{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "from TRIOMA.tools.Circuit import Circuit\n",
    "from TRIOMA.tools.Extractors.PAV import Component,Fluid,Membrane, Geometry\n",
    "import TRIOMA.tools.correlations as corr\n",
    "import TRIOMA.tools.materials as materials\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import griddata\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.colors import LogNorm\n",
    "import matplotlib.lines as mlines\n",
    "from TRIOMA.tools.BreedingBlanket import BreedingBlanket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This analysis is a sweep on two parameters and the analytical efficiency value from it. First we define the d_hyd_v, the first sweep vector. This is going to be sweeped against all others (otherwise the analysis would be very long if we sweep all vectors against each other). Then we define a vector of vectors to sweep. A str_v_vec is used in the \"update_attribute\" method to indicate which attribute is going to be updated, corresponding with the vector that is sweeped in the loop. The Fluid_v_bool vector is used in this code to check if the update attribute must be used for the Component class or the Component.fluid class. The same happens for the solid_v_bool. Color vectors will be used after for plotting contours. Eff_v_vec is used then to store the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Define sweep vectors\n",
    "color_vector = [\"red\", \"blue\", \"green\", \"yellow\", \"purple\", \"orange\"]\n",
    "N_vec=50\n",
    "# d_hyd_v=np.logspace(np.log10(2.5E-2/2),np.log10(2.5E-2*2),N_vec)\n",
    "d_hyd_v=np.linspace(9.2E-3,2.5E-2*2,N_vec)\n",
    "n_pipes_vec=np.linspace(2000,8000,N_vec)\n",
    "# str_v_vec=['T','U0','Solubility','c_in',\"thick\",\"K_S\",\"k_d\",\"k_r\"]\n",
    "variables={'d_Hyd':'Hydraulic Diameter [m]','n_pipes':'Number of Pipes'}\n",
    "# variables = {'T' : 'Temperature [K]', 'U0' : 'Velocity [m/s]', 'Solubility' : 'Solubility [mol/m^3]', 'c_in' : 'Concentration [mol/m^3]', 'thick' : 'Thickness [m]', 'K_S' : 'Partition Coefficient', 'k_d' : 'Desorption Rate [1/s]', 'k_r' : 'Reaction Rate [1/s]'}\n",
    "c_in_vec=np.logspace(-6,-1,N_vec)\n",
    "D_vec=np.logspace(-10,-8,N_vec)\n",
    "thick_vec=np.logspace(-3,-1,N_vec)\n",
    "K_S_vec=np.logspace(-3,-1,N_vec)\n",
    "k_d_vec=np.logspace(2,6,N_vec)\n",
    "k_r_vec=np.logspace(2,6,N_vec)\n",
    "solubility_vec=np.logspace(-3,-1,N_vec)\n",
    "# v_vec=np.array([T_vec, U0_vec, solubility_vec, c_in_vec,  thick_vec, K_S_vec])\n",
    "v_vec=np.array([d_hyd_v])\n",
    "eff_v_vec=np.array([])\n",
    "fluid_v_bool=np.array([True , False , True ,False,False,False,False,False,False])\n",
    "solid_v_bool=np.array([False, False, False,False,True ,True ,True ,True ,True ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here variables for the Breeding Blanket class are defined. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define other HX constraints\n",
    "\n",
    "T_hot_prim=900\n",
    "T_hot_sec=838\n",
    "T_cold_prim=800\n",
    "T_cold_sec=581\n",
    "T_sec_ave=(T_hot_sec+T_cold_sec)/2\n",
    "rho_sec=2263.628-0.636*T_sec_ave\n",
    "mu_sec=0.075439-2.77E-4*(T_sec_ave-273.15)+3.49E-7*(T_sec_ave-273.15)**2-1.474E-10*(T_sec_ave-273.15)**3\n",
    "k_sec=0.45\n",
    "cp_sec=1396.044+0.172*(T_sec_ave)\n",
    "N_HX=2\n",
    "Q=0.625E9\n",
    "m_in_sec=Q/(cp_sec*(T_hot_sec-T_cold_sec))\n",
    "n_pipes_HX=4000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Empty arrays to store results with the append method are defined, and the double sweep takes place. In each iteration, the same component is defined and then one attribute is changed according to the sweep. Then color map plots are displayed, with additional isovariable contours.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025.386635865169 indicates laminar flow\n",
      "1999.6958495144274 indicates laminar flow\n",
      "1966.3947921021054 indicates laminar flow\n",
      "2011.2210722639497 indicates laminar flow\n",
      "1976.574129286256 indicates laminar flow\n",
      "1943.1006839621323 indicates laminar flow\n",
      "1910.7421093067626 indicates laminar flow\n",
      "2026.9254445700262 indicates laminar flow\n",
      "1990.7620030191872 indicates laminar flow\n",
      "1955.866363853016 indicates laminar flow\n",
      "1922.173006461864 indicates laminar flow\n",
      "1889.620848623725 indicates laminar flow\n",
      "1858.1528769405215 indicates laminar flow\n",
      "2009.1299047501614 indicates laminar flow\n",
      "1972.6327987333282 indicates laminar flow\n",
      "1937.4380207954594 indicates laminar flow\n",
      "1903.4770862498099 indicates laminar flow\n",
      "1870.6862295030637 indicates laminar flow\n",
      "1839.0060044641607 indicates laminar flow\n",
      "1808.3809248796147 indicates laminar flow\n",
      "1993.6029094797493 indicates laminar flow\n",
      "1956.7178202784182 indicates laminar flow\n",
      "1921.172812679416 indicates laminar flow\n",
      "1886.8961593834038 indicates laminar flow\n",
      "1853.8211622606843 indicates laminar flow\n",
      "1821.8857191682016 indicates laminar flow\n",
      "1791.0319347824873 indicates laminar flow\n",
      "1761.205770317538 indicates laminar flow\n",
      "2019.037792130242 indicates laminar flow\n",
      "1980.2467100437316 indicates laminar flow\n",
      "1942.9180897472131 indicates laminar flow\n",
      "1906.9707570510013 indicates laminar flow\n",
      "1872.3294360858713 indicates laminar flow\n",
      "1838.9242231278936 indicates laminar flow\n",
      "1806.6901157625314 indicates laminar flow\n",
      "1775.5665907147727 indicates laminar flow\n",
      "1745.4972245761528 indicates laminar flow\n",
      "1716.4293524281093 indicates laminar flow\n",
      "2008.320014355422 indicates laminar flow\n",
      "1968.9790038956078 indicates laminar flow\n",
      "1931.1496841748785 indicates laminar flow\n",
      "1894.7465668609186 indicates laminar flow\n",
      "1859.6904903472578 indicates laminar flow\n",
      "1825.908045108536 indicates laminar flow\n",
      "1793.3310605710037 indicates laminar flow\n",
      "1761.8961459502373 indicates laminar flow\n",
      "1731.5442785482908 indicates laminar flow\n",
      "1702.2204338841823 indicates laminar flow\n",
      "1673.8732527811312 indicates laminar flow\n",
      "1999.686179368856 indicates laminar flow\n",
      "1959.7316269113394 indicates laminar flow\n",
      "1921.3424150916817 indicates laminar flow\n",
      "1884.428320848067 indicates laminar flow\n",
      "1848.9059241142834 indicates laminar flow\n",
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      "1781.7328504688132 indicates laminar flow\n",
      "1749.9440187829955 indicates laminar flow\n",
      "1719.2696262901509 indicates laminar flow\n",
      "1689.6520782608318 indicates laminar flow\n",
      "1661.0376814515005 indicates laminar flow\n",
      "1633.3763192461035 indicates laminar flow\n",
      "1993.0840314886989 indicates laminar flow\n",
      "1952.4494979664419 indicates laminar flow\n",
      "1913.4387538346937 indicates laminar flow\n",
      "1875.9563737903036 indicates laminar flow\n",
      "1839.9142660248842 indicates laminar flow\n",
      "1805.2309810249697 indicates laminar flow\n",
      "1771.831097102505 indicates laminar flow\n",
      "1739.6446728986841 indicates laminar flow\n",
      "1708.6067584967832 indicates laminar flow\n",
      "1678.656957952588 indicates laminar flow\n",
      "1649.7390370420724 indicates laminar flow\n",
      "1621.8005708660319 indicates laminar flow\n",
      "1594.7926266654874 indicates laminar flow\n",
      "1988.4740642560405 indicates laminar flow\n",
      "1947.0897846081903 indicates laminar flow\n",
      "1907.3929710902944 indicates laminar flow\n",
      "1869.2824749000467 indicates laminar flow\n",
      "1832.6650728566817 indicates laminar flow\n",
      "1797.4547060396947 indicates laminar flow\n",
      "1763.5718045397782 indicates laminar flow\n",
      "1730.9426871693704 indicates laminar flow\n",
      "1699.499026601022 indicates laminar flow\n",
      "1669.177371762242 indicates laminar flow\n",
      "1639.9187204613747 indicates laminar flow\n",
      "1611.6681361872552 indicates laminar flow\n",
      "1584.3744038460468 indicates laminar flow\n",
      "1557.9897198962838 indicates laminar flow\n",
      "2029.9104362659905 indicates laminar flow\n",
      "1985.8289237049662 indicates laminar flow\n",
      "1943.6212658141746 indicates laminar flow\n",
      "1903.1704661583808 indicates laminar flow\n",
      "1864.3690694867532 indicates laminar flow\n",
      "1827.1182085489181 indicates laminar flow\n",
      "1791.3267629426207 indicates laminar flow\n",
      "1756.9106149260172 indicates laminar flow\n",
      "1723.791989399783 indicates laminar flow\n",
      "1691.898867158031 indicates laminar flow\n",
      "1661.1644620912243 indicates laminar flow\n",
      "1631.526754354071 indicates laminar flow\n",
      "1602.928072631419 indicates laminar flow\n",
      "1575.3147195815277 indicates laminar flow\n",
      "1548.6366353382407 indicates laminar flow\n",
      "1522.847094635465 indicates laminar flow\n",
      "1985.1330001718875 indicates laminar flow\n",
      "1942.0238739173565 indicates laminar flow\n",
      "1900.7472673035677 indicates laminar flow\n",
      "1861.1887646990044 indicates laminar flow\n",
      "1823.2432811892513 indicates laminar flow\n",
      "1786.814130419162 indicates laminar flow\n",
      "1751.8122019953564 indicates laminar flow\n",
      "1718.1552337144135 indicates laminar flow\n",
      "1685.7671661041993 indicates laminar flow\n",
      "1654.5775686177801 indicates laminar flow\n",
      "1624.5211283686237 indicates laminar flow\n",
      "1595.5371935962605 indicates laminar flow\n",
      "1567.569365146902 indicates laminar flow\n",
      "1540.5651301789937 indicates laminar flow\n",
      "1514.4755330881326 indicates laminar flow\n",
      "1489.2548793126236 indicates laminar flow\n",
      "1986.382197194703 indicates laminar flow\n",
      "1942.2884030458758 indicates laminar flow\n",
      "1900.1096895882056 indicates laminar flow\n",
      "1859.723944987664 indicates laminar flow\n",
      "1821.0192230148539 indicates laminar flow\n",
      "1783.8927067750947 indicates laminar flow\n",
      "1748.2497966691083 indicates laminar flow\n",
      "1714.0033055494139 indicates laminar flow\n",
      "1681.0727466558294 indicates laminar flow\n",
      "1649.3837020875624 indicates laminar flow\n",
      "1618.8672613814256 indicates laminar flow\n",
      "1589.4595212815314 indicates laminar flow\n",
      "1561.101139058212 indicates laminar flow\n",
      "1533.7369328056022 indicates laminar flow\n",
      "1507.315523052829 indicates laminar flow\n",
      "1481.7890107912667 indicates laminar flow\n",
      "1457.112687672783 indicates laminar flow\n",
      "1989.5838710862101 indicates laminar flow\n",
      "1944.4163761272089 indicates laminar flow\n",
      "1901.254141009695 indicates laminar flow\n",
      "1859.9665271321162 indicates laminar flow\n",
      "1820.4340024879243 indicates laminar flow\n",
      "1782.546985908906 indicates laminar flow\n",
      "1746.204832688297 indicates laminar flow\n",
      "1711.3149418099017 indicates laminar flow\n",
      "1677.7919681082292 indicates laminar flow\n",
      "1645.5571252476082 indicates laminar flow\n",
      "1614.5375675364166 indicates laminar flow\n",
      "1584.6658403663248 indicates laminar flow\n",
      "1555.8793905502312 indicates laminar flow\n",
      "1528.120129078109 indicates laminar flow\n",
      "1501.3340398590049 indicates laminar flow\n",
      "1475.4708289038253 indicates laminar flow\n",
      "1450.4836091548316 indicates laminar flow\n",
      "1426.328616806457 indicates laminar flow\n",
      "1994.7569395123305 indicates laminar flow\n",
      "1948.4200668568403 indicates laminar flow\n",
      "1904.1870717935424 indicates laminar flow\n",
      "1861.9178484370807 indicates laminar flow\n",
      "1821.4844610535201 indicates laminar flow\n",
      "1782.769850712312 indicates laminar flow\n",
      "1745.666703441825 indicates laminar flow\n",
      "1710.0764568395737 indicates laminar flow\n",
      "1675.9084257724555 indicates laminar flow\n",
      "1643.0790308370242 indicates laminar flow\n",
      "1611.511115759726 indicates laminar flow\n",
      "1581.13334200118 indicates laminar flow\n",
      "1551.8796505656421 indicates laminar flow\n",
      "1523.6887824698815 indicates laminar flow\n",
      "1496.5038505454581 indicates laminar flow\n",
      "1470.2719562757152 indicates laminar flow\n",
      "1444.9438462368491 indicates laminar flow\n",
      "1420.4736034481796 indicates laminar flow\n",
      "1396.8183695621851 indicates laminar flow\n",
      "2001.9321620773048 indicates laminar flow\n",
      "1954.3226772249186 indicates laminar flow\n",
      "1908.925065501634 indicates laminar flow\n",
      "1865.5886852031329 indicates laminar flow\n",
      "1824.1762704282207 indicates laminar flow\n",
      "1784.562478734868 indicates laminar flow\n",
      "1746.6326240086842 indicates laminar flow\n",
      "1710.2815675612478 indicates laminar flow\n",
      "1675.412744876609 indicates laminar flow\n",
      "1641.9373090338247 indicates laminar flow\n",
      "1609.7733748065443 indicates laminar flow\n",
      "1578.8453498997317 indicates laminar flow\n",
      "1549.0833418254806 indicates laminar flow\n",
      "1520.422630621744 indicates laminar flow\n",
      "1492.8031990414381 indicates laminar flow\n",
      "1466.1693130344015 indicates laminar flow\n",
      "1440.4691463512072 indicates laminar flow\n",
      "1415.654443948265 indicates laminar flow\n",
      "1391.6802195945004 indicates laminar flow\n",
      "1368.5044836926816 indicates laminar flow\n",
      "2011.1526007237205 indicates laminar flow\n",
      "1962.1586754135174 indicates laminar flow\n",
      "1915.4950743661454 indicates laminar flow\n",
      "1870.9994019486214 indicates laminar flow\n",
      "1828.5240093381701 indicates laminar flow\n",
      "1787.9343577707068 indicates laminar flow\n",
      "1749.107595051394 indicates laminar flow\n",
      "1711.9313135979157 indicates laminar flow\n",
      "1676.302463569965 indicates laminar flow\n",
      "1642.1263989519089 indicates laminar flow\n",
      "1609.3160379934175 indicates laminar flow\n",
      "1577.7911223269439 indicates laminar flow\n",
      "1547.477561491128 indicates laminar flow\n",
      "1518.3068515905375 indicates laminar flow\n",
      "1490.2155584901861 indicates laminar flow\n",
      "1463.1448573386285 indicates laminar flow\n",
      "1437.0401213847115 indicates laminar flow\n",
      "1411.850554039594 indicates laminar flow\n",
      "1387.5288589691602 indicates laminar flow\n",
      "1364.0309437085168 indicates laminar flow\n",
      "1341.3156528908403 indicates laminar flow\n",
      "2022.4742729121756 indicates laminar flow\n",
      "1971.9743033070247 indicates laminar flow\n",
      "1923.9348051132542 indicates laminar flow\n",
      "1878.1802352551165 indicates laminar flow\n",
      "1834.551361650921 indicates laminar flow\n",
      "1792.90341175366 indicates laminar flow\n",
      "1753.1044676842646 indicates laminar flow\n",
      "1715.034070472471 indicates laminar flow\n",
      "1678.5820022940604 indicates laminar flow\n",
      "1643.6472207731472 indicates laminar flow\n",
      "1610.1369236476505 indicates laminar flow\n",
      "1577.9657255649745 indicates laminar flow\n",
      "1547.0549316322633 indicates laminar flow\n",
      "1517.331894708833 indicates laminar flow\n",
      "1488.7294453907218 indicates laminar flow\n",
      "1461.1853852728452 indicates laminar flow\n",
      "1434.6420354424213 indicates laminar flow\n",
      "1409.0458333057886 indicates laminar flow\n",
      "1384.3469718180436 indicates laminar flow\n",
      "1360.4990760022283 indicates laminar flow\n",
      "1337.4589123375717 indicates laminar flow\n",
      "1315.1861271851744 indicates laminar flow\n",
      "1983.8282676972929 indicates laminar flow\n",
      "1934.2932656642151 indicates laminar flow\n",
      "1887.17171966523 indicates laminar flow\n",
      "1842.2914409508787 indicates laminar flow\n",
      "1799.496240090712 indicates laminar flow\n",
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      "1719.6056561998516 indicates laminar flow\n",
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      "1646.5071869636001 indicates laminar flow\n",
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      "1517.493372429099 indicates laminar flow\n",
      "1488.3382916252247 indicates laminar flow\n",
      "1460.282385924657 indicates laminar flow\n",
      "1433.264645426867 indicates laminar flow\n",
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      "1382.1213906311557 indicates laminar flow\n",
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      "1334.5022783716124 indicates laminar flow\n",
      "1311.902372611376 indicates laminar flow\n",
      "1290.0551820797252 indicates laminar flow\n",
      "1997.792634738817 indicates laminar flow\n",
      "1946.6314876779684 indicates laminar flow\n",
      "1898.0252669330112 indicates laminar flow\n",
      "1851.7872499215068 indicates laminar flow\n",
      "1807.7484764330495 indicates laminar flow\n",
      "1765.7556855890111 indicates laminar flow\n",
      "1725.6695338128977 indicates laminar flow\n",
      "1687.3630501461041 indicates laminar flow\n",
      "1650.720292829753 indicates laminar flow\n",
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      "1518.7920108562878 indicates laminar flow\n",
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      "1460.4319486572515 indicates laminar flow\n",
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      "1406.390933325113 indicates laminar flow\n",
      "1380.8429591133304 indicates laminar flow\n",
      "1356.2066145568215 indicates laminar flow\n",
      "1332.433960374867 indicates laminar flow\n",
      "1309.4803606521446 indicates laminar flow\n",
      "1287.3042031249129 indicates laminar flow\n",
      "1265.86664741573 indicates laminar flow\n",
      "2013.9539535849813 indicates laminar flow\n",
      "1961.0234451423973 indicates laminar flow\n",
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      "1863.0922865600112 indicates laminar flow\n",
      "1817.7052759965713 indicates laminar flow\n",
      "1774.4770320815212 indicates laminar flow\n",
      "1733.2571146886003 indicates laminar flow\n",
      "1693.9087448470164 indicates laminar flow\n",
      "1656.307288486974 indicates laminar flow\n",
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      "1521.2336579247742 indicates laminar flow\n",
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      "1331.245756620193 indicates laminar flow\n",
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      "1285.3794951186699 indicates laminar flow\n",
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      "1242.5684882608398 indicates laminar flow\n",
      "1977.557195387662 indicates laminar flow\n",
      "1925.5832623988595 indicates laminar flow\n",
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      "1784.8551806472356 indicates laminar flow\n",
      "1742.4081700559516 indicates laminar flow\n",
      "1701.9331909291677 indicates laminar flow\n",
      "1663.295936205203 indicates laminar flow\n",
      "1626.3740242372091 indicates laminar flow\n",
      "1591.055703932292 indicates laminar flow\n",
      "1557.2387250197905 indicates laminar flow\n",
      "1524.829349391956 indicates laminar flow\n",
      "1493.741483383965 indicates laminar flow\n",
      "1463.8959140783497 indicates laminar flow\n",
      "1435.2196353696902 indicates laminar flow\n",
      "1407.6452517178332 indicates laminar flow\n",
