{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "365fc9a2-b911-424a-a313-2af9497544a2",
   "metadata": {},
   "source": [
    "# Pedestrian Graph Plan de l'Aiguille"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b9a7849-f2ca-4b90-b233-6bdd82d3a108",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Ajout dans la variable PATH du système du chemin où est installée la librairie tracklib\n",
    "module_path = os.path.abspath(os.path.join('../../../../tracklib'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "# Alias pour tracklib\n",
    "import tracklib as tkl\n",
    "\n",
    "# Ajout dans la variable PATH du système du chemin où est installée la librairie footprint2graph\n",
    "module_path = os.path.abspath(os.path.join('../../..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a863a91b-593f-4dd9-a139-04427fa7b1f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import time\n",
    "\n",
    "from footprint2graph import run_iteration\n",
    "from footprint2graph import read_config\n",
    "\n",
    "from footprint2graph.util.Outdoorvision import load_raw_tracks_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ed3939-bd5b-4645-a6c0-498d6bc93c6d",
   "metadata": {},
   "source": [
    "## Chargement des paramètres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e69bd82-2a28-47f6-a1df-62e9442e16f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "`````````````````````````````````````````````````````````````````````\n",
      "  Generate a footprint graph                                         \n",
      "             from hiking trajectories                                \n",
      "             in the Plan de l'Aiguille, dans la vallée de Chamonix, summer 2024.  \n",
      "’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "\n",
      "Paramètres relevés dans la configuration: \n",
      "Résultats enregistrés dans le répertoire:  /home/md_vandamme/4_RESEAU/ZTEMPZ3/\n",
      "Number of iterations:  2\n"
     ]
    }
   ],
   "source": [
    "print ('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\n",
    "print ('`````````````````````````````````````````````````````````````````````')\n",
    "print ('  Generate a footprint graph                                         ')\n",
    "print ('             from hiking trajectories                                ')\n",
    "print (\"             in the Plan de l'Aiguille, dans la vallée de Chamonix, summer 2024.  \")\n",
    "print ('’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’')\n",
    "print ('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\n",
    "print ('')\n",
    "\n",
    "\n",
    "\"\"\" ======================================================================= \"\"\"\n",
    "\"\"\"     Load parameters                                                     \"\"\"\n",
    "\"\"\"                                                                         \"\"\"\n",
    "\n",
    "config_path = r'/home/md_vandamme/7_LIB/footprint2graph/data/config_plan_de_l_aiguille.yml'\n",
    "config = read_config(config_path)\n",
    "\n",
    "\n",
    "print('Paramètres relevés dans la configuration: ')\n",
    "print ('Résultats enregistrés dans le répertoire: ', config['output']['RESULT_PATH'])\n",
    "\n",
    "NBITER = int(config['graph_construction']['NUM_ITERATIONS'])\n",
    "print ('Number of iterations: ', NBITER)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b42e48-c89f-4e74-8e72-11ce15651466",
   "metadata": {},
   "source": [
    "## Chargement des données\n",
    "\n",
    "Les données proviennent de la plateforme Outdoorvision. Après avoir été formatées au format CSV, elles sont chargées dans une collection qui servira d’entrée au pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "787afef1-dd96-4406-98f7-da68c5e15639",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading and split outdoorvision track data...\n",
      "Reading track data...\n",
      "     Number files to load:  21211\n",
      "Starting split ...\n",
      "     500 / 21211\n",
      "     1000 / 21211\n",
      "     1500 / 21211\n",
      "     2000 / 21211\n",
      "     2500 / 21211\n",
      "     3000 / 21211\n",
      "     3500 / 21211\n",
      "     4000 / 21211\n",
      "     4500 / 21211\n",
      "     5000 / 21211\n",
      "     5500 / 21211\n",
      "     6000 / 21211\n",
      "     6500 / 21211\n",
      "     7000 / 21211\n",
      "     7500 / 21211\n",
      "     8000 / 21211\n",
      "     8500 / 21211\n",
      "     9000 / 21211\n",
      "     9500 / 21211\n",
      "     10000 / 21211\n",
      "     10500 / 21211\n",
      "     11000 / 21211\n",
      "     11500 / 21211\n",
      "     12000 / 21211\n",
      "     12500 / 21211\n",
      "     13000 / 21211\n",
      "     13500 / 21211\n",
      "     14000 / 21211\n",
      "     14500 / 21211\n",
      "     15000 / 21211\n",
      "     15500 / 21211\n",
      "     16000 / 21211\n",
      "     16500 / 21211\n",
      "     17000 / 21211\n",
      "     17500 / 21211\n",
      "     18000 / 21211\n",
      "     18500 / 21211\n",
      "     19000 / 21211\n",
      "     19500 / 21211\n",
      "     20000 / 21211\n",
      "     20500 / 21211\n",
      "     21000 / 21211\n",
      "     Number of tracks after split: 631\n"
     ]
    }
   ],
   "source": [
    "\"\"\" ======================================================================= \"\"\"\n",
    "\"\"\"     Chargement de la collection de traces                               \"\"\"\n",
    "\"\"\"                                                                         \"\"\"\n",
    "\n",
    "# chemin où sont stockés les traces Outdoorvision:\n",
    "tracespathsource = r'/home/md_vandamme/5_GPS/OV/CHAM/walk/'\n",
    "\n",
    "# Paramètre : Coordonnées de la zone d'étude sur laquelle on construit le réseau\n",
    "#                           Polygone sous la forme d'un tableau de X et de Y\n",
    "X = [1000852, 1001838, 1001852, 1000853, 1000852]\n",
    "Y = [6541520,  6541524,  6540842,  6540839,  6541520]\n",
    "\n",
    "fmt = tkl.TrackFormat({'ext': 'CSV',\n",
    "                       'srid': 'ENU',\n",
    "                       'id_E': 1, 'id_N': 0, 'id_U': 3, 'id_T': 2,\n",
    "                       'time_fmt': '2D/2M/4Y 2h:2m:2s',\n",
    "                       'separator': ';',\n",
    "                       'header': 0,\n",
    "                       'cmt': '#',\n",
    "                       'read_all': True})\n",
    "collection = load_raw_tracks_split(config['output']['RESULT_PATH'],\n",
    "                                   tracespathsource, fmt, X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b03c2f2-7ab4-4bed-9f8c-7534c8112732",
   "metadata": {},
   "source": [
    "## Lancement du pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8f0b991-7c16-4234-9194-dfec5e7b8a78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "              ITERATION  1\n",
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "Starting segmentation and resampling...\n",
      "Starting segmentation ...\n",
      "     500 / 631\n",
      "    Number of tracks after segmentation: 688\n",
      "Finished saving segmented tracks.\n",
      "Starting resampling ...\n",
      "    Number of tracks to resample:  688\n",
      "    Number of tracks after resampling: 688\n",
      "    Number of tracks after resampling: 688\n",
      "Finished saving resampled tracks.\n",
      "Stage 1 finished: segmentation and resampling.\n",
      "Starting rasterization and vectorization (iteration 1) \n",
      "\n",
      "    Loading tracks from :  resample_grid\n",
      "    Number of tracks to load:  688\n",
      "    Building high-resolution geometry density grid G1 :  2 m ...\n",
      "    Building low-resolution contextual density grid G2 :  30 m ...\n",
      "    Assigning track points to the G1 and G2 grids\n",
      "         500 / 688\n",
      "    Computing G1 ...\n",
      "    Computing G2 ...\n",
      "    Number of neighboring cells to consider: 7\n",
      "    Building contrast grid :  2 m\n",
      "    Execution time (seconds): 22.76438069343567\n",
      "    Finished heatmap computation.\n",
      "    Starting morphological closing image ...\n",
      "    Execution time (seconds): 2.9393832683563232\n",
      "    Finished morphological opening.\n",
      "Vectorizing cleaned image ...\n",
      "Extracting road surface vector features ...\n",
      "    Number of polygonize features:  32\n",
      "    Number of polygonize features copied:  10\n",
      "    Execution time (seconds): 0.03627943992614746\n",
      "    Vectorization completed.\n",
      "Smoothing polygon to remove stair-step artifacts ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(341 of 341)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(846 of 846)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(403 of 403)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(663 of 663)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;255;0;0m  0%\u001b[39m \u001b[38;2;255;0;0m(0 of 1273)\u001b[39m |                       | Elapsed Time: 0:00:00 ETA:  --:--:--"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Execution time (seconds): 0.500645637512207\n",
      "    Road surface smoothing completed.\n",
      "    Starting centerline computation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(1273 of 1273)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(621 of 621)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(618 of 618)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(2711 of 2711)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(1113 of 1113)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(967 of 967)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(963 of 963)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Execution time (seconds): 0.7983303070068359\n",
      "    Centerline computed.\n",
      "Stage 2 completed: rasterization and vectorization.\n",
      "Starting topology creation for the network\n",
      "    Number of edges in the skeleton: 1669\n",
      "    Finished loaded skeleton.\n",
      "    /home/md_vandamme/4_RESEAU/ZTEMPZ3/network/tmp_in.csv not exists\n",
      "    /home/md_vandamme/4_RESEAU/ZTEMPZ3/network/tmp_out.csv not exists\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;255;215;0m 50%\u001b[39m \u001b[38;2;255;215;0m(16 of 32)\u001b[39m |############            | Elapsed Time: 0:00:00 ETA:   0:00:00"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Finished removing hooked parts of the skeleton.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(32 of 32)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:000000\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(32 of 32)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Finished simplification of the skeleton.\n",
      "Building [100 x 74] spatial index...\n",
      "    Number of edges in the skeleton (after snapping): 32\n",
      "    Edge count difference after snapping :  0\n",
      "    Number of edges in the simplified skeleton: 22\n",
      "    Number of nodes: 32\n",
      "     Shortest edges limit :  50\n",
      "    Number of edges in the skeleton (after removing the shortest edges): 0\n",
      "    Conflation cannot be performed for node  27 ; the three incident edges are too long: 74 172 76\n",
      "    Edge count after conflation: 12\n",
      "Stage 3 completed: adding topology to the skeleton.\n",
      "Starting map-matching, aggregation, and conflation of GNSS trajectories.\n",
      "    Loading network (1) ...\n",
      "        Number of edges =  12\n",
      "        Number of nodes =  22\n",
      "        Total segment length of the network =  4437.851352451303\n",
      "    Loading collection of tracks ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(22 of 22)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Number of tracks: 688\n",
      "        Execution time (seconds): 2.79937744140625\n",
      "    Starting map-matching ...\n",
      "        Index spatial :  [100 x 74] spatial index centered on [1001326.7471449398; 6541196.7582587]\n",
      "Map-matching preparation...\n",
      "        Parameter search_radius:  25\n",
      "        Map-matching ended.\n",
      "        Execution time (seconds): 78.48061347007751\n",
      "        Prepare map-matching results for candidate segment generation\n",
      "    Number of map-matched points = 143634 (88.37 %)\n",
      "    Map-matching results restructuring completed.\n",
      "        Map-matching results exported.\n",
      "Starting construction of candidate trajectory segments for each topology edge ...\n",
      "    31  candidates for edge 46\n",
      "    115  candidates for edge 44\n",
      "    113  candidates for edge 43\n",
      "    20  candidates for edge 19\n",
      "    29  candidates for edge 3\n",
      "    25  candidates for edge 45\n",
      "    50  candidates for edge 41\n",
      "    24  candidates for edge 21\n",
      "    8  candidates for edge 35\n",
      "    25  candidates for edge 20\n",
      "    1  candidates for edge 37\n",
      "    7  candidates for edge 42\n",
      "    Number of processed edges:  12\n",
      "    Minimum number of candidate tracks per edge:  1\n",
      "    Maximum number of candidate traces per edge:  115\n",
      "    Average number of candidate tracks per edge:  37\n",
      "    Segment construction completed.\n",
      "        Execution time (seconds): 6.564183950424194\n",
      "    Starting track segment aggregation for all network edges ...\n",
      "        Number of candidate tracks / number of sampled tracks 31 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 115 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 113 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 20 / 20\n",
      "        Number of candidate tracks / number of sampled tracks 29 / 29\n",
      "        Number of candidate tracks / number of sampled tracks 25 / 25\n",
      "        Number of candidate tracks / number of sampled tracks 50 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 24 / 24\n",
      "        Number of candidate tracks / number of sampled tracks 8 / 8\n",
      "        Number of candidate tracks / number of sampled tracks 25 / 25\n",
      "        Number of candidate tracks / number of sampled tracks 1 / 1\n",
      "    Only one trajectory available for aggregation: no processing required\n",
      "        Number of candidate tracks / number of sampled tracks 7 / 7\n",
      "        Number of aggregations: 12\n",
      "        Number of aggregations with 30 traces: 4\n",
      "        Number of aggregations with fewer than 30 traces: 8\n",
      "        Minimum number of traces in aggregation: 1\n",
      "        Average number of traces in aggregation: 37\n",
      "        Aggregation process finished.\n",
      "        Execution time (seconds): 9.709306716918945\n",
      "    Starting conflation ...\n",
      "        Conflation process finished.\n",
      "        Execution time (seconds): 0.02811574935913086\n",
      "Stage 4 completed: map-matching, aggregation, and conflation.\n",
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "              ITERATION  2\n",
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "Number of tracks map matched : 688\n",
      "1293\n",
      "Starting rasterization and vectorization (iteration 2) \n",
      "\n",
      "    Loading tracks from :  points_not_mm_2\n",
      "    Number of tracks to load:  1293\n",
      "    Building high-resolution geometry density grid G1 :  2 m ...\n",
      "    Building low-resolution contextual density grid G2 :  30 m ...\n",
      "    Assigning track points to the G1 and G2 grids\n",
      "         500 / 1293\n",
      "         1000 / 1293\n",
      "    Computing G1 ...\n",
      "    Computing G2 ...\n",
      "    Number of neighboring cells to consider: 7\n",
      "    Building contrast grid :  2 m\n",
      "    Execution time (seconds): 8.377056360244751\n",
      "    Finished heatmap computation.\n",
      "    Starting morphological closing image ...\n",
      "    Execution time (seconds): 2.9527673721313477\n",
      "    Finished morphological opening.\n",
      "Vectorizing cleaned image ...\n",
      "Extracting road surface vector features ...\n",
      "    Number of polygonize features:  43\n",
      "    Number of polygonize features copied:  19\n",
      "    Execution time (seconds): 0.022061824798583984\n",
      "    Vectorization completed.\n",
      "Smoothing polygon to remove stair-step artifacts ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(102 of 102)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(186 of 186)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(152 of 152)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(159 of 159)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(113 of 113)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(175 of 175)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(128 of 128)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(219 of 219)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(189 of 189)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(197 of 197)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(163 of 163)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(367 of 367)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(364 of 364)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(391 of 391)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Execution time (seconds): 0.21992087364196777\n",
      "    Road surface smoothing completed.\n",
