diff --git a/22-paper-collection.ipynb b/22-paper-collection.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# This is a collection of all the plots used in the variance reduction paper\n",
+    "\n",
+    "### To help understand things:\n",
+    "$\\phi$ used to be called $h$, and is still called $h$ throughout most of the code and is only relabeled at the very end\n",
+    "\n",
+    "\n",
+    "For now I will disable all save commands, and reduce the amount of experiments per timestep to speed things up \n",
+    "\n",
+    "\n",
+    "\n",
+    "## ToDo:\n",
+    "\n",
+    "\n",
+    "\n",
+    "## Done\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Important parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha_default  = 0.1 #Reguralization coeffcient for sensitivity calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from da_functions import *\n",
+    "from model_functions import *\n",
+    "from plot_functions import *\n",
+    "from misc_functions import *\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "sns.set(color_codes = True)\n",
+    "sns.set_style('whitegrid')\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I need this for some reason so that autocomplete works\n",
+    "%config Completer.use_jedi = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running the default run that is used for the illustration plots and to generate the initial conditions for the variance reduction tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "initialize model and data assimilation setup using the default values\n",
+    "\"\"\"\n",
+    "m_const = set_model_constants_22()\n",
+    "da_const = set_da_constants_22()\n",
+    "sat_operator = reflectance_simulator\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 5.76 s, sys: 153 ms, total: 5.91 s\n",
+      "Wall time: 1.53 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Run the model for the 100 time steps\n",
+    "states   = run_linear_advection_KF_22(m_const,da_const,reflectance_simulator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting the first two and last three timesteps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pgriewank/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 540x216 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = ensemble_plotter_22(states,m_const,da_const,t_start=1,t_end=3)\n",
+    "ax[1,0].set_xticklabels(['0','5','10','15','20','25'])\n",
+    "ax[0,0].set_title('background ensemble')\n",
+    "ax[1,0].set_xlabel('x [km]')\n",
+    "ax[1,1].set_xlabel('x [km]')\n",
+    "ax[0,0].set_ylabel('assim step 1 \\n' +r'$\\phi$')\n",
+    "ax[1,0].set_ylabel('assim step 2 \\n' +r'$\\phi$')\n",
+    "ax[1,1].legend(bbox_to_anchor=(-0.2,0.45),loc=3,framealpha=1.)\n",
+    "plt.subplots_adjust(wspace=0.05,hspace=0.1)\n",
+    "label_axes_abcd(fig,loc=(0.05,0.75))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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A1157bZMPasKECTz//PPdq6rHH3+cHXbYoVvi6/KCW7ly5UYl042198ILL5BKpUilUrzyyisb3W/XXXfl5ZdfpqmpCYCnnnqK6dOnb7btpUuXMmnSJAYOHMgZZ5zBiSeeyIIFC+jfvz8ej6ebALu8D7+vB9+mnksXvut5qqqqiEajXHDBBey1117MmTOHdDrd/bw2hQkTJnSTV11dHXPmzNnofrvuuitPPPEEQgjS6TTPPvssO++8c/fvXRLyokWLqK6uZtttt+Xmm2/mnnvuYZ999uHyyy9n0KBBrFix4ns/hyOPPJJrr72WIUOGUFZWBrDJcQtykll/EQByvK5evZr58+cDsGLFCubOncu4ceM2eV7Lsthrr71IJBIce+yxXHXVVSxbtuw7aV5+CD766CP23HNPjjvuOEaOHMmbb77ZfU2bwsKFC8nPz+fss89m11137SbZbx6nKArHHXccl19+OZMmTcLtdn+n63vnnXc48cQT2W677TjvvPM49NBDNzrGd911Vx599NHu8XHWWWfxxBNPbLbvP9X42BRefvll0uk0qVSKf/7zn+y2227f6vN3Oc/3eS5dYy8QCDB69GgeeeQRQJLWsccey1tvvbXFfm9pDuzyol+0aBFr1qxhhx122OQc2NnZyYIFC7j44ovZb7/9aGhooLq6unteWP9d+SHPcGvgNy3RDhgwgLvuuouamhoKCgq48cYbAbmiPeecc/D5fAQCAXbYYQeqq6txuVzceeedzJw5k1mzZmEYBnfeeecGq8kBAwZw9tlnc8kll/B///d/3HLLLVx22WWoqsrIkSPRdX0D0ujCEUccQX19PUceeSSO49C3b19uvvlmAM466yxmzJjBe++9x4ABA9h+++23eG3HHHMM1dXVTJo0idzcXPr27bvR/XbddVdOO+00Tj75ZBRFIRAIcNddd2121TZ06FAOOOAADj/8cHw+Hx6PhyuuuAKXy8U999zD9ddfz9/+9jcsy+J3v/sdY8eO3SRZbQybei5d2Nx51seQIUPYY489OOCAA3C5XFRWVjJo0CCqqqq+JQGsj6uuuoo//OEPHHDAAZSWlnar/r+JK664guuuu47JkydjmiYTJkzgzDPP7P69pqaGQw89FEVRuOWWW8jNzWX69OnMmDGjW6U1ZMgQDjroIFwu1/d6Doceeii33HILt9xyS/e2TY1bgL322otbbrllA0kgPz+f22+/nWuvvZZkMomiKNxwww3079+fr776aqPn1XWdP/7xj1x88cXouo6iKMycOXOz9/PH4JhjjuH3v/89kydPxrIsdtllF15//fXNLpa6HMYmTpyIoiiMGzeO/Px8qqqqvrXvYYcdxk033dTtZfpdrm+33Xbj/fffZ9KkSfh8PnJycrj22mu/1fbll1/O9ddf3z0+dt55Z0499dTNXu9PNT42hV69enHccccRi8XYd999Oeywwzb4/bvOB9/nuUycOJHjjz+eO++8k5tvvplrr72WyZMnk06nmTRpEgcffDDr1q3bbL+3NAd++eWXPPvssziOw6233kpOTs4m58BQKMTpp5/OYYcdhs/no6SkhDFjxlBVVcX48ePZd999ueSSS7j66qt/0DPcGlDE5nRT/+OIRqPcc889nHfeeXi9XhYtWsQZZ5zBBx988IuoH7LIIosN8fLLL/PPf/6Tv/3tb790V7L4gRgyZAiffPIJ+fn5v3RXthp+0xLt1kYgEMAwDI444gh0XUfXdW677bYsyWaRxa8Axx9/PG1tbdxzzz2/dFeyyGKzyEq0WWSRRRZZZLEV8Zt2hsoiiyyyyCKLXzuyRJtFFllkkUUWWxG/aRut4zjEYjEMw8jaTbPI4gdACIFpmni93u40jtl3KYssvh+63iO/3/+tPALwGyfaWCzG8uXLf+luZJHFbx69evXaYohGFllksXlUVlZuNCf9b5poDcMA5MVtrTjA74OFCxcycuTIX7obQLYvm0K2LxsinU6zfPny7gxN2Xfp19kPyPZlU/g19KXrPeripG/iN020XSoul8vVnQ7tl8avpR+Q7cumkO3Lt9Gl7sq+S9/Gr6UfkO3LpvBr6cumzC5ZZ6gsssgiiyyy2IrIEm0WWWSRRRZZbEVskmgfffTRLSb/ziKLLLLIIov/OqSAGmA1UA/UAauAdcAPqL+xSaKdNWsWJ554Io2NjT+on1lkkcX3h7Bt0h1hUq1t2Jm6u9/5WCGwolFSra2YnRGE4+Ck01jxOM43Kv9kkUUWm0AcWAlEkKT6OTAPcIBw5rfU92tyk0RbWVnJ/vvvz5QpU7j//vs3W4A6iyyy+PFwTJNEXT1mRwd2LEqysYn0ekW/Nwdh2yQbGkm1tGLHYqRaWggvXESsuoZUUzOJ2jrMcHjrXkAWWfzW4QDVSDfhQYABFAMFgCuzTQGqgO+RvHiTXseKojBt2jQmTJjAbbfdxh577ME+++zD+PHjqaioYMyYMT/4WrLIIosNIRyHVFMzAJ6yUlSXi3RbG2ZHOFMQHZx0CkVR0fw+9PUKyQOkWlpw0mnchYVofh/x6hrseALFMPCWl2HHYqTbO0BRMULfjvPLIossgGbABAYC7UjpdghgIVXIuUAvYC3QAhR9t2a3GN7Tt29fbr31VhoaGnj11Vd59dVXqa6u5qWXXvr+F5FFFllsFGZHGMc08ZQUo7ndCNvGyM3F7IzQuXQZ7qIiNI8bx3awYjEsrxd3USGKqmJ2RrATSVwF+egBP6nWVhCCQOUgrM4IdjyBq7AQ4TSTbm9H83pQNxHvl0UW/7MwkUSbi9T11iMl2Vyk9Nqe2VYJBJBEW4iUcLeATRLtN4v6lJaWcuKJJ3LiiSd+3+5nkUUWm4GTTmN2dqIHgwjbJr5uHcKysWJxrFgUEOgBP57iYoQQpFvbSLW04phpXIWF3eRpBINY8ThWJIqRE8KVl4eiKJjhTnS/D3dhAYnaWsyOMO6iwl/6srPI4teFlsxfN/Ah0kbrBpKAByhHOke1INXJq5Hk+x3K6G6SaB9//PEf0eMsssjiu8IMh1FUBRyHVEsrqtuNoumIcKeUPDUNszOKFggQX1tNurWFdFs7ViyKkVuAKy+Ev39/Uu0dJKqqUVw6nvIyAIzcXKxYnHR7O97ycvRgEDPciZETQv0VZIDKIotfBSLAMsAGGpDOTsMyv61C2mb9QBBoRUqyXqQE/GOINhgM8uSTT/Laa6/R3NyMYRj07duXyZMns99++/3wC8oiiyy64aTTWLE4KGDFYujBIJrfR6qxCU9JEUZeHon6ehLraolXrUVRFFSvF09ZKanGZhJ169B8g0jW1WF2RtC8Xlx+L6mGBtwlJWhuN67cXFItLVixGEYohBWJYIY7s1JtFlmkkUS6FuhAMmIn0AcYjHSOWol0kBqEtMl2SbKFyBCgGFs0wm7S6/jOO+9k6dKlnHLKKYwYMYJjjjmG/fffnwceeIBHH330R1zZphGNRpk0adIPTm5up1KkmltI1NeTbGrCisV+4h5mkcVPCzPciZNKIWwHPRDAlZdLuqUVRVOlXdbtxlNSQrKpmdjqtaiBIEYggLuwEE9ZCSBI1NVjpVII28LIycHfvz+oGqmmZoRto/l9qIYhHas0DT0gVcwiGyefxf8qIsBC4CHgH8C7SPurH+iH9DauyvytQKqPWzO/+5Dq4xCSQdu3fLpNEu0777zDtddey+67787MmTP5z3/+w6RJk3jsscd49tlnf+DVbRrz5s3j2GOPZe3atT/o+HRHB8n6BuxEAkXVcNJpUs0tJBubshNKFr9KCNvGjERw0iaa14OrIJ90ezvCsnAXFqJoGgCJ2lrpeawqJOtqseIxUm0dmOEIrqIi0i3NRFetxpVfgGLo2PE4npJihOOQbmtHURSMnBwc08ROJNCDAcjE3GaRxf8c2pFS6odISdaH9CpeCLyDlFhV4Gtk/KwPqTJuzOxXgJSE40AOMrbW2fwpN0m0sVgM0zQBsCyLjkw836bq7f1YPPvss1x11VUUFxd/72PNjjBmRxg9EMDbqwJPSTG+Xr1w5edjJ5MkGxqzAftZ/PqQSGCGw2g+L+7CQpxkUjoyhUJomWo6yaZmYlXVqC4XwhFyLJs2wkrhpFNohgvVMEjV1ZOOdqIYBunWNlAUjJwQbQ0NrF25krq2VtojEdIZu6/mcWNGNkO0VuaTRRb/TYgDtUhptRoYChwE7IhUC68EliAl2K7viyEZTLKufh0182uoj9djKza0AXlIkt2C8nSTmuUdd9yRs88+m4MOOohXXnmFCRMm0NzczJ/+9CdGjx79I6/227j++ut/8LFmNIovNxd3wYZWaSMURHUZpJqapM2rtKRbSsgii18SwnFwOiMQDOEqKEDRNJKNTaiGgZGbA4AZiRBdsRKzrR0UFU9JEYqqYuSEMIIBNH+AVHMLmt9HoqmRto/nkIpE+WDxYt7+8kvmLlxAa2vrBucNBgLsuOOO7LfX3uy34474vxlTG0baqroy37iQE04e3ymMIYssfrWwkeQaRUqqIWAsUnJdhFQn60ATiOcEXwW/4rnFz/HBug9Y3bZ6g6YM3WCbwduw1wF7cdT2R1EY2Ly/gyK+GceTQTqd5v7772fRokWMGjWK0047jXA4zNtvv83hhx+OtpUIa6+99uLvf/87vXr12uK+qVSKhQsX4nR2ooZCm9xPpNOIjg4wDJTc3E2WMsoii58LIpHAqamFoB+1tBSiMUQ8jpKXi+JyIdJp7Lp6RFUNtqqgunTUwiKcWAIl0gmOA24Dx7IhlcYKd/LW11/x/NLFtMfj5AUCbDtsOH0HDiDo9qAG/MQSCWpWrmT+0qU0NDXh9/k4bNIkjpo2jcrKSpYvX45rrQtFU7CCUpzVohpqUsXxOKRL07/xwppZ/C/DaDbQOjS8i7x4qjxEx0RRhIKrxoXWqaGkFYQiWLh0Ibc338781Hw8moexhWOp7FtJQX4BGhqx/Bh1sToWf7WYZTXLUFDYa4e9OP+K8xk5cuRGS/Zt8rVxuVycd955G2wrLCzkqKOO+unvwI/AlVdeyXnnnssBe+652f2sWIxUcwu637/VvC2/+OILxo4du1Xa/r7I9mXj+LX0JbpqFYtq1jF2jz1QDINkfQN6MIi7IB87lSJWVU1HbR1OWSmqpuPKDWEUFJBubiGWNgEHPSg9iNd2Rrnh1VdYWlfLiNIyZkw+hJ1GboMnPxdfr94IHDwFBTIEqLkFKx5jYX09d91+O088+yxHTZsGwNNPP83VF16Nv9y/YWc7kOo2DRiAlHK3Mn4tz+nX0g/I9mVT2GJfIkg1bxJpa/0cGAWMRCaiMIC10Gl3cs3X1/DUuqcoc5dx/fbXc8QuRxAg0JOSsStz1C7y2Oq2ah558xEee/Ixzuf8TXbhN78+DYfDnHraaZxyyilcfvnlmywArPv9CMsi3d6BYui4cnN/3o5mkUUGVjxOqqUNJeBH9flINTSg6BquvFwc0yS+torOpcuwY3EUTcdKRNF8HmjvwJWXj22amJ0RYmtX8+8FC7j3ow/xe71cffTRTKjog7u4CM3lwrFMbNtCsW2iq9ZgJZO48vNxTIvthg3jrquvpmrxUsxMv/7xj3+wbNky7r33XioqKno6nIsM2F+d+QxETk5ZZPFrhkCG34SRC8Uk8AUyfGcP5LhuAObC/Jb5nLr8VOoT9Zxz8DlcWHIh3navdJwykOrmFDIrVAdS9RyAPpE+XHXgVZzV+yxqqNlkV37z9Whvvvlmph11FA899BBTp07tdtraGIycHPRAALMjjBXNhv5k8csg2dAIQqDk52NnvI5d+fk4tk3nsuV0rlhFqrEJx7QwI52ShIMBPCUlKG4Ds62NSNVa7njzLe54/z22HzqMfz3wAFOOPQ4jEMSKRBG6jh1LEl26DDttYpsm8aoa4muria1ZS8v7H5FqaqG4ory7XxdddBHLli7joIMOYv78+Rt22gP0Rzp+rCbrKJXFrx+1SJINIWNeHWABkoDTmb8vw4tLXuSw+YeBBi9c8QIn7nsiVi9L7t+KlIY9yAQVQaRkayJJuAb4EHKKcjbblV8d0b799tvfyT7bBZfLxYzzf8dNl83g87lzOXjSJNYsXoxwNu5v7SrIR/O4SbW2Yqe+Z62jLLL4kXDSadItrRi5udJDuEN6Hau6QXj+AuJr1pBqlsUFVI8bVyhA0a67ULDzznjLSwkvXERnfQN/fulf/HvZEo7aZ1+OOugg1qxeTTKdxsjPxUmmSNSsw07L8S3SJr7yMjxlpZkQnyTpzg4UXSM4eFB33/bYYw+ev+V53IabKVOm8NZbb23YeS8yxtAC1iCdS7LI4teIKJIIi5Exr3VIaVYHypBJKp6CB997kLNrz2ZgaCDb9NuGo246ih3O24GhVw3lgs8ukAxZSo9UuxwZEhTIbM9HkvDAzXdni6rjWCzG3XffzYcffoimaey5556ceeaZuH5F6dtSzS3st91o8v/wRy665a8cM306/7j/fgZsu+23vIwVRcFdVESivoFUU7OslKL/5jXoWfxGkGxqxrEtvGUliHU1KKWlqF4vbXM/J9XSjOMIhGPjKixAOA6Bfv3w9e5FqqWV8OIlpJJpzn/uWVbW13HpsccxeMdxnHbBBQDomsbuI7dhyshtGN5/AJrHC4qCY6Yx4zHsaAxXfj6u/DxQkBWCMqTehcGVg3npry9xwrUncMopp/DAAw9smAnOB/RFTjZrkVLur265nsX/NASSWF1AAjlO/UjJdBQwAZgLt796O7PCszig8AAWi8V8vOZjjtr7KIYGhhKLxRjtGg3N0Gq38n74fQ7LP0xKyL2RamcTSbYJ5PZvuDasjy2+IldccQWNjY384Q9/4JJLLmH16tVcd911P/gebA2kmpvwFBWz8/jx3HfFlYTDYY47/QxWfz1vo8kqFE3DU1IMQpBqatqk9JtFFj8lHNMk2dSEEQzKUpbpNKgaHV99Taq1FT0nJONq3TJ5hTs3l8DggSQbG4nX1rFi2TIOnTWTlfV1jK+s5IgJExial8cT11zHLb+7kCP33Ze5K1dw7pNPUNfchKJrspB8azup+iaEZclSekWFuEI5JOsbSLV/I62NAcWeYp754zOMGDaC008/nddff33DfQLIySaODJf4HnU5s8hiq6MdaU/1AiuQoWl1mW1DgQ64+5W7mRWexWF5h3Hfkffx8CkPM+fiOdww9gaml0zn7AFns/OwncEDT332FOd+dC4Xf3IxiWUJ2X410tbbFxlDW7X5Lm1RlFu8eDGvvfZa9/eddtqJgw466Adc/daD5vXi690LxTAYYxjc+8fLOWvm9Uw/9xyeffgRyoZUonwjyYZqGLiLCkk2NZNqacFdVJQN+8liqyLV0opIp9CLi0g2NuFEo0RXrCLd0Y4e8BOvrUdYNjmjt8VJJFCCQaxwGDMW5z+vvsIf778f23HYb/gIZs28AbsjjDsWpbK0hAFeH+NKSjh13/356PO5FNg2iXXraIgnKFRUvL3K8PnKSTQ0oroMXEX5WIkYqssNznqL0beAYshJ5vCPM/7Bcfcfx5lnnsk//vEPdtppp579cpCp6WqBdUjizSKLXwNa6EmNaCFVxS8h7bQGPPn8k8xcMZMSrYSAK4A+TGfotkNlLO1iJHGqQAlwFpz52JlEP41yZ/OdLIgs4OHIw1RUVkjpuBVps12z+S5tUaItLi6mra2t+3s8HicvL+/7X/zWhKIQXrxEBvx7PIwcOIhbzjuf2vp6Tj3/PNqra75V9g8kQbvy8rDjCcz2jp+/31n8z8CMxYmsXIkZiZKoqSW2Zg1OawuJhgZAIR2JgW2TO3IEnvJy7GQKVBUzkeDfL7zAZffei+04TN1+HFdOOx5NBc3rxkmbCMtCDwZQXS68KEyZNo3AoIEsqqnmhAfv44GPP6SzqoZoVTXp9nbMSBRfnz4EhwzBinzDKdCNJM18yOnI4fFzHqdXRS9OOukklixZsuG++cjJqAMpMWSRxS+NTqTkaiHVuSXAUuQY7QMvv/UyM96ZQYVeQaPdyPDew6Vm5gNkisVK4ABgX6SX8vugqzozAjN4tP+jVJlVHLTyIOavnt+T+3hc5rjNYItEW1payuGHH85NN93EzTffzJFHHommaVx33XW/GhWyk0gQXbkGO5HAXVhIYPBAxo0bx8yzz2HB0qWc+/vfE29s6t7fTqVId4QzhQei2IkksepqWTA7iyx+YpiRCJ2Ll2CGO9G8PlAVVI8HxeXCU1yEp6wEw+clMKAfoW1GkKxdh5WIoSCwOqMsWr4cgOMmTODMQw8lUDmIdFsHqZYWUBR0n49g5SBK99sHT0kx8ZpaQkOGMKRffyaPHMXsBfP4/eznWD1/gcxxHIvjpNOouo6qf2MKCANNSOcPAfnL8/nHJf/A6/Eybdo0amtrN9y/GCkptCKl26waOYtfEi1IohVIfa0BzAdcMHf1XM594VwqjApqrVrOHnY2J1x7gpRgazLH5QLbI+Nkd0C+CxlC3VfZl3+N/xcDcwZSrBdLD+Y3kLG1W1CGblF13LdvX/r27dv9fVNq42/ZcTaBrVJiTzMQVpro0mVSLZabi7e8jIn77ktbOMzMRx/h6quv4sqLfi9zyGZssqphoOg6Rm4OdkOCyLLl2H374lsv5CGLLH4M0h0dJOrqEekURm4uRk4IOxaTdWZVDT3ox06lUQ0Xgf79Eek08eoaVJebN+d+TqKlhcdef42JY8Zy2u57oPkDaLqBnp+H6jJwLAtf715obg/Cssgbtz1tn8whsnIlLl3jrHE7Mqq0lFvee4fzX/4Xv3cs9hgyFDPcgaeomGQsCr3X8/JPIT0rxyPrcX4Cvb7uxRMXP8Hh1x3O9OnTefHFF/H71/P8KEMu2Rszx/fhvyBCP4vfHFJIz2ATSbQBJIHWwjp1Hae+cCoFRgF16TomFk7kDwf+Ab5CSrsx5IJRAb5EanaakDZYraetylQlz+/0PITB7rR59qVnOWr1UVICHrrprm3xdTj33HNJJpNUVVUxePBgUqkUXq/3W/tddtlljBw5crNtLVq0aKsQrbu4EFvYJOvqsed+ibu0GDsWI9nQyAEDB7Nq/C7846WX6F9axtQjj8JdXIQrL3cDb2N3USGRFSuJV1cD4C0vy9pss/hRMCMRaZc1LdB0NAWcVArHshCWhWLoCBRA4Kkow1VYSNtnczHDEf5Tt4Tr7rwDXdMY2acvF+0/EVVR8ZWV4AoFUTLEnGxqxmzrwNWvL3Yiiep2E6wcRLx2nXR+six2Lu/FyAsu4uon/k5dYyNW336klq0g1dSCd+g3dF69kJNTFbAPslLJf2B43XDuO/c+jr/peM477zz+9re/9RQXiSDVbGGk88kyZBhQGVJCyCKLnwPtSNIMZL7bwGKIRWKcNP8k0naaSwZdwuM1j3PL6FtQQ6qMpw0gHacSyEWmjnR2iiHH70Ck5saHlGJTQA68Hn+di+su5pXWV7jdf/uPI9p58+ZxzjnnoOs6Tz/9NIcccgj33nsvY8aM2WC/vn378vjjj2+2rUMPPXRLp/tBcIWCuPLy0H0+4rX1RFesRA8E0Px+7ESCMw88kKqWJm586G8MHjmSnf3bk4jFZOiPokhCVRVcGWkjXlWF6jLwFBVtlf5m8d8PO5Ui1dqGk0iiuAxEJAIuF2Z7B4phoHs8CE0Hx8GVk4c7L49kQxPxqiq+aG7kujvvwG0Y5Hi9/HnKEbh1HV/fPnjKylANA1d+PoqmYeTlkFi3jsjKlXjKyoiuXIXZ2YnuD+BYDqBitneQ29HBbcdOQyTiIAQfr16F7XJz5N57SO/nLhQhJYK1wNPIUIhKYA7sntidq4+8miufvpKbbryJP5zxB6lWSyFnkgFAOVKFXIO0fRUhbb7ZNWsWWxMCORbTyLCeTIiPs8zhd4t+x9LIUv6+/d/ZU9uT44qOQ+2vysVhLnJxuU3m2PlIMm1FJroYilQlh5EOTzmZ311wQPkB3NB5A1e2XMkpX5/CFVyxye5t0UZ700038eijj5Kbm0tpaSmzZs3aaKWd2bNnb/FefJd9fgjMZBItEEDV5QSmBwN4K8pkoP9O48gbPJjrTj6V3vn5nP+HGaxauRLh2KCpaG43qsvojrc1cnOx43HCCxZidnZulf5m8d8N4Tgyp3A0iupxY8fjMpa1pQVhW7gK8lEMA8W20f0B9GAAxWUQXjCfte0d/OH++/C4XCDgusOPpKS4EG9FOZ6iIjSXgR7wY4Y7SDU14SRTuAoLSLe1k6itJbJiJZHlK2RN22CA3JHD8ffri6JpqMkUhteHIgSvr1rJzFdf5veXbzg5pFpTUoodiJQQnqKnZmcETnKfxNSxU7nr7rt4/vHn5QzSGzkhVWSOG4ucvGJIJ6lsCFAWWxsRpK3UjxyTMWAV3PzFzfyn4z8cM+AYlrctx1ZtKckKesbrCOR47Yck27WZNkYhPYot5CJyCNLxaWhmv2I4YfQJ/GPkP/Aq39byro8tEm0ymWTQoJ7sMbvvvjv2RmJTu9RIr7zyCvvvvz/jx49n/Pjx7LTTTowfP36DfX5qHH/OOTx0990k0incRYXkbjcaIxjCikbQ3G40n5fcslJmHjcNHMFZM2bQ0dKKSJugqrgKC/EUF+MtL8ffV3pj2okkrZ9+hhWPb5U+Z/Hfi3R7B2Y4jKLrsphFSzNmJIoQgtDQoSiqghWLg9eL7veiud1EV6zAjMW5/rn/w0ynSabTXLbf/owYPgx3cQmqy4MeCqEYLqxIFEXTcBcX4evTm9xtR+EtLyfR0IBQBHowiKewkPyddiIwcCCFE3YhZ+QI9NwQmteL5vVw3ZFH88cpR7BT5Yaq46l3TOXVV15FLBVSCu2KEQwD7aCsUrg+73rG9xrPJbdfwhfhL6RUIDXgUhpWkbat0sz3NcicsllksbXQgiTbPCQxNsEL/3mB2xtv58jeR/J27ds8W/8stteWY3UYkjj9yMxOa4FXkWaPocj6tB7k+E/TEyebC4xGjvFFQAHsMmwXHh708Ga7t0XVsa7rhMPhbnvl6tWrN7v/X/7yF6644gr69OmzpaZ/MuQYLmY+cD/3/ONJjj3oII6fOo3cUJB0RyfCTOOk07hyc9lm//24Pp3kggce4PczZ/LAzX+BSARhmTKOVlVRDUPG5OoabZ9/SfvnX5A7ZjuMQGDLHcnifx6OaWYq5MQRpikTpjgCIxjAW14uszGtbUb3B1D9flCUbluuKxhk7PDhLFq5guPHjGWvsduDbpBuacbXry+az4eTSsl6tJlyj0IIzHAnGDqphiY0t5vCXcej+wMoCmDoaLpGYNBAnGSaeE0NtgK6z8Pegwfjqqhg/ZQVqqVyyuxTGJ4/nIsmXsQBex8gJzEX0uZaA0ba4IGRDzDpw0mcMvUUXv7zy1SUVUip95s1PTRkRp6vkRPXrywyMIv/AlhIrUkASYBt8PUXX/P7r37Pjv4dcflcNKYaeXDEg7hslyTS0cgFYAhJvEkkmRYB+yOl3UXIvN5JpNbGQY7nGHIR2YZUI+eBUrZ528gWRcwzzzyTadOm0dDQwEUXXcSxxx7LWWedtcn9Kyoq2HvvvRk8ePAGn62Jq6ZMYdb+BzK0sIh7n3mGP//xj7R9+hmR5Ssx43G0QBA7kUDVdXbdc28uOvgQPpz7GbPuvhvVZWAnkqSaWzaItfWWlZEzYhhmNEpk6TLS7e0bjcXNIov1kaxvIFFXh5NKZ7yKQ+ihHNx5ebhyc+Vvlo2rpBiEg5M2STY2MXfBQt6b9zWPv/Qvdh84iJP2359A/75oqooeCKIoOsl16+Ri0O3GSaVIhzuJrV5DbM0aEjXrcOXn4S4tRfV48JaXZdIvWt1hRf7BA9ADAVTbxo7GsSMRoqs2jLR/quwpbiu+jUQywew5GVNPBdgFtgx3GAookO/P59EdHiWRTHDyjScTXxyXiSscpBquF9KLMx9JrlVIiSH8cz2JLP5XoLfr0gmqGIhC/cp6Tn78ZIrUIs4acRZPLnuSUwpOYYxvjByXo4AlyNCfQcg0om1Ijcx4pDbGBWyHtM92SbQKUgIuy2w/GDm2E8j43c31cUsXsddeezFw4EA++ugjHMfhnHPOYeDATWdQPvTQQ7npppvYbbfd0Nfz6t1hhx22dKofDMWBHXYYy/ZjxlDTGUFNJzDDndSkEjz9zD84/7jjKCooIF5Vjbu4iCm7787q+noeffZZBvbqxbHTpmEnEpgdHbjWS8bh69MHKxYn3d5OvGYdnnQad2Hht/InZ5EFQDrcSWT5chTdwMgJogeCpNvaUDQFPRSS5fFaW/GUlqK53Ii0iRmN8s9//5trnnwcl64zuKiIi/fZl0DffmguD2qBC1d+PrrXg6LrCCGIV6/DTiRwUgkUVXouay4XoZ12QlEh2dCE7vPjLSvF7Owk5Tikmpqw4jHcvcpxmcWyapDtYCl0l8kD0FSNIwuPZErBFMJ5YXgHVhas5LjHjuPUEacyddup+IN+6IDK3Eru2fkepr8/nQtevoD7TroPtSrjyVmCnF1ykZKGH/gY+D+kg9UgpHSQRRY/BgJctS7phOeBxKoEp9x9CtF0lH8O+ifnLD2HXq5eXJp/qZRSt0WGABUi7a1uZDjPUqStdjgbip99kZqaecBnSO/kXsAYpDkkgAwR2oLCc4tE+8c//pGZM2duEEt7/vnnc8cdd2x0/zlz5vD+++/z4YcfbrD9pZde2tKpfjCEbaHoboSZYmBFKZrXR7qlhZXrann966/4cMlifj/tBCbtNgHHtglWDua8gyZR1drKn2+/nT5FxYwbvzNOS4uMqw0GAVB1HV9FBcKxEQjpRWpKNbO2ibq3WfxvwjZNwvPnY6dMggMGSH8ERZGq3rw8FE0jUd+Aomh4S0swOyM4ra18snAxM596EpeuE3C7uXLPvckfNBhf3744yQR2Monu8+IuKETz+zJJKkAP+NFLS9D9PiLLV+ApLsLfvy8KYMcTxNasRXUZuAsKMEIhFF0jsnwF3oJC7FSalGWjpFIonm8UBzHkR+vQyG/NhzpI6Sn6aH3486d/5va5t3Ni0YmcXHwyBf4C9vbszRWjr+DaL6/ltorbuGj8RdLjOA85sfmRqjcHKQ0vBt5B2sRGIqWQbG3bLH4owmC0GzAWRLPgwrsvZH7TfB4ueZgRg0YwK2cW9jobf4EfBiNJtAgpxaaRVXw+RS4MJyDHYhJJxonM/7vyJufQUzigFbmI3BYpza7dfDc3SbRXXXUVjY2NfPHFFxukYLQsi5qaTRe4Xbx4Me+///4mC7BvDSiGjrBshGkRX1eHp6wUXG52Ky6h79TjufXN17nqgfv44JOP+eOJJ1JUOYTc4cO45rjjOPO++7jghpk89ddbqSguJtXciqe0BCOT0k71uHHl5mHH4yi6hp1IkmxolHaynBwURWH27NnceOON1NXVUV5ezowZM5gyZcrPdv1Z/LJwTJPI0qWk2zoIjRiKaugoioIZDqN6PGhuF1Y8jtnRjrukmGRjE9FVq2muquKPTz/V7ST454kH0nfECPK33w5hWpiJJJrHgxHKAV0j2dCIoqn4KsrRMgkjoqvXICwL3+BBqBlNS3DoEMILFxFduQphOxg5IVRNIzBwIKquo+g67pJCYitXE49ENryYEUh7qoUMz2mBEdoInvM8xxf9vuCetnu4reE2Hm18lM9LPsereTkjfQbL/Mv460t/ZXDtYCZvN1nGMFpIwg0gJyMHKUkkkLaveqRTSqncb/abPe9RYWEhf/rTn7LvURabx0q6Vbq33n4rLy18iSuKr2DPXnuCBuNax8nf+yCJNY5UMzdnPkuQNWb3oafWcldWUgPpVxCkJ3FFHXIsdxUpALmg3ALdbZJojzjiCFasWMGyZcvYf//9u7drmsbo0aM32WBhYSGWZf2sROstLcNuakQvKMQMd2B2dOApLEYpLWZQfj73nH0uj/773zw65xNK//44J+21N+5eFfgEXDvlcM557BHOv/5ann3sMVxpEysqvTqJSY9jKx7DMW1cHjdCgKLrmB1h7Ficlz94nxlXXEEikQCgtraWSy+9FCA7SfwPwDFN4utqiVevw1tRjruwELMjDJlFmZGTgxCQam1D2A6OaZFYV4clHG5++d+EEwls2+bKAyexTeUgckePAkdIiTeVxD+gH6rbhdnWjub14i4s6DZdpMNh0i0tuAuLcOX0FJ5WdZ3g0CFEV6wktnoNaCqu3Fz8/fthxxOk29pQNQ13YSFaScmG5qVKZMjDauRkshw5MWkwNn8sD01+iJWelcz/aD7eiBea4J74PZzlOotV5iou+OoC+tX1Y5u8bSSBliEnuTHISc5B2sL6Z9pdAkRh9sLZXHr7pSRS8j1qbm7OvkdZbB5xoAbMPJMXn36Rv77+V44sPJLTSk7jsOWHsde6vbgwfaGUOkci7a4DkWE8HqRU6kYu9gRSuu2qV5vDxjUtA5HSbgmSqNePt01suqubJNptttmGbbbZhp133pnS0lIA0uk0LS0tlJdvOkVhSUkJhxxyCDvvvPMGNWuvuGLTwbw/FoW77kxq1SqStQ24i/IwOzrR/T78xf1IrKtD87g575KL2eG11yhPmZitbaQ1lVBZKb1iUa48YBJ/+OdzXHDBhdz9l78gLAthO3h7VyBSadSIm3hVFSnbkmpl28KVl4sVjXHTrFndJNuFRCLBjTfemJ0g/oshhMCOxUi3tRFftw4jFCJYOQizI4yiadjJFKrLJRdlbW2Y4Q6wbNJtbWguF5rfT9+KClbW1zN9113Zo3IIgUGDUYSCFY9jx2O4S0owAgHMjjC634ersLDb+1/YNonaOlRdw9vr2++j7vHg69ObjvkLsdsiqG436Y4OXKEcFF0nXd8uE1/khCCyHtUqSPXa7sjJKIIsmL0IKYF+DoN2GsSg8wZBGqpeqeKvz/6VG2M3MrF4IjXRGk6KnMTLQ16mRC+RUkQ10glqPJJ0FyIzSO2M9GhuhxsfvrGbZLuQfY+y2CyWAQKW1C/hkr9dwg45O3BTn5t4qPkhvox8yanKqVLaPABJsuXIkJ4C5Lj8HKkKDiLtrbmZfTbnO6Bk9g8i244hx7e1+a5u0et4wYIFXHvttUSjUSZOnMghhxzCY489tsn9+/Tpw6GHHkpxcTG5ubndn60JOxYnd9QockaPwhUKYWRI0OyM4ikvw47FURzB/r87n74HTCQe8HPqA/fz5Ftv4RswkJ22Gck5Bx7Iu199yc2334FwBPGaGmKrVqG6XXjLSvEPHACOjZNOY3ZGSIfDGAX51Dc1bbRPdXXZcib/jRBCYEYiJGrrSLW0YsYTKCj4eveS5otMHm2RTqEHAjjpNPHGRhJV1ZixGMJ2qG1q4q333uPtL75grxEjOaZyOHpuLkJRZWpGVUX1evCWlGBFJEmuT7IAqbY2zI4w7tJSmb97PTimSaq1FbMjjK+ijMDQShQB0RWraP/yK8zOCP5+fXGXFqPo31hr74Bc0bcgybEIOVFNRnpa2kjV8mIgBH0v68snt33CaZWn8W7LuzTFm2iKN3HCihNIDExIlVtX0MGbyHCI8UgS/whpo+0DdS0bf1/q6urkOW2kBBNFTpJZ/G8jAVRBbaqWqx+9miJXEQ8Nfoi6ZB2z1s1iX9++HBw8WNpdfUjptAJJsiAXezG6PZUpRIbwfF8HPT9yfG+m6Dt8B2eo+++/n+uvv57XX3+d0aNHc80113DCCScwffr0Dfa75557OPvsszn33HM32VbXPj810u1tJB0HT2kJmtuF3txCzHRINTaiamV4Ksow29uwojEKxu9IWoXBb7/JPa+8TFVTE7+fPJkjdxxPdXs7j7z0IuV9e3P0XnsTXbUWKxojNHQI7sJCnFQKKxoFTUopCEF5efm3K5rAZqX+LH57EI6TWbyFEZaN6nahBXNJrViJHvDjLi0h1diEounSOc8wEJZJePFi4muqUTyyUs+HCxZw4V9vRlVVBhaX8LvtxuLKy8HftxeunFy8/fqQWLsWV04ejmODouIu2pBkHdMksa4W3e/HU1KywXYz3IkVk0YmPRDAlZsDqoodj2NFY6Q7wiiqgqJqeIrzER1haO/xwSCBDGtoR4Y0rEOSYn96gvu7PDAzqRpLikv404w/ce6r5/Lwew/zj9Z/sKhuEWe+cCY3bHMDZZVlKCMUSd7LkGrp7eguQ8Z4KC8up7ZpI+9RQbl0ngohHVK64ELafwvIei//LyCZ+VhINe88aKtq47iHjyOZSvLssGfJS+dxxrozMBSDG3w3oAxXpEo3ivQm7krtsA4ZQ1uAFDULkIvLHwodSeLtm99lsxBCMGTIEB588EF22203AoHARuNJn3/+eQYPHrzZWNPZs2dvFaI1AkFSLa3YiQTeigo0rw/V5yOybBnRVWvQQ0FAEJ4/H80zjqKR2/DXK67k1rvv4olPPqaho50/HzqF8ybsQXu4k5l3301Jv37sNWwYidp6rHiCwMAB0t5m2WgeD6aqkm5r58JTTuXKWTeRSCa7++Nxe7jotNOxojH0wBaWOln86mHF46Tb2jLP3o2eX4Dm9RBduQrHtAgNHoSdyfyEsLHTacxwB8mGRlJNLWh+H4FBA2gQghl3Sm/98qIiZuy+F8GyUvJ2Goeu6Xh798aOREAIXPm5OKk07qLCDYpfACQbGnBSSQKDB6NmQn7Mjg6ZuEJR0AMB6QC13nG634/u9+MuLsKKRDA7Okg1JlG+mYhFIAm2HJkdpxEZY5hGhjVUIMn2C2Re2FHIiUyF/OH5XBy6mIuWXsQT1U/wh+o/8Gnzpwz6eBAXjb+IvbbbC6VUkUT7Bj1ZfFbCjF1mcOnLl5JI96iPvW4vM6bNkIQeR9p885FOKJ2ZvrUgJ8lsIoz/Pgjk2OvKp92FMMTnxjnhmROoaavh3t73MtQ1lMXexXwV+YprA9dSVlEm/QJakX4HI5ELsg5kOE9X8pQgP45kvyO2SLSqqvLKK6/w4Ycfctlll/Hee+9ttKpNeXk5f//73zfbVlnZ1rkid0kxSsBPsq6B6MpVeMpK8PWqwF2QT8e8BURXrpTewo3NOOk0uaNG4a8o56zDj6DM4+PW997m7nff4ZJ99uGyPfcmHI3y+xkz+PtjjzFm2BBiVTVEV6/GW1oqS5Ol091SxsQdd4JLL+PWvz1IXX095eXlXHbxxRw4YTeSzc2obW2oHre0+zoCRdNkWki/Lxsi9CuHEAKzvQOzs1OGypQWdqtpUy0tpFtbZEysx0OyvQNhW5itHTiOzNYkTAvN70d3u0l6PZx61lkk02nyQyH+etw0RHMzoRHD0Q0D3edDdbuIV7Vj5OXhpE00nxfdv+FCzYrHSdQ3oOfk4srPR9g2yaZmnJRUVbvycjcb560oCkZIpmJMt7aSaO/YcId+yPJgtUibVQVSfdyIJDcdOCaz34dI29ZOSOeRTsCWc8YJgRNooIHbq29nVXIVJ7x5Atsu2pYL9r6Affvvi1KjyGNVIA5T4lOgL9xYcyN1yTpKPCVcPvFypuwzRar0wpmPG+mIUoCUvuuREkonciGQlW7/O5BEhoklkarfrlAxB8zXTc749xnMa5nHg7s9yJjwGOgDw+PDea/wPSoCFXIBGEGqdbvSKXZlKBPIRZubn63gxRaJ9rLLLuOuu+7ioosuoqioiHvvvXejjk1bqtyzNZFubSNQUoIRDBJft47EulppvyrIp2Dc9nh7lROet5B4XS2J2nrcZWXkjhxBaUE+U3Sd8oI8Bg4ajK9XBUpdPVftvTcXv/QvTjn1VJ584glGDOhPsrGJVFsbut+HqmmobheeUqm2O3DXXZm8334sXbOGsXvsjqKqMpwjHCbZ1obqduMtK0Vzu+VkHInIydvtxpWflyXcXynSrW1Y0Sh6MIgrP697gWnF48TWrEXz+vD17kWqsYl0Wwd2KoHm9aC5dMzOCBgGdqIVRzj87sorqW9pwed2c+fZ55FnmnQUF+MpLcOJxXAXF5NubkHVZRpQRVFwFxR8q0+xtVUgBP6+fcBxSDY24pgW7qLCb5Hy5qAaBp7SUpzOTuhYT+el0UO2jUgy64O0tSaQ5NiOnPi2Rzo1fQ3shkwAsAvSyaQYLim4hLZ32ni87nEODBzIwtaFnPTESTx2wWPsM34f2ZaVOWcTTKmdwpT8KeBAS7SFQn8hvIVU//VGSrZ1mb8VSFXyAKRU24AM9ejHFkMtsvgVQyDVusuQY6OYnhJ2DWAtsbjgrgt4e+3bzNptFhODE1mXXMdbjW+xd/Pe9NJ6STNHAjkW9s8cX41clHUlU3Ejx/XPtDDbItFuv/32PProo93fn3766a3Znx8ERVNJNTVh5OYQGDSIVHMz6dY2ko1NUo3m9ZKzzXBQFeI11XR8+SW6oRMYNIiiCbuwo22TbGgiFY9z1Ssvs//IkVy330Quefklpk6dyt8fe4xRvXqTam/HSaYRCiStZry6gbuokFRLKwiBSCaJVVWj6BrYDkYwiLuoECsWR1gWWm4uus/XY+8Lh0nWN8iJPC8XZSsVXcji+yPdEcaKRjFyc3Ct58znmCbRFStBgH/gAFItLfKZazp6IIinooyOefNJtTSTbmvHSSRIhIKsqavDpevcedHFVGgqisuPUpiPHYviys1F2A5mNIbq8oCiSmL/hmSabGzE7OjA16cPmtdLKkOynuIitI3UiP4u2OQirxgpQVQjCawcqbbtj3QiaUKq4ZLIsIgXkBPkSGQ4RSEolQrXj7yezvs7eXHti1ySdwkD8gaw57I9IQnPLHiGVGeKw/ofRrAkKLNFCSAF1kJLth9EknmUntCKKHIS7oOUiAuRUk9Vpi992aJzShY/M1JINW4MuVBSkCSnIx2VdCQ5LkKOrRCSKHXkuFoLVtji/H+dz4urXuSP4//I1L5TIQ4Pr3uY+2vv55/+fzKuZJxsrzdwFNKJbjmSYC2klsaLlGh9P8uVA9/B6/i3AHdREXomDMJs78BTWoq/X19cebkIBAIFw+8n0L8/3pJSnLQpCwYsWIgdixOsHIwrN0RHXR2t7W1c8eQTzDHT3HzYEeR7vRx//PHMmTs3MykJHKFgxWIk1tVix2KobhdOKoVIpUg1N5GsrQdNxV1WirugQBaRN1ykmpozzigqRiiIt6IcIxTCikSk3c00t3itWWx92MkkZkcHut//LZKNLF+BnUji7dMLM9xJZMlynHQagYOdTNI+5zM6FyzCisZwEimaPW7O+uvNmOk0d557HpWBAGg6OaO2QXUcEOAuLsLs7ERRQFFB9/nQv2E7tWIxYmur0ANy3KTb2rGTKdwFBT+YZLeI9T0qu2rMOpnv/ZEhQMOR0mwxMhXdv4EPkBNrErQyjTtm3sFhIw7jL+1/YY21Bm2+hnhHMHvBbP6w4A9s99J2XPzCxXy14iuEJcANZpkp21iJlFiXIB2wutYeS5ATaFchMR+SiLsm5m/k4cjiF4JAEudypOrWQJJdCClVCqT9fRU9GcOKkVoMI3NMAqxii3PfPpcXV73I5WMu55wR58Aa+PiLj3mw9kGm6FMY5x4nw3e2QUqyHchx68q05V7v/IU/w7Wvh/8KolVUFXdhgYxtjcVINTaieb14ysrQfX5wHFS3G3//fvj69JL5ihWVZH09iVqZ5N1dUkKOz8+syQez+/AR3P7sM8yuq+H208+iKBjktEsu5pUXXiDV3EKyrpZUayux6mrCi5eQamgi2dyCaGtHD4bw9u6FSJskamowYzFUXcdTWpJZDHSQbGpCOA6KKiUXd3ExwnZI1jdgfyMmN4ufF8JxSGVScboK8ru3W8kknUuWYUWiuEtLSLe0EVm2FCuZwEmlSdTWkmpqIra2Cs3rQff7eWP5Uo6deT3NLS3cevrpjCyvAAV8vXth5OUiEkk8xYXY8QROMolwHHSfb4PzAljRKNFVa1AUlcDggdiJJFYkghEKbX1nOx1JqqVIG+kKegLzfUii7QWcAExE2sIWIaXLMnmMvljn9uNuZ8p2U5jVOotrgtcgthU8ffzTvDz9ZQ4bdRgvtr3IpDcnMXPOTLDBDtgyztaLlIS8yEnzVeTkmwLmAnPocZRxIVXJ7sz5s+Wkf3msQ5og8pDk2Q+pHalAah76I59tNLNtCjKUbACSFAshlhfj5HtO5qV5L3HlHldy9r5nwypY176OM9adQT+1HzMLZsoF3w6Z9m2ktqUUKT13SdEB5Hj9mbFF1fEPQWNjI62trRt4II8YMWJrnGoDGDk5KJpOqrWVZEMj7uIiPKUlmOFOWR/UNAmNHImdNEnU1iEsG1QFVQFXbi5Or94otQozdtmNUr+fp197Dce2efi6mZx/w0wuveduLjryKA7faTx2KomdTAJSOnUVFGDZNrHVa7qlESeVQq2qxp1fgKswH93nx8jNwewIk2xowF1UhGoY6D4vqlFCqqmZZFMz7oL8b0k0Wfw8MMMyfMdTVtqtyjcjEaIrVuKYFq7CAlJNzVjRTqx4CsXQsM0UnqJiVLcLKxrD168PL8yezY0vvYiiqvz13HPZpndfUFV0I4Cvbx+sSARF09BzcrDCnTimharruAp7vIztVEpqaTo7cSwTX/++aC4Xibp6VMPAyMv9+W5MET2q5FVIO1chUjrI1KplD+QE+R5Suv0KOZkaoKFx24m3ESoKcf/r91NLLbfvfjujS0Yz+pDR/GnSn3j+vecZlTMKvNC0ook36t7g+F2Ox/O1R0qpO2TONR9ZXN6HlHKXIT1Mc5Akm4fM2lONVC+Htv7tyWIjqENKlaXI8fNNdGksViM1FX3pWUDVAAY0NTVxwt0nsKhpETdMvoETBpwA88FKWpxSfQqWY3Fn7p0ERwZlhqd4pp18JKk20UOyP7Nddn1skWiTySRvvfUWHR0dG2yfOnXqRve/9dZbeeSRRygs7JHNFUXhrbfe+nE9/Y7QA34UXZOklSFbV24Omtcj64R2hPH364twbFL1DaTa28kZMgQrnsCVl4PmcaEYLk5UxlIeCjFq+7Hked08PGsWl938F25+9hkaEgkuOHYamp0m3thEuq2ddGsrdHSQAPTcXAJ9eqN63DjxJMnmZuxkAiMnJ5MtSMOKJ3HW1UpbnOECx0YPBTHDYVItrTiWtYHaMoufHo5lyQQRmoZqGDIOtTOCHgh02y5TrW3E1qwBFNxFBSRq6zBjcakl0TV0fwAjFCQwaBCtc+bgysvh2Xfe5vonHkdVFO6++BK269MX1WWAI6S3uhCIdBry87Bjcex0CieVxNu7N6quk25vl9V50hlTguPgLizEnZ8vw4wcB3dx0Ua9/7cqfEhV8jqk81EbkmwLkRLJSiQBj0dOakuREnAR4AWtSeO6odfRW+vNtf+5loZ4A/fvdz+l7lKCIsiJY06URNoKbzW8xV1L7+K+ufdx8T4Xc7R5NOrbqvQmNZBSc1cVoK+Rsb3D5XlwkBJNO9ImOAS5IMhi68BB2kA1etIWdiA1EYV8m2RF5reumGols08L3fVk8cPClQs55a5TaE208vDpD7NvcF+5yIuC7tM5v/R8vHEv5XnlkmTbkeNvOJJca5HjMIg0JZSzYSz2z4gtEu2ZZ55JZ2cnvXr1yNuKomySaF966SXefvvtDYj254bm8eApKyXZ2EiqsRFXYRG6z4u3vIx0eztmuBNXSNaoNVvbSDY34+/XF0XXSLe3Exw0AKEp7OfYqB0R4g2N3Pnaa/QrLaP0gAN44qV/sXD1Kv5y6WUUD+gv24xEUVJJ9FAOZmeY6GoT/4ABqG4X6Y4OrEgnZmcUI+DDcQSOmUak0ijrXLjz89D9UoIVQmBFo5idEexkCk9J8c8/of6XwzFNaeNcT02vugyE7aAoYOTmyHJ062qJrVkjtQ55uXQsWIRIpdByc9DdcjyhaXhKikk2NBBtaeHml//Nv954AwWYdeZZbD9kqJw8TBvV68Gdn0e6tQ09GIB4DDudJtXcjIKKk0qRbGwCRUFzy/J4diY+21NUKFXGsThGbu4v56neJXlEkNJCV0KnJJKAq5C20klIFeAypD21AUiB0qJwZvRMKkZWcOHSC9n/yf25c8Kd7Fa6W48Hsg1TK6ey2za7cdPbN3HxixfzdN+nuXH4jQxbOUyqGDuBp5AFvAcjJ+wlSE/oQZnfXZn+fIJMkFG6le/N/xos5BjoquUKcjFWiCQ5Hxvec4Ek4DXI57UGSdJBehL+N4EwBI8vf5yr372aPF8ez1/9PNvmbAtfgB2xWWQuYpRrFAd5DoIyqAnUyOediyTcMD2ZoDSkZiMPKeX+Qtgi0TY2NvKf//znOzeYl5f3i5JsF1TDwFtWJsm2uRky6lh3QYHMVywEtmlix6KEFyxCD4XwlZXhpNII26Zg++2J+AKEly+nc/kKOpsaeXbRIkpCIQ4ftxP/mfcVh591JpefMJ0D9tsPd0EBansbmscNto9UcytmRyfBIYNxFxXJuMuWJuxEAG95OUYoiHAEVqQTKxpHD4ZwFxUiLAvd5yPZ1ExszVqsSAT/gP7dlVl+bRCO0+3Eper6L1Krt6sPjmmiKAqq2/2tJA/d+yaTJOrqZTxpbi6a24VjWqRapAbEW1GBk07LhPzVNai6hu0I2j7/ElXXCFRWYoRCeCvKwbFRNA3Hsvni7be58sEHWF1by4DCQo7ZcSf23XsfHMfGau9A0TQ8JUX888WXuOOJx2lobaEwN5fzDz+C/ceMJThkMEaO1LxoHg8oCum2Nuy4JFbFMEg116O63Rg5vwJdaFe+1yTSZtsVitGGlFA8SFXzTkgJoxk5+XYC62Dy/MkM8Q3hjCVncNzrx3HeoPO4oM8FuJNusMDb6WWXyl148cgXefaTZ7l2ybU8EnyEWTvPkgRajJSA5tMTQ9sAvJz5/45Ie2ARUoX9CZKU+2/9W/ODkUKSkc5WMuptAYKesnAOkig9G99VSSlSg7F+hSYT+Uw+zXzfIfN7BGmn7ap804RcAGlIKbO45/xtZW1c8eIVvLjgRfbovwd3XHUHBWYBfAnmapMLF17IS20vMaPXDB5peoS6VB0l7hIuH3c5Uw6YIsm0GBlnnUISujdznl8QW3yclZWVNDc3U1S0MSV7DxYtWgTA8OHDue6665g8efIGhd9/DhvtN6FoGp7SUlLNzRl1rI0rV6pv/f37oagyi0543nxa3vuAgl3H46koI76mCmyL/B13wF1cSGTNGi4tKGT/MWO589VXef6zTykJhXC7PVx23728OmcOM049FaWoiOCQITipJGYkQmTpciLLVuDr34/A4IGYnZ0k6xuJ11Rj5OWheaQeI93WRrKhHiM3D3///rgLCwjm5pCsbyBRX48djxEYMgTds4lR/xPCMU2cVArHslFURZYKdLlQVBVhZ1w8VTXjkNOJnUzBerZ41e3GCAZ/loxYjmVhhsNS3d7ahtnegZNOg4L09u7dB3dxT/pCO5FAhDtRB8h0iF2LAs0LViSC5veTamsnVlWF1RkBVQFNI9lYj6e0hNCIYdiRKIoiSLe0IGwHy+3itruu4/GX/kUoEOCWE05ihNtN/i7jMXJziK+tAkWg5+Tw0htv8ud77iKZlsl6m9vbuf7vj+EuLWHqIZOBTLGCRAKzowMnbWbCi3JINjYCQlbv+TVpODxsOBkXIyfR1Ugi7rKb9kJOfm1I9V5fqFxQyctlL3P5V5dzx8o7+E/df5jVfxbjGIer2QVxUGoVjh58NPvm7ovqqFAPCzsX0lTQxF477CWloE6kingQPfG01UiV8QBkyNEyZEagWOb71kZXmcEUcuHhQhKXjlRrO0hNh41chITp8aAGec/yM5+t7bIqkATZjCTDWiQ5OvQ4MQ1ELpwy4VfuGrfs20A2VMdqyOefRObDtpDPoss5rYEex6cdkM9IgOgQvND4AlfdfRXhSJhLD7mU804+DxUVPoNEQ4Izvz6TN8NvMrl8Mn+t/SsJR2qkGlINXPrJpbAzTKmcIqXZNNKDWUcuuH5ht98tEu3EiRM54IADqKys3IA4v5kF6rzzztvg+9tvv939/5/TRvtNKKoqkwG0tmF2dCDMNK78fBRNw1VYCIpK8d570vTOu7R9+hl5Y8dg5OZKm6um46koR9g26UiUHctK2Wm33flg4QIefv55rj7mGD5cvZr7//UiB1/wOybusAMzzjqb3IoKVMNAHTWS8KLFhOfNJ756DZ6yMhSPCysWx4xEceXnYgRzMPLzsCNREtU1xKuqceXm4i4vwQiGUF0GsZpaki0tBAYPxlNUhKppKJmkBj8VrJiM6+22C2ZgJ5M4ySSoKprfLx34OjsRti01BEWFaF4vQgicVBonkSDV0oIZieAuyEddr4IT9BCJk0zhWBlJ2OWWDmHf2Hez/Y3GSLe1YkZjpNtlekR3QQGK202ysZHOpctp/+prjFAIT69yjFAuTjKBiMVQNBUrFpOLCMPATiax02mMYJBYdQ3RZcvRfF5cRYVY0Sj+gQPIHTUKq7MTEQyh+7xE11Tx8ptvcNczT1Pb1ER5gSTAwaEQvtJSQsOGEl25CiuRQPe4UTWV2x97rJtku5BMp7ntoYc44ogjMlK5BUKg6Bru4iJ0nw+zsxM7kcRVkI9q/MqrpHuRKe9akKS6sTAbP3ICHwC+Gh+37n8rk5dN5g9P/IHDFh3GUQOO4uTck9nG2kZO0HWQX5wvyacN7ll2Dy+2vMjBXx7Mn/f5M8WhYkmgUeSkWoEk+nnIyT6HHseYJciE8uMzffDy0xaeTyKltwg96lToSRspMn3xZu5PZ+Z+9EJ6aWv0FE+oR97Hcjbu0NV1zclMuy56NA3fp7/Vmf7WZNorQC5Q2pH38BPkIqF/ZnsCXLUuaS9vz1xb1yKiCak6bkPe547MNbqREq8fuSAa23O98+bN44a/38AHn3/AdpXbMevyWQwfP1w+y3egobGBU986la87v2bmjjO5e97d3STbhYSV4MYnbmTK+VNkf9Zk7kk/fhntwDegiM0lJwYmTZrEIYccQp8+fTbYvn6N2l8KqVSKhQsXMnLkyO9U/9YMh2Ucq6biystD8/lI1tfLiiuaRvO77+Ok0gQrB2GlUtidnejBIAgwozGwbZxUGi3ok2Tc3o6wbK566kle++gjAFRFYdiAARx12GEcf+JJ4NiElywhtrYKRdPxFBWheTwI25Jp84KhDCEZoCjE6+pIVK/DjsdQdBea14Pj2KSbW1BQ8VSU4S0vx5WXi5ETQg8E0X3ftvB/8cUXjB07dov3xE6lMNvbM2XdDPRAEM3rwTZNkrW1pNrasZNJFEVBOAInlZTEGPCDrqM4Doqmy5SShoGiKgjb4YVXXuG2h/5GfVMThQUFXPHHP3LwfvthRWMIW0rLQlVxEkmsWAxhWageN+6CwkxeXt9G1dCOZZFqbsbqjMicwp0RNLcLb0UFut9PvLqadFh66ZptbaRaM45FVhpXKIcG22bw8GHogQC634cVT5CsrcOMRkBRSbU046RMXHm5GWnejbeiTOY59nlxlRTzn3+9xN1/f4wV1dUM7NMHbJtVtbWce8ihHLX9DvgqKlA9HuJr1iIUBX+fXrgKi9jmgP03mgtcURSWfzpHLs5cBqrbg+b1oCgKdipFsqERzevBU1z8rWN/LLreocrKSpYvX/6d36XvfgKkdCGQJNIV09gFB2nbDUNcxLnl2Vt4+MmHsW2b48ccz1k5Z1HRUCFJRZP7p/QU97Tfw50td+LW3fxuxO+Y3n863gKvlJa76iw0yna7Y3/dSGmtDkkm2yIJrisZRwHfSsf3Xd+jbntla6af+Uhy1JCS1Wp6pFYNSWg6chHgy2zvIuG8TD8cIAGzP5nNjU/cSF1jHeVl5cw4dwZTxk/ZUFruSgiRyNzfciThhdi4+tfJ9Lcmc2xLpq0BmXuyGrnIcZCkWZVpPwx4oSpURd+xfWX/izL9b0A+y0DmGj/P9Gdspo8q0oY+CuiAhf9ayO3P3M4r814hL5THRcdcxPSDp6OhyWe4HPgC7n3rXm5ZcAt37H4HBxQeQK//64Vg4+/RumXr5MIB5MJg6ysBgS1z0RaJ9sgjj+T//u//tloHfwy+L9GCJJZ0aytO2pQlwlQVs70NzR/AikRo/fRTnLQlwzsUBdVl4OvbD2FbstyeoUvJRsjqKPGqaqx4lHozzd/ffJMP5s0jHI2iKgpH770Px0+bxjtffolXgZFFxfQtKsLw+VB1HSuWwLFNVF1H9XrRMhKdsJ1MSTUL3etDCwZQVY3o6jVYkU4MfxAjN4TqcWPk5OAuKMBTWoKiKJiRKHYizsKv5zF8+HBUlwvdLxMgrJ95yrEs0m3t3UXuXfl5aF4vVixGdPVaUg312Km0JAbbkSklO8Og67iCQdlXRUFYJo4jUFUNLeBD83j5zycfcd3DD28gvXlcLv449XgO3GN3XKGQrKfa1IQdiUk1NTaO46AabtwFBbhyczDy8vCUFqP5fDixOGYsKtMiRiLYqbRMY6lruEvLEFaa2JpqrEQc3e1G9bhwEmmSzc2k2ttQVRU9GKI9HqdsQH85qQmB4nJhx6I4jsAOh1F0Hf/ggVjROHY0gq9fP7AsWjs6ePmLufzfq69R39ZKn5JSdhkzlpfffZukaXLlcdPYffBg7FgM/4AB2NEodiyOf0B/gsOHEq+qYv9TT6W+peVbY7KiooLPPvvsW9uFbZOobwAE3rKyrWL/3upE+13RgZz4U7Jk3lX3XsVrn7yGEIL9++3P9PLp7OLbBTWqyn3jsCqxij+l/sS74Xe5bsx1nFRykiSvcqS01GWniyFJpBhJYvVISU1FOuv4M/v2QkpbfiShdMCiuYsYMWxEj7RYyIYLhS61ayOSbIoynxTS63pB5tpAknEMSUhO5px+etTJZqZPXUkV3DD7q9lc+q9LSVjrFVvQvcyaPIspe2bUpOuQat80PSUFNbpLEHZn9CqR942OTH/rM9+7ikYMlOdkPpJUVeQiQGT2rc60nQON6UZKhpXIvltyG+FMO0qmPwFkhqYuiXdbSA5N8tqbr/HoM4/y2ZLPCPgCnDHpDE4bexrBomC3BsTEZM2CNVTqlThrHWqsGvpW9IUmGPfuOGoT3670VFFWwWePfiafVV9+1lScP5pob731VoqLi9l33303KOS+tWvMfhf8EKKFrqLdcaxYDCeVJN3egRWL4S4qwk6l6Fy0GGFZGLk5OJaF5vHiHzQQuzOCnU6huT3YyQTCdnAV5JFqbCbV2MS61hYGjtqGr1eu4u/PPcv7X3+N7TgYmoaZsW/meH1sN3Ag+40dwx6jtkVYMqOQaugYoZAMAfK4wJF2QzseRwhwF+bjLiwiVlVNqqUZRdVR/R4UR4AQqC43Rn4Ortw8XPn5LF6xglGjRmGnkjipNIquYeTk4KRNUs0t0gZomai6C9VtZByCWkm3NmOlU2iGG1QN1dARti1VyKm0JGtFenarPj9GMICqq5jtYVJNLThmkqPvvZem9YuJZ1Dk9/PIYYfjxBIIx0ZxudDcblSXG0VTZRIP4YCioYcCGIEguAx0t0t6ZTsCK5VAUTTMSKS7HF2qqRk7s2BQ3W4UkLZjTUXVdfRQEHduHqgKdTU1FAVDCFVBpEzSHW2ZTF05suiDywDHRvV46fR7mbN8OW99/RWfffUVjuMweuAgjt1vP/badQKnX3kF8ViUGZMOZkBZOVY8gZETxFVYSLK+EW9ZCbnbjCC6tgqzvYM3Fi3g+kceIbleBjCv18usWbO+VdxcCEGqqUl6npeWbDUv418N0UJPlqBOWDB3AXnBPB5/4XGefOVJ2uPtlLhLOKDsAA4sPJDtXdvjbndDG3zq/pRtx26Lt8DLM18/w/ym+Rw74lhGjBmBUqxIAmtCklwOknSiyLAgix5v1DSSHIqQpFQO82vmM2q7UVLFGs3sV4gkmNrMJ44kpUzSe1qREmEnknS66qEqSCKJZP4vkGQQoqeSTBRJaK2ADeM+GEdteiOkolXwWe5nksTcmX77kSSzPmmHkIsOFz123y6JWum53/iQC4AuL/Jg5vhEpj0Pkvx7yd/qFtdRHijvKVvXiOzL4My5BZLsHIg0RPjI/IiXG1/m9YWvE01F6VvYl+mTpnP0zkeTS65UpWcWIZ+0fsKfnvsTDe0NfLTfR4SaQ3LRkFHHz26bzaULLt1Afex1e5n1u8x7VMHPbpP90UQ7atQo0t+wKymKwpIlS37Sjr700kvce++9WJbF9OnTNxk+tD5+KNF+E45lybCKTE3bdEeY8PwFJFtbwHGwIlF0n5/gyGE4sTh2Ko0e9OMkkziWgxEKoqgay+Z9Tf8+fVC9XgyPl4amBl56+RXe/OwzFlZXAeDSdVRFYZsBA5g+8UB6F+Rz09NPMbJPXwaXlDKgTx/6DR2Cp6AA1e1GmCbJlmYS9Y1g2Rj5uSAEVmcnqseLHvBjhSXJoam4S0rQ/T6qGxqo3GYbUBXMjk7i1dUyMQKim+D0UAgUsDs7SdY1YEUjUrpUVFlv1esGzZCJFVwGWk4IBRU7mURYJjgi4wcl1ceqx4WdTLPnNVdtRLEjMa3/IAq9HiZU9EZxGXze1kbI56U8L5+i/Hw03cDsDMv80IBiGBg+qf9RVQ3F5UZYpnTU8vhwUgmEaUvyd0vHLc3vk/VgTVkxySgqwFdahubzsuLzLygNBjAjMcyONuxYHFCIdYapSSWpSiVZ1tDAvOpq6sJhAApzc6koKMQyTVbU1/HKgw+Sq2jUfPEVOUEfoQEDQdOwI1FAkO5oR/MFCA0fJgsOtLbI4gFmmv989in3/POf1Dc0UFhYyJ/+9KeNk2xzM3Y8gbuwYKsmL/lVEe166FbZmpAMJ3ntydf49/P/5u01b5N0knhUD9sHtmecMY6RykhGBEZQMbiCm5tu5p4F95B20vT29Gafvvuw37j92G233aQktxI5oRcgCbUeSTglyIl8NZJUewNlsKppFQO3GyiJoAMppdYgCajLK7eQnuxGq5HkG0OSsTuznw85+euZc/kz50sgiclCEn0XYQtgHfSau3E1KQLOtc9F0zTOdZ+Lz+uj1lNLypWid25vDK8h+9OSad/DhoTflf7QRpJxVynCLlW0G7koCWX6laLbiY0KWDF/BYPFYCkN19IdA2vHbVZbq1nkXcTCzoV8GvmU+bH52MIm15/LxFETmbzrZHY7ajfUBlWqh00QiuCT9z7hgYUP8Eb1G5R7y7l6yNUcpB3Uo3aOZvrggtnh2dy4+Ebq2uooyi3iynOvZMq0KXLB8QvgRxPtz4HGxkaOPfZYZs+ejcvl4phjjuGWW25h0KBBmz3upyJakN62yYZGUMBTUoIVTxBbs5ZkfQPpSJjEuloURcXXr68M6UglUb0+hGXhxOI4jk19fT0lBYUyFjMYxAiFUD0ezHicmhUr+GzRIr6qXstXa9bQHOnxEtFUFdtxur8bqsqU7ccxfsRwUrbNvNWrKA/lUORxU+QNkJ8Twu/1IxJxHNNE83jQfF5JLGYaxXDT2tZKTiCAE4sjLBMrnZIFEcy09PJTVYRpIhwbYdt0WhYtjkNTMkEslWJi3/44jsWr1dVUJeL4/X4Cfj9ej5ccn4/9hw/HtmxW1dfT0NFO0jTpjMWob2/j9dWriFvWJu/1qIJCrho7DoTDae+/Q1tK5tDTVZVir48JvXpz9JChkErzft06VAGGroPLRVLY9M7JZVivPsQTce6aO4eYY9OZNulMxOmMxzlp3/2YfvBkWto6OP+O2wi6XPg0A5/fi+kI+hcXIdIW1Y0NrG1rpT0WpyOZ6AkFdLkY3bcffk3njaWLAWl7H9irNxPG78TRu++BP9yBcCA4pBLN7cFsaydeX49AoBkGnuJihG1idkbx9u2D7vFgRqIEBvbHmynWvjH7n7Dt7trKrvw8jNDWDeX51RPt+khC7IUYH739ER+t/YiPaj5iaefSbiIKKSH6ePpQHComraVpijaxJrKGSk8ls8pmkWvk8mDbg4TUEEOMIQwKDmJA/gB8pk8Snhc5UWdK9+GCpnATxcFiSbIxesKZ4kiSUjJ/04ADCS1BtbuaGq2GdfY6GpwGjlaPpr/an4XGQp7Rn8Eb8OJ1efHqXty4OTDvQEqUEpZGlvJe+D3CyTDrouuoNqv5wv4CB4eNQUfHwWE5y/Hi5U/an3jIfggNjQq1gj5qH3opvfiL+hdUW+Vz63OaaUZza0TdUTqVTgy3wdTQVFDh6sjVzE3Npc1soy3ZRsJKMKp0FC+d9BJJM8mlz19KXWsdPsOHozuEXCHclhuf7aMmUcOq5CoazAZSIvM+Kzojc0aye97u7NJ3F/pt3w9jlIFWpaGsVWiMN+Lq42LgNgNZ8OoCJj4xkXxPPicPPpkzB5yJt9Mr77uWufflSKLtsvXuBHjgy5VfMmb8mJ9k3P1Q/GCiffHFFznkkEN45JFHNtrwSSed9JN18p///Cdz585l5syZANx9990IITj33HM3e9xPSbQg7bepxiYAXAX5KLqeKQTQTrKlldjKldiJNEZuCDQNTEu+aIqGnU7SWFNDYX4BdiqFSMvctaCiGAaqruJYtsxEBLSGO1kTbmdtLM66WJTa9jZqW1po6+zE+Q5rH5+u49MNmVjBstBUBV1V0VUVQ1HxaRq6pqFk2lIUBaEoWMLBtB1yM2aAhnic5mRig3MqwLalpSgorOvspD2ZkEk2MhOaoaqMLCoGIVjV0U7nehoPBXBrOinb2mAdrgAVgSAFPh+aoaMoUlUcTyZJmSYp2ybt2KQdB6+uU+D1YjsOy9rXK+GWQY7LRb7XiyOgNhpBVRTUjAd21zltx8FyNj5BdbfjdhNOpTbYFnS7uWT//Zkwchuam1qYV7WGPoUl9CkuxO12Y4RyQFPBtvAUl6LnhEi3txOrqUFksn9pHi9GKAi2IzN/uVykWlrxVfQiOHQwmkc6Oq1PJkII7HhcOthljjOC38d99IfhN0W0IB/w18jY2DTEO+MsXbCURcsXsaR5CTVmDevEOmpEDQkS3z5+I/Djp4giDAxaacWluHApLtzCjSpUfPgwhIGKiqM4CEVgKiamMEmKJLlKLn7hp120s0BZsEHbCgojjZHka/m02C0sM5dhY28gpY7RxhBSQtSLepbZywBw48ajSE1OREQ2IFsVlSHaEEo9pQhdIGyBSAuiZpSIiJDI/EuRwsZmpDYSx3FYJVbR0W0wltDRqdQqEYqgwcmQpAICgYODLWwsselFM0Cumksvoxcr0ytJiuQGvx1YfCAPDnkQojBi/gg67A3Pf8wOx/DX0X+FZngl8Qp7jt4Tr+rtibXt8qoejCTWCFKNvD/djk7f2WFtK2JLXLRJx+eqKqnqXL58+dbrXQZNTU0bxOkWFxczf/7873z8woULf7K+CNtGdITBskDXwe0C00QkkjihEHaiGVqaUBQVbBvSpowvdRxwbFrq68CW/0co0BWCoymQcSDCtNCEYJDhZpBXgMcHpeWgKDjCoSOeIJpMEDFNosKh3bJojsWJJBNEkyli6RRFbg8K0JBMUG2ZJC0baz2yHBLKwaWqNCUTNMbj37pOLS8fXVFQNZWgy4VL0zBUFUPT0FWNmGWDppEbCpLrl4UZEA62IxBCEE2nQVHI9XjJcbtRUVBVFU3XwBFE0ilaEwksx0FXVQr8fjwuF7GutIe6hqIbKF4PLo8bt22j2NJGq6BIRzRNZWhevqzA5AhZWUsBQ1HRFQVFVSkuLJLbVVVeg67j6vpoOoau49V1AoaB33bQHYdQIEBpTi6210NVMokeCuB3HPL9flzFxaBptHR0gqaybf+B4DhEWtuIdIbls1VV8PsgWCNJN5UC00TJz0dNpxFeD7oQOIYGnWFobgFNQ1EESn0tqIpMu6lpfP7ee2A7MiWj40hP7lAQpaX5JxvT3wc/5bv0Y/HFF19s8je9Qsez2oORMMjrl8euFbuye/Pu+Fb6cK1zocZVOqwO2rV2OlwdtLnaaNfbCRMmakdpSbfQYrXQ6rQSEAHyySdGjJXqSkmC660S+9CHMq2MqBplobUw40jX83uFWkGRWoSjOJRapd1E7VJdaIqGcAla1VZUW2W4PhxsubASQpJZWk3TqrRiOAYjGIGGhtBF93k6RAcNdgOmMDEUgxKjBE3VaLaapbOiGxSPguIo+E0/wXQQ1VZlDKoiiVlXdAY5g7CxwQHDMdCFjgsXmtBQFIVeai9URcWluPCoHly6C4/mwYULt+bGo3rIUXLINXPJj+VjuAx8fh9leWWY+SZzxBySVhIn4WB6TCxhUWqV0tTZhKvVxcX+i0mJFKbfJFGcoNBbyLDkMJo+byJVmmJYyTCaVzejJBU8azwojoKaUokPjhMdGUVbp6EmVGIlMcxFG4Yhbm6s/BqwSaI9//zzAbjhhhu6t6XTaVpaWigv/2nTbDiOs0FMqBDie8WI/tSrcOksFcOMRHBS6cw2ByeRIlkqcxsrLpcMUbEsnJSJY6apra+jV58+CAGapiEMA93jQfd7QVFlm+FOrGgEK57ATiRJhzswI1GcuKxZKxyHYsNFaTCIlnESQgjp6KPrKG43us+P6vcgTId0uF2GuqTSOECHbdIWjVGmqYQKi6i2LJa0t6FrKh4UQppKSDPok5eP2+NGdbsQqorh90vvXsvEbA9jxaII0wJNkxIYCggH1eNGz8tH83ognZYxqPGEXGyoqsyW5NLRvH5JpJZJY0sLvYcMwVNehqrrxGrWyTJ0Xi/eXhX4KirQAgGEbZGoqyeyYiWp+gacdBrVI++B2dGJGU8gkkmZvlJVpcrcMKQ92eeT4TgeD6pu4Ng2Vns7ZiyCMJ2M/dagvbYWr+HCW16OokK55UhvZxRZRziexLFtVLcbPTcHO5ki3dGOEgzi6tcXq1MWqFA9Xlw5IfmMPG48JcW48gvQdA13YaHMUpWxjyt9+xOsHCRjdhMJWa0nnWbh/HkMHzYcRdcy3uHyGfycCSm6VuJd+NVLtOsjQU8ayHqk9LMj0t7ZAIH6AL2ivaT6scvOCHLWCyIdkPIy+9fLtpxOh2anmVq1lja7jeZoM9vlbMfQ8qFEiiI83/Q8HtNDoV1IUaqIMlcZRb4ilIAi2/Uh7b8m0n7ZipTIoCfspcu5KA9p37WQfezI/KbTkzs4l24ban1bPWWjymQIThBpK16VabsfMhFHaea4RmRlo4X0pCT0ZfrUlQM6mtnXk2mvy7PaT49deX2v6jhSve6D1vpWClIFUpVbIK/xUNehPfbgovXubQwog5PiJ8ln0JW2scvpLEBP7K+RuWe9M8f2hvxJ+fJZrwB2RUq26+HXJNFuClsM5X3jjTf49NNPufDCCzn44IOJRCKce+65TJ8+/SfrZGlpKZ9//nn39+bmZoq3Qszgd4WiyIxReiCAsG0cSyYRQFHwDxpAur0dK9yJlgmbEY5A1TSavvqK4uHDEJaFHU+Qbm8n0dhEvKERx5LhRLrfj6e0FNUwEI4jnYlsh3Q0gh2LyZhVXUfVdYQtHY5QNbky1XQpCWqysLwQNp6CPGwzTbq1jVRLC8VpnZJAkFgqhaYb9FMUBlRUoHl9qB4Phs+HFpD/t9MmVjTa7dhktXeAquDOy8NdVCj7E4shFBXD55ehTfEEVlMjKdtB0zQcHBRVkwSnKKi6V9ZH1TVUTcfILUHPCVEwbgdUQ6ZoDPTvR7KxmURtLWZHB3EE7gKZ+MJTUICnsFA6NCkqyaYmEjXrMKNRdK8XPSeEFgphtbVjJZOoqior6CRSWOFO0h0dmG0yfEsIB0UoKLqacf4KQiyObjvYyQSe0jJEOoWTTqOHghheP45tIxy72+7tLSnFKCrsTnhidobRPV60UBAnkcSMdILtoOoGdiyOMDRobUX3+3HSMr+xr1evbq9hGSMss2apDfX4+/X9xcb5bx5eup1ziNFdkKA765IHSRINSM/aJJLAupyP7MzfrjSCQ0BtVilZW0JJewm4ocPbQW4oFyIQjAc50X+iJJYuT+EQPUnyu4irKdN2HtLjN5bph40kzgCScJozfVt/Fvat9wlm/rqBEmhPtFO2d5kkIw1ZsagNqUqvp6eEYUHm3uyBzDkdyPy+MPPXlelHv0y/u6wzvszxXcTaRa5dHsxdDlUFYNpmz29dGcA66PHabkcuAoz17lNHpr0SerJENWaeX396wnG64n8TSBWxjXRi64WMyf0NYotEe//993P99dfz+uuvM3r0aK655hpOOOGEn5Rod955Z+68807a2trwer28/vrrXHvttT9Z+z8GiqahfSN+0VNcTFrTsaJR7HgCIxSUUp/PixEKyVR/QmCIXEnEAxzsVEpOxJadSdigyrSGjoPicePPCaKHQrhyc6T0m0jI0B7LQtgOwpESo0wc0aUBEDiWjdkZwQjl4ikrB+EgHIdYQx2B4lKcuAxjwhYyGULAj+rxSnWrx4U7VIEWCqIaGrrHD0LIUnHCwV1UKEOekknMcCc4NkLRsKJRkg312PEEmseNnpODaujo/kAm0YQqFxVeL67CIuqXLsHXe8MikO7CQvz9+xKvrSXV0ES6uRk9GMSVL+sKIwTpjg6cVApXfj6BQQNxFxV22y0dyyJeXSMd2DQVd2ERan8DHAsrmSTdHsZJp9FcLlmKLhol3dyMEg7j8fpItbaBsMnffnv0UAA7GsVJm7jy8/D17o1qGKQ7OrDjCSnd9iqno6kJV0EBuduMBAHxdTUoqiZryZom2DauonLcBfmgapk8xTnSXpvF1kNX4occZNxoB3ISN5EONIOQBNtMDzk49KiAncy2DmT4S0XP77F1MXLLciWJtiIJ0oMklC4JVUFKYF3SZABJGl3nK0baGLviSxP0SLZrkF67Bj1pKnOQRKhmPl7Zp8TihFxYrI9CJGGuRqaZ7EqfWJFpz525rnYkeQ3K3JPyTNsCaQtdkLkHRcjc1HbmWruyVxn0xOGuBNu0ZVtLkPfmQLrrDxOlp1xhLHNMYeYerco8m2ZkBaZ4Zt/OTP8MJOkmkNcay+xbipRkf+WJ0TaFLRKtEIIhQ4bw4IMPsttuuxEIBDaa3ebHoKSkhAsvvJATTjgB0zQ54ogjGDVq1E96jp8SiqLgLixA83hId7STapZJCERzC8n6BkDm/PUUF6F5vRukzbNTKexEErGelCy1sg5OIokwLRRVwRUKohYXyaQattNd0k3YtiRT28ZOprDicenhPDyEu6QEJ50m3dxCy+efkzegP4qiIUxTpiyMdmJHY9iJGJrbgxHMwV2Qhx7KRfdmQmgMg+DQwSiq2p2SUdU03IX5OF3n83owBvSTqmHdQPO4N7hGRVXQQyGMYHCzSRZ0n4/gwIF4i4tJNjdjhiMk6upI1NX2tBMM4a6okJma1ksBquo6gQH9cRcXk6ipxuyM4ugaeiCAKy8fX3k5qsslE3O0tCLSKdwFBeimScmY0cRWryHV1IxiaLjz8lFKSsCRDkmp5hZJrj4vCEi1tsr6xbaNOy8PRVFx0kmMQBDd60fzuHBsByMoi1ZY8QSp5mY0nw9X/i9YMuR/FbnISbsVKV3G6Un9ZyAJJUlPSE0USU45wDbIxA0uoBqi70QlQXRp86NI8mlDkoqJJMcKJBHlZ/bVkJmngvRkVIKeZBadmX71Q1ac6UphuD6RdKmhc9Y7/8bgQuYjLkcmr+jKV1y93j4hZGrMisy96IJCT9rKxfSEJuVmPr3pUevWZu5bBUQ9UammbwI+yGwvz+yfoGdBUpJpryNz3t2BjzP37Wh64mO7Yni7Cip0IMk5hVww9EMS928UWyRaVVV55ZVX+PDDD7nssst47733tooNafLkyUyePPknb3drQg/40fw+GU9rWijBAO7iIjS3e5MEo7ndm0w+IITASSax4nHseAIrJp2YVMNAMXQUVUNRFRzTkfGumoa7sAAjJ7c7DaNmGBh+P2pjA/6+/aR0bVm4igrxZYhddbvRDAN0DUUACii6LpNQrEeYut+fWRhIm6LmduPKz0fzuGXeY0WRhJ9IStW4oqAYhpTu18tCtTkoqoqRk4ORk4OdSuGkUthpmV5G93m32JYR8KMPHYoViZBu75Dagky6SDuZxEmbaF4v/pwQroICahYuJGfIEAIDBtDy/oekGpsIDBiAKydHPoO8XKxoVErA7R1ym+OAsPGUlYECnYsWoXo8uHJCmXzJLoSTwgiFsKIxUq2tqIYuCxf8mgoA/C9BRZJaPj220prMb12q167EDgqS0ArZMA1jJUTbo1LC6kQSiFjv4+XbuZINuu2Y3e34M+cJ02Pn7FLv5tEzCzv0VPBx8/0LlIeQ0uggeiTJrjjZAJuf7Q3kwmAgUr0cQ97DLvtxOnP8MCTRLopKNW5Xas0lmWvsishM0EP47sy9qMp8r8/sM2i9vi6jJwf0V5l+7ENPbG/v73kvfmXYItFedtll3HXXXVx00UUUFRVx7733csUVV/wcfftNQFEUNK8XzQuKz4fu8/0EbXkR+UKSTjKJk04jTAtHpFEUVWZ58vnkvpsgbUXXpQoTSeA4Trfq+ftgcwsDkKr1n6pST9e5vq92SFEUjFAIPRjEjsexkylpd0bBCAbRMoS9wbkMg5zR29I257P/Z++84+Qqy/59nTp9tvfdbHoCKSRUaQGlqnRQwUIRUV9FKf4EhFgAEQTBAojgq1RpagBRehEE5KWFhJCE9LK9TS+n//54ZnYTSAPSgHPlk8/uzpw55zkzc873ue/nLqTeepvKaVPQKipKVaLiaKUyka5tI/X2ocVieJ5LsacXO5tFNi0kRSbc2oqdySLpOlYyiZ3LowQDBOrqtknJRJ/3iYIQ3FrEzT/HSBu4cuGGcvDPhi4NFWF11TNS4KHs0n0/aKUxbIqym/jDopf+fxALMIpwcxcQlmY5kCvOSKDUukiIAKUehKUaRVigIYRYphBrvh7ivV+MeB8OLO277ErXEa7hPoSwH4CY3DiI4K+P+KW0WaHdc889ue222wARdXzddddt9ahjn/ciSZIIyNkKrfEkSRJ5vx9zJElaL9hocwRrqolOmEBu2TJyq9cQqKtDi8VEpLMkiYph3T0Yg0Oi1KSuEx0zGjkUwi0UcQoFCl3d2Nksgfp6cETZTq2iwrdkdzbKpRA/+DxY7GMn6ASzXShb61tCGOESfhrRitBCuKLL1aUqEJZxrPSzkhHX/sul108vHW8pwgtRrpvczof7zHYSdoqoYx+fHUV07GicbBornUIJhXDXKWBRbh2oVVYSqKtFjUZHXOvxOK5t4yw3CMaiBOvqRcedT8CExsfnPbQj3MrLGFnnjSEsXA1hnXYigpoM4BVGmjmoCDfzEkTQViPC4zCG91rQH1E26wC5+eab+eIXvzgcdfzss8/y0EMPbY+x+fhsc2RFITp+PEogiGuYyKEgSjgi1q1lmcjo0cR3mYxeVfWeXrB2OoMSCBBubUWNRnyR9flkMwPhFs4jxNVArMe+DbyKsFJlhEu7HSHE5XaC3QgLdwJCYCfwsRFZ2Emijn18diRaLEaotRWjtwc7kxXRzZJEsKEBrapyg25gz3GwMpn1rVwfn08yIYTYvokQ1XKgVzkqejQiErls3uUR67cmwp38KUTU98fQPb/TRB37+OxIAnW1ouGCIoLIZE3bpIVqJpMAaBXbtui/j89HigaEoOYQVq2OCCQrpz2tSxiR95xFBEQ18rEUWdgC1/GFF17I/fffz3nnnTccdXzJJZdsj7H5+Gw3ZFVFq6wSHY1se5Mi6xgGdiYrAqd8a9bHZwSZkUCoGEJ0102ZejflpvI6m4/K/gjzvqKOAe69995tOR4fnx2GFo/h5HOYQ0MiP7bU4WhdPNfFHBwUKVaVFTtglD4+OznlVKDe0u/vvYxGKNeCHsWmi3J8xNms0M6dO5dbbrmFfD4vCiq4Lh0dHfz73//eDsPz8dm+BOrqKHR1U+zrJ9jYsF41KgBzcAjXtAjU129xUQ4fn08cLYhUnbWIPNgNiaiByL+NI1KAPsZs9k4xe/ZsZs6cSTab5eijjyYajXL44Ydvj7H5+Gx3JEUhUF8HrkuxpwenlO7juS7G4CB2LodWOVKJy8fHZwNoCLHNI8pCvhsbUSmq7Gr+mLNZi1aSJL75zW+SSCQYO3YsRx99NCeeeOL2GJuPzw5BCQQINNRj9PVT7O4RnZYcB8910SpKjR98fHw2TQUiOKoXYdGWGxlYCJE1Eak8H9MAqHXZ7ClGSlV2Ro0axdKlS9ljjz2QfZeZz8ccJRAg1NwkOjQZhujDG41ushylj4/PuyiXruxDFK0IICKMJUQu7ccoV3ZTbFZop0+fzrnnnss555zDt771LVatWoWqfgKmID6feCRFEQ0PdvRAfHw+yjQggqOGGOl2VMtHtuXdB2GzinnxxRczb948xowZw8UXX8xLL73Etddeuz3G5uPj4+PzceDD1pn+iLNFa7QzZswA4OCDD+bggw/exkPy8fHx8fH5+PCR9gE7jgNANpvFNM0dPBpBJpPZ0UMYxh/LhvHHMkL5uin/9K+l97KzjAP8sWyMHT2W8jVT1qR3I3kf4cLFvb29dHRsKHbcx8fn/RCPx0mn0zt6GD4+H2laW1tpaGh4z+MfaYs2FosBMHHiRPQNVPHZ3ixYsICpU6fu6GEA/lg2hj+W9TFNkyVLllBTU0M6nfavpZ10HOCPZWPsDGMpX0dlTXo3H2mhVUr1aHVdJ7CTpF3sLOMAfywbwx/Le9FKNZv9a+m97CzjAH8sG2NnGYuykRrpH2mh9fHx+YhRZP2+pP4dyOcTgP819/Hx2T4kWL8cXz+iu8snOO3D55OBX+LJx8dn25NFiGwU2BWYCCjAKkQRAx+fjzGbtGgHBweRJInq6mreeustXnvtNaZPn84ee+yxvca3XfE8DyuVxs6KUHE1GkPWNaxUCs+yUUJBtKqq93R08fHx2QgpRL/RZYjye/sjBFZBWLPLgM7S7z4+H1M2atH+4x//4LDDDuOzn/0sd911F9/97ndZsmQJF154IX/729+25xi3C57nYfT3YyWTyJqOrOkUe3rILFkGrocSDuMUChR7enBte0cP18dn52cIWAPkEDVtK4CViHVaEMLbAGQQFq+Pz8eUjZpm//u//8ujjz5KoVDgqKOO4l//+hft7e0MDQ1x+umnc9JJJ23PcW5zrFQKJ19Ar65Ci8dxbRtjYADPNlFjUfGYGaPY04vR30+wsRFJ+hh3Kvbx+TAUgC6Eq9hAiGwT8DawGNgNUVi+BhhAFJ2P7pCR+vhsczZq0UqSRENDA6NHj6aqqor29nYAqqurcV13uw1we+AYBlYyhRoVggpCeNVolGBDA1Yqjee6yLqOXlONa5jY61QimTNnDnvvvTfHHnsse++9N3PmzNlRp+Ljs+PxEA2/VaASsQYrI6xZCyG0r5Z+lxAF5nMw515xHbW2tnLmmWf615HPx4aNWrS1tbVcf/31ZDIZdF3nvvvu4wtf+AKPPfYYlZWV23GI2xbP8zAHB5FUBb2qEgDXsrCzOZRwGFnXMAeHsLNZtHgcNRLBzuawkkmUcJgH//EPLrjgAgqFAgCdnZ1ccMEFAJxwwgk76rR8fHYcgwgrdnTp92zp7wIQQnRteQNx99kNqIY598zhgt9cQKEorqP+/n7/OvL52LBRi/bKK6+kq6uLZDLJ3XffzcMPP8zUqVP51a9+xY9//OPtOcZtipPL4ZoWelUVUinZ2EqlcYtFnGwWK5HETCTId3RRrlapV1fheWCnM1x11VXDIlumUChw1VVXbfdz8fHZ4diIRt8xxBrsIGINNoGwXEcB+yFyaN8ElgMyXHXbVcMiW8a/jnw+LmzUoq2vr+fKK68c/vuuu+4ikUhQVVW1XQa2PfBcFzOZRA4EUEsN7j3HwRgawkqlUcIh1HAYWVMxBwco9lQQampC1rSSZZuhq6trg/ve2OM+Ph9rBgAXsR6bREQcG4j11w5EOk8YaAZWAwsADbp6/evI5+PLJvNU/vKXv/D444/T39+Ppmm0t7dzzDHHcNhhh22v8W1T7GwWz3YI1NYOP2Zlc+TXrMVzHHQZZFVDjcUx+vrJrV5DoKYGWdfRKuLY2SzNjY10dne/Z9/Nzc3b81R8fHY8DsKCrUBYs71AD8LKtRGBTzUI6zaBsHQ7gTQ01zXT2df5nl3615HPx4GNuo6vv/56Fi9ezJlnnsmUKVM4+eSTOeKII7j55pu57bbbtslgstksRx111FbpyOO5LnY+j5XO4BQKrNukyHMcCn19ZBYvwUqncU1z+Pn04sXkV67CtUxc08IcGsQaGkKtqsbsHyDfKWbYsqahhEOc/61vEwqF1jt2KBTioosu+tDn4OPzkWIQYc3WI6zYRYigqBAwHfg0sDdwODAeEZU8BChw0WkXEdLfdR0FQlz0jYvEPn18PsJs1KJ99tlnh6P+9t13X84880zuvPNOPv3pT/OFL3yB008/fasOZN68ecyePZtVq1Z96H3Z+QLm4CDeOr0BJVVFr67CKRTIr+3AHBzEKRqEWpsxBgdxDQPbcUnNnYcSjVA1YwaBulqsdIb8ypW4poUUDJBfs5ZAXS1aNIoajXH0YYehRiNc/etf09XVRXNzMxdddJEfwOHzycJFuI3jQBDhIn6j9PdewC7rbOsh1nD3AJYAy+GE4AlwDFz12FV05bqora3lJ+f9hBN2P0Hk4o7efqfi47O12ajQ5nI5LMtC0zRs2yaZTAIQiUSQ5a1fufH+++/npz/96XCk4QfFzuUwBgaRNRU1Esa1bJxiETOVotDZCZKEHAigVVUR0HWUYBDHtPCSSRJz5+HaNvX7fYpwawsASl0ANRwmtWABXtoincmy7JlnSesanudhJ5KMaW7i+eefZ8H8+ew2eRfAwzVN5He1G/McB9cS9eY810WSZeRAwM/H9fnoM4hwHdeX/n4B4R4+jPVFFmAJOMsdVrCCNcvWYC43IQ5tR7bx+IzHqcpUMW/CPHb77G5iv12Iush1CJH2Lxefjxgbbfz+k5/8hO7ubj7/+c/zyCOPMH78eM444wx+8pOfUFNTw89//vNtMqDPfOYz3HHHHbS2tm52W8MwWLBgwfDfnmXhJRJ4soKkyGBaIEsgy7iJFO7QEJIsI8XjSK4L0VI182wOJ5nE7ehAbm5G2/dT64mfZVk8/+STPPP4EyxavQp7A3nEuqYxddIkDtn/APaZMQNVVZHCIYhGwTTxcjm8ogH5Ap5pgiwjBQMQDiNHwhAO+4Lrs8OYOHEiS5Ys+WAvdiG4OogbcDGbTeSETNPNTTi1Dj2n9kBpvuk5Hkv+vYQnHn+Cl1e/TKaQ2eDuJtVM4pCphzDrpFnEY3ECawMoKQWr1sLTPSG0EriKixN3cMMuXmCDtzEfn+3K1KlTN9iyb6MW7ezZs7n55pt57LHHmDFjBmeddRapVIpPf/rTnHjiidt0sO+XqVOnomsaxe4e3KYmJFnBc130qirUWBQ7m6XY20dxYIBiZxeu4xBqbSEyahRIEoXubgZefgU7FKZhzz2p23NPAFzX5aGHHuKqq66io6ODUS0tfGnWQUxpa6N90kRio0Zh5IusmD+ft9au4bGXXuSam//A6NGj+eH3vs8hkyaBbSEHgni2jWOaSIqKGgnhuR5OPo9rWiK6ORIhUFe71eoov/766ztNTWp/LBtmZxjLuyer690oPETAkonIfY2x4aiOgdLzExARxXeWtv0mtMwUnqG35r3FZT+7jJfeeIl4OM5nZ32WfYP7MjY8llA6hLfCo8/tY/4u83nqzaf4/XO/546X7+BbJ36L73ztOwT7g0KwqxCWchaRl5sujakOEckc2brvz87wGZXxx7JhdoaxvPs6ejcbvavrus73vve99R6rra3li1/84tYb3VbESqeFkEkSnusSqKtDDYdwbRsrkUDWNbRoFCsYpLh6DbIsi5QeRSG3cjXWQD+erFDsWEuucxQpCb7//e/z3//+lylTpvDLX/6SfadPp+fxJ3GyWaLjJ1C1+0yMvj7GBgIcMWsWx592KslUil/96ld89wfnc/iBs7jo9NNpHjsGSdPRq6oI1NYgl5psO4UCxsAgTj4vIqAdh2BD/fDzPj47jDwikMlc5zEVIWYV6zyWRFR5Kj9vlv5uB2aAaZr88pe/5A9/+APV8Wou/8rlnHLQKYSWhERUch1irXYy8G/4zNBnOPfkc3n69ae5t/terr37Wv7x0j/49bd+zczITCHoraUxqAiXchewAiG+bYh8XR+fnYiPRZs8z3Gw02k8W6x/BmpqUMMigtFKJPA88BwXK5HEyuVQIhFc06TQ1U1hzVqMgX6kQAAtFsUYSPLg73/PoYccwrx587j66qt57LHHOGDvvbEzWSLjx6FUVmIMDjH06muY/YPotdVYqTRyZxeHH3oojzz8MP/vO9/h2Zf/yxf+3w/4vxdfQta094ioEgoRbGxAjUaRJHCMIsXeXr9pgc+OpYAolwhCMHcFxiJEbg1CIG1gIfAEIoWnEWFNPl16fl9YvWY1xxxzDH/4wx849YhTefGyF/n6bl8n1BeiYBXI7pKFT4E31qNQURDlGtcCi2BCdAJ/POGP/OXMv5BNZznmkmP443/+iOd6QkyrEYFW44AZiAlAojS+vm3/Fvn4vB8+FkJrZ7LYRQMkWZRJjAr/kZ0vYGVzuJZN5p0lpJcuxcllCbe1EWxowC0UMfoHQJKJjhtH9Z578M8VS7nw9ttojFdw/29+y4mHHUZhzVpS8xdg9PURamxEliTyq1aRX7katSJOpH20qIE8MEBm8TuYPT1862un8tBf7iYSDvONn/2Uv/71b5iJJI5hrDd2WdMINNQjB4NISLimidHXt17EtI/PdsNGRAwrCHE1EdbiakRksQcsRQjqWwhRUxFW5VOIOsZxmLtyLkd/9mjWrljLn0/8M1fueiXxV+N0vNTBBf97AVNvmMo9/70HdFj64lKm/mwqlyy6hKHAEAyCq7ogwcFjDuaZ2c9wxL5H8LO//oz/d+3/w3zJFEFSZWIIt3UjwpXciUgb8vHZSdjphPaZZ57ZokCodbEyGVzDQA2F0CqFX8vzPMyBAaxkkkJPN4WSpajFK9AiYdR4DCuTxcpmUGNRZF3jD08+yTV3/4VZu+3GHy+8gOpcjtRbb2MXCqjRKOG2Vux0Gqdg4BoGkq6KtdWaaip2nQyRKLlVq8l3duE4NmMbGrjnN79hj6nTuPhXV3PzTb+n2N2DmUisN35ZVQk2NCDrOp4LTtGg2NeP9zFr3uDzEaAXET3cgrBquxGiW4kQ1CHgHUT5xBSizd00hIXZIZ5/JvcMJ/3iJCJ2hIeOfIgjdjkCz/X484o/c/DDB/O31X/jhJoT2GftPvArCDwd4PiG47mj6w4+veLTvLrmVSLzI6I8YwTi7XFu+f0tnHvuudz78r1846pvUFxZFMcrx0AFESlAtYyIbX47vF8+PlvAZoU2l8tx9dVXc8wxx3D88cfzu9/9DtM0N/ey7Yqdy6EEg+i1NUil1CMzmaTQ3YOsaTjZDJ5lEaitoXrP3YnvMhlZUVGCOo5pYSVS/P6RR7jltlv58gkncNVZ30IaSuGZFsWBAbLLliPpGk7RwLEslHgUNRql2NdPYv58rEwGKRhEUmQhlpZFsbMbK5Wivm0Ud/7lLj576KH88qabuOnuv2Cl0hiDg+udg6xpBOrrUHSxbO4UC6JN34aDwn18tg1ZxPpnB8K6HY2wbJsRrlodUYwigbBmAwgh7gYseDb3LGe+cCYTqifw8NkPM378eEjBNU9cw4//+2P2rdiX/3zmP1xz5DVMHzcdgPYx7fzqrF/x2GceI6pE+cKaL/Bc53PCsu4ABkBeK/PD//dDfnnpL3lm/jOcdulp5Lvz4vkyYYRbuQKxdrxajMnHZ0ez2RDX2bNnI8syP/rRj/A8j/vvv5+f//znXHbZZdtjfFuEZ9voVVUopWhJ13HIr1qNpCp4HhQ6upEDAeLjJxBqaaHY04OkygRbmvFkiRvuvZd7Xn2FLx56KD885SuYfb2Y2QyyZeBZDmokjFZdjZVM4OTyhJqbUDSN1MJFpOe/jV5ZiRaN4NkOkqogqSqOYSBrKlp1FVokwo033MB5557Lr2+8kWAwyGlHHY2VzqDouliT9TwkRcHzvJK1rOPkC5gDgwTq/OgOn+2EjBBbCSGw5UyFNEJMPcRdI4gQ2xUMr6s+v/B5zlx6JhMbJnLP9+6hqrNKbJOG46uPRx+jc07bOUh1klgH1hHCmAFegimhKfxr9L84dcWpLJOXCcu63NO2ABjw1eO+SqAQ4Pyrz+cbV32D2y65DT2gj+TvViCsbAchtmtK5+FnzvnsQDYrtAsXLuTxxx8f/vtTn/oUn//857fpoN4vSjgy3OIOoNjZiVMoEBrVxsALL+G6NvExk4i0t4kG70UDz3GQZYW/vPwy97z6CsfutTff+/Rh5JYuw7Utko7DXY8+Rigapq22nhm9PYyZvItIwdE0tMpKomPGkl2+jOTcNwmPHgOFAq7toIVDSLKCGo9jp9Oo4TCBWIxrLv85pmFy5bXXErIcjj3wQEItzWixGMgSnu3gFAsiuGtwCLVC9MZFlgjU1OyYN9fnk0W5R+y6ImsiLEcHIawGQsyqEHeQBLza+SpnLD2DsXVjueef91C1soqOdzr465t/5dzouUxQJ3Du2HNhCkK0+xFuaLe0DwdIQaVSyd/a/kahrwApKKaKBI2gENCSS/gLn/oCzjccfnDLDzjnunO44Rs3oAwqQpBVRMnHCsQ6bgIh1i3b/J3z8dkomxXa+vp6hoaGqK6uBiCfz+90HXyCdTXDLe6cYpFCVw9aZSX5laux02lCzS2EmxqRVBU7kRTbGRZP/N/L/P6v93PkXnvz/eNP4J7/PEeVB5+ePBlZU3lywXwM28YqBSZNax/Nxf/vh0yNRlEUhdjEcdhGnuyiJSixOFIoiKzr2NkcgZpa9IoKXMPESqXQKipQVI3Lv/lN0qkUP7vhd9S0NHF4a4tYIy5FI3ueh15dTW7FKszBQbSKSgAkSUav3rned5+PPk6xuP4DCiJ9plx22AOWIdyw3Yj6xSFEWs4Y4FVY+9Zazpx/Jo3xRu675D6qF1bz5mtvcvrfTsc0TU7a9yTagm1i3ysQAhtHuKN3Rbins8DjQCfoUZ18Os+CNQs47Z3TuD53Pfuxn6iX7AA2nNx8MslDklz+9OVUuBVc+aUrkcZJYpJQDm0oNzMYKI25equ/fT4+W8RmhbaxsZETTzyRI488EkVRePrpp6mtrR2uDDV79uxtPsjNoUSjw7/n16zB8xzwXPIda1HjcYK1teg1NSIISRaRvW8tfJsf33AD08eM4fjDj+CM63/Lmu5uPj9zd0445RQi6SSP/PinFLq7WNPfz6t9vTw6fz709hLYc3dyuTxuZxeSJKMEdDzDxKuIgefh5AvIzTpWKoVr2xS6upBkMRHQg0F+d9llnPXj2Zz/k59w1403MlNTCTY2IkkSkiShhsPEdplEdukyzKEhPKdUttFz0aur/QpSPlsFp1gku2Ll+g/GWT9PdgkikjiMWJONIzrwxIAOyHZkOX3h6Viuxe1n3U7NtBr+dcO/+P6D36der+ev+/6VtjFtIjipiLCG4whruBIhvr2l41QiApgWgVSQaA42E8/FOe3507hXvZc97D1EutEkYAZ8O/BthvQhbnz0RlrHt3L2LmeLdJ8CQlzzCMvZKR0nWDoPH5+tzWbiVjcrtO3t7bS3tw//vTG38RNPPLFF4zn88MO3aLv3Q1l47GwWc2gIWQ9S6OpGUlS0qgq0ykpc2xJrqLrO2hUrOe/aX1EVi/Kp8RP5zpVX0FBVxW9O/zp7T5uKmUjgGkWwbSRJZkxzCxN2ncIX99gbKV8ktWAhF91yM91dnXz7kMOYueuumP19uOkEecsExyWzdBlqJIJWES+l9EhERo9CQoLeHm746U85+fvf538uvJD7briR0YpKsL5u+JxkRSE2aSLZZcsx+vvxvJK4ui56ba0vtj4fmmJPH26xCPo6BVLq19mgB9EvtgEhssnS8/VACzjzHb675LsszS/lrml3MX7peG64+Aau/O+V7B7bnVv3uJXa+lqxhhtFFKVwESKbRVjOOiKCuVxsYlcgDMpTChVuBfeq93KCfQJf+/fXuCN4B3uO31NEFjeI7X4U/xGdiU6u/NOVjNXG8rlDPifc06NLxzEQoushxHYcwyUhfXy2Gms3/fRmhfbss8+mWCyyevVqJkyYgGEY72kLB3DhhRcyderUTe7r7bff3iZCWybf2YmdK6DEVRzTQI1EUPSg6NqTzaFGIiS7uznnFz8nVyhwzlFHceXf/85BM2fys1PPIF5XjVc0KPT04lo2VjIFkoKkari5HHplBU6xSHLefGZUVjF/8WK+f8dtHLDbbnx9r32ImhZuvAIlFMTJF/A8FzuXJVBXT6CmCr2iErUijl0soK9cw2/P+wGn/vgS/ufiH3HbpZdRO2E84ZaWYTe4JElEx48DJIz+PkyEVeu5LoHa2uHtfHzeL65tU+zrRY3HYF33cfkrlQdeQ1iuVcCDpeeaEEK2En7xxC94atVTXPG5K5gVngUWTHImccqYU7jiqCsIGAFxAwoCuyPcuFLp5zhgFCJYaTqiw4+HsG7HQcJOEH0rSkO2gfvS9/ElvsSXHvkS/xj6B1PemgL7AzJIjsS1X7yWNX1r+N6fv0drZSvT7elCtNuBfRANDvrE9siMFN/w8dkaFBDLKtGNb7JZoZ03bx7f/e53UVWVe++9l2OPPZabbrqJ3Xfffb3t2tvbufPOOze5r+OOO24LRv3BsDIZil3dyLqGmy+gqDqu64hCEqaBrGm4Elzw05/wzpo1XPPNb7N3Wxs1dfUcsNsMgpWVBFuawHVRwlEKXZ1IEsgBHdd2wHWRNRW9oh5D0zi29gCOPeYY7n38Me54+inOnD+f7+1/IF/e91PolXGsRBpZkXFNEywTM5HE6BtAr6lGCQZR41FGh4P8+mc/41sXXshFv/k1V3/jmxS7e4mMHU2gRqQqSZJEbMI4JFWm2N2DJHngeriWTbC+7j0dgnx8tgSjtxfPdgjW18OaNes/6QJzES7XScCtiMjgWcCeQB/c+/C9/OHlP3Davqdhpk1uKt7E/xz5Pxz2zGEcph8m1nJ1xLroTIRY9yPc0nlEcFIR4YaejCg2QennasjumxWvnQetfa08lHyIX6u/ZhKTYBV4ioe0jwQhCKpB/nzZn/n8eZ/njD+cwT/P+ydNySYRCDUdIcrPIyz0UkMCxuCLrc/WYS3C27MJod1sHu0vf/lLbrvtNiorK2lsbOTqq6/miiuueM925d61m2JLtvmgZFesws7mkINBMTOWZRRdR41F8WwHraqSa6+5hqdeeYVJrW1UhkJgWszabTcijY2EWppRg2EkVQNZQtZ1IuPHocXi4Lho0SieZVPo7hUFJtpaqB4zmq8ffwJ/u/BivnrIoUwfNw47meK/L7/Cw//5NznbITJuLHaxiDEwSL6ri8zSpVjpNKHmZuRAgN3rGvjRt77F82+8wU1PPI6VyZBdtpx8R8d6pRgjo0cTamnByRex8wXRKKGnBzvvZ+X7vD8cw6DYP4AWi6LGYu/doFxmsZGREovTEKKVg5efeZmL/nkRu7ftzrzV87j0hUt5o+MNvEXeSD3kOMJinYYQ6xRC2NYihC5Y+j+GEZEFsYY6DpxqB/ZFWKWtUFtVyxXKFairVBLpBIffeTi3zbmNQl0BclC3to7bT7qdTD7DGbecQSFTEAUvXkBY4gcgrPPu0rktR7iVfXw+DDaigMtm4lQ3K7TFYlEknZc46KCDcDZQHrDco/aRRx7hiCOOYN9992XfffflU5/6FPvuu+9622xtzFSKwto1aNVV4LggSzimiRqvQPJAi8d5cM4D3HTXXbTU1LB47RoWLV2CpKmE29rQ64Qb1rVMnEIeZAm9pgpFVQk0NxFsbcbOF5A0DSSw0xmKXV3ku3vQqqsIujZfmzSZuAfF7m4efOZpfn7PPRz2/e/yPz+6iIeff450MoFWVYlrmmTeWULijbnkVq6iODjIifsfwNdOPpnbH3yAxxa+Ld733j4K3T3D/WslSSLc1kqotRnPMgEPK52h2NOLmUz6hS18thhzYBDPNAk0Nrx3rd9A1DA2EYK0BmF9TgFkWP3Kar5x+zeIBqLM75zPmsE13LjnjdxywC1IeUlsdxBwIEIko4iSjT2IdVgd2A0hyJWIddp3o4PRZoh9TUGsCY8qbZ+CoTeHCBkhLvn7Jexzyj786qFf0ZvsZZddd+HGb9zIgq4FnHv/ubhVrohwfrV0vAMQE4ABRMrPUkTKkI/PB2UJ4lqZsOnNNus6VlWVVCo1fEGuWLFik9tfc801zJ49m1GjRm3pUD802WXLRN5qLIqVzuK5NrIqGr/LmsZby5fxw9mXUFdRQefgIGcddgSfmziZ6KSJBJubRUMCWRb5q5ksWiiEXF2NMTCApgfQx4zByeVwsjlCzc14hkGhq5tUb59Y4/JA1gMQNJEDOpccfyInzZrFk6+8yvOLF/GfefPYpbWNWy+4ED1WSdHIE4xESp2FUuTWrOXbn9qPZe+8w8+u/RWjr72OaW2jsBJJZEUWEcmyjCSL3wGsdBY5GMDO5fAcG9cw/HVbn81iZ3NYqTRKNIoWi/Ge9hVLECk47Yjc2QBivTMK6ZVpTr/hdCzbIm/lOW7scVzadinV46vFjL4dmIhwDQdLf1cD8xGCmUO4jm2E4I5hZE343agISxiEy9lCNC3og3E943jIfohXoq/we+f3/ObJ33Dj0zfy3+/8l8NaD+OST1/Cz5/5OZNumcT5x5wvSknGEIFW+wKvI0S/o/SzHWFV+/GFPu+HLCLtrR7xPe/a+KabFdpvf/vbfPWrX2VgYIDzzz+fF198cZNVoVpaWjjkkEPe95g/DHY6S7SpCSeXR5IkXNNCjYZRgkEGjCJnfuMbBDWN/lSKLxz8ab40bTrB5iYqdpmMnc7gFg0kRcYpGlCq7OSZBuGWFhzDwM3liY4bR3bxO+B5BJobQVMxBxO4pkGgro7w6HbS895ElVXMRILxkRjjD5zF/8w6iGWWgWHbmINDpPv6OOm6X3HAzN059QtfZHJbK4WebsyhBLM/+3m+3dnJ2T+ezZ0//RktLa3Imoqs6cPVoWRVFetqrofnOCgVFVi5HOZQAteyCNTVDVfI8vFZF89xSuliDlpVBUowiP2uJhe8iYjqdRFrqBGgBhzL4dRLT2XF0Aru/uzdNMYaGZcaJ7bdA2EJVyHWdUEIWxxx8wkhBLUfcVMKIyzUzd19ZMT67SnA7QhhbBH7lnol9sntwz7ePqzUV/Jv7980vtAIoyDeH+fIpiO59sVrmeBN4OhpR4s1ZgdhTe+GyA0uIsR/AcK1PRoxQfDx2Rw2YvnBRkwuN8NmhfYzn/kM48aN48UXX8R1Xb773e8ybty4jW5/3HHH8ctf/pJZs2ahrtPEfK+99tqC0X8w1FgUz3FExxtJwrNd1HAUO6Dzja9/nXw2y+SWVioqKvjOgQeCZVO5224gySKVx3GE69gs4jkeWDah1lYC9XXkVq7CsUyUYJDw6HayK1ZiDQxRMWUKxlCC1Pz5mH29eJaN48moFRXku7ohmyXY1IznOkytrSXY0owky+RWr+aQ6bvx2Kuv8MiLLzBz6lS+cdTRTI9VEDYMrjz5FL79x1s497prufHUM4jX1BAZNxbXtgk21CPJop5yoK6WYl8/KDJ6RQV2LoeVyeI57npVsnx8yhgDg7i2jRwKoUYieJ6HlX6X71RDWHcrEWIbArtg87lvfY63h97mrDFnsX/d/kKkGoHDETN7E2GlJhlpvl5eC61HdPppREQb17Hl1qOEWOc9Gfg7otpTHCHcCSAHY4wxjEmPgbmQfDvJ5c7lGK5BS6CFc18+l9GR0Uwzp4kAL6O0v/bS+MolJ5ch3Mzj3+f4fD6ZdCK+f82IgL7NNFvbrNBefPHF/OIXv1gvl/b73/8+v/vd7za4/f/93//x/PPP88ILL6z3+MMPP7y5Q31g5GAAp2ggyRJOwUAOBFAqYpz/05+ycOFCrjnj6+wzZSpGJouXShEZMxq9uorU/PkUe/vQqyqRFBUkhKCOaiPY1ISVSqEEAug1NaWIZp3YxPGYiSSFri5kRSXaPorC4CDZlSvxerpJ1dSihsNY6SKFjg7UWAxzaAgznSLS3k5tfT0/PPEkzjxwFo+88jJ/nzuX7151JfdcejltdXU09zj85Ohjueiv9/HzfzzAz44+FjuXxUwMERk3lnBzM2o4jBIKEairwxzox5Vc0WbPskQzedfFS6bE5MN1cQ0DxzBFBDQekqwgB3TUSMRvMv8JwRxK4BQKyIEgnmWiRiKYg0NY6cz6G45CWJ5ZwADDMTjmpmN4e+htpkSnMHvKbHGTkYFdEBbrMoT4dSAs2RRCCNOMuIqrgc/wwZqyS4j0Hw2YgxD1CoRQlt3Unjh+ZUclz3nPMdudzSP5R9DQOPXpU3ls/GM0JBrEWByE9Vou69iOsMZXIyz60aX3wbdufTZEL+I7HkB8n8tFVzbBRoX2pz/9Kb29vbz++usMDY00d7Rtm7VrN56du3DhQp5//nkC29F96VkunmUhh0O4Zhq9ppob772XRx99lAnNzbRForjZLF5iCElVUEIhIbJd3YRaWkRaT8mq1auqCTU14lqWaOYeDOC5LkooiATo1TUoepDcqtUYhTxaNEqgshJ5vErKstArKlCiEbSqSgrdPdi5PBKeaDDf04MSjSNJoJkmx4ybxJHjJjI/MUSbruFmczy7eBFTquv49oGz+P3zz3FbYxNn7n8Ahd4+7EyWYq/oiRtsbkINBQk0NGAODuKaFo5RxC2a2NksTl8fiTfmolVWImsaUimSGknBc2ysVBErmUIJhdCrq3zB/RhjpdNY6TRaXBRPKXeistJpPOddq7TlOsQmFIYKnPTkSSwYWsD48Hge+dwjqJ4qbiw1iECpVQiRNUs/ZUTQUrk0dwvCNTuRDyay6zIDIYhPIFzBFYiIz7JYTgeK0PBqA7f03MJD1kNc2HMhfW4fZyw9gzlr5xBcGRQ3xcmMrAEPMrKevBRhhWcQa9N+jKHPugwicrI1RKBfFWKCObipF21CaE866SSWLl3KO++8wxFHHDH8uKIozJgxY6M7rK2txbbt7Sq0rllE0TUhaorKI3Pf5IYbb6Shqorl3d30GwYNto0cDBFsrCfU1oo5OEjFlF2J7TIZc2AQxzRQAkECteIOYQ4NIUmixrCdThOorSXY2IhbLOJ5HrKuogUqiLS0olbE0aIR+tpaaGlsotjbh2tZhJqbyC1fIcYV0PFsC88TqUaaJN76oOtxQEsTsh7EqA5xzeOPoUoyp+6xJ0eMn8idTz9FswefmTQZLR7DTKUxenrRli8nWN9AoL4OrboabAcln8NMprCSpX63moZn22g11WhVVetFmHqOg53NYqVSFLq6CdRUo0Y3kQjm85HELq3fK+EQSjSClU6jhMOYiQSe6wpPzrqkEBbfarjznTt5c+BN6rV6Ht77YdS8OhKkFEREJzciRLRcMaq29LMTcTNKIlzQ49k6tAPHI4K2CgjxfR1x8yuXeGwFSZc4Lnsce7Xvxa0Dt3LTspv4ofNDfrn4l4TTYSGmSxBWeaT0urGI9dsVCDEuQqA3MFytCg9hQecYab+nl17vl3b8+DOASA+LI64RCTHJSyC+e5uwVTYqtNOmTWPatGnst99+NJYiXU3TZGBggObm5o3usKGhgWOPPZb99tsPfZ1iCtuyJrLnOEiajpPPMa+vj9m/uILm2jq6Bvr54UlfYHrbKOxiATUUomLiRPTKCux0mmBLC1YyiWuZyJqGXlmBrGnYuRxOoYgai2IODoIsCvqr4RCOIqNkM1RMm4okybiWhRaPo1XEUUIhIu2jUCNhnGIRNRKldt9PkVn8DvnOLtyigSdJKEEdJRjEyWaxsjmcooUx0E801Mac227jit/+lt+/9AITW1qYUF/Pdc89S3NNDVM0DVdRsPM6sqZS7O/DzudQ+voINTeJyUB9vXARDg4RqKpEkhWsdAbP9dCrq4b79UqKglZRgRqNYgwMiPU7y0LfyRpG+HxwnFL+thwIEKitxUql8FwXJ59HUhQkWcbJZdd/UQ5YBoNLBrlt6W1UyVU8NOkh4sTFjWQiwlrtQhSimIxwF49CCI6EWN/1EBbnUkY6/WwtahFCv6Z0nAmIghRpREGNgjgHeqAl0cLs+GxiXoyrl1/Nk9KT/Lnzz+xX3E9s8yJCYNsQ68jTEa7jANALeocO/0UIark7EOv8LDsE9NJ5Vm7F8/TZeehHpKhVICaSSxHf9QTCw6OzSe/HZhNb33rrLS6//HKy2SxHHnkkxx57LLfffvtGtx81ahTHHXcc9fX1VFZWDv/flsiajlPIs3pggPOuupLqigq6Bvo5fv8DOGb6bjjFIpLnEhkziujEiRR7elHCESRZxjUtJFkpVWuK47kuZiKBrGs4pomVyaBVVqDGYljpNMWeXpAVQk1NBJsaUSMRrGSSYnc3nmEgqaJBgBaP4+TzYNtU77UntXvvRaipETUSQo/GCNTUEJ00iVBTI3pVBbKsUFjbQW0mx5+uuYYbrr+eRLHImkSC6miUSx/5J0Oui6Tr2OkUxYFBscaWz2PncmSXLye7fKVo/xcMQiyKUyxipVN4to2VSlHsGcnLLSMpCoH6epEalUpjDA5t5F32+SjhWhZGX38pSr0OJAk7mxPdeiQJJRjAKRRwnXfdHRbBLx//JV9c/EV6zV7umHgHo8aNEuk5hwB7M1KUYiKiJOMuCLGVEAUpcqXHuxEz/3Fs/eCiKEJgI6Xj7Y4IYlpVemwywooeI35+f//vc1D9QWS8DF8wv8AvUr/A1E1hibwJPAv8G/gjcCOi/ORCCC0IwaPAUwgx70S4mMeXzntXhEgrpXNfid9s/uNGHyMi28Zwj2UshHXrITw3m2CzwVA333wzV1xxBU888QQzZszgsssu49RTT+W0005bb7vf//73fOc73+Hss8/e6L7K22xtHMtgsK+Pc373W3RNIx4M0Tx+PGcfciiOaeFaJqHmFqp23wOjvx/XNAnW1IiC6kggeSiBAOZQAnMogZ3NolXGKfb0iUIQrkexs0us1YZDojxiKV81UFeLEg4JV1wyRWFtB0ooiBwIosZjIn2or59gYwNqRZzk3HkYQ0Mo4TDhMY1E2trIrlghilf095Ga/xbFrm72nzSep/71L954awFVts2Xzz2Hc+68jVvO/CbVzU04uRxWOoOVTKLGYsi6Tr6zm/TChWjV1birVlP0JGGxeB5KMIwSi2DncoSamtZzE0uS6HcrSTJWOi3WqisrNvJu++zseI5DsbcPJIlAfR2Sooio9NJnq1UIj46TL+BJHqyzpPDQGw/xu4wIdLxl5i3sPmN3IWpTEEKWQFiMoxE3nVaEiOYRVm4BIbIyQpQa2Hbt6bTSOMo9Z9sQ4j7IyPqZAxRBqpP407f+xPF/PJ5FvYu4MX8j/1n9H67f/3rGV4wfKapRixDe+eI8ZEcW49cQ69CLEGtyExBiXo+wYisRUcvdCEt5NCOtBn0+uvQjlhGqEPEGeUT0ei9istmP+LwHgE20ad+s0Hqex6RJk/jjH//IrFmziEajG6xC9Pe//50JEyZsskLRnDlztonQZrM5zrnhegYTCW654ELaamvI9Pah2g6O66DHYlTvORM1FCL7zhIkVUWSJDw8rGQCWdexZAXPtjCHEsMRmeZQgmBjPbKmIes6ajSCsoGGCmokghIOI61ZgxIO4xQL2DlRGtG1bexEAmNgkEBtNZW7zyC94G1yq1fjGEWq99qT6r32JNLWSr6zi/TidzAG+knMnYe+YjW7jRlNdOIEvvP1r/Orm27i5Bt+yy9P+Qozp08HxxaWSyKJ5zo4hSKUGg64oSDBxnokuRFrKIGRSGL3ZLEGh7BSGcJtrQTqaoddyQB6dRWe42Alk8iq4q/ZfkQxBgbxHIdgY8NwkJs5NISdyxEeNQrwcIoFHMPAwYXSd9rzPGb3iCWeH+3zIz4/7fMjEcNhhGgNIVzH1QgxXY2wCi3E3WQUwrqdy8ja7LYpCDdCFUJYOxHWbbnco44Qu9LabSgW4o5v38FxvzuOgfQAK42VLF61mPEHjYcTEBWk0ogbaGm/+ba8WLfNIN6LcjDM6tI5lqtX1SLekwjCql6BENtyqpPPR480YvJVifhOdCJEdQXCU1KNWG7oKD23CTYrtLIs88gjj/DCCy9w4YUX8txzz22wRVtzczN33HHHJvfV1NS0ucO9bwzD4Me/vo5la9dy2N570xqOEHQA18XBQ1E1KnebTri1VfR3TSWJtLfj2jbGwACKrosI3kgEc3CQUDCIVllJdvkKImNHExs3dosK90uShBQIDAdTubaNa5ql4hkRjP4B8ms7kPUAgYYG7GyO/Oq1uEWDiim7olVXEWpuRquqotDRQbGnD8c0yCxdRmFtB1/cex+WvfMODz7zDGfffiuH7LIr3/zs52mqrECNRZAlFeoUnHwOK5HC6+qkMKqVcHMLgbo6JE2n2NNDMZmg0N1DdvUqIu2jiY5pR6+sHLbQ9doaPNfBGBxCUlWUoJ/j8FHCSmdwCgX06urhwiV2sUihpwclFEKLxzH6+3EKBcyhISxJGhbau+++m1whx+kTTue7td8V1lm5dV0LQliyCCtVQgiajhDhKMK1piBe14kQ3e215F+2bqPAy4ibZHVpnAWEa0+B+nA99/zPPRx/w/F4psf06HR4FW57/jakuMTJNScTyAVE0FURIgsjYuLQKv4mjJhUuIxYz+UORA2M5AqvQAjuGPxAqY8iFmIpIIT4XJcz0ud4DCIgcAnCwl3GZpV0s0J74YUXcsMNN3D++edTV1fHTTfdtMHAps117tlWXHnllby1dCm7T5zEoy+9xKHTprNHdS1IMhIQGTuW+K67iIL+y5ejV1Uj6TrWwCBKMEh8l8nCbZxM4poWel0t5sAArmEQamn5wN1xZFVFVtXSRVZBqLFRRPj29OHZNrFdJuFJUFgrmgcEGxtQdB3XtlFCIjras2zkYAAzkYR8gR9/7TQmtI3imttv49nFi5jf2cHfL/kxqqogR8KE20YRrK8nv6aDoaeeIvnGmxS7egg1NqLGomgVFcgBDWsohZ3NkHzzTQrdXUTb2wm3taJVVCDJMoHaWoo9vRh9/QSbGv3Un50czxWtEz3HwUwMoYSCaHHRLMC1LNILFmL09BEZN4bssuVY6TT5nm7M3n60tpFiw/fddx+fG/M5fj7t50iyJITrZIR4PIKw4mYiLLwYG757FBCN4kMI9+r2LvxQi6iz/CpCGEMIwR1CuJfroN1u5+6j7ubEv5/IKctPYc5uc3hyzZP8e+m/uT54PWe3n83J+ZMJ1gZR+1URMFWJeD/qERZsBnEj7keIqocQ2zTi/RrDiNiOxc/J/ShgIj7XAuJzyyAmmM+WnivlahNn5LNdgli6mLzpXW9WaPfcc09uu+224b/vvffe9zn6bctbb73FkfvtzyMv/IdTDzmUvVpasTJpFEVDq6wkOnE8uTVrya1chRoIEG5pwkymkENBouPGoQQC2PkCVjKFGo0ieWAODKBXVhLYyhWWtIoKlFAIo38A17Ko2Xsv0vEKEUjlgaRqyJKMncuArODaBl6uQKi5UbTqs2y+fPjhGIbB7+69h1F19UiqgmU5XPe/f+JzU6cxbcYMIu3tMLYdtWCSW7mSYl8fwdpaQm2tSKqGEo3g4eFlsxS7unFSGQqdXQQb6gjUN6DFY6ixKMbgkBDbxga/hvJOhud5Yp0+k8E1TADMRBKnWCDY0EC+oxOnUKDY309+5SoC9XXIqoaR6KfQ3UVu2QpylsF9L73AKeefB8CBBx7Ibyf+FsmShDV6AEI0/o3IV52JaBiwMfF0EGuYGYQY7yi3aQUi7adclj2IuFG+jrBIamCyM5k79r+Dk58/mS8s/AL3nHwPy4eWc92z13HJO5fwu1W/46rJVzG1eqqYLGcYjmRmCeL90RDu5HLRjkWIVoIZxPs2GhEctRK/4fzOjIX4DMtF0gyEt6Ya4bFQEJ/niwgLNoSYYBVKmzcanLP8HM7irI0eYrNCu7Nz77338t3vfpf9J07kzIM/g51MIikykqYSnTgOyfOwczmUUJBATTVO0RDu4qZGka5jGJgD/ci6hlZVSW75SjzHI9jUuE3ERS4d2xxKYKczRNpawPWwM2nUSFik3MSiWIkESlDHTKYxevtRYzGUcAhZ0/nWaafiyRLX3303s//0v3xl1sE88dZ8HnztVXZtaOSomTPZbcxYwvvsQ6C2GnNwCLuQp9jXT7ipEaVWBHOVz8/KZnAdUVWqODCEXlmJXlMNjk2+rx87myUybqyw0H12OJ7jCPdv0RDf28oKPFd8z/XqSpRwBGtoCDubFZPGqkpiEyfh5DIYySSFji7ShsnF/3qYZT3dw0L7wx/+EOVfihCRFoR7bC4wDzFjP5hNi+w7CCtvDMLlvCOpRYhgP8L1NwlhWT6HsHSnwB4de/AX9y987YWvcdKdJ3H/cffzwGUP8MJ/X+D6f19P3YQ6HMuhu74bqU+iUWkUrulKRPrPPogSlG8gLOhlCHfynojtakrHTCBc7rvhi+3ORrkxAAhvRRUjLmMNMTGLICZRHmLyZCImb3lwJIfPvv1Z3km/8/EWWk3TaKmq4tKvn4niOBCJoIRDhNvaiI4Zi1Mo4LluKaApAJ6HWlGBVlkpyhX2D4AsUlzsrCh1GKiv26aBQMI9W4MSDGAODRGoq0FSFdEUwXWQwxGUSBQnnyNYX4uTy2Mmk8JSD4eRZYWvH30Muudx7T33YMkyf7/yap5683X++sSTXP3Yo0SDQW5vaGDMhPFo8ZjIpbVEPehQfR0Vu+6K0deHMTRIobMba2hIFO0gipPNYtgWWlUVSjBIvrMLK5MhOmE8eoUfjfxhmDNnDldddRVdXV3U1tbyk5/8hBNOOGGLX+85DoXuHnAdArU1w99To9RfNlDfgDkkcmdlJPSqasKj2lAiYdIL38bo66c/n+eih/5O59AQv7r44vUPMBkxg98FsS71f4go4kPZsMh6jNQKXoMIGtmFbR8AtSWUO/L0IUR3T8T4nkII8BjYu2pv7gvex1ee/grH//147l57NwfOPJADDz8QNFidWM21b1/L3/7vb5ww5QS+bX6bidpEseZrAp9GFNCYBryNsHqWIizqJCJNqB5hCfcjGjDU49dS/pB82OsIEBbsGsSkqR0xCcqUHs+Vfo8y4pWQSz/7xHNmv8kJK07gnfQ77NG+xyYPtU2Etre3l8HBwfUikKdMmbItDgXA7NO+TjQcxujuRYnH0EJhArW1OPk8TsHAMQz0ilIpQkVBr67CSiSx0mlkTSNQKtafX9OBpCiEmps3GPC1tVGjUeRAQFgn+TwOHrIewC3kQVFBkrCSKbSKCkKRCIW1HeSWL8dzQY2EOeUzhxHSg1x55+2c85vr+M155/OVzx3Fa28v4IGnn2bCnnvgZrP8/clnqIpXsM/o0eRXrcZMpIiOHU2orRUloKNVVGL29ZHv6sbOZFAbGkrFDHLIwSBaRZxibx9WKkWgrl6kNAUDKOGw3ynofTBnzhwuuOACCgXhc+rv7+eCCy4A2KKbhOe6FPv6hMg2NAy/965tY+dySKpKoasTp1BEUmRyy5Yj6zpmKkX2tdcxkwneXrmKS+6/l6Jt86vzz+eg/Q9gtWWOHKQ8ky8iLNkYokZxAZHSYLJ+AfVyZZwC4ma1K5uskLPdaUDcSMvu3UrgMIQFmhB/z5gyg/sj9/PVR7/KMf89hj84f+DgiQdDN4TXhvle7fcI7Bbg3rfu5b7593HYqMP47szvspexl7jpzkSI+FRECtTziEnKpxippxxhZE13MuJ9im2vN+HjxZy/l66j4ge7jgDhAl6D+L6PZqRdYxdiacBGqOMQI92dJISbuQiJZIKzlp/F3MxcdmnYhXsOuIfFLN7o4TYrtMVikaeffppkMrne41/5ylc2uP2vf/1rbr31VmprRwqbSpLE008/vblDfWBqAxqFtR1okQiS66FVVxGorsKzHexsGjUcRquI41oWSkDH6O3Dc13UaHS4WlKhtxc7kyY8evR2FQ9Z0wg2NiLrAbIrVmBnMyjxSjzbAtfFsx2yy5cjeaBWxAmPGYNnGNi5PE6xwOdm7Ealdyo/ve8evvaT2Vxx8leYNmkilQceiJ1MEmhs4L6nnmTJ6tU01dZy/IEHcuzM3fHMIvm1HQSbGlHCYmIioq2XU+zpQa+tIVhfJ4Q2HkerrMDoH8DJFzAHB1GiUaSUmKhoFRUokfB2mZx8lLnqqquGRbZMoVDgqquu2qIbhJlI4Bomgfr1WyGayRTFvn48PHAcZD1Abs0aXMMg2BzHHBxCUhT+s2Y1l955OzXRGFef///4+a1/ojuRYN+jjx45yFqEBbsAsT41GhHclEfcLcqHdRA3HQkhGNMQltrOYMm+m0qE0PUjxNVFuMYdhDUehinhKfxz339y2uuncepLp3KpdymnTz8de9CmPd/OFZVX8IOv/oDb1t7Gn1/4Mw+88wB7Hb6XWM+bjxDW8jrsQQh38v8h3pcZjNRm7kGkhPQg3ts6hIvZn69uGVm46udXDYtsmUKhwFVXXsUJx5+weW+BjXAXa4jJYVlkBxCTpATCG1KqDgaI5ZAokIdly5Zx6kun0m1087uTf8eJNSdiJN/VbvJdbFE/2nQ6TWvrSHSiJEkbFdqHH36YZ555Zj2h3da4nkegvl5Ys+EQsbHjRGqNUUQJhYYLR6DIOJKEEg6hV1YORxQ7pklhzVrUWIxQY8N2G3eZYVdyKER22XKcbBY1GsHTNOSAjhIM4RYNnHwevaYGNA2Q8GyHUG0tnzminubWFn7wh5s4+89/5Mz99+fTraMYKhjo1dXc+bPL+Perr/D3Z57m9w88wO2PPcaPvvI1PjdrFvnVq5EUVbikg0FCzU0UOnsodvZgDSXQ62oJ1NSghsMEamrwbEuk/QQCqNEIdiaDMTCAnNEJ1Nb6EcobwPM8rFSKrq4Nd4be2OPrYufz2JksWkUcNSzyRVzLwkqnSb45DyuVEkVGNI1CZw9WOiUKqQwMkO0f4Pp/PsxDb7zOro2NzP7aqfzstj/T3dvL+KZ3lVM1EDegntJPDyESDYxUf5IYEd0QI80EdmY0hNu4EeEWLCBunN2IcxgLLa+28KDyIGfPPZvZL83m/zr+j3MmnEOT0gS9UF2o5vwp5/M/+/4PheUF6IBX7Vf53cLfcekXLmXs6rFCaIOIGzMI1/EAwuqtKR3TQLiV+xnJzY0hJji+4G6YDMOBaF19G7mOurvEpLBcJnFD6uYhJpMuYv1cLf3eCTyJiCSejAiEGkB8Ro1AC3imx19f/is/evZHGI7B2Z87mxNnnCheV7fp4W9WaHt7e3n00Uc3t9kwVVVV21VkAVQ9QKCiEreQR1EDeK6DGo1iSxKuk8PoHxAu4qra9fILQdwEs8uW4zkOkdGj1yvgsL1RI2Hiu0yi0NMrqlc11CMHArimidE/SHFwAK9YRAoEUCIRzKFBcitWoETDTBg7njtn/5gr7r6bm59/npfb2rj4yKOoHhrCsywOmjqVWRMmsHDJUu584T+0VVcBHs7odtR0Bixb1EhWFcKj2yn29mAMDJJbuYrCmjViwlJRgRoKo8ZEOKmkyASbmsQa8tBQqTlBDWp0x4SblvOWASRVQQ4EdpiV7XkejmHgmqZoWlE0aKqvp6v3vf20mhoaROH/UHCDBVE818UcGioFPlXi2jZWIomRTJBbsRpzcIBgUxNKMIBdKCDpCqGWZvSaGhYvXMiFf7iJlX29HL/rFL603wHMvusu1vT3cd33z2WX1jYG1j3Y5xHuMhcRUFSFuCF9XARARohajJEmCEMIkTsEom9F+dNrf+KmR2/i6pev5tX+V7np+JvYW95b3IxfhdD0EKE9QrAIOl/v5JVVr3DI1YfwnRO/w/cP+D4BIyCs/XLk9VuMVBKazEh7Nbf0exXCNVmuC72Zm/Y2x0ZMRByEQoTZMROpIkIUy9HeK8R4miua6Uy9t0JEc2WzcNM7iIlME+/N4y63gGxFvPf50n7nIiZd5bXaLsQEaBSwG6SWp7jkT5fwwOIHUCWV6lA1p4w6RXgtFITXYhNsVmgnTpxIf38/dXWb/vTffvttAHbddVd+/vOfc/TRR6/X+H1brtHKkTB2No0sK0TGjSZQW4udzmClUtjZLEo4QmR0O2os9p4bb6GjEyuZJDK6HS224yshybpOqLkJc3AQO5tDKhZRQmGCzY2osQiFjk6cfAFZU9ErKnENm2J3L0h9aJrGz445lr3GjOU3D/ydr9/xZ87Y/0COnjoN+jViEycws66eia0tuIZFvmMtV/z+97y5cgXnfeMsDps+HTudQQ2FqZqxG0b/AGY2i51MYfT3YyxfgWc7IHnI0Rih5iaiY8YQamok2NiAOTSEMTCAa5nbtDmBKIxfwM7ncYpFUbPXFMsC6+Y9S7Is8ofj8e2WnmSl0xj9A8LNbpmYAwM4RYNATTXf/8pX+dmNN1A0RtxMQV3neyd/GSORQE4rJVd8HCUSGf6uWqkUnu0QaKrDyeUp9nRjJlPYmQyea6NVVoJrY6WMYbeZKcGNd97OHQ88QDwY5JdfPJl9Zu7OGdf/hs6Bfn574UXsNW4cvDvor5zaEEWIUTsfH5HdEM2IG3N36e/poFQqnD3lbPZ7YT+++cdvcvzdx/PlPb/Mj3b5EdVLqkWTgR7gU3Dc/sexb8W+XL7kcn5z/294+IWHuebka9gnvI/Y78GI6ljzSsdYjRCuJoTVKyFu7OMQYtyDsLjL9ZO3JV7pWOXAnzxChGTWT82SEF6LerZPPrCHENf5jFRcGkBMTkbBRUMXccEDF1BwRtzHIS3ERZ+5SOS5jio9uBohvC0Ipcsj1tQrEQLci5jcrETEHpSDoRSGv//edI85987hsnsuYyg/hCqptIRbuPuUu2nX2oU3pJ4Pb9EeeeSRfPazn2XixInrCee7q0B973vfW+/vZ555Zvj3bb1GK0kSiqoTn7orocZGnGKR3Nq1mIOD6FVVxCaOH3a3rUuhp498Rwd6dRXBbVC16oMiqyrBhoaSuzCDk8viuSKwTK+pwVKSeECwoYH4rpOxMlnya9diZbO4RYPj9tqbtsoK7nrxRW545ikefWseZ8zcg70GBoiPH0/ltN3IrlhGsa+Pw8eMZWVnBxdc8XN2HT+e7x59LDMaG9HXrCG2yySCtbXIDQ2oe8zEGBgk39FBoaOL4kA/Rlc3mUWLiYxuJ9zcDKoKtiMqDqXSYv13K653e46DmUxh9PWKAvlGETuTx7UsJEVCCQbRq6oINDaghEI4OeHNsFKizeHWtrRdy8LO5XGLBcxsjsLq1aVOSQ5KJIaTSeEhEWxsRJJkDp02DePLX+Wmhx6kd2iI2soKzj/tdD63335YySSBmlrsfE6ko4XDYnkjoIu8b13H6OsTkyo8XMcFRcFKpgEXubqWUEsTud4+nly0iBvvv4/unh4+v8eenDXrYOpbmgg2NnLmV77KhEkTmdY+GscwKBby659UHnGTVRA3kI97VSOJkVSksti2A0HYPbI7N4du5pGXH+F///2/PLLwEX6wzw/4cuWXRV/b1cBYaIg1cEPzDZxUcRIXdVzEG++8wT6z9hGW7ApEbu2nEe/takR7wTcRAVkNiAIYBUQwVaQ0jnIJx22xEuMhrLVVpZ/lspXlPNII4rMfW/qZQQjUAGKCsC1W1wqItdEBRGWv1Yj3q4GRilwVQDecoJ8A0+Cqd66iq9BFQ6CBS/a4hBOiJ4gUMwORzpUp7a8PMakZQLyfNYj3f21pWx0h6AlEc4opwDvwWu41rjznSl5e+DK7VO3CQH6A3Sp247aTbqO2rlaMq/z9eVdb53cjeZsqTgwcddRRHHvssYwaNWq9x9ftUbujMAyDBQsW0FQ0CIXDVOw2HWyH9KJFFHv70GvriE2agBZZ/27heR6Fzk7yaztQYzHiu0xG3goWz+uvv84ee2w6zPuD4HkenuOA5wnLzPOG8yi1ykr0ygpx0+ztw0ynsbMZli9cRHtrK8/Oe5Nr77iDnqEhJtfXc8rU6ew9fgLhtlawHYyBPjxF5aXEEDc/8i96Bge54MxvcOzESeA4BJoaCTY1oscrCNbXIakqdiZLoa+XzOIl5NeuwbNsgq3NREePxXMd7EwG17TQKisItbSwaO0a9jrwwPeel+viGgaOYeLZtmhC7nmiqpeiIOsakqohaSpuoUixuwczmUDSNJAk0S1J10VP3kgEYzBBvmMtdjmavK4WNRrDzmZBktGrKlnU0cGM3aaDLCPrwgJ2ikWsRBI7my25nj0kVUUOhURXp1BQFBPRVCRFEa7cZFJErufyGP19FDq78CwHSQE5HAHLQtZ0Ag31wtXuuOQ7u7CTSeRwiEBdHat6uhnb1gaygqJpyIEAemWlGE8qhVM0sLMZPEDRAtjZDFIwiGebOHkD1yzguVC5+0yCbaO4/09/5OZ772VNdze7TprE9z/zGRr1EH/4z785aM+9OPaEEwiU2ig6xSLZZcspFAokx45m4sSJLFmyhKnKVAIDAWEV7IjKTiW21bW0UTxEFGoaYZ00AgV464m3mDZmGu/MfYfZt87mpaUv0Rht5DsTv8Mp2imEk2FhHcWBBOQr8ugH6ajNKk8sfgJznsnn6z6PNEkSkca1CIvqbYTb8W3Ezb4B0RlpH4SoJBBm0BiGPQqbfE/KaVZly9QqPaYixCVY+m8ghKez9HyotD2lc24uHXshQvA9hEXYWtp3EaiBeel57Lb7buJ14dJ+LIRFPsiI61lHeEbK9bFFeMkI+dJrBkpjegMh/HrpNb0Id2+09LqB0rH2R0wElkPHyx201rWKc0uUPr9JiAmTXXpNT2mf5bxmozSuDMJNnAT2AO8Ij1fufIXfPPobnl/4PBXBCi6Zdgmn6Kfw+ODjfGaPzxAYHRDn3FV6fw4AY8BgwegFTJ06dYO92Ddr0YZCIc46a+OJuDsDsiQRbh+FncmSW7GilJtaS3Tc2GGR9TwP1zCwCwWKXV1YqTR6dTXR8eO2ishuSyRJQnpXsYhAQwPmwCBWMoln2+g11YRbmlHDYexsFKm3l2BtLYfvdwAH7703DzzxJH966EF++sxTNL32CkdOmMiRu+9BU2sLxYFBZtU3cvhlP+fBV1/hqM99joYJ43npwYcYevFF9hg3HjUYIlhfR6i5ESUaRYvGqJiyK4GaGtLvvIPR0YVn2sQmTEKvqabY3Ut+TSeFri7sdIZUdTXh0aORJEkEdhnF4YpGINZUJUVFksC1LdxcDteycA0DM53GyWSRFBk5FEayLFzTQNaDqCGZ/Jq1WMkUeKCEQkiBIGY6SXFwCFlRkEMBrFQGO5HEHBxg7YpVyIqMa9vguEiBAFo8hl5ZgacoeMUiTjYvrFXHAllGDQZRYzE8x8EtFrEtC880sbJZPMNAiURQI2GUYBgrk8IcTCAHA2I7y8IziqCqyJqGUyiQW7kKd2iQgqrhFk2USBgtFsN1XNSgjqwHcApFzIEB7HwBxzSRFAXXNHFy+eGI+aFwkNvvvIO///OfJNJpdp08mT/88Ie05ovc9dCD/GP+PAzLYsqUqSAp5NeuwbUc8h1rMQeH0NrXn0CTR4hGuZ7xJwUJMbnoQtzYTaAVzFYTxsCk6CTun3k/L734Etfdcx0/eeMnXKNfw/Gtx/OV5FeYkp+CFJAI94dFLu2+cOdjd/LMW89w+K6Hc1nhMtrWtIkb/TRE6s9uCNflMwg36aMIK3NXhEfBRgjELgiB3hAuI5agiXD7rit8OcTkYWid/w7iM46W/gYhTr2lcRQRIlRVOv6bpcejCAFdC02dTULMyrWfbYSQVSKs3jgjNaYzpbEppX02Iqxmo/RcEiH+axFCXK6ZvaT0eSjr7Kca+sf14yZdantrUfZXGKoeojXWOlK1a1XpnPsYiS5PISYi7zDScKK/NIZRkGhN8MCSB/jLdX9hce9iKsIVjK0Zy8rBlew+sDtyTOazUz4rzrkKcZ04iCC3ntL5bYLNCu1+++3HX/7yFw477LD1Grlv7R6zDz/8MDfddBO2bXPaaadtNKp5QxjJFLnVa3DyeTzPQ6+rI9zUiBqNYKXTYgZfWtOzszmkUsBPqLFxhwY/fRgkSRLddzStJLYWgbo6UcQgFkVZEUevqcGzbdR8nlOOO55jZx3Eo88+w0Mvvcitr7/Gba+/xi4Njew/fjy71zcyulDkuF2nEEgmyC5czD3PP8ejTz1Fa0MDn5k6jQMnTWbymDHE2loJtbaiV1WhV1YQam5maN48csuWUVjbid5YT6ipCUlXsYYGcTu76CsWUIKvEm5vI1BXhxaNoESjqJEoaig4YiWmUkLgCkXsTAYzlcTMFZBlUPQApNMgyyISO5HCWDiInc8Li9i2wQNJK1nBuRx2JodTLOB5gCpDNke6aKCEQ0geuK4HpVZxkqQgBzSUYEh0eAIc1wXDKFmBBVzTRpIlXFlCUUQ9a61CWKHG4GDJlVxEDYdRrRBONiP2Gw4RbW0lPnUKbi5PdtlypMQQriGCt4pd3RhSD2pnl2hyEY1S6OnFsWxcVxj6rmniAqtDAf678C3+PXcuK7q7UWSZA6ZO5cRDD2PWfvvx65tv5q6nn8J1XT49bRpnHXcCkydPptjfgyRrWIkhrKEkWixG4N2xFwbCXfhJrEsiIW7MAYQ1VwC5IA8HT0mWxP5N+7P/4fvz6nOvcuff7uT+Bfdzh3MHo4OjOSJ8BIfqhzIzM5OQE+LWWbfyv63/yzXPXMOBSw7ki7t+kXP6z6FlcYtYky3XjD4MIT4vISxJEO5LFyFybwETIJANiB7AMUailQcRN/xwaR/lSOqO0nNJhMgkEeJTXhMuW7YRhFU2HyG6xdL+XIRolyogDe/LBTTQc7rYPsJIKUoJIYpy6bEoQvDLk4ZCaV+50jl6pW3d0jErSv8HgbXQl+hjrjKXucpccmaOy6suBw/O+vNZvGq8ioxMa7yVXUbvwrEHH8ux448VlndZ4BOlY3SVjh8unXcBPMNjpbuS55zneOzZx3i5+2Vs12ZC1QR2qdmFRYOLkIoSP478mHHKOLHOfiBiAuGWxtjIyPr+vpv8Zm3edTx9+nRMc325liSJRYsWbXrP74Pe3l5OOeUU5syZg67rnHzyyVx33XWMHz9+k68ru45bbBfZNPBsB0mWUAJB1Io4bqEAnosnycKyCQRQYzEC1VUbjO78sGx3d1cJO5vDGBxEkmX06irUSITXX3+d3WfMwM7nRyxI08JKpch3dLFizWqeeuM1/v3GGyzr6wMgouns2tjAtLHjmNjeTmtzC0uGBnnq9dd4ee5cXNdlxvjx3HjWt1GjYf67dg0tEyYwbvx49Fye9MJFpfZ/JnptLaH6OhzLouudJVTHoriWhawqBBuaCNbVIekaSIiavfk8kifWpx3LGrbcHMcCy8EDnHwOr1DAKhp4poXjuhQVCdOTQAbJhUpNRVZkTBfyEriGgWuZOLaDbZpUeKBrGslikYRj4ckykqISCgYIB4PUxkVjBVmSkDRVXKiuIwQvX8B1HTzXJWM7pHHJShKZYpFkKoVtWXxuzFiUQJAn1q6mM5Egoii01dYyYdp0Js+YTiASJlBdg15Vxfw35zKhoVFY7amU6H9smkiahmtbZPr66BxKsDqTYk06w8KOtby9ZjVFy0KWJKaNGsXY2jokz2NRXx9Xn/VNIsUCT730X5YMDXLMgbMY09pGsKZalPasiOMWDRGwZhroNbW4AZ1ORR5xHTOVwKSAuEnuQHbUtTRMDlgLb897mymfmiLcmBFGrHwbSEPy30ke/ufDPP7O47yw+gUsz0JDY5o2jb3q9mLq6KlU1lTyaP+j/O21v/HHs//IodKhJDoT6DU6kf0iYi3WQ1hjzyBc2G2IJgUSw51j1mbW0jaubSRiuhywVoEQyOWItd3yWqtUejyNsLjKsUPl9KKS8HmOhx2ykcMyiqpQNIokzASu5+LKLm7IxTVcGuwGwmaYjJFhaWYpASUgoutlB1d3maBPIBqMUlALFPUiUT2Kpmhi4iIjRKkcfFVg2LXtRTwGKgeoi9ZBP9zUdRN/zv+ZLkek8SiSwoz4DB46+CGkPolnVzzLmuwa+qw+lrhLeMl5ib2r9ubWfW4FB+Zm5jLdmY6SUsACt9GlU+1kUWYR70jv8Fb/W7za/yp9RXHfG18xniOrj+Rg9WC+tvxr6LLOt2q/xdelrxNrjgmPwkGMRIpnEROiOGKyMR6MQw0WLNq463izQrs9eOCBB3j11Vf5xS9+AcCNN96I53mbbCIPI0I7vq4e1XXxJHDzouSia4qbulYRRwmFkTRNpE9sw2IUO/Lm4JomxuAgrmGihIIsWLGCPfddf5rlua7IvcxmRaP5vn5co8jqZct55bXXeKujg0UD/azNZoZfoykK9fEKquIxFFWlMhZnamsbumnwuyefwCl9fSKaRn08zkHjJ3BIYzPk8ixOJ2lvaCKqKlTG4oCHXSjimQaeoiDJkljXlEHRgxDUkDwJCQlkCdd2yOVzdA4lWDs0QEc6zWFNLVQGAzzZ2ckfl7x3svfHE06kJVbBX9+az61vvPae52857EgaQmHuXDCfv61Y9p7n7/r0YYRUlTuXLeGJtatRJRlVloVL2/O4/ajj8CS47r8v8Vzn2vVeG9F0Hv7BBQRbGrj4pj/w4uJFWK47/Hx9NMq1Xz4VpbGeFUaBRDpDS2MDQz19pJIJ+ru76Onro3dwkL50mkSxiFt6fxVZZkxDAzN33ZWmWIyX3pzHoq5OcqaJLEns0tjIOYceRpuq4xgiyjlQVU147JjhJQXXMsmvES5jORgiUFtDMZ+nt6piRGjjUwlM2PFhxjtcaAFcmPf0PHZr2U2IRNnCUxEi4TBS/GApZHoy/HfRf3l14au8mnmVee48zHV8ig2hBpprm2mqa6Kjt4OFXQuZUDmB6e3TmXHADCbuO5G4GSf8RBhtgYau6WiTNfR6Ha1HY+idIRoqG4RIaQj3voxwgZaL4ssIC3Kdu7rhGKzOr6bKq6IuX8eywjKukK6gV+qlV+plQBrAxuYW+RY+H/48zynP8eXUl9/zdvyl7i8cHD2YRwqPcFbPe5cTHww9yF7qXvw1/1fOdc4FIEiQOHFiUow/639mvDyef/Evbrdux8Cg3+2n1+2lSJG31Leolqq5S7uLFwMvMrNmJjPtmUwOTUaNqRhJA6toYYZMTMXETJoUEgV6070klSS2arMmsIYbCjcQIkQ11SDBIIMUveLwOFsjrezVvhdTqqdgDBqsGVzDtTXXIqUlnvOeY2bLTOL5uJggTEf0Kq5ECKyBaE6RLn0GtcDJYLgGC97+AEL70EMPceyxx3Lrrbdu6GnOOOOMDT7+Qbj55pvJ5/Ocd54obv7Xv/6V+fPnc/nll2/ydWWhHVddTbi6GjuVwjEsZE1FjcVE1aftmEe5o28OnudhZ7JYqRRvv/UWU6ZPRwkFRREJSQLXxbVsYeEVDaxUCiudxjUNHMsulXhcQbKvn7W5LF1GgY5Cgf5sjoF8jqFCnsFCActxNj+YDSABiiShSDLg4ZYeW/czUiUZWQLH8yjYNu/+ckY0DVWWcVwPy3PF+nX5/AFNFu0RbccZngSAJA7kgSJLYp3Y84SIeR54iG5GgCpJ4h5afv5dqLJceonYHo9hK2fdcdjrCOyWoioKdaEwtYEAiiQT0jVCmobpOAyaBp+fOp0jp06nO53iojl/ZXpTMzNbWphRV08sGEKJRnELefTqasJtrQQbGgg2NCBpGk4+jzk4iJXLgesSHtUmllN0nQ7XGRHacVMJVPpCu944dt9jpP6tgRBXmZHCHSriJtyN2K4feA7MV01WpVexJLSEpaGlrHJW0Wv30uP20Gl0knfyGz7oJtAkDRkZyRPftvLv5WtILiW8Ghg4OLilfwBRooSkEDY2aS+NXP4nyUiShCZrKJKC4zlYriW+yOtcAqqkIssyjudguzaSJOFJpQ08UDwFCQnHc3BwStdI6Z/koaMjIWGX/gFIpX8ACuL1pSsLDw8be3j8W4qMjIaGgUijixDh1OCpHBE+gmwoy7/sf7HIWMS85Dw8PCZqE/l769+pDleLzzaIcHkfgAhQKyDc72U3cbnRQBtwJFANxlKDBZkPEAy1erVoabBkyZL3dZIfBNd117vZep73vgRyWV8f3vIVeLkcyApyNIwUj8OqldtiuJvk9ddf3+7HfDee5yHFY7z9zmIwLSEmZWQZVFW4RTUNT5HB9UQ/0/pa3GgYdVUHY7q7GJPLgSS2F+ssHp5tU7BMCqZF3rbIex4F1yXv2Fiugw3YSBhATz5HKpclXSiQM01yls3Yqiqa4hWkigX+s2b1e8Y+vrqClkiUnGWxMp0mpCpE9ACRQJBQKIiiaWI8nodUilBGU8D1wDKRbAdcFzQNKRgEzxXvgeOIddhS5SrJskYe9zxQZCgFY1G+9CXWf+/Kz0iln45dcn8p4rWeW1p3ktBUFdUD1fNQZRk1FEIL6KiFIoplMVgo0Fcs0lvIsTabZbBQ4DOtbXxjlym4SJz82D+HjxnRNForq8gXCwz19BDI5fj1gQdDJAKBIOgamWIRenugqgqiEVTHRbJt6OqEXB6vUMQrFMAwIRKGBW8jZTLQ3Iza1Dh8rAXLF2yNr+BWYWe4lgBef2MLxxEELaehhBXkA2SC9UFqn6+lubeZT1ufxg7boIHsyniKR17Pk5bTLDGW8EbuDRJOggOlAynoBa4xr2HQG6SOOircCqJSlAa5gVa1Fc/yeMN7gyJFcl6OlJQiTZrJ6mRmajOxXZs5xhxi5X+S+Fkj1RAOhIW7NuDhBl2QQLZlsYQTcJCQUAoKsiEj2RKe5uHEHZAQj5tCyF3dBRXkojz8uOSIJRwn4IgJsCuEeHgS4Mjib8RzuIi+xx7CJasixlSKTPZUD1VSxQQgokAdKBUKmqQR7g8TMAMEtAAVTgUxKUY4EKY2UUt8II6W0ui3+3mcx3ldeZ2TKk+iUW5kjj2HJwaeYIw6hm+Hvs0h0iFMjEzEDbh013ZjR2xib8awIzaD0iDuchcpK6HkFJScglyQkUwJO2iTq8hhrbJQ31CR07JYAtgI78t1bJomAwMDNDc3b37j98EDDzzAa6+9xhVXXAG8f9fxLmPH4iaSuKaJFosRbG7aIRWBdpZZOKw/Fs9xhicvGyvc4BiGWBM1irjFIoWePpH+0dWFmckIUdM0JNcVqTGhoOiCFI6ItBddR7IczFwKO5XBKbnwlWCQoWKBxuYmPElCsl1so0ghX2BlVydWLofruoQUhXAgSFzTUT1PCF1AR4tEcDwPyTJR9ABqPI4aCICq4BaKGIkknmOjxuOEmhpQgyHQNNRQEDtfwC0auIaB5zkgyXSvXkN1UMezHeSQaEtY7jssqSpqNIpHKX0qlxffI0kGz0OJRUUhDknC7OvFLhhosQiSpuMU8nimWFt2iwaOLCFLEp5lY+VyOOk0kqKgRKPYRhEnkyHf2y/SemQZ03WxZYmYHsD1XBYMDlJVU01tdQ3VFXE80y6dgySWB8JhApWV6A11hGpqyPf2kF68hMioUdQfNAslFKTY1Y1rmEiqgue4OMWiaPQhgZ1MEmlvR2ttYfHq1SMW7UZm5NubneVa+kDjMBkJPlqDaBz+cukxBRGMVF6vLFvIMOKS9uAx+zHmynNZyELW2mtZa67lUOlQbo7eDEGYMjgFV3KpD9YzLjyOsc5YZimzmKXPGhYpyZHE/gOMtPbLM5JyU1F6rGyplbvVjEWk4gRLr8sz0hC9IM6hu6ObJq9ppD9rLSOBZB4iMjeIiCTuKR1DKr03AYTbuxKxxtlVGlMLIoK5gFhbNkvvSXyd/VHaT9lt3wxLVi9hojRRBD/FGHGnL0YEhUmINe0YDBvHbmkbEAI5TuyLJYjo6/J6fANifT5U2u9Q6T06FJE+VASWlbSID5He8+STT/Lyyy9z3nnnccwxx5DJZDj77LM57bTTNvfSLWa//fbj+uuvZ2hoiFAoxBNPPLFZt/G6mMkksmGghELotTV+cft3ISnKZjM1lEBgvfXr8KhRVE6fitE/QG71aoo9vbimhRQMoAaDeI6Na5iiWAQuWBYoEoHKaoJ19aBqeKaJWywytGYtnuthDg1hp9O4toPnuYwJhCASQ9Y01EgIVAXZk5BUBRCC4rk2WiAocnkrK/AMC8cycE0bOR4n3N6GnStgDPRT7OpBi0YJ1NcJt7QHemUcJRqFUrELeWiIyl13JdLeTqilGTUSBs/DyuYwentxLRNZC1A5bRqSLIvqS5aJJ8m4hQJWOoUxmECLVxCf0irWPg0DOysKTViZNG6+KHrFBjS0ujpCtTVYmRzZFStwjSKabWOHQuR1nYqaGtxsnqBtgysipz1gZkVcWMGVVciyjBQWEdKuaSKFIwRbW1BUDc+xySxfgdHfjx6P4xkGqfnz0auqxT3bNLALRSQJPFfkY0uSRHh0O/EpUz7wMoDPJtARN/Zy0YfdEMEzzyL6mqYQgluLuHlHEaJXFpACHJk/kiPTRw43bvDCHp3pTnHjj8B8eT5KRhFuzBxCOEKlfcVLlmKo9FiRkdzUPRFpTBZCmDKM5Mo6iFzWDoSgjC2Nz0OI1GiGe7TaGXukC9GupXNVSsdZiYjKVYG9SmMYZCTqucBIMYkwIpp3HEJY161MFSzt00UIulkai156vhQJbdfa4j0eREwQGkuvsRFNAl4tnW++9L8cFBYpnWM533YRoiJV+bPoLx2vFjE5KKUXcSQiHcpBrM+Xm2tswmTdrNDefPPNXHHFFTzxxBPMmDGDyy67jFNPPXWrCm1DQwPnnXcep556KpZlcdJJJzF9+vQtfr1rmCjl0nU7wWz844CkKKiRCGpElK90DAOjrx8zmcJzbZRgCFnXRYGKbA6nWBTdhlwPSQa3aOEoMpZlIRXy2JqCHo8TGjUKNRQQYioreIaJlUqVLC8VJRJCi0SRQwHUWBxZVVFCIRRdQw6G0OIx1FAYWddwDBMrLXJWzWQCKy3uGnJp7EokCp4rehLLMpG2VtRomMZPf/o9aV16RRwtHsNOpzGTKZxsVkzcqqtAVnCLBWxFQVJVQs0tBGprRBpRyUtQ9hR4joOVSmMMDlLsH8DOZigODKJXVhCdOA5zYLAkeDaJZcuINDehRCK4toPkeeg1tWjVVchAvqsTNRImPHq06C+rKOTXdiLJEpIsWija+QJWIokcCBKoqcHo7yf19mKxRltfhxwMEKqqxs5lMYYSaLEY0bFjiI4ZLU7cF9pti4ywiA5EFKPoQpRj7ETc3BXEzT9NKcig9F9G3MAHgYQQTjkqixzVDlB6lJFayWNKx5BK+yxXP6pGWILVpf8xRkShXDgjzEiqUBfCsu0ubaciRKdcaCKLEKkWSFWnaDuu7b3lOaOIPOF0aX8FRtKmxjFShWqgtO8GRoK6yk0qtiTjsiyky8Fd7Ip0qHJhirIlCvCl0rH7GHHtFkpjy5Xeg3K8mlca034IIU0gUquCCDF2ERHI1aVjr0J4DMrpTOs3FFqPzQqt53lMmjSJP/7xj8yaNYtoNMq2CFQ++uijOXrddl3vA891xezfb0q+zVACAcJtrQTq67CSSdHzVJZQ4jFCTU0gy3iOg1MoiDrT6TRkxBUjNzdRNX4ceBJyQEdWVCRNw3MdPNcRVapkGSwLSVNRQqKtoRaNosZE004rncHOZEQ1pkRSBHeVvodaLEq4rQUlHMbOZjETCexMVqQTKQqBmlr0miq0ykrkN97YaO60JEkiDSYaxUqnsXM5nMGRq0dSFIKNDaJ28sb2Uep3rFVVEmppwejro9Dbi5VKgaKgBAIi4re6GjmXQ43GcT2XcFMjoVFtouRlKV890FhPfuVq7GSKYGMjnuuiV8QJNTch6zqubZNdvgJJkQi2tKCFw5iJJKmFi/FsGymoowSD2JkUVjJNqLaW2OSJBGpqhsfr2pupHeez9dARN/t2hLCtQdycy7mm5QpJGcTNW0ZYn6W+qNJ/JCHQYUS6yViE8BqMiIXOiFhXIASjnJ8qIcSyj5Ec3HUtMQ3RmL4OIf7dCKuugBDeGoTl2grGfGPTNbDXLYhRdg+vS21p7B80jUwunWszSPMlcYzyuHsQ1mpZtPcB/lM6l7L1vRwxCSlb8ktK5z8FYamXGY2wiucicmWDpfPpK72uXG2rCZFatRE2K7SyLPPII4/wwgsvcOGFF/Lcc8/tdK5ZORAQLuOPaPGJjxJKIIDS0IBjGNiZLE4uh53JrreNJMvolZWEGhtQ43G6Fi2ifvfdS68prd/atqj0pOlCXIOic83G2uzplRWip3CxiGuaeK4r8l0DgfW69GixGFpJnMvbvF8kRRHFOKqqcC1reD+Sqm7xd1+SJNRwCHW0cFEL6zOPaxRLaVUGSnU10bFjcU2TQH09wYb69fYfrKsDz6PY00uhtw87k0aPV2Bnc3huBiefw8lmiYxqJzJ2DJIsE25rI9jUSHrB2yh6AK2yAjwIT20j3D4KeZ0KY3Y2R3ED3YR8tjFli7KCkbQcHSFyQYRoDiBu6AOl/3lwq11RFrMaIVAyI7Wpy4JaXs/cWI3qaOl/2U1rlsYTYP12h1HEsSbASHrA+zxPuXSetaXjlDNsgqXz3RpUlAKo+hDn3YqotrUGUWRCQZzbHoiiHCsRIisj1mSHEAJc7q40+l37DyPE2ERYsBZCwMslLTOl89tMTfDNCu2FF17IDTfcwPnnn09dXR033XQTs2fP3tzLtitaNOq7jLcz5TVdz6suFYWw8BxXuDVL/WrXDbySJEmUMQx+8PYfkiQJN/IWFhvZGhOvrdFft1x3WXccHMNADoaw02kIh4hNGI9TKGAMDGL09ROor1tPbAO1teC65Lu6xfmHQ1iZzHCQllpRQaChfr1zDdbV4YwdQ2bpMjwgOrqdQP3629j5PMbgIEpga93xfN435cbj5aL95aYG5VzYGCM3+kro7emlZZ8WcdM3EDf98rYB3l9nHR0h2FvC1rBfdLaeuL4Lq9YaqWlcj/ACrGCkk4+EEOFxiDXVToQ4ljsDaYjJSgMj76HJSLrWGESazzLExKZcfKPISEnJD1uCcc899+S2224TxzZNrrvuuq0edfxhUeOxHT2ETyySJLrmKB9CQD8pSIqCGg6jtIUodHaJ1BtZFpHOnoc5OPQesZVkGTUeR+rsQgoEUCMRtMoKJFWl2NWNGgqhrtPqzvM8nEIBSdOQVVGe0yk2DlvlnuNgpdNYKVHK0vXXaHc85f64BiNFERyEgJYDnDTwhryRohn+5TaMF/SENd6PmDyU2xB2IcS2lZF126WISclohBdAR0SDWwiRziGs3FTpNU2M1JmeinC3lwO2mtjiUqWbnas8+eSTXH755WSzWY488kiOPfZYbr/99i3b+3bCdxnvfMyZM4e9996b1tZWzjzzTObMmbOjh7TTIEkSenU12CJwCoTLW6+pxikUKPb04JbKnjqFAubAIIGGBsKjRmHn8xQ6u8itWElxYABkGSuZxBgYpNDdQ37NWoy+fnBdKnefQWT0aIo9veRWriS/toN8RydWKi2aL4hyVzvyrfBZlwBiHbQZUQyhGaiCOQ+La+nYY49l77339q+lDVGONC6n7NQghDWJcBX3lH7WIoK1cgj3ciciIjyNcCuXS1hWI+pKr9vMQUaI8xiEJ+J9hARtVqFuvvlmvvjFLw5HHT/77LM89NBDW34En08cc+bM4YILLqCzsxPP8+jv7+eCCy7wbxDroIZDSMEAdjqFa4nGAqLIfy2e7VDo6ia/Zi3F3j4RhNVQT6C6SqQkxWOYqTTYNq5hYKUzwoqVSvuoryfU0oIejxMdN5ZgUwOuaYMEWkWp2USpZrNes6X+Q58dwbuvpc7OTv9a2hABhBt3kBE3bj1iwlIWYBXhPh6LWL+tQAhwmpHgrVZEZHEzW7UX8E4Tdezz8eGqq66iUFg/1r1QKHDVVVdxwgkn7KBR7YSUXL5mIkGwvh5ApCUFg6I3rm0jazpqNDLstZFVFUlWCFRXEWxqRNb1TQZoyZpGqLkZo7+/1FQiLapqyTKBujocxfcG7cz419L7oAHh8u1BrM2CSN+p3MC2IYTrN41YA2/ftkPb7FW2btTx/vvvv1NGHfvsXHR1vTuWf9OPf1KRFAWtoqLUwrHwnscDNTVo8dh6SyOe42CnUyjhkAg424JrUdY0gk1NBGprRd/d6mphGYe3fgcrn62Lfy29D8pF/lOMNLPfFAOItfCGzW344dms0F544YXcf//9nHfeecNRx5dccsm2H5nPR5aNBcvtbEF0OwNqPI6saZhDQ3hb0IjATCTxPEQpyPeBJEmo0Qh6VZUQ742U4vTZufCvpfdJHcJP272Z7UyEO7mS7RJYtlmhLUcdf+lLXwLg3nvv3SlqkPrsvFx00UWE3pWCEwqFuOiii3bQiHZeJElCr6nGs22sZGqT2zqFAnY2ixaPbZW0I5+dH/9aep/IiMCocrP6jdGNiCpu3MQ2W5HNrtHOnTuXW265hXw+j+d5uK5LR0cH//73v7fD8Hw+ipTXjq666iq6urqora3lJz/5ib+mtBGUYBA1JqpRycHgBl26nuNgDA4ha5pfAe0TxLuvpebmZi666CL/WtoUVYys1UZ5bwWrIcTabCNbNeBpU2zWop09ezYzZ84km81y9NFHE41GOfzww7fH2Hw+wpxwwgm88sordHR08Kc//cm/MWwGvboaWdcwB/pxDGO95zxPdBPyHMevgPYJpHwtPfTQQ7zyyiv+tbQltCAs1nLR/zI5RH5tlPVTd7Yxm7VoJUnim9/8JolEgrFjx3L00Udz4oknbo+x+fh8YpAkiUB9PcWeXozeXvTqGpRIGM+2MQcHcYoGgdpavwKaj8+WoCGKUqxEVHSqQQQ+DSKKVLTx/ktKfgg2OzWORCIAjBo1iqVLlxIMBpH9GbWPz1ZHVlWCjQ1ImoYxMEB+9RoKnV2iFnJtLWo0sqOH6OPz0SGMyJsNIGoZDyJyZ8eyBSbm1mWzh5s+fTrnnnsu55xzDt/61rdYtWoVqrqdR+nj8wlBiG0jTqEges/KCmok7EcJ+/h8EIIIYS03nt9BNuJmFfPiiy9m3rx5jBkzhosvvpiXXnqJa6+9dnuMzcfnE4no/BOG8GZagvj4+GwZO3ieukVrtDNmzADg4IMP5uCDD97GQ9pyyhWqTHMzrRO2I8a7All2JP5YNow/lhHK145byuH1r6X3srOMA/yxbIwdPZbydbOxqomS9xGup5hKpVi2bNmOHoaPz0eexsZGenp6dvQwfHw+0owfP56KDaTf+VFNPj4+foCjj8825CMd1RQopTpMnDgRXd/xDawXLFjA1KlTd/QwAH8sG8Mfy/qYpsmSJUsIl9aD/Wtp5xwH+GPZGDvDWMrXUWAj6XcfaaEtF1TXdX2jJ7i92VnGAf5YNoY/lvdStmj9a+m97CzjAH8sG2NnGcvGmnx8pIX244TnOFiZDK5hIgd0tHjcrwDk4+Pj8zHAF9qdAM9xKPb04loWsqZhFQo4ubwoXuDnT/r4+Ph8pNmk0A4ODvKf//yHvr4+dF1n1KhR7LfffgSD26Gv0CcIY2AA17YJNjagBIM4xSJGXx9Gfz+BhoYP1f/X8zw8y0JSFF+0fXx8fHYAGxXal19+mZ/97GdMmjSJ119/nYMPPpj//Oc/XHrppfzhD39gl1122Z7j/NhiZ3M4hSJ6TTVKaQKjBIPo1dUYA4OiLVos9oH27RQKGIODeLYDkoQWi6FVVX4o4fbx8dk+uK7LwMAAyWQSx3FQVZVFixbt6GEBfGLHEgwGaW1tRXufbSo3KrS//e1vueuuu6itrWXt2rVcf/31/OlPf+KNN97g0ksv5d577/3Qg/6k43keViol1mTfJaZqNIqVyWKlUqiRyPter/VMk2JfP7KqotdW4hQNrHQaz3MJ1NRszdPw8dn5sIEMoiJQjA9XQN5F1MrNIO6YDcB2KDvd0dGBJEmMHj0aTdPI5/PDted3NLlc7hM3Fs/zGBwcpKOjgzFjxryv12707p3NZqmtFX2E2traWLx4MQC77747+Xz+QwzXp4yTz+NaFlo8vsHn9apKPNvBzmTe1349x8FLp4eL1KvRKIHaGrSKOHYmi136/DzHwUpnMBMJnELhQ5+Pj89OQQ5YAnQg2qQtQwjvB8EFVgADiK4vFqIjTPrDD3Nz5HI5Wlpa0HXd90LtBEiSRE1NDcVi8X2/dqNCG41GefDBB/E8jzlz5tDY2Ijrujz++OOEQu9tTO3z/rHSaWRNQ9lITVslGEQJBbEymfeU9nIMAzuXw7Ws97zOHBoC1yVQV7veuqxWWYmsaZhDCZxikUJ3N+bQEFYqTbFXrAl/hAuF+fiAiRBXFdG5ZVTpsVXAB/hqa30aFIF2RNu1CUAIWFva7zbGLySyc/FBJzwb/RR/+tOf8rvf/Y4pU6Zw8803c+GFF7J8+XJuvfVWrrjiig88UB+Ba5q4hokai23yw1NjMTzbwVnHCi329lLs7sHoH6DQ2SXWYUsCKQQ4jxSJIL+r8IAkSeg11bimSXblSvAg2NRIuH0UWmUldi6POTCw7U7ax2db04UQ1NGINmkViN6jBaB/I6+x2LDFmwYlq0A9UHY6yQjxBvArVvpsIRtdo508eTLPPPMMiUSCqqqq4cf9tdmtQ9l9q0Y23aFFCYVAkTETCSRZxhgcAtdBr65CDgRwcnmsdLpkwdZhDiWQVGWjnV+UYBDXMrFTGSKjRw83EtcrK5AkMBNJlFAWNRrduifs47OtKSDWURsRbt4ycYTg9gPVjNz1CkBn6SeItdzW0vOueM7TPah713E0oBboK702VPrplH73g/t93sUm03v+8pe/8Pjjj9Pf34+mabS3t3PMMcdw2GGHba/xfWyxszmUUHCzKTdOoYA5MEixfwA1FEQJBAg0NWHn8nipNJKqImkqVjaH63q4hkGgtgapt3eD+3NNE0mSUUIhPMsWN4YSWkUFTrGIOTSEHAwi+32HfT5K9CMszuoNPNcApBBrrY2IddxVCFFsQghrP7Ac4XJOADaY9eaGA6nqEI3EO0vHzJUel0v729AYfD6xbPROev3119PX18eZZ57Jww8/zO677048Hufmm2+ms7OT008/fTsO8+OFYxh4to1SseEgqDJmMkVu+TIcy8E1DEzDQA0FcSybQE0Vgdo6XMvEs2ycXBZzcIBAXT1KJCIimtMZnKKYrqvhMEokIizWYBA1EsFKp1Bj0fVc13pNDYXOLqxEkkBd7TZ9H3x8thomQkjr2LBFGUBYtT0IMVzLiGu5HNgULv2+urS/KHjmRhZ2y4L+KsIKHlU6Rj9CfD1gGwT3n3TSSe957KijjuL000+nUCjwta997T3Pf+ELX+BLX/oSQ0NDfPOb33zP81/72tc49thjt+j4t9xyC48++iiWZTFr1ix++MMf0tnZydlnn82ECRNYtGgRNTU1/Pa3vyUSiXDxxRezdOlSAL785S/zxS9+cb39XXTRRYRCIRYuXEg6neb888/noYceYvHixRx66KFcdNFFOI7D1VdfzSuvvILjOJxwwgmcfvrp2LbNz372MxYvXkwikWDSpElcd911DAwMbHA8lZWVW3SO24KNrtE+++yzXH755Rx00EH84he/4NFHH+Woo47i9ttv5/77798mg8lmsxx11FF0dHRsk/3vLDi5PJSbe28EK5cjt3wZnqQQnTCOYEM9sq4jh0Ko4RBKMISs64SamgjU1oCiUOzpxzUtEc08MIg5NIRn2bimhTEwSKGzCzuXQ43H0UoRze+ONpZVVUQn53I4O1G/SR+fTZIs/dyYuHmIoKa5wAPAIkRk8mqENesBWYRluhQYQljBINZwEwgLdt1Lwig9Fy0dN4pYG44B3cDHLDnj+eefZ8GCBfztb3/jnnvuobe3l3/84x8ALF68mDPOOIN//vOfxONxHn74YebOnUsqleLBBx/k5ptv5rXXXtvgfvv6+rjvvvv45je/yY9+9CMuvfRSHnzwQe6//34ymcyw3jzwwAP87W9/4+mnn+a1115j7ty5aJrG7bffzpNPPkkmk+G5557b6Hh2JBu1aHO5HJZloWkatm2TTCYBiEQi2yQSbt68ecyePZtVq1Zt9X3vbNj5HEpw425jz3EorF6N53rEJo/DyeXwHBclFCTU1AiSjFRat5UDOmo0ihIKg2eTensBhZ4uGBxE1nWCTY1IkoSVyZBbsRLPdYm0jwJZRlIV7HT6PYKvxUUakJVMoTTUb4+3xMfnw5FE5LZuqI6Ax4iwWgjLVwaCCOu3FqhECOdKYH7pscmgd+j/v73zDpOrLNv479Tps71nNz2BJECACAEhEJCeEAgqIE1EQCWIIC2IolKEoOSTIiAqKCJICSFIKIrSCSXUkJDetpeZnT5z6vfHO7ObSiipMPd1zbU7M2fOeeeced/7PO1+1o/3grCagwjrdxDC+rXz+5IQyVfLEFbzCL5YDe8GePTRRzf7ns/n+8T3y8vLP/H9LeH111/ngw8+YOrUqTiOg2EY1NfXs++++1JRUcGoUaMAGD58OLFYjOHDh7Ny5UrOOeccJkyYwOWXX77J/U6YMAGA+vp6hg8fTkW+zr+0tJRYLMbrr7/OokWLmDdvHgDpdJrFixdz2mmnUVpayj//+U9aWlpYtWpVX+nppsazI7FZot1///350Y9+xHHHHcfcuXM5+OCD6erq4he/+AVjx47d6gN5+OGHueaaazZ7Mb4sEG5jG7V08wXW2Y5OjFgCf9MAXNshuXI1VjIJLliZDLKiIil+kGVyXd1oZaWs+Ggh73+8hFVLPsZQVLKayvzWVnYfNYr9Jh6Kz+9H0jRc0xBqUyUlaKEQRrQXxzBAlsFxcF0XSZbRwuI9O5frS5gqooidEhkESW6YtFTAagTJFmKnHyIyiRvoL9WRgTBEpSjvrn6XJf9bQuzxGIlsgleHvMrgAwazz/77UEWVcA+vQFixQxFx3V76rWklv++ViG2/JPeqtm1z1llncfbZZ5NKpbBtG0VRiEaj63XPkSQJ13UpKyvjqaee4tVXX+XFF1/kxBNP5KmnniK8gW7AuipL6ibyQmzb5rLLLuPII48EIBKJEAgEeP7557n11ls5+eSTmTp1KtFotK/6YlPj2ZHYLNFeffXV3H333TzzzDOMHTuWc889l1gsxsSJEznppJO2+kC+SMnQggULtuJIvhjmz5//ie+7iQRuJoNUVbXJsh7XsrBXrUZSVTCyuM0tkEwhVVfhOja82QyKDJZFtrKC/774Iv9+4UVWt7f17UOWJJx1flgej4f99t6byV8/iOGDB8GKlVBdhWRZOO2dSIqMtIFV6wKkUkglYeStoCS1pfOyPVEcy+axK82lAtRuFbVXJZvJbhSflTMygfkBXK9LxpfB/7EfrUsjE8jgZB2QwZEd3nr0LWa/NZv3PngPx3XEZyVZ/D8P+IdYsEeNGsXkcZM5ouQIjNEGZtLEs9YDyyDXtH6oRW/TkRfJZAdmP1f7FlVVSaVS67224fPtibFjx3LXXXcxadIkdF3nhz/8IZMnT2bcuHE4jtM3NsMQBcZz585l7ty53Hjjjeyzzz689tprrFixguHDh/ft07IscrkcqVSKbDaLbdt9+3Ech0wmwz777MODDz7Ifvvth2manH766Vx11VW89NJLHH744UyZMoXm5mbmzZvHvvvuSyaT2eR4tta5MwzjM8/bzV5+Xde58MIL13utsrJyo2D2zoAxY8bsFP0I58+fz7777rvZ913XJdPcguL14Knqv/12TBPXtpE1jUxbGxkX/E2N5Lp6yNkOnuoa9NISrHgcM5lECYV47MGH+L+bZxCJxxnV2MhPTjiR8eP2ZcQ++yL19rJ46XIG7DWGD97/gP+9+ipPv/IyL8+bxxEHT+CiKcdTm0jhq6+HykqwHfxNTci6uLN0bRsnZ5Btb8OMJwg0DMBbXfW52/Zt6bxsTxTHsj5yudx65LqrzKX1sBiRiDRog9dd4B1gMLB//n8PsAdi5SuDDz/+kKsev4p3F75LXVkdPzr0Rxx6wKHsPmh3SrpKWCAtoOm4Jpa8sISX332Zx154jBv/eiOz6mdxQ9MNHHD0AWL/LQjrdt37VSM/tkqENf0ZsWjRovWkBXe07OGxxx7LqlWr+hKRJkyYwCmnnEJLSwuyLPeNTc/X7x9xxBG8+OKLfPvb38bj8TBlypSNvKGqquLxeAgEAni9XhRF6duPLMv4fD7OPPNM2traOP3007Esi5NOOolDDjmE2tpaLr30Up599lk8Hg/77rsvnZ2d+Hy+TY5na507XdfZa6+91nttw3m0ISR3R9vUG+Cwww7jb3/7GwMGDNjitoUvt6ssDnYmQ7ajE09VJWoggGNZGD2RvoQkxzTINLeglpahBnwYPRFkXUf1+1ADAdRwCS3Ll3P59dfx2htvMGrgQM4/8CBGVVbiaxyAr64eq7cXM5miva2FgXvuiVZaimsYJDMZHntjHnf99a9IksQlp5/Od751MnppGMcw8VSUo5WUrDdexzSJL/oYXBdPVSWe6urPVfKzMxBKAcWxrI/CHBoxYgRLlizZZeZSH3IIt3A9GydC9QCvIty4JvAmMApoBPMdk5ueuYm7n7+bCn8F06dOZ2p2KpqkiQziRsAPK95awZDjhwiCbhNW1txX53Ld7OtY27WWU487lWtnXotvlU/EeRs2GMNaRCx3JJ/Zql20aNF6zVt2NNGui6/yWDa8LrBlLioWSm5l2LkcZizWV68qe71o4RCypmGl00iyhOLzYRsG2dY20VUnHEJSFJIrVmLEYqAouKYh3Meui+z14amu5q333uP8884jlUrx6+nTmaD7SK9Ygax5kFUNN2vgOi6youD6fHgrK/HWVJPt7kHr6uacyVM48fgpXH3TjVz/pz+xsLmZX15+BZphYGoqaji8njtb1jR8DfWkW1rItneQ7ejAU1WF6g+ghkPFOtsidjyS+b8bNrhyEdnDav7xAaIMZxB0NHfwgz/+gDeXvMlpR57Gz2p+RkmmRCRJ1SEIuwtoBt8aH/wbIcFogWzJTDpgEod/+3BmzpzJH+b+gfc+eo97rryHwYHBQmFKRriwNUTSVC+C9Gso4iuK4kq5leC6Lm4iSbatHUlRULxeXNfBSiaxkknUUBArmULWdbJdXcQXL8Ho6kbWVFGq49HJtLSIkptcFkX3oFdW4q0QZPn4v/7FJZdcwsCmJu6++uc0hcNEF3wEjoOkyVjxONgOgWFDyba04i7qpvfDD/FFG3GyaexMFrO3l0EHjOeBBx7glhkz+P0dd7CiuYU7f/UrwqaJXlGxXgayY4pSITuZEnrMrosRieCYFmYigV5WutmGCEUUsV2QQFibG2YG9yLIMoCQZcwnKC35eAmn/vxUYvEYd/z4Dk7Y+wSRHPVfRGKUgRC10MQ+HRyxjxKExfq+eM/n8XHV+VdxQMUBTHt0GpMumcRff/BXxgXGie3s/IP8ONKIpKhib4CvJLYYdEulUsyYMYPjjz+eE088kVtvvbUvuLwt8N///vdTuY13Jriui9HdjZtOo4ZC+Brq8VRV4q2uxj+gATUYINfZTXr1GjIdHUTnv0O2pRU1GMRbXYMaCmGlM6DpqGWlYLuk1zYTX/gRVibNPff8iYsuuojx48fz8F13U2uYpFevxTVM4Yb2+nBdh0xnB7H338eVAL+PXLSX5IoVpFauBkXBiEaJfbiAbHMLF5x1Fr//xS9YtGQxZ152Ka1tbWRaWvu+k53Nkm1rBxd8dXXo5WWERo5AKylF9ujIXg9GJEquu2eHnfcivuJwEBbtpto1r6Sf6HqBSnhn2TuceNmJODg8cc8TnOA/QViaCqJ+VsrvczXC1VwPSloRsd21iBKhVkTpTgJogonnTeTp3zxNaUkpp9xxCv9d+l/hJh4F7I5wQVcgOgl9mN9/EV85bJFor776ajo6Opg+fTqXXXYZK1as4LrrrtseY9tlYESiQsg/GMBTUb5e0pCkKKL/qwSZzg7iCxdhxhOEd9+dmsMnUjF+P0pG7YadyeBmsiiaB62yEm99HYo/wN1/vIfrZtzENw44gJvPOIvce+9jxHrJ9XSj+by4I0ewJtKDnc7gOi6pZAIcBwYMwFNVjZ3NIQcC+AYNRAsGRMcgrwdZUfjGQRO486qf0d7Rwfd+8XNWffABuXxnn2xHJ5Ki4K2rxVtfB46LawmNZdcwUTwetNISrGRS6C8XUcT2RgrhIl6XaF2EBvFKBFmmAA3ebX6XU645hdKyUmb/ZTajjdHCajWAtxFlP9UIyziNsFwXglluCnfwB4h4bwRBton8cQdC06gmZv9uNkPrhvK9q77H/579nxiLirBu90LEblsQZUGFG4AivjLYout44cKFPPvss33Px48fz3HHHbdNB7UrwUwksBIJtHAYaTMBeTOZJLN2LbKmIrkuWlk5vrqavnhoYsVKHNMAj46VSWFnMkiSzNPLl3P7q2lGCAAAaRdJREFUv+bwjXHj+OXUb2G2tpLp6GBBSwvPL1zIgliUtT09lPp8PHr+BXgqy7n2gb+zuLWF3atrOHzESPaqqCQUHIoViaBXlGF0RzCiUUp22w29qoqv6xp/qark7Esv5Qc3XM+fHIfaYcPxVJTjra3pE9WQPR7MeBxfQz1OzsDsjeGprkIrCWPGRLs/Lbwp06KIIrYREghToTDt0gjL8WOE1RkAXPgo/RGn/fk0Kisqeey2x6iN1worVwHmQku0hXdGv8NIcyQjmkfQRRe3L7+dwWWDGawMZkDZAKROSZDm7ggr+nWE4EWJOEZVqIqH73iYb5//bb7/g+/z93/8nQMOOKB/rMMRMeMuxA3ASD6FmVPElwVbJNrq6moikQjl5UIlO51Or9fN56sMO5fDiERRfD60slJxF73hNoZB7MMFmLEEWkUFSsCHp6wUIxbvkzjMtrQKAYlgkNSatbi2w4tta7nhT/dw6PjxXHfueaQ/XoqViPH3Dz/g76+9ildV2bN+AEcMG87Aujq0khCyrHDImDG4ksSby5bxv6VLqA4EOe3rBzH16wdhxhNIrkv0rXeQJBlPRQVqKMhee43l3j/9iTPOPpsf3TyDOy+9nIbQ3piFGwhZRisJk+vswk6nRas908Do7sFbV4tjmkKlStdQvN7texGK+OoigSBTCUF+qxAWag/CCm2DZWuXceorp+LX/PzztH9S+2wtrBKiBw+vfZhZbbP4yP4IPoKLh17MpdWXEquM8Y+2f5BeK1SGGpQGTlVO5ew9z6Z0r1Kh9rQCYd1WIAh+LZRUlPCPK/7BSdefxFlnnsVD/3yIffbZR1jZNoJkLYRF3Iog7WqKmTJfAWzxEtfW1nLSSSdx9NFHoygKzz//PJWVlX3u46uvvnqbD3JnhGvb5Lq6hGu4smLT4hOuS3LpMtENx+fFyWXQAj5kVcMxDdJrm0mtXIlj2vjqajGjMSRV493WVn5+++3sP3Ys133/PJ5+5mkafUGGVFVx+B57Uq17OHyfffDJCpKuITkuZncPltfDUeMP4LjDDmf5smUsXrGCx+e9Tlckgp3LYmezKGUluNkMicVLUfbQcQEzEmGvpoH84eabOf+SS7jyz/fwp91/i9yrYiWTeKqqUHw+JEUm29aOXlmB7PFi5eLkurrwVFWRM7vIdXXjrastZiMXse2RQ5BqJcJCXIKwUjMIizYDXXYXp807DUmSeGjyQzQuaYQMGGmDw985nM5cJ3vre3NNzTXsH9if3Wt2hzIY5h/Gkh8todVo5YkFT/DKylf4XcvvmNA8gX3f21e4oxWEO3k3BPGqwDKoKK/goZMfYuo/pnLWWWfx5KwnGcQgobNchSDcMoQbeRUi03kg69ff7qRIJBJceeWV3HHHHZ95++bmZs4880z++9//buNR7pzY4oo4cOBABg4c2Pd8c27j55577lMdsCCjtavDiERwbWc99+qGSK9ZQy4SwXVcXMfEW15FcPgwUUNrWljJJHbWwLVskstXIHs9NKdTXHnXHxhUXc05hx3BBddfx/srV3DiPvty6cknU5eIU1XXgJpKYUoyoRHD8NbUCOu6owM3myUbjaH29nLYHntw2Ogx5NIia/iljxYw65/zuPxbJ9OYiGNls/gHNOBaFqmVK/laYxM3XHEll99wPb/8/e+Zcd11YNlk2zuQVBUrmcKIRnEsS/S1NXJYqRSy7sFTXUW2rU2QbW3NJzazL6KIL4xE/m8Q0SigBREHfR/ohUxZhu89+z26c9089qPHGDx0MM/Meoaj3KPQ0fnluF+yW3o3RlaMFBZxEGF5RoBOkJBoKG3gRO+J/OjUH7H247U0ftwIq+DeRfdy4G4HMtI/EuYAX0fEa2sAP9SOqeWBbz7A5L9N5szvnMmc38+hdEypiP8uRdwceBD1tTbCOh5Mvwt8J0UsFmPRokXbbPsvM7ZItNOmTSObzbJ69WqGDx9OLpfD5/NttN0VV1zBmDFjPnFfH3300ZeCaK1kCiuVRist3awOcLazk2xbm+ieY5noZWUEhw1Dz7dqcnIGRm8velUlTjaL67hEIj1ces8f0TWNvXbbnR/M/C0Bj4crpp7Eid84gmxrG046ixoOoqg6kq4hezwEhg1D8/tIrVxNpqWFTHtHXrvYRfb7keNxkbQkwcfNLZzx25s5Y//9OT2TpWr/r4mkpnQao7eXo762H8u/+S3ufvQRGisqufCiH2PFRYMBvbISb12t6ImbbzKf7egk3dJMQB+MXlFBrqsbM9qLXl4MLxSxDZFAENdiYA2iufvHwAfglDlc8tYlvNP5Dvf88B6qRlXxrV98izc63+DBrz/IhOETmPLRFOFi1hGZwGFgX4R1uRSRZdwCSpcCb0HjgEYYCvHWOL9f+3uuXXUtV427inOqz0FKSKI0KIcg6uEwWBnMX1J/4eQHTub7v/4+//jbP9DDuiDTKELFahn9ZUmrEMpSO3Hk5brrrqOzs5MLLriAZcuWUV5ejtfrZfLkybz55pvceOONgGi7N23aNO69996+7adPn042m+Xiiy9m6dKlhMNh7rjjjq9MGHKLRPv+++9zwQUXoKoqDz30EFOmTOHOO+8UsYd1MHDgQO6///5P3NcJJ5zwhQa7M8C1beEK9njQNtNP1ujtJbO2BSQZxzKQZYVAUxOe/I/KdV1Sq1djxVM4Rg5F1zEkiZ/98yEiySQnTp7Mg48+yqT99uPcrx9E09e+hp01MCNRFL+P0LBh+AYNEnrHbW3E3n0PvaxMlN2oCt6aaqR0Gt+AemSvDzeXxYhEOW7CoYwffyC/ufMO/vzaq7y0ZDHT277FyMFDUbweJFnC6Onm/BOn0tLVxa1/v58aj5fJk44Tlq/jIms6jmHg5HLiOLpGasVK0itWERwxHDUUEpnNHh11J1GOKeJLBgfhvk3T3zs2iSBdP/xu0e+Y8/Ecfnb4z9AaNY78yZGYpsnM3WZysHawsCBTCAvUi7BkJUSyUxlCojED+CCrZUV2sguYECbMf3b7D5c2X8o1b17Di6tf5JbqW6hqqoIxiCSpJUAc9huxH78763dc+JcLueLSK7jl2luQyiSRsGUgxDFaEHHaaP67DGPT/XQRbeJmz569dc9lHieffDLf+ta3PnGbq6++mjPPPJPp06dz+OGH8+c//5kBAwYwa9asT9y+4DqORCKcffbZ7Lnnnvz4xz9m7ty5nHbaadvi6+x02GLe20033cR9991HaWkptbW1zJgxY5MNADZ3sj/rNjs7jIjoEOGpKN+ke9SMxYTiEwASdiqDt7ZWaAXnXcxmLE5q5SqsZBInZ+BKEtf99T4+WL6M3/3mN1x+3vnc9bOfcfXpZzB44mE4hknkrbcxeqN46xsIDB6Ev7YGf2MD/qYmPFXVIEti/5qO4vMihwIEBjYRaGwkMHQojm0Te+99KoMBZl5+Bb/+zul0JlMs+ngxsq4RGj5MEKXfj+vYXHH8FMYOG86v/nov78x/BxcJNRjEMQ2R/BSJ4to2nrIyAoMH4ZgGyWXLxbE9HoyeHhzT3I5XpoivDJIIUYkWRIzTR1/3nUd7H+X/Pvg/TtnrFHxBH2dfdTaNciPPHPgM3z7g20i1krAiC67eg4HvAaMRlvH/EOU+SSAIZpUJhwCnAkeIY1SaldxbdS/X117Pq52vctz1x5GbnRMx2zrgI2AhkIGp46by02N/ysPPP8wdM+8QpUcgrOlycQy6858zESS8C6C8vPwz6x1UV1ez5557AjBs2DCi0ei2GNpOiS1atNlslmHDhvU9P+SQQ5g5c+ZG2xV61M6dO5ff//73xONxQFhvkiTx+uuvb5M+ttsTdiaDlUqhlZYg6xtK0YCbyZDt7hFt5xQFo6cbxaPjq69FDQbFNrZNbMFH5KJRHMNALynlL/9+jmfffIPSkhLGjxqN7rrsO2Q4diaNFY8T+3gxdiZNYPBgKg/4GoFBg1F93n4FKstG9ujEFy/ByWQACWwHx7CQvRIle+2BnUrR9errdP/3RUr33pNJxx7LPgMaoaWZxNJlPL/gQwaPGMHoIUOQbAdfeSm//fFFfPeG67j0tlu51+tlyH774Skvw7EszFQaubsHT3UVnvJysB1Sq1aRXLESX0M9tmmS6+zCW1vUnStiK6OL/sbqXoQbdgm8vfxtLnvpMg4cciC/Ofs3vPTaSxxbciy/b/w9vlE+kekbQxBaGBiPqHHVERatH5FF/BYieSkJercuXjeBQxGW6JMgxSW+W/pdxpeO5+Oej/G85MFtdsmSxef3if3IYp8X//Bilvcu58ZHb2R41XCO2uMo8V7easZAuJxr89+rGxHH3QAnnngip59++lY/nZ8Hn9SGztzMDfa6LfB2htZ12xNbJFpVVYnFYn3W24oVKz5x+5tvvpmrr76apqamrTPCnQSu62JEo0iqupH4PoCVSuHGEzhlOSSPBysex06k8Q1swFtbK/bhOCSWLSO9aqUokwmX8PzSxfxx9uMAnD51Kh4ksm3t5Npa0MoqyLa3Y8aieGqrqZ54COGRI9YTxNBCYXLd3cgeD56KCiQXcj09uLKElUqS7ehEVhV8jQMo23dv4gsXEV+yFDPSS2lNNU44SKq1g7tnzaI5GuX48ftzwcmnMmD0KPyuyx2/vpbv/OQiLvvzPdwdCFA2ZLCQXXSc/PkQghyeqkpcVyhaZVvb0MIhbMMg19X1lZpQRWwHfIwg2xAiiUiD1kWtnPv8udSF6rho34vQ5+t8o+cbfGOvb8ABwIsIAhuEIMGDgG8gSNRHP8kFENbtcER7PBOhCOXmt6sCDkP0oG2G3aTd2C2wG7gwp2UO171zHdMnTOeEEScg52T4EKShEr89+besXrOaafdMY/aZsxk9cLQYh4O4YUjmjxsG2tkpE6NUVcWyrI1eLysrY/ny5biuS3NzM4sXL/7E7b+K2KKJ+YMf/IDTTz+d9vZ2LrnkEk499VR++MMfbnb7hoYGDj/8cIYPH77eY1eHnUrhGCZ6WelGLmMrnRFShLawLO1UBqO7ByUUJDh0KLKmiRZ5be1E3/8AI5lCDYdZ0NvD1XfcjgTM+NnVnDdpMunVa8h2tCNpHjwVZeQ6ulD9QSr335/QsKEbtapTAn7RXq+5FcXnw1tXi6++DjkYRA2H0SvLkTQNO5UmNGwYoeHDwLJJrVlDdtVqXMNCD/m547tn880DDuBfb77JiVdezh/+7/fEVq6iMRzmd1dcyZLVq7n2oQfJtHeQ6+7GMXM4hoHREyHb0YljCjlIf2MDuC5mPI4ZT4i64LVrSa1ZS3rNGtLNzWQ7O7GSySIBF/HZ0YlIHHLo0x/OvJLhnIfOIWWmOKDiAE595FTea39PbBMAXkbEZE8CJgNHA1MQVqMfYc2W5/8uRBB4KTABEgckhIu5AmHlxhDu3oK7uie/7xZoWNZAhVrBhU9fyKG/PZS/L/47maoMdIDP9vHnE/5MibeEsx89m650lyDXoYhmB0mE67lQV7uWfFPonQcVFRXU19czffr09V4/8MADqaur4+ijj+b666/v67pU2P6MM87YEcPdqbBFi/awww5j6NChvPrqqziOwwUXXMDQoUM3u/0JJ5zATTfdxIQJE9ZzFXzta1/bOiPeAXAdByPai+zxbJTgY2cy5Lq6xHayhJ1KYcaiuJJLaGBjXwJUtr2d3vffF8IVoTBpXePHN9+M67rcePnlHD5sOJn2TiRNQfbo6NW1pFpbcXAp2XMMoRHDkTVto7FJkoQSCmKuXoMLaAE/weHDkHp6UP0B5NJSJAnMeIJsTzeu66CVhHFNG9nnAUVB1nWClsu0bxzJiYceym2Pz+L2uf9it2CIPYYPY6+aWn763e9y81/+wqDaWs47bhK5zi4kSUYtDaNGRfaxVhJG0lQcy8SMxfNlTTZOTxQ7lcRTXQOOg53LkktnkHp7hVBHqKgoVcSnxLsIN2sJIIHb4nLpny/lw+SHnD3kbP6y4i+cOepM9jL2EqRYhiDJUxFW7GsIctuwcEJGEFsC8TkdGAEZIyNIN4ZIilqCyEr2IvSMlyJixSaMs8Yxt3YuT5Y/yV3dd3HFP65g1jOzmHXYLBgKNSfWcO+QeznhihM45/5zePjbD+Mt84psZzf/3T5AWLabEL/Z0dA0jYceeghYv4m6ruvcdtttm/xMYXtgvRraDXudf9mxRaK96qqruOGGG9arpf3xj3/Mrbfeusnt33jjDV566SVeeeWV9V5/8sknv+BQdxzMeEIk/lRXrfe6ncv1iVa4to2bzmD09mLnDLxVVfiaGgHItLXR+977ZFrawXExsxmuuPsPWLbNNeeex+FDhuJYJr66WoxYDLXcj2uaWJEI3ppqSkYM36S7ugDXtJA1Fas3guLVUTweJF3DyWZxclnM3hi5aAQJmcDAgXhra0mvXIMR7cY2DFS/KCLMdXdT4/Mx44zvsmTNaholidSatTz6n/+wz/ARHH/QQfzpidkMaRzAcYd9A6O7BzMaRa6tQcHBiPSg+Px46+rRSkpxDAPXsaGnGzOWQPEH8DcOQJJl7EwGozcm+vGmUugVFZu8kSiiiD50AO8hiM0DVMAffv8HZrfN5vSm07l/1f1MqJvAteXXIvVIIsFoDEJUYjywIP+5wfn9uQhrNI4g7yUIS7IVGAf0gpJSBKFaCJduvlaWdkQNrB8RazXFXzkmMyU0hePDxzPPPw9DN2AFpNpS/O6p33HOGedw6wW3ct7/ncflT1/O79XfI1VKwqWdRripgwjLtj1/3KL+yy6PzV7Ca665ho6ODubPn08k0i8ab1kWa9eu3ewOFy5cyEsvvbRTNI/eGnAdByuRQPH51quZtXM5ch0dICs42ayIi2azOLaNXlGOv74e1ecj291NdP57GL29KH4vke4eLvnbfSxtb+OqE07k0LIyjFicwMBG7EwaHAczHSfb2YWsewmPHImvoWGzAhC2YZDt6MDOZjHjMUDCyeVwM0IJKtfTgxoKEhg4ENXnQwn40cIlZIcMpve990k3t+BkM0geDwqicYBr24w7+kgUf5CWd9/l3ocf5M633uDE3UYxpr6eX955J7W+APvs9zVy3d1kW1qRBjTira3BtSxcw8CbvykxolEkrw/XsUgtXyEaKowcjuLz4fP5sJJJjEiUTGubsG5LwkWxiyL6kUG4ZxPAK4iM4EpgJPz79X/zm3m/4ZiqY3im8xkafA3cOfhOVEsVVuwQRPccC3gDEVfdE0HY6fzDRbiY1yJIdwT95UItoLfqMD8/hh4Eee+JcDW/jSDasxAZy2+KzxAEqVHigI4DxDF64O342/wl+hf+euVfmbbPNC4+9GJmvjCTkaUjuSB4gXBpF3SUFyMsZhlB4DJFXeRdHJsl2m9+85ssXbqUxYsXc9RRR/W9rigKY8eO3ewOKysrsSzrS0O0VjKFa9vr1cw6hkGuoxPHtrFicaxEAjUYRFIUtFAQLVyCXlGB0dtL5M23MRNx1FCQnpWrOPePd9Iei3Hk2LEcu+/XUEvDKF4fuWgc1zTJdXbgGAZquITQ8GGUjhm9WeUpK5kiuXIlmZZW9JISwmP2AFsQJZKEp7oKNeBD1j0EBg3Mt/PrwYhE0EtKqDr0EJKLFpNasxbHslCDfqx4HDMSJfrWO/jq66ke1MRDv/wVM//+dx75aAGV/gB+XefSO27nL9pPaRw5AiuRILViBUYkgq+uFst1MXp7RavA2lrk8jL8TQPJdnSQXr0aIxqhZMxoPOXlqMEgis+HEYlg9vZip4V1uzkhkCK+IrAQ5BWjP0N3KSJ+eiwsGbqEaRdNY0xwDLdOuJV/LPkHE3wTKPWWiprUgYgs4RzCQlyCIF8QLlon/3oIQawgCHRk/v8167yuIeKoTQhr92OEVXwEQnTCRhB0EHgp/34MkTjlAyQ4pOYQXln7Ctd1Xcdv5/+WgepAJlRM4DfzfsOw4DCOCh4lYsf75sf8IeJGgfw4itNhl8ZmiXaPPfZgjz324MADD6Q2nzVrGAbd3d3U19dvdoc1NTVMmTKFAw88EH2dEphdURPZdV3MeAzZ4+kTy3dMk0x7B1YqhZ1K41oW/sYB2NkcruPguoAL2Y5OIm/Ox4j0oNdU07tqJT++9y+0x2KMaGjgF2eeRcmIkXirKrBSKXLRXuILF+KYFoERIwgObMRbVY2sb+xOLYhmGL0xsp2dePp6xQr3sp3LIXd24G+oR1IUMgVpxHzLO7O3t6/jTmi3ESihIImPP8bJZvHW1qFX12BFerAzacyIRMPgIfz25z9n3htvcNPf/kZzNIKmKFx6z53c9r3vU1pXh1ZaBrZFprkFOa/nnGvvQAn4cSJRpKFD8dbVoQb8pFaupufVeQSGDSY0fDiyqgo95UBaJFe1taMGg6KMqqib/NVDBLxrvCKRqQYRZ/0nwqLcCyJ7RDj7hLPxST5uGHMD/oyf79d+X5BxBEGI5yM+vxqRQGUjXMjB/N8QwpotiF7sgSDLAkYASTB6DOFGLhBdFFHrGkJYt8MQFuhKRNz4KATJL0YQZBChwSzDgL0GcFfvXZy26DR+Gvsp2UiWPdU9mfbfacxOzma0PBqOB/anP+M5v56Q21ont4gdgS2uYh9++CH33HMPF198MccffzyJRIJp06Zx1llnbXL7pqamL01pjyBSGz3fucgxDNItrRjRqKhdVRUCw4fh2jbxRUsgk8U1TVzXIfre+xjRCJ7qKlI9PVx+//0s6+6isqSEu356KeV77omnvAwrnUZSFKxEEsmVKN1rT8rG7okR7UUNBTceUyaTz9pNYcXjqP6AkGBcJ4areDxIXm9fra+nspJsewdGJIqnsgK9rAzF5yPX3Y2VTOGrqUbWNOIfLcLo7kYtCeOtq0PWPSihIDKg+P1MOOpI9h4zhvkvvUS0O8Iv5j7JRX/5EzdNPoGApqOEg+glJdDpABKKz48Sj2P39pJuae3LmNZrqjC7IyQWLyXX1U145Ej08jJUvx/F48GMxUX7wVQK1e9HDYeKFu5XAVmExZgCR3dEiY0HkSD0DiLDeO8MZ597Nm09bZxSeQrfees7PL3v0wzODhZEW4ewDB1EPWwPgvD2QBBwNf3KSy7CjRygP25bgASEwA7Z61uTZflxdiPcuwUlqVEIko8g4sJLEa7lOMK93IzIlq6DgycdzH9W/YfU8hRyROZo62jOfONMnml5hqqlVXAGsDfC1W3kx2uBbMoiSatAvoVxyhTdyzs5tki0d999N9dffz3PPfccY8eO5de//jVnnnnmRkT7hz/8gR/96EdMmzZts/sqbLOrwIzHkHUN1e/HzuVIrVyJGY8jyRpaMIBvQAOSptP96qs4lokUDOJrqBd6v/mMWiuZ4t233uSDtlYCXi9/veFGaocPRc+TrGNa5Hp6yDQ3462pouKA/TF6IoKUXFeUwdg2jmVjRKKYvVFwQQkFkHUdb0M9ni3oCiteL1ppKWZvL4rXI9y1Xi++ujqMSAQrlUYNBAjtPpLUytWYiTh2JoOs6fg0FbWyElwXvayM8nAJB1dUEl+0iKmd7Tz61puc/sDf+OlRR3OgW4ssyfgGNIAsYWeymKkkbmcn8QULQVPAsnAMEyQJXJdsdze5rh6CI4fjq6pEDYUF6YZDmLE4diqZb1ygoYZCqIHARiVOReziMBGEF0WQygDhPcODIM+5QA6c8Q4X3XER8xfO54LqC7ij8w4mByYzaPUgQayDgQkIK7MbQdp+YB8EcW14r9aCsGibEO7hT4taBNkWkrL8+XFX5R85RHLT3giy70ZkEi9CEHECwnuGCY8KwwIY9dYoXrBeYOraqfznnv/gmeeBSQhL2s7vzwHFUMT39NG/cjsINzsIolXzYymmOexU2CLRuq7LyJEjueeee5gwYQLBYHCT9Y+PPfYYw4cP/8TayFmzZu0yRGulMziGiaeyEiMaJbVqFY5hofh9KP4A3qpK1ECAnjffJtfRiaemBmyL5PIVxD5ciOtYyF4fye4u7po3D4/Hw19vmUlTQwNqKIzZE8WxTexMVvSjDYUpG7cvZjxOeu1a1EAA13EAhBJTNIrrOGglJXiqq3AyGdxAAG9V1Ra+iYBWEsbJZvp0mmVNEy3+qqpQfElhQft8eKsq87XASax0muTSpTiOi7+hDteyUUNBgsEgasDPT30+muMx5i1ezK/nPMGIxkbO+vrBHFJRgb++Fre0DLMngmvb2GYOX1UdnooqHMMg29mJGY1iJRKY0Si5ri6CQwejl5ejhcN4qqrQy0qhrBQrlcJKJDB6IpjRKEogiF5astnYdRG7CAo9WrvzzytZ3+IEeBpBwoPh2n9fy1OvPMUFdRfw146/MlobzS1ltyD581m7YxAkm6NfsH9c/vV1icdFZPRG88fbfEL/piEhyHkZ/frE6xK1J/8IIzoKrUAQ+lhETe+HCKIeDBwGN5bdyHee+w4rrBVMtCby7DvPEloZgj/Q35KvMH4DQaye/DEV1rdkDfqt3MJ7xfvSHY4tEq0sy8ydO5dXXnmFK664ghdffHGTWaH19fX87W9/+8R91dXVff6RbmdY8ZgQmehoJ9fZheLR8dbVguOglZaiBoPEFy4isWwZWklY6ABbFvFFi3FNA62ynDuffpqXFy2kNRrljuuuY7DPj5VKY69tRtF1UGSslGguHRw6BGyHTOtaZEXF11CP4vVipdKY8Tje2lr0inJhXWezZOMJtNLST002kiShV1YK1amuLrx1dX3XUQ0GUfx+Ya0rCnY2g2t60T0ejEiUxMJF2KkkwREjsRJJZF3DP6ABSVW4+dzzmXb7bSxYs5pIMsktc//F+BHDodVB0nQ0fwC5tgZPRQV2MoVUVU3pnnugBgPkuntIrlxFcuUKjO4IqTVrQZZFU/meHtRQGG99HXo4hBYKYedyWPGEaC+YSorrEAoVs5R3NbgIcu2ivz9rDRtblR8iXMYBuGfxPfzx+T9yes3pzO2ei+7q/KX+L/gG+gSZjUIkEg3If05B6BgP22CfWYQCVBJB7J9XIVShvwPPCkTi0qasYg+ibnd1/v9vI8Y7j74euo3DGvlf4H+c8vQpvJ58nf3k/XjCfEKcm4KaoQau7AoSzSEsfQVxM6Hl/yr0k7Kdf5B/XnjIm/j/C2Bn6TM7ffp0pk2bRkNDA+eeey7XXXcdNTU7j/zrFon2iiuu4Pbbb+eSSy6hqqqKO++8c5OJTVvq3LMrwYjFSLe0YaeTSIqKt7oavbYWs7tbWLQeL7EPFhB5712wHWSPFzMWw2luxfbo6JWV3Pfyyzz4qqgl/sW0aew/oAnbMtG83rywg4aVEG5RYcUJyULF78dbU40WCpHr7sHOZFD9ftFcXlFE5nBPBElV0MKfTehBVlXRyq6zU8RrK8r73pNkGb20FC0UQg34iS1chJVIopeVCit7zWrM3hgle49FchSMSBQ1EKB8zGhmfO/7TPvD7ayJ9HD5Kaci5XKkojF+9PCDfG3IUA4euRtN1dVYSMQXLsaM9hIaORyttIzSMaPxVlaQXLacdGsr2dY2QruNRPboWMkkyaVL8VTk48oBP56qSjTTxIhGMSJR7HQaT1VV0brdVZBGxCtziISiWjbZGk5tVwWJ5eCBngf45bO/5NiKY/nFgF9wTfwavlX7LRqGNwhX7QhEglMNgkQ/RLyeQbhrC9adTX/Ms57+LOTPi0JN7kpEPLWJTTdw1xFkuxYRsz0YQfLzEK7kVlBllUf2f4Tz3jyPuYm5zCqbxRRpCq7tItkSZEBxFXHMQms/C3E+yX+nvIU769+zuPHWG2lta6W+rp4rf3olU0+YKm5wLNZXnPqSuJvfeOMNLrjgAgDuueeeHTyajbFFoh03bhz33Xdf3/N1lT6+TLBzOexUGiudIrlqDbnOTjwV5XiqK5A8HpKLF+MYBrLHQ659Pum2dqxUCk9lJRIuRm8MUin0hnrueeVl7n/2aQAuOfNMzjj9dBJLl+OtrsZbXS1ir9ksRjSKrMjolRXIHh1kGVmRUbxeMm3tuLbdR8IFmLGYkDusrv5csUrV78MJhzHjcRSP3tfsoABJUfBUVlK291iSy1dg9ERwAw6SqmImE/S8/DLe+np8TU3IpoIky1QPHcxvTvwmP3nwfm7550MM+tE0KhSZpsoqHnljHg/Pe50Jb73Jtw4+hDF1NcSXLiMbiVK6xxg85aV4qiqRFAU5ECCzeg3J5SvzWdRhrGSSbFdeWMPnQ1IVZE0XspYeG6M3hpXJ4hvQgFIUvNi50YMgQg1hDW7qPjFf0xr8IAgJeKzrMa749xUcVnIYN+9+M4E1AX5b/VtBXE0IC7YGkdDUAbyKcNl+PX8cO79PG0FQFQgLemvdl/kR1uwahGVbiqixzZf14CDI3cx/3wwidlyBiOGuRFjZgOSTuNu6m5+88xNuW3sb9jM23/7Ot/HJPqr1amRXFiRp02/FWvlH/jiz/jOLy2+8nEw2A0BLawuX/+xyAKZOndrf8q9QP1y4+ZDot4o/AXfddRdz5sxBkiQOPvhgvvOd75DL5bjoootYuXIlTU1NXH/99ZSUlHDTTTfx6quvIssy3/jGN5g2bRqpVIpf//rXLF26FNu2Offcc5k0aRKzZs3i8ccfp7e3l3322Yf//Oc/vPDCC2iaxpIlS7j00kuZM2cOM2fO5PXXXycWi1FdXc3MmTN56KGH6Ozs5LzzzuOBBx7gpJNO4m9/+xv19fXccMMNvP7660iSxPHHH895553HG2+8wd13343X62X58uWMHDmS3/72txiGwSWXXEJ3t4hlXHDBBRx++OGf95exHr7StROOaWIlhVXpWrZoOdnbi9HRgV5eRqCpCcXvy8drDSRFxUokkHQvWlkZekkJvkEDwbJEVm15GXe9/hr/eEaQ7A+nnsSF06+i9533cC0LT0WFUEqyHexEEjudRisvRw+H0UpKMHoiyF6vkHSUZLy1Netl21qpFGZvDDUYRPVvqCH36aGVleKYBrmeCMgyqn/j23AtFCIwaCBqKIQVT5BpbUHWvUiKTK6zi1xHB1pZmXBfaypDvr4/M1WFaX/9CxfdeQf/9+OL+PW3v00Uib+89CL/nfc6L3y0gNvPO58xVTVkVq3CiEQIDhmMv6EerbSUQOMAFF0jtWIV6TWrKRkzBk9lJbmuLoxIBMpKkTUPdiaLJAGyIPpsewdGTzf+AY1oZaXFDOUdhYLFJLGxhdSJIMIQQkRiUwt6AkFCK0DtUJndNZuLX7yYA30HckDJAUx6cxKPlzxO1eAqYcUORpBEGYIwliEI7AgE2W0v+BAu6g6EhVro/iaxab3iEMJ1XiC3BCJWDMg1MrdU3kL6xTR/uP8PqH6VY6YcQyQboUQpodqpxmf7xPnzIm4qCmSZgxvvurGPZAvIZDPc+LsbmXrUVGHBeuiP2xb+mvl9qOu8v8F9/Isvvsh///tfHnvsMQzD4Morr+Tll1+mp6eHM844g3HjxnHTTTdxxx13cNZZZ/HSSy/x1FNPkclkmD59OrlcjjvvvJPRo0dz0003kUwmOeWUU9hrr70A6OjoYO7cuaiqSmdnJ6+88goTJ07kqaee4vjjj2f16tWsWLGChx56CFmWufzyy5kzZw5nn302s2bN4o9//ON6jeQffPBB2tramDNnDoZhcMYZZzBixAh8Ph/vvvsuTz/9NNXV1Xz729/mlVdeIRaL0dDQwB//+EcWLVrEnDlzikT7ReAYBmYsJuKjkoTi9SIHPeS68gRSWkLZPnujhcPYuRyZllYkRUXxedHKy7DicYyebjwN9WihEIlFH6OoCnZFOe898REA5xx7HNN++AOSS5dhxnsJjhghMm0Bx7bItIsON97K8vw+EziWhZvJoHh0PNXV69WQWuk0Rk8PsseDXvHFVhFJkvBUVZHt6CTX1Y1bXrZJvWG9tFTIOyoKammY1NJlODmD4IjhQsEqHsPJZEX3noGD2GfPPbl3+DDO/dU1XPj7/+OWaRcyprScM/bbn19On87Tzz3Hgfvsi2vkeOKZZ6nq6mKMaWD0xvAPaED1+dDCYYLDh5Jctpz4woVUjN+fcPUo0mvWYiVTSGFNxLcRql2ypqKXl2L0REivWYueyaAFA+jl5UV38vZAGkEsKdav9bQQlpqNIJIMwl07iPUXcDP/fj4bl7XAx/CvN/7Fb9b8hvHaeL5T9R2mrZnGMb5jqCirgP0QMdk2hOvZh4j3Soi2d9uTZAsouKNrEPHfLIJkCy7dQixVzY/TQlj4yxBlQM0IazgM6j4qdwbu5MwlZ3Lr3beiWAqnnHoKUTNKb6qXIf4hhKyQOPcehDWvAn5o7Wjd5PBa21rFtgbicxr9N0KFeK2DuIZ2fn+FR/56zZs3j+OOOw6fz4fjOJx00knMnj2bwYMHM27cOACmTJnClVdeyeWXX47H4+GUU05h4sSJXHrppXg8Hl577TWy2SyPPfYYAOl0mqVLlwIwatSoPn38448/nqeeeoqJEyfy9NNPc//991NTU8MVV1zBI488wsqVK3nvvfc+sZT0jTfe4MQTT0RRFHw+H5MnT+b111/nsMMOY/jw4X36EEOHDiUWi7H33ntzyy230NHRwaGHHtrnit4a2CZE29HRQU9Pz3oZyKNHj94Wh/pMcF23T6xBkiW0UiFo71gWqRWryPV0owSDhHcbiRYKYUZ7++QNlUAASZJxbZvEx4txXRdZUUgsWkTz4iUsifXy0IsvsHDVKi6cPIXvnXoKqu4h3dWDp6oK1etBkiWQJFIrViF7fehVIhaLbWNlMri2g+r3CbewouA6jrC6E0msZBLZ4xEN5LdC8o8ky3hrqsl1dWP0RHCyOfTyso3ISa+sAEnCSiYJ774b6TXN5No78FRW4G9qwnUc7Hic9Jo1yLqHwaN2597rrue8X17DT279PdecdjrDvD6MFSs58dhjAZdMd4S//u+/rG5rZfLYffj+IYdQlk7hra5G8ftRfF58TY2k1zQTeeNNyr/2NULDh5Ft78CxLTyVolmna5o4holsmeC45KIRzFgMADubQ6+o+EKWfxGfgAyC6FKIhTiIIAoVQbzLEMSZQlh6HoRww0L6rarCIu4giLIF3B6X2967jZvW3sQ3tG9wZvWZnNdyHmO9Y7m1+lbkg2VBph30t7eL5481gH5lpx0FBZHFvKVMZhVByjUI6cX/IlzPbUAYtJEaP5v8M6r/XM3MP88kGU9y6Q8vJe1NEwgGwIBoMoqUligxS5C8oqF9fW09LW0tGx2uvjYvMqQhbm4s+vvwFqAhroe1zusFN7UKTr4KYl1YlrVe8xjXdVFVFVVVeeSRR3jzzTd56aWXOOWUU7j//vtxHIebb765jw+6u7spKSnhySefxOvtD9Yffvjh3Hjjjbz11lvU1dVRU1PDggUL+OlPf8p3v/tdjjrqKGRZ/sQqlw3H67outi0yxDbVT3fQoEE8/fTTvPzyy/zvf//jL3/5C3Pnzt0qfdQ/VeP3559/nt7e3vVeP+200za5/cyZM7n33nuprOzvXCxJEs8///wXG+knwLEs2IK70LEscl1dODkDNRgU7e4UBSudJr12LWY8jhoOoYXDSIpKprUV17KxM1msTEZYvmVlxJcsJt3SiqemmtTKVbz7zjv84skniKZSuMDPzzqbk7/9Tbw1NTiOi5Mz0EJBXMcFyya5ciUgEWhsELFGXSfb1Y3R3YOsa8iaQqZ5/YkiyRJaOIy2iRZ9XwSSLOOprhICEbEYVlrU0yo+IXYhKcI166msQNZUjN6YaMHn1cl1R3BMEzUQQKuqQtE07HQGO5Olrq6OU48+mt/+/e9cdd+9+HWdH686nOOPOobg4EHowQAP/uZG7po9iwfmzOGVZUv48VHHMHH0GBSfF0VVUUJBtJIQ2c4ueubNo3y//fDUVJNta89LPdYhrePy9lRWIq/RyHZ0oni8SAGZXGcnTklYuLeLmcnrwc7lsJIpUa/6WdGZfygIgYhy+hfohQjC0PPvdebfr0JYUz0IIm5HWH5JhIWVhZyW46r3r+KhtQ8xWZ/MWVVncXr76QzyDOKvNX/Ft5dPlOukEQQwFCF1mEAkF437fOdih6McOBKRuLUGUZ+7CpRRCjNvmIke0LnnkXv4YM0HXH3l1WiSRlmojEgsQjKXRDd1quwqyp1yrvzRlVx+/eXruY99Xh9X/uhKcd5K6Ldsyf9fyEIuxG2zCDIuhAAMwIbx+4/nzrvu5OSTT8ayLB577DHGjx/PjBkzWLhwIaNGjeKxxx7jwAMPZOHChVx77bXcf//9HHDAASxcuJCVK1cyfvx4HnzwQa677jo6Ozs54YQTNpn3o+s6Bx98MDfccENfs/u33nqL/fbbj1NPPZVoNMoLL7zAkUceCQhp4AKJFjB+/Hhmz57NxIkTMQyDJ598kh/84AebvQx///vfWbt2LdOnT2fChAlMnDiRZDJJOBze7GcwEd6Y1OY3gU9BtD/4wQ+Ix+MMGDCg7zVJkjZLtE8++ST//e9/1yPabY1sRydqZYVoSL4JOKZJtqMDHEfo/+YXaCudJtvWjp1MoVeWC6JIZzERLfFswyTTWujzWkcuEiG1YhVqOISnqoqH5zzBzH892Xfn5AJ/eupJ/GWlnHD88fS+/z44DnplBY5liRpWWcU7tElYjrIs+ru2dyB7vXirq4UalCTuoCQJJFVF8Xq3mRtUkiT00hLUgCjvsVNprGRy/W3k/Ex0bNHJyLSRZAkznsTOZkULQVVDDQfRSkp56oX/MfOf/8TM//DThsGMZ5/BcmHqEUegV1URLAlz4XHHc+See3HD/X/jV489wm5jxjDI48ExLcyeqCB7XSe9thkzHic8egz+pgHYyRTZzi68tTV9BCprGoEhQ0BWyHV2IXt0FL8PMxbHyeXQKyu/snKOruuC4/RnrUdE/bIkSzgb3oA0I2Kfm7qJL4jvxxHWaz39sdYYItu3E1G+MhQRbw0iCDeHWIwKjdYLMVyveL3T7WTqw1NZGRf94d4y3+LrpV/nGPsYflXyKyoq8i5jL4LQByOsYAcR39ybzyY6sbMhjIg7+xDnrwdwIZaJceb3z6SkuoS7776bH1/yY675xTWMGzGOIeVDiGfidCY7acm00G10c+RhR4ILN/7hRlo7WqmvqefKaVcy9bip/V2GQvQnUpmsT7YywhWdRVwzCcESDkw8YCKLFi3ipJNOwjRNDj74YCZOnMhDDz3EHXfcwZo1axgxYgQXX3wxgUCAsWPHMmnSJHw+H/vssw8TJkxgv/3245e//CWTJk3Ctm0uu+wympqaePvttzc6JVOmTGHOnDl9WvvHHnss06ZNY/LkyQCMGTOG5uZmAA499FDOO+88/vSnP/V9/uSTT2bVqlVMmTIF0zSZPHkyRxxxBG+88cYmL8EJJ5zAJZdcwuTJk1EUhcsuu2x9kt3QeO5F3BS5bPG3J7lb6L59zDHH8PTTT3/yXtbBSSed1Od/39bI5XIsWLCAEfX1yDkDrSSMng+Gu46Da1k4tk2uuwfJdfHW1iDrOo5pkuvqJtPWjpWIIwcCSI4DLvgbB6CVlmBEeun98AOsRArfwEZy7R0kly7FTKYIDBvKDY88zNPzxY9jw5wHr8fDz88+h4nDhlO6xxh8A+qFOEM2i6zpKF4vZqwX13IwentRA34CQwbjKd96waX58+f3NWD+LHBdV7S3M03R+s91wXHzLhrxv23ksGIx0q1t2Nkssu4RCZZ5+cnjr5pOe0/PRvtWZZl7zj2f3fbYA19NFXp5Obn2DjK9vbz90UccMO5r6OEQHy5YwLCqSjAtUFVc28Xo6UaSFfxNA/BUVAIOnsoqfA3167XXc0xTxHMTCeE6DgaxEnGQZPSyUt5fvPhznZdtgc97jT4trHQaKx7Hzhmiq1L+2soeD56yMvE7N00xh0aMYMmSJYzRx+DxeQSRKfRnzboI4kyzfmlMGiEtuBphWY5CWK+LEIRcj1jI0/nnLQi3r4pw9frhxcUv8oM7f0A8G19v/D7Zx4yBM5haN1VkER9JvyJTL2Jxa0RoCw9gm2BbX6ON0Iv4fhlYpCwCHQzL6BvLjTfeiOu6/OTCn3DBty4AHVzHJZ6M05ZqoyHcQMgbwsVFcqX+etpCfLhQDhSgPytbZf2bHxDXO0N/m751M7g1SBkpAhv05t5RSKW28VgKpVQ24MKiJYvY3dxdnKsyoBFyruCiMWPGbLKhzhZv8UeMGEFXVxdVW1Ag+ugjkQQ0atQorrvuOiZPnrye735bxmj18nJIJjF6Y9hZkZHh5ITIv9HTg2vbeOvrsHM5zGQKIxIh19WNlUyI7VNpZFkmPHoUemUF6bXNJFeuJNcTQQ2HMSMRXNtBCQbx1NbSkc3y1pIlAHg0jZxprjeebC7HbY/8kyl/vZ/w6N0xIhFcFyRFRdY14dI0LXAd1KAff2PjViXZLwJJkkTW7pYydxsaCA4fTrq5BTMfVpA0Dcc06VinreK6sByH8/98Dz+YeBgnHTqRYGMjgaZGtNJSDvR6yUUjfLxsGWf+5noOGjuWy076NtUBH7LHi3/gQKxkAiPSC66E4tEwe+NCzKOmGq2kRCS1aRremmoyeflKJPBUV2NEe8l19+BEoljpzHaJ3dq5nPgd2jaSLCPrOrLXu83d2K5tk+vpER4aIwe2g+vY5Dq7MeMxtLJycY1VFUfZwHRtQBDqQoQ1mkIsNO0IIm2iv49rB4JkCxnFHoSY/gf5z45EEHJb/vPNCMu3RLyeactw/ZPXc+/H96JuYinKOBlubL+RqSdOFUIPKQSBpxAWWTUixrn5Hie7HkoRVnsrkOgnWYB9992X22+/nRkzZnDDTTew7MNlXHPJNZRWllJSVkLYFxY1tw60pdpwcKgJ1qDJmiAKOb//vKu+QJp9CVCFuHkhYauwBBSIRqdPREPOyeL97VV/u6G+8/Y65rrlU+uWRb2GuE7jEL91fTP7yGOLRHv00UdzzDHHMGLEiPWIc0MVqAsvvHC95+sqhWzrGG1y2TI0jxejsxPXBV9drbhbj0ZRgkKuz7UdkkuXYeWF/5Ek4RKuqQZZQZYlrHic3vZ2jGgvZiQqusqUlKL4PGR7Yzzx7w9YnYwx99VXUWWZq7/5ba579OFNjqkjEiEwqAk7lcJKJHFxcU0DI5kQyVXBEK5h4q2u7kvu2dUgaxqBgU0YwSBmMimsJVWltqqKts7OjbavKitjaG0tt/77OZ754AMuOepo9tx7b4IDG/ENbEL2+BhoWlxy0jf5w7+e5Ds3XMuFJ57IMSNHoaiKiFEHPbi2BbIHSVFE3D2bQytPinhxSYno/BNO97mNjWgUb00NdjoNH38sBDtUFTXgR/H5kD2eTZKf6zg4uZy4KcJFkhURR9c3nlWOaWKn0/nYZxIzngRcFN2D7OsnVyE0UrLJhhGfFU7e61C4FpKiiO5SLa0Y0V6cbAbXBdmjIakqWlkpWnUVRnsH0XfeQS8twQlukG2+lP7+r3UId2YEsZg0IhaUpQhyLSw6JsKibMx/1kSQXwmiR+ty+i2ranArXZ5941l+/fivWZ1Yzferv8+fOvtdfuuiNdMqutnYwEf0KyL58+MbxJdPYtCLqM39CHRJxzD7ybampoYZM2bwzwf/yT8e+gfPv/Y8V19wNd864VtIutQXK5cciUgmQjQdpTpUTZVehZyVxXkMIK5RIQ6rIl7PIojXoj9bWmW9Ot0CubqG219/W1Ck2pzS1LoE5fLJylSF7QrbFn4365Jr4ZjrZER/IRSOVdi3nH9u0Bef7ht3IVSyFyJ08Qr9nZyqN3+ILRLtHXfcwfnnn7/Fjjw7UoKr5/V5KLKCp6wM2eNBDYeQPTqyquGrq0P1+8l1dSF5vTixXsx4Alxh6UiqJiwhXSe5ZjWpxUtxLBOtpBRvVRWeqkp64jHOu+YXLFq7BoBD9xrLT044gYaGAfz5xf/R1tW10Zhqq6pQAwHSrW04hiFIX9NBktFKy7BTKfSyUhFn3IVF8iVZxlNViRLwY8aER+His8/mFzP/j6zRX+/h9Xi49Pvn8o0xe/Dkf/7NrbMe44d/u49vvPkGZx40gWF5F3t4yBCmKjIH7LYbM576F7954AFe3Xsfrj3hRIw1a1ECATwV5TiGhRoM4CqqsNpivdjptOgNHAqKbPKcgWMa2Nkcua5uPNVVSJUVeCorsVJJzHiiLwNd9npRvD4UnxckCbM3hp1OiSS2DSHLKD4vajCEpMiiJ3EqJTKwUykcw0RSZWTdI6xYRUEJ+JE1HTstPCpWMoFrWRvvO49CXNV1hFhIgajNVAqjJ4KdSonMd1XtI37Hscl1domab1lBDfjRKyqQZJl0c7PQx3bBsW2MWILk8hU4Hh3236//wLMRpFqCINUI/YlHNQhrqBex+BYUnmoRvVx1+mOMy4Gn6BfYdwAZPnA+4Po7r+eVBa8wsnokj4x/hJeXvyyIexOoD9WLBf9FBBEUrK4BiO4+X+awuyyItaWlZb0MWk3TuPLKKzljyhlcde1VXHz9xfx99t+57PzLOOjAg5AUibpAHeXectoybbTH2ulRe2gMNRLKhsQ18SHOa4HUCmECa52/Bbfxuu7kHOABV3PFtSgQYSFTuaDCVSCkwn42FaDccPuC5OS6YhqFzxWIvDCewjHVzex7XRSIfl2yLtw4WOu8V1iGpXXeK5Br/qbDzbri2Cvp75P8UX678zY/hC3+TH0+H+eee+6WNtuxkBRcw8BKZ1Acm8i8N1ADfkLDR2KnhHVhJhPgOiheP/4BDYCMa1r5BKAURm9MuHRtC7WkFK20BDfg55e338ajT87BcV0qwmGu+c5pfG3ESPTycgLDhvDDycdz/f1/W8997NV1Lp02jcTS5diZFLgScl7A38qmMaNRZI8XX0PDevHFXRmq34/q9+NYFid///to5RXcfMsttHV2UFlaymU//BHfOu00tFCQM8buxRFHHMFtf7qHWa++xvOLP+bw4SP51vj9GT1mD9SSUmodh1tO/Q7/PngCUihI9cEHEf1wAW1Ll1GeTKEEA3jKy4Wqlq4jfPOiRjrXLdymSBKOYaIG/NgZ0VBBkiTUYECQtG1jZ7PikckKHepUSngcfD4h9xgKibi+YWLEYliJhHikM31ErIVD6OVl2DlDkJwq/GmuZeNqjtC0TiSRZAk1HEYJBDB7Y7gRUY6khsNi/K6LbRiY8YQgUlsQsWs7mIkE2Y4OrGQKyBOsxyN6Jft8yKpKtqsbJ5VGKy8jMLAJf1MjuC7J1WswkgmMjh7sTBJZ9yLrmjh3FeV9YTtALC4pxMrwGqKR+b6IOOgCBCFmEQt1wcLVEC7jHMLSNBGLTxfCglVcXut4jdvn3c5L771EabCU6cdM54SSExigDKBKraI53czTnU+TcdbJlpV9XHnolfAeYrELIBa5IQiB/l33/vRTQdM0vF4vAwYMoL29HcMw0FWd2pJayrxllH29jNmzZ/PP+/7Jb+/+Laf8+BS+NuZrXHDWBRx20GF4ZA+DfINI+pO0pdvQyoUL2YgZKCkFRVX6LVad9UmrQGaF54VYLvRnLBcIcl1iLDwKz2XE76NQOgT95Guus/26NceFkq91hU8Kj4J1q/bvSzbkftItYEO3L/3bk6O/41HhexSOVcieN+kXBSl0Z8qCmTNRc6oIj2gIV3wVIuv9E7DFZKiZM2dSXV3NEUccsV4j99LS0k/e83ZAIRmqUddxOrtIr16LEY1ixeOgqPjqq9HCpULZKZdFVhS08nLhWgwGUfw+XAly7Z1ku7uRJQn/0CEsWLyYx//1JE/Pn086m8WjqpzzjSM5+dCJKI6N4vOjhoLY2Qx2IsmL7W3c9uA/aO/spLq0lB9OmszxRx8DuHjr60UiVk01RiRKprUNSVUJ774beulnbRvy6bHdkzg2Ayud5p2XXmZ4TS2SLOGtqxOSi5JEtqeHZS+9zJ8ffJDZb8zDsCx2q6nluL3GcujeexNUFGRdp2S3kYRHjWLuc89yyfTpHLXXWKaOHcugqmqRAV5ahuL1iUxjXcNVFBRNRdI9ONksrmmhVZQhKyofN69l3IQJmxin6PNrp1LgusLC1TUcZFwzh5PJ9lmXdi6H2duLlUzhOrbwWPREcYyc6OGre5A9GrgujusgyypaRRmeigokTUfWRCb5R++9z9DqasxUGtc2MXq6sVIZIRDiD+BqGnasl2wkipvLIes6nqoK9KoqtEAIM5PG7Ook2xPBisaQJAmtohzZoyOpGpIqY2cMrHgMXKEIFmxsxHEdFJ8XT3kFmUSC9lCgPxmqeQyeDzzijr0DsegMQFi0OYSFC8K6HUO/ZZTOv9+McC0DrRWtPLbiMR6e/zArYiuoKqnilENPIZfI8feX/s5RdUdx+5639yWazIrM4sblN9Iaa6XGV8PPjvkZU3+Qz5aVEaVAQYQK0ydUXGxN7Mh5FI/H6ejooKGhAZ/PRzqdJuAPiPORQxBLAJAgG83y0F8f4va/3k5bVxv11fWcOulUTvjGCQxpHCKuUZnYfvXK1cSTccr0Mir0CnyqTxBNwVUM/RZpwb1bcOPma2/TZhp/iX9j129B9KJgKRZIrECe67phob+mt0CGhaSjwrHXdS/L+ePr67yuQCaRwaf5+l3AOfot4wJ5FtSzCvsu7Kdwc1FQxjLpj12v6w53wTEcWqIteF7xUG1W93dQGgi5VI4FQzefDLVFot1zzz03qrWTJIlFixZ90sc+M5588knuvPNOLMvirLPO2mz50LooEG11tBfZMEk1N5Nra8O1bHAc0DT0cBDZ40Uvr8AxDKHyJEn4Bw1ECwTItLYS6Y2yqLuLN9vb+d/bbxFLJNBVla+PGEmFonLOhAl4y8pwLQct6EcvL0cNhXAzWQJDBhIaORJJlnlz1mwGlpfjravF6O5GCfjBtJEUGdnnE+5rSSI4bCi+bdzJaGchWoC3336bPQYPIdPaip1O462pxt/Y2Fdukli6jLVvvMGcF17kybffYnV3F7IksVdTE/sPGsQeNfWM3nMPsnW13Pf0XB6fM4ecYTBu6FCOGb0Hh40bR7C+rq+1oCQrwjXrOsiygpXK4Do2itdHc3cnow45hEBTI3I+ecuIRPP9dzWUoNhHtr2D1PIV5Hp6wHGRfT7UYADHNLASKWRNQ/b7sGIxjO5u7GwWSfegBQMo4RCK7kGSZexkilxvL1Yqiawo+WQoGccy6GlrJxwIYOdyyJKCFvQjh8NCpCTWixVP4toOki76H0uKhpPNYFsWmCYukijRse2+rGJJklE1Dcs2MdIZFNdB9fjIhkLYfh+5XBZXkvFoGt5ggKpBg4g0Degn2gVj8Cz2iKSjRP4CyohaTz+CYMMIMo0hCHg00APZZVk+7PqQF9Mv8nzv83yw6gMA9m/anz2r92RNdA3/WfEfXNdlUskkflr3U4aVDBPkGQAOQDRtfxFWvbuKQd8bJEhFR/yNI0h/yPb53cKOn0fxeJzOzk5M0xQWbcHYseiPkRYkE12wEhavv/I6T/3nKeZ/OB/XdRnYMJAD9z2QsaPHsvvuu6OGVZJGkkw6g4uLLusE1AABLbC+2xf6rdV162xlsBwL1av2k966Vu268dfCODfloi2Qa+Hz1gavrSsDWSD6Dffhgm3bKCjru4gL32FDF3Rhn+smN7HO92OD11hnOxc8qz2UvVKGnbExMbFki4A/QGBogAVHfYGs4w8++GBLm3xhdHR0MHPmTGbNmoWu65xyyinsv//+DBu2YY+rTeNbV15BJhYnqOuEvF6CPi9XjP86dHcz78Nu2m2bsN9LIBDAlGWiqRTGvNdYHunhvfY2oun1nGc0VVZx1w8vQGlrE9ZGOoOtaQSHDCIweAhqICDKWjSN4LBhSLJMpqMTN5UiOG5fJFlCDw6CfLKOFg6JMh6fD09VlWgs8BWCJEl4KspRgwEya9eSaWvHTCQJDhuG6vMSHjGc4TXVnF3fwMkHH8zHa9fy8pLFvPThh9z14osAeB9/lN1rahnR2MS0U05hdXc3L8+bx59feYlDd9uN5NJlrHUcRn396/g8Iu6e6+nBSiWRVBXXcjFTSey2Djr/81/kUBA9HEbKZ91KioqVyWDFekV8NpMFVRVJRrKCE49hr0iKBtx+P5Kmilh/NivWIU1HNtMYuRxSIiFKnjQtH1t1cG2bTFcPjpFDkhUkTcGNJzCyOVzHpTeTJppKkTKypA2LpJEj7bgcPWQIqqnx7Ecf8W5HO0nTIGNZWI6DIsvcNOFQJFXhngULeHnNKkzLxnJsLMeh1OfjgdPOwltRxo1zZvPqBjfHtaEQ933/PGjqr42ZdNsk6IZKvZJKXyXnBc9jj8getC9v593Qu5SHyykPlJOUk7SarXS80sHyZ5czPzufRalFWK6FJEk01Tax/57785tv/IaRgZH8+tlfM79lPudUnsPZ6tk0qU1i9alAxHBHA4ci3MQdYNQYwkoOIiyUGMIi+zJlGH8KhMPhvlrO+fPn9+kCA8Lj8AaCOMbQpyG9R2APzkufR0tlC890P8Mzrc/w8JMP8+CcB5GRGeUbxV7VezFg+ABanBbmfzyfw2oP46rBV2H2mjyQeoCJUyYycMhA4UXoyR+rEEO1oGVFCw2BBnHTVYW4UUIcn1x++276dZ999NfuxhE3Tn6E6zWWf9j0W8CFeH8wv52GCFl0IWq5C+ENDWKJGCV6CVjg2A5p0sSyMaJWlF6rF6/kZZx3HPhgZnomLXYLvU4vvW4vCRIcpB7Ez/0/BwUOix9G1I1iuiaWa2FiclLZScyomwFVMOCFAbgbBIXPqTyHnyV/Bkdt/jpulmifeOIJpkyZwr333rvJ988+++zN7/Uz4rXXXmP8+PF97uijjjqKZ555hmnTpm3xsx9//DEDvT4ihknGdenOZGhJxPnbx4uIRHr4oL2NSC63yc+W+3woioIiywysq2P8yN0YV1XNbsEgSnuHEO+vLEdWFLzVtaK5u+uKTjO6jre6GlnTMBMJ0itXIfm8qKEgViyOWh7GjPbiq60VjdSzOSS/v0+R6qsIxeMhMHQoaihEatUqYh9+gL+pCW9VFXpJCdWHHEx84ceM8frYrbGJHxwzibauDj5YuZwP1q7lozVrePytNzBef7Vvn5qqcsaf76Hc5+Pj1lac386gpqSEIQMGMGzoMBrLy9EyOVTbRHZcMukUzV2dyLkcsu2gKAqSJIOZAzd/s6woyIqMJKvIjoVrO7iug+VA0raIZ9LEM1kSlsE+dQ2EPDrvdXXx3PJlxDIZ4tkMGdPEAa49+BBqwiXMXbaURxd9tFHexilDhnFIfQOPr1zOcy3NG52z0eXl6JLEsp5ulvRG8KkaXk3Fp3nQZIm2RBIXhzJZZmxFJYqiiO8F+DWVVS1rcdeuZqzuZdBuo1BUVSQKOzayCx8sWsRuEw8BhDZsTaaGDrmD5dZy3o+9j63a+PGzILeAD7MfioVuAwSkALV6LZabjym7LqvbVtPa3oodsGEwXDzgYq4KXYW6WhWL6W6IpCkdQaBDETHgxYAXHNkRJGwgFmEfIqtzEy31vrKoQdycvAO8jyC2QYha5npoeKKBc94/h3OGn0PCSfBO5B3e6n6LNzve5KmWp+hd3du3q8Vti3n040cJykGWJ5fDPChRShhaPpTdBu/G6PrRlCXL8MQ8aIZGKplibclatLiGntPRJA0FBcmQRNxUlpFkCUkVXhfJlpBNGcmVyLk5olKUqBMlYkQwJIMjfUeCDHel7uJ9631yTo6clSNjZ6iRari95HZc3eWy2GW8n3uftJsmh5izGhpXS1dztHw0p9uns1Ra2n+OJNhH2ofb7duhFx53HydGjDBhwmqYkBxCUiVW2CvAhNHSaEzJRJEUFEdBMzWGR4azzF6G2+1yvn4+siujqIpIokSmwWjgzZVv4t9kj8T8MDbnOr711lv58Y9/zPTp0zf5wd/85jef5Sfxibj77rtJp9NcfPHFADzyyCN88MEHXHvttZ/4uVwux5Ahm/YjeVWVsNdLqaYTkGUCmoZXUQloKlWlZZxwxJGUlJeRSSRQHXCyaexsDjuVQtZ0fA31eBsH4FoWiqaj+rw4poESCOa77YTxNdRjpdMkPl4CEiwzcowZMABJ00VGqSThrakm296B2duLVlaKr75+u2QZ72iX17rY1FisdJrkihVYiSRaKIi3thatpARJVcl2dBJ95x2M7giSz4NrWliJBFgWhMK0R7pZunQZzR3tdCdT9KRTdKdStMVjJHI5chtIsRXxyZgzZw4jRoxgt9122+g9r+QlLIepkCoIWAH8+NHRCRGiTC/j22O+zZiGMSxfvZzXzNcIlAaoqa9hkD6IuuY6FFMRiSIBBGHKCDLwIm7zSxGWa6GbTTXQBO963mXvIXv3x4mrES7j7ZwAtbPPI0BYkEsRlp6KyAKvRliDbyMS2gpib1kgA27ApcftYcnSJSzvXE57rp12o512u5015hq6rW5Sbgqb4lz6tJgzZ87nj9GuC8Mw6O7upr5+6/pv7rzzTnK5HD/5yU8AePjhh1mwYAG//vWvP/FzuVyOp59+mvSCj/AkkngdB6+ioPf2IudyoKrg9YKSj3hrmlAbsi3w+iAUglwW0pn+1zUd6mrBoyHF4lBSgtw4ACmdxpVlJK8XsjmoqhSu/p4eJEVGGjAAybZx4glQZCRJRiovg1wOpycKsoRcUY7kK4rcF+A6Dm5vDDfai2uZSB4PUiCA5BfdQZzWNujsxrVtHF0V1ymdFiLgqgo5A2IxyOXAsUFRQAI3a9KWiPFRNEK510dVMMjKRII73n93ozEc3TCAkaVlNKfTPLZyOZAPeykqfk3l4PoG6vwBem2b5bFevIqCT1bwaipej5dgMCDCUWY+jVFVkTRN/NZkCTJZyGSQHIcHFi0kaRobjSHo8XDG+ANBEuVSovhVEd8JkBUVV8uX+KRSkM6K9zQNKT+pJcMAI58BomkQ8IlxWCLoJWmilkO2bBGGcyVUVcZbWsqe3zmVESNG8PTTT1P7fC1lrWV4FS9la8rwRryggh20sfwWkiHh6i6yJaOkFYywgTHMwPbZ6M06akRFySg4Xgc7YGPUGUiKUChS0gq5hhyOz0FJKMhZGSWlIFkSdsAmV5vDDbrkGnM4XgclpYh4YKlFbkDuy13K80XhgBpR0do11LjwGtg+GztgIydl/Iv8aJ0aruMiG7LYBnBVF8mRkGMyWkoTpO0COkhZiWQmyVvOWwyXh5PVsjxiP8ID5gMbEfCN9o3oqs4cdw4v8ELf6378BKQAF6oXorgKC5QFdMgd+PETtsP4ZB8BLUAwGMRVXdS0imRLuIqL43OE8pVH1OyqURU1rfK77O+IEdvoFJQoJfxkwE9wVAdJkZDyQVbJlfpkEh2vg6u6KEkFvVtHzsm4qosdsnEVFyWjoGQUZFPG8TjY4fw5dGRcx8X1CreXmlXRLA3N0VBkBalMovHnjZ+faP/9738zb948Lr74Yo4//ngSiQTTpk3jrLPO+mw/hE/A448/zttvv831118PiNpd13W36DouJEPVpVK48SSZ9naseALXEVmVenkFvtoaJF3F7I5gJuJIXj9WTzfp9nac/MIkKSre6io8NbUoXg9YFtnOLlzXQQuJOJ4a8OO6ElYygSQrKB5dWLW1NfgHD0HCZf7cpxkxdBh6Rbmo2VQUMi0tWPE4enkF3vq67SZuv0vciedhZ7MYkYhISspmcW2nT5c4F4mQbmnB7Ini2BauY4tkNwkUrxfF68U2LZxMBieXw7ZMES+VJaxcDjdrYGfSojWhYRJJpTB9XtKKgurzMbCiirBlkE4kiTkOFZWVlOZlHZ1kMp9oZGOlU7iGKUhMkkS9bDqN64hQgloSQistRdF1HNsRx8wZ4NjIqoZr2xx9z12bLSd85Xe3gO0gKUJIQ9Z0kVSlaSgeDTstBDjsTAbJ4xHZxZKClU6JBUWVUTw+kFzsRAozlcQ1LVzLwkrEcSxb1JYrWr5TlCVCINXVRBrq1k+GWuQRak7Q77IdgrCQehDC9zoi1rYYYZEWSjV8iMQpM/9/IfzbiljsgvkvXIKI7dkIBanB9HevqYYlbUsYMX6E2GYAO0zHeFeaR4Agyh6Eiz+GiIVmERbtGoSSV5z+bF8JEQMNILwFafrbGhYSrTL0S2imRfLRCmsFZolJKpAiHUpzQNUB6BGd1q5WUlKKiooKSppKUMoVMY6CElUqvx8lv+84oi7bpF+5qib/tyCGEs1/Po8BCzeOkwJISDSf29z/vUoRv7cy+st0DISXJJ4/XgNCcCJKf5lTID+2WP5cJulva1hoTejPn5d896rcwBwLDv0CyVB33303119/Pc899xxjx47l17/+NWeeeeZWJdoDDzyQ2267jUgkgs/n47nnntui23hdOIYlYmuyjLe2mor99sNTVYnZ24use/DUVONkc2Tb28m0tGAGfMh+P65toQaD4LhCCKCzE09tNbKqCRWdkhDku+6gyEKvOJFEC4VwXQ0cF0lVyXV0YMbiuJkMnuoqPNVVoq6xo1NkqPr8ogVdsYPMJqF4vfjq6/FUVQl1pWwWK53GyZn4g358dXVYyQS5nihmrFe8n8khqzJaSSmeigrQVZxsDiuRwIzms3x1DwQdkCqQVJWAaaK2dxDyB3AlV9STShJKuJzwqFEMrKrEtSyy7R3YiSSyruGpKEdCxrVMbNPETqawcwaeYAjZ5xGeCkXDxcG1XHBtJNlFC1SheD2ogUC+K5NO7eOP0tbdvdH3r6uro+G4Y/NlQj1k2zuxMmnIZnBcB9c0kDUNX2Oj+H2Vl/eFHwrCFshy3+/LtW0cy+qrEXYtGyMSFaEPrw8tn3dgRntJ98aIrLto2YhFaQAwAaHK1IVYoIYhFr81iLhgQdQ/Rn/2KQgSLSS1ZOiP6w5CJM/shojJxunXKS6Uoe8LVELq7ZRQ39mCEmgRG8CDSBiro19qcd1s3iT90pmt9JNYOP+5QiP5KOKmp6ASFqIvRq4YCmWtZVT6K8V1U/PblEH9mHohJOJB3Dh15I/blN+uUHoTzz+CiN8D9Gc8F0Q0CrWxFflxlQIa1LfU0xLbTCvAH+b334nQ3y6Mn/zr+XIcGvNjWvf3tW7GM4gbwyz9PXyz+f32In7vBc3vbvpvTDeDLRKt67qMHDmSe+65hwkTJggT/9N7mz8VampquPjiiznzzDMxTZNvfvOb7Lnnnp/688FhQ1AsG2nMaHBs1EAQvaQEWVXJdXVjRnvxVJQTHDKY4JDBfY3fjd7evFgAxBd/TKa5FVnVUDQVX30tvoYGoaHrEU3h7ZyBns8gttJZHEPI86nhMFJGR66twVcvynbMuNDhRZbRwiGUost4i5A1DbmkBK1k0/XFrm1jpdKYsV6ynV0i9h2PY3R3oZaE0cIleMpKoXFAvr1hFiwLxxKWsKzrxMsrqGwcgBEVeteSIqGFy8AwSCxchGOaKLoXvboCJRBEdl1cSSR0aK6LW1mB4vOh+ryogSBaoRwnlcLOZISusa6her0i81hV0fOt+q665houv/xyMpl1W5h5uezCC7GSKSFhqXsIjRyOEgwiSRKOZYnkLF1fTyGqAEmShMt83dcURST5eTyQP5f+hnqsZBIj2oubzuB6dDyVFSgV5bBiRf+Hx9IveajSX3qzDEGwQxEW6EjE4hNBxAe9CGJdCryFWNB2QyxS9QiLtQmxCPvy+6tGWBTk91Nou6eDE3SKJPtFICGu26a09ocjrnEvgiBWIpS8OhHXtA5BRsMR1y9vyWIgiFKBRFeCyoGV4rqtQJBYJeI383b+M4VWiXX0l8qsKwrhRxBooWxMRhB8FGEpagiCL2gJh4AGuLL2Si6/coN55PFx5fn5VoCFxvajEUSo028xe+gvh9oQG74mIX6rPvr7Cw/O778t/7cr/x3KEDXkm8EWiVaWZebOncsrr7zCFVdcwYsvvrhNLLPJkyf3tT/6rJAkGW9VGWo4jJVIiDZgyZSoe8wZmPF4n3UBIOs6emWl0Io1TfTKSoIDBxEaPoJMSwuSC77GAUI0wDDI9kRwXVe0sfP70EpLyXV1YSaS2OkMuc4uVL8fKZ+GbyWTQp0ok0EvE0IFRXxxSIqCFg6hhUP4Gxux89KW2ZZWzHgcSQLV70PxBfrKdhzTwEqlcS1LKCm5NoEBA/BWVeEMGSJqD5NJ7GwOraxcdD2RJFxbZOVKui4yeTUNNRRELytD8ftE4paiiC5HjitaCRYaUTsOkiQh5eURC9bn1KlTAbjxxhtpbW2lsqKC6Rf9hMlHHIEkgRYOoQaD20wtTA0GUfx+rGRKSFDG4hgbykBWIojRRpBmK4IgByCsoDb6ydGff7gIq3ZYftsQfVJ9eBHu3xH51y3EwuwgrIpC/WIEsWBtQZy9iK0EDXFdqhDymUkE4S6jnwRL6W8ObyPIMytey6zKCCKOI667Rr+VWug/Uyj1cRBkpSJ+EyWIhK0w4mYrQL/IxSD61aYKtbla/vP5G6+p35wKcv88qiqv4ufTfs7UiVPFOEvzx/jicuKbhh9xw5lA/G5jrK80tQlskWivuOIKbr/9di655BKqqqq48847ufrqq7fCaLcevLU1aHm/uBoKYaXSGBHRSF0rK8XO5TB6eoTubH4RkyQJvbycbHsH2WahJarg4qutQfb5cDIibogkoXi96GWlfZqyomF6NbLHQ7a9g+Sy5UKyMZ0WceJkCieTQQ0F8VRWfGXLebY1FF3HV1ODr6ZGSBS2dwh3qZFF9YuGAbqvHFn34FomuZ4epGwOK5NC8frQK8pxbRutJIwkSchej1CAyeawc1nxm9BUIVbhD6B4vaIRgXedGpMtdTnaAFOnTu0j3B0R/5PyHhYtHMJ1HORsFnqj/RvU0N/BpQbRdacbQcBViDv4AGIxK6AWsdh0IlzFuyEsiCWIhXowYrGM0l+POZj+Up1exEJVvB/dcQgCeyA8FasR17nQwKFAWiPy23aAslQR11NHkA757R0EEan554WHlt9HOP8o6GkXbLbP6PDb0fMIEDeOhZ4cKeDjzW+6RaIdN24c9913HyCyjm+55ZatnnW8NSFJEp6qSrLt7WQ7O0V3nKpKsm3t5Lq68NbW9lkYiteLa9skV6zEW1ODFgwIAs0n4ri2DZK0yXIc0TC9FDubxVtbB46Js7KT9Jo1qP4AajgsxPaLLuPtAi0UQg0EhLWWTOAYpmgTl+53LyFJyJWVlO2990ZWo+u6Ql0p3yD9q9AkXpJl5E+6CaxCLJJtCLdaDWJBaWE9CwMNQcSLEIRZjliA90aQ6rrhNC/CLbnutOjJ72tbWSBFfHroCEu1EXGDFUNc80JbxDyMagMOoZ9o1kUh0aogmfhVSE3ZwnKxxdVke2Qdb23Iqoq3uppsRyfZ9na00jL08jJyXd3kOjuF29gwMaMRMm3tOJaFEgrgrVs/K3hLlqhjmjg5g8DggejlZcimSWj4cBEj8/u/Eov1zoR1rTXHNEVowCq0kcu7cbu7NumalSQpXwJTxHpoRFgpLfTH71Yh3IxDEBZPGkGWCxBWypD8doX6/UJWckHtZ12kEWTeQBE7EwoJcQ30ayu79DWJNwxj0yQL/U0EiujDTpF1vC0g63qf5rARiQhNWMskt7aL1Ko1eVk+C620hMDgwbiWiWtZn2mxNXtjSBLopSV516MXvaxsG36rIj4tZE370nRG2qGQEBZoQVKvF7HgNiNirX4EEccR8bUqRNxtXZGczQvmiH0qrO+KLmLnQaH855OuYRFbxE6RdbytIKsq3tpaUQ6SSiN7LBSfDzudRVJltHAYPS/7mGlpweztxVNV9ck7zaOQaaqFw8UYbBFfbkiIOGwFIvkjg1g5uhGu4UJHnXpE9mob/fWyn4RCRmsFX/qWd0V8tbHTZB1vSxSEDT4JajiM2RtDDedEWcQWYPb2ipKPku3Ur6uIInY0NEScdl2s260FhMt4NYKQt5TcVGj2voVenkUUsatji/eRV1xxBQ8//DAXX3xxX9bxz372s+0xtu2KgmVqRqNb3NbO5bBSaVE/W7Rmi/gqo1AXWUAho7SgUbw5GAg3dDnFeF4RX3p8pqxjgIceemhbjmeHQZJltJISjEgEK51G9W8+KGFEovmazqI1W0QRG6EOUY/ZweaTnApKOp8uUlNEEbs0tki07777Ln/84x9Jp9O4rovjODQ3N/PCCy9sh+FtX6ihIFYygdETQfF4NmmtmvEETi4n6mO3QxeeIorY5eBFuIO76K+XXBcxRGy2lqI1W8RXAltkiquvvpq9996bZDLJ5MmTCQaDHHnkkdtjbNsdkiThqazEdRxyPT0bve+YJmZvVEjwBYtFf0UUsVnUIDJVm1lPEJ4colSoQMZFFPEVwBYtWkmSOO+884hGowwZMoTJkydz0kknbY+x7RDIuo5eVooRiZLrifQ1A3BMk2xHJyChV5Tv6GEWUcTODQkhx7g8/6hC3NYXGgw08dUQMiiiCD4F0Qby+sBNTU0sXbqUfffdF/lL7jLVwmFcy8aMx3GyWWRdw86IvlGemuqiEEURRXwaaAh5vhb6VYX8CCGEYsOAIr5C2CJj7LnnnvzkJz/hoosu4vzzz2fVqlWoXwGi0cvLkD06ViKBnTNQfH600pKiCEIRRXwWaAghC4u+5ttFFPFVwxYZ86qrruL9999n8ODBXHXVVbz22mv87ne/2x5j2+FQA4G+jj9FFFHEF8CX/968iCI2i08Vox07diwAhx56KIceeug2HlIRRRRRRBFFfHmwS99nFqQgDcPYwSPpRy6X29FD6ENxLJtGcSz9KMwdx3HWe74zYEefmwJ2lnFAcSybw44eS2HebE6eWHJ3FeHiTSCRSLBkyZIdPYwiitjlMWDAAJqbm3f0MIooYpfGiBEjCIU2bmu0SxOt4zikUik0Tdvl9JeLKGJngOu6mKaJz+cjk8kU51IRRXwOFOZRIBDYZFXOLk20RRRRRBFFFLGz48tdEFtEEUUUUUQROxhFoi2iiCKKKKKIbYgi0RZRRBFFFFHENkSRaIsooogiiihiG6JItEUUUUQRRRSxDVEk2iKKKKKIIorYhigSbRFFFFFEEUVsQ+zSRPvkk09y7LHHcuSRR/LAAw9sk2OcccYZHHfccUyZMoUpU6bw/vvv89prrzF58mSOPPJIZs6c2bftokWLmDp1KkcddRQ/+9nPsCwLgNbWVk477TSOPvpofvjDH5JKpT7TGJLJJJMmTepT7tlax4/H45x33nkcc8wxnHbaaXR1dW188C2MZfr06Rx55JF95+ff//73dhnL7bffznHHHcdxxx3HjBkzduh52dRYdtR5+TzYHvMIdvxcKs6jjVGcR9sJ7i6K9vZ2d+LEiW40GnVTqZQ7efJkd+nSpVv1GI7juAcddJBrmmbfa5lMxj3kkEPcNWvWuKZput/73vfcF154wXVd1z3uuOPcd99913Vd150+fbr7wAMPuK7ruuedd577r3/9y3Vd17399tvdGTNmfOoxvPfee+6kSZPc0aNHu2vXrt2qx//Vr37l3n333a7ruu7jjz/uXnTRRZ9pLK7rupMmTXI7Ojo22nZbjuXVV191Tz75ZDeXy7mGYbhnnnmm++STT+6Q87KpsTz33HM75Lx8HmyPeeS6O34uFefRxijOo+2HXZZoZ82a5U6fPr3v+e233+7edtttW/UYy5Ytcw866CD3jDPOcCdPnuzef//97htvvOGeeeaZfds8/vjj7pVXXuk2Nze7hx9+eN/rb731lnvGGWe4hmG4e++9d98C09ra6h522GGfegxXXXWV+9Zbb7kTJ050165du1WPP3HiRLe1tdV1Xdc1TdPde++9XcMwPvVY0um0u88++7jnnHOOO2nSJPf3v/+9a9v2Nh/LkiVL+iaZ64pJdNttt+2Q87Kpsdx333075Lx8HmyPeeS6O34uFefRxijOo603j7aEXdZ13NnZSVVVVd/z6upqOjo6tuox4vE4BxxwAHfccQf33XcfDz30EK2trZs87objqaqqoqOjg2g0SjAYRFXV9V7/tLj++usZN25c3/PNfe/Pc/x1P6OqKsFgkEgk8qnH0t3dzfjx47nhhht4+OGHefvtt3n00Ue3+ViGDx/e17px1apVPP3000iStEPOy6bGcvDBB++Q8/J5sD3mEez4uVScRxujOI+23jzaEnZZonUcZz3xc9d1t7oY+t57782MGTMIhUKUl5fzzW9+k1tvvXWTx93ceDY1ri8yzs0dZ2sc33XdTQpibw6NjY3ccccdVFdX4/P5OOOMM3jxxRe321iWLl3K9773PS6//HIaGxt36HlZdyxDhgzZoefls2B7zCPY+eZScR71oziPtj12WaKtra1dL6Dd1dVFdXX1Vj3G22+/zeuvv9733HVdGhoaNnncDcfT3d1NdXU15eXlJBIJbNveKuPc3Pf+PMevrq6mu7sbAMuySKVSlJaWfuqxLF68mGeffbbvueu6qKq6XcYyf/58vvvd7/LTn/6UE088cYeelw3HsiPPy2fF9phHsPPNpeI8EijOo+2DXZZoDzzwQF5//XUikQiZTIbnnnuOCRMmbNVjJBIJZsyYQS6XI5lM8vjjj3PJJZewcuVKVq9ejW3b/Otf/2LChAk0NDTg8XiYP38+AE888QQTJkxA0zTGjRvH3LlzAZg9e/YXGudee+211Y5/yCGHMHv2bADmzp3LuHHj0DTtU4/FdV1uuOEGYrEYpmnyz3/+kyOOOGKbj6WtrY0LLriA3/72txx33HE79Lxsaiw76rx8HmyPeQQ731wqzqPiPNqa82iL2BaB3+2FOXPmuMcdd5x75JFHun/84x+3yTFmzpzpHn300e6RRx7p3nfffa7ruu5rr73mTp482T3yyCPd66+/3nUcx3Vd1120aJF70kknuUcddZR7ySWXuLlcznVd121ubnZPP/1095hjjnG/973vub29vZ95HIXEia15/Gg06p5//vnuscce65588sl9+/8sY/n73//uHnPMMe4RRxzh3nzzzX3bbMuxXHvtte7YsWPd448/vu/xj3/8Y4ecl82NZUecl8+L7TGPXHfnmEvFedSP4jzafij2oy2iiCKKKKKIbYhd1nVcRBFFFFFEEbsCikRbRBFFFFFEEdsQRaItoogiiiiiiG2IItEWUUQRRRRRxDZEkWiLKKKIIoooYhuiSLRFbBOcccYZHHbYYdx+++2fanvbtpkyZQp77703zzzzzDYeXRFF7BoozqMvB4pEW8Q2w+WXX860adM+1baKovDEE08wZsyYbTyqIorYtVCcR7s+1B09gCJ2LTz++OPccccdPPHEE0iSxEknncT555/PCSecsNnP3HbbbaxZs4aOjg66uroYPXo0+++/P7Nnz6a5uZnLLruMSZMmbb8vUUQROxjFefTVQpFoi/hMOPHEE3nllVe4+eabMQyDcePGfeLiUMD8+fN54okn0DSNCRMmUFlZyQMPPMB//vMfbr755uICUcRXCsV59NVCkWiL+Mz41a9+xZQpU/B6vcyaNetTfebAAw8kFAoBQuD74IMPBqCpqYne3t5tNdQiithpUZxHXx0UY7RFfGb09PSQy+WIx+N0dnZ+qs/our7e80LPyCKK+KqiOI++OihepSI+E0zT5JJLLuGiiy7CcRwuvvhiHnzwwe3bCaOIInZxFOfRVwtFi7aIz4RbbrmFyspKvvWtb3HyySdTVlbGzJkzd/Swiihil0JxHn21ULRoi/hMuOKKK9Z7fs8992zxMxdeeOF6z//1r3/1/T9ixAjeeOONrTO4IorYRVCcR18tFC3aIrYZZsyY8ZkL7RcsWLCNR1VEEbsWivNo10exH20RRRRRRBFFbEMULdoiiiiiiCKK2IYoEm0RRRRRRBFFbEMUibaIIooooogitiGKRFtEEUUUUUQR2xBFoi2iiCKKKKKIbYgi0RZRRBFFFFHENsT/A9Sq7NLFGS7lAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 540x324 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = ensemble_plotter_22(states,m_const,da_const,t_start=da_const['ncyc']-3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# plotting the indirect observations in additions to the direct observations for one timestep\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7 µs, sys: 0 ns, total: 7 µs\n",
+      "Wall time: 10 µs\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "t_step = 40\n",
+    "bg = states[0]['bg'][t_step]\n",
+    "an = states[0]['an'][t_step]\n",
+    "truth = states[0]['truth'][t_step]\n",
+    "obs = states[0]['obs'][t_step] \n",
+    "obs_sat = states[0]['obs_sat'][t_step] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 540x360 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plot_ensemble_sat_analysis_paper(bg,an,obs,obs_sat,truth,reflectance_simulator,m_const,da_const,h_c=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YiaSQUiQZZIls0wYSHyzEw0OvrsJXXk504m6okTCZNeswBhIooRBGXz/Z5masErn/dyILLEdEpHyAsJ57ENa4hCDmtWzKAtURBJxFMMFqYDGC+N3iMdcWt2uK71+KIPVg8Tg+YDgiVr2AIPUE/9VlAzKZDDNnztxurRVLpL4dMOjczLe1k29rJ9fcTKGnByeXxzEKOPk8kQnjRYcfxxFWeDZHYMgQgo2NuLaFGgwSHT8OWVPJNzdjp9NYiSRWMo3R3YOVTG7v0yxhWyKFqMViI2STOoSU0symqoqtCMItIEhbQVjaaYRuPgmhl89DVGdcW/zfIFGbCLLuQ6wEhgLrERa8VDy+Xfz/R0vM/Ndg4cKFnHXWWTQ1NW23MZR81NsYnuOQb2vHc1181dV4wP/9/R4eeOghmjo68Gsa0yZM4JuzvsQQnw8lFCbYWI+TyyJ5oEUiWMkknuuihkIE6utJr1mLUyigl8XxlZeBBEZfP2o4jKQo2/uUtwpSqRSpj61ITNPcTqPZzjARhJ5FJA1VFV+vRFjUg+TegSDtDfDKule4o/UOFvUsQkJi8oTJnHv+uRw07CAxQSxE/B4PxBCWeAciOsZE6Omjin/XIuq+PI8Ikxwsx7tzt4n9BDo7O0l+zFiKRqNEo5tKVD744IP8/Oc/31j9cntA8jxvqyyUDMNgyZIlW+PQOzXcgQEoGEgV5RiuyzXXXMP777//if1UWeaco47iiL32AU1DDgbw/H7kWBQyWaTyMiS/H69QwFm6AiQPaegQJMuGinLo7UOKRpDCm9FEcifDpEmTuP3227nppps+8vrYsWO54oorts+gStjlcdFFF9Hb2/uR1y644AIuvPDCT+x72GGH8be//Y3GxsZP/G9rY6tb6pMmTdrpmx0vWLCA6dOnf+HjOPk8+bZ29PJypEiYs88+m/fff5+KsjIuOuMsdg8EMAMB7p/3Jv94+WVueeYZaqqqOWzkaKLDhoNto0YiqA2NwiqvqsIxTXotByyT8PhxeIZJoKEeo68PXI/g0CFb/Dy2Fz5sKHzta1/jlFNO+cj/TdNkYGCAsWPH7vTNjjf7u0oBTyP08QaEldyDkEzK2NSpqAnohp8+9lPuXHQnuqTz/f2+z6njToXd4eH1D/O7P/0O27Y5b9R5/Gzyz+AYRBx7B0JiGY5YERSKn1OH0Np9CCnmOUQUzRBEEbCjd/57DiCdTrNq1SruuuuuT9Rr+rCVvqOgJL9sQ5j9A0iqghaPccUvfsHcuXOpqa7mzl/+kopsHg+QZIlLTzmN0SNHcd1f7uCX/3cfYy67jD18Oo7nYnR344RDuLaDr6oKO51Gi0RwC3ncfAFJlnEKBbRoFKO7B8cwUHbySfXT8PFlL4iHb2BgYDuNaDthEYJkaxEkHkfIMC8gCL0SoYcn4KGFD20k9LsPu5uDqg+is6mTheULGamO5KpTr+Lyhy7ntrW3sceUPTjJd5JgiHYEqZsI7T2L0OJ9xZ8OxIQSR8gybvG1XSxtora2dqcwFkqkvhXgeR6uYeDZNpKiIPv9uKaJk8+jV5Qzf8EC7rjjDlRV5cZf/YoaScZGQi8vw81mkXw63/zqV2lNJfm/hx7iF/fey6OHHY4vEsHO5rASSexMBr2iHDefw1dZgZVMYmdzKKGQiG2vqcGgByeb2yVJvQRE5EoLwkovQ1jH/QgnZRRBvGuAGugJ9PCz538GwNXRqzlIOQi64fHM41xx/RUbDxnzxUgaSX74xA/Za/e9qKutE07QzuLnfR1RRsBFkHwOEW7RgSgZ0ItwwCYpleLdTihFv2xBeI6D0ddPrmkD+dY2Cp1dIrplQzOFzi6QJaRgkB/96EcAnHfOOYyLxXEtEyXkR1JkgsOGovj8uIUCl553HkMaGli1fj0PPP444VEjCTTUowQDOPkCqWUrsJJpfBUVaNEoTiGP59iYff1YqRSSqmDnstv5qpSw1bAaQaq1CEs5hYhYaQUmIMroLgdehqvuvopUIcVhvsM4u/xsEaVSDsceeCyHDD2Eo4ccTW2wlqQhHIFZI8thvzxMhDMmABm6Q914BU84S4OIVYCLkFz6EFY7CKdqDuGULWGbo0TqWwh2JkO2aQP5tjYcy0KvrCAwpBF/bQ0A2aYmPNflvvvvZ/ny5QxpbOSbM0/AMU1knx/P8VD9AfSKCrRIGDudwYfEzy6/HIAb/3IH7QsX4RoGkqqhxuOYvd0YPT14soxrWxg9vaRXr6bQ00uhqxtzYAArkSqVD9gV4SKSfQabVAQRunkTglCrEZEwCVjw7gIeWfIIPnx8y/kWZ3efTV9bHzRBY3sj91bfy5273cn8o+bz5IFPcta4swBImSmWvLAEesGearP3U3uz+4W784tnfsGAMSCscxkxQSSLY1EQYY8yIuLmvxQvvfTSdnGSQonUtwiM3j5yLa0Yvb1Imoosy5h9/Ti5HGo4jBqNovj8FLJZbvzjHwG45LsXoNkWSiCAWzBQA36UUBCzvx8rm6PQ14udy3HsCSewz157kUiluP3uu/HX1eI5Np5pgKJi57Kkli7ByYmaMFb/AEgSvupq1HAYc6APO1uy1nc59CAs8xiC1D9A1HOZz6bkn6WADjcM3ADALH0W/8/9f7yWfY3ru64X71/JxqQhabzElJlTuP6o6/nW7t8C4Jr3rgENuod3E9Ei9Bv93P7u7Rz62KEsWb1EkHgQEcKYF5+HxEYHq2QNFoQpYVuhROpfEIWubsy+PpyCga+qCl9ZGR4S5kCCzLomjN5e7HQaf0M9j73yKh2dnYwdNYrDpk1D1nVcywTXAVXDyeUwBwawUynM3j7Sa9aSXbee73/zmwDc/+QTZPIFfFWV2JkMrmnhuR7WQALJp+Ovr0GvqcLs6ye3YYOIg3dcCp2ldfAuh/WIDM9qhI6+DGG59yLkj5eAp+D91vd5uftl/Ph51nqWHreH/YP787PdfiYkmzyiBoyFIPlxwN7wvYO/R1AN8krmFZZEl1A/sp6FP1nIkwc9yZ7Ve9KT7uGMV85g/Yb1gtQNRIy6H2G1l4vfcrZEMdsapSv+BWD09WGn0yBJaNEISiiIncmiBvwEGurAsUksWkyhoxMzk+a2v90NwNcOPoR8axtmfxIrlcEDPNvGQ8I1LSRNI7rbBLRQmPSatYwJhpkxYTcyuRx/v+dvuIaFk8tjZ1JosSh2Lo+TzuCrqsHLG7imQXZDM0ZXN7LPh9HXW5JgdiWYCK3bX/xZhdCvXURLujI2Jgr9sV+sDGVJptfrZf/o/tw99W4CekCQ+GAEi4JomvEsMABlu5cxe7fZAPxp7p/AAmm6xJSTpvDgwQ9y+MjDSRgJLnjkAiy/JVYGSUQJgRxiBWGAOlCKxdjWKJH654SdyW60kCVZRgn4cdIZ1HCIQEM9gbo6QmPGUOjoJLVsGS/Nm0dbZyfDGhs5ct99AQ/HyFNoa8MpGEiSh4QHrkNwSCPxPXYnOGwoVjKJmUzytRNmAnD3I49gpNNY6TSSqoEkISFKD7iWgezzERw6FC0WxbUdkCTsVBozkdiel6uELYkBRFjhoI7+DoLkOxHkvgwYDhuO3cBzq58DIOfl2D+8P3fPupvA2ICYDKYjyu0qiCzUJMLa7wFa4dx9z0VTNJ5Y+wQb1mwQ1vxhoE/T+eO4P1IfqWdJ7xLea3lPjCWB0NdthASjg95Z6si1rVEi9c8Bz3EwenqQfT5kRQVZxrUd0VquSuRoW+k0Zk8PWjyOHA5zz4MPAnDW8cejhcLIPh1JVcH1RAEuSXwVSiSCXhYXddIryjD6+7HSKfYdvxujhw6lp7eX519/HSQZJRDATqfRq6sw+/qQVQ1/bS2SqiBrGpIio8fjOEaBQk/vvzyfEnYydLAppHBh8Xc/wjG5EkGqY+CuR+7a+JZDyw/l7v3uJpAOCPJtQJD5SEQLu30RjS88hMXeBvUz6jlxzxNxPZf7Hr9PWPYVwN4QV+PcMv4WXjn6Ffau21usDpLF93uIGPUAaP2amAxK2GYokfrngNk/gOe6aGVxnFwOZAknm0UvF/VNC11dGF3duEaBwJBG2i2TdxcvJuDzcczuU3CNAq5pUWjvwPVcZFkGSQJZQQ340eNxPM+j0NEFlo2s+fBVlHPinnsB8Oirr6DHY3hGAVn3I6sajmGgxWKoQZHxJmsqbsFAKytD1jTMnj48q/R07fSwEFa5jbCoDTZJHkOAseAFPXIv5Hjg7QcA+N647/HXCX8lEA4IC19BJCbJCLnmAIRjcwIivt1DZIrWwVd+8BUA7l90P+ZSE55BZLCqMCMxgxH9I8TKIIKYaHIIK70AhEHJKILsS9hmKJH6Z4RrWVipFFo0gpPLY/T1UWjrwOjpodDTQ2bdeqx0BjkYwMoXMDo6mPPKKwAcPWUqkVAQf00tSiBIrqUFO5UhMGQIsj9Atmk9dj6P0dNLaslSjM4OgsOHoZfF0SsqOHLyHvhUlXeWLqG5vUMkImXSWMkB9FgUJ5dDCYfwXBdJUUXyE6DHy7ASCbxcbrteuxK2ABII4sywqXjWAEIXHwEfFD7g2FeO5V71XpJWkqk1U/nhsB+ieZqwzqsRpJsHDkE4NKMIou9FkLsP4Tx1YMYeMxg3fBy9Ri/PPf2cIPQocBoiTr0D3HaXdxe/i+d5m3qU5hFE7yAmoRK2GUpejM8Iq6hNK7EYyfcX4to2it8PcoB8azuOYRCsr6PQ1UVq6XLUaIRn35oHwOHDR2L2DxAeN45cZwdOwUQNCfkms3oNZn8/XT4fr7/4Iqs/WIjr2IzZfXf2bRzK8EKeaCTMIaNG8+zKFTz2wQK+fchhWAMDeIUCwREjRAhlNIIsyYLYfRp2NoteVYnR24uXLNVZ3+nRj5BBYFNzCwmogjdWvMHXX/86eSdP//v9AMyaNEvsO4ZN/UYrEZr6eERy0mqEde4iOiS5CClHAikt8eWpX+anTT/l761/Z+bEmaI6YzmwP/AAnPbOabyTe4cn93ySKYUpgsxzQA14sidWFCVsM5Qs9c8Az3GwUmnUSBirt09YxgG/+K3pqOEQSsCPkUyRa25BLy9jfThId38/9VVVTJk2Fc+26H31dfrenCukl3CIfEsrVjKB5fdx+kUX8ttbb+Wxt+bxxLvvcsNf/sLpv/g5l9z4R5KOw3HjJwDw1Lvv4koSTiGPrOs42Qx2Lgeuh6RpuJaFrCjguiiBALJfx8tkcAqF7XwVS/jccBDW9GBz58E0fA3W9qzlnDfOIe/kOfGQE+ns7USWZGb6Zm7qQboWQbiTEeT9IsJCjxS390bUbylHEH8nsBhO3eNU/Kqf1/teZ8PYDUJX9yPK7UZgsjsZgDn5OWJMyeIYPfD83qb66yVsE5RI/TPASmfA81DDYbLNLdi5PHYmi+zzIWkKWiyGr7aW5OKlZNasRfb5eOyfcwA4bNJkVN2H63gklq+g0NWNhMtAKoWsa6jRGOUjRnD8/gcyc8YMLj72OH4w8wSOmzYNn6bxyooVvLZoIVOnTWdkfT0DySTzOzvAFk00PMfFzmTwbBvFp4kHSgLXcfAcBz1ehlcwMEvNM3ZefLhZRQZBljo4ssMF71xAyk5x3AHHsV/lfjiewwHDD6BySKWQXJYiCHwagpTDCGKuRThJw8X/VyB0+zhiIshDPBPnuD2OA+DRJY9ualh9MLAfnMRJADy24TFc3RWO3DTggu23xVhLi8RthhKpfwbYqRSSromORS2tqEE/ajiEY5hk1jWRWbeO3tdfx+jsQNZUTNPk2ddfA+CAqmrybW1kVq/CzWZRy8p4at06Zt92C8+88gp2OgWOy8UHH8Slp83irJNO4evnnsONf7mTJ+++m+8cfQwnTJ1GdLfxHLvvfgA8+9Y8tFgcSVXwXBfXsLCSSSSfH/AE4SsyODZqOAwSmL2lKks7LZII+QWgDWH9RuHvS//Ootwi6srruOGEG3j83ccBOHnMyULPXozQyI9ECK4awlEaAN5ESDAZhHUdR2jqWWAdImSyB06deSoA/5j3DzzLE5/vA46DadXTGCIPoTPdyQJlwSZZyCpa6ialOjDbECVS30w4hoGdzWKlUuRaWtBiEbSyMjIrV5NvbUXx+8DzQJYJjxlFxYEH8O7SJSQyGYZVVTHjhBOIThiHXl5Bt2Xykxee5cZXXqZgWSxsbsbJZYXTM1fAzRfQy+NEJ+yGGgxQX1nNKaPHYHT34quqYt9RowF49YMPyEuIEEZVw3MsjN5+FF0TEqllgaLgOS6SqiBpGnYqJV4vYeeCh7B2s2wiySDkk3luaBFlAK746hVkV2aZ2zwXTdY4pnCMqOJYAZyIIHEJMRmkEJZ4DcLRKiPkljUIS32w2mINEIcD9z6QyopK1ratZXHL4k2x8mNBGidxlHoUAC8lXxLjzSGIXSkeu0Tq2wwlUt9MGL19GD094IISCCD5fCSXLMVMJgnU1eGrqkKSZPzlFUTGjydQU8NLK1cAcPCo0Vi9fViZDC+uX8uFTz3Owo4OYoEAV55+Bj847TQkVSe1bDlWfz9mIoGkaZj9oqZMrrUF2efHTiZY+PrrXHbnXygLhzEsi5ffeQdcD9mngyRjJRN4roeiKCL6RS5+xZ6HFPCLySlTave+0yGLsNRdBFmmgQpQEyqXNVzG8VOO53jneJ5Y9ASe53Fo7FBihZjQys9EWOAJhBavIWSXycCBCCklDOyB0N8VhBVeD0wBdgN1hcqJx54IwCMvPSIs/lZEfPo0OCxwGAAvdb20yenaDZIjiWP28V/djHpbokTqmwHPccht2IASDKJGQpjJFLmmJqz+AQKNDYSGD0NSZORAADUSQZIVLMfh5bffBuCIKVNJZ5L8/IH7+eUjD5OzLA6ePJmHr/sd+9XU4mRz+CorkVURhmh0ddP94ksMzF9Arq0NO50hMmE8/toaJMOkOzHAQJGYn1+yiHx7J0o4jISEa9s42SySpuPadtFKV/FcFzQdZEV0RSph58JgffLBOHUNkdyT0DhzjzO5/fTbkZZJ/HP1PwE4eezJgpBPRFjbfQgLP4CIZ29gY9YnY9lUbXEKIipmEoLsowjpZiWckhOdph578TGcbkdY30lgOuwT3we/5Kcj30HGyohjpUHOy2LCyBZ/StjqKJH6f4DnurjdPXiWTaCxgUJHJ/m2dlzDxF9fR8XeexEc0ogkK3imiec6qAE/L77wPJl8njF1dex5+peomLEnS1evJqDr/Pj0M7n+4u9TFouI9nSRKEZfH65to1dV4m9swDVM8p3d2NkcwaFDqDpgPyKjRzGstoavHnrYxvHNX7uWrtZmPMdGDvpxLQujv3+js9QxCki6hud4SKqKousbk6dK2IkwGFGSRjgiqxGW92AY4pvQ3NrMe8n3CGgBjtz7SEHWI4vvGZRTxiKs9A/DjyjkpSJkFRCTx0pgLqIpRgSmBqcyvHw43Zlu3nz9TSHXLAeGgb/Oz3OVz/H+8e8TJizGaoJckMUE5FGSYLYRSqT+H2AOJCCfQy8vw0qlyHd0IkmgRWOERgzHV1lZ7EaUwM7lUANBzP4B5sx5DICTTj6ZyNgxxOvquHr2l7nznG8zc999KLS1k1m+Ar2sDDUUIDS0kfLpU/FXVhEZPQpfRQWe5yFpKv6aavRoFC0eR9V1vnLIIQytFOUIPODlFSvIt7Qi+33guljJFK4n4RoGTjaPJMlIioznOKjhMG6+IMIfS9g5YCKs3DyC0G2gDH45/5f8rP9ntHW1wQZ4zBT33FFjjiIYD4q6LoP1XFyEvFL3Lz7Dj8gobSj+XcwopQFRPuB0kI6XOHU/4TB99P1HhdW/lo1lCUbJo1DyijheMaRRykvis2VK8erbCCVS/zdwTVMkG0kysq5h9vZhJZMogQBaZbmosyJJWJk0Rm8fjmlgJBL0Nq3nzaKe3tzXh2uaZFavZsLwkYyZMR1FU5EkGdnvI9BYjxaPoYTCBBsbUfw+PMMgOHwokudSaO8QmXqAGo3iFEzCdXV845BN1vpL69Zi9vfj5ApY6TT59g6Mjg6MgQHy7W0UuotPk22jhIJ4eJj/bb08d2Yk2RSd0gH4IefLcXfX3fyl9y/ke/IAzEmJ8NmTRpwkEoT62DQJDFrt/w4ywoqfiCj2dQxCd/chyhEMg5MPPRmAZ5Y+Q4GCsMIXI2QbCUhBLpDDy3pgFzX1BGIC6KWkq28DlEj930Bozx6epmFnchgDCSRJRg0HUXQf/uoqPM/D7OvH6O7BzubAtpm7ahWmbQMwZ84clr85FyuRQIlGsNMpPAcCw4bir64GF3xVVXiWRb6tnejukwjUN+BJMigKEhLZ9U3Y+byo52JZBIYM48h99mZ4jeiqtLy5ma5sBl9FBXp5OZ7jgk/DX1WJEgjiFgqCxF1XVJT0+UUzjRJ2DgyS+mBjjGHwyqJXyHpZpkWmMTo7mtWF1SzrW0ZUj3LI7ocIMs8jrO3hCCv8syKK0MNtRChlFYwaN4pJIyeRNtK8+sqrm6o7hsX+5yw+hwlvT2B1ZjUkwZVd8d5I8RzyX+hKlLAZKJH6v4BjGDjZHLLfj2QYuK6Nk8mgBAOiwFa5KJTlFgpk163DTCRQw2H8NdX85cknAVBVlT///vdUAU6+gJ1M4hQM9Kpy9LIyFH8AO5cVtc5lCcXvJ1BXT2jEcLxCHkXXUWMRzN4+MmvW4BYKKH4/sqYQGTGCrx1wIAG/H4CXlyxBUmV8lRXIfg0cBzUSQfHpqLEosqriptN4jo0ajWJnsziGsR2vcAmbBRehiScRjTFUoBFebH4RgKPLjoYUzNGFlX7siGPxRXyCyI9BkGm8+PvzoAHhKB2cUGrhpANFstGcD+aI8MjBsru14LN82J7NXHuu0NDl4vj9CJ0+8TnHUcJmo0Tq/wJm/wDIMpKi4BUKeKYlSDUcRtY0fDXVABiJJOk1a1F0DX91Jc8uWEBTRzsA1/361+xeUYHR3YMkKygBH3plJWoghJvPicSlfAGrv59AXT3BYUOxE8KC1uNlqNEIEhIoMpl1TRR6etEqyrBTKUJDh3LYlKlcec65ALywaCF2KoMWjeG5Lk7mQ5q55+KrrATbxi6WOXBNEztdCm3c4ZFGSB/9CJKsBA+PlxIiHvyw8GF4MY85XYLUT97jZKGB748g0TxCH/+8GCwhUECEMFbDCYeeAMBza54jtzInrHUHqIJ9tH0AmGfPgwGQDVk4aLMIqaZU3Guro0TqnwLXNHGyWbRYTJC7omKmUiCBousoPh++8nIAEu+9j5vL46upJa0o/Ox3vwVg2JChHD15D/LNrcihEGpZDGQZPAc7ncI1DFFiQNeQFJXQsKEE6uswE0kKnZ2ERo4gUCu8WnauIKos2g54HlYmgxoJEaisZO/hw4mEQqxqa2Pt2jWokTBaMISVSoPn4XkenmmhxuOg6Ri9fci6hqzrmAP9/+oSlLCjIIkg9mY2OiSXvreUbrebOrWOCZEJLPEtYV3fOir9lex3yn6inO5gwo+GqOXyRdCIqAWzDuiHITOGMH38dPJWnueXPi8cuSEgCvsG9wXgrfxbeFkPNakKPb2fTRZ/CVsVJVL/FFgp0aJOCfixk0k8WcJOplHCEfBctIpyJEXBNk3SK1fhKQrBIQ3ceN//kS8WzDrvG98gvWolniyhRyJYiQRmXx92IoXnuHge+CorCI0YgRoOCfJ1XZxCQYRL1tXiq65E0jXcfA4nn0dSZHA9Ct3dOIUCWnkZuqax7x57APDIyy8hqwpqOISdzYrMUUnCNU0kCaRIGDubFfVh/AHsVBq3qP2XsIMiiSDCZoRuPRxeWPkCAIdVHobkSMxJCCt95uSZqHuoQqIpNpOmCuHA/CJQEY5QGdFlKQYnHVGs97LqMWHBF+vJjAyNpFqtptfqZY2zBrXnQ6QeQ0xQpZpyWxUlUv8YPM/DTqdRQ0HsXA6nYIDjCBKNRECS8VVVApBcvASjtwctHELSdGYfL4oeqYrCfg0NGD19yJpPRKB09YDt4q+rw19fR3BII77KCtRAgOCQIeRbW8m3teOvrcVXU43R1Y2vulqEhckyaiiElc6gRMJYA0kSCxfjq6jEsx0SxUSkp95+GyuTRa+owLUt7EwWRdNwTAvXMJBjMWHp9yfQ4lHhNyiFNu64yCMiXpoQenYdYMPBhYM5J3QOJ1adiKu4zGkuRr2cfJIgThBWerEk7xZBBCHrDADzYebpM5EkiZdWvUSqKyWIPw5SSGJfXVjrc7256AP6pqYZWvF3qbjXVkWJ1D8GJyscl2o0itnTi6xpkMsjKQpIEmooiB6LYefy9L31Lo5hEhw6DH91JW8Wm2Hsu9tE3DXrcYwCViaNa9po0QjhMaOJTpiA6veLqBTDRI1FQZGxEklcyxRkX1ON0ddHesUK/NWVhIYNRQkG8Qp5JFlCDYXIt7Vj9PUiKQrfOFXEDidzOZYuXYoWjyFJMnYmLZpZW6bog+r3oQSDGH19qNEonuOKypMl7JhIAu2ISBYQESxLYao1lStGXsEB4QNYYCygPddOfaSeGafNEPsNluWtYMt2TBiJCJVsgpp8DfvssQ+mY/LMymfEJFIFBGEfXejqb7lvoSQUMTEZxXGplJKQtjJKpP4xiIbOKorfjzkwgOT34WUzqOEgODZaLIYky6RXraLQ1opeFmegoRa1ooJn33oLgMMnTQbJQy8vR9V9+GuqCA0bSmT8eJxcRqTtOzaoKp5p4uYLBIcNQdF9ZNc3YXR1IasqIKPFy9DicQKNjUj+AIrPj14h2ubZ6QxOLseMocMIBUQbu7889KAoZxAOYQwkwXXB80SUja6Ljk35HK5lofh9xVoxpezSHRJ9iKzNPoSlPAZYhHhqo4ADc/qFlX7ificiR4qPcy8iaqZ6C49HQsSw1wHdcNK+H5JgOhGTSBkc4T+CP+7+Ry4fcTmSJW0qa5BC6OrdlOLVtyJKpP4heI6Dk8ujhsMiK9MwcU0bLBs1GMEDfFWVGL19pJavxLNsGDaMs77xDQ45+GAWrVmDT9c5cNpUEVqo+9BiEULDh4toGdfBsx1knw/PspDwcHJ5tPIytGgMM5Eg19yCJMlEJ4wnPGokuC6uaaHoGqrPj6wohEePQouEwXFxbRerp5ejDj0UgDcWLcLK5fGVxXGzWdwiqXumheR5qNEYrm3h5PMofj9uwcDJl4KHdzjYiGzNLkQUSy2gwm3rb+Me5R4GrAFs2ebxTlFm96QzBMFuTMePIrTsLY0ogtTDcPyBx6PICq83vU5/e9HpXg/1Uj2nhU6jobJBjKcTQeoDbIpXL6l+Ww0lUv8Q7GxONMGIhDH7+/AksDPCaerJiJrkiKSkfEc7csDHnGVLGBgYwCrGfB+y5574JRnHMFFUlejkySh+P4quY6czKMEgTi6H57l4toMSDmGnM1gDAwQbGwkOG4Lnuti5HHqFiGd38nmcXB5JVzETA0XrPYYSDiLrKmYywXlf+hIAedPkzRdeQK+pwXVsnHwWJBnHNMCy0GJRJFnBSaVRoxER6VPS1Xc46O26cC4OFsFqAGetwx8Lf+Sy7GX0m/3MTc2l1+xlRPkIJh8hug/Rj5gEvkgY439CI6BCeWM5B005CNu1eWr1U2ICGoVglSwQBk/1hIQks8lSz1PS1bciSqT+IdiZovTi82H2DyCrGnY2g+T3g+OK7XQGq39AWPLxOH//5z8BkCURYnDk1KmYyQSyqhIaNRI9FhUrgIIhYt5tWyQbecKIcYoTib++nkBDPaGhQj83e/sodHSgV1agxaMYvb1IqoKTN0SLOr9fNKQuK8MtGFQiUV1eAcBfH38MNRRClhXMgSSyIuMWDLHiCIWQdR9WJoukaqJcb6ZUPm+HggH+Jv+mei8BYBi898J7JEgw3DecUaFRzOkrOkgPOglJKYa4dBX3j27F8fkR+nkCTjxJlOOds3aO0P6rgBis7lvNpcsv5Sb7JmGZZ9g0QalsasVXwhZHidSL8BwHJ19AjYSx83nsbA4PFzdXwPPpuJaF57lImkquvQ3Jc3m6pZlkKsVuo0bR2tNDOBhk+pDhyI5HoKaG8MjhOLkcjmECopmFa5qiKVE2h+cIKSY4pBE1KNbKkqIQqKvFV1WJUyiQb2tDKytDUhSsdAbXtrEyGWSfDzzw19XhIWF0dXHykUcAsLKlBTOVEmUJEgk8D1zLxjVNlIAfLRLGyWWLk4MPJ5cvZZfuKPCA9cWStTabuhGNhBeXiSzSwysOx3ANnu57GoCTTi9KL0m+eLLR5qIOUOCY6cegqzrzOufRtaFLlPIth0w+w9/X/J0n7Cc2WeZ5RPRLiFLf0q2Iz0TqXV1dzJs3j5UrV26t8Ww32Lmi9BIKYfb349k2riHivD3HxTXy6PE4SjBIvq0Nx+/nvueeBWB4fT0Ah06fjpLLogT9BIcPQwkGsTIZPMtEVhVc08LFw0omANDjcQIN9SKy5mPQYjGCQ4YI3dswkDVRa93JZLBTKdSgiJ7x19SgxeMYvX1850tnUFdZSSKbZf78BejRGHY2JyYS1wXDQNI05EAAz3FwLRO5ePySBLODoBPoAifgiJR6D+GAzMFLmWIWaflhvJp6laSdZELlBMYeMFa8d9Ah+UWTjTYHKlAPUTfKYQcehofHE0uf2FibZhKTCCgBmpwmerI9wlI3ENZ6kE1lhHdhuK7LXnvtRW4bP1ubTep33XUXhx56KD/5yU+YPXs2hx12GE8//fR/fN8TTzwBgGVZzJo1i0ceeQSAfD7PrFmzmDNHLCFTqRSzZs3iqaeeAqC/v59Zs2bx3HPPAdDd3c2sWbN4+eWXAWhra2PWrFm89proAbphwwZmzZrFvHnzAFizZg2zZs3i3XffBWDFihXMmjWLDz74AIAlS5Ywa9YslixZAsDi+fP530svYdX69Zj9/axft45b/ngTff19SLkczZ1dnPPjH7P+nflYqQwPL1lEXyLBuBEjWbp6NQBeZw/ZVAq9ooK3li/ny1/6EgMdnUiaxmuvv84lP/whqdY2HMPg9eVL+fJ3v0OhmKz0yCOPMGvWLKxiq7kHHniA0886i0B9PcGGBubNn8/f/vxnkCUKnV08/tyz/OF3v0UN+gnU1rB22XJuveH3HL2PiBG+6x+P8vcHH8RzPax0mhdeeIEH770Xz7LQImFefPElfnvNNSi6Dnjc/scbufjiizd+b9dccw2XXHLJxu0rr7ySH//4xxu3f/azn/Gzn/1s4/aPf/xjrrzyyo3bl1xyCddcc83G7Ysvvpjrrrtu4/aFF17IDTfcsHH7/PPP56abbtq4fc4553Drrbdu3P7617/OAw88sHE7lUrR2tr6kZ+eHpGu+NJLL+0U99wHH3zArFmzWLFCVPRc8NoCfvjlH9La2Yqck2lb1sZTrzxFr9pLx+sdLPWW4vN8TApOYk63OIe4Fae7rxvSMO/5eXzzx9+kv5gp/NRTTzFr1ixSKSFgz5kzh1mzZpEvOsY/7Z6bNWvWxmt87733csYZZ2zcvuuuu/jyl7+8cfuOOXfw4yt/zIn7Cgnm/jX38/fr/g6jQFM1Jkiiitg71juQhbfffJu/Xv9XIQ8V4Nbf3LrD33N33HHHxu3Ozs5P3HOD1/bTsGbNGurr6wkGg/9yn60ByRus6/ovcMsttzB9+nS+//3vc8sttzB58mQ8z+Odd97h6quv5txzz+WEE074xPsMw2DJkiW8+eab7L///ti2zc9+9jOOPPJIDj30UAzD4Be/+AXHHnssBx54INlsll/+8pfMnDmT/fbbj1QqxbXXXsvJJ5/MXnvtxcDAANdddx2nnXYa06dPp6enhxtuuIHTTz+dKVOm0NnZyR//+EfOPvtsJk2aRGtrKzfffDNf+cpXmDBhAhs2bOC2227jG9/4BmPGjGHdunXccccdfOtb32LEiBFseHc+D8z5J2d+61vUZ3J0tLSwdO5c9po6lUhFJU2yxz3PPMPF++xPMJfl0dYW5syby8xDDuWh558j7Pfz5bHjOGDGXgTHj+G9RIJXH3+cb596GsGaWhYvWsi7b77J2SefTGD4cN5csZynn36an//85/h8Pl5++WWef/55rrzySlRV5cUXX+TFF1/kV7/6FQBvPPoPku8v5KiZx4JpMa+9nbblyzn9gu/i5fO0PDKHtakEddOm8qPbb0OVZc6cuicnTp6IFInwXtN62np6Oe17F+HZNm/ddz+tiQFmff3reNkcz7zyEuvSaS783vcAuPvuu0mn01xwwQUA3HnnnZimybe//W0A/vznPwPiQQC49dZb0XWd//mf/wHgpptuIhKJ8LWvfQ2AP/zhD1RWVjJ79mwAfvvb39LQ0MCZZ54JwHXXXceIESM2kso111zD+PHjOeUU0W3n6quvZu+99+bII49k0qRJ3H777R95IAHGjh3LFVdcsbn3fgklfCZcdNFF9Pb2fuS1Cy64gAsvvPAjr/X39/PLX/6SBQsWkM/nGTp0KDfccAONjY3bZJz/kdSvuOIK3nvvPVatWsWIESPYbbfdmDBhArvtthvRaJQf/vCHn2qxD5L6pEmT8Pl8W+0EtgScQoF8axu+mmpc06T/nQV4eGTXrUMvr6Ajl2H6rNOwc3k23Hs/sk+jYt99cD244a9/4e45czjtwAP53hFHoYRCVOy7N6EhQ0gsXroxHtzJZkFRUENBouPGfark8u9gpdL0v/Mujmli9fcTGjUCSVVRQ2FCI0aw/i934rku6ojhHHT+t/E8j6u//W0OrKjGdRzCY0bR0trGlJNORA2H6H93AUgewcZG7EIBCYlAQz1adGt62L4YPnxPGYbxCSvJNE0GBgYYO3YskcjnLUu4ndCMKAcwFHgbWp5sYYg6RPQAPQFe+NoL3Jm6k5MmnoRf8vOdxd9hauVUnnjzCaFNrym+d0tlkH4WLIXzf3U+j73yGD8Z/RO+c8F34G/wxro3OCNxBpP1yTwz5BnRD9VFtNjLIiz26QjH6w6MdDrNqlWriEajBAIfjRONRqNEP/TMeJ7H7NmzOfPMM1mwYAGTJk1i3bp15PP5bWZw/Ef55YorruCxxx6jvr6eSy65hGnTptHc3MwNN9zAl7/8ZTZs2MA555zDr371K+67775tMeYtDjubE9miwSCF7h7Mvl6sxACyz4e/rhZJ15H9frLNzbj5PIH6erAsJAmefu11AI7cYwqeJ7RwX3kFViqFnUqCLGMNDKBGImjhCP6qqs9M6ABKMIBeXo5czGwtdHUjqzquYSDJEv66OqxUGr+qMbKhAYB7n30WJRbFKeTFpOI42Nksst+P7Pfhmhae56EU+5na2Z0nCiYajdLY2PiRn6qq7cFoWwCDtdJrEI7E7mJ1QxB6+no4onAE95XdxxnjzuCRNiEnnTbjNFEPpg3RyKJyO4wdoA5O2q9YjrdzjjiXITCNaSiSwlJzKZlkRiQvmYg4+hBCV09upzF/DtTW1n7inot+zAhasGABhUKBE088kSVLljBx4kRGjx5NOr3tHAibramfddZZ3HnnnRx66KFceeWVPPTQQzz++OOEw2FOOeUUYrEY77///tYc61aDk88j+3RcxyG7Zg2u4+C54K+uQlJkpGAIO5Mh17SBlnSSv781j56eXuYvXUL3QD/1FRVMrKtDDQXxVZSjhoIUOrpERE1KdEryV1cjayrq57SEZVVFjYSRfTpqLIadyuAYJq5p4hoG4REjQJIwEglmHXo4ACs3bMBTFdGrtGDg2TZ2WlRv1KJRPNcRxC5JyIqCk8+Xsku3NTxEuzkfosTteiADsiVvem0uggwboM/q49WBV1FQOOHkE0Rcer643xct3PV5UQaH7HMIkWCEJZklrF22FhogKAc5MXYi5408D8M0xOSlIBy6EuLcdyJS3xwMErllWTQ1NTFmzBgWL17MpEmTACFLXnvttVx++eUb/WlbGptdGeJb3/oWtm1z/PHHM2LECGKxGEuXLuW0007juOOO2yqD2xbwXBfXMFCjUTJr1lLo7kGNRJEkCb2iAs+0IBzESiQpdHczZ/ly5rw3n87OTpzB2PQpU5E8kH06vtoaXNvG6OnBzuVRwyEi48dhp9LIuobyBaQoLRYj396BXh7H6OzGSifRQqIme2D4UBS/HzuR4MR99+O6e+/BdV0ef/99jiqvwsnnRW/SbB7XMNBiMWiRcPJ5NE0TnZZcr5hRG9pSl7eE/4Q+hHU+ChEd0omQJhxErLkGL77zIhEpwrTKaTy+7nFsz+awqsOonFEpEnsCCJlme0ECf6OfY/Y5hodeeojHFj3GxTMuBh2uVq4mfmgcHkeMdTxiIhok+AziXD/74nWHRDQaZe3atRvl6lWrVvHGG29w0UUX8fjjj1NbW/sRZ/TWwGZb6pIkcf755/PGG2/w3e9+l2OOOYY//elPXHrppVtzfFsdViqF2ddPdv16EgsX4eQN1GgENRwSyTkAqorR20tyYIBnlywC4JSDDuL5uW8CcNQeeyD5fKjBIHosTr6tHSuVQlJkAo2NKIGASMsPhb/QWNVQUFj7/qAoC5xI4ppCNvHFYviqq7FSKTRZYvyIEQA8+tqrSBI4honkORj9vTj5Alosiqzp2LkcHp6IoXdd7NzOI8Hs9PAQCTshRCx6C8LqHvwKdCADV7RewSnuKcyX5vNoy6MAnDr5VEGEBqI70fay0gdRAScdXJRgOubgFTyIgZSThGYeRJQJ8BCTWAJxfha7VHbpMcccQzwe59xzz6WlpYXrr7+em2++mfLycubPn88+++yz1cfwmZOPQqEQhx9+OGeccQZ77rnn1hjTNoOZSJBdvx4rmcTOiGQcf30NgcYG1FAIHBslFEIyTHLt7Ty9bCkF02SfSZNo7+8nk88zrrGR4VU1qP4AWrwMWdfIt3dg5/JosRihYUPFsUHUa/kCUAIBUTfGc1HLy7AyGVzbLJYd8AgNbQTLws5mOeuoowFY29yMHQziZNJ4qorR24eVTonSBX5/MYbdQ5IkZEXGyeb4D77zErYU+hCySn1xex2C4AZX5R6sWbmGdc464nKcqkgVC5ILCMpBjj7saGHVh9lUbnd7QoUDDj2AslAZq3OrWbF0BdSBUlBY0baCu527MZKGIHEQ56kgzn8XkmCCwSA333wzhxxyCJdeeil//etfGTtW5BEcddRR/O53v+M3v/kN8+fP32pj+K/NKLVSaczePpAVlHAINRJGCQYIDR8OlgWqimMYKLqOVyhQ6BtgziJhpX/pgIN4au5cAI6aMg1JU5B1DV9FGVYiidnXixLwExw6FFlVsTMZZF1H1vUvNGZJllEjEdyCQaDoFHSyOZx8AbdQINg4BMnnw0qnOXrPvQgGAniex7v9fbhGAU+S8ByHzLr1eIAaCYtuSq6Lh1dMtHJwS9mlWxcuIlu0GyGdKMDq4s8GhEzhAnXw3DsiZv7wssN5bP1jABxTfgzB4UFBkA3bfPT/ElqdxnH7CSl2zpI5UAsSEuc/fz4/bvkxi4xFgswlhOTSgzj3XYjUB7F48WJ23333j7y2//7787vf/Y5LLrmEGTNmbLXP/q8kddeyMHp7UAIBQBKkZhjIuo5eLgpoSYOOHFnCS6Z4c+liOpMJhtTVMa6ujtfffw9Zkjhi2jRkXUfSVJRinXM7k8NXVYm/phrXEr1NB4uBfVHo8TiuaaLGIkJDz+aw0mmcfB5fdSVaNI6TSSPbFucXtbvXV61A1nRIpVFDYcyeXgrtHWjxOBJg5/PiOiCBJO1UUTA7FTxEl6APgLeA9xCSywqEM7QbUTY3DlajBSPhuXWC1I8afhQPr30YgFNHnCrkjBjCUt9REISTji6W421+DK/Gw5Vc9vLvBcA75juCwC0EqScRDGSzSXLaRfDYY48xevTo7fLZuzyp27k8ueYWMuvWUejsFE7MXlFNSI3HsFMpXMcVckk0iuLzizZwHqLsrW3j5bI8Ol9kCX7p8CN49r0F2I7DvhN2o6aqChQFPRrDs22M3m4kn0Zo0EovEqT6BaWXQWjRCJKqgOuhx6K4RgHXKAinbChEoL4Gp2BiGQZHzBDy2MtvvIEZj0M2h6SruI5Doasbz3aQdA07kwE8cF1kTRPhjyV8MeQQJP3hSLZmRORHBcIS72CTo9RCaMxRYALYMZvedb3Mz81HR8fv99OUbaJOqeOg3Q/amKa/o2GfQ/ehOlzNhsIGFrQswPN77C3vDcDbztuCyLMI2cVCXKddMApme2KXJnUnn6fQ0QF4aJEIdi5HZu06rGQSPV6GnUqJyoyKDJK00dEoKyqebeE5DmZfH57lMPvwIzlsv/05areJPDZPSC+nHHGEKGfrDyAHAljpDFYqi6+sfGPLO7tYfEvWtC1yTrLPh+zz42Sz6OWVeAhL206n8RyHYEOD0MbTaWqqqpgybhymaXLXW3NF/fZMTjTAdj0xIVg2TsHAdYqhjEqxRo1l/dtxlPBv0AEsR1jhqxB10fsQVngtgow7gd0RCTntxf1SiFj1OLi6y4tPvIiHx/6x/fln6z8BODN2Jkq9IuqSb9vs882CUqVw2kGnAXDve/dixS32RpD6/MJ83Jy7qbCXgnCY7qISzPbCLkvqXtEalVSVQEMDvqoqAg0N2Om0aOcWDlPo6sbKZIpNmg1c26bQ3o6dzVAYGMDo78PJ5pACfg7ae2/+cPnlrF27mg3d3VTGYhyw2yShWPh1JBnMxAB4HsGhQ5B1vSi9GFs0RFCSJLRYDDubRa+pRFY1XMPESiSEBFNbgxoKY6UzSJJEQ6WYXJ6cOxdP17ELBVzTxMqm0WIxZEXGTiXxTDGJScUwzZIE8zmRQJB0OYK0GxCEPhdBwvUIucUC9kCQs4PIBG1EtIxzRGYiHVAj13BAzQE81fIUEhJnVp8JQ9g2lRg/DxSY/RVRCuKx5scYiAzQ4DXQEGgg6SRZkV8hCH0AEfWTQqxacmxyopbwhbDLkrqVTOHZNv6a6o0ZnJIsowSDyLqPTNN6UkuWYmeyONkssqrjmRZWMoWRTFJo2iDe39iA5PcjaRpGRwePvSNkmJMOPwLZtUXVQ82HaztYff0oQT+BYkanXWwIvaX09EHoZTHRQUlS0KIRXNPCzuZEaGNFBVpFOW6hgJVMccFZ4gHL5nK819MFjoWs6RgdHSihIIGGBtyCSaGrCyTwbBtZ10Wd9xI+G1yExBIEhiMqJtYinKFJRDJRBtGSTkKEIy5EZIQOQUwExQYTWrfGGb4zeHc3cb8ZrsHB/oNprGqEYewYES//AiOmjOCAhgMouAUeyzwGMuwdENb6O8Y7QkMfbOZhsymkcRcKbdye2CVJ3XMczEQCJRRC8W8qLGEmkrimSO9PLlmG0T+AFo2h+Hz4q6twTAPXsfAcFzUao3zvvXn4lZe5/r57Wb56NT0bWnh5+TIATjrkEJyCgRwI4jk2nmXhGiaB+vqNtdHtTBbZv+Wkl0Fo8ThIEq5RQC+Lg2PjWBbmQAJZ1/FXVwMSViLBkGFDqS9Gyjy0YAGeaaMGA5jJFNnmZoLDhqJXlGF0dm204uVAAKdQEM08Sth89CCIagib4sYdBHGFgfeBZ4EmhOQwD3gVQe4K4mks6uvB9UGQQaqQuL/9fgDO9J8J4xC1zHdkRODLh4lqjv/o+Aeez2MvbS9CWoi8khfXKI+QoEIIglcpSTBbCLskqduZDLguWiyKa9uA0Nczq1fjmga+mhoco4CTzSBJiFoowSCeZaHoPiRFJjh0CGpZnLsefJC5SxbT0tLMP9+ai2nbHDB9Og2RKEgIwvY8rHQGJInQSJH04xiGyFQNb/nCUoquowQDWNkcenk5qBpOPr+xEXWgvg7Z78PJF7ALBrNPFBEJ69rbGcjlAAnFHyC9dDmKz0dgyFAA8u2dAEiyVGxWXbLWNxsewgka4aMRKRsQoYpxBMF3IKxsBUFugz0/W4r/WwU0w4udL9Jf1s9rqddYnVlNrVbLMYFjhGSzLeqlfxFIcPSXj6ZSrWRdbh1vqW8xS5/FklOXcP6Y88W1khDnrCEI3kNY6qUUiS+MXZLUjd4+rFSaQkcnuaYNJJcup2/+fFzTJDxyJGo4hNXXjyQryAE/kbFj8VdWooSENKMGAvjra3niscdpbmujOhplnwm78chbom727BNPxDFMEXfuOciqipVM4KusQI/HgeLEIklfOOHoX0GLx7DTabR4HNmnIbkeTj6HlUrjq6pCi8aEdp4Y4EvHHIMiy3jAXW+8itHXS3DoUIyeXjLNzfgqytHK4ljJJGYyDa4rWueVdPXNx2Co3oe17nbgXYT8MgWYhCDtdQjtfTBVPoiICNkArIWWrhb+t/l/2e+d/bh1g6jv/Y3gN9DKNaHTb+/s0c2APkbnK8O/AsDtydsJ2AH0rC4mJAeRZdpZ/NtBRAk5lCSYLYBdjtSNgQFyLa1IqoIWi4nuQ+k0Vt9AsSenRP/b7+Lk8wRHj0YNBETKfSaNky+IlnYBQe63/e1uAE7eYwpvNq2nN5ViZGMj+4wdB7aNpOvgeNhGAc9yCI0aiSSLSyqaTAc+V0XGzYEej4v4esdGi0axTVO0tevpRY1F0cti4NrYmSy663LwfvsB8PSiRWR6etDKoki6RmL+e0i6jr+hHlyPQmcndi6PEgyKEgKl7NLNQy+CbHMI0n4LoZ0HgP0RFvl7CPKOAbshdPdJiKfQK/7Uwz3ZewDYq2IvXu9/Hb/sZ7Y6WzhRh2+7U/pC8MHXj/w6OjrP9T/HGncNDEDBX6Al3yImMhexitHYFAWT2H5D3lWw05G6YxhYqTTOp1Q4s3M5suubkDWVyLixqJGwiAgpixMeM5pAfR3JFctJLl6M7PMTbGxA8vlILl1Get16sk1N2LkMks/Hi889y4o1a6iIRjl44uSNreu+csqpeJaD69rgiZopdiqDGg6KkrzFcXi2vcUdpB+GHi8TiUK5HL7KShFTb1nYmTSeYRCoq8VTFNyCgVMo8N2zZxMMBjFtm2cXLybXtIHo+HEYvb2iSFgkgh6PYaWSGD09YhXiejjFLjkl/Bs4CKuzD2GdNyHCGDcgyCsOLEUQ2FCElTofMRH0F98zAGiQH53n3iX3AqA4wiD4UtmXKFPLRPjjZpfg2/6oPKiSE0OiK9Kfc39mQecCJj44ke+u+64g8jCbIoFSiHNLUJJgviB2GlL3XJdCVxf5llaM7m7yrW0igaZYKtbJ5yl0doJti/R8TaPQ2YWkKMh+H2o4jK+qCrOzG0nTCY4cjqJr+MrLcQ2TfHsH1kASSVYpdHZye7F12pmHHMqydJJl69YRD4c5/sAD8RwLWdXAcXAtG88wCQ0fhqyKJ85KJpFUZauSuqxrqOEQTjaLr6wMSZaEw9axMfr68NfWofr8uI6NncszprqG7xS7FD2y8H1S6zegV1ehhcNkVq4UIZKVlSi6j3xrm5CPZKkkwWwOOhAEHkOELMYQMkkjgqDeB15CSDSDPUQtRGXFSkQiThCYCo+tf4xEPsHoyGhe6n0JCYlv+b4l9ttjG5/XF8V0+EqVkGAezjxMhVeB4zq8n36fge4B4UsoICa1LGKVswtml25r7BSk7rku+fZ27HQGrayM4NAhaGVlIua8pxfHMMh3dIo64bEYWjSCOTAg+nHGoniWjRLwC0u8UCA4dChaIIgWiSD7fUiaJkrjKgp6dRVthsHypibKYjFO2Xd/Hnz1FQC+dvxM9GKWqSQrSIqCk8sh6yqhkSMBcE0TJ5vbWL53a0KLx3GyeSRNQw2FcDJZ5EAIs6cXLRZFjcXwCqaInuvvZ9+pUxna0EBLXx8vLF5MbkMzoVGjcC2LXGsrbiGHr64Wz7HJbWjGyRewMqUn7N/CARYjrO/dEREwgeJPA6Kzz3zgHQRhRRBhjiFE1MtgA+ahYFfb3Pz8zQDE5Tg2NifFTmK0OhpGIyaJnQkhqB9dz9Hhoyl4Be7sv5O9y/bGxeWF9hfE5BdDlE7oQoR2SohVSwmfGzsFqRe6unELBv7aWnwV5ci6XnTulWEODJBZu7ZY7CoqNGxFwewX3YZcw8DzPKxMFrOvH8UfAElCCYhQRyeVxik2jtAry/GVl7HHwQfyyksvce3//pAlrS2sbm0lHgpx5kkniQJYUrGhBOAYBfwN9ajF5rJGXz/IMnp86wcS6+Vloh68ZaGVxXANU8hBuTxOwcBfXYXnOsVmGCZyNsspJ4lImDvmvk5q3XrkgA9fTTWSLGN09+Jk0qixuEicMi0K7e1YxXj7Ej4FTQgS2g0hv1gIaz2JSCgaQEgzAYQ1biAs0YkI7f1hhDU/CR559BHW9KyhPlbP+8n3kZD4XuR7Qo+fhJBxdjJkJmT4QcUPALgneQ/7RYRv58nuJ8WEONitKQcsQ8hRJVL/QtjhSd3o6xcp8ZWVqOGQqCJoWbiuixwMYCUSmP0D+GtrcLJZrFSa1JJl5Ds6cQoFEZtuGDiZLMjgSaBoGpKmgSzjmBaubRNoqMdf34CTy6GEQtQEg+w1dhy3PSEq43358CMIh8J4joOsa7iugzUwgKL7CI8bBwgt3clm0ePxreYg/TC0SEQkChXy+CurcC0LJye6OBndXfhqqkFRcPIFkGW8vgEojqsvk+Hxt9/C6O5BDRRb5QUCGL2ioiMe+Gpq8GyHzJp1okTvfyscRNjdx8P2exFWZgWCnAbrumQQFqcMvM1G0mYoMAE4AmHVDybeqMBaGOMfw97D9ma4NhwHh5NiJzEmPEZY9tunNtQXRmG3AhPLJnJi+ERMz2RNZg0SEq/1vSZavFUirk8ccS3aEb6HErF/bmxXUvdcFyudxkwkP93xmcmI/p7RCJIik2ttJbu+icSiRXQ99zzdL7yIY1lo8RhmIkGutQ3PNkECX001Rk8PmbXrcBwHFBk3l0fyPBECqMjYmTTZ9evRIhHCo0fRlsvxwFNPYfT0YqUzPP7yyyzfsIF4MMjsWbNwzQKSooqY8HwBz7QJDB2KLxYThcK6u5F1HW0bWOkgYuTVSBgnk0MJBVH8fszEAHp5OXYqg6yJsdjZrFiZ5LJ8ZdYs9GIy1J2vv8ZAZzdOwUCSZfxVlSg+H3a+gNHTi51KEBg6BDefI9/e8d+XjGQi2sstRFiRCxGWuYPQglsQERxhRKTLKoQV3oLIHl2LsNiDiGJdQUTyUBSYU3zvtOLry2BaaBpXHXEV83rnoaBwcexiqEaESe5s0ksRVpUFDfCD2A+QkZnTN4fJsckYrsGLK1/clBkrI/5OI1YzbyMmxxI+M7abL921LAodnRstQM/zQAIlEATXxc7lMAcSKH6fsICNArKq4RgFrIGUSPn3+/FsB2sgRb65FSuTwVdTg4SHhqiZ7jk2dmIAz5ModPeKz7JEqryZGEAJBAhPGEeqrZ0fXvULlq1ZQ6qnlxP2mMIf/v43AGYffAjBSBg3m0f2+zCb+rATA4RHjiC22/jiuXTguR7+uuqNYY3bAr7KSjKr1+DaDmo0gpVMIvt8oMo4xcgYs6cPFAXPcQnkcpx9xhnc9fe/k8zl+PMTj3Hpt84Rq52GetRYVFSu9CC9YjXxGVPRYrGiI7qLQMO2Lw3ouS5OwcBx7C9+sH6EdmsjyLURQcAfRxpByh6CWEMIkulmU7s5GWGRdxePN7T4dzNCeskiLPzBuiZliMngPmANwlEYhbaVbdStrEOaIXHFM1fg4XFm6ExG144W+1QiVgA7I3RgDxi9bDRnRc/i3tS9pCwRjL60Yyknl58srPQuxPXLIcogZBEW+wSEv2J7YCd1J211UvccZ6OE4tqWIDxZxuzpw7Us9MpyJEXD6OrETqZwTKELO5ksjmHguS6KT8dXW4sWADuVRq8sJzp+HIrPR2rFKnrefJPc2nXodTXguejV1WTWN1Foa0crKyPb1oE1MICTzuCvqwNJxkokcQsGemUlySVLuPTmW1i2Zg315eUcu9tu/PWVl+jLZBjf0MDB++xDbt0GJFlC9vvIt7eilZUTnzJFOHFb2/A8D39t7RfqQfp5oEbCQg4yDHxVlZi9fdjJlCj6lc4g6xqyX8fq7cML+Mm3tnHeeedxz33/h+M6PPTG65w68wRG19VSaGtDq6jASiRRQ0HcQh6juwdJUdDCIjzUSiZFf9NtBMcwxIRpO1juZ1gpDGqzgyn4AQRhJBARKEGEBr4GQZhxBMlXFN/XjCD70Wwi/TKEo/M1BNlPQUS2OAjS9xffO4Cw1k3E5KAVP98CXkBY/xFxvLaBNk584ET2COzBkXVHMnfdXOJqnPMazhOOVh9i4gls/qnvcJgB/BMuC13G09mnaco18aPxP+KC+gtE5NAExDXVEJNeGnF9sgi/wwS2bcKVhZjQB0l9yyeFb1VsdZMy19yCNTAgiCESRfb7N9Y3t1JJCu0dpJYtIdfcgp3OFB1/NkooKHqFhoJo4ShmXz+pNWuRNR09Hsfo7iG9eg3Z1lahlysKeBKe6+EWDKz+fpSAX2RZ9vdjJRLguZjJJFYyAbIkIlZ8Pn568y28NP9dwv4A133jf+iLRnhw3lxkSeLS2V9GNgzsbAbHMEivWYfnekQnTwTPw+jqRtJUgo0NG2u+bEsogQBKMIRrWmjhMMgy+c5O9FgcSVVRdB9KOIiVSoHuw8pkqdB1zjrtVABcz+Oq225BicfBA2sggRwK4doWKCpuXjSqttJpZL8fo69vm5XldS2LQnsHIOGrrUGvqPyP7wFgJYI4cwhLEUQp3CcRy/rBSIvVCPJeCCxAJAzdD7yJIO0qhG6+DvGQL0UkEBnF475VPI6LIKQuBDm1FvfpKf7fQFjsPoQZNRyYBs1LmznjpjPozHXSE+rhqmevAuDyMZcTLgsLIvcjHK87M6mPBmqhnHIur7ocgD+v/zP9yX6hodcjJtQWxPWx2JRhmkdcw22FwYSoPGLlMKI4tp0IW53Uzf5+/PV1BBrqkYMB0ZR5IEGgsZHwmNFImi4KaIVCKEE/ajAorETDQJZkImPGEBjSKJpDgJgcYjGMgQSJDxZhdHYQ2W08VYceihYOoYXDqAE/yAp6lYjD1kIRJElGrazEMw1QRDnehGNx/i9+wTPz3yWg6/zyjDMZPnEiV/z5dlzX5Yy992XimDHQ249rCiJzjDx6RRn+yspiDfYqgo2NX7hV3eeFrKpo8SiebeO5LnosgtnXi+zTUYNBJFVGDYWFzGUaKH4/uQ3N/L+LLiJYLHa2ZN06bn/wAfyN9cIgMg284vmaqTR6RTnWQALHEH4Ps69vm5xbrrVNTCKmidHZhZnczIpPzQgdejKCQA2EFh4AxiLIYg3CSjQQFnUKEWu+CEH+9wF3IUrmtiKKbz2BIPxEcb97EWTfjrAwFyHIqBxh5fUiSCKJWDlUIXTjyfBa72scd/9xrE+tZ2JsIrFwjGQhyUFDDuLMMWdih22xwogXf7ZsTbhtizjClyDD6frp7Fu2L71GL//b/L8s+2AZbp8LYxCTsIGQueKI664jJsttlQPXjLgP8sW/W9npSgJvfU1dVck2Ceki396BlUigxcrw11STb2sn396OHi8jPHoUkq6T27ABK5Eg0NCIXlGGJMm4loWkKAQaGnBMk96583ANUf8cRUPx+fBVVyEVS+EqsRh+18XKZERrOp+GVh7HLeRxDQtJlSl0dfHbhx7grfffoywS4Q8/+F+GSxK/uPlm1m3YwIjaWs49+WRR8TAYRCsrw7VM1ECQ+B5TCA0bihIIbPVY9M26xOEwktYnSL2yEmP1Ggqd3ehlMVzLQgkEkWQFEinkMWMx+/spHzWS3111Nf3r1vKTW2/l1nvvZZ8ZM5g2egyeqmC3tYPr4BYd2Fp5nEJ7J/6GelGuOJ8vtgPcshiMIMp3dJFrakKrqMDn86EEAziFzeydqiEs9X6E1d6KIMhpxd/vIYi5prgNwjLvQxB/dXE7XzzOeoRcMwUYhSD2F4v7j2BjDXSaEFJBDYLkIwjZwFc87kroruvm1/f+mvufF5UXDx16KDPKZ3DdB9cRC8S4buJ1SAEJz/LEeTSyQzbD+ExQEPH6r4HUJfH7Eb/nyOyRPNvzLM/2PMudD93J0acfLdjIQXxfU9gUDVOBsOLHbqXxFRD3SidiEo8hfBllxfG4W+lztxK2Oqmb/QPYbe2oRSvaX11NcNhQzIEBXMsiNHw4gcYGjI4urFSqmCEqYRYdfk4+h9ndg53Po0YiGMkk9sAAvpoatLCK6zkYPT04+TzhMWPIrl1HZulyQqNG4rkusqKSb29HQkKWFZSGOlTdj9HZyUWnnoaRznDRKadSGY7w92ef4bn57xLw+7nqrLMJBAIU2ttBkfFVlmMlU2ihELHJEzfGpe8IUEMhYY0bJrJPR5IV8q0tBIcPxU5nhFwVi+B1deEWRJVGo7OLI486ilxTEytWr+FvLzzPRT/7GQ/eehu1gQBOJIqdzWJbJukVK6jYd18yq9bgpFOitnxvH8EhWy4kw3McCt3dopG2YZBdtxYUDa3ouLVTKXLpFGyOzyKFiEQJIgh6HCKOPAq8giDscQjy9SMssiaEhV2J0LKzCKfnKsRDPbH4v8UIK38IgtANBGknitu1iNR3DaGzwyZtPQ9zWuZw//P3oys6F0+9mEkjJ/H1h78OwO9P+j2NaiNUgNqhCmIJs3NLL4PYDXE+HdCYb+Q3+/yGb7/2bQCuev8qjjroKCRPEuQqI+SwgxCTcgDxHQwgiHZLwUNMGp0Ia3wBYrUwAbG6SrAp32Dbusq+ELa6/CKrGmYyRb6jk3TTBtIrV9Hzymv0vT0fz7KLTRoKqJEwWllMtAY1THItzfS/9Q75ri5cx0aPx/E8D3tgADubw+joRCuLUzZlKlo4ip3OUmhtwxwYoNDdTWb1GvBAjUVR/AESqsqf3p7H+XfeAdEwnuehtLbx42OPozIS4Zn57/DHOf8A4Orv/4BGX0BUcfT7oSwOSEgS+GtrUbeChfpFoPj9KD4dSZaQPAk1Esbo7cMpFFDDIdSAH3+5CJ+wBpKAh5lM4aTT6JUVfPuUUxlaXU0ileLbP/oRfek0aAq4NpIERlcPuZZW1HhMtLqzHayM6CBl9PWR7+gQhPwpYambA89xyLe14+TyqNEorm2hhMOER49E0XXh2E6lMfv6N++AQQQBtCEIeDkiTf8BhMU3HTgOQRDrivvlEQQ6aHW7CFIOFfcziseai5BV9gFOLO7fg7DI90MQeAhBCAOQ9+dZZC7a6Oz8qvpVZk+azfPHP88h0w/hvDnn4bgOF8y+gKNGHSWsxBx4sidI0M+uQepxxMQYAAbghNoTOG/aeQCsz6/nztfuFNp6GhFJtAxxnasR1z6BsOC3lNXsIVZgnWyaKAKIeyOCYMYMQl7byUIrt7qlnly6DLurEyedQQ74CA0dihoSJowpufS+9gYeLmoojBoOYxsFrGwGN28IR2RHJ/7qKgIN9di5HHo8TmbdWrKr15LRNbSyMnw1VQRHDqfQ1oaVyQgNtq8XJRxi0ZJF/PPll3nugw+wi+3aFnd3MWPkSPJtbbiOw9PvLeDKu/4KwE9/+lOOmDKV5KIlRMaPE3VcsqIOux4vQy8v2+JNL7YE1FAI1zBBUdDCIfLJFNm164nvPgkrnUGNRZGCQexsBkXXsbWckLVkmRcWL6K5uxtFllm3oYlvXn45N377fMIFA9sw8PJ5BhYvJjZmDE6+gOw4mPkCRncP/poaPNcTYZCWha+qkkBDPVrkoyEDjmFgZzKiY5OuFVsHdmDnctjZHHpFGeExYzC6e8g1t6LHY6iBAJKqIskFjB5j85t3v82mglkaIvGnqvi/WgSRL0LotYP1VwoIK3wVwrHnR5DIYJnY1xGEMugUHXTeHQI8Uvx7MM56NGRXZ7kncw+3PX4bBbPAmye+SblZjm+Vj9+U/Yb57fP56j1fJWfkOPX4U7n0oEuFIxYgC1alJT43yK5B6mHE9/Aq4vq3w+UHX86rza+yoncFV8y7gqkTpzItOE1MkFnENT8Asa0iVk6Dcfubi0zxWDLie1uJ0OgziJXbVDZ99wpC6hnM/O1EfKfVfDLxbAfGVid1WdfFg19dhS8WRw4FsHr7sXM5pGQS2aehhaIUurqw1orMRV9VFRX77YNeXk5i/gI818NMJlH9fpBlgo2N6GXlZNevp+fFlwmNHkl45GiUaAx/TTUMGcLVd/2Vd/58G91F55okSRw+eXfOP3s24ydPRouE8WSJ2++7l9ueeAKAC88/n2994xu0PzoHJRhECYdILVsGriuyRGUZLbZjusKVUAgpmSqWEg6CIlPo7MAdPxY9HsdMDODFY4AnsmgHkjj5AnpFOScedxxznn+eBWvXoCsq61qa+dYfbuC6b57LsGiYQi5LZsUqVN0HsoTVP1DMAXAIDhtGaNhQfOXlOKaJ2duLZ5n4a2vRKyqwMxlyLa3Y6SxKKIASDJJdu558eztqNIIei+KkU5h4ZJwVFLp6kDQNX20t4OEWCtjZHGo4jBqLQ3oznKUSIta5CmEh1iLIthehg68u7jM4N4cQZJ1GOE9dhKbbiCD+xWxKKJqIcL6uRCzdTYSlnhC/l4xfwv1/vJ9H1zxK0hZjnVQ/ic5cJ+VjykGCx199nIufv5i8nefYPY7lt1//LfI8WchGxaqObt4V45LZNUhdQchTYxEWcjvIBZl/nPkPpt82nZyV45S/nMJth9/GMe4x4vt5n03O5WrEamoJYhVmIq7NoMQW4qNhjyZigs4X92tByCs+xL3Rg5B6dDZFKO2OuCfSCBnGh3C2lyHunZ0EW53UY5MnQjqFJElYmWxxiR0hPGYUsu5DCQXRIxHMdJrUsmWiJVxNFVowiJNKEd19Mq5pkF65Ci0YQgmH8FdXEx4/jp5XX2f1iy+z9PkXWD/wIN/a/0DUcIjw8HIWrVtLdzJJRSjEsTNmcMJe+zB6wgSiE3dDi0boSae5+Lrf8Mb77yNJEt87ezbfPvMs+t+ZT76zg9CI4RRaW0XtmIpy1HAYz7ZRfNsrE+LfQwkEkFRFTDxx0aLPGEiQ7+gkPGI4Wm8viqrgi8VxcllcVyK7YQP++jrKJ0/iD9ddx+zvfocNHR0E/X46urv5n+t/zbnHHMvZx59AZtFCUstXEBw+HLdgiEJoioqTy4rVjCThr69DjUUwe3rJrG/CWbIEcyCBa9rIAT+4Dkb/AJ7jokWiSLKMnckSGD4CzzQZ+GARAOFRo5AlCUnVRGen3l6cnEKmpRk2J1t3H4SV5SEssgSCqIciHtQwmxJc3kM8/NXAUQgJ4E2Etb9Hcb8kwlKbj7DwxyIsvDpECKMM+Xiek+aexNJnB81tmFE/g4umX8RhRxyGNFwiq2W55oVr+OuTYlX4pQO/xPVnXI+6QBUk0oAgmV6wo7b428dOUMxjM1GBIMnBVVQ3REdGue3k2/jKQ1/Bdm2++fw3+eqYr3L56MsJpUIirn9PxHc0GBkzBeGwThd/fIgJYzjiu12M+P6yiNVOFjERqIgJoguxGosi2gvmEZO1H3GfDENM7jJCfhmMXNpJsNVJPd/Wik/3Ift8uKaBnU4TqK8n2NiIFo/hFAzyrS0UOnsI1NURHDoUSZYpdHRiJoolAjxJpMAbBTqSCTa0tjDvlpt56623aO7o2PhZpx9+OCOn7IGsafzo698gLCuMHzmSsj12x85m8FVUYuoaN//lL9xy882kMxkiwSC/+fHlHHn0kXieaBKh6D5CQ4fhWhZqNIpcyINUbFrt3zE9JpIkoUYiWAMJ/PV15Ns7MJuayLe24q+uJlBfh6coyIEguA6e62GlU2TXNxEeMZz60aO545prmf2D79Pd10csHCaZyXDT44/x0pLFXHDm2YwaSGAl+onuNhFZknBMQ/Rp9fvw8MiuXScybhMJjI4uXNchPGokodGjMdrayKxZi9nXhyTJaPEokqKCLOHmsuRa23ByeZRwCNc0cEwTORgkvWYtZm+fuE+ymc0j9cEY9UrEA9uNIOADi78H26b1IqzxRsQDHkA89DMQMehJhAQznE2VF9cC90JmtwyvJF/hePd4pKESgeEBgouCxPNxTm04lTNPOJOJEyaCBfZkm4ffepjf/el3tLW1ocoqPz3vp3zzmG8iKZIgqvEIIskAMtgxe1PS1K6COIIwxyCIdTkwEg6rOowz9jqDlo4W3ml7h7+t/hsvdb3ED3b/Aad2n4q6VBWEaxR/3kCQ+e4Ip2YHwvLuYVOBtThiklyN0OdtxIQSQThFjeLrScR1NhD3RQViFbYOce+sQ9w3OxG2OqkrwSDRCRPwVVaQb2sn19qKovswevvJd3bi5PMsX7iIrmSSfCRMev67dLe00NvcQiafZ+KwYXz1uOPRK8r5YPkKzvnd9R85fjAQYOrYcUzfbQKVU6agBIMYHR3sP2MGkqbhr65GCQZYtngxj99zDw8/+igDA6Ja0EHTpnHF939AZSCA7PNj9PSg6D6Cw4cTHDGM5MKFqH4/klFAUmRkVd3mGaOfBVo0ijWQANshPHIEhbY2sq1tBIY0Eh4+HDkWQ3JdFF8AT5IgmyVfjEzSY3GGjBjBvb//PedcdhlNbW3svccerG9rY9n69Xznml+y29ChHDtxEqfsvjvVY0ZjtHeSaWmh0NVNeMRwkGUSCxdhpVMo/iDBoUPwVVVhdHRi9PXjq6kmMGyo0NNbWzENAy0So2/+Aqz+AZRQCK08jhIIkG/vILtuPUZPD4U+0Z5QqayAsZtR2UpCaLEjEXLLO8XXE2zqrPMBkAJvdw/pYEk83EsQ7edkBFmsKr53JFAN+TPzvHLnKzyx8Amemf8MBbfAM4c+w+Tpk6EXbjrxJqpGVOEb6YN9oe2NNh55/hHuv+F+NmzYAMCkIZP47Rm/ZdL0SYJcehETig9hJeaBILghd9eRXgahI6SwfRH+g5biTyNcf/j1yENkli5cysVPX8zS5qVc/MbF/CH0B85KnMWpe55K/R71grEGHdOdiO9tGOI6voeYzA9CrKYWi2MzAnFt1yG+06GI1dhqhHwzGhH51I6YtFsREVHdiO9l/892mo8//ji33HILtm3zta99jdmzZ3+Oi/X5sdVJ/bzf/Y6u3h4c18V1HBzHwXFcHrrsR5THIuQ7u/jtPx5lYVvrp75f0jQ828bo6WHMhPE01tQwvLKS3RuHsOceU9htzBi0gJ9CVw9KaxuFaBonGqEll2XpO2+zaM0aXn/zTTq6ujYec/rUqXz7pJPZb999UYMhshs2YA4MYGfSyKpSLGNrYKez6JUVkMsKh52mbZPqi58XgwW+rFSS4JAh+BsbyKxcTa61HS0SQaqtQS2YGP19KH6/mATSaYzePnAckGUaG4fwf7fcyuW/vJqfnPtt4o31/O3v93Lno4+wrLmZZc3N/P7ZZ5g0Ziz77rUnoyuqqDUNqtauQ5ZEXzbZL+LKPVmm/90FeLYouiY7fmTTRI1ECA4dhlMoUOjsFBKXIuO6Lk4mh50VuQWF7m7yXd309fXy1urVeKEQR+y/73+8Dt999busunMVnuTh2R6e64EDv9/v9+w/fH/ohOvevY4bO27EecTBr/kJ6SFCaogoURpDjfxF/QtUgBf1uOChC9hgb2Bp21JMe1O1yr2G70VhnwJoYDQapCpSvLvwXd5/733e+MkbrFy9cuO+w4cO5wen/oCTJp+EUqeI1UQBQdq1iMnHQBB9NXhasf3PjhM5u2VQgZC4JiGI9T3xmpyVYRRMHDmRv573Vy76y0W09rTSlG7imuw1XPPkNYxvHM8BIw9gSmAK4wvjGVk3El/WJ4qjbUBMGhMRq51nEAQfRxB6nE2x74M5DCBWbAOIyb4cQeirIN+f55X2V/jA+IDvzv0uzNq80+vq6uKGG27g0UcfRdd1zjzzTPbee29Gj952ZTa3Oql3Jwbo6f9kKNqzq1ei5wqYyQSKLDOkrIyArhP0+QkGgwQjYfyBALFIhP97ax6ubeG5HjOnz6Czo4O33n2XZ15/jbysUF9TjWuY9GWzDBgF0rncJz6vvKyM42fO5EunncaYWAyrf4BAXS12OoOTy2GnUsiajieJVnFGr0iHV4JB0T2pWEBsR4deVoadyWIODFA+Ywa55mYy69YSqK5Ccl1CI4ZhJkXdGy0eR3FsnGwGJxjCs0w8PCqHDuFPv7mOQmcHuiRx0U8vZ8Hy5dSXlbGus4P3li5l4coVLFy5YuPnypJEeThMdWUVZcEgfsfBJ4FfkvFrGv5gSEyYwSCaz4eiaciuB5aFoshooRAoCm5HG9abb4jyyobB2v4+nlizGg9oqK3liM24Bu2pdlp6Wz7x+pPZJ1mzYQ3eBo9F2UU4xZCGglWgYBXoQ2TKtplt3PrOrbg+F0/3eGbDMxRsEa4ZcAOE5TDDK4YTyof40e0/osvooj/zyXs86A9y6GGHcuYZZ3Jw5cEoWWWTo+51hFOuDuHkW4Nw7qlAJXj5IqmHPnHYnRsVCMfjgWzKxl2MiPtfB0yB7//4+7y17i1G1o7kmpOu4fX3X+flFS+zonUFK1o33XM8ABV6BTVKDTV6DdFIlFBziGAySMgJ4Yv4UFBQ/ApKUEHRFRRJQc7KKK6CXCkjqRJkQXpYwsk7rEqu4r38eyy2F2MiJvC9fHsR38xi9nPnzmWfffYhXmxAf/TRR/PMM89wwQUXbJnrtxnYblUa/3jffVvsWB2Z9Ee2NUVhaE0NY4YNZ9ywYey9227MOOYYAjXVGF3dGH29aPE4sj+Asb4JWVVwTBNF9yFrGnI4RH7tWuRAEFnTodimbrCxxo4MWdfRYjGsRAJ/XYiyqVPpmzeP9MpVuKqCf8YMAp3dZNasRfbn0cJhrEwaSZWRtQC51lY82yE+bQpIEtmmJh687/94ff67AAwpK+ObM/akzO9ng2WxvreXdV1d9KSS9KbT9KbT/36AXwCWuylIOZVKkUp9tPW8+R9qvt/95t2b9TkD+QGuWnDVp/4vL+fJk6enr+cjr8uSzNCKoUyomcBuI3dj//j+TJ0+Ff1oXViFfQirezAc0kEQvB+h9yY+9FoFeO3FjNIdL3r2i0FDhCT2AnshksEGSxa3AnG4/ujr+Ub/N1jeuZwr/u8KfnDoD/jDhD+waN0i3nTfZFlqGSs6V9Cca6bP7KOPPpbll23ygQyigy2C6+dez9UnXE1nZyfJj5WqiEajRKObIuK6u7upqtrkVa2urmbRokVbZiCbCcnbSu3iDcNgyZIl/PnKq0j39YEsIbkekuchISHJEqgqkqoiuy6SqoKmIZmmaOgciSCHQ8iuC7aD5NORZJl5c+diFgz8ikJYUajz+ynTdBxZ4ZAjjyDu2EQsG8kwQVFB16AsjhwMIWkqUiwKPh+SpoKs4A0MIJWV4aXTeAVDvF5bg7dmHVI4jFxRDsEA5PJIVZWiucYODs/z8Hr7wLbxYlHchYvwBhLQ2IhSVwOhEM6y5UipFFRVg1FAsmwYPQpSadyWVuRIBKqrcJo2YPf08NKqFfxz4Qf0FHuWKpLEbpVVTBw9mtMOOQQH6OvtpaO9nXQ6Q96yyKVS5FyHvKaRNwr4QyE0WUygnT29tKeS5G2bgmNj2JvK6voUhb3rGpBUBTwPC1B9PqIVFZz7o8uYNGkSt99+OzfddNNHznvs2LFcccUV2+w6l/DfhYsuuoje3o/GNl5wwQVceOGFG7dvueUWDMPge9/7HgAPPvggS5Ys4corr9xm49zqlvrFM0/ASyZBVfFsCzstSvCqgQD+xkZkWSoWpSpD8fso9PRhJRKosSiBykoRgRIKIvv8eHicNW8ent+P43mEFYXpsTJkCZalUxx92mlIoQA9z7+E0deLv7YWf1UVajiMk89R6OlB8SR0n5/gsKEAyGPGEBg2jMSC9yj09BAeMRxJksmaNsHaGvz1dSxbtYrxEycSHjlia1+uLYbB5B7XNLHLyumb9xZdLa00VlUTjJfh7rsvA++8i5fPExo5gkJHJ15bO8GRI8jlsmTWrEPt6UavqkEaFuDs+jrOPn4mry5bxjML5vPuqpUs7unGkCQu/spXRS2ffJ59vvyvnULX/+B/Ofboo8iuW88dDzzA7S+/9JH/VwSDjK+sYvf6eo4ePQZdVtErK/DFY3geKFWVDJYS+9rXvsYpp5zykfebpsnAwABj/zmWyLqIiGpwEQ4vA2EV74awhvMIaaMGEQa3sLjPOMT7dEQYWz0cf9LxyJ5M2AtTb9dzdP5owoSZr87nohsuEpb2awgtd7BMbBQhKSxlU5hcGBEmmUbIEOsRT+B6hKUaQDjxjoDl/1jOhMMnCHlmJ8aCBQuYPn36R1/0ENelFeGsXIqw3FU2hQ/WAAPwStcr/GTFT2gymghIARZWLiQ0PgTjYWliKVE7ijVgkexL0pJroa2vjQ3KBs6feD7D4sPgQLjkpUu49+l7P3V8DWoDb9e/jRSWhD9jsH5+PcJRXQHpA9OsCq/irrvuIvCxbPIPW+kAtbW1zJ8/f+N2T08P1dXVn/PqfT5sffnF8wiNHImsqviqq5E8h2xrG7guss+Pr6aa8IiRuIUcqRWrUIMBtLIYTipNvrcXze/DCwWx0ik822ZUdQ0tPWLpOzYUIWlbNOWyFMIRpICP1KIlyH4flfvsgxoOo1eWY6cz5FtzBGpqRaVBTUWNRcFx0WJRnFQSSVXwbBu3YGBnsyLSJRLGV1GBZ5ooO2go47+CrKoEGhuwEkmkdJrQ8OFIrW1km9Zj9PURHCoiYhLLlpFavhJ/fS259k4Kc98SMeiqilXII2XS6OEQejSKXlHO6Xt/jVNOO5Xevj7efmueiElfswajq4uc5xHyB7AcG0WS0GQFVZZQZZlwMITR0kLvq69j5/PsPWIkcU2j0uenHChzwRcMoJaVi9WS46AGgqiRML7aGvw1tVgfapLx8WUvQDqdFpFNFoK8QwhiLXYWohNBtjWIsMUyhJa9DOGslBHEm0CQrA/IwIzwDNoT7QCUuWXYks1ieTHLGpeJ47+FIOSRxWMPR0wMyxDx1D42NYEYXJmbiMiMVYjY9/GIp7GRTanwu5qePggJMVnlEOSZRXxHg/kA/Wzs6XrInofwyomv8Oh7j9KUbCLkC0EfZHoyHPXPo/7lRxyy7hCGTRsGH8Du3buzNraWYeFhDPMNY5g3jKG5oQzLDKPcV44UlEQoq1kcRyXiuxqJ+O6Ki/Pa2loiH8uU/jj2228/brzxRvr7+wkEAjz33HNcddWnS3lbC1s/+WjqHmiuh6TpaDGRcOJ5YPQPgOuhBoNYiQHM/n70YpKPmUhSaG3Dsx2CdfWiYFc2S2D4MI7+xtf59a+vY5yuEVFV2oz/396Zh0dVZYv+d2rOPEJICIIIAZRBpUWQVoRGBr0IIoogKG3wYSvY1+4Wh9ZWnkM//bz62uuEjd19VdTrQBOVoX1cFLvtdgBBRZFBBgkhIfNQSc37/bGrKgmpqiSVVFJV2T++85E6wz77nLP2OvusvfZaNho0jRUzZ1L39be4GhswZ2aSOOQMhNuDvbQMdDrMA/MQLheuY8cQHg/20lPg8WDMSMfdKJNIa4CtvAI8boypqZgzs2RIXadLJqyOMTSdDlOmDG1gGZjH0cpyLCYzLmsTjcePy5deSirWo0dkuN6ERIRHoLk9JOTn4a6RNmtzTg56swlXXT11332PMT2VZJuDS0eejWeIHbfDjtNqJTE5me3/8SSOyiqc1nr0SckIux1HdbUM1CaQM4OTkhiSns6Is84iZcRwmk5VYDt+HF1SEsYkaSbTGUy4Ghtw1tSiMxkxpqVgP1kGhg48hxlIBWFBejT4ersW73rftO9y5AvAF2Ple2SP/RzvPt/J7Vf82xX84e0/kG5NZ5hrGEf1R9mftJ9fTPyFnEhTheyhn4fstTd6y/eFG/gR+RUgkC6TvkiQucAWpBeMBdl7H4X00ID483xpSSbyRZeHvF8JyGfjiz2fh7xHpWDUGVk4ZaH8wnEA30H9yXpGJo2k3lOPMcFIcmIy+e588i35DPIMYqRppPRqOQlLdEtYcuESeWw6cjKTQL6MDcgXsxH5DOqRnjQ67/qjyJdrB/t0OTk53HHHHdxwww04nU4WLFjA2LFju3q3OkXElXrqiBF4TpWjGU04vLGxjWlpMqqg04m7qQl3YyPCIzAlJaFLsKCzNmDJ6Y+tvIKar3ajsyRiTE3GWVnNBRlZPDj3Sg7u3M2xulrqEi38fMlSzhtRgL28koS8gZjSU2n6sRi3wwGahiEpkcSc/tjLyjFlZoFOh7O6Go/bhee778BgwFlZhc5sQWfUAQYS8vMw98vG3SQDOetiwPMlFHqDAcOQIaSmpMhgXo2NuBsasAzMxdS/H/ayMmniMBoQwoOmN2Ie3k/OHjXoMKbLMMhOqxXhFiQXDMPjcFD7zV7cTTYZruN4MTVffy1DPWT3I23cAIwDpEZ1VFXLoGMWM8b0DAzJiSSdMQh3kx0cTix5eRhTU3DW1KDT6UgZVSAzVyUl46qupa5pP4aOfsaeh2yYAtlIv0IqyNFIhWnzLr40dMNpnoxUivSdrkYqGR1MGDSBey++l+P/7zi1zlqKc4pZsGQB45LGyeMmecs5hlRQOqTymOgtwxezpBGpyFzIHv0+pPfHLGSvMNG7nACPydOLbgw9gIbsDe9HKk9fDtdjyGd2GKn4M5AvRV9gNSeQCrmeXP7nJ//T7K54HPliOE9uZzfyXvvuaZa3jAJkr/x77/kHIZ+7DfkcTyEnKNUiTWr5yGfVidR2c+bMYc6cOZ29I91G5Ccfmc3o01KxnarAUVeLq66e1KxMkkYMwVlTS/Xu3bgbG0kZORJzdha20jLc9VaMGZl4nG7spacQaJizMrFVnKLp+AlyhcbgmTNIGVGAOSsLQ2KCTKZgt6PpNEwZmXjsTpwNDWiahkevp+HQD5izskg7ZxTWI0dpqq3FkJZK0/ETuJpsaEKQPHwYSUMGI1wuLAMGoOl0uBubQNNizvwSCM1oJHHQGejNp+QsUI/AY7eDTodn8CAZr1zTEA4HtlPlOOuk8ndWVWFITiFr4gScDVYajx3DcaocoWlYcvrz/cGDfP6PTzDY7QxISWZw/iAGpqTgqq7BlJZG6qiR1H77LR6HS/rHJyeRMf48XPUN1H27D/Q6ks8cSlPpSdA0LAMHUrf3W3RGEwn5A+Xs2MpKDP07OFfbZ8bwRWAsQXpajEUq3I+RPtLDgQuRDfgQsqc2C3gb2ejPRSqDf8A5P57DOePOgQmw8PyFUpmUI71aPN6yDiGVgg6pWA556zLA+7/Pl9onSvuRynwO0gyTgT+0gTsphiJIhUsy8t64kPd5CHKi0JfIr6RvgWuQprTDyHuaDOyBw7sO883ub0ivTydfyycnNYfkgmRp3hqFfN6fInv+Sd5jZ3j/fxv5zH+CfIa+cLtfe/8uQJrg9iKfSY73/DFCj/QFjGlp1HzzLc7aGkzZ2dLTRdPwOOyYs7MRLhfOulqE24WjugZ04KypxW1rJOWckXjsdhxVVQiXi4S8ARjTZeYhc/9+oGnSDc/hIHHQIHRmE3qLhcwJF+CyNdF4+CiNJ06A3YG5f3+M6ekyvOuxH/FYLFjycmV0wcxM0saMxllTi8tqRZ8kv33dTU1gMvVoMulIYkxNkfe5skrGuE9NxdVQD2639Nm32dEnJ5J05hA8NhuOujoaDx+j6otdNJ0sJXHQIDQNXLYmNGDnd9+y7YMPcLhcHGqoY1CTlY8rK7i2YDjnpKXhqK5FZ9TLsQmXC31CEvoEC1Vf7MJltWJKTcOSn0f9oUM4q2swZWTKcAJVVSQNGYIxLRV9QgL2tFQcNhsYOxipMQuZregYspdsQTboemRDHYhs8CVIU4kDaQf/BqkYspANvBLZE5yMNKf4Yo6UIHuAKUhFk4P8EqhBxh05gPTDHgVMR5poPvbWbSqyp6ohZz/6TDMZ3nN6wJMYY5kZwiWP5pguFcgX6WDkM/sf4FXk/cpHDqyehC8Of8H23dtJbkrmkPEQJe4SRKNg3IBxnJNxjnxJmLzlNCGfpQWp5EuRSv4i5CCtL1/sUeRL+Ayae+dpyOe2j5iKpx5xpe6sq6Op5CTCaceUnu795LZRv/8AepOJ5LPOAgRNJSexnSxDZzFj7p+Ns66epDPPwJyVhaO6Buvx4yAEifn5WHL6o+n1uBoaaCo+gSk9jaShQ7H06+c9lxO92YQhMQFzmozcWH/oB5qOy3ypwu3B0i8bXUIiyUOH4LE7sOQOQGc04rJaMSQlyR6+y4XH4UAz906qukhhysgATcNRUYlmd0ib+4ABJA4+A1vZKYTDgSkzUw4i22zYhg+Tgc5KSnBU12DKysCQJD2Kdm/eCm4PBuDCzGwaXC4ON1p5b/P7jFl1u9cObsAyIAens16auWo1PHYHhsQkdKnJ2EpOojOaSC0owF5Ti624GENaGjq9jqYTJ0keOoSU4cOoOXYMAkxka0M1sqddiWzQZyN7z98gG3w6cBWyp3wQqVAGIj/tM5E9ep9HTBWyB3kBsrGfRDby0d79UmiOLZKCfHHkI3t9O711+TvNwaQMNCdYHkHzpBszUtl4favdiX2gpw7yxXYmUgl/jozrMh75IkxCKvbNyPEHE+CELzZ+QaIjEbfOTYWugiZ9E2W6Mvbv3s+an6yRz8OI/HoCqch99nLf+XzeNhNoTpBhRr5Y9yLjxMxEvkiakM8rRoi4Ui/92/8jITWNlFEj0BkNcjp+YxPC6SJttIyYCGCvrAINDEmJ4BEk5ORgycnBUV2NTq8nbeQILAMGoPNOBBIeD+7GRsxZWVhyB/hjtJv7ZdNUfAJ7RQWWnBw0vZ7EQfnoEyzUHTxI04/F6Mxmks86C4/djr2yGktOfwxJSTI5s8eDwTvC7fb6ZHco206MYUpPR2cy46gox152CnQ6dCYTOpMRR30D9YcOoU9IwJiehuYRpI8bgyElGdvJUtzWRgxmM+Z+/fhHyQnqXU7OTEgiy2zC5najAWWVVZjTMzAkJsr8qW4PxuQUcNWC8JA87Cz0CQk4ysvRNL0M35uRgaGqCnNGOoaUJFwNjbgb6rEe+xG3zdbxOPavIyX7XGQDPo5sxDXIQVCf2+JZyB5ahnfRIVPepSB7+BOQSjfLu63SW8ZZSIXhixjhG1A7hezh6Wg23exBKhUrsrefivzkz/DWzYm08/sGc6uRL5QYS8zQJTTkizAZqdT3IHvZZyJnnn6H/ILxmlHK6stwa24MmoHxrvFU6ao4ajjKydqTcp8ByK+vGuSLQEPez+FIc9cPyK+DId7fNUiFnkpzYK993r8zkS9jpdSb0Vss6BLNaELgsdllSriUZAz9c3DW1sqp+cKDcLkw9++Po7ICNA2d2UxjcTEIgTFDenD48oEKt1va3puaMPfv51foIG34powMHFVV2PUGTFmZaJqGKTMTU0oqnn7ZJOQOQGc0YW+oBzyYs7MQQuCorkFnNmNIlB4WrgYrOpMxJiYchYMhMQH9oEFysNpqxW13IBwODEmJCLcLV72MmW4ZkIMxI53E/HycdXU0HvsRR00dmqYnNTOThlNluBHsrKml3GHHrNPRr18/0saOxpiRgWYw4GlsxNXYiKOqGuvhoziraiDVLb+w+vdDb0nA7bBj8nhIPvNMzP2yZbKTqipsJSexl53CqdeDvgNmMF989ESkIi5FKtrBSDNHLVJxH0cqjjrkp3ead79SmgM9JSIV74/eslKQSr1lCKAspGI4QbNJBqSyt9LsomdAvlyKkT39LJo9LfrR7PmRT99S6j7SkUp8P/I+DkAq3gKkfd0bZrdqQBU7anYw1j0WnabjsP4wDp2D7Kxs2cM+i+bBTjvy/u9FPtdDyOd6BvL++6xcZyBf9r74+L5B7BLvthgi4kq9/6VTcFdWIoQgIXcA5uwsnDW16BMsCI+bxmPHsFdVo7eYpPL2JVY4egxDSjKJg8/AlJ6GpmkItxtXQ4N0kXN7pI08ta3fqCkzA+F24aypkQmSLWZc1kZ0ZjMJebnojCbM2dmYvWEDXFYrCIFwOr3JGeSLw22zYUxPg/LyNueIFzRNw5CYGDDnqrOuDvupcoTThSE7GZ3BgCExEXN2Nvbychw1NSy75hq2vv4GNqeDI9YGGtwemswm7vjl7RjT0hBOJ56mJtw2G+6mJnQGAykjC3BaG2RO5ux+mNLT8DgcuK2N6BMSZBA1ZMiDBO8EMmd9PU31DXCqrE092zAf2RibkGaVXJoVtUCaXCqQDbofzYOZVUhbuM+eWk6zvRdvObm0TsbgYzBSGR3ynjMdqdB9CahzkL2+HJqn/zcgXxTZ3t++ae0ZtJ7u3pdIQ74MS5Av0wHI+3M20qTWCIuuWkTinxNJdaSy27ybg/qDpFhSWPTbRTI6pwf53ExIGTAjv8BqkWayfKSi9tA8eO2bV2hCPr885LOpozkyZIwQcaVuSEnGnGDBVlqGo7oGc3Y2ZOhwVFbiamzCZZVxR2QSCjd6kwljbq5MeWZ34Kqtw1VbB5oG3ogGOosZS26/kGFwzf36oTNbcNbW4qxvQG82Y+7fH73ZRNPJk9grKjBlZKCZjDQVl6Azm+QXRLLs9Ttr6+RXQjuTDeIZY2oqml6PrayMphMl8gvHZJITm3JzMWdl8bOF1+IxGPjTa6+jr69n5IBclv18GdPG/4Sm4hP+sjS9Hn1iAoakJJmlSdNwVFfjqKqmyRd6ICEBy4Ac/xdZy2NN6emIhISOKfUUpJnlAFJR5yM/s48ilexJmmNn1yN70VnICUAJ3nVWpEIwIgft2ks+bED2KI/SHFIWpC13hvd3LVJZjEL2BHd6zzuQ5pmvaUjF0pfJRT6nE8j773NHPB+ohovyLkLn0rGhaAPWWivjssexbNkypo2eJsdNfGje4/KRLwY9zV9KB7z7mJE9+9PvueY9JhspD0qpn3aSpCQS8gdiLyvDVlrq9Xxx4mlqRGcwYsrIQGcyok9IxJCS4jd/ALKHZ7MhXG40vQ59QkKHoyUaU1MC9uQTcnOxV1TgqKrC7XBgLy/HlJ3lDwMghMBZV4s+MUFOPurDGJKSSMjLw3aylKYTJZhzcvzPR84zsHHZ1fO5cpWMQifcbjwuF8ItB/o0nU7G9zG0FTVTRgbG1FTp527Qd28UzASkkj6C7I3pkb20U8ge4FlIG67P/zmLZpNKVpjn9A3O+V4IJuQLRqNZ4Z/w/i6nOeOOntb+6wr55eNE3jPfF5WGfFaVMPH6iUx8aKJUyo1Is4nLu48e+TK10NpMBlJJZyC/kgTSjh4fjm1+emx6g95sJmHQINzWRtx2m3QtHDYUndksA3gFcRnUWyzdHvJW0+ux5OTgTk/HY7PJ2N9OF267Xc5wra5BuNwY+6d363ljFb3F4vcXt5WUoLNY0DRNmlPMZiw5zZpI0+vRdyLmvKbX+7+Ouh0zUrHXehcP8jPb5w+uJzINOom2U/x9A7MN3rr0Qyr2EmSvtBzZS++7H4at0SFt34eR4xk1SAVchVTig2mecdvZe6ZH3us4pUfnrGmahiE5qW0j1gIZKSOP3mxGbzZjSE6m6UQJtpOl6BMTcVutGFKSA9qZ+yo6o5HEQfk4a6Ufv/B4B7Az0qPfhz+N6GnEyd4FpGI/SrPZJXbixfUMOuSLsAx5j+qQXz9DkS9mRUDieSJyh9H0ehIGemPM2GwY01IxZYX7DR6/aDodpowM6eeu6DoW5JeEL/CQoi0acrA0B2leiU9HtG5FKXUvml6Pxev5olD0KEqht4+GUugdJMq/mxUKhULRGZRSVygUijhCKXWFQqGII5RSVygUijgiYgOlvnzW7WV4jxXsdntvV6FbiOXr8MlSsFzpHo8M5NHY2NhjdYok9fX1vV2FbiHWr8MnTz75inY0EayFdJH6+noOHDjQ/o4KRScpKCgImCuyrKyM4uK+GjRFEWny8/PJyYn+Kb8RU+oejwer1YrRaGwTy0OhCAchBE6nk6SkJHQBJjy5XC4qKyuxWCwBtysU4eDxeLDZbGRlZWEIEO4i2oiYUlcoFApFz6O6MwqFQhFHKKWuUCgUcUTEDETKpq7obpRNXdEbxJpNPWI1tFqtyvtFERGCeb9UVlYq7xdFRIkF75eIKXWjN69nQUEBphhPNLF3715Gjx4dch8hBDt27GDKlCmtvkyCre8NOnId0YzD4eDAgQN+2Todizfufn5+PokxHjb5wIEDFBQUhNxHCMFnn33GhRde2EbmAq3vDTpyHdFOY2MjxcXFfvmKdiKm1H3CZDKZMIdIOxcrtHcNW7ZsYfny5RQWFrJmzRqZU1UIHnjgAV566SXWrVvH7Nmze6i2wYmHZxFMUflMLomJiQF78rFGe9cQKzIXD88CiBmTXvQbiGKEWbNmUVhYyEsvvQTAmjVr/I2rsLCQWbNm9XINFfGGkjlFIJRS7yY0TWPNmjUAvPTSS/6G1rIXpVB0J0rmFIGIje+JGKFlI/OhGpcikiiZU5yOUurdiM+e2ZIHHnggaAAqhaKrKJlTnI5S6t1EywGqwsJCiouL/fZO1cgUkUDJnCIQyqbeTWzdutXfuHyfvy3tnZMmTYoKTwRF/KBkThEIpdS7iVmzZrFu3TpmzZrlt2f6GtmkSZOUJ4Ki21EypwiEUurdhKZpAXtFwdYrFF1FyZwiEMqmrlAoFHGEUuoKhUIRRyilrlAoFHGEUuoKhUIRRyilrlAoFHGEUuoxgBCCLVu2tJlMEmy9QtFVlMzFLkqpxwBbt25l+fLlrWYJ+mYTLl++nK1bt/ZyDRXxhpK52EX5qccAKsSqoqdRMhe7KKUeA6gQq4qeRslc7KLMLzGCCrGq6GmUzMUmSqnHCCrEqqKnUTIXmyilHgOoEKuKnkbJXOyibOoxgAqxquhplMzFLkqpxwChQqxOnDgRIQRCiFa2TiEEW7dubXWMQtFRlMzFLsr8EgP4Qqme3lA0TUPTNG6++WblT6zoVpTMxS6qpx7jKH9iRU+jZC66UUo9xlH+xIqeRslcdKPML3GA8idW9DRK5qIXpdTjAOVPrOhplMxFL0qpxzjKn1jR0yiZi26UTT3GUf7Eip5GyVx0o5R6jBPKn3jSpEnKE0HR7SiZi27CNr+UlpayY8cO3G43JSUl3VmnuCRSSQdC+RMHWq/oOyiZ65uEpdQ/+ugjrrvuOtasWUNlZSVXXHEF27Zt6+66xRUq6YCip1Ey1/ssXbqUzz77rMvlbNiwgbvvvrtD+4al1J999lnefPNNUlNT6d+/P6+99hpPP/10OEX1GVpO2PA1MjVhQxFJlMz1TcKyqbvdbvr37+//PWrUKPXJ1Q5qwoaip1Ey17MIIXjiiSfYtm0ber2ehQsXttr+wgsv8O6776LX65k8eTJ33nknJ0+e5IYbbmD79u0A/Od//icAq1atYuPGjTz//PMkJyczcOBAEhMTO1SPsJR6QkICJSUlfqHYuXMnZrM5nKKiCntlFc7q6jbrPSUnaUj7gaPrX8d65GjAY101NdABG+VMYNq48f7fxq++5Z/zFgCwrbyMF48daXPMeWnp3DN8ZPBCTaagm4xpaWSefx4AjopTHPpsp39bytmjyLn0knbrHI3U1dVRV1fXap3D4eil2kSQu4DtgTctrVzKdrvcOMYxhq0VzeaUgXkDgxY5/D+Goz0k2+6ria9yV/pdQfc9UXLC//es7Fl8Y/qGabZpvFL1SvA664F58s8zq8+EjBbbpgK3BT80miktLaW2trbVutTUVFJTUwFp7vryyy957733cDqdLF68GLvdDsCOHTvYvn0777zzDkajkVWrVvHGG28wZcqUgOcqKyvjiSeeYOPGjaSnp7NixYrIKvVf//rX3HTTTZSXl7Nw4UKOHj3qf8Oczt69e8M5Ra8g6usR9Q0Bt+3btw9HbR04nUEODq3Qr935acD16UYj1+blM71fTtBjd9fWhCwbjyfoJqfdTnnFKf/vln9XHrFQnJIUuuwo5b/+67945plnWq0rKCjgwQcf5MCBA71Uq+5l165dcC1yCcC/e//592eX/+93eTd02d59RzEq5L4ty3yERwKub48jnNZR6fihUcWyZcuoqKhotW7lypWsWrUKgC+++ILZs2djMpkwmUwUFRWxdOlSAD799FOuuOIKEhISALj66qvZuHFjUKW+e/duzjvvPLKzswGYM2cOn34aWIecTlhK/fzzz+fNN99k9+7deDwezj33XDIyMgLuO3r06JjpxQfrqe/bt49Ro0Zx9Ms9WKuqAh7r0rQO9dRPp8bp5LOaaqb3y/EvLQn2MmiFLvjQiNFsJjNbmsrKK07RL7vZbJZy5pnkjB8f7NCow263+zsJN954I1dddVWr7Q6Hg+rqagoKCkhJSemNKnYbu3btYvz48SF76gNLZG+8ZW+6JQKBC5f/twGD/7cLFxYsaHTOBLM0cynbLa0rdL31eh6vfRyAGzNvZJtlGycmyTpVVVeRmZHZvHMM9tTr6+s5cOAAf/nLX/xK2Yevlw5gMBhambSKi4tpbGwEwBOg4+VyudA0rZUXksvl8pfTcr3B0HFVHZZS//7773nyySd58cUX2b9/P8uWLeOpp55i6NCh4RQXNZizMjFnZbZZr6utIXnYWYx+4L6gxwZ7Ifj40z8/4aGHHmbe3LmsWLHC/9DWrl3LX95+i48mnM+9997bJj71uq1bmTZhAs7qmoDlGjMyAtY5UL2q9+1jwKhRrY6NVVp+9vqor6+nOsQziEkWAmOCbPNZTYL0JbZukd4vLW3oBmHwD5beeuutAWUuVEz0V3gFSoCTgc+57UqvF9w98r+yfWVkjmohn7nBLjT6GTBgQMjOwgUXXMDLL7/Mddddh8vlYvny5TQ0yC//iRMn8vzzz7Nw4UIMBgPvvPMOEydOJDU1lZqaGqqqqkhOTubvf/87U6dOZfz48Tz00EOUlZXRr18/Nm/e3EbegxGWUn/wwQcpLCwEYMSIEaxatYoHHniAV14JYWfr41x00WTuv/9+Lpo0qdWEjRUrViCSEvm/zz2H3W73N76Wngov/eEPPPnss3wbwKxw6cUXs/6NN3r6chQxQKhJQmazmedCyNy6devUrNBOctlll7F3717mz5+Px+PhhhtuYMuWLQBMnTqVffv2cfXVV+NyufjpT3/KkiVLMBgMLF++nAULFjBgwADGjJFv8OzsbO677z6WLVtGQkICw4YN63A9wlLqTU1NXHbZZf7f06dP59lnnw2nqD7B/JuXI4C//nFdm22apnH77bdTL0TQ+NRTp07lySD396O//z2SVVdEMddPuL71IORp+CYDBVp/7733YrfbVUz0buaOO+7gjjvu8P9evHix/+9bb72VW2+9tc0xt912G7fd1tYmNWvWrLCeQ1hKXdM0vv/+e0aOlB4ZP/zwA7oQdt2+gKO6hqbjxQG3+XrYycPOCnp8e65nH3z4YZtjVq9ejaO2jlMf7ghabsKgQaR4z+szIyliiPO9SwAeX/J42MV2yd2xFNgX9qkVESYspf7LX/6SpUuXUlBQAMDhw4d54oknurVifQ1fI/M1Lmg/PvXjjz9O/aHDNB0/3hNVVMQZ4cicIvoJq3s9depUtm7dyrJlyygsLOT9999n8uTJ3V23mGHp0qWMnHIx3x057F/30Et/5LwlizlvyeIQRzaj4lMrOsvXX3/N119/HfbxSubik7CjNP7www+AdMHZtUs6ns6YMaN7ahWlBOsV+2aDWXJySByUD4AhKbnVPpdefHHQck+fvt3SvgnBe0++Bj12amBfV0V847OXnzgR2KUxFOHKHBDSJLRlrBwYZKz8r5FGiB2v2bggLKV+33338fHHHzN48GD/Ok3T4l6pt8foESP8fz905508dOed/t+h3AfDjU/dlUat6NtEKib62LFju7uqik4SllL/17/+xebNm0lOTm5/Z0W7qPjUip5GyVz8EpZSz83NjUuFHsy84jp6hFN1DTitVtwN1rDKdlTXBJ2cZMzICOp6pnyFFZEglLtjV2Ru9erVcBweP1t65gwoGQCbW+wwARkASRExwhooPf/887njjjt49913+eCDD/yLIvqIVKIEhSIQ69evZ/3H69l/YH9Amfv8i8+VzEWYsHrqu3fvBuCtt97yr+sLNnWX1YozSOwXgMYgfuoAQtPQgghzAlrIqf5Vu3bTcPBQ0O2h/NR3Hz/G8nvuobCwkCuvvFLWRc0cjB0eIWjsl2jG87qH0oxS0pPTIUHGoSktL+Xrhq+pHF+pZC6ChKXU4zUcQMqwoaQMaxu/5viuZPqPHx80vss7gwch0PyeL4EQaGhBgnQYM9LDrnN7XHTRZH+ihFOnTjF+/Hg1c1ARcVzDXew8spPc5FzGjxrP3u/2crT+KP2m9FMyF2HCUupHjx7l1VdfpbGxESEEHo+HY8eO8UYfjUEyesQIBBpBIyv1Kq29Gt577z1AJUqIB7YM2QI/6e1aBGb02aMBOHrkKO9veh+AIWcOYfSc0UrmIkzY8dRHjx7N7t27ueKKK/jwww8555xzurtuPU57STKMGRm9Ms0+c/x5ZI4/r836LWdLF8r+IdzI7JVVWH84zJ1Lb2D7680v3TuX3oCjqjqo2SdYtL72ovgpwiBA1MPEfd6ECOlAQeDDxjJWbgsWnzwXyOuOCp5GiCiNPrR7NUaL0dw5u9mtd8srW9DyQsuMkruuE9ZAqdVqZc2aNfz0pz/lkksu4c9//jN79uzp5qrFDqtXr5aj/j3M2LFjGTt2LKtXr2bgwIEBl5tuu9Uf4rcl8nfwLwuVtFjRFYQQvLD2hVbrXlj7QruDpEruuk5YSj09PR2AwYMHc/DgQVJTU/v023P9+vWsX7++t6sRkIqKCtauXcvGoiIuueRitmzezLy5c9lYVMQTTzwRtJGppMXRz+qvVrN6c893JkIx5qwxjDlrDC+sfYGijUVccsklbNmyhbnz5lK0MbTMgZK7bkGEwerVq8XDDz8svv32WzFv3jzx8ssvi8svv7zVPjabTezcuVPYbLZwThFV7Ny5M+T2vLw8kZeX10O16RybN28WeXl54v777xdffPGFEEIIj8cj7r//fpGXlyc2b94c9NiW+/mW+++/X3g8np6qfivak6m6ujqxc+dOUVdX18M1635Ol7mZM2e2eg4tl2ijKzJ3+r7RIHexJldhKfXGxkaxdetWIYQQ69evF7feeqvYtWtXq32UUo8OPB6P2Lx5s/B4PK2uo+X69o5v2bh6q2EJoZR6IIW+ZMmSXqphcLoqc759o0XuYk2uwjK/rF27lpkz5bSwxYsX8+yzz7J58+Z2jlL0Br4Zgqebx4Ktb4lQUfx6Fd+4CEhb84kTJ9os0ehe3BWZAyV3XaVT3i9PP/00dXV1bN682Z97D8DpdPKPf/yD++4LnsMz1lm6dKk/GmPLAFrRbOPzKYQej+Kn6LP4ZC4Qjz32GEuWLAl5vJK7rtMppT5u3Di++eYbdDqdf7AUQK/Xx32SDJ9CD8a0adN6qCY9Q6Si+Cnim2nTprXbVkKh5K4bCMdm89VXX4kff/xRCCFEfX29+Pbbb9vs0579M5h9rTN2t55i586dUW03D8bpdW5vbKAl0fh8umpTj8ZrCkasytzpdEbmhIjOZ9QnbOpfffWVP4FqdXU1q1atahUHpiPEqj9qw6EfAi72yuAxYWKRrtpFo5FYlbl44dVXX+XVV18NuU88yl1PE5ZS/+///m9ef/11AAYNGsTGjRt5+eWXO1WG8kdV9DRK5nqXu+66i7vuuqu3qxH3hBUmwO12t4qnnpKS0uk3aJeymSsUYaBkTtEXCEupDx06lCeeeIKFCxcCsGHDBoYMGdLpcqItm3moJBn33VSIMSOzV2K/BKsXhA7p66PhkMwn64th4z82RORIkMk7QoUEjkWiTeZC8dhjj8G3wEMhdorGpBMtYsP4Y9goeoywzC9r1qzh6NGjzJs3jwULFnD06FEefPDBTpcjYsgf9eppP2OhNx55rHDfTYX87taVvV2NqCKWZG7JkiUsuTS0C6BCcTph9dSzs7N55plnunTi0+2ZLf1RhRB+1yUVqS18rp72Mwypqb1djahByZyiLxCWUi8vL+e3v/0tx44d47XXXmP16tX8/ve/p3///h0uoz1/1D/96U+ttrVskJHK1mPKSA9ojtA11PPe7i8Ru3dz3ZVzAh4bSVNFsHpB+yaUUHHeTRnpcWdeCUU0ylwofJ4iS+6Psd56KbBP/mk5Ymm7PVioYIhcuOA+RFhKfc2aNUyfPp1XX32V1NRURo4cyX333ceLL77Y4TJCZTOfOHEin376aatZZL3tpfC7/5CTq4Ip9Wjkv999F4EWU3WOJLEmcz5PkfZmYSoULQlLqZ84cYJrr72W1157DaPRyJ133smcOZ1THKGymV9++eX+bdHmpfDJP//JRZMmtflE//DD7cy8+uqo+kSPxRdRJIlVmduyZUtcJI048dgJ6NfbtYh/wlLqmqbh8Xj8vxsaGlr9bsnpA1AdFcZQXgrByui6oIvASruF+eKme+4O/omelBSRT3RzVmZQM0l7SakBRky5pNVvXywYe2WV3zMmELHq/RJLMieE4MP1HzJ15NRWxyd8l+D/e9PiTTRc2sCC+Qv8dXl7w9vs+GgHKf87hZ+u+mmnzxtRvgc+kX8mlSdJbxgfFxN93jpxRljeLzNmzOA3v/kN9fX1vPHGG9x4441BldlTTz0V1uy9UF4KkZoZ+OGHH/LQQw+xdu3aVuVu3LjRv08sTV65aNTZvV2FXsE38AnRL3Nbt27lN7/5TausQKfL3JRLp7Djox28veHtVgp9yqVTmHzR5LDOq+g5li5dymeffdZj5wurp37LLbewceNGPB4P//znP1m4cCHXXHNNwH3ffPNNmpqaOmWjDOWlAPDggw/6lSt0j/2zZRTGZ70JtFesWMHatWv5rxYNNpYmr/zH8v9F8vBh/vymu3btYvz48YC0LwuXi3deWBuqiJhk06ZNVFdXR73MgXwOexftpejVIgBuWXELL6x9gY8//hhMcp8F8xcAsOOjHez4aAcgFb2v5x4rzNoxC3bB1sUqHENE6eZYMn58wZceffTRTmcwaZk5xbfv6ZlTujs7Sstyzj3nHDF52HD/4lu/eOFCf11OD+Bvq6gU9QcPBV1sFZVh1aurtKzX5+++5/87FoNFdTSg15NPPhkTMufDU+wRzy1/TszMnulfHpn/SPMz2imE5wtPq+2eLzxC7BRCnOjSqSPDLiHEK3I5/L8P+/9ueT1Blyi8no4G9PJ4POLxxx8XM2bMELNnzxZ/+ctfhBBCLFmyRHz66adCCCHWrl0r5s2bJ+bMmSMee+wxv+w8+eST4pprrhEzZswQS5YsEeXl5UIIIS688EJRWFgorrzySuFwODpU306ZX8477zzOP//8oEsg7rjjjla/O9Kj9XkptNzXZ+9s6b3g6zV3puz22L/jYz7+68ZW6/76wgvs3/Exf3r2uaCf6KGSOCt6lsLCwla/o13mNE3jlhW3tFo3b948/98izCTOip5l69atfPnll7z33nu89dZbbNiwgfLycv/2jz/+mL179/L222+zceNGysrKePfddzl27BiHDx/mjTfe4G9/+xu5ubm8++67gAyYePPNN1NUVITRaOxQPTplfvnlL3/JZZddhs1mw2IJ4H8agKeeeqrV7wceeKDdhhDKS8G3Pphyba9s0c7AlvX4j2x4568MbHF9299+m8HLfo6xtpb//P3v2V5UxB2LFvvNMxtff4PMunoWzZ2LRuBzJ6D1yqBjzTd7qf9OOg27Kk5RerzzCTNijZYDnRC9MudbP3PMTNaubW0G27hxI8VFxbAftt25jbqddfz6J79m+tTpbPtwG1++8SXbDm1j+l3T0fKizARTDhyRf5pKTL1alZ7kiy++YPbs2ZhMJkwmE0VFRa22/+tf/+Lrr79m/vz5ANhsNvLy8pg7dy533XUXb731FkeOHGHPnj2cccYZ/uPGjRvXqXp0qqf+zjvvMHDgQH7zm9/4U221XALx5ptvUlhYSHFxcZtBxnARp9k/O1N2sAEvH4+ve4n1f9vKBTNnsm7DBi6YOZP3//kvXtm0ic/37mVjURHz5s5lxYoVaJrGihUrmDd3Lm+9/z57jh8ncVB+wMWUkR729XYFY0oKpsxMTJmZaC3+jmc2bdoUEzLnG2T9+W9/zsObH2bCLyaw5dQWJvxiAs9++iwPvPsA/6j8B0V7ihg6fSjTb5qONlRj+k3TGTp9KEV7ivjk4CdhX1PEGANcLpe6yXX+v/2MD7HE8MQjg8HQ6qVdXFxMY2Oj/7fb7ebGG2+kqKiIoqIi3nrrLW655Rb27t1LYWEhHo+HmTNnMn369Fby1NEOtL8endk5KSmJmTNnUlZWFtAv/b333muz7tprr+Xuu+/u1gwmXcmO0jL8KjQPeAGckZvLZ59/3kZpV1VXsbGoiNFjxnD//fe3cnn07TNm7BgumjQprOtRdC9XXHEF//7v/x71MvfSSy+RlZXFtm3b/OW0/Mp46aWXuOyuy1i+fDnjxo5rJXML5i9g+LDhjLuoc704ReS44IILePnll7nuuutwuVwsX76c559/3r994sSJPP3001x77bWYzWZuu+02rrrqKmpra5kwYQKLFi2iurqajz76iBkzZoRdD010ovvS0NDAvn37+O1vf8vDDz/cZvuECRP8f9vtdvbu3Yv9D88iamu5duenQcv9X4PPZHq/HAC2lZfx4rEjQfd98ycTAXAC9333DUcarQH3+1l2f1YMGQrAYWsDd+/bG7TMh4eP5My0dIzecgNZrnzrtSBvTVNWFv0vuTjoOVp6oXQnB59bS9233wXdbistBZerzXrf8/Ddz/bu0f8ZNZqhSTLc8tqjh/mfilMB9zszMYnHzh7T5jyB6Mhz99UPQEtLw/zL2xg9ejR2u526urpW+zocDqqrqxleOJzUr1IZmBciX2bNYyxplDM1X018lbvSg8f5PlFyAoHAhYs52XP4xvRNwP2ut17P47WPA/C18Wtm9wv+Anmv/D3GOMdwb9q9vJb0WsB9RjtGs7VqK9pVIcwrU4Hbgm/uFR4BvBntautqSUtNA2Dg996cuSXSBNjePdpSvoWxzrEArE5bzfqk9QH3G+MYw9aKZo+a7njuvjoC1J9fz4EXD5CamkpCQkKr/VJTU0ltEV/pqaeeYvv27Xg8Hq6//noWL17M0qVLWblyJRdeeCHPPfccmzZtwu12c/HFF3Pvvfdy6tQpVq5cic1mA2DUqFF4PB6eeOIJRowYwf79+4NeTyA6pdR9HDlyhNzcXI4dO0ZBQQE2m63NxUZaqQPc1Y1KvSAtPej2VgQbrEhJQT/2nKCH6fLy0EcgbK/t/S3w44/Bd6isggCP+PcHvyfTaOrwPYo2pf7iiy+2CSpXUFAQVrRQhaIj3H777VRUVLRat3LlSlatWtVLNQpMWEp9z549rFy5EoPBwBtvvMHcuXN5/vnnW3nAnK7Ue5L2etu+v3E6mzcajQGPOZ146anHIh3tqRcUFpDyVUqP1UsgsGHDgqXVQLmvd2/wWjldtH0OBgwBB9edODFilKNeV4U4eQz11KF5fSzRmZ56VNAhx8fTWLRokTh48KCYO3euEEKIjz76SMyfP7/VPu35FEeK9vyNN23a5P/bt8/pv0+ns8lzo5VYv46uJp6OFErmghMP19EnEk/bbDaGDRvm/z1lyhTcbne3vWi6Qnt5KIGAA16+Y1TyYUVnUTKn6CjTpk2juLg4oucIK0yAwWCgtrbWPxp/+PDhbq1UV2gvDyUQNPzqpEmToi5+SzwiIhaQrXdQMhf9xJvMhSKsnvqKFStYsmQJpaWl/OpXv2LRokX84he/6O66hU2omX++ySSnP8Bg6xXdT6SCY/UmSuaim56WudLSUpYsWcL8+fNZsGABe/bsabX9yJEjzJgxgz179rB48WI++eQTf51mzJhBWVlZ2OcOS6k//fTTPPPMM6xcuZLzzz+f1157jZkzoyeepggy8090YfKJovsIZa646aabEEIEDJ/70Ucf9U6FO4CSueimPRPZzJkz2bJlS0C5+/TT4B5cwXj77be59NJL2bBhA7fffju7djWneyotLWXlypU8+uijnHvuuVx99dX+2ac7d+7kjDPOICcnJ/yLDccQv3DhQnHy5MmQ+/TWQGmgQaj2BqXaIx4Ge4SIrusIFhxr06ZNQQcdzz333KgcKFUyF5xouo5QAdlCDXb/7Gc/67Rc7dy5U0yaNEn86le/EkVFRX6ZnTp1qrjooovEihUr/PtarVYxefJkYbVaxT333CM2bdrUpesMS6lfeeWV4uyzzxbTpk0T//Zv/+ZfWhKt3i+bN2/udJnRJJhdIdquI1C0y1AK8tFHH41Kpa5kLjjRdh2BZM63PpjcPfnkk2HJVVVVldiwYYNYsWKFWLZsmRBCKvVNmzaJyy+/XHz44Yf+fe+55x6xYcMGMXXq1C7rzLCU+meffRZwaUlv9tR9YVI7sr4jRJtghks0XUeoXlOwbU1NTVGp1JXMBSearqO90MnBttfW1nZarh577DF/6N0TJ06ICRMmCCGkUj9+/Lj4/PPPxdSpU4XVahVCyPt08cUXi9/97nddvs6Ix1PvaaUeCaJJMLtCd15HVxRZR8wVAWPWR6mfeiRQMteWSMucb7/T5S4cuSopKRGLFi0SV155pbjqqqv8vXKfUhdCiLvvvls88sgj/mMuueQS8dVXX3X4HMFQSr0DqAbWlq6YHDozWScWeuqRQMlcWyIpc6GSoITTU+8MHo9HfP/99/7JnF1FKfUOoBpYW7oyOBiqxxVq9mW02tQjgZK5tkRK5jZv3izcbne329Q7yp///GcxefLkbrtXSql3ANXAAhOJ9G6helTR6v0SCZTMBSZSKQW72/ulNwkroFdH8AX0Gj16NGazORKn6DFaJmyOZSJxHUII8vPz/b+Li4u7NJlGhJj598EHH5CdnR1Upurr6zlw4AAFBQWkpPRcQK9IoGQuON0tc74yg8ndtm3byMzMjBm5CmvykUIBkZlwE2r25aWXXhp2uYr4IBIyB6HlbuLEiUGOik6UUleEhRDhp3dTKMJByVzHCCugl0LRlfRuCkU4KJnrGKqnrgiLWbNmsW7dOn/jguagVn/84x+Dxm8JFF9DoegIoWRu3bp1IeO39CW5U0q9DxNM2DvSCELZIDVN4+abb46rKIyK7iFSMjd79mz+9re/xV30z3BQSr0PE6lwpO1FxFPxw/sukQyBq+ROomzqfZiWjQBk/O/uaATtJY1Q8cP7LpGSOVBy5ydSDvBq8lH0Eeg6IjWZw1d2oIh44aJiv8QePS1zvvK7U+5iTa6U+aWPEypjT1cQKmmEIgiRkjlQcgfKpt7niUQjEEL5EyuCEynFq+ROopR6HyZSjSCYP7Gv7L7ihaBoSyQVr5I7L5Gy6yibevRx+nVEImOPr4zuThohhLKpxyI9JXO+ciIhd7EmV0qpd4B4bWCRagSRQin12CPWZU6I2JMr5dLYh/FN2ujoeoWiqyiZizzKpq5QKBRxhFLqCoVCEUcopa5QKBRxhFLqCoVCEUcopa5QKBRxhFLqCoVCEUcopa5QKBRxhFLqCoVCEUdEbPKR8MZwcDgckTpFj2K323u7Ct1CLF+HT5ZEkPggHo8HgMbGxh6rUySpr6/v7Sp0C7F+HT558slXtKOJYC2ki9TX13PgwIFIFK3o4xQUFJCSktJmfVlZGcXFxb1QI0VfID8/n5ycnN6uRrtETKl7PB6sVitGo7HvZBxRRBQhBE6nk6SkJHS6tpZDl8tFZWUlFosl4HaFIhw8Hg82m42srCwMhuiPrBIxpa5QKBSKnkd1ZxQKhSKOUEq9F/nrX//K9OnTsVqtNDY2Mnv2bDZu3Njb1VLEMUrm4h9lfullfv3rX5OSkoLD4UCv1/PQQw/1dpUUcY6SufhGKfVepqGhgblz52KxWNiwYQNms7m3q6SIc5TMxTfK/NLLVFZWYrfbqaur49SpU71dHUUfQMlcfKN66r2I0+nkuuuu47rrrsPj8fDWW2/x+uuvYzQae7tqijhFyVz8o3rqvciTTz5JdnY211xzDQsXLiQjI4Onnnqqt6uliGOUzMU/qqeuUCgUcYTqqSsUCkUcoZS6QqFQxBFKqSsUCkUcoZS6QqFQxBFKqSsUCkUcoZS6QqFQxBFKqSsUCkUcoZS6QqFQxBH/H1Gf7niWh1LuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 396x324 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plot_ensemble_sat_analysis_abstract(bg,an,obs,obs_sat,truth,reflectance_simulator,m_const,da_const,h_c=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running single OSSE experiments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "da_const_vr = set_da_constants_22(obs_loc=np.arange(25,299,50)) #changed direct point observations for variance reduction tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-119.38467652972545 -123.63563524670326\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 540x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t_step=43\n",
+    "truth_idx=7\n",
+    "vr_t, vr_i, vr_r,J_dict,dJdx_inv, quad, dx = vr_reloaded_22(states[0]['bg'][t_step],states[0]['bg'][t_step][:,truth_idx],m_const,da_const_vr,sat_operator,alpha=alpha_default,obs_seed=22)\n",
+    "fig, ax = quad_plotter_paper(quad,m_const,da_const_vr)\n",
+    "label_axes_abcd(fig,loc=(0.95,0.9))\n",
+    "print(vr_t,vr_r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_J_quad_paper(J_dict,quad,sens,dx,bw=0.3,dJ=True):\n",
+    "    \"\"\"\n",
+    "    Plots the forecast metric distributions of the free forecast, forecast, and their linear approximations for the given sensitivity\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    fig = plt.figure(figsize=(4,3))\n",
+    "    nens = len(J_dict['bf'])\n",
+    "    dX_bg=(quad['bg'].T-np.mean(quad['bg'],axis=1)).T\n",
+    "    dX_an=(quad['an'].T-np.mean(quad['an'],axis=1)).T\n",
+    "    dX_an=dx\n",
+    "    dJ_ff=np.dot(sens,dX_bg)\n",
+    "    dJ_fc=np.dot(sens,dX_an)\n",
+    "    print('vr_reductions:',np.var(dJ_fc,ddof=1)-np.var(dJ_ff,ddof=1 ),np.var(J_dict['fc'],ddof=1)-np.var(J_dict['bf'],ddof=1))\n",
+    "    print('variance:',np.var(J_dict['bf'],ddof=1),np.var(dJ_ff,ddof=1),np.var(J_dict['fc'],ddof=1),np.var(dJ_fc,ddof=1 ))\n",
+    "            #'response' : np.hstack([J_dict['bf']-np.mean(J_dict['bf']),dJ_ff,J_dict['fc']-np.mean(J_dict['fc']),J_dict['es']-np.mean(J_dict['es'])]),\n",
+    "    if dJ:\n",
+    "        plot_data = {\n",
+    "            'response' : np.hstack([J_dict['bf']-np.mean(J_dict['bf']),dJ_ff,J_dict['fc']-np.mean(J_dict['fc']),dJ_fc]),\n",
+    "            'x_pos'    : np.hstack([np.zeros(nens)+0,np.zeros(nens)+1,np.zeros(nens)+2,np.zeros(nens)+3]),\n",
+    "            'type'     : ['blindforecast']*nens+['estimated']*nens+['forecast']*nens}\n",
+    "    else:\n",
+    "        plot_data = {\n",
+    "            'response' : np.hstack([J_dict['bf'],dJ_ff+np.mean(J_dict['bf']),J_dict['fc'],dJ_fc+np.mean(J_dict['fc'])]),\n",
+    "            'x_pos'    : np.hstack([np.zeros(nens)+0,np.zeros(nens)+1,np.zeros(nens)+2,np.zeros(nens)+3]),\n",
+    "            'type'     : ['blindforecast']*nens+['estimated']*nens+['forecast']*nens}\n",
+    "\n",
+    "    my_pal = [\"blue\",  \"peru\",\"cyan\",\"orange\"  ]\n",
+    "        \n",
+    "    PROPS = {\n",
+    "    'boxprops':{'facecolor':'none', 'edgecolor':'black'},\n",
+    "    }\n",
+    "    #ax = sns.violinplot(data=plot_data, inner='quartile', orient=\"v\",cut=0,bw=bw,y='response',x='x_pos',palette=my_pal)#sns.color_palette('cool',n_colors=3))#,x='type')#,y='response',x='cyc',hue='type',,split=True,palette={dict_label[left_var]:dict_color[left_var],dict_label[right_var]:dict_color[right_var]}\n",
+    "    ax = sns.stripplot(data=plot_data, y='response',x='x_pos',alpha=0.7,jitter=0.15,size=5,palette=my_pal)#color='0.0')#\n",
+    "    #ax = sns.boxplot(data=plot_data, y='response',x='x_pos',showfliers=False,**PROPS)#,patch_artist=False)#color='0.0')#,palette=my_pal\n",
+    "    #plot errorbars\n",
+    "    plt.errorbar(np.arange(4),np.zeros(4),[np.std(J_dict['bf'],ddof=1),np.std(dJ_ff,ddof=1),np.std(J_dict['fc'],ddof=1),np.std(dJ_fc,ddof=1 )],fmt='.',capsize=15,lw=3,color='k') \n",
+    "    \n",
+    "    #if dJ == False: ax.hlines(J_dict['tr_fc'],-0.5,2.5,'k',ls='--',label='truth'); plt.legend()\n",
+    "    #if dJ: ax.hlines(0,-0.5,3.5,'k',ls='--') \n",
+    "    ax.set_xlim(-0.5,3.5)\n",
+    "    if dJ == False: ax.set_ylabel(r'$j$')\n",
+    "    if dJ: ax.set_ylabel(r'$\\delta j$')\n",
+    "    ax.set_xticklabels(['free-\\nforecast','estimated \\n free-forecast','\\n forecast','estimated \\n forecast'])\n",
+    "    return fig, ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "vr_reductions: -119.38467652972545 -123.63563524670326\n",
+      "variance: 221.43278926594013 176.91208076683142 97.79715401923687 57.52740423710596\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plot_J_quad_paper(J_dict,quad,dJdx_inv,dx,bw=0.2)\n",
+    "#ax.set_ylim(-70,55)\n",
+    "import matplotlib as mpl\n",
+    "ax.set_ylabel(r'$\\mathbf{\\delta j}$')\n",
+    "ax.set_ylabel(r'$ \\delta j$')\n",
+    "ax.set_xticklabels([r'$\\mathbf{\\delta j}_\\mathrm{ff}$',r'$\\widetilde{\\mathbf{\\delta j}_\\mathrm{ff}}$',r'$\\mathbf{\\delta j}_\\mathrm{fc}$',r'$\\widetilde{\\mathbf{\\delta j}_\\mathrm{fc}}$'],va='bottom')\n",
+    "ax.tick_params(axis='x', pad=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sensitivity plots of the regularized sensitivity and the sensitivity which ignores crosscorrelations\n",
+    "\n",
+    "We have to rerun the default run but with 512 ensemble members, and then make two OSSEs for 300 and 600 seconds.\n",
+    "\n",
+    "\n",
+    "Uncomment plot commands to see the intermediate steps plotted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_const_512 = set_da_constants_22(nens=512,ncyc=t_step+1)\n",
+    "da_const_6   = set_da_constants_22(obs_loc=np.arange(25,299,50),nens=512,dt=300)\n",
+    "da_const_62  = set_da_constants_22(obs_loc=np.arange(25,299,50),nens=512,dt=600)\n",
+    "t_step=23\n",
+    "truth_idx=11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pgriewank/pgriewank/code/2021-linear-advection/da_functions.py:328: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  bg[:,i]    = linear_advection_model(analysis[:,i],u_ens,dhdt_ens,m_const[\"dx\"],da_const[\"dt\"],da_const[\"nt\"])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 11.4 s, sys: 292 ms, total: 11.7 s\n",
+      "Wall time: 3 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Run the model and the single OSSEs, from which we only need the quads\n",
+    "states   = run_linear_advection_KF_22(m_const,da_const_512,reflectance_simulator)\n",
+    "vr_t, vr_i, vr_r,J_dict,dJdx_inv, quad , dx = vr_reloaded_22(states[0]['bg'][t_step],states[0]['bg'][t_step][:,truth_idx],m_const,da_const_6,sat_operator,model_seed=505,obs_seed=55)\n",
+    "vr_t, vr_i, vr_r,J_dict,dJdx_inv, quad2, dx = vr_reloaded_22(states[0]['bg'][t_step],states[0]['bg'][t_step][:,truth_idx],m_const,da_const_62,sat_operator,model_seed=505,obs_seed=55)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fig, ax = ensemble_plotter_22(states,m_const,da_const_512,t_end=4)\n",
+    "\n",
+    "#fig, ax = quad_plotter_22(quad,m_const,da_const_6)\n",
+    "\n",
+    "#fig, ax = quad_plotter_22(quad2,m_const,da_const_62)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def L2_ensemble_limit(quad,nens,alpha=None,dt=0):\n",
+    "    \"\"\"\n",
+    "    Simple function that calculates the sensitivity for a given quad\"\"\"\n",
+    "    X =quad['bg'][:,:nens]\n",
+    "    dX =  X.T - np.mean(X,axis=1)\n",
+    "    dX = dX.T\n",
+    "    J = np.zeros(nens)\n",
+    "    if dt == 0:\n",
+    "        X_J =quad['bg'][:,:nens]\n",
+    "    else:    \n",
+    "        X_J =quad['bf'][:,:nens]\n",
+    "    dX_J =  X_J.T - np.mean(X_J,axis=1)\n",
+    "    dX_J = dX_J.T\n",
+    "    for i in range(nens):\n",
+    "        J[i] = sum_mid_tri(X_J[:,i])\n",
+    "        #J[i] = sum_triangle(X_J[:,i])\n",
+    "    dJ = J-np.mean(J)\n",
+    "    B = np.cov(dX,ddof=1)\n",
+    "    cov_dJdX = np.dot(dJ,dX.T)/(nens-1)\n",
+    "    sens = L2_regularized_inversion(B,cov_dJdX,alpha=alpha)\n",
+    "    return sens\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MGfzvf/8jNzc34jabN2/m9ttvbzDLD4fD4WDjxo2tOEJJV2DUqFHNsjTK80zSXDriHBtQuRSLp4ZdqTNxmlIZXPERRq+D7enz8Bjim95BG9Gv6mvi3eXsTZlOvblb2LFKWkZzz7OuxmFhYywsLGTlypWcccYZAKiq2uzMi7Y8EdauXdtmBfjkvtt3/4d6o2mr80x+V51n3x16jq3+HhweRo+bBHEpsGYF2CsZN2o4WDM67vNc+zPU2xkxZiIkCBHE779CrYfRI4ZBcs+W7/sQOVx/H3JiFh0x4w5rjPj4eB5++GHy8/NRVZXXX3+dE088saOHJZFIJIcX7gB3GMROcLQnoIGqTixlr0k6LTEtghYtWsSGDRvIyMjg3nvv5eqrr2b27Nmoqsoll1zS0cOTSCSSwwfV6xcUPhEUI7WCPCHiDPy1gmTBREkbEnPusG+++cb3PLDFwaxZs5g1a1ZHDEkikUgOfzxagLHRAoo2/40FS5DqFWNTFH99IAioFSRFkKTtiGlLkEQikUhaibDWlhiwBPlcdHHBafqyf5ikHZAiSCKRSLoCPldYjMXdhBNn4E+Rl5YgSRsiRZBEIpF0BcJagmLAHRZOnEFsCDRJp0eKIIlEIukKeALcTjqx4A4LNy6Q7jBJuyBFkEQikXQFQjPDAp/HgjvMFCKCpDtM0g5IESSRSCRdgVitxRNau0jHGNBJXiJpI6QIkkgkkq6ALnQMYWKCOrIWT0R3mKwTJGl7pAiSSCSSrkDMusOaCow+TCxBNYWw5gnIe7+jRyJpBlIESSQSSVdA7xSvW1hAdJEPXNcRdJaYoH3fgr0SSjaCraSjRyOJEimCJBKJpCugZ1kZAhoF+IRGB1pbmqoT1JECLVq8bqja4/+/cleHDUXSPKQIkkgkkq6ALnQMgZYgXWjEgggKtQTFgJUqWmylweO0FXfcWCTNQoogiUQi6Qp4w4ggYwyIIHeEmCCfCDoMYoLqNfeX2Soe66QIOlyQIkgikUi6ArqlwhCjMUGHsyWovkw8ZgwRj47KDhuKpHlIESSRSCRdgViPCYoUGH04iCBHtXhM6gWKAZx1h8e4JZia3kQikUgkhz3hssNiwR2miyBDaGD0YeQOc9aIx7hUiEsRWWKOarBmdOiwwuFyuSgoKMBut3f0UNqF+Ph4cnNzMZvNYddLESSRSCRdgXAxQYHWFlVt/zGBPwW+gSXoMHKH6ZaguBSwxLYIKigoIDk5mX79+qEoSkcPp01RVZWysjIKCgro379/2G2kO0wikUi6Ap4w7jDFAAYjqF5QPR00Lgd4PbBuBfz6Dbg0UaQYQVG0sXk7ZmzRoluCLClgSRLPXbUdN55GsNvtZGZmdnoBBKAoCpmZmY1avaQlSCKRSLoC4QKj9f+9no5xO6kqOOqgZD9s3gkq8Nt3cNGdYDILweZxibGFBk7HCh4HuO1irKZ4MGkZYu76jh1XI3QFAaTT1HuVliBJ83DZoPZg7M/MJG2D6u04t4nk0AgXGA0BwdEd4HbyuqG6DDweIYAA9u+E35ZrYzsMXGIOPR4oRViuzAnif5et48YkiRppCZJET+Ue2PKW8OGnDYAR5zS8oEo6L/XlsOk14UIZeT7Ep3f0iCTNQRdBxhBLkLEDA5Dt1WCvg1BP3PoVcMRMTaDVx7YIcmrxQJYU8ahbgqQIOiyQliBJdLjtsO09fxBj5S7Y/3PHjknSvuxbLgI+68sh/4eOHo2kuYSLCYKOrRq9e6OwLHpDrIv7d0J93eGRIRZoCQK/JSiG3WHtidvt5t///jczZsxg8uTJfPzxx7zwwgs888wzHT00IMZEUG1tLXPnzqWgoKDBui1btrBgwQJmzZrFHXfcgdsdwzODzkjR76L2RUoujDxPLCv8ObZnaJLWw+OCsjz//2VbpEv0cKOxmCDoGKFxYLt27DDrCnf5RVBHuOqixVUnHs2J2qN0hwXy2GOPsXr1aj788EP+8Y9/8PTTT/PBBx9w4YUXdvTQgBgSQevWrePcc89lz549YdfffPPN3HXXXXzxxReoqsrbb7/dvgPsyqgqFP0qnveaBmkDIaG7+JFX7u7YsUnah5p8cRNN6gHWdHA7oO5gR49KEi1ejxCtikFkXQXSkTFBxXvFY0QRdBgUTHRrYkd3g5mkJUintraWV155hfvuu4/k5GTGjh3Lrl27OOWUU0hKSuro4QGtLIJOOOEEdu1qWffct99+m7vvvpusrKwG6/bv34/dbmfcuHEALFiwgKVLlx7KUCXNoSZfNAi0JEH6YBH8132UWFeyoWPHJmkfqvPFY0ofSO4jntfs77jxSJqHzwpkEr/fQDrK5aSqUFkknnvCBNuX7j88AqNdmtjR+4aZYz87rL34+eef6devH7179wZEocbk5GQuuOCCDh6Zn1aNav3qq69a/Nq///3vEdcVFxfTvXt33//du3enqKioxcc6XLjuuuv48ssvMZnE17R+/XoMBgP33HMP77zzTtC2VquV1atXA3DLLbfw2WefBa3v1q0b3377rW+/y5cvx+12+/bdt29fPv30UwAuvfRSVq1a5XvtH0/sxczxOQw89iIwGDn77LMpLdjOExcNxubcwGVn3MX4CRN55ZVXAJgzZw4TJkxg4sSJrf+hSMLy22+/cfnll+NwiOq7V111Fddeey0lJSVMnz69wfY33XQTl1xyCXv27OH888/3nQc6f/nLXzj77LPZvHkzZ511Fn+a05vJg1J46rEvSI43cfu5k0irK2b16tVccsklDfb/xBNPMGPGDL777juuvPLKBvt/6aWXfPEBDz30EJ988gmpqamt+IlIgggplKhfAwD+NKc3l5wyFcXr4qWXXuKiiy4KemlLri2BRLq2pJrgjRmZZKf2It4rxrVz1y7s9UI87F67kapRvZg2PJv+I8T458yZwymnnMJVV111yB9Jq+GzBCUEP0p3GEVFRUGGjcWLF5OdnR0zViBogQjaunUrN998Mx6Ph4suuoizzz67LcYVhNfrDcr1V1W12XUONm7c2NrDCmLt2rWtvs8ffviBlJQUxo4d6zuGwWAgJSWFqVOnBm1rNpt9Y+jWrVuD9Var1bc+JyenwfrU1FTf+j59+mA0CpN5nEnlxAkqcRYT64uNuMrWMnDgQFJSUnAZvXRPh1NPnAJJPX2vX7duHYmJiW3ymQTS1vtvCW15njX2fr/66isOHjzI9OnTsVqtmEwm1q5di81ma/Bdg/hNrV27lsrKSo455pgG6x0OB2vXrqWkpISpU6cyfoiXRCtk5A7Hagav10Ppvs0crPOG3X95eTlr166lrKws7P4PHjzI2rVr+frrr9m1axfffPMNffr0aeYnImjL86CznGNmTx0DbTZcBti5di0//PADCQkJjB07lu45Xmw2G4XbtjB48GBcrmCLUFtdW/qpdViTKjAYDDgdLupVM3Fxcb71Q5MUdnXPwWIxsy1vMzWWat+15YgjjmjwHjvqPOhTXUCCy8a+HXuw7bWBqjLMVg/YyFuzCjXU/djM/R/O5OTksHXrVoqLizlw4AAffvghNpsNp9OJxWJpegftgdpMampq1LKyMnXLli3qlClT1HXr1qmqqqrvv/++umjRouburgHTp09X8/Pzg5YVFBSoJ5xwgu//1atXqxdeeGFU+7Pb7eqaNWtUu91+yGOLxJo1a9pkvxMnTlQXLlzYJvtW1SjHXbhKVX+4R1U3/K/huu0fiXX5K4IWz5gxQz399NNbaZThaavPvKXnS1ufZ0293zfeeEPt2bOnWlBQ0Or7Vt12Vf3xXlX98T5V9bhU1V4tvvefHz7k/X/yySdqz5491c2bNzdnyFHt+1DpVOdYXbH4ztb+W1VVcW3505/+JNZt/0SsO7CmfT/Pn5eq6lMXqurbF6vqMwtV9YNnVPX+S1T1nvP9fxveFGMrWq+qqri2XH755U3vuy3HHcrap8UYaw/6l/3yT7HMXnXo+28hkc6Xlv7WWoLD4VBvvvlmdeLEieqMGTPUTZs2qRdffLF6zjnntNsYVLXx99xsS5BuxsrIyOCkk07i+++/Z8yYMWzbto3Bgwe3ukgD6NWrF3Fxcaxdu5aJEyfy4Ycfcuyxx7bJsWKJZ599lv37OzDuQlXhoBYQnT2+4frUfnDwN6jaDbnTfIsVRUGVBfXalRkzZvDOO++QmZnZ+ju3lYhzIbG7iNGwJIlHl00ESIf2fGoGBoMIS/R6ZaZZmxKSHv/ss8+SlpYWtExsE77JZJtQut8flepRIStXBEOXFvq3cWrNVTV3nqIosXeu6LE/uhsMRJC0s1as01PnuyAWi4WHHnooaNnLL7/cQaMJT7NF0JIlS1i8eDGDBw8mISEBp1PUjdm+fTvz5s1r1cEtWrSI66+/ntGjR/PII49w5513Ultby8iRI1m4cGGrHisWmTRpUseWN6/ZD3VFIuUzc1jD9Sl9/dupqi/g0mAwxN6FqpOTnZ1NdnZ22+zcViIeEzTfvqKIQom2ErBXQFJOi3etiyApmtuYkJigSZMm+dcFdZJvRxFUdgCM2vXNC3TrBWndQkSQVpdMC4yOuWuLqvpFkB4QHfhcBkfHPM0SQdu3b+eRRx5h8eLF5Ofnc80113DLLbf41g0ZMuSQB/TNN9/4nr/wwgu+58OGDePdd9895P0fTnz66afU1NR0XIDxQREMSfb48JWhLcnCKuCsBXs5WIUVYtasWVRXV7fjQCU7d+5k/fr1zJ49G6vV2vQLmkN9uXi0BliZWkkE9erVi7PPPlsGRbc1IS0zPv30U3r16iUybjuqTlBlKejGEy+QngWp3YK3sdeLu5QmgmbNmhVb54rXKcoPGM3B10i9z5nH0THjkkRNs1LkV6xYwYwZM+jVqxeTJk0iISGBY489lpqaGkpLSxk4cGBbjbNLcttttx1Sxt0hYa+A0s2irkhOBBGmKJDUUzyv9c/ebrrpJk455ZR2GKRE54cffuDaa6+lrq6u9Xdu10VQhn9ZfJp4dFQc0q5HjRrFo48+6kuhlbQRIYUSb7vtNhYvXhy0zOcya5fxeETPMP0O5FWFFSglxJ2rZYrpAu2mm27i8ssvb79xNoUrpEaQji6C3M72HY+k2TRLBCUmJvrM1k8//TSTJk2iZ8+ebNu2jT59+sROtHcnITQrLmqctYc+A9m7XFyouo/y3/DCkdxLPNYURt5G0ubov0vdvdSq1GtCJz5QBGl9w+yVrX88SeujiyDN9eX1ev3nSkfU4qkuB68XDNr1zZII5jghhAKx29p/bM0hXDwQBFiC7O07HkmzadYVc968eRQXFzN37lzy8vK45557ANo0KLoro6pq8E3NWSPaV9RGqNTrccHmt2DVo/DLP+HA6pYduOh3KNkIBiP0Oa7xbZN6iMfaA75FCxYs4K9//WvLji1pEXqcRKvHkKmq3xIUVgQdmiXo+++/p2/fvqxZs+aQ9iNpghB3WNC1pSPcYVWl2rG1/5O08ynUEmTTLJuaCFqwYEFMFdrDrYkcc4glyCTdYYcLzYoJio+P57nnnmuwPC8vj+HDh7faoCSCIEuQvQLW/Uf0qVEUGHAS9AgIblS9sO19KN8mXFheN+z8HOLSICMKgap6oWoPFK+DYq0K9IDZTXcKT9SCcW3FvuBoj8eDxxPaFlrSlugiKCpLkNshboaGpuuX4KoTTXPN1uALvd4x23HosV9ut1sGRrc1Ie6woGuLfh60p7WlUhNB+imYrImf1FARVAuk+Mbm8Xhiq2+kK6RQoo50hx02HLLtvLa2lh9++CFs8SrJoRFkst7ztbghxaUIsbHrcyjZJNapKuz6QjS4NMXD+Cuh3wyxbuenTc9GyrbCr/+Gja8JAaQYoN8JkWOBAjEniZuj2w5OcUM0GAzyptbORC2C9n0Lvzwkvm894LkxfFagEDGsp/06a5o30BD0G3FMZfx0RjzB2WFB1xZfdlh7iiAt41B3hyVrHQGS0oK3swfHBMVcdpjPHRYfvFwGRh82HFLbjNWrV3PjjTdy6qmnyhYJbcB7771HQUGBiPEp2yrEyZhLoWQ97PkGti8Bo0n0dTqwWszohp8tmptaM8VragqhcBX0bli1F4D9P8PuZeJ5XCpkjYWsMcFBsI2hKJCQLaxIdcUQlypFUAewYMECpk6dSkJCQuSNagpg3/fiub1SCORRTXRyrg/jCgMRCGowCfHrcYKxZfGAsk5QOxGSIv/ee++Rnp4etKxD3WGpWvkFkxniE/yxQB6viE2M1RR53RIU6g6TIqhV8Xg8XHHFFRQUFHDvvfcyefLkVtv3IYmgI444gh9//LG1xiIJYdSoUaIXVNlW4a7KGCJm4L2OEsKocBVs1jI8FAMMOQ1S+/r/73cCbPgf7F8JOZMa/FCtrlLYo8UN9Z8JPY8Ur2suiVlCBNmKIGNwbBY06+R07949qL9eWAq1fnDZ46FsM1TuFsK1McJlhoEQv5Zk4aZ11gSnzzcDKYLaiZCYoFGjRvnXKR3oDtMvN2kBZRYSU/0iSCVIBMVcIdZIgdEyJqhVKSoqIi8vr030Rqs2UJW0Lm+88QaKojAxQbtSZGh1mBRFiBajBYp+Ez/A/idC+qDgHaT2g7QBULkL9q8QokjHVUev2p8hXhHVnntNaflA9SJ62g11zpw57N27t+X7kzSbjRs38vvvv3Puuef6ei8F4fVAxXbxvPfRQuweXAulG4FG6q7Yw2SG6cRpIshR3WIRlJOTw6WXXkpOTstrDUmiICQ77I033mD48OGMHz8+wBLUzu4wX3o8kB4g4JNSRSFFfZ3XA6qIMZwzZ05sCeZwhRLBbxl1SxHUFL/88gsPP/wwXq+XXr16kZCQwPbt2/F4PCxatIi5c+dy5ZVXUllZyYIFC3j//fdb9fhSBMUwt99+O/Pnn8K5AzTrTlp//0rFAH1niL/G6DtDiKADq6HHZHHjUr2wbQkmbz2kDIU+DbuMN4tETQTZhAi69NJLO21DwFjlm2++4cEHH+Sss84KL4JqC8UF2Zop4nsyhwoRVLETmBB5x/URYoJAWILgkOKC+vfvz3333dfi10uiJMQSdPvtt3PllVcKEdTeMUHhagQFFklMChDlKuDxW4IuvfTS9hljtLgipchrMUKHiSXojDPOaLBs7ty5XHzxxdTX13PhhQ3d5meeeSZnn3025eXlXHHFFQ3WX3jhhcyfPz+q4+/Zs4fly5fz3HPPkZWVxYMPPkhtbS3nnHMOY8eO5ZlnnmHhwoWtLoBAiqCYxuv1khqviIBos1VkejWX5J7QbTiUboGCH2DgHMj/Hip24jHEwdDTo8sSaoyE7sI6ZSsFrxun29ugE7WkbWkyMLpyp3hM1wqapvQVN8TaAxjjItQyUVWoLxPPw1l69AwxZ8szxLxeL06nE7PZHF68SVqHxgKjfXWCXNAeXXpqKkSNIP3uo5jBEhBYnBgigrwe8Qc4nU5UVSUuruX96loVd4RiiSbNEuSR2WHR0L9/f5KTk1m5ciV2u5333nsPAJvN1mrdKCLRpAgaOnToIR8kLy/vkPfRFfF6veQkaf7vxB6+3lzNps8fRObYwbXgqILy7aAoFCYeSUprNPczxgmBZq+A+nIuvfJP7N+/n+XLlx/6viVR0WSxxCrNPalbE41mET9WsZNEV1H41wSlx4cJuNYtQY6WW4J+//135s2bx6uvvsqMGU1YNSUtJ0yKvO9cUQKKJbaHDtUzw/S+YaENeMOJINVvCaqsrOSTTz5p+3FGQ0R32OFVLLGxllRWq7XR9RkZGYfc0io+Xohgr9fLww8/zMiRIwEoLS0lNTWVoqII16hWoEkRJAVMx6CqKqqqkp2o+b/19hQtIaE79Dsedn/pE0AMmkddQSvW8knopomgMhkY3QE0WixR9fqLWSb18i9P7Q8VO0lwlYTfqW4FChcPBAFp8i23BMnA6HYiwB2mX1vCVoxuFxEUEhQdKrCD3GFqkDss5q4tPndYJBEkLUHNYcqUKbz55pv87W9/o7i4mFNPPZW33nqrbSrha0S1561btzJv3jzmzJnj7zcjaVP0mX12ki6CehzaDntNhdEXieDoMZdB9rhD218oVs2nX18aexkcXQC9+F1YEWSvEBfjuBTR8FZHyyRMcDchgiIFPbdCTJCsE9ROBARGN7AatndMkK9GkPZ/XFLw+kARpAdGa+6wmBJBqleUiFCUhnWCDGYRt+lx+cYuaZprr70Wu93O3Llzueiii7j55pvp06dPmx4zqpig3NxcXnnlFYqLi7nkkksYPnw4Y8aM4f7776eoqIjHH38cgL/+9a8UFRXx73//u02VW1dAURS+//57Ure/IhYkNJH+HA2pff0p9K2NfqOsL4u9Wh5dgMsuu4zTTjst/ErdCpQYkoGVmANGCxZPpSi5YAm5GfnS49tOBOnXCSma25gAS5B+bfHVCQpMkW+P78FXI0gT7PEh511Yd5gQEjF1bQkslBhaWkRRRIaYXkfLYG34egkAkydP9tX9SUpK4pFHHmmwTW5uLt98802bHD8qEZSUJE7SjIwMTjrpJL7//nvGjBnDlVdeyYknnsj27dv58ccf2bBhA6+99poUQK2AoigM7N+Xup1OUCyNNzGNBRI0S5CtRH7/HUBGRgYZGRHcVnVar7lQa6LBCMm5UFMJ1fug24jg9U25wyxJ4mLvrBOz4hbUmNItQVIEtTEBgdGKojBw4ED/OkURLjGvG4V2sFqEtsxICCnRYE32P1cRQdQBFaNj5lyJlBmmY4zTRJC9YcyQJGaISgQtWbKExYsXM3jwYBISEnA6hZ8zMzOT888/n5tuugmbzcabb76J1Sq+7Ndee41jjz22zU1ZnRW3283il5/ipBw7iZm5fr99rOJzh5Vx8slz6Nu3jSxOkrD8/PPPbNy4kcsvv7zhSq10ga/PWyCpfaFwowicbiCCmrAEGUwinsNZJyxJLQiy7969O9dddx39+/dvemNJywkIjHa73bz44otMmTKFcePGieVGsxBBanuIoBB3WGJI+YWEAMtQSHbYySefTFVVVZsPMSrcEeKBdIwyQ+xwoMmp2/bt23nkkUd45JFHmDNnDm+//TY9e/qDdIcPH05eXh633nprUMXaCy64QAqgQ8DlcvHhWy9hs9W3uBBdu2JOEH8eJ6fPncncuXM7ekRdiq+++op//OMf4Vc2JmZStN9odUhxS9Ub4A5rpIlutGnyrnooXt+g4Wp2djb/7//9v1bJQpU0QkDbDJfLxX333cfKlSv967VJlqElliDV67c0NTkOr6gRBH53WGKIpdESD3q5BBXhotPGf/rpp8dOrSA9Pd4cH369FEGHBU2KoBUrVjBjxgx69erFpEmTSEhI4NhjjwVgy5Yt3H///Zxyyim+vH6dcMWVJNHj9XrplWEV7oJI7ohYQ7MG1ZXto7r60LuLS6InKOU5aIVb9AlTlPAFD5N7oSoGUe1b74MEopSC1yPifoyN1GSJIk1eUT2w4b+wbQmse0FYjTTcbjdlZWXY7YdHKvFhi88SZApfU0oTQc22BHlc8Osz8PODULGj6e31GkEg7j4GY8PAaEUBq7ZM93y5XaB6qampobw8isa/7UFT7rCO6MkmaTZNiqDExESfD/bpp59m0qRJ9OzZk4MHD3LNNdfw97//nbvuuotff/2VdevWAVBeXh45PkESFV6vl54Z2gyjrS1BLif8/h289Sg8fYv4e+Vv8NWbULA9+mDJBDHON196kltvvbUNBywJJaIIclSKmXpcaniXqsFEvUk7v6rz/cvrtLocejXwSESRJp/sLACb5gJx1sGBNb51e/bsYcyYMSxdurTx40gODf1GbDSHL6eg1QoyqM0MOi7dJGLHVC/s+7bp7SsDMhGNiAbQ4UR2QkhckCoaqd56661RVyFuc6J2h0kRFMs0GWgyb948vvrqK+bOnUvfvn25//77qa2tZdGiRVx++eUcd9xxACxcuJDHH3+c//znP+Tl5bVphceugNfrJSdNuziEm8EDVJTA5p/BUQ99h8OAUc0vqJi/HZY8AxUhjTRLC2HvVlj5KfQbDidfCplNpOlrlqBuiTLlub2JKIKaSnMHbKZuQJ1wiWVqbqlaLZg6NKMsFJ87LLIlKMVZIK403UaKm2bpBuj7B0DWCWoXNAGBooBiDG8J0tLkmx0YrVciB6gp1KrbJ0beXs8MAzEFN5r81ZUDsYaJC1LdsZUdpluCwhUSBX/pAekOi2maFEHx8fE899xzDZZ//PHHQf9fe+21vud5eXnSx3+IeL1euqXECU0TF6bB5caf4KPnhZkY4MePYPgRcNo1YDJHdYzEgzvhi8/9+4jEni3wwl/gnD9BvxGRt9NEUKZVZvu0N0HF7wJpKrgZsJm7g3uvv6o0QJ1eXLEJ4atbghwRLEGqVxRjNJmh3wyo2gX1orI41gwpgtqDwL5hAXV2WsUdVrNfPJoThQCq2ifa9EQicLJlUMBk8vfZCiTQEuRFyxDzxFYNsqYsQQZN3HmlCIoGvVfYs88+S25ubtC6LVu2cMcdd1BXV8ekSZO45557MJlaJ1moTXKZt23b1mwR9PHHHzNnzhxmzpzJ66+/3mD9U089xfTp05k/fz7z588Pu01nIjUlmeOPPoLk5JSGWTf7dwrrTah42bIa3n3C73NvjJICev30ftMCSMdphzcfgcJdkbfRbrSZCaq8qbUzN998c/g6GlFYgupNmSK9va7I3/W69gCgwrY8eP0heOMhWL0MPCEF9Xy1giKIoNpCDKoLrBnCopmiZQ3WCNebFEHtQEjfsNTUVH799VfOOecc/zbaOkNzRJDbIeLNDCbIGiOW2YobfUmQO0y3BBmbsgSpWoZYjFmCIvUN05HusKhZt24d5557Lnv27Am7/uabb+auu+7iiy++QFVV3n777VY7dpvkXd9///3N2r6oqIh//etfvP/++1gsFs455xwmT57MoEGDfNts3LiRRx99VHQ97gIYPHYsRgW7MT74IqGq8MVrkYXOtt/g589h2smRd+52wXtPYXA7wRJwCkycAeP/IGbtxQXw27ewe5N/vcsJbz8GV94ffJHSiU8Dg5HkOJW4GM/o72ykpKSQkhImRT0KEaQqJtGWpaZAiJOE7mCvgrIi2LDdv+GO9bB5FZx3M5g1V63PEhTBHVa1RzymainwyT2hbKtwnWSNbb9iiZW7oPBn6HEkpA9qevvOREgHeYPBQHZ2SLkErWBis9xhevZgfLrfbdqUCAqyBCFcRtHEBHm9MewOiySC9MBoaQlqirfffpu7776bW265pcG6/fv3Y7fbfeUcFixYwBNPPMF5553XKseOiVvVypUrmTJlCmlpaQDMmjWLpUuXBrnYNm7cyHPPPcf+/fs54ogjuPXWW2Onk3AbUFdxkJqiIhymNIJubTs3iGDlQMwWIVB0lr8Dg8dB916EZcXHQuQEMvM8mDLH/39Wbxg5BVZ+Al8HtEqpLocvXoVTr264X8UA1kxSUyu56CyZIt+eLFu2jF27dnHVVVcFr9DdYU1lGKb2FSKoai/UlUH5QagIk7G1dysse13EiEGwJUhVG8ak1WoB1snauaj3LqsVbpSUlBRuu+02Ro8eHcW7bCFuB2x5W8RmVO6BI26IHMfRGQlpnlpbW8tzzz3HCSecwNixY8U6PSaoOZagQIGtV7S3lUTeHkICo3V3WJQxQV4PJ598sn/MHY3PEtREdlisW4L+7/9gxYq22fdRR4HWUaIx/v73v0dcV1xcHFR+p3v37q3aUDUmSvuGvsmsrKygN1lXV8fw4cO5+eab+eCDD6iurubpp5/uiKG2G47qIoqKijhYUR+8Yu1Xwf8PHgfXPAzWgGBEjxs+fjG8taikAL58Cw4eJK5wP+zcCQXVsLsKbLbgbRUFjpoHk2cHL1+/AvZFaKxrzSQlOZkTjuoaFrtY4auvvuL5558PXuhxiIBlg6npQoZ6d/mSjbDmXSGq6yJYZ9Z+A3u3iOdGi5gJez3+m0IgdSEB1kk9xHlVVwReNykpKVx77bWMGNFIrNmhUrbZH5zqdYvg7K5EGBH06KOPsmHDBv82upWoOSLIXike49P9Fe0dVRGzSRWPW0yifMdEC4wOExPUQASJmKATTjiBiy++OPoxtiWROsjryDpBrYLeF1FHVdXwPRJbSExYgpp6k4mJibzwwgu+/y+99FJuv/12brzxxqiPsXHjxtYZbATWrl3bqvszFW8GoM5t8u3b4LAxeN1KlABxsydzMPYde0gZdBQ9f1ni38G2dRS9/RwVg4/0LTIfPMDgxf/E4qgEQAE8bhX7rzvgx/twP/EEBxYtoi50ppU+mP7mH4mr8puy6xc/xd7pFzWY+Xez2UivrcGh7m/1zySUtt5/S2jL86yx91tcXIzb7Q7aJs5dQX+bDYcxhd2//tb4vneUM9BhwFq2DZOjFlSwF9WBW6Uuqx+WugrMdf5KvXXvvUD+cecD0L/WTZzHxu5ff8Jh8mcyKqqboeX5gMKvW/ehKsL6M8BuwOKpYffqb7EpKZSUlJCcnExiYiNZRY2NvYnzoGfNT6Q4bdhNGcS7y6nNW0lBYXTzv85wjlldpfS12ah3VrF37VpKS0WGVn5+vu/9ZdcVk263oSR6on7P2XVbSbfbKNpfSkX5JgbXuzGqNravWYnH0FDYWOoqqdMnWgrEKWk4bHa2/vp7g+tIUuF+crVtLa4EcDrYvmk9RfV51NfXN3Tn0bbfVYN9qypDK0tQVC95G7YKl3IIqfZ8ethsVBXs5UBl42Pr0PMsCktNR5KTk0NJid+CWFpaSlZWE6U7mkFMiKCcnBzWrPHXDikpKQl6k4WFhaxcuZIzzjgDECKpuZHho0aNajP32dq1a5k4cWKr7rPy913s3Q02t4kZ+r7XfgPxAReXzB6MPPEUcQGZMAEcpbDtV9/qAbtXw6zTIa07vPkmvPo4pLmFCTotjXqzGevcRSR6EuCNN2DTJlKffhr++Ee45JLgAWWnitpBGon1FXRL8MKII4O3K7FQ8M1KflnzKZc9fF2rfiaBtMVnDuBwOA5JyLTVedbU+83IyCAuLi54m5KNkJdAYrfBZAyL/Nq1a9cycdIkWLUHyneCwQAlHhItVujTh8SL/gIH9wZ//7XFZPXIgJ79YVMeVOxg1OA+kBFQGqOmANYlUO4wMWFSwHmydReUbmFU/2xKlBxOO+007r//fi666KJW/1wAWPU9mBJIHLkANr1BotlJ9oQJTZaT6DTnWOUu2PgLiWk5dBs1kf37hRjt37+///3tKoXCAxhUT/TveetuKE1gwNDR0H0U/P4r1B5g3LC+oidd6OafvEViguY6MgKWeEypGeLcCyUrBX77VDw3mMBgYMSwIfzvgef5/PPPfTXpdNrqu4q4b48LfvoMDCYmTDwy/LlUEgd5m0ns1o2eTf3+YvA8ixV69epFXFyc73P68MMPfQWbW4OYcIdNmzaNn376ifLycurr61m2bFnQm4yPj+fhhx8mPz8fVVV5/fXXOfHEEztwxG2PQau7YvMGpLtv/z14o9HT/D8+RYGTL4H4AP+0ywnvPwN/+hP8+5+Q6obUVBg0ELKzqR4wGo4/FWbNgpdfhmuuETfAf/8bQl0rfYfB0AnBy757r6HLTUuT75neeeO1YpGwdYKaaoAayM4NsOwj2OGC3W4o9UJSKpx9E8RZxfffd1jwa35bLh7j9KrRIRliWsFFhykteLlVc33Xl7R9YLTLJlyCRgukDRCuF1c9uGqbfm1nISQ7LGyxRF+doGYEHeuVvy2a60p3ielushAstQHLDWjxQBGuEwnhssM8sRMY7WuZYY0spmVg9CGxaNEin8v2kUce4R//+AezZ8/GZrOxcOHCVjtOTFiCsrOzufHGG1m4cCEul4szzjiDMWPGsGjRIq6//npGjx7Nvffey9VXX43L5WLChAlcEmqp6GQY3OICY3NrX5HbBXs2B280JESUJKfDieeJeCAAux2++xwqXdDHCLk9IFm7YVkTOTDhJLL1H7DBAJdeCj17wl13CRGUlQWnnurf/4yzhRDTL0Il+2HLKhFArWPNRFEUeqTHtbizuKT5hBVBvt5fjVccTzywE778Qou7AFyqyBA8+0+QGvDaKSeJwGidTT/DzAsi9w/TRJDdGFLnKkFrtmsrQUlWfONvE/RA3YRu4lxMzBK1bOqK/UHdnR1veBEUXCeoBSnyLi0jUBdBej0zR6U4l374AdatE9eW8eMx11b4X2tUIscDQYSYoBjKDnM1USMIwKAJvFgPjI4hAst8BIbADBs2jHfffbdNjhkTIghEZep58+YFLQv8EGbNmsWsWbPae1gdRqLJzahRo3CbjhILCnaIWj06SamQHaZB7bjjRCHFDb/A/v3i4tEtHnrlgjnAqjTnEjz2MEUVZ88GpxPuvRceeAD69wc9Rqh7LyF4NgQ0Xvz+Axh+pLjQARgt1LqMGBXETVjvLi9pUx566CHc7pAaPlGkx7P+R3qvWAzWkIv5/Cuh18DgZYPGQmIK1Glix26D7b9BtzTt/4rg7XVLkDEteLkvk6i07esE1WsVinXrU4ImgmzFkD4w8us6EwF9wwB69+5NXl4eFktAVpZBS5GPVgSpqt8SZNbEpF7Zvngf/L9nYWuAYP7vf+k+IA4G5UC8VbjDTObwhRIB4hOFhUVV/W0z3A4MBkNsFEtsKjMMZMXowwQ5TY9FvG4UVx1Go8k/Ww21Ag0cE94MqyiQOhT25gsBlJoKffsFC6DJs4OtN6Gccgqcdx643XDnnVBX5193zGl+wQPCGrT5l6CXVzg0bW0rRdI+xMfHk5QU4kLwVYuO4A5bvQyWPNswm2fm+eHPD6MJRk0LXrZ1jd/dFiiCVK/fEtTAHaYVZ7RXYNDcL21vCQoQQeDvjdYVCOgbBsIClJSUFCKCmtk2w+sUFg6j2Z8FFZcKLhd88aEQQNnZsGgRXHYZ9OghAu5374HKCn9mWKTMKoNBCCHwN1F11KIEVLzuUPRmw42VWtA/F9lANaaRIigWcdbgcrnYtvcAu3bvEcvytwVv0y9CafpVq+D+h6AAyMkV7q3AuKFpJwuXWVNcfz0MHw4HDsAjj/iXd+sBo6YGb/v9B0GxQT0GjqZbt25N1wyRtBrvvPNOcNkIVx247cLdEG62uuYr+Px/wcsUBWYvFG6vSAw/Ivj/7b/7LQG66AKRKu1xgiWxYaaQwSSsBqqXeGz8/e9/Z9q0EHHVWgS6wwAStayi+i4k0EOKJZaUlHDPPfcEB8021x3mswIlBVxf4iE/Hwwukajx1ltw5ZVw9dWw+C082Zql6MBBqKvRLEGNxA7qcUH6paW+lpNPPpm//OUv0Y3xUKgvh73f+t9nKL70+EZEkN6wWFqCYhopgmIRRzUej5tfN+6gsLBQCIz9O4O3yR3c8HWlpXD77cKCc/r58I83Yc7FMOZomDwLLrsHTjg32JITCZMJ7rsPLBb4+GMITOE85tTgfZQWikauGn2HTRTVi7vSjaaD+fLLL3nnnXf8CwLjgUIthvnbYGmIADKaYMEf4ciZjR8od7Bwiek46qFgj5j1uu3+WAlfF/oIDVg1F53FVcnFF1/MqFGjGj9uS/G5BDURpLtsQl13nZmQwOjy8nKef/55du/e7d+mub3DQoOiAV57BxwOyEiCRx/1xx8C2KpxZ2RAD60XXW0tVFZFjgkCsGqv91mCbBx55JGcf/750Y2xpahe2PIW5H8Pee+F38YVjTtME3jSEhTTSBEUiziqUFUoqRY+cIrzg+OBEpIhI+Tmoqpwzz1QWQlHHgk33CAyxSadAKdeBbMuhJ4DmjeOfv38qfIPPijEFYhu8qFuke+X+KxBZTYFh8MhLUHtSIMGqpEap7pdwgUWYLlTDUYRBN2Yi1THYIBhISnNeWsDXGLacWsLxWMkEaS5p7x1xWzatCmoDkiroXrFzVpR/MUizYnCheOq9/dJ6+yExAQ11kDVEK07zBkSFL1pE7z6lnjevxckhLi5SkSvONLShBBSDFB0EAobabNhDXGHOW0UFRWxaVMbF7usL/O78qv2YvSGqZzu0kIEIrnzIKBitLQExTJSBMUiDlGUrrjKKdJYC3YEr88d3HB2/9138NNPIgbo3nujs/ZEw0UXQW4u7NolagnpHHtqQ2vQpp8A+O/bn1JQUCAuJmoM+O+7AKEFR/0WkJB4oFXLgvs3AfunLoBBY6I/2NCQmiZ5awPSo3URpHeh7xl+H5p7yltXwsyZM3nrrbeiP360OGvE+WdO9LsmFMWfxdRVrEFRZYc10xLkcwclignYP/8JXhXSs0Tgs6suePvANj1padCnrxA3i98XE7dw+ESQpoKc9fznP/9h7tw2bslTsz/o30RXGKHmCnj/kTCYxPnm9cjrYAwjRVAs4qgGVEqqHZoICokHyg1pAOnxwJNPiudXXgndWjEjy2IBvandiy+CPmPPyIHRRwdv+/0H4HbhUo2U12qBk44qJG1PgxT5cDWCXE7RNy6QScdT23MIzaLfCFE7SKeuGuo1a4OtVNy0fCIokjtMWIIM9jLf+FsdvW5RaMsQX4uHytY/ZiyiW4K0wGg9uypcinzUdYJ0EWSKhy+/hPXrISMD+g0Vy0NjaUqDhQXDh0NCApRUiLpk4dDdYfqQnPXtExitu3K1jLk4T5hrmDuKwGhFkdagwwApgmIRp3CHFVdpIig0Hqh3SDzQZ5/B3r3Quzecdlrrj2faNDjuONFbTBdbAMfMD7YGlR2ElZ9gMBjYV6JdJGWGWLsQ2RIUIIjX/wj1ATenOCv84YzmH8xkhsEhveGKte+5rkgIX5dNuAri0sLvI0G46RR7BQalrUSQdvPSLT86cXpcUGXrHzMWCQmMDlss0dc7LKTMQiR0Swhm/zXhqqvAmqatrwnevig/+H9rvHCLuYElS4LT6X3bhLjDXPb2qROknzda9fM4T3XDbaKJCQLZPyxKPv74Y+bMmcPMmTN5/fXXI253yy238P7777fqsaUIikUc1SRYrbz38ZdMGT9OiAsdRYEe/f3/q6rIwgCRihqYCt+a/OlPwir02Wdi1geQkQ1jQ8qX/7CEDHcde0s0c7iMC2oXXnnlFZYuXSr+Ub0BgdH+Xl4Nmu9OnCHiy1pCaFzQLk2o1x4Q7TJAuMIiVtONExYa1UNOenzb1H7RizdapCUI8FklxowZQ0FBQXDV/Wa7w7Q4mRW/iAzSQYNEYVU9RijQElRfK9zlOooCFrO4nsyYKa5hTzzR8BgdJoIqxWO6JoLcYSxB0aTIQ0DVaBkcHYmioiL+9a9/8cYbb7BkyRIWL17Mjh07Gmxz1VVX8cUXX7T68WOmWKIkAM2Mr8SnEl8d4o/O7AGWgIyKDRsgL0/42Wc2kdlzKPTqBRdeCC+9BA8/DK+8IqxAx58t+pXpBfQ8Ho4rX8cTFXZUQJEZYu2Gb2ZvrxBxCHEp/gyVkgI4uM+/scEARxzC+TJwtLAIubWLe2kZqN1FHE7hKrEstX/k1wNYu6E4qumdaW1jd1ioJShNPHYVS1BIdhiEWIEgIDC6Ge4wtxs++1L8/6c/iXMqnAgKtWR3zwXd4nT6OfDm56K0x9atMCygNYteNdonghwYDGJZa3cSD0K3BKUNAIMRs7cGPA7/b0lVA4olNhIYDWDQLUExKoL2bIbP/hssUluTbj1FhnK/ERE3WblyJVOmTCEtLQ0QhZGXLl3Ktdde69vm448/5vjjj/dt05pIS1Cs4baD247d5eWWO+6mdkdIJkRolWi9lPj8+WJm1ZZcfLFopbFlC3z0kViWkAwnBTe+zE0wcuV4rY6RtAS1Cy+88AL/1mMrdOGpFwgE2BRc0JJ+I4JbYjSXOCsMGB28zK5dTnRLUEaYMg6BaMHRt15/GbNnz468ndcNB1ZD8YaGhR0bw+cOi2QJ6iLxaiGB0Xv27OGWW24hLy/Pv40eE9QcS1BxMVTZ4NhjRUYq+Iu7Boqg0MSO3oP9lqTUTFiwQDx/9dXg7eJDRJDbwaxZs3jssceiG2NL8LqFq0/RBJ2eXRl4HfM4xSTDaPFbeiIR6/3DPv1P2wkgEPv+9D+NblJcXEz37v5rVVZWFkVFwcVML7/8cs4888w2GaIUQbGGNnu1eUy8/vobGItDfOk9+vmf19fD11+L56ef3vZjs1pF6j3AU09Bjeb3HzEZxh/n3yzeSn9XPUpdlbh4xEKZ+07O119/zbJly8Q/ehyWfgFXVV/mno/QgpctIbQzdn5AtlVq32ARFg4tXmnyqAGN1wna8Qns/By2fQCFv0TeLhRHBHeYr8dVFxFBHhfU18HKz+CHDynP38Prr78efKNprjustBCqqsBrgD//2b/crAmXwAa1+0LifXoNBI8mgkzxcO65YDSKAOviAMu3XixRv354HIwcOZIzzzyz7axAvqwvrTGqr9lvmX8s+ZvBXgfeKMZgkDFBTREaz9imVr4wSBEUa2hxDC5FmFnTQk322X39z1esEMXJRo0SlaHbgxNPFNVgKyvhuef8y0+62CfQnC4X9hobakU51Ff7a4pIWozB64Si3xuk7+oEZYf5RJAWFF2cHxxXZjQ2THMPR0WFKItw333w9NOiTEIgQyYEB8YXHICsqZA1BoZEEaCviaTS/K3k5+eH38ZWAsXr/f/vXylm4dGgxwSFWoJMVjFDd9s7f60grxf2boLKYtjwEyx/h4FfPs+ABGP4wOho6gQ5HLBTsyKddlbwtSfUHVZXDfvygl+fO0gIM8UgLFDZ2SLxwuuFzz/3b6e3zdA9dG4XhYWFrFq1qu3igkJ7gukxdfUVolfeGw/Du4+KMhM7t8CSZ/wu4XD4+ofFqDvs5EuFy6qt6NZTHKMRcnJyguqElZSUkJWV1XZjCkHGBMUa2uzUoVgxKZDsqIbEgDTnnAAR9JUW6BoY4NjWKIqY+V1wAbz9tnDDDR4s4kPO+D948U4qS3Zz4MBBMuxZ4uJbugt6jWu/MXY2XPX0r/4Ktmsz4iGnCqERQJAICnWH7dwQvL8Bo4O7dIfjiy/gH/8QlX11/vtf4RK96iohfhKSoe9w2B3gst1XKqqSR4Nmqdq5cRVf/ezijjvubLhN0W/iMWciVO0RM/LqfEjr1/i+vW5w1vndGoEoirAO1ZcJoWRqwmJ1OPPzZ1BbARbFZ1ExOmzcOTQZY2DtmkBLkKpGDmgHePSfkO4AaxycFewKbyCCtv0aVJiTrN6QolniTPH+48ydC998A59+CgsXalaYEHeYx8UHH3zA/fffz44dO7CGNv0NxFkLe76EnCMgJTfydqH4LEGaCIrXrKn1ZfD+U7BzPaRoY3arsH4FmCww97Lw+/NVjY5RS1C/EXDNQx06hGnTpvHkk09SXl6O1Wpl2bJl3Hfffe12fGkJijU0EeRU4uhjNWIIdCWlZPhbFtTXww8/iOfHH9++YxwyBM44Q1zc7rxTzAwB0rvDqVcD4iKh2lVRw2jF4uALoaR57PsWs6fOP6vc9XlAirLAZ0JW1YDO6doFPFCkQNOFEV9+Ge64QwigKVNEnSi99MJ//gMPPOB3UYw9Jvi1G1ZEb6kxJ4LZSkKckXhDhNTs8u3iMWs0ZGo1aCp3hN82EJ8rLFkIoVB061BLXGJetxBksR7vVlMB374HBu2mrf8EVciJN5K9a41/W8Xgq4tDYy6xd9+FDz8AkwF692kopgPdYV4vrP0meP3wI0SQMQQHFU+bJpI7du0SiR4g4s4MBr8IUj2+WXuT2YT7vhMxZOv/07xChaH1f/RiowfzYIdmkTRpn6du3Pl1OWz7Lfz+ZJ2gJsnOzubGG29k4cKFnHrqqcydO5cxY8awaNEiNmzY0PQODhFpCYo1tIu325DIiMxEguZjgUHRq1YJ8TFyJOREKEjXllx3Hfz8M+zcKbLF7rhDzN4GjyOv2xBSDhxArfcCRtFC4ddvRAuP1sZZK2axjfUgOpxxO/zWkLGXw66lULkbin+DXv7WJXFxcRiNRvF5uB0ipsGcKEz1oTEZ/RuJv3nrLVG8TlHgppvgnHP8644/Xix7/33o00dYA4dNEtmKeluXmkoxWw6tIxQORfG57FLMYW4S9goxAzfFQXKu5rpaCTVRBHL6XGGp4de3NC7IUQUbX/PHiGSPh4En+StSxxJrvxbfv6IH52rLFVEosfuO1aL3m174Ur9he10N34+qwjvvwEMPQaJJXHOSAizUbhdsWS3ONW+pmAe99SAUhrhQR04JKLQY0DzVZIJZs2DxYli6VGSJGQwQlwBqnW/cFlUojybdYZUBGWn1ZU3Hp+mE1v+xZgAqlAb0WTMHWIJ0vnoTBo1tWKk/1t1hMcK8efOYN29e0LIXXnihwXYPPPBAqx9bWoLKtsK6l+DXZ2D/zx0fxKtdlIeNPZLn7rmd+ECTb6Ar7Cct0PXokKrN7YXVKtwlFosodvbSS75VW3NGsqHaBfXahSpega/egsrWmzkbvQ7Y+g6sehR+fgg2vhr7M/OWULYVvG5s5u7iQt5Dy8IpWhd0rr7xxhu8+uqrUK99Bnrj1IIdolK0TkqGKLMQjq+/Fu0PAO6+O1gAgbAK6WbqJ54QQtwSDyOODN5u5afRv7+EbiiKQpo5zE2iUruBpvYXlookvflmYdOz+0iZYTotEUGqCts/EjfVuBQhFIp+E002Y60tgtvlt8LoV/ljTwNrEkmJSYweNYoUi0lY7nR0S5A3xCqXnw//7/8JAQRwxSXCaqNPPHZthCf/BB88LY5ZXQ31NZAfbIGs7TlExIjomWHGkInLCdok6bvv/Od2QlKQeIvXxtaoCPK6g79XvQJ0NPgEmnbdNSWgOF0ipV/7eLAYRXsQT8AUtbQQNq5suD89MFrWCYpZurYI2v8zbHlbBJvaSmD3Mti7vGPHFFjb5ODe4HW6CFJVWKn94KaFNDJtT4YOhb/9Tcx+nn0WHn8cvF4Uo4lHtteiqhZhyjYr4sL3yUutIzK9bnJrVkDpFnHhNhiFdWTdS1ARhavkcKJ0IwDVlt7i//RBYEkU56vemiIQ/YKfkC0ed4WYk/uPDB/v8dtv8Je/iO/nj38UMRrhmDEDLr9cuDruuEO0UQm18O3dCvnbo3t/WoPVzPhGRFD6QPFoSRLiw+P0F4OMRKSWGTo+ERSmGnAkyraI88ycIKxyYy4VQqAsT7hfYol9ef7aXQpCRB55kiiQGcjqL/2/SZ8lSBNB+fnw17+KzNOvvxYTn7/9DeZq5QxM8fDDh/DGQ8L1pqNrKFPAeRafQNE4LXZRF0Gh1tuxY4W4ys8HvcN9fKJ/fArEqVGIIHtF8HWmOZOj0CKIioKhWrNEWbT3k5UtxtUvxK383fsNXcGyYnTM0zVFkMcNBb8J1wJA/xNg6AJxodi/QgRedgSq12fG37R9H9t/+ApX4Cw+p594zM+HwkLRLHX48PYfZyAzZoibp9Eo6nxcdhmze/Xi8ptuRT3hPLBrFyOrImaM636IvC+XC8rLRVZSY2Kp4Eesbm02PuGPcMSfoPsocaHZ/BaUbGzd99hReFwi9kRRqLFowZ0GI3QbKZ6XbfZt+tBDD/HUU09BnZYFpvfsCo0H6j+ywWEsBQWi2J3TKWK9Lr648XFdcYWoC1NRAbffLjIWQ/f77TvRCd7EHHr37s2EoSEZKqpXCA4QRet0dGtQTRgBGEikQok6LYkJ0otA9jlOCLKkHBh2lrhuFPzoF22xwHbNharrEGsSxCfApBOw2e3s3bcPh8MBJfvhgPY56y6wkiK45x4hfj75RIjm+fOFq2r2bCFivF7YsQmWv9Mw3k93E+kiKCUDzrsZV2Ka+D9cTBCIydQxWozZd5qotCb6Y4IMMHXsaF588UUSEhqp1BwqkJsjdEMtQVWlGKq17FZdBKVrGWNHzgt2f1UUB1vWIMAdJkVQrBKDjuw2pLwIvn8ftq6GPl5hoVCzYUAOdB8gZtEFK2DftzDqwvYfn7NWzCQsiVTs3U1yZTmeJO3HHmeFNM2vrVuBpkxpvW7xh8K8eSJG4M47YcMGcjds4Lxu3TCrJ0NCHMQ7wKJCrQrLXoNeQ8SFcs8e2LEDtm+HbdvE7M+jzaQSEmDyZDjlFOHy060XjmrYr7kCh5zmL3w35DRxYytYCduWACp0Dynm1x78thxcDujWQ8TFmA6hjUlNvjgfknrg8QTET2QOFzfksi3Q93hQFL777jsyMjK4dppW/yexh6gNo9/gdELFSnExfR56SMSXHXecCIJuqkaHwSAsAuedJyxIzz4Lc04JFly7N4sbwpgm3LWJWaSmpoLiEhYI/UZcUyhuttYMiA9o/ZHUQ1he6g4CjXy/Tk3chNYI0mmuO6yuGKr3iZl9YGZeWj/ofYywBG3/CCZc7c8I6ihU1R+oq18erNr7TcmgNC2Xym3b6K43Wv79e+g5ADBiqqqC6/8I+VViYjN/Plx6qagYr1O8RzRELQ+5sSuKsAqm2qF2B2QPgp5TRAaSyQxFa8V2PktQmM/puOPg44+FCLrkEiHeVP/+e6Ql0+MPxzZ8XSB6ZlpcirheOJshgkItQZt+QXVoIs8C9BqAz9TVvS+MO04ERut8vwRGH+V3LQbGWUlikq4jgrb/Dp88D04HZBjAbBRWit0FsP0emH8VDJsmKtNW7hauhqQIsRNthd6zJi6N+Crh1vDdjrL7+G9Oq7QZ6ZQp7Tm6xjniCBEw+/LLON58E8+WLXhLSjCMSoHjc8BeA1u12fsPJ8OBMDdaRRHmcI9HFGJcvlz8jR8Pt90GAwYIAeRxUWPpRWJq3+DX9j0eFBPkfw/bPhQ3g5BU8jbnh49EbSSApFQ4+TIYOqFl+6raIx5T+0Hg5Dalt3CJ1VcI4Z6Ug9frxWJSRLyKYoDELNj2e/AsvXsvSA4QFDU1cN11mCoqxLl0//3Ri+qMDLH9VVeJbLKxY6H/CCF+dL54DfoMbXw/xjhK61QSDXVYbSX+35we2Jo2MHh7vfaRHpgciabcYbo4ctYIq1O4DLJAdKtb91ENRU7vY6BiuxBue74RgdKROLgX8n6HpGakbTeXsoPCKgGaK0yBRL9FrLjnUMx847+4bFwp+mT98iMmq1OI+D/8AW68MVj8AKz7Hn58E7IUgjpsJCTDgmtE+YWClbCnCHr1h/5jG44vsAN9KJMnizjDTZtELbIgEQTlhfmsq1jOtGnTiIuLIDZ1EZSYo4mgZtQp82WHaZagLatQ7boIUmDEJPD8LgScMQ6OOgV+/87/O9OtQXpPRaOMCYp1YsCM0E58/ooQQAagm6bSSzSrg8cjil7t3yOyPcCfkdOe2PVgzjQSqrU0Z1346PFAXi/8/rt4PjGKgnftSWIiXHst715+OYvq6qhdsAC6DxfCJCPB/16SFeiVKsTNmWcKl8p//wvffy9qHy39DB65C+aMhcEmqP4Vbjsb/nEN7PoOUCm1hnEDKgr0/YNwV6he2P6hKDAYibZO26+tgrf/JWbaLSFQBAWiGCBD67FULjK/vF4vvVINQvgldBcWlVBX2ICArDCbDf7v/2DnTpw5OfCvf0Gkm0okJkyAq68Wz//yFxh6bLDlq74WFv8LxdV4QcIPvvyZsrLS4EKQughKHxC8cWuJIKNZCEmvR9QTaoqybeIxI4yoUwwwcK54PLgmvDu99AC8/294/g5Y1fpNIIPYHnDtMiCC1wMER0W3flS7vIAiAqh3boPbroPKalSzGW75MzzySEMB9ONH8OHz+NSPV1MnuYNg0d/8bVQsWpHDwNYZgfisLYkN11mtMG6cOI9XrWoggrav/40LLriAysrKyO/fpX2fuqCONI7GxmZKEDFVhbvw6iIoToG+2vkYr2XGpXdv2ET6uw/8yQgyRT7miRlL0Mcff8wzzzyD2+3moosu4vzzzw9av2XLFu644w7q6uqYNGkS99xzDyZTM4Zvt4sfVoZRRPnbEe4ZHa8X3nsKLvqzKM1fugUGzG56htia6Jag+FQSqksIMtTrImjXLpF9kZ0NPdrZUhUtFgur4+Kou/RSUnJyYPVjUJgHYyygXwss8XDpXaJ4mo7LKTKLVnwsbqAmYOgAKDoIlVVQuw2KskFNIC5xf+Sibn2OE9/b3uWw42MhiNJGCJG1apVoOltUJOrgGAxCvPXoAVnJ0CcHjjvl0N5/qkFYG50qHPSI3jlZvaFn/+j34fVArRbfk5wLbA5enzkUDq4V52mfP+D1ehmYqZ2ruoVsV0hslJ4ab7PBtdfC+vWQnU3+ddeRnhohdqYpLroINm8WFrtb7oSLT4F9v/rXF+2jzw9vCkuRNcxND8g7aGe2ClTthh6TxI2oplB8hyn9gje2Zojl9oqGWUw6HodwuRhM4mbmdok4QHNcsKUrLlUIIEcVxCVHfo+OKuF+M1ogLcJ3mJQDudMg/0fY/QWM0YrnFeyAtV/Bxp/ar1bW9t/9zxVFxAIFNE/1oPBDiYPJlRXiWuLVrosjx+BIcWIaFWaC8ety+OZt8dzg2xFMngXHnxMsfsP1DwskNA09lClTxO/0559h9hS/xckAFk8UKfK6CLJmCreU2x7sam0MX7xSPGz5FVTVbwlKigdFs2Jpfe8AOHq+sJDpY6osgR8/hOlnBgRGS0tQYxzq/f+xxx7DaDRy3XXXNfvYMWEJKioq4l//+hdvvPEGS5YsYfHixezYEZzlc/PNN3PXXXfxxRdfoKoqb7/9dvMOUlAAu3dAkgfikuDkv8CpVwVvU1sFv3wnfjyuOv9MvL3wucNSSbKVYzab/KXt9RpBv2o3mAkTmo7d6CD0ysVer1eMMbUvpHWD5ICLkNMuStDv3SJiV1Yvg6f+JOpt1NcG7gx69ITcXtAvXSvAtp3c7xYLK0ttZfhB9D4G+h0vYl1WvATXnS4sTkuWiNpGeiVkrxds1VCVB0VrIO+n8PuLlrQUyI0XMVDJCvQ0ihvwxy807yZoKxYXbmum3zQfSGo/caG2lUB9KRkZGQzroV1w0/pDVRmUBQQPGwzQdxjU1YkaT5oA4vnncQU0L2w2BoNwi82YIT7Tp16HEpsQWtoU3lq2H16+R1hDwrB5v3ZjqdojBGt5niZc+zeMGzGYhHhRvUII6agqVJQIAfDLx1BdBsVFInX7gcvgwUXw94vg6VuEKN271e8SayouSLfspPZt/Eaae7RI+y7dAZ8/Bk/dJN73+hXB370e99YaeL3CbVRWJgRNXXVwXSgDotaOPu6yMvp8+y2z95RjKq8QAiglGUYNFNmeiiLO10D2boHPXvb/b1S0+J+ZMOvChnFvuoXHFUEEhRYkDEV38//8sxBwAVi0ysuNiiDdsmdJ9gc468KrKXSLjdHiL46oAg5VxGUWa9mW1gARlN69YYbkio/FJMQXEyQtQZE4lPt/TU0Nt99+Oy+//HK4XUdFTFiCVq5cyZQpU0hLSwNg1qxZLF26lGuvvRaA/fv3Y7fbGTduHAALFizgiSee4Lzzzov6GB6jAW+fVHC48f6yi7qXbiT13nvh6FNwfvMuHi21Uf3xU2pmziTV4yC+fBukDWDv3r3U1gb/oOPi4hg0aBAAhYWFxMcH+7etVisDBgjT6Y4dO0QmRgCJiYn069cPgO3bt+N0OulRthur007R3oP09DrpNnwEdTabuNF012IIftNM3eOjKEbXQejCzeUSs59KMohze3H37oVpv/8CHU85yit/x+ly4Qm98CIasYLoRaZmWTEnx0GlDQqrMKsVsPwT2L6B4mPPpCQzID7I6yV540b6rFgBtq14p3SDSUnYkhKp7DYGT7+emFMT6Nk9DfZtw/77j1BvQ3G78SRnNBhHc1DVUrylNXh3VGAcngZmI+6aMgx2O8Z131M7eBJ79+5t8Lo+ffqQnJxMVVUVBQUFJNdto7vdTq1ipnjTJux2EUxaUVFBYaEoFtjdk06yfTfl677ivy8+h3X9U2JnKX1h46rgA/QaBHX1QgDl5QkB9NxzwuVx8CCHhNksqki/9ho88wz8vBdygQQFzBYsqFBWCr+dB+ZciOsmXpOUBLm5pB2opKTKQW9XPWrNAcq3/UiCw06JLYmaTcKll5mZSU5ODm63m6p6hQSHnYOb11C9ejNVix8jqTQfo8uG6vHgzUzAcGQu3ionnq1VeOPjMSUlYTKZ8JYU4CjYCSs+RemfjiHHSunWX7GaepKeno7NZmO3lp69Z+cOklw2Mup+J4N6jC4zti2/cqBwP4qqorhdmOqrMdqq6aY6sJTtx6OWofQwojoLcR6oA6+K4nBg8XgwOOx4bfV4DElwCJ1uij74gD7bt8O6ddi3b0d1+387hlQD5r4WDGYzGA2405JQ88241xfgvPVtLPn5DDIasfTsDwkpkJ0KCZpoKdbckYEWNrsNljwbLOJMRsjIgX4RAtOjtQSFE/cAgwaJmLPiYqjU4nlURIq8JlL0a0tRURGlpaXs3r3bdw3OLT9IigkUcwL1bgXsdgq2rMdp9sfDjRwpEgT279/vd62pHgbY6kBRsKoK7FyH0+nE4XBgqXbi6WZELdmBoijEJ4lsRv3eYMgeQW++xmivRVEMxMfFwdv/ovjIWSR77bhcZeRr53LovSFw7BD+3hBIcnIyffqISfHWrVvxhIjqtLQ0eoW6MmOYQ7n/f/311/Tr149LLrmkxcePCRFUXFxM94DZaFZWFuvXr4+4vnv37sEdkKPg42oHI4enk+pwUfjWahILa0gtLIRLL2HzgVKUqlLftmvzdjD1pGEMTxMn+u233863334btL+hQ4fyzTeiGNljjz3G1q1bMRsVpo/qjserUmnO5aOPPwHgqquuYsuWLUGvP+aYY3jrrbcAuPDCC8nPz+eFq8fRK8PKo6+/zz3DutGvbz+xcbeeYralqsGWoBhFT1/VZ2svf7SS46x5KAosX7WF49PEaTd61CgUiwGbxY4TF/bKeqr2VOB1e7F7VSb/8U4YcSR3P/QoJ8TtYnidSt3WIqwuJxleVWSyVK0nYf1vlO6tZOXOKlIcbkY7nSgmkygfEG9hs6mIbkd2wzzGgHPzV9R8UE58fBwMEfEdBQX7qKsTF+bsIelh3lH0uNOS2LlqJ788u5r5F48h7YjeqIle1M074OV/su6kqznr8kUNXvfaa68xffp0VqxYwaJFi7hh7kBmjs3i2WVf8NHqe3jggQc46qij+PLLL7nxxhsBmDQwjXvPGU5pzUaGGszkql5hrTDFN6wPlNZTZPkUFEBurqgK3ZoXSoNB9HyaOVNUFl7+NdTlg9WBwesFh34h3wx1QCngFGL57oICkn9Kh6wU1M0fsW/9t6gqnP/Y61TXixvyNddcwx133EFtbS1PvvgG50/tiWnz9xyxrxwDKl6TGaPZjKqquA1eTF4v3ooa3IXixq5a4iAlBY/ZTH5xEU5FIdmdSfe4HNS1/6Ni2dekT5xMdWEh65a8S3acgQEWA3ZFwXh0P9ScNPi9AE9RNbW7dqOgYgQMqvCup+bkYImLw1FbC+Y0zElxeO3FKLtKUQE1Lg7cRmr31rKzqhyub/lHXX/33ZAshMbe3bspczhwKQoWVaX/6O6kOdKIB3BB1Z4arP0TqNtSTOE3a3AB5UOGMPOxx8BUK7I1dQ7uEy7hwCDepf8TVsVAho4FtSp8dhdoPcEMkd1QTbnDDAZhDfrsM9iq1ZvSis/HG8Q1Rb+2/Pe//+WJJ54IevnrN0zi2CnjUYzxbMzbhaX+ALffvYTf9wiLn9FoZN++fQD885//ZPHixQAkxRt5+6YjcXjgyOF7ob6OwgMHqKqqwuiowIsTFTCZzIz8g5iU3nHHHSxfLrLDJqSauW9ECtb4OIYOGQpOB3WLn8B7ZE+2ldk593ExSRk/fjyffBL53nD00Uf7xrRw4ULfWHVmz57NS1px2jPPPJPy8uCSAGcsWMDjp50mfusjG5bEaMDmN6C8jeqrZQyCEY0bKw7l/n/qqacC8OSTT7Z4iDEhgrxeb1BHY18fpCjXR0O/eSeQZTlAvi2FX/94NKNWraJuwwZ48inSR+RiDWg9McdggPTu1JXls2PV98yePZupU6cG7c9qtbJ2rUj5XLhwITU11RybWUBOvDDFHnRnsnbNGlAUzjvvPGy2YHNsamqq7/WXXXYZDoedCT23YVBULjipN8mlecIKBBSqFg6sXYv54EEG5ufjSU5me2mpMIEfIvoYWpOsrCzuuusuiouLqaiooN+g4Rjs1fSMr2Xk+WOp3bWfwRW7cZicWPolkGRIFb2A+kLWsJ5sKExmfdJQTGmDoLCcWUePYIRBxe4x8XPvcUwyr8NaV4bJ6cBYW0e8qjKtTwpTe6XgqnThdXhxpyRQO6gnitVAD6cD00EnCQNTSJnQG3tWBvZ8m+/zTUtLJyVZuEbsyZmH9N6daWlsGHIStgdOZ7lawXxzHsbemaibS/EUFTD6wb/w5JlnUqhZEXU8Hg9r167FZDJx2223MTNrD2lmO8fOOZKRJySQm5vL2rVrSU5O5rbbbtNepWLttofRPRxYy3+hzmCg0JBC9ZrVDF63EqNDuJkMdjuex15AKanB3q8f+ddfj+fgwSALUKueB9OmwbRpGKsq6fnLRyQfzPMHtwJKd/Fd26zZ2G1Wks1mEjZX4ZlSDCXFDElIYLsjgz/eMN33mp49e7J27VqU/Hyu6J5JToIJNTsJ74Fq3GYLjpQUXElJeA0KaqYBo9mMy2DBm5KKyelEcXvw1NRgQKW/Hhdk92BUFLKSrXg2bsdZtJs0YF5PTQirKgoq1vQEFLcb98bdxDtcDDcoKAHBugCGkhI8mhAhz4VhUm+sw7Nx76+m1maiImkw9SOOYP/cDPYqClHcmiKiDBrE7iOPxDZ8OKsrK6nVLQWqSu6OL6hTXXgtFhSPl/zufejb3U0lWWz68x+ozMoitXt31lqtGB0qg+x2FE1QmCo8GDLi2LVzOxWFCkn788j9eVnQscuHHEm800OCy8a+7buw7QkfVD6o3oPJW8+ONStxG/2xYGvXrGZYdTmgsHXd5ogu/ZTMTHrabNQv/w5rX5V4TxIoColGJejaMmTIkIDfA4DKsJ7bqK+vJ2/DFlIyskn3qlxx6QTy68VvXFEU3/k+ZcoUn1XGanDRo8dO7F4Lu7/9nCybjeTkZBKsVvJTepDePZMkk5N99gzW/i7i7WbPns2UgCzdkvIdjCvd6ru2pCSlkJIQx5FxFl64+lx2pfYlKTnZd/xw94b0xER+/+YbVLOZSy+9tIEXIT093ff6P/7xj7h1S6DXS++8PMavXk3d11/j7NZNJDzEOO1x/2+MmBBBOTk5rFnjb+ZXUlJCVlZW0PqSEn/Vz9LS0qD10TAu10Kc0oOs8VcxUW8suWoV/PnPJO4pg27doGdAbERSd0hQGdsnkbFHNl0zaGKfOMh7H4xpoKpked0wIAUyhjCxiSyuiRMnijTOVf8CcwInGlPgd/F+62w2eo6bQs+JE+HDD0X9nOOOY+KkSc16/+FYu3Ztk2NrKWaz2bfviRMnQukw2PoOs3JT4IKbYdPnsO9rERtU6wE1DXokYU4wcsRQK0cMO0VUCnbXg7oC7Fkw6GQuPGMieL3seeNJMvesBbdbNJN1OEBVsfQ3QXx8UKZTIlrp/WIFehgx90slOS0ZDggzcmJCAqRmwrSTcYw8ipItW8O8o+hI7DGCc2aeJf5RVfjsZjCXwbihsGM/KWn1LPjxR+jbVwQnmxvWEZp54gnw0z/A6+Hcef8HprjI31XNftEyxOOE9IEMHjEfDuwVcRtWqyg+ebBYWF+mTyfx4YfJDCk015bnAdNnsGvxswzYtUoEKAcQj1fUlPnrNSIVfuU7kHCAxMJaxn2Wx7iR9UJQZWYKwf/iC7B3NfS3gpIEmck4e+VizdBcmGaLKNzY3QPmWkxD5sLl0yAjG6prYc0q+PkbjLvWgasCnOL7N8QbMdvtIlkC0B0THq8XY7pVNCCtcWCw6/Ei2mXTaGz4ZzKJ4G1TOnSLx/K3K8gYPNP3ngcBDoeDjRtbXtCz7/vv+9LDg8RU/nZ4+ZeADziBcRdeDjs/JX3iGAYPObXhzvb/Knp+AZgNuOx1DBjdW1Qm//Ylcb3R6d6LxIU3waZXoKaW4SPHQnIEa+K636CmkLHDBvi6uK9du5aJY4bDqgQwJzZ+DevbF159lcSCAzCsv9bgVSE93siVV17p20w/b33nsMclfjsGExMmHQlpJXBwLfOnnAg9jmhwmKDz3lYKvz4t4vC2OyAhgcSEBOpsNiaedj5k9IHPXiPr+41Q/Qvk5jJx+nRhYQ38nH5ZCl++AV4viUoCmEyYjTDHexCsCTDzZK0ukzj+2rVrmdi/P3zwgajMvX27r9Do+IwMmDTJN7FAP9fry8BewcTxl4PTLaxmr70GutWof39MAZ9TozRhqWlr2uP+3xgxIYKmTZvGk08+SXl5OVarlWXLlnGf3qMI6NWrF3Fx/hvBhx9+yLHHNlEwKxy5R/s7a4Ooevv883DNNbC9FAwefzPSvfthRE9RBTYnihvEQe1L7HeiMAHvXgb5P0D64OgCmO2V4jEuFQ7uCV6nB0UfBvFAEckcCsk9RdbP6n+JzygjW2TU9Jkusji8bpHWXrIJNr8JPSeLgoH2SlHzQy9fYDBQNvxo+s04RZQ2KC0UGV5NUeUVRRr7xUO2GXpkQOpE6D9aXJQMBiGmDoXcgNpNigJ9joCtSyEtDvr1g+QyKCmD11+HdetELE1oA9z6MpEdFp8W2eWgk9wLJv4R6stF/SDFAJt+EcGy5eXi/diAiy8R6exGY+P7a20UhYrBR8IJp8InL4p2DoF4PPDLFyIV+sRzodtAeHsxGD8Url/d/WtUoQeQpojg47g4sMTjjffAkAkiS6nPUCFQNr4qan0NnATp2m8nMw5mnST+AOpqYOXHUPW1cNf06CHGogfzA26XE+PAbIi3gjsJjuitCWztz2AUtaBSMkVV5Jy+IgvQZBbH3/gqlP0G/Y4ODgJ2B8/8W43A1HiAgWPwpVYZGoptQBT700WQFwwuOxzYBd/+4G+7AeK3cdrV4r25td9IY0UhA7vJB6LXCIoUD6TTrRsMHgzbt4lJjqqN32UXgdvGCLeu0BpEemC0XqCxMfSgaIMZ9q0LWKHChp3w3F+Cg9rLysRv+OWXRauZ004T587k2SIG78Pn/MkJ+i2gcBe8dDdMmC6yx9wq2a++Cr/8Iirmg/is09JEckF5OSxbJv4URbj4Jw6AvhUiOaDIDa9tENnPIM7jSy4RBWy9XjgEsd1etNv9PwIxIYKys7O58cYbWbhwIS6XizPOOIMxY8awaNEirr/+ekaPHs0jjzzCnXfeSW1tLSNHjmThwoXNO0hyT8g9quHyYcNE08g/Xg1lFWCNh9Q0KKsFh01czJoopmb21EHVPlF7JGs0oIgy+jX7oXa/luLcBHqmiyVFlLIPJFxm2OGGYoChp4u2FrYScREccJIoPqdjMGmVn1NFwTW9MrQlCYaf1fA76Nkfrvi7aNq48pPg/kWBdO8lbpC9h0DvoWC0w5a3wFUPiUVgHS4ajxavg/J9wCGITL1nl06P0bDrW0ioB7zCqnH1bHjrS3GBOu88UX05sAecr/9XhNlOaSl8+SWsXQt79wqh43aLC7TbDcmVoN9jzGY4dyGcd03L31Nr0K0HXHQn5K2B5e82PMfra+GjF6DPELjgErjiSlHSYONGqCqCmjxR4T0pSQg5rwImA4XT5jP4lIuC96VPKPRq4uFITIYTzoGf9ogb64TrwGEXN6vkDEjNZNPOfUxIOwDF62HgHJG+Hy1p/UW7j8pdsOdLGDxfLLeVwPrXwXJM9PuKlsDUeIDB4/xBzpGy2gaNhV4DYf9Ov8vy16+hOCSD7bgF/rY9gRlUkbBoIig0OFpPX4+UGRbI1KnCKmJzBFWNxm6DxAj1nxqIIE2o6anvjaHHQjld4hgaxvIqePpFQBEVtE8/XfyON28W1pd160SG5Ndfi3pZOTmidtKV98PPn0PhElBUkannRVh51n4Nyz+CXSVk7K0Eg1VUyz79dHF9j48XIqagQDTLXrEC1qwRxxxRCyXa+zICWUZIGSWuJccf75/oHOqErp1ol/t/I8SECAKYN28e8+bNC1r2wgsv+J4PGzaMd999t+UHGHhy5AvB+PHw/26HZ++BAwfFzI84qK2HOLuoEZLUM/xrgRSnlkKbMdQ/O+o+WtQbKtkYnQiq14LbPKagFFW3NUn84IuKRL+wxEQxQzociU+H8VeKlGRLcvjvQzFAvxMgY4iwCJniIOeIyHVcTGZhBTjiRNEionCnuJmZLdCtlxBK1qSGrxt9sRBCdcWiia6OIRFas+tBSm9xfHvATLRsl+izdvfdogXK//2fMKlfeaWYBdq0ar+JISLo4EH4z3+EWzRSmrVBhWzNFZaaKv5OOrMV39AhoCgw7AgYMlFUKV7+TsOg233bREHBicdD/+Fg7AFrNkFcWsjO4qBbd1zxIaIzoP8eliZqHykGUUzR6xZF8AJrvwDqnkJ/AcdIbp/GGHgS/PYcFK2DlD6i3cnWd9umenB5ERQFBNAaDELglGnWIWMES5CiwMwL4L/3BtXjCaL3EDgq4Nrsq6XTAktQU0HRgUydCv/7H1TVgFcLVVAQJTUiiqCQ5qz69dgdhSDQxV2gBaysDGN+qUj7v/tu0TtNJztbVNb+8kt48EFhzTnrLOFZOPNMcW06+hRYuQXK8sFQKT5jm038lh0OiAPzqBQYMwlmng6jpvo9BwYD9Okj/s4+W1xDVn8OFd+JpAJ7BmRUwe2zYNLFTb+/GOZQ7/8tqQ+kEzMiqM0JbdYXyrx58NNy2PM9HCiEfv2hpEo0y6vc06gISnQdADPiIqeTNUYTQZuEi8zQhBtCr4BrC06HtKdmkwp+V9i4cbHRL6ylKIbgXlCRSOkj/qLFYBAz2l4Dm94WRFXlcVcJN2bpZuGiSMyB7Mmwqxldp5vCkgxpuaJuTbwiWrVUlUFtCTz2mKiU/eyz8NJLoi7KrbcCmgjSLUFFRWT/979iJuhyifd63HFi1jdokN86YjDAll/g27f8x8/KFXVMYgmDQfQUG36kmCn/sCQ4XsjrFd3NV38Z/vX9R8DoQVCxlThPdfA6X/+9pMg3/kDiUsUExFHVQAQZvE5wlAmxHskq1xjWTOg/E3Z+Bts/ht1fav3QIl9LWszWNcH/9x4iWlkU65agRj6L3oPh+LNhjTYZCPTeZ+bAWTf4r1+qV4gFRWl8nxEtQU2kxwcydqwQ89V1Woq+UQi0+trIr/FZqTTx47MEReMO04RStVY3qrYWiovx1KmY77sPTjih4WsURWRETpoE//iHKBr6yCOihdD554uGsHGJok7a8PGwdLG4FoCw0mZn4zQaMNdVwAdPw4YfYcG1DeojAcI61N0NSoZo+t1tJKx+HFwHo2v9IglL1xFBTaEocMd9cMVMqLcLX2xKJtjrRCXb3GnhX+e2k+AqE6XiA6vJJuaIi6qtVLw+fVD41+voIqgiuHCbI02b6R7OrrBYxWiGXlPFn47DAbSiCAJI7Q3x2yG+WogggM2rxI3q0kth9Gi46y7RL2nhQrhqLGQmQN438PtzsGoV6TU1wgo4c6bo4q7VEWnAspDK0sOPbN330pqYLXDMfBg1DT7/L+xY1+RLmDxbuLEO/AwVW7F4QvpCBfTfi4pGuslb3dpvMqlH05OYSPSYJCxNe78RAigxBwaeAtvDtNY4FEJF0DAtCFi3OjUmWACmngzeUij8VrhuFAVGTxPFEAMtqYGusMZuuhFFkPa/uZEK3b59WERPwp3fgcMFiUYh0OyNiCD9/equOqNmEYrGEqS7DivLhFtZq8dVdvRMeoUTQIFkZMBDD8EPPwgRtGsX6HEtZ/WDdDO8+yWU26GbGYZnQ6rIeCMwO2zHevjf3+Civ4jijIG4HVC+Xbym2yhx7lozxb2jtjA6j4OkAVIEBZKUBNNPgW/egZISSE8WIqh6X+Sy61V7AFWcgIENARVFuMT2LhcuscZEkOoFu3bBDSlcZ0/XWmMczkHRXZ2kHIhPhPiAG/aWVXDiecIqcsQR8N578OKL8OF7oNqguA5e/EnEQhgMVE+ZQuKdd4omspGoLGlYH2h4w4yYmCO9O5z7Z9i6WjRdrS5vuE1KBsy+0H9z1yr2xoWKIF88UJRtQHzd5KsbrLK6y7UYoUOsp9Rrigjqd9UJK6jTCbSiCKqpgILtwcuGafFLTcUE6SgKDJuM3baFxEHDYPiZ4d3IurWksaBoiOwOc2jflyXMvsMxdSps+077zOLF92GLwhKkiz5f24po3GEOYZF0OmH/fuFyTkrh4KlnEdUZoChw7LGixtGXX8Inn4iJjdMDihmGDoRxfxAB1KkpsH0d/LYcfl8RvJ+D++DdJ8VvItDqX54nvs/Uvn7xnpKriaADUgS1ECmCQjn1Qli9VHTYLiyGQQmQZhexAYFdy3UqdorH9DBumG4jhQgq29p47xpnjUjtNCfAgeCZfH1GT6iogN27RUbM8DB9fSSxTWKOaF2QYEQ0XELc6At3Qq4W35WQANdfD+ecBGufg1oFLjlWpAofdRSFO3fSozEBBKK/kxpQkKdHv+DebLGMogir1aBxkLdWNH+trRSxH/1GiHXmgEBcLcuzoSVIs+hEbQnSRVA4S1C5cHM34gqPGlNc05l+LWVDyE205wBR8gGitwSBdn1SxHUmnACC6IKiIXITVb2je2O92gKZNg3+g7/YpkLj7rBQS5A+MY1KBDnBWQ/VNcI6YzLBtBmRM9EiYbHAySeLP68XNrwKNXvh9POCJ8NDJ8DQCezMGc2YnT/C/l3+dTvXC1fxtJP9y0q0TK9uAYUREnOAdf5kiihp7Vo7sYwaeE0MgxRBoXTvBSPGwaofRS+eSgd0twmLT6gIUlV/t+u0MCLImiFM6bUHoGIHZA4Lf0yb7n6x+rsPAySl4rYm+61Ao0eHrSsjiXESc7QeaklAgJVjy2q/CNLxVEFyikhvDlfXJRJ11bAquLAdE2a0dMQdh9kigkNHTW18u/h0UAyYvbViAqHH/+hZlnHNtQSFiCBVJd5TDmbToVuC2hJVhd++DV42KqBMg08URCuCiNyYFqJLj4fg1hmB8Sq6KLJEKYJ69YLUDPB4/eUL7OELNAKRLUFRucNconSC3qqjZ08YeoiWd4NBNIuGiE1UXcmZcOHt8Oo/RJaezrfvCvGf3l18bpW7xOfYLWAinKiV16iLvvVNfHw8ZWVlZGZmdnohpKoqZWVlDdpaBSJFUDgmTocdm0UdiH0lkJuqub2OC96uvgzslXgMccLlEY5uI4UIKt0UWQTp3cJDe+z1HCh+9Ho137YqaCdpW8wJmv++Diz4v+ctq+CEc4PrSEXKDGsMr1fE1DgDgj+tSU0LicMZg0kIobpasJdDohY7p08oEqIMBtfdCs4QEWSvwOh1igyzaK1KHcGOdVAWcAM0GmH00f7/fZagKC71PhHUSPaazx3WhCXIYBLWIGeduIH7Pmc9cy9Cdlc4Bg8Fzy5xnhsUkR0WcXwhlipjMyxBNZVQWyMEV7duIgavzzAoamglbBb6WBpromqJhzP/D567zf/+3C7RUPrM60XyhuoVWbN6g1rwXyfqiqMOjs7NzaWgoCCoAGFnJj4+ntzcyK5CKYLCMeJI6LZYFJwrrIR6G1TuFT+wwB9/hei3UmfOJiXSyddtOOz5SgS0Bc5YA6nTCmpVhhRR0zOdpAg6/EnM0Yo+mkUdEoDKUpHW3zPAzVWnZ4ZlN9hFA8oOwLbfRLr5gT3B646Z3zCwsrNhzQTyhXBMzBZWEZ8I6tboS31YAmKCvB5/ALSeGp/UK7pipx2BqsL3HwQvGzoxOH1ctz4YmhAtENDxPEL5BQhIj488s/YRlyZEkKMS4lJQVI/IDlMM0dUJ0hk9DrbvFDE6iinK7DDtvQTWCVLVyN+l2w3LPoXuqkht754prJI9+okSB4eC/tlHsAT5SMkQcYIf+VPD2bIK9m6BSi3WL7CuGois5/g0cW2pL4tK/JvNZvr379/kdl0FmVMXjsweovpr9+7g9kJ5LdTXQHVIMKPmCqs1R7ACgZitJvcSP87yvPDb1GoiqDhEmfccgLGmBnbsEH7mUaMavlZyeJCYIy7+uSGulc0BbQ5UNTpLUMl+YTr/982iPH+oAMruA5OayGbpDCRpSQP678dZI35n5oTg2XJjGM3iJuL1CIuSTu0h1AdqL9Z+E+w+AVGXJhCfOyyK+a4xGktQlDFB4C9WqQWrm7yapdKS1Lx07jHjQFXE78PjatwS5AsE18anGMRYVTWyJUZVReX2Si2uJi0TUISrurnxQOHwWdgasQTpjDkmeFIEsPx1IcqNFlGLLhQtScBXa07SLKQIisTwI0TpcosFimuhqlz4ZHU8LqjaC4pCnbmJWXvWGPF48NeG61z14iKhGEShxkB69idhq9bHaswYMRbJ4YnurskMqZG0eZU/mNlZLdKozVZ/dk0oO9bBi38RgcNhj5Mi6rqYukDsmB6wrIug5rrCdPQaQIHBpYdSJLGtUVXYsBK++F/w8iET/FWddXyWoCjOB6U57rAogrx1N6JWtsDk1ao5RxsPpJOY6g+Kt9ubCIwOsQRB4wUTvV4hgJYsESn4loDU/74RwheaizFKSxCIGKJZFwQvq98nuhd0GxHek6DXXdPj4STNQoqgSAw7QphOs7NEurLdBgd+99+wKneJWUdSDzyGJkzD3UeLH0LVnoAgaI3qveJRSdIKgmlk5oA1iYQtW8T/0hV2eKPHjFm8wQKlskS4xMB/E9YDqUMp3A3vPB4cPB9I/5Fw+b2Q3nrNBWOaQEuQ6vX/tqxRusJ0dKubboXzOLVAU6V1MsNam5fvEYX1AquGW+JFCYFQmp0dRuOB0dHGBIE/6NxnCdJFUJTp8ToJSaJXG4hq8PU1kbf1BUYHjC9S64zycrjhBlGewmKB7AwhQvTLcKuJIN3NGGWl8N5DhKDVSTVAdUVwVlggPhGkWYJsNbDxJyGUJU0iY4IikZUrGnwClJWD3Q37d0BNgWiFUPy7WJc5AprKTjTFCyF0cC0cWCPK6evoKfaOkK+ihzCJShHUSYhLExdjdz0MHAF5AXEGm38RJvBGeoYpbpe48YUKoN5DYMRk0ScqI4o4os6EJQm3wSpufPXlomAc+K1u0eKzBGkiqKYAvB7spjQSo4l9aW9qwwTqnnwJpIWxgIWmjDeG72bdmAjSLS1RfC4h7jCzV3NjNTfQ3JqkWYLcQuyWNHLBDY0JgoaWIKcT3n5btKCproaUFLjnL7D7FUARIshoFIkprYEvJigKd5jOjDNFQ9x4RM88u0M09Q5XisWqiaDKQvjweVF12usFawrMuOyQh9/ZkZagSCiKv+BYdjYUVoOtGrZ8LbIdyrcLs2nW6Oj210Mr8lb0m78wm6r6gqspDrmw9RkCFRXEFRTIeKDOgKL401kHhRTO1F1ivnighjfxzLyV/o7UOjPPh4v/InqndTUBpFFv0urhVO0WRU1BTFKag/556yK0ag8ANnOMtRsJh9EEcy+D0WGaQ0Pz3GGGKESQuxmWIF0Eae4wi0dzY1kzmn5t0H4SRNFQk1Fkhx0sDLaaBxJO9OkiyFUvChiedppoWVNdDZMnw1tvQVqc/27oVYUAMkfxHqOhOe4wnaze4jtN1QZVpcL37we3l9GJzxBxUttWwrrvI382krBIEdQYenXa+HhxEiqqqF666Q0tXXFw9P7txCzh0/W6Yd9ysax2v6hPYk6EPbuDt+87TDTkA9EvTMYDHf7oN9uM1IYuscJdAe6wEEFTW0nGtl+Cl43/A0w5KXYzl9oJX1LCvm/F5MKc2PyYIGumsNY6qsRf5R4A6kwx7FY0mWHkZLjibzBhevhtVNUvaKKpE6QYAEVc2yJliDU3JkgxiM/U48Li1URQfDNFkMEorKhGIxgVUcg2f0+E8YXJhjPFieKH990Nf/2raEY9eDA88QQ89RRkZcG+raIjOwhLUGu5wiBAXDbDEgRwzKkBIsgjsklDa4G5XfDtx1BZDKbGiwJKwiPdYY3Rc4BIW6wuh5RusK8Keiiwax30zIW+AcXovF749lvR4TsvT4iWo4+Gyy8XfWUA+v5BZIgVrYP0IX6XmjUXHL/592VNgsye8OOz4v+jIszyJIcXuiXIUSE6fAf2e/p9OaSVi5tG6E38+yUY3C6waBfTxBRhBZJQY+kF7u1ilg+iJEVzG0kqBtGst3wbHPxNTE4MJmzmZsYWtRdn3wg9+zUd/K56hKAxGKP+TLyKpgS87vD90nSXjimalHuTsPrYSqG+RDS7NRubbwkCIWQUN1g1N9wrL8Nd90Ueny766uvhp9XgzofSIujRA66+WnSDD2xJsS8P0rUJhRfoEyYLq6W0xBIEYKgVhVPLq0APZ/r2Xeg3XNybyovg/X+LCdRgE5gUUeHchbAMj5T3jWiQlqDGMBj8LjGDAWrjYGsZrN4La9zgiYf6elK/+w7OOQduuQVWrIDSUtF87+234ayzYLPWCsPaDfoeL55vfUe41IwWqAvxr+uzkJVaYNvRRyPpBARWdx0T8p1u/1nMvq0ZwYXtyg/Cr98Eb3vsqZ2/BlCUeA0W6K0VMbUkQW4LfyuZWhXe/O+FBSVjMKoSoxl22X2iy/5rjitMQ/WJoAg3bN0SZIiyBYhe76piJ0avQ1jcWlJ80qKd70la6YMvl4rSIaF4AwKjt20Tndx/3yAsprNPFEHQc+YECyBbjSg7oS9SDA0ruR8KzQ2M1inLg6Q0qA+4Hrhd8L/7YfGj8PztQgCBED4A8WaYcwlc83Bwyw1JRKQIagrdJQaiv5MxBb4tgVc+hOnT4dhj6fHii6JrcHY2/PnP8NFH8L//icaYlZVwzTWwT4tX6DkZeh8tZlnmRBh2BuSHuML6DIWNG6G6GldWFvTp025vV9KGJHQT37u9HAaMCC5qpzhFO4BQV9jyd4J9/OlZMD6C+6OrkjsNJlwNE67xVyZuLt2G+1+rGCC3E8yim9MyQ38JAZagcDSnThD4SwwUau7cxOyWuXB1EWQyQno6KF644w5h6Qkan/aeP/oELr5YXHdTM6F/Pzju6PBhBXu3iJ5kCiL2KKu3iENqLVoSGK2qwjJpMMKkBcHrnHbI+xWcAdluLlV8z3POh0nHB4s8SaPIT6op+gyBhIC4n4R4uO82IXAMBlAU6gcNEr7mJUuERahnTxgxAp58Ev7wB6ithZtuEn5pRRFutCm3wpE3Qkq/hjVf+g6DH34AoHbs2C4f99FpMJjA2l1c4OxlMO5Y/zqrAnVVfmsRiM7gm0Jigaaf0TVqADWXhO7RVTGOhNECIy+AHpNg+FmxmRrfXJqTHq+h6m6zpkRQNO4wEG5GEJWiAVL7RT2WIOI0UaIgYnhysmDnTpHiXq6lhquqmEjk58ND/xRZYAsWwCWXiRT7SCJk10axX9BcYa3cpLollqD6UlH805IE406Co+Y1vn16LnTvCYnSQtxcZExQUxiMotvvb9/5lznL4ZlnxAzd42Hv+vV0C5fCbjLBvfeKGcmuXSIj4fbbtf1qH31BHjgCZjOJKZDVB5aJALgamRrfuUjKFu6wuoMiuHnlp+LibVVE+nt5LeQiln35ZvBrc/rCiClhdippFRK6wcA5HT2K1qM5LTM0VMUEOJsWQdG6w5JyhItXr2YcruJxNMQlghsxITQY4OIL4IlXRUuhU08lt3dvSE6ASbXgVsGaAHfeCTNn+q1Q4USIqoqO7YaA/weOadkYI6GL0OZYgvTuBCl9xHuecZZwjS1/J7hHYGIK/OEMyEmA3cv8mceSqJGWoGgIdImBCGj1esWPsamu7gkJoiKpyQTvvw+rVgWv37E++P+Bo2HrVti/H7p1wza8lWclko4lUSvwV7MfMnK0opxAvDYV/XGZ8Pv/uhzytwW/9sTzpJlbEj3NaZmhv4QoY4KidYcpBhg0D5JyKLWOjNxouinitQKL+ulvtcB//wtTpoDNRtJvv8HG9YACaRniWjtzZvBYw4mQ8oMi68onggzQtxWDogOP3xxLkE8EaeUeFEWUwvi/x+CM62H2QjjvZrj+MZg4o2GDWknUSEtQNPQfKQJRdYtNXTXk50HfKAXKgAGwaJGwHv3tb6IuRYJm3t0Z0pxv4FhRywLgxBPlTa+zobsHqrRK4cecAnu1LDG7KjJYXvkbFO0Lelltj0Ek9o9QMVYiCUdru8NUb/hihE2R2hfGXUHp2rX0jf5VwVg1EaS7rWw1kJMjUtz37mX/p58yZNQgUFdAQrroAq/js8SEESG7tMakBm3H1hQwR2nlipbGjh+Jmgg1r6xJosF3KBZdBDVSTVsSFnmHjQaTGQaPD17227fN28dFF8GQISJr7KmnxLLqcjgYcLNTFMgZAJ9+Kv6fP7/FQ5bEKIlZ/po09krR62m4lg1Yr9X52L8zuCiaJZ6icTPbe6SSw52WZIc1FhgdWIiwuWUIDpWENPGoi6DaSv+6vn2pmTwZJo7TagmFWKl8lpgwvcO2a5NQ/e0kZ7bKcIMwmMS13atVvG4Ktx3qK8TrEqO0nOmWIEeYauKSRokJEVRYWMj555/P7Nmzufrqq6mra9gleP/+/YwfP5758+czf/58LrusncuBj54W/P+mX4RFKFpMJrj7bvEjffttUQhxc0jQa88BsPRLEUA9YULDysKSwx+9Jg34rUE5GUJo10a4QM65GFdSevh1EkkkwjUTbeoljaXIt8QK1FromZS6CKoLY/Hwvd8QEeTLzgp5T/V1sHujto22LK0NKq8rSvPigvTK8QndoxebliSxrbOu8YrfkgbEhAi65557OO+881i6dCmjRo3i6aefbrDNxo0bmTdvHh9++CEffvghL730UvsOcuCY4MaUHjf89Gnz9jF0KFx5pXj+17/Cr98Frx8wFl5+WTy/6KIWD1US46SJvnCU54kWLPXF0C0X4kIqFBsMcPKlDWsKSSTRcEh1gsLcSH0iqJXdRdGQqE0C9DtWXRiLR6T3GykmaNuv/ia0BsREJKkFhRyjwdgMl5jewy4xq/HtAlEM/sa00iXWLDpcBLlcLlavXs2sWbMAWLBgAUuXLm2w3YYNG9i2bRvz589n4cKF5OXlte9ADQY44sTgZau/hIqS8NtH4qKLYOxYqCiGNT8Gl6df+qPoZzNxIkybFnkfksObzGFidlixHQpWaMuGwBX3w5yLYfxxIiX2yn+IoEeJpCW0ICao0TpBza0R1JokpolHvVxIOCt8JEtQpBT13wMmoQYF4hPb7r3p1qhoWmf4Gik30yol44JahKKqaoc2HCkuLuaMM87g+++/B8DtdjNu3Dg2btwYtN2TTz5JZmYm55xzDj/88AP33Xcfn332GZYmemo5HI4G+2opitvFwM//jcnud9fVZ/Zi37HnoUZbNwMwVlYy5NE7iUuyo5rNeJKT8dSDuuoA3vh4dt93H66cFmZRSFqFUaNGERcX/Yy3uedZr5qVJDv3+/7PTz6aOkuPZo1RcnjT1udYZv1Wuts2UGYdSklCdGnf2XW/kW7fQVHiOCrig6smW10l9K3+Fpu5G/tS2rdgp+J1MnbXs+BVsa8TPcjyFtyCGuCaS3Hso2ftL1RbcilMnupbbvLYGFT5KW6DlR3pcwGIqyyi/5cv+rYxdjfjHdGb8sRhFCWGxH+2Av0rlxHnqWJ36ok4TGmNbtu36hus7jL2pRyLzRy9EOpV8xPJzgIKkyZTHecvsNvc86yr0a7ZYZ9//jn/+Mc/gpb17dsXJaQYYOj/ANddd53v+XHHHcc///lPdu3axbBh0TW6a7UTwWyDT/7j+zexvgLD1y+TMfNs0dMls0fTxQ29HlgzGrZsBIcDU2UlFCMyGh54gDEBVqC1a9cysY1qBR2u+27L/R+qaI76PLMPgPX/ET78rDEMG3xyo+eN/K46z77b7RzbWw35O0nsM4A+faJ7H/t+WE9iQgID+vaG3JDXlG+DzatIzOhJ9xHN/1wO6fP0uiH/P+B2k6hl1k4YMhDSuvv2PXhAX9ixgcTsPvQYHHCcg7uhwg2qnYlZKdBrILz+oD9DF6B3LiQnkJA7gNx+wWNslfNg/TqodjFq+JAGGV8N9v/LN+BKYPiEY6Jv0A2wqxQKyxncJxtyJ7aqAaAz064i6KSTTuKkk04KWuZyuZg8eTIejwej0UhJSQlZWQ19oa+++ipz584lPV34hlVVxWTqgAz/8dNFnaCA+j5x1aXwmRbLk5As2l6M/4NokhnuxrZ5FThs0L8/VFWC3QlTT4YFZ0Bubnu8C0lHE58OE68VMUHxGbIquKT1aUHbjEazwzoyJkgxahX6A5bZanwiCGjo/lNV+P4D+OEDGGoSLTFevgfMFlGYNJB+w8C5r+2CvqMNjHbViz+jBcxJzTtGnHSHtYQOjwkym81MmjSJzz77DIAlS5Zw7LHHNthu9erVvPvuuwCsWrUKr9fLgAED2nWsgLhZnXo15ETo52WrESLpzUfg1fuh9EDweq8HvnvPv6+0dDj5XLj+BimAuhrGOLBmSgEkaRtaVDG6kTpBvmrRHRATpCj+Kvu+DLGQ4OjQmKX1P8J374NX9b9OoaEA6j8SMrODX9vaRFsw0V4hHlsyMfIFRtc273VdnA4XQQB33303b7/9NnPmzGHNmjXccMMNALz55ps8/vjjANxxxx2sXLmSuXPn8uCDD/LPf/4TQ0cVEkxIhgvvaDprZ88W0el35Sf+Jpg/fAhlB/3bGAww+aTwr5dIJJKWckiB0Y2lyHeACAL/+/DVCgoVQQF1jGw1sOx1/zq9+kSorrAmigzMwBpIbUG0liC71l7E2oKSGGbNdeaSlqDmEBMVo3v16sWrr77aYPm5557re56dnc3Levp4LGBNhFOvgqPnU7L0bRIVh2hzENjXBUTRu6/eEi6wzBzY+FPw+rHHQDcZECuRSFqZlrjDFKNwG4W1BOktMzooyFYXKAaEqKkuD16vZ14ZzCJztz7AIuJFxADFO8CmXaN79IdTroCMbChuvmBs0dijtgS1QATJFPkWERMi6LCmWw/Khh9Nv4kThatr/y74/n3YuSF4u8Jd4i+QxBSYflb7jVUikXQdWlAnyNuoCOpgS5DeziJc1Wjwv1/F2LCif2ZPSIiDKxdBnRMscZCe7Xc5eZvZE625RNs6Q280e0giqFbEQ0miIibcYZ0GgxF6D4bzboHTrhbWoojbGkRsUVJq+41PIpF0HQ4pMDoG3WGmEBFUVRa8XrcEFe8PthKZLZCpxVsqKuT0Fc2LA2NuPG3sDvPVKmrKHRYQE9TsY8SJ43hczetY38WRIqgtUBQYfRRc/SAMP6Lh+oRk0QF44Oj2H5tEIukatKiBamO9wzpYBFms4lEXL7UVwet1IbNvR/DyUVPBbA3eJpS2DvqO1LojlENxhymK3xok44KiRrrD2pKkNDjz/+DgXtixTnSh79YDhh0hutJLJBJJW9FSdxiEF0HuNnYZNYUugvSpe3WoCNKETH6ICBo5Beo3BG8TSlv3RTNGERjtcYp4HoPRn+7eXMzJovmqsxbim1FjqAsjRVB7kNNX/EkkEkl70dncYRatuKHuxbLViMQTU0BbDJcDbAENuOMToM8w2LFV/B9JhLR1dlg0gdG6FSguLfrGqaEExgXFt2wXXQ3pDpNIJJLOSIvcYY3UCfJ2YLFEEGLOaApOc68JsAZ5nMLa7g0ICh44RogkQxMipM3dYVEERtsrxWNLXGE6slZQs5EiSCKRSDojnuZbgmK2gSpoVaONwSKoOiA42uvSRFDAej3usil3VHu5wxoLjHZqTWHjDiFZRqbJNxspgiQSiaSzoaottARpERKxKIIMmiUo8K5VWep/7rYLd1igCOo7Qjz63FFhRIjXDapXCCxDG0WIRBMY7dCKP7Y0Hgj8BROlCIoaKYIkEomks6F6hBAymJoVX+IPjI7BmCCDEUym4NT2imL/8/pqTfxp/6d1h3Stt1hjFZtbEEDebKKxBDl0S9AhiCDpDms2UgRJJBJJZ6MFrjAAlQgxQarasQ1UQQg6oznYHVautSBSVbBrAdF6SFD/Ef7t9DGHs8S0R+q/7/hRiCBLK7jDXFIERYsUQRKJRNLZCGwh0Qwi1gnyuYyaZ1lqVQwmEeQcJIKKAFBwN4wH6jfS/7wxS0x7WLh0N1tj7jBna1iCdHeYFEHR0ulT5FWtfLjT2bYVNB0Oh9x3O+67rfavnydqM8vOt8d5Jr+rzrHvdjnH6m2gWMGQBM14DyoGHIYEzbJS7xc8Lm1/xoRm7S+UQ/o8VQuYU8CaDHbts7PVgt2O0VmPQ7UIIWPVBFyvQf6xes1i/C5vw/Hb68U6JTHiezvk88BjEMfwKGGP4bDXg9MltiG+5Z+xahDfuceDs94mFskWGo2iqJ38E6qpqWHbtm0dPQzJYcaQIUNITo6+2Jg8zyTNRZ5jkvaguedZV6PTiyCv10tdXR1msxklMKBOIgmDqqq4XC4SExMxGJoRUCrPM0mUyHNM0h609DzranR6ESSRSCQSiUQSDikPJRKJRCKRdEmkCJJIJBKJRNIlkSJIIpFIJBJJl0SKIIlEIpFIJF0SKYIkEolEIpF0SaQIkkgkEolE0iWRIkgikUgkEkmXRIogiUQikUgkXRIpgiQSiUQikXRJpAiSSCQSiUTSJZEiSCKRSCQSSZdEiiCJRCKRSCRdElNHD6CtkZ2XJc1BdviWtDXyHJO0B7KLfHR0ehFUV1fHtm3bOnoYksOMIUOGkJycHPX28jyTNBd5jknag+aeZ12NTi+CzGYzIE4Ei8XSJsfYuHEjo0aNkvtup3235f6dTifbtm3znTfR0tbnmfyuOs++O/ocewL4Cui1DvpsglPGwMhRwEKozndSj4UkAyQ+Cwxuen/FRfD119CtOyhASQkcfzxkZQdvF+7zLCqCb76GrCzYdIIY18V7YecKKOwDlx8NI6N4T4fjedDW+2/pedbV6PQiSDcbWywW4uLi2uw4ct/tu++23n9z3Q3tcZ7J76pz7bujzrF9QBmQ6oIqJ5gsEKcCO8FUq1LZO460fRB3AIji3uz1gtMJRqMQQU6nWBZuiKHjVlWxvWKAwjgxrsR4sazOAZVxEO07PVzPg7bev3SdNk6nF0ESiUQi8VOsPRpd4tFuBkrFc1eqm/pkQAVKotuf0ykeLRYhggKXNYVbG4PZDOXasjQzGAGTC2qi241E0mKkCJJIJJIuhK5tUjQBUmPyL3SlCxGkqviEUVMcighyucWj2QQV2rIMs0hbNkoRJGkHpAiSSCSSLoIDqEZc+NNd4v86Cz4R5E51Y09qRxGkCTGTGSq1ZRmaJcjohNrodiORtBgpgiQSiaSLUKk9pgNxbiGCbCZ8gseTFiCCisPtoSGt4Q4zmcGGeH2KbglyS0uQpO2RIkgikUi6CFXaYwpg0QSIzYxPHbnT3DgSNRFUGd0+W8MS5NbuRMlAnElYggxSBEnaAVlBSdJlWLp0KRdeeGFHD0Mi6TB0EZSKCDwGqA0QQd4kN06riIv2bdwE7oC4Hj1zv7kxQS4tizsJMJn8lqBaNbr9dAXuvvtuZsyYwb/+9a+OHkqnQlqCJBKJpItQrT2m4s8OqzH7V3iTPThNmiUoShGkCxmTGYyG4GVNobvDdBGUgkiXNxsBD9R4kHcpjcWLF/Ptt9+Sk5PT0UPpVEhLkKRT8/jjj3PCCSdwxhln8OWXX3b0cCSdlG+++YYzzzyTU089lXPOOYfffvuto4cUlkBLkC6Cqsz+FWqyC48JPEbACdib3megJcikCRZd3DSF7g5zaK9Lwb8vgPooxVRn57zzzkNVVRYtWsSaNWs6ejidCqmxJZ2Wr776imXLlrFkyRLi4+P54x//2NFDknRC9uzZw7/+9S/+97//kZ6ezvbt27nkkktYtmwZCQkJHT28IHQRlOwFVRMY1Sb/CjXVDWXgjBMuMaUaiG98n77gZhMYjNqyKMWLK7BWESImCDQR5ACHFEEAvPHGGwwdOpRXXnmFjIyMjh5Op0KKIEmn5aeffuLEE08kKSkJgNNPP51XX321g0cl6WysWLGC4uJiLr74Yt8yRVHYt28fw4YN67iBhUEPNE72iHgbrwlsBnwiyJvkxVABTitCBVUBWY3vUxc8llJI+juMMMCB06Ibj9sjHu0BgdEAFu1/R5QWJYmkpUgRJOnUqKo/stJoNHbgSCSdFa/Xy9SpU3nsscd8yw4cOEBWVhPqoQOo0x6tLvHcY9aWaTFB7mQ3JpMQQaoNlCjignQRlPAamLbB0HKoGh/deDzaa23aT1N3h8VpdyantARJ2piYigmqra1l7ty5FBQUNFj31FNPMX36dObPn8/8+fN5/fXXO2CEksOJY489lqVLl1JdXY3X6+XDDz/s6CFJOiFTp05lxYoV7Ny5E4DvvvuOU045Bbs9ioCadsamPca7RBq6x6xlctUDRlDjVIxGcMZrwdHVEXflw+UCVDD/CnqbqvQom917NEtQXYglKE5zj7ncWqaaRNJGxIwlaN26ddx5553s2bMn7PqNGzfy6KOPMn58lFMMSZfnuOOOIy8vj9NPP52UlBSGDRtGRUVF0y+USJrBoEGDuPfee/nTn/7E/2fvvMPjKK+2/5tt2qLeLcmSXCRXXLCNTTOYgE2xMTEQQxJMNQRCCCQhCZBAgBcIhMBHyEteQgghhBo6BEwzAYwptnHFtmRbruq97Gr7fH88M9u0K62aJdlzX5euWe1OeXZ3dp577nOfc2RZxmAw8Je//AWbzTbUQ+sCVQlK8Ig7YG9IZhgpgERACSLODDGvF8ztoGsiUCgoZV9841FVJEcUY7SEqBXkJv4mqho09BbDhgS99NJL3HHHHfzyl7+M+vq2bdt4/PHHqaysZM6cOfzqV78a9M6+GkY+rr76aq6++uqhHoaGIxxnnXUWZ5111lAPo0dEkiC/EfStigk6Rbym1yvhsF6QoIwGRQVKBxogsR5kv0h372lbgHYlHBZqjNYBOo9Qr7QrPZSVlQ31EI5IDJtw2D333MPs2bOjvma325k0aRI333wzr732Gm1tbTz22GOHeYQaNGjQMLKhkiCjajg2gq0V/BCQYQwG8JiVMFQP4TDZL0JatmZAAukEQAeWVvDau98WguGw9ohwWGjBxM543pgGDX3EsFGCuoPNZuOJJ54I/H/FFVdw6623ctNNN8W9j23btg3G0ALYsGGDtu/DuO/Dsf++YDDPM+27OrL23Vf05xyrGjsWh8nEofJaHI4cvH4Dxho/dodMp1PkjrW0NGDUJdLZKVO3o4maDTUx9+fzSjgcE0ioN+BweKmnHn9yPrYmHVvfK0MeE05hIj/PlpYJ+P0S+1udOPxmDu7bh8nppK4uF78vB4/dy7qtuyiIowT1SD4PhuN5drRgRJCgqqoq1q5dywUXXAAQiLv3BlOnTh208NmGDRuYNWuWtu/DtO/B3L/L5erXJDNY55n2XR05+x7Kc8wIWIHxBWPp2AOWRKEEJVghudTKIQ6Rk5OJnArmBEi25ZM/Kz/m/hwO2LQJMtxgsyZgO9HG7jWga4EJ6ZOxhXx8kZ+nLMM3G8TSkG3DqoN5U6ZQCLhcsLkNrHoD4445hqk9vK+ReB4M9v77e54dLRg24bDuYDab+cMf/sDBgweRZZlnn32WM844Y6iHpUGDBg0jCmqEyqC2ujCApUMJhymxKL0+JBzWQwdT1dNjUXecDe408dBf1f22fp8gQDqdUqsI0TsMQjxBWjhMwyBjWJOglStXsnXrVtLT07nrrru49tprOfPMM5Flmcsvv3yoh6dBgwYNIwZ+RBcMCUTKFaLfl8WukCAlmU1vAI9JMUb34OtRq0WbVbKUCd5U5Xj1PWyr+IEMBuhQnlPra2ueIA2HC8MuHLZ69erA41Af0KJFi1i0aNFQDEmDBg0aRjwChRIJ6fdlBGtHOAkyKEoQ8ZAgr1jP3IFQkjLBk6G8GNtKBAQLJer0gpPpCGaBGTQlSMNhwrBWgjRo0KBBw8BALZRoI9izK8EIZkcECTKA16SEw3ogQR4vGJ2g8yFiWWbwKa2tpIbut1WVIEm5FU8kUGYoqAR5guPWoGEwoJEgDRo0aDgKoPIZG8EwVoIRzFHCYd4EJRzW0WU3YfB6IcEeUiMI8GeKpdRDOExVgmRDcFwqNCVIw+GCRoI0aNCg4SiASoKshCtBkZ4ggx58BpAlRJyqmyamXg+YnAoJUuoM+ZVwmK4nJUghQX6lUGIYCTJqniANhwcaCdKgQYOGowDRwmGWKJ4gvQGQwGeO2DAKvEo4TJIIZJfJGYJASa0EDNhRt1XCYbGUILVthkaCNAwmNBKkQYMGDUcBQsNhoSQoMhxmUJSZAAnqxhfk9QoliBASZDSBy4YwFbV0vy1EV4KMmidIw2GCRoI0aNCg4ShAtHCYNUaKPIBXJUHd+II8oUqQ2nbDCG6r4inqpl+x6gny9eAJcvb4zjRo6Ds0EqRBgwYNRwFCw2GqCmM1CBLkg0ClQlUJ8qr56j0oQUZXOAkyGsAVBwlSw2HeaCQoxBM0kpQgB91GADUMQ2gkSIMGDRqOAqjeGgugtuKyKSRIhkClwoASFAcJ8nnB1KmktqskKk4SpCpBnmjG6BAlaKSQoDXAAuBHKJ+nhhEBjQRp0KBBw1EANaxkJqgEJfpA8oM7gUDpXFUJ8piUDboJh3l9XZUggwHcFoUIdKcEKWOIpgQZFWO03guuHt/Z8MDTCEVtC6B17Bo50EiQBg0a4kJ1FXz4IezYMdQj0dAXqGQiwR/0BNkURcgZwkDU3tSeeJQgn/AEEUGCAkpQS+xt1XCYqgQlhrwWqgSNBBLUCWwO+X/jUA1EQ68x7NpmaNCgYfih0wGrVonJs2IPJNpgdOFQj0pDb6AqQSaFWBiNYO0UHhZHCAPRRypBPYXDnEo4TMkOU0lQT9lhajjMpcxC1pDXdHrQSaIStcvPsL9d34tiLlewe6gGoqHXGOanlgYNGoYDtm8PqgcAGzcN2VA09BGqomKK6CAP4AhhIKonyG1UnugmHBamBIWQoHiyw1QlyBUlHCZJIYqUN/Y+hgtU0jNKWe4ZqoFo6DU0EqRBg4YeUVEhlmecIZSC2hqhDmkYOVCVIIMSAjMaRcsLAEdoOKwXSpDX1zVFXq+GwyAuY7QrijEahC8Iwsn3cMVBZXmSsqweqoFo6DW0cJgGDRq6hb0DmpvFpFlUBKPy4NBBOHQISkqHenQa4oVKgowKqQglQfYQBhJQgtTZIZ7sMAtdwmE9KkER4bDEiNdNI0gJqlWWkwAT0IbwCVmGbESx4ff7OXToEHZ7D91xRzBsNhsFBQXodD3rPBoJ0qBBQ7eoUm5rR40SXo3RowUJOnBAI0EjCWo4TB9CgkzKPNgexRPkjscT5FaUJRthFafdvTBGd0YqQUp+uaoEeUcACVJ7xeYA2cAhBDEqHqoBdYOGhgYkSWLChAlxkYSRBr/fT2VlJQ0NDWRnZ/e4/pH3CWjQoGFA0aBc4XNyxHJ0gVhWVysTnYYRAVUJ0oWQIKNCcDpswdo2ajjMpXqCuiFBUmgvDmU2UVPkkRGSiD/qpoFwmCPUGL0JWAqcBuO+DF9vOENVgrKAXOVxzRCNpSe0tLSQk5NzRBIgAJ1OR05ODq2trfGtP8jj0aBBwwhHvdINPDNTLFNSISEBHA7ojaIuyxppGkp0UYJMoOsQnuZOW7BZvKoEqWGq7ozROvW1pOBzBgPIOnCb6TZDLKAEqcboNuBnQBXQDqVvwfidIHlgOPMgGahTHucA6crjbiKBQwqfz4fRaOx5xREMo9GIN04JUSNBGjQcBZBlqKmGxoZebucPbpOZCWwB6S6YtxqSa6GurtvNA3A44JWX4R9PQXlZ78agYWDQRQkyAA4xCXTagq+rniBXHJ4gnfpacvA5dXunaohpib6tqvDY1TpBLyKUo1nADYKcLfgIEhzDu1ZQB+KzsyIEsVTl+ZYhGk88kCRpqIcwqOjN+9M8QRrihuyH7W44lAAnS+F1PTQMb2xYD998Ix5/5zswbnx827W2iewcmw0sX4H31yD5oMABKZ9DYyZwXc/7+eILaGoSjz//XNQYsgxH1+gRDJVI6EKUIOxBJShAknQi28ttFCqH1F04TFGCpBASpG7vsoLsBCmGJOL1ikiZzwBmGXTvKi9cBsyFzqcgqQLmrAZXadfsseGCUBUIgiQovmCMhqGGpgRp6BGyDDt3wN/+BM2LoGAGvPkb6NDaO48I2Dtg06bg/199BX5ffNs2NYplvgz238BuHzy5DBrPEC0NRj0KlHe/D7dLT8UeMTmmpQULLmo4vAj8XEOVILuiBCUGX5ckERLzJCjhy27CYXqVIIWEw9QaPy5L9xliPp8gQX49TNwJHEDEko4DdFB/uiBoJ3wwvJWgUD8QQIqybDn8QznseP7557n66qu58847mTt3LieddBKff/75UA+rV9BI0EiEH/gG0aBmkD0Wsgxffw1fvgXzn4DC/ZDcAsc/C9vuGNxjaxgY7NoFfj8Uj4GUFOjogKqq+LZtaRHLiW9CowvWLoK/3gJ/vhf2Twd/J8i3ETSUREFjUxKyLLLKZswUz+3b1483pKFPUImEFKoEdXQNh4EgQT7VNuJEaTPfFXqlVpSUGv68WjCxW0+QogT5DTBzrfLkAkAJj7XPFRlqY3aCd3+Pb2/IoGaGqXlIqcryaFCCysrK2LRpE6eddhpffPEFF110EU888cRQD6tXGFYkqKOjg8WLF3Po0KEur+3YsYNly5axaNEibrvttrhNT0caZAe0XwyuS0G+DLgWaBq845WVweaNMOU9Uca+8gzY8mMhYWe/Ds5vBu/YGgYG+w+IZUkJjBunPBfnpNLSAmlVkLwDGmzwzC8ACdbYoOy70J4Gvt3AU7H30dgoZILiYijIF8/V1golQMPhgUyQ5Mgh2WFqOMxp7UqCkEBWY94xQmIqCdKlhD8fT62ggBJkgBK18das4Os6G+wtBUkG/Zqe3uHQQb38ZijLo0kJKisrY+XKlZx88snodDrGqReYEYRhQ4I2b97MxRdfzL4Yt4g333wzt99+O++99x6yLPPSSy8d3gEOA8gy7LoJnF9CvRca3OBfB1xFMDAdgYYG2LwJtm6B5ibE1XA/sBaMdd1nCDQ2wOdrIH8HZNRCaxaY74cTV8LmeWBwwqF/Duhb1DDA8Lihvk6EKPLzIF9Jb6+JM3+3tRVKvgK3Hj46H8alwThEvyn/KPjmbKWOy9+Biq7bez3Q0mJFkqCwECxWSE0V2zQNInnXEA4vgnDoAV8ECVKVoNCQk5oh5ldJUJSQmN8Phk7xONQTBPGRIK9XCEyyBMUqCZoRvo8DypxqGsYRFlXxUclPasTzRzLKy8s57bTTAv/v2rWL8ePjNBwOEwwoCTr99NOpqIhyJYwDL730EnfccUfU4kaVlZU4nU5mzJgBwLJly1i1alV/hjoi4PF4aGtro6mpiaamJvZvlUn5AJBkvvyhh7cu8VJp9eGp8OK+1B0oTNHR0UF9fROr3nXw75e8rF3rZc0aL+88DpVLwbvUh/c6L8U3FtP5s06aDjXRosY9gLa2Nmprm3jnXTdyq5fpa3w4E2Q++CnMSAdJ10r1OW5kScb0vp/mXc20tbUFtm9tbUXWcqEPO9rb2wPnSmenmJ2qq/14PF6Skl102Jsw6Jvw+700NPhxu0S6bOg5pv45nU5kGVwVMqN2yjiMPt65wMsEu50ShwPZ76c9ExpG+6k8zo3X6cV1i4umerG92y16Mxyq9OD1+ElKctHpFK9ZrZ3Isp+mJnC5XIGxahg8BDrIA24PyLIfZ2cb7hY3yH46rTJOoLOzk6amJvx+Nz6fF5fBLVR3RfHp6OgInCMNDU0YHD5ADpAg9Rz0+Vw4zT68Pi/OmqDGFHquOZ0ePH4fORVeLHZgFLQmtAZed7k6ODDGj4yM+RvAMTyvLZEk6GhRgg4ePIjP52PMmDGB57Zv387EiRMBeP3111mxYgXLli3jyy9F0acLLriA++67j6VLl/LPf4o7aI/Hw80338xFF13EhRdeSF28KacDhAHNDvvwww/7vO0999wT87W6ujqysrIC/2dlZVFbWxtz/SMFy5cv56uvvgr8/8aKQxT5YHfmLm59+vucMv+PbJ+YzlnvSmTvlDnm6mPgf+GWB/6HpsZZpKWV4vN7qDzwXxY1zuT0hu+g88B+uZNN9s8oas/C+GcjFf9XwWPHPMYHX30AwJVXXoXPdxp5o47nlM+N1DdnsXmuifxzU5CAiy++mAOtFkrGvMD4PRK3nPBb6k6u4tVXXwVgwYIFzJs3j9mzZw/Fx3ZU4tNPP+Xiiy8O/H/jjTdy8803s2u3nZ1b9pK3ppx/raziW+O3dCw8jclTF1Bbl43Hs58f/vCHXfZ33333cf6yFRR86qXT7uS5abv4vF7P1p/dhz85mfT776cuM40Uh4M7m9/k0p05ZG7P5J/v/ZMXbC/w97//nUWLFrFmzW727nXzwYdv8PNfPA7AhAkXsfic39HclMS6df/mjjvu4Msvvwz7jWsYWKg0xIxQ5yoqKvjL/93I/9v5C5KzJ+O0ZuEEXnjqKVatWsWCU/9Eaup43l9bSUnnaI6xHwPAr3/9a1577TUAjMYk/pr+KZ2dNkgWuVs33HAD77//PvNP/gOzPXMo3iNTXv8t333ouwBceeWVrF0rDEDnLX0LOSUNs8+Ejnw4RlxbNm8WstC4secy5cy72JeTRG67GbbBgh8tYOnSpdxxx/AxJMZSgloO+0gOL8rKyigtLQ0rurhjxw6uv/56ysvL+eyzz3j66aex2+387Gc/o7S0lJaWFn7yk59w3XXX8bOf/YwVK1awdetWEhMTeeGFF5Bl+bCn7/eaBO3cuZObb74Zn8/HpZdeyvLlywdjXGHw+/1hH0xfPqht27YN9LDCsGHDhgHfZ0VFBaWlpZx66qkgmzCv8uL362g76SA/SF8C8nZgMV9fYuDsd33Yd9lxny1x6oxf8O14N7LUTsGhcq47sIjM5kysiQ52T4KPpnViN07nQOtuzlyVzdzWuZR6S9m4ZiM+s5+5x12H3V5KbrXM8Q1G6rJT+NvPW7hryyY2+HwsXLiQ1pZWyuo9TNhj5qIpP2XrglWBz6C2tpb6+vpB+UxCMdj77wsG8zzr7v1+8cUXgJhEkpKSyMvLY8OGDezfk8cl68eR15QHSW4u4AIO7vXw6VQD677eT0bmQa6++uou+7PZbHz9URmlW8YhGfV8dEMamVkJLJ83D2dCAh9IEt/om5jv0zNu2vFUZHzIpP9M4mfSzzhm4TF4vV42bNhAW1smWVkpZGUVcfzxynHkIvx+PzvL6tixYytOp5M1a9ZQWFg44J9Lf3GknGN1RiOOceOwejzU1PhwuVwUFGQzpnkMTrOFJsnBt7urOemkk8T3II8Fsigo0ZFRmcHODTuxe+1Mnz49SFZlK2n/sWEw6NlZuRP7Bjtz586luLgY5PEkNKSQlioxMXti4HM85ZRTmDp1qoiBkYvDbGZUvQGf280+YxULFy5k7ty5yv4n0GqxcHCsxPSvnDS+VUNtbS3btm2L+r0M1Xmwp6gIh8VC5f79bOjsRAY8EybgkCTWlpWREIdyNRzPs55QVlYWUH0AmpubaWhooLS0lCeeeILdu3ezYsUKALKzsykrK+Occ84hMTGRvXv3UlAgYvNTp05l9erV3HTTTSxdulTMd4cRvSZBBQUFPP3009TV1XH55ZczadIkpk2bxmuvvca7777LX//61wEfZG5uLvX19YH/4+0JEoqpU6eSkJAw0EMDxAk8a9asnlfsJQwGA4WFhdxxxx3s2wrWf4DRCovu+w6LrN8BYM9uWL0aNlwB8n8gfyfM/9TKgg2Qkg565wShgU8HboPp88C/0crGb6AjJYXtP7Gx8N+Q25aH/0n46vuQnAyZCbB8DdhzYdVKOGFuNqcp+Q/qe334XZDehtKmEk5dWSJuM4GJEyciy/KgfCYqBuszd7lc/SIyg3We9fR+y8tFnvpNN91Efr5wH8sy2P8KRfWQNtmG4TLgH5BeC/qvoHF2OqecUkRiYmLUfVfeCSYZHMcbaD19DMXAH2+8kTZgHdCaIlppdHamsPxnK0meA/wVLvv2MpgF/tmwZTOkpNi55ppLsSj+krZWeOEFkaI9f/58/vWvfzFx4kQmTZo04J9Lf3AknWN7EHW9chC/b0mqZdKEcRTVFFEN6DOgIGs8Y1pbufzyy3n7baiqhElz8kjyQ15BHswi7PNoa4WaVWA2Q95xE2FK8PX33oO6LZCRAXm5WTBLfJ7XX389ILxqTz0FjQYoWQ9mE2SfXsyNJ98Y2P/uXfDMaqgrgYTNkN1azMSJE0lNTe3yvQzleWBEfLbHT57MWOW5fIRNc+yxxwbaaPR1/31Ff8+znvDjH/847P+0tDS+/fZbQIRFf/vb3waiAV6vl3/9619MnjwZEIrRhAkTABGS/8UvfkFbWxvXXXfd8CdBiYmi0156ejpnnXUWn376KdOmTaO8vJySkpIBHyBAfn4+CQkJgZPljTfeYP78+YNyrOGEVatWBU6q1s/AJoNvImFVCseNFxehL7+Er78LJXtg5hZIaQTJCeQBFwDLgQRhAps1C4qL4NXXXDT6bbxxJsx/BiyrIW0PZM2H72wHcwt8MRneuAL+X5TxjZ8GNaNgVD0iZf8E8bwkScMubn+k47zzzuO0004jU+1tAXRUQclqUZ9HfwdwMnAcGC6Boi3Q/l/gzBg7dIHtbZH5vm+ZeGo84vxJQZyCdgmSM6HzoOgvlrwS4cD9O/AP8P0NjrfB/mNkLObgrpOShCnXboeUFNGl0++P0WBKw4BA9QSZEXWaSkpK+O4ZP4FPwGcVbS66ZIcBPvV7i5Id5vOByYlIL0sKfy1gjAYRF4q4HKjJvX4DjN6tmFMj/LQGg3i+slBpP7YNdJJu2J0rajgsNeS5FAQJaoEeSdCRiOXLl3PrrbdiMBgwGo3cf//9YSbq7du3s2DBAgBuvfVWqqur8Xq93HDDDYd9rL0mQa+//jovvvgiJSUlWK3WgAFy165dLFmyZEAHt3LlSm644QaOOeYYHnzwQX7zm9/Q0dHBlClTAjLbkYzMzMwA6fStF8+Z5nRdL78Azr9AlKFXS9bThrjwJCMuUhHIyISZM/eSlpbO9u3wxQo46WnIr4bSt8UFqCkPHvg9ZBthbpTxnZADn4yB3GpwrgOzQoJ0uuF3oTrSYbFYsESUYO58WXT4tk+BjJOVJ0tB90vg5zDhNfBcH2OHr4HUAi2jYJ/yvarJrxJQgKiRqMsCDoosxLHjENWjTwKeAfcHkHEIsqpNcAPwEGACSSfUiMZG8HjFmLXzZXChEpwEBAkyGIykm0V2qJoBFpodZoiDBHl9YHQql5coJMhvAH8Cghh3dN0WwNQJSc3gt9GFLRgMYt8dSeDMABoh05qJbxjVVpAJkqDQj0BNluumzuQRjTFjxvD888+HPXfvvfcGHv/iF78IPH744YcP27iioVckaNeuXTz44IO8+OKLHDx4kOuuu45f/vKXgddKS0v7PaDVq1cHHocWXZo4cSIvv/xyv/c/kvDYY4+h0+mYNm0W1nJAAttJsdfXh36byTFXC0CSRN2YceOAJcAPgCeB7cBkePBaaMiCnxA9jTDDAAemwZy10PwVjFKev/LKK48K4/pwwsaNG/noo4+49tprsdlES3DDmyIF2X5O+Lq6ZdDyd0jdBp7b6dr2wg08LSaqnSdCQ5p4OtSxo5IgpyI8qU1WAZgG/AHWvg6+D2D2Wi98aYL/Ae4SqyQmCRKUnT2WW2+9lRy1Rb2GQUHAGC0LElRfX8dXH5czn5MCJCiaEuTtTgnyKCTIQFcSpG6fBLTTJU1e7RuWolQkd42jy82aqgTpfNBSCqO+gJWnrsRxnKPnN3yY0IFQqayIsJgKW8jrGoY3epUi//nnn3PaaaeRn5/P7NmzsVqtzJ8/n/b2dhoaGkZkoaThjL/85S+sX7+e5gZIrREXJv3MQTxgPnA78AKU3Q7vZ4EJWNrNJvZ5YiltI9Dqefny5Zx44omDOFANkdi8eTMPP/xwMN28AvQHRUjCeFrEyhLUXAVuC0hfQ+pHqeGvvwJyHTRnQu0EqFQmuNEhqyg1D2lVSFBjQ3iHeNkPVS2CJO//1QGwAO8ASrJjsrLPRFseP/7xj3vt8dPQOwRS5H2ivk9tbTUbPlsHgCzE5ujhMNV6FIUE+dtFIUOfmS630wblf6/KBlrCX1eVoESFHHmLu+5fVYJ0XmhSnBanjT6NxYsXR3mHQ4NooTAA5SPtrveshmGCXpEgm80W8Ho89thjzJ49m7y8PMrLyyksLMRkMg3KII9WqFlwrdtB7wE5k2AeZgx4EfacvlVrEvADjyiPL6TrDzwUOROhOQ38dmC3eK66uprGxsZ+jEBDb6H+LgPpqp+Iu+2aEkiLknmePAY2niXWyXkuR/RtAqgC/iL8HjtOhuQUOKRMiNFIUHWiaITqdAqjrIrmZnC5IDER5PF2UdAThLlMFr4ggKZmD/v27dNqBQ0yAuEwpVCi19eJWYl1yVGUoACJUUlQtGKJSmkwX5RGuOr2HpUNtIS/ripBSUrBTDlKYqCqBOm90Kj4hTo2dlATb6XPw4DI9HgVKvfTSNDwR69I0JIlS6irq2Px4sWUlZVx5513AgyqKfpohizL6HQ6nIrBv30c/An4gOgtw5qA7wNXA98DHoyxXrfHBP4P+BoRUbuih/WnZULtKPB7wbdDPHfNNdfwyCOPdL+hhgGF6qlRS0f4VwsiU10qKjRHIj0DqiZC1XSQPJLw7LyNCI05oH2Osm0aqE1sopGgKglyFS9HdXXwdfVx7igRduViRF+BXcA3QRK0Z3ctJ5544qCXsDjaEVCCFBLk8zoxeZWb1iieIFUJ8nSjBMkKCfJHae/ehQRFhMNUY3Sycq+ki0GCVCWoTpletr62lZtuuqnrykOEnkiQFg4b/uiVJ8hsNvP44493eb6srKxP6a0auodaH8lfJtSZdyeB2qViG3AjwTB6p/J/BZCJ8EW/gEi4OC+OY7UDq4C3EJYgCbibHoUnJhrhlbEwcTvYN0Lyd4UacbT2dhsqqCRIp9NBG/i/Fd25ndPCvWJ+oAxIThf/rzsVMlpd2A7Z4HfKShNh7/eBctCnCotQOmFJiagOnlogLw/27oWqapioXAZUEjQqFxydiLjq+cBfgRch6VfidbdbzLJaNuHgoosS5O0MkiBlxo5GgtRVopKgFrH0RSFB6jnnVl9rRhjJFKjhsBTFS2bshgTpvVBfDOgg05kpSPswgaYEjXz0u21GR0cHn332GXPmRElb0tAv+P1+dDodhv0izLVrivCcGoBnCRIiH3ArgrzkAc8hrD0AD9N9f1U/8DRwFnC/so9k4A9APK6eZKBalH6gQ2Tza9lhQ4AwErRJ6c2VBykhfmMZ+C1wCXCRVZhe7Too//Uh+BEihf4q4HFoVDisK1UsQ1UgCHbMrgdy88Tj6irhC/L5QO2BnJcXstF5iFltDSQpk6wgQZJ2vgwyVIJjUkiQx+sgQYl1SdGUIFXJ6UYJIg4lyK0y5yjGaMkfVIISIk8wwGBUjNFe6DQCBSAhkWZPizKYoYFGgkY++kWC1q1bx5lnnslZZ501qIXxjlZs3LiRiy++FHM1+CSomSzCYXcj5pJHgWeAW4DPEITkUcRd+yIEibEjyrbEwh+VbZzAccq+/wOc2otxdipmbd0uwC8mYu3O/vDiiiuuoLy8XJRUWC8mmYYiSAuZL9YC7ymPOyXYpahBHR6TID8PI8iQDVpbxGstqWIZOUfZEF7nTsCUBjabqPtTWwOVh8DthvR0SEkN2SgbUbTTDcavICEB/LKOhIQUjQQNMlQlyKiQoPPPP5fzFp0HiG7tIBQ/FaoS5O5GCVJjPaqxOhQqCXKpfqGW8Ne9PrC2CGN1Uw7YotR+1OuVcJgPXH6gUJCgLPvwaa8SiwRpxuiRg371DpszZw5r1qwZqLFoiIDVasXvtmFuAY8eJhSLH9cZQAOCwKjOm0SE57RI+V8CrkdMfK8gst9HEY4vk5J4EXESPIgo79IXpI8HeyKk2IEq4UvRJrXDC6PRiNGoJOl+I9SYhkI4JoQEqQUmrkacE5XpUFwFdnv4DOT3i+7xAPWpYhlJgiQgC+GnrpegpAQ2bYKycnFsUOoGReJ0YBPwEdjmiE7yZnOGdr4MMlSVRyVBFosRk8J6dNGM0aonSCVBUcwtcrtY+qORIGV7VzdKUGKzUCdrigLF5sMgSYoi5QWPF1GjQYIMe0aUtYcGmhI08jGgXeQ1DCzuvvtudq+pAR+0ZsC8kCvFxYiSK8cCpyHUnmkR25cgFCEP8ETEaweAJ0cJWvQz+k6AAErSoS5XmKM938JVV13F0qXdJdZrGGh8+umn/O53v8Nr98Iu8PhFOExVglwEstO5ADgHsGeIiIbdHj4FtbcJImOzwSFlEowSrQiExOoAtURY2U7R7kCSYPz4KBuphd7XgdUCpoQEbrjh14wdOzbKyhoGCirBMSgk6IsvPqV8s2i1ou8uHKYWv4kym0tKOKw7JcgZSwnygrVJtBBrGh21nmvYfjxeoEgUkD1zcqwy54cfLcpSM0YPLnw+H1deeSWLFi0Kayo+ENBI0DDG3/72NzrLZfxAbb4gPKE4G+EzfQCINYVcg/iS3wb2Kc+5gF8DTp2O0xFp8P1BqQEOjhF3dZ2bYNGiRRx//PH93KuG3mDjxo088cQTyGUysleEsfwJwXDURkS4YwIiXHoyYE8XF+lIJailRSxT04KZ8wV0RSgJSk0LEiGAqVNFVeguyFN21g5Z9WA0GDll/tnkhZmHNAw0VBKkV0jQuq/XUFshCpqq4bBoxmi3SoIcKL0rgpDUGT6iUCIESVSnSoIiKmZ4fWBrAiRoKSImIklQSnIKJabhk4msKUGHB7W1tZSVlfHee+8FG+wOEPoVDtMwuPD7/SS7xOTQXCTCD73FaIQf9VXgfxHm53sQ1X5z3G5+a7PFvAuLF8XAayUgfwDOMmjev5/KykrNJ3YYoYaT9OV6fD5oHi1IiDqJfKmsp1LTYwApDdwStHWa8fmCE1+ABKXCQWX9aPNUqDka4OT5MEpJie+2YsZc4BBk7QZ/gY9du2ooGJ1MUlKU2VTDgEAlOHqPUIY93k5MSqzLqChBoZ6gQMVnmaD5y0lYiqAUhyeoU12/gTAS5fNCoqIEdUSTGSP241XCYS63C1+ZD2tYrmIQ7W2wdi2MLgSlV+egQhHDutRS00hQ/Pjqq6/4wx/+gN/vJz8/H6vVyq5du/D5fKxcuZLFixdzzTXX0NLSwrJly3j11VcH9Pg9kiC102t/UFZW1u99HI3w+/2kOERJXn8/ogVXIczOHyNCIQcQPYR+UlmJLdQ520eYgPaJ4rFnL9x2221UVlZy7rnn9nvfGuKDakSXdkh4fdCSK9QZFZuU5XHK0gDMMUJbMnQ6dbS0iI7fINpZgEKSEMpRlASgAAlSG6To9TBhYhyDPQ54BZIrwJnp5IknniMxaVaguaKGgYeqBOnU7DC3PUCCDLbwdSCkYrQX8eV3ImTDEO6hU0iQFEXxC2SHSYiMjTbQt+sDr3t9kN4seJG9GyXIGEqCMmF/9X6kAxIljhKi8aA1a+DgQdi/H3Jyguf0YEFVgiI/gpFmjL7gggu6PLd48WIuu+wyOjs7ueSSS7q8fuGFF7J8+XKampq4+uqru7x+ySWXxG2L2LdvHx9//DGPP/442dnZ3H///XR0dHDRRRcxffp0/vKXv7BixYoBJ0AQBwnSCMzQQJ3UUtrFz8kazV8RJ7IRWV+/QRAgC8JUrXe5utusV5Anibs6QxXo8/Sa0fUwI1AscYcklKA8KFZIkA+h/AGEVvOaDbyVDs4GHQ0NwQmjXpF2nArLiTVHqcpkfYzXY+IYsbBWAHMkzBbNGD3YUH/pkgtAxuN1YFQMP4ZuiiX6fAgS1ECXGV2n/h+NBIVunwW0gaElON34O8HaCm1J4OkmEhpGgnTQamkl1Z0KNXTxALicwdIMABUVg0+CWpSlFg7rH8aMGUNSUhJr167F6XTyyiuvAOBwOAasL2ksxBUO27lzJzfffDM+n49LL72U5cuXD9qANAiISUEitU34NXL72ZbtNOA1YCdiDkoHNvRvl2HIHQVtKZDRJgqa7ZX3DuDeNfQEWZaxyBbYC14/tGYHlaC9CEWngHD7xrHAsznQuVtHTQ1MmCAmktZWMQk2RmmcGopQT1CvkA3kgL4SUpokLFp22KAjoPIoMS+Pu0OQIH0wHBbNGB0gQdA7EhRKXrKAPWBoDk43+mpAhoZcsHQzC6kkSG2z0WptJaU1BSrpQoJqa8P714VWMB8MeBBWKR1B5UeFCdAjPm638v9wRnfNyS0WS7evp6en97u5udkskjP8fj9/+MMfmDJlCgANDQ2kpKQMakPuuIzRBQUFPP300zz00EP8v//3/9iyZQsA9957Lz/96U8D6/3ud7/j2muv1S5oAwC9Xk/5zkOktOvx62B0N3HzeJEDnIIgQAONsYnQmCXSq3M6C7Vz4DDj5ptvZtd/doEMLZngNwQzw3Yq60RGqsYCvlzwSRL7lWuMqgJlZsJB5W4+lhIU6QnqFY4BvQ4ya3SYLZlaXalBRoDguAEkXnrpX5QWiLtrY6LIzvIStO2EKUExYjt65f/uwmFeL4ETxdgc7LNurBLLuvzooVYVJmU/foUEtdhaRAZGFILToFSfVrMSGxtEI9/BguoHSqFrdptE8H0Nn573wx/z5s3j+eefB6Curo5zzz2X6kFms3GRoMTERNLT05k4cSJnnXUWn376KSB6RH322Wfs2rWLp556iq1bt/LQQw8Fmzhq6BcaD4HeB52JkBvdBzhsMEaC6kJxfcrtHKtNakMAaY+EDDSkC3NyqqLRxyJBOmBCpmivUdsMzk5xNw2QlQX7lfViKUEZyj6aEHfFvcI0kHSQWSVhMibi9Wrny2BCVYJkhQ2ZTCDZxdQtWYVHEMCt9J4zRIbDIJwEyaBXZncpSm8dfQgJkpWQVKgSZKwR14raOEmQqgS12dqQkYUSFAHVyzZ6NFit4PFA+yDmqMfKDFOhckctTT5+XH/99TidThYvXsyll17KzTffTGFhrCvQwCCucNjrr7/Oiy++SElJCVarFbdbaKoZGRn84Ac/4Oc//zkOh4Pnn38ei0XkRP7rX/9i/vz5g/4GjlS43W7++fhqVsgLac+Shn0tgyLg3bEgfwSzM0/AftoPh3pIRxXefvttDP9r4HTfmbRmiu7tBuXGu0JZJ5qt7Fg9vJfpx1Gvp7Iy6KnIzw+mx8f6BesQferqEGpQr5Lcp4q75dx6A6Pnj2bChOweN9HQd6hKkN8NsuznoYcf4Laaa0mxpoBVhGucgEe5gQ0lMVGL3rgAL/gMQU9RKHQ68ef3gz9DhIZCSVCC0gi+tiCoKEZDQoQSNPe7c8mtyY2qBLUp0kxqqvhzOERoN2qphgFAi7KMRYLUj0VTgrrH3LlzA2nviYmJPPjgg13WKSgoYPXq1YNy/B7n1l27dvHggw/y4IMPcvbZZ/PSSy+F1fSYNGkSZWVl/OpXvyIrK5jE/cMf/lAjQP2A1+ulYZdb1N6JLPU8DGEDGhWpIbktg+OOO67b9TUMLDZu3EjrhlZ8XmjLCm+XoZKZaGGtY4G6Qh8OYMMGoQTp9ZCdJ262JaLXCFIRb0jsAPAY8K36xETAACnNOjJt6aSkjICTfARDVYJ8buEfe/Pll3DZXeI22BSs2KwqQV2M0RCuBLUBMnjM0KEXxvtILU8NiflUJSjEGJ1Qp1SLLuxdOGzKwimkJKd0UYJkOUiCkpMhWWEmba0MGnpSgtQSSZ2DNwQNA4AeSdDnn3/OaaedRn5+PrNnz8ZqtTJ/vij7umPHDu69917OPffcgJtbRbSUOg3xw+/3M0onSKRvhMwPnqnKg/0+dpXvGtKxHG3w+/wUeYvw+oQpOrRSdA3iTjyaUlMKNBd6cBqgoUU8N3481JqEPySX7k2dkWny0dCp07ESUdX8apTaQyZgLOiQMe13UFsziLOVhjASBGBwK2YZRa5Qw2Ge7sJhoUpQhyAe7gT4uR6+D7wVccyAL0gxIRobgp4gs+Kmry6KjwRJXnE+VnRW4Oh0QFX4eh6PHo9H9KNLMEOKov6oxGgwEOoJigaNBI0M9EiCbDZbwN/x2GOPMXv2bPLy8qipqeG6667jnnvu4fbbb+ebb75h8+bNADQ1NZGePhj226MHfr+fHE8mEmDs7lZ8GCGjSBRHkzv8/OOBp4d6OEcVLB0WEv2JuBLAZQtmhqnFDgsQRCgSemAMnVTMA4ck7qJnzwmm1PdUmzeeDLHPUlICBYNdwAvqCyUgyz5c2+rY8M2OHo6koT9wAZIPfIp5y+TVISEFGEjAE6SGw9RiiV6CKYXtITtsE0qO3QJtClGJ/MUHSJBqjK43io3awNgOXgM05XZPgowGpYmqV3i6H3zyQXYf2i3GEjIel0sQrETFiKMuO4bQE6SqaxoJGt7okQQtWbKEuro6Fi9eTFlZGXfeeScdHR2sXLmSq666ilNOOYWkpCRWrFjBI4+Idp5lZWWDmtd/NMDv95NtF7cztuKhHUu8GGOB+hwAiUKf9v0fTqQ1poEkeswhBZWg7kJhKiY6HFRPAfcK+N5y0TMslpk6EvGQoI3KjPQD5f+PUEInpSDpZLJbEvB6tOL1gwUvolaU0S0IhdHoxypbxT+KEqSqfdHCYbLqqYkgQchgTxDGehBG+pqQVQIkyAokg86lEy76nUJFasgBn7F7EmRQSJBeIUE6vY56oxJ8DfEFud3iYFZlZzZl6RhEQ06LsuzJE6SRoOGNHq88ZrOZxx9/vMvzb70VLn5ef/31gcdlZWUDUmn6aIYsy2S1i3uJjDFDPJg4UQwcHA2j90gU+w9DzXoNAWS1ZaHT6QNd39OUZU8ZXiBI0NvAJnPwriheEpSjLGORIDdQZrViBC4F3kf4h3YjWmvoJMhuTaDKa4yxBw3xwucNGppDoZqibUoozGiSSbekI0lBJUhVLVRjtKQTRMjnE13i9RCUPgDaFSXILEhQEeJc2wSo7U3D1KQCBEM6gDi5ZKXpsqEHEmQU56SqBEmSRINRyYWvQsRzAbdLvHGbwjxUMmQfxGqFmifoyMCgJB2Vl5f3mgS99dZbnH322SxcuJBnn322y+t//vOfWbBgAUuXLmXp0qVR1zmSYLWmkWE3gSSRM4JIUNVYQJLI942QQR8hOG/qeZSWTqJFyQwzKrf2PWV4AYzp7CQBkUXWjJjcBkoJ2oLwmZQg6lOp3eS2ApSKDKLMVhNet0aC+gpZhs/XwN//Dp/8N7xgIAT9QBaFDSXaTLz53JtkZmR28QSpShAESYxfZSmh/ppW8MsiHGbWwxnK07tDVgkoQT5gXMgKihJUmyuyy3qlBOl0QRIUogS5FCVIVYBUMuRwDF6tIJUEpcZ4XSNBIwODokHfe++9vVq/traWhx9+mFdffRWTycRFF13E3LlzGa9WvQK2bdvGQw89xMyZMwd6uMMSldVCvu60yiQk97fF6eFBFlBTIibRPN8AVHfUED92i7v2tqzwnmGqEtRdOMwATAPWIbrNj0PMd+n03LRXVYJiGaO/UZZzlOUUYBUiS2xZKvgy/JiadFgbB7cQ1psIY/YlwPmDeqTDj5pq+FZJuysrg+JiKCoOvq4qQVZFCTIlEMz0imGMhhAlJxGMEE6CWgSR6bRCnj7oHYtKgrwEV9iFIEEIEuTXx0eCdCEkqN4QLRwmSLRVeT96gzBJu1zgdIJlEE6vWH3DVGgkKH488sgjvPfee0iSxAUXXMDll1/Oiy++yDPPPIMkSUydOpU777wTk2nga28Pi/Iza9euZd68eaSmpmK1Wlm0aBGrVq0KW2fbtm08/vjjLFmyhLvuugvXAPa9Go44+K0Dv+ynOc071EOJGzrANVlI1sX64qEeztEDP9Svq6ehvom2zGAoDOILhwGotxbrga+Ux7PpWgk3EpnKMqJJeABq58EpEUs1VV4/QY/JZGJ0Z24PR+o7GoF7gEPAfUQtMTOiUa642FXSsnNn+OuqEpSgkCBZdvKXP/6F9o72AAMJeIJCCt0GfEEqgQgJh8nNQglyWmC0AVTdd39wlWCKvJegpPghyAeEAlSfBXIvSdAVV1zBd6/9rngxxIAUCIeF7Ex9bB8kX5AWDhsYfP3113z55Ze8+eabvPLKKzzzzDNUVFTw5JNP8sILL/Dmm2/i9/t57rnnBuX4w4IE1dXVhdUYys7ODusVYrfbmTRpEjfffDOvvfYabW1tPPbYY0Mx1MOGjv2ALNOcPLLInnUi+AwSNnsCslYq9fDgEDjbnNRLHXjNQVN0q/JnIUhWYuFEZfk+8HrEc93BBKQhjLdNUV5XCyWoQsAExEWnAlFEzjTZgF5vIKneHGXrgcF7yvhC/z+SoHYVOF2JSVVWKuqLApUEmRUSJEketn21TRS9VQhOZJ0gCFGCVBKkmKEBaBYPHVbI1wXLL1QTJMNhStAx4E/wi6wyGerGiPR6pKjN4AMIDYd5gGOOOYbpZ04PHkyBK8IYHfrYMUi+oBZlqZGg/uG4447jn//8JwaDgcbGRnw+HwkJCdxxxx0kJiYiSRKlpaVUVVX1vLM+YFikZPj9fmHSUyDLctj/NpuNJ554IvD/FVdcwa233spNN90U9zG2bds2MIONgQ0bBrIdKXSUjwESaElzsGFDeY/r9xUDPW4yMmjIKGRUtczG13ciTxo8Z+KAj30AMJjnWaz3m7g+EZPXREM2OBx2qmv2Y3d0sttsxlFcTJbTyTf79nW7784NG8gpLmav2UwVYPH7Sd21iw1xtD8xFBfjMJv5eN8+xjoDrTqx63TsLi3FKMs0bNhAs/J8+pgxHEhI4M39+5mOjlTvOKT9Ptav/xapD5Hfns6D/xQU4EhMZEZHB5sSE3nb4eCYAwe63SbefQ8FQs8xt1tPTU0Jer2f+vpyJGkMbW0JfPrpflJSxPS702LBUVSEu8GPw6Gjvr4Si2zB7XZzoPEA9Rvqqc/JwZGWhkenC7zntrYxOBwJbC7byyxPMZJDomxtGbJZZvTuIvxyOh02GV/Ffr5tbsZQUkKbXs9Hu3eT7vVSW5uLw5FKWXkNbe0t5M7LRfeJDlmW2HGChBsT7o5OtpaVRX2fAO3tZvzeUmQnbNq+i/KyMhwHHJznOA9fmY9dGwTNdrtK8Hrt7Nq1i/37BeVtbR2Fw5HClq011KtFsPqIyPNABqonTMAnSVSUlXEwyu+kMiUFx6hR7GltZUMP/a+G8jz7KfD5IO37ROCRONYzGo386U9/4u9//ztnnnkmeXl55OfnA6LkzrPPPst99903KGMcFiQoNzeX9evXB/6vr68nOztYTL2qqoq1a9dywQUXAIIkGQy9G/rUqVNJSEjoecU+YMOGDcyaNavnFXuBhsdEQY/21I4B37eKwRh3C1A5yk12pUSBdyLZgzP0QRk7gMvl6heRGazzrNv3uxEO6g/SlqnDarVxwvGTSTCLaIEVmGmzMSsjo9t9z541iz8C1yFq4v0OOPHYY+Ma2yRExlfmlCmEjvAb5fjZjY0cFzL2WYjwmXXyZHKammhzO0lqMFE8eVbA0xEvejoP1F6bVuA2m41LgTqbjWOzsnoM9Y2Ec2xvhfDB5OfD7Nmz6HTAjh2QkTGZ6Ypg4ka8/3SDWLeoKJsK2UJCQgKjJo2icFYhnwFfIJQg9T0fPCBUm4kTp5KYB9TDseOPhRzw6qFWgs5kOHHsWGYhIl7bgKzp05mB8OO0tUFR4TiOmQYbOzaSX5xPZwE0tYOUAFmJid1+xk1N8GGZUBzHT57Ma//8J6veWcUPUn4AHpg1eRY+I3z2qZ3ERBvz5s5AUuIbfj+0t0Fu7jj68zVGOw8cCB+VGZgX43fShPjc02w2ZoV0WYhn/wOB/p5nhxs33HADK1eu5Ec/+hEvvfQSy5cvp7a2lquuuorzzz8/0FpjoDEsSNAJJ5zAo48+SlNTExaLhffff5+777478LrZbOYPf/gDc+fOpaCggGeffZYzzjijmz2OfFjqxC/ZkdTcw5rDC8XAOwVeZn6TgHvwBCwNoagAGR1NaX6sVlExF+IzRYdiIsK07CS22TMa1EB2ZOsMNRRWGOHfG6ssK4ATi2Rkv4+UFh2d7fSaBPWERkQUJxmYjDB7NyGIUa96nQ1T1CppeTmKpSo7W5CgupB0PfXTNykindHoE3WCoEudoGjGaJ8P8QHWI+KrOQhjNGBPCoZa8xAkqAqYQbDqtBqa8yf64VfgbAbfSyI9Xq3DGAvRjNE+2SdKmR8EqqFTuV82mwkQIACrEo9yDkI8qkVZxgqFwcgJh8Wj1Awm9uzZg9vtZtKkSVgsFhYuXEhZWRl79uzhqquu4pJLLuGKK64YtOMPC09QTk4ON910EytWrOC8885j8eLFTJs2jZUrV7J161bS09O56667uPbaaznzzDORZZnLL798qIc9qEhskECS8CWPLBI0Gtg7Rlz1fBXdr6thgLAb9HoTLel+0kIKtceTHh8JE70jQBDMEKuJeF7lwIUhITIImmgrAMkm0WzqQOcD995eHjgOqKfgWMRkquab7o6++ohDs2LEylSEPlVArw8hQeqnb1BmY7NZJsuWhU6n61InKMwYHWpsVmd71RfUIv51hJAgtbuP6twIS5EPgU9pgeHT93yuRUuR9/v9wYPVBEmOxRK+rVl5U53hp9+AoCdTNIwcEjTUOHToEL/5zW9wu9243W4++ugjpk2bxpVXXslPf/rTQSVAMEyUIBCVqZcsWRL2XKgPaNGiRSxatOhwD2tIIPshpUmHToKZJw1e1sxgwARU5bUCNuT4bBca+gMvcABSU7NIOxYyopCgeJWgviJfWR6KeF5VgooiSJCqBO1FTGrV5kZGuXPw7QbmDezYVF6lHnM88DWCBM0f2EMNCVpaxFIti5CaCkajaBfhcAhlTVWCVBJUVJjNnIuugs8Apb1Ed3WCvKoSBIIEdQA+cJnAkwBqoFU9D7qQoIgEV69XGNX9hsDhYyKaEiTLslCCQChBSjUOc4S3Xv3fOQgkqEVZpnazjkaC4sMpp5zCli1bOO+889Dr9SxcuJCWlhYaGhp46qmneOqppwA47bTT+OlPfzrgxx82JEhDEC11Qrp2J0Bionuoh9NrNGTUAHmYakH2gqSdZYOHA4AXHCngM4Fq/fETJEGDXbFJ3X8oCfIRVFtGR4TDChAXnmrAqdNRmVDLse7J+AdBOVR3qapPR5IS5PUIsqPTQbISV5J04hyoqYHGxnASpFPIgNlCsBmqwkIC4bAQJcgQGQ4DIYG0iO+30yp+2+pkr4ozqgU4TEkKHbcvSIJ6DIfpgxWjXbIovxGmBFUHlaBYJMg1xCRoEDt3HDH4yU9+wk9+8pOw5y677LLDcuxhEQ7TEI6aCpCRaUh2sf3bkWNsU3HSscW0pUpIXrBrzeQHF3vEYq++hUOVh1D7FjcgwiDp9DzR9Bdqf9+DBDOoDyDu3EcBNn94BSEDwRBdvdXKlHOmoNfr0e1nwBFNCQp9fiSjtVUYl5OTQRfSHVclwk1K11ongAySQhaam6v48oMv6bB3xKUE+bwEZ/tmoAW8MnRawBRyXNUbptRzjqkEqeGweEiQTi+InSSD2wcrVqwQykAoCQoldyFQ/+/sgxSzBbiFIImORIuyTO1mH+pwBoGDaRhAaCRoGKJ1n1jWWjtoUfXuEYTjsrKozZfxA/Zve1xdQ39QobQgsPqwd7STmiqejrdI4kAgWfnrJFgrqKcu9CopOWgwkH9iPpKkw3Awxsr9QKQ5XA3ZVBIkbCMV6qUhJTX8eZUENYSQIL0HJK8gJp2d7dhr7Hi93gAJCvQOi2WMDq2K2aKQICskhJIvZakctosxWoUaDovHEwRBJdnlhXHjxnH88ceHkSDV82OJVIIUZud0dm0l0h28wPXAB8Av1CdlBKtXoDo1U7vZjxYOGxnQSNAwhEuZDGqtrcK8OMLg272bA/leZMCpZYgNLvaI8EJTqhenqyYQguiLKbo/CFWDAHYoy1i9x1QStMvvZ13jF/hlP6YqBpSZeBCkTEdQpUhGJEQ5CO8CMRIR8ANFuHOjKUFGp/gcLBaQZX8wO0yRYgJKUGg4TA1nRZKgekFi7Imib5iKFESj1TYEX4hljO6NJwhAF0KCtm/fzttvvx1ujFaVoAgSpDcIf5Qsg7sXroJdBENYB4D2OissA04C/gDIQSUorevmAWgkaGRg5M2wRwHkSrGsNtWHFY0cKdj42mvsyG5DBnx7hno0Rzj2iEmlIc2DszNYUbW36fH9heoLUknQdmU5Ocb6xcqywu/nd//v19iNHhGuidWJtQ+oR3CqLJQu6AiTbagaNJLR3i6WySoJWgPcDGk7QZIESfJ6hSfI2Cneu9kMfq9CgiS6tM2I2jsslAQ1AnXgk6E9KZwE6QiqQQ10Y4z2xR8OA9ArvXXdXnjjjTeEdyRbOWA9uJTPITIcBsGMsd6kyYcaECQ/5D5WIE5sP/AieJ+HtgrI2AeJ3cS6jMoQvQhCrmF4QiNBwxBGxVlYKR8YkSTI5PNRnibanvgGweehQYEbOAheP7SkeHC6giTocCtBKtnai5gr1PZVk2KsH+g1pdPhdDbTkubD72dAzTpqyn5OxPNqfaDBKcJ/+NChmJsTExHmlV8AH4Ph1+I9y7JIoXciSJAOQRQku7im+M3+wAwQTQkK8wSFsps60TesIwkSIpIeQrmSIZYxWlWC9L0kQZ4QY7QBwW5l8CsdliLDYdC3DDH1FEwBZn4Gtv0J4gO9UxC4+nvB/B+Y/B7seha+/rrrewTBMTU1aPhDI0HDEFblR+1MbMQSWfxiBECn07HTIAIiCVW9i8dr6AX2AX7oSAGd2UhycvB+83ArQaXKcifiptmBuFmPVae6CHHxqZQkXD57gATJA5i2pZKgyCITkancIxUqCUpKBF5ASA4Abpi4RTxsbBRKkMkhJmWrBUxuk6g4HRKLito7LDQcpjLJWqBaEN325HBPEIRHzXoyRvsM8XmCDAoJ8nhC6gRBICQmKTeNkeEwCBYO7Q0JUrPbTgZOfgvcOgkuBv+ZcDAVDO1QUg6to4TPatNGePVVUZ06EmrtT80cPXyhkaBhiMRGkJC49fc/Yrpa+34EQafTUdOyDZcZjHboGOlxh+GKChHuaUyF/PwCHvzDrYAQiCoRP+6C2FsPKCYoy52IdhkQOxQGIvyShxi/Z1Q2LWkikds7gOFTtQVzLCVoJJ+Wsj9Igmw24BPlhZ+LRbYixTU2ignY3CHOh8REGJ87nokTJpJeFCwqpSpBMcNhZoTy4gXWCyWoLQUs3ZAgfTfhsN54gtT9xCJBeqVUebRwWF+UIJUcz3fD1K+Uz2Qh7KmArbPE51J8ELYsgTOWitpMzc3w5ptdm7VqafLDHxoJGmZwt4oLltcA+YdrBhtg6HQ6DAf2U5cnMsQ6tAyxwUG56I/UnC4u9mrXbNW+kEfQ6zHYyFX+2oB/KM/11OmnGERV9MJCWtLENOEbQCWoJxI0kpUgR6f47s1mMB5AxJ8ygQsBG1jrwNweVIIS2oUSlJQEKB6aUAainifuWHWCIMioZfDpoCWtKwmKyxPkjb9OEHRVgkD0j2SUINFGJSdfzQYLhRoi603VaFUJOm4TWDphz3hoy4Zd5VA7DowFkH4IJn4D43LhvO+KtiV2O3z0UbjyrYpTWjisZ3R0dLB48WIOHRIVx1588UUWL17MkiVLuOWWW3Ar7vZvv/2W888/n3PPPZdrrrmGtrb+pThoJGiYoV65E25N83Pp986nvHzkpVdddNFF3Py971FTKC5Szp09bqIBRPPL7aLQXVzYKQrmNY+CvXu/5tZbbwFElAyCvpvDAQk4QXlcqfx/cg/bqOO79v77SZ8lpk+pgi4ZYi2IpJz/o3cG01gkKCSxaMQiEApLAr5SnpyL8MpMFgQkrVohQX5BiHTK+vu/3c/uPbup6wy60LtTggJ+l+Lg8RtzhKfHGkMJaiR2inygTlCcniCjQoK8HnFteeutt8QTuUIRs7aBweAL6xsWeF/KG3O7ur4WDU6EamMCbGvF57Jpro+dnVBZiXDYXyBO0YWvCfOzyQRnnCFM2NXVsC/E16Z5guLD5s2bufjii9m3bx8Ae/fu5cknn+SFF17gzTffxO/389xzzwFwzz33cMMNN/Dmm28yZswYnnzyyX4dWyNBwwyN+8SyNdPPl19+id1u73b94Yi8vDxOGDWKyrGSCHccCeV5Bxkd7fDKK/DZZ/DmG9BjjUwZQYK80JIDNTVb2b5d5GSp1+DDSYJAiBDqBeU7dPXiREIdn7+4mMQxNjxmkNsIFppBvM1bgReBvyGIULyIRYLUcdUwcmsFdShqji0R2KA8qUpvk0UV6exGoZ54OsDSKr6b5GRwNbiw2+106oNTc7e9w1QlKKRZ+gGlxoE1hjE6VAkKbK8gUCcoTiXIFEKC8vLyOPbYY0XCyCgRlrO2iqawUbdVSJArThIU2hNM2iLI0NZZfnYeEApPfj60XgCyBMetJlBnwWol0Kl+/fqgGqR5guLDSy+9xB133EG20vzOZDJxxx13kJiYiCRJlJaWUlUltFu/3x+YFzs7OzFHM4P1AlpDg2EGx35Re6ItU/yoR2KdoO3bt/P5qlW4JkwHdFoj1Tjw5Zei11NiorjL//JLKC5WJjmgymTic0TxwTNRzKBt4DCDMwk6nQcD54pKgooP83soAR5DVNu9KI71i5XlJ/v3M6PFQFtmPqktiEwnZTbdguj1peJl4CqCd9jdIZYxOpFgraB2et8wdjggzBStKq1Twpc5CpnUHxB1gkwJImSqs4vzxG8NVvKOpgQZQj1BIKS+TKABNpwAuCCxm3CYPkQJCg0RubyCfOoNweN2B5UE+ZU6QZs3b+bCCy/EMMoglKBuSJCqBLnirBOkkqA0P1AuSFDFBD81u0RhxILRUJMHe+fAnK+B9xDsH5g4ETZuFP6g2hrIHTUyPEHvvgsHB6nP4+hCOOusnte75557wv7Pz88nP1+kMDQ1NfHss89y3333AfDrX/+aK664gnvvvReLxcJLL73UrzGOvBn2CIeaUt6RI4T/kZgi//HHH/PnP/8Z10w/MmA+IO5INURHQz1UVIhJY+lSGDNW3D1/oziMq4A7i4v5J/Bb4O8AO8VE0pAJSNDZeWDISRDAbOAKgnfA3UFVgtY3NLBj50baskR4Q20FAvC2srwUYbS2A+vi2Hcn4ibdRNeCdhLhatBIhEqCkv2I2koWgsWalLoE6bWADElKplhKuqgfFEiRtwVJkMIz8EhSQB3rEg5LAV4A+UXYpfQfiSRBoeEwSRdRdVpBp/I4MrMsFgJKkFtcW37xi1/g8XggV/iiLG1g0IWToP3ACmCbYnaKNxzWoizH7gOcII8S2ZftiqyYkyM+7v+epygIbwS31emhREmT3FkmlponqH+ora3l0ksv5fzzz2fu3Lk4nU5uu+02/vGPf7BmzRq+//3v86tf/apfx9CUoGEGo1JtrjNPCKgjUQlSx5xZ5Mdpg6Q2aNwFud2lCx3F2KGUV548WSg/s2fD3grYtQvmzoNHTeDQ6ShFNCn9G3DBTkj0QWOm8CJ4va2ihgrB9PjioXgzvUASinJgMuFI8NOeCf4dBBo2ycDnyrqLEKrBduBLeu4Ar4bCshGkJxK5ymFqCKb3x4sGRDJWOnAKQ3MnqZKgNNXWUxIykFwgESzNkOAAgySSunIV5hdNCdIhCKMDkV2YQHQCQyp0pgKHxDaRREbNN2tE+H4MBrF9qC/IqTw2xzn7qCRI9kYYoy3gTQJdG9ic4YHNxxDnysEEoUrGGw5rUZbjFHVNNwkMLvA0i88jM0MU4dxwKviTESpcOYGTaEKpSJnftxf8Jwc9U8OZBMWj1AwF9uzZw1VXXcUll1zCFVdcAUB5eTkJCQlMmzYNgOXLl/PII4/06zgjb4YdBPgRMerhAKuSmqArdjN9+vQRWSdIVa9KZB+HxorPt2P9wB/HB2wCvmXkVmT1emGPonxMUHpMpKXBqFHitS0V8BHih/o4sBDxXvfvBI8XWnIhK0v0VBo3bhw1iGygdEZGmEdVg9psetqyxJ19oCks4q47HdH4dIay7g56Riw/kIq+KkHlwPeA+4CbgR8RnDgPJwKFEtUUt9D+JBJQJLKq8tzi9wdQrHzYFo8Fq8WKMdNIKNTQlMoXuniCFLQBOp84J/URJMiEOO/8iM8lWoaYSoIscZKgBDUc5gleW9Q0eZfCupIirJNblaU3QbyfeNtmtCjLQoUE2SZCSoMON5CRKT6TesBrgsYzlZXfDG6fkip+v2431NRqxui+oqOjgyuvvJKf/vSnAQIEUFRURE1NDRUV4k7po48+4phjjunXsY5qJcgP/FP56wQWIy5shyutOBKyD5KUO7spC/J45/x32LBhQ/cbDUOod2slXi9rJ0HJVnBtGdhj1BiNXEKwUWce8GuCGUojBQcPigtmZiaBDvAApRNEpsmacvBPhGkdHeTYbJwPfOAHeZsIMTaPginZ8MADDwBB5eRwm6L7irEAkkRTmok2lzC6omSIfalIOPMQE646z5chlI1oF6+ODmEarvBCkhFy0ohqPOkLCfIAtyNIwDREFtw3wA0Igno4b1dUEmRRZb8JESsUgfQtHJcLL6VCQxHkKw3UchNyyS3JDbJKBV1IUDQlCIUEeUWilD7Kl5CprBNmjg4hQS5lf7Y4Zx9zCAlSry0qCXKmilBeYkiWdGj3Fa9JvJ/eKkGjFKZtmQhZTUKVtCixPnXf9qXAS8A7iJNAmTgKC4Uv6MB+sOQFx6Qhfrz88ss0NDTw1FNP8dRTTwFw2mmn8dOf/pT77ruPG2+8EVmWycjI4N577+3XsY5aEiQj0m7/HfLcawgX/91DMiJw7hfdntuTIC+95/WHK9QLVa7Px4GZwEvgH8BM/zbgwdGjaUdccE0I38yNiM4B3xu4Qw06DiiT2JgxiDiEKfj/ms+grhpMHXBiayvk5DATOLYCjG3QZIPOFGHAVKFYEXod4hkqlCKEi7rMRFxt4EoAOoB6+FIkijBPWTcZYXs5iOBJgfdoh+qdsLYCGpU29g2IOT5Bgo/GwvHHB+soQd9I0FvAbkS5nP9VhnklIuzye+DOXuyrP/B4xKRuMIBBTTqIJEHFYpHUAhsvE59xQPeJ0QJdvflT+UKXOkEKVCVIT1clCMRvsoLw1hmhSpBbeRyZWRYLamsOOQoJcqQIq5K1LRjUOBiyraoEuVzCnN2TxbIV0S8sU/0hTYSsL0Soza1ck5XajNgmID73MuBdYKl4fnQhbN4Mhw5pnqDeYvXq1QBcdtllXHbZZVHXOeWUUzjllFMG7JhHbThsFYIAmYCHgOcQJ+y7wH+HaEztZYrZNRcOrF/PokWLAnUTRhKWLVvGn//8Z5KTkvAfK9JJLQdEMbGBwP8CtSYTpcCrwOvA1Qhl7wEU4/AIgOyHAwcguwIm/w9CxroQ2Chqj+QWiotnTgXMUG79dcCyb0CSYX+ukgqdDTfccAO33XZbIFEoVvf24YYJwPjx40k8YSZI0JKhVJGu6Jr5DcFeZGqD1vR30nGdDIbzYepDkOoQ5lVPPnRkgVES4cY33wR7R3A/vSVBMiJNH+BahOqTBfw/hILyH4Im7sGG+jtKMYF0AMFGxkasVCwWaqKFmaA3qnlfM+W7ytnVsCtsE3XCjgyHRdb5CSVBhhgkCGIXTPQojyNN1bFgCbi2xbXl448/JilJJNd3KM1jbS3BqexQyLayXhSelWWRYt8TWoCcg8JLRTaQjshYBNpSxVIlQdkAP1D++SeBuGNOtiCHzc1gUnLjNRI0fHFUkqA2hAoE8CuEybIU+LHy3F8ZmvohzYpa0pQPjtZWtm3bFqiSOZKQnp5OYWEher2ezPHgtoCtGarLet62J+xFKHYS8D+ILCQdggTdrjz/GPBE/w816Kivh4z1cPLLYFJTVPciZtkvwKNk4JTsBnNIjvHsjUIx3DsaMrPERLNnzx4OHDgw4kjQOMBqNlOdaEZvhNYMMWEdqBCTcSnh/cfU97Ub4CPIei6H9mbwGiG/GS58F5bOhfrFsHEZzL5IeKba2mD1x0r2Gb0nQVsQVqV04LSQ58ciriEADyIm/sGG2pohsw1xoRpD1xi+0jRO3ieWYRHBFlFfxZEQnrgdbzjMTmxPEAS/r0aiE6kACYpTCQolQenp6ZSWlqJXDtympP7Z6oIDUctMqclyrl7UCmoBxuwQBI+J4nyxtIppsj5d8ByVBGWBMOnlIrIRlNYleoM452SZgDlNqxM0fHFUkqBnEERoFnBuyPPnIy5y5cSXhjvQcCszWGtJUO4diSnyO3bs4I033sDhcDDJAPsmiItH22f93/fTiH2d2tLS5eb3XERIQjURP87wLoZX+ykc+x9h/JQuB9YgYnle4DaoMoHPCCn10Nmp5glD+hdgcsO+IgLdQP1+Px6LhSrEfFh8+N9On2AC9Pv20d7ejs9KwBxdr4gU8yLWL1aWVR3A/eB2G9h0Omy5B2wng9QE3AjNClEoTBLZLxYLVFeBWoBdzRprINh3tDusVpZnERJWUrAEOAkRHnsgrnfdP6hKUKYa1hofZaXRiDdYKQhzgAS5Qe/U48MHtvBN4g2HOeieBIUpQZG1hkIep/RBCdqxYwdPPPEEDocgcM2K+99SF2RUKglSI4TOXpCgVqB4p0KCJkGHHYxe8FjggFns24cIwZlAGEp+qGz8FIELjpqJ51FY9oApQX6CqXcaBgRHHQlqBJ5XHv+E8PRZE3CB8vhwSduhMChZMc7JSgooIzNFfv369Tz55JO0t7czA9h2vPjNetcF78T7gjpEGFMnw9l1TVH3dTbC06VDqEF/YZgSIS+kPgx6L/iWImRIM8LUdBLQBiX3QmORmKvq65Wr/TdAh/BCtKTDLuWO3+/30zZa3PuWolzERwj2vvMOLa2tdFigNUeQIL9yQxCLBBW8Cv5GqM6SqDgW5nwHpD+KFeQ98N1bxESdg2isOU/Z0caNYv8GxJ186J19LMgE+5OeGuV1CbgFoUquZvDD6XZFwElRB14SZSWlQ63sh5wQbwotICPTqmtFpw+/tsRSgiLDYXa6N0aHKkGR4TDZH3ycFqcSZFVIkOQV15bf/e53tLeLktmtCcL8bLTrA5UOFVtYQDW0q7WC4hDVWwhRgiZBSwsY/X4cqUJ9VH3oRaEbnYe4e94OfCGeUn16TiXbd0CKJX6GyN5ZhLjQfToQO9UwbGbYt956i7PPPpuFCxfy7LPPdnl9x44dLFu2jEWLFnHbbbfhjfxlxoknEdLkKcA4h7g7bG0Jvq6WTPgvwYtBv9GOML12BxlsiqNPmjGylaBQ82I2UHOS0idoTy/6YkXBCz4Y9Q2c+Szs+u9Y/vlPWPs5uCK05kXAPYiT++/AnxETmSyLi1pVVbg/ZCAhI4jXSYgbxEMx1nP+DcyV4EiHxNtDXtABt4M/BQq+hgm7xQQWIEGrxSRSOxbcVvg0UxzT7/fTOG4cIBTOkQTbjh0gy9QpJMgDpOyBJFeXBCbygAQvzH0enC7YdpyXUfmQnoFwTj8CvlSYvBauvg8SFaI8fjykpIiwWIVysxFvSGw/4ntMBabHWCeHYDj9EeJTl/oKRQQhUa0DEE0JAigSJG/UvhAlSFGPWnWtXa4tkZ4gSSeMxLKslC5Q0EF8SlC0cJhHbZlhhLQ4Zx+VBOnc4dcW2S8qQXdkgIQcaJqnKkGFCC7oTBCfQzxKkMMPRTuD4bD2NjDKMu4UkXyhetGKQzcyE1SD/gbIwpcmSdBZLwhjv5Wgd4CbEHeCVoTM9ktEnFZDvzAsSFBtbS0PP/wwzz33HK+//jovvvgiu3eHN5y6+eabuf3223nvvfeQZblPpbKrEEZakwNOfA9eeQzeeQVefBFee1VM0KMRlWkdBNONoyEeRcPzLdSdC43ToW4KVPwqPFU0FK4DYHCAwwYZRZCWlsYJJ5wwIusERWZwpM4UvqCUatizKY4dNCBST+8Cfg7cCi0PgvQ7mPEeZHQAkriobdsGL78s/DWhOAP4vQ+mrgPvH2HzD+HgyVB9Luz9GfznD/DuO9DWyoDiLYJEeycibb/LqVIFvsfFw9rLQB8RliAd9v1KkJsL/wXpneCwJ1CzC3gHnE6onQSdY6BaJ44zffp03DNmAD13bx9usCnVIg9ZhLenOk9k6Jxb3tXqYgBO/xzS6qE+BaqL/YH6SgDkQ+UfwWOCBa+DdCOwHaQOmDYB9G4o3wjI8ZOgLQij7EnNYiKOhQsQCsFBxHVmsGDvAGSwqAw7mhIEQhUDRu0PUYKaQa/XYx1lJTExMWz1SCVIkqJUjUZcGyWFBEUzRkfrJK9u73EHSVBqd28ydFwGQBLHlHVih36/H7dbEDS7GtvcG3iLgBBnUhEZYrjB1QMTkQFbJVjsIGUCmdDWLnadojQ5e11Ztzhy4wsQMbItwDrRriM9HfBDYn0/PUHbEddCgGsQd+ghYfMeb7A1dIthkSK/du1a5s2bR2pqKgCLFi1i1apVXH/99QBUVlbidDqZoVzkly1bxp/+9Ce+//3vx32MDz74gGfHjGfKttFc+oSRknIdBp0BgwFq09zsK/Ty2jo3pnH7ST3WT9u4sXyeksJpTvj2b99i+MyAtTIRt9tGXaqB3ZPBODeRMWNgy5ZyDh0Kv+cftXUSY58pRXKCCy86N1ifha92N1N3yVqys7M44YQTAmPTfZZBqTyDyjyZPZ9/ToHZzL///e8RXSeos1NcdbIaDrBpRi7zPjNQ+3I7r3SuwWDwcs4552AwGNi6dSsVFRXoPDoKPyhk9H9Ho/PpSE1NRZahtdWF3WVgqU4Hkh+TrZ3ajAOMWzKNTW7YWm/nH/9wkZtbRpqxkdSKVHLLcjmtcjyzm6G+3Y/RJaGTZdJkH+n7/Iz/XE/TRwY+mw+e2duQ9EIemDy5f2Wt/+710tLZyfnl5XxYVMSXCQn8fts2Lh0zhvz8fOrq6mj/UTtJjQXsGSezO20j1W/UMW/ePHJycqiqqmLdunV8kpfPxBOmcvpqmWPesHPoLBO1f4bUZjd70vwcSHAimepoacnmoYMHufX22/lvcjImuqonwx3GlhbYtw+HZTQOZDZndlJaY6Lg9T28USHiYuPHj2fKlCm43W5mPmPH70liQ6mHDns727Z9jCyXMGHCBBwOB8+1beCre2Zw1y0GWt5xwTtgtpiZYDKT2SDj8/mo/107Z06S0Z1kZmvOASbPsVBUUkRTUxOfffYZxnYjnWs7MT9qJqtmNH+rSSTLoMPj81Bvrsc+yk57YTtto9voGN3BnFPmkJOTw0UNDdxiMHCv2w1ff02CYqg56aSTyMjI4ODBg5SXlweudX1BVXUL5o4cDHZopplP134aFtfPylKuLcXQbreTvlPPV3ureWPLFrK/yWYa01i4fGGXmTzSEwSCxHi9whekWnPiNUaHeYJClCAvvSNBkgSyASQPeHWCqnV2duJ0gtPZSa3Vxah2F/te28duaTc75sxBHjWKFEmC5hbOeN7Gond1+P7WzkfXbMBeYOfcc4UbdOPGjRw4ILISPJJEuu80/D4zuoliatxVXk1Hhw4a62hpSaMF0EkS01JEWtoXX3xBXZ2oHFQ4pZAx74zBfped/Lfzyc6G3QfsGPZCBW288bXogpeWlsb8+aLm+ccff8zmzZvD5o/A9wd8+M6HTP6fyVgbrFSeWMnu7N2MWj+K4352HGyA1o2t7Pr1LipPqQxsX1BQwKxZI0sPvuSSS2hqasKgsOa77rqL6dOn4/F4uOqqq7juuuuYO1fc3n344Yc8+uijyLJMQUEB9913HynK99EXDAsSVFdXR1ZWVuD/7OxstmzZEvP1rKwsamtr6Q3W372Xa+QFFNeYkVxuOn0dpI0zYnRIGPfUMbbMxKTP0qlNLYTsSozzO5nQZKb9Sw+mfQn4fSb85hSMko58oGCzRNM6D5tO9rDTM5N3313NvqYvsZ98Atevm8+xmwvxGX0cnOniLvsNJO+zcVfH/1DwmY1/7SpjX8Hj3H23qEj0y1/+ksvbb2O8fxrbRjt48Oc/58vCQm699VaAQSVCg7HvpiYRlS8rK6O1tZVNL75I45SrmbG2iNRNXh6s2sCWrY/z73//m8qUFO6prqbGbuLRB8ei32Wgxt/MltQ9FC89ljqvmaaqDhK8KRjcPrLWV5BWJaPbo0NqsDNThtGNLpwmIzAVXYcdp7MRt85DS76TRquejeM72F2awLrSFjo2r+bEF/ayhHMprJpM+jM6Dr4+mldtm3m98vdceeNlnPqdU/v+3tvbqXntNf71P/+D48ILsV9+Ofd98w2J33zDiSeeSPWL1RS+V4yc4eG14gpeuv0yPJ4O7rjjDmbNmsXatWv5/e9/T9uvfoX1AjMTXtaTtsvHouZxpDf7aPLKfDDhAOvWfcYn7z9Hy0MP8U+7HW9FBY6SEiZ0dLD1UKwgXGwMNtnubv9Wq5W8nTvpmDWHA34jLVkuip1+qh/6iPutorHismXLuOyyy3BWOyl4txB/ooUvRjVzcNtq/vJ/v+IHP/gBy5cvp76+nj+88god1yez6czPuPLhZua45zAxeyKZqZl4jTocbhnfIQdJ9V6+u9OM1JqCXu+jYXoDzfZmrJ9byfaLIkUGcwp5yVb8RrCbvfg726ivErKjHj1ppJFKKvZj7FRPrybNvZ2i8TLrz8zm6k8/xaaE9n//+98zefJkVq9ezQcffBBoBtkXHDrYwJS2ZJxOP582fcp1P74u7PXp06dz9913Y+m04Guwkro9mc8++IAtd97JUsdSfsbPMHeYqd0Qfg1tyM2F1FS279lDXksLAB328bhdBr7ZuJuEBMFkKgoKSHNn4nF52LJlO3pDuOtOBjwTJtAgSeyvbsThyGBPRT2FhbBp07d4/BNxSzL7Nm6lzh+HpA649dMwunQ0tIobq7KyMg4edFFfn0x9czm5tZmU/buMmz68iYbnn2dMcjIVu3cz/m0D81aX4vF4cO7twPczHzdl3hRozvnII4/w0UcfAeBPTOSi81bjdsJ+ayMNGxr45hsHHk8yHz7+Vw5kXw0Ij5BTltkgy9x77718ozT5s/gt/KvpX6TUpND0RhPtRgt1tRK6iiQ2uXZy3XXieyotLeXBBx8E4De/+U2XMijq9wfwybWfkFWdxSH9Ia7/4HrcH7qZN28et956K4mnJ+J500P7Y+385IWf4JME4V6wYAE33XRTXJ/rcIAsy+zbt4+PP/44QIIAKioquPXWW9m+fXvguY6ODn73u9/xyiuvkJOTwyOPPMKjjz7Kb37zmz4ff1iQIL/fHxaflmU57P+eXo8Hl+qvQl9ppiNNpuNkH2krWkg7biw4wfmeE+kDCePnOqxNyRQdSOGM5yQ8ZgkfCciTx3BgrI/aQpm0FBeTmz3k/tdGboeRMa8ZmZhfTNWkq2gedQ1TP9SRWw2YoeVKiZm/tvJo1W9xu914nvZg+6eNX8o/x//gDxk7WZQTffXVV5Evz0Paq6fiOxZW3/ocaTYbeXl5bNiwYdBY/WDt+9hjj6WwsJBFixZhMBgoKiri/iY9npf0lDanseyCX3LjjVezb3w2LzToOL5xIlc/pCOjEdrGwBfneHGMyqctQYj4LXNNvD1Pxpzt49EqC8ZGPdUfVmNLscF2MGwy4GqWcHYa8KXZ8GZlUTXez+bJRtozITnVRP70Fl4qTsMjXcDB+xwk1raT/rUR+1+h8GASP+24mJ+ZLkL/gYuK78TTmCE6UlJSeOPMMyk8/XRq9Hquy8vDXFrKD7xeMoxJeLd56Sz28/k8mH56IStvFRb8vLw8bDYbpaWlnHnmmdyQm8shoxH935sYf2cq7nrwoKfsBEj+TgG/Of407rXO546sLLaazWwxmbDqdNxgszErJ1aziOgYzHMsnv3/5z//Yb/Tya87zKCTaZ2SxKjVMldOuZSz/n4GSOLuOTMzE/+zfhozvawbq8c0LpuFUyZx9//8l4yMDNLT0/F4PNxw7LG8kpzM8mtzWf4DUUo4JTOFxLREmg64eed1P8neVPIK7GyqMHLy1xnk7tVhqDKQ7k9ndMlo5ASZfcntHMxJY+NYHQdKJKa6DFgNqRyX7SCpUo9ptwnTbhPGfUZMsgn9Fj3Z/mxmb/Gyb5WR9Qvu4bgXfowl101BQQEWi4Xx48fz3e9+l9bWvsdhR48uZPQ3Fmw2mH/efP57xX/DXrdarWKSHwMND3jobNXxg/Mv4BcLFpDybAqpr6VimmqiYFZB2HafAB/b7RSMGxfwle0qFz6qKZOnk5IqnksEDDJYEvTMnnMs0XI3CoFqIHuMjbYGyM+3AY2MKZ0CZSBZ4KSZM6P2dYuGp/aCzglLvnsRFy46gfz8fCorDezY7iHroiQK98iUJpUy49mZXDh2NMaEBE4eN4OiVT5kk8TXS30cvz2L+S3zWXP1GkbNEs7lBx54IGCyrtfrOXB/DjaLRNGZRRTNKmLDehctLQ5evvZn3Dk6k7KEBC7s6GBepnA+/d///V8gUw0g7f/SSPogiYTdCRReCbsr3Dh8epqOm8uz/xXfU0JCAoWFhQA899xzbNy4kSlTpgT2YbFYKCgogCaYkjMFkiHtjjTen/5++Pd7LHS80sH4yvGs/cVaOk8QBDEpKYnc3FxcLhfbtm2L8xMeOqgtMK644gpaWlr43ve+xw9/+ENefvllrrrqKp5++unAuh6PhzvuuIMc5Ro3YcIE3nrrrX4df1iQoNzcXNavDzaXqq+vJzs7O+z1+hDTR0NDQ9jr8aCsRMfOBRLHfU/i+GmJSJISDzdD5tJMUe3TBe6Pofwd2NcAlRngKgRzsp6sLD2nHgcFBUrkvA34BxifhylVfsbWmLF7RBG7llHw6D1w3ndEmmZRkcglkO+E6jVg2wstT+bBH8WuinPGUH8QOiWoOs3IlLxYQf6RAUmSyM3NDbD6zMxMLsqE9QvhxP/AhE1mPsnIpWk/zG6Ai1/Qk9wGdaPgyWuhOcfIXLORGRYw5cHP8k04dHAnCUwoFZ9Ns6k54ABO8CeQ0AK+Zti8Gw5Vgs+nJysL5kyBceNtSJKNqQhv4UazmQfT0/njJEhcDr6ndTQ8B5ZqcHn6l1e1QKfjO2NE04oS4BhEyYWdwIl/AUOdgfZRcHAenDDdRElJatj2SUlJJCQl0YgoyDfnzDwS5kDV07vJnD6ezHw9l4y2YrWJYzwAXIfwu13IyPMDgXjPU5OSuEaGtUgkpBjIzQFdo5ESY0mwB4gMujd12IwmyicLs/3YsWZKSoK/F6PRiD87mwRgmtlMSQQhHD3aRHY2tLaCdZ6NF78H64EXm4HdoJN1mHPMtCbCB//y4Tcb+eh0yCiEUe9CTbWe3brRnH1NSPVht9iWXaDfrSd1q570bXDCBzrSVheR/SPYfzI0t4DRlEJOtrlfJMhoNJGhpEClzUkjrSQt+oppQLqRxHYYK6VTUqKUPDaiVPoLR6QnCKLXCrL7wSqDXiIqAQJhjq4GHCHGaL0eWhT/isEYvbFtTCixuk63xATleupygsFgJKMoFYqasbRaGE0JRrN4L6Z/g82p55vpUDFTR8k4KHgJRn0yCq4HJDG35Cr57Ho/GHeBQenRIhIuErBY3EydMo7nJOG9Hm8OOKwCilIAVyPY5H8g+ceQnGjC7IQEn5XxJSVd3nNRURENDQ1h53AAfwez3wynQeEFhWEvfQlUSHDe5YmY/wAFXxXApb35QEPwU7o3wPYHJyIyBbpBW1sbxx9/PL/97W/xeDysWLGCMWPG8Mtf/hIgjASlpaVxxhlnAOB0OvnrX//KJZdc0q8hDgsSdMIJJ/Doo4/S1NSExWLh/fffD8iBIE60hISEwB3lG2+8EYipxouXr4XTJ8EJ3YUOE8B0JkxZBDvr4J0aMZHdnCuq8oaJT8mIfjHfg8Y/1/Oto4gGPcyYBYfOhY1WkaVwFkH3uaSHpLvAeQkkvw7Nl0PaVGj/RHRIri0A2yiOSBwLfHgZuFZD5m5gLhS2wIVvQYYf9PMh+39hcrIoVvktsAvhdWxDFFM+NdbOdUA6pKXDGeOUImWyyG4JxUxE8sb1iGrEVwOPmiHjGrBeAWVl0Nwqki/6iu9H5OOfiCBBe7fDif8Anx++OBkkAyjJXF2wF2GmLkKZmDKg9ZRWxs/q6qMoAF5BJCCO4E4rAJxmExOnxQG6WcD7wNcESdB2YA8YkuFgviBBFltXy6mSlUy0n5IkQWkprFsHreVAoWKMTgPmBNdb9yH4/RJSCTSMEwVVT/+OMOFXHhLnykTVkG1CZFOE2Mk8VfDp3+C0l8F4N7SPh41LwW8A2QSzp/XxQ1KQolZljJUZBiBBezGwFbL3IU6QbjrLxvIEQTgJcij+HlM3s4fqC7KHGqMToEXdNrLYUk8IkKDgU07lqzeboXNcJ2nfpOHaDEyGTCfwnMjyWrUcRtVC7WQE+duHKDURIU76DoKtTVQaJwvalTSzhAQPkiSG0GM7mgni+GwH6VMlS+wA+ZOf6AAAKEpJREFUJNaBJ7kXfSmbEVVhQVywQvABoiQDwIdnwZMPg7QeMeFkMOIwc+ZMZs6cGfj/ggsu4JNPPuHEE0+MuU17ezs//vGPmThxIt/97nf7dfxhkR2Wk5PDTTfdxIoVKzjvvPNYvHgx06ZNY+XKlWzdKvoBP/jgg9x3332ceeaZOBwOVqxY0atjFM+E5XF6pyQJFuRA5XT4Yjqk53TTcyYXNl7Uzu0PwT/+ABMvgoVWkcp7gK5FF5Pmg3OBSJtsVlKjO14RcfTNx0PeyMuIjwsScN1k2He+ICc/eB5+9C4UGMCyCExPgJQcrPxsQJTFOIS48NxD/HeOktSVAKkYj0ibLyTYEfxp4BMjPDEV7pvdjzcJlH8avDiDSKlOcMCk3wA+qDoFGgugqEjUr4kGNS+yu/ktFEZGPgECsFqUtOJO8B2vPPl+yApviIX9JNDpoS0TWqLMxCoJyotxnJJScZzqfZDoFEbf0IoJTU2wtwIknUzlceK5qYjeY4pfla++DKaqR8O4PKi6FN49E9qMMHoPnPshpBXCvn7eeup8YlJFoseTpLVYLDP3KU90Q4KiKUG6KMUOO5XHxm5EUzVNviMiRb5VITG9JUGSMrjOkME5lcfmBLBPUSpIrhGL77wKtED7ZPj2OJGR5vQi6uyA6HMSCSVyVDMVkILfrymhlwUP1O7yHykkCEiq7WWG2POIL+JkwrL/PMCjIattSYZ9JyDumj7s3TADeAQhhw7GXw8qEIjaT1988UXgf1mWw7xBkairq+P73/8+EyZM4J577unVW42GYUGCAJYsWcLbb7/Ne++9x8qVKwF44oknOOaYYwCYOHEiL7/8MqtWreKPf/wjJlPcnBoQzTV7wy8yECXxXQR+GzHxZbKo4XIGgukbCP7W3o2yfuZd4DND0gaouxOM/wWfBB8ujyjCdYQhCTj7lzBpBYxNA0shotbFg4Tk8IrKz28CNyMyQ/+hbDtQyEOksc9B1Fd7FJHK/jlRG473CpWV8OorUKdMNtN8cP0tYD0AnmL4rxL6nzo19j5UEjSyg6K9h6QDqyLDOeYgzonNiHbt9QQmrkPHit9Zew5UJ4R/Y15EKRUdUSM+ACQmQl6+UDfGKQ1IQy3C32wQamJubgvblKi5+nWNGw+jR4vyDGu7CSEcPAD5H8DBcfDw3dA2HlIPQuYzsPv0OD6MbpBUr9SxGUOPsmVLsVim70PcaalvNMqHE1knCKJXjXYqj03dkCBVkGiPIEHtytLcVxIUogS5QpQg+zQ76MG4DnL3w+mi8TiHVoLXLDiC240oMgiCMESwEoO436ZB+bI7VRJk6iUJUvuqfA45yeJcTK7tRa0gO6JECMDl4S+9hwh9FyMunQDvLAp5cQSivb2dBx54AJfLRUdHB6+99log5BUJn8/Hj370I8466yxuu+22AamjN2xI0GCjt+orBNXxnlpobLGJQi+nhjyn3gyspusdgKUQ3Er8Vvo74IGd82DP7JHT7qDP0CPCiB8giql8j6hnYTawHHHN6h3djQ9piB5jDyMI60nAFYhCh/1BRgZ0dIiGnVvWQ9Jv4bjPoT0F3lsu7qJH5Ym/WFDbWsarBB1JUH5K2CE4mTyN+LJcwAI4aBJktS0bqiNuhuoQE14W3f/mJyhxjWyln51aK6ixASoqhH8laUwrdYiK3cXK65IEJ50MRqNYb/++rvs+dAjef1/UtTl2Knx1IfzoKfh8NOSUw29/Ht9nEQupdaDXEWRm3aBJuatK24sgki5EPZvErutG9QRF1PnxAZ44lCCVBLVGkKAOhcRYenlB1ilfc2jBQ1VxTTCDL9kHp4qijg+cD4nNwDRwnSQqSgeKJRYTLAQXUXHZrCQkN4v77qASZOwlCcoFpgBOyNkDOgkSG6Ej3t28gZAmjxXvIRTvKMsfItqWAfx7PvjNiBpFVb0b6nDAggULOOWUUzjvvPM4//zzOf/888PCY6FYvXo127dv57333mPp0qUsXbqU2267rV/HHxaeoOGK4xCdo9chwjTR0ATsM5tJRZyzKgoRv4NvEb+1hRHbjf4V7LGDbhU4xsEzDwHSka0EDTdICLX55JDnXAjhoa/4TiJsz4XqT8D6BDQ3giEFXloBWU2QqYcTju9+H0erEgQi5ARKf6zLEH1S1MqDBpCvhfpPBTFuy4GqlnASpM4BPVnriovBZAJznQgt1SjKiJqfMWkybE0Rl8cphPP0pCSYPRu++AI+/RTOS4ckpaD3oYPw3ntCOZk8GU48AQokeDAP7n4K7v85TDkEfc8/hJQaxbAcBwmqGy/aR6SVEaPnQxDRPEH6iHCYWiNIT/QaQSrUcFiLXnwX6vaqEpTUWxKUIIQsZxQSZFbZ24/B+wXgAK8NuBUSJVEs0UdI24xzEP6y/xC8MDeBuVwU2WxV1FqHIt30WgkC+A7wLRg+A+8MkBqhth7G9XRiygTP94vDX2pCRJgMiPuDZIRVoNwCtafAqPcQalCEejQScOONN3LjjTdGfe2ZZ54JPD7jjDPYuXNn1PX6iqNGCeoLjkV8QFuJLWV+qSxn0TWUorbgiKZSSnoYfw8Ufw2TXoSdijJQGGVdDSMH+vth9t2w8ENIa4D2RHh/Kfic4FRUhIzM2Nu3IIrMWeh5Ij8SYVPDYXZEPPp2xIeRCNwNrenijj7RCm5b13CYSoK6EdoAkZ00ebJQi4rXQbUsQpn79wsz8IwZsFup1h6Na0ydCvn5wr/05pvw7bfw5ZewapUgQBMnwoknCuVoPiK8+1EqzPkL6H/Yhw8mBGk1ilcnDhLUmA/2ZDA3IUzmEJMERVOCIsNhat8wHUHTdDQESFCEkqQqQSm9JEF6ZXCh/b9CjdEAFMK65+GpW+HVF4DxQsXzG8CvU4o+ehHER4/o86U2GvtSqEU7Zotqz9CPcBjAAmX5OQHDXn2M0nayHNKfbTPCuJ1B+N0ZIoLnRySJKJw7cApsVUMP/2GYNkscvtBIUDdIQtxFeYFNMdZRSVC0m/vvINSGtYQbL0Oh00O1JI6RjbjeaxjBmAjkQMIsSLsdPM9AygKomgL7l8KECd1vHmqKPhp/nGo4rEPxubIY+Bj4CDgj6LXKywGkruGw7jLDIjFtOiQYIe0Q1H0B//1YPD99hvAm7emGBEk6OGOh6BZut8Pna2DLZhGOmTEDTj65qzlfAiG3fC+OwXUDiwOkNOKKlzol2DNVOZfUTOMYKU7RPEGRbTNCq0Xr4lCCmiLCYWpmWVpvSZDyNbtDBqcUpMcS4otqyoePl4FfOQESASSh8IBCotIQaZt+hNIIsEZwh63HB21WqhJkNIUYouLFaIRnqx3ylWoI9Qe7rtbRAZs3FfP3J0UbH9/Lygvn0iVOo95Mh0YV1MvJ2uMRZGsfIvygIW4cjdfZXqE7X5CfIAmK7HYNwpcwE+Ho/6SbYyj9HI98P9DRgAcQd2NPge4yGD8NzpkPe06CHTk9N9Y8mv1AIEzLICaHAAwoTmCoFR0KGJMtVJxmgyGsQ7daKzuiektUmM0w6STx2LdVkJncUTBzpvht71MkhliCi8kEi5cIwjNmLEyaBEvPg+Pmxs5OHAjo9Yi7rjiO4QK2zlMImFqcObrdIq46QaEkKFrfMBXpiGM2G5QK0h7xvGpsTu8lCTIqg/O4g+NxOoXSZglJqlAVe/UptS2fO1JJUg3SbyPqS3wiPp6NJwe37ZcSBEICBErKQZagrSY4fgC/D95/D9rbxRFrdkGHkgHJ0vBdVSFEooTgboEgn91pCHlP/asdeNRBI0E9oDsSVIZQUzM8nkApk0iorP39GK+DKKQHQkTQcOTBhqjn4yHQ4zEmepsef6RB9da0t0V/XVWCcrPFzTYEmocDoCR7xfw9RmJaKXy7CJrGCPJz1pmiAGAF4NTpGEX35Qd0OuEfOuMMOHm+SIkebOglhJM/DjiBdd8JyYzNI6YSFNUTFFEnqKe+YSoMiJtAt0kQf5V8uBQ+kdVLEmRQBudRBhdQgSzhhFMlQaqirvq/XaqxWiUh8xFFt8qBSwAXVM6BuoIQJai/JOgUsSj5HNqzRI2w0I42W7dCQwOYzR6WLoXC7eBuA/cMxAUjBKpgtYDwhMDxBHvHuheHrBzj96NClo/smFlv3p9GgnrADMQPeiddz6u1ynKa3R4z/f40xIf8ZZTtVagkaFLfh6lhmEOVrct7WO9oNkUDJCu1ENralKKXIfB4RA0fSYLMLGEZguBn5idIMuMlQdmINPINC2H6cWBUJku1LEYctpvDDysinBMHnEBTDtTdijAu3krMq353niBvFE9QdyQIREjSZxLk3+0GJxJ+t5i0exsOMymD8yokRiUoVmv4eioJskYsXZHhNBOibgoI+VAHX/44uI3HLc43vR70+vj6m3XBVCANUqoEifMDqqe3vR3Udnrjx9eQkwNTFRl4X4RSJxMsa3R2+EtYEDcDPmDveMRdux14KvawzGYzjY2NRywRkmWZxsZGzCGVvbuDlh3WA8yILMVvEFWGF4S8FiBBHbEcP+IucjbCk/ghsCzKOpoSdORjAsLWUoZITokGP8HQ6NGqBJktIv3c7RYTVkLIdayhXhCjjAyxziSE4XgHwkJRjehgkU3UDPCoMCGSEfYhCJRKVoczCXKdSjDO0wPU8hzOZUS/+IQgbk+QV0QnDT0QmXxgsx68BlERv1EyoveKSae3xRJV65dPVYIUEmSJQYJUJUiHIDXehJA0eRWLlRW+Bs6C/cqXbSaoNFmt3RTK7Qk64GTQvQkFVSDr4eBBaG6GNWuET2rsWEhLt8N2kUhRZ4XNKVAiB4+7A5HYl070tjjFiMK8B4EJPwFWAM8oG67sun5BQQGHDh0Ka0V1pMFsNov+a3FAI0FxYA6CBH1BkAS1IbLG9MCU7krHIn5rXwMvA98lvGhjI6J8h434fAwaRibUybWsm3UOISatLILZH0cbJAmSk6GxUahBWSEkqE7xA2UrISdVOVXTzXsbClMxHkGCdjMySJC5FynQkaSgO8QTDnMA+jiywyBoTveYAC80Y0LvFpNOTwSqy9iMok2KzysUmoASFPHGIj1BIAhxlzR5FWcTkFfUq7iVoCk6UmnqNRQSNP0L2HUZsANe/rcg8xaLyCDcvgN4TaiQNcdAs12c/0qP1kDB3UUErHFhUKf6QyBqIN0A/JmYNYOMRiNjxvT2V3LkQguHxQEltMtqgsbWNYg7i+mAxd+9XHo6wfDz1ojX1KzVqWhfxpEM1YZRTuwM1qM9FKYiSQ2JtYc/r5qic5SaPqpyWo74XaokaCy9g6q6qZ9/G0KR08vysFRnrb3oD6UqQfEEBrozRqvhsA7iS5GHYJkCNRTVjAmDWxjaIyob9Dw2CTwW8dvp7Ow5HBbKjRIJKZgYSYJCoH5WVkKUpv6m684VveLGbQXLeEhLEwTIaITTTxdKluSU4D1xc+xdIjarVLxDPoJZYWdF2T0EvXEBu9EKhC/o7/0c+1ECbd6NAyUIybGFIGlRDfiLoqwfCRNwnvL4xYjX1JBanCF+DSMUmYjM3HaCadyRUDPDemzSeIRDNUe3hZjoZDlois5WSFAykO3x4EaQFlW96e3nF0mCNiEm2/Gdnf1uozLU6C8JilYnKF4lSCVBDoUENcoJ6D1glAQJ6A1MgNuqkCBH/J4gCCdBoSn2kVCVIDPB/UeG23oNK7TMAUmGrPWwbBksXQrf/36wanzKmhQx8GMhS+ldqBqov0Ik3xQR2zOqKkGhGfjfVsNbH/dz7EcJNBIUBySCLPxlgo1RTXStBB0L5yOkzA8I3rH6ESE2iF5nSMORA4mezdEqCTralaBUpdFxS3PwufZ2MTGZzZCSGnx+gjJbfUGwlteMXh5PJUFliElWKRrNpB7C3CMBfSVBqloZ6QlyEPQEGeMkQe0KCWp1iSNYzL332ZgAt6IEOXqpBNkIhsO6U4KihsMGoHBbqxJKyPtEhBdzckO8bjKkfZQmHl8AecqHVlMjPENqm4xziN37MiwcpqC8LKLMhIaY0EhQnPgu4iLxKUGv2ZnE39hzlLIPP6JflYxg+S2Ii0XxwA1VwzBFaEgsGjQSJKBW1G5oCD5XozT3yskNn0DVpIQ/A82I31J8dsgg8hGm0yaEAVVVe0c6CZLpHQnSIyYEGZHRBdFT5ONVgnJRGt0qpmS7QoIS+yCvmQBPL5SgLp6gOJSg0HBYrP33BQ6lnEH+FwgpOBSbIOFQgjgBFwjlKSNDfN77a0SdUAj2ooyGUYjvow6RGCD7hflaQ3zQSFCcSAeuVx43Kv//qJf7WImQ8L8A/kqwWWekWVrDkYnuzNEORM8yI1rrlPR0QXRaWoKVhlUSlJsbvu6xHR2khPx/Jr3/LUmIDE4QSTW7ERNniZoiNELhRpAGE/Ff6E2Kv1EVTCJT5HvjCdIjbu48FqEuue1CEkrtIwlyWQWR6bAHQ6XJERkE0cJhqhIU6CQfAyrltRA7+6wvMGTDt8eBzk1Q2lGhtsU6j0DH33yFxa+tFJ/bMXTfBsaAIJwyItTe1i5+N/32Mx0l0EhQL3AxoiDwSkQZhuxebp8B3KI8fgLRwy8L0S1dw5EPVQmKRoJUP8oYtJRNgwFSU4UPqKlJLKuUrraRJMgky/wKMeFOQHhC+4IzlOUbIf8bR3gdld6oQCpMyntWBZPIcFgn8StBIEKNbosQQEztOgxASh9IUALgTBYTfUO9SHU3GkVJhVBEC4dZCVGCYpAgmQgSNIDhMAtKKw8QplC1C8d24FOQTTJcFFw/X0kT3q2c86FlWWJB/VnUAvVKAkFad1U+NQSgkaBe4jTgGvqezn4GcBfiDmk68Cjhdy0ajlwUIS7mNXQtnKmGyI72UJiK0JBYS7O48zebITur67oLEWHqfxF/faBInEyQpFqAS/u4n+GEaKGhnqAqQV1IUC8bqKqYDnjMQj1PbJVIILz2U7wwAs4kQVYqFXKQlNTVWxSNBJkRRRu71AkKgQfxuhFxEzKQ4TALsOFUaChAGEpfRkhtd4vXm85oCitLnpsrqmB3NIDeBafGcQy1UHkdwVISGb3IIjyaoZGgIcDZiN/Bkxy9RfGORugIkpxIX5Da83Dy4RvOsEauclWvPAT79onHRUWxe3Il0L+QsgERnv4FQuXtra9oOKIvSpBBUYLUbaPVCeoNCToWQYIAkAVJTYyz0GMoQpUgVaBLj5jk/UR/z6HFEmMpQaHkSfaHt+XoLyyAzwBv3Kg88QhwGcIEWAAN5zWErW80QkI2+GUYVx1feFyNSoSSILXOkIbuoZEgDRoOI2KFxIZzcb6hwGjlyn/ggOixBFBcPLjHTEFEJY6UG5P+hMNUrhCpBPWmWCIIxXt0CJFIAmx9IEFqirw3ZNusiElefb8JhE9sFkLqBMVQgkJDYS4X+P2ilpF+AGLT6tvfcArCU+FG3AWlAL8H2dw17OpQTEAlMQoeRkJVgmq8otCiJEGGFg6LCxoJ0qDhMEJVejaHPNeOaNlgQqsRpCIpCQoKgt3Ck5Nh9Oiet9MQhEoKeiNmRIbDIusEOQCdRyFBcdT6kYCbk0RCSJbHg5G+kaAEZWeuUcHn8iM8CbHerwXwG0DWifeh+ptCEWqoHrBq0RHj6ZSAnyOKGN4NvErMXkkHlPeWFicJUpWgxirxHtPTg33wNHQPjQRp0HAYcayy3EiwFst2ZTmBQIKIBkRLgdRUMWmeugB0PTTs1BCOPnmCIo3RihLi9QbDTb1RggBKbVBkgESFSfWFBKm/i8bZ4pyYOKlrOCxWixALgAQ+xZAdrVZQ6LaBQokDlF2lfv6BXMNpiMJzKVFXB2BztmgTYmgMhua6g6oEuZQOwoOtmh5JGBaJKFVVVdx88800NjYyZswYHnzwQWwRv5TKykoWL15MYaHQyTMzM3nyySeHYrgaNPQZoxFZgo2IflVjgC+V12YMzZCGLVJS4cLvicd9bmJ5FKMv4TCjogQFPEEhSpA6FxsVJSVeEiRJ4W0y0tJ6MSAF6uaOFPhejHTaWCRIFXS8JrGS29VV5Rms9HgQBE6HaO3ipedJtwmoNUBeLpgqoaoKxo3rfpscQPKBYb/4v1hrDRY3hoUSdOedd/L973+fVatWMXXqVB577LEu62zbto0lS5bwxhtv8MYbb2gESMOIhATMUh6vU5ZrlOXJh384wx6SpBGgvqJPJCjCExQaDrOr6/SSBAGUKnHegoK++WzUyE43ZX7CiEwo1P+9cShBYYUSB0gJkkLGEE/lKbVchllx5+/f3/M2qcCoXSB1QkqaCIdpiA9DToI8Hg/r1q1j0SLRhWvZsmWsWrWqy3pbt26lvLycpUuXsmLFCsrKuuvHrUHD8MU8ZflfRKn7vYismWlDNSANRyT64glKiPAE6UJJkNIn2tAHEjR7NkyYWMXJfWT68ZCg7jxBoHSzJ3rV6LBw2AB7gkLH0BsSlKuoOQf2R/cxqZBlqKmEEkVSHj1Tu3HoDYacBDU3N5OYmIhB+UVlZWVRW1vbZb2EhATOPfdcXnvtNa688kp+/OMf4+6u/KcGDcMUpyIk8nXAQ8pzJzFMYtMajhj0xRNkjPAESVIwJNbhA/ygV0zS+l54tCQdZGe3BZrj9hYqCeqm60X3niBA6doRNU0+WjhsMEiQs9u1BFQSVJIiav243XDwUNf17B3w9VfwwvPw9ttgdkFjERiPlPTGw4TDet199913ue+++8KeKyoqQoqgrZH/A/zkJz8JPD7llFP44x//SEVFBRMnxrDXR2Dbtm09r9QPbNiwQdv3Ydz34dh/XxDveTYjN5dPUlNRNc9j9u1jg7P7S6T2XR1Z++4r4j3HdmZk4MjKoq6xkQ319XFtY8zJwWG3s6O2lg1KAyqnswSPR8+Gbw/hKi7A7zXh9Lr45pveq/F9/TydkoRjwgR8fj8byqN339u8axeOvDxa29rYUBVMq2oyGHCMH0+brMfh8LFjRw0trS1h225PS8ORk0NTUxMHDphxOKzsP3CAtnZHv8atonPMGBwJCazbu5f6KHn6ofv/qrgYh9mMZ/9+EowWHI5sPl7tYNr0A4F1amtT2L0rF79fzJWmBC/tJfDNXANrd1eha4ssx6ohFg4rCTrrrLM466yzwp7zeDzMnTsXn8+HXq+nvr6e7OyuDSmeeeYZFi9eTJriqpNlOaAexYOpU6eSEOrOG0Bs2LCBWbNm9byitu9hv3+Xy9UvwhzveVYCXIcoF3I1sHzKlG7X176rI2ffh+sc+wrhcZlgszGrML6OdC8cOIDVZiN/7NiAd21XuajYnTdmAolGMBkgLcnY68+mP5+nF/FedBB1Hxs2bKCgpAQrMMZmY9aoYC59m7KtnCTUnYKCccyYGb79JmWdEpsNm02oL8fOnER6xsCcB3lAAzB26lQiDh22fzlkvIsnT8YyHp57FtxuG7k5WWRnw9q1cOigqKBePAaOmSqqTD+qg7VAUnIJs+j/eXa0YMgVeKPRyOzZs3nnnXdYsmQJr7/+OvPnz++y3rp163A6naxcuZKvv/4av9/P2LFjh2DEGjT0H8mI3okywyAmreGIxED0DgNRwRjA7gad1Lv0+IGC2uHej2i9FS0S15MnyNmNMTo0HOYY4Oyw0DH05AlqVtZJRsmgN8G0abB+Pbz/vvjcOzvF8sQTYUJIIETtKNMQuVMN3WJYXH/vuOMOXnrpJc4++2zWr1/PjTfeCMDzzz/PI488AsBtt93G2rVrWbx4Mffffz9//OMf0emGxfA1aOgTJIbJD1DDEYn+ZIeFkSDFkOPw9L5G0EBBomdzdLQO8hDsB+Y2iZuObo3RXlExWpLAPICBg3g9QQeVZWjblhkzYMxY8HgEAcrKgvPOCydAAGoBbY0E9Q5DrgQB5Ofn88wzz3R5/uKLLw48zsnJ4amnnjqcw9KgQYOGEYuBqBMEYApVggxDQ4JAkCAnggRFy3hT1Zxo79dC9/3DAjWQQjLDYvWp6wviVYJU/3MoCdLp4fTToakRfH7RLiTa2DQS1DcMCxKkQYMGDRoGFn1qmxFRJwiCSpBTUYL0DExPrd6ipwyxWNlh6nMeRQnqLhxmGIT0+NAx9USCoilBoPQC66EhqvpyfBZ4DSo0NV6DBg0ajkD0yRMUUScIgkqQ0w16jxKaGoL+LioJ8sR4vTvSF9pEtbtwmH6AW2aEHj/0OLGgKkF9aZMXqgR1bcmqIRY0EqRBgwYNRyAGok4QBJUglwf0bqEEDUVzTtWi05MSFE3EsRIMh0XrJK9uKw2CKRr6Fw7rzTGsCJLY3oftj1ZoJEiDBg0ajkD0JzssmifI7QaD0kF+KJQg9ZCxjNH98QSp28pqOGyQlKC+GKN7Ay1DrPfQSJAGDRo0HIHojzE6mifIoyhBOoZWCeopO6zHcJhbtJqItq08CNWiQ8fUnRLUDrQi3mcP9p+Y0MzRvYdGgjRo0KDhCERfwmEJUcJhqurjcQdJ0FB6gmKRIJX0ReMvFkA2gKwXfdB8vvDX1c/KN0jhMPU76I4EhYbC+tr6SyNBvYdGgjRo0KDhCER/lKBoniCfJxgOGwolqCcSFKuLPASJkRSjiaq6rXeQw2HdkSC10Ud+P46jkaDeQyNBGjRo0HAEoi8p8sZuPEG+Ya4Edad8qc/JUapGyyHbuocwHKa2Dc/tx3G0NPneQyNBGjRo0HCEQSZIZHpT+DhqnSCF8Pg9IkVeB5hGmCdI5TRyFCXIg/AKmeRgB/nBSpHvzhhdoywHggRpSlD80EiQBg0aNBxhUOd4E727yEetE6QQB9kDBtfQkSBVfIqVIt+TJwjApzCp0AwxNRRmdQuvkNE48OG+3ihBOf04jkaCeg+tYrQGDRo0HGHoSygMuq8TJLnBqDRQNffGaDRAUJWgaMUSfQiFKLTHWCgCJEiteRRCglRikjxI1aIhPmO0RoKGBhoJ0qBBg4YjDN3VzOkOphi9w/wE/UAACUNAgrprm+FSmmlbiJ5ZpZIgb5RwmEpMEgcpFAZBdcrRzToaCRoaHPEkSFZj3NEqZA0gXNHKkGr7HrR9D9b+1fNEjiwk0gMOx3mmfVdHxr4PxznWAWQAo4gdPooGPZDpciEjJmw9IPvBmADJssgOM5lAJ0WvvNwT+vN5JiHek0zX9+SRJDJcLlKivAaQqGxrSBDjdzqD47crr2V1iNdsiV3fW3/PA5NyDFOM8TmUzzwTYr6HeGBEfOduoK2P59nRBkk+wj+h9vZ2ysvLh3oYGkYYSktLSUpKint97TzT0Fto55iGw4HenmdHG454EuT3+7Hb7RiNRiSpryWoNBwtkGUZj8eDzWZDp4vfUqqdZxrihXaOaTgc6Ot5drThiCdBGjRo0KBBgwYN0aDRQw0aNGjQoEHDUQmNBGnQoEGDBg0ajkpoJEiDBg0aNGjQcFRCI0EaNGjQoEGDhqMSGgnSoEGDBg0aNByV0EiQBg0aNGjQoOGohEaCNGjQoEGDBg1HJY54EvTWW29x9tlns3DhQp599tl+7++SSy7hnHPOYenSpSxdupTNmzezdu1alixZwsKFC3n44Yd7vc+Ojg4WL17MoUOHAGLub8eOHSxbtoxFixZx22234fV6e73vW265hYULFwbG/8EHH/R533/+858555xzOOecc3jggQcGdOzR9j2QYx9IHO3nWLT9D9R3pZ1jQRzt59lIPMdi7X84n2dHHeQjGDU1NfKCBQvk5uZm2W63y0uWLJF37drV5/35/X75pJNOkj0eT+C5zs5O+ZRTTpEPHDggezwe+YorrpD/+9//xr3PTZs2yYsXL5anTJkiHzx4sNv9nXPOOfLGjRtlWZblW265RX722Wd7tW9ZluXFixfLtbW1Xdbt7b4///xzefny5bLL5ZLdbre8YsUK+a233hqQsUfb9/vvvz9gYx9IHO3nWLT9y/LAnGfaORbE0X6ejcRzLNb+h/N5djTiiFaC1q5dy7x580hNTcVqtbJo0SJWrVrV5/1VVFQAcMUVV3Duuefyr3/9iy1btlBUVMTo0aMxGAwsWbKkV8d46aWXuOOOO8jOzgaIub/KykqcTiczZswAYNmyZT0eJ3LfnZ2dVFVVceutt7JkyRL+9Kc/4ff7+7TvrKwsfv3rX2MymTAajYwbN459+/YNyNij7buqqmrAxj6QONrPsWj7H6jzTDvHgjjaz7OReI7F2v9wPs+ORhzRXeTr6urIysoK/J+dnc2WLVv6vL+2tjaOP/54fvvb3+LxeFixYgVXXXVVl2PU1tbGvc977rmnxzHX1tZ2eT4rK6vH40Tuu6GhgXnz5nHHHXeQlJTENddcw8svv0xJSUmv911SUhJ4vG/fPt59911++MMfDsjYo+372Wef5euvvx6QsQ8kjvZzLNr+B+o8086xII7282wknmOx9j+cz7OjEUe0EuT3+8MaDcqy3K/GgzNnzuSBBx4gKSmJ9PR0LrjgAv70pz8N6DFijXkg3svo0aP53//9X7Kzs7FYLFxyySV88skn/dr3rl27uOKKK/jlL3/J6NGjB3TsofseO3bsgI99IKCdY10x0OfZ0X6OgXaeRWIknWOR+x/O59nRiCOaBOXm5lJfXx/4v76+PiCn9gXr16/niy++CPwvyzL5+fkDeoxYY458vqGhodfHKSsr47333gsbv8Fg6PO+N2zYwGWXXcbPf/5z/n979+/SvBqGcfw6SzM5OzgXREVxVTAoWBQVKohah0IVXPwBdhOdpZT+C06CIAgiCEIHRxXEwcnFIuIgLgVpscRoe4YDvvRUD9jzkNo+38/YwJ0bcg0XSUinp6eN7v7v2aZ3N4WM1TJ5rcjYP8hZtWbJ2Ffzf3PObNTSJWhgYEAXFxfK5/MqlUrKZrMaGhqqe16hUFA6nZbneSoWizo6OlIymdT9/b0eHh708fGhk5OT/3WOvr6+L+d1dHTIcRxdX19Lko6Pj398nkqlop2dHb28vMj3fR0cHGh0dLSu2U9PT1pZWVEmk9HExITR3b+abXJ3k8hYLVPXioz9Qc6qNUPGvpv/m3Nmo5Z+J6i9vV0bGxuKx+PyfV8zMzPq7e2te97w8LBubm4UjUZVLpe1sLCg/v5+pVIpra2tyfM8ua6rsbGxus/hOM638zKZjLa3t1UsFtXd3a14PP6j2Z2dnVpeXlYsFtP7+7sikYgmJyfrmr27uyvP85RKpT5/m5+fN7L7d7NN7W4SGatlKmdk7A9yVq0ZMvZf839rzmz0V6VSqTR6CQAAgKC19OMwAACA71CCAACAlShBAADASpQgAABgJUoQAACwEiUIAABYiRJkkb29PY2MjOjt7U2SlMvlNDg4qNPT0wZvhlZBxhAEcgZTKEEWicViCoVC2t/f1+PjoxYXF5VMJjU+Pt7o1dAiyBiCQM5gCh9LtMzZ2Zk2NzfV1tameDxe9UXSbDYrx3Hkum4DN0SzI2MIAjmDCS39txmoFQ6HVSqV1NPTU/NJ9kgk0qCt0ErIGIJAzmACj8Ms8vz8rEQiobm5OV1dXSmXy1Udn52d/XzGDtSDjCEI5AymUIIskc/nlUgkFI1GtbW1pampKaXT6c/jvu+rXC4rFAo1cEs0MzKGIJAzmEQJskChUNDS0pJc19Xq6qokaX19XZeXlzo/P5ck3d3dKRwON3JNNDEyhiCQM5jGi9GQJB0eHur19bXm2TpgChlDEMgZfoI7QZAk3d7eqqurq9FroIWRMQSBnOEnuBMEAACsxJ0gAABgJUoQAACwEiUIAABYiRIEAACsRAkCAABWogQBAAArUYIAAICVKEEAAMBKlCAAAGClvwHOhfUHgvYvrAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x432 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots(2,3,sharex='all',sharey='all',figsize=(8,6))\n",
+    "\n",
+    "alpha_list = [1.,0.1,0.01]\n",
+    "nens_list = [2**5,2**7,2**9]\n",
+    "#Reference lines\n",
+    "ax[0,0].plot([0,99,100,199,200,299],[0,0,1,1,0,0],'k--',label='ref')\n",
+    "ax[1,0].plot([0,99,100,199,200,299],[0,0,1,1,0,0],'k--',label='ref')\n",
+    "\n",
+    "x_advected = np.array([0,99,100,199,200,299])-da_const_6['dt']*m_const['u_ref']/m_const['dx']\n",
+    "x_advected[-1] = 299\n",
+    "ax[0,1].plot(x_advected,[0,0,1,1,0,0],'k--',label='ref')\n",
+    "ax[1,1].plot(x_advected,[0,0,1,1,0,0],'k--',label='ref')\n",
+    "x_advected = np.array([0,99,100,199,200,299])-da_const_62['dt']*m_const['u_ref']/m_const['dx']\n",
+    "x_advected[-1] = 299\n",
+    "ax[0,2].plot(x_advected,[0,0,1,1,0,0],'k--',label='ref')\n",
+    "ax[1,2].plot(x_advected,[0,0,1,1,0,0],'k--',label='ref')\n",
+    "#ax[2,1].plot(x_advected,[0,0,1,1,0,0],'k--',label='ref')\n",
+    "\n",
+    "#First all the ensemble ones\n",
+    "n_lines = len(nens_list)\n",
+    "for i in range(n_lines):\n",
+    "    nens = nens_list[i]\n",
+    "    linewidth=2\n",
+    "#     if nens==32: linewidth=4\n",
+    "    ax[1,0].plot(L2_ensemble_limit(quad ,nens,dt=0,alpha=alpha_list[2]),alpha=0.8,          color=plt.cm.cool(i/(n_lines-1)),label=str(nens),linewidth=linewidth)\n",
+    "    ax[1,1].plot(L2_ensemble_limit(quad ,nens,dt=1,alpha=alpha_list[2]),alpha=0.8,          color=plt.cm.cool(i/(n_lines-1)),label=str(nens),linewidth=linewidth)\n",
+    "    ax[1,2].plot(L2_ensemble_limit(quad2,nens,dt=1,alpha=alpha_list[2]),alpha=0.8,          color=plt.cm.cool(i/(n_lines-1)),label=str(nens),linewidth=linewidth)\n",
+    "\n",
+    "\n",
+    "n_lines = len(alpha_list)\n",
+    "for i in range(n_lines):\n",
+    "    linewidth=2\n",
+    "    if alpha_list[i]==0.1: linewidth=4\n",
+    "    ax[0,0].plot(L2_ensemble_limit(quad ,32,dt=0,alpha=alpha_list[i]),alpha=0.8,color=plt.cm.rainbow_r((i)/(n_lines+5)),label=str(alpha_list[i]),linewidth=linewidth)\n",
+    "    ax[0,1].plot(L2_ensemble_limit(quad ,32,dt=1,alpha=alpha_list[i]),alpha=0.8,color=plt.cm.rainbow_r((i)/(n_lines+5)),label=str(alpha_list[i]),linewidth=linewidth)\n",
+    "    ax[0,2].plot(L2_ensemble_limit(quad2,32,dt=1,alpha=alpha_list[i]),alpha=0.8,color=plt.cm.rainbow_r((i)/(n_lines+5)),label=str(alpha_list[i]),linewidth=linewidth)\n",
+    "\n",
+    "ax[0,1].set_ylim(-0.8,1.8)\n",
+    "\n",
+    "ax[0,1].set_xlim(0.,300)\n",
+    "ax[0,1].set_xticks(50*np.arange(0,6));\n",
+    "plt.subplots_adjust(hspace=0.1,wspace=0.1)\n",
+    "\n",
+    "\n",
+    "ax[1,2].legend(title=r'$n_{ens}$',ncol=1,bbox_to_anchor=(1., 0.8),loc='upper left')\n",
+    "ax[0,2].legend(title=r'$\\alpha$' ,ncol=1,bbox_to_anchor=(1., 0.8),loc='upper left')\n",
+    "ax[1,0].set_xlabel(r'$x_i$',size=13)\n",
+    "ax[1,1].set_xlabel(r'$x_i$',size=13)\n",
+    "ax[1,2].set_xlabel(r'$x_i$',size=13)\n",
+    "ax[0,0].set_ylabel(r'$\\frac{\\partial j}{\\partial x_i}$',rotation=0,size=17,labelpad=10)\n",
+    "ax[1,0].set_ylabel(r'$\\frac{\\partial j}{\\partial x_i}$',rotation=0,size=17,labelpad=10)\n",
+    "\n",
+    "ax[0,0].set_title('lead time: 0 s')\n",
+    "ax[0,1].set_title('lead time: '+str(da_const_6['dt']) +' s')\n",
+    "ax[0,2].set_title('lead time: '+str(da_const_62['dt'])+' s')\n",
+    "label_axes_abcd(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def var_ensemble_limit(quad,t,nens,dt=0,response_func = sum_mid_tri):\n",
+    "    \"\"\"\n",
+    "    Looking into the cov(dJ,dX)/var(dX) simplification\n",
+    "    \"\"\"\n",
+    "    X =quad['bg'][:,:nens]\n",
+    "    dX =  X.T - np.mean(X,axis=1)\n",
+    "    dX = dX.T\n",
+    "    J = np.zeros(nens)\n",
+    "    if dt == 0:\n",
+    "        X_J =quad['bg'][:,:nens]\n",
+    "    else:    \n",
+    "        X_J =quad['bf'][:,:nens]\n",
+    "    dX_J =  X_J.T - np.mean(X_J,axis=1)\n",
+    "    dX_J = dX_J.T\n",
+    "    for i in range(nens):\n",
+    "        J[i] = response_func(X_J[:,i])\n",
+    "        #J[i] = sum_triangle(X_J[:,i])\n",
+    "        \n",
+    "    dJ = J-np.mean(J)\n",
+    "    cov_dJdX = np.dot(dJ,dX.T)/(nens-1)\n",
+    "    return cov_dJdX/np.var(dX,axis=1,ddof=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RNyTsqv+Y3x4FTz1MGlD/1/r6wYABpxk2DG6cDJ06V9/Hz4rFCOBeRMQ8COwB/g48B/QBfq7nPEwmEy+88AI/3HwzaTfcwIQJE0hIaIQJTaGwQmFhIQ8++CCTJ09mxowZnD59uqmnpHBBvv32WyZOnEh8fDwzZ84kOTm57hcpqqGEUQVcWRhduCBup8xosGTE+wA9vCG5H5w9KzFItlJUChf3g19biK0jE60mvLyM9Olb3vKjKj4+UuSxqjAKAz4HngW+AiaYnx8CHAMK6zGHffv2kZqaSvclS4hbuZKpU6fywQcf1O+NKBR1kJyczOzZs1m2bBmTJk3isccea+opKVyMo0eP8uqrr/Lf//6XH3/8keuuu4533nmnqafllDhlgUdHkVv3Lk5LagqY/EEfZBZGJmAJ/OkgfDQKPL1h1y6YcINt431/EtwK4No4x81ZpwM/PyiwonRCgfgqz/VH3tZhwNYi9wMGDCA4OJgZX31F+PnzXNi+HX+LD0+hsBOxsbFceeWVAEydOpXnnnuOvLw81bxTYTe2bt3K8OHDadNGio7Mnj27aSfkxCiLUQVcWRilpUFphHzhbQHeB/4F3bbC/U9Ar1Q4fx4uXap9HJD0+X17wRQGo9s7dNr4+UGRjSagrubHxHqMv2HDBubNm0c+0Of667ntttvqOUOFom7c3CqfanU6HR4e6r5UYT/c3d0r9T0rLi7m1KlTTTgj50UJowq4qitNXywxRoXh4obySAE+AW4Az7WwdwR0/BrapcPm3yvXDbLGuiNQkgN9BoK7g/sP+vpBoY3CKAwIQJrB2srmzZsZMmoUnn/8I7379GHdunUYjfaukqRo6Rw7dowjR44AsGTJEgYOHIivryM6/ilaKoMHD2br1q2kpkpMxFdffcU///nPJp6Vc6JuWSrgqhajNHMuekY4RAIsQyok3gt+HrDlBYi9FUZugi9DYN9+GFBDQLW+GLbsgvx2cGsH6/vYEz9fuGRj/KAO6IjUNLKVW2+9lT//5S8UxMfzbmkpY665hrVr12Iymard5SsUDaVz58689dZbnD9/ntatW7Nw4cKmnlKD0Ol1UmHVG+gJuDfxhBRlxMbG8uijj3LXXXcBEB4ezksvvdTEs3JOlDCqgKtajDIy5PFSGHQC2AhcQVmZ56F+8J8n4N0HYch52LoTIsKrj6NpsOZ3yCmBbkMg0MHWIhBXWnExGI0SiF0XbZEYI1vp0qULT//wAw8jjXV7AwsWLGjQXBUKawwePJgff/yxqafReH6Dro90lYqqAK2BecAUlO+hmTB58mQmT57c1NNwetRyroCrWoyys8HXF5J9IDobOI6kcJmJA/YPg8Rh0GsrRHjBmjWQnh5Qto+mSUr/3lNwbhDcfpkqIfr6yWNRUe37WWiDVLGuT3PcLPNjPUsxKRQthwPA/4EhzACLgIVAB+AlpH5GZhPOTaGwM8piVAFXtRhlZ0NAKygCYnchd3xXl29vD/QDXpsP790KN1yAFb3gyOH2GEshPBySkuHcJTgaC1dfAe1sObCGVE70BFo1bO5+ZmFUWAgBAbXvC2IxKgWy6xHYajmnN3CKCoVrYwAWAJFw7v/O0Xp4a3n+euAn4B/AHUhCh4OTMRSKy4GyGFXAFS1GmibCyD1E/u6QAPgBvSrvdzuwuzOcuAk8l8ONfaBjxzRycmDPHsgvgr3XQsYImGfLqvkZmIaUqR4DPEyD7ir9zPGptmamWUoqZdZDGGUhNZ186jMxhaKl8ANwEXgCTP4VbLE6pOT8R0jbgD8B9aiFplA0V5QwqoArWoyKikCvB6PZHNL6GNCDakGTI5FYyv+bB8X+4P46REdncNsf4fq7YdWtcLAX/N1Nqk7XSAnwV+BJRGk8BtwDbEdM7vWpvkhli5EtWNxhufUURsqNplBYwYRksPZHGhVaIxZ4C8gBnkBMtgqFE6OEUQUa2oC0OZNtDqApDgGdCQJOUl7wpwLuwItAfit48T44vxM2nOjI7cAtbnLD+C8qeeCqU4D06ViJBGUuBv6ACKNFSLrYK/WbvyWj2VZhZDby18uVlkUdYk+haKn8DqQgJuXaki16Iu62/cBnl2FeCoUDcWph9PLLL/PEE08AsGXLFuLj4xk7diyvv/56g8YrpH5Bu85ArtkMlhsE4UngWQh0t75vDPAl4DkNEnvA8A+DiMyD+5DmrMNrO1AmIoYszcvuprJV6mpgJrACOXnaiJu7tAYptDH42hInlGtLCpuZLFR8kUJhlaVAOJKhURdjEdPzfxExpVA4KU4rjLZu3cr3338PSIXPp556irfffpuVK1dy8OBBNm7cWO8xNVzPapSXJ601sgKgxwnzF96t5v0jgb+6QdxT0OdSCa/9HeZqdQiHJOAupLLia5Q3L6vKXMSk82793oNfPYo8egFB1N9ipISRQlGFHGAr8nu29T7jEeTuclH9Dxe8IVhS/+9FCStFk+KUwig7O5vXX3+de++9F4D9+/fToUMHoqOj8fDwID4+ntWrVzdo7Bx7TrQZkJ8n2VyZbhB7AjGHW+lkX41ekHpzKqyndtP4cWAOoi7eBq6pZV8/xCS/g3oVG/KtR1sQEO2VY6Mw0lAxRgqFVX5FCsGOrcdr2gKzkeSLffV43U/Q5sM24tM+jMQoarW/RKFwFE4pjP7617/y8MMPExQUBEBqairh4eUVCSMiIkhJadgth6tlpuXlizBKB6LPIyYhGzsRZN6QKSm5bwCfUvlEpQE/IpYid+BDJECzLqYBgUhAp434+dpexwhE5NgafF2ExIuH2D68QtEy+BlJv4+t5+tmIO6317AtNuEcsBAKexTKeeQviLt9az2Pq1DYCaerY/TNN9/Qpk0bhg4dytKlSwEwmUyVmudpmlbpb1spKixkx+nTFBcU2G2+FhISEuw+pi1jX7zQleCQAk5m+xF83Is07wLOJZyzbWA32D11N21T2hK4MJCib4vIuzoPjBCYEIjvSV8KexSS9KckSrNKpVWADYQPDqf1j63xjPO06XPJyAgnNS2UXbuOYcvXamjblhwfH5vGTvX0pLBLFzKSk0nIcby98ODBgw4b25FrzNHjq7HtS2PXmXuuO902dCMjPoO03WmVttnynoPHBtPmgzYkvZ1E7tBabjdLoePzHfEs9iTpT0mc23sOLQpa+fTi7JclBHiftFvXEfX7UNiK0wmjlStXkpaWxuTJk8nJyaGwsJCLFy/iXiHYNi0tjYiIiHqP7evnR9sePRhozwkji3rgQHuPWvfYJiPsToBu3YIpDYHoTAiP8yN8oJV+HzWMfeWQK2EwsAz8P/En7AdzyeuOwAvgP8WfcDfbxiujI7ARQleH0v7NuivCeXlCZib07TMQbxuKDfUG9ubl2fSZH0A8fFd17WrT967X6xt10enTpw/e3t4Nfn1NOHKNOXp8NXZ1mnydfQf4gv+d/sR0iyl72ub3PADYBd1Wd5NEjJqmsgipffQqlAaWMnDgQL4HEsbAwA2+HO8/kD/a4Sqlfh/Vaewac2WcThh9/PHHZf9funQpO3bs4Pnnn2fs2LGcPXuW9u3bs2LFCqZNm9ag8V0pxqigQAo8egaCrhD8s2hYZVodEhQ5BQnIcQOCGzGx1sAECPkuBLKp04/lW6GWkS3CKBQodnOjmLqLNlruZeuYgkLRsvgZafnR1VwkNgvc63O1cEOKut4L/A8p2VGVX5H4xZuBkaDtgsREWFsK7a6BsB/h633wR8fpGYXCKk4njKzh7e3NwoULeeCBB9Dr9cTFxTF+/PgGjeVKMUZ55hS70gCIvGD+shtbst9e6Vt3gO4rHXyLxCnVQlmRxyLbDm8JpM6krE9ujViEcJAN4yoULYJMYDcwB0warF8PiadkU3BIJFcOAJ0t0amDkIy2/wIDzf8snAKeRcy7D4v4On6sDZn5cjPTujMEukHodrg0EKLs9+4UijpxamF00003cdNNNwEwdOhQli9f3qjxfHAtYWRJcS/0g4jjdhJG9qIz5PfPx3+Jv9Q38qp51/q2BbEYs2ypZG7ZRwkjhcLMBiRoejTs2CGiaMCVUFICO3e0YstWuKa27NOKPAEcQaxHfwVGIEHVf0OSQF4BvOD4UUhNDUYbDsnF0O0IuHWALgdFo91g7/eoUNSCU2alOYpAXEsYWYREvh9EXDQLI5u6v14eMidmimtuSe371bctiEXk2OIWtewTaNvQCoXrsw6IgewwOLAfevSEq64SMdSuXSaHDpZbkOrEH6lbFo2IpGFI1llrJAMtEnKyYcsWCA4pZNNAaH0FeGhQGAldD8EJV6u6q2j2OLXFyN4E4FrCqLAQ3N0hyxtapYG7D/ImmwmFPQulnPZ/gfFIiq8VPL3kfdha/dpiMbLlu8xFPhJ7Zb4oFE5NFrALmA0JCeDhAVcNKt/csVMqXl7RbNwIYeEQZIupNQwp97EeSAQ6AdcBHpIgsn49uLmBR98MzruFMycQDK0hKQRaF0DWWfNrFIrLhLIYVSAQ1wq+LiwUa0uGDkLTwT2M2vsdNQWPIEXknqHG5pM6Xf2qX1vO1bYKI+VGUyjM/AqYoPgaOH0aevQoT34AETDXXS+P638Bo9HGcd2BMUjboLGU3ZLvSoC0NBgxAnZEBuCFaKa2beG0vySzaYfs9u4UCptQwqgC/tgWl+IsFBZKE9YMIDIddGFNPSMrxCAm9l3AU0jFRSv41aP6tcViZIvIVcJIoajAOiAajhrBZIKePavvEhgoQiY1FTZukP0qYiiRYOq6SEqCfXtFfEV3hm1BQYxALLhhYZATAh5uEHTStSz5iuaPcqVVIAjXshgVFUFgkFS9Dkun1h5pTcok5Mz3OtJi5GEkSLOCdcvXT2IRbMEL8NI0JYwUivqQCuwCbRYcPQZt2kBIDWmgnTrD1VdLcHZODkTHwKl8SL4EbjkQ7A3dY+HKAdZLbBTkwy/rIDgYhg6VeOw8d/eyNouhoaC5gSES2p+Ck8CVjnnXCkU1lMWoApYYI1dp0VPmSgNaZSC+/ubKH5EgTQ8kOPM+JB7BjJ+v7a40AH+j0aa7zBwaV5JJoXAZVgAmSB8CubliyamNKwbA9ddDdgksTYDfzsLuVvDrVXA0GvYdgK+/huPHKluQ9MWwdi2UlsKYsRJDuAwIMhrLWi2GhIgLvSQC2p+UriEKxeVCWYwqEAgYgGJsbifWbDEaobhYBEVOMfgV0LyFEUidk6+QqrvvIc1p3wZ6S783vV7M9J61pPZbCDAalcVIobAVE6JOBkKiQWKIYjrU/bKMrvBpV3A3wn3ucBXwC3KP06U/3Pg7bNggFqhBg+SctHMH5OfDmDHQqpUYqjYBcdnZeJijud09xJqUHwqh++CQ+qFeVgwGAxcuXKC4uLipp+IQ3N3dCQkJISwsDDe36vYhJYwq4G9+zMP5hZGl6aq3H+jSzV90cxdGIBO9BRiJtBJ4DPhG4hoA8vIgtHXdw9hiMdIQi5E63ypaPLuBi6DdA2fOQJu2UFdHkTVIzkQn4HX38mKqs4E+wCNh8MWN8PgxOLEdVvwo2wMDYcINEmANkrCmA67PzoaY8vYjoaGQ6i8dhEqUL+2ycuHCBQIDA+nYsWOD+o42ZzRNw2AwkJKSwoULF4ipsOYsKFdaBSy1bFwhzsgijAy+EOJMwshCJPACcjv538rCyBZssRgVITfKShgpWjzLgADIvlJihjp1rH33tYgougL4iOoV5gcB7wB5bvD3njDkVhg/HuLj4Q+3lIuiC8BSJMww3GCoNEZwMKT4gSdgPNuod6eoJ8XFxbRu3drlRBGATqfDy8uLdu3aUVBDw3gljCpgEUaukAFhsYAW+TipMALoh6T2fgeB5kJDtgojWyxGFuGkYowULZpcxP81Ac4ky1MdanGjbQcWIKLoDcot7VXpDbyPVOP4sw8UdhBLlKXfdwlSDNsLyeKvSmAQFASCuxu4n6/ne1I0GlcURRWx5kIr23YZ59HssdQ+dAWLkd4sjPKdWRiBBGUXgM8vUmyuPhajugLpLcJJWYwaR042XLwAxhrqUCmaOasRlTJZ3GgREeBfQyHYFE9PngQ6A4uoO+SgK1K/1QeYi1iRTgH7gD8DB5AqHRFWXhsYALhJAHbwOciv37tSKBqMijGqgOVc4BIWI7085vhAcAZ4uOOcppHeQDfQrYPAMfUTRiXUHkivGsg2nmPHYNNGyToKCYHRo22LAVM0I5YB3SG/PaRthKsHW9/NCPynnfQUeg3ws75bNWKAT4BXkS4gH5qfD0Bapo2t4XWB5h9maRhEnYOLQKyNx1QoGoOyGFXApYRRsaS7ZnuLxci9Nc77bY8C9kEotgsjP3PVudp2Vw1kG0dRIWzZLPVuRo8GgwF++sn2elOKZsBR4BgwRaxFAB07Wt/1M+C0jw9PUD2mqC7CgIWIBnsB6R27HMrqFlkjwF/OYYZQiLgAF1XPNMVlQlmMKuCDfCCuIIz0xeDlBcluEJsObs7oRrMwEngf2p6Gc+GgmUBXh8jzN/cqyMW6mR6UxaixHD0qYmj4tRACROXBzoPwiydMvllSrhXNnGVIkM94OLNJ0udDQqrvloik4A/Ky2OMf01RRXXTDtv7WLu5g78/lASDdwmkpwJRDT60ohlSWlrKe++9x3fffUdBQQELFizg0qVLlJaW8qc//anJ5qVOXRXQIRdJVxBGxcXg4yPFHSPSgTZNPaNG0A1oDeGnwRAiNVAC61AzFYVRTVi2OaOHsTmQmAhRURBSAtwJfskwpAROHoK9XWHgVU09Q0WtlCDxRaOg2AuSk6D/FdV3KwWeQ1xnsy9dQhd1+dRJYCDkBogeKjyHEkYuxqJFizh48CDLli1j586d/POf/0Sn0/H111836byc1bniMFylLUhFYRSajnMGXlvQAQMg4JT8mZVd90v8bXCl5SI3y3WUa1FYoSAfMjLM2Ut/Q7qy/w+8n4HoJMhaDIXWM2EVzYUNyA/kRjh3TuLErKXpfwQcBp4Egm3uGmsfAgIhzUd+p6UqM82lyM/P53//+x9///vfCQwMpH///iQmJnLjjTcSEFBD9P9lwiHC6PXXX2fu3LmOGNrhBOEajWT1einQllsKQVk4tzACuBK8MsE3B7Iy697dVouRcqM1jEsp8tjhBLANeAgJlJ8OPldDj18gYVvTzU9hAz8i9cKugjOnxW0VVuU8sRPJKpsIjL7sE5Q4oyx30HmDp6pl5FJs27aNjh07Eh0dDUi17cDAQO64444mnlkjXWklJSV4eVXvz3DnnXfWWiOgOROM1BR0doqLpXKsKRPcwfmF0RXgpoN2qZCVVffufmZhVJfFSAmjhpGaAu46CF6C5G5PM2/Qgdd9EDIX8n+EgsE1p34rmpBUYBtoc2GdERIuQFQsZLuBpW/sVuBxpPL0Y000zYAA0HRQ3AZ8Lkj5DdeurtNySElJISKiPAJ0yZIlREZGNrm1CBphMYqPj6dv374MHTqUJUuWVNoWFBTULN5cQ3ClGCMvH3BPNwsjZ0+h7gp4Q1S2jcLIZEJH7d+laiDbcFJTJahfdwa4i8pnkiHg0xk67oaDB5tmfoo6WAlo8J94ePUCpJbC5x1hDHCb+d8DSGjiW9RcxNHRWER1aRi0SnINa75CiIqK4ujRo6SmprJv3z6WLVtGRkYGJSUlTT21hguj9957jwMHDrBgwQL+9re/sX//fgByc3OJjY3loAPPiG+99RYTJ05k4sSJvPLKKwBs2bKF+Ph4xo4dy+uvv97gsYNx/hij0lL5ZzIXd3QJi5EbEAth6RLbUlpHMUE3pJK5cqXZH6MR0tOhyz5kXV1XZQc38JgC7VIgcQc0g/Ocoio/Q0Zf+KQ9jDwD/bzhtTZwH1IWIxSYD/yPmrM6LwcBZkVmbAVhSXCptoqtLQkXKF1w7bXXcs0113DDDTfwyCOP8NZbb9GzZ09mzZrV1FNruCutrbnZzcSJE1m6dCnr1q2jX79+HDlyBA8PD7p162a3SVZky5Yt/P7773z//ffodDruuusuVqxYwauvvsrixYtp06YN8+bNY+PGjcTFxdV7/ECgEMnEcNaUPUvV61JvCDntxFWvq9IDgr4FUymkpUn9nNoIpG5XWg87Tq+lkJMt8V6tjiK9HKz9UEaD79sQfgCOHoF+/S/vHBW1cAE4BusehmAjRJ2FjjHQz1268DSn6FCLxUgLAp9CSMuF7i3ZzKsBTwPrEOX6xyadTaPw8vIqM2xY+Pjjj5toNpVpkMXo0KFD3HzzzVx//fW8//77hISEkJ2dDcCRI0fo1KkT3nW1Zm4g4eHhPPHEE3h5eeHp6UmXLl04c+YMHTp0IDo6Gg8PD+Lj41m9enWDxrf85pzZZGu5Q9d7V7AYhTbljOxED/AqBf9siXGpi7oyDJXFqGFkZUGH/eDhBkypYafO4NkNup0Xd5rJBe5wXYZfxODwxXUwKgkMeujUuaknZR0vL/D0hFKzQMpLatr5NDm/Ih18A5CeLC3983AQ9TaKGI1G5s+fz/Tp07n99tt55ZVX2LBhAzNnzgTg6NGj9OjhuPvwipaoM2fOsGrVKu644w7Cw8PLno+IiCAlxYYrZxUOHjxISlAQhW3bsjkxkTZ29AEkJCTYbay6xs7J8aWwsAPHUzLwT4qiwKOEo/uP2WVse1Of8b0N3nQq7kRIsol9+wspNV6odf/i1FTOuLmRcLZ6OotBpyMjNpastDQSMjLqPe+G8t+zZ4ktKqJVXb7ABnC5vqszZ8Loe7AdmZ2zOJ98DpLlQlv1LiusWxgh30WQc9rE2jUXCY+o+Xbjcv4+nGXsxlBbKEPHJR3JbefOiSBPRqwpRK/3ITX1BBkZtvmpLvfnaTB04nyRke6mUM7uSSehsPbffX3GtieX43OJfi8aL18vzj5+lq6PdCXj3xmk3ZzmsOO2VOotjPbs2UNycjJz5szBy8uLRx99lG+++YbYWOlic/ToUeLj4+0+0aqcOHGCefPm8dhjj+Hu7s4ZSz17QNO0BnUG7tOnD3pvb/yAmL596WenuSYkJDBw4EA7jVb32GfPwskTENDVH+1bCOro1aDjO3LeDRq/L/AyxHrBb6WBXNE/ssbqygkJCXSJiOAEMLBqDjJS38kP6Ovvz8CaeiBYQa/XNyp+bnmHDvh6e/MldTfgrA+X87sqPARheRB6azg5A8N5GWkKGogYkO5F6s4QDNpa6JsJaUXdufJKafFwOefurGM3dp316dPHutU+CUiFYw+Bny+0yvcnphdcffWVNo3bFJ9nSgoYA8DbDUKNkQwcGGm3se3FZflcCoGzwG0QMi4ErgH/c/7EDIxp0LiNXWOuTL1daampqcTExJSl6W/YsAEfHx+GDx+OwWDg5MmT9OzZ0+4TrUhCQgKzZ8/mL3/5C1OnTiUqKoq0tHLVnJaWVikNsD5YXCtO7UozN5DN9xJXmqcrxBeBXG3bQ0SBtKJIqsOMXFtNKouL7XKHK4xDQjxWXObj2hPvzdKU+OwouBN5PzOBwcCnwLNIKARdQNceeqZITFjKpaabs8LMBvPD9dDlEpQWQedOTTqjOgnwh2wjGAOB5KaeTROyBwl+HWr+ezhwHNeoL9PMqLcwio2NJS0tjczMTC5evMiLL77I/fffT0BAAKdOncJgMDjUlZacnMyf//xnXn31VSZOnAhA//79OX36NGfPnsVoNLJixQpGjBjRoPEtwsiZM9MsHsA8bwjNMDeQdRU6QUC6xB2cPlP7rpasNGsOgqYSRn9COoT/dJmPay+MpRC6B/Q94MVwMTl/iqR2/wPJavoZ2AxScGYUhJ4Efw32H2iyaSssbAE6wZa20D0R3N3BXF+v2eIfAEVFoI8Cr5YcU3MIuWL3Mf893Py4pWmm48rU25XWpUsXHnjgAaZPn46bmxvz5s1jzpw5gAReR0REEBrquEjfDz/8EL1ez8KFC8ueu/XWW1m4cCEPPPAAer2euLg4xo8f36DxLRdKZ65lpDcLoxxPEUYukZFmoTO4/Q6douHUSRgyRAI0rRGE3GAVU91t1VR90tyA64G3gUycLyY+9wgEp0DSLbAbeILK6dwzge+ALzGft0eBbjEMKoFNZyAzUwqPKmpmC3AJ6GDvgYuB3aCfDilGabHToSN41vD7aS5YUvZLw8E/CYyYE0paGoeATkgMAOb/hwD7qTkJQtEgGpSRPnPmzLJg64ocOHCA/v0dm5e7YMECFixYYHXb8uXLGz1+oPnRmYVRiV4sKvp8yeJyNWGEEfoEw/FEOH4c+vSxvqvlu8yjujCyWIyaIittkPlxPzCyCY7fGPSr5KK0drwkxkyqst0DuAl4BzgHxPQBQqHLOdjaG3btgrFjL++cnY2PkIKK1c+wjWQ3UAJJw6DVefAohu6OqapiVyy1grVWELYb0jWIbGnlrzVEGFV0hOiQuMsWYIl94403WLNmDTqdjunTpzNnzhyWLFnC4sWL0el09OnTh+eff95qJ46GYJe+HcXFxRw6dIi1a9c22IXVXHBDTvjO7ErT68WKYnSV4o4VMacVh+VBZBTs3QOGGpIHLaLHmsi1fL8h9p2dTfQAPBFh5Gy4b4LsNvBDDxgP+FjZ50bz488gP6iR4LED+vWUnlypKiaiRkqQ659DghE2A95w8kqIPA4BvtC+vSMOZF/KhFEIeBVDug2V712OZCAb6FXl+T7AaZw7KLYOduzYwbZt21i+fDnfffcdixcvJjExkQ8//JCvvvqK5cuXYzKZ+OKLL+x2TLsIo48++oh7772X6667jqlTp9pjyCYlEMhv6kk0gpISs3vJFYVRB+ROKRGGDIbCQtiz1/qutQmjXOSzsWdmmK14AT2RTC6nIhW8jsO5AaB3l/YR1ghH+slusjwxEiiEvoXg6wubfwdN1TWyyknAgIOE0VZgEJwxQeg56NkV3JzAJ+VvdqVpZoGU3RKD+E+ZH7tXed5iLT98Gedymbn66qv59NNP8fDwICMjA6PRiLe3N88++ywBAQHodDq6d+9OUl3ZOPXALsWd77vvPu677z57DNUsqKticnNHXwJe3tInzWWqXlvwAdoCiWIx6t4d9u2Fzp2rdwavLcPQ0ietqSzy/YCvcbIK6+sl+PrAcAlzqM1pPgJxp2UAra8C/MFzMwy7DX75BQ4drtkF2pKxVBvrDKTbc+CLiG/zFrhwCryM0KvqRbaZ4uEJPj5QahZxBZeobjlxdU6bH6tmEFoSwI8iaaEO4CdgmWOGZjIw0Yb9PD09+fe//81HH33E+PHjadu2Le3atQMgMzOTzz//nH/84x92m5ddLEauRgBObjHSg4cX+GS4SAPZqnRC6nkAQ4eKFWLjBjAZK+9WW4ZhUzeQ7Y64Tc434Rzqi2kdZIXCnh4whNoF3bXmx80gfsPhwCZJDY+Ohh3bIfPy1dV0Go4j8UV2709mrj2oDYKiQ+AWBq2d6Lzg7w/5PnI+07dEi9Fp5AY3sMrzQUAUcOKyz+iy8+CDD7J161aSk5P5+uuvAUhJSWHWrFlMmzaNwYPtpwyd5mb1cuLs5TJKSsCrldQwwo/yLAZXIQbYBWjg7QPDh8PatbB3H1xZoU5dxeDrquTQtO1ALDd+p6l+E9gccc9xx5gAZ3vDpVAJsK6Nboge34o55mgUsAZ0+yBuJCz9Dn7+GaY4v+fdrhxDRLPd71j3AsGQ7ANaFgTGWS+22VwJCICcXDD5Iil7LY1Eaj5RdEMUtYOYiG1WHUdx6tQpSkpK6NmzJ76+vowdO5Zjx45x6tQp7rrrLmbMmMHcufbt8FenMLJUtLYXx441rDXF5SQAJ3el6cHHXNxRcyU3moVoJPU4DYiAjp2gSxfYs1tcaiEhsps/4iqrKcYo6vLM1iodzY+na9upGRGYEEipAY7HQkEoDKtjfx2yz0bM6dXDkOCqteB3JVw/Gn5aAatXQZs2TnSFdiAm5MZ/siMG3wv0hwOHoMgHunZ1xEEch38AXLoERVHg0dKEkYacKKqmgFrohphmSzCXnHctLly4wL///W++/PJLAH755RduvPFG7rzzTubPn8+UKVPsfsw6hZEzCBl748wxRpomFiODuYGszhWFkaXAy3nKfA7DhkkrlH17xSIBctdd03eZjRRabCp8KQuVcgoCdwRS2BrOtYWYQAmwrothwI9IllU/P+A6YA3wCLRpI+Jo3c+QltaBXr3Ls49aKueBIqrH1zaaTOAcFI+HU2fgUn+43sl8BQH+csNXEg7eLUwYeWR5SDuQjjXs0A1R1WdwwOJpeuLi4ti/fz9TpkzB3d2dsWPHkp2dTXp6Oh9//DEff/wxANdddx0PPfSQXY5Zr59HSUmJ3eoENGcCgQKsN8Zs7hgMZnHkBWHp4OHY7ixNg6VS71nA3J7I1w9iY+HoUbjq6vJdLdWvq5JLLa60Y8C/gCuBeTgsQrsjTmIxSgb/w/4cGwmpoTDcxh/FYOT3swUJNiceWI2YkcZAp05S02j5ci+WfgeDrpLv0P0yZUqlpkBiolhPwmxReg7Gcgtq94w0c/rjMR8oLYbkXs6Xj2ERzaWtwf+kGFFaip3RM9VT/lNTSzRLLarjuKQwAnjggQd44IEHKj03e/Zshx3P5ut+fHw8ffv2ZejQoSxZssRhE2oOBCA/vMKmnkgDsPRJKzZbjLyc7QxoC5GIybhK5HLv3mA0wqlT5c8FUV0YlSCeOKvB1yZgAVIM77/Ab/aZsjU6ITd5zT5z/UdAg53doKA1xNn4siAkm7isY8FViIXvx/J9OnSEKwacoVUr+P03+Gwx/LIOEnbB4cNwOlFcKKWldns3AGRnwYoVsH+/POY3AxPxceRO1e4xZ/vA6AF7iiCoG+httPg1J/wttYyCITATcvVNO5/LiVeq2RjRroYdopHzYQsIwL5c2CyM3nvvPQ4cOMCCBQv429/+xv79Up5u1apV9OnTh4sXL5bt+8ILLzB69GjS0+2acHrZqC1ot7ljaQdSYgTvIvBxRWHkBrRH0o8rENJK2k2crmCGseZKq7VP2k7EjPM3oA3wlR3mWwPtEJGW6bhDNB4TsBxyexZx0RfcQsszhG1hGHAEyAL53iYjSuls+T5+fiVMiocJEyA6RgpAJiSIUPr5Z1i+DBZ/Crt3V888bCjbt4ObG0yeImJ69277jNsYjiFp+p72HngvZESCQQeeA+QpZzstBFSpZZSV0nRzudx4pnrKb6dNDTu4A11RwsiO2CyM2rZti5eXFxMnTmTIkCGsW7cOgPHjx9O9e3feeecdQHqZ/fTTT/z3v/8lrGphGSfBEurgjMLIYjHS8uX34uacX0HdxFBNGIG4Z1IuQUmJ+GOsWYxq7ZP2C5LFNxqYgGS/Oag/jOUGsFn3xfwNuATnry6iAOgVWj/38jDE+rrN8sTNyN3t55X30+lEFF13Hdz2R7jzLrj9Dpg2DcaNg3btYddOWLNWaik1hvQ0iUfrfwVERkLXbnDyZHnz5aZAQ4SR3ePeisF0CBIDJDEhN0S+P2drV+fvL2vEaD4557WgOCOvVC/JFKkt8KUrUh1UYRdsOscdOnSIm2++meuvv57333+fkJAQsrOzAdDpdDzyyCN8//33vP/++7z11lu89957dOzYEYBt27bx4YcfOmr+DsFiMXLGWkaWk7sp30VrGFmIAS5QzQ8VEyMxVjnZUqMgiJotRlZjjLYiwTFeSO0dEyKOHIDlBrDZCiMN+BhoC9t6emLSwbX1FNo9gFZIWBEgV+SJwApqNZW5u8vFsHWYuNvGjoVrr4Xz5+D33+U7bih7johrqZe5SGDPHuKqO9WEF5Z05OOwuzA6BIV5kNoOBlwpiZytcb7YSTd3qVdmMPegKXLmeir1xDPVs2Y3moWuyAJq1uZn56HO34fRaGT+/PmMHj2aZcuWceHCBTZs2EDrCtXBhg8fTt++fVm0aBGLFi2iX79+ZduGDBnCnXfe6ZjZOwiLxcgZhZHFlablumDV64rEIL0Tqtw5tg6TBro5ueXCKAe5xluo0ZWWhhSwMgd00xMRSA5qatbW/NhshdHvwEFgFpzRB2JoBUPrmXvhhvRU24hkAgJwB1Lyu573Sz17SZ2qY8cqu0vrw5oSWHESfusCL3pLKYHwCGjVSqxGTcVR86O9A6+LtkJRMbQeKW7mdJz3lBAQAHpP0NzA0NIsRnX1tLOUX1BWI7tQpzDas2cPycnJzJkzh4CAAB599FEKCwsr1TfaunUrR48eRdO0au6ze++91+lS/p05xsjiSnPLccE+aRWxZGhUcae5uYl7JCenXBgZkTRoCzUKI0u/IUu7AU9EHDlIGPkgBpRmKYxKgDeAGEiPB32uD9GRDYt/mYxo2BWWJzoAU4FvseoOrY0rB0rrly2ba24eXBOJwLunwN8Ag3rASmAx4qLp0kWCvAua6G7oGJJlZe+korR1kBcOA8wR8+k4X+C1BX9/EXl5YaC1FGFUCO557rZZjEAJIztRpzBKTU0lJiamLE1/w4YN+Pj4MHz4cACOHj3KAw88wIIFCxg9ejSvvfZapdefPn2aTp2cobZvOc7sStNXEEY6T5q2vLMjqUEYgdTIKSzwRl9sXeRaQoaqfTSHkF9ERX9GPyR62EHxJ21ppsLov0jK3P/B4nzwKIFrIhs2VFfECPcpFTI95wHewCtUNufVgZsbXDNcmgcfPFS/ebwJRB2B3qHwcKSUVXofaSPWpYu45041UWGpo0hykT2L1Kckgcdh8L4a/MzBy85uMcrPh6JIcG8prrQL5se6LEah5n9KGNmFOoVRbGwsaWlpZGZmcvHiRV588UXuv/9+AgICuHjxInfffTezZ89m+vTpPPDAA2zevJnt27cDkJ+fj5eXl9PVPnLq4OsScSV5Z4ChNa5b7KM1chWxIozCzUUf0zPKxU/F+OlMxEPmW/WFh5GruHeF5/oi5g4HnXDaIhfmZsVvSGzRjZA+DH6/CAFGI/1ryoqxgT8jn/u7lidCgQeAbRDyS0i9xoqMhJgOsH9f+Y1AXRwDdqdDdBr07ylWokeRE+DbQHAIhIc3XZzRUezrRtM0OPA9eBsgcpw8V4p8B84sjEpLW1iRR8vJoS6LEbh8AHZ+fj6TJk3iwgVRi0uWLGHSpEnEx8fz5JNPUmIOsD106BDTpk3jxhtvZN68eeTm1j97pk5h1KVLFx544AGmT5/OrFmzmDdvHnfeeSfZ2dncddddjBo1ivvvvx+A7t27M378+DKr0YkTJ+jWrVttwzdL3JGLpjMKI30JeHhDQAaYnPUMaAs6rKbsQ3lzzIx06xajDOTiUE0zHqV6LrqDTdSRSGhTI2KJ7csh4GmgB2iPwT+B4PMQ4VVEYCOsj/2QhLQvEA8aANOBoRD5RWS5G9NGBg0SUXTwgG37fwtEH4Uwd+hm/k7DgT8ixbiPAp27QFoa5FjrOuxAcpBQOXsKo3PnQNsnliKPQfKcpWev07rSzHesxhDwTcEJCoDZAVstRiDnqlO45Oeyb98+brvtNs6cOQOIJ+rDDz/kq6++Yvny5ZhMJr744gsAXnzxRR588EGWL19Op06dGpT8ZVPl65kzZzJz5sxKz4WEhLBq1apq+y5atKjs/ydOnKB7d+csxemsbUFK9IC5T1pZhWhXpQPlUasV8PUFL+9SMjLKM78q3jNYhFElcpDo4M5Vnm+PWJAcJIzCES9dDhDimEPYzibgScQa9y9Y4gPrS+GmZGgTnEcNBQ5s5mHkBngh0topXgf9ngePKaXwCPAJNjewCwuDDh3g4EHo1w88azFKFwPrDBB3Arp1lsbDFmYC3yFutpc7w/ZtkHhKMrguF46oeL1vL/RNB59oyn4Elqpyznq/ZKllZAwGDKBlumjLo4pcBKO/sfwOrza6AnpETNVUJbsBHD8Ox6ycZ+1BbA+wRSJ8/fXXPPvsszz22GMAeHl58eyzzxJgLonevXt3kpIkKMFkMlFQUABAUVERwcH1P285tGPO8ePHGTp0qCMPUY0ff/yRd955h9LSUmbNmsXtt9/eoHECcI4YI71eT35++UxzcqFY0xGS7o/bFbIwjMbKVfF0Oh3+/nKWsbbdzc0NPz+JdigsLMRkMtVru7u7O76+4qgqKChAq5JbXXF7o4hG6g6VUm0lB/gXk5ERXHaxya6wLZ3ydmtlWAoOVt3gBnTBYcLI7PUjFccLI5PJRGFh9XruXl5eeP3ghfGfRoq6FcHLsNEPXs2HEWchRO9Nq5h8jEYjRUVF1V7v7e2Np6cnpaWlFBcXV9vu4+ODh4cHOoOBv+v1fAV8iTmF3wsi3nDj5Yeh6C4Di/+lp7Sd/P78kc9nrK8v3a30CbniCli2TNrA9O1XbXMZGwDf0xBSAj2qqI8A4E7gNeBQIERFSXba5RRGFqOXPYVRQQF0yAHdYMpMo2nmbbZajKydGyp+//Y+txQWFlJYWFjjdg0wGNwxBfmiAamnC/D3cdC5pblwAQwRBtv2rWjdtqMwag68+OKLlf5u164d7dqJfzEzM5PPP/+cf/zjHwA88cQTzJ07l5deeglfX1++/vrreh/PocJowYIFjhy+GikpKbz++ussXboULy8vbr31VgYPHkzXBrSSdhZh9Pbbb/Prr79Wes4vIJRlgQfwDJMeM1Ute9HR0WzbJiX35syZw2+/Ve570bNnz7ICnn/4wx/Ys2dPpe2DBg1i2bJlAEycOJHjx49X2j5y5Eg+/1wq+I0aNapSVXTLa95///2GvN3KxCBm4ySqnQj8A4rJzIAQs2iqWIM9nfKM/DLOmB+rKSbkhPN746drDYswSsfxbY4uXLhg9UblxetfZPbh2Rzrf4wxq8fAkPJtS4Cbp7/OoKu6sHv3bqudrD/44ANuuOEGfvvtN+64445q27/88ktGjBjB6tWruffee6ttv+KDD9j27g3ob1/KD9c9Un389esZGRvLk1Q2KEVGSaD9/v1Sk8i9hrPZj0CXIxAVDFFW4qSmI0LtTeDxLrB1M2RmSnr75WAvor3tmSfRwQ98c4D+5c/V12L0xBNP8O2331Z6LigoiCNHjgBNc27p2mUkDz0g55Yb7hzFpQwHnVuaCxehJMLGzI/OyI3cSSSzwE50726bVacpSElJ4a677mLatGkMHjyY4uJinn76aT755BP69evHxx9/zOOPP17vNeFkPZZrZ8uWLQwZMoSQkBAAxo0bx+rVq8tioOri0UcfJS9PHGgHgI7PPQcdOrB+/XoWL15cbf+FCxcSGRnJqlWrrKrSRYsWERwczMaNG3nrrbeqbX/nnXfw8fHhyy+/ZO3atZW26XQ6PvroIwA+/vhjNm3aVGm7r68vb7/9NiNGjCjLEATYswc8fH3gd2kHcvPNNzNo0KBKrw0MLLfL3n777YwcObLS9tAKV4Q777yTlJTK9fejosovT/feey9ZWVmVtkdHl/vwHnzwwUoWLYDOncVftWjRIjw8Ki/BwYMHl1087733XvRVomvj4uLKmgfO+XiORJPeT1khy/Hjx3PLLbfg6ZHLl1/OYcNGyPMSL81mYOLkyeROmYJvVhZzHqlwEU4EsuGWA7cwPno8ycnJPPXUU7LtAuK7vwNm3z27zHzbUObNm1cWKFiMvIVvR45k2KxZmEwmq3W/LO+rsLCQP//5z9W29+vXj4EDB5KZmclf/vKXattvvfVWhg4dyjPPPFP+pBFYDYP2DYIZEDErkvFXPcNmpMHt+FLYnwBDhvTHzS2ftm3bV369mR5mM0yXLl2sbrd83z179rS+3cODsT3g6Mv98XryGamtcD3QU/7rHR7OR7/+Stynn9KXyl6F++9/ieTkNnz8ySo2b67+G5wydy4Hs6DTuh/45uIy1qypvP3tt9/G19eXvl99xVdr1vCoEUouwKpV0mLG0rn7k08+YePGjZVeW1hYWNY38t133y1LOrEQHBxcFlrw+uuvl7VRshAZGclLCxeyHwheuJA5FcqavPTSS9XeS33oZtID3nyZ9CVr58i55QzSmuVRnY6PbTi3TJ48mZ49KwfdpaWllf3f3ueWCxcuVBrP2rnl7JloDObUvRlxD+LT2/q5Zf78+eRUCBbLzs5m3Lhxtp9b5syhKpbfYFFREffdd1+lbdnZ2cyaNYspU6aQlZXFI49UF/i33HIL48dXObdUYNasWYwcOZLTp0/zt7/9TUxk+6EorAjfOb7MmzePIUOGcOTIEV555ZVqr3/ooYe4ov0V7Nm6h39v/Xe17Y899hg9e/Zk69atvP/++4wfP75BRoPmwqlTp7jrrruYMWMGc+fOBcRL5e3tXVZL8ZZbbuGNN96o99guJYxSU1MJDy83FEdERFQ7GdXGmTNnyMyU0qH5np4kp6eTkJ7OgQMHOHGieiOaPXv2EB4eXuP23bt3ExQUREFBQY3bvb29OXToULXtOp2OhIQEAKvbfX19SUhIYODAyraPUkN33HVGTJuMXMg8RVhMmNXWLJax27ZtS9u2bWvcHhMTQ0xMdbusZXtNPyzL9qon1orbMzIyqmUMBAYGlr32xIkT1U5e4eHhZdtPJp/E2+BN6dlSSjOlT0T79u3p2rUrPr4msrLPU1hkoNTfgzRNg5ISdh0+TOGYMWSfOlXpM/VK80Kn6Th4+CDhkeGkpKSUbXcvcsfT4EnJsRKOHTtW7TOvDwcPHuTEiRNlwgjA4OPD8TNnSEhIwGQyWV0r0dHRJCQkUFhYaHV7165dSUhIICsry+r2AwcOEBYWxlVXXQWAW5Eb7V9rj1+iHyl/TGHnqAT+VxLF1hkzGJ6by91JSSSdaw2GcNq0lWqKSUlJZa+vSFZWVtl3Ym17SkpK2QXQ2nYwr5dWMOSVIbT7Tzv8fvYjy5RFyh0pcPo06YcP8/G5c+zU6YgsKcHL7GJJSdmLzs2L3QkpVt/3Lj8/Wu8pwZifzqWUk6SkVna97NmzB29vb3QHD+J29ixH3NxonWsiO1tHWrq+1t+gt7d32fbDhw9X2x4cHFy2/ejRo9W25+TksOzgQdI6dcK3yno8dOgQrVq1svpZ2ULhnkvkmXQcSiufd4anJ0Y3N06WlNh0bgkODrb6fVleGxZm33OL5Vi1nVu8vGJIS8ukwCeYQX7j8L2qevZFQkICx48fr3ZuOXz4sM3nFmtryXJuKS4utrr94MGDREdHk52dXeP28PDK55aKHDhwgMDAQM6dO8eJEyfQlerwLvHGoDdgPGFk//79eHp6cuLECauv37dvH1EBUaQdTuOEp/XthYWFZderLl26OK0wys/P584772T+/PmVrNgdOnTg0qVLJCYm0rlzZ3755Rf69u1b/wNoLsTbb7+tvf7662V/L1myRHvmmWfqfF1xcbG2a9curbi4uOy5hZqmXW+nee3atctOI9U+tsmoae+/p2lfvatphwdqmumw/cZ2BI0e36Rp2khN016qvmnHjgTt/fc0bcd2TXtA07QZ5ucPapo2UNO0TVVfME3TtP+r4TjnzS9aZn2t2EJtrxujadoL9RqtOvX6LHM0TbtD07SrNU1bo2lGTdOe0OQt/luTv0v0mvbp/zRt5U8NGL+eVBu7VNO0180TukvTtAx5OknTtEmapo3TNC2twu6nTmrae+9q2qlT1ceefD5de/wDTdu4oe55JGmynO5O1LR3axiv1nnXhVHTtC81TbtH07RnNE07r2mfafI2k6vs2uh1dk+xpv258rYHNPnaG8NlXQdW+GWdpn36uaat+YOmHX7EvmM3BoeMv13TtIGadvh/9TiRv6dp2iBN0wrr3jUzo+Y1dvhwIy8eDmLUqFHa+fPntY8//ljr3bu3duONN5b9W7RokaZpmrZhwwYtPj5emzRpkjZr1izt3LlzNY5X0/t0KYtRVFQUu3aVN7ZKS0sjIiKillfUTACSlabhPKWASgxSv8StQEoOuHzGhg6JLTpffZObm0ZwMGRlSUyF5f7JkrJcqYWcEXGXjazhOG0R2+rZGrY3knAk+PqyUAzMR+IQ/gXacHgR+Bl4CJhh3m3nbigqgkYYxxqOu3mOPYC/IS1E3oA23SRIeg7wOFITyRNpHBwcDHv3yP915h/sRaD0XADBxtqDsy20AZ4D/tIB2gVKxlvnqlmKDcUI/AWJVYtFsim3wsHF0DHK5mQ82zkLTKv8VDoOOM5lxj8A9ImQFQUxrl7LyBw+ZXPwNUA35KJ1mvIK/lZIS4WVK6FPA4wpTcn69esBmD17dpnbsypxcXHExcU16jjO1kuwVoYNG8bWrVvJzMykqKiItWvXMmLEiAaN5Y+cy+qxJJscSzsQ9zxw0+F8LbQbQgw1tpVo1UqEUTgiiEzUEIB6EclssxZ4DfIricZhwiiC8owhh2JE0vEPIGpouNQVWoZkZllE0fnzEtAcGwsRDax2bRfGI4UmQSplH5Xz/l+BfcDr5k06N+jfH9LT4eKF8pevLIQOhz2I7SBrwRbigLlusLsPHE2GVHsp1jcQUfQo8BnwCZj0cM1TMNQRRaw04IrKTzlz1WsLQYFgMoE+FLxcvfr1RcADDK3rcRWyeMaqe9IqcfKkVJFXWMelPprIyEgefvhhZs6cyZQpU5g0aVKlhrb1wVKa3xky0yxYwlY886CkFeZmaS5ONJCC1ZYdrVpBbi60LhVRlEnZuabyBcIirGoSRlCrAGssl8VipCFi6DfE3HId7EGu19cBllyxrEz4ZZ1kZA0b5uhJ2UAs8AHyg7wfuABjkcKMXwOrzbt16ya9tLZuhVIDGDVI2Ar+JSauG2J96Jq4F+jQA877wJpdde5eNz8hCvQ24BbE0tkJDv8fdN4P49fb4RhVcQP6lP/p7FWvLQSZU/eMQeCRS4UeMy7IBcRaXZ+rdDukCWMt5UU0EyQmSmkKhXVcShgBxMfHs2LFCtasWcPdd9/d4HEseUfO9LvTW4RRDuid/QxoKx2Qi/6F6ptatRLXYmC2/J2GCKNq5xpLs7Layu53QFx2DqgqG4HUWXJQOzbhHWA5cBcwXTxqzyPuo2eRa3VREaxeDe7uMG5c7UUTLyvtkL4dJsTflw8PIgaRF5CEQncPiIuD7GxYswY+2wi+JyGqUwbmJFWbcQNe8IL8/rD/PBxtjMvmECJIB5nnXoGvJ0FqZ+j5PvYvfd6NSj1vMs2Pzn5aCDLX6jMGiNgjpba9nZyL2NYKpCI21F1LSZU6VzG13Qi2cFxOGNkLc5FVCpp0FvXD4krzzgGjs58BbcVSGcCKm8viPvHOlsdLiH6qdq65hDRPq83d0gHxqzrA51WxlpFD+AL4COloP0+eeg/5LJ7B7DYuhbVrpDnruPEQYEul3ctJDPAqIk5fAg9NKmj7IUWz04D20XDtCHF/HT0OuX3hmvCGdaILAh7vDXpf+HSb3GXXmwzEdRaGTLZCRKce2OAGqX8Et1OIb9Ce9K78Z32LOzZXAvxFuJt8zMLIld1pDRFGIO60E9Qotk8nymfYviFjtxCUMKoBiyvNmYSRxWLkm+XifdIqYsn2tRKAHRwsgbje5tvlk4h+qlZ8IAmJSq3t12B5kRXLVGOxXKwc4k5biUQsX4fEF+mkLdnnwE2IIUPTYOMmSEmBUaOggfkKjudKRNitBVaK3vgXYg35E3Id6dEDPGbC+tlwyzDwsJY5oUfireqghyf0GgxFKbDseN37V6IQ6YGSgwi6kMqbN5t36TQOMU9/U8/x66JP5T9dRRjp3CAwEAxe8hWaXDUAO9f8ryHipTtigq7hJu7CBSl06uHZ0Mm5PkoY1YCzWoyMJgjMagEZaRYCkCBzK/E/7h4ijgqyxGX0K1IwsFrrhWTqTtexmJ2Tat2rQVRsC2JXViJpVoMQn5ObGL2eR0TFg+bdDhyAkydg0FXSSLVZMwcYALwMJEFfYBFyDfgD0vvsDQ+41gvGVH1tInAfcC0wFDE1JdZ+uLndwBgJW7aLNc0mCoH/QzLP/oHVkuYrkWV7pS8wCWltk1l9vwZTpXyYs/dJq0hQMJTowOQGha4qjCyGTluax1bFYi08VH1TYYEkpChrUe0oYVQDlhgjpxJGJeBZLPEiHq5wBrSVaOrMTOsJWG76q2WxJlPebbYmWiGLwgHCyHIXb1cv3XIkeOhKJH3LHC/0MVLE+0nk7SQnS+PUTp1gwAB7TsBBuCEp/CBiT5O3uASYjKTv3w28RJUyG/uBWcgimIUEQu9FSgH8UvPhvNxgxAjQG+CrjWJdq5WzwFxgF/L5W0mKTUX69cZjzo+4CfELra6+b4Pxr/xnGvLRuUKianAQkA8ZkaB3dWHUUIuRB1aFkaU7U7uGCK4mZMaMGUycOJHJkyczefJk9u0T37PBYGDWrFmVqs6vW7eOyZMnc+ONN3LfffdVqoBuK0oY1YCzutJ8zD0evZzdZl4f6kjZz82F66QwNu2RlkJllCB36nUJI0vNpIaFrNRKIJJIYrc40qWIeBiMmFPMQbiHgP8CExCjicEAGzeIayJuZHn9n2ZPGySQeQfwgzwVBTwGfIh42yrFjWcibq1wpCnan81/f4so5icQIVkDU0IhYwicPgeHDtawUykSx3UbonzeBCZa3/V7JPyjrMxQZ0StL8f+Qdhm0hFR5Aon/KAgcDNAVhiUurowaoiA8ULEUQ3CyMcHWjuRQtY0jTNnzrBs2bKyf/379ycxMZEZM2ZU6reXn5/Pc889x/vvv8/y5cuJjY3lzTffrPcxXeF34hCc0ZWm14Ovubm5T3ONE3EE0ciZ34qrw5KZNihbvBqvUsWSYDmx1iWMQNxpDhBGOuxYy+hrxFwyHIkt8pGn85BA6whEQADs2imiMS4OvJpLBpqtTEVchK9T/h1aQ0O++CLky694wxAK/AcRkH8H1lR7NSA339f2hnMdYNM2K7WNTiBWqLeRQkjfmMe0QikijIYi2ZFl3IgEwR2z8iI7kIbzxxdZCAqW7yQ3BDRXFkatKL9Dry+9EWFUIWlA00QYtW0nsVrOQmKi+Lvnzp3LjTfeyGeffQbAt99+y1133UX//uWdkg0GA88++yyRkVKALTY2luTk+kfou1Tla3vig6hGZxJGJXrwNluMAlzlLGgLlvifC1SL57BkpmVnwxhr7kXLb6aN+N8B/Pyt7AdiMdrRiHnWgl1qGX2OCIWRiBgwB1fmIQaSJCRrPxBxLx48CD17Qpvq7ayaP25IpcdbkHT4f2O1RH3QtiAJLnuQKqZCMz6IYHoQUY4+iLipwlQdfDISei6VOk83Wdxf/zX/CwL+CYyqfdqbEA1frYXoWETILsdKEFzjcYWq1xaCg8QFmRcI7inIxd+JLvQ2YTV9th70RgT6WaCTPJWdLWn67dohNwwJgLcNY/2EVIF1BJOp0bJqITc3t6wBtsFgYObMmXTq1InHHpNbvP/9739l+7Zq1YoxYyS6sLi4mPfff58ZM2ZYHbc2XG052Q0dItadShiVgHcBaDoIal33/i5DLSn7lsy0rJoCW83C6GQ+fP45fPGFVIWt9TgOIJJGWIw0CPs+TETRaCQ13CyKtiIVrQ8iRhFLGNGOHeDhIQHXTktb4AHkTf5oZXsGRP4vUiK076hlHB/E5Whxq22vvksE0NcHDoyWi8v2L6Djcx2lL8l1iKWuDlEE4r2LRAx6lQhCBO1qHFLQyhWqXlsICARPd9D7gdGIA+tcNCH2EEYgVe7NWOKL2rdDYuCeb8T4l5EBAwbwyiuvEBgYSGhoKNOnT2fjxo21viYvL4977rmHHj16MHXq1HofU1mMasEf5yrwWFICwQWQHwo+LaHqtQWLYLGSsm/JTMvKquG1yVIledMRsZyUlsJvmyC6PXj7VNm3Wp6//bBYjOp981sKvAxhS8MkLesZwF3Cal5G4opjkOv3FeaXJCfD2TNw1VXg62tlTGdiOrAOsbYMoTzFzwT8HdxK3CQzr64P1Q+JC5qHZKu9RbmKNDMWeDUEFpyB0C9BC/aVegHX2TbVRMTgeF9N04lHShH8hhXl1HBcpeq1BTc3828611zL6BLl37srUIq8pwnWN58/Jyn3XbrU0rKnAxAM7EbctEi7nKAgCAxC2tPYevWfSJ1WHUeya9cuDAYDQ4cOBSTmyMOj5smnpqZy5513MmTIEJ56qppt1iaUxagW/HGuliB6PXjnQUGE8zS+tQu+yImxjsw0qyRDpgfoPGHUdVIg0GCAY9ZiPToid/YOIAI5H2bX50X7gNuB7yF9crpkQbnL07cgbpv7gK8oF0WaBju2S/uMvk7WQNIqbogYNABPI3cyGlLB8ndIvS219lYvFQlCYo6ikODunzBfeYFMGP8lvDoVWq+BzEGw7HYDxnq0YvwSiYu9qaYdBiMLwZr1qxG4StXrirRqBUY3czkqV4szuoQIeysWozOnYdUqKbGxfDlcqum9uyExeDsBDUxGSEoyu9FACmk5ye8/Ly+PV155Bb1eT35+Pt9//32Zu6wqRqORe++9lwkTJvD000+ja2BGiRJGteCPc7nS9HrwzofilhRfZKGWlP0Qc2aasbT6tpJzkOEJffqIWGjdGsLC4PRpKwP5AZ/acc4VqFfKfg6Sqn4notxfg/Tp6aATMfQnJI7ocyRzvGJc9elEKeQ4cJALFXiLRkThPuBm5AP4ELgRskbXpIhrIBQJxOpkHnMUcAMwDgL+Bbo28Mp7EPga5Ll7cKKW1gsVyUJ01iSq1Xosxw25M99ifoGdcJXijhUJaQUlHlCq4XrVr2vISDMZYcsWOUfdcYdkk/6yDgw1uV6vQlJdL0jCgMEA7dsj58kzwEDHTN/ejBo1iri4OKZMmcK0adOYNm0aA2qoLbJ+/XoOHz7MmjVrylL7n3766XofU7nSasGZhJGxVBa+bw4YXOkMaCsxSJCtFULNmWnZ2dC6ym1z/kkoCoFeFVoodOwkGVuFBVYCsR10K2GxiKcifVOtoiFX10VIVdwZSNEePyABfgYWILG7b1D9Amwshe3bpUFsrJWig07NWKA1EgidjjScnYl0yq0vEUjBp81IvFEBIr6uheSusEEHf9LAP0DPgf3+xMbWXergOyR06La6jh1vPvavWC0M2RAswsiVLEahrUDzhHw/XM9iVEMNo7NnIT8frrlGzkujRsEPP8C+/TBokJVxrjY/boELnWWNtm0HfGZ+/hqcRlTOnz+f+fPnW922ePHisv+PGTOGo0ePNvp4ymJUC84UfF1cDG6l4FUAppYojKIRP1Ru9U2h5kD0tCpBmoYiMCaBb2exFlno2FEez9ZggXIEdbYFyUDcO88hIvBz89/mdN7fg4J4GuiHGDxCrAxx8BDk5cHgIc6VrmszA5E3/w0wm8ad3dyQYk//h1iO5gLdYKRO3NQbdNC2bSZZWZBaRwGqAsSNdg1lCUI1E4N8iVYCwBuKZdm70mkhpJXc1WeFgNHVhNEFJHmiyhd28pTEBEabYx0jIqVS/f59khBQjRikoezPEpMUEQHeXkhZigHIjYTCKq54erQbAThPjFGxHnzywagDzZUCEW3FEkdiJQA7JEROKElVqlYn7gCMEHVF5edbtZL9a/TfN4YaCvi1Rn6MVoVRAhI0tAspQvRfpHs6EmOxCHivbVsGIVnr1kqf5GRDwi7pqB3twOw6VycMCc1YD4SF5eHuTp3utM8R7+c8Ww/yPLVn0dUTV6p6bSE4CDzcJGXf4CRWD5u5gGRcVrg6G0rg3Fno3FmCzy1cfRWYTLBndw1jjQXTHsg/ZXaj7UGyAGoI7FYIShjVgjNlpRUXS3yR0a2FtQOxYLnYW7Hy6HSScZacVN7SQdPg3DZJWQ/uWX3/yChIcYQwSrD+tDsijqrFGK1D3EKtkCvsHyj71aYA9yKW8dFZWRWLXFeiRA8//ywdta+1Y7ZTS2UkUocxy9edjh0h8ZTEf1jjNOIZG4uVVjQ1EY1dA2Ndqeq1BTd3CAiGAj8XtRhViS9KSpLSBJ2qmByDgqVx8tGjEkdZjfGSrdxpl1kYfYIshibMMnMGXOm3Ynf8KE9yae7oi8ErX2oYebuSzdxW2iM+DisWI4C2bcXcbGmbk5wEpefFMqSzUuAwKkpONIX29qV+To0Lqg1VWrF9jTQ164UEE1c4KW5B4lWOIXHYs1JSKrfBMJOdDT/+KI+jR4N/gJWdFPXCUq4oISCALl3kpiTJitUiBzHw+SEeuabClapeVyS0FRR5gikf5zHt14UJqcdWJZMyKUlubCKtpOcPuFJu5hKs3XS1g4t9oXsCRHyInDjuwLbCji0YJYwqYqr8p7/5qeKmmEs9KS4G71zQ3MC/JbrSvBBlUUNcUEyMnDxOnRRrUcJuCC4Cb2+slgSOMp+AUuzWwMzMKaS1uhXaY9Z1lnTzV5AmpG9TqUzAOqSSdRRiLRpvZSy9HrZugW+/kYDNceOdr3FkcyUa6ArsCgykfTR4ekq2X0UKkWLaF5GC2E3pxnKl4o4VaRMGxV5gcKXMtDRAj1VhFBUlddmq4u8PvfvAyROQmVF5m74YNl8NuhjQfY+cT253zNRdCSWMKlLFj2G5uXaGAOxiPfjkQrGfuft0S6SWlP2AADElHzoEuxPEYtQ1AHStsXr31Lq1+PKr9cVqLD2QooBWxo0G0kuh9GXgA6Qw2yuV57cXaSfRx7yLtZqTp07B10uk5Uf3WPjDH1Rckb25Djju50eWh4juM2dAM99YlQKPAkeQIuRXNtUkzaTjmhajmDDICpWirDX97p0OS/X+CsKopMSdjAxzRlkNXHGF3OT99lv5OgQ4fASKfYEvgBXIuaclFf9tIE4njBISEpg+fTqTJ09m1qxZXDTXOc/NzeWee+5hwoQJ3H777aSlNaDBwoXKf1qCWJ1CGBWDfx6kt4XWLaq6YwVikBNkDa6qqweLnz4hAdq0gfBSamwg5e4h4qghy6hWHkSunI9QLYOuaxI8eS8Yv0VSzc1VrC1kI6KoLZKOX7WSgGaSOie/rBMhOHUqjBgBvg1tRKmokQnIMlsGdOoMRUWQbI51eQ1JKluA3KA3Ja5W9boibcIgt7VZGFmrO+aMWBFGOTnyA25XS09DHx8YMlQs3BaXWkkJHNgvN0Vhkci5rqVeG+qJ0wmjRx99lBdeeIFly5YRHx/PCy+8AMCiRYsYNGgQq1at4uabb+bFF1+s/+BVspYsFx5nEEZ6izBq41rZJ/UiGok1yLa+uXVrmDYNxo6FG24A3SXE/VYD4REijDRTzfvUm/bAS4hL7Q4kjmg98DIMngYdjsHxFxEBVeUk9m/kIreQcmumBWOpjjVr4eAB6NMXJk+BMFc0EzQTooE+BQUsBSKjJYj/dCL8gHylt1PWiaFJccWq1xZ8fMHQCnKCkEwrV+Asckde4QvLyfbH07Pu33O3bhAbC7t3w4YNsGa1uNSt1jhyMYxGI3feeSfjxo1j+/bG17pwKmFUUlLCQw89RI8e0n46NjaW5GRxLm/YsIH4+HgAJk2axKZNmzAYDPU7gBMLo+Ji8MuF3DbSE7NFYrnLqsWsHhQsBRzd3ZHCcLUIo4hwKZqZnWPHOYL0wXoPUTevIBG6y8BtPDy6FPaPq/6Sk0iniFuo3ny9IB/27e/A+XNS/G3YsMopvQrHMDEjg1Tga0+5K997GhaapGXbg009OTOuWPW6Im4RkBmMa1mMYqh0U5Sd7UebtnX/pnU6uPZa6NcPThyH9HSIGyk3eK5OSkoKx44dY82aNQwePLjR4zlV5WsvLy8mT54MgMlk4q233mL06NGANI4LD5efv4eHBwEBAWRmZhJpLYy/BjL2Z5CcUB7Fd8bbm8JOndh94QJu+Y1Le0iwmjJgHxISEkg53ZE+hSFkhBSTkND4yp8Vx3Yk9hzfM9uTLoVdSF6fDCNqH9s9x51uWd1IKUohK8F6/4WCAi8KCzuzdWsykZH2UUcHDx4s/+MR8EzzxK3QjZKoEjQfjRzvbmy5mEePKkWU/hkdjebry5UnT5JgKjdh5WT7cuRIe0wmL3r0PEqxvsB6doodcPQadrax+wC9Ll3i9YAAxgQVYTwVStSJfG4uTGSvyZ5mxvpjWWcJAQEUtm9PypkzJBTbJ42kOX1XRT6dSQsMIvdgMSd2Hq31Vt8ZzmVd9nWhqGsRSQlyl67Xe1BU1JUSfSIJNZynquLpBf2v0KHTQV6eZpfzwfTp06s9N2nSJGbPnk1RUREzZsyotv3mm2/mlltuITMzk3vuuafa9hkzZpRdz2tj+/bt/POf/8RkMtGuXTv8/Pw4ceIERqORu+++m0mTJjFv3jyys7O56aabWLp0acPeZAWarTBatWoV//jHPyo917lzZz755BNKSkp44oknKC0tZd4862XTNE3DrZ63za2LW9N2YLkjNxKxakbHxjaqrUxCQgIDBzqmMY1l7IsbAA/w7uxvt2M5ct4OGb8UeB66enclgTrGPgz4QedrOtfYM0gzSRZbUFBXLEPp9frK4qae9OnTB2/vmnNlBwAFQUEMbFceabkDsRg9BMSZewSVGmD3HkhMlN5ubdoeYMQIx3WFvBxr2BnHfisqiieB368MYPRB+HNGMHHDrPdxqg/2WmenkXPYyN697eJOa27f1a/pkBkOXkf9GRg1sLyemR3Grg92Gb8EKIKQISG0GSim7GNHAQoYPrxzWQV/e9HYNXY5OXPmDL/++ivvvfceERERvPzyy+Tn53PrrbfSv39/3nnnHWbOnGkXUQTNWBhNmDCBCROql+csKCjgT3/6EyEhIbzzzjt4ekonzIiICNLT04mKiqK0tJSCggJCQkLqd1BLqqT5umVxpTX3EhmaBm4pYHKjVteQy+OBxPCcQzqV14bFbVpD8DVI24ywcEizd2ZaLfREOlqUIm/HhARaRyFuNE2TBrfbt0l7j+7dxXV24GBNnSQVjiQI+A9g9IJ17SE1EbRm1HLFFateVyQ8FC62gRID+JymRmHkFJxHIvorBF5fvAienkZaNfEX+O2339a4zdfXt9btoaGhtW63hU6dOhEYGMiWLVsoLi7mu+++A6CwsJATJ07Qvbt9mz82W2FUE48++igdOnTg+eefr2QRiouL44cffuDee+9l5cqVDBo0qEw01YvzSJESnCfGSK8H3wwodQOPWlI6WwS1pOxXwuIxrUNIRoTDgQPSgNVaDZGGoGni/8/IkCy54GAIDwNvH2mT9Tli0OqHtDU6BvwNyEqBbVsl8yQ0FCbFS+FKRdPjjrRrOHsWkpOtp1bri2HvPjh/TuJFOnSQeBBPa5U57YQrVr2uSGs3+K0f6D8GLRF0TZ0G2BgsGWnmGhyaSYRRq1YF6HQttQaL4OMjkbMmk4l//vOf9O4tXb/T09MJDg4mxc4F55xKGB0+fJhffvmFrl27MnXqVEAsRR988AEPPfQQTzzxBBMnTiQwMJBXX321YQc5S5kw8kQ+oObeFqSwEAIyIS9I+ny1aGKA3dRdrjwZUb6Bte8WHiG9iDIz7RPEePGi9CzLqhIuoNNJk8ewaAiKhk1h0NkN3jBBvxTwOADLToOfn6Tgx8Y2H6uEQujUGbZtg337qgujjHRYvVp+q+3ai9BOSIATJ+D66x0XIOuqVa8tRABJPSDfD/wOVC9h4VRUSdXPzJIyEO2jm/ut+eVjyJAhfPnll7zwwgukpqYyZcoUvvrqq3qHzdSFUwmjXr16cezYMavbQkJCePfddxt/kLPl/9Uh/vnmviwLC8A/Ay7EQFRLr1MRDRSBR3YdSzsJsMG6FmG+qqSm2ufitWmjtCGJixNrj7u7iKTkZDh/Hg4nwDW74KQPvOQL3Qugawkke8OVA6G/gy0Miobj4QF9+sDOnXDxQnml8bNnYf0v4OUFU6aUr6NLybB+PSxbJjVoevcSsWssFUuiPUhHYiVdlfZAZgykR0HwHicXRmeQNH1z3bHzZst3SEhzvwJdPu6//36ee+45Jk2ahNFo5NFHHyUmJoYLFy7U/eJ64FTCyOG0ppobJoDmH2NUWAgBGZA0DK5q6sk0Nea7La9LdaiHJKyXja6Cf4AIGXsVehw4SC6AbhUKN/r5y0V00FVyh7j9Anx0ETIMMKgNTIiCjh2l9YSiedO3r1iBfv1VxG/yJdi3V2pojRsv7RssRLWRulq//gpbNsOhg1JOIjUFIiLFgthY0pHsOVclCtD5Qlov6LwCTHngVocVuNlykjJvBUgF+8hI8PYubbIpNQcGDx5cloIfEBBg1RvUvn171q9fb7djKmFUkfZUKxTmT/N3pRVnQGA+nO/WPIrKNSnm4MtahZGGNLEaWvdwOl15oUd70L17ZVFUFV9fGNkN+nWTdafamzkXHp4wegys/AlWrZLnunWD4ddaF7bePjBuHJxKhOPHxPobEwM9e4nbtTGUAlm4ZnFHC27Ib+TMCLhqGSSvh3Z1Z4A3P4xILSbznW12lsQgDh0mFawVlxcljCoSA2xFUoHMLktncKUZEwEdpHZ23ewTm4kCPMEzpRbzSiaSfWhjoHpEuJi1S/R2mJ+NhKK+S2clNBRuuQUuXYLAQAipI+5P5wZdu8o/C3p944VRJnIP4MrCCOS0vXMU3OIOF3+GtvFOGH93HknX7yJ/HjwoAfpdOsMR+5WlU9iIsy0fxxIDFCEVkc340/yFkS4RTO5g6KK+UNyA6DosRpYLjo0ZXW3aSCZZYy9UipaDpxdEx9QtihyJq1e9thADHGsN3l3B56iICmfBaIS8XDBaQme7yt9Hj0JsD3GzNxWaVlcGi3NjqqUIa4u/jlbCUj/iVPlTzhBj5HVCOih7Kr+LYKswstFiFBkpgbPnzzd6ZgrFZSPd/NgSLEYlgDEO2qfAjq0S/N7cSUqCLz6HL7+EbZ9BTj4c1Uv2oocHDGh8ndAG4+PjQ0ZGhkuKI03TKCkp4eLFi/j7W1eeypVWEUsw7ingWvmvM1iMvE/D2c4QrWSuEANe67wquUQrYSnuaKPFyM0d2rUTYXR149vwKBSXhZZiMeplfjx+FQxcCu0LRVwMGQK9ejVPt1pxEfy8VspvDBoEgb9CdgBs2i5xhmPGQkDVTtGXkfbt23PhwgXS7BVc2czw8PAgODiYsDDrtw1KGFXED4lRqWIxas7CyKTX4Z8EiUOhc1NPprkQAzqDTlyi1sTPRSQDsebOHNXo2FEqTtu5jphC4TDSce2q1xa6IqfuzYPgKh1c5wc/t4XNm+H4cSmiaTQ2rzomu3ZJg+rrR0tMGsWgjYRbb5PMRfdaEjQuB56ennTq1KlpJ9GENEMt3cR0ppIw8geKkQyP5ojuRCA6IyT2VsKoDHMAIydr2H4Rm91oFjp1Bm9v6Z2mUDgDabh21WsL7kiV+O2hQF/w3AQTJsDIkZLR9csvsG1rd1avhqNHoKiJ04wNJVLSoVt3sygqBC6CLhaCgppeFClc/zdTf7oiaZPmAmsWa2ZzTdn33ROIUQf7h0FsU0+muWDJ7qlJxNhY3LEiHh4SDKnijBTOgqtXva7IFcjPvWA0cAx0F6B7LPzhDzBxEkS1ySYrEzZtgs8+g1/XS/23piDxtFiLelhO2EeQ9MFetbxIcVlRwqgCmoZYGwyAOXivuTeSDdzvz6U24BdTZ3eLloMfGMIMcMLKthIghQYVCLqivxTqUyicgXRcP/DaQhyiLdaORloW/CjP69wkPrBLlxRuvU0KavbtC4mJ8N23DmoQfRz4O/A2kFd9c+IpsQxFWEqSHzI/KmHUbFDCqAJr1sA5T3ObLbM7rVkLo3TwP+vN8R5whU9TT6Z5oY/WW7cYnUGCshvgd/QxB0UqFM5AOi3HYtQNcad9EAklI4FvkdIrFdDpoHWYtF+56SaxAv/0E2Rl2nEiJ4C5wM/AJ8A9VHI3mIxS36p9e5kPIB2j2wItvc9lM0IJowoYS2HtceldVbBXnrO40pplAPZ3YNDc2D0EhjX1XJoZxTHF0veuatXY0+ZHFZClcGGMSNVrVw+8rsh8IAN4bQYYc4HF1ffJBjYBR0JhzI0Sz7N2rbi2Go0GvIpEgi8FFiE32AvLd0lLl2O1rZgUcghlLWpmKGFUgRtugFETIK81nF4jHbEtwqjZWYwKQPsWTnaGlJ5KGFVF314vlqHTVTYkUlYEUqFwVfKQ63RLEkb9gCeApf3gm/GQ/hGkH5TkmUN+fjwDTAAeAR4EZgdAp9GQkwMJCXaYQIL531zEhzkMmAOsBPbILknmUiFtLMIo2fyvvx2Or7AbShhVQOcGXbpAm+sg9JK41rzNbSCalcXIBCyEwkz4baiOvmGgGq5XRh9j/uKqutNOI6JIfWAKFybb/NjSQuKmAv8Ftv0FDkXC6QfhgU2wMDqG38zbPwL+jVz8nm8L0T3gwH4RSI3ieyAIuKnCc3OASOBfgEmEUWio1CoCYKf58epGHlthV5QwsoJXfwjXgfESnNohzzUbi1ESMA+KVsFnN0FaG41pLSXCsh6URJZInaIjVTacAlpueQ5FC8FyjW+JYSv9gEWtoMM7UrX+H4/AjzebWPcaPLYG+p2DYSYRR3pg/SDpS7a7EVYjXbEONgBjqHzT5QPcBxwF4xpIuVTBWgSwC/mSlGu/WaGEkTV6gacHDPCBM0fAN6sZWIw04CfQboXkY/D087DtemhjKCGmJdnLbcUd6AvsrfBcHhJ31LMpJqRQXD6yzY8tzWJUkY5tIeZ/EPYUBPkW4/Et8DRi0ZkEHd+Gu3LhV38I6QWnTkFBA0/0gXsCRWVNsLJxAtAVDG+AUV8hvsgIbAOuQjLpFM0GJYys0R1wg+5GEUidEprYYmQE/gHGZ2Frd5jzFfhOhAlJEBpQiLuqX26dK5DUWUtWyAHzY78mmY1CcdnINj+2RItRJbyAm+Dc0+ck6voLYAFyjv8Ebp8KQ7fAjt5gMkkByIbgv98fgrF+bnEDHgDjOei8F9q2MT+/HcgEVKZrs0MJI2v4Ap3B6yT06g3hiZBvpR7FZUEDXgHTUvh6NjzyPtzTFp4uhOw0CA1tNk6+5scVSDyWRRDtQ1Z876aakEJxechB+j2p2mYV8EAE0RQkY+wLcI+Ap+dD4XYIi5au9iZjPcfVwP+gPwym5ivqMEiPhj7bwdvS1H0FEpN0Tb3ficLBOK0wOnz4MH369Cn7Ozc3l3vuuYcJEyZw++23N775XS/gMPTuKV6ZwsONG67B/AJ8B5tnwr/uh2fdxBJ8/pxsVsKoFvohJ8Nt5r/3IidGv6aakEJxechGMtKUh6YWugIfQcAVMO8ZMOjElVbv6vaJ4JHtAUNq3qW0FBKGgb8eSd8/AKwDbgQ8GzZ9heNwSmFUVFTE3//+dwwVik8sWrSIQYMGsWrVKm6++WZefPHFxh2kH5ANAZmg7wimo7K4LyuFwCuQ1xMe/bP8hiYgFbqPHoXAQPD311/mSTkRfki2xzqkwMkeaj15KRSuQjYtK1W/wfhCwL/AEAWx/4ZAo8Qa1QvLjddgeTh/DnbuhJzs8l0uXIT0KDDchaTvzwEizI+KZodTCqOFCxcya9asSs9t2LCB+Ph4ACZNmsSmTZsqCad6M9D8uAtKe4OpGM5UrYnjaL4GMuH9x8HLHR4yP30pWbq89+tXoXqqwjo3InVCZiFutRuadjoKxeUgGyWMbCYQzr8C7lkwcCecPQul9bl0bIOSNiUQKe0+Vq2CPbvhhx+kWDDA2TPg5QXB84GXgXlI3YBg+74VhX1wurDdX375heLiYsaPH1/p+dTUVMLDpQC+h4cHAQEBZGZmEhkZaW0Yqxw8eLD8Dw26enel8KdCsoa74endis1bssnJbVgX0YR6VhDTGXR0facr6bElfNjJl8ln0zmZno6xVMeePZ0wGt0oKDyFu3v9x64Pjhzb0eMnJCRAMMR0jMHvsB9Z12eRkpUiRdiaiEprzM44/XelxrYbF3JziTQYSEhOtvvYzvp51ja2t4cH39/Sgzs/0OEVauSXX84TFm5DYGkpxP4WS0FcASd3nmbnji54eZcSG5vE/n0dWPKViX79z3LgYCdahRSwZ18ShABXAufN/xo5d4X9abbCaNWqVfzjH/+o9Fznzp3Jz8/nk08+qfP1mqbh5lY/g1ifPn3w9vYuf+J6CN4WTOdIuNQLSvZG0KtnBL71jFFJSEhg4MCBde9YkXWAEX56AAL94VF/f7xad2DtGqm5ceON0KbtlQ0b25HzbibjVxp7CZAE/u39aa9rQPfYCuj1+kaJm2przE64zHelxgYav86MQUH08fZmYKXeE43HWT9PW8b+NhZSfoFhW+DUuO7YNJW9gAdc7HWRVq2uxMMDxo2D6OjWdO8OK36EQwdD8PKE60cHERXVpq4RGzT3htDYNebKNFtX2oQJE9i0aVOlfxMnTiQ7O5vbb7+dyZMnAzB58mTy8/OJiIggPT0dgNLSUgoKCggJCWncJAYCmdDuDCR3k9iek9YakzqC5WCKhA+vhpGAIQW+Xwq5uTBufJUiYYracUeqXSu3o6KFYECl6teXa33gf/dBaCYY19qYnbYL0EFhj0JOJ4K/P7RvJ5siI2HMGIiKgmHXyKPCOWi2wsgaN998M+vWrWPZsmUsW7YMgGXLlhEQEEBcXBw//PADACtXrmTQoEF4ejYy3H+QPHTaBpmtIDwcTpxo3JA2kQJshdPxkO0GI1Nh5U/g6QmTp0BMzGWYg0KhcGpacnHHhjAS2DIeMrtCt/WQnGTDi3YC3cHgq3H+PHToKK2lLMR0gPgboUICtcIJcCphVBsPPfQQe/fuZeLEiXzxxRf89a9/bfyg7YDO0OlXuQPr2BXS0yErs/FD18paQIPlkyBIDxk/g4+P/MBaqdtAhUJhA+pUUT9igA5usOxBCMyE3CV1vECPpN1fBdnZ/pSWQseODp+m4jLQbGOMbOHYsWNl/w8JCeHdd9+1/0FGQdjHEJgFEV1Atw1OnoKrHJnysQG0WPixPQzfDEUFMHmymGkVCoXCFpTFqP7EAZ+NhWmdIOgrYD41XyX3AyXAIMhJ9sPdHdood5lL4DIWI4cxCjxMMOA3MPlLn5tTJyXeyCFkAvsheSQU50PwEYjtARG2J9cpFAqFshg1gJGAUQdH5oJXGhTUZjXaiVxBB0Buri9h4aj2TC6CEkZ1EQulbeCqX6RfWteuEgDd2MLaNfIboMGWOGh3EPw1GHCFg46lUChcFiWM6k9vxNL262TIbAvG95A4Cmv8BvSDUm/Iz/MpC642IJ2cFM6LEkZ1oYPCcdBvKxSlQqdO4O7uwOy0DUBbWNcZOh2HLh0gMMhBx1IoFC5JAE4eJ9FEuAEjgM1BcH4SmJKBZVZ2vAicAEZBWipomo6oSPgMaX32Z8TLpnBOlDCyAdMU0JnA+0fw8oboGLM7zVTnS+tHIbAdDCOlX09QEcTG2vkYCoXC5VHWooYzEjkVZ4yGlEgw/pfqKucX8+MouHRJ/lsSBf9Ggrh3AF9cltkqHIESRjYQ2B4ODobWXwMl4k4rKoIkW9I568M2Gf9oHISchmAfaB9t52MoFAqXRxmZG85VgC9wsgMcGgGGi1RWOSZgKdJPsy1cSgE/Pz0rfKRk2vtIS8Yl5l1rooSGWZU0IKsBr1PYjhJGNhAMrJgN7hnAD1JHyNPTAe60DXKwzX0h9Bz0iJEq1wqFQlEflMWo4XgBQ4HfIyA/FlJ7IWrH0itzE3ABuE28BqkpEBhUxDrz60KBSUAaks1vjR3AWPO/XfWc3xfAGESbKRyDuuzagDtwfhAkXwF8AB5FEmt0+jQYS+10kFIkmG8EHE6HAD107WCnsRUKRYtC9SZtHCOBNDfwiYEt14Dmh3TxXg/8E+gAXAdZ2aDXQ0G4kRRgtPn11wKelHvcKlIILECCvFsBz2K75UgD/mf+/+eoIG9HoYSRjYTo4Jf/Q9pWvynutJISONewnrLV2Q3kgWkkZJwBX3do37i2XgqFooWiLEaNYzhycTwfA7nekP4MUAA8BhQBLwLu5fFFiW3dcDO/DsAfGIw4AaqKl/8hVVn+BjyONDr42cZ5nTK/NhY4a36twv4oYWQjwcCpHsAMYCm0PQC+vnZ0p/0KeEPi1RB0FsLbgaeXncZWKBQtChVj1DiCkOyy9e2lxUeiD/AD8B/ge6CH7HfpklwHjoR504PKn/u1QBIiYCyUAN8Co5DSAIOBtli3LFnD4pq73fx4rKYdFY1CCSMbCUGMRfwJuBLcnof++XDurFiOGkUJsAaIg11F4JMLfZQbTaFQNJCQpp6ACxAPpHiDWxs5zxOIKBmzn1LTpJ9aRFs46edraa1ZxjDz4+YKz60DcoCbzX/rEAG2E9vcaSeRwPCR5tcer+d7UtiGEkY2EowsaDyB14Du0OMjCD8qqfuNYgOQC0yBY2clpmmAahSrUCgaSEhTT8AFuBY57yfGQFaWFPatSF4uFBRAcRsw6nTVhFEboDPwe4XnvkPS+SvuOxTxzu2zYU6ngC6AHxCOlFNS2B8ljGwkBLPFCKR62n/AsxcMXwbpXzWiRYgJ+ARpWDsIMs+CdxgEBDRywgqFosUS0tQTcAE8gQnA5o5gBE4nVt5uKddytq1cSK+wMsZwJHy0ADiMiJ/pVL7wDkJuhnfaMKeLgKWCSxSQbMNrFPVHCSMbCUZUfZm5Mwh074Bbf4j9FLK+bODAKxF76L2Qqge3VIhUbjSFQtEIVFaafYgH8oOgOLJ6POn5C+DnB3tCoHNREX5WXj8SEVVrkaDrQGBylX38ECvQ4TrmYkKCrduY/26LEkaOQgkjGwkxP2ZXfNIf/D+CrI7g/ndss4UC6JEouk+BfyCFwsbB1nOABn2VMFIoFI3Ap6kn4CLEIqfn/V0hIwPS0+V5gwHOn4N2HeGQDnoWFlp9fV/zGC8iAda3IBlrVekJHKH29Ps0RByZW7LRBhFK9m7AoFDCyGZCzY+ZVZ73DIKcpyHLG4yPUHv+5G7gfiAOmIPUj+8N/Atwg+NnocQPrm5t58krFIoWha6pJ+BC3Aoc6QqFnrDPfPN77iyUloK+iwiTXgUFVl+rA55DxNH1wNwajtETiWG9VMs8LNsqCiMjIpgU9kUJIxsJNz+mWtnWczBsuxnyM4CHkQpeFTEiIugeMJ6Eoqmg/RNxo70HtILiIsg+C3QBf/WtKBQKRbPgOiDUB873gsRTkJEOBw5IHOixKGnW262oqMbXd0OKMb6MVNW2Rk/z45Fa5mFxm7Wp8mjvzlQKJYxsJsL8aE0YBQZBm2tg0wQwHkeqdll+J5kQszAG7VNIGgafTobFIfBDFmR7UnZrd+wEFJsguoeD34hCoVAobMYDSa/f3B9MvvDdd5CaCoOugt/doD/g3eDsG6ErcimoLcHZIowsFqO2VZ5X2A8ljGykNfJhWRNGAF0HwLFOsGMasB24DSltOh18T/lycgas6A3tu8I110BeHixbJnUwDCWwbT9kR8KVoTUcQKFQKBRNwjTA2xdOxkPPnjBiBLh3l/Zpo+t6sQ14I0LnTC37XEKC6n3Nf0eaH1X1a/vj0dQTqC+pqaksWLCA1NRUfHx8ePXVV2nfvj25ubn83//9H+fPnyc0NJRFixYRHh5e94A24gaEYd0HfAaYFwytesKFI3Dp7zB1CVLA4grY1Sud48YAuneHuJGg00FMB1i1En76Cfz9IbsQEkfL3YdCoVAomg9BSNODd0PglhFS+Po/yHXhesr7yzaGjtQujJIptxaBBNj7UT3uVdF4nM5i9NhjjzFq1Ch++OEHJk+ezKuvvgrAokWLGDRoEKtWreLmm2/mxRdftPux2yNNlSuiB55AAvAevgqCvWFVLux8D1gLyY/Cvvww2rWDa0eIKAIIDITJkyG2B/gHQM5o8I8qvwtQKBQKRfPhDqQ443NIHs03wAjKE3MaS0dEGNWUZXaJ8rgiC62BDDsdX1GOUwmjzMxMjh49yq233grAtGnTmD9/PgAbNmwgPj4egEmTJrFp0yYMBoNdjx8DVO0Z+wXiF34eGOwDM0ZBSBZ8vgISdsPqVeDjY2D0aHB3r/xabx+49lqIvxF2d7ZeIEyhUCgUTY8P8Hcke+weRMA8YMfxOyJ18qx5JTTz81FVng9FCSNH4FSutPPnz9O2bVsWLlzIrl27CA8P55lnngHExWZxnXl4eBAQEEBmZiaRkXXbYDRz4FxJHU3PugC/IQsxAMgHfgTGAwMR61F0JAwbBdu3w7Z90DkKgkPOgs4Pvd76uEnIwh9kHqO+6Gsa2A44cmxHj++IsS1rRKtnsKWta6wxqO/KdcZuqeusuY/dFVgM7ELO+VGUn7MbO34HxAJ0xvxYkUy9Hl/k5rziUaIRL0ZDjtzQNdYS0GnN9FNZtWoV//jHPyo916FDB3bu3Mk777zDqFGj+Oabb1i+fDmLFy+mT58+7N27Fw8P0XrXXnstS5cutSnOKC8vj+PHVTs+he10796dwMBAm/dXa0zRENQ6Uzia+q6xlkCzFUbWOHfuHFOnTiUhIQGAoqIihgwZwr59+7juuuv44osviIqKorS0lKuvvprt27fj6elZ57gmk4mCggI8PT3R6VRpNEXNaJqGwWDA398fNzfbPdFqjSnqg1pnCkfT0DXWEnAqV1pMTAxRUVFs3LiRuLg4fv31V3r37g1AXFwcP/zwA/feey8rV65k0KBBNokiADc3N6WYFTbj41P/hgtqjSnqi1pnCkfTkDXWEnAqixFAYmIizz77LFlZWQQEBLBw4UI6duxIdnY2TzzxBOfPnycwMLAsjV+hUCgUCoXCVpxOGCkUCoVCoVA4CuVYVCgUCoVCoTCjhJFCoVAoFAqFGSWMFAqFQqFQKMwoYaRQKBQKhUJhRgkjhUKhUCgUCjMtXhj9+OOP3HDDDYwdO5bPP/+80ePNmDGDiRMnMnnyZCZPnsy+ffvYsmUL8fHxjB07ltdff73eY+bn5zNp0iQuXJAWtjWNd+TIEW666SbGjRvH008/TWlpab3HfvLJJxk7dmzZ/H/++ecGj/3WW28xceJEJk6cyCuvvGLXuVsb255ztzdqnTlmnak1Vo5aY+pcprATWgvm0qVL2qhRo7SsrCytoKBAi4+P106cONHg8UwmkzZ8+HDNYDCUPVdUVKTFxcVp586d0wwGgzZ37lxtw4YNNo+5d+9ebdKkSVrv3r218+fP1zrexIkTtT179miapmlPPvmk9vnnn9drbE3TtEmTJmkpKSnV9q3v2Js3b9ZuueUWTa/XayUlJdrMmTO1H3/80S5ztzb22rVr7TZ3e6PWmWPWmVpj5ag1ps5lCvvRoi1GW7ZsYciQIYSEhODn58e4ceNYvXp1g8dLTEwEYO7cudx444189tln7N+/nw4dOhAdHY2Hhwfx8fH1OsbXX3/Ns88+S0REBECN4128eJHi4mKuuOIKAG666aY6j1N17KKiIpKSknjqqaeIj4/n3//+NyaTqUFjh4eH88QTT+Dl5YWnpyddunThzJkzdpm7tbGTkpLsNnd7o9aZY9aZWmPlqDWmzmUK++FULUHsTWpqaqUmsxEREezfv7/B4+Xm5jJ06FCeeeYZDAYDM2fO5K677qp2jJSUFJvHfPHFF+ucc0pKSrXnw8PD6zxO1bHT09MZMmQIzz77LIGBgcybN49vv/2Wbt261Xvsbt26lf3/zJkzrFq1ijvuuMMuc7c29ueff86OHTvsMnd7o9aZY9aZWmPlqDWmzmUK+9GiLUYmk6lSo0VN0xrVeHHAgAG88sorBAYGEhoayvTp0/n3v/9t12PUNGd7vJfo6Gj+85//EBERga+vLzNmzGDjxo2NGvvEiRPMnTuXxx57jOjoaLvOveLYnTt3tvvc7YVaZ5Wx9zpTa0ytsaqoc5miMbRoYRQVFUVaWlrZ32lpaWWm2Iawa9cutm7dWva3pmm0a9fOrseoac5Vn09PT6/3cY4dO8aaNWsqzd/Dw6PBYyckJDB79mz+8pe/MHXqVLvOverY9p67PVHrrDL2/K7UGhPUGquMOpcpGkOLFkbDhg1j69atZGZmUlRUxNq1axkxYkSDx8vLy+OVV15Br9eTn5/P999/zyOPPMLp06c5e/YsRqORFStWNOoY/fv3tzpeu3bt8Pb2JiEhAYBly5bV+ziapvHSSy+Rk5ODwWBgyZIljBkzpkFjJycn8+c//5lXX32ViRMn2nXu1sa259ztjVpnlbHXd6XWWDlqjVVGncsUjaFFxxhFRkby8MMPM3PmTAwGA9OnT6dfv34NHm/UqFHs27ePKVOmYDKZ+OMf/8iAAQNYuHAhDzzwAHq9nri4OMaPH9/gY3h7e9c43quvvsqCBQvIz8+nd+/ezJw5s15j9+jRg3vuuYfbbruN0tJSxo4dy6RJkxo09ocffoher2fhwoVlz9166612mXtNY9tr7vZGrbPK2GudqTVWjlpjlVHnMkVj0GmapjX1JBQKhUKhUCiaAy3alaZQKBQKhUJRESWMFAqFQqFQKMwoYaRQKBQKhUJhRgkjhUKhUCgUCjNKGCkUCoVCoVCYUcJIoVAoFAqFwowSRgoAduzYQWxsLBs3bix77vz58wwdOpQXXnihCWemcCXUOlM4GrXGFI1F1TFSlDFz5kxKSkr46quvyMvL49Zbb6V9+/a8/fbbuLu7N/X0FC6CWmcKR6PWmKIxKIuRoowHH3yQPXv28PvvvzN//nw8PDx47bXX1IlEYVfUOlM4GrXGFI1BWYwUlZgzZw579uwhMDCQb775hqioqLJtp06d4uOPP1bmaEWjUetM4WjUGlM0FGUxUlQiJiaGoqIiHnzwwUonEoAuXbqoE4nCLqh1pnA0ao0pGooSRooylixZwnfffUePHj345ptvqm3/61//ysqVK5tgZgpXQq0zhaNRa0zRGJQwUgCwefNm/v73v/PCCy/w/PPPs2/fvkpZHQBHjhyhR48eTTRDhSug1pnC0ag1pmgsShgpOHHiBA899BB33nknU6ZM4YorrmDYsGG8+eabZfuYTCYuXLhAx44dm26iCqdGrTOFo1FrTGEPlDBq4WRkZDBv3jyGDRvG/Pnzy56/7777OHDgABs2bADg9OnTxMTE4Oamloyi/qh1pnA0ao0p7IXKSlPYxIoVK9i5cyfPP/98U09F4cKodaZwNGqNKepCSWaFTRw9epSePXs29TQULo5aZwpHo9aYoi6UxUihUCgUCoXCjLIYKRQKhUKhUJhRwkihUCgUCoXCjBJGCoVCoVAoFGaUMFIoFAqFQqEwo4SRQqFQKBQKhRkljBQKhUKhUCjMKGGkUCgUCoVCYUYJI4VCoVAoFAozShgpFAqFQqFQmPl/E+zPQ3onhogAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x216 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Looking into the var simplification that we know doesn't work, but might be worth showing anyway \n",
+    "fig,ax = plt.subplots(1,3,sharex='all',sharey='all',figsize=(8,3))\n",
+    "#nens_list = [2**3,2**5,2**7,2**9]\n",
+    "nens_list = [2**5,2**7,2**9]\n",
+    "n_lines = len(nens_list)\n",
+    "for i in range(n_lines):\n",
+    "    nens = nens_list[i]\n",
+    "    ax[0].plot(var_ensemble_limit(quad,20,nens,dt=0),alpha=0.8,color=plt.cm.cool(i/(n_lines-1)),label=str(nens))\n",
+    "    ax[1].plot(var_ensemble_limit(quad,20,nens,dt=1),alpha=0.8,color=plt.cm.cool(i/(n_lines-1)),label=str(nens))\n",
+    "    ax[2].plot(var_ensemble_limit(quad2,20,nens,dt=1),alpha=0.8,color=plt.cm.cool(i/(n_lines-1)),label=str(nens))\n",
+    "\n",
+    "x_advected = np.array([0,99,100,199,200,299])-da_const_6['dt']*m_const['u_ref']/m_const['dx']\n",
+    "x_advected[-1] = 299\n",
+    "ax[1].plot(x_advected,[0,0,1,1,0,0],'k--',label='ref')\n",
+    "x_advected = np.array([0,99,100,199,200,299])-da_const_62['dt']*m_const['u_ref']/m_const['dx']\n",
+    "x_advected[-1] = 299\n",
+    "ax[2].plot(x_advected,[0,0,1,1,0,0],'k--',label='ref')\n",
+    "\n",
+    "plt.subplots_adjust(hspace=0.1,wspace=0.1)\n",
+    "ax[0].set_xlim(0.,300)\n",
+    "ax[0].set_xticks(50*np.arange(0,6));\n",
+    "\n",
+    "ax[0].plot([0,99,100,199,200,299],[0,0,1,1,0,0],'k--',label='ref')\n",
+    "# ax[0].legend(title=r'$n_{ens}$',ncol=3,bbox_to_anchor=(0,1.0),loc='lower left')\n",
+    "ax[2].legend(title=r'$\\alpha$' ,ncol=1,bbox_to_anchor=(1., 0.8),loc='upper left')\n",
+    "ax[0].set_xlabel(r'$x_i$',size=15)\n",
+    "ax[1].set_xlabel(r'$x_i$',size=15)\n",
+    "ax[2].set_xlabel(r'$x_i$',size=15)\n",
+    "ax[0].set_title('lead time: 0 s')\n",
+    "ax[1].set_title('lead time: '+str(da_const_6['dt']) +' s')\n",
+    "ax[2].set_title('lead time: '+str(da_const_62['dt'])+' s')\n",
+    "ax[0].set_ylabel(r'$\\frac{\\partial j}{\\partial x_i}$',rotation=0,size=20,labelpad=20)\n",
+    "ax[0].set_xlim(0.,300)\n",
+    "#plt.xticks(32*np.arange(9));\n",
+    "ax[0].set_ylim(-60,60)\n",
+    "label_axes_abcd(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Now making the localization advection scetch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Reinitializing to the default values, and calculating the localization matrices\n",
+    "\"\"\"\n",
+    "m_const = set_model_constants_22()\n",
+    "da_const = set_da_constants_22()\n",
+    "L_obs,L_os = localization_matrices_observation_space(m_const,da_const)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 30.0)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This one has the plus 50 and minus 50 error, and also follows the color scheme of the other plot\n",
+    "o = 2\n",
+    "np.random.seed(2)\n",
+    "u_advect=10\n",
+    "dt = 800\n",
+    "L_adv_mean = semi_lagrangian_advection(L_os[:,o],m_const['dx'],-u_advect,dt)\n",
+    "L_adv_plus = semi_lagrangian_advection(L_os[:,o],m_const['dx'],-u_advect*1.4,dt)\n",
+    "L_adv_minu = semi_lagrangian_advection(L_os[:,o],m_const['dx'],-u_advect*0.6,dt)\n",
+    "L_adv_ens = np.zeros([m_const['nx'],da_const['nens']])\n",
+    "for n in range(da_const['nens']):\n",
+    "    u_ens = np.random.normal(m_const['u_ref'],da_const['u_std_ens'])\n",
+    "    L_adv_ens[:,n] =semi_lagrangian_advection(L_os[:,o],m_const['dx'],-u_ens,dt)  \n",
+    "\n",
+    "    \n",
+    "fig, ax = plt.subplots(1,1,figsize=(8,3))\n",
+    "\n",
+    "\n",
+    "linew = (10-np.abs(60-100)/10)/2\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.vlines(da_const['obs_loc'][o]/10+0.1,-0.1,1.1,ls='--',color='k',label='obs location')\n",
+    "plt.plot(m_const['x_grid']/1000+0.1,L_os[:,o],lw=3,color='k',label='analysis')\n",
+    "for n in range(da_const['nens']-1):\n",
+    "    plt.plot(m_const['x_grid']/1000+0.1,L_adv_ens[:,n],'grey',alpha=0.2)\n",
+    "plt.plot(m_const['x_grid']/1000+0.1,L_adv_ens[:,n+1],'grey',alpha=0.2,label='ens members')\n",
+    "plt.plot(m_const['x_grid']/1000+0.1,np.mean(L_adv_ens[:,:],axis=1),'grey',lw=3,label='ens mean')\n",
+    "\n",
+    "plt.plot(m_const['x_grid']/1000+0.1,L_adv_minu,lw=linew,color=plt.cm.viridis((2)/(5)))\n",
+    "plt.plot(m_const['x_grid']/1000+0.1,L_adv_mean,lw=5,color=plt.cm.viridis((4)/(5)),label=' $\\overline{u}$ advection')\n",
+    "plt.plot(m_const['x_grid']/1000+0.1,L_adv_plus,lw=linew,color=plt.cm.viridis((2)/(5)),label='$\\overline{u}$ error +- 40%')\n",
+    "   \n",
+    "#plt.plot(m_const['x_grid']/1000,L_adv_mean,lw=3,color='r')\n",
+    "#plt.vlines(da_const['obs_loc'][o]/10+u_advect*dt/1000,-0.1,1.1,ls='--',color='r')\n",
+    "#plt.legend(loc='upper center',ncol=3)\n",
+    "plt.legend(framealpha=1)\n",
+    "ax.set_xlabel('x [km]')\n",
+    "ax.set_ylabel('localization weights')\n",
+    "ax.set_ylim(-0.1,1.1)\n",
+    "ax.set_xlim(0,30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculating the lead time dependent plots from the paper with and without advection errors\n",
+    "\n",
+    "this is relatively time consuming, because for each of the 11 forecast lead times the 900 OSSEs need to be done with the explicit, implicit, and the implicit with 8 different lead times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4.46 s, sys: 51.7 ms, total: 4.51 s\n",
+      "Wall time: 1.16 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# just resetting a\n",
+    "m_const = set_model_constants_22()\n",
+    "da_const = set_da_constants_22()\n",
+    "sat_operator = reflectance_simulator\n",
+    "da_const_vr = set_da_constants_22(obs_loc=np.arange(25,299,50)) #changed direct point observations for variance reduction tests\n",
+    "# Run the model\n",
+    "states   = run_linear_advection_KF_22(m_const,da_const,reflectance_simulator)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "completed timesteps: 1  seconds spent computing so far: 61.0\n",
+      "completed timesteps: 11  seconds spent computing so far: 727.0\n",
+      "completed timesteps: 21  seconds spent computing so far: 1421.0\n",
+      "completed timesteps: 31  seconds spent computing so far: 2131.0\n",
+      "completed timesteps: 41  seconds spent computing so far: 2775.0\n",
+      "completed timesteps: 51  seconds spent computing so far: 3465.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "import time\n",
+    "start_time=time.process_time()\n",
+    "\n",
+    "t_start= 40\n",
+    "t_end = 100\n",
+    "n_ens = 15\n",
+    "#n_ens = 5 # Comment out for full data\n",
+    "ndt_steps=11\n",
+    "nerrors = 8\n",
+    "error_vec = [20,40,60,80,120,140,160,180]\n",
+    "t_vec=np.linspace(0,1000,ndt_steps)\n",
+    "n_samples = (t_end-t_start)*n_ens\n",
+    "truth_idx = 0\n",
+    "sens_all     = np.zeros([ndt_steps,300])\n",
+    "vr_es        = np.zeros([ndt_steps,n_samples])\n",
+    "vr_es_hi     = np.zeros([ndt_steps,n_samples])\n",
+    "vr_is_ca     = np.zeros([ndt_steps,n_samples])\n",
+    "vr_is_cw     = np.zeros([nerrors,ndt_steps,n_samples])\n",
+    "vr_real      = np.zeros([ndt_steps,n_samples]) \n",
+    "var_total     = np.zeros([ndt_steps,n_samples]) \n",
+    "ref_t = da_const['dt']\n",
+    "t_vec[0] = 1\n",
+    "counter = 0\n",
+    "for t in range(t_start,t_end):\n",
+    "    for n in range(n_ens):\n",
+    "        i = (t-t_start)*n_ens+n\n",
+    "        counter = counter+1\n",
+    "        truth_idx = n\n",
+    "        for dt in range(len(t_vec)): \n",
+    "            da_const_vr['dt'] = t_vec[dt]\n",
+    "            \n",
+    "            vr_t, vr_i, vr_r,J_dict,dJdx_inv, quad, dx = vr_reloaded_22(\n",
+    "                states[0]['bg'][t],states[0]['bg'][t][:,n],m_const,da_const_vr,reflectance_simulator,\n",
+    "                obs_seed=i,model_seed=i,alpha=alpha_default)\n",
+    "            sens_all[dt,:]= sens_all[dt,:]+dJdx_inv/n_ens/(t_end-t_start)\n",
+    "            var_total[dt,i] = np.var(J_dict['bf'],ddof=1)\n",
+    "            vr_es[dt,i]     = vr_t\n",
+    "            vr_real[dt,i]   = vr_r\n",
+    "            \n",
+    "            \n",
+    "            vr_t, vr_r, quad, J_dict= vr_individual_loc_22(states[0]['bg'][t],states[0]['bg'][t][:,n],m_const,da_const_vr,reflectance_simulator,\n",
+    "                                                  advect_flag=1,obs_seed=i,model_seed=i,quad_state=quad)\n",
+    "            vr_is_ca[dt,i]  = vr_t\n",
+    "            \n",
+    "            for e in range(nerrors):\n",
+    "                vr_t, vr_r, quad, J_dict= vr_individual_loc_22(states[0]['bg'][t],states[0]['bg'][t][:,n],m_const,da_const_vr,reflectance_simulator,\n",
+    "                                                  advect_flag=1,obs_seed=i,model_seed=i,error_u=error_vec[e],quad_state=quad)\n",
+    "                vr_is_cw[e,dt,i]  = vr_t\n",
+    "            \n",
+    "    if np.floor(t/10) == t/10: print('completed timesteps:',t-39,' seconds spent computing so far:',np.floor(time.process_time()-start_time))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculating the mean properties over all experiments\n",
+    "es    =np.sum(vr_es    ,axis=1)/counter\n",
+    "is_ca =np.sum(vr_is_ca ,axis=1)/counter\n",
+    "real  =np.sum(vr_real  ,axis=1)/counter\n",
+    "total =np.sum(var_total,axis=1)/counter\n",
+    "\n",
+    "me_es    =np.sum((vr_es    -vr_real),axis=1)/np.sum(vr_real,axis=1)\n",
+    "me_is_ca =np.sum((vr_is_ca -vr_real),axis=1)/np.sum(vr_real,axis=1)\n",
+    "\n",
+    "rmse_es    =np.power(np.sum(np.power((vr_es    -vr_real),2),axis=1)/counter,0.5)/np.sum(vr_real,axis=1)*counter\n",
+    "rmse_is_ca =np.power(np.sum(np.power((vr_is_ca -vr_real),2),axis=1)/counter,0.5)/np.sum(vr_real,axis=1)*counter\n",
+    "\n",
+    "std_real    =np.std(vr_real,axis=1)\n",
+    "\n",
+    "rmse_is_cw =np.zeros([nerrors,ndt_steps])\n",
+    "is_cw =np.zeros([nerrors,ndt_steps])\n",
+    "\n",
+    "for e in range(nerrors):\n",
+    "    rmse_is_cw[e,:] =np.power(np.sum(np.power((vr_is_cw[e,:,:] -vr_real),2),axis=1)/counter,0.5)/np.sum(vr_real,axis=1)*counter\n",
+    "    is_cw[e,:] =np.sum(vr_is_cw[e,:,:] ,axis=1)/counter#/var_total\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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E4YEjEhxBEARBEB44IsG5x/zwww+V1lG5n6Snp/P8888zadIkZsyYQVJSElBWvfT555/Hz8+PJ598koyMDKCs4uaoUaOYMGECcXFxQFlp9fLy/kLt3e+fncDAQKZNm8bEiROZO3cuycllSxCIz87dd+tn69YK1FUpfz04OJivv/66yWMThOqIBEdoVP/6178YOnQoO3fuZOLEiYaibMuXL8fHx4f9+/czffp0Q5Gu1atX8/XXX/Piiy8aKun++uuvPP3003ftPQh3R/lqzrt27WL8+PF8+umngPjs3Gv+WYG6utejo6PJysq6EyEJQpVEJeM7RKvV8tFHHxEVFUVmZiaenp589913mJqa8uuvvxpWrC1fzHDt2rUkJCTw/vvvA/DFF1/g7OzMU089ZThnUVERS5YsISoqCp1Ox3PPPce4cePYvn07O3bsIDc3l6FDh5Kenk5ubi4JCQksWrQIe3t7PvvsM1QqFXZ2dixZsgQ3Nzdmz56NjY0NUVFRLF++vMqiSsOGDau2Im12djbh4eGsXr0aKFsN2NfXF4Djx4+zYcMGAMaNG8eSJUsM66QUFxdTVFSEUqkkPT2d+Ph4Fi5c2Ji//grCc9PYEB1Eakl+k12jtpzNrHnSoycdbZ2q3edh+Oyo1WoWLlxIx44dgbKlGtavXw/cW5+dcl97JdbrOKfOSub87lzla2sfSyUttOr1eqqyKMS1VvutWrWK/fv3o9PpGDhwIIsWLeLo0aN89dVX7N69m9TUVGbPns0ff/zBsmXLMDEx4erVqxQVFfHiiy8yadKkCufz9PQkIiKC3Nxc3n33XWJjYzE2Nmbx4sX4+vri6enJxYsXWbFiBcXFxfz0008VVnkPDw/ngw8+QKvVYmJiwtKlS2nTpg0nT55kxYoVaLVaXFxc+OSTT7Czs2PYsGFMmDCB06dPU1JSwpdffomXlxerV69mx44dGBkZ4e3tzZIlS2r9uxMeDvd1gvPbutNERN6422Hg2aEFc2cPvO0+ly5dMix6ptfrmTt3LidOnKBly5Zs27aNHTt2IJPJePzxx/H29mbcuHFMmjSJd955ByMjIw4ePMiWLVsqnPOnn36iS5cufPnllxQWFjJjxgzDCrdpaWns27cPhULB4sWLsbW15eeff0atVjN69GiWL1+Ot7c3+/fv54033jCsKu3p6cnKlSvr9XtITEykZcuWfPHFFwQEBODg4GD4kk1PTzesaK1QKLC0tCQ7O5sXXniB999/HzMzM7755htWrlxZ4WbYFNZFBZJeWtCk16it1JJ81kUF8lnvMdXu8zB8doyNjQ1dG3q9npUrVxoWbb2XPjv3m5MnTxISEmJY2HLRokXs3r2biRMncvDgQX7++WfOnz/PW2+9hZNTWZKdmJjIli1byMrKYsqUKQwYMKDKc3///fe0bt2aH3/8kYiICD744APDA421tTWvvvoqFy5cqPRv8ttvvzFv3jz8/PzYsWMHly9fxtramm+//Za1a9diY2PD5s2b+eabbwytdba2tmzdupV169bxn//8h+XLl/Of//yHU6dOIZfLeffdd0lLSzO8B0GA+zzBuZ/07t0bW1tbNmzYQGxsLPHx8RQXF3PhwgWGDBliWC9o9OjR6PV67O3t6dixI+fPn0epVNK2bVvDTb7c2bNnKS0tNXzBFBcXExUVBUDnzp0rrNJavhx9fHy84UkfwM/Pjw8++ICCgoIK+93qxo0bhnEN6enphi+i7du3V1hJWKvVEhoayiuvvMLbb7/NH3/8weLFi6tcGVeSJIyMjOjUqRNbt24FICYmBii7mb3yyitoNBreeust2rZtW6ff9YPmYfjslFOr1SxevBitVssLL7xQ5e9DfHZqz9/fn+DgYKZMmQJAaWmpYc2yd999lzFjxtCzZ0/Gjh1rOGbKlCkolUqcnZ3p2bMngYGBVZ774sWLhi5oT0/PSkl0dYYMGcKSJUs4deoUw4YNY+jQoZw8eZIbN24wZ84coCzJtbGxMRwzaNAgANq3b8/BgweRy+X06NGDadOmMXz4cObNmyeSG6GS+zrBqanV5F5y5MgRVqxYwZw5c5gyZQo5OTlIklRpeXiFQoFarQbKBuvt27cPpVLJ+PGVVwzW6/V8/fXXdOnSBYDMzExsbGzYs2dPhWXr4eYy9nq9vtJ5JElCp9NV2O9WLVq0MPSrDxs2rEIf/K0DDn/44QcsLCwYOnQoUNadUD6OwtHRkczMTJydndFqtRQVFVVahfqHH35g8eLFrF27lpEjR+Li4sLKlSv59ttvq/qV1tvs9r3YGB3EjXugi6qFmTUzPXredp+H4bOza9cuQ5eIra0tP/30k2HF8Hvps3O/0el0zJ07l3nz5gFlA7bLE8vMzEzkcjmxsbGoVCpMTEwAKiSeer2+QrJ7K4VCUWHBypiYmFollKNHj6ZHjx4cO3aMNWvWcPz4cR555BF69uzJzz//DIBKpaKoqMhwTHlst17v3//+N5cvX+bkyZM8++yzfPPNN4YFPgUB7vME537i7++Pn58fU6dOJTExkfPnz+Pr64uvry8LFy5kwYIFGBsbc+jQIYYMGQLA8OHDWbFiBZIk8eabb1Y6Z79+/di0aROffvop6enpTJo0yTDYsjru7u7k5uYSHByMt7c3+/bto2XLlpW+MGrrnwMOnZ2dOXHiBEOGDOHYsWOGL9AhQ4awc+dO5s+fz759+/Dx8TF8gUHZ06CrqyvOzs5oNBrkcjlGRkaoVKp6xXU7HW2dWOLj1+jnbSoPy2dn0aJFuLm58fHHH2NkdHP+w7302THEWsvxL3VR3dichujXrx8rVqzgsccew8TEhJdffpnJkyczceJE3n77bd59913OnTvH999/z7/+9S8A9u/fz+jRo0lJSSE4OJjPPvuMsLCwSuf28fFh7969eHp6EhMTw3PPPceRI0cMr8vlcrRabaXjXnvtNcaNG8eMGTNo164dS5cuZeHChbz33nvExcXRtm1b/v3vf5OWlsYXX3xR5fvKzs7mySefZOvWrfTo0YPU1FQiIiJEgiNUIBKcO2T69Om8+eab7N27F6VSSc+ePUlKSmL69OnMnTuXadOmYW1tbWg+hrIn4p49e6JWqw3dELdasGABH330EePGjUOn07Fo0SJat25NQEBAtXEYGxuzbNkyPvnkE0pKSrCxsWHZsmW1fh/VDRIt98MPP/Dhhx/y9ddfY2lpabhBLVy4kMWLFzN27FisrKwMTdvlVq1axXfffWf4XS1cuBC9Xs/nn39e69geVA/DZyc0NJQjR47g4eHB5MmTgbKWm19++UV8dhpg2LBhhIeH89hjj6HT6Rg0aBCTJ0/m119/pVmzZowcOZL+/fszbtw4Ro4cCZR1Y02dOhW1Ws2SJUuws7Or8tyvvvoq7733HhMmTEChUPDVV19VaGHx9vZm5cqVfPPNNxWS7Pnz5/Puu+/y448/olQq+eijj3BwcODzzz/ntddeQ6/X4+TkdNsp5vb29jz++ONMmzYNMzMz2rZty9SpUxvptyY8KGTSrW3c97j6LJcuCIIg1M7ixYvp06ePYcyOINwr6vP9L+rgCIIgCILwwBFdVIIgCAJAtWNeBOF+JFpwBEEQBEF44Nx3CU5VU1UFQRAEQXgw1Xeo8H2V4FhYWJCcnIxara73GxYEQRAE4f4gSRJZWVlV1tmqyX01i0qv15OZmUleXl6V9RUEQRAEQXiwmJqa4uLiUqH+VW3cVwmOIAiCIAhCbdxXXVSCIAiCIAi1IRIcQRAEQRAeOCLBEQRBEAThgSMSHEEQBEEQHjgiwREEQRAE4YEjEhxBEARBEB44IsERBEEQBOGBIxIcQRAEQRAeOCLBEQRBEAThgSMSHEEQBEEQHjgiwREEQRAE4YFzTyU4e/bsYcyYMYwcOZINGzbc7XAEQRAEQbhPKe52AOXS0tJYtmwZ27dvx9jYmBkzZtC3b188PDzudmiCIAiCINxn7pkWnLNnz9KvXz9sbW0xNzdn1KhRHDhw4G6HJQiCIAjCfeieacFJT0/HwcHB8LOjoyPBwcE1HqfX6ykqKkKpVCKTyZoyREEQakGSJDQaDRYWFhgZ3TPPULcl7iOCcG9pjPvIPZPg6PX6CjcWSZJqdaMpKioiMjKyKUMTBKEeOnTogJWV1d0Oo1bEfUQQ7k0NuY/cMwmOs7MzAQEBhp8zMjJwdHSs8TilUgmU/RKMjY0JCQnBy8uryeJsbPdbvCBivlPut5jL41Wr1URGRhr+Nu8H4j5y54mY74z7LebGvI/cMwlO//79+eGHH8jOzsbMzIyDBw/yySef1HhceSuPsbExJiYmAIb/f7+43+IFEfOdcr/FfGu8Td3VM3v2bLKzs1Eoym5jS5YsoaioiKVLl6JSqfDz8+P111+v1bnEfeTuEDHfGfdbzI11H7lnEhwnJydef/115syZg0ajYdq0aXh7e9/tsARBuAdJkkR8fDzHjh0zJDilpaWMHj2adevW0aJFC1544QVOnDjBkCFD7nK0Zc6fP88nn3zCn3/+ebdDEYSHwj2T4ACMHz+e8ePH3+0wBEG4x8XGxgLw9NNPk5uby2OPPUaHDh1wc3PD1dUVKLufHDhw4J5JcARBuLPuqQTnXqXX6/n888+5cuUKRUVFSJLEp59+Sq9eve52aILwUMrPz8fX15f3338fjUbDnDlzePbZZyvNxExLS7uLUVZWXFzMq6++SkJCAtbW1ixZsoS2bdve7bAE4YEkEpxauHLlCunp6WzZsgUjIyNWrVrFL7/8IhIcQbhLevToQY8ePQw/T5s2jRUrVlT4m6ztTMxbhYSEGP47MDCw4YHeIjIykpSUFJ577jnmzp3LkSNHePnll2s11rA2GjveO0HEfGfcbzE3VrwiwamFHj16YGNjw+bNm0lMTOT8+fNYWFjc7bAE4aEVEBCARqPB19cXKEtmWrVqRUZGhmGf2s7EvJWXlxcmJiYEBgY2+gOMVqulY8eOPPHEEwB07dqV1atXN8p0+qaIt6mJmO+M+y3m8nhVKlWFB476uD+qcN1lx48f54UXXgBg+PDhhhuUIAh3R0FBAV999RUqlYrCwkJ27NjBG2+8QVxcHAkJCeh0Ov78808GDx58t0Ot4J8Fy2QymWGQtCAIjUv8ZdXCmTNnGDp0KDNnzqS0tJRffvkFnU53t8MShIfW0KFDuXLlCpMmTUKv1zNz5kx69OjBF198wSuvvIJKpWLIkCGMHj36bodaQUREBGFhYXTq1IktW7bQq1cvzMzM7nZYgnDXqUpUBB4MJjQ4tNFanESCUwszZszg//7v/xg/fjxarZYBAwZw8OBB9Hr9fVOKXhAeNK+99hqvvfZahW2+vr7s3r377gRUC+7u7qxcuZLExESaNWvGF198cbdDEoS7Kj+rAP89AQQeuoK7dxvcurk02rlFglML7dq1Y+fOnRW2vffee3cnGEEQ7kt9+/Zlz549dzsMQbgnpManc2bHBcIvRtNjqBcvfvcUdk62jTogWiQ4giAIgiA0OUmSiLkcz+nt50lPzKTfuF6MeW44ZpZN000rEhxBEARBEJqMTqvjyolQzu68AED/SX3oNqQzcoW8Sa8rEhxBEARBEBpdSWEJFw9c5tyfgTi6Nmf008No171Nk69TV04kOIIgCIIgNJqc9Dz8d13k0rEQOvb2YPaH02nR1um2x6jVWk6cDOf8hWjat++MiUnDJ/CIBEcQBEEQhAZLirrB6e3niQ2Op9ej3Viw4mlsmlvXeFx4xA32/HmJnNwi8vMKKC5RY2Ji2uB4RIIjCIIgCEK9SJJE+IVozu68QG56Hr4TejP5VT9MzExqPDY3t5g/914mNDwZgBbOtgwa4ISzkw0qlarBsYkERxAEQRCEOtGoNVw+GsKZnRcxMTNm4JS+dO7fAbm85oHDOp2eM2ejOHosFLVGi7FSwaMjutCvrweXL19qtBhFgiMIgiAIQq1IkoT/ngBObvWnlUcLJi4YTZsurrUeOBwXn8Hu3ZdIy8gDoGsXV8aM6YaNdeNPFRcJjiAIgiAINcrLzGf78r1o1Vqe/mwmjq7Na31sYVEpB/66StCleACa2VsyflwPOrR3bqJoRYIjCIIgCEINQk6H8ed/DuE73oeBU/vWqisKylp8AgLiOHDwKiWlauRyI4YM6siQwR1RKkUdHEEQBEEQ7oLSYhV7/3OIxPBkZn0wHZf2LWp9bMqNXHbtDiIxKQsAj3ZOTBjXg+bNrZoq3ApEgiMIgiAIQiUJoYls/e5PPHq05aXv52Fsalyr41QqDYePhHLWPwoJCWsrM8b4daOrl8sdK/IHIsERBEEQBOEWOq2Oo5tOE3Q4mAkvjaZT3/a1Ok6SJEKuJbF33xXyC0qQIaO/b3tGDOuCqamyiaOuTCQ4giAIgiAAkJmcxdZv92BhY8FLy+dhZWdZq+OysgrZ8+clIqNTAXB1acbECT1p2cK2CaO9PZHgCIIgCMJDTpIkAv66zOH1Jxk2cxB9/HrUqjtJo9Fx8lQEJ06Go9XpMDM1ZtTIrvT2aXtHu6OqIhIcQRAEQXiIFeUVsWPFfvKzCnhm6ZO1nv4dHZ3Grj1BZGUXAtCjuxt+o72xtGj4MguNQSQ4giAIgvCQirgYza4fD9B9qBdPvD0ZuaLmqdv5+SXs3XeFq9cSAXB0sGbi+J60bevQ1OHWiUhwBEEQBOEho1Zp+Ot/R4kMiGH6mxNo69W6VsfFxKSxcfM5SkrVKBVyhg3tzMABHZDLG776d2MTCY4gCIIgPESSo2/wxzd7aNW+BS+teBqzWnQpSZKE/7lo9u2/gl6S6NDemYnje2JnZ3EHIq4fkeAIgiAIwkNAr9dzatt5/HdfZMxzI/Ae3LlWx+l0enbtCSIgMA6ARwZ34tERXe76IOKaiARHEARBEB5wOel5bP12D0ZyI+Z/NxdbB5taHVdYVMrGTeeIT8hAqZAzZbIP3bxr1511t4kERxAEQRAeUJIkcflYCPv/e4SBU/oxYFJvjIxqN17mxo1c1m88S05uETZWZjw5sz8uLvZNHHHjqTbBWb16da1OMG/evEYLRhCE+0tubjExsenExqYTHhFLW/eOWJiL5yZBuBeUFJZwct15FBpjnloygxbuTrU+NuRaElu3XUSt0eLq0ownn/DF2tqsCaNtfNXeiX7++WeGDRt224OPHj1a7wRn+fLlyOVyXnnlFQDy8/N58803SUxMxN7enuXLl+PgcG9NOROEh11hUSlxcRlEx6QTGpJIanIOJUWllBaq0Og05OUVY2FufbfDFISHXtaNHNa8twkzZ1Oe+3AOSuPaLZUgSRLHjodx+Og1AHp0c2PSxF5NvvJ3U6g2wfHy8mLp0qW3PfiZZ56p8wULCgpYunQpe/fu5dlnnzVsX758OT4+PqxatYqdO3fy2WefsXz58jqfXxCExqNSaYiLzyQ0JJGQK9dJSsyitEhFaVEpMiMZVtbmtHFrRhfv1pjbaGjbxgGVSnW3wxaEh1pGUhZrPtjM0BkDkTXT1jq5Uau1bN1+kZBrSciQMXpUVwYO6HDPDyauTrUJzn//+99K29RqNRqNBgsLi2r3qcmRI0do06ZNpZaf48ePs2HDBgDGjRvHkiVL0Gg0KJV3foEuQXhYaTQ6YmLSuHQ+mtCQJJKSsykpLEWv1WNqYYKFtRle3VrTtbsbXt3caNnC1lD/IjAw8C5HLwhC+vUM1nywhRGzh9BzeNda/13m5hazfsMZUlJzMTVR8vj0vnh6tmjiaJtWrTvL9+zZw6pVq9Dr9YwfP5758+fX64KTJk0C4IcffqiwPT093dAlpVAosLS0JDs7Gyen2vcZCoJQNxqNlpDAOC5djCEiPIXkG3moStQYmxljZmmKpa0F3r3c6eLdmnbtHGnt2uy+bKoWhIdBanw6az/6nVHzhtJtSJdaH5eQkMmGTf4UFpXSzN6S2U8OwNHx7nQ1a/X6RjtXtQlObm4utra2hp93797N7t27ARg9enSNCc7+/fsrdXG5u7uzZs2aWgUmSVKtR3oDhISEGP77fnuSvN/iBRHzndLYMRfmFBMXmkpsVCbJyQXkFKiQKRQYmykxNlNiZm+KWwsHWrW0omULK5ycLDE2lgMqcnMSyc1JvKPxCoJQOykxqaz9+HfGPf8oXgM71fq4gMA4du0JQqfT49HOiSce74eZmXETRlq1qNRMtpwPJjwhCbW1A33aNLz1qNoEZ/78+YwfP56ZM2cik8lwcHDg559/Ri6XV0h8quPn54efn1+tA3F0dCQzMxNnZ2e0Wi1FRUW1uk45Ly8vTExMCAwMpFevXrU+7m673+IFEfOd0hgxF+YWEXf1OlfORXHpUjypBSoU5qaYWZpg5eKMg4UJDo7WtHN3pF07R9q2daj3Qnnl8apUqgoPHIIgNK2kqBusX/IHE14aRWdfz1odo9PpOfDXVc74RwLQ37c9fqO87/iSC0UqNTsDr3EmKgEACYnL11OaNsFZv349v/32G7NmzeKNN97ggw8+YPfu3ajVan7++ecGX/ifhgwZws6dO5k/fz779u3Dx8dHjL8RhDoqLigh7up1YoMTiLwUR2JGATprS7RKBeYtmuHmboy1tRnt3B1xd3fEva1Dg0qt6/R6riamcjIinrC4eLS2jnR3cWzEdyQIwu1cD09m42fbmPSKHx37tK/VMSUlajZtOUd0TBpyuRETx/fEp1fbJo60IkmSCEpI4Y/zweSX3pyYYCyX4+fdoVGuUW2Co1AoeOaZZ5gwYQJfffUVMpmMRYsWNdnU7YULF7J48WLGjh2LlZUV33zzTZNcRxAeJCVFpSRcSyQ2+DpxwQlkpeVg2rI5paYm5FlaYu5gDzIwMzWmS5dW9OjmRps2zRs8KyK/pJQzUQmcjownp6ikbJtKw/Gw2AYnOBcuXCAvLw9JkgzbRo4c2aBzCsKDKCE0kU1LdzDltbF06NWuVsdkZOSzbsNZMrMKsLQwZeYTvrRxa97EkVaUXVjM5vPBhCSlVtjew60l7Y31eDg1b5TZmNUmOIWFhezatQtjY2M+/fRTrl69ysKFCxk+fDhz585FoWhYMa/y+jflbG1tm6RlSBAeJOpSNfF/JzTxVxNIT8rCtUNLLFo1w7hTa/TN7cjVaAEwN1bSob0z3bu3pqNnywYPDpYkiZj0bE5GxHE5IaXSYEAZMMizTYOu8d5773Hy5Enc3NxunlcmEwmOIPxD3NUEtny1i2n/Nx6P7rVrfYmMTGXLH+cpKVXTwtmW2U8OwNbWvIkjvUmvlzgeHsueS2GotFrDdhszU2b060a31i0adRxftVnKiy++yKBBg0hPT2fx4sUsW7aM9evXs3HjRmbNmsXmzZsbLQhBEKqmUWu4HpZMbHACccEJpCZk0KqdM2293fCZ3Jf0QhVXQ5LJS8kzHOPayp7u3d3o2tWl3uNpbqXSaLkQm8TJiDiSc/IqvW5lasKA9m5YlebRv71bg568/P392bdvH5aWlrU+5ssvvyQnJ4cvvviCs2fPsnTpUlQqFX5+frz++uv1jkUQ7lXRl+PY+u0eHv/XRNp2datxf0mSOH0mkgN/XUVCwquzC9Om9sbY+M5VHU/KzmPD2cskZOUYtslkMgZ1aMOEnp0xr2Wtnrqo9t2lpaXx/PPPo1KpmDp1KgBGRkbMmjWLsWPHNnoggiCUrfabHHWDqKA4/A+eZ2/RcVq0caRN19YMnzUYO5dmhEXc4NKlBFJCbw7ktbO1oHu31nTv7oZDc6tGiSU1r4CTEXGcj06kRKOp9Lq7oz1DPN3p7tYCpVzeKE9eLVq0qFNy4+/vz44dO3jkkUcoLS3lnXfeYd26dbRo0YIXXniBEydOMGTIkAbHJQj3isjAGLYv38sTb0/GrbNrjftrtXq27Qgg6FI8AMOHdmHY0E53rHifWqtj35VwDl+LRn9Lt3MLW2tm+najnWOzJrt2tQnOmDFjGDt2LJIk8eSTT1Z4zc7OrskCEoSHTUFOIdGX4ogKjCX6chxWdpa07+mO1/CO+E17FIyMCAtL4dSVBKK3BRhuEmamxnh1caF799a0cWv4uBooGzQcnJjKyfA4IlIzKr1urJDTx92VQZ5tcLW3bfD1/qlnz568/vrrDB06FFPTm61PVXVR5ebmsmzZMubPn094eDjBwcG4ubnh6lp20x8/fjwHDhwQCY7wwAi/EMXOH/bz5HtTcfVsVeP++fkl7P8rGrVGgbFSwbSpvfHq4nIHIi0TlpLO5nNXyCgoMmxTGBnh182TR7u0R9HEM7aqTXBee+01nn32WRQKRYUbjSAIDaPT6rgenkx0UByRgTHkpufRrlsb2vdyZ9S8odg0t0avl9i79wS7910hJCQJ9d/jauRGRnTybEGPbm54erZotKJ7eSWlnIksGzScW1xS6XUna0sGebaln0frJmlKLnfp0iUA/vjjD8O26sbgfPDBB7z++uvcuHEDqFgsFMpKT6SlpTVZrIJwJ107G8Gen/5i1gfTcWlf8xTq9PR8Vq85SUZGEW5uLZj15ABatrBt+kCBwlIV2wOucS7meoXt7Z2aM9O3O042tW+lbYhqE5xp06axdevW2x5cm30EQYDcjDyiguKIDoojNjgee2c7PHq2ZdwLj+Li2RK5vCxRKSws5eixUC4GxHH9+g1sbG0AcHVpRo/urenq5YqFhUmjxFQ2aDiLE+FxXL5+A90/Bw3LZHRzbcEgzzZ0bOFwR5q0161bB4BWq0WSpGpLRfzxxx+0aNECX19ftm/fDpR1790aoyRJdY5ZFAy9s0TMtRN3KZELOy4z4vlBpOWnkBaYctv9i4s1/Lk3ksIiNU5OlvT3bcaNlBhu3P6wBpMkibCMPE7E3aBEqzNsN5HLGdLWmS7NTUmKjiCphvM01u+42gQnPj6+xmrFCQkJjRKEIDxoNGoNCaFJRAXGEhUYS1F+MR7d29KpX3vGvzgSS9uKtWcSk7I5dy6a4JBEdLqyRMPK0phhj3SmW7fWjTauBsoHDSdyMiK+ykHD1qYmDOjQhoEd2mBnYdZo162NrKws3nrrLc6dO4dOp6N37958/fXXlZZs2bdvHxkZGUycOJG8vDyKi4tJTk42JIoAGRkZODrWbcq6KBh654iYa+fK8WvEnk7ijR9fwrlNzZ9nlUrDL78eR640w8vLhZ7drejbt3eTx5lRUMTmc1cISy/A2MKS8lrIvdq0YlqfrtiY1a4nqDELhlab4Lz77rs1Hjxq1KgGXVwQHiRZN3LKEpqgWOKvJeLUujnte7VjyuvjaNnOqdLSI1qtjqshSZw7F01icjYAMmR06tgS374e5OYm4uNT+/VkapJTVMKha1Gci75OqUZb6fV2js0Y0rEt3Vu3bPK+8eosWbKE7t27891336HT6Vi3bh0fffQRP/30U4X9Vq9ebfjv7du3c+HCBT7++GNGjhxJQkICLi4u/Pnnn4YJEoJwPwo6cpXD607w1CczcHStuVaNTqdn05ZzpKTmlq0pNas/EeHXmjRGnV7PsbCyqd8a3c1WGzsLM2b07UZXV+cmvf7tVJvgTJ48+U7GIQj3HY1aQ+yVBKKC4ogKjEGj1uLRoy09hnkx9fVxmFtV3fqRn1/ChYuxXLgYS2FRKVA2YNinVxv69vXA/u/KwoGBNTXk1k5WYTF/XY3kXPT1SrVrTBQKeru7MNizLS72No1yvYaIj4/n+++/N/z86quv1nrWpomJCV988QWvvPIKKpWKIUOGMHr06KYKVRCa1MW/LnN8yxme/uwJmreqeaaRJEns2hNEZFQqFuYmPDVnUKOUibidhMxcNvpfIjH7ZkuwTCZjaCd3xnfvhInyzk1Dr8rdvbog3Gd0Wh3Rl+K4eiqMiIvROLdxpH0vd2a8PRnnNo7VjvmQJImE61mcOxfNtdBkw3gXZycbfPt60K1b60avSZFRUMTBq5Gci0msNL7G2caKQZ5t6NuuaQcN15VWq0WlUmFiUjbOqKSkpMZxNFOmTGHKlCkA+Pr6GhYFFoT71fl9QZzaeo6nP5tJsxa1m7V8/EQ4AYFxKBVyZs8aQLNmTTeQV6XR8uflcI6GxVSoON7KzoZZ/bvj1vzemGktEhxBqIFOqyM2OIGrp8IIvxCFg0tzug7qxKh5Q7Gyu/1NRKPRcSX4OufORZOSmguAkUyGVxcXfPt6NMqyCf+UkV/IgauRnI9JrFB3Aspq14zx9qRTy+qTsbtpzJgxPPXUU0yZMgWZTMa2bdtEV7jwUDm7+yL+uy7yzNKZ2DnZ1uqYoEvxHDoSggwZj0/vS2vXpqstE5WaydozQWQVFhu2KeVyxnTzZHhnj7vWvV0VkeAIQhX0ej3xIYlcPRVGqH8E9s52dB3UieFPDsKmuXWNx+fmFnP+QgwXA+IoLimr7GthbkJvH3f69nHHxqbxy6On5RWyPziCi3FJFZ6qoGx65phunnRwbvyEqjG9/PLLODs7c+rUKfR6PVOmTGHatGl3OyxBuCNO7zjPhX2XeHrpk9g51q7LOCYmjR07y2YdjRvbnc6da66PUx86vZ79VyLYfzWywv3Fs4UDM/t1w8H6zkz9rosaE5zMzEw2b95Mbm5uhe3vvfdeU8UkCHeFJEnEX0sk5FQY1/wjsLa3ouugTsz/7qla3WwkSSI2LoNz56IJDUtBouwm0KqlHb59Peja1bXR6tbc6kZuAQeCIwiIT66U2Hg6OzCmmyftne/sYnp1VVhYiKWlJbm5uQwfPpzhw4cbXsvLy8PW1vbuBScId8CJ389y6chVnlk6s1YPUQCpqbls2OSPTq9n0IAO+PbzaJLYsgqLWX0ygNiMbMM2CxNjpvb2oq+76z370FRjgrNo0SJMTU3p3LnzPfsmBKG+JEkiMSKFkFNhhJwJx9zKjK6DOvHcl7Nr3fetVmu5fOU6/ueiSUsvG2wnNzKiSxdX+vfzwNXVvkn+dlJy8tkfHEFQQkqlxKZjC0fGdPPEw6npmqob0+zZs9mxYwf9+vWrspZNWFjYXYxOEJqOJEkc23Saq6fCeHrpTKzta1cSIi+vmN/WnqZUpaFrF1dGj/JukvgC4pLY5H+lwnItns4OzB3YE9s7XEairmpMcFJTU9m/f/+diEUQ7ghJkkiOTi1Lak6HozRR0HVQp1pPxSyXm1vMWf8oAoPiKSlVA2BlaUqf3u709nHH2rpp/viTc/LYdyWCSwmVq3Z1aeWEn7cn7o72TXLtprJjxw4AQkNDK02n/2frsSA8KCRJYv9/jxJ9KY5nlj5ZqT5WdUpK1Py27jR5BSW0ad2caVN7N/pDVKlGy+/ngytUIzaSyRjfoxOPdmmPkdG93+BRY4LTsmVLiouLMTe/c0uqC0JjkySJG3FphJwK5+qpMGQy8BrUiVkfTMPJrW5VektK1Jw8FcGZs1Fo/6770Nq1Gb79PPDq4oK8iQbZJWbnsu9KBFeu36j0mpeLM2O8PWnjcG/MXqivqVOnGpKdcrNmzeLPP/+8SxEJQtPQqDVs/fZPivOLee7LJzGzrN0DUXmtm9S0PByaWTHryf6N3vWdkJnD/04GVFhDqrmVBfMG9aKtw/3z8FRjguPo6MikSZPo06dPhTWpxBgc4X6g0+o4vzeIfesOYmdjh9egTsxYPIkW7k51fuLR6fScvxDD0WNhhoHDXbu4MmhgB1xcmu6PPiEzh31XIrialFrpNW/XFvh5e+LW3LbJrl+ThMIcDidHcjkjiuxkSwY3b1Pnc8ydO5erV69SWlpKz549Ddv1ej1du3ZtxGgF4e4ryi9mwydbsXW0Ye6Sx1HUsl6MJEls3xlAdEwalhamzJ07CHPzxlm6pfz8h65Fs+dSWIXSEn3cXXm8rzdm91BJidqo8bfaqlUrWrVqmlHZgtCUEkIT2f3vv7BuZsXAJ3ozasqj9WrGlSSJa6HJ/HXwKlnZhQC0cXPAb7Q3rk2Y2NwoKObfh/0JSa68YGQPt5aM9u7QJCt611ZkXgb7E8MIySlrUcrVFPN77GXamNa9YOCPP/5Ibm4u77zzDkuXLjVsVygUFRbQFIT7XdaNHNZ99Dud+3vy6JwhdbonHTkayqXLCRgrFcyZNcBQFLQx5BaXsvZ0EOE30g3bTJUKHu/bjb7tXBvtOndSjQnOggULKCoq4tq1a2i1Wry9vbG0vPemgwlCuaL8Yg6uOU7UpVjGPDOcLgM6EhQUVK/k5npiFvsPBJNwPRMAh2ZWjB7lTceOLZps0H1CZg67L4VxLiwWG5ubyYJMJqOHW0v8vDvQyu7uVB2WJImrOTfYlxhGTH5mlfuodJWXgaiJpaUllpaW/Pvf/2bDhg288MILJCcn8+uvv/L666+LLnLhgZAYkczGz7cz7ImB9B7do07HBgTGcfR4KDJkzHi8X6O2GgcnprL+zCUKVSrDtjbN7Zg3qNc9Of27tmpMcIKDg3nppZdo3rw5Op2OtLQ0fv755wrNyIJwL5AkiaDDwRxaewLvwZ159cfnMK1n821WViEHD4Vw9VoiAJYWpgwb2pnePm2bbIxNRn4huy6FERSfXGG7TCajV5tWjPbuQEvb2k0fbWx6SSIoM4m9iaEkFeVWu1/3Zq1oa2VPOJVbnWrj7bffxsXFBQBra2tkMhnvv/8+3377bb3OJwj3ilD/CHb9eIApC8fi2btu07kjI1PZuaus1s3ECT3p6NmiUWJSa3XsDLzG8fBYwzaZTMZIr/aM7dbxniraVx81Jjhffvkl33zzDf369QPA39+fL774gt9//73JgxOE2kqNT2fPv/9Cp9Mz9+PHaeHuVPNBVSguVnHseDjnLkSj0+lRKuQM6N+BwYM8MTVtmv7nglIV+69EcCoyvkK/t4yyvu/R3h1wtmm81cTrQqvXcy49nv2J4aSXFlS7Xzvr5rgbOTC90wDUanW9rxcfH88PP/wAgJWVFe+88w4TJkyo9/kE4V7gvyeAU9vOMfvDx3BpX7fkJOVGLhs3+6OXJB4Z3JE+vd0bJaaU3HxWnwwkOefmOlI2ZqY8NagXni0ejG7hGhOcoqIiQ3IDZWu9fP75500alCDUlrpUzdFNZ7h89CrDZg7CZ1S3StOMa0Oj0eF/LpoTJ8MpKVUjQ0bPHm14dHiXJqk6DGXruRwJjeFQSBQqbcVunR5uLWmjcOTRgb2a5No1xqbTcio1loNJEeSoi6vdz8uuBX6unehg40BgYGCDu+20Wq2h6B+U3X/+WeNHEO4X5dPAowJjeO6r2bWuTlwuJ6eI39aeQq3R0s27NY+O8GqUmE5FxLMtIKTC6t/eri2YPaAHFibGDb7GvaLGBEcmk5GcnGwYaJyUlIRc3vjVWAWhrsLOR7H3P4do4+XKgh+eqXUNiVtJkkRwcCIHD4eQk1s2JdKjnRN+o7xp0cK2kSMuo9PrORt1nX1XwskrKa3wWnun5kzq1Zm2DvYEBgY2yfVvp1ir5lhKNIeTIynUqqrcR4aMns1d8HPthJtl405LnzRpEtOnT2f06NHIZDIOHTpkWEhTEO4nt04Df/7r2bWeBl6uvNZNQWEp7m0cmTrZp8EPEEUqNevPXqpQakIplzOttxcDO7R54Ir51pjgvPzyyzz++OP4+voCcObMGT788MMmD0wQqpOTnsfe/xwiMzmbKa+Nxd3brV7niYvLYP9fwSQll5Ufd3K0wW+0Nx3aOzdmuAaSJBGcmMquoFBS8yp29zjbWDGpVxe6utR9+npjyFeXcig5kuM3oinVaarcx0hmRD8HN0a7dqSFedOMBXrhhRfw8PDA398fhULBm2++yZAhQ5rkWoLQVIryi9n46TZsHKzrNA28nFarY/3Gs6Rn5OPoYM2TM31RKBrWsBBxI4M1pwIrPFS1srNh3uBed21sX1Or8bc+YsQI3N3dOXfuHHq9nvnz59OuXbs7EZsgVKDT6jiz8wKnd5xnwMQ+zFg8qc43DoCMjHwOHLxKWHhZJWBrKzNGDO9Czx5tmqw6Z0x6FjsCrxGbnl1hu42ZKeN7dKJvO1fk9ehaa6jM0iIOJoVzOi0OjV5X5T5KIzmDnN0Z2cqTZqaNNy21Ov9ci0oQ7icNmQYOZQ9CW7cHEBefgbWVGU/NGYSZWf27jXR6iV1BoRwMiarQ3ftIR3cm9eqCcQMTp3tZtd8O/v7++Pr6cvDgQQCaNy8rYR8TE0NMTAwjR468MxEKAhAXcp09//4LW0cb5n87F3vnuneNFBaWcuRoKBcDYtFLEsZKBYMHeTJwQAeMjeueKNVGal4Bu4JCK1UfNlMqGdm1PUM7uWOsaJpr305KcT4HEsM4n3EdvaSvch9TuZKhLTx4tFUHrIxNq9ynsfXo0aPKL4SgoKA7cn1BaIjyaeBDZwykj1/dpoGXO3gohOCr18tq3cweiK1t/ccAZuQXsuVqLCXymwmShYkxswf0xNu1aVqq7yXV3ln37t2Lr68v69atq/SaTCYTCY5wRxTlFfHX6uPEBMcz5tkRdPbtUOcnorLFMFPZs/c6ao0WI5mMPj7uDB/WBSurpvnizi0uZd+VcM5GJaC/5alJYWTEIM+2+Hl3wNK08SqQ1lZcQTYHEsO4lJVsWO38n6yUpoxo1YFHWrTDXFG7J0dJklBT0uABwbcuyaBWq9m7dy9mZvf2gn6CABB6LpJdK/fXaxp4ufMXYjhxKhwjmYyZT/jSsgHjAC8npLD2TBBphSXY2JT9HXds4cicgT2xNb8zDyx3W7UJzqeffgqUrSbu7V1xldKzZ882bVTCQ0+SJAIPBXN43Qm8h3Th1R+fxcSs7glByo1c1m84Q0LCDWxsbejk2ZJRI7vi6Ng0fc6lGi2HQ6I4HBqNWluxy8enrQvje3TCwarpu3luJUkSkXkZ7EsMIzS38nIP5eyMzRnl4slAZ3dM5LVrVSrS5hOWf5HogstkyNPITAljoN3kesf6z6rpCxYsYPr06TzzzDP1PqcgNLWGTAMvFx5xg917LgEwaWKvBo0FjLiRwf9OBqD9u+yE3MiICT06MaKLxwM3kPh2qr2LhYaGIkkSb731Ft9++63hyUyr1fLRRx8Zuq4EobGlxqez+8cDSBINqmkTHnGDzVvOodZoaWZvxlNPDcG9rWMjR1tGq9NzOjKe/cERFJRWnH3k6ezApF5d7vh6UZIkcTX7BvuSqq86DOBkZoWfayf6OrihqOU4oGxVKqH5F4gvCq3QxZWjzuBKzmmsaJzS7jExMWRlZTXKuQShsUmSxIH/HSUyIIbnvpyFnZNtvc6TlJTN5i3nkJAY9khnfHq1rXdMKbn5/HL8giG5sTE15k2/Qbg1v78X4q2PahOcTZs2cebMGdLT01mwYMHNAxQKHn300TsSnPBwUZeqObrxNJePhTD8yUH4jOpe77WjzvpHs2//FSQkunu3po2bUZMkN5IkEZSQwu6g0Aor70LZDIVJvTrTuaXjHX1qqm3VYVcLO8a4dqJncxeMahGfJEmklMQSmn+eGyXx1e5XqM2pd4Jz6xgcSZLQaDQsWrSoXucShKakUWvY9t2fFOUV89xXszG3ql9XanZ2IWvXn0Gt0dKzRxuGD+tc75jySkr59+FzFKvLZkLamJkysr3TQ5ncwG0SnE8++QSAZcuW8frrr9+xgISHU0ZSFms/3EIbr9a8svIZLGzq142j0+n5c99lzl+IAWDEsC4MfaRTkwxSjU7LYntACPGZORW221mYMb5HJ/q0dW2yWVlVqW3VYQ9rB8a4dsLLzrlWiZdO0hFfeI3Q/PPkqDNq3L+Faf2fPm8dgyOTybC2thZr3wn3nIZOAy9XXKzit7WnKSwqxaOdE5Mn9qr3w5BKo+WnI+fILiorzGmiUPDSiH6kx8XU63wPghr/Vfz8/Hj++edZtWoVERER/Otf/2LZsmW4u9evXHRgYCBLly5Fo9Fga2vL559/TqtWrcjPz+fNN98kMTERe3t7li9fLlYRfkikX89gzQdbGDF7CD2Hd633eUpLNWzeco7I6FQUcjlTp/jQzbt1I0ZaRq3V8eflMI6ExlQYVGturGRU1w4M6eh+R6deqnRaTqfG8lcdqg7XhlpfSmR+EOEFARRrC2vc3wg5Xja+dDLvQ2hSWK3jB7h48WKV25OTy9bl6t27d53OJwhNpaHTwMtpNDrWb/QnI6sAZycbnni8X73XudPp9fz3ZADXs3IBMJLJeGaID672tqTH1euUd9yN4nwupF/nam4CtvluuJg0fHmaGhOcjz76yDDAz9PTk1deeYUPP/ywytlVtbFo0SL+/e9/07FjR7Zu3cqnn37KTz/9xPLly/Hx8WHVqlXs3LmTzz77jOXLl9frGsL9IzU+nd8+3MLoecPo9kiXep8nJ6eItevPkJaeh4W5CbNm9sfNrXkjRlomKTuPNacCScnNN2xTyuUM6diWUV073NEy58VaNcdvxHA4OZICTWmV+5RXHR7j2onWtaw6XKjNJSzvItGFl9Hoqy76dytTuTkdrX0ozpbR034AKlXVFZBvZ8mSJQCUlJSQkpKCh4cHCoWCyMhI2rVrx65du+p8TkFobBnxWRxecZZHHh9A3zH1X3BakiS27QggPiEDGysz5s4eWO9aN5Ik8ceFq4Qk3ZxA8Hjfbni53PvTwAs1Ki5mJOKfFk9cYdlYu9ziPBKDj7G0+6gGn7/GBKekpKTCmJsRI0bw448/1utiarWahQsX0rFjR6AsYVq/fj0Ax48fZ8OGDQCMGzeOJUuWoNFoUCqbZoFD4e67EZvGbx9tYexzj9J1UKd6n+d6YhbrN5ylsKgURwdr5swagL1943Zr6PUSR0Kj2XMpzDB4D6BLKydm9OtGM8umWa+qKgXqUg6nRHEsJZoSXdULW9an6nCW6gaheeeJLw6r1XRva6U9nW364G7RFYWRkkDqv7TEnj17AHjttdf46quv6Nmz7Mvj2rVr/Pzzz/U+ryA0lpAz4Rz59QzPLpld72ng5cpr3ZgYK5kze2CD1rs7EhrDyYibzTQjvdozyLNNg+JrSlq9nms5NzibFs+V7BR0VdTh0kt60kuLqji6bmq1FlV4eLghKYmJianXYoYAxsbGTJw4EQC9Xs/KlSsZMWIEAOnp6YYuKYVCgaWlJdnZ2Tg51W8GjXBvS4lJZe1HvzNu/ki8BnSs93muhiSyddtFNFodHu2ceOLxfg2q+lmVrMJi1p4OIirt5kwkpVzOVB8vBnneufVbslXFHEyK4GRqzG2rDg90cmekiyfNa1F1WJIkkktiCM0/T2pJQq3icDJ1pbNNX1zM2hvee0BAAFu3bkWSJLp2rX83Y1xcnCG5AejSpQsJCbWLSxCaQlF+MftWHSYxIoURzw9scHJTsdZNvwateRcYn8z2gBDDzz5tXZjYs/6DlJvS9cIc/NPiOZ9xvdoW53JtrZrR2sKWMFIadM0aE5yFCxcye/ZsOnToAEBsbCzffPNNjSfev38/S5curbDN3d2dNWvWoFarWbx4MVqtlhdeeKHK4yVJqlMiFRJy8x/5bixS2BD3W7zQsJgzr2dz+Jcz+E7vicq0qF7nkiSJ4KtpBAaVVQj29GyOV2czQkOvNlrMkiQRlpHL0dhU1LesuutkaYZfOxcsirIJCsq+zRkaLjAwkBxtKecK0wgpyUJXTcuKsZGcnuYO9LZwxCJPIiEvnNulBXp0ZMgSSJFFUCLLv82e5WQ0k1xoqffEimakpxSQThCpqans2rWLffv2IUkSmzdv5quvvqr3ci6mpqZs376diRMnljW9//EH1tYP5jo5wr1NkiSunQln7y+H6TqoMwt+eJqr16q/v9TGP2vdtPeofzdSTHoWa0/fnDzRzrEZs/pXXQn8bslTl3AuPQH/tASSi3Nr3N/TxpHm2PGk91D0Gm2Dr19jgjN06FAOHDhAUFAQcrmcbt260axZsxpP7Ofnh5+fX6XtRUVFvPjii9ja2vLTTz8ZuqAcHR3JzMzE2dkZrVZLUVERtra2tX4jXl5emJiYEBgYSK9evWp93N12v8ULDYs5MSKZQ9vP8PSHT9Kpb/t6nUOn07NjVyDRscXY2toyelRXBg64fYXjusZcpFKz0f8yl9ILMbO0xIyygXuju3ZgtLcninoOBqyL/edOkWirICAjGclYwsq48he9hcKEEa06MLSFBxbKmluuVLoSIguCCM8PoERXhDFGGGNb7f4KIwUelt3pZN0bK6UdkiQRHh7OgQMH2LdvH6GhoRX21+l05Obm1vWtGnz++ee8+eabvPfee8hkMrp06cK3335b7/MJQn0U5BSy56eDZCRm8sTbU2jdsVXNB9UgOTnHUOtm6JBODap1k55fyM9Hz6P5+8HLydqS+cP63hPrSql1OoKzUzibFse1nFT01VRML+dgakl/p7b0c3SjuakFgYGBKI3kqLgDCc61a9cAaNmyJQCpqamkpqbSpUv9BoQuWrQINzc3Pv744wotNEOGDGHnzp3Mnz+fffv24ePjI8bfPGASwpLY9Pl2Ji8ci6dP/Z7wi4tVbNjkT1x8BsZKBY9N60Pnzg2/+dwqNDmNdWcuVVh118HKgqcG9aKtg32jXqsqcQXZ/Hn9GiczwrDV2FS5j42xGaNcPBns3K5WVYdz1RlEFAQSUxiMVl/zjcNMbkFHax86WPVEKTPh0qVLhqQmPj7esJ+lpSU9evTg3Llz6HQ6jI2NK3Qx1VW7du3YsWOHIUmqy0OOIDSUJElcPhbCX6uP0WtkN6a/OR6lccO/h8omQZxGrdHSo5sbI4bXf0JFQamKHw/7U6QqG39nZWrCSyN87+gEh3+SJInYgizOpsVzMSOx2rGB5czkxvR2cMXXqQ3trJo1WatTjXfGV155xfDfGo2GjIwMvLy82Lp1a50vFhoaypEjR/Dw8GDy5LJy7o6Ojvzyyy8sXLiQxYsXM3bsWKysrGrVDSbcP+KvJbL5ix1MfX0c7XvWr8RAZmYBa9efITOrAGsrM2bPGkCrlo1XwEqt1bIzMJTj4bEVtg/s0IapPl6Y1LPWRW2lFuezMyGEwMzEavdpbmqJn0tHfJ3aoDS6/dOaXtKRVBxNeEFArcfX2Bg3o4t1X1oZdyDgQiAf7/+Ev/76i9TUmzM07O3tGTVqFH5+fgwcOBATExPDGJxp06bRtWvXCl3GdZGRkcG7775LQkICGzdu5JlnnmHp0qU4OjZNBWpBKJeXmc+ulQfIzypgzkeP0bJd48xCKilR89u60xQUltKurSNTJvvU+wtdrdXx89HzhqKiSrmc+cP63vHlX8pllRaVdUGlx5NWUn3tLSib0dnFzpn+Tm3oZt8KY7mcokwdCf4qMiLVZERq6Pts43ZH13jHPnr0aIWfz58/b5jxUFedO3cmIiKiytdsbW3FbIkHVNzVBDZ/uZPpb07Ao3v9mmXj4jLYsMmf4hIVLZ1tmT1rQINmHvxTQmYuv50OJDXv5h+plakJs/r3oGsTr7qbqyphz/VrnE6NrbY5t5W5DX6unfBp7oq8hrFppbpiogsuE1EQRJG2NuNrwNm0Ne4m3Ym5mMzK/Ws4dOhQha6mli1bGrqde/fujeIfK6D7+JTdtHv16lWvaeLlPv74Y0aMGMH69euxtramY8eOvPfee6xatare5xSE25EkiYC/LnN4/Un6jfNh8LR+yBupq0er1bFhoz/pGfk4Olgz8wnfete6kSSJ304HEpdRNu5PJpMx7w61Kt8qW1VMWG4a/mnxROZlVLtob7lW5jb0tWtDh8JWlMQYkbFfw86ILDIiNRRnV5xB1bqvKbg0Xqx1fiTt27cvX3zxReNFIDzQYq7E8/vXu3j8X5Nw93ar1zmCguLZsTsQnU5PJ8+WPP5YX4yNG6c1RafXczAkin1XItDdMv3b27UFT/bvjlUTrvhdrFVzIDGcwymR1c6KamvZjDGtO9HNvmWNT31ZqhuE5wcQXxSKTqr6fLeSyWQ46tqQHlTK1kMnOXbsY4qLbxYKbNeuHX5+fowZMwZvb+9qr1+YriP5igoa4T6bnJzMY489xsaNG1EqlSxatIjx48c3/MSCUIXs1Bx2/rAfdYmapz+biZNb4xWXlSSJHTsDiY1Px8rStEG1bgB2BF7jUsLNWUVTfbzo7tayyn31WomAdQVEHy2hWG2LNK4An9lVF85TF+tRmsmq/PuWJIkbJQVE5WUQnZ9JVF4GWarbT982KpJjd90e95yW2KTZUBQL1+K0hOhya3yPGZEarO9kglM+BgfK3mxISAilpbef4iUIANGX4vjj293MWDyZtl51rygsSRKHDl/j+MmyqrgDfDvgN9q70ZY/yMgv5LfTQcRm3JwJZaJQML1PV3w9WjdZv7Bap+PYjSj2JYZRrK26r9rN0p5Rckemdn/ktnHoJB0JRWFE5AeQoap5SmVCSCqRZ69jp3AkIzYf/zP/QaO5WczP29vb0FLTvn3Ng8APf57Dld8LkRlBv68bPvBaJpOhvyXRLCwsrPCzIDQGvV7PuT8DOb7lDIOn+eI7wQe5vHEH6B4+co1LVxIwViqYM3sgdnb170Y6ER7L4WvRhp8f6eTOsM5Vj2PMua5h79vZ3LhSfm8xJrtL1QU7Jb3ECt9k5AoZZnZGmNkbYWSjQ2WhosCsmCyTAkrMS9BaadD9/T+ZpRGSceW/SbnMiG72LWmX5caVFRJ5QF4dBwpnRKqxHlanQ26rTmNwZDIZ9vb2fPTRR40XgfBAigyMYfvyvcx8dypuneqekms0OrZuu8jVa4kYyWSMG9uDfn3rNzD5nyRJ4kxUAtsuhqDS3vwDdHe0Z+6AnjhYN83aR3pJwj8tnl0JIdUuqeBgasmUNt70au5CUFBQtclNsbaAyIIgogouU6KruSBWQXYx5zeHcXTT+QpF/GQyGf369cPPz4/Ro0fj4lK3fytJD+VjlpMOmcHwOh1eyciRI3nzzTcpKChg8+bN/PHHH1XOxhSE+spIymLnin0gk/H8V7Np3qrmWcF1FRAYx7ETYciQ8cTj/Ro0VjA4MZXfL9ycnt6tdQum+VSuNSVJEle3FXH0y1w0JRW7jcztq07e8nI0SDrQ6iQKUnUUpJa3/CoBm7//rzK9iY60mTEU9MmkjaU9/Z3a0NuhNZZKE3KttFzhRq3em8JERrN2Chw6GOPgqcS5izFpUvVjEOuqzmNwBKEmERej2bFiHzPfnVqv6ZWFhaWs23CWxKQsTE2UzHi8Hx3aN844mIJSFRvOXiY48eYfoNzIiLHdPHnUq32N41vqQ5IkrmSnsCP+KinFeVXuY600ZVzrLgxydkdRTQySJJGuSiQiP4DrxZHoq6gAequi3BKunYgl4ngy4YExlVpC/Pz8+OKLL2jevP5LWvSea8WV3wuR9JB81Ax1kb4eHd83zZ8/n507d6LX6zl79iyPP/4406dPr/8JBeFvOp2OMzsucHrHeYY9MYi+Y3s2SSttZFQqO3eV1dyaOKEnnp4t6n2uhMwc/nfyouGhpE1zO+YN6lWpFbsoS8fBj7KJPlZ174p5s7J7SoFGRXReJtH5GUTlZZISXYIb3escl5FKTqe81ozt1ZuW/6iUbtNKjtJchqa4YpJl3VKOQwdlWTLTQYlDByV2rRUYKSq+l7RGLAtX7a1o5cqVtz1wwYIFjReF8MAIOx/FrpX7mfX+NFw6VN0/fDupaXmsW3+GnNwi7GwtmD1rAM5OVU+VrqvgxFQ2nL1EQenNQbDONlbMHdgLt+a2jXKNf4rKy2BbfDAx+ZlVvm4qVzLKpSOPtupQ7XRvrV5DXFEI4fkBNa7mXZxfyrWTsVw7FkdUwHX0urKkRqlU0rNnT65cuYJer0epVDJ//vwakxudRiJsXzE2reS4+phWet3WVYHnSDNSr2lwGpaNkUJGzaN/qvevf/2Lr776ikmTJtW47/fff89ff/2FTCZj2rRpzJs3j7Nnz7J06VJUKhV+fn68/vrrDYhGeFCkxqez4/t9mFma8uKyedg5Ns495Z9SbuSycZM/ekliyKCO9OldvxmjUFZB/acj51Bry/6imlta8OLwfhj/Y4B/zIkSDryfXWnALoBTDwXFXa9zpXUGOwPSuFFScdKBabEleqUOI03du+dsU20rJTcAMiMZncaYgwwcPcsSmuYeSkytm7522D9Vm+Dk5OQAZZWL4+LiGDFiBAqFgiNHjuDp6XnHAhTuH9fORrDn57+Y/eF0WnnU/aklMiqVTZvPoVJrcHVpxuwn+2NpWflLta5UGi2HopO5HlKx6fORTu5M6tmlSYpjJRflsT0+mODsqsfFyGVGDG3hwZjWnbFSVj2QuZRCArKPEF1wGbW++plJJQUqQk/FEnw0muiLSYakRqFQMGToEMaPH8+oUaOwtbUlICAAf39/fH198fHxqfac6mI9wVuLCPitgII0HS4+Jjyxpup/ixHv22FiacSlyykoTGTo6j+JirCwsnWwanqyvnDhAufOnWP37t1otVrGjBmDr68v77zzDuvWraNFixa88MILnDhxgiFDhtQ/IOG+ptPqOPGHP+f3BjJy7iP0HFH9YPmGyssrZu26slo33l1bM/JRr3qfq0il5sfD/uT//TBmbmzMyyN8K0x6UBfrOf5NLld+r9xFrTAF5mRyqnskuXl52OptoKTydUrdC4n+/jwylRGKAiXyAiVWJRa00Nphr7LCssQco3wFJTl6irP1FGfrKMnRo9dCRlT1tW5GfVS/GQfF2gJKyK/VJInaqDbBef/99wGYM2cO27dvx96+LOAXX3yRl156qVEuLjw4Qk6H8eeqQ8z96HFauNd9/bArV67zx7YL6CWJrl1cmTa1N0plwxOPlJx8/nPsPNFpOdjYlD212ZiZMmdgTzq1bPzaKlmlRexOuIZ/enyV0ydlyOjn6MYEN68q14qSJInU0njC8i9yTR6ITV7VT5qlRWrCTscRfCSaqAvX0WnLkhq5XM7gwYMZP348o0ePNvzdlvPx8bltYlOUpSNoQyGXNxdSmn/ziTApQEXKFRUtu1VOxsxsGi9BdHR0ZOzYsXTr1g0Li5u/n/fee6/Cfn369GHt2rUoFArS0tLQ6XTk5+fj5uaGq6srAOPHj+fAgQMiwXlIJUXdYOeKfdg4WPPy909j3azqWUSNobzWTX5BCW3cHJg2pf61bjQ6HauOXTCUrFAYGTF/WB+cbG6ODbxxVcXexdnkJFQexOvgJSduVhgpNlW3Gv+To5kV7Z0daG/dnPY2zXEwtbxt7JIkocqXkDewrqAkSeRqMkgvTSRdlUh6aRJF2nzy5LlkpUQy0HZSwy5ALXrLMzIyKtwkra2tycrKavCFhQfHlRPXOPC/ozy1ZAbObeqeNIRH3DAkN48M7sijI7wa5SkrLiObHw+fo1h980mjZ5tWPNGvW6NX/SzUqNiXGMaxlGi01Tx9eNu3ZHKbrrhY2FZ6rTyxuZJ7ivTSpPKtFfZRFasJOxPP1aPRRJ6/jlZddh0jIyP69+/PhAkTGDNmTK2WUvmnnOtaAn7LJ2RnMVpV1XUtLvyvgEnfN920eYAePXrQo0ePWu2rVCpZsWIF//vf/xg9enSFBXuhLFlKS0ur0/XFmnZ3VlPErNXouPJXKFHn4+kzqRtte7oSFR8J8Y1z/n/GrNPpOXQklpSUAmxsTOjS2YwrVy7X69ySJHEgKpmwjFzDtjEdXMhLSiAwKQFJD/F7zInfaYGkr3iPlBlJOIzL5bhvEIWSGm6egtzcsrF/MsBRaY6rsSUuf//P0kgJBUBBNonJ2TTeEN+K9OgoJJsCWSb5skwKZJloqboV6HpGDMfS9tCW2t0LqlNjguPp6cnbb79tWPxu69atdOvWrUEXFR4cl4+FcPC34zy1ZEa9akjExWWwabO/IbkZ+Wj9V6K+VcSNDH4+et4wS8pYbsTcgb3o4+7SqE3UKp2Ww8mRHEgKp1RX9VRMd6vmTG3rTQebqn8/qSUJXMk9RVrp9UqvqUs0RPgnEHw0mvCz8YakRiaT0bdvX0NSU99Kv2mhas7/N5/IQyVUN2ZZYSKj6xQLfOY03RNwubqO7Xv11Vd57rnnmD9/PvHx8RX+bWvT1fVPYk27O6cpYs66kcP6JX/g1MaRDzcuwtK2cSv8/jNmSZLYtiOAomIjWrVyYv4Lw7BvwHTw3UGhpKglQ2vzxJ6dGdW1Q4XrJazORNJXHExs21pB13dlbJTFoNCZYYuZ4TXjIjUjOnanvXVz2lk3x0xxZ5ZAUulKyFAlkV6aRLoqkUxVSoWJERaYA5WLtebl5WJjY4ut0hrq9nxSSY0JzqeffsqKFSv47LPPABg8eHCFqePCwyvoyFUOrz/BU5/MwNG17jNxklNyWLfhDBqtjj4+7jw6ov591rcKvn6D/54MMCxEZ2liwrg2zenbzrVRzg9lN5qTqbHsTrhGvqaKzm2ghZk1k9t4071Z1UX60koTuJJzitR/JDY6rZ5Tmy8RdCCc7JQCdOqbLUI9enVj8sSpjB07Fmfn+s0skySJBH8VF/6XT8K56gfMmFob0WOmJT1nWlY7zfRuiYmJQa1W06lTJ8zMzBg5ciQHDhyoUMskIyNDLPHwEEm/nsGaD7cw9PEB9B7dsCf/2jp2PIygS/EoFXLmzBrQoOTmbFQCB65GGn4e2KENI70q1qKSyWSMXmLPmimphkHF3aZbYDOviNXXz1UoVgrg09yV7mZK+rZpnAfH6kiSRJEuryyZKU0kXZVEbg0TImriYt6Omgtg3F6NCY6lpSVvvPEG8fHxdOjQAZVKhalpwwd+Cve3gINXOLb5NE9/+kS96khkZBbw29rTlKo0eHu5MmF840zZPB+TyLozQej/nlZpa27GqyP7kxwdWcORtZerKuF/kecJy6368cLW2JyJbl3wdWxT5bTz9NIkruSe5EZJfIXtWo2Oy39FcOi/F8jPuPmn7dbOhVlPzmHiuEm0atWwhUVL8/T8/lw6aaFVtzYBWDnL6T3Xiq5TLTA2v/MzH2ojKSmJFStWsGnTJgCOHDnCjBkz+Oqrr0hISMDFxYU///yTqVOn3uVIhTshOfoG65b8gd8zw+k2pP4LWdZFUFA8h49eQ4aMGY/1w8Wl/qW8w1LS2eh/2fBzl1ZOPN636gHRFs3ljPrYnr8+zGbUEnsSOyTx35hLlcb8DWvZnhnuPQgKCqp3XNXRS3ryNJllyczfY2iKtLdfi6o2ZDIZlpI9A5qPoZWyA9e4VvNBt1FjgnP58mUWLFiAQqFg8+bNTJw4kZ9++qlBKwYL97eIs7EkX0rn6c9m0qxF3QtY5eUVs3rNSQqLSunQ3pnp0/o0SnXiE+GxbDkfbPjZwcqCV0cOoJmlOckNPnuZ4KwUVkdeoFBbueXDTG7M2NadGNqiPcZVVEXNKE3ichWJjUalJeDPME5uDCI3rbDCa0ZGRjwxfRYvvfByo8RvYi3DSFn177p5eyV9nrai42hz5NXsc68YMmQIwcHBTJo0CblczsiRIxk7diz29va88sorqFQqhgwZwujRo+92qEITSwhNZNPSHUxc4EenvjVX324MMTFp7Pi71s24sd3p1KnuJTHKJWXn8cvxC4aHMld7G54Z0pucWB3NPap+wPAYaoZrb2f2ZYayLya00usT3TriYpXH/htrSDFK4nrSBQCkv7uIypIhyZAUSZJ0yzbg758r7Cdh2CZJ1a2aVzdKIyXNTVrhZNoaRxMXmpm0JDjrKu2svBu0pl25GhOcr776ijVr1vDmm2/i7OzMV199xWeffca2bdsafHHh/hN05CpXj4Sz6OcF2DvXPbkpLCrlf2tOkZtXjFvr5sycUf/F58pJksRfVyPZfSnMsK2VnQ0LHvXFxqxxWhvVOh3b4q9wNCWq0mtKIznDW3ZgtEtHLJSVBy9nqlK4knOS5JKKq5SritVc2HWNU5svU5BVVtnYuW1z/Mb6senXP1Cr1RgbG+Pr69so7wHKnpD6zLNi12s3Jwq4+pjQ52kr2g4ybbIptLVVVFTEN998Q2xsLN9//z3fffcdb731VoUZVeVeeeWVSt3lvr6+7N69+06FK9xl0Zfj2PrtHqb93/h6L+RbV6lpeWzY5I9Or2fQgA749vOo97nyS0r595FzlGrKxgraWZjx3IB+nPoin8tbipj67+a4DzardJxWr2djSiBn0+IqbDdCxmR3V4r1pwn+e2BxiayAgnrUuWkKZnILHE1dcTRxwdHUFTtjR4xkTRdbjQlOaWkpHh43/wGHDBnCsmXLmiwg4d6VEJbEwTXHePSFQfVKbkpLNfy29jQZmfk4O9kwZ9aABi+aKUkSOwNDOXTtZuLR1sGel4b3a7SZUinF+fwS7k9SUW6l1zrYOPJ0hz40q2LKd6YqhSu5p0gujqmwvbRQhf+2q5z+/QrFeWWDBV06ODN/wXPMnvQsCrmCSY9OY+vWrUybNu2207qrUpqn59LmArwmWWDlVPn3236YGc3cFdi3VdLnGStaejftzKi6+PTTT3F0dCQrKwsTExMKCwv54IMP+Pbbb+92aMI9JvRcJLt/PMATb0/GrXPjja+7neJijaFr3auLC6NHedf7XHq9xOpTgeQWl43hM1MqmdGyNzvn5JITX5bw7H8/m3k7nCuMgVPptPwn7CxXcyouh2BsZMRoN2My1McapXWlMVgr7XE0dcXJxBUHUxesFHZ39CGqxm8XhUJBXl6eIajY2NgajhAeRLkZeWz5cidTXx9HvpRT5+M1Gh3rNpwhOSWHZvaWzJs7uEEr60LZDWLTuSuciYo3bPNs4cD8oX0xUTZ8tXFJkjiVGsvm2EuVVvs2QsZ4Ny/GuHbC6B9/sFmqVK7kniSpOLrC9qLcEs78EYz/tmBKC8umR7bp0ooXX32BJ8Y8hdzo5k3Mx6esjkZdZpkUZuoIXFvA5S2FqIskSvP1DF1UORGVGcmYvcUJpdm9N74mLCyMpUuXcuLECczMzPjmm28YN27c3Q5LuMeUl6aob1HR+lCpNBw6HINWb4xb6+ZMn9qnQV/WB65GEnGjbCCuTJIxOLkHB78qMqztBlCcpefQJzlMXFY2iaNAo+KHkFPEFVYs1WKp1NHXOYccTTZ3i5HMiGbGzjgYWmhcMJU37iy2uqrxW2D+/PnMmjWLzMxM3njjDc6cOcOSJUvuRGzCPUJdqmbDp9sYMKkP7Xu617l2hU6nZ/Pv54iLz8Dayox5Tw3GyqphXUdanZ41pwMJir85usbbtQVPD/ZplMrERRo1a6MuEpSVVOm1ZiYWPNexH+2sK84cy1alciX3FInFFbuxCrKKOLX5Mud3hqAuKbt7te/pxkuvzmfq8JnIjRqWjOUla7mwuoCr2wvR3VJW4srvRfR7zhoz28q/j3sxuYGyMUe30ul0lbYJD7eLBy5xbMsZ5n0yA8fWdS9NUR8ajY5NW86RlV1Cu3bNmDWzf4MKkUbcyGDvlXAAFDnGdDjemcjoyvWzbFsr6D2vrDxDZmkhy0NOklZy62BeiWZm+XjaZaK+w802SiNjHExaGbqcmpm0RGnUuPXFGqrGO+ugQYNo164dZ86cQa/X8/LLL9OuXeOs6izc+yRJYvvyvbRo60T/ib3rd/zOAMLCUzA3M2HeU4MaNJUSQK3V8svxi1xLvjmLqW+71szq371RFsuMzMvg1/BzVa743duhNbM8emGuuPmHnKNOJzj3FAlFERX2zU0r4OTGS1zcE2qoX9PZ152XFrzIxCGPN7jvOStGw/n/5hO6t5iqagtqSiQubSqk/4tNs+5OU+jduzdff/01paWlnDp1ivXr19O3b9+7HZZwjzi94zzn/wzkmc+frNcEh/rIzy9hw8azJCZnY2qqYO7sgVhY1L9bN7+klDWnApEkCdNES1rsaE+JqvJ9y3u6BUMX2WJsbsT1why+Dzn1j5IUWlpYJtHGWl2h9fdWjqYuuGX3podLWUuwDJmh1UmG0c1tyEAmQ3brFtnNn8q3AX9vl2GE0V0fs1eTGhOcadOmsWvXLtzc3O5EPMI95tjmM+RnFfD05zPr/GGWJIm9+65w6XICxkoFc2YPwKmBi9wVqzX8fPQc0Wk3m2gf6eTO9N5dG/zHptPr+fN6KHsTQytNuTQxUvCER0/6O7YxXKcssTlNQlF4hX2zkvM4sT6IoP3hhiUUvId04OUFL+HnOwV5AxOb1BA1537NJ+pIyT+LHRuY2xvhM8eK7o9bVr3DPerNN99k1apVWFlZsWzZMgYNGiSWhhGQJIljm88QfCKUZ7+chU3zyos8NoXEpGw2bDxLfkEJdrYWdPduQbNm9f+b0usl1pwKIq+kFKMSOY5/usM/khtzeyNGfWyPx9CywcXhuWn8GHqmQiFRuVEeLayu42ZpWuWDkgzwth1EV9v+XEq5jLWy/lPY72c1JjhmZmakpqbWu6iYcP+6djaCoEPBvPDtHBT1GNNy7HgYZ89FIZcbMWtmf1q71r1ezq0KSlWsPORPYnauYdsYb0/Gdu/Y4OQms7SIXyPOVbnqd2sLO57r2A/nv1fO1eo1XMo5Tnj+xQr5RXp8NsfWBXLlUBSSXkJmJKPno51ZsOBlRvQa36DERpIkkgJUnPulgPizpdXuZ+Usp8/TVnSdbHHPdkPdjlKppE+fPrz88svk5uYSEBCAicm9MwhauPMkSeKv1ceIvhTHs1882ejViatz6XICO3YGotXpaNvGgSdm9CMivGF1Wf4KiST8RjoAzY62Rl5Ysaqw+2BTRi+xx6J52b3iYsZ1/htxHp2hArAeE+V1nC0ycbW0NbSq3MpSYcNAh4k4mro0KNYHQY3fWiUlJQwfPhxnZ2fMzW+WVd6zZ0+TBibcXTdi09j97wPM/fhxrOzq/sRy1j/KUATr8el98fCo+wKct8opKuGHQ2cNC9ABTPHxYkSX+k/RLBeQkcjaqABKdJXXRRnZypNJbbqi/LsJOEedzumMXeTcUqUzJSqDY78Fcu1EDJIERnIZfcZ25eUFL/NI1zENbrHJSdCw791sUi5Xv3qvfVsFfZ+xptPYe7+Gze0sW7aMoKAg1q1bR2lpKatWrSIyMlK04jyk9Ho9e346yI3YNJ7+fCbmVpWnTDf+NSUOHrrKydNlXc59fNwZP65Hg8tZRKVm8uflstZe80hbLEP/sRDuXCseedPG8LB2ODmSLbGXDK8byUowN4nAyVxPS3NbqCK5cbf0ok+zURgbiYcCqEWC8+67796JOIR7SFFeERs/28b4+SNp2a7uLXeXLifw577LAEye1AuvLg17kkjPL+SHQ2fJKiwbEyOTyZjZrzsDOjSs21Sl07Ip5hJn0irPDLRWmjKvQx+87MtmaEiSRERBIIHZR9D9PeDl0l8RHFsbQEZCLgBypRH9xnXj5ZdfYmDHkchlDZ/JBWDeTE5WbNWVhx07Ken3nDXth5thJL9/E5tyR44cYceOHQA4Ozuzfv16pkyZIhKch5BOq2P78r3kZxcy79MZmJg1/Zd2SYma37deICLyBkYyGePG9qBf34aPOS0oVfG/kwFIkoRRsQKnw20qvO7QQcng18qSG0mS2B5/lQNJ5XW9JJTydMxMomlpbomDaeVufmMjY/o286Ot5Z2p4ny/qPEO3KdPnzsRh3CP0Gl1bPxsO92HdcVrYKc6Hx8WnsK27RcB8BvljU+vhhXfSs7J44eDZ8kvLatqqTAyYu6gXvRq07AlC64X5rAq3P8fMxLKeNm1YF6HPlgbl830KtEVcTbzT0M9G0mSOPCTPyc33ny66j6iI4vf+xf9PYY3WmJTzsTSiJ4zrfD/Od+wzaWXCf2es6LNgLtfnK8xaTQalMqbzfZKpfKBen9C7Wg1WrZ8uQudVsecj6ajNG76BSIzMwtYt+EsGZn5mJuZ8MSMfrRzb/haZpIksfpkIHklpSCB89E2UHSzVddIAX6f2yNXytDq9ayNuoh/ejwAMjSYGUdjrCjrkrI1rrw4paOpCwMdJmCpsG1wrA+axr0TC/c1SZLY/e+/sLC1YNjMgXU+Pi4ug81bzv29MngnBg30bFA8cRnZ/HjYn2J1WeuFUi7n+aF96NKq/t1dkiRxOCWSbXHBt/Rrl5HLjJja1psRLTsYvlSTiqM4m7mXUl1Z65GqWM2Or49z5dDNqeBGRkaM7DWeQe1HNSiuBH8Vbr4mVX6h93zSkoDfCnD1MaHvc9a49Hwwm6B79uzJ//3f/zFt2jRkMhk7d+6kW7dudzss4Q5Sl6rZ+Nl2TC1MmLF4EvJGKPtQk+joNDZtOUdJqRonRxtmP9kfe/vGGaD/19Wb425kaiMcNLbk3zJ6z3e+NU4djVHptPwUdoZrOakAyI1yMTeJQGGkwc3SHitlxdIaMpmMbraD8LLxbdJqwPczkeAIBv57AkiOusHzX8+u81NzckoOa9eXrQzet3c7Hh3RsKbSsJR0Vh27gEpbVjfGTKnkxeF98XCq+6rl5QrUpfwv8gIh/6gACuBkZsXzHX1pbVk29VSr1xCUc5Tw/Js1f1Jjstj4/gEyruciN5aDXgJJhlKpZED/uieE5a5fKOXksjxuXFUzcXkzOoyo/JRmbifn2f0tsGz+YN/I3n//fb7//nuWLl2KQqHA19eXBQsW3O2whDukpKiU9Uu20qyFHRNfGV1hdfimIEkSZ/2j2bf/ChISnTq25LFpfTAxaZwWo6jUTPZcvjnLcqSPB+PmteLcqnzO/ZKPQwclfZ+xpkBdyoprp4gvzAb0mCqvY6JMRGFkRFvLZpgpKtaXsVTYMMhhIg5iIPFt1TrByc/Px9r6zkzNE+686EtxnNp2jue/noOxad2KNWVk5LPmt1Oo1Bq8u7Zm/LgeDepWuJyQwv9OBqDVl7WwWJqYsOBRX1o3s633OeNU+WwM+ot8TeUZSAOc3HmiXQ9M5GV/DjnqdE5m7CBPXTYVXZIkAveGsXvZKTQqLU5t7Xl32Ru40IkL5y7i6+tb5+UUAFKvqTm5PJcE/5uLyp1ekYfH0KrH0zzoyQ2Aubk5b7/99t0OQ7gLivKLWfvh77h4tmTcC482edekVqtj955LBASVref0yOBOPDqiS6Nd99ZxNwDtHJsxrntH5EYyBrxsg8dQMxQmMrK1RSwLOUFGaeHfA4nDkRsVYmwkx926Gcb/KAQqBhLXXo0JTmxsLAsWLKCgoICtW7fy1FNPsXLlSlHs7wGSmZzF1u/2MGPxZOzqWKcmN7eY/605RVGxqmxl8Km9G7Qy+LmY66w/c8mwsq6dhRmvPNofZxurep1Pq9ezIz6Y37OisLWt+N7M5MbMae+Dj0PZOjaSJBGWf5GgnKPo/+6+Updo2PntCS4d+HtGxVgvvvr8a9o3L1uDpm/vfnWOKTtOw+mVeUT8VVLptaxYLaF7ivGadHdLnN8tly5d4rvvviMvL8/wxQBi1uaDLj+7gDXvbaZj3/Y8OmdIkyc3hYWlbNjkT8L1TJQKOVOn9Ma7a+OtZyVJEr+d+nvcDWBhYszTg30qFCJ16mxMSlEe31w5ToGmxDCQWIYeM4WStpb2KG4p4GdsZELfZqPFQOI6qDHB+fTTT3n33Xf5+uuvcXJyYtasWXzwwQds2LDhTsQnNLGSolLWf7KN4bMG06ZL3f7AC4tKWb3mJHn5jbMy+LGwGP64cNXws4OVBa+OHEAzy8pdNrWKT6Pix9AzROdnVHqtnXVznvXsR/O/F8ks1hZwNvNPUkpurs6bFpfNxvcPkB6fg9JEwVOLp/LWM59gVs/1VQpStZz9KZ+rO4uqrDwM4DHMDOeu91a58zvpgw8+YMqUKXTu3FkMLn5I5KTnsea9TfR8tBtDpvs2+fWSU3JYv+EsefnF2FibM2tmf1q1atyqyAdDoghNSTf8/NSgXthZVJzirtJp+XfYGQo1BZgbR6NUlNXgslSa0MbSDiPZzXupGEhcPzUmOLm5uQwYMICvv/4agCeffJLff/+9yQMTmp5er+ePr3fTvmdbeo/qXqdjS0s1rPntNBlZBbRwtm3wyuAHgiPYfSnM8HMrOxsWPOqLjVn91qzKLC1ieciJSrOkZMgY69qZca07G56mEosj8c/cS6nuZotK0P5wdn57Ak2pFkc3Oz5b8RF+vabW60u3JFfH+V8LCNpYQBWldgBw7W3C4NdsaNnt4W52VigUzJs3726HIdwhmclZrHl/CwMm98F3fN27eevqakgiW7ddRKPV0dq1GU8+0b/B6+L9U3Ra5s17mQSPOHaocmLE9vhgMlUpWJqFYiQruzHYGptVKOAnBhI3TK2+kVQqleHGnpGRgV6vr+EI4X5wcM1x9Do9o58ZVqfjtFo96zacIeVG+crgg+q9MrgkSfx5OZz9wTfXcWrrYM9Lw/thYVK/c1a9bgvYGZvzbMd+dLApW6BPq9cQkH2YyIKb073VpRr2LDtFwN6yG1QfP29Wfv0Treza1DkOdbGewHUFXFhdgLqw6jUVHDspGfyaLW36Vz176mHTvn17IiIi8PRs2Aw84d6XnZLLkR82MfzJQfR6tGlnykmSxOEj1zh2ouzvulePNkyc0BNFI8/QKht3E2joXm0b70biciv8E/Pp+4wVRoqyv/HQnFSO3biKlelVZLKy5lw7EzNcLWwpL+BnpbRlYPOJOJg2rCTGw6zGBGfmzJk888wzZGVl8e2337J3716effbZOxGb0ISCjlwl7FwkL3w7t04zFXQ6PcdOxJNfANZWZjz91GAsLev3BCRJEruCQjkYcnPKtWcLB+YP7YtJPZaGAAjJvsHPYWdR6bUVtrc2tuSDniOxVJa1kGSrUjmVsYs8zc01rdITctj4/gHSYrNRGMt54Z05LHr6A5TyuidahZk6fpuSSnF21Q8Ddm4KBr5ig+dIM2QNGLP0oElMTGTq1Km0bNmywhINYgzOgyUlJpWDP51i7juP16veVl2oVBp+33qBsPAUZMgY49eN/r4ejf5AIUkSa08HkVtc9mBlVWKBcp8DGq3E6R/yiD5azNgvm2HSSmJ15AXMjSMMyY2xkZxW5jaUJzftLLvSu9lIMZC4gWq12KabmxvHjx9Hq9Xy6aef0r9//3pfMCAggM8//xyNRkOrVq348ssvsbGxIT8/nzfffJPExETs7e1Zvnw5Dg4O9b6OUL3r4ckcXHOszqXPJUli+44AEhPzaNHCkaefGoxdPVcGlySJ7QHXOBIabdjWpZUTzz3SB+N6PlWdSYtjbVSAYYBwOZ/mrelepMBSaYIkSYTmn+dSzvEK+10+GMmOr4+hLtHi0NqO5T9+xyM9R9YrDiib8eTgqawwQwrA0lFO/5es8ZpocV8vqdBUXn/99bsdgtDESopK2fT5dvpN7d7kyU12ThHr1p8hLT0PM1NjZjzel/YeTbOu4sGQKK4lp5X9IIHn6U5kFd9suc2K1WIkl7ExJpASfSSmxjcLd7pa2mIkMxIDiRtZjSNCCwsLCQoKYtGiRcyaNYtjx45RXFxc7wu+/fbbfPXVV+zZswcPDw/++9//ArB8+XJ8fHzYv38/06dP57PPPqv3NYTq5WXms/mLHUxeOBZH19rXlJEkiX37g7l0JQGl0oi5cwbi6Fi/sgGSJPHHxasVkpuuLs48P7R+yY0kSfx5PZQ1kRcqJTcjW3nyfMd+KGRGFGsLOJy2icDsm7OkNCotO746xpYlh1CXaOk3qgeHDxxpUHJTbvBCW8N/m1obMeQNG57d50y3aZYiualGnz596NChA66urri4uNCiRQs0mqqXqRDuP5IksWvlATr09qBN98abtVSV2Lh0/v3TEdLS83Bobs2LLwxrsuQmOi2LPbeMIeyd1pmsyxW7pYf8ny1RpikEZF7D1DjBsN3B1BILhQkWCivGtXpGJDeNqMYWnLfffhsXl7JiQtbW1shkMt5//32+/fbbel1w3759KJVKNBoNaWlphr7248ePG2ZmjRs3jiVLllQq2y40jFqlYcOn2/Cd0BtPn7pN8z9xMoIz/pHI5UYMG+qOq4t9zQdVQZIkNp8L5lTkzdlK3Vu35OnBPijqMQNLp9ezMSaIk6kxFbbLkPGYe3dGtOoAQJYsiT0pJ1HdMpA4MzGXje8f4EZ0FgpjOa+88wJvPPM2Rka1jyMpUIV1SznWLSr/KTl7GeM1yRxLRwW9n7LC1Pr+W937Tvv+++9ZtWoVAHK5HI1Gg4eHh+iiekAE/HWZzKQspr4+luCrwU12nXPnY9i77zI6vR7PDi14bFqfeo8TrEnh3/VuyktbtDVyJPcPc7ilWrFbPxPcJsn4+NIFzE0iDK+ZyhU4m1shAwY0F7OkGluNCU58fDw//PADAFZWVrzzzjtMmDCh3hdUKpVEREQwb948FAoFb7zxBgDp6emGLimFQoGlpSXZ2dk4OTVsFWqhjCRJbF++F8fWzRk4uW7ri128GMvBw1fLkoZpfVCr0ms+qJoYNpy9zNnom08vPdu04qmBveqV3Kh0WlaF+xOcnVJhu0Im5xnPvvg4uKLRqwnIPkSE0RlsdLaGfYKPRLHti2OoSzQ4utrz75/+jW+PQbW+dm6SliOf5xB7spQuE80Z81mzKvfz+7Tq7ULVdu3axbFjx/jiiy/417/+xblz5zhx4sTdDktoBOnXMzi8/iTPfjGrydaW0un0/Ln3Mucvlj3wDB7oychHuzaoNtftSJLEb7eMu7EwNsbpz7akld4cA2hsIWPUEjtWR59FL4tEafT3osFAa0s7ZMjoZNMXZ7OGLR4sVFZjgqPVaiksLMTSsmxdjqKiogoFuKqzf/9+li5dWmGbu7s7a9aswdPTk7Nnz7J582Zef/11Nm/eXOl4SZLq9CQdEhJi+O/AwMDb7HnvuRPxXjkYRlLoDUa9PISgoKBaHxefkMux43FIEvT3dTUkN3WNWS9JHIxKJjQj17CtY3MbulnIuHL5UvUHVqNYp2FrTgwp6qIK202N5Ey1a4fsejqnr8cQbnSaEllZX3deXi5atY4jvwRyaV/ZwObuA7rwr1fewVhvXqv3JOkh+ZgZMVss0KnKPp/Xdhdh0ScRS5dqits0wMP2Wba3t8fR0RF3d3fCw8OZNGkSv/zySyNFJ9wtapWGzV/uYtS8YTi4NE3SX1KiZuMmf2Li0lHI5Uye1Ise3Zs2aTh0LfrmuBtgcEY3wq9UnOAw9F+2BJFARF4YFqY3H8acza0xlSuxM3agh92QJo3zYVVjgjNp0iSmT5/O6NGjkclkHDp0iClTptR4Yj8/P/z8/CpsU6lUHD58mBEjRgAwYcIEvvzySwAcHR3JzMzE2dkZrVZLUVERtra2tX4jXl5emJiYEBgYSK9evWp93N12J+IN9Y8gKyqP176fj7V97SsCx8Smc3lPPNY2NowY1oVhQzsDdY9Zp9fz2+kgktUSNjZl1YT7tWvNrP496vVklV5SwPKQkxSbK7A1v1md2N7EnIVeQ2hpbk1qSQIn0k9grDfCGNuy5KZQxsb3j5ASmYFcKeeNd15l4XP/V+vZFHkpWg58kM31cxUHDiPJyD7iwpAV9V8nqyr362dZpVJVeOCoC4VCwfXr13F3dycgIICBAweiUqlqPlC4p+3/9Qgt3J3oMcyrSc6fnVPE2nWnSc/Ix9LClFlP9qe1a9O2nsakZ7E7KNTw8xB7T6JXVHz4dx9kisNoHT9fvoiZSaRhu4XSGAdTC4xkRgx0mIhcJpaFbAo1/lZfeOEFPDw88Pf3R6FQ8OabbzJkSP2yTYVCwccff4yzszNeXl7s37+fnj17AjBkyBB27tzJ/Pnz2bdvHz4+PmL8TSNIjU9n148HmP3hY3VKbsqrfep0enz7ejD0kfrNdtDq9Kw5HUhQfLJh24D2bsz07V6vaZpxBVn8cO00Bf9YU8rFwpaFXQZja2JGVMFlzmcdMAwkTghJ5eTmQKLOJaEp1eLQqhm//OdXeveoXVedJEkEbyvi+Ne5qIsqt1626mFM76fqt5SEUNELL7zA+++/z08//cTy5cvZuXNnve83wr0h5HQYsVfieen7eU1S6+l6YhbrN5ylsKgUJwcb5sweUO/ZnbVVpFJXHHdjb496ox1a1c0B8SbWMkZ8aMuPkSdQKiINxfyMZDJDvZsedo9gZ+zYpLE+zGqVNg4aNAgfHx9D11Rubm6dWlfKyeVyli1bxgcffIBOp8PJyckwW2rhwoUsXryYsWPHYmVlxTfffFPn8wsVFeUVseGTrYx9/lFc2reo9XEZmQX8tva0YfHMcWPrl4xodXr+dzKAy9dvNssO9mzL432963W+4KwUfg4/i0ZfsSuok60TL3YagIlcTkD2YULzLhhei7+Swi+v7kSvK/vsduvtxcY1W2r9+c2/oeWvD3OIP1t5kU6luYxH3rSl23QLUaSvkQwdOpShQ4cCZeNxEhIS6Nix412OSqivnLRc/vzPIWZ/OB0Ts8av6RJyLYk/tl5Ao9Xh0c6JJx7v12SDicuVj7vJKSobd2NubIxPYhcCrlacXTz8bTtOqiNJKgnD3OTmcjGtzG0wNlLgbNqaztZ9mzTWh12NCc6mTZtYunSpYaqmJEnIZDLCwsJqOLJqPj4+bN++vdJ2W1tbfv7553qdU6hMp9WxaekOvB/pgvfgzrU+Lj+/hDW/naKwqJQOHmWLZ9bny1uj0/HL8YuEJKUatg3t1I5pvb3qdb4TN2LYEB2IRMUWFF/HNsxp3xsJDcfTd5BUfHPquVatY+c3JwzJjUwmw2/4uFolN5IkEbKjiKNf5VZZhbh1HxNGf2KPTSvRtNwYfvnlF5577jk++eSTKj8f77333l2ISmgInVbHlq92MXiaL608av+AVRuSJHHqdCQHDpbNxOrdy50J43s0aC282jp8LbrCfW2ySw/Of1UxufEYZobZkFL2BgdhYXrznmRtbIqdiRnGRsYMcBgvHoyaWI