      "1381.1104493383805 indicates laminar flow\n",
      "1355.5575260964947 indicates laminar flow\n",
      "1330.9329726393546 indicates laminar flow\n",
      "1307.1870983680208 indicates laminar flow\n",
      "1284.2736967468595 indicates laminar flow\n",
      "1262.1497452068866 indicates laminar flow\n",
      "1240.7751355420844 indicates laminar flow\n",
      "1220.1124312440777 indicates laminar flow\n",
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      "1033.3605285026372 indicates laminar flow\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Gabriele\\AppData\\Local\\Temp\\ipykernel_23112\\89788373.py:87: UserWarning: Log scale: values of z <= 0 have been masked\n",
      "  contour1=plt.contour(X, Y, Z, levels=np.linspace(np.nanmin(Z), np.nanmax(Z), 7), norm=LogNorm(), colors=color_vector[0])\n"
     ]
    },
    {
     "data": {
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yuk9ZgTc9dH9S6F3KE5fV38TRo0eFZ8dwy9IzY1wbMoLndX4GbovfkRkadMxwWzTs0KrHRE3GWjIu3NgfI/ElK9ACSktoXngfi03pFV7c//e//4kDmS5Wfok8KdFXz+d5Be+UaLljUCihlYp/cF4EmHXKAyCzA6q4QwsJ7ywZt8Z5MyxtQysErXu0QvAuj3eevDvNbFsUkIzx44WGooiCh642XqAI72Zp9eJJhCUOjOMhI3jCosWGd8V0XZkzt3iXxhMBYwBZ2uRycLyRbZUbuC888Ziz4pgJzPgehgrw8/Bzm2NzGO9iXNwVeQetIkzA4O/ffOzm1XmHf2P+TXmRpeeAsYD8G9JDwPfjNhkgziQmisrMXEK++8zQEt4MmOOKsnPc8jdF6zFd17wZY/wrrR++7mBa6rMbo6XIOjwGKKSZnMDvmscKPQy05PFvzOcUeEwwYvwXrf485/HcQBclE9c4nut4U0wBb5w3GMd59uxZEX/O4/fTTz8V17Pc7tOloECiwGSCGr0XPNZ5fLOo96Vqk2b1NxGkXxvoWuW+cZ/o1s4sLp9CkNvO6LxN4Ug3OI9/Jr/wt23EgdNdzBslLjM+C2PNswr3jXF+V8QToxVhXC6XNnHiRK1evXqav7+/VqZMGe3666/Xli5d6h4TGRmpff31116vy2hZRiQlJWl169bVNm3a5LV88uTJWrly5bSqVatq8+bNy8NPVLRwOp1ahQoV6C/QDhw4kG79woULtY4dO2rBwcFaRESE1rZtW+3LL790r+frZs6c6X6+detW7dprr9WCgoK0UqVKaffcc48WHx/vtc3PP//cfTzwvR9++OFMt8e/Y5UqVTSr1ap169bNazvPPfecZrPZtJMnT172c54/f17s0+7du93LXnjhBa1Zs2Ze4+644w7tpptu8lrG9x03bpyYHz58uPbMM894rX/ooYe0WrVqaYGBgeL4HjFihHbu3Dn3+tdff10c84qM8f3Ozd+3AddznJmdO3eK46VatWriPJOd886///4rXnvx4kX3a3gO4bJDhw6J5ykpKdrAgQO1EiVKiOXG+ejIkSNa//79tdDQUC08PFwbPHiwdurUqUseV2Z4fPC3we1fjh07dojfXkxMjHuZ3W4Xxxx/jyVLltQmTJgg9mHo0KHuMcnJyeIcunr16su+hyLj331W1kdHR2sjR47USpcuLX7/NWvWFOe82NhYcUzcfPPN4hwXEBAgjtPnn39enHNTU1PF34vnNq6rWLGi+Jvy72bw2WefifU8zvger732mthGbvYpI3i88/g2ppCQEK1BgwbaAw88oO3bt89rbGbHdlZ/E5MmTRKflefiQYMGaRcuXNAuxfz587VKlSqJ78x8fuC+jRkzxv0bePrpp73OAT/++KNWvXp18fk7dOigzZkzR3w2Qydk9Ps3uO6667Q33nhDuxJY+L+8l5AKRdGGWbm8+zVqYV0OZr4x5Z8ZmTmB4QC0UPIO2xwgfykYI0L3Al032S2MqyiaMEGJmYpZLdxKFzXdc7QSZeZuo0WIVhLDgsEwA2YrX6qsi0JxtaBHZ9asWdnuhkRpxCoIhpv7SkMPFuvE0lOYUcm33FJk3bgKxZWA7lya9imgzHFxl4M11ZhJltMAfAbt8qSTnWLddDc8/fTTSugphKuVAoxuNSPkJCuwSLy5XATdfnQD84LEkhPMBKe7zlxHkvGLmYVHKBSFBYvFIkKxrlb3Idb9ZejSlRB6RFn2FIpswHgWxoXcd999XtnDCkVBhjGAFHys9s+YwpzCQHiWSGJcLC8dLCbPYHRVS1RR1Cx7RQ0l9hQKhUKhUCiKMMqNq1AoFAqFQlGEUWJPoVAoFAqFogijxJ5CoVAoFApFEUaJPYVCoVAoFIoijBJ7CoVCoVAoFEUYJfYUCoVCoVAoijBK7CkUCoVCoVAUYZTYUygUCoVCoSjCKLGnUCgUCoVCUYRRYk+hUCgUCoWiCKPEnkKhUCgUCkURRok9hUKhUCgUiiKMEnsKhUKhUCgURRgl9hQKhUKhUCiKMErsKRQKhUKhUBRhlNhTKBQKhUKhKMIosadQKBQKhUJRhFFiT6FQKBQKhaIIo8SeQqFQKBQKRRFGiT2FQqFQKBSKIowSewqFQqFQKBRFGCX2FAqFQqFQKIowSuwpFAqFQqFQFGGU2FMoFAqFQqEowiixp1AoFAqFQlGEUWJPoVAoFAqFogijxJ5CoVAoFApFEUaJPYVCoVAoFIoijBJ7CoVCoVAoFEUYJfYUCoVCoVAoijBK7CkUCoVCoVAUYZTYUygUCoVCoSjCKLGnUCgUCoVCUYRRYk+hUCgUCoWiCKPEnkKhUCgUCkURRok9hUKhUCgUiiKMEnsKhUKhUCgURRgl9hQKhUKhUCiKMErs5RKn04nnnnsONWrUQHBwMGrVqoVXXnkFmqa5x3D++eefR4UKFcSYnj17Yt++fV7buXDhAm6//XZERESgRIkSuOuuu5CQkOA1ZuvWrejSpQuCgoJQpUoVvP3221ftcyoUCoVCUdivx77ceeedsFgs6aZGjRq5x8THx+ORRx5BtWrVxHY7duyI9evXIzPGjBkjtjFx4kQUGDRFrnjttde0qKgobd68edqhQ4e0GTNmaGFhYdqHH37oHvPmm29qkZGR2qxZs7QtW7Zo/fv312rUqKElJye7x/Tu3Vtr1qyZtmbNGm358uVa7dq1tWHDhrnXx8bGauXKldNuv/12bfv27dpPP/2kBQcHa1988cVV/8wKhUKhUBTG67EvMTExWnR0tHs6duyYVqpUKe2FF15wj7n11lu1hg0bakuXLtX27dsn1kVERGjHjx9Pt73ff/9dXMsrVqyoffDBB1pBQYm9XNK3b19t9OjRXssGDBggRBlxuVxa+fLltXfeecfr4AoMDBSCjezcuZO3Hdr69evdYxYsWKBZLBbtxIkT4vmkSZO0kiVLaqmpqe4xEyZM0OrVq3fFP6NCoVAoFIX9epwVZs6cKa69hw8fFs+TkpI0m80mBKSZli1bas8884zXMoq/SpUqCYNMtWrVCpTYU27cXEJz7t9//429e/eK51u2bMGKFStwww03iOeHDh3CqVOnhOvWIDIyEu3atcPq1avFcz7Sddu6dWv3GI63Wq1Yu3ate0zXrl0REBDgHnP99ddjz549uHjxYob7lpqairi4OPcUGxuLs2fPXtKkrVAoFApFXpCSkuJ1DcrJxOuW7zJe23JyPc4KU6dOFddfumyJw+EQ7mGGT5mhO5fbNnC5XBgxYgQef/xxLxdwQcEvv3egsPPkk0+Kg69+/fqw2WzioHjttddE/B2h0CPlypXzeh2fG+v4WLZsWa/1fn5+KFWqlNcYxiH4bsNYV7JkyXT79sYbb+Cll15Kt5w/HsYGKhQKhUJxpYRejWphOHXGmavthIWFpYtff+GFF/Diiy9m+3p8OU6ePIkFCxbgxx9/dC8LDw9Hhw4dROxfgwYNxHX3p59+EgaY2rVru8e99dZb4ro9duxYFESU2Mslv/zyC3744QdxcFDNb968WQRyVqxYEXfccUe+7ttTTz2F8ePHu5/zR8DEDoVCoVAoriRpaWlC6B3aUA0R4TlzIsbFu1Cj1REcO3bMy0ARGBh4Ra7H3377rfCy3XzzzV7Lp02bhtGjR6NSpUpCRLZs2RLDhg3Dhg0bxHo+fvjhh9i4caNIzCiIKLGXS2iy5d3E0KFDxfMmTZrgyJEjwqrGg6t8+fJi+enTp0U2rgGfN2/eXMxzzJkzZ7y2S9MxM3SN1/ORrzFjPDfG+MIfRGY/CoVCoVAorjQUejkVe+5tRERkyRt1uevxpWB401dffSVcseZwKcKs3qVLlyIxMVEYTXgtHzJkCGrWrCnWL1++XFzDq1at6n4NrYqPPfaYyMg9fPgw8hsVs5dLkpKSRGydGSp/+u8JXa8UY4wjMODBwlg8moYJH2NiYtx3CeSff/4R22BsnzFm2bJlsNvt7jGLFy9GvXr1MnThKhQKhUKR3zg1V66mvLweXwqKuf3794uyZ5kRGhoqhB7j5P/880/cdNNNYjkFIkuj0ZJoTLQmUnxyXEFAWfZySb9+/URMABU9zcabNm3C+++/L0y+hCZdmpFfffVV1KlTR4g/1gHigWCYihkH0Lt3b9xzzz34/PPPhaB76KGHxN0Jx5HbbrtNxN/xQJwwYQK2b98uzMYffPBBvn5+hUKhUCgywwVNTDl9bV5ej43wphMnTuC7776Db2IGjSuNGzeGLxRstPzRuEJBSBHHuMBRo0aJ9VFRUWIy4+/vLww9fE1BQIm9XPLxxx8L8fbAAw8IMy7F2X333SeKKBs88cQTwvx77733Cgte586dsXDhQq/sHsYZUOD16NFD3JkMHDgQH330kVcG76JFi/Dggw+iVatWKF26tHgPblOhuCTMvuadrc2Wq83EHIlBWLkw+AX5nDZ27QLq1+edjddYv13bEda7c67eU6FQFG5c4l/OX5vX1+Po6GgcPXo0XdLib7/9JgwoGcH1FInHjx8XiZO8PlNUUtAVFiysv5LfO6G4OtB9TNGY02zczz4Dfv8dGDYMMN0ogVnu4/ttQUjgYfzywofAwH8yfP3Zs8A770hN8NZbufkkAJOlvvoK6NMHMOWgePPbb3Kne/UCJkzwWhV3Ig6zR81GQGgAhswcggLDpglA9J9AuylAlKcUT46ZOhV49135Jd1zT443c3jpYfwy8BfU6VMHN397sycI+csvgQceAN57Dxg3Tiy6eOgivm31MQIunsYdL9VA6PP/y/3nUCgUhfJ6c3JP5VwlaFSsd1xVkMgDlGVPkSkHDwLnzgHMLi9VCmjTBmCFGN8SQixH9E3DJ1Bq4GIgpUSm22P2PMVecHDuxR7fkzG4bdte5gMwVrJSpXSr7Il2HFx8EIERBSyBJXanuJ+FPS5vtscajLt3S9F7991e1rfs4HK4kBKTgosHLsKR4oB/sH5HGx/PSGQpJuvWBW64AZpTg8uhwQkbnC+8DJQNY/+gvPk8CoWiUOHUNDHl9LWKvEGJPUWmsDTRmjXAzJkAwwtZ89lU99kNE34DY5KARzXgk08z3R4FIzVBaGju961TJzldkuRk+ehTDJOElQ/DLdNugdW/gOUodZsNWPJwnxhT8uyzwKZNAHs5XlIdZyzyYg7HoGaPmsICWuPaGh6hR/gHpRuXFsQhQ1j9G6UaNcId6x9EwKQPEP5RvLT8lS4NDBqUd59LoVAUCq5mzJ4icwrYlU5RkKhcWVrQfJKbMibZDpyjsArJdEhkpPT2vfxynu7m5cUeTYk+0KLXdHhTNB6SPhg3X8lLoUcYNHzrrXL+88+z99rvvsPGZnfi0/qfYMlLS1CvXz0EhHlKEpzbc05aCidNArp1k1a+G29klW9E1SuN8ImvAvfdJ2IGz932MFyLPRnpCoWieEDB5szhpMRe3qHEniJTZswAWB6of3/5nGX9li8Htm5NP/azE/3xCD7A/vNXpwwMrfuMFVy0iMU7sy/2CjwuO81qebOt+++Xj9OnS7duVvjjD2EVPLEzDi6nhuDZP7O8vHv1th+3YVKjSVj9/mqANakYH1mrljxguneXBwuF4Kef4kDnOzDZPgp/9PkU2n//5c1nUigUCkWWUWJPkWVmzwa6dgWeey79uq/O9ceHeAS79i0Cjv2e6TYcDk+YV25p0YL9gQGfxKosiT17sh0n1p/Aqc2yHV2BYsUQYEYEcGZJ3myvfXugaVP5fXz77WWH2zdtQ9LQUTjrikL/ShswEt+g9aYvRcZt2hdfI+l8Ei4cuCBi885sPyN7LdOCyHpSNAfTrUvBx4wcmw1pD41HGgJw0REO5w39gOPH8+ZzKRSKQuPGzemkyBuU2FNkGdZuZsFwnxa9gttCZ2NCwzdRo8Qs4OyqTLdRpgyroQN79uRuX2g0athQJov4tE30kJKSqdhjosGUtlMw7bppKHhYAGcKcG5NHm3OIt2rxNS4OzPOfDgdHybcjWkYgT9PNEINHIYNLuyIr4q/xs3F9Jumo17/ehg6eyj6T+nvycylZe/ff2VCzM6dwJ13ChNsgyFNMXzmQNzebDv8KpSRWT4KhaJYJWjkdFLkDUrsKTKFhppbbgFefVU+HzwYOHAAmDgx/dhHgz7Dm1WeQuMqO4DEzFvDVK8uYwBPnMj9/jHfYPt2QO86l57UVPmYQcs4i9WCEtVLILJKJAocpWVnFZxbnTfb4wlzzhw5f+21lx1e8Y2H0KGdE6mWYDTFNhm42bMnYtpfj42u5oioHIFyTcsJwcfv0Qumbi9cKL/z+fNlFjB14M1NYFu8EPjpJ+H2XTxhMXbP2p03n0+hUBRYXLmcFHmDysZVZEp0NDBrlscbekloRTPa+yYcynQYr//0+Pm0HrwyXELslWlYBuMOybpwBVfsrQHYLii3SRurV0tVTAsnU6wvg6VCBVyz5i10iEtFYNjL7gydamuO4+bDMWg4uKFXs+/ki8lIOpuEqLp6BXlWoGdtnUceAR57TCr8G26QZt0yZXDon0NY9fYqYcB8cOeDKF2/dO4+n0KhUCguibLsKTKFpU2YaOlTjzhjUlORdtYf5+NLXdKyV6HCVRJ6+j5lJvYKNCWbA7YgIO0CELc399tj4WPCwoQlMq+D6IuoQWhKxa7cvjIaD20Mq82zbMeMHXg76m3Mu2+e94sffhi47jp5E9C3r4zhW7dOrKrSsQo6P9UZHR7roISeQlHEyWkmrjEp8gYl9hSZUqeOTOQ0e/7YOYOl2lin18ycpJ4IPJ6Gfu/NlSIlr4oCXwLmAVBH0NVcpMSeLQAo1TpvXLnMvv35Zzl/BVrrlW9WHjwf07qnuUwnZopEpnPTskd1v2QJ0K6dqLXnd+IIerzeA73e7uUenpaYJhJmFApF0cKp5W5S5A1K7CmyxcaNMlaOFTbcOJ0o7TotZs8l6MH3CYcz1R6s8Wtut5ZTqCfoFl68WIalZSr2MjAlHlt9DD/1/wn/PJtxa7ciE7f3ww/SutakiRRbOSB6U7TIvnU5XUKUMQM3PjperCtVpxTGnxiPMZvHpI/fYyYOW7Xt2yeTNej6ZYkW7ssnn7hdwU67E7/e+iu+7vK1iuNTKIoYKmavYKBi9hSXhJ43NqBo0ABgz2cma7BsSsuWpkGpqWiN/3AWpRH1ZVUg3gYkRwMlm6bbnp8f8Nprcp6JHrlpd8is4C++kEmgFHvpOoFdwrIXezQWe+fuRWqcPqagUa4HEL8XKNMxd9tZulQ+sj9wDlqlsawKRVjVTlVxZPkRBJcKFoktZRuXxY2f3ygEW3jFcPf4Za8tQ63raqFSG1OLuqpVga+/llY+9s/95x/p5qX4bNNGlHBhJ5OSVcNx8ou5qH/ib+DBB3P3uRUKhULhxqKJIlmK4tSYOqtNpV0uUSZNcOaMjK/PkJgYWZdFvMlxILQsYDW11PLhqadk6Bi7aIV7dELew7osLAFCceGThXrx4EWRKBBSJgT1b6qPIgvj9djFgn8fWtiYHZNN1n+2Hv888w9K1iyJtPg08d3dve5uVGhRwWscrX7vlnsXzlQnRq8cjUpt0/ckFqqcvfeYHcwD4fXXxWJaDbU/F8HW9wZZuuXYsRz38VUoFAXnerNxZzmEhefMiZgQ70LLhqezfM1SZI4Se8WI7Io9ZuGyll1SEnDw4CV62rJbQvnyHoVYUC7SNP3R38wGvzl0YRZ6DDMs25489BDw8cc52gwtoEnnkoTLlYkb4RXSq/S0hDTMuWuOiN8bvnB4ereuwfffAyNGyKzdbdu8Dzg2UKbbmcu5XqFQFOrrzX87cif2WjdSYi8vUDF7ikxhpY5Dh6SWM4Qe6+wtW+YTs6f3K/vGdhfGjrOIuL6rBUUoY/a4n+mgSiUhmffrLdDwPizxCHBhU863QdOsURiRNe927MjRZijwaNkrzZ63JqHncrgQfzIeKbEpom/uwOkDMXTWULfQ473k+b3nvTfGMiwMuGQ5GPOBFBwMrds1iEUkMM8nu1ehUBRKnLDkalLkDUrsKbLF22/LnvfTpqUXe79gsDAcbZoxBVg+6JIt0+gWjo3N/f4w2YMVPmbOzJ7YizsRJxINEs8mosByZDowuzrw30O52w5d2ExZppVv/PhMsllyxi+DfsH7ld7H9unbxXORdGEB5t47Fwf/Poi/n/4bnzX9DPsX7ve8iK7kzp3l/Ny57sWp8an4cH1HfIhxSJ7hWa5QKAovSuwVDJTYU2SLihWBevU8IXpmsTco6A88+VgcmoR8CRyfBbjsGW5j1CigXDlg8uS8KQ/D0Lx0LmZhFdOFXAb+5zUT1+CzJp9h5dsrUWCJaisfL6wHHFmpbH0J3nlHZiUvWiRTmPOIsAphsNgsSI1NBf77D/joI+yZuQsbJ2/E7FGzcW7XORHDx2xeL/r1Syf2AsMD4V8qAla4cGbj8bxps6JQKBQKJfYUmUOX7U03yUQKgxdekDX2GP7lK/ZGh8/AG++EoW3dbYDmlC7IDChdWob1GVosN7z0kvQGMgchXSauYcHKoDeuX6CfSM4ILpl+XYEhrCYQVF6KZgq+3MCU5TFj5DwzY/OI6969Ds+mPotOT3SS6n33bpQ5sx0t722Jtg+1xeAZgzFs7jC0fVAXrr5ij/X3WI9HZ/CsYXii7RJUw1Hgm2/ybD8VCkX+4NIsuZoUeYMSe4pMYVgVkyZ5zb2k589c4oStvShSSPyBDIez9Ar1IYXjFcNw4Wbixu3+anc8fuZxdHm6CwosVMRG6RW2TsstcXqha7YvywWnt53Gwb8Oili9gNAAT0eNPn1EmnW523uh3xf9hAC0+dtQ98a66TdC8zATMOx22ZNPp2yjsgh4WFfu772XN75+hUKRbyg3bsFAiT3FJVub8XpLg80lxZ5u2aPY4+yJ5DbyeULGYo/ai/X2rihGQ1++EQsEFlbyqrgys6QXLPCIslyw4csNmNZrGtZ+tNZ7Bc3ALKWi1+jZPXs39szZg8QziaK7xvl95xF7zCTe2L6NTJ/uvZ1hw0Rhx9iLLuCDD3K1rwqFIn9xwpqrSZE3qG9SkSkspsx4/ttv97RI3bNHtigbODC9ZW+PVlcY9xqPnnRJsZfXmo77wxIxrNiRzrKXgQu30Iq93CRWbNok06rDwjzJETkkpHQISjcofcm+tqzFx64Y02+ajlObT2HBuAX4pO4nWP+pyR09ZIh8/OsvuW86dN38En63SNQ4++63wAWfeD+FQqFQZAsl9hTZhvH9f/+dXuxVDJVWm+TUACSmhGQq9s6eBR5/PG+aJFCQLl8u++R6lYMxLHuZlF1hYsZvt/2GQ/9mVLOlAFGqlSxQnXI60xjILGEkZfTsmWH7uOxwzQvX4MGdD6L5nc0vKQjbj2+Puv3qomavmijXpBxsgTZReNlN7dqy/iGtjkb/Xp6U/KyisLIGCw4mlpUt1xQKRaFEy0W8Hl+ryBtUuzTFJWGJFCZFsqlB2bKy8xXduszKdbco09244cEOMb502l+wLEvJNIOUpVd4/aa18KOPPF06cgLff+pUWYu3SpUMxF4mlr0jS49g3/x9QogUaGxBQLM3gdAqQGD2u1+4ySMXbnbq8vV8o6eos8dyLE1HNEXzUc1FDJ8XNBuvXStr+Ywd617c462euLaHBWUeegn4cLtss8YUboVCUajITeyditnLO5RlT3FJ7rlHNmD49VePdrr7bqkZ3I0yTAkaDNeyVOgB3JoMdP8zw21yzKOPAm++KYVfbhk8GOjRw6fCymXEXqsxrXD9xOtRuV1lFHgajAeqDgb8c9hbjpYzCirCLyoPiN4UjZkjZopuGe63cbpEPF/yBc8yUXcPgH+wv5fQczfuoSuXcZUs28L2afryqDpRKPPAYNE7V7jk2WItPj5P9l2hUFw9nJo1V5Mib1DfpOKS1K0rEzVYjzdTfIsX0+1oy9xVyGv7++9LVy5j/K4IRgBfJmKvXr96aD+uPco0zKzhbxGCJlTji86DVnZMtph1xyxs/X4r/n7K48+n0Jt33zwxXaoL46avNmF6/+miBZswFzMLiFD907pHcWrs69SpiImoiunrayFpzZZc77tCoVAUR5TYU1wSXn9PngQeftizjD3q//lH1uHzFXtMrOT1muuvFjExsmWa4an0EnsM6ivsUDidXgJsfxWw59C6xeKG5Ny5XO8OW6H1m9wP1bpWEyVsDNguzT/UH1W7VM30tbQE/vnon9g7by+2fKeLNx4wn38uxd0nn0jTsX53oTVujF+rjsceZx388WV0rvddoVBcXVywwCVKpedkUm7cvEKJPcUlySiejvXx6A10x9SbxN6ff0K0TFs35x9gcRfgxB+Z6pfoaJ+kihyyfr1smfbYY1kXexcPXUTM4Rg4UvPAj3yloQhaMxrY+hxwdlXOtsEWZeS8T5/aHEL3951L7xSJGAaNhzTGhAsT0G5sO7f71hcWsR6xeATaPNQGbR7US/QQVsX+9ltphWTRZ8YPuFxiOzf9fJsQkHS7KxSKwoWqs1cwUGJPkaMWZfXrm2LkTG3JWAaFoVcd6qwCzq4AYmXPVF8++0wmeTDuPi/2p0EDWaM3q2Lv++u+x4c1PsTJ/06iUFC2q3w8uyx3Yi8PLHuZYQuwielyVGpbCX0+7uMWhIz12zVzF7Thw2XNPUPw0ZysacLVTmEZUSniiu27QqG4MqiYvYKBysZVXBaKN7plJ04EOnSQzzm5MVn2Bg2CmLAlFdjBWnsZm+7YQIHXdCOPIjewIcTOnT4LLyP2WAbEL9gPfkF+hUfsHfoWOLMsd27cPLLs5QQWVF78v8WiyHJ4pXBU7lAZtXrVwuZvN2PF6ytEKZd+UwbC+t13wIgRwKRJskzMe+/BYhR6BHB05VH899l/6PN8KwSlxfuofIVCoVD4UkiudIr8ZPNmYN06YMcOKfbSkVEB47Aa8jHhYIbb7NpVGgSvWEidoSIzeYMHtpsa/hYGynaTj+fXypI2ftksFh0R4d0yLR9gnB5j+mj92/bDNjEN+HEAQqJC4B/ij4ptKop4QFGOhWKdsXu8w2A9n6++EkkmqfGpIrmDGb+lN/+FrnunAE8+CTz9dNGIz1QoimTMXs7csSpmL+9QYk9xWf73P2D0aKCtTy/7jMqcsOQeJ3+/GhD5n0kZFwJmB7Mr2sXMcC2zY0RRgP2GgysCySeB82uActdm7/Xm/sX5RMu7WqLFqBbYv3A/QsuGinjJEtVLoMmwJmhxVwsERZrE2l13ybRtCr4ffwS2bxdJHIEdOmDonKFY+foydLRsAnbYgVdeAX75Rcb8sUizQqEoMLhy0fbMhVx0DVJ4oRziisvCZAzWsqtWTT5nZ6vevaV1zrfMyaefAuHhwF1PtJbLEo/mrs1XFqFbmd68n37SFyQkyEev4nuFGMa3Gda900uz//rL1B28GtCiR7d5/Zvr45Zpt2DwL4NRpYOshG0WekeWHcGmrzfBPvg2mWLNitlbtwIdOwL33ouqDcIw7I/h8Js7E5gxQxRbnr2nHnZ0HePT2kWhUOQ3KmavYKC+SUW2YRgVs27ZpkwYjExCwiiRZg1glqYFcCYDqWcz3M733wO33iqv17mFmb10Mx896iP2MrDsOe1OzBw5E3PvnQt7sh2F0pVbCMVeVlnxxgrMGT0Hy15dJtu77d4NjBolV7J9CwM+v/lGPh80CPs/XoDNaInf0vrjQp/h8uBUKBQKhRsl9hSXhSF5jNkzaudFRspkyblzZZKFOT6O2bUcP3mKDQipIqfUjJMCNm2SQm9VDquJmHnkEVlrj+Fel3Pj2pPs2DptKzZO3phpiZACSZUBwPXrgW5zi6zYYzHm6t2ro2TNksK1KyhTBolvfYxz0xYAjRrJjGKKP2bvJiai5i3N0PreFujb9BhKpZ0C+vf39AJWKBT5Ss5r7MlJkTeomD3FZaFhhaFQ5ctLCxoF3p13mgaY3LgMs+IkuOkQK/Bmul12wGLP3U6dcr+PzZv7LLiEG5fuxF7v9BJWvayUCikwBJWR0xXoKFJQoPju9HgndPxfRyx7ZRmWvLgEre5rBaufVWTgXvvip+hiWwU895yM5du2DdZ//kHfL/oDab2BoTHAzJny4OLdSefO+f2RFIpijVOziCmnr1XkDUrsKS4L6+HVqCF72jL8Lp0xzBASvsH/lxB6pEsXOV0RDMue0cLNBPu0UkwUKy7h1i4osN4esdqsQvSJlmv8T9OQEJ0g2rRVal8F6PmUFHGMAdi2DbjpJuCvv6SQ/flnJPcfgpMLt6AWA0iV2FMo8hVnLhI0nCpBI89QYk9xWWjRO+hTQeXECVnbrnJloIFdj3sLCBAu3A8/lH3tmSCZUQeOKwEzgBlDuHcvcP/9hceSlW1idwO73pHz7adm/XUXL8rHkiVRUGGB6686fSWKLt+16i50fKwjWo9pLcR5YEQgzu0+h4TTCVj5zkp0fKwzLLTcMWmDcQB33CEKMsefTcHExTTzNsbj879EkMNhMjUrFApF8USdBRU57pnLNqYTJgBvGmLP318kb7z+ujQksVpGs2aXFmjss8vrMWPucwN3gbH8hEWdy1wiRo0lP5LOJonabsGlCpkY1OzAwa8AWwjQ9nPA6p89sVeiBAoqsUdjoTk1dxwle+1yMgirEIZvu38rrHyOFAeOrz6OGz75EaVG3SSDP+vVQ/grryCqXhQse3YjNg4IWrNGWfcUinzEpVnFlLPXKsteXqHEniJHsD0ZJ6EdTGKPRhQKQJZfKVfu0tugFa5JE6BsWVnOJTcwNK97d1k7mEKzzCU6aJzadApTO0xFiRolMO5gHvRru5pENgICSgFpF4ALG4HSWagrRzUdH1/gLXsNBjTAo8cfFQk0XujVt2ndu+bFa7Dhiw2I3hCN/Qv2Y+bFyhg9ZQostOy99Zaoy3fX6rsR+Ml7gN94GX+gUCjyDeXGLRgosafIEsyyZfbs228D7dsDDzwgJ8FUj9gjzz6bwQZ8g/0Sj6JM0mpERgxEaGjeHIZeJdYuYdlj6RWrv7XwtErzjYMs2wU4Phs4szRrYi8mxjNfgC17jNVL1//2t9+A224Ts5a6ddGqQQO0uKE+kpq2hz2xJvpM6gNLnSiALdZ4ALzyCgKnTJEdNRQKRb7jykWihV7JS5EHqLxmRZag0GNMnLuOnRmTZS8dx+cAFzZ4hJ5hltccKHfhPcQc2pIuHjBPyCxpBEC1LtXwXNpzha9lmkEZvZo1xV52XLg0txam+DX28WUtHaMtC+MCZsyA9bVXEDakLzp0DxJW2u0/bwdeflm+hvX3EhLSWwcVCoWiGKPEniJLvPSS7E6RYcs0U4KGwalTwIoVgGvL88CSvsCBrwFnqhR9Lqds/1W6vRSCeYzQk4Zl7xIdNEQf1sKI0SrtzHLA5SgSyRlk1bursObDNUg4Zcocpo8/A3YEt8Kvr+/Fr0N/FbF87ruQiAjhMnq/8vvCVe/elkKhyBdUnb2CQSG6zVfkJ9dm0IqViRCMu5udXB41cNLLstewodQYJ/++HRVC/gYubgROzAWavQ5E1peD/COA+H15to/79gH9+kkj0MFLlF4p9JRsBgSUBNIuSrF8OVeu4cYtwC5co3NG8oVk1OheA2Hlw6RVljXzWGKFwo/ZPiyo2KwZqlSsB2erb0VplrPbzqD6pFfkRh59FKd2XUTKxRSc33ceIWWK4N9foShE5KbtmWqXlncosafIMWxXSoF1LLQcavhY9phdS+terL0KKpSuD7SaCOx4E1g+AKjcHwipKoTe79vG4IdXgQcflAkWuSEqCtizR84nw4XgTMTe0ZVHse3HbajQogJa3t0ShTJuj9a92F1AmikerxBb9lhjjx0zYg7FoER1kyht1Qo4cEDMst4ea+0xti9c09DjjR7Y8u0WNDv7l6wDxNYuY8eiUmQkHhmTjAun42Ddtxeor99cKBSKq44LFjHl9LWKvEGJPUWWu2hQM9Sq5fGsTZwo6+g1uXmzXGCy7K1cqbdSi20OrHxdLmz0pGz5tedD4Mh0oMpA/LL0Wvz+u6yOkVuxRy3D0mvVK6QisIEes5eB2Du99TT+m/SfyP4slGKPdPyJrUCyNrYQiD0KuF5v98p0fVpiGubdN09Y6np/0FuUZ2k/rj3a1DgH280srAjgnXek4HO5EDljCiIZ8/fYgKv3IRQKhaKAomykuaR69eriwuM7PUhTlcgTSBHzUVFRCAsLw8CBA3Hap87I0aNH0bdvX4SEhKBs2bJ4/PHH4WC5DBNLlixBy5YtERgYiNq1a+MboxH8VWL8eFm/dsECz7I+fYDrrwdK2s/IBSbLnhB6JKIeUG2o50URdYE2nwK9lgH1x2HkSODjjz018nIDwwHpbq5RNhFWI2U/g2zciq0rotsL3dBgUAMUWrIq9AqRG/dSHF1xFNt+2Ia1E9fi9Hb993PgAGwjh8sgzXvvBe65Ry7fsgUahR5dvxkGmSoUiqvtxs3ppMgblGUvl6xfvx5Op9P9fPv27ejVqxcGDx4snj/66KP4448/MGPGDERGRuKhhx7CgAEDsJKmL/4QnE4h9MqXL49Vq1YhOjoaI0eOhL+/P15ndWIAhw4dEmPGjBmDH374AX///TfuvvtuVKhQAddTbV0FaDCpUwcoVcpnhcvFD5F5Ni4VWJ37AWcKYAvylGDRXGK+T58r0GKDbTwIM08z2KdKbSqJqUjgTANcKTL+MTNiYz1/xAIMXbmM22t5T0uElfNu61brulqIqBKBuGNxQvSVe6OcLLfCz9a6NfDRR2LcqS2nsO5/yxCFTmhT8hQC0vX2UygUhafOnhJ7eYX6JnNJmTJlhFAzpnnz5qFWrVro1q0bYmNjMXXqVLz//vvo3r07WrVqha+//lqIujWs7A9g0aJF2LlzJ77//ns0b94cN9xwA1555RV8+umnSGOmAYDPP/8cNWrUwHvvvYcGDRoIwTho0CB88MEHV+1zMhOXyRhMgDA4dw5YPN+OpeiazorGMCvq3SFDABz8xpN1S5FnxJ1Z817ocR8/n+qP2WBcYBEPzt/xBvBrCWDX+5cel5qaaYHpgsSChxfg3+f+xYzBM3Dwr4OY2nEqZo+a7V7f59M+qNqlKto80MY705oBonqJndL1SuPQfhf+Qi9sOlZamqR5Q6JQKPIFl2bJ1aTIG5TYy0MozijaRo8eLVy5GzZsgN1uR0+Tj7J+/fqoWrUqVq9eLZ7zsUmTJihnajdBa11cXBx27NjhHmPehjHG2EZmpKamiu2Yp7yE8XHX9QvEs3g1nZjg9fXXX4F58wCtQl8gWLekmQWe5hLj2DKN7U3zgiVLgPtfLIfJuCfTvrip8alIiUkRxZULNYGlAWcycPqfS4/TbxrMbvaCSLtx7UQZlbYPtRVWPrZDY79cwt9TvX71MGrZKERW0S2UVarIx+PH3dtgoezeH/dBn5FRaIP1MkZg6FBP3UWFQqEohig3bh4ya9YsxMTE4M477xTPT506hYCAAJTwiZWisOM6Y4xZ6BnrjXWXGkPxlpycjOBMRM0bb7yBl1gg7wpRvTrQqJ4dtffsl5kapoK9vA5/+CFQqRKghdcVhjwkRwO73gMq9gai2gP+YYiNAapVc0HTrKIrVm6NcazOcWOni+i8cnmmYu/vp//G+k/Wo8uzXdD9lVxmheQn5fR9P78GcCQCfpnUFMygDmJBhFa5sQfGwj/YH0nnkjD418Hembm+VK6cTuyRev3rAZyuLwXwt8i+udHRwOzZGcQhKBSKKwlr5eXUHavq7OUd6pvMQ+iypRu2YsWKKAg89dRTwpVsTMdoQsshmzdLF+6oUZ5ljH3fPv8YvsbodMKKRr6xY4GBA03JGrYQmYXLVl+rbgcO/YASfodRqpQVNWtKt3BuadcOmPvKZkzA25mKPUeKTH4plO3SzLAwNUvYuOzAWRkDeknLXkYxlfkAS6iYsSfb3cso9EhI6RA0HNgQFVpWwKw7ZmHr91vhcvi4Y82WvYwapt92G5zzFmCO/0CcX7EL6NQJOHz4Sn0shUKRAS7NmqtJkTcU8qtdweHIkSP466+/8DvriOgwho+uXVr7zNY9ZuNynTFm3bp1XtsysnXNY3wzePk8IiIiU6seYeYup7yAYV90yRrXVzdGp4rLxYOx00NApEzSaPQMYAsEdrwOS8ppnFkZBGuN24CAPMoWvURfXNLvy37o80kf4Ros1HD/y3eXMZF05Va4rsC7cRPPJmLZq8sQezgWNXvVROX2lYWY6/pcVzQe2jjd+K3TtmLLd1vExGLL4RXDPSt5U8XvgAfnv/9mWLvn78VObLI3wTG/Knhg94ewsLEzW7vUrn2lP6pCoRBJFhYx5fS1irxByeY8gokXLJvCrFkDJmQwq5bZswZ79uwRpVY6dOggnvNx27ZtOHNGL18CYPHixULINWQbCn2MeRvGGGMbVwNm4rKM2fTpWRd7NLisXQucOMEjTb+vCKslu2ZYbEDlAcDJP2Dd8BBwcmHe7WxSkii8Yg8yCQMTFHl+gX6wBVyBTOD8cuWe8j4+vChI3UQ04MjSIzj07yH89/l/oqXZ2Z1nkZagC1IfLDYLavSogZ5v93QLvTPbz0hLIMXrdbrA5eMLLwDx8V6v7/R4J1Fqp+fkwbA0acy7JGmiNsrRKBQKRTHAovn6VBTZxuVyiWzZYcOG4c033/Rad//992P+/PmiLh4F3MMPPyyWMyPXKL3CLFy6ft9++20RnzdixAhRWsVceqVx48aiXh+TP/755x+MHTtWlHTJTukVxvix/AtdutyXvODRW0/grxkX8E7Fieh9YqrXumHDpDhk0vAjty4A9nwks3LZ5otiL6gsUKYLUKoVUHUgEFotT/bpnm578f2yKvi00WcYvX08ijRJJ4BZjF2zAIPOyzZqvnTrBixbBvz8s2w9ls8wOWbLtC2ivEqVjlWwftJ6dH22K/xDMnYzM5HG5i+FecyRGHxU6yOUbVwWo5aPQqDVAdx3H/DDD3Jw6dKMX+APz23ZpfvX6meVcXtt2sB5Ihq263oCf/zhFWeqUCjyDuN689LanggKy9nvLCXBgRfa/ZWn16ziirLs5QF039JaRyHmC8uj3HjjjaKYcteuXYVL1uzqtdlsolwLH2mpGz58uKiz9/LLL7vHUEhS2NGa16xZM1GCZcqUKVetxt6lOHrSD9vRBPtRO8P4+WrVdO8h3bgR9WWrtHLXAENTgJuPAp1+wJx94zFwZDXRkSMv8HOlIQXBOOTw9TlL1n2yDoufWIzT27xd44WSkEpAzVFAc++bjIJcVDmoRBDaPdwOjW5thIjKEejxeo9MhR4xhB6J3hgtrLKM6QsMD5TlV6ZNg8a7irp1ZeDnY49JN+0XXwAOhxR6pEIFxHzxMyZbxmD7ohOij65CobiyOE2u3OxPirxCWfaKEbm17LF6Bau9nDwJ3H67XLbmneWIe+IVNG3hh/Ib519+I45k4OImoExHd829jz62YNw4i6jL98svyDVHn/kCjtffQuXbuiHgh6/TrWf9Npb1GDJzCOrfXAz6plJxHz0qfepFoKPE+X3nsWnqJlTpVAXVulSDLdCGya0no/GQhuhQdj/833hFfl5y443Soqm7sJe8uARLX1qKEriIh/AJbJ9+BDzwQP5+IIWiCF9vnl1zHYLCcpYclpJgx6vtFynLXh6gLHuKLLNvn4yBHzNGGEwE7auexHVYjPKReuzepaC48wuWQo+wHovFimuvteCTTwC9w1yuqRpwCjVxCAG0/GRA0+FN0eGxDihVp5iU4SgEvXGzCl26JWuUxJZvt2B6/+ki3m/79O3icePUzbCOHiUra7PuD+NImVXEg/bCBfF6JoJ0+F8H3PlEWdhoN2DK+OLF+f2xFAqF4oqiAlYUWYb5Ij16yPmzZ4VXzFOsNivdGUSxvfQ0aSKnPG+XlklCgrsDQ1Ei+ZRM0qCLnK5dA7ayM5IWCni7tKxAq9zhJYeFVe/CvguIqhuFf5//F01HNkXdG+vqSTc2KeJatZLJGLRoDhjAdjWwBgTguneuA7RewMldwPffy/pAHNOgEPdKVigKKLnpcat64+Yd6ptUZBnWTf7rLzkJoUeNEWfHYvTET+e8O3wQetKYD2BKUL4qaIlJmIK78PjqAcjjpiEFl5VDgdXDgRPzvJebv4ACErOXU5xpTmycvBHHVh5Dk9uaYMyWMSJ2L3pDNLZ+txV7Zu1xj72w/wLSmreRLVXCw4GlS6VJ2ohaYcmWyZNxvPmNmBZ/M+z9BqgMXYXiCqDBAlcOJ75WkTcosafIFRcuWoQbd8SGR9yuXQOW+GPzggULdAPgsVnAlueAJO+OB4SVZ1gqzVSBJsdYkpPwPF7Gu2s6Y/fu9OvTEtPSF+gt7JTXxfYpH5dkbKzH8loA6uzlBlrt7tt0H7q/1t0r1vLGL27EbfNvQ9/P5V0Fs3snNZ6EpS8vBZo2lTF7rOz99dfAW2+5X+e0+eO3C91xELWw7EAlYPhw1UdXobhClr2cToq8QX2TilxRPuACmmArelXcgYQE73Vly0Jk2LJLleiisf0VYMerwPn16bYzaJAMraLVMNckJ2M4vsfYLpsyNGa9X/F9vOL/igj0L3Jij8WVXc70Yq+QunCZP3Z+r+fvxFp7dMPbk/QWcIDI6q1zQx2ZncuPWi0SzlQnzu44C82lATfcAHz0kRzMsizffefO8u3/zc1odF1FdA5cL0uxsFafQqHIM1yaJVeTIm9QYk+RLRj61LgxsF7Xa7a0ZGxFMyzo+2k6YUVP2bhxMmxKGJVK6IF5MdvSbZfbrFVLhpjlmqQkvI0J+HD4elGNwxe25zK35ioSlGoN+EfIGobMdi4CYi96UzQ+rv0xJreZ7LX8z0f/xBsRb2DpK0szfF3dvnVx57I7MWzuMFis+sWC2T9GqRX2/NPLH9W4tgYG/XkPAqd8Kte9+irw229X8mMpFArFVUclaCiynZG7Y4dHQ4hWVSQrbdlKNJWPMVvTrfr0UykO8zRBI5N2aU/GPCkEH+u9FRnYoaTctbLvMF25Ua0LvdgrWbMk4o7Hibg8WviM9nbCBa8BJarLuwt23wgI83ZRsyRLOt59V34fX30l+uaK1i5RUXIdXbibNgHvvw/cdZcs2ZJHrQYViuKME1Yx5fS1irxBfZOKbMFrIVuLssVoVsQes3ZXrgS2Ut9F6tmOcZ5AeoM8bVOrt3DTgkPE+/vCAr4hUSGw2orY4V8ug7g9I0GjEIq9oMggjPhrBB7a+5BXH+Nbpt2C/53+n4jbo/D7uO7H+Lb7t4g7EZcuNvPkhpNiEjCW4MsvgVKl5HEr+vhJTm89jTcml8Zk3CMFodEGUKFQ5Arlxi0YFLGrneJK06sX0KkTEBamL0hNxY8YhibfPoZHHkk//ttvgc6dgbffZsBVHbkw4YC7oPIVITkZB1EDocNvFq7hYlM2vILeJ/bsKlm82mzZK0QFSc/u8vTKpYUuIDR9Yklo2VARo3di3QkkRCeIfrlcZoYCjsWWf731V++UckM4mlqlJZ1PQlp8GtIQIMcUQnGsUBREXLDmalLkDcqNq8gdqamwwx/bz5ZH+R3pV1etKmPxhLeMvW/ZE9eZDCSfBELY09XjeWVXjmPHpCUwVx605GRUxEmkpFnB1qnnz8uWqWLVxWSsemcVAiMC0fnJzihSUEx3+hko21UWry6kbtxpPachPjoe96y7BxVbV7zkWPbWHXd4HM7vOe/VVo3wbxxeKRxhFYw7Ex0jbdwk9qp0qIKH5vSCs//N0vKXp6ZmhUKhyF+U2FNkCwonijFWqLj5Zin2emExFo7+BXWfvTXdeNbZ4yTxB0JrAAn7gfj9XmKP4XULF8oSLfSu1ayZi51MTkYQUnHg182o3K8F/E15GElnk7DijRUIjCyCYo8CpZrP36CQuXFT4/SwAACl6+sK/TKUqFZCTL6UbVQW44+PT/+CDMSeX5AfosL43meB0qq4skKRVzg1i5hy+lpF3qDEniJbMPbupptkz3lD7FVENCo2jQZqZGEDnX4E/EsAYdW9Fus1bkVGL0u25Aq9q0eNWlahL80EhAeg3bh2sPoXE/dAIbPs0Ro3/sR4JJ1LSpd0kZG7t0yDMtl/kwzEnuDcOUSjAspbrLBwjO96hUKRbXITe6di9vKOYnLFU+QVNWoAHTsCzZrpC7LTLo1EtQEi6gDW9GVPmBDJJEh3POAVyMYNrxCO3hN7y5ZZRZXdE4G/ewCJRwplzB5hBm78yXj82PdH7J23V/TENbPzt534rMlnWPLiEpGpmxEUjBmuMzJwN2/2WnzyQhCm4G78tLMZ7I1byCrfCoUiV2iaFa4cTnytIm9Q36QiW1SvLt24v/yiL0hMFA+LDtYWtWrp5vWFblxm757UkyKvKLy469WdtxwtKWrofvwxihfHfpXFlaP/LHSWPTPrPl2HffP34ad+P+G1oNdExq25HZrm1BB71KgB5A1F3nc9v8OUtlNwZodPW5Zhw+QjS7CYOBdWDRZ/P/j7AX57trvby/kKTYVCoShsKD+FIk+saPd/2w4HT0shSMufmdWrgePH5VSx9EVg7yTZMq3tZ17j2JqUbmJWyGAGb45gSQ29MvPek2F4802gQwfg4YflatFRwUK3cRF2D1ToDZxdCZxc4On3Wgj74ra8q6XolBF3LA67ftsl2qUZdJ7QGeWalEOt62tl+LdkwgY7b7CoMq25XrCo8nvvyY4Z7M+nxw00vb0pyjUth4gwFyxr+wHNmyP2WKywIDYY0AD9JvcreuV6FIorjBMWMeX0tYq8QYk9Re7QLXtdmsSidrOwDOsYf/CBDH+qXZvPrMDWZ+WK5m8AAR4Rwh66rHXbtavsW58jTD3bmrcNwEMPyfaoBrtm7sKMQTNQ/drquOOfO1AkqXgDsPU54NTfQHyVQl1UufcHvYVATziVAEeKd/PlOn30Uj4mOJYCj8kdjxx5BNEbohFcyuegbNQIaNNGtoH5/ntgvCeJgwJSUGOoeNg9azdSY1Nx8eBFJfQUihzA++ucx+zl+e4UW5TYU2Sb/v2BXbuAX38Fmuli75sXDgGdK2U4nn1vPUTKLFxa9mJ3AmU8ZkCKQZZpqaLrkxyh7w9VZ536tnQuXEeyFAxF+sJdsgUQWAZIPQuUPFtoxZ6BsM5V9LHO+eBIdeD3238XZVb6fNxHLAstE4ravcUdRnpo3aPYmzIFGDFCxpzSpGxM7O9nsaDtg21RoUWF9LF/jFcwYv8UCkWmGPF3OX2tIm9Q36Qi27AW3v798tHdaSAkJOsbiGwkH2O3ey2msYXbpbElxxiWvVDvArsGDQc3FN0XBvw4AEUWixWocL2cr144EzSyy/E1x4Wrd/0n65ESqycNXQrG7fEY4V0L3bj8fpgZxOOYwq9BA5EebrGnoWrnql7t17bd/SH2Vu4OrFlzZT+UQqFQ5BFK7Cly1DKNcXjXXsueVLLTgbCEZMLFizKWb+1aX7G3K+93zsjE1cUeDTLR0bJtG/EL9BOdFmj1KdJUlNYt1E8rFmKPxZW7PNtFtFZjm7XLwhjGuXOBOuldwYI9e4B775UZSW+95U502Tt3D36fGoPfUvriwo0jgaNH8/iTKBRFCxcsuZqyQ/Xq1UUMr+/04IMPZjj+999/R+vWrVGiRAmEhoaiefPmmDZtmteYhIQEPPTQQ6hcuTKCg4PRsGFDfP755+71Fy5cwMMPP4x69eqJ9VWrVsXYsWMR624gXzBQYk+RbSjymF0r9JTdLpYtXhcpQqFE7T0f5s2TCRfPPKMviGwoH+nGzWsMS6MePDhmDFCxIvDFFyhesHWaLRQ4rwdrhF/aDVrYYfeM7q90R1SdzF2rLMUisniNQCAeyBR1LB/E44YhAPHxImkj9rl3oVWqDJw6BTz5pIwteO891OpdGzW6VUXrMkdQ4vx+WSuISR4KheKSRZVzOmWH9evXIzo62j0tXiz7hA8ePDjD8aVKlcIzzzyD1atXY+vWrRg1apSY/vzzT/eY8ePHY+HChfj++++xa9cuPPLII0L8zZkzR6w/efKkmN59911s374d33zzjRh/1113oSChxJ4id+iWvcBQP+zcCWzblnFtPnbEqFBBXxChi7249GLvww+Bli2BSZNyadnT3co0zDAE68IFufjQv4ew/PXlOPTPIRRpAqOALjuBd3m2tWTq1i6q7J69GzMGz/CKtWMJl4/rfIxlry5zL3PaXdj0405sn82OLiG4eNaO9TMO46tvrJgUNgFnXpssEzooAv/3P9imfYvbF41Er/Wvw1qurDzgmVEkYhoUCoUvOa2xl5NYvzJlyqB8+fLuad68eahVqxa6deuW4fhrrrkGt9xyCxo0aCDGjRs3Dk2bNsWKFSvcY1atWoU77rhDjKXl8N5770WzZs2wbt06sb5x48b47bff0K9fP7GN7t2747XXXsPcuXPhMAq4FwCU2FPkSN/RA8bqFVqatOy1aOYS7c7++iv9eFr1DhwA3NbxSL0dVfIpwO7JniV0t27aJEuw5IXYY8kVLqLrmexfuB//PPMP9v6xF0WeRL31GGPRinKpGR9Ye49CjzX6nGmeGnmVO1aG1c+KCi2Nuw7gzPYzmDN6Dla9vUpk+05qNAnzH5yPlJgUJJxJRImxI+XB+KyeQT5mDGzrVgPVqomUca1yFSzbUxZJHXpIK6FCochz4uLivKZUlti6DGlpacIaN3r06CyV2uKN4d9//409e/agK2/gdDp27CiseCdOnBBj/v33X+zduxfXXZd5YX66cCMiIuBXgLrwFJw9URQaaCyhu5b9cW9HKZTHaYSX9MP19bO4AZZb6bsLCKsJ2ALSxc23betdLiU33TN8u3FUbF0RzUc3R+V2nr68RRZao0ilYMCZlu67LqpEVo1E38/6YumLS4XrNqKSjFdk15Reb/XysvZdPHQRlTtURo0eNWALtCEwPBAVW1XE9R9cLzJ83S3bXnoJ2L1bpqBff73MIrrlFvxz04dY8elWbD9xBmPadYB1xs9Ar1759dEVigKHiL3LaekVPWavik+JhhdeeAEvvvjiJV87a9YsxMTE4M4777zkOAqzSpUqCQFps9kwadIk9DL9hj/++GNhzWPMHsWb1WrF5MmTvQShmXPnzuGVV14RrylIKLGnyDaBgUDfvkCgnxOOmfohlFGBvUsRmbEypMeMU65Lr2Titmw0uJGYigUMEH4SQJMzwNnlQPkeKC6wIDMn3xIunMw0HNhQTIRZvC6HC0dXHMXkNpNxz3/3iOXn953Hzhk7Edn7CTSlgGY8z4ABwBtvoOn9o7F97iF0DtgK6/6LwA038EoE8K6/fv1CXfJGocgLtBwkWphfS44dOyYsZQaBvAhdhqlTp+KGG25ARQZtX4Lw8HBs3rxZJGLQsscYvZo1awq3rSH21qxZI6x71apVw7Jly0TCB7fbs2dPr23R6ti3b1+RxHE5MXq1UW5cRY5gbOqMr+JRGSfkgqAgbNggqlWIR1/YtoylVejqvaJkIPY++ki2bPvvPxQv2D3DaF934o983pmCAztrLPrfIqx8Z6XX8iXPL4F/qD/8gv0QWi4Up7eeFssZ30nX/76/j8psI6Mdy1NPocw7T+CBzfei6fYfgTvuEN1b0p5/VWYw1auXHx9PoShQ0KqXm4lQ6Jmny4m9I0eO4K+//sLdd9992f2zWq2oXbu2yMR97LHHMGjQILzxxhtiXXJyMp5++mm8//77IiaP8XxMzhgyZIhIyDATHx+P3r17C/E4c+ZM+Pun7/+enyixp8h95ivjIfz9RcYrLdeM5/OF9fMottxhTRc2AavvBDY9nm7skiXSS2Z4ZHMk9kx1/2iImTEjYxFa5C17m/X5k/PyeWcKDqe3ncbq91Zj3Ufr3Jm5yReTRS9etmYb/udwjD0wFi1GtRDr6t5YF1Z/K7o93022gvnoI5x97kPssjaC/dsf4H9zX1nf8euvYf/4c3wT/jA2R3aTtfoUCsVV5+uvv0bZsmWFlS27uFwud0yg3W4XEwWhGbp7Oc5s0WMMX0BAgLAABrFWZwFDuXEVOYclKwwXrsUiLHcnTsguGL6MHQsMHw60aqUvsMcCh74FwmoBLd7xGjtwoMyebdYMaNIk95Y9hmz06CETRZiRyYzc/lP6o/HQxijyYo91q11WIH4fELcXiKiL4sSJ9Sew8s2VaDCwAZrcJg+mun3rounwpmgwqIGI37PQzeRwodMTnXBu9zmvAsprJq6BPdmOezfcK1qwGWyIr4u1rsEoZz2D+5ZNgoWWvPnzsTOiPaLjT2FRVB80+PV+XN7ZpFAUba52Bw2KMIo9ZtD6JkiMHDlSxOcZljs+ss4es2gp8ObPny/q7H32mezbTisiM3kff/xxUUOPbtylS5fiu+++E9Y+s9BLSkoSCSFGEomRHUxhWBBQYk+RI77+GnjxmUrog0n4LFAW0LvnHjllRJcuPguMWnsJBwFHEuDnscR17OgxGmabDMSeucTS+vhU2BPtKBb9tenG5fcYWwEoeQI4+UexEnvMxJ11xyyc23UOiWcS3WLPL8gPt0y7xWssi2z3eN07ppEJGn9N+Etsh3X83H1zOb5sKMIrh+PiRT+4omrARtP1E0+g6W+/4+R/J9HynpYIjCratQ0Viqxgdsfm5LXZhe7bo0ePiixcX44ePeplpUtMTMQDDzyA48ePCzFXv359IdjopjWYPn06nnrqKdx+++2igDIFH0urjGERVwAbN27EWr1jAN3BZg4dOiTKtRQElNhT5AgmNB6NDsARVMteqzQD9m4NLA2knpPFlaNau1dl5AbOaQcNXwbPGIzUuFRxsS7yGBXck+pJsce4vfqPorhgC7Dhge0PiJp7lyq2nBks19D1ua6IqByBMo3KeK3r8GgHHFt1DPvm7YPj09dgu/M20S+XCSA3fHRDHn4KhaJwk5NOGObXZhda2dL1stZZwhghE6+++qqYLgXr9dFSmBlM5Mjs/QoSSuwpcgRDIdZ8vhlVxtwNBF9eOLG2JGtQHj4sy6uIukclmgCn/wVitnmJvVyRQcwef4es30fjS9u2xaBVmq/Y05oD+Ac4sxSwxwH+Rbt1mhmKrwa3NMixWOz6bMblFSw2Cyq0qCAmm+t4zjLSFQqF4iqhEjQUOaJcOaBdzbOoiGj3RY5GNVabYIa8b3IFu6p16gTcfrswgEgi9YA8ir28IgPLHsUeLel8f4rNYoMh9kJrAg3+B3T6EbAWj1p77pZoV2r7Tg11+taBPcmOTX/pB7QelH105VEseWkJ9sxVRZYVirzIxlXkHmXZU+RZH1o+sGsU9VZ0tHeiBte1aycNbkxcLM1Yd1r2SKy32GPbtZEj5Xy2y6VkYNljiAb73TOEbfkXO3C+aoKI3wqJyoH7uTCKPdZ680mCKeosf2M5tv+0HR3Gd0CL0TKrNiccX3NcxPhF1YuCf7CnlMKOX3aIeEBSvrINbUxi78iyI6Kgc/NRzVGvnyq/oijeXO2YPUXGKLGnyDFzVpTCIYzF7X6HQe1GzyzbpZUqBVStmn78mjU+C4TYs8gEDRPh4bJMChOp6P7NVseZTIoqG9t7q+Q8HI1JQa1etYqX2CtG0Nq29KWlIsOW8Zkn1p0QrdOqdKoi/u5ZhUkd31zzDZypTozZMgblmnoSNFiKhYdunT51UE07DBz3iD26dluNaYUqHbyr/isUxREl9goGSuwpcszj05pgLzqjadoTuFZf1qFDNjZQqhVwa4JXJi6pVImtbqRl0Ke8UY7FniEYGw5uiNTYVASXKgbxVb5ij+7yYzOByv2BkozjK5rQEnfX6rsQVj4MYeXC8Pczf4vet8yQzY7Yoyu41nW1EHskNl2CRu3ra4tEjLYPtZVNoueLmg9yXe/aYlIoFIqCghJ7ihxzQ8OjaH5qJ8JDPM3ms4XVT06+i63ATTddmWzcfl/2Q7HBV+zteAM48hPgTCrSYo9JGexva0DBFn8iHjV71nQvO7PjDErWLOnlmvWFYnHo7KFIiUmB1eZ918GbBSH0iFEpn4GpPjBLj6VYKrWplAefTKEofCjLXsFAJWgocszEm/7FzxiK1hVOmNrUAFOmsDZR+vGLF8uWaUY83hUhE8seizSPGCGTNApBlvyVEXuV+svH47NRnKjZoyYGfD8AjW6VPZEPLz2Mrzp+hV+H/Cpcvb6Yyygwazy4ZHorMGvwsY2aeL0h9tLSvMa4nC7MvWcuprSbgt2zduf9B1MoCgGaqfxKdqficqq+Giixp8g5eksZmPoUMjaOhZU/+CD9cHq5mHCx2WjhRY7PARZ3Sdc2jWVSpk0D/vknb8ReWJgUoKtWySSSIg+Fh9HhxBB7FXsDFj8gbrfsplFMoeWPhZIZz8fuGL7MGDwDX3X+Sgi6zFjzwRp81+M7vBHxBlIdtgwte3wflm+hYEw4lZD3H0ShKASobNyCgRJ7ipyjiwkt0NMHsFEj4MYbgW7d0g9v3RqYPRv44QfTQiZnnF0BnFnhNXbOHGkB/PLLbOwPLTKZuHEDAoB3Xk7GyJBfMb3rpyg2Vj3CWjgkoARQTo+uPD4TxZXK7Spj5D8jMXzhcASGezc0i4+Ox67fduHYymM4temUe3nciTgvYVj7htrCzdv5qc4ILKuL6eXLgUWL3GMo8hjXN2r5KLQek0d1JBUKhSIHKLGnyDFbDkWgCo6iwa8vu5fVqyc7YLz9dvrxUVFA//4+/W6N2DGWX9E8LrWmTVmZXD5mS3wazakziNm7/eYE1EzaAcTr1r+iDOvMGKnN5t6MVW4plq5cw626+v3VmFh9ouiKwUQOXyjgmMhR98a6qNy+sljGmL0PKn8gSq0YLl5m5o4/OR7dnusmD2rWFaLAvuEG4IUX3AUdrX5WVOnoycqltTDxbDE4/hQKHWXZKxgosafIMeFaHI6jCo7El8p5HFx4HcAWDDgSgfgD7sU9ewL//gs8/XQ2tsUCfgYZtHArVasUHtrzEO5cdieKjWWvRAnv5Ubc3rk1QLLHclUcoFuVsXMJ0Qn477OMCzjSGscknmFzh7mXHV1xVDzS2sdED2Oc6AJjFJFcuhS46y55s/Hyy0CNGkCDBsCjjwILF4qalMkXk/H99d/jh94/IC3BO75PoSiqKLFXMFBiT5FjqthOYi3aYu8jn6VbR/FnGNnMbN0q3bi7jXh1q81TXDnGHMyXA4x4PdY7M1uzdOwuGw7ERGHdobIoNpY93xp7IZWAUm1kyzT2JC6i0ALHRIyZI2e6hZXhVu0/tT+ufcUoFnR5aOWjFY83CrQIZgjjVidPBr79FujcWR5/PMgnTpTWvrJlkfLrPJzdeRbn950XjwpFcUCJvYKBEnuKHOOfmoC2WI8q5e2ioLLB8OHSe/jLL+lf8/rrcv181iXzdeVe3JyhaHQ6c5ecYbBvn/S28f2LfEauIfZ8LXukywxgwBmgfHcUVQ78eQDTek7D1mlbsfWHre7l5ZuXFx01fEupXI7wCuEICAvItFvH580+x5bvt8pA0+XLkXb4JN4r8Sq+LvcUHJWqCatzybEjcdvL9TF65WhUaqtKsSgUiquHEnuKnGMkQ/g0gKdFj7rruN4f3kyrVjIWj7113ZRolqHYGzdOduOgsSQvxF5p6wWUK5mKBtWS3EOLnWWPhFYDbEW7Ry6LGj8W/Rg6P90ZVTtn0M4lD2Gm7emtp3F6y2n3svNnnEiIceCsowT8Du8H+vUTMaWVHr0V5fatdI87tuoY4k9K17BCURTRNEuuJkXeoMSeIuckJ2MFOmHiitZYv96zmCFLe/cCDz+c/iWPPy5j8W6/3bSwZDMguBIQ5O1eZas0apYDnlC+rMXssc5KBpzfdhL3X3wTY6JmZDak6MfsmaF50150S4KElA5Bj9d6oGyjK+u2bzGqBYbNG4YSNUrg3+f/Ff10f7/9d2EJHPjjQNm+5eefZZo6k4gGDRLuXSZ+/DLwF3za4FPR0k2hKIrktMaeMSnyBtVBQ5FzkpPxNUbhq5/a4pWGsmAyqZ3dTlGlOwK3pDcDjh8PjBkD1PQ0Prg0hrkuEyUXViEMDQY2SNf6qti5ccmJP4ANY4GodkCnH6/qrhU16BqOqhuFj+t+LBI4mIhx8dBF+AX6ieVu6/fMmcDYscBnn4nEjeQtRxFZpQFSYlO9+u4qFEUJ1UGjYKDEniLnpKSgE1YioVNv1K+fixgkc8CfCfbGzRaGZS8TN271btXFVCy4nGUvoBSQcBBIPQc404qcW/fA4gPYPXM3KrWrhOZ3XPnWcH7Bfuj9YW+s+2gder3dS7RoY8mVkChTVjgtfJ9+KjN1n3gCJb/5AKN7XYf4jye6y8AwsWT79O2i20d24woVioJIbtyxyo2bd6izSR5w4sQJDB8+HFFRUQgODkaTJk3wH1tF6PAE/vzzz6NChQpifc+ePbGP2QImLly4gNtvvx0REREoUaIE7rrrLiSYS4mITNat6NKlC4KCglClShW8nVExu6tJcjJG42v8/PwO4Zkyh/IxMfGllzL2HLJlWZky/N4y2KYrfUeDLHOZmD2ybBnQvr30pBVry17pdkBwBcAeB5zObpuSgg/j51he5eCig1fl/Zjp23BgQ9y59E7Rb7dev3reQk/n2JrjmPRtKH5s8Y7IGrcuXoTI69rLXoIAdv2+C7/f9jtmjpgJzVXUs4gUCsXVQln2csnFixfRqVMnXHvttViwYAHKlCkjhFzJkiXdYyjKPvroI3z77beoUaMGnnvuOVx//fXYuXOnEG6EQi86OhqLFy+G3W7HqFGjcO+99+LHH6WLLS4uDtddd50Qip9//jm2bduG0aNHC2HIcflCcrJ81D+DWdAZu8QkC7PeoBEvOho4d07G4lUyDIIHv4W2+QmgbC246leAw3kGaa40/DyrF/YcqII77/kJQeFJSNMssMMGu8sCh8UfqZofHJoNds0flY8eBrufHsUWrDo0DGkuP6S5rHCCY/zg0qzYe6IO1q4dj12HY/Dqztdhd2pwwganWG8R8w6XVcxr8IPdpdd6tgQgzemCBgssCIDd5RR3nVYEINXlBDQrbPBHitMJKyzwt/oj2eGARbMiyBqABLsdFg0I8wtCfFqauAGI8A9Bkj0NdpcLEf5BsDtcSLLbEernL15zMSUZ/hYrogJDcSE5CWlOJ8oGh8LudCEmJRlhfoEoGRSM0/EJcLpcqBgegbQ0J84mJuKFHTvRCsDknXvxx9TvUSkyAgFWG45eiIXVAtSKikKf5Gboimgs+ec9nG9YGX2b1EeQf9E4JdToUQPdXuyG0vVKo0ChAWd3nIWjVklg0yZg1ChgzRoZzLp5MzSnJgoxl6xVUtQFVCgKO8qNWzAoGmf2fOStt94SVravv/7avYyCzoAX9YkTJ+LZZ5/FTTfdJJZ99913KFeuHGbNmoWhQ4di165dWLhwIdavX4/W7CkG4OOPP0afPn3w7rvvomLFivjhhx+QlpaGr776CgEBAWjUqBE2b96M999/P//FXnCwKI9ilLajYW3IEJlJ69MuVMDsWnbwYrcNAw3RsKScgXYxFklp5eDSXEiCE8+9/ABOnaqI5l3nonHz/UjS/EGDR5IWADv84dAsSHYFCoEZlSizGhMD03A85bxYluoKgAM28RrHZ5URNiMB/XtPQeCofTiWdBKpTn8h8FyaJsbSluLULEh1yp8GhZ/daXPPOzXOa3Lepc87bXC6aCTX4HLxxGbMW+FyypOV5rKISczzBOae5xtaAHFSY3FCCywuKzTKSi4zys5oXA7swlmxjxSOFqfFM84F7DjJ2m2aGOu8cFG8bFtyKnZGn8Wuk57Xcey2o6dxJqQaulYBmrpWo8vshZi84j98e+cglIso/NkrFVpUEFNBg/GiIxaPwIKxC7BpVTKaz54NS/nywJYtwtRN921UvSgVw6coMig3bsFAuXFzyZw5c4RAGzx4MMqWLYsWLVpgMn2YOocOHcKpU6eERc4gMjIS7dq1w+rVq8VzPtJCZwg9wvFWqxVr1651j+natasQega0Du7Zs0dYFzMiNTVVWATNU56SnIxUBKBq/2aipqy5Hev06cCkSdJd60uXLkCzZh6DoOa6AM3yjXySkArN5UIqXHBAQ+++f+CWIT8hOCRJCD1qlVT4ww5a4iCEnvismj9sybKKc3Kg/I7stPrBKgRVsisAlos2BJ3TUKfSVpSqdRJ2lx+c+vo0l9y2ZhJ6vKs0hB4FnBR6EMJOCj1j3mq6g5XzFHZS6OklBDIQelwnhZ7+VBd6Yo1Z6NGaaPLoiXkaE3U7o9xxkyh0AuEpUojHBwZL/SffzWvsuqTaiHGGoJRfIlqHHMSxizEY9/O8HBwIiqwSFBkksnDP7TqHOXfNwd8fbAXatpUrFywQD+WblXd356Ard849c3Bk2ZH83G2FIsdouSiorMRe3lFsxd6xY8dw3FQIbt26dXjkkUfw5ZdfZms7Bw8exGeffYY6dergzz//xP3334+xY8cKly2h0CO05Jnhc2MdHykUzfj5+aFUqVJeYzLahvk9fHnjjTeEsDQmWiDzlORkBCINiSk2Ydk7diyH20maAQRp0Pys0mKVmCbEHpnwwgt46Z3/oUbdfULaCDGm+QvpQjFHNN2165ciX+MItollaWI9XbO0lFmRNvo8Lnx/GEm3xMmqI0K8GSn+/ClY4NDFmph3med1y5su8NzzTs88rXre8xJD6OlyS3/kZzGG68tp/TOUmHu5R8y5t6gPcQs9lz6vr+NcOEt8CLEnFbVb6ImxElo8F8fL7iW9w7fA6dKw+Xg0th4v/G3UXA6XyIpNvqBbnwsQDQY0QI83e6DnWz3RekxroG9fucKr0rhk/aT12DRlE3688ccC+VkUCkXhoNiKvdtuuw3/suCbLpZ69eolBN8zzzyDl1koLou4XC60bNkSr7/+urDq0aV6zz33iLi6/Oapp55CbGyse6LAzTOEuUxefP799YJItmjYMP0Qo+6yGVoAf/pJJiaKcWkrhbnKpXcosMR7+oY6dMEjRZgmhBlFH3HqwoxijmP8dMuePciq12cyj9OglXUgtb4dcZYI7F3YEcdXNjXFhRhCziPS6IY1RJl8z0vPS0wWPJMA88x73st7ufFq83OPOc/r/tanDZ37NbrVj6+K0C17cUHBXq/1vU+eE9cK02M6YF5cS/HcZrFg9UHZC7Ywc+jfQ3i71Nv49tqsVuS+ejAWr/OEzuj0RCeUqFYC6NNHrli0CNi+3Wssu300HNQQfSf1RXAp7+LlCkVhQHpMcjjl984XIYqt2Nu+fTva6u6TX375BY0bN8aqVatEbNw33+guxSzADNuGPiqnQYMGOHpUXjDLMx6H2YGnPdX1jefGOj6eOXPGa73D4RAZuuYxGW3D/B6+BAYGiuxe85RnMBhPb37btKUfKlYErKajiWKOLdO8iifrnD1LsS2qT+jWLemvNMSeX6In0M/4sScmZJZh65EvfklyO/Zg71BU3xPG8dVNseS5+7F7eq9LfsTsn2hy53Iwi7bcjLW4XAhLTRXz8UGXFgjrk2vjxdODsSFZL2ZIL3MR6SVHUWWxFQI3UIsWQNOmMpucqeJMytJ/W/4h/hj0yyA0HS5vTBSKwoYqqlwwKLZijxmvFEPkr7/+Qv/+/cV8/fr1RVZsVmEmLuPmzOzduxfVqlVzJ2tQjP3999/u9YydYyxehw4dxHM+xsTEYMOGDe4x//zzj7AaMrbPGLNs2TKx3wbM3K1Xr55X5u9VT87IoF0a4S7x2pVR94vKlWXLNJY/SaMRL4CxihZYwuXfwy/B8xlPH6+Ebi02o2+7dXr2qyfozMaMBeGglY8BCVLspYXZvMeJ9RbYloUh9I8wlClzFOWa7UFUw0NivVUEwcmTis3qETpyuSYyiHV7nT7v2bYxb/ENiHM/95n3RM+lG0MXrtuN63OO85Jf+ljPOtO8BQhPTYVVF2y07JlfezkZR1dui6oVUdip1asWnrM/h1HLR6HAw7sknh8Y18sfDe+QmL30zjvizsiI3yNJ55Mw/ebpOL3N+8ZPoSioqHZpBYNiK/aYzUpX6/Lly4Vo6t27t1h+8uRJUS8vqzz66KNYs2aNcOPu379flEph3N+DDz4o1vNEzVjAV199VSRzsGTKyJEjRYbtzTff7LYE8v3p/qUreeXKlXjooYdEpi7HGW5nJmew/t6OHTvw888/48MPP8R4tpnID/SYMKqcI6cC2f1JJGQYdO4M7N4NrzZqBkzMoAedYY1Cb/vThci6JEFwRAbCGRmEAP3QrFzuFOJiSiApMQwXzpaGxaIhEA4x3p+ZCLooY6qFfwKXA6lhfmKcXK/BTxeFgVNLI+KFCqiYdgY3T30VbR74VReLIhrQNJZo8LdKkUhsGczzGmyzeXyqZtFoNY23uAWkEXhnEohmwWfVEy7Ei+D9GlMeh/Gr1bzWeUSikZyR7O8Pe4CfZ1O+23EvdqFF8CGMLz0PfhYNLSoXvCzWnFr2AkILSbHo0qVlgsZzz0mT+P790vTNO6Nhw+QPRtOw+PHF2DN7j2jHpurwKRSKrGItziVTvvjiC1xzzTUYNmwYmjE9VM+uNdy7WaFNmzaYOXMmfvrpJ+EKfuWVV0SpFdbNM3jiiSfw8MMPi3g+jmexZJZaMWrsEbqPaVXs0aOHKLnSuXNnr2QRJlgsWrRIZPe2atUKjz32mCjUnO9lV4KCcOCghd2fhOAzYMcyGid04+mlSWVRXyssYYHQWlRCas1IBIF164DQQDtmL+yFNbvqokq5k/ItLWnMxRXiivNymR0BiVL8WcLkRTDAYhdWP4qqYGsqnE2T4OiQAGsZ+ZoAK9dTdGkItEprIucDrLqItLrgb8zDpVsSKer43uZ53cpnMUQjX6vpgk9frgs+zlPUSTSaHT3zFg2avi3x6P51crl7lJw3Cz7Tr5ivi0hNclv1hLgzhwGax+qP/C4/qzQF90b9g5ZBB/Dv3qtTiFjhTdzpJOzvOJJ3nLIqObPzafpmanv37kL09XqrB2r2rIkBPwxQdfgUhYKcZuLmpj6fIj3Fts4eRd65c+eES9XsBqV4CglJX/n+Utx4441iygxa95j0canED2beGgWUM6Np06bCElkgMIm9Bg2AgQPTJ2hkBRGa5NgrIjvSNIf4roI1f9gtToSyTh5caNZgN9JcQAqVimZHmsWGUKSIgsoWqz8sLk2UYwlIlJY9LRwIs6QgGf4ItqSJMi2Uh/jfUaRp/rBqNgTqJVb8HBo0f6cYQ8HIDF2eXwKtGuwum7DiUaClOa2wanKe5Vxsushjxi7H0G3KeUPwMRuXAo8lnUWihxBoso6eJ5NCn6fgE8ZHi0fwMTNXiERZQ08KPt1SaBgNKdwMwyK/Gk0T64zkDMbr0ZvLl3nle4ixnuf87H8nNMHAyHXoHbEFc7fsQuda1RAWlBWlXjDZ+v1WnPzvJOr1r4ca3T11LwsyX3X6CrFHYtHn0z5o88DdwN13Axs3Arzpo/j7+WeExMZixI/fedU0ctqdsPm77xoUigKFkWyR09cq8oZia9kjvDgyTo4Wvvh4WZCXrtLsir1iiSH2QkJQoQLw66+Ar5ZlJYlnnwVWrEj/cloBWW2GnipYmHwhA+NSNDvi7Mk4n5SEi5oDZ1zARZcN51yBOO8MQqwrGOdc4YjVQnHOGYGLzjDEuEJxwRkOW6I8M1wMDEWcKwSxzjAkasGIdcgpwRWMi/YQxDuCsPH3Xvim52dY+tZoJDqDkOQMQIoWgERHAJKd/kh1+SPJHoAUhz/SnH5IcQSIWnycT3Wws4afqMGXauejnLc7WFxZTg7OO2xwuWxwOazQWE+PxZJZqsU9bwEceq09ikCnrLMnJs4byxmr6LIIrchJeKcdsqiyhaLSeBSFly2ITJR/m9jgYCH+xCQNh+K17uemv8fCeGnZ7hW2FUt27kfXN7/ET2u2oLByYNEBrP1wLU5ukNbgwkDjYY3FY50+dTwLW7YEmNn/888y/mHhQqB5c9n3jzGtW0/jk3qf4PCSw/m12wrFJVExewWDYmvZO3LkiIiTY9Ysiw+z9Ep4eLhw7/J5QSidUqAxaqpcQhjPmAEwsZnXKMbwmWFPeGblMjTJEnQ9tLRl8IMNWnQsQvZcRHCpQJxpXBJ+0LD/UA0smHMTrCGpGDzqGxFjRvsZY/LYKs2mx9z5JevZuCE2vUCLPs7qQpreyYIxeSzjEhCcgtTYcMQeriBdsnrBZD+OdcmfBS12staeRVjxGCJFy6MsnkL3L+P2NN165pk34vl4MyHmrZqn1h7VmqjNZ47X0zNA9NeIdxBvYqrVpz+KJXy5u+CyJ16Pr2OVmQhdiItMXLFNj7Dz2o6JNYl1EOsMRhm/eLQKPoj/kmrjlbn/ICTQHze1yIHJNp+pd1M9hFcKR5UOeVxb8grS842eaD+uPcLKZ9DBhNlMjIu49VYZDHvttaL59NKNdRBzKAar3l2F6tdUz4/dVigUhYBia9kbN26c6FjB7hPBpmzSW265xStzVpF1scfCyiK7VqdXL+D++2XokS90+9JDNW0as3lvBKyVYLX4AyEyoD5Qz8hlA7MTx6pg0nuPY95PQ0wJGhYEWFiFT8bcieZputhzBEt3KteLLFzG28GJ8JtrovSgqrCes6FSu+3o990L6P7xB6JXrJHRa3PH3WlC+Ek47+5bpidoGMs9855MXk+MXobVkM0JGuZfoP4ad+KFqZqy8PAa4yzpEzTMpVgMNy4te+lK/ekeZV+EKzdeWpZuiNzsXj5x0UrRd7ew0XBgQyGeqnauisJEhkLPoEkTmfE0cqSMf3juOdwS8xU63N8MN38rk70uBd29G6duxNYftuLMdu9STwrFlUJZ9goGxVbsMfaN/WrN7cdI9erVcYIVghXZEnujR9MFDkyZ4hnCWnrM0NUTnb2g65flxZh4aLEEw1LqO2iWCASGhwphYktzIcSuCSHWqO5u9B/0CwbcOl0kE/hbXKJzBwVdmDVZJFkEOhww9JhfiCy9TItegEWOC0UqbEcCYTsciMCANARFJqJ8g8MIDubn4FjRWE0IsECbXReRQKCfjAPkfnCMnPeIPCZi+Nn0xBCLRwhy3qpn6opyLaZ5mDJ43f5VPUHDLfhoJbSZAlasmvTo6gY/3RApx9JyyPEirk9DRIqeoBEcIo2DPlWVxevNdZv1t5kf10I8Xh+xRYhjcjouAVuPFf6OGkUGZj59+y2OP/cFplnvxOx/I9Dh98cREp15Ug2txSfWncDnTT/Hrl93ifkZt87A2V3spaxQXFlUgkbBoNiKPdawc9IU5QNbqNGdq8hizJ5uFaXmo7EhpzrZ4lcF9qCbkUzhEsJ2aEBQPAWYFRXLn8Eb7z2Kkfd9IUqsGOVSZOSvRXTK9U+RQoykBdG169Ldl/znEl02Ur7fi4TJh+AKc4ntCLesnmnLk4p0w2qi4wa3z/VsjUY3L+dp8+N22WVDjIcmW6cxpo5jRLszKfTEvJFwQW2nt1KTiRH6cvEZTCY7rz65jK0zzHKmxA4xTteHhtvXtI4xeYYbV3TPMOlKsR+mMn+6MVPqTAewNr4OzjvCRJu5ygHn3a+LS5EFmgsTrEdnT7LrrvSiR3L7a3HQVR2JIWVw4rQf8OSTmY49uuIoFoxdIFzbty+4HTd8eAOa39kcK99cWWS/H0XBIcfdM3KR2KFIT7GN2bvuuutEiRSjvAljrlgS5YUXXhClTxTZs+w984ycfNr3ih/r+fPMNvbusEH+/BP47z/gppuAxo0Bq38NpMIOe7gfApPscManIrZEkNAkzLxN1WxCZMVpFJgWJLv8RX9Xl2aFJU6eFRz+FsT7hQixlehiNqkFaS4b0pjy2siJJCZLwF9s59jW+ohe2xAl6h1FVPtdcqzTqrdmsyCVCRa6nzTNafP04XXJnrr8bA4Rf6e3ZdPj+3z74Yp5w49qZN26+9qaRByTLNx9cg0Pbsb9b728wy4fN64h9ijEdbHnPdaD2wstNKQNtx0ci+P2UkJOG1QpFYnCxqSGk5B4JhFjto5BuSY+B2URgJnGpMa9vVB/4mfAgr0Au/ZU9XZbxx6LxW/DfkPr+1uj6zNd3csDIwMRXDrYq2CzQqEouhRby957770nihez1VlKSoooWmy4cJmkocieZY9uWU5mQUdLH2vFskpERhY/9sZltq5RTcY/sI+wnjn1ThqB8XYE6KYpfziRGM/CymXEvKeoskyeCEiSlj17qBRidLvKzhkslOyx4Lrr5lmA6NWNsG3yTTixtLm+1nDPZlZIWbphGddnxNwZXTbkNk2dNUydOOS8b7FkA5MflUZC07xvgWXvdch4nQWINFn2MmqMa27ja3p3wTF7aS+hVyIkCDXLlEJhIy1BBo8G6sdSUSP2aKx4tJYvI9vR8Mf21VcZlqCpdX0tL6F3bNUxLH1pKSq3q3xV91lRPJEWupzG7OX33hcdiq1lr3LlytiyZQumT5+OrVu3Cqseu1OwGLI5YUOR82xcCj9a9C5cAI4dA6r4JEZef71cX7u2fO6wr6fzFM5wf4/Y01xg6ePvvxqNd19+CdffMhNPvvc4HFqwiCvzg0NY98KSZUcPR4gNtPfZYUOQ1YFEV4AUg3GAtiICtggnHO3ThAys3GIXEvpEoXzrXaKQMrNwRTyekeVrofvXJax7Rrk7TReELidzgFk4GXCJTF/veVpMjHQLGbQnu3qL5XqNPeE2Zu9WoT/14sjCFay/nskiPOGJhA+5A551pk5r1LdOzzqjg4Zb7OkNxd2ZvGI7ev09Ckfjuelvw+82wpaM2GTgXEIiSodl1pu4YPJk3JPCjcveskWRfpP7ocGABqjduzZQ7T5gyRIZMMu7J6a66/A7KFXbI9YP/nUQS19eivaPtEejWxvl094rihO5SbRQCRp5R7EVe8TPzw/Dhw/P790oEpY9tuylpe7wYXYn8XTO+OcfadkzNQtxo3eUc+NI20gphZRQF/zLhcAR7g9/CxAKJ6pWOi7GXDhTRiRohEEWTQZYNNkfgUl62ZVQm+iWYdP8kQo/hFrTkOLyQ8BxG/yeqQRX+TQ45h8Qrt3K7XagYtudQuQ5ZeESsZyJHdRjdpcVATanKL/C6EEmYtBVS9HH5XZ93s8m4/igz9OFzOVWm+HO1TtlUFTRjWqla5dP9BItulgTastPL6jskgJPxFT5FFF2iz/dvSvWmQRfhK/Y8xF47mLMXCBDDmXtPp3u4dvxYsVfsDGpBh45Ngq7Tp5Fl7qFS+xZbdYia9UjFPXueny33CJN6DSfs5XNu++6f4DstjHvvnkiyzf+RDw2fLkBHca1RZMR0potYj6VK1dxBfH1HmT3tYq8oViLvT179uDjjz/Grl273D1q2ZOWbcsUWbTs6WKPxgTG7HHxAw8AdevK1b7WvEsiTFQaXDYLTtaLEJokmUWKWVu22wos2NIMweFJiHcFCXHGQsi04NEKF5ooXY+pwX5IcAWJmLwkFwu3WJHmsiItwIqQdgmwl3CJoslcz5g/XuYo2lJdrM3Hrho2kaBhxOyJVBANSHFwvUgLEXF9QhpqQJrDM+8Qwk7uh9PumXcJD7Mplk+P3xMJE3r9P7FMCD65XhRUNldqMSfwshCzMW+8znjutHjH7JnXGQWZDfTiyp69k5y0l0RpvwR0C9slsp1tqi1XwYbCjj11x40DPvlEFlxmN55GjVC9W3Vc9+512Dd/n7Dy3fRcU9R4fQQs7acBZTsJoZcan4q0+DTs/WMvgiKDlMVPoSiCFFux99tvv2Ho0KGi1l6HDh3EsjVr1qBJkybCtTuQheAUWbbsUaOwTS/Lr2TXCx4XB4SGMmavG5Lj33e3ixVJrMx4hQWBQSlwBDKb1SLctlIvWeAUZjELrPruOIIZqSeKqIjqenyExQpXbQcSPj+GFKcfNF3MyYp6FqTGhSLVEYDAEonSRaonUPAziV7zFrpxmYEr3lS3Acrl5nlPCCzf0yPqLLSeCbFmFEs2F1U2LYcpKUN4fr1FljuGT7fMeYfjeeL7IpM9pVdM7+CzLf1/Pp00yO6UijiQUg61gk7j+sidaFalAgoT8SfjsfKdlcKa1XmCTzXvosrYsUCNGsBddwFbtwLs782euv36oe6NdcUkGDIEOHwI2pSpsHTqhJjDMdgweQPO7zmPlIspCAgPwLYftmHo7KH5/YkURQTlxi0YFNsEjSeeeAJPPfUUVq9ejffff19Mq1atwtNPPy3WKS5Ds2bAgAGy0KvOBx9IF67ZmnfokDQ6vPRS+k3QGsZ4vchI4MABarLy7nV0QgUk2FHiolRxdLTSaWouliwfZamToGRZhNkZpPePdY/TRKKGdKyynp6nJh4TPDZ+NAizek/EwRnX6MkcGRdMzjRxw+aZt5qWW21ZLKrsLp6cPilDr5zn6a5hSsJwv0rXmMa6QEcaghwOT1Flq/dYQ2em244XFvyh19zrG7kBwf6FK+4t7ngc1k5ci/8m/YdiRb9+Uuj17ClN7DffDHz2mXs1CynPCx+GtBdeheXLL4TQWzNxDVJiUtB0YB2MfLMBhs4aipTYFOyZuydfP4qiCPpxczop8oRiK/aio6MxkpXofWAMH9cpLgMtCL/9Bgy9tAWAZVdefVX2cveFYoc1YsnBg4A95Q+3AglJcKDyxvMovycGwmlrAVbMvR5vP/E6Ni/tIJIw6F0Ms8rEjKCUNHeCRrBVzvtbHEIk8rXBVhZKZiatCwFWKQwp9sIryMKyaeciPIWUbZ7iyQFGIWWrS++iwULP7LThKapsZOpyuWi9pnf1sOiCT8wbxjxRONkjIkWBZUMMssag1VMc2TBxup8br/Ja56nkQoyCyg6rFfGBQV7r3OLQ8+5eZwD3O2jA/Fgp9tqE7MGug9tQmAgtG4pOT3ZCy3tboriRHBAJ19x5iB92D465KsqYigkTxJ1V2cZl0W58JzjHjQf8/UVWriPFgQY31UN9bRcwZgzinnkLfkF+iKxS+MrtKAoouemeoSx7eUaxdeNec801ootGbSMVVGfFihXo0qVLvu1XYYdGpZgYGS9O+PXSvcsYPtlD1nv8778DUVHSupcUd0HPA3UhOcQPJVjmxKEhONUBLdCCTevaYf6MwYgqcxZtu63Q481sCLWkIEBvlUbLHi2AgZY0pMFftlTTNDjml0H4lHKwd0mEa+xZBFjsYn3dPitQs/cqWMPsIsnCLnrT2sTr7JpNuHUpCllbj0LOz6rBIeL7GM3nFG5k0S3D6hQZurL0MgsyyzGi84W5F67RvsKQViIdVr+F5ThDnLlMwfMm165XwobhhjU2ZdFQItGcnKG/l5G5q0+aOUFDx7xL5GhqGWxNqoqmIUexaN4rWNHkfxjRpSVCAgq+la9E9RKiVVpxZNYds7B33l4AldCs+T2osvkF4O23gTNngMmTUaZhGTHOkerAmg/WoPFtjVHzOp4DawPr1uHYuz/D2ulBBJUIcpewCQjz7jKkUCgKH8VW7PXv3x8TJkzAhg0b0L59e3fM3owZM/DSSy9hzpw5