      "    Starting centerline computation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(175 of 175)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(86 of 86)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(135 of 135)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(966 of 966)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(193 of 193)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Execution time (seconds): 0.45319151878356934\n",
      "    Centerline computed.\n",
      "Stage 2 completed: rasterization and vectorization.\n",
      "Starting topology creation for the network\n",
      "    Number of edges in the skeleton: 631\n",
      "    Finished loaded skeleton.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(50 of 50)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:000000\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(50 of 50)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Finished removing hooked parts of the skeleton.\n",
      "    Finished simplification of the skeleton.\n",
      "Building [100 x 87] spatial index...\n",
      "    Number of edges in the skeleton (after snapping): 50\n",
      "    Edge count difference after snapping :  0\n",
      "    Number of edges in the simplified skeleton: 46\n",
      "    Number of nodes: 65\n",
      "     Shortest edges limit :  50\n",
      "    Number of edges in the skeleton (after removing the shortest edges): 5\n",
      "    Conflation cannot be performed for node  54 ; the three incident edges are too long: 30 42 59\n",
      "    Edge count after conflation: 19\n",
      "Stage 3 completed: adding topology to the skeleton.\n",
      "Starting map-matching, aggregation, and conflation of GNSS trajectories.\n",
      "    Loading network (2) ...\n",
      "        Number of edges =  19\n",
      "        Number of nodes =  33\n",
      "        Total segment length of the network =  1439.7164877840992\n",
      "    Loading collection of tracks ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(33 of 33)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Number of tracks: 1293\n",
      "        Execution time (seconds): 1.837005853652954\n",
      "    Starting map-matching ...\n",
      "        Index spatial :  [100 x 87] spatial index centered on [1001240.6166657063; 6541182.303244937]\n",
      "Map-matching preparation...\n",
      "        Parameter search_radius:  25\n",
      "        Map-matching ended.\n",
      "        Execution time (seconds): 12.17409062385559\n",
      "        Prepare map-matching results for candidate segment generation\n",
      "    Number of map-matched points = 73193 (49.96 %)\n",
      "    Map-matching results restructuring completed.\n",
      "        Map-matching results exported.\n",
      "Starting construction of candidate trajectory segments for each topology edge ...\n",
      "    16  candidates for edge 52\n",
      "    50  candidates for edge 56\n",
      "    3  candidates for edge 61\n",
      "    51  candidates for edge 5\n",
      "    89  candidates for edge 6\n",
      "    18  candidates for edge 38\n",
      "    68  candidates for edge 12\n",
      "    3  candidates for edge 28\n",
      "    1  candidates for edge 53\n",
      "    3  candidates for edge 64\n",
      "    26  candidates for edge 63\n",
      "    3  candidates for edge 46\n",
      "    6  candidates for edge 27\n",
      "    2  candidates for edge 58\n",
      "    2  candidates for edge 55\n",
      "    35  candidates for edge 24\n",
      "    13  candidates for edge 59\n",
      "    7  candidates for edge 36\n",
      "    18  candidates for edge 57\n",
      "    Number of processed edges:  19\n",
      "    Minimum number of candidate tracks per edge:  1\n",
      "    Maximum number of candidate traces per edge:  89\n",
      "    Average number of candidate tracks per edge:  22\n",
      "    Segment construction completed.\n",
      "        Execution time (seconds): 4.859700441360474\n",
      "    Starting track segment aggregation for all network edges ...\n",
      "        Number of candidate tracks / number of sampled tracks 16 / 16\n",
      "        Number of candidate tracks / number of sampled tracks 50 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 3 / 3\n",
      "        Number of candidate tracks / number of sampled tracks 18 / 18\n",
      "        Number of candidate tracks / number of sampled tracks 68 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 3 / 3\n",
      "        Number of candidate tracks / number of sampled tracks 1 / 1\n",
      "    Only one trajectory available for aggregation: no processing required\n",
      "        Number of candidate tracks / number of sampled tracks 3 / 3\n",
      "        Number of candidate tracks / number of sampled tracks 26 / 26\n",
      "        Number of candidate tracks / number of sampled tracks 3 / 3\n",
      "        Number of candidate tracks / number of sampled tracks 6 / 6\n",
      "        Number of candidate tracks / number of sampled tracks 2 / 2\n",
      "        Number of candidate tracks / number of sampled tracks 2 / 2\n",
      "        Number of candidate tracks / number of sampled tracks 35 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 13 / 13\n",
      "        Number of candidate tracks / number of sampled tracks 7 / 7\n",
      "        Number of candidate tracks / number of sampled tracks 18 / 18\n",
      "        Number of aggregations: 19\n",
      "        Number of aggregations with 30 traces: 3\n",
      "        Number of aggregations with fewer than 30 traces: 14\n",
      "        Minimum number of traces in aggregation: 1\n",
      "        Average number of traces in aggregation: 14\n",
      "        Aggregation process finished.\n",
      "        Execution time (seconds): 1.9753568172454834\n",
      "    Starting conflation ...\n",
      "        Conflation process finished.\n",
      "        Execution time (seconds): 0.01727581024169922\n",
      "Stage 4 completed: map-matching, aggregation, and conflation.\n",
      "Merging the mobility network with the result of iteration 2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(31 of 31)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of collection In  :  12\n",
      "Size of collection In+1:  19\n",
      "Size of collection In+In+1:  31\n",
      "Building [100 x 75] spatial index...\n",
      "Size of collection In+In+1 avec intersection:  31\n",
      "Size of collection In+In+1 avec intersection et raccordement:  46\n",
      "Nombre de géométries :  46\n",
      "Size of reseau de mobilité:  46\n",
      "End building the mobility network.\n",
      "==================================================================\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "`````````````````````````````````````````````````````````````````````\n",
      "                           FIN                                       \n",
      "’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"
     ]
    }
   ],
   "source": [
    "for idx in range(0, NBITER):\n",
    "    iteration_index = int(idx) + 1\n",
    "\n",
    "    # run pipeline for the ith iteration\n",
    "    run_iteration(iteration_index, config, collection)\n",
    "\n",
    "print ('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\n",
    "print ('`````````````````````````````````````````````````````````````````````')\n",
    "print ('                           FIN                                       ')\n",
    "print ('’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’')\n",
    "print ('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d060ac54-e013-4cc0-8a30-44175e841b68",
   "metadata": {},
   "source": [
    "## On affiche le résultat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd93d2b4-3b36-430b-9833-3f1feb5bc45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f263c291030>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zbtwA9u+XsZ8fUKGC3niIiIiI3CkkBKhZ0zWZVKsWsHs3k0lERBTvopNQ8gdQGsB00zRLAbgHYMBjj9kJIKdpmiUATAOwGAAMw2gM4IppmjtiENNFADkevdaHAH5+tErKhWmaM0zTLGuaZtn06dPH4OnJZ2zZopZ9lyoFJEumNx4iIiIid1q4EDh+XMYJEgCjRkkx7syZ9cZFREQ+IToJpXMAzpmm+e+j+UJIgun/TNO8bZrm3Ufj5QACDMNIB6AKgKaGYZwCMA9ALcMwfnrWi5mm+dA0zeBH4x0ATgAoEP0fieiRTZvUuGpVfXEQERERuZtpAp99pubDhwNDhsiqbCIiIg94bkLJNM1LAM4ahlHw0aHaAA46P8YwjEzGo2JIhmGUf/S8waZpDjRNM5tpmrkAtAKwzjTNds96PcMw0j/qCgfDMPIAyA/gZMx+LCK4JpSqRblzkoiIiMiaFiwAtm+XcWAg0KWL3niIiMjnRLfL23sA5jzq8HYSQCfDMLoBgGmaXwFoAaC7YRjhAB4AaGWajr1GUTMMozlke1x6AH8YhrHbNM36kALgIx89VwSAbqZpXo/Fz0a+7MYNYNs2Na9SRV8sRERERO4UEgL076/mPXsCmTLpi4eIiHyS8Zy8jyWULVvWDAoK0h0GeZOePdUy8KJFgX379MZDRERE5C7jxwMDHpU0TZcOOHYMSJVKa0hERGRPhmHsME2zbFT3RaeGEpG1HDwIfPGFmg8fri0UIiIiIre6cgUYM0bNR4xgMomIiLRgQonsxTSBDz4AIiJkXrMm8Mor0f/+U6eA774D2rQBcucGSpQAhg4Fbt+Oj2iJiIiIYmbYMODOHRkXLgx07ao3HiIi8lnRraFEZA1//CHtcgFpnztlCiD14qN25Qqwbh3w55/y9WQU9d/37gUOHwZ++SVeQiYiIiKKlgMHgBkz1PyTTwB/ns4TEZEefAci+wgNldVJDl27ygojZ6YpJ2OLFgGLFwM7d0bvuUNC3BYmERERUaz06QNERsq4bl2gQQO98RARkU9jQonsY/p04PhxNc+TB1izBggLk4TQ5s2SRDpx4unPkSQJUL06UKsW0K+fOt6tW7yFTURERPRcK1fKDZBV2JMmPXsVNhERUTxjQons459/XOfOCaGn8fcHKlYEateWW4UKQGAg4Nw1MHFioH5998ZKREREFF3h4UDv3mr+5ptAsWL64iEiIgITSmQnvXoB27dLYe1nSZ4caNgQaNZMloqnTPnkY2bPVuOWLVmfgIiIyApOnwb27wdu3gTattUdjft8+610sQWAZMmAkSP1xkNERAQmlMhOKleWotr79smS8H/+Ae7dAwIC5JYtG9CkiWxnS5jw6c8TGgrMnavmb7wR/7ETERFR7EVGSn3EDh2A+/fl2MKFQJkyQKlSQMmSQJYs1twidusW8NFHaj5wIJApk754iIiIHmFCiezFMIDixeUWW8uXA8HBMs6eHahZ0y2hERERkZudPw/MnAl8992TK5QXL5abQ/r0klwqXRro3BnIn9+DgcbBqFHA1asyzpHDtQEJERGRRkwoET3Oebtb+/ZS+JKIiIi8y7x5soo4NDR6j796FVi9Wm6zZwOHDwMpUsRvjHE1Z44U33b4+GOp7UhEROQF+EmZyNm1a8Aff6h5hw76YiEiIqKoBQUBHTu6JpPSpAG6dwcyZFDHSpUCqlaVukPOLl4Exo71SKixtnYt0KmTmteqBbRqpS8eIiKixzChRORs5kwgLEzGFSsCBQvqjYeIiIhcXb0KvPIK8PChzPPnB37+Wba/ffklMHmyeuy+fVLQ+tYt4NgxYPRodd/kyVJ70Rvt3i0/o+OcpEgR4NdfrVkDioiIbIsJJSJACni+/z7Qr586xmLcRERE3iU8XFbpnD0r85QpZWVx69ZAokRyrHVroEoV9fhevSQRky8fMGiQXDACZHWT8/u+tzh1SrrQ3rkj82zZpNlIqlQ6oyIiInoCE0pEQUGyJH7aNHUsd245ISUiIiLvMWAAsG6dmv/005PFtQ1D3tMdq3lWrlTb2Q3DdQXTr78Cf/0VvzHHxO7dQPXqwKVLMk+ZElixQpJKREREXoYJJfJt338vVzGPHlXHmjQBNm+WkzgiIiLyDkuWuBaoHj4caNw46seWKgV06aLmH3ygtshVrAi0bet6X0SE28ONltBQ4N9/gSlTgNdfl3MSx+qrwEDpUle0qJ7YiIiInsMwTVN3DHFWtmxZMygoSHcYZCVhYXIC+cUX6ljy5HJC16kTaxQQERF5k0uXgGLFpHkGIBd/Fi9+difWq1eBAgWAmzdl/vHHQP/+Mj57VuokPngg86++At5+2/1xh4dLzJcvy+3KFfl67hywbRuwY4dKdDlLkQJYsACoX9/9MREREcWAYRg7TNMsG+V9TCiRz7l8GWjZEti4UR0rVgxYtAjIm1dfXERERPQk0wQaNZKtXwCQNSuwd690dXueadOkRiIAJE0qK5KzZJH58OHAiBHqsZUrS1KpZUsgceKYx/nff7K1btUqGV++DAQHS/wxUbiwbMUrXDjmMRAREbkZE0pEDtu2SdeU8+fVsZYtpbtb0qT64iIiIqKoTZ8O9Oih5mvXArVrR+97w8OBkiWBAwdk3r498MMPMr53D3jhBeDMGdfvSZ0a6NgR6NoVKFTI9b4HDyRZdPKk6+3wYekiFxt58gCVKqlbiRKAn1/snouIiMjNmFAiAoDTp+XE8f59mRsGMG6cdHjhFjciIiLvc+SI1ENybE3r1cu1qHZ0/PknUKeOmv/zj6xGAqSjWv/+sko5LOzJ761RA8iRQyWOLl6M+c+QNi2QMaPcMmRQ4yJFpJ5Txowxf04iIiIPYUKJCADmz5dWw4C03p07F3jpJa0hERER0VOEhUnix3GOV6SIjBMlivlztWgh28gAoEwZWbHsXH/p8mVZrTxjhqxAio1EiYBatWR7XqVKQKZMQPr0gL9/7J6PiIjICzwrocR3OPIdt26p8auvMplERETkzcaNU8mkwEBgzpzYJZMA4JNPpL5RSIgUwp45E3jzTXV/xozAgAGyannNGuDrr4GlS5/s/ubnB+TMKdvUnG+5c8sq6CRJYhcfERGRBTGhRL7DOaGUMqW+OIiIiOj55s1T42HDpLZQbOXKJcmikSNlPnCgXFxKlcr1cQkSSGe1+vWl3uKyZZJEciSOsmfniiMiIqJHntFrlchmIiN1R0BERETRVaSIGsd2ZZKz/v0lIQQAV6+6dniLStasQLduQJcuUgQ8d24mk4iIiJwwoUS+I3NmNY5NUU0iIiLynHr11HjVqrg/X5IksvXN4fPPgYMH4/68REREPooJJfIdWbKo8fnz+uIgIiKi56tfX43//lt1eouLli2lcxsAhIcDPXsCNmhQQ0REpAMTSuQ7smZV4wsX9MVBREREz5cjB1CokIxDQoCNG+P+nIYBfPaZ6vC2di2wZEncn5eIiMgHMaFEvuPxFUq8IklEROTdnFcpuWPbGwAULy61kRw+/FASVkRERBQjTCiR70iRAkiaVMYPHrh2fSMiIiLvEx8JJUC6vaVJI+P//gMmTXLfcxMREfkIJpTIdxiG6yolbnsjIiLybjVqAIGBMj5wADh3zj3PmzYtMHq0mo8d677nJiIi8hFMKJFvCQhQY3cU9yQiIqL4kyQJUK2amm/e7L7n7tpVtr8BwP37QL9+7ntuIiIiH8CEEvmOc+eAY8fUPG9efbEQERFR9GTPrsb37rnvef38pEC3w9y57in8TURE5COYUCLfMWQIEBYm4/LlgVSptIZDRERE0eDvr8aO93F3qVEDeP11NX//fSAiwr2vQUREZFNMKJFv2LMH+OEHNR83Tl8sREREFH3O29XDw93//BMnAokTy3j3buDbb93/GkRERDbEhBL5hr59AdOUcaNGQK1aeuMhIiKi6HFeoRQfCaXs2YGBA9V88GDgxg33vw4REZHNMKFE9rdqFbBmjYwTJAAmTNAbDxEREUVffCeUAKBPHyBXLhkHBwPDhsXP6xAREdkIE0pkb5GRrl1b3nwTeOEFffEQERFR9F296trZLb4SSokTA5MmqfmXXwL79sXPaxEREdkEE0pkbytXAnv3yjhpUmDECL3xEBERUfRs3AiULAn8+686liNH/L1e8+ZA7doyjogAevZU2+WJiIjoCf7PfwiRhX3xhRp36wZkzqwvFiIiIru7fVsu5OzZI7e9e4FDh2QFULZscsua1XWcIYMU3vb3V19nzQKGDpWVxg4DBgCvvRZ/sRsGMHUqUKKEJJTWrwd++w149dX4e00iIiILM0wbXHkpW7asGRQUpDsM8jYnTgD588vVRcMAjh8H8uTRHZVvuHsXOHsWOHNGfQ0OBpInB1KnBlKlkq+pUwNp0qhbihTyd0VERNZx+rQkfzZtAv77z/3PnzYt8OOPQIMG7n/uqPTsCXz2mYxz5JCEWJIknnltIiIiL2MYxg7TNMtGdR9XKJF9TZ+ulqo3aMBkkrtFRkqSLihIbseOqQRSbLvjJEjgmmTKlQsoVAgoWFBuBQoAyZK59ccgIqI4WLkSaNsWuH49fp6/alVg7lxZzeQpw4cDP/8MXLsm72sTJ7JINxERURS4Qons6f59Ofl0JDb++ANo2FBvTFZmmnLV2ZE8CgoCduyQrQ2eZBjyweXLL2W1ExER6REZCYwaJbUJnc8l/f3lQkCJEupWrBgQFgacPw+cOyc35/H161Js23ELC5Mtcm+8AQwe7NrlzVO++Qbo2lXGiRIBhw8DOXN6Pg4iIiLNnrVCiQklsifnmgd58sjqmQSsQR8tpikn+M7Jo6CgmF19DgwEsmeXW44c8jV9etkKd/OmJPoct+vX1de7d6P3/EWLAkuWcNUZEZEOwcFAu3ayOskhSxZg5kygRg0gYUJ9sblLRARQrhywa5fMW7YEFizQGxMREZEG3PJGvufkSTUuV47JpOiIjATmz5el/kePRu970qcHypaVW/HicvU2Rw45Hps/89BQlWi6elUSgUeOyO3QIRXX/v3y9zp/PlCnTsxfh4iIYicoCGjRQuomObz4omxLy5hRX1zu5ucHTJsmW+4A4JdfpEj3iy/qjYuIiMiLcIUS2c8ffwCNG6t53brA6tX64rGCNWuA/v3VldiopE6tkkeOW/bsni2iPXs28PbbwMOHMk+QAJg0SQqospg3EVH8MU3ZBvbee5L8d+jfHxg9Ws+2NE9o1w6YM0fGxYoBO3fa92clIiKKAre8ke84eVJO+O7fV8e+/BLo3l1fTN5sxw5pw7x2revxpEllBVC5cip5lDu3dyRt/v0XaN4cuHhRHWvSBPj6ayBzZn1xERHZ2dChkjhySJFCkvzNmmkLySPOn5emEPfuyfzzz4F33tEbExERkQcxoUS+ITJStj+tXy/z7Nnlamr9+nrj8iYhIcDevbJl4c8/pdaUs8SJgQ8+APr1A1Km1BNjdFy4IDWytm5Vx1Knlu0Jbdp4R+KLiMguFiwAXn9dzYsXB379FciXT19MnjRuHDBokIxTp5bt2GnT6o2JiIjIQ5hQIt8wfTrQo4eM/fwk2VA2yn/3viE0VGoNORfW3rdPOug8LkEC4M03pX5SliweDzVWHj4E+vSRq8XOmjUDZsyQOk5ERBQ3e/cClSqplb/168vFiCRJ9MblSSEh0gzixAmZd+8uq5+JiIh8ABNKZH9LlkiRUEeyZMAAuaJod+Hh0pHtv/9ku5/j67Fj8iHAuc7F0zRrBowdCxQuHO/hxov164HOnYFTp9SxfPmkblbu3NrCIiKyvOvXZeuzo9FF/vzAtm1AqlRaw9Ji6VLg5ZdlnCCBbBkvWVJrSERERJ7AhBLZ29KlkkwKC5N5kSKyGidRIr1xuduNG3Iiv3Wr1BE6elS67ES14uhZ8udXdZFq1bLHCfGdO1IYdvp0dSxzZmDVKqmpRUREMRMRATRsqJpaJEsm7z0vvKA3Ll1ME2jQQN5XAKBaNeCvv7jFmoiIbI8JJbKvrVuB6tVVMil/fmDDButs23qamzclWbRtG7Bli/ychw7F/Hny5HHtylaqlL2vLP/2m9RQcnSBS5UKWLZMtX0mIqInhYXJdq4jR+R28aLMly1Tj/ntN2mI4MsOH5aLFI4LOfPmudaWIiIisiEmlMieTBOoUQPYuFHmefPK1cKsWfXGFRdnzwJ9+0oB1Oj+38yUSbZ25cnj+rVYMd8sGrphg2xLuH1b5okSyZ9nkyZawyIi8hpTpwI//wwEB8vq1xs3nv2eM2QIMGqU5+LzZr17A59+KuNs2STJlDSp3piIiIjiERNKZE/r18uWLQDw95cVPFbtOBMSAkyaJLWMHIVPH+fvD5QoIcVRK1aUrWq5c/tWYdTo2r0beOkl4PJlmfv5Ad99B7zxhtawiIi027YNqFAh+o9v1EjqFPr5xV9MVnLrFlCgAHDlisyZbCMiIpt7VkLJ39PBELnNiBFq3LGjNZNJpik1oD74QApqO8uSRRJHFStKEql0aSaPoqtkSeCff4B69aSYbESE/BtJl04+HBER+arZs6M+nj07UKgQULAgkDOnJJBSpwZat2YyyVnKlMDHH0szCACYOFHGbAJBREQ+iCuUyJr++guoWVPG/v5SoNpqJ3OHDwO9eqkCnw4lSgDTpknBT4qbixeliOqePTJPnRrYtUs+LBER+ZrQUGlYcP26zBcskPfSVKmAgACdkVlLZKRc7Nm+XebNm0uNKSIiIht61gqlBJ4OhsgtnFcndehgrWTS7dtSJ6lYMddkUpo0wJdfSitiJpPcI3NmYO1aqXMBSJ2Q11+XD1VERL5mxQqVTMqRA3j1VSB9eiaTYipBAuCzz9R80SJ5ryEiIvIxTCiR9WzcKPWTAFmGP3iw3niiKzIS+OEH2U7wySeqS0yCBED37rLKqnt3bi1wt3TpgPnzZSUbIG2v+/fXGxMRkadERACnTgFr1gCTJ6vj7drJ+w/FTsWKckHLoWdP1XGWiIjIR3DLG1lPnTrAn3/KuGNHYOZMreE804EDwLFj0sZ+6lRgyxbX+6tWle1tJUtqCc+nfPqpdOdxWLFCCncTEVlJSAhw86YUh3Z8ffx28yZw5oxcqDh5Ut6DHnfokNRMoti7eFEKdN+9K/MpUySxREREZCPs8kb2MWeOXFUFZCXP4cPeV4z7yhVpxzx7tnQbi0qWLLJKqVUrwDA8Gp7PMk2pc7FkicxffBFYt05vTEREjzNNeR85cSLqm6O7WFzUrKlW+lLcTJwI9Osn45Qp5SJS+vR6YyIiInIjJpTIHjZulNVJjvo33rQ6KSQE+OMP4Mcf5atjO9vjAgOBDz+UbXrJknk2RgLOn5eC3BERMj94EChcWG9MROSbTFNWEW3eLM0Cjh+XhNHJk2rFi7tkyADkzy+3IkVU10uKu9BQoGhRSSQBQJcuwIwZemMiIiJyIyaUyPqOHgUqVVLFRF94QdrCp0qlL6bwcLnC+/PP0t3l9u0nH5MoEVCjhnzNmlW6uuXP7/FQycmrr6puPO++K1sOiYji2+XLQPv28v518aK8h50/H/Pn8feXjpUpU8p7YMqU6uY8z5RJJZFSpHD3T0POli8HGjWSsWEAQUFA6dJ6YyIiInITJpTI2q5elWTSiRMyz5hRCivraP1umsC2bZJEmj9fPiBEpUoV4I03gNdekxN78h5//ikr3QAgeXLgwgWuFiOi+HPmjDQCmDcv+t+TIgWQN2/Ut2zZ2LzBGzVuLCuUAaByZWDTJm5pJyIiW3hWQsnf08EQxUhICNCsmUomJU4MLFvm2WTSw4fAjh1SxPnnn2U7QlTy5gVat5Yr0AUKeC4+iplataTT3pEjwJ07wO+/Sy0rIiJ3CQuT3y3ffAOsXCkXI6KSLJl0C6tUSQpkO5JGadMyGWE1kycDq1fL3/3mzXK+0Lat7qiIiIjiFRNK5L0iI2WVz+bNMjcMOUErVy5+Xzc4WF7zn3/kCmNQUNQdcgBZLdWqFdCmjcTFDwDezzCksPvQoTJfsIAJJSJyjxMngG+/BWbNAi5devZjW7SQ97SAAI+ERvEsf37ggw+ACRNk3q8f8PLLXAFLRES2xi1v5L0GDQLGjVPzTz+VkzV3O3kS+PtvSSD984+0Un6WFCmkDk+bNtIpjFsPrOfIEdUuO2FC2VaZPLnemIjImiIipC7b11/Lltqo1K0rK1g3bnRtJvHBB8CkSbwYYRd37sgKZUcyceBAYOxYvTERERHFEWsokfV8+610SnF45x0pnuyuk+5bt6QG0vffSz2m58mXT+oiNW0KNGwoRbbJ2kqWBPbskfGcOZIgJCKKiTVrgL591e8SZ5kzA507A2++CeTOLcciImTl7Zw56nFDhgCjRnkmXop/P/wgf8eAdHY9cEDOIYiIiCyKNZTIOiIigNGjgREj1LFGjYApU+KeTDJNWYn0/ffAL78ADx5E/Th/f6BMGUkgOW4ZM8bttcn7vPaa+hC4YAETSkQUfXv3ypamVatcjydIADRoIBdEGjWS9xNnfn6yHS4kBPj1Vzk2erTUBxw0yCOhUzxr1w6YPh3YuhUIDQV69waWLNEdFRERUbzgCiXyLl27ShFTh5IlZYtAXGoQnDsHzJ4t2wwcxb2dBQQAtWsD1apJ8qhcOSBJkti/HlnDsWOqeHpgoGx7Y2ttInoW0wTGjAE++si10HaSJECvXkD37tKF7XlCQ4HmzaXdvMPkyfIcZH3btwPly6v5ypVA/fr64iEiIoqDZ61QSuDpYIieqVs3qWkDADVrytXf2CSTTFO6sjVoIB3hhgx5MplUrJisfLpwQR47aBBQowaTSb4if36gVCkZh4YCS5fqjYeIvFtkpNQ8GjpUJZMSJADeeksS1GPGRC+ZBEgS+9df5WKGwwcfSB0msr5y5WS7o0PPnvI+Q0REZDNMKJF3KV0a+OwzYPBgqU2RIUPMn+OffyQx1LChXBWMjFT3pUwpV5C3b5ftTj17AunSuS9+spbXXlPjBQv0xUFE3i08XGohTZ2qjlWvLu8j33wDZMkS8+dMlEi2QlWtqo517y41eMj6xo5Vq16PHAE+/1xvPERERPGAW97IPvbtk0TUsmVP3le7tlwtbN5calUQAbJqzVEsNTAQuHwZSJVKa0hE5GUePpQaa7/9po61aAH89JNaURsXt28DderIhQ5AVj3Nneua8CZr+vRTqaEESCfREyeA9On1xkRERBRD3PJG9vbff0CHDkCJEq7JJH9/6Q538iSwdq18IGAyiZzlzSsF2AFueyOiJ929CzRp4ppMevNNYN489ySTAFnFsnIlULy4zCMjgbZt+fvIDt59FyhUSMZ37sg2eyIiIhthQoms6/Jl4P33gYIFgR9/VDUtDENOxh1LzB3tmomiwm1vRBSVGzeAunVl+7VD796yxc3Pz72vlSaNvI4j+RAeDrRsCaxe7d7XIc8KDARGjVLzadPk3xUREZFNMKFE1nPrlhRFzZtXTs7CwtR9jRoBu3bJVoQ8efTFSNbRsqUar17Nk30iAi5dklp8W7eqY6NHAxMnykWL+JAhA/Dnn/LeBsiqyWbNgL/+ip/XI8945RWgcGEZ37nDWkpERGQrTCiRdYSEAJMmSaJo9Gjg3j11X5UqwN9/A7//LlvfiKIrd27pyANIcnLJEr3xEJFep04B1apJXT6HadOkRl98JZMcsmSRpFL27DJ/8ABo3Ng1sUXWkiCBdJF1mDJFtlISERHZABNKZA1r1sjWtj59gOvX1fFixSSJtHGjfAAgig3nbW/z5+uLg4j0unZNCmQfPy5zPz/puvbuu56LIWdOYN06IHNmmd+9C7z0ErBzp+diIPdq1Uqtmr5+HfjqK73xEBERuQkTSuTdHjwAevYE6tUDzpxRx3Pnlm1tu3bJNrf4vmpM9ua87W3tWiA4WF8sRKRHSIhsMTtxQuYJEwK//gq0b+/5WPLlk5VKjo5gt27J++CBA56PheLO3x8YMEDNP/lEzm+IiIgsjgkl8l47dgClSwOffaaOpUsn9QcOH5bC2+4ujEq+KWdOoEIFGYeHA4sXaw2HiDzMNKV72z//yNwwpJPbyy