135//+979s2rSJLl3E3Pz7yd5VhzGzNGPErMG1PqakRM3q306Rk1uEq0szZj5Rv8Uz1Vodvxy/UGHw3YguHkzu1aXOf9CSJLErIYS9iaGVXhvj2plJbl4U6fI4lvYHOeqbT0nqEg3r3z1AWlw2UJbcmJiY4Otb82J+BWla/vooh7hTVbTamMkY8n82dH/MUlQgbkRWVmVdfHZ2jbvooXD3HF5/Egsbc3wnNO4aUzqdnj1/XuJCQCwAo0d6M2hghzuSLCTnF3MwMcnw8/BOHsR8I0N3S6kmM1sjHnnPmm8jD2NqHG6oVqwwMsLl72rFfZqNwkJhg9C0akxwmjdvLpKb+8zVU2HEXI7jxeW17/NWq7Ws/bvip6ODNXNn12/xTLVWy89HLxB+4+ZU8lFdOzChR6c634C0ej3rogI4mx5XYbsMGTM9evJICw8ySpM4lr6VUt3NJ6iSAhW//WsvCVdvYG1nxYfvf8jlS1eYNm0aPj63v9kmX1Kx7aUMVAWVW21cfcpabWxdRatNY5sxYwYA169f56uvvrrL0QgNFRUUy5UT13i5Dveg2igpUbN5yzmiYtJQKuRMn9oHLy+XRjv/7RSp1OyLSMTIzBwoWzNvYq/OxL+o4q+PsinOKhvzN+I9Ow4UhJCjCcfMOM9wvIuFLQojOW0sOtLWomkGWwsV1XinHjhwIBs3bmT48OEV1oapzxgcoellp+awd9Uh5nz0GKbmtevz1un0bNpyjoTrmdhYmzNv7iDMa3nsrVQaLT8dPUdkaqZh25huHRnbzbPON7lSrYafws4SmptaYbvSSM7zHX3p3qwVsYUh+GfuRSfdHJNTmFPM/97Yw42oTJo7N2Pblu14eHjQ3qNDrWYkOXRQYmJlhKrg5jkVpjKGvG5DjydEq01TCw8PN3SDC/en/OwCtn+/l8fenICFTeMN9s3NLea3dadJS8+7YzOlypWPuylQa7AxKxt3U16c1GOoGa16OHP4s1wkvYS+bz7Hr13G0uzmg5m9iTnWSlPMFZb0beYnPt93SI0JzqpVq1Cr1SxZssSwrSFjcISmo9Pq+P3r3Qye5kvLdrUrSV6+vlRE5A3MzUx4+qlB2NiY1/napRotPx72Jyb95uKV43t0ws+77qtC56pKWHHtFIlFORW2WypMeKXLINpa2XM55wTBuWcqHpdawH9f301mYi6ubVzYumWboQp3bRlbGDH6E3t+f6asu6tVT2P8PrXHrrWY0XcnODg4MHbsWLp164aFxc0vRzEG5/4gSRLbl+3FZ2R32nZtvBo0SUnZrNtwhoLCUhyaWzN3zkDsm3imVDlJktgReK3CuJs5A3rQzPLmfdLMVs74r5uRX6zik6sHMDeJQPb3eEFjIzktzcuWtenffDwm8tqv/yc0TI0JTnBw05XTFhpXXfu8b11fylip4Kk5A3FwqPv6UsVqDT8e9icuI9uwbVKvLoz0al/nc90ozuf7kJNkqSoW8GtuaslrXoNpZmLKyYwdJBSFV3g943ou/31tF3nphXh26sCWTb/Xe7FWt76m+My1wspZTq8nRavNndSjRw969Ohxt8MQ6unUtnNoNVoemVH/BZn/KTQ0mS1/nEej1dGurSMzn/Bt8plS5TQ6HevOXCIg7pZxN5098G5d9aDpLdcvUSqFYWJ08/5VvpBmR2sfWpq1bfKYhZtqTHDUajUnTpygqKjsH0yn03H9+nWx6u89JiooluCTobxUhz7vW9eXmjWzPy71WF+qSKVm5SF/ErJutrZM692VYZ3rttYVQHR+JiuvnaZIW7GwWxtLe17pMgiFkYaDqevJVN2o8HpKVAar39hDYU4JPXp2Z/26DbftQpUkifD9xUgSdB5b9VPg0EXVHy80nQULFlTa1pDFfYU753p4Mmd3XeTFZU8hl9d90dx/kiSJs/5R7NsfjIRErx5tmDSx1x2ZKQVl97ZVxy4QlXazy93dzoqJ/9/eeYdHWWV//PNOb8mkFxJ66KGH3hGlW0BAFEGRVdfVXXVdl/WnYldcC64FxIKI2KU36TXUUANJgJAGIaT3ZDLl/f0xMGFIQkILIdzP8/A8yX3bmTBz59xzzznfLpVXpe7LSCEq6xAmXbkzdEFI06zxpYv3oBtus8Cdah2c5557jpSUFDIyMmjbti2HDh2ie/cr0zIS3FgKcgpZ/L9VjPvnaIyeNdteulRfKizsyjW/iixl/G/tDlKyyxPpJvTowIDWza74XlGZp/k6dhc22b3HTXvvYJ5o05tCWyZrz/5Gsa3A7Xji4bPMf3EFpYVl9O/fj2+++RaDoeq/QVGWnXVv5nBifQkak0RoF22lQpmCm8P69ev53//+R3FxMbIs43A4yM3N5cCBK5fzENQeJYUl/Pbfpdzz9HDMflceBb4Uu93BilUH2b3HqSl115BwBvRvXWu5K1mFxXy+fidpeeXzTf9WTWnisLL1gzx6TPPE6FvuxOVaSlhwcheGi7oVXxDSVEgK+vrdg0ohtrlrm2pd4ZiYGBYtWsQdd9zBSy+9xE8//UReXl51lwlqCVmW+eOjFXS9s2ON97yjj55myTKnHtXdozvTPrzhFT+3oNTCrD/dnZuJPTtdsXMjyzLLk4/yZUxkBeemb2Az/ta2L+dK4/kz7fsKzs3xPcl8+/wySgvLGDFiBN99N/+yzk3smmLm3ZvGifXOBl1lhTJrXs2ukbaaoHZ4//33efLJJwkODmbGjBn069fPVWF1KZ999hkjR45k5MiRrsqryMhIRo8ezV133cXHH39cm6bftsiyzJJP19C6Rwva9LjybelLsVisLFi4g9174lEplUwY14OBA668CvNqScrM5b8rt7g5N/d2bceEHh1IWmwiakEh8+5NI26tM7IoyzLfndiDrIhBITmjzxcLaXb06oevtmY5kYLrS7UOTkBAACqViiZNmnD8+HFatGhBQUFBdZcJaokr3fOOP5XOL7/tRkZmyOB29Oh+5VtJRWVWPl6znTM5TudGkiQm9e5Mv1ZNrug+pTYrX8TsYFlSdIUGfnc3DufhsK7E5O9ic/of2BzuAnbRm+P5/sWVWEttjB8/ntmzZ7tV+V1Mcbad6M88Wf5CFiU5Drdjp6MsZBy3VnqdoPbR6/WMGDGCTp06odVqee2119i8eXOF8yIjI9m+fTuLFy9myZIlHD16lBUrVvDSSy/xxRdfsGrVKqKjo4USeS2wd81BstNyuOuRgdd8r7y8YuZ+vZnjJ9Iw6LU89mh/OnZodO1G1pAjKWl8/Oc28kudjopKoWBq/wjuCm9B6sEyktc4F1AlOQ6WPZ/Fod8K2Xw2nrj8aDSq8tYYF4Q0A3ShtDNX33tLcGOo1sExGAwsX76c1q1bs3r1auLi4sSeeB3hwp73uBfurtGe9/XQl8otLuXX6ETX6kaSJCb36ULvFldWMXGupIB3Dq7nYNYZt3EFEo+06M6Ihq3YmbWS/TmbK1y7b2UMP776J3abg8cee4wPP/wQlarybaaTm0uYd28a6Xt1FY4Ft9cw5bcgAlrVTsKioHq0Wi1lZWU0atSImJgYFApFpSt3f39/pk+fjkajQa1W07x5cxITE2ncuDENGzZEpVIxevRo1qxZcxNexe1DWmI6GxZuZcKL96DWXNsWTOrZXGZ/uZGzabn4+3rw1ycG07jx9RWwvRzb4hKZs2k3ZTZnJNmg0fDMnb2JaBqKtcTB6pezQS5/L3oEKfEeYOf3hN0YNCdc4xeENNUKNX38RqOQaidnSFCRav/yr7zyCjExMfTp0weFQsGkSZOYOnVqbdgmuAwlRaVXtOd9PfSlMvIL+XjNNnJKnKsbhSTxaL+u9Gh+ZVtch7NTefvAes6W5LuNm1Ranms/gK7+AaxL+5H4wiMVrt3+6yH+eHcjskPmn//8J6+//joKRcW3seyQ2fF5HoufzqQ42z1qo1RD/2fNPLggAN/mYl+8LjF48GAef/xx+vfvz3fffcczzzxTaXfjFi1a0KlTJwASExNZvXo1kiS5Vc4FBARw7ty5CtcKrg9lpWX8+v5Shk0djF/ItfWjiY07y9yvNpFfUELTJv488fggfH1N18nSyyPLMkuijvLTroOu7Wpfk4EXRvSjRZDTwdo6K4+cJPco8l1veLHgzB7U6lgkyXnsYiHNbj534qEWnblvJtVmVzZt2pQXX3wRgFmzZt1oewQ1QJZlln66mlbda7bnfT30pU6kZTJ38x6KLE5FXKVCwaP9utKlScgV2b0qJYallWxJNTR681TbPqgUxaxK/Y5CW16Fazd8u5cN8/YC8PrrrzNt2rRKn2MpdLDqP1mc3FRRaiGwrZoR7/jiFyYcm7rIk08+yd13301gYCCff/45+/btY9SoUVWef+LECZ544glefPFFlEoliYmJrmNX0zAwOjra9XNUVNQV238zqW17d/y8D4fOgcNcdtXPjoqK4tixDHbvPY0sQ/PmPnRsbyQmJrr6i68DNoeDP0+cIS6zfL4JNOm5s0EwZ04e5wyQvldL9EJ3WYWQwcWsKDvEiaJDmD3SsZ5fQwWo9ZQUFKKXQ8jNthFF3XgP3a7v5SodnH/84x988sknjB49utLjy5cvvy4GCK6cfX8eJCs1h7HPVz3xX+B66EvtOpnMjzsPYnM4P8VKSeIvA7pV2QuiMkptVuYd38P+rNMVjvXwb8zDLSLItCSx/txiyhxlbscdDpmVn24n8rfDKBQKPvjgAyZMmFDpc7ITrCz+eybZCe6rLUkh0+dvXvR4zAOFSvS1qatMmDCB8ePHM2LECNq1a3dZmZioqCj+/ve/89JLLzFy5Ej27NlDRka5HllGRgYBAQFX9Pzw8HC0Wi1RUVE16nxdV6htew9vPYYjH/46a+pVq4Tv3buPc+kqYo4X4Gk2M2RwOwYNrL1k4gtl4GlWMJudDkx4aBCP9Y9Aq3Z+NWacKGPbN+lw0YLMHKqk2//58dHJXfh6nEPCuVjyVOto6OGNXmlidMg0dMraaURYHbfqe9lisbgtOK6GKh2cv/zlLwD8+9//RqMROQp1hXNJGaz/YSt/mTmp2j3va9WXkmWZpfuPsTa6fH/ZU6dlROOmV+TcpJcU8NnR7RW2pBRI3N+sI3cEtyCuMIp9Weu4tJ7JbnOw+P3NRK2KQaPR8PnnnzNixIhKn3Nycwkrp2dVUP82+Cho9UQWvR6qvWRFwdXx1FNPsWTJEj788EOGDBnCuHHjaN++fYXzzp49y9/+9jc+/vhjl4Bqx44dSUhIICkpidDQUFasWMHYsWNr+yXUe7LTclj11Xomvzb+qp2bsjIbGzYlkF8ASqWCMfdG0LnT9et8XB1VlYGP694e5fkt79I8B0v+noW1pHw+kZQyQ9/x5quUjeg0MUg4F30qhYJQo1NIs5ffiDrj3NzuVPltFx7uFAP773//y9KlS2vNIEHVlFms/PL+UoY+Wv2e97XqS1msNuZv38/B5FTXWIi3mb8O7kFCXM1lOo5kn+Wr2F2U2N2jMkaVlida96K52cyOzGUkFFVUC7eV2fn19Q0c2XICvV7Pt99+S//+Vaujpx0pq+DcBLZVc+8nfpxITaviKkFdYsCAAQwYMID8/HyWL1/Oq6++6syRWLLE7bxvvvkGi8XCe++95xp74IEHeO+993jmmWewWCwMGDCAYcOG1fIrqN/YbXZ+fX8pA8b3rrEczKWUllr59rutpKTkERwcwEMTe9G06dV1Hb8akjJzmb1hp6tSCpxl4He2C3NFjxx2mRX/ziI3xT0S3PLhAiLNmeRmRKNTF7rGLwhptvToTKjh2kvlBdeHapfzer2etLQ0goJEHf/NZvXXGwhuFkjnwZdXor1WfancohJmb9zl1uMmPDSIqf0j0KlVJFzm2ottWH06liWJR6rMt3GQw4oz31TItwEoK7Hy0/+tJ3bPKcxmM/Pnz6dbt26XfWafv3lyLqaMU1uduTft7jZw56veqHUKSL3spYI6hM1mY9euXWzfvp2srKxKI3Yvv/xylfpUy5Ytu9Em3ras+34LJm8TPUdd3ZbHhajy6TPZeJg0PPH4IPz9PK6zlVVzJCWNb7budVVKqRQKJvftQkRTd826HZ/lkbDdPYevw/1GCnufZEvaSUy6FNf4BSFNT7U3XX3uuPEvQlBjqnVwSkpKuOOOOwgKCnJroiZycGqX6O0xnDqUyFOfXF6K4Vr1pZIyc5izcTd5JeUf7iHtwri3SzsUNdRksthtzDu+h6jMlArHuvk34uGwrsQV7ORI7o4KW1IAJfml/Dh9PScPJ+Hn58ePP/542VyMC0gKiZHv+fLjw+foOM5El4dMQrX3FuOtt95i1apVtGrVinHjxvHJJ5+ILfI6wvGoeI5sj+Fvn0y9qs+V3e7gx593kpiUgdlDT88eobXq3GyLS+Tn3YdclVIGjYYnBnV3VUpdIG5dMbu+cu/11qCjht4vGnl28ynMAXGu8QtCmpIk0cfvbtQK8V6tS1Tr4Pzf//1fbdghuAw553JZ8eU6Jr06rto9723bj1+1vtSBpFS+2xaF1e5c3SgVCib06EDflk1qfI/0kgK+OLaDM8XuURkJibFNO9A7IJDN6T+RYTlT6fUF2cUsfGEdScdPExISwk8//UTz5jVvRqjzVDDl9yCUauHY3IoYjUZ++eUXGja88u7aghtHfnYBi/+3ivH/ugeDx5WrYTscMr/+tofjJ9IwGrRMfbQ/ycknqr/wOlBZLqGvycBTd/Qi2MvdwcqKt7L6pWy3MaOfgns+9uPn5L0oDKdQSOWLvwtCmh3MffDX1byiVFA7VOvgdO/endzcXEpKSpBl2SW2Kagd7DY7v/53Gf3v70Voi8sn9u7fn8iatU719/vHdKuxvpQsy6yNPsHS/eV5MAaNmr8M7E6r4JrvjUdnn2VuFfk2j7fuiU6dwYqz32C9pErqAjFbk1jy/hbycwto3rw5P/30EyEh7pOG7JDZOdeZrNz7SXNltxHOzS2MEPGtezgcDn7/cDndhnWmafiVJ+rLssySpVEcOZqCTqvmkSn98Pf3pDa+RipTA2/k68Vf7+iJWV+x+adHsJKm/XQcX+uUc1Go4J6P/YiVUonK3o9Blwnnq6YuCGn6aYMJ9+pz41+M4Iqp1sH55JNPmDt3LgBKpRKr1UpYWJjYoqolNizchsFDT6+7Iy57XmzcWRYt2QfAqBGdatze3Gq38+POQ+yOL59tAjxN/HVwTwLNNWu0Jcsya07HsriSfJsQgxdPtIngZNE2EnIrJhJfIH5jOt+/ugJwdkeeMWNGBefGUuhg1UvZnNzonHwCWmkIG3Tlq0mBQFBztv2+C9khM3BCzeRgLubClvm+/QmoVUomP9yXkAa10/yuMjXwS8vAL0VjUHD3h77s+aaArZ/kccdL3pQ0L2B+9A4MmhPYzuccXxDSVClU9PG/G6V07erpgutPtQ1Rli5dyqZNmxg6dChr167l3XffJSwsrDZsu+05eSCBg5ujGfPsiMvueScnZ/HTzztxyDID+7ehd6+aZfEXlFr439pIN+emVZA//xrRv8bOjcVuY27sThYlHq7g3ET4NWJam1bszPqZhMLKnRsJKNirZv6bS1xjCoWCY8fcz89OtLLwwXMu5wZg5X+yyE4QOlICwY0i6VgKO1fs4/5/jq60Y3h1bNwU49oyf+jB3jSpJemFrMJiPly9zc256d+qKU8M6l6lc3MBSZLoMc2TRxYFYhhawqwjm1GojiFJzrnmYiHNrt5DMKuvrYuz4MZRbQTHx8eHgIAAmjVrRmxsLPfeey9fffVVbdh2W1OYW8SiT1Zy//OjMZqr7qmQnp7P/AXbsdrsRHRpyp1Dqk/GBTibW8DsDbvILCxyjfVp0ZgJPTqiqmEjwIySQj4/toMzxblu4xISY5qEE2TKZNO5HytNJAYwSB4c/uEs38yeD+DSHVKr1a7eJgDxW0pY8e+K/W2UaoniHAc+TWtkruAWJTs7Gx+fmueSCa4PJUWl/P7hcu6toRzMpeyIPM6GTUeRkJgwrgctW9ROJW5SZi5zNu5yK5S4tAy8JmT55/Bp9HYkRSJqZXleTvB5Ic0QQ3NaenS+rrYLri/VfpOpVCqSk5Np1qwZ+/btw2azYbFYqrtMcA3IsszvHy2ny5AONOtQdfOrvLxi5s3fRklpGW1aNeCeu7vU6AN87Mw5Pli11eXcSJLEmIhwHuzVqcbOzbGcNN46uK6Cc2NQaXiiTWdQ7eNw7vYqnRt/exMWv7yTb2bPR6FQ8Oqrr7J48WL+9a9/8csvvxAREeHMt/kyj0VPZ1ZwbgLaqHn4l0BCu1xdozFB3eJifbsvv/zS7dhjjz1W2+YIgDXfbCSsSzNad7/yvi77ohJYufoQAGPviyC8XWg1V1wfDiefZdaf213OzcVq4JXNjQ575TPUsZw0/nd0GyhS0GkSXeM+Kh1+OhM6pZ7eviNFlWYdp9oIzhNPPMErr7zC7Nmz+eSTT1iyZAkDBw6sBdNuX7Yv2o211MqgiVUnrhUXW5g3fxt5+cU0buTHhPE9aiTBsCX2FL/tOYLjfKmkVqXi0X5da9yZWJZldhemcTA6vtJ8m3ua+BJXuLSC3MIF1AoN/lmtePXpd0lKSsLb25s5c+bQt29fACIinLlGZUXOfJsTG0oq3KPNSANDX/NGrRcqvfWF7OzyFfKaNWt44oknXL9fKOsV1B6xe05w6nAST3965cLKR6JTWLzEqSU0cngnunRpcp2tq0hucSm/7z3C/sTy6syqysAvUJrn4OdH0+kxzZM2I8pboBzJPssXx3YgKdLQa066xoMMHmgtzgrTnr4j0KtqRwxUcPVU6+BEREQwaNAgAJYsWUJSUhKtWrW64YbdrqTEnWHHkj08+dEUlMrKE9fKymwsWBhJekY+gf5mJk+qXoLB7nDwx95oNseeco15G/X8dXBPQn0qr0a6lFKble9P7mNT/hm8vNyv6eIbRBvfHI7kr63yen9tAwr36vnrC89TXFxMeHg433zzDaGh7qu7nCSnnlRW/CV6UkoY+E8vuj4s+tvUNy7+/7zUoRH/17VLcUEJy774k3Ev3H3FUgzHj6fx6+97kJEZMrgdfXrf2K6+siyzLS6RpfuPUWItz8erqgz8Ahc6FWcct7LixSzOxZTR/x9movPO8kXMDiQpC4O2vN9NsMETf52JPEsubTy70cgovgNvBap1cO644w4GDhzI+PHjiYiIoHXr1rVh121JSVEpv/53GXc/NRQv/8qdDrvdwc/nJRi8zAYeeaQfev3lm0sVl1n5dstejqWmu8aa+HnzxOAelZZKXoosy+zNSOHXhIPklblHVCQkhjYMQlIeIbm4Ykdi5znQ1qMXa+ZG8sXnXwAwZswY3n//ffR69yqoU1ud+TaWAvcvOb2XgtEf+tK4R/X2Cm49LnZqhENzc1n2xZ+079fmikvCExIzWPhTJHa7g769WzJoYJsbZKGT09l5/LTrEAkZ7n1rejRvxNiIdph0VTtn2z9171S8d14BeYZCVrffCVIeBt0xLghsNjB44qdzRmv85cZE+Ay5/i9GcEOo1sHZsGEDK1asYObMmRQUFDBu3Djuu+8+kfR3nZFlmaWfraFlRHPa9qp8dSDLMouXRhF7XoLh0Sn9MHtevkw6o6CI2Rt2uYnKdWkSwuQ+XdCoqi9tTC3O58eTUcTlpVc4pleqGdpITbZtB7Kt8m0Eo8qT9uoBvP6P99i8eTNKpZJXXnmFadOmVVi17/6qgG2f5nFp4k5Aa6eelDmk5kKhglsL4dTUDQ5vPUZ6UgZjnxt5RdedSc1hwQ87nMUOXZsyfFiHG/Z/arHaWHUojg3HTrq22sHZ3mJiz47V9u6KW1vM7q/dOxV7toVVrXcBhZi00S4RzRCDGV+ds8gj1BCGR3ZT8V69haj2G8PDw4OJEycyceJEYmNjefXVV5k1axZHjhypDftuG6LWHSbzdNZlJ5a166LZfyARjVrF5If7VCvBcPJcFnM37aHwoqTwER1aMbJT62o/pBa7jeXJR1l35jgO2VHheLBBQ6eALLKsFR2fCzQ1tsWc0ZRH/vIESUlJ+Pj4MGfOHPr0qZhblB5jZftnFZ0bkW9ze3Dq1ClGjx4NQHJysutngJSUipIfgutPfnYBq75az6RXx6HWqGt8XXp6Pt/N30apxUr7dg259+6uN8wJiD6dxi+7D5NVWOwaUykU3NW+JXeFt6h20ZZxoozV/+ce8dH4yBx6OApZXYxJdwRJcm6NhxjN+Gqdzk2ALpT+/vdx6PTh6/yKBDeSGi2Jjx49yuLFi1mzZg3h4eF88sknN9qu24r05AzWfb+Zx959qMqJZUfkcbZsi0UhSTz4QC8aNbx874Xd8SksjDyAzeF0TtRKJQ/17kT3ZpdvgS/LMlGZp/n11EFyyoorHFdKCrp5QahvPIW2qhKJ1XT3HcbRjQk8+vxYSkpKaN++PV9//XWFfJsLBLbV0O9ZM1s/cm5zSQoY8E8vIiaLfJvbAdF64uYiyzJLP11Dt2Gdq+2YfjHZOUV8+91WioottGwRxPhx3WusWXcl5BaX8tuewxxIclfNbRHox8ReHQkyV69pVZrnYMnfs7CWXLQdqpKJnxqNzasIo/YICsk5p4Uazficd268Nf4MDhyPSlFzp09QN6jWwRk9ejQlJSWMGTOGP/74g8DAmrX/F9QMm9XOzzOXcteUgQQ0rDzb/9Ch5PKSyzHdaNmy6n4Ssiyz7EAMfx457hrz0Gl5YlAPmgVcflsxrTifn+IPcCw3rdLjrc0+tPXL5WT6SWyyV6Xn+Gsb0NNnJLM/+orPP//cafPYscycObNCvs2ldH/Ug/RjZSTtsjD6A18a9xT5NrcL3bt3rzCWm5uL2WwWDm4tsH/9YfKzC3hwwpgaX5OfX8K387aSX1BC0yb+PPhArxpVcl4JDofMtuMJLN1/jFJredGBUathbEQ4PZo3rNH740JScW6Ke+FC2vhTlDTPwaSLRqlw5hc2NHrhrXVWVZlUZu4IfACNQsxFtyLVOjjTp0+vdEvhWjl27Bjjx48nOjoagPz8fF544QVSUlLw8fFh1qxZ+PvXXAfpVmXvkkMENQmgy5AOlR4/cTKN3xftBWD40A507lR1X5xSq43vtkVxOOWsa6yBlyd/vaMnviZDlddZ7DZWpcTw5+lY7JVsR3lrNPQOVlIqHyajih5IEhDu1ZtGcnv+NvUZtmzZglKpZMaMGUydOrVCvk1lk5IkSQx9w4eSHIfIt7nNKCws5LXXXmP8+PF0796d559/ntWrVxMSEsI333xD48ZVv+8F10ZOeh5r529m6tsPoqxBXh6cb1Px3TaycwoJDfHh4Yeqr+S8UlKyc/kx8hBJWTlu4z2bN2JMNUnEl3JpUjFAXt9z5PU7i1Ebg1JRCEAjkxdeGudcqVcaGRI0EYOq9hTPBdeXat+RN8K5KSkp4c0338R6UVnfrFmziIiIYO7cuSxZsoS3336bWbNmXfdn1yWORsaRGneOaQsnV/qFf/p0Ngt/3Ind4aBfn5b061t1aWJWYTFzNu7mTE55JVN4SCCP9o9AX8W2lyzLHMpO5ef4A2RZiiocV0gyEQEyJu0p8u1VN3c0qjzo6383WaeKGPXYKJKTk/H19WXOnDn07u2uX3NiQzH7fyxk7Bf+qLQVX7PGoEBjEPk2txszZ87EaDQSFhbGli1b2LlzJxs3buT48ePMnDmTL7744mabWC+RZZlFs1bS974eBDau2YLSYrHy3ffbOZeRR4C/J1Me7otOd/22byxWGysPxbLxWLxbEnGgp4mJvTrRsoq+NlVRWVJxSdMC0sfHY9DGolLmuuQXzBpnlFmj0HJH0AN4qkUxza3MTVkmv/fee0yZMoX9+/e7xjZv3szChQsBGDVqFG+88QZWqxW1un7ue+Zl5rN89p/0n9QdnaHiSiQjs4D5C7ZTZrXRuWNjhg2tPMIDcPJcJnM37XVLJh7SLox7u7Srcj88vaSAn+MPcCTnbCVH7TQ1F9HAlINCsmGtGNRx0cTYmh6+w1mzYi3//Oc/KSkpoUOHDnz99dduYpnWUgebP8jj4M/OldLWj3MZPL12RPcEdZ+DBw+ybNkyJEli69at3HnnnQQHBxMcHMzMmTNvtnn1lp3L9+Gw2elzX8UtwsqwWu18/8MOTp/JxsfLyNRH+mM0Xr9u4kdSnEnE2UXuScTDOrTizvAw1FX0BquKypKKbZ5lpD4ei854ArUyq4Jzo5JUDAoch49GpGPc6tS6g7NhwwZKS0sZNmyY23h6erprS0qlUmEymcjOzq6XOT8Oh4M/Pl5Br9ERmJpUnBzy80v4bv42V+LemPsiqtxnjjyRxM+7DrmSiVUKBQ/26kTPsMp7WJTZ7aw5HcPqlFhssv2So3a8dVk0NedjVINz46ly1Ao13X2G0kjXhpnvzmT27NkA3H///bz33ntu+TYZJ8pY8WI2mSfKI3ZRPxTSuKeO5gOFGrgAlEql6z1+4MABN+kG0cn4xpB5JovNv+zgiQ+m1EhI02538NPPO0lIzMDTQ8/UR/vjWU2bipqSW1TCb3uPVEgibhXkzwM9O9ZY/PdiKksqlpUOUh+PQ+1/Ao0qDQlo7OGDp9qZYyNJEv0D7iNQd2U9gAR1kxo5OGfOnCEvL89tomnX7vKijqtXr+bdd991G2vWrBmFhYV899131T5TluUrUq+9kMsDEBUVVePrbgZHNsRyNjWN9ve2BNztLSuzs2r1CbJzSvD3N9K6pZaDBw9UuIdDltmamMb+1CzXmEGtYljrRqjzMoiKyqhwzcnSPNbnp5Bru3S7yYHJkEljj1y8VRK2YonKW/Y5G/sFyM0IzWxL8qksnvnvPRw6dAiFQsG0adMYOXKkSwlcliF1k44TP3rgsLo7S5JSJnpPArkeFaUYbhR1/X1RGbeazVdrr0KhoKCggOLiYuLi4ujRowcA586dq7dR3JuJ3W7n949WcMeD/fANrj6S6nDI/PbHXlcPrqmP9MfH59qlChwOmS1xp1h+IMYtidik1TK2Wzjdm4VedZL50eVFFZKK0yck4GgVi16dggQ08fDBQ12eQNzHbxShhhvbfVlQe1Tr4HzyySd8++23+PqWlyVLksSGDRsue93w4cMZPny429hvv/3Gl19+yUMPPeQau+eee1i4cCEBAQFkZmYSFBSEzWajqKgILy+vGr+Q8PBwtFotUVFRdO3atcbX1TanT5xl3ZEd/O2jaXj5m93stVrtzJu/DbusISzMjyf+MhBDJdtXFzoTxxfZMJudHY9Dvc08ObgHPpUkE2eWFvFz/AEOFWeASYcXFz7QdjSqc/gb0mlg1KFTVr3fLAHNTO1p79WHE4cT0Ol0THt6mivf5ssvv3RTAC/Js/PnqzmVakl5NVQx6n0fgtvX3iqprr8vKuNWs/mCvRaLxW3BURMmTZrEfffdhyzLDB8+HH9/fzZu3MiHH37IpEmTbpDFty/b/9iNzqCl+4gu1Z4ryzJLl+3n8JFktBo1j0zuS0DAlauLX0p6YQn/XbW1QhJx77DG3BfRDqP28h3aq6PLQyYUKlj/bg7YJXL7plE88DAGzSkkCZqY3J2bCJ8hNDO1v6ZnCuoW1To4S5cuZe3atddlq2jcuHGMGzfO9XurVq1YunQpAAMGDGDJkiU8+eSTrFq1ioiIiHq3cisrLeP3D5Yx6ok7K0gxOBwyv/y6m8SkDMweeh6d0q9S5yY9v5DZG3ZxLr/QNdapUQOm9O2CVu3+32l12Fl7Oo6VKcewOi7ejnKgUaVh0qbSwKDFW2uiqu0oCWhqCqeDV1881T7s27ePDz74gN27d1NWVkbHjh356quv3PJtUvaVsvLf2RScu3QLDNqOMjDkZW+0JpFILChnzJgxhIWFkZmZSf/+/QHIyclh2rRp3HfffTfZuvrF2YRzRC7by1OzHq02OiLLMmv+PMzeqFOoVUomT+pDaOi1Jd6W2WwsPxDL4kPxeJrL58EgswcTe3asUhzzakjplUzKP5Lw3tCA7IlRGLXHkSRoavLFpC6fX9t79aatuWZ5SIJbh2odnODg4FrJg/nHP/7B9OnTGTlyJB4eHnzwwQc3/Jm1zcq562nUJpTwvu4aLbIss2z5fo7FnkGv0zBlSj+8vCpGYmJS0/lmy16Ky8pzWarqTHw0J40fT+4nvfTi6gEHGtU5dOoU/PUqAvVmlFLljoYENDG1pYNXP8xqZ/Ru8+bNTJ48Gbvd6bgMHjyYr776Cp3OuQpy2GQi5+Sza24+l1abqw0Sd77sTbu7jTX5UwluQzp0cE+kHzt27E2ypP5is9r446MVDH10MGa/6qMwW7bGsm3HcZQKBQ8+0IumTa+tdcep9Gy+37Gf9PxCV9NytVLJsA4tGdLuypOIq0KWZZYkRbMq5Ri0gLJWuzHqjqGQJJqafDBe5Ny09OhMJ68B1+W5grpFtQ5Or169eP/997njjjtcX2RQfQ5OTYiLK1dr9fLyYs6cOdd8z7pK9I5YEqOTeeqTRysc27DxGHv2OVdID0/qQ1Cge3RHlmW2xiXw254jrrJJtVLJ5D6d6drUvTNwrqWEX04dYF/mxe3tHWhU6WjVyZjUMiFGM3pl5dExCWhsbEMHr354aZwrKavVyg8//MBbb73lcm4kSaJ79+6u90Reqo2V/87izIGK3Y2D2qkZ9V9fvBvVr4ic4PpxsTRDZSxfvryWLKnfbPppB96BXnQeHF7tuXv2nmLt+mgkJMaP606rVjXvcHwpVrudlQdjWXf0pFsuZ6tgfyb27EiA57Xn85TmO9B5KpBlmT8SD/Pn6VgAFFIhRu0xlJJMUw8fjKpy56axsTXdfYeKZpL1lGodnEWLFgGwZs0a11hNcnAE5eRl5rNizlomvToOrd592ykmNoNjsQUoJIkHJvSkSWP38KzN7uDXPYfZfjzRNWbW63hycA8a+5UnBzpkmU2pJ1iSFE2p/UKEx4FamY5Ok4xGYSXI4IGP1kBV21GNja3p4NUXb02Aa2zTpk28/vrrnDhxAsCV+K3RaFw5N2cOWvjjqQws+RWrXbo96kG/v5tRqsUEIqia4uJiLBYLd999N/369UN5nVbygnJS4s4Qtf4QT//vsWq/0GPjzrJ0mbONx713d6F9+OUlXi773Oxcvt9+wK1Hl06tokdYCJPv7H3NzoWl0MGOL/I4sqiIRxYFsqYkmvWpzk7uCqnkfJdiB808fDGoyvN6gvVN6Ot/N4oqotiCW59qHZyNGzfWhh31FofDwe8fLqfX3d0qaLwciU5h1+7TeJrN3HtPV9q0buB2vMhSxleb93A8LdM11tjXmycG98DLUB5NSyrM4YcT+0gsvNDvwYFamYFOk4xCKsVXayDI4F3ldlQjY0s6evV3c2xOnDjBG2+84fr/b9KkCa+88gp+fn78/vvv3H///URERADgF6ZG56nAkl+ec2PwVTDiHV+a9hEtzgXVs2HDBvbt28fixYt5/fXXGTx4sCsvR3DtlFms/PHxSkY9cRcmr8tvE58+nc3Pv+xCRmbwwLZ069bsqp5pdzhYG32CVYfisDvK96xbBfkzqU9nEuNirsm5kWWZ2NUlbPpvDkUZzvvPfyWRI484nRtJsmDUHUGlsNH0EufGTxvMwID7UUqiY3p9ptr/3ezsbJYtW0ZRURGyLONwOEhKSuLDDz+sDftuebb9sRuAfmN7uI3Hn0rn19/3IMtw15BwIro2dTuempvPnI27ySwo7zDcrWkoD/Xu7FLMLbVZWZIUzcbUE8jIgIxKmYVenYhCUYJepSbE4Of2wb6YhoYWdPTqh4+2XNsqOzubjz76iO+//x673Y6HhwfPPvssjz76KFqtM/okSZJbdY/WpGDU+778NDkdhw2a9tUx/G0fjL5iFS6oOREREURERFBaWsq6det49913KSws5J577uHBBx+82ebd0qybv5mQsCDC+7S+7HlZWYV8/8MOyqw2unRuwh2D217V89LyCpi/bb9bhZRaqeS+ru0Y0LopkiSReFV3Pm/nKSvr384hebd7ywvLbh3GCC+KwzMwaqNRK6w08/Rz25I3a3wZHDgBteLaqrQEdZ9qHZxnn30WnU7HyZMn6d27N5GRkbdU6erN5PTxVHYu28tfP37EradP6tlcflgYid3uoG0bfwb0d590jqSkMW/bPre+EPd0actd4S2QJAlZltmfdYaf4w+Qe17xW6nIR6dJQKXIRyFJBOvN+Ooq344KNYTR0asfvtryiJLVamX+/Pl89NFH5OXloVAoePjhh3nhhRfw86u+qqFBBy39nzUjKSS6TjIh3QBFYcHtgU6nY/jw4RgMBubNm8fHH38sHJxr4NThJI7ujOOZTx+77HlFRRbmL9hOYVEpLZoHct89Xa84wiLLMptiTrF0/zGs9vKIbjN/Hyb37XLNuTZlxQ52fZnP3vkFOGwVj1t9SpGVdgy6Y2iVpTTz9EV3kXNjVHkwJHAiOmXV2nyC+kO1Dk5qairr16/ntdde44EHHuCZZ57hqaeeqg3bbmksJRZ++2AZo568y61aITu7kO/mb8NSZqVDeEPCmqtck4gsy6w/epIl+4+5EvG0KhWP9OtKx0ZOZySztIif4vdzONvZ8VMhFaPTJKJWOhv+mTU6QgxmVIqK0ZMQfTM6evfHT1u+FSbLMuvXr+fNN98kPj4egH79+jFjxgzatHGv9io4ZyMnyQZVBGa6PXLtvTEEtzcHDx5kyZIlrFu3jnbt2jFx4kSGDBlys826ZSkpKmXxJyu59+nh6E1Vdx22Wu0sWLiDzKwCGgR5MfEqlMEzC4pYsOMAJ86Vb6mrFApGdmrNne1aVCkbUxNkWebkxhI2vpdL/tmK7SdkpYPsO1PJHp6MwSManaqQZh7uzo1OqWdI4ESMKjFP3S5U6+BcWL03adKE48ePc/fdd2OzVeI6C9xY+eV6moQ3cgsJFxaVMm/+NgqLSmneLJD7x3bj0KGDAJTZ7Py06xC745Nd5/uaDDw5uAch3mZsDgcbUo+zLOkoZQ4bEmXoNEloVGkAaBRKQoxmt8ZVF2igb0pHr/7460LcxmNjY3n99dfZunUr4Ow0/eqrrzJkyBC3lVtZsYO98wrYM68AhRK6viaS8gTXl88++4xly5ZhMBi49957Wbp0aY0ih4LLs+abjYR1aUbLrs2rPMfhkPn1t90kp2ThZTYw+QrFM2VZZvvxJBbti8Zy0XdDQx8zk/t2IcTbfJmrqycn2caGd3NI2FZa6fGi1rmkTziFNagEL108AUYbfroAt5xDtULN4MAJmDXiPXU7Ua2D4+vry9dff02nTp349NNPMZlMlJZW/kYTOIneHkNyzGm3knCLxcr877eTlV1Ig2BvHprYC9X5XJq8klK+2rSHUxnlonDNA3x5fFB3PHRa4vMzWXAiijPFuYANrfo0WvUZJBxIgJ/ORKDeVKEaIFDXiM7eAwnQuZeSZ2Vl8cEHH/DDDz/gcDgwm80899xzTJkyBY2mfF/aYZc5uqyIbf/LcyXxARydbabXEFlURgmuG5999hkNGjQgKCiIXbt2sWvXLrfj9bmFxI0ids8JTh1O4ulPp1Z5jizLrFp9iKMxzh5cj0zud0X6UrlFJSzceZCjZ865xhSSxLD2LRnWoRWqK4wCXYzNIrPrm3x2f5WPw1rJcbOF9HGJFHbJwNdQQCPPXAwqGYXk4XaeQlIwKGCcW+RacHtQrYPzxhtvsHLlSiIiIggPD+d///sfL7zwQm3YdkuSk57Hii/XMenVcWh0TmfBbnew8KednEnNwdfHxCOTy1dI6YUlLFq5hZyickmDXmGNeKBnR8ocdhac2MfWtHgudB/WqZORJOen3aDSEGo0u4VhAbw0fnTxHkSIPsw9ElNWxrfffssnn3xCfn4+SqWSRx99lOeffx4fH/fupEm7S9n831zSYyvOLPmn1Bz6rZAuD3pUOCYQXA2X6tYJro3ighKWffEn4164u0JriovZvuM4kbtOoFQqmPRg7xpLMMiyzN6E0/y6+7Bb49EgswdT+nZxa2FxNVhsNr6ZdIaimIoOkqyQyRmcStbIJIJ8c2nlkYNeZUdCAtzPl4B+/vcSpG9yTfYIbk1qFMEZP348cXFx/POf/+Tpp592U4oWlONwOPjjo+X0vqe8JFyWnSJ1J+PPYTLqeGRyP0wm5zbSgaRUfjmSgMHD6ShIksTYiHAGtm7K3swUfjl1kAJrCWplJjp1IgqFM3KmlCSCDZ4VetoYVB508upHM1MHt2iOLMusXbuWN954g8TERAAGDRrEq6++SsuWLd1eQ3aClS0f5XJyU+VROoOPgoZ359JpfGilxwWCq+Fycgw7duyoRUvqB8u++JPwvm1oGl613tuhw8ms/vMwAOPGdq9xl+KCUgs/7zrkpvwtSRKD2zRndOc2rirPq6HYVsaWs/GsP3McIjwJinFvE1AclkfGAycIbnuOJvps9CoZ5xxYeTS5p98IGhsvXzkmqL9U6+AcPHiQp59+GpVKxc8//8w999zD7Nmz6dKlepG2242tv+1EoVTQb2xPwOlYrFx9qFykbko/fH1NyLLMhmPxLNoXjfV8fwi9Ws3UARH4+xiYdXQrMbnnUCryMOkSUCrK5Ra8tXqC9Z5uScQahYZ25t608eyGSuEezTl69CivvfYakZGRALRo0YJXX32VwYMHu51XkmsncnY+B38prLQ6QamBiIc96PEXT6Lj0lCoxPaU4Ppx9OhR3nzzTby8vHjnnXfw8fEhNTWVd955h61bt3L48OGbbeItw5FtMZxLTGfscyOrPCchIYPfF+0FYPjQDnRoX7NGfoeSz/LjzoMUlJaXZ/uZjDzcp/M1aUjlWkpYn3qcLWfjyxuV9izFvCMA/SlPbB5lZI49SeDQeDrqs9G6tr6qnoe6+gymhUenq7ZJcOtTrYPz/vvv89133/HCCy8QFBTE+++/z9tvv80ff/xRG/bdMqTEnWHXiig3Abut2+KI3Hk+/PtQbxoEe2F3OPhtzxG2xiW4rvX3MDJtYDcOFp7hs6gYHBRg0CagVpbn5GiVSkIMXm4CcQpJQSuPrrT36lOh7HH9+vV89NFHHDp0CHBKYbzwwgtMmjTJTcTUViZz4KdCdn6ZV2knYoA2Iwz0+4cZc4hoiiW4Mbz22msMHz6c1NRUZs+eTY8ePZg+fTodO3Z0CfJWRmFhIQ888ABz5swhNDSUyMhI3n33XSwWC8OHD+e5556rxVdx88nPLmDlXOcWuVpTeaJweno+P/zobFPRu2cL+vZpWel5F1NcZuXX3YfZcyrFbbxfy6aMiWhXQei3pqQV57M65ji7SxKwXypgp4D0B07hHemL95QjtPFJR6NQcuk21KUE6RrRwauv2JYSVO/glJaWunUTHTBgAB9//PENNepWo7TYwm8fLOfup4bi6evcbtq/P5E/1x1x6rjc353mzQKwWG18s3Uf0afTXNeGeBi4t29bvjwVSXppJjp1EhpVecKeBAToPQjQm87vMTtpamxLJ+8BeKjd97rLysp44403mDdvnmts1KhRvPfee3h7u5+bk2Tl9yczyU2pvCquQUcNg170okHHqvfwBYLrQUFBAVOnTsVutzN06FBWr17N66+/zsiRVUchDh06xMsvv+zadi0tLeWll15iwYIFBAcH88QTT7BlyxYGDLg9hBRlWWbpZ2voNqxzha7pF8jPL+G777dRUlpGuzYhjBjesdpeNzGp6SzYcYDc4vI8QS+Dnkm9O9E25OqEmBMKslmSkIz0qQL9CW/kGYlwSeaDXllMiy7nMPQ4iFqhpMr+FOdpaGhJuLlXhWpRwe1LtQ6OSqUiLy/P9SE4derUDTfqVmPll+to1rExbXu1Apw6LouW7ANg5IiOtA9vSG5xKbM37CQlu1yPJbxxIOesZ/gibjM69Wk89M7KqAuY1BpCDGa0FyURB+kb09V7sFuTvgts2bKFV155xdXPBpzaUeHh4RWcGwDPBioqU28whyjp/5wXrYbqhQidoFa4kNenVCqxWCzMnTuXtm0v30X3119/ZcaMGbz44osAHD58mMaNG9OwoXO7ZfTo0axZs+a2cXD2rz9MflYBD740ptLjFouV7xdsJzevmEYNfRk/rsdle9NYrDYW7TvKtuMJbuPdmzVkXPf2GLVX1gnY5nCwP/M0G1NOkLVCje+yTigtzq8g3xUNyRiXCMh4aopp7pWPXl14vtS7asdGISloZgqnrWdPlziwQHCBah2cv/71r0yaNInMzEyef/55duzYwRtvvFEbtt0SHN56jJS4VJ6a9QgAyclZ/PTzThyyzMD+bejdqwWpOfl8vmGnW6VUqzA/jitSybXF4qvPRJLKoygqhYJgvSfeWj0X9pi9Nf508R5MA32zCk5HUlISr7/+On/++ScADRo0IDMzE7vdjlqtdoliXopSLTHgn14s+buzMZfGJNHzcU+6PuSBSiscG0HtcbHCtLe3d7XODcDbb7/t9nt6ejr+/uWJsgEBAZw7d+7Sy+olhdlFRM4/wKNvTURZSZKv3e7gx592kpqWi5+vBw8/1Ae1umrHIbOgiDkbd5Oam+8a89BpeaBnRzo3vrJy6/yyUrakxbP1VALSJjNeGxoTkOseFfbaFIxiQCKh7c+gV5Wen+Oq3opSK9SEmTrR1txDNO4TVEm1Ds6gQYNo1qwZO3bswOFw8Le//Y3mzatuGnU7kZOex6qv1jP5tfFodBrS0/P5/ocdWG12Iro05c4h7Yg9m8FXm/ZQYnUmzsmSTGBzA7FEo1efxGwqQJLKIzQ+WgPBBk9XkyqjyoNOXgNoagqv0OemuLiYzz77jDlz5mCxWDAajTz77LNMmzaNw4cPs3PnTnr16kWHtl2wWeRKnZawQToa9dTi01hF76fMQj9KcFNwOBzk5eW5HJ2LfwZnDllN7nGx8y/L8hVHIKOjo10/R0VFXdG1NwtZltn+0z5COgRyJiuFM1kpFY/vSObEyWx0OhWdO4UQGxtdxd3gdF4Ry2KTKbWVdwwO8/FkSGMfHJlnico8WyO7UssKiSrK4GRGEf47GhOwqy2q0ioaCIZm4adMwlqcSSUtb1yo0BLsaEGQHIYiU0ssJ2pky/XkVnlfXMytZvP1srdGmWEGg4Hu3bsD51tmnzx526v82u12fv9gGX3H9KBB8yDy8oqZN38bxSUW2rRqwD13d2F3fAoLdx50KenaVHbUoTayVdEYz+fZXJhCdEoVoUYvlzCmRqGlvVcfWnl0rVAZJcsyy5cv58033yQ11VmqOXbsWF566SWCgpzCmREREXTp3JXoJUV8/cJZujzkQc+/VFzpSJLEuC/9UShFxEZw8zh+/Dg9e/Z0OTU9epSL00qSRExMTLX3CAoKIiMjw/V7RkYGAQEBV2RHeHg4Wq2WqKioW0JzT5ZlVsxZC7LM5BcmumneXWD9hqOkZ9rx9/dl2tQBhIb6VHInJ9viEll/7AxaowktzmjyAz070iusUY2cRavDzr6MFDamnuBMajHe6xrTflcAClsV0RhDGR4PHUQ/8jiSUga8Kj3NqPKgrbknYaaON1Uk81Z5X1zMrWbzBXstFovbguNqqNbBeffdd1m4cCEeHh6uyUeSJHbu3HlND77V2frbLtRaNX3u7U5JSRnfzd9OXn4xjRv5MX5cd1YfjmPV4TgAZGSK9BY0gdlojSdRSGWu+0gSBOk98D+fRKyQFLT27EZ7c2+0yor9hmJiYnjllVdcf//w8HDeeustunXr5nZe4k5no76M48710K6v8ml/nxGjX8UIjXBuBDeb2NjYa75Hx44dSUhIICkpidDQUFasWMHYsWOvg3V1E1mWWTl3Panx5xg8rU+lzs2+qAQ2bj6GhMQDE3pW6dzYHQ7+2BvN5tjyHEtPnZa/DOpO8wDfam3JtZSw5Ww8W9LiKTupxGdtCE0O+CLJVcwtGhvKgUfxnnQcpXfVnfHNGl/Czb1pYmyLUhLRZcGVUa2Ds27dOrZt21ZpkurtSlLMaXavcpaE22wOvv9hB+cy8gjw92TixJ78tKe8nLIMGzkeeQQFn0avyXC7j1apJEjSEqB3Vl41M4XTybs/JpVXhWfm5OTw4YcfMn/+fBwOB97e3kyfPp2JEyeiVJZ/8DNPWtnyYS6nLtFtsRbLbP88j6Ezql69CQS3Mlqtlvfee49nnnkGi8XCgAEDGDZs2M0264YgyzKrv97A6bgzTHnzAY7FHq1wzvHjaSxZ6gz133N3F1q3qryyqshSxtdb9hJ3tnx+auhj5snBPfE2Vt3UVZZl4guy2HDmBPuzTuOQHZgO+NB4btWN9SSPUgyj4zCMiKOAcyjNXpWe568NIdyrF6H6FqLQQXDVVOvgNGnSBE9PkcR1gZKiUn7/cDn3/G04RrORH3/eSVJyJmZPAw9M7Mm8HfuJS3NOFLlSMWV+Z2jkn4pS4b6z7KM10MDgSUF+PoG6RnTzGYKPNqjC8+x2Oz/99BPvvfceOTk5KBQKHn30UV544QW3vITCDDs7PsvjyOIiLm0nAaDSShh9lVeVlyAQ1GU2btzo+rlXr14sW7bsJlpz45FlmdXfbCQ55jRT3nwAvbGiwO6ZMzn8eFGxQ/duzSq919ncAuZs3EVGQZFrrEuTEB7u3bnK3jZldjt7M5LZkHqClKIct2NF7XKxmayoCt231RUBhRjvPYb+zngk3fmCijwqEGJoTri5FwHahmKeElwz1To4Dz/8MJMmTaJHjx6oVOWnP/300zfUsLrKijnrCOvclNbdw1i8NIqY2FT0Og33jotg7o59pOUV4MBBqjoT7+BEGnjmu12vlCRCjV6YNXrUCjX+jq7cFTS+0g/z3r17eeWVVzhy5AjgnLzfeOMNtwqTC0rfe78rwFpSeaO+tqOcjfo8g0WjPoHgVkaWZf6ct4nE6GQefaty5yYnp4j5C7ZTZrXRuWNj7hzSrtJ7RZ9OY97WKFcBBMCoTm0Y3qFlpfNRVmkRm8/Gsy3tFEVWS6VNhCVtISVDYvFY0h4AVZNsDGOPouubhKSqfH6SgCamtrQz98JHc3V9dQSCyqj2G2/u3LmYTCYKCgqqO7Xec2jzUVJPnuWvsx5l/Yaj7ItKQK1SMmR0B+bt3k9+qYViqYxMUzyNglMxaNw/0Ca1hoZGb9QKJcH6JvTyG0FcZnyFySQtLY23336bRYsWARAcHMyrr77K6NGjXec6bDJHFhex4/M8ijIrCdkAIV00DPqXF8HtRaM+geBWR5Zl1n2/hfhDiTz61kT0porbR8XFFr6bv43ColKaNwtkzH0RFeaXC1Ixi6OOuvIqNSolU/p2rVACLssyx/My2Jh6ggNZZ5AKlXhtDcL3gC8pLx5GVsuADY0qEy9dDn46K+b7MylI8cEwOhZNl1SqCsQoUNHKswttPXtUaFgqEFwPqnVwSkpK+Omnn2rDljpNzrlcVn+zgSmvTyDqQBKbtsSgkCQi7mjJL0eisdhsZKqyUfjH0tyrEPVFeTESEGjwIEBnQq3QEuEzhDBTxQ6iZWVlfP3118yaNYuioiK0Wi1PPvkkTz/9NAZDuRTDqW0lbP4gl6z4yjsQezdRMeA5M2GDRaM+gaA+IMsy6xds5fi+eKa+8yAGj4rOjdVqZ8HCSDKyCggKNPPgAz1RKt0Tj8tsdn7edYhd8cmuMW+jnicH96Chj5fb86IyT7Mi+RhninNRZWvwW98Yc2QgCotzbjPvNVI2cD+++gL8dLrzMjIa0Fnxfm0jVRGgCyXM1IHs7FK6+/a8tj+MQHAZqnVwmjZtSmxsLK1b376KrHa7nd8+WEa/sT3JLC5jxcqDADTtEcqaU/FYZCtZnvH4+ibioZdQuglhKmlk8sag0tBA34xefsMxqswVnrFx40ZmzJjh6hQ9dOhQZsyYQePGjSucmxhZWqlzY/BR0PspTzqMNaFUC8dGIKgPyLLMhoXbiNt7kkffnlipcyPLMr/9sceVDzjl4b7o9e7l1HklpczdtIeEjHKNu2YBPjw+sDue+vKtrvSSAhae3M+x3DQ0ZwwErQvDY68fksPdWfLf0BDvMVFoKymKuBSDykQzY3uae3TArHZWZUVxa/VmEdx6VOvgnD17lvvvv5+QkBA0mvIPzPLly2+oYXWFovxiVn65Do1OQ2D7xsxfsB2H7MCjjTdRmWnkKfNwBEQTaMrDoFGjuChiciGRWKfUE+EzhOamDm4RlX379jFv3jzeeOMN9u1zSjs0b96c119/nUGDBlVpU68nPIleUoSlwBleVmklIiab6P6YJ1rT5YXoBALBrcWmn7ZzbOdxHntnIkZPQ6Xn7Nl7htOpZei0aqY83Aez2f285Kxcvty0262beq+wRjzQs6Mr2mxzOFh7Jo4VyUchTU3w4lZ4HKy6RFyRa0SZ6guNKskWximj0NDQguamDjTQN0MhyrwFtUy1Ds7zzz9fG3bUOWRZJmrdYdYv2EKHAe3ofn9P5v+wE6vNjrWhlmRbPjkeCZj8TqJROTBo1C7n5eJE4lBDGD19h2NQebjdPzIykokTJ2KzOSMxOp2Of/3rX0ydOtXNkawMvZeSnn/xZMvHeYTfY6Dv02Y8gkQCsUBQ39j08w6it8cy9Z0HMZqNFY47HDIbNx3j6LEMfHy9eejB3gQFebmdE5V4hu+378dqd7YVlSSJsRHhDGpTLvtyMj+TBSf2kZZRhO+qhnhtCUKyV75YUvgUY7gnBv2wEygMFfsOe2v8aW7qSDNTO3TKijYLBLVFtd+KFzoY306cPXWOZV84dZ2mvD4BrZeROXM3UWixkO0LsqkAi/8hzIYc1EolOnW5c2NUa2hk9MaoMtDN9y6aGcMr5MFERkby+OOPu5wbgGnTpvHkk08CYLfKHPi5kMTIUsZ+4VdpHk2Xhzxo0kdHQKub19VTIBDcOLb8GsnhLUeZ+s6DmLwqOgp5+SX89tseTiWmAzD2vgiaNyvv3CzLMisPxroajgIYNGoe7R9Bu/Mq4EXWMv5IPMS20wl4bQmi6aqWKIsrl1NQhuRhHHMM3aBTSGr3wgaNQktTUzhhpg74aIJE7p+gTiCW/RdRWmxh48JtHN56jCEPD6Bdvzbsi0pg2697yCgsItVkQd0oC63/cYwKGxqVCt35XhESEKj3IEBvoqGhJT18h1WI2mRnZ/PWW2/xyy+/OK85PwlotVruvPNOZ8XC2hK2fJxL3mnnaituTQmth1cMS6u0knBuBIJ6ytbfd3JgYzRT35mIh7epwvGYmFT+WLyP4hILJqOOHt386NSxPF/PYrXx/Y79HEhKdY0FeJr46+CeBJpNyLLMnoxkfjl1ENtRDU1+6IQmo/KmfqqwTIzjo9H2SOFiOTwJCNY3JcyjI6H6FhUkZQSCm41wcHCudKK3x7L62w206NyMR96bxKGjZ/jvh6sotVjJl62cNudh7piA1pANSOjUajTnVXsvJBJ7a81097mLJsa2FUT/Fi9ezGuvvUZWVhYajYZnnnmGXr16sXTpUu6//36CFO1ZOCmds4fK3Gzb+kkuYXfoUWnEikgguB3Ytmg3+9cdZuq7D+Lp475IslrtrF5zmF17TgLQskUQ94/pRlxceSfjrMJivty4m9M55bkxrYMDeGxABEathvSSAn44GUVMrlMPT6dUVOrcKPwL8XjkANp+iW6l3h5qL5qbOtDc1L7SggmBoK5w2zs4mWeyWDFnHYW5Rdz1+F2cOpfP519tosxmJ4cy7GYFZaFn8Q85gaSwIUkSerUK1fnEPG+tnhCDmaamNnT3GYpe5b7aSkpK4j//+Q9btmwBoGfPnsycOdMlVmrL1nN6QSibNqRXal9JjoP02DIadBC9bASC+s72xbvZt+YAj737UAXnJj09n59/3UXauTyUSgVD72xPn97uUgbx6VnM3bSHglKLa2xQm+aMiWiHDKxMPsbKlGNYHXZARq08hzo8idLuWnR72gAg6cswjovGcHcsktYZSVZJKhob2xDm0YEAbc2ENwWCm81t6+BYy6xs/W0Xu1dF0XZIR5RqDb+sPEQuZWSrCnA0zUMfWIDGlINe5ZwsFJKEXqNGqVCglCRCjF4E6X3o7juMxobWbh96q9XK3Llz+eijjygtLcXLy4uXX36ZCRMmoFAoKMq0s/PLfA7+6oNsL6lgn6SEjuNM9P6rJ0ZfUX0gENR3IpfuZe/qA0x950E8fcudG1mW2bsvgZWrDmK12fH39WD8+B6ENHBvjhd5Iomfdx3C5nDmx6gUCib06Eiflo05npfBD7H7OFuWD8iolNno1IkoFcVIgG7yPjgUhn7wKUwPHkbh5dSy0yn1hJv7EObREY1CLLIEtxa17uAsXryYDz/8EF9fZ/nhwIEDee6558jPz+eFF14gJSUFHx8fZs2ahb+//w2x4XhUPMtnr0Ud4IWpR2s2JJwm2yudstaZGPwLMRsrOhwqhQLd+TJwo0pDQ5MXLT3C6e47tEKlwIEDB/jXv/5FTEwMAPfddx8zZszA398fS4GDPfPyiFpwQVqh4koobLCeAc+Z8Wkq9rQFgtuBncv3sXP5Ph5790HMfuXaf8XFFpYs3U/0sdMAdO3chNGjOqPRlE/ddoeDzQlniS9KcY2ZtFoeH9SNIB8PvovZS/SSQnxWtUD33G6UocdQKZ3bVya1hhCDGa0POL5dhMLTuUWuklS0NfegrbmncGwEtyy17uBER0czffp0Ro0a5TY+a9YsIiIimDt3LkuWLOHtt99m1qxZ1/XZeZn5rPhyPUfjTmMN15Dhexybbzba8CL0ahnjJWFXpUKBSqlArVSikCRXInFDUyA9fYfS2NjG7fyCggLef/995s2bhyzLNGrUiHfffZeBAwdiLXWw59t8dn9TQGle5dIKwe01DHzBi9CuYkIRCG4Xdq+MInLJHqa++xBe/uU5LQkJGfz6+x7y8ovRadXcc3cXOnZo5HZtQkY2S6KOsT81C7PZeW2It5knBnXnRHEGX39/GNOvDQg84xTyDVgSRN4/dqKSFAQbPPHW6rmwyFJ4liFJEi1Mnejg1bdCkYRAcKtR6w7OkSNHSExM5Msvv6RVq1a88sormM1mNm/ezMKFCwEYNWoUb7zxBlarFbX62qMYNquNdSvWs/zAVkpaW1B2L0RW21BKoFZcqGY6/yGXJFRKJWqlAqXCWTIg4Sz/DtJ70s7ciW6+d6FTulc2/fnnn7z00kukpaWhVCp54okneP7559Hrncl76bFWtnxUeUMsc6iS/s960WqokFYQCG4ndq/az/ZFu5n6zoN4BzgdFLvdweYtMWzcFIOMTMNQXyaM74GPd3mkODkrlxUHY4k+neZ2v46NghnRtSXfbT9I4Xce+B5t4XZct78lqvgwgiIKUUrufW4aGlrSxXsgZo3fjXmxAkEtU+sOjr+/P1OnTqVLly589NFHvPHGG3z44Yekp6e7tqRUKhUmk4ns7GwCA69OXbbYVkBqSQLrj27lYPoRbJ5W5DtAAchIKBSSqzJAkiTU5yM1F5walUKBh1qLp1qHp0ZPkK4Rbc09aGhwnzDOnj3LK6+8wurVqwHo1KkTM2fOJDw83O28kE5amg/UEb+51DVm8FEQOiKPUf9sI6QVBILbjL1rDrDt911MfWci3oFegFMJ/NffnZILEhID+7fhjsFtXZpSZ3LyWHkwjoPJqW73koCh7VvgUMLc6fF4bG+I0VHJnKKx4Z3dAKV00jUUoAuli/dgAnShN+qlCgQ3hRvm4KxevZp3333XbaxZs2Z89913rt+nTZvGnXfeWen1siyjUNRcduBI9BFypLNkcIb1exeTbskkz1qGAxk8JVemiyRdcGxklJLkShgG0MhglJUYlCq88cFcFuj8V+iPEjXpqfmkn9dPsdvtrFmzhu+//56SkhL0ej2TJk1ixIgRWCwWoqIq6qz4DlESv8UHpU6m8YhiQu8qQaWTOXh4f41fZ12hstdX1xE233huNXtvFvvWHmLzr5FMfftBfIKcycLR0adZvDSKktIyPD30jLu/u6txX1peASsPxrI/KdWlAA7O+axrkxCkEiOHlxWjXuGHZ6lnpc/UDYrH9PBBlP7FAHiqfejiPYiGhpYiciyol9wwB2f48OEMHz7cbaygoIDvvvuORx55BHA6Mcrz5dYBAQFkZmYSFBSEzWajqKgILy+vGj/vlNdBDuQdIaekCKvd4ZwEJOm8QyM5NaIkCZVSQq1UolIoUCuUeGi0eKh1BOh8aWhoTrC+CUG6Jpfdf46JieHFF19k/36nY3LXXXfx1ltvIWX5sf3TPO582RvvxpVsrXWFAE0RTXrr0Hs5X3dUVBRdu3at8eusCwiba4dbzeYL9losFqKjo2+2OXWWqHWH2PTzdh59ayK+wd6UldlYueoQe6OcQrttWjdgzL0RGI1aMvILWXkojr0Jp90cG4CODYNp6RnIsZ0F5PzSCF1u5TpVynZpmB+LQt3CKbKpVxrp6N2PMFNHoQ8lqNfU6haVwWDg66+/pnPnznTs2JEffvjBFcEZMGAAS5Ys4cknn2TVqlVERERcUf7NkazjpJcWIp8vkXQ5NgpnlEZ9Pq/GoNbgqdbirfWkmakFDfRNCdY1wVPtW+0qpqSkhFmzZjFnzhxsNhtBQUG8+eabdGsxhO0z8zm50dnLZvtn+Yz+b+UidW1GCG0WgeB2Zf+GI2z4cRtT35qIXwMfzp7N5ZffdpOekY9KqWTE8A706N6c7KISFu84wK74ZByXODbNG/jgH6Ln9OtG9h1VAl5UVpYgBeXhOXU/2p6nkSRQKzS0M/ekjWd31ArRBV1Q/6lVB0epVDJr1ixee+01SktLadKkCe+//z4A//jHP5g+fTojR47Ew8ODDz744IrubXU4wOEsu1YoJWeERqVEp1Jh1ujx1OhpamxGY1MYwbqm+GqDarx62bdvHz/++CNbtmwhLS0NSZKYMmUKTz3yAofny8yffg75osKo2NXFdJ/qQWAbMYkIBAIoKSpl7+oD7FoR5YzcNPAhcucJ1vx5BJvdToC/Jw+M74HWQ8vPuw6z82QSNqsDTZaesoAS7DjwCtCh9VYQZz3DydwcAvxboqZVhWfJxlJMEw9jHHECSe1AISlo6dGFDl59hPil4Lai1pOMIyIiWLx4cYVxLy8v5syZc9X3DTSaKKQAu92Bl16Pt9ZAA0MorTxb0UDfjABdwytatVitVo4ePcqiRYuYN28ejvORocaNG/P+W7Ow7G7BrxMKsVcU0wXgxPoS4eAIBLc5aYnp7F65n+jtMTTv1JSpb09E72Xih4WRxMQ5E4W7RzSjz4BWbDhyiqgtmaiTjfidbo72rBGFVUnc/+3BHlRIgS6FUkUWHoZcJBzYW8uwudzBkZV2NCOO4TXxGAoPZz+bJsY2dPIegKfa56a8foHgZlJvOhlPaPwoccUHSD2TRu+W/QjWN7mi1UpBQQFRUVHs3buXPXv2cODAAUpK3Bv+SZJEj8b3sO/fDbCWFFZ6n+D2Gvo9a6ZxD901vR6BQHBrYrfZORoZx+6V+8k5l0u3YZ155vNpePp4EH8qna8/X0d+QQl6hZ6IxuGc3m7jy89Po0kzE+DwrnA///xMrC2PVBgva3kaWW1FDjsHLU/jNzIVdXARAEH6xnTxHoSftsENf70CQV2l3jg4vtogBnneS9SZKJqa2lV7/pkzZ9i7d6/LoYmNjXVFaS7QrFkzmjZtytatW7Hb7ChQw4H2WFVyhfv5NlPR7x9mwgaLXjYCwe1IXmY+e9ccJGrdIfxDfel9Tzdadw/DIUNiQiZrl50ieksWysxAfPK9kbM1RMsSoERbaRaNE+0JP6wR7mN6lRqfhgrMP/+MSgN5ebmozV54a/zp4j2YBvpmYh4S3PbUGwfnctjtdmJjY90cmtRU9z4SarWaTp060a1bN7p3705ERAR+fn5Ezs4jJDaSk1lRhKq70EDV3u06z2Alff5mpu1oAwqlmFAEgvqC7JApK5KxFDqwFDooK5QpK3JgKZCxFDmwFDjHMxLzOHsii8LMMhr2UTPljQmUKZScOpXOrgU7SE7OxJ6jxry+E0acW0UVl0iVYzc7m5KCszeXt8aAj1aPVllegKGQFJjlQPr6D6WJsS0KqebtNQSC+ky9cnD27dvH77//Tmmps5nenj172LdvH1FRURQUFLid6+npSUREBF06RRAW0Jk7xkZgMFQssyzJdeBTHE53vXvjPr23gl6Pe9JxggmVRjg2AkF94eTmEra84MfG0tM1vEIB+KMA8gpymbsgklKLMznPLstYsGPyq5n8ii0gB2vL05S1SsHaKgVHYC5mjZ6mWh881FoudFzXKvWE6JvT0NCCYH0zjmRF08wUfvmbCwS3GfXGwTly5Ahjx47FZrOxYMGCCscbNmxI1y4RtG7YhRBtB7RZjUk/ZidrvpVYBwy8SwOVtJEIaueeKKwxSnR7xIOIyR5ojGKlJBDUN5RqsJde3Wc7KSOb002SUZgdSCYZtA7UepkiewpmOridK0sy1pB0bK1OU9bqNNaWp3F4O3P7DCoNwVo9Zk2QS1LBrPYl1BBGQ0NL/LQhIlIjEFRDvXFwIiMjsdlsrt8DAwMZ2HMYTc2d8CtrjyXRm8wdVkrtEA9Aqdv1aUeteARW/HMEnndwVDqJjuON9JzmicFHNMcSCOojdtlOvioNuDoNPJVfLoa+hysekMHqm4PNq4DSFinYW53G0eosstHiOkWjUOKtNeGtNaBRqJAkiUBdI0L1LQg1hIlKKIHgCqk3Dk7v3r1RqzXYrFaUCg2DLG8RtLU9pYAz0FxFPfd5zh0to8VgfYVxnyYqJn4fQEBrNRqDWDEJBPWZ35O/5nh6DmamAuDQlGHXl+LQWZz/9KXYdRYceuc/WWfBYbhwzEJZQNYld3Q2G1VIkDrzC5QKZyf1CygkCbNGh7fGgEmtQaPQEaJvTqihBQ30zdAqK85JAoGgZtQbB6d9+/bMfPp7fp+9hVB1F4Kk9tVfdB7vxirUxsrzaBRKidAuNds/FwgEtzaxOSmcM+aS9v47OLRloJTdRHmdii+XzhXlToxCUqCT1M6fFQqnREwlmNQavDUGzBodZo0vofowGhpa4K9riFLIJwgE14V64+AADBrVg+Tvmlz2HHOokqB2Gue/cA0BrTXoPEVkRiAQgN3hAIUdh8GGdN5BkXDq2F3sxCgk6bJOjCSBSlKilJxd1ZWS859WqcJLoyfE0JhQQwtC9S0w10AmRiAQXDn1ysHxba5GoZZxWJ2ThWewksALzkw7DYHt1OjNYnUkEAgqJ8DoQZFUhN1uR61SObeUFEqXQK9SUqA676woFZLrZ61Ci1apQ6/UoVfp0Sn1aBRaVJIGtUKLRqFFrdBiUHoQrG+KTlm5MKZAILh+1CsHR6mWaDmpgHbdmxHYVoPRVzgzAoGg5jzQZDKnSmM5lRJPi0Yt0Ct1aJQ61AoNaknr5qyUj2mEKrdAUAepVw4OQIOBpTTrKhLzBALBleOnDSHEsxmGlCi6+ne92eYIBIJrQCSfCAQCgUAgqHcIB0cgENQrli9fzogRI7jrrrtYuHDhzTZHIBDcJOrdFpVAILh9OXfuHB9//DGLFi1Co9HwwAMP0KNHD8LCwm62aQKBoJYRERyBQFBviIyMpGfPnnh5eWEwGBg6dChr1qy52WYJBIKbwC0fwZFlpy5vWVmZa8xisVR1ep3kVrMXhM21xa1ms8VicX0WL3w2a5P09HT8/f1dvwcEBHD4cCXSCZcg5pGbg7C5drjVbL5e88gt7+BYrU4JhuPHj7vGoqOjb5Y5V8WtZi8Im2uLW83mi+21Wq3odLpafb7D4XBrmifLco2a6Il55OYgbK4dbjWbr9c8css7OEajkZYtW6JWq0U3UIGgDiDLMlarFaPRWOvPDgoKYt++fa7fMzIyCAgIqPY6MY8IBHWL6zGP3PIOjkKhwMPD42abIRAILqK2IzcX6N27N59++inZ2dno9XrWrl3Lm2++We11Yh4RCOoe1zqP3PIOjkAgEFwgMDCQ5557jsmTJ2O1Wrn//vvp0KHDzTZLIBDcBCT5ZmQCCgQCgUAgENxARJm4QCAQCASCeodwcAQCgUAgENQ7hIMjEAgEAoGg3iEcHIFAIBAIBPUO4eAIBAKBQCCod9QbB6euKgh/9tlnjBw5kpEjR/L+++8DTr2c0aNHc9ddd/Hxxx+7zo2JiWHMmDEMHTqU//u//8Nms90sswGYOXMm06dPB+q+zRs3bmTMmDEMHz6ct95665aweenSpa73xsyZM+uszYWFhYwaNYrTp09flY2pqak89NBDDBs2jL/+9a8UFRXVmu1XiphHrj9iHrmxiHnkMsj1gLS0NHnQoEFyTk6OXFRUJI8ePVo+ceLEzTZL3rFjhzxhwgTZYrHIZWVl8uTJk+Xly5fLAwYMkJOTk2Wr1SpPnTpV3rx5syzLsjxy5Ej5wIEDsizL8n/+8x954cKFN832yMhIuUePHvK///1vuaSkpE7bnJycLPft21c+e/asXFZWJk+cOFHevHlznba5uLhY7tatm5yVlSVbrVb5/vvvlzds2FDnbD548KA8atQouV27dnJKSspVvRcef/xxecWKFbIsy/Jnn30mv//++7Vi+5Ui5pHrj5hHbixiHrk89SKCU1cVhP39/Zk+fToajQa1Wk3z5s1JTEykcePGNGzYEJVKxejRo1mzZg1nzpyhtLSUTp06ATBmzJib9hpyc3P5+OOPefLJJwE4fPhwnbZ53bp1jBgxgqCgINRqNR9//DF6vb5O22y323E4HJSUlGCz2bDZbJhMpjpn86+//sqMGTNccgdX+l6wWq3s3buXoUOH1rrtV4qYR64vYh658Yh55PLUi07GV6sgfKNp0aKF6+fExERWr17NpEmTKth67ty5Cq/B39+fc+fO1aq9F3j11Vd57rnnOHv2LFD537cu2ZyUlIRarebJJ5/k7NmzDBw4kBYtWtRpm00mE//4xz8YPnw4er2ebt261cm/89tvv+32+5XamJOTg8lkQqVS1brtV4qYR64vYh658Yh55PLUiwjO1SoI1xYnTpxg6tSpvPjiizRs2LBSW+vKa/jtt98IDg6mV69errGqbKsrNtvtdnbu3Mk777zDL7/8wuHDh0lJSanTNsfGxvLHH3+wadMmtm3bhkKhIDExsU7bDFf+XqjM1rr02byYuvR3rgwxj9xYxDxSe9TWPFIvIjhXqyBcG0RFRfH3v/+dl156iZEjR7Jnzx4yMjJcxy/YGhQU5DaemZl5U17DqlWryMjI4J577iEvL4/i4mLOnDmDUqmsszb7+fnRq1cvfHx8ABgyZAhr1qyp0zZv376dXr164evrCzhDrt98802dthmoYEt1Nvr4+FBQUIDdbkepVNapz+aliHnk+iHmkdpBzCOXp15EcHr37s3OnTvJzs6mpKSEtWvX0r9//5ttFmfPnuVvf/sbH3zwASNHjgSgY8eOJCQkkJSUhN1uZ8WKFfTv35+QkBC0Wi1RUVGAMzP+ZryGefPmsWLFCpYuXcrf//53Bg8ezNdff12nbR40aBDbt28nPz8fu93Otm3bGDZsWJ22uXXr1kRGRlJcXIwsy2zcuLHOvzfgyt+/arWaiIgIVq1aBcCSJUvqxGezMsQ8cv0Q80jtIOaRy1MvIjh1VUH4m2++wWKx8N5777nGHnjgAd577z2eeeYZLBYLAwYMYNiwYQB88MEHvPzyyxQWFtKuXTsmT558s0x3Q6vV1mmbO3bsyLRp03jwwQexWq306dOHiRMn0qxZszprc9++fTl27BhjxoxBrVbTvn17nnnmGfr06VNnbYarey/MmDGD6dOnM3v2bIKDg/noo49uiu3VIeaRG4uYR64/Yh65PEJNXCAQCAQCQb2jXmxRCQQCgUAgEFyMcHAEAoFAIBDUO4SDIxAIBAKBoN4hHByBQCAQCAT1DuHgCAQCgUAgqHcIB0fgYvfu3YwaNeq63W/NmjU8/PDDlR6bOnUq2dnZAPzlL3/h5MmT1+25F9OqVStGjx7Nli1bKj1+6NAh7rnnHlq1auWyRyAQXD1iHhHzSF2hXvTBEdx67Nixw/XzV199dUOfNX/+fFd30kvp2LEjS5cupVWrVjfUBoFAcP0R84jgcggHR1ApZWVlfPDBB+zduxe73U7btm15+eWXMZlMbNq0iS+//JKysjKys7O59957efbZZwH45JNPWL58OV5eXjRu3LjSe//nP/8BYMqUKcydO5eHHnqITz75hOLiYj766COCg4NJSEhAr9fz+OOPs2DBAhISErjrrrt46aWXANi4cSOzZ8/GarWi0+n497//TefOnS/7mtauXcvs2bORJAmlUsmLL75It27drt8fTSAQuCHmEcFNRRYIzrNr1y555MiRsizL8qeffiq/9957ssPhkGVZlj/88EN5xowZssPhkCdNmiQnJCTIsizLaWlpcps2beSsrCx53bp18ogRI+SCggLZarXKjz/+uDxp0qRKn9WyZUs5KytLlmVZHjRokHz48GF5165dcps2beSjR4/KsizLjz32mDxhwgTZYrHIWVlZcrt27eS0tDQ5ISFBHjVqlJydnS3LsiwfP35c7tOnj1xUVHTZ59xxxx3ygQMHZFmW5W3btsmffvpppecJBIKrR8wjYh6pK4gIjqBSNm/eTEFBAZGRkQBYrVZ8fX2RJIk5c+awefNmVqxYQXx8PLIsU1JSws6dO7nzzjsxmUwAjB07lgULFlzRc0NDQ2nbti0AjRo1wsPDA41Gg4+PD0ajkby8PPbu3Ut6ejqPPPKI6zpJkkhOTqZ169ZV3nvkyJE8/fTTDBgwgD59+vCXv/zlCv8qAoHgShDziOBmIhwcQaU4HA5eeuklBgwYAEBRUREWi4Xi4mLuu+8+hgwZQkREBGPHjmX9+vXI5xU/5IuUPy5WtK0pGo3G7XeVquJb1OFw0KtXL2bNmuUaO3v2bLXqss899xxjx45lx44dLFq0iG+//Zbff//9im0UCAQ1Q8wjgpuJqKISVErfvn1ZuHAhZWVlOBwOXnnlFT766COSkpIoLCzk2WefZfDgwezevdt1Tv/+/VmzZg35+fk4HA6WLl1a5f2VSiU2m+2qbOvVqxc7duwgPj4egC1btnD33XdTWlpa5TU2m43BgwdTUlLCxIkTmTFjBnFxcZSVlV2VDQKBoHrEPCK4mYgIjqBSnnrqKWbOnMl9992H3W6nTZs2TJ8+HYPBwMCBAxk+fDgajYaWLVsSFhZGUlISAwYMIC4ujrFjx+Lp6Unr1q3Jycmp9P7Dhg3j4Ycf5tNPP71i28LCwnjjjTd4/vnnkWUZlUrF7NmzMRqNVV6jUql46aWXeOGFF1CpVEiSxDvvvFNhpScQCK4fYh4R3EyEmrigXtOqVSt27txZZXnnlZ4nEAhuP8Q8cmsitqgE9Z4pU6ZU26BLIBAILoeYR249RARHIBAIBAJBvUNEcAQCgUAgENQ7hIMjEAgEAoGg3iEcHIFAIBAIBPUO4eAIBAKBQCCodwgHRyAQCAQCQb1DODgCgUAgEAjqHf8Pp5H62KrIPKkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 648x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots(1,2,figsize=(9,4),sharex='all')\n",
+    "for e in range(4):\n",
+    "    alpha = np.abs(error_vec[e]-100)/100\n",
+    "    alpha = 1-alpha/2\n",
+    "    alpha = 1.0\n",
+    "    linew = 10-np.abs(error_vec[e]-100)/10\n",
+    "    linew = linew/2\n",
+    "    label='adv error +-'+str(np.abs(error_vec[e]-100))+'%'\n",
+    "    #ax[0].plot(t_vec, 100*(is_cw[e,:]+is_cw[-e-1,:] )/total/2  , lw=linew,color='mediumseagreen',alpha=alpha,linestyle='-.',label=label)\n",
+    "    ax[0].plot(t_vec, 100*(is_cw[e,:]+is_cw[-e-1,:] )/total/2  , lw=linew,alpha=0.7,linestyle='-',label=label,color=plt.cm.viridis((e)/(5)))\n",
+    "    #ax[0].plot(t_vec, 100*(is_cw[e,:]+is_cw[-e-1,:] )/total/2  , lw=linew,alpha=alpha,linestyle='-.',label=label,color=plt.cm.viridis(e/(6-1)))\n",
+    "    ax[1].plot(t_vec, rmse_is_cw[e,:] *real  , lw=linew,linestyle='-',label=label,color=plt.cm.viridis((e)/(5)),alpha=0.7)\n",
+    "    \n",
+    "ax[0].plot(t_vec,100* is_ca/total   , lw=5,alpha=0.7,label='no advection error',color=plt.cm.viridis((4)/(6-1)))\n",
+    "ax[0].plot(t_vec,100* es/total   , lw=4,label='explicit sens'              ,color='blueviolet',ls='--')      \n",
+    "ax[0].plot(t_vec,100* real /total   , lw=2,alpha=1,  color='k'  ,ls='-' ,marker='.',label='truth')\n",
+    "# ax[0].errorbar(t_vec,100* real /total   ,std_real*100/total,lw=2,alpha=1,  color='k'  ,ls='-' ,marker='.',label='truth')\n",
+    "ax[1].plot(t_vec,rmse_is_ca*real    , lw=5,alpha=0.7,label='implicit sens',color=plt.cm.viridis((4)/(6-1)))\n",
+    "ax[1].plot(t_vec,rmse_es*real    , lw=4,alpha=1.0,label='exp sens'              ,color='blueviolet',ls='--')      \n",
+    "\n",
+    "\n",
+    "ax[0].set_xlabel('lead time [s]');\n",
+    "ax[1].set_xlabel('lead time [s]');\n",
+    "ax[0].set_ylabel('mean variance reduction [%]');\n",
+    "ax[1].set_ylabel('RMSE variance reduction',labelpad=0.1);\n",
+    "ax[1].set_ylim(bottom=0)\n",
+    "ax[0].set_ylim(top=0)\n",
+    "ax[0].set_xlim(left=-10,right=1010)\n",
+    "\n",
+    "ax[0].legend(bbox_to_anchor=(-0.05,1.05),loc='lower left',ncol=4,handlelength=3);\n",
+    "plt.subplots_adjust(hspace=0.2)\n",
+    "label_axes_abcd(fig,loc=(0.1,0.95))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
+      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
+      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind_list = [2,6,10]\n",
+    "\n",
+    "fig,ax = vr_scatter_v6(vr_es[ind_list[0],:],vr_real[ind_list[0],:],vr_es[ind_list[1],:],vr_real[ind_list[1],:],vr_es[ind_list[2],:],vr_real[ind_list[2],:],\n",
+    "                       vr_is_ca[ind_list[0],:],vr_is_ca[ind_list[1],:],vr_is_ca[ind_list[2],:],\n",
+    "                       alpha=0.5,\n",
+    "                       label1='lead time: ' +str(int(t_vec[ind_list[0]]))+' s',\n",
+    "                       label2='lead time: ' +str(int(t_vec[ind_list[1]]))+' s',\n",
+    "                       label3='lead time: ' +str(int(t_vec[ind_list[2]]))+' s')\n",
+    "ax[0,0].set_xlim(-190,75)\n",
+    "ax[0,0].set_ylim(-190,75)\n",
+    "\n",
+    "for i in range(3):\n",
+    "    rmse = int(10*rmse_es[ind_list[i]]*real[ind_list[i]])\n",
+    "    me = int(10*me_es[ind_list[i]]*real[ind_list[i]])\n",
+    "    ax[0,i].text(-180,30,'RMSE: '+str(rmse/10),ha='left')\n",
+    "    ax[0,i].text(-180,50,'ME: '+str(me/10),ha='left')\n",
+    "for i in range(3):\n",
+    "    rmse = int(10*rmse_is_ca[ind_list[i]]*real[ind_list[i]])\n",
+    "    me = int(10*me_is_ca[ind_list[i]]*real[ind_list[i]])\n",
+    "    ax[1,i].text(-180,30,'RMSE: '+str(rmse/10),ha='left')\n",
+    "    ax[1,i].text(-180,50,'ME: '+str(me/10),ha='left')\n",
+    "label_axes_abcd(fig,loc=(0.9,0.05))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Now looking into the effect of the localization length scale\n",
+    "\n",
+    "Very similar to the advection speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "completed timesteps: 1  seconds spent computing so far: 287.0\n",
+      "completed timesteps: 11  seconds spent computing so far: 321.0\n",
+      "completed timesteps: 21  seconds spent computing so far: 356.0\n",
+      "completed timesteps: 31  seconds spent computing so far: 393.0\n",
+      "completed timesteps: 41  seconds spent computing so far: 432.0\n",
+      "completed timesteps: 51  seconds spent computing so far: 473.0\n",
+      "CPU times: user 3min 40s, sys: 3.5 s, total: 3min 44s\n",
+      "Wall time: 56.2 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "import time\n",
+    "start_time=time.process_time()\n",
+    "\n",
+    "t_start= 40\n",
+    "t_end = 100\n",
+    "n_ens = 15\n",
+    "n_ens = 2\n",
+    "\n",
+    "n_samples = (t_end-t_start)*n_ens\n",
+    "truth_idx = 0\n",
+    "\n",
+    "loc_values = 2.**np.arange(11)*m_const['dx']\n",
+    "vr_es        = np.zeros([11,n_samples])\n",
+    "vr_is_cl     = np.zeros([11,n_samples])\n",
+    "vr_is_ca     = np.zeros([11,n_samples])\n",
+    "vr_is_it     = np.zeros([11,n_samples])\n",
+    "vr_real      = np.zeros([11,n_samples]) \n",
+    "var_total     = np.zeros([11,n_samples]) \n",
+    "counter = 0\n",
+    "for t in range(t_start,t_end):\n",
+    "    for r in range(n_ens):\n",
+    "        n = (t-t_start)*n_ens+r\n",
+    "        np.random.seed(n)\n",
+    "        counter = counter+1\n",
+    "        truth_idx = r\n",
+    "        for i in range(len(loc_values)): \n",
+    "                \n",
+    "            da_const_vr = set_da_constants_22(loc_length=loc_values[i],obs_loc=np.arange(25,299,50))\n",
+    "            \n",
+    "            vr_t, vr_i, vr_r, J_dict_LA,bla, quad,dx_ni =vr_reloaded_22(states[0]['bg'][t],states[0]['bg'][t][:,truth_idx],m_const,da_const_vr,reflectance_simulator,iterative_flag = 0,\n",
+    "                                                                        obs_seed=counter,model_seed=counter,alpha=alpha_default)\n",
+    "            var_total[i,n] = np.var(J_dict_LA['bf'],ddof=1)\n",
+    "            vr_es[i,n]     = vr_t\n",
+    "            vr_real[i,n]   = vr_r\n",
+    "            \n",
+    "            \n",
+    "            vr_t, vr_r, quad, J_dict_LA     = vr_individual_loc_22(states[0]['bg'][t][:,:],states[0]['bg'][t][:,truth_idx],m_const,da_const_vr,reflectance_simulator,quad_state=quad,advect_flag=0,obs_seed=counter,model_seed=counter)\n",
+    "            vr_is_cl[i,n]  = vr_t\n",
+    "            vr_t, vr_r, quad, J_dict_LA     = vr_individual_loc_22(states[0]['bg'][t][:,:],states[0]['bg'][t][:,truth_idx],m_const,da_const_vr,reflectance_simulator,advect_flag=1,quad_state=quad,obs_seed=counter,model_seed=counter)\n",
+    "            vr_is_ca[i,n]  = vr_t\n",
+    "    if np.floor(t/10) == t/10: print('completed timesteps:',t-39,' seconds spent computing so far:',np.floor(time.process_time()-start_time))\n",
+    "       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "es    =np.sum(vr_es    ,axis=1)/counter#/var_total\n",
+    "is_cl =np.sum(vr_is_cl ,axis=1)/counter#/var_total\n",
+    "is_ca =np.sum(vr_is_ca ,axis=1)/counter#/var_total\n",
+    "real  =np.sum(vr_real  ,axis=1)/counter#/var_total\n",
+    "total =np.sum(var_total,axis=1)/counter#/var_total\n",
+    "\n",
+    "sample_uncertainty =np.std(vr_real[:,:] ,axis=1)/np.mean(vr_real,axis=1)/30\n",
+    "sample_uncertainty_es =np.std(vr_es[:,:] ,axis=1)/np.mean(vr_real,axis=1)/30\n",
+    "sample_uncertainty_ca =np.std(vr_is_ca[:,:] ,axis=1)/np.mean(vr_real,axis=1)/30\n",
+    "\n",
+    "me_es    =np.sum((vr_es    -vr_real),axis=1)/np.sum(vr_real,axis=1)\n",
+    "me_is_cl =np.sum((vr_is_cl -vr_real),axis=1)/np.sum(vr_real,axis=1)\n",
+    "me_is_ca =np.sum((vr_is_ca -vr_real),axis=1)/np.sum(vr_real,axis=1)\n",
+    "\n",
+    "rmse_es    =np.power(np.sum(np.power((vr_es    -vr_real),2),axis=1)/counter,0.5)#/np.sum(vr_real,axis=1)*counter\n",
+    "rmse_is_cl =np.power(np.sum(np.power((vr_is_cl -vr_real),2),axis=1)/counter,0.5)#/np.sum(vr_real,axis=1)*counter\n",
+    "rmse_is_ca =np.power(np.sum(np.power((vr_is_ca -vr_real),2),axis=1)/counter,0.5)#/np.sum(vr_real,axis=1)*counter\n",
+    "\n",
+    "std_real    =np.std(vr_real,axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Lets see what changes when we change the localization used to create the background\n",
+    "fig,ax = plt.subplots(1,figsize=(5,4),sharex='all')\n",
+    "#plt.yscale('symlog',linthresh=0.001)\n",
+    "# ax.plot(np.arange(11,0,-1), is_it/total*100    , lw=3,alpha=0.8,label='imp sens, iterative    ',color='darkolivegreen')\n",
+    "# ax.plot(np.arange(11,0,-1), is_cl/total*100    , lw=4,alpha=0.8,label='imp sens, localized    '           ,color='green',zorder=2) \n",
+    "ax.plot(np.arange(11,0,-1), is_ca/total*100    , lw=4,alpha=1.0,label='implicit sensitivity',color=plt.cm.viridis((4)/(6-1)))\n",
+    "ax.plot(np.arange(11,0,-1), es   /total*100    , lw=4,alpha=1.0,label='explicit sensitivity'              ,color='blueviolet',ls='--')      \n",
+    "ax.plot(np.arange(11,0,-1), real /total*100    , lw=2,alpha=1,  color='k',ls='-',marker='.',label='truth')\n",
+    "\n",
+    "#ax.errorbar(np.arange(11,0,-1),real /total*100   ,yerr=sample_uncertainty*100,color='k',label='truth',capsize=5)\n",
+    "\n",
+    "ax.fill_between(np.arange(11,0,-1),real /total*100  -sample_uncertainty*100 ,real /total*100+sample_uncertainty*100,color='k',alpha=0.15,label='sample size mean std')\n",
+    "\n",
+    "ax.set_xlabel('localization length in dx');\n",
+    "ax.set_ylabel('mean variance reduction [%]');\n",
+    "ax.set_xticks(np.arange(1,12));\n",
+    "ax.set_xticklabels(2**np.arange(10,-1,-1));\n",
+    "#ax.legend(bbox_to_anchor=(-0.3,1.05),loc='lower left',ncol=5);\n",
+    "l=ax.legend(loc='upper left',ncol=1);\n",
+    "l.set_zorder(1)\n",
+    "plt.subplots_adjust(hspace=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparison between observations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "completed timesteps: 1  seconds spent computing so far: 3.0\n",
+      "completed timesteps: 11  seconds spent computing so far: 51.0\n",
+      "completed timesteps: 21  seconds spent computing so far: 97.0\n",
+      "completed timesteps: 31  seconds spent computing so far: 150.0\n",
+      "completed timesteps: 41  seconds spent computing so far: 203.0\n",
+      "completed timesteps: 51  seconds spent computing so far: 255.0\n",
+      "CPU times: user 4min 58s, sys: 4.4 s, total: 5min 2s\n",
+      "Wall time: 1min 15s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "import time\n",
+    "start_time=time.process_time()\n",
+    "da_const_sur = set_da_constants_22(obs_loc=np.arange(25,299,50),obs_loc_sat=np.array([]))\n",
+    "da_const_sat = set_da_constants_22(obs_loc=np.array([]))\n",
+    "t_start= 40\n",
+    "t_end = 100\n",
+    "n_ens = 15\n",
+    "# n_ens = 2\n",
+    "n_samples = (t_end-t_start)*n_ens\n",
+    "truth_idx = 0\n",
+    "vr_es         = np.zeros(n_samples)\n",
+    "vr_is_ca      = np.zeros(n_samples)\n",
+    "vr_real       = np.zeros(n_samples) \n",
+    "var_total     = np.zeros(n_samples) \n",
+    "sat_vr_es         = np.zeros(n_samples)\n",
+    "sat_vr_is_ca      = np.zeros(n_samples)\n",
+    "sat_vr_real       = np.zeros(n_samples) \n",
+    "sat_var_total     = np.zeros(n_samples) \n",
+    "sur_vr_es         = np.zeros(n_samples)\n",
+    "sur_vr_is_ca      = np.zeros(n_samples)\n",
+    "sur_vr_real       = np.zeros(n_samples) \n",
+    "sur_var_total     = np.zeros(n_samples) \n",
+    "counter = 0\n",
+    "for t in range(t_start,t_end):\n",
+    "    for n in range(n_ens):\n",
+    "        i = (t-t_start)*n_ens+n\n",
+    "        #print(i,n_obs)\n",
+    "        counter = counter+1\n",
+    "        truth_idx = n\n",
+    "\n",
+    "        # point obs\n",
+    "            \n",
+    "        vr_t, vr_i, vr_r,J_dict,dJdx_inv, quad, dx = vr_reloaded_22(\n",
+    "            states[0]['bg'][t],states[0]['bg'][t][:,n],m_const,da_const_sur,reflectance_simulator,\n",
+    "            obs_seed=i,model_seed=i,alpha=alpha_default)\n",
+    "        sur_var_total[i] = np.var(J_dict['bf'],ddof=1)\n",
+    "        sur_vr_es[i]     = vr_t\n",
+    "        sur_vr_real[i]   = vr_r\n",
+    "        \n",
+    "        vr_t, vr_r, quad, J_dict= vr_individual_loc_22(states[0]['bg'][t],states[0]['bg'][t][:,n],m_const,da_const_sur,reflectance_simulator,\n",
+    "                                              advect_flag=1,obs_seed=i,model_seed=i,quad_state=quad)\n",
+    "        sur_vr_is_ca[i]  = vr_t\n",
+    "        \n",
+    "        \n",
+    "        # sat obs\n",
+    "        vr_t, vr_i, vr_r,J_dict,dJdx_inv, quad, dx = vr_reloaded_22(\n",
+    "            states[0]['bg'][t],states[0]['bg'][t][:,n],m_const,da_const_sat,reflectance_simulator,\n",
+    "            obs_seed=i,model_seed=i,alpha=alpha_default)\n",
+    "        sat_var_total[i] = np.var(J_dict['bf'],ddof=1)\n",
+    "        sat_vr_es[i]     = vr_t\n",
+    "        sat_vr_real[i]   = vr_r\n",
+    "        \n",
+    "        vr_t, vr_r, quad, J_dict= vr_individual_loc_22(states[0]['bg'][t],states[0]['bg'][t][:,n],m_const,da_const_sat,reflectance_simulator,\n",
+    "                                              advect_flag=1,obs_seed=i,model_seed=i,quad_state=quad)\n",
+    "        sat_vr_is_ca[i]  = vr_t\n",
+    "        \n",
+    "        \n",
+    "        # both obs\n",
+    "        vr_t, vr_i, vr_r,J_dict,dJdx_inv, quad, dx = vr_reloaded_22(\n",
+    "            states[0]['bg'][t],states[0]['bg'][t][:,n],m_const,da_const_vr,reflectance_simulator,\n",
+    "            obs_seed=i,model_seed=i,alpha=alpha_default)\n",
+    "        var_total[i] = np.var(J_dict['bf'],ddof=1)\n",
+    "        vr_es[i]     = vr_t\n",
+    "        vr_real[i]   = vr_r\n",
+    "        \n",
+    "        vr_t, vr_r, quad, J_dict= vr_individual_loc_22(states[0]['bg'][t],states[0]['bg'][t][:,n],m_const,da_const_vr,reflectance_simulator,\n",
+    "                                              advect_flag=1,obs_seed=i,model_seed=i,quad_state=quad)\n",
+    "        vr_is_ca[i]  = vr_t\n",
+    "        \n",
+    "    if np.floor(t/10) == t/10: print('completed timesteps:',t-39,' seconds spent computing so far:',np.floor(time.process_time()-start_time))\n",
+    "       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sur_es    =np.sum(sur_vr_es    )/counter#/var_total\n",
+    "sur_is_ca =np.sum(sur_vr_is_ca )/counter#/var_total\n",
+    "sur_real  =np.sum(sur_vr_real  )/counter#/var_total\n",
+    "sat_es    =np.sum(sat_vr_es    )/counter#/var_total\n",
+    "sat_is_ca =np.sum(sat_vr_is_ca )/counter#/var_total\n",
+    "sat_real  =np.sum(sat_vr_real  )/counter#/var_total\n",
+    "es    =np.sum(vr_es    )/counter#/var_total\n",
+    "is_ca =np.sum(vr_is_ca )/counter#/var_total\n",
+    "real  =np.sum(vr_real  )/counter#/var_total\n",
+    "total =np.sum(var_total)/counter#/var_total\n",
+    "\n",
+    "sur_me_es    =np.sum((sur_vr_es    -sur_vr_real))/np.sum(sur_vr_real)\n",
+    "sur_me_is_ca =np.sum((sur_vr_is_ca -sur_vr_real))/np.sum(sur_vr_real)\n",
+    "sat_me_es    =np.sum((sat_vr_es    -sat_vr_real))/np.sum(sat_vr_real)\n",
+    "sat_me_is_ca =np.sum((sat_vr_is_ca -sat_vr_real))/np.sum(sat_vr_real)\n",
+    "me_es    =np.sum((vr_es    -vr_real))/np.sum(vr_real)\n",
+    "me_is_ca =np.sum((vr_is_ca -vr_real))/np.sum(vr_real)\n",
+    "\n",
+    "sat_rmse_es    =np.power(np.sum(np.power((sat_vr_es    -sat_vr_real),2))/counter,0.5)/np.sum(sat_vr_real)*counter\n",
+    "sat_rmse_is_ca =np.power(np.sum(np.power((sat_vr_is_ca -sat_vr_real),2))/counter,0.5)/np.sum(sat_vr_real)*counter\n",
+    "sur_rmse_es    =np.power(np.sum(np.power((sur_vr_es    -sur_vr_real),2))/counter,0.5)/np.sum(sur_vr_real)*counter\n",
+    "sur_rmse_is_ca =np.power(np.sum(np.power((sur_vr_is_ca -sur_vr_real),2))/counter,0.5)/np.sum(sur_vr_real)*counter\n",
+    "rmse_es    =np.power(np.sum(np.power((vr_es    -vr_real),2))/counter,0.5)/np.sum(vr_real)*counter\n",
+    "rmse_is_ca =np.power(np.sum(np.power((vr_is_ca -vr_real),2))/counter,0.5)/np.sum(vr_real)*counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
+      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 396x396 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig,ax = vr_scatter_v4(sur_vr_es,sur_vr_real,sat_vr_es,sat_vr_real,\n",
+    "                       sur_vr_is_ca,sat_vr_is_ca,\n",
+    "                       alpha=0.5,\n",
+    "                       label1='direct observations',\n",
+    "                       label2='indirect observations')\n",
+    "ax[0,0].set_xlim(-135,35)\n",
+    "ax[0,0].set_ylim(-135,35)\n",
+    "\n",
+    "# for i in range(3):\n",
+    "rmse = int(10*sur_rmse_es*sur_real)\n",
+    "me = int(  10*sur_me_es  *sur_real)\n",
+    "ax[0,0].text(-130,5,'RMSE: '+str(rmse/10),ha='left')\n",
+    "ax[0,0].text(-130,20,'ME: '+str(me/10),ha='left')\n",
+    "rmse = int(10*sat_rmse_es*sat_real)\n",
+    "me = int(  10*sat_me_es  *sat_real)\n",
+    "ax[0,1].text(-130,5,'RMSE: '+str(rmse/10),ha='left')\n",
+    "ax[0,1].text(-130,20,'ME: '+str(me/10),ha='left')\n",
+    "rmse = int(10*sur_rmse_is_ca*sur_real)\n",
+    "me = int(  10*sur_me_is_ca  *sur_real)\n",
+    "ax[1,0].text(-130,5,'RMSE: '+str(rmse/10),ha='left')\n",
+    "ax[1,0].text(-130,20,'ME: '+str(me/10),ha='left')\n",
+    "rmse = int(10*sat_rmse_is_ca*sat_real)\n",
+    "me = int(  10*sat_me_is_ca  *sat_real)\n",
+    "ax[1,1].text(-130,5,'RMSE: '+str(rmse/10),ha='left')\n",
+    "ax[1,1].text(-130,20,'ME: '+str(me/10),ha='left')\n",
+    "label_axes_abcd(fig,loc=(0.9,0.05))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Next up testing for different regularization parameters alpha\n",
+    "\n",
+    "This takes long enough (hour), that I will simply load the data instead of running it by default \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "completed t: 40\n",
+      "completed t: 41\n",
+      "completed t: 42\n",
+      "completed t: 43\n",
+      "completed t: 44\n",
+      "completed t: 45\n",
+      "completed t: 46\n",
+      "completed t: 47\n",
+      "completed t: 48\n",
+      "completed t: 49\n",
+      "completed t: 50\n",
+      "completed t: 51\n",
+      "completed t: 52\n",
+      "completed t: 53\n",
+      "completed t: 54\n",
+      "completed t: 55\n",
+      "completed t: 56\n",
+      "completed t: 57\n",
+      "completed t: 58\n",
+      "completed t: 59\n",
+      "completed t: 60\n",
+      "completed t: 61\n",
+      "completed t: 62\n",
+      "completed t: 63\n",
+      "completed t: 64\n",
+      "completed t: 65\n",
+      "completed t: 66\n",
+      "completed t: 67\n",
+      "completed t: 68\n",
+      "completed t: 69\n",
+      "completed t: 70\n",
+      "completed t: 71\n",
+      "completed t: 72\n",
+      "completed t: 73\n",
+      "completed t: 74\n",
+      "completed t: 75\n",
+      "completed t: 76\n",
+      "completed t: 77\n",
+      "completed t: 78\n",
+      "completed t: 79\n",
+      "completed t: 80\n",
+      "completed t: 81\n",
+      "completed t: 82\n",
+      "completed t: 83\n",
+      "completed t: 84\n",
+      "completed t: 85\n",
+      "completed t: 86\n",
+      "completed t: 87\n",
+      "completed t: 88\n",
+      "completed t: 89\n",
+      "completed t: 90\n",
+      "completed t: 91\n",
+      "completed t: 92\n",
+      "completed t: 93\n",
+      "completed t: 94\n",
+      "completed t: 95\n",
+      "completed t: 96\n",
+      "completed t: 97\n",
+      "completed t: 98\n",
+      "completed t: 99\n",
+      "CPU times: user 2h 59min 31s, sys: 4min 23s, total: 3h 3min 55s\n",
+      "Wall time: 46min 1s\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %%time\n",
+    "# da_const_vr = set_da_constants_22(obs_loc=np.arange(25,299,50))\n",
+    "\n",
+    "# nalpha_steps=23\n",
+    "# alpha_vec=np.logspace(-9,2,nalpha_steps)\n",
+    "# t_vec=np.array([200,600,1000,1400,1800])\n",
+    "# ndt_steps=len(t_vec)\n",
+    "# t_start= 40\n",
+    "# t_end = 100\n",
+    "# n_rand = 15\n",
+    "# n_samples = (t_end-t_start)*n_rand\n",
+    "# sens_all     = np.zeros([ndt_steps,300])\n",
+    "# vr_es_alpha  = np.zeros([nalpha_steps,ndt_steps,n_samples])\n",
+    "# vr_real      = np.zeros([ndt_steps,n_samples]) \n",
+    "# var_total     = np.zeros([ndt_steps,n_samples]) \n",
+    "# counter = 0\n",
+    "# total_variance = 0.\n",
+    "# for t in range(t_start,t_end):\n",
+    "#     for r in range(n_rand):\n",
+    "#         n = (t-t_start)*n_rand+r\n",
+    "#         np.random.seed(n)\n",
+    "#         #print(i,n_obs)\n",
+    "#         counter = counter+1\n",
+    "#         truth_idx = r\n",
+    "#         for i in range(len(t_vec)): \n",
+    "#             da_const_vr['dt'] = t_vec[i]\n",
+    "            \n",
+    "            \n",
+    "#             for a in range(nalpha_steps):\n",
+    "#                 if a ==0:\n",
+    "#                     vr_t, vr_i, vr_r, J_dict_LA,bla, quad,dx_ni =vr_reloaded_22(states[0]['bg'][t],\n",
+    "#                     states[0]['bg'][t][:,truth_idx],m_const,da_const_vr,reflectance_simulator,iterative_flag = 0,\n",
+    "#                     alpha=alpha_vec[a],obs_seed=counter,model_seed=counter)\n",
+    "#                 else:\n",
+    "#                     vr_t, vr_i, vr_r, J_dict_LA,bla, quad,dx_ni =vr_reloaded_22(states[0]['bg'][t],\n",
+    "#                     states[0]['bg'][t][:,truth_idx],m_const,da_const_vr,reflectance_simulator,iterative_flag = 0,\n",
+    "#                     alpha=alpha_vec[a],obs_seed=counter,model_seed=counter,quad_state=quad)\n",
+    "            \n",
+    "#                 vr_es_alpha[a,i,n]  = vr_t\n",
+    "#             var_total[i,n] = np.var(J_dict_LA['bf'],ddof=1)\n",
+    "#             vr_real[i,n]   = vr_r\n",
+    "            \n",
+    "            \n",
+    "            \n",
+    "#     print('completed t:',t)\n",
+    "       "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loading raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a_file = open(\"plot-data/alpha_OSSEs.pkl\",'rb')\n",
+    "bla=pickle.load(a_file)\n",
+    "dict_raw=bla\n",
+    "a_file.close()\n",
+    "vr_es_alpha=dict_raw['vr_es-alpha']\n",
+    "vr_real    =dict_raw['vr_real ']\n",
+    "var_total  =dict_raw['var_total']\n",
+    "t_vec      =dict_raw['t_vec']\n",
+    "alpha_vec  =dict_raw['alpha_vec']\n",
+    "\n",
+    "counter = vr_real.shape[1]\n",
+    "nalpha_steps = vr_es_alpha.shape[0]\n",
+    "nalpha_steps=23\n",
+    "alpha_vec=np.logspace(-9,2,nalpha_steps)\n",
+    "t_vec=np.array([200,600,1000,1400,1800])\n",
+    "ndt_steps=len(t_vec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "real  =np.sum(vr_real  ,axis=1)/counter#/var_total\n",
+    "total =np.sum(var_total,axis=1)/counter#/var_total\n",
+    "\n",
+    "rmse_es_alpha =np.zeros([nalpha_steps,ndt_steps])\n",
+    "me_es_alpha =np.zeros([nalpha_steps,ndt_steps])\n",
+    "es_alpha =np.zeros([nalpha_steps,ndt_steps])\n",
+    "\n",
+    "for e in range(nalpha_steps):\n",
+    "    rmse_es_alpha[e,:] =np.power(np.sum(np.power((vr_es_alpha[e,:,:] -vr_real),2),axis=1)/counter,0.5)/np.sum(vr_real,axis=1)*counter\n",
+    "    me_es_alpha[e,:] =np.sum(vr_es_alpha [e,:,:] -vr_real,axis=1)/np.sum(vr_real,axis=1)\n",
+    "    es_alpha[e,:] =np.sum(vr_es_alpha[e,:,:] ,axis=1)/counter#/var_total\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax=plt.subplots(2,1,figsize=(4,6),sharex='all')\n",
+    "\n",
+    "for t in range(ndt_steps):\n",
+    "    ax[0].plot(alpha_vec,es_alpha[:,t]/total[t]*100,label=str(t_vec[t]),color=plt.cm.copper(t/(ndt_steps-1)),marker='.')\n",
+    "    ax[0].hlines(real[t]/total[t]*100,alpha_vec[0],alpha_vec[-1],linestyle='--',color=plt.cm.copper(t/(ndt_steps-1)))\n",
+    "    \n",
+    "    ax[1].plot(alpha_vec,rmse_es_alpha[:,t]*real[t],label=str(t_vec[t]),color=plt.cm.copper(t/(ndt_steps-1)),marker='.')\n",
+    "    \n",
+    "    \n",
+    "    #plt.hlines(es[t]/total[t]*100,alpha_vec[0],alpha_vec[-1],linestyle=':',color=plt.cm.copper(t/(ndt_steps-1)))\n",
+    "t=2\n",
+    "ax[0].hlines(real[t]/total[t]*100,alpha_vec[0],alpha_vec[-1],linestyle='--',color=plt.cm.copper(t/(ndt_steps-1)),label='truth')\n",
+    "ax[1].hlines(real[t]/total[t]*100,alpha_vec[0],alpha_vec[-1],linestyle='--',color=plt.cm.copper(t/(ndt_steps-1)),label='truth')\n",
+    "\n",
+    "ax[0].vlines(alpha_default,-1000,1000,linestyle=':',color='k',lw=3)\n",
+    "ax[1].vlines(alpha_default,-1000,1000,linestyle=':',color='k',lw=3)\n",
+    "\n",
+    "#plt.legend(title='lead time [s]',bbox_to_anchor=(1,1))\n",
+    "# ax[0].legend(title='lead time [s]',ncol=2,bbox_to_anchor=(2,1),loc='upper right')\n",
+    "ax[1].legend(title='lead time [s]',ncol=2,loc='upper right',handlelength=1)\n",
+    "ax[0].set_xscale('log')\n",
+    "# ax[0].set_xlabel(r'$\\alpha$')\n",
+    "ax[1].set_xlabel(r'$\\alpha$')\n",
+    "ax[1].set_ylabel('RMSE variance reduction',labelpad=0.1)\n",
+    "ax[0].set_ylabel('mean variance reduction [%]')\n",
+    "ax[0].set_ylim(top=1,bottom=-50)\n",
+    "ax[1].set_ylim(top=200,bottom=0)\n",
+    "ax[0].set_xlim(left=1e-8,right=100)\n",
+    "label_axes_abcd(fig,loc=(1.02,0.95))\n",
+    "plt.subplots_adjust(hspace=0.1)\n",
+    "fig.align_labels()\n",
+    "ax[0].yaxis.get_label().set_verticalalignment(\"baseline\")\n",
+    "ax[0].yaxis.get_label().set_verticalalignment(\"baseline\")\n",
+    "# plt.legend(title='lead time [s]',bbox_to_anchor=(-0.05,1.05,1,0.1),ncol=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ensemble size plot.\n",
+    "This again requires running a large ensemble and it takes about 30 minutes to run everything, so I reload the conducted experiments instead. uncomment the ensemble_plotter commands below to show the full ensemble "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "initialize model and data assimilation setup using the default values\n",
+    "\"\"\"\n",
+    "\n",
+    "m_const = set_model_constants_22()\n",
+    "da_const = set_da_constants_22(nens=512,ncyc=100)\n",
+    "sat_operator = reflectance_simulator\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# slight difference now, instead of running a new state for each ensemble size, i instead first run the max size ensemble, and then randomly select the desired number of ensembles from the large state.\n",
+    "# This is to avoid having differences in the default model run overpower the differences between how many ensembles are used for the vr  test\n",
+    "\n",
+    "n_steps=8\n",
+    "ens_values = 2**np.arange(2,n_steps+2)\n",
+    "ens_values = ens_values.astype(int)\n",
+    "#da_const['nens'] = ens_values[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pgriewank/pgriewank/code/2021-linear-advection/da_functions.py:328: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  bg[:,i]    = linear_advection_model(analysis[:,i],u_ens,dhdt_ens,m_const[\"dx\"],da_const[\"dt\"],da_const[\"nt\"])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 19.3 s, sys: 296 ms, total: 19.6 s\n",
+      "Wall time: 4.95 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Run the model\n",
+    "states_512   = run_linear_advection_KF_22(m_const,da_const,reflectance_simulator)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fig, ax = ensemble_plotter_22(states_512,m_const,da_const,t_end=3,t_start=1)\n",
+    "# fig, ax = ensemble_plotter_22(states_512,m_const,da_const,t_end=29,t_start=27)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!-- a_file = open(\"plot-data/state_letkf_512_1220.pkl\", \"rb\") -->\n",
+    "<!-- states_512 = pickle.load(a_file) -->\n",
+    "<!-- a_file.close() -->"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "completed nens: 4\n",
+      "completed nens: 8\n",
+      "completed nens: 16\n",
+      "completed nens: 32\n",
+      "completed nens: 64\n",
+      "completed nens: 128\n",
+      "completed nens: 256\n",
+      "completed nens: 512\n",
+      "CPU times: user 4min 28s, sys: 4.17 s, total: 4min 32s\n",
+      "Wall time: 1min 8s\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %%time\n",
+    "\n",
+    "# t_start= 40\n",
+    "# t_end = 100\n",
+    "# n_rand = 15\n",
+    "# n_samples = (t_end-t_start)*n_rand\n",
+    "# counter =n_samples\n",
+    "# vr_real       = np.zeros([n_steps,n_samples]) \n",
+    "# vr_es         = np.zeros([n_steps,n_samples]) \n",
+    "# vr_is_ca      = np.zeros([n_steps,n_samples]) \n",
+    "# var_total     = np.zeros([n_steps,n_samples]) \n",
+    "# for i in range(n_steps): \n",
+    "#     n_ens=ens_values[i]\n",
+    "#     for t in range(t_start,t_end):\n",
+    "#         for r in range(n_rand):\n",
+    "#             n = (t-t_start)*n_rand+r\n",
+    "#             np.random.seed(n)\n",
+    "#             # selecting random ensemble members for the ensemble\n",
+    "#             idx_ens = randomized_obs_loc(n_ens,start=0,end=ens_values[-1],seed=n)\n",
+    "#             truth_idx = r\n",
+    "            \n",
+    "#             da_const_vr['nens'] = n_ens\n",
+    "            \n",
+    "            \n",
+    "#             vr_t, vr_i, vr_r, J_dict_LA,bla, quad,dx_ni =vr_reloaded_22(states_512[0]['bg'][t][:,idx_ens],\n",
+    "#                                 states_512[0]['bg'][t][:,truth_idx],m_const,da_const_vr,sat_operator,\n",
+    "#                                                 obs_seed=counter,model_seed=counter,alpha=alpha_default)\n",
+    "                                                \n",
+    "#             var_total[i,n] = np.var(J_dict_LA['bf'],ddof=1)\n",
+    "#             vr_es[i,n]     = vr_t\n",
+    "#             vr_real[i,n]   = vr_r\n",
+    "#             vr_t, vr_r, quad, J_dict_LA     = vr_individual_loc_22(states_512[0]['bg'][t][:,:],\n",
+    "#                             states_512[0]['bg'][t][:,truth_idx],m_const,da_const_vr,sat_operator,\n",
+    "#                                                                    advect_flag=1,quad_state=quad,\n",
+    "#                                                                    obs_seed=counter,model_seed=counter)\n",
+    "#             vr_is_ca[i,n]  = vr_t\n",
+    "            \n",
+    "            \n",
+    "#     print('completed nens:',ens_values[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # got to put this in a dictionary, otherwise this is just to many variables\n",
+    "# dict_raw = {\n",
+    "\n",
+    "# 'vr_es   ':vr_es   ,\n",
+    "# 'vr_is_ca':vr_is_ca,\n",
+    "# 'vr_real ':vr_real ,\n",
+    "# 'var_total':var_total,\n",
+    "# 'ens_values':ens_values\n",
+    "# }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a_file = open(\"plot-data/dict_raw_sqenkf_ens_0401.pkl\", \"wb\")\n",
+    "pickle.dump(dict_raw, a_file)\n",
+    "a_file.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a_file = open(\"plot-data/state_letkf_512_1220.pkl\", \"wb\")\n",
+    "pickle.dump(states_512, a_file)\n",
+    "a_file.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8, 900)"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_file = open(\"plot-data/ensemble-size-OSSEs.pkl\",'rb')\n",
+    "bla=pickle.load(a_file)\n",
+    "dict_raw=bla\n",
+    "a_file.close()\n",
+    "vr_es      =dict_raw['vr_es   ']\n",
+    "vr_is_ca   =dict_raw['vr_is_ca']\n",
+    "vr_real    =dict_raw['vr_real ']\n",
+    "var_total  =dict_raw['var_total']\n",
+    "ens_values =dict_raw['ens_values']\n",
+    "\n",
+    "counter = vr_real.shape[1]\n",
+    "vr_real.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "es    =np.sum(vr_es    ,axis=1)/counter#/var_total\n",
+    "is_ca =np.sum(vr_is_ca ,axis=1)/counter#/var_total\n",
+    "real  =np.sum(vr_real  ,axis=1)/counter#/var_total\n",
+    "total =np.sum(var_total,axis=1)/counter#/var_total\n",
+    "\n",
+    "me_es    =np.sum((vr_es    -vr_real),axis=1)/np.sum(vr_real,axis=1)\n",
+    "me_is_ca =np.sum((vr_is_ca -vr_real),axis=1)/np.sum(vr_real,axis=1)\n",
+    "\n",
+    "rmse_es    =np.power(np.sum(np.power((vr_es    -vr_real),2),axis=1)/counter,0.5)/np.sum(vr_real,axis=1)*counter\n",
+    "rmse_is_ca =np.power(np.sum(np.power((vr_is_ca -vr_real),2),axis=1)/counter,0.5)/np.sum(vr_real,axis=1)*counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Lets see what changes when we change the localization used to create the background\n",
+    "fig,ax = plt.subplots(2,1,figsize=(4,6),sharex='all')\n",
+    "ax[0].plot( is_ca  , lw=4,alpha=1.,label='implicit sens',color=plt.cm.viridis(0.8))\n",
+    "ax[0].plot( es     , lw=4,alpha=1.0,label='explicit sens'              ,color='blueviolet',ls='--')      \n",
+    "ax[0].plot( real     , lw=2,alpha=1,  color='k',ls='-',marker='.',label='truth')\n",
+    "ax[0].plot(-total    , lw=2,alpha=1,  color='k',ls='--',marker='.',label='limit')\n",
+    "ax[1].plot(rmse_is_ca*real    , lw=4,alpha=1.0,label='implicit sensitivity',color=plt.cm.viridis(0.8))\n",
+    "ax[1].plot(rmse_es*real       , lw=4,alpha=1.0,label='explicit sensitivity'              ,color='blueviolet',ls='--',zorder=2)      \n",
+    "\n",
+    "\n",
+    "ax[1].set_xlabel('ensemble size');\n",
+    "ax[0].set_ylabel('mean variance reduction');\n",
+    "ax[1].set_ylabel('RMSE variance reduction');\n",
+    "\n",
+    "ax[0].set_xticks(np.arange(n_steps));\n",
+    "ax[0].set_xticklabels(ens_values);\n",
+    "ax[1].set_ylim(bottom=10)\n",
+    "ax[0].set_ylim(top=-20)\n",
+    "    \n",
+    "lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]\n",
+    "lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]\n",
+    "#fig.legend(lines, labels, loc='upper center',ncol=6)\n",
+    "#ax[0].legend(bbox_to_anchor=(-0.3,1.05),loc='lower left',ncol=5);\n",
+    "ax[0].legend(loc='center right',ncol=2);\n",
+    "#ax[1].legend(lines,labels)#ncol=2,bbox_to_anchor=(1.3,.05),loc='lower left').set_zorder(100);\n",
+    "plt.subplots_adjust(hspace=0.05)\n",
+    "fig.align_labels()\n",
+    "ax[0].yaxis.get_label().set_verticalalignment(\"baseline\")\n",
+    "ax[1].yaxis.get_label().set_verticalalignment(\"baseline\")\n",
+    "label_axes_abcd(fig,loc=(1.02,0.95))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Graveyard to be removed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def vr_scatter_v4(vr_tot1,vr_rea1,vr_tot2,vr_rea2,vr_tot3,vr_tot4,alpha=0.3,alpha2=0.5,color1='blueviolet',color2=plt.cm.viridis(0.8),\n",
+    "                  label1='',label2='',label3='',llabel1='explicit sens',llabel2='implicit sens'):\n",
+    "    \"\"\"\n",
+    "    Just a 2x2 scatterplot with shared axis and a linear regressions \"\"\"\n",
+    "    \n",
+    "    fig, ax = plt.subplots(2,2,figsize=(5.5,5.5),sharex='all',sharey='all')\n",
+    "    \n",
+    "    color = color1\n",
+    "    vr_rea = vr_rea1\n",
+    "    vr_tot = vr_tot1\n",
+    "    m, b = np.polyfit(vr_rea, vr_tot, 1)\n",
+    "    ax[0,0].plot([-1000,1000],[-1000,1000],'k--',zorder=0)\n",
+    "    ax[0,0].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1,label=llabel1)\n",
+    "    ax[0,0].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) \n",
+    "    ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)\n",
+    "    ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)\n",
+    "    ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)\n",
+    "    ax[0,0].set_aspect('equal', 'box')\n",
+    "    ax[0,0].legend(loc='lower center')\n",
+    "    \n",
+    "            \n",
+    "    vr_rea = vr_rea2\n",
+    "    vr_tot = vr_tot2\n",
+    "    m, b = np.polyfit(vr_rea, vr_tot, 1)\n",
+    "    ax[0,1].plot([-1000,1000],[-1000,1000],'k--',zorder=0)\n",
+    "    ax[0,1].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1)\n",
+    "    ax[0,1].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) \n",
+    "    ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)\n",
+    "    ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)\n",
+    "    ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)\n",
+    "    ax[0,1].set_aspect('equal', 'box')\n",
+    "    \n",
+    "    color = color2\n",
+    "    vr_rea = vr_rea1\n",
+    "    vr_tot = vr_tot3\n",
+    "    m, b = np.polyfit(vr_rea, vr_tot, 1)\n",
+    "    ax[1,0].plot([-1000,1000],[-1000,1000],'k--',zorder=0)\n",
+    "    ax[1,0].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1,label=llabel2)\n",
+    "    ax[1,0].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) \n",
+    "    ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)\n",
+    "    ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)\n",
+    "    ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)\n",
+    "    ax[1,0].set_aspect('equal', 'box')\n",
+    "    ax[1,0].legend(loc='lower center')\n",
+    "    \n",
+    "            \n",
+    "    vr_rea = vr_rea2\n",
+    "    vr_tot = vr_tot4\n",
+    "    m, b = np.polyfit(vr_rea, vr_tot, 1)\n",
+    "    ax[1,1].plot([-1000,1000],[-1000,1000],'k--',zorder=0)\n",
+    "    ax[1,1].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1)\n",
+    "    ax[1,1].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) \n",
+    "    ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)\n",
+    "    ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)\n",
+    "    ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)\n",
+    "    ax[1,1].set_aspect('equal', 'box')\n",
+    "            \n",
+    "    \n",
+    "    plt.subplots_adjust(wspace=0.05,hspace=0.05)\n",
+    "    \n",
+    "    ax[1,0].set_xlabel('variance reduction')\n",
+    "    ax[1,1].set_xlabel('variance reduction')\n",
+    "    ax[1,0].set_ylabel('estimated var reduction')\n",
+    "    ax[0,0].set_ylabel('estimated var reduction')\n",
+    "    \n",
+    "    ax[0,0].set_title(label1)\n",
+    "    ax[0,1].set_title(label2)\n",
+    "    \n",
+    "    x_max = np.max(np.hstack([vr_rea1,vr_rea2,vr_tot1,vr_tot2]))\n",
+    "    x_min = np.min(np.hstack([vr_rea1,vr_rea2,vr_tot1,vr_tot2]))\n",
+    "    \n",
+    "   \n",
+    "    ax[0,0].set_xlim(x_min,x_max)\n",
+    "    ax[0,0].set_ylim(x_min,x_max)\n",
+    "    plt.locator_params(axis='y', nbins=4)\n",
+    "    plt.locator_params(axis='x', nbins=4)\n",
+    "    return fig, ax"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/da_functions.py b/da_functions.py
index 1e14c26aa3e8519b73bf5466409b818f4afb65b9..fefebff77abbf3ffe39b00d6b435ed0f0575d762 100644
--- a/da_functions.py
+++ b/da_functions.py
@@ -11,7 +11,7 @@ from misc_functions import *
 def set_da_constants_22(ncyc=100,nt=1,dt=550,u_std=2.0,dhdt_std=0.,
                      True_std_obs=0.15,used_std_obs=0.15,pert_std_obs=0.15,
                      obs_loc=np.arange(49,299,100).astype(int),
-                     True_std_obs_sat=0.25,used_std_obs_sat=0.25,pert_std_obs_sat=0.25,
+                     True_std_obs_sat=0.15,used_std_obs_sat=0.15,pert_std_obs_sat=0.15,
                      obs_loc_sat=np.arange(7,299,15),
                      nens=32,nexp=1,
                      init_noise=0.,init_spread = False, init_spread_h=0.5,init_spread_x = 500.,
@@ -285,6 +285,8 @@ def generate_obs_22(truth,truth_init,m_const,da_const,j,t,sat_operator):
     if len(da_const['obs_loc_sat'])>0:
         truth_sat = sat_operator(truth)
         obs_sat = truth_sat + np.random.normal(0,da_const["True_std_obs_sat"],m_const["nx"]) 
+        obs_sat[obs_sat>1.]= 1. 
+        obs_sat[obs_sat<0.]= 0. 
     else:
         obs_sat = np.zeros(m_const['nx'])
         