XmMVl4cthVlOhZ7dTZvkshIlgC++yPw1NWt65q02+n/prgXsViA1zB9B8XY441y4WDoQrXsuQViZC6jXYSNitRAkOYV9TyRUOBNixDYSQwIR5wqWCRpOT1Fl6+lghBwIhqOeA4nOQJmg4QwAQphJCyQ7AtIVVWYhZd02hxSH/KnwbpNjfIsqc95pKrDscnji90Q5Fq+iynJe1JAyhKBv4WSfosrmLhmeselfZ07OEEt8iirT8Gj2Kps7bPgyN6aVEHu9wtbitkWr8ff2/fhmzGCEBKqLf0EkPjoeR1cedT9v/OZtQHRV4O67gW++kTWQGMcXHCxctyGlQ9BhbFsx1j5jFnb/sgt/RwzCtXe2FoI5+WIyVr61EsFRwej0eKd8/GSKwkxuOmGoOnt5R7EVew/QvcFK+5MmiSmjdYSWlYzaqinSU748BQ9wUhb3zzYBwf2QGPsCrJYU0ANqiL2Q+DT4l3aiXbflaNdtBRJcASJJgy5WhytQFDkOSJTuVmeY1auoMqUj6+YlXx8HR/1U2CONAsh6mzTa8YTikVY5Gy13eoYslzPTlzBmjyVYjKLKTr1mHl23ou2axWi3ZiRlsE2aEcvna90zf2ojDs9juRPCjUY+I1PXFK/nWZe+qDLXlTCSM2jZ830/kzvXSNDwWeXF/JgWqBRwAfPOt4JL07D75Fl8ungNHr/RU6C3IELXJLEFSstscSG8QjjGHhiLw0sOo16/erCwhqOltixmycQM3sAyceOZZxA2eDDCyofihxuno06TQMR+OR17grqgx8Qb0eT2pmJ7rFFYtXNVzBwxE8Elg9Hy7uLnFlfkHpWgUTAo1r1xszIpoZd16tWTpb4yCnlk7F5Gy0+fBt54Q5ZtsVojEBr5olgeBCtS9eLKoQlpMn5OT8gItsiYPIo5o1duUJKMw3OEWkU8H5eZY+/8y6fC0TYJqJukF1DRRK08cmJ5U2x+dQSO/9VaCDybxRObZyR2MC5PL54iBKGE7tuM50Vsnj4vXa6mHmeGhhOxd6ZxbiOfHGfE6Wm+68zeYPlFuNcZmbgxISFeMXrudzHF+rlr8GUUB60BF51heOf4TdidLDstUPD9unYbUu2ePsQFkdXvr8Zrwa+J+nLFDYqyBrc0gNWPNyb6H5+eCfYmpJl9+3Zg2DBhUr+p/WlUqh+G81NmIrRdE9z863C30DPwC/ZDeMVwUbZFocgRRuxdTidFnlBsxZ4i72GtvYoV0/fAffNNGcNHQecLkwaffprt62THp6DQ4QgIe1Ra0CKkuzAkgZ00nAi0OBBkscPPoiHckiKKKzMZg8vdlr1Qq0jG4DIKtxAbu+IyKcOBQItdWPSCrHb4sTCLxYUgmwMxe6ri6J/tceG/uiLOLsDqkoWVKRKtHCfFW4Afk0L0eZvLPc9CyiIJg4kbpnmr17yRpKELN1oBjYothuAziiMbljxm2RoC0qeyi5gXWbim1wk3rhR7sUbZFV9BZxRxNidoZHQW0Gvy+Z5qE1PTcComHgWZ1LhU8egfWvDjC68Waa3a4/een2FZ9xelpY99dMeMwTXfjcYNI0qjw8/jULlzdTGW1uW0xDTs/HUnFj++GK2G10e7YaZ4C4VCUegoVm7cjz76CPfeey+CgoLE/KUYy7pVComRWZGaCuzbJ2vssTBeRITMrrhMYT2jNzsTNzJaN2KETOTg5rkp/8BrcSruPdiCbShhs8Dm1KAlaEgM9RfxdClagHDPsjNGmsYiyDZYE6UoSgwOEG5eTS+a7NKssGtWOLaEw3LBD2m1HHBUcInlSU4/4fos12EHWKe4dIt9SHXKGECLu6iyjIYTRZVFDT4g1WHxFFi2e5ZLI7BeVNnhU2DZUE3mxAjRUcNkepMVZTxFleFVcNCDaZxX3J0GRCaleNy4esHlzGL0LL7bdaYzPgpahh7EoKg1+P18W/yXWBsB/gX7tNHj9R7o8rRKsjLDDFtLUCD+ZUebH/9GgwPzgOefl3F8QUGwlCiBxDOJIpPZ5XBh24/bROmatnc1RovKZ0UZF9fTz8La94b8/iiKQoaK2SsYFOyzdh7zwQcfiN63FHuczwxevJXYM0Ghx0C8adOA5cvlfEKCVGZMbvnf/4A6snXT/PnA4sVA9+6y7JfRzYkWvIw0oc0GfPed97LAgCawWNgkLREna5ZASoANKcH+oi0af/vsmqG582HlvC1Jqpa0ED93UWWKQL2iHQK+L43AxRFw/u8s4obEu1/Px6hGhxFa/4R4PbtgUFByOa2ADtGb1uJdbFkXekbhZSNOz5QG4RaAnqLK+nojI9d4iTFjzq41iiqbkzI8W05fVNlt/LMgUi+9EhcUIjeZSeFkd1Fl825YAb0MoRd9S25E/1KyZt0xv+YoH6nXyymgsJVcYETRbZWW0/Zx4ZXDEVU3ClFNKwLDngWqV5d3WszWbdsW8753wBZgRZ0b6wqh1/HxjmhyWxN5F+PvD9fjE5B86BTONeuOiMoRKFmjZH5/LEVhQPVLKxAUK7F3iBV+M5hXZIHXXgNiY1mNWhZUpoo4e1am2jKhhUkudeqIfuwTJ0qXrCH2sttRQ6Jb6soHIYmWO02DTXPCabGC6RkprgBREiVV8xMu0WMxNfAruqKC32rxOuFqtTqQ6vIXclCrnAZH42RYyqWZ1jtFX1xRH8+drOF5f8bpyWxbY14mcTBZgy3WPAkdcp7ua/bFFfKSAk9Y5/Qet0KLmuugyJsKofuEKtRFnN7fVqwzjfPqqGFOtNALJVtd3tm4oqCyrisZ+ud2GevP3dvRxeqlEjZmX2iNIaVXoWfkVnwRp7cqURQKki8kizIqPAavfelaMdkCdD8++4Jv3ixjKJ55BgOWr8a3/X7D7FGzMWr5KFTpoFdH1zScKNsce9o/gX3PbkN4exvO7L6AwTMGo1KbSvn6+RQFH5WgUTBQMXt6jIqoX6bInBUrZHXkrl1lUTy6cGvVklaBlBTg3DkxrEcPaejr3Tt7m09M9GTxpqZtg6ZJCxXFV5DonAGEWlKxYmFP7NvYBIHWNFks2ZIqhEmptIv4CcOw53QThOjLmIXrb7FLkTPuOBK+OwRHj3gE6kWVud5PT/pgLkfs7ipIPF4GQTZZgJmWvQBTkocsqiyza43uGiy2bNPHGPF5xrzsqKHP+xk1UGTtPTfuAsuyqLKxjskZmjt5Q3/uTs6QxZc96zzJHJ6YPY/YM86XvkXpMyqybO6wYYzdmlQNB1PKivjHltZVWLnnMAoy6yetx5IXl+DcHnlMFld4Tvvhhh/wY98fkXg2UYg8t9AzGD9ePu7dC/+pX2D43CEoWbMkzu897x5ydO1J/Pf5f3AcOobujc7gtvnD0Xtib/zz9D+ir65CoSj4FGuxN3XqVDRu3Fi4dTlxfsqUKfm9WwWT1q2Br78GduyQBVoZgEdLH617VGoUf5B19t55B7jBJ7Rn8mTg9tuBlSvTb5o929lJ48475XMN0vpG4pw2hJ5PQeXDsbC5NPz8+b145q4vMP+7oUhNCBBi0M/lROXUaFyLf7HveEMp0kDBxk4XbJcmg9xk5i4D5WjSdoi7Rr6eZZy3ThqIpfc8iSOzOonlFIKyArE+L6xjsmAza/QRblta8ujydZnuQuVyKaY4r9+hUqC5W14YBZFlmRZZVFnvqasXVRaWPD1Zwzzvrqtn1EyWu6ln40rLW3xwsFxnjsnzteAZ64zt6M+NTF+PY9qCORdai7l+Jf/DmC9nYcj7P2D/qYIppjZN3YSlLy3Fhf0s0l18Yezd6a2nsX/hfpzZdsa9fN6YeVg8YbGoy4cyssiyMO/eeiuCokJx5883IHntNqTNnAfHdz9i75NfI2TFIjTVtqDOgMbSbK+5EJRyEYEqCUaRFVSrtHynWLlxzTz//POiH+7DDz+MDh06iGXsk/voo4/i6NGjePnll/N7FwsWTJl9+GHZe7NaNSAgAIiLA+bNA8aNc8fsZcaiRcCvv0rN2MmnPis3R6gbib8ft0ULhFOIjQr7YxBgd+FYiUj0uGU2lv3VC8dPVsEr93+Mmx+ZjOpNdsGW6EQA0nDsXF3UdwUKcRXnpHXLIlyudNdynkkZdNdqRlFlWEScXnDNUwiIjAc9tan6WCZoyIIrFqSJZA0p2qQLV85L1y6x6MLPmPekyop4PXcqLK/C7ma3UuAZosotzDyFlY2iyu5YPvHGJuGmJ10Y6wyxF2vE7JmSeT3b9OBbVNlsaDQz72IrPFJxPtqGH0D5gIvYc9KCER/9jF/G344qpUugIMGuEJXaVUKp2qVQnLH52zDgxwGo2KoiIqtGuvsGb5yyEZpTQ9PhTRFuvygHs/8xY/hOnkT4vcPQfsNGWEIeRfRRO3Ztj8RNw6qg/NiHgUqV4HAC0RuiUTLpBFw9esHy83RYyuqiUaHwQblxCwbFVux99tlnmDx5Moax5pSpU0bTpk2FAFRizwe6bJl98c8/wMaNQFqaLKz30ktAuXJeQxnPzcoOZcvKpF3Cr5lC79pr02+6TRsp9IwWa362KIQF90NC8lwEWl1IDA9EwIVkRMSnoEK1YyhT/jRGPPEhFv08AF899QzqttqCY0mbsQXN0O7GBe7ECfbGtWt+8GOJlQcrw5Johev5M0iqJnvgsvQKM29p3avSew2q9FktrHfsuCH2w+rSRSITSWT8nqeosnwPxvI5dfEniyobhZRdevs0Jgxo0MxFlQVG7zhTlJzRxsw0zojfc8+bYvvc40zFmI3euEbpFRnP5zvWsxteMXqXKLJ8yl4Sa+Nro134ftwU9R++jO6F5DQ7Jv+1Di8PvQ4FiY6PdczvXSgwsOaembAKYRjy+xAcXXEU5ZqUk21viFEzqXJlujxgYRxu1aqI6dQLVcP2ouqkm8Qx5kx1YNW7q7D9l50Y9PMjsP7zI9C/H/DLL560e4XCjErQKBAUW7Fnt9vRmurDh1atWsHBBq+K9LAqcrt2MtWWMCOXFgEf+LUy7nvhQunWJQMGZL5ZGgkNoWdQpsTLSE3bDJfjMJLD/VHyQjJKJSShTqOd+OyVp0U+7HVDfkerXsuwcMoQbEdj3ITZSG57EMmWQKRq/qIGn9NpE4Y0v92BsMb4wc/FQswuvQOHHSm07jEuMMCBVKfsZ+und86w6gWWRWkXC+vqWUS3DNk1Q6R9yO4Y4s5VT5aQGRfSGytcuxSIfJELGkWhmDeseyyqbNGFm7lxsK7G6I8WXmeZ5OGet3iEoVErT3iaNRdKJHli9szpIO5HdxKI/nam7RjJHrT2ubts6LtDZl9og7L+sThnDxfPnS4N8zbswnODesDfzycWTFHgcDldIiu3Xv96YhKwojkx34U1bQr89hswcCCi6p7EvvmlsW/+PtFiLXpTNPbO2YtBI4JRoUUFaM3Hw8If+/79SuwpFAWYYhuzN2LECGHd8+XLL78U5VkUPvz3H/DCC8Bjj8kTOzP4Bg0CJkyQMXsmDC8vQ/tyip+tNKqU+wN2BMIeIe9JwuNTUblsNHreNAeBdllguXSpc7hzxEf4FYMx0vYtbEGy0wa7bNByF2ZNFgWW7e8cQfIHR2CpmIpAUWCZBZIhkjEYk2cUWKaEo0XP3+qUSRkWzsuxLJ7MdRRFfJRFleVyWvKEBrPKJA0xLwop60kaonixqVGtuXOGXkhZYJ73LXhsFEfWjILLnlUslBzksCNA7/gSF6IXVdateyYPs17U2ZME7GXmM23XK2fJBcw72wr9tz+J38/JXtLE7nQhPqXgBOnzu3Gkqps1M2d3ncXc++ZiaoepopaeG8bcGpY934rntNb/+y/KVg/BLW+3x9qJq7Hvj724sPc8bvqmPyqs/h1ax06wMGmLN4F6+IBCkR5LLidFXlBsLXtGgsaiRYvQnrXiAKxdu1bE640cORLjjSw1QMT2FWvsduDxx2WqLfub3XEH0KSJFH7ffy/j+T780D2cdfOYcOHbSYPXlj17gMaNAZMWEbCbE40JjOfj5onNWhIuW1WcCzuEqriAgDQnUpKsuPHBb5AMfyQ72RjND6EJspBwWqgfLjhZn88qrG2JriC53GWDvSUFgAOJzgBRBoVFlVOcrMWmiRg8u8uG44ta49Dv3VCq3U7UGPEXUh1+7np6jNkzBFCa0889b8TsyaLKHuuW0yHLsYj4Pc4bysorfs+w6ulZEeYad4zlM+LtuM6tE/V59zpPn9ySSfKC67BakeAf6BUHaDTvMN7DHe9nei7Kw5iLKpuey+Hp7w0D/GwIDwosUKVG3in9jsg6fTrxadE2rLgTGB6IAwsPIPZoLBY9tgg3f3eztA6Hh8sfKi30DMvwhVb7559HLQCVSqUgzlYCn/X5A4eXHMET5+fBek1XWShz4ECgZ8/8+GiKwoBy4xYIiq3Y2759O1q2lI29Dxw4IB5Lly4tJq4zKE6N1DOFFwMqtWeflVa88uU9abV063bx7lagJ+amo1Ej2Tt39WpZi9nMli0yY5dvZYg9EuLfGBfsR5AUGoDQxDSUTExBbIAUdGyXxqLHgUlSsdhDbKK9mVPUzpO9czmO1jlZbMUiXsOuG1xv1NZj+zWudySGIHZnDfiFUTxaZA09EY/HbFvpruXxkNm8J2ZPZtgahZTTxezpY7wwFVU24vfcXTQyqIjsVStPJ9Ltwg3xHLeZjM3oeboCzLp72JcAix09Sm7D8tgGqFyuaoFy4Rqt0ijylNCTsADyiL9G4Nzuc/APNoVd8G6MN238QfIH2LBhptsIPHMUJV4fi9r1b4dWrTKsyYkyKevWW3E2LRKlZ8+GZd06mcRlZFwpFESJvQJBsRV7//77b37vQuGBAo/lFghF3803e9bFx2e5anL9+nIzF/UEQDMMGWL3Jj0x2k2y/Yh4TAiXYi8iMRWBpRyITQnFiUPVEF7xPPyTY8UYe7BNlFHhRJFHd22SKwDWVCBoTTAc7CDWMgV2Pd7O38Y4PX93D9yy7Xeg+XPfIKTucVkwmQkcIoqPVkbGPEk1JBI39ALLVtNyzjuNQsqiKLIpFs9c2dgcSOdOyhDBgF7xe6J2npGF646tM29b35RNijKjxl5McIgn5k5/L/dzmeTsU5DZu+iyeeN6CKKXLpxc93O0DD+Elw8Pwp/nS8DpcsHma8bNJ0pUK4EJFycgLSEDS1UxJqpOlJjSwQLpFHuM3aN1jzcMdMkaj8TPDxabDQGN6+L29ZPgCmwD3DMP2L0bCeeS8OW1P6Os9RyGJ32BYJrmldhTKAocxVbsKbKJUS+FVj2a4Ayo3BigZ4J6kEm6tOJNn+7JO1iwIMN8DndGLidfUhyHhfY4USkS0RUiEB8UJCx2Hz36OmBz4bbHPkFQomx15gph3TwNQVa7LJ9iAUKsaUg5G4rgcVWhBbtwfsU+BLvXuxBoswu3LK1/kZXOIqTCeXd5FQesCLA5hYuXesxfz8jldsVyMW8RMXtOfblNnzdi9igERYKGTe+o4U6F1YvjUeCJnTdi+UyZtobA07yTMtzjxDpdtNlYdkVvlUbxLVq0ZZB0kcF7iE361NxzCzzdGy22pQ/5O6aJEHu3lFmLGec6YsQH0zG8W0v0blkPVuPz5BNMZGHHCE6KLECxR7Zt8xS6vAzWObPlzODBOHGKN0uArVolBLUc6Gmbo1AYmCu75+S1ijxBiT3F5alQAWApGprlaMEJ0i+kfM7uGY8+6jWcqxnCx9VsncaXk8yE3qWwWYLh0hIRHxAMO6wiC9euWfHkpPFIdvkLl6xlizRFpYT5izIoqRpte1Yki5p6VlEmxdEwGVqAhhQKN/a+FTF7fnoPXQvShECzwOmyiO3TmkZhx9eTNGGxkz1x7Q7pthXL7fq8iN+jmJJCz0HDkj7GZdeteeKJKWbPFHtnjqcTOE35sKI2X/pxRlydIdS83LjGtk0iTbhzzXX0ROFm7+deLhfTe5pPuXPPt8Yjlf5Ak9BjqBt0EjuPAU9NW4B/tx/AmyNvKDBWPkUWYDIa78J408Y6SbxRYEAtHzmJg9kBzeHE0eM27DgQhJ419iNg4RxgxgzUi43FuC1fIdkZCEt9mVLPmxMmgoSVK9g9lBVXB96Q5rRBlWpslXcosae4PAzCjsrABcSLOuus+JSwCQyUibsszp+dvrj0IjGBg2VYDIEYFtgCF5MXiz64aZofHEyQ0PxFrB0dtmkIgC1enhFSw9gXg/0y/GTBY00WUHZWdiJ+Gl2zdLlyGxR7FHmyUDJj7lzC9AYkny2BM5vrwj8iCREt9+ndMqTQ857n1qTQM+L0RAFlY7m7Hy7naRnTxwi3qCl+Tx/jXfhO74XrTqE1JWTo67yKH+vrIpNlokpcULC3iMugjIrbxWsusize0/tFvkWXyUVHGP6NaYzrSm0R1r13Dt8ili/atBcta1bCsK7NkV9Eb4zGnrl7ULZxWTQcmHkMmkKHSRqzdUvdpdA0zK79MS4evIiqL76GxqOHS0vgokUI7XMNQufMYdSzGLrmgzVY/tpyDPxpIGrVtQElSshJUTxRMXsFAnULrsgdhw/LQss+MP7u/vu9z/GnTrHkTeaenttukyW+6Po1cDhjxKNF01DmYhya7T2OOqdZ08UiSqw4U/0RkOhwZ+OyjAoj2ug6ZcyeSK4QeaTSbMUyKmJ7Ik6PJjKLO1mD688sb4qdr43A8Zmd3f1vjTg9Y95q88zbzMvd/WpZcsVksvNVWuYWGOZfoDvPwYj20/vh6uPcHTQ8TTW81hluXFr23OdIk3HQM9a0zlRI2Wu73nuajt/OthOP/aL+Ewkbxua+X7IxX/tMH197HEtfXIptP2zLt30o7NAq99uw3/Bdj+/cf0ve1LS8tyWaj2qOEjVL4PfZNhz5aDZ+CblTVGISiVrz5olafjt/3Smyos9tOQH07SvjM0xJb4pi6sbN6aTIE4qV2GP27UU9O4AdMpJ0t5cii2R0ET9yBNi0KUsvp8WPlVrYYc2nNJ+AyYCRkTI23CDJvks80psUmZqCchcSUO5iPJwJ/hh73Uzc32oxXBekgdoRJgsYs5gyBV+A1YkgzuvLGHkna+vJ4H1ZT4/iTwo/isKSDY+gRMNDCK9zXLxngE2KQ75OikM5b4g8Cjw/m1zO92G9Pfe8LgpFToYhEIXgcpkElmiQq4s/veCysdwtGGkM1ETChvjHR691PCdq7gQNFlR2r8sgA9fofevTpldO5jOCb9KwqWflmri6OJlaEpF+yehZaqt79fHzsYhPzr+6e2UalEGrMa1Q63oWDFHkBNYp3D59Ow79c0hY6TRpxkanJzqJbOfvun8nxPS0B9ZgV1J1/IjbEBtvZQsiWBcuwB3/3IH+U/ujbb+yMr2eapDp96yvlBnHjwMrVly9D6lQFDOKldjbtWsXEnWV8dJLLyGBJyJF1jEyLcwdRrp1k/X2fGBtX+pAxn0blCwJvPWWCPURnmFfWNmFWtyrvqtFDkzW/HA2XMYARSYkIzQ0AfExkXDa/XHylMz+c4TZhAWQ7lZRVkUYxTQELA9B8J01EfpJaSFY6XZlVJ9cr5dgEeVTNETWP4J2kz5AnVHzhbblclHCRSQ4yHnRB15PU2WMIOfZLcO8nIkZYp7CTBgNPfOe5XwDk5oyt0AzXqPHQAmXrmHYE+OYrasnaOhJG27LXkiIFIDmxAujjp5Jcxrr3F5lw6VrStIwxno2oicWu6yYdbatWNQw9JjX3zE/Y/aqX1MdN352I1rfl747jiJrnNnmqYa+7NVl7gPg7I6z2PXbLtgT7bjxyxtRvkV53Prbrah9Qx3433qLPBjvvRd+qYloMboFLEy/37ABzmt7YkFiV8TfNNxTxNm3jueSJcCYMTKzS1GkMO5nczop8oZiFbPXvHlzjBo1Cp07dxYX0HfffRdhLCqaAc/TD6nwdteuWgWcPy9FH2P46HPlCT0D5cZzOlul0Vq3Y4dn+RNPZP4WPkm9ghJB1+J80lxRHy8mJAQOqwX+ThdsyRoen/oIwspeQKP3pIsoMSQQSa5AEdtHQcWyKxRo1lPBCN4SCktJJmj4i364XJ/iZMaITNAQ2bm87mgssCxr67HGnhGnR1HH+D8x75SxfnLebYqDy9SmQgg6t+/UFLNH9F66crnJPesWZhZPMWUD8zg9ls7is66kkaARFOqJ6fMRbIbA836t52196+qZDIj6PnnmfzndCX+eb47DKZ7eyJWiIhAalMEfUlFoKNOoDK6feD2OrTyGyKqR7mSkiCoRCK8UDpfDJcRcy7tbinUNBjQAkgcCG1dIKx4DdpmZRUqXxj8tHsO6f9ficGp13Pf3v7CyOLsZZm4NHy57b0+bBrAvLwN+FUUDFbNXIChWYu+bb77BCy+8gHnz5omT1IIFC+Dnl/4r4Dol9kyw4CrTa6na2EaJ2XrHjkkBOHq0NMUZGbo6NWvKczi/XqM8SE4oHzEK55PmiH62tLzFhgcjKjYJUQmJqNtku+iSEZBsFFW2uhM5REyemLfB1TEese+cgLOkU7hiZWkUowcuM3zNBZZZgJliUANS/IBApx6bp8GlizSWVzE6Z9Cd63TJeatpjCykbBSpM2VfGJWLxSBprROWO3ctPVNcnileSpZS0ceZXK9Gf1uuM/riXqRlzxB0mfS+Nd7DvR3PHppXXfI5EzU4mXHpLr/87v+qyF2twvbj2ovJjNVmgSvNgcSzydj8zl9oOaGXZyVDBz79VN7hffwxMGqUu6xL6wfbYc/cfehxYzlYX7vP+82MDP9Jk6QbgGJPCb2ihSq9UiAoVmKvXr16mK5H/1utVvz9998oW7Zsfu9WwWfKFJkiu3at93KWXRk8GPjhB+Cuu7xW1aola7Jm5K69FDQKLF8OvP22TPINC2yKWlHv4sD5xxCqpSEmTIq9svHxOFS2DIKtqfBPlGLPEWoV8XSMz0t2BcDf4hCuWkclO6wVLyLVFSByIAKsdqS5/EW8nubyE1m9gTaHtPRZgJhVDbHj3aGIaHgYjV75Gg6XTYg6yhha+yjq2NqWIo/zmnDtyuXQ50VtPT0L13deEy/WC9jxQe+UwTg8WU5FL8LMD6MXUhYaTy/xIoot2yzCCudO3rUAJZJkiEJMqOxFZ17nLqzsk+ArevYa7lwRW+hxJ4vzrKlGn9iWSTzqiwSl/OKR6AxE9MV4EbMXHpw/LdR+GfAL9v+5Hzd+cSOa35F/WcFFDpcLAaOG47Gzv+I8ohD61JvAtgHyt29w3XWiX3bir/MRNGQYbN99A7Rti5I1S+L+HQ/A5m/LWOgtXAi88w77Uqavqq5QFEPOnTsn2rc6nU60adMGFYzyFLmg2N4Cu1wuJfSyCmMbK1VKv5zWPN6FZ5C4IQqt5qCLFov5s7nJVhnzLygddgvCQm6F3eKH5HBZrC8qPhFpcUFY/N1gvLv3afmeoRr8kSasc0GWNNEBg9Y9dtBlW7NAS5qw4LHwMjNIuY/M3rVZZAwixR/XB5aMhz02DAkHKorlVjjFR2QCB8dTBVG02SgWhbFOg9UixwhjnbuCsfR7yjg+kw9Uj9nzFFbWBZ0Rl2d02zBl8gqXrrhBZvyeZzPmzZbQY/YuhoR6boh9zXb6W5pj8tyz5s4Z5t01FmdSzuWRKnOxqMXL6FN6oxyfj9m4ZZuUReX2lRFWXtV4y1Mo6n79VfzNS+M8gpECzJqVftwHH2CW/634fE93nGg3QMTwMfTDLPSSzifhtw7v4BRjA/ftAx58UMb9mjvzKIoOWi6nYsZvv/2G2rVri7wCeiJr1aqFr7/+OtfbLVaWPV/YE3fixIkicYM0bNgQ48aNE1+uwgRdM3PnAl98IeP0WHyV9bnodmEWBmum5BFsrcnwna5dvZeXCu6K4wlzcCosAg0sp5HiH4D45BB8/coERsvhNbyI2NBQJLhChOuWAivRxdgxC5z7guGICYCzih2JJTlaxuwlGzF7LlPMnsuKgFrRaPnJhwiofkp01+AYdssw4vTSHPLCJWr5mWL2ZKs0Oe8yzYt4PT32TyZJeBdLzjBmz12dxZJunFEc2WjEwfkAhxNhqTILNi4wxBNr5xOj5xXr50720DGSPUzPvWL2MgmYvugIFe7xQWVXY0Vaj3yz6pHur3bPt/cusjCp7amn5Pybb8qYOjbAZthAYiKcAUHYOGUjmt/ZHCnWSJwMr4+UmGQEuZJlt53ff5evY8gHPSrj56Hxmik402M+ytVKgoWFnVmzL7NYj4MHgZgYllO4qh9bkUeomL1LwkRRc+4ARd66detQt25d8fyPP/7APffcI/INckOxtez9+eefQtzxS23atKmYaDZt1KgRFi9enN+7V7AYOlS6aL75RhbPo8u2SxeZPvv000DPnhm+jDf+t9wCfPRR+nV798pcDzM0CLEG38iRQPXq3utsFumadPjZML91Q/zTqC5Klr2Idv3+xPiwd0Vx5bQwm3DdEiNmj2eL4G9LodQ9VRA0P1yUY5HFkGnVk7X1aPWT3WZlHJ8twIGIBkcQEMT6cfICJEut6POijIrcBmP5jOXSjasLsnTz4hOaFJo+L7ScZvo1emL25Mj044zsWXf+hwUI1616LotFFFV2nyNzU1fPd30G48nss22FWG4cdgyNQo65A/oVRYR335W9D/mjHDcO4IXJiNE9cwabv96M+Q/Mx1cdv0JYhTA8fGAcbp01DFFLZwKNG4sf+v573kTq3Q+KH3nPif2wo+8TaFA5ARYmdFBIZpIoJ0QeK63TQsj+vYrCh7LsXZJWrVphtqmwOfMIzpzxZMSfPn0aARllL2aTYmvZe/LJJ/Hoo4/iTd5x+iyfMGECevUyBR8rgDvukBPv8uPiZAwfMzDY9iITmL9BwUd37tix3uuoEenBYc1VCkJeEy7VbeNCyhp3U1fWxxPdai0WjH3/WQxosxlBcMARwgLJ7I2bhhRXgCi6LFqjRTngrJ4KV5RDiDa6de0uP+G2dTn9hUBij1zG7BnFmClerBYX/KxOEbPH7fI5t8dHzWIR83TZilA6MS+LOYtSLHr8HkuUyJg9zRSzZ9Hj4vS4PKc5Ls/k5jXF7LEjhyeWTq5ze4utHhduXFAQnP5Wdzyf2Dd9c5oh/oztGJVtzLF/mSR0mJNB3Ov1v02MPQyLLzTDjaU34JqgRXjlx24Y2q056lSSHRUUhZhz52QALWHdJEPkMVGLVv3oaASXKoHIapFodmczIfTZl7hev3qMkgY2bkTcSx/gl9diUeXrYxhe41UEP/ccBsxjjO9dwIABQPfu2P3EVwirGI7KbSt5W/hYlf2GG+QJgjeZbLztc85WKAq74enBBx8UCaSffvopPvzwQwwZMkTE6zkcDpFfwHW5pdha9ui6vcsnqYCMHj0aO3fuzJd9KtAwmJrQhctgUaOWGqvjM1s3A2jwo1XPXIaPJbUIPY60SvfvD7z+uozNZs1VhgfS6keRqHslBRocwo3JvUiDPwJhR4gzRQg6I0HDGuaSzy0uBFtk7F4guzuMP46UWXth6XdeiERa/AKsaULABVgd8LMwpg8IsjpEnB+Xa+dDceT7njgytbe78DJj/WyiQwc/vgtWq1Ncl4QQ1OfdhZUNNWTUQBECzVQPxfzLk5Ve3LXzpDLztvAZ40TMnr5tcwHkEimJ7u4ZXtY/Y6w5McOqe5KNu2ajzp4pZs9tgDQXXva5yxbP9WUzTnUUj72jNuHvdWtx6+vT8Onclfkav6fIA+LjZaYVMcc4N2okH6dPR8NBDfHQnofQ5v426V/v74+EW4ZD8w9AIkKR/PybspaecVz8/jvinn8bf46Yhl87vo8jy496XmuM4YmgShXg889lJw4KPkXhQXXQuCTVq1cXrtpbb70V3bp1w+bNm7F//37hYfzrr79w9OhR9OnTB7ml2Iq9MmXKiC/VFy5TiRs+vPce8MorUpHxhMs2GFRjzM69cEHG72UAb8YZg2dOsKMxkDAmjwWUWVj/yy+lS3fgQODxx2XnJVr76L0xiAxoKgQfD1i704YO2w6j94ZdSEnwh8tuwwHUxIXQcMQ4QxDrDEaCKwgXnaFI0IKR4AxGnCNIlGmJswcjwRGIZEcg4h1Boi5fsjNALGNdPsbxcUpKCMOhr/vgxMwuSLMHIM3hjzSnDXanH9IcfqL0CuvtMX6PGbrMzHU4rKLWHrNxuc7lsELjI+vyMbbPZZPxe2JeP5E5ZQ9cJmBYHMywpTVPrue81SUneqet+nMuN56L1zosKBUvL8gxIaHCckdd6S6c7DIVKdXX0fAo1ov394g7q/FaY7x5LK+9TtM60996c0J17E2sgGCbHTeWWS+WTVm4DnPXXtkbJyUmrzA1agD36eVS+GM27taMOzjG5J06Bb9AP9gCbDj07yEcWX7EaxMVW1XEPRvvx32vV0MIkoAXX5TlmvS/nX/71mjV1h8dqxxHldamc69h4WPrHY796Sfp7jXKZam/faFAFVXOGsOGDcP69euxZcsWXHPNNSKJlLWBg3zKmuWUYiv2GPB477334q233sLy5cvFRJfufffdJ9ZllRdffFG4LsxTfRYa1klJSREm2qioKBGEOXDgQOGDN0Pl3rdvX4SEhAih+fjjjwvzrZklS5aIdm+BgYEiUycvzLpZ5skngZUrgWXLZPsL1sR67TV5EWAsTyZiLzNDAXM9mBPD68SNN8okvKNHgSFDZNJv27aMY/BuqVYu9Hr4W1ng1Qo/mwv+Tidsmgbn5pIIRzxaYQNSg2WdPd+YPRmbRzz9cEW8HdLH7NF6R0KqnEG569ejyqg/oTms7pg9YcnTY/A8MXvwic2Tlj9TKq3ng/jG7BlxecTmO847Ls87ts87ns9ddoU1EM2hLtmJ2cskJs+83uu5Fxb8fKqTmLupLEv0yDIyXy9af0UFGX9viWcTcXrbaTjt3hWhlRDMI/hbZxF1WtVYS49ce628i2P5Jd4EsnPisiP4se+P+KbrNzix7oTXJso2LgvLU0+6x4r4O97ZaRqCSwaj8z8vofmGKbAG+AvXr/blZJy8+zloEyZIVy/vCnlzyYLM9C5QdNKtnFFHDkWxjdm73PXYF7vdLlqnMimToqpZs2ZYyFJAPpY3321y4nU9O9f5SzF//ny89957+O+//zBlyhS8/fbbuP3224UWSDYs67mk2MbsPffccwgPDxdf8FN6plnFihXFwTLWN8DsMjCpg+ZWA3OhZsYF0kQ7Y8YMREZG4qGHHsKAAQOwkuJJtBVzCqFXvnx5rFq1CtHR0Rg5ciT8/f3xOv2bAA4dOiTGjBkzBj/88IOoD3j33XeL2jvXM1P2SsMT7dSp0pXiCzOXLxFsx+Odwo7D+HJeGz75RHqCGfpHT3qpUrKCCxP8GArIm37fGH+bNRDNy36MDf9n7zrgo6jW79mW3gud0HtvUsSKFQVUULH3/qzPLs/37O1vwV6wY8OuqAhKE6nSe+8hEBJC+mbL/H/fLbN3ZmdTIJBo5vhbMzP3zp3ZJdk9+33fOd/eaxGNCuxPSkBirhcD4kP92IoOZCCxUT5L3Xo1D7NXoW4ZUQ+0QGyhCwfu2g+tlQ9uhwN+zc1SuNRRg1gJ99kTZsxObqzc5d7PWL0ePYg4OEWdHq/Z44peVscntlk9nvDCI4LncIZq9kQXNV4Hp9bsiW2zlx6zbDHV5RnmqR58TgdSykp025UwI2WLmj09Y6vU7PF7C42rXtCV1ezJnZ/290OcswI/7KN0HqfP2/YewL6CYjROrf4Xgppg57ydWPzmYrZNdWMDbhmAhMa82P+PJ//AoDsGISre7uhxWKA/UCJnJJIgI0ySy8s/VPq2RgSwZUuknXkBel3RC3nr89BsALctUnFg6wF4LroOuVuBNq/cxTMGVPtHpI/8L9OT+JsCFfROnYo12jBsa9cUg+8+HY42rTm5JKInUwT0IU4RRhKNHKZS0cY/B5V9Hpsxbtw4TJw4Ee+88w4jhVQ/d+6557LP4j59+rA5FG2jz2mJVatWsZr+88ljtpqf85Xh3//+N7uHk046Ca+//jquvPJKxk+WLFmCxx57jN3Hiy++iDOpdvUw4NDsr78oonATKEBV8w8kIoffffedZUr44MGDLF386aefYsyYMezYunXr0KVLF8ybNw+DBg1iXTzOPvtsZGdnozG98YEypW8ykUhubi5T4dA2/SLRL5nE2LFjUVBQEPYtpDIUFhayX0S6ryRiVtXF0qX8Dd9M9qhu7z//4SneCKB0LGV8qXaPskCEzZt5+Q+V7lBGJ5IQzwp7SmZiUc5tyMrNQ+8t2TjojMHBizKRlr4Xn845hlz2GNEjCxXqpEFkz3FyTzj2e1D4+WaUd/CztKtfc8EXdLE2aJQcpn2yYKGOGnSMWqjxdmm8ZRqtR+lZilex40HeEo3G5Dadw1qmETlSt0X/XP5NleaKOhbZ/1Z+gxXzubVKaL6eztDnmSxZNOCeX37CDbOm44PBx+GJs841zFctWgyBRZN9ix6AlBYs6rnyuNV8Gbw0pXYlJj9yNZpnJONI4KNTPkKbYW2Q0joFy95bxgjfaf93GhMJvNXnLVy74FqWXrRRCzW71BGD3oPI84usUugXmXpjkwu6bH59660I3nc/nHH8C2CgIoAfr/8RA24egM9GfoboxGjERGu4bsc4HuanfrhvvGG8VlkZ8k8egz3rC7HpnLtx+ounIyZZpLLoQ5cUX9KQmVICFPkj4klvNjbqDeTnTdYzj8MZe2ipyGBZOXbcN67an1mVfR5bgQI8Dz30kCFKR1G52NhYRsCscMcdd7AuXBs3bmRfzqvzOV8ZKBo4depUpsrNz89n8zdQ4boAaQgo40jZx8NBg03jqiCSdyhET4L+0emXpm3btiz0SmlZwuLFi1mY+BTFmoS+PWRlZbFfAgL97NGjh070CBStoz+U1aKpLM1R15Bz5BqR4PV62Trq45BA33BateJvruqDUAnRI1BvXIrqyVo9Au3Ty02iupoQPUJm7CB4tQTsTExn+4mBcmTF7ERFohuFGtXrxaEoEIOyYDQKA3EoCcai+KG9KPhfDkobO1Dsi0FpIBregId1fCjXoth2qT8K3qAH5X4Pylh9ngdevwclBxOQv7oNq9kjckj1gl6fm/30qTV7mhN+H9XnERF0IeDjdXoaq9OTNXq07wD8FM0jsse3HdS+jSKA5po9qteTRE+t7WN1f8p+wIFUkfMuiI3nJE6pyeNza1izp2Sc5XboPpT5tJZyPRUkfImN8qBR6pExOPaV+VC8pxjHPXAcelzUA5dNuwzlB8ox85GZ8JX62A3ZRK+WQH/vpKYiSGsqCvWSqorKOuiPmopwH30UzuOG8h65AGY9OgvLP1yOSWMmYeBtA+FxB3Hhvpc50aN6Dan0VREbi7R5P6Hr6M4YtfdtZpCuQzq13303FVgDWVnAsbx8wEb9hHzvOKSHWMP8OUafbTX9PLYCrWOuiSOiN2fOHMv5FRUVjASSkFNaTFXnc74yxMfHs+wdYefOnWH3QxZxh0v0CDbZO0wMHDiQ1c9RhO2NN95g/2jHHXccixbm5OSwyFwK2QcoIGJHYwT6qRI9OS7HKptDv/SV5fOfeuop9s1KPlpapWFrCjJSpdq9an5zevxx/r5PX+DNiBRdp7/jM87g7+OkzlXhdLgBhxsl0dEojo5mKlp0BCN7rCZPr9mDXrOnnViIsrOLoCUGqqzZI1sXDg3+/AT8NfoRrPn3jXD4nCF/PjFHKnH1e1Nq7lSfvfCaPVjX7Kl/jTWu2ePWKwUizXVUavYizO+btBkf9hiPf7f+Hm6XE275xaCW4T3o1e8p6Of/JmO+GIP8Dfn47f7f4IlVvmHYOHxIOypKkcmEEJVwkPcmqamoFSXV9i1Zwg2QJ03CMbcegzYnt8FZr5+FoZdk4Zri8UjK3czN2X/5pdJ6XwcV9ZLdU3Y2f1NQ3+vofKrfo5Qytduh+hAb/1g1Ln12qZ9l9NlW089jK1DQ5IUXXmAEkQQRpID95ptvWDmVFShqSBk1SrVKVOdzvjLQc6HSLSKopMal1O2RgE32DhOUR6fcPZky0y8OFVrSL8OkSZPq+tZYLSKFmOWDvjUcNsg8j1I39EZbDRyKvy6J74hL0u2K5iY6Cit2wKfxN/Z9iTxitKd1EzyU+zQ+v/8eVkPH7FbAW6KxxCvz3uMqQrJOoYgTgWr2iOQxbz2XYsbs4oQxJuMgPKlFiGpUAH9+Ilsb0nZFEDjmocegCc89sa9IyRzmbT3YRNtaGHGT5xvMkxWCx332lH1nSKBxgAQaJtNl3TpFtVGR4y7TXNN+GOk0r63uC1C34Z5J2zGy8SJo3gJc+tRn+GHuavj8RgHF4cLhcqDTyE4o2FYAp9vJBBr089yPz8WmKZtQll87hc02BKhmjn6/yPCVCJ0KiriRwor+cIcO5ZG7Cy9Ewr+uwmXj+6Jj3wQ4TjkFnl1bAeoMMHUqLw2pChdcAG+zNgBFWtSSFfqwJQd2eh966SWSMtb+87VRb0CfXepnmayzP9zP4/Hjx6NDhw4sEkeEjWrtqFMFedtZ4d1332XXIGJWW6DoIz0/Mlbetm0bRo0ahSMBm+zVMojdU5sT8skh0QWFfemXTQWpdGiMQD/Nqh25X9UcqmGgkHMkkHKX5qiPw4aUyMpC6SMEsmOh2layb1GhMWUD+e05sD05DXlFcXBtC+C9nJuw5MdhCJZ4WPRO9saNqfAhemk0ojZGIdbpY1E56qIR5fSJfrnksxfg28Jnj9S7US5+bMAHT2PAh08jtnkeG2M+e0yFG4RL8dljhsoiokfCDEZyVW893QdFHHcpxE7kajmRUyOBYt/sx6d7+IVeFWmqTGlcnZhZkDJ2zNwbV45JXz3Vo0997c3XtfLh04CFBztgc0ljxLkqMKLxIqzbsRf/+3AqbnjhK5R5hXVHLSA2LZYJMGQfXNl/lY5fMeMKDLqz8loZG4fwLUz6fZFQwwotWvDm1vRhTH8E1E+XonBU70chfurCQZFBU6bCClPvnoqnk5/GByd8AH9qBhduSDUwlaQQ8aR1aG2CXX7+j1Xjmj/H6LOtpp/HVqBaO4rWlZSUYPv27azWjtS0lAI2g8ZJ+EHiSBXV+ZyvClS3N2DAgLDoYG2iQZI9yq8PGzaMhW6PRJ876rlLSlkquCRVLalnJdavX89qCAYL8zn6uXLlSkN7FAol0y805erlHHUNOUeucVRxCGSPWmnS+/1iLpqsFqg8aMiQcKFvkicLTvCetttSM7Fxc2M0WpGL6zqMxwVPPI+iYAwO+uOYtx71bC3Zk4ioq9oj9uq2KAkIn70A99ZTffbCa/bcrJYvEB1g3TbKfR54A262LWv2yGvP5+ciD6rT8/t5zR7V6JHHHqvJI3EG+eqJWjyNftKYrNljXnlOOEhIwraleKNmNXspes1enKGGjmWygxFq9rQqavYUIYb03ENlvnz6v5IDX+wZyrbGNp0DjTUEBlZs2YMXvpqF2oLT5UR8o3jsXbEXq75YhY0/b0T2X9kozilGUvMk9Lu+X61dy4ZSl0FRvJ9+4ulTK1B9BjkJkNk6mWcSCaNOHFT3S0SwmuUkQ+4eAle0i5H3iqwOvFbwiy9YtI/VhVAdk3iPZLDb9KGhW69U9nlcGahOrnnz5szy7Ouvv7aMrr3//vvMGo2cMVRU53O+PqBBWq/QP8yKSG9UNcTdd9+NESNGoFWrVkxR+9///hcul4sZJFJtAXXpuOuuu5CWlsYI3K233sp+AaRC57TTTmOk7rLLLmPeOpTjJzk4qYPktxeyXHn11Vdx7733ssLQ6dOns7A0KXT/DmSPUrErV5KqiPvnSVEdOSbQP8MPP/CuSNUCD5mxTarJk90zbun/HKad05WpZ0mgIX32AkEgkOVFICbks0ekjkXnHEEmrOABM14RR5E/ElzI+j0ifQTaJoEGgbz1SLkrPfeY6lZE9ZgYw7TNmFVA8V6R72Diw4m1Q6PnxGxNlM4ZquUJa8mmzBM2LLxIWInsxYvInnyVRLsz/QBbR2/YYZxrsa/fQ4R9w3wFk/f1x62tf0JW7H4cm7YOf+bTv42GH+auwa3nDEVS/OEbhRZlF2Hpe0uxZ/Eepvos3V/KUrnJLZPR78Z+6HBmB/6a2SSg9tCpEydwlBYbP55bMkVCjx4sssdIH5lrUv1dDeqGKWJ79Z9XI61dGrcaQhwwfTo3V6Y0Gn2AU6RQKnNt1EscjjlyTc+r7POYQLVxROpkzd+CBQuwe/duZl5MP0nNS7V79Fmrgo4R2bviiivCrFyq8zlfH9Bg/0IuvfRSln8/XOzatYv9InXq1Im1O6Fw7Pz581l4mED+OGStQnLu448/noV1qQBUgn4RScZNP+mXg+6LfiHJ6FGiTZs2jNhRNI9MH8kbkIwXj4rHXi2QvXHjeCCARBcSFBwgkkdfzhVHGR3UcpdKdMiDVc3OFFXsQBBcnUcELbqsAmgGxDSpYPV5VFcX6+RKLWqdhtZeFH2/GUWfb2HHKEUrRRxqzR5vicZr6iiFS3CKtO3WV0Zh1Q13wL+X24eotXlOZdtQm6eH5MRxZ9U1e6wWT6n5q27NXpzfiyjhA3Ug3lSzx1qshfbVNY3rivSuq/IavbCaPXW+8rRIDf19zkC2e0mL2foQ1e2t3RGKYh8O1ny9BnuW7MGQe4dgzKQxuGrOVUyR23FER/z59J9Y+81am+gdCdxxB//5ySc8nVoVKM1KbwKHIBBL75AuiB7gLfTi++t/wqZGg6GdfDLQpQtvnWYTPRvV/DzesWOHQXxBZsgUXKGAC/nrEREkJa45nSrbllGwxQpVfc7XBzTIyB6BwrXvvfce+0ekMCzJn1WQQqc6+JwUaFWEh6m5MT0igb6FUCFpZaD2KUvJ766ucQhkL5KWg0RHRPo6dAgfoy/rFC2nnxdfTHURciT04e3V3EhPLQGeA5oWFqJkVzpydjZHm8Er4UQ5yoNUp1cBn+Zi/nsxtB3kZsrOIFFGF6vTI289P1yIcgTYNpGpKJefpWopole6qRnKdzZC2boWSG2azyJ/rH+uI8C899RtSfjIYoVF94ipUrSOESWxzfKfwmcP4jg7TcyjNdgYN2nWv93ygCM3NWamzXwNmcL1ut0oc0cZo3C0LhE+cR5bVaxjiPgpPJPGmZmzEgE0p1TMvXOZabOayg0Cn+46Dhc1n41BqRvQPj4bm0p4UXNt8a+cZTloe2pbtBwcIhFx6XHoe21f5G3Iw/51+2vnQjaMoH7YRLBIIUtCjNqoBa4G1n67FsveX4Zd83fh5tU386ghKYGpwwfVUUlbFhv1C4eTjq3heVV9Hs+cOdOwT+pX8rGrCpSBq8ySuDqf89UBuWuUlpayCGFtf1FtsGSPDIqp/RhBNTAk2NGAapC9mhrkWYCyOpFAVkNkGE6cUnVVSIpqhWhnCrxBXgzr3eEB+gEHKjLx8Gmfw+n2474FY+F0B1ASpM4J3ASZUrcE6pIRoHie5mA9cGmczJCpHo9A5sk8jeuAj+rvNAeaXTydbcd13cG6afio5o7BYdgOkH+e2A7Stgh/MW89fVtJ1dIx8taTFEl0yGP7uqmxOJdq5eSLIPvdirG0Ip7CzY9LYMdE4JJDbOueeeo6sqZPQtGTyH1DGsVqXN1XNCiEHG8a3tl+GjaVNMGWEs7WqQtJ11ZVF+dXB1lDs7D+u/WspqtJ7yasUwapcekJ5m/KZxE+G0cAJJOnb2FUZlJFLVRtommfpuh/U39kdM7g79HUMjIvD3k3Poj0t94CXnnF9txr4GTv74zx48fj/vvvZ2IPUgZ3796dpZfpQV00KKtnDkrVBA2W7M2gQmEbNYc0vqvBLx19Lsyfz2v2Lr8ciKpm96rJk8OPkc9ep5SxWJH/FkvTOnM0IAfIarwLyal5iG9yACV5yUhqkscidcE/khH/RRrKentRdEUBq9kLiJo9SulSJI9TuyCrnGPHRAiNFLckwkgduI6JMIw1e86wbV6/p9TskTCD7VDYK0LNnqkWj4kvDPVxoubMVGtHkGOpZfzf5IDJY0/W7OkRRKnKDUauuQur4ZO3Wt35pvG3tyulBuL5kCI3IbZ6arrK0P3C7kyMsfrz1dgybQsr5tcCGrb+vpURvc6jIvfEtHEY2MJLItCmzVFNoTbu2Zj59en4/ntsvv1lTHy9EP2WLsZZQ4fCQb571DO3Fq0xbPx9avb+znj66adZrT55+FH3LOoCQo+33nqLqYSpbpBUwkT+DsXarcGSPQmSZJNah/LsZGNiF3TXfhqXXk6q16OMD4mTunXjx/1+iqryjkdqPV9V6Jp6GQoqNmFH8W+IKfYBawBHE2Dqp/0xM6sz/BS1C0bB4/AjsNMNz5wEBGODIFtNEmWQ/16FRhYtfgQDHgQdDuazR+pbiBQuCTMoPUuEj8gc/WSBuKCL1eyxDCopaNVtsl3RKHXLU7gUxmPpXJ1kif65ht64GlfnsvRpKJ2rpml5/1uKwglBB+uNK3vUaiFD5dg40xhnXez6qiZEFVqI3rhyXSno0M81rUX1hnpvXfM4LPbF05Qb9Npszs5DZsrhR4Y9cR7WPYOEGjnLc5hAgxS6Jzx8AhKbHZlevDZYs+4Q2atLuN3YkU79S2fD3a0THGt+AqjF1bffctWwrC20YeNvAOrmcfPNN+u2L9QrV4KifZSNpH65y0nwdAhosGQvLy+PFXBShI/IHdmw0ItMqprU1FQmgrBRO2lcIhhUt0echAieBHVXIuJH41TnHWlJSuOqHWQounds48exrzwbnuKVAHWVOxloW5qLP9GOddiIdXjhhQfaoIPw/i8AX5Mgq9mjfrluxnh88FGdnlqz5/SzNK6mb7u4ebIzgKJNzVG4thUSem2Gu3EBi+IhGEAQztA21eyxbV6zx8UMsk5PjfCpETeSBdN8MY/q62hbjerJiJ5aeyfFEkEgtZRH9vKJgKssS36jNtfkiQicnnJl11TGTTV5VudbfVsPaxQi9ol0X9pyJs5qvBhXLLkd0cIPrzZAhsq5a3KZwXJml0zEZcaxvrg2jiCkKKM6pshHGCc9ehLan9EemV0zgU1juSULeTzZX9jrD5ROGId0bgPBhRdeiEWLFll6/FFal8rOZOnZoaDBkr0777yTWbCQwoYaFqsvOEmobbJXe2lcAn3ZNoMEUtRSkzos5eaGkz2yHiQz/t27uehOVbwTCcqM7Qd3yWcsjUu8IqO8BKCacXcMiyCRn57WGgi0KmViDQRjUB5wMbsVGvcGaUEHcTOUs/o9itw5RLqW1+zxFK0D298/A4WLOqP5DZORfOYiESlzwB9QavZoWxwP8k5toTo97pbMt+U5ei2ew1RfJwieWttnqr0z1OwVi8heXLzBu5nN12v/Qufp64hrGnybAzWo2TNdi+2b1mOX0JwY2WQRWsXl4pymC9Gt9T2oDWz4aQNWfrISAW8ApXmlTK3pjnGzWr6h9w9ltXw2jgB8whhbbXh9NLF5M2+dJhzXWw4RAp3+/YGFC1H47pdIumRE3dybjXDYNXvVQosWLZhVDIkzTpWtCWsRDZbsTZ06Fb/++it7gVVQ6xRyyrZx5AUaBPK1jvQlPCMDIJU8RQQp3av6p7Jb8ecjqtgPFAN5rnhEFzvw+W0PYf2Wbrjph5sR7fajPOjhliwIsq4bUc4gygJcNctr9ih6B32cMqz8oyyUwqXtpF5bEPS74U4tErV8/Di1TiNDZX1bHKcUrCZTtSI6V2XNnkipSt892QuXRffMfnlKzV6oVVp4zR5T1oq0q0zh6gSuKt+9atTkhfnyqevpc5yYuPMEPNTpK1zUYhben/wnRp/UDxmHmcqd9cgs9L+xP1PkJjZNZP1xSZgx74V5+OnmnzD609G6bYeNWgT5IhGqW3xbG6BfqnfeQemt9+HXipNQgSic9+e/4RkyQJ9CZP/H63/Emq/W4yytvbWpNqk1qV8p+QW2b899+uqKtDYQ2DV71VcSb9myhVmqkQl0//79dYEGPciC7XDQYMketUeJo3Y7JuTn51e7FUuDxCFG9iQYEVA+fyvLtlDtN5mS0/txo0bh40UVe3VT5aUpLVGYFYPVq/qipCAZuRtao0m3zdB2RMFfHoWYxhUoS+B1eGS3QlE98tBjQTPNJWr23KJ+z8fq94gneJgFixtNR/+BxufNEUpd4mVBFsljaV4SgsiaPRJ6CDLJ06OiTo+ld/lxzR2q0wsZLgs7FUEMpZeejO5xDz5ZzxeqjSPINC5F9kQA0ZByNdThyddd8cpT07cqqZTpWsO/nzou//3kHJGlDksHA/hpb3/c3OYXNIs5gO2LJuCsn5bggmG9cceFJ8B1CEX+FSUVKNpdhD5XU82WeCndTjTq3gij3huF8W3H20TvSH/hO5rvk2SmfMMNmI6zsAK90Ah7EfxlCqCQPRLoeA96mUiHxBwSW6dvxezHZ6P9me1x7C9v8y4e+kku3tmD/J9IEWYyzLVh42iBOmnJ2jyqyyNxxqxZs/Dyyy+jsLAQAeGleqhosL/Zxx13HD766CM8RmZvInpCahfqYqEWRtqoncgepWlJhLFtG0/PVtcS65hjIo8lenlrMMLqzGZArANjHn0BMSklaNR+MzTqjftKY7h+T0HJ/TnwjznIPPcccLE6Mh/ccGoa0TZG+Ei8wUrsNCczXpbpXLeTtnnql6KEkvAR0ZNKXIrDSSUuEThmvcKYFHiLNAZRy8eUt8JLT1Pq9GQtHoHV0CkdNgSzUv3yZFQwTfyb5MUnGIKHal0eI2GqClekVsz7cq653s+cilGJnJ4iNp+v/Ft5g1H4YvdQ3NjmV1yWNRPTcnrji2lLWQTzrotORI2hAS2Pbcn6p3Y5rwvrtkAf9gRqnxaTbNftHTFIF3SKjh0tDBwIXHYZ+n78GzahA87Bt4g+5m3DFHe0Gxf/fDFyluagad+QJczuhbuxbcY23kOZzDtTU1mf3rlrU5Hpy0abLVvhpsJgm+gdGdhp3GojUm1ebWQbG+xvN5E66o/7119/MTZN7VFWr17NInt//vlnXd/ePy6yR7XcZL1C76nk3CCNlEmJe801APWQXrSoZrfSEZwJBtwOlEe7oWlOtD1pMSMW5KVX6o9BbBzgSPejLNGJCi0KFdTPVtTskecei8WJbem5R4pbgl/x3AsE+FiQ+uKWRsEZT1066AsC9+yT23pojSJwUoAh21NIqDV7svZO7ktvPZbCUI6bau30ej4W2SuJXLNnInXWtX/KfNN+VTV65nHzeiq+3D0EV2TNQJekXRiYtgEL8jth0u9LcfnwAchIrtnvU1RCFIY+MBTz/m8elkxYwogeRfYKdxQy0cbpL9ZBd5mGgiVL+M/DKBavMejL5UcfodmrhbiDOhN4OwNnnx02jb60q0SP0PX8rohvHI/krGRg2Gjg3/9GWX4ZpmU8y8bv+noIEqN41x0bRwCHkcZtaGSvssYLh4sGS/bIsJDMlKnnbGJiImuYfN555zGfm6qaJjdoHGJkjyJ5lCWhjkmq2Cg5mVrR8G0SYdC+Wfj34Yfc7cHc1KRVgEcWfPHUCSOAlJJidD6wB9tiMrAquTlT1JY8wlvjVDABhpNH6USalTz3KqimjyxWLPrksto8UbPncgJ7vxuMPR+ehrRTF6PRNVNEnR71+XXo20GxzbpWyG4ZivcJfRixlKzMgao+eM7Ka/akpQnP9IoxOPTIHlPjOiqp2ZPXUNYJ5XOrUcNnta9G8czrqdA0FPgT8e3uQbg4azaubP07I3vUL3fG4o04/+TeOBST3XM+PAebp25m9it+rx+dRnRiNXy2fdIRAhXR0oN+2Xv2PPrXp24dV15Zo1Ooty49VPhKfehzTR8U7SpC4nmhYvjCXYVIanF0OoI0GNiRvXqBBkv2ZAPjhx56qK5vo8HU7Fm1TSNyR9ZYRAAtSigZIbntNr794INctCHhKuQkx58YC48jiA5Fe3HM3u1Yt7UfFuSfhQGXT0aM08dEGrxOj0ziXIh1+Vgkz8361fpQwVqoBeELUhMLJ/PfYylchwNRbh98fjfbjkk/CM0bhfKtTeFyBRAIkOceDNtOVwBBue0OIki1eaxuTrFgoXCYEHLodiqCjUlvPU6kKH0rSB8tIyOEiu0KnRwm0JDty+QLZRZ3qARTSbvKcbar7su1zCliWaMn5wvSF1ovlH5mSwU0fLL9BKR4SvDx9hP1jhrFpYcWVaEUNkXzOgw39tujlLhuW2OjdrFiBf/ZseMh1+3WBcoPlmP+vd+iZddEtLvlDEboRr4z0jBn3+p9eHfQu+hzbR+c9txpvBuLDRv/EDRosnfgwAG8++67WLt2LdunZshXXXUVkz7bqILsJdaeae0ll0Qeo8tQmpd644a1JhR+XzFpLVlrtK2J6RiCLbjtyfdQXJ6IVkOXIrVdNouRUeqWeJQvANE1Q4M3yFO4JKrwaeJPgbQTJKQgEhkgTsSjgHReQp9N6PjqeLib5QkFLkX16P+cuPEIX2ibpWNZ6lRT6vcUhqS2TiOQB6HM3AaEQtdUv8cvyn/QkcTSMnioRQlrlxbP/fLkoKmdGYvYqWlXxVvPqi4vzFbF/Pqb56vH5Xx2P8Ismj5QvSn47+qLQ08lqKF5pimcWwMEA0FmpMwuG+Qq6Ny1uSg/UM4sWGzUMtat4z/N0vh6jEB2DiZ0fhl5RdG4Eu8BjxRRs1Mc6H8q9mZ2R7vz+zKDbhJyVBRXYN+KfZX2QbVRQ9iRvXqBBkv2Zs+ejREjRrDoHkmcCaR6efTRR/Hjjz+yjho2KiF7h2C9kp8P/PgjT9fKaF11MGFChAFaiOhVchqCcGFnTBrKPW6M7PsD1sc25WKMKSlwe50oOb4CWqKGAJzsOPPIC5KmltfcyVZotO8XxI+2A+I4oyqxPsS02s9sV1h9nshbSs897qPHfzKuJecwYieNleV2iMjxwwqxE+cYWqeptXYUNRSijzQR1SuJioLP7THW6Im5qjBDJ2L6vSj7CB831PCJ+ZZrW57vYB1FDOuHgSJw1FKP0uDVj8TJTjdkpkwkj1K4VKBPKNhagJLcEpvsHUmydzTFGYeDL7+E8+ZbkFF0MvLQGVGJ0cCBHcAXX2D2F+VYhl1oc+vHuPzOdAy8+GKk/DAWLQe3hKsWjb8bOmzrlfqBBkv2qDaPDJTfeOMNuIQ0lKTN1K6ExkgGbcMEan9BCotDJHukyKVyG0rX/utfobaa5KM3cyYXa5ABfrUhyF55vJMZI9OH/9aEDHxyy6WYkdkRUxt1Q2k77gXmCbpF94wAS9USx6G2aGSmTJG7KFa/J1qkObg61+ng3nua8N/j29x/T3rr8XK8UM1eQK3TY1AlraJmT6Z06Z1MtE5jaUensi1q+2Qal0gNH5OpUh4tU1O4SsY0lGpVNB5sU/fyE7V/ii2L6sOnj5v25VOJeL5aE0hTlfZqKpVrFpOHq9v8xpTPD73mwKtpc3DZWf0xelivaqVfac6W37Zg489k1MgFG/GN4llxfsezO9bgl8jGIZG9zn+DvsMvvYTAnXdjHzKw09mG/TKnblkCbFgB/PwzDow/yDw6HUWFwKOvkWoPnebOBTJC9SQLX1vI7Hxan9C6Tp+KDRuHC2dD7on773//Wyd6BNqm7hk0ZqOSqB7hEOp1qFvGyScDV10V4owEsmIhRwSK9qnt1Mym/evXW6dxA0zJyUnZpsRMdqxj8V64HBrinBXME4/q8KIdPkbmYlx+ZqjMt30spet2Bpmgg2iGx0nzA4y8uF18HhN0uILw70/CnvdPw943z2LH6MHapTECosHlDnK/PRqjvreMIPGaOymBZR55UsZK20rwkG1LAYao2zOMiZo4Iln0UJW4fHVFOCG39TVN64jrs3EJ2jfIcY2ZZstx9V1ECVqKF4X3BTZlZFKiSjCq+UKMaLYQTWPykZNXiOc+mo4XJioeaJVgxcQVTIVbXlDOFiZ15cbJG/HNJd9gztNzEKg4PE8qG1X0xSVD4nqOYH4BvsW5+ASX4YTTojD81eGITo8HhgxhvXP7vnkdupzVGn1vPIaLTcrLsf/hl/XfnV3zd2HKbVPw2dmfIW9jXl0/HRs2DgsNNrJHPjZUq9fJlI6gY7169aqz+/pbKHHJj+oQDFXpNDJJNiMri9tokUF4URG3wVJBhvekPKfSNBrX++SKyF5ManM4sY/55W1IaEJUAE3LDqIsLwYVCQ5GRkikQfCydmkuRgxL/W6WMiWbFjpOnMQXVKxX/FTTx1laBUubOhD0u7D/2+MAVwCZV02F5g6xU78vlNrlPnsCAYVpiTo9XdRAnytyqkJ0WQpXradTuAtFymSqNuBwYW3jpticwV2nWVpVZVWyDlCmUky2LGwteQvs3pR9Jf0Scdy0b7R24d6BZv5HWFOYhQV5HTAwfSMuazMDz60ZzY5PmrYMZwzpgm7tmlZJ9npf2Rvdx/KWWarZ8sTTJqLZgGZoOyy8x6SNwwAVqFLvQgLJ6us5Km67G3lvPoOyXAfSp3yCdmmbELj0bfz14SrWeaXnJT3Zg2HRQHx3zJNYPrk1Rr09D73/NRRNejdBqxNaIaNLRpia10YNYNfs1Qs0KLK3QirJQFGk23D77bezKN6gQYPYsfnz5+O1117D008/XYd3+Tcge8zio/aUjpT+nD8/8njjxlyoQVE/8pbU+bmI7HlSM9EjpTtWFPyO8igPcqKTMOY/kzF33bE4/+P/ILPzNkbu6H2DiTREKIsyo37RpJvLNPgxn6Am3FZFdMxwcuGGp3EB0kf9iaisXHacp3GFybKLeuIKk2VmvSJNlgXhkycIsHSlnuGVLdGUOj0tfJ60T5EkbU6nzpjTvrPR/kQlaGYlrtkqRUVV/6Q1HaeaOoMs2Ij3tp7CyN7IFgvwweZTkOtNhsvpwLczVlRJ9vzlfp7aNiEqPoqpKOmnjVrG3r38j5D+MP4G9lQxGQm4YuNDyP7v22j72nbg082Y8Rvw576O2PTzJlzyS0gZti82C8vBid+eSX8wskd9li/5+RLm4Wgruw8dds1e/UCDInvUX47VQilKKzJTNuPiiy9m9Xw2KiF7hwF6+SlCR5ZZ1QG9z1IJJSlyDe+5IrJHC53a5DrsK9+GPeUbMbHNQGSnJjASlrchC426bGMWLGVBD6u384CsVdwsVUu+eMSvKM1LNXu8fo9vU3rWpQWYSIOoIq/hc6LZNb8wqxWK+tHvEv99ImIT1MkfpXIprMUIn0zjmur0uPWKsq3XyIl6PWHDotbzsZmiNs5R5ZggnSLiZ25vxn4oNXWsXs9c0yctVuS5ldX8wTgun1NY+zSxv+RAOyzNb4s+aVtwRdvf8X9rz2Pq3G3Z+VX+Tgy6cxBWfLQCuxfsZq2x4jLjEJ0YjZxlOewiic1rTy1uQ4CKagnNmv1tuk1QJ5W2L90GjOwOnH8+Uvetgxtt0Dt2HeD16hmKzK6N0GeAG2sXFaNzcSgoQIRPgv7O5z43F72u6IWExrXTG7zBwCZtdY6/x19sLWGrrDexUWe2K7NmAaNG8fq9xYvDxylVa9Uq1TKQIMlecjKiXXG4vM0zeG3dXShwb8dZ97wFZ6IX0elFzGqFftVjyVNPsBuHMyj89QIsuueHC1Ek3iACR/56Tj9L57pY/Z2fKXB58pfEG044nUGmNKV2aSx6RbSLIoQ0Xwvw1mmMeAUFyVO99gTJU82UFa89VqcniZxkXOIc3T5FIVphY6rXnvR0NnnpseOShMl15Lju4SfGZUDQFD2U+4ZrKeP6+eq1ZdRRtIp7Z9NpeP2YNzGq5Xx8tOVkZs0SF1N1VK7zqM4I+oJY//16rP5iNTPJJWPltPZpGPHOCCS3PHQ7FxsRQK1vCIfZkL1OcPLJ0FatxvKeL8K/34Nt3y5EN3Jh+PBD5Ma0ZF8WRiTOwhmYjeDAmy2X+OPJPzBj3Aws+2AZblh6g67+tlEF7DRuvUCD+m2tjZYjDRoUjjtEJa4EReeIo5EGRhIRAqlxSbhBpUCzZ1dzMYXsEdzOKDRN6II9BTmIa7Mf5X43/H4nyh1RKC+NQvJ7yTh4A5E/l9IuDcZ2aUGyYeF/FrTN1bUO1nWD5jHLloADvvxElK5qg9gB6+Bwc/bFfPZEiIx10pDt0mQPXKmSkCldvcZNsWGR7dIYcTPPC0XGnJHGzC3M9DXFz6rapZl0DUJ7Ennc3C4tYFovrM2b0RpmcX57Pbp3cZtZeGndKDROq96Xia5jurJHwBdA0B+EJ5bXZdo4wmSPvqn9DeFo2gTHvHIZCm75HoOxAVi1Cb4BQ/BF6v0o16IxMn87OrqCwP13Wp7f/cLuWPL2Egy6Y5BN9Gz87dCgf2Ozs7MxZ84c7Nu3D0FhTKvW9NmoPY89CfqcoJQs9cZVU7LE17ZtMwp+VZSV8Q4ay5cDU6ZQw2iF7KWksB/eQi+yP9oPpzMFnR27cIxvI1Y1aYLZHdogeocLcbPi4L6pAH7W4UK2SyO7Fcqsasz7mLZlozKeHeVhKWa3EuB2K3R86x03I1AUh6ynJiCq3Z5qtEsztaBAzdulhUyO5YhxTE29sjHDmqG0KxE01UrF0B5NrekzBgPD9y3apxnX4zYz+vVkFNFgxeLAmxvPwID0jfh8G/e2JGVuTUCeaLYv2lHA5s1/a7JHIEFP53M7w114PXDzzSj56jd484oQRCkmYSyapflwcUpjSA2YCooa37zmZkM9aPbibKR3TEf0HTdzI9G77waOPfaoPqf6Drtmr36gwZK9Dz74ADfccAOioqKQnp5uKMClbZvs1W6rNAkq9eluFFAydOnCU7yRjPlJgUs9cg8cAFavBvr0CY/sUbujg49ocJ2SiH372uOHPT2RX5KG2PRypJT44c8IMKIW46qANxDF2qVprH7PJer0XAhQOtflQwVF+hwOeFzUS5c89xDadgLxPbbAm5MGrTyKpXQpbct4l7INdxCaX03nivw0WbLogg2ZCxXsR22XRilhEcFj3nwsHUIXEQISP+W8XYYxvfZOSaEyqxdFpCFLB1Wipr+nKucaiJu8RXUt2QxEXsvUTo2RXqVU0UD4ZLoZwLID7dhDoszrQ01Akb3CnYWITYtFTIrVx7SNWsE/gOwRWFQuM5MZLrve/AQlN22gr2psLNg4A9FJkZ0GVKJH3Vs+H/U5KooqMDZlI1rv+IM3AH/rLZ6msEUdHHYat16gwZK9//znP3j44YfxwAMPwGlVJGYjchq3FlulqWSusqYl9L756KP80rrrQ0GBgeyV5ZUhqqMLJffvQWlpOTaNOxc/LzkbfUZORs+0RYiay9uJUc0d1d75RbqWLJbJcJn89AJakAkyaNsX1Hg7NYfGUrcs2ket1QIuNP/3V6y2jtXmCbrEo3r8DT7I0pmyXRp7BiEWxBei/LHRxE6xYWGtv8QO2xbmdjdN/x0Ho2Px2aAh0FgoMTSm26OIHUa+1DXlN2ylpk6/uvrtW55rbrUWoebPalxfT7mGnjpWzgv/ONTQNK1mhO3g9oN4pcMrzFj5gaIHanSujRpA+o9SWP4fgoqTz0CnMwMo27ADJ13bDkkXnFFt5W3hrkIWUU5ulYyUl14CHrqF2wr8/DMnezYY7Mhe/UCDJXulpaUYO3asTfSOchqXQObIn3zC1biU9aguqOuGgV2YInuUZkm6yIMDdKvNPDhu2DS0aLEbzoHbkH8gCs4k6pHrZhE8VocXdOrt0vgxXr8n26XRcem5R/PUFmm0TyEqmhNqlxZqkcb4jozksfCW8numkyhFpKHU7akkUW+dJmrlTlm9Gq5gEMdv2IDfunbHjM5dcTA6Poy46SuKlmbygLlmLqxdWtD63IjjgcrGHXAQoTWROnMdoES35O24vfMPyPG3wQ8z2uOUwZ2qJdagfqbuWDdiUu2o3hH929+z5x8R2VNBKdgLf76c1XySZU9NkNIqhaV16VxSgmPOHOq5yZt921E9G/UMDZbpXHPNNfjyyy/r+jYaJNkjr7zHHgPeecd4nEpe6L3yvvuqsQi14KC2GgrZy+icgeD5RUAKZyBthi3Gkxc+hFGtpsJ3ZiH2PbaftUKjjhnEeNxOPo8id9FimyJ68jhZrdCD1+kxeidq83h6UoapNJ9onab/NSmKCBniEl9RmdJWnad/3QoxMNk9Q86jfdnlgojey6eehk2NGuHMlctxx9SfMXDrRiSUl4U6ZCjaDVlXpz/UbhbSSkXdN42zNK06btoPW8/ULSOse4bpflREu3zolboNJ6XPxrsfT8LZN7+J6Qs2VPmrQOa3D5U+hNs226UXRzyFm5YW7nr+D0BNiZ4EiYIY0SO4XNgxYDS8sbYS3ADtMB82agUNNrL31FNP4eyzz8aUKVPQo0cPeDxGJd8LL7xQZ/f2T7ZeIVC93dVXUxcT43HySL79doD+KYgMMhGGAoqgkYjjr7+A0cce5LyCIrMJ5KnHbUqiglFwLouFVuDGSndn9CzJQ7v0PEQ388ERRalZN6JdfmaxQrV6RPIowkc1ddFkqBxwMrJHSVuK6lHrtACL8DnhcgVZ5I+0PG53APu+G4K8b4ci9awFSD1nLieCbhHdo4gW54B8X+Y0Wc2deEKiNo/9Faq+e4p/HiN+sobPxU+f0aUrZnbuij5bt2Hswnl45IevsKBNe3xyzLHY0LiZQQOiEzpppSLWYYIOzdTPVu6r51OmWO3KwYQnxnG2rIVxs/w34T1+FRsYYTVjTtEsyW+PxXnt0C99M67uOA3PrDgf/3l5MlLGnY++Xaru2GCLNI4g/oEp3NoG9Wr+9OxPWX/mS6dcWmntH8PGjQ3j9bRr9uoFGjTZ+/XXX/V2aWaBho0jZ6pMtdHvvht+nJxxLrqId8ggv1Mz2aNuTd26cWXuup9Lwf7lKBfspBo84MCWA4id0AyeJYVAsRP5hY3xVUUTRCdXIGaMB8WnkTDDxX33NAdKSZVLVXjUWzXAW6f5yW5FhNF8AdrmNXk+P6Vr+Ta1UWOxParxK0hA6ZosJI+ca2iRxur3RLiLkz++rbYcU21T+BNUfgf1c7jJMs1LKi1FvNfLW6L5g1ia1RrLWrZBy9w8XD/nd3z4wZsYcu+jxlSqQswc5rZrFrYtZosXQxo3aNxX6/pUKxfDen5TGle0eosUR3l9w5l4d/CrOLvlQny2+QTsLGuM97+Zj74P1f/2XA2C7P0NeuLWFUgcRJG++EbxrKzAgJ9+4uINeoOj4mTKTFAR8m+/8WNPPXVILSj/DrBr9uoHGizZe/755/Hee+/hyiuvrOtbaXBp3Eggjv3pp5UreY85hutECrJLDbYrhIWvLkTCgTS4byhDaet8VJB/32OnIOfnTui1exGiWmxAoAsZJPNcpMfJe+GqDijMbkW8wVBmh3nnyXZpAaWNWtCBxMFrENVqLzxts/n9U+RNzGHWK7riVv8ftyOppF2aKrAIvTA8gkYU9MNjjxPpVaqHo34eDuzMSMd/R16Ax4efZ/GimqJ8wnpF3zePV/oPVOlu+AH2wnLrFXWOqsw1QNOwqqA1ZuV0wwlNVuP6zr/gocVX4K/VO1BUUo7EeOuavLXfrsWmKZvQ7tR2zHfPxhGATfaqRLP+zXDNvGuQ0iZFjzKXF5QzgVXUwr/gevNNoGdPBAYfC9/+Yjhen4DoW64D1qyxa/xsHHE02Jq96OhoHGv7IR116xUV+/eHPkOqixkzeOeNgS12G+r1CNQ6a+DNg3DDqPsRlxAPd0wARccVY35cf1R43HDnOVlNXrTTz+a7HQH2IFBql2r5HKJ1GoG2o1yifs+pwa1sk++eO60Y8d23wx0j51PqVVO2OcthHTbIrJXtCOsVAbJNCdWzKXV7DqrTU+Y5NBTEx+KzwUNEJjjI1LhBJ3X8oDWCqHC72ZgQ54rzTDV8wgpFL4lR58r7U841j8saPPPabF+X5OoVe6F11KPq/dA5SvtC+ud4a92ZLF1+ctOV6Ji0ix0vK49sx7Jz7k5mdrtz3s6Ic2wcJmyyVy1Q3bBquPzDNT/g2fRnsSO1N/DII0D//tj480Y80/I1TDx9IveRooCDOY1BoD7Esi757wy7Zq9eoMGSvdtvvx2vvPJKXd/G3wsm9evh4IsveDqXavfMoAzHhgh1+foXYGm7ohSLtxnWBoteW4TSuX5c6RkH774YdOi9GDd+eT0yMrOBFB+zXKHYmtvBCRqZKZO7HqVlXSTIoG2NJXdF31siheSRJxxTqD+u3iuWrFfkfWlCictJm1TlcpsSveltyH6FESBew8eFHaI2T4LVyYnz6e40oM/2bbjij9kshRt0uQxjzIZFLGPwxVM4pqGuTp0r6u7Mb6xqT1tzJI7tq9Yr5kgdI3FiUw1wWr15M4sYjRE9mrO1uAl+3U1GisDIrAVwu5xITY5DJHQ4swNOfOREtD/dJiJHDHbNXo1RfrAcW6eLFp29egIPPwwMGKB/uWlUtIlsIYDjjrNegIggKXvPPx/4/nv8bWGTvXqBBpvGXbhwIaZPn47JkyejW7duYQKNb775ps7urd6CFBS1RPbIRJlA73Uq1q4FevTgGhBS50bKbmgHONlzKGncY249BjMensEIX1R6FGKKMuCocCB6XhxiztqNxBY5WB5owdqlEa2g+j1pvcLbpXHrlYog/10gUietV0ikQX1wme1K0KG3TvPlJaJwdk9mrpx6/qwQyZO1erqlihLO0gmYhfWKZmG9IojagC1bccvvv+HYjRvwZ/tOmNyzL/ITEsL87PQaPbFrSNuKmrlINXaSwBn895TXndULGv4hTOdXZtWiXMO8pnnOuxtOw/x9nfFbdm8WtXz4pZ9w6rGdcPyA9nC7jUKMNie3YQ8bRwhUJLuLR1jtyF71EZMcg3vz7uU+mcIIndBpeHuMqxgHx403AJ4TgGbNsHfFXvboeWlPPokIIb3PUl0fefe9/jp3nf8bCgftmr36gQZL9lJSUnDeeRY1TjaqjuzVgqkyCS2IO5qXatuWEzzi3nl5QEZG+LnkVzrl60vwAyZggEI84zPjcfYbZyN3bS7WLFkN3zovtBgN3S5diMvL/8T6ksZYldyMiS7oPYRStETyKCoXxVqncTNlsmchkQaXYQTZ/yl1SwbKFElziXo8ljwtiUX+p8PgiPIh5dw5glkp/cdkzIuF38RPJ9Xb8fMdLi6+4OfwfCc7rhQSsgigy4FmBw7goyFDsTstDSevWY2u2bsxs1MXLGzdDgdiE0W00VQXV0X7tNA1xFzTubqyVo6bWrMZxx0s9Wxoh6aur7wkof3wlmqE3aUZ7CExe9FGzFq4EW1apuPl/5yP9JTaKSWwUQPbFfpbI+sVG9UGkTyV6LFjUR7ueDR7FjB+PIr2FOPjUz9Gyb4SuGPcvO6U/rhat+YndO4MDBkCXHcdJ90tWvA+xWQdNnYsV7bZsFEFGizZe//99+v6Fv6+NXukgD1MUBbSijOSII3sVZo1ixzV27sXyClKwEIcgwEpxogsKXKLc4qR1DgRPo8XweQAfl13At5/6zE0T9uF5Gd/wUFnHCN8VKUX4+Lt0oht0OXIhsXtDMKpcesVaqlGET5K/pLdCkX3mPWKizptOBDdai8Sj1uB6PbZLHrn8PAULpNhUJ0esSf6QW/49G2dqUAEgZPWK/TOzzpdVG69klpagu/79mMq3FXNW+D8hQtw/ezpOGHDOnzV9xgsadmWEyZaX0bthNWKVOU6lOuFET4aF9YqlVmtsGXVfr7quCBvBmsXC8JouB+noNTG9tSh3wlnBTKiCpFdmoEdu/Nx/3Pf4+3HL9JV89TJgLpnkNWF+YPVRi2ALEII5obWNmoOImvSioC+yQ4fjgRNQ/eLumP7rK2sFEUH/QFR3R598/3jD07yyNiaftIbKBG+Cy7gpSz/+U/97clrW6/UCzRYsmej/qlxJZo3r3ycSl8einkefb99D0i+Vz++/sf1WPXZKngLvSjdX4qMvObwxVfA3yYac9YORaPUfXiw+D1MT+jESFhZgKdzKSVb7ufWK0TgfCyqZ7Re8RM5Um1YRKqWLFYa/es7th2g434lhatbrygqW/opVbq69Ykj3PaECKPJoqXJwYPwutzs+OrGLbBmRAs0yz+Ay+bNwScTXsP519+B1c1aGlOnStrW0nrFnMY1Wa9UasUSNs6JqUG3YWXtolqvUIrLIo0r0SNlG57o/xEOlCfgqpl3MLK9euMerNq4Bz06NmNzJgyagKLdRbjur+vQrB8/ZqMWYdfr1R6o7ISI2scf82+t//oXHE8/jdNfPB2+Eh/70qJ/S6IHzaXa8mee4Q+qcSFQNI968BLIlHTaNE729G9Y9Qd2Grd+oMGSvTZt2lTqp7eFvjXZCKGiIqQMqyWyt3w5f/+iCJ9836oOBg0CELuESqD1+kEieLMfnY0BtwxA21PbspTuopx5+GT6B0j5KA13d38Cl1z6FYpKSzE9obOwXtFY6pZlTIUPMnuPFVEol8F6hQif2HYpNiwu4aknt6XXHotmhexWdCh2K6FUrRgXKdLwU/i86V26YUdGJht0Bnkkck9aGp49cyTeOX4YiqItrEloTUnwRBQvTKihX6j6/wbW54uIpjoudSmm+7G0XjFDA7YXZyLGVYGOKdk4veUSTNnZn/1bzF+6VSd7AS9/grFpsTV8AjZqHNmzcXig985x4/hj6VJei0fm46+9hqj+/YGBA9nf0aaP5qLs1XfR1bUOrmZNgIkTUd5nEIq3FyMm2Y+EeI1bE4wcyd+QqK6Pon5Nm/LUCB3Tm4jXMezIXr1AgyV7d9xxh2Hf5/Nh6dKlrKPGPffcU2f3VW9B5nYStUT2yDj5s8+4KpcsqCTBoVq+++/nPXTpC6tl+2KpxhVkjyJ5RPh6X9lbnzIoayjKhpfiy9afovFVjdG95WqUl7mRgDIUI5alaDXwNG6Uy4+KgJsJNsh6hTptMEJIaV5W10eEj1K3ZN9CHnxB1lWD6v2ons9fGo2ytVmIbpcNR2wFV+eSDQubQ6lUygULokfpXb8z1D5NEiK9Zo66ThiJGa314bFDUe4hiwYNQTev++MvWRAHyA5HKc7TlbTmdUzeemq3Da2Kc0P3J8ZF2ldGB7n1ilHFq9cMqiphdU1YrKn8Mxd64/HxhpNxc7efcUOXXzB9dy/4tCj4KNQqcE/uPfCX++GKsjtoHBHYZO/IgFoJyZ6RZCBKb3QOB0peeAvN/30nVqMbcr98AU3GcLXuxk9X4ptLvmGp3stfHwT873+sjq+w51A4zhyFRCJ6BPoGTW+oVBhNc8aMqcMnaaO+wN2QrVes8Nprr+Ev6sdlw5rsxcVxd+NaAGUknniCv+ep2Qe6xHvvcTJIAVYrAeCf21vgD9yH4YWtQfo1KmxOa5+GP5/7k5nrUpTHFe3CAAzB5tU7sTl5I/a745HhL0Gvsl1YENuWR/XIcoU86jQ3s16h+6DULUWomMWKxi1ZfKTcFayF1Licm2lcoasB2Y9cDu+WZsj81zeIH7wWWkAyVKrhU03uQkINVldH1iuScMkuG2xeSNDASFQQKI+OUrpYCELI5jmZwbJGzEtRy+q2d+Z9eT1VuaHW6YnywjBrlUhWK3K+eV+qhA0me5F/H/Q1FdsF2p+0+Tic12YumsQV4MJ2s/HxxmFIS45HMEhEm1+V/v1tHCFIH6SOHev6Tv65ILInEN+jLcr79kbbbfsQO/VLoIVHpDNI4RvFVL7s32LJEgQfeRR/PfcX/vhtD64auANZx2YBBw5w4kjWLWTbcu+9wJNP8mhfXcCO7NULODT2SWJDTd/27t0bhdJm5B8Eek7Jyck4ePAgkmoqslixAujVC2jcGMjJwZHG+PE84jd8uKFJho4Lkn7Bl0Vn4unrN+O+t9rpvSkXjF+AuEZx8MR5WLF+3vo87M7JxvobdqLFHiB6XRJ6Xj4ZH6Qdy6xXyFKFWa/4OVmgba9iw6JbrwQpjcu3SaAhrVcohUvkL2/iMJTM74Lk8/5A4vGrhEBCRO9om9n6iTCX7LJB+9zuj48JwhVeDycUu1TfxhQUPBVKqVzy10sqKcWgzRsxrVsvyzo7QwcL1XqF5ojrG0jWodbssZo/LbwG0GQLYxBiaBY1e+ZzAJzeYjH+2/8zlPiiccHU+1kNX0ZyPEYP74OxI/sjOsome0esTlcqqcgLSfG1tHGEQTV9JOZYsgS48UbgxBND3lQCpZN/R/7V9+Ab52j8a9cDcFLbH7Loe28BKr78Hr2nPMX/jk47Dfjqq1pxUqjp503Xm5+Ey6rEpBoIeMux5vUHD+0zy4YB9jukCV999RXSbHuBcEjye5TeLCIEXnWchmkUbkT37v31Y21PaYvmA5tj89TNKN5TjGAgiM6jOmN6mzlYvHEPHrvleZZDHHGpFx6UIsrpR3nAwxgWddAgkkfpV48zwFK7ZMPCTZQpdRuE00FiDp665WE10TFDcyHtwplIvfh3nqMUYzJWxVO6wlOPKW55nV4k6xW2R8E/xXqFqUyJmEoWxBSs/I09qbwMnffuwVQie4bzDOYvulJXKnEZyJtZVeaKW4lozWK2XlGVvUqK1rCvXC/sflT1roU9jDxn6q4+LKrXOXU3ruo8DS8uOxf5BSX47K0/sPaJ+Rg+tj+Ov29o7f0C2jCKM9LTbaJ3tEFfrB98kG9TvTT5HZJYg9qrXX890K4d4r6eiLjBLXHzF/fBSe8lAvPeXondC6JReP6rOOGne4GpUzlhnDjx6As47MhevUCDJXt9+vQxCDQowJmTk4Pc3Fy8TkWzNo6YobIKev8ioQZZ+J1+evXPu7bidVyLF4GR2wz/htGJ0eg62tgfNX7bEniSi9Hi+GWITiuEM+BElMOHCrhZ6zQ/2bA4nYiCn6dlRQaWavIohcvStXCymj2HxlO3RARlDMvpDEKL5pYrlFpkJJClWcW7HOtvJkgas1ghQkOpWZEOpuuxqZpoLeYI2fIJstU8Px+xFRXY1KgJI0YZxQdxzJYtKIqOxZKsNnh52Bn8poXbi6GWTnBIg12KosTVCZopmqan1k32KroXn9KBg1u3cI2yedxcoyfTvPpF1Lo9xeKFnaPX/znx6qoRePW4N5HkKdMpY2xRALHLSjG3dLFN9o5kCrdTp7q+k4YNsl+hx9tvA7/9Bnz4ISdu5BU7bBgvY6CUA3150jT2xTexWSK6/u9k4PbewAknAJMmcfJItXw2GhwaLNk755xzDPtOpxOZmZk48cQT0ZlMLG1Yk71aDqWTiOzkk7mTAInIoHAACiosWsR9Qw0iDSrmo4cF+aRonpNktLSGcK73r/Eheq8Hxz/zBvPRo6gdiTFKAx69g0aFYsNCYwQ/pW5F1wxS5crUrbpNaVzOV/g243Y+N0B9dEUHDW69otqtEBWUKV1555zg6elLlqoNUa8zly1nPzdnNkWbfftw8fy56L99K+K95ZjWpQdePukM+GUtpWg7Jq9n6HoRlnY17qs1f2EpV/o7UaxbJGFUCaIhJeswpYnlOZV15ZDnmOYQlu5vj4t/uwfbCxvpJLA0wYnNXTyISuft7SpT2Ns4BNj1evUPp5zCH4GAsQ5PvEnS38DJj5+snJBJxei8OLoOiJ5tvVI/0GDJ3n//+9+6voW/F45QGrdvX6BRI/4eRO4ush84eYlSiSBF/vr1MwUWZCcPlshNROleWoN/0FNalD70AxUBXZ15YNMBRBV54O3qQ6JWjitz56Krdw+ub3YpioPRLM3Klba8awZP3ZIwQ5aPkRBAWqwYt1kJndguX9MKee+fAXejA2j076/EHcrwmCJ/VXOZegSOonoioidTpmKHiGHLA/lY16QZu5fRSxbCpQVx0XW3wBMI4rkvP8HJ61fj1269DCbG/Hr6XYR3tBBpWb17hcW5MiPtiLBvnM+jmSyzLYfUNHGEDhr6k5avGJ3jt7Zn2V7UmNf5if2SJCe2JNEvTQAVFX5ERxtNtm3UEtmzlbj1DzURXNxwA+oMdhq3XqDBkj0bh6jGreXIHgXmSO9hDshQxmLwYN47V3o56yC1GYBnY/6D+9NcrBTl9dcd2DZzG3sE/UF44j2IbxSPpn2bInVUGkpyKPUHlDs8SN0TREy0hj7lO7Ewtg2L6pENiwMBFsmjmj36SalbaqlG0UDiY6xrBqlsHZqyTf56GiN/rqQy+LIz4D+QANAYWayIbhoaMUeWluQ1e1xhq7RSkt0u1Jo3JT2aVlKMwnhe5Nx6fy4+GnIcvFFR8GpAcUwsgkq9Dl9fRN1EBw1DRwslxRvWEcNslSLJopKSNUT0zPOplpAiqiphFIRP7ptbqOlpZpVUkvONLl5B6CbEB0ej2AKMbj8Hb686k7e2I2scU89cG7UA8j8i2GncvyWoo9DqSauR2TUTnUZ2qjvTZZu01TkaHNmjdG1VqR4a91NoycZR6Z4R6Z+DSlMsxwTZa5OYB60c2LEDWPX5Kqz9Zi07HpseC22fhi3TtuCPx/9A6xvaIHicxn7bf7/jTny6cAK+ueNcHJO2DnOjqcWYBi+pcSmSp1EXDZG6DcoOGhRplF0zwDposN8hmu8LRbjczfYh8/avENN1B2dPsibOYL2ipHTVzhoypavasCjnNCk4iPt/nIxzFy3GoC2b8NGg4/R5jQ8WYG+CSGcrtXgMkmTJVIp6PXNK1Xyuss/mSHsWVTEctOigoY6bv9Urz0ueI2vzDGsa7ku0jBPnUO/iN056FY3jDmJPbiYm7zgGMSnRcIv0vY1aAr3uNtn7W2PVF6sw/cHpaD8kE53eu4+3aJswoa5vy0YdoMGRvW+//Tbi2Lx58/Dyyy8jSIWuNqwje0dQjWv+0hmRk5MFBNWxNVuOPSuAJk2AD09ejAE3D+BNxBWU5Zdh4pkT0aFFG2zusB3xTfPgcAawNbcNri3/RYS1OEnQ6/AEwaOgmy8Y6o5BhA+ys0bQuoNGfH+e9qI6QJ3U0RPRI2chxa3+pUOkatWIGxtTumnceOVVyNqfj8YHD7JOGjtT0rmtihaE1+PB3qQUI2lT0raGqJrDGMUz19jpr73V604HTUKLKjtoqK3RlGMGqPviHikVHAkUyftk/Um4q893aLrQg5MWl2JtLz8qfH5EeRrcW9qRw759vGSCfgGtzC5t1Ht0Oa8Lts/ajq79YoAnv+cmpnfeeVRr9+yavfqBBvfOOGrUqLBj69evx/33348ff/wRl1xyCR599NE6ubeGar1CQoyLLuKXkIEEFVK5qZM/EdlLyIhBQhPoLbModWsGM1d2u3BpxwvxcfwklFz/Hfr86wtcvf99JAW96F+xHQui24Q6aDgchq4ZlMalNC/rmuEKwh8QXTOEJQtts7o9lpLlYSwyVDZ20KA0rvDXo+fgVjpoUPpYkEWqd2MkjylxNd0WhU7an5iIvPjEEJET5souTcOdF1yGkijhY2WhxJXKVqEjCavLUzteGPZFe05DPZ3ajcNUjijVxOYOGtJxBhHq9qzWJJ5sdY5c9/stgzCyzXxoPp669UXZwoxah/xjJPVUzKH5pNmoW2R0ysClUy7lf1jTBnDF29ChwI8/8p9HA3bNXr1Ag857ZGdn47rrrkOPHj1Y2nbZsmX48MMP0Yre3A4RTz/9NPvAVtuxlZeX45ZbbkF6ejoSEhIwevRo7CXDTAU7duzAWWedhbi4ODRq1Ii1bDOnkmfOnIm+ffsiOjoa7du3xwcffICjBimKqGXrFQKZJ1PTEqoFF0E7HRdcwMfJDD6sVZri+9X/pv5Y/sFyzH58NtZ8vYbV7u1etBuL3ljEjEYbt2yE/3W9H8npHjjiApgX34add0LpRtZBg6J6zEtPbDtYNw3e5YLsVxiRYdE+OsaJBavFk9E7lq7lTKp4Tnfse/lc+HPptRJzZJ9clqbkoSu2IluUi0AMUTOWDuVsS9bE6ZE0pYOGz+kKET19LASjgbHSQUOuKWvmxJg6ZCBbpjdsdRl9XL+ocr5mPc/q276+pik4aDWHonvjl4/CpfdNxP3vP47U7vvx07SV2Jv7zzNDrzOsW8d/dulS13di43BB36CmTIF/4BD+/nnqqcB33x2dS2uH97BRO2iQZI/cuO+77z5GmFavXo3ff/+dRfW6d+9+WOsuWrQIb731Fnr2pAZeIdx5551s/S+//BKzZs1iJPM88kcSCAQCjOhVVFRg7ty5jHASkXv44Yf1OVu3bmVzTjrpJEZKiUxee+21+PXXX/F3J3u05A8/8ECCuVsGZZLy8oCFC5WDIrJHk8lc/tprgc/X9ET7M9sje1E2/nrjL0wfNx1fj/0aG37cgBETRiCxaSIj4WWBACqCbsyK5TVIg8q2wBHUWC9cv0YdNZwsqkfKXLbNflKrNLJcoZ9ORvaCmot5v1Edn0a2LcJMmaJ6RTP6oHRRF5QtbycUDSKqx1iVCFsx4Qapb0XnDPkfEU1mxCwFHSIYp4siHEj0luOSeX/i1NUrDUbIkqxRNFB/s6Q/ciGmUNuyyX0ZITQTOp2gqfdgtlYJG6d+vUZxh1xXJW36/cgPAotUsko85fNTr0tYmtse03f2RHSUH7f0+hYvvjUNF1z7Fp548Wd4vVRMaeOwYNfr/aOw+rc9GL/tXKzocwVFIICrrqrrW7JxFNHg0rjPPvssnnnmGTRp0gSfffaZZVr3UFBcXMxSwO+88w4ef/xxA7F899138emnn+JkMpQD8P7776NLly6YP38+Bg0ahKlTp2LNmjX47bff0LhxY9au7bHHHmOE9H//+x+ioqLw5ptvok2bNnj++efZGnT+nDlz8OKLL+L0mrgRHypkNO0IkD3CiBHWx6mlIylzDfxZIXsUCaSOQllZwJPbe6P3lb2Z7UrAF0BUvPBxURBEEPsWdsai766Ft20f9BzzM8htL8rlgzfg4R00RBqXonguhwbqAEZ2K1QfF2RpWTJX5ka/LAKo5yI5Q0k8cTlium5DTJcd7Jo8pSsieiw9yz1d9M4YYjtEdLiFieyuoStzRe1diwN5eOTHr5GbkIjfuvYwpFLZvZi6Yugmx6Z12CnKXJWc6Slfc4cNs3WLer6wopHj/MmY0rqk/mW1hnINUaCndtBQGJ0+RzwBsx3LayvOxpCma9EncwtOaLESs3b2xLSZa1Ba6sUTD51bg99AGxEjezbZ+9uDshTLPliG4r0lKLj5SqCfh6dMjsrF7TRufUCDI3tUmxcbG8uiehRBo4cVvvnmmxqtS2lairydcsopBrK3ePFi+Hw+dlyCTJuzsrKYIITIHv2kVDIRPQkicDfddBOLPFK3D5qjriHnqOliM7xeL3tIHHeo/IYAAJBPSURBVFa/X0mwjnLLpCFDKrmXtDTWH/yee4DjjgvVe5G/nvTYMyMrrik25KRjzx+98eHBBAy5ei8zVnYEHax9GkXzNIeL1fD5WK0eG2VdNlxUX+cIMMsVVquHAK/bY8yFTJR5942EE1YIyxSRz2QkL9Q1g6sWxJhI48pt3nXD1CZNsWKhN+1G4t8xV9RP6sRLeVM1dMVQxkLrhHfQCEufRuqYEWFcrQNU3+AN3eMIzJtQbJtbc5vPkXMshB3OgIa0P+Pw8par0PjkTViwh5OSoKbhjwWbsG5jDjp3EEWdNmoOO437jwF9Ybrg6wvw15t/oc9VfYDkE0LG9Ef62rZAo16gwZG9yy+/vNZd9j///HMsWbKEpXHNoBZsFJlLMeUnidjRmJyjEj05Lscqm0MErqysjBFYM5566ik88sgjtfAMjzzZo+V/+omnbKvqi6sX9qWmMieYZ59FWBeNrdO3IiYlhvnsyY4ahLOanYDlA39E5+t+QEq/jfAG3YyUlYuuGbRNJI9br1AXDU4aSX0bFDV5QdO2rNULUoRLbPMUrBBeMFsVp9FiRaQ9Ddt6jZv4/VS7W+jpUQcyi7gNzv6ExNCYmlpVzzN3waiig4YhpapVvbZ53LBPJJC7UhvGw5S2VZ1j8RwJUV4NLbb7UbKjGSZktgu9bsxv1oHfZq+1yd6hgtJ8W7fybbuj0D8CnlgPBt85OHTgaIlu7MhevUCDI3u1LWrYuXMnbr/9dkybNg0x9Uyx9sADD+Cuu+7S94kYtmzZ8vBq9sxFdbWE3Fzgssv4+89NN4U6aRD++AOYMQM491ygR4+qiaf3oBcTT5vItsd5x7FUpMTxmf2xsOtKzG08lbeS9GsYVL4Z+5xJ+MvdytBNw6mobumnBqrTo+gevQPRonybjssUJo/mcWFHxZamCJZGI6b7NhEaEyEwPVqnpmoVJa5gOhTVMxgsi3mZxTyytz8hKTRm6oqhn6d0vYDVXHPK13SuQTlrNW5YLzwly+abOmiowbzKz+E9hhEhjUv8eVNnD5ysH7EUzQSRGFWGIl88Cou4kbaNQ8DGjfwfiso2qMWNDRs2/tZokAKN2gSlafft28dUsm63mz1IhEF+fbRN0TcSXhTImjcBUuNS3SCBfprVuXK/qjlJSUmWUT0CqXZpXH0cEqgHI/UyI5BP0xEAdWOiLDURvZIS49iLL1J7O0DXoihpXAJ9Jq1dy71Cadvv9aNRj0ZI75gels51OZy4u/NVuKDlmYwfXFy6EA8W/IoLS/5CtMvPJBdE9tzOAOMY1FnD7Qzy9LArCCdL5Rq3na4gS+XybSJ9GkoXd0LOY1cg/1Oeene4iOQFQ3916m3xQCJP4XI/Z32e1HfI+jn6mSnSuPuShA2OJF2S0ynnyX1D/lWuKTiSPlfuS+GwTDGb9vV7kuvp4yJnbJD0mu5F6lTUfxTDOZzoMtsauaa8jimt44t2YFtHD7Z0imLDrZL24o1TX8PjQz9i5xUVl2PHrjzjL5ONmqVwKapn9xu2URuRvUN92KgVNLjIXm1j2LBhWLlypeHYVVddxerySGBBkTSPx8MUv2S5In39yGplMPUDA7UFG4wnnniCkUayXSFQpJDIWdeuXfU5P//8s+E6NEeuccRTOhIRiOXhgj5Ppk2zHhs+nHNMFtUzpXEJRA6pj67PB5x4Ivm/JuKmFTdFvBYRviGpA/Di95vw0vqBGHvs5Rjg3Y4YnxdliGfiDILaQSMQCHXWYKlbwVzoeCiNK1mMA7Fdt8MZX4aoFrkIlnvgdEtVhAOQjjpKSpedxSxaxDbNU1Om/tB2pjC4zo1PMnbFkGSI1LiRUq1iHVYmaEgpC6jnwmLcX8nabJw/AVmGqN43lHS0zhdl1w39CdDrI3sCy3N4lw2VDzKVhynVW+6PQtvkHMS4fTi11RJMmw/MnbsJXTo1xW03DEPXzs3Mvwo2qlLi2ilcG4cJu2avfsAme4eJxMTEMMuW+Ph45qknj19zzTUsnZqWlsYI3K233spIGokzCKeddhojdZdddhlTC1N93rhx45jog6JzhBtvvBGvvvoq7r33Xlx99dWYPn06Jk2ahJ+o0O1Io0xJh9VBqpqsVeihw5TGpbq9007jnNQUQI2IbfuKsfiOW1nYaW6vZzAkYTWGlW/AN3F9WFqQ++7pnIKpcWXXDC544NuG40x4II7He9Fi/Mssoqd305DZRpYK5YpbFq1TO1yoIgdVRav0l80sEgKNhCTr88ypVhVVqGsN17Tat0jzhsZ5SlYfR3gfXMN8+WKaU7R0j36TOldNJ4tjrgregzdAXtoOB/aWpuKjNcNwfc8puLn3T5i3qwtKvLFYvzEHt937GcY/exG62YSverCVuDZqC3bNXr2AncY9CiB7lLPPPptF9o4//niWklXVvi6XC5MnT2Y/iQReeumlTEiidvIg2xUidhTN69WrF7NgmTBhwtGxXZGt0iiq5zqyzeYpakPmyhE71pGCrLTUkMYlTJ7Me+n271+967RrGo/UvhvR6MSlmOrgIcMzvKsZkaBOGZTKpXo8SuFS7R6zZHFTapdvUwqXwGr83Hwue1C6VhjIOTyi9o6lZ5Xmr2TLR/PYAkpK15CO1cLSuKwLB5G9YhHZS+Y1e8ZUKl+DBSHV9UNBR30d/Vz1XYDOVQmiktaV+4Zxbi+o7DvC07zmNK66L89RMrnMSFq9RxEQNd6XA1lb/Tjx1zJ0Whny1Pti3fHYXpiJ9NhiXNeL5/2DQQ2BYBDj3/hNWcBGpbCVuDZs/KNgR/aOAKjThQoSbrz22mvsEQnUtcOcpjXjxBNPxNKlS3HUUVx8xPviSqLSti2wbRuwalV4+0bK3hZvOYgsGRGK4Pm3YfIGzH9xPlqf1BrHjzveco7b6Uaf595i2yv9SfDnOtDVl4PmvgPYggy2PqVr/SJ162fRMGcodSv66FI5o2REoTSuSFeKThfB4ig4iBB6AtD04+FpXJniNKdxGZRmKhl6Glf8e5jVt0LNGramaR2Z8g39AxgNlI3KYaOy13o81AUkYhrYfD90jjCBDr0OphSt+bpsXQ1uHz/Jp3TJI3PsF/46F+NPfhujOszHlM39sC6vJSN8FOHbtmM/WmdlKBezEQb6N1Fr9mzYOAw4NIrAH1qI7lDPsxEOO7Jno/qRPcqXHkEQf2vdmitxKbqn4qWXgPR04OHHo0KqYMqhmkAd5vZtyGfWK/tW7Yt4rX3lB0JZYVc8FkfxFnmnetfqilGeAuXtypwUfSKzY2GwLGkHHZehLaZ01ZUEnDLtf3c4dt5xO0qXduCkxcENQlgdC0t7qi+AaVsuJVObFFyt8CKhgvtjUa9cmSrV9RfKGnoKVIVYR5+gnMuikOophh2LtWQaV9/nO3q9nrg3qPvqfHGOvGd5OanXUM8JgxPY2C0Kvw+PxbYOxp7IS/e2w5StfVlE9r5BX8HtDDHcnL12O7UqsXs3L4SlKD59+7Jh43BgCzTqBWyyZ6NqkPkdgdjWEcbEiWQRw21WVHTsyH/uzQlGvJcrr+QcMDu5K8775DzWLzcSkj3x+ra/JBq/Onkqt3mwAB6Xkrp1BRjdYyldUt0KMkJqXIJU4MptlsZlO0SsNLiSi1lOtWJ7k1A4zCn/8tRt65SqIf3qBDJElLXM40FxTHRYatTqvLA0qpqJN6lr1fLCsLWELYq6L89XU7LmNLCZ0PLrid7AYk3DGmYSLK+L8Dmay4GgW7kgsXENeO2vs3GgPB7lAQ+So0XanzWAOTICo38UZFSvfXujB5ING4cAuzdu/YCdxrVRr8he8+bWx6nTHHnxZcydA4wy1utJUJ0fBSTW7U7CBQ9L6a41Wsc3Qau4xvj5wVOxd2ZvFDz4AVYMbYI9jmT4hQEyZRNJfEG8glK3JNqgd5+An8egmHBUqFNZLRsFkDRj+jLxlCVIGLISnsZCOaKqZ01KWnWbrEOYOtc0T6Zw91MKV/jysWs5KllTfdNUx8xzlTSuYVxneqFOGQY1rr4v0q+GlKzp27lUvCjnGPrpVjeNGzZHpJDFsUJvPG6beiN2FWUwn0R5D7ExxiigDQvYKVwbNv5xsCN7NqrG/v1HjexFAomAMzLCbVdUPPAAr/V76KGq16MU6g3tR8CdWMoib4W7mmKPM4XV5VG6logd/UdEgW0LBS49KI2rpnRlWzSWxpUmciwP6YA7pRiepgWhlITMWcptxh+VPKZm0VFDUb2mC3FGPqXUJdkRbXn1NcT6qvpVDknxa+iA3tLXkGI1p4D1fa2ytK5IZ5tTwYY5JjJolaJVXqKIcADt11Sg3doKRJWH/P3U5XYUNgoRPTGw107jVg0yrSTY4gwbtQE7jVsvYJM9G1VD+pkcoe4ZZrz1FvfWmz3bYlCSPQviSZ9NJOrIXZWD3Yt2o7xA8Qe0wMD0rnh+XBpOnPQk2l42DS7qg+vQkOkoglvjylvqkStJktvFw3U8jRvaJlNluU1CDL5Df13KNrNgERfW+4Vxla7eD1dlSex8keZUUp1SibtP6YtrVswK7Qg/z5zilWsrUFOo5p/ynti+aAKi35MkUSaTZEMamD1f0/XUNLJmul/5IlWSxpVzWm7zo/UmP+uRK19cg2Gz2Ihy+XB9719wfuc/sGNnHvbtswlfpbDJno1ahJ3GrR+wyZ6NqiE6NkRSv9Y2qD3aL78A06cbj69ZA1z+wcm4Eu9bpnElptw+BROOmYBNv26q8loX9u+Ka/oPQQXrhwvcVzQF3+e/gUH+rSyiR2pcIn2UIuamyryhSJBtU0pX9sOlnryhbb03LpEPrxP5E09F9kPXI1gSxdmQjJIJXz6CxkiL2CYjYRZd5ClM8p2jMWmoTK3SpNDDnIpV3yBlilQnZHJfTdFaMT1zCleuZ5HSDV1b1C6a07h0/+o3dZOtjpoali3jLOfo0UlugLi1vQc72rhRERU6h71Wpud0XMvVuKT7TFzb51d8+8mXGHvZG3jw4a+Ru18Ij2yE/6ERbLJnozZgR/bqBWyyZ6P6ZO9Q263VEJdfDowfD1x0kfE4kayPV/bGlzgfvmRr+4zly4Fv9wzCqtRjEZdRvdZupYEKlr71B10oRTTcCOJs70rWmVVN4xLBo5QtkQ1pveIkzzwRymJZTEn2RBqXHY8Konx9S/j3p6B0RXuFzIi0p0gZUy/cUD0b758r12WPYKhVGqvZU2xSqkzjKvv6G6i81QgpX32uVt196zSuOXBZ3TSuAeY3facD2zt4mCJXc/N/E6niNT+mb+uFxXvas84adw/5mkUGFy7cjH/d/jEKCkLiDRsici7bMto1ezZqAXZkr37AJns2qoY0MY4PKViPJKgbxm23hZv3U4r24a5f4VucC0e6dWRv8WLghw2dkd3lFLQdVj3biOLt6dj0wmhsfuk8TI7hwo4TfBuRrhUbUrdutz+UuhUp2rA0rsuYxpVp1pQxM5H5r68Q12cjS+mGlLk8jcv+k71xxTl6f1iZxnQAjUT3jH0K8TaYGiv7lmlcszpWSX1q5nOVWjc9jauocc3jMr0q07h6r93K0sjVSuPKVLd4nWTK1pAONqZxjZ8RDvzf/PNQ7vegb9MtGNFxIQJBDfvzijHp64Wmm2vgkCncli2PuK+mDRs2jh5ssmej+u3S6qBVmgoSQzzS9E2chmlwN7Ime8OGAVdfDdx6a/XX7Z3QCfumHIP90/tgfaApVrqasejeSO8KPXVLKdqgktIlw2UZbWRRPhb5C6VxNSWlS+nZuB5bEddrC5xRFaE0LltYuRElbcrUuLKthDRKVvviUqs0qQVR1L1k2sx4kYzaqWuarhExRasEJ80wpIjNY2JNQyTRnAq2+KYelsa1SP3qc8Q8Z4UGNwkzVNNVdR3TNfYUpWHCUt5t5qYBP6FJQj4zWp788/IQwbQRSuGKntw2bBw27DRuvYBN9mxUn+xRu7SjBLKT+/VXYNasCDYwEWr2WrUC3n0XGDu2+tc66ZgEnHL9RnR8+GPmmfd1dB92/NyKZazXLSdzDiWNG1Ljsm01jStDYwZlroORMNY9w1CTJixcZBpXjkVQ49K2jOzlJiZZpmb5PVikcVGDNK7Vusr8yONSXWydxtUnVpXGVdTFOnEzvek3ygmwVmm9F3CDaSs1rg7xD/X16iFYntMasZ4K3HvsV5SkR1FROXw+tY1IA4ddr2fjCMBO4dY9bLJno97V7BEmTADOOAN45hnjcS0vHyvQAy/82g3lFmJbUuB+ePKHmDR6EhM5VAfEE6a82Q63jG7K+txOj+6EYkSjRbAAQ7BZn2NU44anbs3bUFO6Lg3+AwkonN4HJUs7Cu5iTOMaUqwWaVwiJ9J6ZV9SKMUm06ChJySOqyng2krjmlOu4h2Z3b9M4wp1rx5APIQ0bqRz5H25/XyeL8oROY2rRuzY74ITz/55Psp8HnRM342s5FxGsD/55E8sXbLNjvCpaVw7smejtsD8Nw/jYaNWYJsq26gaBw8eVTUu4cQTees0MvE3ID8fZ2Ae9oxvht4judmyitK8UmybsQ3lsSlwf+fAeedV73ouhxP/6jQcy/N3Y2nBVkyJ7oox3qUY4V2BeXHtmHkyM1UmBS6L5IUUuJKJ8G3OUlgUTzInZgDsQNnyDjjwxSmIbrcL8X02iAifosYVCly2rWlwmtK4qSUl8ASDCDocyIsXaVzFcJifZ3R2UVmXIeUrD6jcS434yXSoaa2wNC6bFzJK1qN+akTOnMZV15HblLaVZ1VC0mVEcVdrD3a3cOt82rC2eoLJfDm7KB2PzboIWw40xd6SVPaCffrpfEz8eC6ystLx+BNj0Lx5ZKV3g4nsmRtT27Bh428Nm+zZqJdkr1cvYOtW08GKCjhKijEaX2PTsBvgcIS3ciIF7qnvjMFx13dFcDSQnQ00bVq9a1I/3jWTeqC8zwF8njoAmx2Z+MHVk9XnUQSI263wqJ2s2SMjZUr1mrf5JBi2Y3tuQsn8bojvv1YQoJAalyJjer2amsZVCE7jgzzCeiAuHn4mA1bSqfI8c6pVbOvr0FMImEidieQZ1lXXUq8jLiaJnprWNadtw/YNa4TbrKhzQq+fucCQMXSQY04ohUxk2bSWBW+ct0uJWlF3FD9vg7drVz7uvOMTvPvetUhMjG2YEfydO/m2nca1UUs4nJSsncqtPdhpXBv1zlTZQFos6vVecd6BX6a6cdJJ4VNikmMw5Npu6NvPgb59gZyc6l/z2muBuS8NwsEFnbHFlYnPoweg1B0thANgPXMdlfTDDU/jyifDt91pRWhy/0QknrRMUeNS0E9hY2oal86XnM8JNCrmZG9vUjJP8Vr0v2WrKmnRsLStxb5qgizPMaRgVaNiNa1rvpY812SqbE7JGlS04rmpa4adw8KVpjlWKl+nI+zaVmur6Nd0A87uvIBtk2AjP78Yv/yyAg0Sq1fzn82aWXaosWHjkGALNOoF7MiejarFGV5RBF9HHwBUm8eEwLJtG90HSXMrwdy5gKeGbVDPOQfYUrYbnjROqiiYJCN4fjI8hlDj+lk8i8+hKJk5pUs1ePI4DdG5IioolaacZSlRNy2U7uUqVlNv3ADQRERY9yYlhVqsyTUlzG+QinGyZRrXUFwXIW1rYILq+Ur61mo99d7Mc9R7MPe9tXqTN6WcW633wePTkN3SjfIE1rPO2KfXdH2r7w5dMrbj/05/D76AC+tzW2JTXjO2zPTfV+OCCwaiwZK97t3r+k5s/IPAjM6Dh36ujdqBHdmzUb0ULoWqqB/rUcTKldzXlVK6BiUua5LLSSCpdlUU7irkrdJya94d4a67gBGvTkXKYN4InjyOz/Ctwael72JAcAer2WN1e7Ifrkzdip65XGkrHlJZyxhWiLQxQ+aAE2Wr2sKXl8xToSI8xoyBJclSzyGq4nSgkUjjMtsVc7pVScXKWruIilo1bau+CYgoneFcVREbth9SwJrHdah9e+VlTGndsHPMqlpFpSzRbKcfrTf7Ee1V8tdKYxL9WCRoGtbmtsScbV3hcQXw0EmfI8ZdwYYOHhTq84YGaixNsOv1bNj4x8EmezYqh2RTZKhcRTStttGiBa+j27hR8DwZ2cvMxP3386wy9dFVsWLiCtYqbfpDvNcaRWp8vupfs3VCJsXv9P2B2lb0CGbjUv9CuITygaJ7oTSuBif1wxX8Tqoj6DhL40rmJYyUqTYv75MzsO+N81A8p5cxHamkfpm6lY4zZSs/t1GxiOxR7aSaXjWncZ0hRay+ZqS0rpirQ9yPmqYNVpampXFlPk9Lm9ZT07ZEds2pVXGOStLCU7TGdPGuNm7saONCWZzTmMZluwozlMMGPz5RZxgAnv/jXOwvSUJWyn7cPGgyG3a5nA2b7PXgxuI2bNQK7DRuvUADfVezUW2UlBzV7hkqKFv7++9Abi6Qnk4hrVw+kJGBJk14dpk6ZqhwRbuQ1DIJCc0S8MQTYPPef7/61zyvZX/4fU6UbWvMsqoTXcew48MC69A4cJBnZQMOBJkQw4GAX25zNa1MG7I0Lkvlip9SuKEBcT03wplQCkd0BR+TqQo9xaukN+WbXQBoVChsVxKTw8b0fWlIbF5TRuvEXL2nrnp95Xx1X4/0qevLtLPcl+Pq+vpzV15g5TUyrKmmX1naW03rhqdod7Z2Y0OXKFREC/FGIAj4qYGxEGmwc+S+eKHEMUdAg5PS5BQJ9sbjqZnns4jtWZ3/wvGtV8LfUH33KJROsNO4Nv4B7dKefvppliG54447Is754IMPRKvF0CPG1DzgyiuvDJtzBvmCKViyZAlOPfVUpKSkID09Hddffz2KzWmnOoZN9mxUL417FD32VJAIgxE9Uxr34ou5S8QnnxjnD75zMO7ccSeGPTGMdbfYtw+YMaP61/PuaIJ1F/8Hmx66iqVXN7kaYYGzNVzQcL5/CeMmFODUTZVZP9uQebJxOzyNSwwjtudmtHj8LaScOZ+nOeXF1ZSrblAcyrU2LuT/FvvIUFlNfTIDZ2UNdcyQ1zQZFsueu+rcCPua5b7DeHnT+VLMUnmK1rSmvEdzOraacxDhPOVSxjkAlu1ph8+XH8+27xz6HRLdediTXcAEGw0G9IdCD4KtxLXxN/fZW7RoEd566y307NmzyrlJSUnYs2eP/ti+fXvYHCJ36pzPPvtMH8vOzsYpp5yC9u3bY8GCBZgyZQpWr17NSGJ9gi3QsFE5JMHSGVf9uJdGjcAeleHyy4ETTgAGD67+JTp2BDxOF2I9UQjkpcCTeQBfRfXBwPJtGBNcgtdxAnwOFzNfZuINijiRCwrZs1DClVKW+ra0UhEpWRbFcsDhobQu9V8LpU0pEse4IDtftEujtCQdFfMaFXGyl5PE07jsfVBTUrXUok3UERJPYdE9OcbWFByU7ErkXDP7kSlZGd2jtWSkTo6r6RUnWdIolififD2yKBmWFIiwFC2JT0K3HnZNmiNsVOQ9sxStuA5F5twBwOfiIhZ9HWG/YmBy4p8gZC8TmqOvTd/w/zoFfZtvQufM3eifMR+XjXWjSdMUjL14MM4a2SdUQ/lPT+G2bXvUa3Nt2KhNUETtkksuwTvvvIPHH3+8yvkOhwNNKAVUCaKjoyPOmTx5MjweD1577TU4RanTm2++yYjmpk2bGAmsD7AjezbqNdmjD+R33gGL5OVleyttlWYGmTIT2YsKt+OLCJq7YoUD+XujkNjID5/fhWnOrshDHBppxTjBv5GlcSl9SwSP9cD1CzKlpG6ZqbJM47J0plL8JuYTfDnJ0CpEu7VgaIwpcZV5rooAMkRaYB+RPXNqVmQeGamh8wKmVK3VXKu0rrw+e/GVfXVcT+tS+lTx2VPnyxdUuU95DlPnoZJzZBpX76+rAX5+Hs1P3R/ECb+UYeAsr2Fdw73IY+o6VsfEv09Ac+HJ6Rfi8ekXYtKK49jUnJwCvPT8L3jztd/QYFK4dr2ejXqYxi0sLDQ8vNIhwgK33HILzjrrLBZtqy45bNWqFVq2bIlRo0axqJwZM2fORKNGjdCpUyfcdNNNyJOfi5QN8noRFRWlEz1CrGgtOmfOHNQX2GTPRvXSuHVku0IBlRdeAChq/ueWJgayt20bcOedwHXXheb/eP2PrFXavlUiJXUIaNOGXzfOFcVquXwON7528365lwYWiGiQImWVUZ9IElYh3GCqWkUgse+Nc5H9+LXwbmoZerLiRJa9Vf46G5UUwqlpqHC5kEf1k2pa0+yxZ05xqtlcJW2snmsWb6j7jojjjvBxYXKsCjgqdVMxr6nco2a6jgRZrshWaVZzqpPaNRwT2F2YgRmbpfQ7NOHrSQuxbm02/tEg36GJE4Gbb67rO7HxT0MtCDSIiCUnJ+uPp556yvJSn3/+OaufizRuBpG39957D99//z0mTpyIYDCIIUOGYNeuXYYU7kcffYTff/8dzzzzDGbNmoUzzzwTAaoTAnVxOhk5OTl47rnnUFFRgQMHDuB+UhACLOVbX2CncW3Uu+4ZZtxwA/d17vStSDVlZrIfpLJ96SUejXv5Zfo2BWz+dTMO7jiIY+87ls0hTcd77/HGAK++WrPrDm3cHl9uW8JSqp+7BzBV7oeuQZwvkOpWsBVmcMwiczz9yI+GtmWeUXMGeXRPEBxXShEL53mzGyG28w6eupWpX7UGTyOPvQMhQ2UH+f3Jlmyh1K3+5qikUtXyQbkmU6zKb81qmlWmNU37eprZZOfC7lcQTD3lCtN8ZT3+fPjrorTVDdXtSWGFsoYKebv7mrsxvYkTLr86GKG2R7aUU+exTicR/tGFUjc5pgTXDZqCt+ediRJ/PH6evBSduzTDPxatWvGHDRv1sIPGzp07WW2dmlY1g+bcfvvtmDZtWpjIIhIGDx7MHhJE9Lp06cLq/R577DF2bOzYsfp4jx49WHq2Xbt2LNo3bNgwdOvWDR9++CHuuusuPPDAA3C5XLjtttvQuHFjQ7SvrmGTPRtVt1AiJCbW2S3oYqqJSwwpZSqFuPtuoF+/UHDttOdPQ/HeYqS245FIivbTlywaf+SR6mejKXX8xStnoPB4DcmnLEG2IxlXeHjBLU/fyh64si6PCzZkSwm2rataRUqWvXlRfy6+nXL6QqSeMQ/OxFJ+XKpf2TlqutWBpgdC3TPYG6CailVTumJMFSMY1K9irv4WpK4TYT/iWlbj6r7DQmkr0rj6GmH3y2vz9P6+Vuewl1RplcYG6HWVRDvyeZbPUZkvf4479XP0ab4VqbElGPfLpdi+Vdj+2LBh46iDiJ5K9qywePFi7Nu3D32pdZIARd9mz56NV199laVbiYhVBqq969OnD6u1i4S2bdsiIyODzSGyR7j44ovZY+/evYiPj2d1gC+88AKbW19gkz0blaOoqE7VuAbIOgkR2aPP9+eeM07pOqZrmFcfZaZIYOiuwW87CRPXrfRgQKMTUXbqMh5Go+4ZmpNF9cjOQwvybWaozKJYGud61AWDtp2KOoAidgEhPGDbgCu1UI8IEphwQSgKWLRMF11oaFzIW9btSU7hY8TWROROj4rJKJsi1oBprkOdKyOC0i7Gat1K1gobN68nM9Pq9VTxhRxX12Q9gpUoocU5jMSRYENT1hH3U9m1+O8MpXt53Z5+nliTQViyvDF3OF459y0MbLUBF/b+A9+tHYa7bvoQxwxujzNG9EZK6tG3IrJh42+Jw1DV1uQ8Il4rZe2pwFVXXYXOnTvjvvvuq5LoSXJIawwfPjziHErxUs1eU4um6xTNI1BqmKKLZMdSX2CTPRuVQ3oF1bFCL+D1Y7GvN5KwB51FB43q4rXXan49EoQQURwxIgml0bfitF9fgcMRRKZ2EGN9i1GAOHzoHBQifCyCJQvpgnqHDT3PSWAkQ6n1UxSrQZ8TDlLoCvcWVQ1L200PFoSUuKYUryQ6ampUplZ1RqMSIUNBXOhctYhNj7qZ15LjyrYcV0mkeU5oX2FxcrISYuPN4iTtU8QXyjlZW/2IKtewt5kLxQnOsDmRztOPqc9RHhMpZHlsS15TvDrnbPz7xO9w1TG/YU1OS6xc7sOqFbvw6Ud/4skXLkL3nqLW8u8OJreWruA2bNS/NG51kJiYiO4mj0iKspHvXXdx/PLLL0fz5s31mr5HH30UgwYNYorZgoICVndH1ivXUqN0Id545JFHMHr0aKbG3bx5M+699142//TTT9evQ5FDSgEnJCSwNPI999zDfP7Id6++oP4klG3UT9Sxz57EfQ+5MbBkBl6+fnWYGjc7m9fl5eyoYK3SCrZxYnQ4IJHGFVfwSzWPS4E/6EAg4MIA/w7cGpyJG4Oz4QlwUqeR0lZ+eWXb4iEVuMHQNiMzUgFL234X9k88AzvG3YRAYXy4clbMk31xSYlrlbp1KgpTslXRt+U6prn6vqqWpc97uY58mNYy7jsM+zLCaEjj0vUEUWTHpepY1a9ESqsqUM9pvCuA1pv8iClW2WeovYa6jmUaN1i9CMIv6/ph6vrerHPKg6d+iZTYYhZNLC/34aF/f4bCwrK/P8kjUF2RTfRs1GOBRm1hx44dBtEEiSmuu+46VqdH0TxS+s6dOxddu/IMEUUDV6xYgZEjR6Jjx4645ppr0K9fP/zxxx+GusGFCxeyKB7V9L399tus5o/q9uoT7MiejcqhdK2oSxx3HPDuu4AnRi3U4qAvWGQT9sr/CpH3vwmsXu+2TcY/tNJSks8D9DdMliw1AfNhik1CTlkhfnV0RQ6S0ASFGI6V+M7Rl7EZ5rOnizCEgMJFHRyEtJTapclOG+QPJztquAOo2JcGrTwapavbImnQSl3EIf3wCI11jz2exjWkbqWvXkgzYojwMXWrGkVU7OgMylyLc81pWHUtlkZVxxWypQfxrK5nPse8pjJHnqNHF4nct3ThYKoDJUnKL4JyccsIpwqRejaApdzNEx14+Y+R6Ji5G63TcnH/qV/hwR8vRzDoRFmpD79OXobzL66BiWN9AjmS//QTMGsWVzYdeyxAKSe7L66NfxBIRFHZ/osvvsgekUAWKr/++muV1yG1bn2HHdmz8bcge2edxVvjjh8fPkblFcccA0S5gqxVWnLLcOXwZZfxNT79tPrXrKjg86+5Bjg/qy/rmRtwuvCJYwAbv0qbx8QEPGVLwgI6KggdSw0qMlWR4mUmybpilteOpY6YjSa3f4qEY1bqKV9RIqjPaywiezKNq5I03TxZHNA3RRTNQOgsGZ4x4sbv06iWNWRbq9hXs7VhqWET1GOyzs7qHHVedhsPNnaL4n1x1W//UuBSxTUtI3sRumWU+6Pw6LSLUObzoEnSAaTGFev/vosXbsHfEsuXA9dfD2zZwp3HqcsAfQjeeit9atX13dn4h6Gu2qXZMMKO7NmIDGILe/fy7araVRxhVCauePppmYVqBIy703IOBS0WLapZi1/KblEknnQhP40diDaJq7CtOA9fuPrjFv8sdEc2BmIr5gXbiTOkMlduK1RFTycKzSnr/ECRMQdi2+0KU+qy58OIiwOOYBCNihQ1Lm1oEVKhso5PbSQRKW0qywqD5muGIoSGtZQ0L9/nYTSr+SrC3rDVfaXukJFfdhETIZPXMSxqTvFwYq23gpP/N59nvn5V0DTsyMvEuMmXYXNuE5RUcLNUQlFhOf6W+PJLYNAg4P/+D/D7+R8XydZ//pl7GJHN0qhRdX2XNv4pYP2wD5G1NaSWhUcYdmTPRmTk5/PwFqFZ/fEYo88nFdUpN6LoHLU8vP326l+HPgNJyUvWLZ3bReHTE6/AiJY9cMARj6+d3GT5evwBp1spSBN/UaTGZalbeZxShzKyRwEp6S1C86VIjKV7Q5EyLtbQkFpSjKhAAEGHA7nUF1cqWNV5qtBMeOMhglFyxLnifqSeRB9Xs6WUMlbHSRVr2DetZ17fyvyYrWmqvzNFH+ka7LoBDR6vOOgyp6BN9yKvr5bkqdfXU7/h1xMTOOkMAit2tzEQPacjEDr/7waqNfJ4jN+i6Ni553IfI0rx2rDxD6zZa8iwyZ6NyJCFrKRSsDCxPCoF5PKhadi6FRg6lFzPrRX5NE1mnc2gz7ZDqUF/9FGAhFtkl5QaHYeH+5yBgN+JCdpQBODASdiA9gER/RTEQG5TXZ65ZZnakgxKa7NgeRQO/DoQe94YzQx/dS89RYmbm5AIv9MVuo58DZQWaFDOC92HkhJR58pzdT/AcDFHWDs1wzhvY6aPR2qXpu5LAoVIa4i2Z0Eh7GBr0DG+TvKBII7/tQyDp5dbePxZtEsLu3++Plnn6NcPBNlrJIUroXmmVmviRRrVYx5ePf8txLj/ppG9Sy4Bpk3jPyltO3s2QC2iSOk0bx7QS+kiYsOGjX8E7DSujcrN5gjCO+iow+Q+3mzddOz5qxO2eJtj40agY8fQ2JQpwNjRPrSMz8dX7xag04hOlT6tQ81KR7voT8aB7cjAD+iFMs2NEkQr9Xk0S3i5MeLAPff01KSMtEnCJS1YnEEcnNkPWnkMvFtbIKbdLuZZR2nTprJeL5nL+KXDi072aF4g3GdPX19JxYb53qniCAtPPtb+TE37GsYdLHppUNMaPPNCvn6h/VC6VV+D/AWFV5+q6oWFN55slVYRFaJg+nmyc4fFeaG1QzcUOk+oOgypcelHY7gCEqNKcVH/2UiPL8bIgrdw9rEF6NW/DUZfPBh9B9YfA9VKQd9cZszgKdvp0/m3JAqXUx/PG2/kNQ82bNQSZHLjUM+1UTuwyZ6NyCBFhGJifFRx4YWkk+fXJmZ2zDGIfvxx/HD5v5F4y+XIakdWMC7D59fBUg/8pfHI27I9Isk76SSwCCHV4Yle1VWCPvOppp1u58yz5UHg3xhDVXdCHau0QhPCC91zRMpRpcmygRTxuj1ntB8pp8+DK7YcUVl7DSnQptQrjpG95LB6NlWRy0idaX1Gaii1KsYMhE9V7Jrbl0Vqf1aNdml6PZ2VIlZMCilmxb6pPZruBSjPEcht4sb0s1xwEekz8DHNQMv0qJ3500L0cDOkYMkgW6kdNFzTQPiAIm8cHvvlQvzfue/jxI6rsDGnKb5ZEMRfczfhuttPxZhLh+BvAeqIQ/UJ1GCa6nIpck/+RfTTtmGx8Tc0VbZROew0ro2qO1aYfO2OCkpKeFpp4EAe4fvsM3Y/3b5+FFl90rmSUEGHDsCkl7MxdfxatDvZuscn8UbyiKYyxCWi81p1QEGQPn14j95Sn1+JjEkxgSgok4VlbNsJ+JXjzGePzjL67DEBhniknLAUif3WwunxhTzugkCTQhHZS0wJ+dipvnqU8hRpSDX9q8tDxDoGnzvTOqwEUBJJ1WtPPTfMl8/os6efr+4Lnz013erwq35+jtC+8pobhRZGkQl1JvFTZM8UiTN4/ElYefiJom/D9Srx3nOYHmv2tMKbc85kY1cP/Q39Wq5n2++Mn4ZN6+tP43NLTJoErF3Lt6mjQLt2XGlPNbnUT9QmejZqGbYat37AJns2qjZUrgsXcIoy/Pe/PNLw9tuccWVlcQJKaSdidwroM+r8W5thyG0D0LiHddqZ5nzzDQ9Ykq1YdUF1gtQZhwSMvqIopEYbQ4JdnNl40TEJqY5iUfglxRZa+DaM2yTA0P9jrCt0TlAELpuImr09Kck8YGYhmpCtdaUHX2h9o0BCPdfsWqKfbzFXzjGsTRExp/U4X4+/W5P4Qv7HxhSBCIuwGdYMXVtf0+K6DKrQg+7F9FzUOYa1KMJqgkG0Usk8iR+WD8DPq/oyw+UHhn+F1ul74XQ58OOXi1Cv8cwzoYj93LnA6NHAuHHAQw+FfYGyYcPGPwc22bMRGSJ9yKwYjjYodUsGetSb9557gD//BMp4x4LffuMq2T/+qPmyJDasKXeNiuIp3G+/pSCIA5d26AsnRZKIJAQ1PINvcK5zGa7Bn6GCM8Vbj+c81W3FZy9UDsbTun4XSlZ0QO6kU/UeubJ7xt4kceNqBlJGpUxqUnkpQ2pURszUuYqtndoJQ5+r7iuvicrRJNE0pIHZD8lAlTSxSWFnWNNUU2geJ7Tc4kP7tT4kHAwV54WtoR6zWstS3VND2weHE6/+PhzLd7ZGfHQFHj3nU8S4yrB6+U7UWwQCQFwcdygnPPggNRQFbrmFR/nIikX8jdmwUWs4FAWuxXuFjcODTfZsRMaBA/xnaurRvzZ9IDdpAvznP5ydkZuyMHb+/DMNb7wBfPedOl3D7oW78fR/S3DcUA1Llx45n7/rugxCh6QMTvgcDrwWPJEdv9w5D0nB8lB4Tao7DWE3AnnnCSc41mJCpj4dQLkbuZ+fgaJFPeDd1gxOzRGK7AmyJ51CdPKi1PHp3nkKETK3Jwubq67DXkzjfhiJU8Nt5lo/E1kziy3YIbOHnpnUmc9R5jTeHUDrzX7ElKlGgqEXxXB5q2PK8zOcXt0PFUkUgxoCQTce+/EC7MxPx/dLB6K0Ihq7tu3HyEGP4d9Xv4s/pq2uX/YsZDS5bBmP4NFPujf61nTCCcArr/AvVNUtZLVho5pgyvfDeNioHdgCDRv1k+xJJkIsi1JMFMbbyaMm54/REBPrwMiRoem+Uh8mDJyAz3EB1qELfvyR19lZgZYi7ti3Lw9u1PQlKS+PxhenXoYXVszCh+sXY5qzC9ZrjdDJsQ+Xu+fi1cAwTiVcsk5PiDOk7YjSPYOneHltGaVxHQkVSBq6lCl4PemF0BAM1eylCjUue31MkTq5Bg868aVV9a9JYavPVdZh0Tr59U8RelDWXCdDomlFaJw6h2iRCR+JIUhtq1xftoFT1bC0hkoyVXGzOmcPa5XmREmi0yBCkUWC7DWUEQFxLwaxh1kebHWPpnnmqCZZtcjzSbBx08Qb4Qt4RCpZg9frx5rlO7Fq6Q4MH90Ptz00gpP6ugbVvg4ZwotPqWj1+ONDY6TEpTIJGzZqG6ol1aGca6NWYJM9G/WT7JHH3/vv8ygEWb8MGMDFGl4vTj8zGqfz+ngd3kIvklsl49j8lbj16c4YOTLyhyulZL/+mluL1YTsUfDj7ruBq64C3nwzBnf2OAEfrFkCDS685jgJL7u+wNXOP/GefyhKtWjuOUeQogm5rb6BSf89Rs4o4geknzlHF1qkFZUgWrhI74tNCkuF6qIJGa1TriVTuYZ9xZ+P7Vc212rf7JGn7ivPR4iDWQTM4FUXtqbFGor4hJ8jPO8A7G4lzIBpXXGM1+wJbzzDOhZrmy1WIt1DpHnma9AXDSJ67KADcZ5y9M/aiNnreY/Zn79ejF4D2uLE07ujzkE1DF98wWtxSZKeRIp2hezZ/no2bPxjYZM9G1WrcdPTj+51SYzx1ltAQgKvMVq5EvjlF+6MTGmnK64IOyWxaSLu2HZHtZY/+2zggQeAMWN0J45qoUsXruRdz8WXSPBEId4dgxK/Fz8Ge+JO529o48jDJa4FeCdwvB5B0wUapMhlkTZux0L/sYhSILTNU7whv7ymBznh3puYBL/HzYNNpvI/mapkxEecZ/DdU73vRMDKUYXPHqpYS93Xz5fr6abNZCtjTJuavfrYLamRN/kPwtI3oZZy0i+PTaX0joiU6k0w5P8M1zJF5+TaamRPiVga/PiU9QzHrOYJxLrL8eLYd9E6IxeOH4BZ67uxe/ju03n1g+xRXR6VRNCjVatQSprq9Ijo9ehR13do4x+Iw0nH2mnc2oNds2ej/pE9KsYjtS0RPjJ+/eEHnnaiAvIPPuBhOfCgH6VrawrSmzz5JE/j1iS7Rh59K1ZwLkpwOZ24sENPuBxOaA4nXg/w2r3rXbMRo1XordEYdIYmIFunSZGE0tdVlvZ5d2dgz5yhbMqe5JRQyzD1/U/WzMk2bSLqJ8sEw+rv1FSsQowMc9V9ZS21rk3fF09B981T5ysX1U811++pP/X8NPe9Y6ROLk63HdDg9iqdL9TnpOzrRExap6gfNoJE6hFS031Z/ToYjlUyr8wfgyXbuLHy3cO/Q7fmO9hz2LA6G4UFpQj41dYldQDWrYSsg+STEM+C6vSoVVr79nV6ezb+odAO82GjVmCTPRuV98atC7JHLI6iDBSJUEEqQvIDKy3FqlXcImzsWKqhM06jnu6TJwOvvVa7t0W3Yw5+3Nx9EBrFJjDC943WF0uCWXjTfwKC5EkSoDozwd709CkvIiN/uTDFqojqscBTeRR2v34xFi0fjfkYhOyUVIO3nupnx85U2piZHFzC5kYSaMgIm2FfekPLifp4KCSqj8tp5jdoE0EyCkbE66DMUUUl6pop+UEcP60Mx8wW/+AWQg/jSfQ6c3KoP2hf8dmTr5elQKO6x9hx3uz9nZmn4s+NnRDlDuB/536BFqn7EQgEcMHxT2LM0Cfw1rM/oyCvGHUCeo2oBla+VrLJdE4O8NVXdXNPNhqOqfKhPmzUCmyyZ8MalK8kB+K6MFW+7TYexbvrLu53QvVEFNmbNYuLNNq2RbduQOvWrLEGawCw5us1mDR6Eha/s5jV4o0YAdx7L+OFEUFdMahmb9Ommt8iiRZ8PiAjNh7fDb8Mp7fsiABcODdwEyYEj4PX4eZpXBG1k9uyoI0dk3+BktMK8QJLtcZUIKHvGgxIn44EFGN3alrIC06mQmXETJ4nofrjSUGGwjUNf/UmHz5+b8ZxIRxW1lbegJ0Oi3F1n1KwpnH1fizXCEVB9WwrdWajHrr0qxntMKyrWZwnQQbMzNe6krUNx9Sol9U88zX5SaKeUWMk/5nJ52FtdnMkxZbh8TGfIiWW/x2VlVbg+8/m4baL30BebhGOOihC/vHHoT8KKTGn1jJC/GTDho1/JmyyZ6NycQZ9wB1tU2Xy/nriCb5NeVoyVX7sMeDaa3nvzkGD2G1R7RylVKn8KGdpDtZ+sxY5y3KYCpdMk6++mjfiiATqFvXUU7ypQE1AmWUimp9+yvcbxyXixePOhkaBkoASqZPiC3WbmAfNkwpXdk5IWMEidDQWBDJH/YbHGt+BHliFbOqLaxZ5SMZB89UxsYY+zyTQMHSrkHPlueq9yXNlNE8KFPRxSgtyomMWaJhFEgaBhsgkRlxDKHNl5E0+j31N3ZgxPBar+kUb1g1d27SOuN8wsYelQINHEUKikWD1hRzsNQnRP6/fg/9+MxbZB1LRNKUAj47+AtGeCv7UAhry9hWxCN9RBXlVUvSOfuGJ9L30Ev8joS9W1GT6zjuP7v3YaDCwO2jUD9hk7zDxxhtvoGfPnkhKSmKPwYMH4xcSEwiUl5fjlltuQXp6OhISEjB69GjspVCUgh07duCss85CXFwcGjVqhHvuuQd+mWIRmDlzJvr27Yvo6Gi0b98eH1Dt2tEyVCbLhqMJuh41Yydnf/owIpL38MPAxo3AxRfr6V0yO5bofG5nDH9tOLqdT0XxPBhI6tnK2vpecglw3nlcpFgTFBbyQIjq8xftcsNJ6gRRsHaGczW+in0DySjVo2marNsjEYP4j21Ljz3ZV0wXRzjQvICT7t0paXqwiUXOTF0xpEiDbYvImb6vRqjMqVt2L8Zz9VOkAEO9jhiH1Tj7t1P3Td0x5IXFHM3khWfZUUOeIzvTOR3wUUDKaRV5M59njMSp1zOcp5xrmblVU0lWkT2LP4+DZfF46KuLcbA0FmkJRUhPCKVuA4Eg5vy2GgX5lXwTqU3Q/f/6K/D007ze9fbbuVk5mSlTEeq77x6d+7DRMGGncesFbDXuYaJFixZ4+umn0aFDB/ah8OGHH2LUqFFYunQpunXrhjvvvBM//fQTvvzySyQnJ+Nf//oXzjvvPPxJBqbsjT/AiF6TJk0wd+5c7NmzB5dffjk8Hg+eJBUByCVhK5tz44034pNPPsHvv/+Oa6+9Fk2bNsXpp59+ZCN7ddE9g0B/5JQ+VlPIFG0hEqyyPJFxTurUDM36NavRJS69lD9qChIDUzBk1CjlHgIBBPVeqhru8ExDF2cOro+ajee8ZwqlKlfhsqcn2Y4UaNCGjLDpYxqaFRxAPlIxb8dwBDvsYGtY1bPpogjFd47djlQrqOIK1Z9OsxZkmAukdQ4m1cBa5YINzvPETTCWZlK56ieGpMFsDanEVU41PwfrOaZ15DHlRFW0Yb4N62NKpFAXeFjMi/B5lF2QjnFfXYz84njkFSYaxoKBIHZv34+UtHgccVAJBHWkoXpXVnuQwc3KCaRSIi8hIn42bBwBsEzCIfrlHep5NsJhR/YOEyNGjMDw4cMZ2evYsSOeeOIJFsGbP38+Dh48iHfffRcvvPACTj75ZPTr1w/vv/8+I3U0Tpg6dSrWrFmDiRMnonfv3jjzzDPx2GOP4bXXXkMFsRiQp9ubaNOmDZ5//nl06dKFEcYxY8bgxRdfPHJPTPbPFF0r6kw5SASP2jzJaOM77ximUVaKbjGSGIPq8bZtq91ba9kSuOAC3rZXwu10wqlLF5x4wXca277K8ycyUMI7ZjCix4vadAGFQqwYOVK6acRWVCC5tAy9sQyrZp+H0vVtOMmyIE+6eENG7swCDSuBhBqSU4iV4d4MggvOKK0FG0rU0CzYUK7P7lFNKwtX5zDipa4hrtNmow/t11QgtjhYY4FGxLVrckxJAVd6XWX+hpxm2K8QvSbJ4ksUyEnIqiHvESJ7pGaSYgzVuoj+po62AMuGDRtHHTbZq0VQlO7zzz9HSUkJS+cuXrwYPp8Pp5xyij6nc+fOyMrKwrx589g+/ezRowcak3GwAEXrCgsLsZqUBmKOuoacI9eIBK/Xy9ZRH9VGbi7/WVke9EincqmAnH5KVS5FJUwfruQLSxmpGb+UI39TPgK+kL0FBS/IwYW0HpWBPGa/+eYwb9fhwKmt2ut/UFMDXbE80ALxjgrcFP07/0tzhgQauiBBSbvq2yLS1/RgAVwIYrTrS0Q12QeHO2BIl6oiDIPogcbkjSgpX13Moe6rAhGxbxBLmAUbZjFF2LhJ8MG6WhivH3qOEda0SrU6gSa7/Gi1xY9or2C1zsiiCgndrqaytasQe4QfI38/hfVZ8T2ZggoG9eFjO63D29e9hfMGLGD7/kDg6LRTo3qF//2Pb5MxOSmXJOgX3zZTtnEkYadx6wVsslcLWLlyJYvmUT0dpVq//fZbdO3aFTk5OYiKikKKSeBAxI7GCPRTJXpyXI5VNofIW1kljcufeuopljqWj5YUkqrvZI+YF9XnkeqClINEeKldGoXwKLVMpsoKKMJGQdKTVr2CVzq8gv3rRERSfK4RT6QlI4GEiS1aAKNHA2vW1OxWSaBx8sn8+oSbew3i0SS258CzXp5iv8wzDy20/FDqVOmawbbN4gqWInWgeT6PAl2V/gqybv4E8e2385UVEYZB2CHHpMgibM2QQIPfYej6lgILuZZZoFGZIEPel9xXOmiE3U+kNUUELXSOBvg07Gzlxo7WLpTGOnjLsoBJVGG1jlU3juoKLyIKOZT7osizVaqJCTyMUcXmqfnMkuX6Yb/h9J7LcNdFb+Gcvv/Fc/dNwtYN/G/9iICMyZs2DT0H1aOIwtNnmtrR2LBRm9AO82GjVmCTvVpAp06dsGzZMixYsAA33XQTrrjiCpaarWs88MADLJUsHztrYq9AdgwEE8k84qAWaZR3ffRRYNo04JFHgA0bgNmzuRJ37VrDdOKixxyjITYlGp54D+IzQzVQp53GOStxxso+B8ksuWvX0FOuLkiHQ2rgiRP5fu/Mpnj95FFMrEGYo3XEbH8HRDsCuCdOiHZENwhdFCHNkKnSz6GJLhrc3qT5Qe5zmJuWoFut8DFj9Iy2tUhjiiDCDFWwoe6rgoywtQxiCnP9nWlcsCJZT6i/d4eJJkwCDXHM8D7vAna1dmNT1yh4ZfpcjbzpBYrqzZmELGr6OWyeTMErsDpG/Y4rexeV0QiL9O6k+YPx1YJBbPu2M37GcV3WosLrx8yfV+C281/D4j834ohDvS/qSEN/X0OHHvnr2miwkKbmh/qwUTuwyV4tgKJ3pJClmjyKpvXq1Qvjx49noguquyuQylYBUuPSGIF+mtW5cr+qOaT+jSX3+wigSKNUCctHtSGZz9GO7FGRHZknU10R1SySod6XX3Jm1akTFTmGnUIF+7dtug0PFj+IhCYJ+nHScVSnrS9F6Mik+UTeAKPaIO5JGhpytZA4s3VHLLzoZrgFS3q64kwENQdGeZahvXOvHjHSqUlQdrSQViLiwzgItBCm1rtTUvk5QQeK17aDvyhWV8vqogwJGVWz6HohDnOo3Eh01NA7eEi9gxIRCxuvRKRg6KBhynYaMqrKvn5NeY7SkUNO0K9rIlLG+zClfjSHzgONUTvzPEXEob+WkY8ZEKxuysmBCTNOxi/LesPl1HDvOT+gf7vNTKwR8Afx1J2fobyM1+keFVCJxNFW2ttoeLDTuPUC9l/6EUAwGGT1ckT+SFVL6lmJ9evXM6sVqukj0E9KA+9TwkrTpk1jxIxSwXKOuoacI9c4ogKNo032iNQmioL27du5GkKCTPMo52oClfJRgIIMliOVJVK6NtL7BrXgrUnbNAmyKaMeu5RxVpEcHYNmcUlwBJxYE2iJlytOwZXFV2NzgEdJmemKJCB0YeHNx4iOTPE6QmRvVyq3XcmZNBx7PhuJg4t4jZVO5IRAw8DAVJJlFkCYXwdVBWxBEEMLGW1SzPMleTOcL5S7hssqvXnNHTTUew4t4YDbr/FaPQNBU36Y7k2/F+qta3jKIUFI6H5Cqdkw/YW5Zi/CMXU+fw0shCHiZl+ZcgZmrekCjyuIcWO+Qa/W21hEs6S4HLN/WYkjitdf547jZFZuw4aNBgOb7NVCqnT27NnYtm0bI220T554l1xyCauTu+aaa3DXXXdhxowZTLBx1VVXMZI2aBBP55x22mmM1F122WVYvnw5fv31V4wbN45581FkjkB1gFu2bMG9996LdevW4fXXX8ekSZOYrcsRJ3tHW6l3663Ac8+xLhno2RNYuBD44gtuakdpaHIzNoF0HBSdW7QI+NnCq/a66zhnXcDr4iOCyqxqS7k7tktPXWn7kvc0zAx2MQkX+A8W0XMpQga90wbQooCTPWqVRuQioftGOGPL4YzyhQkh1FSuGjJjnFIRXeipW6u5NKisq6dp9XGentSqO5+tz+fzVGtoXI8USgsVk4jCsE/fOXICOO73cvReVBFRjKFfU4U5nEhwEgW0PtcQTdQjlHKCSLGbjykkUH0O+rhu28J/BoMOPPfDCMzf0B7RHj+Gdl6nnzbt27/wzXuzsWzepiMj3qDSCOoluHVr7a9tw4YVRK3wIT3swF6twfbZO0xQRI588cgfj8gdGSwTYTuVTIEBZo/idDqZmTJF+0hFS2RNwuVyYfLkyazWj0hgfHw8q/l7lGrWBMh2hbz6iNxRepi8/SZMmHDkPPbqUqBBKdy33uLtMYYP5ync997j6V3qotG7d9gp22ZsxamJezHmwnScemoHy6gePSgTLDh2GNat43XqlDnesSO8LW9lIJJJ1i+PPx4KPF7arTc+X7sS2cWFCFCaTwgo0hylKAzEsNZqespSigOkeEFEs2Rkb2dqOhtL6LQR8Xdtgcvj58IJlWCo3STMIgtzpwlVeCB78Up+EjBZq5htUswdKczzpWhEv1bIF1C/nrImi3/RmiYCZbCOofKyCn6PrFVaRAEF//Zq+Hww9wWWIotK0tD6fiRGaIZVajdCmlj+9PtdePLrc3F67+X4aXFffWzVoq1Ys2grgkENzVql44Hxl6B91+aoNdAvK2HAgNpb04aNSnA4tXd2zV7twaEdFe2/jfoAUu8SISWxRpX1e5RKpd64JI4g/5K6hFQNRsDCVxfil1t/QZfRXXDBVxeEja9cCZBomT7fIqVr6RLkOUvWfuRoIzLo1QLV+lHbXmr4QXxUYm9JMe6Z8Qtm79rOSMLF7nm4P/ZnPFs6HJ+UDwmlbsW3WFWtGldRgZUPP8DW6fvQEyim2kyFuBlIotjnrdYccBBTUkkO/TS3LDMoZk3kUJ2vKCukAtVhNd/Q7sxEsEiRa7o+3WtValjjORocPg1OEr+6FcKnzpHXCmuZFr52GOEzt3Vjx/hNhR0z36vVMXnc/PZqdc8CTkcQjRMPYE8+V+87XQ7ExEbh1e9uR9OsWoiwZ2cDzZvzOj2qd4g/CobONtDQP29O7n0/3K7I79+VwR/wYvqyp6v3mWWjUthpXBvWzIeIXl2aKquohOgRWp/UmrVK631VeNSP0KMHr+errC6PLkHaD3K7qQnRI9xxB3D55bwUSkXj+AR8dPb5yPTEM8JBnnlJznLcGfcrEh3CMke1YZEEzgE0F1G9wpgYFBHRU1Ma5migsFPxF8cjZ8owFK5tH7oJMa6LVc2pEUnM1FSL+TpqL1yxliURhGm+OMcY6RP3o+9bROgEOQydI65LfnyVEb0I1itVEj1lnvmaxkek9UzH2L0EakT0XA4/7j3ne7x47cdo1ShX76NbXubDV+/ORq2ASiIIJHqyiZ4NGw0KNtmzEY68PP6Tcpkmj8CjDmorRx0AKA8bAY26NcKAmwegxUkdWXnfyy8f2qWoR24l4uaIOOcc4MMPOaG0gsfpZL55n5cPxiZ/I6Q7S3BL7O/cboVyoKKYX69T04DmBYo4Q45JEYHDJHwQxMqTUIK4Ntux5+fTwueqmUmTGEGPhIl5DotzDePyNqVAI3Tb+j4/JoieWfxgvh8FsjeubCOsnmOYI44ZSKPB9sXiPNMxw7Ur6XmrCksM92Bh7xK6FxOlM92zfr44Hu32o0nKQSTHleHJyychK5PXzJJS9/fvFtdO/Z4sWiUDShs2jhZsNW69gE32bEQWZ1Bf2kORqdYmxo7lLIy8UaoA9XSn6eTBbPaapqwVKXbJU09k5yoFKXxrCzFuD/vphwtPlPDw31Wxf6C1a59eLMfe0kRUj9BCGCrvTkkLiRuERYquQiXiQGlb2nEABas7IX9BP7iTilFRkBw2V0zToZNFVUAhofS+1a1UFGWCvi9Jm9V8hR0aRA3qPRgvabBVUc/puLIC7df54CknYhyaExJUSHpZvWuF7WvVPMaOm5S21fk8ks+LRTrN52so9UZh3CcXYPOeRkhLKMGzV36GNo25Qt9b5mOkr9bIXqRvJTZsHAkED/Nho1Zgkz0b9cd2xQxiZaKLiN4BwAJ7V+5lrdL69wkwMkeC3vJy4xzy3KNWwjNncn/mSKD6PtLWHEpTAbpVavRBAg8VndIz9A/3mb7OmFHRGVGOAP4b/50uWpDRL0pfOjUHWhwIRfYIsh+u5BYHl3TnETinhrIdzbHj4zHInzsAyT3WoNUlkxCVwtuGyEidLqIwKU7luH4NJWIoI3QhixcT7THNN6yvQiGFclynZhFUtWa7luY7AqxVmppiVlO9+r0aFjESR/WYIWoo16vqGOvja7ZyUaerL6TVBD4eFtEUx4tLo/HARxdiQ3ZjJMeX4ZkrP0fHZnsYAXa5D7OPLqWVpTgjkkrJho0jANtUuX7AVuPaiEz26rpej9LJpJioopPHpPMmMbJ35awrMX26yfROICaGiycoWFmZEDE5GfjtN/55TZZ/NWkgQnV75GxBUUVFTI2x3XpiymbZHcGBR4rPwZDU53BC9HqcEr0Kv5X30BkAj4ppOtkj2xUdgkMQcfPuzcDur86GK7YMpVuzkNR1PVJ6r4QnqShE5tRIm/TiU6N8qr+ebPxAm9RxQ63N08dFREoSPLm+aT67llT4KueL2fx+9EigZkkS5RpczKFhcxc3oks1VEQb08j6ORb+ewYVifqZQZFU84eIvKfKjtGO0wlN6Xcbuq6sTVQIX9g1Qi+E4XwZGgVQVBaLBz+6EI9e8hW6tszGk5d/gXEfj8FlQx5BQlIcThzZB2eMHYRk6qpSE1DnGarDJVPJmhal2rBh428PO7Jno/7YrpixZ0/oPig0FwHUJo21SmtcedE5RfyoJ3xltelZWbxjGzm91LRT3DXXcCJJWWcVx2e1wpntOujEZ3sgAxNKT4Rfc6I9pXIVUQRToGpASyHQ2JGaYVTIivHME/9E6ZYsBIrjkXXpl8g4dj4nenQScRLZE1Y9T9yPftxU4Ca5CJ2r7ocif47QuBqJVOerkUL5Apj2mRLXJOqQkUe9ZlC5huZyYkdrDzZ1iTJEzQzXEfemv5bsYWEVU13rlUjHzDUAktSFFx+iWrAghSXlMRj38flYua0F3K4gYjw+7N9zENvW78GHz/+CG057Fjs2GTvqVAnZwLl//5r5Ctmwcbiwa/bqBezIno360yrNyiqiihQu4cZlN7KfMlpDP5Yv52bL3bvX/LJXXnkI9wrggguACy8MP04kafzpZ+GVRfPxxl8L4XcG8VrpyfihvDc2BppxI2VS5dLnvosTnRYHuEhmZzpP47IxEUGjbWesD2lDF6JkQ1t4Uoo52SABKA2TibKM3DnEVzqhgGXcQkbiJEET4wzqufK67EUN7QfViBrVEaoKXhlFVFO/xOT09RzQ3BojfHrUjK6p+vOx1G6orZzD8jq8l7Ahgsh62ZqUtaweUlHpquRKnqjeoxRXKMdCEUpxXOMJW50sKgLhSitcledlPK4WPvIVyrxR+M/EMaxub90u8tnjEW4tqKGooBT/u2YCJsx4gHl4Vgu2OMNGXeFwSJtN9moNdmTPRuTIXqNG9YPskTdYNSCL+6lfbZ8+Rs87CRL1UrcNq7HDRWVaFo/LhbsGHYuLu/ZiRKcsGI0NgaacYAgfOEnYEktLkSIUJjuThb+aiFrpxCsApA/+C560AwiWe6CpJsvMb8+4ph6hE0XPOlmR15fRMH8lHnxy7cr2ZURR39fYmow8smvxfa2yNUx2LJ7yIKJKeUQu0hz9WiZvPt63NtwqRW8vJ++JRQA1ZQ73zjPUASofWub1DNeQx82IFAGUvXVN1/D6PJzoif2sRvsxoONWJtbYsyMPf80Kdd6oErY4w0ZdwY7s1QvYZM9G5MhefSF7VUT2zDjjDO6bRw/zewW1Q6NULtXUydJEK9C8m28+tCgfKXmp8YeVgPj09u1Z6Iv3x+XihE6ePbgn8ScWqaK/yKx8HtXLTUhEuUhfqzYoMspHaDZiGhzRPhbN06FakChzVRgyuKoViWqLooSv+D6/P5kxNZ/LI6uiJo/+k4zOZRJRqPenma8p/sGUe87a4sdx08vRYZ3PKG5wVmJtos+xYODm+2HRUIfFMYv1rSJpLkdk6xW1fpJFC8NPt7xHdS1NQ0ZyEZ686hs8fOkPOKEnJ3kfvzAFL9z9Kb54dRry93JBjiWoVm/1ar5tizNs2GiQsNO4NupvGlfW7FVC9rb/sR3znp+HFoNbYOh9Q9mxvn25uILEFmZQbfqoUbzrWmUZsJIS4I03eCr46aeBJk2qf9t33QW8+ioXbJD/norBLVuiR6PGWJO7DwFNQ5JWiq/SX0a8swJrKprj59Leer0eU+LKujklYqWLG6SAQDUxVoQesjZPVcOaFatW2/q58iTxU66lrxOmP5D1a0o0zaSarYx0sn8OWkNE4yTcfp429UarpitKKlqep15XwioyoJ6nH7OYRxE/81pm2xSreaytnOocXYWpstW11XsPBnGgKA4rtrTESb3X4d7zf0FibDkmLwC2rtrFCPJHz/+M68adg3OuOSF8HVLhUqSyZUveJsaGjaMJxVLqkM61USuwI3s26n9kr5IPqNw1uVj//Xrs/HOn4bPWiuhJfPcd8L//cWVuJFCTAWp/NmVKzV+Gyy7j53TsGD5GhGjCOeegQzpPzxZqcZhQfCLbfjjlOySiDFnC1HpHWnrI6kOSPraIhcmyLP1iUUOjMbJu1kxQUp9m0apeDmcyVQ67jmlfrqenONX1TNdTaY26HyKyUvkRmrO+RxRmnBmDXa3Ed1NJXlXjZROpq1RvYTZsVm5AixBxC0UlLT61LE2VLY6J6J75dYDLtICavhKCkEDQhee+OgM/zO/FvqTcMnIGLj5pHgKBAOujS9023nrkW8z+cWlkccbgweFjNmwcYdjWK/UDNtmz8beO7LU6vhXOeuMs9L1ONJM3gYJkBQWHdnmq6xs2rPIIoBWoLGrnTm7ubIXM+Hj8eMmluKYPv+c3SoZhsy8Tma4i3JP6s57G3ZlKhFCkF3VbFhPpE2PeAynY+e1Z2D83VJNlmCuhpjlVUYWyr2sFTOMGE2VzZE/J6YZMlUPE1CqqGDpDzXQ6rMepVRqlS5Vr6upe9r/QecaoncWWKuqIcD98nhbxmAFhgguH9TFxftjrEEnha2q5RkT+jR9PwsTfeSr2slPm48YRs3gvZIFPx/8abitDzZ4JdgrXRl3ArtmrF7DJng0jKiqAA7x7Q429R+pAoJHZJRP9b+yPTiM6hY09/DBPv77zTvh59B7y11/AJ5/giKASpxgGl9OJ9qnpzLbEH3Tj4YIx7PhF8fPQNW4X296ekWFoN6ana/UnIX4SNyj3IKXbWmQcuyA8jGbOMmqR12FRL4XUqRuGukF1X5mopnf1ZUXqkvNBE8XS1xT1fopS1nyOwexZeV0sPw70ujnNFNUL1eYd6jHjkwu9IFaRwrB70jdV1hohx2V53IFPpg/G6z+exPZGHbsc5x23RB/dviEH+3blG68pyZ4d2bNho8HCJns2rJW45MVVWZ7zSIOiGjKyd4h1RsQRSSxBrXWtxInkiUciDHNrtZrA3KnDjKVLI3fsSIiO0knZgor2+LJkAJwODV1GZAMensaV4goZODNH5CQdiG6Wi/hOW0IcQ/5lKyEslaQZYklKT1w5HrqoWc0RgQSyaaYLKdez6mHLCZsgeWI9M72JKtfQbbEX7UicYcF99AikSjhVZasSGQ2FLE23KeYZon2CLIYdoz1V6axewzzPDGnbIlXFqgWMFSJFNTQNP87rhWc+Ox3rdjTGz/N7GIbLy5Ref2QYSUokUiuRRN2GjaMNppI/jIeNWoEt0LBhBCkbCFR0VtP8ZW2nkql7BpHOStK4OctzEBUfheSsZLiijGax1Cd3yBCgh/GzUE+1du7MhRqU5o2NrfktEokkm5evvrIeJ4sXUv6S1x/17TUHajqqHUo04OmCkTgpei0ymhYDJwLb0jJ1cYNBLEE70j5EEhnVF8/vQLA8Cq4YbygiqPI+c6TPfA0ZQdMjhCKdKcQTBjNjc5RQWV9Ps1pcL+SPZ2FKrKwZWxpEk+wAymMc2NLBY3kdlVqFGzqbLVqMLc/UeWHHBCkLpaTp+Ss96+R64phhHjMFNL8uoXsxlPXVJFWl1/JpmLm8M2av6IigLrfWkBhbht++nI/k1Hh0HdAOXVbN4dchM2UifDZsHG3YPnv1AjbZsxGZ7NUldvFUJsvDVuL4/8U5X6BgWwGu/vNqtBzS0jBGIg0rokcgHkvWKIfTTIBUv5Tx/uEHYOTI8PHhw4HUVH4P5H6RmGgc75CRjn7Nm2NpdjaCmoaDWhxe2XYqHpn/LYr/iEL+CbzVh8EAWWEVYebIGlC6ozn2TBmGmEb70eLsnw0RQZ1gyXUkb6l2ezQ+IWJ7NGW+Whenm0ErkUC95k+xTFEJn1zTG+PAxi5uaDJdqr73q/vSUNvcQs18jnwOZoJZ3XZp5o4X8pjaQo2da5FgjtQuLWJrNRPkPStzQ0QPOP/4vzBq6HL8992D2JqdyYQb4xwLcSwNHsv+b8NGHeBwau9ssldbsNO4NqzFGXVdr7d7d7UMlVmrtDgPEppW3iuUgoSyza5EdYkepYJVT74//wQ++4xv33kn8NJL1uelpADbt/MIn5noSTx1xqlIjI6Gi0zdNAdyNqYBvwDbUzPh0JyhVmT0P6VOjX4axgSZckV7UZGfhpLtLREoi9bTpeZoHFtHpjNN7c5CkT3OytTWZbL1mjrf0E5NPV8sRybS6n0bag9N50jI8YpYJ3a2i8Ku1u7w9331Oqa6Pn1tq64WooWaZcTNYn3DMXNaSRC1MGpX3fSToaOHiURazY1QiOlx+XBSn/VITyrBszd9i97tt7PjLXM3s5+lPa0FTDZs2GgYsMmeDevIXl2TPRnZq4Ls3bzqZjxQ/ABSWqdEnPPii0CrVsDkyZHLFInARQJ55T33XGifbomsW8ghpVcvICkp9LKZEYnkSbRNT8P3V1yC87rz5vStDvCaye3pGYh2VeDYeNElgT7n1fo9ajEm/3ppjIirA4imiN6on9D+uvfhivXy+ZxHhq+jRAjVfV1wwEyUHUZTZtpW2ZNi7cJtU1QFhxgXpFrT25yZxQyOyGsqx4xzLIQRYm0DrK7ltDgmV9IjjSEDZH3JSKbIorbPfF1LmI/XNLKnpr0Vkujzu3HPm2OwbFMLxMX48Oi1P+G8QcuQ5ediq1+3R7ohGzaOMGw1br2ATfZsGJGTc0hdK+qyVRpFlCz9z5Sl6GFVWzd3LtCiBa/vM0f+JE46Cfj119B+69a8/OmWW4DTT+f6kaq4MRFD8vezQovkZDx15mlIj4pBq1weQsxploLvmz6PtxtPQI/oHeFtzwKKWbE6BiCp40a4PRUhUYLazss0V7ZP46+jss/GyRxYM5oiq/1pYTFftiyTc5R2cLoowWilZ6gD1J+PUGzEFQeZSCN8jkW7NObLZXpxrUyWw+aYjaBD7dLC5lmZvJpTuJXBfG2rtHCkDzkrUqjsl5RH4+F3R+L3vzrC5dJw3flzgcuBHZ5kfPXFUnz0zI/47IWfsXE5j/rZsHFUYAs06gVssmfDmuz9TSJ71cH11wNffw1MmBA+1q8fr+0jwibFv2a0a8f7x5PJMoGcLCii98QTvFvG669XnZGm5gXnn8/bsEXCJf16o3UeJ3vrE5pira853I4gnmv0KWKcFXqbL70OT43WqWMUuRJ/2eW5maI9W+g88zrmiKHhXYEie45KInvm+aYoHds1KIpD0TEt0jUUaXC3JV4M/a0MGblBMUerPLJnfkeLFEWsRpRQsmPdJqUmkT2JsIid9fnVRhWt1Xx+J/7vs2H44OeB/NjpQMydAeTvLcCkl6dg4v/9hNtOexr3nvMCDuYVV/+6NmwcKkisdDgPG7UCm+zZMELmI2vSH+xIkj1iSRGw8ZeN+HzU51jwsmjyHgEdOgDnnQfExISPUYSOlLJkxVLJpfDgg9zFgkQZFOkjcSORwGuvrfqpEF8dOpQLNUQnNEtcPbAvOhzghsrbMzLxv9zR2OtPQtuofbgn7UdjhEyJqIVFz0RdXfbUU7D540tRsLZzaExG9tR9c+2fIZJnipaJtfWooRphtIq4mddXomNqZC9MREH3qKiIy6LpOkr6Uj9HaV9G9xqsRmRPjXRa3bM+rxrRPoEwCmZKs8qH3oauMlPlylCN1mp0hS9+74edryQD5cDyVWRd5EDAH0SQ2rgBWL1wMx668GUE/OKXwYYNG/9o2GpcG/UzskctKAiUY42AvSv2Yv0P6xGdXH1LCTVII1EdXks1fxMnclJIdi3xXChbbXzxBRdsVBbESQgGEX8gX6/ZO6jF4/7ci/B+07dwacqfmFHaDXOKOvNSOKdCpGR0TipzRU2aJ4Vah2jw5nO/RF2JS5G9gLIvzjV8/dP3HdwXTnIMWlukktlTkeeqggsp+4XFvbHoGCdcskSQRavUa9AcFyd8C4fGwBkkHz6TWsJhIlNsXYqwhRM+2VpOX9t8feWeDccizjPLmi00gzJ6aKHwrTKVW9kvLL1WEXr2qqQxWvOhyfwiYAPwqXOAHs11uwLwB1yM9G1euRMLpq7EkOG9ra9tw0ZtwLZeqRewI3s2rMleXUb26A+8GpG9DsM74Kw3z0LPy3pWa9nvv+fp2EhCDVLdLl8e+Xy3mztY1JToEciCpcps3ebN3BA4JQUjjz+ekZY/Szvh44KhbPipRp8j1VUc8tUTka9I9XzpfZai/WUfo8mxf4bGLKKA8lx9naoie+aavQi1dIgU2VPvlV1TEDS9xlDM0a1bBHOyuI7BZoWiVupzEY+wWj9zZDFiZM/qmPL81IidObqn1x2aYHmsCqKnXitSFNB0vHPFXngQRG5BPHKcXCWUEFeO1+/5Emcfu0q/kZnfLrJez4aN2oJds1cvYJM9G8ZWaTLPWJeRPVIzyNYUldTsNe7RGP1v6I92p7ar1rJz5gCLFlnX2G3dCrRpA5xwAvfEqwqMWxxCBozO+fzzCOrd9ev5zw4dcNGAXjpR+b+8s7GpojEauQtxf+YPPFIlauUs6/CE+tbpCSCmUZ6xRk6p0YNljZ65Ji+8Zq/SGj4LZa28Nz0aJpTDVjV7Dim4Mdf1VVJnpw87Q+epAzzbG153p3OpSDV7YfdgrNmTt2TJ4SMOWEQ6atIurZpze1RwgdOqqGbcVFLTcPrAdWjZuAC3jJmDO8fOhMftx+7NEWTkNmzUFmw1br2ATfZshCAZiMdTt63SZAqXjJ1r0fWf1LOPPQa8/751mjYujl9u7drK1/nyS94VY9Kkmt8DddS46CJuBxOR7HXujNbpqbjpOF5kX65F4a49l2FWcWc8n3sWj4KpEThzHZ7SoUJG1vxlMShY08UQBdSjbiJVaa22NUXdAlVE9oQa17C+X1Xn8qidwazYSmkbBDL3BNB9aQUa7/aHR8qsonHy2ipYnZyS7qUImMn3T43WVR3ZMx2LhIiRPYtoXXUie5Udszje3cfJ3kpPU33s6xm98M73gxEIOnDawPX4v1u/h+/AFvzrpMdw91nP4ts3fkPxwdKqnpkNGzb+hrDJno3wFG5dt0qTZK8yxQRpOObvQv6mfAT91StwJ8sUUtRaZajp6VInjB07eM/cykBzyXfvwgtRY1x8MefRlg1KJNnr1In9uP3EwXjkrGFIi4vFel8zXL/neuT6kxnToAiWHsyJENnjdWaA3xuNjR9eiV1Tz0Dp3sZsrn6qXEt/clWrcStX55oUq6ZIoozaOSpV4/I1kguCaLwngMSDweopaM3XFvOs1jacp9beaVXMMx9TYYjW1YLy9hAjex7Nz9K4hJUU2Qs9KXwzsxfGvXkWDhbHoGNWLp676TPElc7Hqnkb8fa4L3DNgHHYtlYYmtuwURuQYqVDetT1zf9zYJM9GyFI75G69tgjxiXDbRFAooH3j38fr3R4BUXZRbVyWeqVW51A4ujRvBXaofDhESN4Vw2ybKmK7BExGtuvJ54ddTocFB1TonfHx61BHLyVqnHZZ3wQcEd7kdhmK2IycvXonC46UGrqIM8NmOrpqqjZM6hzzdG1aqpxrSJ7+5q4sLGzG/sbuw4jsmdRsxcpiiefB3s9axjZs1De1qg+T9xrbUT2Ovv2IhoB5DvjsMuZHDZ12YbmuO2F0di4MwPJCeV46l+/YHDPbWyJovxiPDTmJfgqIphO2rBRU9hp3HoBm+zZCDcyri9kr5LInrfIy7pmRCVEIaFJ5a3SzFi4ELjqqshCDcJm3mWqWjV4NXk/ogBMgtXt0iKS7HXsaBhKiBZtz8TjX+lT8FarCXi42dehfKTJK4/1o2UX5I+mJ01Hu4s/QVzTvXpkL1INn/Sqk3VwZl+9sEifOp8ZXKtP2HRv1fLZ43MKU53Y0caDgnQXj9odUmTPdL+Kz16V9XkCVj571f4nN/9yVFbHZ141YmTP4kNQmdvTyyNzKz3NQkpf0/x9eQm4e/woTFvQAfsL4rFyEw93Uz/dvD0F+OOHxTiw7yCKC+y0ro3DBLMsOoyHjVqBTfZshEf2asHI+EhH9mKSY3Drhltxf+H9cEVVs8mtAHWy+OAD4IUXrDUqJ54ItG8f4l6RQD1vu3YFpk/HIWHZMuDttxVRSkFByBhQQZuMNLiUD/P5pR0Q0BwYlfIXLkidZ6nGNdTzBQGXxwcnDQYtIntqJM/CZ89YkxfqlWs5P6IaV/jhKdGxiNdQFbv6GtXroFHtyF4Eha5xnkKSZL1fsIapK/NakRS6hsioVP1GiOzJw+b7E+glxBnLKYWrexMqa4up3goXnp94Am7/v5EoLpUmlBqaZhzEi7e8j4va34ExWbfgrlOfwMJfK5Gp27Bho97DJns2jK0eCNROoi5BeU5CVlaVUytrkxYJN97IhRLPPBM+FhXFe92SRmX+/MrXofENG4BXXqnxLWD1aqBPHy4aIbNmtpCMZsbGGuamxMXgrJ6d9T/Wv0rb4aXc4Wz7oabfomfcdh6tk4pWcz2fqa6uYH0X5P41wDKyxwJf5hq8CJE8ff3KIm5sfc2yd27Iuy88akeOeUkHAvB4Q1E1RlCtFLRhx9Qet9XsoGFR6xcWWVPv3XzcjJr8Wtbod9hirkL0oqhez8drb5dHVeNLmwYUFMXpu2cMXo+3x32N4UOW6Yx43aLNePj8l/DTuzNqcJ82bAjYadx6AZvs2Qgne3Ud2ZNkr5LI3uGAOCQZJEcSYjz/PCdgV1xR+Tp3382FGh9/XPN76NaN1/1RCzVWJyjJnqjXM+P+M05AVnoqnIJwTNh/MqYd7IEoZwAvZX2AVBSHFK5q5EpE1iRJKs1uhp3TzkDOgiHw5qVb1NRZq2sjRv6qqqUzR+nEeoL/Gev8lAhUfKGGAfMrMPAPYcGjRvbkOTKCaaizixD5UhHBe8/okxfyzzM/N55INalqw65RxYeUWZVb7XGtUsLYpSKH+evtd8Yj25kUgRuGRwMlenTIgccdxE3nL8B/bpiB+FgvS+0SXrt7IvL2HKj8edmwYYZN9uoF7A4aNuoX2fN6Q+nkSiJ7i15fhE1TNqHHJT3Q/cLutXoLpixqRNDtEeE7VFA6mSKIDOvWWdbrSaTGx+KL68fig7lL8M4fi+BHEA/suQjtYvaibfQ+PNd6Iq7fcj00zck/92Vpl4j0MSKoAfHNs5HSaS1i0vbDk3LA2OFCBN70c4nsMENjpcWYYdy0LyOtDkHMNGM3DLlL84k/GAJsqjzX4YA7qKE8xgGvFMyI0jOHRYcLAwwyX+WwvraITqoeiWItasNpjtix18C0FuvSoefBxfmWNXRViyn0J2WeSufLVK68TjU++HpW8L/hFdJfT7RHC7uFCCaRz310AtZvy8B15y3EsX12oH1WHp5573is2dwYwWAQ7//vKzTJSofT5USfk7qj8zHtDim6bqMBgX1ZOETSZpsq1xpssmejRi3Kjjhk5wxqZJuZGXna/F3Y8OMGtBxSuT3L4WD//tCtWIoqLOr9KA1cXehEj7BmDf9JRYARkBQbg9uGDUGiJwrP/fIHShGDO7Zdic87vIQhiRswJGED/izqbBmtI17E3jY1IOuUKSF+oUbq5EH9XNHGTO7LcUWvwKNknPnISJ5UDUdUyAp+aXgbN0XaClJcmHuSy0C2wqJsSu9c8zpqTaKxd69pXz2unsfIVjD8GIK6h6Hu3yeLyFUCqBCz0L2YIhWVRNiM9XjKnCo+/HoJssdSuJKNm9evlDQ68MPs7li7ORMPXDsTzTKL8Ny/p+DTn3rhs196Ytonc+ASL8qHj3yNjv3a4r9f3I6M5nXoy2nDho0qYadxbXCUloYEAnVJ9tR6vUoiBgNuHsBapbU/o32t38K0acDgwUDPnryEbsKEyudv3AiMHAlccMGhXY9e9gf/OBMLcEylZE/i3H7dkBDtYX+8myua4KGdY3HntivwZ3Hn0CRTlwihkTD48MnxYNClE0HjuaLWLqw2Th2XCU0tNN/cZUL9ZxTMJ1gV8ZC1dgpZCZtprsczDulRMfW2GUFjZMWUKpI1gep9VfeY+oR0MsxnGKOXkertIqluLeZG+pvQNMQEKtDJt4/trvA0j9xzt7JInHg9Nm5Px7+eGIHf57eFy6nh4rOWo01z6q7jQMAfREBEDHM3rcN7N9wE/6K7geJtkde10WChacHDetioHdiRPRtG2xVqI0EKhXper9diUAv2qE0UFvI6vVWrgFtvBV57jWeU580D7rgj8nmUEfvpJ/4ZSrdf01LDh+7z4/WCm/AHumN25w5V1vanxMfizSvPxY0ffItSrw9TC3obIlMMarROqdmTPnJ0uLwgGbtnDUNUfBFanTSNkzolsicXNKRALSN74ZEzQ12bem/yfszpSlhE5PRzRIRRnSMubnjOgtjo0bhI5yl2DkbSZriD8HvS90zRPvmBZK51NCNSpE1/nZQXtqZROYeDdc1wI8h64ea4kyKniNVrWF1HpHhLy6Pw3AfH4681zZGRUorNOzNMBZvAQzfOxJ59Cche70PWnmFAt4eAdldb36ONhgn2jfIQ07F2zV6twY7s2TB2z6D2EnVZg7NNRAeoUe1RKhGUAc3ERCA5GTj9dOC224BXX+X1e9TLtiozZuq3SwrbQ9GUPDB6A/piMe6Lf63aPYn7tm6OX++5BucP6MEPiAhWY3cBnmv9EZLcJVBZieEtU+z7y+JRvCsLBZs7wVucYAyByXPVUjplEV6TxyeqfW3lvpLYNawRdj9qTZqyRvelXnRbWoHYEllMaLo95Zg5cmZIm6r3rx8LRd30e5A/1ZSp6Z7UxQznRiJTlscipWyVvnNq9LGqNSWCQfQWKdxlqgo30mclEV5zGpmJacJr+WYsbIcvp/bgkVsx1ekIYuzwVYiPrcDz7w/B+A/6Av3GAwdFOYINGxK2QKNewCZ7Noxkr5pk44iTPeptFgG+Uh+2ztiK/M2UVjo8vPce8Mgjoc/Sl18G/viDR/lOPZUrZsmEuSrccENEIW2VaJG/An+hP87utVMIIqoHEm2MHtCdR9JY9E7DC60/wpmpy/BC24/gYa0wQlEyM9WIb5qN5sdPR6cxExEdX8yOSc7Bo1YW54pInqRVsnuG1bgWiS2JOjz1HH2aIFuZ+4JonMPvn62ndEwLf/83fjDo6yiXNz53q9SmcS69loYUrJ7itvj3MR+zqs0z58AN56tvwyJ0aiLB+joq4VPXdToVstcizCrG8FSZIMXird/KbsZ8nohiNskswXEDtuPNz/uz/ezNOdiyeCWKN0xGzrbciGvYaICwTZXrBWyyZ8OYxq1r25WtW6ske7lrc/HRyR/h/aHvH/blqNaOTJFJXEEgGxS69MqVfP/xx4EuXfi4PFYVDh6s4U2sWcM/k+lCNcxcNEtNUj7PHXh05xiUBqIwKGkjHsz6WhcRyOyr+lFOJCqj2wpEpRboY1Itq0bmZLBLDfhZRe50gmiOuIkT1MigZmltErqvVb2jsLGTG94YhYDIdUKLmCJrjpAKWFmzEpoVglWaSYpLDAbVEeapkPcrTYzN92tGBGWspULXKuKhaUj2l6Cdf7/RX0+jOsrwZdhrb/UhWkkkhc7RWDSQ75914gbkF8Ri+TreeSM/5yAOLhyPTz6NxeWdbsd/Rj6JfTvzQgtU2JYtNmzUJWyyZ8Oogq1rsleNyJ6/3I/0Tunscbigp0sRvJtu4t56H33EU7v9ecCCqXCpbo8cUSi9Wy5s3yKBooSkb6E6v2pDKHG1Ll3xzTdAv35AbjWDI2kJcRjWo70eXNpQ3gz3bL0MQc2B8zPn4/Ims9hxPUolI1gKaZIkzPv/7Z0HeBTl1sdPGoEkEFoCoSQQEiAGCBARolcUUKqCoB+CSlcpoqACipcrYkEsgNi4iBhUEAsX5dKUqpfela6AhACBIKGl1/me/7s7szOzs5td2CS76/k9z5DM7DvvnHmz7Jw99VooZaXJBbUtD329QmalNOkSMDTtycyvWyxm2qQFJUxNbd3z8aFLEX50pnGAKn5NJ5UpAE9riVNNrFzLx6AunlGxZL11Tm8VkwMOjRQwWxY4czKHPmnD+tq2rGzkGD4+ilXvlH9NuupnLpKsKMlmhV+tyDkRqmFSDuHiNSmIPj4SxUReppU/W8oEdUk6SUXFPrT7QD1xr7vXHaHxHafStVN7iQ68TLT5/4h2jSIqMMdMMH8f2I3rFrCyx2hblDnQtaLMKCy01Pqzo+xF3hFJY4+NpaE/D3XJZadONcXrjRlD9OqrpiQNUeiYLOGDskdBrpBir3pNVpaplZrDICMEhqBbWghL4v79pmLNjjLhvo4UGlRF0Vd+uRZP757tLX5/vsEK6lzdZJJUKx56a1dWWn06unQQpWzsRcV5laxi12ykFJjmUBdEFjdiPlW1ac7X6xxGn+f6OniGY1QqnGL1MjhPHYtnFCwuYtUMjtlroabZdOPU19Wfb4TRcVuxgDbOb5NvKpu0r1JDnfXU9MeR1D9LjBs6K9nUhrJYFkOSfOi3Y3UoNspkuatZPYcG9TlIv+yMEskaYkxJMWWcv0xH/vMB0dkfiNp/QlSpBtGGTkRXHTSRM14BLMI3szGugbNxGYf70ZaLDPjPjcJ2SBQpJ6DovfeeybhpVHUGtfPWrCFq0sQkWmmKY48eRP36OXjx3FxT7RYi8ktoIWIG164leuEFx+VvUDOUljwzkGav2kJrD/whSp98cbEjRQX+RQ+Hb6MZTRbTsKNP0ZHshibtDu3U1EWOkYQdfoEqBWdRQEgmlZT4kz/lW8XtkVy2RZXdCw1TFBmWM2z1AXJmJUyTIWvW/hRXr27OkOsS+RZLlB3kQ8X+ZmcxNFl1dwxzIWRc1+JCNgf46TVLA3nEK8Ia6KMp0CzZK9osj4VLszS3sFGQpCKSbMaUzZA2SqQ4OlaSKLFAVvZ0b2BbBZ9tlGSRH66Wv5Wxsr3rYH0aM3AP3dYyjTKEO7cOrd0SLax+JpFM52z76ThlnymkmX1fotDwUHrlxXBqGHmAgqubE4sY70f55nej5zKugJU9RqvsobBcRfHnnxarXgVkBNsrL4j2Zo6A5XNqCdE5Aw/YmjWJIiLoH/WI/vEPchoofDMH9aL1vzWlZxeuFJrGjNN9qX7gZaoTcJUu5Ve1WNrUSg1+KSby9SummPu+o4AqWValVLQlRfTt0nQKkWTwuoFZUKMHaaxgPtTojwIKTy+hP+IC6GyUv/E85utq57FhDTNI6FB0Qvm6mnIxBlZCFbZ0OE2konGwnHXihvqnI2MNLB2RRVeodkk2FZAfHQqIsD7HaG57x/VuX4PxJ07XoudmdKN2Lc/RydTqlHGlCvn6FlNJiclZ1DDiOiW2OE9t4y9QbNRlmj4hgw4fr0VZZ67Son/Np75zulF4Q5RyYRimPGA3LmMKEJezcSsyZu/kSdPPGPuFkv/zyH9oSe8llH4wvUzEyMy05KsYsXOnJaHDHkVFli5oNpGzPlq2NFRwL5pq5DpM55YxFFc/THQ5KCY/mnBiMA09NpYuFlcXr2tCxxTrnOlYQHC2Ju5N0Svk8DFZl9EVXbaKp/PV7Wti37RlVDTf+s1KX1ElH8oLJMpB6Jm9uB1YBO0thj57VXVMa/wzywRLn5H7VSOrPvlEpRCpXcW24vNszOnQWBvHE/NTlRZpBT7+FhmcLapsz8Jog10H6lHG1SBqWPcajR+6myLCMsXxLkkpVC88iz5b2ooGPH0fnUmrRnXDcmhOcmta9t8wmjniY6euw3gwcqLSjW6MS2BljzFlA0Dhw4d9RZZekZU9+EvtDVt7UrRKk8rgg+C//zXF6I0da/z60KFEHTqY6uqVZqREB46OHS11/OzF61ELbX/fjAyiBx80zXHZiQozvr4+9NETD1DjOqbklZziypRZGKRY2+6ufoiq+eYIDyUQoViKC9WssJQQXfitHZ1c94DJvaouyizvix2zlU6TIWuxkskKovp1JW5QjVxaxayjHGsRQNs6VabLtf3Mr1s+9E1zqpRDZQ6LJc3HwCplZQG0Qhevpkk/Vimcqlp++nM1ySKSqdWadTJLKUqgwbU0rxlwa76pEPleVbye5R5sXE+uYaPa7JdksT6srHFJCZ05X43mftWWrmebgl2RrIE5U86YvmRs3N6QAgOKRQZvSYlE+9YfpLPHzT2wGe9Gjvm8oY2VPVfByh5jSjcF4eFE/v4V78a1o+zhAdNvUT/q+XFPqhnj+n6cMCqidAoaicAypwcuVj8/S99cey5h+TmKYssOWfZUBAebkkFwHZSGcYaw0BD6bsJjNGdEb41Ltm/YDno/7jN6/5ZPKdA3XxxTVzWRTXj5maF0fn8Hun42mq6mxqiKJFsw7ZvGy7Y5dbav+iDOL9ErhUoGrqq8i4FypMTPkbGVT+gzOnOlup+uekbDudU3pD8mYgSt4+NsnWuZ2FDN05xvWOBY77Z1INYusKSQWhaYzNC7KxkkV9lyESOzVncNCV/4bJZkMb4d9fjcvADKzjE1h06/FESxjS9TZL0rVC0kn9q2SKdaNXKpoNBPWZtju0yxqox3gy/lN7MxroFj9hhLBmxFl12RlT073TNgfSiLfrgyaE2Llmf15AokOoYNI7rrLlNnDXsgqWPpUtM8NWo4oOzpLHtIBFm82KR7w7rnLP5+vtSpRRMKCQigrPxCcexwdiRdL6pCbaql0HtxyTTuyAgqoACrJIzA0GsUeft60eS2euQJbQyfUcCaJgFCn9ShP8G0r1f8rJIo9Cm85twLeaj2F9V5VsqNzvWqUhztJnaojmkVOwMFzDxOo6zKvwgtV5XooJfR6JgtmfQy+PhQQsFZCkCLNL+qdM4vVDeFD0m2XLNywovRPTmLgSVy3ZZoKiz0pWeH7xE/s7ID6NOvTV9okMRRp3YOpZ0wh44wDFPmsGXvJnnzzTepXbt2VLVqVQoPD6cHHniAfv/9d82YvLw8euqpp6hWrVoUEhJCDz74IKWna+PNUlNTqVevXhQUFCTmmThxIhXpTEs///wztW3blgIDAykmJoYWLlzo2uQMexkKZQ0eGLKyFx1dcXKQbUUPwKpXmqKnTuqwq+hduWJRtHXKHmjb9sYUPTVdEmIU/et4Vj0ac+QJU9HlGn/QjOZfkj+yM2SzmmqrGXuUajc9rOhgmmQKNWa3rTr5Q6OnWSVwWLJeNXOadyLOFFHi1jxqcEpnVrVKsoV1SieSjWQQzWaOA7JS4PTtbW0dM8Ko9qC6xItS8sWGW8qmtc/4cur5bss11aXcAxeuzmVrKoJsoNDZioMsNaHDhrVRXlyz6w1lV1DiZdOOSBo/rRPNXZRAr33QgQ7+HiZOSYj7ixbOXEO31/ucqMDZCuSMx3HDLlzz5gRz586lVq1aUbVq1cSWlJREa1BKwQaHDx8Wz+NGjRqJL0fvoSyDHWbMmCHGjVc1S09JSRHHjLbvvvuO3AVW9m6SX375RShyO3bsoHXr1lFhYSF17dqVsrPNvUmJ6Nlnn6UVK1aIPzzGp6WlUT9VbY7i4mKh6BUUFNC2bdvo888/F4rcyy+/rIw5deqUGNOpUyf69ddfxZvt8ccfp59++sl1XSvKqR+tIfBXokdZKXL8deQv0Sot64KpvVdZglg59Me19QyE99tRfRst16wKLctWPdQ2RFNeO+BaiCd0lkc7JSr19/BcPpTdiJ45OoLyS/ypU61D9ErTJeTjY4ovk+O2FOMc7tsXOoUfXf6zubJvSdiQTBYidUKG+WTFUKeK99LvK6jmCLleQqHXJArM18XQ6T2jIr7M4IaNfLX68+R7k+zMpTpmsdbZcCeL8jMG5zuCvbg6o7G6fTleb1egQV1KuWSKUYcPydmkEFsKYomNtmqWLh1/plZXsnRBwi0Xyc+XKCZ4NdHK5kSnv+HYLC+mPN24DRo0EArZ3r17ac+ePdS5c2fq06ePUOqMyMnJoejoaHFO3VLKfe3evZvmzZsnlEk1DRs2pPPnz2u2adOmCcNOD9ThchNY2btJfvzxRxo6dCjFx8dTQkKCUNJgpcObDVy7do0WLFhAs2bNEm+8xMRESk5OFkodFESwdu1aOnLkCC1atIhat24t3iCvvfYaffTRR0IBBP/+97+pcePGNHPmTIqLi6OxY8fSQw89RLNnz3ZdYkRFWtTMteaEdbFKFZvD9n26T7RK2/butjIVB100YJl7+mmiDRvIMEsWFr7hw0svtLxoEVH79kSjRuk6Y/32m+lnKea7P/4w9d0dONDkYnaGZg3C6K1hvYRbVzzKS4j2XI2lCUeHUmGJL/UK30eTon8goa4oQfuW+Dup2Id+X/0wndrcky6nNLUkYEjalmJGCRqmfQNXqFFsnPmc1Mb+dLBNAKXX9dPqaap2a7atjJZrGVkNtZZBlSUSBZWVlmhqa5z+egbxe6LatoHlq7QsW70rV49R2RXd2EZFGVSnJIvyyU9k4hpfT3W/6gxHg2sqNQftya2y4Nmyumg6dRjw+dIWNHF6R7peEEGUd4Fo6wCijfcSZZm75zDeRTla9u6//37q2bMnxcbGUtOmTemNN94QStcO87NWD7xy77zzDg0YMEB4zGyRlZVFjz76KM2fP59q6Nw1fn5+QlFUb99//z31799fXNtd4Jg9FwPlDtRE3TRkyO3dK6x999xzjzKmefPmFBkZSdu3b6cOHTqIny1btqQ6qkzYbt260ejRo8U3kjZt2ogx6jnkMWpzsp78/Hyx6WW7LlvQZNALDNoIlD39a+UF5EQ/slq17MoQUC+A6txZh0KahVjfh4vp04do61ZTizT9pRBT17mzyeKGzFl7oiCpA58P8NSiwg2KOAvwQYB03Xbt7E6AtwXyN7BEuJZd17AB7ZvUpW+e/z9atu0Qfblxr/j8/CU9ml4seoimxn5LGTm+VFiQJxIuRC9d1ecrFKyqdQ9TPipqSNepOD9PPO+VIebx0GGV8izClSuRUC+VkiTyfJZ9JW5PvqZEVORPlF3LPLhQrRRaFELlHCPFUp21q+5vqz9PncRhdEw+17AXr04R049T1sGqBYfSckwz2Mi9K9zUNlyt5nmLSwpoY6UGVED+lC2OFVgrXUZ9d1VzWI23laBR7MR48Zr9TMo9h2vQ8nNvUZ/OR4mOzSI6u4uoRSFRSQV9/vyNQQiSUkC7DCjCf2TpJs41eGZBMbOnnMkeM3jT4GVLSkqimwHeO3jW8Ax+HW2O7IBnPrxvMNa4FRLjMoqLi6VevXpJd9xxh3Js8eLFUqVKlazGtmvXTpo0aZL4/YknnpC6du2qeT07O1s8flavXi32Y2NjpenTp2vGrFq1SozJyckxlGfq1KkG0Vi88cYbb7zxZtouXrwolQW5ublS3bp1b1q+kJAQq2N4ttniwIEDUnBwsOTn5yeFhoaK56QjREVFSbNnz7Y6vmTJEqlFixbifsBdd90ljRs3zuY8o0ePluLi4iR3gy17LgTa/6FDh2jLli3kDkyePJmee+45Zb+kpIQuX74sEkWMvsnh2xPiD86cOSOCWz0JT5bd0+X3ZNk9XX5Plt3T5fdk2dXyV0LpgDKgcuXKItZcDkW6UZS2hirsWfWaNWsmLGvwZC1dupSGDBkiYuVvQakFJ8Hfdty4cSIeH/dTGrm5ufTVV1/Rv/71L3I3WNlzEYihW7lyJf3vf/8TQaIy8N/jzX716lWqXt1UYBQgG1cOCMXPXYjgVyFn66rH6DN4sY8PmSo2YtyMTN1qGWwhZzJ5Ip4su6fL78mye7r8niy7p8vvybKDsnThQkFyRElyJVBeUa0CIEYeiRVz5swRyRXOApfsxYsXRRUMtXsYz/kPP/xQhEkhZk8GyiWSPgYPHkzuBido3CT41gFFDwGZGzduFEkUavBmCwgIoA2qKH+UZkEShxxHgJ8HDx4UbyoZfJPAB4j8bQRj1HPIY242FoFhGIZhvBV4tPJVsevO0KVLF/FshqVQ3m699VaRrIHf1YoeQDJm7969KSzMVGbInWDLngtctzDbLl++XAS6XjD3mA0NDRUWN/wcMWKEcKciaQMK3NNPPy2UNCRnAJRqgVI3aNAgevvtt8UcU6ZMEXPLlrlRo0aJbxKTJk2i4cOHC8Xy22+/pVWrVlXo/TMMwzCMu4QuoZoFEiAzMzPFsxn1aX8ylyiDxa1+/fqiPi6A1w2VMOTfz507J5Q4ZNHCOohnegtdDdTg4GARCqU/fuLECWHxW716NbklFR006OnYCipNTk5WxiCwc8yYMVKNGjWkoKAgqW/fvtL58+c186SkpEg9evSQqlSpItWuXVt6/vnnpcLCQs2YTZs2Sa1btxYJH9HR0ZpruIK8vDwR+IqfnoYny+7p8nuy7J4uvyfL7unye7Ls3iC/EcOHDxeJFnhGhoWFSV26dJHWrl2rvI7kiiFDhij7p06dMnx+Y5wtbCVoTJ48WWrYsKFI1HRHfPBPRSucDMMwDMMwTNnAMXsMwzAMwzBeDCt7DMMwDMMwXgwrewzDMAzDMF4MK3sMwzAMwzBeDCt7XgR68TVq1EgUsWzfvr1VoWY96BuIPr0Yj968+pTxZcuWibIwcscNpKTrycvLEyViMAbp6g8++KBV8Wd3lf3uu+8Wr6k3lLi5EVwpP3opv/DCC+I40vzr1asnSgakpaVp5kA3FNR7QjkfFMtGiR807PYE2XE9/drPmDHDadldLT945ZVXxOuQH03P0Q9z586dbr/2jsruzmuvBv8XIdt7773nEWvviOzuvPZDhw61kq179+5lsvZMBVDR6cCMa/j6669Fuvlnn30mHT58WPTbrV69upSenm44fuvWraJ34Ntvvy0dOXJEmjJlihQQECAdPHhQGfPFF19I06ZNk+bPny/S0ffv3281z6hRo0S6+YYNG6Q9e/ZIHTp0kG6//XaPkB0p9LgWyuDI27Vr15ySvSzkv3r1qnTPPfdI33zzjXTs2DFp+/bt0m233SYlJiZq5unevbuUkJAg7dixQ9q8ebMUExMjDRw40CNkR3mEV199VbP2WVlZTsleFvLL/azXrVsnnTx5Ujp06JA0YsQIqVq1apoeou649o7K7s5rL7Ns2TKxvvXq1bPqV+qua++I7O689ihJgrVVy3b58mWXrz1TMbCy5yXggfrUU08p+6j1gw+bN99803B8//79pV69emmOtW/fXho5cqTVWLkWkV5hwoMdHxjfffedcuzo0aNiLB7y7iy7Iw2t3UF+mV27don7OH36tNjHBzb2d+/erYxZs2aN5OPjI507d86tZbfXdNxZykN+fAGA/OvXr/e4tdfL7glrf/bsWal+/fpCWdXL6u5rb092d197KHt9+vSxeU1XrT1TMbAb1wtA5W/08IPLRsbX11fsb9++3fAcHFePB926dbM53ghcE2479TxwE6B6uaPzVJTsMosXL6batWuLauiovo6+hs5QXvKjqTfcKnJvY4zF72jdI4M5cW29287dZJeB+wpu9jZt2tA777xDRUVFDsldnvLjGp988onohJOQkOBRa28ku7uvPVpboZPQxIkTKT4+3nAOd1370mR397UH6DYRHh5OzZo1o9GjR1NGRoZL156pOLhdmhdw6dIl0Zy5Tp06muPYP3bsmOE5aMlmNF5u9+YIGIum0/qHuDPzVJTs4JFHHqGoqCgRV3bgwAERa4a+xYj3c5TykB9xkZBt4MCBSsN1jMWHshp/f3/Rks+d1t5IdvDMM8+I5uKQd9u2bULRPn/+PM2aNcsh2cta/pUrV9KAAQOE8h8RESH6UONLgSesvT3Z3X3t33rrLbGWkNHWHO669qXJ7u5rj/i8fv36if7uJ0+epJdeekm0HoOShx6wrlh7puJgZY/52/Lkk08qvyNgGQ9GNL7GB12TJk3IHYDltH///gi3oLlz55InYU929IqWadWqlfjSMHLkSNGzUu4HXZF06tRJJPXgwTp//nxxH7Be6B927khpsrvr2sNaNWfOHNq3b5+wBHsSjsrurmsP8AVB/XkI+fA5CGsfPhcZz4bduF4AvrXjm5c+Cxb7devWNTwHx50Zb2sOuBSuXr16w/NUlOxGIKNNbmjtKGUpv6wsnT59Wlhn1JYxjL148aJmPNxByJZzh7W3J7uttYf8KSkpDsle1vIjmxWN0Dt06EALFiwQFgz89IS1tye7O6/95s2bxboiDAQyY8P75/nnnxdZp+689o7I7s5rb0R0dLS4lvx56Iq1ZyoOVva8AHw7TExMpA0bNmjiR7CflJRkeA6Oq8cDPJRtjTcC1wwICNDMAzdoamqqw/NUlOxGyOVZYOFzlLKSX1aWjh8/TuvXrxcxPvo5oGTDoiCzceNGcW1ZaXVX2W2tPWJ/nLGcled7B/Pm5+e7/dqXJrs7rz3i3RBOAXnkDSEWiIH76aef3HrtHZHdndfeiLNnz4qYPfnz0BVrz1QgFZQYwrgYpOIHBgZKCxcuFFlTTz75pEjFv3Dhgnh90KBB0osvvqhJxff395feffddkUE7depUq1T8jIwMkcW6atUqkYWFa2AfKfnq0iuRkZHSxo0bRemVpKQksbm77CdOnBAlECAzMnaXL18uRUdHSx07dqzwtS8oKJB69+4tNWjQQPr11181pRDy8/M1ZRDatGkj7dy5U9qyZYsUGxt7QyUoylv2bdu2iYxEvI4SIYsWLZLCwsKkwYMHV/jaowzG5MmTRTZ5SkqKeH8MGzZMXAMZlu689o7I7s5rb4RR9qo7rr0jsrvz2mdmZkoTJkwQ7x18HiJ7u23btmJt8/LyXLr2TMXAyp4X8cEHHwjFC/WXkJqPWkjqMiNIrVfz7bffSk2bNhXj4+PjhWKkJjk5WShK+g0fFDK5ubnSmDFjpBo1akhBQUFS3759Ncqgu8qempoqFLuaNWuKD03Ui5o4ceIN1dlztfxyuRijbdOmTRqFFh+0ISEhopYaHuz40HZ32ffu3SvKPoSGhkqVK1eW4uLipOnTp2seKhUlP97PeA+jjAVej4iIEMoryseocce1d0R2d157R5U9d1x7R2R357XPycmRunbtKpRPKIGQHbX7ZOXR1WvPlD8++KciLYsMwzAMwzBM2cExewzDMAzDMF4MK3sMwzAMwzBeDCt7DMMwDMMwXgwrewzDMAzDMF4MK3sMwzAMwzBeDCt7DMMwDMMwXgwrewzDMAzDMF4MK3sMw2hAn040c5fbx5XntdB0Hfv6fss3w8KFC6l69erK/iuvvEKtW7emvwPoy4r1dMWa3n333cpc5fHeYBjGdbCyxzAeyNChQ+mBBx6wOl4WylJ5cvvtt9P58+cpNDS0zK4xYcIEqz6hFQX+Vj/88EOZXuPVV191yZouW7aMdu3a5TK5GIYpP/zL8VoMw3gBaLpTXFxM/v6u//hAk/e6detSWRISEiI2b6KwsJACAgIMX6tatapL1rRmzZp0/fr1m56HYZjyhy17DOOlZGdnU7Vq1Wjp0qWa47AkBQcHU2ZmptiHtaZNmzZUuXJluvXWW2n//v2G1sI1a9ZQYmIiBQYG0pYtW+jkyZPUp08fqlOnjlCe2rVrR+vXry/VcgWXKlyrRhhZJrdu3SpciEFBQVSjRg3q1q0bXblyxeZ9Y+7IyEgxvm/fvpSRkaF5Xe/G3b17N917771Uu3ZtYf266667aN++fVb3MW/ePLrvvvvEvHFxcbR9+3Y6ceKEkA3rCask1kTN8uXLqW3btmJto6Ojadq0aVRUVKS4WAFkxPzyfmnnyfLMnTuXevfuLa79xhtv2FwPo/XB32DlypXUrFkzcT8PPfQQ5eTk0Oeffy7kwDo/88wzQqlnGMbzYWWPYbwUKAEDBgyg5ORkzXHs4+EOi09WVpZQYG655Rbau3evUITg5jTixRdfpBkzZtDRo0epVatW4tyePXsKlygUxO7du9P9999PqampLrsHxIZ16dJFyAflCkomrmFLCdm5cyeNGDGCxo4dK87t1KkTvf7663avAaV3yJAhYu4dO3ZQbGysuC9ZGZZ57bXXaPDgwWLe5s2b0yOPPEIjR46kyZMn0549e4TFE9eV2bx5sxg/btw4OnLkiFAWoWjJihmUTPnvATervF/aeTL4W0FRPHjwIA0fPtypdYVi9/7779PXX39NP/74o1CyMdfq1avF9uWXX4rr6r8oMAzjoUgMw3gcQ4YMkfz8/KTg4GDNVrlyZQn/ra9cuSLG7dy5U4xLS0sT++np6ZK/v7/0888/i/158+ZJtWrVknJzc5W5586dK+bYv3+/2N+0aZPY/+GHH0qVKz4+Xvrggw+UfZz3/fffa8aEhoZKycnJ4vdTp04ZXkuWf+DAgdIdd9zh8LpgfM+ePTXHHn74YXFNmalTp0oJCQk25yguLpaqVq0qrVixQnMfU6ZMUfa3b98uji1YsEA5tmTJErH+Ml26dJGmT5+umfvLL7+UIiIi7K6Po+eNHz9eKo2oqChp9uzZmmNYe5x/4sQJ5djIkSOloKAgKTMzUznWrVs3cVyN/u/FMIxnwJY9hvFQYLWClUm9ffrpp5oxt912G8XHxwv3HFi0aBFFRUVRx44dxb5spYO7UCYpKcnwenDxqoFlD1ZAuDThFoQrF/OVhWXPUXD99u3ba47Zuh+Z9PR0euKJJ4RFD25cuL5xb/r7wDrJwHUNWrZsqTmWl5enxLX99ttvIjlCjhHEhuvAigfLmi0cPU//93AGuG6bNGmikR3uW3UsI45dvHjxhq/BMIz7wAkaDOPBbtqYmBjNsbNnz1qNe/zxx+mjjz4Sbli4DIcNGyZivm7kemqg6K1bt47effddIUeVKlWEe7igoEAZg+uYDFHaZAJHwZxlDVy4iOubM2eOUIQRkwgFUX0fQJ0AIa+f0bGSkhLxEwojYu369etndU21cq3H0fP0fw9n0CdzQHajY/K9MAzj2bCyxzBezmOPPUaTJk0SMVqIAYNyIwOrHOKzYJGSFQnErTkCEidQAgaxXrKSgrp5asLCwoRFSub48eN2rVp6YE1DTCCUH0fA/SBuT01p94P7+Pjjj0WcHjhz5gxdunSJbhYkWPz+++9WCrkaKFj6+ENHzmMYhnEGduMyjJeDzEpYiSZOnEhdu3alBg0aKK8hyQAWHLgJoQgiOB+WOkeA2xO11+BqhesRc+ktQZ07d6YPP/xQJHAgiWHUqFE2S4QYgeQHJC6MGTOGDhw4QMeOHRNZqLaUMWSQIuEA9wDFEtfGfmn3AYUXLmAoio8++qhLLIovv/wyffHFF0JRPXz4sJgfCRFTpkxRxsB1CmX2woULSoaxI+cxDMM4Ayt7DPM3ABmqcEvqszYRo7VixQqR0YnyK//85z/prbfecmjOWbNmCUUSJUeQIYuSKLBKqZk5cyY1bNiQ7rzzTqEMwvWLeDFHadq0Ka1du1Yok4g/hHsVZUls1fjr0KEDzZ8/X7hkExISxLmlKUkLFiwQihZkHzRokFAYw8PD6WbBeqC8CWRAWRrINnv2bOEqVq8PXOFYI6y/o+cxDMM4gw+yNJw6g2EYjwOWq2effZbS0tJE4WLm7wEsh+PHjxebK4CbvnHjxsJS+3dpOccw3gBb9hjGi0F8HAr9oj4easKxovf344UXXhAW3GvXrt3UPD169BCZ3QzDeB5s2WMYLwaFd1GMF6VW4P70tjZhjH1Onz6tZD+jE4ev741/vz937hzl5uaK39GhhL84MIznwMoewzAMwzCMF8NuXIZhGIZhGC+GlT2GYRiGYRgvhpU9hmEYhmEYL4aVPYZhGIZhGC+GlT2GYRiGYRgvhpU9hmEYhmEYL4aVPYZhGIZhGC+GlT2GYRiGYRgvhpU9hmEYhmEY8l7+H8YqFAPXOY0SAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import contour\n",
    "\n",
    "\n",
    "for j,vec in enumerate(v_vec):\n",
    "        eff_v=np.array([])\n",
    "        var_str=list(variables.keys())[j]\n",
    "        T=800\n",
    "        res_vec=np.array([])\n",
    "        d_hyd_v_res=np.array([])\n",
    "        n_pipes_v_res=np.array([])\n",
    "        deltap_vec=np.array([])\n",
    "        L_vec=np.array([])\n",
    "        U0_v=np.array([])\n",
    "        inv_v=np.array([])\n",
    "        res_vec=np.array([])\n",
    "        power_v=np.array([])\n",
    "        for n_pipes in n_pipes_vec:\n",
    "                for i,d_hyd in enumerate(d_hyd_v):\n",
    "                        mat=materials.Flibe(T)\n",
    "                        flibe=Fluid(T=T, Solubility=mat.Solubility, MS=False,D=mat.D, d_Hyd=d_hyd ,mu=mat.mu,rho=mat.rho,U0=1,k=mat.k,\n",
    "                         cp=mat.cp)\n",
    "                        BB=BreedingBlanket(Q=Q,TBR=1.08,T_out=T_hot_prim,T_in=T_cold_prim,fluid=flibe, c_in=1E-10 )\n",
    "                        BB.get_flowrate()\n",
    "                        BB.get_cout()\n",
    "                        c0=BB.c_out\n",
    "                        Steel = Membrane( T=T,\n",
    "                                D=1E-6,\n",
    "                                # D=1E-4,\n",
    "                                thick=2.1E-3,\n",
    "                                K_S=4.41E-3,\n",
    "                                k_d=1E6,\n",
    "                                k_r=1E6,k=21)\n",
    "                        Steel_HX = Membrane( T=T,\n",
    "                                D=1E-9,\n",
    "                                # D=1E-4,\n",
    "                                thick=2.1E-3,\n",
    "                                K_S=4.41E-3,\n",
    "                                k_d=1E6,\n",
    "                                k_r=1E6,k=21)\n",
    "                        geom_PAV=Geometry(thick=2.1E-3, D=d_hyd,L=15,n_pipes=n_pipes)\n",
    "                        geom_HX=Geometry(thick=2.1E-3, D=2.5E-2, L=10, n_pipes=n_pipes_HX)\n",
    "                        PAV = Component(c_in=c0, geometry=geom_PAV, fluid=flibe, membrane=Steel)\n",
    "                        PAV.fluid.update_attribute('U0',BB.m_coolant/(PAV.fluid.rho*PAV.geometry.n_pipes*PAV.fluid.d_Hyd**2/4)/N_HX)\n",
    "                        Q_HX=Q/n_pipes_HX/N_HX\n",
    "                        flibe_HX=Fluid(T=T, Solubility=mat.Solubility, MS=False,D=mat.D, d_Hyd=2.5E-2 ,mu=mat.mu,rho=mat.rho,U0=2.5,k=mat.k,cp=mat.cp)\n",
    "                        HX=Component(c_in=1, fluid=flibe_HX, membrane=Steel_HX, geometry=geom_HX)\n",
    "                        # PAV.get_adimensionals()\n",
    "                        d_h_sec=2.5E-2 \n",
    "                        V_sec=m_in_sec/(rho_sec*d_h_sec**2*np.pi/4)/N_HX/n_pipes_HX\n",
    "                        Re_sec=corr.Re(rho=rho_sec,u=V_sec,mu=mu_sec,L=d_h_sec)\n",
    "                        Pr_sec=corr.Pr(c_p=cp_sec,mu=mu_sec,k=k_sec)\n",
    "                        h_coeff_sec=corr.get_h_from_Nu(corr.Nu_DittusBoelter(Re_sec, Pr_sec), k_sec,d_h_sec)\n",
    "                        R_sec=1/h_coeff_sec\n",
    "                        U = HX.get_global_HX_coeff(R_conv_sec=R_sec)\n",
    "                        L= corr.get_length_HX(corr.get_deltaTML(T_hot_prim, T_cold_prim, T_cold_sec, T_hot_sec), HX.fluid.d_Hyd, HX.U, Q_HX)\n",
    "                        HX.geometry.update_attribute('L',L)\n",
    "                        FC=Circuit(components=[BB,PAV, HX],closed=True)\n",
    "                        FC.solve_circuit()\n",
    "                        out_flux=(PAV.c_in*(1-PAV.eff)*PAV.fluid.U0*PAV.fluid.d_Hyd**2/4)\n",
    "                        eff_v=np.append(eff_v, PAV.eff)\n",
    "                        d_hyd_v_res=np.append(d_hyd_v_res, d_hyd)\n",
    "                        n_pipes_v_res=np.append(n_pipes_v_res, n_pipes)\n",
    "                        res_vec=np.append(res_vec, PAV.eff)\n",
    "                        L_vec=np.append(L_vec, L)\n",
    "                        U0_v=np.append(U0_v, PAV.fluid.U0)\n",
    "                        PAV.get_pumping_power()  \n",
    "                        PAV.get_pressure_drop()\n",
    "                        deltap_vec=np.append(deltap_vec, PAV.delta_p/1E5)\n",
    "                        FC.get_inventory(flag_an=True)\n",
    "                        power_v=np.append(power_v, PAV.pumping_power)\n",
    "                        inv_v=np.append(inv_v, FC.inv*9)\n",
    "                eff_v_vec=np.append(eff_v_vec, eff_v)\n",
    "        plt.figure(j)\n",
    "        plt.xlabel('Hydraulic diameter [m]')\n",
    "        plt.ylabel('Number of pipes')\n",
    "        x = np.logspace(np.log10(min(d_hyd_v_res[:])), np.log10(max(d_hyd_v_res[:])), num=100)\n",
    "        y = np.logspace(np.log10(min(n_pipes_v_res[:])), np.log10(max(n_pipes_v_res[:])), num=100)\n",
    "        X, Y = np.meshgrid(x, y)\n",
    "        Z = griddata((d_hyd_v_res, n_pipes_v_res), (res_vec)*100, (X, Y), method='cubic')\n",
    "        dZdX, dZdY = np.gradient(Z, x, y, edge_order=2)\n",
    "        ZL=griddata((d_hyd_v_res, n_pipes_v_res), L_vec, (X, Y), method='cubic')\n",
    "        Z_U0=griddata((d_hyd_v_res, n_pipes_v_res), U0_v, (X, Y), method='cubic')\n",
    "        Zpower=griddata((d_hyd_v_res, n_pipes_v_res), power_v, (X, Y), method='cubic')\n",
    "        Zinv=griddata((d_hyd_v_res, n_pipes_v_res), inv_v, (X, Y), method='cubic')\n",
    "        Z_deltap=griddata((d_hyd_v_res, n_pipes_v_res), deltap_vec, (X, Y), method='cubic')\n",
    "        Z_npipes=griddata((d_hyd_v_res, n_pipes_v_res), n_pipes_v_res, (X, Y), method='cubic')\n",
    "        contour1=plt.contour(X, Y, Z, levels=np.linspace(np.nanmin(Z), np.nanmax(Z), 7), norm=LogNorm(), colors=color_vector[0])\n",
    "        contour3=plt.contour(X, Y, Z_U0, levels=np.linspace(0.8, 2.4, 2), colors=color_vector[5], linestyles='dashed')\n",
    "        # contour7=plt.contour(X, Y, Zpower, levels=np.linspace(1E5, 1E6, 2), colors=color_vector[1], linestyles='dotted')\n",
    "        contour8=plt.contour(X, Y, Zinv, levels=[1E-3,2E-3,1E-2,1E-1,1], colors=color_vector[4], linestyles='dotted')\n",
    "        contour4=plt.contour(X, Y, Z_deltap, levels=np.linspace(1, 5, 5), colors=color_vector[1], linestyles='dotted')\n",
    "        ## add mask for a range of values\n",
    "        mask = (Z_U0 > 2.4) | (Z_U0 < 0.8) | (Zpower > 0.8E6) | (Z_npipes < 2000) | (Z_npipes > 6000) |(Z_deltap>3E5)\n",
    "        Z[mask] = np.nan\n",
    "        scatter=plt.scatter(X,Y, c=Z ) #,norm=colors.LogNorm() \n",
    "        plt.clabel(contour1, inline=True, fontsize=8)\n",
    "        plt.clabel(contour3, inline=True, fontsize=8)\n",
    "        # plt.clabel(contour7, inline=True, fontsize=8)\n",
    "        plt.clabel(contour8, inline=True, fontsize=8)\n",
    "        plt.clabel(contour4, inline=True, fontsize=8)\n",
    "        if scatter:\n",
    "                if np.nanmin(scatter.get_array()) < np.nanmax(scatter.get_array()):\n",
    "                        cbar = plt.colorbar(scatter, ticks=np.linspace(np.nanmin(scatter.get_array()), np.nanmax(scatter.get_array()), 5))  # Show color scale for scatter with 5 ticks\n",
    "                        cbar.set_label(r'$\\eta$ %')\n",
    "                else:\n",
    "                        print(\"Cannot create colorbar: data does not have a valid range of values.\")\n",
    "        line1 = mlines.Line2D([], [], color=color_vector[0], markersize=15, label='eff %')\n",
    "        line3 = mlines.Line2D([], [], color=color_vector[5], markersize=15, label='velocity (m/s)')\n",
    "        # line7= mlines.Line2D([], [], color=color_vector[1], markersize=15, label='Pumping Power (W)')\n",
    "        line8= mlines.Line2D([], [], color=color_vector[4], markersize=15, label='Inventory (g) ')\n",
    "        line9= mlines.Line2D([], [], color=color_vector[1], markersize=15, label='Pressure Drop (bar) ')\n",
    "        plt.legend(handles=[line1, line3, line8,line9], bbox_to_anchor=(0.5, 1.15), loc='center', ncol=4, frameon=False)\n",
    "        plt.gca().spines['top'].set_visible(False)\n",
    "        plt.gca().spines['right'].set_visible(False)\n",
    "        plt.tight_layout()\n",
    "        plt.savefig('feasibility_map_.png', dpi=300)\n",
    "        \n",
    "        plt.show()"
   ]
  }
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