo7S0FBcuXMDmzZtRUFAAlUqF5cuXN7m5REREnVvj9RnCw8ObjI5rvD5DQEAA3NzcAOSbOj2L5RPcBxlx9dNJ5abEyZhN8+ycXBE8cAySYg6KsfhjO9F3/GzYqOxvsGfHF3d4KypLpD+fg2cuQnVFKbISzkvi4SMmG21EKBHRzbCyViJs6ERcPLBRjCXHHMKgGfdAaWt3U8f09vbGY489ho8++kiMpaSkYNWqVUabcpeIiIiIzMtshYalS5e2e5/a2lq89tpr+PDDD/H5559j165d6NOnT+s7EhFRp9B4REN4eHiTPo2nTeK0C4B3o3UaygqyUV1eAjsnV3kSakHf8XOQFHMIQN2IRU1tNRKO70G/CbfLm5gJVZYW4eKBTZKYT3AfBPUfgbM71uH6vwUAKFX26BE52swZEhE11XP4RFw6uElcV0ajrkHKuaMIHzH5po95zz33YNu2bYiLqy+Gf/fdd5g2bRr8/W9uWiYiIiIiko9Zp05qL1tbW3zwwQd47rnnkJmZifnz56OqqkrutIiIyEyaG9HQkMFgQGxsrCTGQgPg3q0HlDYqSawo85o8ydyAi3c3BPYdJonFRW+DVl0rU0amd2rTj9BqGn5/AobOXgK9Tourpw9K+oYOjoK1ja15EyQiaoajmxe69x4oiSUc331LU9sqFAq8/PLLUCjqP5Kq1Wq89957nDKXiIiIqAMy24iGW/HGG2/gu+++w5UrV/DDDz/gr3/9q9wpERGRiVVWViIzM1MSa1xoyMrKQmFhoSQWGRmJ3Nxck+fXmI26GtOOrUVUQRZsKpOgyjyOXaMWQW1zc9NK3AqFwgo9h0+GoBDgFRQOz8CeFrvIcr8JtyPt0kmxXVNZhqtnDqL3qGkyZmUaaRdPIf3yaUms5/BJcO8WhOSzh1FbJV34nItA0624fk1qSK5rEnUO4SOmSKblK85JQ0FaIryCmo42bEyn1eLy5cvNbhs1ahR27doltvfu3YtPP/0UY8eObbZ/TU2NOOLR3d0dKpWq2X7XRUZGwsbGptUciaj99JUVKP3P+5KYyxN/h8LBUaaMiIhITh2i0GBnZ4eoqChs2rQJ69atY6GBiKgLaDxtkkKhQEiIdFHcxqMZ3NzcEBAQIEuhwUqvRa+0c3WN6jygCNg7YoHZ87huyG33yXbu9vDwD0G3ngOQlVi/NsGlA5vQc9hEWFkrZczMuNQ11Ti1+SdJTOXgjEHT635GEo7vlWzzDe0HF69uZsuPOh/JNen/yXlNoo6vW88BcHT3RkVRnhiLP767TYWGkpxU/JwDeGQ2/fipdRuAMv1B1FSUibH3//sdjhbawdbeqYUj1q3dd+5w2g3PW5iRhDcfAIYNG3bDfkR0k7Qa1B45II099owsqRARkfw6RKEBAHx9fQEAV69elTkTIiJqK7Va3aQY0Fa7d+9GZWWl2Pb3929yrK1bt0r69OrVC6dPn/7/pyY7zFtcl9dvwh2SQkNVWRESTuxBnzEzZczKuM7t/AVVZUWS2JDbFsPW3glFWdeQn5Yg2daLoxmIyMIICgXCh09GzI51Yizzyjlo1bVtmubNwz8UfmF9m9027t4ncGDVRw0iBqSeP47xi5+6pZxvNJLC1DiSgoiIiLqaDnMXpqCgAABQVlbWSk8iIrIUsbGxWL5iOzz8Q9u974X9Z5FXVj+XvbXWAf/eLb0ZG304BuUN+qSonfDv3QlIjjmKkMFRN584mZV3cG/4BEcgN6X+ZtDFA5sQNnQClLYdf5qX/LSriD++WxLz6zkAwQPHAAASju+RbLNzdoN/xBCz5UdE1FahQycgds9vcHDxRPjIKQgZPM4oa8kERAxBjwGjcO38MTGWdukkUi+cQFD/ETd93BuNpDAljqQgIiKirqhDFBpKSkqwd2/dlALe3t4yZ0NERO1xoycYb+TsjnVQNph3OaDvUMlxNOoaqKsrJX3Ch0+CV1A4CjOSbi1pMitBEDBo+gLs+Op1MVZTUYorR3ei/8S5suVlDHqdFic2fAegfmFTK2slRtzxAARBgFZdi2uxRyX79Bw2CQqFlZkzJSJqncrBCbP++k+4eHeHIAhGPfawOUuRffWiZL2ak5t+hG9oxA2mUGrdzf4dQkRERETto5A7gdYUFRXhnnvuQWlpKQRBwLhx4+ROiYiITEyv16EkN10Sc/cLkrSVNiosWP41pjz0CiKnzEf3XgPh3j3YnGmSEXkFhaN7r0GS2KVDW1BbVSFTRsZx6eBmFOdI5xAfMOUuOHn4AACsbWwx64l/ImLsbbCxc4QgKNBz+EQ5UiUiahNXH3+jFxkAQOXojOG3L5PEaipKcWrLKqOfi4iIiIiMz2wjGlauXNnmvlqtFoWFhTh37hy2bNmCior6mwxPPPGEKdIjIiILUlaQA51WI4m5+gU26ae0tYNfWF8+qdiK6opS5KcmojAjGQOn3W2SG0TGMHDaAmTGnxXbmpoqXDq4CYNnLpIxq5tXlHUNsXv/J4m5+QYiYuwsSczZ0w9DbrsPkdPuRn5qIuyd3c2ZJhGRxQgaMBIpsUeQERcjxlLORiN4wGh07z1QvsSIiIiIqFVmKzQsW7bspm5sGAz1Uw28/vrrGDHi5ufoJCKijqE4K1XStnN2g52ji0zZdFy1VRXY/sVylBfmiLGQwWPh4tVNxqxa5t4tqMkc3XHROxA2bCKcPf1kzKz9dFoNjqz/Ega9TowJggIj7vwTFFbN//llrbRh0YyIujRBEDBi7oPITbkCTU2VGD++4TvMefpfsFF1/HV7iIiIiDors06dZDAY2v0FAKGhoVi/fj1ee+01c6ZLREQyKc6WFhrcfJuOZqDW2dg5QKuulcTyUxNa6G0ZIqfOh0JRfyNer9fizNY1MmZ0c2J3/9pk+q++42+HV2CYTBkREZmWQa83ynHsnd0xdNZ9klhVWRFOb2n7CHkiIiIiMj+zjWhYunRpm/sqlUo4OTkhODgYw4cPx/Dhw02YGRERWZrGc9q7d+shTyIdnCAI8AoKR9rFE2IsPzUBYUMnyJdUK5w9/dB7zAxcPrxFjGVciUFm/Dl07zVQvsTaIfvqRVw6tFUSc/MLwoDJd8qUERGR6RRnpyLh+B5kXb2A25/+F6yslbd8zNChE5ASeww5SRfFWNKZgwiIGIqAiCG3fHwiIiIiMj6zFRpWrFhhrlMREVEH13jqJI5ouHneTQoNiTJm0zb9J92J5LOHUVNRKsZOb14F39C+RrmBZUrVFaWI/uVzAPVTPyqsrDFmwWMWnzsRUXtoNWrs/f5d5KXGi7HUCycQMmjsLR9bEASMmvcnbPn3i9Coa8T48Q3fwSuwJ1SOzrd8DiIiIiIyLrMVGjoSnU6HS5cu4dSpUzh9+jROnTqF8+fPQ6OpW5h0/PjxOHDgQJuOde3aNQQHB7fr/JMnT8aePXva3D8uLg4//PADdu7ciYyMDNTW1qJ79+4YNWoUlixZgsmTJ7fr/EREcqquKEV1RYkk5tYtSNK+emo/FNbW8ArqBUc3L4td3NgSeAWFS9ql+ZmoraqArb2jTBm1zkZlh0HT78Gx378RY2WF2bh4YBMip9wlY2Y3ZtDrEf3z55ICCQAMnHq3pFim1+uw6+u30L33QIQNm8j1R4ioQ7JW2kBhZSWJJZzYY5RCAwA4untj6JwlkveCmopSHN/wHcYvfprv/UQ3qba2FpmZmSgoKEBpaSlKS0tRUlKC6upq6HQ66PV66HQ6CIIAe3t7ODg4wN7eHo6OjvDx8YGvry+8vLygVJrvAQq1Wo3Y2Fizna+hyMhI2NjYyHJuIqKOhoWGRjZu3Ij77rsPVVVVrXe2AO+88w5ef/11sQhyXWJiIhITE7Fy5Urce++9+Prrr+Hk5CRTlkREbdd4NIOVtQ2cPXwlsdi9/0NVaSEAwM7RFWMWPAa/nv3NlmNH4t4tCFbWNtBp1WIsPy0R/r0HyZhV60IHRyHh+B4UZiaLsQv7NyIgYijcGxWeLMXFg5sk03wAQLfwSESMu00Sy7h8BvlpCchPS8D5Pf9DYL9hGH33nznigYg6nPCRU5GTfFls56cmoCgr1WjX6dAh45F++TQy4mLEWPrl00iOOYTQIeONcg6izup6QeHXX3/FtWvXkJaWhoyMDBQUFNzysQVBgJeXF/oGBuBZI+TamtjYWCxfsR0e/qFmOFu9wowkvPkAMGzYMLOel4ioo2KhoZGSkhKTFRmcnJywZMmSVvv17t27Tcdbvnw53nrrLbHdrVs3jB07FiqVCmfOnMGlS5cAAOvWrUNhYSG2bt0Ka2v+Lyciy9ZkIWi/QAgKhdiuLCkUiwwAUF1RAjtnN7Pl19EorKzhGRCK3JQ4MZafmmDxhQZBocCIuQ9i+xfLYTDULTBq0Otw7LevMfMvb0JhZVnvZxlXzuLcrl8lMTtnN4y5+zHJzy8AxB+vH7Wo12tRUVzAIgMRdUgBEUNg5+SG6vJiMZZwYg9G3vmQUY4vCAJGznsYWz79O2oqy8T4qU0/wSckAo5uXkY5D1FnkJubizNnzuDMmTM4ffq0eD/A2dkZikZ/i9wqg8GAvLw81BTkAT52km2vvvYaIoaPxNChQxEeHm60c3v4h8IvrK9RjkVERKZhWZ/SUfeGVVhYCIVCAXd3d9ny8PHxwbBhw8SvnTt34t///vctHdPd3R3//e9/jZLf3r17JUWGF154AW+99ZZkSN+6devw4IMPoqamBrt27cI777yD5cuXG+X8RESmUpwtXQjazU/6VGR+aoKkbWPnCBevbibPqyPzCgpvUmjoCDz8Q9B3/O24eGCjGCvKvoZLh7ag/8S5suXVWEleJqJ//i8arssgCAqMu+evTeYRL83PajLqodeoKeZIk4jI6BRW1ug5bCLO7/ufGEs5dwSDZy6CjcruBnu2nZ2jC0bc+SccXP2xGNOoa3D0168x9U8vNynmEnUVarUap0+fxuHDh3H06FFkZmaK2/R6vWx5HT9+HLuOHgcAuLm5YcKECZg4cSKGDRtm1umWiIjI/MxSaCgvL0dlZSU8PDxafGM5dOgQPvzwQxw4cACVlZUAAJVKhUmTJuGZZ57BxIkTzZEqZsyYgdTUVAQGShcePXHiRAt7yOPll18WXy9cuBDvv/9+kz733nsvSktL8dhjjwEAPvzwQzz++OPw9PQ0W55ERO1V1MyIhobyrsVL2l6BYRZxk0GvsEK6TxgqSwqhtFXBxs4BeoVV6zuagVdgT0m7IP0q9DqtxY0KaM6AyXci/fJplOZliLHYPb/DNyRCxqzq1VZV4ODKj6GprZbEB05bAJ+QPk36J57YK2nb2jshqP9Ik+ZIXdP1a1LjGJGxhQ2fhAv7N4qjz7TqGiSfPYzeo6YZ7RyBfYcidPB4JMUcFGO5KZcRd2QHIsbNMtp5iCxdVVUVDh48iH379uH48eOorq5ufacWuLi4wNXVVfyvg4MDrKysxC+dTofq6mpUVFSgqqoKJSUlyMnJQU1N/QLtOgNwoVYnOa6u/rkLFBcXY8OGDdiwYQMcHR0xfvx43HHHHRg0aBDXWSEi6oRMdoehvLwcb731FtauXYvs7Oy6k1lbY+TIkXjhhRdw22318xW/+uqrePfddwHUjWi4rrq6Gtu2bcO2bdvw9NNP48MPPzRVuiJfX9/WO8ns1KlTOHnyJADAysoKH3zwQYt9H330UXz88cdITExEeXk5Vq1ahaefftpcqRIRtYtOq0FZfpYk5t5oRENB+lVJu/Fix3KptbHH+ml/w8UDmyxuaHfjfyOdVoOirFR4Bph3ntubYWWtxOj5j2LHl/+QTKF0aO1nGDK79ekITUmrUePAqo9RVpgtifeIHI2+4+c07a+uRdKZQ5JY2NDxnDaJTOL6NYnI1Bxc3OEfMQTpl06JsYTje9Br5FSj3kgcOmcJcpIvo7IkX4yd3fkzfEMj4N6th9HOQ2RpNBoNjh07hh07duDgwYOora1t874KhQJhYWGIiIhAWFgYAgIC0L17d3Tv3h12du0fdWQwGFBeXo6cnBykpaUhISEB2xITkZCQgNzc3BvuW1FRga1bt2Lr1q0IDAzEHXfcgdmzZ8PDw6PdeRARkWUySaEhKysLEyZMQFJSkqRwoNFocPjwYURHR+OTTz7Bk08+iW+//RbvvPMOBEGQ9G3IYDDgk08+QUBAAP72N35g2rhxo/h68uTJCAgIaLGvIAhYtmwZXnnlFQDAhg0bWGggIotVkpMu3kyuI8DVt/4ap9Wom4x48AyQPq1PTdnaO8LFqztK8+uH1BekJXaIQgMAeAaEot+E23Fh/0YxVlVWhNhd66GfHSlLTnq9HofXfYa8a1ckcY/uIRg17+Fmb65dO38M6prKBhEBPYdPNnGmRESm12vkVEmhoTQvA3kpV5od2XWzbFR2GH33o9j97T9xfao6vU6Lw2v/g1lPvA2lrXGmaiKyFKmpqdiwYQO2bNmCkpKSNu3j7OyMwYMHo1+/fqioqICvry9uu+22myoqNEcQBDg7O8PZ2Rnh4eGYMqV++sf8/HzExMTg9OnTOHPmDNLS0lo8TlpaGv7zn//giy++wIwZM3D//fcjLCysxf5ERNQxmKTQsHjxYly9erXFJ1gMBgNeeOEFzJgxQ7wBbjAYMHr0aEyfPh1+fn6oqqpCTEwMfvvtN1RXV8NgMODVV1/FkiVL4ObWtRf93L9/v/h6woQJrfZv2Ofo0aOora2Fra2tCTIjIro1jReCdvLwkdw4KM66BoO+4fBsAZ4BIWbKrmPzDAyTFhrSk2TMpv0GTLkL+akJyEm+LMYKM5Lx66+/YsSIEWbNRa/X4+L+jci7FieJ2zm6Yvz9z8Dapvn32IQGi0ADQLfwAXDy8DFZnkRE5uIbEgFnDz/JCK+EE3uMWmi4fp6+Ubfh0qEtYqysMBsn//gRYxY8ZtRzEclBq9Vi7969+P333xETE9Nqf0EQ0L9/f4wbNw5jxoxBWFgYFAoFqqursWvXLqPmplarERsbe8M+7u7umDZtGqZNm4bc3Fyx6HD16tUW91m/fj3Wr1+PAQMGYNasWejTp0+Te0mXL1+GBS4xSkREjRj9Sr1v3z4cOHBAfGN4/PHHsXDhQvj6+iI7Oxvr1q3D119/DY1Gg4cffhgFBQVQKBT4/vvvsXTp0ibHe+uttzBjxgzExcWhqqoKq1evxhNPPGHstM1Cq9Vi165dOHPmDAoKCmBnZwcvLy8MHz4cQ4cObfPCSHFx9Tc2Bg8e3Gr/hn10Oh0SEhLQv3//9n8DREQmVtTKQtCNb467+vjzCcY28gwIQ9KZ+rmt89MTZcym/RQKK4y99wls/exlVJcXi/GtW7di8ODBWLRokVny0Ol0+O6775B55SyUKpUYV6rsMfnBv8PBxb3Z/QoyklCYmSyJhY/gItBE1DkICgXCR07B6a2rxFjaxVOoLi+BnZOrUc8VOfVu5CTHoTCj/m+C5LOH4RvWD6GDxxn1XETmUlZWho0bN+Lnn39GXl7eDfsqFAqMHDkSU6dOxdixY832IGZsbCyWr9gOD/92jIi16QmXUT3Rt38pshIvICPuDCqLC5rtuif6FPZEn4J7tx4IHzUVbr7167QlxxxFyOCoW/0WiIjIxIxeaPjll1/E1++//z6ee+45sR0WFoZx48YhODgYf//73xEdHQ1BELBkyZJmiwwAEBAQgJUrV2LYsGEA6goZHbXQkJmZienTpze7zdvbG0899RSeffZZ2NjYtHiMvLw8ybDJoKCgFvtep1Kp4OXlhfz8uvlMr1y5wkIDEVmkxiMa3BstBJ2fJr057hnAIdZt1fjfqqIoDzUVZVA5OsuUUfvZObpg3L1PYPe3b0um2Pr444/h5uaGmTNnmvT8Go0Gr776KqKjoyVxK2slJi55rklhrKGEY9LRDA6unvDvPcgkeRIRySFk8Dic3fkLdFo1AECv1+Lq6QPoP3GuUc9jZa3EuHufwNbPXoKmtn4h3JN/rIBXYBicPf2Mej4iU0pPT8e6deuwefPmVhd2joyMxMyZMzF58mTZZnm4lXXIggeOhsFgQEFaIhJP7ce12KPQaTVN+pUX5eDM1lXw7zMYA6feDTe/IElhkYiILJfRCw2nTtXNzenh4dHiWgBPP/00PvjgAxQWFkIQhBaLDNcNGTIEffv2xaVLl3D+/Hljp2wR8vLy8PLLL2PDhg34448/4OfX/B/IhYWFkraPT9umXPD19RULDUVFRe3KLSMj44bbry/2DdQt4N3aH0hEnUlNTU2zr6lOe/5NDAZDk0JDayMaPANZaGgrV19/WCttodNq4OLdHZ4BYc1+uLN0PsG9MfLOh3Dsf98CqPu50ev1WL58OdRqNaZNm2aS85aVleEf//gHTp48KVlTSlBYYdy9T8InuHeL+9ZWlePa+aOSWM/hkyEoFCbJlYhIDrb2jugROUoyei7hxF70G3+70c/l5O6NkXf+CYd//o8Y06prcGjtZ5j5+Juwsm7bSHFTqqmpkeVzkZx/j8r1PXdEKSkpWLFiBfbt29fiWpUA4OrqilmzZmHOnDkIDKx/AKe1f2dTfEYxxnEEQYBXUDi8gsIxeMa9iD++G/HHdqG2qrxJ34y4GGTEnUXokCijj4xqD/5cd178LE9dmamua0YvNKSlpUEQBIwaNQpWVlbNn9TaGqNHj8bmzZsBAAMGDGj1uJGRkbh06RIKCpofZmfJXFxcsGDBAsyYMQMDBw6Er68vrK2tkZOTg+joaPznP//B8ePHAdQVambPno3Dhw/D3t6+ybEqKiok7bYu6tSwX+NjtOZGi003dujQIXh6erbr+ESdxaFDh+ROweIkJiYCaNuThZXF+ZInEwFpoaG6vASVJfmS7V4WNKJBqalBVMwmDCxOgUqdBYfCSzg0+HZolKrWdzYDhcIK0x5dDmdP3w4/3VTYsImoqSzHyT9+RGVlJbRaLQDg2WefxezZszFhwoQW14m6GdnZ2VixYoVY7K+trQWghJW1ElH3PdXqyISkM4ckRR2FwhphwyYYLT+i5ly/JjVkSdck6pzCR06RFBqqSguRceWsSc7VI3IUcpIuIfHUPjFWnJ2KmO3rMGzOEpOcsz1OnDjR7ge8jKE9f3sZm1zfc0eSlZWF3bt3t/oAZY8ePTBu3Dj069cP1tbWuHLlCq5cuQKgboTltWvX2nzOup+JW5eeng74DJfEbuW9RuXojMgpd6Fv1GwknTmES4c2o7Kk8f0eA5LOHISmphoRUbPhE9IbCkXz95lMhT/XXQM/y1NXY6r760YvNJSWlgJAi0/kX9dwe1uG/bm4uABo/01yufn5+SEzMxMODg5NtgUGBmLRokW499578eabb+L1118HAMTExOCjjz7Ca6+91mSfxlXWG02z1FDDxZ9ZjSciS9R4fQYbO0fYN5jvviBduoic0kYFF+/uZsmtLax1GgxMOFzXqMgAcoGjkTMt6qaeR/dguVMwmr7j5yDvWjzyLkg/FGzZsgWFhYW444472rz2UUsMBgNOnDiBP/74A2q1WrLNSqnExKUvtDp9gEGvR8KJvZJYYP/hsHN0uaXciFojuSb9P0u7JlHn4+kfCo/uISjMTIaVtQ16RI6Ck4cPSvMyTXK+obPvR15qAkrz6kdgXzm6Az7BfRDYb5hJzkl0MzIzM7F7925cuHChxT4KhQIDBgxAVFTUDadIvnbtGvYmV7VvrQQjSM5MRIhP3d82NVXlqK0og01FUZP3mu3dB8HGv2ebj2ttY4teo6YibNgEJJzYg4v7/0BNZZmkj1ajxuVDW1GQfhUj7ngAXkHhRvmeiIjIuIxeaNDpdBAEodUb4O398K/ooNML2NraSm7yN0cQBPzjH//A1atXsXr1agB1802/9NJLsLaW/i9SqaQfDtVqdZNYc+qevqzT1lEQ16Wnp99we3Z2NoYPr3uyISoqCv7+/u06PlFHVlNTIz79EBUV1abfx67E3d0d5w6ntd4Rza/P0PCp9MbTJnkEhHLqmS5MEAT0HD4Fwfa1uHjxomTbqVOnkJOTgwceeKBdo/Iays3NxapVqxAfHw8AkvdjGxsbDJiypE1zFGdfvYjywhxJrNfIqTeVExFRR9Bv4lxUFucjZPA42No7AqibAsUUrG1sEXXvE9j2+auSkWNHf/0Srr7yfiYZMWIEhg4davbztudvL2OT63u2ZFlZWfj222+xa9cuAICzc9O1sezt7TF37lzcdddd8PX1bfWY7u7uOKdOu+m1EtpKp9WgOCcNpbkZKMnJQGb8OaRdPAWdTgODXgcAcLMS8Fovd8l+V0/sRUQLhYbCjGTotBq4+QU2GWFrZa1EnzEzETZ0AuKit+PyoS3QqKUPWhZnp2LHV2+gz9iZGDj1bljb3PheizHw57rz4md56spamyb/Zhm90EA374033hALDSUlJTh27BjGjRsn6ePo6ChpV1dXt+li2HAUQ+NjtKY9hQM7O7t2FzKIOguVSsWf/0ba88daq+szcCFoaqQ0Lx0Gr/5w7++F+KM7JdvyLyfh7AuvwT9iCEKHTmjzCIKK4gKknD2MzIRzMOj0Tba7+HSHR7dguPm2rYChVNnBr+cAZCfWTZHg6hPAp/CIqFML7GveG3KuvgEYOnsJTmz8Xoxp1DU4sOoTBPUbfoM9TUuuvwvlvFHGv4XrlZSU4IcffsCvv/4KjUbT7IOTjo6OWLRoERYuXNhsAaIlpv5/XFlSiKO/fY381ARxcXcAqC4vhbXSFspWzq9UtfwzcOnQFqReqJs22s0vCH5h/eEX1hfePXqLRQOlrR0GTJ6H0KETcHb7OqTEHml0FAPiorchM+4sRs1/BN49et3cN9pG/LnuGvj/mboaU/28s9BgQUJCQtCjRw9xvsW4uLgmhQYPDw9JOzc3t01TT+Xk1D9N6e7ufoOeRETyaK3Q4BUUDp1Wi6KsFOi0GngGmHe4OFkmz4Aw9J94B7r17I9jv3/dZIHrnKSLyE2+jO69ByGo/3B49+gNB1dPcbSMQa9HWWEOcpPjkH7pFLISLwAwwFppAzQafBk2dCKG37EMcdHb25yfV2BPTHnwRZQVZCPxxD64NhqpQ0REt67n8EnIS41HytloMVaal4GkMzXw6tFbxsyoq6mpqcG6devw449160g1x9nZGYsWLcI999wDJycnM2fYOpWjM/JT45v8TdVWN1oLrDir/u/94uxUFGen4vLhLVBYWcM3tC96RI5GQMRQ2Kjs4ODijrEL/4Kewydh93f/hLq6SnKsssJs7Pz6TUSMnYWB0xdYxCLwRERdHQsNFsbPz08sNFxfdLIhb29vuLq6oqSkBACQmpqK3r1v/MdzTU0N8vPrF1BtrT8RkbmpqytRUSxd6LlxoWHgtAUA6odxO3t2M1t+nZlBr+8UU1AFDxwNV19/HF73X8lc3QBgMOiREXcGGXFnAABW1jawtXeEAQbUVpZDr9Pe8NgOrl4YMfcBdO818Kbzc/b0w5Db7rvp/YmIqGWCIGDk3IdQkp2G4pz6aYMKMpKQev4YuvXsJ2N21BXo9Xps2bIFX375peSzd0MODg5YsmQJFi5c2OwajuZUlJUKe2c3qBybjqSwslbCMyAMuSlxrR7HStl06iJVC6NINeoalDWaTvI6vU6LrIRYZCXEwspaie69ByNk0Fj49x4En5A+iJwyH6UF2Ug9f6zR320GXI7eipzkyxh371/h7CnPQuhERFTHZIWG8vJypKW1PDdkeXm5+Do9PR0Gg6HV43UFDZ96aOmPjz59+uDYsWMAgLNnz2L69Ok3PGZMTP2cqFZWVggP55QNRGRZihstBK1QWMPFu/lCgpW1Ep5mXvyuM6mpLEdWwjkUpCXVLbAtCJj1l7fkTsso3HwDcdsT/8TFA5tw6eCmFp/E02nVqCoravV4VtZK9Bo5DQOmzLvh03lERCQ/axtbjF/8NLb+9xVoauqffL5ydCdCBo2FT0gfGbOjzuz8+fP417/+hbi45m/MW1tbY8GCBXjwwQfh6upq3uQa0Gk1SL1wHAnH9yI/LQGRU+ZjwOR5zfb1CYkQCw1OHr5w9Q1AVUkhfEP7IbD/MNg5ucHOyQUO6irg15cl+wYPHI3qZo5ZU14KVx9/lOZlwmBoOj1lwzzTLp5A2sUTcHD1Qq+RU6DTqhE6OAr9JtyOY799Xfc3bANFWSnY9p9XMHzuAwgZNK6FIxMRkamZrNCwcuVKrFy5stV+BoMBPXr0MFUaHUp1dTUSEhLEtp9f89X4iRMnioWGAwcO4MUXX7zhcQ8ePCi+Hj16dKuLUxMRmVvDpw+BunnwOfzZNEpzM3Bk/ZdiWxAU0KhroLTpHIufWVkrETnlLoQNm4hLBzYhKeYQtI0WEmyN0tYOvUZNQ+8xM9q8tgMREbVMq1Ej9cIJ2Dk6w7WNa9zcDCcPH4xd8Dj2r/xQjBn0ehxc8ylm/eUtOLp7m+zc1PXk5eXhP//5D7Zvb3lKxZkzZ+Kxxx5Dt27yjcTV1FYj4fgeXI7ehpqKUjGeeHIf+k28AwqFVZN9ggeOhptvIHxCesPWvm56p4sHNsHDP/SmH/hx8vDBnKfeh06rQUF6EnKuXkT21YsoSL/aYuGhsiQfMTvWobaqAv0nzcOIO5Zhxp9fx+XobYjd/avkoRKNugZH1n+J7MSLGDH3QbMsFE1ERFImnTrpRqMUBEGonx+5ldEM1/t3dmvXrkVNTd3NEEEQEBUV1Wy/uXPn4p133gEA7NmzBxkZGTdcsPmnn36S7EtEZGmKsq5J2m6+gfIk0gW4+wdDEBTiBzqDQY+ijJRO96Sng4s7ht+xDAOnL0DaxZNIv3waedcSoK6uaLa/naMrfEL6wL/PEPj3GcQRDERERlCSm4H4Y7tw8cCmuvcevQ6j7nrYpOf07zMY/SfdiQv7Noix2qpy7F/5EWY89jqv73TL1Go1Vq9ejRUrVqC6urln94GRI0fiqwJ3xgABAABJREFUiSeeQK9epl2o+EbUNdWIP7YTcdHbUVvVdIaIqrIiZMSdbXYBd2dPP5NNQ2RlrYRPcG/4BPdG5NT5UNdUIfPKWVyLPYbMhFgY9Lom++h1Orh6dwcACAoF+kbNhl9Yfxz++T8oy8+S9E0+exjFOWmYsPhpFheJiMzMJIWGthQO2tLnVvpbArVaDQCwsbFptW9SUpJkZMKUKVPg4+PTbN9hw4Zh2LBhOHXqFHQ6HV588UWsXr262b7ffPMN4uPjAQBOTk5YsmRJe78NIiKTK8lJl7Qbr89AxqO0UcHVN0Cy+HZB+tVOV2i4zkZlj7ChExA2dAIMBgOqSotQWVIATW3djQEbO0c4uXs3O0fxzSoryEFpXib8ew/qFOtfEBHdrLSLJ5FwYg90Wg2slbZIOXcEQ2Ytgo2daeenj5x8F4oyr+Hq6QNirCQ3HdG/fIEJi5/mtZlu2qFDh/DRRx8hMzOz2e09evTAM888g9GjR5s5s3o6rQYJx/fg/L4NLT5gAQB2Tm7tHvVpCjYqewQPHIPggWNQW1WO1AsncfXUfhRmJot93Lv1gLOXdFSIe7cgzPrr2zi9eRWunt4v2VacnYqt/30VUfc+Ab+e/c3yfRARkQkKDSkpKcY+ZIeVlZWFcePG4W9/+xvuueceBAQ0HSas1+uxfv16PPnkkygoKAAAKJVKvPvuuzc89rvvvospU6YAANasWYOAgAC8+eabUCrrpxr55Zdf8NRTT4nt5557Dp6enkb4zoiIjEev16Ekt+VCg8Fg6BKj2szJMyCsUaEhScZszEcQBDi4esDB1cOk57lyZAfij++Cg6sneg6fhLBhEzkFExF1SWHDJuB8g5EFOq0ayTGH0XvMDJOeV1AoMHbhX5GVEAt1df16DRlxZ3Bu928YNH2BSc9PnU9OTg4+/PBDHDhwoNntDg4OePTRR7FgwQJYW5t04ogWGQwGZMSdQcy2dSgrzG6xn29oX/QaORX+fQZDYSVPri2xtXdC+IjJ6Dl8EgrSEnHl2C6kXTyJgIghzfZX2qgwYMpd8Anug5N//ABNg8KJuroCe1e8j4HTF6Bv1Bx+niAiMgOjv6sEBXX8p1BnzZqFrCzp8LucnBzx9enTpzFw4MAm+23btq3J3IsZGRl4/vnn8cILLyA4OBj9+vWDh4cHlEolcnJycPz4ceTl5Yn9FQoFvvvuOwwZ0vwb6XWTJ0/Gq6++irfffhsA8N5772HlypWIioqCra0tzpw5g4sXL4r9p06dipdffrmlwxERyaYsP7vJor3u3eqnTrp0cDOSYg7BMyAMXoFh8AmOaHGhaGobz4BQJJ7cK7YbL6hHN09TW43kmEMAgMqSApzbtR5adS0GTb9H5syIiMzP3tkdgX2H4srRXWIs/vhu9Bo93eQ3/WxUdug9ejrijuwAUD86/uKBjXDx9uOCsdQmWq0Wa9euxTfffCNOc9yQIAi444478Pjjj8Pd3V2GDOuU5GXi1KYfkZN0qcU+/n2GoP+kuTe9xoI5CYIAr6BweAWFo7aqAokn9zXbz6DX4+DqT6CpqcLgmYsQd3SHZColg0GPszt+RlleNkbc+WC714DTabW4fPnyLX0vNysyMrJNs2MQEVkSyypfW4jLly8jNTW1xe2VlZWIjY1tEr8+VVJzDAYDkpOTkZyc3GKfkJAQ/PDDDxg/fnyb8nzzzTdha2uLN998ExqNBllZWfj555+b9Fu4cCG+/vpr2Z6sICK6kYZP1gN1NyWuLzoHAPlpCSjLz0JZfhaSYw6h18hpGH7HMjNn2bl4BvaUtKvKilBZWgQHF/k+IHcWKeeOSp6mAwT0HD5JtnyIiOQWPmKKpNBQVpCNnKTL8Avra/JzqxxdMGj6QpzbvV4y7/ux376FnZO7WXKgjis2NhbvvPMOkpKaH/k5YMAAPP/88+jTR97pJy8d3Ixzu3+FXqdtdntg3+HoP+lOuHfrmA+F2to7trgt+Ww0CjPq/v+c+OMH+PceDAdXT2Qnnpf0S4o5iMqSfETd99QNj9dYSU4qfs4BPDLNey+lMCMJbz5QN202EVFHwjvPJhQUFIQLFy7g6NGjOHbsGC5duoT8/HwUFhaipqYGzs7O6NatG0aMGIE5c+Zg9uzZULRjvlBBEPDqq6/irrvuwnfffYddu3YhPT0dGo0Gfn5+GDVqFJYuXSpOsUREZIkaFxrc/OpHMxgMhibT+ngGWv5TWJbOxdMPSpU9NDX100kUpl+Fg8twGbPq+AwGA+KP7ZLE/PsMgqObl0wZERHJzyckAnZOrtDU1C+am3B8t9lu8nv4h2D47UtxYuMPYkyv1+Lg6o8x/c//gJtv4A32pq6otLQUn332Gf74449mt7u5ueGpp57CrFmzLGI6HkGhaLbI4BkQhqGz74dXowdMOgt1TTVidqyTxDKuxMDWzhHdew9GRlwMGv7vyUm+jB1fvY5Jy16AUzsWifbwD2VRkoiojVhoaMa1a9eMchxBENCvXz/069cPjzzyiFGO2Zw+ffrgo48+MtnxiYhMqTg7TdJ28+shvq4szkdNRalku2dA5/ywZE6CQgGP7iHISaqfYq8g/SoC+7HQcCvyrsU3WW8kfASL/UTUtQmCAN/Qvki/dFqMpV8+jaqyItg7m2ckXfiIKSjNy8KVozvEmKa2GvtWfIAZj7/JEX0EoG79xC1btuDf//43SktLm2wXBAF33nkn/vrXv8LZ2VmGDJvXe8wMXIs9Ji6ebO/igcEzFqJH5GiLKISYSm1VORxcPZt8VqitrkDmlRi4endHWUEODIb60Uxl+VnY8cVyTFz2fIeYQoqIqKNp++PzREREJtC00FD/ZGHj0Qy29k5w8vAxS17tZRAUKHDxRaa1A3Ls3FDg4guDYLlvs16BYZJ2V1kQ2pQaj2Zw8vBFt54DZMqGurrr16SGX5Z8TaLOzSuoJ6yU9XOjGwx6JJ7cb9Ycht62GIF9pQX1qrIi7FvxPtQNRvhR15ScnIxHHnkEb775ZrNFhvDwcPzwww94+eWXLarIAAAKhRVGznsYVtZK9I2ajTue+RDBA8eYpcgg53uNk7s3Zj72BkbPf1Qy7ep1pfmZEAQBQqN8airLsOfbfyL76sUm+xAR0a3hiAYiIpJNdUUpqitKJDFpoUG6SLFnQKjFPplVY+uAn25/BRcPbOoQQ6w9A6SFhsKMZOj1OigUVjJl1LFVlRUh7dIpSazXyKkQ2jElIpExXb8mEVkCa6UtuvWMRE5y/Y29xFP70H/iHVBYmecjqaBQYMw9j6P6uxLkpyWI8ZLcdBxY+REmPfB3WCu58GpXU1tbi++//x4rV66EVtt0+iF7e3v8+c9/xj333AMrK/n+RjIYDEhJSUFISEiz2927BeHOv38GO0cXs+Yl93uNoFAgdMh4dOs1EKc2/YTUC8cl2/V6LfRaLSAIEBQK8XOERl2DfT9+gHEL/8oRvURERsRPv0REJJviLOn6DFbWNnD28BXb+WmNCw3Sm+N08xr/W2o1tSjJyZApm44v8eR+yUKj1kpbhAweJ2NGRESWJbC/9GZedVkxMuJizJqDtdIGE5c+C2cPP0k8NyUOh9Z8Cp1WY9Z8SF4nT57EwoUL8cMPPzRbZJg8eTJ+++03LFq0SNYiQ1VVFV555RUsXrwYV69ebbGfuYsMlsTO0QVRi57E+PuegqrRv4PC2hqCoIC6ukryO67XaXFo7WdIPLnP3OkSEXVaLDQQEZFsGi8E7eobID4BrtNqUJx9TbKdhQbjUTk6w7HRQniNR5BQ2+i0GiSe2CuJBQ8cA1t7R5kyIiKyPM6efvAKDJfE4o/vMXsetvZOmPzg35vcjMyMP4cj67+EQa83e05kXsXFxVi+fDkef/xxpKenN9nevXt3fPbZZ3j//ffh7d32RYNNIS0tDcuWLcOuXbugVqvx8ssvQ61Wy5qTJQvsNxy3P/0vBPQdJokLCgE2dvaorSyHurp+qjSDQY/jG77D5cPbzJ0qEVGnxKmTiIhINsU50vUZ3P2CJNsaP1no4d/8cHG6OZ4BYagoyhPbBelXET5isowZdUzpl043mQIsfORUeZIhIrJg4SMnS6Ytykm6iNL8LLh4dTNrHo7u3pj8wIvY/e3bUNdUivHUC8ehtFVh5LyHLXaqRkul02px+fJlWc4dGRkJG5vWp70yGAzYvHkzPv30U5SVlTXZbmVlhaVLl+Khhx6Cra2tKVJtl4MHD2L58uWorKz/GU1OTsbPP/8M+I2UMTPLZmvviPH3PYXEk3txessq8fOEIAiwd3ZHZUkBDAa95IGQM9tWQ6/Xot/42+VKm4ioU2ChgYiIZNN0Iej6QkNhRrJkm7OHH58QNzLPgFBciz0qtrkg9M1pvAi0d4/ecO8W1EJvIqKuK6j/SJzeshq1VeViLPHEXgydfb/Zc3HvFoRJy57Hnh/eg1ZdI8avnj4AK6Uths1ZwmJDO5TkpOLnHMAj07y3GAozkvDmA8CwYcNu2C81NRX//Oc/ERPT/HRdAwYMwCuvvILQ0FBTpNkuer0e3377Lb799tsm25ycnNC3b19EF8mQWAciCALCR0yBV1A4Dq/7L0rz/n96UAHw69kf/r0H4XK0dBTD2R0/w6DTof+kO2XImIioc2ChgYiIZKHTalCalymJNVwIunGhwSOAoxmMzSsoHD7BfeDRPQQe/iHwCJD/w3VHU5SVirzUeEms16hpMmVDRGTZrKyVCBs6HpcObRFjpflZsuXjFRSOCfc/g30/fgC9rn6O/vhjO2Ew6DF8zlJxSkdqnYd/KPzC+sqdhoRarcaPP/6IFStWQKNpugaHo6MjnnzyScydOxcKC/h/rVar8frrr2PXrl1NtvXs2RP/+te/kJ2djejdCc3sTY25+QZi1l/ewvEN3yHl3BEobVSYcP+zcPHuBpWTK2K2r5X0P7f7V+h1OgyYchcLjUREN4GFBiIikkVJbgYMBuk8yDcsNFj4tEnWmloMu7wXoaVJsDcUwbEyFaciJkOrlH/ofUs8/UMx7ZHX5E6jQ0s4vlvStnNyQ2DfoTJlQ1Tv+jWpIUu/JlHX0HPEFFw5ugs9BoxCr1FTZX9/9wvrh6hFf8PB1Z9I/i5JOL4bBr0eI+54gMWGDurMmTP45z//ibS0tGa3T506Fc8++yw8PT3NnFnziouL8eyzz+L8+fNNts2cOROvvPIKVCoVsrOzZciueR3hvcbaxhZjFjwO927BcPLwgYt33VRtfaNmQ6GwwumtqyT9z+/7HyAIiJxylxzpEhF1aCw0EBGRLBovBO3o7g2lrR0AQKOuaTLawdPfsp+2V+rUGH1+e12jDEAGcK7XOIv6oEXGZdDrkXdNOpqh5/BJUFjxzyuSn+Sa9P94TSJL4OTujfmvfAkblZ3cqYgCIoZg7MK/Ivrn/0qKDYkn90Kv02LUvIdZbOhASktL8e9//xubNm1qdrufnx9efPFFjBkzxsyZtSwlJQV/+9vfkJUlHeGjUCjwzDPP4J577rHIJ+w7ynuNIAiIGDerSbzP2JkQrKxwatOPMBgM4r/x+b2/w1rZ+rofREQkxU/CREQkixutz1CUeU3yQV8QFJLtRJZAUCgw56n3kXElBleO7kJeyhX0HD5J7rSIiCyeJRUZrusxYCQUCgUOrfsPDHqdGE86c7Cu2HDXI7CyVsqYIbXGYDBg+/bt+Pjjj1FSUtJku0KhwOLFi/Hwww/Dzu7WfwbVajViY2Nv+TiJiYn4+OOPJYs+A4BKpcJjjz2G0NBQnD59WozXLbrNWznG0nvUNAiCgL0/vAelrR1Ujs4AgJgd6+AX1h8eFv6wExGRJeG7ExERyaJxocFdUmhIkWxz9Q2AtY1lPRlFBNQVGwIihiIgYiiqy0tg5+Qqd0pERHSTAvsNR9Siv+Hw2s+g19ev2ZBy7ghqK8sRdd/fxNGXZFmSkpLw/vvvt7jYc0REBF599VWEh4cb7ZyxsbFYvmL7Ld2Izk9NQMyOddBrtZK4yskF4ZPux/5cFfY3Wo8hOeYoQgZH3fQ5ScpgMKA0NxNKlT0qiwug1+tg5+QKQRCQfDYaLt7+Frf2CBGRpTJroWHPnj04evQoAGD58uXmPDUREVkQg8HQZOqkhuszBA8aCycPHxRmJKMgIwluvgHmTpGo3VhkICLq+AL7DkXUfX/DobX/liwQnZV4Hru//ScmLXtBfOKZ5KdV12LdunWIjo6GXq9vst3e3h5//etfMX/+fJMs9nwrC2DnXYvH+X3/g5W1Nays62/NeHQPwcSlz7X4d0VhRtJNnY+ad+XIDsQf3wWVgxNgMKCypBAGvR72Lu4AgIv7N8IrqCd6DBgpc6ZERJbPrIWGnTt34qOPPoIgCCw0EBF1YVWlhVDXSIeHu/nWFxpUDk7w7zMY/n0Gmzu1Lq22qgJFmSlwcPOEs6ef3OkQEVEXYjAYUFmcD0d3b7lTQUDEEEy4/xkcWvNvaDW1YrwwMxk7v3oDkx/8u0Xk2ZUZDAaknj+OI79+BRcrNRwcHJr0mTRpEp577jl4e1vm/ysP/xD4hfVDVkL99Evdew1E1KK/cSSvGWlqq8XXKkdnGPR6VJUVw6DXQ2GthMFgQPQvn8PKWomAiCEyZkpEZPksauqkHTt24OTJkwA44oGIqDMrypKOZlCq7OHg5iVTNnR+7/+QfDYa5YU5AIABk+9C5JS7ZM6KiIi6Ak1tNZJjohF/bBdqq8ox78X/WMRaCN17DcSUP72C/T/9C7VV5WK8rDAb27/8ByYsfhpeQcabhofariQ3A6c2/Yic5MvQ1NQAztKb8n5+fnj++ecRFWXZ0wtZWSsRdd/fsPf795CfloDgQWMx+q5HoLCyqNs0nd6AyfOgcnTBiY0/ADDAztkVBoMe1eWlUFhZwVppC4Neh8PrPsOUh16Gd49ecqdMRGSxjD928BZs3boVr7/+Ot544w25UyEiIhMqyWm0ELRvIARBkCkbUldXikUGACjMSJYxG8unVde23omIiFpVU1mO3997Aic3rUBpfiZqKsuQeuG43GmJvALDMP3P/4CDq6ckXlNRil3fvo2kMwdlyqxr0tRWI2b7Wmz57CXkJF9usl2pVOKhhx7Cr7/+avFFhuuUNipMXPocBs1YiDHz/8wig0zCR0zGuIV/haCwAgDYu7hD5egMrUaNqrIiGAwG6LQa7F/5EUpyM2TOlojIchm90BAWFob7778fX3zxBc6dO9fsPIlERNS1FWU3LTT8H3v3HR5VmbYB/J6aSe+VFEJCekILvTcFBRQLYNe17ura26fYO7qWddeyNrCjoiJFeoeETiCkUBJCQnpvM5l2vj+QIWdCCZDMmcncv+vyuvI+OXPmRnTKec77viQd//A+onHNiQIIgiBRGvtm1Ldhz58/YOcfC1CcsxsCP+cQEV00jbsn/MN6i2p5W1dKE+YsvAPDcPl9L8InWLxflNlkxLZfPsXuZd/xvaCbCWYzjuxcj8XvPIaDm5ZCMJs6HDNs2DAsXLgQf//736HRaCRIefFc3DyQMnYGZN2whwR1Xu9+wzH+lscsM6rcvf2hUmtg1OvQXFMBQRCg1zZj7VdvoaWhVuK0RET2qcvfyQoKCvD999/jn//8JwYNGgRvb29MnDgRc+fORU5Ox7sOiIjI+dRbNRr8wqIkSkJAx0aDrrkBrQ01EqWxb4VZ22A0tKG6+Ag2fPMv/P6vR2HQ66SORUTksOKHXy4a15woQHWxfW126+7th8vvfR5hfdM6/C5nyzKsnT8PuuZGCZL1fGVHDmLZf55Fxq+fQdtc3+H3Gg8v/POf/8SHH36IyEj7vHFFEAQc2bVBtBcA2adeCf0x8W//B5VaA8hONoFUag30Oi2a/mo2tDbUYN1Xb0GvbTn/CYmInEyXNxomTZoET09PCIJwckOvlhZs2LABb7zxBlasWGE57qmnnsKyZcvQ0NDQ1RGIiMiOGdq0aGy3TA8A+ISe/mJ4Im8f7xKyMU//EKg14k0UuXxSR4IgID9jlajmFRB68ssoERFdlPCkgR2WJsrLsK9ZDQCgdnXH+NufQOLIqR1+V3Z4P5Z++H+oKMiVIFnP1FhdhvVfv4M1X7yGurKiDr+Xy5VIHjsDo298CIMHD7brJTizNyxGxqL/Yf2Cd3hzggMIjk7AxL89/VezQQZXL18o1S4wtGs21FcUY8M378FkNEgdl4jIrnR5o2HVqlWoq6tDVlYWPvroI9x8883o3bu3pfFwyjvvvIMZM2YgICAAAwcOxCOPPMIZD0RETqC+vBjA6fcDmUxuWY6gtbEO6xbMw69vPoBfXr8f679+B22tzRIldR4ymQx+vaJFNXu7m9QeVBUd6nCxI8HqTlwiIrowcrkCcUMniWpF+zOhbba/G9LkcgXSp92C4dfeA7lcvJa+trEOqz9/DQfW/callC6BrqUJu5Z+gz/eexIluXvOeEyv+P6Y9tCbGDhlDpQqtY0TXpi8rSuwb9VPAICKwlxsWPAO93pyAIFRcZj4t6ehUKogk8nh6R8ChVIFg06L5tpKCIKAisIcbP3pY/7/TkTUTrcsAiiTyZCamor77rsPX3/9NY4ePYqysjJcf/31ouMEQYDJZMK+ffvw73//Gxs2bLD87h//+Ad++OEHnDhxojsiEhGRROqsNoL2Cgi1fElsfxe9tqkOFUdzoNa42TSfswqIiBGNa05wRoM169kMHn5BCIvruIwGERFdmNjB4y3rogMn9z84smOdhInOLTZ9HCbf/SxcPXxEdUEwY9/qn7Hjj/moqqqSJpyD0uu0yFqzCL/Pewi5W/884z4M3kHhmHjH05hw+5PwDgqTIOWFObJrA3Yu/VpUKy/IQdmRbIkS0YUIjIpD0ugroVCrIVfI4RUQArlCAb22FS311QCAogOZ2LvyR4mTEhHZD5vtNhQcHCxaM7GmpgaLFy/Go48+ikGDBkEul0MQBMhkMgiCgE8//RQ333wzIiMjERMTg9tvvx1ffvkljhw5YqvIRETUDepKxXeE+7ZbNqmmRHwXvV+vaG6MZyP+4VaNhpIC3qHVTmtjHYqyd4hq8cMm879PIqIuoHH3RO+0EaJa/vY1MJuMEiU6v6De8bjyoTcQGpva4Xe1Jwrx7LPP4tdffxXN6qeOjPo2HNy4BL/Newj71y4649JCGncvDL36Tkx76A2HafAf25+JzF8/71AfPP02RCQNkiARXQxP/2AMnn47VGoN5EolPP1DIJfLRTdCHdy0FEd2bZAuJBGRHZHs27GPjw+mT5+Od955Bzt37kRdXR2uu+46S7NBpVJZllsqLCzEN998g7vvvhsJCQlSRSYioi5QV14sGvu22wjael8A64vf1H2sN4Q+014azuzwjnWiuyvlSiViBo2VMBERUc8SP+Iy0VjbWIfig7skStM5rh7emHjHU+h/2SzIZOKv1jqdDq+//jruv/9+lJaWSpTQfhn1bcjbthK/v/Mo9qz4AXptx6Uy5QolksdMw1WPv4u4oRMhlyskSHrhSvL2YsvC/0IQxDds9L9sFhJGcMlFR+MbEoHxtz0BhVIFjYcXxt/+BFzcvUTHZP72BfdoISICoDz/IRdm7ty5GD58OEaMGAFfX99OP87DwwPBwcGWcX19PbZv347Nmzdj8+bNyMjIQHMz1+kmInJkgtncYekk35CTMxoEQUDNiULR76wvfts3GVpdPGAytEEmV0CuUAKw340Jrbl7+8HV0xfapjpLrabkKLwD7X9pgu5mNhlx2GoJj7C+aXBx85AoEVFnnHxNsq4R2Sv/XtEIjIxD1fFDllpexipEpQ2TMNX5yeRypI6/GkFR8diy8L9obawV/X7Hjh24/vrr8be//Q233HIL1Gr73lOgu+l1rcjPWI28rX9C19J4lqNkiB4wEv0nXQcPvyCb5rtU5QU52PTd+x2WfkoeOwOp46+WJlS3co73muA+iRh78yNQu7ojMLIvXD28kfHrZ5bfC2YTNnz7Hqb+42V4BYRImJSISFpd3mh4/fXXIZOdfGOJi4vDiBEjLP9cyLRRjUaDsWPHYuzYk3cLms1m7N27F5s3b+7qyEREZCNNtRUwWk2J9w09OaOhpa4Kba1Not8FOFCjQavxwMez3kD2hj/gHx6D0NhkqSNdMP/waJTknm40VBcXoM+A0RImsg/HD+4SNWAAICp1qERpiDrn1GsSkSOJH36ZqNFQeSwPtaVF8Gs3+9FeBfdJxPRH5mH3su+Qu+VP0e/a2trw8ccfW5YOHjt2rOU7s7PQNTcid+sK5GeugkHXetbjIpOHoN/k6+ATHG7DdF2jrqwIG77+F0xGg6geP+wyDLh8tkSpupczvdf0iu9v+Tl28Hg0VJUhZ/NSS02vbcb6BW9jyt9f4s0oROS0urzRAMDSUMjPz8ehQ4cwf/58ABB9mNq1axcGDhwIeSfXNpbL5Rg0aBAGDeJ6hkREjqquTDybQePuBVdPHwBAtdWySS5unnD3DbRVNAIQEB6Dktw9lrH1UlbOKm/bCtHY0z8YXpzpQUTU5SJTBsN1mQ+0zfWWWn7GKgy/9m7pQl0AtcYNw6+9G+6+gWg+sBptbW2i35eWluLxxx/H4MGD8fe//x1paY6x38ClqC09hrxtK3Esa1uHC/DthfZNQ//LrkeAgy6b2VJfg7Xz58HQphXV+wwcg8HTb3W6xpIzGDhlDhqrSy2fnQWzGY3VZdj0/b8x8Y4n/5rdTETkXLr8la+4uBgZGRnIzMxERkYG9u7dC53u5N2r7Td7Hjp0KDw8PDBq1CiMGzcO48aNg9Fov5t9ERHRpasrE28E7RMSafni1XF/hj78UmZj1ktV1ZUdg9lkdOovSjUlBagqOiSqhcSkSJSGiKhnUyhV6Dt0IvavXWSpFe7bioFTb3CoO4QDI/vimRvGIyMjA4sWLeows3/nzp3YuXMnRowYgXvvvRfJyY43C/JcTEYDig/uQt62laIZKmcSFtcPKeOuQnC04+7FqNe2YO1Xb0HbKJ79GJE8GMOvvRuyTt5cSY5FJpdj1Oz7sfLTl1FRkIOW+hp4+gej/Gg2di75GkOv/pvUEYmIbK7Lrxz06tUL1113Ha677joAgNFoxN69e5GRkYGvvvoKWVlZlmZDU1MTVqxYgRUrVnQ4T0ZGBtLT06FSqbo6IhERScR6fwa/0PYbQR8V/c6x9mfoGaz/nZuMBtRXlMAvrLc0gexA3raVorGrly/8w6MlSkNE1PP1HTIBB9b/blnj3mTUo2DPZiSOmipxsgvj5uaGp59+GjNnzsTbb7+Nffv2dThm27Zt2LZtG0aMGIEbbrgBQ4cO7fSMf3vUXFuJPX/uR8GezaJZKR3JEJk8GCnjr4J/L8d+TzUZjdjwzbtoqCwR1YOi4jF6zgMOs4E1XRyViysiktJRsGczzCYTmmoq4BUYikPb18AvrDf6DpkgdUQiIpvq9lsUlUolBg8ejMGDB6O4uBhZWVkAgI8++ggbNmzAxo0bUVFRYTn+1N2ro0aNgouLC4YMGYLRo0dj9OjRGDFiBDw8HOdOFiIiEqstFc9o8A37ayNosxm11htB92KjwdZc3Dzh6ReMptqT78ue/iFoa22WOJV0tM0NOLY/Q1SLHzYZMpnjXgQiIrJ3bl6+iEoZgmP7M+Dfqw/iR1yO3na+IfS5xMfH47PPPsOqVavwwQcfoLKyssMxpxoOkZGRmD17NqZNmwZ3d3cJ0l44bVM9irJ3YP/aX6FrboRKoznrsXK5Er37DUfyuBnwCeplw5TdJ2fzUlQU5opqXoFhGHfrY1AoedNkT3csKwP71y6Cp38wGqvKRM2GHYvnwyc4HIFRcVLHJCKyGcnWQrjvvvtw3333AQDy8vKwYcMGfPTRR8jOzrbMeNDpdNi8ebNlA2i5XI7+/ftj586dUsUmIqKL1NbajNaGGlHt1EbQDVWlMFhtEs0ZDdIYeMVNUKld4Ncr2qGWqegOh7evhdl0ellHhVKFvoMn4MiuDdKFIiJyAqkTr0HCyKkIjIyVOkqXkMlkuPzyyzFu3DgsWrQI8+fPR21tbYfjjh8/jrfffhsffvghxowZgylTpmD48OF2N8u/ubYSxTm7cTx7ByqLDgEQoG1qgFLlcsbjXb18ETdkIvoOmWDZm6uniEodisaqUmibTi6b5Orhg4m3P+n0n6GcRURyOoKjk1BRmAMP30A01VbCZDCguaYSngEh2PjdB7jigVfh5uUrdVQiIpuwi0WXExISkJCQgIMHDyI7OxsA8O2332LTpk3YtGkT8vLyAAAmkwl79uw516mIiMhOWS+bJFco4f3Xhro1VrMZ3Lz8HO4DudKoR8rRTAQ1FcO9XAtvUw2yY4bBqFRLHe2CRCanSx3BbsQNmwyZXIFDmavR2liL3mkjoPHwkjoWUaecek1qzxFfk8g59ZS73a25uLjgxhtvxDXXXINffvkFCxYsQF1dXYfjdDodVq1ahVWrVsHLywvjxo3DsGHDMGTIEPj4+Ng8t16nRUVBDsqPHkTZkewOywSdTVDvBMQPvwyRyek9dr8nr4BQTP3HS1j71Ty01ldjwh1PwsMvSOpYNuPs7zUKpQrjbnkEKz55CQ2VJXA1eEPb1ABDmw7NdVUAgE3ffYDJdz/LGS5E5BTs9t3+xhtvxI033ggAqKmpwaZNm7Bx40Zs2bJF4mRERHQxrDeC9g7qZfnSWVPs+PszqIxtmLjj55ODegCFQH7UAKf5otUTadw9kTr+KiSPuRLHD+6CT3C41JGIOk30mvQXviYR2QeNRoObb74Z1157Lf7880/8+OOPKCgoOOOxjY2N+OOPP/DHH39AJpMhMTER6enpSExMREJCAnr16tWl+zqYTUY0VJWipqQA1cVHUVNSgLqyIgiCuVOPd/XwQfSAUegzcBR8QyK7LJc9c/cJwOX3Po/GqlKn29eK7zWA2tUdE+94En9+9AIgAEa9HoY2LfStLdAqVag6fgg7l3yNYTPvlDoqEVG3s9tGQ3v+/v6YOXMmZs6cKXUUIiK6SHVl4hkNvu03gj4h/nLtiI0G6rnkCqVDrw9ORET2ydXVFddccw1mzpyJ3bt3Y+HChdi4cSPM5jNf1BcEATk5OcjJybHUPDw8EBMTg7CwMISGhqKlpQWVRc1QKJVQqjVQaVyhUCphNpkgmM0wm0ww6nXQtTRC19KEtpZGtDbUorG6DI3V5Wiurex0U+EUlVoD9/AAxA4ej5TxM5xyA2QXNw+uxe/E3H0CMOH2J7Hq05fg6R+EhsoTMBmN0DbWQ6FU4fCOtfDvFc3NoYmox7Npo+Gyyy7jZs5ERE6qzmojaL+/Gg2CIAAAZDK55YttQHiMbcMRERERSUQmkyE9PR3p6emoq6vDmjVrsHLlSuzbt++8j21ubkZWVhaysrIA4GSjobHtnJsydwUXN0+EJw5EZMoQhMamIHfLn/APj+nRTQaz2dyls0eoZ/ELi8KYmx7Cuvlvw8MvGI1VpRAEAS111VAoVSc3hw6JQGBkX6mjEhF1G5s2GiZPnozJkyfb8imJiMgOmE1G1Fut5+sbenI6vUwmw9R/vAyjvg11ZUWoLjnKGQ1ERERkIQgCKgvzkLdtJYKjE5AwcorUkbqNr68vrr/+elx//fUoLy/H+vXrkZmZid27d0On00mWS65QIjAyDqF9UxASkwL/8Oge3VSwZjKZ8PDDD2PQoEG47bbbIJPJpI5Edigsrh/Sp9+KnX/Mt2wOLQgCmmoq4B0Uhk3f/xtX/vN1aNw9pY5KRNQt7GrppFtuuQXp6dyEkoiop2msKoPZZBTV2i+dBABKtQsCo+I47dwOnLz7qgrVJQWoKSmAtrEOo+bcL3UsIiJyQuUFOdi99FvUlh0DANSWFiJ++GWQOcGd5SEhIbjhhhtwww03QK/X48CBA9ixYwdyc3ORm5t7xo2ku4qHbyD8w/vAv1cfBETEwi88Gip1986SsGf/+te/kJGRgYyMDBQUFGDu3LlSRyI7FT9sMurLi3F4x1rL5tBmkwlNNZWQyeXYuvC/mHD7k07xGkZEzseuGg1DhgzBkCFDpI5BRERdrNZqI2g3Lz+4uHEpPXtVW3oMy//zrKg2eMZtTvF3lr3xD2gb6xE//DJ4BYRIHYeIyOnJZHJLkwEAmuuqUJy7B5HJznWDmlqtxqBBgzBo0CAAJ28KqKqqQl5eHkpKSlBWVoaysjIcOHAAjccqIFcqYWzTnXG/BZlMDo27FzQeXnBx94Krhzc8/YPhGRACr4BQePoHO8V7fmctWrQIP/30k2W8fPlyaLVazJo1S8JUZK9kMhmGzLgNjVWlqCjIhVHfBkObDkZ9G1rqqnHiUBYOrP8daROvkToqEVGXs6tGAxER9Uz15WffCJrsj09wOOQKpWgWSu2JQoT2TZUwVfczGQ3I3bwcupZG5G1bifCEAeh/+Sz4hkRKHY2IyGkF9Y6HX1g0aksLLbW8rSucrtFgTSaTISgoCEFBQaL6zp078cHqQwiNTYYgCDAa2mA2GiFXKCBXKCGTyyGTybn0Tyft378f8+bNE9XUajVuvfVWSZeyIvsmVygx5qaH8ed/58JsNqGh8gTMJhPaWlug0rgha80iBEbFITQ2ReqoRERdinO1iIio29VabQTNRoN9UyhVHf6OqkuOSpTGdo5lZUDX0vjXSEBJ3h7IwAsxRERSkslkSBwl3pOhojCnw2cL6kgmk0Gl1sDFzQMqF1colCrI5Qo2GTqpuroaTzzxBEwmk6j+3HPPITW1Z998QZdO4+6J8bc9ARdXd3j4BQEywNXLB2pXdwACtvz4H7Q01Eodk4ioS7HRQERE3a7OakaDXyjvELd3AeExonFNSYFESWxDEATkbV0hqoXEJMMnJEKiREREdEpU6jBoPLxFNevXbKKuZDAY8OSTT6KmpkZUv/XWWzF16lSJUpGj8QkOx6g5D8DDNxCDrrgJbl6+lkafrqURm3/4d4d97IiIHBkbDURE1K3aWpuga24Q1XzDTt4tf2x/Jrb/9gWO7NqAuvLjEMwd1xEmafiH9xGNe3qjoarokGgNcABIGDHlzAcTEZFNKZQqxA+bLKoVZm2F1urzBVFXeffdd7F//35RbejQoXjggQckSkSOKjxxIGY++T5GzfoHwhMHin5XVXQIe1cslCgZEVHXY6OBiIi6VWN1uWisVLnA0y8YAFCSuxuHdqxFxqL/YekHT2P7719KEZHOwLrR0NpYi9bGOonSdL+8beI7Yz18AxGeMECiNEREZK3v0ImQK05vMWg2GXF4+1oJE1FPtWTJEvz888+iWlhYGF5//XXI5byEQhdO5eIKmVyOEdfdBw/fQNHvcrYsw4m8fdIEIyLqYnyXJCKibtVUI240+IREQPbXl7SaYvFd8qdmOpD0vAPDoFJrRLWeOquhpb4Gx7N3imrxwy+3/HdKRETSc/XwRnS/kaLaocw1MBkNEiWinig3NxdvvPGGqKZWq/H222/D29v7LI8i6hwXNw+MufEhUdNUEARs/fljtDZyvwYicnz8Bk1ERN2qsapMND61ybBe24LGGvHvrO+iJ+nI5HL49YoW1Wp66IbQ+ZmrIQinl+1SqjWITR8rYSIiIjqThJHiJe20zfUoOpApURrqaerr6/HEE09Ar9eL6nPnzkV8fLxEqain8Q/vg0FX3gwAMOrb0FBRgua6Kmxd+BGXkSUih8dGAxERdSvrGQ2+f20EXXOiUFSXK5TwDeEm0fbE3wk2hDYa9Di8Y52o1mfgaKhd3SVKREREZ+MXFoXg6ERRLXfLCgiCIFEi6inMZjOef/55lJeLP7fOnj0bV1xxhUSpqKeKHzYZHn5BaKwqg8loRHNtJUoPH0D2xj+kjkZEdEnYaCAiom5jMBjQUlctqvn9NaPB+qK1b0gkFEqVzbLR+XXYEPpEYY+7mFO4byv02mZRLWH45RKlISKi80kYOVU0ri0tRFXRIYnSUE/x9ddfY9u2baLagAED8Mgjj0iUiHqy4oM70VRdDplcAQAwm0xorqvCvlU/o/JYvsTpiIgunvL8hxAREV2ckpKSDhemfUIiAHRsNPhHiO+edzRajSf+dcuHyN7wB/zDYxAamyx1pEsWYNVoaGttQktdFTz8giRK1LUEQUDeVvEm0GF90+AdFCZRIqKuc+o1iainiUgcCA/fQDTXVVlquVv+RFBvLm1DFy84OBgajQY6nQ4A4OfnhzfffBNKJS+ZnAvfay5OeNIghPZNhclgQEN1KSAABp0W2sY6bFn4X6RfeYvUEYmILgpnNBARUbcpKioSjT39Q6BycQUA1JwQNxqsL2qT9Nx9A+Hi5imqVfeg5ZPKjx5EfUWxqGa9/jcREdkXmVyOhBHi1+rinF2ixgPRhZo6dSq++eYb9OnTB3K5HK+//jr8/f2ljkU9lFyuwOg5/4RnYAjcPH0t9damOtRXFOPAhsU9bhYxETkHNhqIiKjbWDca/MJOLpukbW5AS714SSX/Xmw02BuZTNZx+aQe1GjI3fKnaOzlH4qwvmkSpSEios6KSR8LlVoDAFC7eiBpzDQoVGqJU5Gji46OxoIFC/DOO+8gPT1d6jjUw2k8vDB69gNw8/aDyuXk6xkEoLm2CmWHD2D9+vXSBiQiugicB0hERN3m+PHjorHvWfZnUKo18A7qZbNc1Hn+4X1QeijLMq4pOSphmq7TUFmKE/l7RbWEkVMgk/MeDCIie6fWuCFt0rVQubgiuv9IKNUuUkeiHsLV1RVjxoyROgY5ieA+ieg3+Trs+fMHNFSUwGw2w2wyQdtYh2+//RbXXnstYmNjpY5JRNRp/DZNRETdwmw2d2g0+IX1BtCx0eAX1psXeO2Uf68+kCuU8O/VB3HDJiNu6CSpI3UJuVKJPgNGQy4/ec+F2tUDfQaNljgVERF1VtLoK9F3yAQ2GYjIoaWMnYFeCQPg7htoqRkNbaiursYzzzwDvV4vYToiogvDGQ1ERNQtSkpK0NbWJqqdbjSI74q3Xp6H7EdYXBrmvPgFFEqV1FG6lKdfEEbO+jsGTJmD/IzVUGk0lmU4iIiIqOeqqanBwYMHOXOB7IJMLsfIWX9HfUUxDG1a6JobAQD19fXIzs7Gf/7zHzz66KMSpyQi6hw2GoiIqFvk5eWJxhoPb7h6+kAQhA4zGgLCY2wZrVsoTAbElGRD01oBzxrAV6XH0fAUmBSOfYG+pzUYrLl5+WLA5bOkjkHU5U69JrXXE16TiIguhdlsxty5c7Fz507ceOON+Oc//wmViq+LF4vvNV3D1cMbo2c/gFWfvQpjmw4GvQ4AcOLECXz99dcYOXIkhg4dKnFKIqLzY6OBiIi6RX5+vmh8ajZDS301dC2Not/1hBkNaoMO0zd9eXJQA+AQ8NH1r0PLL1pEJAHRa9Jf+JpERM5u/vz52LlzJwDg+++/x/79+zFv3jwEBQVJnMwx8b2m6wT3SUT/y67H7mXfob6iGABgNBpRWlqKF154AQsXLoS3t7fEKYmIzo0LYhMRUbc4dOiQaHy2jaDVrh7w8OOXOyIiIro0rY212LfqJ9RXnpA6Ctmh7OxsfPLJJ6JaZWUl1Gq1RImIxFLGzkB40iBo3L0stZaWFuTn5+O1116DIAgSpiMiOj82GoiIqMsJgnDWGQ1eASFIHjMNwdFJUKk18O8VDZlMJkFKIiIi6glqThRiy8L/4re3HsaB9b8jb8ufUkciO9PS0oJnnnkGZrPZUpPL5Xjttdfg4+MjXTCidk7t1+AZEApXV1dLva2tDWvXrsXSpUslTEdEdH5cOomIiLpcTU0NamtrRTW/sJMzGnxDoyyzGwSzGXpdi83z0aUx6HWAIEDl4nr+g+1IVdEh1JYVoc/A0dz4mYioBzmevROF+7ZaxgV7N6PfZddLmIjszZtvvonS0lJR7a677sKAAQMkSkR0Zq4e3ug/+XocXfMNKioqEBgYCC8vL8hkMrz99tsYOHAgevXqJXVMIqIz4owGIiLqctazGVRqDTz9gjscJ5PL4eLmaatYdAmO7c/Etl8+xZL3n8LCF+8SXdBxFNkb/sCOxV/h1zcfxN6VC9HaWHv+BxERkd2LHz4ZcsXpe+hMRgMOZa6RMBHZk+XLl+PPP8WzXPr164c777xTokRE5+bXKxp33XUX/ve//8Hb29sy+7u1tRXPPfccTCaTxAmJiM6MjQYiIupy1o0Gn9BIyOR8y3FkJTm7cXT3RtRXFEMQzKguPip1pAvSUFWKkrw9AAC9thnZGxbj+MFdEqciIqKu4Obli+h+I0W1/IxVMJuMEiUie1FSUoI333xTVPPw8MCrr74KhUIhUSqi8xs3bhxmzZqFq666SlTfv38/5s+fL00oIqLz4FUfIiLqch32ZwjtLU0Q6jL+4X1EY+tNve1d3tYVorHa1QMxg8ZIlIaIiLpa4uiponFbaxMqiw5JlIbsgdFoxLPPPovW1lZR/dlnn0VoaKhEqYguzGOPPYbw8HBR7dNPP8XBgwclSkREdHZsNBARUZfruBF0lERJqKtYNxoaKk+c3KvBAbS1NuHo7k2iWt8hE7hPAxFRD+IbEonQvmmiWtmh/RDabf5LzuVMF2NnzJiByZMnS5SI6MK5ubnhlVdegbzd7HC9Xo/nnnsObW1tEiYjIuqIjQYiIupSzc3NKCkpEdV8w3oDAAxtWgkSUVfwDYuCTHb6Y4MgmFFXWiRhos47tH0dTEa9ZSyTKxA//DIJExERUXdIGnWFaKxtbuCsBie1c+fODsvLREZG4vHHH5cmENElSE1NxV133QUAaGpqQkFBAfbs2YOPPvpI4mRERGLK8x9CRETUeYcPHxaNZXI5fILDYTYZ8fOrf4erpzf8w2PgH94Hsenj4OLmIVFSuhAqtQbeQb1QX1FsqTnC8kkmowH5GatEtd5pw+Du7SdRIiIi6i6hfVPhExwheq86tm8r+k++VsJUZGuNjY144YUXIAiCpaZUKvH666/Dzc1NwmREnWMyGpGTkyOqJSQkoKmpCcePHwcAVFRU4MMPP0RAQAASEhK69Pn79esHtVrdpeckIufARgMREXUp62WTPPwCoVCqUFtaBJNRj+a6KjTXVaHoQCZi08dKlJIuhn94H9HFm+qSo/ANiZQw0fkVHciEtqlOVEscOfUsRxMRkSOTyWRIGn0ltv3yiaVWW3oMNSUFHZYApJ7rrbfeQmVlpaj2wAMPdPnFWKLuUl9ehB/LAf8Tpy/Z1VeUoLJRB6NZsDTRSsoq8NSr/8KYGx6EUu3SJc9dU3IUL98BDB48uEvOR0TOhY0GIiLqUocOiZco8Ao4udlezQnx3e8efkFwcfO0WS66dAERMTi6e6NlXFNSYNeNBkEQkLv5T1EtODqRF5uIiHqw3v2GY+/KhaImc86W5Rg95wEJU5EtXXHFFdi1axdqamoAAEOHDsWNN94ocSqiC+MfHoPQ2GTLODQ2GXptM7b//iVaG2oBAILZjMaqUpTm78XQmXdKFZWIyIJ7NBARUZfKy8sTjT1PNRqsltnx78WLvY7G+u+sqaYcRjveELqiIAe1ZcdEtQTOZiAi6tEUSlWHfXiK9meipb5aokRkayNHjsTChQsxYcIEeHl54YUXXhBtpEvkqFInXI2IpHSoXDSWWltLE7I3LkHpoSwJkxERncR3WyIi6jIGgwEFBeKGgvdfjYbq4iOiekBEjM1yUdfwCYmAXCGeDNlcZ78Xbg5uWioae/oFIyJxoERpiIjIVuKGToRSdXoZEUEwI3frCgkTka35+PjgrbfewnfffYegoCCp4xB1CblcgVGz/wHvoF6QyWWWekt9NbYs/Bh6bYuE6YiI2GggIqIuVFhYCKPRKKp5BoTCaNCjvrxYVOeMBsejUKrgGxolqjXXVUmU5tzqy4s73NmVMGoqZLyjkYiox3Nx80DMIPE+UEd2rINe1ypRIpKCTCZDaGio1DGIupSnfzCGXv03uHn7W2pmkwnVxYex448FEiYjIuIeDURE1IWsl00KDAyEykWDutJjEASzpS6TyeHXq7eN03UvrYs7Prr+deRtXQnfsN4Ijo6H1sVd6lhdLiA8BjUlRy3j5trKcxwtnYObl4nGLm6eiB3EzcfJeZx6TbKuETmLxFFTsW/1z3+NZAjtmwaDTgu1xk3SXEQ9Cd9rpBE7eDyKc3Yjd+ufMOi0AAC9thV52/5EZMoQRCanS5yQiJwVGw1ERNRlrDeCjoqKggCgut2FaQDwDuoFlYurDZPZgEwOrcYTTQo11CpXaDU9c6Nr642U7XFGQ0tDLY7t2yaqxQ+bDKXa5SyPIOqB/npNInJWnv7BCIzsC8+AUAyZcRu8AkKkjkTdwGwyYefOnUhPT4dMJjv/A6hr8b1GEjKZDMOvvRsVBbmoLMqHYD55Q1dLfS22/fwxgqLeg8bDS+KUROSMuH4AERF1mfz8fNE4KurkMjsdNoIO57JJjsr6706vbUFba5NEac6sraVJNGPmTBuDEhFRzxc7eDySx0xjk6EHO7p7Az788EM8/vjjqK2tlToOkc24evpg5Kz74O5zegklwWxGTUkBMn/7AoIgSJiOiJwVGw1ERNQlzGZzh0ZDZGQkAKCmmI2GnsI7MAwqtUZUa6wqkyjNmfmFRWHK31/CZfc8j/DEgYhNH8+7uoiInBDvcO/Zqo4fwdFdGwEAGzduxJw5c7Bjxw6JUxHZTmTKECSMnAK16+kl4QxtOhzasQbHsrad45FERN2DSycREVGXKC0tRWureJPFqKgoGE4Uo7FGfCE6IDzGltGoC8nkcsQNmwS5Qgn/8BhUFR1CYFSc1LE6kMlkCI5OQHB0gmU6OREREfUMBr0O2376WHTXdkNDAzw8PCRMRWR7Q2bcjrLDB1B2aD/Mf33m1TbUYdsvnyIkJhmunj7SBiQip8IZDURE1CWsZzP4+vrC19cXDZWlorpcoYRPSIQto1EXGzj1RvS/bBYikgZB7Wr/G/7J5Py4Q0RE1JPs+fOHDjey3H333UhKSpIoEZE01Bo3jJ7zANx9Ayw1AQJaG2qxY/F8LqFERDbFGQ1ERNQlrBsNcXFxkMlkaKgsEdV9Q6OgUKpsGc0m5CYjwqoKYdDVwrtBA79yNUoDo2FW8K2WiGzv1GtSe3xNIgJMRgMK921FQGRf+AT1kjoOXYSywwdwKHO1qJaSkoI77rhDokTOi+819iEkJhn9Jl2HnUsWwKBvg4dvAFQurjh+cAeKDmxH77RhUkckIifBV38iIuoSeXl5onF8fDwAoKHyhKjeU5dNcjFoMXv1v08OqnYDOcBH178OrcJT2mBE5JREr0l/4WsSOTO9TovD29cgd9sKaBvrED1gFEbN+ofUsegC6XVabFv0P8tYgACTyYRrr70We/bssWmWnJwcOPslFb7X2I8BU+ZAEAQc3bMJBt3p5Wx3LP4KIX2SuF8ZEdmEc78rEhFRlxAEoUOjISEhAUDHRoN/RM9sNJC0qo4fgVGvQ0hMMjf/JCKiDvK2/omsNb9YxseyMjDgsllw9wk4x6PI3uxe9i1aG2osY5NBD/foIVh4oBE40GjTLAV7tqHPwDE2fU6is1EoVRg8/VYERvbF5h8/tNTbWpuwc8kCjL7hnxKmIyJnwUYDERFdsqqqKtTW1opqiYmJKC8vx6g5D0DlokFNSQFqSgoQGBkrUUrqyfau+AEVhbnwC4tG8phpiEwdArlcIXUsIiKyE3HDJiN7wx8wGfUAAMFsQs7m5Rg8/VaJk1FnncjfhyO71otq3oFhSBo9DaGxyTbPU1Ny1ObPSXQ+UWnDcOxAJooP7rTUju3PQFTqMESmDJYwGRE5A+6OSEREl8x6NoO7uzt69Tq57rHKxRWhsSlIGTcDY29+GF4BoVJEpG6i17agojAPh3euP//B3aS65CgqCnMBALWlhdj844c4lpUhWR4iIrI/GndPxKaPE9WO7FyPttYmaQLRBdFrW5D56+eimkqtQUz6WMjkvKxBdIpMJsPQq+6A2tVDVM/8/Qu0tTZLlIqInAXfkYmI6JLl5uaKxgkJCZDzS1+PVl1yFL+8cT92LfsWe5Z/h91Lv4FgNkuSJWfTUtHY1dMXUalDJclCRET2K2n0FZDJTn8+MRrakJ+x+hyPIHuxa9m3aG0Uz54deMVN0Lhz3Xkia66ePhg8/RYAJ5e41TU3orIwD9v/+EriZETU0/EqEBERXTLrRkNiYqJESchW3Lz8oG2ss4wNeh0aqkptnqO1oQbHs3eKagkjL4dCqbJ5FiIism8efkHonTZcVMvbthJGfZtEiagzSvL24ujujaJaaGwq+g6ZIFEiIvsX3X8UgnonoKm6HC31NTDq25C7aRlK8vZKHY2IejA2GoiI6JKx0eB83Lx84eblJ6rVlBTYPEdh1jYIwumZFCq1BnFDJto8BxEROYakMdNE47bWJhyxuohN9qOttfmMSyYNv/ZuyGQyiVIR2T+ZTAaT0QCjQW+paZsbsOn7D6DXtkiYjIh6MjYaiIjoklRXV6OmpkZUS0hIkCgN2ZJ/eB/R2NaNBkObFiW5e0S1vkMmQu3qbtMcRETkOPzCohAW109Uy928DGazSaJEdC5trU0d3tfTp90Cd58AiRIROY4R190LD9/A0wUBqCkuwI4/FkgXioh6NDYazsBkMmH//v344osv8Pe//x3p6elQq9WQyWSQyWQYN27cRZ977dq1uPXWWxEXFwd3d3f4+fkhLS0NTzzxRIfNVDsrNzcXTzzxBNLS0uDn5wd3d3fExcXhtttuw9q1ay86KxFRZ1jPZnBzc0NERATy8vKQkZGBlvoaCIIgUTrqTtaNhuqSozZ9/vKjB2E2Gi1jmVyBhJFTbJqBiIgcT/LY6aJxc10VivZnSpSGzsUrIBRX/vM1pIy7CjKZHGFx/RBjtak3EZ2ZT3A4hlx1B1QaV0vNZDQge+MfKD2UJWEyIuqplFIHsDe///47brrpJrS2tnbpeRsbG3HPPfdg4cKFonprayvq6upw4MABfPDBB3jppZfwf//3f50+7+uvv44XX3wRBoNBVD98+DAOHz6Mr7/+GjfccAM+/fRTeHp6dsmfhYiovbNtBL18+XJ89tlnqGxsw+7l3yJ+2CT0v2yWRCmpOwSEx4jGdWVFMBkNNtkfwdCmRdnhbFGtd7/hcPfx7/bnJiIixxYcnYiAiFhUFx+x1A5uWore/UZwOR47pFCqMODy2YhISoerly//joguQPLYaSjctxWF+7ZAMJ+8+UvX3IAN376P6575COp2TQgiokvFGQ1W6uvru7zJYDAYcM0114iaDCkpKbjtttswa9YshISEWI575pln8PLLL3fqvM8//zyeffZZS5MhLCwMs2bNwq233ork5GTLcT/88AOuu+46GNvd9UlE1FWsZ2OdWjYpJyfHUtNrmyGTK2yai7qfX69o0dhsMqK+vNgmz314x3oYDeLNO1PGzrDJcxMRkWOTyWRIHiOe1VBXVsQ7fO1cQEQM3L39zn8gEVnI5QqMufFBePgGnS4KQE3xUexa+rV0wYioR2Kj4SyCg4Mxbdo0vPTSS1i+fDkeeuihiz7XK6+8YlnCSKPR4IcffsCBAwcwf/58LFy4EEVFRXjiiScsx7/wwgvYuPHcG5KtXbsWr7zyimX85JNPorCwEAsXLsSCBQuQnZ2N77//HhqNBgCwatUqvP766xf9ZyAiOpszNRpMJlOHmQ7Wd7+T43Nx84DGw0tUqzlR2O3PazIakLNlmagWnjgQPsHh3f7cRETUM0QkDYJXQKiodnDjUonSEBF1H5/gcAy9+m9QuWgsNZPRgP1rf0X50YMSJiOinoaNBitTpkxBUVERysvLsWTJEjz//POYOnUqfHx8Lup8lZWVePfddy3j999/H3PmzBEdo1arMW/ePMyePdtSO9/ySc8884zl5zlz5uCtt96CWq0WHXPDDTfgvffes4zfeecdVFdXX9Sfg4joTGpra1FZWSmqJSYmoqCgAG1t4rvNrdfzp55BtMEcgBob7NNQsGcztI11olrKuKu6/XmJiKjnkMnlSB4zTVSrKMxBdbFt9xsiMUOblhtzE3WD5DHTEJE8WLT0mK6lERu/fQ9Gfds5HklE1HlsNFgJCQlBZGRkl51vwYIFaGlpAQDExcXhnnvuOeux8+bNg1x+8q8kIyMDe/fuPeNxO3fuxI4dOwAACoUC8+bNO+s57733XvTt2xcA0NTUhG+++eai/hxERGdiPWvB1dUVUVFRomWTAMDdJ7DDne/UM4imYQOoKSno1ucTzGYc3CS+4zQ4OgmBkX279XmJiKjniR4wCq5evpaxd1B4h2X5yLYyf/sCKz56AfUVJVJHIepRZHI5xt70MDz82t0kJACVx/Kxd+XCsz+QiOgCsNHQzX7//XfLz7fffvs5N66KjIzExIkTLePffvvtvOecOHEiIiIiznpOmUyG22+//bznJCK6GNaNhvj4eMjlchw8KJ6CGxDB2Qw9lejLCoD6ipJuvSuq6MB2NNWUi2op47g3AxERXTiFUoXEEVMQFBWP8bc+jukPvYmQPklSx3JaxTm7cSxrG2pOFGDZh88ge+MfnN1A1IW8g8IwbOZdUKpdLDWT0Yg9f36PahvMSiaink8pdYCeTKfTITMz0zIeN27ceR8zbtw4rF69GgCwbt26M24MvX79+gs+5ynbtm1DW1sbXFxczv4AIqJOOttG0NaNBv9ePb/RoFO7Yf70Z3B453r4BEcgMDIWOrWb1LG6nbtPgKiJLghm1JYeQ1Dv+G55PrPZCI2HN3TNDQAAr8BQhPZN7ZbnInJkp16TrGtEJJY0+kokj51+/gMvgMlo7DC701ZOPq/jfc3Xa1uwffGXlrHZZMTBDUsQM3AMXD19pAtG58T3GseTNPpKHN2zCYV7t0IQBACAtrkBG75+F9c89YHE6YjI0TneJxAHkp+fD7PZDODkzIIBAwac9zEDBw60/Gx9p/CZ6u2P78w5TSYTDh06hNRUXpQhoktn/TqVmJgIvV6PI0eOiOr+ET1/I2hBrkCNTyhKVR5oc/OD3Cf0/A/qARRKFTz8gqBrabDUakoKuq3R0GfAaESmDMXR3RuR+esXiBk05pyzBYmc1anXJCI6N5m86yf515cX4cdywP+E7b9uF+zZhj4Dx9j8eS/V7uXfd9h/KX3azWwy2Dm+1zgemVyOsTc/gorCPLTU/bWHpwCUH8lG9sYlCIyMkzYgETk0Nhq6UX5+vuXnoKAgaDSa8z6m/f4QtbW1qKqqQmDg6WUpKisrUV9fbxlHRUWd95wajQaBgYGoqqoCcPIOZDYaiOhS1dXVoaKiQlRLTExEfn4+TKZ209xlgH+vaBunI1vyDuoFXaG40dCdlCo14odNhr61Bf7hPb+JRUREjsc/PAahsck2f94aB1z+pOxINo7sWi+qhfVNc8iGCZEj8A4Mw7CZd2LDN+/BZNBDqXaBh28Astf/jsEz/gaAzQYiujhsNHSjmpoay8/BwcGdekxISIhoXFtbK2o0tD/nhZ73VKOhtra2U485paTk3BtxlZWVWX7WarXQarUXdH4iR6bT6c74szPIysqyzNoCABcXFwQFBeHXX3+11AVBgIdvIFQurlLFJBvwDuqFisIcyGRyeAf16rBvQ3eRyeXdcicqERER2YZBr0Pmr5+Jaiq1BkNn3skZi0TdKHnsdBTu3YqKwly4uHtCJpPBZDQge/1vaJ3W3ymu6zjzd3mi7vp/nI2GbtTc3Gz52dW1cxfZrI9rf44zjS/mvNbnOJ9zbTZtbdOmTQgICLig8xP1FJs2bZI6gk2tWbMGjY2NlnHv3r2xdu1aLFu2zFJva2uDbxw3VezpgqITEd1/BHzDoqBSn3/2HhERkb2rPJaPptpKxAwcLXWUHi1r1c9orqsS1fpPmQMPX9vctEDkrORyBaY/Mg+7l3+H3C3LLfW6suP44osvRCtpOANn+y5PVF1d3S3nZaOhG7XviKrV6k49xnqTZusOk3WX9WLO6wydaSLqftaznXr16gUAOH78uKjuHdTLZplIGhp3z27bk4GIiMhWBEFA+dGDOLDuN1QU5kKl1iA8YQBc3DykjtYjVRUdQu7WFaJaUO8ExA+dJFEiIucik8nQb/J1KD64U9Tw27JlCyZOnAgfHx/pwhGRQ2KjoRu135NBr9d36jFtbW2isfWMBet9HvR6faf2fmh/3s7OgjiluLj4nL8vKyvDkCFDAABjxoxBeHj4BZ2fyJHpdDrL3Q9jxozp1P+PPcWnn34KLy8vy3j69OkYMWIE2traLPXW1lb4BHd+VpQjk5lN8GusRJihGT6ttfCvL0OtVxAEuULqaA7vyK4NCOmTBA+/IKmjEDmMU69J7fE1iejcWuqqsPbLNyEIJ5eANOh1yM9YhbSJ10icrOcxGQ3IWPQZAMFSUyhVGH7t3VwW0YHwvcbxqdQaDLvmbqz54nVLTa1WY9euXXjrrbd69BJmzvxdnuh8y+RfLDYaupGHx+k7Xzo7i8D6uPbnONNYq9V26sWw/Xmtz3E+F9I4cHV1veBGBlFPodFonOa//4aGBlRWVkLe7otgv379UFhYKKqpVCp4+HVuLxlHp9G34vYlf31AL88AsoCPrn8dWo2ntMEcXGN1OTJ//RwA0Lv/CKSMnQGfYDa0ic5H9Jr0F74mEZ2bh18QolKH4tj+DEstd+sKJI6ayv2mutj+tb+hoeqEqJY26Vp4BYRKlIguBt9reobQ2BTEDBqLo7s3WmrLli3DlVdeiSlTpkiYzHac6bs8EXDhN6F3Fm8V6Eb+/v6WnysqKjr1mPLyctHYz8/vrOe82PNan5OI6ELl5eWJxmq1Gn369AFwsuFwalm3iIgIKJTsadPFy96wGIJghiCYUbh3C1Z8/AKM+rbzP5CIiOgipIybIRrrtc04tH2tRGl6ptrSIhzc+Ieo5t+rD5JGXylRIiIadMVN0Hh4w2jQo7S0FBUVFXj88cedbq8GIro0bDR0o/j40+tVV1ZWdmoX+/Zrm/v5+SEwULwJVlBQkGidvKKiovOeU6fToarq9Hp7CQkJ530MEdG55ObmisZxcXFQKBQYMmQIvvjiC2zatAnffPMNbrzxRokSUk/QXFuJgr1bRLW4oROhVLuc5RFERESXxjc0CuGJA0W13M3LYTR0bilcOjezyYiMXz61LE8FADK5AsOuuRtyLrdDJBkXNw8E9U5AS30VDAYDAODYsWN48cUXpQ1GRA6FjYZuFB8fb1lCRBAE7Nu377yP2bNnj+XnxMTEMx7Tvr53794LOqdCoUBcXNx5H0NEdC7WjQbr1yulUonExERRw5V6vsbqMhzdvQnbf/8Syz58FsU5uy/pfNkbl0AwmyxjhVKNRN7tSERE3Sx1/EzRWNtcjyO7NkgTpocpzMpAbdkxUS1l3Az4hUVJE4iILOKHT4bK5fTS3IIg4LvvvsPWrVslTEVEjoSNhm6k0WgwbNgwy3jDhg3nfczGjafXxJswYcIZjxk/fvxFn3PEiBFwceGdoER0aayXTuJMKQKA7b9/hW2/fIJD29egtrQQVUX5F32ulvoaHN21UVSLGzoRrh7elxqTiIjonAIiYhASkyKq5WxaCrPJKFGinqNP/5EYevXfoFKfvJjpHRSO1PFXSxuKiAAAIX2SEDt4gmjPvdbWVjz44INobW2VMBkROQo2GrrZ1Vdfbfl5/vz55zy2pKQEa9eeXv+z/WPPds41a9acd6fwBQsWnPecRESd1djYiBMnxJv3nW0GFjmXgIhY0bi6+OhFn+vgxiUwm09f0FEoVUgaw9kMRERkG6kTrhaNW+qrOyznRxdOJpcjbugkTH/kbYQnDMTwa++BQqmSOhYR/SV5zHRERkaKaocOHcJbb70lUSIiciRsNHSz2267De7u7gCA/Px8fP7552c99sknn4TJdHKJiOHDh2PgwIFnPG7w4MEYPHgwAMBkMuHpp58+6zn/97//IT//5B2lnp6euPXWWy/qz0FEdIr1skntN4Im5xYYKW401JQUwNxu6aPOam2sw5Fd60W12PTxcPPyu6R8REREnRUcnYjAKPGSswc3LIFgNp/lEXQh3H38Mf62xzt8diAiaSnVLnj88cfh5uZmqZnNZnzyySfIysqSMBkROQI2GrpZUFAQHn30Ucv4wQcfxE8//SQ6Rq/X4+mnn8YPP/xgqb3xxhvnPG/733/33Xf4v//7P8uGPacsXLgQDz/8sGX8+OOPIyAg4GL+GEREFgcPHhSN4+PjoVQqJUpD9sR6RoPR0Ib68nPPujuTnM3LYDKefk+Ty5VIHjv9kvMRERF1lkwm67CkT2NNGYoObJcmEBGRjaSlpeHmm2+GTCaz1Jqbm3H//fdDr9dLmIyI7B2vDJ3BFVdcgdLSUlGtvLzc8vOuXbvQv3//Do9bvnw5wsLCOtSfe+45bN26FevWrYNWq8Xs2bPx6quvYuDAgdDpdNi0aRPKysosx7/00ksYO3bsOTNOnDgRc+fOxauvvgoAePPNN/H1119jzJgxcHFxwe7du5GdnW05fvLkyXjmmWc69ecnIjoX60ZDUlISDAYDbrnlFsTFxSE1NRWpqanceN4JaTy84OEXhObaSkutuvjIBW3wqG1uwKHMNaJaTPpYuPv4d1lOIiKizgiL6we/sGjUlhZaavvX/Yao1KGQyXnPHhH1XC+99BLWrFmDgoICS23//v34z3/+I7qZloioPTYaziAnJwdFRUVn/X1LS8sZp4ydrbOrUqnw66+/4p577rHMZjhw4AAOHDjQ4bgXX3yx0w2Bl19+GS4uLnj55ZdhMBhQWlqKH3/8scNxc+bMwaeffso7jomoS+Tk5IjGycnJOHz4MI4cOYIjR45g+fLlAIB169ZJEY8kFhAR26HREDd0Yqcfn7t5OUzG0++nMpkcKZzNQEREEjg5q+EqbPzufUutobIEx7N3ICptmHTBHMjx7B1Qu3kgpE+S1FGI6AJ4eXnhvffew+zZs6HT6QCcXLr7nXfewYwZMxAby2XPiKgj3oZhI97e3li4cCFWr16Nm2++GTExMXBzc4O3tzdSUlLw2GOPISsr64JmHchkMsydOxdZWVl49NFHkZKSAm9vb7i5uSEmJgY333wzVq9ejR9++AFeXl7d+KcjImdRWVmJqqoqUS0pKUk0gwoAoqKi+LrjpAIiYkTjC9kQuq21CfmZq0W1PgNGw8MvqEuyERERXaiIpHT4BEeIavvX/ca9GjqhtbEW2xb9D6s/exWZv30Bva5V6khEdAEmTZqEa6+9VlRraGjA/fffDzNfA4noDHiL+xkcO3as2849adIkTJo0qUvPmZiYiH/9619dek4iojOxns3g7u6OyMhIfPnll6J6SkqKLWORHQmI6CsaN1SegF7XCrXG7SyPOC13y58w6nWWsUwmR8r4GV2ekYiIqLNkcjnSJl6DTd9/AABQa9wRlToUZrMJCi6fdFaCIGD771/B8Fdz4fCOtTiRtxfTHnoDLm6eEqcjos568803sXHjRpSUnN53LTMzE19++SXuuusuCZMRkT3iJyMiIuo060ZDUlIS5HJ5hxkNbDQ4L7+wKMgV7e9jEFBTUnDW40/Ra1uQt22lqNa733B4BYR2cUIiIqILE5k8GEG9E9Bv0nWY+dQHSJt4DRRKldSx7FrR/kyU5O4W1YL7JLLJQORg/Pz8MG/ePKjVakvNaDTi5Zdf7rC3KRERGw1ERNRpZ9oIurGxEcePHxfVU1NTbRmL7IhCqYJfWG9Rrbr4yHkfZzIaEJk8BDLZqY8mMqSMv7rL8xEREV0omVyOy+55DmkTr+nUDD1np2tpws4lC0Q1jbsX0qfdKlEiIroUM2bMwNSpU0U1vV6Pl19+GYIgSJSKiOwRGw1ERNQpgiCccSNo69kMarWam4M5uYAI8d9/9fHzNxpcPX0w4vp7cdXj76Lv4AmIHjASPkG9uisiERHRBZHJZFJHcBi7ln4NXUujqDZ4xu3QuHM2A5EjkslkeP/99xEcHAyFQoFevXohPDwcu3btwtKlS6WOR0R2hHs0EBFRpxQXF6OpqUlUS05OxuLFi0W1xMREKJXO9/bSpnLFwskPonDfVngHhsGvVzTaVK5Sx5JExw2hj0AQhE5dpPH0C8Kwa+7i3VFEl+jUa5J1jYioO5Xk7UXhvq2iWkRSOqJSh0qUiLoT32ucR1BQEBYsWICXX34ZdXV1lvp7772HkSNHws/PT8J0RGQvOKOBiIg6xXo2g5+fH4KCgrg/w1/MCiVKQvoiX+OHo969UBLSF2aF8zVcACAgUrwhtK6lEc11VRd0Dt45SnRpTr0mtf/HWV+TiMg2DG06bP/tC1FNrXHHkKvu4Pt6D8X3GucyevRovPDCC6JaY2Mj5s2bJ1EiIrI3bDQQEVGnWO/PcKqhYN1o4P4M5OEb2GGzx84sn0RERORIWhvrsGvpNyjO2SV1FLuQn7ESrY21otqgK2+Cm5evRImIqKuNGjUKl19+uai2Zs0abNy4UaJERGRP2GomIqJOOdNG0MXFxWhsFK/B66wzGug0mUyGkJhkaJvqERARi8DIWARFJ0odi4iIqEu0NtYhZ9NSHNq+BiajAWVHshGeMBAyufPex9dQeQLFB3dBpdFYaqGxqYgZNFbCVETUHR5//HFkZmaioaHBUnv99dcxaNAgeHh4SJiMiKTmvJ+EiIio04xGI/Lz80W1pKSkDrMZ/P39ERwcbMtoZKfG3PggLr/3eQy64kZEpgw54waQBzctRc7m5TDq2yRISEREdHEqj+Ujd+ufMBkNAID6imKnntVg0OtwdLf4bmalWoNhM+/kkklEPZCvry8ee+wxy7ixsRGZmZmYO3euhKmIyB6w0UBEROdVUFCAtjbxxeAzNRpSU1P5hZI6RdfShP1rf8Xu5d/i97cfQd7WFZYLNkRERPYsKmUIvIPCRbX9a3+FYDZLlEhaWat+hq6lSVQbcPlsePgFSZSIiLrb1KlTMWDAAJSUlODEiRMwGo34+uuvsX37dqmjEZGE2GggIqLzsl42qVevXvDx8eFG0O0JZrjqmuBp0sPdoIWrrgkQnPOCQ2fkbFoCo14HANA212PXsm8veMNoIjqHv16T2v/D1ySiriGTy5E2YaaoVld+3ClnNVQdP4zcrStEtcCoOMQPmyxRIrIpvtc4LZlMhuTkZLS0tFhqbW1tuO+++zrcoEZEzoN7NBAR0Xnl5OSIxsnJyWhra+uwnJIzNxpc21rwj5+fOTko3QjsAj66/nVoNR2XDHJ22uYG5GesFtWi+4+Ed2CYRImIeh7Ra9Jf+JpE1HWiUodi/7rf0FBZYqntW/0LIpLSJUxleyaDHm5evtA21QMAFEoVhl97j1PvV+FM+F7j3B544AH8/PPP2Ldvn6V2+PBhvPbaa3j55ZelC0ZEkuG7PxERndeZNoKWy+V4//33cc8992DEiBHw9fVFUlKSRAnJkRzcuARGw+k7nWQyOVKt7gwlIiKyZ2ea1dBQWYLCrG0SJZJGSEwypj8yD8HRiQCA1AnX8MYBIiehVCrxv//9D25ubpaaIAj46KOPOtyoRkTOgY0GIiI6J51OhyNHjohqycnJUKlUGD58OO655x78+9//xqpVq0QfMonaa2ttQtmRbLQ21uFQpng2Q5+Bo+EVECJRMiIioosTlToUviGRotr+NYtgNpskSiQNtcYNMYPGYNi1dyN5zJVSxyEiG0pOTsY//vEP0T59Wq0Wd911F4xGo4TJiEgKbDQQEdE55efnw9xuc0O5XI6EhIQOx3ETaLLW1tqMLT99hN/feRQ/vXIv1nzxOvat+lm06bNMruhwRygREZEjkMnl6Df5elGtqbYCVccOSZRIWr4hkZAruDozkbN55plnEBcXJ6rt378fH3zwgUSJiEgqbDQQEdE5HThwQDTu06cPXF1dJUpDjkSlcUVx9k401ZQDAMwmI/IzVomOiU0fBw+/ICniERERXbLwxIHwD48R1Ypzd8PEO3mJyEm4uLjg008/hYuLi6UmCALmzZuHY8eOSReMiGyOjQYiIjon60ZDWlqaREnI0cjlCviH97GMtU310Gtb2v1eidTxV0kRjYiIqEvIZDIMuGyWqKbXtqA4Z6dEibqXXtcKQRCkjkFEdmbw4MG4/fbbRbXm5mbcfffdotnxRNSzsdFARETnZN1oSElJkSgJOaKAyL4AAJPRgLaWJhj1OsvvYoeMh7tPgFTRiIiIukRIbIplM+RTju7eCEO797yeQDCbsX7+21g3fx5a6mukjkNEdubVV19F7969RbXMzEzMnz9fkjxEZHtsNBAR0VlVVlaisrJSVEtNTUVubi5KS0t5RxudV+BfjQZtYz0EATDq2wABUChVSBnH2QxEROT4ZDIZ+lvPamhtQf62VWd5hGPK374GlUX5KD2UhSXvPYFD29dA4J3KRPQXNzc3fPTRR1CpVJaa2WzGc889h4qKCgmTEZGtsNFARERnZT2bwdPTE1FRUXjzzTcxY8YMTJkyBU8++ST2798vUUKyd4FRcTAa9GjTNgM4uV6r0aBH3NBJcPf2kzgdERFR1wjqHY+wuH6iWs7mZTAZDRIl6lrNtZXYu+JHy9ig1+HgpqU95s9HRF1j7NixuP7660W1+vp63HfffbxJjcgJsNFARERndaZlkwwGA/Ly8gAANTU1WLduHXS6nrU0AHUdjbsnTHo90O57hSCYOZuBiIh6nP6TT19cC45JwmX3PAeFUnWORzgGQRCQ+dsXouUPAWD4NXdDqXY5y6OIyFn961//QmhoqKi2du1aLF++XKJERGQrbDQQEdFZWTcaTi2bZDKZLDW5XI7k5GRbRyMHUVd+vMMa1d6BYdB4eEmUiIiIqHv4h/fBgMvnIG3iNRg45Qb4BIdLHalLHN29EWVHxJ8J+w6ZiJAYfv4joo68vLzwwQcfQKFQAADUajXCw8PxwQcfoKGhQeJ0RNSd2GggIqIzMhgMyM3NFdVSUlI6LJMUGxsLd3d3W0YjB+ITHIGBU2+AUq0GAMgVcnDSNBER9VQp42bAwzdQ6hhdprWxFruXfSequXn5YeDUGyRKRESO4Morr8T06dMREBCA6OhouLm5oba2Fu+9957U0YioG7HRQEREZ3T48GHo9XpRLSUlBVlZWaJaWlqaLWORg5HJZIgfNhlegWHw8A+Cm7c/tI21aGmolToaERERnYMgCNj++1fQ61pE9aEz74Ra4yZRKiJyFN988w2uu+46yOWnLz0uXboUmZmZEqYiou7ERgMREZ1Rdna2aBwVFQVPT88OMxrYaKDz8Q4Mg9rVHS6u7nBx8wAAVB8/LHEqIiIiOpei/Zkoyd0tqkX3H4nwhAESJSIiRyKXy/HUU091mP3++uuvo7W1VaJURNSd2GggIqIzsm4opKam4sSJE6irqxPV2Wig85HJ5QiM7CuqVRUdkigNERGR7RkNemibHWdtcl1LE3YuWSCqaTy8MXj6rRIlIiJHFBQUhIceekhUKy0txSeffCJRIiLqTkqpAxARkX0600bQ1s0HPz8/9OrVy5ax7JZepcGSMX/D8YO74OkfDN+QCOhVGqlj2Y3AyL4oPXR62a0qzmgg6lanXpOsa0RkW4LZjKN7NiFr9S8IiIjF2JsfljpSp+xa8jV0LY2i2pDpt8HFzVOiRGSP+F5DnXH11VdjxYoV2LNnD4CTy7L997//xahRozBkyBCJ0xFRV2KjgYiIOqitrcWJEydEtdTUVCxatEhUS0tLg0wms2U0u2VSqHAoagCyC4vh7x+D0KhkqSNJpqWhFm5evqL/NgKj4kTH1J44BqNBD6VKbet4RE7h1GsSEUmnvqIEm3/4EPUVxQCA4wd3oKroUIf3RHtTkrsHhVlbRbWI5MGITB0qUSKyV3yvoc6Qy+WYO3cu5syZg6amJpSVlUGr1eKuu+7C7t27oVKppI5IRF2ESycREVEH1vszuLq6IiYmhvsz0HkZDXqs+Oh5rPj4RZQX5Fjq/uExkMlOfeyQwSswFNrGujOfhIiIqAdw8/aDtqleVNuz4kcIgiBNoE7Q61qx/fcvRTW1xh1DZtzOm0uI6KJFRkZi+PDhKCwshFarBQAcPXoUL7/8ssTJiKgrsdFAREQdWDcakpKSoNPpcPToUVG9X79+toxFDuBQ5mq0NtaiuvgwVn/2KtZ8+SYMeh3UGlekT78VE+94CrOf/x+mP/wWPP2DpY5LRETUbdQaN6SOv1pUqzyWh5LcPdIE6oS8rSvQ2lgrqqVPuxluXr4SJSKinuLBBx+Eq6urqPbf//4Xubm5EiUioq7GRgMREXVwpv0ZDh48CLPZbKkplUokJibaOhrZsbbWZhxYv1hUE8wmqNQn1+pNGH4ZwuL6Qe3qLkU8IiIim4sbNgkevoGi2t6VCyG0+0xlT1LGzUD/yddDrji5ynJY3zT0GThG4lRE1BNERUXhqaeeEtXa2tpwxx13iL5nEpHj4h4NREROQK/XIysr6/wHAjCbzcjMzIROp7PUVCoVFi9ejJaWFkstJibmvOfMyckB32qcR/b6xdBrm0W1/pfNligNERGR9BRKFfpNvh5bf/rIUmuoLMHRPZsQmz5OumBnIVcokTphJiKSB2PX0m8wdOadXDKJyImYjMa/vsN1j9GjRyM6Olo0Uz4rKwsPP/ww3nnnHajV3L+NyJHx6g8RkRPIysrC81/9Cf/wmPMe21hdjuOVDaLammIBB9bvQlVjm6XmZvbGB6sPnfNcBXu28S44J9FcW4m8jJWiWlTqUARGxkqUiIiIyD5E9xuBnM3LUFdWZKllrf4FvfuNgFJlnxfVfILDMenO/5M6BhHZWH15EX4sB/xPdN/lwuhJt+JwwcswGQ2W2ieffYEJEybg6quv7rbnJaLux0YDEZGT8A+PQWhs8nmPa6opg0qjsYw9fAPRO20YCvduQVNNueUDYczA0ec9X03J0XP+vidx1TXhHz8/c3JQvBrIAD66/nVoNZ7SBrORvSsXwmwyWsZyuRIDLp8jYSIi5yZ6TfqLM70mEdkTmVyOgVPmYO1Xb1lqrY21yN+2Esljp0uYjOjS8L2mZ+rs98aLFRqbjOriI9i19BtLzWwy4qWXXsKMGTMgl3OVdyJHxf97iYhIpPJYvmgcGBUHABh/2+OY/cLnuPzeFzDg8jkI6sP9Geik+ooSHNufIarFD5/MzZ6JiIj+Eto3DSExKaJa9oY/0NbafJZHEBH1XCOuuwe+oZGiWl5eHv773/9KlIiIugIbDUREJFJVJF4O6VSjATi5znBQ73ikjJsBVw9vW0cjOyQIAvK2rRDVVBo3pE64+qyPaW2sxbGsDOxYPB8bvn2vmxMSERFJTyaTYcAU8b5Fel0Lsjf8IVGik3K3/ImakgJJMxCR85ErlJhy34tQKFWi+quvvoqSkhKJUhHRpWKjgYiILFoaatFcVyWqBfWOlygNOYK6siLUlRaJaqnjr4aL25mnzNeWHsOiNx7A5h8/RH7mKhQf3AldS5MtohIREUkqIDwGvdOGi2r5GSvRXFspSZ7KY/nYtexb/PnR89i78ifReulERN0tuE8iUsZfJao1NzfjtttugyAIEqUiokvBRgMREVlUWS2bpNK4wScoXKI0ZO/MJiOK9meKau4+gUgYcflZH+MTHA6lykVUs16ui4iIqKfqf9ksyOQKy9hkNGDvyoU2z2HUtyHjl/8BECAIZmRv+B0rP3kJgtls8yxE5LxG3/BPeAeFiWrbt2/HJ598IlEiIroUbDQQEZFFpfWySZF9IeNmXHQWh3esh7a5QVQbcPmsDlOg25MrlAiIjBXVKo/ldUs+IiIie+PpH4y4oZNEtWP7MzosXdnd9q36CY01ZaJaVNowfu4jIptSqtS47N4XIG/XgBUEAS+++CKXUCJyQPwUQUREFtYzGrhsEp2NXqfF/rWLRDX/Xn06LAlxJkG9E0TjykI2GoiIyHn0m3QN1Bp3y9gvLBoyheIcj+halcfykbtVvL9SQERfJI26wmYZiIhO6RWXhj4Dx4hqgiDgpZde4hJKRA5GKXUAIiKyD4Y2LerKj4tqQVHx2LfqJ7TU1yAoOgFBUfHwCgyFTCaTKCXZi4Mbl0DX0iiqDbripk7dCWndwKotPQaDXgeVWtOlGYmIiOyRi5sn0iZeg4OblmLA5bPQZ8Bom80kaL9k0ikKpQojrruXsxmISDIDpsxBW1k+qqurERQUBB8fH+zduxeLFy/G1VdfLXU8IuokfpIgIiIAQPXxIxCE0+vyyuVK+EfEoGj/dhTs3YzMXz/DH+89jvzM1RKmJHvhFRAMjYe3ZRyeOBDBfRI79diAyFjR+tSCYEb18SNdnpGIiMhexQ2bhKseewcxg8ba9AL/vtU/d1gyqd+k6zqskU5EZEsKpQqvvPIKBg8eDF9fX8uNbe+++y4qKiokTkdEncVGAxERAei4Ia9fr2gY2rQdvowGRMTYMhbZqZhBY3H14+8iImkQlGoXDJxyY6cfq1Jr4BfWW1TjhtBERORMFEoVVC6uNn3OymP5yN3yp6gWENEXSaOvtGkOIqIziY+Px6uvviqqtba24rXXXuMSSkQOgo0GIiIC0HEj6KCouA57NpzpAjE5L5WLKyKS0jH+ticu+E7IYOt9GrghNBERUbcxGvRcMomI7N6IESMwffp0UW3btm1YsmSJRImI6ELwEwUREcFsMqK6WLx0TWDv+A53mQdE9oVcbrvNCskxKNUuF/yYQKt9GqqPH4HZZOyqSERERA6rO+7c3bfqJy6ZREQO4ZFHHkFgYKCo9vbbb6O0tFSiRETUWWw0EBERasuKYNTrRLWgqLgOjQbrTXyJLpb1f0tGQxtqS4skSkNERGQfKo/l48+Pnkdt6bEuO2dV0SEumUREDsPLywvPPPOMZdzS0oIDBw7g1ltv5RJKRHaOjQYiIkKV1bJJXv6hUKhUHb7kstHgvASz+fwHXQCNuye8A3uJalw+iYiInFVzbSU2fvcBVn76EmpKjmL3sm+75IKa0aDHtp8/BZdMIiJHMnr0aEyePBllZWU4fvw4DAYDMjMz8fnnn0sdjYjOgZ8siIgIlcfEjYbA3nGoLj4KQTh9cVkmVyAgItbW0chOZK1dhI3fvo/m2souO2dQtHifhopCNhqIiMg5FWZtw/Hs7ZZxeUEOSnJ3X/J5961cyCWTiMghzZw5E83NzZaxIAiYO3cuysvLJUxFROeilDoAERFJSxCEDjMagnrHo9Lqoq9/r+iLWovfWRiULlg75HqUHjoAd98AeAeGwqDsGf++muuqkLNpKUxGA07k70XiqCuQMm4GVC6ul3TeoN5xOLxjrWVcVXQIgtnMOyyJusCp1yTrGhHZp8RRV+DwjnVoqa+21HYv/x5hcf2gUKou+rxBveNRsHcL2lqbAAABEbFcMom6DN9rqDsNGTIEd9xxBz777DNLrbm5GTfffDNWr14NmUwmYToiOhN+kycicnLNdVXQNtWJakG94zssYxPUW3z3OYkZlWrsix+DdZ4R2BaSgn3xY2BUqqWO1SX2/PkDTEYDAMBkNODgxiVoqa+55PMGRYmX4mprbUJDFTd5I+oKp16T2v/TU16TiHoipUqNAZfPEdWaaso77K1woSJThmD6I/MQnjgQCqUKw7lkEnUhvtdQd5s3bx4iIyNFtYyMDHzyyScSJSKic+EnDCIiJ2c9c0Hj7gU3b39UHT8iqnN/BudUUZiHogOZolrc0EnwCQ6/5HO7+wbCwzcQ/r36IHHkVIy96WG4+/hf8nmJiIgcUe9+wxEYGSeqHVj/O1oaai/pvK4e3hh3y2O44p+vwyeo1/kfQERkJ9RqNRYsWAClUrwgywsvvIBjx45JE4qIzoqNBiIiJ1dRmCsaB/aOR+2JQpiM+nZVGYJ6i7/4Us8nmM3YtfQbUU3t6oF+k6/tkvPLZDJc9di/cMUDryJ92i2ITBlyycsxEREROSqZTIbB028FcHo5EKNehz1/ft8l52aTgYgc0ZAhQ3DXXXeJai0tLbjppptgNpvP8igikgIbDURETs660RASnYiKghxRzTc0Ei5unraMRXbg8M51qC0tFNX6Tbq2S/9bkCu4XRQREdEp/uF90HfweFHtWNY2VBTknuURREQ935tvvok+ffqIavv27cPbb78tUSIiOhM2GoiInFhLfTWaaytFteA+iSi3+jIbHJ1oy1hkB3QtTdi78idRzTsoHHFDJ0qUiIiIyDn0v2wW1Bp3UW3nkgUwm03nfJwgCGisLu/OaEREklCpVPj222+hVov3AHnrrbeQl5d3lkcRka2x0UBE5MSsZzOoXT3g6R+CqqJDonpITJItY5Ed2LdyIfTaZlFt8PTbOAOBiIiom2k8vNDvsutFtbry4zi0fe05H3csKwPZGxZj36qFaGttPuexRESOpl+/fnjwwQdFtba2Ntx4440wmc7diCUi2+DVAiIiJ1Z+VLxEUnB0AuRKJSbc8SQqCnJRUZCD6uKjCOqdIFFCx+Gqa8btf7wGk6ENsrLNkO9RYv6MZ6HVeEgd7YJVFx/F4Z3rRbXeacMRGpssUSIiulCnXpPac9TXJCJnFDd0Ig5vX4v6imJLLWvVz+idOgwaD68Ox7c21mLHH/MBAGWHs7Hk/acw4rp7ERaXZqvI5IT4XkO29sILL+DPP//EwYMHLbX8/HzMnTsXb7zxhoTJiAjgjAYiIqdmPaMhuE8i5HIFQvokod+ka3HZPc9h9gufwcWNXxbOT4BbWzM8zQZ4GHVwa2sGIEgd6oIJZjN2LP4K7bMr1RoMvOImmzy/rrkRxw/ugiA43r87Ivty8jWp/T+O+JpE5KzkcgWGzLhdVNPrWrBv1U8djhUEARmLPhPNRNQ21UGva+3umOT0+F5DtiWXy/H999/D1dVVVP/444+xe/duiVIR0SlsNBAROamW+pqO+zNEd1wiSaFU2SoS2YHDO9ej5kSBqJY2YSbcvf267TlNRgN2LJ6PJe8/hZ9fuw8bv30XjdVl3fZ8REREjiC4TyJ6pw0X1Q7vXI/qkqPi2o51KD2UJar1ThuO3mnDuj0jEZGtxcbG4tlnnxXVDAYDHnnkEej1eolSERHARgMRkdOqKBQvm6R29YBvSIREacgetLU2Ye/KhaKaV2AYEkZO6dbnlSuUOJ6zU7Q8RIXVhuRERETOaOAVN0GpcrGM3bz9YNS3WcZNtZXYvfw70WNc3D0w5Ko7bJaRiMjWHn74YQwZMgQAoFQqERkZidbWVnz22WcSJyNybmw0EBE5KesLuUG94yGT823Bme1d+VOHDaCHzLi922e1yGQyhPQRz6apKMg5y9FERETOw93bD6kTZkImVyB5zDTMeGSe5T3TbDZh608fwajXiR6TMn4ml70koh5NJpPhu+++Q2xsLPr06QN3d3cAwIIFC7B//36J0xE5L15RIiJyUtaNBusLveR83H0CoFCqLeOo1KEIjU2xyXMHW/33V16Qw30aiIiIACSOmoppD76BgVNvhMrl9LrkBzcuQVXRIdGxwdEJCIqKs3VEIiKbCwsLw88//wwXl9OzvsxmM55//nm0tnKPGiIpsNFAROSEWhpq0VRbIaoF90nCibx9aK6t5AVeJ5U6/irMePRtRCQPhlLlgkFX3Gyz5w6OThSNdc0NaKziPg1EREQKpQo+weGiWnXJUWStWSSqefgGdtjTgYioJ4uLi8M999wjqpWUlODdd9+VKBGRc1NKHYCIiGzPelkatasHvAJCsOLj52EyGuDuE4Dg6ET0v2wW3H38JUpJUvDwDcS4mx9BS321Tf/uPf2D4eblh9bGWkutvOAgvIPCbJaBiIjIERjatNj640cQzCZLTSaTY9Ts+1FRmCdhMiKii2MyGpGTc3FLpyYlJSE0NBRHjhyx1L777jv4+vpa9nE4E51Oh8OHD6N3794X9bxE1BEbDURETqjj/gxxqCkpgMloAAC01FejYO8WpE+7RYp4ZAfcfQJs+nwymQzBMUko3LvFUqsoyEX8sMk2zUFERGTvdi//Ho017Wb9CUDqhKsRGBXHRgMROaT68iL8WA74n7i4y5SqpMmoyT4Mk14PQRCg17Xg8afnYur9r8DNy/esj6spacVEHLvI1ERkjY0GIiInVFFovT9DcoeaX2gUNxIkmwrpY9VoKMyFIAiQyWQSpiIiIrIfxTm7cHjHWgAn7wBuqa+Gb0gEUifOlDgZEdGl8Q+PQWhs8kU/XhDM2PrTx2iuq4JBpwUA7Fj8Jea88Dlk8nOtHM/lWom6CvdoICJyMi0NtWiqKRfVgvskdlhOKTiGm0P3dAa9TuoIItYbQuuaG9BQeUKiNERERPZF21SPjEWfQRAEaBvr0VBRAqNeB5NBj+baKqnjERFJKmbQWAAyS5MBODlDOvP3L6ULReRk2GggInIylVYzF9Qad3gFhKLq+GFR3XpzXupZDG1aLH3/Kexc8jX07T6MS8nDN7DDkk3Wy3wRERE5K72uFa6ePjAbDWhtqoMgCHDz9gdkMuz4Yz4EQZA6IhGRZGQyGabc9zwUSpWovmvJ16g8li9RKiLnwkYDEZGTKTuSLRoHRcej6vhhy/4MwMkNBYOjE2wdjWwoa80iNNdVIW/bCvzx3uM4nr1D8gsUMpmsQ4Or/OhBidIQERHZF+/AMFzxwKtIHjsDGncvqF3dLMtclh3ej8J9W85zBiKini24TxLSr7xZVDMZDVj6wdMwGvQSpSJyHmw0EBE5EUEQUHb4gKgWEpPcoeYf3gdqV3dbRiMbqi09hrytKyxjbWMd8jPXSJjotBCrJbvKC3IgmM0SpSEiIrIvCqUKg6ffiiseeA0BkbGifYx2Lf0Whjb7mKVIRCSVYdfc1eGmucbqcqz5/HWJEhE5DzYaiIicSGNVGVoba0W10NjUDneNh8Sk2DIW2ZDZbELmr59DEE5fvFcoVRh69R12selySGyqaKzXNqOmtFCiNERERPYpMjkdw6+5W1Rra23CsaxtEiUiIrIPMrkc0x+ZB7Wrm6ien7EKh3dulCgVkXNQSh2AiIhsp+yoeNkkVy9faDy8UHNCfCE3rK/4Yi+dn0Ghxra0qag8lg83L194+AXBoFBLHauDvK0rUHOiQFRLGXcVvAJCJUok5u7tB6/AMDRWlVpqZYcPICA8RsJURI7n1GuSdY2Ieo6IpHREpgzF8eztllrV8SOoKjqE0NhkCZORs+B7DdkrD99AjLv1Maz+36uW5WEFQcCaL15Dr/h+cPPykTYgUQ/FRgMRkRMpO7xfNA6NTUXF0RwAp9fmV6pcEBAZa+Nkjs+ockFGvyuQXWeEf3iMXX7Bb6wux75VP4lqXoFhSB47XaJEZxYam2ppNPiF9obGw1viRESO59RrEhE5rvqKEnj6B3fY2LS9ITNuQ/mRbOh1LZZa9sY/kDhqClQurraISU6M7zVkz5JGXYGjuzbi6O5NllpbSxOW/vtpXP/sx3Yxm5uop+HSSURETsJsMv3VVDgtNDb5DJtDJ5zzCy05JsFsRuavn4k2/QZkGH7N3Xb39x2bPg6j5/wT1z/7Ca588HX0HTxe6khEREQ2pW2qx+rPX8OKj15AY3XZWY9z9fTBoCtuFNV0TQ3Yt+rn7o5IRGT3ptz3Itx9AkS10kNZ2Ln0G4kSEfVsbDQQETmJhsoSGPQ6US00NhVlRw50qFHPc3jnelQU5opq8cMvQ1DveIkSnZ1fWBR69xsOjYeX1FGIiIhsTjCbsfWnj6FrbkBt2TEs+/BZHN29ybL8h7WY9HEI6ZMkquVtW4mq44dtEZeIyG6pNK644p+vQq5QnC4KwPZfP0flsXzpghH1UGw0EBE5ierio6KxT3AEjPo2NNdVieqhfbkRdE/TUl+DPcu/E9XcfQIx4PJZEiUiIiKis8nZvEx0I4hRr8Px7B1nPV4mk2HozLusZigKyFhkPZORiMj59IrrhwGXzxHVTEYDlrz/FIwGvUSpiHomNhqIiJxETYm40RDat+NsBo2HN3yCI2wZi7qZIAjY/vuXHWazDJt5J9duJiIisjNVx49gr9V+Sq5evhh+3b3nXE/cKyAE/SZdJ6o1VJYge/3ibslJRORIRs3+BwKj+opqzXWVOLDuV4kSEfVMbDQQETkBrVaL+vJiUS00NgUady8ERydBLldaatwUq2cp3LcVJ/L3imoxg8YiLC5NokRERER0JnpdK7b8+CEEs6ldVYZRs/4BjbvneR+fOPoKuPv4i2oHNixGfeWJLk5KRORYZHI5Zjz6DtSubgAApdoF3kG9UH7kII4cOSJxOqKeQyl1ACIi6n65ubmidX3lCiWCohOgUmsQmTIEBr0OlYV5cHE7/5dYOjNNWwtmr3wfbS1NUFTvgjLHBQsvfxg6F3fJMmmbG7DLaqMzVw8fDLriJokSXRqzyQi5gh9diDrj1GtSe1K/JhHR2QmCgO2/fdFhScvU8VchJCa5U+eQyxWIGTQOuVuWAwCUKhcMmHoDvANCuzwvEcD3GnIsnn5BmPi3/8PGb9+DxsMLMpkMZqMJa9asQXl5OaKjo6WOSOTw+G2diMgJZGdni8aBkXFQqTWWsUqtQa/4/jZO1bPIBDMCGspPDowtgPZkTUoH1v6GttYmUW3IVXfAxc1DokQXxtCmRUVBLsqOZKPsSDb8w/tg5PX3SR2LyCGIXpPa1YjIPh3dvRHH9meIaoFRcUibdO0FncfDNwAxg8ZA19yA4dfeA0//4K6MSSTC9xpyNPHDJkGuUCDz188stba2Nrz00kv44osvoGi/aTQRXTA2GoiInEBOTo5ozA2fncOAKbMhCGYc2r4GABCZMhSRKYMlTtV5R/dsxs4/5lvGel0LBEHg8l5ERNSjNFSWYucfC0Q1tcYdo2bfD7n8wi96xaSPR1hsMmRyrpRMRGQtNn0cyg4fQNGBTEtt//79+Pzzz3HvvfdKmIzI8fGTBxFRD1dZWYnS0lJRLbRvqkRpyJZULq4YevXfMPGOp+EbGoUhV90udaQLEhorbohpG+vQwHWmiYioBzHq27Dp+w9gNLSJ6sOvvRsevoEXdU65XM4mAxHRWchkMgy75q4Or7FffPEFtm7dKlEqop6Bnz6IiHq4zMxM0Vjt6gH/MK4/6UzC4tJw5T9fh6uHt9RRLohXQCjcvMWbWpYdOSBRGiIioq6344/5qK8oFtXihkxEZMoQiRIREfV8ao0bRs15wDJT2mw248SJE7juuus63KRHRJ3HRgMRUQ9n3WgI6ZMEo6ENgpnrpzoTR1xuSCaTdZjVUHaYjQYiIuoZjuzagKO7N4pqPsERGDTtlm57zoqCXFSXHO228xMROYrAyL7oO2wSjEYjioqKUF9fj8bGRsycORNmflcmuihsNNjA/PnzIZPJLuifV199tdPnX7t2LW699VbExcXB3d0dfn5+SEtLwxNPPIG8vLxu/JMRkb0zmUzIyBBvLBjaNxV7VvyIX964H9t+/hTH9mdCr22RKCHRuYX1TRONy4/mwGQ0SJSGiIioa9RXlGDH4vmimkqtwdibH4ZSpe7y5zPoddjxxwKs+uwVbF34EYz6tvM/iIioh4vuNxJmsxltbadfE7Ozs/HQQw9JmIrIcbHR4MAaGxsxZ84cTJo0Cd988w0OHz6M1tZW1NXV4cCBA3jnnXeQlpaGN954Q+qoRCSR7OxsNDU1iWph8f1Rmr8PuuYGHN2zEZt/+DcO7VgrUULqKoJgRt7WFWiurZQ6SpcK7ZsCmez0xxWTUY/yowclTERERHTp3Lz9EZ44QFQbds3d8AoI7fLnaq6txLIP/g/5GSsBAI3VZdjz5w9d/jxERI5GrlDgpptugouLi6j+1Vdf4ZdffpEoFZHjUkodwNkkJCRg4sSJ5z1u8ODB5/y9wWDANddcg7VrT18cTElJwaBBg6DVarFp0yaUl5fDYDDgmWeegcFgwPPPP3/J+YnIsVhvZuUTHAGTvg3NdVWieq/4/jZMRd2h7HA2SnL3oKLgIAZNuwWx6eMccrkkay5ungiIiEXV8UOW2on8LP43S0REDk2tccXoGx5EUPRq7F76LfoOmYDe/YZ3y3O5eftB7eouquVnrkKvhP58PyUipxcWFobnn38ec+fOtdQEQcA//vEPpKeno3fv3tKFI3IwbDTY2NChQ/Gf//znks/zyiuvWJoMGo0GX331FebMmWP5vV6vx9y5c/H2228DAF544QWMHTsWY8eOveTnJiLHYd1o6BXfHyfy94lqbl5+8AmOsGEq6mp15cdxPHsH5AolDHodMn/9DDUlBRg2806po3WJXgn9rRoNeyEIt0qYiIiI6NLJZDIkDL8MQVHx8A4K67bnkSuUGDn7H1j272dgMuot9Yxf/odpD70JjYdXtz03EZEjuO+++7Bt2zYsX77cUmtpacFVV12F3bt3Q6nk5VOizuDSSQ6osrIS7777rmX8/vvvi5oMAKBWqzFv3jzMnj3bUvu///s/m2UkIulVVVUhPz9fVDtTo6FXfP8ecee7szIa9Njy439hNptE9e66K1IK1ndbNtdWoqmmXJowREREXcwvLAoKpapbn8M7MAyDrrxJVNM21yPzt88hCEK3PjcRkSP49ttvERkZKaodOXIEd911l0SJiBwPGw0OaMGCBWhpOblxa1xcHO65556zHjtv3jzI5Sf/mjMyMrB3716bZCQi6VlvAq1Uu8AnJAKVheJN4sM4Zd6h7fnzB9RXFItqiaOuQEifJIkSdT3f0Ci4evqKaify9kkThoiIyEHFDZ3UoXlfnLMLR3dvlCYQEZEdcXFxwe+//w5XV1dR/eeff8b8+fOlCUXkYNhocEC///675efbb7/9nHciR0ZGivaE+O2337ozGhHZEetlkwIiYlF5LA9ms9FSk8uVCI1NtnU06iIn8vZZNnY8xTc0CgMun32WRzgmmUyGsLg0Uc16Zg4REZG9Esxm7F25EC0NtZLmkMlkGH7tPXBx8xTVdy75Gk01FRKlIiKyH/Hx8Xjvvfc6XGd77LHHkJubK1EqIsfBRoOD0el0yMzMtIzHjRt33se0P2bdunXdkIqI7I3RaBS9VgBAYFRfnMjPEtWCouOhchHfsUGOQdvcgG2LPhXV5EolRs2+v9uXX5BCeMIA0biiMBcmg/4sRxMREdmPA+t/R/aGxVj+n2dRXpAjaRZXTx8Mv/ZuUc2o12HrTx93WIaRiMgZ3XLLLaJlyIGT1+KuvvpqaLVaiVIROQY2Gmysvr4eP/30E1588UU88sgjePHFF/HZZ591ujOan58Ps9kM4OQdKQMGDDjPI4CBAwdafmYHlsg57N+/37LE2in+EbE4kSdePs16+jw5BkEQkPHLp9A1N4jqiSOnwic4XKJU3SskNgUyucIyNpuMaKgqlTARERHR+Z3I24esNYsAALrmBqz5/HUc2r5W0kwRSemITR8vqlUdP4T9a3+VKBERkX359NNPERcXJ6qVlJRg1qxZ3NeG6By4bbqNLV68GIsXLz7j71JTUzF37lzMmjXrrI9vv7FrUFAQNBrNeZ+z/WY2tbW1qKqqQmBg4AWkJiJHs3GjeK3dyMhIGNu0aG0UT9nn/gxdx6hQYV/caNSUFkLj7g13bz8YFd0zsyA/c3WHpYN8Q6MQkTy4W57PHqg1bgiKikdF4ck7QWVyBbRNDed5FJHzOvWaZF0jIttpqqnAloX/BSC+KOXpHyJNoHbSp92MioIcNNWeXjLpwLrfERydxGU1qdP4XkM9lVKpxB9//IH09HQ0Nzdb6uvWrcPLL7+MF154QcJ0RPaLjQY7cuDAAcyePRt//vknPvvsMyiVHf96ampqLD8HBwd36rwhIeIPsrW1tRfUaCgpKTnn78vKyiw/a7VaTiUjp6LT6c74s5QEQcC6desss5+Ak43MbUWHRcd5+AbCOzDM1vF6LINKg7VDZyF7wx/wD4/pti/pdeXHsWf5d6KaxsMbseljz7lnT08QPWAkPPwCEZ4wACGxKTiUuUbqSER269RrEhFJw2TQY+O370GvE88wHTBljl1cyFe5uGLk7Pux8tOXIFiWTBKwdeF/ceVDb8DVw1vSfOQY+F5DPcWZvssHBATgo48+wu233w6T6eTrpFqtxqJFizB16lSkpqbaOiZRl+mua7dsNNhIdHQ0Zs+ejUmTJiEpKQn+/v4wGAwoKirCypUr8f777+P48eMAgPnz50Oj0eDjjz/ucJ72nVRX186tq259XPtzdEZERESnj920aRMCAgIu6PxEPcWmTZukjgAAqKio6LBMmkwmQ9WxQ6JaWFy/Hn9huqcxtGmx6bsPYDIaRPWR19+H2tIiiVLZTt/B49F38PjzH0hERCQhQRBwYMNi1JUfF9WjUociafSVEqXqKDAyFgMum4U9K36w1LTN9cj89TOMv/VxCZMREdnW2b7LK5VKTJkyBUuWLIFGo4Gfnx9aW1tx77334tFHH4WXl5eNkxJ1jerq6m45L/dosIGrr74aR44cwRtvvIGJEyciNDQUarUa7u7uSEpKwiOPPILs7GxMmzbN8phPPvkEmzdv7nCu9l1WtVrdqed3cXERjTnjgKhnO3jwoGjs7e0Nb29v1JWJL0RzfwbHIggCti/+Co3VZaJ6wogpCIvrJ1EqIiIislZ+JBtlh/aLat5B4Rh+7T12d5NH0ugrEdY3zTJ29wlEytgZEiYiIrIvN910EyZNmgR/f3/I5ScvozY1NWHBggUwGo0SpyOyL5zRYAM+Pj7nPcbT0xM//fQT+vfvj0OHTt51/NZbb2H0aPF6h+33ZNDr9Z16/ra2NtG4szMhTikuLj7n78vKyjBkyBAAwJgxYxAe3jM3IiU6E51OZ7n7YcyYMZ3aN6W7LVq0SHRnxVVXXQVXV1fRplUKpQrBMUlSxKOLVFGQg8K9W0Q1v7BoDJx6g0SJiIiIyFpFQS6O7c+AQnn6pjCViyvG3fIIVC4X9j3MFmRyOUbM+juWffB/CIyKw/Br74ba1V3qWERENmEyGlFcUYwBAwac82beJ554Ah988AFycnIstZKSEqxZswY33njjRT9/Wlpap28iJupK51sm/2Kx0WBHXF1d8dRTT+HOO+8EAKxfvx5tbW2iGQkeHh6Wnzs7M8H6uPbn6IwLaRy4urpecCODqKfQaDSS//dfW1uL3Nxcy50WADBp0iRs2LABSvXp15LgPklQqaVvilDnBfdJwtCr78SupV/DZDRA5eKKMTc+CIWSG+4RERHZg5aGWmz64d+imzsAYOSsf8ArIFSiVOfn6uGNqQ+8CjcvX7ubcUFE1J3qy4tQD398tb38vMcqU6aiOfcYtI31ltpPf6xEbos7whMGXPBz15QcxcsaDQYPHnzBjyW6VN117YqNBjszceJEy8+tra0oKipCXFycpebv72/5uaKiolPnLC8Xv2D6+fldYkoisldbtmwRfbl1dXVFeno6VCoVsk29IJPJUJy7G8G9EyRMSRdDJpMhbuhEBETEYvP3/0b/y66Hp3+w1LGIiIgIgFHfhg1f/wu65gZRPXXCTEQkDZIoVee5e/M7IhE5J//wGITGJnfqWE+/QKz4+AXLnnmCIGDvih8QEBmD6LTh3RmTyCGw0WBnQkPFd7rU1NSIxvHx8ZafKysrodPpzrtUy6lNpoGTTYbAwMAuSEpE9sh6E6vhw4dbpmLKFUqExiYjLC7tTA+lS+Sib8VVGz7DlfU1UDVkQ12wBovH3Y02tVuXPo9fWBSmPfym089kMOrbUHOiECfy96Fw7xaMuP5eqSMR2ZVTr0ntdcdrEhGdvNC07ZdPUVtaKKqHxfVDv4nXSpSKqPvxvYacjV9Ybwy75i5s/eljmE0mNNdWwtCmw5//mYubXvsa3oG9pI5IJCk2GuxMS0uLaOzuLl4fMz4+HnK5HGazGYIgYN++fRg2bNg5z7lnzx7Lz4mJiV0Xlojsik6nQ2Zmpqg2ZswYidI4H7nZhIiKIycHbQAaT9a6g7M3GcqPHsS6+W+jua4KSpULNB6eGHzVbVwOjKgd0WtSuxoRdb0D635D0QHxZzB3H3+Mmn0/ZO2Ws3REgtmMrLWLENY3DUG948//AHIqfK8hZ9RnwGiU5O3DvpULYTad/O9dr23Fotf/iVve/B4qF34nIefl2J96eqC9e/eKxtYzHDQajaixsGHDhvOec+PGjZafJ0yYcGkBichuZWRkQKfTWcZyuRwjR46UMBFdCsFsljqC3fINjYLZZLSMTUYDyg7tlzARERE5K8FsRm1ZkaimUKkx8Iqb4OJ2YXvj2RtdcyPWfPkmDqz7DZu+/ze0VstCERE5q2FX/w1uXuIl5xqry/D72w932KeHyJmw0WBnvvrqK8vPiYmJZ1zm6Oqrr7b8PH/+/HOer6SkBGvXrj3jY4moZ1m3bp1oPGDAAPj6+kqUhi5FW2sTlv3nWRTn7JY6il1ycfNAcJ8kUe14zi6J0hARkTOTyeUYe+NDSBl39cmxTI64oRPh4evYy9U211Vh2YfPoPxoNgBA21SHzd9/CDPvVicigtrVHTOf+qDD7IUT+VlYN3+eRKmIpMdGQzdrbm7u9LG//fYbvvvuO8v45ptvPuNxt912m2VJpfz8fHz++ednPeeTTz4J019TuYYPH46BAwd2Og8ROQ69Xt9hf4aJEyfybgoHJJjN2PLjf1FXVoQN3/wL+1b/zNkNZxCRnC4an8jdK5rlQEREZCsyuRwDLp+F0XP+ifRpt8A3JFLqSJfM3dsf3sHholpFYQ72rfxJokRERPbFv1c0Lr/vBcitlsjLXv87slb/IlEqImmx0dDNfvnlFwwdOhTffvstGhsbz3hMU1MTXnnlFcyaNctyUTAiIgIPPfTQGY8PCgrCo48+ahk/+OCD+Okn8Qc+vV6Pp59+Gj/88IOl9sYbb1zqH4eI7NSuXbs67PEybtw43H333XjiiSewefNm6LUtZ3k02ZOsNb+g9PDpZYAOrPsNe1culDCRfYpIHCQa63UtqCjMkygNERER0LvfcCSMuFzqGF1CJpdj1Oz74ebtL6of3LQExw9yFiEREQDEpo/D0Jl3iWqCAGz6/gOU5HJ2OjkfbgZtAzt27MAtt9wClUqFxMRExMfHw8fHB0ajEcePH0dGRgZaW1stx/v6+mL58uUdNoJu77nnnsPWrVuxbt06aLVazJ49G6+++ioGDhwInU6HTZs2oayszHL8Sy+9hLFjx3brn5OIpNN+iTQASEtLg8FgwL59+wCc3Gi+qkmPGY+9A/9e0RIkpM4oztmFA+t/F9U0Ht5IGNkzLlp0JXcff3j4BkLXfLqJX3xwF0JjUyRMRURE1HNo3D0x5saHsOrTl2E2n541uO3nj+Ed9Aq8A8MkTEdEZB+GXn0HqkuO4PD200sZm4xGLHn/adz42tfwDgg9x6OJehbOaLAhg8GA/fv34+eff8Znn32Gr776CmvXrhU1GSZMmIC9e/ciJeXcF0pUKhV+/fVXzJo1y1I7cOAAFixYgIULF1qaDCqVCq+99hqef/757vlDEZHkTCZTh43hJ0yY0KH5oNK4wjfU8afy91SN1WXY+tPHoppMrsCYGx/qsNEYneQX1ls0Ls7ZxeXCiIioWx3ZtQEtDbVSx7CZwMhYpE+7RVQztGmxYcG/OFuWiOgvU//+MgIj+4pqba3NWPT6/TDodRKlIrI9Nhq62Q033IAtW7bg7bffxjXXXIN+/fohPDwcrq6ucHFxQXBwMEaMGIHHHnsMO3fuxNq1axEVFdWpc3t7e2PhwoVYvXo1br75ZsTExMDNzQ3e3t5ISUnBY489hqysLDzzzDPd/KckIint2bMHDQ0NotqZGg3B0UmQyxW2jEadZGjTYsM378HQphXVB11xI4KjEyRKZf/8evUWjVsba1FzokCaMERE1OMdy8pAxqL/4c+PnkNtaZHUcWwmbtgkRPcfKao11pRhy8L/ch8pIiIAcoUC1z7zX7hbLTfXWFWG3958kK+V5DS4dFI3c3FxwciRIzFy5MjzH3yRJk2ahEmTJnXb+YnIvq1bt040jo+Ph1wux8GDB0X1kJhkW8aiThLMZmxZ+F80VJaI6tH9RiJhxBSJUjkGV09fuPv4Q687fUdl8cHdCAiPkTAVERH1RJXH8rHtl08AANrGOqz69CWMvvFB9IrvL20wG5DJZBg28y40VJxAbdkxS/1E/j7sXbkQA6feIF04IiI7oXH3xMyn/40fX7wTxrbTsxhKDx/Ayk9fxpS/vyhdOCIb4YwGIiIHZjKZOsxcmDhxYofmg7u7O/y4N4Nd2rf6Z5Tk7hHVfIIjMPSaOyGTySRK5RhkMhmCohNFteKDOyVKQ0REPVVjdRnWf/0vmIwGS82g16Gh8oSEqWxLqXbBuFsfhcbdS1Q/uGkJCvdtkygVEZF9CQjvgyn3vQi5XHy5NS9jJTIW/U+iVES2w0YDEZED2717N2prxesET5o0qUOjYeDAgZAruGySvSnYuxnZGxaLampXD4y75VGo1BqJUjmW4D7iRkND1QnUlxdLlIaIiHoaXUsT1s1/G3pts6geN2wyEkddIVEqabj7BGDMTQ9DZrUUZ8aiT9FcVyVRKiIi+xKbPhbDr78XaHfPmEKhxKHta3Eif59kuYhsgY0GIiIHtmLFCtE4ISEBrq6uyMrKEtUHDx5sy1jUCVXHDyPz189FNZlcgbE3PQRP/2CJUjken6DwDptlH9ufIVEaIiLqSUxGAzZ8/S801ZSL6r3iB2Dw9FudcuZhcHQChsy4XVQzGQ3I27YSba3NZ34QEZGTGTztViSPmQ7g5Iwwr8AwyBUKbP7+3061xw85HzYaiIgclF6vx/r160W1yy67DOvXr4cgCJaau7s7kpO5P4M9aamvwYZv3hUtwQAAQ2bczr00LpBMLkdU6lBRrWj/dtH/A0RERBdKMJux9aePUXX8kKjuF9obo294AHK5884UjRs6EXFDxXsEymQyGNq0EiUiIrI/k+78PySMnAKvgBDL6gIGvQ7rFsxDS32NxOmIugcbDUREDiozMxNNTU2i2mWXXYY1a9aIamPGjIFKpbJlNDqHU3dH6pobRPX4YZchbuhEiVI5tqi04aJxm7YZ2qY6idIQEVFPsHfljyg6kCmquXn7Y/ztj0Pl4ipRKvuRPu0WBP+1T5J/eAxSJ8yEh2+gxKmIiOyHTCbD5fc8h+gBo0R1bWMd1s2fB6Ned5ZHEjkupdQBiIjo4qxcuVI07tevH+RyOfbu3SuqT5zIi9e2YJIrkR/ZH41VpVC7ekDj4QWTvOPbrEKpQuzg8di5ZAEEwQwACIlJQfr0W2wduccIiIiBX1g0/MKi0DttOIJjkpz6TlMi4PRrknWNiM4vd8ufOLhpqaimcnHFhNuf6LBcn7NSKFUYc+ND2L/2VwycegPytq08/4Oox+F7DdG5yeRyjLjuXmgb61BRmGup11cUY/fy79E2KV7CdERdj+8AREQOSKvVYuPGjaLa5ZdfjlWrVomWjPHw8MCIESM67NlAXU+vdsXSsXcie8Mf8A+PQWjs2ZdAih8+GV6Bodj0/b+hcfPEmBsf5IXxSyCTyXDFA6865VrZRGdz6jWJiC5Mwd4t2LXsG1Ht5B5KD8M3JFKiVPZJ4+GFIVfdLnUMkhDfa4jOT6FUYezNj2DFxy+gsboMACAIAk7k7cVDDz2EHTt2QKHgd0HqGbh0EhGRA9q8eTN0utNTLeVyOSZNmtRhlsOECROgVqttHY86ITQ2BVP/8RLG3fYYXNw8pI7j8NhkICKiS3Uifx+2/fJph/qwmXcitG+qBImIiKgncHHzwIQ7noLG3QuC2Yzm2krodS3IycnBrFmzuL8c9RhsNBAROaBly5aJxoMHD0ZzczNyc3NF9csvv9yWsegCeQWEwjswTOoYRERETq+2tAibvvsAgtkkqg+YMgex6eOkCeXABLMZe1f+hPqKEqmjEBHZBU+/IIy77XG01FVDr2211FesWIF7771XwmREXYeNBiIiB1NTU4OMjAxRberUqSgvL0dY2OmL1n5+fkhPT7d1PLJiMhqkjkBERETn4RUQghCrZQ8TR05F8pjpEiVyXEZ9GzZ+/wGyN/yOdfPfRmtjrdSRiIjsQmBELIZec2eH2djfffcdnnnmGYlSEXUdNhqIiBzMihUrYDabLWONRoMJEyZgyJAhWLx4Mb788kvMnj0b1113Hdd6lFhjdTn+ePdxnMjbJ3UUpyW0+3+FiIjobJRqF4y9+RHEDBoLAIjuNxKDrriJS/NdIF1zI1b97xUUH9wJAGipr8LaL9+CXtsicTIiIvswcMoNGHTlTR3qH3zwAd577z0JEhF1HW4GTUTkYJYuXSoaT5gwAW5ubgBOrlOflpaGtLQ0KaJRO9rmBqybPw/NdVVY//U7GHLVHYgbOlHqWE7B0KbF8eydKNi7BR6+ARh+7T1SRyIiIgcglysw/Np7ENQ7HtH9R0Im5315F0rpounw762+ohjrv/4XJv7taShV3DuMiGjU7PtRXVKIkv1bRfXnn38efn5+uO222yRKRnRp2GggInIghw4dwuHDh0W1adOmSZSG2lPrtbgs43uMqS6FsukQGjaWYEVdFQBAEMzY/vsXUChViBk0RuKkPVtJ7h5s+v7fMBn1AACVWoPB02+DUu0icTIi2zr1mtTequE3Qq92lSgRkWOQyWTck+ESKFVqjLv1Maz65GU01pRZ6pXH8rDlx/9g7E0Ps4HTg/C9hujiDZlxO6I8IVoW2Ww248EHH4Sfnx+mT+fSfeR4+A5PRORArGczBAcHcx8GO6EwGxF/fB8GaysxoL4I49QmqNqttuAbGoXIlMHSBXQSfr16i/bFMOh1KMndI2EiImmcek1q/4/CbJQ6FpFdMLRp0dbaLHWMHsvVwxsT//YUXD18RPXinF3YvvgrCIIgTTDqcnyvIbp4MpkMr776KkaMGCGqG41G3HrrrVizZo1EyYguHhsNREQOwmg0YsWKFaLalVdeCTnvCrN7Hr6BmHD7k1C58O6u7ubm5YeQGPFmngV7t0iUhoiI7I1R34b1C97B6s9fg665Ueo4PZaHXxAm3PFUh88+h3esxf41iyRKRURkXxQKBZYtW4aUlBRRXa/XY/bs2di8ebNEyYguDq9OERE5iG3btqG2tlZUu/LKK1FTUyNRImpPMJ/57jyNhzcm3fkM3Lx8bZzIefUZOFo0Lj2UxYtJREQEk9GAjd+9j4rCXNSVFWHl/15GS0Pt+R9IF8UvLArjb30cCqVKVN+/7lfkbV1xlkcRETkXtVqNdevWITo6WlTX6XS45pprsH37domSEV04NhqIiBzEr7/+KhqnpKTA19cX06dPx2233Yaff/4ZjY28mCoFQRCQteaXDnWliysm/e1pePoHS5DKeUUmp0OhPL3ZpCCYUZi19RyPICKins5sMmLzj/9B6aEsS62xqhSbvvuAS/l0o+A+iRg1+37IZOJLDzuXfo3DO9dLlIqIyL64u7tj06ZNiIiIENVbW1sxY8YM7Nu3T5pgRBeIjQYiIgdQUVGBbdu2iWozZ87E6tWrodfrcfDgQbz11luYNm0adDqdRCmd1/61v+LwjrUd6qNnPwDf0CgJEjk3lYsrIpPF+2Ec3bWRF5KIiJyU2WTEph8+RPHBnaK6WuOOoVf/DTKZ7CyPpK4QmTIEQ666o0M989fPubwhEdFf/Pz8sGnTJoSGhorqWq0WDz/8MMrKyiRKRtR5bDQQETmAxYsXw2w2W8Zubm6YPHlyh82hhw0bBo1GY+t4Tu3A+t+xf+2Z1xoOiIyxcRo6JSZ9rGhcV34cNScKJEpDRERSMZtMZ2wyqNQaTLjjKfiF8YYAW4gbOhEDLp9jVRWw7edPcDx7hySZiIjsTVBQEDZt2oSgoCAAJ/dwiIyMRHNzM+677z5UVFRInJDo3NhoICKyc2azGYsXLxbVpkyZgsrKShw4cEBUnzZtmi2jOb2DG5dg36qf/r+9+46O4jrbAP5sVe9dqEuoUITovYpmbDBgcI/BPS6fW+IkLjE2tuPEcRzHcbcDjlswNhjTO4heRJFAHTXUe2/b5vtDsGjZVVukHQk9v3N02LlzZ+4r4O7sznvnXrHDIBO8Q4bAztnDoCzz9EFxgiEiIlHodFqc37PeKMkgV1hh5ooX4REQJlJkA9OwGYswfOZigzJB0OHU5v9Co2oRJygioj7G19cXBw4cgK+vLwIDA/UDCQsKCvDoo4/yyQbq05hoICLq444fP240cmHp0qX45ZdfDMpcXFwwadIkS4Y2oKUc2YGzO/8ndhjUDolUitDR0wzKshOO8UYGEdEAodNqkHFyH0oykw3K5QorzFr5B3iFRIkU2cA2Ys5yRE2+Rb9tbe+E2Q/9CXKllYhRERH1LUFBQTh9+jSGDBliUF5YWIjHHnsMhYWFIkVG1DEmGoiI+rjrF4GOiopCSEiI0bRJCxcuhFwut2RoA5pWqxY7BOpE2JjpAK7Nu61uacLlJE7PQER0s9NpNTj8v49QUZBtUM4kg/gkEglG33o/wsfFwtbRFfMefw3O3v6dH0hENMA4Ozvj008/RUhIiEF5UVERHn30UaSlpYkUGVH7eEeKiMhCVCoVEhISunVMeXk5du7cabA+w/Dhw/H5558bjWIIDg7G6dOnrz8FACA5ORl8y+9Zw6YvglQqx5nt3wFoHaGHy8YLQpN47Jzd4TN4OIoyEvVll07HIWTkVBGjIiKi3qTVqHHoh38hP+WsQblcYYWZK19kkqEPkEgkGHf7g4iefQdsHJzFDoeIqM9ydXXFZ599hieeeAKZmZn68osXL2LatGnYunUrxo4dK2KERIZ414mIyEISEhLw2todcPPr+gLBacd3o7i6Sb8tUypxttENZ37+AZW116aAcR0UjB8v1AIXak2eJ+vsMYSMmmZyH5lvyNQFkMpk0KhbED5+JhMNfVDY6OkGiYaS7GTUlhfB0d1HxKiIiKg3qFXNOPjN+yjOvGhQfjXJ4B0ypJ0jydIkUimTDEREXXA12fDkk08iIyMD5eXlKC8vBwDceuut2Lp1K8aNGydylEStmGggIrIgN79Q+IQN7VJdjVqFQz/8C4oriz8BQMSEuXDy9EVdRZFB+YjZd3R43or8zHb30Y2JnDSv9UVznbiBkEn+Q8dAaWMPVVO9viz95D6MufV+EaMiIqLecH7XeqMkg0yhwMwVv2eSoR8RBAHnd/8Ej4Aw+EWNEjscIiLRubi44LPPPsNtt92GsrIyfXlDQwMWLFiAdevWYfbs2SJGSNSKazQQEfVROQnHDG6OAkDExLm4dPqAQZmVrQMChvFxyd4iCAIaqsvFDoPMJJMrEDZ6un7b2csfLj6BIkZERES9ZcScZXD1DdZvy+QKjF24At6hXRvkQeK7mmS4eHAT4r77AHnJ8WKHRETUJzg5OeHbb7+Fu7u7QXlTUxOWL1+ODRs2iBQZ0TVMNBAR9UGCICD12C6DMp/B0bB39cCl+DiD8pBRUyGTKywZ3oAhCALO7foRW/75B5Tn8amQ/mrw+FgERU/EvMdX4bZn/4rQUVyjgYjoZqS0tkXsQ3+Ck6cflDb2GDrtNiaX+5nEfRtx8eAmAIBOp0Hc9/9CTsJxcYMiIuojgoKCcPjwYXh6ehqUq1QqPPTQQ1izZo1IkRG14tRJRER9UFluOqqKcg3KIifORU7CMbQ0Gk7REzZ2piVDGzAEQUD81m+RemwnAGDfmr9izqOvwNU3yGR9nVSGPK8wNFRXQGFlDaWNHXRSmQUjpvY4untj6j3/J3YYRBZ19T3p+jKim521nQNmP/wSVI31yE89J3Y41A2mniIVdFocXvcRWprqETFhjkiRUXt4rSGyvICAABw/fhwzZsxAXl6evlyj0eDZZ59FdXU1XnjhBREjpIGMiQYioj4o5cgOg217V0/4ho/Ajk9eMyj3Dh0GZ89BlgxtQNBpNTi+8StknT2kL1M1N2Dfmr/i9t+/D6W1rdExLUpbrJ/7LC4e3NyttTiIiHrD1fckooHI1tEFto4uTDT0MxKJBBOXPgqJRIpL8W2nChVw6te1UDU1YNiM2yGRSESLkQzxWkMkDm9vb5w4cQLTp0/HpUuX9OU6nQ6vvfYaqqqq8Oabb4oYIQ1UnDqJiKiPqS0vxuWk0wZlERPmoiI/E5WF2QblkZPmWjK0AUGjasHBb/9pkGRoJcHI+XebTDIQERGR5RRduoh9a/8GjapF7FCoh0mkUkxY8jAiJs4z2nd+93qc2fYdBJ1OhMiIiPoWZ2dnHD9+HCNGjDAoFwQB77//Ph5++GHo+H5JFsZEAxFRH5NyeBsAQb+tsLJB2NgZcB0UjEnLfqtf5NDexQN+kaNEivLm1NJYhz1f/QUFaYYjICUSKabc9RTCxswQJzAiIiICAOQkHMf+te+iMD0Bh9f9GzqdVuyQqIdJpFKMXfgAomPvMNqXcnQHjm34gv/uREQAbG1tERcXh0mTJhntW7duHW699Vao1WoRIqOBiokGIqI+pKm+BpnXjaQPHx8LpbUtZHIFQkdPw4Kn38L8376OsYtWQiLl23hPaaiuwK7PVqM8L8OgXCZXYPr9zyM4xvjDG/VfgiCgJDuVC0wSEfUjqUd3XkkuaAAA+SlncWLjVxAEoZMjqb+RSCQYMfsOjF24wmhf1tlDiPvun1CrmkWIjIiob1EoFNi5cyfmz59vtO/QoUOYOHEiampqRIiMBiLeoSIi6kPSju2CVnNtxIFUKkfkZMMPDBKJBB6B4fCLHGnp8G5a1SX52PnpKtSUFRiUK23sMeeRV+A/ZLRIkVFP02k1yD5/DDs+/jN2f7EapzZ/DY1aJXZYRETUAUGnw+kt3+D01m+M9mlULRA4uv2mFTlpHibf+SQkEsNbF/kpZ7Hni7egam4UKTIior5DJpPh559/xooVxsnZ1NRUrFixApWVlSJERgMNEw1ERH2EuqUJaSf2GpQFj5wMW0dXkSIaGIouXcTOz15HY63hBy9bR1fMe/w1eASGixQZ9YbasiIc+fEjVBRkAWidLiv73BGRoyIiovaoW5pw8Lv3kXpsp9G+iAlzMfXupyGVyUWIjCwlZOQUzHjgd5DJFQblFQVZuLD/F9RVlooUGRFR3yGRSPDJJ5/g1VdfhbTNzAd+fn7Iz8/HQw89hMuXL4sYIQ0E/ERGRNRHZJzaD1VTvUHZkGm3iRTNwJBxaj9O/rrWaCSkk8cgxD70J9g5u3X5XAp1M6ad3YyYqmxYqwphV5GEQ6MWQa2w7umw6QY4e/vDZ3A0ijIS9WUpR3YgbOxMSCQSESMj6llX35Pa4nsS9TeNtZU48PV7qCzKMdoXM/dODJtxO9+7Bwi/yJGY/fDLOPDNPww+L7c01uPs9u8RNmYapFKZiBEOTLzWEPU9L730Evz9/fH000/D3d0d9vb2AID8/HysXLkSf/vb3zB27FiRo6SbFZ9oICLqAzSqFiQd2mpQ5hc1Cs6eg0SK6OYmCALO7vgBJ375yijJ4O4/GPN++1q3kgwAINeqEZN+GLH1+ZhckoSY9MOQa7nwVl80ZMotBts1ZQUoTE8QKRqi3nH1PantD9+TqD+pLMzFjo9fM0oySGVyTL7zCQyfuZhJhgHGMygC8594HfYuHvoyqVSG6NilTDKIhNcaor7p/vvvx8GDBxEVFWVQXltbi6effhobN24UKTK62THRQETUB6Sf3IfmesMFmobNWIychOPY//XfUZKVwoUOe5BEIoFcaTzSKmDoOMx55GVY2TqIEBVZis/gaDh5+hmUJcVtESkaIiK6Xl7yGez+/A2jaQ2VNvaY/fDLCBk5VaTISGxOHr6Y/+RquPuHAQDCxs6Ai0+gyFEREfU9MTExWLt2LSIiIgzKtVot/vKXv+Dpp5+GRqMRKTq6WTHRQEQkstanGQxvcvqGj4C7fyguxm1GQdo57P7yTez45DUUZyaJFOXNZ/isJQgeOUW/PXT6Iky79xnIlVYiRkWWIJFIjJ5qKMlOQWlOmkgRERER0Lro84X9v+Dgt+9DrWo22Ofg5o1bnnwDXsGRIkVHfYWNvRPmPPIKpt//gj7hQERExtzd3fHll19ixowZBuVVVVVYu3Ytxo0bh7KyMnGCo5sSEw1ERCIz9TRDdOwdKMpIRFVRrr6sIj8TqqYGS4d305JIJJi49FF4hw7FxDsew6j5d0Mi5WVxoAgeOcVoofULBzaJEwwREUGtasahHz7E+T0/ATB8itMzKBK3PPkGHN19xAmO+hy50goBQ8e0u1/Q6dBQU9nufiKigcLW1hbvvvsuVq5cCQBoaGhAcXExACAtLQ2jR4/GqVOnRIyQbia8o0JEJCK1qrndpxkS9/1iUO7o5gP/Ie1/oaLuk8kVmP3QSwgbM0PsUMjCZHIFhl632HphegIq8rNEioiIaGCTSKRoqC43Kg8eOQWzH36J0xpSt1w4sAlbP/gjCtMTxQ6FiEh0UqkUTz/9NF5++WUUFhYa7KuoqMDcuXPxySefiBQd3UyYaCAiElHq0Z0mnmZYiqKMCyi7nG5QPnTGQo647yatRo3jG75ESVZKu3X4dzpwhY2dCWs7R4OyCwd/FSkaIqKBTa5QYvr9z8Pa3glAa+Jh9IL7MXn5E5DJFSJHR/3J5YunkbD3Z6iaG7D/63eRdGgr1zojIgKwdOlSfPLJJ7CyMpwuWK1W48UXX8QDDzzAdRvohvDuChGRSJrra5F0cLNBWevTDGFI3LfRoNzexQMhbdYToM411ddg12dv4FL8ARz64V9oqK4QOyTqY+RKK0RNWWBQlpd0GtXFeSJFREQ0sNk5u2H6fc/B2t4JsQ/+EUOmLoBEIhE7LOpHqkvycfSnT/XbgqDD2R0/IO67f3IKUiIiAPfddx927doFNzc3o30bNmzAuHHjUFRUJEJkdDNgooGISCQXDmy6bqFDCUbOuxvFmUlGTzMMm7kYUpncsgH2Y1VFl3H0x49RUdA6DU5zQy3ivvsnNGqVyJFRXxM+YQ6U1nYGZef3/ixSNERE5BkUgSUvfgCfwcPFDoX6ITtnN/gOjjYqz0uOx7aPXkFlYY7lgyIi6mPGjh2Lc+fOITra+P3y6roNe/bsESEy6u+YaCAiEkFdZSnST+w1KAseORkuPgFIuO4mJ59m6DqdVoOzO35AytEdUDc3GeyrKStEVdFlkSKjvkppbYPIyfMNyvKSTqM8P1OkiIiIbm5luenY89VfoG5pareOXGnV7j6ijiisbDDt3mcwYvYyAIZPw9RXlmLnp6uQcfoAp1IiogHPzc0NR48exQMPPGD09GBNTQ2WLl2KF198ETqdTqQIqT9iooGISATnd/0Ine7a3IdSmRwxs5ehIO08ynKve5phxu2cm7gLGqrLsfuLt5B0aKvRPgc3b9zy5Gp4BISJEBn1dVFTFkBpY29Qdn73TyJFQ0R0cxIEASlHdmDXF2+iOPMiTmz8ijd7qVdIpFJExy5F7IN/MFpEXKtR48TGL3Hsp886THYREQ0EUqkUn376KT755BNYW1sb7NPpdPjkk08wZcoUlJSUiBQh9TdMNBARWVhpThpyEo8blEVMmAM7Z3ec27nOoNzO2QMho6ZaMrx+KT/lLLZ++LLRlFMAEDB0HBY89SacvfxEiIz6A6W1DYZNX6jftrF3hn/UaN4AIyLqIarmRpzZ/h3it30LQacFAOQkHjd6upOoJ/mGj8Ct//cXuPsPNtqXde4wtv37FT7BSEQE4IEHHsCBAwfg7e1ttC8hIQExMTHYuXOnCJFRf8NEAxGRBQk6HU5t/tqgTGFti2EzFyPr3BFUlxguQhszdxmfZuiARtWCk7+uxYFv3oOqqd5gn1Qmw9hFKzHtvmehtLFr5wxErSImzoWzlz9Gzb8Hi1/8JyImzuECpEREPaAwPQEJe35GWY7xYID81LNM6lKvsnN2w9zHXkXkpPlG++oqirHz09dx8eBmCJwahIgGuOjoaCQmJiI2NtZoX319PV5++WV89913nEqJOsREAxGRBeUlx6OqKNegLGb2MiisrHF+j+FULS7eAQgeMdmS4fUr5fmZ2Pbhy0g/YbxIlbWdAyYsfRSRE+fyZjF1iVxphdue/SuGTl/IucGJiHqAVqNG/NZvsW/t30xOUTNsxmLMWvEir9PU62RyBcYufADT7nkGCqXh1CCCTotzu9Zhz1d/QVNdtTgBEhH1EXZ2dti8eTPefvttKJVKfbmPjw+kUik++OADPPXUU5xKidolFzsAIqKBor6+HuknDacIcPL0Q/jEOUg5vB2NNRUG+0bOvxsSKfPB19PptLh44Fck7v9FP/1CWwHDxsPJ0xdOnoNEiI76M97sIiLqGdWlBTiy7iOjwRUAYG3vhEnLHsegiBjLB0YDWmD0BLj6heDIuo9RnpdhsK++qoxPERMRXfHcc89h5syZWLZsGZqamuDo6Kjfd/r0adx9993405/+hHnz5okYJfVFTDQQEVnIjz/+CHVzExRtFlkat2gFJBIpci+eMqjrFTwEvuEjLB1iv1BbVoQL+zcZJRnkCiuMvvU+DB4Xi6S4LRaPS5BIUe7kjZaGOsgUSsiVVhAkTBQRkTiuviddX0bUmwSdDsmHtyFh78/QatRG+wdFxGDissdhY+8kQnREgIOrJ+Y9/mck7v8FFw/8CkHQQSKRYspdT3KqTTPwWkN08xoxYgQSExPx+eef4/vvvzeY6rCurg6vvPIK9uzZgxdeeAG+vr4iRkp9CRMNREQWEB8fj7i4OIOywOHj4R06FAAw7/HXkHpsFy7s2wi1qhmjbrmHo6vb4ezlhxFzlxksnO3mF4opdz0JR3cf0eJqtrLDfxe9gosHN8PNLxQ+YUNFi4V6nk6nhVQqEzsMoi67+p5EZCk1ZYU4/vMXKLtsvBaDVCpD1NRbMWHJQ/x8Q6KTyuSImbMcPmHDcXT9JwgdNQ2eQRFih9Uv8VpDdHOzsbHBc889h6lTp+K1114zmjLp+++/xzfffIO3334bjz76qEhRUl/CRAMRUS9raWnB22+/bVCmUFpj9IL79dsyuQJDp92G4JjJKEg9B3f/UEuH2a8MnXobClLPoyw3HcNnLcHwmbdDKuMljXpebXkRzmz/ATYOzpiw5GGxwyEi6pNSj+7E2Z3/M/kUg7OXPzyDIxEwdByTDNSneAVH4rZn3oHcyrrdOvVVZZAplHwKh4gGtNGjR2PdunV49913sWPHDgBATU0NamtrAbROtfT999/j+++/x6BBnMJ4IONdGSKiXvbFF18gLy/PoCxm/t2wc3Yzqmvr6ILB42ZZKrQ+TatRQxAEyBVKo30SqRSTl/8WTfW18AgIEyE6utmpmhqQuG8j0o7vgU6nASBB6Ohp8AgYLHZoRER9TlN9rYkkgwRRU25BzNw7kXp0pyhxEXWmo+mSBJ0OR378GDWlhRi94F6Ejp7OZBkR9XsqlQoJCQlmHbto0SJ4enriq6++QmFhIXQ6nX7fyZMnMXToUDz44IO49957TR4/YsQIg0Wm6ebDRAMRUS9KTk7Gt99+a1DmERCOiPGzRYqofyjJSsHJX9fCNzwaY26932Qde1dP2Lt6WjgyGijUqhZknNp/JckAAAJOblqDBU+/JWpcRER9UXTsEuSnnEF1SevACgc3b0xa9jino6F+LfXYLpTltk4FdnzDF8g6exjjFj8EZ0+O1iWi/ishIQGvrd0BNz9zZ1Fwhf+Me5FT9G80VJcZ7FE3NuHfH3+CbzdsxeTlT8DBzUu/ryI/E6sfBMaOHXsD0VNfx0QDEVEvaWpqwquvvmqQ5ZfIpJiw9BFIpFwkzZSm+hqc2f49ss8dAQDUlhUicPgEPrVAFmfn5Iro2Xfg7I4f9GVVRblIO7ZbxKiIiPommVyBSct/i52frEL4hNkYOe8uyJVWYodFZLba8iKc2/WjQVlJdgq2/eslDJ1+G4bNXCxOYEREPeBG1xT0CRuKiAmzcWTdRzi/52fotBqD/TUl+dj12esYPmsxpt3zDKRy3n4eKHini4iol3zwwQe4fPmyQVnoqOlw9vLD2R3/04+QotZH09OO78Gv//idPskAAIKgw/ENX5ic85mot0VNng9nL3+DsvN7fkJLY71IERERiae5oQ65iSfa3e82KBiLX/wnxi58gEkG6vcU1rbwixplVK7TaXDhwCZs/eCPqCrKFSEyIqK+QSqTYdp9z+KeN9fCxSfQaL9Wo8b53T/hP88vRubZQyJESGJgSomIqBccOXIEGzZsMCgLDg6G8+jpuJwUj6RDW5B0aCsiJs7FyHl3QmFlI1Kk4ivLTcfpLd+goiDL5H6pTIbm+lqTa1r0JXJ1C8Ym70NoTSZshUrYN+Ti9JBYaBS82dJfSWVyTFjyMHZ+9rq+TKNqRuaZOPgMjhYvMKIuuPqe1Bbfk8gcOp0W6Sf3IWHPz1A3N8LB3QeuvsY3FAD0+Ws1UVfZ2Dth2r3PoCBtGk79uhb1VYbTg9RVlqA0Nx3VJfmYdu+zcPL0FSlScfFaQ0Qe/mH4zV9/wImNX+LM9u+hVRsOEmyorsCWD/4Iz8AI3DfyHZGiJEthooGIqIeVlpbijTfeMCiztrbGE088ge9OF+PUlq+vlApIO9469+uCp98acIvL1VeW4uzOdci9YHp0pMLKBjFz70T4hNmQSmUWjq77FFoVJiXuaN2oBZAPnI+Yyi9a/ZxHYDgGj52FjNP79WXVJfnIT46H7+BhIkZG1DGD96Qr+J5E3VWcmYTTW77Rr70AACc2folbnlzNaSBpQBgUEYOFz72LxH0bkXxkOwSd1mB/WW4Gtvzrj4icOBfRsUs7XFz6ZsRrDZH5tBoNkpOTLd5ua5s9eztYKpVi0rLHETXlFuz4ZBVKs1MNKwit08/dfffdeO6557Bq1SpI+TnipsREAxFRD9JoNHjppZdQVVVlUP7CCy/Ay8sLSYe+RFOt4b6oKfMHVJJB1dyEiwd/RerRHe1OiRQcMxmjF9wHGwdnywZHZMLI+XcjP+2cQd9NObYTQ6Yu4ILkRHRTaqgux5nt3yP3wkmjfRUFWUg7uReRE+eKEBmR5cmVVhh1yz0IHjkZJ39Zg7LLhtOfCjotUo7uQNa5IxgxZxkGj50JqYy3WoioY9XFuVhXDLgVWPb9IuvsMYSMmtYr53bxDsC9q9fi4sHNOLzuI7Q01BnsV6lU+O6775Ceno4XXniBC0PfhHj1IyLqQR9++CESEhIMyqZNm4YlS5bgww8/RFHGRSisrfX7fAdHIzhmiqXDFIVOq8Gl+INI2LsBzfU1Jus4efph3O0r4R0yxMLREbXPytYeE5c+iv1fv6sv06pUOPbzF5jzyMsc1UtENw1VcyMuHtzc4WCAoOiJ8B8yxsKREYnPxTsA8x5/DdkJR3F2xzo01Rl+nm1prMOpX9eiMD0BMx/4vUhRElF/cqOLMpujIj+z19sYNmMRwifMxr41f0P6yb0QdDoAgEKhgLOzMzIyMvDEE09g+vTpePbZZxEQENDrMZFlMNFARNRD9u/fjx9++MGgzNfXF6+//jqKi4vxzTffGOxT2thjwh2PDYinGQSdDts//nO7i+YpbewRHbsE4eNnQyZXWDg6os4NiohB2JgZuBR/UF9Wkp2Mi3FbMHzm7eIFRkTUA7QaNdJP7MWFA5vQ0lhnso6LdwDGLlwBr5AoC0dH1HdIpFKEjJwK/yFjsPvLt1CWk25UJ2zMDMsHRkTUxyitbXHLk29g5Py7sPvz1agoyIarq6vB/Y+4uDgcPXoUy5Ytw0MPPQRXV1cRI6aewEQDEVEPyMvLw/fff29QplAo8Le//Q22trb43e9+h8bGRoP94xc/BDungXEhlUil8IscaZRokEhliJgwB9GxS2Flay9SdERdM/rW36Do0kWDEYwJe36CV3AkPIMiRIyMiMg8gk6H3AsncW7XOqPFbq9S2tgjZu5yDB43q1+smURkCQorGwQOG4/IifORl3waecnxAACPgHD4RY0WOToior7DO2QIHvjbOhzf8BVsKtOMppnWaDRYt24dPvzwQ0ybNg3//Oc/4eHhIVK0dKP4rD8R0Q2qrq7GmjVr0NLSYlD+4osvIioqCh9//DHOnTtnsC945BQERU+wZJiiGzLtNihtriUT/KJGYeFzf8PYhQ8wyUD9gtLaBpOXP2EwCkcQdK3zj7YzApiIqK8qSDuPbR+9gsPr/m0yySCRSBE+YQ4W//4fiJgwh0kGIhNsnVwx4zcvYM4jr8BtUAhG3XJPu08rl+dnojyv96csISLqi4JGTMSbb76Jl19+Gc7Ozgb7mpqaUFFRgV9++QURERF46qmnUF9fL06gdEOYaCAiugGNjY34z3/+g9raWoPy22+/HUuWLMH+/fvx7bffGuyzc/bAuEUrLRilZQg6HS4nxaOhptLkfqW1LYZNXwh3/8GY88grmPnA7+Hk4WvhKIlujFdIFPyiRhmUNdZUoDA9UaSIiIjMU1V8ud0pDQOGjsPC59/F+NsfhJWtg4UjI+p/vEOH4pan3mz3CUdBEBC/5Vvs+OTP2Lf2byi7fMnCERIRiU8mk2Hp0qXYtGkTfvOb30ChUEAQBJSWlurrtLS04Ouvv0ZISAiee+451NVxQFd/wkQDEZGZVCoVXnrpJRQWFhqUjxkzBn/605+Ql5eHN954w2CfVCbDtPuegdLa1pKh9ipBp0NOwnFs/fAlxH33PpIPbW23btTUBZj/xOvwDrXsgldEPckvahRcBwUBAOQKK0y/7zkEx0wWNygiom6KmDDHKIngERiO+b99HdPvf46DAYi6qaN114ovXUTZ5db1HArTE7Dz09ew56u/oCjjAgRBsFSIRER9gr29PZ599lls3LgRkydPNppmGgAaGhrw5ZdfIjg4GE8//TSqq6stHyh1GxMNRERm0Gq1ePXVVxEfH29QHhAQgHfffRdNTU147rnn0NDQYLA/asoCuPuFWjLUXqNuaULq8d349R+/w+F1/0Z1SR4AIOPUPjTVVZs8RiqVDYjFr+nmJpFIMWLOcrj5hWL+E68jYNg4sUMiIjJJp9OipdH01AMKKxsMnb4QAODk6YcZv3kB8x5fBY/AcEuGSHTTEwQBCXs3GJUXZ17E3jXvYNu/X0bWuSPQaTUiREdEJB4fHx98+OGH2Lp1K6KiokzWaWpqwtq1axEaGorHH38cZWWm15SivoGJBiKibtLpdHj77bexf/9+g3JnZ2d88MEHsLGxwYsvvojLly8b7J88eTL8h461ZKi9oqGmEmd3/A8b//oMTm/+GnWVJQb7tRo1Uo5sFyk6IsuwtnPELU+uhotPoNihEBEZ0ahVSDuxB7++9wLit33Xbr2ICXMw9e7/w8Jn/wr/IWM4GICoNwgC/IeOgbWdo8ndVUW5OLr+E2x67wWkHNkBdUuThQMkIhLXzJkzER8fj2+++QaBgaa/XzU3N+O7777D4MGDsXTpUly8eNHCUVJXyMUOgIioP9HpdFi9ejW2bjWcHsjKygr/+Mc/EBAQgNTUVCQnJxvsDw8Px4oVK/D5YcPkQ39SUZCNlCPbkZN4AoJO2249J08/uPuHWTAyInHwhhwR9TVNddVIP7kP6Sf3orm+BkDrAIGYOctM1pcrrRA0YqIlQyQacCRSKYZOuw3hE2Yj4+Q+JB3aqu+fbTVUlyN+27dI3LcRYWNmYPD4WDi6e4sQMRGROO644w7ccccd+OGHH/CXv/wF2dnZRnXUajV27dqF3bt3Y+TIkXjvvfcwfvx4EaLtnEqlQkJCgihtjxgxAkql0uLtMtFARNRFWq0Wq1atws6dOw3K5XI5Hn74YURGRgIAIiMj8dVXX+H5559HaWkp3N3d8cEHHyA31/SCi32ZRq1CbuIJZJzar59Xtj2uvsEYPvP21hGRUj4wRwNb0aWLcPb2h429k9ihENEAUJ6fidRju5CbcAI6neH0K4JOi+TD22Hj4CxOcEQEAFAorTFk6q0InzAHWWcPI+XwdtRWFBnVUzU3IPnINiQf2QbfwdEYPmtJu4tMExHdjO69917ce++9+Omnn/DWW2/h0qVLRnUEQUBCQgKeeOIJjBs3Dvfffz8mTZoEaR+6F5GQkIDX1u6Am4Wnz67Iz8TqB4GxYy0/owYTDUREXdDc3IxXXnkFcXFxBuVyuRwPPPAAQkMNLxwRERH4+uuv8fLLL+N3v/sdPD09+12iIWHvBqQe3QlVc0OH9fyiRiFqygJ4BUdxhDcRgMyzh3F8wxdw9QnE7EdegdLaRuyQiOgmpNWocfniaaQe24XyvIwO61YVXYY1E59EfYJcoUT4+FgMHjsTeclnkHRoa7t9uDAjESGjplk4QiKivmH58uVYvnw5fv31V7z11ltGM0e4uLhAKpUiPj4e8fHx8PX1xdKlS7Fo0SK4urqKFLUhN79Q+IQNFTsMi2GigYgGnO4+vlZXV4d//vOfRll0hUKB3/72t2hsbERGRgZcXV1hbW1tUOexxx5DfX09Tp8+feWi2H/edtXNje0mGWRyJUJHT0PUlFvg6O5j4ciI+iZBEJB0aAvO7VwHAKgoyMK+NX9F7EN/YrKBiHpMbXkRLsXHIevMITTVV3dY12dwNIZNXwivkCFIittimQCJqEskUikCho2F/9AxKMtNR9KhLchPOWtQx9reCQHD+v8ab0REN+L222/H7bffjhMnTuCNN97A0aNHodPp4OLiYlCvsLAQH330ET777DO4u7vj3nvvxd13392nnnK42fWfO15ERD2kO4+vNVSX48y279BQXWFQLpXLMfqWu3Gs2gmC4AiJRILznay/kHX2WJ8bkSQIQrtPIQweF4uUozsMymwcXBAxcQ7Cx8fCytbBEiH2IxI0WtlDq26BRCqDVCYHwCc8BhJ1SxMyTu4zKCvPy8D+tX/DrAf/yGQDWVjre9L1ZdR/5SWfQcqR7SjJTumwnkyuQHDMZERMnAdXXy5YT9TXSSQSeAZFwDMoArXlRUg/uQ+ZZw5B1VSPsDEzIZMrTB5XkHoeRZcuIHT0NLj4iNXXea0hIsuZMGECduzYgdzcXHz22Wc4c+YMKisrjeo1NjbiwIEDOHDgAP74xz9i8eLFeP755xESEiJC1AMLEw1ENCB15fG1vOQziN/6DdQtTVC0eVJBaW2HGb95AV4hUUg6tBWNNZUYc9tvOp02qCI/s0di7wnVpQXIPncE2eePYe6jr8De1dOojpOnL7yCo1CSnQKfwdEIHxcLv6iRV26g0/WarO3x6Z3v4OLBzQPu8UhqpbS2RexDf8Kuz1cbLPJYdjkd+9a8g1krX2SCjizm6nsS3TyqS/I6TDLYOroiYuJchI2dCWs7vtcQ9UeO7j4Yc+v9iJmzHDmJx+ETNqzdumkn9qAg7RxSju6Aq08QQkZNRcDw8bBzstx0IbzWEJEYAgMD8c4770ClUmHnzp1Yt24d0tOvrSlZXV2tf11ZWYk1a9Zg7dq1iIiIwD333IPf/va3sLe/PklKPYF3i4iIriPodEjctxGJ+zca7bN1ckPsg3+Ek+cgnNu1HhcPbgLQOnpw5Py7+/QaBQ3VFbgYtxm5iSdRWZitL88+fxTDZy0xecyY234DhZUNHNy8LBUmUb/m6O6DOY++ij1fvmWQbCjPu4Sdn72B2Q/9CXbO7iJGSET9VcioaUjY8zMEQWdQ7hkUichJ8+A/dAykUplI0RFRT5IrrRA2Zka7+5sb6lCYfm0q2MqiHFRuy0H8tu/gGRSBwOgJCBg6FraOLu2eg4iov1MqlVi0aBEWLlyIpKQk/Pzzz9i5c6dBouEqQRCQmpqKVatW4e2338b48ePx6KOPYsmSJZxaqQcx0UBE1EZDdTmO/PgJSnNSjfa5+ATqRyQf+fFj5CQc0+9LOrQFcqUVomOXWjLcTtWUFuLyxZNI3PcLWhrrDZ7MuCrr7BEMm7nYZJLE1TfIAlES3VycPQdhziOvYM9XbxskG2rLCrHj01WYteIPnM6EiAw01FQiN/E4ss8fw/jFD8Hd33h6RzsnV/iGj0BB2jlY2TogZOQUhI2ZAWdvfxEiJiIxFaafN0o6thJQmpOK0pxUnN78X3gFRyEwejwCho2DDReEJyIRaTUao8Wce9qtt96KiRMn4ssvv0RcXBzq6+sBtCYZAOjveTQ3NyMuLg5xcXGwtbXFiBEjcOutt2LKlCk9mnTob+t09oSB9dsSEbVDEATkJBzDyV/XQt3caLQ/eMRkTFj6CNSqZuz96i8ozU0zqqOwEn/+dUGnQ3l+FgrSzuHyxdOoKc0HADTV1UCusDJ5TG1FEaqKcplUIOpBzl5+mPPoq9j3n3fQWHtt3tCm2irs+ux1TFr+WwQOHy9ihEQktrqKEuQlx+Ny0mmU5WYAaP0SnJNwzGSiAQCGzViEkFFT4T9kdLvzthPRzc8reAhsHV2RdfaQwecMQwJKspNRkp2MU79+DXf/MPhFjYL/kNFw8hzUp5/EJqKbT3VxLtYVA24FvX8r2nrUEswZsQhZ544g60wcKq7M6GDqfa+mrh6HjhzFoSNHobCyxqDIkYicdEuPvE/2xXU6exsTDUQ04DVUV+DkpjUoSDtntE8ikWL0gvsQOXk+yi9nIO6Hf6GptsqozvjFD2HwuFmWCtlIWW460k/tR2F6gsEI6o7Y2DsjKGYSQkZO5ehqol7g7DkI8594A/vW/k2f9AMAjboFh374F4bNWIyYOcsg4aO6RAOCIAioLMzB5aTTSD6yHS0NtSbr5SSewOgF95l8b/AMiujtMImoH7BzdkPYmGmImbMMxZlJyDp3BHnJ8VC3NLVzhIDyvAyU52Xg/O4fsfjFD+BgYo02IqLeZOm1DAdFRGPq3U/izPYfUJqThqKMRNSWF7dbX6NSoTwvE/Fb/ws7Zw/4RY2CX+RIeIVEmTXAoy+t02kpTDQQ0YCl1aiRenQnLuz/BWpVs9F+excPTLnrKbj7hyH16E6c2fEDBJ3WoI5CaY2p9z6DQRExForatNqKEmSdPdRpPWt7JwQMGYOA4ePhFRLFuZyJepmdsxvmPf4aDn7zD6MnoS4e3ISy3HRMvutJiy7cSESWo25pQnFmMgrTE1CQdh4N1eX6pwxNTWcIAE11VSjJToF3qOW+iBNR/ySRSuEzeDh8Bg+HVqNGYXoici+cQH7yGZPfbwDAydOv3SSDVqOGVCbn0w5EdFOxsrVHzNw7ccuTb6AkKwVnd/yAnMQTaGmsN6woAZTWtgCAhuoypB3fhbTjuyBXWsMrZAgUVtYYNn0hXHw4ULM9TDQQ0YAjCAKKLl3EiY1foL6qzGSd4BGTMW7xg1C3NGPvmr+iOPOiUR1bR1fMXPFirz8NIAgC6ipKUJyZhKARk6C0Np6iaVD4CAASXJ12oS0bBxc4efohdNQ0DJm6gKOne4lco8KwzBPwrMuDXXETnLQVuBg6ARq5UuzQSGRWtvaY/cjLOLlpDTLPxBnsK8lORtx3/8QtT67ml3rqUVffk9rie5LlFF26iIsHNqE0Jx06naZLxyisbBAwbByCYybDMziylyMkopuNTK6A/5DR8B8yGhq1CoVpCci9cAIFqecMkg5+UaPaPce5XT/i8sVT8AkbBp/B0fAKiepwbQdea4iov/EKicItT70JnU6HnIRjuHhwMwpSz7WuaWllA6nMeDCmRtWM7PNHUFdegjPbvoedsxu8Q4ciZOQUDB4fq09OEBMNRDSACIKAM2fO4K233sL5MxdNjiS0sXfGuNtXwn/oWGSdPYz4rd9C1dxgVM87dBim3v00rO0dez5OnQ41ZYUozUlDaW4aSrPT0FDdmhCxcXCG/5DRRsdY2zvC3T8M5XkZAABHD1/4RbbOweoRMBhJh7bCdVAwkwy9SKFpQeypn1o3qgFkA2mBI/lFiwC0fvmfeMdjcB0UjPgt3+gXcJRIZRi3aCWTDNTjDN6TruB7kuXoNBoUZ3W+4KG1nSP8okbDf+gY+IQN47oLRNQj5AolAoaNRcCwsdBq1CjJSkZeylnkp5yFfweJhqKMC2ioLsel+IO4FH8QAODo5gPP4Eg01dfAxtEVgiDoP7fwWkNE/ZVUKkXIyCkIGTkFOp0OuYknkJ96DpX5WaitKDKq33Ytz4bqCmSeOYTMM4ewd81f4eThA9/BIxA6dgaCoydCKh+4t9sH7m9ORAOGIAg4fPgw1q5diwsXLqChwThxAACho6dj9IL70FRXjT1fvoWS7BST9YbNuB0xc5b32E17rUaNivwslOamoSwnHaW56VA11ZusW5yZZDLRAABRk+ejuWESBkWO5JyrRH2QRCJB5MS5cPUJxOF1H6GxpgIxc5e3u+grEfU9rU8ZFqMkOxUlWckYOe8u2Dm7G9XzDImETK6AVqM22mdt5wD/IWMxdPpt8AgYzEEARNSrZHIFfMNHwDd8BMYtWtluvcbaKlSX5BmV11YUobaiCE11Ncg5fwznd/8Ir6BIeAZHItDbtxcjJyKyDKlUiuCYSQiOmQRBEFBdkof8lLMoSD2PsssZEAQdVM2m18ARdDpUlxSguqQAyUe2Q6ZQwMljkH5dBwf3QRb+bcTFRAMR3bSampqwe/durFu3DhkZGe3W8wyMwOjb7oe7X+vNvuTD200mGWwdXTFp2ePwGTz8huKqLi1ARd4lVORno6IgC1VFuSZvRJhSdMl4CqergkZMvKG4iMgyPIMicNsz7yD9xB4MnXpbu/VaGuthZWtvwciI6Hqq5iZU5Gei/HIGyi5noDwvEy2Ndfr9PmHDETp6mtFxCqU1PIMiUXTpAqRSOTyDwuETHo1BETHITzkHd/8wLuxMRBbX0ROUJVmmB1ldr7GmAtkJR5GdcBTpMglejuA6U0R085BIJHDxDoCLdwCGz1yM5vpa5CadRvzmr1FRkA2dVtvh8Vq1GpWFOagszIFOp8XFg5vh7h+KodMXwSMgDG7+oR1OSdffMdFARDedS5cuYePGjdi2bVu7Ty8AgL2rJ0bfci/8h441+NA9fObtyEk4arB+Q9CISRi3aGWP3PQ7tWlNu09LdMTa3gmuPoHQaTWQyvj2TdSfWdnaY/isJe3uV7c0Ycu//ggXL39Exy6FR2C4BaMjGpi0GjWqS/JRWZiD8suXUHY5AzWlBTC1/tFVxVnJJhMNADB0+kJETJwL79AhUFhdW1+pIPV8D0dORHTjgkZMhItPAIoyLqAwIxFlOWntLijdHTqdFvWVpXBw9eITXETU71jbOyJifCwixsdC1dyE9BN7kHXuCEqyktFQU9HRx0QAgCDoUF9djosHN+nLbJ3c4O4XCje/ECht7OAbHg17F4/e/UUshHeqbgIqlQo//vgj/ve//yEpKQklJSVwcXFBcHAwli5dipUrV8Ld3fiRbqKrVCoVEhISRGl7xIgRUCpvfA7PgoIC7NmzB7t370Z6enqHdT08POAeE4Mxt94HudLKaL9caYVxix7E/v++C3sXD4xdtBJ+kSPbPZ8gCK2PGhdfRnVxHqpK8mBt64Axt/3GZH23QSFdSjTYOrnBMygCnoER8AyOgLOXP+dxJxogkuK2oKm2Ck21VSjMSIRP2HDYOrnBzY/TLBH1tLM7fkBheiKqSwsg6DoepXa9jkYA+4QNu9HQiIgsRiKRwNnLD85efoiacgt0Oi2qCnNRmpOGkpxUZJ093OkNNVNqy4qw5YM/QK6wgrO3P1y8A+DsEwBXn0A4e/lBaWPX878MEVEvUFrbYNiMRRg2YxEAoK6iBKnHdiH3wgmU5V4yeOq1LcV1950aaypwuaYCl5NOoao4DzqNBla2DnDy8oXboBB4hw5FwNAxcPEJ7PXfqacx0dDPpaam4t5778W5c+cMyouLi1FcXIzjx4/j73//O9auXYsFCxaIFCX1dQkJCXht7Q6L38CqyM/E6geBsWPHdvtYnU6H9PR0HDt2DAcPHkRycucLLgYHB+PBBx9EVVUV/rZmA/b+5x3M++0qkzfvB0XGYPKdTyJg6Fh9MkKrUaO+sgy15UWoLStEbXkxasuLUF2Sb7Smgr2LR/uJBr8QE6USuHj7w+NqYiEo3OScz0R086uvKkPy4W0GZUWXLqCprgaFGYmImX0HgkZMMpkoJSJD6pYm1JQVQtAJ8AgIM1mnprQQVcWXu3VeG3tneIVEwStkCASdjqN0ieimI5XK4OYXAje/EERNuQVug0JgZecICDqUZqei7HI6UFve6XmqinIBABp1C8rzLqE875LBfht7Zzh6+MLRwwdObf60c3bneysR9WkObl4Yu/ABjF34AACg7PIlXIo/iIK0c6jIy2p94gGAXGlt8nidVgudRgMAaGmsQ2l2Gkqz05ByZAcAwNUvGL6DozF8xu3wComywG9045ho6Mfy8/MRGxuLwsJCAK0jEKZNm4awsDCUlpZi7969aGpqQmlpKRYvXowdO3YgNjZW5Kipr3LzC4VP2FCxw2iXIAgoKirC+fPnER8fj6NHj6KioqLT4yQSCSZNmoS5c+eisbER69atw+nTp1Fa2wKFtTXK8zLbvfHg4OqJ87vX6xMK9ZWlEARdl+KtryqDqrkJSmsbo32ug4Jh5+wOt0EhcB0UDPcrH+A5moeIAKC2rBAKa1to62uM95UW4vjGLxG//XuEjpqG4JGT4TYohE870YCm02nRUF2B+ooS1FWWoLasCDVlhagpLUBDdetNMO/QoZjzyCsmj3fxDUR+6tl2zy+VyuE6KAju/mHwCBgMd/9Q2Ll4sN8R0YAikUjg4OoJn7ChCB/fel9BWlEEbP9Lh8ddTTS0p6m+Gk311SjJNhw4JpMr4Ojhi3mPv3ZjgRMRWYhHQJjB/aX4rd+isa4GDq4eKMtNR0VBNjRtpqTTqFs6PJ9OrUZRRiKGTmt/Xb++homGfuy+++7TJxkCAwOxefNmREdH6/eXl5fj7rvvxr59+6BWq3HnnXciMzMTzs7OIkVM1HUqlQqZmZm4cOECzp8/j/Pnz6O0tLTLx7u7u2Pu3LlwdXVFfHw83njjDeh0Ouh0OqjVamhULdBq1Tiy7iMs+cMHJs9RVZyHlKM7zP4dakryTM6r7ujujaV//NDs8xLRzc03fASWvPgBMk7tb51Cqb7aqI66uRGpx3Yi9dhO2Lt4IHD4BARGT4CrbxBvftJNq6GmEpUFWairKEVdZcmVxEIp6qvKOp3yqKakoN19rtc9lu7g6gXXQUHwCAiHu38oXAcFQyZX9MjvQER0M7Gy63z9uobqzgeHmaLVqNFQXdHuSODLSfFIO74b9i4esHf1hL2LB2ydXGHn5AobRxe+bxOR6KztnTAocpR+UK+g06GmrBAV+Zkoz89C9rkjrQNadcbz0kkkEkjlrbftXbwDLBr3jWCioZ/avn07Dh06BABQKpXYsmULhg8fblDH3d0dv/76K6Kjo5GVlYXKykq8++67+MtfOh5x0FfcDOsGUOd0Oh2Ki4uRm5uL9PR0/U92djZ0uq49PQC0PvFgZ2eHwMDWmwV5eXn417/+BY1G05pY0Gig0Wig1Wpbkw1aHSQSKRprKtFcXwNreyejczp6+HTrd1EoreHsHQBnb384e/vDllMfEZGZ5EorRE25BYPHxyLj1D4kH96OpjrjJxyA1ieokg5tQdKhLbBxdMG8x1fBwdXTwhETmU+jVqG2vAgN1ZXQqJrgP2SMyXqZ8QeRsPdns9poqq9GS2M9rGyNb4p5BIZj7G0PwMU3CC4+gSafRiQiIvNMvef/MGbhA6guuozKolxUFV9GVWHulWntOk4SO3n4tjuAoqooF8WZF9s91trOEbaOrrB1dm3908kVtk5usHV0gZ2zGxzdu/ddj4joRkmkUv1aOKGjp2P87Q9C1dyIvIunkZdyBmW5GagpLUBjbSWkMjkkEglsHFxgbe8oduhdxkRDP/Xxxx/rX69YscIoyXCVnZ0dVq9ejfvvvx8A8Pnnn2P16tWQy/v+P31/XDeATNOoWtBYW4mG6krUV5agrqIYteXFKM1JQ+K6JpNJHUEQoNPp9MkBjUYDBwcHow+a/v7+aGlpwYkTJ6BSqXDmzJkuxSSVymDj6AIrW3s0N9SZTjS08+HT2t4Jju4+V3684ejhCxdvf84jSkQ9Tq5QImryLYicOA+H132EmtICVJfko73VGLUqFeyc3Uzv06ghlcr4PkW9ThAEaNUqtDTWoamuBk111Wiub/2zqb4GzXU1aKqvRnNdDSoKsiGRSKGwbh2xamXr0G