@@ -2228,6 +2230,9 @@ def generate_obs_22_single(truth,m_const,da_const,sat_operator,obs_seed):
     if len(da_const['obs_loc_sat'])>0:
         truth_sat = sat_operator(truth)
         obs_sat = truth_sat + np.random.normal(0,da_const["True_std_obs_sat"],m_const["nx"]) 
+        
+        obs_sat[obs_sat>1.]= 1. 
+        obs_sat[obs_sat<0.]= 0. 
     else:
         obs_sat = np.zeros(m_const['nx'])
         
@@ -2355,7 +2360,7 @@ def create_states_dict_22(j,states,m_const,da_const,sat_operator):
 def vr_reloaded_22(background,truth,m_const,da_const,sat_operator,
                 func_J=sum_mid_tri,
                 reduc = 1,reg_flag=1,
-                quad_state = None,dJdx_inv=None,alpha=None,mismatch_threshold=0.1,
+                quad_state = None,dJdx_inv=None,alpha=0.01,mismatch_threshold=0.1,
                 iterative_flag=0,explicit_sens_flag = 1,exp_num=0,obs_seed=0,model_seed=0):
 
     """
diff --git a/misc_functions.py b/misc_functions.py
index 6e7cf63ea4d1e2c636ba8e6a4c4215ac29782f0f..db78f49199ab9db025e56fcb16b5d9bde32c5136 100644
--- a/misc_functions.py
+++ b/misc_functions.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-def reflectance_simulator(h,h_c=0.5,window=7,clear_sky=0.3,cloud=0.9):
+def reflectance_simulator(h,h_c=0.5,window=7,clear_sky=0.3,cloud=0.7):
     """
     calculates the "reflectance" which is a fixed clear sky value where h is below h_c, and a cloud  value where h>h_c.
     As a spatial averaging defined by window, with the total width over the averaging being 2*window+1
diff --git a/plot-data/alpha_OSSEs.pkl b/plot-data/alpha_OSSEs.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..39675770ffa55240da01d38b7eed72afb6fdfdf5
Binary files /dev/null and b/plot-data/alpha_OSSEs.pkl differ
diff --git a/plot-data/ensemble-size-OSSEs.pkl b/plot-data/ensemble-size-OSSEs.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..4454a0c4e0e99bbd5bb2fe3b320adcdb789190be
Binary files /dev/null and b/plot-data/ensemble-size-OSSEs.pkl differ
diff --git a/plot_functions.py b/plot_functions.py
index a14d7180ace7e761407d3b79453495f33ad9f278..a5112e8737323bec40faedafed967c464177a912 100644
--- a/plot_functions.py
+++ b/plot_functions.py
@@ -183,6 +183,67 @@ def quad_plotter_v2(quad_state,m_const,da_const):
     