/ri6lwYVmdmyqVzIODgdq1gaNH9cVEsdehA5Atm4wvXwa++05vPERERG7AhBJ5n/BwYMwYoGJFSRw5NG4M7N8PvPOOdE4hcid2eyPyXcOHAz//rOaffCKrlXQrUQJYtQpInlzmly9LUunUKa1hUSwkTAj066fm48dL4XUiIiILY0KJvMuJE0D16sCQIZJYAoCkSaVN89KlQMaMeuMj+2rRQo3//NO16DsR2dcPPwAjR6p5t27ABx/oi+dx5csDy5cDSZLI/Nw5oFYt+UrW8tZb0s0PkL+/H37QGw8REVEcMaFE3sE0JWlUogSwZYs6XqkSsHu3nISxThLFpxw5pG4JAEREACdP6o2HiOLXlStAr16y1c2hfn3p6OZt7zdVq0oHyoQJZf7ff1I8/PJlvXFRzCROLM1FHMaNUxfPiIiILIgJJdLv8mWgaVOga1e1KsTfHxg9Gvj7b/Uhnyi+5c6txv/9py8OIoo/t24BH30kXbemTlUf6IsWle2u/v5643uaOnWkSHhAgMyPHAHq1mUTAavp1g1Ik0bGJ09KrS4iIiKLYkKJ9FqyBChWDPj9d3WsUCFg61Zg8GDvPbEne3K0dQaYUCKym5AQqY2UJw8wapTrttbKlWVbWYoU+uKLjkaNgLlzgQSPTt/27ZPubzdvag2LYiB5clkZ5zBmDBAZqS0cIiKiuGBCifS4c0e2sTVrBly9qo6//z6wcydQpoy20MiHOa9Q4pY3Ivs4cUI6OfbtC1y/ro4XLSoXNjZtArJn1xdfTLz6KjB7ttqWt3Mn0LAhcPeu3rgo+t57TyUvDx8GfvtNbzxERESxxIQSed7Nm9LBzbllbtas0h556lSpMUCkA7e8EdnPsmVykWLvXnUsd27gxx+lRl/Tpt5XM+l52rUDvv5azbdsAZo3l3qE5P1SpQLefVfNp0zRFQkREVGcMKFEnhUZCbRvDxw8qI61aiXL9uvU0RcXEcCEEpGdREQAQ4dKwujWLTkWGAhMniyrQtq1A/z89MYYF126AJ99puZr18pqJbIG54TSzp1MBhIRkSUxoUSeNWqUa72kGTOkHkTq1PpiInJ4vIYST/CJrCk4WLaBjR6tjuXIIVvbevWSxJIdvPceUL26mrNAt3VkzqzOfR48AC5c0BsPERFRLDChRJ7z++/A8OFq3qePXGEl8hZp0kjBVEAK9jrX9yIiawgKAkqXBlavVsfq1QN27ADKldMXV3xxdH0DrLd1z9flz6/Gx47pi4OIiCiWmFAizzh2TLYXONSqBYwbpy8eoqgYBre9EVnZt98CVaoAZ86oY0OGSAe3dOn0xUUUFeeE0vHj+uIgIiKKJSaUKP7dvSvd3Bw1LHLkAObNA/z9tYZFFCUmlIis58ED4M03ZdVraKgcS5kSWLpUtlpbuVbS8zhvzeUKJWvJl0+NuUKJiIgsiJ/oKf69844qwp0wobTHTZ9eb0xET+NcR+nECX1xEFH0nDoFvPqqa0Hq4sWBX391/cBuV0woWRe3vBERkcVxhRLFr82bgR9+UPOvvpL2zUTeyvkE/+hRfXEQ0fOtXCnvKc7JpHbtgC1bfCOZBDChZGXc8kZERBbHhBLFn8hI6abj0KIF0LGjrmiIoqdQITU+fFhfHET0dBcvyvtLw4bA9etyLCAA+OILuYiRJInW8IiixTnpefy4nDcRERFZCLe8UfyZMwfYvl3GCRMCEyfqjYcoOh5PKJkmr/oTeYuzZ4EJE4BvvgEePlTHs2YFFi4EKlbUF5suXKFkXWnSyO36dakDduECkC2b7qiIiIiijSuUKH7cuwcMGKDmvXsDuXJpC4co2jJlAlKkkPHt28ClS3rjISLg5Emga1cgb17g889dk0m1asmWN19MJgFARIQa27n4uF1x2xsREVkYE0oUPyZMkCttgHxAd04uEXkzw+C2NyJvcfgw8MYbQIECsiopLEzdV7YssGQJsHYtkCGDvhh1Y0LJ2tjpjYiILIwJJXK/M2ckoeQwdiyQPLm+eIhiqmBBNT5yRF8cRL5q3z6gVSvghRekJpJz0qRKFSnGvW0b0LQpt3kxoWRt7PRGREQWxhpK5H4DBwIhITIuXVquLhNZCVcoEekzYwbQvfuTBYpr1QKGDgVq1GASyRkTStbGLW9ERGRhTCiRe505A/z8s5pPmQIk4EI4shgmlIj0+OUXoFs310LTDRoAQ4YAlSvri8ubOSfemFCyHkd5AAAID9cXBxERUSwwoUTude6cGpcoAVSrpi8WothiQonI89auBdq2Vcmk0qWBr7+WWkn0dM4rlHgBx1pMU1bkOTRrpi0UIiKi2GBCidwraVI1dj7JJbKSvHnlSn9EhKy6u38fSJJEd1RE9hUUBDRvropuFywIrFoFpEunNy4r4JY361q/XtVNSpECeP11vfEQERHFEC9lkXs5J5Tu39cXB1FcJEwI5MkjY9NkoVSi+HTgAFC/PnD3rsyzZQNWr2YyKbqYULKur79W4/btXc+hiIiILIAJJXIv51Uc9+7pi4Morpy3ve3cqS8OIjs7eRKoWxe4fl3madLIyqQcOfTGZRWm6fpey4SSdVy5AixapOZvv60vFiIiolhiQoncy/nqGhNKZGVVq6rxihX64iCyq/Pngdq1gYsXZZ48uSSTXnhBb1zxKSQEGDQI6NgRmDMHuHkzbs+3fj1w9qyMEyeW1V1kDTNnqi2elSoBxYrpjYeIiCgWWEOJ3Mt5hdL9+3L1lO2dyYpq11bjffv0xUFkR2fPyv+xU6dknigRsGyZvQtwh4VJjZylS2U+ezYQEAC8+KLUj3r5ZSBz5pg956RJatyxI7dMWUVkpGsxbq5OIiIiizJM59a8FlW2bFkzKChIdxjkEBiorro9eCAfFIis5uZNIHVqGSdOLCvumBwlirv//gNq1VLJJH9/YMkSoGFDrWHFq8hISfj8+OOzH1exoiSXmjcH8ud/9mMPHgSKFJGxYQBHjjz/e8g7rFkD1Ksn41SpgAsX5H2GiIjICxmGscM0zSiv+nHLG7mf8xXSy5f1xUEUF6lSSdcdQBKjV69qDYfIFo4eBapVU8mkgABg/nx7J5NME+jVyzWZ1Lx51Kuxtm4F+vcHChQAihYFhgwBduyQ53jcp5+qcdOmTCZZiXMx7g4dmEwiIiLLYkKJ3K9kSTX+6SdtYRDFWa5canz6tLYwiGxh/36genWpnQRIN8XFi4FXXtEaVrwbMQKYNk3Nu3QBfv0V2L4dOHMG+Owz2fb2eEHtAweAMWMk8ZQrF9Czp9RMCg+XizXOCarevT3yo5AbnDsnK/IcuN2NiIgsjAklcr+33lLjb75xbWlMZCU5c6qxY0UFEcXcrl1AzZpq1WqSJMAff9h7ZRIATJ0qCSWH114Dpk9X22ezZwfeew9Yt07+bGbNklpKj28VdySeatUCMmWSP7fQULmvXDnXJgLk3YYPl6QgIH9vdi5CT0REtseEErnfq68CadPK+PRp6dpDZEV586rxv//qi4PIyv79VxIhwcEyT54cWLnStfC9Hc2eLVvdHF56SVYVPb4SySFtWuCNN2TV1rVrwG+/Ae3by/ZbZ8HBwM6dat67N+u7WcXBg9LdzWH4cG2hEBERuUO0EkqGYaQyDGOhYRiHDcM4ZBhGpcfur2kYxi3DMHY/un302P1+hmHsMgzjd6djLQ3DOGAYRqRhGE8UEjAMI4dhGHcNw+gT2x+ONEmUCOjUSc2/+kpfLERx4SiaCsiHPBs0MSDyqL//BurUkSL3gCRH1q6VOkp2tngx8Oabal65MrBwoTStiI6kSaXO0g8/AFeuSBHnd94BsmZ1fVyuXHIRh6xh0CAp0A7I+4vdk6pERGR70eryZhjGbAAbTdP81jCMQABJTNO86XR/TQB9TNNs/JTv/xBAWQApHI8xDKMwgEgAXz/63qDHvufXR/f/a5rmJ8+Kj13evNCxY1JUFAASJJDtQtmzaw2JKMYePgTSpQPu3pX5vn1SKJeInm/NGtm+9eCBzNOlk2POdfbsaN06oEEDtSWteHFgwwbVNTIuIiOBoCBg0SLg4kWgb1/V6Y282z//uG5N3LkTKFVKXzxERETRFKcub4ZhpABQHcB3AGCaZqhzMika358NQCMA3zofN03zkGmaR57yPc0AnARwILqvQ14mf3515S0yEvj222c/nsgbJUwoHwwdnAupEtHTLVgANGqkkkmZMklSxe7JpLAw2abmSCblywesXu2eZBIgF2jKlwfGjZN6S0wmWYNpAv36qXnbtkwmERGRLURny1seAFcBzHy0be1bwzCSRvG4SoZh7DEMY4VhGM5nOFMA9IOsNnquR8/dH8CI5z2WvFy3bmr87beqCCWRlTRrpsaLF+uKgsg6vvwSaNVKkisAkC2bbH3zheTHoUPAhQsyTp1aVmRlzKg3JtJvyRJg82YZBwQAo0bpjYeIiMhNopNQ8gdQGsB00zRLAbgHYMBjj9kJIKdpmiUATAOwGAAMw2gM4IppmjtiENMIAJNN07z7rAcZhtHVMIwgwzCCrl69GoOnJ495+WV1In3hArB7t9ZwiGKlYUPA31/GQUHAyZN64yHyVqYpRYbfeUfVGytUSD5I58+vNTSP2eF0ulOjhtQ48oRjx1yLPZP3CA8HBg5U8x49gNy59cVDRETkRtFJKJ0DcM40TUeLo4WQBNP/maZ525EAMk1zOYAAwzDSAagCoKlhGKcAzANQyzCMn57zehUATHj0Pb0ADDIM493HH2Sa5gzTNMuaplk2ffr00fgxyOMCAmSbg0MCNhUkC0qVyrXuxTvvqKKqRCQiIoB33wVGOC0urlAB2LTJt+rnOXdfK1366Y9zl+vXpZPcCy8AXbsCR4/G/2tSzMycCRw+LOPkyYEhQ/TGQ0RE5EbP/YRvmuYlAGcNwyj46FBtAAedH2MYRibDkJ61hmGUf/S8waZpDjRNM5tpmrkAtAKwzjTNds95vWqmaeZ69D1TAIw1TfPzmP1Y5DUcbaIBaYlMZEWDBqnxypXA1Kn6YiHyNg8fAm3ayFY3h/r1pZubr/3ed16hVKZM/L3Ow4fAp58CefPK76PwcLk51+kh/e7fB4YNU/P+/aU4PRERkU1Ed8nIewDmGIaxF0BJAGMNw+hmGIajSE4LAPsNw9gD4DMArczntI8zDKO5YRjnAFQC8IdhGKti9ROQd3NOKKVJoy8OorioWxfo00fN+/d3XYlA5Kvu3AEaN5Yi3A6tWwNLlwLJkumLS4eICNet3fGxQsk0gYULZUVS797AzZvqvmrVgMGD3f+aFHtTp0o3PgDInFlWkxEREdmI8Zy8jyWULVvWDAoK0h0GPe7BAyBJEhkHBMgVVVnIRmQ9oaFA5cpqBUKBAjL2tQ/NRA5Xr0qNMef33/feA6ZM8c0tzgcOAEWLyjhLFuD8efc+/7ZtwIcfSvt5Z/nyARMmSAMBvsd6j+BgIE8e4PZtmX/9tWxLJCIishjDMHaYplk2qvt88IyPPOb6dTVOm5YnumRtgYHA3LlA0kdNLo8eBd5/X29MRLqcPi21xZyTSaNHy4oMX0wmAfG33e30adlSWKGCazIpdWpg8mRJZDVvzvdYb/PVVyqZVKAA0Lmz3niIiIjigY+e9ZFHcLsb2U3+/K51YmbOBObN0xcPkQ47d8pqPUcB6AQJZPXF4MG+ndRwd0HuW7eAAQOAggUlme0QEAB88AFw/LhsoQoMjPtrkXuFhwPTp6v50KGqWygREZGNMKFE8ee//9Q4Y0Z9cRC5U/v2QNu2av72267/1ons7LvvJJl04YLMAwOlfhK38rhvhZIjGZE/PzB+vGwXd3j1VeDgQSnIzQs13mvJErXlMWNG4LXX9MZDREQUT5hQovjz779q7In2yUSeYBiySilPHpnfvi3bUcLC9MZFFJ9CQoC33pKbI8GRIgWwYoUkOXxdZCSwa5eax/Y9zzQlYd2jh9SocihXDvj7bynInS9f3GKl+Ddtmhp37cpVZEREZFtMKFH8cU4olS+vLw4id0uRAvj5Z7WFYetWYMQIvTERxZf//gOqVJHVSQ5FiwLbtwO1aumLy5scPQrcuyfjjBmlKHdsLFzo2jEvRw5gzhz5HVOtWtzjpPi3bx/w118y9vOTVaxEREQ2xYQSxY+wMPmw4VChgr5YiOJDhQpShNhh7Fhg/Xp98RDFh+XLZfuWc32gtm0lwVGggL64vM3j9ZNiW0vqk0/U+PXXgcOHZQWkrxY6t6IvvlDjV14BsmbVFwsREVE84xkKxY9Ro4A7d2ScObNcZSWym759gdq1ZWyaQIcO6t89kZVFRADDhgGNGgE3bsixgAD5sPzjj6rbIQl31E/atk1uAJAwoWybSpw47rGR59y8Kf8/HN59V1soREREnsCEErnfpk3AmDFq/sEHvt35h+wrQQLghx+AdOlkfu6cdPMhsrLgYEkkjRypjmXLJjV8evTg7/OoOK9Qim1CyXllS6tWQPr0cYuJPG/WLOD+fRkXL85tikREZHtMKJF73boFtGsnBUoBoGZN4MMPtYZEFK+yZAGmTlXzadOAoCB98RDFxf79smVr1Sp1rHZtSZhUrKgvLm8WGfnklreYunULmDdPzbmyxXoiI12Tgu++y+QrERHZHhNK5F49egCnT8s4dWpZveHnpzcmovjWujVQt66MIyOlCGt4uN6YiGLq/HngpZeAM2fUsUGDJLnE1TJPd/KkdHsEZLVi9uwxf459+4DQUBkXKQKULeu++MgzVq0Cjh+XcapUUvuKiIjI5phQIveZM0c6Xzl8/XXsTqyJrMYwgOnTgUSJZL5zJ/D553pjIoqJu3eBJk0kqQQAyZIBS5bI9mVeFHg25/pJsS3IfeyYGhctGveYyPOcf+d37sw6Y0RE5BOYUCL3+O8/WZ3k0KkT0LKlvniIPC1vXtf6SUOGuK70IPJWERGyym7XLpn7+QG//go0bao3LqtwR0Fu54RS/vxxi4c87/hxYMUKGRsG0L273niIiIg8hAklirvwcKB9e7XkP29e15oyRL6iTx/ghRdkfO+e1NAwTb0xET3LunVAhQrA77+rY9OnA/Xq6YvJarZuVePY1E8CXBNKBQrELR7yvOnT1e/6Bg2AfPn0xkNEROQhTChR3I0bB/zzj4z9/GTrW/LkemMi0iEwULZ6OixbJjcib7Nnj9RLql3bdYVNv35Aly764rKaLVuAjRtlbBixL1x+9Kgac4WStdy7B3z/vZq/956+WIiIiDyMCSWKm61bgREj1Hz4cLnaTeSrqlZ1/UDu3LmJSLfTp4EOHYBSpVw7uSVMKFs2x43TF5vVmKYULXdo3RrIli12z+Mo5gwwoWQ1c+YAN2/KOF8+ru4jIiKfwoQSxV5YGNCundTfAOSD9MCBemMi8gYNGqjxgwf64iByuH5dtmQWLAj8+KPanmMYUvPu2DFg5EggAU8Lou3PP4ENG2Ts5+d6cSUmzp4F7t+XcerUQNq0bgmPPMA0XYtxv/MO/w8REZFP8dcdAFnYmTPAiRNqPns2uwERAa7/Dw4fluRrQIC+eMh3PXgATJsmK48cqygcGjUCPv6YXcViwzSBwYPVvHPn2NfNcd5yWKJE3OIiz9q0Cdi3T8ZJkgAdO2oNh4iIyNOYUKLYy50byJgRuHxZ5hcvAnnyyDgsDFi+HNi7F4iMlJNvxw1wnUdEABcuAKdOyXaM+fNjX4eCyBtUrAgkTiwf5g8fliL1ffrojop8zcaNQNu2sgLGWfnywIQJQI0aeuKyg6VLgW3bZOzYLhhbQUFqXLZs3OIiz9qzR40zZpSkEhERkQ9hQoliL0ECaSv9zTcyX7wYSJcO+O47Wa105UrsnnfJEiaUyNoyZJB6Yv37y3zYMKBlSyBnTq1hkQ/Zs0dWIN25o47lyweMHQu0aCFb3Sh2IiKAIUPUvEcPIHv22D8fE0rW1by5bPW/exf47z9J1Dr/2yAiIrI5bvSmuHn5ZTX+7DOgUCFg4sTYJ5MAYP/+uMdFpNsHHwDFisn4/n3p/ONYoUcUn06fljpejmRS2rTAF18ABw9KYpPJpLiZN0+9TyVLFrfagabpuuWNCSVryZoVGDNGzUePdu3YR0REZHOGaYMPOGXLljWDnK/wkeeEhMiqpHv3nrwvIEBqyYSGyra3Z0mcGMicWW5Nm0oHosBAeY7AQCBFCul8ww9CZCWbNwNVqqj5b7/JFW2i+HL9ujRIOHRI5ilSyNa34sX1xmUXYWFA4cKqfuCQIcCoUbF/vlOnZPs4AKRKJX9/fJ+zlogIoFIlYPt2mb/4ohRs598jERHZhGEYO0zTjPKqF7e8UcydOwesWCFFuc+elSu0USWUwsLkFh0PHgAnT8rtn3+ifsxrr0l9JSKrqFwZ6NoVmDFD5u+9B9SpAyRPrjcusqeQEKBZM5VMCgiQrchMJrnPzJkqmZQ6NdC7d9yez/liWJkyTEJYkZ+f/I4vW1aSS+vXAz/8ALzxhu7IiIiI4h23vFHM/P671OHo2lWWds+erYpyx7dVqzzzOkTu9PHHUlMJAM6fj1vxXqKniYgA2reX1UgOs2fLaglyj5AQYORINe/fX1YVxQXrJ9lDyZKyzdmhd2/g2jVt4RAREXkKVyhR9P32G9Cq1bNXHaVODeTIIQVKz5yRlUvZs8uV1+TJ5XtDQ+XmGD/+1Xns/OGocuX4/xmJ3C11auDTT4F27WQ+bRrQoQNQurTeuMg+TBP48ENg4UJ1bOJEoHVrfTHZjWlKMun8eZlnygS8+27cn5cJJfsYPhz45RepYRYcLEml2bN1R0VERBSvmFCi6Jk7V65+R0TIPFcu+YCcI4dKIGXP7v6tPJUrA1u2yPjVV9373ESe0qYNMGsWsHat1BN7+21g61bZKkEUF6Ypq+A++0wd69kz7luxSLl/H+jSBfj5Z3Vs8GAgadK4Pa9pMqFkJ0mTAl9+Kd0VAdn21qEDULu23riIiIjiEYty0/PNmgV07qw6VBUoAKxbJ91N4tPZs5KsAuSD96VLUgCcyIqOHZOubw8fyvyzz6SmElFsmKbUSurTR2raObRoIbXmEnBHu1ucPCmF9PfuVceqVpXkcMKEcXvu48el2QQgnfiuXmUNJTt4/XVgwQIZ58sn/3YSJ9YbExERURw8qyg3zzjp2b7+GujUSSWTihQB/vor/pNJAPDRR2pcsyaTSWRt+fMDgwap+eDBavsMUXRt2iS1WgoUkN/HzsmkatWAH39kMsldVq2SVUPOyaQuXdyTTAKAHTvUuGxZJpPsYupUIGVKGR8/DowZozceIiKieMSzTnq6qVOBbt3UvGRJYMMGqR0R33bskJVRDgMGxP9rEsW3/v2BggVlfOeOaxFXomeJjATeeUeSRlOmyAdVB8OQ1W4rVgCJEmkL0TZMExg3DmjQALhxQ44FBkonrxkz3JNMAp7s8Eb2kCkTMH68mo8fDxw4oC8eIiKieMSEEkVt/HigVy81L1dOtrl5YpWQabq+dtOm0mqdyOoSJgS++krNf/kF2L9fXzxkDaYpBaC//NL1eNKksh1r0ybZQhnXmj4kid5XX5XVhI6VuVmzysrcLl3c+1qsn2RfXboAVarIODxcak7evKk1JCIiovjAhBK5Mk1gxAjXFUFVqsgS/9SpPRPDwoXyAQkAAgKATz7xzOsSeULNmsArr6j5pEnaQiELME1ZmTR9ujrWpAmwcqV0kvrtN3bAdJcjR4Dy5YFFi9SxatVkxWzFiu59rcjIJ7e8kX0kSCAlAwICZL57t6x4u31ba1hERETuxoQSKaYpV2WHD1fHXnxRPrikSOGZGB48APr2VfP33lNFS4nswvnf+Jw5wMWL+mIh7+VYmeScTGrTRhIe9eu7b+sVAUuWyErcw4fVsfffB/78E8iY0f2vd+yYrIYCgAwZgGzZ3P8apFeRIq7/d7dulQ5wd+/qi4mIiMjNmFAiYZpSz+Xjj9Wx+vWBP/4AkiXzXByTJwOnT8s4bVpg6FDPvTaRp1SsqFaVhIUB06bpjYe8j2lKQt15m1vr1sDs2dL1ktwjIkLeZ5o1UwmeRImkuPnUqWqFibs9vt2NBbnt6c03gS++UPNNm2SF4f37+mIiIiJyIyaUSJbe9+ghJ88OTZrIFVtPtrq9eBEYO1bNR40CUqXy3OsTeVKfPmo8fTqvWpNy757UXHH+INqqFfDDD4C/v7647ObGDXmvGz1aHcuZE9i8Wf784xO3u/mOHj3kYpnDhg3Ayy8DISHaQiIiInIXJpR8XUSEXEFzLhTcooXUMfL0dorBg+WDFAAULer+AqhE3qRpUyBfPhnfvAl8/73WcMhLHD0qK9h+/lkde/11WTHDZJL77NsnW9xWrFDH6tSRRE+pUvH/+uzw5lt69QImTFDztWullt7Dh9pCIiIicgcmlHxZeDjQvj0wa5Y61rYtMHeutEj2pPPnXeP49FN+eCJ78/MDPvxQzSdPlv+T5LsWLZIkh3Pnvy5dgJ9+4u9Dd5o/X5J2J06oY/37S73AtGnj//UjIoCdO9WcK5R8Q9++rqvhVqyQC3hMKhERkYUxoeSrQkPlqvfcuepY585Sn0PHB5dff1UtmmvUAOrW9XwMRJ72xhvqA+ypU9Kxi3xPeLh01nzlFdUFKmFC4LvvgBkzmExypyFDZPugo4ZN0qTAL79I/UBP1aY6ckStxs2cGciSxTOvS/oNHgx89JGa//67bH978EBfTERERHHAhJIvCgmRDy7OH167dwe++UZfsdcFC9S4TRs9MRB5WpIkUl/D4ZNPVGKVfMOVK0C9esD48epYrlxSx6dzZ21h2dJPPwFjxqh5/vzAv//KKhFPerwgN/mW4cOBgQPVfNUqdn8jIiLLYkLJ19y/L7Vb/vhDHfvgAyn+mkDTP4dz54B//pGxnx/QvLmeOIh0ePddVa9s+3bg77/1xkOeYZqy5aV0aWD9enW8QQOp41O6tL7Y7OjwYaBbNzWvXx/Ytk1au3saE0q+zTAksTl8uDq2fj3w0ktqhSIREZFFMKHkS+7eBRo2BNasUccGDQImTdLbsnjhQjWuVQtIn15fLESeliGDbH1zmDhRXyzkGbt3y7behg2lfhwgv4OHD5ctMGnS6IzOfh48AF57TW0zK1BAtrnp6iLKDm9kGMCwYbLV0uGff+T3wo0b+uIiIiKKIRZmsJOHD4Fdu+Rk5NYtudJ165Yab9woH2QcRo4Ehg7VFu7/OW93e+01fXEQ6dK7t2w5NU1ZPXjwIPDCC7qjInc7e1Zq+Pz4o+vWxtSpgTlzZHUSuV+vXtLVDZDVgAsWAMmT64klPFzepx3Y4c239e8PJEok/0YBWTVXq5Zc+EuXTmtoRERE0cGEkl08eABUqgTs2RO9x0+YIB1HdDtzBtiyRcZ+fkCzZlrDIdKiQAEpzLp4scwnTZKCzGQPt25JjaTJk6WGnYOfH/DWW7IyKVMmbeHZ2rx5UtjcYepUoEQJffEcOqQKMGfLBmTMqC8W8g49e0qis3t3me/eDdSsCaxdy98LRETk9bjlzS6GDYt+MmnqVO9IJgGu291q1+YVOfJdzv8nf/rJta04WdODB8DnnwP58gHjxrkmk5o0kVUzX33FD43x5dgxoEsXNX/9daBrV33xAKyfRFHr1g2YOVOVHzhwQDreOrbEEhEReSmuULKDhQtd6674+UkXt5QpgRQpXL9WqSKrIbzFL7+oMbe7kS+rXFn+f/7zDxAaKl1//v0XyJFDd2QUUwcOyBbGH354sh5KmTLSza9mTS2h+YyQEKBlS9U5K18+Wamks14gwIQSPV3HjkBgINChAxARARw9ClSvDqxbB+TMqTs6IiKiKDGhZHXnzwPt26t5kSJywpookb6Youv0aWDrVhn7+3O7G9G338rW1Zs3gUuXpGjzpk36igdT9EVEAPPnS8fMzZufvD9nTmDsWKBVK30dNX3Jhx+qVbuBgVI3KUUKvTEBTCjRs7VpI9vfWrcGwsKAkyclqfTnn5IUJSIi8jI8q7W6zz5T2yjy55eTDiskkwDX7W516gBp0+qLhcgbFCokdZQCA2V+4ADw6quyYom8k2kCS5ZIXZ62bZ9MJuXKJSuSDh+WD4tMJsW/BQuA6dPV/NNPgVKl9MXjEBbmujWdBbkpKq++Cvz2m3ofOHMGqFpVOkASERF5GZ7ZWtmdO8DXX6v5xInWKvDJ7m5ET6pRQ2ppOKxbJ3VgnLuCkXfYsEG2KjZrJsk/B39/oEULYPVq4MQJ6eJnlUS/1Z04IYXOHVq0AHr00BePswMHpBsrIIlG1gykp2ncGFi2DEicWOaXL0vdtU6dZAUrERGRl2BCycq++066BwGyOqlJE73xxMSpU9IeFwACArjdjchZmzbAmDFq/sMPwIgR+uIhV2Fh8nf04otq2y4AJEsGfPQRcO6c1IerW5crkjzp4UO5OHHnjsxz55ZtpLrrJjk4b3fj6iR6nnr1gJUrXS8UzpoFFCsGrFqlLSwiIiJnPNO1qvBwYMoUNf/wQ2t9cHEuxl2nDpA6tb5YiLzRwIGuKy1GjJAPE6SXaQJvvw3MnauOBQYCvXpJvZMRI6y1UtRO+vZV3REDAmQVbMqUemNyxvpJFFPVqwP790vtNYdz54CXXpKOhY7kKRERkSYWykCQi99+k6LWgNQe6tBBbzwxxe1uRM9mGMCXXwL166tjXboAa9fqi4mAYcNctyS2bi3dmCZPBtKn1xeXr/vtN2DaNDWfONH7kjZMKFFspEsnCexffnHdJvnNN7Jaad06fbEREZHPY0LJikxTirw69OgBJEmiL56YOnlSnVgHBAAvvxy357t2TWqV/PADawuQvQQEyIeIEiVkHh4uBVv37dMbl6/6+mtg1Cg179QJmDOHLb11++8/oHNnNW/WDHj/fW3hROnhQ2DvXjXnljeKqRYtpA7XK6+oY6dPA7VrA+++C9y9qy82IiLyWUwoWdGmTcD27TJOmBB45x298cSU83a3evVitt0tNBRYswYYPRpo3lw+yKVPL6s43nhDTtKPHXN/zES6JE8O/PEHkC2bzG/fBho2BM6f1xuXr1m61LW480svSYLJW+rz+CrTlGSSo55gzpzA999739/Lvn1SewsA8ublNm+KnQwZpEPuzz+7/hv64gu58LBpk77YiIjIJzGhZEWTJqlx+/bWq9cR0+1uYWFSmLJzZ/lZ69UDhg6V9upnzrg+9uRJ6br0779uDZlIq6xZJamUPLnMz52Tq9Ts/OYZW7ZIDZPISJmXKSOJ8YAAvXGR/D1s2CBjPz9g/nzvTNbs2KHG3O5GcWEYstX2wAHXZiwnTwK1agF//qkvNiIi8jlMKFnN0aNypdzhww/1xRIbJ06ooqmBgUDTpk9/7PHjQLduQKZMQIMGUrckqi1tCRPKCbqjve61a9J96fff3R4+kTbFiwO//iot6QHpkvjPP3pj8gVHjsiHtgcPZJ4njyT3kiXTGxcB9+8Dffqo+XvvARUq6IvnWdjhjdwtc2ZgyRLZ7u8oPh8WJhcbuC2aiIg8hAklq5k8Wa1KaNQIKFxYbzwx9fh2t1SpnnzM8eNSm6RQIdlScv266/05csjWk++/B3bvli4n27fLVbm0aeUxDx5IbaYZM+LrJyHyvLp1gTffVHN2fYtfly7J1rbgYJmnS/dkG2/SZ8IE4OxZGadPLwXTvRULclN8MAxZqb5nj6xkBWRbdIMGspKViIgonjGhZCXXrrl+gOzdW1sosbZ+vRq3aOF63/HjQMeOkkiaNQuIiFD3Zcsmq7G2bgVOnZJ6AZ06Sc0Ax7aTSpWAzZuB3LllHhkp7b25/Y3spGNHNV6wQFZpkPstXy7bZ0+dknmSJLIyKX9+rWHRI6dPA+PHq/mYMVFfoPAGDx5I63eH0qX1xUL2lDOn/M5KkULm589LUslRW4yIiCieMKFkJdOnAyEhMi5VCqhZU2s4seK4mgzIzwDIBzZHImn2bNdEUu3a0hL39GmpHVWhwrOLrRYoIEklx3MD3PpG9lKhgvw7B2R13qJFeuOxmzNnZMtIo0bSPQyQ2jwLFgDly+uNjZQ+fdT7YenSrl3evM3evdKhEZD/u47tSUTuVLw48Ntvalv0/v3SFTQ0VG9cRERka0woWUVICPD552rep4/3dbGJDkeXGwBIlAjYuFE+DDyeSKpTR+5bu1bqISWIwT/VTJmkha7DiRNxj5vIWxiG6yql2bO1hWIroaHAxx/LNmLnJF3q1MDcuZJgIu+wfr10unL47DNJ+nmrb79V44IF9cVB9le7tpQDcPjzT9kmzQYOREQUT5hQsoqffgKuXJFxtmxAy5Z644kt56TRb79JTZgbN9QxRyJpzRqgatXYv07evGrMhBLZTfv2KqG8dq3ryj+KmTt3pGNkiRLAwIGuWwg7dZKi3Fb9fWtH4eHA+++redu2QJUq+uKJjlWr1Dh9en1xkG9o3x4YPVrNf/oJGDJEXzxERGRrTChZQWQk8Omnat6zp3XbVTsnlAYOBB4+lHHGjHLVOa6JJIc8edSYCSWym2zZ5Eo0IFeef/pJbzxWcvmydMvr1Uu6baVKBTRvDhw+rB5TvDiwaZNc6WcCwLt8/bWqR5Q0qWsdJW/l3J00Xz5tYZAPGTQI6NpVzceOZZMSIiKKF0woWcHKlcChQzJOnhzo0kVvPHHhqCPhrGBBYMsW99aESp1ajYODo35dIitz3vY2axa3NDyNaQLbtsk24YIFZUtsixbA1KnAzp2SsHdInlw6ae7Y4f2rXnxRcDAwdKiaDx6sOlt5M+dC7izqTp5gGNK8xHmrbvfurClJRERux4SSFUyapMZduli7oKfzCiVAuij984/qzOYua9eqceHCqkglkV00by4JEAA4epTdDJ2ZpiSF+veX1YoVKsjv0aNHn3ysYch2tw8/lFVKvXrx94W3GjpUbZHOkwf44AO98URXunRq7Pg/SxTf/P2BefNkJSYgyfPXX5eVl5cu6Y2NiIhsg2fN3m7XLulyBkjR0Z499cYTV2nTypYTQLqP/PgjkDix+19n8WI1bt7c/c9PpFuSJMBrrwHffSfzWbOAihW1hqSVacrvy19+kdvTtroGBkq3tmrV5Fapkve2mydlzx7Z7uYwebI0drAC5wspMWkwQRRXyZLJqqRKlaSj7v37UqQbkK299erJrWrV+DkXIyIi22NCyds5r0567TUgRw59sbjDhAnAuHFAw4ayeiA+OvOEhwPLlql5s2bufw0ib/DGGyqhNH8+MGWKdT5ku4NpAkFB0vFr4ULg5MmoH5cypSSWX3tNukb60p+RHTx4AHTooLYn1qsHNGmiN6aYcE4oeXM3OrKnTJmkdEKVKrJt1GHvXrl98on8TqxeXSWYiha1ZidhIiLyOCaUvNm5c/Ih0aF3b32xuEujRvHffnvTJuD6dRlnzaqWexPZTdWqsvXn5Ekp/NuihWxnyJBBd2Tx684d4Msvga++kqvuUUmeXJLJr70m3SQTJvRkhOROvXvLB19A/h6nTLHWh13nOl1MKJEOBQsC+/bJStbVq6XUQFiYuj8kRI6vXi3zzJnl92b9+tJ91+7vKUREFGtce+3Nli1TxaRr1GBiJLqct7u9/DK3GJB9GYYUWnX44w+gWDFg+XJ9McWnGzeAkSOBnDmBAQOeTCalSAG0aye/A65cAX74AWjcmMkkK/vlF2D6dDX/9FOpi2cl3PJG3iBzZumuu369rFRatgx47z1JNj3u4kX5/dm2rXThLV1afueuW6e68xIREYErlLzb8eNqXK+evjisJDJSWoI7vPyyvliIPOGDD2Q149SpMr9yRVYBfvyxbCu1g6tXpWbO55/L6iRnqVLJ//OWLeVKOpNH9nHyJPDWW2r+6quuCVSr4JY38jbJk0uyvXFjmZ8+DaxZIyuU1q5Vxe8ddu2S2/jxUmupZk05L+3QAUiTxuPhExGR9+ClMm/2339q7O4uaHa1aZN8uAakAPiLL+qNhyi++fnJFqCVK6VWhsPAgcDff2sLyy0uXpTtTrlySe0152RS3rzAt99Kt6JZsySJxmSSfYSGAq1aAbdvyzxXLvn7ttJWNwdueSNvlzOnJG8XLJAE/r//AqNGSeOCx7tePngArFghFzMqV5ZadkRE5LO4QsmbMaEUc3PnqnGLFkBAgL5YiDypfn2pkdG8uSRWTVO2f23fLlsWvJlpArduAWfOqNuuXdIF8vHtFYULA4MHS/vrxz/okH0MHCj/dgH5e54/37rd+LjljazEz086YZYvDwwZIkndDRtUjaVjx9Rjn1bDjoiIfAbPxr2Vabp2LGJC6fnCwqTTk0Pr1vpiIdIhXTpg3jxpB339OnD2LFCgANC3L9Crl7SQ1uXePWDnTvkAcvasa/LozJknt7I9rkQJ+XDzyiv8UG53y5ZJrSSH8ePlw61VMaFEVpYiBdC0qdwAYPhwYMQIGdepY81Vg0RE5DZMKHmrGzfUUv8kSdhhIzr+/BO4dk3GWbPKUm0iX5M1q2wNeuUVmd++DQwdKvWHhg4FunQBAgPjP467d6WT0F9/yW3bNtVkICbKl5e4GzXiBxdfcPYs0LGjmjduLFtrrCxFCjXesIENNsja1q9XY9apJCLyeUwoeSvn7W65cvGDVHQ4r056/XVeCSbf1by5dHzr0wc4dEiOXb4MvPsuMGmS1MZo3dq9/0du31YJpA0bgKAg15UZz5M4sdTxyJFDbtmzS1K4Zk3+/vMV4eHy7/L6dZlnyyb1saz+99+unapn9umn8v+Q9b7Iiq5dky3VgPy/bNJEbzxERKQdE0reivWTYu7SJTWuVElfHETeoGFD6cLz44/AsGGy8gOQ3y3t2gETJshWuJIlgXz5gESJov/ct27JltwTJ6R4619/ATt2uBYfjkqRInJzJI2cb2nSWD9xQHEzbJgkJQGp4zJ3rjRXsLr27YGPPpL3qAsXgDlzgM6ddUdFFHO//65+z1eq5NoIgoiIfBITSt6KCaWYcy48HBysLw4ib+HvD3TqJKs+vvwSGDNGrf7Yu1c+6AKyUilXLqBQIbkVLCi3iAhJHDluJ07IV8dzPE/x4kCNGrLKqFo1IH36+PgpyQ7WrJFOfg4jRwJVq+qLx50SJZIaZgMGyHzCBNnWx1W0ZDWLF6txs2a6oiAiIi/ChJK3YkIp5pwTSpcv64uDyNskSgR8+CHw5pvAxInA5MnA/fvq/shIlTRavjx2r2EYstqpRg25Vatmj9UlFP8uXZJVc47243XrquSLXXTrBowdK1tDjxwBli7lB3Kylvv3pcubA+snERERmFDyTqYp20gcmFCKHuel187b34hIpEwJjB4tNVy++EK2qR05Iglsx4f56EiUCMiTR26FCgHVq8tqktSp4y92sqeICKBtW+DKFZlnzCjbNO22eidlSqB7d+lYBwAffywfyLnNk6xizRrgwQMZFy4sHUSJiMjnMaHkjX76SdprA0BAAFChgt54rIIrlIiiJ1MmKcztEBICHDsmyaXDh+Xr0aOyZS5PHiBvXpVAypNHvt9uH/hJj3HjgHXrZGwY8v7n/LvcTnr2lNWBoaFy0ejvv2U1H5EVcLsbERFFgQklb3P7NtCvn5r36gVkyaItHEvhCiWi2EmUCChWTG5EnrJ2rRTidhg8GKhTR1888S1zZqmdNGOGzMePZ0KJrCE8HFi2TM253Y2IiB7hJWZvM3q0SoZkzgwMHao3HivhCiUiIms4dAho0UJ1jKpWzTW5ZFd9+qhtbitWAAcO6I2HKDo2b1bNTjJnBsqV0xsPERF5DSaUvMmRI8CUKWo+cSKQPLm2cCyHCSUiIu939SrQqBFw65bMs2QB5s6VLZZ2lz8/0Ly5mv/yi75YiKLLebvbyy9zyzMREf0f3xG8hWlKfYWwMJlXrQq0aaM3JqtJlQoIDJTx3bvAvXtawyEioseEhEj9FUcn0yRJgN9/B7Jm1RqWR7VqpcZLl+qLgyg6TPPJhBIREdEjTCh