Genus 2 curve data - 110976.b.110976.1

Formats: - HTML - YAML - JSON - 2026-05-08T11:10:51.045071

• 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': '199398194071111667', 'abs_disc': 110976, 'analytic_rank': 2, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[4,2]', 'aut_grp_label': '4.2', 'aut_grp_tex': 'C_2^2', 'bad_lfactors': '[[2,[1,1]],[3,[1,1,3,3]],[17,[1,2,1]]]', 'bad_primes': [2, 3, 17], 'class': '110976.b', 'cond': 110976, 'disc_sign': 1, 'end_alg': 'Q x Q', 'eqn': '[[6,0,-11,0,6,0,-1],[0,1]]', 'g2_inv': "['7692386305499136/289','319046421956544/289','17569237294080/289']", 'geom_aut_grp_id': '[4,2]', 'geom_aut_grp_label': '4.2', 'geom_aut_grp_tex': 'C_2^2', 'geom_end_alg': 'Q x Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['4944','7005','11407566','13872']", 'igusa_inv': "['4944','1013794','276011520','84205670111','110976']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '110976.b.110976.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '1.642896395839170847500841632206434808822412968608', 'prec': 167}, 'locally_solvable': True, 'modell_images': ['2.90.5', '3.90.1'], 'mw_rank': 2, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 4, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '16.027498766180152245406493501', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.410019409717', 'prec': 47}, 'root_number': 1, 'st_group': 'SU(2)xSU(2)', 'st_label': '1.4.B.1.1a', 'st_label_components': [1, 4, 1, 1, 1, 0], 'tamagawa_product': 1, 'torsion_order': 2, 'torsion_subgroup': '[2]', 'two_selmer_rank': 3, 'two_torsion_field': ['8.8.443364212736.1', [-50, 180, -38, -260, 71, 68, -18, -4, 1], [8, 3], True]}
• 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], ['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR', 'RR'], 'factorsRR_geom': ['RR', 'RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': False, 'is_simple_geom': False, 'label': '110976.b.110976.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], ['RR', 'RR'], [2, -1], 'G_{3,3}']], 'ring_base': [2, -1], 'ring_geom': [2, -1], 'spl_facs_coeffs': [[[60], [-432]], [[121], [-845]]], 'spl_facs_condnorms': [1088, 102], 'spl_facs_labels': ['1088.f1', '102.a1'], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'G_{3,3}', 'st_group_geom': 'G_{3,3}'}
