Genus 2 curve data - 18289.a.18289.1

Formats: - HTML - YAML - JSON - 2026-04-17T08:33:24.238087

• g2c_curves •

  Column	Type
  Lhash	text
  abs_disc	bigint
  analytic_rank	smallint
  analytic_rank_proved	boolean
  analytic_sha	smallint
  aut_grp_id	text
  aut_grp_label	text
  aut_grp_tex	text
  bad_lfactors	text
  bad_primes	integer[]
  class	text
  cond	bigint
  disc_sign	smallint
  end_alg	text
  eqn	text
  g2_inv	text
  geom_aut_grp_id	text
  geom_aut_grp_label	text
  geom_aut_grp_tex	text
  geom_end_alg	text
  globally_solvable	smallint
  has_square_sha	boolean
  hasse_weil_proved	boolean
  igusa_clebsch_inv	text
  igusa_inv	text
  is_gl2_type	boolean
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  leading_coeff	numeric
  locally_solvable	boolean
  modell_images	text[]
  mw_rank	smallint
  mw_rank_proved	boolean
  non_maximal_primes	integer[]
  non_solvable_places	jsonb
  num_rat_pts	smallint
  num_rat_wpts	smallint
  real_geom_end_alg	text
  real_period	numeric
  regulator	numeric
  root_number	smallint
  st_group	text
  st_label	text
  st_label_components	integer[]
  tamagawa_product	smallint
  torsion_order	smallint
  torsion_subgroup	text
  two_selmer_rank	smallint
  two_torsion_field	jsonb

  
  {'Lhash': '390668727339126280', 'abs_disc': 18289, 'analytic_rank': 2, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[18289,[1,-103,18185,18289]]]', 'bad_primes': [18289], 'class': '18289.a', 'cond': 18289, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[0,3,2,0,-3,1],[1,1]]', 'g2_inv': "['30019840638976/18289','1591680221184/18289','80787964160/18289']", 'geom_aut_grp_id': '[2,1]', 'geom_aut_grp_label': '2.1', 'geom_aut_grp_tex': 'C_2', 'geom_end_alg': 'Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': False, 'igusa_clebsch_inv': "['992','-16760','-3877033','73156']", 'igusa_inv': "['496','13044','328385','-1816744','18289']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '18289.a.18289.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.700999171439383494437499083368019470221306922168', 'prec': 167}, 'locally_solvable': True, 'modell_images': ['2.6.1'], 'mw_rank': 2, 'mw_rank_proved': True, 'non_maximal_primes': [2], 'non_solvable_places': [], 'num_rat_pts': 11, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '12.101190720585245327865480257', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.0579281153091', 'prec': 50}, 'root_number': 1, 'st_group': 'USp(4)', 'st_label': '1.4.A.1.1a', 'st_label_components': [1, 4, 0, 1, 1, 0], 'tamagawa_product': 1, 'torsion_order': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 2, 'two_torsion_field': ['5.1.73156.1', [1, 4, -1, -1, 0, 1], [5, 5], False]}
• g2c_endomorphisms •

  Column	Type
  factorsQQ_base	jsonb
  factorsQQ_geom	jsonb
  factorsRR_base	jsonb
  factorsRR_geom	jsonb
  fod_coeffs	jsonb
  fod_label	text
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  lattice	jsonb
  ring_base	jsonb
  ring_geom	jsonb
  spl_facs_coeffs	jsonb
  spl_facs_condnorms	jsonb
  spl_facs_labels	jsonb
  spl_fod_coeffs	jsonb
  spl_fod_gen	jsonb
  spl_fod_label	text
  st_group_base	text
  st_group_geom	text

  
