From 912cc020aff1b7e20e15eb73d7feb461eb1f04db Mon Sep 17 00:00:00 2001 From: Alexciane Lima Date: Fri, 30 Aug 2024 16:11:33 -0300 Subject: [PATCH] Subindo_ProjGuiadoII --- exercicios/para-casa/2017.csv | 156 +++ exercicios/para-casa/2018.csv | 157 +++ exercicios/para-casa/2019.csv | 157 +++ exercicios/para-casa/Projeto_II.ipynb | 1123 ++++++++++++++++++++ exercicios/para-casa/dados_2017_limpos.csv | 157 +++ exercicios/para-casa/dados_2018_limpos.csv | 157 +++ exercicios/para-casa/dados_2019_limpos.csv | 157 +++ 7 files changed, 2064 insertions(+) create mode 100644 exercicios/para-casa/2017.csv create mode 100644 exercicios/para-casa/2018.csv create mode 100644 exercicios/para-casa/2019.csv create mode 100644 exercicios/para-casa/Projeto_II.ipynb create mode 100644 exercicios/para-casa/dados_2017_limpos.csv create mode 100644 exercicios/para-casa/dados_2018_limpos.csv create mode 100644 exercicios/para-casa/dados_2019_limpos.csv diff --git a/exercicios/para-casa/2017.csv b/exercicios/para-casa/2017.csv new file mode 100644 index 0000000..efbb9ec --- /dev/null +++ b/exercicios/para-casa/2017.csv @@ -0,0 +1,156 @@ +"Country","Happiness.Rank","Happiness.Score","Whisker.high","Whisker.low","Economy..GDP.per.Capita.","Family","Health..Life.Expectancy.","Freedom","Generosity","Trust..Government.Corruption.","Dystopia.Residual" +"Norway",1,7.53700017929077,7.59444482058287,7.47955553799868,1.61646318435669,1.53352355957031,0.796666502952576,0.635422587394714,0.36201223731041,0.315963834524155,2.27702665328979 +"Denmark",2,7.52199983596802,7.58172806486487,7.46227160707116,1.48238301277161,1.55112159252167,0.792565524578094,0.626006722450256,0.355280488729477,0.40077006816864,2.31370735168457 +"Iceland",3,7.50400018692017,7.62203047305346,7.38596990078688,1.480633020401,1.6105740070343,0.833552122116089,0.627162635326385,0.475540220737457,0.153526559472084,2.32271528244019 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Faso",134,4.03200006484985,4.12405906438828,3.93994106531143,0.3502277135849,1.04328000545502,0.215844258666039,0.324367851018906,0.250864684581757,0.120328105986118,1.72721290588379 +"Niger",135,4.02799987792969,4.11194681972265,3.94405293613672,0.161925330758095,0.993025004863739,0.26850500702858,0.36365869641304,0.228673845529556,0.138572946190834,1.87398338317871 +"Malawi",136,3.97000002861023,4.07747881740332,3.86252123981714,0.233442038297653,0.512568831443787,0.315089583396912,0.466914653778076,0.287170469760895,0.0727116540074348,2.08178615570068 +"Chad",137,3.93600010871887,4.0347115239501,3.83728869348764,0.438012987375259,0.953855872154236,0.0411347150802612,0.16234202682972,0.216113850474358,0.0535818822681904,2.07123804092407 +"Zimbabwe",138,3.875,3.97869964271784,3.77130035728216,0.375846534967422,1.08309590816498,0.196763753890991,0.336384207010269,0.189143493771553,0.0953753814101219,1.59797024726868 +"Lesotho",139,3.80800008773804,4.04434397548437,3.5716561999917,0.521021246910095,1.19009518623352,0,0.390661299228668,0.157497271895409,0.119094640016556,1.42983531951904 +"Angola",140,3.79500007629395,3.95164193540812,3.63835821717978,0.858428180217743,1.10441195964813,0.0498686656355858,0,0.097926490008831,0.0697203353047371,1.61448240280151 +"Afghanistan",141,3.79399991035461,3.87366141527891,3.71433840543032,0.401477217674255,0.581543326377869,0.180746778845787,0.106179520487785,0.311870932579041,0.0611578300595284,2.15080118179321 +"Botswana",142,3.76600003242493,3.87412266626954,3.65787739858031,1.12209415435791,1.22155499458313,0.341755509376526,0.505196332931519,0.0993484482169151,0.0985831990838051,0.3779137134552 +"Benin",143,3.65700006484985,3.74578355133533,3.56821657836437,0.431085407733917,0.435299843549728,0.209930211305618,0.425962775945663,0.207948461174965,0.0609290152788162,1.88563096523285 +"Madagascar",144,3.64400005340576,3.71431910589337,3.57368100091815,0.305808693170547,0.913020372390747,0.375223308801651,0.189196765422821,0.208732530474663,0.0672319754958153,1.58461260795593 +"Haiti",145,3.6029999256134,3.73471479773521,3.47128505349159,0.368610262870789,0.640449821949005,0.277321130037308,0.0303698573261499,0.489203780889511,0.0998721495270729,1.69716763496399 +"Yemen",146,3.59299993515015,3.69275031983852,3.49324955046177,0.591683447360992,0.93538224697113,0.310080915689468,0.249463722109795,0.104125209152699,0.0567674227058887,1.34560060501099 +"South Sudan",147,3.59100008010864,3.72553858578205,3.45646157443523,0.39724862575531,0.601323127746582,0.163486003875732,0.147062435746193,0.285670816898346,0.116793513298035,1.87956738471985 +"Liberia",148,3.53299999237061,3.65375626087189,3.41224372386932,0.119041793048382,0.872117936611176,0.229918196797371,0.332881182432175,0.26654988527298,0.0389482490718365,1.67328596115112 +"Guinea",149,3.50699996948242,3.58442812889814,3.4295718100667,0.244549930095673,0.791244685649872,0.194129139184952,0.348587512969971,0.264815092086792,0.110937617719173,1.55231189727783 +"Togo",150,3.49499988555908,3.59403811171651,3.39596165940166,0.305444717407227,0.431882530450821,0.247105568647385,0.38042613863945,0.196896150708199,0.0956650152802467,1.83722925186157 +"Rwanda",151,3.47099995613098,3.54303023353219,3.39896967872977,0.368745893239975,0.945707023143768,0.326424807310104,0.581843852996826,0.252756029367447,0.455220013856888,0.540061235427856 +"Syria",152,3.46199989318848,3.66366855680943,3.26033122956753,0.777153134346008,0.396102607250214,0.50053334236145,0.0815394446253777,0.493663728237152,0.151347130537033,1.06157350540161 +"Tanzania",153,3.34899997711182,3.46142975538969,3.23657019883394,0.511135876178741,1.04198980331421,0.364509284496307,0.390017777681351,0.354256361722946,0.0660351067781448,0.621130466461182 +"Burundi",154,2.90499997138977,3.07469033300877,2.73530960977077,0.091622568666935,0.629793584346771,0.151610791683197,0.0599007532000542,0.204435184597969,0.0841479450464249,1.68302416801453 +"Central African Republic",155,2.69300007820129,2.86488426923752,2.52111588716507,0,0,0.0187726859003305,0.270842045545578,0.280876487493515,0.0565650761127472,2.06600475311279 diff --git a/exercicios/para-casa/2018.csv b/exercicios/para-casa/2018.csv new file mode 100644 index 0000000..19d77f1 --- /dev/null +++ b/exercicios/para-casa/2018.csv @@ -0,0 +1,157 @@ +Overall rank,Country or region,Score,GDP per capita,Social support,Healthy life expectancy,Freedom to make life choices,Generosity,Perceptions of corruption +1,Finland,7.632,1.305,1.592,0.874,0.681,0.202,0.393 +2,Norway,7.594,1.456,1.582,0.861,0.686,0.286,0.340 +3,Denmark,7.555,1.351,1.590,0.868,0.683,0.284,0.408 +4,Iceland,7.495,1.343,1.644,0.914,0.677,0.353,0.138 +5,Switzerland,7.487,1.420,1.549,0.927,0.660,0.256,0.357 +6,Netherlands,7.441,1.361,1.488,0.878,0.638,0.333,0.295 +7,Canada,7.328,1.330,1.532,0.896,0.653,0.321,0.291 +8,New Zealand,7.324,1.268,1.601,0.876,0.669,0.365,0.389 +9,Sweden,7.314,1.355,1.501,0.913,0.659,0.285,0.383 +10,Australia,7.272,1.340,1.573,0.910,0.647,0.361,0.302 +11,United Kingdom,7.190,1.244,1.433,0.888,0.464,0.262,0.082 +12,Austria,7.139,1.341,1.504,0.891,0.617,0.242,0.224 +13,Costa Rica,7.072,1.010,1.459,0.817,0.632,0.143,0.101 +14,Ireland,6.977,1.448,1.583,0.876,0.614,0.307,0.306 +15,Germany,6.965,1.340,1.474,0.861,0.586,0.273,0.280 +16,Belgium,6.927,1.324,1.483,0.894,0.583,0.188,0.240 +17,Luxembourg,6.910,1.576,1.520,0.896,0.632,0.196,0.321 +18,United States,6.886,1.398,1.471,0.819,0.547,0.291,0.133 +19,Israel,6.814,1.301,1.559,0.883,0.533,0.354,0.272 +20,United Arab Emirates,6.774,2.096,0.776,0.670,0.284,0.186,N/A +21,Czech Republic,6.711,1.233,1.489,0.854,0.543,0.064,0.034 +22,Malta,6.627,1.270,1.525,0.884,0.645,0.376,0.142 +23,France,6.489,1.293,1.466,0.908,0.520,0.098,0.176 +24,Mexico,6.488,1.038,1.252,0.761,0.479,0.069,0.095 +25,Chile,6.476,1.131,1.331,0.808,0.431,0.197,0.061 +26,Taiwan,6.441,1.365,1.436,0.857,0.418,0.151,0.078 +27,Panama,6.430,1.112,1.438,0.759,0.597,0.125,0.063 +28,Brazil,6.419,0.986,1.474,0.675,0.493,0.110,0.088 +29,Argentina,6.388,1.073,1.468,0.744,0.570,0.062,0.054 +30,Guatemala,6.382,0.781,1.268,0.608,0.604,0.179,0.071 +31,Uruguay,6.379,1.093,1.459,0.771,0.625,0.130,0.155 +32,Qatar,6.374,1.649,1.303,0.748,0.654,0.256,0.171 +33,Saudi Arabia,6.371,1.379,1.331,0.633,0.509,0.098,0.127 +34,Singapore,6.343,1.529,1.451,1.008,0.631,0.261,0.457 +35,Malaysia,6.322,1.161,1.258,0.669,0.356,0.311,0.059 +36,Spain,6.310,1.251,1.538,0.965,0.449,0.142,0.074 +37,Colombia,6.260,0.960,1.439,0.635,0.531,0.099,0.039 +38,Trinidad & Tobago,6.192,1.223,1.492,0.564,0.575,0.171,0.019 +39,Slovakia,6.173,1.210,1.537,0.776,0.354,0.118,0.014 +40,El Salvador,6.167,0.806,1.231,0.639,0.461,0.065,0.082 +41,Nicaragua,6.141,0.668,1.319,0.700,0.527,0.208,0.128 +42,Poland,6.123,1.176,1.448,0.781,0.546,0.108,0.064 +43,Bahrain,6.105,1.338,1.366,0.698,0.594,0.243,0.123 +44,Uzbekistan,6.096,0.719,1.584,0.605,0.724,0.328,0.259 +45,Kuwait,6.083,1.474,1.301,0.675,0.554,0.167,0.106 +46,Thailand,6.072,1.016,1.417,0.707,0.637,0.364,0.029 +47,Italy,6.000,1.264,1.501,0.946,0.281,0.137,0.028 +48,Ecuador,5.973,0.889,1.330,0.736,0.556,0.114,0.120 +49,Belize,5.956,0.807,1.101,0.474,0.593,0.183,0.089 +50,Lithuania,5.952,1.197,1.527,0.716,0.350,0.026,0.006 +51,Slovenia,5.948,1.219,1.506,0.856,0.633,0.160,0.051 +52,Romania,5.945,1.116,1.219,0.726,0.528,0.088,0.001 +53,Latvia,5.933,1.148,1.454,0.671,0.363,0.092,0.066 +54,Japan,5.915,1.294,1.462,0.988,0.553,0.079,0.150 +55,Mauritius,5.891,1.090,1.387,0.684,0.584,0.245,0.050 +56,Jamaica,5.890,0.819,1.493,0.693,0.575,0.096,0.031 +57,South Korea,5.875,1.266,1.204,0.955,0.244,0.175,0.051 +58,Northern Cyprus,5.835,1.229,1.211,0.909,0.495,0.179,0.154 +59,Russia,5.810,1.151,1.479,0.599,0.399,0.065,0.025 +60,Kazakhstan,5.790,1.143,1.516,0.631,0.454,0.148,0.121 +61,Cyprus,5.762,1.229,1.191,0.909,0.423,0.202,0.035 +62,Bolivia,5.752,0.751,1.223,0.508,0.606,0.141,0.054 +63,Estonia,5.739,1.200,1.532,0.737,0.553,0.086,0.174 +64,Paraguay,5.681,0.835,1.522,0.615,0.541,0.162,0.074 +65,Peru,5.663,0.934,1.249,0.674,0.530,0.092,0.034 +66,Kosovo,5.662,0.855,1.230,0.578,0.448,0.274,0.023 +67,Moldova,5.640,0.657,1.301,0.620,0.232,0.171,0.000 +68,Turkmenistan,5.636,1.016,1.533,0.517,0.417,0.199,0.037 +69,Hungary,5.620,1.171,1.401,0.732,0.259,0.061,0.022 +70,Libya,5.566,0.985,1.350,0.553,0.496,0.116,0.148 +71,Philippines,5.524,0.775,1.312,0.513,0.643,0.120,0.105 +72,Honduras,5.504,0.620,1.205,0.622,0.459,0.197,0.074 +73,Belarus,5.483,1.039,1.498,0.700,0.307,0.101,0.154 +74,Turkey,5.483,1.148,1.380,0.686,0.324,0.106,0.109 +75,Pakistan,5.472,0.652,0.810,0.424,0.334,0.216,0.113 +76,Hong Kong,5.430,1.405,1.290,1.030,0.524,0.246,0.291 +77,Portugal,5.410,1.188,1.429,0.884,0.562,0.055,0.017 +78,Serbia,5.398,0.975,1.369,0.685,0.288,0.134,0.043 +79,Greece,5.358,1.154,1.202,0.879,0.131,0.000,0.044 +80,Lebanon,5.358,0.965,1.179,0.785,0.503,0.214,0.136 +81,Montenegro,5.347,1.017,1.279,0.729,0.259,0.111,0.081 +82,Croatia,5.321,1.115,1.161,0.737,0.380,0.120,0.039 +83,Dominican Republic,5.302,0.982,1.441,0.614,0.578,0.120,0.106 +84,Algeria,5.295,0.979,1.154,0.687,0.077,0.055,0.135 +85,Morocco,5.254,0.779,0.797,0.669,0.460,0.026,0.074 +86,China,5.246,0.989,1.142,0.799,0.597,0.029,0.103 +87,Azerbaijan,5.201,1.024,1.161,0.603,0.430,0.031,0.176 +88,Tajikistan,5.199,0.474,1.166,0.598,0.292,0.187,0.034 +89,Macedonia,5.185,0.959,1.239,0.691,0.394,0.173,0.052 +90,Jordan,5.161,0.822,1.265,0.645,0.468,0.130,0.134 +91,Nigeria,5.155,0.689,1.172,0.048,0.462,0.201,0.032 +92,Kyrgyzstan,5.131,0.530,1.416,0.594,0.540,0.281,0.035 +93,Bosnia and Herzegovina,5.129,0.915,1.078,0.758,0.280,0.216,0.000 +94,Mongolia,5.125,0.914,1.517,0.575,0.395,0.253,0.032 +95,Vietnam,5.103,0.715,1.365,0.702,0.618,0.177,0.079 +96,Indonesia,5.093,0.899,1.215,0.522,0.538,0.484,0.018 +97,Bhutan,5.082,0.796,1.335,0.527,0.541,0.364,0.171 +98,Somalia,4.982,0.000,0.712,0.115,0.674,0.238,0.282 +99,Cameroon,4.975,0.535,0.891,0.182,0.454,0.183,0.043 +100,Bulgaria,4.933,1.054,1.515,0.712,0.359,0.064,0.009 +101,Nepal,4.880,0.425,1.228,0.539,0.526,0.302,0.078 +102,Venezuela,4.806,0.996,1.469,0.657,0.133,0.056,0.052 +103,Gabon,4.758,1.036,1.164,0.404,0.356,0.032,0.052 +104,Palestinian Territories,4.743,0.642,1.217,0.602,0.266,0.086,0.076 +105,South Africa,4.724,0.940,1.410,0.330,0.516,0.103,0.056 +106,Iran,4.707,1.059,0.771,0.691,0.459,0.282,0.129 +107,Ivory Coast,4.671,0.541,0.872,0.080,0.467,0.146,0.103 +108,Ghana,4.657,0.592,0.896,0.337,0.499,0.212,0.029 +109,Senegal,4.631,0.429,1.117,0.433,0.406,0.138,0.082 +110,Laos,4.623,0.720,1.034,0.441,0.626,0.230,0.174 +111,Tunisia,4.592,0.900,0.906,0.690,0.271,0.040,0.063 +112,Albania,4.586,0.916,0.817,0.790,0.419,0.149,0.032 +113,Sierra Leone,4.571,0.256,0.813,0.000,0.355,0.238,0.053 +114,Congo (Brazzaville),4.559,0.682,0.811,0.343,0.514,0.091,0.077 +115,Bangladesh,4.500,0.532,0.850,0.579,0.580,0.153,0.144 +116,Sri Lanka,4.471,0.918,1.314,0.672,0.585,0.307,0.050 +117,Iraq,4.456,1.010,0.971,0.536,0.304,0.148,0.095 +118,Mali,4.447,0.370,1.233,0.152,0.367,0.139,0.056 +119,Namibia,4.441,0.874,1.281,0.365,0.519,0.051,0.064 +120,Cambodia,4.433,0.549,1.088,0.457,0.696,0.256,0.065 +121,Burkina Faso,4.424,0.314,1.097,0.254,0.312,0.175,0.128 +122,Egypt,4.419,0.885,1.025,0.553,0.312,0.092,0.107 +123,Mozambique,4.417,0.198,0.902,0.173,0.531,0.206,0.158 +124,Kenya,4.410,0.493,1.048,0.454,0.504,0.352,0.055 +125,Zambia,4.377,0.562,1.047,0.295,0.503,0.221,0.082 +126,Mauritania,4.356,0.557,1.245,0.292,0.129,0.134,0.093 +127,Ethiopia,4.350,0.308,0.950,0.391,0.452,0.220,0.146 +128,Georgia,4.340,0.853,0.592,0.643,0.375,0.038,0.215 +129,Armenia,4.321,0.816,0.990,0.666,0.260,0.077,0.028 +130,Myanmar,4.308,0.682,1.174,0.429,0.580,0.598,0.178 +131,Chad,4.301,0.358,0.907,0.053,0.189,0.181,0.060 +132,Congo (Kinshasa),4.245,0.069,1.136,0.204,0.312,0.197,0.052 +133,India,4.190,0.721,0.747,0.485,0.539,0.172,0.093 +134,Niger,4.166,0.131,0.867,0.221,0.390,0.175,0.099 +135,Uganda,4.161,0.322,1.090,0.237,0.450,0.259,0.061 +136,Benin,4.141,0.378,0.372,0.240,0.440,0.163,0.067 +137,Sudan,4.139,0.605,1.240,0.312,0.016,0.134,0.082 +138,Ukraine,4.103,0.793,1.413,0.609,0.163,0.187,0.011 +139,Togo,3.999,0.259,0.474,0.253,0.434,0.158,0.101 +140,Guinea,3.964,0.344,0.792,0.211,0.394,0.185,0.094 +141,Lesotho,3.808,0.472,1.215,0.079,0.423,0.116,0.112 +142,Angola,3.795,0.730,1.125,0.269,0.000,0.079,0.061 +143,Madagascar,3.774,0.262,0.908,0.402,0.221,0.155,0.049 +144,Zimbabwe,3.692,0.357,1.094,0.248,0.406,0.132,0.099 +145,Afghanistan,3.632,0.332,0.537,0.255,0.085,0.191,0.036 +146,Botswana,3.590,1.017,1.174,0.417,0.557,0.042,0.092 +147,Malawi,3.587,0.186,0.541,0.306,0.531,0.210,0.080 +148,Haiti,3.582,0.315,0.714,0.289,0.025,0.392,0.104 +149,Liberia,3.495,0.076,0.858,0.267,0.419,0.206,0.030 +150,Syria,3.462,0.689,0.382,0.539,0.088,0.376,0.144 +151,Rwanda,3.408,0.332,0.896,0.400,0.636,0.200,0.444 +152,Yemen,3.355,0.442,1.073,0.343,0.244,0.083,0.064 +153,Tanzania,3.303,0.455,0.991,0.381,0.481,0.270,0.097 +154,South Sudan,3.254,0.337,0.608,0.177,0.112,0.224,0.106 +155,Central African Republic,3.083,0.024,0.000,0.010,0.305,0.218,0.038 +156,Burundi,2.905,0.091,0.627,0.145,0.065,0.149,0.076 diff --git a/exercicios/para-casa/2019.csv b/exercicios/para-casa/2019.csv new file mode 100644 index 0000000..1e6eea4 --- /dev/null +++ b/exercicios/para-casa/2019.csv @@ -0,0 +1,157 @@ +Overall rank,Country or region,Score,GDP per capita,Social support,Healthy life expectancy,Freedom to make life choices,Generosity,Perceptions of corruption +1,Finland,7.769,1.340,1.587,0.986,0.596,0.153,0.393 +2,Denmark,7.600,1.383,1.573,0.996,0.592,0.252,0.410 +3,Norway,7.554,1.488,1.582,1.028,0.603,0.271,0.341 +4,Iceland,7.494,1.380,1.624,1.026,0.591,0.354,0.118 +5,Netherlands,7.488,1.396,1.522,0.999,0.557,0.322,0.298 +6,Switzerland,7.480,1.452,1.526,1.052,0.572,0.263,0.343 +7,Sweden,7.343,1.387,1.487,1.009,0.574,0.267,0.373 +8,New Zealand,7.307,1.303,1.557,1.026,0.585,0.330,0.380 +9,Canada,7.278,1.365,1.505,1.039,0.584,0.285,0.308 +10,Austria,7.246,1.376,1.475,1.016,0.532,0.244,0.226 +11,Australia,7.228,1.372,1.548,1.036,0.557,0.332,0.290 +12,Costa Rica,7.167,1.034,1.441,0.963,0.558,0.144,0.093 +13,Israel,7.139,1.276,1.455,1.029,0.371,0.261,0.082 +14,Luxembourg,7.090,1.609,1.479,1.012,0.526,0.194,0.316 +15,United Kingdom,7.054,1.333,1.538,0.996,0.450,0.348,0.278 +16,Ireland,7.021,1.499,1.553,0.999,0.516,0.298,0.310 +17,Germany,6.985,1.373,1.454,0.987,0.495,0.261,0.265 +18,Belgium,6.923,1.356,1.504,0.986,0.473,0.160,0.210 +19,United States,6.892,1.433,1.457,0.874,0.454,0.280,0.128 +20,Czech Republic,6.852,1.269,1.487,0.920,0.457,0.046,0.036 +21,United Arab Emirates,6.825,1.503,1.310,0.825,0.598,0.262,0.182 +22,Malta,6.726,1.300,1.520,0.999,0.564,0.375,0.151 +23,Mexico,6.595,1.070,1.323,0.861,0.433,0.074,0.073 +24,France,6.592,1.324,1.472,1.045,0.436,0.111,0.183 +25,Taiwan,6.446,1.368,1.430,0.914,0.351,0.242,0.097 +26,Chile,6.444,1.159,1.369,0.920,0.357,0.187,0.056 +27,Guatemala,6.436,0.800,1.269,0.746,0.535,0.175,0.078 +28,Saudi Arabia,6.375,1.403,1.357,0.795,0.439,0.080,0.132 +29,Qatar,6.374,1.684,1.313,0.871,0.555,0.220,0.167 +30,Spain,6.354,1.286,1.484,1.062,0.362,0.153,0.079 +31,Panama,6.321,1.149,1.442,0.910,0.516,0.109,0.054 +32,Brazil,6.300,1.004,1.439,0.802,0.390,0.099,0.086 +33,Uruguay,6.293,1.124,1.465,0.891,0.523,0.127,0.150 +34,Singapore,6.262,1.572,1.463,1.141,0.556,0.271,0.453 +35,El Salvador,6.253,0.794,1.242,0.789,0.430,0.093,0.074 +36,Italy,6.223,1.294,1.488,1.039,0.231,0.158,0.030 +37,Bahrain,6.199,1.362,1.368,0.871,0.536,0.255,0.110 +38,Slovakia,6.198,1.246,1.504,0.881,0.334,0.121,0.014 +39,Trinidad & Tobago,6.192,1.231,1.477,0.713,0.489,0.185,0.016 +40,Poland,6.182,1.206,1.438,0.884,0.483,0.117,0.050 +41,Uzbekistan,6.174,0.745,1.529,0.756,0.631,0.322,0.240 +42,Lithuania,6.149,1.238,1.515,0.818,0.291,0.043,0.042 +43,Colombia,6.125,0.985,1.410,0.841,0.470,0.099,0.034 +44,Slovenia,6.118,1.258,1.523,0.953,0.564,0.144,0.057 +45,Nicaragua,6.105,0.694,1.325,0.835,0.435,0.200,0.127 +46,Kosovo,6.100,0.882,1.232,0.758,0.489,0.262,0.006 +47,Argentina,6.086,1.092,1.432,0.881,0.471,0.066,0.050 +48,Romania,6.070,1.162,1.232,0.825,0.462,0.083,0.005 +49,Cyprus,6.046,1.263,1.223,1.042,0.406,0.190,0.041 +50,Ecuador,6.028,0.912,1.312,0.868,0.498,0.126,0.087 +51,Kuwait,6.021,1.500,1.319,0.808,0.493,0.142,0.097 +52,Thailand,6.008,1.050,1.409,0.828,0.557,0.359,0.028 +53,Latvia,5.940,1.187,1.465,0.812,0.264,0.075,0.064 +54,South Korea,5.895,1.301,1.219,1.036,0.159,0.175,0.056 +55,Estonia,5.893,1.237,1.528,0.874,0.495,0.103,0.161 +56,Jamaica,5.890,0.831,1.478,0.831,0.490,0.107,0.028 +57,Mauritius,5.888,1.120,1.402,0.798,0.498,0.215,0.060 +58,Japan,5.886,1.327,1.419,1.088,0.445,0.069,0.140 +59,Honduras,5.860,0.642,1.236,0.828,0.507,0.246,0.078 +60,Kazakhstan,5.809,1.173,1.508,0.729,0.410,0.146,0.096 +61,Bolivia,5.779,0.776,1.209,0.706,0.511,0.137,0.064 +62,Hungary,5.758,1.201,1.410,0.828,0.199,0.081,0.020 +63,Paraguay,5.743,0.855,1.475,0.777,0.514,0.184,0.080 +64,Northern Cyprus,5.718,1.263,1.252,1.042,0.417,0.191,0.162 +65,Peru,5.697,0.960,1.274,0.854,0.455,0.083,0.027 +66,Portugal,5.693,1.221,1.431,0.999,0.508,0.047,0.025 +67,Pakistan,5.653,0.677,0.886,0.535,0.313,0.220,0.098 +68,Russia,5.648,1.183,1.452,0.726,0.334,0.082,0.031 +69,Philippines,5.631,0.807,1.293,0.657,0.558,0.117,0.107 +70,Serbia,5.603,1.004,1.383,0.854,0.282,0.137,0.039 +71,Moldova,5.529,0.685,1.328,0.739,0.245,0.181,0.000 +72,Libya,5.525,1.044,1.303,0.673,0.416,0.133,0.152 +73,Montenegro,5.523,1.051,1.361,0.871,0.197,0.142,0.080 +74,Tajikistan,5.467,0.493,1.098,0.718,0.389,0.230,0.144 +75,Croatia,5.432,1.155,1.266,0.914,0.296,0.119,0.022 +76,Hong Kong,5.430,1.438,1.277,1.122,0.440,0.258,0.287 +77,Dominican Republic,5.425,1.015,1.401,0.779,0.497,0.113,0.101 +78,Bosnia and Herzegovina,5.386,0.945,1.212,0.845,0.212,0.263,0.006 +79,Turkey,5.373,1.183,1.360,0.808,0.195,0.083,0.106 +80,Malaysia,5.339,1.221,1.171,0.828,0.508,0.260,0.024 +81,Belarus,5.323,1.067,1.465,0.789,0.235,0.094,0.142 +82,Greece,5.287,1.181,1.156,0.999,0.067,0.000,0.034 +83,Mongolia,5.285,0.948,1.531,0.667,0.317,0.235,0.038 +84,North Macedonia,5.274,0.983,1.294,0.838,0.345,0.185,0.034 +85,Nigeria,5.265,0.696,1.111,0.245,0.426,0.215,0.041 +86,Kyrgyzstan,5.261,0.551,1.438,0.723,0.508,0.300,0.023 +87,Turkmenistan,5.247,1.052,1.538,0.657,0.394,0.244,0.028 +88,Algeria,5.211,1.002,1.160,0.785,0.086,0.073,0.114 +89,Morocco,5.208,0.801,0.782,0.782,0.418,0.036,0.076 +90,Azerbaijan,5.208,1.043,1.147,0.769,0.351,0.035,0.182 +91,Lebanon,5.197,0.987,1.224,0.815,0.216,0.166,0.027 +92,Indonesia,5.192,0.931,1.203,0.660,0.491,0.498,0.028 +93,China,5.191,1.029,1.125,0.893,0.521,0.058,0.100 +94,Vietnam,5.175,0.741,1.346,0.851,0.543,0.147,0.073 +95,Bhutan,5.082,0.813,1.321,0.604,0.457,0.370,0.167 +96,Cameroon,5.044,0.549,0.910,0.331,0.381,0.187,0.037 +97,Bulgaria,5.011,1.092,1.513,0.815,0.311,0.081,0.004 +98,Ghana,4.996,0.611,0.868,0.486,0.381,0.245,0.040 +99,Ivory Coast,4.944,0.569,0.808,0.232,0.352,0.154,0.090 +100,Nepal,4.913,0.446,1.226,0.677,0.439,0.285,0.089 +101,Jordan,4.906,0.837,1.225,0.815,0.383,0.110,0.130 +102,Benin,4.883,0.393,0.437,0.397,0.349,0.175,0.082 +103,Congo (Brazzaville),4.812,0.673,0.799,0.508,0.372,0.105,0.093 +104,Gabon,4.799,1.057,1.183,0.571,0.295,0.043,0.055 +105,Laos,4.796,0.764,1.030,0.551,0.547,0.266,0.164 +106,South Africa,4.722,0.960,1.351,0.469,0.389,0.130,0.055 +107,Albania,4.719,0.947,0.848,0.874,0.383,0.178,0.027 +108,Venezuela,4.707,0.960,1.427,0.805,0.154,0.064,0.047 +109,Cambodia,4.700,0.574,1.122,0.637,0.609,0.232,0.062 +110,Palestinian Territories,4.696,0.657,1.247,0.672,0.225,0.103,0.066 +111,Senegal,4.681,0.450,1.134,0.571,0.292,0.153,0.072 +112,Somalia,4.668,0.000,0.698,0.268,0.559,0.243,0.270 +113,Namibia,4.639,0.879,1.313,0.477,0.401,0.070,0.056 +114,Niger,4.628,0.138,0.774,0.366,0.318,0.188,0.102 +115,Burkina Faso,4.587,0.331,1.056,0.380,0.255,0.177,0.113 +116,Armenia,4.559,0.850,1.055,0.815,0.283,0.095,0.064 +117,Iran,4.548,1.100,0.842,0.785,0.305,0.270,0.125 +118,Guinea,4.534,0.380,0.829,0.375,0.332,0.207,0.086 +119,Georgia,4.519,0.886,0.666,0.752,0.346,0.043,0.164 +120,Gambia,4.516,0.308,0.939,0.428,0.382,0.269,0.167 +121,Kenya,4.509,0.512,0.983,0.581,0.431,0.372,0.053 +122,Mauritania,4.490,0.570,1.167,0.489,0.066,0.106,0.088 +123,Mozambique,4.466,0.204,0.986,0.390,0.494,0.197,0.138 +124,Tunisia,4.461,0.921,1.000,0.815,0.167,0.059,0.055 +125,Bangladesh,4.456,0.562,0.928,0.723,0.527,0.166,0.143 +126,Iraq,4.437,1.043,0.980,0.574,0.241,0.148,0.089 +127,Congo (Kinshasa),4.418,0.094,1.125,0.357,0.269,0.212,0.053 +128,Mali,4.390,0.385,1.105,0.308,0.327,0.153,0.052 +129,Sierra Leone,4.374,0.268,0.841,0.242,0.309,0.252,0.045 +130,Sri Lanka,4.366,0.949,1.265,0.831,0.470,0.244,0.047 +131,Myanmar,4.360,0.710,1.181,0.555,0.525,0.566,0.172 +132,Chad,4.350,0.350,0.766,0.192,0.174,0.198,0.078 +133,Ukraine,4.332,0.820,1.390,0.739,0.178,0.187,0.010 +134,Ethiopia,4.286,0.336,1.033,0.532,0.344,0.209,0.100 +135,Swaziland,4.212,0.811,1.149,0.000,0.313,0.074,0.135 +136,Uganda,4.189,0.332,1.069,0.443,0.356,0.252,0.060 +137,Egypt,4.166,0.913,1.039,0.644,0.241,0.076,0.067 +138,Zambia,4.107,0.578,1.058,0.426,0.431,0.247,0.087 +139,Togo,4.085,0.275,0.572,0.410,0.293,0.177,0.085 +140,India,4.015,0.755,0.765,0.588,0.498,0.200,0.085 +141,Liberia,3.975,0.073,0.922,0.443,0.370,0.233,0.033 +142,Comoros,3.973,0.274,0.757,0.505,0.142,0.275,0.078 +143,Madagascar,3.933,0.274,0.916,0.555,0.148,0.169,0.041 +144,Lesotho,3.802,0.489,1.169,0.168,0.359,0.107,0.093 +145,Burundi,3.775,0.046,0.447,0.380,0.220,0.176,0.180 +146,Zimbabwe,3.663,0.366,1.114,0.433,0.361,0.151,0.089 +147,Haiti,3.597,0.323,0.688,0.449,0.026,0.419,0.110 +148,Botswana,3.488,1.041,1.145,0.538,0.455,0.025,0.100 +149,Syria,3.462,0.619,0.378,0.440,0.013,0.331,0.141 +150,Malawi,3.410,0.191,0.560,0.495,0.443,0.218,0.089 +151,Yemen,3.380,0.287,1.163,0.463,0.143,0.108,0.077 +152,Rwanda,3.334,0.359,0.711,0.614,0.555,0.217,0.411 +153,Tanzania,3.231,0.476,0.885,0.499,0.417,0.276,0.147 +154,Afghanistan,3.203,0.350,0.517,0.361,0.000,0.158,0.025 +155,Central African Republic,3.083,0.026,0.000,0.105,0.225,0.235,0.035 +156,South Sudan,2.853,0.306,0.575,0.295,0.010,0.202,0.091 diff --git a/exercicios/para-casa/Projeto_II.ipynb b/exercicios/para-casa/Projeto_II.ipynb new file mode 100644 index 0000000..0d75cbd --- /dev/null +++ b/exercicios/para-casa/Projeto_II.ipynb @@ -0,0 +1,1123 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "df_2017 = pd.read_csv(\"2017.csv\")\n", + "df_2018 = pd.read_csv(\"2018.csv\")\n", + "df_2019 = pd.read_csv(\"2019.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dados de 2017:\n", + " Country Happiness.Rank Happiness.Score Whisker.high Whisker.low \\\n", + "0 Norway 1 7.537 7.594445 7.479556 \n", + "1 Denmark 2 7.522 7.581728 7.462272 \n", + "2 Iceland 3 7.504 7.622030 7.385970 \n", + "3 Switzerland 4 7.494 7.561772 7.426227 \n", + "4 Finland 5 7.469 7.527542 7.410458 \n", + "5 Netherlands 6 7.377 7.427426 7.326574 \n", + "6 Canada 7 7.316 7.384403 7.247597 \n", + "7 New Zealand 8 7.314 7.379510 7.248490 \n", + "8 Sweden 9 7.284 7.344095 7.223905 \n", + "9 Australia 10 7.284 7.356651 7.211349 \n", + "\n", + " Economy..GDP.per.Capita. Family Health..Life.Expectancy. Freedom \\\n", + "0 1.616463 1.533524 0.796667 0.635423 \n", + "1 1.482383 1.551122 0.792566 0.626007 \n", + "2 1.480633 1.610574 0.833552 0.627163 \n", + "3 1.564980 1.516912 0.858131 0.620071 \n", + "4 1.443572 1.540247 0.809158 0.617951 \n", + "5 1.503945 1.428939 0.810696 0.585384 \n", + "6 1.479204 1.481349 0.834558 0.611101 \n", + "7 1.405706 1.548195 0.816760 0.614062 \n", + "8 1.494387 1.478162 0.830875 0.612924 \n", + "9 1.484415 1.510042 0.843887 0.601607 \n", + "\n", + " Generosity Trust..Government.Corruption. Dystopia.Residual \n", + "0 0.362012 0.315964 2.277027 \n", + "1 0.355280 0.400770 2.313707 \n", + "2 0.475540 0.153527 2.322715 \n", + "3 0.290549 0.367007 2.276716 \n", + "4 0.245483 0.382612 2.430182 \n", + "5 0.470490 0.282662 2.294804 \n", + "6 0.435540 0.287372 2.187264 \n", + "7 0.500005 0.382817 2.046456 \n", + "8 0.385399 0.384399 2.097538 \n", + "9 0.477699 0.301184 2.065211 \n", + "\n", + "Dados de 2018:\n", + " Overall rank Country or region Score GDP per capita Social support \\\n", + "0 1 Finland 7.632 1.305 1.592 \n", + "1 2 Norway 7.594 1.456 1.582 \n", + "2 3 Denmark 7.555 1.351 1.590 \n", + "3 4 Iceland 7.495 1.343 1.644 \n", + "4 5 Switzerland 7.487 1.420 1.549 \n", + "5 6 Netherlands 7.441 1.361 1.488 \n", + "6 7 Canada 7.328 1.330 1.532 \n", + "7 8 New Zealand 7.324 1.268 1.601 \n", + "8 9 Sweden 7.314 1.355 1.501 \n", + "9 10 Australia 7.272 1.340 1.573 \n", + "\n", + " Healthy life expectancy Freedom to make life choices Generosity \\\n", + "0 0.874 0.681 0.202 \n", + "1 0.861 0.686 0.286 \n", + "2 0.868 0.683 0.284 \n", + "3 0.914 0.677 0.353 \n", + "4 0.927 0.660 0.256 \n", + "5 0.878 0.638 0.333 \n", + "6 0.896 0.653 0.321 \n", + "7 0.876 0.669 0.365 \n", + "8 0.913 0.659 0.285 \n", + "9 0.910 0.647 0.361 \n", + "\n", + " Perceptions of corruption \n", + "0 0.393 \n", + "1 0.340 \n", + "2 0.408 \n", + "3 0.138 \n", + "4 0.357 \n", + "5 0.295 \n", + "6 0.291 \n", + "7 0.389 \n", + "8 0.383 \n", + "9 0.302 \n", + "\n", + "Dados de 2019:\n", + " Overall rank Country or region Score GDP per capita Social support \\\n", + "0 1 Finland 7.769 1.340 1.587 \n", + "1 2 Denmark 7.600 1.383 1.573 \n", + "2 3 Norway 7.554 1.488 1.582 \n", + "3 4 Iceland 7.494 1.380 1.624 \n", + "4 5 Netherlands 7.488 1.396 1.522 \n", + "5 6 Switzerland 7.480 1.452 1.526 \n", + "6 7 Sweden 7.343 1.387 1.487 \n", + "7 8 New Zealand 7.307 1.303 1.557 \n", + "8 9 Canada 7.278 1.365 1.505 \n", + "9 10 Austria 7.246 1.376 1.475 \n", + "\n", + " Healthy life expectancy Freedom to make life choices Generosity \\\n", + "0 0.986 0.596 0.153 \n", + "1 0.996 0.592 0.252 \n", + "2 1.028 0.603 0.271 \n", + "3 1.026 0.591 0.354 \n", + "4 0.999 0.557 0.322 \n", + "5 1.052 0.572 0.263 \n", + "6 1.009 0.574 0.267 \n", + "7 1.026 0.585 0.330 \n", + "8 1.039 0.584 0.285 \n", + "9 1.016 0.532 0.244 \n", + "\n", + " Perceptions of corruption \n", + "0 0.393 \n", + "1 0.410 \n", + "2 0.341 \n", + "3 0.118 \n", + "4 0.298 \n", + "5 0.343 \n", + "6 0.373 \n", + "7 0.380 \n", + "8 0.308 \n", + "9 0.226 \n" + ] + } + ], + "source": [ + "print(\"Dados de 2017:\")\n", + "print(df_2017.head(10))\n", + "\n", + "print(\"\\nDados de 2018:\")\n", + "print(df_2018.head(10))\n", + "\n", + "print(\"\\nDados de 2019:\")\n", + "print(df_2019.head(10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chamar o shape para verificar o número de linhas e colunas do DataFrame analisado." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tamanho dos dados de 2017:\n", + "(155, 12)\n", + "\n", + "Tamanho dos dados de 2018:\n", + "(156, 9)\n", + "\n", + "Tamanho dos dados de 2019:\n", + "(156, 9)\n" + ] + } + ], + "source": [ + "print(\"Tamanho dos dados de 2017:\")\n", + "print(df_2017.shape)\n", + "\n", + "print(\"\\nTamanho dos dados de 2018:\")\n", + "print(df_2018.shape)\n", + "\n", + "print(\"\\nTamanho dos dados de 2019:\")\n", + "print(df_2019.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Contar dados nulos por linha no DataFrame." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Seguindo, contar dados nulos por linha em um DataFrame, referentes aos anos de 2017, 2018 e 2019.\n", + "Obs: df.isnull() cria um DataFrame de valores booleanos onde True indica um valor nulo, ou seja: 0 para ausência, 1 para presença.\n", + ".sum(axis=1) soma os valores True (que representam os nulos) ao longo de cada linha. \n", + "O argumento axis=1 especifica que a soma deve ser feita horizontalmente, ou seja, por linha." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "nulos_por_linha_2017 = df_2017.isnull().sum(axis=1)\n", + "nulos_por_linha_2018 = df_2018.isnull().sum(axis=1)\n", + "nulos_por_linha_2019 = df_2019.isnull().sum(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dados nulos por linha em 2017:\n", + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + " ..\n", + "150 0\n", + "151 0\n", + "152 0\n", + "153 0\n", + "154 0\n", + "Length: 155, dtype: int64\n", + "\n", + "Dados nulos por linha em 2018:\n", + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + " ..\n", + "151 0\n", + "152 0\n", + "153 0\n", + "154 0\n", + "155 0\n", + "Length: 156, dtype: int64\n", + "\n", + "Dados nulos por linha em 2019:\n", + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + " ..\n", + "151 0\n", + "152 0\n", + "153 0\n", + "154 0\n", + "155 0\n", + "Length: 156, dtype: int64\n" + ] + } + ], + "source": [ + "print(\"Dados nulos por linha em 2017:\")\n", + "print(nulos_por_linha_2017)\n", + "\n", + "print(\"\\nDados nulos por linha em 2018:\")\n", + "print(nulos_por_linha_2018)\n", + "\n", + "print(\"\\nDados nulos por linha em 2019:\")\n", + "print(nulos_por_linha_2019)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O df.isnull() é utilizado para retornar um DataFrame do mesmo tamanho, com valores booleanos, onde True indica que o valor é nulo.\n", + "Já o df.isnull().sum() é utilizado para somar os valores True para cada coluna, resultando no número de valores nulos por coluna.\n", + "Abaixo, segue o número de valores nulos para cada coluna em cada um dos DataFrames de 2017, 2018, e 2019. Então o valor 0 impressos nas colunas significa a ausência de valores nulos e 1 a presença" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "nulos_por_coluna_2017 = df_2017.isnull().sum()\n", + "nulos_por_coluna_2018 = df_2018.isnull().sum()\n", + "nulos_por_coluna_2019 = df_2019.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dados nulos por coluna em 2017:\n", + "Country 0\n", + "Happiness.Rank 0\n", + "Happiness.Score 0\n", + "Whisker.high 0\n", + "Whisker.low 0\n", + "Economy..GDP.per.Capita. 0\n", + "Family 0\n", + "Health..Life.Expectancy. 0\n", + "Freedom 0\n", + "Generosity 0\n", + "Trust..Government.Corruption. 0\n", + "Dystopia.Residual 0\n", + "dtype: int64\n", + "\n", + "Dados nulos por coluna em 2018:\n", + "Overall rank 0\n", + "Country or region 0\n", + "Score 0\n", + "GDP per capita 0\n", + "Social support 0\n", + "Healthy life expectancy 0\n", + "Freedom to make life choices 0\n", + "Generosity 0\n", + "Perceptions of corruption 1\n", + "dtype: int64\n", + "\n", + "Dados nulos por coluna em 2019:\n", + "Overall rank 0\n", + "Country or region 0\n", + "Score 0\n", + "GDP per capita 0\n", + "Social support 0\n", + "Healthy life expectancy 0\n", + "Freedom to make life choices 0\n", + "Generosity 0\n", + "Perceptions of corruption 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(\"Dados nulos por coluna em 2017:\")\n", + "print(nulos_por_coluna_2017)\n", + "\n", + "print(\"\\nDados nulos por coluna em 2018:\")\n", + "print(nulos_por_coluna_2018)\n", + "\n", + "print(\"\\nDados nulos por coluna em 2019:\")\n", + "print(nulos_por_coluna_2019)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vamos descrever estatíticamente os dados, considerando o DataFrame por ano?\n", + "O .describe()' é um método que quando utilizado fornece estatísticas como a contagem (count), média (mean), desvio padrão (std), valores mínimo (min), máximo (max), e os percentis (25%, 50%, 75%). Esse processo retorna um DataFrame com as estatísticas descritivas para as colunas numéricas." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "descricao_2017 = df_2017.describe()\n", + "descricao_2018 = df_2018.describe()\n", + "descricao_2019 = df_2019.describe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descrição dos dados em 2017:\n", + " Happiness.Rank Happiness.Score Whisker.high Whisker.low \\\n", + "count 155.000000 155.000000 155.000000 155.000000 \n", + "mean 78.000000 5.354019 5.452326 5.255713 \n", + "std 44.888751 1.131230 1.118542 1.145030 \n", + "min 1.000000 2.693000 2.864884 2.521116 \n", + "25% 39.500000 4.505500 4.608172 4.374955 \n", + "50% 78.000000 5.279000 5.370032 5.193152 \n", + "75% 116.500000 6.101500 6.194600 6.006527 \n", + "max 155.000000 7.537000 7.622030 7.479556 \n", + "\n", + " Economy..GDP.per.Capita. Family Health..Life.Expectancy. \\\n", + "count 155.000000 155.000000 155.000000 \n", + "mean 0.984718 1.188898 0.551341 \n", + "std 0.420793 0.287263 0.237073 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.663371 1.042635 0.369866 \n", + "50% 1.064578 1.253918 0.606042 \n", + "75% 1.318027 1.414316 0.723008 \n", + "max 1.870766 1.610574 0.949492 \n", + "\n", + " Freedom Generosity Trust..Government.Corruption. \\\n", + "count 155.000000 155.000000 155.000000 \n", + "mean 0.408786 0.246883 0.123120 \n", + "std 0.149997 0.134780 0.101661 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.303677 0.154106 0.057271 \n", + "50% 0.437454 0.231538 0.089848 \n", + "75% 0.516561 0.323762 0.153296 \n", + "max 0.658249 0.838075 0.464308 \n", + "\n", + " Dystopia.Residual \n", + "count 155.000000 \n", + "mean 1.850238 \n", + "std 0.500028 \n", + "min 0.377914 \n", + "25% 1.591291 \n", + "50% 1.832910 \n", + "75% 2.144654 \n", + "max 3.117485 \n", + "\n", + "Descrição dos dados em 2018:\n", + " Overall rank Score GDP per capita Social support \\\n", + "count 156.000000 156.000000 156.000000 156.000000 \n", + "mean 78.500000 5.375917 0.891449 1.213237 \n", + "std 45.177428 1.119506 0.391921 0.302372 \n", + "min 1.000000 2.905000 0.000000 0.000000 \n", + "25% 39.750000 4.453750 0.616250 1.066750 \n", + "50% 78.500000 5.378000 0.949500 1.255000 \n", + "75% 117.250000 6.168500 1.197750 1.463000 \n", + "max 156.000000 7.632000 2.096000 1.644000 \n", + "\n", + " Healthy life expectancy Freedom to make life choices Generosity \\\n", + "count 156.000000 156.000000 156.000000 \n", + "mean 0.597346 0.454506 0.181006 \n", + "std 0.247579 0.162424 0.098471 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.422250 0.356000 0.109500 \n", + "50% 0.644000 0.487000 0.174000 \n", + "75% 0.777250 0.578500 0.239000 \n", + "max 1.030000 0.724000 0.598000 \n", + "\n", + " Perceptions of corruption \n", + "count 155.000000 \n", + "mean 0.112000 \n", + "std 0.096492 \n", + "min 0.000000 \n", + "25% 0.051000 \n", + "50% 0.082000 \n", + "75% 0.137000 \n", + "max 0.457000 \n", + "\n", + "Descrição dos dados em 2019:\n", + " Overall rank Score GDP per capita Social support \\\n", + "count 156.000000 156.000000 156.000000 156.000000 \n", + "mean 78.500000 5.407096 0.905147 1.208814 \n", + "std 45.177428 1.113120 0.398389 0.299191 \n", + "min 1.000000 2.853000 0.000000 0.000000 \n", + "25% 39.750000 4.544500 0.602750 1.055750 \n", + "50% 78.500000 5.379500 0.960000 1.271500 \n", + "75% 117.250000 6.184500 1.232500 1.452500 \n", + "max 156.000000 7.769000 1.684000 1.624000 \n", + "\n", + " Healthy life expectancy Freedom to make life choices Generosity \\\n", + "count 156.000000 156.000000 156.000000 \n", + "mean 0.725244 0.392571 0.184846 \n", + "std 0.242124 0.143289 0.095254 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.547750 0.308000 0.108750 \n", + "50% 0.789000 0.417000 0.177500 \n", + "75% 0.881750 0.507250 0.248250 \n", + "max 1.141000 0.631000 0.566000 \n", + "\n", + " Perceptions of corruption \n", + "count 156.000000 \n", + "mean 0.110603 \n", + "std 0.094538 \n", + "min 0.000000 \n", + "25% 0.047000 \n", + "50% 0.085500 \n", + "75% 0.141250 \n", + "max 0.453000 \n" + ] + } + ], + "source": [ + "print(\"Descrição dos dados em 2017:\")\n", + "print(descricao_2017)\n", + "\n", + "print(\"\\nDescrição dos dados em 2018:\")\n", + "print(descricao_2018)\n", + "\n", + "print(\"\\nDescrição dos dados em 2019:\")\n", + "print(descricao_2019)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para verificar a informação do objeto" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Informação do DataFrame para 2017:\n", + "\n", + "RangeIndex: 155 entries, 0 to 154\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Country 155 non-null object \n", + " 1 Happiness.Rank 155 non-null int64 \n", + " 2 Happiness.Score 155 non-null float64\n", + " 3 Whisker.high 155 non-null float64\n", + " 4 Whisker.low 155 non-null float64\n", + " 5 Economy..GDP.per.Capita. 155 non-null float64\n", + " 6 Family 155 non-null float64\n", + " 7 Health..Life.Expectancy. 155 non-null float64\n", + " 8 Freedom 155 non-null float64\n", + " 9 Generosity 155 non-null float64\n", + " 10 Trust..Government.Corruption. 155 non-null float64\n", + " 11 Dystopia.Residual 155 non-null float64\n", + "dtypes: float64(10), int64(1), object(1)\n", + "memory usage: 14.7+ KB\n", + "\n", + "Informação do DataFrame para 2018:\n", + "\n", + "RangeIndex: 156 entries, 0 to 155\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Overall rank 156 non-null int64 \n", + " 1 Country or region 156 non-null object \n", + " 2 Score 156 non-null float64\n", + " 3 GDP per capita 156 non-null float64\n", + " 4 Social support 156 non-null float64\n", + " 5 Healthy life expectancy 156 non-null float64\n", + " 6 Freedom to make life choices 156 non-null float64\n", + " 7 Generosity 156 non-null float64\n", + " 8 Perceptions of corruption 155 non-null float64\n", + "dtypes: float64(7), int64(1), object(1)\n", + "memory usage: 11.1+ KB\n", + "\n", + "Informação do DataFrame para 2019:\n", + "\n", + "RangeIndex: 156 entries, 0 to 155\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Overall rank 156 non-null int64 \n", + " 1 Country or region 156 non-null object \n", + " 2 Score 156 non-null float64\n", + " 3 GDP per capita 156 non-null float64\n", + " 4 Social support 156 non-null float64\n", + " 5 Healthy life expectancy 156 non-null float64\n", + " 6 Freedom to make life choices 156 non-null float64\n", + " 7 Generosity 156 non-null float64\n", + " 8 Perceptions of corruption 156 non-null float64\n", + "dtypes: float64(7), int64(1), object(1)\n", + "memory usage: 11.1+ KB\n" + ] + } + ], + "source": [ + "print(\"\\nInformação do DataFrame para 2017:\")\n", + "info_2017 = df_2017.info()\n", + "\n", + "print(\"\\nInformação do DataFrame para 2018:\")\n", + "info_2018 = df_2018.info()\n", + "\n", + "print(\"\\nInformação do DataFrame para 2019:\")\n", + "info_2019 = df_2019.info()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "non-null: quantidade de dados válidos (não ausentes) em cada coluna;\n", + "int64: indica que a coluna contém dados inteiros (números inteiros) armazenados em 64 bits;\n", + "object:Refere-se a colunas que contêm dados de tipo texto (strings) ou dados mistos (podem conter textos e números, mas tratados como texto). Em pandas, object é o tipo de dados usado para strings;\n", + "float64:Indica que a coluna contém dados numéricos de ponto flutuante (números decimais) armazenados em 64 bits. Isso é usado para representar números com casas decimais\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Removendo linhas duplicadas do DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "df_2017 = df_2017.drop_duplicates()\n", + "df_2018 = df_2018.drop_duplicates()\n", + "df_2019 = df_2019.drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(156, 9)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2017.shape\n", + "df_2018.shape\n", + "df_2019.shape\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualizar colunas duplicadas do Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty DataFrame\n", + "Columns: [Country, Happiness.Rank, Happiness.Score, Whisker.high, Whisker.low, Economy..GDP.per.Capita., Family, Health..Life.Expectancy., Freedom, Generosity, Trust..Government.Corruption., Dystopia.Residual]\n", + "Index: []\n" + ] + } + ], + "source": [ + "def visualiza_as_duplicadas(df_2017):\n", + " duplicados = df_2017[df_2017.duplicated(keep=False)]\n", + " return duplicados\n", + "linhas_duplicadas_2017 = visualiza_as_duplicadas(df_2017)\n", + "print(linhas_duplicadas_2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty DataFrame\n", + "Columns: [Overall rank, Country or region, Score, GDP per capita, Social support, Healthy life expectancy, Freedom to make life choices, Generosity, Perceptions of corruption]\n", + "Index: []\n" + ] + } + ], + "source": [ + "def visualiza_as_duplicadas(df_2018):\n", + " duplicados = df_2018[df_2018.duplicated(keep=False)]\n", + " return duplicados\n", + "linhas_duplicadas_2018 = visualiza_as_duplicadas(df_2018)\n", + "print(linhas_duplicadas_2018)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty DataFrame\n", + "Columns: [Overall rank, Country or region, Score, GDP per capita, Social support, Healthy life expectancy, Freedom to make life choices, Generosity, Perceptions of corruption]\n", + "Index: []\n" + ] + } + ], + "source": [ + "def visualiza_as_duplicadas(df_2019):\n", + " duplicados = df_2019[df_2019.duplicated(keep=False)]\n", + " return duplicados\n", + "linhas_duplicadas_2019 = visualiza_as_duplicadas(df_2019)\n", + "print(linhas_duplicadas_2019)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coluna duplicada: Generosity\n", + "Coluna duplicada: Overall rank\n", + "Coluna duplicada: Country or region\n", + "Coluna duplicada: Score\n", + "Coluna duplicada: GDP per capita\n", + "Coluna duplicada: Social support\n", + "Coluna duplicada: Healthy life expectancy\n", + "Coluna duplicada: Freedom to make life choices\n", + "Coluna duplicada: Generosity\n", + "Coluna duplicada: Perceptions of corruption\n" + ] + } + ], + "source": [ + "colunas_unicas = set()\n", + "\n", + "for df in [df_2017, df_2018, df_2019]:\n", + " for coluna in df.columns:\n", + " if coluna in colunas_unicas:\n", + " print(f\"Coluna duplicada: {coluna}\")\n", + " else:\n", + " colunas_unicas.add(coluna)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Generosity', 'Overall rank', 'Country or region', 'Score',\n", + " 'GDP per capita', 'Social support', 'Healthy life expectancy',\n", + " 'Freedom to make life choices', 'Generosity',\n", + " 'Perceptions of corruption'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "df_concatenado = pd.concat([df_2017, df_2018, df_2019], axis=1)\n", + "colunas_duplicadas = df_concatenado.columns[df_concatenado.columns.duplicated()]\n", + "print(colunas_duplicadas)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# Removendo as colunas duplicadas do DataFrame concatenado\n", + "df_concatenado = df_concatenado.loc[:, ~df_concatenado.columns.duplicated()]\n", + "\n", + "# Separando os DataFrames novamente (se necessário)\n", + "df_2017_novo = df_concatenado.iloc[:, :df_2017.shape[1]]\n", + "df_2018_novo = df_concatenado.iloc[:, df_2017.shape[1]:df_2017.shape[1] + df_2018.shape[1]]\n", + "df_2019_novo = df_concatenado.iloc[:, df_2017.shape[1] + df_2018.shape[1]:]" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(156, 9)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2017.shape\n", + "df_2018.shape\n", + "df_2019.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "# Salvando os DataFrames sem duplicatas (opcional)\n", + "df_2017_novo.to_csv('dados_2017_limpos.csv', index=False)\n", + "df_2018_novo.to_csv('dados_2018_limpos.csv', index=False)\n", + "df_2019_novo.to_csv('dados_2019_limpos.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Qual o país tem a menor taxa de felicidades (Happiness.Rank)?" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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xbNmyYotkAUC9evUAXFvg6p9Onz6N+vXrlzuGikhKSkJgYCCioqLKfE5OTg7uueeeKpl8E9GdhZNhIiK6Y61atQr5+flYuXIlxo4di/j4eMTGxpb5H7WHDh0qMYEoukbs9av4Av/v6G2RS5cu4fTp0yWedzOaNm0K4NqRx6KJ6D9v15+iXa9ePYwbNw4rVqxAZmYmfH19SxxlCwsLw6RJk/Dbb79h06ZNOHnyJGbPnl3psdWrVw8vvPACVq1aha1bt6r3L1u2DMOHD8dHH32Efv364V//+hc6dOiAixcv3lyE/5+fnx/Wrl0LNzc3dO3atczTwUtT0e9n9+7dUbt2bSxevBjff/89rly5UqFFsYouw7No0aIbPrdRo0YArl0P958yMjLUx4FrR3ZL61baKeQVdbNHNUNDQxEZGYnFixdj06ZNOHbsWIXaNGzYENWrVy/zDxiHDx9G9+7dUadOHSQnJ5dYcRoAjEYjgGunmF/v1KlTOHHihPr4zdq2bRsOHTpU7lFhAMjMzESLFi1u6bWI6M7EyTAREd2xio6Y/fOU3fnz55f6/FOnTuH7779Xv7ZYLFiwYAGMRmOxU6SBaysbX7969RdffAGr1YoePXrc8rijoqLQtGlTfPjhh7h06VKJx4suQWSz2UqcglynTh3Ur19fPbXUYrHAarUWe05YWBi0Wm2x00+PHTuGAwcOVGh848ePxz333IP3339fvU+n05WYeM6aNavMo5mV0aBBA6xduxZ5eXn417/+pX4e9UYq+v3U6/V49NFH8e233yIxMRFhYWGlfv78n/r164ewsDC8++67SE1NLfF4bm4uXnvtNQDXVkSuU6cOZs+eXaz7Tz/9hP3796Nnz57qfU2bNsWBAweKXWpq586dt7SicY0aNQDgpv44MXToUPz666/4+OOP4evrW6GfcTc3N0RHR5eYyALXVo7u1q0btFotfvnllxKfVS4SEhKCoKAgfPnll8V+jr744gtoNBr069ev0u/leklJSQCAwYMHl/u8P//8E+3bt7+l1yKiOxM/M0xERHesbt26oVq1avj3v/+NsWPH4tKlS/jqq69Qp06dUk+9vP/++zFq1Chs374d9957L77++mv8/fffpU6eCwoK0LVrVwwYMAAZGRn4z3/+gw4dOqBXr163PG6tVou5c+eiR48eCAkJwciRI3Hffffh5MmTWL9+Pby8vLBq1Srk5uaiQYMG6NevHyIiIuDh4YG1a9di+/bt+OijjwBcu0TTM888g/79++P++++H1WrFwoULodPp0LdvX/U1hw0bho0bN1bo1FpfX1+MHDkS//nPf7B//360aNECDz30EBYuXAhvb28EBwcjNTUVa9euVU+XvVXNmjXDr7/+is6dOyMuLg4pKSnw8vIqd5vKfD+HDRuGTz/9FOvXr8e0adMqNCY3Nzd89913iI2NRadOnTBgwAA88MADcHNzw969e5GUlISaNWvi3XffhZubG6ZNm4aRI0fiwQcfxKOPPqpeWikgIKDYpZ0ef/xxzJgxA3FxcRg1ahTOnj2L2bNnIyQkBBaLpXLh/n9FpwE/++yziIuLg06nw6BBgyq07eDBg/F///d/+P777/HUU08VOyuhPA8//DBee+01WCyWYt+r7t2748iRI/i///s/bN68udjnpe+9995ilzGbPn06evXqhW7dumHQoEHYs2cPPvvsM4wePbrE0dqiy23t3bsXALBw4UJ13/9ceM1ms2HJkiVo27ateiZGaXbs2IELFy6Ue4ktIrqLySxiTUREVFLRZXm2b99e6uMPPvhgiUsrrVy5UgkPD1fc3d2VgIAAZdq0acrXX3+tAFAyMzPV5xVdxueXX35RwsPDFYPBoAQFBZW4JFHRGDZu3KiMGTNGqVmzpuLh4aEMGTJEyc7OLjGe0i6t9M99Fl3eZv78+cXuT09PV/r06aP4+voqBoNBadSokTJgwABl3bp1iqIoSn5+vvLKK68oERERiqenp1KjRg0lIiJC+c9//qPu48iRI8rjjz+uNG3aVHF3d1dq1aqldOnSRVm7dm2Jsf7z//aLLq1UmsOHDys6nU69/FBOTo4ycuRIxc/PT/Hw8FDi4uKUAwcOKI0aNSp2iaKyvodFba6/HM71l1Yqsm3bNsXT01Pp1KlTqZdz+ue2N/p+Xi8kJETRarXKiRMnynxOaXJycpQ33nhDCQsLU+655x7F3d1dCQ0NVRISEpTTp08Xe+6SJUuUyMhIxWAwKLVq1VKGDBlS6ustWrRIadKkiVKtWjXFaDQqv/zyS5mXVpo+fXqJ7QEob775pvq11WpVxo8fr9SuXVvRaDTq97q8fVwvPj5eAaBs2bKlwl3+/vtvRa/XKwsXLiwxtrJupV2y6fvvv1eMRqNiMBiUBg0aKJMmTSp2WbOK7Peffv75ZwWA8umnn5b7HiZMmKA0bNiw2GXAiMh1aBSlEqsvEBER3aECAgIQGhqKH3/8sdznJSYmYuTIkdi+fXupK1rTnSsyMhK1atXCunXrpIdy23nkkUewe/duHDp0qFLbjRo1Cn/99Rc2bdrkoJE5Tn5+PgICAjBx4kQ899xz0sMhIgH8zDARERHd9dLS0mAymTBs2DDpodx2Tp8+jdWrV1do4ax/evPNN7F9+/Zb+ryzlPnz58PNzQ1PPvmk9FCISAg/M0xERER3rT179mDHjh346KOPUK9ePQwcOFB6SLeNzMxM/P7775g7dy7c3NxKXE6rIho2bIirV686YHSO9+STT3IiTOTieGSYiIiI7lrLli3DyJEjUVhYiP/+979wd3eXHtJtY+PGjRg6dCgyMzPxzTfflFhRnYjobsfPDBMREREREZHL4ZFhIiIiIiIicjmcDBMREREREZHL4QJadMez2+04deoUPD09odFopIdDRERERERCFEVBbm4u6tevD622/GO/nAzTHe/UqVPw9/eXHgYREREREd0mjh8/jgYNGpT7HE6G6Y7n6ekJADh69Ch8fHxkB+Ni7HY7Tpw4gQYNGtzwL29U9dhfDtvLYXs5bC+H7WWxv5ybaW+xWODv76/OEcrDyTDd8YpOja5Rowa8vLyER+NaCgsLcfjwYQQGBsLNzU16OC6H/eWwvRy2l8P2ctheFvvLuZX2Ffn4JP+0QURERERERC6Hk2EiIiIiIiJyOZwM012DK0k7n0ajQe3atdleCPvLYXs5bC+H7eWwvSz2l+Po9hpFURSH7JnISSwWC7y9vWE2m/mZYSIiIiIiF1aZuQGPDNNdw2azSQ/B5dhsNhw4cIDthbC/HLaXw/Zy2F4O28tifzmObs/JMN017Ha79BBcjt1uR0ZGBtsLYX85bC+H7eWwvRy2l8X+chzdnpNhIiIiIiIicjmcDBMREREREZHL4WSY7hpaLX+cnU2r1aJhw4ZsL4T95bC9HLaXw/Zy2F4W+8txdHuuJk13PK4mTUREREREAFeTJhfFFf6cz2azIT09ne2FsL8ctpfD9nLYXg7by2J/OY5uz8kw3TW4wp/z2e12HDt2jO2FsL8ctpfD9nLYXg7by2J/OY5uz8kwERERERERuRxOhomIiIiIiMjlcDJMdw2u8Od8Wq0WgYGBbC+E/eWwvRy2l8P2ctheFvvLcXR7riZNdzyuJk1ERERERABXkyYXZbVapYfgcqxWK7Zs2cL2QthfDtvLYXs5bC+H7WWxvxxHt+dkmO4aPMnB+RRFwblz59heCPvLYXs5bC+H7eWwvSz2l+Po9pwMExERERERkcvhZJiIiIiIiIhcDifDdNfQ6XTSQ3A5Op0ORqOR7YWwvxy2l8P2ctheDtvLYn85jm7P1aTpjsfVpImIiIiICOBq0uSiuMKf81mtVqSkpLC9EPaXw/Zy2F4O28the1nsL8fR7TkZprsGT3JwPkVRkJuby/ZC2F8O28thezlsL4ftZbG/HEe352SYiIiIiIiIXA4nw0RERERERORyuIAW3fGKPiSfk5MDHx8f6eG4FLvdjvPnz8PPzw9aLf+25mzsL4ft5bC9HLaXw/ay2F/OzbSvzAJanAzTHY+rSRMREREREcDVpMlFFRYWSg/B5RQWFmL16tVsL4T95bC9HLaXw/Zy2F4W+8txdHtOhonolvAyA7LYXw7by2F7OWwvh+1lsb8cR7bnZJiIiIiIiIhcDifDRERERERE5HK4gBbd8Yo+JH/x4kV4e3tLD8elFF0I3dPTExqNRno4Lof95bC9HLaXw/Zy2F4W+8u5mfZcQIuInKZ69erSQ3Bp7C+H7eWwvRy2l8P2sthfjiPbczJMdw0ubOB8VqsVycnJbC+E/eWwvRy2l8P2ctheFvvLcXR7ToaJiIiIiIjI5XAyTERERERERC6Hk2EiIiIiIiJyOVxNmu54XE1ajqIosFqt0Ov1XF1RAPvLYXs5bC+H7eWwvSz2l3Mz7bmaNBE5TV5envQQXBr7y2F7OWwvh+3lsL0s9pfjyPacDNNdgyv8OZ/VasX69evZXgj7y2F7OWwvh+3lsL0s9pfj6PacDBMREREREZHL4WSYiIiIiIiIXA4nw0R0S/R6vfQQXBr7y2F7OWwvh+3lsL0s9pfjyPZcTZrueJVZMY6IiIiIiO5eXE2aXJLdbpcegsux2+04e/Ys2wthfzlsL4ft5bC9HLaXxf5yHN2ek2G6a9hsNukhuBybzYbU1FS2F8L+ctheDtvLYXs5bC+L/eU4uj0nw0RERERERORyOBkmIiIiIiIil8PJMN01NBqN9BBcjkajgaenJ9sLYX85bC+H7eWwvRy2l8X+chzdnqtJ0x2Pq0kTERERERHA1aTJRXGFP+ez2+04evQo2wthfzlsL4ft5bC9HLaXxf5yHN2ek2G6a3CFP+ez2WwwmUxsL4T95bC9HLaXw/Zy2F4W+8txdHtOhomIiIiIiMjlcDJMRERERERELoeTYbprcIU/59NoNKhduzbbC2F/OWwvh+3lsL0ctpfF/nIc3Z6rSdMdj6tJExERERERwNWkyUVxUQPns9lsOHDgANsLYX85bC+H7eWwvRy2l8X+chzdnpNhumtwuXvns9vtyMjIYHsh7C+H7eWwvRy2l8P2sthfjqPbczJMRERERERELoeTYSIiIiIiInI5nAzTXUOr5Y+zs2m1WjRs2JDthbC/HLaXw/Zy2F4O28tifzmObs/VpOmOx9WkiYiIiIgI4GrS5KK4wp/z2Ww2pKens70Q9pfD9nLYXg7by2F7Wewvx9HtORmmuwZX+HM+u92OY8eOsb0Q9pfD9nLYXg7by2F7Wewvx9HtORkmIiIiIiIil8PJMBEREREREbkcTobprsEV/pxPq9UiMDCQ7YWwvxy2l8P2ctheDtvLYn85jm7P1aTpjsfVpImIiIiICOBq0uSirFar9BBcjtVqxZYtW9heCPvLYXs5bC+H7eWwvSz2l+Po9pwM012DJzk4n6IoOHfuHNsLYX85bC+H7eWwvRy2l8X+chzdnpNhIiIiIiIicjmcDBMREREREZHL4WSY7ho6nU56CC5Hp9PBaDSyvRD2l8P2ctheDtvLYXtZ7C/H0e25mjTd8biaNBERERERAVxNmlwUV/hzPqvVipSUFLYXwv5y2F4O28thezlsL4v95Ti6PSfDdNfgSQ7OpygKcnNz2V4I+8thezlsL4ft5bC9LPaX4+j2nAwTERERERGRy+FkmIiIiIiIiFwOF9CiO17Rh+RzcnLg4+MjPRyXYrfbcf78efj5+UGr5d/WnI395bC9HLaXw/Zy2F4W+8u5mfaVWUCLk2G643E1aSIiIiIiAriaNLmowsJC6SG4nMLCQqxevZrthbC/HLaXw/Zy2F4O28tifzmObs/JMBHdEl5mQBb7y2F7OWwvh+3lsL0s9pfjyPacDBMREREREZHL0UsPgKiqfL7nAqp58SPwzqS1WxEIYOaubNi1/HXibOwvh+3lsL0ctpfD9rLYv3wTI/2kh3DTbqsjw1arFVOmTEFQUBBCQ0NhNBoxZswYXLx48ab3mZWVhdmzZ9/09omJiejdu3e5z3nwwQfRrFmzYheDLigowEMPPYSwsDA8/fTTpW4XHx+PjIyMmx5bZWg0GoSFhSEiIgLBwcGYP3++Q18vICAAJpPpho+NHj0a69evr5LXtGt0VbIfqji7RofDdSPZXgj7y2F7OWwvh+3lsL0s9pej1+vRpUsX6PWO+SPEbfWnjVGjRuHChQtITU1FzZo1oSgKli1bhgsXLtz0JXOKJsNPPvlkqY9brdZbinvw4EEcPHgQNWvWxMaNG9G5c2cAQHp6Og4ePFjqZNdutwMAkpOTb/p1b8amTZvg4+ODnTt3olWrVujevTvq1avn1DH809y5c0Vfn26dVV9Neggujf3lsL0ctpfD9nLYXhb7y6levbrD9n3bHBk+dOgQli5divnz56NmzZoArh3N7N+/P5o0aQIAWLhwIdq0aYOWLVuiU6dO2LlzJ4BrR29jY2Px6KOPIiwsDNHR0Thy5AgA4Mknn0RGRgaMRiN69eoF4NqRyQkTJqB169YYPnw4zpw5gy5duiAqKgohISF45pln1AnrjXz99dd47LHHMHr0aMybNw8AsG/fPgwZMgTHjh2D0WjEggULMHnyZPTt2xdxcXEIDQ3F6dOnix0hPXnyJPr164ewsDCEh4fj9ddfBwAkJSWhTZs2iIyMREREBFatWqW+dufOnfHyyy+jY8eOaNq0aZkT/n+KiIhAzZo1ceLECQDXJvQ9e/ZEq1atEB4ejs8++0x9rkajwaRJkxAZGYn7778fixcvLvbY9Uft/fz8kJWVpX69ePFiREVFoVmzZpg+fXqpY+ncuTNWrFgBADCbzRg9ejRCQ0MRERGBxx9/vELvp4hWsVXq+XTrtIoNgSe2sb0Q9pfD9nLYXg7by2F7Wewvx2q1Ijk52WGLaN02R4b//PNPNG/eHH5+pZ9z/vvvv+O///0vfvvtNxgMBmzatAmDBw/G3r17AQDbt2+HyWRC48aNMXHiREybNg1z5szB7Nmz8fzzz5c4ZTc7Oxvbtm2DRqPB1atXsWrVKnh4eMBms+Hhhx/Gt99+i0GDBpU7ZpvNhm+++QYpKSnw8/PDlClTYDabERwcjLlz5xZ73cmTJyM1NRXp6em49957S+zrscceQ7du3bBs2TIAwLlz5wAAcXFxePTRR6HRaJCVlYW2bdvi6NGjMBgMAIDDhw9j/fr1KCwsRHBwMFJTU9GuXbtyx71x40b4+fkhIiICNpsNjz76KBYtWoSgoCBcuXIFbdu2RZs2bdCqVSsA1ya96enpOHLkCKKjo/HAAw8gICCg3NcAgL///htpaWnIzs5Gy5Yt8cADD6B9+/ZlPv/5559H9erVsWvXLmi1WrXBP+Xn5yM/P1/92mKx3HAsRERERERE17ttJsM38sMPP2Dnzp1o06aNet+FCxeQl5cHAGjXrh0aN26s/nvWrFnl7m/EiBHQaDQArp22PGHCBGzevBmKouDs2bMIDQ294WQ4OTkZAQEBCAoKAgDExsYiKSkJTz31VKnPj4+PL3UifOnSJWzevBm//PKLel/t2rUBAJmZmRgyZAhOnDgBvV6PCxcuIDMzU33NgQMHQq/XQ6/Xw2g04vDhw2VOhjt27Ii8vDxkZmZi2bJlqFatGvbt24e9e/cWe6+5ubnYt2+fOhkePXo0AKBJkybo1KkTfvvttwpNhkeNGgWNRgM/Pz/06dMHa9euLXcy/OOPP2Lbtm3QarXFGvzT1KlTMWXKlBu+PhERERERUVlum8lwy5YtcfDgQWRnZ8PX17fE44qiYPjw4XjvvfdK3d7d3V39t06nu+GhdA8PD/XfM2bMwNmzZ7Ft2za4u7vjxRdfxNWrV2845nnz5uGvv/5SJ4Z5eXnIysoqczJ8/WtW1KBBg/D++++jX79+AIBatWoVG1tl3nfRZ4YTExMxYsQItG/fHoqioFatWmUudlWaoj8i6HQ62Gz/73SRGzUr2u5WJSQk4MUXX1S/tlgs8Pf3r5J9ExERERGRa7htPjPcrFkz9O3bF6NGjVI/h6ooCpYvX44jR46gV69eWLRoEY4dOwbg2tHctLS0G+7Xy8sLZrO53Ofk5OSgbt26cHd3x5kzZ7B06dIb7vfvv//GunXrcOjQIWRlZSErKwunT5/GqVOn1M8yV5SHhwc6deqEjz76SL2v6BThnJwc9Yj3okWLkJOTU6l9l2bEiBHo2rUr3nvvPQQGBsLLy6vY6tKHDh3ChQsX1K+LHsvKysKmTZvQsWNHANe+Z9u2bQMAfPfdd7h8+XKx10lMTARw7Qj+999/j65du5Y7rl69euHDDz9UP69d1mnSBoMBXl5exW4AV5OWYNfokNGgDdsLYX85bC+H7eWwvRy2l8X+cvR6PeLj4x22mvRtMxkGri1GFRERgTZt2iAkJATBwcH49ddfUatWLXTs2BEffPABHnnkEURERCAkJAT/+9//brjP8PBwhISEIDQ0VF1A65+ee+45bNu2DSEhIRg6dChiY2NvuN9vvvkG3bp1K7bKtVarxaBBg9SFtCpj4cKFSEtLQ0hICIxGo7qI1SeffIJ+/fohMjIS6enpaNiwYaX3XZpp06Zh/vz5+Pvvv/Hjjz/iu+++U1uNGjVKPf0cuPbZ6MjISHTr1g2ffvqpeiR85syZeO6559CyZUukp6eXOKJfu3ZtREVFoXXr1njmmWfKPUW6aH/5+fkICwuD0WjEq6++WiXvlRxLby2QHoJLY385bC+H7eWwvRy2l8X+cq6fl1Q1jXL9xXGJ/kGj0SAnJ+emL23lDBaLBd7e3jh//nypp9iT4xQWFiI5ORnx8fFwc3OTHo7LYX85bC+H7eWwvRy2l8X+cm6mfdHcwGw2q2eQluW2OjJMRERERERE5Ay3zQJadHviiQNERERERHQ34mSY7hqf77mAal6cvDuT1m5FM40OM3dlw67lrxNnY385bC+H7eWwvRy2l8X+/8/ESD+nv6ajFs8CeJq0ywoICEBgYCCMRiNatGiBwYMHl1gN2pl+/PFHdO7cGQCQlpaGgQMHVnofrv7LSYJdq8df/m3ZXgj7y2F7OWwvh+3lsL0s9pfj5uaGnj17Ouyz2pwMu7AlS5bAZDJh7969MJvN6qWQKupG13K+WdHR0ViyZEnlN+Qp3c6nKKiRl8P2UthfDtvLYXs5bC+H7WWxvxi73Y6zZ8+ql16tapwMEwoKCnDlyhXUrFkTiYmJ6N27t/rY9UdsN2zYoF56yWg04vvvv8eIESPw8ccfq89/+eWXMXnyZADA5MmTMXDgQPz73/9GcHAwYmJi1OsXFxYWYty4cWjevDlat26N9evXq/vYsGEDjEZjmePNz8+HxWIpdgMArWKrkh5UcVrFhobn9rG9EPaXw/Zy2F4O28the1nsL8dmsyE1NRU2m2PaczLswgYOHAij0Yi6detCq9ViwIABN9xm//79GDZsGEwmE/r373/D52/btg2JiYnYt28f6tSpgzlz5gAAvvzyS2RkZGDv3r3YvHkz/vzzzwqPe+rUqfD29lZv/v7+Fd6WiIiIiIgI4GTYpRWdJn3+/HkEBARgwoQJN9ymSZMmePDBByv8Gt27d1ev/duuXTscPnwYALBu3ToMGzYM1apVQ7Vq1fD4449XeJ8JCQkwm83q7fjx4xXeloiIiIiICOBkmHBthba+ffvi559/hl6vL3YawtWrV4s918PDo8S25T3f3d1d/bdOpyvzc8YajabC4zUYDPDy8ip2AwAFFd8HVQ0FGuS7VWd7Iewvh+3lsL0ctpfD9rLYX45Go4Gnp2el5gqVwckwAQBSUlIQGBiIZs2aYdeuXcjLy4PVakVSUlK52zVr1gx//PEHACA7OxvJyckVer3Y2FgsWrQIhYWFKCgowPz582/5PSha3S3vgypH0epwpF5LthfC/nLYXg7by2F7OWwvi/3l6PV6xMTEOOzySpwMu7CizwyHhoZi//79+OSTT9C2bVvEx8cjNDQUnTt3RvPmzcvdx5gxY3Du3Dm0aNECw4YNQ9u2bSv02k888QSaN2+O4OBgdOjQodwFsypMccwqc1QOxQ6fS2fYXgr7y2F7OWwvh+3lsL0s9hdjt9tx9OhRh60mrVEUrhFOdzaLxQJvb2+cP39e/XwyOUdhYSGSk5MRHx/vsOu/UdnYXw7by2F7OWwvh+1lsb+cm2lfNDcwm83qxynLwiPDRERERERE5HIcc/I1kYDP91xANS+e6OBMWrsVgQBm7sqGXctfJ87G/nLYXg7by2F7OWwvy5X7T4z0kx6CQ/HI8F3OaDTCaDQiODgYOp1O/XrgwIHSQ0NaWlqVjoMr/DmfAg0uufuwvRD2l8P2ctheDtvLYXtZ7C9Ho9Ggdu3aDltNmp8ZdhFZWVkwGo24ePGi9FCqXNHnAt787QjcPTylh0NEREREdFe4E48M8zPDVC6r1Yq4uDhER0cjJCQEgwcPxuXLlwEAGzZsQGhoKMaNG4eIiAiEhIQgLS0NADB37lz1yLLRaIROp8PGjRtx5swZdOnSBVFRUQgJCcEzzzyjrviWmJiI2NhYPProowgLC0N0dDSOHDmivlbRKtLljemf8vPzYbFYit0AQMMV/pxOo9jhd/EY2wthfzlsL4ft5bC9HLaXxf5ybDYbDhw4AJvN5pD9czLsgnQ6HZKSkpCWloY9e/bA29sbs2bNUh8/cOAAhg8fjp07d2L8+PF47bXXAACjR4+GyWSCyWRC//79ERsbiwceeAA+Pj5YtWoVduzYgV27diErKwvffvutur/t27fjvffew+7duxEbG4tp06ZVekzXmzp1Kry9vdWbv78/AE6GJWgUO2pbjrO9EPaXw/Zy2F4O28the1nsL8dutyMjI8Nhl1biZNgFKYqCmTNnIjIyEuHh4Vi9ejVMJpP6eLNmzdCmTRsAQLt27XD48OFi2y9cuBDLly/HsmXLoNfrYbfbMWHCBERERCAyMhJpaWnF9teuXTs0bty4zP1VZEzXS0hIgNlsVm/Hjx+/tSBERERERORyXGs5NAIAJCUlISUlBRs3boSXlxc+/fRTpKSkqI+7u7ur/9bpdLBarerXKSkpeOutt/Dbb7/B0/Pa53NnzJiBs2fPYtu2bXB3d8eLL76Iq1evVmh/FR3T9QwGAwwGw80HICIiIiIil8cjwy4oJycHfn5+8PLyQm5uLhITEyu03e7du/H4449jxYoVqFevXrH91a1bF+7u7jhz5gyWLl3qtDFdjyv8OZ8CDS7WqMP2QthfDtvLYXs5bC+H7WWxvxytVouGDRtCq3XMtJVHhl3QsGHD8MMPPyAwMBC1a9dGx44dcfTo0RtuN2PGDFy+fBlDhgxR75s7dy6ee+459OvXDyEhIahfvz5iY2OdNqbrKVpdpV+Xbo2i1eG0b3PpYbgs9pfD9nLYXg7by2F7WewvR6fTITIy0mH756WV6I5XtHz6hQsXULNmTenhuBSbzYZdu3YhPDwcOh3/GOFs7C+H7eWwvRy2l8P2sthfzs2056WVyCU5apU5KpvdbsexY8fYXgj7y2F7OWwvh+3lsL0s9pfj6PY8TZruGp/vuYBqXjzRwZm0disCAczclQ27lr9OnI395bC9HLaXw/Zy2F6Wq/SfGOknPQSn45FhuinfffcdoqKiYDQaERQUhJiYmEr/xeaNN97A4sWLHTRCIiIiIiKist29f9oghzl9+jTGjBmDHTt2oFGjRgCAP//8ExpNxVfYs1qteOutt6p0XIqGf9txNkWjxTkvf7YXwv5y2F4O28thezlsL4v95Wi1WgQGBjpsNWl+R6nS/v77b+h0OtSqVUu9r2XLlli+fDm6deum3mez2dCoUSPs27cPGzZsQEhICEaNGgWj0Yjvv/8eI0aMwMcffwwAWLduHdq1a4fIyEiEhIRg3rx5lR4Xf0E5n6LR4rxPQ7YXwv5y2F4O28thezlsL4v95eh0OgQFBTls4TJ+R6nSwsPD0aFDBzRq1AiPPPIIpk+fjpMnT+KRRx7BX3/9hYyMDADAypUr0axZMwQHBwMA9u/fj2HDhsFkMqF///7F9tmyZUts3rwZ6enp2LRpE9566y2cOHGi1NfPz8+HxWIpdgMAjd3mwHdNpdHYbfA/u5fthbC/HLaXw/Zy2F4O28tifzlWqxVbtmyB1Wp1yP45GaZK02q1WL58ObZs2YLu3bvj999/R0hICDIzMzFu3Dh8/vnnAIDPP/8czzzzjLpdkyZN8OCDD5a6z+zsbPTv3x+hoaGIiYlBdnY29uzZU+pzp06dCm9vb/Xm7+8PANCAi2c5mwYKPK5eZHsh7C+H7eWwvRy2l8P2sthfjqIoOHfuHBx1NWBOhummBQUFYezYsVixYgXatm2LlStX4oknnsDSpUuRlpaGQ4cOoVevXurzPTw8ytzXk08+iQ4dOmD37t0wmUy4//77cfXq1VKfm5CQALPZrN6OHz9e5e+NiIiIiIjublxAiyrt5MmTyMrKwgMPPAAAyMnJQWZmJpo2bYqaNWvi4YcfxiOPPIJx48ZV+Pz+nJwcNGrUCBqNBr/99ht27txZ5nMNBgMMBkOVvBciIiIiInJNnAxTpRWtBJ2ZmYl77rkHVqsVw4cPx8MPPwwAeOKJJ5CYmIgnnniiwvt8//33MW7cOLz99tswGo1o06ZNpcdl56IGTmfXaHG6VlO2F8L+ctheDtvLYXs5bC+L/eXodDoYjUaHLaClURx1Aja5rA8//BD79++/qRWhb4bFYoG3tzfMZjO8vLyc8ppERERERHT7qczcgH/eoCoVEhKCxMRETJkyxemv7ahV5qhsVqsVKSkpbC+E/eWwvRy2l8P2ctheFvvLcXR7niZNVWrv3r1ir/3Z7mxU87KLvb4r0tqtCMzNxYyd52HX8teJs7G/HLaXw/Zy2F4O28u6G/tPjPSTHkKFKIqC3NxcriZNREREREREVFU4GXZBAQEBCAwMhNFoRGBgIN5//32nj+HSpUvQaDROf10iIiIiIiKAp0m7rCVLlsBoNOLkyZMIDg5GTEwMWrduLT2sW2LXOGaVOSqbXaPDsdrBbC+E/eWwvRy2l8P2ctheFvvL0el0aNeuncNWk+aRYRd33333ISgoCEePHsXgwYORlJQEAPjPf/6DatWq4fLlywCAmJgY/Pbbb7BarYiLi0N0dDRCQkIwePBg9TkbNmxAaGgoxo0bh4iICISEhCAtLU19rTlz5qB58+aIjIzEzJkzi41jyJAhiI6ORnh4OHr27IkzZ86UOeb8/HxYLJZiNwAAjzQ7n0aDy9Vrsr0U9pfD9nLYXg7by2F7WewvRqvVok6dOtBqHTNt5WTYxR04cADZ2dno3LkzYmNjsXbtWgDAmjVrEB0djY0bN+LKlSvYuXOn+leZpKQkpKWlYc+ePfD29sasWbOK7W/48OHYuXMnxo8fj9deew0AsGfPHrz55pv47bffkJ6ejry8vGLj+Pjjj5GWloZdu3ahY8eOmDx5cpljnjp1Kry9vdWbv78/gGuLG5Bzae1W3H98K9sLYX85bC+H7eWwvRy2l8X+cgoLC7F69WoUFhY6ZP+cDLuogQMHokWLFggODsb48eNRu3ZtxMbGYt26dbDZbNi3bx9efPFFrF27Fps2bULr1q3h5uYGRVEwc+ZMREZGIjw8HKtXr4bJZFL326xZM7Rp0wYA0K5dOxw+fBgAkJKSgh49eqBevXoAgKeeeqrYeJKSkhAdHY3Q0FDMnTu32D7/KSEhAWazWb0dP368auNQpegUm/QQXBr7y2F7OWwvh+3lsL0s9pfjyEtacTLsopYsWYL9+/fj119/xcSJE7F79240bNgQBoMBixcvRlRUFLp27Yr169dj7dq16Nq1K4Brk9aUlBRs3LgRu3fvxssvv4yrV6+q+3V3d1f/rdPpyvzhvX7xrM2bN+PTTz9FcnIy9uzZgxkzZhTb5z8ZDAZ4eXkVuxEREREREVUGJ8MuLjY2Fk899RQmTZqkfv3GG28gNjYWNWvWhJubG5YuXYrY2FgAQE5ODvz8/ODl5YXc3FwkJiZW6HViYmLw888/q58Fnj17tvpYTk4OPD094evri4KCAsyZM6dq3yQREREREdE/cDVpwuuvv45mzZphx44diI2NxRdffKFOfmNjY/HVV18hIiICADBs2DD88MMPCAwMRO3atdGxY0ccPXr0hq8RGhqKyZMno2PHjvDw8ECfPn3Ux7p3745FixYhMDAQvr6+iI2NxcmTJyv9Pp4z1oG3t3elt6ObpygKcpt3QS9PT14qSwD7y2F7OWwvh+3lsL0s9pej1+vRpUsX6PWOmbZqFEVRHLJnIiexWCzw9vbGxYsXORl2MkVRYLVaodfr+X8OAthfDtvLYXs5bC+H7WWxv5ybaV80NzCbzTf8OCVPk6a7hiM/XE+ls1qtSE5OZnsh7C+H7eWwvRy2l8P2sthfjqPb8zRpumt8vucCqnnxRAdn0tqtCAQwc1c27Fr+OnE29pfD9nLYXg7by2F7WXd6/4mRftJDuG3ded9Num0YjUYAQEFBATIyMhAWFgYACAwMxJIlSwRHRkREREREVD5OhummFV0LOCsrC0ajsdxrAxMREREREd1O+JlhqnILFy5EeHg4wsPD0bNnT3Vl6MLCQowbNw73338/2rZti5deegmdO3dWt5s+fTpCQkIQFhaGIUOGwGw2l7r//Px8WCyWYjcAsGt0Dn9vVJxdo0NGgzZsL4T95bC9HLaXw/Zy2F4W+8vR6/WIj4932GrSnAxTldqzZw9eeeUV/PTTT9i1axfat2+P0aNHAwC+/PJLHDx4EHv37sWmTZuwa9cudbuffvoJX3/9NX7//Xfs3r0bNWrUwMSJE0t9jalTp8Lb21u9+fv7O+W9Uen01gLpIbg09pfD9nLYXg7by2F7WewvJy8vz2H75mSYqtT69evRvXt33HfffQCAcePGISUlBTabDevWrcNjjz0GNzc3uLm5Yfjw4ep2a9euxcCBA+Hj4wMAeOqpp7BmzZpSXyMhIQFms1m9HT9+HACgVWyOfXNUglaxoemZdLYXwv5y2F4O28thezlsL4v95VitVqxfv56rSdOdqbzrgd3sYwaDAQaD4ZbGRUREREREro1HhqlKdenSBT///DNOnToFAJg9eza6du0KnU6HmJgYJCUlobCwEIWFhViwYIG6XWxsLL799lv1879z5sxBt27dRN4DERERERHd/XhkmKpUaGgopk+fju7duwMA/P398dVXXwEAxo4di927dyM4OBg1a9ZEdHS0Omnu0aMH9uzZg3bt2kGr1SI8PBz/+c9/xN4HVZyNi0mIYn85bC+H7eWwvRy2l8X+chy1eBYAaBRFURy2d6J/yM3NhaenJwoLCzFkyBBERUVhwoQJt7RPi8UCb29vmM1meHl5VdFIiYiIiIjoTlOZuQFPkyanio2NhdFoRFhYGLy8vPDss89W2b7tdnuV7Ysqxm634+zZs2wvhP3lsL0ctpfD9nLYXhb7y3F0e54mTU61bds2h+37053nUM3LMSvNUem0disCT2y7du09LX+dOBv7y2F7OWwvh+3lsL2sO63/xEg/6SFUGZvNhtTUVMTHx0OrrfrjuDwyTACAwsJCTJkyBUFBQQgJCUFkZCR69+4Nk8lU7nZpaWkYOHCgcwZJRERERERURW7/P22QU4wcORKXLl1CamoqatasCeDatX8zMjJgNBrL3C46OhpLlixx0iiJiIiIiIiqBo8MEw4ePIjvv/8eX3/9tToRBq59vnfgwIFITExE79691ft//PFHdO7cGQCwYcMGdbKclZUFHx8fvPnmm4iKikKzZs2QnJysbrd9+3bExMQgOjoakZGRWLp0KYBrF9OOi4tDdHQ0QkJCMHjwYFy+fLnS70NB2dcmJsdQoEG+W3W2F8L+ctheDtvLYXs5bC+L/eVoNBp4enpCo3FMe06GCenp6WjWrBlq1ap1y/sym80IDw/Hjh078Nlnn+GFF14AAFy8eBFjxozB4sWLkZaWhjVr1uCll17CyZMnodPpkJSUhLS0NOzZswfe3t6YNWtWma+Rn58Pi8VS7AYAipZL3jubotXhSL2WbC+E/eWwvRy2l8P2ctheFvvL0ev1iImJcdjllTgZphIOHz4Mo9GIwMBAjBw5slLburu7o0+fPgCAdu3a4fDhwwCALVu24MiRI+jRoweMRiNiY2MBABkZGVAUBTNnzkRkZCTCw8OxevXqcj+rPHXqVHh7e6s3f3//aw8oXOHP6RQ7fC6dYXsp7C+H7eWwvRy2l8P2sthfjN1ux9GjRx22mjQnw4TIyEgcOnQIOTk5AICmTZvCZDIhISEBOTk50Ov1sNls6vOvXr1a5r4MBoN6GoNOp1O3UxQFISEhMJlM6u3YsWOIiYlBUlISUlJSsHHjRuzevRsvv/xyua+RkJAAs9ms3o4fPw4A0PIXlNNpFTvqXTjM9kLYXw7by2F7OWwvh+1lsb8cm80Gk8lUbC5SlTgZJjRv3hwPP/wwRo0ahYsXL6r3F31ut1mzZti1axfy8vJgtVqRlJRU6ddo3749MjMzsXbtWvU+k8mEgoIC5OTkwM/PD15eXsjNzUViYmK5+zIYDPDy8ip2IyIiIiIiqgxOhgkAkJiYiLCwMLRp0wYhISHo0KED1q5diwkTJqBt27aIj49HaGgoOnfujObNm1d6/zVr1sTq1avx3nvvISIiAsHBwZg4cSLsdjuGDRuGK1euIDAwED169EDHjh0d8A6JiIiIiIj+H42iKIr0IIhuhcVigbe3N7Kzs6tkETCqOKvVij/++AOtW7d22MIGVDb2l8P2ctheDtvLYXtZ7C/nZtoXzQ3MZvMNzyDlZJjueJX5gSciIiIiortXZeYG/NMG3TVmms7B4FkgPQyXolHs8DWfQLZ3AygafurC2dhfDtvLYXs5bC+H7WXdzv0nRvpJD8GhbDYbDh48iObNm0Onq/pLW91e300SFxAQUOKyRqNHj8b69esBACNGjMDHH398y69z6tSpKv9ssIYr/DmdRrGjtuU42wthfzlsL4ft5bC9HLaXxf5y7HY7MjIyHHZpJR4ZphuaO3dule7ParWifv362LRpU5Xul4iIiIiIqKJ4ZJhuqHPnzlixYoX69a5du9C+fXvcf//9GD58OPLy8gAAubm5eOKJJ9C6dWuEh4djzJgxKCgoUPfx7LPPol27dujWrRuysrLg4+Oj7nPIkCGIjo5GeHg4evbsiTNnzjjzLRIRERERkYvhZJgqbdu2bfjll1+wf/9+XLhwATNnzgQAvPTSS+jYsSP++OMP7Ny5E3a7HZ988om63V9//YXffvsNKSkpJfb58ccfIy0tDbt27ULHjh0xefLkMl8/Pz8fFoul2A0AFGiq9o3SDSnQ4GKNOmwvhP3lsL0ctpfD9nLYXhb7y9FqtWjYsCG0WsdMW3maNFXagAED4OnpCQAYNWoUPv30U7z66qtYsWIFUlNTMWPGDABAXl5esQ+6P/bYY3Bzcyt1n0lJSVi4cCGuXr2Kq1evws+v7MUApk6diilTppS4X9FW/YfqqXyKVofTvpW/7jRVDfaXw/Zy2F4O28the1nsL0en0yEyMtJh++eRYbplGs21v5IpioLly5fDZDLBZDIhIyMDc+bMUZ/n4eFR6vabN2/Gp59+iuTkZOzZswczZszA1atXy3y9hIQEmM1m9Xb8+PFr47DbqvBdUUVo7DbUyz7I9kLYXw7by2F7OWwvh+1lsb8cm82G9PR02GyOac/JMFXasmXLcOnSJdhsNsyfPx+xsbEAgN69e2PatGmwWq0AgJycHBw6dOiG+8vJyYGnpyd8fX1RUFBQbAJdGoPBAC8vr2I3ANCAl8x2Ng0U+Fw+y/ZC2F8O28thezlsL4ftZbG/HLvdjmPHjjlsNWlOhqmEuLg4NGjQQL2dOHGi2OOtWrVCXFwcWrRoAR8fHzz//PMAgJkzZ6J69eowGo0IDw9H165dkZWVdcPX6969OwIDAxEYGIiOHTvCaDRW/ZsiIiIiIiK6Dj8zTMXcaPKamJhY5mMeHh747LPPSn1sw4YNxb4OCAjAxYsXAQBubm5YsmRJscfffffdGw21hKdDa8HX17fS29HNKywsRPIJ4IVw3zI/D06Ow/5y2F4O28thezlsL4v97148Mkx3DUetMkdl02q1CAwMZHsh7C+H7eWwvRy2l8P2sthfjqPbaxRF4cnvdEezWCzw9vaG2WxWPz9MRERERESupzJzA54mTXeNmelnYfAqkB6GS9HYbWhw/gBO+AXx0lYC2F8O28thezlsL4ftZd0u/SdGln3p0buV1WrFH3/8gdatW0Ovr/qpK4/13ya+++47REVFwWg0IigoCDExMbe8atrkyZOLXaJoxIgR+Pjjj506nn79+pX7OeOqxBX+nE8DBR5XL7K9EPaXw/Zy2F4O28the1nsL0dRFJw7dw6OOpmZR4ZvA6dPn8aYMWOwY8cONGrUCADw559/qtfvvVlTpkzB888/D3d399tiPERERERERLcLHhm+Dfz999/Q6XSoVauWel/Lli3VyWdaWhrat2+P8PBwtG7dGr///juAays/+/j4qNtcunRJ3ebJJ58EAPVSRWfPngUA7N+/H127dsX999+PPn36oKCg5GnFNxpPQEAATCaT+lh0dLS6WvSBAwfQvn17hISEoHfv3rBYLOrzkpKS0KZNG0RGRiIiIgKrVq1SH+vcuTNefvlldOzYEU2bNlXHX5r8/HxYLJZiNyIiIiIiosrgZPg2EB4ejg4dOqBRo0Z45JFHMH36dJw8eRIAUFBQgD59+uDNN9/Erl27MGPGDPTt2xeXLl0qd5+zZ88GAGzatAkmkwl16tQBAJhMJqxatQr79+/H33//jeXLl1dqPDcydOhQjBo1Cnv37sXbb7+NjRs3qo/FxcVh69atSE9Pxw8//IAnnngC+fn56uOHDx/G+vXrsWfPHvzyyy9ITU0t9TWmTp0Kb29v9ebv7w8AsGv44+xsdo0Wp2s1ZXsh7C+H7eWwvRy2l8P2sthfjk6ng9FohE7nmM9q8zt6G9BqtVi+fDm2bNmC7t274/fff0dISAgOHTqEjIwMaLVaxMXFAQA6dOiAe++9t9iR2cp45JFHcM8990Cn06F169Y4fPhwpcZTHovFApPJhBEjRgAAwsLC0KFDB/XxzMxM9OjRA6GhoejduzcuXLiAzMxM9fGBAwdCr9ejevXqMBqNpY4NABISEmA2m9Xb8ePHrz3AX1DOp9HiokddtpfC/nLYXg7by2F7OWwvi/3FaLVaNGrUyGGXVuJ39DYSFBSEsWPHYsWKFWjbti1WrlxZ6vOKTlfW6/Ww2Wzq/dcvllWW6z8/rNPpYLVaKz2eyrzu9Z8zHjRoEEaPHo09e/bAZDLBw8Oj2LYVHZvBYICXl1exG3BtpT9yLo3dhian/2R7Iewvh+3lsL0ctpfD9rLYX47VakVKSkq5c5ZbwcnwbeDkyZPq54ABICcnB5mZmWjatCkCAwNht9uxZs0aAMCWLVtw5swZGI1G1K1bF4qiYN++fQCABQsWFNuvp6cnzGZzlY4HAJo1a4Zt27YBAP744w9kZGQAALy8vBAZGamOY+/evdi8eXOx/TRu3BgAsGjRIuTk5FR6bOXhCn/Op4ECQ2Ee2wthfzlsL4ft5bC9HLaXxf5yFEVBbm4uV5O+m1mtVrz11lvIzMzEPffcA6vViuHDh+Phhx8GcO0yR88++yxeeukluLu7Y9myZfDw8AAAzJo1Cw899BB8fX3Rr1+/Yvt96aWX8K9//Qv33HMPfv311yobzzvvvIPhw4djzpw5aNeuHUJCQtRtFyxYgJEjR+Kjjz5C8+bN0alTJ/WxTz75BP369YOPjw9iYmLQsGHDm25GRERERER0KzSKo6bZRE5isVjg7e2N8+fPw9fXV3o4LqWwsBDJycmIj4+Hm5ub9HBcDvvLYXs5bC+H7eWwvSz2l3Mz7YvmBmazWf04ZVk4GaY7XtEPfE5OTrFLTZHj2e12nD9/Hn5+fg5b2IDKxv5y2F4O28thezlsL4v95dxMe06GyaUU/cC/+dsRuHt4Sg+HiIiIiOiWTIz0kx7CHasyk2H+aYNUQ4YMwauvvlrsvp49e+LDDz8UGlHlaO2OWWWOyqa1W3H/8a1sL4T95bC9HLaXw/Zy2F4W+8spLCzE6tWrUVhY6JD9czJMqs8++wyLFy/G9u3bAQDz5s2DxWLBiy++KDwyup3pFF5mQBL7y2F7OWwvh+3lsL0s9pfjqMsqAZwM03Vq1qyJOXPmYMSIETh48CDeeOMNTJs2DYMGDULr1q0RFhaGSZMmqc8PCAjApEmT0L59e/j7+2P27NmYP38+2rVrh4CAAPzvf/9Tn7t9+3bExMQgOjoakZGRWLp0KQAgKysLPj4+ePPNNxEVFYVmzZohOTm53HHm5+fDYrEUuxEREREREVUGJ8NUTPfu3dGxY0e0atUKb7zxBqZMmYKnn34af/zxB9LT05GWlqZOZAHg8uXL2LJlC9avX48XXngBJ0+eRGpqKpYuXYrx48cDAC5evIgxY8Zg8eLFSEtLw5o1a/DSSy/h5MmTAACz2Yzw8HDs2LEDn332GV544YVyxzh16lR4e3urN39/f8cFISIiIiKiuxIX0KISDh8+jMjISJw+fRre3t4IDQ1VH7t06RJGjBiBSZMmqUd/27ZtC+DakeXU1FQEBQVBURTo9XpkZ2djy5YtePTRR9G4cWN1PxcuXEBiYiKaNGmCFi1a4MqVK9BoNDCbzfD19S33dIj8/Hzk5+erX1ssFvj7++PNjYfh7ln+h+SpiikKqhXmocCtOqDRSI/G9bC/HLaXw/Zy2F4O28sS6M8FtK5RFAW5ubnw9PSEpoLtK7OAlr4qBkl3F51OB61Wi6K/k2zduhXu7u6lPvf6+3U6nfq1RqOBRqOB1WqFoigICQnBli1bSmyflZUFg8Gg/nDrdDrYbOV/JsNgMMBgMNzUe6OqZ9VXkx6CS2N/OWwvh+3lsL0ctpfF/nKqV6/usH3zNGkqk4eHB7p06YL3339fve/UqVM4ceJEpfbTvn17ZGZmYu3atep9JpMJBQUFVTZWANByYQOn0yo2BJ7YxvZC2F8O28thezlsL4ftZbG/HKvViuTkZIctosXJMJVr8eLFOHToEEJDQxEWFoY+ffogOzu7UvuoWbMmVq9ejffeew8REREIDg7GxIkTYbfbHTRqIiIiIiKi8vE0aSohICAAFy9eBADUqVMHixYtKvV5WVlZxb4+f/58sa+v/wtOy5YtkZKSUu5rAdeORt/sx9ifDq0FX1/fm9qWbk5hYSGSTwAvhPvCzc1Nejguh/3lsL0ctpfD9nLYXhb73714ZJiIiIiIiIhcDleTpjte0YpxXE1agKJAq9hg1+i4uqUE9pfD9nLYXg7by2F7WU7oz9WjS6coCqxWK/R6vUNWk+aRYbolAQEBMJlMxe7r3LkzVqxYUe52o0ePxvr16wEAK1aswNatW9XH0tLSMHDgwKoeKjmI3lq1C6FR5bC/HLaXw/Zy2F4O28tifzl5eXkO2zcnwyRi7ty56NKlC4CSk+Ho6GgsWbKk0vvkCn/Op1VsaHomne2FsL8ctpfD9nLYXg7by2J/OVarFevXr+dq0nTnSUpKQps2bRAZGYmIiAisWrVKfazo6HFycjJWrlyJ6dOnw2g0Yu7cudiwYQOMRqPcwImIiIiI6K7H1aTplg0cOLDYxbAPHToEAIiLi8Ojjz4KjUaDrKwstG3bFkePHoXBYFCfGx8fj169esFoNOL5558HAGzYsKHc18vPz0d+fr76tcViqbo3Q0RERERELoGTYbplS5YsKXYkt3PnzgCAzMxMDBkyBCdOnIBer8eFCxeQmZmJoKCgW3q9qVOnYsqUKbe0D6o6No1Oeggujf3lsL0ctpfD9nLYXhb7y9HrHTdl5WnS5DCDBg3C6NGjsWfPHphMJnh4eODq1au3vN+EhASYzWb1dvz4cQCAXcu/7TibXavHX/5t2V4I+8thezlsL4ft5bC9LPaX4+bmhp49ezrs+s6cDJPD5OTkoHHjxgCARYsWIScnp9TneXl5wWw2V3i/BoMBXl5exW4AAF4lzPkUBTXyctheCvvLYXs5bC+H7eWwvSz2F2O323H27FnY7XaH7J+TYXKYTz75BP369UNkZCTS09PRsGHDUp83dOhQfPvtt4iMjMTcuXNv+vW4wp/zaRUbGp7bx/ZC2F8O28thezlsL4ftZbG/HJvNhtTUVNhsjmnPY/10S7Kyskrcd/0CWI899pj6748++qjU57Rq1Qp79+4tto9/Xru4Ip4OrQVfX99Kb0c3r7CwEMkngBfCfR12+gqVjf3lsL0ctpfD9nLYXhb73714ZJiIiIiIiIhcDifDdNfQaDTSQ3A5Go0Gnp6ebC+E/eWwvRy2l8P2ctheFvvLcXR7jaLwk+B0Z7NYLPD29sabvx2Bu4en9HCIiIiIiMo0MdJPegh3taK5gdls/n8L7ZaBR4ZvUwEBAahTpw4KCwvV+9avXw+NRoPnn3++Uvv67LPPMGLEiKod4O1Iccwqc1QOxQ6fS2fYXgr7y2F7OWwvh+3lsL0s9hdjt9tx9OhRribtiho2bIiVK1eqX8+bNw/R0dGCI3Iuq9Vaqedr+QvK6bSKHfUuHGZ7Iewvh+3lsL0ctpfD9rLYX47NZoPJZHLYatKcDN/GRo4cia+//hoAYDabsXXrVnTv3h0AsHv3bnTo0AEtW7ZEcHAw3nnnHXW73NxcDBw4EIGBgejQoQN2796tPlaR7YKCgtCxY0eMHTtWPaJc3narVq1CeHg4jEYjQkND8cMPPwAATp48iX79+iEsLAzh4eF4/fXXAQBJSUlo06YNIiMjERERgVWrVqn76ty5M5599lm0a9cO3bp1K7VLfn4+LBZLsRsREREREVFl8NJKt7EHHngA//nPf3Dq1CmsXLkS/fv3h06nA3DtNOp169bBYDAgLy8P7du3R2xsLNq2bYu33noLBoMBBw4cgMViQdu2bdGmTZsKbVe9enXs378fly5dQvv27REVFXXD7SZNmoQ5c+agXbt2sNvt6uT0scceQ7du3bBs2TIAwLlz5wAAcXFxePTRR6HRaJCVlYW2bdvi6NGjMBgMAIC//voLv/32W5lL10+dOhVTpkxxXHgiIiIiIrrr8cjwbW7o0KFITEzE119/jccff1y9Py8vD6NHj0ZYWJg6mSy6Nu+6deswatQoaDQaeHt7Y/DgwRXebuTIkeqqbQMHDqzQdl27dsVzzz2HDz74ALt27YKPjw8uXbqEzZs346WXXlL3Ubt2bQBAZmYmevTogdDQUPTu3RsXLlxAZmam+rzHHnus3Gu4JSQkwGw2q7fjx48DABRwhT9nU6DBJXcfthfC/nLYXg7by2F7OWwvi/3laDQa1K5d22GrSfPI8G1u2LBhaNmyJe6//340b95cvf/VV1+Fn58f0tPTodfr0adPH1y9erXUfVz/w+OI7WbMmIG9e/di/fr1GD58OIYMGYJx48aV+Z4GDRqE999/H/369QMA1KpVq9gYPDw8ym1iMBjUo8jXU7S6crejqqdodTheJ0R6GC6L/eWwvRy2l8P2ctheFvvL0ev1aN++vcP2zyPDt7n69etj6tSpmDZtWrH7c3Jy0KBBA+j1emRkZGDNmjXqY7GxsZg/fz4URYHFYsF///vfCm0XExODb775Boqi4NKlS/j2228rtN2BAwcQEhKCZ555Bk899RS2bt0KDw8PdOrUCR999JH6vKLTpHNyctC4cWMAwKJFi5CTk1MlrTRc1MDpNIodfhePsb0Q9pfD9nLYXg7by2F7Wewvx2az4cCBA1xAy5WNHDkS7dq1K3bfpEmTMH/+fISHh2PixImIiYlRH3v99deRl5eHoKAgxMfHo0OHDhXa7o033kBubi5atGiB7t27IyIiAj4+Pjfc7tVXX0VISAgiIyOxcOFCTJ48GQCwcOFCpKWlISQkBEajEZ999hkA4JNPPkG/fv0QGRmJ9PR0NGzYsEo68ReU82kUO2pbjrO9EPaXw/Zy2F4O28the1nsL8dutyMjI8Nhl1bSKIqiOGTPdMcpLCyEzWaDu7s7Ll++jLi4OIwfP77YZ4dvR0UX1j5//jx8fX2lh+NSCgsLkZycjPj4+HI/502Owf5y2F4O28thezlsL4v95dxM+6K5gdlshpeXV7nP5WeGSZWTk4MePXrAZrPh6tWrePjhhzFgwADpYREREREREVU5HhmmO17RX38mbzgIg5eP9HBcisZuQ92cIzhTswkXMBPA/nLYXg7by2F7OWwv61b6T4z0c9CoXIPNZsOuXbsQHh6uXmL2RipzZJiTYaqQgIAAGAwGVK9eHfn5+YiMjMRXX32FGjVq3NT+Vq5cifXr12PmzJm3PLaiH/g3fzsCdw/PW94fEREREVFV4GTY+SozGeYCWlRhS5Ysgclkwt69e2E2m5GYmHjT++rVq1eVTISvp7E7ZpU5KpvGbkO97INsL4T95bC9HLaXw/Zy2F4W+8ux2WxIT0/natJ0+ygoKMCVK1dQs2ZNAMCHH36I1q1bo2XLlujevTuOHj0KAJg8eTIGDhyIf//73wgODkZMTAwuXLgAAEhMTETv3r0BABs2bEBoaCjGjRuHiIgIhISEIC0trczXz8/Ph8ViKXYDAA14koOzaaDA5/JZthfC/nLYXg7by2F7OWwvi/3l2O12HDt2zGGrSXMyTBU2cOBAGI1G1K1bF1qtFgMGDEBSUhIyMjKQmpqKP//8E0OGDMG4cePUbbZt24bExETs27cPderUwZw5c0rd94EDBzB8+HDs3LkT48ePx2uvvVbmOKZOnQpvb2/15u/vX+XvlYiIiIiI7m5cTZoqbMmSJTAajbBarRg7diwmTJiA48ePY/v27YiKigKAEqcwdO/eXb3cUbt27bB79+5S992sWTO0adNGfd6HH35Y5jgSEhLw4osvql9bLBZOiImIiIiIqFI4GaZK0+v16Nu3L1555RUEBwcjISEBY8aMKfW57u7u6r91Oh2sVustPQ8ADAYDDAZDifsVDU90cDZFo8U5L3+2F8L+ctheDtvLYXs5bC+L/eVotVoEBgZCq3VMe35H6aakpKQgMDAQvXv3xuzZs9XPAhcWFiI9PV1kTPwF5XyKRovzPg3ZXgj7y2F7OWwvh+3lsL0s9pej0+kQFBRU4csqVRaPDFOFDRw4ENWrV4fVakWjRo0we/Zs+Pv7Izs7G126dAEAWK1WPP7444iMjHT6+LjCn/Np7DY0OH8AJ/yCeN1DAewvh+3lsL0ctpfD9rLYX47VasUff/yB1q1bQ6+v+qkrrzNMd7yia4mdP39e/XwyOUdhYSGSk5MRHx8PNzc36eG4HPaXw/Zy2F4O28the1nsL+dm2vM6w0RERERERETl4GnSdNf4fM8FVPPiiQ7OpLVbEQhg5q5s2LX8deJs7C+H7eWwvRy2l8P2siraf2Kkn/MGRVWC/2u6iwQEBMBgMKB69erIz89HZGQkvvrqK9SoUaPMbVasWIG6deuibdu2ThypY9i5qIHT2TVanK7VlO2FsL8ctpfD9nLYXg7by2J/OTqdDkaj0WELaPE7epdZsmQJTCYT9u7dC7PZjMTExHKfv2LFCmzdutU5g3M0/oJyPo0WFz3qsr0U9pfD9nLYXg7by2F7WewvRqvVolGjRry0ElVOQUEBrly5gpo1a8Jms+GVV15BaGgoQkNDMX78eBQUFCA5ORkrV67E9OnTYTQaMXfuXBw8eBAPPPAAIiIiEBYWhkmTJgEA7rvvPpw6dQoAMGDAALRv3x4AkJ+fD19fX+Tn52P37t3o0KEDWrZsieDgYLzzzjvqeCZPnoyBAwfi3//+N4KDgxETE6Nejqm87SqDq0k7n8ZuQ5PTf7K9EPaXw/Zy2F4O28the1nsL8dqtSIlJQVWq9Uh++dp0neZossfZWVlISoqCgMGDMCXX36J7du3Y8eOHdDpdOjVqxdmzpyJCRMmoFevXjAajXj++ecBAM899xweeughJCQkAIA6Ye3atSvWrl2Lxx57DDt37oRer4fFYsH27dsRFRUFg8GAgIAArFu3DgaDAXl5eWjfvj1iY2PVU7C3bduGHTt2wNfXF4MGDcKcOXOQkJBww+3+KT8/H/n5+erXFosFAKABPy/sbBooMBTmQQOF9QWwvxy2l8P2ctheDtvLYn85iqIgNzcXjroAEo8M32WKTpM+f/48AgICMGHCBKxduxYjRoyAwWCAXq/HE088gTVr1pS6fadOnfDVV1/htddew6+//gofHx8AQGxsLNauXYv09HREREQgJiYGGzZswNq1a9G1a1cAQF5eHkaPHo2wsDC0bdsWR48ehclkUvfdvXt39dJH7dq1w+HDhyu03T9NnToV3t7e6s3f3//WwxERERERkUvhZPgupdfr0bdvX/z8888lHtNoNGVu17dvX/z+++8IDAzEZ599hoceegjAtcnwunXrsGbNGsTGxqqT4+snw6+++ir8/PyQnp6OnTt3onPnzrh69aq6b3d3d/XfOp1OPd3hRtv9U0JCAsxms3o7fvx45eIQEREREZHL42T4LpaSkoLAwEDExsZiwYIFKCgogNVqxdy5c9GtWzcAgJeXF8xms7rNwYMHce+992LYsGH44IMP1MW16tevD29vb8yePRuxsbHo0qULfvzxR2RlZaFly5YAgJycHDRo0AB6vR4ZGRllHn3+p8puZzAY4OXlVewGAHaNY1aZo7LZNTocqx3M9kLYXw7by2F7OWwvh+1lsb8cnU6Hdu3aOWw1aX5m+C5T9Jlhq9WKRo0aYfbs2ahfvz4OHz6sTlo7d+6sfkZ46NChGDFiBFasWIGnn34a586dw6JFi1CtWjXY7XbMnj1b3XdsbCx+/PFHNGnSBABQt25dREZGqqu7TZo0CUOHDsU333yDpk2bIiYmpkJjvtntSijniDc5iEaDy9VrSo/CdbG/HLaXw/Zy2F4O28tifzFarRZ16tRx2P41iqM+jUzkJBaLBd7e3jh//rz6mWRyjsLCQvz666/o1q0b3NzcpIfjcthfDtvLYXs5bC+H7WWxv5ybaV80NzCbzeoZpGXhadJEdEsctdQ9VQz7y2F7OWwvh+3lsL0s9pfjyPY8TZruGp/vuYBqXjzRwZm0disCAczclQ27lr9OnI395bC9HLaXw/Zy2F5Waf0nRvrJDoqqBI8MExERERERkcvhZPg2l5ubCw8PD4waNUq9r2gxrMjISMyfP7/c7UeMGIGPP/64Ssc0e/ZsTJ8+vdznrFixQl2J2lm4wp/z2TU6HK4byfZC2F8O28thezlsL4ftZbG/HL1ejy5dukCvd8wZETzP4ja3ZMkSREVF4bvvvsMnn3wCDw8PLFu2DK1atcKcOXNExvTkk0/e8DkrVqyA0WhE27ZtnTAikmTVV5MegktjfzlsL4ft5bC9HLaXxf5yqlev7rB988jwbW7evHmYMGECOnXqhCVLlmDBggWYOXMmvvvuOxiNRuzbtw8HDhxAu3btEBISgj59+qBbt25ITExU97F//3507doV999/P/r06YOCggIAwLp169CuXTtERkYiJCQE8+bNU7cZMWIExo4dW+p2kydPVi/NtHXrVkRFRcFoNCI0NBRffPEFkpOTsXLlSkyfPh1GoxFz587FmTNn0KVLF0RFRSEkJATPPPMM7HY7ACAxMRGxsbF49NFHERYWhujoaBw5cqTMJvn5+bBYLMVuAKBVbFWZnipAq9gQeGIb2wthfzlsL4ft5bC9HLaXxf5yrFYrkpOTHbaIFo8M38b27duH48ePIy4uDlarFe+//z62bNmCI0eO4OLFi+rpz61atcK4ceMwcuRI7N+/H5GRkRg8eLC6H5PJhPXr18NgMKBTp05Yvnw5Hn30UbRs2RKbN2+GTqfDhQsXEBkZibi4ODRo0KDc7a43depUvPzyy+r9OTk5qFmzJnr16gWj0ahOmq9evYpVq1bBw8MDNpsNDz/8ML799lsMGjQIALB9+3aYTCY0btwYEydOxLRp08o88j116lRMmTKlKlMTEREREZGL4ZHh29i8efMwbNgw6HQ6xMfHIzMzE/v37y/2HIvFApPJhGHDhgEAWrRogQ4dOhR7ziOPPIJ77rkHOp0OrVu3xuHDhwEA2dnZ6N+/P0JDQxETE4Ps7Gzs2bPnhttdr0uXLnj77bfx1ltvYfPmzahZs/QLktvtdkyYMAERERGIjIxEWloaTCaT+ni7du3QuHFj9d+lvVaRhIQEmM1m9Xb8+PFyKhIREREREZXEI8O3qcLCQixcuBBubm5ISkoCAFy5cgXz5s2Dh4dHudtqNJpiX7u7u6v/1ul06mkGTz75JOLj47F8+XJoNBq0bNkSV69eveF213v++efx8MMPY+3atXj11VcRGhqK//znPyWeN2PGDJw9exbbtm2Du7s7XnzxxUq/VhGDwQCDwVBeAiIiIiIionLxyPBtauXKlWjSpAlOnjyJrKwsZGVlYevWrVi4cCEKCwvV53l5eSEiIgKLFi0CAGRkZGDz5s0Veo2cnBw0atQIGo0Gv/32G3bu3FnpcWZkZKBx48Z44okn8Oqrr6orSHt5ecFsNhd7rbp168Ld3R1nzpzB0qVLK/1aN8IV/pzPrtEho0EbthfC/nLYXg7by2F7OWwvi/3l6PV6xMfHczVpVzNv3jwMGTKk2H0tWrTAfffdh1WrViEmJka9f8GCBXj88ccxffp0NGvWDK1atYKPj88NX+P999/HuHHj8Pbbb8NoNKJNmzaVHudnn32GlJQUVKtWDTqdDh999BEAYOjQoRgxYgRWrFiBp59+Gs899xz69euHkJAQ1K9fH7GxsZV+Lbo96a0FKHBz3Cp/VD72l8P2ctheDtvLYXtZ7C8nLy8Pnp6eDtm3RlEUxSF7Jqe5dOkSatSoAY1Gg8zMTLRr1w7bt2+Hv7+/9NCcwmKxwNvbG+fPn4evr6/0cFxKYWEhkpOTER8fDzc3N+nhuBz2l8P2ctheDtvLYXtZ7C/nZtoXzQ3MZjO8vLzKfS6PDN8FtmzZgldeeQUAYLPZMHPmTJeZCBMREREREd0MTobvAt26dUO3bt2khyHu8z0XUM2LJzo4k9ZuRSCAmbuyYdfy14mzsb8ctpfD9nLYXg7byyrqT3cfLqBFxeTm5sLDwwOjRo1y6OskJiaid+/eDn0Ncg4bF5MQxf5y2F4O28thezlsL8tRCzjRjTmyPSfDVMySJUsQFRWF7777DpcuXZIeTqXwL6XOZ9fq8Zd/W7YXwv5y2F4O28thezlsL8uu1aNnz578vLAANzc3h7bnZJiKmTdvHiZMmIBOnTphyZIlAK4dxY2NjcWjjz6KsLAwREdH48iRI+o2b775prqK9aRJkxAQEKA+tnDhQoSHhyM8PBw9e/bEyZMnS7zmmTNn0KVLF0RFRSEkJATPPPMM7HZ7mWPMz8+HxWIpdgMAcC0451MU1MjLYXsp7C+H7eWwvRy2l8P2shQFZ8+eLfe/T8kx7Ha7Q9tzMkyqffv24fjx44iLi8OoUaMwb9489bHt27fjvffew+7duxEbG4tp06YBAFavXo3ly5cjPT0df/zxR7HJ7p49e/DKK6/gp59+wq5du9C+fXuMHj26xOv6+Phg1apV2LFjB3bt2oWsrCx8++23ZY5z6tSp8Pb2Vm9Fi4VpFVtVpaAK0io2NDy3j+2FsL8ctpfD9nLYXg7by9IqNqSmpsJmY39ns9kc256TYVLNmzcPw4YNg06nQ3x8PDIzM7F//34AQLt27dC4cWP134cPHwYArFu3Dv3794enpyc0Gk2xzxqvX78e3bt3x3333QcAGDduHFJSUkr8MNvtdkyYMAERERGIjIxEWloaTCZTmeNMSEiA2WxWb8ePH6/KDERERERE5AL4wQMCcO0aXgsXLoSbmxuSkpIAAFeuXMG8efMQGhoKd3d39bk6nQ5Wq7XU/Wg0mjJfo6zHZsyYgbNnz2Lbtm1wd3fHiy++iKtXr5a5H4PBAIPBUJG3RUREREREVCoeGSYAwMqVK9GkSROcPHkSWVlZyMrKwtatW7Fw4UIUFhaWuV1MTAyWL1+OS5cuQVEUfP311+pjXbp0wc8//4xTp04BAGbPno2uXbtCpyu+GmJOTg7q1q0Ld3d3nDlzBkuXLr2p96Cg7Ik4OYYCDfLdqrO9EPaXw/Zy2F4O28the1kKNOpZkORcGo1j2/PIMAG4dor0kCFDit3XokUL3HfffcjNzS1zu4ceegjbtm2D0WiEj48PHnzwQfj4+AAAQkNDMX36dHTv3h0A4O/vj6+++qrEPp577jn069cPISEhqF+/PmJjY2/qPShaXnLA2RStDkfqtZQehstifzlsL4ft5bC9HLaXpWh1iImJkR6GS9Lr9Q5tr1EULktHtyY3Nxeenp5QFAUvvfQS8vLy8MUXXzjt9S0WC7y9vZGTk6NOxMk57HY7jh8/Dn9/f2i1PNHE2dhfDtvLYXs5bC+H7WWxv5ybaV80NzCbzfDy8ir3ufxu0i0bNmwYIiMjERwcjGPHjuHtt98WGQdX+HM+m80Gk8nE9kLYXw7by2F7OWwvh+1lsb8cR7fnadJ0y77//nvpIQAAPt9zAdW8eKKDM2ntVgQCmLkrG3Ytf504G/vLYXs5bC+H7eWwvXNNjPSTHgI5CY8Mu6iAgIByL190q1asWIGtW7eqX2/YsAFGo9Fhr0dERERERFQZnAyTQ/xzMuwMXGHR+RRocMndh+2FsL8ctpfD9nLYXg7by9JoNKhduzZXkxbg6PacDJNq+/btiImJQXR0NCIjI9VLHJ07dw7dunVDWFgYwsPDMXLkSADXzuF/5ZVXEBoaitDQUIwfPx4FBQVITk7GypUrMX36dBiNRsydOxcAYLVaMW7cOERERCAkJARpaWnqay9cuBDh4eEIDw9Hz549cfLkyUqPn6tJO5+i1eF4nRC2F8L+ctheDtvLYXs5bC9Lr9ejffv20Ot5irqzObo9J8MEALh48SLGjBmDxYsXIy0tDWvWrMFLL72EkydPYtGiRWjcuDF2796NXbt24aOPPgIAfPnll9i+fTt27NgBk8mEw4cPY+bMmYiPj0evXr3wyiuvwGQyYfTo0QCAAwcOYPjw4di5cyfGjx+P1157DQCwZ88evPLKK/jpp5+wa9cutG/fXt2mNPn5+bBYLMVuAKBR7A6uRP+kUezwu3iM7YWwvxy2l8P2ctheDtvLstlsOHDgABfQEuDo9pwMEwBgy5YtOHLkCHr06AGj0ahe6zcjIwNt27bFTz/9hJdeegk//PADatSoAQBYu3YtRowYAYPBAL1ejyeeeAJr1qwp8zWaNWuGNm3aAADatWuHw4cPAwDWr1+P7t2747777gMAjBs3DikpKWX+0E+dOhXe3t7qzd/fHwAnwxI0ih21LcfZXgj7y2F7OWwvh+3lsL0su92OjIwM2O3s72yObs9j/QQAUBQFISEh2LJlS6mPm0wmrF27Ft999x1ef/11pKenl3jOjc7ld3d3V/+t0+lgtVpLfd6N9pOQkIAXX3xR/dpisagTYiIiIiIioorgkWECALRv3x6ZmZlYu3atep/JZEJBQQEyMzPh4eGBAQMGYNasWfjrr79w6dIlxMbGYsGCBSgoKIDVasXcuXPRrVs3AICXlxfMZnOFXrtLly74+eefcerUKQDA7Nmz0bVrV+h0pX8uxmAwwMvLq9iNiIiIiIioMnhk2IXFxcXBzc1N/XrJkiWYPHkyXnrpJRQWFqJhw4ZYsWIFNmzYgBkzZqhHc6dPnw5vb2+MGTMGhw8fRsuWLQEAnTt3xvPPPw8AGDp0KEaMGIEVK1bg6aefRrNmzcocR2hoKKZPn47u3bsDAPz9/fHVV19V+v1whUXnU6DBxRp12F4I+8thezlsL4ft5bC9LK1Wi4YNG0Kr5XFEZ3N0e42iKIpD9kzkJBaLBd7e3jCbzTxKTERERETkwiozN+CfN+iuwRX+nM9msyE9PZ3thbC/HLaXw/Zy2F4O28tifzmObs/TpOmuMWvXeVTz4i8pZ9LarQg8cQxr7PVg1/LXibOxvxy2l8P2ctheDts7zsRIvxs+x26349ixYwgNDS1zTRtyDEe355FhIiIiIiIicjmcDN+hVq5cCaPRWOx23333wd3dHStXrsQLL7xQ6X0mJiaid+/eld5u8uTJ6sJZREREREREdwKeZ3GH6tWrF3r16qV+ffHiRbRq1QpvvfVWicdchaLh33acTdFocc7Ln+2FsL8ctpfD9nLYXg7by9JqtQgMDORq0gIc3Z7f0buA3W7HkCFD0LVrV4waNarYEd4NGzYgNDQUTz31FMLDwxEWFoZdu3ZhxIgRCAsLQ5s2bXDy5El1XxaLBb169UJwcDA6deqErKwsAMDu3bvRoUMHtGzZEsHBwXjnnXeKjeH48eOIiYlBUFAQ/v3vfyM7OxsAcN9996nXDx4wYADat28PAMjPz4evry/y8/MBAB9++CFat26Nli1bonv37jh69GiZ7zc/Px8Wi6XYDeBkWIKi0eK8T0O2F8L+ctheDtvLYXs5bC9Lp9MhKCiInxcW4Oj2/F/UXeDNN9/EhQsX8Omnn5b6+IEDBzB69Gjs2rULvXv3RkxMDCZOnIjdu3cjOjoaH3/8sfrc33//HdOmTcO+ffvw0EMPYcyYMQCAgIAArFu3Dn/++Sd27NiB5cuXY+vWrep2mzZtQlJSEg4cOAB/f38kJCQAALp27Yq1a9fCbrdj586dMJvNsFgs2Lx5M6KiomAwGJCUlISMjAykpqbizz//xJAhQzBu3Lgy3+/UqVPh7e2t3vz9/QEAGjsXz3I2jd0G/7N72V4I+8thezlsL4ft5bC9LKvVii1btsBqtUoPxeU4uj1Pk77D/fDDD5g3bx7S0tJQrVq1Up/TrFkzREVFAQCio6PRrFkzBAUFAQBat26N77//Xn1u+/bt0aJFCwDAmDFjMGnSJNhsNuTl5WHcuHEwmUzQarU4fvw4TCYT2rZtCwDo2bMn6tatq27Xp08fAEBsbCzWrl2LkJAQRERE4N5778WGDRuQmpqKrl27AgBWrFiB7du3q2O80dLpCQkJePHFF9WvLRYL/P39oQEvme1sGijwuHoRGiisL4D95bC9HLaXw/Zy2F6Woig4d+4cFIX1nc3R7TkZvoNlZGRg1KhRWLFiBerXr1/m89zd3dV/63S6El9X5C8tr776Kvz8/JCeng69Xo8+ffrg6tWrZT5fo9EAuDYZTkhIQHBwMGJjY3Hvvfdi7dq1SE1NxRdffAHg2g95QkKCehT6RgwGAwwGQ4WeS0REREREVBqeJn2Hys3NxSOPPIIpU6agQ4cOVbbf1NRUHDhwAAAwd+5cdOnSBTqdDjk5OWjQoAH0ej0yMjKwZs2aYtslJyfj77//VreLjY0FANSvXx/e3t6YPXs2YmNj0aVLF/z444/IyspCy5YtAQC9e/fG7NmzceHCBQBAYWEh0tPTq+w9ERERERER/ROPDN+hPv/8c2RkZOCrr77CV199VeyxAQMG3PR+27dvjwkTJuDQoUPw9fXFggULAACTJk3C0KFD8c0336Bp06aIiYkptl3Hjh0xePBgnDx5Es2bN0diYqL6WGxsLH788Uc0adIEAFC3bl1ERkaqq8INGTIE2dnZ6NKlC4Brnw14/PHHERkZWamxPxtRGz4+Pjf5zulm2O12HPcz4t/+dbjCogD2l8P2ctheDtvLYXtZOp0ORqORC2gJcHR7jcKT3+kOZ7FY4O3tDbPZDC8vL+nhEBERERGRkMrMDfinJbprcIU/57NarUhJSWF7Iewvh+3lsL0ctpfD9rLYX46j2/M0abprfLY7G9W87NLDcClauxWBubmYsfM87Fr+OnE29pfD9nLYXg7by2H7qjEx0u+mtlMUBbm5uVxNWoCj2/PIMFWa1WrFlClTEBQUhNDQUBiNRowZMwYrVqyA0Wi85f3v2bMHAQEBt7wfIiIiIiKisvBPS1Rpo0aNwoULF5CamoqaNWtCURQsW7ZMXQ2aiIiIiIjodsfJMFXKoUOHsHTpUhw7dgw1a9YEcO2awv3798eGDRtgtVoxbtw4/P7777Barfjmm28QHR0Nq9WKnj17Ijs7G3l5eYiIiMBXX32FGjVqAAAmT56MxYsXw8vLCz169Ch3DPn5+cjPz1e/tlgsAAC7hiv8OZtdo8Ox2sFsL4T95bC9HLaXw/Zy2F6WTqdDu3btuJq0AEe352nSVCl//vknmjdvDj+/0j9zceDAAQwfPhw7d+7E+PHj8dprrwG49oOclJSEtLQ07NmzB97e3pg1axYAYPXq1Vi6dCl27NiBtLQ0ZGVllTuGqVOnwtvbW735+/tfe0CjqbL3SRWk0eBy9ZpsL4X95bC9HLaXw/Zy2F6UVqtFnTq8rJUER7fnd5SqVLNmzdCmTRsAQLt27XD48GEA1z78PnPmTERGRiI8PByrV6+GyWQCAKxbtw4DBgyAl5cXNBoNxo4dW+5rJCQkwGw2q7fjx48DuLa4BDmX1m7F/ce3sr0Q9pfD9nLYXg7by2F7WYWFhVi9ejUKCwulh+JyHN2ep0lTpbRs2RIHDx5EdnY2fH19Szzu7u6u/lun06nLoCclJSElJQUbN26El5cXPv30U6SkpJT6Gpob/NXTYDDAYDDcwrugqqRTbNJDcGnsL4ft5bC9HLaXw/ayeFklOY5szyPDVCnNmjVD3759MWrUKFy8eBHAtaO+y5cvx5EjR8rcLicnB35+fvDy8kJubi4SExPVx2JjY7F06VJ12fQvv/zSwe+CiIiIiIhcHSfDVGlff/01IiIi0KZNG4SEhCA4OBi//voratWqVeY2w4YNw5UrVxAYGIgePXqgY8eO6mPx8fHo168fWrZsiejoaDRs2NAZb4OIiIiIiFyYRuHVo+kOZ7FY4O3tjYsXL8Lb21t6OC6l6ELonp6eNzy9naoe+8thezlsL4ft5bC9LPaXczPti+YGZrMZXl5e5T6XR4aJ6JZUr15deggujf3lsL0ctpfD9nLYXhb7y3Fkey6gRXeNT0xnUc2Lq/w5k9ZuReCJbcho0AZ2LX+dOBv7y2F7OWwvh+3lsP3NmRhZ+qVAK8tqtSI5ORnx8fFwc3Orkn1SxTi6PY8MkyogIAB16tQptnT5+vXrodFo8Pzzz5e77eTJk2/4nIpauXIlXnjhhSrZFxERERERUWk4GaZiGjZsiJUrV6pfz5s3D9HR0U4dQ69evTBz5kynviYREREREbkWToapmJEjR+Lrr78GAJjNZmzduhXdu3cHAOzevRsdOnRAy5YtERwcjHfeeafUfZT3vPvuuw+nTp0CAAwYMADt27cHAOTn58PX1xf5+flITExE7969HfguiYiIiIjI1XEyTMU88MADyMrKwqlTp/Df//4X/fv3h06nA3DtNOp169bhzz//xI4dO7B8+XJs3bq1xD7Ke17Xrl2xdu1a2O127Ny5E2azGRaLBZs3b0ZUVBQMBsMNx5ifnw+LxVLsBgB2ja4KS1BF2DW6a59fYnsR7C+H7eWwvRy2l8P2svR6PeLj46HX8/Pazubo9pwMUwlDhw5FYmIivv76azz++OPq/Xl5eRg9ejTCwsLQtm1bHD16FCaTqcT25T0vNjYWa9euRXp6OiIiIhATE4MNGzZg7dq16Nq1a4XGN3XqVHh7e6s3f3//qnjbdJP01gLpIbg09pfD9nLYXg7by2F7WXl5edJDcFmObM/JMJUwbNgwfPrpp3B3d0fz5s3V+1999VX4+fkhPT0dO3fuROfOnXH16tUS25f3vNjYWKxbtw5r1qxBbGysOjmuzGQ4ISEBZrNZvR0/fhwAoFVsVfDuqTK0ig1Nz6SzvRD2l8P2ctheDtvLYXtZVqsV69evh9VqlR6Ky3F0ex7rpxLq16+PqVOnIigoqNj9OTk5aNGiBfR6PTIyMrBmzRp06tSpxPblPa9+/frw9vbG7NmzkZKSAj8/P7zwwgvIzc1Fy5YtKzQ+g8FQodOpiYiIiIiIysLJMJVq5MiRJe6bNGkShg4dim+++QZNmzZFTExMqdve6HmxsbH48ccf0aRJEwBA3bp1ERkZCa2WJyoQEREREZFzaBRFUaQHQXQrLBYLvL29cf78efj6+koPx6UUFhbi119/Rbdu3XgRegHsL4ft5bC9HLaXw/ay2F/OzbQvmhuYzWZ4eXmV+1xOhumOV5kfeCIiIiIiuntVZm7A06TprjHDdB7unlxp0akUBTWuXsRldx9Ao5EejethfzlsL4ft5bC9HLa/oYmRfg7bt91ux/nz5+Hn58eP9TmZo9vzu0kVFhAQgKCgoGKruUVHR2PDhg144403sHjxYsHRcTVpCVrFhobn9rG9EPaXw/Zy2F4O28the1k2mw2pqamw2djf2RzdnpNhqpT8/HzMmzevxP1vvfUWhgwZUmWvY7fbYbfbq2x/RERERERE1+NkmCpl8uTJePvtt3HlypVi948YMQIff/wxACA3NxcDBw5EUFAQOnbsiLFjx2LEiBHqcz/88EO0bt0aLVu2RPfu3XH06FF133379kVcXBxCQ0Nx+vRpZ70tIiIiIiJyMZwMU6VERESgS5cumDlzZpnPeeutt1C9enXs378fycnJ2LJli/pYUlISMjIykJqaij///BNDhgzBuHHj1MdTU1OxYMEC7Nu3D/fdd1+p+8/Pz4fFYil2AwAF/AyNsynQIN+tOtsLYX85bC+H7eWwvRy2l6XRaODp6QkNP6/tdI5uzwW0qNLefvtttG7dGk8++WSpj69btw4zZ85Uf3gHDhyIQ4cOAQBWrFiB7du3IyoqCgBKnP8fHx+Pe++9t9zXnzp1KqZMmVLifkWru5m3Q7dA0epwpF5L6WG4LPaXw/Zy2F4O28the1l6vR4xMTHSw3BJjm7PI8NUaQEBARg8eDDeeeedCj3/+r/kKIqChIQEmEwmmEwm7N69G7t371Yf9/DwuOH+EhISYDab1dvx48f//53zM8ZOp9jhc+kM20thfzlsL4ft5bC9HLYXZbfbcfToUa5nI8DR7TkZppsyadIkLFq0CKdOnSrxWExMDL755hsoioJLly7h22+/VR/r3bs3Zs+ejQsXLgC4diHt9PT0Sr22wWCAl5dXsRsAaPl/EE6nVeyod+Ew2wthfzlsL4ft5bC9HLaXZbPZYDKZuJq0AEe352SYboqfnx+effbZUhe5euONN5Cbm4sWLVqge/fuiIiIgI+PDwBgyJAhGDFiBLp06YKIiAgYjUakpKQ4efREREREROTq+JlhqrCsrKxiX7/++ut4/fXXAQCdO3dW769evToWLlwId3d3XL58GXFxcejZs6f6+LPPPotnn322xP4nT558S+N7OrQWfH19b2kfVDmFhYVIPgG8EO4LNzc36eG4HPaXw/Zy2F4O28theyLH4GSYqlxOTg569OgBm82Gq1ev4uGHH8aAAQMc/rpc4c/5NBoNateuzfZC2F8O28thezlsL4ftZbG/HEe31yiKojhkz0ROYrFY4O3tDbPZrH5+mIiIiIiIXE9l5gY8Mkx3jZmmczB4FkgPw6VoFDt8zSeQ7d0AioZLEDgb+8thezlsL4ft5bB9cRMj/Zz6ejabDQcPHkTz5s2h0/FSns7k6Pb8X9NtIiAgAIGBgTAajWjRogUGDx6My5cv39S+RowYgY8//rjE/adOnULHjh1vcaTFJSYmwtvbG0ajUb2lpaVV6WtUlIYrLDqdRrGjtuU42wthfzlsL4ft5bC9HLaXZbfbkZGRwUsrCXB0e06GbyNLliyByWTC3r17YTabkZiYWOl9WK3WMh+rX78+Nm3adAsjLF2XLl3U6wabTCZER0dX+WsQERERERFVJU6Gb0MFBQW4cuUKatasCeDa0dfevXurj//444/q6s0bNmxASEgIRo0aBaPRiO+//77YvjZt2oTg4GCkpaUhKytLvcQRcO0D6e+99x5at26Nxo0bY/78+epjL7/8Mlq1agWj0YhOnTohIyOjwuO3Wq2Ii4tDdHQ0QkJCih3lPnjwIB544AFEREQgLCwMkyZNAgBcunQJjz/+OEJDQxEaGoopU6aUuf/8/HxYLJZiNyIiIiIiosrgZPg2MnDgQBiNRtStWxdarbbCKzDv378fw4YNg8lkQv/+/dX7lyxZgvHjx2P16tVlHq01GAz4448/8NNPP+HZZ59VjyxPmDAB27dvh8lkwrhx4/Dcc8+V+frr169XT5GOi4uDTqdDUlIS0tLSsGfPHnh7e2PWrFkAgM8++wwPPfQQdu7cid27d+PFF18EALz99tvIz8/Hrl27sG3bNqxYsQJLliwp9fWmTp0Kb29v9ebv7w8AUMAV/pxNgQYXa9RheyHsL4ft5bC9HLaXw/aytFotGjZsCK2WUydnc3R7LqB1G1myZAmMRiOsVivGjh2LCRMm4KOPPrrhdk2aNMGDDz5Y7L6FCxdCp9Nh/fr16hHm0gwZMgQAEBQUBL1ejzNnzqBBgwZYs2YNZs2ahdzcXNjtdly4cKHMfXTp0gUrVqxQv7bb7Zg5cyZWr14Nq9UKs9mM9u3bAwA6deqEV155BZcuXcKDDz6I2NhYAMDatWvx0UcfQavVokaNGhg2bBjWrFmDgQMHlni9hIQEdRINXFsxzt/fH4qWCxo4m6LV4bRvc+lhuCz2l8P2ctheDtvLYXtZOp0OkZGR0sNwSY5uzz9v3Ib0ej369u2Ln3/+Wf3aZrOpj1+9erXY8z08PErsIzw8HBcuXMDu3bvLfS13d3f13zqdDlarFceOHcMzzzyDRYsWYc+ePfjf//5X4jXLk5SUhJSUFGzcuBG7d+/Gyy+/rG7ft29f/P777wgMDFSPEpemvGuJGQwGeHl5FbsBgMZuK3MbcgyN3YZ62QfZXgj7y2F7OWwvh+3lsL0sm82G9PT0Yv89Ts7h6PacDN+mUlJSEBgYCABo1qwZdu3ahby8PFitViQlJd1w+4iICKxatQqPP/64OqmuKLPZDDc3N9SrVw+KouCzzz6r1PY5OTnw8/ODl5cXcnNziy0EdvDgQdx7770YNmwYPvjgA2zduhUAEBsbi3nz5kFRFFy+fBkLFy5Et27dKvW6GvCS2c6mgQKfy2fZXgj7y2F7OWwvh+3lsL0su92OY8eOcTVpAY5uz8nwbaToM8OhoaHYv38/PvnkEwBA27ZtER8fj9DQUHTu3BnNm1fsNJkWLVrgl19+wXPPPYfly5dXeBxhYWEYNGgQQkJC0KpVKzRs2LBS72PYsGG4cuUKAgMD0aNHj2KXc1q2bBnCwsIQGRmJgQMHYvbs2QCA119/HW5ubggLC0ObNm3Qq1evCn9mmoiIiIiIqLI0iqLwT0x0R7NYLPD29sb58+fh6+srPRyXUlhYiOTkZMTHx8PNzU16OC6H/eWwvRy2l8P2ctheFvvLuZn2RXMDs9msfpyyLDwyTHcNrvDnfFqtFoGBgWwvhP3lsL0ctpfD9nLYXhb7y3F0ex4Zpjte0V9/3vztCNw9PKWHQ0RERERVZGKkn/QQ6A7DI8N0QxqNBhcvXix2X0BAAEwmk9PG0K9fv2KLa90qrrDofBq7Df5n97K9EPaXw/Zy2F4O28the1lWqxVbtmyB1WqVHorLcXR7TobprsEVFp1PAwUeVy+yvRD2l8P2ctheDtvLYXtZiqLg3Llz4Am1zufo9pwMU6m2bNkCo9GIsLAwPP7444iIiMCGDRsAADNmzECrVq1gNBrRqlUrpKamqtsFBATgjTfeQLt27dC4cWO888476mMHDhxA+/btERISgt69e8NisaiPJSUloU2bNoiMjFQvC1WW/Px8WCyWYjciIiIiIqLK4GSYSigoKMDAgQMxc+ZM7N69G0OHDsWuXbvUx4cOHYrt27fDZDJh1qxZGDlyZLHtL168iNTUVGzfvh3Tp0/HyZMn1e1GjRqFvXv34u2338bGjRvVbeLi4rB161akp6fjhx9+wBNPPIH8/PxSxzd16lR4e3urN39/fwdUICIiIiKiu5leegB0e9FoNDhw4AD0ej26dOkCAOjSpQuaNm2qPic9PR3vvvsusrOzodfrkZGRgby8PFSvXh0AMHjwYACAn58fmjRpgszMTHh6esJkMmHEiBEArl3LuEOHDuo+MzMzMWTIEJw4cQJ6vR4XLlxAZmYmgoKCSowxISEBL774ovq1xWKBv78/7Br+bcfZ7BotTtdqyvZC2F8O28thezlsL4ftZel0OhiNRuh0OumhuBxHt+dk2EXVrl0b2dnZ8PHxUe87f/486tSpg3PnzpV4vkajAXDtqHGfPn2wfv16tGrVSl2tLT8/X50Mu7u7q9vpdLoyP/BetE8AGDRoEN5//33069cPAFCrVi1cvXq11O0MBgMMBkMpO+T/QTidRouLHnWlR+G62F8O28thezlsL4ftRWm1WjRq1Eh6GC7J0e05e3BRcXFxmDNnjvr1ggUL0KRJE9SrVw+BgYEoLCxUT2PeuHEjDh06BAC4evUqCgoK0LBhQwDArFmzKvR6Xl5eiIyMxIIFCwAAe/fuxebNm9XHc3Jy0LhxYwDAokWLkJOTU+n3xBUWnU9jt6HJ6T/ZXgj7y2F7OWwvh+3lsL0sq9WKlJQUriYtwNHteWTYRX388cd4/vnnER4eDq1Wi7p162Lp0qUArh15/d///oenn34adrsdUVFRCAwMhI+PD7y8vPDOO++gdevW8PPzw6BBgyr8mgsWLMDIkSPx0UcfoXnz5ujUqZP62CeffIJ+/frBx8cHMTEx6mS7MrjCovNpoMBQmAcNFNYXwP5y2F4O28thezlsL0tRFOTm5nI1aQGObq9R+F2lUuTm5sLT0xMAsH37dvTq1QuHDx/GPffcIzyykopO1T5//jx8fX2lh+NSCgsLkZycjPj4eLi5uUkPx+Wwvxy2l8P2ctheDtvLYn85N9O+aG5gNpvh5eVV7nN5ZJhKtXz5csycOROKokCv12PhwoW35USYiIiIiIjoZvDIMN3xiv768+bGw3D3LP+vP1TFFAU1rl7EZXcf4LoF0chJ2F8O28thezlsL8eF2k+M9JMeQgl2ux3nz5+Hn58ftFouueRMN9O+MkeG+d10URqNBhcvXix2X0BAAEwmU4l/34r4+HhkZGTc8n4q5C7/P4fbkkaDy9Vrsr0U9pfD9nLYXg7by2F7UVqtFnXq1OFEWICj2/M7SrfEZit/VcPk5GQEBgY6ZSxaO1f4czat3Yr7j29leyHsL4ft5bC9HLaXw/ayCgsLsXr1ahQWFkoPxeU4uj0nw3RDs2bNwgMPPIBz584hMTERXbp0Qd++fREWFoY//vgDM2bMQKtWrWA0GtGqVSukpqaq215/hLlz5854+eWX0bFjRzRt2hRPPvmk+rzc3Fw88cQTaN26NcLDwzFmzBgUFBQ4+63STdApvMyDJPaX8/+xd+9xUdb5//+fc0AwEVDRzETxCHJyQFAxcBFJTTt41jTPmq5r5pb7QcxKy2Jbd3XL2tVKpTR2TW3NVsz1gGmGKMoIaJKSGHlIBRxQAZmZ6/eHP69vk6CCzLzUed5vt7ndYA7X9Z6Hhr25rnlfbC+H7eWwvRy2l8XLKsmxZ3suoEXVslqteOmll3Dy5Els374d9evXBwCkp6cjMzNTPeLbvn17vPTSSwCAffv2Yfz48Th27FiV28zLy0NqaioqKysREBCAtLQ0REZG4uWXX0Z0dDQ++ugjKIqCKVOm4N1338Wf/vSnm7ZRUVGBiooK9fuSkpK6futERERERPSA42SYbGh+9VmU559/HqGhodiwYYPNefo9evSwOfU5MzMTb731FgoLC6HX65Gbm4uysjJ18vxrI0aMgF6vh16vh8FgQF5eHiIjI7Fx40akpaVh8eLFAICysjLodLoqx5iYmIgFCxbU1VsmIiIiIiInxMmwk2ratCkKCwvh5eWl3nfx4kU0a9ZM/f53v/sdtm3bhnPnzqFFixbq/e7u7urX165dw+DBg5GamoqIiAh19baKiooqJ8Nubm7q1zqdTj3tQVEUbNiwAR07drzt2BMSEtQj0cD1I8M+Pj6waqqePJP9WDU65DUPZXsh7C+H7eWwvRy2l8P2svR6PXr16gW9nlMnR7N3e35m2En17dsXy5cvV7//9NNP0bZtWzzyyCPqfWPGjMFrr72G2NhYnDx5ssrtlJeX49q1a2jVqhWA658vro2BAwfinXfeUSfHxcXFOHHiRJXPdXV1hYeHh82N5Jj19aSH4NTYXw7by2F7OWwvh+1lVXWQhxzDnu05GXZSf//733H27FmEhITAYDAgOTkZ69atu+l5Q4cOxd/+9jf06dOnys8Be3h4YOHChejatSu6dOmCevVq94N6yZIlqF+/PgwGA0JCQtC7d2/k5+fXaBtaLizhcFrFAr+f09leCPvLYXs5bC+H7eWwvSyz2YyUlBQuoiXA3u01iqIodtkykYPcODX74sWLaNKkifRwnEplZSVSUlLQv39/uLi4SA/H6bC/HLaXw/Zy2F4O28tifzm1aX9jbmAymW57BimPDBMREREREZHT4WSYiIiIiIiInA5Pk6b73o1TIV7/Jg9uDbmYlkMpCrSK5frqlr+6LBc5CPvLYXs5bC+H7eU8oO3nhHpLD+GOKIoCs9kMvV5vcxlSsr/atOdp0nRXKisrsWDBAvj7+yMwMBChoaEYOHAgjEYjkpKSMHDgQOkh0j1Eb74mPQSnxv5y2F4O28thezlsL6usrEx6CE7Lnu05GaabTJgwAZmZmUhLS8ORI0eQmZmJGTNmIDc3V3pot8QVFh1Pq1jQ7lwm2wthfzlsL4ft5bC9HLaXZTabkZqaytWkBdi7Pa8cTTaOHz+O//znPygoKECjRo3U++Pi4gAASUlJuHz5Mp599lnk5OTA1dUVn3/+Odq2bYtz587h2WefRUlJCcrLy9GrVy+899570Gq1SEpKwpo1a9C0adMava4qFRUVqKioUL8vKSmxbxQiIiIiInrg8Mgw2cjMzET79u3RuHHjap9z4MABvP3228jOzkZcXBzeeecdAICXlxe++uorHDx4EFlZWcjPz8fnn39+16/7rcTERHh6eqo3Hx+fOnr3RERERETkLDgZplvKy8uDwWCAn58fJkyYAACIjIxEmzZt1K/z8vIAAFarFfHx8ejcuTNCQ0ORkZEBo9Gobqu2r/uthIQEmEwm9VZQUGCHd053yqLRSQ/BqbG/HLaXw/Zy2F4O28vS63lCrRR7tuefKtkIDQ3FiRMnUFxcjEaNGqFdu3bqwlkbN24EALi5uanP1+l06jn8ixcvxvnz55Geng43Nze89NJLKC8vV59b29f9lqurK1xdXW+636rlX2dHs2r1+MGnu/QwnBb7y2F7OWwvh+3lsL0sFxcXDBgwQHoYTsne7XlkmGx06NABzzzzDCZNmoRLly6p91+5cuW2ry0uLkbz5s3h5uaGc+fOYd26dXe0z9q+7ia8SpjjKQoalBWzvRT2l8P2ctheDtvLYXtRVqsV58+fh9VqlR6K07F3e06G6SZJSUkIDg5Gt27dEBgYiKioKGzfvh3x8fG3fN2LL76I9PR0BAYGYsyYMeqiW7dT29f9FldYdDytYkGrC0fZXgj7y2F7OWwvh+3lsL0si8WCtLQ0WCzs72j2bq9RFP6Kie5vNy6sffHiRTRp0kR6OE6lsrISKSkp6N+/P1xcXKSH43TYXw7by2F7OWwvh+1lsb+c2rS/MTcwmUzw8PC45XN5ZJiIiIiIiIicDlccogfG+znFcPXgiQ6OpLFa0MalPhZnFUHRcpVLR2N/OWwvh+3lsL2cB6H9nFBv6SHUmkajQcOGDaHRaKSH4nTs3Z6nSVO1vvjiC7z11luwWCwoLy9HixYtsH37dmi1d35CwdChQ/Hkk09i/PjxdhvnjVMhXt/9I9zcG9ptP0RERERUO/fzZJjuLzU5TZpHhqlKZ8+exfPPP4+DBw+idevWAIBDhw7d278RU7jCn8MpVnhdOY9LDZoBGn7qwuHYXw7by2F7OWwvh+1FWa1WFBQUwMfHp0YHheju2bs9/zSpSr/88gt0Oh0aN26s3hcWFgaNRgNfX18YjUb1/vDwcOzatQsAcOzYMfTo0QOBgYEYOHAgSkpK1OclJyejW7duCA0NRefOnfHVV1+pj8XExGD27NmIjo5Gu3btMG3atGrHVlFRgZKSEpsbAGg5GXY4rWLFI0V5bC+E/eWwvRy2l8P2cthelsVigdFo5GrSAuzdnpNhqlJISAiioqLQunVrDBo0CIsWLcLp06dv+7oxY8Zg0qRJOHLkCN58801888036mN9+/bFvn37kJmZiS+//BJTpkxBRUWF+nheXh5SU1ORk5ODrVu3Ii0trcp9JCYmwtPTU735+Pjc/RsmIiIiIiKnwskwVUmr1WLDhg347rvv0K9fP+zduxeBgYE4ceJEta8pKSmB0WhUPx8cHByMqKgo9fGTJ0/iiSeeQFBQEAYOHIiioiKcPHlSfXzEiBHQ6/WoX78+DAYD8vLyqtxPQkICTCaTeisoKKibN01ERERERE6DnxmmW/L394e/vz+mTp2Kfv36YdOmTdDr9TanKpSXl1f7+l9/xnjkyJH485//jKFDhwIAGjdubPNaNzc39WudTgez2VzlNl1dXeHq6nrT/Qru4c8zP6AUaHDZzYvthbC/HLaXw/Zy2F4O28vSaDRo2rTpvb12zgPK3u15ZJiqdPr0aezdu1f9vri4GCdPnkS7du3Qvn17pKenAwD279+P3NxcAICHhwdCQ0Px6aefAgCOHDmCb7/91mYbbdq0AQCsWbMGxcXFdTrm+/VSA/czRatDQbNAthfC/nLYXg7by2F7OWwvS6/Xo0ePHtDreRzR0ezdnn+iVCWz2Yw33ngDJ0+exEMPPQSz2Yxx48bhmWeewaOPPopx48Zh+fLliIyMRGBgoPq6Tz/9FBMmTMDf/vY3dOjQAT179lQfe/fddzF06FB4eXkhNjYWrVq1qtMxa7iohMNpFCuamH5GoWdLKFzd0uHYXw7by2F7OWwvh+1lWSwWHD9+HB06dIBOx19IOJK92/M6w3Tfu3EtsYsXL6JJkybSw3EqlZWVSElJQf/+/eHi4iI9HKfD/nLYXg7by2F7OWwvi/3l1KZ9Ta4zzF8tERERERERkdPhadL0wPggpwj1PHiigyNprWb4AViSVQirlj9OHI395bC9HLaXw/Zy7rf2c0K9pYdAdEd4ZPgBYTabsWDBAvj7+yMoKAgGgwHPP/88Ll26VCfbnz9/PmbNmnXX2xk6dCiSkpIAAK+99ho+++yzu97mDVxh0fEUaHCpQTO2F8L+ctheDtvLYXs5bC9Lq9WiVatW0Go5dXI0e7e/93+1RHdk0qRJKCoqQlpaGho1agRFUbB+/XoUFRXBy8tLenhVeuONN+p0e1xh0fEUrQ5nm3SQHobTYn85bC+H7eWwvRy2l6XT6RAaGio9DKdk7/b89cYD4MSJE1i3bh1WrVqFRo0aAbh+Ta5hw4bhoYceQq9evdClSxcEBgZixowZsFqvr7qclJSEuLg4PPvsswgICECPHj1w9OhRDBo0CJ06dUKfPn1w+fJldT8FBQWIjY2Fv78/nnrqKRQWFgIALl++jIkTJyIoKAhBQUFYsGCB+ppjx46hR48eCAwMxMCBA1FSUqI+Nn78ePz9738HAOzYsQORkZEIDQ1FYGAgVqxYUeMOGqvl9k+iOqWxWvBI4XG2F8L+ctheDtvLYXs5bC/LYrEgMzMTFgv7O5q923My/AA4dOgQOnToAG/vmz+f4eXlha+++goHDx5EVlYW8vPz8fnnn6uPHzhwAO+88w6OHj2Kdu3a4amnnsKyZcvw/fffo169evjkk0/U5+7ZswfJyck4duwYfHx8kJCQAAB48803UVFRgaysLKSnp2Pjxo1Yu3YtAGDMmDGYNGkSjhw5gjfffBPffPNNle8hLCwM3377LTIzM7Fnzx688cYb+Pnnn6t8bkVFBUpKSmxuAKABPy/saBoo8Lpynu2FsL8ctpfD9nLYXg7by7Jarfjpp5/UA0rkOPZuz8nwA85qtSI+Ph6dO3dGaGgoMjIyYDQa1ccjIyPV6/2Gh4cjIiICDz/8MAAgIiICx48fV587YMAANG/eHADw/PPPY/v27QCA7du3Y8qUKdBqtWjQoAHGjh2Lbdu2oaSkBEajEePHjwcABAcHIyoqqspxFhYWYtiwYQgKCkJsbCwKCwuRk5NT5XMTExPh6emp3nx8fO6qEREREREROR9Ohh8AYWFhOH78uHra8q8tXrwY58+fR3p6OrKysjBq1CiUl5erj7u5ualf63S6m743m83V7lejqXoRh+ruv9Vj06ZNQ1RUFLKzs2E0GtGxY0ebcf5aQkICTCaTeisoKKh2f0RERERERFXhZPgB0L59ewwZMgSTJk1SV49WFAUbNmzAwYMH0bx5c7i5ueHcuXNYt25drfeTkpKCX375BQDw8ccfIy4uDgAQFxeHFStWQFEUXLlyBatXr0afPn3g4eGB0NBQfPrppwCAI0eO4Ntvv61y28XFxWjdujU0Gg12796Nw4cPVzsOV1dXeHh42NwAQNHwr7OjKRotLnj4sL0Q9pfD9nLYXg7by2F7WVqtFn5+flxNWoC923M16QfEypUrsXDhQnTr1g16vR5WqxU9e/bEu+++i6FDhyIwMBAtWrRQJ7C1ER0djVGjRuH06dPo0KGDeomkV199FTNnzkRwcDAAYNiwYRg+fDgA4NNPP8WECRPwt7/9DR06dEDPnj2r3Paf//xnTJ8+HW+++SYMBgO6detW4/HxHwjHUzRaXPRqJT0Mp8X+ctheDtvLYXs5bC9Lp9PB399fehhOyd7tNYqi8JP4dF8rKSmBp6cnCgsL0bhxY+nhOBWz2Yz9+/eja9eu0Ov5uzVHY385bC+H7eWwvRy2l8X+cmrT/sbcwGQyqWeQVoeH0uiBwd/rOJ6iKLhw4QLbC2F/OWwvh+3lsL0ctpfF/nLs3Z6/2qAHxgc5RajnwR9SjqS1muEHYElWIaxa/jhxNPaXw/Zy2F4O28u519vPCb358p5E9wMeGSYiIiIiIiKnw8nwfcLX19fm+sCOtn37dkRHR6Ndu3YIDw9H7969sWfPnjrfz65du/D111/X6rVWLqDlcFaNFmcbt2N7Iewvh+3lsL0ctpfD9rJ0Oh0MBgN0Op30UJyOvdvfe+dZkENYLJY7/ku1fft2jBkzBhs2bECPHj0AAMePH7/l5Y9qa9euXbh06RL69etX8xfzHwjH02hxyb259CicF/vLYXs5bC+H7eWwvSitVovWrVtLD8Mp2bs9Zw/3mb1796qXMLohJiYGX375JQBg9erVCAkJQUhICAYMGIDTp08DAJKSktCrVy8MGTIEwcHB+O677+Dv72/zYfQePXpgy5YtN+1zwYIFePXVV9WJMAB06NABQ4cOBQBkZGSgR48eCAkJQdeuXbF3714A11d/69u3L8LDwxEYGIhRo0bhypUrAK5Pph977DF07twZwcHBmDdvHoxGI5YtW4bPPvsMBoMBb7zxRpUNKioqUFJSYnMDAI3VUqumVHsaqwVtzx5ieyHsL4ft5bC9HLaXw/ayzGYzdu7cCbPZLD0Up2Pv9pwM32cee+wxVFRUICMjAwDw448/Ijc3FwMGDEBOTg7+9Kc/YcuWLcjKykKPHj0wefJk9bXp6el4++23kZ2djejoaDRp0gTbtm0DAGRmZuLChQtVHpE9ePAgIiMjqxzPtWvXMHjwYLz++uvIysrC4sWLMWTIEFy+fBk6nQ7JycnIyMhATk4OPD09sXTpUgDA+++/jyeffBKHDx9GdnY2XnrpJRgMBkybNg2jR4+G0WjEa6+9VuU+ExMT4enpqd58fHwAABpw8SxH00CBa2UZ2wthfzlsL4ft5bC9HLaXpSgKSktLuZq0AHu352T4PjRhwgSsWrUKAPDJJ59g9OjR0Ov1SE1NRb9+/fDoo48CAKZPn46dO3fCYrn+W8QePXrAz89P3c6LL76I999/HwDwwQcfYPr06dBoNDUaS25uLrRaLfr27QsAiIqKwsMPPwyj0QhFUbBkyRKEhoYiJCQEmzdvVj/33LNnT3z00Ud45ZVX8L///Q9eXl53vM+EhASYTCb1VlBQUKMxExERERERcTJ8Hxo3bhw+//xzlJWV4dNPP8WECROqfN5vJ7bu7u423w8ePBhZWVnIzMzEpk2bqt1Oly5dkJaWdsfju7Hf5ORk7Ny5E9988w2ys7Mxe/ZslJeXAwCGDBmCvXv3ws/PTz1KfKdcXV3h4eFhcyMiIiIiIqoJTobvQy1atEBERAT++Mc/olmzZggMDAQA9OrVC19//TXOnDkDAFi2bBl69+5d7UJZer0e06ZNw9NPP41BgwZVe3T21VdfxcKFC7Fv3z71vry8PKxfvx5+fn6wWq3q6dbfffcdzp07B4PBgOLiYnh7e8PDwwOlpaVISkpSX3/8+HE8/PDDGDt2LP7yl7+o2/bw8IDJZKpVF6uGK/w5mlWjw09NA9heCPvLYXs5bC+H7eWwvSydTofIyEiuJi3A3u25mvR9asKECRg+fDj++c9/qvcFBQVh0aJF6ud+fXx88NFHH91yO5MmTcLcuXMxY8aMap/Tp08frFq1CrNnz8a5c+dQv359NGvWDAsWLEC9evXwxRdfYObMmXj55Zfh5uaG9evXw93dHWPHjsWXX34JPz8/NG3aFNHR0Th16hQAYP369VizZg3q1asHq9WKZcuWAQAGDRqE1atXw2AwYPDgwdV+brgqLxm8eZRYRFPpATg59pfD9nLYXg7by2F7KVqtFs2aNZMehlOyd3uNwk+CO7X169fjn//8J3bs2CE9lForKSmBp6cnLl68iCZNmkgPx6lUVlbif//7H/r06QMXFxfp4Tgd9pfD9nLYXg7by2F7Wewvpzbtb8wNTCbTbQ+U8ciwE+vXrx9++OEH/Oc//5EeCt3HeJkBWewvh+3lsL0ctpfD9rLYX44923My7MS+/vpr6SHUqQ9yilDPgyc6OJLWaoYfgCVZhbBq+ePE0dhfDtvLYXs5bC/nXmo/J9RbdP9EdYkLaFGd8fX1RbNmzVBZWanel5qaCo1Gg1mzZt3ytfPnz1efs2nTJvzxj3+040iJiIiIiMjZcTJMdapVq1bYtGmT+v2KFSsQHh5eo208/fTTWLJkSY33zRUWHc+q0SGveSjbC2F/OWwvh+3lsL0ctpel1+vRq1cv6PU8I8LR7N2ek2GqUxMmTMDKlSsBACaTCfv27VNXt87OzkZUVBTCwsIQEBCAhQsXVrmNpKQkDBw4sNp9VFRUoKSkxOZGcsz6etJDcGrsL4ft5bC9HLaXw/ay6tevLz0Ep2XP9pwMU5167LHHkJ+fjzNnzuBf//oXhg0bpl4XzNfXFzt27MChQ4dw8OBBbNiwwebaxXcqMTERnp6e6s3HxwcAoFUsdfpe6Pa0igV+P6ezvRD2l8P2ctheDtvLYXtZZrMZKSkpXERLgL3bczJMdW7MmDFISkrCypUrMXHiRPX+srIyTJ48GcHBwejevTtOnToFo9FY4+0nJCTAZDKpt4KCgjocPREREREROQOe+E51buzYsQgLC0PHjh3RoUMH9f65c+fC29sbmZmZ0Ov1GDx4MMrLy2u8fVdXV7i6utblkImIiIiIyMlwMkx1rkWLFkhMTIS/v7/N/cXFxejUqRP0ej1yc3Oxbds29OzZU2iURERERETkzDgZJruYMGHCTffNmzcPY8aMwSeffIJ27dohNja2TvfJFRYdz6rRIbdlN7YXwv5y2F4O28thezlsL0uv16N///5cTVqAvdtrFEVR7LJlIgcpKSmBp6cnLl26BE9PT+nhOBVFUVBaWoqGDRtCo9FID8fpsL8ctpfD9nLYXg7by2J/ObVpf2NuYDKZ4OHhccvncgEtemBwhT/HM5vNSE1NZXsh7C+H7eWwvRy2l8P2sthfjr3b81g/PTA+yClCPQ+e6OBIWqsZfgCWZBXCquWPE0djfzlsL4ft5bC9HKn2c0K9HbYvIgk8MnwbZrMZCxYsgL+/P4KCgmAwGPD888/j0qVL0kMDAAwbNgxpaWkAgPnz52PWrFnqY6tXr0br1q2Rk5ODTZs24Y9//GOt95OUlISBAwfe5WjvzOzZs5GcnOyQfRERERERkXPir/VuY9KkSSgqKkJaWhoaNWoERVGwfv16FBUVwcvLS3Rs+/fvR1FRESIjI2967L333sP777+PXbt2oU2bNggKCsLTTz8tMMqa+7//+z9ERUVhxIgR0Om4UMS9zsLFPESxvxy2l8P2ctheDtvL4uJZcuzZnkeGb+HEiRNYt24dVq1ahUaNGgEANBoNhg0bhrZt2wIAFi1ahMDAQAQHB2P06NEwmUwArh+lHTFiBJ566ikEBAQgNjYWRUVFAIDKykpMnz4dHTt2RPfu3fHyyy8jJiZG3W912/yt5cuXY9SoUTfdv2DBAqxcuRK7d+9GmzZtANge2d21axeCgoIwffp0dO7cGYGBgcjIyAAAXLhwAX369EFwcDBCQkJsVoW+fPkynn32WQQHByM8PBw//vgjAODcuXPo1asXunTpgsDAQMyYMQNWqxUAsG/fPnTp0gUGgwFBQUH45z//CQBITk5Gt27dEBoais6dO+Orr75S99OsWTO0a9cO//vf/2rwpwWesiXAqtXjB5/ubC+E/eWwvRy2l8P2cthelouLCwYMGAAXFxfpoTgde7fnZPgWDh06hA4dOsDbu+rPS2zZsgUrV67E3r17kZ2djQYNGmDOnDnq4+np6UhKSsLRo0fRrFkzLF++HADw4Ycf4vjx4zhy5Aj27NmDrKysO97mr+3atQvdunWzuW/NmjX497//jV27dqF58+bVvrdjx45h3LhxOHz4MF544QW88sor6uvbtGmD7OxsZGVl4W9/+5v6mgMHDuDtt99GdnY24uLi8M477wAAvLy88NVXX+HgwYPIyspCfn4+Pv/8cwBAYmIiZs+eDaPRiJycHIwcORIA0LdvX+zbtw+ZmZn48ssvMWXKFFRUVKj7ioyMxI4dO6oce0VFBUpKSmxuAAAujO54ioIGZcVsL4X95bC9HLaXw/Zy2F6U1WrF+fPn1YM95Dj2bs/J8F3Yvn07RowYoZ4u/fvf/x7btm1TH+/Xrx+aNGkC4PrkLi8vDwCwY8cOPPfcc3BxcYGLiwvGjRt3x9v8tZ9//hkPP/ywzX3h4eEoKSnBf//731uOvX379upE+tdj6969O7Zs2YKXX34ZX375JRo0aKC+JjIyUj3S/OvXWK1WxMfHo3PnzggNDUVGRgaMRiMAoFevXnjzzTfxxhtv4Ntvv1WPsJ88eRJPPPEEgoKCMHDgQBQVFeHkyZPqvpo3b46ff/65yrEnJibC09NTvfn4+AAAtIrllu+Z6p5WsaDVhaNsL4T95bC9HLaXw/Zy2F6WxWJBWloaLBb2dzR7t+dk+BbCwsJw/PhxFBYW3tHzf3vtKzc3N/VrnU5X7ZLgt7pm1q0ee+ihh1BeXm5zn7+/P7Zv3474+Hh8+umn1b62urFFRkbCaDSiW7du+OKLLxAREaH+5avuNYsXL8b58+eRnp6OrKwsjBo1Sh3XrFmzsHnzZjzyyCOYO3cupk+fDgAYOXIkJk+ejJycHBiNRri7u9u8l/LyctSvX7/KsSckJMBkMqm3goKCat8nERERERFRVTgZvoX27dtjyJAhmDRpkrp6tKIo2LBhA3788UfExcXh888/V0/TXb58Ofr06XPb7cbGxiI5ORmVlZWorKy0mbTWZJshISHIzc296f5OnTphx44dSEhIwKpVq2r0nk+ePAl3d3cMHz4cS5cuxQ8//IDLly/f8jXFxcVo3rw53NzccO7cOaxbt059LDc3F23atMGUKVMwd+5c7Nu3T33NjaPMa9asQXFxsc02v//+e3Tu3LnK/bm6usLDw8PmRkREREREVBP8FP5trFy5EgsXLkS3bt2g1+thtVrRs2dP9O7dG0888QRycnIQGRkJrVaLkJAQ/OMf/7jtNqdOnYrs7GwEBASgUaNGCA8Px5kzZwCgRtscOnQotm7diri4uJse8/f3x86dO9G7d29YLJY7XoVt165dWLx4sXrkd9GiRfD09Lzla1588UUMHToUgYGBaNGihc143n//fezcuRP16tWDTqdTP4P87rvvYujQofDy8kJsbCxatWqlvkZRFOzYsaPaz0pXR0H1R9HJPhRoUOFSn+2FsL8ctpfD9nLYXg7by9JoNGjYsOEtz9gk+7B3e42i8JP4EkpLS9GwYUNUVlZi9OjR6NKlC+Lj42u0jcuXL6NHjx5IS0uz+Wzv/e7rr7/GmjVrsGbNmjt6fklJCTw9PWEymXiUmIiIiIjIidVkbsDTpIXExcXBYDAgODgYHh4emDlzZo234e7ujiVLltgsPPUgMJlM+Mtf/lLj13GFP8ezWq04deoU2wthfzlsL4ft5bC9HLaXxf5y7N2ep0kLSU9Pr5Pt9O7du062cy8ZMWJErV733uELqOdR9SJlZB9aqxl+Pxux9qIrr30ogP3lsL0ctpfD9nIc0X5OaNWXEqXrKxobjUa0aNECWi2PJTqSvdvzT5OIiIiIiIicDifDTqqyshILFiyAv78/AgMDERoaioEDB6rXB7a3jIyMWh8BJiIiIiIiuls8x8VJTZgwAZcvX0ZaWhoaNWoEANi+fTtyc3NhMBjuevtms/mWK1iHh4dj7dq1d72fX+MKi46nQIPLbl5sL4T95bC9HLaXw/Zy2F6WRqNB06ZNuZq0AHu352rSTuj48eMwGAwoKChA48aNq3zOX//6V3z++ecwm81o1qwZli9fjtatW+Py5cuYOXMm9u/fDwAYNmwYXn/9dQBATEwMQkJCcODAAdSvXx9bt27Fiy++iO3bt6Nx48Z47LHHcPDgQezatQu7du3CrFmzYDQaYTabMWDAABQWFqKsrAydO3fGRx99VO0K2RUVFaioqFC/LykpgY+PD17f/SPc3BvWcS0iIiKiBx8/M0wPCq4mTbeUmZmJ9u3bVzsRTk5ORm5uLtLS0nDo0CGMHj0a06dPBwC8+eabqKioQFZWFtLT07Fx40abI7w//PADdu/ejZ07d+LDDz/E8ePHceTIEezZswdZWVlV7k+n0yE5ORkZGRnIycmBp6cnli5dWu34ExMT4enpqd58fHwAABqFK/w5mkaxwvvST2wvhP3lsL0ctpfD9nLYXpbFYsGxY8dgsVikh+J07N2ek2FCXl4eDAYD/Pz8MGHCBGzcuBHbt29Hly5dYDAY8Je//AU//fQTgOunUk+ZMgVarRYNGjTA2LFjsW3bNnVbzz33HFxcXAAAO3bsUL93cXHBuHHjqty/oihYsmQJQkNDERISgs2bN9/ys8sJCQkwmUzqraCgAAAnwxI0ihVNSwrYXgj7y2F7OWwvh+3lsL0sq9WK3NxcXlpJgL3b8zPDTig0NBQnTpxAcXExGjVqhHbt2sFoNCIpKQkbN26Ei4sLEhIS8Pzzz992W789f9/d3f2On3tDcnIydu7ciW+++QYeHh547733sHPnzmq34+rqCldX19uOjYiIiIiIqDo8MuyEOnTogGeeeQaTJk3CpUuX1PuvXLkCABg4cCCWLVuGoqIiANdXns7MzAQAxMXFYcWKFVAUBVeuXMHq1avRp0+fKvcTGxuL5ORkVFZWorKyEp9++mmVzysuLoa3tzc8PDxQWlqKpKSkunuzREREREREVeCRYSeVlJSEt956C926dYNer0ejRo3QtGlTxMfHIzIyEoWFhejVqxeA6ytDT5w4EaGhoXj11Vcxc+ZMBAcHA7i+gNbw4cOr3MfUqVORnZ2NgIAANGrUCOHh4Thz5sxNzxs7diy+/PJL+Pn5oWnTpoiOjsapU6dq/J5eCPFWV8Ymx7BYLMjStsKTIU2h0+mkh+N02F8O28thezlsL4ftZWm1WrRq1QpaLY8jOpq923M1abKr0tJSNGzYEJWVlRg9ejS6dOmC+Pj4Ot1HTVaMIyIiIiKiBxdXk6Z7RlxcHAwGA4KDg+Hh4YGZM2fabV9c4c/xLBYLMjMz2V4I+8thezlsL4ft5bC9LPaXY+/2PE2a7Co9Pd1h+1qadRH1PPhDypG0VjP8fv4J26yPwKrljxNHY385bC+H7eWwvRx7tOd1he+c1WrFTz/9hKCgIJ6m7mD2bu+UR4bNZjMWLFgAf39/BAUFwWAw4Pnnn7dZTErSsGHDkJaWBgCYP38+mjZtCoPBgM6dOyMiIgLfffed8Airl5GRgREjRgAA8vPz4eXlpT6m0WjuqHF0dDROnjxppxESERERERE56WR40qRJyMjIQFpaGnJycpCZmYnHH39cXT1Z0v79+1FUVITIyEj1vtGjR8NoNOLw4cN4+eWX8eKLL1b5WrPZ7KhhVis8PBxr1669q228/PLLeP311+toRERERERERDdzusnwiRMnsG7dOqxatUpdeVij0WDYsGFo27YtAGDRokUIDAxEcHAwRo8eDZPJBOD6UdoRI0bgqaeeQkBAAGJjY20uPzR9+nR07NgR3bt3x8svv4yYmBh1v9Vt87eWL1+OUaNGVTt+k8lks2KyRqPB66+/joiICCQkJCA7OxtRUVEICwtDQEAAFi5cqD736aefhsFggMFggK+vL9q0aQPg+gT2xv0tWrRQV5FevHgxIiIiYDAYEBERoR6t/uyzz/Dkk0+q21UUBW3btsXhw4exa9cuGAyG2/45HD9+HAMGDEBERARCQkLw/vvvq48NGDAAW7ZsqbZRRUUFSkpKbG4AoGic7q+zOEWjxQUPH7YXwv5y2F4O28thezlsL0ur1cLPz4+rSQuwd3un+xM9dOgQOnToAG/vqj8nsWXLFqxcuRJ79+5FdnY2GjRogDlz5qiPp6enIykpCUePHkWzZs2wfPlyAMCHH36I48eP48iRI9izZw+ysrLueJu/tmvXLnTr1s3mvs8++wwGgwFt2rTB3Llz8fbbb9s8rtPpcODAASxatAi+vr7YsWMHDh06hIMHD2LDhg3Yt28fAGDTpk0wGo349ttv0aRJEyxYsADA9VObjUYjtmzZggYNGmDevHkAgDFjxuDAgQMwGo1YunQpJkyYAAAYPHgw9u3bh3PnzqljbtSoETp37nxHfwYWiwXPPvss/va3v+HAgQPYt28fPvzwQxw4cAAA4OLiguDgYOzZs6fK1ycmJsLT01O9+fj4AOBkWIKi0eKiVyu2F8L+ctheDtvLYXs5bC9Lp9PB39+fnxcWYO/2/C/qN7Zv344RI0aon3X9/e9/j23btqmP9+vXD02aNAEAREZGIi8vDwCwY8cOPPfcc3BxcYGLiwvGjRt3x9v8tZ9//hkPP/ywzX03TpM+efIkPv/8cwwePBhlZWXq4xMnTlS/Lisrw+TJkxEcHIzu3bvj1KlTMBqN6uNmsxnDhg3DoEGDMHbsWPX+0tJSDBgwAK+99hp69+4NAMjMzMTvfvc7BAUFYdq0acjNzUVZWRnq16+PIUOGYPXq1QCuX7P4xkT5TuTm5uLIkSMYOXIkDAYDevTogdLSUhw9elR9TvPmzfHzzz9X+fqEhASYTCb1VlBQAADQWLl4lqNprBb4nD/C9kLYXw7by2F7OWwvh+1lmc1mfPfdd/fERxKdjb3bO91SgGFhYTh+/DgKCwvVSe2taDQam+/d3NzUr3U6XbV/ML993Z0+9tBDD6G8vLzax3v37o3y8nLk5OQgIiICAODu7q4+PnfuXHh7eyMzMxN6vR6DBw+22d60adPQsmVL9egv8P8myEOGDMGYMWMAANeuXcPgwYORmpqKiIgI9XpdFRUVqF+/PiZOnIgJEybg97//Pf773/9iyZIl1Y75txRFQePGjW0m6b9VXl6O+vXrV/mYq6srXF1db7pfA14y29E0UOBefgkaKKwvgP3lsL0ctpfD9nLYXpaiKLhw4QIUhfUdzd7tne7IcPv27TFkyBBMmjRJXdlYURRs2LABP/74I+Li4vD555+rn0Ndvnw5+vTpc9vtxsbGIjk5GZWVlaisrMSnn36qPlaTbYaEhCA3N7fa/Rw+fBiXL1+Gr69vlY8XFxejZcuW0Ov1yM3NtTkC/cYbb+DMmTP45z//afOaqVOnolWrVnjllVfU+8rLy3Ht2jW0atUKALB06VKb19w4lXv27NmIi4tD48aNqx3zb/n5+cHDwwOrVq1S7ztx4oTNAmbff//9HZ92TUREREREVFNOd2QYAFauXImFCxeiW7du0Ov1sFqt6NmzJ3r37o0nnngCOTk5iIyMhFarRUhICP7xj3/cdptTp05FdnY2AgIC0KhRI4SHh+PMmTMAUKNtDh06FFu3bkVcXJx632effYZdu3ZBURRoNBqsXr0aTZs2rfL18+bNw5gxY/DJJ5+gXbt2iI2NVR97/fXX0bFjR4SHhwMAWrRogX/+859YuXKleokp4PqCWh9//DEWLlyIrl27wtvbGyNHjrxpXxMmTMD//d//YcuWLbft82t6vR7//e9/MWvWLCxZsgQWiwXe3t5ITk4GcP2STBaLhZNhIiIiIiKyG43C4/11prS0FA0bNkRlZSVGjx6NLl26ID4+vkbbuHz5Mnr06IG0tDQ0aNDATiO9t82ZMwft27fH5MmT7+j5N07hLi4utrmuMdmf1WpFQUEBfHx8uMKiAPaXw/Zy2F4O28the1nsL6c27W/MDUwmEzw8PG75XE6G61C3bt1QUVGB8vJyREVFYenSpdV+7vVWduzYgYcffhhBQUF2GOW977333sOMGTPs8heeiIiIiIgeXJwMk1O58Rd+/q7jcPXwkh6OU9FYLWjzy2GcfLgzFC0vN+Bo7C+H7eWwvRy2l3O37eeEVn1JUbozZrMZu3fvRs+ePaHXO+WnTMXUpn1NJsM8zn+f8vX1RbNmzVBZWanel5qaCo1Gg1mzZtXpviZPnozU1FQAwMaNG9XrFt+NjIwMjBgx4q6382tcTdrxNFDgWlnG9kLYXw7by2F7OWwvh+1lKYqC0tJSriYtwN7tORm+j7Vq1QqbNm1Sv1+xYoW6OFZdsVgs+Pjjj9GrVy8AdTcZDg8Px9q1a+96O0RERERERLXByfB9bMKECVi5ciUAwGQyYd++fejXrx8AIDs7G1FRUQgLC0NAQAAWLlyovm7+/Pk2R4/ff/99jB8/HgCQlJSEXr16YciQIQgODsb+/fsRExODjRs3IiUlBZs2bcKiRYtgMBjw8ccf49y5c+jVqxe6dOmCwMBAzJgxA1arVd1WXFwcnn32WQQHByM8PBw//vgjAGDXrl3q6tVmsxl9+/ZFeHg4AgMDMWrUKFy5csXO9YiIiIiIyJnxpPf72GOPPYZ//OMfOHPmDDZt2oRhw4ZBp7v+ORJfX1/s2LEDrq6uKCsrQ48ePRAXF4fu3bvfdrvp6enIzMyEn5+fzf39+/fH008/DYPBoE6my8vL8dVXX8Hd3R0WiwXPPPMMPv/8c/VSTAcOHIDRaESbNm0wZ84cvPPOO1i+fLnNdnU6HZKTk9GkSRMoioLp06dj6dKlmDNnTpXjq6ioQEVFhfr9jes3WzX8/JKjWTU6/NQ0gO2FsL8ctpfD9nLYXg7by9LpdIiMjFT/P5scx97teWT4PjdmzBgkJSVh5cqVmDhxonp/WVkZJk+ejODgYHTv3h2nTp2C0Wi8o2326NHjpolwdaxWK+Lj49G5c2eEhoYiIyPDZj+RkZFo06aN+nVeXt5N21AUBUuWLEFoaChCQkKwefPmW441MTERnp6e6s3Hx+f6AxrNHY2Z6pBGgyv1G7G9FPaXw/Zy2F4O28the1FarRbNmjXjZZUE2Ls9/0Tvc2PHjsV7770HNzc3dOjQQb1/7ty58Pb2RmZmJg4fPoyYmBiUl5cDAPR6PSwWi/rcG/ff4O7ufsf7X7x4Mc6fP4/09HRkZWVh1KhRNttzc3NTv9bpdDCbzTdtIzk5GTt37sQ333yD7OxszJ49+6Yx/VpCQgJMJpN6KygoAABorTdvm+xLazWjY8E+thfC/nLYXg7by2F7OWwvq7KyEps3b7ZZuJYcw97tORm+z7Vo0QKJiYl45513bO4vLi5Gy5YtodfrkZubi23btqmPtW/fHhkZGbBYLLh69So2bNhwx/vz8PCAyWSy2U/z5s3h5uaGc+fOYd26dTV+D8XFxfD29oaHhwdKS0uRlJR0y+e7urrCw8PD5kZydIrl9k8iu2F/OWwvh+3lsL0ctpdV1QEdcgx7tudnhh8AEyZMuOm+efPmYcyYMfjkk0/Qrl07xMbGqo8NHjwY69atQ6dOndCyZUuEhobi6tWrd7SvMWPGYPz48di4cSP+8Ic/4MUXX8TQoUMRGBiIFi1aIC4ursbjHzt2LL788kv4+fmhadOmiI6OxqlTp2q8HSIiIiIiojulUXjBLLrP3biw9sWLF9GkSRPp4TiVyspKpKSkoH///nBxcZEejtNhfzlsL4ft5bC9HLaXxf5yatP+xtzAZDLd9gxSTobpvnfjL/ylS5fg6ekpPRyncuNC6A0bNoSGi3o4HPvLYXs5bC+H7eWwvSz2l1Ob9jWZDPM0aXpgLDlcCLeGXNjAoRQFWsUCq6aCK1xKYH85bC+H7eWwvZwatJ8T6u2gQTmX+vXrSw/BadmzPRfQolrx9fW940s13TB79mzMnz8fALBs2TIsWrSoTsek5cISDqdVLPD7OZ3thbC/HLaXw/Zy2F4O28sym81ISUnhIloC7N2eR4ZJxLRp06SHQEREREREToxHhumuxMTEYPbs2YiOjka7du1sJrlnz55F3759ERAQgLi4OPz888/qY/Pnz8esWbMAANnZ2YiKikJYWBgCAgKwcOFCR78NIiIiIiJyMjwyTHctLy8PqampqKysREBAANLS0hAZGYmZM2eia9eu2Lp1K06fPg2DwQB/f/+bXu/r64sdO3bA1dUVZWVl6NGjB+Li4tC9e/cq91dRUYGKigr1+5KSEru9NyIiIiIiejDxyDDdtREjRkCv16N+/fowGAzIy8sDAOzYsQOTJ08GADz66KN4+umnq3x9WVkZJk+ejODgYHTv3h2nTp265eeRExMT4enpqd58fHwAAFaNrm7fGN2WVaNDbstubC+E/eWwvRy2l8P2cthell6vR//+/aHX8ziio9m7PSfDdNfc3NzUr3U6XbUfcK9uOfS5c+fC29sbmZmZOHz4MGJiYlBeXl7t/hISEmAymdRbQUHB3b0Buit68zXpITg19pfD9nLYXg7by2F7WWVlZdJDcFr2bM/JMNlNXFwcVq5cCeD654c3bdpU5fOKi4vRsmVL6PV65ObmYtu2bbfcrqurKzw8PGxuAFeTlqBVLGh3LpPthbC/HLaXw/Zy2F4O28sym81ITU3latIC7N2ex/rJbt59912MHz8eAQEBePTRRxEbG1vl8+bNm4cxY8bgk08+Qbt27ap9HhERERERUV3hZJhqJT8/HwCwa9cum/vXr1+vfv3II49g69atVb7+xvWGASA0NBQ5OTl3PaY/BDVGkyZN7no7dOcqKyuR8jPwx5AmcHFxkR6O02F/OWwvh+3lsL0ctieyD54mTUR3hYtJyGJ/OWwvh+3lsL0ctpfF/nLs2V6jKIpit60TOUBJSQk8PT1hMpnUzw8TEREREZHzqcncgL/ioAfGYuNFuDXkSosOpShoUH4JV9y8gGpWCyc7Yn85bC+H7eWwvZxq2s8J9ZYbkxOxWq24ePEivL29odXyxFpHsnd7/mk6mdLSUri7u2PSpEmi4zhz5gyio6PrdJtcYdHxtIoFrS4cZXsh7C+H7eWwvRy2l8P2siwWC9LS0mCxsL+j2bs9J8NOZu3atejSpQu++OILXL58ucrnOOI/9BYtWmDPnj123w8REREREVFVOBl2MitWrEB8fDx69uyJtWvXAgCSkpLQq1cvDBkyBMHBwdi/fz80Gg3eeustdOvWDb6+vti4cSMSExMRHh6ODh062KwivXXrVkRFRaFLly7o2rUrUlNTAVxfaTooKAjTp09H586dERgYiIyMDADXV6P28vJStzF69GiEh4cjJCQEAwYMwLlz56p9DxUVFSgpKbG5ERERERER1QQnw07k6NGjKCgoQN++fTFp0iSsWLFCfSw9PR1vv/02srOzERkZCQBwd3dHeno6VqxYgeeeew6PPPIIMjIy8Pbbb+NPf/oTAODHH3/E/PnzkZKSgoMHDyI5ORmjRo1CRUUFAODYsWMYN24cDh8+jBdeeAGvvPJKlWP7+9//joyMDGRlZSE6Otrm0ku/lZiYCE9PT/Xm4+MDAFDAzy85mgINKlzqs70Q9pfD9nLYXg7by2F7WRqNBg0bNoSGn5V3OHu352rSTuTll1+Gq6sr3n77bZjNZvj4+GDnzp1IT0/HmjVrsH37dvW5Go0GZ8+eRfPmzWEymeDl5YWysjK4ubnh1KlT6Ny5My5duoR//OMfeP311/Hoo4+qr71w4QJ27dqF06dPY9q0aTh27BgA4PDhwxgyZAhOnDiB/Px8GAwGXLp0CQDw7rvvYvXq1SgvL0d5eTm8vb2xb9++Kt9HRUWFOtkGrq8Y5+Pjg9d3/wg394Z2KEdERER07+ECWkQ3q8lq0jwy7CQqKyuxevVqfPLJJ/D19UX79u1x9epV9eiwu7v7Ta9xc3MDAOh0upu+N5vNAABFUfD444/DaDSqt9OnT6NDhw42r/nt637t22+/xXvvvYeUlBTk5ORg8eLFKC8vr/a9uLq6wsPDw+Z2fTDWmmahu6VY4XX5HNtLYX85bC+H7eWwvRy2F2W1WnHq1ClYrezvaPZuz8mwk9i0aRPatm2L06dPIz8/H/n5+di3bx9Wr16NysrKWm+3b9++2L59O7KystT79u/fX6NtFBcXo2HDhmjSpAmuXbuG5cuX12osWv4D4XBaxYpHivLYXgj7y2F7OWwvh+3lsL0si8UCo9HI1aQF2Ls9J8NOYsWKFRg9erTNfZ06dcKjjz6K0tLSWm+3ffv2SE5OxtSpU9G5c2d06tQJf//732u0jX79+sHPzw9+fn6Ijo6GwWCo9XiIiIiIiIjuBD8zTPe9G58LuHjxIpo0aSI9HKdSWVmJlJQU9O/fHy4uLtLDcTrsL4ft5bC9HLaXw/ay2F9ObdrzM8PklLjCn+NpNBo0bdqU7YWwvxy2l8P2ctheDtvLYn859m7PI8N037vx2x+uJk1EREQPKq4cTXRneGSY8MUXX6BLly4wGAzw9/dHbGxstauwZWRkYMSIEVU+Nn/+fMyaNavOxpWUlISBAwfW2fZ+TcNFJRxOo1jhfeknthfC/nLYXg7by2F7ORrFimPHjnEBJyEWi4X9hdi7PSfDD6CzZ8/i+eefxxdffAGj0Yhjx47hr3/9a5WnF5jNZoSHh2Pt2rUCI61b/MfZ8TSKFU1LCtheCPvLYXs5bC+H7eVoFCtyc3N5aR8hViv7S7F3e06GH0C//PILdDodGjdurN4XFhamToZ9fX0RHx+Prl27Yty4cdi1a1eNV3DOzs5GVFQUwsLCEBAQgIULF6qPzZ8/HyNGjMBTTz2FgIAAxMbGoqio6KZtnDlzBhEREVi5ciXMZjP69u2L8PBwBAYGYtSoUbhy5UqV+66oqEBJSYnNjYiIiIiIqCY4GX4AhYSEICoqCq1bt8agQYOwaNEinD592uY5hYWFSE9Px2effVarffj6+mLHjh04dOgQDh48iA0bNmDfvn3q4+np6UhKSsLRo0fRrFmzm64dnJ2djccffxxvvfUWJk6cCJ1Oh+TkZGRkZCAnJweenp5YunRplftOTEyEp6enevPx8anVeyAiIiIiIufFyfADSKvVYsOGDfjuu+/Qr18/7N27F4GBgThx4oT6nPHjx9/VqmxlZWWYPHkygoOD0b17d5w6dQpGo1F9vF+/fupljiIjI5GXl6c+duTIETz99NNITk5Gnz59AACKomDJkiUIDQ1FSEgINm/ebLO9X0tISIDJZFJvBQUF17cBrvDnaAo0uNSgGdsLYX85bC+H7eWwvRwFGrRq1QpaLf/XXYJWq2V/IfZuzz/RB5i/vz+mTp2KjRs3onv37ti0aZP6mLu7+11te+7cufD29kZmZiYOHz6MmJgYlJeXq4+7ubmpX+t0OpjNZvX7Fi1a4OGHH8bOnTvV+5KTk7Fz50588803yM7OxuzZs22292uurq7w8PCwuQGAotXd1XuimlO0Opxt0oHthbC/HLaXw/Zy2F6OotUhNDQUOh3bS9Dp2F+KvdtzMvwAOn36NPbu3at+X1xcjJMnT6Jdu3Z1to/i4mK0bNkSer0eubm52LZt2x2/tlGjRti2bRs2btyIN954Q92et7c3PDw8UFpaiqSkpBqPSWPlCn+OprFa8EjhcbYXwv5y2F4O28thezkaqwWZmZlczViIxcL+UuzdnpPhB5DZbMYbb7yBjh07wmAwIDo6GuPGjcMzzzxTq+2tWLECLVu2VG+LFy/GvHnzsGrVKoSEhGDOnDmIjY2t0TYbNmyIr7/+Gt999x3+9Kc/YezYsbh69Sr8/PzwxBNPIDo6usbj1ICXzHY0DRR4XTnP9kLYXw7by2F7OWwvRwMFP/30E1czFmK1WtlfiL3baxRF4U80uq/duLD2xYsX1c8pk2NUVlYiJSUF/fv3h4uLi/RwnA77y2F7OWwvh+3lsL0s9pdTm/Y35gYmk0n9OGV1eGSYiIiIiIiInI5eegBEdeX9I5fg2pAnOjiSRrGiiYcPFmcXQ9Hwd2uOxv5y2F4O28the8eaE+qtfq3VauHn58fVjIWwvxx7t+efqJPx9fWFn58fDAaDesvOzsb8+fNtVm8eP348/v73v1e5jWXLlmHRokV3NY7XXnut1tc4rg7/YXY8RaPFRa9WbC+E/eWwvRy2l8P2cnQ6Hfz9/bmasRD2l2Pv9jwy7ITWrl0Lg8Fgc19ISAhmzZplc0mk6kybNu2ux3BjFem6xNUtHU9jtaDlxWP42dufl9oQwP5y2F4O28thezlmsxn79+9H165dodfzf98djf3l2Ls9f7VH6uQ2OjoaBoMB58+fBwB8//336N27Nzp27IjBgwfj2rVrAID58+dj1qxZAK4vd/6nP/0JQUFBCAoKwgsvvKA+b/z48Zg4cSJ69OiBjh07Yty4cSgrK1Mfu3HkeceOHYiMjERoaCgCAwOxYsWKWr0Prm7peBoocC+/xPZC2F8O28thezlsL0dRFFy4cAFc91YG+8uxd3tOhp3QiBEjbE6TXrJkCQBgz549MBqNaNasGQDAaDTiq6++wvfff49ffvkFGzZsuGlbH374IQ4cOICDBw/CaDQiLy9P3R4ApKenY+vWrfj+++9RVFRk89gNYWFh+Pbbb5GZmYk9e/bgjTfewM8//1zt+CsqKlBSUmJzIyIiIiIiqglOhp3Q2rVrYTQa1Vv9+vWrfN6gQYPw0EMPQafToWvXrsjLy7vpOdu3b8f48ePh6uoKvV6PKVOmYNu2berjw4cPR8OGDaHT6TBp0iRs3779pm0UFhZi2LBhCAoKQmxsLAoLC5GTk1Pt+BMTE+Hp6anefHx8alGBiIiIiIicGSfDVK1ff35Yp9PBbDbf9jUajabGj0+bNg1RUVHIzs6G0WhEx44dbRbz+q2EhASYTCb1VlBQAACwckEPh7NqtDjbuB3bC2F/OWwvh+3lsL0cnU4Hg8HABZyEsL8ce7fnTzMCADRs2BAmk6nGr4uLi8Onn36Ka9euwWw24+OPP0afPn3Ux9evX4/Lly/DYrFg1apViIuLu2kbxcXFaN26NTQaDXbv3o3Dhw/fcp+urq7w8PCwuQEA+I+z42m0uOTenO2lsL8ctpfD9nLYXoxWq0Xr1q15aR8h7C/H3u35J+qEfvuZ4dTUVLz88st4/PHHbRbQuhPPP/88wsLCEBYWBoPBAF9fX3VxLQCIiIhA37590alTJ3h5edk8dsOf//xnzJkzBwaDAStXrkS3bt1q9b64mrTjaawWtD17iO2FsL8ctpfD9nLYXo7ZbMbOnTvv6Cw9qnvsL8fe7TUKl0UjOxk/fjwMBkOVE+C6VFJSAk9PT1y8eBFNmjSx677IVmVlJVJSUtC/f3+4uLhID8fpsL8ctpfD9nLYXg7by2J/ObVpf2NuYDKZ/t8ZpNXgkWEiIiIiIiJyOrxqNNlNUlKS9BCIiIiIiIiqxNOk6b5341SI17/Jg1vDW58KQXVMUdCg/BKuuHkBt1lJnOyA/eWwvRy2l8P2djMn1PuWj1utVly8eBHe3t5cxEkA+8upTXueJk13rbKyEgsWLIC/vz8CAwMRGhqKgQMHwmg03vJ18+fPt/tnhKvFf5gdT6PBlfqN2F4K+8thezlsL4ftxWi1WjRr1owTMSHsL8fe7fknSlWaMGECMjMzkZaWhiNHjiAzMxMzZsxAbm6u9NCqpbVyhT9H01rN6Fiwj+2FsL8ctpfD9nLYXk5lZSU2b96MyspK6aE4JfaXY+/2/Mww3eT48eP4z3/+g4KCAjRq1Ei9/8Y1grOzs/H73/8eV69eRXl5OUaNGoV58+apzysoKEBsbCzOnDmDDh06ICkpCU2aNMHly5cxc+ZM7N+/HwAwbNgwvP766wCAmJgYhIeHIz09HWfOnMHjjz+OZcuWVTm+iooKVFRUqN+XlJTUeQO6czqFl9iQxP5y2F4O28thezm8rI8s9pdjz/Y8Mkw3yczMRPv27dG4ceMqH/f19cWOHTtw6NAhHDx4EBs2bMC+ffvUx/fs2YPk5GQcO3YMPj4+SEhIAAC8+eabqKioQFZWFtLT07Fx40asXbtWfV1eXh5SU1ORk5ODrVu3Ii0trcr9JyYmwtPTU735+PjU4bsnIiIiIiJnwMkw3VZeXh4MBgP8/PwwYcIElJWVYfLkyQgODkb37t1x6tQpm88SDxgwAM2bNwcAPP/889i+fTsAYPv27ZgyZQq0Wi0aNGiAsWPHYtu2berrRowYAb1ej/r168NgMCAvL6/K8SQkJMBkMqm3goIC+715IiIiIiJ6IPE0abpJaGgoTpw4geLiYjRq1Ajt2rWD0WhEUlISNm7ciLlz58Lb2xuZmZnQ6/UYPHgwysvLq92eppqFNn57v5ubm/q1Tqer9pQIV1dXuLq63nS/VaO7k7dHdciq0SGveSjbC2F/OWwvh+3lsL0cvV6PXr16Qa/n/7pLYH859m7PI8N0kw4dOuCZZ57BpEmTcOnSJfX+K1euAACKi4vRsmVL6PV65Obm2hzdBYCUlBT88ssvAICPP/5Y/axxXFwcVqxYAUVRcOXKFaxevRp9+vRxzJsiuzHr60kPwamxvxy2l8P2ctheTv369aWH4NTYX44923MyTFVKSkpCcHAwunXrhsDAQERFRWH79u2Ij4/HvHnzsGrVKoSEhGDOnDmIjY21eW10dDRGjRoFf39/nDp1Cm+//TYA4NVXX4WLi4u63aeffhrDhw+vszFruaiHw2kVC/x+Tmd7Iewvh+3lsL0ctpdjNpuRkpLCRZyEsL8ce7fnsX6qUr169bBgwQIsWLCgysdzcnKqvH/+/PnVbtPd3R0rV66s8rFdu3bZfL9+/fo7Guev/SGoMZo0aVLj11HtVVZWIuVn4I8hTeDi4iI9HKfD/nLYXg7by2F7InrQ8MgwEREREREROR0eGaYHxgc5RajnoUgPw6lorWb4AViSVQirlj9OHI395bC9HLaXw/Z1Y06ot/QQiOj/p1EUhbMHqjO+vr7YuHEjDAaDw/ZZUlICT09PvP5NHtwaejhsvwRAUaBVLNdXFq1m1XCyI/aXw/Zy2F4O29eJ2kyGFUWB2WyGXq+v9iodZD/sL6c27W/MDUwmEzw8bj034GnSZHdcbODBpjdfkx6CU2N/OWwvh+3lsL2csrIy6SE4NfaXY8/2nAyTXcTExGDmzJmIjIxEnz59YDab0bdvX4SHhyMwMBCjRo1SL9UEXF94q0OHDujSpQvmzZsHX1/farddUVGBkpISmxvA1aQlaBUL2p3LZHsh7C+H7eWwvRy2l2M2m5GamsoDDELYX46923MyTHbzww8/YPfu3di5cyd0Oh2Sk5ORkZGBnJwceHp6YunSpQCAzZs3Y926dTh48CAyMjKQn59/y+0mJibC09NTvfn4+Djg3RARERER0YOEk2Gym+eee0699IKiKFiyZAlCQ0MREhKCzZs3w2g0AgB27NiB4cOHw8PDAxqNBlOnTr3ldhMSEmAymdRbQUGBvd8KERERERE9YLgUINmNu7u7+nVycjJ27tyJb775Bh4eHnjvvfewc+fOKl93uw/Hu7q6wtXVtU7HSrVn0eikh+DU2F8O28thezlsL0ev5/+2S2J/OfZszyPD5BDFxcXw9vaGh4cHSktLkZSUpD4WFxeHdevWobS0FIqi4MMPP6zVPniZB8ezavX4wac72wthfzlsL4ft5bC9HBcXFwwYMEA9444ci/3l2Ls9J8PkEGPHjsXVq1fh5+eHJ554AtHR0epj/fv3x9ChQxEWFobw8HC0atWqdjvhVcIcT1HQoKyY7aWwvxy2l8P2cthejNVqxfnz52G1WqWH4pTYX4692/M6w3TPycnJwZNPPnnbhbRuuHEtsYsXL6JJkyb2HRzZqKysREpKCvr378/flgpgfzlsL4ft5bC9HLaXxf5yatOe1xkmIiIiIiIiugV+6IPuOUFBQXd8VPjXPsgpQj0PnujgSFqrGX4AlmQV8jNkAthfDtvLYXs5bF9zc0K9pYdARLfAI8NOatOmTTAYDDa3Rx99FG5ubnW2j/Hjx+Pvf/97lY+99tpr+Oyzz+psXwCg4NarUFPdU6BBhUt9thfC/nLYXg7by2F7ORqNBg0bNrztFTfIPthfjr3b8zPDBAC4dOkSIiIiMGfOHEyaNKlOtjl+/HgYDAbMmjWrTrZXnRufC3h9949wc29o130RERER3SkeGSZyPH5mmGrEarVi9OjR6N27NyZNmoTs7GxERUUhLCwMAQEBWLhwofrc+fPnY/jw4XjqqafQsWNHPPnkk8jJyUHfvn3RsWNHPPvsszarvWVlZaFHjx7o2LEjxo0bh7KyMgC2R4137NiByMhIhIaGIjAwECtWrKjdG1G4wp/DKVZ4XT7H9lLYXw7by2F7OWwvxmq14tSpU1zNWAj7y7F3e06GCa+//jqKiorw3nvvAQB8fX2xY8cOHDp0CAcPHsSGDRuwb98+9fkZGRn49NNPkZubi9LSUkyePBnr16/H0aNH8f3332PLli3qc9PT07F161Z8//33KCoqwpIlS27af1hYGL799ltkZmZiz549eOONN/Dzzz9XO96KigqUlJTY3ABAy3+cHU6rWPFIUR7bC2F/OWwvh+3lsL0ci8UCo9EIi8UiPRSnxP5y7N2ek2En9+WXX2LFihXYsGED6tWrBwAoKyvD5MmTERwcjO7du+PUqVMwGo3qa/r06YNGjRpBo9EgLCwMMTExaNiwIfR6PUJDQ3H8+HH1ucOHD0fDhg2h0+kwadIkbN++/aYxFBYWYtiwYQgKCkJsbCwKCwuRk5NT7ZgTExPh6emp3nx8fOouCBEREREROQVOhp1Ybm4uJk2ahM8//xwtWrRQ7587dy68vb2RmZmJw4cPIyYmBuXl5erjv15kS6fT3fS92Wyudp9Vffh92rRpiIqKQnZ2NoxGIzp27Gizv99KSEiAyWRSbwUFBXf8nomIiIiIiABOhp1WaWkpBg0ahAULFiAqKsrmseLiYrRs2RJ6vR65ubnYtm1brfezfv16XL58GRaLBatWrUJcXNxNzykuLkbr1q2h0Wiwe/duHD58+JbbdHV1hYeHh80N4GrSEhRocNnNi+2FsL8ctpfD9nLYXo5Go0HTpk25mrEQ9pdj7/a8SJyT+uCDD5Cbm4uPPvoIH3300U2PTZ06FZ988gnatWuH2NjYWu8nIiICffv2xYULFxAZGVnlytJ//vOfMX36dLz55pswGAzo1q1brfalaHW1HifVjqLVoaBZoPQwnBb7y2F7OWwvh+3l6PV69OjRQ3oYTov95di7PS+tRPe9G8unFxUVoVGjRtLDcSoWiwXHjx9Hhw4doNPxlxGOxv5y2F4O28thezlsL4v95dSmPS+tRE6Jy907ntVqRW5uLtsLYX85bC+H7eWwvRy2l8X+cuzdnqdJ0wPjg5wi1PPgiQ6OpLWa4QdgSVYhrFr+OHE09pfD9nLYXg7b396cUG/pIRBRDfDIMBERERERETkdToad0BdffIEuXbrAYDDA398fsbGx99xpHxqNBpcuXarRa7i6peMp0OBSg2ZsL4T95bC9HLaXw/ZytFotWrVqBa2W/+sugf3l2Ls9F9ByMmfPnkVwcDAOHjyI1q1bAwAOHTqE0NDQe2q5eI1Gg+LiYnh5ed32uTc+JP/67h/h5t7Q/oMjIiIiqgJPkyaSxwW0qFq//PILdDodGjdurN4XFhYGjUaD48ePY8CAAYiIiEBISAjef/999TkajQZvv/02unbtijZt2mDVqlXqY7d63ZdffolOnTqhc+fOiI+Ph7e3N/Lz8wEAs2fPRkREBAwGA3r27Inc3Nw7eg8VFRUoKSmxuQGAxmq5mzRUCxqrBY8UHmd7Iewvh+3lsL0ctpdjsViQmZkJi4XtJbC/HHu352TYyYSEhCAqKgqtW7fGoEGDsGjRIpw+fRoWiwXPPvss/va3v+HAgQPYt28fPvzwQxw4cEB9raurK/bv348tW7Zg5syZMJvNt3zd+fPnMXHiRPznP//B4cOH4e/vj8LCQnV78fHxOHDgAIxGI6ZPn44XX3zxjt5DYmIiPD091ZuPjw8AQAOe5OBoGijwunKe7YWwvxy2l8P2cthejtVqxU8//XTPfazNWbC/HHu351KATkar1WLDhg04duwYvvnmG2zZsgVvvfUWdu/ejSNHjmDkyJHqc0tLS3H06FFEREQAAEaPHg0A8Pf3h16vx7lz51BSUlLt686ePYuQkBD4+/sDAMaNG4dp06apz9u2bRuWLl2K0tJSWK1WFBUV3dF7SEhIwEsvvaR+X1JSok6IiYiIiIiI7gQnw07K398f/v7+mDp1Kvr164evvvoKjRs3htForPY1bm5u6tc6nQ5msxmKolT7uk2bNlW7rZ9++gkzZszAgQMH0K5dO2RlZaFnz553NHZXV1e4urre0XOJiIiIiIiqwtOknczp06exd+9e9fvi4mKcPHkSQUFB8PDwsPks8IkTJ257tNbPz6/a13Xv3h1ZWVnqZ4HXrFmDa9euAQBMJhNcXFzwyCOPQFEUm88Z15ai4V9nR1M0Wlzw8GF7Iewvh+3lsL0ctpej1Wrh5+fH1YyFsL8ce7fnkWEnYzab8cYbb+DkyZN46KGHYDabMW7cODzzzDMICgrCrFmzsGTJElgsFnh7eyM5OfmW29Pr9fjvf/9b5eseffRRfPzxxxg4cCBcXV3x+OOPw93dHV5eXvD19cXIkSMRGBiIJk2aYODAgXf93v5oaHrbFePIHppJD8DJsb8ctpfD9nLYXoJOp1M/dkaOx/5y7N2el1YiuyotLUXDhtcvd7Rx40YkJCTg+++/r9N93Fg+vbCw0GaVbLI/s9mM/fv3o2vXrtDr+bs1R2N/OWwvh+3lsL0ctpfF/nJq074ml1binybZ1dKlS7F27VpYLBZ4eHjgs88+s9u++Hsdx1MUBRcuXGB7Iewvh+3lsL0ctpfD9rLYX46923MyTHY1d+5czJ071yH7+iCnCPU8+EPKkbRWM/wALMkqhFXLHyeOxv5y2F4O28the1tzQr2lh0BEd4mfAn8A+fr6olmzZqisrFTvS01NhUajwaxZs+QGRkREREREdI/gZPgB1apVK5tLG61YsQLh4eGCI6o5s9lco+dbubqlw1k1Wpxt3I7thbC/HLaXw/Zy2F6OTqeDwWCATqeTHopTYn859m7Pn2YPqAkTJmDlypUArl/GaN++fejXrx8AIDg4GN9995363A8//BAjRowAACxevBgREREwGAyIiIhAWlqa+jxfX1+89tpriIyMRJs2bbBw4UL1sZiYGLz88svo2bMnWrVqhVdffRUpKSmIioqCr68vFi9erD539uzZ6j569uypXnoJADQaDV5//XVEREQgISGhyvdWUVGBkpISm9v1F/Ovs8NptLjk3pztpbC/HLaXw/Zy2F6MVqtF69ateWkfIewvx97t+Sf6gHrssceQn5+PM2fO4F//+heGDRum/kZl5syZNtf1/eCDDzBjxgwAwJgxY3DgwAEYjUYsXboUEyZMsNnupUuXkJaWhgMHDmDRokU4ffq0+tipU6eQmpqKw4cP47333kNKSgr27NmDvXv34rXXXsOlS5cAAPHx8eo+pk+fjhdffNFmHzqdTt1+VRITE+Hp6anefHx8AAAaq+XuolGNaawWtD17iO2FsL8ctpfD9nLYXo7ZbMbOnTtrfNYc1Q32l2Pv9pwMP8DGjBmDpKQkrFy5EhMnTlTvf+6555CamopffvkF3377LTQaDaKjowEAmZmZ+N3vfoegoCBMmzYNubm5KCsrU187atQoAIC3tzfatm2LkydPqo8NHToUOp0OjRo1Qtu2bfHkk09Co9Hg0UcfRdOmTZGfnw8A2LZtGyIjIxEUFIQ33ngDRqPRZty/HmtVEhISYDKZ1FtBQQEAQAMunuVoGihwrSxjeyHsL4ft5bC9HLaXoygKSktLuZqxEPaXY+/2XArwATZ27FiEhYWhY8eO6NChg3p//fr1MX78eCxfvhzff/89/vCHPwAArl27hsGDByM1NRURERHqNboqKipQv359AICbm5u6HZ1OZ/Nbmt8+VtVzf/rpJ8yYMQMHDhxAu3btkJWVhZ49e9qM293d/Zbvy9XVFa6urrUoQkREREREdB0nww+wFi1aIDExEf7+/jc99oc//AHdu3dHZWUlVqxYAQAoLy/HtWvX0KpVKwDXrxFc10wmE1xcXPDII49AURSb07WJiIiIiIgchZPhB9xvP/N7Q8uWLREaGoqOHTvioYceAgB4eHhg4cKF6Nq1K7y9vTFy5Mg6H09wcDBGjhyJwMBANGnSBAMHDqyzbVs1XOHP0awaHX5qGsD2QthfDtvLYXs5bC9Hp9MhMjKSqxkLYX859m6vUXjyu1O6cuUK/Pz8sGfPHrRp00Z6OHflxuncJpMJHh4e0sMhIiIiIiIhNZkbcAEtJ7Rs2TL4+/tj+vTp9/1E+NcqKyulh+B0KisrsXnzZrYXwv5y2F4O28thezlsL4v95di7PU+TdkLTpk3DtGnTpIdR5z7IKUI9D57o4Ehaqxl+ZjOWZBXCquWPE0djfzlsL4ft5Thz+zmh3tJD4GV9hLG/HHu255Hhe4Svry+aNWtm81uP1NRUaDQazJo1S25gADQajXqN4N8yGAwoLS117ICIiIiIiIjuEifD95BWrVph06ZN6vcrVqxAeHi44Ihuz2g0omHDhtLDICIiIiIiqhFOhu8hEyZMwMqVKwFcvwTRvn370K9fPwCAxWLBn/70JwQFBSEoKAgvvPACrl27BgAIDw+HwWCAwWBAixYt0KtXLwDA4sWLERERAYPBgIiICKSlpan78vX1xbx589CjRw/4+Phg2bJlWLVqFSIjI+Hr64t///vfNmP761//qq4+/dlnn6n3//qo8XfffQeDwYCgoCBMmDABBoMBu3btAgDExMRg48aN6uuGDh2KpKQkAEBpaSmmTJmCrl27IiQkBM8//7z63mqCq1s6nlWjQ17zULYXwv5y2F4O28thezl6vR69evWCXu9cp6ffK9hfjr3bczJ8D3nssceQn5+PM2fO4F//+heGDRumLiP+4Ycf4sCBAzh48CCMRiPy8vKwZMkSAEBGRgaMRiO2bNmCBg0aYN68eQCAMWPG4MCBAzAajVi6dOlNl1m6cuUKvvvuO6SmpuKPf/wjTp8+jbS0NKxbtw4vvPCCzXM1Gg0yMzPx9ddf44UXXkB+fr7N49euXcOIESPw17/+FTk5OXj22Wdx+PDhO3rfL7/8MqKjo7F//34cPnwYVqsV7777brXPr6ioQElJic2N5Jj19aSH4NTYXw7by2F7OWwvp379+tJDcGrsL8ee7TkZvseMGTMGSUlJWLlyJSZOnKjev337dowfPx6urq7Q6/WYMmUKtm3bpj5eWlqKAQMG4LXXXkPv3r0BAJmZmfjd736HoKAgTJs2Dbm5uSgrK1NfM2LECABA+/bt4ebmhqFDhwK4fqS5qKjI5nPCkydPBgC0bdsWPXv2xO7du23GfezYMej1esTFxQEA+vTpg7Zt297Re964cSMWLVoEg8GA0NBQ7NmzBydOnKj2+YmJifD09FRvPj4+AACtYrmj/VHd0SoW+P2czvZC2F8O28thezlsL8dsNiMlJYWLOAlhfzn2bs9j/feYsWPHIiwsDB07dkSHDh2qfZ5Go1G/NpvNGDZsGIYMGYIxY8YAuH6kdvDgwUhNTUVERIR6va2Kigr1tytubm7qNnQ6nfq9RqOBRqO55V+6X+//Tp6j1+thsfy/fzzLy8vVrxVFwYYNG9CxY8fbbhMAEhIS8NJLL6nfl5SUqBNiIiIiIiKiO8Ejw/eYFi1aIDExEe+8847N/XFxcfj0009x7do1mM1mfPzxx+jTpw8AYOrUqWjVqhVeeeUV9fnl5eW4du0aWrVqBQBYunTpXY1r1apVAID8/Hzs2bMH0dHRNo/7+/vDbDYjNTUVwPUj2Xl5eerj7du3R3p6OgDg5MmT+Pbbb9XHBg4ciHfeeUedfBcXF9/yyLCrqys8PDxsbkRERERERDXBI8P3oN9+thcAnn/+eeTl5SEsLAzA9QWpZs2ahVOnTmHlypUICgqCwWAAcP00548//hgLFy5E165d4e3tjZEjR97VmCwWC0JDQ3HlyhW899578PX1tXm8Xr16WLt2LaZPnw6LxYKIiAh07txZffz//u//MGLECAQHByMwMBDdunVTH1uyZAnmzJkDg8EArVYLvV6Pv/zlL2jfvv1djZmIiIiIiKg6GkVRFOlB0IMpPDwcf/3rXxETE2PX/dw4BfzSpUvw9PS0677IlqIoMJvN0Ov1d3TqPNUt9pfD9nLYXg7by2F7Wewvpzbtb8wNTCbTbc8g5WnSRHRXfr0oGzke+8thezlsL4ft5bC9LPaXY8/2PE2a7CYjI8Oh+3vXeB71PCoduk9np7Wa4fdzOnJbdoNVyx8njsb+ctheDtvLcZb2c0K9pYdwkxvrsvTv3x8uLi7Sw3E67C/H3u15ZJiq5evrC6PRCOD6glzPPPMMhg0bhmvXrskOjIiIiIiI6C5xMky3VVpaiieeeAIPP/ww1q5di3r16kkPiYiIiIiI6K5wMky3VFhYiNjYWHTr1g0ffvghtFotVq9ejW7duiEsLAw9e/bE4cOHAQBJSUmIi4vDs88+i+DgYISHh+PHH38EADz55JNITk5Wt/u///1PXVE6OTkZ3bp1Q2hoKDp37oyvvvrK8W+Uas2i0UkPwamxvxy2l8P2cthejl7/4J6afj9gfzn2bM/VpKlavr6+uHz5MqZMmYLExEQAwN69e/HWW2/hP//5D1xdXbFnzx5MmzYNR44cQVJSEl588UUYjUa0adMGc+bMQXFxMZYvX45t27bh9ddfx3fffQcAeOaZZzB06FCMGTMGhYWFaNy4MTQaDfLz89G9e3ecOnUKrq6uVY6roqICFRUV6vclJSXw8fHB67t/hJt7Q/uHISIiogfWvfiZYSK6c1xNmurMgAEDsH79ehQUFAAAvvzySxw+fBjdunWDwWDACy+8gKKiInWVt8jISLRp00b9Oi8vDwDw+OOPw2QyITMzE6dOncL+/fsxfPhwAMDJkyfxxBNPICgoCAMHDkRRURFOnjxZ7ZgSExPh6emp3nx8fK4/wN/rOJ6ioEFZMdtLYX85bC+H7eWwvRir1Yrz58/DarVKD8Upsb8ce7fnZJhu6Y9//CN+//vfIyYmBj/99BMURcG4ceNgNBrV29mzZ1G/fn0AgJubm/panU4Hs9msfj9z5kwsXboUy5Ytw8SJE9UjvyNHjsTkyZORk5MDo9EId3d3lJeXVzumhIQEmEwm9XZjoq5VLPZIQLegVSxodeEo2wthfzlsL4ft5bC9HIvFgrS0NFgsbC+B/eXYuz1Pfqfbeumll6DVahETE4NVq1ZhzJgxmDZtGlq1agWr1YpDhw4hPDz8ttsZM2YM3njjDVgsFhw4cEC9v7i4WD2avGbNGhQXF99yO66urtWeQk1ERERERHQnOBmmOzJr1ixotVqMGzcOv//97zFo0CCYzWZcu3YNAwYMuKPJ8EMPPYTBgwfjzJkz/+/UZgDvvvsuhg4dCi8vL8TGxqJVq1b2fCtERERERERcQIscx2KxoEuXLli6dCmio6PrbLs3PiR/YyEuchyz2Yzdu3ejZ8+eXGVRAPvLYXs5bC+H7eWwvSz2l1Ob9jVZQIuTYXKITZs2YebMmXjiiSfwz3/+s063XZO/8ERERERE9ODiatJ0z3n66aeRn59f5xPhX+MKf45ntVpx6tQpthfC/nLYXg7by2F7OWwvi/3l2Ls9j/PTA+O9wxdQz8N8+ydSndFazfD72Yi1F11h1fLHiaOxvxy2l8P2ch7U9vfDdYUtFguMRiNatGgBrZbHshyN/eXYuz3/NJ2Ir68v/P39bS53FB4ejl27dtXZPvr374/c3FwAQExMDDZu3Fjl8yZPnozU1NQ62y8REREREVFNPDi/1qM7UlFRgRUrVmDq1Kl22X5KSsodPe/jjz+2y/6JiIiIiIjuBI8MO5n58+fjzTffxNWrV23uT05ORrdu3RAaGorOnTvjq6++Uh+LiYnByy+/jJ49e6JVq1Z49dVXkZKSgqioKPj6+mLx4sXqc319fWE0GtXvd+zYgYiICLRv3x4vv/wybqzX9uujxrfad1UqKipQUlJicwMABZq7SUO1oECDy25ebC+E/eWwvRy2l8P2cjQaDZo2bQqNhu0lsL8ce7fnkWEn07lzZ/Tq1QtLlizBK6+8ot7ft29fPPvss9BoNMjPz0f37t1x6tQpuLq6AgBOnTqF1NRUlJSUwNfXF8XFxdizZw/OnDkDPz8/TJw4EV5eXjft7+jRo/juu+9QWVmJnj174l//+hdGjRpl85zb7fu3EhMTsWDBgpvuV7S6uyhDtaFodShoFig9DKfF/nLYXg7by2F7OXq9Hj169JAehtNifzn2bs8jw07ozTffxLvvvovCwkL1vpMnT+KJJ55AUFAQBg4ciKKiIpw8eVJ9fOjQodDpdGjUqBHatm2LJ598EhqNBo8++iiaNm2K/Pz8Kvc1duxYuLi44KGHHsJzzz2H7du33/Sc2+37txISEmAymdRbQUEBAECjcIU/R9MoVnhf+onthbC/HLaXw/Zy2F6OxWLBsWPHYLFYpIfilNhfjr3bczLshHx9fTFq1CgsXLhQvW/kyJGYPHkycnJyYDQa4e7ujvLycvVxNzc39WudTnfT979elOtWqjrF4Xb7/i1XV1d4eHjY3ABOhiVoFCualhSwvRD2l8P2ctheDtvLsVqtyM3N5aV9hLC/HHu352TYSc2bNw9r1qzBmTNnAADFxcVo06YNAGDNmjUoLi6uk/2sWbMGlZWVKCsrQ3JyMuLi4m56jr32TUREREREVB1Ohp2Ut7c3Zs6cibNnzwIA3n33XQwdOhShoaHIzMxEq1at6mQ/nTp1wmOPPYbg4GBER0dj5MiRNz3HXvsmIiIiIiKqjka5sbwv0X2qpKQEnp6eKCoqQqNGjaSH41QsFguysrIQEhICnY4LmDka+8thezlsL4ft5bC9LPaXU5v2N+YGJpNJ/ThldTgZpvteTf7CExERERHRg6smcwNeWokeGEsyz8PV45r0MJyKxmpB8+Ifca5RW17aSgD7y2F7OWwv50FoPyfUW3oItcIjk7LYX4692/Mzw/c4X19f+Pn5wWAwoFOnThg1ahSuXLlil33t2rULBoOhxq/LycmBr69vrfc7f/58zJo1q9avv0EDnuTgaBoo8Lpynu2FsL8ctpfD9nLYXo7VasVPP/3E1YyFsL8ce7fnZPg+sHbtWhiNRhw5cgQmkwlJSUnSQyIiIiIiIrqvcTJ8H7l27RquXr2KRo0aITs7G1FRUQgLC0NAQIDNNYPnz5+PESNG4KmnnkJAQABiY2NRVFQEAKisrMT06dPRsWNHdO/eHS+//DJiYmJu2pfZbEbfvn0RHh6OwMDAm45Iz58/Hx06dECXLl3w73//2+a1W7duRVRUFLp06YKuXbsiNTUVAHD8+HE89thj6Ny5M4KDgzFv3jz1NWfPnq1yvFWpqKhASUmJzY2IiIiIiKgmOBm+D4wYMQIGgwHNmzeHVqvF8OHD4evrix07duDQoUM4ePAgNmzYgH379qmvSU9PR1JSEo4ePYpmzZph+fLlAIAPP/wQx48fx5EjR7Bnzx5kZWVVuU+dTofk5GRkZGQgJycHnp6eWLp0KQBg8+bNWLduHQ4ePIiMjAzk5+err/vxxx8xf/58pKSk4ODBg0hOTsaoUaNQUVGB999/H08++SQOHz6M7OxsvPTSS7cdb1USExPh6emp3nx8fAAAioZ/nR1N0WhxwcOH7YWwvxy2l8P2cthejlarhZ+fH7RatpfA/nLs3Z5/oveBG6dJX7x4Eb6+voiPj0dZWRkmT56M4OBgdO/eHadOnYLRaFRf069fPzRp0gQAEBkZiby8PADAjh078Nxzz8HFxQUuLi4YN25clftUFAVLlixBaGgoQkJCsHnzZnX7O3bswPDhw+Hh4QGNRoOpU6eqr/v6669x4sQJ9OzZEwaDAUOHDoVWq8VPP/2Enj174qOPPsIrr7yC//3vf/Dy8rrteKuSkJAAk8mk3goKCq6Pmf84O5yi0eKiVyu2F8L+ctheDtvLYXs5Op0O/v7+XLxJCPvLsXd7/jS7j+j1egwZMgRff/015s6dC29vb2RmZuLw4cOIiYlBeXm5+lw3Nzf1a51OB7PZXOU2NRpNlfcnJydj586d+Oabb5CdnY3Zs2fbbL+6bSiKgscffxxGo1G9nT59Gh06dMCQIUOwd+9e+Pn5qUeJazpeAHB1dYWHh4fNDbi+yiU5lsZqgc/5I2wvhP3lsL0ctpfD9nLMZjO+++67W/7/EdkP+8uxd3tOhu8zO3fuhJ+fH4qLi9GyZUvo9Xrk5uZi27Ztd/T62NhYJCcno7KyEpWVlfj000+rfF5xcTG8vb3h4eGB0tJSm0W74uLisG7dOpSWlkJRFHz44YfqY3379sX27dttTr/ev38/gOufGX744YcxduxY/OUvf7E5rbsucHVLx9NAgXv5JbYXwv5y2F4O28thezmKouDChQtQFLaXwP5y7N2e1xm+D4wYMQL169eH2WxG69atsWzZMly8eBFjxozBJ598gnbt2iE2NvaOtjV16lRkZ2cjICAAjRo1Qnh4OM6cOXPT88aOHYsvv/wSfn5+aNq0KaKjo3Hq1CkAQP/+/bF//36EhYXBw8MDTzzxhPq69u3bIzk5GVOnTsXVq1dx7do1hIaGIjk5GevXr8eaNWtQr149WK1WLFu2rG4CERERERER1ZBG4a84nE5paSkaNmyIyspKjB49Gl26dEF8fLz0sGqtpKQEnp6euHjxovq5Y3KMyspKpKSkoH///nBxcZEejtNhfzlsL4ft5bC9HLaXxf5yatP+xtzAZDKpH6esDo8MO6G4uDhUVFSgvLwcUVFRmDlzpvSQ6gQXNXA8nU4Hg8HA9kLYXw7by2F7OWwvh+1lsb8ce7fnkWG679347c/ru3+Em3tD6eEQERHRPWBOqLf0EIhIQE2ODHMBLUJpaSnc3d0xadIk9b6kpCQMHDgQALBr1y4YDIY63WdGRgZGjBhRp9vk6paOp7Fa0PbsIbYXwv5y2F4O28thezlmsxk7d+7kasZC2F+OvdtzMkxYu3YtunTpgi+++AKXL192yD7Dw8Oxdu3aOt0mV7d0PA0UuFaWsb0Q9pfD9nLYXg7by1EURb2KBzke+8uxd3tOhgkrVqxAfHw8evbsWe0E1Ww2Y+zYsQgKCkKXLl1gNBoBAOfOnUOvXr3QpUsXBAYGYsaMGbBarQCuH12Oi4vDs88+i+DgYISHh+PHH38EYHu02Ww2o2/fvggPD0dgYCBGjRqFK1eu2P19ExERERGR8+Jk2MkdPXoUBQUF6Nu3LyZNmoQVK1ZU+bwjR45g3LhxyMnJQXx8PEaOHAlFUeDl5YWvvvoKBw8eRFZWFvLz8/H555+rrztw4ADefvttZGdnIy4uDu+8885N29bpdEhOTkZGRgZycnLg6emJpUuXVjvmiooKlJSU2NyIiIiIiIhqgpNhJ7dixQqMHTsWOp0O/fv3x8mTJ/H999/f9DxfX1/07t0bADB8+HCcO3cOBQUFsFqtiI+PR+fOnREaGoqMjAz1qDEAREZGok2bNurXeXl5N21bURQsWbIEoaGhCAkJwebNm2228VuJiYnw9PRUbz4+PgAAq4Yr/DmaVaPDT00D2F4I+8thezlsL4ft5eh0OkRGRnI1YyHsL8fe7TkZdmKVlZVYvXo1PvnkE/j6+qJ9+/a4evVqtUeHf02j0UCj0WDx4sU4f/480tPTkZWVhVGjRqG8vFx9npubm/q1Tqer8sPvycnJ2LlzJ7755htkZ2dj9uzZNtv4rYSEBJhMJvVWUFBwY1A1ePdUJzQaXKnfiO2lsL8ctpfD9nLYXoxWq0WzZs2g1fJ/3SWwvxx7t+efqBPbtGkT2rZti9OnTyM/Px/5+fnYt28fVq9ejcrKSpvn5ufnIzU1FQCwfv16PPzww2jZsiWKi4vRvHlzuLm54dy5c1i3bl2Nx1FcXAxvb294eHigtLQUSUlJt3y+q6srPDw8bG4AoLVyhT9H01rN6Fiwj+2FsL8ctpfD9nLYXk5lZSU2b9580/+fkWOwvxx7t9fbZat0X1ixYgVGjx5tc1+nTp3w6KOPorS01Ob+wMBAJCUlYebMmahXrx7+9a9/QaPR4MUXX8TQoUMRGBiIFi1aIC4ursbjGDt2LL788kv4+fmhadOmiI6OxqlTp+7qvZHj6BReYkMS+8thezlsL4ft5fCyPrLYX44922sUrhFO97kbF9a+ePEimjRpIj0cp1JZWYmUlBT0798fLi4u0sNxOuwvh+3lsL0ctpfD9rLYX05t2t+YG5hMJvUM0urwNGkiIiIiIiJyOjwyTPe9G7/9uXTpEjw9PaWH41RuXAi9YcOG0HBBFYdjfzlsL4ft5bC9HLaXxf5yatO+JkeG+ZlhemAsOVwIt4Zc2MChFAVaxQKrpoKri0pgfzlsL4ft5dzD7eeEeksPwe7q168vPQSnxv5y7Nmep0lTnfL19b3lNYLtSctFPRxOq1jg93M62wthfzlsL4ft5bC9HLPZjJSUFC7iJIT95di7PSfDZHf8wUFERERERPcaniZNdhETE4OQkBAcOHAA9evXx//+9z8MGDAAhYWFKCsrQ+fOnfHRRx+hQYMG2LVrF2bMmIGePXti7969MJvN+OSTTxAeHl7ltisqKlBRUaF+X1JS4qi3RUREREREDwgeGSa7+eGHH7B7927s3LkTOp0OycnJyMjIQE5ODjw9PbF06VL1uceOHcO4ceNw+PBhvPDCC3jllVeq3W5iYiI8PT3Vm4+PjyPeDhERERERPUA4GSa7ee6559TrgSmKgiVLliA0NBQhISHYvHmzzWeL27dvj27dugEAIiMjkZeXV+12ExISYDKZ1FtBQQEAwKrR2e/NUJWsGh1yW3ZjeyHsL4ft5bC9HLaXo9fr0b9/f+j1PKlTAvvLsXd7TobJbtzd3dWvk5OTsXPnTnzzzTfIzs7G7NmzUV5erj7u5uamfq3T6W75OWNXV1d4eHjY3EiO3nxNeghOjf3lsL0ctpfD9nLKysqkh+DU2F+OPdtzMkwOUVxcDG9vb3h4eKC0tBRJSUl1vg+ubul4WsWCducy2V4I+8thezlsL4ft5ZjNZqSmpnJRUiHsL8fe7TkZJocYO3Ysrl69Cj8/PzzxxBOIjo6WHhIRERERETkxnvhOdSo/Px8AsGvXLpv7PT09sX379ipfExMTY/P54aCgIHU7NfGHoMZo0qRJjV9HtVdZWYmUn4E/hjRRPx9OjsP+ctheDtvLYXsietDwyDAR3RUuJiGL/eWwvRy2l8P2ctheFvvLsWd7jaIoit22TuQAJSUl8PT0xOu7f4Sbe0Pp4RAREZGdzQn1lh4CEd2jbswNTCbTbRfa5ZHhB4DZbMaCBQvg7++PoKAgGAwGPP/887h06ZL00ByLv9dxPEVBg7JitpfC/nLYXg7by2F7MVarFefPn4fVapUeilNifzn2bs/J8ANg0qRJyMjIQFpaGnJycpCZmYnHH38cRUVFDh2H9Ap7XN3S8bSKBa0uHGV7Iewvh+3lsL0ctpdjsViQlpYGi4XtJbC/HHu352T4PnfixAmsW7cOq1atQqNGjQAAGo0Gw4YNw0MPPYRevXqhS5cuCAwMxIwZM9TfqiQlJSEuLg7PPvssAgIC0KNHDxw9ehSDBg1Cp06d0KdPH1y+fBnA9QUz5syZg65du8JgMGD48OEoLi4GAIwfPx4TJ05Ez549ERQUBABYtGgRAgMDERwcjNGjR8NkMgEALl++jIkTJyIoKAhBQUFYsGCB+j5iYmIwe/ZsREdHo127dpg2bVq177miogIlJSU2NyIiIiIioprgZPg+d+jQIXTo0AHe3jd/dsbLywtfffUVDh48iKysLOTn5+Pzzz9XHz9w4ADeeecdHD16FO3atcNTTz2FZcuW4fvvv0e9evXwySefALg+uW3QoAH2798Po9GI4OBgzJs3T93OwYMHsXnzZhw7dgxbtmzBypUrsXfvXmRnZ6NBgwaYM2cOAODNN99ERUUFsrKykJ6ejo0bN2Lt2rXqdvLy8pCamoqcnBxs3boVaWlpVb7nxMREeHp6qjcfH586aUlERERERM6Dk+EHmNVqRXx8PDp37ozQ0FBkZGTYXMIoMjISrVq1AgCEh4cjIiICDz/8MAAgIiICx48fBwBs3LgRa9asgcFggMFgwL/+9S+cPHlS3c6wYcPQsOH1hau2b9+OESNGwMvLCwDw+9//Htu2bVMfmzJlCrRaLRo0aICxY8eqjwHAiBEjoNfrUb9+fRgMBuTl5VX5vhISEmAymdRbQUEBAECBpg6qUU0o0KDCpT7bC2F/OWwvh+3lsL0cjUaDhg0bQqNhewnsL8fe7blG+H0uLCwMx48fR2Fh4U3X2F28eDHOnz+P9PR0uLm54aWXXkJ5ebn6uJubm/q1Tqe76fsbnwFWFAVLly5Fnz59qhyDu7t7teO71V/c3z5W3f5/y9XVFa6urjfdr2h11e6L7EPR6vDjI2HSw3Ba7C+H7eWwvRy2l6PX6xEbGys9DKfF/nLs3Z5Hhu9z7du3x5AhQzBp0iR19WhFUbBhwwYcPHgQzZs3h5ubG86dO4d169bVah8DBw7EkiVLcPXqVQDA1atXceTIkSqfGxcXh88//1z9HO/y5cvVSXRcXBxWrFgBRVFw5coVrF69utoJdq0oXOHP4RQrvC6fY3sp7C+H7eWwvRy2F2O1WnHq1CmuZiyE/eXYuz0nww+AlStXonPnzujWrRsCAwMREBCA//3vf3j33XeRnp6OwMBAjBkzBnFxcbXafnx8PCIiItCtWzeEhISge/fuNqdb/9oTTzyBCRMmIDIyEsHBwSgpKUFiYiIA4NVXX4WLiwuCg4PRrVs3PP300xg+fHht3/ZNtPzH2eG0ihWPFOWxvRD2l8P2ctheDtvLsVgsMBqNXM1YCPvLsXd7jaLwYnF0f7txYe2LFy/edKo42VdlZSVSUlLQv39/uLi4SA/H6bC/HLaXw/Zy2F4O28tifzm1aX9jbmAymeDh4XHL5/LIMBERERERETkdLqBFD4z3c4rh6sETHRxJY7WgpZsXFmcVcQEzAewvh+3lsL0cqfZzQm++fKSz0Wg0aNq0KVczFsL+cuzdnkeG7yFffPEFunTpAoPBAH9/f8TGxqofFu/fvz9yc3MdOp6kpCQMHDjQofu8G/yfIsdTtDoUNAtkeyHsL4ft5bC9HLaXo9fr0aNHD+j1PI4lgf3l2Ls9J8P3iLNnz+L555/HF198AaPRiGPHjuGvf/2r+luQlJQU+Pn51Wibv700kdVqfaBXwdNwQQ+H0yhWeF/6ie2FsL8ctpfD9nLYXo7FYsGxY8e4gJMQ9pdj7/acDN8jfvnlF+h0OjRu3Fi9LywsTJ0M+/r6qis4nzt3DsOHD0fXrl0RHByMefPmqa/x9fVFfHw8unbtinHjxmH+/PkYMmQI+vbti6CgIJw9exazZ89GREQEDAYDevbsWeMjzpWVlZgzZw669a3NLAABAABJREFUdu0Kg8GA4cOHo7i4GABw/vx5DB48GMHBwQgKCsLy5cttxvbaa68hMjISbdq0wcKFC9XHbvWe7hT/cXY8jWJF05ICthfC/nLYXg7by2F7OVarFbm5uQ/0QY17GfvLsXd7TobvESEhIYiKikLr1q0xaNAgLFq0CKdPn67yuePGjcMf/vAH7N+/H5mZmcjIyLC5hnBhYSHS09Px2WefAQDS0tLw6aef4ujRo3j00UcRHx+PAwcOwGg0Yvr06XjxxRdrNNZFixahQYMG2L9/P4xGo83k9YUXXoCfnx+ys7Oxc+dOLFy4EPv27VNfe+nSJaSlpeHAgQM27/F27+nXKioqUFJSYnMjIiIiIiKqCZ74fo/QarXYsGEDjh07hm+++QZbtmzBW2+9hYyMDLRv31593pUrV7Bjxw788ssv6n2XL1+2Obo7fvx4mw+Z9+/fHw8//LD6/bZt27B06VKUlpbCarWiqKioRmPduHEjTCYTNmzYAAC4du0afH19AQDbt2/HwYMHAQDNmjXD4MGDsX37dnTv3h0AMGrUKACAt7c32rZti5MnT8LLy+u27+nXEhMTsWDBghqNmYiIiIiI6Nc4Gb7H+Pv7w9/fH1OnTkW/fv2wadMmvPTSS+rjNy4LvW/fPri5uVW5DXd392q//+mnnzBjxgwcOHAA7dq1Q1ZWFnr27FmjMSqKgqVLl6JPnz63fe5vV3779Zh1Oh3MZvMdvadfS0hIsGlSUlICHx8fKOAKf46mQINLDZqxvRD2l8P2ctheDtvL0Wq1aNWqFbRantQpgf3l2Ls9/0TvEadPn8bevXvV74uLi3Hy5Em0a9fO5nnu7u7o1asX/vznP6v3nTlzBj///PMd7cdkMsHFxQWPPPIIFEXB+++/X+OxDhw4EEuWLMHVq1cBAFevXsWRI0cAAHFxcfjoo48AABcuXMAXX3yBxx9//Jbbq+l7cnV1hYeHh80N4GrSEhStDmebdGB7Iewvh+3lsL0ctpej0+kQGhoKnY7tJbC/HHu352T4HmE2m/HGG2+gY8eOMBgMiI6Oxrhx4/DMM8/c9NzPPvsMJ06cQFBQEIKDgzF48GAUFhbe0X6Cg4MxcuRIBAYGIiIiAq1atbrl87du3YqWLVuqt5deegnx8fGIiIhAt27dEBISgu7du6uLe7333nv4/vvvERwcjF69euGVV15Bt27dbjuuu3lPN2isXOHP0TRWCx4pPM72QthfDtvLYXs5bC/HYrEgMzOTqxkLYX859m6vUW6co0p0nyopKYGnpycuXryIJk2aSA/HqVRWViIlJQX9+/eHi4uL9HCcDvvLYXs5bC+H7eWwvSz2l1Ob9jfmBiaTST2DtDo8MkxEREREREROhwto0QPjg5wi1PPgiQ6OpLWa4QdgSVYhrFr+OHE09pfD9nLYXo4j2s8J9bbLdomIqsIjw/TAUDT86+xoikaLCx4+bC+E/eWwvRy2l8P2crRaLfz8/LiasRD2l2Pv9vwTdSK+vr7w8/ODwWBAp06dMGrUKFy5cuWWr9m1axcMBkOdj2XZsmVYtGhRnW6T/zg7nqLR4qJXK7YXwv5y2F4O28thezk6nQ7+/v5czVgI+8uxd3v+NHMya9euhdFoxJEjR2AymZCUlOTwMZjNZkybNg1/+tOf6nS7XN3S8TRWC3zOH2F7Iewvh+3lsL0ctpdjNpvx3XffwWw2Sw/FKbG/HHu352TYSV27dg1Xr15Fo0aNkJSUhIEDB6qP/fe//0VMTEyVr1u+fDk6duyIsLAwvPnmm9BoNOpjo0ePRnh4OEJCQjBgwACcO3cOAJCfnw8vLy/Ex8cjLCwM77//PubPn49Zs2YBALKzsxEVFYWwsDAEBARg4cKFtxx7RUUFSkpKbG4AoAE/L+xoGihwL7/E9kLYXw7by2F7OWwvR1EUXLhwAbwIjAz2l2Pv9pwMO5kRI0bAYDCgefPm0Gq1GD58+B2/NicnB/Pnz8fu3btx6NChm35D8/e//x0ZGRnIyspCdHQ05s+frz5mMpkQGBiIQ4cOqZPgG3x9fbFjxw4cOnQIBw8exIYNG7Bv375qx5GYmAhPT0/15uPjc8fvgYiIiIiICOBk2OncOE364sWL8PX1RXx8/B2/dufOnejXrx+aN28OAJgyZYrN48nJyQgPD0dQUBA+/vhjGI1G9TEXFxc899xzVW63rKwMkydPRnBwMLp3745Tp07ZvPa3EhISYDKZ1FtBQcEdvwciIiIiIiKAk2GnpdfrMWTIEHz99dfQ6/WwWP7f53/Ky8vvaBu/PkX622+/xXvvvYeUlBTk5ORg8eLFNtt56KGHql0Fbu7cufD29kZmZiYOHz6MmJiYW47B1dUVHh4eNjcAsHJBD4ezarQ427gd2wthfzlsL4ft5bC9HJ1OB4PBwAWchLC/HHu3508zJ7Zz5074+fmhffv2yMrKQllZGcxmM5KTk6t8fq9evbB161acP38eALBixQr1seLiYjRs2BBNmjTBtWvXsHz58jseR3FxMVq2bAm9Xo/c3Fxs27atdm+I/zg7nkaLS+7N2V4K+8thezlsL4ftxWi1WrRu3ZqX9hHC/nLs3Z5/ok7mxmeGg4KC8P333+Pdd99F9+7d0b9/fwQFBSEmJgYdOnSo8rXBwcGYN28eHnvsMYSFhaG8vByenp4AgH79+sHPzw9+fn6Ijo6u0eWY5s2bh1WrViEkJARz5sxBbGxsrd4bV7d0PI3VgrZnD7G9EPaXw/Zy2F4O28sxm83YuXMnVzMWwv5y7N1eo3BZNKqB0tJSNGzYEADw7rvv4uuvv8aWLVtEx1RSUgJPT09cvHgRTZo0ER2Ls6msrERKSgr69+8PFxcX6eE4HfaXw/Zy2F4O28the1nsL6c27W/MDUwmk/pxyuro62KQ5DzmzJmDvXv3orKyEi1atKjR6dBERERERET3Ck6GqUY++OAD6SFU64OcItTz4IkOjqS1muEHYElWIaxa/jhxNPaXw/Zy2F5OXbefE+p994MiIroL/FeEbsvX1xeurq6oX7++et/q1asRHBxcp/vJz8/H119/jWnTptXq9VYNV/hzNKtGh5+aBrC9EPaXw/Zy2F4O28vR6XSIjIzkasZC2F+OvdtzMkx3ZO3atTVaFKs28vPzsWzZslpPhvGrSz2Rg2g0uFK/kfQonBf7y2F7OWwvh+3FaLVaNGvWTHoYTov95di7PVeTplr78ssv0alTJ3Tu3Bnx8fHw9vZGfn4+1q9fjz59+qjPs1gsaN26NY4ePYpdu3YhKCgIY8eORVBQELp06QKj0QgAmDZtGnJzc2EwGPD0009Xu9+KigqUlJTY3IDrp2+RY2mtZnQs2Mf2QthfDtvLYXs5bC+nsrISmzdvRmVlpfRQnBL7y7F3e06G6Y7cuCTTjdupU6cwceJE/Oc//8Hhw4fh7++PwsJCAMCgQYPwww8/IDc3FwCwadMmtG/fHgEBAQCAI0eOYNy4ccjJyUF8fDxGjhwJRVGwbNky+Pn5wWg0YtOmTdWOJTExEZ6enurNx8fH/gGoWjqFl9iQxP5y2F4O28thezm8rI8s9pdjz/acDNMdWbt2LYxGo3o7fPgwQkJC4O/vDwAYN24c6tWrB+D6uf3Tp09XF9v64IMPMGPGDHVbvr6+6N27NwBg+PDhOHfuHAoKCu54LAkJCTCZTOqtJq8lIiIiIiIC+JlhspMpU6YgICAAY8eOxYkTJ2552rNGo4GmBp/3dXV1haura10Mk4iIiIiInBSPDFOtdO/eHVlZWeqp0GvWrMG1a9fUxxs1aoRnnnkGgwYNwtSpU21WgMvPz0dqaioAYP369Xj44YfRsmVLeHh4wGQy1XpMXN3S8awaHfKah7K9EPaXw/Zy2F4O28vR6/Xo1asX9Hoex5LA/nLs3Z5/onRHRowYYXNppSVLluDjjz/GwIED4erqiscffxzu7u7w8vJSnzNlyhQkJSVhypQpNtsKDAxEUlISZs6ciXr16uFf//oXNBoNQkJCEBgYiKCgILRt2/aWnxume4dZX096CE6N/eWwvRy2l8P2cn79/2HkeOwvx57tORmm28rPz6/y/tLSUgwaNAgAsHHjRvz3v/+1mQynpqZi9OjR8Pb2tnmdXq/HJ598ctP29Ho9/vvf/9Z6nC8EeqJJkya1fj3VXGVlJVJSUtC/f3+4uLhID8fpsL8ctpfD9nLYXo7ZbGZ7Qewvx97tORmmWlu6dCnWrl0Li8UCDw8PfPbZZ+pjgYGB0Gg0+PrrrwVHSEREREREVDVOhqnW5s6di7lz51b52JEjR6q8PyYmRr2ucF37IKcI9TwUu2ybqqa1muEHYElWIaxa/jhxNPaXw/Zy2F7O3bafE+p9+ycRETnQA7OAlq+vL/z8/GAwGBAQEKBe1ud2r6ntxOzSpUv485//bHPf5MmT1YWhbmXZsmVYtGhRrfZblf79+6vX/9VoNAgODobBYEB0dHStt3nmzBmb18+fPx/l5eW12lZGRgZGjBhR67EQERERERHVNY2iKA/EoTRfX19s3LgRBoMBp06dQkhICPbs2YOQkJA7ek1N5efnw2Aw4NKlS7UftB1oNBoUFxfbfHb3dsxms80Kbb/9vrbbrW5bda2kpASenp54/Zs8uDX0sOu+6DcUBVrFcn1l0RpcHovqCPvLYXs5bC/nLtvzyHDtKYqi/j9VTS5HSXWD/eXUpv2NuYHJZIKHx63nBg/MkeFfa926Nfz8/PDDDz9g8eLFiIiIgMFgQEREBNLS0qp8zblz5zB8+HB07doVwcHBmDdvHgDAarVixowZ6NSpEzp37owuXbqgvLwc06ZNQ2lpKQwGA8LDwwFcPwV448aNAIDx48dj6tSp6N27Nzp27IjBgwerlx6aP38+Zs2aBQDIzs5GVFQUwsLCEBAQgIULF6pjmj9/PkaMGIGnnnoKAQEBiI2NRVFR0R13OHDgAGJjYxEeHo7Q0FCsW7cOwPWJvJeXF+Lj4xEWFob3338fMTExmDlzJiIjI9GnTx/1OQAwbdo0AEB0dDQMBgPOnz+P8+fPY/DgwQgODkZQUBCWL1+u7tfX1xfx8fHo2rUrxo0bh127dtn8wmHr1q2IiopCly5d0LVrV/Vo+vHjx/HYY4+hc+fONn8GdG/Tm6/d/klkN+wvh+3lsL0ctpdTVlYmPQSnxv5y7Nn+gZwMZ2dn49ixY+jcuTPGjBmDAwcOwGg0YunS/4+9O4+Lqt4fP/6aBUHFQROtLAi3UNkGBFxSUyMX6pqphWnuueTNsrSr2GZmcbt2pdK6tmiUStdMr9mV6rpm5QbqiKRycyfT3GBAZZvl94c/zzeugILMfNR5Px+PeTxk5pwzn3npQ/3M2eYwYsSIctcZNmwYf/7zn9m2bRs7d+4kIyODpUuXsmvXLtauXcvPP//Mrl27WLduHbVq1WLevHnUq1cPi8VCRkZGudu0WCx8/fXX7N27l99//51ly5ZdtkxQUBBr165lx44dbN++nWXLlrFlyxbt9a1bt5KSksKePXto3LhxmUlnZfLy8hgzZgyLFy8mIyOD1atXM2nSJI4dOwaA1WolJCSEHTt2aBPz//73v2zcuJF169aV2da8efMA+OGHH7BYLDRu3JgJEyYQHBzM7t27WbduHTNnziwz7jNnzrB169YyF9UCOHjwINOnTyctLY3t27eTmprKoEGDKC4uZu7cuTz44IPs2rWL3bt389xzz5X72YqLi8nPzy/zANA77VfVRtQcvdNO8xM7pb0i0l8daa+OtFdH2qtjs9lYv349NptN9VA8kvRXx9Xtb6orT1y6F26dOnVYsGABLVu25D//+Q+vv/46Z86cwWg0kp2dTWFhYZn7VZ0/f561a9fy+++/a8+dO3eO7OxsevTogc1mY+TIkXTr1o0HHngAvf7qvkN4+OGHqVOnDgCxsbEcOHDgsmUKCwsZP348FosFvV5PTk4OFouF9u3bA9CrVy/tdkEdOnRg9+7dV/XemzZt4uDBg/Tu3bvM89nZ2TRr1gwvLy8ef/zxMq89/vjjV33J8jVr1rB9+3YAGjduTL9+/VizZo027uHDh5d7KMO3337L/v376dKli/acXq/n6NGjdOnSheeff55z585x7733EhcXV+57JyUl8eqrr17VOIUQQgghhBCiPDfVZHjJkiVlDsctKSmhX79+rF+/npiYGO348eLi4jKT4UunTW/ZsgUfH5/LtpuVlcX333/P+vXrSUxMZOPGjVd1Huwft2UwGMr9RmPatGn4+/uzc+dOjEYj/fr1K3OhqqvZRnmcTichISFs2rTpstcOHz5MnTp1LpvU+/r6XtW2y/O/E9+KtuV0Orn//vtJTU297LWWLVvSsWNHVq9ezdy5c3n77bdJS0u7bLnExMQye43z8/MJCAio9tiFEEIIIYQQnuemPEz6kqKiIkpKSggMDAQu3he3PL6+vnTr1q3M1aF/++03fv31V06dOsX58+fp0aMHb7zxBkFBQezZsweTyURhYaF2HnB15ebmcuedd2p7rVevXn1N27ukY8eOHDp0iDVr1mjPWSyWao+3Xr16WK1W7ee4uDg++ugjAE6dOsXy5cu5//77r7idnj17smbNGjIzM7Xntm3bBlw8Z/jWW29l6NCh/O1vfytz2PUfeXt7YzKZyjyEOnadQfUQPJr0V0faqyPt1ZH26rj6gqSictJfHVe2v6l/V00mEzNnziQ2NhZ/f38GDhxY4bKLFy/mueeeIzQ0FJ1OR926dfnggw+w2+2MHj2a0tJS7HY799xzD71798bLy4uhQ4cSHh6Or69vhecNX8mLL77IkCFD+PTTT2nevDndu3ev7scto0GDBqxatYrJkyczadIkSktLCQwM1C7wVVWTJk3i/vvvp06dOvznP//h3Xff5cknnyQsLAyn08kLL7xAu3btrridFi1akJqaytixY7lw4QIlJSVERkaSmprKl19+yaJFi6hVqxYOh0M7V/lqyf0m3c+hN/LfgPaqh+GxpL860l4daa+OtFfHy8uLBx54QPUwPJb0V8fV7W+aWysJz3Xp8Pfq3PpJXBuHw8Hp06fx9/e/6nPpRc2R/upIe3WkvTrSXh1pr5b0V6c67T3+1krCM9ntcnVLd7Pb7WzevFnaKyL91ZH26kh7daS9OtJeLemvjqvby3Gl4qbxXtZZapnkQAd30jtsBAPJmWfkMHUFpL860l4daa9OVdpPjfR3z6CEEOIayJ5hIYQQQgghhBAeRybDAgCz2YzZbKZNmzYYDAbt54SEhGpt74knnmD9+vWVLhMfH092dna1tl8eJ5ff11i4lhMdxV61pb0i0l8daa+OtFdH2quj0+moV6/eZbeyFO4h/dVxdXu5gJYo4/Dhw5jNZvLy8lQP5apdOkn+lY0H8fGtp3o4QgghhMeTw6SFEKrIBbREjdHpdGUmxv7+/hw+fBiAoKAgXn75ZTp06EDTpk2ZOXOmtlzXrl212zh9/PHHtGnTBrPZTFhYGFu3btXWt1gsAMyePZuYmBjMZjMxMTFs3ry5wjEVFxeTn59f5gGA01Fjn1tcJaeD+udOSHtVpL860l4daa+OtFfG4XBw5MgRHA5pr4L0V8fV7WUyLK5JXl4emzdvJj09nVmzZnHs2LHLlpk0aRJr167FYrGwY8cOQkJCLltmyJAhpKenY7FYmDNnDiNGjKjwPZOSkvDz89MeAQEBAOjlH2e30zsd3H72gLRXRPqrI+3VkfbqSHt17HY7FotFrmasiPRXx9XtZTIsrsmgQYOAi3uMmzVrxqFDhy5b5r777mPIkCG88847HDp0CF9f38uW2blzJ/feey+hoaGMGzeO7OxsCgsLy33PxMRErFar9sjJyanZDyWEEEIIIYS46ck9CUSlDAZDmW9iioqKyrzu4+NTZlmbzXbZNpYtW8b27dvZsGED8fHxzJw5k4EDB2qvl5SU0K9fP9avX09MTIx2nH9xcTG1a9e+bHve3t54e3vXxMcTQgghhBBCeCjZMywq1aJFC+0c3+XLl3P+/PkqrW+z2Thw4ADR0dFMnjyZAQMGsG3btjLLFBUVUVJSQmBgIABz5syp1ljl6pbu50THOZ/60l4R6a+OtFdH2qsj7dXR6XQ0atRIrmasiPRXx9XtZc+wqFRycjJPP/00L774Ig888AANGzas0vp2u52RI0dy9uxZjEYjjRo14pNPPimzjMlkYubMmcTGxuLv719mr3FVPBvZ+IpXjBOucKvqAXg46a+OtFdH2qsj7VUwGo107NhR9TA8lvRXx9Xt5dZK4oZ36bDqs2fP0qBBA9XD8Sh2u51ffvmFli1bYjAYVA/H40h/daS9OtJeHWmvjrRXS/qrU532cmsl4ZHkcvfu53A4yM7OlvaKSH91pL060l4daa+OtFdL+qvj6vZymLS4abyXdZZaJjnQwZ30DhvBQHLmGRx6+evE3aS/OtJeHWmvTkXtp0b6qxuUEEJcA9kzfB1avnw5bdu2xWw206pVK7p3737N34ZMnz69zJWghw8fzttvv33V6xcUFODr68uoUaPKPH/gwAGioqKIjIy87FxggN9++43OnTtXe9xCCCGEEEII4QoyGb7OHD9+nDFjxrB8+XIsFgv79u3jrbfeuuYrqL366quX3RapKpYsWULbtm1Zvnw5586d057/8ssviYmJYefOnYwYMaLMOjabjSZNmvDDDz9U+32rQq5u6X5OdOTVbSztFZH+6kh7daS9OtJeHb1eT2BgIHq9/NddBemvjqvby+/odeb333/HYDBwyy23aM9FRUVpk+GMjAw6duxIeHg4sbGx/PTTTwAcPnyY+vXra+ucO3dOW2fcuHEAdO7cGbPZzMmTJwHYu3cv9913H3fffTf9+vWjpKSkwnHNnz+fKVOm0KVLF5YsWQLAZ599RnJyMsuXL8dsNrNnzx66du3K008/TYcOHejRo8dl49q8eTOdOnUiIiKC8PBwvvrqKwAmT55MTEwMZrOZLl26kJ2dXeFYiouLyc/PL/MAcOrlggbu5tQbON6wpbRXRPqrI+3VkfbqSHt1DAYDkZGRcvEmRaS/Oq5uL5Ph60x4eDidOnXirrvu4uGHH2bWrFkcO3YMgJKSEvr168crr7xCZmYms2fPpn///mX21JZn3rx5APzwww9YLBYaN24MgMVi4euvv2bv3r38/vvvLFu2rNz19+zZQ05ODj179mTUqFHMnz8fgKFDhzJu3DgGDx6MxWKhTZs2APz3v/9l48aNrFu3rsx2zp49S9++fUlKSmLXrl1YLBbtEOopU6aQnp6OxWJh/PjxPPPMMxV+nqSkJPz8/LRHQEAAADqHvdIOoubpHHZuP/OLtFdE+qsj7dWR9upIe3Xsdjs7d+7Ebpf2Kkh/dVzdXibD1xm9Xs+yZcvYtGkTvXr14qeffiIkJIT9+/eTnZ2NXq+nZ8+eAHTq1Ilbb70Vi8VSrfd6+OGHqVOnDgaDgdjYWA4cOFDucvPnz2fo0KEYDAbi4+M5dOgQe/furXC7jz/+OF5eXpc9v3nzZoKDg7UJsF6v1/aAr169mg4dOhAaGsqMGTMq/UyJiYlYrVbtkZOTA4AOuXiWu+lwUv/8SWmviPRXR9qrI+3VkfbqOBwOjh49KlczVkT6q+Pq9jIZvk61atWKsWPHsmLFCtq3b8/KlSvLXe7SodBGo7HMNyZXc36wj4+P9muDwYDNZrtsmdLSUhYuXMinn35KUFAQLVq04MKFC9re4fL4+vpe8b3/6OjRozz11FMsWrSIrKws/vnPf1Y6fm9vb0wmU5mHEEIIIYQQQlSFTIavM8eOHdPOAwbIzc3l0KFDNG/enODgYBwOB6tXrwZg06ZNnDhxArPZzG233YbT6WTPnj3AxfN5/6hevXpYrdYqj2flypU0a9aMY8eOcfjwYQ4fPsyWLVtYuHAhpaWlVdpWx44d+eWXX7QLajkcDs6ePYvVasXLy4vbb78dp9PJ3LlzqzxOIYQQQgghhKgKuUHfdcZmszFjxgwOHTpEnTp1sNlsDBs2jIceegi4eNulp59+mkmTJuHj48OXX36p7YmdM2cODz74IA0bNmTAgAFltjtp0iTuv/9+6tSpw3/+85+rHs/8+fMZPHhwmedat27NHXfcwddff12lz9agQQP+9a9/MWnSJAoKCtDr9bz22mv86U9/YuDAgYSEhNCwYUP69u1bpe1e4tTJdzvu5tTpOWUKkPaKSH91pL060l4daa+OXq8nODhYrmasiPRXx9XtdU6nU078EDe0/Px8/Pz8sFqtcsi0EEIIIYQQHqwqcwP5ekPcNMo751m4ls1mY9OmTdJeEemvjrRXR9qrI+3VkfZqSX91XN1eDpMWN425u89QyyRX+XMnvcNG8KlTzN51Gode/jpxN+mvjrRXR9qrc6m9HFTofk6nk1PSXhnpr46r28ue4ZtYUFAQjRs3LnOhq/Xr16PT6Zg4caLbxzN8+HDefvtt4OK9j2fNmuX2MQghhBBCCCEEyJ7hm15gYCArV66kf//+wMULYkVHRyseFYwbN071EIQQQgghhBAeTPYM3+RGjBjBggULALBarWzZsoVevXoBYLfbef755wkNDSU0NJQJEyZQUlICXNyLO3bsWO677z7uvvtu+vXrp71WUFBAQkICrVq1onPnzowdO5bhw4dfcZt/NH36dG3v9O7du+nUqRNRUVG0adOGmTNnVuuzOuTqlm7n0Ok5fktzaa+I9FdH2qsj7dVx6PSYzWYMBoPqoXgcg8Eg7RWS/uq4ur38S3KTu+eeezh8+DC//fYbn3/+OY888oj2h+nDDz8kPT2d7du3Y7FYOHDgAMnJydq6FouFr7/+mr179/L777+zbNkyAGbMmEHt2rXZu3cvaWlpbNq0SVvnStssT1BQEGvXrmXHjh1s376dZcuWsWXLlgqXLy4uJj8/v8wDAPmPkfvp9OT53ibtVZH+6kh7daS9Ojo9d911l9xeRgG9XtqrJP3VcXV7+R31AEOGDCElJYUFCxYwcuRI7fk1a9YwfPhwvL29MRqNjB49mtWrV2uvP/zww9SpUweDwUBsbCwHDhwAYO3atYwYMQKdTke9evVISEi46m2Wp7CwkCeeeIKwsDDat2/PkSNHsFgsFS6flJSEn5+f9ggICABA57BXJ4+4BjqHnWbHd0h7RaS/OtJeHWmvjs5hZ926dXJFXQVsNpu0V0j6q+Pq9jIZ9gBDhw7l3XffxcfHh5YtW1a4nE6nK/Ozj4+P