5i6VLgVWrZJwgATBtGru/xJRhABkyqPm1a/piISIiV6YJdO4s22cA+Z09dy5QqpTeuDytfn1puAEAu3YB587pjYfoWfbtUwng5MmBF1/UGw8REXkVJpS8xbhxavz220DJktpCsbTEidU4JERfHERE5Gr4cEkgOUyaBDRtqi0cbVKkAGrWVHPnYsdE3sZ5W2bDhkDChPpiISIir8OEkrdYuRJ4/31ZYePczptixnHVF2BCiYjIW/z0EzBypJp37y5dTH2VcyKN297IW5mma0KpZUt9sRARkVcyTNPUHUOclS1b1gwKCtIdhnvcucNC3LFlmlJH6fZtmZ8/L8VeiYhIj8hI2cLdt6+qEVivHvDHH75RhPtpTp8GcuWScWCgdNBKlkxrSERP2L8fKFZMxkmSSEH9JEn0xkRERB5nGMYO0zTLRnUfVyh5GyaTYu/yZZVMSpZMWtsSEZEeFy/KFplevVQyqUgRYMEC304mAUDOnEDx4jIODQVWr9YbD1FUFi5U40aNmEwiIqInMKFE9nH0qBoXLMii5kREuixbJgkTR7MJQIpvL18OpEypLy5vwm1v5O243Y2IiJ6DCSWyjyNH1LhgQX1xEBH5qvv3gR49JFni6LRpGEC/fsDWrUCOHHrj8yZNmqjxH38AERH6YiF63MGDcgOk4UnDhnrjISIir8SEEtnHoUNqzIQSEZFn7d4NlC0LTJ+ujmXJAqxdC4wfL7WCSClbFsiUScbXrknCjchbPL7dLWlSfbEQEZHXYkKJ7CE83HVpdokS+mIhIvIlkZHApElA+fKuif3mzYG9e4FatfTF5s0SJHBdpcRtb+RNnM+pWrTQFwcREXk1JpTIHn7/HTh3TsYZMgAvvaQ3HiIiX3DhAlC/PtCnjyq8nSQJ8M03wK+/AmnT6o3P2zknlJYt0xcHkbPDh6XDGwAkSiQrlIiIiKLAhBLZw5dfqvGbbwIJE+qLhYjIFyxeLC3F165Vx8qUAXbtAt56i40RoqN2balPA8jqrmPH9MZDBLhud2vYUDrnEhERRYEJJbK+Y8eANWtkbBjA22/rjYeIyO5mzJAtbdevy9wwgAEDgM2bgQIF9MZmJUmSAHXrqjlXKZE34HY3IiKKJiaUyPq++kqNGzcGcubUFwsRkd0tXw50767mWbMCf/4JjBvHwtuxwW1v5E2OHpXaZ4Cs9m7cWG88RETk1fx1B0AUJw8eADNnqrnzhxwiInKvHTuA116TQtyAbHFbvRpIk0ZvXFbm/IF940ZZ9cU/T9LFebtbgwZA8uT6YiEiIq/HFUpkbfPnAzduyDh3bikOS0RE7nf6tCQ/7t2Tec6c0hCByY+4yZRJOuQBQEQEsGKF3njItzknlLjdjYiInoMJJbI252Lc3btLG2YiInKvGzdktcKlSzJPlUoSH5kyaQ3LNpo2VWNueyNdTpyQovqAbHdz3o5JREQUBX76JusKCgK2b5dxwoRAp0564yEisqOHD4FXXpEuZIDUSVq8GChcWGtYtuKcUFqxAggN1RcL+a4vvlDj+vWBFCn0xUJERJbAhBJZ1+efq/FrrwHp0umLhYjIjkwTePNNYMMGdWzmTKBGDW0h2VLRokCuXDK+fRtYt05rOOSDdu0CPvtMzTt00BcLERFZBhNKZE3z5gGzZ6t5jx76YiGyoqtXdUdAVjB0KDBnjpqPHQu0aaMvHrsyDKB5czX/9Vd9sZDviYgA3n5bvgJArVqyKpGIiOg5mFAia/rgAzWuUEFuRPR8x44BL78MFC8O3LmjOxryZt98A4wZo+ZduwIDBuiLx+6cCyAvXgyEh2sLhXzM9OmuJQSmT5ckJxER0XMwoUTWdP26Gs+bxxMfoue5eRPo0wcoUgRYulSKK3/8se6oyFutWCGNDhwaNpT6KvxdG38qVgSyZJHxtWvAxo164yHfcP48MGiQmg8aBBQooC8eIiKyFCaUyHpM07VgaY4c+mIh8nbh4cBXXwH58wOTJgFhYeq+4GB9cZH32rVL6tI5tr+UKgXMnw/4++uNy+4SJOC2N/K8nj3VatWCBYH+/fXGQ0RElsKEElmP40MOAPj5yUk4ET3pwQNZWdK9u6x4cKhSBdi2TRJNRM7OnAEaNQLu3pV5jhzA778DyZLpjctXvPqqGv/2GxAZqS8Wsr/ff3dNXH79tWx5IyIiiiZ+EifrMU01joxkHRiiqDx8KB9O16xRx3LmlJUmGzcC5crpi428082bkoC8eFHmKVMCy5erbVgU/6pVUx1LL14Etm7VGw/Z1717wDvvqHmnTuzeSEREMcaEEllPQABQsqSMTRNYv15rOEReJywMaN1a6uA49OsHHDokW5lYB4ce9+CBJCAPHJB5QACwaJHU3CLP8fcHmjVTc257o/gyfLisSAQkiTlxotZwiIjImphQImuqX1+NV6/WFweRt4mIADp0kGSAw9ChwPjxQOLE+uIi73XvHtCkCbBunTr2/ffAiy/qi8mXOW97+/VX11W5RO6wezcwebKaT5oEpE2rLRwiIrIuJpTImmrVUuNt2/TFQeRNIiOBt96SzocOvXsDI0boi4m82507ss3tzz/VsbFjgXbt9MXk62rVku2GAHD6NLBzp954yF4iIoC331b1KF98EWjfXm9MRERkWUwokTWVKaPGe/e6dn0j8lX9+gGzZql5jx6yjYFb3Cgqt28DL70E/P23OjZ6NDBwoL6YCAgMBJo2VXNueyN3+uordSEuMFDmfI8gIqJYYkKJrCltWiBXLhk/fKjqfhD5qgMHgE8/VfNOnYBp0/hBgaJ24wZQty6webM6NmECMHiwvphIcd72tnAht72R+4wdq8aDBwMFCuiLhYiILC9aCSXDMFIZhrHQMIzDhmEcMgyj0mP31zQM45ZhGLsf3T567H4/wzB2GYbxu9OxloZhHDAMI9IwjLJOx8s7Pc8ewzCax/WHJJsqW1aNd+zQFweRNxg2TH3orFMH+OYbIAGvGVAUgoPl34jzduEpU4C+fbWFRI+pVw9ImlTGx44B+/frjYfsITgYuHBBxkmSAP37642HiIgsL7qfNqYCWGmaZiEAJQAciuIxG03TLPnoNvKx+3pG8T37AbwC4O8ojpc1TbMkgJcAfG0Yhn804yRf4rztLShIXxxEuu3e7botZvx4wM9PWzjkxS5ckBo9znV5vvgC6NlTX0z0pMSJgcaN1Zzb3sgdjh9X43z5gIQJ9cVCRES28NyEkmEYKQBUB/AdAJimGWqa5s3ovoBhGNkANALwrfNx0zQPmaZ55PHHm6Z53zTN8EfTRAC4zpui5pxQ4gol8mUfOS0Kbd4cKF1aXyzkvf79V1Z27t0rc8OQlWw9euiNi6L2eLc3orh6PKFEREQUR9FZoZQHwFUAMx9tW/vWMIykUTyu0qMtaisMwyjidHwKgH4AIqMblGEYFQzDOABgH4BuTgkmIoWFuYlk29KyZTI2DHZ0o6j98ANQowZw8aLM/fyAmTOlKyB5pwYNgESJZLx/P3D0qN54yPqOHVPj/Pn1xUFERLYRnYSSP4DSAKabplkKwD0AAx57zE4AOU3TLAFgGoDFAGAYRmMAV0zTjNHyEdM0/zVNswiAcgAGGoaR6PHHGIbR1TCMIMMwgq5evRqTpye7SJMGyJ1bxqGhrDFBvsl5ddJrrwHFiumLhbxPeDjQuzfwxhvSwAAAUqcGVq2SY+S9kiWTLnwOXKVEccUVSkRE5GbRSSidA3DONM1/H80XQhJM/2ea5m3TNO8+Gi8HEGAYRjoAVQA0NQzjFIB5AGoZhvFTdIMzTfMQJIFVNIr7ZpimWdY0zbLp06eP7lOS3XDbG/myf/6RxAAgBbiHD9caDnmZGzekDo9z978XXgC2bwdq19YXF0Uft72ROzmvUGJCiYiI3OC5CSXTNC8BOGsYRsFHh2oDOOj8GMMwMhmG9KY2DKP8o+cNNk1zoGma2UzTzAWgFYB1pmm2e9brGYaR21GE2zCMnAAKAjgVo5+KfAc7vZEvGzpUjdu1AwoV0hcLeZfDh4EKFVTCEQCaNgW2bAHy5tUXF8VM48ZAQICMd+wATp3SGg5ZnPMKJW55IyIiN4hul7f3AMwxDGMvgJIAxhqG0c0wjG6P7m8BYL9hGHsAfAaglWmazyymbRhGc8MwzgGoBOAPwzAcZ71VAewxDGM3gEUAepimeS0mPxT5EHZ6I1+1fr3cAKmH47z1jXzb8uWSTHJejTBkCLBoEZAihb64KOZSpQLq1FFzrlKi2Lp+XW6AdBHMnFlvPEREZAvGc/I+llC2bFkziMkE33T9OpA2rYwDAoA7d9gGl+zPNIFq1WTLGyCFlb/5Rm9MpJ9pAhMnAgMGyBiQD46zZkl9LbKm775TxdOzZgUOHQKSJ9cbE1nPtm2SaAak1p6j2yMREdFzGIaxwzTNslHdF90VSkTeKU0aIE8eGYeFsTA3+YZVq1QyKSBAVp+Qb3vwAGjfHujfXyWTsmeXfydMJllby5ZAxowyPn8eGDZMbzxkTSzITURE8YAJJbI+521v27fri4PIEyIigMGD1bxLFyBnTn3xkH7nzwPVqwNz5qhjVavKNuBSpfTFRe6RIgUwaZKaf/YZsGePvnjImpy3wLJ+EhERuQkTSmR9jiXcgBScJbKz774Ddu6UcaJEwKBBeuMhvbZuleYEztu+u3QB/vwTyJBBX1zkXm3aAC++KOOICKB7dyAyUm9MZC1coUREFDtnzsj5dvHicsGuf385z7JB6SB3YEKJrK9yZTV2bAMisqPr110TSAMGSE0V8k2zZgE1agCXLsnczw/4/HPg66+BwECtoZGbGQbw5Zeq49uWLcDMmXpjIms5c0aNc+fWFwcRkVUcPAi0aCG/M8eNA/btk8+aEyZIw4yePXVH6BWYUCLrK11aFeI+cQK4fFlvPETxZdgwIDhYxjlzAv366Y2HPOvePWDNGtnyWLky0KkTEBoq96VNK/e9844kH8h+ChUC+vZV8379gGtsgkvRdOuWGqdJoy8OIiIrmDcPKFdOuqs+bUXwtGnAjBnPfh7TtP1KJiaUyPoSJpQtHw6bN+uLhSi+7N0rKxQcPv1UOniR/V26JFfB0qUD6tUDxo513d5btKjUj3NsiSL7GjwYyJVLxtevy7J7oui4fVuNU6TQFwcRkTcLDwf69AFatwbu31fHa9WS5NKiRcBLL6njPXrIBZ/SpYHy5eWcLE8eIFMm6cjq7y9dyG3MX3cARG5RpYra7rZ5M9C8ud54iNzJNIH33lNXSOrU4b9xX3D1qiyr/uIL6eIWlTZtgK++Yht5X5EkiVwRbdJE5t9/D3TuLO+BRM/ivEIpZUp9cRAReas9e6QOpXOTpwIFgLlzJWHkUK+e1FLatUvqGh458uznvX/f1ol8rlAie2AdJbKz+fOBv/+Wsb+/dHnitib7unEDGDJErnB98olrMqlQIeDdd4GFCyXhNGcOk0m+pnFjoFkzNe/eHQgL0xYOWYBpcoUSEdHT3L8vdUnLlHFNJjVtCmzb5ppMAuTizuLF0a9Hd++e20L1RoZpgz19ZcuWNYOcO9yQ77l6VXU0Mgw5cUqWTG9MRO5w964kEc6fl/mHH7q2ECf7uH0bmDJFtjM6ryYA5GRm1CigQQMmE0kKLBcurJbjT5okvxuIovLggXwAAqRMQEiI3niIiHQzTeDYMenW9sknwMmT6r7AQGDECKlVmOAZ628iIoBz5+S9+P59ubiTNKn8vk2SRI39rb8pzDCMHaZplo3qPuv/dESAtE83DFX4bPx4+fBFZHXjxqlkUsaMUpib7OXePenONmGC1MVxVrQoMHKkrEhhIokccuSQ3wWOGkrDhgGvvQZky6Y3LvJOzglqrk4iIl91+jSwbp3c1q9X59fOqleXQtsFCz7/+fz8pEmOj+MKJbIH05QMsOOqW4oU8sHMz09vXERxsXYt0LCh2s4ycybQsaPWkMiNQkKk/tG4ccCVK673FSwIDB8uSYJnXR0j3xUWBpQsKW2NAWlt/MsvWkMiL3X0qPpwlC+fXJUnIrK7S5dcE0jOq5AelyqVrFTq1InnXVHgCiWyP8MAGjWS6vuAbB358kspZExkRZs2AS+/rJJJFSsCHTrojYnc58wZ+fvdvdv1eJ48stqkTRtbLJGmeBQQAEyfDtSoIfOFC4GVK127zxABXKFERL7BNIEDB4ClS4ElS6T+0bOkSCHvobVqyXmXo3wKxQjPVsk+pk5VCSUAGDhQOmFxCwBZTVCQJEgd9VGyZwfmzeMVE7vYtAl45RWp/eaQPTswdKisQAsI0BYaWUz16sAbbwCzZ8v8nXeA/fuBxIn1xkXexbkgNzu8EZEdrV0LdOsGnDjx9MckSSLd2WrVklupUrx45wb8dEL2kTWrFCl1uHePK5TIevbtA+rXVx8AMmaUN0nu0baHb76RkxhHMsnfX5ZYHzsmrWqZTKKYmjgRSJ1axidPAh9/rDce8j5coUREduZY9f14MsnPD6hWTQpsb9woXXRXrZL6g+XKMZnkJkwokb3UqeM6X7xYbkRWcPQoULeuKsycJg2wZg1QoIDeuCjuwsIkwd21q9rGmD697Ovv3Vs6LxHFRvr0UofL4eOP5XcJkcOpU2qcNq22MIiI4kXv3mpVf7JkQMuWwE8/SX3Kv/8GPvpIViYFBuqN06aYUCJ7eTyhBADvvuu63JvIG506BdSuDVy+LPPkyeUqSrFiWsMiNwgOllVnn3+ujpUsCWzfLlfOiOKqSxegfHkZh4bK1jcbNF0hN9m0SY3LldMXBxGRu61dKzUEHVasABYsANq2lQuzFO+YUCJ7qVHjyc5u588DQ4boiYcoOi5ckGTSuXMyT5wYWL4cKBtlMwWykv375QPc+vXqWMuW8gGP2xjJXRIkkI6Bjjpra9fKCTWRabomlJjEJiK7CA11LW/Srp2sRCKPYkKJ7CVlyqivvn3++fMr/RPpcO2arKxztDINDJTOFHxDtL4lS4BKlYD//lPHRo0C5s8HkibVFxfZU6lSsiLX4YMPuDqXZPujo2Zb6tSutSaJiKxs+nTg8GEZJ08OTJigNx4fxYQS2Y/ztrfs2eWrabrWLiHyBqGh0u3r0CGZ+/vLst26dfXGRXFjmsDo0UCzZsDdu3IsWTJg0SJZLWkYWsMjGxs1CsicWcYXL8q/Q/JtzquTqlRht1Aisg/nrW7Dhqn3P/IovquQ/dSurcYJEqj2yXv2AFOmaAmJ6AmmCXTvLl0nAEky/Pgj0KSJ3rgobu7dA15/HRg6VB3LnRvYskUSTETxKUUK4NNP1fyrr7hKydc53mMAbncjIvuIjAR271bz1q21heLrmFAi+6lUSSWRTp8GevZU9w0b5rr9hEiXyZOB779X83HjgFat9MVDcXf6tGxV/OUXdezFF6X4dtGi+uIi3/Laa0ChQjK+cwf47ju98ZBeziuUuJWaiOzi+HG1CjxjRq5O0ogJJbKfhAmB6tXVPE8eoHhxGT94APTowe43pNfy5UCfPmreoQPQr5++eChubt0CBg8GXnjB9WrZu+9Kpz626SZPSpBA6ic5fPYZEBGhLx7S5+JF4MQJGSdKBJQpozceIiJ32blTjUuXZjkBjZhQInty3vb211/AN9+oXzQrV0pRXCIdzp8H2rdXSc0qVYAZM/hGaEUhIcCkSZK0HjsWuH9fjgcEyN/ptGkyJvK09u1VIvPUKWDxYp3RkC7Oq5PKl5cLbkREdvB4Qom0YUKJ7Mm53frp03Ii9c476ljPnsCNG56Pi3xbZCTwxhvA9esyz54d+O03nuRbTUQEMHMmUKCArDRz/H0CQMmSwIYNQJcuuqIjkm3f3bqpuXNdJfId3O5GRHbFhJLXYEKJ7ClVKjW+c0e+jhkDZM0q4ytXgP79PR4W+bgpU4A//5SxYQA//QRkyKA1JIoB0wSWLJEttJ07A2fPqvvy5AF+/hnYsQOoXFlfjEQO77yjVsht3gxs26Y3HvI8FuQmIjsyTdeEUqlS+mIhJpTIppInV2NHh5sUKWQLisM337iebBHFpz17gIED1XzAANdaX+TdNm2SK/zNmgEHD6rjGTIAn38OHDokHUbYkpu8RebMrl1vJk/WFwt53u3b8r4DyAWMSpX0xkNE5C6nT6udJqlSAbly6YzG5/HMl+wpRQo1dm6Z3Lw58PLLav7228DDh56Li3zTgwdAmzZAaKjMy5YFhg/XGhJF0759QJMmcnV/82Z1PFkyYMQIKXj7zjtAYKC+GImexrk49y+/uK6qI3vbulW2WQNAiRJAypR64yEichcW5PYqTCiRPTknlBxb3hymTZMPg4CsKpgwwXNxkW/q10+takmSBJgzhwkIb3f6tNS7KlEC+P13dTwgQGqwnTwJfPSR+l1C5I1KlgRq1pRxRISspiPf4LwCm/WTiMhOWD/JqzChRPaUMKGqHREa6roKKXt2YPRoNR8zBjh61LPxke9Yvtz1Q9zkyVLMmbzTtWuyqqNAAeCHH1Q3PsOQzllHj0otrPTptYZJFG3Oq5S+/hq4e1dfLOQ5zgW5WT+JiOzk33/VuEwZfXEQACaUyK4MI+o6Sg7vvqs6wT18KN1wHB8cidzlyhWgUyc1f/lldv/yVjdvyjbEPHkkYeTYnggAjRoBu3dLgon79MlqGjcG8uWT8a1bwKxZWsMhDzlwQI1z59YXBxGRO0VEuCaUKlbUFwsBYEKJ7OxpdZQAwM8PmDFDvgLA+vXA7Nmei43szzSlE9iVKzLPlAn49lvu8/Y2t2/LisXcuaUmkvMW2YoVgb/+ki1vxYvri5EoLhIkAHr1UvOpU1VtHbKvokXVeORIfXEQEbnT4cPqXC1TJiBnTr3xEBNKZGPPqqMESItJ55PsPn1kuwtRXF29CnTtCvzxhzo2axaQLp22kOgxd+8CH38siaShQ2WFkkPhwsCiRVKEm534yA7eeEM64QDA8eOudcHInj7+WF3A+P13YNkyvfEQEbnDli1qXLEiL9R6ASaUyL6eteXNYcQIldkODgZ6947/uMi+QkKkyHu+fLIayaFnT6B+fX1xkXL/PvDJJ5JIGjgQuH5d3Zc/P/DTT9LZrVkznqSQfSRLJkluh8mT9cVCnlG+PPDWW2r+/vvScZSIyMq2blVjbnfzCoZpg7oxZcuWNYOCgnSHQd6mYUNgxQoZL1smdSSisny51EhxWLsWqF07/uMj97tzB7hxQxI7Dx/KzTF2fE2QQFYKpUsnhZVTp5ZjcWGawPz5wIAB0h3MWaNGwMKFQKJEcXsNipsHD6Qg8ccfA5cvu96XJ490bGvbFvD31xMfUXw7e1YSqRERMt+5U1bqkn0FB0uDAUfi/KOP5EIaEZFVFS2qasT99RdXknuIYRg7TNMsG9V9PHMm+3pWDSVnDRsCr70GLFgg827dgL17gcSJ4zc+iruHD4F//gHWrJHbzp0xL67u5wekTSvJJeebI+GUPr3cHxYm26Tu3ZOvzrctW4Bt21yft0ABYOJEoEkTrnTxpLAwSeqdPCm3Eyfk6+bNwKVLro/NmVO2u3XooLpCEtlV9uzyXjd3rswnT5ZC82RfadNKEt2xOm38eOlW6SjSTkRkJbduAQcPytjPjx3evAQTSmRfz6uh5GzqVGDVKvlFdfw4MGaMFOol7xIRAezZA6xbJyvJ/v477kv4IyKkcLajeHZcpU0r3cLefptJivhy48aTCSPH1zNnnl9wOFs2YMgQ6cAXGOiZmIm8wQcfqITSvHmSYMicWW9MFL/efFO2YG/bJhdh3n9f6vvxQgcRWc22berCcfHiQNKkeuMhAEwokZ1Fp4aSQ6ZMcmLdrZvMx48HWrcGihSJv/jo+UxTrkSsWye3DRtciyc/zs9PPhwlSgQkTBj11/BwKZrtuD3v30Z0BQZKraRBg1TxW4od05S/m4MHgWPHnkwe3bgRu+fNnBkYPFjqiiRM6N6YiaygXDmgShVZ2RkWJs0CBg7UHRXFpwQJgC++kJpKpimlAJYskTpxRERW4lw/qVIlfXGQCyaUyL6iu+XNoUsX4Mcf5UQ7PFyWiG/cGPf6OhQ99+5JjY/QUODff1US6Xkrh/LnB+rWlduLLwIpU8bsdR8+lO5+V6+qr4/fgoMlAZEsWdS3lCmBOnWAHDli//OTrDgbNQrYtUv+zGMrWzapi5Q3r3x1jEuV4ookorfflvc5AFi6lAklX1C2rPy9f/WVzHv2BOrVA5Ik0RsXEVFMPN7hjbwCE0pkXzFNKCVIIEV7S5WSK7ebN8u8e/f4i9HXmaYk7b79FvjlFymc/TyZMgG1aknyqE4dIFeuuMWQMCGQNavcSJ+vvwbefVeSuc+TOHHUCaM8eeTfAwugEz1dw4byfhcZKcn7K1eADBl0R0XxbcwYeZ8NDpatwWPHcms/EVmHabLDm5diQonsK6YJJUC2uPXrJydegNSbSJcOaNnS/fH5ssuXgdmzge++A44effZj06SR5NGLL0oiqVAh1n6wk/Bw4MMPgWnTXI8nSwa88IIUN8+bVyWM8uYFMmbkvwGi2EqbFqhcGdi0SU7Qly8HOnbUHRXFtzRpZDv/W2/JfOJEoFUr6ZhEROTtjh1TJQ/SpmVzAS/ChBLZV6ZMavzff9H/viFDgEWLpH7Lw4fSFaddO/nAy9o4cWOawLBhwLhxUa9EyZFDVp/kyyfJo1q1pOgetx3a082bwOuvA6tXq2OlSwM//yyJJCaNiOJHkyaSUAKAZcuYUPIVnTrJiuCtW2V7ee3a0uCiWDHdkRERPdvj2914jug1+CmN7OuFF9T4wIHof1+iRMCCBdJi2eGnn+SE688/Y96WnpQxY6RGjnMyKUUKKYYeFAScOgUcPgz8/rusWilZkskku7p0SQoqOieTWrSQLZAFC/JEgSg+NWmixqtXy8UTsj/H1n5HZ6QrV4CaNYGdO7WGRUT0XMePq3GpUvrioCfwkxrZV86cquDktWsxawtfpAiwdy/QoYM6du6c1OwpUkTawh886NZwbW/GDGDoUDUvW1Y6DF24AEyfDpQpwySCL+ndW5KHDh99BMyfzyKxRJ5QqJBsHwWAu3eBv/7SGw95TvHikkR0lAW4fl1WAzvXJiEi8jZ376oxd4x4FSaUyL4SJIj9KiVAflnNng0sXCi1BxwOHQJGjJDEUrFisuLmyBG3hGxbv/3mWty8dm3ZbvHGG+pKKfmOAweAuXPV/Kef5P8UV6MReYZhuK5SWrZMXyzkeZUry4rr1KllfuuWdEr9+2+9cRERPc29e2qcLJm+OOgJPHsneytSRI1ju6Lo1VdlO1br1k+unti/X1ZWFCoElCghW7qOHYt9vHb0119AmzbSUQiQlUiLFkl3NfJNw4erraONGgFt22oNh8gnNW6sxsuWcTu3rylbFli/XhqPAHL1/6WXpKYSEZG3cV6hxISSV2FCiezNOaEU0xVKznLnlkLBV69K292WLaV4tLO9e6Wgd4ECsrd33DjZJufLdu8GmjZV9Tny5wdWrACSJ9caFmm0e7es+nMYOVJbKEQ+rVo1te3p9Gm5QEK+pUQJuejjaGLy4IEkGpcv1xsXEdHjnBNK/BzhVZhQIntzV0LJIUkSKRy8YIHUZJo3D3jlFSnk7Wz3bmDQICBXLkk+7d4d99e2mhMn5Grn7dsyz5xZ6jakT683LtLro4/U+JVXpKsbEXleYKD8jnb4/Xd9sZA+L7wgW92yZZP5w4dAs2aykpiIyFtcvKjGLJfhVZhQInt7PKHkziX9yZJJy/Nff5Xk0s8/y0mY81auiAhZjVGpErBhg/te29tdvgzUry9fASBlSmDlSkmwke/atk3VajEMqZtERPqwjhIBsnr477/Ve3RYmFwMmzdPa1hERACkA3BQkIz9/IDChfXGQy6YUCJ7y5FDrR4KDgY2b46f10meXGosLVokyaUffwRefFHdHxIiy8i3bImf1/cmt28DDRrICiVA/vyXLZPOMuTbnLv8vf46ULSovliISH5XO4rhb90as26oZC+5c0tSKX9+mUdESH272bP1xkVENGyYGrdvD2TJoi8WegITSmRvhgGEhqr5jBnx/5opUgDt2gHr1gG7dqlfevfuyfaCHTviPwZdQkJkldauXTL385NW8NWqaQ2LvMDGjbLlEZAPsMOHaw2HiACkTSsdvwBZwcvaOb4te3apqeTokBsZCXTsCHz9tdawiMiH/fWXNBAA5HPFkCF646EnMKFE9ufcmW3fPs++dsmS0prXUTfo9m2gXj37FT8NCQFmzQLKl1e/9AFJ4DVtqi0s8hKm6bo6qX17oGBBffEQkeK87Y11lChzZtmiX6KEOtatm7ogQETkKeHhQM+eat6xI5A3r7ZwKGpMKJH9pUmjxrduef71CxWSNrypU8v8+nXZZnD/vudjcbeLF6XIco4cQKdOrgm7ceOAzp31xUbeY906ucIEAP7+roW5iUgv54TSqlWqKyf5rvTp5fd22bLq2A8/6IuHiHzT558De/bIOHFi14uT5DWYUCL7y5NHjc+ckba4nla8uFzdc7RoPncO+PZbz8fhLkFBssokZ05g1Cjg6lV1X5IkwNixQP/++uIj7/H46qTOnV3/TxKRXoUKqSu+d++q5C/5tjRpgKlT1dwXu9USkT7nz7ueP370kXzuIK/DhBLZn5+fGoeHx19h7ucpWxYYM0bNJ0yQQuFWER4OLFgAVKkClCsH/PSTdIJxyJ4dGD8eOHsWGDhQ6lcRrVihitEHBnLvO5G3MQxpGuHAbm/kUKyYei8/fFjPBTki8k0ffigXOQDp6vbhh3rjoadiQonszznpAcgybl3efBPImFHG588DFSoAhw7piyc6rl6VRFGePNKZ6/GEXJUqwC+/ACdPAv36uW4xJN/2+Oqkt9+WxCMReZfH6yiZpr5YyHskTw7kyyfjiAjgwAG98RCRb1i9Wi5iO3z5pVyUJK/EhBLZn3OXN0BvQilxYuCTT9T8xAmgYkVg5Up9MUUlPFy6/bRoAWTNCgwYICuPHAICpJPd9u3Apk3yOH9/ffGSd1q8GNi5U8aJEsnKNSLyPtWqqS3Zp04B33+vNRzyIqVKqTG3vRFRfAsJAd55R83btwdq1tQWDj0fE0pkf4+vUNq+Xbqt6dKuHfDbb6r73O3bQKNGUqtA91XhY8eAQYNkj3KjRsCvv7r++aVPLytOTp8GfvzRtWAnkbMTJ4A+fdT8nXekexAReZ/AQOC119S8a1dg6VJ98ZD3KFlSjZlQIqL49vHHwPHjMk6VCpg4UWs49HxMKJH9PZ5QiogADh7UE4tD8+bAP/+o7T+RkUCvXtKa9/F4PeG334Dq1YECBaQ724ULrvdXrChXrM+cAUaOZGKAnm31akk2njwp86RJWaSdyNt98olKHkRGSoLp77+1hkRegAklIvKU48cloeQwdqwqFUJeiwklsr/Ht7wBwJ07no/jcSVLAtu2SR0lhxkzgHr1PFuse8QI4NVXgY0bXY9nyCArTA4ckKLKnTrJtiWipzFN+VDaoAFw86YcS5gQmDVLVrcRkfdKmVK2Xzs6vj18KLWVHC2byTc5J5T27JFkIxFRfPjwQ3nvAaQBUNeueuOhaGFCiewvqhU/jq4BumXKBGzYALRtq45t2OC5Yt3Dh8vNwc8PaNpUat+cOyfLTF94If7jIOu7f1/+Hfftqz5wZM0qKxxatNAbGxFFT8aMssIwUyaZ374N1K8vW1jJN2XKJBeYADl34r8FIooPq1apLqOGIYW4nTt1k9diQonsL6qEkjesUHJIlEjqEY0dq455olj38OGyOsmhXj1JIi1ZArz8shTeJoqOEyeAqlWBuXPVsSpVgKAgoHx5fXERUczlySMn9ilTyvzyZXl/uHRJb1ykh2Fw2xsRxa+wMOCDD9S8UyfWabUQJpTI/qLa8uYtK5QcDEM6YHmiWPedO0C/fq7JpPr1JZHkuCpNFB2mCcycCZQoAezapY6//bZ0U+S/JyJrKl5crhQ7tjmfPAm89JLaykq+hQklIopP06ernRnJkwNjxuiNh2KECSWyP2/e8va4+CzWfeKEZP+zZXPtmFC/vmxxY30kiongYKBlS6BzZ+DePTkWEAB89ZXcAgP1xkdEcVOtGrBggdpysGePrF598EBvXOR5pUqpMRNKRORO164Bw4ap+dChvCBpMUwokf15a1Hup3lWsW7HB/foMk1g7Vqpi5Q/PzBliqx8cmAyiWJj7VpZwfDrr+pYwYJSvP3tt/XFRUTu1aQJ8N13av7330CrVkB4uL6YyPO4QomI4suwYWr1a758wPvvaw2HYo4JJbI/K61Qcnhase7Ro6P3/eHhwLffAkWLAnXrytYF521zBQsCX3zhuqWB6HkePgR695Z/UxcuqOPdugE7dwJlyuiLjYjixxtvuK5qXbpUOu+4cys2ebf8+YHEiWV84QJw5YreeIjIHg4elFXtDp9+Kt2ByVKYUCL78/ai3E/jKNbtvAz0yy+BW7ee/X0rVsjqkS5d5Be1swYNpND3wYNAjx4svE3Rd+CAFNj+9FN1LH16+XA5fbqq/UVE9tOnj9Tec5g5U+r+kW/w85PzCgeuUiIid/jsM9UZuG5doHFjvfFQrDChRPYWGQlERDx53NtXKDkYBvDRR7KiCJDtas6ZfGf370t9i4YNVWE7AEiWDHjvPeDIEWD5ctnmloD/9SmaTBOYNk26bezdq46/9JLMmzTRFxsRec7HH0vnHYfx44FfftEXD3kWt70RkTvdugX89JOaDx0qn3vIcvipkuzNeXWSo7AoYI0VSg4JErheGZ48GQgJcX1MZKRsS1i6VB1LnhwYOxY4d06uABQo4Jl4yT4uXZIE5fvvq39ziRJJgmn5chZNJPIlhiH1/Jo2VccWLNAXD3lW/vxqPHOmvjiIyB5++EHVhi1aFKhaVW88FGtMKJG9OSeU/P3V2CorlBzatgWyZpXx5cuuGX0AGD4cWLhQzd98Ezh2TLYkpEzpsTDJRpYvB4oVky2SDiVKAEFBwLvv8ioSkS/y95f6SQ737+uLhTzL+Vzi8GFg82Z9sRCRtZmmlPFw6NGD55UWxoQS2ZtzhzfnekFRdX7zZgkTAj17qvnvv6vxsmXAqFFq/v77UpA7Y0bPxUf2YZpSJ6lxY2nl6tCnD/Dvv0CRIvpiIyL9AgPVOKoahWQ/pgnMnet6rHBhPbEQkfVt2CCJaUB2VLRrpzUcihsmlMjenrZCyVEAzkoaNVLjjRvlZzBNYNAgdbx+fWDSJM/HRvYQFiYd23r3Vh2csmYF1q6VLk/svEFEVr44Q7Hzyy/AunVqHhCgur4REcWUcz3YDh0kqUSW5f/8hxBZ2NMSSlEV6vZ2hQtLV62rV4Hr16Xr1pkzwP79cn+yZMCcOa4/J1F03bgBtGwJ/PmnOla5MrBoEZAhg764iMi7OK9QYkLJ/m7ckJXPzsLCeIGBiGInNFTKKji8/ba+WMgtuEKJ7O1pW96suELJMIDq1dX8r7+k645D165A2rSej4us78QJSR45J5PatJE5k0lE5IwJJd/St6/Ubnzc481BiIiiY8sWVcs2d24pyE2WxoQS2ZvzCiXnhJIVVygBQI0aavzrr8CmTTIOCAA++EBPTGRtGzcCFSqovewAMGKEFH5PlEhfXETknZhQ8h0bNgDffafmSZKo8YMHHg+HiGxg1So1rl+fxbhtgAklsjc71VACXBNKW7aocfv2QLZsno+HrO3HH4E6dYDgYJknTCiFVz/6iG/wRBQ1JpR8w4MHrh39XnnFtdMbE0pEFBvO3YNfeklfHOQ2TCiRvT1ty5tVVygVLQqkSSPjhw/V8cfrGxA9z5dfSiFEx/+RDBnkanSrVlrDIiIvxy5vvmH0aODYMRmnTAlMm+ZaiPv+fT1xEZF1Xb4M7NolY39/4MUX9cZDbsGEEtmb3ba8JUgAVKvmeix7dqB4cT3xkDXt2AH06qXmRYoA//4LVKyoLSQisgh2ebO/vXuBCRPUfPx4IEsW14QSVygRUUz9+qsaV6kCpEihLxZyGyaUyN6ellCy6pY3wHXbGwA0aMDtSRR9d+7IKiTH/43SpYF//gFy5dIaFhFZBLe82VtEBNClCxAeLvNq1WQOsIYSEcVeRAQwZYqav/KKtlDIvZhQIntzTig5nwRbdYUS8GRCifuPKSbeew84flzGyZIB8+e71sUgInoWJpTs7YsvgG3bZBwYCMyYIaujAW55I6LYW7ZMbaNNlQro1ElrOOQ+TCiRvTmf7DqfCF28aN2k0uNt3LmyhKJrzhxg9mw1nz4dyJdPXzxEZD1MKNnXmTPAoEFqPngwUKiQmnPLGxHF1qRJavz220Dy5PpiIbdiQonszXmFUpIkQKZMMg4JAU6e1BNTXPn7A/nzy4ldtmyS5Sd6nhMngG7d1Lx9e6BdO33xEJE1MaFkT6YJ9OgB3Lsn8xdeAAYMcH0ME0pEFBvr1wObNsk4IEBWy5Nt+D//IUQW9viWtyJFgEuXZH7ggCRmrCZTJuDQIcDPT07mnU/uiaISGgq0bg3cvSvzfPlkWwMRUUz5O506hodLIoJ1/KxvwQLgjz9kbBjAN988eX7BGkpEFFMXLwJt2qh569ZA1qz64iG34wolsjfnq6cBAZJQcjhwwPPxuIufn3xlMomiY8gQYPt2GQcEAPPmcakxEcWOYbg2uXC+cEPWdP068P77at69O1C58pOPYw0lIoqJsDDg9dfVxfx06YDRo/XGRG7HhBLZW1QrlBysnFAiiq7Vq4GJE9V83DigTBl98RCRtZmmKtIMMKFkB337AleuyDhrVnmfiAq3vBFRTAwYAGzcKOMECeSCZvbsemMit2NCiezN+UTXTiuUiKLj6FGgbVs1f+kl4IMP9MVDRNZ34wbw8KGMkyVz3QZF1rN+PfD992r+xRdAihRRP5Zb3ogouhYsAD79VM1HjwZq19YXD8UbJpTI3s6eVeMkSVwTSocP88oq2dfZs0DdusC1azLPmBGYNct1ZQERUUydOqXGuXKxfpKVPXgAdO2q5q++Crz88tMfzy1vRBQdp04Bb76p5i+/DPTvry0cil/8ZEH2tnixGlerJh3RsmSReWgoULUqsGOHjsiI4s+1a0C9etICGpBk6qJFklQiIoqLxxNKZF2jRgHHj8s4ZUpg2rRnP55b3ogoOnr1cm0EM3s2L2jaGP9myb5OnQJ27ZJxQADQoIGMHV8BYNs2oFw54J13ZBk/kdXduQM0bCgr8AD5t//bb0ClSnrjIiJ7cE4o5cypLQyKo717XevrTZgAZM787O9xTijxnImIorJ8ObBkiZr/+KMkrMm2mFAi+3JenVS7tvpl9vnnwNChqkOaaQJffgkULChbgiIjPR0pkXs8fAg0b646uhmGvJHXr683LiKyD65Qsr6ICOCtt4DwcJlXqybz53HuDvrdd9INzrEKgYgo5H/s3Xd4FNXXB/DvJCH03ntHpEkJIKJUEaUjSBHkR1GKdEUQUaQIUqUJSJUmHRFBQVAQEKQXQXrvvbeQMu8fx3nvTgiQsrt3y/fzPHmcOxuTAyS7s2fOPeeRfWJkmzbAyy/ri4fcggkl8k3h4dJY0lK/vjpOlAgYMADYv9/+RvvqVaBVK6BCBblzR+RNwsOBd98F/vhDnZs4Uca1EhE5CxNK3u/bb9WNh+BgYPLkmG1HqV4dyJRJrb/7DihWDNiwwTVxEpF3GTECOH5cjlOlevrESPIpTCiRb5ozR/UFSJUKaNToyc/Jnx9YuRJYssQ+wnLTJqBkSdn/e/u2O6Ilih/TBNq3l61tlkGDgHbt9MVERL6JCSXvdvo00KePWn/+OVCwYMz+3yxZgL17gXr11LmTJ4FKlYCPPmJfJSJ/duqUXHtaBg0C0qfXFg65DxNK5HvCwqQCyfLxx5JUio5hAG+/DRw8KNMHgoLkfEQEMGYMULasmpJF5Kl69ZLtB5aPPgJ699YXDxH5JtNkQsmbmSbw4YfA