  {'factorsQQ_base': [['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR'], 'factorsRR_geom': ['RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': True, 'is_simple_geom': True, 'label': '18289.a.18289.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1]], ['RR'], [1, -1], 'USp(4)']], 'ring_base': [1, -1], 'ring_geom': [1, -1], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'USp(4)', 'st_group_geom': 'USp(4)'}
• g2c_ratpts •

  Column	Type
  label	text
  mw_gens	jsonb
  mw_gens_v	boolean
  mw_heights	numeric[]
  mw_invs	smallint[]
  num_rat_pts	smallint
  rat_pts	jsonb
  rat_pts_v	boolean

  
  {'label': '18289.a.18289.1', 'mw_gens': [[[[0, 1], [-2, 1], [1, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]], [[[-2, 1], [1, 1]], [[-2, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.28654149047338283265366011544844', 'prec': 113}, {'__RealLiteral__': 0, 'data': '0.21516959488905870369038357364467', 'prec': 113}], 'mw_invs': [0, 0], 'num_rat_pts': 11, 'rat_pts': [[0, -1, 1], [0, 0, 1], [1, -3, 1], [1, 0, 0], [1, 1, 1], [2, -2, 1], [2, -1, 1], [4, -20, 1], [4, 15, 1], [33, -7396, 16], [33, -5148, 16]], 'rat_pts_v': False}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 18289, 'lmfdb_label': '18289.a.18289.1', 'modell_image': '2.6.1', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  local_root_number	numeric
  p	bigint
  tamagawa_number	smallint

  
  {'cluster_label': 'c3c2_1~2_0', 'label': '18289.a.18289.1', 'local_root_number': 1, 'p': 18289, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '18289.a.18289.1', 'plot': 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SAEeWmSm1by/NnWvvUQsX2pkHRC4CoA8QAAvGcWzu3XffSd9/L/3wgx2pqYdfVBEfHzotecopFkBOOcVOWUb8S710qbRggZ1rvvRSG64sAbt2SStWWBhftsyOH36QNm069P1PPtkGLVu0sKNpU1pZAAdyHOnGG6UJE%2Bw97euvbUQdkY0A6AMEwIM5js3LW7JE%2BvZbO5YuPfyp20DA3hBPO82OxEQ7atZkfky42LLFwnvu3%2BfixfZ3/HdxcRYIW7eWzj3XtrSKjy/5eoFw8eijts1bTIz00UdSx45uV4SSQAD0AQKgjewtXGjHokUWDv788%2BD7RUfbaF7TpnY0aWJHrVoEPS/avt3%2BvhcutD5m33xz8N97TIydKm7b1na0O/dc63sG%2BMFbb0nXXWeXR4%2BW%2BvZ1tx6UHAKgD/gtAO7bZ6N58%2BeHfumvW3fw/WJjLdw1by6deabNGzv9dBshQmQKBqWff5b%2B9z/pq6%2BkefNsruGBYmNtAvyFF9qcqObN6VmIyPTVV1K7djb/7447pKefdrsilCQCoA9EegDcujX0C/3rr%2B20bmZm3vtERdmp27POkpKS7Dj9dOuLB3/7/Xeb%2BD5njvU/%2B/33vLdXqmRB8OKLbdvjKlXcqRMoSitX2vSHHTtsd8f33guzjgModgRAH4i0ALhunY3czJ1rf65cefB9qla1UZwWLezP5s2Z54X8OY6t7p41y1oezp5tu6zkioqSzj5b6tzZjsREpgbAe7ZssffF336zD8Vz5thaLvgLAdAHvB4A16%2B3N6g5c6Qvvjj0xP7ERJvU36qVfapt0IBfzCi87Gxb6PzJJ9LHH9sikwOddJKNnlx%2BuQVDRlAQ7vbskS64wObFnniiTZOh3Ys/EQB9wGsBcMcOC3uzZtkpuV9%2ByXt7TIzN2TvvPDtatbIdMoDitn69rZKcPt1GBzMyQrfVqiVdcYV0zTX2IYQwiHCTnW3/Rj/80KY2zJ9vbavgTwRAHwj3AJiVZXP3Zs60Y8kSOxWXKzo67yrN1q05nQv37dolffqpNGWKhcJdu0K31aol/etf0rXX2r9dRqPhNseR%2BvSRxo61vu3//a99UIF/EQB9IBwD4O%2B/22m1Tz6xkZQDf3lKUqNGtgrzggukNm3YzQHhbd8%2B6fPPpXfflaZNs72gc518su2p2q2bTU0A3PDww9L999uHkffft2kL8DcCYARLSUlRSkqKcnJylJqa6moAzM620w0ffSTNmGG7OByoalULfLmtN44/3pUygULbt0/67DPrrzZ9urR3b%2Bi2Vq2knj3tNHGYfBaDD4wfL910k11OSZH69XO3HoQHAqAPuDUCuGuX/SKcPt1C344doduio22F7kUX2XHGGcyZQuTZtUuaOlV64w075RYM2vVxcdJVV0n//rfNY%2BUUMYrL9Ok22hcMSvfcY7t%2BABIB0BdKMgBu3WqnwKZMsV94B06Sr1TJthjq1Enq0KHEto4FwsKGDdKkSbbf6ooVoetPOknq3dtGBlmNiaL01Vd2VmXfPqlXLxsJ5MMGchEAfaC4A%2BCmTdIHH9j8p3nzQqMckv1yu%2Bwy65l2zjlSqVJF/u0BT3Eca8ExfrydJt69266PjZWuvNK24jr3XH5Ro3B%2B/NFGl3futA/dU6bw/ou8CIA%2BUBwBcPt2m0g8ebI1ZD4w9J15prUa6NKFRrnAkezeLb39tq3MXLgwdH3jxlL//lL37uxLjKO3erXNN9240f6cOZNGzzgYAdAHiioA7t1r80kmTrT2F9nZodvOOku6%2BmobwahfvwiKBnxm6VJpzBg7TfzXX3ZdIGDzBAcM4P8VCmbTJmuVtWqVfZCYN0%2BqWNHtqhCOCIA%2BUJgA6Di2z%2B6ECdI77%2BRt19KsmfU5u/pqfjkBRWXnTvv/lpIi/fqrXRcdbSPqd9xhIzrAoezcaW2zfvjB3pO/%2BoqOCjg8AqAPHEsA3LxZeu01m6eUmhq6vl69UE%2BzRo2KqWAACgZtpP3ZZ63HYK4WLaQ777RAGBPjXn0IL3v22IKP%2BfOlGjUs/NF3EkdCAPSBggZAx7H5fKNH24Th3FO85cpZ37IePWxyOu1agJK1fLn0zDPWTiYz0647%2BWQLgj16SGXKuFsf3JWRIV16qX1QqFDB3sebNHG7KoQ7AqAP5BcA9%2B61XyyjRuVt0Hz22dLNN1v4K1%2B%2BBAsGcEibNkkvvGAf0v78066rWVP6z3%2BkW27h/6kfZWfbNJypU%2B3D%2BuefSy1bul0VvIAAGOlWrlT66NEKjBqltNtuU0K/fvt3/9661X6ZpKTYql7J3kCuv95aUTRt6mLdAA5r925p3Dhp5Ehp/Xq7rnJl6fbbbcFIIOBufSgZwaB0ww22cKhMGWu4366d21XBKwiAkezxx6WhQ5XuOApISpOUEBWltHuf0PBdd%2Bqll0LbVJ1wgnTrrdYslH13AW/IzLTR%2BxEjQgtGKlSwIHjrrfxfjmSOY6O%2B48ZZf78PPrDTwEBBEQAj1axZNiNYUroUCoD/f3NbzdFctVXz5tLgwbZVEE1CAW/KzrZ%2Bgo8%2BGtplpEIFWzV8223sOxxpHEcaNMim7URHS2%2B%2BKf3rX25XBa8hAEaqLl1sTzYdOgB%2BUfkKZb75vi68kEbNQKTIyZHee0966CHpp5/susqV7UPegAG2BzG8zXGkIUOkJ56wr1991bYRBI4WAbAQHMfRrgMb44WJjIwMxZ5xhqI3bJBkAbCOpHUKBUDnhBMU9f33LlUIoDjl5NgpweRkawgsWWuQu%2B%2B2OWOxse7Wh2P36KOh8DdypDUKx7GLj49XlE9HQQiAhZC7uhYAAHhPUW6R6jUEwEI4lhHApKQkLVq06Ji%2BX0EfO21aln66%2BQUN3Tdc0qFHAPXoo3ZOqIi/dyQ8Nj09XXXq1NG6deuO6Y3Baz9vYR7La1Vwbr5WzZufo969v9aTT0rbttl1Z54pPfywbRtWXN/Xi69VuD72ySelRx6xyw8/bIt8JF6ro3Go18rPI4BM%2By%2BEqKioo/4PFxMTc8yfNvJ7bE6ONHSovVHE63b9u%2By7OmlfqLFfwv8fatJEGjhQio8Pi7rD8bGSlJCQcEyP9%2BLPy2tVMo%2BV3HmtYmODuvvuBPXrJz39tL1HfPutdMkl0mWXWcOAU08t%2Bu/rxdcqHB%2BbnBwKfyNG2JzOv%2BO1Krhjfa0iDXs6lLD%2B/fsXy2P/%2BstW8j75pH3d%2B44E1Vk1V%2BrbV87/d4d14uOl/v2lL744qvCX3/eOxMcWhhd/Xl6rknlsYRRFzeXLSw88YPMC%2B/a1reSmTZMaN7bVwjt2FM/3LWle/LdxuMc%2B9ph0zz2hy4cKf4URbj9vcT8WIZwCjgB79tgn%2BblzpbJlbSP5A1sCrF%2B9WnVOPFHrfvtNtevXd61OrziWvZP9iteq4MLxtfr5Z%2Bmuu6SPPrKvK1WSHnxQ6tPH3bZQ4fhaueHRR6V777XLjzwiDRt28H14rQqO1yqvmOHDhw93uwgcu5wc6corbfufhATbPP7ii/PeJyM7W08%2B%2BaSGDhumcuXKuVOox8TExKht27YqRXPEfPFaFVy4vVZVqkjXXWfzAJculdaulT75xPYCb9RIcvPzYri9ViXJcayVz/3329cHjgIeip9fq6PFaxXCCKDH3XuvfUqMi7Pez%2Becc/B9%2BNQDID/Z2barxH33hbaGvPpqmzNYp467tfmJ49jfwaOP2teHm/MHFBZzAD1syRKbHCxJ48cfOvwBQEGUKmXzAlNTbapwdLT07rtSw4a2SCQz0%2B0KI5/jWK/G3PD39NOEPxQfAqCH3X23bQbetat07bVuVwMgElSqJL3wgq0Sbt3aFpgNGSI1a2brx1A8gkFrzvDUU/b1qFG2lR9QXAiAHvXjj9Ls2fap/fHH3a4GQKRp2lSaN0967TWpalXbY/j8823bsa1b3a4usuTkSL17SykptjXn2LEWBoHiRAD0qA8%2BsD8vuUSqW9fdWrxo9OjRql%2B/vsqWLavmzZvryy%2B/POx9J0yYoKioqIOOffv2lWDF4WXevHm69NJLdfzxxysqKkpTp051uyRXHe3r8cUXXxzy39TPP/9cQhUXTFSUbR23cqWtDI6KskDYsKF1GyjqGeTJyclKSkpSfHy8qlWrpi5dumjlypVF%2B03CTGam1K2b9Mordtr9tdcsDErH9nrwfnWwMWPGqEmTJvv7/7Vs2VKffPKJ22W5jgDoUQsX2p8XXuhuHV709ttva9CgQRo2bJiWLl2qc889VxdddJF%2B//33wz4mISFBGzduzHOULVu2BKsOL3v27FHTpk31wgsvuF1KWDjW12PlypV5/k2dfPLJxVRh4VSsKI0ZI339tfWR37FD6tVL%2Buc/pV9/LbrvM3fuXPXv318LFizQ559/ruzsbLVv31579uwpum8SRvbutS4Ob79t%2BzO/847UvXvo9mN9PXi/yqt27doaMWKEFi9erMWLF%2BuCCy7QZZddpuXLl%2Bf/4EjmwJOaNXMcyXE%2B/jj/%2B6alpTmSnLS0tOIvzAPOOussp0%2BfPnmua9iwoTNkyJBD3v/VV191AoFASZTmSZKcKVOmuF1G2CjI6zFnzhxHkvPnn3%2BWUFVFJzPTcR5/3HHi4uw9KC7OcZ54wnGysor%2Be23ZssWR5MydO7fon9xlaWmO06aNvYZlyxbsvbwgrwfvVwVTsWJF5%2BWXX3a7DFcxAuhRuVsX0sTn6GRmZmrJkiVq3759nuvbt2%2Bvr7/%2B%2BrCP2717t%2BrVq6fatWurU6dOWrp0aXGXCh8444wzVLNmTbVr105z5sxxu5wCiY21BWg//ii1a2ejWHffLbVsadcVpbS0NElSpUqVivaJXbZ1q3TBBda8PyFB%2Buwz6aKL8n9cQV8P3q8OLycnR5MnT9aePXvUsmVLt8txFQHQo44/3v5cvfrw90lJSVFiYqKSkpJKpigP2LZtm3JyclS9evU811evXl2bNm065GMaNmyoCRMmaPr06XrrrbdUtmxZtWrVSr/88ktJlIwIVLNmTY0dO1bvv/%2B%2BPvjgA5166qlq166d5s2b53ZpBdaggTWgHz9eqlBBWrxYat5cevhhKSur8M/vOI7uuOMOtW7dWo0bNy78E4aJ33%2BXzj3X2nhVqWKL%2Bc47L//HFfT14P3q0H788UeVL19eZcqUUZ8%2BfTRlyhQlJia6XZa73B6CxLF54AE7dXDllfnfl1PAIRs2bHAkOV9//XWe6x955BHn1FNPLdBz5OTkOE2bNnUGDhxYHCV6jjgFnMexvh6dOnVyLr300mKoqPht2OA4nTvbe5LkOGec4Tg//FC45%2BzXr59Tr149Z926dUVTZBhYvtxxate216hOHcdZsaLgjz3W14P3K5ORkeH88ssvzqJFi5whQ4Y4VapUcZYvX%2B52Wa5iBNCjLr3U/pwxI9S1H/mrUqWKYmJiDhrt27Jly0GjgocTHR2tpKQk33%2BiRtFq0aKFZ/9NHX%2B8NHWqNGmSLRhZutRGA0eMsB1GjtbAgQM1ffp0zZkzR7Vr1y76gl0wf771VVy/3rbZ%2B9//bDV1QRTm9eD9ypQuXVonnXSS/vGPfyg5OVlNmzbVc88953ZZriIAetSZZ1pj1n37pGefdbsa7yhdurSaN2%2Buzz//PM/1n3/%2Buc4p4FYqjuPou%2B%2B%2BU82aNYujRPjU0qVLPf1vKirK9hX%2B6Sf7gJqVJQ0daqc3C7pS2HEcDRgwQB988IFmz56t%2Bm5uRlyEPvzQ5kv%2B%2Bad09tnSl18WbHu9ong9eL86NMdxlJGR4XYZ7nJ1/BGF8v77oVV4q1Yd/n6cAs5r8uTJTmxsrDN%2B/Hjnp59%2BcgYNGuSUK1fOWbNmjeM4jtO9e/c8K4KHDx/ufPrpp86qVaucpUuXOr169XJKlSrlfPPNN279CK7btWuXs3TpUmfp0qWOJGfkyJHO0qVLnbVr17pdmivyez2GDBnidO/eff/9n3nmGWfKlClOamqqs2zZMmfIkCGOJOf9999360coUsGg40yY4DgJCfYeddxxjvPii3b9kfTt29cJBALOF1984WzcuHH/8ddff5VM4cVg7FjHiYmx1%2BHiix1n9%2B6CP7YgrwfvV/kbOnSoM2/ePGf16tXODz/84Nxzzz1OdHS0M3PmTLdLcxUB0MOCQcdp29beWM47z3Gysw99PwLgwVJSUpx69eo5pUuXds4888w8bRXatGnj9OjRY//XgwYNcurWreuULl3aqVq1qtO%2BffuD5hD6TW4bk78fB75ufpLf69GjRw%2BnTZs2%2B%2B//%2BOOPOw0aNHDKli3rVKxY0WndurUzY8YMd4ovRms0YNqEAAAgAElEQVTXOs7554fmBnbq5DibNx/%2B/od6DSU5r776aonVXFSCQce5//7Qz96rl7XQORoFeT14v8rfjTfeuP/9vmrVqk67du18H/4cx3GiHIdGIl62apWdCt692zYNHzHi4Pukp6crEAgoLS1NCQkJJV8kAN8KBm2aytChtutFtWrSq69KF1/sdmXFJzNTuuUW2y1Fku69V3rooVD7LiAcMAfQ4xo0kMaNs8uPP27bCQFAuIiOlu64w9rEnH66tGWLbWF56602hznSpKXZzzdhghQTY/v6Pvww4Q/hhwAYAbp2le65xy737i2995679QDA351%2Bum1hedtt9vXzz0tnnSWtWOFuXUVp7VqpVStp1iypfHlp%2BvTQvr5AuCEARohHHpFuvNFOt3TtantLAkA4KVvWTgd//LFUtartHNK8ufTyy97f1WjhQlvhu3y5VLOmNG9eZJ/mhvcRACNEVJSdaujeXcrJka69Vnrp2b1ScrLUtKndqVkzmyQYieddAHjGRRdJP/wgXXihbSXXu7fUrZu0e8lKqVcvKRCQypSx3ikff%2Bx2ufl6912pTRtp82apSRPpm2%2BkM85wuyrgyFgEEmGCQal/f%2BmVFzM0U%2B3VRvOULikgKU1SgiSdf770ySf2BgsALgkGpSeflIYNk07PWaq50ecrIZh28B2ff14aMKDkC8yH49jZl/vvt68vuUR66y0pPt7duoCCIABGIMeRPu2coos%2BsjfMgwKgJL34oi1TAwCXzZ8vxbZtpX9kfn3oO5QpY1toVKlSsoUdwd690r//bYFPkgYNkp56yhZ%2BAF7AKeAIFBUlXbT5tSPfKbc/AQC4rGXVXw8f/iQpIyOUtMLAhg22w8lbb0mlSkkvvSQ98wzhD95Syu0CUEw2by7c7QBQUgryfhQm71nz50uXX27lVK4svf%2B%2Bzf8DvIYRwEh16qlHvDmj/pFvB4AS06CBDaUdST7vaSXh5ZdDiz0aN5YWLSL8wbsIgJGqT58j3tx7SV9Nm1ZCtQDAkdSoIV122WFv3qbKumvh1crKKsGaDpCRYW%2BpvXtLWVnSFVfYSGD9%2Bu7UAxQFFoFEsoEDpRdeOGgRyBuVbtMNO56VJF1/vfXlqlzZxToBYPNm61Dwt87QGbHldFHWdM3RBTr3XOmdd6QaORtsP7nVq6Vatax1TDGlsfXrpauustYuUVG2q8fQobbDCeBlBMBIN3u20seMUeC995R29dVK6NdP%2B1q01QMP2Iq1YND25hw1SrrmGrYrAuCiPXukiRNtYt2%2BfVLr1lKfPpr6bV3dcIO0a5f0n8DLemJ3X0XnZIceFx1tPU7vuqtIy5k92xrrb90qVaggTZpEc2dEDgKgD6SnpysQCCgtLU0JCfsbwWjBAmtj8NNP9vXFF0spKdIJJ7hTJwAcTmqqdF/HRXpzdQvFKHjoO332mdS%2BfaG/VzBoe6vfe69dbtrUMmmDBoV%2BaiBsMIjtYy1aSN9%2BKz3wgBQbaw33ExNt85DMTLerA4CQU06RJrZ4/vDhT7JTGYW0bZvUqZPtrx4MSj172nw/wh8iDQHQ58qUkYYPt22Z2ra15qb33GMbt3/2mdvVAUBI7PdLjnyHxYsL9fxffmk7Zn7yie1b/PLL0iuvSHFxhXpaICwRACFJatjQ5ru8/rpUvbqdbunY0RbmrVrldnUAoPz3WDtgisvRyM6WHnzQPgRv2GAdZ775xqbIMC8akYoAiP2ioqTu3aWVK6Xbb7e2XNOn22nhu%2B%2BW0g6xRScAlJiuXY9488IGXXW0s9rXrLHgN3y4nfK94Qbr79ekybEWCXgDARAHCQSkkSPttHD79jYf8MknpZNOkkaPlmu9uAD43E032fyUQ/hN9XXxp7eqe3dbTJwfx5HGj7eg97//2eDixInSa6/lP9AIRAICIA6rUSPp00%2Bljz6yU8Tbtkn9%2B9v779SpOupP2gBQKOXLS3PmWBA87ji7rkwZOddfr5n3faWdMVU0aZJ01lnSsmWHf5o1a6SLLrKn2bXLus18/73UrVuJ/BRAWKANjA8crg3M0cjKksaOtXkyW7fadeecY60SWrcuwmIBoCD27JG2bJGqVNk/ZDdvnp0l3rgxtMBt0CBb0CFJO3dKzzwjPfGEtRksU8YaO99xhxQT496PAriBAOgDRREAQ89lb54jR9qKYUm65BLpsceYMwPAfVu2SDfeKM2YYV/Xri116GB5ccYMG/GTbN7fSy9ZexnAjwiAPlCUATDXH3/YaOD48VJOji0g%2Bde/7DreUAG4yXGso8GwYbaq90CJiTYyeNVVrPCFvxEAI1hKSopSUlKUk5Oj1NTUIg2AuVJTpfvus/05JTuNcsMNdh0bpQNw0969No/5hx/sdO8550jnnkvwAyQCoC8Uxwjg3333nYW%2Bjz6yr0uVstMww4ZJdesWy7cEAADHiFXAKBLNmkkffmhbJl14oTVWHTvWWsf06SOtXet2hQD8ZssW62X65pvSihVuVwOEFwIgilSLFtLMmbalUrt2tnr4pZcsCPbuLf32m9sVAoh0CxZIl14q1ahhuxl162Zz/1q0sB0%2BABAAUUxat5ZmzbK2DO3a2Yjgyy/bApEbbpB%2B/tntCgF4kuPYkF7btlK9elKrVrZhb3a2fv1VuuIKqWVLm47iONa39JxzpNhYC38tW0r33mvvSYCfMQfQB0piDmB%2Bvv5aeugh6bPP7OuoKOnKK6WhQ6Uzz3SlJABe4zj2CXLixINu%2BvmkTjpz7RTtzSqlmBipRw9pyBDp5JPt9k2bpDvvlCZNsq8vuEB6912pUqUSrB8IIwRAHwiHAJhr0SLp0UeladNC13XoYG/UbdqwOg/AEbz3nnT11Ye9%2BRa9qNUX3qKRI6XGjQ99n8mTbQeQPXukE0%2B03oANGxZTvUAYIwD6QDgFwFzLlkkjRtibcU6OXXf22dLgwTZnJ5rJCQD%2BrmPH0GmEQ/jzpH%2BoQuqifD9ILlsmde4srV4tVahgW1u2aVPEtQJhjl%2BzcEXjxnYWJzVV6tvXenR9843N32nUyFYQ79vndpUAwko%2B7QQqpv9eoLMIjRvbQpGWLW17uPbtQ71MAb8gAMJVJ54ojR5t7%2Bv33GOfxlNTpVtusfndDz8sbdvmdpUASkxGhq0g%2B%2Bgj6%2BNygC3lTjjyY%2BvVK/C3qVZN%2Bu9/7UNnZqbtIfzCC8dQL%2BBRBMAwl5WVpcGDB%2Bv0009XuXLldPzxx%2BuGG27QH3/84XZpRap6dZsb%2BPvvts9wnTr23n///dZIum9fC4YAIlhKiv3nv/BC6%2BNSp450001as2KvunSR%2Bi656ciP7937qL5dXJyN/PXrZ%2BtLBg6UHnjALgORjjmAYS4tLU1XXXWVevfuraZNm%2BrPP//UoEGDlJ2drcWLFxfoOcJxDmB%2BsrJshd7TT0vffhu6vlMnadAgW8HHghEggowda0P/hzAtuou6BKcoJtrR/IY9lfTT6wffqXNn6YMPbD/Ko%2BQ4drbhgQfs6wEDpOeeYy4yIhsB0IMWLVqks846S2vXrlXdAuyz5sUAmMtxpLlzbVQwt6%2BXZL29brtNuu46%2BxQPwMNycqQTTpDWrz/sXXonfafbJzRVYiNHevttayz6229S7dq272T37scU/g40erSFP8exp3vlFdvWEohEBEAPmjVrltq3b6%2BdO3ceMtBlZGQoIyNj/9fp6emqU6eOJwPggVJTpVGjpFdflf76y66rXFm6%2BWbbbo49hwGPWrxYSko64l2cBx9S1P33FXspkyZZD8GcHJsf%2BOabtkgNiDQMcHvMvn37NGTIEF133XWHDXPJyckKBAL7jzp16pRwlcXjlFNskvb69dKTT9p87%2B3bpeRkqX596aqrpDlzmL8DeE4BtuWIyimZrTu6dZPef18qXdrOKF9%2BubR3b4l8a6BEEQDDzKRJk1S%2BfPn9x5dffrn/tqysLHXt2lXBYFCjR48%2B7HMMHTpUaWlp%2B49169aVROklpmJF6%2Bi/apW9QZ9/vhQM2pv2BRdYi4eUFCk93e1KAeQnLU26/4Nm2q58tuRo165kCpL1Iv3oI5te8sknNvd4z54S%2B/ZAieAUcJjZtWuXNm/evP/rWrVqKS4uTllZWbrmmmv022%2B/afbs2apcuXKBn9PLcwALatkyC31vvBF6oy5Xzubx9OkjNW3qbn0A8srIkF580RZfbN8u3auH9bDuP/SdzzlH%2Bt//SrZASV9%2BKV18sbR7t3TuubZrSHx8iZcBFAsCoAfkhr9ffvlFc%2BbMUdWqVY/q8X4IgLnS0qTXX7fJ3D//HLq%2BRQsLgldfLR13nHv1AX6Xk2Pz6u6/X1qzxq5r2FAakeyo89z/KOqF5/OeEj7/fFv0cZTve0VlwQLbrjI93RpHf/qpFOFvo/AJAmCYy87O1pVXXqlvv/1WH330kapXr77/tkqVKql06dL5PoefAmAux5G%2B%2BEIaM0aaMiX0%2B6RCBRsV7N3bVhIDKBmOY1uu3XeftHy5XVezpjR8uC3i3b/aduNGafp02wqodWupeXO3St5vyRLpn/%2B0XUPOPtt2owsE3K4KKBwCYJhbs2aN6tevf8jb5syZo7Zt2%2Bb7HH4MgAfatMlWDo8dGxpxkOyN/KabpH/9i9M6QHFxHOnjj63H3pIldl2FCtKQIdZ42Ssj8kuXWgjcsUM66yxp5kxCILyNAOgDfg%2BAuYJB6fPPpXHjpGnTQqOC5cpZCLzxRptqRINpoPAcxxZQDB8uLVpk15Uvb/0777zTQqDXfPedrUXZsYORQHgfAdAHCIAH27xZeu01afz4vFvMnXKK1KuXnSauVcu9%2BgCvCgbtDO4jj4RG/OLirMHyXXe5NpWvyHz/vXUb2LHD5hZ/9hlzAuFNBEAfIAAenuNIX31lHf/feSfUYDo62k739OghdenindNUgFuys%2B3/UHKyrcqX7P9Nv34W/KpVc7e%2BonTgSGCrVjbSyTQSeA0B0AcIgAWza5ftP/zqqxYKc8XH2%2Brh66%2BX2rRhf1DgQH/9JU2YID31lLR6tV0XH28jfrff7v0Rv8P59lsLgTt3SuedZyGQD4rwEgKgDxAAj96qVdZO5vXX8y4cqV3b9h/u1k1q0sS18gDXbdli7ZZSUqRt2%2By6KlVsjt%2BAAd6c43e0Fi2yMwXp6RYGP/pIKlvW7aqAgiEA%2BgAB8NgFgzYa%2BMYbNjqYlha67bTTpGuvlbp2lRo0cK9GoCQtWyY9%2B6w0caI1c5akE06Q/vMfW0jlt1Gw%2BfOl9u2tWfTFF1vbqQJ05wJcRwD0AQJg0di3z3YCePNN%2B6SfmRm6LSnJVhJfc40UIVsvA/vl5Egffig9/7w0e3bo%2BrPOsuB3xRUH9PHzoblzpYsusj2Dr7jC%2Blb7%2BfWANxAAfYAAWPR27rR9iN96y34hBoOh21q2tCB45ZWEQXjbli22Uv6ll6S1a%2B26mBjp8svtVG%2BrVrRNyjVzpnTppfbBsHt3mxfJfGGEMwJgBEtJSVFKSopycnKUmppKACwmmzdL771nKyC//NJWFuc6%2B2wLgldcwWlieEMwaLvojB1rH3Kysuz6ypWtcXrfvlK9eq6WGLamTZOuuspWRPfta/MjCcgIVwRAH2AEsOT88YeFwffes7mDB/7vatLERk4uv9wu84sB4eSPP0K9MVetCl1/9tkWZq65xvr54cjeessWiTmONHiwNGKE2xUBh0YA9AECoDs2brQJ4R98YCMqOTmh2%2BrVky67TOrcWTr3XCaNwx1799rcvgkTrKFx7lSG%2BHgLMTffLJ1xhqsletK4cfbaSdYXccgQd%2BsBDoUA6AMEQPdt326/aKdOtblCe/eGbktIkDp0kDp1sonkkdo3DeEhGJTmzZMmTbJpC%2Bnpodtat7aVvNdcY1sk4tg99ZQ1wJakF1%2BUbrnF3XqAvyMA%2BgABMLz89ZftSTxtmq0q3rIldFtUlK2svOgiO5o3t0n3QGE4jvWsmzzZQt%2BGDaHb6ta1Juc9e0onn%2BxaiRFp2DDpscfs//XkyRasgXBBAPQBAmD4CgalhQutrcyMGbbF1IEqV5YuvND6jLVvz/7EKLhgUPrmG5uC8N57eRuaBwK2u023braLBatVi4fj2FZ4L74oxcba//P27d2uCjAEQB8gAHrHhg3Sp59KH38szZqV9/ScJDVqZDsOtGsntW3rj90WUHAZGdaTbupUG2H%2B44/QbeXKWZuSrl2ljh2lMmXcq9NPcnJs96B33rG/g9mzbZQfcBsB0AcIgN6UlWUjOJ99ZvMGFy/O228wOtom6J9/voXB1q1tZAf%2BsmmTfWiYMcP%2BrezaFbotPl665BJrTXLRRf7bpSNcZGbaHN/PP7dR/a%2B%2Bkho2dLsq%2BB0B0AcIgJFhxw5bTTxrlvTf/0qpqXlvj46WmjWzVcXnnmtNemvUcKVUFKOMDNt%2B7LPP7Fi6NO/tNWrYSF%2BXLjZSzEhfeNi92z6sLV5s8y6//popHXAXAdAHCICRacMGac4cC4VffJG3d1uuBg2kc86x3UlatJBOP50tqrwmO9tC3uzZdnz1lS0kOlDz5rYPbadO0j/%2BwZy%2BcLV1q43Up6ba/8V585jGAfcQAH2AAOgPGzbYTiS5x7JleRtRS3YKsHlzm4OUlGRh4cQTj6Ep9e7d1kdk/nyb2HTVVTa8gUL76y9bsfvVV3b87395T%2BtKUvXqocVBHTpI1aq5UyuO3po19oFs0yabuvHpp4zSwh0EQB8gAPrTzp3SggWW0ebPt/mEf19UItkIxBlnhI5mzaRTT7VVi4f07bc2oezA/jWSXffBB1LZskX%2Bs0SqYFD65RdbCb5wof19ffedjfodqEIFW617wQV2NG7MTjJe9v33Nk1j1y5rDfPWW4zaouQRAH2AAAjJwsbPP4fCxuLF9osoM/Pg%2B5YubSuOmzSxsHHaaVJiolSvRoaiTzox7/LSAw0aJD3zTPH%2BIB6VkSGtWGGv%2BXffWY5euvTg0T1JqlnTThW2bm3B7/TT6QcZaWbPttXYWVnS7bdLI0e6XRH8hgDoAwRAHE5mprR8uQWRb7%2B1YPLDD4cOJZLUM3aSXs26/vBPGB9v4bB8%2BeIp2AP%2B%2BsvmeP38swW%2BFSvsNU5NPXhkT7L9dc84w07Ln322nR6sW5cRPj94803rxSjZ56ZBg9ytB/5CAPQBAiCORjAorV1rQfDHH20uYW6ASc68Q3foyCN8d5y/VGrWTHXrWpCpU8dWO1avHhmjWFlZlnHXrbPXafVq6bff7Pj117y7bPxdhQpS06Z2nHGGzcds1IiFOX72xBPS4MEW%2BN99V7rySrcrgl8QACNYSkqKUlJSlJOTo9TUVAIgCiU7W0q740FVfn74Ee9XX79pjeofdH1MjIXAmjXtqF7d9j2uWtV6o%2BUeFSrYEQjY6Fhxj4RlZ9vcyD//tGPHDtu7eds2m%2Ba4ebNN2N%2B40cLdpk0HL675u0qVrM9bw4Z26jwx0U7j1qrFyB7ychxpwABp9GibPjt7to0CA8WNAOgDjACiyKSmWqo5zNvGn6eerUkDFmjtWhsd%2B/13GynbtClvE%2BuCiomxs8rlytkRF2e/JMuWtXmKsbE2ehYTY5Poc8OV49gODDk5NmKXlWVz8Pbtk/butdO0u3fbsXfv0dcVGyvVrm0jnPXr29GggXTSSXZUrnz0zwn/ys6WrrhC%2BvBDqUoVW7R10kluV4VIRwD0AQIgitSAAVJKysHXlyljW5acd95BN2Vn20jaxo12bNpkX2/dasf27Xbs2GGjcGlp%2BY%2ByFbXjjpMqVrTRu8qVQ6OT1apZc%2BWaNaXjj7fgV60aqzZRtPbskdq0kZYskU4%2B2UIgHyRQnAiAPkAARJFyHJux/txzNsQnWW%2BShx%2B2rtNFIBi0X4jp6aGRuj17QiN4GRl2ZGVZuMzJyTvCGBVlo4IxMTZCWLq05dMyZWwU8bjjbJ1K%2BfJSQoIdh217A5SQTZtsIdDvv9vnqJkz6RGI4kMA9AECIIpFMGirIeLiGKoAisiyZbaNY3q6dP310uuvM28UxYOTGACOTXS0nQ8l/AFFpnFj6b33bPR64kQbWAeKAwEQAIAwcuGF0pgxdvmBB6TJk92tB5GJAAgAQJjp3Vv6z3/scs%2Betk0gUJQIgAAAhKHHH5c6d7YFT126hNZcAUWBAAgAQBiKiZEmTbKdYzZvli691FbEA0WBAAgAQJgqX16aPt12zvnhB1sZfCxN1YG/IwACABDG6taVpk61noDTpkn33ed2RYgEBEAAAMJcixbSuHF2%2BbHHpLfecrceeB8BEAAAD%2BjeXbr7brt8443S4sXu1gNvIwBGsJSUFCUmJiopKcntUgAAReCxx6RLLrFtEbt0se3jgGPBVnA%2BwFZwABA50tNtz%2BCff5ZatpTmzGHPYBw9RgABAPCQhARbGRwISPPnSwMGSAzl4GgRAAEA8JiTT7Yt4qKjpZdfDm0dBxQUARAAAA/q2FFKTrbLt90mffmlu/XAWwiAAAB41F13SV27StnZ0lVXSevXu10RvIIACACAR0VF2SngJk2kLVukK6%2B0vYOB/BAAAQDwsHLlbKeQihWlhQttUQiQHwIgAAAeV7%2B%2B7Q6SOyKYu2sIcDgEQAAAIkCHDtKjj9rlAQNsNBA4HAKgx9xyyy2KiorSs88%2B63YpAIAwM3iw7RCSmWmLQrZudbsihCsCoIdMnTpV33zzjY4//ni3SwEAhKHoaOm116RTTpHWrZOuvdZWCAN/RwD0iA0bNmjAgAGaNGmSYmNj3S4HABCmEhKkDz6wxSH//a/0wANuV4RwRAD0gGAwqO7du%2Buuu%2B7Saaedlu/9MzIylJ6enucAAPjHaaeFFoI89pj04Yfu1oPwQwD0gMcff1ylSpXSrbfeWqD7JycnKxAI7D/q1KlTzBUCAMLNtddKAwfa5RtukH77zd16EF4IgGFm0qRJKl%2B%2B/P5j7ty5eu655zRhwgRFRUUV6DmGDh2qtLS0/ce6deuKuWoAQDh66impRQtp507p6qulffvcrgjhIspxHMftIhCya9cubd68ef/X7777roYNG6bo6FBWz8nJUXR0tOrUqaM1a9bk%2B5zp6ekKBAJKS0tTQkJCcZQNAAhT69ZJZ54pbdsm3XKL9OKLbleEcEAADHPbt2/Xxo0b81zXoUMHde/eXb169dKpp56a73MQAAHA32bOlDp2lBxHmjhR6tbN7YrgtlJuF4Ajq1y5sipXrpznutjYWNWoUaNA4Q8AgPbtpfvukx56SLr5ZhsRbNTI7argJuYAAgDgA/ffL11wgfTXXzYfcM8etyuCmzgF7AOcAgYASNLmzVKzZtKmTVLPntKrr7pdEdzCCCAAAD5Rvbr01lu2Y8iECbZrCPyJAAgAgI%2B0bSsNH26X%2B/WTVqxwsxq4hQAIAIDP3HOP9M9/huYD/vWX2xWhpBEAAQDwmZgYawdTvbq0fLk0aJDbFaGkEQABAPCh6tWlSZOkqCjbN/jtt92uCCWJAAgAgE%2B1a2engyXrD8h%2Bwf5BAAQAwMeGD5datZLS06WuXaXMTLcrQkkgAAIA4GOlSklvvilVrCgtWmQ7hiDyEQAjWEpKihITE5WUlOR2KQCAMFa3rjR%2BvF1%2B4gnbOxiRjZ1AfICdQAAABdG3r/Tii7ZA5IcfpGrV3K4IxYURQAAAIEkaOVI67TTbMq5XL4khoshFAAQAAJKkuDhp8mSpTBnp44%2Bl5593uyIUFwIgAADYr3Fj6amn7PLdd9upYEQeAiAAAMijf3%2BpUycpI0O67jpp7163K0JRIwACAIA8oqKkV14JbRU3eLDbFaGoEQABAMBBqlaVJkywy88/L33yiavloIgRAAEAwCF17Cjdeqtd7tVL2rLF3XpQdAiAAADgsEaMCLWG6d2b1jCRggAIAAAOKy5OmjRJKl1amj5devlltytCUSAAAgCAI2raVHr0Ubt8%2B%2B3Sr7%2B6Ww8KjwAIAADydccdUtu20p49UvfuUna22xWhMAiAAAAgX9HR0muvSQkJ0oIFNjcQ3kUAjGApKSlKTExUUlKS26UAACJA3brSCy/Y5QcflJYscbceHLsox2E9T6RLT09XIBBQWlqaEhIS3C4HAOBhjiNdc4303ntSo0YWAuPi3K4KR4sRQAAAUGBRUdKYMVKNGtKKFdKwYW5XhGNBAAQAAEelSpVQO5hnn5W%2B%2BMLVcnAMCIAAAOCoXXJJqDF0r17Srl1uV4SjQQAEAADH5OmnpRNOkNassTYx8A4CIAAAOCbx8dKECXb55ZelTz5xtRwcBQIgAAA4Zm3aSIMG2eWbbpL%2B/NPdelAwBEAAAFAojz0mnXKK9Mcf0m23uV0NCoIACAAACiUuznYJiY6W3nhDmj7d7YqQHwIgAAAotBYtpDvvtMs33yxt3%2B5uPTgyAiAAACgSDz5ou4Ns3izdeqvb1eBICIAAAKBIlC1rq4Kjo6U335SmTnW7IhwOARAAABSZs86S7rrLLvfpI%2B3Y4W49ODQCYARLSUlRYmKikpKS3C4FAOAjw4eHTgXntohBeIlyHMdxuwgUr/T0dAUCAaWlpSkhIcHtcgAAPrBggdSqlRQMSh9%2BKHXq5HZFOBAjgAAAoMi1aBHaHu6WW6SdO92tB3kRAAEAQLF46CHp5JOtQXRuixiEBwIgAAAoFnFx0vjxdnn8eGnWLHfrQQgBEAAAFJtzz5UGDLDLvXtLe/a4Ww8MARAAABSr5GSpbl1pzRpp2DC3q4FEAAQAAMWsfHlp7Fi7PGqUNH%2B%2Bu/WAAAgAAEpAhw7SDTdIjiPddJOUkeF2Rf5GAAQAACVi5EipWjXpp5/stDDcQwD0iBUrVqhz584KBAKKj49XixYt9Pvvv7tdFgAABVa5svT883b5scek5cvdrcfPCIAesGrVKrVu3VoNGzbUF198oe%2B//1733XefypYt63ZpAAAclauvli69VMrKslXBwaDbFfkTW8F5QNeuXRUbG6s33njjmB7PVnAAgHCyfr2UmCjt2mUjgu8clZQAAAf3SURBVLltYlByGAEMc8FgUDNmzNApp5yiDh06qFq1ajr77LM1depUt0sDAOCY1K4tjRhhl4cOldatc7cePyIAhrktW7Zo9%2B7dGjFihDp27KiZM2fq8ssv1xVXXKG5c%2Bce8jEZGRlKT0/PcwAAEE769JHOOUfavdtGADkfWbIIgGFm0qRJKl%2B%2B/P5j5cqVkqTLLrtMt99%2Bu5o1a6YhQ4aoU6dOevHFFw/5HMnJyQoEAvuPOnXqlOSPAABAvqKjrTdgbKw0fbr0wQduV%2BQvBMAw07lzZ3333Xf7j2bNmqlUqVJKTEzMc79GjRoddhXw0KFDlZaWtv9Yx9g6ACAMnXaaNHiwXR44UEpLc7cePynldgHIKz4%2BXvHx8XmuS0pK2j8SmCs1NVX16tU75HOUKVNGZcqUKbYaAQAoKsOGSe%2B8I6WmSvfcI6WkuF2RPzAC6AF33XWX3n77bY0bN06//vqrXnjhBX344Yfq16%2Bf26UBAFAoZctKuTOaxoxhm7iSQhsYj3jllVeUnJys9evX69RTT9WDDz6oyy67rECPpQ0MACDc9ewpvfaadPrp0pIlNjcQxYcA6AMEQABAuNu2TWrYUNq%2BXXr8cenuu92uKLJxChgAALiuShXpqafs8vDh0po1blYT%2BQiAAAAgLPToIbVtK%2B3dS2/A4kYABAAAYSEqSho92ub/zZghTZnidkWRiwAIAADCRqNGod6At95q%2BwWj6BEAAQBAWLnnHunEE6UNG6QHHnC7mshEAAQAAGElLi7UEHrUKOn7792tJxIRAAEAQNjp2FG66iopJ0fq21cKBt2uKLIQAAEAQFh69lmpfHnbHeSVV9yuJrIQACNYSkqKEhMTlZSU5HYpAAActVq1pAcftMuDB1uTaBQNdgLxAXYCAQB4VVaW1Ly59OOP0k03SePGuV1RZGAEEAAAhK3YWOsNKEkvvywtWOBuPZGCAAgAAMJa69a2S4gk9e9vC0NQOARAAAAQ9p54QqpQQfr2W%2Bmll9yuxvsIgAAAIOxVqyY98ohdHjZM2rrV3Xq8jgAIAAA8oU8fqVkzaedOacgQt6vxNgIgAADwhJiY0IKQV16RvvnG3Xq8jAAIAAA8o2VLFoQUBQIgAADwlMcflxISpCVLpPHj3a7GmwiAAADAU6pXlx56yC4PHSrt2OFuPV5EAAQAAJ7Tv7/UuLGFv3vvdbsa7yEAAgAAzylVSnr%2Bebv80kvSd9%2B5W4/XEAABAIAntW0rXXONFAxKt94qOY7bFXkHATCCpaSkKDExUUlJSW6XAgBAsXjqKSkuTvryS2nyZLer8Y4oxyEvR7r09HQFAgGlpaUpISHB7XIAAChSjzwi3XefVKuWtHKlVK6c2xWFP0YAAQCAp915p1S/vrRhg/TYY25X4w0EQAAA4Glly0pPP22Xn35aWr3a3Xq8oJTbBQAAABRWly7S5ZdLrVrZqWAcGQEQAAB4XlSU9P779ifyxylgAAAQEQh/BUcABAAA8BkCIAAAgM8QAAEAAHyGAAgAAOAzBEAAAACfIQACAAD4DAEQAADAZwiAAAAAPkMAjGApKSlKTExUUlKS26UAAIAwEuU4juN2EShe6enpCgQCSktLU0JCgtvlAAAAlzECCAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYA6AG7d%2B/WgAEDVLt2bcXFxalRo0YaM2aM22UBAACPKuV2Acjf7bffrjlz5mjixIk64YQTNHPmTPXr10/HH3%2B8LrvsMrfLAwAAHsMIoAfMnz9fPXr0UNu2bXXCCSfo5ptvVtOmTbV48WK3SwMAAB5EAPSA1q1ba/r06dqwYYMcx9GcOXOUmpqqDh06HPL%2BGRkZSk9Pz3MAAADkIgB6wKhRo5SYmKjatWurdOnS6tixo0aPHq3WrVsf8v7JyckKBAL7jzp16pRwxQAAIJwRAMPMpEmTVL58%2Bf3Hl19%2BqVGjRmnBggWaPn26lixZoqefflr9%2BvXTrFmzDvkcQ4cOVVpa2v5j3bp1JfxTAACAcBblOI7jdhEI2bVrlzZv3rz/61q1aikQCGjKlCm65JJL9l9/0003af369fr000/zfc709HQFAgGlpaUpISGhWOoGAADewSrgMBMfH6/4%2BPj9X6enpysrK0vR0XkHa2NiYhQMBku6PAAAEAEIgGEuISFBbdq00V133aW4uDjVq1dPc%2BfO1euvv66RI0e6XR4AAPAgTgF7wKZNmzR06FDNnDlTO3bsUL169XTzzTfr9ttvV1RUVL6P5xQwAAA4EAHQBwiAAADgQKwCBgD8Xzt3UAMwDMRAMASOP9AAuFDouzuDYiVLBmIEIABAjAAEAIgRgAAAMQIQACBGAAIAxLiBCdjdc%2B89M/PpNxAA%2BDcBCAAQYwIGAIgRgAAAMQIQACBGAAIAxAhAAIAYAQgAECMAAQBiBCAAQIwABACIEYAAADECEAAgRgACAMQIQACAGAEIABAjAAEAYgQgAECMAAQAiBGAAAAxAhAAIEYAAgDECEAAgBgBCAAQIwABAGIEIABAjAAEAIgRgAAAMQIQACBGAAIAxAhAAIAYAQgAECMAAQBiBCAAQIwABACIEYAAADECEAAgRgACAMQIQACAGAEIABAjAAEAYgQgAECMAAQAiBGAAAAxAhAAIEYAAgDECEAAgBgBCAAQIwABAGIEIABAjAAEAIgRgAAAMQ9eEt527enQhwAAAABJRU5ErkJggg%3D%3D'}