9muDwVDhH8L/Xe9qX7tk2rRp+Pv7s3PnTnbt2kXXrl0pKiqqcPnExESsVqv2yMnJufheyBX+3E2HE+/SQmmviPRXR9qrI+3V0eGkoKBArqirgNMp7VWS/uq4ur1Mhj1AkyZNSEpK4s033yzzfFxcHJ999hklJSXYbDY+/vhjevToccXtde/enU8//RSn08m5c+f44osvrmmbubm53HnnnRiNRrKzs6+4J9nb2xuTyVTmIYQQQgghhBBVIVeT9hAjRoy47LkxY8Zw4MABoqKiAOjatetV3XLp5ZdfZtSoUbRu3Rp/f38iIiKoX79+tbf54osvMmTIED799FOaN29O9+7dq/TZhBBCCCGEEKKqdE7Z3y+qqLS0FLvdjo+PD+fPn6dnz55MmDChzLnD7pSfn4+fnx+5ubnapFy4h8Ph4PTp0/j7+8tFJRSQ/upIe3WkvTrSXh1pr5b0V6c67S/NDaxW6xWPIJXJsKiykydP0rt3b+x2O0VFRTz00EP89a9/varzg12hKn/ghRBCCCGEEDevqswN5DBpUWWNGzdm+/btqodxmbd3/k4t0+X3NBauo3fYaHEsg/13ROPQy18n7ib91ZH26kh795ka6V/m59LSUv7zn//Qo0cPvLy8FI3KM0l7taS/Oq5uL/v5xRUFBQXRqlWrMleTjo6OZsOGDS57v8purSSuLwan3N5EJemvjrRXR9qrI7eWUUfaqyX91XFle5kMi6tSXFzM/PnzVQ9DCCGEEEIIIWqETIbFVZk+fTqvvfYaFy5cKPN8QUEBo0ePJjY2lvDwcMaMGUNJycVDlbt27cqECROIiYmhRYsWTJo0SbtH2OzZs4mJicFsNhMTE8PmzZvd/pmEEEIIIYQQnksmw+KqRERE0K1bN5KTk8s8P2nSJDp37sy2bdvYtWsXDoeDd955R3t9z549bNq0iczMTL7//ns+//xzAIYMGUJ6ejoWi4U5c+aUe+unihQXF5Ofn1/mAeDQGWrgk4qqcOgMHLgtUtorIv3VkfbqSHt1jEYj3bp1w2iUc7XdTdqrJf3VcXV7+R0VV+21114jNjaWcePGac+tWLGCzZs3M3v2bAAKCwsxGP7vPyhDhw7Fy8sLLy8vHn/8cdasWcOgQYPYuXMnr7/+OmfOnMFoNJKdnU1hYSG1a9e+4jiSkpJ49dVXa/4DimqxGWupHoJHk/7qSHt1pL06V/PvtHANaa+W9FfHle1lz7C4akFBQQwaNIiZM2dqzzmdTpYtW4bFYsFisZCdnc0HH3xQ4TZ0Oh0lJSX069ePt956i6ysLDZu3Ahc3ON7NRITE7FardojJycHAL1cUMXt9E47wb9ulfaKSH91pL060l4dm81GWlqaXEhIAWmvlvRXx9XtZTIsquTFF19k0aJF/PbbbwD07duXN998U/sDmpuby/79+7XlFy1aRGlpKYWFhaSmphIXF0dRURElJSUEBgYCMGfOnCqNwdvbG5PJVOYhhBBCCCGEEFUhk2FRJf7+/jz99NMcP34cgOTkZGrXro3ZbCY8PJz77ruPw4cPa8u3bt2ae+65h7CwMDp37szAgQMxmUzMnDmT2NhY2rZtS61acribEEIIIYQQwr3knGFxRX+c3AK89NJLvPTSS9rPc+fOrXDd7t278+677172/F/+8hf+8pe/aD8///zzFb7f1fpz6C00bNiwWuuK6iktLSXtV3g2vKHchF4B6a+OtFdH2gshhKgpOuele90IUcO6du3KxIkT6du3r0vfJz8/Hz8/P/Ly8vDz83Ppe4mynE4nNpsNo9GITqdTPRyPI/3VkfbqSHt1pL060l4t6a9OddpfmhtYrdYrnk4ph0kLl9mwYYPLJ8JCvcLCQtVD8GjSXx1pr460V0faqyPt1ZL+6riyvRwmLW4a71hOUstUqnoYHkXvsBH861ay72yHQy9/nbib9FdH2qsj7V1naqR/pa/bbDbWr19PfHy8HKLuZtJeLemvjqvby57hG0xQUBAWiwWAoqIiHnroIR555BFKSkrUDuwP8vLy+Otf/6p6GEIIIYQQQghRIZkM36AKCgro3bs3t956K0uWLLmqKzK7695oMhkWQgghhBBCXO9kMnwDOnPmDN27d6ddu3a8++67NGnShJycHO31adOmMWXKFODinuQpU6YQGxvLsGHDKCgoICEhgVatWtG5c2fGjh3L8OHDAQgLC2PTpk3adj788EMSEhI4efIkZrNZe/j7+zNixAgcDgdPPfUUrVu3JiIigrZt21JUVMS4ceMoKCjAbDYTHR0NwOzZs4mJicFsNhMTE8PmzZu19wkKCuLll1+mQ4cONG3alJkzZ1b6+YuLi8nPzy/zEOrYdQbVQ/Bo0l8daa+OtFfHaJRD01WR9mpJf3Vc2V6uJn2DCQoK4ty5c4wePZqkpCQAXnjhBZxOJ2+88QbFxcUEBQWxZcsW7rrrLoKCgoiLi+Ojjz5Cp9Px/PPPc+rUKT755BPOnTtHx44dadu2LSkpKXz00UesX7+e1NRUACIiIpg7dy6dO3fW3v/nn3/mgQce4N///jelpaUMGjSIn3/+Gb1ej9VqpV69ehw9ehSz2UxeXp623qlTp2jUqBEAW7ZsYfjw4ezbt0/7TH369OHdd9/l9OnTNG/enD179nDHHXeU22D69Om8+uqrlz3/ysaD+PjWq5HOQgghhKe50jnDQghxI5CrSd/kHnjgAb788kttb/D48eP59NNPKS4uZunSpcTGxnLXXXdpyw8fPly7FPnatWsZMWIEOp2OevXqkZCQoC33+OOPs379en7//Xd+/PFHdDpdmYnwb7/9xkMPPcSCBQsIDQ2lWbNm2Gw2Ro4cyaeffkppaSl6ffl/pHbu3Mm9995LaGgo48aNIzs7u8yV4QYNGgSAv78/zZo149ChQxV+/sTERKxWq/bQ9orL9zru53RStzBX2qsi/dWR9upIe2UcDgcnT57E4XCoHorHkfZqSX91XN1eJsM3oGeffZYnn3ySrl27cvToUe644w66dOnCkiVLeO+993jqqafKLO/r61vhtv54v67atWszfPhwPvjgA9577z3+/Oc/a68VFBTw4IMPMn36dLp37w6An58fWVlZDBo0iH379hEeHs7+/fsve4+SkhL69evHW2+9RVZWFhs3bgQuHu58iY+Pj/Zrg8FQ6fnN3t7emEymMg8AvdNe4TrCNfROO4Gn9kh7RaS/OtJeHWmvjt1uZ/Pmzdjt0t7dpL1a0l8dV7eXg99vUM899xx6vZ6uXbuyfv16nnnmGR555BF8fX2Ji4urcL3u3bvz6aef0qVLF86fP88XX3xBZGSk9vqf//xn2rdvT2lpKfPnzwcuXnhrwIABDBgwgMcff1xb9tSpUxgMBnr06MH999/P999/z549e+jUqROFhYWUlJRQq1YtioqKKCkpITAwEIA5c+a4qIoQQgghhBBCXB2ZDN/AJk6ciF6v595772X9+vX4+fkxduzYMnt7/9fLL7/MqFGjaN26Nf7+/kRERFC/fn3t9TvvvJPIyEjuvvtu6tSpA8BPP/3EmjVr+P333/niiy8A6NOnD3379mX06NGUlpZit9u555576N27N15eXgwdOpTw8HB8fX3JyMhg5syZxMbG4u/vz8CBA13aRQghhBBCCCGuRC6gdZM4duwY0dHR/Pe//6VevYovInVp4urj48P58+fp2bMnEyZM0M4dPn/+PMHBwfzwww80bdrUXcO/JpdOkj9z5gy33HKL6uF4FJvNxsaNG+nSpYtcZVEB6a+OtFdH2qsj7dWR9mpJf3Wq074qF9CSyfBN4OWXX2bBggW8/vrrDBs2rNJlT548Se/evbHb7RQVFfHQQw/x17/+FZ1Ox7x583j99dd58sknmTZtmptGf+2q8gdeCCGEEEIIcfOSybDwKJf+wL/y/X586vmpHo5ncTqof/4keXUbg06ux+d20l8daa+OtK8R1bmNksPhICcnh4CAgArvHiFcQ9qrJf3VqU57ubWSqJKgoCCCg4Mxm820bt2aQYMGcf78eVJSUujbt2+Vt5eXl8df//rXmh/oFeidcrl7d9M7Hdx+9oC0V0T6qyPt1ZH26tjtdiwWi1xRVwFpr5b0V8fV7WUyLABYsmQJFouFn3/+GavVSkpKSrW3pWoyLIQQQgghhBBXSybDooySkhIuXLhAgwYNADh37hyPPfYYYWFhREdHc/DgQQA2bNiA2WzW1svKyiIoKAiAcePGUVBQgNlsJjo6GoDZs2cTExOD2WwmJiaGzZs3a+sGBQXx8ssv06FDB5o2bcrMmTMrHWNxcTH5+fllHkIIIYQQQghRFTIZFgAkJCRgNpu57bbb0Ov1PProowCkp6fzxhtvsHv3buLi4njzzTevuK158+ZRr149LBYLGRkZAAwZMoT09HQsFgtz5sxhxIgRZdbJy8tj8+bNpKenM2vWLI4dO1bh9pOSkvDz89MeAQEBADip+JZSwjWc6DjnU1/aKyL91ZH26kh7dXQ6HY0aNar0Fo7CNaS9WtJfHVe3l8mwAP7vMOnTp08TFBTElClTALS9tZd+feDAgWptf+fOndx7772EhoYybtw4srOzKSws1F4fNGgQAP7+/jRr1oxDhw5VuK3ExESsVqv2yMnJAcCpN1RrbKL6nHoDOY1DpL0i0l8daa+OtFfHaDTSsWNHubWMAtJeLemvjqvby2RYlGE0Gunfvz/ffvstAD4+PtprBoMBm82mLffHE9mLiooq3GZJSQn9+vXjrbfeIisri40bNwIXD3e+pKL3KY+3tzcmk6nMA0AnF1NxO53TgX/eUWmviPRXR9qrI+3Vsdvt7Nu3Ty4ipIC0V0v6q+Pq9jIZFpdZt24dwcHBlS7TrFkzjhw5wqlTpwBYuHCh9prJZKKwsJCSkhLg4kS5pKSEwMBAAObMmeOScct/jNxP53TQKD9H2isi/dWR9upIe3UcDgfZ2dk4HNLe3aS9WtJfHVe3l8mwAP7vnOHQ0FD27t3LO++8U+nyTZo04S9/+QuxsbG0b9+eW265RXvtlltuYejQoYSHhxMdHY3JZGLmzJnExsbStm1batWq5eqPI4QQQgghhBCV0jmdTqfqQQhxLS7dWPv06dM0bNhQ9XA8SmlpKWlpacTHx+Pl5aV6OB5H+qsj7dWR9upIe3WkvVrSX53qtL80N7BardrplBWRPcPipqHXyx9nd9Pr9QQGBkp7RaS/OtJeHWmvjrRXR9qrJf3VcXV72TMsbniXvv15ZeNBfHzrqR6OEEIIcV2ZGumveghCCOE2smfYw5nNZsxmM23atMFgMGg/JyQklLt8RkaG9tpvv/1G586dtdd0Oh15eXmXrTNv3jxmzZpV6ThWrFjBli1bqv9BqkjnkCv8uZvOYef2M79Ie0WkvzrSXh1pr47dbmfnzp1yRV0FpL1a0l8dV7eXyfBNyGKxYLFYSEtLo169etrPS5YsKXf56Oho7bUmTZrwww8/XPE9xo0bx/PPP1/pMm6fDCMHObibDif1z5+U9opIf3WkvTrSXh2Hw8HRo0fliroKSHu1pL86rm4vk2EPYbPZ6NmzJ9HR0YSEhDBo0CDOnz8PwIYNGzCbzQAcPnyY+vXrX7a+0+lkypQp9OnThwsXLjB9+nQmTpwIwJYtW2jbtq12Nep//OMfpKWlsXLlSmbNmoXZbObjjz/mxIkTdOvWjbZt2xISEsJTTz2l/cFOSUkhLi6Oxx57jLCwMKKjozl48KA70gghhBBCCCE8kFH1AIR7GAwGUlNTadiwIU6nk/HjxzNnzhymTp16xXWLi4t57LHHaNiwIf/6178wGAxlXk9KSmLy5Mk89thjAOTm5tKgQQP69OmD2WzWJs1FRUV8/fXX+Pr6Yrfbeeihh/jiiy8YOHAgAOnp6VgsFpo2bcrUqVN58803+eCDD8odT3FxsfZzfn5+dbMIIYQQQgghPJTsGfYQTqeT5ORkIiMjCQ8PZ9WqVVgslqta94EHHiAkJIT33nvvsokwQLdu3XjttdeYMWMGP/74Iw0aNCh3Ow6HgylTphAREUFkZCQZGRllxtChQweaNm2q/frAgQPlbicpKQk/Pz/tERAQcPEz6uSPs7s5dXpOmQKkvSLSXx1pr460V0ev1xMcHCxX1FVA2qsl/dVxdXv5HfUQqamprFu3ju+//57du3czefJkioqKrmrd7t27s3r16gr3wE6cOJFVq1Zx++23M23aNMaPH1/ucrNnz+bkyZNs3bqVzMxMBg0aVGYMPj4+2q8NBgM2m63c7SQmJmK1WrVHTk4OIJNhFZw6PafrB0p7RaS/OtJeHWmvjsFgoFWrVuV+MS5cS9qrJf3VcXV7+ZfEQ+Tm5uLv74/JZKKgoICUlJSrXnfatGn069ePuLg4zpw5c9nr2dnZNG3alNGjRzNt2jTtolkmkwmr1VpmDLfddhs+Pj6cOHGCpUuXVuuzeHt7YzKZyjxAriatgs5hJ+Dkz9JeEemvjrRXR9qrY7PZ2LRpU4VfVgvXkfZqSX91XN1ezhn2EEOHDuWrr74iODiYRo0a0blzZ44cOaK9rtPpKl1/4sSJ1K1bl+7du/Pdd9+VeW3u3LmsW7eOWrVqYTAY+Pvf/w7AkCFDGD58OCtWrODPf/4zzzzzDAMGDCAkJIQmTZoQFxdXo59Rrizqfjqc+BblocMp9RWQ/upIe3WkvTpOp5NTp07hdEp5d5P2akl/dVzdXibDN7GgoCDtHsF+fn6sWbOm3OVOnjyJv7//ZesAZf7gjR49mtGjRwMwffp07fk5c+aUu92YmBh+/vnnMs9t27at3GWHDx/O8OHDtZ8ffPBBHnzwwXKXrcifQ2+hYcOGVVpHXJvS0lLSfoVnwxvi5eWlejgeR/qrI+3VkfZCCCFqikyGPdz7779PcnIy7733nuqhCCGEEEIIIYTb6Jyyv1/c4PLz8/Hz8yM3N7fceyQL13E4HOTk5BAQECBXWFRA+qsj7dWR9upIe3WkvVrSX53qtL80N7Bardq1hSoik2Fxw7v0B/6VjQfx8a2nejhCCCGEMlMj/VUPQQghlKrKZFi+2hAEBQXRqlWrMldpi46OZsOGDbz88sssXry4xt8zPj6e7OzsGt2mXFnU/XQOO82O75D2ikh/daS9OtJeHZvNxrp16+SKugpIe7Wkvzqubi/nDAsAiouLmT9/PmPHji3z/IwZM2r0fRwOBwBpaWk1ul2Qq0mroMOJd2mhXNVVEemvjrRXR9qr43Q6KSgokCvqKiDt1ZL+6ri6vewZFsDFq0O/9tprXLhwoczzw4cP5+233wagoKCAhIQEWrVqRefOnRk7dmyZK0C/9dZbxMbGEhUVRa9evbRbN02fPp3+/fvTs2dPQkNDOX78OEFBQVgsFgBmz55NTEwMZrOZmJgYNm/eXOlYi4uLyc/PL/MQQgghhBBCiKqQybAAICIigm7dupGcnFzhMjNmzKB27drs3buXtLQ0Nm3apL2WmppKdnY2mzdvZseOHQwePJjx48drr2/evJnPPvuMPXv2cMcdd5TZ7pAhQ0hPT8disTBnzhxGjBhR6ViTkpLw8/PTHgEBAdX81EIIIYQQQghPJYdJC81rr71GbGws48aNK/f1tWvXkpycjE6no169eiQkJLB//34AVqxYQXp6Om3btgXAbi97Lld8fDy33nprudvduXMnr7/+OmfOnMFoNJKdnU1hYSG1a9cud/nExESee+457ef8/HwCAgJw6AxV/szi2jh0Bo42aiPtFZH+6kh7daS9OgaDgQ4dOmAwSHt3k/ZqSX91XN1eJsNCExQUxKBBg5g5c+ZVLa/T6bRfO51OEhMTGTNmTLnL+vr6lvt8SUkJ/fr1Y/369cTExGhXfysuLq5wMuzt7Y23t3d5A7qqcYsapNNxvnYD1aPwXNJfHWmvjrRXRq/X07hxY9XD8EjSXi3pr46r28th0qKMF198kUWLFvHbb79d9lr37t359NNPcTqdnDt3ji+++EJ7rW/fvsybN4+zZ88CUFpays6dO6/4fkVFRZSUlBAYGAjAnDlzqj12vUOu8OdueoeNu3O2SHtFpL860l4daa9OaWkpq1atorS0VPVQPI60V0v6q+Pq9jIZFmX4+/vz9NNPc/z48ctee/nllykoKKB169b06tWLiIgI6tevD8DgwYMZPnw43bp1IyIiArPZzLp16674fiaTiZkzZxIbG0vbtm2pVatWTX8k4WIGp9zeRCXpr460V0faqyO3llFH2qsl/dVxZXudU64RLq5SaWkpdrsdHx8fzp8/T8+ePZkwYQIJCQlKx3Xp0OrTp0/TsGFDpWPxNKWlpaSlpREfH4+Xl5fq4Xgc6a+OtFdH2qsj7dWR9mpJf3Wq0/7S3MBqtWIymSpdVs4ZFlctNzeX3r17Y7fbKSoq4qGHHuLRRx9VPSwhhBBCCCGEqDLZMyxueJe+/Xnl+wP41Kv82x9Rw5xOapUWUuJVWy5gpoL0V0faqyPty5ga6e+293I6nRQUFFCvXr0yF9EUrift1ZL+6lSnfVX2DMs5wx4iPj6euXPnXvZ8REQE4eHhLF68+IrbWLFiBVu2bHHF8MQNzGaU87xVkv7qSHt1pL06Fd3pQbietFdL+qvjyvYyGfYQo0aN4pNPPinzXEZGBsePH2f79u0MHjz4itu43ifDermgitvpnXaCf90q7RWR/upIe3WkvTo2m420tDS5kJAC0l4t6a+Oq9vLZNhD9OnTh5ycHDIzM7XnFixYwNChQxk9ejRvv/02cPEk9alTpxIbG4vZbObRRx8lNzeXtLQ0Vq5cyaxZszCbzXz88cds2LCB0NBQxo8fT0REBCEhIWRkZAAX/+D27NmT6OhoQkJCGDRoEOfPnwfQ1nvyyScJDw8nLCyMzMxMhg8fTlhYGO3atePYsWMVfpbi4mLy8/PLPIQQQgghhBCiKmQy7CG8vLwYMmQICxYsAC7e3/fzzz9n1KhRZZabNWsWdevWZdu2bVgsFsLCwnjxxReJj4+nT58+PP/881gsFp544gkA9u3bx7Bhw9i1axcTJkzghRdeAMBgMJCamkpGRgZZWVn4+fmVuYfwvn37eOKJJ8jMzKRv3750796dqVOnsnv3bqKjo7XJeXmSkpLw8/PTHgEBATVcSwghhBBCCHGzk8mwBxk1ahSLFy+mpKSE5cuX07p1a1q3bl1mmRUrVrBo0SLMZjNms5nPP/+cQ4cOVbjNFi1a0K5dOwA6dOjAgQMHgIsnuycnJxMZGUl4eDirVq3CYrGUWa9t27YAREdH06JFC1q1agVAbGwsv/zyS4XvmZiYiNVq1R45OTnV6iGEEEIIIYTwXHJrJQ/Spk0bWrRowddff82CBQsu2ysMFyexc+bMoUePHle1TR8fH+3XBoNBO54/NTWVdevW8f3332MymXj33XdZt25dhetVtJ3yeHt74+3tfdnzDp3hqsYsao5DZyD7znbSXhHpr460V0faq2M0GomPj8dolP8+upu0V0v6q+Pq9rJn2MOMGjWKN954g23btpGQkHDZ63379iU5OZkLFy4AcOHCBX7++WcATCYTVqv1qt4nNzcXf39/TCYTBQUFpKSk1NhnENcXo61E9RA8mvRXR9qrI+3VKSwsVD0EjyXt1ZL+6riyvUyGPUxCQgLZ2dk88sgj+Pr6Xvb6lClTiImJoV27doSHh9O+fXvt8OYhQ4bwxRdfEBkZyccff1zp+wwdOpQLFy4QHBxM79696dy5sys+ThlyZVH30zvtND+xU9orIv3VkfbqSHt1bDYb69evlyvqKiDt1ZL+6ri6vc7pdDpdsmUh3OTSjbVPnz5Nw4YNVQ/Ho5SWlpKWlkZ8fDxeXl6qh+NxpL860l4daa+OtFdH2qsl/dWpTvtLcwOr1YrJZKp0WdkzLIQQQgghhBDC48hZ4OKm8V7WWWqZ5EAHd9I7bLTQGUjOPINDL3+duJv0V0faq+PJ7adG+qseglxASCFpr5b0V8eV7eUwaQ+0fPlyXn/9dex2O0VFRTRp0oQ1a9ag17v2QIGUlBRWrFjBihUryMjIYNasWSxZsuSat3vpUIhXNh7Ex7deDYxUCCGEuP5cD5NhIYS43lXlMGn5isPDHD9+nDFjxrB9+3buuusuAHbs2IFOp3PrOKKjo2tkIlyGfK/jfk4ndYvyOO9TH9z8Z0gg/VWS9upIe2UcDgenT5/G39/f5V+gi7KkvVrSXx1Xt5ffTQ/z+++/YzAYuOWWW7TnoqKi0Ol0ZGRk0LFjR8LDw4mNjeWnn34C4PDhw9SvX5+XXnqJqKgoWrZsyU8//cSzzz6L2WwmNDSUrKwsAE6cOEG3bt1o27YtISEhPPXUUzgcjsvGsWHDBsxmM3DxKnE9e/YkOjqakJAQBg0axPnz56v82eTKou6nd9oJPLVH2isi/dWR9upIe3XsdjubN2/Gbpf27ibt1ZL+6ri6vUyGPUx4eDidOnXirrvu4uGHH2bWrFkcO3aMkpIS+vXrxyuvvEJmZiazZ8+mf//+nDt3DgCr1Urbtm3ZsWMHU6dOpWfPnvTp0weLxcKwYcN49dVXAahfvz5ff/0127dvJzMzk8OHD/PFF19UOiaDwUBqaioZGRlkZWXh5+fHnDlzKly+uLiY/Pz8Mg8hhBBCCCGEqAqZDHsYvV7PsmXL2LRpE7169eKnn34iJCSE7Oxs9Ho9PXv2BKBTp07ceuut2j2GfXx86Nu3L3DxEGdfX1+6desGQGxsLL/88gtw8VCGKVOmEBERQWRkJBkZGdo2KuJ0OklOTiYyMpLw8HBWrVpV6TpJSUn4+flpj4CAgGtqIoQQQgghhPA8Mhn2UK1atWLs2LGsWLGC9u3bs3z58suW+eN5xN7e3tqvDQYDPj4+ZX6+dCPs2bNnc/LkSbZu3UpmZiaDBg2iqKio0rGkpqaybt06vv/+e3bv3s3kyZMrXScxMRGr1ao9cnJyAHAi5465mxMdxV61pb0i0l8daa+OtFdHp9NRr149t19nREh71aS/Oq5uL5NhD3Ps2DHtXGCA3NxcDh06hNlsxuFwsHr1agA2bdrEiRMntPN6r1Zubi633XYbPj4+nDhxgqVLl17VOv7+/phMJgoKCkhJSal0eW9vb0wmU5kHgFNvqNJYxbVz6g0cvD1K2isi/dWR9upIe3WMRiPdu3eXW8woIO3Vkv7quLq9/I56GJvNxowZMzh06BB16tTBZrMxbNgwHnroIe644w6efvppJk2ahI+PD19++SW+vr6cPn36qrf/zDPPMGDAAEJCQmjSpAlxcXFXXGfo0KF89dVXBAcH06hRIzp37syRI0eq/uGcl1+oS7iY00H98yfJq9sYdPLdmttJf3WkvTrSXhmHw0FOTg4BAQFyRV03k/ZqSX91XN1e7jMsbniX7iV2+vRpGjZsqHo4HqW0tJS0tDTi4+Px8vJSPRyPI/3VkfbqSHt1pL060l4t6a9OddpX5T7D8tWGEEIIIYQQQgiPI4dJi5vGe1lnqWWSAx3cSe+wEQwkZ57BoZe/TtxN+qsj7dXxlPZTI/1VD0EIIW56smdY3DTkyqLu50THOZ/60l4R6a+OtFdH2quj0+lo1KiRXFFXAWmvlvRXx9XtZTJ8AwoKCqJx48aUlpZqz61fvx6dTsfEiRNd/v5z585l+PDh17SNefPmMWvWrJoZ0P8nVxZ1P6feQE7jEGmviPRXR9qrI+3VMRqNdOzYUa6oq4C0V0v6q+Pq9jIZvkEFBgaycuVK7ef58+cTHR2tcERVM27cOJ5//vka3aZOribtdjqnA/+8o9JeEemvjrRXR9qrY7fb2bdvH3a7XfVQPI60V0v6q+Pq9jIZvkGNGDGCBQsWAGC1WtmyZQu9evUCYPfu3XTq1ImoqCjatGnDzJkztfVKSkp4/vnnCQ0NJSIiQlsH4K233iI2NpaoqCh69eql3d6ooKCAhIQEgoOD6dSpE7t379bWsdvt2vZCQ0OZMGECJSUlAAwfPpyxY8dy3333cffdd9OvXz/ttenTp2t7sSsbb3mKi4vJz88v8wCZDKugczpolJ8j7RWR/upIe3WkvToOh4Ps7GwcDmnvbtJeLemvjqvby2T4BnXPPfdw+PBhfvvtNz7//HMeeeQRDIaLh4wFBQWxdu1aduzYwfbt21m2bBlbtmwBICkpif/+979s376dXbt2sXDhQgBSU1PJzs5m8+bN7Nixg8GDBzN+/HgAZsyYgbe3N/v27WPVqlVs3LhRG8eHH35Ieno627dvx2KxcODAAZKTk7XXLRYLX3/9NXv37uX3339n2bJll32WysZbnqSkJPz8/LRHQEDAtQcVQgghhBBCeBSZDN/AhgwZQkpKCgsWLGDkyJHa84WFhTzxxBOEhYXRvn17jhw5gsViAeDf//43zzzzDN7e3gA0atQIgBUrVrBmzRratm2L2Wzmb3/7G0ePHgVg7dq1jBo1Cp1Oh5+fH4MGDdLea82aNQwfPhxvb2+MRiOjR49m9erV2usPP/wwderUwWAwEBsby4EDBy77HJWNtzyJiYlYrVbtkZOTU+2GQgghhBBCCM8kZ4HfwIYOHUpUVBR33303LVu21J6fNm0a/v7+7Ny5E6PRSL9+/SgqKqp0W06nk8TERMaMGXPF963sam7/+5qPj4/2a4PBgM1mu2ydqo7X29tbm8yX+QxyZVG3c6Ijr25jaa+I9FdH2qsj7dXR6/UEBgai18u+FHeT9mpJf3Vc3V5+R29gTZo0ISkpiTfffLPM87m5udx5550YjUays7PL7Knt06cP77zzDsXFxQCcOnUKgL59+zJv3jzOnj0LQGlpKTt37gQgLi6OTz75BKfTSX5+Pp9//rm2vbi4OD777DNKSkqw2Wx8/PHH9OjRo0qfo7LxVoVcWdT9nHoDxxu2lPaKSH91pL060l4dg8FAZGSkdlqWcB9pr5b0V8fV7WXP8A1uxIgRlz334osvMmTIED799FOaN29O9+7dtdemTJnCCy+8QFRUFF5eXjRp0oS0tDQGDx7MmTNn6NatGwA2m42RI0cSGRnJSy+9xBNPPEGrVq1o1KgRnTp10ibTY8aM4cCBA0RFRQHQtWvXKt/eqbLxVsUzofVp0KBBtdYV1WO328nMzCQ8PFz+gVBA+qsj7dWR9upIe3WkvVrSXx1Xt9c5nU5njW9VCDfKz8/Hz8+P06dP07BhQ9XD8SilpaWkpaURHx+Pl5eX6uF4HOmvjrRXR9qrI+3VkfZqSX91qtP+0tzAarViMpkqXVYOkxZCCCGEEEII4XHkMGlx03gv6yy1THKggzvpHTaCgeTMMzj08teJu0l/daS9Ojdj+6mR/qqHIIQQHkn2DN9kgoKCLrstUdeuXVmxYkWNvceAAQNISUmpse3VFKdO/ji7m1On55QpQNorIv3VkfbqSHt19Ho9wcHBckVdBaS9WtJfHVe3vzm+UhXXPYfDAeDSv0TkP0bu59TpOV0/UPUwPJb0V0faqyPt1TEYDLRq1Ur1MDyStFdL+qvj6vYye/AgJ0+epF+/foSFhREaGsoHH3ygvRYUFMTLL79Mhw4daNq0KTNnztRe27dvHx07diQkJIS+ffuSn5+vvTZ8+HDefvtt7efJkyczffp0AKZPn07//v3p2bMnoaGhHD9+nMmTJxMTE4PZbKZLly5kZ2cDUFhYSEJCAm3atCEiIqLS2zMVFxeTn59f5gGgc9hrIpOoAp3DTsDJn6W9ItJfHWmvjrRXx2azsWnTJmw2m+qheBxpr5b0V8fV7WXP8E0oISGB2rVraz/v378fgAkTJhAcHMzy5cs5efIkbdu2JSIigvbt2wOQl5fH5s2bOX36NM2bN2fEiBHccccdDBkyhHHjxjFq1Ch2795NdHQ0gwYNuqqxbN68mZ07d3LrrbcCF2/t9NZbbwHwz3/+k2eeeYZvv/2Wb7/9lry8PPbs2QOg3e+4PElJSbz66quXPa9Dzhd2Nx1OfIvy0OGU+gpIf3WkvTrSXh2n08mpU6eQG5G4n7RXS/qr4+r2Mhm+CS1ZsgSz2az93LVrVwDWrFnD9u3bAWjcuDH9+vVjzZo12mT40gTX39+fZs2acejQIerVq4fFYmH48OEAhIWF0alTp6seS3x8vDYRBli9ejVz5syhoKAAh8OhTXojIiLYu3cv48eP59577yU+Pr7CbSYmJvLcc89pP+fn5xMQEHDVYxJCCCGEEEIIOUzag+l0ujI/+/j4aL82GAwVHo7wx/WMRiN2+/8dqlZUVFRmWV9fX+3XR48e5amnnmLRokVkZWXxz3/+U1u+WbNm7Nmzh169evHTTz8RGhpKbm5uue/v7e2NyWQq8xBCCCGEEEKIqpDJsAeJi4vjo48+AuDUqVMsX76c+++/v9J1TCYTkZGRfPbZZwD8/PPP/Pjjj9rrLVq0YNu2bQCcOXOGtLS0CrdltVrx8vLi9ttvx+l0MnfuXO21X3/9FZ1OR58+fXjrrbdwOp3k5ORU6fM55AJabufQ6Tl+S3Npr4j0V0faqyPt1TEYDJjNZgwGg+qheBxpr5b0V8fV7eUwaQ/y7rvv8uSTTxIWFobT6eSFF16gXbt2V1zvs88+Y8SIEfz973+nZcuWdOnSRXttzJgxDBgwgNatW9OsWTPtkOvyhIWFMXDgQEJCQmjYsCF9+/bVXtu9ezeJiYk4nU5sNhtDhgwhPDy8ah9Q/mPkfjo9eb63qR6F55L+6kh7daS9Mnq9nrvuukv1MDyStFdL+qvj6vY6p5wJLm5w+fn5+Pn5cebMGW655RbVw/EoNpuNjRs30qVLF4xG+W7N3aS/OtJeHWmvjrRXR9qrJf3VqU77S3MDq9V6xdMpZVeauGnI9zru53Q6KSgokPaKSH91pL060l4daa+OtFdL+qvj6vby1Ya4abyXdZZaJvlLyp30DhvBQHLmGRx6+evE3aS/OtJenZuh/dRIf9VDEEIIwXW0ZzgoKIjg4GDMZjOtW7dm0KBBnD9/3iXv9cQTT7B+/XqXbPtKsrKyCAoKKve16dOnM3HixDLPpaSklDm39kYzb948Zs2apXoYQgghhBBCCFHGdfWV6qX74zocDv70pz+RkpLCn//85xp/n48//rjGt3m9sdvt18UV78aNG+e293Lo1H9eT+PQGTjaqI20V0T6qyPt1ZH26hgMBjp06HBd/P/C00h7taS/Oq5uf93sGf6jkpISLly4QIMGDYCLE7vnn3+e0NBQQkNDmTBhAiUlJcDFiW2bNm0wm82EhYWxdetW4OKe5pdffpkOHTrQtGlTZs6cqW2/a9eurFixAoDU1FTatWtHZGQkERERfP311+WO6cSJE3Tr1o22bdsSEhLCU089hcPhAC7uvY2Li+Oxxx4jLCyM6OhoDh48qK07ffp0WrZsSdu2bfnnP/95TW0WLlxIu3btiIqKokuXLuzatUsbQ7du3ejfvz9hYWH8+OOPmM1m7eHv78+IESMA+OWXX3jggQeIiYkhPDy8zC2OvvrqK1q3bk1ERARTpkzB39+fw4cPA5CRkUHHjh0JDw8nNjaWn376CYDRo0fz1ltvads4dOgQt912G6WlpWX2dlfWqbK+V+1/7pss3ECn43ztBtJeFemvjrRXR9oro9frady4MXr9dfnfx5uatFdL+qvj6vbX1e9oQkICZrOZ2267Db1ez6OPPgrAhx9+SHp6Otu3b8disXDgwAGSk5MBmDRpEmvXrsVisbBjxw5CQkK07eXl5bF582bS09OZNWsWx44du+w9e/bsyZYtW9i5cydfffUVo0ePpri4+LLl6tevz9dff8327dvJzMzk8OHDfPHFF9rr6enpvPHGG+zevZu4uDjefPNNAFatWsXSpUvZvn07GRkZ2sSyIosXLy4ziX355Ze113766Sc+//xzNm7cyI4dO3j99dcZNGiQ9vrWrVu1Mdx7771YLBYsFguLFy/G19eXSZMmYbfbeeyxx/j73/9Oeno6W7Zs0fqePHmSkSNH8q9//Ytdu3bRqlUrzpw5A1z8gqJfv3688sorZGZmMnv2bPr378+5c+cYMWIEKSkp2jhSUlIYPHgwXl5el32+ijpdqe8fFRcXk5+fX+YBF88jE+6ld9i4O2eLtFdE+qsj7dWR9uqUlpayatUqSktLVQ/F40h7taS/Oq5uf11NhpcsWYLFYuH06dMEBQUxZcoUANasWcPw4cPx9vbGaDQyevRoVq9eDcB9993HkCFDeOeddzh06BC+vr7a9i5NFP39/WnWrBmHDh267D0PHTpE7969CQ0NpW/fvpw9e7bc5RwOB1OmTCEiIoLIyEgyMjKwWCza65f2QF/69YEDBwBYu3Ytjz76KCaTCZ1Ox9ixYyttMHjwYG0Sa7FYmDFjhvbaV199xa5du2jXrh1ms5kJEyZw9uxZCgsLAejYsSPBwcFltvfbb7/x0EMPsWDBAkJDQ8nOzubnn39m4MCBmM1mOnbsSEFBAXv27GHLli2Eh4fTqlUrAIYNG0atWrUAyM7ORq/X07NnTwA6derErbfeisVioWPHjthsNtLT03E6ndp9ictTUacr9f2jpKQk/Pz8tEdAQEClTYVrGZx21UPwaNJfHWmvjrRXx2aTLyFUkfZqSX91XNn+upoMX2I0Gunfvz/ffvttua/r/nBo1LJly/jrX/9KaWkp8fHxZQ5D9vHx0X5tMBjKDTlw4ECeeOIJsrKysFgs+Pr6UlRUdNlys2fP5uTJk2zdupXMzEwGDRpUZrmrea//HXtVOZ1Ohg0bVmayfPz4cWrXrg1Q5osAgIKCAh588EGmT59O9+7dtW3ccsstZbZx6NAhhg0bVuXx/PGzjBgxgk8++YQNGzbg7+9PaGhouetU1OlKff8oMTERq9WqPXJycqo8diGEEEIIIYRnuy4nwwDr1q3T9nLGxcXx2WefUVJSgs1m4+OPP6ZHjx7YbDYOHDhAdHQ0kydPZsCAAWzbtq1K75Obm6vtqVy0aBG5ubkVLnfbbbfh4+PDiRMnWLp06VVtPy4ujqVLl2r3x/rwww+rNL4/6tOnD4sWLeLo0aPAxb2pGRkZ5S5rs9kYMGAAAwYM4PHHH9eeDw4OxmQy8cknn2jP7d+/n7Nnz9K+fXsyMzPJzs4GLva4dG52cHAwDodD2yO/adMmTpw4gdlsBmDIkCEsXbqUefPmMXLkyCp/tqr09fb2xmQylXkIIYQQQgghRFVcV1eTTkhIoHbt2thsNu666y7mzZsHwJgxYzhw4ABRUVHAxQtgTZw4EbvdzsiRIzl79ixGo5FGjRqVmeRdjXfeeYcBAwZQv359unfvTmBgYLnLPfPMMwwYMICQkBCaNGlCXFzcVW0/Pj6ebdu2ERUVhclkonfv3lUa3x917tyZv/3tbzz88MPYbDZKSkp44IEHiI6OvmzZn376iTVr1vD7779r59726dOHGTNm8O9//5uJEyeSnJyM3W7H39+f1NRU7rjjDj7++GP69u2Lt7c3999/P76+vtSvX59atWqxfPlynn76aSZNmoSPjw9ffvmltje6SZMmxMbGsnLlSj744IMqf7bq9v0jubKo+zl0Bg7cFintFZH+6kh7daS9OkajkW7dumE0Xlf/ffQI0l4t6a+Oq9vrnE6n0yVbFjekgoIC6tWrB8CKFStITExk7969ikdVufz8fPz8/MjLy8PPz0/1cDyK0+nEZrNhNBqv6RQAUT3SXx1pr460V0faqyPt1ZL+6lSn/aW5gdVqveIRpNftYdJCjTlz5hAREUFoaCh/+9vfWLx4seohXTW5sIH72Ww20tLSpL0i0l8daa+OtFdH2qsj7dWS/uq4ur3s6xdlTJs2jWnTpqkeRrW8l3WWWiY50MGd9A4bwUBy5hkcevnrxN2kvzrSXp0brf3USH/VQxBCCFEB2TPsQYKCgggODsZsNtOmTRvee++9Kq1/+PBh7TxuV+ratSsrVqxw+fsIIYQQQgghPJdMhj3MpXs5f/PNN0ybNo3MzMyrWs9ms7ltMiyEEEIIIYQQriaTYQ911113ERwczL59++jXrx9hYWGEhoaWuRJ0UFAQU6ZMITY2lmHDhjFu3Diys7Mxm8306dNHW8ZisWjrREdHs2HDBgD27dtHhw4dCAkJoV+/fvTo0YOUlBQAUlNTadeuHZGRkURERPD1119f82eSK4u6n0NnIPvOdtJeEemvjrRXR9qrYzQaiY+PlyvqKiDt1ZL+6ri6vfyOeqjdu3ezb98+lixZQnBwMMuXL+fkyZO0bduWiIgI2rdvD8CZM2fYunUrOp2ODRs2MHHixDKT38oMGTKE8ePHM2LECPbu3UtkZCSDBg0CoGfPnjz22GPodDoOHz5M+/btOXLkCN7e3lfcbnFxMcXFxdrP+fn5VQ8gaozRVkKJV23Vw/BY0l8daa+OtFensLBQu+uEcC9pr5b0V8eV7WXPsIdJSEjAbDYzduxYFixYwIYNGxg7diwAjRs3pl+/fqxZs0Zbfvjw4dW6hHx+fj4Wi4WhQ4cC0Lp1azp16qS9fujQIXr37k1oaCh9+/bl7NmzHDp06Kq2nZSUhJ+fn/YICAgAQO+0V3mc4tronXaan9gp7RWR/upIe3WkvTo2m43169fLFXUVkPZqSX91XN1eJsMe5tI5w5s2bWLAgAGXvf6/E19fX99Kt2c0GrHb/+8/JEVFRRUu+8dtDxw4kCeeeIKsrCwsFgu+vr6VrvtHiYmJWK1W7ZGTk3NV6wkhhBBCCCHEJTIZ9nBxcXF89NFHAJw6dYrly5dz//33l7usyWTCarWWea5FixZs3boVgG3btpGdna0tGxERwaJFiwDIzs7mxx9/1NbLzc2ladOmACxatIjc3NyrHrO3tzcmk6nMQwghhBBCCCGqQibDHu7dd99l7969hIWF0a1bN1544QXatWtX7rLh4eGEhIQQGhqqXUBr5syZvPfee0RERLBgwQJCQkK05T/77DP+8Y9/EBoaypQpU4iJiaF+/foAvPPOOwwYMIDIyEh27txJYGCgyz+rcA27XMRGKemvjrRXR9qrIxcQUkfaqyX91XFle53T6XS6bOvCo507d466deui0+k4dOgQHTp0ID09XTvHt6bk5+fj5+eH1WqVvcRCCCGEEEJ4sKrMDWTPsHCZTZs2YTabiYiI4E9/+hPJyck1PhH+I4fD4bJti/I5HA5Onjwp7RWR/upIe3WkvTrSXh1pr5b0V8fV7WUyLFymR48e7Nq1i127dpGVlcVjjz3m0vf744W8hHvY7XY2b94s7RWR/upIe3WkvTrSXh1pr5b0V8fV7eXgd3HTeC/rLLVMctS/O+kdNoKB5MwzOPTy14m7SX91pL0613P7qZH+qocghBCiCmTPsIcICgrCYrFc1bKHDx/WLnQlhBBCCCGEEDcjmQyLm4YT3ZUXEjXKiY5ir9rSXhHpr460V0faq6PT6ahXrx46nbR3N2mvlvRXx9XtZTLswdLT0+nevTvR0dFERkaydOnSMq9PnjxZu53SmjVrALDZbPTs2ZPo6GhCQkIYNGgQ58+fB2DDhg2EhoYyfvx4IiIiCAkJISMjQ9vewoULCQ8PJzw8nAceeIBjx44BkJKSQlxcHI899hhhYWFER0dz8ODBCsddXFxMfn5+mQeAUy+32nA3p97AwdujpL0i0l8daa+OtFfHaDTSvXt3ucWMAtJeLemvjqvby2TYQ+Xl5TFmzBgWL15MRkYGq1evZtKkSdoE1Wq10rp1azIzM5k/fz6DBg2ioKAAg8FAamoqGRkZZGVl4efnx5w5c7Tt7tu3j2HDhrFr1y4mTJjACy+8AEBWVhbPP/8833zzDZmZmXTs2JEnnnhCWy89PZ033niD3bt3ExcXx5tvvlnh2JOSkvDz89Me2hWqnXKFP7dzOqh/7oS0V0X6qyPt1ZH2yjgcDo4cOSJX1FVA2qsl/dVxdXuZDHuoTZs2cfDgQXr37o3ZbCYuLg6A7Oxs4OK3MMOHDwegffv2NGnShJ07d+J0OklOTiYyMpLw8HBWrVpV5lzkFi1a0K5dOwA6dOjAgQMHAFi/fj29evXijjvuAGD8+PGsW7dOuzJchw4daNq06WXrlScxMRGr1ao9cnJyANDLf4zcTu90cPvZA9JeEemvjrRXR9qrY7fbsVgsckVdBaS9WtJfHVe3l339HsrpdBISEsKmTZsue+3w4cPlrqPT6UhNTWXdunV8//33mEwm3n33XdatW6ct4+Pjo/3aYDBgs9kq3NYfXe16AN7e3nh7e1f4uhBCCCGEEEJciewZ9lAdO3bk0KFD2rnAABaLhZKSEuDiucELFy4EYNu2bfz222+YzWZyc3Px9/fHZDJRUFBASkrKVb1ft27d+Pbbb/ntt98AmDdvHvfddx8Gg5zzJYQQQgghhHA/2TPsQXr27ImXl5f285IlS5g+fTqTJk2itLSUwMBAVqxYAYCfnx9ZWVlERERgs9lITU2lXr16DB06lK+++org4GAaNWpE586dOXLkyBXfOzQ0lFmzZtGrVy8AAgIC+Oijj2r088mVRd3PiY5zPvWlvSLSXx1pr460V0en09GoUSO5oq4C0l4t6a+Oq9vrnE6n0yVbFsJN8vPz8fPzw2q1YjKZVA9HCCGEEEIIoUhV5gZymLS4achFDdzPbrezb98+aa+I9FdH2qsj7dWR9upIe7Wkvzqubi+HSYubxpzM09QyyV9S7qR32Aj+NZuV5+vj0MtfJ+4m/dWR9upcL+2nRvore29VHA4H2dnZNG/eXK754WbSXi3pr46r28ueYQFAaWkpr776Kq1atSIkJITIyEj69u1b5rZJQgghhBBCCHGzkK+zBQAjRozg3LlzbN68mQYNGgCwZs0asrOzMZvNZZa12+3yrZgQQgghhBDihiZ7hgW//PIL//rXv1iwYIE2EQaIi4sjISGBlJQUunXrRv/+/QkLC2Pbtm2kp6fTvXt3oqOjiYyMZOnSpdp63333HZ06daJt27bExsayfv167bVPPvkEs9lMREQE0dHR2j2NK1vnfxUXF5Ofn1/mAXI1aRWc6Mir21jaKyL91ZH26kh7dfR6PYGBgej18t9Hd5P2akl/dVzdXvYMC3bu3EmLFi245ZZbKlxm69at7Ny5k+DgYPLy8ujWrRtpaWncfvvtnD59mqioKDp27EhxcTHTp0/nu+++w2QysX//fjp37szhw4fZvHkzM2bMYNOmTdx+++1cuHABgIMHD1a4jre392VjSUpK4tVXX73seade9la7m1Nv4HjDlqqH4bGkvzrSXh1pr47BYCAyMlL1MDyStFdL+qvj6vYyGRaXOXDgAP3796ewsJCOHTty77330rFjR4KDgwHYtGkTBw8epHfv3mXWy87OZt++fezfv58uXbpoz+v1eo4ePcqqVasYMmQIt99+OwB16tQB4Ntvv61wnZYtL/8PT2JiIs8995z2c35+PgEBAegccvEsd9M57NyWe5ATDZrJlxEKSH91pL060l4du91OZmYm4eHhcrqUm0l7taS/Oq5uL5NhQWRkJPv37yc3N5cGDRrQvHlzLBYLKSkprFixAgBfX19teafTSUhICJs2bbpsW3v37uX+++8nNTX1qt/f6XRWaR1vb+9y9xjrkFtmu5sOJ/XPn+T3Bk2lvgLSXx1pr460V8fhcHD06FFCQ0NlQuBm0l4t6a+Oq9vLge+Cli1b8tBDDzFq1Cjy8vK058+fP1/u8h07duTQoUOsWbNGe85isVBSUkLPnj1Zs2YNmZmZ2mvbtm0D4E9/+hOLFi3i+PHjAFy4cIELFy5Uuo4QQgghhBBCuILsGRYApKSk8Prrr9OuXTuMRiMNGjSgUaNGTJkyhezs7DLLNmjQgFWrVjF58mQmTZpEaWkpgYGBrFixghYtWpCamsrYsWO5cOECJSUlREZGkpqaSpcuXXjllVfo2bMnOp2OWrVq8eWXX1a6jhBCCCGEEEK4gs7pdMpRRuKGlp+fj5+fH2fPni1zNWzhena7nV9++YWWLVvKYUMKSH91pL060l4daa+OtFdL+qtTnfaX5gZWqxWTyVTpsjIZFje8qvyBF0IIIYQQQty8qjI3kMOkxU0jeedJvE0lqofhUXQOO3ee3sev/q3kqq4KSH91pL06qtpPjfR323tdr2w2G9u2bSM2NhajUf4L6U7SXi3pr46r23vUBbSCgoIIDg4mIiKCFi1a8NBDD5V7ReRrFR8ff9l5tuV5+eWXWbx4cY2/f3m6du1K06ZNMZvNBAcH8+yzz+JwOFz2fsOHD+ftt9++4mvz5s1j1qxZNfKecjVp99PhxLcoT9orIv3VkfbqSHt1nE4np06dQg4qdD9pr5b0V8fV7T3uq40lS5ZgNpsBWL58OfHx8Xz33Xe0a9euxt4jLS3tqpabMWNGjb3n1UhOTqZv377k5+djNpvp0KEDjz76qFvH8L/GjRun9P2FEEIIIYQQnsmj9gz/r379+jFu3DjeeustAM6dO8fIkSMJDQ0lNDSUV199VVu2a9euTJo0iS5duhAYGMhLL71EWloanTp1IigoiNmzZ2vLBgUFYbFYtPUmT55M586dad68eZnJ3x/3kJaUlPD8888TGhpKREQEvXr1AmD37t106tSJqKgo2rRpw8yZM7X1p0+fTkJCAn/6059o06YN3bt35+zZs1f83CaTiZiYGI4cOQJAQUEBo0ePJjY2lvDwcMaMGUNJSYk2/gkTJhATE0OLFi2YNGmS9s1M165dtfsQAwwYMICUlBTt58zMTDp27Mjdd9/NsGHDKCwsvGws06dPZ+LEidrPb775JmFhYURERNC+fXsuXLhwxc8jhBBCCCGEEFXl0ZNhgHbt2vHzzz8D8Nprr1FcXExmZiZbt25lxYoVLFmyRFv2yJEjrF+/nl27dvHuu++SlpbGDz/8wE8//cTLL79c5h69f3TgwAHWr19PVlYW3333HZs3b75smaSkJP773/+yfft2du3axcKFC4GLE+u1a9eyY8cOtm/fzrJly9iyZYu23tatW0lJSWHPnj00btyYDz744Iqf+fjx4+zatYsHH3wQgEmTJtG5c2e2bdvGrl27cDgcvPPOO9rye/bsYdOmTWRmZvL999/z+eefXzns/x/bd999x969ezl79izJycmVLv/pp5+ybNkyfvzxR3bt2sU333yDt7f3ZcsVFxeTn59f5gHg0Hn8H2e3c+j0HL+lubRXRPqrI+3VkfbqGAwGzGazXE1XAWmvlvRXx9XtPf5fkj8ef75mzRpGjx6NXq+nbt26DB06lNWrV2uvDxgwAIPBQIMGDWjWrBkPPvggOp2OO+64g0aNGnH48OFy3yMhIQGj0Ujt2rUxm80cOHDgsmX+/e9/88wzz2iTv0aNGgFQWFjIE088QVhYGO3bt+fIkSPaXmeAXr160bBhQwA6dOhQ7rYvefbZZwkNDSUwMJDevXvTunVrAFasWMGsWbMwm81ERkbyww8/sH//fm29oUOH4uXlRZ06dXj88cdZs2bNFape9Oijj1KvXj0MBgOjRo264nr//ve/GTduHH5+fsDF+xmX9wc/KSkJPz8/7REQEHDxBfmPkfvp9OT53ibtVZH+6kh7daS9Mnq9nrvuugu9Xtq7m7RXS/qr4+r2Hv87mp6eTmhoaLmv6XS6Mj/7+PhovzYYDJf9bLPZyt3O1S5XnmnTpuHv78/OnTvZtWsXXbt2paioqFrbTk5OJisri+3bt7NgwQK++eYb4OIXAsuWLcNisWCxWMjOzq50D/OlLkajEbvdrj3/x3FVtt61SkxMxGq1ao+cnJyL23fYr7CmqGk6h51mx3dIe0WkvzrSXh1pr47NZmPdunVV+n+MqBnSXi3pr46r23v0ZPirr77iH//4B5MmTQIgLi6O+fPn43Q6OX/+PAsXLqRHjx5uGUufPn145513KC4uBuDUqVMA5Obmcuedd2I0GsnOzi6zp7q6wsPDee2115g2bRpOp5O+ffvy5ptvan/IcnNzy+wZXrRoEaWlpRQWFpKamkpcXBwALVq0YOvWrQAcOnSIH3/8scz7fPnll5w7dw673c4nn3yirVdZg3nz5mG1WgHIy8srM9m+xNvbG5PJVOYBcjVpFXQ48S4tlPaKSH91pL060l4dp9NJQUGBXFFXAWmvlvRXx9XtPW4ynJCQoN1aaf78+aSlpWlXkn7ppZfw8vIiLCyMdu3a0adPH7ddbXnKlCncfffdREVFYTabGTZsGAAvvvgin3zyCeHh4UydOpXu3bvXyPs9+eSTnD9/nuXLl5OcnKwdwh0eHs59991X5pDv1q1bc8899xAWFkbnzp0ZOHAgAH/5y19Yv349YWFhJCYmXnZF7piYGHr27Enr1q2pX79+mQtllWfIkCH079+fjh07EhERQXx8vPblgBBCCCGEEELUJJ1TvuIQlejatSsTJ06kb9++qodSofz8fPz8/Dh9+rR2/rRwj9LSUtLS0oiPj8fLy0v1cDyO9FdH2qsj7dWR9upIe7WkvzrVaX9pbmC1WrUjSCvicXuGxc1LrvDnfgaDgQ4dOkh7RaS/OtJeHWmvjrRXR9qrJf3VcXV72TMsbnhV+fZHCCGEEEIIcfOqytzA6KYxCeFyb+/8nVqmEtXD8Ch6h40WxzLYf0c0Dr38deJu0l8daa+OO9pPjfR3yXZvdKWlpfznP/+hR48ecqiom0l7taS/Oq5uL4dJ38AKCgrw9fVl1KhRNb5tnU5HXl5eldb57bff6Ny5s/az2WymoKCghkcmrjcGp9zeRCXpr460V0faqyO3llFH2qsl/dVxZXuZDN/AlixZQtu2bVm+fDnnzp2rkW1eyx+2Jk2a8MMPP2g/WywW6tWrVxPDEkIIIYQQQogaJZPhG9j8+fOZMmUKXbp0YcmSJQCMGzcOs9mM2Wzm7rvvRqfTARcPMZg6dSqxsbGYzWYeffRRcnNzARg+fDgjR46kS5cuhIaGatt/6623iIyM5O6772bx4sXa84MHDyY6Oprw8HAeeOABTpw4AcDhw4epX7++ttwf9y5PnjyZmJgYzGYzXbp0ITs7u8xyb7zxBrGxsTRt2pRPPvmk0s9dXFxMfn5+mYcQQgghhBBCVIVMhm9Qe/bsIScnh549ezJq1Cjmz58PwLx587BYLGRkZNC8eXNmzJgBwKxZs6hbty7btm3DYrEQFhbGiy++qG1v+/btrFq1in379mnP6XQ6du7cybfffsuECRO0ew+//fbbZGRkkJmZSefOnZk+ffoVxztlyhTS09OxWCyMHz+eZ555pszr3t7ebNu2jW+++Yann3660j3USUlJ+Pn5aY+AgAAAHDq5wp+7OXQGDtwWKe0Vkf7qSHt1pL06RqORbt26YTTKefLuJu3Vkv7quLq9/I7eoObPn8/QoUMxGAzEx8czduxY9u7dS+vWrQF48sknueOOO3jppZcAWLFiBVarlWXLlgFQUlJCUFCQtr1HHnnkskOan3jiCQCaNWtGly5d2LhxI0FBQaSmprJw4UKKioooKirC3//KFxpZvXo1c+bMoaCgAIfDwdmzZ8u8PnjwYABatWqF0WjkxIkT3HnnneVuKzExkeeee077OT8/X5sQC/ezGWupHoJHk/7qSHt1pL06tWvXVj0EjyXt1ZL+6riyvewZvgGVlpaycOFCPv30U4KCgmjRogUXLlzQ9g6/9tpr/Prrr8ybN09bx+l0MmfOHCwWCxaLhT179pCWlqa97uvre8X31el0/Pjjj7z77rukpaWRlZXF7NmzKSoqqnS9o0eP8tRTT7Fo0SKysrL45z//edk6Pj4+2q8NBkOle4a9vb0xmUxlHgB6uaCK2+mddoJ/3SrtFZH+6kh7daS9OjabjbS0NLmQkALSXi3pr46r28tk+Aa0cuVKmjVrxrFjxzh8+DCHDx9my5Yt2gR5xYoVLF26tMzhBH379iU5OZkLFy4AcOHCBX7++edK3+fSubuHDx/mhx9+oHPnzuTm5lKvXj0aNmxISUkJH3zwwRXHa7Va8fLy4vbbb8fpdDJ37txr+PRCCCGEEEIIce3kMOkb0Pz587XDii9p3bo1d9xxh3b+bqdOnbTXLBYLU6ZMobi4mHbt2mkX1ZoyZQohISEVvo/dbicyMpLz58/z7rvvEhQUxB133MGiRYsIDg6mYcOGxMXFcezYsQq3odPpCAsLY+DAgYSEhNCwYUP69u1b/Q8vhBBCCCGEEDVA53Q6naoHIW4+J0+eJCAggMLCQvR61x6AkJ+fj5+fH6dPn6Zhw4YufS9RVmlpKWlpacTHx8tN6BWQ/upIe3WkvTrSXh1pr5b0V6c67S/NDaxWq3Y6ZUVkMixqXHp6OgkJCYwePZrExESXv9+lP/B5eXn4+fm5/P3E/3E6ndhsNoxGo3bEgXAf6a+OtFdH2qsj7dWR9mpJf3Wq074qk2E5TFrUuJiYGA4ePOj2903edQafeqVuf1+P5nRSq7SQEq/aIP84uJ/0V0faq1PD7adGXvmOCOL/FBYWXnb3CeEe0l4t6a+OK9vLBbQ8SFBQEI0bN6a09P8mjOvXr0en0zFx4kR1A6shcmVR99M77TQ/sVPaKyL91ZH26kh7dWw2G+vXr5cr6iog7dWS/uq4ur1Mhj1MYGAgK1eu1H6eP38+0dHRVdqG/EUghBBCCCGEuNHJZNjDjBgxggULFgAXb3m0ZcsWevXqBVy8evTzzz9PaGgooaGhTJgwgZKSEgCGDx/OyJEj6dKlC6GhoQDMmjWLkJAQwsLCGDx4MFarFYCSkhJtOxEREdr2Ad58803CwsKIiIigffv22q2eKtpWeYqLi8nPzy/zEEIIIYQQQoiqkMmwh7nnnns4fPgwv/32G59//jmPPPIIBoMBgA8//JD09HS2b9+OxWLhwIEDJCcna+tu376dVatWsW/fPr755hsWLFjATz/9xO7du6lbty5Tp04FICkpif/+979s376dXbt2sXDhQgA+/fRTli1bxo8//siuXbv45ptv8Pb2rnRb5UlKSsLPz097BAQEuLCYuBK7zqB6CB5N+qsj7dWR9uoYjXK5GVWkvVrSXx1XtpfJsAcaMmQIKSkpLFiwgJEjR2rPr1mzhuHDh+Pt7Y3RaGT06NGsXr1ae/2RRx7RTl5fs2YNCQkJ1K9fH4Ann3xSW/bf//43zzzzDN7e3gA0atRIe37cuHHaFZ8bNGiAwWCodFvlSUxMxGq1ao+cnBwAHHr5S8rdHHoj/w1oL+0Vkf7qSHt1pL06Xl5ePPDAA3JrGQWkvVrSXx1Xt5fJsAcaOnQo7777Lj4+PrRs2bLC5f738uW+vr5Xvey1uNK2vL29MZlMZR4AyF3C3M/ppG5hrrRXRfqrI+3VkfbKOBwOTp48icPhUD0UjyPt1ZL+6ri6vUyGPVCTJk1ISkrizTffLPN8XFwcn332GSUlJdhsNj7++GN69OhR7jbi4uL44osvtPN1P/jgA23ZPn368M4771BcXAzAqVOntOfnzZunnQ+cl5eH3W6vdFtVIVcWdT+9007gqT3SXhHpr460V0faq2O329m8eTN2u7R3N2mvlvRXx9Xt5RgjDzVixIjLnhszZgwHDhwgKioKgK5du1Z4y6XevXuTlZVFhw4d0Ov1hIeH8/777wMwZcoUXnjhBaKiovDy8qJJkyakpaUxZMgQfvvtNzp27IjRaKRu3bqsWbOm0m0JIYQQQgghhCvonE45zkjc2PLz8/Hz8+P06dM0bNhQ9XA8SmlpKWlpacTHx8t5NApIf3WkvTrSXh1pr460V0v6q1Od9pfmBlar9f9Op6yAHCYtbho1ed6yuDo6nY569epJe0WkvzrSXh1pr460V0faqyX91XF1e9kzLG54l779eWXjQXx866kejhBCiOvU1Eh/1UMQQgjhYrJn+CYQFBRE48aNKS0t1Z5bv349Op2uwvN4r8aGDRv49ttva2CE1yGnXOHP7ZwO6p87Ie1Vkf7qSHt1pL0yDoeDI0eOyBV1FZD2akl/dVzdXibD17HAwEBWrlyp/Tx//nyio6OvaZvX22TYZrPV2Lb08h8jt9M7Hdx+9oC0V0T6qyPt1ZH26tjtdiwWi1xRVwFpr5b0V8fV7WUyfB0bMWIECxYsAMBqtbJlyxZ69eoFXPyD8fzzzxMaGkpoaCgTJkygpKQEgOHDhzN27Fjuu+8+7r77bvr160dJSQkWi4V58+axePFizGYzM2bMAOC7776jU6dOtG3bltjYWNavXw9cnDiHhoYyfvx4IiIiCAkJISMjQxtfResBvPLKK7Ro0YKYmBhefPFFgoKCADh8+DD169dnypQpREVFMXfuXPbv309cXBzh4eGYzWZWrFjh6rRCCCGEEEIIDye3VrqO3XPPPbz//vv89ttvrFy5kkceeQSDwQDAhx9+SHp6Otu3b8dgMNCnTx+Sk5OZMmUKABaLhfXr1+Pt7U2XLl1YtmwZjz32GOPGjSMvL4+3334bgIMHDzJ9+nS+++47TCYT+/fvp3Pnzhw+fBiAffv2MX/+fN5//33mzZvHCy+8wHfffVfpemvWrGHZsmXs3LkTX19fRo4cWeZzWa1WQkJCtPsct2vXjpEjRzJ27Fh++eUX2rdvT2RkJHfddVe5XYqLi7V7GAPa/YmFEEIIIYQQ4mrJnuHr3JAhQ0hJSWHBggVlJpVr1qxh+PDheHt7YzQaGT16NKtXr9Zef/jhh6lTpw4Gg4HY2FgOHDhQ7va//fZb9u/fT5cuXTCbzQwYMAC9Xs/Ro0cBaNGiBe3atQOgQ4cO2nYqW2/t2rU88sgj2pXfRo0aVeY9vby8ePzxxwEoKChgx44d2jItW7akU6dO/PDDDxU2SUpKws/PT3sEBAQA4ESu8OduTnSc86kv7RWR/upIe3WkvTo6nY5GjRrJFXUVkPZqSX91XN1e9gxf54YOHUpUVBR33303LVu2rHC5//0D4uPjo/3aYDBUeG6u0+nk/vvvJzU19bLXjh07VuF2KlvvSmOrU6cOen3F38Nc6Q97YmIizz33nPZzfn4+AQEBOPWGK45F1Cyn3kBO4xDVw/BY0l8daa+OtFfHaDTSsWNH1cPwSNJeLemvjqvby57h61yTJk1ISkrSDim+JC4ujs8++4ySkhJsNhsff/wxPXr0uOL2TCYTVqtV+7lnz56sWbOGzMxM7blt27ZdcTuVrde9e3eWLVvGuXPncDqd2nnP5alXrx5RUVF88sknAOzfv58ff/yRLl26VLiOt7c3JpOpzANAJxdTcTud04F/3lFpr4j0V0faqyPt1bHb7ezbt08uIqSAtFdL+qvj6vYyGb4BjBgxgg4dOpR5bsyYMURFRREVFYXZbCYoKOiqbrn08MMPY7FYtAtotWjRgtTUVMaOHUtERAStW7fWzieuTGXrPfjggzz00EOYzWZiYmKoX78+9evXr3BbixcvZsmSJURERDBgwAA+/vhjAgMDrziG/yX/MXI/ndNBo/wcaa+I9FdH2qsj7dVxOBxkZ2fL7WUUkPZqSX91XN1e53Q6nS7ZsvBoBQUF1KtXD6fTyaRJkygsLOQf//iHS97r0o21T58+TcOGDV3yHqJ8paWlpKWlER8fj5eXl+rheBzpr460V0faqyPt1ZH2akl/darT/tLcwGq1akeQVkTOGRYuMXToUA4fPkxRUREhISHMmzdP9ZCEEEIIIYQQQiOTYeES//rXv9z+nnOzcvE2yYEO7qRz2LmtbmNmZ56VC5gpIP3VkfbqVKX91Eh/N43KM+j1egIDAyu9CKZwDWmvlvRXx9Xt5TBpccO7dCjEKxsP4uNbT/VwhBBCXCdkMiyEEJ6nKodJy9cbN7D4+Hjmzp172fMREREsX75cwYiu3vTp06/qgl9VoXPIFf7cTeewc/uZX6S9ItJfHWmvjrRXx263s3PnTrmirgLSXi3pr46r28tk+AY2atQo7ZZEl2RkZHD8+HH+9Kc/KRqVOjrkIAd30+Gk/vmT0l4R6a+OtFdH2qvjcDg4evSoXFFXAWmvlvRXx9XtZTJ8A+vTpw85OTll7vW7YMEChg4dyj//+U/atWtHVFQUXbp0YdeuXQCkpKQQFxfHY489RlhYGNHR0Rw8eFBbf+HCheWuN27cOMxmM2azmbvvvhudTgdcvod37ty5DB8+XPv5rbfeIjY2lqioKHr16sWRI0cu+xy7d++mU6dOREVF0aZNG2bOnFnp5y4uLiY/P7/MQwghhBBCCCGqQibDNzAvLy+GDBnCggULACgqKuLzzz/n4Ycf5vPPP2fjxo3s2LGD119/nUGDBmnrpaen88Ybb7B7927i4uJ48803Afjpp58qXG/evHlYLBYyMjJo3rw5M2bMuOL4UlNTyc7OZvPmzezYsYPBgwczfvz4y5YLCgpi7dq17Nixg+3bt7Ns2TK2bNlS4XaTkpLw8/PTHgEBAVXqJoQQQgghhBByNekb3KhRo7j33nv529/+xvLly2ndujVfffUVu3btol27dtpyZ8+epbCwEIAOHTrQtGlT7ddz5swBqHS92rVrA/Dkk09yxx138NJLL11xbCtWrCA9PZ22bdsCVHisf2FhIePHj8disaDX68nJycFisdC+fftyl09MTOS5557Tfs7PzycgIACnTr7bcTenTs8pk7RXRfqrI+3Vkfbq6PV6goOD5Yq6Ckh7taS/Oq5uL5PhG1ybNm1o0aIFX3/9NQsWLGDUqFHs27ePYcOG8cYbb5S7jo+Pj/Zrg8GAzWYDwOl0Vrrea6+9xq+//srXX3+tPWc0GstMcouKirRfO51OEhMTGTNmTKWfYdq0afj7+7Nz506MRiP9+vUrs53/5e3tjbe392XPy3+M3M+p03O6fqDqYXgs6a+OtFdH2qtjMBho1aqV6mF4JGmvlvRXx9XtZfZwExg1ahRvvPEG27ZtIyEhgT59+rBo0SKOHj0KXDzxPCMj44rbqWy9Tz/9lBUrVrB06VKMxv/7DqVFixZkZGRgt9u5cOECy5Yt017r27cv8+bN4+zZswCUlpayc+fOy943NzeXO++8E6PRSHZ2NqtXr65WB7myqPvpHHYCTv4s7RWR/upIe3WkvTo2m41NmzZpX6IL95H2akl/dVzdXvYM3wQSEhKYOHEiCQkJ+Pr60rlzZ/72t7/x8MMPY7PZKCkp4YEHHiA6OrrS7VS23vTp0wHo1KmTtrzFYqFfv34sXbqU1q1bc+eddxIZGcmFCxcAGDx4MGfOnKFbt27AxT/MI0eOJDIyssz7vvjiiwwZMoRPP/2U5s2b071792p1kCuLup8OJ75FeehwSn0FpL860l4daa+O0+nk1KlTOJ1S3t2kvVrSXx1Xt9c55XdV3OAu3Vj79OnTNGzYUPVwPEppaSlpaWnEx8fj5eWlejgeR/qrI+3VkfbqSHt1pL1a0l+d6rS/NDewWq2YTKZKl5XDpIUQQgghhBBCeBzZMyxueJe+/Xnl+/341PNTPRzP4nRQ//xJ8uo2BrmAmftJf3WkvToVtJ8a6a9wUJ7B4XCQk5NDQECAXFXXzaS9WtJfneq0lz3DHiooKAiLxVKldSZPnqydD+xOZrOZgoKCmt2o/IfU/XR68nxvk/aqSH91pL060l4ZvV7PXXfdJZMBBaS9WtJfHVe3l99RoYTFYqFevXo1uk25sqj76Rx2mh3fIe0Vkf7qSHt1pL06NpuNdevWyRV1FZD2akl/dVzdXibDN6GuXbsyefJkOnfuTPPmzRk3bpz22vHjx+nZsydt2rQhLi6OX3/9VXvt3LlzjBw5ktDQUEJDQ3n11VevapsFBQWMHj2a2NhYwsPDGTNmDCUlJQDMnDmT1q1bYzabMZvNHDlyBACdTkdeXh5wce90TEwMZrOZLl26kJ2dXennKy4uJj8/v8wD5GrSKuhw4l1aKO0Vkf7qSHt1pL06TqeTgoICuaKuAtJeLemvjqvby2T4JnXgwAHWr19PVlYW3333HZs3bwbg6aefJjY2lj179vDpp5+ydu1abZ3XXnuN4uJiMjMz2bp1KytWrGDJkiVX3OakSZPo3Lkz27ZtY9euXTgcDt555x1yc3N566232LFjBxaLhU2bNnHrrbdeNtYpU6aQnp6OxWJh/PjxPPPMM5V+tqSkJPz8/LRHQEBATSQTQgghhBBCeBCZDN+kEhISMBqN1K5dG7PZzIEDBwBYu3YtTzzxBAB33HEHffr00dZZs2YNo0ePRq/XU7duXYYOHcrq1auvuM0VK1Ywa9YszGYzkZGR/PDDD+zfvx+TyUTLli15/PHH+eCDDzh79iw+Pj6XjXX16tV06NCB0NBQZsyYccXznhMTE7FardojJyfnWnMJIYQQQgghPIxR9QCEa/xx0mkwGCo8zl6n01W4jf99raJtOp1Oli1bxt13333ZNrZs2cKmTZvYsGED7du35/PPP6dz587a60ePHuWpp54iPT2d5s2bk5mZSZcuXSr9bN7e3nh7e1/2vENnqHQ9UfMcOgNHG7WR9opIf3WkvTrSXh2DwUCHDh0wGKS9u0l7taS/Oq5uL3uGPUxcXBwLFiwALp4/vHLlyjKvzZ8/H6fTyfnz51m4cCE9evS44jb79u3Lm2++qU2Oc3Nz2b9/PwUFBfz+++907tyZl156iU6dOrFz584y61qtVry8vLj99ttxOp3MnTu3+h+ukom9cBGdjvO1G0h7VaS/OtJeHWmvjF6vp3HjxnJFXQWkvVrSXx1Xt5ffUQ/zzjvvsGXLFtq0acPQoUPp3r279tpLL72El5cXYWFhtGvXjj59+vDoo49ecZvJycnaodPh4eHcd999HD58GKvVSr9+/QgLCyM8PJzS0lKGDRtWZt2wsDAGDhxISEgIMTExBAYGVvuz6R1yhT930zts3J2zRdorIv3VkfbqSHt1SktLWbVqFaWlpaqH4nGkvVrSXx1Xt9c55bJo4gZ36cbap0+fpmHDhqqH41FKS0tJS0sjPj4eLy8v1cPxONJfHWmvjrRXR9qrI+3Vkv7qVKf9pbmB1WrFZDJVuqzsGRZCCCGEEEII4XHkAlripvFe1llqmeRAB3fSO2wEA8mZZ3Do5a8Td5P+6kh7dS61F0IIIa6V7Bn2IEFBQQQHB2M2m2nTpg3vvffeFZcv7zZHK1eu5Nlnn6103Q0bNvDtt99ey3CrTK4s6n4OnYEDt0VKe0WkvzrSXh2HzkC3bt0wGuVLCHczGo3SXhFpr5b0V8fV7as1GR42bBgbN26s6bEIN1iyZAkWi4VvvvmGadOmkZmZWeVt9OnTh+Tk5EqXUTEZFmrYjLVUD8GjSX91pL06tWvXVj0EjyXt1ZH2akl/dVzZvlqTYavVSlxcHC1btuSNN97g2LFjNT0u4WJ33XUXwcHBfPvtt8TExGA2m4mJiWHz5s3lLj9nzhzuueceTp06RUpKCn379gXgl19+4Z577iEiIoKwsDBefPFFLBYL8+bNY/HixZjNZmbMmIHNZqNnz55ER0cTEhLCoEGDOH/+PHBx4hwaGsr48eOJiIggJCSEjIyMKn8mvdNe7R6ievROO8G/bpX2ikh/daS9OnqnnbS0NO12fsJ9bDabtFdE2qsl/dVxdftqTYZXrFjBsWPHePLJJ1myZAlBQUH07t2bL7/8Ui45foPYvXs3+/bto1u3bqSnp2OxWJgzZw4jRowos5zD4eC5555j3bp1rFmzhkaNGpV5fe7cuTz44IPs2rWL3bt389xzz2E2mxk3bhyDBw/GYrHw8ssvYzAYSE1NJSMjg6ysLPz8/JgzZ462nX379jFs2DB27drFhAkTeOGFFyoce3FxMfn5+WUeQgghhBBCCFEV1T74ulGjRjz33HM899xz7Nixg08++YQhQ4bg6+vL448/zvjx42nZsmVNjlXUgISEBGrXrk2dOnVYsGABubm53HvvvZw5cwaj0Uh2djaFhYXa4QhjxowhMjKSZcuWlXuz6y5duvD8889z7tw57r33XuLi4sp9X6fTSXJyMqtWrcJms2G1WunYsaP2eosWLWjXrh0AHTp04K233qrwMyQlJfHqq69eSwYhhBBCCCGEh7vmC2gdP36c1atXs3r1agwGA/Hx8ezevZs2bdpc8bxS4X6XzhnetGkTffr0oV+/frz11ltkZWVp54EXFxdry997771s3bqVEydOlLu9/v3789NPPxEcHKztJS5Pamoq69at4/vvv2f37t1MnjyZoqIi7XUfHx/t1waDodJDIRITE7FardojJyenSg2EEEIIIYQQolp7hktLS1m5ciWffPIJ//nPfwgPD2fixIkMGjRIu7Hxv/71L0aOHHnFqw4LdYqKiigpKSEwMBCgzGHLlwwZMoQOHTrQvXt3vvnmG5o2bVrm9V9++YXmzZszdOhQYmNjtb29JpOJI0eOaMvl5ubi7++PyWSioKCAlJQU7X2rytvbG29v78uel6u6up9DZyD7znbSXhHpr460V8ehu/jFu1zV1f2MRqO0V0TaqyX91XF1+2pt9fbbb8fhcPDYY4+xbds2zGbzZct069aN+vXrX+PwhCuZTCZmzpxJbGws/v7+DBw4sNzlBgwYQO3atenRowdff/11mde+/PJLFi1aRK1atXA4HMybNw+Ahx9+mIULF2I2m+nXrx/PPPMMX331FcHBwTRq1IjOnTuXmSyLG5fRVkKJl1xhURXpr460V6ewsJB69eqpHoZHkvbqSHu1pL86rmyvczqdzqqutHDhQh555JEyh7YKoUp+fj5+fn6cPn2ahg0bqh6ORyktLSUtLY34+Hi8vLxUD8fjSH91pL060l4daa+OtFdL+qtTnfaX5gZWq1U7arkiVT5nuLS0lBEjRrB///6qriqEEEIIIYQQQlwXqnyYtJeXF4GBgdjtcm9FcX15L+sstUxVPtBBXAO9w0YwkJx5BodezqNxN+mvjrR3n6mR/qqHIIQQ4iZVratJv/DCC0ybNo2zZ8/W9HiEEDcYu1xASCnpr460V0cuYqOOtFdH2qsl/dVxZftqnTMcGRnJ/v37KS0t5a677qJu3bplXt+xY0eNDVBcH2w2G6+//jqff/45RqMRo9FIbGws8fHxTJ8+HYvFctk6v/32GwkJCfzwww8uHdul8wJe2XgQH1+5sIEQQtxMZM+wEEKIqqjKOcPVmmY/9NBD6HS6ag1O3JhGjRrF2bNn2bx5Mw0aNMDpdPLll19WenRAkyZNXD4RLqPq3+uIa+V0Urcoj/M+9UH+TnA/6a+OtFfG4XBw+vRp/P390eurdYCbqCZpr460V0v6q+Pq9tWaDE+fPr2GhyGuZ/v372fp0qUcPXqUBg0aAKDT6XjkkUfYsGEDNpuN8ePH89NPP2Gz2fj000+Jjo7m8OHDmM1m8vLytHVef/11VqxYwalTp3j55ZcZMWIEAJMnT+b777+ntLQUk8nERx99RHBwcLnjKS4upri4WPs5Pz8fAL1TzmN3N73TTuCpPf//fqty+JC7SX91pL06drudzZs3Ex8fL/8pdTNpr460V0v6q+Pq9tXaYrNmzThz5sxlz+fl5dGsWbNrHpS4vuzYsYOWLVvi71/+oWr79u1j2LBh7Nq1iwkTJvDCCy9UuC1vb2+2bdvGN998w9NPP43NZgNgypQppKenY7FYGD9+PM8880yF20hKSsLPz097BAQEXNsHFEIIIYQQQnicak2GDx8+XO7VpIuLi/n111+veVDixtKiRQvatWsHQIcOHThw4ECFyw4ePBiAVq1aYTQaOXHiBACrV6+mQ4cOhIaGMmPGjHLPQb4kMTERq9WqPXJycmruwwghhBBCCCE8QpWO7Vq5cqX26++++w4/Pz/tZ7vdztq1a2natGnNjU5cF6Kiovjll184c+YMDRs2vOx1Hx8f7dcGg0Hb21ue8pY9evQoTz31FOnp6TRv3pzMzEy6dOlS4Ta8vb3x9va+7Hknct6euznRUexVW9orIv3Vkfbq6HQ66tWrJ9cuUUDaqyPt1ZL+6ri6fZUmw3379gUuDmrYsGFlXvPy8iIoKIi///3vNTY4cX1o0aIF/fv3Z9SoUaSkpFC/fn2cTifLly/HarVe8/atViteXl7cfvvtOJ1O5s6dW63tOPVymxN3c+oNHLw9SvUwPJb0V0faq2M0GunevbvqYXgkaa+OtFdL+qvj6vZVmgw7HA4AmjZtSnp6eoXnkIqbz4IFC5g5cybt2rXDaDTicDjo0qULvXv3vuZth4WFMXDgQEJCQmjYsKH2pUtVTQxrQP369a95POLqORwOcnJyCAgIkAtKKCD91ZH26kh7daS9OtJeLemvjqvbV+s+w0JcTy7dS+z06dPlHsYtXKe0tJS0tDTi4+Px8vJSPRyPI/3VkfbqSHt1pL060l4t6a9Oddq7/D7DAGvXrmXt2rWcPHlS22N8yYIFC6q7WSGEEEIIIYQQwuWqNRl+9dVXmTFjBtHR0dx+++1yMrm4LryXdZZaJjnQwZ30DhvBQHLmGRx6udequ0l/daS9a0yNlNOvhBBCuE+1/gWfN28eKSkpDBkypKbHIxQJCgpixYoVmM1mioqKSEhIoFatWixevJhatWqpHt5Vkau6up8THed86kt7RaS/OtJeHZ1OR6NGjeSLeAWkvTrSXi3pr46r21frLOSSkhI6duxY02MR14GCggJ69+7NrbfeypIlS26YiTDI1aRVcOoN5DQOkfaKSH91pL06RqORjh07YjTKHnl3k/bqSHu1pL86rm5frcnwE088QWpqak2PRSh25swZunfvTrt27fjwww/R6/XMmjWLkJAQwsLCGDx4sHYrpa+//prw8HDMZjOhoaF89dVXAOzfv5+4uDjttRUrVgDw+uuv89RTT2nvde7cOW655RZOnTqF3W7n+eefJzQ0lNDQUCZMmEBJSUmF4ywuLiY/P7/MA0DndFS4jnANndOBf95Raa+I9FdH2qtjt9vZt28fdrtd9VA8jrRXR9qrJf3VcXX7ak2Gi4qKmD17Nvfeey8TJkzgueeeK/MQN6aEhATi4uL461//CsA333zDggUL+Omnn9i9ezd169Zl6tSpALz44ot88MEHWCwWMjMzuffeewEYPHgwjzzyCJmZmSxdupRRo0Zx5MgRhg4dyhdffEFxcTEAS5cupVu3bjRq1IgPP/yQ9PR0tm/fjsVi4cCBAyQnJ1c4zqSkJPz8/LRHQEAAIJNhFXROB43yc6S9ItJfHWmvjsPhIDs7+7KLdwrXk/bqSHu1pL86rm5frclwZmYmZrMZvV5PVlYWO3fu1B4Wi6WGhyjc5YEHHuDLL78kJycHgDVr1pCQkKDdu/fJJ59k9erVANx3330888wz/O1vfyMzM5P69etTUFDAjh07GDVqFAAtW7akU6dO/PDDDwQEBBAZGcnKlSsBSElJYcSIEdr7DB8+HG9vb4xGI6NHj9bepzyJiYlYrVbtcWm8QgghhBBCCHG1qnXw9fr162t6HOI68OyzzxIREUHXrl3L/T3+44nrs2fP5ueff2b9+vUMGzaMwYMH8+STT1a6zsiRI/nkk09o27Yt+/fvp1evXuWO40onyHt7e+Pt7X21H0sIIYQQQgghLlOtPcPi5vXcc88xYcIEunbtSnh4OF988YV2Tu4HH3xAjx49ANi3bx8hISE89dRTPPnkk2zZsoV69eoRFRXFJ598Alw8f/jHH3+kS5cuAPTt25f09HSSkpJ4/PHHtRPh4+Li+OyzzygpKcFms/Hxxx9r71MVclVX93OiI69uY2mviPRXR9qro9frCQwMRK+X/8K4m7RXR9qrJf3VcXX7au0Z7tatW6V779atW1ftAQn1Jk6ciF6v55VXXuHPf/4zHTp0QK/XEx4ezvvvvw/AtGnTyM7OplatWtSpU4d//OMfACxevJhx48Yxd+5cdDodH3/8MYGBgcDFPbqPPvoo77//Pnv37tXeb8yYMRw4cICoqCgAunbtysSJE6s8brmqq/s59QaON2ypehgeS/qrI+3VMRgMREZGqh6GR5L26kh7taS/Oq5ur3M6nc6qrvTss8+W+bm0tBSLxUJWVhbDhg3jnXfeqbEBCnEl+fn5+Pn5cfbsWRo0aKB6OB7FbreTmZlJeHg4BoN8GeFu0l8daa+OtFdH2qsj7dWS/upUp/2luYHVasVkMlW6bLX2DFd0pd/p06dz7ty56mxSiGsmV/hzP4fDwdGjRwkNDZV/HBSQ/upIe3WkvTrSXh1pr5b0V8fV7Wv07sWPP/44sbGxvPXWWzW5WSGuyntZZ6llqvKBDuIa6B02goHkzDM49HIjeneT/upI+5oxNdJf9RCEEEJ4sBo9E3nz5s34+PjU5CaFm9hsNl599VVatWpFaGgoZrOZMWPGkJeXVyPbP3z4MPPmzSvzXHx8PNnZ2TWyfSGEEEIIIYSoimp9nd2vX78yPzudTo4fP05GRgYvvfRSjQxMuNeoUaM4e/YsmzdvpkGDBjidTr788kvOnj2r3WcYLk6aL10FuiouTYbHjRunPZeWllYTQ9c4dXKFP3dz6vScMgVIe0WkvzrSXh29Xk9wcLBc1VUBaa+OtFdL+qvj6vbVuoDWiBEjyvys1+tp1KgR3bt3r9YtcYRa+/fvJzw8nKNHj+LvX/aQtQ0bNvDnP/+Z9u3bs337dl544QWaNm3K008/zblz5/Dx8SE5OZl77rkHm83GAw88wJkzZygsLCQiIoKPPvqIunXr0qpVK44cOUJwcDCBgYGsXLmSoKAgVqxYgdlsZvbs2Xz++eeUlpbi5eXFu+++S4cOHa5q/JdOkn9l40F8fOu5IpEQQggXkMOkhRBC1DSXX0Dr0n1kxc1hx44dtGzZ8rKJ8CV79+7l/fffZ/78+ZSUlNCiRQs++ugjevbsyY8//kj//v3Zv38/devWJTU1lYYNG+J0Ohk/fjxz5sxh6tSpzJs3j4kTJ2KxWMp9jyFDhvDcc88BsGXLFoYPH86+ffvKXba4uJji4mLt50v3QdY57NdQQVSHzmHnztP7+NW/ldzaSgHpr460V8dms7Ft2zZiY2OrdaSSqD5pr460V0v6q+Pq9te0xe3bt2v3iw0JCZH7b92kmjVrxr333gtAdnY2er2enj17AtCpUyduvfVWLBYLHTt2JDk5mVWrVmGz2bBarXTs2PGq3mPnzp28/vrrnDlzBqPRSHZ2NoWFhdSuXfuyZZOSknj11Vcve16HXDzL3XQ48S3KQ4dT6isg/dWR9uo4nU5OnTpFNQ5sE9dI2qsj7dWS/uq4un21JsMnT55k4MCBbNiwQTufNC8vj27duvHPf/6TRo0a1eQYhYtFRUXxyy+/cObMGRo2bHjZ676+vpWur9PpAEhNTWXdunV8//33mEwm3n33XdatW3fF9y8pKaFfv36sX7+emJgY7dCG4uLicifDiYmJ2l5kuLhnOCAg4IrvI4QQQgghhBCXVOtM5AkTJlBQUMDPP//M2bNnOXv2LFlZWeTn5/P000/X9BiFi7Vo0YL+/fszatQo7erRTqeTZcuWcfDgwTLLBgcH43A4WL16NQCbNm3ixIkTmM1mcnNz8ff3x2QyUVBQQEpKiraeyWTCarWW+/5FRUWUlJQQGBgIwJw5cyodr7e3NyaTqcxDCCGEEEIIIaqiWpPhb7/9lvfff5/WrVtrz7Vp04b33nuPb775psYGJ9xnwYIFRERE0K5dO0JCQmjTpg3/+c9/uOWWW8osV6tWLZYvX84rr7xCeHg4EydO5Msvv8TX15ehQ4dy4cIFgoOD6d27N507d9bWCw8PJyQkhNDQUPr06VNmmyaTiZkzZxIbG0vbtm2pVatWtT6DQ67q6nYOnZ7jtzSX9opIf3WkvToGgwGz2YzBIOdqu5u0V0faqyX91XF1+2pdTbpevXr88MMPmM3mMs/v3Pn/2Pvz+Bjvxf//f14zExOajYSiwtQ2JJFMZCEqSsRSelSVRq2xFHVs3Q5ptVLVpufUF7V8Dj1KjmpO05bjcOR0sbUUsWXEmqJCiliyzMSSZJbr94ef691pEpLIzAt53m+3ud1krmuueeXBDa9c17yuDDz99NPKgkZErlCVFeOIiIiIiOjRVZW5QbV+pB0TE4Pp06fj4sWLynMXLlzAq6++ip49e1bnkET3zWq1ih5CrWO1WrFt2za2F4T9xWF7cdheHLYXh+3FYn9xnN2+WgtoLV26FAMGDIBOp1MWLsrJyUFQUBDWrl1bowMkqqylR/JQx8suehi1ispuhb6oCAsOX4NdxVsNuBr7i8P2VVdT9xSWZRlFRUVc1VUAtheH7cVif3Gc3b5a/4L7+/vj0KFD2LJli3Iv2Pbt2yM2NrZGB0fOo9PpsGHDhjKXute0ixcvIi4uDjt37nTq+xAREREREVVFlS6T3rZtGwICAmA2myFJEnr16oWpU6di6tSpiIiIQGBgICc95KBp06b8M0FERERERA+cKk2GFy1ahJdffrncDyJ7e3tj4sSJWLBgQY0NjpxvwYIFiIiIgMFgQEREBPbs2aNs0+l0mD17Nrp06QJ/f38sX74cq1evRlRUFHQ6Hb788ktl3+HDhyM8PBzBwcHo378/cnNzAQDZ2dnKvagBYM+ePejatStCQkIQHByM//znPwCAN954QxlHt27dkJWVVeXvxS5xhT9Xs0tqnG8YwPaCsL84bC+OWq1GVFQUV3UVgO3FYXux2F8cZ7ev0mrSLVq0wLfffutwS6XfO3nyJHr37o3z58/X2ADJOe5cJv3EE0+gYcOGAIC9e/ciPj5eufRdp9Ph+eefx8KFC3H69Gl06NABb7/9NmbPno39+/ejX79+uHr1KgDg6tWrynE++ugjZGdnY/ny5cjOzobBYEBhYSHy8/PRvn17fPPNN4iOjobdbkdhYSEaNGjg8Povv/wSycnJ+Pbbb8sde0lJCUpKSpSvzWYz/P39MeenX+Hu4em0ZkREVH019ZlhIiKiu3HaatKXL1+Gm5tbhds1Go0yOaKHw53bYQUFBWHSpEnIysrCrVu3lO1xcXEAgNatW8Pd3R2DBw8GAISHhyM/Px+FhYUAgJSUFISHhyMoKAgrV66E0Wgs81579uyBXq9X7j+sUqmU+xj/8MMPiIqKQlBQEObOnVvu6+9ISkqCt7e38riziJvKzhX+XE1lt6Jtzl62F4T9xWF7cSwWCzZv3gyLxSJ6KLUO24vD9mKxvzjObl+lyfATTzyBo0ePVrg9MzMTTZo0ue9BkWuUlpZi0KBBmD9/Po4ePYqffvoJABzOurq7uyu/VqvVyteSJEGSJFitVuzatQuLFy9GWloajh49igULFqC4uLjS4zh//jymTJmCtWvX4ujRo/jyyy/v+vqEhASYTCblkZOTU9VvnWqQWraJHkKtxv7isL04vL2JOGwvDtuLxf7iOLN9lSbD/fr1wzvvvFPuROXWrVuYM2cOnn322RobHDlXcXExSktL0bx5cwDAkiVLqnWcgoICeHp6wtfXF6WlpVixYkW5+3Xp0gWnTp1SFtSy2+3Iz8+HyWSCm5sbmjRpAlmWsXTp0ru+n1arhZeXl8ODiIiIiIioKqo0GZ49ezby8/PRtm1b/O1vf8N//vMf/Oc//8Ff//pX6PV65Ofn4+2333bWWKkGWa1WNGrUCPPmzUNkZCTCwsJQp06dah2rb9++0Ov1yiXQFd2uqX79+vj3v/+NWbNmITg4GB07dsTPP/+MDh06YOjQoQgMDERERIQyOSciIiIiInKWKi2gBQDnzp3DK6+8gu+++065+bEkSejTpw+WLVuGJ5980ikDpZpz6dIl6PV65Obmol69eqKHc9/ufEi+sLAQ3t7eoodTq9y5EbqnpyckSRI9nFqH/cVhe3HYXhy2F4ftxWJ/carTvioLaGmqOqAWLVogLS0NBQUFOH36NGRZRps2bVC/fv2qHooEWLBgAVasWIH58+c/EhNhEq9u3bqih1Crsb84bC8O24vD9uKwvVjsL44z21fpMunfq1+/PiIiIhAZGcmJ8EPktddeQ1ZWFiZMmCB6KDWOCxu4ntVqRVpaGtsLwv7isL04bC8O24vD9mKxvzjObl/lM8NED6plR/NRx6tKV/3TfVLZrdADWJiZB7uKf524GvuLw/Z3x3sKExHRw6DaZ4bp4aLT6crcu7d79+7YsGGDkPEQERERERGJxMkwCWez8V6dRERERETkWpwME+Lj47Fo0SLl6zfeeAOJiYkAgMTERMTFxeFPf/oTAgICEBMTg/z8fACAxWLB5MmT0bZtW3Tu3Bmvv/46unfvDgDIzc1Fjx49EBYWhsDAQEyZMgV2ux0AkJycjB49euCFF15Ahw4dsHv3brRr1w6/X9i8S5cu+N///lfueEtKSmA2mx0eAGCX1DVchu7FLqmR1awT2wvC/uKwvTgajQb9+vWDRsPL012N7cVhe7HYXxxnt+dkuBaJi4uDwWBQHgcOHKjU69LT05GcnIzjx4+jUaNGWLFiBQDg008/xalTp3Ds2DHs3LkTmZmZymt8fHywadMmHDx4EJmZmcjOzsZXX33lcMwPP/wQR44cQXR0NHx9ffHDDz8AADIyMnD16lX07du33PEkJSXB29tbefj7+1c3CdUAjbVU9BBqNfYXh+3FuXXrlugh1FpsLw7bi8X+4jizPSfDtUhqaiqMRqPyCA8Pr9Tr+vbtC19fXwBAVFQUzpw5AwDYunUrRowYATc3N7i5uWH06NHKa+x2O2bOnImQkBCEhobiwIEDDp9Z7tKlC/R6vfL19OnTsXTpUgDAsmXLMHny5ArvJZaQkACTyaQ8cnJyAAAqmZdbu5pKtqFVbgbbC8L+4rC9OFarFdu3b+eqrgKwvThsLxb7i+Ps9pwMEzQajcPndouLix22u7u7K79Wq9UV/mH8/eR1wYIFuHLlCtLT05GZmYlhw4Y5HNfDw8PhtYMGDUJmZiYyMjKwceNGjBkzpsLxarVaeHl5OTyIiIiIiIiqgpNhQuvWrbFv3z4AQF5eHtLS0ir1upiYGKSkpMBiscBisWDNmjXKtoKCAjRu3Bju7u7Izc3F119/fddjaTQaTJo0CQMGDMDzzz8PHx+fan8/RERERERE98LJMGHChAm4evUq2rdvj1GjRqFz586Vet3EiROh0+kQEBCAp556Cq1atVImsdOnT0d6ejoCAwMxcuRIxMbG3vN448aNw4ULFzBlypT7+XbIxWxcQEgo9heH7cXhIjbisL04bC8W+4vjzPaS/PslfImqqKioCJ6enrBYLBg+fDjCwsIwc+bMah3rm2++wd///nds3bq1Sq8zm83w9vaGyWTiJdNERERERLVYVeYG/BEH3ZfY2FiUlJSguLgYXbt2xbRp06p1nL59++KXX37Bv//972qP5c6tm8h17HY7rl27Bj8/P6hUvNDE1dhfHLYXh+3FYXtx2F4s9hfH2e05Gab7kp6eXiPH+fbbb+/7GIsPX0UdL67y50oquxX639Jv329Vxb9OXI39xWH7/zMr1M+l72ez2bBnzx7069eP/yl1MbYXh+3FYn9xnN2ev5tULp1O53ArpOp44403kJiYWCPjISIiIiIiqkmcDFON4H3XiIiIiIjoYcLJMN3V6dOnERsbi+DgYBgMBmzYsEHZJkkS5syZg4iICCQkJODSpUvo06cPAgICEBsbi99++03Zd+vWrYiKikJoaCgCAwPx2WefKdvi4+MxceJE9OzZE23btsWgQYNQWlpa4ZhKSkpgNpsdHgAgQ6rwNeQcMiSUuNVle0HYXxy2F0eSJHh6ejrc255cg+3FYXux2F8cZ7ev3R90onsaPnw4xo4di4kTJ+LUqVPo3LkzQkND0aJFCwCAWq3G/v37AQBDhgxBZGQkvvvuO1y4cAEGgwHt2rUDAHTs2BG7du2CWq1Gfn4+QkND0adPHzRr1gwAYDQasX37dmi1WnTr1g3r1q3DSy+9VO6YkpKS8N5775V5XlbxNieuJqvU+LVJR9HDqLXYXxy2F0ej0SAmJkb0MGoltheH7cVif3Gc3Z5nhqlCRUVFOHToEMaNGwcAaNOmDbp27YqdO3cq+4wdO1b59datWzF+/HgAwBNPPIEBAwYo2/Ly8jBkyBAEBQUhJiYGeXl5OHr0qLL9+eefR7169aBWqxEZGYkzZ85UOK6EhASYTCblkZOTc3uDzNWkXU62w+d6LtuLwv7isL0wdrsd586d4x0EBGB7cdheLPYXx9ntORmmKvnjJQoeHh6V2nfSpEno2rUrjhw5AqPRiLZt26K4uFjZ7u7urvxarVbf9TPIWq0WXl5eDg8AUPE/pS6nku1okn+G7QVhf3HYXhybzQaj0QibzSZ6KLUO24vD9mKxvzjObs/JMFXI09MTHTt2xOrVqwHc/vzwrl270K1bt3L3j42NxapVqwAAly5dwsaNG5VtBQUFaNGiBSRJwk8//YTDhw87/xsgIiIiIiKqAD8zTOWyWq1wd3fHF198gUmTJmHp0qWQJAkrV65E8+bNy33NJ598gvj4eAQEBOCJJ55wuL7/o48+wuTJk/H+++/DYDCgU6dOrvpWiIiIiIiIyuBkmMq4dOkSzGYzmjdvjnr16mHLli3l7ifLssPXTZo0wXfffVfuvr169cKpU6fK3ZacnOzw9fz586s+aABTOviiQYMG1XotVY/VasW+koboH+IHjYZ/nbga+4vD9uJIkoSGDRtyVVcB2F4ctheL/cVxdntJ/uOMhmq1BQsWYMWKFXj99dcxYcIE0cOpFLPZDG9vb5hMJuXzw0REREREVPtUZW7AyTA99O78gU/88TS0nt6ih1OrSLIdvqbfkOfdDLLEJQhcjf3Fqc3tZ4X6CX1/m82GU6dOoU2bNlCreUs9V2J7cdheLPYXpzrtqzIZrl3/glOlrV+/HmFhYcq9gmNiYqq8pHlycjIGDhxY7raNGzfi1VdfrYGR/h+Jq7q6nCTb0dCcw/aCsL84bC+O3W5HVlYWb3EiANuLw/Zisb84zm7PDzpRGZcuXcKECRNw8OBBtGjRAgBw6NChKl2rf7dbIwHAgAEDHO5DTERERERE5Eo8M0xlXL58GWq12mExqo4dO0KSJJw6dQr9+/dHREQEgoODsXTpUmUfSZIwZ84cREREICEhAcDtyxQGDBiAgIAAdOvWDdnZ2QAczxrn5uaiR48eCAsLQ2BgIKZMmcKfvBERERERkVPxzDCVERwcjK5du6JFixZ4+umn0aVLFwwbNgyNGzfGSy+9hLVr16Jdu3a4efMmOnfujE6dOiEiIgIAoFarsX//fgC3J7w///wzjEYj2rdvj7/97W+YMGECvv/+e4f38/HxwaZNm+Dh4QGbzYbnnnsOX331FYYOHVru+EpKSlBSUqJ8bTabAQAyuMKfq8mQUPhYI7YXhP3FYXtxVCoVmjdvDpWKP893NbYXh+3FYn9xnN2ev6NUhkqlwrp167B792707dsXP//8MwIDA3Hs2DEcO3YMQ4cOhcFgQJcuXVBUVITjx48rrx07dqzDsbp06YL27dsDACZMmIAdO3bAZrM57GO32zFz5kyEhIQgNDQUBw4cgNForHB8SUlJ8Pb2Vh7+/v4AAFnFBQ1cTVapccm3DdsLwv7isL04arUaoaGhXMRGALYXh+3FYn9xnN2ek2GqULt27TBx4kRs2LABnTt3xqZNm9CgQQMYjUblcfbsWYwePVp5jYeHR5XfZ8GCBbhy5QrS09ORmZmJYcOGobi4uML9ExISYDKZlEdOTg4AQLLbKnwNOYdkt6FJ3im2F4T9xWF7cWw2GzIyMsr8YJWcj+3FYXux2F8cZ7fnZJjKuHDhAn7++Wfl64KCApw9exZBQUHw8vLC6tWrlW2nT59Gfn5+hcfas2cPTp48CQBYuXIlevToUeYnOwUFBWjcuDHc3d2Rm5uLr7/++q7j02q18PLycngAgATeJczVJMjwuXGF7QVhf3HYXhy73Y7z589zbQkB2F4ctheL/cVxdnt+ZpjKsFqtmDt3Ls6ePYt69erBarVi9OjReO655xAUFIQZM2Zg4cKFsNls8PPzQ0pKSoXH6tKlC2bOnInTp0/D19cXa9asKbPP9OnTMXjwYAQGBqJp06aIjY115rdHRERERETEyTCV1aJFC3z33XflbmvVqhU2bdpU7jZZdjxDEh8fj/j4+HL3/f225s2bY9++fdUe7x1/DmoAX1/f+z4OVZ7FYkHab8Crwb5wc3MTPZxah/3FYXsiIqKHHy+TpkcGV/hzPZVKBb1ez/aCsL84bC8O24vD9uKwvVjsL46z20vyH0/nET1kzGYzvL29YTKZlM8PExERERFR7VOVuQEvk6ZHxsKMK9B6lYoeRq0i2W1odu0kfvNrx1vMCMD+4tSW9rNC/UQPoQyr1Yp9+/YhMjISGg3/G+NKbC8O24vF/uI4uz3P9T+EdDod9Ho9DAYDAgICsGzZMuzYsQMGg6Hc/S9evIjo6Gjla0mSUFhYCADo168fsrKy7ms848ePx/bt2+/rGDWBq7q6ngQZHsWFbC8I+4vD9uLIsoyrV6+WWaeCnI/txWF7sdhfHGe35482HlKpqakwGAw4d+4cgoOD8f7771e4b9OmTbFz585yt6Wlpd33WFauXHnfxyAiIiIiInIlnhl+yLVo0QJ6vR5NmzaF1WrF5MmTERISgsDAQBw4cAAAkJ2dDR8fn3Jfr9PpYDQaAQDdu3fH1KlTERERgdatW+P1119Xfgpzr20bNmwAcHuV6IkTJ6Jnz55o27YtBg0ahNLS25cuWywWzJo1C5GRkTAYDHjxxRdRUFAA4PaEOiAgAAaDAR06dEB6enqF33NJSQnMZrPDg4iIiIiIqCo4GX7IHTlyBCdPnkRBQQFOnjyJ0aNH4/Dhw5g6dSrefvvtKh/v+PHj2L17NzIzM/Hjjz/iX//6V6W2/Z7RaMSmTZtw4sQJXL58GevWrQMAfPzxx3jsscewb98+GI1GdOjQAbNnzwYAvP7669i6dSuMRiMOHTqEwMDACseYlJQEb29v5eHv7w8AsEv84+xqdkmFSw1asb0g7C8O24ujVqthMBigVj+6n9V+ULG9OGwvFvuL4+z2/Ff8IRUXFweDwYCJEydi1apVaNOmDVq3bo1OnToBAKKionDmzJkqH3fUqFFwc3NDvXr1MGLECGzZsqVS237v+eefR7169aBWqxEZGamMY8OGDVi7di0MBgMMBgP+9a9/4ezZswCAnj17YuTIkfjkk09w9uxZeHh4VDjGhIQEmEwm5ZGTk3N7A/9T6nqSCoUejdleFPYXh+2FUalUaNGiBW9xIgDbi8P2YrG/OM5uz9/Rh1RqaiqMRiN2796NwYMHAwDc3d2V7Wq1Glar9b7fR5KkKm+raByyLGPJkiUwGo0wGo04fvy48pnldevW4aOPPoLFYkG/fv3w5ZdfVvi+Wq0WXl5eDg/g9uqu5FqS3YaWlw6xvSDsLw7bi2O1WrFt27Ya+TeOqobtxWF7sdhfHGe352SYHKxduxYWiwW3bt1CSkoKYmNjK7WtMgYOHIiFCxfi5s2bAICbN2/i2LFjsFqtOHPmDMLDw/HGG29g8ODB2LdvX5XHzlVdXU+CDK3lFtsLwv7isL04siyjqKiIq7oKwPbisL1Y7C+Os9tzNWly0L59ezz11FPIz8/Hc889h6FDh1ZqW2XMnDkTJSUl6NSpk3JWeebMmWjdujXGjh2L/Px8aDQaNGzYEKtXr67R74uIiIiIiOj3OBl+CGVnZ5d5rnv37sqq0AAQFBSk7KfT6ZT7CgNw+MnKH48VExODxYsXl/u+FW3bsWOH8uvk5GSHbfPnz1d+rdFoMHfuXMydO7fMMX766ady37Mq/hzUAL6+vvd9HKo8i8WCtN+AV4N94ebmJno4tQ77i8P2REREDz9eJk2PDK7w53pqtRpRUVFsLwj7i8P24rC9OGwvDtuLxf7iOLu9JPPid3rImc1meHt7Y85Pv8Ldw1P0cIiIHkqzQv1ED4GIiOi+3ZkbmEwmZaHdijwQZ4Z1Op3DJb7A7ct+N2zY4LIxSJKkXEpcUFCAqKgozJgxwyUflP/xxx8RFRUFg8GAgIAAPPXUU7h8+XKF+//6669QqVR4//3373pcSZLQoUMHhISEICAgoNqfw71+/TpmzJiB1q1bIyQkBKGhoXjjjTdgsViQnJyMgQMHlvu6AwcOIC4urlrvWR0qO1f4czWV3Yq2OXvZXhD2F4ftxbFYLNi8eTMsFovoodQ6bC8O24vF/uI4uz0/M/wHly5dQp8+fTBkyBC88847Tn8/q9WK559/Hlu2bEHHjh0BAFlZWXjssccqfM2qVasQExOD1atXY/bs2Xe9/dHOnTvh4+ODw4cPIyIiAn379kWTJk0qPT5ZlvHss8+iTZs2OHLkCOrWrQuLxYLPPvsMJSUld31teHg4UlNTK/1e9HBSy7y1jEjsLw7bi8Pbm4jD9uKwvVjsL44z2z8QZ4bv5cqVKxg0aBA6dOiAoKAgrFixQtmm0+nw7rvvIioqCk8++STmzZunbDt58iSioqIQGBiIQYMGoXfv3mUWePq9s2fPolu3bpg0aZIyEbbZbHjzzTcRFBSEoKAgTJ06FaWlpQCA+Ph4TJw4ET179kTbtm0xaNAgZVtRURHi4uLQrl07REdHY+LEiYiPjy/znkVFRTCbzWjcuLHynF6vh4eHR7ljtNlsSE5OxuLFi+Hp6Ylt27ZVqmFISAjq16+P3377rVL737Ft2zacPn0ay5YtQ926dQEAbm5umDRpkjLG69ev46WXXkKHDh0QHh6OX3/9FcDthbUMBgOA2wt1+fj4YM6cOQgLC0Pr1q2VewwDwPDhwxEeHo7g4GD0798fubm5FY6ppKQEZrPZ4UFERERERFQVD8xkOC4uDgaDQXkcOHBA2TZ16lTo9XocOXIE27Ztw7x587B3715le2FhIfbs2YP9+/fj448/xoULFwAAI0eOxIQJE3Ds2DF88MEH91yxuFevXnjjjTcwefJk5blPP/0U+/fvx8GDB2E0GnHmzBksXLhQ2W40GrFp0yacOHECly9fxrp16wAAc+fORd26dXHixAmkpaVh9+7d5b5n/fr1MWXKFOj1evTr1w/vv/8+fvnllwrH+N1336FZs2YICAjAuHHj8Nlnn931e7rjxx9/hJ+fH0JCQiq1/x0HDx5EWFgY6tSpU+E++/fvx4cffogjR44gNjYWf/3rX8vdz2QyITg4GAcPHsTSpUvx6quvKtsWLVqEAwcOIDMzE9HR0UhMTKzw/ZKSkuDt7a08/P39q/Q9ERERERERPTCT4dTUVBiNRuURHh6ubNuyZQsmTpwIAGjUqBEGDRqELVu2KNuHDRsGAPDz80PLli1x9uxZmM1mGI1GjBo1CsDte+R27dr1rmPo378/Vq1aBZPJ5PDe8fHx0Gq10Gg0ePnll/HDDz8o259//nnUq1cParUakZGROHPmDABg69atGDNmDCRJgqen510/O7to0SIcPXoUL774In755ReEhoZi165d5e772WefYezYsQBun01NS0tDQUFBhceOjo5G69atERMTg3nz5t11Ultdd87K3/n1nQZ/5O7ujkGDBpW7X0pKCsLDwxEUFISVK1eW+Qz57yUkJMBkMimPnJwcAIBd4gp/rmaX1DjTOJTtBWF/cdheHI1Ggx49ekCj4Se9XI3txWF7sdhfHGe3f2Amw1Xxx8/Iuru7K79Wq9UVXld+t8/WAsAnn3yCzp07IzY21uG+vK547xYtWiA+Ph6ff/45Ro4cia+++qrMPlevXsXmzZvx/vvvQ6fTISwsDBaLBV988UWFx925cydOnz6Nzz77DPHx8XddmKs8YWFhOHTokHL5d3kq20Cr1Sod1Go1bLbbn7fbtWsXFi9ejLS0NBw9ehQLFixAcXFxhe+n1Wrh5eXl8CBxrJqa/wELVR77i8P24tz52A65HtuLw/Zisb84zmz/UEyGY2Nj8Y9//APA7Qnh+vXr0atXr7u+xsvLCyEhIVi7di2A24tSVXS29fc++eQTdO3aFT179kRBQQFiY2OxZs0alJaWwmq1YuXKlejdu/c9jxMTE4N//vOfkGUZ169fL3dyC9z+vO3//vc/ZdXqW7du4cSJE2jVqlWZfdesWYOBAwciJycH2dnZyM7OxjfffFOpS6Xj4+PRs2dPfPjhh+Vu79mzJ/bt21fu9/Hkk09i2rRpygTVarXi008/xfXr1+/5vpVRUFAAT09P+Pr6orS01OEz4VWh4mI2LqeSbdD/ls72grC/OGwvjtVqRVpaGhezEYDtxWF7sdhfHGe3fygmw4sXL8aJEyfQoUMH9OjRA2+//TY6dep0z9etWbMGf//73xEUFISZM2ciIiICPj4+93zdwoUL0b17d/Ts2RMvvvgiOnbsiI4dO8JgMECn02HGjBn3PMa7776LoqIitG/fHn379kVISEi57y3LMpYvXw69Xo+QkBCEhYUhLCwMf/7znwEAy5cvx7vvvgvg9iXSw4cPd3h9r169cPHiRRw6dMhh3/L89a9/xerVq3HhwgWHfW02Gw4fPoxmzZqVeY0kSdi8eTPq1KmDwMBABAUFITg4GKdOnXI4I3w/+vbtC71eD71ej+joaGXRLSIiIiIiImeRZFfcSFeQ69ev47HHHoMkSTh79iyioqKwf/9+lyy4ZLFYYLPZ4O7ujhs3bqBPnz6YOnWqS++7W1n79+/HihUrsHLlStFDqZY7N9a+du0afH19RQ+nVrFYLEhLS0O/fv3g5uYmeji1DvuLw/bisL04bC8O24vF/uJUp/2duYHJZLrnxykf6U+B7969G2+++SaA22c/Fy5c6LKVhwsKCvDMM8/AZrOhuLgYzz33HF588UWXvHdVRUREICIiQvQwiIiIiIiIXOaRPjNMtcOdn/7M+fEM3D25mJZLyTJUsu32irr3WCSOnID9xXnI288K9RM9hGqTZRlWqxUajeaei1NSzWJ7cdheLPYXpzrtq3Jm+KH4zDBVjU6ng16vh8FgQEBAAJYtWyZ6SPQI01grXmmcnI/9xWF7cW7duiV6CLUW24vD9mKxvzjObM/J8CPqzn2b//e//+Gtt95CZmZmpV73MK+Sx1VdXU8l29AqN4PtBWF/cdheHKvViu3btz/U/149rNheHLYXi/3FcXZ7ToYfcS1atIBer8e3336LiIgIGAwGREREYM+ePco+Op0OM2fORGRkJEaPHo3c3Fz06NEDYWFhCAwMxJQpU2C32wHc/hD75MmT0bZtW3Tu3Bmvv/46unfvDgDYsWOHw0rQR48ehU6nA3D7D3KfPn0QHh6OwMBADBs2DDdu3AAAPPvss0hJSVFe9/3331dqtXAiIiIiIqLq4mT4EXfkyBGcPHkSPXr0wP79+2E0GrFkyRKMGTPGYb+8vDykp6fjiy++gI+PDzZt2oSDBw8iMzMT2dnZyn2SP/30U5w6dQrHjh3Dzp07K33GWa1WIyUlBQcOHMDRo0fh7e2NJUuWAACmT5+OpUuXKvsuW7YMU6ZMqfBYJSUlMJvNDg8iIiIiIqKqeKRXk67N4uLiULduXdSrVw+rVq1CQUEBnn76aeTl5UGj0SArKwu3bt1C3bp1AQDx8fHKh9LtdjtmzpyJXbt2QZZlXLlyBUFBQRg6dCi2bt2KESNGKEubjx49ulK3ZJJlGQsXLsTmzZthtVphMpnQpUsXALfvlTxjxgxkZGSgQYMG2LdvnzL5Lk9SUhLee++9+01ENcQmqUUPoVZjf3HYXhyNhv99EYXtxWF7sdhfHGe25+/qIyo1NVW5ZLm0tBQNGjTA9u3bERERoaywVlJSokyGPTw8lNcuWLAAV65cQXp6Otzd3fHaa6+huLi43Pf5/apuGo0GNtv/fX7u969JSUnBtm3b8OOPP8LLywuLFy/Gtm3blO3Tpk3DkiVL8Pjjj2Ps2LHQarUVfm8JCQl47bXXlK/NZjP8/f1hV/GPs6vZVRr84t9Z9DBqLfYXh+3FcXNzQ//+/UUPo1Zie3HYXiz2F8fZ7XmZdC1QXFyM0tJSNG/eHACUy5MrUlBQgMaNG8Pd3R25ubn4+uuvlW0xMTFISUmBxWKBxWLBmjVrlG0tW7bEuXPncPXqVQDA559/7nBMPz8/eHl5oaioCMnJyQ7vOXLkSHz33XdYvXo1Jk2adNfxabVaeHl5OTwAALxLmOvJMh67VcD2orC/OGwvjN1ux5UrV5S1LMh12F4ctheL/cVxdntOhmsBLy8vzJs3D5GRkQgLC0OdOnXuuv/06dORnp6OwMBAjBw5ErGxscq2iRMnQqfTISAgAE899RRatWoFHx8fAEDTpk3xl7/8BZGRkejcuTMaNGigvG7UqFG4efMm9Ho9nnnmGURHRzu8Z7169TBo0CA89dRT8Pf3r9b3yVVdXU8l29D86nG2F4T9xWF7cWw2G/bs2eNwJRK5BtuLw/Zisb84zm7P60ofQdnZ2WWe+8tf/oK//OUvytdvvvlmhfs3b94c+/btK/fYbm5umD9/Pjw9PWGxWDB8+HBERUUp22fPno3Zs2crX8+ZMwcA4O3tjS1btlQ4ZpvNhp07d97zrPXd/DmoAXx9fav9eqo6i8WCtN+AV4N9lc+Rk+uwvzhsT0RE9PDjmWGqstjYWBgMBnTo0AFeXl6YNm3afR1v48aNaNWqFaKiosqcMSYiIiIiInIGnhmmKktPT6/R4w0YMAADBgy47+MsPVoArRc/v+dKkt2GJ93qYkFmPmQVV9Z1NfYX52FrPyvUT/QQaowkSfD09HRYwJFcg+3FYXux2F8cZ7eXZJmrf9DD7c7q2HN++hXuHp6ih0NE9MB5lCbDREREd3NnbmAymf5vod0K8DJpcmAwGGAwGBAQEAC1Wq18HRcXV+lj7NixQ7mtk0vJXOHP5WQ7fK7nsr0o7C8O2wtjt9tx7tw5ruoqANuLw/Zisb84zm7Py6TJgdFoBHB7US2DwaB8XVlWq7XmB1VJKv6n1OVUsh1N8s/AXM8Pdok/W3M19heH7cWx2WwwGo1o2rQpVCq2dyW2F4ftxWJ/cZzdnr+bVCmSJKGwsFD52s/PT1mFWqfTYebMmYiMjMTo0aMdXmc2m9G7d2/MnTsXAPDdd9+ha9euCAsLQ2RkJLZv3w4AePbZZ5GSkqK87vvvv0enTp3KHUtJSQnMZrPDg4iIiIiIqCo4GaYakZeXh/T0dHzxxRfKczk5OejRowdGjBiBd999F7/++isSExORlpaGgwcPIiUlBcOGDUNJSQmmT5+OpUuXKq9dtmwZpkyZUu57JSUlwdvbW3lU977ERERERERUe/EyaaoR8fHxDqu8Xb58Gd26dcPKlSvRs2dPAMC3336L06dPo1u3bsp+KpUK58+fR69evTBjxgxkZGSgQYMG2LdvH7766qty3yshIQGvvfaa8rXZbIa/vz9kcIU/V5Mh4bq7D9sLwv7isL04kiShYcOGXNVVALYXh+3FYn9xnN2ek2GqFLVaDZvNpnxdXFzssN3Dw8Phax8fH7Ru3Rr//e9/ERMTA0mSIMsyevXq5XA59O9NmzYNS5YsweOPP46xY8dCq9WWu59Wqy1328Nwe5NHjaxSI6dRoOhh1FrsLw7bi6PRaNClSxfRw6iV2F4ctheL/cVxdnteJk2V0rp1a+X+wuvXr8eNGzfuur9Wq8X69etx8eJFvPzyy7Db7ejTpw+2bNmCzMxMZb99+/Ypvx45ciS+++47rF69GpMmTaryGCUuoOVykmyHX+F5theE/cVhe3FsNhtOnjzp8ANacg22F4ftxWJ/cZzdnmeGqVIWLlyIadOmYfbs2ejfvz98fX3v+Ro3NzekpKRg/PjxGD58OD7//HOkpKRg4sSJuHnzJkpLSxEaGqqcKa5Xrx4GDRqEixcvVutzwFMCfSo1Lqo5FosFaWl7MLprB7i5uYkeTq3D/uKwvTh2ux1ZWVlo1aoV1GpeEeRKbC8O24vF/uI4uz0nw1QunU7nsHr0M888g1OnTilfv//++8qv76wqfUf37t2VWzKp1WqsXr1a2RYbG4vY2Nhy39Nms2Hnzp1YsmTJ/X8DREREREREd8HLpOmBsHHjRrRq1QpRUVGIjo4WPRwiIiIiInrE8cwwPRAGDBiAAQMG3Ncxlh4tgNZLrqERUWVIdhsaP9YICzLzuYCZAOwvzoPcflaon+ghOJVKpULz5s2hUvHn+a7G9uKwvVjsL46z2/N3lKpEp9Mpl0DfMX78eGzfvh3A7VssLVq0yPUDA1eTFkFWqXHJtw3bC8L+4rC9OGq1GqGhofzcngBsLw7bi8X+4ji7PSfDdN9WrlyJHj16iB4GJDtX+HM1yW5Dk7xTbC8I+4vD9uLYbDZkZGRwVVcB2F4ctheL/cVxdntOhum+de/eHRs2bFC+zszMRJcuXdC2bVuMHj0at27dwsWLF/H444/j5s2byn7Dhg3D3//+dwDA8OHDER4ejuDgYPTv3x+5ubkVvl9JSQnMZrPDAwAk8BJpV5Mgw+fGFbYXhP3FYXtx7HY7zp8/D7udt7VyNbYXh+3FYn9xnN2ek2Gqcenp6fjuu+9w4sQJ5OfnY+HChWjatCliY2Oxdu1aAMDly5exZcsWjBw5EgCwaNEiHDhwAJmZmYiOjkZiYmKFx09KSoK3t7fyqM5tmIiIiIiIqHbjZJhq3IsvvghPT0+o1WqMGzcOW7ZsAQBMnz4dy5YtAwD84x//wEsvvQQPDw8AQEpKCsLDwxEUFISVK1eW+Vzy7yUkJMBkMimPnJwcp39PRERERET0aOFq0uR0kiQBACIjI1GvXj1s374dn376qTJJ3rVrFxYvXow9e/agUaNG2LhxI959990Kj6fVaqHVass8L0v82Y6ryZIKV7382V4Q9heH7cVRqVTQ6/Vc1VUAtheH7cVif3Gc3Z6/o1TjvvnmG1y/fh02mw2rV69GbGyssm369OkYNWoU2rdvj7Zt2wIACgoK4OnpCV9fX5SWlmLFihXVel/+p9T1ZEmFaz7N2V4Q9heH7cVRq9Vo164dV3UVgO3FYXux2F8cZ7fnv+JUZX369EGzZs2Ux2+//eawPSIiAn369EH79u3h4+ODGTNmKNsGDx6M69evY8qUKcpzffv2hV6vh16vR3R0NAwGQ7XGxVVdXU+y2+B/5RjbC8L+4rC9OFarFbt374bVahU9lFqH7cVhe7HYXxxnt+dl0lQl2dnZd92enJx81+1GoxENGzZE//79lefc3NyQmprqsN8HH3xQ5bFNCaoPX1/fKr+Oqs9isSAtrRCvBTeAm5ub6OHUOuwvDtuLI8syrl69ClnmSt6uxvbisL1Y7C+Os9tzMkwuM378eHz//fdYuXIlP3NBRERERERCcTJMLrNy5UqnHn/Z0XzU8eJP7FxJZbdCD2BhZh7sKv514mrsL86D0n5WqJ+w9yYiInrY8X9PtZhOp4NWq0XdunVRUlKC0NBQ/OMf/8Bjjz1W4WsSExNRWFiIRYsWuW6glWTnQjYuZ5dUuNSgFdsLwv7isL04arUaBoOBC9kIwPbisL1Y7C+Os9vzX/FaLjU1FUajEceOHYPJZLrnZ36rw263w2631/hxy+B/Sl1PUqHQozHbi8L+4rC9MCqVCi1atODHbQRge3HYXiz2F8fZ7fk7SgCA0tJS3Lx5E/Xr1wcAzJ8/H5GRkejYsSP69u2Lc+fOlXnNkSNH0LVrV3Ts2BEBAQGYN2+esi0xMREvvPAC+vTpg6CgIFy6dAk6nQ5Go1HZJzw8HDt27AAAzJs3D+3bt4fBYIDBYCj3/e6Fq7q6nmS3oeWlQ2wvCPuLw/biWK1WbNu2jau6CsD24rC9WOwvjrPb8zLpWi4uLg5169ZFdnY2wsLC8OKLLyIlJQVZWVnYs2cP1Go1Pv/8c0yePBmbN292eK1Op8PWrVuh1Wpx69YtdOnSBbGxsejcuTMAYM+ePcjIyMDjjz9+1zEUFBRg/vz5uHTpEurWrYubN2/e9ac/JSUlKCkpUb42m80AAAn8vLCrSZChtdyCBJn1BWB/cdheHFmWUVRUxFVdBWB7cdheLPYXx9ntORmu5VJTU2EwGGC1WjFx4kTMnDkTOTk52L9/P8LCwgAANlv5Zz5u3bqFyZMnw2g0QqVSIScnB0ajUZkM9+vX754TYQDw8vJCmzZtMGLECPTu3Rv9+/dHs2bNKtw/KSkJ7733XjW+WyIiIiIiott4mTQBADQaDV544QV8++23kGUZCQkJMBqNMBqNOHLkCI4cOVLmNW+99Rb8/PyQkZGBw4cPo3v37iguLla2e3h4lHmP30+s7+yrVquxd+9ezJgxA1euXEHnzp2xc+fOCseakJAAk8mkPHJycu732yciIiIiolqGk2FSbNu2DXq9HgMHDsTy5cuRn58PALBYLMjIyCizf0FBAZo1awaNRoOsrCz88MMPdz1+69atkZ6eDgDYt28fsrKyAABFRUW4fPkyoqOj8c4776Br167lvt8dWq0WXl5eDg8AsEtc4c/V7JIa5xsGsL0g7C8O24ujVqsRFRXFVV0FYHtx2F4s9hfH2e15mXQtd+czw1arFS1atMDy5cvh7++PvLw89OjRA8DtD66PHTsWoaGhDq+dPXs2Ro4ciX/+859o1aoVYmJi7vpe8+bNw+jRo7FixQpERUUhMDAQAGAymTB48GDcuHEDkiShTZs2GD16dNW/GUmq+mvo/kgSbtStL3oUtRf7i8P2wqhUKjRq1Ej0MGoltheH7cVif3Gc3V6S+UlwesiZzWZ4e3vj2rVr8PX1FT2cWsViseD7779H79694ebmJno4tQ77i8P24rC9OGwvDtuLxf7iVKf9nbmByWRSriCtCC+TJqL7wtsMiMX+4rC9OGwvDtuLw/Zisb84zmzPy6TpkbHsaD7qePFCB1dS2a3QA1iYmQe7in+duBr7iyOi/axQP5e8DxERUW3BM8O1nE6ng16vh8FgUB7lrRwNANnZ2Vi+fHmNvK/BYEBRUVGNHIuIiIiIiKiq+JnhWk6n02HDhg0wGAz33HfHjh2YMWMGjEaj08dVFXc+FzDnxzNw97z75wKohsky6lhuodStLhcwE4H9xRHQnmeGb5NlGUVFRfD09ITEP/cuxfbisL1Y7C9OddrzM8N0X27duoW4uDgEBAQgJCQEvXv3BgBMmjQJWVlZMBgMGDBgAADgwIED6NKlC4KDgxEZGYmff/4ZwO2zyD4+PpgzZw7CwsLQunVrpKWlKe8hSRIKCwsBAG+88QYiIiJgMBjQrVs35ZZL9HCwauqIHkKtxv7isL04devWFT2EWovtxWF7sdhfHGe252SYEBcX53CZ9IYNG1BYWIjjx4/j8OHD+PLLLwEAy5cvh16vh9FoxMaNG1FaWopBgwZhzpw5yMzMxIIFC/DCCy/g+vXrAG7fMik4OBgHDx7E0qVL8eqrr5b7/jNnzsT+/fthNBoxefJkTJ8+/a7jLSkpgdlsdngAgEq21WAVqgyVbIP+t3S2F4T9xWF7caxWK9LS0riYjQBsLw7bi8X+4ji7PVdcIaSmpjpcJv3rr79i5syZmDx5Mp5++mn069ev3NdlZWVBpVKhT58+AICuXbvi8ccfh9FoRLNmzeDu7o5BgwYBAKKionDmzJlyj/PDDz9gyZIlKCoqgt1uR35+/l3Hm5SUhPfee68a3ykREREREdFtPDNMZbRs2RLHjx9H37598fPPPyMoKAgFBQWVeu3vr+XXarXK12q1GjZb2TMo58+fx5QpU7B27VocPXoUX375JYqLi+/6HgkJCTCZTMojJyenCt8dERERERERJ8NUjt9++w2SJGHAgAGYP38+ZFlGTk4OvLy8YDKZlP30ej3sdjt++OEHAMDu3buRm5tbqcW47jCZTHBzc0OTJk0gyzKWLl16z9dotVp4eXk5PIiIiIiIiKqCl0kT4uLiHD6YnpSUhISEBMiyDKvVipEjRyI4OBhWqxWBgYEICgpCy5YtsXHjRqxfvx7Tpk3D66+/Dnd3d3zzzTfw8PDAtWvXKvXeHTp0wNChQxEYGAhfX18MHDiw2t+HXVJX+7VUPXZJjaxmndheEPYXh+3F0Wg06NevHzQa/hfG1dheHLYXi/3FcXZ73lqJHnp3lk8vLCyEt7e36OHUKrzVgFjsLw7bi8P24rC9OGwvFvuLw1srEVUSV/hzPavViu3bt7O9IOwvDtuLw/bisL04bC8W+4vj7PacDBMREREREVGtwwvf6ZGx7Gg+6njxqn9XUtmt0ANYmJkHu4p/nbga+4vjivazQv2cclwiIiK6jWeGSWG1WvHee++hXbt2CAoKgsFgwIQJE1BYWCh6aPQAs3EBIaHYXxy2F4eL2IjD9uKwvVjsL44z23MBLVKMHj0a+fn5WLNmDerXrw9ZlvHNN98gLCwMLVu2vK9jW61Wp/1BvvMh+Tk//Qp3D0+nvAcRkavxzDAREVHVcQEtqrLTp0/j66+/xurVq1G/fn0AgCRJGDJkCKZNm4aUlBRl3++//x6dOnUCAMTHx2Ps2LHo0qUL2rZti9GjR+PWrVsO27p164agoCDlmL8/0+zn54fs7GzY7XZMmTIF7du3R0hICMLCwlBcXFzuWEtKSmA2mx0eAAD+XMf1ZBmP3Spge1HYXxy2F8Zut+PKlSuw2+2ih1LrsL04bC8W+4vj7PacDBMA4NChQ2jTpg38/MqeiZg+fTqWLl2qfL1s2TJMmTJF+To9PR3fffcdTpw4gfz8fCxcuFDZdvDgQWzevBknT5686/sfPnwYW7duxbFjx3D48GFs27YNderUKXffpKQkeHt7Kw9/f38AgEq2Vel7pvunkm1ofvU42wvC/uKwvTg2mw179uyBzcb2rsb24rC9WOwvjrPbczJM99SrVy+YTCZkZGTg3Llz2LdvH1588UVl+4svvghPT0+o1WqMGzcOW7ZsUbYNGTIEnp73vnS5ZcuWsFqtGDt2LP75z3/CYrFApSr/j2dCQgJMJpPyyMnJuf9vkoiIiIiIahVOhgkA0LFjR5w6dQp5eXnlbp82bRqWLFmC5cuXY+zYsdBqtRUe6/c3xPbw8HDYplarHX6yc+dSaG9vbxw9ehTDhg3DyZMnERwcjNOnT5d7fK1WCy8vL4cHERERERFRVXAyTACA1q1b44UXXsC4ceOUz/TKsox169bh119/xciRI/Hdd99h9erVmDRpksNrv/nmG1y/fh02mw2rV69GbGzsXd8nPT0dALB+/XrcuHEDAHD16lXcuHEDvXv3xocffgidTofjx49X6XuQId17J6pRMiSUuNVle0HYXxy2F0eSJHh6ejr84JVcg+3FYXux2F8cZ7fnZJgUq1atQkhICDp16oTAwEAEBATg+++/R4MGDVCvXj0MGjQITz31lPIZ3TsiIiLQp08ftG/fHj4+PpgxY0aF77Fw4UJMnz4dHTt2REZGBnx9fQEAOTk56NWrF4KDgxEUFISgoCA888wzVRq/rOJtTlxNVqnxa5OObC8I+4vD9uJoNBrExMTwNicCsL04bC8W+4vj7Pa8tRJVis1mQ1hYGJYsWYLo6Gjl+fj4eBgMhrtOgJ3tzvLpBQUF8PHxETaO2shutyMnJwf+/v4VfsabnIf9xWF7cdheHLYXh+3FYn9xqtOet1aiGrVx40a0atUKUVFRDhPhBw1X+HM9m80Go9HI9oKwvzhsLw7bi8P24rC9WOwvjrPb81w/3dOAAQMwYMCAcrclJye7djB3sexoPup48UIHV1LZrdADWJiZB7uKf524GvuLU9PtZ4WWva0dERERORfPDD+idDodGjVqBIvFojy3fft2SJJU45c0jx8/Htu3bwcAbNiwAXv37lW2HThwAHFxcTX6fkRERERERPeLk+FHWPPmzbFx40bl688++wzh4eE1+h42mw0rV65Ejx49AJSdDIeHhyM1NbVG37MiXNXV9WRIuO7uw/aCsL84bC+OJElo2LAhV3UVgO3FYXux2F8cZ7fnZPgRNmbMGKxatQoAYDKZsHfvXvTt2xcAcOTIEXTt2hUdO3ZEQEAA5s2bp7wuMTHR4ezx0qVLER8fD+D2ZdE9evTACy+8gA4dOmDfvn3o3r07NmzYgLS0NGzcuBEff/wxDAYDVq5ciR07dsBgMAAAsrOzHRa4un79uvIH+9atW4iLi0NAQABCQkLQu3fvCr+vkpISmM1mhwfA1aRFkFVq5DQKZHtB2F8cthdHo9GgS5cuXNVVALYXh+3FYn9xnN2ek+FH2FNPPYXs7GxcvHgR//rXvzBkyBCo1bf/46bT6bB161YcOnQIBw8exLp16xzO6N5Neno6PvzwQxw5cgRRUVHK8/369cOAAQPw5ptvwmg0Yvz48ZUe67fffovCwkIcP34chw8fxpdfflnhvklJSfD29lYed271JMn2Sr8f1QxJtsOv8DzbC8L+4rC9ODabDSdPnuRCNgKwvThsLxb7i+Ps9pwMP+JGjhyJ5ORkrFq1CmPHjlWev3XrFsaPH48OHTqgc+fOOHfuHIxGY6WO2aVLF+j1+hodZ0hICE6cOIHJkycjNTUVbm5uFe6bkJAAk8mkPHJycgBwMiyCJNvR0JzD9oKwvzhsL47dbkdWVhbsdrZ3NbYXh+3FYn9xnN2ek+FH3KhRo7B48WK4u7ujTZs2yvNvvfUW/Pz8kJGRgcOHD6N79+4oLi4GcPtyhN//9OXO83d4eHhUayx3O27Lli1x/Phx9O3bFz///DOCgoJQUFBQ7nG0Wi28vLwcHkRERERERFXByfAjrmnTpkhKSsJf//pXh+cLCgrQrFkzaDQaZGVl4YcfflC2tW7dGgcOHIDNZsPNmzexbt26Sr+fl5cXTCZTudsaN24MWZZx/PhxAMCaNWuUbb/99hskScKAAQMwf/58yLKsnPElIiIiIiKqaZwM1wJjxoxx+GwvAMyePRurV69GcHAwZs2ahZiYGGXboEGD0LRpU7Rv3x7PPvssQkNDK/1eI0eOxFdffYXQ0FCsXLnSYZtGo8GSJUvw7LPPIiIiwuG2T0eOHMFTTz2FkJAQhIaGYuTIkQgODq7S98lVXV1PhoTCxxqxvSDsLw7bi6NSqdC8eXOoVPwvjKuxvThsLxb7i+Ps9pIsy7JTjkzkImazGd7e3jCZTLxkmoiIiIioFqvK3IA/3qBHBlf4cz2bzYaMjAy2F4T9xWF7cdheHLYXh+3FYn9xnN2eN8uiR8aSzGuo48W/pFxJZbdC/9t5/GBvAruKf524GvuLcz/tZ4X6OWlUtYPdbsf58+cRFBSk3C6QXIPtxWF7sdhfHGe355lhui86nQ56vR4GgwEBAQFYtmyZ6CERERERERHdE08l0H1LTU2FwWDAuXPnEBwcjOjo6EotfmW1WqHR8I8gERERERG5Hs8MU41p0aIF9Ho9Dh8+jJdffhmRkZEIDg7GhAkTUFpaCgDo3r07pk2bhqioKPTu3RvZ2dnw8fFRjnH9+nVIUvVWZ5Ul/nF2NVlS4aqXP9sLwv7isL04KpUKer2eq7oKwPbisL1Y7C+Os9vzd5RqzJEjR3Dy5EmsWrUK0dHR2LdvHw4fPgy73Y5PPvlE2e+XX37BTz/9hG3btlXrfUpKSmA2mx0eACfDIsiSCtd8mrO9IOwvDtuLo1ar0a5dO35uTwC2F4ftxWJ/cZzdnv+K032Li4uDwWDAxIkTsWrVKhw7dgwff/wxDAYDQkNDsXPnTpw+fVrZf8SIEXBzc6v2+yUlJcHb21t5+Pv7AwAkOxfPcjXJboP/lWNsLwj7i8P24litVuzevRtWq1X0UGodtheH7cVif3Gc3Z4f2KT7duczw3e88sorWLduHdq2bVvu/h4eHsqvNRqNw1LpxcXF93y/hIQEvPbaa8rXZrMZ/v7+kMBbZruaBBkexYWQILO+AOwvDtuLI8syrl69CllmeVdje3HYXiz2F8fZ7XlmmGrcwIED8de//lX5CU5BQYHDmeHfa9y4MWRZxvHjxwEAa9asuefxtVotvLy8HB5ERERERERVwckw1biFCxeibt26MBgMCA4ORs+ePZGdnV3uvhqNBkuWLMGzzz6LiIgIWCwW1w6WiIiIiIhqJUnm+X56yJnNZnh7e6OgoMBhZWpyPrvdjpycHPj7+3OFRQHYXxy2F4ftxWF7cdheLPYXpzrt78wNTCbTPa8g5WSYHnpV+QNPRERERESPrqrMDfijDXpkcIU/17Nardi2bRvbC8L+4rC9OGwvDtuLw/Zisb84zm7P1aTpkbH0SB7qeNlFD6NWUdmt0BcVYcHha7Cr+NeJq7G/OJVtPyvUz4Wjqh1kWUZRURFXdRWA7cVhe7HYXxxnt+eZ4Vpi/fr1CAsLg8FgQLt27RATEwO73Q6dTgej0ejy8WzcuBGvvvqqy9+XiIiIiIgI4JnhWuHSpUuYMGECDh48iBYtWgAADh06BEmShI1pwIABGDBggLD3JyIiIiKi2o1nhmuBy5cvQ61Wo0GDBspzHTt2LDMZPn36NGJjYxEcHAyDwYANGzYAAD744ANMmTJF2e/69eto0KABrl69CgCYP38+IiMj0bFjR/Tt2xfnzp0DACQmJiIuLg5/+tOfEBAQgJiYGOTn5wMAkpOTMXDgQABAbm4uevTogbCwMAQGBmLKlCmw2yu+3LmkpARms9nhAQB2SX1/oajK7JIa5xsGsL0g7C8O24ujVqsRFRUFtZrtXY3txWF7sdhfHGe352S4FggODkbXrl3RokULPP/88/j4449x4cKFMvsNHz4cQ4YMQWZmJr7++muMGzcO586dw6hRo/DVV1+hpKQEAPD111+jR48eaNiwIVJSUpCVlYU9e/bg0KFDGD58OCZPnqwcMz09HcnJyTh+/DgaNWqEFStWlHlfHx8fbNq0CQcPHkRmZiays7Px1VdfVfj9JCUlwdvbW3n4+/vf3iDwTHetJUm4Ubc+24vC/uKwvTAqlQqNGjXi7U0EYHtx2F4s9hfH2e35O1oLqFQqrFu3Drt370bfvn3x888/IzAwEKdPn1b2KSoqwqFDhzBu3DgAQJs2bdC1a1fs3LkT/v7+CA0NxcaNGwHcPqs7ZswYAMCGDRuwZcsW5fPIf/vb33D+/HnluH379oWvry8AICoqCmfOnCkzPrvdjpkzZyIkJAShoaE4cODAXT/HnJCQAJPJpDxycnJuf592rvDnaiq7FW1z9rK9IOwvDtuLY7FYsHnzZlgsFtFDqXXYXhy2F4v9xXF2e35muBZp164d2rVrh4kTJ6Jv377K5LYiv7+MeuzYsVi9ejXCwsJw+vRp9O3bF8DtFd4SEhIwYcKEco/h7u6u/FqtVpe7LPqCBQtw5coVpKenw93dHa+99hqKi4srHJdWq4VWq73r2Ml11LJN9BBqNfYXh+3F4e1NxGF7cdheLPYXx5nteWa4Frhw4QJ+/vln5euCggKcPXsWrVq1Up7z9PREx44dsXr1agC3Pz+8a9cudOvWDQAwcOBA7N+/H0lJSRgxYgQ0Go3y/PLly5XPAlssFmRkZFRpfAUFBWjcuDHc3d2Rm5uLr7/++r6+XyIiIiIionvhmeFawGq1Yu7cuTh79izq1asHq9WK0aNH47nnnsP06dOV/b744gtMmjQJS5cuhSRJWLlyJZo3bw7g9tnYF198Ef/v//0/nDhxQnnN8OHDkZeXhx49eijvNXbsWISGhlZ6fNOnT8fgwYMRGBiIpk2bIjY2toa+cyIiIiIiovJJMu8eTQ85s9kMb29vFBYWwtvbW/RwapU7N0L39PQUequu2or9xWF7cdheHLYXh+3FYn9xqtP+ztzAZDLBy8vrrvvyMmkiui9169YVPYRajf3FYXtx2F4ctheH7cVif3Gc2Z6XSdMj4xPjFdTx4ip/rqSyW6H/LR1ZzTrBruJfJ67G/uJU1H5WqJ/AUdUOVqsVaWlp6NevH9zc3EQPp1Zhe3HYXiz2F8fZ7XlmWDCdTod27do5rJIWHh6OHTt2VPlY2dnZWL58eZnj3+02Rb935coVjBkzBi1btkRoaCg6duyIDz/8sMrjICIiIiIietBxMvwAKCkpwWeffXZfx7BareVOhivr1q1bePrpp9GiRQucOnUKGRkZ2LVrFx577LH7Gtcfx0hERERERPQg4GT4AZCYmIj3338fN2/eLLPtypUrGDRoEDp06ICgoCCsWLFC2abT6TBz5kxERkZi9OjRmDRpErKysmAwGDBgwABlv/Xr1yMqKgpPPvkk5s2bV+4YUlJS4OnpicTERKjVagBAvXr1MH36dBQXF6Nx48bIyclR9n/rrbcwc+ZMZRxvvvkmwsLC0Lp1a3z88ccVjnHHjh0wGAzK9qNHj0Kn0wEArl69it69e6NDhw4IDg7GmDFjyh1rSUkJzGazw4OIiIiIiKgq+CGzB0BISAh69OiBhQsX4u2333bYNnXqVOj1eqxfvx5XrlxBWFgYQkJC0LlzZwBAXl4e0tPTIUkSduzYgRkzZpS5LLqwsBB79uzBtWvX0KpVK4wZMwZPPPGEwz4HDx5EVFRUueNzd3fHuHHj8Pe//x0ffvghSkpKsHr1auzdu1fZ5/Llyzhw4ADy8vLQsWNHPPXUU+jSpUu5Y6zI2rVr8eSTT+L7778HAOXexX+UlJSE9957r8zzdkld4bHJOeyS+vZnJtleCPYXh+3F0Wg06Nevn3K/e3IdtheH7cVif3Gc3Z5nhh8Q77//Pj755BPk5eU5PL9lyxZMnDgRANCoUSMMGjQIW7ZsUbbHx8ffc5nxYcOGAQD8/PzQsmVLnD17tsrjmzx5Mv75z3+ipKQEX3/9NSIjI9GiRQtl+7hx4yBJEvz8/Ko1RgDo3Lkz/ve//+H111/Hf/7znwov0U5ISIDJZFIevz9jTa6nsZaKHkKtxv7isL04t27dEj2EWovtxWF7sdhfHGe252T4AaHT6TBs2LAKL2O+44+TSg8Pj3se293dXfm1Wq0u97O7YWFhDmd6/+iJJ55At27dkJqaimXLlmHKlCmVHufvx6jRaGCz2ZSvi4uLlV9HRUXBaDSiU6dOWL9+PSIiIhz2vUOr1cLLy8vhAQAquey+5Fwq2YZWuRlsLwj7i8P24litVmzfvp3rUAjA9uKwvVjsL46z23My/ACZPXs21q5di4sXLyrPxcbG4h//+AeA25+pXb9+PXr16lXu6728vGAymar13i+99BIKCwvx/vvvKxPQW7duYfHixco+06dPx9tvv43CwkLExsY6vD45ORnA7Uub//3vf6Nnz57lvk/Lli1x7tw5XL16FQDw+eefK9vOnj0LDw8PvPjii1iyZAl++eUXXL9+vVrfDxERERER0d1wMvwA8fPzw7Rp03Dp0iXlucWLF+PEiRPo0KEDevTogbfffhudOnUq9/XBwcEIDAxEUFCQwwJalVGvXj38+OOPOHPmDFq3bo0OHTqgU6dODot6de7cGd7e3pg8eXKZM9QNGzZEWFgYIiMjMWXKFOXzwn/UtGlT/OUvf0FkZCQ6d+6MBg0aKNt27NiBsLAwGAwGdOnSBR9//DG8vb2r9H0QERERERFVhiTLsix6EPRwuHDhAsLDw/HLL7/A09NTeV6n02HDhg0Oq0S7ktlshre3N65duwZfX18hY6itLBYLvv/+e/Tu3Zs3oReA/cVhe3HYXhy2F4ftxWJ/carT/s7cwGQyKR+nrAgnw1Qp7777LlatWoUPPvgAo0ePdtj2oEyGK/MHnoiIiIiIHl2cDFOtcucP/Jwfz8Ddk5Nhl5JlPFZciBvuPkAlVgynGsb+4sgyxjaT4efnB5WKnzhyJbvdjmvXrrG9AGwvDtuLxf7iVKd9VSbD/N18COh0OuXewcXFxXjuuecwZMgQlJbW3G09kpOTMXDgwHu+f2UlJiY6rBTtClzV1fVUsg3Nrx5ne0HYXxyVbMOePXvKXfGenMtmY3tR2F4ctheL/cVxdntOhh8iRUVFeOaZZ/D4448jNTUVderUET2kCr333nsunwwTERERERFVFifDD4m8vDzExMSgU6dO+PTTT6FSqTB8+HCEh4cjODgY/fv3R25uLgBg5cqVMBgMykOtVuPHH39Ebm4uevTogbCwMAQGBmLKlCmw2+1l3uvixYuIiIjAqlWrlOfWr1+PqKgoPPnkkw73Qp43bx7at2+vvNe5c+cwadIkAEB0dDQMBgOuXLmClJQUdOrUCaGhoQgJCcGmTZuUY3Tv3h1vvPEGoqOj0apVK+X1REREREREzqIRPQCqnLi4OLz88stISkpSnlu0aBEaNmwIAPjoo4+QmJiI5cuXY/z48Rg/fjwA4IMPPsDjjz+Op556ClarFZs2bYKHhwdsNhuee+45fPXVVxg6dKhyzCNHjmDo0KFYuHAhevfurTxfWFiIPXv24Nq1a2jVqhXGjBmDevXqYf78+bh06RLq1q2LmzdvQqVSYfny5VixYgV27twJHx8fAECfPn3w0ksvQZIkZGdno3Pnzjh37hy0Wi0A4MyZM9i+fTssFgsCAgKwZ88eREVFlduipKQEJSUlytdmsxkAIIOfmXQ1GRJK3OqyvSDsL44MCZ6enmVuM0fOJ0lsLwrbi8P2YrG/OM5uzzPDD4n+/fvjm2++QU5OjvJcSkoKwsPDERQUhJUrV5b5XO/nn3+OdevW4ZtvvoFGo4HdbsfMmTMREhKC0NBQHDhwwOE1x44dw4ABA5CSkuIwEQaAYcOGAbh9L+SWLVvi7Nmz8PLyQps2bTBixAisWLEC+fn5cHd3L3f8Z8+exTPPPIOgoCAMHDgQ+fn5OHv2rLI9Li4OGo0GdevWhcFgwJkzZypskZSUBG9vb+Xh7+8PAJBV6kq1pJojq9T4tUlHtheE/cWRVWrExMRAo+HPlF1No9GwvSBsLw7bi8X+4ji7PSfDD4lXX30Vr7zyCrp3747z589j165dWLx4MdLS0nD06FEsWLDA4TO627Ztw9y5c7F582blnsALFizAlStXkJ6ejszMTAwbNszhNU2bNsXjjz+Obdu2lXn/309y1Wo1rFYr1Go19u7dixkzZuDKlSvo3Lkzdu7cWe74hw4divHjx+Po0aMwGo3w8PBweO/yjl+RhIQEmEwm5aH8gEAue8k3OZlsh8/1XLYXhf3Fke04d+5cuR81Ieey29leFLYXh+3FYn9xnN2ek+GHyGuvvYapU6eie/fuOHPmDDw9PeHr64vS0lKsWLFC2e/IkSMYO3YsNmzYgCZNmijPFxQUoHHjxnB3d0dubi6+/vprh+PXr18fP/zwAzZs2IC5c+feczxFRUW4fPkyoqOj8c4776Br167IyMgAAHh6esJkMjm895NPPgkAWLt2LQoKCqrdQavVwsvLy+EBACpOCFxOJdvRJP8M2wvC/uKoZDuMRiNXFhXAZrOxvSBsLw7bi8X+4ji7Pc/1P2RmzJgBlUqFt956C127doVer4evry9iY2Nx4cIFALfPAN+4cQPDhw9XXrdy5UpMnz4dgwcPRmBgIJo2bYrY2Ngyx/f09MS3336L559/Hm+++SY+/vjjCsdiMpkwePBg3LhxA5IkoU2bNhg9ejQA4PXXX0evXr1Qr149fP/99/jkk08wePBg+Pj4ICYmBs2bN6/hMkRERERERJUnybIsix4E0f24c2Pta9euwdfXV/RwahWLxYK0tDT069cPbm5uoodT67C/OGwvDtuLw/bisL1Y7C9OddrfmRuYTCblCtKK8DJpemRwhT/XkyQJDRs2ZHtB2F8ctheH7cVhe3HYXiz2F8fZ7XlmmB56d376M+enX+Hu4Sl6OET0CJoV6id6CERERFQJPDNMtZLERYRcTpLt8Cs8z/aCsL84NpsNJ0+e5GIqArC9OGwvDtuLxf7iOLs9J8MCGAwGGAwGBAQEQK1WK1/HxcVV+hg7duyAwWBw3iDvoV+/fsjKyrrrPomJiQ63T3I2TghcT5LtaGjOYXtB2F8cu92OrKws3mZDALYXh+3FYXux2F8cZ7fnatICGI1GAEB2djYMBoPydWXd7R68rpKWlnbPfd577z3MmDHD4R7CREREREREDwKeGX6ASJKEwsJC5Ws/Pz9kZ2cDAHQ6HWbOnInIyEjl9kV3mM1m9O7dW7k3cPfu3fH666+jW7duaN68Od555x2kpaWha9eu0Ol0WLBggfLaU6dOoX///oiIiEBwcDCWLl3qMJ4PP/wQkZGRePLJJ7F69Wplm06nUybx8+bNQ/v27ZUz3OfOncOkSZMAANHR0TAYDLhy5QpSUlLQqVMnhIaGIiQkBJs2bVKO1717d7zxxhuIjo5Gq1atlNeXp6SkBGaz2eFBRERERERUFTwz/BDJy8tDeno6JEnCjh07AAA5OTkYOHAgpk+fjlGjRin7njt3Dtu3b4fZbIZOp0NBQQF27tyJixcvQq/XY+zYsfD09MRLL72EtWvXol27drh58yY6d+6MTp06ISIiAgCg1Wqxb98+nDx5EhERERg5ciQ0mv/7Y1NQUID58+fj0qVLqFu3Lm7evAmVSoXly5djxYoV2LlzJ3x8fAAAffr0wUsvvQRJkpCdnY3OnTvj3Llz0Gq1AIAzZ85g+/btsFgsCAgIwJ49exAVFVWmQ1JSEt57770yz8vgCn+uJkNC4WON2F4Q9hdHpVKhefPmUKn4M2VXY3tx2F4ctheL/cVxdnv+jj5E4uPjHZYVv3z5Mrp164a//e1vDhNhABg8eDDUajXq16+Pli1b4tlnn4UkSXjiiSfQsGFDZGdnIysrC8eOHcPQoUNhMBjQpUsXFBUV4fjx48pxhg8fDgBo164dNBoNcnNzHd7Hy8sLbdq0wYgRI7BixQrk5+dXeFn02bNn8cwzzyAoKAgDBw5Efn4+zp49q2yPi4uDRqNB3bp1YTAYcObMmXKPk5CQAJPJpDxycnIAALJKXYWaVBNklRqXfNuwvSDsL45arUZoaCjUarZ3NbYXh+3FYXux2F8cZ7fnZPgBolarHVZK++PiUx4eHg5f+/j4ICgoCP/973/xxztk/X5Cqlary3xttVohyzIaNGgAo9GoPM6ePetwGXZ5r/vjmPfu3YsZM2bgypUr6Ny5M3bu3Fnu9zd06FCMHz8eR48ehdFohIeHh8P3eK/3ukOr1cLLy8vhAQCSnSv8uZpkt6FJ3im2F4T9xbHZbMjIyODKogKwvThsLw7bi8X+4ji7PSfDD5DWrVsjPT0dALB+/XrcuHHjrvtrtVqsX78eFy9exMsvv1zlVdb0ej28vLwcPgt8+vRp5OfnV/oYRUVFuHz5MqKjo/HOO++ga9euyMjIAAB4enrCZDIp+xYUFODJJ58EAKxduxYFBQVVGu+9SOAts11NggyfG1fYXhD2F8dut+P8+fNcWVQAtheH7cVhe7HYXxxnt+dk+AGycOFCTJ8+HR07dkRGRgZ8fX3v+Ro3NzekpKTAZrNh+PDhVVppWqPR4L///S/Wr1+P4OBgBAYGYty4cbh161alj2EymTBo0CB06NABwcHBsFgsypnl119/Hb169VIW0Prkk08wePBghIaGIiMjA82bN6/0+xAREREREdUkSf7j9bVEDxmz2Qxvb29cu3atUj9AoJpjsViQlpaGfv36wc3NTfRwah32F4ftxWF7cdheHLYXi/3FqU77O3MDk8mkfJyyIjwzTI8MrvDneiqVCnq9nu0FYX9x2F4ctheH7cVhe7HYXxxnt+eZYXro3fnpz5yffoW7h6fo4RDRQ25WqJ/oIRAREVE18cwwVZtOp4Ner4fBYED79u0xbNiwey7kFR8fj0WLFgEAli9fjo8//vie7zN+/Hhs3769Joas4Iq6rifZbfC/coztBWF/caxWK3bv3l2ldRqoZrC9OGwvDtuLxf7iOLu9xilHpYdaamoqDAYD7HY7/vSnPyE5ORl//vOfK/XaSZMmVWq/lStX3s8Qy8UVdV1PggyP4kJIkFlfAPYXR5ZlXL16tcxt7cj52F4ctheH7cVif3Gc3Z5nhqlCpaWluHnzJurXrw+bzYY333wTQUFBCAoKwtSpU1FaWlrmNYmJiZgxYwYAoG3btjhw4ICyLTk5Gc8//zwAoHv37tiwYQMAICUlBZ06dUJoaChCQkKwadOmu46rpKQEZrPZ4UFERERERFQVnAxTGXFxcTAYDGjcuDFUKhVefPFFfPrpp9i/fz8OHjwIo9GIM2fOYOHChXc9Tnx8PJKTk5WvV69ejbFjx5bZr0+fPti7dy8yMjLwn//8By+//DJKSkoqPG5SUhK8vb2Vh7+/f7W/VyIiIiIiqp04GaYyUlNTYTQace3aNeh0OsycORNbtmxBfHw8tFotNBoNXn75Zfzwww93Pc6oUaOQmpqKkpIS/Prrr/jll1/wzDPPlNnv7NmzeOaZZxAUFISBAwciPz8fZ8+erfC4CQkJMJlMyiMnJwcAYJf4x9nV7JIKlxq0YntB2F8ctVoNg8EAtVoteii1DtuLw/bisL1Y7C+Os9vzM8NUIY1GgxdeeAFvvvkm2rVr57BNkqR7vr5Zs2YIDw/Hf/7zHxw7dgwjRoyARlP2j9zQoUPx0UcfYfDgwQCABg0aoLi4uMLjarVaaLXashs4IXA9SYVCj8aiR1F7sb8wKpUKLVq0ED2MWontxWF7cdheLPYXx9ntOXugu9q2bRv0ej1iY2OxZs0alJaWwmq1YuXKlejdu/c9Xz9mzBisWrUKa9asKfcSaQAoKCjAk08+CQBYu3YtCgoKqjVWrqjrepLdhpaXDrG9IOwvjtVqxbZt27iyqABsLw7bi8P2YrG/OM5uzzPDVEZcXBzq1q0Lq9WKFi1aYPny5WjatCnOnDmDjh07Ari9ANadhbLu5rnnnsMrr7yCNm3aoH379uXu88knn2Dw4MHw8fFBTEwMmjdvXq1xczVp15MgQ2u5xdWMBWF/cWRZRlFREVcWFYDtxWF7cdheLPYXx9ntORkmB9nZ2RVumz9/PubPn1/m+d8vkpWYmOiwTavVIi8vr8xrduzYofx6xIgRGDFihPL1//f//X+VHu/v/TmoAXx9fav1Wqoei8WCtN+AV4N94ebmJno4tQ77ExEREVUfL5MmIiIiIiKiWkeSeb6fHnJmsxne3t6Y8+MZuHt6iR5O7SLLeKy4EDfcfYBKLKpGNYz979usUL9qvc5ut+PatWvw8/ODSsWfK7sS24vD9uKwvVjsL0512t+ZG5hMJnh53X1uwN/NWkCn00Gv18NgMECv1+Ojjz4SPSRcvHgR0dHRNXtQTgZcT5Jwo259theF/YVRqVRo1KgR/1MkANuLw/bisL1Y7C+Os9vzd7SWuHPv4G3btiEpKQn79u0TOp6mTZti586dNXpMlZ0r/Lmaym5F25y9bC8I+4tjsViwefNmWCwW0UOpddheHLYXh+3FYn9xnN2ek+Fa5oknnkC7du1w7tw5nD59GrGxsQgODobBYMCGDRuU/SRJwgcffIBOnTpBp9Nhw4YNSEpKQnh4ONq0aaMsgGW1WtGnTx+Eh4cjMDAQw4YNw40bNwDcXiQrKCgIkydPRkhICAIDA3HgwAEAtxfq8vHxUd5v+PDhCA8PR3BwMPr374/c3FxXJaH7pJZ5Wx+R2F8c3mJDHLYXh+3FYXux2F8cZ7bnZLiWOXnyJPLy8tC9e3cMHz4cQ4YMQWZmJr7++muMGzcO586dU/b18PBAeno6PvvsM4wYMQJNmjTBgQMH8OGHH+LNN98EAKjVaqSkpODAgQM4evQovL29sWTJEof3Gz16NA4fPoypU6fi7bffLndcixYtwoEDB5CZmYno6Ogyq1L/XklJCcxms8ODiIiIiIioKnhrpVoiLi4OKpUKWVlZWLhwIdzd3XHo0CH8/PPPAIA2bdqga9eu2LlzJ1q0aKG8BgDCw8Nx48YNDB06FAAQGRmJU6dOAbh976+FCxdi8+bNsFqtMJlM6NKli/K+rVu3RqdOnQAAUVFR5d6aCQBSUlLw+eefo7i4GMXFxfDzq3hRm6SkJLz33nv3WYSIiIiIiGoznhmuJVJTU3HixAl8//33mDVrVrn3E5b+sAiPu7s7gNtnf//49Z3LFVJSUrBt2zb8+OOPOHLkCN544w0UFxeXOcYfX/d7u3btwuLFi5GWloajR49iwYIFDsf4o4SEBJhMJuWRk5MDALBL6sqkoBpkl9Q40ziU7QVhf3E0Gg169OgBjYY/U3Y1theH7cVhe7HYXxxnt+dkuJaJjY3FK6+8gtmzZ6Njx45YvXo1AOD06dPYtWsXunXrVqXjFRQUwM/PD15eXigqKkJycnKVx1RQUABPT0/4+vqitLQUK1asuOv+Wq0WXl5eDg8Sx6qpI3oItRr7i1O3bl3RQ6i12F4ctheH7cVif3Gc2Z6T4VronXfeUc7GpqamIiQkBIMHD8bKlSvRvHnzKh1r1KhRuHnzJvR6PZ555plq3S6pb9++0Ov10Ov1iI6OhsFgqPIxAEDFhYRcTiXboP8tne0FYX9xrFYr0tLSuKCKAGwvDtuLw/Zisb84zm7Pc/21wB8via5fvz7y8vIAAFu2bCn3NbIsK7/28PBw+LpZs2a4fv06AMDb27vCY3Tv3h1Go1H5OigoSBmLTqdDYWEhAMDNzQ2pqakOr/3ggw/u+X390Z+DGsDX17fKr6Pqs1gsSPsNeDXYF25ubqKHU+uwPxEREVH18cwwERERERER1To8M0yPjGVH81HHS773jlRjVHYr9AAWZubBruJfJ67G/lU3K7TileqJiIiodpHk31//SvQQMpvN8Pb2xpwfz8Ddk4tpuZQsQyXbbq9m/IfVyMkF2L/KamoyLMsyrFYrNBpNmZX4ybnYXhy2F4ftxWJ/carT/s7cwGQy3XOhXV4mXYvodDo0atQIFotFeW779u2QJAkzZsy462vj4+OxaNGicrctXboU8fHxNTdQeqhorKWih1Crsb84t27dEj2EWovtxWF7cdheLPYXx5ntORmuZZo3b46NGzcqX3/22WcIDw8XOKKawxV1XU8l29AqN4PtBWF/caxWK7Zv386VRQVge3HYXhy2F4v9xXF2e06Ga5kxY8Zg1apVAACTyYS9e/eib9++AACbzYY333wTQUFBCAoKwtSpU1FaWvasU1FREeLi4qDX69G1a1ccOXJE2VbRMXJyctCoUSOH48XHx+OTTz4BAAwfPhzh4eEIDg5G//79kZubW+H3UFJSArPZ7PAgIiIiIiKqCk6Ga5mnnnoK2dnZuHjxIv71r39hyJAhUKvVAIBPP/0U+/fvx8GDB2E0GnHmzBksXLiwzDHmzp0LrVaLkydPYvPmzfjpp5+UbRUdw9/fHwaDQTkrff36dWzcuBEjRowAACxatAgHDhxAZmYmoqOjkZiYWOH3kJSUBG9vb+Xh7+9fg4WIiIiIiKg24GS4Fho5ciSSk5OxatUqjB07Vnl+y5YtiI+Ph1arhUajwcsvv4wffvihzOu3bt2KcePGQZIkeHt7Y9iwYZU6xpgxY7B69WoAwNdff42YmBjlvsApKSkIDw9HUFAQVq5c6XB/4j9KSEiAyWRSHjk5OTWRharJJqlFD6FWY39xNBqu4C0K24vD9uKwvVjsL44z23MyXAuNGjUKixcvhru7O9q0aVPhfpVdse1u+/1+2/PPP499+/bh0qVLSE5OxpgxYwAAu3btwuLFi5GWloajR49iwYIFKC4urvCYWq0WXl5eDg8AvLWMAHaVBr/4d2Z7QdhfHDc3N/Tv3x9ubm6ih1LrsL04bC8O24vF/uI4uz0nw7VQ06ZNkZSUhL/+9a8Oz8fGxmLNmjUoLS2F1WrFypUr0bt37zKvj42NxerVqyHLMsxmM/71r39V6hju7u4YMmQIEhMTcebMGeWzygUFBfD09ISvry9KS0uxYsWK6n1jvEuY68kyHrtVwPaisL8wdrsdV65cgd1uFz2UWoftxWF7cdheLPYXx9nteTqhlrpzVvb3JkyYgDNnzqBjx44AgO7du5d7y6V33nkH48ePR7t27dCwYUN07doVJSUllTrGmDFjEBkZiZkzZyqfVe7bty/Wrl0LvV4PX19fxMbG4sKFC1X+nqYGeiuXXZNrWCwWpKXtRr9+/fjTUgHYXxybzYY9e/agX79+UKn4c2VXYntx2F4ctheL/cVxdntOhmuR7Ozscp///WJV8+fPx/z588vsk5ycrPza09MTqamp5R5LrVZXeAwAiIiIgPyHs1hubm5ljvfBBx+U+3oiIiIiIqKawB9tEBERERERUa3DM8P0yFh6tABaL3520pUkuw1PutXFgsx8yCquauxq7H93s0L9nHZsSZLg6elZ6YUGqeawvThsLw7bi8X+4ji7Pc8MP6DWr1+PsLAwGAwGtGvXDjExMff9wfHExESHVZrj4+OxaNGiSr1Wp9MptzsqLi7Gc889hyFDhqC0tPS+xlSTOBlwPVmlxq9NOrK9IOwvjkajQUxMDG+1IQDbi8P24rC9WOwvjrPbczL8ALp06RImTJiA9evXw2g04uTJk5g/f/59/0Tkvffeu+stiyqjqKgIzzzzDB5//HGkpqaiTp06lXqd3W53/gp8Mlf4cznZDp/ruWwvCvsLY7fbce7cOa4sKgDbi8P24rC9WOwvjrPbczL8ALp8+TLUajUaNGigPNexY0dlMnzgwAF06dIFwcHBiIyMxM8//wzg9gJZPj4+ymuuX7+uvGbSpEkAgOjoaBgMBly5cgUAcOLECfTs2RNt27bFoEGD7nqmNy8vDzExMejUqRM+/fRTZUW3jz/+GIGBgejQoQOGDx8Ok8kE4PaZ6BdeeAF9+vRBUFAQLl26hO+++w5du3ZFWFgYIiMjsX37dgBAbm4uevTogbCwMAQGBmLKlCkV/qEvKSmB2Wx2eACAihMCl1PJdjTJP8P2grC/ODabDUajETabTfRQah22F4ftxWF7sdhfHGe352T4ARQcHIyuXbuiRYsWeP755/Hxxx8rtxoqLS3FoEGDMGfOHGRmZmLBggV44YUXcP369bsec/ny5QCAnTt3wmg0olGjRgAAo9GITZs24cSJE7h8+TLWrVtX4THi4uIQGxuLjz76SHnuf//7H1atWoWff/4ZR44cwWOPPYZZs2Yp2/fs2YM1a9bg+PHjKCkpQWJiItLS0nDw4EGkpKRg2LBhKCkpgY+PDzZt2oSDBw8iMzMT2dnZ+Oqrr8odR1JSEry9vZWHv79/5cISERERERH9/3Ey/ABSqVRYt24ddu/ejb59++Lnn39GYGAgTp8+jaysLKhUKvTp0wcA0LVrVzz++OPK53mr6vnnn0e9evWgVqsRGRmJM2fOVLhv//798c033yAnJ0d5bsuWLYiLi1POSL/yyiv44YcflO39+vXD448/DgD49ttvcfr0aXTr1g0GgwGDBw+GSqXC+fPnYbfbMXPmTISEhCA0NBQHDhyo8HtKSEiAyWRSHr8fDxERERERUWXwU+APsHbt2qFdu3aYOHEi+vbti40bN6JXr15l9rtzKbRGo3G4hKAynw92d3dXfq1Wq2G1Wivc99VXX0VISAi6d++O7du3o3nz5hWO5Q4PDw/l17Iso1evXkhJSSnzunnz5uHKlStIT0+Hu7s7XnvttQrHr9VqodVqyzwvgyv8uZoMCdfdfdheEPYXR5IkNGzYkCuLCsD24rC9OGwvFvuL4+z2PDP8ALpw4YLyOWAAKCgowNmzZ9GqVSvo9XrY7Xbl7Ovu3buRm5sLg8GAxo0bQ5ZlHD9+HACwZs0ah+N6enoqn+etrtdeew1Tp05F9+7dce7cOcTGxuKrr75SPre7YsUK9O7du9zX9unTB1u2bEFmZqby3L59+5TvsXHjxnB3d0dubi6+/vrrKo+NK+q6nqxSI6dRINsLwv7iaDQadOnShSuLCsD24rC9OGwvFvuL4+z2/B19AFmtVsydOxdnz55FvXr1YLVaMXr0aDz33HMAbt92adq0aXj99dfh7u6Ob775RjkDu2TJEjz77LPw9fXF4MGDHY77+uuvo1evXqhXrx6+//77ao9vxowZUKlUePrpp7F9+3aMGTMGUVFRUKlUCA4Oxv/7f/+v3Ne1bt0aKSkpmDhxIm7evInS0lKEhoYiJSUF06dPx+DBgxEYGIimTZsiNja2yuOSuIiQy0myHb6m35Dn3QyyxJ+tuRr7i2Oz2XDq1Cm0adMGajV/GOFKbC8O24vD9mKxvzjObi/JsizX+FGJXMhsNsPb2xvXrl2Dr6+v6OHUKhaLBWlpaejXrx/c3NxED6fWYX9x2F4ctheH7cVhe7HYX5zqtL8zNzCZTPDy8rrrvjyVQERERERERLUOL5OmR8ayozTry0sAAQAASURBVPmo48ULHVxJZbdCD2BhZh7sKv514mrs/39mhfqJHgIRERE9ZHhmmCqk0+mqdMumRYsWITc3t1L79uvXD1lZWdUcWfm4oq7ryZBQ+FgjtheE/cVRqVRo3rw5VCr+M+pqbC8O24vD9mKxvzjObs/PDFOFdDodNmzYAIPB4JT9a8qdzwXM+elXuHt4uvS9iejBwDPDREREBPAzw+RECxYsQEREBAwGAyIiIrBnzx4AwNy5c3Hx4kXExcXBYDBg9+7d8PX1dThTnJiYiFdffRWA41nnio5ZVZLddu+dqEZJdhua5J1ie0HYXxybzYaMjAyHe7uTa7C9OGwvDtuLxf7iOLs9J8NUJSNHjsT+/fthNBqxZMkSjBkzBgDw7rvvomnTpkhNTYXRaESXLl3wwgsvYO3atQAAWZbxz3/+E2PHjq30MStSUlICs9ns8AAACbzIwdUkyPC5cYXtBWF/cex2O86fPw+7nbd0czW2F4ftxWF7sdhfHGe352SYqiQjIwNPP/00goKCMGnSJGRlZeHWrVvl7jtmzBisXr0aALBjxw74+vqiQ4cO93VMAEhKSoK3t7fy8Pf3r5lvjoiIiIiIao3avfwoVUlpaSkGDRqE7du3IyIiQrkev6SkBHXr1i2zf1RUFOx2O/bt24fk5ORyz/hW9ZgAkJCQgNdee0352mw2c0JMRERERERVwskwVVpxcTFKS0vRvHlzAMCSJUsctnt5ecFkMjk8N2bMGCxZsgT/+9//sGjRoiofszxarRZarbbM87LECx1cTZZUuOrlz/aCsL84KpUKer2eK4sKwPbisL04bC8W+4vj7Pb8HaW76tOnD5o1a4ZmzZohICAAs2fPRmRkJMLCwlCnTh2HfadNm4aXX34ZBoNBWRxr5MiR+PLLLxEbG4v69euXOb6XlxfmzZtX4TGrghMC15MlFa75NGd7QdhfHLVajXbt2kGtVoseSq3D9uKwvThsLxb7i+Ps9ry1Ej307lxanZeXhwYNGogeTq1itVqxb98+REZGQqPhhSauxv7isL04bC8O24vD9mKxvzjVac9bK1GtxJ/ruJ4sy7h69SrbC8L+4rC9OGwvDtuLw/Zisb84zm7PH23QI2PZ0XzU8eJfUq6ksluhB7AwMw92Ff86cbXa2n9WqJ/oIRAREdEjgGeGSVFUVAQPDw+MGzdOeS45ORkDBw6s8fd699138cUXX9T4cYmIiIiIiCqDk2FSpKamIiwsDOvXr8f169ed9j5WqxVz587F8OHDa/S4di4i5HJ2SYVLDVqxvSDsL45arYbBYOBiKgKwvThsLw7bi8X+4ji7Pf8HRYrPPvsMM2fORLdu3ZCamlruPnPmzEHr1q0RERGB2bNnQ6fTKdu+++47dO3aFWFhYYiMjMT27dsBADt27EBgYCDGjRsHg8GAf//734iPj1dutbR161ZERUUhNDQUgYGB+Oyzz6r3DXBC4HqSCoUejdleFPYXRqVSoUWLFrzNhgBsLw7bi8P2YrG/OM5uz99RAgAcP34cOTk56NOnD8aNG1fuhHTz5s1Yt24dMjIysG/fPly4cEHZ9uuvvyIxMRFpaWk4ePAgUlJSMGzYMJSUlAAATpw4gVGjRsFoNGLIkCEOx+3YsSN27dqFjIwM7Ny5E3PnzsVvv/1W4VhLSkpgNpsdHgAg2W01kYKqQLLb0PLSIbYXhP3FsVqt2LZtG6xWq+ih1DpsLw7bi8P2YrG/OM5uz8kwAbh9VnjUqFFQq9Xo168fzp49ixMnTjjss3XrVgwZMgSenp6QJMnhs8XffvstTp8+jW7dusFgMGDw4MFQqVQ4f/48AKBly5Z4+umny33vvLw8DBkyBEFBQYiJiUFeXh6OHj1a4ViTkpLg7e2tPPz9/QEAErh4lqtJkKG13GJ7QdhfHFmWUVRUxJVFBWB7cdheHLYXi/3FcXb72rP8KFXIYrHg888/h5ubG1JSUgAAN2/exGeffYagoKAKXydJkvJrWZbRq1cv5fW/d+HCBXh4eFR4nEmTJqFfv35Yt24dJElCx44dUVxcXOH+CQkJeO2115SvzWazMiEmIiIiIiKqDJ4ZJmzcuBEtW7bEhQsXkJ2djezsbOzduxeff/45LBaLsl9MTAzWrVuH69evQ5ZlrFq1StnWp08fbNmyBZmZmcpz+/btq9T7FxQUoEWLFpAkCT/99BMOHz581/21Wi28vLwcHkRERERERFXBM8OEzz77rMzKzu3bt8cTTzyBoqIi5blnn30W6enpMBgM8PHxwdNPPw0fHx8AQOvWrZGSkoKJEyfi5s2bKC0tRWhoaLlniv/oo48+wuTJk/H+++/DYDCgU6dO1fo+7BJX+HM1u6TG+YYBbC8I+4ujVqsRFRXFlUUFYHtx2F4ctheL/cVxdntJ5sXvVAVFRUXw9PSELMt4/fXXcevWLfz9738XOiaz2Qxvb2+YTCaeJSYiIiIiqsWqMjfgZdJUJaNGjUJoaCgCAgJw/vx5vP/++6KHpPj9Jd3kGhaLBZs3b2Z7QdhfHLYXh+3FYXtx2F4s9hfH2e15mTRVyb///W/RQ6AHDG8zIBb7i8P24rC9OGwvDtuLxf7iOLM9J8P0yFh2NB91vHjVvyup7FboASzMzINdxb9OXK229J8V6id6CERERPQI4mXSjyCdTgej0ejw3Pjx47F9+3YAwIYNG7B3715l244dO2AwGFw4QsBgMDgszkVERERERORKj+6pBHKwcuVK5dcbNmyAwWBA586dhY3nj5P1msAVdV3PLqlxpnEo2wvC/uJoNBr06NEDGg3/GXU1theH7cVhe7HYXxxnt+eZ4Vqie/fu2LBhA9LS0rBx40Z8/PHHMBgMyiTZarVi8uTJCAkJQWBgIA4cOAAAyM7OVm6fBADXr1+HJEnK18OHD0d4eDiCg4PRv39/5ObmOrxuzpw5CAsLQ+vWrZGWlqa8TpIkFBYWAgDeeOMNREREwGAwoFu3bsjKyrrr91JSUgKz2ezwIHGsmjqih1Crsb84devWFT2EWovtxWF7cdheLPYXx5ntORmuZfr164cBAwbgzTffhNFoxPjx4wEAJ0+exOjRo3H48GFMnToVb7/9dqWOt2jRIhw4cACZmZmIjo5GYmKiss1kMiE4OBgHDx7E0qVL8eqrr5Z7jJkzZ2L//v0wGo2YPHkypk+fftf3TEpKgre3t/Lw9/cHAKhkW6XGTDVHJdug/y2d7QVhf3GsVivS0tK4oIoAbC8O24vD9mKxvzjObs9z/QQAaN26NTp16gQAiIqKwvz58yv1upSUFHz++ecoLi5GcXEx/Pz+b6Ebd3d3DBo0SDnmmTNnyj3GDz/8gCVLlqCoqAh2ux35+fl3fc+EhAS89tprytdms1mZEBMREREREVUGJ8ME4PbE9Q61Wq389EWj0cBm+7+zTsXFxcqvd+3ahcWLF2PPnj1o1KgRNm7ciHfffVfZrtVqlUuq1Wq1w3HuOH/+PKZMmYL9+/ejVatWyMzMRLdu3e46Vq1WC61WW71vlIiIiIiICLxMulby8vKCyWSq1L6NGzeGLMs4fvw4AGDNmjXKtoKCAnh6esLX1xelpaVYsWJFlcdiMpng5uaGJk2aQJZlLF26tMrHICIiIiIiqipOhh9Rffr0QbNmzZTHb7/9pmwbOXIkvvrqK4SGhjqsMl0ejUaDJUuW4Nlnn0VERAQsFouyrW/fvtDr9dDr9YiOjq7W7Zk6dOiAoUOHIjAwEBEREWjevHmVj3EHV9R1PbukRlazTmwvCPuLo9Fo0K9fP64sKgDbi8P24rC9WOwvjrPbS7Isy045MpGLmM1meHt7o7CwEN7e3qKHU6vIsoyioiJ4eno6rDJOrsH+4rC9OGwvDtuLw/Zisb841Wl/Z25gMpng5eV11315ZpgeGVzhz/WsViu2b9/O9oKwvzhsLw7bi8P24rC9WOwvjrPb81w/PTKWHc1HHS9e6OBKKrsVegALM/NgV/GvE1d7FPvPCvW7905ERERENYBnhqnSdDodjEaj6GEQERERERHdN06Giei+2Lh4k1DsLw4XUhGH7cVhe3HYXiz2F8eZ7bmAFlWaTqfDhg0bsG3bNvzrX/+CxWKBm5sbFi9ejKioKGWfIUOGYNu2bTCZTJg4cSLefPNNAMAbb7yBH3/8ERaLBV5eXvjHP/4BvV4PAJAkCR988AE2bNiAq1ev4t1338WYMWPKHUdJSQlKSkqUr81mM/z9/THnp1/h7uHp5ApE5Ey8TJqIiIjuBxfQIqcaOXIk9u/fD6PRiCVLlpSZtF6+fBkHDhzA3r17sWTJEuzevRsAMHPmTOV1kydPxvTp0x1ep9VqsW/fPvzvf//DtGnTKvygfFJSEry9vZWHv7//7Q38uY7ryTIeu1XA9qKwvzB2ux1XrlyB3W4XPZRah+3FYXtx2F4s9hfH2e05GaYqy8jIwNNPP42goCBMmjQJWVlZuHXrlrJ93LhxkCQJfn5+GDRoELZs2QIA+OGHHxAVFYWgoCDMnTu3zOePhw8fDgBo164dNBoNcnNzy33/hIQEmEwm5ZGTkwMAUMk2J3y3dDcq2YbmV4+zvSDsL47NZsOePXtgs7G9q7G9OGwvDtuLxf7iOLs9L36nKiktLcWgQYOwfft2REREKJchlJSUoG7duuW+RpIknD9/HlOmTMH+/fvRqlUrZGZmolu3bg77ubu7K79Wq9UVnhnWarXQarU1900REREREVGtwzPDVCXFxcUoLS1F8+bNAQBLliwps09ycjIAID8/H//+97/Rs2dPmEwmuLm5oUmTJpBlGUuXLnXlsImIiIiIiBzwzDBVmtVqRaNGjTBv3jxERkbCz88PQ4cOLbNfw4YNERYWBpPJhClTpqBLly4AgKFDhyIwMBC+vr4YOHBgjY9PhlTjx6S7kyGhxK0u2wvC/uJIkgRPT09IEtu7GtuLw/bisL1Y7C+Os9tzNWmqlEuXLkGv1yM3Nxf16tWrcL87K04bDAaXja0qK8YREREREdGji6tJU41asGABunfvjvnz5991IiwaV/hzPbvdjnPnzrG9IOwvDtuLw/bisL04bC8W+4vj7Pa8TJru6bXXXsNrr71WqX2zs7OdO5i7WHz4Kup4lb/oFjmHym6F/jcjUq9pYVfxrxNXe9j7P8z3FLbZbDAajWjatClUKv5c2ZXYXhy2F4ftxWJ/cZzdnr+bdFc6nQ56vR4GgwHt27fHsGHDcOPGjbu+JjExETNmzAAAbNy4Ea+++qoLRkpERERERFR5nAzTPaWmpsJoNOLYsWMwmUzKatGVMWDAACxcuNB5gyMiIiIiIqoGToap0kpLS3Hz5k3Ur18fADB//nxERkaiY8eO6Nu3L86dO1fmNcnJycrK0XPnzoXBYIDBYEBQUBAkSVJeU5lj3QtX1HU9GRKuu/uwvSDsL44kSWjYsCFXFhWA7cVhe3HYXiz2F8fZ7TkZpnuKi4uDwWBA48aNoVKp8OKLLyIlJQVZWVnYs2cPDh06hOHDh2Py5Ml3Pc67774Lo9EIo9GILl26YOzYsWjRokWVj1VSUgKz2ezwAABZpa7R75vuTVapkdMokO0FYX9xNBoNunTpAo3m4fus9sOO7cVhe3HYXiz2F8fZ7fk7SveUmpoKg8EAq9WKiRMnYubMmcjJycH+/fsRFhYG4PaH2ytr3rx5OH/+PP773/8CADZs2FClYyUlJeG9994r87wkc4U/V5NkO3xNvyHPuxlkiT9bczX2F8dms+HUqVNo06YN1Gr+MMKV2F4ctheH7cVif3Gc3Z7/e6JK02g0eOGFF/Dtt99ClmUkJCQoZ3qPHDmCI0eO3PMYa9aswfr16/HNN98oP+Gp6rESEhJgMpmUR05ODgBOhkWQZDsamnPYXhD2F8dutyMrK4u32RCA7cVhe3HYXiz2F8fZ7TkZpirZtm0b9Ho9Bg4ciOXLlyM/Px8AYLFYkJGRcdfXbtmyBe+//z42b94MDw8P5fmqHkur1cLLy8vhQUREREREVBW8TJruKS4uDnXr1oXVakWLFi2wfPly+Pv7Iy8vDz169AAAWK1WjB07FqGhoRUe54MPPsDNmzfxzDPPKM+lpaVh+PDhVT4WERERERHR/ZBkWZZFD4LofpjNZnh7eyM/P19Z6Zpcw2azITMzE8HBwfwMjQDsLw7bi8P24rC9OGwvFvuLU532d+YGJpPpnleQcjJMD72q/IEnIiIiIqJHV1XmBvzMMD0yqrKiNdUMm82GjIwMtheE/cVhe3HYXhy2F4ftxWJ/cZzdnp8ZpkfGksxrqOPFv6RcSWW3Qv/befxgbwK7in+duNrD1H9WqJ/oIdQou92O8+fPIygoiJfMuRjbi8P24rC9WOwvjrPb88wwlUun00Gv18NgMECv1+Ojjz6q0eNfvHgR0dHRNXpMIiIiIiKiynqwTyWQUKmpqTAYDLhw4QICAgIQExODyMjIGjl206ZNsXPnzho5FhERERERUVXxzDDd0xNPPIF27drh3Llz6N69OzZs2KBsGzx4MJKTkwEAK1euREBAAAwGAzp06ID09HTY7XZMmTIF7du3R0hICMLCwlBcXIzs7Gz4+Pgoxxk+fDjCw8MRHByM/v37Izc3t8LxlJSUwGw2OzwAQJb4x9nVZEmFq17+bC8I+4ujUqmg1+uhUrG9q7G9OGwvDtuLxf7iOLs9zwzTPZ08eRJ5eXno3r07li1bVuF+r7/+Ok6ePIkmTZrAYrGgpKQEhw8fxtatW3Hs2DGoVCqYTCbUqVOnzGsXLVqEhg0bAgA++ugjJCYmYvny5eW+T1JSEt57770yz3NC4HqypMI1n+aih1Frsb84arUa7dq1Ez2MWontxWF7cdheLPYXx9ntOXugCsXFxaF9+/YICAjA1KlTlclqRXr27ImRI0fik08+wdmzZ+Hh4YGWLVvCarVi7Nix+Oc//wmLxVLuT3ZSUlIQHh6OoKAgrFy5EkajscL3SUhIgMlkUh45OTkAAMnOxbNcTbLb4H/lGNsLwv7iWK1W7N69G1arVfRQah22F4ftxWF7sdhfHGe352SYKpSamooTJ07g+++/x6xZs3DkyBFoNBqHpc2Li4uVX69btw4fffQRLBYL+vXrhy+//BLe3t44evQohg0bhpMnTyI4OBinT592eJ9du3Zh8eLFSEtLw9GjR7FgwQKH4/6RVquFl5eXwwMAJPCW2a4mQYZHcSHbC8L+4siyjKtXr0KW2d7V2F4ctheH7cVif3Gc3Z6TYbqn2NhYvPLKK5g9ezZat26N9PR0AMDZs2exa9cuALd/anPmzBmEh4fjjTfewODBg7Fv3z5cvXoVN27cQO/evfHhhx9Cp9Ph+PHjDscvKCiAp6cnfH19UVpaihUrVrj8eyQiIiIiotqFnxmmSnnnnXfQunVrfP/995g0aRI6dOiAwMBAdOrUCcDtG2KPHTsW+fn50Gg0aNiwIVavXo2cnBy8/PLLsFgssNlseOqpp/DMM8/gwoULyrH79u2LtWvXQq/Xw9fXF7GxsQ7biYiIiIiIapok83w/PeTMZjO8vb1RUFDgsEI1OZ/dbkdOTg78/f25wqIA7C8O24vD9uKwvThsLxb7i1Od9nfmBiaTSfk4ZUU4GaaHXlX+wBMRERER0aOrKnMDXiZNj4yFGVeg9SoVPYxaRbLb8OTlwzj7eAhklVr0cGqdB7n/rFA/0UNwKqvVip9++gndunWDRsN/Sl2J7cVhe3HYXiz2F8fZ7Xmen+6bTqeD0WhEcnIyTp48WanXJCYmYsaMGTU6Dq6o63oSZGgtt9heEPYXR5ZlFBUVcWVRAdheHLYXh+3FYn9xnN2ek2GqMVWZDBMREREREYnEyTDViC1btuDAgQN49dVXYTAYkJaWhiNHjqBr167o2LEjAgICMG/evHJf26FDB+zevVv5+tNPP0VcXFyF71VSUgKz2ezwICIiIiIiqgpOhqlGxMbGIjw8HAsXLoTRaES/fv2g0+mwdetWHDp0CAcPHsS6deuwd+/eMq+dNm0ali5dqny9bNkyTJkypcL3SkpKgre3t/Lw9/cHANilB+szk7WBXVLjfMMAtheE/cVRq9WIioqCWs32rsb24rC9OGwvFvuL4+z2nAyT09y6dQvjx49Hhw4d0LlzZ5w7dw5Go7HMfiNGjMD27dtx+fJl7Nq1C5IkITo6usLjJiQkwGQyKY+cnJzbGyTJSd8JVUiScKNufbYXhf2FUalUaNSoEW+xIQDbi8P24rC9WOwvjrPb83eUnOatt96Cn58fMjIycPjwYXTv3h3FxcVl9qtbty7i4+OxYsUKLFu2DH/+85/velytVgsvLy+HBwCo7FanfB9UMZXdirY5e9leEPYXx2KxYPPmzbBYLKKHUuuwvThsLw7bi8X+4ji7PdcGpxrj5eUFk8mkfF1QUID27dtDo9EgKysLP/zwA7p161bua//85z+jc+fOsFgs+Oyzz1w1ZKoBatkmegi1GvuLY7XyhxCisL04bC8O24vF/uI4sz3PDFONmTBhAj788ENlAa3Zs2dj9erVCA4OxqxZsxATE1Pha5s1a4bQ0FCMGDEC9erVc+GoiYiIiIioNuKZYbpv2dnZAACDwYBnn33WYdvRo0fLfU1iYqLD1zdu3EBGRgYWL15c7XH8OagBfH19q/16qjqLxYK034BXg33h5uYmeji1DvsTERERVZ8k8+7RJNjy5cvxwQcf4JVXXsFbb71V5debzWZ4e3ujsLAQ3t7eThghVeTOjdA9PT0hcREnl2N/cdheHLYXh+3FYXux2F+c6rS/MzcwmUzK2kIV4WSYHnp3/sDP+fEM3D3v/geeapgsQyXbbt/ah/84uN4D0n9WqJ+w9xZFlmVYrVZoNBr+x8jF2F4ctheH7cVif3Gq074qk2F+ZvghI0kSCgsLHZ7T6XTl3rLoXq+7l+zsbPj4+JS77d1338UXX3xx19cnJyfj5MmTVXrP+6HiQkIup5Jt0P+WzvaCsL84VqsVaWlpXFBFALYXh+3FYXux2F8cZ7fnZ4apWubOnXvPfZKTk+Hj44N27dq5YERERERERESVxzPDjxCj0QiDwaA8vLy88N577ynb58+fj9DQULRt29bhrO7+/fsRExOD8PBwhIaG4uuvvy5z7NLSUowYMQITJ06EzWZDfHw8Fi1aBADYtGkTgoODYTAYEBQUhP/85z9YuXIlDhw4gFdffVVZXfrIkSPo2rUrOnbsiICAAMybN085fmJiIuLi4vCnP/0JAQEBiImJQX5+vvNiERERERFRrcYzw48Qg8GgXC69fft2vPzyyxg/fryyXZIkZGRk4Ndff0V4eDieeuop+Pj4YMKECUhLS0OTJk1w7do1dOzYEV26dFFeV1hYiEGDBiE2NrbcBa5mz56NFStWICoqCna7HWazGT4+Pli7di1mzJiBgQMHAgCKioqwdetWaLVa3Lp1C126dEFsbCw6d+4MAEhPT8fBgwfh6+uLoUOHYsWKFUhISCjzfiUlJSgpKVG+NpvNNZGPiIiIiIhqEU6GHxG//0D50aNHMWbMGPz3v//FE088oTx/Z2LcsmVLdOvWDT/99BP8/Pzw66+/4plnnnE4XlZWFlq2bInS0lI89dRTmDlzJkaNGlXue/fs2RPTp0/H4MGD0bt3bxgMhnL3u3XrFiZPngyj0QiVSoWcnBwYjUZlMty3b1/l1khRUVE4cuRIucdJSkpyOON9h11SV1CHnMUuqZHVrBPbC8L+4mg0GvTr1w8aDf8ZdTW2F4ftxWF7sdhfHGe352XSD5mGDRsiLy/P4blr166hUaNGAICLFy9i4MCBWL16NYKCgu56LEmSIMsyAgMDYTQalcf58+cRExMDAHBzc0PXrl2xadMmWCyWco+zYMECrF69GvXq1cPo0aPxt7/9rdz93nrrLfz/2Lvz6KjKw//j75lJSFCSsASqaCCyGCQLk5AAoQQBoyC0SnFBocgqIl8UtSqgFAHR1KKggP2JZROQSlGKC7R+QVBAArJkCMESAQlQhAIhJKjZJjO/PzjcrzEJJMDkEfJ5nTPnZGbu3HnmDaU+uXeeGxoaSlpaGjt37qRLly4UFBRYzwcGBlo/OxyOCr8oP27cOHJzc63b4cOHz/s5xbf83EWmh1Cjqb85+fn5podQY6m9OWpvjtqbpf7m+LK9JsNXmO7duzN79mzr/sKFC2nWrBnXX389Z86coVevXkyaNImuXbuWee38+fOBs6tEb9iwgaSkJDp27MiBAwdYs2aNtZ3L5aKo6Ox/YNtsNmbPnk1YWBi9e/cu9y/jnj17iIyMZNSoUTz66KNs3rwZgODgYHJzc63tcnJyuPHGG/Hz8yMzM5PVq1dfVIOAgACCg4NL3UCrSZtg95bQ/Fia2hui/ua43W7WrVunlUUNUHtz1N4ctTdL/c3xdXtNhq8wr7/+OkePHrUWrFqyZIm14NXy5cvZs2cPU6dOtRbReuutt6zXlpSUEBsbyx133MGMGTMIDw+nXr16rFy5kpdffpk2bdrQunVrxo4di8fjKfW+06ZNIy4ujp49e/L999+Xeu65554jMjKS2NhYFi1axMSJEwEYPnw4L7/8srWA1vjx45k/fz4xMTGMHTvWOvosIiIiIiJS3Wxer9drehAil+LchbVPnjxpfedYqkdxcTGrVq2iZ8+e+Pv7mx5OjaP+5qi9OWpvjtqbo/Zmqb85F9P+3NwgNzfXOoO0IjoyLCKXRItJmKX+5qi9OWpvjtqbo/Zmqb85vmyvI8NyxTv3258X1n9LYJ0g08MRuaqNjQ01PQQRERGRCunIsNRM+r1O9fN6uTY/R+1NUX9jPB4Px48fL7O+gvie2puj9uaovVnqb46v22syLKWEh4cTERGB0+mkdevWvPnmmxfc3uVyXdYx3HvvvSxYsKDKr9OKutXP7i2hyYmv1d4Q9TenpKSE1NRUSkrUvrqpvTlqb47am6X+5vi6vU5+lzKWLl2K0+nk4MGDxMTEkJSURExMjOlhiYiIiIiIXDY6MiwVatq0KREREezZs4c+ffoQHR1NVFRUqesc/9S0adNISEjA6XSSkJBAamqq9Vx4eDgTJkwgMTGRm266iSlTpljP7dmzh44dOxIZGUnv3r3Jy8s777gKCwvJy8srdRMREREREakKTYalQrt27WLPnj0sXbqUiIgIdu3axdq1a5kyZQqbN28us/2AAQPYunUrLpeLmTNnMnjw4FLPnz59mtTUVLZu3crUqVM5cuSI9bqhQ4eye/duXnzxRb744ovzjislJYWQkBDrFhYWBoAX22X65FJZXmwU+tdWe0PU3xybzUZQUBA2m9pXN7U3R+3NUXuz1N8cX7fXatJSSnh4OAEBAdSuXZtrrrmGp556ikceeYTt27cTHh4OwOjRo2nYsCHjx48nPDycFStW4HQ6+d///V9eeuklsrOz8fPzY+fOnfz444/Url2b8PBw3nvvPTp06ABAbGwsM2fOJCYmhgYNGlBQUIDD4QDgtttuY8CAAQwaNKjcMRYWFlJYWGjdz8vLIywsTKtJi1QDrSYtIiIiv2RaTVouydKlS3G5XGzatIl77723zPPl/WamqKiIPn368Oqrr5KRkcH69esBSk1aAwMDrZ8dDgdut7vc97/Qb34CAgIIDg4udQPAqxX+qp3XQ93vj6m9KepvjMfj4eDBg1pZ1AC1N0ftzVF7s9TfHF+312RYLig5OZm//vWvAJw4cYLly5dz++23l9qmoKCAoqIimjRpAsDMmTMrte/g4GBiY2NZuHAhALt372bjxo0XNU67JgTVzu71cP2p/WpviPqbU1JSgsvl0sqiBqi9OWpvjtqbpf7m+Lq9JsNyQTNmzODf//430dHRdO3aleeff5727duX2iY4OJgpU6bQrl072rZtS61atSq9/4ULF/L2228TFRXF+PHj6dy58+X+CCIiIiIiIqXo0kpSSlZWVpnHfvWrX7F8+fILbv/ss8/y7LPPWvefeeaZCve7bds26+dWrVqVWnn6Yv1PVH0aNGhwyfuRyisuLmbVf+DJmAb4+/ubHk6No/4iIiIiF09HhuWqoRX+qp/NZqNhw4Zqb4j6m6P25qi9OWpvjtqbpf7m+Lq9VpOWK965FeO0mrTI5aWVo0VERORKo9Wkr1Lh4eFERETgdDqt265du4CzvzU5ffp0mdcUFxfz+OOPExkZSZs2bWjdujXTpk274HstWLCA3r17X9bx33vvvSxYsOCy7vOnbFpEqNrZvB5CTx9Se0PU35ySkhL27NmjxVQMUHtz1N4ctTdL/c3xdXt9Z/gKs3TpUpxOZ6W3f+ONN/juu+/YuXMnfn5+FBQUsH//ft8N8DJzu934+VXur6kmBNXP5vXQMO8wp4Ib47Xpd2vVTf3N8Xg8ZGZm0rx5c+sa6VI91N4ctTdH7c1Sf3N83V7/9XSV+89//kOjRo2sCWVgYCCRkZEAHDt2jK5du9K2bVsiIyMZNWpUudfwuv3223n//fet+59//jmxsbEALFmyhPbt2xMbG0ubNm34+OOPre327NlDx44diYyMpHfv3uTl5VnPHT9+nD59+hAdHU1UVBSzZ8+2ngsPD2fMmDG0a9eOgQMHlhlPYWEheXl5pW4iIiIiIiJVocnwFaZv376lTpPOz88/7/YPP/wwH3/8MbfccgsPP/ww7733nnWaQd26dfn444/Zvn076enpZGVl8fe//73MPgYPHlzq9Ob58+czZMgQALp3787mzZtJS0vjww8/5OGHH6awsBCAAQMGMHToUHbv3s2LL77IF198Ye3jscceIyIigl27drF27VqmTJnC5s2breezs7PZsmUL7777bpnxpKSkEBISYt3CwsIqH1BERERERARNhq84S5cuxeVyWbfatWufd/vIyEj279/Pm2++SdOmTXnhhRe46667gLOnHYwZM4Y2bdoQGxvLtm3bcLlcZfbxu9/9js2bN3P06FG+//57PvnkE/r16wfAgQMHuPPOO4mKiqJ3796cOnWKAwcOkJeXh8vlYtCgQQBER0fTqVMna59r1qzhkUceAaBRo0b06dOHNWvWWM8PGjSowlXjxo0bR25urnU7fPgwAF60wl9182Lj9LWN1N4Q9TfHbrfTpEkT7Hb932h1U3tz1N4ctTdL/c3xdXt9Z7gGqFWrFt26daNbt24MGzaM66+/nlOnTvGXv/yF48ePs2XLFgIDA3nqqacoKCgo8/ratWtz3333sWjRIho2bEi3bt2s6/k+8MAD/OlPf+Lee+8FoH79+uXuA85/6aOfP1enTp0Ktw0ICCAgIKDM4167vsNR3bx2B0cbtDQ9jBpL/c1xOBzW10Wkeqm9OWpvjtqbpf7m+Lq9fr1xlVu/fj1Hjx617m/fvp369etTt25dcnJyuO666wgMDOTYsWMsW7aswv0MHjyY+fPns2DBAusUaYCcnBxuuukmABYvXkxOTg4AwcHBxMbGsnDhQgB2797Nxo0brdclJyfz17/+FYATJ06wfPlybr/99kv6rDaPVvirbjZPCddn71V7Q9TfnJKSEtLS0rSyqAFqb47am6P2Zqm/Ob5ur8nwFebn3xlet27debc/dOgQvXr1onXr1jidTlJSUvjwww+x2+2MHj2aLVu2EBkZyYABA0hOTq5wP+3atcPhcLBv3z7uuOMO6/E33niDe++9l9jYWNLS0mjSpIn13MKFC3n77beJiopi/PjxdO7c2XpuxowZ/Pvf/yY6OpquXbvy/PPP0759+0soAzZ0yezqZsNL3R+Oq70h6m+Ox+Ph0KFD5S46KL6l9uaovTlqb5b6m+Pr9jpN+gqSlZVV4XNeb/n/Mfz73/+e3//+9+U+16RJE7766qtynxs0aJD1fd9zMjIyLrj/1157zfq5VatWpKamlrv/X/3qVyxfvrzc5873Oc/nf6LqW6dvS/UoLi5m1X/gyZgG+Pv7mx5OjaP+IiIiIhdPR4ZFRERERESkxtGRYblqzNp9moAgnS5anWxeDw2Cw5i2KwevTb9bq26Xu//Y2NDLMKqawW63ExERoZVFDVB7c9TeHLU3S/3N8XV7m7ei82vlqhQeHk5AQECpSzItWrSI6Ohon75vfHw8r776Kl26dLns+87LyyMkJIQX1n9LYJ2gy75/kZpCk2ERERG50p2bG+Tm5hIcHHzebfXrjRro59cq9vVE+GK53e4qba8VdaufzVNC2PHdam+I+pvjdrvZtGlTlf+dkkun9uaovTlqb5b6m+Pr9poMCwCpqal06tSJNm3aEBMTw4cffgicPZLscrms7eLj4/n8888BmDZtGgkJCTidThISEkotlrVp0yacTidRUVEMHjy41F/gffv2kZycTExMDE6nkxUrVljP2Ww2XnjhBRISEhg3blyVPoNW1K1+NrzUKTit9oaovzler5cTJ05UuHih+I7am6P25qi9Wepvjq/b6zvDNVDfvn1LnSa9cuVKevfuzfvvv09SUhIej4fTp09fcD8DBgzgqaeeAmDz5s0MGjSIPXv2UFRURN++fZk/fz7Jycn87//+LwsWLLBe179/f4YMGcIjjzzC3r176dChA7GxsTRt2hQ4e3HtrVu3Vvi+hYWFFBYWWvfz8vKqWEBERERERGo6TYZroKVLl+J0Oq37K1euJCIigqSkJODsF9Xr169/wf2kpaXx0ksvkZ2djZ+fH5mZmeTn57N37178/Pys6xbfcccdNGvWDIAzZ86wY8cOvvzySwBatmxJp06d2LBhgzUZHjJkyHnfNyUlhUmTJlX5c4uIiIiIiJyj06TlvPz8/Cgp+b/vIxYUFABQVFREnz59ePXVV8nIyGD9+vUApY7Y/pTNZqvwPX7+XJ06dc47pnHjxpGbm2vdDh8+DIBHqxlXO4/NztH6zdXeEPU3x+Fw4HQ6cTgcpodS46i9OWpvjtqbpf7m+Lq9/gtK6NixI3v37mXDhg0AeDweTp06BUCLFi3YsmULAF999RWZmZnA2UlxUVERTZo0AWDmzJnW/lq1aoXb7WbdunUArFmzhv379wMQFBREXFwc8+fPB85+f3jjxo107ty50uMNCAggODi41A0ATQiqn83O6TrXqb0p6m+M3W6nadOmusyGAWpvjtqbo/Zmqb85vm6vP9EaqG/fvjidTuvmcrn4xz/+wdixY4mJiSEuLs46jXnKlCm8+eabtGnThnnz5hEZGQlAcHAwU6ZMoV27drRt25ZatWpZ+69VqxZLly7lySefJDo6miVLltCmTRvr+XfffZelS5fSpk0b7r33XubMmWNNqi+FVtStfjZPCc2O7lB7Q9TfHLfbzdq1a7WyqAFqb47am6P2Zqm/Ob5ur+8M1zBZWVkVPnduAvxT8fHx7N69u9ztn332WZ599lnr/jPPPGP93LFjx1KrUP9UixYtWLNmTbnPXcpKcaOi6tGgQYOLfr1UXXFxMatW5fNUTH38/f1ND6fGUX9zvF4vZ86c0cqiBqi9OWpvjtqbpf7m+Lq9jgyLiIiIiIhIjaMjw3LVeDPjFLWC9Ru76mT3uIkApqdn47Hrn5Pqdin9x8aG+mZQIiIiIlcIHRmWq4bHphX+qpvH5uBQw9Zqb4j6m+NwOEhMTNTKogaovTlqb47am6X+5vi6vSbDV7jw8HBr9eZz4uPj+fzzz5kwYQLvvvuuwdFVs/Ncvkl8xGbjh9r11N4U9TfGbrfTqFEjrSxqgNqbo/bmqL1Z6m+Or9vrT/QqUFhYyNy5c8s8PnnyZPr3739Z3sPj8eDxeC7LvnzF7tEKf9XN7nFz8+HNam+I+ptTXFzMypUrKS4uNj2UGkftzVF7c9TeLPU3x9ftNRm+CkycOJEXX3yRH3/8sdTjgwYN4vXXXwegqKiIZ555hqioKNq0aUOPHj0A2LVrF506dSIuLo7WrVszZcqUUvu955576N69O1FRURw9epSnn36ahIQEnE4nnTt3tq47DPDhhx9yyy230KZNG8aMGUNoaKi1enV4eHip1aXPHb0GmDZtmrXPhIQEUlNTz/t5CwsLycvLK3UTcxxeXdbHJPU3R5fYMEftzVF7c9TeLPU3x5ftNRm+CrRp04auXbsyffr0CrdJSUnhm2++Yfv27ezcuZNFixYBZyepn332GTt27GD79u188MEHbN682XpdamoqCxcu5Ouvv+aGG25gzJgxbN26FZfLxciRIxk9ejQAx48fZ8iQIfzjH/9g586dtGrViuzs7EqNf8CAAdY+Z86cyeDBg8+7fUpKCiEhIdYtLCysUu8jIiIiIiJyjibDV4kXX3yRN954o8IJ6CeffMLo0aMJCAgAoGHDhgDk5+czbNgwoqOj6dChAwcPHix1BLdnz5786le/su6vXr2axMREoqKimDx5srXt5s2biYmJoVWrVgAMHDiQWrVqVWrsaWlp3HrrrURFRTFixAgyMzPJz8+vcPtx48aRm5tr3Q4fPlyp9xERERERETlH10K5SoSHh9OvX79SpzlXxnPPPUdoaChpaWn4+fnRp08fCgoKrOfr1Klj/Xzo0CFGjRrF1q1bad68Oenp6XTu3LlS7+Pn50dJyf+dznnuPYqKiujTpw/r1q0jISGBvLw8QkJCKCwspHbt2uXuKyAgwJrU/5RW1K1+HpuD/dfFqr0h6m+On58fXbt2xc9P/zda3dTeHLU3R+3NUn9zfN1eR4avIuPHj2fx4sV89913ZZ676667eOONNygsLATgxIkTAOTk5HDjjTfi5+dHZmYmq1evrnD/ubm5+Pv7c/311+P1epk1a5b1XIcOHUhPT7e+Q7x48WKKioqs51u0aMGWLVsA+Oqrr6ztCgoKKCoqokmTJgDMnDnzUhKIAW6/yp0BIL6h/uZU9As78T21N0ftzVF7s9TfHF+21683riKhoaE8/vjjTJgwocxzY8aM4fnnnycuLg5/f38aN27MqlWrGD9+PAMGDOCdd96hefPmdOvWrcL9R0dH88ADDxAZGUmDBg3o3bu39VyjRo2YM2cOvXv3JiAggNtvv506depQt25dAKZMmcLAgQOZPXs2iYmJREZGAhAcHMyUKVNo164doaGhPPDAAxf9+R+LDKFBgwYX/XqpuuLiYlatWkXPnj3x9/c3PZwaR/3Ncbvdam+I2puj9uaovVnqb46v29u8Xq/3su9VaqQzZ84QFBQEwIoVKxg3bhz//ve/ff6+506tPnnypCbD1UyTMbPU3xy1N0ftzVF7c9TeLPU352Lan5sb5ObmEhwcfN5tdWRYLpuZM2eydOlSSkpKCA4O5t133zU9JBERERERkXJpMiyXzXPPPcdzzz1n7P3fzDhFrWCd6FCd7B43EcD09Gw8dv1zUt0q239sbGj1DUpERETkCqEFtGqA8PBwGjVqRHFxsfXYunXrsNlsPPHEE+d97cSJEy+4zcX47rvvSEpKuqz71Iq61c9jc5B5Y3u1N0T9zfHz86Nnz55aWdQAtTdH7c1Re7PU3xxft9dkuIZo0qQJH330kXV/7ty5xMfHGxtP48aN2bBhg7H3l8vHz1104Y3EZ9TfnPNdD118S+3NUXtz1N4s9TfHl+01Ga4hBg8ezLx584Czl0javHkzPXr0AGDXrl106tSJuLg4WrduXeG1iivarqCggOuuu47Dhw9b2z733HOMGTMGj8fDqFGjuOWWW2jTpg1t27aloKCArKwsa6VpgP79+xMfH09MTAy9evXi2LFjFX6WwsJC8vLySt0A7N6SCl8jvmH3ltD8WJraG6L+5rjdbtatW4fb7TY9lBpH7c1Re3PU3iz1N8fX7TUZriF+/etfk5WVxXfffcff/vY37rvvPhyOs6dWhoeH89lnn7Fjxw62b9/OBx98wObNm8vso6LtAgMDGTp0KP/v//0/4Oxkdf78+YwcOZKdO3fy2WefsXv3bnbu3MnatWupVavsdVFff/11tm3bRnp6OklJSUycOLHCz5KSkkJISIh1CwsLuzyRRERERESkxtBkuAYZMGAACxYsYN68eQwZMsR6PD8/n2HDhhEdHU2HDh04ePAgLperzOvPt93IkSN55513KCwsZNmyZbRr146mTZvSrFkz3G43Q4YM4Z133qG4uBi7vexfuyVLlhAfH09UVBRz5swp9/3PGTduHLm5udbtp0ekRUREREREKkOT4RrkoYceYsaMGQQGBtKyZUvr8eeee47Q0FDS0tLYuXMnXbp0oaCgoMzrz7fdDTfcQOfOnVm6dClvvvkmo0aNAiAkJISMjAz69evHnj17iImJYd++faX2u3HjRmbMmMGqVavIyMhg2rRp5b7/OQEBAQQHB5e6iTklWrzJKPU3RwupmKP25qi9OWpvlvqb48v2mgzXII0bNyYlJYVXXnml1OM5OTnceOON+Pn5kZmZyerVq8t9/YW2Gz16NM8//zynT58mOTkZgBMnTvDDDz9wxx138PLLLxMeHs7XX39dZr9BQUE0aNCAoqIiZs+efVGfT5f2qX4eux/fhHVQe0PU3xx/f3969eqFv7+/6aHUOGpvjtqbo/Zmqb85vm6vyXANM3jwYBITE0s9Nn78eObPn09MTAxjx46lW7du5b72Qtt16NCBkJAQRo4cic1mA+Dw4cPcfvvtxMTEEBUVRVRUFHfeeWep1/Xo0YOIiAgiIiJISkrC6XRe3Ifz6hrD1c7r5dr8HLU3Rf2N8Xg8HD9+HI/HY3ooNY7am6P25qi9Wepvjq/b27xe/VeUXB5HjhwhPj6eb775hqCgoGp737y8PEJCQjh58iQNGjSotvcVKC4uZtWqVfTs2VO/LTVA/c1Re3PU3hy1N0ftzVJ/cy6m/bm5QW5u7gW/Tqkjw3JZTJgwgfbt2/OnP/2pWifCIiIiIiIiF0NfNJPLYvLkyUyePNnoGN7MOEWtYJ3oUJ3sHjcRwPT0bH1v1YDy+o+NDTU7KBEREZErhI4M11Dh4eFERETgdDq55ZZb6NevHz/88MN5XzNx4kSeeOKJy/L+PXv2JDMz87Ls6xwvtsu6P7kwLzYK/WurvSHqb47NZiMoKMhaH0Gqj9qbo/bmqL1Z6m+Or9vrO8M1VHh4OCtWrMDpdOLxePjtb39Lz549+Z//+Z8KXzNx4kROnz7N66+/fsH9l5SU4HBUzyVfzn0v4IX13xJYR6doS82mI8MiIiJSk+k7w1IlRUVF/Pjjj9SrV49du3bRqVMn4uLiaN26NVOmTCm17dGjR/ntb39L69at6datG6dOnQJgwYIFdO3alXvuuYfo6Gi++uorpk2bRkJCAk6nk4SEBFJTU639hIeH43K5AOjSpQtPP/00SUlJNG/enBEjRlzcB/Fqhb9q5/VQ9/tjam+K+hvj8Xg4ePCgVhY1QO3NUXtz1N4s9TfH1+01Ga7B+vbti9Pp5LrrrsNut3P//fcTHh7OZ599xo4dO9i+fTsffPABmzdvtl6zZcsWFixYwNdff02jRo1KXRN4y5YtvPzyy+zatYvExEQGDBjA1q1bcblczJw5k8GDB1c4lv3797Nu3ToyMjL49NNPS02cf66wsJC8vLxSNwC7JgTVzu71cP2p/WpviPqbU1JSgsvloqSkxPRQahy1N0ftzVF7s9TfHF+312S4Blu6dCkul4uTJ08SHh7OmDFjyM/PZ9iwYURHR9OhQwcOHjxoHcGFs9cEPnf5osTERPbv328917FjRyIiIqz7aWlp3HrrrURFRTFixAgyMzPJz88vdyx9+/bFz8+P2rVr43Q6S+3351JSUggJCbFuYWFhl1hCRERERERqGi3/Kvj5+XHPPffwzDPPkJubS2hoKGlpafj5+dGnTx8KCgqsbQMDA62fHQ4Hbrfbul+nTh3r56KiIvr06cO6detISEiwzt0vLCykdu3aZcZwvv3+3Lhx43jqqaes+3l5eZoQi4iIiIhIlejIsACwdu1aIiIiyMnJ4cYbb8TPz4/MzExWr159UfsrKCigqKiIJk2aADBz5szLNtaAgACCg4NL3UCrSZvgxcb3gXXV3hD1N8dms9GwYUOtLGqA2puj9uaovVnqb46v2+vIcA3Wt29fateujdvtpmnTprz11lucPHmSAQMG8M4779C8eXO6det2UfsODg5mypQptGvXjtDQUB544IHLPPqyvPbqWb1a/o/X7uBwo0jTw6ix1N8cPz8/OnbsaHoYNZLam6P25qi9Wepvjq/b69JKcsU7dwr2qVOnqFevnunh1CglJSXs3buXli1bVtultOT/qL85am+O2puj9uaovVnqb87FtNellaRG0nL31c/j8ZCZman2hqi/OWpvjtqbo/bmqL1Z6m+Or9vrNGm5aryZcYpawTrRoTrZPW4igOnp2Xjs+uekup3rLyIiIiJVpyPDNcjy5ctp27YtTqeTVq1a0a1bNzweD+Hh4aUun3Q5TZw4kSeeeKLc55xOJ2fOnPHJ+4qIiIiIiJyPDuXUEEePHmX48OFs376dpk2bArBjxw6jq+Jd7gm4VtStfl5snL62kdob4sVGkyZNsNv1e83qZrfb1d4QtTdH7c1Re7PU3xxft9efaA3x3//+F4fDQf369a3H4uLiykyG9+3bR3JyMjExMTidTlasWAHASy+9xKhRo6ztvv/+e+rXr8+JEyfYtWsXnTp1Ii4ujtatWzNlypRyx/D1118TFRXFP//5T+DsUumnT58G4OmnnyYhIQGn00nnzp3JzMys8mfUatLVz2t3cLRBS7U3xGt3EBsbq8U8DHA41N4UtTdH7c1Re7PU3xxft9dkuIaIiYmhU6dONG3alN/97ndMnTqVI0eOlNmuf//+3HfffaSnp7Ns2TKGDh3KwYMHeeihh/j73/9OYWEhAMuWLaNr1640bNiQ8PBwPvvsM3bs2MH27dv54IMP2Lx5c6n9fv7559x7770sXLiQO++8s8z7jhkzhq1bt+JyuRg5ciSjR4+u8LMUFhaSl5dX6gZg85RcSiK5CDZPCddn71V7Q2yeEtLS0igpUf/qVlKi9qaovTlqb47am6X+5vi6vSbDNYTdbueDDz5g06ZN9OjRgy+//JLIyEj27dtnbXPmzBl27NjB0KFDAWjZsiWdOnViw4YNhIWFERsby0cffQTAggULGDx4MAD5+fkMGzaM6OhoOnTowMGDB0udAr127VpGjBjBv/71L+Li4sod3+rVq0lMTCQqKorJkyef9xTqlJQUQkJCrFtYWBgANrR4VnWz4aXuD8fV3hAbXg4dOqTVLQ3weDxqb4jam6P25qi9Wepvjq/bazJcw7Rq1YpHHnmEFStW0KFDB2tyW5GfnkY9ZMgQ5s+fz7fffsu+ffvo0aMHAM899xyhoaGkpaWxc+dOunTpQkFBgfW6Fi1aYLfbyxwtPufQoUOMGjWKxYsXk5GRwXvvvVfq9T83btw4cnNzrdvhw4erkkBEREREREST4ZriyJEjfPnll9b9nJwcDhw4QPPmza3HgoKCiIuLY/78+cDZ7w9v3LiRzp07A9C7d2+2bt1KSkoKv//97/Hz87P2deONN+Ln50dmZiarV68u9d5NmjThs88+Y8qUKda+fyo3Nxd/f3+uv/56vF4vs2bNOu9nCQgIIDg4uNRNRERERESkKrSadA3hdruZPHkyBw4c4JprrsHtdjNw4EDuvvvuUt/PfffddxkxYgSzZs3CZrMxZ84cmjRpApydhN5///385S9/4d///rf1mvHjxzNgwADeeecdmjdvTrdu3cq8//XXX8/atWvp0aMHZ86c4fHHH7eei46O5oEHHiAyMpIGDRrQu3fvi/qMXpt+t1PdvDY7J4LD1N4Qr81ORESEVrc0wG5Xe1PU3hy1N0ftzVJ/c3zd3ub1evVlP7mi5eXlERISQm5uro4Si4iIiIjUYFWZG+jXG3LVcLvdpodQ47jdbjZt2qT2hqi/OWpvjtqbo/bmqL1Z6m+Or9trMixXDZ3kUP28Xi8nTpxQe0PU3xy1N0ftzVF7c9TeLPU3x9ft9Z1huWq8mXGKWsH6R6o62T1uIoDp6dl47PrnxNfGxoaaHoKIiIjIVUNHhqWU8PBw6xq/BQUF3H333dx3330UFRX55P2+++47kpKSrPsTJ04872WVRERERERELgdNhqVcZ86c4c477+RXv/oVS5cupVatWj55n8aNG7Nhwwbr/qRJky56MuzRisbVzmOzc7R+c7U3xOFw4HQ6cTgcpodS46i9OWpvjtqbo/Zmqb85vm6v/4KVMrKzs+nWrRvt27fn7bffZv369TidTuv5jIwMwsPDAXjuued4+eWXAVi1ahU2m41vvvkGgCFDhrBw4UIA+vfvT3x8PDExMfTq1Ytjx44BkJWVRd26dQEYMWIEAElJSTidTo4fP17u+AoLC8nLyyt1A0ATsupns3O6znVqb4jdbqdp06a61IMBam+O2puj9uaovVnqb46v2+tPVMro27cvycnJ/OlPf7rgtsnJyaxZswaA1atXk5iYaN1fs2YNycnJALz++uts27aN9PR0kpKSmDhxYpl9vfXWWwBs2LABl8tFo0aNyn3PlJQUQkJCrFtYWBgANk9JlT+rXBqbp4RmR3eovSFut5u1a9dqdUsD1N4ctTdH7c1Re7PU3xxft9dkWMro1asX77//PocPH77gtr/+9a9xuVzk5+fzxRdfMGXKFNasWUNmZibXXnstjRs3BmDJkiXEx8cTFRXFnDlzrO8lX4xx48aRm5tr3c6N04YWz6puNrwEFOervSFer5czZ85odUsD1N4ctTdH7c1Re7PU3xxft9dkWMp48sknefTRR+nSpQuHDh3Cz8+PkpL/O/L30+/0BgQEEB8fz7Jly6hduzZdunQhPT2dTz/9lNtuuw2AjRs3MmPGDFatWkVGRgbTpk27pEWyAgICCA4OLnUTERERERGpCk2GpVxPPfUUjz32GF26dCEsLIyDBw9y4sQJABYtWlRq2+TkZCZMmMBtt92G3W4nNjaWadOmWadI5+TkEBQURIMGDSgqKmL27NkVvm9QUBC5ubm++2AiIiIiIiJoMizn8cQTT/DEE09w66238swzz9CuXTs6dOhA/fr1S22XnJzMwYMHrcnv7bffzuHDh+nSpQsAPXr0ICIigoiICGtxrIr84Q9/4Pbbbz/vAloV8di0wl9189gcHGrYWu0NcTgcJCYmanVLA9TeHLU3R+3NUXuz1N8cX7e3eXXyu1zh8vLyCAkJITc3V6dMi4iIiIjUYFWZG+jIsFw1iouLTQ+hxikuLmblypVqb4j6m6P25qi9OWpvjtqbpf7m+Lq9n0/2KmLAmxmnqBWsEx2qk93jJsLtZnp6Nh67/jm5nMbGhlZqO13mwRy1N0ftzVF7c9TeLPU3x5fta8SRYafTidPppHXr1jgcDut+3759S223bdu2Mo+db59nzpwp97n4+Hg+//zzKo/zYl9XWRkZGYSHh5d5fMKECVaTOnXqcNNNN1n3MzMzy92XzWbj9OnTPhuriIiIiIiIL9WIQznnrmmblZWF0+ks9xq3breb+Ph4li5dWqV9Xg0mT57M5MmTAejSpQtPPPEEvXv3NjsoERERERERH6oRR4YrEh4ezpgxY2jXrh0DBw7k888/t1Y6zsrKom7durzwwgu0bduWFi1asGrVKuu1Pz0yumnTJpxOJ1FRUQwePLjUofxp06aRkJCA0+kkISGB1NRU67nzve6njh07RteuXWnbti2RkZGMGjUKj8cDwIIFC0hOTubBBx8kOjqa+Ph4vv32W+u1EydOpGXLlrRt25b33nuvSn327dtHcnIyMTExOJ1OVqxYUer5V199ldjYWG6++Wbeffdd6/H+/fsTHx9PTEwMvXr14tixY9Zzs2fP5uabbyYuLo4XX3wRm81mPffpp58SFxdHTEwMt956K19//XW54yosLCQvL6/UDbSatAkem4P918WqvSF+fn507doVP78a8XvNXxS1N0ftzVF7c9TeLPU3x9fta/RkGCA7O5stW7aUmsydk5ubS0xMDNu3b2fWrFk8+eSTZbYpKiqib9++vPrqq2RkZPDggw+yc+dO6/kBAwawdetWXC4XM2fOZPDgwZV63U/VrVuXjz/+mO3bt5Oenk5WVhZ///vfree3bt3Kyy+/zK5du0hOTuaVV14BYOXKlSxbtozt27ezbds2srKyqtSmf//+3HfffaSnp7Ns2TKGDh3KwYMHredtNhtpaWn861//4rHHHrP2//rrr7Nt2zbS09NJSkpi4sSJwNnTtCdOnMj69evZsWNHqcn/8ePH6devH++88w7p6ekMHz6ce++9l/IWO09JSSEkJMS6hYWFVelzyeXl9qtlegg1Wu3atU0PocZSe3PU3hy1N0ftzVJ/c3zZvsZPhgcNGlTq6ORPBQYG0qdPHwASExPZv39/mW327NmDn5+fdY3dO+64g2bNmlnPp6WlceuttxIVFcWIESPIzMwkPz//gq/7KY/Hw5gxY2jTpg2xsbFs27at1GnaiYmJ3HTTTWXG+dlnn3H//fcTHByMzWbjkUceqXSXM2fOsGPHDoYOHQpAy5Yt6dSpExs2bLC2GTZsGADNmjWjc+fOrF+/HoAlS5YQHx9PVFQUc+bMsca6du1aevTowXXXXQfAww8/bO1ry5YtREdHEx0dDZydiH/33XccOXKkzNjGjRtHbm6udTt8+DAAdm9JpT+fXB52bwkR/9mi9oa43W5WrVqlRT0MUHtz1N4ctTdH7c1Sf3N83b7GH+uvU6dOhc8FBARYE2WHw0FJSeX+g//ca4qKiujTpw/r1q0jISHBuuZVYWHheV/3c9OmTeP48eNs2bKFwMBAnnrqKQoKCqznAwMDrZ8dDkeFf1kq2n9lXej1NpuNjRs3MmPGDFJTU2nUqBEfffQREyZMuKzjCQgIICAg4KJeKyIiIiIiAjoyfMlatWqF2+1m3bp1AKxZs8Y6MltQUEBRURFNmjQBYObMmZV63c/l5ORw3XXXERgYyLFjx1i2bFmlxpacnMyyZcs4c+YMXq+Xt99+u9KfKygoiLi4OObPnw+c/f7wxo0b6dy5s7XNueeysrLYsGEDSUlJ5OTkEBQURIMGDSgqKmL27NnW9l27duXTTz/l+PHjAMydO9d6rkOHDuzatYuMjAwA3nvvPW644QZuuOGGSo9ZRERERESksmr8keFLVatWLZYuXcrIkSMpKSkhISGBNm3aABAcHMyUKVNo164doaGhPPDAA5V63c+NHj2ae++9l8jISBo3bmydWn0hPXv25KuvviIuLo7g4GDuvPPOKn22d999lxEjRjBr1ixsNhtz5syxJvYAJSUlxMbG8sMPPzBjxgzCw8O54YYbWLx4MRERETRo0IDk5GTrVOfo6GjGjx/Pr3/9a4KCgujRowchISEANGzYkHfffZeHHnoIt9tNvXr1WLZs2SUfzRYRERERESmPzVveCkUiPnLmzBmCgoIAeOONN/jXv/7FP//5z0va57nTz0+fPm1NrqV6eL1e3G43fn5++sWFAepvjtqbo/bmqL05am+W+ptzMe3PzQ1yc3MJDg4+77Y6TVqq1dixY3E6nURGRvLJJ5/w5ptvmh6SXKL8/HzTQ6jR1N8ctTdH7c1Re3PU3iz1N8eX7XWatFQrX05+33Adp1Zwsc/2L2XZPW4i/rOFzBvb47Hrn5OLNTY29KJed27dgZ49e+Lv73+ZRyXno/bmqL05am+O2pul/ub4ur2ODP9CLF++nLZt2+J0OmnVqhXdunXD4/Fc1veIj4/n888/B85eFunc4l3l+fbbb7Hb7bz44ouX9J42m43Tp0+X+5zT6eTMmTOXtH8REREREZGLoUM5vwBHjx5l+PDhbN++naZNmwKwY8cOn34nYc6cOed9ft68eXTr1o358+czfvz4csdybrJut1/c71R+eq1kERERERGR6qQjw78A//3vf3E4HNSvX996LC4uzpqAPv300yQkJOB0OuncuTOZmZnWdj8/8hoaGkpWVhYAmzZtwul0EhUVxeDBg0tdf7hLly6sWLGi3PGUlJSwYMECZsyYQVBQEGvXrrWemzhxIvfccw/du3cnKiqKo0ePnnd8AK+++iqxsbHcfPPNvPvuu+WO/UL7kF+uEpvD9BBqND8//U7TFLU3R+3NUXtz1N4s9TfHl+01Gf4FiImJoVOnTjRt2pTf/e53TJ061bocEcCYMWPYunUrLpeLkSNHMnr06Avus6ioiL59+/Lqq6+SkZHBgw8+yM6dOys1nk8//ZQbb7yR1q1bM3To0FLXAwZITU1l4cKFfP3119xwww0XHJ/NZiMtLY1//etfPPbYY9Zk/aeq8hkLCwvJy8srdQP0nVUDPHY/vgnroPaG+Pv706tXL31/yQC1N0ftzVF7c9TeLPU3x9ftNRn+BbDb7XzwwQds2rSJHj168OWXXxIZGcm+ffsAWL16NYmJiURFRTF58uRKnV68Z88e/Pz8rGsS33HHHTRr1qxS45k7dy5DhgwBoH///qxatYqcnBzr+Z49e/KrX/3Kun+h8Q0bNgyAZs2a0blzZ9avX1/mPavyGVNSUggJCbFuYWFhZ5/QVcKqn9fLtfk5am+Ix+Ph+PHjl319AbkwtTdH7c1Re3PU3iz1N8fX7TUZ/gVp1aoVjzzyCCtWrKBDhw589NFHHDp0iFGjRrF48WIyMjJ47733KCgosF7jcDgoKSmx7v/0uZ+rzHeQT5w4wcqVK3nxxRcJDw+nbdu2FBcXlzq9uU6dOtbPFxpfZcZR1X2MGzeO3Nxc63b48GEA7N6SCl8jvmH3ltDkxNdqb0hJSQmpqaml/g2Q6qH25qi9OWpvjtqbpf7m+Lq9JsO/AEeOHOHLL7+07ufk5HDgwAGaN29Obm4u/v7+XH/99Xi9XmbNmlXqtS1atGDLli3A2RWpf/jhB+DsxPrcUuQAa9asYf/+/Rccy8KFC+nduzeHDx8mKyuLrKws3n///TKnSp9zofEBzJ8/H4CsrCw2bNhAUlJSlffxUwEBAQQHB5e6iYiIiIiIVIW+6PcL4Ha7mTx5MgcOHOCaa67B7XYzcOBA7r77bgAeeOABIiMjadCgAb179y712unTp/P4448zfvx4evXqRYMGDQCoVasWS5cuZeTIkZSUlJCQkECbNm0uOJa5c+fyyiuvlHrs9ttvZ9CgQezYsaPM9tHR0ecdH5z9jU5sbCw//PADM2bMIDw8vMr7EBERERERuZxsXq++7CdXtry8PEJCQsjOzi61Irf4ntvtZv369XTu3FmrLBqg/uaovTlqb47am6P2Zqm/ORfT/tzcIDc394JnkGoyLFe8qvyFFxERERGRq1dV5gb6zrBcNbTCX/XzeDwcPHhQ7Q1Rf3PU3hy1N0ftzVF7s9TfHF+313F+uWrM2HmCWsFu08OoUeweNxH/cbH0ZICuNVxJY2NDL9u+SkpKcLlcNG7cGLtdv9usTmpvjtqbo/bmqL1Z6m+Or9vrT/Mq5Xa7mTRpEq1atSIqKgqn08nw4cM5ffp0ha+ZOHEiTzzxxGUdR5cuXVixYkW5zw0bNsxa7VpERERERKQ66VDOVWro0KGcOnWK1NRU6tWrh9fr5f333+fUqVPUrVvX9PAAmDNnjukhiIiIiIhIDaUjw1ehffv2sWzZMubPn0+9evUAsNls3HfffTRr1oypU6cSGRlJdHQ0/fv3Jzc3t8w+SkpKeOaZZ4iKiiIqKorHHnuMoqIiAAYNGsTw4cNJTk7mpptuYsiQIXz11Vd06dKFZs2a8dRTT5Xa12effUZCQgItWrTgD3/4A+fWbPvpUeMlS5bQvn17YmNjadOmDR9//HGFn6+wsJC8vLxSNwAvtktuJ1Xjxcb3gXXV3hCbzUbDhg2x2dS/uqm9OWpvjtqbo/Zmqb85vm6vyfBVaMeOHbRs2ZLQ0LLfTfznP//JvHnz+PLLL9m1axfXXnstY8eOLbPd22+/zdatW9m+fTsul4v9+/czffp06/ldu3bxySefkJmZyfr160lJSWH16tXs2rWLd999l927d1vbfv3112zatIn09HS++OIL/va3v5V5v+7du7N582bS0tL48MMPefjhhyksLCz386WkpBASEmLdwsLCAPDaHVVuJZfGa3dwuFGk2hvi5+dHx44ddZkHA9TeHLU3R+3NUXuz1N8cX7fXZLiGWbNmDX379rVOlX700UdZvXp1udsNGjSIgIAA/Pz8ePjhh0ttd/fddxMYGEitWrWIjo6me/fu+Pv7c+2119K6dWv27t1rbfvQQw/h7+/PNddcw+9//3vWrFlT5v0OHDjAnXfeSVRUFL179+bUqVMcOHCg3M8wbtw4cnNzrdvhw4cBsHm1wl91s3k9hJ4+pPaGlJSUsGfPHkpKSkwPpcZRe3PU3hy1N0ftzVJ/c3zdXpPhq1BcXBx79+4lOzv7gttW9pSDn28XGBho/exwOMrcd7srXtW5vPd84IEHGDZsGBkZGbhcLurUqUNBQUG5rw8ICCA4OLjUDTQZNsHm9dAw77DaG+LxeMjMzNSlHgxQe3PU3hy1N0ftzVJ/c3zdXpPhq1CLFi245557GDp0qLV6tNfr5YMPPqBZs2b8/e9/t75nO3v2bO64444y+0hOTmbhwoUUFRXhdruZM2dOudtVxuLFiykuLiY/P58lS5aQnJxcZpucnBxuuukma/ucnJyLei8REREREZHK0InvV6l58+YxZcoU2rdvj5+fHx6Ph86dO/PKK6/w448/kpiYiN1uJyYmhr/85S9lXj98+HD2799PXFwccHaxq4u97NItt9zCr3/9a06dOsXdd9/NAw88UGabN954g3vvvZe6devSrVs3mjRpclHvJSIiIiIiUhk277mlfUWuUHl5eYSEhHDq1Clr9WypHiUlJaSnpxMTE4PDoUW0qpv6m6P25qi9OWpvjtqbpf7mXEz7c3OD3Nxc6+uUFdFkWK54VfkLLyIiIiIiV6+qzA10mrRcNaanHScguMj0MGoUm6eE63K+5Vi9Zrq8UgXGxpa9xNnlot9Um6P25qi9OWpvjtqbpf7m+Lq9FtCSSikqKmLMmDG0aNGCW265haioKObOnXvB17lcLt57771qGCHY0EkO1c2Gl7o/HFd7QzweD4cOHdLqlgaovTlqb47am6P2Zqm/Ob5uryPDUimDBg2isLCQnTt3cu2115KVlcWdd95JUVERjz76aIWvc7lcrFixotxFsy7E7Xbr4uYiIiIiIuITOjIsF7R3715WrFjB22+/zbXXXgtAeHg4r732Gi+++CLHjh2ja9eutG3blsjISEaNGoXH4+H48eNMmDCBdevW4XQ6GTFiBAD9+/cnPj6emJgYevXqxbFjxwDIysqibt26jBkzhri4OGbNmlXueAoLC8nLyyt1ExERERERqQpNhuWC0tLSaNmyJQ0aNCj1eGJiIkePHqWwsJCPP/6Y7du3k56eTlZWFn//+99p1KgRkydPpmvXrrhcLt566y0AXn/9dbZt20Z6ejpJSUlMnDjR2mdubi6RkZHs2LGjwks5paSkEBISYt3CwsIA8Nr017m6eW12TgSHqb0hdrudiIgI7Hb1r25qb47am6P25qi9Wepvjq/b6xxUuWT16tVjzJgxbNy4Ea/Xy/Hjx4mKiqrw1OglS5awaNEiCgoKKCgoIDT0/xYY8vf35/e///1532/cuHE89dRT1v28vDzCwjQhM8Frs3Oyrq4JbYrD4aBVq1amh1Ejqb05am+O2puj9mapvzm+bq/Zg1xQbGwse/fuJTs7u9TjqampREZGMmPGDI4fP86WLVtIT0+nX79+FBQUlLuvjRs3MmPGDFatWkVGRgbTpk0rte0111xzwd/8BAQEEBwcXOoGZ1c2lupl85QQdny32hvidrvZtGkTbrfb9FBqHLU3R+3NUXtz1N4s9TfH1+01GZYLatmyJb/97W8ZPnw4P/74I3D2+71jxozhtddeIycnh+uuu47AwECOHTvGsmXLrNcGBweTm5tr3c/JySEoKIgGDRpQVFTE7NmzL9s4taJx9bPhpU7BabU3xOv1cuLECXS5+Oqn9uaovTlqb47am6X+5vi6vSbDUikLFy6kefPmREdH07JlS1q0aMHUqVPp3r07o0ePZsuWLURGRjJgwACSk5Ot1912220UFhYSExPDiBEj6NGjBxEREURERJCUlITT6TT3oUREREREpMbSd4alUgICAvjzn//Mn//8ZzweD2PHjmXcuHG0a9eOJk2a8NVXX5X7upCQEDZt2lTqsaVLl5a6/9JLLwFnV6g+ffr0RY/xf6Lql1nkS3yruLiYVf+BJ2Ma4O/vb3o4IiIiIiKVpsmwVJndbufPf/6z6WGU4XA4TA+hxnE4HDidTrU3RP3NUXtz1N4ctTdH7c1Sf3N83d7m1cnvcoXLy8sjJCSEF9Z/S2CdINPDkRpsbGzohTcSEREREZ85NzfIzc21FtqtiL4zLJW2fPly2rZti9PppFWrVnTr1g2Px3PR+3M6nZw5c+ayjU8rGlc/m6eEZkd3qL0hbrebtWvXanVLA9TeHLU3R+3NUXuz1N8cX7fXadJSKUePHmX48OFs376dpk2bArBjxw5sNttF79Plcl2m0Z2lFY2rnw0vAcX52PCqvgFer5czZ85odUsD1N4ctTdH7c1Re7PU3xxft9eRYamU//73vzgcDurXr289FhcXh81mIzw8nGeeeYa2bdtaq0yf8/TTT5OQkIDT6aRz585kZmZaz9lsNmvBrPDwcCZMmEBiYiI33XQTU6ZMqbbPJiIiIiIiNY+ODEulxMTE0KlTJ5o2bcqtt95Kx44d6devHzfccANwdrK8bds2srOziYuL49e//jUdO3ZkzJgxvPrqqwC89957jB49mn/961/lvsfp06dJTU3l5MmTNG/enMGDB1v7/6nCwkIKCwut+3l5eT74xCIiIiIicjXTkWGpFLvdzgcffMCmTZvo0aMHX375JZGRkezbtw+AoUOHYrPZCA0NpU+fPqxZswaA1atXk5iYSFRUFJMnTz7vqdH9+vUDIDQ0lGbNmnHgwIFyt0tJSSEkJMS6hYWFAeCxaYW/6uaxOTjUsLXaG+JwOEhMTNTqlgaovTlqb47am6P2Zqm/Ob5ur8mwVEmrVq145JFHWLFiBR06dOCjjz4qdzubzcahQ4cYNWoUixcvJiMjg/fee4+CgoIK9x0YGGj97HA4Kvyi/Lhx48jNzbVuhw8fPvemF//B5OLYbPxQu57aG2K322nUqBF2u/4pr25qb47am6P25qi9Wepvjq/b609UKuXIkSN8+eWX1v2cnBwOHDhA8+bNAViwYAEAp06d4h//+Ae33XYbubm5+Pv7c/311+P1epk1a9ZlGUtAQADBwcGlbgB2j1b4q252j5ubD29We0OKi4tZuXIlxcXFpodS46i9OWpvjtqbo/Zmqb85vm6v7wxLpbjdbiZPnsyBAwe45pprcLvdDBw4kLvvvpvRo0fTsGFD2rZtS25uLqNGjaJjx44APPDAA0RGRtKgQQN69+5t9kOITzi8uqySSbrMgzlqb47am6P25qi9Wepvji/bazIsldK0aVM+/fTTCp/v168ff/7zn8s8/sYbb/DGG29Y98ePH2/9/NMl0rOyskq9btu2bVUe4/9E1adBgwZVfp1cvOLiYlb9B56MaYC/v7/p4YiIiIiIVJpOkxYREREREZEax+bV1aPlCpeXl0dISAgvfLGfwKBg08OpWbxeahXnU+Rfu8YtojU2NtT0EKwL0QcFBWGrYf1NU3tz1N4ctTdH7c1Sf3Mupv25uUFubq61tlBFdGRYRC6J26+W6SHUaLVr1zY9hBpL7c1Re3PU3hy1N0v9zfFle2OTYafTidPppHXr1jgcDut+3759y91+27ZtFT7nSwsWLCAkJASn00lkZCR33nknhw4dumz7N/W5fm7//v3ExcURGxvL/Pnzyzx//PhxBg8eTLNmzYiNjSUuLo6XX34ZgIkTJ/LEE0+Uu9+PPvqIJ5980pdDt9i1kFO1s3tLiPjPFrU3xO12s2rVKi3qYYDam6P25qi9OWpvlvqb4+v2xhbQcrlcwNmFk5xOp3W/IvHx8SxdutT3AytH165dWbFiBQCjR4/mySef5IMPPii1jdvtxs+v6jlNfq6fev/990lISGD27NllnsvPz+fWW2+lb9++7N27F4fDwY8//shf//rXC+73rrvu4q677vLFkEVERERERC7aL+o0abfbTffu3YmPjycyMpJ+/frxww8/APD555/jdDoBeO6556yjkqtWrcJms/HNN98AMGTIEBYuXAhA//79iY+PJyYmhl69enHs2DHg7AS8bt26vPDCC7Rt25YWLVqwatWqSo2xe/fuZGZmAmCz2XjhhRdISEhg3LhxHD9+nD59+hAdHU1UVJQ1sXz33Xf5zW9+Y+3D6/XSrFkzdu7cWepzXWhcqampdOrUiTZt2hATE8OHH34IwN69e+nVqxcJCQnExMRUeD3f77//niFDhhAVFUVUVBSTJk0CYOHChUyfPp3ly5fjdDr5+uuvS71uyZIlBAUFMXHiRBwOBwDXXHMNo0ePtrY5evQov/3tb2ndujXdunXj1KlTwNkj6+cuqfT5558TFRXFyJEjadOmDZGRkdaq0ef7s/+5wsJC8vLySt1ERERERESq4hc1GXY4HCxZsoRt27aRkZFBSEgIM2fOLLNdcnIya9asAWD16tUkJiZa99esWUNycjIAr7/+Otu2bSM9PZ2kpCQmTpxo7SM3N5eYmBi2b9/OrFmzKnUqb0lJCcuWLaNt27alxrx161amTp3KY489RkREBLt27WLt2rVMmTKFzZs306dPHzZv3mxNxj///HPq1atHmzZtyrxHReM6deoUvXv3JiUlhZ07d+JyuUhKSqKkpIQHH3yQ1157ja1bt7J582befvtttm7dWmbfL774IoWFhaSnp7NlyxZWrFjB0qVLeeihhxgxYgT9+/fH5XLRunXrUq/bvn07iYmJ522zZcsWFixYwNdff02jRo3KPcIMsGfPHgYOHMjOnTt57LHHeP75562OlfmzB0hJSSEkJMS6hYWFnXdsIiIiIiIiP/eLmgx7vV6mT59ObGwsMTExrFy5stzTp3/961/jcrnIz8/niy++YMqUKaxZs4bMzEyuvfZaGjduDJw9ohkfH09UVBRz5swpta/AwED69OkDQGJiIvv3769wXOvWrcPpdNK2bVtsNhuvvfaa9dyQIUOsn9esWcMjjzwCQKNGjejTpw9r1qyhdu3a3HPPPSxatAg4e7R08ODB5b5XReNKTU0lIiKCpKQkAOx2O/Xr1yczM5Pdu3fzwAMP4HQ66dixI2fOnClzdPfc+B5++GHsdjvXXnstDz30EKtXr67wc1dFjx49rGv8nq9nixYtaN++fZntKvtnDzBu3Dhyc3Ot2+HDhwHw2ByX5bNI5XlsDjJvbK/2hvj5+dGzZ8+L+oqGXBq1N0ftzVF7c9TeLPU3x9ftf1F/okuWLGHt2rV88cUXBAcHM2PGDNauXVtmu4CAAOLj41m2bBm1a9emS5cuDB8+nE8//ZTbbrsNgI0bNzJjxgxSU1Np1KgRH330ERMmTCi1j3PLczscDkpKKl4A6KffGf65OnXqVPi6ny7/PWTIEAYPHsyjjz7KJ598wvTp08t9TVXGBWcnkfXr17/gd64vNL7zadu2LW+//fZ5twkMDLR+djgcFX7JvaLtKvtnD2cbBQQEVGrs4nt+7qKzl1YSI/Lz8wkKCjI9jBpJ7c1Re3PU3hy1N0v9zfFl+1/UkeGcnBxCQ0MJDg7mzJkzLFiwoMJtk5OTmTBhArfddht2u53Y2FimTZtmnSKdk5NDUFAQDRo0oKioqMLTdi+n5ORka1GpEydOsHz5cm6//XYA62jo008/TXJyMvXr16/Svjt27MjevXvZsGEDAB6Ph1OnThEREUFwcHCpFaD37dtnfWf35+ObO3cuXq+XH374gUWLFnHHHXdc8L0ffPBBTp8+zYsvvmhNzvPz85kxY0aVPsP5VOXPviJa0bj62b0lND+WpvaGuN1u1q1bp9UtDVB7c9TeHLU3R+3NUn9zfN3+F3Vk+KGHHuLDDz8kIiKChg0bkpSUxMGDB8vdNjk5mTFjxliT39tvv53ly5fTpUsX4Oxpu4sXLyYiIoIGDRqQnJzMkSNHfDr+GTNm8OijjxIdHY3X6+X555+3JsEAgwcP5tlnn+Wf//xnlfddr149/vGPf/CHP/yBM2fOYLfbefHFF/ntb3/LJ598whNPPMH06dMpKSkhNDSUJUuWlNnHH//4Rx5//HGio6MBuO+++7j//vsv+N7XXHMNX3zxBWPHjqVFixbUqVMHm81Gv379qvw5KlKVP/uK/E9UfetUbakexcXFrPoPPBnTAH9/f9PDERERERGpNJvX6/WaHoTIpcjLyyMkJISTJ09qMlzNiouLWbVqFT179tRk2AD1N0ftzVF7c9TeHLU3S/3NuZj25+YGubm5BAcHn3fbX9Rp0iJy5dFiEmapvzlqb47am6P25qi9Wepvji/b68iwXPHO/fbnhfXfElhHCxvI5Tc2NtT0EERERESkEnRkWCxOpxOn00nr1q1xOBzW/b59+/L555/jdDqrvM9t27bRt29fALKysqhbt+7lHTQQGhpKVlZW1V6k3+tUP6+Xa/Nz1N4Qj8fD8ePH8Xg8podS46i9OWpvjtqbo/Zmqb85vm6vyfBVzuVy4XK5WLVqFUFBQdb9pUuXXvQ+4+PjL+n1vqIVjauf3VtCkxNfq70hJSUlpKamXvASbHL5qb05am+O2puj9mapvzm+bq/JcA3ndrsZOXIkbdq0ITIykm3btlmPd+/enfj4eCIjI+nXrx8//PADwHmPKPfv35/4+HhiYmLo1asXx44dA/7vCPILL7xA27ZtadGiBatWrbJe99FHH3HLLbcQExPDs88+e94xFxYWkpeXV+omIiIiIiJSFZoM13B79uxh4MCB7Ny5k8cee4znn38eAIfDwZIlS9i2bRsZGRmEhIQwc+bMC+7v9ddfZ9u2baSnp5OUlMTEiROt53Jzc4mJiWH79u3MmjWLJ598EoDjx48zePBgPvjgA9LT02nRogXZ2dkVvkdKSgohISHWLSws7NIiiIiIiIhIjaPJcA3XokUL61rIiYmJ7N+/HwCv18v06dOJjY0lJiaGlStX4nK5Lri/JUuWEB8fT1RUFHPmzCn1msDAQPr06VPmvTZv3kxMTAytW7cGYOjQodSqVavC9xg3bhy5ubnW7fDhw2fHjK3Kn18ujRcbhf611d4Qm81GUFAQNpv6Vze1N0ftzVF7c9TeLPU3x9fttUZ4DRcYGGj97HA4cLvdwNlJ7dq1a/niiy8IDg5mxowZrF279rz72rhxIzNmzCA1NZVGjRrx0UcfMWHCBOv5gIAA6y+yw+Go8Nz/C/1lDwgIICAgoMzjXrvjvK+Ty89rd/Dt9XGmh1Fj+fn50a1bN9PDqJHU3hy1N0ftzVF7s9TfHF+315FhKVdOTg6hoaEEBwdz5swZFixYUKnXBAUF0aBBA4qKipg9e3al3isxMZH09HT27NkDwLx58ygqKqr6oL1a4a/aeT3U/f6Y2hvi8Xg4ePCgVrc0QO3NUXtz1N4ctTdL/c3xdXtNhqVcDz30ED/++CMRERHceeedJCUlXfA1PXr0ICIigoiICJKSkip92aaGDRsyb948fve739GmTRv27t1LgwYNqjxmuyZk1c7u9XD9qf1qb0hJSQkul0urWxqg9uaovTlqb47am6X+5vi6vU6TriHCw8M5ffp0qce6dOlS6ju9UVFR1rV9Q0JCWLNmTbn7+unrfrpff3//Mpdceumll8p9/zp16uD9ybVp7777bu6++27r/iuvvFKFT3fW/0TVv6hJtFy84uJiVv0HnoxpgL+/v+nhiIiIiIhUmo4Mi4iIiIiISI2jI8Ny1ZiVkUNAsPfCG8plY/OUcGNgXaaln7qqFjAbGxtqegiVYrPZaNiwoVa3NEDtzVF7c9TeHLU3S/3N8XV7m/en56pKjRceHk5AQAC1a9cGID4+njlz5hge1fnl5eUREhLCC+u/JbBOkOnhyFXgSpkMi4iIiEhp5+YGubm5BAcHn3dbnSYtZSxduhSXy4XL5SozEf4lLxxg0yJO1c7m9RB6+pDaG1JSUsKePXt+0f+7vFqpvTlqb47am6P2Zqm/Ob5ur8mwnNeCBQvo2rUr99xzD9HR0Xz11VdMmzaNhIQEnE4nCQkJpKamWtuHh4czYcIEEhMTuemmm5gyZYr13JEjR7j33nuJjo4mJiaGP/7xjwCcOXOGhx9+mHbt2hETE8Pw4cMv6tJKmpBVP5vXQ8O8w2pviMfjITMzU5d6MEDtzVF7c9TeHLU3S/3N8XV7fWdYyujbt691mvTAgQPZsmULaWlpREREANCiRQueeuopADZv3sygQYOsawQDnD59mtTUVE6ePEnz5s0ZPHgwN9xwA7///e+54447eP/99wE4ceIEAH/4wx9ISkrir3/9K16vl4cffpg33niDZ555ptzxFRYWUlhYaN3Py8u7/BFEREREROSqpsmwlLF06VLrGsELFiygY8eO1kQYIC0tjZdeeons7Gz8/PzIzMwkPz/fmkD369cPgNDQUJo1a8aBAwcICQlh48aNfPrpp9Z+GjZsCMCKFStITU1l2rRpAOTn5+NwVLwYU0pKCpMmTbqsn1lERERERGoWTYblgurUqWP9XFRURJ8+fVi3bh0JCQnWF9QLCwutyXBgYKC1vcPhwO12n3f/Xq+XDz74gJtvvrlS4xk3bpx1ZBrOHhkOCwvDi1b4q25ebJy+tpHaG2K322nSpAl2u77xUt3U3hy1N0ftzVF7s9TfHF+315+oVElBQQFFRUU0adIEgJkzZ1bqdXXq1KFz58689tpr1mPnTpPu3bs3r7zyijVpzsnJYd++fRXuKyAggODg4FI34Kq6tM+Vwmt3cLRBS7U3xOFwEBsbe94zKcQ31N4ctTdH7c1Re7PU3xxft9dkWKokODiYKVOm0K5dO9q2bUutWrUq/dpFixaxbds2IiMjcTqdzJo1C4Dp06dTu3ZtnE4nMTEx3HbbbWRlZVV5bDaPVvirbjZPCddn71V7Q0pKSkhLS9PqlgaovTlqb47am6P2Zqm/Ob5ur+sMyxXv3KnaJ0+epEGDBqaHU6MUFxezatUqevbsib+/v+nh1Djqb47am6P25qi9OWpvlvqbczHtdZ1hERERERERkfPQAlpy1Xgz4xS1gnWiQ3Wye9xEANPTs/HYr8x/TsbGhpoegoiIiIgYoCPDNVB4eDgRERE4nU5at27Nm2++aXpIl4XXpr/O1c1rs3MiOEztDbHb7URERGh1SwPU3hy1N0ftzVF7s9TfHF+313eGa6Dw8HBWrFiB0+nk4MGDxMTEsGHDBmJiYkwP7aKc+17AC+u/JbBOkOnhyBVGR4ZFRERErh76zrBUWtOmTYmIiOBf//oXCQkJOJ1OEhISSE1NtbYJDw9nwoQJJCYmctNNNzFlyhTruWnTpp33dePHj6djx46EhYXx1ltvMX/+fBITEwkPD+e9996ztu3fvz/x8fHExMTQq1cvjh07VuXPohWNq5/NU0LY8d1qb4jb7WbTpk0XvJa3XH5qb47am6P25qi9Wepvjq/bazJcw+3atYs9e/bQtWtXtm7disvlYubMmQwePLjUdqdPnyY1NZWtW7cydepUjhw5AsCAAQPO+7offviBTZs2sW7dOp588kmOHDlCamoqy5Yt47HHHrO2e/3119m2bRvp6ekkJSUxceLECsdcWFhIXl5eqRuADZ3kUN1seKlTcFrtDfF6vZw4cQKd4FP91N4ctTdH7c1Re7PU3xxft78yV7yRS9a3b19q167NNddcw7x588jJyeHWW28lOzsbPz8/MjMzyc/Pp3bt2gD069cPgNDQUJo1a8aBAwe44YYbSEtL46WXXqrwdX379gWgRYsWBAYGcu+99wIQHx/PqVOnOH36NHXr1mXJkiUsWrSIgoICCgoKCA2t+NTVlJQUJk2a5Ms8IiIiIiJyldNkuIZaunQpTqcTgKKiIurXr8+6detISEiwzrMvLCy0JrWBgYHWax0OB263m6KiIvr06VOl1527b7PZsNlsuN1uNm7cyIwZM0hNTaVRo0Z89NFHTJgwocKxjxs3jqeeesq6n5eXR1hY2GVrIyIiIiIiVz9NhoWCggKKiopo0qQJADNnzvTp634uJyeHoKAgGjRoQFFREbNnzz7v9gEBAQQEBJR53KMVjaudx2bnaP3mam+Iw+HA6XTicDhMD6XGUXtz1N4ctTdH7c1Sf3N83V7/BSsEBwczZcoU2rVrR9u2balVq5ZPX/dzPXr0ICIigoiICJKSkqwj1lWmCVn1s9k5Xec6tTfEbrfTtGlTXerBALU3R+3NUXtz1N4s9TfH1+11aSW54p07PTs7O5v69eubHk6N4na7Wb9+PZ07d8bPTyeaVDf1N0ftzVF7c9TeHLU3S/3NuZj2urSS1Ej6vU7183q9nDlzRu0NUX9z1N4ctTdH7c1Re7PU3xxft9dkWERERERERGocHeeXq8abGaeoFazf2FUnu8dNBDA9PRuP/Zf/z8nY2Iov2SUiIiIiNYuODF9hiouLmTRpEq1atSIyMpLY2Fh69+6Ny+W66H26XC7ee++9yzfISliwYAG9e/e+rPv02LTCX3Xz2Bwcatha7Q1xOBwkJiZqdUsD1N4ctTdH7c1Re7PU3xxft//lH8qRUgYPHsz3339Pamoq9erVA2DNmjVkZmZe9CrMLpeLFStW8MADD1zGkRpgs5keQc1js/FD7XqmR1Fj2e12GjVqZHoYNZLam6P25qi9OWpvlvqb4+v2OjJ8Bdm7dy//+Mc/mDdvnjURBkhOTqZv375ljrZ+8skndOnSBYBjx47RtWtX2rZtS2RkJKNGjcLj8XD8+HEmTJjAunXrcDqdjBgxAoCtW7fSrVs34uPjiY2NZdmyZQBkZWVRt25d/vjHPxIXF0fLli358ssvefLJJ3E6nURFRZGRkXHe9/y5ym53TmFhIXl5eaVucPaUXaledo+bmw9vVntDiouLWblyJcXFxaaHUuOovTlqb47am6P2Zqm/Ob5ur8nwFSQtLY0WLVpc1OWD6taty8cff8z27dtJT08nKyuLv//97zRq1IjJkyfTtWtXXC4Xb731FqdPn2b48OG8++67bNu2jdWrV/OHP/yBI0eOAJCbm0vbtm3ZsWMHY8eOpXv37tx11124XC4GDhzIpEmTzvuelR1bRVJSUggJCbFuYWFhVe4hl4/DW2J6CDWa261fRJii9uaovTlqb47am6X+5viyvSbDV7D9+/fjdDqJiIhg8ODB593W4/EwZswY2rRpQ2xsLNu2bavwe8abNm3i22+/5c4778TpdJKcnAxAZmYmAIGBgdYR6Pj4eOrUqUPXrl0BaNeuHXv37q3Se1ZlbADjxo0jNzfXuh0+fPi8n11EREREROTn9J3hK0hsbCz79u0jJyeHevXq0bx5c1wuFwsWLGDFihX4+flRUvJ/R+kKCgqsn6dNm8bx48fZsmULgYGBPPXUU6We/ymv10tkZCSbNm0q81xWVhYBAQHWfYfDQWBgYKn75357U9n3rMrYAAICAkqNQUREREREpKp0ZPgK0rJlS+6++26GDh3K6dOnrcd/+OEHAFq0aEF6ejr5+fm43W6WLFlibZOTk8N1111HYGAgx44ds74DDBAcHExubq51v2PHjhw4cIA1a9ZYj7lcLoqKiqo03vO958VsdyFa0bj6eWwO9l8Xq/aG+Pn50bVrV/z89HvN6qb25qi9OWpvjtqbpf7m+Lq9JsNXmAULFhAdHU379u2JjIykU6dOrFmzhjFjxtChQwd69uxJVFQUXbp0oWXLltbrRo8ezZYtW4iMjGTAgAHWqc8At912G4WFhcTExDBixAjq1avHypUrefnll2nTpg2tW7dm7Nix513Uqjzne8+L2U5+mdx+tUwPoUarXbu26SHUWGpvjtqbo/bmqL1Z6m+OL9vbvF6v12d7F6kGeXl5hISEcPLkSRo0aGB6ODVKcXExq1atomfPnvj7+5seTo2j/uaovTlqb47am6P2Zqm/ORfT/tzcIDc3l+Dg4PNuqyPDIiIiIiIiUuPoxHe5aryZcYpawTrRoTrZPW4igOnp2Xjsv6x/TsbGhpoegoiIiIj8gunIcA1hs9lKLboFEB4eft5LGImIiIiIiFyt9J3hGsJms5GTk0PdunWtx8LDw1mxYgVOp7PUtm63+4paLe/c9wJe+GI/gUHn/16AXGZeL3ZvydnVpG0206MppSYcGfZ6vdb/Xm2/sP5XO7U3R+3NUXtz1N4s9TfnYtrrO8NSZeHh4YwZM4Z27doxcOBAPv/881KT5IyMDMLDw637s2fP5uabbyYuLo4XX3yx1F/Onx+FDg0NJSsrC4Cnn36ahIQEnE4nnTt3JjMzE4BXX32V4cOHW685ffo0oaGhnDp1qsxYCwsLycvLK3UTc/zcVbvkllxe+fn5podQY6m9OWpvjtqbo/Zmqb85vmyvybBYsrOz2bJlC+++++55t8vIyGDixImsX7+eHTt24Ha7K/0eY8aMYevWrbhcLkaOHMno0aMBGDZsGCtWrLAm0fPnz+fuu++mfv36ZfaRkpJCSEiIdQsLCwPA7i2p9Djk8rB7S2h+LE3tDXG73axbt65K/xuUy0PtzVF7c9TeHLU3S/3N8XV7TYZruJ8e0R00aFClTj9Yu3YtPXr04LrrrgPg4YcfrvT7rV69msTERKKiopg8ebL1neW6dety7733Mm/ePLxeL//v//0/Ro0aVe4+xo0bR25urnU7fPhwpd9fREREREQEtJp0jdGwYUOys7NLfWf45MmTNGrUyLpfp04d62c/Pz9KSv7vaF9BQUGF+/75BNrhcJT72kOHDjFq1Ci2bt1K8+bNSU9Pp3PnztZ2jz/+OHfddRe33HILDRs2JDY2ttz3CwgIICAg4AKfWEREREREpGI6MlxDdO/endmzZ1v3Fy5cSLNmzbj++uvL3b5Zs2YcPHiQEydOALBo0SLrua5du/Lpp59y/PhxAObOnVvqtS1atGDLli0ALF++nB9++AGA3Nxc/P39uf766/F6vcyaNavU61q1akWzZs0YPnx4hUeF5ZenxOYwPYQa7Upa7O5qo/bmqL05am+O2pul/ub4sr0mwzXE66+/ztGjR4mJicHpdLJkyRKWLVtW4faNGzfm2WefpV27dnTo0KHUd3ejo6MZP348v/71r4mLi6OgoICQkBDr+enTpzN69Gji4uJIS0ujQYMG1useeOABIiMjSUhIoEmTJmXe9+GHH8btdnPvvfdW+TP+0q5zWxN47H58E9ZB7Q3x9/enV69e+Pv7mx5KjaP25qi9OWpvjtqbpf7m+Lq9Lq0kF+XMmTMEBQUB8MYbb/Cvf/2Lf/7zn5e831GjRvGrX/2KP/7xj5V+zbnl039+6SjxPY/Hw8mTJwkNDcVu1+/Wqpv6m6P25qi9OWpvjtqbpf7mXEx7XVpJfG7s2LE4nU4iIyP55JNPePPNNy9pf9999x2tWrVix44dPPHEExe1j59+T1mqR0lJCampqWpviPqbo/bmqL05am+O2pul/ub4ur3ObZSLcqmT359r3Lgxe/bsuaR9vJlxilrBOtGhOtk9biKA6enZxk+VHhsbavT9RUREROTKoiPDUmnh4eE0atSI4uJi67F169Zhs9kueDR34sSJ1jYul4v33nvPhyMVERERERE5P02GpUqaNGnCRx99ZN2fO3cu8fHxVdqHrybDXi58jWS5vLzYKPSvrfaG2Gw2goKCKnV9cLm81N4ctTdH7c1Re7PU3xxft9dkWKpk8ODBzJs3Dzh7qaTNmzfTo0cPAHbt2kWnTp2Ii4ujdevWTJkypczrjx8/zoQJE1i3bh1Op5MRI0YA0L9/f+Lj44mJiaFXr14cO3asymPz2nWJn+rmtTv49vo4tTfEz8+Pbt266XIPBqi9OWpvjtqbo/Zmqb85vm6vybBUya9//WuysrL47rvv+Nvf/sZ9992Hw3F2IhQeHs5nn33Gjh072L59Ox988AGbN28u9fpGjRoxefJkunbtisvl4q233gLOXvpp27ZtpKenk5SUxMSJEyscQ2FhIXl5eaVuAHg9PvnMch5eD3W/P6b2hng8Hg4ePIjHo/7VTe3NUXtz1N4ctTdL/c3xdXtNhqXKBgwYwIIFC5g3bx5DhgyxHs/Pz2fYsGFER0fToUMHDh48iMvlqtQ+lyxZQnx8PFFRUcyZM+e8r0tJSSEkJMS6hYWFAWDXhKza2b0erj+1X+0NKSkpweVyaXVLA9TeHLU3R+3NUXuz1N8cX7fXZFiq7KGHHmLGjBkEBgbSsmVL6/HnnnuO0NBQ0tLS2LlzJ126dKGgoOCC+9u4cSMzZsxg1apVZGRkMG3atPO+bty4ceTm5lq3w4cPX5bPJSIiIiIiNYdOfJcqa9y4MSkpKbRq1arU4zk5Odxyyy34+fmRmZnJ6tWr6dy5c5nXBwcHk5ubW+p1QUFBNGjQgKKiImbPnn3e9w8ICCAgIODyfBgREREREamRdGRYLsrgwYNJTEws9dj48eOZP38+MTExjB07lm7dupX72ttuu43CwkJiYmIYMWIEPXr0ICIigoiICJKSknA6nRc1Jq1oXP282Pg+sK7aG2Kz2WjYsKFWtzRA7c1Re3PU3hy1N0v9zfF1e5vX6/X6ZM8i1SQvL4+QkBByc3MJDg42PRwRERERETGkKnMDHRmWq4YWNah+JSUl7NmzR+0NUX9z1N4ctTdH7c1Re7PU3xxft9dkWK4aWu6++nk8HjIzM9XeEPU3R+3NUXtz1N4ctTdL/c3xdXstoCVXjTczTlErWGf9Vye7x00EMD09G4+9+v45GRsbWm3vJSIiIiJXJx0ZvkosX76ctm3b4nQ6adWqFd26dcPj8dCzZ08yMzNND09EREREROQXRUeGrwJHjx5l+PDhbN++naZNmwKwY8cObDYbq1atMjy6C3O73fj5XfpfRa1oXP282Dh9bSO1N8Rut9OkSRPsdv1es7qpvTlqb47am6P2Zqm/Ob5urz/Rq8B///tfHA4H9evXtx6Li4vDZrMRHh6Oy+UCoEuXLjz99NMkJSXRvHlzRowYYW1/9OhR7rjjDlq3bs0dd9zBAw88wMSJEwH47LPPSExMJDY2lsjISObOnWu9btCgQQwZMoSOHTty8803M3DgQPLz8wE4fvw4ffr0ITo6mqioqFLXDw4PD2fMmDG0a9eOgQMHUlxczNixY2nXrh1Op5P777+fnJyccj9vYWEheXl5pW4AXrvjsvSUyvPaHRxt0FLtDXE4HMTGxuJwqH91U3tz1N4ctTdH7c1Sf3N83V6T4atATEwMnTp1omnTpvzud79j6tSpHDlypNxt9+/fz7p168jIyODTTz8lNTUVgMcff5zExES+/vprFi5cyOeff269Ji4ujo0bN5KWlsaGDRuYPHky//nPf6znt2zZwqeffsq///1vTp06xfTp0wF47LHHiIiIYNeuXaxdu5YpU6awefNm63XZ2dls2bKFd999l6lTp3Lttdfy1Vdf4XK5iI6OZvz48eV+hpSUFEJCQqxbWFgYADaPVvirbjZPCddn71V7Q0pKSkhLS9PqlgaovTlqb47am6P2Zqm/Ob5ur8nwVcBut/PBBx+wadMmevTowZdffklkZCT79u0rs23fvn3x8/Ojdu3aOJ1O9u/fD5w9+jtkyBAArrvuOn7zm99Yr8nOzua+++4jKiqKbt26kZ2dTUZGhvX8/fffT1BQEA6Hg6FDh7JmzRoA1qxZwyOPPAJAo0aN6NOnj/UcnD2qfO4C2itWrGDx4sU4nU6cTid/+9vfOHDgQLmfd9y4ceTm5lq3w4cPA2BDi2dVNxte6v5wXO0N8Xg8HDp0SKtbGqD25qi9OWpvjtqbpf7m+Lq9vjN8FWnVqhWtWrXikUceoUePHnz00UdltgkMDLR+djgcuN3ucvd1bpIKMGLECHr27MkHH3yAzWYjLi6OgoKCCsfx09ee7/E6depYP3u9XmbOnMkdd9xR4X7PCQgIICAg4ILbiYiIiIiIVERHhq8CR44c4csvv7Tu5+TkcODAAZo3b17pfXTr1o0FCxYAZ7+D/Mknn5TaX9OmTbHZbKxfv56dO3eWeu3777/P999/T0lJCfPnzyc5ORmA5ORk/vrXvwJw4sQJli9fzu23317u+/fu3Zvp06fz448/AvDjjz+ye/fuSo9fRERERESkKnRk+CrgdruZPHkyBw4c4JprrsHtdjNw4EDuvvtuRo8eXal9vPHGGwwcOJDWrVvTuHFj2rdvT926dQH405/+xMiRI3nxxRdxOp20b9++1GsTEhLo3r07J06cIDExkSeeeAKAGTNm8OijjxIdHY3X6+X5558v89pzxowZQ2FhIe3bt7eOII8ZM4bIyMhKd/Da9Lud6ua12TkRHKb2htjtdiIiIrS6pQFqb47am6P25qi9Wepvjq/b27xer77sJ+Tn5+Pv74+fnx/Z2dl06NCBxYsXVzh5PWfQoEE4nU5rAmxCXl4eISEh5ObmEhwcbGwcIiIiIiJiVlXmBvr1hgCwd+9e4uPjadOmDZ06dWLkyJEXnAj/0lT0/WfxHbfbzaZNm9TeEPU3R+3NUXtz1N4ctTdL/c3xdXudJi3A2csznbsecVWc+57xL8GsXdnUCtYqf9XJ7nETceIE03aexGP3zT8nY2NDfbLfq4HX6+XEiRPoBJ/qp/bmqL05am+O2pul/ub4ur2ODIslPDycRo0aUVxcbD22bt06bDbbRZ8G/d1335GUlHSZRigiIiIiInJ5aDIspTRp0qTUJZnmzp1LfHz8Re+vcePGbNiw4XIMTURERERE5LLRZFhKGTx4MPPmzQMgNzeXzZs306NHD+v5V199lXbt2hEXF0ePHj04ePAg+fn5tGnThvfffx+A1NRUwsPDOXHiBFlZWdaq1Oee69SpE23atCEmJoYPP/wQgG3bttGxY0diYmJo165dqUtF/VxhYSF5eXmlbgAerWhc7Tw2O0frN1d7QxwOB06nE4fDYXooNY7am6P25qi9OWpvlvqb4+v2+s6wlPLrX/+av/zlL3z33Xd89NFH3HfffdZfviVLlpCZmUlqaioOh4NFixYxcuRIVq5cybJly7jtttto2rQp/fv3Z9GiRTRs2JAffvjB2vepU6fo3bs377//PklJSXg8Hk6fPk1RURF9+vThr3/9K927d2fjxo3cc8897Nu3jzp16pQZY0pKCpMmTSo7eE3Iqp/Nzuk615keRY1lt9tp2rSp6WHUSGpvjtqbo/bmqL1Z6m+Or9tr9iBlDBgwgAULFjBv3jyGDBliPb5ixQrWrFlD27ZtcTqd/PnPf+bQoUMA3HzzzbzyyiskJiYybNiwcr8nnJqaSkREhPWc3W6nfv36ZGZmYrfb6d69OwCdOnXiV7/6VYULeo0bN47c3FzrdvjwYQBsnpLLmUEqweYpodnRHWpviNvtZu3atVrd0gC1N0ftzVF7c9TeLPU3x9ftdWRYynjooYeIi4vj5ptvpmXLltbjXq+XcePGMXz48HJft2PHDho2bGhNTi+FzWar8LmAgAACAgLKvgat8FfdbHgJKM7Hhlf1DfB6vZw5c0arWxqg9uaovTlqb47am6X+5vi6vY4MSxmNGzcmJSWFV155pdTjvXv35q233uLUqVMAFBcXk5aWBsAnn3zCp59+yu7du9myZQtLly4ts9+OHTuyd+9ea0Etj8fDqVOniIiIwOPxsHr1agA2bdrEsWPHcDqdPvyUIiIiIiJSk+nIsJRr8ODBZR7r378/2dnZdO3aFTh72sKQIUNo0KABjz76KJ9++in169dn2bJldOnShbi4OPz9/a3X16tXj3/84x/84Q9/4MyZM9jtdl588UV++9vfsnz5ch5//HH+8Ic/EBgYyPvvv1/u94VFREREREQuB5tXx/vlCpeXl0dISAg5OTmlVq4W3/N4PJw8eZLQ0FDsdp1oUt3U3xy1N0ftzVF7c9TeLPU352Lan5sb5ObmEhwcfN5tNRmWK15V/sKLiIiIiMjVqypzA50mLVeN19P+S63gItPDqFHsHjctjmxj3w3xeOyX/s/J2NjQyzCqmqO4uJj//d//5Y477ij1lQTxPbU3R+3NUXtz1N4s9TfH1+11nP8qc+bMGerUqcPQoUOr/b1ff/11jh07Zt1/6623mDp1arWPQ6qXw6vLKpmkyzyYo/bmqL05am+O2pul/ub4sr0mw1eZpUuX0rZtW5YvX873339f7jYlJb6ZvPx8MjxixAieeeYZn7yXiIiIiIjIpdBk+Cozd+5cxowZQ+fOna3LGy1YsICuXbtyzz33EB0dzVdffcWHH37ILbfcQps2bRgzZgyhoaFkZWUBsHfvXnr16kVCQgIxMTHMmjXL2r/NZuPll1+mXbt23HTTTcyfPx+AyZMn891339G3b1+cTicul4uJEyfyxBNPWGNITk7mwQcfJDo6mvj4eL799lsAjh07RteuXWnbti2RkZGMGjUKj8dTfdFERERERKTG0WT4KvL1119z+PBhunfvztChQ5k7d6713JYtW3j55ZfZtWsXzZs3Z8iQIfzjH/9g586dtGrViuzsbODsUeMHH3yQ1157ja1bt7J582befvtttm7dau0rICCAr776in/+8588/vjjuN1uJkyYQOPGjVm6dCkul6vcawRv3brVGkNycrJ1HeO6devy8ccfs337dtLT08nKyuLvf/97hZ+zsLCQvLy8UjcAj81xOTJKFXhsDvZfF6v2hvj5+dG1a1f8/LT8Q3VTe3PU3hy1N0ftzVJ/c3zdXpPhq8jcuXN56KGHcDgc9OzZkwMHDvDvf/8bgI4dOxIREQHA5s2biYmJoVWrVgAMHDiQWrVqAZCZmcnu3bt54IEHcDqddOzYkTNnzvD1119b79O/f38AWrVqhZ+fX6lTo88nMTGRm266yfp5//79wNkl08eMGUObNm2IjY1l27ZtuFyuCveTkpJCSEiIdQsLC6tCJbnc3H61TA+hRqtdu7bpIdRYam+O2puj9uaovVnqb44v22syfJUoLi5m0aJFvPPOO4SHh9OiRQt+/PFH6+hwnTp1KrUfr9dL/fr1cblc1u3AgQMMHDjQ2iYwMND62eFwVPpL7RW9btq0aRw/fpwtW7aQnp5Ov379KCgoqHA/48aNIzc317odPnwYALsWcqp2dm8JEf/ZovaGuN1uVq1apUU9DFB7c9TeHLU3R+3NUn9zfN1ek+GrxEcffUSzZs04cuQIWVlZZGVlsXnzZhYtWkRxcXGpbTt06EB6ejqZmZkALF68mKKis5ckioiIIDg42PouMMC+ffs4derUBccQHBxMbm5ulceek5PDddddR2BgIMeOHWPZsmXn3T4gIIDg4OBSNxERERERkarQZPgqMXfuXOv05XNuueUWbrjhBs6cOVPq8UaNGjFnzhx69+6N0+lk165d1KlTh7p16+Ln58cnn3zC8uXLiYmJITIykqFDh5Kfn3/BMTz++OM8/PDD1gJalTV69Gi2bNlCZGQkAwYMIDk5udKvFRERERERuRj6FvhVYtWqVeU+vmPHDgCeeuqpUo8nJyfzu9/9DoAVK1bwySefULduXQCaN2/Oxx9/XO7+vF5vqfsnT560fh42bBjDhg2z7v90Ea1BgwYxaNAg6/5vfvMbfvOb3wDQpEkTvvrqq/N8usr5n6j6NGjQ4JL3I5VXXFzMqv/AkzENdBF6EREREbmi2Lw/n91IjfDyyy+zdOlSSkpKCA4OZtasWcTFxZke1kXJy8sjJCSE06dPExISYno4NYrX68XtduPn54fNZjM9nBpH/c1Re3PU3hy1N0ftzVJ/cy6m/bm5QW5u7gW/TqnJsFzxzv2Ff+GL/QQG6fvD1crrpVZxPkX+teEi/s9hbGyoDwZVc3i9Xs6cOUNQUJD+z7maqb05am+O2puj9mapvzkX074qk2F9Z1iuGlrRuPrZvSU0P5am9oa43W7WrVun1S0NUHtz1N4ctTdH7c1Sf3N83V6T4cvM7XYzadIkWrVqRVRUFE6nk+HDh3P69OnL+j4LFiygd+/eldp20KBB3HDDDTidTlq1asWAAQP48ccfL+t4fmrixIk88cQTF3zuo48+4sknn/TZOERERERERCqiyfBlNnToULZt20ZqaioZGRmkpaVx++23V+rSRL70zDPP4HK52LlzJ99++y2zZs0yOh6Au+66i+nTp5sehoiIiIiI1ECaDF9G+/btY9myZcyfP5969eoBYLPZuO+++2jWrBmTJ0/G6XTidDqJiorCZrNx8OBBAF599VXatWtHXFwcPXr0sB4vKirimWeeISoqijZt2tCjRw/r/b7//nsefPBBoqOjiY+P59tvv73gGAMCAujUqZO1/+LiYsaOHUu7du1wOp3cf//95OTkAGePKA8ZMoSOHTty8803M3DgQOsSS4MGDeL111+39vv0008zceJE6/7hw4fp1q0brVq14re//S3Z2dllxvLzo9vz58/H6XTSpk0b4uPjycrKKvczFBYWkpeXV+om5pTYHKaHUKP5+emiAKaovTlqb47am6P2Zqm/Ob5sr8nwZbRjxw5atmxJaGj5iwJNmDABl8uFy+WiY8eODBkyhKZNm7JkyRIyMzNJTU1lx44d9O/fn5EjRwKQkpLCN998w/bt29m5cyeLFi2y9rd161Zefvlldu3aRXJyMq+88soFx5ibm8vnn3/OPffcA8DUqVO59tpr+eqrr3C5XERHRzN+/Hhr+y1btvDpp5/y73//m1OnTlX6SO6GDRtYsmQJe/bsISwsjHHjxp13+88//5zJkyfzz3/+k507d7J+/XoaNWpU7rYpKSmEhIRYt7CwMAA8dv0jVd08dj++Ceug9ob4+/vTq1cvXdbKALU3R+3NUXtz1N4s9TfH1+01GTZgypQpHDp0iNmzZwNnr/O7Zs0a2rZti9Pp5M9//jOHDh0C4JNPPmH06NEEBAQA0LBhQ2s/iYmJ3HTTTdbP+/fvr/A9p06dSkxMDL/61a+48cYb6dq1q/Xeixcvto5Y/+1vf+PAgQPW6+6//36CgoJwOBwMHTqUNWvWVOoz9urVi+uuuw6A4cOHX/B1K1euZMCAAVx//fUAXHPNNVxzzTXlbjtu3Dhyc3Ot2+HDh88+oYXRq5/Xy7X5OWpviMfj4fjx43g8HtNDqXHU3hy1N0ftzVF7s9TfHF+312T4MoqLi2Pv3r3lnhJ8zsKFC1m+fDnvv/++dcjf6/Uybtw466jxrl272LVr1wXfLzAw0PrZ4XCcd5W1Z555hvT0dL755hu2bdvGW2+9Zb33zJkzrff++uuvWbVqVYX7ObekuZ+fHyUl/7eCcEFBwXnHejmXoQ8ICCA4OLjUDbSatAl2bwlNTnyt9oaUlJSQmppa6n+LUj3U3hy1N0ftzVF7s9TfHF+312T4MmrRogX33HMPQ4cOtVaP9nq9fPDBB3z77besWbOGF198kZUrV1KnTh3rdb179+att96yFtkqLi4mLS0NOLvI1BtvvEFhYSEAJ06cuKQxNmnShJkzZzJ58mTy8/Pp3bs306dPt1aX/vHHH9m9e7e1/fvvv8/3339PSUkJ8+fPJzk52fqsX331FQDZ2dllJtCrVq3iv//9LwBz5syxXleR3/72tyxevJijR49a4/DlitciIiIiIlKz6Yt+l9m8efOYMmUK7du3x8/PD4/HQ+fOnbntttsYOnQoP/74I3feeae1/apVq+jfvz/Z2dnWqctut5shQ4YQGxvLmDFjeP7554mLi8Pf35/GjRuf98htZZxbxfkvf/kLY8aMobCwkPbt21tHb8eMGUNkZCQACQkJdO/enRMnTpCYmGhdFmn48OHce++93HLLLTRr1owOHTqUeo+kpCT69evHkSNHaNmyJQsWLDjvmDp37swLL7xA9+7dsdls1KpVi/fff5+mTZte0mcVEREREREpj83r1Zf9pHyDBg3C6XRWeM3gX4q8vDxCQkLIzs6mfv36podTo7jdbtavX0/nzp21yqIB6m+O2puj9uaovTlqb5b6m3Mx7c/NDXJzc62vU1ZEk2Gp0JU2Ga7MX3gREREREbl6VWVuoF9tSIUudGrzL8001wkCg4pMD6Nm8Xqo+8NxTl/bCGznX4JgbGz5lxyTi+fxeDh8+DBhYWHY7VoCojqpvTlqb47am6P2Zqm/Ob5urz9NqZTw8HAiIiJwOp3ccsst9OvXjx9++OGy7d/pdHLmzBnrvVwuV5X3YfdqufvqZvd6uP7UfrU3pKSkBJfLpdUtDVB7c9TeHLU3R+3NUn9zfN1ek2GptKVLl+Jyudi9eze5ubnlHjm+2L+oLpeLoKCgSxyhiIiIiIhI5WgyLFVWVFTEjz/+SL169ViwYAFdu3blnnvuITo6mq+++opp06aRkJCA0+kkISGB1NRU4OyE1+l0Wrfg4GAmTZoEnL0O8bnLUV1IYWEheXl5pW4iIiIiIiJVoe8MS6X17duX2rVrk5WVRdu2bbn//vtZvHgxW7ZsIS0tjYiICODsNYifeuopADZv3sygQYPYs2cPTqfTOv153bp1PPzwwwwbNqzK40hJSbEm0T/lxXbxH04uihcb3wfWVXtDbDYbDRs2tC6LJtVH7c1Re3PU3hy1N0v9zfF1e60mLZUSHh7OihUrcDqduN1uHnnkEerWrUt0dDSLFy9mzZo11rb/+7//y0svvUR2djZ+fn7s3LmTH3/8kdq1awOQkZHBb37zGz755BOioqKAs3/Rc3JyqFu3bqn3Kk9hYSGFhYXW/by8PMLCwnhh/bcE1tGp1r9UWkBLRERERHytKqtJ6zRpqTI/Pz/uuece/vWvfwFQp04d67mioiL69OnDq6++SkZGBuvXrwewJq/fffcdvXv3Zv78+dZEuKoCAgIIDg4udQOwaRGnamfzegg9fUjtDSkpKWHPnj1a0MMAtTdH7c1Re3PU3iz1N8fX7TUZlouydu1a67TonyooKKCoqIgmTZoAMHPmTOu5M2fO0KtXLyZNmkTXrl0v+5g0Iat+Nq+HhnmH1d4Qj8dDZmYmHo/6Vze1N0ftzVF7c9TeLPU3x9ftNRmWSuvbty9Op5OoqCj+/e9/88Ybb5TZJjg4mClTptCuXTvatm1LrVq1rOeWL1/Onj17mDp1qrWI1ltvvVWdH0FERERERATQAlpSSVlZWeU+PmjQIAYNGlTqsWeffZZnn33Wuv/MM88AMHDgQAYOHFjufn761fWK3utC/ieqPg0aNLio18rFKS4uZtV/4MmYBvj7+5sejoiIiIhIpenIsFw17Hb9da5udrudJk2aqL0h6m+O2puj9uaovTlqb5b6m+Pr9lpNWq5451aM02rSvxxaOVpERERETKiRq0nbbDZOnz5d6rHw8HDrurbnM2zYMNatWwfAihUr2Lx580WN4ZNPPqFLly7n3ebWW2+lRYsWXMrvIAYNGsTrr79eqW1tNhvR0dHWd3SdTifZ2dlVer9t27bRt2/fixjppfWsKptHK/xVN5unhOuz96q9ISUlJaSlpWl1SwPU3hy1N0ftzVF7s9TfHF+3v2omw5dizpw51urGvpy87d27l7179xIQEMAXX3xR4XZut/uyvu+GDRtwuVzWrarfq42Pj2fp0qXlPnehsVbrZBid5FDdbHip+8NxtTfE4/Fw6NAhrW5pgNqbo/bmqL05am+W+pvj6/Y1ZjIcHh7OhAkTSExM5KabbmLKlCnWc126dGHFihWsWrWKjz76yFrteM6cOQAsWrSI9u3bExcXR+fOndm5cydwdvGgkSNH0rJlS9q1a2cdXa7IvHnz+P3vf8+wYcOYO3eu9XhWVhZ169ZlzJgxxMXFMWvWLD777DMSExOJjY0lMjKy1PYA6enpdOzYkZtvvpmBAweSn59fpR7n3vOPf/wjcXFxtGzZki+//JInn3zSWjE6IyMDgM8//xyn01nlsVa15+bNm2nbtq31/v/v//2/Kn0mERERERGRyqpRq0mfPn2a1NRUTp48SfPmzRk8eDA33HCD9XzPnj256667cDqdPPHEEwB8+eWX/O1vf2P9+vUEBASwYcMG+vXrx+7du3n77bfJzMxk9+7dAHTv3r3C9y4pKeGdd95h7dq1hIaGMmnSJHJzcwkJCQEgNzeXyMhIXnnlFQBycnLYuHEjDoeDU6dOERsbS/fu3bnxxhsB2LJlC5s3b+aaa66hd+/eTJ8+neeee67c905KSsLhcABQr149a9Kem5tL27ZtefHFF5k7dy7du3fn448/Zvr06UydOpVJkyaxbNmyMvur7Fir2jMlJYWnn36aBx980NpveQoLCyksLLTu5+XlVdhdRERERESkPFf9ZNhms1k/9+vXD4DQ0FCaNWvGgQMHSk2Gy/Phhx+yc+dO2rdvbz126tQp8vPz+eyzz3jooYesa+kOGTKkzBHcc1atWkV4eDitWrUCIDk5mSVLlvDoo48C4O/vz+9//3tr++zsbIYOHco333yDn58f2dnZZGRkWJPh+++/n6Cgs4tFDR06lBkzZlQ4Gd6wYQN169Yt83hgYCC9e/cGzp4KXadOHet08Xbt2vHuu++Wu7+qjvWnzteza9euvPjii+zdu5du3brRqVOnct8/JSWFSZMmlXnca6sxJzr8Ynhtdk4Eh6m9IXa7nYiICK1uaYDam6P25qi9OWpvlvqb4+v2V82faMOGDcssDHXy5EkaNWpk3Q8MDLR+djgclfpurtfrZeDAgaW+c3v06FFq165dZtufTrx/bu7cuXzzzTeEh4cTHh7Ohg0bSk2cr7nmmlJ/yCNGjKBTp07s2rULl8vFzTffTEFBQYX7P997VyQgIMD62eFwVLrPpYz1fD2feOIJVq5cyfXXX89zzz3HyJEjy93HuHHjyM3NtW6HDx8+u29NyKqd12bnZN0mam+Iw+GgVatW1pkfUn3U3hy1N0ftzVF7s9TfHF+3v2r+C7Z79+7Mnj3bur9w4UKaNWvG9ddfX6X9BAcHk5uba92/6667WLx4MYcOHQLOfol727ZtwNmju4sXL6a4uJiioiLmz59f7j7/+9//8tlnn7Fv3z6ysrLIysri6NGjfPfdd9b3ZX8uJyeHpk2bYrPZWL9+fZnt3n//fb7//ntKSkqYP38+ycnJVfqcl9P5xlqVnpmZmdx00008/PDDPPfccxUuvBUQEEBwcHCpG2g1aRNsnhLCju9We0PcbjebNm267IvuyYWpvTlqb47am6P2Zqm/Ob5uf9VMhl9//XWOHj1KTEwMTqeTJUuWlPt91wsZMGAAf//734mNjWXOnDkkJSXx5z//md/97ne0adOGyMhI3nvvPQAefvhhWrZsSevWrenUqZO1yNTPvfPOO9xxxx2lTlW22+088MADFZ5W/ac//YmxY8fidDqZN29eqdOKARISEujevTu33HILdevWtb6TW56kpKRSl1bKzMysUpMLOd9Yq9Jz1qxZREZGEhsby/jx43nttdeqNA6taFz9bHipU3Ba7Q3xer2cOHHiki7VJhdH7c1Re3PU3hy1N0v9zfF1e5tXf6pyhTt3Ye2TJ09W+bJRcmmKi4tZtWoVPXv2xN/f3/Rwahz1N0ftzVF7c9TeHLU3S/3NuZj25+YGubm51hmkFblqjgyLiIiIiIiIVNZVv5q01Bwzd58mMEgnOlQrr4cHnU4tKGGIw+HAqf5GqL05am+O2puj9mapvzm+bq/TpOWKd+5UiBfWf0tgnSDTw6lxxsaGmh6CiIiIiAig06SlGrjdbiZNmkSrVq2IiorC6XQyfPhwVqxYUeFCYt999x1JSUnWfZvNxunTpy/bmLSicfWzeUpYu3atVlc0xO12q78ham+O2puj9uaovVnqb46v2+s0abkoQ4cO5dSpU6SmplKvXj28Xi/vv/8+p06dqvA1jRs3ZsOGDT4bk1Y0rn42vJw5c0arKxri9aq/KWpvjtqbo/bmqL1Z6m+Or9vryLBU2b59+1i2bBnz58+nXr16wNmjvPfddx/NmjXD7XYzcuRI69JJ564jnJWVVeryUj+1d+9eevXqRUJCAjExMcyaNavC9y8sLCQvL6/UTUREREREpCo0GZYq27FjBy1btiQ0tPzviu7Zs4eBAweyc+dOHnvsMZ5//vnz7q+kpIQHH3yQ1157ja1bt7J582befvtttm7dWu72KSkphISEWLewsLBL/kwiIiIiIlKzaDIsl12LFi1o3749AImJiezfv/+822dmZrJ7924eeOABnE4nHTt25MyZM3z99dflbj9u3Dhyc3Ot2+HDhwHw2LTCX3Xz2BwkJiZqdUVDHA71N0XtzVF7c9TeHLU3S/3N8XV7fWdYqiwuLo69e/eSnZ1NgwYNyjwfGBho/exwOC74hXev10v9+vVxuVyVev+AgAACAgLKPmGzVer1chnZbDRq1ND0KGosu91Oo0aNTA+jRlJ7c9TeHLU3R+3NUn9zfN1eR4alylq0aME999zD0KFDrdWgvV4vH3zwAd9++22V9xcREUFwcDDz58+3Htu3b995F+Mqj92jFf6qm93jZuXKlRQXF5seSo1UXFys/oaovTlqb47am6P2Zqm/Ob5uryPDclHmzZvHlClTaN++PX5+fng8Hjp37sydd95Z5X35+fnxySef8MQTTzB9+nRKSkoIDQ1lyZIlVdrP/0TVL/dItfhOcXExq/6jX0KYpMs8mKP25qi9OWpvjtqbpf7m+LK9JsNyUfz9/Zk0aRKTJk0q81zv3r2tn6OiosjKygIgPDy81HWFf7pEevPmzfn44499NVwREREREZFSdJq0iIiIiIiI1Dg2r64eLVe4vLw8QkJCeOGL/QQGBZsezlVvbOz/XVLr3IXQg4KCsGkBs2qn/uaovTlqb47am6P2Zqm/ORfT/tzcIDc3l+Dg888NdGT4EtlstlKn/sLZ04ErszLysGHDWLduHQArVqxg8+bNFzWGTz75hC5dupT73KBBg3j99det+ykpKURGRnLkyBHeeustpk6delHvWZHvv/9e/0jUMLVr1zY9hBpN/c1Re3PU3hy1N0ftzVJ/c3zZXpNhg+bMmUPXrl2BS5sMV9YzzzzDihUrWL9+PTfccAMjRozgmWee8el7Vie7t8T0EGoct9vNqlWrtKiEIepvjtqbo/bmqL05am+W+pvj6/aaDPtYeHg4EyZMIDExkZtuuokpU6ZYz3Xp0oUVK1awatUqPvroI6ZOnYrT6WTOnDkALFq0iPbt2xMXF0fnzp3ZuXMncHYF35EjR9KyZUvatWtnHV2uSElJCcOGDSMtLY3PPvvMWnF54sSJPPHEEwAsWLCA5ORkHnzwQaKjo4mPjy91maQXXniBFi1akJCQwPjx4wkPD7eemz17Ni1btiQ2Npbp06eXeu9PP/2UuLg4YmJiuPXWW/n6668B+Pzzz4mKiuLRRx8lJiaG6Oho0tPTGTRoENHR0bRv354jR46U+3kKCwvJy8srdRMREREREakKTYarwenTp0lNTWXr1q1MnTq1zCSvZ8+e3HXXXTzzzDO4XC6GDRvGl19+yd/+9jfWr1/Pjh07eOmll+jXrx8Ab7/9NpmZmezevZuNGzeyY8eO875/SkoK+/btY+XKldSpU6fC7bZu3crLL7/Mrl27SE5O5pVXXgFg5cqVfPDBB6SlpfHVV1+VGn9GRgYvvPAC69evJy0tjfz8fOu548eP069fP9555x3S09MZPnw49957r7WK9J49exg2bBjp6en07t2bbt26MXbsWHbt2kV8fHyp07t//nlCQkKsW1hY2Hk/v4iIiIiIyM9pMuwjP/3e7LlJbGhoKM2aNePAgQMXfP2HH37Izp07ad++PU6nk8cee4xTp06Rn5/PZ/+/vTuPj+nu////nMxEgmyIpbXFnmadSCSiaJCKqstWrRJbKVV1obQf0qKUorioaq/SUqpoVRFrL42tigQhY5emmqBKLVlVtsmc3x9+zrfTLJLI5CWZ5/12m9sts5wz73lwufrOOfM+e/diyJAhqFKlCqpUqYLhw4cXua+QkBCcP38ehw4dKvJ1D45eP/j50qVLAIC9e/fixRdfVL+4PmLECHWbffv24bnnnsMTTzwBAHj99dfV544ePQpvb294e3sDAMLDw/HHH3+ok+nmzZvD398fABAQEIDmzZvD3d0dABAYGIiEhIQCxxkREYG0tDT1dvXq1SI/FxERERER0T9xMvyIateujTt37pg9dvv2bdSpU0e9b29vr/6s1WqLdc67oigYOnQoDAaDert+/XqBXyB/2IJV7du3x/fff48BAwYgKiqq0NcVd5xFvV9JFs/65/sV9/3t7Ozg5ORkdgMAk0Zb7PemsqHT6dC9e3fodLxkuQT2l8P2ctheDtvLYXtZ7C/H0u05GX5EYWFhWL58uXp/zZo1aNq0qXqktLicnJyQlpam3u/ZsyfWrl2LK1euAABMJhNiY2MBAKGhoVi7di1yc3ORk5ODVatWPXT/HTt2xObNmxEeHo7du3eXaGydO3fGpk2bcPfuXSiKgi+//NLsuf/973+4ceMGAGDZsmXqc23btsWZM2dw9uxZAMC3336L+vXro379+iV6f3q8/f3UeCp/7C+H7eWwvRy2l8P2sthfjiXbczL8iD766CNcv34dPj4+0Ov1WL9+PTZu3Fji/QwePBjfffcd/Pz8sGLFCnTo0AHz589Hnz594OvrC09PT3z77bcAgJEjR6JFixbw8PBA+/btodfri/Ue7du3x5YtWzB48GD88MMPxR5bjx490KtXL+j1erRp0wYuLi5wcXEBAHh5eWHGjBno0KED/Pz8YGdnp25Xu3ZtrFu3DkOGDIGPjw8+++wzbNy40WKXXuJq0uXPaDRi//79XF1RCPvLYXs5bC+H7eWwvSz2l2Pp9hrlwWpGREV4cLFrRVEwadIkZGZm4rPPPpMeFoD/d2Ht27dvqytlU/nIzc3Frl270L17d9ja2koPx+qwvxy2l8P2ctheDtvLYn85pWn/YG6Qlpamfp2yMDzxnYplyJAhSEpKQlZWFjw9Pc1OhyYiIiIiIqpoOBmmYtmyZYv0EB7q07PJqOLEEx3K0hQ/14e+hotJyGJ/OWwvh+3lsL0ctpfF/nIs2Z6nST9m3NzcEBkZWezvAVva4zaegjw4FeK9g7/B3sFRejiVSnEmw0REREREj4uSnCbNBbRIVJl+GZ6/1yl3JpMJN2/ehMlkkh6KVWJ/OWwvh+3lsL0ctpfF/nIs3Z6T4QpAo9EgNTVVve/q6oqkpCTcunULbm5uiImJAQB8//338PX1RWZmJjIyMjBy5EgEBgbCx8cHo0aNQk5ODgAgJCQEkyZNQseOHdGoUSNMmzYNu3btQvv27eHm5oZFixaZvf+6devg7++P5s2bY8GCBerjsbGxaNeuHXx8fBAYGIjDhw8DAJKSktTVpgHg7t27ZitIazQavPfee2jTpg0iIiJw/fp1dO3aFR4eHujatStefvllzJgxo8SduJp0+cvLy0N0dDTy8theAvvLYXs5bC+H7eWwvSz2l2Pp9jz5vQKrXbs2vv76a4SHh+Obb77BhAkTsG/fPlStWhWjRo1Chw4d8MUXX0BRFIwcORJLlizB22+/DQC4fPky9u/fj/T0dLi5uSElJQU///wz/vjjD7Rq1QrDhw9XJ7R//vknYmNjcefOHbRu3RpPP/00AgIC0LdvX3zxxRcICwvDoUOH8MILL+DXX38t1ti1Wi2OHz8OAHjxxRcRHByMmTNn4saNG9Dr9XB3dy902+zsbGRnZ6v309PTS1mQiIiIiIisFSfDFVyHDh0wYsQItGvXDmvWrEHLli0BAJGRkYiOjlaP8mZmZkKr1arb9evXD1qtFjVq1EDTpk3Ro0cPaDQa1K9fH7Vr10ZSUpL6PeERI0ZAo9HA1dUVffv2xZ49e+Do6AgbGxuEhYUBuH8N47p168JgMKBBgwYPHffw4cPVn/fu3YuFCxcCAOrVq4cePXoUue3cuXMxc+bM4kciIiIiIiL6B06GKwCtVmt2akBWVpbZ83FxcahduzauXr2qPqYoCjZt2qROjv/J3t7ebP//vF/Ud3n/fspzQY/rdLoixwsADg4OJd7/AxEREZg4caJ6Pz09HQ0bNoSCorejsqfRaODo6PjQPzOyDPaXw/Zy2F4O28the1nsL8fS7fmd4QqgefPmOHr0KABg8+bN+Ouvv9TnPvnkE6SkpODUqVNYvny5+r3d3r1748MPP1QntSkpKcU+hfmfVq9eDQBITk7Gli1b0KVLF7Rq1QomkwlRUVEAgCNHjqinONerVw+KouD8+fMAgDVr1hS5/86dO6vv8eeff2LHjh1Fvt7Ozg5OTk5mNwBQbLRFbkdlT6fToXPnzrzcgBD2l8P2ctheDtvLYXtZ7C/H0u05GX4MhYWFoUGDBurt3Xffxfjx49G6dWvExcWhVq1aAICTJ09i4cKFWLduHerUqYO1a9di8ODBuHPnDhYvXoyqVatCr9fDx8cHXbp0QVJSUqnGU7t2bfj7+yMwMBBjx45Fu3btUKVKFWzevBnvvfcefHx8MGHCBHz//fdwcHCATqfD0qVL0aNHD7Rp0wa5ublF7n/JkiX4+eef4eHhgfDwcAQFBZktwFVsClf4K28mkwmXL1/m6opC2F8O28thezlsL4ftZbG/HEu353WGSVxmZiZsbW2h0+lw584dtG3bFmvXrkVQUFCxtn9wLbHbt2+rvyig8pGbm4tdu3ahe/fusLW1lR6O1WF/OWwvh+3lsL0ctpfF/nJK074k1xnmsX4Sl5CQgCFDhkBRFOTk5GDMmDHFnggTERERERGVBifDJM7HxwcGg+GR9/Pp2WRUceKJDqU1xc9VeghEREREROWG3xm2Mm5ubmjVqhX0ej2eeuopDBw40GxBroLMmDEDEyZMeOi+Y2Nj0b9//zIaaclxNenyp9FoULt2ba6uKIT95bC9HLaXw/Zy2F4W+8uxdHt+Z9jKuLm5ITIyEnq9HiaTCf/617/QvXt3vPHGG4VuM2PGDKSmpuKjjz4qv4GWwIPvBbx38DfYOzhKD6fC4pFhIiIiIqroSvKdYR4ZtmI5OTm4d+8eatSoAQBYuHAhAgMD0bp1a3Tr1g2XL1/Ot01ubi7GjBmDli1bom3btpg0aRJCQkIAAAcOHIBerwcAJCUlma0IfffuXbPf6Gg0GnzwwQcICgpSJ+hz585FQEAAWrRogQMHDpT482i4mnS5y8vLw8WLF82uK03lh/3lsL0ctpfD9nLYXhb7y7F0e06GrVD//v3V6wHb2NjgpZdewvr16xEfH4/o6GicPHkS4eHhGDNmTL5tP//8cyQkJODcuXP4+eefcfr06VKPw8HBAUePHsXKlSsxaNAgPPHEE4iNjcWcOXPw9ttvF7pddnY20tPTzW4AJ8MSTCYT4uPjeakBIewvh+3lsL0ctpfD9rLYX46l23MBLSu0YcMG6PV6GI1GvPbaa5g8eTKuXr2K48ePw9/fHwAK/e3L3r17MWjQIHVp86FDh2LFihWlGseD7xcHBATgr7/+wssvvwwACAwMREJCQqHbzZ07FzNnzizVexIREREREQE8MmzVdDodXnjhBfzvf/+DoiiIiIiAwWCAwWDAmTNncObMmYfuo7Avs+t0OrMJdVZWVr7X2NvbAwC0Wm2++0ajsdD3jIiIQFpamnq7evXqQ8dJRERERET0d5wMW7l9+/ahVatW6N27N5YtW4bk5GQA978bHBcXl+/1nTt3xvr165Gbm4vc3FysWbOmwP3Wq1cPiqLg/PnzAFDo60rDzs4OTk5OZjeAq0lLsLGxQaNGjWBjw39KJLC/HLaXw/Zy2F4O28tifzmWbs/TpK1Q//79UbVqVRiNRjRu3BjLli1Dw4YNcefOHXTq1AkAYDQaMXz4cPj5+Zlt+9prr+HMmTPw8PBAjRo1EBAQgD/++CPfe+h0OixduhQ9evRArVq10K9fP4t/LsVGa/H3IHNarTbf3xEqP+wvh+3lsL0ctpfD9rLYX46l2/PSSlRiGRkZcHR0RG5uLsLDw+Hv74/JkyeLjefB8unJycnqythUPvLy8nD69Gn4+Piop7tT+WF/OWwvh+3lsL0ctpfF/nJK056XViKLCg0NhV6vh7e3N5ycnDBu3DjpIQEAV/gTYDKZcOXKFbYXwv5y2F4O28thezlsL4v95Vi6PU+TphI7evSo9BCIiIiIiIgeCSfDVGl8ejYZVZx41n9xTPFzlR4CEREREZEoniZdieXm5mLmzJlwd3eHp6cn/Pz80Lt3bxgMBumhWYSi4V/n8mZjY4NWrVpxdUUh7C+H7eWwvRy2l8P2sthfjqXbcwGtSmzQoEG4e/cuVq1apS4stWfPHty5cwf9+/cv1j4enJ//OP+P/8GX5N87+BvsHRylh1Mh8MgwEREREVVGXECLkJCQgC1btuDLL780W2E5NDRUnQgvXLgQgYGBaN26Nbp164bLly8DAGbMmIEXXngBYWFh8PLyQnR0NFxcXDBt2jS0bt0aLVq0wOHDh/Hmm29Cr9fDy8sLZ8+eBQDcuHEDnTp1gr+/Pzw9PTF27Fh1Qr169WqEhoZiwIAB8Pb2RkBAAH777TcAQI8ePbB+/Xp1nD/++COCgoIK/GzZ2dlIT083uwGAxpRXxhXpYYxGI44cOQKj0Sg9FKvE/nLYXg7by2F7OWwvi/3lWLo9J8OVVFxcHJo3b46aNWsW+Pz69esRHx+P6OhonDx5EuHh4RgzZoz6fHR0NNasWYPz58+jfv36SEtLg7+/P06ePIkpU6YgLCwMPXv2hMFgwNChQzFz5kwAgIuLC7Zv344TJ07g9OnTSEpKwnfffafu9/jx45gzZw7OnDmD0NBQfPjhhwCA8ePH45NPPlFf9+mnn2Ls2LEFjn3u3LlwdnZWbw0bNgQAaMCTHMqboii4desWeIKJDPaXw/Zy2F4O28the1nsL8fS7TkZthKXLl2CXq9Hq1at8MorryAyMhJ79uyBv78/9Ho95s+fjytXrqiv7969O+rWravet7e3R+/evQEAAQEBcHBwQKdOnQAAgYGBSEhIAHD/tOrJkyfD19cXfn5+iI2NNfuOcnBwMJo0aaL+fOnSJQDAs88+i7S0NMTFxeHy5cs4duwYXnrppQI/S0REBNLS0tTb1atXy6wTERERERFZB64mXUn5+fnh119/RUpKCmrUqIFmzZrBYDBg9erViIyMhK2tLSIiIjBq1KgCt3dwcDC7b2dnp/6s1Wphb29vdv/BqQuLFi3CzZs3cfToUdjb22PixInIyspSX1vYdgAwbtw4LF26FHXr1sXw4cPN3vOfYynsOSIiIiIiouLgkeFKqkWLFujVqxdGjBiB1NRU9fG//voLANC7d28sW7YMycnJAO6vPB0XF/fI75uSkoJ69erB3t4eN27cwMaNG4u97eDBg7F7926sWrUKo0ePLvF7m7iadLnTarXQ6/XQarXSQ7FK7C+H7eWwvRy2l8P2sthfjqXb88hwJbZ69Wp88MEHCAoKgk6nQ40aNVC7dm1MnjwZwcHBuHPnjnqqs9FoxPDhw+Hn5/dI7zl+/Hj069cPnp6eePLJJxEaGlrsbatVq4a+ffvijz/+UL8HXCKcDJc7GxsbNG7cWHoYVov95bC9HLaXw/Zy2F4W+8uxdHteWokeG3l5efD398fSpUvRoUOHYm/3YPn0O3fuFLpgGFmG0WjEwYMH0bFjR+h0/N1aeWN/OWwvh+3lsL0ctpfF/nJK056XVqIKZ9u2bWjWrBmCg4NLNBH+O/5ep/wpioKMjAy2F8L+ctheDtvLYXs5bC+L/eVYuj1/tUGPhZ49e6Jnz56PtI9PzyajihP/kfqnKX6u0kMgIiIiInrs8Mgwqdzc3Mwug1QcM2bMwIQJE8p0HDt27EBISEiZ7pOIiIiIiOjvOBmmIv390kePO5OGK/yVN61Wi+DgYK6uKIT95bC9HLaXw/Zy2F4W+8uxdHueJk35hISEwMfHB8ePH0fVqlWxb98+LFy4EN999x2MRiPq1KmD5cuX51vZ7cyZM3j99ddx7949ZGVlYeDAgZg6dSqA+0eQL1y4gHv37uHSpUuoV68evv/+e9SsWRO5ubkYP348oqKiUKNGjYd+Zzg7OxvZ2dnq/fT09Ps/aDRlG4IeysbGBnXq1JEehtVifzlsL4ft5bC9HLaXxf5yLN2eR4apQL/88gsOHjyIffv2Yf369YiPj0d0dDROnjyJ8PBwjBkzJt82bm5u2Lt3L06ePIkTJ05g06ZNiImJUZ8/evQoVq9ejfPnz6sTagD4/PPPER8fj3PnzuHQoUM4efJkkWObO3cunJ2d1duDyzDZmCrOUezKIjc3Fzt37kRubq70UKwS+8thezlsL4ft5bC9LPaXY+n2PDJMBRo0aBBsbW0BAJGRkTh+/Dj8/f0B3L8EUkEyMzMxZswYGAwG2NjY4OrVqzAYDGjbti0AoFu3bqhVqxYAIDg4GGfOnAEA7N27F0OGDEGVKlUAAMOHD8fKlSsLHVtERAQmTpyo3k9PTy/ddYmpTFSkU+krI/aXw/Zy2F4O28the1nsL8eS7TkZpgI5ODioPyuKgoiICIwaNarIbd555x24uroiLi4OOp0Offv2RVZWlvq8vb29+rNWqy30L7bmIac729nZwc7Orjgfg4iIiIiIqEA8TZoeqnfv3li2bBmSk5MB3D9dIS4uLt/rUlJS0KBBA+h0OsTHxyMqKqpY+w8NDcXatWuRm5uLnJwcrFq1qkzHT0RERERE9E88MkwPFR4ejjt37qBTp04A7p+qMHz4cPj5+Zm9burUqRg8eDC++uorNGvWDJ07dy7W/keOHImzZ8/Cw8NDXUDrxIkTJR4nV5MufzqdDp06dYJOx39KJLC/HLaXw/Zy2F4O28tifzmWbq9RFEWxyJ6Jykl6ejqcnZ2RmpoKZ2dn6eFYFUVRYDQaodPpHnp6O5U99pfD9nLYXg7by2F7WewvpzTtH8wN0tLS4OTkVORreZo0VRpc2KD8GY1G7Nq1i+2FsL8ctpfD9nLYXg7by2J/OZZuz2P9VGl8ejYZVZx4osMUP1fpIRARERERPfZ4ZFiIRqNBamqq2WNubm4wGAwP3fbVV1/F/v37Ady/7NHfr+VbEjt27EBISEiBz505cwadO3eGr68vvLy80KZNG5w9e7bE72kwGPDtt9+WanxERERERESWwslwBbRixQp1MatHmQwXZcCAAfj3v/+NU6dO4ezZs9i8eTPq1KlT4vfkZJiIiIiIiB5HnAw/ptzc3DB9+nQEBwejSZMmmD17tvpcSEgIIiMjsWvXLmzbtg0LFiyAXq/HihUrAABff/01goKC0Lp1a3Ts2BGnTp0CcP+SSGPGjEGLFi0QGBioHl0uyO+//4769eur9xs2bIg6deoU+J43btxAp06d4O/vD09PT4wdOxYmkwk3b97E9OnTsX//fuj1eowePRoAcPz4cXTu3BkBAQHw8/PDxo0bAQC3bt1C165d4e3tDR8fH7zyyislasbVpMufTqdD9+7dubqiEPaXw/Zy2F4O28the1nsL8fS7fkn+hhLTU1FdHQ0bt++jWbNmuGVV14xm6B2794dPXv2hF6vx4QJEwAAhw8fxjfffIODBw/Czs4OP//8MwYOHIhz587h888/R3x8PM6dOwcACAsLK/S9p02bhk6dOqFt27Zo27Yt+vXrBz8/vwLfMysrC9u3b4eDgwPy8vLQq1cvfPfdd3j55Zfx/vvvIzIyEpGRkepnGjVqFHbt2oUnnngCt2/fRuvWrdGuXTt89913aNKkCX788UcAUK9r/E/Z2dnIzs5W76enp5c2MZWBzMxMODo6Sg/DarG/HLaXw/Zy2F4O28tifzmWbM8jw4+Zvy8ZPnDgQACAq6srmjZtisTExIduv3XrVpw6dQpBQUHQ6/X497//jeTkZGRmZmLv3r0YMmQIqlSpgipVqmD48OGF7mfSpEn47bff8OqrryI5ORkdOnTAhg0bCnytyWTC5MmT4evrCz8/P8TGxhb63ecjR47gt99+w3PPPQe9Xo/Q0FAAQHx8PNq2bYsffvgBkyZNwtatW1G9evUC9zF37lw4Ozurt4YNGwIAbJS8h/ahsmU0GrF//36uriiE/eWwvRy2l8P2ctheFvvLsXR7HhkWUrt2bdy5cwcuLi7qY7dv31a/lwsA9vb26s9arbZYfwkURcHQoUMxZ86ch772Ydfqqlu3LgYMGIABAwagcePGWLduHfr375/vdYsWLcLNmzdx9OhR2NvbY+LEicjKyip0fJ6enjhy5EiBzxsMBuzZswebN2/GtGnTEBcXB63W/PTniIgITJw4Ub2fnp6uToiJiIiIiIiKg0eGhYSFhWH58uXq/TVr1qBp06Z44oknSrQfJycnpKWlqfd79uyJtWvX4sqVKwDuH7WNjY0FAISGhmLt2rXIzc1FTk4OVq1aVeh+t2zZgtzcXAD3fyNz+vRpNGvWrMD3TElJQb169WBvb48bN26o3wEu6LXt2rVDYmIi9uzZoz5mMBiQk5ODxMREODg44KWXXsLSpUvxyy+/4O7du/nGZmdnBycnJ7MbERERERFRSXAyLOSjjz7C9evX4ePjA71ej/Xr15tNIotr8ODB+O677+Dn54cVK1agQ4cOmD9/Pvr06QNfX194enqqqzmPHDkSLVq0gIeHB9q3bw+9Xl/ofjdv3gwvLy/4+PjA19cXdnZ2mDlzZoHvOX78eBw9ehSenp4YPHiweuozAHTp0gXZ2dnw8fHB6NGjUaNGDezcuRNz5syBr68vPDw8MGXKFJhMJhw4cAD+/v7Q6/Vo164dFixYAGdn5xI3ofLFxSRksb8ctpfD9nLYXg7by2J/OZZsr1EURbHY3onKQXp6OpydnZGWlsajxEREREREVqwkcwMeGaZKw2QySQ/B6jy4hBbby2B/OWwvh+3lsL0ctpfF/nIs3Z6TYao08vK4mnR5y8vLQ3R0NNsLYX85bC+H7eWwvRy2l8X+cizdnie/U6Xx6dlkVHGyrrP+p/i5Sg+BiIiIiKhC4pFhUmVkZMDBwQEjRox4pP0cOHAA//vf/4p8zbJly7BgwYJHeh8iIiIiIqLS4mSYVBs2bIC/vz82b95c4CWNiuthk2Gj0YjRo0fj7bffLvV7FERB0ddNprKn0Wjg6Oj40GtWk2Wwvxy2l8P2ctheDtvLYn85lm7P1aRJFRwcjGnTpmH58uXo2bMnRowYgdWrVyMyMhKRkZEAgB07dmDhwoU4cOAAEhISMGzYMNy9excmkwm9evVCv3790K1bN+Tl5aF+/fro27cvhgwZAr1ej9deew1RUVEYMmQIUlNTkZqaio8++ghnzpzB66+/jnv37iErKwsDBw7E1KlTCx1ndnY2srOz1fvp6elo2LAh3jv4G+wdHC2d6bHC06SJiIiIiP4friZNJXb+/HlcvXoVYWFhGDFiBFauXPnQbT755BP06NEDp06dwpkzZzBx4kTo9XqMHj0a4eHhMBgMmD59OgAgLS0Nnp6eOHnyJCZMmGC2Hzc3N+zduxcnT57EiRMnsGnTJsTExBT6vnPnzoWzs7N6a9iw4f0nFK7wV95MJhMuX77M1RWFsL8ctpfD9nLYXg7by2J/OZZuz8kwAQBWrlyJIUOGQKvVonv37khMTMSFCxeK3KZjx4744osv8O677+LHH3+Ei4tLoa+1tbXFoEGDCnwuMzMTr776Kry9vdG2bVtcvnwZBoOh0H1FREQgLS1NvV29ehUAYMPJcLnLy8uDwWDg6opC2F8O28thezlsL4ftZbG/HEu352SYkJubi6+//hpfffUV3Nzc0Lx5c9y7dw8rV66ETqcz+8uXlZWl/vzCCy/g8OHDaNWqlXqUuDDVqlWDjU3Bf93eeecduLq6Ii4uDqdOnUJISIjZ+/yTnZ0dnJyczG5EREREREQlwckwYdu2bWjatCmuXbuGpKQkJCUlISYmBl9//TUaN26M06dPIzMzE0ajEevXr1e3S0hIQN26dTFkyBDMnz9fPbXZyckJaWlpxX7/lJQUNGjQADqdDvHx8YiKiirzz0hERERERPR3nAwTVq5cifDwcLPHnnrqKdSvXx+3bt1C9+7d4eXlhZCQELRo0UJ9zffffw9vb2/4+fmhf//+WLZsGQCgT58+MBgM0Ov1eP/99x/6/lOnTsWqVavg4+ODKVOmoHPnzqX6HFxNuvxpNBrUrl2bqysKYX85bC+H7eWwvRy2l8X+cizdnqtJU4VXkhXjiIiIiIio8uJq0mSVuKhB+cvLy8PFixfZXgj7y2F7OWwvh+3lsL0s9pdj6fY6i+yVSMDS07dRxany/iP1OF5T2GQyIT4+Hs2aNYNWq5UejtVhfzlsL4ft5bC9HLaXxf5yLN2eR4YrKKPRiJkzZ8Ld3R1eXl7Q6/UYNWoUUlNTS7W/1atX4+LFi6Xa1s3NDa1atYJer0erVq0wb9489bnY2Fj079+/VPslIiIiIiKyFB4ZrqBGjBiB5ORkREdHo0aNGlAUBd9//z2Sk5OLvN5vYVavXg0XFxe4u7uXajwbNmyAXq/HtWvX4OHhgc6dOyMwMBABAQHYsGFDqfZJRERERERkKTwyXAH9+uuv2LhxI1atWoUaNWoAuL/S2osvvoimTZsCABYsWABPT094e3sjPDxcvdTR9u3b4ePjA71eDy8vL2zduhUrVqxAbGws3nzzTej1euzatQtnzpxB+/bt0bp1a3h4eGD27NnFGlv9+vXh7u6Oy5cvAwAOHDgAvV6vPr9z5060adMGvr6+0Ov1OHr0KAAgPDwcAQEB8PHxwfPPP48bN24U+h7Z2dlIT083uwFcTVqCjY0NGjVqVOg1pMmy2F8O28thezlsL4ftZbG/HEu352rSFdB3332HDz74AKdOnSrw+R9++AETJ05EdHQ0XFxcMGrUKGi1Wnz22Wfw9fXFsmXLEBwcDJPJhPT0dLi4uCAkJAQTJkxA7969AQAZGRmoUqUK7OzskJmZiXbt2uGzzz5D27Zt872fm5sbIiMjodfrcfHiRfTo0QPR0dGoXbs2Dhw4gAkTJsBgMOCXX35B+/btcfDgQbi7uyM3Nxf37t2Ds7Mzbt26hdq1awMA5s2bh6SkJPVSTf80Y8YMzJw5M9/j7x38DfYOjqWs+vh7HL8zTERERET0OOFq0lZuz5496N+/v3q69Ouvv46oqCgAQJcuXTB+/HjMnz8fp0+fLvSU6szMTLz66qvw9vZG27ZtcfnyZRgMhkLfs3///njqqafg4eGBf//73+rE9u+ioqLQrVs39VRsW1tbODs7AwDWr1+PgIAAeHl5YcWKFUW+V0REBNLS0tTb1atXAQAaU+VdPOtxlZeXh7i4OK6uKIT95bC9HLaXw/Zy2F4W+8uxdHtOhiug1q1bIyEhAXfu3CnW6/9+kepFixZh1apVqFatGoYOHYr58+cXuM0777wDV1dXxMXF4dSpUwgJCUFWVlah77FhwwZcuHABP/74I6ZMmYIzZ84U+/McOnQIH3/8MXbt2oWzZ89i0aJFRb6XnZ0dnJyczG4AoAFPcihvJpMJV65cgclkkh6KVWJ/OWwvh+3lsL0ctpfF/nIs3Z6T4QqoefPmeOGFFzBixAh19WhFUbBp0yb89ttvCA0NxXfffad+l3b58uXo2rUrAODixYvw9PTE2LFj8frrryMmJgYA4OTkpH6vGABSUlLQoEED6HQ6xMfHq0eWHyY0NBSvv/46pk6dmu+5sLAw7N69W121Ojc3F2lpaUhJSYGjoyNq1aqFnJwcLF++vNRtiIiIiIiIioOrSVdQX375JWbPno2goCDodDqYTCZ07NgRXbp0wXPPPYezZ88iODgYNjY28PHxwX//+18A94/4xsfHo0qVKqhWrRo+++wzAMCoUaMwadIkLF68GHPmzMHUqVMxePBgfPXVV2jWrBk6d+5c7LFNmzYNzZs3x4kTJ8web968OVatWoVBgwYhNzcXWq0Wy5YtQ7du3bB27Vq0atUKtWrVQmhoKK5du1Z2sYiIiIiIiP6BC2hRhffgS/LJycnq6tpUPvLy8pCQkIAWLVrwIvQC2F8O28thezlsL4ftZbG/nNK0L8kCWpwMU4VXkr/wRERERERUeZVkbsDTpKnSWBx3E3ZOOdLDKBMV5TJKRqMRx44dQ2BgIHQ6/nNS3thfDtvLYXs5bC+H7WWxvxxLt+cCWlRsbm5uRV7yqKwMGzYMH330UYm342rS5U9RFNy6dQs8wUQG+8thezlsL4ft5bC9LPaXY+n2nAyTRRiNRukhEBERERERFYqTYSqx2bNn46mnnoJer4der8fly5cB3L+e8XvvvYc2bdogIiICZ86cQfv27dG6dWt4eHhg9uzZ6j5yc3MxZcoUBAYGQq/X46WXXkJKSorURyIiIiIiIivDk96pRBRFwcKFC3H9+nVUrVoV9+7dg43N//udilarxfHjxwEAGRkZ2Lt3L+zs7JCZmYl27dohNDQUbdu2xYIFC1C9enUcO3YMADBr1ixMnToVn3766UPHkJ2djezsbPX+g+spmzT83U5502q10Ov1XFlRCPvLYXs5bC+H7eWwvSz2l2Pp9pwMU4mYTCa0aNECgwYNQteuXfH888+jQYMG6vPDhw9Xf87MzMSYMWNgMBhgY2ODq1evwmAwoG3btoiMjERaWho2bdoEAMjJyYGbm1uxxjB37lzMnDkz/xOcDJc7GxsbNG7cWHoYVov95bC9HLaXw/Zy2F4W+8uxdHvOHqhEtFotYmJiMGHCBNy8eRNt27bFzz//rD7v4OCg/vzOO+/A1dUVcXFxOHXqFEJCQpCVlQXg/hHmpUuXwmAwwGAw4Pz589i1a1exxhAREYG0tDT1dvXqVQCAxpRXhp+UisNoNGLfvn38jrgQ9pfD9nLYXg7by2F7Wewvx9LtORmmEsnIyMCff/6JDh06YNq0aWjfvj3i4uIKfG1KSgoaNGgAnU6H+Ph4REVFqc/17t0bixcvxr179wAA9+7dw7lz54o1Bjs7Ozg5OZndAK4mLUFRFGRkZHB1RSHsL4ft5bC9HLaXw/ay2F+OpdvzNGkqEY1Gg759++Kvv/6CRqNBixYtMHTo0AJfO3XqVAwePBhfffUVmjVrhs6dO6vPTZ48GdnZ2QgKCoJGo1Ef8/T0LJfPQURERERE1o2TYSq2pKQkAEBMTEyBz//zNzZ+fn44e/Zsga/V6XR4//338f777+d7bvXq1aUa3xteNVGrVq1SbUtERERERNZFo/B4P1Vw6enpcHZ2RkpKClxcXKSHY1VMJhNu374NV1dXs1XFqXywvxy2l8P2ctheDtvLYn85pWn/YG6Qlpamfp2yMJwMU4X34C/8ewd/g72Do/RwSmWKn6v0EIiIiIiIKrySTIb5qw2qNGxMXOGvvOXm5mLnzp3Izc2VHopVYn85bC+H7eWwvRy2l8X+cizdnpNhK5CRkQEHBweMGDHikfZz4MAB/O9//yv19sOGDcNHH30EAFi2bBkWLFjwSOOhxwMvMyCL/eWwvRy2l8P2ctheFvvLsWR7ToatwIYNG+Dv74/Nmzfj7t27pd7PwybDJfmLOnr0aLz99tulHgsREREREdGj4GTYCqxcuRKTJ09Gx44dsWHDBgD3V2zu3bu3+podO3YgJCQEAJCQkICnn34avr6+8Pb2xtSpU2EwGLBs2TKsW7cOer0e77//PpKSkuDi4oLJkyejdevW+OSTT7B3714EBwfDz88Pnp6eWLlyZYFjmjFjBiZMmAAAOHPmDNq3b4/WrVvDw8MDs2fPLvLzZGdnIz093exGRERERERUEry0UiV3/vx5XL16FWFhYTAajZg3b95DT5f+5JNP0KNHD0RERAAAkpOTUbNmTYwePRqpqanqqc5JSUlIS0uDp6cnPvzwQwBASkoKDh06BK1Wi+TkZPj5+SEsLAwNGjQo9P3c3Nywd+9e2NnZITMzE+3atUNoaCjatm1b4Ovnzp2LmTNn5nvcpNEWJwmVIZ1Oh06dOkGn4z8lEthfDtvLYXs5bC+H7WWxvxxLt+eR4Upu5cqVGDJkCLRaLbp3747ExERcuHChyG06duyIL774Au+++y5+/PHHIi9XZGtri0GDBqn379y5gxdffBFeXl7o3Lkz7ty5U+i1hh/IzMzEq6++Cm9vb7Rt2xaXL1+GwWAo9PURERFIS0tTb1evXi1y/2RZVatWlR6CVWN/OWwvh+3lsL0ctpfF/nIs2Z6T4UosNzcXX3/9Nb766iu4ubmhefPmuHfvHlauXAmdToe8vDz1tVlZWerPL7zwAg4fPoxWrVqpR4kLU61aNbNrfo0ePRrt27fHmTNnYDAY0LJlS7N9F+Sdd96Bq6sr4uLicOrUKYSEhBS5jZ2dHZycnMxuAGCj5BW6DVmG0WjErl27uKiEEPaXw/Zy2F4O28the1nsL8fS7TkZrsS2bduGpk2b4tq1a0hKSkJSUhJiYmLw9ddfo3Hjxjh9+jQyMzNhNBqxfv16dbuEhATUrVsXQ4YMwfz58xETEwMAcHJyQlpaWpHvmZKSgsaNG0Oj0eDgwYM4derUQ8eZkpKCBg0aQKfTIT4+HlFRUY/2wYmIiIiIiB6CJ75XYitXrkR4eLjZY0899RTq16+PW7duoXv37vDy8sITTzyBp59+GkePHgUAfP/991i7di2qVKkCk8mEZcuWAQD69OmDr7/+Gnq9Hn379sWQIUPyvee8efMwZswYzJo1C3q9HkFBQQ8d59SpUzF48GB89dVXaNasGTp37lyqz/uGV03UqlWrVNsSEREREZF14WS4Etu1a1eBj588eRIA0Ldv3wKfj4iIUBfP+rsmTZogLi7O7LHU1FSz+88++ywSEhIK3O/q1avVn2fMmKH+7Ofn99DvFRMREREREZUljaIoivQgiB5Feno6nJ2dkZqaCmdnZ+nhWBVFUWA0GqHT6aDRaKSHY3XYXw7by2F7OWwvh+1lsb+c0rR/MDdIS0tT1xYqDI8MU6Wx+NQd2DvmSg/joab4uUoPoUxlZmbC0dFRehhWi/3lsL0ctpfD9nLYXhb7y7Fkey6gZaU2b94Mf39/6PV6uLu7o3PnzjCZTGX+PgEBAThw4ECZ77cgXE26/BmNRuzfv5+rKwphfzlsL4ft5bC9HLaXxf5yLN2eR4at0PXr1zFq1CicOHECjRs3BnD/e8Q87YOIiIiIiKwFjwxboT///BNarRY1a9ZUH2vdujWioqLQtWtXAPfPtbe1tcXnn38OAFizZg2GDx8OALhx4wZeeuklBAYGwtvbG1OnTlX3c+TIEej1enh5eeGVV14x+y1OUdu5ublh+vTpCA4ORpMmTTB79uxCx5+dnY309HSzGxERERERUUlwMmyFfHx80L59ezRu3Bh9+vTBggULcO3aNXTo0AGxsbHIzs7G/v370aZNG+zZswcAEBUVhdDQUADA0KFD8cYbb+DYsWOIi4tDbGwsNm7ciJycHPTv3x8LFy7E2bNnMWDAALPrDBe23QOpqamIjo7G8ePH1TEVZO7cuXB2dlZvDRs2tGAtehidjieYSGJ/OWwvh+3lsL0ctpfF/nIs2Z6rSVuxixcv4qeffsIPP/yAAwcOIDY2FqNGjcLUqVOxZcsWPPvss5g4cSJ++eUXNGzYECdOnICjoyOcnZ3h5eWl7ufu3bsYNmwYevbsiV69eiExMVF9rlmzZli5ciXatGlT6HZTp06Fm5sbvv32W7Rt2xbA/cstLV26FO3bt8837uzsbGRnZ6v309PT0bBhQ7x38DfYOzz+CxtUtgW0iIiIiIgeF1xNmorF3d0d7u7ueO2119CtWzds27YNoaGh2LNnDw4ePIh58+bB29sba9euRY0aNVCvXj3cvXsXABATEwN7e3uz/Z0+fTrfezz4HvKD37kUtN0Df39cq9UW+kV5Ozs72NnZ5X+Cv9cpdyaTCbdv34arqytsbHiiSXljfzlsL4ft5bC9HLaXxf5yLN2ef5pW6Nq1azh8+LB6PyUlBYmJiWjWrBlCQ0Oxfv16uLi4oHr16ggNDcX06dPVU6QdHBzQqVMnzJs3T93+jz/+wO+//w53d3d1xTcA2LNnDy5duvTQ7coKV5Muf3l5eYiOjkZeHttLYH85bC+H7eWwvRy2l8X+cizdnkeGrZDRaMT777+PxMREVKtWDUajEUOHDkWvXr1gMpmQlpaGLl26AACeffZZjB07Vr0PAOvWrcPEiRPh5eUFjUaD6tWrY/ny5WjQoAE2bNiAMWPGIC8vD23atIGvr2+xtiMiIiIiIipPnAxbocaNG2P37t0FPmdjY4OUlBT1fsuWLfHPr5XXqVMHa9euLXD7du3awWAwFPhcUdslJSWZ3Y+NjS1k9IV7w6smatWqVeLtiIiIiIjI+vA0aao0eJ3k8qfRaODo6Mj2QthfDtvLYXs5bC+H7WWxvxxLt+dq0lThPVgx7nFcTZorRxMRERERlZ+SrCbNI8PC3Nzc1IWnHggICMCBAwfK/L2WLVsGvV5vdqtduzbc3d0fab8hISGIjIwsm0H+/9566y3MmDGjZBsppjIdAz2cyWTC5cuXYTKxvQT2l8P2ctheDtvLYXtZ7C/H0u05GX4MZGdnY+XKlRZ/n9GjR8NgMKi3rVu3wsbGBh9//LHF37s82HAyXO7y8vJgMBi4uqIQ9pfD9nLYXg7by2F7Wewvx9LtORl+DMyYMQOzZs3CvXv38j2XkZGBkSNHIjAwED4+Phg1ahRycnLwyy+/oGXLlgDuX8O3bt26eOeddwAABw8eROfOnYt8z8zMTPTp0wdvvvkmunbtCgDIzc3FlClTEBgYCL1ej5deekldTGv9+vUICgqCn58ffH19sX379gL3W9TrQkJC8NZbb6FDhw5o1qwZRo8erT53/fp1hIWFwcPDA6GhoWV6ySUiIiIiIqJ/4mT4MeDr64tOnTph8eLF+Z6bNGkSOnTogGPHjuHUqVMwmUxYsmQJWrZsiezsbFy5cgWnT59G06ZNsXfvXgBAVFSUel3gwrz22mto2rQppkyZoj62YMECVK9eHceOHYPBYIC3tzemTp0KAAgLC0NMTAzi4uKwdetWjBw5EtnZ2fn2+7DXXbp0Cfv378fZs2exe/duREdHAwDGjRuHwMBAnD9/Hl999ZX6WQqSnZ2N9PR0sxsREREREVFJ8NJKj4lZs2YhMDDQ7GgpAERGRiI6OhqLFi0CcP+IrlarBQB06dIFe/bsQUpKCgYPHozPP/8cqamp2LNnDz766KNC3+vjjz/GiRMncPTo0XzvlZaWhk2bNgEAcnJy4ObmBgBITExEeHg4fv/9d+h0OiQnJyMxMTHf940f9rr+/ftDp9NBp9NBr9fj0qVLCA4Oxt69e7Fw4UIAQP369dGzZ89Cxz937lzMnDkz3+MKuMJfedNoNKhduzZXVxTC/nLYXg7by2F7OWwvi/3lWLo9J8OPCTc3NwwcOBCzZ882e1xRFGzatEk9JfrvQkNDsWPHDqSkpGDJkiVISEjAli1bkJCQgICAgALf5+DBg5g1axYOHz4MBweHfO+1dOlS9bTpv3v55Zcxb9489OvXDwBQs2ZNZGVllfh19vb26s9ardZs4bC/K+ovfEREBCZOnKjeT09PR8OGDaHYaAvdhixDp9OhXbt20sOwWuwvh+3lsL0ctpfD9rLYX46l2/M06cfI1KlTsXbtWvzxxx/qY71798aHH36oThpTUlLw66+/Arh/ZHjv3r1ISkpCy5YtERoaipkzZ6J9+/bq0eO/u3btGvr3748vv/yywMl17969sXjxYvW7y/fu3cO5c+fU923SpAkAYO3atep3if+puK/7p9DQUHz55ZcA7n9/eNu2bYW+1s7ODk5OTmY3ANBwAa1yl5eXh4sXL3JBCSHsL4ft5bC9HLaXw/ay2F+OpdtzMvwYcXV1xbhx43D9+nX1scWLF6Nq1arQ6/Xw8fFBly5dkJSUBACoW7cu6tati+DgYADAM888gz/++ANdunQpcP+zZs1Camoqpk2blu8SSwAwefJktGnTBkFBQfDx8UHbtm1hMBgAAEuWLEG/fv3g5+eHuLg4NGrUqMD3KO7rCtouJiYGHh4eGDJkyEMXACsIJ8Plz2QyIT4+npcaEML+ctheDtvLYXs5bC+L/eVYur1GURTFInsmKicPLqx9+/Zt1KpVS3o4ViU3Nxe7du1C9+7dYWtrKz0cq8P+ctheDtvLYXs5bC+L/eWUpv2DuUFaWpp6BmlheGSYiIiIiIiIrA4X0KJK45OzKbBzkjvRYYqfq9h7S7GxsUGjRo1gY8Pfq0lgfzlsL4ft5bC9HLaXxf5yLN2ep0lbMaPRiA8++ADffPONermjwMBAzJ8/Hy4uLhZ97x07dmDhwoU4cODAI+/rwakQ7x38DfYOjo8+uFKyxskwEREREdHjhKdJU7GMGDECsbGxiI6OxtmzZxEXF4dnn30WycnJ0kMrFY2JK/yVt7y8PMTFxXF1RSHsL4ft5bC9HLaXw/ay2F+OpdtzMmylfv31V2zcuBGrVq1CjRo1ANy/tu+LL76IatWqoVOnTvD394enpyfGjh2rruC2evVqhIaGYsCAAfD29kZAQAB+++03AMCNGzcK3S43NxdjxoxBixYtEBgYiP3796tjKWq7ktCAJzmUN5PJhCtXrnB1RSHsL4ft5bC9HLaXw/ay2F+OpdtzMmylTp48iRYtWsDVNf+pvS4uLti+fTtOnDiB06dPIykpCd999536/PHjxzFnzhycOXMGoaGh+PDDDx+63eeff474+HicO3cOhw4dwsmTJ4v9fv+UnZ2N9PR0sxsREREREVFJcDJM+ZhMJkyePBm+vr7w8/NDbGyser1hAAgODkaTJk3Uny9duvTQ7fbu3YshQ4agSpUqqFKlCoYPH17s9/unuXPnwtnZWb01bNiwzBsQEREREVHlxsmwlWrdujUSEhJw586dfM8tWrQIN2/exNGjR3H69GkMHDgQWVlZ6vP29vbqz1qtFkajsVjb/Z1Goyn2+/1TREQE0tLS1NvVq1cBAIqGf53Lm42NDVq1asXVFYWwvxy2l8P2ctheDtvLYn85lm7PP1Er1bx5c7zwwgsYMWIEUlNTAQCKomDTpk04ceIE6tWrB3t7e9y4cQMbN24s1j5TUlIK3S40NBRr165Fbm4ucnJysGrVqmJtVxA7Ozs4OTmZ3QBOhiVotVq4u7tDq9VKD8Uqsb8ctpfD9nLYXg7by2J/OZZuz9mDFfvyyy/h6+uLoKAgeHp6wsPDAz/++COWLFmCo0ePwtPTE4MHD0ZoaGix9jd+/PhCtxs5ciRatGgBDw8PtG/fHnq9vljblQRXky5/RqMRR44cUc8OoPLF/nLYXg7by2F7OWwvi/3lWLq9ziJ7pQrB1tYWM2fOxMyZM/M9d+zYsQK3GTZsGIYNG6be79GjB3r06AEAaNSoUaHb2dra4r///W+BzxW1XUmM9aqBWrVqPfJ+qPgURcGtW7fAy5XLYH85bC+H7eWwvRy2l8X+cizdnkeGiYiIiIiIyOpwMkxERERERERWh6dJU6Wx9Fwq7B3L7/SVKX75r9FsbbRaLfR6PReUEML+ctheDtvLYXs5bC+L/eVYuj2PDD+G3Nzc4O7ubvZF8YCAABw4cKDI7VavXo2LFy+a3e/du3eZjCkkJASRkZFlsq8H3nrrLcyYMaPsdsjVpMudjY0NGjduzEsNCGF/OWwvh+3lsL0ctpfF/nIs3Z5/oo+p7OxsrFy5skTb/HMyXFby8irGKs1cTbr8GY1G7Nu3j6srCmF/OWwvh+3lsL0ctpfF/nIs3Z6T4cfUjBkzMGvWLNy7d8/s8YyMDIwcORKBgYHw8fHBqFGjkJOTgxUrViA2NhZvvvkm9Ho9du3aBQC4e/cuBgwYAG9vbwQEBOC3335T9/X1118jKCgIrVu3RseOHXHq1CkA9yfVnTp1wgsvvABvb+98Kz2vX78eQUFB8PPzg6+vL7Zv364+FxISgrfeegsdOnRAs2bNMHr0aPW569evIywsDB4eHggNDcXvv/+uPrd9+3b4+PhAr9fDy8sLW7duLbRNdnY20tPTzW4AoAFX+CtviqIgIyODqysKYX85bC+H7eWwvRy2l8X+cizdnpPhx5Svry86deqExYsXmz0+adIkdOjQAceOHcOpU6dgMpmwZMkSvPrqqwgICMDixYthMBjQvXt3AMDx48cxZ84cnDlzBqGhofjwww8BAIcPH8Y333yDgwcP4uTJk/jggw8wcOBA9X2OHj2qbhccHGw2hrCwMMTExCAuLg5bt27FyJEjkZ2drT5/6dIl7N+/H2fPnsXu3bsRHR0NABg3bhwCAwNx/vx5fPXVV9i7d6+6zdSpU7F8+XIYDAacPn0azzzzTKFt5s6dC2dnZ/XWsGHDUlYmIiIiIiJrxQW0HmOzZs1CYGCg2dHVyMhIREdHY9GiRQCAzMzMIr9QHhwcjCZNmqg/L126FACwdetWnDp1CkFBQeprk5OTkZmZCQBo164dWrVqVeA+ExMTER4ejt9//x06nQ7JyclITEyEu7s7AKB///7Q6XTQ6XTQ6/W4dOkSgoODsXfvXixcuBAAUL9+ffTs2VPdZ5cuXTB+/Hj069cPXbt2hV6vL/QzRUREYOLEier99PR0ToiJiIiIiKhEOBl+jLm5uWHgwIGYPXu2+piiKNi0aRNatmxZrH3Y29urP2u1WvV8e0VRMHToUMyZM6fA7RwcHArd58svv4x58+ahX79+AICaNWsiKyvroe/5TxqNRv150aJFOHfuHPbv34+hQ4ciPDwc//d//1fgdnZ2drCzs8v3uEnDFf7Km1arRXBwMFdXFML+ctheDtvLYXs5bC+L/eVYuj1Pk37MTZ06FWvXrsUff/wBAOjduzc+/PBDdYKZkpKCX3/9FQDg5OSEtLS0Yu23Z8+eWLt2La5cuQIAMJlMiI2NLda2KSkp6tHmtWvXIiUlpVjbhYaG4ssvvwRw//vD27ZtU5+7ePEiPD09MXbsWLz++uuIiYkp1j7N/G1yTeXDxsYGderU4eqKQthfDtvLYXs5bC+H7WWxvxxLt+ef6GPO1dUV48aNw/Xr1wEAixcvRtWqVaHX6+Hj44MuXbogKSkJADBq1CjMmTPHbAGtwnTo0AHz589Hnz594OvrC09PT3z77bfFGtOSJUvQr18/+Pn5IS4uDo0aNSr2djExMfDw8MCQIUPQuXNn9bl33nkHnp6e8PPzw9dff12qSy7ZmLjCX3nLzc3Fzp07kZubKz0Uq8T+ctheDtvLYXs5bC+L/eVYur1G4bJoVMGlp6fD2dkZt2/fRq1ataSHY1Vyc3Oxa9cudO/eHba2ttLDsTrsL4ft5bC9HLaXw/ay2F9Oado/mBukpaXBycmpyNfyyDARERERERFZHS6gRZXGp2eTUcXJMic6TPFztch+iYiIiIhIBk+TJri5ucHOzg5Vq1YFcP/Ugl69euW7xvE/JSUlQa/XIzU1tUzH4+rqitjYWLi5uRXr9Q9OhXjvp0uwdyz6VIjS4mS4YA8uhO7o6Gi2OjiVD/aXw/Zy2F4O28the1nsL6c07UtymjSPDBMAYMOGDUVe25eoMA9+iUIy2F8O28thezlsL4ftZbG/HEu253eGKZ/Vq1ejd+/eAIADBw7Ay8sLY8aMUVedLuwSTOHh4QgICICPjw+ef/553LhxA8D9I8guLi5477334O/vj+bNm5utdr1t2zY89dRT8PHxKfTawn+XnZ2N9PR0sxsA2Ch5j/jJqaSMRiN27dpV6LWkybLYXw7by2F7OWwvh+1lsb8cS7fnZJgAAP3794der4der8933eCLFy9i6NChOHXqFP7973/j3XffLXAfH330EWJjY3H69Gl06NDB7PJIaWlp8PHxwYkTJ/DJJ5/gzTffBADcvHkTr7zyCjZt2oTTp0+jefPmuHPnTpFjnTt3LpydndVbw4YNH+3DExERERGR1eFkmADcP03aYDDAYDCgRo0aZs81b94cQUFBAIDg4GBcunSpwH2sX78eAQEB8PLywooVK2AwGNTn7O3t0bdv33z7iImJgY+PDzw8PAAAI0aMQJUqVYoca0REBNLS0tTb1atXS/WZiYiIiIjIevE7w/RQ9vb26s9arbbA0xQOHTqEjz/+GNHR0ahTpw62bduG6dOnq8/b2dmpX3rXarXIyyv4lObifDHezs4OdnZ2Jf0YREREREREKh4ZpjKRkpICR0dH1KpVCzk5OVi+fHmxtgsODsbp06dx8eJFAMCXX36JnJycUo3BpNGWajsqPZ1Oh+7du0On4+/VJLC/HLaXw/Zy2F4O28tifzmWbs/JMJWJbt26oVWrVmjVqhU6dOhQ7JWpa9eujS+//BJ9+vSBr68vEhISUKtWLcsOlspUZmam9BCsGvvLYXs5bC+H7eWwvSz2l2PJ9rzOMFV4D64ldvv2bU6ky1lubi527dqF7t27w9bWVno4Vof95bC9HLaXw/Zy2F4W+8spTfuSXGeYR4aJiIiIiIjI6vDEd6o0Pj2bjCpOj36iwxQ/1zIYDRERERERPc54ZLgC2Lx5M/z9/aHX6+Hu7o7OnTvDZDIVa9tXX30V+/fvBwBERkYiJiamzMfn5uZmdhmlstCvXz+sXr26TPdJlsHFJGSxvxy2l8P2ctheDtvLYn85lmzP7ww/5q5fvw5vb2+cOHECjRs3BgCcPHkSfn5+xboM0d8NGzYMer0eEyZMKJOxPZiQN23aFJGRkcVeNKs4+vXrhx49emDYsGEPfe2D7wW8d/A32Ds4PvJ788gwEREREVHFxO8MVyJ//vkntFotatasqT7WunVrREVFoWvXrgDu/4Hb2tri888/BwCsWbMGw4cPBwCEhIQgMjISu3btwrZt27BgwQLo9XqsWLEC77//PvR6PfR6Pby8vKDRaHD58mUAwMKFCxEYGIjWrVujW7du6uMzZszACy+8gLCwMHh5eeH69etm4120aBHatGkDvV6PNm3aIDo6Wn3Ozc0N06dPR3BwMJo0aYLZs2erz128eBHt2rWDp6cnevfujfT09JLH4u91yp3JZMLNmzeLfaYClS32l8P2ctheDtvLYXtZ7C/H0u05GX7M+fj4oH379mjcuDH69OmDBQsW4Nq1a+jQoQNiY2ORnZ2N/fv3o02bNtizZw8AICoqCqGhoWb76d69O3r27Im3334bBoMBr776KqZPnw6DwQCDwYB27dph+PDhaNy4MdavX4/4+HhER0fj5MmTCA8Px5gxY9R9RUdHY82aNTh//jzq169v9j6DBw/G8ePHYTAYsHTpUrzyyitmz6empiI6OhrHjx9XP8uD7UaMGIFz585h1qxZ+Omnnwptkp2djfT0dLMbANgoeaUPTaWSl5eH6Oho5OWxvQT2l8P2ctheDtvLYXtZ7C/H0u158vtjzsbGBps2bcLFixfx008/4YcffsAHH3yA2NhY6PV6HD58GHv27MGUKVMwceJEmEwm7Nu3DwsWLCj2e8yePRtXrlzBjh07ANz/bvHx48fh7+8PAPn+8nXv3h1169YtcF9xcXH44IMPcOfOHeh0OsTHxyMzMxNVq1YFAAwcOBAA4OrqiqZNmyIxMRGOjo4wGAzqKdHe3t5o3759oeOdO3cuZs6cWezPR0RERERE9E+cDFcQ7u7ucHd3x2uvvYZu3bph27ZtCA0NxZ49e3Dw4EHMmzcP3t7eWLt2LWrUqIF69eoVa79r1qzB5s2bcfDgQfXL6YqiICIiAqNGjSpwGwcHhwIfz8nJQd++fdUj1Q/O18/OzlYnw/b29urrtVotjEZjgfsq6vvQERERmDhxono/PT0dDRs2LPqDEhERERER/Q1Pk37MXbt2DYcPH1bvp6SkIDExEc2aNUNoaCjWr18PFxcXVK9eHaGhoZg+fXq+U6QfcHJyQlpamnp/z549mDVrFnbu3Gk2we3duzeWLVuG5ORkAPcvdh0XF/fQsWZlZSEnJweNGjUCACxdurRYn9HJyQl+fn5Ys2YNAODcuXM4dOhQoa+3s7ODk5OT2Q0AFJRsQTF6dBqNBo6OjiVezI3KBvvLYXs5bC+H7eWwvSz2l2Pp9jwy/JgzGo14//33kZiYiGrVqsFoNGLo0KHo1asXTCYT0tLS0KVLFwDAs88+i7Fjx6r3/2nw4MEYNmwYIiMj8cYbb2DdunW4d+8ennvuOfU1u3btQnh4OO7cuYNOnTqpYxg+fDj8/PyKHKuTkxNmz56NwMBAuLq64uWXXy7251yzZg1eeeUV/Oc//0GLFi3QsWPHYm/7gGKjLfE29Gh0Oh06d+4sPQyrxf5y2F4O28thezlsL4v95Vi6PS+tRBXeg9OxU1JS4OLiIj0cq2IymXD16lU0bNgQNjY80aS8sb8ctpfD9nLYXg7by2J/OaVpz0srkVXiCn/lLy8vDwaDge2FsL8ctpfD9nLYXg7by2J/OZZuz8kwERERERERWR1+Z5gqjU/PJqOKU8nO+p/i52qh0RARERER0eOMR4YJbm5uMBgMpdo2JCQEkZGRZTqet956CzNmzCjxdlxNuvxpNBrUrl2bqysKYX85bC+H7eWwvRy2l8X+cizdnkeGqdLgatLlT6fToV27dtLDsFrsL4ft5bC9HLaXw/ay2F+OpdvzyDCprl27hn79+sHb2xs+Pj6YNm0aACAjIwMjR45EYGAgfHx8MGrUKOTk5OTbfv369QgKCoKfnx98fX2xfft29bmQkBC89dZb6NChA5o1a4bRo0erz12/fh1hYWHw8PBAaGgofv/99yLHmZ2djfT0dLMbAGgUU1lkoBLIy8vDxYsXuaCEEPaXw/Zy2F4O28the1nsL8fS7TkZJtWgQYPg7++PM2fO4PTp0xg3bhwAYNKkSejQoQOOHTuGU6dOwWQyYcmSJfm2DwsLQ0xMDOLi4rB161aMHDkS2dnZ6vOXLl3C/v37cfbsWezevRvR0dEAgHHjxiEwMBDnz5/HV199hb179xY5zrlz58LZ2Vm9NWzYEAAnwxJMJhPi4+NhMrG9BPaXw/Zy2F4O28the1nsL8fS7XmaNAEA7t69i0OHDmH37t3qY7Vr1wYAREZGIjo6GosWLQIAZGZmQqvNf0pyYmIiwsPD8fvvv0On0yE5ORmJiYlwd3cHAPTv3x86nQ46nQ56vR6XLl1CcHAw9u7di4ULFwIA6tevj549exY51oiICEycOFG9n56erk6IiYiIiIiIioOTYXooRVGwadMmtGzZssjXvfzyy5g3bx769esHAKhZsyaysrLU5+3t7dWftVotjEZjgft52Bfk7ezsYGdnV9zhExERERER5cPTpAkA4ODggI4dO+I///mP+titW7cAAL1798aHH36oTl5TUlLw66+/5ttHSkoKmjRpAgBYu3YtUlJSivXeoaGh+PLLLwHc//7wtm3bSvUZuJp0+bOxsUGjRo1gY8N/SiSwvxy2l8P2ctheDtvLYn85lm7PP1FSff3114iNjYWnpyf0ej0++eQTAMDixYtRtWpV6PV6+Pj4oEuXLkhKSsq3/ZIlS9CvXz/4+fkhLi4OjRo1Ktb7LlmyBDExMfDw8MCQIUPQuXPnUo2fq0mXP61WCz8/vwJPmyfLY385bC+H7eWwvRy2l8X+cizdXqMoimKRPROVk/T0dDg7OyM5ORk1atSQHo5VycvLw+nTp+Hj48P/gxDA/nLYXg7by2F7OWwvi/3llKb9g7lBWloanJycinwtjwxTpcEV/sqfyWTClStX2F4I+8thezlsL4ft5bC9LPaXY+n2XECLKo1PzyajilPRJzpM8XMtp9EQEREREdHjjEeGKyk3Nze0atUKvr6+aN68OXr16oUjR45ID6tASUlJcHFxkR4GERERERFZEU6GK7ENGzbg1KlT+PXXXzF06FB0794dR48elR6WmcIur1QaioZ/ncubjY0NWrVqxdUVhbC/HLaXw/Zy2F4O28tifzmWbs8/USvRt29fjB49GgsXLkRubi6mTJmCwMBA6PV6vPTSS+plkIYNG4bXXnsNXbp0QcuWLdG3b1/k5OQAAGbMmIGXXnoJ//rXv9CyZUv06NEDZ8+eRVhYGFq2bIkBAwao5/OvX78eQUFB8PPzg6+vL7Zv366OJSQkBOPGjUNwcDC6du1qNs6cnBwMGjQIr732GvLy8gr8LNnZ2UhPTze7AZwMS9BqtXB3d+diEkLYXw7by2F7OWwvh+1lsb8cS7fn7MGKBAUF4dy5c1iwYAGqV6+OY8eOwWAwwNvbG1OnTlVfZzAYsH37dly4cAF//vknNm3apD4XGxuLNWvWID4+HhkZGXj11Vfx/fff4/z587hw4QJ++OEHAEBYWBhiYmIQFxeHrVu3YuTIkcjOzlb388svv+DgwYPYt2+f+lhqaiq6desGDw8PLF++vNC/9HPnzoWzs7N6a9iwIQBAYyp48kyWYzQaceTIkTI9wk/Fx/5y2F4O28thezlsL4v95Vi6PRfQsiIPrqIVGRmJtLQ0dZKbk5MDNzc39XV9+vRBtWrVAACBgYG4dOmS+lzXrl3Vyxe1bt0adnZ2cHR0BAD4+fkhISEBAJCYmIjw8HD8/vvv0Ol0SE5ORmJiItzd3QEAgwYNgq2trbrfnJwcPP3005g8eTKGDBlS5OeIiIjAxIkT1fvp6elo2LAhNOBVwsqboii4desWeIU2Gewvh+3lsL0ctpfD9rLYX46l23MybEWOHz8OLy8vJCYmYunSpflOUX7A3t5e/Vmr1Zr9JuafzxX22pdffhnz5s1Dv379AAA1a9ZEVlaW+loHBwez97S1tUX79u2xfft2DBgwwGyi/E92dnaws7MrzkcmIiIiIiIqEE+TthJbt27FZ599hkmTJqF3795YvHgx7t27BwC4d+8ezp07V6bvl5KSgiZNmgAA1q5dq34nuTAajQbLly9Hw4YN0bt3b2RmZpbpeIiIiIiIiP6OR4Yrsf79+8Pe3h5//fUXPDw8sGvXLgQFBcHf3x/Z2dkICgqCRqMBAEyePBmenp5l9t5LlixBv3794OLigs6dO6NRo0bF2m7RokWYNm0aunfvju3bt+c7glwUExfQKndarRZ6vZ4LSghhfzlsL4ft5bC9HLaXxf5yLN1eo/Dkd6rg0tPT4ezsjLS0NDg5OUkPh4iIiIiIhJRkbsBDaVRpcIW/8mc0GrFv3z62F8L+ctheDtvLYXs5bC+L/eVYuj1Pk6ZK45Mzd1DFyaTen+LnKjga66AoCjIyMri6ohD2l8P2ctheDtvLYXtZ7C/H0u15ZNiKubm5wd3d3ew3LQEBAThw4IDcoIiIiIiIiMoBJ8NWLjs7GytXriz19jxdhIiIiIiIKiJOhq3cjBkzMGvWLPUySw/cvHkTffv2hbe3N7y8vLB8+XL1OTc3N0yePBmBgYEYOnQoBg4ciPXr1wMA/vvf/6JKlSr466+/AACdO3fGwYMHYTQaERYWhoCAAHh6emLgwIHqa3r06KFuDwA//vgjgoKCSvxZTBqu8FfetFotgoODubqiEPaXw/Zy2F4O28the1nsL8fS7TkZtnK+vr7o1KkTFi9ebPb4v//9b7Rq1QpnzpzBvn37MHv2bMTExKjP37lzB0ePHsW6desQGhqKPXv2AACioqIQEBCAn376Cffu3cOpU6fUv8Dr169HbGwszp49C2dnZyxduhQAMH78eHzyySfqvj/99FOMHTu20DFnZ2cjPT3d7AYA+P8vE0Xlx8bGBnXq1IGNDf8pkcD+ctheDtvLYXs5bC+L/eVYuj3/RAmzZs3CkiVLcOfOHfWxPXv24LXXXgMA1KlTB3379lUnvAAwbNgw9RrFoaGh2Lt3L/Ly8nD+/HlMnDgRe/bswc8//4zAwEDY2tpCURQsXrwYfn5+8PHxwc6dO2EwGAAAzz77LNLS0hAXF4fLly/j2LFjeOmllwod79y5c+Hs7KzeGjZsCACwMfGU7fKWm5uLnTt3Ijc3V3ooVon95bC9HLaXw/Zy2F4W+8uxdHtOhglubm4YOHAgZs+eXehrNP846urg4KD+3KhRI9jZ2WHdunXw9/dHly5dsH//fuzZswddunQBAKxfvx779u3DTz/9hDNnzuCtt95CVlaWuo9x48Zh6dKlWLZsGYYPHw47O7tCxxIREYG0tDT1dvXq1dJ+dCoD/N64LPaXw/Zy2F4O28the1nsL8eS7TkZJgDA1KlTsXbtWvzxxx8A7h/t/eKLLwAAt27dwubNm/Hss88Wun1oaCimT5+O0NBQ1KhRA7a2tti4cSNCQ0MBACkpKXB1dYWTkxMyMjKwevVqs+0HDx6M3bt3Y9WqVRg9enSRY7Wzs4OTk5PZjYiIiIiIqCQ4GSYAgKurK8aNG4fr168DAD7++GNcuHAB3t7e6NSpE959990iF7UKDQ3F5cuX1clvaGgo/vrrL/j6+gIAhgwZgnv37qFVq1Z47rnn0KFDB7Ptq1Wrhr59++Lpp59WT3smIiIiIiKyFI3Cq0fTYyAvLw/+/v5YunRpvonyw6Snp8PZ2Rmpqalwdna20AipIA8uhO7o6JjvVHqyPPaXw/Zy2F4O28the1nsL6c07R/MDdLS0h56BimPDJO4bdu2oVmzZggODi7xRJjkVa1aVXoIVo395bC9HLaXw/Zy2F4W+8uxZHtOhklcz549kZSUhM8+++yR9sOFDcqf0WjErl272F4I+8thezlsL4ft5bC9LPaXY+n2nAwTERERERGR1eFkmIiIiIiIiKwOJ8NERERERERkdbiaNFV4XE1ajqIoMBqN0Ol0XF1RAPvLYXs5bC+H7eWwvSz2l1Oa9lxNmojKTWZmpvQQrBr7y2F7OWwvh+3lsL0s9pdjyfacDFOlwRX+yp/RaMT+/fvZXgj7y2F7OWwvh+3lsL0s9pdj6facDBMREREREZHV4WSYiIiIiIiIrA4nw0T0SHQ6nfQQrBr7y2F7OWwvh+3lsL0s9pdjyfZcTZoqvJKsGEdERERERJUXV5Mmq2QymaSHYHVMJhNu3rzJ9kLYXw7by2F7OWwvh+1lsb8cS7fnZJgqjby8POkhWJ28vDxER0ezvRD2l8P2ctheDtvLYXtZ7C/H0u05GSYiIiIiIiKrw8kwERERERERWR1OhqnS0Gg00kOwOhqNBo6OjmwvhP3lsL0ctpfD9nLYXhb7y7F0e64mTRUeV5MmIiIiIiKAq0mTleIKf+XPZDLh8uXLbC+E/eWwvRy2l8P2ctheFvvLsXR7Toap0uAKf+UvLy8PBoOB7YWwvxy2l8P2ctheDtvLYn85lm7PyTARERERERFZHU6GiYiIiIiIyOpwMkyVBlf4K38ajQa1a9dmeyHsL4ft5bC9HLaXw/ay2F+OpdtzNWmq8LiaNBERERERAVxNmqwUFzUof3l5ebh48SLbC2F/OWwvh+3lsL0ctpfF/nIs3Z6TYao0uNx9+TOZTIiPj2d7Iewvh+3lsL0ctpfD9rLYX46l23MyTERERERERFaHk2EiIiIiIiKyOpwMU6VhY8O/zuXNxsYGjRo1Ynsh7C+H7eWwvRy2l8P2sthfjqXbczVpqvC4mjQREREREQFcTZqsFFf4K395eXmIi4tjeyHsL4ft5bC9HLaXw/ay2F+OpdtzMkyVBlf4K38mkwlXrlxheyHsL4ft5bC9HLaXw/ay2F+OpdtzMkxERERERERWh5NhIiIiIiIisjqcDFOlwRX+yp+NjQ1atWrF9kLYXw7by2F7OWwvh+1lsb8cS7fnatJU4XE1aSIiIiIiAriaNFkpo9EoPQSrYzQaceTIEbYXwv5y2F4O28thezlsL4v95Vi6PSfDVGnwJIfypygKbt26xfZC2F8O28thezlsL4ftZbG/HEu352SYiIiIiIiIrA4nw0RERERERGR1OBmmSkOr1UoPwepotVro9Xq2F8L+ctheDtvLYXs5bC+L/eVYuj1Xk6YKj6tJExERERERwNWkyUpxhb/yZzQasW/fPrYXwv5y2F4O28thezlsL4v95Vi6PSfDVGnwJIfypygKMjIy2F4I+8thezlsL4ft5bC9LPaXY+n2nAwTERERERGR1eFkmIiIiIiIiKwOF9CiCu/Bl+RTUlLg4uIiPRyrYjKZcPv2bbi6usLGhr9bK2/sL4ft5bC9HLaXw/ay2F9OadqXZAEtToapwuNq0kREREREBHA1abJSubm50kOwOrm5udi5cyfbC2F/OWwvh+3lsL0ctpfF/nIs3Z6TYSJ6JLzMgCz2l8P2ctheDtvLYXtZ7C/Hku05GSYiIiIiIiKrw8kwERERERERWR0uoEUV3oMvyaempsLZ2Vl6OFblwYXQHR0dodFopIdjddhfDtvLYXs5bC+H7WWxv5zStOcCWkRUbqpWrSo9BKvG/nLYXg7by2F7OWwvi/3lWLI9J8NUaXBhg/JnNBqxa9cuthfC/nLYXg7by2F7OWwvi/3lWLo9J8NERERERERkdTgZJiIiIiIiIqvDyTARERERERFZHa4mTRUeV5OWoygKjEYjdDodV1cUwP5y2F4O28thezlsL4v95ZSmPVeTJqJyk5mZKT0Eq8b+ctheDtvLYXs5bC+L/eVYsj0nw1RpcIW/8mc0GrF//362F8L+ctheDtvLYXs5bC+L/eVYuj0nw0RERERERGR1OBkmIiIiIiIiq8PJMBE9Ep1OJz0Eq8b+ctheDtvLYXs5bC+L/eVYsj1Xk6YKryQrxhERERERUeXF1aTJKplMJukhWB2TyYSbN2+yvRD2l8P2ctheDtvLYXtZ7C/H0u05GaZKIy8vT3oIVicvLw/R0dFsL4T95bC9HLaXw/Zy2F4W+8uxdHtOhomIiIiIiMjqcDJMREREREREVoeTYao0NBqN9BCsjkajgaOjI9sLYX85bC+H7eWwvRy2l8X+cizdnqtJU4XH1aSJiIiIiAjgatJkpbjCX/kzmUy4fPky2wthfzlsL4ft5bC9HLaXxf5yLN2ek2GqNLjCX/nLy8uDwWBgeyHsL4ft5bC9HLaXw/ay2F+OpdtzMkxERERERERWh5NhIiIiIiIisjqcDFOlwRX+yp9Go0Ht2rXZXgj7y2F7OWwvh+3lsL0s9pdj6fZcTZoqPK4mTUREREREAFeTJivFRQ3KX15eHi5evMj2QthfDtvLYXs5bC+H7WWxvxxLt+dkmCoNLndf/kwmE+Lj49leCPvLYXs5bC+H7eWwvSz2l2Pp9pwMExERERERkdXhZJiIiIiIiIisDifDVGnY2PCvc3mzsbFBo0aN2F4I+8thezlsL4ft5bC9LPaXY+n2XE2aKjyuJk1EREQBh7SSAAAUQklEQVRERABXkyYrxRX+yl9eXh7i4uLYXgj7y2F7OWwvh+3lsL0s9pdj6facDFOlwRX+yp/JZMKVK1fYXgj7y2F7OWwvh+3lsL0s9pdj6facDBMREREREZHV0UkPgOhRPfjae0ZGBmxtbYVHY11yc3Nx7949pKens70A9pfD9nLYXg7by2F7WewvpzTt09PTAfy/OUJROBmmCu/OnTsAgCZNmgiPhIiIiIiIHgcZGRlwdnYu8jWcDFOFV7NmTQDAlStXHvoXnspWeno6GjZsiKtXr3IlbwHsL4ft5bC9HLaXw/ay2F9OadorioKMjAw8+eSTD30tJ8NU4T247pizszP/gRLi5OTE9oLYXw7by2F7OWwvh+1lsb+ckrYv7gEyLqBFREREREREVoeTYSIiIiIiIrI6nAxThWdnZ4f33nsPdnZ20kOxOmwvi/3lsL0ctpfD9nLYXhb7y7F0e41SnDWniYiIiIiIiCoRHhkmIiIiIiIiq8PJMBEREREREVkdToaJiIiIiIjI6nAyTERERERERFaHk2Gq8D799FO4ubnB3t4eQUFBOHbsmPSQKp25c+eiTZs2cHR0RJ06ddC7d2/Ex8ebvSYrKwtvvPEGatWqBQcHB7zwwgv4888/hUZcOc2bNw8ajQYTJkxQH2N3y7p27RoGDRqEWrVqoWrVqvD29kZsbKz6vKIomD59Op544glUrVoVoaGhSEhIEBxx5ZCXl4dp06ahSZMmqFq1Kpo1a4ZZs2bh72t+sn3ZOXjwIP71r3/hySefhEajQWRkpNnzxWmdnJyM8PBwODk5wcXFBSNGjMDdu3fL8VNUTEW1z83NxeTJk+Ht7Y3q1avjySefxJAhQ/DHH3+Y7YPtS+dhf+//bvTo0dBoNPjoo4/MHmf70ilO+wsXLqBnz55wdnZG9erV0aZNG1y5ckV9vqz++4eTYarQNmzYgIkTJ+K9997DyZMn4evri7CwMNy8eVN6aJXKTz/9hDfeeAMxMTGIiopCbm4uunbtir/++kt9zZtvvont27dj48aN+Omnn/DHH3+gb9++gqOuXI4fP47ly5fDx8fH7HF2t5yUlBQ8/fTTsLW1xQ8//IDz58/jP//5D2rUqKG+Zv78+fj444+xbNkyHD16FNWrV0dYWBiysrIER17xffjhh/jss8/wySef4MKFC/jwww8xf/58LF26VH0N25edv/76C76+vvj0008LfL44rcPDw3Hu3DlERUVhx44dOHjwIEaNGlVeH6HCKqr9vXv3cPLkSUybNg0nT57E5s2bER8fj549e5q9ju1L52F/7x/YsmULYmJi8OSTT+Z7ju1L52HtL126hPbt28Pd3R0HDhzA6dOnMW3aNNjb26uvKbP//lGIKrDAwEDljTfeUO/n5eUpTz75pDJ37lzBUVV+N2/eVAAoP/30k6IoipKamqrY2toqGzduVF9z4cIFBYASHR0tNcxKIyMjQ2nRooUSFRWlPPPMM8r48eMVRWF3S5s8ebLSvn37Qp83mUxKvXr1lAULFqiPpaamKnZ2dso333xTHkOstJ5//nll+PDhZo/17dtXCQ8PVxSF7S0JgLJlyxb1fnFanz9/XgGgHD9+XH3NDz/8oGg0GuXatWvlNvaK7p/tC3Ls2DEFgHL58mVFUdi+rBTW/vfff1fq16+vnD17VmncuLGyePFi9Tm2LxsFte/fv78yaNCgQrcpy//+4ZFhqrBycnJw4sQJhIaGqo/Z2NggNDQU0dHRgiOr/NLS0gAANWvWBACcOHECubm5Zn8W7u7uaNSoEf8sysAbb7yB559/3qwvwO6Wtm3bNgQEBODFF19EnTp14Ofnhy+++EJ9PjExETdu3DDr7+zsjKCgIPZ/RO3atcPevXvxyy+/AABOnTqFQ4cO4bnnngPA9uWpOK2jo6Ph4uKCgIAA9TWhoaGwsbHB0aNHy33MlVlaWho0Gg1cXFwAsL0lmUwmDB48GG+//TY8PT3zPc/2lmEymbBz5060bNkSYWFhqFOnDoKCgsxOpS7L//7hZJgqrNu3byMvLw9169Y1e7xu3bq4ceOG0KgqP5PJhAkTJuDpp5+Gl5cXAODGjRuoUqWK+n/OD/DP4tF9++23OHnyJObOnZvvOXa3rN9++w2fffYZWrRogd27d+P111/HuHHj8NVXXwGA2pj/BpW9KVOm4OWXX4a7uztsbW3h5+eHCRMmIDw8HADbl6fitL5x4wbq1Klj9rxOp0PNmjX551GGsrKyMHnyZAwYMABOTk4A2N6SPvzwQ+h0OowbN67A59neMm7evIm7d+9i3rx56NatG3788Uf06dMHffv2xU8//QSgbP/7R1dWAyci6/DGG2/g7NmzOHTokPRQKr2rV69i/PjxiIqKMvueDJUPk8mEgIAAzJkzBwDg5+eHs2fPYtmyZRg6dKjw6Cq37777DuvWrcP69evh6ekJg8GACRMm4Mknn2R7skq5ubl46aWXoCgKPvvsM+nhVHonTpzAkiVLcPLkSWg0GunhWBWTyQQA6NWrF958800AgF6vx5EjR7Bs2TI888wzZfp+PDJMFZarqyu0Wm2+leP+/PNP1KtXT2hUldvYsWOxY8cO7N+/Hw0aNFAfr1evHnJycpCammr2ev5ZPJoTJ07g5s2baN26NXQ6HXQ6HX766Sd8/PHH0Ol0qFu3Lrtb0BNPPAEPDw+zx5566il1NcsHjflvUNl7++231aPD3t7eGDx4MN588031DAm2Lz/FaV2vXr18C1cajUYkJyfzz6MMPJgIX758GVFRUepRYYDtLeXnn3/GzZs30ahRI/X/fy9fvoxJkybBzc0NANtbiqurK3Q63UP//7es/vuHk2GqsKpUqQJ/f3/s3btXfcxkMmHv3r0IDg4WHFnloygKxo4diy1btmDfvn1o0qSJ2fP+/v6wtbU1+7OIj4/HlStX+GfxCLp06YIzZ87AYDCot4CAAISHh6s/s7vlPP300/kuIfbLL7+gcePGAIAmTZqgXr16Zv3T09Nx9OhR9n9E9+7dg42N+X+iaLVa9YgB25ef4rQODg5GamoqTpw4ob5m3759MJlMCAoKKvcxVyYPJsIJCQnYs2cPatWqZfY821vG4MGDcfr0abP//33yySfx9ttvY/fu3QDY3lKqVKmCNm3aFPn/v2X6350lWm6L6DHz7bffKnZ2dsrq1auV8+fPK6NGjVJcXFyUGzduSA+tUnn99dcVZ2dn5cCBA8r169fV271799TXjB49WmnUqJGyb98+JTY2VgkODlaCg4MFR105/X01aUVhd0s6duyYotPplA8++EBJSEhQ1q1bp1SrVk1Zu3at+pp58+YpLi4uytatW5XTp08rvXr1Upo0aaJkZmYKjrziGzp0qFK/fn1lx44dSmJiorJ582bF1dVV+b//+z/1NWxfdjIyMpS4uDglLi5OAaAsWrRIiYuLU1csLk7rbt26KX5+fsrRo0eVQ4cOKS1atFAGDBgg9ZEqjKLa5+TkKD179lQaNGigGAwGs///zc7OVvfB9qXzsL/3//TP1aQVhe1L62HtN2/erNja2iqff/65kpCQoCxdulTRarXKzz//rO6jrP77h5NhqvCWLl2qNGrUSKlSpYoSGBioxMTESA+p0gFQ4G3VqlXqazIzM5UxY8YoNWrUUKpVq6b06dNHuX79utygK6l/TobZ3bK2b9+ueHl5KXZ2doq7u7vy+eefmz1vMpmUadOmKXXr1lXs7OyULl26KPHx8UKjrTzS09OV8ePHK40aNVLs7e2Vpk2bKu+++67ZBIDty87+/fsL/Dd+6NChiqIUr/WdO3eUAQMGKA4ODoqTk5PyyiuvKBkZGQKfpmIpqn1iYmKh//+7f/9+dR9sXzoP+3v/TwVNhtm+dIrTfuXKlUrz5s0Ve3t7xdfXV4mMjDTbR1n9949GURSlZMeSiYiIiIiIiCo2fmeYiIiIiIiIrA4nw0RERERERGR1OBkmIiIiIiIiq8PJMBEREREREVkdToaJiIiIiIjI6nAyTERERERERFaHk2EiIiIiIiKyOpwMExERERERkdXhZJiIiIhEaTQaREZGSg9D3IwZM6DX66WHQURkNTgZJiIisjLDhg1D79698z1+4MABaDQapKamlut4rl+/jueee65c37O4kpKSoNFo1FvNmjXxzDPP4Oeff5YeGhERPSJOhomIiEhUvXr1YGdnJz2MIu3ZswfXr1/HwYMH8eSTT6JHjx74888/pYdFRESPgJNhIiIiyufOnTsYMGAA6tevj2rVqsHb2xvffPON2WtCQkIwduxYjB07Fs7OznB1dcW0adOgKIr6Gjc3N8yaNQsDBgxA9erVUb9+fXz66adm+/n7adIPjsRu3rwZnTp1QrVq1eDr64vo6GizbQ4dOoQOHTqgatWqaNiwIcaNG4e//vpLff6///0vWrRoAXt7e9StWxf9+vVTn/v+++/h7e2NqlWrolatWggNDTXbtiC1atVCvXr14OXlhXfeeQfp6ek4evSo+vzXX3+NgIAAODo6ol69ehg4cCBu3rypPv/gqPvevXsREBCAatWqoV27doiPjy/0PS9duoSmTZti7NixZk2JiKhscDJMRERE+WRlZcHf3x87d+7E2bNnMWrUKAwePBjHjh0ze91XX30FnU6HY8eOYcmSJVi0aBFWrFhh9poFCxbA19cXcXFxmDJlCsaPH4+oqKgi3//dd9/FW2+9BYPBgJYtW2LAgAEwGo0A7k8Su3XrhhdeeAGnT5/Ghg0bcOjQIYwdOxYAEBsbi3HjxuH9999HfHw8/ve//6Fjx44A7p+SPWDAAAwfPhwXLlzAgQMH0Ldv32JPNjMzM7FmzRoAQJUqVdTHc3NzMWvWLJw6dQqRkZFISkrCsGHDCvxc//nPfxAbGwudTofhw4cX+D6nT59G+/btMXDgQHzyySfQaDTFGh8REZWAQkRERFZl6NChilarVapXr252s7e3VwAoKSkpBW73/PPPK5MmTVLvP/PMM8pTTz2lmEwm9bHJkycrTz31lHq/cePGSrdu3cz2079/f+W5555T7wNQtmzZoiiKoiQmJioAlBUrVqjPnzt3TgGgXLhwQVEURRkxYoQyatQos33+/PPPio2NjZKZmals2rRJcXJyUtLT0/N9hhMnTigAlKSkpIdUUszGU7VqVaV69eqKRqNRACj+/v5KTk5OodsdP35cAaBkZGQoiqIo+/fvVwAoe/bsUV+zc+dOBYCSmZmpKIqivPfee4qvr69y+PBhpUaNGsrChQuLNUYiIiodHhkmIiKyQp06dYLBYDC7/f2Ibl5eHmbNmgVvb2/UrFkTDg4O2L17N65cuWK2n7Zt25odtQwODkZCQgLy8vLMHvu74OBgXLhwocjx+fj4qD8/8cQTAKCednzq1CmsXr0aDg4O6i0sLAwmkwmJiYl49tln0bhxYzRt2hSDBw/GunXrcO/ePQCAr68vunTpAm9vb7z44ov44osvkJKS8tBeGzZsQFxcHDZt2oTmzZtj9erVsLW1VZ8/ceIE/vWvf6FRo0ZwdHTEM888AwD5ehX1uR68/tlnn8X06dMxadKkh46LiIhKj5NhIiIiK1S9enU0b97c7Fa/fn31+QULFmDJkiWYPHky9u/fD4PBgLCwMOTk5JTL+P4+0Xww2TaZTACAu3fv4rXXXjObyJ86dQoJCQlo1qwZHB0dcfLkSXzzzTd44oknMH36dPj6+iI1NRVarRZRUVH44Ycf4OHhgaVLl6JVq1ZITEwscjwNGzZEixYt0KdPH8yZMwd9+vRBdnY2AOCvv/5CWFgYnJycsG7dOhw/fhxbtmwBgHy9ivpcAFC7dm0EBgbim2++QXp6emnzERFRMXAyTERERPkcPnwYvXr1wqBBg+Dr64umTZvil19+yfe6vy8iBQAxMTFo0aIFtFqt2WP/fM1TTz1V6rG1bt0a58+fzzeZb968ufo9Xp1Oh9DQUMyfPx+nT59GUlIS9u3bB+D+JPTpp5/GzJkzERcXhypVqqiT1+Lo168fdDod/vvf/wIALl68iDt37mDevHno0KED3N3dzY72lkTVqlWxY8cO2NvbIywsDBkZGaXaDxERPRwnw0RERJRPixYtEBUVhSNHjuDChQt47bXXCryU0JUrVzBx4kTEx8fjm2++wdKlSzF+/Hiz1xw+fBjz58/HL7/8gk8//RQbN27M95qSmDx5Mo4cOYKxY8fCYDAgISEBW7duVRfQ2rFjBz7++GMYDAZcvnwZa9asgclkQqtWrXD06FHMmTMHsbGxuHLlCjZv3oxbt26pk/MtW7bA3d29yPfXaDQYN24c5s2bh3v37qFRo0aoUqUKli5dit9++w3btm3DrFmzSv35qlevjp07d0Kn0+G5557D3bt3S70vIiIqHCfDRERElM/UqVPRunVrhIWFISQkBPXq1UPv3r3zvW7IkCHIzMxEYGAg3njjDYwfPx6jRo0ye82kSZMQGxsLPz8/zJ49G4sWLUJYWFipx+bj44OffvoJv/zyCzp06AA/Pz9Mnz4dTz75JADAxcUFmzdvRufOnfHUU09h2bJl+Oabb+Dp6QknJyccPHgQ3bt3R8uWLTF16lT85z//wXPPPQcASEtLK/JyRw8MHToUubm5+OSTT1C7dm2sXr0aGzduhIeHB+bNm4eFCxeW+vMBgIODA3744QcoioLnn3/+oZd+IiKiktMoCi9cR0RERCUXEhICvV6Pjz76qNDXuLm5YcKECZgwYUK5jYuIiKg4eGSYiIiIiIiIrA4nw0RERERERGR1eJo0ERERERERWR0eGSYiIiIiIiKrw8kwERERERERWR1OhomIiIiIiMjqcDJMREREREREVoeTYSIiIiIiIrI6nAwTERERERGR1eFkmIiIiIiIiKwOJ8NERERERERkdf4/xBhCQmvfGt8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Country Happiness.Rank Happiness.Score Whisker.high Whisker.low \\\n", + "0 Norway 1 7.537 7.594445 7.479556 \n", + "\n", + " Economy..GDP.per.Capita. Family Health..Life.Expectancy. Freedom \\\n", + "0 1.616463 1.533524 0.796667 0.635423 \n", + "\n", + " Generosity Trust..Government.Corruption. Dystopia.Residual \n", + "0 0.362012 0.315964 2.277027 \n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "df_2017_sorted = df_2017.sort_values('Happiness.Rank')\n", + "\n", + "plt.figure(figsize=(10, 25))\n", + "plt.barh(df_2017_sorted['Country'], df_2017_sorted['Happiness.Rank'], color='skyblue')\n", + "plt.xlabel('Happiness.Rank')\n", + "plt.ylabel('Country')\n", + "plt.title('Happiness.Rank by Country (2017)')\n", + "plt.grid(axis='x', linestyle='--')\n", + "plt.yticks(fontsize=8)\n", + "plt.show()\n", + "\n", + "print(df_2017_sorted.head(1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Teste de hipótesse." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "H1 = O rank de felicidade pode ter relação com o nível de economia do país.\n", + "H0 = Não existe relação entre o rank de felicidade e o nível de economia do país." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para testar a hipótese de que o rank de felicidade tem relação com o nível de economia do país, foi utilizado o coeficiente de correlação de Pearson." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rejeitamos a hipótese nula. Há uma correlação significativa entre renda per capita e felicidade.\n", + "A correlação é positiva, indicando que países com maior renda per capita tendem a ser mais felizes.\n" + ] + } + ], + "source": [ + "from scipy.stats import pearsonr\n", + "\n", + "corr_coef, p_value = pearsonr(df_2017['Economy..GDP.per.Capita.'], df_2017['Happiness.Score'])\n", + "\n", + "if p_value < 0.05:\n", + " print(\"Rejeitamos a hipótese nula. Há uma correlação significativa entre renda per capita e felicidade.\")\n", + " if corr_coef > 0:\n", + " print(\"A correlação é positiva, indicando que países com maior renda per capita tendem a ser mais felizes.\")\n", + " else:\n", + " print(\"A correlação é negativa, indicando que países com maior renda per capita tendem a ser menos felizes.\")\n", + "else:\n", + " print(\"Não rejeitamos a hipótese nula. Não há evidências suficientes para concluir que existe uma correlação entre renda per capita e felicidade.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O coeficiente de correlação de Pearson é um teste que mede a relação estatística entre duas variáveis contínuas. Se a associação entre os elementos não for linear, o coeficiente não será representado adequadamente. O coeficiente de correlação de Pearson pode ter um intervalo de valores de +1 a -1." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coeficiente de correlação: 0.81\n", + "Valor-p: 0.000\n", + "Rejeitamos a hipótese nula. Há uma correlação significativa entre renda per capita e felicidade.\n", + "A correlação é positiva, indicando que países com maior renda per capita tendem a ser mais felizes.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from scipy.stats import pearsonr\n", + "\n", + "df_2017 = pd.read_csv(\"2017.csv\")\n", + "\n", + "renda = df_2017['Economy..GDP.per.Capita.']\n", + "felicidade = df_2017['Happiness.Score']\n", + "\n", + "corr_coef, p_value = pearsonr(renda, felicidade)\n", + "\n", + "print(f\"Coeficiente de correlação: {corr_coef:.2f}\")\n", + "print(f\"Valor-p: {p_value:.3f}\")\n", + "\n", + "if p_value < 0.05:\n", + " print(\"Rejeitamos a hipótese nula. Há uma correlação significativa entre renda per capita e felicidade.\")\n", + " if corr_coef > 0:\n", + " print(\"A correlação é positiva, indicando que países com maior renda per capita tendem a ser mais felizes.\")\n", + " else:\n", + " print(\"A correlação é negativa, indicando que países com maior renda per capita tendem a ser menos felizes.\")\n", + "else:\n", + " print(\"Não rejeitamos a hipótese nula. Não há evidências suficientes para concluir que existe uma correlação entre renda per capita e felicidade.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1cZmVRUdHm+2zcp1iYmIkn+2xY8cQFxeHatWqSbZr2rSp2f7Pnz+PGTNmYPny5WbvqeJnasuoUaNw7733IigoCNWrV0eLFi1M17Dx/VkaRlDxOBWHNjRs2NCh40ZHR+PixYsObQsYhsQ8++yzyMrKQnFxsVkdKqpbt67ZpKubbroJgGE4hjNplr755hu8+OKL2Ldvn2Tcor3hEs6cQ0uaNGmC1NRUs/KdO3e69B1TWW5uLurWrYsaNWo4tL2Rte8NS9etI5/lsWPHAMDsuyQkJASNGjWSlCnx907EwJT8Qvv27U1jru6880506dIF999/Pw4ePIhq1aqZWkImTZqEtLQ0i/to3LixxXKdTofevXvj/PnzmDJlCpo1a4aqVavi1KlTGDFihOwUL//973+xePFiPPHEE0hJSYFWq4VGo8GQIUOcShdjiV6vh0ajwffff4/g4GCz5ysHW5a2AWBzbGJllsaw6fV6tGrVCnPmzLH4morj06wJDg6WBAJpaWlo1qwZRo8ebZpUYjxvn3zyCWJjY832UaWK9KvO2vt11uDBg/HLL7/gqaeeQnJysumaS09Pd/gztRbwANff36uvvmo1dVTlz9TS52FJs2bNsG/fPly9etXsxqKywsJCdO/eHdHR0Zg5cyYSExMRHh6OPXv2YMqUKYpdv9b8/PPPGDBgALp164b33nsPcXFxCAkJweLFi/HZZ5/ZfK0z51AOV75jvMEdn6USf+9EDEzJ7wQHB2PWrFno2bMn3nnnHTz99NOmO/uQkBCrP/7W7N+/H4cOHcJHH32EBx980FS+YcMGyXbGY+Tk5Njc38qVKzF8+HC8/vrrprLS0lKz2d4NGjTAwYMHzV5v7GKrmHKqssTERAgh0LBhQ1MLlaucmcCTmJiI3377DbfddptTr7ckLi4OTz75JGbMmIHt27ejY8eOSExMBADUqVNH9udrTYMGDbBp0yaUlJRIApbKn8mFCxewadMmzJgxA88995yp3NhCpwTj+4uOjlbs/Rn1798f2dnZWLVqFYYOHWpz2y1btqCgoACrV69Gt27dTOUVMwdUdPr0abNUVYcOHQIAm6scWbtWVq1ahfDwcPzwww+SHpHFixfbrDfg3nMIwKXvGEsSExPxww8/4Pz587JaTRs0aGDx2qt83Tr6WRq/Zw4fPizpkbh27Rry8vJw8803S+qs9N87BR6OMSW/1KNHD7Rv3x5vvvkmSktLUadOHfTo0QMLFixAfn6+2fbnzp2zui9j61rF1kMhBObOnSvZrnbt2ujWrRs+/PBDHD9+XPJcxdcGBwebtUS+/fbbZila+vbti507dyI7O9tUdunSJSxcuBAJCQlISkqyWue7774bwcHBmDFjhtmxhBBOpTEyBhdyVgEaPHgwTp06ZTEP5pUrV3Dp0iXZ9QAMrc6RkZF45ZVXABhaUaOjo/Hyyy/j2rVrZtvb+nyt6du3L8rLyzFv3jxTmU6nw9tvvy3ZztL1AUDRJWbbtm2LxMREvPbaaygpKTF73pn3ZzRmzBjExcVh4sSJpqCxorNnz+LFF18EYPm9Xr16Fe+9957FfZeXl0tysF69ehULFixA7dq1TUn8LbF2rQUHB0Oj0Uj+Vo4ePerQbG93nkMALn3HWHLPPfdACIEZM2aYPWerJ6Nv377Yvn07du7cKTl25cUoHP0s27Vrh9q1a2P+/Pm4evWqqXzJkiVmn4+7/t4psLDFlPzWU089hXvvvRdLlizBmDFj8O6776JLly5o1aoVRo4ciUaNGuGff/5BdnY2Tp48id9++83ifpo1a4bExERMmjQJp06dQnR0NFatWmU21hQA3nrrLXTp0gVt2rTBqFGj0LBhQxw9ehTffvutaXnJO+64A5988gm0Wi2SkpKQnZ2NjRs3mtICGT399NNYtmwZ+vTpg3HjxqFGjRr46KOPkJeXh1WrVtlc7SUxMREvvvgipk6diqNHj+LOO+9EVFQU8vLy8OWXX2LUqFGYNGmSrPOZmJiI6tWrY/78+YiKikLVqlXRoUMHm2MZH3jgAaxYsQJjxozB5s2b0blzZ+h0Ovz1119YsWIFfvjhB6tpb2ypWbMmMjIy8N577+HPP/9E8+bNMW/ePDzwwANo06YNhgwZgtq1a+P48eP49ttv0blzZ7zzzjuyjtG/f3907twZTz/9NI4ePYqkpCSsXr3abBxldHQ0unXrhtmzZ+PatWuIj4/H+vXrrbYiOiMoKAjvv/8++vTpgxYtWiAjIwPx8fE4deoUNm/ejOjoaNM4XLliYmLw5Zdfom/fvkhOTpas/LRnzx4sW7YMKSkpAAzpwWJiYjB8+HCMGzcOGo0Gn3zyidVAqW7dusjMzMTRo0dx00034fPPP8e+ffuwcOFCmwnljccfN24c0tLSEBwcjCFDhqBfv36YM2cO0tPTcf/99+Ps2bN499130bhxY7vjod15Do2c/Y6xpGfPnnjggQfw1ltv4fDhw6ZhIT///DN69uyJxx9/3OLrJk+ejE8++QTp6ekYP368KV1UgwYNJOfI0c8yJCQEL774IkaPHo1evXrhvvvuQ15eHhYvXmw2xtRdf+8UYDybBIBIWcZ0Ub/++qvZczqdTiQmJorExERRXl4uhBAiNzdXPPjggyI2NlaEhISI+Ph4cccdd4iVK1eaXmcplcuBAwdEamqqqFatmqhVq5YYOXKk+O233yymT8rJyRF33XWXiI6OFgBE06ZNxbRp00zPX7hwQWRkZIhatWqJatWqibS0NPHXX3+ZpX0x1nfQoEGievXqIjw8XLRv31588803Dp+fVatWiS5duoiqVauKqlWrimbNmonHHntMHDx40LRN9+7dRYsWLcxeWzl9jRBCrFmzRiQlJYkqVapI3ru1fQhhSDeTmZkpWrRoIcLCwkRMTIxo27atmDFjhigqKrJZf2O6KEtyc3NFcHCw5Jxt3rxZpKWlCa1WK8LDw0ViYqIYMWKE2LVrl919GtMsVVRQUCAeeOABER0dLbRarXjggQfE3r17zT73kydPirvuuktUr15daLVace+994rTp08LAGL69Ok236MxDdGrr75qczshhNi7d6+4++67Rc2aNUVYWJho0KCBGDx4sNi0aZPZ+zh37pzd/VV0+vRp8eSTT4qbbrpJhIeHi8jISNG2bVvx0ksvST6nbdu2iY4dO4qIiAhRt25dMXnyZPHDDz+Y/c0Yr4ldu3aJlJQUER4eLho0aCDeeecdi++/4vksLy8X//3vf0Xt2rWFRqORfC4ffPCBaNKkiQgLCxPNmjUTixcvtvjZuXIOLXH0c3L2O8bS31t5ebl49dVXRbNmzURoaKioXbu26NOnj9i9e7dpG0vfG7///rvo3r27CA8PF/Hx8eKFF14QH3zwgVm6KEc/SyGEeO+990TDhg1FWFiYaNeunfjpp59E9+7dJemihHDt751ICCE0QsiY3UBEsqSmpmLy5Mm4/fbbvV0VIo/q0aMH/v33X7tjromIKuIYUyI36t+/P5YuXertahAREfkEjjElcoNly5bh0qVL+OKLL1CnTh1vV4eIiMgnsMWUyA3++OMPPP744zh16pTsSUZERESBimNMiYiIiEgV2GJKRERERKrAwJSIiIiIVMGnJz/p9XqcPn0aUVFRXP6MiIiISIWEELh48SLq1q1rc3EYwMcD09OnT6N+/frergYRERER2XHixAnUq1fP5jY+HZhGRUUBMLzR6OhoL9eGiIiIiCorLi5G/fr1TXGbLT4dmBq776OjoxmYEhEREamYI8MuOfmJiIiIiFSBgSkRERERqQIDUyIiIiJSBQamRERERKQKDEyJiIiISBUYmBIRERGRKjAwJSIiIiJVYGBKRERERKrAwJSIiIiIVIGBKRERERGpAgNTIiIiIlIFBqZEREREpAoMTImIiIhIFap4uwJEREREztDpBXbmncfZi6WoExWO9g1rIDhI4+1qkQsYmBIREZFFag781uXkY8baA8gvKjWVxWnDMb1/EtJbxnmxZuQKBqZERERkRs2B37qcfIxdugeiUvmZolKMXboH84a18XodyTkcY0pEREQS63LyMWbpHklQClwP/Nbl5Ct2LJ1eIDu3AGv2nUJ2bgF0+srhpvn2M9YeMAtKAZjKZqw9YHc/pE5sMSUiIiITnV7g6dX7LT4nAGhgCPx6J8W63K3vTKvszrzzZgFz5TrmF5ViZ955pCTWdKl+5HlsMSUiIiKTd7KOoPDyNavPVwz8XGHsjpfbKnv2ovWg1JntSF0YmBIREQUIe93mOr3A4m15Du3LlcDPle74OlHhDh3D0e1IXdiVT0REFAAc6TbfmXcehVest5ZW5Erg50p3fPuGNRCnDceZolKLga0GQKzWkEGAfA9bTImIiPyco93mjraCVo8IcSnwc6U7PjhIg+n9kwAYgtCKjI+n909STVorkoeBKRERkR+T023uaCtoRucElwI/V7vj01vGYd6wNojVSp+P1YYzVZSPY1c+ERGRH5PTbW6vmxwAYiJD8HivJi7VSYnu+PSWceidFKvaBQDIOWwxJSIi8mNyus1tdZMby2bd3crl4E+p7vjgIA1SEmtiYHI8UhJrMij1AwxMiYiI/JjcbnNr3eRxCneTszueLGFXPhERkR9zptvcU93k7I6nyhiYEhER+TFjt/nYpXugASTBqa1uc2M3uSU6vVAsmLR1HAo8DEyJiIj8nLHbvHIe01g7y39a4swyokSO0gghrE28U73i4mJotVoUFRUhOjra29UhIiJSNVdbOo35UCsHDsY9cGwoWSInXmOLKRERUYBwpdvcXj5UDQz5UHsnxXKMKDmNs/KJiIjILjn5UImcxRZTIiIiH6bkRCRb+z1TdMWh1zmaN5XIEgamREREPspdE5Es7bdG1VCHXuto3lQiS9iVT0RE5IOME5Eqd6+fKSrF2KV7sC4nX9H9Xrh01ebrNDAExbaWESWyh4EpERGRj7E3EQkwTETS6eUl3nFkv5bIWUaUyBYGpkRERD7GXROR7O3XGi4jSkphYEpERORjHJ1gJHcikrMTl6b1Y3J9UgYDUyIiIh/j6AQjuRORnJm4pAHwwrfyhw2Ql3z/PaDRGP6pEANTIiIiH9O+YQ3EacNhLbRwdiKScb9yMH+pjygpASIigL59vV0TmxiYEhER+ZjgIA2m908CALPg1JWJSBX3Kxfzl6rY7NlAVBRQWuEz2rDBe/WxgYEpERGRSun0Atm5BViz7xSycwsk3eXpLeMwb1gbxFZq4XR1IlJ6yzg8mdpE9uuYv1SF8vIMXfZTplwv69cP0OuB1FTv1csGJtgnIiJSIUeS56e3jEPvpFjFV356vFcTLNt5AmeK7beCamAIhpm/VEWEAAYNAlavlpYfPgw0buydOjmILaZEREQqIyd5fnCQBimJNTEwOR4piTUVySMaHKTB8wOSoIH5UIGKmL9UhbZsAYKCpEHpCy8YglWVB6UAA1MiIiJVcVfyfLmsDRWoiPlLVaS0FIiNBXr2vF4WFAQUFQHPPuu9esnErnwiIiIVkZM8PyWxplvrUnmoQK1qYYAA/r1UptiwAVLAe+8Bjz0mLfvqK2DgQK9UxxUMTImIiFTEXcnznWUcKkAqdPo0EB8vLeva9Xp3vg/yzVoTERH5KXclzyc/k5FhHpTm5AA//eSzQSnAwJSIiEhV3JU8n/zEjh2GFFBLllwve+opw+SmFi28Vi2lMDAlIiJSEXclzycfd+0a0LQp0LGjtLygwJBA308wMCUiIlIZdyXPJx/18cdAaChw6ND1ss8+M7SS1vCvlnNOfiIiIlIhdyXPJx9y7hxQp460rHVrYPduoIp/hnD++a6IiEgVdHrBwMoFnBEfwJ54Apg7V1q2axfQtq1XquMpDEyJiMgtHFlSk4gq+e03IDlZWjZmDDBvnleq42kcY0pERIqTs6QmEQHQ6YBbbzUPSvPzAyYoBRiYEhGRwtSypCaRz1i50jBmdNeu62ULFxomN8XGeq9eXsCufCIiUpSaltQkUrXCQiAmRlrWoAFw8CAQFuaVKnkbW0yJiEhRaltSk0iVpk0zD0q3bgWOHg3YoBRgiykRESmMS2oS2XDwINCsmbTs/vuBpUsNKzoFOAamRESkKOOSmmeKSi2OM9XAkCieS2r6Bqb8UoheD9x+O7Bpk7T8+HGgfn3v1EmFvNqVn5CQAI1GY/bvscce82a1iIjIBVxS03+sy8lHl8wsDF20HeOX78PQRdvRJTPLqawKOr1Adm4B1uw7hezcgsCa/Pbdd0BwsDQoffNNw+QmBqUSGiGE166Mc+fOQafTmR7n5OSgd+/e2Lx5M3r06GH39cXFxdBqtSgqKkJ0dLQba0pERHIxj6lvM6b8qhwkGG8nHFka1djauvHAGXy57xTOX7pmei4groWSEqB2baC0wnjqmBjg5EkgMtJ79fIwOfGaVwPTyp544gl88803OHz4MDQOjLNgYEpEpG7sBvZNOr1Al8wsq9kVjMMxtk7pZfXztHRjUnkfgGMBrk+aPRuYMkVatn490Lu3d+rjRXLiNdWMMb169SqWLl2KCRMmWA1Ky8rKUFZWZnpcXFzsqeoREZETuKSmb3I15Ze11tbK+9DAkNO2d1Ks/9yw5OUBjRpJy/r1A9au5eQmB6gmXdRXX32FwsJCjBgxwuo2s2bNglarNf2rz3EZREREinMl5ZetBRYqqxjg+jwhgHvuMQ9KDx8GvvmGQamDVBOYfvDBB+jTpw/q1q1rdZupU6eiqKjI9O/EiRMerCEREVFgcCXll73WVkt8Pqftli1AUBCwevX1shdeMASrjRt7rVq+SBVd+ceOHcPGjRuxuuIHakFYWBjCAjjpLBERkScYU37ZCzAvXLpqVuZMkOmzOW1LS4GEBOCff66XBQUBFy4AnPviFFW0mC5evBh16tRBv379vF0VIiKigBccpMG0fs3tbvfCtwfM0j7JCTI1MMzO98mctu+9B0RESIPSr74CdDoGpS7weoupXq/H4sWLMXz4cFSp4vXqEBEREYCYqvZ7KC1NgLK3wEJlPpfT9vRpID5eWta16/XufHKJ18/gxo0bcfz4cTz00EPergoREVFAspT83tkJULYWWKgoThvue6miMjLMg9KcHOCnnxiUKsTrTZS33347VJRKlYiIKKBYWwhhyK03OvR6S1336S3jMG9YG7P91qwaioHJddE7Kda3ctru2AF07Cgte+opQ65SUpSqEuzLxQT7REREzrO3upM2MgRFl69Z7JJ3JMm+zy+wcO0a0LIlcOiQtLygAKhhfVysz79vhflkgn0iIiLyHFv5Ro3J7zUV/r/idsYQy974UJ9eYOHjj4Hhw6Vln30GDB1q82Vcitc1HBBBREQUgBxZ3enC5Wt4MrUJYrXS7vpYXxwf6qhz5wzJ8CsGpa1bG1pPHQhKxy7dY3ZezxSVYuzSPViXk++OGvsVtpgSEREFIEcnNyXUqoqtU3oFRtf0E08Ac+dKy3btAtq2tftSR1qg/W75VTdgYEpERBSA5Kzu5NNd8o747TcgOVlaNmYMMG+ew7twpAXaUnotkmJgSkREFIDs5Rs1Tm7yyeT3jtLpDLPtd+2SlufnA7GxsnblbHotkuIYUyIiogBkK9+oo5ObfNrKlUCVKtKgdOFCw/r2MoNSQF4LNFnHwJSIiChAGfONBtTkpsJCw+Sme++9XtaggWHd+5Ejnd6tsQXaWhjv08uvehC78omIiAJYess49E6KDYzJTdOmAS++KC3buhXo3NnlXRtboMcu3eN0ei1ign0iIiLydwcPAs2aScvuvx9YutTQeqog5jE1xwT7RERERHo9cPvtwKZN0vLjx4H69d1yyIBqgXYDBqZERETkf777DujXT1o2Zw7w5JNuP7Tfp9dyIwamRERE5D9KSoDatQ2TmYxiYoCTJ4HISO/VixzCWflEREQBQqcXyM4twJp9p5CdWwCd3menmVg2ezYQFSUNStevB86fZ1DqI9hiSkREFAD8elLO338DiYnSsn79gLVrFZ/cRO7FFlMiIiI/ty4nH2OX7jFbMvNMUSnGLt2DdTn5XqqZi4QwBJ6Vg9LDh4FvvmFQ6oMYmBIREfkxnV5gxtoDFpcdNZbNWHvA97r1lywBgiqFMS+8YAhWGzf2SpXIdezKJyIi8mM7886btZRWJADkF5ViZ95535hJXlJiGEda2blzQK1anq8PKYotpkRERPDfiUFnL1oPSp3ZzqsyMsyD0sxMQyspg1K/wBZTIiIKeP48MahOVLii23mFpZWbAKC8HAgOBmC4sWBSe9/HwJSIiNzCVwIF48Sgyu2jxolB84a18engtH3DGojThuNMUanFcaYaALFaw+ejSjVqABcuSMuysoCePU0P/fnGItCwK5+IiBS3LicfXTKzMHTRdoxfvg9DF21Hl8ws1c3+9tuJQRUEB2kwvX8SAEMQWpHx8fT+Seq7aVi1yjCrvmJQmpxs6LavFJT6ZcaBAMXAlIiIFOVLgYKciUHu4omxrekt4zBvWBvEaqXd9bHacPW1CJeWGgLSQYOk5SdPAnv3SooC4cYi0LArn4iIFGMvUNDAECj0Top1qYVOqWEC3p4Y5Mku6PSWceidFKvu4RUTJxrWs6/omWeAl16yuLnfZRwgBqZERKQcTwQKSgZz3pwY5I2xrcFBGnUGaMeOAQkJ5uVlZUBoqNWXefvGgpTHrnwiIlKMEoGCra5tpYcJGCcGWWsz1MAQ9Co9MYhd0BU0bWoelK5daxhLaiMoBdx7Y+Gv6cPUji2mRESkGFcDBVutob2TYhUfJmCcGDR26R5oAMm+3TkxiF3QAH74AUhPl5bVqwecOOHwLtyVcYCz/L2HLaZERKQYV1og7bWGvpN1xC0TlbwxMSigu6DLyw2TmyoHpUeOyApKAfdkHPClyXv+iIEpEREpxtlAwZGu7cW/5DlUB2eCufSWcdg6pReWjeyIuUOSsWxkR2yd0sttrWN+kfTeGTNnAiEh0rIxYwzd9omJTu1SyRsLDrHwPnblExGRooyBQuWu0FgbXaGOdG0XXr7m0PGdDeY8OTGobYMY1KgagvOXLL8n1Se9l+vMGSDOQoB46RIQGeny7pXKOMAhFt7HwJSIiBQnN1BwtJWzekQIiq5c880VjP7HOH7RVlAKqDTpvTO6dAG2bZOWffYZMHSooodR4sYioIdYqAQDUyIicgs5gYKjrZwZnRPw5sbDHp2opCRrKaIqstWy7FO2bTMEpRWFhQFXrhjGmKpQwA6xUBEGpkRE5HWOzq5+vFcTNI2NkjVMQC1sjV80qlk1FD8+1ROhVXx4CoheDwQHm5fv3w+0bOn5+sjgrln+5DgGpkRE5HVy0jb5xApGFtgbvwgABZeuYvexC747fvGtt4Dx46VlgwcDn3/unfrI5K30YXQdA1MiIlIFOZOmVLuCkQ1+PX7xwgWghoVWxMJCQKv1eHVc4czkPVIOA1MiIlINX20NdYTfjl8cMMCwUlNF8+cDo0d7pz4K8OfrUO0YmBIRuUCnF/zxUpiSraGWPh8AXvnM/G784t69QJs25uV6vWonN8nhi63y/oCBKRGRk7hsobpZ+nyqRxqSu1fMieqpz8wb4xfdcuMkBBBkYXLWjh1A+/au7ZsCnkYI4bPLFxQXF0Or1aKoqAjR0dHerg4RBRBraX+MP/nuWsrSH3iildmRtExGnv7MPHVDY+k4NaqG4MWBLdG3dV3ndrpkCZCRIS1LTQU2bHC+ouT35MRrDEyJiGTS6QW6ZGZZnWFt7JLdOqUXu/Ur8URQZu/zscTTn5m7g3N7gfnobg0xtW+S4zssKQGioszLz50DatVyqo4UOOTEaz6cKI2IyDvkLFtI1xmDpcrn7kxRKcYu3YN1OfmKHMeRtEyVefozM45fHJgcj5TEmnaDUp1eIDu3AGv2nUJ2boHNtdodyZe64Kc8fPe7g+c7I8M8KM3MNHTpMyglhXGMKRGRTH6d9sdNbAVLAoYWyxlrD6B3UqzLLYeunHc1fmZyW5kdDcynrclBWksb5/vgQaBZM/Py8nLLCfSJFMAWUyIimfw27Y8bebKV2ZXzrrbPzJlWZkeD64JLV62f7xo1zIPSrCxDKymDUp8mp/XdG9hiSkQkk9+l/fEAT7Yy2/t8LFHjZ+ZsK7Oc4NrsfK9aBQwaJC1LTjakhiKf5wuZRNhiSkQkkzHtD3B9RreRvy9b6GxriydbmW19Ppao9TNztpW5fcMaqFE1xKFjmM53aakh92jloPTkSQalfsJTY7xdxcCUiMgJxmULY7XSQCpWG+63qaLW5eSjS2YWhi7ajvHL92Hoou3okpnl0A+asRXTWtingaHlRqkWS2ufT/XIEFMuUyO1fmbOtjIHB2nw4sCWdl9nOt8TJwIREdInn3nG0G0fH+9wfStSe3dxoLHX+g4YWt/V8DmxK5+IyEmBtGyhtfRDxtYWe4GdN5LLW/t8AO+s/CSXK63MfVvXxeiThVjwU57F12gAzGobjeBgC+1TZWVAaKicqkr4QndxoJHT+u7t1a7YYkpE5AK5aX98kVKtLd5oZbb0+fjKZ+ZqK/PUvkl47/42qFFVGmTGacOR8/k49EirtErT2rWGVlIXg1Jf6C4ONL6USYQtpkREZJOSrS2B1MrsKiVamfu2jkNay+vnu8neX5D00B3SjerVA06ccLm+nkwJRvL4UiYRBqZERGST0q0txhZLss/Yyly5azxWRtd4cJAGKQ20QIiFZPhHjgCJiYrU1Ze6iwONL2USYWBKREQ2+VJriz9yuZV55kxg+nRp2ZgxwLx5itbTl7qLA403xng7i4EpERHZ5EutLf7KqVbmM2eAOAstqpcuAZGRylSsAt7AqJsSre+ewMCUiIhs8qXWFvqfLl2AbdukZZ99Bgwd6rZD8gZG/XxhjDdn5RMRkV2BmLfVJ23bZkiUXzEoDQsD9Hq3BqVAYC884UvUnpVCI4TwfjZVJxUXF0Or1aKoqAjR0dHerg4Rkd/T6YWqW1sCll5veQ37/fuBlvaT7SuJeUypMjnxGgNTIiIiX/bWW8D48dKywYOBzz/3Tn3AGxiSkhOvcYwpERGRL7pwAahhYbxmYSGg1Xq8OhUxJRg5i2NMiYiIfM2AAeZB6fz5hpWbvByUErmCLaZERES+Yu9eoE0b83K93jDpicjHscWUiIhI7YQwBJ6Vg9IdO64/R+QHGJgSERGpgE4vkJ1bgDX7TiE7twA6/f/mJi9ZAgRV+rlOTTUEpO3be7yeRO7ErnwiIiIvs5RiqVG4QNaM/uYbnzsH1LKw7j2RH2CLKRERkRety8nH2KV7JEHpq9++aR6UZmYaWkkZlJIfY4spERGRl+j0AjPWHjAt4dmo4CSy3h9jvt3VawgO4U82+T9e5URERF6yM++8qaV039whqF5aInl+6JCXkd2gNZYdL2JeUAoIDEyJiIi85OzFUqQf3Ib5X82SlP9RpxH6Zbwl2Y4oEDAwJSIiciOry3OWlmLgLfUwsNL2HR5dgn+ipONI60SFe67CPo7Lofo2BqZERERuYmm2fZw2HJ/99QUafrxAsu07KYPxWrcHJWUaALFaQ3DlTb4S7Fk739P7JyG9ZZwXa0aOYmBKREQkkyOBmnG2vahQFl90FtsyHzLb302TvsS14BBJmXFv0/sneTUI9JVgz9L5BoAzRaUYu3QP5g1ro6r6kmUMTIlItXyllYYCiyOBWuXZ9gCwadFoJJ4/JdmXbs3XCB7QH29Z2GesCoI/Xwn2LJ1vIwFDkD9j7QH0Torld4jKMTAlIlXylVYaCiyOBmoVZ9t3+3s3Pv5iumT7/Go1kfLYR1jWoiNSAKS3jEPvpFhV3Yj5UrBX8XxbIgDkF5ViZ955ZjdQOQamRKQ6vtJKQ4FFTqB29mIpgvU65L5aeWoT0G3UIhyPMVy/FWfbBwdpVBU0+VKw52jWAmY3UD+u/EREqmLvxx8w/Pib1hEn8hA5gVqbJW+bBaVLk/sgYco3pqAUUPdse18K9hw9j2o+32TAFlMiUhVfaqWhwOJIAFa75AJSGpsvGdpswkqUhlwPitQy294WXwr22jesgThtOM4UlVq8qfWF800GLrWYlpZ6/y6JiPyLL7XSqJ1OL5CdW4A1+04hO7eArcwusheAfbF0Mn599wFJ2bj+T6HhlG/MglLA+7Pt7TEGe9ZqqIFh3Lcagr3gIA2m908CALP6+sr5JgPZgaler8cLL7yA+Ph4VKtWDX///TcAYNq0afjggw8UryARBRZfaqWRy5OB4rqcfHTJzMLQRdsxfvk+DF20HV0ys7AuJ99tx/R31gK1ticP4GjmHbj11IHrhWFhgF6Pvi8/iVit9FqN1Yb7xDhpXwv20lvGYd6wNj57vslAI4SQ9c04c+ZMfPTRR5g5cyZGjhyJnJwcNGrUCJ9//jnefPNNZGdny6rAqVOnMGXKFHz//fe4fPkyGjdujMWLF6Ndu3Z2X1tcXAytVouioiJER0fLOi4RqZNOL9AlM8tul9zWKb1U84PoCE9mGbA2ecx4tvgj7TzjuQUACD3yZg8w32j/fqBlS9NDX0975msZMnz9fPsjOfGa7MC0cePGWLBgAW677TZERUXht99+Q6NGjfDXX38hJSUFFy5ccHhfFy5cwC233IKePXti7NixqF27Ng4fPozExEQkJibafT0DUyL/VPHHv+IXlK8GVp4MFI2BvbVxur4a2KvJupx8HJj6EiZ8866k/PTt/VH3h6+9VCv3YrBHrpATr8me/HTq1Ck0btzYrFyv1+PatWuy9pWZmYn69etj8eLFprKGDRvKrRIR+Rljl5wrCcfV8kPq6VyQnDzmXrqC80hvVRfplcpbPfE5qtWpiek5+R65afL09a22VFbkv2QHpklJSfj555/RoEEDSfnKlStxyy23yNrX119/jbS0NNx777348ccfER8fj0cffRQjR460uH1ZWRnKyspMj4uLi+VWn4h8hCsJx9XU9ejpQJGTx9znbM/bUWfLBknZM2mP4bPkPgCAEg/l2VXT9U2kNNmB6XPPPYfhw4fj1KlT0Ov1WL16NQ4ePIiPP/4Y33zzjax9/f3335g3bx4mTJiAZ555Br/++ivGjRuH0NBQDB8+3Gz7WbNmYcaMGXKrTEQ+yplWGrUl5/d0oOjPk8e8Zu9eoE0b1KlUnDB5LaC5fqPkidWQ1HZ9EylN9qz8gQMHYu3atdi4cSOqVq2K5557Dn/++SfWrl2L3r17y9qXXq9HmzZt8PLLL+OWW27BqFGjMHLkSMyfP9/i9lOnTkVRUZHp34kTJ+RWn4j8mBqT83s6UPSlFD+qJ4Qh8GzTRlI88IHXkTDlG0lQanoJrreAK02N1zeR0pxKsN+1a1ds2LDB/oZ2xMXFISkpSVLWvHlzrFq1yuL2YWFhCAsLc/m4ROSf1Di+sm2DGARpAFuxQpDGsJ0SjCl+xi7dAw0sTx5TU4of1VqyBMjIkBT93CAZDwx50aGXu2OohKPX95JteagVFcZJSuSTvLryU+fOnXHw4EFJ2aFDh8zGrxIROUKN4yt3H7tgMygFDEHr7mMXFAuWlZg8FrBKSoCoKLPiW/77KS5Eah3ezb8Xy7Bm3ylFg0NHr9sXvv3T9P8ce0q+xqHANCYmBhoLXRaWnD/vePfFk08+iU6dOuHll1/G4MGDsXPnTixcuBALFy50eB9EREZqHF/prWDZlcljASsjw9BSWlFmJrLveRgXFm13eDdBGvcEh85ctxx7Sr7GocD0zTffNP1/QUEBXnzxRaSlpSElJQUAkJ2djR9++AHTpk2TdfBbb70VX375JaZOnYqZM2eiYcOGePPNN/Gf//xH1n6IiAB1rpftzWCZKX4cdPAg0KyZeXl5ORAcjPZ6YfO6qqxyC7lSwaG969sST0zIIlKS7AT799xzD3r27InHH39cUv7OO+9g48aN+Oqrr5Ssn01MsE9ElaktOb+/rmRVmVryxspWowZQeWGYrCygZ09JkbXrqiJbY4mV+pwdqYc1y0Z25I0KeYWceE32rPwffvgB6emVUwsD6enp2Lhxo9zdEREpSm3rZfvaeuPOWJeTjy6ZWRi6aDvGL9+HoYu2o0tmFtbl5Hu7atatWmWYVV8xKE1ONszErxSUAtavq5pVQ/FQ5wRM69fc5lhipWbrW6uHI5i7lnyB7MlPNWvWxJo1azBx4kRJ+Zo1a1CzJu/EiMj71Da+0p8nI/lcXs3SUiAiwrz85EkgPh6A9dZfW9fVmn2nHDq8EsFh5Xr8e7FMMqbVGuauJV8gOzCdMWMGHnnkEWzZsgUdOnQAAOzYsQPr1q3DokWLFK8gEQUeJbqF1Ta+Um3BshI8vdyqyyZOBObMkZY98wzw0kumh/ZWVbJ2XXl6LLGxHjq9wPa/C1A9IgSFVywvC+6NsdVEzpIdmI4YMQLNmzfHW2+9hdWrVwMw5B7dunWrKVAlInKWGpZbdNd4SbUFy65yJW+sp8ak6vQC+7b+hrbdLSyZXVYGhIaaHrrS+uuNiXeW/lYsHRfw/eEiFDicymPaoUMHfPrpp0rXhYgCnBq6hdUQGPsKZ1Nheeocr8vJR/Oe7dH235OS8t3vfIS2jz0oKXO09bdXsxuw+9gFs4DamYUNXAnOrf2tVOYPw0UosLiUYL+0tBRXr16VlHF2PBE5Qw3dwmoIjH2JM93XnjrHuxYsQ/qY+yVl+dVqotNjHwEngHk5+ZLjONr623HWJpy/dP13r2JALWcssSvBua2/FaPqkSF4d2gbdEysyZZS8imyA9PLly9j8uTJWLFiBQoKCsye1+l0ilSMiAKLt5cTVUNg7Gvkdl975ByXlwMhIWhXqbjbqEU4HhNnqlfl4zja+lsxKAXMA2pHxhK7Gpzb+1sBgMLL1xD0v5ZcIl8iO13UU089haysLMybNw9hYWF4//33MWPGDNStWxcff/yxO+pIRAHA28uJygmMyUBuKiy3n+OZM4GQEEnR0uQ+SJjyjSkotXYcZyclGYPLGWsPQPe/fFHGscQDk+ORUqnF0l5wXnlflnj7b4XInWS3mK5duxYff/wxevTogYyMDHTt2hWNGzdGgwYN8Omnn3LVJiJyireXE+WPvXPkdF+77RyfOQPEmbcwNpuwEqUh1q+XisdxZlUlIzmt+Ur0DHj7b4XInWQHpufPn0ejRo0AGMaTnj9vuOPs0qULxo4dq2ztiChgeHs5Uf7YO8/RVFhuOcddugDbtkmKDr2xALefiZd1HFuTlxzlSECtRHDu7b8VIneS3ZXfqFEj5OXlAQCaNWuGFStWADC0pFavXl3RyhFR4PD2CknGH3tre9fAMDmFP/aW2eq+NlL0HG/bZli5qWJQGhYG6PVIHDfSqeNYW1WpRlXp8ABrHAmoj/572eV9eftvhcidZAemGRkZ+O233wAATz/9NN59912Eh4fjySefxFNPPaV4BYkocHhzOVH+2LufIudYrzcEpF26SMv37zes6qTRuHSc9JZx2DqlF5aN7Ii5Q5KxbGRHbJ+aqkhAvS4nH29uPGRzG0f3pbald4mUohFCONNjYXLs2DHs3r0bjRs3RuvWrZWql0OKi4uh1WpRVFTENFVEfsRTydctYR5T93P6HL/1FjB+vLRs8GDg88+VPY6VfY1dugeA5Ryl9oJBnV6gS2aW3dn0ADBfRmDpzb8VIkfJiddcDky9iYEpEblDoP3Ye+P9yjrmhQtADQstiIWFgFar3HHscCXQzc4twNBF2+0e48nUJhifepNT9SNSKznxmkOTn9566y2HDz5u3DiHtyUiUiN/WzrUFm+1EDt8jgcMANaulZbNnw+MHq3scRzg6CQvSxyd9JRQq6qr1STyaQ4Fpm+88Ybk8blz53D58mXTZKfCwkJERkaiTp06DEyJiHyEqle62rsXaNPGvNw4xtRLnA10mfWByDEOTX7Ky8sz/XvppZeQnJyMP//8E+fPn8f58+fx559/ok2bNnjhhRfcXV8iIlKAEone3UIIQ+BZOSjdseP6cz6IWR+IHCN7Vv60adPw9ttvo2nTpqaypk2b4o033sCzzz6raOWIiMg9VLnS1ZIlQFCln6XUVENA2r695+rhBsz6QOQY2Qn28/PzUV5eblau0+nwzz//KFIpIiJyL1WtdFVSAkRFmZefOwfUquX+43uInFWyiAKV7MD0tttuw+jRo/H++++jzf+6Wnbv3o2xY8ciNTVV8QoSEZHyVDPmMSPD0FJaUWYmMHmye4/rJa5MoCIKBLID0w8//BDDhw9Hu3btEBJiWA2jvLwcaWlpeP/99xWvIBERKc/ry1oePAg0a2ZeXl4OBAc7vBtfTO0VSFkfiOSSHZjWrl0b3333HQ4dOoS//voLgGFp0ptuYt41IiJfYWtteLePeaxRw5CbtKKsLKBnT1m78afFEHwxwCZyBybYJyIKYB4N7latAgYNkpYlJxtSQ8lkLdWVoysxeYKjwaY/BdhElii+8tOECRPwwgsvoGrVqpgwYYLNbefMmSOvti5gYEpE5Dq3t9aVlgIREeblJ08C8fGyd2dveU/jMIStU3p5rdXR0WDTFwJsIlcpvvLT3r17ce3aNdP/W6Px0fxyRESBzK1jHidOBCo3WDzzDPDSS07vUk6qK2+M5XR04QJ7uWQ1MOSS7Z0Uy259ChgOBaabN2+2+P9EREQWHTsGJCSYl5eVAaGhdl9uqxVXVamuKpETbKo9wCbyBtmTn4qKiqDT6VCjhnSm5vnz51GlShV2qRMRBbqmTYFDh6Rla9cCd9zh0MvtdYOrJtWVBXKCTTUH2ETeInvlpyFDhmD58uVm5StWrMCQIUMUqRQREfmgH34wLBlaMSiNjzes3CQjKB27dI9ZcGfsBl+Xk6/q5T3lBJtqDrCJvEV2YLpjxw70tJDSo0ePHtixY4cilSIiIh9SXm4ISNPTpeVHjhgmODnIXjc4YOgGB6Da5T3lBJtqDrCJvEV2YFpWVmZxSdJr167hypUrilSKiIh8xMyZwP8WWzHSjx6N7CP/Ys3FcGTnFkCndywroZxucOPynrFaaSAYqw336kx2OcGmMZessbzydoD3Amwib5E9xrR9+/ZYuHAh3n77bUn5/Pnz0bZtW8UqRkREKnbmDBBnHvyt33kE0zceRf6i7aYyR3Nyyh1zqcblPeUuXGAMsCuPqY1lHlMKULIT7G/btg2pqam49dZbcdtttwEANm3ahF9//RXr169H165d3VJRS5jHlIjIC7p0AbZtk5Z99hnWterhUk7O7NwCDK0Q0FqzbGRHs1nqals5SW7SfLXVn0hJiifYr2zfvn149dVXsW/fPkRERKB169aYOnUqmjRp4nSlncHAlIj8hU8EJlu3ApUbH8LCgCtXoBNwOem9MXH+maJSi+NMre1DrSsn+cRnSuQBbg9M1YKBKRH5A7UGViZ6PRAcbF6+fz/QsiUA11o7KzLOygcsd4NXbnXlyklE6icnXnNo8lNxcbHk/239IyIixzmSHsmr3nrLPCgdPNiQAup/QSmgXNJ7OZOaHJ3F7+jkK/IMnV4gO7cAa/adkjU5jgKDQ5OfYmJikJ+fjzp16qB69eoWlx4VQkCj0UCn0yleSSIif6TqJSkvXABqWEhTVFgIaLVmxUrm5HR0UhNXTvI9qu8dIK9zKDDNysoyrfTEJUmJiJSh2sBqwADDSk0VzZ8PjB5t9SXGNEn2xoc6mpMzOEhj9z1z5STfYm3YhbF3gMMuCHAwMO3evbvF/yciCjRKTmhRXWC1dy/Qpo15uV5vSKBvg9w0SUrgykm+Q9W9A6QqDgWmv//+u8M7bN26tdOVISJSM6W7IVUTWAkBBFmYcrBjB9C+vcO78XROTqVbacl9VNs7QKrjUGCanJwMjUYDexP4OcaUiPyVO7ohVRFYLVkCZGRIy1JTgQ0bnNqdJ5Pee6OVlpyjut4BUi2HAtO8vDx314OISLXc1Q3p1cCqpASIijIvP3cOqFXL6sscGcrgyPhQpXDlJN+gmt4BUj2HAtMGDRq4ux5ERKrlzm5IrwRWGRmGltKKMjOByZNtvkytM6rVuDQpSamid4B8gkOBaWWffPIJ5s+fj7y8PGRnZ6NBgwZ488030bBhQwwcOFDpOhIReZW7uyE9FlgdPAg0a2ZeXl5uOYF+BWqfUe3JVlqSj8MuyFEOJdivaN68eZgwYQL69u2LwsJC05jS6tWr480331S6fkREXueJbkhjYDUwOR4piTWV/4GOiTEPSrOyDBOf7ASlTGRPSpCzeAIFLtktpm+//TYWLVqEO++8E6+88oqpvF27dpg0aZKilSMiUgOf7oZctQoYNEhalpxsSA3lIM6oJqVw2AXZIzswzcvLwy233GJWHhYWhkuXLilSKSIiNfHJbsjSUiAiwrz85EkgPl7WrjijmpTEYRdki+yu/IYNG2Lfvn1m5evWrUPz5s2VqBMRker4SjekTi9w+uFHzYPSZ54xdNvLDEoBzqgmIs+R3WI6YcIEPPbYYygtLYUQAjt37sSyZcswa9YsvP/+++6oIxGRKqi9G3LL+l/RI6096lYq/2HPUaTd4nx2FZ8eyuAGSq7+RURSsgPTRx55BBEREXj22Wdx+fJl3H///ahbty7mzp2LIUOGuKOORESqoXQ3pFJBzqWGiehx9G9J2UP3PIfNjdsDn+dgXkio0626PjmUwU3UmjKLyF9ohL3lnP5Hr9cjqNKSdZcvX0ZJSQnq1KnjlsrZU1xcDK1Wi6KiIkRHR3ulDkREzlIkyFm/HkhLkxTlV6uJlMc+Mj02tmhundLLpeDRl4MyJW4ArKXMMu5FTUM6iNRETrzmcGAaHByM/Px8UxD61FNPYerUqahRw3tdNwxMichXuRzklJcDISFmxd1GLcLxGMuvWzayo8utvb7Yja1EQK3TC3TJzLKanUCp4J/IH8mJ1xye/FQ5fl2wYAEKCwudqiARkTU6vUB2bgHW7DuF7NwCv8yNKScvqMXzMXOmWVC6NLkPEqZ8YzUoBZSZNV8x32r7hjWwM++8qj8r4w1A5YDSuDDAupx8h/YjJ2UWETnPqZWfAPNAlYjIVb7cVSyHo0HOO1lHsPzX46Zta5dcwK/vPmC2/Y79x/Hs0t/tHlfJWfO+8FnZuwHQwHAD0Dsp1m4rJ1NmEXmG7HRRRETuoFTLli9wNHh5Y+Mh0/n4Yulk86D0s88AIdAuqR7itOGwFVpVjwxRbNa8q5+Vp1rFlWzlZMosIs+Q1WL63HPPITIyEgBw9epVvPTSS9BqtZJt5syZo1ztiCggKNmy5S1yxl7KCV7anfwDKz+dIikrCw5Bj5nfYet9tyEY12fNj1m6x+p+Ci9fw4YDZ1xuzXTls9LpBd7JOozF246i8Mo1U3nlllalxrEq2crJlFlEnuFwYNqtWzccPHjQ9LhTp074+29pahKNRp0/GESkbr6+5KXcbm17QQ4AaIQeebMHmJXf/tA7OFQ7ASguk5yP3kmxqB4ZgsLL18xeAygX3Dv7Wa3LycfTq/dbrJ+xpXXesDbA/+qpxBABJVs5baXMwv8eB0rKLCJ3cjgw3bJlixurQUSBzJfH71mbXV8x2KocUNnLCzp819d4ftNCyWu+adYVjw+UtpxWPB87885bDUoB5YL7DQfOOLRdxbpZO0cV66YB8PTq/Si6fE3WubRF6VZO4+pflgLs6pHmGRKISD6OMSUir/PV8XtyZtdXZmmJ0+jSEuRl3mEWlLZ64nOzoBSQng9PBPfrcvLx4bajDm1rrJutc1SRgGG4gTPn0hrjDQAAs/G3riwMUGThBqDo8jW/GwtN5A0MTInI64wtW9bCAw0M3blqG7/n6uSa9JZx2DqlF5aN7Ijs7Ln4fa509bxn0h5DwpRvcDGsqqTc0vlwd3BvDDAdEaQB2jaIAWD/HDnK2XRMlm4AAENLqdwWWJfTfBGRXU6niyIiUoqvjt9TopUy+Ld9SGnTxqx83e+nsOzTvQ4tAarTC+iFQPWIEMmkIlR6nSuTc+QEmHoB7D52ASmJNRUffuHM/tJbxqF3UqzLE6qcTfMFqC+VFpFascWUiFTB2LKltTBWT63j91xqpRQC0GiAykHpjh2AEEhvVdehlr51OfnokpmF/7y/w2ZQCrgW3J8puiJre2MAqfTwC2f3V3FhgJTEmm6d5V8xzZeRP6Y9I3IHtpgSkarYGr+ntrXInZ5cs2QJkJEhLUtNBTZskBTZa+mzN6nIKNbF1rp1Ofl44ds/Zb3GGEA6koHAqHpkiMXJT4A60jG5EmT7StozIm9zqsX0559/xrBhw5CSkoJTp04BAD755BNs3bpV0cr5Co4lInKdKxOJvEX25JqSEkMraeWg9Nw5s6C04jEstfQ5MqmoemQIPn24A7ZO6eVSUDp26R6cv3TV4ddUHP9q6xxVrOf8YW3wyt2tLG6nRIuvEuyNhbaHy5YS2Sc7MF21ahXS0tIQERGBvXv3oqysDABQVFSEl19+WfEKqp2xG23oou0Yv3wfhi7aji6ZWeyuIZLJV9cid3hyTUYGEBUlfXFmpqFLv1Yt2cd1ZMxn4eVrCArSOB3MOTqjviINzANIa+eoemQInky9Cbuf7Y30lnGKTlRyB0duRByhxrRnRGohuyv/xRdfxPz58/Hggw9i+fLlpvLOnTvjxRdfVLRyaudM/kIissyXc5na7HI/eBBo1sz8ReXlQHCw08f0xPmSO6Pe1gQfRycgKTVRyV2MwXPlRQBiteEYcmt9vLHxsN19qC3tGZGayA5MDx48iG7dupmVa7VaFBYWKlEnn+APSygSqYmv5jI1Mna5S8TEAJW/F7OygJ49XT6eJ86Xo0HtAx1vRN9Wde0GkBbPkQvbeYu14BkAlv96gsuWErlAdld+bGwsjhw5Yla+detWNGrUSJFK+QJf7XYkUitfzWVq0apVhrGkFYPS5GRDt70CQSngmfPlaFDbt1Vdp2e6+ypLY3/dldCfKJDIDkxHjhyJ8ePHY8eOHdBoNDh9+jQ+/fRTTJo0CWPHjnVHHVXJl7sdidTIL37US0sNAemgQdLyEyeAvXsVPZQnzpdf3Sx4iNrHyRKpneyu/Keffhp6vR633XYbLl++jG7duiEsLAyTJk3Cf//7X3fUUZV8vduRSI1sjd+Tk+5IpxfYmXceZ4pLcb6kDDWqhiJWG+HesYoTJwJz5kjLnnkGeOkl9xwPyp0va2wtfOAzNwteoPZxskRqphFCOJV75erVqzhy5AhKSkqQlJSEatWqKV03u4qLi6HValFUVITo6GiPHlunF+iSmWV3LNHWKb34ZUQkkzGwdOZHfV1OvlmgZuSW1XeOHQMSEszLy8qA0FDljmODK+fLEZbOKVcyIiJHyYnXnA5M1cCbgSlwfVY+YLklgd02RJ7lSMJ5DRT822zaFDh0SFq2di1wxx2u71tl3B38EpH/Ujwwvfvuux0++OrVqx3e1lXeDkwBtiSQezEYcJyxF8OR9EZxrvZmrF8PpKVJy+LjgZMnndsfEZEfkxOvOTTGVKvVmv5fCIEvv/wSWq0W7dq1AwDs3r0bhYWFsgJYf8GxROQuvOmRR07OTWPGDNkpicrLgZAQ8/IjR4DERHn7IiIiMw4FposXLzb9/5QpUzB48GDMnz8fwf9LDq3T6fDoo496rdXS29Sec498DxdvkE9uBgzZGTNmzgSmT5eWjRkDzJsnbz9ERGSV7Fn5H374IbZu3WoKSgEgODgYEyZMQKdOnfDqq68qWkGiQOOJxRv8cYiA3AwYDm9/5gwQZ+Em4NIlIDLSrNgfzy0RkafIDkzLy8vx119/oWnTppLyv/76C3q9XrGKEQUqOYs3ONNS769DBIw5N61lyqjI4dybXboA27ZJyz77DBg61OLm/npuiYg8RXaC/YyMDDz88MOYM2cOtm7diq1bt+L111/HI488goyMDFn7ev7556HRaCT/mllaU5oogLhz8QbjEIHKga9xiMC6nHzZ+1SLignnbdHAgdybW7caEuVXDErDwgC93mZQ6q/nlojIU2S3mL722muIjY3F66+/jvx8wxdtXFwcnnrqKUycOFF2BVq0aIGNGzder1AV2VUi8ivuWrzBE0MEvM1awnkju62Xej1QYZiSyf79QMuWVo8bCOeWiMgTZEeBQUFBmDx5MiZPnozi4mIAcGnSU5UqVRAbG+v064l8haNjD+11SRsXb5C7DKS7hwioRcVMGbJWfnrrLWD8eGnZ4MHA55/bPWagnFsiIndzqXlSiVn4hw8fRt26dREeHo6UlBTMmjULN954o8Vty8rKUFZWZnpsDIyJ1E7O2EN3LQPpziECaiMrU8aFC0ANC0F+YSFQIVWeLb56bjlRi4jURvYYUyV16NABS5Yswbp16zBv3jzk5eWha9euuHjxosXtZ82aBa1Wa/pXv359D9eYSD5nxh4au6RjtdLu+lhtuNOpotw1RMCnDRhgHpTOnw8I4XBQCvjmuV2Xk48umVkYumg7xi/fh6GLtqNLZhbHwhKRV6lqSdLCwkI0aNAAc+bMwcMPP2z2vKUW0/r163t15SciW+ytRmTslre2CpGSLVrGutgbIuDSiki+Yu9eoE0b83K93jDpSSZfO7fW8uRyOWUicgc5Kz95tcW0surVq+Omm27CkSNHLD4fFhaG6OhoyT8iNZMz9tASY5f0wOR4pCTWdCmoqThrvfJeXBki4FOEMASelYPSHTuuP+cEXzq39iZqAYaJWjq9atosiCiAqCowLSkpQW5uLuIsJbMm8kFqG3vojiECPmPJEiCo0ldeaqohIG3f3uXd+8q5dfVmiYjInZya/PTjjz/itddew59//gkASEpKwlNPPYWuXbvK2s+kSZPQv39/NGjQAKdPn8b06dMRHByMoVbyBBL5GjWOPaw4az0gJr2UlABRUebl584BtWopeihfOLdqu1kiIqpIdovp0qVLkZqaisjISIwbNw7jxo1DREQEbrvtNnz22Wey9nXy5EkMHToUTZs2xeDBg1GzZk1s374dtWvXllstIlUypn6yFpZoIGMVIgUpOURA1TIyzIPSzExDK6nCQamR2s+tGm+WiIiMZE9+at68OUaNGoUnn3xSUj5nzhwsWrTI1IrqCXIG0xJ5i3GiCWA59ZOaunn9xsGDgKVV5MrLLSfQDyC+NlGLiHyfWyc//f333+jfv79Z+YABA5CXlyd3d0R+z1fGHvqNmBjzoDQry9BKGuBBKeBbE7WIKPDIHmNav359bNq0CY0bN5aUb9y4kXlFiazwhbGHPm/VKmDQIGlZcrIhNRRJWFu6Ndbekq1ERG4mOzCdOHEixo0bh3379qFTp04AgG3btmHJkiWYO3eu4hUk8heyViMix5WWAhER5uUnTgD16nm+Pj6CN0tEpEayA9OxY8ciNjYWr7/+OlasWAHAMO70888/x8CBAxWvIBGRVRMnAnPmSMumTgVeftk79fEx9m6WuGQpEXmaqlZ+kouTn4gC1LFjQEKCeXlZGRAa6vHq+KN1OflmXf1x7OonIif47MpPRER2NW1qHpSuXWuY3MSg1CKdXiA7twBr9p1Cdm6B3VWdjJkkKifiP1NUirFL92BdTr47q0tEAUx2V75Op8Mbb7yBFStW4Pjx47h69ark+fPnuVoIEbnB+vVAWpq0LD4eOHnSO/XxEXJbPu0tWaqBYcnS3kmx7NYnIsU51GLapk0bLFy4EAAwY8YMzJkzB/fddx+KioowYcIE3H333QgKCsLzzz/vzroSkZ+y2aJ37ZphDfvKQemRIwxK7XCm5ZNLlhKRNzkUmP7www/IzMwEAHz66adYtGgRJk6ciCpVqmDo0KF4//338dxzz2H79u1urSwR+Z91OfnokpmFoYu2Y/zyfRi6aDu6ZGYZgqaZM82758eMMXTbJyZ6p8I+wl7LJ2Bo+azcrc8lS4nImxzqyh85ciQef/xxAMCZM2fQqlUrAEC1atVQVFQEALjjjjswbdo0N1WTiPyRsUWvcvBUfiof6a1SzV9w6RIQGemRuvk6OS2fFWfmc8lSIvImh1pMd+3ahcuXLwMA6tWrh/x8Q/dPYmIi1q9fDwD49ddfERYW5qZqEpG/sdai98XSyfj13QekhZ99ZmglZVDqMGdbPts3rIE4bbjZqlBGGhjGqLZvWMO1ChIRWeBQYPrzzz+jVq1aAIC77roLmzZtAgD897//xbRp09CkSRM8+OCDeOihh9xXUyLyK5Vb9Nqd/ANHM+/AracOmMrKgkOQffgcMHSoN6rok4zjdQ//U+LQ9pVbPrlkKRF5k8t5TLdv345ffvkFTZo0Qf/+/ZWql0OYx5TId63Zdwrjl++DRuiRN3uA2fO3P/QODtVOwNwhyRiYHO+FGvoeSzPwrdHAsATp1im9LAaZashjygT/RP5BTrwmO11UZR07dkTHjh1d3Q2R2/DHTZ3qRIVjxK6v8fymhZLyb5p1xeMDp0i2I/usjde1xJGWT28vWaqGwJiIPE92YDpr1izccMMNZt32H374Ic6dO4cpU6ZYeSWR5wXaj5vPBOEXLiClcS2kVCpu9cTnuBhWFcD1Fj2OZbTP1gx8S2Id/Buwt2Spu6zLyceYpXvMyo1pruYNa+OXf79E5ERgumDBAnz22Wdm5S1atMDgwYNRp04dfP3117jnnnswbNgwRSpJ5AxrLUj++uPmM0H4gAGGlZoqeCbtMXyW3Mf0mGMZ5bE3A9/o8Z6J6Ny4tnpvWGAIsp9evd/ic0zwT+T/ZC9JeubMGcTFmf/I1a5dGydOnMDZs2eRkpKC//73v4pUkMgZzuZw9FU+sYTk3r2GRPmVgtJ1v5/C5u53ScpiteF+d+PgTo7OwG9yQxRSEmuqOqB7J+sICi9fs/o8E/wT+TfZLab169fHtm3b0LBhQ0n5tm3bkJCQgClTpuCvv/7Cq6++qlglieRyNoejL1L9EpJCAEEW7oF37ADat0c6gN4t4nxjCIJK+UvuUZ1eYPG2PIe2ZYJ/Iv8kOzAdOXIknnjiCVy7dg29evUCAGzatAmTJ0/GxIkTARjym+bm5ipbUyIZAmn1GlUH4UuWABkZ0rLUVGDDBkmRt8Yy+gtj7tEzRaUWb1B8ZbzuzrzzKLxivbW0IrUH2UTkHNmB6VNPPYWCggI8+uijuHr1KgAgPDwcU6ZMwdSpUwEAISEhCAkJUbamRDL4SwuSI1QZhJeUAFFR5uXnzgH/y4lMyjHmHh27dA80gFlwKuAb43UdvUarR4SoPsgmIufIHmOq0WiQmZmJc+fOYfv27fjtt99w/vx5PPfcc+6oH5FTAmn1GtUF4RkZ5kFpZqahS59Bqdukt4zDvGFtoI00bxSobqFMjRy9RjM6J6g+yCYi58gOTI2qVauGW2+9FS1btuRSpKQ6gbR6jWqC8IMHDZObliyRlpeXA5Mnu/fYZFJkYeJQ0eVr6pkEZ4O9axkAYiJD8HivJh6rExF5llMJ9nft2oUVK1bg+PHjpu58o9WrVytSMSJXGVuQKqdQcjSHo6+w1Y1bMQgHgOzcAvdMMIqJAQoLpWVZWUDPnsrsn+xS/SQ4B9gbkqABMOvuVqqtPxG5TnZgunz5cjz44INIS0vD+vXrcfvtt+PQoUP4559/cNddd9nfAZEHeXv1Gk+xF4QDQJfMLOVznK5aBQwaJC1LTjakhiKPUvUkOBmsXcuqzMlLRIqTHZi+/PLLeOONN/DYY48hKioKc+fORcOGDTF69GiL+U2JvC1QZnxbC8I3HDij/EIDpaVARIR5+YkTQL16Tr8Hcp4qJ8E5KVBuKInInOwxprm5uejXrx8AIDQ0FJcuXYJGo8GTTz6JhQsX2nk1EbmTMQgfmBxvCsYVX2hg4kTzoHTqVMPkJgalbqfTC2TnFmDNvlPIzi0wfXaqmwTnosrXMoNSosAgu8U0JiYGFy9eBADEx8cjJycHrVq1QmFhIS5fvqx4BYnIMTq9MGthUrR799gxICHBvLysDAgNdanu5Bhby872Tor1i1ymRBTYZAem3bp1w4YNG9CqVSvce++9GD9+PLKysrBhwwbcdttt7qgjqZylgEhNrRtqr58SrAUsfVrGOvR6u927TZsChw5Jy9auBe64Q25VyUnGZWdtDclwZBKcv137RORfNEIIWYuFnz9/HqWlpahbty70ej1mz56NX375BU2aNMGzzz6LmJgYd9XVTHFxMbRaLYqKihAdHe2x49J1tlpw1DBJQU31c1eAbC1gsTSr2ZplIztabjFdvx5IS5OWxccDJ086UVNylk4vzCavVWRsDd06pRc2HDijmmueiAiQF6/JDkzVhIGpd9kKiAA4N6lGQWqqn7sCZEcCFo0GsDaEtGJAIwmSr12z3D1/5AiQmOh0fck52bkFGLpou93tjDcYgdBLQES+Q068Jnvy0/Hjx23+o8BgL2ci4MSkGgWpqX7GALly8GjsgnUl6bkjY0iNb9HhhQZmzjQLSvWjRyP7yL9YczFcMuGGPEPujHtOHCIiXyV7jGlCQgI0GutfcjqdzqUKkW9Qe85EtdTP3UnPHQ1YHu6cgO9yztheaODMGcBCyrf1O49g+sajyK/QYseuYc/yxRn3bLUlImfIDkz3Vkqcfe3aNezduxdz5szBSy+9pFjFSN3UnjNRLfVzd4DsaCCSmhSLZ/olWQ8UunQBtm2Tvuizz7CuVQ/lc6CSbMalOn1lxr2axnYTkW+RHZjefPPNZmXt2rVD3bp18eqrr+Luu+9WpGKkbmpvwVFL/dwdIMsJWCwuNLB1K9C1q7QsLAy4cgU6AczIzPLpJS79haPLzqrhc3AkewCDUyKyRvYYU2uaNm2KX3/9VandkcoZAyJrP4MaGFpIvNWCo5b6uTtANgYsgIwxpACg1xtmRVUOSvfvN6zqpNHIau0l9zMu1RmrlV4rsdpw1QR73hzbbW3hASLyLbJbTIuLiyWPhRDIz8/H888/jyZNmihWMVI3NbXgWBvLpob6eaIL1tra4mZjSI3eegsYP15aNngw8PnnkiK1DIeg69S+VKe3xnZz6ACR/5AdmFavXt1s8pMQAvXr18fy5csVqxipn+yAyA3s/SB5u36eCpAdClguXABqWAiACwsBrdasWC3DIUjK4pAMB3hiMpI3bmY4dIDIv8gOTDdv3ix5HBQUhNq1a6Nx48aoUkX27sjHebMFx9EfJG+3MHkqQLYZsAwYYFipqaL584HRo63uz9cm3JB1nmpR9PTNjLuzXhCR58mOJDUaDTp16mQWhJaXl+Onn35Ct27dFKsc+QZnW3BcIfcHyRspqyryWoC8dy/Qpo15uXGMqQ1qGQ5BrvFki6Knb2bUkhaOiJQje/JTz549cf68+WSHoqIi9OzZU5FKEdnjixNzPJr0XAhD4Fk5KN2x4/pzDvCFCTdknacnIzk9Gc9JHAdN5H9kt5gKISwm2C8oKEDVqlUVqRSRPfxBsmHJEiAjQ1qWmgps2ODU7tQwHIKc440WRU+O7eY4aCL/43BgasxPqtFoMGLECISFhZme0+l0+P3339GpUyfla0hkAX+QLCgpAaKizMvPnQNq1XJp12oYDkHyeesGzlM3MxwHTeR/HA5Mtf+btSuEQFRUFCIiIkzPhYaGomPHjhg5cqTyNSSygD9IlWRkGFpKK8rMBCZP9kp1SMpby3N68wbOEzczHAdN5H8cDkwXL14MAEhISMCkSZPYbU9exR+k/zl4EGjWzLy8vBwIDvZ8fciMN3NsBsINnBrSwhGRcjRCCJ9dHqO4uBharRZFRUWIjo72dnXICwI6sXZMjCEHaUVZWQAnIaqGtRnxxtslT0wgM9YBsHwD5y+T2LzVKk1E9smJ12QHpv/88w8mTZqETZs24ezZs6j8cp1OJ7/GTmJgSkAA/iCtWgUMGiQtS042pIYi1dDpBbpkZlmdfGRsrdw6pZfbr9eAvoEjIq+TE6/JnpU/YsQIHD9+HNOmTUNcXJzFGfpEnhQwE3NKS4EKY7tNTpwA6tXzfH3IJjXl2GRmBSLyFbID061bt+Lnn39GcnKyG6pDRBZNnAjMmSMtmzoVePll79THh3irRV1tKc0C5gaOiHya7MC0fv36Zt33ROQmx44BCQnm5WVlQGiox6vja7zZhc2UZkRE8sle+enNN9/E008/jaNHj7qhOkRk0rSpeVC6dq1h5SYvB6U6vUB2bgHW7DuF7NwCxVYOUpJx0k/l7vQzRaUYs3QP5m485Nb6G2fEW2ub1cAQJPvyjHgiIqXJnvwUExODy5cvo7y8HJGRkQgJCZE8b2m5Unfx58lPATehh65bvx5IS5OWxccDJ096pz6V+MJEGnsTjypzV/2VmBHP7wIi8nVunZX/0Ucf2Xx++PDhcnbnEn8NTH3hh5/c4No1yy2hhw8DjRt7vj4WqCH9kSOycwswdNF2h7d3Z/1d+XvmdwER+QO3BqZq4o+Bqa/88JPCZs4Epk+Xlo0ZA8yb5536WKCm9Ef2rNl3CuOX75P1GnfW35lWT34XEJG/cGu6KMCQq/Srr77Cn3/+CQBo0aIFBgwYgGCuNOMSnV5gxtoDFldoETD8IM1YewC9k2K9/sNPCjlzBoizEFxcugRERnq+PjaoKf2RPc5MKHJn/eXOiOd3AREFKtmTn44cOYLmzZvjwQcfxOrVq7F69WoMGzYMLVq0QG5urjvqGDDk/PCTH+jSxTwo/fRTw+QmlQWlgPrSH9lib+KRLWqoP78LiChQyQ5Mx40bh8TERJw4cQJ79uzBnj17cPz4cTRs2BDjxo1zRx0Dhi/98JMLtm4FNBpg27brZaGhgF4P3H+/9+plhy+lPwoO0mB6/yQAkB2cqqH+/C4gokAlOzD98ccfMXv2bNSocT3FSc2aNfHKK6/gxx9/VLRygcaXfvjJCXq9ISDt2lVavn+/IS+pyldR87X0R+kt4zBvWBvEah37e1FT/fldQESBSnZgGhYWhosXL5qVl5SUIJQJv13iaz/8JMNbbwGVx2APHmzotm/Z0jt1kslWK6Tx8fT+Saoa85jeMg5bp/TCspEdMXdIMp5MvQkaqL/+/C4gokAlOzC94447MGrUKOzYsQNCCAghsH37dowZMwYDBgxwRx0Dhi/+8JMdFy4YWkLHj5cU685fAD7/3EuVcp61VshYbbhqZ4kbJx4NTI7H+NQmPlF/fhcQUaCSnS6qsLAQw4cPx9q1a03J9cvLyzFgwAAsWbIEWq3WLRW1xB/TRQHMXeg3BgwwrNRUwTNpj+Gz5D6q+TydTd7u60nffaX+/C4gIn/gkTymR44cMaWLat68ORp7IQG4vwamgO/8cJIFe/cCbdqYFSdMXmsaR6qGXJTrcvLx/Nd/4ExxmaksNjoMzw9owaBHRfhdQES+zi2BqV6vx6uvvoqvv/4aV69exW233Ybp06cjIiJCkUo7w58DU/JBQgBB5qNjBj7wOn6r29Ss3JsJ6dfl5GPM/5bKtGS+irq1iYjIt8mJ1xweY/rSSy/hmWeeQbVq1RAfH4+5c+fisccec7myRH5h8WKzoLSwc3ckTPnGYlAKeC8XpU4v8PTq/Ta3eXr1fuj0jnWm6PQC2bkFWLPvFLJzCxx+HRERUWUOr/z08ccf47333sPo0aMBABs3bkS/fv3w/vvvI8hCKxFRQCgpAaKizMvPncOPJ8sAB5bF9HQuyu25BSi8fM3mNoWXr2F7bgE6N6llczuOgSQiIiU5HFEeP34cffv2NT1OTU2FRqPB6dOn3VIxItXLyDAPSjMzDV36tWqpNhdl9t//KrKdcS33yisU5ReVYszSPfjud343EBGRPA63mJaXlyM8XPoDGhISgmvXbLe8EPmdgweBZs3My8vLJblKjbkozxSVWlzz3DjG1PO5KB0dz2p9O1truRs9vmwv3oEGfVuz5ZSIiBzjcGAqhMCIESMQFhZmKistLcWYMWNQtWpVU9nq1auVrSGRmsTEAIWF0rKsLKBnT7NNjbkoxy7dAw0gCeK8mYsyJbEm3tl8xKHtrLG3ljsA6AXw6Gd7MD8ocCdScUY9EZE8Dgemw4cPNysbNmyYopUh8gSngoVVq4BBg6RlycmG1FA2GBPSVx6HGevFcZgdG9VE9cgQm+NMYyJD0LGR9cBUzrjYGWsPoHdSbMAFZBx/S0Qkn9N5TNWA6aJILtnBQmkpYCkl2okTQL16Dh9XbS1nrqaLys4twNBF2x0+3rKRHW22wPob4/jbyl+uashfS0TkaW5JF0Xk66xN1jlTVIqxS/dgXU6+9AUTJpgHpVOnGiY3yQhKAemymCmJNb3eepjeMg7zh7VBbLR03HicNtyhHKbG8bOO8nTmAW+yNf7WWDZj7QGm1SIissDhrnwiX2YvWNCgQpfzieNAQoL5hmVlQGioeyvqQekt49A7Kdapllzj+Flbra4VeTrzgDfZG39bMX9tILUiExE5gi2mFBAcDRauNm5iHpSuXWtoJfWjoNTIlZbc9JZxeO/+W2DrJRoYWmE9n3nAexxtHQ6kVmQiIkexxTSAqG2coztYe4/2goCueXvwyYrnpIXx8cDJk26sre/r27ou3oEGj35m3nLqzcwD3qTW/LVERL6AgWmACIQZwrbeo7UgoIquHEdeu9P8icOHgcaN3VRT/9K3dRzmB6kr84A3qTd/LRGR+nFWfgAIhBnC9t7ju/e3wQvfHpAEC+O2LcOErZ9KttePHo2g+fPdXV2/FAgt8o4yXo+A5fy1/vA3R0TkKDnxGgNTP6fTC3TJzLI6vtLYerN1Si+fDSIcfY/T+jXHY5/tRa2SC/j13QfMtlu/8whuvzXRzbWlQBEIvRRERI6QE6+ppiv/lVdewdSpUzF+/Hi8+eab3q6O3wiEGcKOvseYqmHYs34GYvb+Knl++uCpSJn2XwYLpChXsh4QEQUqVQSmv/76KxYsWIDWrVt7uyp+xxMzhL3dhetI3dud/AMpje+QlOlCQrHzwCk818j7eUXJPxmzHhARkWO8HpiWlJTgP//5DxYtWoQXX3zR5rZlZWUoKyszPS4uLnZ39Xyeu2cIq6G70lbdNUKPvNkDzJ/Yvx/BLVsixY31IiIiInm8nsf0scceQ79+/ZCammp321mzZkGr1Zr+1a9f3wM19G3GGcLW2gNdyTMpeyUlN7H2Hkfs+to8KB082JCTtGVLj9SNiIiIHOfVwHT58uXYs2cPZs2a5dD2U6dORVFRkenfiRMn3FxD32dcoQeAWeDmSp5JNS27WPk9aq9cxNHMO/D8poXSDQsLgc8/V+SYOr1Adm4B1uw7hezcAi4vSUREpACvdeWfOHEC48ePx4YNGxAe7lg3clhYGMLCwtxcM/+T3jIO84Ypm2dSbZOqjO+x6r33oOtf2ZLn/piWiRYzJyt2LDUMXyAiIvJHXgtMd+/ejbNnz6JNmzamMp1Oh59++gnvvPMOysrKEBwc7K3q+R2lZwirbtnFvXuRXuFaMtKV69AiWLmOAWv5Uo3DF5ifkoiIyHleC0xvu+027N+/X1KWkZGBZs2aYcqUKQxK3UDJGcKqWXZRCCDIQuC5YwfQvj2UvIrsDV/QwDB8oXdSLGf5ExEROcFrgWlUVBRaVpqAUrVqVdSsWdOsnNRHFcsuLl4MPPSQtCw1FdiwwS2HU9vwBSIiIn/j9XRR5JuME47GLt0DDSwvu+jMpCqHlJQAUVHm5efOAbVq2XypKzlXVTd8gYiIyM+oKjDdsmWLt6tAMrhjUpVdGRnAkiXSssxMYLL9yU2uTlpSzfAFIiIiP6WqwJR8j8eWXTx4EGjWzLy8vBxwYDyyEpOWVDF8gYiIyI95PcE++T7jpKqByfFISXTD8p4xMeZBaVaWYeKTA0GpUjlX3ZUTloiIiAwYmJJ6rVoFaDSGxPhGycmGgLRnT4d3I2fSkj3G4QuxWml3faw2nKmiiIiIXMSufFKf0lIgIsK8/MQJoF492btTetKSx4YvEBERBRi2mJK6TJhgHpROnWpoJXUiKAXcM2nJ7cMXwGVPiYgo8LDFlNTh2DEgIcG8vKwMCA11adeOTlpq2yAG2bkFqmgF5bKn6uVKyjEiIrJNI4Tw2WaY4uJiaLVaFBUVITo62tvVIWc1bQocOiQtW7sWuOMOxQ5hnJUPWM65OqpbQ3z9W74qAkFrGQSMdeVYVu/hDQMRkXxy4jV25ZP3rF9vmNxUMSiNjzd02ysYlAK2Jy2N6tYQC3/KM5sgZUwltS4nX9G62KJUBgFSnvGGQQ3XCRGRv2JXPnnetWuWu+cPHwYaN3bbYS1NWmrbIAbdX91sNRDUwBAI9k6K9Uh3LZc9VSd7Nwyevk58FYdBEJE9DEzJs2bOBKZPl5aNGQPMm+eRwxsnLRll5xaoKhDksqfqxBsG13EYBBE5goEpecaZM0CchR+fS5eAyEjP1+d/1BYIctlTdVLbdeJrlFh5jYgCA8eYkvt16WIelH76qWEsqReDUkB9gaAxg4C1zk0NDK1MXPbUs9R2nfgSjpsmIjkYmJL7bN1qmNy0bdv1stBQQK8H7r/fe/WqQG2BIJc9VSe1XSe+RMmV14jI/zEwJeXp9YaAtGtXafn+/Ya8pBr1BFVqDAS57Kn6qPE68RUcBkFEcnCMKSnrrbeA8eOlZYMHA59/7p36OMAYCFaemBHrxYkZXPZUfdR4nfgCDoMgIjmYYJ+Ucf48UNPCbOTCQkCr9Xh1nMFUNuQIXify6PQCXTKz7K68tnVKL55HIj8lJ15jiym5bsAAw0pNFc2bZ0gD5UMqp5IisoTXiTzGYRBjl+6BBpZXXuMwCCIyYmBKztu7F2jTxrzcOMbUD7G1jEg+DoMgIkcxMCX5hACCLMyb27EDaN9ekUOoMQBkgnAi53HcNBE5gmNMSZ7Fi4GHHpKWpaYCGzYodgg1BoDWEoQbf1I5W56IiMgyOfEa00WRY0pKDN3zlYPSc+cUD0rHLt1jlvfQuELMupx8xY7lKCYIJyIi8gwGpmRfRgYQFSUty8w0dOnXqqXYYdQaADJBOBERkWdwjClZd/Ag0KyZeXl5ORAcrPjh5ASAcmZFuzpelQnCiYiIPIOBKVkWE2PIQVpRVhbQs6fbDumOANDR8aq2gld/SxCuxollREREAANTqmzVKmDQIGlZcrIhNZSbKR0AWpuwZByvapywZC94Na6Tbi9BuC+sk67GiWVERERGHGNKBqWlhslNlYPSEyc8EpQCMAWA1truNDAEUY4EgI6OV/3ud/uTrfxlnXQ1TiwjIiKqiIEpARMmABER0rKpUw2Tm+rV81g1lAwAHR2v+uyaHIcmWxkThMdqpa21sdpwn0gVpdaJZURERBWxKz+QHTsGJCSYl5eVAaGhHq8OoNwKMY6OQz1/6arV5ypPtvLlBOHumlhGRESkJAamPsrlCSxNmwKHDknL1q4F7rhD2Yo6QYkAUMmJSBWDXF9dJ52ZBYiIyBcwMPVBLk1gWb8eSEuTlsXHAydPuqGmznM1AHRkwlJM1RCcv3TN7r58Zba9Lf6WWYCIiPwTx5j6GKcnsFy7ZpjcVDkoPXxY0aBUpxfIzi3Amn2nkJ1b4LUxi46MV31xYEvFJlupnZITy4iIiNyFgakPcXoCy8yZ5mNGx4wxTG5q3Fix+q3LyUeXzCwMXbQd45fvw9BF29ElM8up2d5KBLj2Jiz1bV3XL2bbO8JfMgsQEZF/0wghfHYabnFxMbRaLYqKihAdHe3t6rhddm4Bhi7abne7ZSM7GrrBz5wB4ix07V+6BERGKlo3azlDjWGOnJnrSufatDceN5ByewbSeyUiInWQE69xjKkPkTWBpUsXYNs26ROffgrcf7/i9bLXkquBoSW3d1Ks3RY5R5PiK8mXZ9vLFUjvlYiIfA8DUx/iyMSUdif/wMBbKs2sDw29nkDfDZRKRaRkgGvkaAuhr862d4Y/v1cut0pE5NsYmPoQWzPNNUKPvNkDzF+0fz/QsqVb66VUKiKlc216o/WVvIfDFIiIfB8nP/kQaxNYRuz62jwoHTzYMLnJzUEpoFwqIiVzbXKlo8DC5VaJiPwDA1MfU3GmufbKRRzNvAPPb1oo3aiwEPj8c4/VSalURErm2pTT+kq+jTchRET+g4GpD0pvGYdffpmL394aKn1i3jxDK6lW69H6KJWKSMlcm1zpKHDwJoSIyH8wMPU1e/cCGg0036yVluv1htykXmIvZ6gjY/yUzLXJlY4CB29CiIj8Byc/+QohgCAL9xE7dgDt23u+PhYokYrIGOBWnsQSK3MSiyNLksZypSO/wJsQIiL/wcDUFyxeDDz0kLQsNRXYsME79bFBiVRESgS4xtbXsUv3QANIglOudORfeBNCROQ/GJiqWUkJEBVlXn7uHFCrlufr40FKBbhKtL6SuvEmhIjIf3BJUrXKyACWLJGWZWYCkyd7pTq+jEnXAwPzmBIRqZOceI2BqdocPAg0a2ZeXl4OBAd7vj7kUQyiXcPzR0SkPnLiNXblq0lMjCEHaUVZWUDPnl6pDnkWW/xc58/LrRIRBQKmi1KDVasM69hXDEqTkw0z8RmUBgSuXERERMQWU+8qLQUiIszLT5wA6tXzfH3IK+ytXKSBYeWi3kmx7JYmIiK/xhZTb5kwwTwonTrV0ErKoDSgcOUiIiIiA7aYetqxY0BCgnl5WRkQGurx6pD3ceUiIiIiA7aYelLTpuZB6dq1hlZSBqUBiysXERERGTAw9YT16w2Tmw4dul4WH28ISO+4w3v1IlUwrlxkbfSoBobZ+Vy5iIiI/B0DU3e6ds0QkKalScsPHwZOnvROnUh1jCsXATALTrlyERERBRIGpu4yc6Z59/yYMYZW0saNvVMnUi3j8qmxWml3faw2HPOGtWEeUyIiCgic/KS0M2eAOAtBxKVLQGSk5+tDPiO9ZRx6J8Vy5SIiIgpYDEyV1KULsG2btOzTT4H77/dOfcjncOUiIiIKZAxMlbB1K9C1q7QsNNSQQF/D1i4iIiIiRzAwdYVeDwQHm5fv3w+0bOn5+hARERH5ME5+ctZbb5kHpYMHGyY3MSglIiIiko0tpnKdPw/UtDAGsLAQ0Go9Xh0iIiIif8EWUznmzDEPSufNM7SSMiglIiIicglbTB2l0wETJ0rL9HpObiIiIiJSCFtMHRUcDCxaZPj/HTsMraQMSomIiIgUw8BUjkceMQSk7dt7uyZEREREfoeBKRERERGpAgNTIiIiIlIFBqZEREREpAoMTImIiIhIFRiYEhEREZEqMDAlIiIiIlVgYEpEREREqsDAlIiIiIhUgYEpEREREakCA1MiIiIiUgUGpkRERESkCl4NTOfNm4fWrVsjOjoa0dHRSElJwffff+/NKhERERGRl3g1MK1Xrx5eeeUV7N69G7t27UKvXr0wcOBA/PHHH96sFhERERF5gUYIIbxdiYpq1KiBV199FQ8//LDdbYuLi6HValFUVITo6GgP1I6IiIiI5JATr1XxUJ3s0ul0+OKLL3Dp0iWkpKRY3KasrAxlZWWmx8XFxZ6qHhERERG5mdcnP+3fvx/VqlVDWFgYxowZgy+//BJJSUkWt501axa0Wq3pX/369T1cWyIiIiJyF6935V+9ehXHjx9HUVERVq5ciffffx8//vijxeDUUotp/fr12ZVPREREpFJyuvK9HphWlpqaisTERCxYsMDuthxjSkRERKRuPjnG1Eiv10taRQOVTi+wM+88zl4sRZ2ocLRvWAPBQRpvV4uIiIjIbbwamE6dOhV9+vTBjTfeiIsXL+Kzzz7Dli1b8MMPP3izWl63LicfM9YeQH5RqaksThuO6f2TkN4yzos1IyIiInIfrwamZ8+exYMPPoj8/HxotVq0bt0aP/zwA3r37u3NannVupx8jF26B5XHV5wpKsXYpXswb1gbBqdERETkl7wamH7wwQfePLzq6PQCM9YeMAtKAUAA0ACYsfYAeifFslufiIiI/I7X00XRdTvzzku67ysTAPKLSrEz77znKkVERETkIQxMVeTsRetBqTPbEREREfkSBqYqUicqXNHtiIiIiHwJA1MVad+wBuK04bA2elQDw+z89g1reLJaRERERB7BwFRFgoM0mN7fsOJV5eDU+Hh6/yROfCIiIiK/xMBUZdJbxmHesDaI1Uq762O14UwVRURERH5NdSs/kSE47Z0Uy5WfiIiIKKAwMFWp4CANUhJrersaRERERB7DrnwiIiIiUgUGpkRERESkCgxMiYiIiEgVGJgSERERkSowMCUiIiIiVWBgSkRERESqwMCUiIiIiFSBeUwdpNMLJrwnIiIiciMGpg5Yl5OPGWsPIL+o1FQWpw3H9P5JXCKUiIiISCHsyrdjXU4+xi7dIwlKAeBMUSnGLt2DdTn5XqoZERERkX9hYGqDTi8wY+0BCAvPGctmrD0And7SFkREREQkBwNTG3bmnTdrKa1IAMgvKsXOvPOeqxQRERGRn2JgasPZi9aDUme2IyIiIiLrGJjaUCcqXNHtiIiIiMg6BqY2tG9YA3HacFhLCqWBYXZ++4Y1PFktIiIiIr/EwNSG4CANpvdPAgCz4NT4eHr/JOYzJSIiIlIAA1M70lvGYd6wNojVSrvrY7XhmDesDfOYEhERESmECfYdkN4yDr2TYrnyExEREZEbMTB1UHCQBimJNb1dDSIiIiK/xa58IiIiIlIFBqZEREREpAoMTImIiIhIFRiYEhEREZEqMDAlIiIiIlVgYEpEREREqsDAlIiIiIhUgYEpEREREakCA1MiIiIiUgUGpkRERESkCgxMiYiIiEgVGJgSERERkSowMCUiIiIiVaji7Qq4QggBACguLvZyTYiIiIjIEmOcZozbbPHpwPTixYsAgPr163u5JkRERERky8WLF6HVam1uoxGOhK8qpdfrcfr0aURFRUGj0bj9eMXFxahfvz5OnDiB6Ohotx/P1/F8OY7nynE8V/LwfDmO58pxPFfyBPr5EkLg4sWLqFu3LoKCbI8i9ekW06CgINSrV8/jx42Ojg7IC8tZPF+O47lyHM+VPDxfjuO5chzPlTyBfL7stZQacfITEREREakCA1MiIiIiUgUGpjKEhYVh+vTpCAsL83ZVfALPl+N4rhzHcyUPz5fjeK4cx3MlD8+X43x68hMRERER+Q+2mBIRERGRKjAwJSIiIiJVYGBKRERERKrAwJSIiIiIVCHgA9N3330XCQkJCA8PR4cOHbBz506b23/xxRdo1qwZwsPD0apVK3z33XeS54UQeO655xAXF4eIiAikpqbi8OHD7nwLHiPnXC1atAhdu3ZFTEwMYmJikJqaarb9iBEjoNFoJP/S09Pd/TY8Rs75WrJkidm5CA8Pl2zDa8ugR48eZudKo9GgX79+pm389dr66aef0L9/f9StWxcajQZfffWV3dds2bIFbdq0QVhYGBo3bowlS5aYbSP3e9AXyD1Xq1evRu/evVG7dm1ER0cjJSUFP/zwg2Sb559/3uy6atasmRvfhWfIPVdbtmyx+Dd45swZyXb+eF0B8s+Xpe8jjUaDFi1amLbx12vLGQEdmH7++eeYMGECpk+fjj179uDmm29GWloazp49a3H7X375BUOHDsXDDz+MvXv34s4778Sdd96JnJwc0zazZ8/GW2+9hfnz52PHjh2oWrUq0tLSUFpa6qm35RZyz9WWLVswdOhQbN68GdnZ2ahfvz5uv/12nDp1SrJdeno68vPzTf+WLVvmibfjdnLPF2BYEaTiuTh27JjkeV5bBqtXr5acp5ycHAQHB+Pee++VbOeP19alS5dw8803491333Vo+7y8PPTr1w89e/bEvn378MQTT+CRRx6RBFzOXKu+QO65+umnn9C7d29899132L17N3r27In+/ftj7969ku1atGghua62bt3qjup7lNxzZXTw4EHJuahTp47pOX+9rgD552vu3LmS83TixAnUqFHD7DvLH68tp4gA1r59e/HYY4+ZHut0OlG3bl0xa9Ysi9sPHjxY9OvXT1LWoUMHMXr0aCGEEHq9XsTGxopXX33V9HxhYaEICwsTy5Ytc8M78By556qy8vJyERUVJT766CNT2fDhw8XAgQOVrqoqyD1fixcvFlqt1ur+eG1Z98Ybb4ioqChRUlJiKvPna8sIgPjyyy9tbjN58mTRokULSdl9990n0tLSTI9dPf++wJFzZUlSUpKYMWOG6fH06dPFzTffrFzFVMiRc7V582YBQFy4cMHqNoFwXQnh3LX15ZdfCo1GI44ePWoqC4Rry1EB22J69epV7N69G6mpqaayoKAgpKamIjs72+JrsrOzJdsDQFpammn7vLw8nDlzRrKNVqtFhw4drO7TFzhzriq7fPkyrl27hho1akjKt2zZgjp16qBp06YYO3YsCgoKFK27Nzh7vkpKStCgQQPUr18fAwcOxB9//GF6jteWdR988AGGDBmCqlWrSsr98dqSy953lhLn31/p9XpcvHjR7Dvr8OHDqFu3Lho1aoT//Oc/OH78uJdq6H3JycmIi4tD7969sW3bNlM5ryvbPvjgA6SmpqJBgwaScl5bBgEbmP7777/Q6XS44YYbJOU33HCD2TgZozNnztjc3vhfOfv0Bc6cq8qmTJmCunXrSr6o0tPT8fHHH2PTpk3IzMzEjz/+iD59+kCn0ylaf09z5nw1bdoUH374IdasWYOlS5dCr9ejU6dOOHnyJABeW9bs3LkTOTk5eOSRRyTl/nptyWXtO6u4uBhXrlxR5G/bX7322msoKSnB4MGDTWUdOnTAkiVLsG7dOsybNw95eXno2rUrLl686MWael5cXBzmz5+PVatWYdWqVahfvz569OiBPXv2AFDmN8NfnT59Gt9//73ZdxavreuqeLsC5P9eeeUVLF++HFu2bJFM6BkyZIjp/1u1aoXWrVsjMTERW7ZswW233eaNqnpNSkoKUlJSTI87deqE5s2bY8GCBXjhhRe8WDN1++CDD9CqVSu0b99eUs5ri1zx2WefYcaMGVizZo1k3GSfPn1M/9+6dWt06NABDRo0wIoVK/Dwww97o6pe0bRpUzRt2tT0uFOnTsjNzcUbb7yBTz75xIs1U7+PPvoI1atXx5133ikp57V1XcC2mNaqVQvBwcH4559/JOX//PMPYmNjLb4mNjbW5vbG/8rZpy9w5lwZvfbaa3jllVewfv16tG7d2ua2jRo1Qq1atXDkyBGX6+xNrpwvo5CQENxyyy2mc8Fry9ylS5ewfPlyh760/eXaksvad1Z0dDQiIiIUuVb9zfLly/HII49gxYoVZsMgKqtevTpuuummgLuuLGnfvr3pPPC6skwIgQ8//BAPPPAAQkNDbW4byNdWwAamoaGhaNu2LTZt2mQq0+v12LRpk6TlqqKUlBTJ9gCwYcMG0/YNGzZEbGysZJvi4mLs2LHD6j59gTPnCjDMIn/hhRewbt06tGvXzu5xTp48iYKCAsTFxSlSb29x9nxVpNPpsH//ftO54LVl7osvvkBZWRmGDRtm9zj+cm3JZe87S4lr1Z8sW7YMGRkZWLZsmST9mDUlJSXIzc0NuOvKkn379pnOA68ry3788UccOXLEoZvpgL62vD37ypuWL18uwsLCxJIlS8SBAwfEqFGjRPXq1cWZM2eEEEI88MAD4umnnzZtv23bNlGlShXx2muviT///FNMnz5dhISEiP3795u2eeWVV0T16tXFmjVrxO+//y4GDhwoGjZsKK5cueLx96ckuefqlVdeEaGhoWLlypUiPz/f9O/ixYtCCCEuXrwoJk2aJLKzs0VeXp7YuHGjaNOmjWjSpIkoLS31yntUktzzNWPGDPHDDz+I3NxcsXv3bjFkyBARHh4u/vjjD9M2vLakunTpIu677z6zcn++ti5evCj27t0r9u7dKwCIOXPmiL1794pjx44JIYR4+umnxQMPPGDa/u+//xaRkZHiqaeeEn/++ad49913RXBwsFi3bp1pG3vn31fJPVeffvqpqFKlinj33Xcl31mFhYWmbSZOnCi2bNki8vLyxLZt20RqaqqoVauWOHv2rMffn5Lknqs33nhDfPXVV+Lw4cNi//79Yvz48SIoKEhs3LjRtI2/XldCyD9fRsOGDRMdOnSwuE9/vbacEdCBqRBCvP322+LGG28UoaGhon379mL79u2m57p37y6GDx8u2X7FihXipptuEqGhoaJFixbi22+/lTyv1+vFtGnTxA033CDCwsLEbbfdJg4ePOiJt+J2cs5VgwYNBACzf9OnTxdCCHH58mVx++23i9q1a4uQkBDRoEEDMXLkSL/40jKSc76eeOIJ07Y33HCD6Nu3r9izZ49kf7y2rvvrr78EALF+/XqzffnztWVM01P5n/H8DB8+XHTv3t3sNcnJySI0NFQ0atRILF682Gy/ts6/r5J7rrp3725zeyEMqbbi4uJEaGioiI+PF/fdd584cuSIZ9+YG8g9V5mZmSIxMVGEh4eLGjVqiB49eoisrCyz/frjdSWEc3+HhYWFIiIiQixcuNDiPv312nKGRggh3NwoS0RERERkV8COMSUiIiIidWFgSkRERESqwMCUiIiIiFSBgSkRERERqQIDUyIiIiJSBQamRERERKQKDEyJiIiISBUYmBIRERGRKjAwJSKy4/nnn0dycrK3q6FKCQkJePPNN71dDSLyEwxMicgnjBgxAhqNBhqNBiEhIWjYsCEmT56M0tJSb1fNIc8//7yp/lWqVEFCQgKefPJJlJSUuOV4mzdvRt++fVGzZk1ERkYiKSkJEydOxKlTpxQ9zq+//opRo0aZHms0Gnz11VeKHoOIAgcDUyLyGenp6cjPz8fff/+NN954AwsWLMD06dO9XS2HtWjRAvn5+Th69CgyMzOxcOFCTJw40al9CSFQXl5u8bkFCxYgNTUVsbGxWLVqFQ4cOID58+ejqKgIr7/+uitvwUzt2rURGRmp6D6JKHAxMCUinxEWFobY2FjUr18fd955J1JTU7FhwwbT83q9HrNmzULDhg0RERGBm2++GStXrjQ9v2XLFmg0GmzatAnt2rVDZGQkOnXqhIMHD0qO88orr+CGG25AVFQUHn74YbNW2V9//RW9e/dGrVq1oNVq0b17d+zZs8du/atUqYLY2FjUq1cP9913H/7zn//g66+/llX377//Hm3btkVYWBi2bt1qdoyTJ09i3LhxGDduHD788EP06NEDCQkJ6NatG95//30899xzAICCggIMHToU8fHxiIyMRKtWrbBs2TLJvnr06IHHH38cjz/+OLRaLWrVqoVp06ZBCGHapmJXfkJCAgDgrrvugkajMT3Ozc3FwIEDccMNN6BatWq49dZbsXHjRrvni4gCDwNTIvJJOTk5+OWXXxAaGmoqmzVrFj7++GPMnz8ff/zxB5588kkMGzYMP/74o+S1//d//4fXX38du3btQpUqVfDQQw+ZnluxYgWef/55vPzyy9i1axfi4uLw3nvvSV5/8eJFDB8+HFu3bsX27dvRpEkT9O3bFxcvXpT1HiIiInD16lVZdX/66afxyiuv4M8//0Tr1q3N9vnFF1/g6tWrmDx5ssVjVq9eHQBQWlqKtm3b4ttvv0VOTg5GjRqFBx54ADt37pRs/9FHH6FKlSrYuXMn5s6dizlz5uD999+3uO9ff/0VALB48WLk5+ebHpeUlKBv377YtGkT9u7di/T0dPTv3x/Hjx93/GQRUWAQREQ+YPjw4SI4OFhUrVpVhIWFCQAiKChIrFy5UgghRGlpqYiMjBS//PKL5HUPP/ywGDp0qBBCiM2bNwsAYuPGjabnv/32WwFAXLlyRQghREpKinj00Ucl++jQoYO4+eabrdZNp9OJqKgosXbtWqvbTJ8+XbKPXbt2iVq1aolBgwbJqvtXX31l9RhCCDF27FgRHR1tcxtr+vXrJyZOnGh63L17d9G8eXOh1+tNZVOmTBHNmzc3PW7QoIF44403TI8BiC+//NLusVq0aCHefvttp+pJRP6rilejYiIiGXr27Il58+bh0qVLeOONN1ClShXcc889AIAjR47g8uXL6N27t+Q1V69exS233CIpq9jSGBcXBwA4e/YsbrzxRvz5558YM2aMZPuUlBRs3rzZ9Piff/7Bs88+iy1btuDs2bPQ6XS4fPmy3RbA/fv3o1q1atDpdLh69Sr69euHd955R1bd27VrZ/MYQghoNBqb2wCATqfDyy+/jBUrVuDUqVO4evUqysrKzMaLduzYUbK/lJQUvP7669DpdAgODrZ7HMDQYvr888/j22+/RX5+PsrLy3HlyhW2mBKRGQamROQzqlatisaNGwMAPvzwQ9x888344IMP8PDDD5tmt3/77beIj4+XvC4sLEzyOCQkxPT/xqBLr9c7XI/hw4ejoKAAc+fORYMGDRAWFoaUlBRTt7w1TZs2xddff40qVaqgbt26pmEIR48edbjuVatWtXmMm266CUVFRcjPzzcF3Za8+uqrmDt3Lt588020atUKVatWxRNPPGH3PThj0qRJ2LBhA1577TU0btwYERERGDRokFuORUS+jWNMicgnBQUF4ZlnnsGzzz6LK1euICkpCWFhYTh+/DgaN24s+Ve/fn2H99u8eXPs2LFDUrZ9+3bJ423btmHcuHHo27cvWrRogbCwMPz777929x0aGorGjRsjISFBMjZWqboDwKBBgxAaGorZs2dbfL6wsND0HgYOHIhhw4bh5ptvRqNGjXDo0CGz7S2diyZNmlhtLQ0JCYFOp5OUbdu2DSNGjMBdd92FVq1aITY21hSMExFVxBZTIvJZ9957L5566im8++67mDRpEiZNmoQnn3wSer0eXbp0QVFREbZt24bo6GgMHz7coX2OHz8eI0aMQLt27dC5c2d8+umn+OOPP9CoUSPTNk2aNMEnn3yCdu3aobi4GE899RQiIiKcfh9RUVGK1B0A6tevjzfeeAOPP/44iouL8eCDDyIhIQEnT57Exx9/jGrVquH1119HkyZNsHLlSvzyyy+IiYnBnDlz8M8//yApKUmyv+PHj2PChAkYPXo09uzZg7fffttmyqmEhARs2rQJnTt3RlhYGGJiYtCkSROsXr0a/fv3h0ajwbRp02S1UBNR4GCLKRH5rCpVquDxxx/H7NmzcenSJbzwwguYNm0aZs2ahebNmyM9PR3ffvstGjZs6PA+77vvPkybNg2TJ09G27ZtcezYMYwdO1ayzQcffIALFy6gTZs2eOCBBzBu3DjUqVPHpfeiRN2NHn30Uaxfvx6nTp3CXXfdhWbNmuGRRx5BdHQ0Jk2aBAB49tln0aZNG6SlpaFHjx6IjY3FnXfeabavBx98EFeuXEH79u3x2GOPYfz48ZKE+pW9/vrr2LBhA+rXr28aHztnzhzExMSgU6dO6N+/P9LS0tCmTRvZ74uI/J9GiAoJ6YiIiP6nR48eSE5O5pKjROQxbDElIiIiIlVgYEpEREREqsCufCIiIiJSBbaYEhEREZEqMDAlIiIiIlVgYEpEREREqsDAlIiIiIhUgYEpEREREakCA1MiIiIiUgUGpkRERESkCgxMiYiIiEgV/h/0f7pj4fMstAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Para calcula a reta de regressão\n", + "x, y = np.polyfit(df_2017['Economy..GDP.per.Capita.'], df_2017['Happiness.Score'], 1)\n", + "\n", + "# Para Criar o gráfico de dispersão\n", + "plt.figure(figsize=(8, 6))\n", + "plt.scatter(df_2017['Economy..GDP.per.Capita.'], df_2017['Happiness.Score'])\n", + "\n", + "# Plotando a reta de regressão\n", + "plt.plot(df_2017['Economy..GDP.per.Capita.'], x * df_2017['Economy..GDP.per.Capita.'] + y, color='red')\n", + "\n", + "# Configurações do gráfico\n", + "plt.xlabel('Renda Per Capita')\n", + "plt.ylabel('Pontuação de Felicidade')\n", + "plt.title('Relação entre Renda Per Capita e Felicidade')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Executando consulta SQL para identificar o país com a maior taxa de expectativa de vida (df_2017) em um DataFrame do Pandas, utilizando a biblioteca pandasql e o banco de dados SQLite." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import sqlite3\n", + "from pandasql import sqldf" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "155" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conn = sqlite3.connect(':memory:') \n", + "df_2017.to_sql('df_2017', conn, if_exists='replace', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "query = \"\"\"\n", + " SELECT Country, `Health..Life.Expectancy.`\n", + " FROM df_2017\n", + " ORDER BY `Health..Life.Expectancy.` DESC\n", + " LIMIT 10;\n", + " \"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Country Health..Life.Expectancy.\n", + "0 Singapore 0.949492\n", + "1 Hong Kong S.A.R., China 0.943062\n", + "2 Japan 0.913476\n", + "3 South Korea 0.900214\n", + "4 Spain 0.888961\n", + "5 Switzerland 0.858131\n", + "6 Italy 0.853144\n", + "7 Luxembourg 0.845089\n", + "8 Cyprus 0.844715\n", + "9 France 0.844466\n" + ] + } + ], + "source": [ + "result = sqldf(query, globals())\n", + "print(result)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/exercicios/para-casa/dados_2017_limpos.csv b/exercicios/para-casa/dados_2017_limpos.csv new file mode 100644 index 0000000..cdd232c --- /dev/null +++ b/exercicios/para-casa/dados_2017_limpos.csv @@ -0,0 +1,157 @@ +Country,Happiness.Rank,Happiness.Score,Whisker.high,Whisker.low,Economy..GDP.per.Capita.,Family,Health..Life.Expectancy.,Freedom,Generosity,Trust..Government.Corruption.,Dystopia.Residual +Norway,1.0,7.53700017929077,7.59444482058287,7.47955553799868,1.61646318435669,1.53352355957031,0.796666502952576,0.635422587394714,0.36201223731041,0.315963834524155,2.27702665328979 +Denmark,2.0,7.52199983596802,7.58172806486487,7.46227160707116,1.48238301277161,1.55112159252167,0.792565524578094,0.626006722450256,0.355280488729477,0.40077006816864,2.31370735168457 +Iceland,3.0,7.50400018692017,7.62203047305346,7.38596990078688,1.480633020401,1.6105740070343,0.833552122116089,0.627162635326385,0.475540220737457,0.153526559472084,2.32271528244019 +Switzerland,4.0,7.49399995803833,7.56177242040634,7.42622749567032,1.56497955322266,1.51691174507141,0.858131289482117,0.620070576667786,0.290549278259277,0.367007285356522,2.2767162322998 +Finland,5.0,7.4689998626709,7.52754207581282,7.41045764952898,1.44357192516327,1.5402467250824,0.80915766954422,0.617950856685638,0.24548277258873,0.38261154294014,2.4301815032959 +Netherlands,6.0,7.3769998550415,7.42742584124207,7.32657386884093,1.50394463539124,1.42893922328949,0.810696125030518,0.585384488105774,0.470489829778671,0.282661825418472,2.29480409622192 +Canada,7.0,7.31599998474121,7.38440283536911,7.24759713411331,1.47920441627502,1.48134899139404,0.83455765247345,0.611100912094116,0.435539722442627,0.287371516227722,2.18726444244385 +New Zealand,8.0,7.31400012969971,7.3795104418695,7.24848981752992,1.40570604801178,1.54819512367249,0.816759705543518,0.614062130451202,0.500005125999451,0.382816702127457,2.0464563369751 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+Guinea,149.0,3.50699996948242,3.58442812889814,3.4295718100667,0.244549930095673,0.791244685649872,0.194129139184952,0.348587512969971,0.264815092086792,0.110937617719173,1.55231189727783 +Togo,150.0,3.49499988555908,3.59403811171651,3.39596165940166,0.305444717407227,0.431882530450821,0.247105568647385,0.38042613863945,0.196896150708199,0.0956650152802467,1.83722925186157 +Rwanda,151.0,3.47099995613098,3.54303023353219,3.39896967872977,0.368745893239975,0.945707023143768,0.326424807310104,0.581843852996826,0.252756029367447,0.455220013856888,0.540061235427856 +Syria,152.0,3.46199989318848,3.66366855680943,3.26033122956753,0.777153134346008,0.396102607250214,0.50053334236145,0.0815394446253777,0.493663728237152,0.151347130537033,1.06157350540161 +Tanzania,153.0,3.34899997711182,3.46142975538969,3.23657019883394,0.511135876178741,1.04198980331421,0.364509284496307,0.390017777681351,0.354256361722946,0.0660351067781448,0.621130466461182 +Burundi,154.0,2.90499997138977,3.07469033300877,2.73530960977077,0.091622568666935,0.629793584346771,0.151610791683197,0.0599007532000542,0.204435184597969,0.0841479450464249,1.68302416801453 +Central African Republic,155.0,2.69300007820129,2.86488426923752,2.52111588716507,0.0,0.0,0.0187726859003305,0.270842045545578,0.280876487493515,0.0565650761127472,2.06600475311279 +,,,,,,,,,,, diff --git a/exercicios/para-casa/dados_2018_limpos.csv b/exercicios/para-casa/dados_2018_limpos.csv new file mode 100644 index 0000000..5032e58 --- /dev/null +++ b/exercicios/para-casa/dados_2018_limpos.csv @@ -0,0 +1,157 @@ +Overall rank,Country or region,Score,GDP per capita,Social support,Healthy life expectancy,Freedom to make life choices,Perceptions of corruption +1,Finland,7.632,1.305,1.592,0.874,0.681,0.393 +2,Norway,7.594,1.456,1.582,0.861,0.686,0.34 +3,Denmark,7.555,1.351,1.59,0.868,0.683,0.408 +4,Iceland,7.495,1.343,1.644,0.914,0.677,0.138 +5,Switzerland,7.487,1.42,1.549,0.927,0.66,0.357 +6,Netherlands,7.441,1.361,1.488,0.878,0.638,0.295 +7,Canada,7.328,1.33,1.532,0.896,0.653,0.291 +8,New Zealand,7.324,1.268,1.601,0.876,0.669,0.389 +9,Sweden,7.314,1.355,1.501,0.913,0.659,0.383 +10,Australia,7.272,1.34,1.573,0.91,0.647,0.302 +11,United Kingdom,7.19,1.244,1.433,0.888,0.464,0.082 +12,Austria,7.139,1.341,1.504,0.891,0.617,0.224 +13,Costa Rica,7.072,1.01,1.459,0.817,0.632,0.101 +14,Ireland,6.977,1.448,1.583,0.876,0.614,0.306 +15,Germany,6.965,1.34,1.474,0.861,0.586,0.28 +16,Belgium,6.927,1.324,1.483,0.894,0.583,0.24 +17,Luxembourg,6.91,1.576,1.52,0.896,0.632,0.321 +18,United States,6.886,1.398,1.471,0.819,0.547,0.133 +19,Israel,6.814,1.301,1.559,0.883,0.533,0.272 +20,United Arab Emirates,6.774,2.096,0.776,0.67,0.284, +21,Czech Republic,6.711,1.233,1.489,0.854,0.543,0.034 +22,Malta,6.627,1.27,1.525,0.884,0.645,0.142 +23,France,6.489,1.293,1.466,0.908,0.52,0.176 +24,Mexico,6.488,1.038,1.252,0.761,0.479,0.095 +25,Chile,6.476,1.131,1.331,0.808,0.431,0.061 +26,Taiwan,6.441,1.365,1.436,0.857,0.418,0.078 +27,Panama,6.43,1.112,1.438,0.759,0.597,0.063 +28,Brazil,6.419,0.986,1.474,0.675,0.493,0.088 +29,Argentina,6.388,1.073,1.468,0.744,0.57,0.054 +30,Guatemala,6.382,0.781,1.268,0.608,0.604,0.071 +31,Uruguay,6.379,1.093,1.459,0.771,0.625,0.155 +32,Qatar,6.374,1.649,1.303,0.748,0.654,0.171 +33,Saudi Arabia,6.371,1.379,1.331,0.633,0.509,0.127 +34,Singapore,6.343,1.529,1.451,1.008,0.631,0.457 +35,Malaysia,6.322,1.161,1.258,0.669,0.356,0.059 +36,Spain,6.31,1.251,1.538,0.965,0.449,0.074 +37,Colombia,6.26,0.96,1.439,0.635,0.531,0.039 +38,Trinidad & Tobago,6.192,1.223,1.492,0.564,0.575,0.019 +39,Slovakia,6.173,1.21,1.537,0.776,0.354,0.014 +40,El Salvador,6.167,0.806,1.231,0.639,0.461,0.082 +41,Nicaragua,6.141,0.668,1.319,0.7,0.527,0.128 +42,Poland,6.123,1.176,1.448,0.781,0.546,0.064 +43,Bahrain,6.105,1.338,1.366,0.698,0.594,0.123 +44,Uzbekistan,6.096,0.719,1.584,0.605,0.724,0.259 +45,Kuwait,6.083,1.474,1.301,0.675,0.554,0.106 +46,Thailand,6.072,1.016,1.417,0.707,0.637,0.029 +47,Italy,6.0,1.264,1.501,0.946,0.281,0.028 +48,Ecuador,5.973,0.889,1.33,0.736,0.556,0.12 +49,Belize,5.956,0.807,1.101,0.474,0.593,0.089 +50,Lithuania,5.952,1.197,1.527,0.716,0.35,0.006 +51,Slovenia,5.948,1.219,1.506,0.856,0.633,0.051 +52,Romania,5.945,1.116,1.219,0.726,0.528,0.001 +53,Latvia,5.933,1.148,1.454,0.671,0.363,0.066 +54,Japan,5.915,1.294,1.462,0.988,0.553,0.15 +55,Mauritius,5.891,1.09,1.387,0.684,0.584,0.05 +56,Jamaica,5.89,0.819,1.493,0.693,0.575,0.031 +57,South Korea,5.875,1.266,1.204,0.955,0.244,0.051 +58,Northern Cyprus,5.835,1.229,1.211,0.909,0.495,0.154 +59,Russia,5.81,1.151,1.479,0.599,0.399,0.025 +60,Kazakhstan,5.79,1.143,1.516,0.631,0.454,0.121 +61,Cyprus,5.762,1.229,1.191,0.909,0.423,0.035 +62,Bolivia,5.752,0.751,1.223,0.508,0.606,0.054 +63,Estonia,5.739,1.2,1.532,0.737,0.553,0.174 +64,Paraguay,5.681,0.835,1.522,0.615,0.541,0.074 +65,Peru,5.663,0.934,1.249,0.674,0.53,0.034 +66,Kosovo,5.662,0.855,1.23,0.578,0.448,0.023 +67,Moldova,5.64,0.657,1.301,0.62,0.232,0.0 +68,Turkmenistan,5.636,1.016,1.533,0.517,0.417,0.037 +69,Hungary,5.62,1.171,1.401,0.732,0.259,0.022 +70,Libya,5.566,0.985,1.35,0.553,0.496,0.148 +71,Philippines,5.524,0.775,1.312,0.513,0.643,0.105 +72,Honduras,5.504,0.62,1.205,0.622,0.459,0.074 +73,Belarus,5.483,1.039,1.498,0.7,0.307,0.154 +74,Turkey,5.483,1.148,1.38,0.686,0.324,0.109 +75,Pakistan,5.472,0.652,0.81,0.424,0.334,0.113 +76,Hong Kong,5.43,1.405,1.29,1.03,0.524,0.291 +77,Portugal,5.41,1.188,1.429,0.884,0.562,0.017 +78,Serbia,5.398,0.975,1.369,0.685,0.288,0.043 +79,Greece,5.358,1.154,1.202,0.879,0.131,0.044 +80,Lebanon,5.358,0.965,1.179,0.785,0.503,0.136 +81,Montenegro,5.347,1.017,1.279,0.729,0.259,0.081 +82,Croatia,5.321,1.115,1.161,0.737,0.38,0.039 +83,Dominican Republic,5.302,0.982,1.441,0.614,0.578,0.106 +84,Algeria,5.295,0.979,1.154,0.687,0.077,0.135 +85,Morocco,5.254,0.779,0.797,0.669,0.46,0.074 +86,China,5.246,0.989,1.142,0.799,0.597,0.103 +87,Azerbaijan,5.201,1.024,1.161,0.603,0.43,0.176 +88,Tajikistan,5.199,0.474,1.166,0.598,0.292,0.034 +89,Macedonia,5.185,0.959,1.239,0.691,0.394,0.052 +90,Jordan,5.161,0.822,1.265,0.645,0.468,0.134 +91,Nigeria,5.155,0.689,1.172,0.048,0.462,0.032 +92,Kyrgyzstan,5.131,0.53,1.416,0.594,0.54,0.035 +93,Bosnia and Herzegovina,5.129,0.915,1.078,0.758,0.28,0.0 +94,Mongolia,5.125,0.914,1.517,0.575,0.395,0.032 +95,Vietnam,5.103,0.715,1.365,0.702,0.618,0.079 +96,Indonesia,5.093,0.899,1.215,0.522,0.538,0.018 +97,Bhutan,5.082,0.796,1.335,0.527,0.541,0.171 +98,Somalia,4.982,0.0,0.712,0.115,0.674,0.282 +99,Cameroon,4.975,0.535,0.891,0.182,0.454,0.043 +100,Bulgaria,4.933,1.054,1.515,0.712,0.359,0.009 +101,Nepal,4.88,0.425,1.228,0.539,0.526,0.078 +102,Venezuela,4.806,0.996,1.469,0.657,0.133,0.052 +103,Gabon,4.758,1.036,1.164,0.404,0.356,0.052 +104,Palestinian Territories,4.743,0.642,1.217,0.602,0.266,0.076 +105,South Africa,4.724,0.94,1.41,0.33,0.516,0.056 +106,Iran,4.707,1.059,0.771,0.691,0.459,0.129 +107,Ivory Coast,4.671,0.541,0.872,0.08,0.467,0.103 +108,Ghana,4.657,0.592,0.896,0.337,0.499,0.029 +109,Senegal,4.631,0.429,1.117,0.433,0.406,0.082 +110,Laos,4.623,0.72,1.034,0.441,0.626,0.174 +111,Tunisia,4.592,0.9,0.906,0.69,0.271,0.063 +112,Albania,4.586,0.916,0.817,0.79,0.419,0.032 +113,Sierra Leone,4.571,0.256,0.813,0.0,0.355,0.053 +114,Congo (Brazzaville),4.559,0.682,0.811,0.343,0.514,0.077 +115,Bangladesh,4.5,0.532,0.85,0.579,0.58,0.144 +116,Sri Lanka,4.471,0.918,1.314,0.672,0.585,0.05 +117,Iraq,4.456,1.01,0.971,0.536,0.304,0.095 +118,Mali,4.447,0.37,1.233,0.152,0.367,0.056 +119,Namibia,4.441,0.874,1.281,0.365,0.519,0.064 +120,Cambodia,4.433,0.549,1.088,0.457,0.696,0.065 +121,Burkina Faso,4.424,0.314,1.097,0.254,0.312,0.128 +122,Egypt,4.419,0.885,1.025,0.553,0.312,0.107 +123,Mozambique,4.417,0.198,0.902,0.173,0.531,0.158 +124,Kenya,4.41,0.493,1.048,0.454,0.504,0.055 +125,Zambia,4.377,0.562,1.047,0.295,0.503,0.082 +126,Mauritania,4.356,0.557,1.245,0.292,0.129,0.093 +127,Ethiopia,4.35,0.308,0.95,0.391,0.452,0.146 +128,Georgia,4.34,0.853,0.592,0.643,0.375,0.215 +129,Armenia,4.321,0.816,0.99,0.666,0.26,0.028 +130,Myanmar,4.308,0.682,1.174,0.429,0.58,0.178 +131,Chad,4.301,0.358,0.907,0.053,0.189,0.06 +132,Congo (Kinshasa),4.245,0.069,1.136,0.204,0.312,0.052 +133,India,4.19,0.721,0.747,0.485,0.539,0.093 +134,Niger,4.166,0.131,0.867,0.221,0.39,0.099 +135,Uganda,4.161,0.322,1.09,0.237,0.45,0.061 +136,Benin,4.141,0.378,0.372,0.24,0.44,0.067 +137,Sudan,4.139,0.605,1.24,0.312,0.016,0.082 +138,Ukraine,4.103,0.793,1.413,0.609,0.163,0.011 +139,Togo,3.999,0.259,0.474,0.253,0.434,0.101 +140,Guinea,3.964,0.344,0.792,0.211,0.394,0.094 +141,Lesotho,3.808,0.472,1.215,0.079,0.423,0.112 +142,Angola,3.795,0.73,1.125,0.269,0.0,0.061 +143,Madagascar,3.774,0.262,0.908,0.402,0.221,0.049 +144,Zimbabwe,3.692,0.357,1.094,0.248,0.406,0.099 +145,Afghanistan,3.632,0.332,0.537,0.255,0.085,0.036 +146,Botswana,3.59,1.017,1.174,0.417,0.557,0.092 +147,Malawi,3.587,0.186,0.541,0.306,0.531,0.08 +148,Haiti,3.582,0.315,0.714,0.289,0.025,0.104 +149,Liberia,3.495,0.076,0.858,0.267,0.419,0.03 +150,Syria,3.462,0.689,0.382,0.539,0.088,0.144 +151,Rwanda,3.408,0.332,0.896,0.4,0.636,0.444 +152,Yemen,3.355,0.442,1.073,0.343,0.244,0.064 +153,Tanzania,3.303,0.455,0.991,0.381,0.481,0.097 +154,South Sudan,3.254,0.337,0.608,0.177,0.112,0.106 +155,Central African Republic,3.083,0.024,0.0,0.01,0.305,0.038 +156,Burundi,2.905,0.091,0.627,0.145,0.065,0.076 diff --git a/exercicios/para-casa/dados_2019_limpos.csv b/exercicios/para-casa/dados_2019_limpos.csv new file mode 100644 index 0000000..b86ff12 --- /dev/null +++ b/exercicios/para-casa/dados_2019_limpos.csv @@ -0,0 +1,157 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +