0.1.Uncalibrated_Cs_Spectrum.ipynb

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   "source": [
    "# The Uncalibrated Spectrum of Cs137\n",
    "\n",
    "** **\n",
    "\n",
    "* This project uses the lmfit package. Lmfit provides curve fiting using non-linear least squares minimization and was developed by Matt Newville: https://github.com/newville."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24ad9e8-a1c3-4c57-ad13-f288557a5b39",
   "metadata": {},
   "source": [
    "## Install and import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44958a06-8e41-47a5-84e4-eccd6b6158d2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#uncomment the following line to install lmfit\n",
    "#!pip install lmfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d67ea34c-d2aa-4fec-90ef-e022cf41e6ad",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "#import libraries\n",
    "from numpy import sqrt, pi, exp, linspace, loadtxt\n",
    "from lmfit.models import GaussianModel, MoffatModel, StudentsTModel\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import matplotlib.lines as mlines\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98bcbd50-6c0b-4573-aeba-c74d5ecf0c0f",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d811099b-2529-4e30-a8f9-baed191d2d23",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#define a function to read the data files\n",
    "#the original data was previously split into subsets using spreadsheet applications\n",
    "\n",
    "def read_csv_data(n):\n",
    "    '''\n",
    "    The function reads different ranges of data points from separate csv files and returns\n",
    "    x and y pairs for each range.\n",
    "    '''\n",
    "    data_frames = []\n",
    "    x_values = []\n",
    "    y_values = []\n",
    "    \n",
    "    for i in range(1, n + 1):\n",
    "        file_path = f\"./Data/csu{i}.csv\"\n",
    "        df = pd.read_csv(file_path, sep=\" \")\n",
    "        \n",
    "        # Assuming the column indices for x and y are 0 and 1\n",
    "        x = df.iloc[:, 0:1]\n",
    "        y = df.iloc[:, 1:2]\n",
    "        \n",
    "        data_frames.append(df)\n",
    "        x_values.append(x)\n",
    "        y_values.append(y)\n",
    "    \n",
    "    return data_frames, x_values, y_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6607fe1e-555e-4ccd-80fa-3093473c9ae8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#load the data\n",
    "\n",
    "n = 11  # there are 11 csv files in the data folder\n",
    "dfs, x_vals, y_vals = read_csv_data(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fe450d11-33e1-442d-82bc-ea3d6fd47562",
   "metadata": {
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   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>0.1</th>\n",
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       "         0  0.1\n",
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   "source": [
    "#verify the data\n",
    "dfs[0].head() #preview the first dataframe from the list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7a1009e-4f6d-4049-962e-a08047ca4d06",
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   "source": [
    "## Define the composite model, fit and print the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a6e0571-857d-408b-8aab-3de221ca691d",
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   "source": [
    "#use builtin NameModel, specify prefix for each:\n",
    "\n",
    "mod2 = MoffatModel(prefix='g2_') \n",
    "\n",
    "mod4 = GaussianModel(prefix='g4_') \n",
    "       \n",
    "mod6 = GaussianModel(prefix='g6_') \n",
    "       \n",
    "mod8 = GaussianModel(prefix='g8_') \n",
    "      \n",
    "mod10 = GaussianModel(prefix='g10_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "66aece87-da38-4360-be5a-b586e85e2d2d",
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   "source": [
    "#initialize the parameters to be optimized\n",
    "pars2  = mod2.make_params(g2_amplitude=1678, g2_center=44.3862, g2_sigma=4.68938)\n",
    "                         \n",
    "pars4  = mod4.make_params(g4_amplitude=583.8, g4_center=100.1304, g4_sigma=8.46)\n",
    "                       \n",
    "pars6  = mod6.make_params(g6_amplitude=478.657, g6_center=240.4850, g6_sigma=21.898)\n",
    "                        \n",
    "pars8  = mod8.make_params(g8_amplitude=364.214, g8_center=436.2977, g8_sigma=21.94)\n",
    "                        \n",
    "pars10 = mod10.make_params(g10_amplitude=1836.15, g10_center=641.54, g10_sigma=24.5728)                       \n",
    "                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9876839d-8cdc-49d4-9641-43b1e646710c",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "[[Model]]\n",
      "    Model(moffat, prefix='g2_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 36\n",
      "    # data points      = 20\n",
      "    # variables        = 4\n",
      "    chi-square         = 16.1885755\n",
      "    reduced chi-square = 1.01178597\n",
      "    Akaike info crit   = 3.77147004\n",
      "    Bayesian info crit = 7.75439913\n",
      "    R-squared          = 0.99999687\n",
      "[[Variables]]\n",
      "    g2_amplitude:  1704.05966 +/- 39.6638942 (2.33%) (init = 1678)\n",
      "    g2_center:     43.7999655 +/- 0.11515168 (0.26%) (init = 44.3862)\n",
      "    g2_sigma:      7.24523198 +/- 1.15609445 (15.96%) (init = 4.68938)\n",
      "    g2_beta:       1.50129232 +/- 0.31589092 (21.04%) (init = 1)\n",
      "    g2_fwhm:       11.0998255 +/- 0.42484909 (3.83%) == '2*g2_sigma*sqrt(2**(1.0/max(1e-3, g2_beta))-1)'\n",
      "    g2_height:     1704.05966 +/- 39.6638942 (2.33%) == 'g2_amplitude'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g2_sigma, g2_beta)      = +0.9840\n",
      "    C(g2_amplitude, g2_sigma) = -0.5652\n",
      "    C(g2_amplitude, g2_beta)  = -0.4677\n",
      "------------------------------------------------------\n",
      "[[Model]]\n",
      "    Model(gaussian, prefix='g4_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 42\n",
      "    # data points      = 32\n",
      "    # variables        = 3\n",
      "    chi-square         = 29.5128204\n",
      "    reduced chi-square = 1.01768346\n",
      "    Akaike info crit   = 3.41084339\n",
      "    Bayesian info crit = 7.80805110\n",
      "    R-squared          = 0.99988892\n",
      "[[Variables]]\n",
      "    g4_amplitude:  24884.9834 +/- 978.833353 (3.93%) (init = 583.8)\n",
      "    g4_center:     102.071953 +/- 0.62348440 (0.61%) (init = 100.1304)\n",
      "    g4_sigma:      19.0162029 +/- 0.97861811 (5.15%) (init = 8.46)\n",
      "    g4_fwhm:       44.7797344 +/- 2.30446948 (5.15%) == '2.3548200*g4_sigma'\n",
      "    g4_height:     522.063878 +/- 13.1619132 (2.52%) == '0.3989423*g4_amplitude/max(1e-15, g4_sigma)'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g4_amplitude, g4_sigma) = +0.8793\n",
      "------------------------------------------------------\n",
      "[[Model]]\n",
      "    Model(gaussian, prefix='g6_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 39\n",
      "    # data points      = 67\n",
      "    # variables        = 3\n",
      "    chi-square         = 64.8993151\n",
      "    reduced chi-square = 1.01405180\n",
      "    Akaike info crit   = 3.86567817\n",
      "    Bayesian info crit = 10.4797560\n",
      "    R-squared          = 0.99935067\n",
      "[[Variables]]\n",
      "    g6_amplitude:  69154.8831 +/- 2667.28045 (3.86%) (init = 478.657)\n",
      "    g6_center:     240.434069 +/- 1.19238152 (0.50%) (init = 240.485)\n",
      "    g6_sigma:      64.0402502 +/- 2.90062984 (4.53%) (init = 21.898)\n",
      "    g6_fwhm:       150.803260 +/- 6.83046116 (4.53%) == '2.3548200*g6_sigma'\n",
      "    g6_height:     430.804197 +/- 4.58023442 (1.06%) == '0.3989423*g6_amplitude/max(1e-15, g6_sigma)'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g6_amplitude, g6_sigma)  = +0.9806\n",
      "    C(g6_amplitude, g6_center) = +0.2541\n",
      "    C(g6_center, g6_sigma)     = +0.2165\n",
      "------------------------------------------------------\n",
      "[[Model]]\n",
      "    Model(gaussian, prefix='g8_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 53\n",
      "    # data points      = 57\n",
      "    # variables        = 3\n",
      "    chi-square         = 57.3287311\n",
      "    reduced chi-square = 1.06164317\n",
      "    Akaike info crit   = 6.32778676\n",
      "    Bayesian info crit = 12.4569406\n",
      "    R-squared          = 0.99926567\n",
      "[[Variables]]\n",
      "    g8_amplitude:  49600.4459 +/- 2410.37813 (4.86%) (init = 364.214)\n",
      "    g8_center:     422.838655 +/- 1.43880785 (0.34%) (init = 436.2977)\n",
      "    g8_sigma:      57.8111288 +/- 3.19999692 (5.54%) (init = 21.94)\n",
      "    g8_fwhm:       136.134800 +/- 7.53541679 (5.54%) == '2.3548200*g8_sigma'\n",
      "    g8_height:     342.282129 +/- 3.78709137 (1.11%) == '0.3989423*g8_amplitude/max(1e-15, g8_sigma)'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g8_amplitude, g8_sigma)  = +0.9857\n",
      "    C(g8_amplitude, g8_center) = -0.5607\n",
      "    C(g8_center, g8_sigma)     = -0.5414\n",
      "------------------------------------------------------\n",
      "[[Model]]\n",
      "    Model(gaussian, prefix='g10_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 63\n",
      "    # data points      = 120\n",
      "    # variables        = 3\n",
      "    chi-square         = 117.732127\n",
      "    reduced chi-square = 1.00625750\n",
      "    Akaike info crit   = 3.71042332\n",
      "    Bayesian info crit = 12.0728986\n",
      "    R-squared          = 0.99999728\n",
      "[[Variables]]\n",
      "    g10_amplitude:  108140.187 +/- 1071.06251 (0.99%) (init = 1836.15)\n",
      "    g10_center:     638.540552 +/- 0.28312335 (0.04%) (init = 641.54)\n",
      "    g10_sigma:      24.7796145 +/- 0.28384485 (1.15%) (init = 24.5728)\n",
      "    g10_fwhm:       58.3515310 +/- 0.66840353 (1.15%) == '2.3548200*g10_sigma'\n",
      "    g10_height:     1741.01561 +/- 17.2086352 (0.99%) == '0.3989423*g10_amplitude/max(1e-15, g10_sigma)'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g10_amplitude, g10_sigma) = +0.5800\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#fit with weights=1./yerr\n",
    "result2 = mod2.fit(y_vals[1], pars2, x=x_vals[1], weights = 1 / (x_vals[1] * 1.42))\n",
    "result4 = mod4.fit(y_vals[3], pars4, x=x_vals[3], weights = 1 / (x_vals[3] * 0.435))\n",
    "result6 = mod6.fit(y_vals[5], pars6, x=x_vals[5], weights = 1 / (x_vals[5] * 0.1))\n",
    "result8 = mod8.fit(y_vals[7], pars8, x=x_vals[7], weights = 1 / (x_vals[7] * 0.043))\n",
    "result10 = mod10.fit(y_vals[9], pars10, x=x_vals[9], weights = 1 / (x_vals[9] * 0.12))\n",
    "\n",
    "#print the Fit Statistics and parameters\n",
    "print('------------------------------------------------------')\n",
    "print(result2.fit_report())\n",
    "print('------------------------------------------------------')\n",
    "print(result4.fit_report())\n",
    "print('------------------------------------------------------')\n",
    "print(result6.fit_report())\n",
    "print('------------------------------------------------------')\n",
    "print(result8.fit_report())\n",
    "print('------------------------------------------------------')\n",
    "print(result10.fit_report())\n",
    "print('------------------------------------------------------')"
   ]
  },
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   "execution_count": 141,
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ZswbHjh3DyZMnMX78eKSkpMjOHT9+POzs7HTDkH744QfY2dlh/Pjx+d6jVq1a2Lt3Lzw9PfHWW2+hVq1aqFWrFr777rsC32/WsbS0NCQkJCAyMhLp6elYvHhxjvfft2/fIr//wsjtz1xRubq6yh5bW1vne9zwe68v6/+J3Ory8fGBVqvVDbErimrVquHWrVuFOlelUuHQoUNo2rQpPvroIzRs2BA+Pj749NNPdUMxs4ZLZn117969yDVlcXFxQcuWLdG1a1d89NFHWL58ObZv355jrlKWP//8E+Hh4Zx8TmaNq2ARmSl3d3cAwOLFi/P8KVxuHzoNX59bSLGxsUFqamqO44Yf2Asra1Ln/fv34evrm+s5//77L86dO4fg4GCMGTNGd/z69evFumeW3CZaZx0zDE65/aR9zZo1GD16NObMmSM7/uTJEzg7Oxd4/6zv84wZM2STbPVlLQLg5uaWb725iY6O1t0jL25ubjhx4kSB1y1KrR4eHgVOCF6zZg1q1KiBDRs2yL63uf3ZUqlUGDNmDH766SdMnz4dQUFBGDlyZKG+x8899xyee+45ZGRk4NSpU1i8eDGmTJmCKlWqYPjw4Xm+36xj1tbWcHBwgEKhgKWlJQIDA/Ps1tWoUaPQ778wcvszZ2tri9jY2BzHC7MYQ0ll/T8RFhaW47mHDx/CwsICLi4uRb5ur169sHjxYhw/frxQXYPGjRtj/fr1EELg/PnzCA4OxmeffQY7Ozt8+OGHmDVrlmyPH0dHxyLXlJfWrVsDyFzlKzcrV66EtbU1AgMDS+2eRBUNOyBEZqpDhw5wdnbGxYsX0bJly1y/sn7imZus4Rg3btzI8Zyfnx/Onz8vO7Z//34kJCQUq9aePXvC0tISS5YsyfOcrA9iWd2TLMuWLSvWPbPs27dP95N9AMjIyMCGDRtQq1atQv0EW5KkHDX98ccfOYY9GXZ9stStWxe1a9fGuXPn8vx9yvrw1LVr1zzrzc3Dhw+RkpKS6xKh+rp27Yr4+Hhs375ddtxw+eWi1NqnTx8cOHAg15XZskiSBGtra9mH7PDw8Dx/sjx58mQ8efIEQ4YMQUxMTJE3kbS0tESbNm10k6MN99TZvHmz7Kf/8fHx2LFjB5577jlYWlpCqVSia9euOHv2LJ599tlc33/WB/Q+ffrg6tWrOfby0ZfXn4mC+Pn54erVq7KgFhkZiaNHjxbpOsVRt25dPPPMM1i3bp1sVbLExET89ttvupWximrq1Kmwt7fHm2++mWu4EkLkugyvJElo0qQJFi5cCGdnZ93vqZ+fn+z3pTT3wjlw4ACAzKFghsLDw/Hnn3/ihRdeyPEDDCJzwg4IkZlycHDA4sWLMWbMGERFRWHIkCHw9PREREQEzp07h4iIiHw/8FetWhU1a9bE8ePHMXnyZNlzgYGBmDlzJj755BN07twZFy9exPfff5/r8pmF4efnh48++giff/45kpOTMWLECKhUKly8eBFPnjzB7NmzUa9ePdSqVQsffvghhBBwdXXFjh07sGfPnmLdM4u7uzu6deuGmTNnwt7eHj/++CMuX76cYynevAQEBCA4OBj16tXDs88+i9OnT+Prr7/OEV5q1aoFOzs7rF27FvXr14eDgwN8fHzg4+ODZcuWoU+fPujVqxfGjh2LZ555BlFRUbh06RLOnDmDTZs2AchcQWv79u3o1q0bPvnkEyiVSvzwww95zuXJGp5W0Epmo0ePxsKFCzF69Gh8+eWXqF27Nv7880/s2rUrx7mFrfWzzz7Dzp070alTJ3z00Udo3LgxYmJiEBISgmnTpqFevXoICAjA5s2b8eabb2LIkCG4d+8ePv/8c3h7e+PatWs57l2nTh307t0bO3fuRMeOHdGkSZMCf3+WLl2K/fv3o1+/fqhWrRpSUlKgVqsBIMf8DEtLSzz//POYNm0atFot5s2bh7i4ONnqVt999x06duyI5557Dm+88Qb8/PwQHx+P69evY8eOHbrAMWXKFGzYsAEDBw7Ehx9+iNatWyM5ORmHDh1CQEAAunbtCkdHR1SvXh3btm1D9+7d4erqCnd39zyXi84SGBiIZcuWYdSoUZg4cSIiIyMxf/58ODk5Ffj9KCkLCwvMnz8fL7/8MgICAvDaa68hNTUVX3/9NWJiYjB37txiXbdGjRpYv349hg0bhqZNm+o2IgQyVx5Tq9UQQmDQoEH4/fff8eOPP+KFF15AzZo1IYTA5s2bERMTg+eff77Ae0VERODQoUMAoFsue+fOnfDw8ICHh4duz5Jly5bh8OHD6NmzJ3x9fZGYmIjDhw9j8eLFaN++PQYOHJjj2qtWrUJ6ejqHXxEZcQI8ERVTYVbBMlxZJmvFG8NVlg4dOiT69esnXF1dhUKhEM8884zo169foVammTlzpnBxcREpKSmy46mpqeL9998Xvr6+ws7OTnTu3FmEhobmuQrWyZMnc30PBw4ckB1fvXq1aNWqlbC1tRUODg6iWbNmsvdz8eJF8fzzzwtHR0fh4uIiXnrpJXH37t0cKwkVZRWst956S/z444+iVq1aQqFQiHr16om1a9fKzsvrfQghRHR0tJgwYYLw9PQUSqVSdOzYURw+fDjXlYp++eUXUa9ePaFQKHLUfO7cOTF06FDh6ekpFAqF8PLyEt26dRNLly6VXePIkSOibdu2wsbGRnh5eYn33ntPLF++PNeVfAIDA0Xjxo1z1Jyb+/fvi8GDBwsHBwfh6OgoBg8eLI4ePZrrn6nC1nrv3j0xfvx44eXlJRQKhfDx8RFDhw4Vjx490p0zd+5c4efnJ2xsbET9+vXFihUrdCtA5SY4ODjP1dlyc+zYMTFo0CBRvXp1YWNjI9zc3ETnzp3F9u3bdedk/b8zb948MXv2bFG1alVhbW0tmjVrJnbt2pXjmrdu3RLjx48XzzzzjFAoFMLDw0O0b99efPHFF7LzoqOjxTvvvCOqVasmFAqF8PT0FP369ROXL1/WnbN3717RrFkzYWNjIwDo/v/J+h5ERETk+r5WrVol6tevL2xtbUWDBg3Ehg0b8lwF6+uvv5a9Nq+/Q/L7c25o69atok2bNsLW1lbY29uL7t27iyNHjhTqPvm5ceOGePPNN4W/v7+wsbERdnZ2okGDBmLatGm6P9+XL18WI0aMELVq1RJ2dnZCpVKJ1q1bi+Dg4ELdI6uu3L70/589cuSICAgIED4+PsLa2loolUrRpEkT8fnnn4vExMRcr12nTh3h5+eX78psROZAEsJg5yYiokJ6+PAhatSogdWrV3MzrQomLi4OPj4+WLhwISZOnGjsckpN1kpLt2/fhkKhKJVr3r59GzVq1MDXX3+N6dOnl8o1iYjMGeeAEFGx+fj4YMqUKfjyyy/zXHKSTNPChQtRrVo1jBs3ztillFhqaiqOHTuG7777Dlu2bMF7771XauGDiIhKH+eAEFGJ/N///R+USiUePHiQ5wpVZHqcnJwQHBwMK6uK/89AWFgY2rdvDycnJ7z22mt4++23jV0SERHlg0OwiIiIiIio3HAIFhERERERlRsGECIiIiIiKjcMIEREREREVG4YQIiIiIiIqNwwgBARERERUblhACEiIiIionLDAEJEREREROWGAYSIiIiIiMoNAwgREREREZUbBhAiIiIiIio3DCBERERERFRuGECIiIiIiKjcMIAQEREREVG5YQAhIiIiIqJywwBCRERERETlhgGEiIjIiI4cOYLGjRtDoVDghRdeyPNYeejSpQumTJlSbvcjIvPEAEJEZOLGjh0LSZIgSRIUCgWqVKmC559/Hmq1GlqtttDXCQ4OhrOzc9kVagayfi9ef/31HM+9+eabkCQJY8eOLdI1p02bhqZNm+LWrVsIDg7O81hhaitMWNH/86T/df36dWzevBmff/657lw/Pz8sWrSoSO+HiKggDCBERBVA7969ERYWhtu3b2Pnzp3o2rUr3nnnHQQEBCA9Pd3Y5ZkVX19frF+/HsnJybpjKSkp+OWXX1CtWrUiX+/GjRvo1q0bqlatqguIuR0rTVl/nvS/atSoAVdXVzg6Opb6/YiI9ElCCGHsIoiIypsQAhqNce6tUACSJBX6/LFjxyImJgZbt26VHd+/fz+6d++OFStW4JVXXsG3336LoKAg3Lx5E66urujfvz/mz58PBwcHHDx4EF27dpW9/tNPP8WsWbOwZs0aLFq0CFeuXIG9vT26deuGRYsWwdPTszTebuHExgIXLpTf/Qw1bgyoVAWelvV7cfPmTXzwwQd4+eWXAQDr1q3D3LlzUbNmTTg7O+u6FqmpqXjvvfewfv16xMXFoWXLlli4cCFatWqF27dvo0aNGrLrBwUFYdy4cTmOBQYG4tVXX8X+/fsRHh6OatWq4c0338Q777wDAJg1axZmz54te92BAwfQpUuXPN+D4Z8nIHMIVtOmTbFo0SJ06dIFhw4dkj3PjwxEVBqsjF0AEZExaDTA5/8zzoepmZMlWFuX/DrdunVDkyZNsHnzZrzyyiuwsLDA//73P/j5+eHWrVt488038f777+PHH39E+/btsWjRInzyySe4cuUKAMDBwQEAkJaWhs8//xx169bF48ePMXXqVIwdOxZ//vlnyYssrAsXgOeeK7/7GTp8GOjYsdCnjxs3DkFBQboAolarMX78eBw8eFB23vvvv4/ffvsNq1atQvXq1TF//nz06tUL169fh6+vL8LCwlC3bl189tlnGDZsGBwdHdG7d2/ZMZVKBa1Wi6pVq2Ljxo1wd3fH0aNH8eqrr8Lb2xtDhw7F9OnTcenSJcTFxSEoKAgA4OrqWqJvyebNm9GkSRO8+uqrmDhxYomuRUSkjwGEiKgCq1evHs6fPw8AssnDNWrUwOeff4433ngDP/74I6ytraFSqSBJEry8vGTXGD9+vO7XNWvWxP/+9z+0bt0aCQkJupBCcoGBgZgxYwZu374NSZJw5MgRrF+/XhZAEhMTsWTJEgQHB6NPnz4AgBUrVmDPnj1YuXIl3nvvPXh5eUGSJKhUKt3vi729fY5jAGQdjho1auDo0aPYuHEjhg4dCgcHB9jZ2SE1NTXH729ufv/9d9nvbZ8+fbBp0ybZOa6urrC0tISjo2OhrklEVFgMIERklhSKzE6Ese5dWoQQuuFcBw4cwJw5c3Dx4kXExcUhPT0dKSkpSExMhL29fZ7XOHv2LGbNmoXQ0FBERUXpJrbfvXsXDRo0KL1iKxF3d3f069cPq1atghAC/fr1g7u7u+ycGzduQKPRoEOHDrpjCoUCrVu3xqVLl4p8z6VLl+Knn37CnTt3kJycjLS0NDRt2rRY9Xft2hVLlizRPc7vzwcRUWljACEisyRJpTMMytguXbqEGjVq4M6dO+jbty9ef/11fP7553B1dcXff/+NCRMmQJPPZJfExET07NkTPXv2xJo1a+Dh4YG7d++iV69eSEtLK7830rhx5jAoY2ncuMgvGT9+PCZNmgQA+OGHH3I8nzVfwnC+j35oLKyNGzdi6tSpWLBgAdq1awdHR0d8/fXX+Oeff4pcN5AZOPz9/Yv1WiKikmIAISKqoPbv348LFy5g6tSpOHXqFNLT07FgwQJYWGQucLhx40bZ+dbW1sjIyJAdu3z5Mp48eYK5c+fC19cXAHDq1KnyeQP6VKoizcEwBb1799aFtF69euV43t/fH9bW1vj7778xcuRIAIBGo8GpU6eKvNfG4cOH0b59e7z55pu6Yzdu3JCdk9vvb0mVxTWJiLgMLxFRBZCamorw8HA8ePAAZ86cwZw5czBw4EAEBARg9OjRqFWrFtLT07F48WLcvHkTP//8M5YuXSq7hp+fHxISErBv3z48efIESUlJqFatGqytrXWv2759u2wfCMqbpaUlLl26hEuXLsHS0jLH8/b29njjjTfw3nvvISQkBBcvXsTEiRORlJSECRMmFOle/v7+OHXqFHbt2oWrV69i5syZOHnypOwcPz8/nD9/HleuXMGTJ0/y7XwVlp+fH/766y88ePAAT548KfH1iIgABhAiogohJCQE3t7e8PPzQ+/evXHgwAH873//w7Zt22BpaYmmTZvi22+/xbx589CoUSOsXbsWX331lewa7du3x+uvv45hw4bBw8MD8+fPh4eHB4KDg7Fp0yY0aNAAc+fOxTfffGOkd1nxODk5wcnJKc/n586di8GDByMwMBDNmzfH9evXsWvXLri4uBTpPq+//jpefPFFDBs2DG3atEFkZKSsGwIAEydORN26ddGyZUt4eHjgyJEjxXpP+j777DPcvn0btWrVgoeHR4mvR0QEcB8QIiIiIiIqR+yAEBERERFRuWEAISIiIiKicsMAQkRERERE5YYBhIiIiIiIyg0DCBERERERlRujBpCvvvoKrVq1gqOjIzw9PfHCCy/gypUrsnOEEJg1axZ8fHxgZ2eHLl264L///pOdk5qairfffhvu7u6wt7fHgAEDcP/+fdk50dHRCAwMhEqlgkqlQmBgIGJiYsr6LRIRERERkR6jBpBDhw7hrbfewvHjx7Fnzx6kp6ejZ8+eSExM1J0zf/58fPvtt/j+++9x8uRJeHl54fnnn0d8fLzunClTpmDLli1Yv349/v77byQkJCAgIEC2e+vIkSMRGhqKkJAQhISEIDQ0FIGBgeX6fomIiIiIzJ1J7QMSEREBT09PHDp0CJ06dYIQAj4+PpgyZQo++OADAJndjipVqmDevHl47bXXEBsbCw8PD/z8888YNmwYAODhw4fw9fXFn3/+iV69euHSpUto0KABjh8/jjZt2gAAjh8/jnbt2uHy5cuoW7eu0d4zEREREZE5sTJ2AfpiY2MBAK6urgCAW7duITw8HD179tSdY2Njg86dO+Po0aN47bXXcPr0aWg0Gtk5Pj4+aNSoEY4ePYpevXrh2LFjUKlUuvABAG3btoVKpcLRo0dzDSCpqalITU3VPdZqtYiKioKbmxskSSr1905EREREVN6EEIiPj4ePjw8sLMpncJTJBBAhBKZNm4aOHTuiUaNGAIDw8HAAQJUqVWTnVqlSBXfu3NGdY21tDRcXlxznZL0+PDwcnp6eOe7p6empO8fQV199hdmzZ5fsTRERERERVQD37t1D1apVy+VeJhNAJk2ahPPnz+Pvv//O8Zxhx0EIUWAXwvCc3M7P7zozZszAtGnTdI9jY2NRrVo13Lt3D05OTvnem4iIiIioIoiLi4Ovry8cHR3L7Z4mEUDefvttbN++HX/99ZcseXl5eQHI7GB4e3vrjj9+/FjXFfHy8kJaWhqio6NlXZDHjx+jffv2unMePXqU474RERE5uitZbGxsYGNjk+O4k5MTAwgRERERVSrlOcXAqKtgCSEwadIkbN68Gfv370eNGjVkz9eoUQNeXl7Ys2eP7lhaWhoOHTqkCxctWrSAQqGQnRMWFoZ///1Xd067du0QGxuLEydO6M75559/EBsbqzuHiIiIiIjKnlE7IG+99RbWrVuHbdu2wdHRUTcfQ6VSwc7ODpIkYcqUKZgzZw5q166N2rVrY86cOVAqlRg5cqTu3AkTJuDdd9+Fm5sbXF1dMX36dDRu3Bg9evQAANSvXx+9e/fGxIkTsWzZMgDAq6++ioCAAK6ARURERERUjowaQJYsWQIA6NKli+x4UFAQxo4dCwB4//33kZycjDfffBPR0dFo06YNdu/eLRuntnDhQlhZWWHo0KFITk5G9+7dERwcDEtLS905a9euxeTJk3WrZQ0YMADff/992b5BIiIiIiKSMal9QExZXFwcVCoVYmNjOQeEiIiIiCoFY3zGNeocECIiIiIiMi8MIEREREREVG4YQIiIiIiIqNwwgBARERERUblhACEiIiIionLDAEJEREREROWGAYSIiIiIiMoNAwgREREREZUbBhAiIiIiIio3DCBERERERFRuGECIiIiIiKjcMIAQEREREVG5YQAhIiIiIqJywwBCRERERETlhgGEiIiIiIjKDQMIERERERGVGwYQMgljx46FJEm6Lzc3N/Tu3Rvnz58v0XXPnTsHGxsbbN++XXb8t99+g62tLf79998SXZ+IiIiIioYBhExG7969ERYWhrCwMOzbtw9WVlYICAgo0TWbNGmCmTNn4tVXX0VkZCQA4PHjx3j99dcxe/ZsNGrUqFDX0Wg0JaqDiIiIiDIxgJDJsLGxgZeXF7y8vNC0aVN88MEHuHfvHiIiInTnfPDBB6hTpw6USiVq1qyJmTNnFhgOZsyYgWrVquGtt94CALz22muoXbs2pk+fnudrZs2ahaZNm0KtVqNmzZqwsbGBEAIhISHo2LEjnJ2d4ebmhoCAANy4cUP3um7dumHSpEmya0VGRsLGxgb79+8vzreFiIiIqFJhACGTlJCQgLVr18Lf3x9ubm66446OjggODsbFixfx3XffYcWKFVi4cGG+17K0tMSqVauwbds2jBw5Ert27UJwcDAsLS3zfd3169exceNG/PbbbwgNDQUAJCYmYtq0aTh58iT27dsHCwsLDBo0CFqtFgDwyiuvYN26dUhNTdVdZ+3atfDx8UHXrl2L+d0gIiIiqjysjF0AUZbff/8dDg4OADI/6Ht7e+P333+HhUV2Tv6///s/3a/9/Pzw7rvvYsOGDXj//ffzvXb9+vUxZcoUzJ07F/PmzUOdOnUKrCctLQ0///wzPDw8dMcGDx4sO2flypXw9PTExYsX0ahRIwwePBhvv/02tm3bhqFDhwIAgoKCdHNciIiIiMwdOyBkMrp27YrQ0FCEhobin3/+Qc+ePdGnTx/cuXNHd86vv/6Kjh07wsvLCw4ODpg5cybu3r1b4LUTEhKwYcMGKJVKHD58WPacg4OD7uv111/XHa9evbosfADAjRs3MHLkSNSsWRNOTk6oUaMGAOhqsLGxwahRo6BWqwEAoaGhOHfuHMaOHVus7wkRERFRZcMOCJkMe3t7+Pv76x63aNECKpUKK1aswBdffIHjx49j+PDhmD17Nnr16gWVSoX169djwYIFBV77vffeg7W1NY4ePYp27dph9erVGD16NADohlcBgJOTk6weQ/3794evry9WrFgBHx8faLVaNGrUCGlpabpzXnnlFTRt2hT379+HWq1G9+7dUb169eJ8S4iIyk1svEBkNODmAqgc2bElorLDAEImS5IkWFhYIDk5GQBw5MgRVK9eHR9//LHuHP3uSF727NmDn376CYcPH0aTJk0wZ84cTJkyBc8//zy8vb1loSc/kZGRuHTpEpYtW4bnnnsOAPD333/nOK9x48Zo2bIlVqxYgXXr1mHx4sWFuj4RkbGcviCwbbeAEIAkAQN7Ai0aM4QQUdngECwyGampqQgPD0d4eDguXbqEt99+GwkJCejfvz8AwN/fH3fv3sX69etx48YN/O9//8OWLVvyvWZcXBwmTJiA6dOno23btgCAyZMno2HDhnj11VeLVJ+Liwvc3NywfPlyXL9+Hfv378e0adNyPfeVV17B3LlzkZGRgUGDBhXpPkRE5Sk2Pjt8AIAQwLbdArHxwriFEVGlxQBCJiMkJATe3t7w9vZGmzZtcPLkSWzatAldunQBAAwcOBBTp07FpEmT0LRpUxw9ehQzZ87M95pTpkyBSqXC7NmzdccsLCwQFBSE/fv3Y/Xq1YWuz8LCAuvXr8fp06fRqFEjTJ06FV9//XWu544YMQJWVlYYOXIkbG1tC30PIqLyFhkNXfjIIgQQFW2ceoio8pOEMPxrh3ITFxcHlUqF2NhY2TwBotzcu3cPfn5+OHnyJJo3b27scoiI8hQbL7BguZCFEEkC3n1V4lwQIjNgjM+47IAQlSKNRoO7d+/igw8+QNu2bRk+iMjkqRwlDOwpDxoDezJ8EFHZYQAhKkVZE+VPnz6NpUuXGrscIqJC0Z9w7urMCehEVLa4ChZRKerSpQs4qpGIKjIFPxkQURljB4SIiIh0LCwy54XcvMuVsIiobPDnHERERKSTnALdpHTuCUJEZYEdEKr0Tp48iQsXLhi7DCIik5Wekd3piIkD9wQhojLFAEKVWkZGBgYPHoxVq1YZuxQiIpOVlpb3c9wThIhKGwMIVWr79u3DvXv3MGTIEGOXQkRkslLzCSCSBLi6lF8tRFT5MYBQpaZWq1G/fn20adPG2KUQEZks/Q6Ik4P8Oe4JQkSljQGEKq2oqChs2bIF48ePhyTxH08iorykarJ/bWWwPA0noBNRaWMAoUrrl19+QUZGBgIDA41dChGRSdPvgKSnA9YK49VCRJUfAwhVWmq1GgEBAahSpYqxSyEiMmn6c0A0BgEkLY0rYBFR6WIAoUopNDQUZ86cwfjx441dChGRyTPsgOiLTyzfWoio8mMAoUopKCgIVapUQZ8+fYxdChGRyTPsgKToPQ57zA4ImZ7bt29DkiSEhoYauxQqBgYQqnRSU1OxZs0ajB49GgoFBzITERVEfxI6IO+CbNgBnL7AEGJMQgj89ttvGDhwIKpVqwZPT0+0atUKn332GaKjc9+k5cGDBxg1ahTc3NygVCrRtGlTnD59WnbOpUuXMGDAAKhUKjg6OqJt27a4e/eu7vnly5ejS5cucHJygiRJiImJKVS9Bd177NixkCRJ9tW2bVvd81nhIrevTZs2FeE7l7u0tDS4u7vjiy++yPX5r776Cu7u7kjLb4McKhEGEKp0tm/fjqioKIwbN87YpRARVQjxBex0zt3QjScyMhI9evTA+++/j86dO2PNmjXYv38/Zs6ciYsXL6JBgwY4fvy47DXR0dHo0KEDFAoFdu7ciYsXL2LBggVwdnbWnXPjxg107NgR9erVw8GDB3Hu3DnMnDkTtra2unOSkpLQu3dvfPTRR4WutzD3BoDevXsjLCxM9/Xnn3/qnvP19ZU9FxYWhtmzZ8Pe3r5URjZYW1tj1KhRCA4OhhA5/1wHBQUhMDAQ1tbWJb4X5Y4BhCodtVqNtm3bon79+sYuhYjI5J2+IPBPaP7nVJTd0OPj4/Hyyy/D3t4e3t7eWLhwIbp06YIpU6YAANasWYOWLVvC0dERXl5eGDlyJB4/fqx7/cGDByFJEnbt2oVmzZrBzs4O3bp1w+PHj7Fz507Ur18fTk5OGDFiBJKSknSv69KlC95++21MmTIFLi4uqFKlCpYvX47ExESMGzcOjo6OqFWrFnbu3Kl7TUZGBiZMmIAaNWrAzs4OdevWxXfffSd7P+np6Rg4cCD8/Pxw+fJlTJs2DZ06dUKjRo0wYMAArF+/Hj/99BMGDRqEsLAw3evmzZsHX19fBAUFoXXr1vDz80P37t1Rq1Yt3Tkff/wx+vbti/nz56NZs2aoWbMm+vXrB09PT905U6ZMwYcffijrThSkMPcGABsbG3h5eem+XF1ddc9ZWlrKnvPy8sKWLVswbNgwODg4GN4SAKDVajFx4kTUqVMHd+7cAQDs2LEDLVq0gK2tLWrWrInZs2cj/Wl7b8KECbhx4wb++usv2XUOHz6Ma9euYcKECYV+z1R0DCBUqdy7dw+7du3i5HMiokKIjRfYtrvgzkZF2Q192rRpOHLkCLZv3449e/bg8OHDOHPmjO75tLQ0fP755zh37hy2bt2KW7duYezYsTmuM2vWLHz//fc4evQo7t27h6FDh2LRokVYt24d/vjjD+zZsweLFy+WvWbVqlVwd3fHiRMn8Pbbb+ONN97ASy+9hPbt2+PMmTPo1asXAgMDdcFFq9WiatWq2LhxIy5evIhPPvkEH330ETZu3Ki7ZlBQEIQQWL58OYQQmDp1Knx9fdGgQQNs3LgRDg4O6NevH1555RXMnj1b97rt27ejZcuWeOmll+Dp6YlmzZphxYoVuue1Wi3++OMP1KlTB7169YKnpyfatGmDrVu3lvj3oKB7Zzl48CA8PT1Rp04dTJw4URYEDZ0+fRqhoaF5hoK0tDQMHToUp06dwt9//43q1atj165dGDVqFCZPnoyLFy9i2bJlCA4OxpdffgkAaNy4MVq1aoWgoCDZtdRqNVq3bo1GjRqV4LtABRJUKLGxsQKAiI2NNXYplI8vvvhC2NnZ8feJiKgQdh7IEP/3dcFfp85rjV1qgeLi4oRCoRCbNm3SHYuJiRFKpVK88847ub7mxIkTAoCIj48XQghx4MABAUDs3btXd85XX30lAIgbN27ojr322muiV69eusedO3cWHTt21D1OT08X9vb2IjAwUHcsLCxMABDHjh3L8z28+eabYvDgwbrHHTp0EDt27BBCZP775u/vL/bs2SOOHDki2rRpI7I+xl2/fl1UqVJF9zobGxthY2MjZsyYIc6cOSOWLl0qbG1txapVq2S1KJVK8e2334qzZ8+Kr776SkiSJA4ePJijrqzvS3R0dJ61F/beQgixfv168fvvv4sLFy6I7du3iyZNmoiGDRuKlJSUXK/5xhtviPr168uO3bp1SwAQhw8fFj169BAdOnQQMTExuuefe+45MWfOHNlrfv75Z+Ht7a17vGTJEmFvb6/7/Y+Pjxf29vZi2bJlBb7PysQYn3Gt8gomRBWNVquFWq3GSy+9BCcnJ2OXQ0Rk0mLjBY6cKvg8T7eKsRv6zZs3odFo0Lp1a90xlUqFunXr6h6fPXsWs2bNQmhoKKKioqDVagEAd+/eRYMGDXTnPfvss7pfV6lSBUqlEjVr1pQdO3HihOz++q+xtLSEm5