/fuyLlw49pOXMmSQmxeOfVFMExg1CihRQnpTEpH/GTdOtrwB8lzAm5p+wzBNU3cM8RYSEmLu2LFDdxjkKaZOBT74QI7TpJG7ZylSxOz/PXgQ6NQJWLtWnXv1VeD334GECZ0fK1F8DR0KfPqpWrdsCUyfzlHeROR8N24AadPKcZIk0j+HzzXeY/58oGlTOTYMqciOz8CG8+el99KqVepcQICcy5ZNWgzE9SMoiD9bRN6kUiVg/Xo5/vFHe7sR8nqGYew0TTMk2seYUCKf8vixbGWzxqUPGRL7u2+mKW/IHRtUvvuubKPjxQ15kilTgLZt1bpePWDRIlVpR0TkTLt3y5ZwAChUCPj3X73xUMzduAG8+CJw5YqsO3aUXkrxZZpyI++jj5zboDtzZmDwYLlJQkSeL0MG6UcLyM18VrD6lGcllLjljXzL9OkqmZQ+vVwwxZZhyFSCYcPUublzgX79nBIikVMsXix9kyyVKwPz5jGZRESuw+1u3qtHD5VMyppVkjXOYBhSFb5vn1QoOMvFizIoZcgQSVoRkee6elUlk5IkAXLk0BsPuRXfeZDvePQI+Oorte7VC0iWLO5fr0cPmVQwaZKsBwyQC6fevbn9jfQxTWDkSNnmFhkp50qVAn76SbYJEBG5ChNK3mntWuD779V6woSYtwKIqVy5ZMrozz/LFN1Hj57+ERr67McfPFCvb717yxvV4cNjNomOiNzPsVq1UCH+rvoZJpTId0yZIvv5ASBjRqBDh/h9PcOQcvBTp4DffpNz/ftLFci33wLVqsXv6xPF1p07csfWcZrbCy/ItEJnvzkgIoqKCSXv8/ChfWt0w4ZAnTqu+V4BAbL12nEKXFzcvStfw+pn+c03MiBl6lQgQYJ4BklETueYUCpcWF8cpAXTh+QbHj60l2/37i0ll/EVFAQsXGhvWnnkCPDGG0DjxsDZs/H/HkQxsX8/EBJiTya9/LJccHMsKxG5w/796jhnTn1xUMwNGCDV1oBMZRs7Vm88MZE8OfDLLzKF1zJrlqwfPtQXFxFFL2qFEvkVJpTIN3z3HXDpkhxnyWK/GxdfKVIAGzfKOEzHKpCFC4HcuYGaNYEFC3iRQ67zww9A2bLA0aPqXOfOMk0jSxZ9cRGR/zh7Fli3To4NQxLa5Nn27pWtYpbhw6XZtTdIlEius6ypvQCwYgVQvTpw65a2sIgoGgcOqGNWKPkdJpTI+92/L00bLZ99BiRO7NzvERgIdOoEHD4MNG+uzkdEAL/+CjRpIhdpbdsCf/3FBpLkHKGh0li+eXPpKQFI5d3cuXKXOThYb3xE5D9mzlSvba+/zqarni4iQpIxERGyrlBBBo54k8BA6WPZu7c6t3GjNP+2biISkV4REcA//6g1E0p+hwkl8n4TJqjJJdmzA++/77rvlSkTMHu23KWtUMH+2O3b0sfptdeAfPmk39KJE66LhXzb2bNAxYry82154QVg2zagaVN9cRGR/4mMtDd1bt1aXywUM+PGAdu3y3HChMDkyd7ZKNcwpKXByJHq3N69QPnyvMYi8gSbNgE3b8px5sy82eCHvPCVhcjB3bvA0KFq/fnn7pnAVqmSbDc6cUISR3nz2h8/cQLo10/OV6ggjSTDwlwfF/mGP/4ASpQAtm5V5955R94c8M4PEbnbhg3qzXuqVPFvukyudfq0XA9ZPv9cbkh4s48+kiq5wEBZnzghSaW9e/XGReTvfvpJHdet652Ja4oX/ouTd/v2W+D6dTnOlQto2dK93z93bqBvX+lts2mTbHlLmdL+ORs3Stl5jRpAeLh74yPvc/o0ULu2+rkOCgJGjZI+XcmT642NiPzT9OnquFkz6W9Dnsk0Zcrt/fuyLlIE6NlTb0zO0qIFsHSp+vm7dAmoWlVeN4nI/UxTfict9evri4W0YUKJvNedO/Zmk198oa+njGEAr7wie/0vXZJGkjVrqjtpAPD778Cnn+qJj7zH+PGqwXvmzLK9sls3+RkjInK327eBxYvVmtvdPNv8+cDKlXJsGLIV35f67dWuDaxerYakXL8ONGwIPHqkNy4if7R3L3DqlBynTCk7OMjvMKFE3mv0aLVnN29e4L33tIbz/xIlku1JK1YA58/LNC7LyJFyseepIiOBM2eAP/+U8fR79/IizZ3u35eLf8vkycCrr+qLh4jIcYrpSy/JdlzyTNevA127qnXHjr45je+112QgSlCQrHfssP+5icg9HKuTatb0reQ1xRgTSuSdzp2z907q2xdIkEBfPE+TMaMkvurUUefatAH27dMW0hMePACWLZO4MmcGcuYEKlcGGjQAihcHkiYFChQAPv7YPhaUnG/OHDUOOW9e2SZJRKST43a31q1ZLenJevQArl6V42zZgEGD9MbjSuXL2xt1T55sbxxPRK7H7W4EwDB9YLx5SEiIuWPHDt1hkDs1ayaj0wHpD7B7t7pT5Ylu3wZKl5ZeS4AkC3bskOamOly+LBVUy5YBa9bErgrpgw9kax/fVDjP48fyotyzp1SIAZKI5B1XItLp33/lNRaQO88XLgBp0+qNiaL3xx/A66+r9c8/y/YwX2aacj04b56sEyYENm8GSpbUGxeRPzh+XKZaA/K7d+0akCyZ3pjIZQzD2GmaZkh0j7FCibzPX3+pZBIAjB3r2ckkQPYVL10q1T6APAk3by5bzNwpMlKaWmbODLz/PrB8+ZPJpNSppUT+zTcl8RU1cTRlCvDNN+6L2ZedOCF9tbJnB5o0UcmkZMnc32CeiCgqx4qPunWZTPJUDx8C7dqp9Tvv+H4yCVA9oqykZ2ioVFffuKE3LiJ/4DjdrVo1JpP8GBNK5F0iIuw9id55R7ZneYPChe0X57/8AgwY4N4Y1q0DZs+Wu3qOXnxREhubN8sdhr//lqaex45JX5+VK4E33lCf36eP9Iei2AsLk/5U1atLwm7oUODKFfV4YCAwYcKT0wKJiNwpLAyYNUut2Yzbc/XvLzeqAKl8HjtWazhulTQpsGSJatJ96pSeG3ZE/mbFCnXM7W5+LUYJJcMwUhmGsdgwjEOGYRw0DKNclMcrGYZx2zCMPf999I3yeKBhGLsNw1jhcO4dwzD+NQwj0jCMEIfz1QzD2GkYxr7//lslvn9I8iFbtgB79shx4sT2KW/e4J137ON7+/e3PyG7mmND8MKFgREjgCNHpDfS118D5coBAVGeFhInlmql5ctVGXloqHw+xdyZMzKJMGdOuYO6erX98WzZgH795GLYUxrME5H/+uUX1Y8na1a5A02eZ88eeS23DB8OZMqkLRwtChSwJz9XrgQGDtQXD5GvCw8Htm1T6+rV9cVC2sW0QmkMgFWmaRYE8BKAg9F8zkbTNIv/9xG17KJrNP/PfgBvA9gQ5fw1ALVN0ywK4H8AZscwRvIHjlUxb74pb869zaBBQNWqat28uUwrcTWrMsYyaZI02s6fP2b/f3AwMHiwWk+ZApw969wYfU1EhCTiatUCcucGvvoKuHhRPW4Y0nh72TLg5Engyy8lsUREpJtjM+6WLaV6kjxLRIT0NYyIkHXFijJgwx/VrSuV1pb+/d1zbUXkj/79V4b6AHLdmjWr3nhIq+cmlAzDSAGgAoBpAGCa5mPTNG/F9BsYhpENQE0AUx3Pm6Z50DTNw1E/3zTN3aZpXvhv+S+ARIZhJIzp9yMfd/u2Ok6TRl8c8REUJJVCOXLI+vZtGbXZvLlsN3OV339XfQWyZ5dqpNh64w3glVfk+PFje4KJlPPnZTtj7twy4e+XX+zl95kyybbBEyfksTp1PL8PGBH5j4sX7W/G2dPNM40dKwM+AGmKO3myfw/MGDhQ3bCzGnZzez6R823dqo7LltUXB3mEmFQo5QFwFcD3/21bm2oYRtJoPq+cYRh7DcNYaRhGYYfzowH0BBCXzcwNAOw2TTM0Dv8v+SJrpDrg3T1m0qWTZnbp06tzP/wgvYzmzXuyx5EzLFigjhs1enJrW0wYhtz1s0ybBpw+Hf/YfEFkJLBqlewjz5lTqo2iVnBVqwYsXizb3776CsiVS0uoRETPNGuWverFmuRDnuPUKeDzz9X6iy9k65c/CwqSayir0vfWLff3qiTyB0wokYOYvKMMAlASwETTNEsAuA/g0yifswtATtM0XwIwDsBPAGAYRi0AV0zT3BnbwP5LSg0F0O4pj7c1DGOHYRg7rlp7/Mn3OVYoeXNCCQBKlJDeRc2aqXPXrgHvvisVK+fOOe97hYbapzE0bhz3r1W1KvDqq3IcFiZb+PzZgwfAuHHyhuutt+Tv2XojBkjysGdP4OhR6ZvUoAGQIIG2cImInsk05WaBhc24PcO1a9IbaOJEoHdvoF49teWkSBHgk0+0hucx0qe3b9ecNk0GjBCRc5imDPGxMKHk92KSUDoH4JxpmlYqcjEkwfT/TNO8Y5rmvf+OfwWQwDCMdADKA6hjGMYpAPMBVDEMY87zvuF/2+SWAmhhmubx6D7HNM3JpmmGmKYZkt6xyoN8my8llABJNsyZI9ueHHvnrFgBFCokfY6cMankt9/U312ePEBIyLM//1kMw37H7/vvpf+Pv7l+Xf4ecuYEunR58u+gYkW5U3runExy4x1+IvIGGzdKAhyQyVkNG+qNx189fAisWSM3JEqWlERJjRrAhx8CQ4YAe/fK5xkGMHWq9DkkUa0aUOW/mT4REUDfvs/+fCKKuW3bgEOH5DhRIqBUKb3xkHbPTSiZpnkJwFnDMF7471RVAAccP8cwjEyGIZu2DcMo89/XvW6aZm/TNLOZppkLQBMAa03TbP6s72cYRioAvwDobZrmplj+ecjX+VpCyVKjhjS4+/BDde7uXaB9e7kosi7u48pxu1vjxvHvsVC5siRMAJn04E9VSmfPAt27q21tjn2vUqcGunUDDh4E/vwTaNJE+loQEXmLqQ4tL5s1A5Ik0ReLP4mIAHbulGTR66/L68kbb8jUtt27n/7/9ejBCoHoOF6XzJunEnBEFD8TJqjjpk2BpNF1wiF/Ypgx6NViGEZxSFPtYAAnALQC0BgATNP8zjCMTgA6AAgH8BDAR6Zpbo7yNSoB6GGaZq3/1vUh2+PSA7gFYI9pmtUNw/gcQG8Aju+g3zBN88rT4gsJCTF3WE0JybfVqSNTswBg6VIp+fY1GzbI1JYjR9S5RImkd9FHH8WteXPGjMCV/36Fdu8GihePf5zr1wOVKslxYCBw+DCQN2/8v66nOnAAGDZMel2Fh9sfy5lTJua1bs0XViLyXrduAZkzA48eyXrnTqmOIdc4cUIGZqxZA6xdqwZnRCcwEChTRnot5swpgz0KFpRkkj834n6WunWBn3+W49q11TERxc25c7LTISxM1tu3x2/XA3kNwzB2mqYZ7T92jBJKno4JJT9SsaIkXAC5+KpcWW88rvLokWynGjbM3o+nRAmZ4hLbJ++ECWUqGyBl9IkSOSfOKlWAdevkuGVL2f7mazZvljvGViLTUdGish2hcWP2RSIi7zdhAtCxoxyXKAHs2qU3Hl9z/bq8Zq5ZI4mkEyee/fkvvijbt15/Xa5/UqRwT5y+4p9/5Aaa9V5n8+a4TbglItG9OzB6tBy/8gqwiZuJ/AUTSuQ7ihdXZcv+cOd0926petmzR50LCJCePQMHAsmSPf9rRESoqibDkLWz7mZu3AhUqCDHgYGyp9oXegWZpozMHjIE+OuvJx+vUAHo1UuacPPOMBH5ipIl1faq8ePt27Apbg4cAGbPliTSrl3PnuKaKZMkj6yPrFndF6evatYMmDtXjitVkpuRfN0mir2rV2U6sTUM4JdfpGUH+YVnJZTiMDecSCNf7aH0NCVKSPO7r79WVUWRkXJ3oHBheTJ/HmvrAgAkTuzcC6nXXpOLXkASVQMHOu9r6xAWJhf+xYoBtWo9mUyqW1fucK5fLy+ivCglIl+xa5dKJiVKJBNHKX4mT5YbYUOGyE2wqMmkpEnltWTUKGDfPuDCBXkN+t//mExylv791U21P/+UyjAiir0xY1Qy6aWX5KYqEZhQIm9y7Rpw+rQcBwRIXyB/kCAB8OmnwP79KnkDAGfOSNKjcWPg0qWn///Wkz8gCSVn699fHc+ZI72UvM39+8DYsVJd1aKF/F1bgoJkO9+//wI//cRyeSLyTY7NuN95B0iVSlsoXu/RI+mF2K6d6jUCyLXLyy8DX3whNyZu3JAbQ926AUWK8CaFK+TLB7Rpo9afffbsKjEietLt28C336r1Z5/x+Yr+HxNK5D1+/11dBLz8csy2e/mSvHmB1auBWbOAdOnU+YULpVrpaZPgHj5Ux65IKL3yClC9uhxHRnpXldL165IQy5kT6NpVknSWpEllr/iJE9IbqlAhfXESEbnSgwcycMDy/vv6YvF2Z89KvyPHBN1LL8kgkRs3gL//lh6JFSoAwcH64vQnX3yhJq7u2CE3h4go5iZOVLtEChQAGjTQGw95FCaUyHv89ps6fuMNfXHoZBjAe+/JWPr//U+dv3FDLlCj4+qEEmCvUpo3T+LzZKdPyx3hHDmAfv0ksWRJl06SYmfOAN98A2TPritKIiL3WLwYuHNHjvPnl+3MFHvr18vQjG3b1LlmzWSrdL16/rFV3xNlzQp06qTWn39uH3hCRE/34IFcD1s+/VT6phL9hwkl8g6mKdU5Fqsixl+lSwfMmAEsW6bOLVpkT4xYHLe8JUnimnjKllV7qSMjn57c0sm6A1+tGpA7t30vOCCNBr/9VpJNn38OpEmjLVQiIrdyrKZ5/31uZYgt0wTGjZNt6VeuyLmgINlKPXu26157KeY+/RRInlyODxyQLfpE9HzTpklDbkBusjZrpjce8jhMKJF3sJpVAkDq1EDp0nrj8RR16sjdUAAIDZXtcFE5Vii58qK2Xz91vGCB9BzSzTRlEl2bNjI9p3lz+9ZJQBpw//CDbBns2JEX/kTkXw4fludJQJIgLVrojcfbPHoEtGol01fDw+VchgzAH38AnTszOecp0qWTymTL8uXaQiHyGo8fA8OHq/Unn3CrLj2BCSXyDoMGqeNq1Vhq6ahtW3U8efKTzSZd3ZTbUqaMNAkHJAadVUqnTsn3z5dP+lRMnw7cvaseNwz5Ofr1V2DPHplmZE2BISLyJxMnquNatST5TjFz7py8xsycqc6VLi0T3SpU0BcXRc/xGojbD4meb8YM6QsHAOnT2xvcE/2HCSXyfH/9JY2nLV266IvFEzVtqsq4Dx1Sd5ot7uihZHGsUlq4UCrL3OXePXnhq1RJtrR9+aU01HZUoIAkJ0+fli2Ub73Fu8dE5L+uXJEbEZZ27fTF4m02bgRKlQK2b1fnWrUCNmwAsmXTFxc9neP1EfuEET3bo0f2QTs9erCKn6LFhBJ5tshIe4ly48ZA+fLawvFIyZLZ9zNPmmR/3B09lCylSsk2PItjs25XiIwE1q6VBuUZM8rF/Pr19s9JmRJo314m6xw6JKNO2WibiEgarVo3HUqUYH/CmDBNYPx4oEoVe7+kb7+VXiOJEumNj6IXEQFs2qTWTCgRPdvkyVKFCcg1dseOeuMhj8U9HuTZZs+W0nFARr4OHao3Hk/Vti3w3XdyvHixNJxOl07W7qxQAqRK6eef5XjJEmDvXhmZHFd37gAnT8o2Nuu/1vHJk/atbJaAAHlj1LKlJLh4gU9EZHfjhiRGLJ9/zorN53n0CPjwQ+D779W59OnldZdb3Dzbvn1qkmHmzECePHrjIfJk9+8Dgwer9WefAUmT6ouHPBoTSuS57t2TJzBLjx5Azpz64vFkJUpI34bt26WB3syZwMcfy2PurFCyYqlfH1i6VNZffinHsXmjcuCA/H9//AHcvBnz/69wYUkiNWsmF4xERBS9MWPkdRaQ58569bSG4/HOnQMaNAC2bVPnQkKAH39k1as32LBBHb/2GpOnRM8yZAhw+bIcZ8tm79dKFAW3vJHnGjZMTXbLlElGvtLTOfa+mDRJtoMB7q9QAuROt2XZMtm2aMXzNKYp1Uzt2gFFi8od35gkk9KlAzp1AnbskDuQPXowmURE9Cy3b8tIe0ufPlLZSdH76y9JHjkmk/73P0lSMJnkHRz7J7GajOjp9uyRhJKlb19W+tMzsUKJPNOZM/YxlV9/Lb2C6OmaNAE++khKuo8eBdaskW1f7q5QAoCSJaVZ+Lx5sh47VuKaMuXJaWrHjwOzZgELFsj46qgSJQJy5ZKP3LmfPE6XjncaiYhiY8IE4NYtOc6fH2jUSGs4Hss0ZTt5ly5AeLicCwwERo2SGxl87fEOpsmG3EQxERYGtG6tnu9efZWT3ei5mFAiz/Tpp9KrAJDkRIsWeuPxBkmTyovA6NGyHjtWEko6KpQAmbgWHg4sWqTWd+8CP/wg/bAAYNcuoFw52aYX1euvS8+sEiV40U5E5Cz370szbstnn0mShOxCQ6UJ7bRp6ly6dPKaVqmStrAoDo4dU9t3UqUCihTRGg6RxxoxAti9W44TJZLnP1av0nPwJ4Q8z+bNqrIFkAQJn8xipmNHlXz59VepVNKVUAoOln9HxzsbS5YAdevKGxoAmDvXnkxKmlQqrVavlo+SJZlMIiJypkmTgGvX5DhnTvuUUBLnzwMVK9qTSSVLypAQJpO8j2N1UvnyvKYkis7BgzJYxzJgAFCggLZwyHvwGZU8S2Qk0L27Wr/zDkuTYyNfPqBGDbUeP17PljdLYKBsc3P8N/3tN6mcun0b+PNPdX7IEBnBPG8eUK0aE0lERM726JF9O/mnnwIJEuiLxxNt2iT9krZuVefee0/6KOXIoS8uiruoDbmJyG77drk2t27yhoTYr92JnoEJJfIsq1erppcJE8qWJ4qdzp3V8fffq2ogwL0VShbDAEaOtN/12LQJyJtX7vZa2rVzf8KLiMifTJsGXLokx1myyFRMUiZNAipXVn9HgYFSJT1zpp7XT3IONuQmerpp06RX0tmzsg4OBqZPf7LnKdFTMKFEnmX1anX8wQfSeJlip1o1NeXszh01KQ/Ql7AxDODLL6WRqeX6dXWcJo30NSAiItd49Mh+k6ZnT07usUREyE2N9u2lKS0g/ZLWrAG6dmXFrDe7cAE4cUKOEycGSpXSGw+RpwgNlee9999XlUmpUsl05qJFtYZG3oUJJfIs69er47fe0heHNwsIkDusFutOK6D/Dmu3bsCcOSrhZcmUSUs4RER+Y8wYdQc6fXq5aUNi2DBg8mS1LlEC2LHD/lpK3mnvXnUcEiLVF0T+7tw5qdZzfN4rWlSe9958U19c5JWYUCLPceuWmiwQECDllxQ3r7yijq3mq4BnbClr1kxeyObNA954Q6rQatXSHRURke+6dAkYNEitv/zSM14PPMGZM8DAgWr97rvSLylnTn0xkfOcOaOO8+bVFweRp/jzTxkyYLUYAeR57++/+TtCccLNkeQ5/voLME05LlECSJFCbzzerFw5dXz7tjrWXaFkCQiQaW5NmuiOhIjI933xBXD3rhwXKiTbHEh0766mob70kvRLYu8Q33H6tDpmkpD8mWlK64mePWWbLyB94kaOBLp04dZeijO+YpLn+PlndcyxvPFTrJjcfX7wQO2LBjwnoURERO6xZ480XbV88w0TJpbffgN+/FGtx4/n342vcaxQ4pQ+8me9e9v76GXIACxcCFSsqC8m8gnc8kaeYdMmYOpUtX7jDX2x+IKgIKBMmSfPc4sDEZH/ME3pXWdV/9aoIaOhSRrSOk5F/d//gPLl9cVDrsEKJSIZMOCYTHr5ZWDXLiaTyCmYUCL9Hj4EWrVSF7zVq8ukMoofxz5KFlYoERH5j59+UsMurK0NJEaOBI4eleOUKe1vtsh3sEKJ/N3160DLlmr95pvSRylrVl0RkY9hQon0++ILdVGXPDkwZQr38TqDYx8lCyuUiIj8Q2go0KOHWnfsCBQsqC8eT3LmDPDVV2o9cCCQMaO+eMg1wsOB8+fVOnt2fbEQ6WCaQNu2wIULsk6fHpgxA0iYUGtY5FuYUCK9Nm+Wfg6Wb77hC76zvPzyk+dYoURE5B/GjgVOnJDj1KllshuJqI24O3TQGw+5xoULqvlwpkxAokR64yFytxkz7H3ipk9n8pycjgkl0ufhQ6B1a7XVrVo1oE0bvTH5knTpgBdesJ/jxRQRke+7fFmqbiz9+wNp0uiLx5OsWsVG3P7CsX8St7uRv1m61J4sb98eqFVLXzzks5hQIn2+/BI4fFiOkyWTptzc6uZcpUur48BAIIC/8kREPq9vX+DuXTkuWFDeSNCTjbhbtmQjbl/m2D+JDbnJn0ycCDRsKM95AFCgADBihN6YyGfx3SXpsXWrvTnoiBG8e+QKefOqY6sSjIiIfNfevfapqd98AyRIoC8eTzJyJHDsmByzEbfvY4US+RvTlN60H34IREbKuXz5gJUrgaRJ9cZGPosJJXK/R49kqpv1RFelijSMI+e7dUsdR0YCp07pioSIiFzNNIFu3dTr65tvAm+9pTUkj3H6tL0R91dfARky6IuHXI8VSuRPzpyR91eOz3OlS0u/2jx59MVFPo8JJXK//v2BgwflOGlSYNo0bnVzlffft6+7dNETBxERud6yZTIOGpBtzo6VwP7OsRF38eLcBugPWKFEvu7OHeD774HKlSVpOnOmeuytt4C1a2WyG5ELMaFE7rV9OzBsmFoPHw7kyqUtHJ9XqJB9it7y5cAff+iLh4iIXCM0FOjRQ607dJDXAJLtHkuXqjUbcfsHViiRL4qMBFavBpo1k+mFrVurGwmWli3lBkOyZDoiJD/DhBK5T2ioPMFZpfiVKwPt2mkNyecFBMhd2ebN1bmOHVWzViIi8g3jxgHHj8txqlRAv346o/EcoaH26tyWLYFXXtEWDrnRuXPqOFs2fXEQOUNkJLB4MVCsGFC9OjB3rqq6BOSav3p1YOFCYPp09s4jt2FCidxnwADgwAE5tra6ceqYewwbpu5SHD4sdzUiIvTGREREznH3LvD112rdrx+QNq22cDzKiBFsxO2vUqRQxzdv6ouDKD5ME/jpJ6BECeCdd4B//7U/XqSI7Pg4exZYtUo+h61EyI34bp7cY8cO+0Xc0KFA7tz64vE3mTNLib9l+XLg88/1xUNERM4zcSJw44Yc584tE35IBlEMGqTWgwaxEbc/yZdPHVtJRSJvYZrAihVAqVJA/frAP/+ox5IlkwEMu3fL+R49gCxZtIVK/o0JJXK90FCZOmBVxFSsKL0dyL1atAA++USthwwB5szRFw8REcXfgwf25tu9e3Org4WNuP1b/vzqmAkl8hamKZVGZcsCtWtL0siSJAnw6afAyZPAqFHyvMZqJNKMCSVyvYkTgf375ThJEm510+nrr4FatdT6/feBLVv0xUNERPEzZQpw5YocZ8sG/O9/euPxFCtXyjYRy/jxMvmO/IdjhdLRo/riIIqpv/4CypeXCW3bt6vziRNLFdLJk3Itny6dvhiJouC7enKtx4/td04HDgTy5tUXj78LDAR++AEoXFjWoaFAvXqy75qIiLzLo0f2yam9egHBwfri8RSPHgGdO6t1q1ZsxO2PuOWNvIVpylTmihWBv/9W5xMmlK1tJ05InyRu2SUPxIQSuda8eWrKRoYM3OrmCVKkAH7+WTVsvXwZqFsXuH9fb1xERBQ7338PXLggx5kyAW3a6I3HU4wYYZ94N2SI1nBIE255I0/24IG0A3n0SJLeH3+sJmEHBwOdOkkiadQoeX4n8lBMKJHrREbaG3F37Solm6RfnjzAkiVAUJCsd++Wu7fbtumNi4iIYubxY3ui5JNP+BoLPNmI+6uveFffX+XJo45PngTCwvTFQgQA4eFy/f3aazLxOkkSIHt2YOZM9TmvvCJbNMeNY6Nt8gpMKJHrrFsHHDwox8mTc+qMp6lYUfpbWf75B3j5ZSmtvXdPW1hERBQDc+YAZ87Icbp0QLt2euPxFN26yR1/QMZssxG3/0qaFMiRQ47Dw4GNG/XGQ/7JNKWnW5kyMjChYUPplQTIjYFr19TntmoFrF2rfm6JvAATSuQ6v/6qjlu0kLJz8izvvw98+626q22awJgx0mPJ8d+PiIg8R3g4MHiwWn/8sbx59ne//gosW6bWbMRNdeuq43nz9MVB/mnXLuD114EaNexNtgH7dLbAQGD0aBlclDChW0Mkii8mlMh1fvtNHdeooS8OeraOHYF//wXeeEOdO3MGqFkTaNJEeiwREZHnmD9f9QhKnZoVwIBUJXXpotatWwPlyumLhzxD06bqePFiGUZC5GqnTwPvvQeUKiUVR47SpgX69JEes3fuyA6BU6ekNYhjkonISzChRK5x/rwkKQBpLFexot546Nly5wZWrQJmz7aPIl2wAChQQCb1PX6sLz4iIhIREfYeQV27yrAFfzd3Lhtx05NefhnIlUuOb92y3+wkcoUpU4AXXpBtyZbAQNl+e/IkcPWq9HbLkkVaghQtCmTLpi9eonhiQolcY/VqdfzqqyzF9waGATRvLn2vWrRQ5+/cAXr0AIoUAZYvl21xRESkx48/AocOyXHy5PaqHH9mDZkApALAmmRK/s0wpNrawm1v5GqffWavhKtbF9i/X/qW5srFKiTyOUwokWv8/rs6rl5dXxwUe+nSybSJ1avlDovl6FGgTh3gzTeBAwf0xUdE5K9M016d1LmzbHkjoEED1avx+HFpgksE2Le9/fwzcP++vljI91lDAQCpiPvpJ6BgQW3hELkaE0rkGqdOqeOyZbWFQfFQrRqwbx8wahSQMqU6v3o1UKyY9CIgIiL3+fNPYO9eOU6SBOjeXWs4HiVpUhk0YRk7Vl8s5FmKFgUKFZLjBw8kqUTkKo5NtUuU0BcHkZswoUSucfOmOrYmiJH3SZBARjAfPSp7vwP+e8qIiADatpV94ERE5B7ffquO//c/e887kiET1uvU6tWyhZvIMOxVStz2Rq6UKJE6dqxWIvJRhukD/VBCQkLMHTt26A6DHJUvD2zerNb588sdorx5pTw/VSr5sI7TpZPG0Bzv69n++Uf2glsVaC1ayPY4IiJyrTNn5HUyMlLW//6rqi5IqV9ftpgAQIcOwIQJWsMhD3H8OJAvnxwnSABcugSkSaM3JvJNefMCJ07I8dGj6ueOyIsZhrHTNM2Q6B4Liu4kUbyVKWNPKB09Kh/Pkjw5ULq0JJmCgtQHAGzZIln+wEDgl1+4F1mXYsWkqeBbb8l61iygZUugcmWtYRER+byJE1UyqWpVJpOepksXlVCaOVN6TrHPFOXNK9em27YBYWHAkiXABx/ojop8Ua1aUsGfMKFsTSbycaxQIte4fBn4/HNg40ZJJFkXwc6wZQv7MunWqBGwaJEcv/CC9PRw3DNORETO8/AhkD07cP26rJcuBerV0xqSxzJN4KWXpAcgAAwfLpNKiUaPVn3HKlcG1q7VGg4Rkbd4VoUSE0rkeg8fylSwffukxPjmTeDWLft/z56VJFRM7NolTe4iI4F792SsvfXx+DGQJw+QLZsr/0R04YJUid29K+uBAyWBSEREzjdjBtCqlRznzCnbd7hF/OmmTVMNunPmBI4dUxXP5L8uXgSyZpWkY2AgcPu2NHMnIqJnYkKJPJ9pAqdPS9IpNBQID5ePsDB5vH17SRYBQMaMMvL13r2nf72XXwaaNQMaNwbSp3d9/P5o3DjZWgBIddL+/dwnTkTkbKYJhITIzRQAGDoU6NlTb0yeLmpF1+LFQIMGemMiz1CkiPQfA4D164EKFfTGQ0TkBZhQIu9mmtJAMSIi9v9vYKBUz/Tu7fy4/F1EhGw93LlT1m+8AaxaJdNUiIjIOTZvlkEXgEwPOncOSJtWb0ze4PPPpX8SALz2GrBhg954yDO8/75UsAFMzhIRxdCzEkoB7g6GKNYMA6hZM/rHkiUDsmSR7VdlykiCw7GsPSIC6Nv32dVMFDeBgcB339lHNC9cqDcmIiJfM26cOn73XSaTYurDD9X1wMaNwO7deuMhz+DYg3PLFn1xEBH5CCaUyDssWSIj6/fvl9HJt27Jlri7d4Hz54GDB4GtW+Xi4OJFGROcMaP8v+Hh9olz5DwhIUDHjmrdrZv0JCAiovi7eFG2a1k6d9YXi7fJkgV45x21HjNGXyzkOV5+WR3//bdUwRMRUZwxoUTeISgIKFoUKFxY+iKkTPn0hqTp0gEdOsgkMsuff7olTL80cCCQObMcX7oEfPaZ3niIiHzFpElyUwQAXn0VKF5cazhep1s3dTxvXsyHf5DvKlRIqtsBuWY5e1ZvPEREXo4JJfJdFSuq4/Xr9cXh61KmtN/5nTABGDCAd/2IiOLj8WNJKFlYnRR7ZcqoipTHj2WbNvm3wED5ubBw2xsRUbwwoUS+y3Fyx9atsl2OXKNhQ3ufqy+/BDp1ilsjdSIikq1uly7JcZYsQP36euPxVl27quOJE2WSLPk3x21vTCgREcULE0rku9KnB15/XY4jIqTXD6tmXMMwZDtBtWrq3IQJQJMmvHgnIooLx2bc7dvLtFOKvQYNgKxZ5fjyZWDBAr3xkH6OCaWtW/XFQUTkA5hQIt82erSa8rJhA/DDD1rD8WnJkwMrVgBNm6pzixcDb73FRt1ERLGxcaOqnEiQAGjbVm883ixBAvvwiDFjeHPJ3zlOetu5U7ZDEhFRnDChRL6tcGF7U84ePWRCHLlGcDAwZ459i8G6dUClSmrrBhERPdvXX6vjFi3U1FKKm7ZtgUSJ5HjXLmDTJr3xkF4ZMgB58shxaCiwd6/eeIiIvBgTSuT7+va1l7v37as3Hl8XEACMGmV/Q7RnD/Daa8DNm9rCIiLyCnv3AitXyrFhAD176o3HF6RNCzRvrtajR2sLhTwE+ygRETkFE0rk+5InB775Rq0nTAD27dMXjz8wDODTT4Hp02WiCgAcOwZ88AG3GhARPcuQIeq4QQOgQAF9sfgSx8rZpUuB06f1xUL6MaFEROQUTCiRf3jnHaBKFTmOiJDxy0xsuF6rVsDcuWq9ZAkwe7a+eIiIPNnx48DChWr96af6YvE1RYoAVavKcWQkMH683nhIL8c+SkwoERHFGRNK5B8MQybmWA2616/npBd3adQI6NBBrXv2BO7c0RcPEZGnGj5ckh2ATM0sVUpvPL7GsUppyhTg/n19sZBexYsDCRPK8YkTwNWrWsMhIvJWTCiR/yhUSCqTLD17SrUSud6wYfY+VgMH6o2HiMjTXLwIfP+9WvfurS8WX1WzJpA3rxzfugXMmqU1HNIoOBgoWVKtt27VFwsRkRdjQon8y5dfAunSyfHZs8Aff+iNx18kSyZ33i2jR0tPJSIiEqNHq/HlZcrIdExyroAAoEsXtR47VlWEkf9hHyUionhjQon8S8qU9kkvc+boi8XfNGkik94AIDwc+O47vfEQEXmKW7eAiRPVundv2apNzteypQzrAIBDh4A1a7SGQxo5JpQ2b9YXBxGRF2NCifzPe++p4x9/ZA8FdzEM4LPP1HrmTCA0VF88RESeYsIE4O5dOX7xRaBOHb3x+LIUKYA2bdR69GhtoZBmr7yijtevl22nREQUK0wokf8pUUIu2AFJJv30k9Zw/Eq1akD27HJ87Rrw88964yEi0u3hQ3tSo1cv2ZpFruM4KGLVKuntR/4nWzagYkU5jozkFFoiojjgFQv5H8OwVykNHiwX9OR6gYFA69ZqPWWKvliIiDzB9OlqwlT27EDTpnrj8Qe//66OkyWTD/JPrVqp4++/B0xTXyxERF6ICSXyTy1aAEmSyPGBA/atWORarVqp3iBr1gBnzuiNh4hIl7Aw+8CCHj1k+hS5zs2bQN++at2nD5A0qb54SK+GDVVC8dAhNuf2VrduAePHS1KQ7RSI3IoJJfJPWbMCI0eq9ejRUql05Yq2kPxGzpxA2bJqfeiQvliIiHRasAA4fVqO06UD3n9fbzz+oH9/4Pp1Oc6dG+jWTWs4pFnSpEDjxmr9/ff6YqHYu3ULGDBAfpc7dZIq+AIFpE9nRITu6Ij8AhNK5L/atQNq1lTrPn0k0dSgAbByJV+IXCkwUB0nTqwvDiIiXW7eBAYOVOsuXVTlLLnGoUNSxWAZPhxIlEhfPOQZHLe9zZ/PYS3e4NYtSQ7nygV8+aWsLWfOyDTHYsWAZcu4jZHIxZhQIv9lGMC0aUC+fOpceLhMfqtRQ16kvvgCOHJEW4g+68EDdcw3UETkb27cAF5/Xb2+JEsmd9fJdUwT6NxZXucBoEIF4O239cZEnuGVV6SqBZBpiz/+qDceejrHRFK/fsDt2+qxfPmk0tNy4ABQr578+65f7944ifwIE0rk3zJmBPbskcSS4/hYADh3DvjqK+CFF2SL1rhx3BLnLEwoEZG/spJJu3bJ2jCkaiZ1ar1x+bofflDNuAMCZKu71c+P/Jth2KuUpk/XFwtF71mJpPz5ZULfwYPAiRPyuGOj/S1bgEqVgLfeAnbvdmvYRP6ACSWipEllz/WmTcC//wIff2y/wwEA27bJdoQsWaR6ae5clkQ7y+PHuiMgInIPK5lkvakxDGDqVBkUQa5z/TrQvbtad+kClCihLx7yPC1aSKIRAP78Ezh2TGs49J9btyRB9KxE0oEDQPPmQFAQkDy5bIE7cUL6ozkOOVi1CihZEhg2zK1/BCJfx4QSkaNChYARI4Dz54HFi4G6dYEECdTjERHSX6lZM6lu+vxzJpbiIk8edXz0qL44iIjc5fp1oGpVezJp2jS5oUGu1bMncO2aHGfLJk18iRxlyQK8+aZad+gAREbqi8ffOSaS+ve3J5IKFHgykRRV+vTAqFGyrbhlS5UsBOQ8ETkNE0pE0QkOlubcP/0EXLwITJwIvPqq/XPu3wcGDQJefBFYuJBN/2LjhRfU8eHD+uIgInKH69elMmnPHlkbhmyrcdxmQ66xfr19C9P48VLFQBTVZ5+pbZC//y6tDsi9rl2T/qVPSyTNmfPsRFJUOXPK5L4PP1Tnihd3dtREfo0JJaLnSZsWaN8e2LhRSmitvkqWs2dl5GyVKsC+ffri9CaOf39sek5EvuzaNalMippMatlSZ1T+ITRUJrpa3n4bqFNHXzy+7uFDYOdO+8Qtb1K+PNCrl1r36gXs368vHn9y/jzw0UeSAPrqq6cnkpo1s08KjonHj4EFC9S6QwfnxExEAJhQIoqd3LmBPn3kRW36dCBDBvXYn3/KXY/atYHly9UkGXoSK5SIyB9YyaS9e2VtGHK3nMkk9xgyRL3GJE8OjB2rNx5f9MYbcm2UOrX0pAwJAQoXBo4f1x1Z3PTvr/prhYZKJUxoqN6YfNmxY0DbtvIzNGqUfWhLfBNJlh9/BK5eleNs2aQXKhE5DRNKRHERECBbFQ4flqZ/1otcZCSwYoXcAc2VSxoDnjmjM1LPZI3nBeTvkNsFicjXWMmkf/6RtZVM+t//9MblLw4dAgYPVuvBg4GsWfXF46vOngVOnZKqJOu1/MIFedN+/brOyOImOFiSGIkSyXrvXtmCRc61f78kiV54AZgyBQgLU4+99JJUFMU3kWT57jt13LZtzLbKEVGMMaFEFB+pUskdlb177c0cASnfHTBAEks1awLLlrFqyZIlixrpeuuWunNEROQLoksmzZjBZJK7mKZsVbemiJYpw20urpIqVfTnjxwB6tUDHj1yZzTOUaiQfRLYiBFShU7xt22b/FwULSoTkx0bn5crJzdld+8GGjWKfyIJkKTU+vVyHBgItGkT/69JRDZMKBE5Q+HCMv3t6FHZc++4Fc40gV9/lRfQHDmAMWNYkWMY9iol9lEiIl9x9ar01HNMJs2cKWPJyT1mzLC/iZw82TlvTulJs2fL9rZr16TKZNEi9dhff0k1tzdOS+vYUbbzAXLN1qKF9/aG0s00gbVrZTBB2bJyg9VRtWqSsNu0SW7AWo3RnWHSJHVcr57c0CQip2JCiTxXZKRcFHpTVU++fNKz4exZuaiqVs3++MWLskXu7bftDQf9kWMfJatZLRGRN7t6VSqTrAENAQHArFnAe+/pjcufXL0K9Oih1h99JFtoyDXy5QPy5JEBJkFBQMOGwPDh6vH5871zy1hAgGxRTZNG1mfPAp066Y3J2xw4IJX6xYrJ8+Iff9gfr19fKpZWrwYqVnRuIgmQacwzZ6p1+/bO/fpEBIAJJfJUDx8CTZrIna2uXb2voic4WC6qVq+WhoO9ewMZM6rHf/pJGldad7D9UZky6njMGO9KHBIRRXX3rtxEcEwmzZwpTX3JfT76CLhxQ46tXobkXh9/bH/zPniwJGe8TZYsUt1m+eEHSZBR9ExTrmv79pVtg4ULy++f46S8wEB5Tty/X5plly7tungWLFA3b/Pnl8pRInI6JpTIM82cqcqmJ0wARo/WGk685M0rF1Nnzkh1kuXYMeDll+XutT9q1Ur1Xjh2DJg3T2s4RERxFhkp/ZGsaW5WZRKTSe61Zo00VLZMmCCTx8i9DAMYNw546y11rkMH4OBBfTHFVYMG9qmMHTpItRIJ05SeR336AAULSjXgwIFP/lsnSiQNsY8ckW2ShQu7PraJE9Vx+/byvExETsffLPJMbdsCjRur9ccfA0uX6ovHGYKDpYH3ggWqIfXDh/ImpH17/6vQSZlS7iRbBg70v78DIvINAwfaX6OmTJHpROQ+Dx/aG283bmxPaJB7BQXJ9U6OHLIODfXe6p4xY6TaDZA+Si1bemdfKGcxTWD7dukZmi8fULKk3DiN2g8zSRLgnXfk5+DqVelnlCePe2LcsUM+ACBhQg5EIHIhJpTIMwUESP+kV16RtWnKxfn27VrDcopGjWTP+IsvqnOTJnl3FVZcdemiqpSOHvXei00i8l9LlwL9+ql1t25A69a6ovFfX30lzaEBeV3xx9dUT5M8ufTJsXhrEiZFCqmqsSpc1q71v5+vyEhgyxa5wZs7t7QtGDYMOHHC/nnJkknLisWLgStXgIUL5brXupHqLuPHq+PGjaXHFxG5BBNK5LkSJZJJEHnzyvrhQ6B2beDUKa1hOcWLL0pSqVEjdW7UKJmQ4k9SprRvAxw4EIiI0BYOEVGs7Ntnb7hdtaq9ITG5x/799jHvQ4cCmTLpi4eUR4/UsWMvSW/z6qvAp5+qde/ewOnT+uJxlwMH5DotZ06gXDngm2+e/HOnSCHbe3/6SZJI8+bJVkFd202vX7ffoPzwQz1xEPkJJpTIs6VLB/z6q5qycfmyjBT1hdGtyZJJjw3rovfCBfu4XX/RtasklgApl2aVEhF5g+vXgbp1ZZIQIFs5FiyQrT7kPpGRQLt2ast0+fLA++/rjYmUkyfVcbp0+uJwhi+/lO1dAPD4MTB1qt54XCkiQhKzxYvLlr9z5+yPp0ol28iWL5ck0uzZ8nyYOLGOaO2mT1eJzFKl7ENgiMjpmFAiz1eggNz1CA6W9YEDMkHNF6p5Eia03zkZNcr7JtrFV6pUklSysEqJiDxdeLhso7DeLCdNKhW13FbhflOmAJs3y3GCBLKFnM139QsLk6boq1erc97+2h4cLM2nLd9/75u9H0+dAipXloosx2vtNGmANm2AlSvlBu+MGUCtWnIt6ykiIuzNuDt2lCbxROQyfMUl7/Daa3LHwfLHH9LI2heSL+3bqxfjHTvUhbE/6dZNSqYB4PBh2XNPROSpPvlEXocss2cDRYroi8dfXbwojYEtPXu6Z3oUPduWLUCxYvJm3lGBAnricabatYEMGeT4/Hlg1Sq98TiTaUqSqFgxYONGdb50aUkMXrokVVlvvqlu8nqaVatUoj9NGunnREQuxYQSeY9mzYABA9R6+nTg66/1xeMs6dPbR0uPGqUvFl1Sp2aVEhF5hxkz7A15+/WzNx4m9+neHbh9W47z5bNXj5A+DRoAhw6pdY4c0qS5dGl9MTlLggQy5c3iK9verl2T6v9WrYC7d+VcYKBs89u0CahWTf7sns6xGXfr1p6xBY/IxxmmD1R4hISEmDus0ZDk20xTXuxmzlTnGjQAChUCXngBKFhQ7oAlT64vxrjYvx8oWlSOAwJkUo01otZf3Lghf2brQmb5cimlJiLyFFu3AhUqSP8UQBJJixdzi5UOK1cCNWqo9e+/S1N00i9tWnlNB6SC7MsvfeuN/dGjqtoqMBA4exbInFlvTPGxcqUkXy5dUufy55fKy7Jl9cUVW8ePS9ymKdvcjh2T3nZEFG+GYew0TTMkusd4BUTexTCAyZNlb7dlyRKpaGneHAgJka1Tb71lvzvm6YoUkbs/gDQYHTdObzw6pEkj2/8sjklDIiLdLlyQBJKVTCpSRJ6nmExyv/v3gQ4d1Pq995hM8iSlSqnjkiV9K5kESNKiYkU5joiQqkVvdP++9PGsUcOeTGrfHti927uSSYD0TrIKJd56i8kkIjfhVRB5n+BgSSI9a2rDqlWyB/yzz4AHD9wXW3x066aOp05VlTr+xLGM/OefgZs3tYVCRPT/Hj0C3n5bevYAkgBftsz7qmF9Rb9+anR5mjTAyJFaw6EoHLe2/fWXvjhcyXGS4NSpcjPQm2zbJsk+xwbWGTMCK1bIuaRJ9cUWFw8e2HutRu3fRUQuw4QSeafUqYG//5YG1tOnS0l1vXrAiy+qu8VhYdJjqVAhufD39O2db74p2/YA4M4dmR7ibwoVkiozQKoAFizQGw8REQB06SLb3QDZ4rJwIe9+67Jnj73X4IgR0ouQPIdVvQMA69bpi8OVGjSQKbUAcOKEVMpv26bG1Xuq8HDpR/rKK8CRI+p8vXrAvn1AzZraQouX+fPVTcg8eeSamojcgj2UyPfs3SslvFGnpdWsCYwd69lvAiZOlNgBifPIEXnz4k++/Rbo3FmOy5Xzz6l3ROQ5/vzTvs161Ch7RSm5T0SEvBHetk3WFStKwoJjwT3L/fty488aOX/pklS/+JrOneWaxVFQkGyHLVVKPkJCpEdmokR6YnR09KhsD7WS4wCQLJlcG7ds6b2/R6Ypf8+7dsl6+HCgRw+9MRH5mGf1UGJCiXxTZKTsae/VSyZXWBIlkm1wn3ziGS/uUd2/D2TPru6y/PQTULeu1pDc7to1IEsWdSF6+LBvjBomIu8TFgYULw4cOCDrBg2ARYu8942Xtxs5Ur1RDA4G/vlHVfaSZ6lQQY2enzfPN8e3Hz4MvPQSEBr67M+zkkylSwNNmwKVKrnvOcQ0Zdvh1KlSWelYQfXqq8CsWUDu3O6JxVW2bJEbkIBc258/L1thichp2JSb/E9AgEysOHwYaNdOvXA/egT07St3i377TW+M0UmaFGjbVq0dy/r9Rbp09pLr2bP1xUJE/m30aJVMSpYMGDOGySRdDh8GPv9crfv0YTLJkzk2SV+7Vl8crvTCC1IVP2KEJMzy54/+88LDZavmlClAlSqSpJ4+3bXb465elbhefFGSe7Nmqe+XIIG0hPjzT+9PJgHA+PHquGlTJpOI3IwVSuQftm2TiTBWOaylYUNJ2mTLpieu6Jw7B+TKJaX9gMRcooTWkNxu6VJpgAsAOXNKfwJOUiIidzp3DihYUCpHAXlz9vHHemPyVxER8qbY2gJdvLi8ridIoDUseoaNG+XfDADy5pUR7v7g9m2ZkLZjB7Bzp3wcPRr956ZPLxPVOnQAMmeO//eOjAR+/12qkX76SVV6OwoJkWnJvnJdeeWKVPZb0zd37LBPGSQip2CFElGZMnLxOX48kDKlOr94sbxhWLRIX2xRZcsGvPOOWo8erS0UbWrWVHeYTp8GNmzQGw8R+Z/u3VUyqXBhacxNeowdq5JJQUGypZ3JJM9WtiyQOLEcHz+upvL5upQpZUtbjx6y1e/IEeDWLanS6tgRSJJEfe7Vq9LMO2dO6W20c2fcvue5c/J18uQBqleXa1rHZFLy5FKtv2OHXAv7SjIJAKZNU8mksmWZTCLSgAkl8h+BgdLw+vBhoEULdf7+fSmR9aSJYt27q+P584HLl/XFokNwsPybWGbO1BcLEfmfefPkhoNlwgQmMHQ5ckR6H1o+/1z61pBnCw4GXntNrX1121tMpEwpjf2//VaSP8OHAzlyqMfDwoA5c6R66LXX5LknPFw9Hh4uvTVPn5ZJbJs2AStXyja22rUlIdW375NJu3LlZGvdhQvAd99JssWXtuw+emRvit6xo75YiPwYt7yR/9q4EWjTRpUiBwYCc+cCjRrpjctSrpw0GgSA/v3lYsGfbNsmd5sA6S116ZL0MCEicqUjR+SN1717sv7f/6QihtwvIkLeYP/9t6xfekleG4KD9cZFMTNsmAxHAYDmzdkT0VF4uGxLGz1aEkRRpUsn1Xh37gAPHsT866ZJIzdN339fKit92dixQNeucpw5s7RH8MSBO0Q+gFveiKLz2muSVHrxRVlHRADvvus5298ct1dMnKhKev1F6dLq3+b+fWDJEr3xEJHve/RIbipYyaR8+eRNC+kxapRKJllb3ZhM8h5VqqjjtWtl4hiJoCDp4/nXX8D27ZJwCwpSj1+7JjfSYppMqlJFKivPn5ffG19PJj18KI3FLZ9+ymQSkSZMKJF/y5gRWLfOnlRq2tQzkkoNGwJZssjxpUsy7tWfGAbQqpVaf/+9vliIyD989JFMbQIkcbFwIZAihd6Y/NWBA/apbn37SjNu8h4lSqi+lRcuSMsBelJIiFRvnT4tP/Pp0tkfNwz5e8yeXRJF5cpJr6R33pHfi2PHgD/+kElz/pJUmTRJro0BuVZ2nJBMRG4Vo4SSYRipDMNYbBjGIcMwDhqGUS7K45UMw7htGMae/z76Rnk80DCM3YZhrHA4945hGP8ahhFpGEaIw/m0hmGsMwzjnmEY34LI1TJmlDtnBQvK2lOSSgkSSM8ny5gx/nd3r3lz2YoIAOvXSzkzEZErLFwo1aCWUaN8q3mtNwkPB1q2BEJDZV2qlFQgkHcJDAReeUWt9+/XF4s3yJJFmmufPy/tGM6fB+7elevSW7eAM2fk73DzZmDVKnnO6t9fpuj5kwcPgCFD1Pqzz/wnkUbkgWJaoTQGwCrTNAsCeAnAwWg+Z6NpmsX/+xgQ5bGu0fw/+wG8DSDq+KZHAL4A0COGsRHFX6ZMUqkUNank2JRVh7ZtgYQJ5XjHDtVTyV9kzgy8+aZaszk3EbnCsWPSc8Tyzjsyypv0GDZMtgEBUinGqW7ey3GqGcVMcLBst82SRXpH+lIjbWeYOFENq8mWzf7cTURu99yEkmEYKQBUADANAEzTfGya5q2YfgPDMLIBqAlgquN50zQPmqb5RO2raZr3TdP8C5JYInKf6JJKTZro7d2TPr30dbKMGaMvFl0ct73NnAlERuqLhYh8j9U36e5dWefJA0yZwjdxuvzzD9Cvn1r37w8UKaItHIonx8pq/k5RfN2/DwwdqtaffaZuvBKRFjGpUMoD4CqA7//btjbVMIyk0XxeOcMw9hqGsdIwDMdOcKMB9ATAd4Hk+TJlku1vL7wg64gIoHFj4Ndf9cXk2Jx78WKZ+OFPatcG0qaV49OnJelHROQsPXoAu3fLsdU3yer7Qu4VFiZT9cLCZF22rPz7kPdyvAnEhBLF14QJwNWrcpw9O9C6td54iChGCaUgACUBTDRNswSA+wCibmTfBSCnaZovARgH4CcAMAyjFoArpmnudFrE/zEMo61hGDsMw9hx1XpiIXKGzJklaeGYVGrbVu6K6FC8OFCggIrF3/oIBQcDzZqpNZtzE5GzLF4MjB+v1iNHSr8e0mPQIGDPHjlOlEi2ujlOviLv41ihFMBZQBQP9+7JdlhLnz6sTiLyADF5Zj8H4Jxpmlv/Wy+GJJj+n2mad0zTvPff8a8AEhiGkQ5AeQB1DMM4BWA+gCqGYcxxRuCmaU42TTPENM2Q9OnTO+NLEilWUilDBlmfPw+MGKEvnly51PHZs9rC0KZlS3X844/A7dvaQiEiH3H8ONCmjVq//TbQsaO+ePzdrl2SULIMGqS2oJP3YoUSOcv48cC1a3KcM6e9JQIRafPchJJpmpcAnDUM479yDVQFcMDxcwzDyGQY8iphGEaZ/77uddM0e5ummc00zVwAmgBYa5pmc2f+AYhcJnNm+8XtsGGSWNIhRw51fOaMnhh0KlECeOklOX74ULakEBHFVWiobGe2thDnzg1Mm8Y3vLqEhspWt/BwWZcvD3Ttqjcmcg5/m05LrnH3LjB8uFr36SMV7ESkXUxrTzsD+MEwjH8AFAcw2DCM9oZhtP/v8YYA9huGsRfAWABNTPPZryCGYdQ3DOMcgHIAfjEM4zeHx04B+AZAS8MwzhmGUSg2fygip2nVSiUyHjyQ5n86+HtCCbDfieK2NyKKj549gZ3/7cZPkECS1KlSaQ3Jr/Xvr0bKJ0kiW90CA7WGRE6SJo06/uMPfXGQd5s1C7h+XY5z57ZXrhORVsZz8j5eISQkxNyxY4fuMMhXrV0LVK2q1tu2AaVLuzeGmTPVi2eTJsC8ee79/p7g6lUZoWvdwT50SPW5IiKKqR9/BBo0UOvRo1kNo9O2bUC5cmpr1LhxQKdOemMi51mzBnjjDTlOmhQ4d47JW4q9119XCUk+RxC5nWEYO03TDInuMXbHI3qeKlWAunXVunt395dwZ8+ujv2xhxIApE8vE98sM2ZoC4WIvNTJk/apQPXq2Sdpkns9fChb3axkUuXKwIcf6o2JnOv114EiReT4/n1gyhS98ZD3uXULWL9erd9+W1soRPQkJpSIYmL4cNkWAQCbNslkIHfiljfhuO1t1iyZekdEFBOPH0vfJKupf65cwPTp7JukU9++Um0KAMmSyb8HJ4H5FsOQG3GWsWOBsDB98ZD3WblSVaeXLi3V6kTkMfiqTRQT+fPby2t79gQePXLf98+WTR2fP69eWP3Nm28CGTPK8YULwOrVeuMhIu/RqxewfbscBwUB8+cDqVPrjcmfbdoEjByp1iNG2Ceaku949101NffcOWDJEr3xkHdZtkwd16mjLw4iihYTSkQx9cUXQNq0cnzqlPTdcJdEiVQiJTISuHjRfd/bkyRIADR3GBTJ5txEFBPLltmfs4cNA8qW1RaO33vwQPoCWtvHq1UD2rbVGhK5UKJE9q2M33zD6W8UM48fS4WSxbEFBRF5BCaUiGIqdWqZRGMZPBi4fNl939+xjxK3vYmffwZu3tQXCxF5vlOn7BOB6tQBunXTFAwBAHr3Bo4dk+MUKYBp07j10Nd16AAkTCjH27cDmzfrjYe8w/r1wJ07cpwrl+rHRUQegwklotho1w548UU5vntXqpbchX2UROHCQMh/QwZCQ2XcNxFRdB4/lsmYt27JOkcOqWxk8kKfP/+UPjqW0aPtN0zIN2XIYK8w/uYbfbGQ9/j5Z3Vcty6fu4k8EBNKRLERFGS/CJo2Ddi71z3fmwklpUULdTxrlr44iMizffYZsHWrHAcFAQsWAGnS6I3Jn927Z68yrVnTXj1GrqdzmIVjc+6ffgJOnNAWCnkB02T/JCIvwIQSUWy9+aZ8ANLP6KOP3NMLwDGhdPas67+fJ2vSRN4cAlI2b22dICKyLF9ub/r89dfAyy/ri4eATz6RLYiAbCOfPJkVB+60cydQqBCwf7+e71+4MPDGG3IcGQnkzcvrGXq6PXvUz0eqVMBrr+mMhoieggklorgYORIIDJTjtWvljYursUJJSZ8eqFFDrWfP1hcLEXmeM2eA//1PrWvVkuQ/6bNmDfDdd2o9bhzHf7vT+vVA5crAkSOS1NFVHRT199CdrQPIuzhud6tRQwazEJHHYUKJKC4KFQLat1frHj2kV4crsSm3neObxVmz5G4nEVFYmFQxWg37s2cHZswAAnjJo83t20CbNmpdr56Mkif3SZZMHV+8KEmlS5fcH8cbbwBFi6r1smWuv34i7+S43Y3T3Yg8Fq+uiOKqXz8gZUo5PnoUGD/etd+PFUp2NWvKlglAtlD89ZfWcIjIA5im9Gn5+29ZBwYC8+cDadPqjcvfffyx2rqSNq1UKnGrm3uVKiXV1NaktePHgerV3T8p1TCk92TixLK+dQtYt869MZDnO3sW2L1bjhMkUK0miMjjMKFEFFfp0gF9+6p1376qN4QrZMgABAfL8c2b0tzUnyVMKFUIFjbnJvJvERFA27b25P7gwcArr+iLiYBff5UBFpYJE4CMGfXF488qVgQWLVJb9v/5R27O3L/v3jgMw16xtmOHe78/eT7H7W6VKwMpUuiLhYieiQklovjo1AkoWFCOrek1rtp6FRBg3/bGRpb2aW8LFwIPH+qLhYj0CQ2VBPPUqepcgwayHZn0uXkT+OADtW7USD5In9q1ge+/V+u//5aGx23bAgcPui+OwoXV8bJl7hluQt7DMaHE7W5EHo0JJaL4CA4GZs5UvTn+/FMajboK+yjZlS0L5M8vx3fvyhhiIvIv9+7Jm+TFi9W5Fi1kqxv7JunVtStw4YIcZ8jg+q3hFDPvvQeMGaPW4eHAlCnSH7J2bWng7eoET926qup6+3b5nkSA/Dw6boOsXVtfLET0XLzSIoqvMmWA3r3V+tNPgcOHXfO9HPsosUJJyuajNucmIv9x8iTw+usyQczSpYtUYAQF6YuLpOrEcQLnpEmyVZw8Q5cucgMsTRr7+RUrgEqVpBeZK2XODLRsqdZff+3a70feIyxMPgBpb+B4M5WIPA4TSkTO0LcvUKyYHD96JEmO8HDnf5+cOdWxrpG/nqZ5c3W8erVMryEi3xYeDowYIdtmtm5V5/v3B0aPZmWSbteuyRYqS/PmMtmNPEunTsDVq8CGDVIx5NgofcwYYNMm137/Tz5Rv6urVwO7drn2+5F3sHp8AdIbj4g8Gq+4iJwhOFiqYxIkkPXWrcDw4c7/PgUKqGNXVUF5m5w55W4qIP2r5s7VGg4RudiuXbLd9ZNPVN+0gACptujbl9PDPEGnTsCVK3KcOTMwdqzeeHxRWJhs8xw9WialxVVAAPDaa7Jl/OBB4KWX1GNffBHPIJ8jXz7gnXfUeuhQ134/8g6O1aVMKBF5PCaUiJzlpZeAfv3U+ssvZYKKM1kNwAHg0CHnfm1v5tice+ZMNvck8kUPHkgSqUwZeyVD0aLSWLhTJ32xkbJoEbBggVpPmQKkTq0vHl9z5Qrw1VdArlySjOneXRJC16/H/2u/8ALw44/qDf26da7vbdSrlzpevBg4etS13488n2OFqWnymo7IwzGhRORMPXvKmx1A7h62aAE8fuy8r//CC+r46FHXbKvzRg0aAIkTy/G+fcDevXrjIaL4Cw0FLl+WasylS4EiRWSbm3XHOmFCYPBgYOdO9bxLel25Anz4oVq3aiVj6Sn+du2SnkPZs0vlkNXsHAD27wfefBO4fTv+3ydPHntvQscbZa5QogRQvbocR0a6prqbvI9jldK9e/riIKLnYkKJyJmCgqRCJlEiWe/dK0kmZ5XsJk8OZM0qx2Fh0pCWgBQpgLffVms25ybyHj/9JFN8ypUDXnxRtkglTizPo5kySWXm22/bn+8qV5bkce/eaqsx6WWaQPv20j8JALJlA0aN0huTtwsLAxYuBF59FShVSq4vHG9SZcigtnju2CHJu/v34/99P/tMvaH/80/5cKVPP1XHM2fak2Xkn/LmVcczZmgLg4iejwklImcrWNA+rWTMGLkQ3LjReV/fwm1viuO2tx9+YPUWkTc4fx5o1EgmS23ZIs9ply7JcIPopE4NTJ8O/PEHkD+/e2OlZ5s3TyrJLNOmASlT6ovHm129KtV3uXMDjRs/2Ry7bFl5nTt7FvjuO3V+0yZpfv6035+Yilql1L9//L7e81SsCLz8shw/fix9oci/OW5hHjlSTX0jIo/DhBKRK3TpArzxhlrv3QtUqAC8+668gYoPx21vTCgpVatKZQMg2y5WrNAbDxE934IFT3+jEBQkY+bz5ZOk/IcfStPgVq3YeNvTXLhgfwPYrp39NZBiZs8eoHVr2dbWp4/9eiFBAqBZM0m8btki1xPBwTJNz7ES7PffJUkb3zfg7qxSMgx7ldLEicDNm677fuT5WreW538AOH1aKvWIyCMxoUTkCgEBwLJlwMCBqrcPIHdwX3gBGDJE+oPExYsvquM9e+IVpk8JDJTR1JauXZ3TT4KInM80gVWr7P1ZBgwA/v0XOHdOemY8fiyVGkePynae8eOBjBm1hUxPYZqSQLISALlysQ9ObP3+u9x0KlEC+P57+/VBhgwy5OP0aWDOHKlOiqpbN7nesCxfLq+H8dlu7+4qpdq1gUKF5PjePWDCBNd+P/JsSZIAnTur9bBhbM5N5KGYUCJylUSJgM8/lyqiRo3U+fv3pe9H4cKybSO2ypVTx+vW8QXWUffuQJo0cnzmjCSViMhzPH4sPVKKFQPeegu4e1c9VquWvKHMmhVImpRVSN5i5kx7Rej06dLvj54vNBT46COgWrUnt8WHhACzZ8trWb9+qgL3afr0sVf5LFwY/4baffq4r0opIMA+8W3MGEkqLVwoSeXISNd9b/JMHTtKYgmQqcmrVumNh4iiZZg+8GY0JCTE3LFjh+4wiJ5t3TrZCrd/vzoXFARs3QqULBnzrxMRIWXAt27J+uBBe18lf7dwofScsPz4I1C/vr54iEieryZPljeJ0TXczZoV2LBBqiLIe5w7JzdH7tyRdefOwNixemPyFocPA02bArt3q3NBQUDDhnIzpGzZ2CdVTVOqlax/gyxZ5N8oPsnZDz4Apk6V44oVXZtUCguTZsxnzz75WHCwTLKbPVsGcZB/6NZNXjcAqeDbtUtrOET+yjCMnaZphkT7GBNKRG4UHi69Afr2VQmhokVlO0dwcMy/Tv36MhkJkG0gjmOaSfpMzJ0rx+nSSRKPW2WI3O/4cWDQIGDRoidHPydNKm9W331XmjcXKKAnRoob05Q3+KtXyzpfPtmGnTSp1rA8nmnK1KpOnYAHD9T5mjWlwXa2bPH7+hERQKpU6vft5EnZhhhXJ0/K76Y16GLdOqBSpfjF+CwTJkhlyrOcOSN9pki/sDD5vb99WypQQ0PVfx2P4/rY/fvAkSPq+9WrZ2/+T0RuwYQSkac5ehR46SXg4UNZf/GF9A+JqXHjpNoJkHHaS5Y4P0ZvdvOmJOqshqa1a0tPK26hIXKPu3clkTRqlH3MOQBkyiQVGO3aydQ28k6TJ8u/ISDPrRs2yHh7errbt4H27YH589W54GDpOdW5s/Neo6pVk75MgPRdatYsfl/PnVVKpglMmSJbnMLDgVOngN9+s39OypRyHdS8OV/Xdfr5Z/nZuHLFfd8zUSJJxPLfncitnpVQYg8lIh3y5we+/lqtBw2SPgoxbSJdpYo6XrfuyTds/i51amlsalm+3L4mIteIjJTqiwIFgKFD7c9NhQpJf51Tp6TXC5NJ3uvUKeDjj9W6e3cmk55nyxageHF7MqlgQdn23qWLc98gly+vjjdtiv/Xc5z4tn696ye+tW0LfPutVGytWiXN+h17SN2+DbRoATRoII37yb0iI6VJe9267k0mAcCjR1LxSkQegxVKRLpERkrZuGMjzgwZJNHUsqU0qHwa05SyeKsXyYQJQIcOrozWO3XqJFsCASBZMrnjmTu33piIfNXff8sb46ivx2XLSgXGq6/yrrIviIwEXn9dbmYAMrl09277RFNSIiIkudq3r33q2vvvA6NHu2aL4Jo1wBtvyHGxYsDevfH/mu6sUnqaTZtk8tzx4+pcxozAX3/JlktyvatXJeFntV0AgLRp5ecsYUKpuEuY0H7sjHOtW6vkaI4c0j/UathNRC7HLW9EnuriRWnKuX69/XxIiDTVdJzoFtWIEcAnn8hxlizAsWO8oI/qwQNp4mjtv69QAVi7FggM1BsXkS85d04qjn74wX4+SxZ5I/3uu89OkJN3GT9ekvWA/Ltu3hz9KHuSbdfvvaeSb4Bs15o82T791dnu3pU+SpGRksS9eVO+b3ycOiXV1VYvpbVrgcqV4xtp7N27J9c+332nzjVubK/8Iud58ECqxDZulITOrl32xGjVqsCCBZJUcqVbt+Tn79o1WffrB3z5pWu/JxH9P255I/JUmTPLheaCBfZGnDt2AK+8InfirOk5UX34ofQiAaRSacIE18frbZIkkYkwVgJpwwZ54+sDiXQi7R4+BAYOlAoVx2RSwoTA55/LFKvmzZlM8iXHjgE9e6p1z55MJj3N8uXSK9ExmVSunDQwdmUyCQCSJ5fvDcjr3ZYt8f+auXIBrVqpdb9+el5LkyWT4SYjRqhzvKnsfKYpW5SzZpWthaNHA9u325NJH30kySZXJ5MASZAOHqzWQ4YAp0+7/vsS0XPxKo9IN8OQi8tDh6Q5d8KE6rFZs6SZZnQXbUmSyJs2y5AhcleS7MqUkf4PlhEjJBnneFFERDFnmtLD4sUXZRuP46Sqhg1lK8LAgfLGj3xHZKQkFKx/78KFJalAdo8eSYPtOnWA69flnGHI6/uGDfGbuBYbzu6jBNh7KW3YoGfbmyVvXnWcM6e+OHzRrVtSPd+mjZpI7Kh4cWnIPXKk+nlwh9atgZIl5fjRI1WlT0RaMaFE5CmSJpVJbwcPAvXrq/MrVgDffBP9//PBB+pC6to1uYNET/riC6BWLbX+7jtJ1LGZOVHs7Nkjvd8aNbLfHS5WTCoxFi1inzJfNWaM9KoBpOpz5kz7DRCSqrwyZaShtCVrVtkeNmCAe998uyKh5ClVSoA8F1nWrpXkgjU5l+Ju40ZJGC1YoM7lyiXV3StWSJJ0926ZnutugYHSDsKyaJG9ApCItGBCicjT5M4N/Pij3OG09OghY3SjCg627yEfMQK4ccP1MXqbBAnk7/Tdd9W5BQuAevXs1RVEFL0rV6QRa8mSUplgSZdOErS7dkmiiXzT4cP2Ss8+fYBSpfTF44kePZJG2Pv2qXP16klDbB2/G44Jpa1bVe+j+PKUKqWojcat6mNuaY+7FSuk4b7jzYK2bWXK3tdfAzVrAmnS6IsPkJ/rZs3UumtX5/1sE1GcMKFE5KmGDrWPYW7XDpg798nPe+896WECSL8lx74CpCRIIP2UOnZU51aulDcA0ZV0E5FU8X3zjTRDnTJFvVkLCpJR8UePynMTG937rogI6ef36JGsixeXhBLZTZ8OnDkjxwkTSl/DH390T3+Z6GTPLh8AcP++cya9AVKt0rq1WuuqUrJ+Hh3NmMFtmHH1yy/SK8mq3E6dWiqAJk3yvGlqQ4eq6Yj79kmMRKQNE0pEnipxYrlbFPJfQ33TBFq0sI9qBeSNXf/+aj1mDHD5stvC9CoBAcC4cbIFzrJpk9w95t8Zkd2vvwJFiwIff2wfDvDmm3IR/8030iiVfNuIEVLhAkhifsYMqY4lJSwMGDZMrb/+GujQQXon6eSKbW/Ak1VKOrYdffWV3BBq2NB+fsAA+7Yoer5ffgHeflslk3Lnlm1tUf9uPUXWrPaKyS++UP3KiMjtmFAi8mQpU8oEjSJFZB0RIeNxf/vN/nnvvKMmujx4IBezFD3DkAtOx75Ue/dKNdipU9rCIvIYly7JneqaNYEjR9T5AgXkjcfKlUDBgvriI/f5919pvG7p21e91pAyd67aJpQunWwT8gSuSijlzKm/SqlUKbkWWrQICA2VRLela1dgzhz3xuOtfv31yWTSn396fqPzjz4C8uSR45s37TcKicitmFAi8nRp0wJr1siWE0Be9OvXt/cxCQiQqUqW77/nnvLn6d5d/p6skebHjklS6cABvXER6WKasi20UCHZqmNJkUISsPv2ATVq6IuP3CssTLa6WW80Q0KkMS/ZRUTYb+J066a24+gWNaHkzKSPY5XSxo3AH38472vHVnAwsGQJ8Mor6lzLlpIAp6f79Ve5noyaTMqRQ2tYMZIokf3G4KRJztvWSUSxwoQSkTfIlAn4/Xf1Iv/woUwt27ZNfU6tWkC2bHJ85w6wc6f74/Q2LVvKRai1feP8eaByZW5/I/9z7pxM7WnRQu72Wlq3lj5J3btzm5O/GTJEvY4EB8tUN3dOKfMWP/4oTcsBSb469unTrWhRIFkyOT5/3t5sOb6iVil98IHebUdJkkibAMeK7oYNJdlFT4oumbRunXckkyx16si2RwCIjAS6dGFTdiINmFAi8hY5csgdwEyZZH33rpR4//OPrA0DqFpVfT5HqcZMvXpyYWXdUb5yhZNiyH+YJjB1KlC4sP1ufq5cUhk5bRqQIYO28EiTPXtka7Bl4ECpXCM70wQGD1brTp08q69YUJC9amftWud+/S++kK35gGwZr1NHbnjpkjo1sHq1JEcAadxdq5b8PJOycqU9mZQrl1wzevo2t6gMAxg92t7PS2elHJGfYkKJyJvkyyeVStbUmJs3gWrV1N3RihXV527e7P74vFXVqlKpZPnxR+nLQOTLTp6U548PPrA33e7cWba3vf66vthIn8ePZaubtW365ZelMTs9aeVKlaxInFi2u3kax9/j1aud+7WzZZOt45bNm4GmTfVuuc+cWZLhGTPK+s4dufl27Ji+mDzJihVyI80xmeQNPZOe5sUX5TXM8tVX+mIh8lNMKBF5m8KF5aIwRQpZX7kiCZGTJ+13IjdvZpVNbFSvDrz/vlp37Ch/t0S+aOFC2Q7jeDc3Xz65wzt2rNomQ/7nq69U5WuiRDLVLTBQa0geyTSBQYPUum1bIH16ffE8jbUlCJAbUpGRzv369esDo0ap9bJlUqml8/ojb165TrKqpy5fluT5hQv6YvIEBw7INkBfSSZZevVSVUrr13ObI5GbMaFE5I1KlpQ7o0mSyPr8ebkLmSwZkCaNnLt+XXqfUMyNHAlkzy7H1655Vi8MImdZvRpo1gy4f1/WAQFAjx7S0PS11/TGRnrt2GHfwvX118ALL+iLx5OtX68qgRMkkN8hT1S0qKrWuX5dxsE7W7du9j//pEn6K0WKFZNqnMSJZX3qlNw4unFDa1jaPH4MNG8uE/EAmZDmC8kkQP4MLVqote6fPSI/w4QSkbd65RXg55+BhAllfeKE3IErVUp9Dre9xU6KFMCUKWq9eDG3vpFv2b0baNBAbUkpUECeJ4YPVwlq8k+hobLVLSJC1q+9Jk1uKXqO1UktW6qhGJ4mIECuDSzO3vZmGTpUEtWWvn2lP5tOr74qr+NW9cr+/dJTyUqm+5MBA1QyMWFCqSTzhWSSpXdvNbV39Wr70BoicikmlIi8WdWq9oulgwflgsnChFLsVa8OtGmj1h9+CFy9qi8eImc5fRqoUQO4d0/W2bNLk96yZfXGRZ7hyy9lSwwgycXvv1dv0Mhu2zbZPgbI31GvXnrjeR53JJQCAoDp0+09m9q3lyohnWrUkG2blr//tm/78nV37gDDhtkToIMHq2l4viJfPukNZVm8WFsoRP6GVwpE3q5WLeCHH9SF/8WL6rHly4GwMD1xebORI9Xd5mvXOPWNvN+NG9KY9tIlWadKJdtms2bVGhZ5iC1bpErNMmyY9KGh6DluC2za1PP/rhwTSps2qaSyswUHy1CLEiVkHREBNGokP186NWsmveEsq1bZq/F8TXi4TO1s0kS2OzomPCtV8szm8c5w4oQ65lRKIrdhQonIFzRqJOO9o7p0Sf/dQW+UMiUwebJaL14MDBmiLx6i+Hj0CKhbFzh0SNbBwcBPP0mDf6KHD+XNtdWsuUoVoEMHvTF5sn37ZLuQpXdvfbHEVObM0ksJkJtMGza47nslTw78+iuQO7esHz6UG1+7drnue8ZE585ShWeZP1+2dPrazaJNm+TfulYtYMECef63ZM4MzJzpm5WHBw6oiYsJE0qzeCJyCx98RiHyUy1bAt9+++T5iRPdHopPeOst+9S3zz4DZs/WFw9RXERGAu+9B/z1lzo3axZQsaK+mMiz9OkDHDkix8mSybYlX3zD6Szjx6vj+vW9JzHrOO3NVdveLJkySRVQunSyvn5d+j6OGKG3KujLL+3DNiZMAPr10xaO061dK1sOrZsHlmLFpOpw714gRw49sbnaDz+o49q11YQ/InI5XjEQ+ZKOHZ+spFmzRkaEU+x9+y1QubJat24tf59E3uLjj+29JEaOBBo31hcPeZaNG4HRo9X6m298q1Gvs0VGSnWfpWtXbaHEmjsTSoA0/P/lFxl2AUjT908+kWT2sWOu//7RMQzZ+ta0qTo3YIB9O5y3Wr9eqpKsiqTkyeXve+9e+fjkEyB9er0xuoppAnPnqrVjc3gicjnD9IFSz5CQEHPHjh26wyDyHF98YR+bmi4dcPky7zrHxa1bMu3IanaePLlsFyheXGdURM/3zTeSULJ07QqMGiVvqoju3wdeegk4flzW1atLXy3+fDzd339LpQ0gb84vXgQCA/XGFFMPHwKpU6ux8WfOSGN+Vzt6VHr5OG55S5JEpsJ9+KGe65LHj6WB88qV6tzs2UDz5u6PxRk2bpQeeQ8eyDprVkkweXpvL2fZvBkoX16OU6WSdg/WBGQicgrDMHaaphkS3WN8d0nkiwYMAEqXVutr1+SDYi9q8+K7d2VqzOnTWsMieqYFC+zJpIYNJcHEZAFZevZUyaSUKWXEO38+ns2xOqlOHe9JJgFA4sRyc8Ti+Gdxpfz5pSl3//5qIu2DB9LT6PXXgVOn3BOHo+Bgqdy0koOAtA345Rf3xxJfmzbJFn0rmZQlC7Bunf8kkwD7dreGDZlMInIzJpSIfJFhSFNMx7V1IUexly2bJJWs0v2LF+UC7uZNvXERRWf9eqBFC7V+9VW5+84KRbL8+qv0j7GMGaMmW1L0TBNYulStHUeUe4s331THn34K/POPe75vggRA377A1q32cfXr1kkD6SlT3N8cO0kSGVpiNSuPiJBkxMaN7o0jPrZskWuR+/dlnSmT9FHKn19vXO4UFmZv68DtbkRuxy1vRL6sbFlg2zY5XrIEePttvfF4u3Xr5IL88WNZv/aa9KJIlEhvXESWf/+VBNKtW7IuWFDuYKdJozUs8iBXrsib6CtXZF23riRKWJ30bAcPqlHkSZNK1a+3PfffuQOUKqV6GOXKBWzfrppnu0NoqFQrDR2qJgsC0q+wWTOpHoqIAMLD5SNNGqBmTfk7d4WLF2W71MmTsk6RQpLynr6tfds2oFo1+TcFgIwZ5RrlxRf1xuVOkZHAoEGSrASkkvzMGd48IXIBbnkj8levv66O//hDXxy+onJlGblr2bhRKkEcL4qJdDl/Xu5WW8mkTJmkso7JJLKYpgwXsJJJmTJxq1tMOVYnvfWW9yWTAEmWLFsmvQAB2W7WqJFUebhLwoTA4MHS9+aFF9T5detksmqLFkCrVsAHHwAdOsgQgSJFgN9+c008mTPLsI2MGWV9547cONLVODwm/v1XmqxbyaT06eUaz1+SSZcvA19/DeTLp5JJAFCiBJNJRBrwt47Il1Wpoo7XrtUXhy9p0gQYPlytFy2SMdtEOt2+Lb29zp6VdbJkkkzKlUtrWORhvvvO3idmxgz3Vqd4M8eeQ9643c1SqJD0nLGSiOvW2futuUvZssDu3UD37s9PaJ46JUme5s1VryBnyptXqo2tUfOXL0v1z4ULzv9ecRUZKZVTH3wgzfRv35bz6dJJMqlwYb3xuVpkJPD778A778j23M8+U1VlFuvfj4jcilveiHxZ1Kku586p5tIUd6YJdOwITJwo6/r1gR9/1BsT+a/HjyWZZFUhBgVJ0sBxTDjR7t3ShNgaK961KzB6tNaQvMa5c2oiWlCQVHilTq03pvgaNAj4/HO1njoVaNNGTyybNgHffw/cuyf9lgID5e/5wQPp92UlTwCgSxfp+eUKf/0lz5sPH8q6SBFJ4uis8ty/H5gzB5g7V90wsCROLH93JUroic0drlyRxPeUKdFXjaVKJdXjadLI57DaksglnrXljQklIl9XpYrcgQSAWbOA997TG4+v2LtX9VjImVPulPFChtzNNGWLyJw56tyMGcD//qctJPJAV67I5M8zZ2RdpIj0zvHGbVs6TJggNxEA2Uq+Zo3eeJzBNGU72aJFsk6QAPjzT/vkM09w5YpshVu+XNZp0sg5V03Y+/VX6SsWHi7rcuXk39tVPZyic/48MG+ePK/v3Rv95+TNKze1qlVzX1zuZJrARx8B48dHvyWzfHmgXTtppJ44sfvjI/Iz7KFE5M+qVlXHy5bpi8PXFCokzUMB4PRpaSZq3dUkcpc+fezJpK++YjKJ7MLCZJuIlUxKkUKSCEwmxZyvbHdzZBhSFVSsmKzDwmRwx7lzeuOKKkMG+fu3tmbeuCFVO65So4Yk5S1//y1JC2sYh6s8fCj/HlWrSjXcJ588mUxKm1YSm3//DRw96rvJJEB6VI4ebU8mpUolFWr790s12XvvMZlE5AGYUCLydY6T3ZYv56h7Z0mQwL49YN48oGJFmRhD5A4TJ0pjUkvbttJXgshRt27Ahg1ybBjSP6dgQa0heZVbt1SVLwDUqaMtFKdLmlRuNKVNK+vLl2ULt6fdHAkIkG1Nlr/+it3/Hx4ObN0KjBwJDBsm/ZLu33/65zdrBowdq9arVkmiPiIidt83JiIjZbthnjzSMH/tWqnOsSRKJJVky5fL9cW33wIvv+z7FdHz56vjwoUlyXf+vGx39PV+UURehgklIl/34osyJhiQO2yLF+uNx5eMGQO0b6/W27fLtpJdu/TFRP5h2TKgUye1rlVLtgb4+psMip0pU2S7luWrr+RnhWLu11/V9qeQENVLyVfkyiXXBdYWsh075M+5aJFnTTAtX14db9r07M8NAEmPbAAAYoRJREFUDwe2bZPkUY0a0u/q5ZeBHj2AXr2A6tXlXIUKQP/+kqCKuq2qc2fgyy/Vev58qY5xZquQ7dslrg8+AC5dUucNQ7ZWfv+9JPnmz5ff2wQJnPe9PVl4uP1adfx4SeglSaIvJiJ6KiaUiPyBY9+k2bP1xeFrEiSQN2vjxqlRtefPA6++CixZojc28l1btgBNm6o3e2XKyBuOoCC9cZFn2bRJ9f0BZNtb79764vFWvrjdLapKleyNrg8cABo1kj6BS5c6N4kSV6++qo7nzZNE0M6d8jwYHi7JmeHDgZo1pc9S2bKSPFq5Upp9RxUWJtuq+vUDXntNEkw1akgV05498nW//NL+OzRhgnx+fN2+LRWlZctK3JYsWSQJdvas9G1q2VK2qPqbdeuAq1flOEsW+789EXkcNuUm8geXL8t0N6tc++RJjhN3ttWr5QLccRrNgAEyRYdVI+QsR45I09zr12WdNy+webP0GSGynDsnVSaXL8v6pZckweTOxsK+4NEjIH16lZDYv993t9uYpiRT+vd/MgFTooQkUmrX1vd6Fh4uSZ+osaVMKY89awsbAOTIIRVJiRNLD6Ln9WFKl06qhBo3loT9ggXqsTFjpFopLh49ku17W7aocwkTAj17Ap9+yiocQJqwT5smx5xGSeQROOWNiOTO28qVcjxwoH1cMDnHoUNywe042rZJE2D6dDaOpPi7fFmSSSdOyDpdOnljlC+f3rjIszx8KG+creuitGnlmDcRYu/XX6XiBZDfsyNHfP8GwbVrklgaOxZ48MD+WEiIJJZq1NDz9zBmjGxbs7YgPkv27JK4qVRJPnLntj9++bL0K/rjD/k4derpXytLFnkNP35cnZs9G2jePHbxm6b0Z5o3T52rWVP+XHnzxu5r+arHj4FMmVS/z82bZdIeEWnFhBIRyQXMu+/Kcd68UtJuTSkj57lxQ7aWrF2rzpUuLT1vMmfWFxd5t/v35U2R9VqXOLFsCyhbVmtY5GFME2jRQk3+CwwEfv9dfnYo9j74QBomA5LIGD5cbzzudOWK/HnHj3+ySXeZMlKB+8Yb7k8s3bolTbJ/+UVuklnVmlmyAFWqqCRS7tyxi+3ECZVcWrtWbbl6moAA4OefVcIxJgYOBPr2VeuhQ6UyiZRfflF93nLkkESfrydxibwAE0pEJHcaM2cG7tyRdZ8+0qCVnC8sTMrhv/tOncuaVS5+ixbVFxd5p/BwoG5dqZYA5I3MTz9JNRyRo5EjJfFhGTfO3rydYu76dSBnTrWV6q+/7I2h/cWlS9LXZ+JE2a7l6JVXJElSpYqe2CIigH/+kX6GhQs7L/EQGSlfd/58mS5mbR2NKiBAXuc/+OD5X3PuXKlOsrRrJ3+nTJbYtWihen1+8on87BGRds9KKLEpN5G/SJLE3kzy66/te/jJeZ7WrLthQyA0VG9s5F1ME+jQQSWTAPnZYjKJolq92l7t0KaNvaEwxc748SqZVKSI/267yZQJ+OYbqeDp0kX6/Vg2bwaqVpVm7zomwgUGSn+nIkWcm5gJCJCG5EOGSIPsRYukn1JUkZHSXDtjRrlBt2/fkw3Mz5yRz3HcHle1qlwfMJlk9+iRvQl+48baQiGimGOFEpE/iYyUO4nr18s6f35g9242anWl1auBBg1UI9HBgzlpiWIu6haJzz4DBg3SFw95pmPHZGvtrVuyLldOtkQ6vvmnmLt/X6qTrO1Uc+bYq0v82blzckNqyhSpxrXUri1/T748lezYMWDyZNkGafX4iSpPHqkorV5dtsRF/Xt68UVpkJ86tXti9iZLlwJvvy3HefMCR48y6UbkIVihREQiIACYORNInlzWR4/KWF1ynTfesG8tHDgQOH1aXzzkPb7/3p5MatGC21TpSXfvyhtYK5mUJQuwZAmTSfExZYpKJuXOzUoJR9mySfXWsWPAW2+p88uX24cG+KJ8+WQL1unTQJ060X/OiRPAqFHAm29KNaljMqlyZUn0MpkUPcdJek2aMJlE5CWYUCLyNzlzyvQWy/jxUkVDrtOxo4ztBqS5affueuMhz/fbb/a+HK+/Lm9yeYFNjiIjgffekyELgCSRli7lAID4ePxYelFZPvkECArSF4+nypFDkkiffKLO/fuvVMqtW6cvLndInlwGbZw4IU2j584FGjVSN+uiKldOmuP/8Ydsj6Mn3b8vP08WJnGJvAYTSkT+6H//kzvallatnl6+TfEXFCSJO8vSpdKgmyg6u3bJNsmICFkXLy4VJ5zKSFH17y9vbC2TJ8sELoq7H36QbV0AkCED0LKl1nA8WmCgVOzMnKmen27ckMrciRP1xuYOuXPLTbqmTaW65upVeW1v3x4oVEhuBKxaJVvcqlblDYFnWbFChscAsi2wSBG98RBRjDGhROSPDEPeeKRPL+sLFzgJyNXKl7e/Menc+cmJOUSnTgE1aqhmwDlyyBhlX+5LQnHz448yut3Svbtsi6S4i4iQUe6W7t2BxIn1xeMtWrSQ3oyZMsk6PBz48EP5cNzy5esSJpStbhMnSrXWmjXSS4mJpOdbtUodN27MvzMiL8KEEpG/ypBBkkqWuXOBhQv1xeMPhg4FUqWS4+PHOQ6X7K5flzcj1ojqVKnkbneWLFrDIg+0b589efT663w+cYaffgIOH5bjFClkwiLFzMsvA9u3A6VKqXMTJ0pCxepHRfQ027ap4ypV9MVBRLHGhBKRP6tXz14106EDcPGirmh8X4YM9gldX3/t2w1MKeYePpQmr9ab2eBgmRBUqJDeuMjzXL8uW5atKrY8eWS7Dfv8xI9pyph4S8eOQMqU+uLxRtmyARs2SENly7p10lfp33/1xUWe7e5d4OBBOQ4IAEqW1BsPEcUKE0pE/m70aNlWA0jvgzZt5MKaXKNdO3Wx9OgR0LWr3nhIP6ux8ubNsjYMGb/92mt64yLPEx4u20FOnpR10qTSQylNGr1x+YI//gB27JDjRIn43BxXSZJIxbPjRMqTJ6WCybHpMpFl1y513Vm4sDyvEZHXYEKJyN+lTAnMmKHWK1fKNClyjcBAGSVs9QdYsYIX2f5u2DBpum355hvgnXf0xUOe65NPJPFhmT2bzWud5euv1XHr1pzGFR+GAfTpIwMorOTAvXtSWTd0KG9akd327eq4dGl9cRBRnDChRERA5cr2UfYffSQ9fsg1ypYF3n9frbt0UdNNyL9s3w588YVad+smH0RRzZwpFaWWL78E6tfXFo5P2bYNWLtWjgMDJXFH8VevnlRe5swpa9MEPv1UthNaUyyJHPsnMaFE5HWYUCIiMWiQjGoFpDfH//7HCz5XGjxYbVM5dcp+d5z8w717QLNmso0JkC0hw4frjYk809atsl3WUq8e0LevtnB8jmPvpKZNgVy5tIXic4oVk8R5hQrq3MSJQMOG0juO/JNpArt3A7162Se8MaFE5HWYUCIikTixbJ+wGrtu2gSMGKE3Jl+WLp39TczQoaopJfmHrl2Bo0flOHly4Icf2FiZnnTxIvD220BoqKwLFwZmzZLmtRR/Bw/K1ixLr176YvFV6dMDa9ZIss7y008ynZAT4PzP5csyDbBkSdnyffeunE+SBChaVG9sRBRrvBohIqVUKftd7y++AP75R188vq5NG6lKAYCwMKB9e/aW8BeLFwPTp6v1+PEyrYvIUWioJJMuXJB16tTShDt5cr1x+RLH6tDatdmTylWCg2XYQI8e6tzmzcCrrwKnT+uLi9xv8GCpTnKUKhUwbpz8nBCRV2FCiYjsevcGypSR47AwoHlzdWecnCsgAJg0SXp2ADJu2bFBOvmms2eBDz5Q66ZN5feMyJFpAh9+CGzZIuuAAGDBAiBvXr1x+ZLt2yXJYendW18s/iAgQLb1jhqlBlMcOgSUKwfs2aM1NHKTmzeBadPU+p13ZDDJ5cvSDJ+IvA4TSkRkFxQk2ykSJZL1vn3S/JVco1gx4OOP1bpHD+DqVX3xkGtFRADvvQfcuiXrnDmln4j15orIMn68vYpt+HCgWjV98fiaR4+ATp1UVWitWpLYINfr1g2YP19Vo1y8KD2WHCcYkm+aNEn6dAKyvW3BAvndY2USkddiQomInvTCC7Kv3TJsGPDXX/ri8XV9+6omsDdu2LcEkG8ZOhRYv16OAwKkb1LKlHpjIs+zbp192t9779kncVL83LgBvPGGmi4VHCxVM+Q+jRoBv/2mnv/u3gXeeguYO1dvXN7u0SNg/3417MGThIYCY8eq9ccf82YKkQ9gQomIotexozTMBOQO7muv2cebk/MkTQpMmKDWs2bJG0ryLdu22av9Pv8cKF9eXzzkmU6elG0g1pTN0qXlrj7feDnH6dPSt2fjRnWuXz8gXz5tIfmtSpXkZlXWrLIOC5PJlyNGsJ9gXBw6BBQqJJU/OXMCn30GHDumOypl3jypRgOALFnsTdqJyGsxoURE0QsIkO0WjpOEvvpK7pRfvqwvLl/11lvyJtLSubNn3mGkuLl7F3j3XfVvWq4cE7T0pPv3gXr11OSrjBmBH3+UKZwUf3v2yO+e40TNESOATz/VFpLfK1IE+PtvmV5o+eQTqciLjNQXl7fZulUSpSdPyvrCBWk4nz8/ULmy9Ap7+FBffKZpnxzcpQu3uRH5CCaUiOjpsmeXNzSO5swBChYEvvuOF3vONnq0VCsBwL//Sm8d8n6mKQnC48dlnTy5bHULCtIbF3kW0wRatVKTNRMkkGRStmx64/IVv/8ufXqsCokECaRigttu9MueXSrGKlRQ58aMAZo0kS1c9GyrVgFVqqhEdFR//ik3AzNnlkb/O3a4vwLs99/lugYAkiUD2rVz7/cnIpdhQomInq1+/SfP3boFdOggd3qjjn6luMuSRbZBWfr2Ba5d0xcPOceECcDMmWo9cSKQO7e+eMgzff01sGiRWk+YALzyir54fMmcOVIFeveurFOmlP49TZrojYuU1Knl36RhQ3Vu0SLgzTfVEAN60pw5QO3awIMHsk6bFti0CVi6VJpdO1aZ374trz+lSwPFi0s/oxs33BOnY8P1//0PSJXKPd+XiFyOCSUierYsWdRx48b2kdXbtgEhIdI89s4dt4fmk7p3V3/Ht25xW5S3++MPoGtXtX7vPekRQuRo+XJ7MrljR+D99/XF4ytMExgyRH7vrO2mWbNKNUzlynpjoyclSiTT3zp3VufWrwfKlgWmTuV1RlTffGP/2c6RQ5JJr7wiW2eXLwfOngUGD36yR9g//8hrU+bMklhds8a1VecHDqhj9g4k8ilMKBHRsyVPro7Tp5fpIV9+qfa+R0ZKafqLLwILF7KRZnwlTGifNjRpkvT9IO9z9Ki9uXJIiPx7Ejk6eFCSjNZzZ8WKnDjmDBERQKdOQO/e6lyRIsCWLdK0mDxTYKBcUzhOmj1yBPjgA0l+tGolCUF/vtYwTaBnT9muaSlSBNi8Wab0OsqSRX4HjhxRW98ce7I9fgwsWCBTD/PkAfr3B86ccX7Mjn3LChVy/tcnIm2YUCKiZ0uWTB3fvSt3EPv1A/btU1PgAGkA2bixbCuwesVQ3NSqBVSvLsemKc0r/fni2Rvdvg3UqQPcvCnrzJmBn35ic2Wyu3ULqFtXbcXKmVO2+SRIoDUsr/fwoWydcpyeWamSJCLYk8rzGYY05p492/6c+eABMGOG9Fp64QWpPrtwQVuYWoSFSVJt+HB17tVXgQ0b1LS86BiGJKtnzZI+YhMnyk0OR6dPy/VdrlxyDbJwIRAaGv+YHz0CTpxQcRQoEP+vSUQegwklIno2xwol600PIBcEq1dLU9NMmdT5336TaS0DBjjnQsQfGYY06LaaNm/cKBd25B0iImQc8qFDsk6YUJJJz7rYJ/9j/ZwcPSrrxInl5yR9eq1heb1r14CqVeXv0tKkiTQuZt8W79K8uSQ5vvnGPgUOkN+b3r2loXft2tIz6PFjPXG6U6dO9p58derItVjq1DH/GilTAu3bA9u3A3v3yta3NGnU46YpX7NxY3nd6tZNbiLG1u+/S7Vljx5qO51hAFevxv5rEZHHYkKJiJ7NMaF0+7b9McOQC/VDh+Qix5qUExoq2+KKFbM3YqSYK1hQKpMsPXrISHHyfL16AStXqvX06UCZMvriIc/Up48kOSwzZkijXIq7kyelP8vff6tzPXrIVMWECfXFRXGXPr30Fty3D9i6VaaDpUihHo+MBFasAN5+W6rPuncHfv31yesVXzBtGjB5slq3aQMsWRK/ytdixeQG1oULauub49TD69dlC2KxYvI6NmlSzP9u584FPvoIGD9enYuMVNVKROQTmFAiomfLkUMdO+6Bd5QyJTBunDTpLlVKnT9yRLbFvfuu/5WlO0PfvkCGDHJ87py9pwR5ppkzgZEj1bp3b/n5J3I0bx4wdKha9+4NNGqkLx5fsGePTB49ckTWhiFvhIcPt0+6Iu9kGJLQ+O472bI1a5ZsY3R09aokR2rWlIqbUqUkwbR0qfdPTN2xQ5r1W5o2BaZMUZXM8ZUwoTwH/fabJGb79ZMtuI62b5fKpsyZZVLbhg3P3o7/tCSuYyKdiLyeYfpAX46QkBBzx44dusMg8k0REZIwsqpjzp+3T36L7vO/+w747DP7RJbgYClf//hjNmSMjWnT1LSnRImkGizqRR55hs2bZXKUte2iTh15I8M3s+Ro1y6ponn0SNY1awLLlkkzYoqbW7ek0fa5c7JOmFCqkho00BoWucHx48D330uF3/nzz/7c4sWl/9B778Vui5hu165Jcsxqll20qFThJU3q2u8bGSlV5tOmPX1LYf78QOvWkmDKnNn+2KJFMnUuYULp2WS1TahenUklIi9jGMZO0zRDon2MCSUieq4KFaSPDyBvfOrUef7/c/GiJI/mzXvysZo1ZRtCxYr20mp6UkSE3JXdtUvWjRpJWTp5ljNngNKlgStXZG1N3HHcMkp05Yo0wj17VtYvvCDbeFKm1BuXt2vRQho4A/J3uXw58NpremMi94qIkJ49v/0mlTO7d6u+PVElSiRN29u2lYbWnnwdEhEBvPmm/NkA+fnesQPIl8+9cVy/LknaadOAf/558vHAQKBGDdmGV6PGk4MF9u2TbXOAJJguXWJPMyIvwoQSEcXPRx+pMdZffCENt2Pqjz+Azz+XUc1RFSsmPZjeecf9F0fe5K+/7G+O1q+XJB95hvv35U3Jnj2yTpdOtn/mzq01LPIwjx/LFmArOZ8ihfycRB3zTbGzdKn0z7EsXszKJJI+P5s3S3JpwwbZrhUW9uTnFSwoVcClSgFp08pWubRpJemkQ2SkJG8uXZKPxYvtfZOWL5dJsLqYJrBzpySW5s61V6JbMmaUJG+bNvbntxIl1Ovk5MnABx+4JWQiij8mlIgofubOBZo1k+PSpeWOemzv6G3eLL0sli2Lfs998eKSWHrnHSmhJrt331XVXsWLyx1KbpHRLzJSqsaWLJF1UJDcSa5YUW9c5Hk6dJDtwIA8f65YIXfyKe6uXJFqQGtqVLNmwJw5emMiz3TnjryGTp6sKn6fJUkSSSw97yN1atnSHxwsVTkJEtiPrXVYGHD5slRvW8mi6D4uX5aqpOj07Qv07+/cv5f4ePBAXvumTZMbXdEpX14SS40aSc+n7t3V+b/+cl+sRBQvTCgRUfxcvizNua3986tXA9Wqxe1rHTkiI4BnzlQ9RKJ66SWVXCpQIG7fx9ecPSt3+h4+lDXv7nmGfv3sF/j8d6HoTJokzWwtX38NfPqpvnh8gWlKJdLSpbLOmlW21XhTbxzSY+dOSW7Mnav6+ni6t96SJLSn9uQ7dkwmms6YIUmzqJIlA+rWBebPVwmzY8eAvHndGiYRxQ0TSkQUfx07AhMmyHH58rJtIz59B+7ckdLtRYukOWNoaPSfV6yYSi75+9aQAQOAL7+U4/TpJTnHHgT6LFpkn8zVuTMwdqy+eMgz/fWXNGsPD5d148ZSKeHJfVu8wezZsq3GsmqVNPsliql796Qn4cqVUu12/br6sH5fdUidGsiUSX0UKCB9J5Ml0xdTTIWHy+/i9Olyjfesv8cuXWQSIxF5PCaUiCj+zpyRPkdWD4K1a+VNkjPENLlUuLD0qgkJka13hQo92fjRlz14ALz4opr0wosxfXbskD5WVsXY66/LmxJnjXAm33D2rDxfWc3aS5SQBFOSJHrj8nZnz8qkq9u3Zd2+vUyRInIG05TKJccE09M+bt2S6u2wMPVf68NxHRBgTxI97SNjRn39m5zt8mVJ/E6bJhNqo0qRQv0OE5FHY0KJiJyjXTvVHLJSJWDdOud/jzt3pKx70SJ5g/605BIgF13Fi8sbNuujYEHf7i20cKFUOFhq1JCG6dwa6D7r18ukQ6sZaf780leMW23I0YMH0kzf6teSLp0kInPm1BuXtzNNqURas0bWefIAe/d6R/UGkT8yTeDvvyWxNHOm2vKWIwdw+rTe2IgoRphQIiLnOHVK3jxbJcyunjZ29649ufS0nkuOkiYFSpZUCabSpaWyyle2l5imVMOsXavOJUggjS4//5xj6l0pMlL6Q3z4oUp0pkolF8oFC+qMjDyNaQL/+58aZc9m7c4zYYJswQbkeX3DBqlcJSLP16KFel5s3lwdE5FHe1ZCyUM7uxGRR8qVy96zYuBA136/5MmBpk2BH3+UKT5r1kgz2wYNnn6X//596e80apRM/ClQQD569ZIqkshI18bsaoYhfx9t26okWVgYMGyY9JiaMyf6KXoUP7t2yZvWNm1UMilTJkmqMplEUU2dan+jNHYsk0nOcOwY8Mknat2jB5NJ5H1u3/bf1+ncudUxG3IT+QRWKBFR7Bw/LokLq2R50ybglVf0xHLlikxr2bFDfVy48Oz/J2tWoH594O23ZTuKN/e82bVLGkFv3mw/X64cMG4cUKqUnrh8yY0bUvn13Xf2NwD58wO//Wa/OCYCgD17gJdfVonHVq1kq4evVEnqEhEhFbHW813hwvKc7yv9Zsh/5MkD3LwpNyMKFpTeiAULymt21qy6o3Otvn3Vzch+/dSgESLyaKxQIiLnyZsXePddtXZ1ldKzZMggo3S/+AJYtgw4f14+fv5ZLlreeuvJvhrnzwPffgtUqSIVJq1bA6tXe+fdwpIlpcHvnDlA5szq/N9/y1a/tm2laSjFXkSEjJUuUECa/Vo/HwkSAL17SzKPySSK6vZtoGFDlUwqWlSeb5hMir+RI1UyKSgImDWLySTyPg8fSvuAW7eALVtkG3WvXkDdukD27FJ19/ix5iBdyPFai8+LRD6BCSUiir0+fdSFwKpVwLZteuNxlCULULs20L8/8OuvslVu+XKpEkiTxv65168D338vDV7r1QMuXdIScrwYhmztO3xYLkqtqXemKQmRChVkGyDF3LZtUmESNSFXvTqwfz8weDAbANOTTFO2RB4/LutkyaT/Gye6xd++fXLjwPLFF5JQJ/I25849/fXDNCVxWq4ccOSIe+NyFyaUiHwOE0pEFHsvvAA0aaLWOquUnidRIqBWLWD6dBlhu3Yt0KmTJJ4c/fyzbKFYsEBPnPGVPDkwZAjw778y+c1y4ADQtau+uLzJ1avA++8DZcvKVhpLrlzATz9JY3hO06OnGTkSWLJEradNk+dKip/Hj6V3n1W1ERIiVYJE3ih/fqlkPH9eGvV/+600mQ9x2Emya5ckTGfM8M7q6WdhQonI5zChRERx41iltGKF9DLydEFBQOXK0l/o7FkpN3//ffX4jRuSKGvUCLh2TV+c8ZE/P/DLLzIJyTJtGrBwob6YPFFoqDQ3f+89oE4d4J13JFk0bZr6nIQJpb/DgQOyHYEXv/Q0v/8uFYKWTp3keYTiJyxMKjD37JF1woSy1c2qxCTyRoYhN7WqVpVk0rffSmXs6NFAcLB8zv37Uln97ruSgPIVTCgR+RwmlIgobgoXll4hlh49vOtOWkCAVKJMmSLT43LkUI8tWiR/vl9+0RdffLVvb68ia9tW+jb4M9OUnlPt2kn/rAYNpP/U8uXA4sXS08JSp44kkvr1AxIn1hUxeYNTp+R3zZogWb68VCtR/ISHy1jxxYvVuSFDpIExka8xDKkm3rLFXtk4fz5QvLic9wXnzqnjtGn1xUFETsOEEhHF3ZdfAoGBcvznn/Lm3Bu9/rr06GjTRp27ckWSCt76ZzIMmUyWK5esb9+WO/3h4VrD0uLwYem5kiePTPabPNmePHKUN68kEpctk88nepaHD2VipNVrK3NmSUhbVQYUN+HhUj3oWFnZtSu375LvK1FCKr4dq6dPnQJefRUYNEhN2PVW//6rjgsV0hcHETlNjBJKhmGkMgxjsWEYhwzDOGgYRrkoj1cyDOO2YRh7/vvoG+XxQMMwdhuGscLh3DuGYfxrGEakYRghUT6/t2EYxwzDOGwYRvX4/AGJyIUKFwa6dVPrjz+WUbjeKEUKYOpUSSZY/ZUiI6V3x8yZemOLq5QpgXnzVNJv82ZgwAC9MbnLlSuytbFMGRnH/NVXT1Zo5coFfP659EdasECauEftQUX0NKYplYC7d8s6QQKppnGcuEixFx4uz7vz56tznTsDo0Zxiwz5h6RJpXp64UJ5HQckkfT553ID7Px5vfHF1dq1wN69as1qQyKfYJgx2KJiGMZMABtN05xqGEYwgCSmad5yeLwSgB6madZ6yv//EYAQACmszzEM40UAkQAm/ff/7vjvfCEA8wCUAZAFwO8ACpim+dSUfEhIiLnDsYEqEbnPvXtyUWCVMbdvL2PWvdmVK9LbYP9+WRuGXNw5VjB5k8GDpecVIFv91q4FKlbUG5MrPHggzdXnzJHpg9HdyU2VCmjcWLbSlC/PN6gUd99+K4kOy4QJQIcO+uLxBRERwP/+B/zwgzrXsaMkh/m7Sv7o9Gnpo7R5szqXJo0MGqlbV19csXXkiExPtW46VqoErFunNSQiijnDMHaaphkS3WPPrVAyDCMFgAoApgGAaZqPHZNJMfj/swGoCWCq43nTNA+apnk4mv+lLoD5pmmGmqZ5EsAxSHKJiDxRsmTAmDFqPWkSsH27vnicIUMGudB56SVZm6aUn0+apDeuuOrVSy7eAKm6at5cGpD7gogISZC1aiV9kZo2lSozx2RSggRA/foygevSJdkK+OqrfINKcbdxI9C9u1q3bi3JdIq7iAj5PXZMJnXowGQS+becOYH164G+feWGECCv3/XqSbL14UOt4cXIzZtA7doqmZQ5s/e2EyCiJ8Rky1seAFcBfP/ftrWphmEkjebzyhmGsdcwjJWGYRR2OD8aQE9INVJMZAVw1mF97r9zROSp6tdX24RMU+6cebt06SRRUbKkOte+PTB+vL6Y4iowUC7e0qSR9blzkiDzpibqUZ09K4mynDmlmmzGDODuXfvnlC8vyaNLl2Si29tvy5Qoovg4f14GElj9yEqXlucFJj3iLiJCKkBnz1bn2reXKjD+vZK/CwoC+veXXpXZs6vzEybItm6rmtoThYXJFNUjR2SdOLFUEmflWzsiXxGThFIQgJIAJpqmWQLAfQCfRvmcXQBymqb5EoBxAH4CAMMwagG4YppmbOaJR3fl8MS7HsMw2hqGscMwjB1Xr16NxZcnIqczDBmTbTl4UF8szpQmjYwDL11anevUSUb7epusWe2JvqVL7Q1vvckffwDFigHDhj3ZS6JAAekTdfy4muhmJdKI4is0VKYDXrki6/TppfItUSK9cXmzyEjggw/sveratpUkXQBnxxD9v9deA/bskZsjlv375RplwgTPu0lkmkCXLvKabZk5EwiJdtcMEXmpmLxSnwNwzjTNrf+tF0MSTP/PNM07pmne++/4VwAJDMNIB6A8gDqGYZwCMB9AFcMwnlfjeA6AQ/od2QBciPpJpmlONk0zxDTNkPTp08fgj0FELuU45ta6E+ULUqcG1qyRvf+W7t2BESP0xRRXdevae7x88on0HfImkyYB1avbp7SlSye9bLZuBQ4dUhPdiJytSxf5OQOk8m/hQnvFAMVOZKQkj77/Xp17/33pw8dkEtGT0qSR5v+TJkm1DwA8eiTb3956Czh6VG98jsaPlyphy4ABUq1ERD7lua/WpmleAnDWMAzr3WJVAAccP8cwjEyGITXJhmGU+e/rXjdNs7dpmtlM08wFoAmAtaZpNn/Ot/wZQBPDMBIahpEbQH4A22LzhyIiDXLmlF41AHDxInDnjt54nCllSuC332QLleWTT4AhQ/TFFFdffy09ogDZNjZ0qN54YurRI0nktW+v+iNlySIT2i5cAMaOldJ/bo8hV5k6FZg8Wa2HD1e9ySj2IiPl93naNHWudWt5o8xkEtHTGYYkYnfsAIoWVed/+w0oUkRuqui+WbR6NdC1q1o3aSJT6ojI58T0FbszgB8Mw/gHQHEAgw3DaG8YhtWBsiGA/YZh7AUwFkAT8znj4wzDqG8YxjkA5QD8YhjGbwBgmua/ABZCklarAHR81oQ3IvIQgYFAvnxq7Ul3yZwhRQqZHFahgjrXu7f0jrK2v3iDlCll6ptl2DDg1Clt4TzT7dvA3LlyRzNdOvtWw5IlgW3bpOrKSmQSucrWrVIBYHn3XaBbN23heL3ISODDD2V6pqVlS1kzmUQUM4UKyetg167qZsrjx8BXX8ljy5bp2QYXESHJ4cj/2ueWLStb7nnDh8gnGc/J+3iFkJAQc8eOHbrDIKL69aViBJBEQNOmWsNxifv3gTp1pGG3pXJl6RHgLRdLkZFygWc9bzZoICX0nuDiRbkIXrpUJu2FhT35OW+/DcyaBSSNbj4EkZNdvgyUKqX6db30kozwTpJEb1zeyjQlOTdxojrXooW84QwM1BcXkTfbuVN+r6wtuZYaNaSCN29e98Xyxx/A66/Lcbp0wL59MoWViLyWYRg7TdOMtgEabwMRkfMUKKCODx/WF4crJU0KLF8uZeWWP//03Cqf6AQEyAWmZckSe4LM3Y4elUqpcuVkG1uHDlIuHzWZlD8/MHIksGgRk0nkHtaEIiuZlDq1TAxkMilurCa9jsmk5s2ZTCKKr1KlJNE9dSqQNq06/+uvQOHCsg0u6hALV5k3Tx2/+y6TSUQ+jgklInIex4SSLzXmjipJEmDTJjWC3jSl7NyblCsHvPeeWnftqsagu5ppyt3Uzz+XxFyBAkCvXsCWLU9+bqlSUr7/77+SpPzoI26JIffp0QPYuFGODUPeKLHhe9wNGwZ8+61av/suMGMGk0lEzhAQALRpI9df7durqunQUHkdzZYNePVVuaHkquTS48dyk8rSpIlrvg8ReQxelROR8/jqpLfopEghjbktjhU/3mLIEFXps3+/fRqLq2zaJL22QkKAQYMkUeQoMFC2EI4dC5w5I9vy+vSRfhDesqWQfMOcOfbf60GDZMIgxc3ChcCnn6p148YyQpzJJCLnSpNGqgC3bQNKl7Y/tmmT3EDKnh147TVg3DgZbOEs//yjprDmyGGfkEtEPok9lIjIea5cATJmlOPkyaWpsi8nAS5dkgsma2vW9u2SKPEmQ4eqN3mpUsnFoKvGoJ89CxQvDty4YT+fKJG8Ua9fH6hVy16uT6TDkSNAiRJqUtLbb0ufMV9+PnOlv/+WRHFoqKwrVZKJVMHBWsMi8nmRkcAPP0jfwbVrVaNsR4YhlUuNGklPxcyZ4/79GjeW5DEgfZTWrIn71yIij8EeSkTkHunTyxQxALh7VxIuvixTJrl4snhjlVK3bmo6361b0nB4/nznf5+wMPm7spJJyZPLlrslS4Br16SZ+//+x2QS6RcWJj+bVjKpYEHZlsVkUtycOCGDDKxk0gsvSB8qJpOIXC8gQJ7P1qyRa7JJk4CqVe1bx01TtvZ27gxkzQpUrAiMHx/7a7gLF1QyCZAtdkTk85hQIiLnMQz/2vYGSINZy/z53pdES5hQLhyti8ubN2U6X+PGkuhxlj59pEoBkC0uK1fKHdO332aDbfIsX32leqIlSCB9k5In1xuTt7p5U6ZMWc8l6dNLk+DUqfXGReSP0qcH2rYFfv9dJqp+9x1QpcqTyaUNG4BOnWRIRqVK0ug7Jj0Wf/vNvu7Tx6nhE5FnYkKJiJzLXxpzW0qXlgbXgFQ2TJ6sN564eOMNGfObM6c6t3ChNMxesSL+X/+XX4Dhw9V68GCgfPn4f10iZ9uyRRJKlkGDZJsmxd7Ro7LNzZr4mTAhsGwZm5oTeYIMGYB27eS1/8IF6blUufKTyaX164EPPgDq1QPu3Xv213RMKA0dqqqficinMaFERM7lmFCK2nDZVzlWKU2cKFNOvE2lStI/qU0bde7yZaB2bTl3507cvu65c7KVzVKjhkzOIvI0Dx7Iz6rVY6RSJZkqSLG3cKFMaNy7V52bNUsl34nIc2TMKFPh1q6V5NKECfL857jN95dfpIn306bDRUTY+yVxgAGR32BCiYicq0QJdbx1q7443KlBAykNB2TL2+LFeuOJqxQppLR9xQrpD2WZPl2ajR88GPuv+fHHwPXrcpw1q0x1CuBLD3mg3r1VVWXy5BxnHxePHgEdO8qW2bt35VxwsDyvNGqkNzYier6MGYEOHYB16yS59PHH6rE9e4CyZe2JYsvOnapHYqZMQLFibgmXiPTjVT0ROZfjiNhdu1QjVl+WIIFcgFm8sTm3o5o1gf377W8Ajx6VC8nYbIF7+BBYvlyt58wB0qVzXpxEzrJunf33dvRo+xZQer7jx2Ur64QJ6lyePNI7zbHykYi8Q6ZMwIgRkhAOCpJz58/LRLhff7V/7qpV6rh6dQ4xIPIjTCgRkXOlSwfkzy/Hjx9LUskftG2rphZt3er91Vlp0wILFkgSKEkSOXf3rkxrGjxYeis8z7p1klQCpFl7pUouC5cozu7cAVq1Uutatexrer4lS4CSJe3P9w0ayLpkSX1xEVH8tWkjgzRSpJD1vXuyHX7iRPU5jv2TuN2NyK8woUREzleqlDq2GrL6ugwZZDqaxdurlCzNmgGbNqlqDdOUyS1NmgD37z/7/3WsZqpd23UxEsXHRx8Bp0/LcZo00lifd9djJjQU6NoVaNhQ9VlLkECe/xYtAlKm1BsfETnH668DmzcDOXLIOjIS+PBD6Yl4/boMNADkubNaNX1xEpHbMaFERM7n2H/n6lV9cbibY3PuxYtVPwFvV7w4sH07ULGiOrdwoVxgPm2UsGnaE0q1ark0RKI4+eUXYNo0tZ4wAcicWV883uTkSWnS65g8z5VLEtCdOzMpR+RrCheW6uuQEHVu5EgZtmENMyhVilvbifwME0pE5Hzp06tjf0oolSypLrQePwbGjdMbjzOlTy8TXDp2VOe2bHmyj4Lln3+As2flOFUq4JVXXB4iUaxcvw68/75aN2okzaTp+X76SZ7vtm9X5+rVky1upUvrioqIXC1TJuDPP4G6ddW5bdvU8Ztvuj0kItKLCSUicj5/TSgBUgJuGTIEOHVKWyhOlyAB8O230i/K8tdf0X/uxo3quHp1+X+JPEnnzjKVEZDJRuPH643HG4SFyRbB+vWBW7fkXFAQMGoU8OOPQOrUWsMjIjdImlT6pnXv/uRjnOJK5Hf4W09EzufPCaUWLVQT2keP5M2Xr6lRQx07Jo4c/fOPOi5TxrXxEMXWokXAvHlqPWUKt2k8z/Xr0htl1Ch1LkcOeQ7o1o1b3Ij8SWAg8M03QPv29vNVquiJh4i0YUKJiJzPnxNKgYFSxWNZuhRYvVpfPK5Qvrw63rkTePDgyc9xTCgVLer6mIhi6tIloEMHtW7Vik3jn+fAAUkMr1+vztWuDezeDbz8sr64iEivrFnVcdq09l6LROQXmFAiIufz54QSAJQrB/zvf2rdpYv0VPIV6dIBL74ox2Fh9v4JgDTn3LdPrYsVc19sRM9imkC7dlJtAwDZs9srbuhJK1fKc9qJE+rcoEHAsmUyFY+I/JfjtvdBg/TFQUTaMKFERM7n7wklQPonpUghx4cPA2PG6I3H2RyrlDZtsj924oSqWsqQQfrTEHmCWbOAn39W6+nTOdr+aUwTGD1aJjTeuSPnkiSRXkmffcYtbkT+7uFDe9Vi9er6YiEibZhQIiLnS5VKGrUCwL176s2IP8mUCejfX60HDADOn9cXj7O9+qo6jppQctzuxuok8iTBwSrR27Ej8PrreuPxVI8fSyVX9+5qHHj27PK7Xr++3tiIyDNs3Ci9IgHghReAXLm0hkNEejChRETOZxhA4cJq/fvv+mLRqWNH9fdw7x7Qs6feeJzJsUJp82b1phOwb3dj/yTyJE2bAvv3A23aAEOH6o7GM12/DrzxhjQqt5QtK1tbixfXFhYReZjfflPHrE4i8ltMKBGRa9SsqY5XrNAXh04JEgDjxqn13LnAhg364nGmvHnVVrbbt4F//1WPOVYovfSSe+Miep7s2YGpU2X0NdlF13y7WTPgzz+l6pKIyMKEEhGBCSUichXHqUm//GKvYPEnlSsDjRurdadOQHi4vnicxTCe3keJW96IvE90zbcHDwZmzwYSJdIXFxF5nsuX1Y2k4GBOdyPyY0woEZFrlCkjDZkB4MoVYPt2vfHoNGKENLMFZDvYxIl643GW6Poo3bsHHD8ux4GBahocEXmmZzXf7t2bzbeJ6EkREeo4QQIgYUJ9sRCRVkwoEZFrBATYt70tX64vFt2yZQO++EKtv/hCkmzeLiREHR85Iv/99195gwpIk05WNhB5LjbfJqK4yJJFrm0A4P59e2UyEfkVJpSIyHVq1VLH8+YBZ8/qi0W37t2B/Pnl+PZtGbvt7RzHrT94IP/ldjci78Dm20QUH0/b9k5EfoUJJSJynTfeUGXQJ04ARYoAM2eqChZ/kjAhMGaMWk+bBmzdqi8eZ0icWB0/fCj/ZUKJyPNdvgy8/DKbbxNR3DGhRERgQomIXClZMunNYfXguHMHaNkSqFcPuHRJY2CavPUWUKeOWn/8sXcn156XUCpa1L3xENHzPXggQxOOHVPn2HybiGLr5ZfV8f79+uIgIq2YUCIi12rfHti4EciXT537+WepVlq4UF9cuowaJQ0sAbmjt2KF3njiw2o0DsibVNNkhRKRJ4uMBJo3V0MSAgKARYvYfJuIYi97dnXsjzcJiQgAE0pE5A7lywN79gCdOqlz168DjRsDXbtqC0uLPHmADh3Uundv+7QUbxK1QuncOeDWLVmnTGm/2CQi/Xr2BJYuVetx44CGDfXFQ0TeK316SUoDck33+LHeeIhICyaUiMg9kiaVNy+//25PNIwdK3fI/UmfPrIdEJCpaLNn640nrk6csK/37lXHxYqx4oHIk0ycCIwcqdbduwMffqgvHiLyboGBQIYMan35sr5YiEgbJpSIyL2qVgX27bP3EmrXDjh/Xl9M7pYhA/DJJ2rdty/w6JG+eOJq+HB1/Oab9h4K3O5G5DlWrrRXiNarZ//9JSKKi8yZ1TG3vRH5JSaUiMj9UqYEZs0CcuaU9c2bQKtW0t/DX3z0kbqzd/YsMH683nhi69Il4Icf1LpXL/ZPIvJEe/cCjRqp59eQEGDOHKkuICKKD8epkBcv6ouDiLRhQomI9LCSSta2qDVrgG+/1RuTOyVLBnzxhVoPGqT6D3mDU6eA8HA5zpYNKFeOCSUiT3P+PFCzJnDvnqxz5ACWL5ctyERE8cUKJSK/x4QSEelToYJ961evXsCBA/ricbe2baVJNyBVWsOG6Y0nNooUAYKD5fjcOeDwYeDQIfvjRKTPvXtA7dpqO3GKFMAvv9grCoiI4oMVSkR+jwklItJrwADgpZfk+NEjoFkz/5kUEhwMfPWVWo8eDVy4oC2cWEmWTPphWaZNU9Pq8uZVTceJSI8ePYDdu+U4KAhYvJiJXiJyLlYoEfk9JpSISK+ECaUXT8KEst6zB/jyS60huVXjxkCJEnL88CHQv7/eeGLDsbH6L7+oY253I9Lr9GlJ8lomTgSqVdMXDxH5JlYoEfk9JpSISL/ChYEhQ9R66FBg40Z98bhTQID9zz5tmmwf8wa1a6tjx+1uRYu6PxYiUr7+WvU4q1ABeP99vfEQkW9yrFA6f16uX8LC9MVDRG7HhBIReYYuXdQWKtMEWrdWb4h8XbVq6s8eEQH06aM3npjKmlUmRgH2CX2sUCLS5+xZYPp0te7bV18sROTbHCuUduwAChaUm03+NLWXyM8xoUREniEgAJgxA0iVCsifX7bBBQXpjso9DMNepbRkCbB1q754YqNu3SfPMaFEpM+QIapCoHx5oEoVvfEQke+Krsn/b78B8+e7PxYi0oIJJSLyHNmyyYXI7t1AmTK6o3GvkBCgUSO17tVLKrU8nWMfJQBIlEhNriMi9zp/Hpg6Va379pWENRGRKyRNKtduUX32GRAa6v54iMjtmFAiIs9SpoxcoPijr75SVVnr1wOrVumNJyaKFsX/tXffYVZVZ9/Hv4sqFlQERdEo2LsIir1jwx4JxBJs+BjLo0ZNNImamOJjUGOJ5VURCxasUcReiC1RUYoFCxpUFFFABUOH9f6xzmTPwFDOwMw+58z3c13nmr3X3rPPfQw7zPxY696svHK2v/rq0LRpfvVIjdlf/pI9JbNbNxtxS6p/t94K220HrVplY59+Ctddl19NkhqMgZIklYoNN4S+fbP9888v/T4EIWRP6AOYMiW/WqTGbPx4uOmmbP/ii52dJKn+de8Or78O06bB1Vdn43/8I7z7Ljz9NHz1VX71SapXBkqSVEouugiWXz5tjxoFd9+dbz1LYqONsu0pU2Dq1PxqkRqrfv1gxoy03bUr7L9/vvVIanxOOSVb9v7tt7DFFrDffrDeevDOO7mWJql+GChJUilp3x5+8Yts/8ILS78PwXPPpaVukGZU2YxTalgTJsCNN2b7zk6SlIfp02t/oMrMmfDGGw1fj6R6Z6AkSaXmvPNgtdXS9tixNX9RLEUtWsAFF2T7N9+cXy1SYzNpEhx+ePpFDqBzZ+jRI9+aJDUuM2bAlVfC+uvDhx/Wfs6PftSwNUlqEAZKklRqWreG3/422//jH0t/Gdmxx6ZgCdK/Qo4YkWs5UqPw6aew887wz39mY3/4g7OTJDWcl15KS9/POScF3LVZe23YcceGrUtSgzBQkqRS9POfw7rrpu2JE6F//3zrWZzVVoMjj8z2H3ggv1qkxuDtt2GnneCDD9J+CHDttc5OktSw1lsPvv462+/YEQYOhLlz01Mnhw1L/39V1R9SUkUxUJKkUtSyZXrKW5W//hVmz86vniVxxBHZ9rPP5leHVOn+8Q/YdVf48su036IFDBoEp5+eb12SGp911kn/37P66inUfv99OPpoaNIEmjeHLl1glVXyrlJSPQkxxrxrWGpdu3aNw4YNy7sMSVq2pk9PPQcmTkz7d90FRx2Vb02LMnkytG0LMaYfJCdN8odIaVl78MH0y1pVs/7WreHvf4c998y1LEmN2JQp6e/9FVfMuxJJ9SCE8GaMsWttx5yhJEmlqlWrmjMOLr88hTWlqk2b9C+RkJ72NnRoruVIFeeGG6BnzyxMat8eXnzRMElSvlq3NkySGikDJUkqZaedloIlgOHD4fnn861ncfbZJ9t22Zu0bMQIF14Ip56ahcobbZSacW+9db61SZKkRstASZJKWdu2cPzx2X6/fvnVsiS6d8+2n3kmvzqkSjFnDvTtm572WGX77eGVV1IzXEmSpJwYKElSqfvFL1JvAoCnnoJRo/KtZ1F22gmWWy5tf/ghjB2bazlSWZs2LTW7r/6UxwMOSDMV27bNry5JkiQMlCSp9K2/fs0nqF1+eX61LM5yy9Xs5/LII/nVIpWzSZPSEtLBg7OxPn3SPbXCCvnVJUmSVGCgJEnl4Lzzsu177oHPP8+vlsU57LBs++GHcytDKluffQa77pp6JFW54AIYMCA9hluSJKkEGChJUjnYfnvYbbe0PWcOXH11vvUsyiGHQAhp+6WXYOLEfOuRysnbb8OOO8Lo0Wk/BLjmGvjzn7P7SpIkqQQYKElSuag+S+mmm+D77/OrZVHWWAPWWittz5sHjz2Wbz1Sqfv2Wxg4EHr2hG7d4Msv03iLFnDvvXDGGfnWJ0mSVAsDJUkqFwceCJtumranTk2hUim64AL44ots//XX86tFKlWffQbXXgt77w3t2sGxx8IDD8D06en4SivBE0/AT36Sb52SJEkLYaAkSeWiSRM499xs/+qrYdas/Oqpzeuvw2WX1Rzbf/98apFKSYwwciRccglsuy2suy787/+mJ7bNnVvz3C23hBdfhL32yqdWSZKkJWCgJEnl5OijoX37tP3FF6lBdymZMWPBsVtvhZkzG74WKW9z5sDQoXD22dCpE2yzDVx8MQwfvuC5228Pf/oTvPtuCp622aaBi5UkSSqOgZIklZOWLdOshiqXX55mPpSKv/xlwbFHHoEjjqg9bJIq0fffw5lnpn5ie+4JV10FY8fWPKd5c9hvP7jhBhg3Dl57DX79a9hsM5tvS5KksmCgJEnl5pRTYIUV0vY776RfSEvBqFEwZEi2f+KJ2fbjj8PBB8O0aQ1fl9SQnngCttgiPZlt8uSax1q3ht69U6Ptb76BJ59M93OHDvnUKkmStBQMlCSp3Ky6as1Gvb/9bX61VFd9dtIRR8DNN8OFF2Zjzz4LPXrADz80fG1Sffv2Wzj++NQ8f9y4bLxDBzj1VHjqqRQi3XMP9OoFK6+cX62SJEnLgIGSJJWj447Ltr/9Fm67La9Kkn//O826qHL++WnZziWXwB/+kI0PHZqadE+Z0uAlSvVm8GDYfPOa92G7dnDfffD553DddbDvvtCiRW4lSpIkLWsGSpJUjnbbDdZbL9sfMCC3UgC44orsSVV77w3bbZcd++1va85eeuUV6N49BWFSOZs8GY49Fg45BMaPz8Z794b33oOePe2HJEmSKpaBkiSVq+uvz35ZffHF2p8c1VAeeijb/tWvFjx+3nmpMXGV119PwdOkSfVemlQv/v731EB74MBsbI010r1wzz3Qtm1upUmSJDUEAyVJKlcHHAA//nG2/+c/51PH3Lnw1VfZ/h571H7emWfWbCA+fHh6AtbXX9dredIyNXEiHHUUHH44TJiQjR9zDLz7bhqXJElqBAyUJKmc/eY32faDD6ZlNg1t0iSIMW2vump6HPrCnHIK9O+fzax6++0UQFVfLiSVqgcfTL2S7rknG1tzTXjkEbjzTlhttfxqkyRJamAGSpJUzrbZBg46KG3HCJde2vA1fPNNtr366os//4QT4I47oEnhr6DRo2H33Ws+GUsqJV9/nZ6seOSRNWfU9emTZiUdckh+tUmSJOXEQEmSyl31WUp33w1jxjTs+1f/BXtJAiVIy4PuvhuaNk37H32UQqVPP1329UlL47HH0qyk++/PxtZaK43fdlualSdJktQIGShJUrnbYQfYZ5+0PW8eXHllw733xInQr1+2v6SBEkCvXumX9Kolcp98kp5e98kny7ZGqa5uuQUOPTT9Oa9ywglpVlKPHvnVJUmSVAIMlCSpElxwQbb9//5f+qW3vpeQPfMMbLklPPFENta5c3HXOPzw9FSsFi3S/mefpVDpww+XXZ1SsWJMTe779k0hLcDaa6c/6/37wyqr5FqeJElSKTBQkqRKsOeesP32aXvePBgwADbcEH75y2X/FLWZM+Gcc2DffWs+3e2MM+C884q/3kEHwaOPwnLLpf0vvkjL3/JoMC7NmwdnnVVzKWnnzjBsGOy/f25lSZIklRoDJUmqBCGkmT7Vf+GdMSMtR1t3XTj9dBg7dunf5733oFu3msvqVl8dhgyBa67JZhoVa7/90jWWXz7tf/VVevrbqFFLXbK0xGbNSv29rrkmG9tzTxg6FNZYI7eyJEmSSpGBkiRVig4d0pKc556Drl2z8Rkz4LrrYIMN4NhjU/+XunjtNejSBUaOzMYOPDCFPgceuHS1A+y1Fzz5JKy4Ytr/5pv0y/xbby39taXF+eEHOPhguOeebOzII+Hxx6F16/zqkiRJKlEGSpJUafbaC15/HR58MAVAVebOhYEDYYstUqPhf/6zuOvedlsKpyA10r722vSkq2U5c2PXXeHpp7Nf4CdPhr33Tp9Hqi8TJ6Y/Z08/nY39/Odw773ZUkxJkiTVYKAkSZUoBDjiCHjjjfRL8l571Tz+6KOw005pWdmTT6YmxIuz777Z9pw5qWdTCMu0bAB23DHNsqp6HPt336Wn2L3yyrJ/L+nTT2GXXWqGlr/7XZrV17RpbmVJkiSVOgMlSapkIUD37img+de/4LDDah7/xz/ggAPSTKb77kuzmBbmsMPSuZACqJNPTsFSfejaFZ5/Htq2TftTp6Y+S0OH1s/7qXF6913YeWf44IO0HwJcfz1cfHH9hKWSJEkVxEBJkhqLbt3g4YdTY+0+faBZs+zY8OHQqxdssgncfHN6ktv8QkizNlq1SvsjR8LVV9dfvdtsAy+8kC2p+89/Uq+mZ5+tv/dU4/Hqq2mJ5RdfpP0WLWDQoLTUTZIkSYtloCRJjc2mm6Z+SGPGwBlnZAERpLGTT4aOHeHyy1OIU13Hjmk5UJWLLlo2T49bmC22SLOS1lwz7U+fDgcdlBolS3U1ZEhaRvntt2l/xRVTQ/uePfOtS5IkqYyEuCR9M0pc165d47Bhw/IuQ5LK0zffpMek/+1vqV9RdRtvnJp7b755NjZ7Nmy4Yeo9A3DUUXDXXfVb45gxqQ/U55+n/ebN4f77U3NxaXFiTH+GXn4ZXnwR7rwzW97Zrl0Kk6o3sJckSRIAIYQ3Y4xdazvmDCVJauzatYM//CEFRP36ZbOBIPWW6dYtLQWCNEPoqadg/PjsnPqcoVRlgw1SELDeeml/9uz0SPf776//91b5mTsX3norBaU9e8Jaa8FGG8EJJ6TZeVVh0nrrpWbvhkmSJElFc4aSJKmmGTOgf38477wUIFVZd12YMCEdr+666+DUUxumts8/TzOVxoxJ+02apNkmRx3VMO+v0jB1KkyalJasVX99+WXqjfTqq+mcRenSBQYPrhmgSpIkqYZFzVBqVtugJKkRW245OO002G03OOKILLypWuI2vx/9qOFqW2ed9GS6vfeG99+HefPgmGNg1iw47riGq0P5mDIFTjoJHnggLWMrxsorpye67bJLasa9447QtGn91ClJktQIGChJkmq35ZYwbFj2C3yVTTdNM4V++CE7ryGttVZq1L3PPvDOOylYOP74FCqdfHLD1qKG89lnqSH7228v2flrrZWCo113TSHSFlsYIEmSJC1DBkqSpIVbeeXUp2jChBQgrbQSrL46bLJJ6q8ECz4JriGssQa88AJ07w4jRqSx//mfFCqdfnrD11NJpkxJoc0PP8B+++VdTfLmmylM+uqrbGyddWDVVbPXKqtAmzaw1VYpROrYEULIrWRJkqRKZ6AkSVq8NdZIryorrJBt5xEoAbRtC889l0KPqj56Z5wBM2fCOefkU1M5mTMHPvwwhUejRmVfq5Y2brABfPRRvjUCPPJI6pE1bVrab94cbr4Z+vTJty5JkqRGzkBJklS8FVfMtquWvuWhTRt49lk44AD45z/T2LnnplDp17/Or65SEmOa2VM9NHr7bXjvvTSja2E+/jiFhdXDw4YUI1x1VQoHq/olrbIKPPww7LFHPjVJkiTpvwyUJEnFK4UZSlVWXhmeeiotiXrxxTT2m9+ksOTiixvfsqdp0+D11+Hll9Nr2LD0RLQl1axZWtK41VbpSWl5BEpz5sCZZ8L112djnTrBkCGpNkmSJOXOQEmSVLxSCpQg9XZ6/HE45BB4/vk09vvfp1DpT3+q7FDp66/hlVeyAOmtt1IgsyTWXjs1Vd9qq+zrxhtDixb1W/OiTJ0KvXrBE09kYzvtBH//O7Rrl1tZkiRJqslASZJUvFJZ8lbdCivAY4/BEUfAk0+msUsvTcvfLr+8ckKlCRPSTJ2qEOnDDxf/PSuumAKjqldVgLTqqvVfbzE+/zzNNBs1Khvr1Qtuuw2WWy63siRJkrQgAyVJUvFKbYZSlVat0kyWnj1h8OA0duWVaabS1VdDkya5lrfU7rkHTj558SHeZpvBLrvAzjun2T2dOpX+Z3/rrRQmjR+fjf3mN3DJJaVfuyRJUiNkoCRJKl6pBkoALVvCAw/AT38KDz2Uxv72tzRT6cYbyzOcmD4dzjoLbrppwWMtWsB226XwaJddUoC02moNXuJSGTwYevfOnuTWrFn6rMcfn29dkiRJWigDJUlS8UpxyVt1LVrAvffCz36WvkJ61PysWdC/PzRtmm99xfjoozTjauTIbGz99eGkk1KA1LVr+S4H+/jjFPJdcUXNJ7k9+CDstVeupUmSJGnRDJQkScUr5RlKVZo3h4ED09c770xjt98Os2enr83K4K/AQYNScFQ9tOvVK83ead06v7qWxowZ8PDDcMstWQP1Kh07pubqPslNkiSp5JXBT9OSpJJTDoESpJlIAwakGUv9+6exu+9OM5XuvjuFTaVoxgw4++w0e6dKixZw1VVwyinl2WD8nXdSiHTnnTB58oLHfZKbJElSWTFQkiQVr/qSt1IOlCCFSjfdlAKZG25IYw88kEKl++5LPZdKyZgx8JOfwPDh2dj668P990PnzvnVVRc//JBmWd1yC/zrXwseb9IEDjwwzcLq0aM8Zo1JkiQJMFCSJNVF9RlKpdhDaX5NmsB116Xw6Kqr0tijj8Lhh6d+Pa1a5Vref91/P5x4Ikydmo317JkCmXJZ4hYjvPFGqvmee2r/87HeeulzHnccrL12Q1coSZKkZcBASZJUvHJZ8lZdCHDllSlUuuyyNPbEE3DIISlcyjNUmjkTzjknhV5VWrRI9Z56avkscXv33TTbqLbZSM2bpwCvb9/UcLscn7YnSZKk//KnOUlS8coxUIIUzFx6KVx0UTb27LPw+9/nV9PHH6f+QdXDpE6d4NVX4bTTyiNMmjsX/vIX2HbbBcOkTTdNwdiXX6blb/vsY5gkSZJUAfyJTpJUvOo9lMphyVt1IaQA6ZJLsrGrr4Zx4xq+lgcfTCHMW29lYz/+cdrv0qXh66mLMWNgt93gV79KfakgzQI7/nh45ZU0a+nss6Ft23zrlCRJ0jJloCRJKl65zlCq7je/ga5d0/aMGfC73zXce8+cCf/7v3DkkTBlShpr3hyuvTb1UVp55Yarpa7mzUuzqrbeOs2mqtK1awrEbr01zbwqhxlWkiRJKpqBkiSpeJUQKDVpkvVSAhgwAN5+u37fc+ZMeOYZ2GWXFB5V6dgxhTKnn14eAcxnn8G++6Z6p01LY82apVlfr74Km22Wb32SJEmqdzblliQVr/qSt3INlCA1h95nn9RHad68NNvmJz+B9u2z1xprZNvt2hX/aPuvvoLHH4fHHkth0vxLBA8/PM3mWWWVZfax6k2McNttcNZZ2cwqgC22gDvugM6d86pMkiRJDcxASZJUvOWXz7anTUthTLk2Wq5qJg0pMBk0aOHnhpB6AdUWNlXfnj49hUhDhsAbb9R+rebNoV+/tPStHGYljR8PJ5+cgrEqTZrAeeelnlQtW+ZXmyRJkhqcgZIkqXhNmkCrVik4gRQqVZ+1VE622WbJz40Rvvkmveq6PK5TJzjoIDjpJNhyy7pdo6ENGgSnngqTJ2djG2wAt9+e+iRJkiSp0TFQkiTVzYorZoHSf/5TvoESpGVu992Xtn/8Y9hhh7RUreo1YUL6OnFi8ddu2hR23RV69EhB0sYbl8eMJEif97TTsv82Vc44Ay69tGYvLUmSJDUqBkqSpLpZYYU0UwdSX6A11si3nroKITWYrgpNQoBzz6393Nmz02eeP2iaf3vmzCxE2m+/8uiPNL/Bg6Fv3/S5qvzoR6l5+V575VeXJEmSSoKBkiSpbirhSW9Vtt462x45cuHnNW8Oa62VXpVq3jw4+2y45pqa4yecAFdeCSuvnE9dkiRJKilL1EE1hLBKCOGBEML7IYTRIYQd5zu+Rwjh+xDCiMLrovmONw0hDA8hPFZtrE0I4ZkQwkeFr6sWxluEEAaEEN4OIYwMIeyx9B9TkrTMVcqT3gA23zxrKj5mTPl/nrqaNy8tcaseJrVvn2Yr9e9vmCRJkqT/WtJH8lwNPBlj3ATYGhhdyzkvxRi3Kbwume/YmbV8z/nAczHGDYHnCvsAfQFijFsC3YErQghl+uggSapglTRDqVWr1NsIUuPtujbcLmdVYdKNN2Zjhx0G77yTej9JkiRJ1Sw2qAkhtAZ2A/oDxBhnxRi/W9I3CCGsDfQAbpnv0KHA7YXt24HDCtubkQImYoxfA98BXZf0/SRJDaR6oPTDD/nVsaws6bK3ShQjnH56zTDp6KPhgQdgtdXyq0uSJEkla0lm/nQCvgEGFJat3RJCqO2xLjsWlqg9EULYvNr4VcAvgXnznb9GjHE8QOHr6oXxkcChIYRmIYSOQBdgnfnfLIRwcghhWAhh2DdVTWElSQ2nkpa8Qc1AadSo/OpoaDGmmUk33JCNHX003H57ekKdJEmSVIslCZSaAdsCN8QYOwP/IVueVuUtYN0Y49bAtcDfAUIIBwFfxxjfLKKmW4FxwDBSGPUqMGf+k2KMN8UYu8YYu7Zr166Iy0uSlolKWvIGsNVW2XZjmaFUW5h01FGGSZIkSVqsJQmUxgHjYoyvFfYfIAVM/xVjnBJj/KGw/TjQPITQFtgZOCSEMBa4F9grhDCw8G0TQghrAhS+fl34/jkxxrMLvZgOBVYBPlqKzyhJqg+VvORt1KjUU6iSVS1zM0ySJElSHSw2UIoxfgV8HkIodCtlb+C96ueEENqHEEJhe/vCdSfFGC+IMa4dY1wP6A08H2M8pvBtjwJ9Ctt9gEcK37981ZK6EEJ3YE6Mscb7SZJKQKXNUFprraxf0NSpMHZsruXUqxjhjDPg+uuzsZ/+NIVJzZrlV5ckSZLKxpL+1HgGcFcIoQXwCXB8COEUgBjjjcCRwM9DCHOA6UDvGGNczDX/D7gvhHAi8BnQszC+OvBUCGEe8AVwbDEfSJLUQCqth1IIaZbS88+n/ZEjoVOnfGuqD1Vh0nXXZWO9e8MddxgmSZIkaYkt0U+OMcYRLPiktRurHf8b8LfFXGMoMLTa/iTSbKf5zxsLbDz/uCSpxFTakjdYMFA6/PB861nWpk+H885bMEy6807DJEmSJBXFnx4lSXVTaUveoGYfpUpqzP3BB3DjjXDbbfDdd9m4YZIkSZLqyJ8gJUl1U2lL3qCyAqXZs+GRR1LT7apZV9X16mWYJEmSpDrzp0hJUt1U4gylTTdNAcucOfDvf8OUKdC6dd5VFefzz+Hmm+GWW2D8+AWPd+qUeiidfrphkiRJkurMnyQlSXVTiT2UWrZModLbb6f9UaNgl13yrWlJvfQSXHEFDB4M8+bVPNakCRx8MPz859C9e9qXJEmSloKBkiSpbipxyRukZW9VgdKbb5Z+oPTll6nR9t13L3isfXvo2ze91lmn4WuTJElSxfKfKCVJdVOJS94Adtgh237qqfzqWJzZs+HKK2GTTRYMk/bcE+67Dz77DC65xDBJkiRJy5wzlCRJdVOJS94ADjgg237hBZg2DZZfPr96avPii3DaafDOOzXHe/WC3/0uhUySJElSPXKGkiSpbip1yVunTlkgM2MGDB2aazk1jB8Pxx4Lu+9eM0zaZBN49lm4917DJEmSJDUIAyVJUt1U6pI3gB49su3HH8+vjipz5sBVV8HGG8PAgdn4CivAZZfByJGw9965lSdJkqTGx0BJklQ3LVtmTwubNSv19KkUBx6YbQ8ZAjHmV8tLL8G228LZZ8PUqdl4z54wejT88pfQokV+9UmSJKlRMlCSJNVNCJW77G2XXbLPNnYsfPBBw9fwzTfQpw/stlv21DlIs5SeeSY13bbZtiRJknJioCRJqrtKXfbWogV0757tN/SytwcfhM03hzvuyMaWXx7+7/9g1CjYZ5+GrUeSJEmaj4GSJKnuKvVJb1Bz2VtDBUqTJsFRR8GRR6YZSlWOPBLefx9+9SuXt0mSJKkkNMu7AElSGavUGUoABxyQbb/4YupftNJK9fd+jz4KJ58MEyZkYx06wM0316xFkiRJKgEGSpKkuqvUHkqQwpxttoERI1LD8V/8AnbfHTbYANZfH9q2TX2k6mrePBg+PDX9HjIEXn+95vHjjoO//hVWWaXu7yFJkiTVEwMlSVLdVfIMJUjL3kaMSNu33JJeVVq3TsHS+utnIVPVdocO2RPwqps6NTXUHjIkLaP76qsFz2nfHm66CQ4+uF4+kiRJkrQsGChJkuquknsoARx7LPTrl2YozW/KlDTDaPjwBY+1bAkdO9aczTR0aFo6V9u1IAVQRx+dZiWtttoy/RiSJEnSsmagJEmqu+pL3saPz6+O+rLJJjB6NDz3HHz8MYwZk75+/PGiA7SZM1MT7fffX/T127RJ/ZF69IB99zVIkiRJUtkwUJIk1d2mm2bb/frBiSemx9tXkqqlbNXFCF9/nYVL1YOmMWNg4sSFX2/rrVOA1KMHdOsGTZvWb/2SJElSPQgxxrxrWGpdu3aNw4YNy7sMSWp8vvsONtooe8T92WdDnz5pyVeHDvX7VLRSNmVKzaBp/HjYfPPUk2nttfOuTpIkSVoiIYQ3Y4xdaz1moCRJWio335wedz+/li3hySdhjz0avCRJkiRJS29RgVItj6CRJKkIJ5wAnTsvOD5zJpxzTloeJkmSJKmiGChJkpZO06YwaBAcdBB06QJbbgnNm6djb70F/fvDvHn51ihJkiRpmTJQkiQtvQ03hMGDYdgwGDWq5hK4vn1TI+q77oI5c/KrUZIkSdIyY6AkSVr2zj8f1lor23/nHTjmmNTA+8YbYcaMND5rFkyYAKNHwyuvpFDq8cdh2rR86pYkSZK0RGzKLUmqH199BVdcATfcAP/5T81jK66YeivNP16lfXu48EI46SRo0aL+a5UkSZK0AJtyS5IaXvv20K8ffPYZ/P730KZNduyHHxYeJkEKo047DTbeGO64A+bOrf96JUmSJC0xAyVJUv1q0wYuugg+/RSuvBI6dMiONW0K7dqlpXA77AAHHlhzqdzYsdCnT2r0/dBDPjFOkiRJKhEueZMkNay5c9MMpJVWSq8Qah6fPh2uvx4uvRQmTap5rEsX+POfoXv3Bb9PkiRJ0jLlkjdJUulo2jTNUmrduvZQqFUrOOcc+OQT+N3vUuhU5c03Yb/9YM89UxNvSZIkSbkwUJIklabWreHii1OwdO65sNxy2bF//AN22QUOOghGjMitREmSJKmxMlCSJJW2tm1Tc+8xY+CUU6BZs+zYkCHQuTP07g0ffphfjZIkSVIjY6AkSSoPHTrADTfA++/DMcfUXC43aBBsthmcdFJ6qpwkSZKkemWgJEkqL+uvD3feCaNGwWGHZeNz50L//umJcX/6E8yenVuJkiRJUqUzUJIklacttoCHH4bXXktPfasycyb89rfQrVsKnSRJkiQtcwZKkqTytv328PTT8Pzz0KVLNj58OHTtCpdc4mwlSZIkaRkzUJIkVYY994R//Ss18K56Itzs2elJcdtvDyNH5lufJEmSVEEMlCRJlaNZMzj3XBgxAnbcMRsfMcLZSpIkSdIyZKAkSao8G28ML70El1+ezVaaMyebrTR6dL71SZIkSWXOQEmSVJmaNoVzzql9ttLOO8Mrr+RVmSRJklT2DJQkSZWtttlK334L++wDjz6ab22SJElSmTJQkiRVvqrZSi+/DO3apbEZM+Dww+GWW/KtTZIkSSpDBkqSpMajSxd49VXo1Cntz5sHffvCH/8IMeZbmyRJklRGDJQkSY3LBhuk/kmdO2djF14Ip58Oc+fmV5ckSZJURgyUJEmNT/v2MHQo7L13Nnb99dCrV1oKJ0mSJGmRDJQkSY1T69YwZAj07p2NPfgg7L8/fP99fnVJkiRJZcBASZLUeLVsCXfdBWedlY394x+w227wySe5lSVJkiSVOgMlSVLj1qQJXHklXHZZNjZqFGy8MZx0ksGSJEmSVAsDJUmSQoBf/hJuvx2aNk1jc+ZA//6w0UZwwgkwZky+NUqSJEklxEBJkqQqP/sZvPQS7L57NjZ3LgwYAJtsAn36wEcf5VefJEmSVCIMlCRJqm7HHdMT4IYOhT33zMbnzoU77kjB0rHHwgcf5FWhJEmSlDsDJUmSarP77vD88/Dii7D33tn4vHkwcCBstll6QtxDD8GMGfnVKUmSJOXAQEmSpEXZdVd49ll4+eWa4/PmwaBB8OMfwzbbwMcf51KeJEmSlAcDJUmSlsSmm0KrVrUf++AD6NZtwdBJkiRJqlAGSpIkLYnBg2H69IUfnzQpLY0bOLDhapIkSZJyYqAkSdKS6NMH3ngDfvpTaNq09nNmzUoNuy+6CGJs2PokSZKkBmSgJEnSkuraFe6+G/79bzj3XGjduvbz/vAH2G671GdJkiRJqkAGSpIkFWuddaBfPxg3Dv76V1h33QXPefPN9AQ4SZIkqQIZKEmSVFcrrQRnnQVjxsB990GXLjWPT56cS1mSJElSfWuWdwGSJJW9Zs2gZ8/0evVVGDAAllsOjjkm78okSZKkemGgJEnSsrTTTuklSZIkVTCXvEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSimKgJEmSJEmSpKIYKEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSimKgJEmSJEmSpKIYKEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSimKgJEmSJEmSpKIYKEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSimKgJEmSJEmSpKIYKEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSimKgJEmSJEmSpKIYKEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSimKgJEmSJEmSpKIYKEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSimKgJEmSJEmSpKIYKEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSimKgJEmSJEmSpKIYKEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSimKgJEmSJEmSpKIYKEmSJEmSJKkoIcaYdw1LLYTwDfBp3nVIZa4tMDHvIiQB3o9SqfBelEqH96OUj3VjjO1qO1ARgZKkpRdCGBZj7Jp3HZK8H6VS4b0olQ7vR6n0uORNkiRJkiRJRTFQkiRJkiRJUlEMlCRVuSnvAiT9l/ejVBq8F6XS4f0olRh7KEmSJEmSJKkozlCSJEmSJElSUQyUJBFC2D+E8EEIYUwI4fy865EqWQhhnRDCCyGE0SGEd0MIZxbG24QQngkhfFT4umq177mgcH9+EELYL7/qpcoTQmgaQhgeQnissO+9KOUghLBKCOGBEML7hb8jd/R+lEqbgZLUyIUQmgLXAQcAmwE/DSFslm9VUkWbA5wTY9wU2AE4rXDPnQ88F2PcEHiusE/hWG9gc2B/4PrCfStp2TgTGF1t33tRysfVwJMxxk2ArUn3pfejVMIMlCRtD4yJMX4SY5wF3AscmnNNUsWKMY6PMb5V2J5K+oG5A+m+u71w2u3AYYXtQ4F7Y4wzY4z/BsaQ7ltJSymEsDbQA7il2rD3otTAQgitgd2A/gAxxlkxxu/wfpRKmoGSpA7A59X2xxXGJNWzEMJ6QGfgNWCNGON4SKETsHrhNO9Rqf5cBfwSmFdtzHtRanidgG+AAYUlqLeEEFbA+1EqaQZKkkItYz7+UapnIYQVgQeBs2KMUxZ1ai1j3qPSUgohHAR8HWN8c0m/pZYx70Vp2WgGbAvcEGPsDPyHwvK2hfB+lEqAgZKkccA61fbXBr7MqRapUQghNCeFSXfFGB8qDE8IIaxZOL4m8HVh3HtUqh87A4eEEMaSlnvvFUIYiPeilIdxwLgY42uF/QdIAZP3o1TCDJQkvQFsGELoGEJoQWpw+GjONUkVK4QQSD0iRscYr6x26FGgT2G7D/BItfHeIYSWIYSOwIbA6w1Vr1SpYowXxBjXjjGuR/q77/kY4zF4L0oNLsb4FfB5CGHjwtDewHt4P0olrVneBUjKV4xxTgjhdOApoClwa4zx3ZzLkirZzsCxwNshhBGFsV8D/wfcF0I4EfgM6AkQY3w3hHAf6QfrOcBpMca5DV611Hh4L0r5OAO4q/APnJ8Ax5MmQHg/SiUqxOhSU0mSJEmSJC05l7xJkiRJkiSpKAZKkiRJkiRJKoqBkiRJkiRJkopioCRJkiRJkqSiGChJkiRJkqSyFkLoGUJ4N4QwL4TQdRHn7R9C+CCEMCaEcH618TYhhGdCCB8Vvq5aGF8thPBCCOGHEMLf5rtWlxDC24VrXRNCCIXxliGEQYXx10II61X7nj6F9/gohNCn2njHwrkfFb63RWE8FK49JoQwKoSwbV0/y2L++/0ohPB0CGF0COG96jUvjIGSJEmSJEkqGyGEPUIIt803/A5wBPDiIr6vKXAdcACwGfDTEMJmhcPnA8/FGDcEnivsA8wALgTOreWSNwAnAxsWXvsXxk8Evo0xbgD8Fbis8P5tgIuBbsD2wMXVwp7LgL8W3v/bwjUo1Fp1/ZML71nXz7IodwD9YoybFmr7enHfYKAkSZIkSZLKWoxxdIzxg8Wctj0wJsb4SYxxFnAvcGjh2KHA7YXt24HDCtf9T4zxZVKw9F8hhDWB1jHGf8YYIymQOayWaz0A7F2YvbQf8EyMcXKM8VvgGWD/wrG9CufWeP/Cte6Iyb+AVQrvXfRnCSE0DSH0CyG8UZjt9D+F8c2AZjHGZwqf+YcY47TF/Lc0UJIkSZIkSY1CB+DzavvjCmMAa8QYxwMUvq6+BNcat5Br/fd9YoxzgO+B1Rbx/qsB3xXOXei15jtWl89yIvB9jHE7YDugbwihI7AR8F0I4aEQwvBC6NR0MZ+fZos7QZIkSZIkKW8hhNeAlsCKQJsQwojCoV/FGJ9akkvUMhbrWs4irrWwY8WO1+Vai7IvsFUI4cjC/sqkpXTNgF2BzsBnwCDgOKD/oi5moCRJkiRJkkpejLEbpB5KwHExxuOKvMQ4YJ1q+2sDXxa2J4QQ1owxji8sKVtcD6Fxhe+v7VpV7zMuhNCMFNxMLozvMd/3DAUmkpayNSvMUqrtWvO/T4s6fJYAnDF/+BZC2AEYHmP8pLD/d2AHFhMoueRNkiRJkiQ1Bm8AGxaeqNYC6A08Wjj2KFD11LU+wCOLulBhKdnUEMIOhR5IP6v2PdWvdSTwfKHP0lPAviGEVQvNuPcFnioce6Fw7vzv/yjws8LT3nYgLVkbX8fP8hTw8xBCc4AQwkYhhBUK11o1hNCucN5ewHuL+vwAIdUtSZIkSZJU+mqboRRCOBy4FmgHfAeMiDHuF0JYC7glxnhg4bwDgauApsCtMcY/FcZXA+4DfkRa9tUzxji5cGws0Jo0K+g7YN8Y43shhK7AbUAr4AnS7J8YQlgOuJO0hGwy0Lva7J8TgF8Xyv5TjHFAYbwTqbF2G2A4cEyMcWYhrPob6Qly04DjY4zD6vJZQghNgD8CB5NmK30DHBZj/D6E0B24ojD+JnByodn3wv93MFCSJEmSJElSMVzyJkmSJEmSpKIYKEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSimKgJEmSJEmSpKIYKEmSJEmSJKkoBkqSJEmSJEkqioGSJEmSJEmSivL/AfqqArwiZnbiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1440x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 16))\n",
    "\n",
    "fmt = tkl.NetworkFormat({\n",
    "           \"pos_edge_id\": 0,\n",
    "           \"pos_source\": 1,\n",
    "           \"pos_target\": 2,\n",
    "           \"pos_wkt\": 4,\n",
    "           \"srid\": \"ENU\",\n",
    "           \"separator\": \",\",\n",
    "           \"header\": 1})\n",
    "networkpath = config['output']['RESULT_PATH'] + 'merge_2/reseau_mobilite_2.csv'\n",
    "squelette = tkl.NetworkReader.readFromFile(networkpath, fmt, verbose=False)\n",
    "\n",
    "L = list(squelette.EDGES.items())\n",
    "for i in range(len(L)):\n",
    "    x1d = []\n",
    "    y1d = []\n",
    "    edge = L[i][1]\n",
    "    for j in range(edge.geom.size()):\n",
    "        x1d.append(edge.geom.getX()[j])\n",
    "        y1d.append(edge.geom.getY()[j])\n",
    "    ax.plot(x1d, y1d, 'r-', linewidth=3, label='Mobility Network')\n",
    "\n",
    "\n",
    "# Supprime les doublons dans la légende\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "by_label = dict(zip(labels, handles))\n",
    "ax.legend(by_label.values(), by_label.keys())"
   ]
  }
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