6iwdy1iyQSKexdPdFcX2sy0WDj4IzIyfPNOjcREXXOxt4JNoOHw2fwtfsWV9e/qykrRG1ZocGf6uZGAO1/HwOA+sqOn3ZvbqhFc0MtKotyjPY5uHlj8e/fN3lccVYyKvIyYW3vhKriy5ArrWHv6gErGzsorGz4OYqIepTS2hahY6YjdMx0fZlOo0FFYRYaqys7nV6pr+n7d5vJSH19Pfbt26fffvDBBzusv2zZMjzxxBOoq6tDZWUlDh06hFmzZvVKbOfPn0dRUVGPnCs5ObnPrxswkOm0GrQ01rd+gKuvQ0tjHZobatHSUIemumo01lSgsaYSDTWV+oWSdVoN1C3NEAQdBJ0O6pYW1Ep1kEql+mmN2v5cJQitN9QCAgLg7OyM8PBwREVFITo6Gt7e3vj444/R1NTUacwSiQS2trZQKBRQSa2huDJisb6qDM5efkb1bRycETxyCuyd3eHo4QNHd184untzLQUisjiJVApX3yAMHhcLe1cPXDp9ELkXTqCuotignnfYMEilMpPnSIrbgqS4LXDyHARnb3+4eAfA2TsATp4+sLF35hdnMnL1uq1uboS6pQmqpka0NNajpakeWrUKkZPmmTwu/eRenPp1bZfa0GrUkCuuLWze0lgHjarF5GLntk6mk2hXKaxt4eDqBQc3Tzh6+MLZ0w9Onr5w9PDlFBpERH2MTK6Ak6cvnDx9DcoFQUBzfQ1qy4qgsLZt9/jOEg0d6Wh0cEHqeSQf3goAaKqrQYZivz4ZLpFIobSxg5WNPZS2dq2vbe2htLGHla097JzdEDZmhsnz6nRaSCRSTnFJRJ2SyuXwCAgH+s+MSXpMNPRDx44dQ0tLa0bLzs6u05H3VlZWmDBhAvbs2QMA2L9/f68lGtYczoKtk+mpHbor6+wxhIya1iPnolY6nQ7Nzc1obm5GU1MTWlpa0NLSgrS0NJTmZqCloRaq5kY01lSguqQAquZG/c2F1j+br71WtbQujKwTWhMHgg7ClddOnoMglRnf6NKqW0etXItHixYAElwZmytcHaErtL4UhNadkEAqlcJ/6nJ4h0QBEglSAKRcqAUu1CJX5QS11vQ0SxKJBHKlFRRWtpArrSBIpKhrrIOtsxWcvfxh7+oBhZXpKRIkEgmm3Pmk2X/fRES9wcHVEyPn3YmYuctRWZiD3MTjyL1wEvVVZfANj273uLLcdGjULagoyEJFQZbBPplcAXsXT9i7euj/tHFwhrW9M2wcnPrdSJqBRtDpoFG3QKNWQatq/bOpoarT44ouJSHzbBzUzY0oyU6FXGkNhcIKqpZGqJuboNWo2j1WKpUjYuJckzdMrGw6n7O7I421lSZHsdo5u8HG3hkObl6wd/OCg6tn62tXLzi4ecHK1p43cIiI+rnWqUKcYePg3GG9odMXYlDJSNRXlqKusrR1oFttlcFCq+2xsW//3M317d/PEAQdWhpbB9nBxNITzl7+7SYaEvZsQFLcZsitrKGwsrn2Y20DhdIacitryBVKyBVWKMlJRXVpAWpK8iBXWsHKzhH+Q0abPK9Wo4YgCJDJFbwGEpHomGjoh1JSUvSvhw8f3qVpkEaNGqVPNLQ9vqd5BkXAwc2rR85VkZ/ZI+fpLq1Gg+TkZP22IAj6qXuu/tn252qZTqeDo6MjdDodtFqt/jhBEFBbW4uKigqDum3XC9BoNGhpaYFKpdIvVtx2XYHIyEiT7ZeVlSErK8tkfNeXXW1LEAT9T0BAAKRSKZqbm1GrkuhHaqiaG1FXXtLJ39S1aTta8wPXtnU6relEg1YDrUatP14Qrh3Tmk9oHeEhkUghkbb+2Zazpy98Bw8zOq+9qydObPhCfyKltR0c3Lzg4hMId/8Q2Dm7w9bRBbaOrQuD5SQeh2dQlMlzERH1FxKJBG6DguE2KBgj59+DuopiWNk6mKwr6HQou5zR7rm0GjVqygpQU2Z6wVxHD194BQ8xuS/lyA6omhqgsLaF0sYWCisbyBRKyORKyBUKyBRWkCkUkF8pa31t1aeeoBAEARAE6HRa6HRaaNQqqJsb0VRfA0Gnu/LTuk8qlcG+nTUw6ipLUVdeDEGng06nhaDTXnmtg06rgU6rgVargU5z5bVGDZ1WC51GDZlSiZg5y02eNzfxBFKO7tQfo9WooVG3QKtWQ6tuaXNtvcZFJsGrEa4d/t4NVaXIPncEQOuoTbnCSv9ZoDM6nQYaVbPJZL2p6Ym6QmFtCzsnN2hUphMcju7eWPbKJ2adm4iIbi5+UaPgFzXKoEwQBKibG9FYW2XwhH1jTSUaa6/+WdXhEw0dJRo609H1T93cCEHQtQ7auzI1VHua6mogv5SE7CvXZEd3n3YTDcmHt+H87vUAAKlMDplMDqlcAan82muZXAGpVNb6p1wBmVwOqUwOaztHTFj6iMnzludnojgzCVKpHFKpFJIr029KZTJIZTJIJDIT5fLW11IZPIMiTP89qJqhamps/d4vlUICCSRSCTSqZmhUrQMbW+8HtH5OlEplgETCJApRP8FEQz+Ulpamf3114dvOBARce94mNTW1W+3l5+d3uD8vL0//ur6qdbT6hYO/orokHzkJx9DSUIfr7kNfv6HfFNqUS6VSeIcOa/0Q0ObmOAAUZ15Ac33ddYcL7U1ZbdCQVCqHz+ChaL2ncLVhAQIEVBfmormhFus6O007nL38TZY31FR0+mGiIw7uvpCZuHHfVFfdOprCTIVltZArFWhpqoeVjb3+RkVLY32XRoK0R9VYD4md8c0uiURqYsEvCSCR6J9oaJt8uHZc6weL3Asn4REQZtyeqhnu/qGwsnOAnYs7FIprN0nqykuMkiaVhTnISTwBO8fWGzChY6bDI2Cwyd/l5KY10HWySFl7AoePh+/ga6OLm+tr0FhTgbqKEpzZ/j1UTQ1mndc3YgQCh403uS9h7wY0VJcblVcV5cLKzhG2ji7tntczOBJho6eb3Jd8eNuVeeG7p7IwFx5BEbB3MT2vdvqpfSjPvdTt8wKtCabo2CUm9+Uln0H6qf2wdWj/922Ptb0jRi+41/R5U87i8oWTpuOBFotlhqO+96x9F/Vo7bsyhRITljxs8tjizCRcOhPX7Vivmrj0UYP/X1dV5Gch5egOs8879rYHYGWiL9eWFSFh/0YAQHVxHqxsHGDj5Nzl88bMWW5yseLGumqc2fZdl89TXZwHpa0DbB1b2x46fSFcfYyvi2p1C47//EWXz3u9yIlzDb4stf27PvLjx2a/R4SMnAq/yJEm953ctMbo/b26pABWtvawcTBeU6Yt/yFjEBwz2aCstqwIzQ21aKiphKDRdDvW5voapBzdYbIvJx3ehrKcNGjV7Y9+b0u4cr2WSCRw9PBF2NgZGD5zsVG9Yz9/jqyzh1vXlpArWhPSkBicyei8Vyit7eEXGYPJdz5hdN6LcVuQeSYONaUFECC0PlV33aVHq1a1rmdx3bVXobSGu38Y5j72qtF5M04fQMbJvSg3Y6CETKaEk9cghI6aZtSXcxKPI+34bpRkd+/zm1QmQ2GA4VR/tZWlaLZq/TxSkHYeqUd3ora8dfotjaoFUqkM0i4MYJFIJLB1ckVFQTbsrpvOqDQnDanHd+nPK5FIIFdYQaa0glxpBZlC2TpiU2kFuUIJiVQGGwdXOLp7QSqTY9yiFQBg8F4GABUFOci8gffJmHl3QnldUqS5vgZluelIPrzN7PMOm3k7bE2Mum2sq8bFA7+aPKayIAvWdk6wbWc9FQCInDQPju7eRuWqlmac3/WjWbFWFmQhZMzMdq/Jpzb/16zzAkDQ8PHwDI40ue/crh9RkpXc6e9syqCIERgUEWNy34UDm9BU1/ENwfb+rj2DwhEUPdHkManHrv3/7S5Xn0CEjZ1h8pp8KT4OlYU5Zp3X0d273anKci+cQEn2te+HXfn/dZWNg5PJ91+g9T2iIC2hyzG2bVehtMLI+XebrFeak4acxBNdPu/1rr5HXHX17zon8WSPv0cAQG15MVKP7TJ5TFf+rs15j+hMZUEWIibOM9mXb+Q9AgBCR0+D26Bgk/tSjmw3+n0doMHi6556jPvfv1F33a2mzt4j1C0df/ds7++6vfcImVyBnMQT171HSFoXg3ZyQ11VGfatfddkW2V5l6C0toPS1h4tTY3QCTqDhH5zfa3JBD8ACDqh3fMWZSSisaYKVnamkxEtDfX6J0i1qhZI2lyTW5oa2483Nx0N1RWwtneEVqPG9ZGpGhugbue7vVxphYaaSoOyq3/XLY21KLt8CTZXPmdfT93cCFWz6emLJVIpIibMNbmvpjQfxZlJsHUyHAzRXF8HuaJ17caWK1MvG59YgvBxsZDKjD+rNFaXoyA9EU6evtemqJK0zowAiQSq5iY01lz7niy58pmypakBMrkSwTGTIFcaD7ZoaaxDYcYFuPoEXkmwtPksKpFA3dKMmtKCKwMnjZMh3mHDTD7pqVE1I/v8Ubj6BsPe1aM1mdLmHFqVqvUJ5HYSLO7+obC2M/5OIOi0KEhPgKtPoMkpn3U6LS5fOAWZwgpKa2sYfrYGXHwCjf5tripMOw8nLz/YmFjfEgCKLrW/OLuTly9UjQ0m+3FxVhLsnd3bXWeuOCsVgtb09xd719bpMk0py02HlZ0DHNy8UJmfDWsHR4NpOMty09u972Xr6Apnb9PzFlUWZEOqUMDZ03j6baB10LSqsfX/cFNdNWRKK/31xcreEa6+pt9jq4ovA4IOLj5B+rLRC+41a/rPxpoKXL58Gc7Ozu3WKS6+9llHY8b3w/ZIBFN39ahPu+uuu7B+fWvG+plnnsG//vWvTo/ZuHEj7rjjDgCAt7d3t9ZRYOaYiIiIiIiIiIiI6OZy6tSpTqfl76q+88w6dVl9/bXsro2N6bnlr9e2XtvjiYiIiIiIiIiIiIhuBKdO6oeam6892qNUKrt0jJWVlf51U5Ppx9va03ZqpPbiSU1NhZeXFzw8PLq0ZgTRzaKoqAjjxo0D0JoF9vExXrySiNhXiLqC/YSoc+wnRJ1jPyHqHPsJDWRX130FWtf/7Sm8I9wPWbdZpE/VzmJ512tpuTZvYlefgrjKz8/0vGNthYUZz5tPNND4+Ph0qb8QDXTsK0SdYz8h6hz7CVHn2E+IOsd+QgNRUFBQj5+TUyf1Q/b21xaS6erTCW3rtT2eiIiIiIiIiIiIiOhGMNHQD7m5XVslvaSkpEvHtF1N3NXV9AryRERERERERERERETdxURDPxQREaF/nZub26VjLl++rH8dGRnZ4zERERERERERERER0cDEREM/FBUVpX994cIFaDSaTo85e/asyeOJiIiIiIiIiIiIiG4EEw390KRJk2BlZQUAaGhoQHx8fIf1W1pacOLECf32rFmzejU+IiIiIiIiIiIiIho4mGjoh+zt7REbG6vf/vrrrzusv3HjRtTV1QEAXFxcMG3atN4Mj4iIiIiIiIiIiIgGECYa+qknn3xS/3rt2rVISkoyWa+xsRGvvfaafvvxxx+HXC7v9fiIiIiIiIiIiIiIaGCQCIIgiB0EmWfatGk4fPgwACAoKAibN2/G8OHD9fsrKipwzz33YM+ePQAAV1dXZGZmwtnZWYxwiYiIiIiIiIiIiOgmxERDP5afn49x48ahqKgIACCVSjF9+nSEhISgrKwMe/fuRWNjIwBALpdj586dBlMuERERERERERERERHdKCYa+rnU1FTcc889OH/+fLt1PDw8sHbtWtx6662WC4yIiIiIiIiIiIiIBgQmGm4CKpUK69atw//+9z8kJSWhpKQEzs7OCAkJwZIlS/DQQw/B3d1d7DCJiIiIiIiIiIiI6CbERAMREREREREREREREZlNKnYARERERERERERERETUfzHRQEREREREREREREREZmOigYiIiIiIiIiIiIiIzMZEAxERERERERERERERmY2JBiIiIiIiIiIiIiIiMhsTDUREREREREREREREZDYmGohoQFKpVPj222+xYMECBAYGwtraGj4+Ppg0aRLee+89lJeX91rb9fX1+PTTTzFz5kz4+fnBysoKfn5+mDVrFj777DPU19f3WttE3SFGP0lJScGHH36IO++8E0OGDIGTkxMUCgXc3d0xZswYPPvsszh37lyPt0tkLjGvJ6YsXboUEolE/zNjxgyLtk/Unr7QVw4dOoT/+7//w4gRI+Dp6Qlra2v4+/tj3LhxePLJJ/HTTz+hqqqq1+Mgao9Y/UQQBOzZswcPP/wwhg0bBmdnZ8jlcjg7O2PIkCH4zW9+g82bN0Or1fZK+0RdodVqkZiYiP/85z944oknMGbMGCiVSot+5uF3eaJOCEREA0xKSoowcuRIAUC7P56ensK2bdt6vO1jx44JwcHBHbYdEhIinDhxosfbJuoOS/eT3bt3C0OHDu2wvbY/y5cvFyoqKnqkbSJziXk9MeXnn382an/69OkWaZuoI2L3lczMTGHevHldur78/e9/75UYiDojVj/Jzc0Vpk2b1qX+MWrUKCE5OblH2yfqil9++UWwtbXt8P9nb3/m4Xd5os5JBEEQup6WICLq3/Lz8zF+/HgUFhYCACQSCaZNm4awsDCUlpZi7969aGpqAgAoFArs2LEDsbGxPdJ2YmIipkyZgrq6Ov35Z82aBT8/P+Tl5WH//v3QaDQAAEdHRxw9ehTDhg3rkbaJukOMfvLXv/4VL730kn5bIpEgOjoa4eHhcHFxQVlZGY4cOYKysjJ9naFDhyIuLg5ubm431DaROcS8nphSXV2NqKgoFBcXG5RPnz4dBw8e7LV2iTojdl9JSEhAbGwsKioq9GVDhgzB0KFD4erqirq6OqSlpSEhIQEajQZ///vf8fvf/77H2ifqCrH6SWlpKSZMmIDs7Gx9mZ+fH6Kjo+Hj44PCwkIkJCTo4wIADw8PnDx5EsHBwTfcPlFXff3113jwwQc7rNObn3n4XZ6oi8TOdBARWVLb0TqBgYFCQkKCwf6ysjIhNjZWX8fV1VWoqqq64XZVKpUQGhqqP++IESOEnJwcgzrZ2dnCiBEj9HXCw8MFtVp9w20TdZcY/eSdd94RAAgxMTHCxx9/LJSXlxvVaWlpEd555x1BJpPp277zzjtvqF0ic4l1PWnPww8/LAAQFAqFsGzZMj7RQH2GmH0lOztbcHNz0587NjZWuHDhgsm6FRUVwpo1a4SdO3f2SNtE3SFWP3nwwQf157S2thY+//xzo+8fKpVK+OijjwSlUqmvu2jRohtum6g71q5dKwAQvLy8hNtuu0144403hO3btwvPPvtsr3/m4Xd5oq5jooGIBoxt27bpL/xKpVJITEw0Wa++vl4ICQnR133ppZduuO2PP/5Yfz4XFxehsLDQZL3CwkLBxcVFX/fzzz+/4baJukOsfrJhwwbhl19+6VLd999/3+AR5ZSUlBtqm6i7xLyemLJv3z59G6+88oqwatUqJhqoTxC7r8yZM0d/zrvuukvQaDQ9cl6iniRWP2lsbDSYiuaDDz7osP57772nryuTyYTKysobap+oO4qKioTc3Fyjckt85uF3eaKu42LQRDRgfPzxx/rXK1aswPDhw03Ws7Ozw+rVq/Xbn3/+uf4xyJ5o+/e//z18fHxM1vPx8cHvfvc7k8cRWYJY/WTp0qVYvHhxl+o+88wz8PX11W9v377d7HaJzCHm9eR6TU1NeOyxxwAAYWFhePXVV3v0/EQ3Qsy+8uuvv2LPnj0AgICAAHz55ZeQyWQ3dE6i3iBWP8nIyEBjY6N++5577umw/n333ad/rdVqkZWVZXbbRN3l7e2NgIAAUdrmd3mirmOigYgGhPr6euzbt0+/3dn8jsuWLYODgwMAoLKyEocOHTK77UuXLiE5OVm/vXLlyg7rt92fmJiIzMxMs9sm6g4x+0l3yGQyjB8/Xr+dk5NjkXaJgL7XT1atWqW/Tnz66aewtrbu0fMTmUvsvvLpp5/qXz///PP6cxP1JWL2k/r6eoNtZ2fnDuu7uLgYbOt0OrPbJuov+F2eqHuYaCCiAeHYsWNoaWkB0DoaaOzYsR3Wt7KywoQJE/Tb+/fvN7vttseGh4cbjMQ2ZdCgQRg8eLB++8CBA2a3TdQdYvaT7pJIJPrXWq3WYu0S9aV+cubMGbz//vsAgPvvvx+zZ8/usXMT3Sgx+0ppaan+aQYAuPfee80+F1FvErOfXD86PCkpqcP6Fy9e1L9WKBSIiooyu22i/oLf5Ym6h4kGIhoQUlJS9K+HDx8OuVze6TGjRo0yefyNtN32nJZom6g7xOwn3XXhwgX9a39/f4u1S9RX+olGo8EjjzwCrVYLV1dXfcKBqK8Qs68cP35cP9o6IiICnp6eqKmpwT/+8Q+MHz8ebm5usLW1RWBgIJYvX45169ZxdDaJQsx+4ufnh5EjR+q3X3311XYHb2g0Grz00kv67QceeAD29vZmt03UX/C7PFH3MNFARANCWlqa/nVgYGCXjmk7yic1NbVftk3UHf3l/+qxY8eQkZGh3+YobrKkvtJP3nvvPZw/fx4A8Pe//x0eHh49cl6iniJmXzl9+rT+9dChQ3H8+HFER0fj97//PU6dOoXKyko0NTXh8uXL+Pnnn3HPPfdg9OjRyM7ONrtNInOIfU15//33oVAoALSueTVmzBhs2LABOTk5aG5uRnZ2NtavX49Ro0bpnxKaPHky/vGPf9xQu0T9hdh9lKi/6TxdTkR0E6ioqNC/9vLy6tIx3t7e+teVlZX9sm2i7ugP/1d1Oh2ef/55/fb48eMxZsyYXm+X6Kq+0E8yMjLwxhtvAACmT5/e6ZzeRGIQs6/k5eXpX9fU1GDBggWorq4G0DrSNDo6GlqtFqdPn9bfBDp//jwmTpyI+Ph4+Pn5md02UXeIfU2ZMWMGdu7ciWXLlqGqqgrnz5/HsmXL2m334YcfxmuvvQalUnlD7RL1F2L3UaL+hk80ENGA0HaxMxsbmy4d07be9Yul9Ze2ibqjP/xfffPNN3Hq1CkAgFQqxXvvvdfrbRK1JXY/EQQBjz76KJqbm6FUKvHZZ58ZrFlC1FeI2VeuJhUAYN++faiuroa7uzv279+PM2fOYO3atfjmm2+QkpKCH3/8Ud9uSUkJ7r//frPbJeousa8pADBr1izk5ORg1apV7U7dJJPJsHDhQtxzzz1MMtCA0hf6KFF/wkQDEQ0Izc3N+tdd/XBsZWWlf93U1NQv2ybqjr7+f3XLli36UdwA8OKLL2LKlCm92ibR9cTuJ19++SXi4uIAAC+99BIiIyNv6HxEvUXMvtLQ0GCwLZPJsGXLFsycOdOo7p133omvv/5avx0XF4eDBw+a3TZRd4h9TQGArKwsrFixAqtXr4ZGo0FQUBDuuusuPPbYY7jjjjvg7e0NrVaLL7/8EtHR0Vi9evUNt0nUX/SFPkrUnzDRQEQDgrW1tf61SqXq0jEtLS36110dvdDX2ibqjr78f/X06dO45557IAgCACA2NhZvvvlmr7VH1B4x+0lhYSH+8Ic/AADCw8MNFuYk6mv6ymcvAFi6dCkmTJjQbv0777zTYBq+devWmd02UXeI/dnrxIkTGDlyJDZt2gQnJyf89NNPyMrKwrp16/D555/j559/Rl5eHj766CNYWVlBp9Nh1apVePfdd2+oXaL+Quw+StTfMNFARAOCvb29/nVXRxW0rdf2+P7UNlF39NX/q8nJybjlllv0I1THjh2LTZs26RcvJLIkMfvJU089hZqaGgDA559/bjBijqiv6SufvQBgyZIlnR7Tts6xY8fMbpuoO8TsJ1VVVVi6dClqa2shkUiwadMmLFu2zGg6PrlcjqeeegqffvqpvuzPf/4z8vPzzW6bqL/oq9+PiPoqJhqIaEBwc3PTvy4pKenSMcXFxfrXrq6u/bJtou7oi/9Xs7OzMWfOHP1CbEOGDMGOHTv4oZ1EI1Y/+fXXX7Fp0yYAwMqVKzFjxgyzzkNkKX3lsxfQeu3oTNs6BQUFZrdN1B1i9pMvvvgCRUVFAIC5c+di+vTpHdZfuXIlIiIiALSO7P7+++/Nbpuov+iL34+I+jLTK/0QEd1krn4oBoDc3NwuHXP58mX96xuZAzsiIgI7duwQpW2i7hCzn5hSUFCA2NhYFBYWAgBCQ0OxZ88eoxtIRJYkVj85d+6c/vWpU6c6nAam7SjTs2fPGtT95JNPMGrUKLNiIOoOMa8p1x/bleR02zp1dXVmt03UHWL2k507d+pfm1q/5HoSiQQzZsxAWloaACA+Pt7ston6C36XJ+oeJhqIaECIiorSv75w4QI0Gg3k8o7fAs+ePWvy+Btpu+2NIku0TdQdYvaT65WWliI2NhbZ2dkAAD8/P+zduxe+vr491gaROfpCP0lOTu5y3bq6Opw8eVK/XVtbe8PtE3WFmH1l2LBhBttdSRy0rePk5GR220TdIWY/afvkTlcHcbStd3UqP6KbGb/LE3UPp04iogFh0qRJ+rmsGxoaOh2B09LSghMnTui3Z82aZXbbbUcIpaWl6R9Rbk9hYSEyMjJMHk/Um8TsJ21VVFRg9uzZ+hFznp6e2Lt3L4KCgnrk/EQ3oq/0E6K+Tsy+MmHCBNjZ2em3u5Kca1vH39/f7LaJukPMftJ2kdrKysouHXN1KksAcHZ2Nrttov6C3+WJuoeJBiIaEOzt7REbG6vf/vrrrzusv3HjRv3INhcXF0ybNs3stgcPHmww7+9///vfDuu33T98+HCEhoaa3TZRd4jZT66qra3F/PnzceHCBf159+zZYzC1AJGYxOonr7/+OgRB6NLPqlWr9MdNnz7dYB/XdiBLEfOaYmNjg/nz5+u3f/nll06PaVuns7nqiXqKmP0kICBA/3r//v2d1hcEAQcOHNBvh4WFmd02UX/B7/JE3cNEAxENGE8++aT+9dq1a5GUlGSyXmNjI1577TX99uOPP97pI8zdafu9995rdyGp4uJivPfee/rtp5566obaJeouMftJY2Mjbr31Vv1oPgcHB+zcuRPR0dE3dF6iniZmPyHqT8TsK88++6z+9caNGw1GgV9v/fr1OHPmjH57xYoVN9Q2UXeI1U9mz56tf717924cOnSow/pr165Fenq6fnvevHlmt03Un/C7PFE3CEREA8jUqVMFAAIAISgoSEhMTDTYX15eLsyZM0dfx9XVVaiqqjJ5ruzsbH09AMKBAwfabVelUgmhoaH6uiNHjhRyc3MN6uTk5AgxMTH6OuHh4YJarb7RX5mo28ToJ83NzQbntLGxEeLi4nr4NyPqOWJdT7pi1apV+nNNnz79hs5FdKPE7Cu33367vq6Hh4fJ+uvXrxdsbGz09e666y4zf1Mi84nRT6qrqwVXV1d9PRcXF+Gnn34yqqdWq4WPPvpIsLKy0tcdN27cjf7KRD3C3M88/C5P1Ds4pIqIBpQffvgB48aNQ1FREXJychATE4Pp06cjJCQEZWVl2Lt3LxobGwEAcrkc69ev75H5RxUKBTZs2IApU6agvr4e586dQ1hYGGJjY+Hn54e8vDzs378farUaQOtI7g0bNnDkK4lCjH7y5z//GXv27NFvR0VFYf369Vi/fn2nxw4ePNhg5CqRJYh1PSHqb8TsK2vWrMHkyZORmpqKsrIyzJw5E6NHj0Z0dDS0Wi1Onz6NlJQUff0hQ4bgiy++6JG2ibpDjH7i5OSENWvW4I477oBWq0VVVRWWL1+OoKAgTJgwAU5OTigvL8fRo0dRXFysP87V1RXffPPNDbVNZI4FCxagsLDQoKzt/834+HjExMQYHbd9+3b4+vqa3S6/yxN1g9iZDiIiS0tJSTEYbWDqx8PDQ9i6dWuH5zFnBOqxY8eE4ODgDtsOCQkRjh8/3kO/LZF5LN1PVqxY0WFbHf1wxDaJRczrSUf4RAP1NWL2lYKCAoOR4O39LFiwQKisrOyh35io+8TqJ9u3bxd8fHy69JkrJiZGSEpK6sHfmqjrAgMDzfqukJ2dbXQufpcn6h1MrxHRgBMZGYmTJ09i3bp1+N///oekpCSUlJTA2dkZISEhWLJkCR566CG4u7v3eNsTJ05EYmIivvnmG6xfvx7p6emoqKiAm5sbwsPDceedd+KBBx6Avb19j7dN1B1i9hOi/oL9hKhrxOwrvr6+2L17N7Zu3Yrvv/8ep0+fRlFRESQSCXx8fDB16lTcf//9mDVrVo+3TdQdYvWTW265BVlZWfjxxx+xbds2nDt3DiUlJWhsbISDgwN8fX0xbtw43HHHHViwYAGkUi71SQMTv8sTdU4iCIIgdhBERERERERERERERNQ/MRVNRERERERERERERERmY6KBiIiIiIiIiIiIiIjMxkQDERERERERERERERGZjYkGIiIiIiIiIiIiIiIyGxMNRERERERERERERERkNiYaiIiIiIiIiIiIiIjIbEw0EBERERERERERERGR2ZhoICIiIiIiIiIiIiIiszHRQEREREREREREREREZmOigYiIiIiIiIiIiIiIzMZEAxERERERERERERERmY2JBiIiIiIiIiIiIiIiMhsTDUREREREREREREREZDYmGoiIiIiIiIiIiIiIyGxMNBARERERERERERERkdmYaCAiIiIiIiIiIiIiIrMx0UBERERERERERERERGZjooGIiIiIiIiIiIiIiMzGRAMREREREQ1oM2bMgEQiMetn5cqVYodPRERERCQ6JhqIiIiIiIiIiIiIiMhscrEDICIiIiIi6ivGjh2LcePGdbn+hAkTejEaIiIiIqL+gYkGIiIiIiKiKxYsWIDXX39d7DCIiIiIiPoVTp1ERERERERERERERERmY6KBiIiIiIiIiIiIiIjMxkQDERERERERERERERGZjYkGIiIiIiIiIiIiIiIyGxMNRERERERERERERERkNiYaiIiIiIiIiIiIiIjIbHKxAyAiIiIiIuortm/fjvLy8i7XX716NVxdXXsxIiIiIiKivk8iCIIgdhBERERERERimTFjBuLi4sw6Njs7G0FBQT0bEBERERFRP8Opk4iIiIiIiIiIiIiIyGxMNBAREREREV2xatUqCILQ5R8+zUBERERExEQDERERERERERERERHdACYaiIiIiIiIiIiIiIjIbEw0EBERERERERERERGR2ZhoICIiIiIiIiIiIiIiszHRQEREREREREREREREZmOigYiIiIiIiIiIiIiIzMZEAxERERERERERERERmY2JBiIiIiIiIiIiIiIiMptc7ACIiIiIiIj6iu3bt6O8vLzL9W1tbfHuu+/2YkRERERERH2fRBAEQewgiIiIiIiIxDJjxgzExcWZdayTkxOqq6t7NiAiIiIion6GUycREREREREREREREZHZ+EQDERERERERERERERGZjU80EBERERERERERERGR2ZhoICIiIiIiIiIiIiIiszHRQERERIzu8acAAAFrSURBVEREREREREREZmOigYiIiIiIiIiIiIiIzMZEAxERERERERERERERmY2JBiIiIiIiIiIiIiIiMhsTDUREREREREREREREZDYmGoiIiIiIiIiIiIiIyGxMNBARERERERERERERkdmYaCAiIiIiIiIiIiIiIrMx0UBERERERERERERERGZjooGIiIiIiIiIiIiIiMzGRAMREREREREREREREZmNiQYiIiIiIiIiIiIiIjIbEw1ERERERERERERERGQ2JhqIiIiIiIiIiIiIiMhsTDQQEREREREREREREZHZmGggIiIiIiIiIiIiIiKzMdFARERERERERERERERmY6KBiIiIiIiIiIiIiIjMxkQDERERERERERERERGZjYkGIiIiIiIiIiIiIiIyGxMNRERERERERERERERkNiYaiIiIiIiIiIiIiIjIbEw0EBERERERERERERGR2ZhoICIiIiIiIiIiIiIis/0/7hSM3/RphskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 436,
       "width": 781
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "E_fitter = bext.bursts_fitter(ds, 'E', binwidth=0.03)\n",
    "E_fitter.fit_histogram(model=model, pdf=False, method='nelder')\n",
    "E_fitter.fit_histogram(model=model, pdf=False, method='leastsq')\n",
    "dplot(ds, hist_fret, show_model=True, pdf=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dplot(ds, hist_fret, show_model=True, pdf=False, figsize=(6, 4.5));\n",
    "# plt.xlim(-0.1, 1.1)\n",