sbGjduLHsNANlP+pcuXYqffvoJd+7cQXJyMtLS0tC0aVPd8+fPn0f79u0BANu2bcOsWbPQo0cPAMA333yD5557DgDg7e0tm4yu1WrRsmVLzJkzBwDQrFkz/Pfff1iyZAlGjx6te98DBw7E1KlTAQBNmzbF0aNHsXTpUnTu3Dmvb3OBCro3AAwbNkx3fqNGjdCyZUtUr14df/zxB1588UXZ9ZKTk7Fu3TrMnDkz1/uNGDECVatWxb59+6BUKnXHT58+jZMnT+o6HkDmsLeUlBQkJSVBqVRixIgRmDZtGjZs2IAJEyZgw4YNEEJg+PDhxX7/VDhGHYL1119/oX///vDx8YEkSTlaf3mtgPD111/rzunSpUuO5w3/4ERHRyMwMBAqlQoqlQqBgYGFXsmBKo7Dhw/j5s2bHH5FRFQIkQXM6cjajDAltexrKQ3i6WRiSZJyPZ6YmIiePXvCwcEBa9aswcmTJ7FlyxYAyLHakf4KipIk5VhRUZIk3Yf43F6T2+uy6sp63caNGzF16lSMHz8eu3fvRmhoKMaNGyerJT09XTcpPC0tDfb29rrn9OdCnDt3TjbHwtvbWxaoAKB+/fq6Fa7c3d1hZWWV7znFVdC983pN9erVce3atRzP/frrr0hKStKFF0N9+/bF+fPnc0zE12q1mD17NkJDQ3VfFy5cwLVr13TfU5VKhSFDhuiGYQUFBWHIkCH8IWY5MGoASUxMRJMmTfD999/n+rzhCghqtRqSJGHw4MGy8yZOnCg7b9myZbLnR44cidDQUISEhCAkJAShoaEIDAwss/dFxqFWq1GrVi106tTJ2KUQEZk8N5fMuR15cXXO/G9iEnJdKcjU1KpVCwqFQtaZiIuL032ovXz5Mp48eYK5c+fiueeeQ7169fKdd1DWDh8+jPbt2+PNN99Es2bN4O/vjxs3bsjO8ff3x/nz5wEAnTp1wqJFixAZGYnExETMnTsXQGbnZ9KkSZg2bZrudR06dMCVK1dk17p69SqqV68OIHMVqFatWuV7TnEVdO/cREZG4t69e/D29s7x3MqVKzFgwAB4eHjk+to33ngDc+fOxYABA3Do0CHd8ebNm+PKlSvw9/fP8WVhkf3xd8KECThy5Ah+//13HDlyhJPPy4lRh2D16dMn3+XUvLy8ZI+3bduGrl27ytqgAKBUKnOcm+XSpUsICQnB8ePH0aZNGwDAihUr0K5dO1y5ckXWmtWXmpqK1NTsH/vExcUV6j2RccTFxWHTpk34+OOPc/z0i4iIclI5ShjYE9i6K6tzALg4AVGxmc872gPhEUCGNnOjQlsbIxZbCI6OjhgzZgzee+89uLq6wtPTE59++iksLCwgSRKqVasGa2trLF68GK+//jr+/fdffP7550ar19/fH6tXr8auXbtQo0YN/Pzzzzh58iRq1KihO2fQoEFYvHgx2rZti08//RRDhgyBh4cHbGxs8MEHHwDI/MD/0Ucf4ZVXXtG9burUqWjfvj3mzJmDoUOH4sSJE1i+fDmWL1+uO+e9997DsGHD0KlTJ3Tt2hUhISHYsWMHDh48qDsnPDwc4eHhuH79OgDgwoULcHR0RLVq1XSrVnXv3h2DBg3CpEmTCnXvhIQEzJo1C4MHD4a3tzdu376Njz76CO7u7hg0aJDse3T9+nX89ddfsiV6c/P2228jIyMDAQEB2LlzJzp27IhPPvkEAQEB8PX1xUsvvQQLCwucP38eFy5ckO3/0blzZ/j7+2P06NHw9/fnDzHLS7nNNikAALFly5Y8nw8PDxdWVlZi7dq1suOdO3cW7u7uws3NTTRo0EC8++67Ii4uTvf8ypUrhUqlynE9lUol1Gp1nvf79NNPBYAcX5zcbJqWL18uJEkS9+7dM3YpREQVytwfMyeaX7qeIRatzPz1zHka8flnUWLWgnTxf19niCdRpj8JXYjMiegjR44USqVSeHl5iW+//Va0bt1afPjhh0IIIdatWyf8/PyEjY2NaNeundi+fbsAIM6ePSuEyH2ydVBQUI7PEZ9++qlo0qSJ7nHnzp1zTHSvXr26WLhwoeyY/medlJQUMXbsWKFSqYSzs7N44403xIcffii7bnR0tHjmmWfEkiVLdMeePHmimzT94MEDodXm/nuzY8cO0ahRI2FjYyPq1asnli9fnuOclStXCn9/f2FrayuaNGkitm7dmuN95vZZKCgoSPY+P/3000LfOykpSfTs2VN4eHgIhUIhqlWrJsaMGSPu3r2bo74ZM2aIqlWrioyMjBzPZU1Cz/q9E0KIBQsWCEdHR3HkyBEhhBAhISGiffv2ws7OTjg5OYnWrVvn+n2YM2eOAJBj0rq5MMYkdEkI0+irSpKELVu24IUXXsj1+fnz52Pu3Ll4+PChbJOcFStWoEaNGvDy8sK///6LGTNmwN/fH3v27AEAzJkzB8HBwbh69arsenXq1MG4ceMwY8aMXO+XWwfE19cXsbGxHBtogtq1awdnZ2fZGutERFSwz7/TIk0DTBgOrPxFoOuez9Hm6A+wT3yCy/X7YeOo9Rgzxh7Vn6l43eXExEQ888wzWLBgQYUdWnPu3Dn07dsXnTp1wuTJk9GqVStYWVnh1q1bWL58Oc6ePYuQkBBjl0kVWFxcHFQqVbl+xq0wq2Cp1Wq8/PLLsvABZM7/yNKoUSPUrl0bLVu2xJkzZ9C8eXMAOSekAZnjWfMbqmNjYwMbGxPvNxOAzGF2x48fl62dTkREBcvQCqRpMn994RLQOHQ9uu3J3k+i3qU/MGTdKNzs/BucnSygcjTtEHL27FlcvnwZrVu3RmxsLD777DMAmas9VVRNmjTBuXPn8OWXX2LQoEF4/PgxrKysoFQqMWTIENmwKqKKokIEkMOHD+PKlSvYsGFDgec2b94cCoUC165dQ/PmzeHl5YVHjx7lOC8iIkK3JB5VbEFBQXB1dcWAAQOMXQoRUYWSkpL969B/4jFlx/Qc5zT4bxtOrNqLA3Wfx8Cepr8k7zfffIMrV67A2toaLVq0wOHDh+Hu7m7sskrE3d0dCxcuxLfffosnT54gLS0NXl5esLS0NHZpRMVSIQLIypUr0aJFCzRp0qTAc//77z9oNBrdSgrt2rVDbGwsTpw4oVsb/J9//kFsbKxubW2quDQaDVavXo1Ro0axY0VEVERZS+xaWQKNzm+EY3x4rue1Or4cN+o8j227Bfz9YLKdkGbNmuH06dPGLqPMSJKU52pQRBWJUZfhTUhI0K3NDAC3bt1CaGiobK3orNWN9Fd3yHLjxg189tlnOHXqFG7fvo0///wTL730Epo1a4YOHToAyFx7unfv3pg4cSKOHz+O48ePY+LEiQgICMhzBSyqOHbu3IlHjx5x7w8iomJIftoBsbUFGp3/VXc8SemKy/X76R7X+28bHOLCIQQQVcD+IabMRKa9Epk9o3ZATp06ha5du+oeZ61hPWbMGAQHBwMA1q9fDyEERowYkeP11tbW2LdvH7777jskJCTA19cX/fr1w6effiprS65duxaTJ09Gz549AQADBgzIc+8RqljUajWaN29eqO4YERHJZXVA3DIi4X99n+74pUYvwHr0MGDSHwAAS20G/K/uwrlWY+DqYoxKC0+r1eL+/fu4dOkSLl++rPu6dOkS6tSpg7/++svYJRKZPZNZBcvUGWOFAMpfeHg4qlatiu+++w5vvfWWscshIqpw/r0isGGHQNc7v6Db96N0xxM374R9/x7QOLlCkRwPAAhtMQoZQavLZA7I77//jg8//BAbNmxAw4YNC/WalJQUXLt2LUfIuHLlCpKSkgBkLihTt25d1KtXD/Xr10fPnj05/JrIAFfBIiqCNWvWwMrKKtfuGBERFSxrCNYz149kH7S1hX2/boCVFZLbdoHiwA4AwLMP9sGiUenePykpCe+99x5+/PFHBAQEyDbhyxIVFSXrZmT9+tatW9BqtQAyJ2nXq1cPLVu2xKhRo1CvXj3Uq1cP1atX50RtIhPEAGLqoqKAs2eBtm0Be3tjV2MyhBBQq9UYNGiQbjdWIiIqmqwhWFWu6AWQNm0Aa2sAQOpz3YCnAcQiPAz39l+CU+v6pTIJ/dy5cxgxYgRu3bqF77//Hn369MGhQ4dkIePy5cuIiIgAkDkBu0aNGqhfvz5eeOEFXcioV69ehV/lisjcMICYssRE4NlngQcPMv979ixgYdR1A0zGP//8g0uXLuG7774zdilERBVWTJyATXIsVLcvZB98uogLAGR07iY7/+Ty4wgNrVfi5XgnTZqEpUuXwtHREdWrV8f06dMxadIkAICdnZ0uWPTo0UM3fKp27do59gIjooqJAcSULV+eGT4A4Px54NAhQG/SvjlTq9Xw9fVFt27dCj6ZiIhyOH1B4EQoUOvuP5D0p4PqBRCrxg2QprCDtSYZAOD94CzOirElWo43JSUFP/74I4QQSE9Ph7OzMwYPHoxOnTqhXr168PX1hQV/2EZUqfH/cBOWdvaC/EAuGyqao8TERKxfvx5jx47l2F4iomKIjRfYtjszdPg8DJU/2bat7pe2SkuE+zTVPfZ5cBYASrQcr62tLR4/foxVq1ahR48eOH/+PObMmYPp06dj7dq1xbsoEVUoDCAm6vQFgYsXNfKDCoVxijExv/32G+Lj4zF27Fhjl0JEVCHdfSCQ1fSoEnZedzzdxxfQm1dnawM8fKaZ7rHXw1BI2gxIEkq0HK+7uztGjx6NLVu2ICIiAps3b0bTpk3xyy+/ICYmpvgXJqIKgUOwTFDWT6aGZMgDSKLGEpyGnjn8qmvXrqhZs6axSyEiqnBOXxDYuiv7sVdYdrddNH5Wdq6VlYTH1ZoBRzMf26Qlwv3JVXQIbFBqu6Hb29tj0KBBGDRoUKlcj4hMHwOICYqMzmxvWxoEkIToNLMPINevX8ehQ4fw888/G7sUIqIKR3/oFQBYpqfB/fFl3WNF82dzvCaqRjPZ41caXICyceH26iAiyg2HYJkgNxdAkgDLjDTZcUeLZCNVZDqCg4Ph5OSEF1980dilEBFVOFk/4Mri/vgyLLXp2QcaN87xmsRq9aCVsrsdyjtXyrJEIjIDDCAmSOUoYWBPCRYGHRClMO8AkpGRgeDgYIwYMQJKpdLY5RARVThZP+DKUiXcYLGTZ3N2QLS2dohx0dsg8PLlHOcQERUFA4iJatFYgq1FuvxgsnkHkD179uDBgwcYP368sUshIqqQsn7AlcU94lr2k5aWQO3aOV6j0QARnnWzDzCAEFEJMYCYMBuRKj9g5gEkKCgIDRs2RKtWrYxdChFRhaW/gaDbk6vZT9SoodsBXV9qGvDEs172gStXAK22LEskokqOAcSEWSbGyR6H3U40UiXGFxkZia1bt2L8+PGQpNJZeYWIyBzFxmdPAnF7cl33a02NnN0PAEhJBSL0A0hyMnDvXpnVR0SVHwOIiYqNF7BMkAeQm1eSZf9wmJN169ZBq9Vi1KhRxi6FiKhCi4h8+u+IEHCLyO6AJPnmDCCx8QJarUEAAZB4+mKZ1khElRsDiImKjAZsUmJlxxRpScXeebaiU6vV6N+/Pzw9PY1dChFRhebokPlf+4THsE2N1x23bpAzgEQ+/Tcn0qOO7HjyxZtlVh8RVX4MICbKzVnAJlXeAVGkJ5do59mK6uzZswgNDeXkcyKiUmBrkzmM1V2v+wEAds/WzXFu1qpZifYeSLXO3onK8TEDCBEVHwOIiVJZJsNSmyE7Vt0tpdR2nq1I1Go1vLy80Lt3b2OXQkRU4aU93WKqSsw1+RO5rICVtWqWZCEh2rWm7rjN/VtlWSIRVXIMIKYqNjbHIVdFkhEKMa6UlBSsXbsWY8aMgZWVlbHLISKq8NKebjHlFnsn+6CVFeDrm+v5LRpLmDoRiHbT2wvkJjsgRFR8DCCmKi4u5zEzXIZ327ZtiI6Oxrhx44xdChFRpZAVQFyi9LoY1apl7gOSBxcnC8S6Z3dAcPOmfEt1IqIiYAAxVbl0QJBkfh0QtVqNDh06oG7dnGOTiYio6LKGYDlH6XVA/PwKfF2sh14AiY8HIiNLtzAiMhsMIKYqtwBiZh2Qu3fvYs+ePZx8TkRUirI6II5PbmcfLEQAia9SQ36Aw7CIqJgYQExVQkLOY2YWQFatWgU7Ozu89NJLxi6FiKjSSNMAlulpsI9+kH2wEAEk0aum/AADCBEVEwOIqdJoch4zoyFYWq0WQUFBGDp0KBwdHY1dDhFRpZGmAZxi7kHSn8NRiACS4llNfuDu3dItjIjMBgOIqcotgJhRB+TQoUO4desWh18REZWytDTAJfq2/GAhAojkoESi0i37wL17pVoXEZkPBhBTlZ6e85gZdUDUajX8/f3RsWNHY5dCRFSppGkEnIsRQBRWQKyLXheEHRAiKiYGEFOVVwfEDJY9jI2Nxa+//orx48dDksxv40UiorKUpgGco/W6F5aWgI9Pga9TWAGxqqq6xxm3GUCIqHgYQExVbgFEiOz1Eyux9evXIy0tDaNHjzZ2KURElUpsvEBkFOAUez/7oI9PvnuAZFEo5B2QlOv3cPpC5f+hGBGVPgYQU5VbAAHMYhhWUFAQevfujWeeecbYpRARVRqnLwgsWC5w7bZBAMljB3RDQgAxztkBxD4pEn/uSEBsPEMIERUNA4ipyiuAVPKJ6P/99x/++ecfTj4nIipFsfEC23YL3SheVYxeAKlaNfcXGcjIAOKc5ec6xdxDVHRpVUlE5oIBxFSZaQAJCgqCu7s7+vfvb+xSiIgqjcho+RRC/Q5Iqmfhus32SnkHBACcY+7C1aVUSiQiM8IAYqKEGQ7B0mg0WL16NUaNGgVra2tjl0NEVGm46YUEm5Q42KbG6x4nuBZuCNaDR0Css/zczs/ch8qRi4UQUdEwgJiqvAJISkr51lGO/vjjD0RERHD4FRFRKVM5SujV+emvY+T7d9j6FzwEKzZeIPwxkODoBa3e6oRemoelWicRmQcGEBMl0vIIILntD1JJqNVqtGzZEo0bNzZ2KURElU7HVhawsjKYgA7Avk61PF6RLfLpPA+tpQKJDlV0x9NuPyjVGonIPDCAmKg8A0henZEKLiwsDH/++Se7H0REZcywA1KYSehuLkBW4yPOKXvPELuosNIsjYjMBAOIicpzDkgl7YD8/PPPUCgUGDFihLFLISKqlLRCID0dcIzTCw0WFkCVKnm/6CmVo4SBPSVIEhCvyg4gikfsgBBR0VkZuwDKndDkETQqYQARQkCtVuPFF1+Es7OzscshIqqUsn6u5RinN2+jShXAqnAfBVo0luDtKXD/V71d0x9yDggRFR07IKbKjIZgHTt2DFeuXOHwKyKiMpT1z4eTfgDx8cn95Dy4uUiId9JbtvfRo0r57xIRlS0GEBNlTh0QtVqN6tWro2vXrsYuhYio0tJ1QOLDsw96exfpGgoFEKfSe40QmSGEiKgIGEBMVJ5zQCrZT5oSEhKwYcMGjBs3DhYW/ONIRFRW0p7+/KokHRALSUKyq8HGhQ84D4SIioaf+EyVmSzD++uvvyIhIQFjxowxdilERJWaRgNI2gzYx+t1LIrYAQGAZDeDAMJ5IERURAwgpiqvoFHJAoharUb37t3h5+dn7FKIiCo1jQawT4iAhTYj+2AROyAAkOpu8BoGECIqIgYQU5XHUKvbt9LKuZCyc/XqVRw+fJiTz4mIykGaBnCMN9i3oxgdEK2zC9ItFdkHwsPzPpmIKBcMICYqrzkg585rEBsvyrmashEcHAyVSoVBgwYZuxQiokpPk26wBC9QrA6ItY2FbDd0BhAiKioGEFOVRwCxyEhHVHQ511IGMjIysGrVKowcORJ2dnbGLoeIqNJL0xhsQggUqwNirQDinfRexwBCREXEAGKq8gog2nS4upRzLWVg9+7dePjwIYdfERGVE40GcIzTCwuSBHh6Fvk61tZAgiM7IERUfAwgpiqPAPJs7XSoHKVyLqb0qdVqNG7cGC1atDB2KUREZkGjARz09wDx8Cj0Luj6rBVAvKNX9gEGECIqIgYQEyXlsdqVr3vFXwXryZMn2LZtG8aPHw9JqvhhioioIkhLBxwS9Jbg9fLK++R8WCuABEeDIVhabQmrIyJzYtQA8tdff6F///7w8fGBJEnYunWr7PmxY8dCkiTZV9u2bWXnpKam4u2334a7uzvs7e0xYMAA3L9/X3ZOdHQ0AgMDoVKpoFKpEBgYiJiYmDJ+dyWU14aDlWAZ3rVr1wIAXn75ZSNXQkRkPjQaAQf9PUCqVMn75HxkdkD0XpueDkRFlbA6IjInRg0giYmJaNKkCb7//vs8z+nduzfCwsJ0X3/++afs+SlTpmDLli1Yv349/v77byQkJCAgIAAZGdnrnI8cORKhoaEICQlBSEgIQkNDERgYWGbvq1SkV86d0IUQWLlyJQYMGAAPDw9jl0NEZDY0mtLpgDyJAhIcDV7LYVhEVARFH/xZivr06YM+ffrke46NjQ288vhLMjY2FitXrsTPP/+MHj16AADWrFkDX19f7N27F7169cKlS5cQEhKC48ePo02bNgCAFStWoF27drhy5Qrq1q2b67VTU1ORmpqqexwXF1ect1h8lbQDcubMGVy4cAFz5841dilERGYlzXAOSDE6ILHxAlduAr4GASThZhgcGjUqaYlEZCZMfg7IwYMH4enpiTp16mDixIl4/Pix7rnTp09Do9GgZ8+eumM+Pj5o1KgRjh49CgA4duwYVCqVLnwAQNu2baFSqXTn5Oarr77SDdlSqVTw9fUtg3eXN6mSBhC1Wg0fHx/Z7xkREZU9bWISbFITsg8UI4BEPl0GXrYML4Dkm2G5nE1ElDuTDiB9+vTB2rVrsX//fixYsAAnT55Et27ddJ2J8PBwWFtbw8VFvi5tlSpVEP60HRweHg7PXJYZ9PT01J2TmxkzZiA2Nlb3de/evVJ8Z4WQkUfQqMBDsJKTk7Fu3TqMGTMGVsVYeYWIiIpPG/5IfqAYQ7Dcnv5zK1uGF4BD4qNcziYiyp1JfwocNmyY7teNGjVCy5YtUb16dfzxxx948cUX83ydEEK2ulJuKy0ZnmPIxsYGNjY2xay85CpjB2Tr1q2IiYnBuHHjjF0KEZFZOX1BIOaywQ/ditEBUTlK6NBS4MgpO6TYOMI2NR4AYBcbURplEpGZMOkOiCFvb29Ur14d165dAwB4eXkhLS0N0dHyrcEfP36MKk//YvXy8sKjRzl/MhMREaE7xxRJlXASulqtxnPPPYfatWsbuxQiIrMRGy+wbbeQT0CHwUpWRdCkQeYP75Ic9UYX5PLvLBFRXipUAImMjMS9e/fg7Z059rRFixZQKBTYs2eP7pywsDD8+++/aN++PQCgXbt2iI2NxYkTJ3Tn/PPPP4iNjdWdY4ryDCAVtANy+/Zt7Nu3jzufExGVs8hoQAiDCegAoqyLF0CUtpn/TbTXCyB68zOJiApi1CFYCQkJuH79uu7xrVu3EBoaCldXV7i6umLWrFkYPHgwvL29cfv2bXz00Udwd3fHoEGDAAAqlQoTJkzAu+++Czc3N7i6umL69Olo3LixblWs+vXro3fv3pg4cSKWLVsGAHj11VcREBCQ5wpYJqGSBZBVq1bB3t4eQ4YMMXYpRERm5eEjAQBwiM8OCVrJAs413Yp1PTu7zP/GO7ADQkTFY9QAcurUKXTt2lX3eNq0aQCAMWPGYMmSJbhw4QJWr16NmJgYeHt7o2vXrtiwYQMcHR11r1m4cCGsrKwwdOhQJCcno3v37ggODoalpaXunLVr12Ly5Mm6lZcGDBiQ794jpiCvndAr4hAsrVaLoKAgDB06FA4ODsYuh4jIbMTGC+z+K/PX9gnZASTdxQMq5+J9BFBYAVaWQKIDOyBEVDxGDSBdunSBECLP53ft2lXgNWxtbbF48WIsXrw4z3NcXV2xZs2aYtVoFELAohJ1QA4cOIA7d+5w+BURUTnLGn4FAPaJeiEhl9UhC0uSJNjZCflKWI8fZ94on8VdiIiyVKg5IGZDbxf3HCpgB0StVqNOnTomPeeGiKgycnPJzgT2CU90xy28ih9AAMDO1qADotEAMTEluiYRmQ8GEFOUX8ioYB2QmJgYbN68GePHj8932WMiIip9KkcJA3tm/t2rPwTLyrtkAURpCyQ4GFyD80CIqJAYQExRJQog69evh0ajwejRo41dChGRWWrRWIKFJA8gJRmCBWRORE80DCCcB0JEhcQAYoryCyAVbAiWWq1Gnz59dEsnExFR+crQCiA9HfZJkdkHSxhAlIZDsAAGECIqNJPeCd1s5dflqEAdkAsXLuDkyZPYvHmzsUshIjJb6emAMvGJ/GAJA0hcAuST0AEOwSKiQmMHxBRVkg5IUFAQPDw80K9fP2OXQkRktjQag+FXQIkCSGy8wLVbQIqtMzIsspe8T7nPDggRFQ4DiCmqBHNA0tLS8PPPPyMwMBDW1tbGLoeIyGylpwP2iRHygyUIIJHRmf8VFhZIUrrrjqc9iMjjFUREcgwgpqgSBJDff/8dT548wbhx44xdChGRWUtLL90OiJtL9q/154HYxTOAEFHhMICYIoMAIhSKPJ8zVWq1Gq1bt0ajRo2MXQoRkVlLTwfsEwzCgYdHsa+ncpTQtlnmrxMdsq+jiGYAIaLCYQAxRQZdDmFrl+dzpujhw4fYuXMndz4nIjIBGg3goN8BsbUFHBxKdM0GdTL3Fklzzh6ChQgGECIqHAYQU2TY5bCzy/s5E7R69WpYW1tj+PDhxi6FiMjsadIBpf4cEA+P7O3Ri8n6aWM+Wa8DwgBCRIXFAGKCLl9Okz1OtVJmPzDxDogQAmq1GkOGDIFKpTJ2OUREZk+TDtjrL8NbguFXWbLWFom307tWZCSg1Zb42kRU+TGAmJjYeIEjR+UBJD6j4nRAjhw5gmvXrnH4FRGRidAY7gPi7p73yYVkowsgetfSaoGoqBJfm4gqPwYQExMZDVhkyEOGRpEdQISJd0DUajVq1KiBzp07G7sUIiICkK4xmIReGh2Qp0OwEuwNrsVhWERUCAwgJsbNBbAU8pCRrqgYk9Dj4+OxceNGjBs3DhYW/KNFRGQKcnRASjOAOBgs5/uYmxESUcH4KdHEqBwltH9WHjJUVWyzH5jwEKxNmzYhKSkJY8aMMXYpRET0lCY1A3bJekOjSmEIloWFBIUVkMQOCBEVAwOICfJ/Rh5AnDztsx+YcAdErVajR48eqFatmrFLISKiLJGRsBAi+3EpdECAzIno+vuAAGAAIaJCsTJ2AZQLwy6Hrel3QK5evYojR47gl19+MXYpRESkR3piMCyqFDogQOYwrBg7VwgLC0hZq189eZL/i4iIwA6IaTLockhK058DEhQUBGdnZ7zwwgvGLoWIiPRYRhmEglLqgNhYA8LCAlpn1+yDDCBEVAgMIKbIMIDobUQopacD+q10E5Ceno5Vq1bh5Zdfhq1+t4aIiIzOIspgWFQpDsECgHT93dAZQIioEBhATFFGhvyx4Yd6w+eNbNeuXQgLC+PeH0REJsjKMICU4hAsANAwgBBRETGAmCLDYVaGAcTEhmGp1Wo0adIEzZo1M3YpRERkwCo6MvuBhQXg6pr3yUWQtRlhmooBhIiKhgHEFBkGDL0hWABMaiJ6REQEtm/fjvHjx0OSJGOXQ0REBhSxeqHA1TUzhJSCrA5IqpNeAOEqWERUCAwgpqgCdUDWrFkDCwsLvPzyy8YuhYiIcmEdW7qbEOqu+7QDkuLIDggRFQ0DiCkqqANiIgFECIGVK1di4MCBcHNzM3Y5RESUC1kHpBT/rs4agpXioBdAkpOBpKRSuwcRVU4MIKbIMGDY2Mgfm8gQrFOnTuG///7j5HMiIhN1+oKApd4ckGjr0pmADgDWisxht1FWBqGGXRAiKgADiCmqIEOw1Go1nnnmGTz//PPGLoWIiAzExgts2y1gn5gdCG4kuiE2vnSWcn/0JPM6N5IMQg0DCBEVgAHEFOkFDGFhkT3QNosJdECSkpKwbt06jB07FpaWlsYuh4iIDERGZ24bZZeU3QFJUrojKrrk146NF7hw+ek17RlAiKhoGEBMkf4+H1ZWmV/6TKADsmXLFsTFxWHs2LHGLoWIiHLh5gJYaZJhk5aoO5Zk7w5Xl5JfO1IvxDCAEFFRMYCYIv2AYWUFKBR5P28karUanTt3hr+/v7FLISKiXFy/DSgTI2XH6rZxh8qx5Eumu7kAWVfJEUC4FC8RFYABxBTpD8GyzKUDYuQhWLdu3cL+/fs5+ZyIyERlzf9QJsq7EZ51SmcVLJWjhB7PZf461cYR6ZZ6PyiLjMz9RURETzGAmCLDDoiJDcEKDg6Go6MjBg8ebNQ6iIgod1nzPwwDSGwproLVtvnTHogkQdJf3pdDsIioAAwgpqigIVhG7IBkZGQgKCgIw4cPh729vdHqICKivLm5AJIEKJPk3QhHv9JdhjdrN3ThqhdA2AEhogIwgJgi/QBiaZmzA6I/Sb2c7d+/H/fu3ePwKyIiE6ZylNClXc45II5+pbcTOgDYKzP/m+6sF2wYQIioAAwgJkjodzhMbAhWUFAQ6tWrhzZt2hitBiIiKpi3pyTbAwSWloBKVar3cHgaQNKcOASLiAqPAcQECROdAxIdHY3Nmzdj/PjxkKSSr6JCRERlJyYOUCbphQE3t8xxWaUoqwOS4sghWERUeAwgJkika7MfmFAA+eWXX5Ceno7AwECj3J+IiAovPELIh2C5lc4KWPocnk4FjLZkACGiwmMAMUWa7IAhKUwngKjVavTr1w9eXl5GuT8RERXO6QsCZy7Id0EviwASl5D535tJetdOTgaSkkr9XkRUeTCAmKAcQ7AsLeUnGCGAnDt3DqdPn+bkcyIiE5e1BwgAKJOidMc1qtINILHxAldvZv46yd7g2uyCEFE+GEBMkcb05oAEBQXB09MTffv2Lfd7ExFR4WXtAQLI9wFJsXct9ftkSVIygBBR4TGAmCD9DoiUWwAp52V4U1NTsWbNGowePRoKwz1JiIjIpGTtAQLI9wGx9i7dDoibC5A1pT3J3mB/Ea6ERUT5YAAxRSbWAdmxYwciIyMxbty4cr0vEREVncpRwsCeEizT02CTmqA7buNTunuAqBwl9O6S+Wt2QIioKBhATJAw2Ijw/DX5HJBbt8p3J3S1Wo22bduiQYMG5XpfIiIqnmYNDSagA2UyCb1dCwkKKyCZc0CIqAgYQEyRXgBJl6yw87A8gJw7r0FsvCiXUu7fv49du3Zx8jkRUQWSni4ffgWgTAKIJElwdQZSbJ0hLPQ+UnAIFhHlgwHEFOnN8UgTVsiwkA/BkjLSERVt+KKysXr1atjY2GDYsGHlc0MiIiqx9AzI9wAByiSAAICLChAWFkiyy57k/ugqOyBElDcGEBOkPwRLYWsFYSkPIJYiA64u5VCHEFCr1XjppZfg5ORU9jckIqJSUV4dEABISs78b7IyO4CEXYost049EVU8Rg0gf/31F/r37w8fHx9IkoStW7fqntNoNPjggw/QuHFj2Nvbw8fHB6NHj8bDhw9l1+jSpQskSZJ9DR8+XHZOdHQ0AgMDoVKpoFKpEBgYiJiYmHJ4h8WkkQeQvj3kAaRJ7XSoHCXDV5W6w4cP48aNGxx+RURUwWR2QAyGQZVBAImNF7j79J9l/YnoyqTIcuvUE1HFY9QAkpiYiCZNmuD777/P8VxSUhLOnDmDmTNn4syZM9i8eTOuXr2KAQMG5Dh34sSJCAsL030tW7ZM9vzIkSMRGhqKkJAQhISEIDQ0FIGBgWX2vkosQ74KVrOm8qVvfatoy6UMtVqNWrVqoVOnTuVyPyIiKh2adPkmhAAA19LdBwQw3Ask+/rKxKhy6dQTUcVkVfApZadPnz7o06dPrs+pVCrs2bNHdmzx4sVo3bo17t69i2rVqumOK5VKeHl55XqdS5cuISQkBMePH0ebNm0AACtWrEC7du1w5coV1K1bt5TeTSky3AndCMvwxsXFYdOmTfjoo48gSWXfbSEiotKTng7Y6c8BcXICymAfp6w9R4SQd0DcRCTsyqFTT0QVU4WaAxIbGwtJkuDs7Cw7vnbtWri7u6Nhw4aYPn064uPjdc8dO3YMKpVKFz4AoG3btlCpVDh69Gie90pNTUVcXJzsq9wYBhALi7yfLyMbN25EcnIyxowZU+b3IiKi0pWeYTAHpIzmf2TtOSJJ8qV47RKj8nkVEZk7o3ZAiiIlJQUffvghRo4cKZsQ/fLLL6NGjRrw8vLCv//+ixkzZuDcuXO67kl4eDg8PT1zXM/T0xPh4eF53u+rr77C7NmzS/+NFIJksA8IJAlaKytYZB0vhwASFBSEXr16oWrVqmV+LyIiKl3p6YCd/hCsMgogANCisYQa1QRO79W7R0xM5oqOlpZ5vo6IzFeFCCAajQbDhw+HVqvFjz/+KHtu4sSJul83atQItWvXRsuWLXHmzBk0b94cAHIdQiSEyHdo0YwZMzBt2jTd47i4OPj6+pb0rRSOYQcEACytso+XcQC5fPkyjh49io0bN5bpfYiIqGykG84BKYP5H/pcVRbQOOpN+hACiI4G3N3L9L5EVDGZ/BAsjUaDoUOH4tatW9izZ0+By8E2b94cCoUC165dAwB4eXnh0aNHOc6LiIhAlSpV8ryOjY0NnJycZF/lJpcAIluKt4wDSFBQEFxdXXOd8E9ERKZPY7gMbxl2QLJkuHA3dCIqHJMOIFnh49q1a9i7dy/cCvEX6H///QeNRgNvb28AQLt27RAbG4sTJ07ozvnnn38QGxuL9u3bl1ntJaLN3ohQ1wGxKp8AotFosGrVKrz88suwsbEps/sQEVHZSc8ovyFYWbQMIERUSEYdgpWQkIDr16/rHt+6dQuhoaFwdXWFj48PhgwZgjNnzuD3339HRkaGbs6Gq6srrK2tcePGDaxduxZ9+/aFu7s7Ll68iHfffRfNmjVDhw4dAAD169dH7969MXHiRN3yvK+++ioCAgJMcwUsGMwBKecOSEhICB49esS9P4iIKrB0jZAHkDIeggUAwpUBhIgKx6gB5NSpU+jatavucdacizFjxmDWrFnYvn07AKBp06ay1x04cABdunSBtbU19u3bh++++w4JCQnw9fVFv3798Omnn8JSb+Lb2rVrMXnyZPTs2RMAMGDAgFz3HjEZGbnNAdGbyJeRgbKiVqvRrFmzHN9zIiKqOERsPCy1ev+WlEMHRHI3uEcUV8IiotwZNYB06dIFQog8n8/vOQDw9fXFoUOHCryPq6sr1qxZU+T6jCW3Dkh5DMF69OgRfv/9dyxcuLBMrk9EROUkyqD7UA4BxMKDHRAiKhyTngNitnLrgJRDAFmzZg0sLCwwcuTIMrk+ERGVDwvDAFIOQ7BsVHZIU9hlH2AAIaI8MICYoBz7gABlHkCEEFCr1Rg0aBBcy+EfKiIiKkPR5d8BsbOVkKzU+/eDQ7CIKA8MICZIMkIH5MSJE7h48SInnxMRVQKWRgggj58IJCuz7xN1kx0QIsodA4gJMkYAUavV8PX1Rffu3Uv92kREVL4sYwy6D2Xc2Y6NFwi9CCTpdUCib0UhNj7/uZxEZJ4YQEyNEJC02uzH5RBAkpKS8Msvv2Ds2LGy1cOIiKhisozVCyAWFoBKVab3i4zO/G+yXfZu6MrESERFl+ltiaiCMuoqWJQLwyV2s4KHwirvc0rot99+Q3x8PMaOHVuq1yUiIuNQxOoNf3J1zQwhZcjNBZAAJNlnD8GyS4qC0iXv1xCR+WIHxNQYdjeeBhBJrzMhSrkDolar0bVrV9SsWbNUr0tERMZhFVe+mxCqHCV0aQ/ZJHTH1EioHKUyvzcRVTwMIKYmjwCiPwRLaEovgNy4cQMHDx7k5HMiokrEOk6vA1IOE9ABoGkDCUl6k9AtkxOB1NRyuTcRVSwMIKYmrw6I/hCsUgwgwcHBcHJywosvvlhq1yQiIuNSxOtNviinpdWtFUCy4ZgrLsVLRLlgADE1hgHk6dAr/QBSWkOwMjIysGrVKowYMQJKpbJUrklERMZnnaD3wb+cOiDWCiDZziDsMIAQUS4YQExNXh2QMhiCtW/fPty7d4/Dr4iIKhnbeINJ6OXASiGfhA6AAYSIcsUAYmoKMQektJbhVavVaNiwIVq1alUq1yMiIhOg1cImqfyHYFlIEtKdDO4Vyc0IiSgnBhBTk9cyvPodkPSSL8MbFRWFLVu2YNy4cZAkrlJCRFRZhB6LgSSyNwC8m1I+AQQANCp2QIioYAwgpqacOiDr1q2DVqvFqFGjSnwtIiIyDbHxAgd+l3cdjt9xKbcdybUqg0no7IAQUS4YQExNXgFEf4fyUgggarUaAQEBqFKlSomvRUREpiEyGrBLlH/oT7JzLbcdyS2VtkhT6C1qwgBCRLlgADEx/13UyB5fv/80eFiV3ipYZ8+exdmzZzn5nIioknFzAZTJ8mFPyQ5ucC2nHcmtDSeiR5dT8iGiCoUBxITExgsc/FseQI6dt8xsnZfiEKygoCB4eXmhT58+JboOERGZFpWjhHY15AGkY0+3ctuR3NraYCledkCIKBcMICYkMhqwyJCHiwzJKrN1XkoBJCUlBWvWrMHo0aNhpX9NIiKqFKoq5AGkcQf3cru3QgEkK/UCCCehE1EuGEBMiJsLYKnNuRGhqwtkAUQqQQDZvn07oqOjMW7cuGJfg4iITFfGE70P/ZaWgKNjud07cwgWAwgR5Y8BxISoHCV0ailfYrddG0Vm67yUOiBqtRrt27dHvXr1in0NIiIyXSJS70O/qytQjkut59gNnQGEiHLBAGJi6vlpZY/r1lFk/kI/gBjuFVJI9+7dw+7duzn5nIioEhOR5b8JYRZrDsEiokJgADE1hdkHJKN4HZBVq1bBzs4OQ4cOLWZxRERk6iT9id9ubnmfWAasFUCSfgBJTARSU8u1BiIyfQwgpqYQ+4AUZw6IVqtFUFAQhg4dCsdyHA9MRETlS4oxGIJVjhQKCclKgzV/2QUhIgMMIKamEB0QqRgdkL/++gs3b97k8CsiokrO0ogBxMYaSFbKuy7/HedSvEQkxwBiagozBKsYHRC1Wg1/f3907NixBMUREZGps4wzXgBJzxDyIVgAju+NzNzPiojoKQYQU1MGHZC4uDj8+uuvGD9+PKRyXA2FiIjKmVYLRbzxJqHfumcwCR2AbWJU5n5WRERPMYCYGsMAkjX3wyCAFOWnSRs2bEBqaipGjx5dGhUSEZGpio2FpNVbTbEcJ6HHxgtcuWGwDC8AZXJU5n5WRERPMYCYmsJ0QITAt0szcPpC4UKIWq1G79698cwzz5RWlUREZIoMJ3yXYwcka/Vfww5IfbfozP2siIieYgAxNXl0QJIzLOXHtRnYtlvk2glJSUnBrVu3AAAXL17E8ePHOfmciMgcGDGAuLlk7nmYrrBFmkKpO+6n5CR0IpJjADE1hgFEkbkRYWKaleywhTYdQiDXcbUbN25E8+bNIYRAUFAQ3N3d0b9//7KqmIiITIURA4jKUcLAnhIkSd4FsU3gBBAikitWADlz5gwuXLige7xt2za88MIL+Oijj5CWllZqxZmlPAKIvYO8A2KRkQ5JQq7jau3s7BATE4OIiAisXr0aL7/8MiwtLXHp0qWyqpqIiEzAzVB5t+FCRPlOvmjRWMKUCdwNnYjyV6wA8tprr+Hq1asAgJs3b2L48OFQKpXYtGkT3n///VIt0OzkMQfEzkEhO2wp0jGwp5TruFpvb28AmZ2Qx48f4+WXX8bw4cPRpEkTJCUllU3dRERkVLHxAhcN9tzYcdql3JfAdVFJDCBElK9iBZCrV6+iadOmAIBNmzahU6dOWLduHYKDg/Hbb7+VZn3mpzD7gACYNCoDLRrnPqkvK4CsW7cOTZs2xYcffogdO3bg119/hVKpzPU1RERUsUVGA3ZJ2R/2tZIFUqydyn0JXEmSkGKv13lhACEiA1YFn5KTEALap8v87d27FwEBAQAAX19fPHnypPSqM0cajfxxHgHEyTYjz0tkBZDjx4/D19cXN2/exO7du9GpU6dSLZWIiEyHmwvwKDn7w36ynQtgaWGUJXBTHfWW/2UAISIDxQogLVu2xBdffIEePXrg0KFDWLJkCQDg1q1bqFKlSqkWaHYK2QFBRt4BRKlUwtbWFikpKUhJScFff/2FJk2alHKhRERkSlSOEmqr9AKI0jXPobplLc1RbwhWJFfBIiK5YgWQhQsXYtSoUdi6dSs+/vhj+Pv7AwB+/fVXtG/fvlQLNDt6AURIEqRcNiI0PC83CoUCVlZWOHbsGGrWrFnaVRIRkQlyycgeb+Xs5wb3PIbqljWNfgBJSADS0gBra6PUQkSmp1gBpEmTJrJVsLJ8/fXXsDL8oExFox8s9L+XRQwgBw8ehI+PD7y8vEqxOCIiMml6w50sPctvCV5D6U4G476iowGOkCCip4o1Cb1mzZqIzKWlmpKSgjp16pS4KHMm9OeAlCCANG/enOGDiMjc6P3bLJXjHiCG0lUG9+Y8ECLSU6wAcvv2bWTkMgchNTUV9+/fL3FRZi2vAGJpsBN6AQGEiIjMjxSt90HfiAEkgwGEiPJRpPFS27dv1/16165dUKlUuscZGRnYt28fatSoUXrVmSGhSYduxG4JOiBERGRmtFqTCSBaZ4N7cyI6EekpUgB54YUXAGSu8T1mzBjZcwqFAn5+fliwYEGpFWeOhEYvWCj0Nh9kACEiovzEx0N6ukQ+AMDNLe9zy5hwYQeEiPJWpACStfdHjRo1cPLkSbi7u5dJUeZMFHYSej7L8BIRkRky/JBvxA4IAwgR5adYS1bdunWrtOugp2QdEA7BIiKiwjIc5mTEAGLpqITGyhaK9JTMAwwgRKSn2Gvm7tu3D/v27cPjx491nZEsarW6xIWZLb1gIeUXQAx3TCciIvNmQh0QhVXmRoiKuIeZBzgHhIj0FCuAzJ49G5999hlatmwJb29vSJJxNjqqjEQaOyBERFQMphRAFBKSla5wygog0dH5v4CIzEqxAsjSpUsRHByMwMDA0q6H0vOYhK7/a8PziIiITCmAWAFJyuz7ax5HQpHP+URkXoq1D0haWhrat29f2rUQijAJnUOwiIhIn14AEZIEODsbrRRrBZBsl70besTVKJy+IIxWDxGZlmIFkFdeeQXr1q0r8c3/+usv9O/fHz4+PpAkCVu3bpU9L4TArFmz4OPjAzs7O3Tp0gX//fef7JzU1FS8/fbbcHd3h729PQYMGJBjM8To6GgEBgZCpVJBpVIhMDAQMTExJa6/THASOhERFYdeAElzcAEsivVPfKnQpAsk63VA7JKisG23QGw8QwgRFXMIVkpKCpYvX469e/fi2WefhcJgeNC3335bqOskJiaiSZMmGDduHAYPHpzj+fnz5+Pbb79FcHAw6tSpgy+++ALPP/88rly5AkdHRwDAlClTsGPHDqxfvx5ubm549913ERAQgNOnT8Py6e7hI0eOxP379xESEgIAePXVVxEYGIgdO3YU5+2XqTw7IByCRURE+dGb6K1xdIWNEUu5fgeopszeh8QuOQpCAFHRgMrRiIURkUkoVgA5f/48mjZtCgD4999/Zc8VZUJ6nz590KdPn1yfE0Jg0aJF+Pjjj/Hiiy8CAFatWoUqVapg3bp1eO211xAbG4uVK1fi559/Ro8ePQAAa9asga+vL/bu3YtevXrh0qVLCAkJwfHjx9GmTRsAwIoVK9CuXTtcuXIFdevWLerbL1v6Q6vy24iQQ7CIiEifXgdE4+iSz4llKzZe4PJ1wEOvA2KbEgdLrQauLtZGq4uITEexAsiBAwdKu44cbt26hfDwcPTs2VN3zMbGBp07d8bRo0fx2muv4fTp09BoNLJzfHx80KhRIxw9ehS9evXCsWPHoFKpdOEDANq2bQuVSoWjR4/mGUBSU1ORmpqqexwXF1cG7zIXHIJFRETFoRdA0lXGm4Ae+XTBq2SlPAR1rh0NlaOXESoiIlNjvAGiBQgPDwcAVKlSRXa8SpUquufCw8NhbW0NFxeXfM/x9PTMcX1PT0/dObn56quvdHNGVCoVfH19S/R+Cq2wQ7DYASEiIn2yAOKWz4lly80FkCTI5oAAQMtqXIqXiDIVqwPStWvXfIda7d+/v9gFGTK8jxCiwGFehufkdn5B15kxYwamTZumexwXF1c+IaSwq2CxA0JERPr0AkiGETsgKkcJA3sC56/Ja3BMZQAhokzFCiBZ8z+yaDQahIaG4t9//8WYMWNKoy54eWW2acPDw+Ht7a07/vjxY11XxMvLC2lpaYiOjpZ1QR4/fqxbJtjLywuPHj3Kcf2IiIgc3RV9NjY2sLExwhQ+TkInIqKiEkIWQLTOxgsgANCisYTagW7AMr2DhvuUEJHZKlYAWbhwYa7HZ82ahYSEhBIVlKVGjRrw8vLCnj170KxZMwCZ+48cOnQI8+bNAwC0aNECCoUCe/bswdChQwEAYWFh+PfffzF//nwAQLt27RAbG4sTJ06gdevWAIB//vkHsbGxprmXSTonoRMRURHFx8t+MGXsAAIATtXc5Qf0VukiIvNWrACSl1GjRqF169b45ptvCnV+QkICrl+/rnt869YthIaGwtXVFdWqVcOUKVMwZ84c1K5dG7Vr18acOXOgVCoxcuRIAIBKpcKECRPw7rvvws3NDa6urpg+fToaN26sWxWrfv366N27NyZOnIhlyzJ/FPPqq68iICDA9FbAAjgEi4iIis6gu6B1Nd4cEB3DndjZASGip0o1gBw7dgy2traFPv/UqVPo2rWr7nHWnIsxY8YgODgY77//PpKTk/Hmm28iOjoabdq0we7du3V7gACZ3RgrKysMHToUycnJ6N69O4KDg3V7gADA2rVrMXnyZN1qWQMGDMD3339f0rdbJiQGECIiKiqDD/fC8MO/MSiVSLeyhlV6WuZjBhAieqpYASRrX44sQgiEhYXh1KlTmDlzZqGv06VLFwiR966okiRh1qxZmDVrVp7n2NraYvHixVi8eHGe57i6umLNmjWFrsuo8gogkgRhaQkpIyPzMYdgERFRFsMP9y4mEEAkCSn2bnCIDct8zCFYRPRUsQKISqWSPbawsEDdunXx2WefyfbkoGLIyCOAZD3OCiDsgBARURaDD/eSmwkEEACpDq7ZAYQdECJ6qlgBJCgoqLTroKekvHZCz3qctTkiOyBERJTF4MO95G4Cc0AApDnoBSEGECJ6qkRzQE6fPo1Lly5BkiQ0aNBAt1oVlUBBHZAs7IAQEVEW/SV4JQkWzqp8Ti4/Gke9jYIZQIjoqWIFkMePH2P48OE4ePAgnJ2dIYRAbGwsunbtivXr18PDw6O06zQbeU5CByAsraDbOpEBhIiIsuh9uE+xdUZKuoURi8mWpr8jO+eAENFTxfob6u2330ZcXBz+++8/REVFITo6Gv/++y/i4uIwefLk0q7RvOTXAdEbkiXS0sqpICIiMnl6ASTZ3g1rtwKnL+S9yEt5SXfiECwiyqlYHZCQkBDs3bsX9evX1x1r0KABfvjhB05CL6H8OiD6j0V6enY3hIiIzJrmUSSyfkSVbOcCIYBtuwX8/QCVo/H+tUjX74DExWXOXzSc30hEZqdYHRCtVgtFLn+BKBQKaLXaEhdlziT9DojB91jSf5zGSeil7fbt25AkCaGhocYuhYioSDIisrsLScrMroMQQFS0sSrKlOHsIj8QbeSCiMgkFCuAdOvWDe+88w4ePnyoO/bgwQNMnToV3bt3L7XizJGUrhcs8u2AZJRTRcYxduxYvPDCC8YuQyYhIQG1atXSbZiZ5fbt23BycsJPP/1kpMqIyNwp4rLnVyQrM7sOkgS4uuT1ivKhdeZu6ESUU7ECyPfff4/4+Hj4+fmhVq1a8Pf3R40aNRAfH5/vhoBUMCnfOSB6AUTDSejlzcHBAUFBQVi8eDEOHz4MIHMTznHjxqFDhw545ZVXCnUdIQTSuYgAEZUiyxh5B0SSgIE9JaMOvwIA4WKwHDAnohMRihlAfH19cebMGfzxxx+YMmUKJk+ejD///BOnT59G1apVS7tG86HVQtIfwpbPJHRz3wfk4sWL6Nu3LxwcHFClShUEBgbiyZMnuudDQkLQsWNHODs7w83NDQEBAbhx44bsGidOnECzZs1ga2uLli1b4uzZswXet1OnTnj77bcxbtw4JCYm4rvvvkNoaGi+3Y+DBw9CkiTs2rULLVu2hI2NDQ4fPowbN25g4MCBqFKlChwcHNCqVSvs3btX97rPPvsMjRs3znG9Fi1a4JNPPinMt4mIzIEQ8s6CqyvefVVCi8bGnymodTUIIOyAEBGKGED279+PBg0aIC4uDgDw/PPP4+2338bkyZPRqlUrNGzYUPeTYSqGDINhVQYBRNJ/bMYdkLCwMHTu3BlNmzbFqVOnEBISgkePHmHo0KG6cxITEzFt2jScPHkS+/btg4WFBQYNGqSbo5SYmIiAgADUrVsXp0+fxqxZszB9+vRC3X/OnDlQKBQYNWoUPvroIyxevBjPPPNMga97//338dVXX+HSpUt49tlnkZCQgL59+2Lv3r04e/YsevXqhf79++Pu3bsAgPHjx+PixYs4efKk7hrnz5/H2bNnMXbs2CJ8x4ioUouPly3NbunhZvTORxbJhUOwiCinIq2CtWjRIkycOBFOTk45nlOpVHjttdfw7bff4rnnniu1As2K4bAcw4n++nNAzLgDsmTJEjRv3hxz5szRHVOr1fD19cXVq1dRp04dDB48WPaalStXwtPTExcvXkSjRo2wdu1aZGRkQK1WQ6lUomHDhrh//z7eeOONAu9va2uLRYsWoXfv3ujTpw9GjRpVqLo/++wzPP/887rHbm5uaNKkie7xF198gS1btmD79u2YNGkSqlatil69eiEoKAitWrUCAAQFBaFz586oWbNmoe5JRGbAYFiThbuRJ37oc+MQLCLKqUgdkHPnzqF37955Pt+zZ0+cPn26xEWZLcNQkd8QLDOeQ3D69GkcOHAADg4Ouq969eoBgG6Y1Y0bNzBy5EjUrFkTTk5OqFGjBgDouguXLl1CkyZNoFQqdddt165doWtYuXIllEolLly4gNjYWN3xPn366Gpq2LCh7DUtW7aUPU5MTMT777+PBg0awNnZGQ4ODrh8+bKuRgCYOHEifvnlF6SkpECj0WDt2rUYP358oeskosrv0gn5h/pYa7c8zix/lo520FjZZB9gB4SIUMQOyKNHj3Jdfld3MSsrRERElLgos2UYKvJZBcucA4hWq0X//v0xb968HM95e3sDAPr37w9fX1+sWLECPj4+0Gq1aNSoEdKebuAoRPE36NqwYQO2b9+OY8eOITAwEFOnToVarQYA/PTTT0hOTgaAHP+v2Nvbyx6/99572LVrF7755hv4+/vDzs4OQ4YM0dWY9T5sbGywZcsW2NjYIDU1NUd3h4jMV2y8wIl9T1Bf79j1WFe0jBcmMQxLoZCQrHSDIu7pqpnsgBARihhAnnnmGVy4cAH+/v65Pn/+/HndB0AqhoICCCehAwCaN2+O3377DX5+frAy/B4BiIyMxKVLl7Bs2TLdcMC///5bdk6DBg3w888/Izk5GXZ2dgCA48ePF3jvR48e4a233sIXX3yBZs2aITg4GO3atcNLL72EPn36FGouSJbDhw9j7NixGDRoEIDMZX5v374tO8fKygpjxoxBUFAQbGxsMHz4cFnXhojMW2Q0YJck7yokKV0RFQ2oHI1UlB6FFZCsdIVTVgBhB4SIUMQhWH379sUnn3yClJSUHM8lJyfj008/RUBAQKkVZ3aK0AERZtABiY2NRWhoqOzr7t27eOuttxAVFYURI0bgxIkTuHnzJnbv3o3x48cjIyMDLi4ucHNzw/Lly3H9+nXs378/x94dI0eOhIWFBSZMmICLFy/izz//xDfffFNgTa+99hrq1q2ru17Lli3x/vvv49VXX5UNxSoMf39/bN68GaGhoTh37hxGjhyZ60aer7zyCvbv34+dO3dy+BURybi5AMocAcQNDx4Vv8tbmm7dFUhSZg8Ji7vDDggRFTGA/N///R+ioqJQp04dzJ8/H9u2bcP27dsxb9481K1bF1FRUfj444/LqtbKrwiT0M1hFayDBw+iWbNmsq9PPvkEPj4+OHLkCDIyMtCrVy80atQI77zzDlQqFSwsLGBhYYH169fj9OnTaNSoEaZOnYqvv/5adm0HBwfs2LEDFy9eRLNmzfDxxx/nOqRL3+rVq7Fnzx4EBwfDwiL7f51PP/0Uzs7OmDp1apHe38KFC+Hi4oL27dujf//+6NWrF5o3b57jvNq1a6N9+/aoW7cu2rRpU6R7EFHlpnKUUN81O4BoJQuk2qqw+6/M4VnGFBsvcOQUkKzMnhSfcD/K6HURkfFJooiD4e/cuYM33ngDu3bt0o2jlyQJvXr1wo8//gg/P7+yqNPo4uLioFKpEBsbm+sqYKXixg1Af3jbhg2A3tKyGDwY2LwZAJDWpAWsQ0+VTR1kUoQQqFevHl577bUcnRwioiej34H7z/8DACQq3TB39mMAwPihEmpUM948kJt3BYI2Cgzc9CpanlgJAIhxroboc7eNWhcRyZXLZ1wDRZoDAgDVq1fHn3/+iejoaFy/fh1CCNSuXRsuLia07F9FxUnoZODx48f4+eef8eDBA4wbN87Y5RCRCbLVG4KVrMzcd0OSAFcj/7Ps5pJZR5J99hAsu6RISPy4QGT2ihxAsri4uOj2JqBSUoQAIpnxJHRzUqVKFbi7u2P58uUM+USUK6tYeQCRJGBgT8noq2CpHCUM7Ak82Z+9GaFNWiJsrNMA2OT9QiKq9IodQKgMFGUVLHZAzEJJlgsmIvMgRWVP7Har5YZ3XzV++MjSorGEw1Xlu6GfOxKJJt18jFQREZmCIk1Cp7J16bK8q3H1PodgERFR/iyiszsgyqpuJhM+gMyJ6PdS5RsjHv7zCSeiE5k5BhATERsvcPhImuzYkbOW8r+k9TogUjqHYBEREWARrbe0rZvp7IIOZO5TkqSUd0Bsk6IQFW2kgojIJDCAmIjIaEDKkHc1MiQr+V/S7ICUKa1Wi8WLFyMiIsLYpRARFU5GBixi9f6hMLEA4uYCJNvLa7JPijT6BHkiMi4GEBPh5gJYauWhQlhayf+SlgUQdkBK244dOzB58mQ8ePDA2KUQERVOTAwk/blirq55n2sEKkcJnfq6y461qxFtUsPEiKj8MYCYCJWjhI7NM2THOnawlv8lLRuCxQ5IaRJCYM6cOejUqROaNm1q7HKIiAon0mBncRPrgABAkw7ymvzsovI4k4jMBVfBMiF1fOUBpH69fHZCz2AAKU379+/HiRMnEBISYuxSiIgKrwIEEFhbI83WAdYpCZmPDWsmIrPDDogp4T4gRvPll1+iRYsW6Nmzp7FLISIqvCiDboIpBhAAaY56dRnWTERmhx0QU2IYKvLZB8RwwjoV37Fjx3DgwAH89ttvkCSOSyaiCqQidEAApDu5AhF3Mh+wA0Jk9tgBMSVF6YBkZADcpK5UfPXVV6hXrx5eeOEFY5dCRFQ0hh/mTWwSepZ0Z726GECIzB47IKbEMIAoFPk/Tk/PeYyK5Pz589ixYwdWrVoFCwvmcSKqYPQ+zGcorGFpb2/EYvImXPVWwmIAITJ7/MRlSorQAcn1fCqyuXPnonr16hgxYoSxSyEiKjq9D/MaJzfAVIeR6nVmtE8YQIjMHQOIKTGcA2JpKX9sGEA4Eb1Erl+/jg0bNuD999+Hgp0kIqqI9AJIuso0538AQIwiuzYR8QR/n8jI52wiquwYQEyJYaCwtpY/zm0IFhXbvHnz4OHhgfHjxxu7FCKi4tFbUcpUA0hsvMC1uOzaLLXpOLg7Hn+f1BqxKiIyJgYQU2IYQAwDB4dglZr79+9j1apVePfdd2Fra2vscoiIiufJE90vM5xNM4BERgNJSvnkeLukSOz+KzOcEJH5YQAxJUUNIByCVWwLFiyAvb09Xn/9dWOXQkRUfPqT0F1McwUsNxcgyV4ejpSJkRACiIo2UlFEZFQMIKYkLU3+uDCrYFGRRUREYPny5Zg8eTIcHR2NXQ4RUfHpBRCti2l2QFSOEp7taBBAkiIhSYCri5GKIiKjYgAxJXodDWFhwUnoZeS7776DJEmYPHmysUshIiq+5OTMr6eEm3s+JxtX0+c8ZI+VSZEY2FOCytFEV+0iojLFfUBMiX6gyG1VJs4BKbHY2Fh8//33eP311+FmojsGExEVisF+GsLVhP9OM/j7tlu9KLg1ZvggMlfsgJgSvSFYwiqXAMIhWCW2ZMkSJCcnY9q0acYuhYioZPQmoAPI8SHfpKhUsq6+TRz3AiEyZwwgpkS/A2K4BC/AIVgllJSUhG+//Rbjxo2Dj4+PscshIioZwx3F3U13CFbmhI/sSfKCu6ETmTUGEFNS0BAsdkBKRK1WIzIyEu+//76xSyEiKjmDD/GSuwl3QAB5h4a7oROZNQYQU6I/CT23IVjsgBRbWloa5s+fjxEjRqBmzZrGLoeIqOQqWgDR69BIUQwgROaMAcSU6C/DW4hJ6AlxDCCFtXbtWty7dw8zZswwdilERKVDbw6IVpJg6eZsvFoKQy+AWEQzgBCZMwYQU1LQHBCDULJpqwanL3AX2YJkZGRg7ty5eOGFF9CwYUNjl0NEVDr0OiDJdq5ISTPxf9L1hmBZRj/J50QiquxM/G8rM1PAHJD4NHkHxCI9Ddt2C8TGM4TkZ/Pmzbh69Sq7H0RUuegHEHs3rFgP0/6hlF4HxCqGHRAic8YAYkJEAUOwYpLlxyy1GggBREWXdWUVlxACc+bMQY8ePdC6dWtjl0NEVGo0j7I/xCcp3SAETPuHUnoBxDI5EUhJMWIxRGRMJh9A/Pz8IElSjq+33noLADB27Ngcz7Vt21Z2jdTUVLz99ttwd3eHvb09BgwYgPv37xvj7eQvLf8OiLO7fFiWZXpa5sqGLmVdWMW1c+dOhIaG4qOPPjJ2KUREpSojQj+AZC5xa9I/lDLcp4RL8RKZLZMPICdPnkRYWJjua8+ePQCAl156SXdO7969Zef8+eefsmtMmTIFW7Zswfr16/H3338jISEBAQEByMjIKNf3UhChNwRLymUOiKOzQQDRajCwpwSVI3eTzY0QAl9++SXatWuHLl26GLscIqJSpYjJnkeR6OABAKb9QynDfUoMN1IkIrNhVfApxuXh4SF7PHfuXNSqVQudO3fWHbOxsYGXl1eur4+NjcXKlSvx888/o0ePHgCANWvWwNfXF3v37kWvXr1yfV1qaipSU1N1j+Pi4kr6Vgqm3wGxzmUVLINQEvCcBsrGDB95OXz4MI4ePYodO3ZAkvh9IqLKxTIq+wN8ktIdkgTT/qEUOyBE9JTJd0D0paWlYc2aNRg/frzsA+XBgwfh6emJOnXqYOLEiXj8+LHuudOnT0Oj0aBnz566Yz4+PmjUqBGOHj2a572++uorqFQq3Zevr2/ZvCl9BS3DaxBAlFbciDA/c+bMwbPPPot+/foZuxQiotKVlgbo/WBM6+qOd1+V0MKUfyjFDggRPVWhAsjWrVsRExODsWPH6o716dMHa9euxf79+7FgwQKcPHkS3bp103UvwsPDYW1tDRcXeU+6SpUqCA8Pz/NeM2bMQGxsrO7r3r17ZfKe9MmGYBVmJ3T9wEIyp0+fxq5duzBjxgx2P4io8jHoHghXV9PtfGRhACGip0x+CJa+lStXok+fPvDx8dEdGzZsmO7XjRo1QsuWLVG9enX88ccfePHFF/O8lhAi3w+mNjY2sLGxKZ3CC0t/GV6bXPYBMZwXwgCSpzlz5sDf3182V4iIqNIw+PCe4eqex4kmxNkZwsICklab+ZhDsIjMVoXpgNy5cwd79+7FK6+8ku953t7eqF69Oq5duwYA8PLyQlpaGqKj5cuCPH78GFWqVCmzeouloA6IYQDRcCf03Fy8eBGbN2/Ghx9+CEtLS2OXQ0RU+gwCiLYiBBALC2SoXLMfswNCZLYqTAAJCgqCp6dngeP5IyMjce/ePXh7ewMAWrRoAYVCoVs9CwDCwsLw77//on379mVac5EVNAfEyqBhxQ5IrubNm4eqVasiMDDQ2KUQEZUNw+6B4fAmEyULSuyAEJmtChFAtFotgoKCMGbMGFjpfQhPSEjA9OnTcezYMdy+fRsHDx5E//794e7ujkGDBgEAVCoVJkyYgHfffRf79u3D2bNnMWrUKDRu3Fi3KpbJKGAndEgShP5xBpAcbt++jbVr12L69OmwzmUpYyKiSsGwe1BBAohw1VsJKyLCeIUQkVFViDkge/fuxd27dzF+/HjZcUtLS1y4cAGrV69GTEwMvL290bVrV2zYsAGOjo668xYuXAgrKysMHToUycnJ6N69O4KDg01veI5+AMnjw7OwUkDKOo9DsHL4+uuv4eLiUuBQPSKiCk0vgGglC1i6OhuvliIQ7tlL66dHRFaMDyFEVOoqxP/7PXv2hBAix3E7Ozvs2rWrwNfb2tpi8eLFWLx4cVmUV3oKGoIFQFhbA8lJOc8nhIWFYeXKlZg5cybs7e2NXQ4RUdnRCyDJSlfYKivEP+eIVrjB++mvE25F4MYFYdpLBxNRmagQQ7DMhZRewBAsAFDodUbYAZFZuHAhbGxs8NZbbxm7FCKisvVEfxNCN5T3oo3FERsvcC0he6iYMvEJtu0WiI3P+QNGIqrcGEBMSUFzQAyPswOiExUVhSVLluCtt96Cs7OzscshIipb+gHE3j3XldtNTWQ0kKjMDiDWmiRYpSYhKjqfFxFRpcQAYkoKMQdEdpwBROf7779Heno6pkyZYuxSiIjKnqwD4grbCtABcXMBkhzkk+WVyZFwdcnjBURUaTGAmBBJU/AcENlxDsECkLka2nfffYeJEyfC09PT2OUQEZU9vRWkEh08KkQAUTlKePY5D9mxvo2emP4O7kRU6hhATIUQkNLTsx/nFUDYAclh2bJliIuLw/Tp041dChFR+ZANwfJAmqZizKOo3VIeQBq4cC8QInPEAGIq9MMHUKgAIhhAkJKSggULFmD06NGoVq2ascshIip7SUmZX08l2nsgeBNw+kIFCCGG+5VwN3Qis8QAYioMw0Qec0Ak2SR0DsFatWoVwsPD8cEHHxi7FCKi8mHwoT3J3g1CoGKsKOUh74BwM0Ii88QAYioM53Pk1QHRW+pEpJp3ByQ9PR3z5s3DSy+9hDp16hi7HCKi8mHwoT3RIfNDvRAw/RWlHByg1V9Onh0QIrNUMXYuMgeFDCCS/hAsM5+EvmHDBty6dQubN282dilEROXHIIAk2WcOa5IkmP6KUpKEdBd3WD9+mPmYAYTILLEDYioMw0Sey/DqBRMz7oBotVrMmTMHffv2RdOmTY1dDhFR+TH40J5o7wFJAgb2lCrEilLCLXsYliacAYTIHLEDYioM54AUpgNixpPQt2/fjosXL2LFihXGLoWIqHwZdECUVd3xSmDFCB9A5maEWasG3z8fgagLAi0aV4zaiah0sANiKgo7B4T7gEAIgTlz5qBz585o3769scshIipfeh0QjZUt7D3tK0z4iI0XuJ+RvRKWfWJExZg8T0Slih0QU1HYAMJ9QLBv3z6cPHkSu3btMnYpRETlz2ATQju7ihE+ACAyOnPIWBZlQoRu8rzK0YiFEVG5YgfEVBR6DggDyJw5c9CiRQs8//zzxi6FiKj8yTYhdK8Qu6BncXMBkhz0AkhSJCyE1vQnzxNRqWIHxFQUcg6IuQ/BOnbsGA4cOIDffvsNklRxfupHRFRq9Dsg9u6wszViLUWkcpTg38oDeNrAthBaDGoTDZWjR/4vJKJKhR0QU8EhWIUyZ84c1K9fHy+88IKxSyEiMg69AJJk7w47m4r1w5jqTT1lj5u6czNCInPDDoipKEYAkdLNqwNy7tw5/P7771i9ejUsLJidichMyeaAeMK2AnVAAHA3dCJiB8RkGHYz8poDoh9MzKwDMnfuXPj5+WH48OHGLoWIyDg0GiAqSvcw0d6jQg3BAsAAQkTsgJiM4nRANOYTQK5du4aNGzfi+++/hyKv7w0RUWUXGSl7mOjgDrsKNAkdQI4AknzvMeyMVAoRGQc7IKaiGPuAmNMQrHnz5sHT0xPjxo0zdilUTH5+fli0aJGxyyCq2B4/lj1MtPdEekYF20PD1RVCbxjtsZAInL5Qwd4DEZUIA4ipKNYckHRAVP6/tO/du4fVq1fj3XffhW2FG+xcMYwdOxaSJOm+3Nzc0Lt3b5w/f97YpZWK4OBgODs75zjOUEQVjsFwpUQHD/y8GRXqA3xsooREOzfdYyU3IyQyOwwgpqKwc0AMj5vBUrwLFiyAg4MDXnvtNWOXUqn17t0bYWFhCAsLw759+2BlZYWAgABjl1UhpJnZfCwyIsMAYu8BIVChPsBHRmdOns9ir7cZIRGZBwYQU1GMIVgAKv1E9IiICCxfvhyTJ0+GoyO3yS1LNjY28PLygpeXF5o2bYoPPvgA9+7dQ8TTDzwffPAB6tSpA6VSiZo1a2LmzJnQGPy53b59O1q2bAlbW1u4u7vjxRdfzPN+QUFBUKlU2LNnDwDg119/RePGjWFnZwc3Nzf06NEDiYmJuvPVajUaNmwIGxsbeHt7Y9KkSbrnvv32WzRu3Bj29vbw9fXFm2++iYSEBADAwYMHMW7cOMTGxuo6PLNmzUKXLl1w584dTJ06VXc8y9GjR9GpUyfY2dnB19cXkydPltXi5+eHL774AmPHjoVKpcLEiRNL8J0nKoL/b+/M46Ko/z/+muU+lpVDLhEEwRNQA2/zSIW80sw8stKstDTL1I5vd31/aYeWZWlapvbVvmrfvNIUlRTvCy+8RVE8QBRhOeXaz++PYWdn9oAFll2WfT8fDx7OZ+YzM5/ZHWc/r3lf2i5YlRN5a5rA6xQjLLwPjgMVIyQIG4IESEOhNnVAgEYvQL777jvIZDJMnz7d0kOxKQoKCrB69WqEh4fD25t3lZDL5VixYgXOnz+P7777Dj///DO+/fZbYZ+tW7di5MiRGDJkCE6ePInExETExsbqPf68efMwe/ZsJCQkYODAgcjIyMC4ceMwadIkXLhwAXv27MHIkSPBKl0MFy9ejGnTpmHy5MlISUnB5s2bER4eLhxPJpPh+++/x9mzZ7Fy5Ur8888/ePvttwEAPXr0wIIFC+Dh4SFYeGbPno3169cjKCgIn332mbAeAFJSUhAfH4+RI0fizJkzWLt2Lfbv3y8RPADw9ddfIzIyEsnJyfjwww9N9+ETRFWILCDldg4ocfYAAKuawCvkHBThIgtI4T0Mj+OgkFtXPROCIOoAI4xCqVQyAEypVNbPCX78kTH+JRb/l5Ojv98vv0j7ZWbWz3gaALm5uUyhULBZs2ZZeiiNngkTJjA7Ozvm5ubG3NzcGAAWEBDAkpOTDe7z1VdfsZiYGKHdvXt3Nn78eIP9Q0JC2LfffsveffddFhAQwM6cOSNsS05OZgDY9evX9e4bGBjI3n//faOvZ926dczb21toL1++nCkUCoNjEvPcc8+xyZMnS9bt27ePyWQyVlxcLOw3YsQIo8dDECZjyhTh+Z+raMY++LqCfTivgh0/o7L0yGrGa68J11Hq7Wfp0RCETVPvc1w9UBrehkJJibTtZCCvog1ZQBYtWoTi4mLMmjXL0kOxCfr164fFixcDAB48eIBFixZh0KBBOHr0KEJCQvC///0PCxYsQGpqKgoKClBeXg4PDw9h/1OnTlXrijR//nwUFhbi+PHjCAsLE9Z36NAB/fv3R1RUFOLj4xEXF4dRo0bB09MTWVlZuHPnDvr372/wuLt378acOXNw/vx55OXloby8HA8fPkRhYSHc3Nxq9DkkJycjNTUVq1evFtYxxqBSqZCWloa2bdsCgEHrDkHUKyIXrCK3pvDxBCaOtkLrga/GAmKfcw9QqQAqMEsQNgP9b28okACRUFRUhG+//RaTJk1CQECApYdjE7i5uSE8PBzh4eHo0qULli1bhsLCQvz88884fPgwxo4di0GDBmHLli04efIk3n//fUnwtYtL9Zn8H330UVRUVGDdunWS9XZ2dti5cye2bduGdu3aYeHChWjdujXS0tKqPe6NGzcwePBgREZG4s8//0RycjJ+/PFHANCJUTEGlUqFKVOm4NSpU8Lf6dOnceXKFbRs2VLoV1NhQxAmQVwF3c0HTRSwPvEBSAQIp1Lh+plsqwmiJwii7pAAaSg8fCgsquzsDb8J0o4NaaRZsJYtW4YHDx7grbfesvRQbBaO4yCTyVBcXIwDBw4gJCQE77//PmJjYxEREYEbN25I+kdHRyMxMbHKY3bp0gXbt2/HnDlz8PXXX+ucr2fPnvj0009x8uRJODo6YsOGDZDL5WjRooXBYx8/fhzl5eWYP38+unXrhlatWuHOnTuSPo6OjqioqNDZV9/6Rx55BOfOnRPEmPjP0VB2OoIwFyILSKG7FVZBVyMSIACwefVdzF/KrCqdMEEQtYdcsBoKIguIysHJsDLUmgDl55TAUrmhlPkM2Tl8RhNTvoErLS3F119/jXHjxkncdIj6paSkBJmZmQCAnJwc/PDDDygoKMCwYcOgVCqRnp6ONWvWoHPnzti6dSs2bNgg2f/jjz9G//790bJlS4wdOxbl5eXYtm2bEAyupnv37ti2bRsef/xx2Nvb480338SRI0eQmJiIuLg4+Pr64siRI7h3757g7vTJJ5/glVdega+vLwYNGoT8/HwcOHAA06dPR8uWLVFeXo6FCxdi2LBhOHDgAH766SfJOVu0aIGCggIkJiaiQ4cOcHV1haurK1q0aIG9e/di7NixcHJygo+PD9555x1069YN06ZNw8svvww3NzdcuHABO3fuxMKFC+vxGyAIIxAJkAJ3v0YjQNwKsnCPtcOmHQzhLazUqkMQhNGQBaShIBYgjgbcrwAdAbJqXalF3hjtP6bCvCUMy9cxk7+1WrVqFW7evIl3333XZMckqmf79u0ICAhAQEAAunbtimPHjuGPP/5A3759MXz4cLz55pt47bXX0LFjRxw8eFAn81Pfvn3xxx9/YPPmzejYsSMee+wxHDlyRO+5evbsia1bt+LDDz/E999/Dw8PD+zduxeDBw9Gq1at8MEHH2D+/PkYNGgQAGDChAlYsGABFi1ahPbt22Po0KG4cuUKAKBjx4745ptv8OWXXyIyMhKrV6/G3LlzJefr0aMHXnnlFYwZMwZNmzbFV199BQD47LPPcP36dbRs2RJNm/JpQaOjo5GUlIQrV67g0UcfRadOnfDhhx+SKyBheUpKgNxcoVkobzwCxL2AF1bWlE6YIIjawzFmA6W0TUBeXh4UCgWUSqUk8NZkTJsGLFoEAHjoHQDn+3f0divY+g/ch2qCcZdO249bod0xa7L5ghD3H1MhIUm6juNgkjFUVFSgbdu2iIyMxPr16+t0LIIgiEbFrVtA8+ZCc/3oX+A3exJ6drZCa4FSCTRpIjS3jPgeR3pOM9lvCUEQxlPvc1w9kAtWQ0FkAWFVWECUDx3hLmrbVZQKb4wUZvDFUuYzHfEBwGRj+PPPP3HlyhVJBiKCIAgCwN27kmahuy+crdUC4uEBlYMjZGV8Igu3gixwANUDIQgbgVywGgpaMSCGUHhJg9BlFWVmLUCVbcA0booxMMYwZ84cDBw4EJ07d67bwQiCIBobWlXQrToGhOOg8tG4Ybnn30W3GCAmisQHQdgCZAFpKIgFiJPhXxR3T6k4cagoMesbI29PXmxoO+7F9a570OC2bdtw+vRp7N69u07HIQiCaJTosYBYrQABUOzhC3nGLQC8BeR0poUHRBCE2SALSENB7IJVhQVEuz7Ik/3LzPrGSCHnMGyAdF2L5kCvznW7lRhj+Pzzz9G9e3f06dOnTsciCIJolGhZQKxZgCjzGTLRVGi7F2Thxm1QLRCCsBHIAtJQMDIGRNvh140rMdCxflDmM7hq1YXz9a77cffu3YuDBw9iy5Yt4DgywRMEQeggEiAPnT1Q7uCM8jIGwPqemdk5QIG7xgXLrTIL1s3bDIo21nc9BEHUDLKANBRqKUDEBQzrm+QUPuXums3S9SUmKMY+Z84cREdHY/DgwXU/GNHgqKiowJdffomkJD0ZDAiCMA6RC1Zh5eR96X9hlcX7vD35NMJq3Av4a1u31TqvhyCImkECpKEgEhJVChAtFyyxcKlPlPkMm3YwndgPACitowA5fvw4duzYgffee4+sH42Q/Px8DB8+HO+//z6KioosPRyCsF60ihACfDzeph3M6lyXFHIOITEaAeJUUgCH0iKrvR6CIGoGCZCGQgO3gGTn6AaeqyksAq6l1/4HY+7cuQgPD8eoUaPqMEKiIXLr1i08+uij2Lt3L7Zu3SoUFiQIohZILCCa+AlrLd7XvKO/pO1WaQWx1ushCMJ4KAakoSASIGV2job7WcgCYij7FQCk3wGWr2PgOGB4XM3SKJ4/fx7r16/HL7/8Ajs7OxOOmLA0ycnJGDZsGBwcHHDw4EFERkZaekgEYd2IBIjaAgKYJg26RfDzkzTleZnI9Qq13ushCMJoyALSQHiYrxESt3OcDPvA2tmB2Yt0o5ksIPqyX8ldpe3amM6/+OILBAUF4bnnnjPBKImGwqZNm9C7d28EBQXhyJEjJD4Ioq5UVAD37gnNAjlvPeBf/Fhp8T4tAaKOA7Ha6yEIwmhIgDQAlPkMJXkaAVJu71TlRJ6J64SYMQi9fSvpD0K+Hnf+mpjO09LS8Pvvv+Ott96Co2MVVh/CamCM4dtvv8WTTz6JQYMGYc+ePfD3969+R4IgqiY7mxchlRTIfRHVBpg1mbPe4n1azwa3/Lto34qKERKELUACpAGQnQPYlWsESIW9U9UTebEblplcsADg7v3qLRs1MZ1//fXX8PT0xEsvvVTHkRENgfLyckydOhUzZ87E22+/jXXr1sHV1bX6HQmCqJ5MaZW+Ark/QppZuaXA2xuQaaYh7gVZuPeAaoEQhC1AMSANAG9PwL5CbAFxrnoiLwpEZ8XFZssAf/e+7rqAWyfQ+sJWMJkdzkeOQM/n2xv1g5iRkYFff/0VH330EU1SGwF5eXkYPXo0EhMT8fPPP5OoJAhTo1UFPV/uDw+5hcZiKuzsgKZNhWtzz7+LrPvA/KWsxvGEBEFYFyRAGgAKOQeVSmoBqdIHVixASkrMJkAcHUQNxtBv52d4bOdnwqrHdnwCmff/AVHv6t3/iy++wP379zFv3jx88803cHJywtSpU+t51ER9c+PGDQwdOhQ3b97E9u3b0b9/f0sPiSAaHzoWED/YyayzCKEEPz+NABFlwdq0gyG8BazbwkMQhEHIBashwBhkpRoB0qadU5VvfjhnjQsWKzZnJXTNmDofXiIRHwAgU1UA//oXsGaN3r3Pnj2LY8eO4cGDB1i8eDFee+01NGnSpD4HTNQzx44dQ9euXVFYWIhDhw6R+CCI+kLLAlLo7odVGxpB0T5RILq6GjpAqXgJorFDAqQhUFYmaTp5VFEHBJBaQIrME4SuzGe4dYf/oYvxuIGh29822LfipcnAfV1/LQcHB5SXl2PhwoVQqVR444038Pfff2Pnzp31Nm6i/vjzzz/Rp08fhIWF4fDhw2jbtq2lh0QQjReRBeShkxxljq6No2ifKBBdnq+5RkrFSxCNmwYtQD755BNwHCf5E2fUYYzhk08+QWBgIFxcXNC3b1+cO3dOcoySkhJMnz4dPj4+cHNzwxNPPIFbt26Z+1KqRiuTlcy5agHCiYLQmRmC0JNTGOYvZTh2hm+3XTcXsqJCYfuOQXOwt59GkNgV5qPk8690jmNvb4+HDx/iu+++w6RJk/DFF19gyJAh2L9/f71fA2E6GGP46quvMGrUKDzxxBP4559/4Ovra+lhEUTjRlwDRC7+HbRyS4HoN929UoBYdWphgiCMokELEABo3749MjIyhL+UlBRh21dffYVvvvkGP/zwA44dOwZ/f38MHDgQ+fn5Qp8ZM2Zgw4YNWLNmDfbv34+CggIMHToUFaJ0hhZHS0RwLjWwgBTXrwVEmc+waQcTChC652Wg5Z6Vwvabzbtgf9+3kBj/b9xr2lpY7/DTD8CDB5JjOTg4IDMzE3l5eTh27BgWLlyI7777Dp988km9XgNhOsrKyjB58mS88847+OCDD/D777/DWXQ/EgRRT4gsIGIBYvWWgoAAYdGppACOJQUY0IsC0AmisdPgBYi9vT38/f2Fv6ZNmwLg38IuWLAA77//PkaOHInIyEisXLkSRUVF+P333wEASqUSy5Ytw/z58zFgwAB06tQJq1atQkpKCnbt2lXleUtKSpCXlyf5qze0BIisBgKkvtPwZudIq593Pvwz7CtKhXbSgPfAZDKo7OyxO+4jYb3sYTHS562WHIvjOGRkZMDR0RE3b97E7t278frrr4Pj6IfGGsjNzcWgQYOwcuVKrFixAv/+978hkzX4RwhBNA4kFhDe4tgoLAVatUDc8zOhYlZ8PQRBGEWDnz1cuXIFgYGBCA0NxdixY3Ht2jUAfBG7zMxMxMXFCX2dnJzQp08fHDx4EACQnJyMsrIySZ/AwEBERkYKfQwxd+5cKBQK4a958+b1cHWV1FSAiOuA1HMhQm9P/kcOAMAYOpxYJWy717Q1LrcZIrTPRY1CvujNnMPKZVDmqYT2tWvXwBhDZGQkTpw4gV69etXr2AnTkZaWhh49euDEiRPYsWMHJkyYYOkhEYRtkZEhLKotIHG9G4GlQEuAyPMyUFRsxTEtBEEYRYMWIF27dsVvv/2GhIQE/Pzzz8jMzESPHj2QnZ2NzEpztJ8og4a6rd6WmZkJR0dHeHp6GuxjiH/9619QKpXC382bN014ZVpoCRC7amJAJBaQehYgCjmHrp345eY3DsM7+6qw7VTs82CiN+AqO3ucjNVMTAPunEb+EU1MzgsvvICxY8fiwIEDVB27jijzGa6lmyf49NChQ+jatSvKyspw+PBh9O3bt97PSRCEiNJSSWKPfDnvtrRjbyMo2idywQJ4C0hRsYXGQhCE2WjQdUAGDRokLEdFRaF79+5o2bIlVq5ciW7dugGAjvsOY6xalx5j+jg5OcHJqRohYCq0LSCuDccFCwBcKk8XeeYPyfozncbp9D0Z+zx67/5SaPskbQAGRgEARo8ejdGjR9ffQG2E5BRNXA7vglF/b0HXrl2LCRMmoHPnztiwYQN8fHzq5TwEQVSBdhFCD/4FjjoAXWHNBQm1LSD5mbhfZKGxEARhNhq0BUQbNzc3REVF4cqVK8IbdG1LRlZWlmAV8ff3R2lpKXJycgz2aRBoW0BcqwnqFQkjrqT+0/BWVABgDK3PbxHWpYd0R9cRIYJ7lvrf+75tcM+3jdDP+e+N9T4+W0I7KUB9peFkjOHzzz/H2LFj8fTTT2PXrl0kPgjCUojcrwCNBcTqA9ABQKGQ/KaRBYQgbAOrEiAlJSW4cOECAgICEBoaCn9/f0kNidLSUiQlJaFHjx4AgJiYGDg4OEj6ZGRk4OzZs0KfBoFODEg1AkRkATGHAMlVAk2zLkrcr2QjhqFXZxlmTeYwaTSHuN6a/hfaD9c0Tp4E0tPrfYy2gnZSAMD0aThLS0vxwgsv4IMPPsAnn3yC3377zXzWQIIgdNESIAUeAY0jAB3gVZTIDYsECEHYBg1agMyePRtJSUlIS0vDkSNHMGrUKOTl5WHChAngOA4zZszAnDlzsGHDBpw9exYTJ06Eq6srnnnmGQCAQqHAiy++iFmzZiExMREnT57Es88+i6ioKAwYMMDCVydC242qusmeWICU1q8LVnIKw5mLQKsLWyXrg15+AgAfI+Llyfsiq7nQ/glJ36LNCfU6RltCkhSgkrq8BS0oKJDUz3nw4AHi4uLw3//+F6tWrcLHH39MWcoIwtJoCRCPCH/MmsxZfwC6GnExwrwMFBQ2gtgWgiCqpEHHgNy6dQvjxo3D/fv30bRpU3Tr1g2HDx9GSEgIAODtt99GcXExpk6dipycHHTt2hU7duyAXK5xiP32229hb2+P0aNHo7i4GP3798eKFStgZ2dnqcvSpaYCxEwuWGp3HwBoeUWTtlgVHAJZu3ZCW/ut/O3mnVHs0gQuxbl8/x2JwGsv19s4bQmFnMPwOGBjguYDr8tb0FWrVmHfvn2Qy+VITU3FkCFDkJ2djcTERMpSRhANBZEAqZDZwT2kqfVbPsSILCDyvEyUVwDzl7J6jW8jCMKyNGgBsmbNmiq3cxyHTz75pMpCds7Ozli4cCEWLlxo4tGZEO1MVjWwgMjKSiFEI5sYtbCwKy9FSJqmWnlh9/6Qi86nfisvxCXI7HAt/DG0T1kPAHA9mAioVADVjDAKZT5Ddg7/ueqbZDwSCWysNCo1D6j9DzRjDD/88AOGDx+O9PR0jBgxAt7e3jh8+DDCw8PrcgkEQZgSkQApdPeDi2sje5aKBUg+f63q+LbwFvqfgwRBWDeN7ClmpdTBAqJ3fxOhFhZB6YfhWKZxyrWL6y/px7+V5yQaKDVC4+Imy74PnD5dL2NsbCSnMMxfyrB8Hf9vcoquG0JpmWbZyan2KXn37NmDc+fOoW3btujfvz+ioqJw6NAhEh8E0dAQCZB8jwC4ODeyCXlgoLDoXpAFWQX/kGMMuHmbXLEIojHSoC0gNkMdYkAA8BYU7XUmQCHnMKgvQ3HCbsl618GP6fSNieIQ3oIPhr59l+HYfalIwb59QKdOJh+jtSO2dgDQm+FK+w1goShFZa6Sd1WoaUpeZT7Dl18thLdPU8yZMwfjx4/H2LFj8fXXX0Mul+O9994z4VUSBFEnxAJE7o/q8pRYHSIBAvCB6HlN+OK/a7cAufkq9OpM70sJojFBAqQhUEcBkpf9EB5NTDskNa1bcshJO6BZ0batTt52NQo5B4UccHQEErxbosDDH+55lWmS9+4FXn+9fgZppYjreQBA+1aGM1yJ8/wXiATI/RxpX2NcFpJTGH79/QoStm8EwODnF4Q1a9Zg9erV8PPzwwsvvFDXSyMIwpRoWUBcXSw4lvpAS4B45N0RBAgAJCQBAIkQgmhM0P/mhoB2DEh11gwtgfLzr8V6XXVMQXFBOYLSD2tWGBGY7OEOgOOQFvqoZuW+fbqzaxtGu54HAJy7rNtPX4arwiqKdFWXkld93uMHFgFgcHJuAp+Anpg3/wdcvHgRGRkZmDt3bo2uhSCIeqSiAhDVu8r3aNboLSBy5R2dLo2i6jtBEAJkAWkIFGlmlIzjwFUjQAqZE9xEbbvyknoL1lOdOg2n0kLNCiMEiJsrIOOAG6GPIup0ZfX0rCzg8mWgdWuTjs9a0VfPQx/6MlxVJUC0BYt2QLv6vP2HfIWOXV5CU7+24DgOw5/kEBrcyPzKCaIxcPcun8SjkjxFQOMXIHkZOl0aRdV3giAESIA0BIo1Ad4Vji6wryajVV6Zs0SA2Jc9rLeHs/2h/dIVPXtWu49MxsHVleFGC62+hw+TAKnE27P6Ph7uQHgL6TplPkPqDcPKRS1YlPkM+48yHD7Jr1fHh6iPZ2dnD1//dsI2q6+mTBCNlTtSa0CeRzNUlDMAjeiFgZcX77tbWgqAd8HShp5TBNG4IAHSEBBZQMqdXKv9UuTeUhcs+/KH9fZwdkoWuV/5+QFhYdXuk5zCUFAIFPlHosTRTWNBOXwYmDDB9IO0QhRyDq1bMly6arhPXgEfYB7Xm0EhB66lA8fPAJyqAk3vXUbTrAtQ5N6ES1EO7CpKUWHniBgHb1xNbI4td9og2ycCkPH1btTxIbMmc1B4AMo8/hyNppoyQTRWbt+WNPM9AvHzGmB4HGs8NTLU1dBv3AAAyLUECAd6ThFEY4MESENAJEAqnFyr7e7uJY1AdKgoqbeHs2vKUU2jW7dq642Iixeq7Oxxu3lnhF3dw288fNjwjjZIUy9UKUAAXjgkJAFu+XfRLmU9nr2wFSFp++Fckq9/h11ASwBvAHjo7IHrYb1xqe0QnI8aiSI3HzzIARwdNN1nvgw08aAfdYJosGhbQBTNGmeNjMBAkQCRumA92pUKEhJEY4MESENAJEBUztULEO0g9LFxJXCrj4fzvXtwvpOmaXftWu0u2rENt4K7agTImTNAYSHg5qZ3X1sjN6+aDowh9OoedNu/EK0vbIGdqqJGx3d+mIc257egzfktGLphOi5EDodb+HSUl/WC2n3D2Yl+1AmiQSOygJTbOaDY1RtAI4yJEMWBaLtgUf4Sgmh8UBashkBNBYhWkLqbrH4KEeLoUWnbCAGiLl6o5lZwF01DpQKOHzfR4Kyb5BSGs5c07WBpDCbCriRi8g89MWnJALQ7t6lK8VFu54ASJ3eU2zkY7GOnKkfkmT/h+2RfPPVFX4Rc2wsAKCo2uAtBEA0BkQUk3yMQTMb/bDe6mIhmzYRFuVLqdlb8ULszQRDWDllAGgKiIHSVsxEJ3l20+hTX0yzyyBFhkXEcuNjYanfhq6JrCurdDNYSLcnJQJ8+ph6pVXErQ4VNCdJ1NyvnGJ73r2LI5hlofeFvvfsWuPvicuvHcSPsUWQ064QH3i1R4iTnZyOMwelhHryzUxFw+yRaXNuHiEvb4VZ4X3KMkLT9eGlxP5yPHIGSPt9C2SZEkinLVGhn4CIIohZoCRCgkcZuiQSIa3EOHEqLUObIv5AjAUIQjQ8SIA0BcRpeYwSItgtTYaH+fnVFZK14GNYWLh4eRu0WE8Vnwfp9A8AFBPCmdfWPqI1bQJJTGDYm6NmgqkDPvQvQP+EjOJRLf20rZHa4EPkkjnV9GWnh/cAqA8t14DiUuChwJygGd4JikNz1JcgqyhF2ZRc6H/kZbc5tgkzky9Du7EaUPboDOx6fgyM9pgF2MqMrqRtznWoRWpMK7QRBaCFywcpTBCLQD3hmRCMTHwAQFCRpypW38aBpBAAgv8ASAyIIoj4hAdIQELtguRgRH+EqddNSFRaa3peOMYlYKGwXg5oU323mxwFgKCgCWEwsuDub+Q3Hjpl0mNbErQyVXvGhyEnHqP8+jxZp+yTrVZwMJ2MnYM+A95HrFVqrc6rs7JHa5nGktnkcXveuoN+u/0P0ydWCEHEoKcKQTTPQ5txf+HPsCmzaEVjnwFbtIouNMmCWIMyFSIDkewSiiUcj/X+kJUAUyluCACkkV1GCaHRQDEhDQJyG19GIab62AMmrBwvIrVvAvXtC84gspkbV1t3dADsZP/nMahGj2ZCaCuRUUaq7kZKcwrBkte76iIvbMPXbR3TEx7WWffHjzJPYOPqXWosPbR40jcCf41Zi8RvHcSOkh2Rby9REfhxX/sHN2wzX0lmtqw7rK7JYXYV2giD0UFgoeV4qFUFwdqqivzUjcsECAA9RHEhJCer0TCIIouFBAqQBUJKrESC38lyrn+g7OIA5aAKOK/KrKI1dSwr3S12l7jR7BJt2GP8DIOM4Id1rQlGMdOOJE6YYotUgTk0swBh6//MFnv11GFyLNROMh05ybBy1BMun7IJH10hJQL96keOA+D7VZkQ2SGazjlg2NQl/jViIUgeNmHUvvIeJS+Nx850FWL5WhflLGZJT+O+8Jj/+jg66/QwFzNb02ARhU2jVAMlrYlsCRP2MKygClq9jwjOJIAjrh1ywLIwyn0GWVwT1b0qZgyu2GeGuwrm5Abm5AIDy/EKY+jep9OBxodq6ipMho1nHGqV93H9MheLK5Fx3grQEyMmTQP/+Jh1vQ0WZz3D2EpNYBOzKSzH8f5PRKfk/kr43g7ti3fjVgsVjxOP89/8gRzN5Vy8r5BxcnKWuTjWByWQ42nMqrrYagNGrn0Hgbb5kuoypMGjzLPjcvYgtT/6AjQn26vh2g7Ec4mDz1OvAph2659MXMEtxIgRRDbduSZpKRRCaNtbU2c7OgLc3kJ0NAOjqexuhI4Hf/tR0IXdOgmg8kACxMNk5QGCZxsG13MHFuIm+SICwAtO7YLlfOiks32/aGmWObkanfVTmMyQkadqF7r5QKppBoTap24gFJDmFYVMCg1gfOD7Mx7jfnkL4lURJ36xnXsOv0V+j3M5RJ8ON+D4QL8dEcQhvoREl/OS/ZoIku2kr/DxtP4ZsfB2xR5cJ6zsf+RnyvDtY9+waIRMNY8DGBAZHB4bgZvz4xCLCEAG++kULxYkQRDXcvClp5jVmFyyAjwOpFCAK5W3c0/MoaHT1TwjCRiEBYmG8PQGHUo0LVZmDq3ETfVEciKrA9BF6DmdPCcsZzTrVKO1jth5f/4zAjhoBcvKkbodGhjDBFq1zKczG88uGIOimJhBfZe8A2ZKf4DtpEmbkM4mFwxgUck74IY6JgiBIfl2nOfPoocC6LYaPUe7gjE1PL0V5dCd0/vUNoeZImwtbMeHnx/GfSX+hxEUh9F+3BeA4hrjeDDv2Vl8kLCOL/zzE13QoWVe00MSCILQQWUAYxyHfI1C7Dm3jolkz4PRpfvnWLWTe0+3S6OqfEISNQjEgFkbhXA47VbnQLnN0NW6iL0rFa3ILyN27QEaG0Cxq0wmzJnNGu8doFyMEgIxmj2galy6ZPHWwvlgCS8YXaAdiuxVkYdJP/SXi46GzB07O+xuYNAkALyZCg+uWXlN9DDGeCuPiRba2fhWrX9iEEkfNvRVy/QBeWDoQLkUPJH0ZAxKSjK9QLA5AV+YzHNCTjZkmFgShhUiAFCv8UGHv2PgtIJWobt7Cjr26XeJ6k5WUIBoDJEAsTZE0gLxjZ1fjJvriTFimrgOiZaFgHTvW6IHPFyPkJJPejGYdRQdkmrdcJiA5hQ9OFAcp6ltnTrxFE2m3giy88NMA+GemCOvy3f2w7NU9KOlV/7EwS38HOrQzToRcaTMIy19JRKGrt7Cu2a1kTFwapyNCaoJYWOizkAFAz5i6p/+lgHaiUSESIPlN+Ml5oxYgwcHCouxuJmRlJTpd+BTvBEFYOyRALI2WAHHzdjXQUQuRBYQrNnEWLC0BUtK+Y40PERPFYdZkDpNGc/BwB+406yTtcOpU7ccnQl8swcYE/fEF5pyYKuQc2obzblcTl8TB7+45YVtuk+b4ZWoSMgM7ADD9mLSvkzHg9Hlg8jPApNEcZk/hMCKeMyhIbjfvjGVT9yBf7i+sC7x9Es//MhhOxUphHccBHdtL9x0Rz+lk6HJ2kgoLfRYyAOgWU/uJhaUFZ0OGhJkVI4oByVPwWaKcHC01GDPQvLmkqci7rdOFrKQE0TggAWJpiqXxG3buRpb7E1dDLzKxBUQkDnKbBOOfC161mtAp5By8PIG8AkDZJBjFLk2EbQUHTRMHkn5bfwB0Q6hD4VqWj+eXDZFYPnI8Q7Ds1d1Cga2KCtOfV5+FgTGgrEzj4qUWiGOG6j/GPb92WPbqbuR5BAjrgm4ew7PLnxBilmKiAKbS7NOxPS88e3WWYdZkDk8P5teXl0tz+KstZGJ8vGpv/TAU0G6rE26x4CBhZuWILCA5HrZlAQGAwRG3JC8rAnzJ/YogGgskQCyNlgXEXm6kBUTkgsUKikw62ao4cUpYVrtO1XZCJ0yGOQ4ZgR2F9bl7z9R5MpScwgwGV+t7w+7gwJByUYWUi6r6jxUpKUHnT0dKYj6UiiD8+kqipLCgm5Ffd03QZ2HQF1+hkHOIbCPDiHj9P+jZTVth+ZRE5Lv7CetapO3HmFVjIKsow/EzwOkLmv4PRd4SCjmHVi3545ZX6Obwj4niLWNq6vJWlwofahALjnlLGDYmkDCzWgoLhYxQAPDAg5+c25IAaW1/E7Mmc+jTjW836msnCBuDBIil0RYgHjV3wVIVFJru7WZhIWRXrwjNzIAOAGo/oRPHQvAuRzx+GWfw17Yyg5Oh6kSB3uJ+lYyI5zCgl+76Jav5DE7rtgDzlujGisxbwrB9j6ruEzSVCnj+eTQ794+wKt/dD8un7NSpar4xASZ/K60dg1NdBrOYKN4tq3O07rb7vq2xYsoOFLl6CetaX/gbw/83RWfWfydTuu/DEl1XMPEEuKRUs028XFO89bhk2EJAu/b/EW1LkD5sVZhZJenpkqayCe+eVFLaiAWkVjFC3LwJhZxDSDP+2VWsGxJCEISVQml4LY2WAOHErlVVUGLvIhQfdCwtNF0dhZQUcKIZTEalaKjthE4h5/BYD4Z/DmqOBQAO5Q/hde8KHuS000m7Wl2BOn3F/cSEtwDu3K1+bBsTGDhIozAOHAcOJrO6FcV76y1g3TqhWeyswG8vb0N201bo2A44dV7Ttb7qX2jXCKnu2Ao5hyfiOIQGq3SsSln+kfjfa1sxZsEAOJXy7n6PHF+JPEUQEh//TOiXVwAcOalCm3Be7BzWU+5FPQF2c2VVChB1cUNHB4bSMg7eVVyDQs6hQzuG05Wfa01SRlsr2v9H4noz3MqoPiuZLQizRoOWAMn1DAEALFgGDI9jjbNop5MT4O8PZFa+zaj8DFyc+Wax6TPOEwRhIUiAWBotASLJblXVbjI3QYCoffJNUkdBKztVRrOOdZ7Q9erM4Z+DTLCmqPG/cwoODm0BSMVFVQXqjCl8d/w0wz0jEzbpO0xNRYG4Erhi5Y/AN98I28rsnbD6hY2C9SfAVypA1Oerj/oX4hohxhLcjAPH6X6+V5p2wX+f/x+e+3WYkDa6b+LnyPFqgRNdJgn9tiQCWxIZ4vvoT7UL8BPg4ofSdaUiAaL7HbNqK6X7+3A4XfltTnwaCAu2/ORMcl8Y+X/HmH30/R8RF/6sih4xQH5BzcdFWAAdCwjvntToi3Y2b64RIJVB+K6VoZHazw2CIKwXEiCWRvuVjpECxMVbYylxLCsCp1IBdrK6v90UBaA/dPaAXVgLzBpTt7fJDg4cXF0Y7vm1RYXMXpjA+mekYOnv4yRv86r2569efADAnsO1Hqrec1Y1WRNPlltd/BvPLn9DkFMqjsOf437DjbDeQv9te3TP1ZDeSvPuW/orql9tHYcNo3/BqDUThXVP/PkqcrxaIC38MUnfqibEh5IZIqSeaCgtBVSMIb9A/7nV2c38fBjk7pzOd1JYrNlBJjPdZL+2VGfF0zcGcSX7qvYRijgyBsfSQtiVl4DJ7FDq6A6VnfSR3isW2C8SggeOo1IYVi/qCAtz44awWG7ngAJRVrpGXbQzOBg4Vhk7p2UBKS0DyisY7O3oniUIa4cEiKXRtoC4GJcFy9lT6qrlUF6MwYPc6z6ROnNGWMwMiIaPl2lcWUpKgQp7J9zzbStkhfLPOC2ZWAYFyPT68wNAVjbDtXTjC9/VFY4DbmYwLF/HW0kMuYKpJ4y+mWfx9KpneCFYScKQr3EuepTB42smmg3LXUjtvrVpB8OVNOm20zHPoUlOOgYkfAQAsFOVY+xvo7Fk+iEhs5cabfc2NZpJMOAhB/Ly+X5lZfoFqJglqyEcVe16FOjHITdP06dQ67+UPqFhrECoDdVZ8dRUZc3T2aeiAjh+HMpNiQj+6xhi7l5Ak5zrcCjXOMWrOA6F7n7I9onAnWadkB7aE7Ej+2P/cf3/qfSNqz5FGVFDRBaQPEVzMJkmZLMhvbQwOeJA9Bs3AMYk1d+z7vP/5wmCsG5IgFiaWrpgQStW5I1xxfAIrePrMJVKR4DI3avobyTKfCakm80MjNYIkDuacy1dDQyP5y0hwYEM6Xekx9iSaPj4zfyBkGbAweS6j1VNXG/pW3x9kzX1ZNm18D7GLx8B55J8of+R7q/iYO8ZBo8/egjg5soZFZ9hKbTFh5qk/u/B634qHkn+DQDgWpyDZ1eMwJLXDqLERQFALQ6qdw1ycwXyC/jP8UoaQ+p148encT2SzuCLREZFfUJDLa6qEgj6YlAAGDU5r8qKp35jbWzA+PXNJ9Bm73I4bfwDyMqCAoDCQH8ZY5DnZ0Ken4kWafvQY//3YL/b4fmWj+FUzHM4H/UUyh2cDY6rPkUZUQtEAqTAWzMpb4gvLUxKixaa5fx8IDcXJ281EVb9tKoRx8AQhA1BAsTS1NICoi1U5DBBLZC0NKCgQGhmBkajwASHFdelyAyIBrAaAOCRdweuhfdR5OYDBs0kUFbD3Gy3M4HmAYa394yFwXgEQ+hL96g9ifT2BGQVZRjzn7HweqCZradGDMDfwxcYLD3OcUDzZg17AmGoWjkAgOOwedRP8HpwDS3S9gMAmmZdxNO/j8fqFzYBMjsMj+NrjbQIUmHpasPlFu1lgJ0dXytk7V+mGjtvLXN0YNi0AzpC4+khVbv57TnEcPyMZKukb3WTc0NWPO1q8AbFB2NodWEreu/+CiHXDxjoZBxcRQUiLu9ExOWdKNg8C0d7TMWhXtPx0JUfjPpN+q0MFTYmSIbQuOMMrAGRC1a2h6ZAX1zvRi4MxQIEQP65NGw60lFo071JEI0DEiCWJl/z1lwls4PMWAGiZQEpVRahrinSC4+chviodwOicesq/7a2Lg96dV0KxtQCRIP/ndO4FtEfAL/95m2GB8qan+PIScPbauO2tWmH7jpttweFnMOTO99C2NXdwrr7TVth7bNrJL74fboCe482XJcrfYi/MzVqq8aOvbw73ZoJ/8ObS7vC6Q4/UWp9cRteu/oRnObNEa4vKECGuD4qg5YQe3tefJgS3sVL/5fOGO8apu/abt9l+HWd3t10jrExgcHRgSFYj5BUyDk09dJNhJB6nS/eCBgWKcFp+/H4lrfRPP2IwfM/8ApFRrNOyPaJQIHcD2X2zpAxFVyKHqBJzg34ZZ5FwO2TsK+QphZzL7yHx3Z+iu77v8O+fu/gUK/XMXSoCy6nMWzWc7836jiDhk5FhaQIoToAHeD//0W1qdszuUETEiJpFp6/DsY6StbRvUkQ1g8JEEuTp3FeL3eVw9HAW3MdtAVITmGdBUjpMY0AUXEc7vpHAgAOJzPE9639j506sHljApPUAgEAv4wUQYAAwFoDhQWroyqNcUiUDrapF4zOkKVNTJRWBqGNq9AxcaGw/aGzB1ZP3CC8XVbTMoRD547Gp8RtCGgHo6uFU0wUh6g2rPJafOH05Gage3fBkue79AsgLhZ46inhWLy/tv5vKO2mOa5GSmExEBYMXNW8YDbKXUybdVsAjtOfsrlcpdtf/dYW4C0goc011+9WkIXH/5qNjidW6z3X9dBeSOk4FpfaDoHSM1hvHzH2ZcVocTUJ7VP+ROSZ/8H5oeY541Kci7i//4WYI7+gosUPWHg7zuBxbt9lCG0AGcVsjlu3JMo8x6uFsNzoJ99aAkSRfR2co+4Lg0YbA0MQNgIJEEsjsoBUuHoYv5+WC1ZJTiHq+nvknqrxO3ngHY4yR16OHEgGusXU7Y1bTBQHPx+Gpav9UODuC/eCLAC8BaS+Ef9w1VZ8AMDxM6h0zWEIvH0CUxZPFip5qjgOfzyzCvd92+jsx0/iZFY3YTBUS0SS3jc6Gli5Enj6ac2OEyYAbdoA7dsD0G9NsSTa8UQ+ntW4nFWB2hoidgdhjInfK0j6Hk5mOJAs/SwiT6/D0PWvwa0oW9K/3M4BJ2Mn4NCjM3DPr22NxlXu4ILUNo8jtc3j+Hv4AnQ8/ht6Jc2XuAp6Z18FnhmEEZ0nYtsT36LEWff5k5AEtAhSISiAataalTRpAFaOqIBpo598N2nC/+XmAgBc7qZj+IscNiZo/tNYgxWZIIiqoV8VSyOaqVS410CAaFlAyvKKDHQ0HofzGgFyNyBKWDZV9eSgABmGx3MSNyx1QLopMNZ4ZAr8b5+UFK/YP+RTXG47RG/fHXtR9+rqFkIh5xAaXM2P/ahRwLvvatpNmgAPNQn7tSuzG0u3TuIKMfXH/RxoxXzUnMPJ/PerzGc4f4WhQo8FBOBT4qrFh2NJAUaumYgxq8bpiI9TjzyLBe9cwuZRSwyKjwBfzT0v/mwD/aT9yhzdcKzHq/ju7QvYNHIRCtx9Jdtjjq3AtG86IeiG/vzVS1YD2/eorPYetkoMCBBrceGsM+I4kOvXERPFYUAvvhkW0shjYAjCRiALiKXRcsEyGi0LSFlugYGONRjHtWtCU1w00JRv3GKiOOQ/Fg1c2QUA8M08B1lFGVR2DnU6LscBk58Byso43L7LsGNv/b5xP9HlReR6tsDoVeOQHtoTux79l8G+jd5lAgD+7/+AEyd44bFuHeAnnQWrLWBL9HsYCfTuCoSHaLKD+fsaV/vF0uw/DtjZqZBkOHRDQtO7FzDut1FomnVRsj7TPwqbRv2EWyHdqj1GUAAw/kmOryzvxrBwOb/ezweICAWStPSEys4Bx7tPQUrHsRiQ8CG6HFwEWeUH65lzHS8t6oPtw+bhcM/XdNT8gePAwWT97mZEPXD9urCoktkhTxGE3l2ALp1sQHwAvBuWuiZV5WehduUsNEFiFIIgLA8JEAuTn5kvuE7dK5XjaoqR6QU9pNaSitx8Ax2N5OxZSTMzkLdS1McbN3nPjsASftm+ohQ+9y4jy799tfupIwk4DujQDjh9XhrYHRTAjzE0WBOn4ODAsPT3+hEj1yL6Y/GMY3jo4inN0Q9pxEOjd5kA+FRW69bxwthBv5gsLTMcC6Jm31GgS0eNO5O2G1h+QfUixlIYKz5an/8Lo35/TpK2uUJmh72P/QtJ/d9Hhb2jUcdxc9W4wx0/o/lcT57jM78ZosRFga0jvkdKh9F4as0L8HrAv3iwU5VjyKYZCLyVjM1P/aQ3Za+2uxlRT4gsIEpFc6js7LHvGP9/ICaqiv0aC2ILSFoawBiaVOafzs4BcvNUaOJBDhwEYc2QALEgynyGosw8QYCUOHsYn15QIa0GoLylRMpFld6sPEZxRuqDkhkQjZ6dge6P1MMbt2itTFgZpw0KkNgOfMBwcCA/BnE8woBezGBgtyZOgcPwOOPeovftxk8iayJWlJ4hOusYpGLJJlwmAJ17UhtjYkH0WYvEMSfZlaly65t+PYDdB018UMbQY+8CxG99S7A8AIBSEYR1439HemjPGh3OrnL+pcxn2LxTuu3Acf2ZvoY8pomBSQ/thUVvJuOJP19F9Kk1Qr9Oyf+B1/2r+H3iehS5N9U5b12TUhBGIBIgavcrm0o/GxamWc7LAx48QFo6/xanvAKYvxSI76NCr84kQgjCWqH/vRYkOwdwEmWneejsYXy8hbOz5E1zzg0l1m0B5i1hSE6p+QSt5PgpyThyPUNwsIa1M4ymbVvJ2P0y9MeBTBkPDB8oQ1RrWeUkVBqPYFR8Avi36LMmcxgz1HCcCMcBsR34WAVt+lTvDaMDAzB6GDBrMkcuK5UYEwtSnbVILWLqm9LS6vvUBE6lwqDNMzFoy2yJ+EiN6I9FbybXWHwAQOIBvnigoZoiPWOkMSLD4zi0CZd+/iXOHvjjmVX4a8RCVMg076NCbhzE5B97wTP7GrQ5kGy9MU1Wg0SAaF5ymCoer8HTsqWkWZByFVv+kXZJSAL2HzMQbEUQRIOHBIgF8faUCpBSJ7nx7jocB5Vc44bl9FBTPGPTDlbjCQI7pREBmQHRAMfV34+doyMvQirpbncGvWKlk6UR8ZxJM+8o5BxcXTm9EzWxlSK8he4EV9uX3ljcXWzE8lED1GJw0mgOs6dwGBHP6UySq/rMtEUMxwHxfYAxQ3nBN2U8v2wMfasQlgeT9a+vjfixKy/BS1ufRY/930vP0et1/OfFv1Hk5qNz7PatjTv2ph18PRLtcXEc0C1G81mrhbBeEchxONpzKpZP2YkiVy9htff9VLz8Qy/43ZFaR9X1eoh64uFD4M4doZnraUMZsNSILSAACs9e1fvstuYEHwRh65ALlgVRyDmoyjR+4CXOHjVy16lwV0D2gM+e4ywSILUJerZXlULFcZAxhrv+vJNxvf7YRUcLbl8O588gvq8M3WIMu1SZAkMuQJOfgRA/YuhtclXRC7EdgOQzlKfeWMQuVTFR0JvqtyoMpQdWExQA5OYbLn4IaCxeTRSQpPfkAPSI5V2YtBnUF1Axab2Q6qNagEd3f4XgvWuFNpPJsPWJBTjSc5pkPOokCur75vzl6t0GGeP30VezRWMplO6j/vxu3mZYt1Vz394I642lrx/C9HVDYHctFQAgL7iLF3/qh99e+hu3grsKx1i3FSgpMzJejagZlTEPah748JNxm3Ln1KqGrrh3DfryzNtEgg+CaKSQBcSSlJRAVqbx9Yjq4lGjH3SZp8bnXmxJqc3k1/7EMaz7bx5+mn4Ih3u9Vv8/dh1EBQnv3AHu3zfapaq26Ht7rm1p0efiw3FAXB/Db7+Tz/CF7GryJp/QUJvvvbp9enWWIV7rO1Mvir+fmCjeEqO2oMyawqF7jK6bGMcBwc34N65iqhMfXgrgQJ9ZuNGCd7FSOTqB++MPBHz2ms79EhQgE67JkKVH37i8PIFO7Rl8XZajW/vTmPly9ZmqFHIOkW1kOud49JkI2B0+iPJOmih2l+JcTFgaj+C0A5rrZrWztBJGcPWqpJntHY4ObW3MndPFBQgMFJrOt9MQ30e3G73oIQjrhSwglkSrWpmLbw3qgACwEwkQdaVjDrWf/JY7uOJ2cBdEtwUm9q7nCbRYgADA6dNA//76+5qQ6t6eV1cB/Nwlhm17pMdkDGjmx2HWZOuqdt7Y6dVZJqrazq/T9/0o5BwUbaTfl757oLSMLzJoDIP68oJlyWoAjq5Y/cJGjP91OBIH/x+eGtgXMfKq70NA/73q4sz0WjpycnIx9/OPcOfOHURERGD06NF4+umnER0dDa4KvzH9/x+a4uaqRLAnhiPs6h4AgHNJPp7/ZRB+e2mbEK9Cb5/ridRUSfOBd0t0CrTBFxotW2pc0a5dqww4l1o26UUPQVgvJEAsSb40da5dTX/JFWILCO+C9cpz6nzpxqHM54NY79xluFQZb3rmAp95ql7TPWplwjKXAAG0KnnroaoK4O1bA9uTmF53q+qOS5gf7e/E2O9H3z2gzGd6Xfj0ZZtq35qTZOwqdvXCL9P2AhwnTNqNuV+0+xi6Nz09PXH9+nX8888/WLt2LX744Qd8/vnn8PPzQ58+fbB27Vr9J9BzDgDwai7Hdy9twdgVT6HVpQQAgFNpIZ5bNgQrJycI7lj09rkeEFlASlyboNjVCx62+FwJCwP27eOXKz+TXp1l8HBX4Y+tgGcTqklDENYMCRBLomUBsfeqmQVEXAvEtZQXIBUVxj+Qk1MMp6et93SPfn783927fPv06fo5Ty0xNDk0ZCGht3CND13xov+7BwzdD1qCheNM4jKikHNwd61Aeno6jh5ORWqq9O/q1asoKSkBANy9exebN29GeXk57O2Nf9wr5ByGDHHFGqzHmJWj0PriNgBqS8hg/DolEZnNOiLlIkOvznTvmxSRBSTHpyXAcZC7WXA8lkKcCevmTT4439kZwc34/1tKJfBAqYKXgjzJCcIaIQFiSbQEiF2TGgoQkQXEuYQ/1qWrDB7y6oWDMr/q2hhmca/o2BFI4N+uNjQBUhXVuXERjRdD370hi1ldxGpZWRmuX7+uIzBSU1ORlpaGsrIyAIC9vT1CQ0MRHh6O/v37Y/z48di6dSsOHjyIZ555Bj/88EONxIf0Wl1w/tH/4cqkJxFxeQeAypiQnx/HsqlJSEhqjewcFfp2JxFuMkQWkHue/CScE6oL2RAREZplxvjPpX17XLnO/2ipGLDgF2B4HCVDIAhrhASIJdFyweKqKeSmg6i/YxFvAUk6Auw9yjA8rmrztKFsT8JYzBHc16GDRoCcP88XX3A0rgq0pSF3K9tF33dv6H6oTqyWlJQgLS0NV65c0REZN27cQEVFBQDA0dERLVu2RHh4OIYMGYLw8HDhLzg4WBAYW7ZswUsvvYSysjL88ccfGDVqVJ2vtV20C757YT3GL3sCLVP5Ygzuhfcw4efH8fO0fTh+JgjJKdU/cwgjKC8Hrl8Xmg+8eQGy5HcbnGiLBQgAXLkCZXA7/CUquskYn8XOz4eZNG07QRD1T4MWIHPnzsX69etx8eJFuLi4oEePHvjyyy/RurUmSf7EiROxcuVKyX5du3bF4cOa4g0lJSWYPXs2/vvf/6K4uBj9+/fHokWLEBQUZLZr0YuWBQTy2seA2Jc9hF15KSrsHY2qmHvnrmH1YTa3oo4dNctlZcCFC7rB6QRh5TjYFaMw7xpOn9C1ZKSnpwuB7S4uLoKoeOqppyQio1mzZrCzs6vyPLt378awYcMwePBg/PLLLwgICDDJ+NXuWL9XbMDEpXFonn4EANAkNx0TfhmEX6YmodjVy3aqdNcn16/zz8JKsn3CAdhYFXQ12gLk8mVkP6L/xdnS1cDweBsTaARh5TRoAZKUlIRp06ahc+fOKC8vx/vvv4+4uDicP38ebm4ap9jHH38cy5cvF9qOWm/RZ8yYgb/++gtr1qyBt7c3Zs2ahaFDhyI5ObnaH/V6RVuAeNQ+BgTgA9GL3JsCMOxCpcxnSL/NdNKJAnzF75bBnPncirTFxsmTJEAIqyQ/Px9Xr17V6y51+/ZtoZ+7u7sgKsaNGycRGQEBAZDJav8WNzY2Frt27cJjjz1WZear2sBbcuQ42HoLHF/rC7+75wAAvnfPY/zyEVgxOQHlDi6UFauuXL4sad5vqnnZZnNZxzw8pHGCV64YrOXEYIMCjSCsnAYtQLZv3y5pL1++HL6+vkhOTkbv3r2F9U5OTvD399d7DKVSiWXLluE///kPBgwYAABYtWoVmjdvjl27diE+Pr7+LqA6TGgBAfhihGoBAgAFRQzKfM0DuaqgcwDw9wFCg8348G7VCnB25oMLAauKAyFsj9zcXL0CIzU1FXfVkyQACoUCERERCA8Px6OPPioRGb6+viYXB2rkcjn612MmOYWcw6BRPthntw2OLz4Kz5wbAICQ6wfw9O/PYu3z6+Dl2aB/Uho+OgKklbBskzUvIiIkAkQdVyUuHqrG5gQaQVg5VvVroVTycQ5eXl6S9Xv27IGvry+aNGmCPn364PPPP4evry8AIDk5GWVlZYiLixP6BwYGIjIyEgcPHjQoQEpKSoRMMgCQpy0WTIEoBqTcwQn2NY1/0BIg4mKEALBuC8BxvG+2nw/DxoSqD2f26sb29kBUFHDsGN8+edI85yUIPTDGkJ2dbVBkZGdnC319fHwEUTFw4ECJyPDy8qo3kdEQaBYThN9e2oaXf+wF16IHAIB2Zzdi0uFZULz1vYVHZ+VcuiQsFrt5o9jNG4ANZ9uLiAD27+eXK8VZeAv9XW1SoBGEFWM1AoQxhpkzZ6JXr16IjIwU1g8aNAhPP/00QkJCkJaWhg8//BCPPfYYkpOT4eTkhMzMTDg6OsLTU/pk8vPzQ2ZmpsHzzZ07F59++mm9XQ8A4NFHkTnpbdy4kIcmcobW1e8hRY8FRBt1kJ4xWMTPuFMnjQA5dQpCuiCCqAcYY8jKykJqaqrewG/1Sw4A8Pf3R3h4ONq2bYthw4YJAqNly5Zo0qSJ5S7Cwnh7Atl+rbHqhc14YckAOJTzFsyQ9QuBBWHAjBmWHaA1I7KA3PPRWD/iettogL8o3hMZGUBeHrJz9Zs4esaQ+xVBWBNWI0Bee+01nDlzBvvVb0MqGTNmjLAcGRmJ2NhYhISEYOvWrRg5cqTB4zHGqnxL+a9//QszZ84U2nl5eWjevHkdrkAPAwfiuvcAbP2HIbI16ixAPFS6AqSmmN2M3amTZlmp5IMwQ0PNdHKiMaJSqZCRkWHQklFQUCD0bdasGSIiItCpUyc8/fTTgsgICwuDvKYukTaCJr1wd/wxfjXG/jYKskq/TjZzJrgWLYARIyw6RqtFZAG576MJwt6xF4hqw2xvgt2mjbR98SK823bWGwfSLcbGPhuCsHKsQoBMnz4dmzdvxt69e6vNXBUQEICQkBBcuXIFAP8Ws7S0FDk5ORIrSFZWFnr06GHwOE5OTnBycjLNBVRBaWXCE0eHWuysJUA8VTl1Ho/ZzdjiTFgA74ZFAoSohoqKCty6dUuvwLh69SqKi4sBABzHITg4GOHh4ejatSvGjx8vERmurq4WvhLrJCaKg58Pw5LVI7DtiW8xZNMMAADHGNgzz4DbuxeIjbXsIK2NggJAlLAgWxT/YbPxDW3bStsXLkDRpYukvg4AeDUh6wdBWBsNWoAwxjB9+nRs2LABe/bsQagRE9Ps7GzcvHlTSEEZExMDBwcH7Ny5E6NHjwYAZGRk4OzZs/jqq6/qdfzGUFrGP0FrJUB8fCTNJmW8P3ZYMHAtveaHs4ifcXQ0IJMBKhXfPnECqMJyRdgO5eXluHHjhl6Rce3aNZSWlgIA7Ozs0KJFC4SHh6NPnz548cUXBZERGhpqlhcJtkhpGV+R+nCv6fDMvoYe+/n4D664GBg2DDhyBAgOtuwgrYkLFyTNe36aybfNxjeEhQEODprUxBcvAtDU1zl3mWHbbv4nhCAI66JBC5Bp06bh999/x6ZNmyCXy4WYDYVCARcXFxQUFOCTTz7BU089hYCAAFy/fh3vvfcefHx88OSTTwp9X3zxRcyaNQve3t7w8vLC7NmzERUVJWTFsiSCBaQ29fecnQF3d/7NGQCHnHsAaic+BvUF2re2QJCjqytvZj9/nm+fOGHe8xMWpbS0FGlpaXpFxvXr11FeXg4AcHBwQFhYGMLDwxEfHy8J+g4JCYGDQ20UPFEXxClRtw+bB88HaWh7/i9+Y8eOgA3HydQK9TOwkiy/dgBsOAAd4BOVRERoPhuRSFPIOYSHAABDUbFFRkcQRB1o0AJk8eLFAIC+fftK1i9fvhwTJ06EnZ0dUlJS8NtvvyE3NxcBAQHo168f1q5dK/Hf/vbbb2Fvb4/Ro0cLhQhXrFhh2RoglVS+xIWjQy1/XHx8BAFSlnmvVofgOAuJDzUxMZofmORkCkRvZDx8+BDXrl3TG/idnp4OVaX1y9nZWaj2PXz4cInIaN68eYP4/0poEKdEZTI7/DF+NWb+py/c+3UFvv+enzwSxiMSIBX2jsjxCkO3TkCvLjYqPtS0bav5bCotIGpcXfh/i4sBlYpBJrPhz4kgrIwG/QvBDBWsqMTFxQUJCdXklgU/sVm4cCEWLlxoqqGZjMIi/t8KFQNQi4dn06Z84DYAt8L71Xbv2RmwlwF7j2rm+RZ/u/bII8B//sMvZ2UBd+4AzZpZbjxEjSksLDRYiO/WrVvC/2VXV1eEh4cjIiICY8aMkYiMwMDAOhXiI8xPTBSHS1cZLqQCZY5uWPDcbgwa6o4Ye/oea4xIgOQGtIbKzh4tW9i4+ACkgeipqXzdKGdnAIBLpQBhAIofAm4U0kUQVkODFiCNneQUhotX+eXdBwGFvBY1OERxINUJkCnjgaAAfmLQuSPDgxyYr+p5VcTESNsnTpAAaYDk5eUZzCyVkZEh9PPw8BBERffu3SUiw9/fv1HXyLA1lPkMF1M17RJHd2zaCYSHajI2KfMZ0m/zAjS4GU2oDSISIHeb8vEfNhd0rg9R2n1UVPCZwjp0AADYyTi4ODMUPwQKi0mAEIQ1QQLEQijz+arkYmpVg6OppvJ5U3Zfb3pCNWVlmuMq5FzD+XHTzoR14gQfxEo0GIYMGYK///5baHt5eQmiol+/fhKR4ePjQyLDRsjO4d8+i2EMuHmbAc2APYcYjp+RbMWIeButaVEVhYVAWprQzPDh4z9kslpaxhsTUVHS9tmzggABACcn3vpxL5vB19vGPyuCsCJIgFiI7BxdoVCrVIsiC4iz8h5mTeZw8zbDuq3S4zfoLCpyOdCqFV+ESyYD7tUuloWoP2bMmIFnn31WKMTn5eVl6SERDQBxILqYtVsAXWnCY/Zip9bAuXOSD1EdgP7DCmBEfC0s442JVq2kmbBSUoRNySkMuZXlr9ZsBuL7qNCrM7n/EYQ1QALEQuj74a6VSBBZQKBUQuFcDkUbB5SUMSFPeoOI86iOhQsBDw8+LS/VZmhwDBw40NJDIBog4kB0Y7HZmhZVcUZiJkJmoOYNv80LNgcHPg5ELTzOngWg34sgIQl4WKLCgF4kQgiioUP/Sy0E/8PNCcmeai0StGqB4D4fBxITxWHWZA6TRvP/Nvg3aHFxQLduJD5qyJ49e8BxHHJzcy09FMJGiYniEOBrfH9DL1qU+QzX0hmU+caLmUaDSICUOLohxytMaKsFm00jjgOpFCL6vAgAIOkwsP+YykwDIwiitpAAsSAmEQliCwggCBCAFzmhwQ3b8pGZmYnp06cjLCwMTk5OaN68OYYNG4bExERLDw3Xr18Hx3E4deqUWc7XokULcByn8/fFF1+Y5fwEUVv8fKrvo0b9okUtOG5lqLB9jwrzlzIsX8cwfylDcoqNiRCRALnrHwUmygbXoN1nzYU4DiQ9HcjJgXcVn0lCEnArg0QIQTRkyAXLQijzGbJzeFes0OA6CARtC4gVxU9cv34dPXv2RJMmTfDVV18hOjoaZWVlSEhIwLRp03BRK+e7LfDZZ5/h5ZdflqwT17QhiIaIrw9fFb06unQEwlsA2/eocDBZ/xtsxmzM7YgxqQAJ0Ey2OViB+6w50E5UcuoUFP36Ib4PQ0KS/l2WrgaG23r8DEE0YMgCYgH2H1Nh/hITve3TtoBYkQCZOnUqOI7D0aNHMWrUKLRq1Qrt27fHzJkzcfjwYaFfeno6hg8fDnd3d3h4eGD06NG4e/eusP2TTz5Bx44d8euvvyI4OBju7u549dVXUVFRga+++gr+/v7w9fXF559/Ljk/x3FYvHgxBg0aBBcXF4SGhuKPP/4QtoeGhgIAOnXqBI7jhIKYKpUKn332GYKCguDk5ISOHTti+/btwn5qy8n69evRr18/uLq6okOHDjh06FC1n4lcLoe/v7/kz83NTdj+999/o1WrVnBxcUG/fv1wvbIGjJiff/4ZzZs3h6urK5588kl88803aKJVlfqvv/5CTEwMnJ2dERYWhk8//VSoOk4QNeVBruFnWOcOQJ9u/PLtTGDeEoYDxw1n6wNszO3o5k0gR3OxdwOi0CMWGD0MmDXFCtxnzUGnTtL2yZMAgF6dZcK9pQ0DL2Rt0qWPIKwAEiBmZv8xFRKSNO8K1W/7av2Q9NVyvhZNzBsyDx48wPbt2zFt2jTJBFuNesLMGMOIESPw4MEDJCUlYefOnbh69SrGjBkj6X/16lVs27YN27dvx3//+1/8+uuvGDJkCG7duoWkpCR8+eWX+OCDDyTCBgA+/PBDPPXUUzh9+jSeffZZjBs3DhcuXAAAHD16FACwa9cuZGRkYP369QCA7777DvPnz8e8efNw5swZxMfH44knnsCVK1ckx37//fcxe/ZsnDp1Cq1atcK4cePqNMm/efMmRo4cicGDB+PUqVN46aWX8O6770r6HDhwAK+88greeOMNnDp1CgMHDtQRXgkJCXj22Wfx+uuv4/z581iyZAlWrFih048gjEGZz5B8Rnf96GHA7Ckcnhgog39TfhJ9O9O4Y9qU21FysqR5p1kndGzHIaq1jCwfavz9+T81lQIEAAb0kiG+j/7dbErIEoSVQQLEjCjz9ZuL6/SQVCgA8QT+1q1aHsi8pKamgjGGNuIqt3rYtWsXzpw5g99//x0xMTHo2rUr/vOf/yApKQnHjh0T+qlUKvz6669o164dhg0bhn79+uHSpUtYsGABWrdujRdeeAGtW7fGnj17JMd/+umn8dJLL6FVq1b497//jdjYWCxcuBAA0LTSuuTt7Q1/f38h9ey8efPwzjvvYOzYsWjdujW+/PJLdOzYEQsWLJAce/bs2RgyZAhatWqFTz/9FDdu3EBqaiqq4p133oG7u7vkTz3mxYsXIywsDN9++y1at26N8ePHY+LEiZL9Fy5ciEGDBmH27Nlo1aoVpk6dikGDBkn6fP7553j33XcxYcIEhIWFYeDAgfj3v/+NJUuWVDk2gtCHvlogAODuonEdauJRs2PalNuRSICoOBkyAztW1v8gJIitICIBAvCWkIlP6+5iU0KWIKwMEiBmJNuAyKjTQ5LjgKAgTfvmzVoeyLywSv+L6grWXbhwAc2bN0fz5s2Fde3atUOTJk0ESwXAB3CLYyX8/PzQrl07yETBnH5+fsjKypIcv3v37jpt8XG1ycvLw507d9CzZ0/J+p49e+rsFx0dLSwHBAQAgM75tXnrrbdw6tQpyV/Xrl0B8J9Ft27dJJ+Z9vgvXbqELl26SNZpt5OTk/HZZ59JRM7LL7+MjIwMFBUVVTk+gtBGnVJcjPYz7cbtmk2ow1vUfVxWg0iA3PNtizJHV/y4ErYXiF8dYgFy4QJfvFFEbp7ub4lNCVmCsDIoCN2MeHvyQYXaPytxvesYbBkUBFy6xC9biQUkIiICHMfhwoULGDFihMF+jDG9IkV7vYODg2Q7x3F616lU1WdGMaaKt3YffeMUn1+9rbrz+/j4IDw8XO82VpXTfBXj0N5PpVLh008/xciRI3X2d3Z2rvYcBCFGXQvEUN0hQ5bfqrCZOiGMSQTInaBHhNU2FYhvDLGxmmWVireC9OoFQH9NEMDGhCxBWBlkATEjCjmHVmHSdfF9UPfKrWILiJUIEC8vL8THx+PHH39EodabLABCXYt27dohPT0dN0WWnfPnz0OpVKJt27Z1Hod2TMjhw4cFtzBHR0cAQEVFhbDdw8MDgYGB2L9/v2S/gwcPmmQ8VdGuXTu94xXTpk0bIXZFzfHjxyXtRx55BJcuXUJ4eLjOn9hiRBDGUlVKcUP1GqrCy9NG6oLcvClJHHKn2SPCMsUvaKFlyYXoOWfoHsu634jvHYKwcsgCYkaSUxguXdO0TSI+AKkAuX2bfztkBRPJRYsWoUePHujSpQs+++wzREdHo7y8HDt37sTixYtx4cIFDBgwANHR0Rg/fjwWLFiA8vJyTJ06FX369EGs+I1YLfnjjz8QGxuLXr16YfXq1Th69CiWLVsGAPD19YWLiwu2b9+OoKAgODs7Q6FQ4K233sLHH3+Mli1bomPHjli+fDlOnTqF1atX13k8+fn5yMyURuq6urrCw8MDr7zyCubPn4+ZM2diypQpSE5OxooVKyR9p0+fjt69e+Obb77BsGHD8M8//2Dbtm0Sq8hHH32EoUOHonnz5nj66achk8lw5swZpKSk4P/+7//qfA2EbaKQc3qtFlXVazDEweMMh05oJpU9Yxm6x/D3sDp9eaOwDGi9QLjdvLOwTPELWjRrBgQGAnfu8G2RAFG7AWqLkLJy4Fo6azz3C0E0Ihr+LLWRoM9EvGMvTPN2TyxAysqsJhVvaGgoTpw4gX79+mHWrFmIjIzEwIEDkZiYiMWLFwPgXZc2btwIT09P9O7dGwMGDEBYWBjWrl1rkjF8+umnWLNmDaKjo7Fy5UqsXr0a7dq1AwDY29vj+++/x5IlSxAYGIjhw4cDAF5//XXMmjULs2bNQlRUFLZv347NmzcjIiKizuP56KOPEBAQIPl7++23AQDBwcH4888/8ddff6FDhw746aefMGfOHMn+PXv2xE8//YRvvvkGHTp0wPbt2/Hmm29KXKvi4+OxZcsW7Ny5E507d0a3bt3wzTffICQkpM7jJwhtFHLOYJYiQ2jXCDlwnE/fO89A+nKrtZaIUnOX2zkKLlhU/8MAYiuISIDwboCcTizSfzfBdotbEkQDh2PGOJYTyMvLg0KhgFKphIdHDVO6gH8Ls3yd7kc9aTRXt0KEALBlCzBsmKZ9/DgQE1O3Y9oAHMdhw4YNVcagNAZefvllXLx4Efv27bP0UAgbZv8xFXbshRAn0qEdcPq8pt02Ajh/2fjjcRwwazKH1Ou68SdWUzuje3fBCnIzuCuWTj8obJo9hQSIDnPnAu+9p2lnZgJ+fkJTmc9wOJlh/3HdXdX3C32mBKFLXee4tYFcsMyEPhOxyUzsYgsIwMeBkACxWebNm4eBAwfCzc0N27Ztw8qVK7Fo0SJLD4uwcXp1liGqDcODHP65p5BzGNCLbzs4MCypoQcjY8Cpswy7DkjXWU3wdkkJVMknBDeEm8FdJZttJhC/JvToIW0fOABoJdM4IC2rIqCOqaHPlCAaBuSCZSbUJmI12pli6oQoRS0A4MaNuh+TsFqOHj2KgQMHIioqCj/99BO+//57vPTSS5YeFkFAIectvurnnrpdWla756BYfKixluDtgr3HICsrFdo3QzQlvSn+wwCdOwPi7IZayUCqS3hw+y45fBBEQ4EsIGYkJooTXAWmPAM0CzDRGzovL6BJE6AycxS0KnIT+mms3ofr1q2z9BAIokYYCiKuLbfvsrq7ttYzpTuluYlvhD4qLFP8hwFcXXnrvjp4X0uAVHcf7dgLRLVh9NkSRAOALCBmRKViwoPRs4kJH4AcB7RqpWlfroEjNUEQhIXRthDXlYQk4FYGX3OnoQaoy09oYrKyfcKRrwgU2lS/ogoqa38AAE6cAPLzhaahYHQ11mIdIwhbgCwgZqRcU04CdnYmPnhEhCYrCAkQgiCsjJgoDn4+DEtX6xZrrQ1LVgPhISpcTYcoQJ0/jzKfWSydrzKfIfteOUKPaPzHrousHwDFKlRJ797AvHn8ckUFbwUZNEjYHBPFIbwFcPM2w9ot0l3JtY0gGg4kQMyIqJ4d7E39yYstIDduAA8fAlTVmiAIKyIoQIbh8UxwVa0rqaJwOMaAjQkMObkMe4/qipK6YKygSU7hry0o7SgmFxQI66+HaQQITZKroXdvvs6VirdwITFRIkCAypo0bTjk5quQUOnpZtK4S4Ig6gwJEDNSXs7/y3GAnczED0GxAGEMuHoVaN/etOcgCIKoZ9RvsNXZsXKVwLqtposPSTqiWTaUNUtbUCjzGdJv8wMIbiadxKpFRXWCRpnPsDGBP0b45Z2SbWkR/QHQJNkoFAo+GP1I5ReZmGiwa89YDnuPMBQ/BB7rSa5tBNGQIAFiRtQuWPamdr8CpAIE4N2wSIAQBGGFaKqqcygtY/WaMEI7PatYUABAqzCGy9cke2BEvMaVS9y3qjTAB45prqHllV3CcpZvW0QNCEKrUE5IT0xUw2OPaQTIqVNAVhbg66vTjeM4yN14AZK4H0jczxDfh6FXZwp/JQhLQ/8LzYjaAmJy9yuAjwERQ3EgBEE0AtSZjaqjLtP281f4QPWLV1XYmCB1/