     return fig,ax
 
+def quad_plotter_paper(quad_state,m_const,da_const):
+    """
+    Plots the initial background and blind forecast, as well as the analysis and forecast with observations.
+
+    Swapped free forecast and analysis figure locations, also changed h to phi
+
+    Returns: 
+    figure and axes
+    """
+    sns.set()
+    sns.set_style('whitegrid')
+
+
+    alpha = np.sqrt(1/da_const['nens'])+0.1
+    
+    fig, ax = plt.subplots(2,2,figsize=(7.5,4),sharex='all',sharey='all')
+  
+    
+    
+    for i in range(da_const["nens"]):
+        ax[0,0].plot(m_const['x_grid']/1000.,quad_state['bg'][:,i],'r',alpha =alpha,zorder=1)
+        ax[1,0].plot(m_const['x_grid']/1000.,quad_state['bf'][:,i],'b',alpha =alpha,zorder=1)
+        ax[0,1].plot(m_const['x_grid']/1000.,quad_state['an'][:,i],'magenta',alpha =alpha,zorder=1)
+        ax[1,1].plot(m_const['x_grid']/1000.,quad_state['fc'][:,i],'c',alpha =alpha,zorder=1)
+    
+
+    ax[0,0].plot(m_const["x_grid"]/1000.,quad_state['tr_bg'],'k',zorder=10,label='truth')
+    ax[0,1].plot(m_const["x_grid"]/1000.,quad_state['tr_bg'],'k',zorder=10,label='truth')
+    #ax[0,1].plot(m_const["x_grid"],quad_stat/1000.e['tr_fc'],'k',zorder=10,label='truth')
+    #ax[1,1].plot(m_const["x_grid"],quad_stat/1000.e['tr_fc'],'k',zorder=10,label='truth')
+    
+    ax[0,0].plot(m_const['x_grid']/1000.,np.mean(quad_state['bg'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean')
+    ax[0,1].plot(m_const['x_grid']/1000.,np.mean(quad_state['an'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean')
+    ax[1,0].plot(m_const['x_grid']/1000.,np.mean(quad_state['bf'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean')
+    ax[1,1].plot(m_const['x_grid']/1000.,np.mean(quad_state['fc'][:,:],axis=1),'k--',alpha =1,zorder=2,label='ens mean')
+
+    if da_const['n_obs_h']:
+        ax[0,0].scatter(m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]],zorder=3,label='observations',color='k')
+        ax[0,1].scatter(m_const["x_grid"][da_const["obs_loc"]]/1000.,quad_state['obs'][da_const["obs_loc"]],zorder=3,label='observations',color='k')
+    
+    
+    ax[0,0].set_title('background')
+    ax[1,0].set_title('free-forecast')
+    ax[0,1].set_title('analysis')
+    ax[1,1].set_title('forecast')
+    ax[1,0].set_xlim([m_const["x_grid"][0],m_const["x_grid"][-1]/1000])
+    plt.subplots_adjust(wspace=0.03,hspace=0.20)
+    ax[1,1].set_xlabel('x [km]')
+    ax[1,0].set_xlabel('x [km]')
+    ax[0,0].set_ylabel(r'$\phi$')
+    ax[1,0].set_ylabel(r'$\phi$')
+    ax[0,1].legend()
+    
+    # now to add in shading for response domain
+    ylimits = ax[0,0].get_ylim()
+    ax[1,0].fill_between([10,20], ylimits[0], y2=ylimits[1],color='grey',alpha=0.2,zorder=-1)
+    ax[1,1].fill_between([10,20], ylimits[0], y2=ylimits[1],color='grey',alpha=0.2,zorder=-1)
+    ax[1,1].set_ylim(ylimits)
+    
+    return fig,ax
+
 def B_plotter(states,j=0,ncyc=0,matrix='bg'):
     """
     Plots the covariance plotter of either the forecast/background ensemble ('bg'), analysis 'an', or the free/blind forecast 'bf'
@@ -546,16 +607,108 @@ def quad_plotter_22(quad_state,m_const,da_const):
     
     return fig,ax
 
+
 def plot_ensemble_sat_analysis(bg,an,obs,obs_sat,truth,sat_operator,m_const,da_const,h_c=None):
-    fig ,ax = plt.subplots(2,2,figsize=(8,6),sharey='col',sharex='all')
+    fig ,ax = plt.subplots(2,2,figsize=(10,6),sharey='col',sharex='all')
     """
     Plots the background and analysis ensemble, as well as the satetlitte obs equivalents. Includes observations as well.
     
     Is still quite rough around the edges, and will hopefully be improced upon
     """
+
+    sat_an    = sat_operator(an)
+    sat_bg    = sat_operator(bg)
+    sat_tr_bg = sat_operator(truth)
+    n_obs_h  =len(da_const["obs_loc"])
+    n_obs_sat =len(da_const["obs_loc_sat"])
+     
+    dY_b, y_ol_b = state_to_observation_space(bg,m_const,da_const,sat_operator)
+    y_b = dY_b.T + y_ol_b
+    y_b = y_b.T
+    
+    
+    # for plotting the pixels
+    window = m_const['x_grid'][da_const["obs_loc_sat"][1]]-m_const['x_grid'][da_const["obs_loc_sat"][0]]
+    xpixmin = m_const['x_grid'][da_const["obs_loc_sat"]]-window/2 
+    xpixmax = m_const['x_grid'][da_const["obs_loc_sat"]]+window/2 
+    
+    for i in range(bg.shape[1]):
+        #ax[0].plot(m_const['x_grid'],x_a[:,i],'r',alpha=0.2)
+        ax[1,0].plot(m_const['x_grid']/1000,an [:,i],'magenta',alpha=0.2)
+        ax[0,0].plot(m_const['x_grid']/1000,bg [:,i],'r',alpha=0.2)
+        
+        ax[1,1].hlines(
+                     sat_an [da_const["obs_loc_sat"],i],
+                     xpixmin/1000,xpixmax/1000,
+                     color='magenta',alpha=0.2,lw=3)
+        ax[0,1].hlines(
+                     sat_bg [da_const["obs_loc_sat"],i],
+                     xpixmin/1000,xpixmax/1000,
+                     color='r',alpha=0.2,lw=3)
+    ax[1,0].plot(m_const['x_grid']/1000+1000,an [:,-1],'magenta',alpha=1,label='background ensemble')
+    ax[0,0].plot(m_const['x_grid']/1000+1000,bg [:,-1],'r',alpha=1,label='analysis ensemble')
+    
     #ax[0].plot(m_const['x_grid'],x_ol_a,'r')
-    #ax[0].plot(m_const['x_grid'],x_ol_a_loc[:],'b')
-    #ax[0].plot(m_const['x_grid'],states[0]['truth'][t],'k')
+    ax[1,0].plot(m_const['x_grid']/1000,truth,'k',linewidth=2,label='truth')
+    ax[0,0].plot(m_const['x_grid']/1000,truth,'k',linewidth=2)
+
+
+    if n_obs_h>0:
+        ax[1,0].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']],
+                        c='k',label='h point observation')
+        ax[0,0].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']],c='k')#,label='h point obs')
+    if n_obs_sat>0:
+        ax[1,1].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']],
+                        marker='x',s=50,c='k',label='reflectance observation')
+        ax[0,1].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']],marker='x',s=50,c='k')
+
+
+    ax[0,0].plot(m_const['x_grid']/1000,np.mean(bg,axis=1),'k--',lw=2,label='ensemble mean')
+    ax[1,0].plot(m_const['x_grid']/1000,np.mean(an,axis=1),'k--',lw=2)#,label='ens mean')
+    
+    if h_c is not None:  
+        ax[0,0].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':')
+        ax[1,0].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':',label=r'cloud threshold $h_c$')
+    
+    label_axes_abcd(fig)
+
+    ax1 = ax[0,1].twinx()
+    ax1.set_yticks([0.3,0.7])
+    ax1.set_yticklabels(['clear \n sky','cloud'])
+    ax2 = ax[1,1].twinx()
+    ax2.set_yticks([0.3,0.7])
+    ax2.set_yticklabels(['clear \n sky','cloud'])
+    ax1.set_ylim(-0.05,1.05)
+    ax2.set_ylim(-0.05,1.05)
+    ax[1,1].set_ylim(-0.05,1.05)
+    ax[0,0].set_yticks([0,0.5,1])
+    ax[0,1].set_yticks([0,0.3,0.7,1.0])
+    ax[0,0].set_yticklabels(['0',r'$h_c$','1'])
+    
+    ax[1,0].set_xlabel('x [km]')
+    ax[1,1].set_xlabel('x [km]')
+    ax[1,1].set_xlim(m_const['x_grid'][0]/1000,30)#m_const['x_grid'][-1]/1000)
+    ax[0,0].set_ylabel('background \n h [m]')
+    ax[1,0].set_ylabel('analysis \n h[m]')
+    ax[0,1].set_ylabel('reflectance')
+    ax[1,1].set_ylabel('reflectance')
+    plt.subplots_adjust(wspace=0.2,hspace=0.1)
+    
+    ax[0,1].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_bg[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean')
+    ax[1,1].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_an[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean')
+    
+    lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]
+    lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
+    fig.legend(lines, labels, loc='upper center',ncol=4)
+    return fig, ax
+
+
+
+def plot_ensemble_sat_analysis_paper(bg,an,obs,obs_sat,truth,sat_operator,m_const,da_const,h_c=None):
+    fig ,ax = plt.subplots(2,2,figsize=(7.5,5),sharey='row',sharex='all')
+    """
+    Plots the background and analysis ensemble of the state, as well as the satetlitte obs equivalents. Includes observations as well.
+    """
 
     sat_an    = sat_operator(an)
     sat_bg    = sat_operator(bg)
@@ -567,6 +720,7 @@ def plot_ensemble_sat_analysis(bg,an,obs,obs_sat,truth,sat_operator,m_const,da_c
     y_b = dY_b.T + y_ol_b
     y_b = y_b.T
     
+    
     # for plotting the pixels
     window = m_const['x_grid'][da_const["obs_loc_sat"][1]]-m_const['x_grid'][da_const["obs_loc_sat"][0]]
     xpixmin = m_const['x_grid'][da_const["obs_loc_sat"]]-window/2 
@@ -574,8 +728,8 @@ def plot_ensemble_sat_analysis(bg,an,obs,obs_sat,truth,sat_operator,m_const,da_c
     
     for i in range(bg.shape[1]):
         #ax[0].plot(m_const['x_grid'],x_a[:,i],'r',alpha=0.2)
-        ax[1,0].plot(m_const['x_grid'],an [:,i],'magenta',alpha=0.2)
-        ax[0,0].plot(m_const['x_grid'],bg [:,i],'r',alpha=0.2)
+        ax[0,1].plot(m_const['x_grid']/1000,an [:,i],'magenta',alpha=0.2)
+        ax[0,0].plot(m_const['x_grid']/1000,bg [:,i],'r',alpha=0.2)
         
         # plotting the pixels
          
@@ -583,54 +737,389 @@ def plot_ensemble_sat_analysis(bg,an,obs,obs_sat,truth,sat_operator,m_const,da_c
         #ax[0,1].plot(m_const['x_grid'],sat_bg [:,i],'r',alpha=0.2)
         ax[1,1].hlines(
                      sat_an [da_const["obs_loc_sat"],i],
-                     xpixmin,xpixmax,
+                     xpixmin/1000,xpixmax/1000,
                      color='magenta',alpha=0.2,lw=3)
-        ax[0,1].hlines(
+        ax[1,0].hlines(
                      sat_bg [da_const["obs_loc_sat"],i],
-                     xpixmin,xpixmax,
+                     xpixmin/1000,xpixmax/1000,
                      color='r',alpha=0.2,lw=3)
+    ax[0,1].plot(m_const['x_grid']/1000+1000,an [:,-1],'magenta',alpha=1,label='analysis ensemble')
+    ax[0,0].plot(m_const['x_grid']/1000+1000,bg [:,-1],'r',alpha=1,label='background ensemble')
     
     #ax[0].plot(m_const['x_grid'],x_ol_a,'r')
-    ax[1,0].plot(m_const['x_grid'],truth,'k',linewidth=2)
-    ax[0,0].plot(m_const['x_grid'],truth,'k',linewidth=2)
-    #ax[0,1].hlines(
-    #         sat_tr_bg [da_const["obs_loc_sat"]],
-    #         xpixmin,xpixmax,
-    #         color='k')
-    #ax[1,1].plot(m_const['x_grid'],sat_tr_bg,'k',linewidth=2)
-    #ax[0,1].plot(m_const['x_grid'],sat_tr_bg,'k',linewidth=2)
+    ax[0,1].plot(m_const['x_grid']/1000,truth,'k',linewidth=2,label='truth')
+    ax[0,0].plot(m_const['x_grid']/1000,truth,'k',linewidth=2)
+
+
+    if n_obs_h>0:
+        ax[0,1].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']],
+                        c='k',label=r'$\phi$ point observation')
+        ax[0,0].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']],c='k')#,label='h point obs')
+    if n_obs_sat>0:
+        ax[1,1].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']],
+                        marker='x',s=50,c='k',label='reflectance observation')
+        ax[1,0].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']],marker='x',s=50,c='k')
 
+    ax[0,0].plot(m_const['x_grid']/1000,np.mean(bg,axis=1),'k--',lw=2,label='ensemble mean')
+    ax[0,1].plot(m_const['x_grid']/1000,np.mean(an,axis=1),'k--',lw=2)#,label='ens mean')
+    
+    if h_c is not None:  
+        ax[0,0].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':')
+        ax[0,1].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':',label=r'cloud threshold $\phi_c$')
+    
+    label_axes_abcd(fig)
+
+    ax1 = ax[0,1].twinx()
+    ax1.set_yticks([0.,0.5,1])
+    ax1.set_yticklabels(['0',r'$\phi_c$','1'])
+    ax1.set_ylim(-0.3,1.3)
+    ax[0,0].set_ylim(-0.3,1.3)
+    ax2 = ax[1,1].twinx()
+    ax2.set_yticks([0.3,0.7])
+    ax2.set_yticklabels(['clear \n sky','cloud'])
+    ax2.set_ylim(-0.05,1.05)
+    ax[1,1].set_ylim(-0.05,1.05)
+    ax[0,0].set_yticks([0,0.5,1])
+    ax[1,0].set_yticks([0,0.3,0.7,1.0])
+    ax[0,0].set_yticklabels(['0',r'$\phi_c$','1'])
+    
+    
+    ax[1,0].set_xlabel('x [km]')
+    ax[1,1].set_xlabel('x [km]')
+    ax[0,1].set_xlim(m_const['x_grid'][0]/1000,30)#m_const['x_grid'][-1]/1000)
+    ax[0,0].set_title('background')
+    ax[0,1].set_title('analysis')
+    ax[1,0].set_ylabel('reflectance')
+    ax[0,0].set_ylabel(r'$\phi$')
+    plt.subplots_adjust(wspace=0.05,hspace=0.05)
+    
+    ax[1,0].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_bg[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean')
+    ax[1,1].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_an[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean')
+    
+    lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]
+    lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
+    fig.legend(lines, labels, loc='upper center',bbox_to_anchor=(0.5,1.1),ncol=3)
+    return fig, ax
+
+
+def plot_ensemble_sat_analysis_abstract(bg,an,obs,obs_sat,truth,sat_operator,m_const,da_const,h_c=None):
+    fig ,ax = plt.subplots(2,2,figsize=(5.5,4.5),sharey='row',sharex='all')
+    """
+    Modified for paper visual abstract
+    Plots the background and analysis ensemble, as well as the satetlitte obs equivalents. Includes observations as well.
+    """
+
+    sat_an    = sat_operator(an)
+    sat_bg    = sat_operator(bg)
+    sat_tr_bg = sat_operator(truth)
+    n_obs_h  =len(da_const["obs_loc"])
+    n_obs_sat =len(da_const["obs_loc_sat"])
+     
+    dY_b, y_ol_b = state_to_observation_space(bg,m_const,da_const,sat_operator)
+    y_b = dY_b.T + y_ol_b
+    y_b = y_b.T
+    
+    
+    # for plotting the pixels
+    window = m_const['x_grid'][da_const["obs_loc_sat"][1]]-m_const['x_grid'][da_const["obs_loc_sat"][0]]
+    xpixmin = m_const['x_grid'][da_const["obs_loc_sat"]]-window/2 
+    xpixmax = m_const['x_grid'][da_const["obs_loc_sat"]]+window/2 
+    
+    for i in range(bg.shape[1]):
+        #ax[0].plot(m_const['x_grid'],x_a[:,i],'r',alpha=0.2)
+        ax[0,1].plot(m_const['x_grid']/1000,an [:,i],'magenta',alpha=0.2)
+        ax[0,0].plot(m_const['x_grid']/1000,bg [:,i],'r',alpha=0.2)
+        
+        ax[1,1].hlines(
+                     sat_an [da_const["obs_loc_sat"],i],
+                     xpixmin/1000,xpixmax/1000,
+                     color='magenta',alpha=0.2,lw=3)
+        ax[1,0].hlines(
+                     sat_bg [da_const["obs_loc_sat"],i],
+                     xpixmin/1000,xpixmax/1000,
+                     color='r',alpha=0.2,lw=3)
+    ax[0,1].plot(m_const['x_grid']/1000+1000,an [:,-1],'magenta',alpha=1,label='analysis ensemble')
+    ax[0,0].plot(m_const['x_grid']/1000+1000,bg [:,-1],'r',alpha=1,label='background ensemble')
+    
+    #ax[0].plot(m_const['x_grid'],x_ol_a,'r')
+    ax[0,1].plot(m_const['x_grid']/1000,truth,'k',linewidth=2,label='truth')
+    ax[0,0].plot(m_const['x_grid']/1000,truth,'k',linewidth=2)
 
     if n_obs_h>0:
-        ax[1,0].scatter(m_const['x_grid'][da_const['obs_loc']],obs[da_const['obs_loc']],c='k')
-        ax[0,0].scatter(m_const['x_grid'][da_const['obs_loc']],obs[da_const['obs_loc']],c='k')
+        ax[0,1].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']],
+                        c='k',label=r'$\phi$ point observation')
+        ax[0,0].scatter(m_const['x_grid'][da_const['obs_loc']]/1000,obs[da_const['obs_loc']],c='k')#,label='h point obs')
     if n_obs_sat>0:
-        ax[1,1].scatter(m_const['x_grid'][da_const['obs_loc_sat']],obs_sat[da_const['obs_loc_sat']],marker='x',s=50,c='k')
-        ax[0,1].scatter(m_const['x_grid'][da_const['obs_loc_sat']],obs_sat[da_const['obs_loc_sat']],marker='x',s=50,c='k')
+        ax[1,1].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']],
+                        marker='x',s=50,c='k',label='reflectance observation')
+        ax[1,0].scatter(m_const['x_grid'][da_const['obs_loc_sat']]/1000,obs_sat[da_const['obs_loc_sat']],marker='x',s=50,c='k')
 
-    ax[0,1].plot(m_const['x_grid'],np.mean(sat_bg,axis=1),'k--',lw=2)
-    #ax[1,1].plot(m_const['x_grid'],np.mean(sat_an,axis=1),'magenta',lw=2)
-    #ax[0,1].plot(m_const['x_grid'],np.mean(sat_bg,axis=1),'r',lw=2)
-    ax[1,1].plot(m_const['x_grid'],np.mean(sat_an,axis=1),'k--',lw=2)
 
-    ax[0,0].plot(m_const['x_grid'],np.mean(bg,axis=1),'k--',lw=2)
-#     ax[0,0].plot(m_const['x_grid'],np.mean(an,axis=1),'magenta',lw=2)
-    ax[1,0].plot(m_const['x_grid'],np.mean(an,axis=1),'k--',lw=2)
-    #ax[1,0].plot(m_const['x_grid'],np.mean(bg,axis=1),'r',lw=2)
+    ax[0,0].plot(m_const['x_grid']/1000,np.mean(bg,axis=1),'k--',lw=2,label='ensemble mean')
+    ax[0,1].plot(m_const['x_grid']/1000,np.mean(an,axis=1),'k--',lw=2)#,label='ens mean')
     
     if h_c is not None:  
-        ax[0,0].hlines(h_c,m_const['x_grid'][0],m_const['x_grid'][-1],color='k',ls=':')
-        ax[1,0].hlines(h_c,m_const['x_grid'][0],m_const['x_grid'][-1],color='k',ls=':')
+        ax[0,0].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':')
+        ax[0,1].hlines(h_c,m_const['x_grid'][0]/1000,m_const['x_grid'][-1]/1000,color='k',ls=':',label=r'cloud threshold $\phi_c$')
+    
 
+    ax1 = ax[0,1].twinx()
+    ax1.set_yticks([0.,0.5,1])
+    ax1.set_yticklabels(['0',r'$\phi_c$','1'])
+    ax1.set_ylim(-0.3,1.3)
+    ax[0,0].set_ylim(-0.3,1.3)
+    ax2 = ax[1,1].twinx()
+    ax2.set_yticks([0.3,0.7])
+    ax2.set_yticklabels(['clear \n sky','cloud'])
+    ax2.set_ylim(-0.05,1.05)
+    ax[1,1].set_ylim(-0.05,1.05)
+    ax[0,0].set_yticks([0,0.5,1])
+    ax[1,0].set_yticks([0.3,0.7])
+    ax[1,0].set_yticklabels([])
+    ax[0,0].set_yticklabels([])
+    ax[1,0].set_xticklabels([])
+    ax[1,1].set_xticklabels([])
+    
+    
+    ax[1,0].set_xlabel('x',labelpad=-8)
+    ax[1,1].set_xlabel('x',labelpad=-8)
+    ax[0,1].set_xlim(m_const['x_grid'][0]/1000,30)#m_const['x_grid'][-1]/1000)
+    ax[0,0].set_title('background')
+    ax[0,1].set_title('analysis')
+    ax[0,0].set_ylabel(r'$\phi$',labelpad=-10)
+    ax[1,0].set_ylabel('reflectance',labelpad=-8)
+    plt.subplots_adjust(wspace=0.05,hspace=0.05)
+    
+    ax[1,0].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_bg[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean')
+    ax[1,1].step(m_const['x_grid'][da_const['obs_loc_sat']]/1000,np.mean(sat_an[da_const['obs_loc_sat']],axis=1),'k--',lw=2,where='mid')#,marker='s',markersize=4,label='ens mean')
+    
+    lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]
+    lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
+    return fig, ax
 
-    ax[0,0].set_title('height h')
-    ax[0,1].set_title('reflectance')
-    ax[1,0].set_xlabel('x [m]')
-    ax[1,1].set_xlabel('x [m]')
-    ax[1,1].set_xlim(m_const['x_grid'][0],m_const['x_grid'][-1])
-    #ax[1,0].set_ylabel('Analysis \n  h [m]')
-    #ax[0,1].set_ylabel('Background \n cloud fraction')
-    ax[0,0].set_ylabel('Background')
-    ax[1,0].set_ylabel('Analysis')
-    #plt.subplots_adjust(wspace=0.1,hspace=0.1)
+def plot_J_quad_paper(J_dict,quad,sens,dx,bw=0.3,dJ=True):
+    """
+    Plots the forecast metric distributions of the free forecast, forecast, and their linear approximations for the given sensitivity
+    """
+    
+    fig = plt.figure(figsize=(4,3))
+    nens = len(J_dict['bf'])
+    dX_bg=(quad['bg'].T-np.mean(quad['bg'],axis=1)).T
+    dX_an=(quad['an'].T-np.mean(quad['an'],axis=1)).T
+    dX_an=dx
+    dJ_ff=np.dot(sens,dX_bg)
+    dJ_fc=np.dot(sens,dX_an)
+    print('vr_reductions:',np.var(dJ_fc,ddof=1)-np.var(dJ_ff,ddof=1 ),np.var(J_dict['fc'],ddof=1)-np.var(J_dict['bf'],ddof=1))
+    print('variance:',np.var(J_dict['bf'],ddof=1),np.var(dJ_ff,ddof=1),np.var(J_dict['fc'],ddof=1),np.var(dJ_fc,ddof=1 ))
+            #'response' : np.hstack([J_dict['bf']-np.mean(J_dict['bf']),dJ_ff,J_dict['fc']-np.mean(J_dict['fc']),J_dict['es']-np.mean(J_dict['es'])]),
+    if dJ:
+        plot_data = {
+            'response' : np.hstack([J_dict['bf']-np.mean(J_dict['bf']),dJ_ff,J_dict['fc']-np.mean(J_dict['fc']),dJ_fc]),
+            'x_pos'    : np.hstack([np.zeros(nens)+0,np.zeros(nens)+1,np.zeros(nens)+2,np.zeros(nens)+3]),
+            'type'     : ['blindforecast']*nens+['estimated']*nens+['forecast']*nens}
+    else:
+        plot_data = {
+            'response' : np.hstack([J_dict['bf'],dJ_ff+np.mean(J_dict['bf']),J_dict['fc'],dJ_fc+np.mean(J_dict['fc'])]),
+            'x_pos'    : np.hstack([np.zeros(nens)+0,np.zeros(nens)+1,np.zeros(nens)+2,np.zeros(nens)+3]),
+            'type'     : ['blindforecast']*nens+['estimated']*nens+['forecast']*nens}
+
+    my_pal = ["blue",  "peru","cyan","orange"  ]
+        
+    PROPS = {
+    'boxprops':{'facecolor':'none', 'edgecolor':'black'},
+    }
+    #ax = sns.violinplot(data=plot_data, inner='quartile', orient="v",cut=0,bw=bw,y='response',x='x_pos',palette=my_pal)#sns.color_palette('cool',n_colors=3))#,x='type')#,y='response',x='cyc',hue='type',,split=True,palette={dict_label[left_var]:dict_color[left_var],dict_label[right_var]:dict_color[right_var]}
+    ax = sns.stripplot(data=plot_data, y='response',x='x_pos',alpha=0.7,jitter=0.15,size=5,palette=my_pal)#color='0.0')#
+    #ax = sns.boxplot(data=plot_data, y='response',x='x_pos',showfliers=False,**PROPS)#,patch_artist=False)#color='0.0')#,palette=my_pal
+    #plot errorbars
+    plt.errorbar(np.arange(4),np.zeros(4),[np.std(J_dict['bf'],ddof=1),np.std(dJ_ff,ddof=1),np.std(J_dict['fc'],ddof=1),np.std(dJ_fc,ddof=1 )],fmt='.',capsize=15,lw=3,color='k') 
+    
+    #if dJ == False: ax.hlines(J_dict['tr_fc'],-0.5,2.5,'k',ls='--',label='truth'); plt.legend()
+    #if dJ: ax.hlines(0,-0.5,3.5,'k',ls='--') 
+    ax.set_xlim(-0.5,3.5)
+    if dJ == False: ax.set_ylabel(r'$j$')
+    if dJ: ax.set_ylabel(r'$\delta j$')
+    ax.set_xticklabels(['free-\nforecast','estimated \n free-forecast','\n forecast','estimated \n forecast'])
+    return fig, ax
+
+def vr_scatter_v6(vr_tot1,vr_rea1,vr_tot2,vr_rea2,vr_tot3,vr_rea3,vr_tot4,vr_tot5,vr_tot6,alpha=0.3,alpha2=0.5,color1='blueviolet',color2=plt.cm.viridis(0.8),
+                  label1='',label2='',label3='',llabel1='explicit sens',llabel2='implicit sens'):
+    """
+    Just a 2x3 scatterplot with shared axis and a linear regressions """
+    
+    fig, ax = plt.subplots(2,3,figsize=(8,5.5),sharex='all',sharey='all')
+    
+    color = color1
+    vr_rea = vr_rea1
+    vr_tot = vr_tot1
+    m, b = np.polyfit(vr_rea, vr_tot, 1)
+    ax[0,0].plot([-1000,1000],[-1000,1000],'k--',zorder=0)
+    ax[0,0].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1,label=llabel1)
+    ax[0,0].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) 
+    ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)
+    ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)
+    ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)
+    ax[0,0].set_aspect('equal', 'box')
+    ax[0,0].legend(loc='lower center')
+    
+            
+    vr_rea = vr_rea2
+    vr_tot = vr_tot2
+    m, b = np.polyfit(vr_rea, vr_tot, 1)
+    ax[0,1].plot([-1000,1000],[-1000,1000],'k--',zorder=0)
+    ax[0,1].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1)
+    ax[0,1].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) 
+    ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)
+    ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)
+    ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)
+    ax[0,1].set_aspect('equal', 'box')
+    
+    vr_rea = vr_rea3
+    vr_tot = vr_tot3
+    m, b = np.polyfit(vr_rea, vr_tot, 1)
+    ax[0,2].plot([-1000,1000],[-1000,1000],'k--',zorder=0)
+    ax[0,2].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1)
+    ax[0,2].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) 
+    ax[0,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)
+    ax[0,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)
+    ax[0,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)
+    ax[0,2].set_aspect('equal', 'box')
+    
+    color = color2
+    vr_rea = vr_rea1
+    vr_tot = vr_tot4
+    m, b = np.polyfit(vr_rea, vr_tot, 1)
+    ax[1,0].plot([-1000,1000],[-1000,1000],'k--',zorder=0)
+    ax[1,0].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1,label=llabel2)
+    ax[1,0].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) 
+    ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)
+    ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)
+    ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)
+    ax[1,0].set_aspect('equal', 'box')
+    ax[1,0].legend(loc='lower center')
+    
+            
+    vr_rea = vr_rea2
+    vr_tot = vr_tot5
+    m, b = np.polyfit(vr_rea, vr_tot, 1)
+    ax[1,1].plot([-1000,1000],[-1000,1000],'k--',zorder=0)
+    ax[1,1].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1)
+    ax[1,1].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) 
+    ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)
+    ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)
+    ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)
+    ax[1,1].set_aspect('equal', 'box')
+    
+    vr_rea = vr_rea3
+    vr_tot = vr_tot6
+    m, b = np.polyfit(vr_rea, vr_tot, 1)
+    ax[1,2].plot([-1000,1000],[-1000,1000],'k--',zorder=0)
+    ax[1,2].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1)
+    ax[1,2].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) 
+    ax[1,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)
+    ax[1,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)
+    ax[1,2].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)
+    ax[1,2].set_aspect('equal', 'box')
+    
+            
+    
+    plt.subplots_adjust(wspace=0.05,hspace=0.05)
+    
+    ax[1,0].set_xlabel('variance reduction')
+    ax[1,1].set_xlabel('variance reduction')
+    ax[1,2].set_xlabel('variance reduction')
+    ax[1,0].set_ylabel('estimated var reduction')
+    ax[0,0].set_ylabel('estimated var reduction')
+    
+    ax[0,0].set_title(label1)
+    ax[0,1].set_title(label2)
+    ax[0,2].set_title(label3)
+    
+    x_max = np.max(np.hstack([vr_rea1,vr_rea2,vr_tot1,vr_tot2]))
+    x_min = np.min(np.hstack([vr_rea1,vr_rea2,vr_tot1,vr_tot2]))
+    
+   
+    ax[0,0].set_xlim(x_min,x_max)
+    ax[0,0].set_ylim(x_min,x_max)
+    plt.locator_params(axis='y', nbins=4)
+    plt.locator_params(axis='x', nbins=4)
+    return fig, ax
+
+
+def vr_scatter_v4(vr_tot1,vr_rea1,vr_tot2,vr_rea2,vr_tot3,vr_tot4,alpha=0.3,alpha2=0.5,color1='blueviolet',color2=plt.cm.viridis(0.8),
+                  label1='',label2='',label3='',llabel1='explicit sens',llabel2='implicit sens'):
+    """
+    Just a 2x2 scatterplot with shared axis and a linear regressions """
+    
+    fig, ax = plt.subplots(2,2,figsize=(5.5,5.5),sharex='all',sharey='all')
+    
+    color = color1
+    vr_rea = vr_rea1
+    vr_tot = vr_tot1
+    m, b = np.polyfit(vr_rea, vr_tot, 1)
+    ax[0,0].plot([-1000,1000],[-1000,1000],'k--',zorder=0)
+    ax[0,0].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1,label=llabel1)
+    ax[0,0].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) 
+    ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)
+    ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)
+    ax[0,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)
+    ax[0,0].set_aspect('equal', 'box')
+    ax[0,0].legend(loc='lower center')
+    
+            
+    vr_rea = vr_rea2
+    vr_tot = vr_tot2
+    m, b = np.polyfit(vr_rea, vr_tot, 1)
+    ax[0,1].plot([-1000,1000],[-1000,1000],'k--',zorder=0)
+    ax[0,1].scatter(vr_rea,vr_tot,c=color,alpha=alpha,s=5,zorder=1)
+    ax[0,1].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) 
+    ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)
+    ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)
+    ax[0,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)
+    ax[0,1].set_aspect('equal', 'box')
+    
+    color = color2
+    vr_rea = vr_rea1
+    vr_tot = vr_tot3
+    m, b = np.polyfit(vr_rea, vr_tot, 1)
+    ax[1,0].plot([-1000,1000],[-1000,1000],'k--',zorder=0)
+    ax[1,0].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1,label=llabel2)
+    ax[1,0].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) 
+    ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)
+    ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)
+    ax[1,0].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)
+    ax[1,0].set_aspect('equal', 'box')
+    ax[1,0].legend(loc='lower center')
+    
+            
+    vr_rea = vr_rea2
+    vr_tot = vr_tot4
+    m, b = np.polyfit(vr_rea, vr_tot, 1)
+    ax[1,1].plot([-1000,1000],[-1000,1000],'k--',zorder=0)
+    ax[1,1].scatter(vr_rea,vr_tot,c=color,alpha=alpha2,s=5,zorder=1)
+    ax[1,1].plot(vr_rea, m*np.array(vr_rea) + b,'k',zorder=2) 
+    ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=100,zorder=2)
+    ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='k',s=50,zorder=3)
+    ax[1,1].scatter(np.mean(vr_rea),np.mean(vr_tot),c='w',s=10,zorder=4)
+    ax[1,1].set_aspect('equal', 'box')
+            
+    
+    plt.subplots_adjust(wspace=0.05,hspace=0.05)
+    
+    ax[1,0].set_xlabel('variance reduction')
+    ax[1,1].set_xlabel('variance reduction')
+    ax[1,0].set_ylabel('estimated var reduction')
+    ax[0,0].set_ylabel('estimated var reduction')
+    
+    ax[0,0].set_title(label1)
+    ax[0,1].set_title(label2)
+    
+    x_max = np.max(np.hstack([vr_rea1,vr_rea2,vr_tot1,vr_tot2]))
+    x_min = np.min(np.hstack([vr_rea1,vr_rea2,vr_tot1,vr_tot2]))
+    
+   
+    ax[0,0].set_xlim(x_min,x_max)
+    ax[0,0].set_ylim(x_min,x_max)
+    plt.locator_params(axis='y', nbins=4)
+    plt.locator_params(axis='x', nbins=4)
     return fig, ax
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