    "# plt.savefig('fret_hist_fit.png', bbox_inches='tight', dpi=200, transparent=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are in `E_fitter`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name             Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "p1_amplitude     26.43     0.01      inf    2.665     True     None     None\n",
      "p1_center       0.1304     -0.1      0.3 0.003665     True     None     None\n",
      "p1_fwhm         0.1247     -inf      inf 0.009604    False 2.3548200*p1_sigma     None\n",
      "p1_height          199     -inf      inf     12.7    False 0.3989423*p1_amplitude/max(1e-15, p1_sigma)     None\n",
      "p1_sigma       0.05296     0.02      0.2 0.004079     True     None     None\n",
      "p2_amplitude     97.73     0.01      inf    5.766     True     None     None\n",
      "p2_center         0.42      0.3      0.7 0.006083     True     None     None\n",
      "p2_fwhm         0.3394     -inf      inf  0.02357    False 2.3548200*p2_sigma     None\n",
      "p2_height        270.5     -inf      inf    6.868    False 0.3989423*p2_amplitude/max(1e-15, p2_sigma)     None\n",
      "p2_sigma        0.1441     0.02     0.18  0.01001     True     None     None\n",
      "p3_amplitude     46.61     0.01      inf      4.2     True     None     None\n",
      "p3_center       0.8268      0.7      1.1  0.01187     True     None     None\n",
      "p3_fwhm         0.2946     -inf      inf  0.02508    False 2.3548200*p3_sigma     None\n",
      "p3_height        148.6     -inf      inf    6.991    False 0.3989423*p3_amplitude/max(1e-15, p3_sigma)     None\n",
      "p3_sigma        0.1251     0.02      0.2  0.01065     True     None     None\n"
     ]
    }
   ],
   "source": [
    "res = E_fitter.fit_res[0]\n",
    "res.params.pretty_print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a dictionary of values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'p1_amplitude': 26.425112571102087,\n",
       " 'p1_center': 0.13042117805610684,\n",
       " 'p1_sigma': 0.052962724398903344,\n",
       " 'p2_amplitude': 97.72510948676253,\n",
       " 'p2_center': 0.4199740995810512,\n",
       " 'p2_sigma': 0.14414401587742645,\n",
       " 'p3_amplitude': 46.61031832136763,\n",
       " 'p3_center': 0.8267695386960199,\n",
       " 'p3_sigma': 0.12509313036967762,\n",
       " 'p1_fwhm': 0.12471768266902558,\n",
       " 'p1_height': 199.04744906009154,\n",
       " 'p2_fwhm': 0.3394332114684814,\n",
       " 'p2_height': 270.47033280627744,\n",
       " 'p3_fwhm': 0.2945718052571243,\n",
       " 'p3_height': 148.647871708916}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the startndard lmfit's fit report:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    ((Model(gaussian, prefix='p1_') + Model(gaussian, prefix='p2_')) + Model(gaussian, prefix='p3_'))\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 154\n",
      "    # data points      = 46\n",
      "    # variables        = 9\n",
      "    chi-square         = 7950.00085\n",
      "    reduced chi-square = 214.864888\n",
      "    Akaike info crit   = 255.005152\n",
      "    Bayesian info crit = 271.462925\n",
      "[[Variables]]\n",
      "    p1_amplitude:  26.4251126 +/- 2.66475790 (10.08%) (init = 1)\n",
      "    p1_center:     0.13042118 +/- 0.00366511 (2.81%) (init = 0.1)\n",
      "    p1_sigma:      0.05296272 +/- 0.00407857 (7.70%) (init = 0.05)\n",
      "    p2_amplitude:  97.7251095 +/- 5.76640353 (5.90%) (init = 1)\n",
      "    p2_center:     0.41997410 +/- 0.00608253 (1.45%) (init = 0.4)\n",
      "    p2_sigma:      0.14414402 +/- 0.01001133 (6.95%) (init = 0.04)\n",
      "    p3_amplitude:  46.6103183 +/- 4.20009275 (9.01%) (init = 1)\n",
      "    p3_center:     0.82676954 +/- 0.01187091 (1.44%) (init = 0.85)\n",
      "    p3_sigma:      0.12509313 +/- 0.01064950 (8.51%) (init = 0.05)\n",
      "    p1_fwhm:       0.12471768 +/- 0.00960429 (7.70%) == '2.3548200*p1_sigma'\n",
      "    p1_height:     199.047449 +/- 12.7008735 (6.38%) == '0.3989423*p1_amplitude/max(1e-15, p1_sigma)'\n",
      "    p2_fwhm:       0.33943321 +/- 0.02357489 (6.95%) == '2.3548200*p2_sigma'\n",
      "    p2_height:     270.470333 +/- 6.86806312 (2.54%) == '0.3989423*p2_amplitude/max(1e-15, p2_sigma)'\n",
      "    p3_fwhm:       0.29457181 +/- 0.02507766 (8.51%) == '2.3548200*p3_sigma'\n",
      "    p3_height:     148.647872 +/- 6.99134733 (4.70%) == '0.3989423*p3_amplitude/max(1e-15, p3_sigma)'\n",
      "[[Correlations]] (unreported correlations are < 0.500)\n",
      "    C(p2_amplitude, p2_sigma)     = 0.935\n",
      "    C(p3_amplitude, p3_sigma)     = 0.857\n",
      "    C(p2_amplitude, p3_amplitude) = -0.806\n",
      "    C(p1_amplitude, p2_sigma)     = -0.792\n",
      "    C(p2_sigma, p3_amplitude)     = -0.789\n",
      "    C(p2_amplitude, p3_center)    = 0.779\n",
      "    C(p1_amplitude, p1_sigma)     = 0.774\n",
      "    C(p2_sigma, p3_center)        = 0.768\n",
      "    C(p1_amplitude, p2_amplitude) = -0.762\n",
      "    C(p3_amplitude, p3_center)    = -0.731\n",
      "    C(p2_amplitude, p3_sigma)     = -0.713\n",
      "    C(p2_sigma, p3_sigma)         = -0.665\n",
      "    C(p3_center, p3_sigma)        = -0.660\n",
      "    C(p1_sigma, p2_sigma)         = -0.554\n",
      "    C(p2_center, p3_sigma)        = -0.553\n",
      "    C(p1_sigma, p2_amplitude)     = -0.547\n",
      "    C(p2_center, p3_amplitude)    = -0.534\n",
      "    C(p1_amplitude, p3_amplitude) = 0.531\n",
      "    C(p1_amplitude, p3_center)    = -0.518\n",
      "    C(p2_center, p3_center)       = 0.511\n"
     ]
    }
   ],
   "source": [
    "print(res.fit_report(min_correl=0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous cell reports error ranges computed from the covariance matrix.\n",
    "[More accurare confidence intervals](https://lmfit.github.io/lmfit-py/confidence.html) \n",
    "can be obtained with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paul/anaconda3/envs/Py38/lib/python3.8/site-packages/lmfit/confidence.py:317: UserWarning: Bound reached with prob(p2_sigma=0.18) = 0.992812065990089 < max(sigmas)\n",
      "  warn(errmsg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 99.73%    95.45%    68.27%    _BEST_    68.27%    95.45%    99.73%\n",
      " p1_amplitude:  -9.30991  -6.10472  -3.00234  26.42511  +2.97612  +6.05976  +9.43949\n",
      " p1_center   :  -0.01193  -0.00771  -0.00382   0.13042  +0.00397  +0.00835  +0.01354\n",
      " p1_sigma    :  -0.01374  -0.00894  -0.00442   0.05296  +0.00456  +0.00953  +0.01531\n",
      " p2_amplitude: -22.75290 -14.16482  -6.83120  97.72511  +6.80352 +13.90510 +21.30745\n",
      " p2_center   :  -0.02278  -0.01400  -0.00664   0.41997  +0.00641  +0.01298  +0.02009\n",
      " p2_sigma    :  -0.03326  -0.02232  -0.01138   0.14414  +0.01224  +0.02572      +inf\n",
      " p3_amplitude: -13.74698  -9.15839  -4.65290  46.61032  +5.09442 +11.07792 +18.71141\n",
      " p3_center   :  -0.05671  -0.03303  -0.01497   0.82677  +0.01330  +0.02587  +0.03840\n",
      " p3_sigma    :  -0.03201  -0.02169  -0.01125   0.12509  +0.01292  +0.02877  +0.04959\n"
     ]
    }
   ],
   "source": [
    "ci = res.conf_interval()\n",
    "lmfit.report_ci(ci)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tidy fit results\n",
    "\n",
    "It is convenient to put the fit results in a DataFrame for further analysis.\n",
    "A dataframe of fitted parameters is already in `E_fitter`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p1_amplitude</th>\n",
       "      <th>p1_center</th>\n",
       "      <th>p1_fwhm</th>\n",
       "      <th>p1_height</th>\n",
       "      <th>p1_sigma</th>\n",
       "      <th>p2_amplitude</th>\n",
       "      <th>p2_center</th>\n",
       "      <th>p2_fwhm</th>\n",
       "      <th>p2_height</th>\n",
       "      <th>p2_sigma</th>\n",
       "      <th>p3_amplitude</th>\n",
       "      <th>p3_center</th>\n",
       "      <th>p3_fwhm</th>\n",
       "      <th>p3_height</th>\n",
       "      <th>p3_sigma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.425113</td>\n",
       "      <td>0.130421</td>\n",
       "      <td>0.124718</td>\n",
       "      <td>199.047449</td>\n",
       "      <td>0.052963</td>\n",
       "      <td>97.725109</td>\n",
       "      <td>0.419974</td>\n",
       "      <td>0.339433</td>\n",
       "      <td>270.470333</td>\n",
       "      <td>0.144144</td>\n",
       "      <td>46.610318</td>\n",
       "      <td>0.82677</td>\n",
       "      <td>0.294572</td>\n",
       "      <td>148.647872</td>\n",
       "      <td>0.125093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p1_amplitude  p1_center   p1_fwhm   p1_height  p1_sigma  p2_amplitude  \\\n",
       "0     26.425113   0.130421  0.124718  199.047449  0.052963     97.725109   \n",
       "\n",
       "   p2_center   p2_fwhm   p2_height  p2_sigma  p3_amplitude  p3_center  \\\n",
       "0   0.419974  0.339433  270.470333  0.144144     46.610318    0.82677   \n",
       "\n",
       "    p3_fwhm   p3_height  p3_sigma  \n",
       "0  0.294572  148.647872  0.125093  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_fitter.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With [`pybroom`](http://pybroom.readthedocs.io/) we can get a \"tidy\" DataFrame\n",
    "with more complete fit results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pybroom as br"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>vary</th>\n",
       "      <th>expr</th>\n",
       "      <th>stderr</th>\n",
       "      <th>init_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p1_amplitude</td>\n",
       "      <td>26.425113</td>\n",
       "      <td>0.01</td>\n",
       "      <td>inf</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>2.664758</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p1_center</td>\n",
       "      <td>0.130421</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.3</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.003665</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p1_fwhm</td>\n",
       "      <td>0.124718</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>2.3548200*p1_sigma</td>\n",
       "      <td>0.009604</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p1_height</td>\n",
       "      <td>199.047449</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.3989423*p1_amplitude/max(1e-15, p1_sigma)</td>\n",
       "      <td>12.700874</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p1_sigma</td>\n",
       "      <td>0.052963</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.2</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.004079</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>p3_amplitude</td>\n",
       "      <td>46.610318</td>\n",
       "      <td>0.01</td>\n",
       "      <td>inf</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>4.200093</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>p3_center</td>\n",
       "      <td>0.826770</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.1</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.011871</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>p3_fwhm</td>\n",
       "      <td>0.294572</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>2.3548200*p3_sigma</td>\n",
       "      <td>0.025078</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>p3_height</td>\n",
       "      <td>148.647872</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.3989423*p3_amplitude/max(1e-15, p3_sigma)</td>\n",
       "      <td>6.991347</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>p3_sigma</td>\n",
       "      <td>0.125093</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.2</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.010650</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name       value   min  max   vary  \\\n",
       "0   p1_amplitude   26.425113  0.01  inf   True   \n",
       "1      p1_center    0.130421 -0.10  0.3   True   \n",
       "2        p1_fwhm    0.124718  -inf  inf  False   \n",
       "3      p1_height  199.047449  -inf  inf  False   \n",
       "4       p1_sigma    0.052963  0.02  0.2   True   \n",
       "..           ...         ...   ...  ...    ...   \n",
       "10  p3_amplitude   46.610318  0.01  inf   True   \n",
       "11     p3_center    0.826770  0.70  1.1   True   \n",
       "12       p3_fwhm    0.294572  -inf  inf  False   \n",
       "13     p3_height  148.647872  -inf  inf  False   \n",
       "14      p3_sigma    0.125093  0.02  0.2   True   \n",
       "\n",
       "                                           expr     stderr  init_value  \n",
       "0                                          None   2.664758        1.00  \n",
       "1                                          None   0.003665        0.10  \n",
       "2                            2.3548200*p1_sigma   0.009604         NaN  \n",
       "3   0.3989423*p1_amplitude/max(1e-15, p1_sigma)  12.700874         NaN  \n",
       "4                                          None   0.004079        0.05  \n",
       "..                                          ...        ...         ...  \n",
       "10                                         None   4.200093        1.00  \n",
       "11                                         None   0.011871        0.85  \n",
       "12                           2.3548200*p3_sigma   0.025078         NaN  \n",
       "13  0.3989423*p3_amplitude/max(1e-15, p3_sigma)   6.991347         NaN  \n",
       "14                                         None   0.010650        0.05  \n",
       "\n",
       "[15 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = br.tidy(res)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, for example, we can easily select parameters by name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "         name     value  min  max  vary  expr    stderr  init_value\n",
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       "6   p2_center  0.419974  0.3  0.7  True  None  0.006083        0.40\n",
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       "        name     value   min   max  vary  expr    stderr  init_value\n",
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       "9   p2_sigma  0.144144  0.02  0.18  True  None  0.010011        0.04\n",
       "14  p3_sigma  0.125093  0.02  0.20  True  None  0.010650        0.05"
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