5KKD55NO5hgJdHWRvoCnZX5DIdO8MtOxUoEpR8WtqW2Goj9x/jMXSQ+jCQuTtrevl1vN2U+Q1a2dF1CErBrv6qeBkYQhLGQADEj9WoBkcuBgAB+2cEBUCrr4SQEQRDmRTuzEccBHdtD0h4Rz2F4fO0n74dPAsvXMazeYFx/tcjwNhCrkZXNkHJRJWTeyhYJkohL22Gn0gQEprYaCIBPEdvQMnU1WHr2BMTVmv/+W2+37By9q5F0GNh/jEQIQVgScsEyI4IAqa9Pff16oGlTICSkHk9CEARhXsRxIdpV1MVuS+EtgMPJDPuP1/+YCooYbt/Vv22LEJbAu2t5KjTCos35v4TlUgdXpIX343tSpW7jcXDgrSD/+x/fTkgAysqkRQphWCACVBOEICwNWUDMiNoFy+QpeNV06wa0bEnigyCIRoehKuriCaRCziG+rwxTxtfNJcsY1m2BkGGpKjYmMCyvrA0qqyhDq4vbhG2prQai3MEFAGW/qjGDB2uWc3OBJN0vQyHnEN9H/+5UE4QgLAsJEDNSUd8WEIIgCKIyna/hgnSWIjR1N1yKc4X2pfbDAFD2q1oxbJj0bZ7aGqJFr84y9Omm/xC375LLG0FYChIgZqRMHYReXxYQgiAIAgDvtjVrMoegAOl6S07xo06v1TTs7ND/yycwaTQ/zrrWIrE5fHyAfv007fXrNW4GWgzopV+EJCQBtzIoFoQgLAEJEDNSUZ9B6ARBEIQOtzKkbQZg9DBg9hTD7jkA0Kercdm3jMWuvATtUkRR7gMHwqNFUx03MqIGjBqlWb53r8qaIGHB+j/jpav51MsEQZgXEiBmpF7T8BIEQRASDGVBcnfhJ/29OsswewqHXrG6WbUGPCrDrMkcJo3mMGV83cfS5txmuDwUZSccO7buB7V1nnpKGni+cqXBrobSOTNo0ioTBGE+aCpsRuo1DS9BEAQhQT3pFNfq0A725gPXOXSL0c2qJS6IOCJeU8lcH326Af4+wN1sYM8h3e2PHFuuabi6Ak8+WbeLI3g3rCFDgI0b+faGDXxAepMmOl0Vcg5xvZnexAGUgYwgzA9ZQMxIvWfBIgiCIAT01RAxFOytL6uWmJgoDrOncBgzFIjtIH2bHt+HjzOIbCNDbLSeY+fcQPjlHZoVo0dL61gQtef55zXLDx8CK1YY7BroZ9jVjQLSCcK8kAXEjFAWLIIgCPOir4ZIbVHIOSjacIhsA/TtpmsxUfcZEQ+JtaTbgR8hE5thXnyx1mMgtBg6FAgMBO7c4duLFgGvvw7IdN+v6rOIqaG6IARhXsgCYkbKKgWIAwkQgiAIs1GddcPUxxRbS8Y+VoCep37RbOzQga/kTZgGBwdg8mRN+8oV4K+/9HZVW8T0wRhwKZXhWjrFgxCEOSABYkbKy/mHGrlgEQRBNG4Ucg6RbWRof/A3cEpR8Pmbb5o2vRYBTJkCODlp2nPm6DdzgBeHhgpV/pUILF/HMH8po8xYBFHPkAAxI5SGlyAIwsZwcgL8/fllPz/KflUf+PsDL7ygaR89CmzdarB7UIAMcVWkYGaMMmMRRH1DAsSMUBpegiAIG+Pll4Hr14HffuPfzIvf1BOm4+23pT+u77xjsDAhUHVAOqDJjEUQRP1AAsSMaNLwkvmdIAjCZnByAp57Dpg0ydIjabyEhgKvvqppnz8PLFxosLu3p8FNAg4OZAEhiPqCBIgZESwg5IJFEARBEKblww8BhULavnpVb1c+W1nVLwOXrAb2H1NRYDpB1APkDGRGyikNL0EQBEHUD02bAl98obGEFBYCzzwD7NsHODrqdFenaE46xHDsjP5D8oULefHRpxtDWDAH7zqmcyYIgiwgZkUtQCgLFkEQBEHUA5MnA31EEeZHjwJTpxrMiqWQc3giTobRQ6s/dNJhPkvWvCUM+4+pTDRggrBNSICYkQoKQicIgiCI+kMmA/7zH8BTFOSxbBnw7rsGRQgABDermUUjIQnYtENlFa5Zynz99U0MrSfqF+3P3Va/B5oKm5FySsNLEARBEPVL8+bAunXA449r8t9/9RXw4AHwww96M5Hpq2BfHcfPAMfPMES3YQhuBri68EJGIeegzGfIzgEcHRhyKsvAeCogLKv7qVHmM6TfZnq3aaM+ttoVTHyu0jKpi1hyCsOmHQyM8eVn4nozKOTAxavAmQv88TgOGB4HhLeAweNUdV5xv7qsq2p9dZ+F9phrcs7qjqXdx5jvydCxxN8HALRvxXD+CoTvZ3gc7xpoDIausTbjc7Azv/ghAWJGKAaEIAiCIMzAgAG85WPiRM26X34BTp4Eli8HoqJ0domJ4uDowLBuS81OdeYi/8fDJJNKwzD0jGWIbA0kp/BiRrwtNpohLFgjWoqKeYGTkQXsO6rp2SqM4co1dZQKvy8AxEbzQiPxgOioTB3TojUSpk948W21uEq/oxEsABAewnA1XXON8X34hR1JmrHERjOUlkmFTlxvfuIrvt6esQzdYzikXodELKlFkXpCLRZwngr9nxsAnc+/ZyyDvR2w9wjfQyzEAH6iLj63+Fh9ujH4+fBrlPnan5/0eyot4+8fQ+Nq05LholZOhHOXRb0qv4fycgZXF93rVQsZADiUzHAwWXON0W0YGICUi+KjM8T3YejVWSYRJnfvaT4Ldb+Sh+YXIBxjVf8XIXjy8vKgUCigVCrh4eFRq2MsXK5CVjbw1CCgY3vyfiMIgiCIemXFCuCllzSWEIAPxHzxRWD2bCAiQtJdmc/HeBBEY8HfF8jMqrpPycM8fP2hZ53muDXFpmbBixYtQmhoKJydnRETE4N9+/bV+znVvn37j/HiAwDWb+PNogRBEARB1CMTJwJ//w14e2vWVVQAS5cCrVrxAesLFgApKYBKZVR6XoKwJqoTH5bCZpyB1q5dixkzZmDRokXo2bMnlixZgkGDBuH8+fMIDg42+jjnLqsgszMu+4W2yVINA2/qC29BqfwIgiAIol6JiwNOnwZeeQXYouVftXcv/wcA7u5A+/aIiYhAe/9gPHAOQKG7Nw5dbYKHjh4oc3RFmYMLyu2doJI5oMLOAUxmBxVnJ/xb7uDM+/gQBFElNuOC1bVrVzzyyCNYvHixsK5t27YYMWIE5s6dW+3+ahest/6dAydn05inJo3mEBpMDyqCIAiCMAsJCcAnnwCHD5v80Nda9sXyVxJNflyCqG8s4YJlExaQ0tJSJCcn491335Wsj4uLw8GDB/XuU1JSgpKSEqGtVPKRQCUP80wyJo4D7O045OWRACEIgiAIs9C9O7B9O3DiBPDf//IWkYwMkxza4d4llBblgMlqn+oyOJD3niAIc6Ke25rTJmETAuT+/fuoqKiAn5+fZL2fnx8yMzP17jN37lx8+umnOuu//zzEZOP66gOTHYogCIIgCEuSlwF87GPpURBErcnOzoZCoTDLuWxCgKjhtPwyGWM669T861//wsyZM4V2bm4uQkJCkJ6ebrYvh7BO8vLy0Lx5c9y8edNspkzCeqH7hTAWuleImkD3C2EsSqUSwcHB8PLyMts5bUKA+Pj4wM7OTsfakZWVpWMVUePk5AQnfcWKFAr6j0wYhYeHB90rhNHQ/UIYC90rRE2g+4UwFpnMfMlxbSINr6OjI2JiYrBz507J+p07d6JHjx4WGhVBEARBEARB2B42YQEBgJkzZ+K5555DbGwsunfvjqVLlyI9PR2vvPKKpYdGEARBEARBEDaDzQiQMWPGIDs7G5999hkyMjIQGRmJv//+GyEhxgWVOzk54eOPP9brlkUQYuheIWoC3S+EsdC9QtQEul8IY7HEvWIzdUAIgiAIgiAIgrA8NhEDQhAEQRAEQRBEw4AECEEQBEEQBEEQZoMECEEQBEEQBEEQZoMECEEQBEEQBEEQZoMEiBEsWrQIoaGhcHZ2RkxMDPbt22fpIRFmZu7cuejcuTPkcjl8fX0xYsQIXLp0SdKHMYZPPvkEgYGBcHFxQd++fXHu3DlJn5KSEkyfPh0+Pj5wc3PDE088gVu3bpnzUggzM3fuXHAchxkzZgjr6F4hxNy+fRvPPvssvL294erqio4dOyI5OVnYTvcLAQDl5eX44IMPEBoaChcXF4SFheGzzz6DSqUS+tC9Yrvs3bsXw4YNQ2BgIDiOw8aNGyXbTXVv5OTk4LnnnoNCoYBCocBzzz2H3Nzcmg+YEVWyZs0a5uDgwH7++Wd2/vx59sYbbzA3Nzd248YNSw+NMCPx8fFs+fLl7OzZs+zUqVNsyJAhLDg4mBUUFAh9vvjiCyaXy9mff/7JUlJS2JgxY1hAQADLy8sT+rzyyiusWbNmbOfOnezEiROsX79+rEOHDqy8vNwSl0XUM0ePHmUtWrRg0dHR7I033hDW071CqHnw4AELCQlhEydOZEeOHGFpaWls165dLDU1VehD9wvBGGP/93//x7y9vdmWLVtYWloa++OPP5i7uztbsGCB0IfuFdvl77//Zu+//z77888/GQC2YcMGyXZT3RuPP/44i4yMZAcPHmQHDx5kkZGRbOjQoTUeLwmQaujSpQt75ZVXJOvatGnD3n33XQuNiGgIZGVlMQAsKSmJMcaYSqVi/v7+7IsvvhD6PHz4kCkUCvbTTz8xxhjLzc1lDg4ObM2aNUKf27dvM5lMxrZv327eCyDqnfz8fBYREcF27tzJ+vTpIwgQulcIMe+88w7r1auXwe10vxBqhgwZwiZNmiRZN3LkSPbss88yxuheITRoCxBT3Rvnz59nANjhw4eFPocOHWIA2MWLF2s0RnLBqoLS0lIkJycjLi5Osj4uLg4HDx600KiIhoBSqQQAeHl5AQDS0tKQmZkpuVecnJzQp08f4V5JTk5GWVmZpE9gYCAiIyPpfmqETJs2DUOGDMGAAQMk6+leIcRs3rwZsbGxePrpp+Hr64tOnTrh559/FrbT/UKo6dWrFxITE3H58mUAwOnTp7F//34MHjwYAN0rhGFMdW8cOnQICoUCXbt2Ffp069YNCoWixvePzVRCrw33799HRUUF/Pz8JOv9/PyQmZlpoVERloYxhpkzZ6JXr16IjIwEAOF+0Hev3LhxQ+jj6OgIT09PnT50PzUu1qxZgxMnTuDYsWM62+heIcRcu3YNixcvxsyZM/Hee+/h6NGjeP311+Hk5ITnn3+e7hdC4J133oFSqUSbNm1gZ2eHiooKfP755xg3bhwAerYQhjHVvZGZmQlfX1+d4/v6+tb4/iEBYgQcx0najDGddYTt8Nprr+HMmTPYv3+/zrba3Ct0PzUubt68iTfeeAM7duyAs7OzwX50rxAAoFKpEBsbizlz5gAAOnXqhHPnzmHx4sV4/vnnhX50vxBr167FqlWr8Pvvv6N9+/Y4deoUZsyYgcDAQEyYMEHoR/cKYQhT3Bv6+tfm/iEXrCrw8fGBnZ2djqrLysrSUZGEbTB9+nRs3rwZu3fvRlBQkLDe398fAKq8V/z9/VFaWoqcnByDfQjrJzk5GVlZWYiJiYG9vT3s7e2RlJSE77//Hvb29sJ3TfcKAQABAQFo166dZF3btm2Rnp4OgJ4thIa33noL7777LsaOHYuoqCg899xzePPNNzF37lwAdK8QhjHVveHv74+7d+/qHP/evXs1vn9IgFSBo6MjYmJisHPnTsn6nTt3okePHhYaFWEJGGN47bXXsH79evzzzz8IDQ2VbA8NDYW/v7/kXiktLUVSUpJwr8TExMDBwUHSJyMjA2fPnqX7qRHRv39/pKSk4NSpU8JfbGwsxo8fj1OnTiEsLIzuFUKgZ8+eOim9L1++jJCQEAD0bCE0FBUVQSaTTtvs7OyENLx0rxCGMNW90b17dyiVShw9elToc+TIESiVyprfPzUKWbdB1Gl4ly1bxs6fP89mzJjB3Nzc2PXr1y09NMKMvPrqq0yhULA9e/awjIwM4a+oqEjo88UXXzCFQsHWr1/PUlJS2Lhx4/SmuAsKCmK7du1iJ06cYI899hilP7QBxFmwGKN7hdBw9OhRZm9vzz7//HN25coVtnr1aubq6spWrVol9KH7hWCMsQkTJrBmzZoJaXjXr1/PfHx82Ntvvy30oXvFdsnPz2cnT55kJ0+eZADYN998w06ePCmUjTDVvfH444+z6OhodujQIXbo0CEWFRVFaXjrix9//JGFhIQwR0dH9sgjjwipVwnbAYDev+XLlwt9VCoV+/jjj5m/vz9zcnJivXv3ZikpKZLjFBcXs9dee415eXkxFxcXNnToUJaenm7mqyHMjbYAoXuFEPPXX3+xyMhI5uTkxNq0acOWLl0q2U73C8EYY3l5eeyNN95gwcHBzNnZmYWFhbH333+flZSUCH3oXrFddu/erXeeMmHCBMaY6e6N7OxsNn78eCaXy5lcLmfjx49nOTk5NR4vxxhjNbTkEARBEARBEARB1AqKASEIgiAIgiAIwmyQACEIgiAIgiAIwmyQACEIgiAIgiAIwmyQACEIgiAIgiAIwmyQACEIgiAIgiAIwmyQACEIgiAIgiAIwmyQACEIgiAIgiAIwmyQACEIgiAIgiAIwmyQACEIgiBMCsdx2Lhxo6WHYRQTJ07EiBEjLD0MgiAIm4IECEEQBFEjMjMzMX36dISFhcHJyQnNmzfHsGHDkJiYaOmhEQRBEFaAvaUHQBAEQVgP169fR8+ePdGkSRN89dVXiI6ORllZGRISEjBt2jRcvHjR0kMkCIIgGjhkASEIgiCMZurUqeA4DkePHsWoUaPQqlUrtG/fHjNnzsThw4eFfvfv38eTTz4JV1dXREREYPPmzcK2iooKvPjiiwgNDYWLiwtat26N7777TnIetWvUvHnzEBAQAG9vb0ybNg1lZWVCnxYtWmDOnDmYNGkS5HI5goODsXTpUslxbt++jTFjxsDT0xPe3t4YPnw4rl+/Xj8fDkEQBGEUJEAIgiAIo3jw4AG2b9+OadOmwc3NTWd7kyZNhOVPP/0Uo0ePxpkzZzB48GCMHz8eDx48AACoVCoEBQVh3bp1OH/+PD766CO89957WLduneR4u3fvxtWrV7F7926sXLkSK1aswIoVKyR95s+fj9jYWJw8eRJTp07Fq6++KlhhioqK0K9fP7i7u2Pv3r3Yv38/3N3d8fjjj6O0tNS0Hw5BEARhNCRACIIgCKNITU0FYwxt2rSptu/EiRMxbtw4hIeHY86cOSgsLMTRo0cBAA4ODvj000/RuXNnhIaGYvz48Zg4caKOAPH09MQPP/yANm3aYOjQoRgyZIhOnMngwYMxdepUhIeH45133oGPjw/27NkDAFizZg1kMhl++eUXREVFoW3btli+fDnS09OFPgRBEIT5oRgQgiAIwigYYwD4LFfVER0dLSy7ublBLpcjKytLWPfTTz/hl19+wY0bN1BcXIzS0lJ07NhRcoz27dvDzs5OaAcEBCAlJcXgeTiOg7+/v3Ce5ORkpKamQi6XS/Z5+PAhrl69Wu01EARBEPUDCRCCIAjCKCIiIsBxHC5cuFBt6loHBwdJm+M4qFQqAMC6devw5ptvYv78+ejevTvkcjm+/vprHDlyxOhjGNNHpVIhJiYGq1ev1hlf06ZNqxw/QRAEUX+QACEIgiCMwsvLC/Hx8fjxxx/x+uuv68SB5ObmSuJADLFv3z706NEDU6dOFdbVh0XikUcewdq1a+Hr6wsPDw+TH58gCIKoHRQDQhAEQRjNokWLUFFRgS5duuDPP//ElStXcOHCBXz//ffo3r27UccIDw/H8ePHkZCQgMuXL+PDDz/EsWPHTD7W8ePHw8fHB8OHD8e+ffuQlpaGpKQkvPHGG7h165bJz0cQBEEYBwkQgiAIwmhCQ0Nx4sQJ9OvXD7NmzUJkZCQGDhyIxMRELF682KhjvPLKKxg5ciTGjBmDrl27Ijs7W2INMRWurq7Yu3cvgoODMXLkSLRt2xaTJk1CcXExWUQIgiAsCMfUUYUEQRAEQRAEQRD1DFlACIIgCIIgCIIwGyRACIIgCIIgCIIwGyRACIIgCIIgCIIwGyRACIIgCIIgCIIwGyRACIIgCIIgCIIwGyRACIIgCIIgCIIwGyRACIIgCIIgCIIwGyRACIIgCIIgCIIwGyRACIIgCIIgCIIwGyRACIIgCIIgCIIwGyRACIIgCIIgCIIwG/8P+U65Sw/wvysAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib widget\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "#plot data and models\n",
    "plt.plot(x_vals[0], y_vals[0], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.plot(x_vals[1], y_vals[1], color = '#7c8bfd', marker = \".\")\n",
    "plt.plot(x_vals[1], result2.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.plot(x_vals[2], y_vals[2], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.plot(x_vals[3], y_vals[3], color = '#7c8bfd', marker = \".\")\n",
    "plt.plot(x_vals[3], result4.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.plot(x_vals[4], y_vals[4], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.plot(x_vals[5], y_vals[5], color = '#7c8bfd', marker = \".\")\n",
    "plt.plot(x_vals[5], result6.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.plot(x_vals[6], y_vals[6], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.plot(x_vals[7], y_vals[7], color = '#7c8bfd', marker = \".\")\n",
    "plt.plot(x_vals[7], result8.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.plot(x_vals[8], y_vals[8], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.plot(x_vals[9], y_vals[9], color = '#7c8bfd', marker = \".\")\n",
    "plt.plot(x_vals[9], result10.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.plot(x_vals[10], y_vals[10], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Channel')\n",
    "\n",
    "\n",
    "plt.ylim(0, 2000) \n",
    "plt.xlim(0, 1000)\n",
    "#plt.yscale('log')\n",
    "\n",
    "#make annotations\n",
    "plt.annotate('Ba X-ray', xy=(61, 1048), xytext=(124, 1861),\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\"),\n",
    "            )\n",
    "plt.annotate('Lead X-ray', xy=(114, 480), xytext=(130, 900),\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\"),\n",
    "            )\n",
    "plt.annotate('Backscatter', xy=(254, 439), xytext=(260, 590),\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\"),\n",
    "            )\n",
    "plt.annotate('Compton Edge', xy=(440, 282), xytext=(120, 160), horizontalalignment=\"left\",\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\"),\n",
    "            )\n",
    "plt.annotate('gamma@661.657keV', xy=(666, 1700), xytext=(700, 1750),\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\"),\n",
    "            )\n",
    "\n",
    "# Create legend entries for the blue and red lines (data and model) and display the legend\n",
    "blue_line = mlines.Line2D([], [], color='#7c8bfd', label='Data')\n",
    "\n",
    "red_line = mlines.Line2D([], [], color='red', label='Moffat Fit', linewidth=2.5)\n",
    "\n",
    "plt.legend([blue_line, red_line], ['Data', 'Moffat Fit'], loc='lower center', bbox_to_anchor= (0.5, 1.005), ncol=2,\n",
    "            borderaxespad=0, frameon=False)\n",
    "\n",
    "plt.title('The (uncalibrated) decay spectrum of Cs-137', y = 1.07)\n",
    "\n",
    "#show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d8d88b4-fb3a-4cac-b0a8-c4c1204e3b66",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "## References\n",
    "\n",
    "### **The images below are included as a reference for the decay scheme and gamma ray energies** "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00cda79c-9451-47f0-9385-019fb3bb113e",
   "metadata": {},
   "source": [
    "![](./img/Cs_spectrum.png)\n",
    "\n",
    "https://gammaray.inl.gov/SiteAssets/catalogs/nai/pdf/Cs137.pdf\n",
    "\n",
    "\n",
    "![](./img/Cs_gamma.png)\n",
    "\n",
    "https://gammaray.inl.gov/SiteAssets/catalogs/nai/pdf/Cs137.pdf\n",
    "\n",
    "\n",
    "![](./img/Cs_energy_table.png)\n",
    "\n",
    "\n",
    "https://nucleardata.nuclear.lu.se/toi/nuclide.asp?iZA=550137"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed95a0c-ae4c-42fe-90cc-bd697e501f6c",
   "metadata": {},
   "source": [
    "## Author\n",
    "** **\n",
    "\n",
    "### <a href=\"https://www.linkedin.com/in/alin-airinei/\">Alin Airinei</a>"
   ]
  }
 ],
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