• 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': '110976.b.110976.1', 'mw_gens': [[[[-1, 1], [0, 1], [1, 1]], [[1, 2], [-1, 2], [0, 1], [0, 1]]], [[[-1, 1], [0, 1], [1, 1]], [[0, 1], [-1, 1], [0, 1], [0, 1]]], [[[-3, 2], [0, 1], [1, 1]], [[0, 1], [-1, 2], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '1.4310934289467562511600528717465', 'prec': 110}, {'__RealLiteral__': 0, 'data': '0.28650778588176014295254882606367', 'prec': 113}], 'mw_invs': [0, 0, 2], 'num_rat_pts': 4, 'rat_pts': [[-1, 0, 1], [-1, 1, 1], [1, -1, 1], [1, 0, 1]], 'rat_pts_v': False}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 20973
    {'conductor': 110976, 'lmfdb_label': '110976.b.110976.1', 'modell_image': '2.90.5', 'prime': 2}
  • id: 20974
    {'conductor': 110976, 'lmfdb_label': '110976.b.110976.1', 'modell_image': '3.90.1', 'prime': 3}
• g2c_tamagawa •

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

  
  
  • id: 4724
    {'label': '110976.b.110976.1', 'local_root_number': 1, 'p': 2, 'tamagawa_number': 1}
  • id: 4725
    {'cluster_label': 'c4c2_1~2_0', 'label': '110976.b.110976.1', 'local_root_number': 1, 'p': 3, 'tamagawa_number': 1}
  • id: 4726
    {'cluster_label': 'c2c2_1~2c2_1~2_0', 'label': '110976.b.110976.1', 'local_root_number': 1, 'p': 17, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '110976.b.110976.1', 'plot': 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GMCCEiJmIYNZdS4QAFpE1i8uOqoiEhvTABNQtOAWbOkntuNG/JvPj7S/D0kBPD1lX8/cQI4cgRISZHPsdlkJ%2BiIEUDFiuriJ%2BexT/seOAAEBUnyV7as6qjcj7smgIDcSNatC/z%2BuyR/27fLc4mIrIMJoAncvAl07AisXi3vV6wIvPOOlPzIlu3%2Bz79%2BXco%2BfPGFbAgAJBHs2lU6Q%2BTJo1/s5FzXr0vyt38/kz9Xc%2BcEEJB2cbVqSe/gypVlOjgwUHVURKQXbgIxuOvXpW7X6tVS223qVPnj36FD6skfAOTMCbz8stSDO3QIeOklGUH87DOZJly2TN/vgZwjJgZo0kT%2B//PmlTV/TP6cLyIiAuXLl0dYWJjqUFyqQAG5QcyfX0aTW7SQuoFEZA0cATSwW7dktGfXLlmw/d13QPXqGXus7duB11%2BXhBAAOncGIiIenESSscTFSfK3bZs8FzZu5JS%2Bq7n7CKBdZCRQp46sDXzpJeCrr6SLDBG5N/6aG5SmAd27S/KXM6f8wc9o8gfIVM/evcCwYfLi/sUXwJNPAidPOi9mco34eOD55yX5y5FDRm2Y/JGzPP64zDBkyQIsXw707686IiLSAxNAg5o7F1iyBPDyAlatcs4ffG9vYMwYSSaDgoDDh4Fq1aRhPBlTYqL09v3pJxmtXbdONv4QOVODBnJTCAAffSRvROTemAAa0JkzQL9%2Bcj5%2BvPPbNtWpI0Vgn3xS1hg2aiTTPmQsKSlSxmfNGtnl/e23mRsFJnqYdu2ASZPkvF8/ufEkIvfFBNCA%2BvaVnb9PPeW66ZiCBWUk8MUXZZSpfXvg009dcy1KP02T58HixTIKvHw5UL%2B%2B6qjI3Q0YAPTsKc%2B/l18G9uxRHRERuQoTQIPZtEnW43h6AjNnunYxtp8fsHQp0KuXvOC/8QYwZYrrrkdpN2oUMGOGlO/54gvg2WdVR0RWYLMB06c7%2BgY3by4zEkTkfpgAGoimAUOHynmPHvq0aPLwkBd8%2B3X795dSM6TOJ59IAgjI/0379mrjIWvx8pIbw4oVgYsXJQm8eVN1VETkbEwADeSXX4CdO2W917Bh%2Bl3XZgPGjgWGD5f3%2B/WT0UfS3/LlQO/ecj5ihIzOEunN31/KTuXPL%2B0GX34ZSE5WHRURORMTQAOZPFmO3bpJkVY92WzAyJHSag6Q6eAvv9Q3BqvbuFEKfGua1GwcMUJ1RGRlRYoA33wjLSfXrNH3ppSIXI%2BFoA3ijz%2BAMmUkETt5Ul1zdk0D3nxT1p95eQFr18ouYXKtQ4dk009MDNCqlUzBeXqqjsrarFII%2BlEWL5YbEwBYtEhGA4nI/DgCaBCzZ8uxWTN1yR8gCejHHwNt2wJJSZKMREaqi8cKzp6VRfcxMUDt2vJHlskfGcXLLwODB8t5t25SUJ6IzI8JoAEkJQELF8p59%2B5qYwFkY8j8%2BVIc9uZNSUrPnVMdlXu6cQNo2lR%2BvuXLy5Sbr6/qqKzNKr2A02PsWNmJHh8PvPCCbA4hInPjFLABrF8vI0B580oikCWL6ojEjRvSQu7IEaBKFWDLFvYOdqY7d%2BT//ZdfZM3nrl2y7oqMgVPA94qOlkLkx47JSPXPPxvntYqI0o8jgAawbJkcX3rJWC%2BoOXLITsC8eYHffpOuFLxdcA57r%2BdffgGyZwe%2B/57JHxlbYKDUKA0IALZuZc9gIrNjAqhYUpJM%2BwHSlcNoQkKAlSsdjeLHj1cdkXsYMwZYsEDW%2Bi1fDjz%2BuOqIiB6tTBnHcpXp0x3nRGQ%2BTAAV27kTuHYNyJVLplWM6KmngIgIOX/vPWDdOrXxmN3ixY4SLxERQJMmauMhSo/nnnPUDO3RAzhwQG08RJQxTAAV%2B/57OYaHS9kVo%2BreHXjtNUeP0NOnVUdkTtu2AV27yvnAgfIHlMhsRoxwtItr2VLWCxORuTABVOzHH%2BXYuLHaONJi2jSgWjXg%2BnVZrxgfrzoiczl1Cnj%2Bedn80bIlMHGi6oiIMsbDQ8oVFSsmz%2BvOnbk%2BmMhsmAAqdP06sH%2B/nDdsqDaWtPDxkfVquXMD%2B/ZxEXh6REdLGY2rV4EnnpC1Ux787SMTy5UL%2BPprR6cQeycjIjIH/glSaOtWuWsuU0b/1m8ZVaSIY%2BH3J5/IHwB6uKQkoE0b4OhRoFAh%2BWOZNavqqIgy74knZGYAAIYOldc0IjIHJoAKbdsmxzp11MaRXuHhwKBBct6tG/DXX0rDMbwBA4AffgD8/CT5K1hQdUREztO9u7SKS06WDkKXLqmOiIjSggmgQjt3yrFmTbVxZMSYMUCNGjK12b69jHLR/ebOldZ6gIycVqmiNh4iZ7PZgE8/BcqVA86fBzp2BFJSVEdFRI/CBFCRpCRZRwcATz6pNpaMyJIFWLJEisLu3CkJId1r61bgjTfkfPRo6atM5I6yZ5f1wX5%2BsrGNG5yIjI8JoCJHj0oJhezZZQ2gGRUrBsycKedjxwI7digNx1D%2B/lsSvsREoHVrYNgw1RFRWrEXcMZUqADMmCHnw4cD27erjYeIHo69gBVZuBDo1EmKP2/ZojqazOnYUUpCFC8OREYC/v6qI1Lr1i3poRwZCYSGylpPbvowH/YCTj9Nk9eDxYuB4GD5HciVS3VURJQajgAqYq%2BeX7my2jicYcYM2R186pRseLAyTZNCz5GRQL580juVyR9ZhX09YMmSwNmzwKuvsj4gkVExAVTk99/lWLGi2jicITAQmD9fzmfPtnaruMmTgaVLpavL119LYkxkJf7%2B8jvg7S03QPZlIkRkLEwAFTlyRI4VKqiNw1nq1wfeekvOu3WTItdW88MPwODBcj5tmnF7OxO5WpUqjo0g/fo5bniJyDiYACoQFyfTIwBQtqzaWJxp3DigdGkpBWFPBq3i1CmgXTuZ7urWDejZU3VERGr17Qs0aSItI9u1Y%2BtIIqNhAqjAyZNyzJVL2qq5i6xZgXnzZB3QggXA99%2BrjkgfcXHS4/f6dSnpM2OG/AyIrMzDQ5aG5MsnI4D24vFEZAxMABWwJ4AlS6qNwxVq1gTeflvOX3tNCkW7M02T7/PQIflDt2KF9EYlIiB/frkpBGRZxPr1auMhIgcmgAqcPi3H4sXVxuEqY8YAJUoA58451sS5q2nTpCC2l5cUwi1USHVERMbStCnQp4%2Bcd%2BkCXL6sNBwi%2Bj8mgArYe%2BeGhCgNw2WyZpUWaIDsADR7ncMH2bYN6N9fzidPNl9PZyK9vP8%2BUL48cPGijJizNAyRekwAFThzRo7uXCKkXj1pEg/IC767LQCPigJeeglITgbatpUF70SUOj8/KQ6dJYuUhrFPCxOROkwAFTh3To6FC6uNw9UmTQKCgoDjx92rN2hSEtCmjSSBFSoAc%2BZw0wfRozz%2BuLSMBOSG6dQptfEQWR0TQAXsCaC7rxfLkUPWyAHAhAmSCLqDd9%2BVaW1/f9n0kT276ojImdgL2HX695f6mDdvAp07ywg6EanBXsA6S06WCvkpKcCFCzJC5s40DXj2WSkJU78%2B8PPP5h4t%2B%2BYbKfkCyKaPF19UGw%2B5DnsBu8ZffwGVKgGxsbI28J13VEdEZE0cAdTZlSuS/NlsQJ48qqNxPZtN6uL5%2BQEbN8o6ILM6dUpGLQApdM3kjyj9ihUDpk6V8/feY5cQIlWYAOrMXgIhVy4pHWIFISHAsGFyPmCAOWsDJiQArVtL7DVqyPpGIsqYrl1lZuDOHaBTJzkSkb6YAOrsyhU5WmH0798GDADKlJEyEMOHq44m/fr3B/btk84tS5fKbkYiyhibTTZP5coF7N8vbSSJSF9MAHV2/bocc%2BVSG4fevL1lKhgAIiKAgwfVxpMey5dLzACwcCEQHKw2HiJ3EBQEfPqpnI8bJzdYRKQfJoA6syeAOXOqjUOFhg2BVq1kI8ybb5qjGOyffwLdusn54MFAeLjaeIjcSevWjnqaXbrIUgsi0gcTQJ3Z178FBqqNQ5UPP5QNIZs3y8ia4WzaJDs9nnkGyd16YGTzfYiJAWrVkhZ3ZB5btmxB8%2BbNUbBgQdhsNqxevVp1SJSKiAggb17ZDDJ6tOpoiKyDCaDOYmPl6O%2BvNg5VihYFBg2S84EDgdu31cZzj9dfl1o1CxYAP/0Ez89mY%2BHRqhjrNw5ffmmdTTvuIi4uDpUrV8YM%2B9oDMqS8eR1Twe%2B/z6lgIr0wAdTZzZtytHLx4IEDZR3dmTMyImgIS5ZI4%2BJUvHt7GIJPu2lDYzcWHh6OsWPHomXLlqpDoUdo1Uq663AqmEg/TAB1duuWHLNlUxuHSlmzyp0%2BIC3iLlxQGw8A4JNPHv5x%2BxAFEbnE9OlSHeH337krmEgPTAB1Zp/y9PNTG4dqbdsC1asDcXFSDFa5o0cz93EyvYSEBMTExNzzRvrJm9dxHzZhAnDggNp4iNwdE0CdxcfL0cdHbRyq2WyO6d958wzQDeBRhRmtVrjRgiZMmIDAwMC7b8Gs96O7F18EWrYEkpKAV16RIxG5BhNAndkr3nt7q43DCGrWlLU/KSlSYkWpTp0y93EyvSFDhiA6Ovru29mzZ1WHZDk2m%2BwKzplTCkR/8IHqiIjcFxNAndnvaNlJQowfL7tr164FtijcZ3Hr1T444vN46h9s2BBo107fgEh3Pj4%2BCAgIuOeN9BcU5OgVPHIk8McfSsMhcltMAHWWnCxHT0%2B1cRhF6dKOQstDhqgrDj1oXABqJGzC9GyDkZI3n/xjkSKyGv2775ixm9DNmzcRGRmJyMhIAMDp06cRGRmJM2fOKI6MHqVTJ6BRI9kN3K2bzBIQkXMxAdSZ/YXMgz/5u957D/D1BXbsANav1//6GzZIm7oYBKL0ignwuHRRhmr//hsYOpQLNk1q7969CA0NRWhoKACgX79%2BCA0NxXAzNqO2GJsNmDVLqiVs3QrMnq06IiL3wzSElCtYEOjVS86HD9d3FPDGDaBrVzl/4w2gceP/f4BDtKZXr149aJp239v8%2BfNVh0ZpUKyYoxzMoEHAuXNKwyFyO0wAdWazydEMfXD19M47Uh9w715ZD6iXt98G/vkHKFkSmDRJv%2BsS0aP17g08%2BSQQEyPnROQ8TAB1Zp/65ZqWe%2BXL5xgFHDtWnwR53Tpg/nxJyufPt3ZxbiIj8vQE5syRjWKrVwMrV6qOiMh9MAHUmb2fLOtb3a9/f1kL%2BOuvwKZNrr1WbCzQo4ec9%2B0L1Krl2usRUcZUrOjoH96nDxAdrTYeInfBBFBn9s2kiYlq4zCi/Pkd6/HsreJcZehQ4OxZICRERhyJyLiGDQNKlQLOn5ffXSLKPCaAOrMXgGaz89T17y/T5D/8ABw54ppr7N4txWYB2V3IqV8iY/P1lV3BgLTl3rlTbTxE7oAJoM58feVobwlH9ypeHGjRQs6nT3f%2B4ycnA6%2B/LmsMO3SQGs9EZHz16wNdusjv7muvcRaFKLOYAOosa1Y53r6tNg4j69NHjosWyVo9Z5o7F/jtNyAwkG2mKHUREREoX748wsLCVIdC//HBB0Du3NI7fMoU1dEQmRsTQJ3ZE8C4OLVxGFm9etIh5OZNYPly5z1udLSsJQKAUaNkzSHRf/Xq1QtHjhzBnj17VIdC/5E7N/Dhh3I%2BahRw%2BrTaeIjMjAmgzrJnl%2BPNm2rjMDKbTaZ6AGDhQuc97vvvA1euAGXKSNFnIjKfTp3kJvH2bakNyJqqRBnDBFBn9v7yMTFq4zC69u3luHkzcPFi5h/v4kXgo4/kfOJEtvYlMiubTTaCeHsD33/P2oBEGcUEUGf2BJC1rB6uaFGgalW5u//uu8w/3ocfyohBWJhjkwkRmVPZso7agG%2B%2B6fy1wkRWwARQZzlzyvHGDbVxmEGzZnLcsCFzjxMdDcycKefDhzva8RGReQ0ZApQoIbUBR4xQHQ2R%2BTAB1Jk9Abx2TW0cZlCvnhy3bcvc4yxcKCME5coBTZtmOiwiMgA/P0c9z2nTgAMH1MZDZDZMAHWWJ48cr1xRG4cZVK0qo3XnzgGXL2f8cT77TI6vv%2B7oxUxE5te4MfDii476nuyxTpR2/HOoM3sCGB0N3LmjNhajy54dCA6W8xMnMvYYJ04AkZHSg9m%2BsYSI3MfUqfJasXMnMG%2Be6miIzIMJoM5y5QI8PeU8M6NaVlGokBwvXMjY169dK8e6daWGGBG5l8KFpSYgIBtDrl5VGw%2BRWTAB1JmHB5Avn5xHRamNxQz8/eWY0cLZW7bIsVEj58RDRMbTpw/w2GOS/A0dqjoaInPvSrKbAAAgAElEQVRgAqhAgQJyzOiolpUkJ8vRPmqaXvaF4ezqReS%2BsmRxbAiZMwdgExeiR2MCqIB9WvPcObVxmIF9s4x993R6JCUBf/8t56VLOy8mcm/sBWxOdeoAHTtK7dBevbghhOhRmAAqULiwHM%2BeVRuH0aWkAH/%2BKechIen/%2Buhoxwiifdqd6FHYC9i8Jk2SYvt79gBz56qOhsjYmAAqUKSIHM%2BcURuH0R0/Lj2T/fyAkiXT//X2XdY2G1u/EVlBUBAwerScDxnCDSFED8MEUIGiReX4119KwzC8H3%2BUY40aGUvgfH3lqGksuUNkFb16ARUrSrH9YcNUR0NkXEwAFbBPZ54%2BrTYOo/v6aznaW8KlV2CgI3Hkjmsia/DyAqZPl/NZs4B9%2B9TGQ2RUTAAVKFFCjufOAfHxamMxqj/%2BkBZwNhvQpk3GHsPDAyhWTM4zWkiaiMynbl2gXTsZ/e/ThxtCiFLDBFCBPHlkobKmOTY50L2mTZNjs2aOXdMZ8dhjcoyMzHxMRGQekycD2bJJh5BFi1RHQ2Q8TAAVsNmAMmXk/PhxtbEYUVSUo3/v229n7rGefFKOW7dm7nGIyFwKFQLee0/OBw0CYmLUxkNkNEwAFSlbVo5Hj6qNw4jGjZOp8erVgfr1M/dY9q/ftAlITMx0aERkIm%2B9BZQqJTeVY8aojobIWJgAKlKunByPHFEbh9EcPw7MnCnn48bJaGlmPPGETLlHR3MUkMhqfHyAjz6S848%2B4owL0b8xAVTEvjbt99/VxmEkmiZ37ElJsvavQYPMP6anJ9CihZwvW5b5xyMic2naVF5PkpLk9UXTVEdEZAxMABWpVEmOR4%2ByRp3d8uXA%2BvWAtzcwdarzHte%2Bi3j5ciAhwXmPS%2B6JreDcz9SpUhJq/Xpg7VrV0RAZg03TeD%2BkgqYBuXIBN24A%2B/cDjz%2BuOiK1rl0DypcHLl4ERowARo503mMnJ0v3lfPnpbZgq1bOe2xyXzExMQgMDER0dDQCAgJUh0OZNGiQtIorVUpmXry9VUdEpBZHABWx2YDQUDlnoVKp1XXxoqyNHDLEuY/t6Ql07izns2Y597GJyByGDQPy55eaoB9/rDoaIvWYACpUtaoc9%2B5VG4dqy5cDS5ZI4eZ582ThtrN17y5J94YNUmSaiKzF3x%2BYOFHOx4yRG04iK2MCqJB9idGvv6qNQ6WzZ4EePeR86FBH3T5nCwlxtJSbMcM11yAiY%2BvUSW68Y2OBd99VHQ2RWlwDqNA//wDBwTJFeeMGkD276oj0lZQkO323bpVkePt2R%2B9eV9iwAWjUSLoDnD0L5MzpumuR%2BXENoHvasQOoVUtmBPbuBapUUR0RkRocAVSocGFJAJOTrTkKOHKkJH/%2B/jIF7MrkDwAaNgQqVgTi4rgWkMiqatYE2rd3lJ3iEAhZFRNAxWrXluPmzWrj0Nu6dVLoGQBmzwZKlnT9NW02YMAAOf/4Y%2Bk2QkTWM3Ei4OcnN6ArVqiOhkgNJoCK1asnx40blYahq9OngZdflvPXXwfattXv2m3byqhrVBQwf75%2B1yUi4wgOBgYOlPOBA3kzSNbEBFAxe7eLXbuAmzfVxqKHuDjghReA69eBatWcW/A5Lby9HaOA77/P/sBEVvXOO0ChQsBffznaxRFZCTeBKKZpQIkSMiq2Zg3QvLnqiFxH02QEbtkyIF8%2BqX9YuLD%2Bcdy6JbuCL12SsjNduugfAxkfN4G4v4ULZWewv7/UB8yfX3VE5pWSIhsb//5bbvCTk2VjY6FCssSHhbeNhyOAitlsQJMmcv7992pjcbWxYyX5y5JFOnKoSP4AIGtWxyjguHGyG5mIrOfll6UCQWws8N57qqMxn2PHZCblmWeAHDmAokWBOnWk/3rLllJ1oUIFSbBr1wYmT5aOTGQMHAE0gLVrgWeflYTozBlJCt3NsmWOnryzZ0thZpVu3pRRwCtXZC2gvVMIUUREBCIiIpCcnIw//viDI4Bubts2SU48PIDISKkUQA8WFycjp3PmAL/9du/HsmSRtpu5c0t5s9hYGRGMjXV8jqen7MIeNUpeg0kdJoAGcPs2kDev/GLt2ePoEOIudu6UtY7x8VJ2Qe91fw8yaZL0By1eXO5kXV2GhsyFU8DW8dJLMivxzDPADz%2B45014ZkVHS/WEjz%2BW3u0A4OUl5bWaNZORv3Ll7n8d1TTgzz%2BlDuvixVLvFZCOT8OHy1pMLy99vxcSTAANwv4CNHgwMGGC6mic58QJqbt15Yqsb1y1Su4AjSAuTu5AL18G5s4FXn1VdURkJEwArePUKUle7tyRGZmmTVVHZByJicCnnwKjRwNXr8q/lSgB9OoFdOwI5MmTvsfbs0f6vf/8s7xfp460A82Xz7lx06NxDaBBvPSSHJcudZ/CpBcvAuHhkvw98YQUezZK8gdIR5DBg%2BV89GggIUFtPESkRvHiwJtvyvmAAVwXbLd9u3RK6dtXkr%2ByZYGvvgKOHwfefjv9yR8gay43bAAWLJC1gVu2SAvQEyecHz89HBNAg3j2Wdkxdfq0tCoyu5gYuYv%2B808ZZfvuO2O2unv9daBgQVl7OWeO6miISJV335W1a0ePAp99pjoatW7dkqTvqaeA33%2BXn8vMmcChQ7KWO7M38jabjB7u3i2jiX/9BdStC5w86ZTwKY2YABpE1qxAq1ZybvYCxbdvyy6w336TtY0//AAEBamOKnV%2BfsCwYXI%2BbpxMCxOR9eTIIWvSADn%2Be%2BOClfz2m4z6TZsm73ftKiN%2BPXo4f61e2bIyyvjYY8CFC7Jr%2BOJF516DHowJoIG88oocv/zSvC8%2Bd%2B7IdPamTTK8v349UKqU6qge7tVXZZQyKgqYPl11NESkSs%2Be8np16ZKUN7ESTQNmzABq1JCEr2BBef3%2B7DMZAXSV/PmBn35y1MNt1Ur%2BjpDrMQE0kDp1gDJlHNvszSYxUbb3r10L%2BPrKtG%2BVKqqjejRvb1kDCMiL/vXrauMhIjW8vR2J35QpUtjYCm7dAjp0APr0keTr%2BedlurdxY32unz%2B//N0IDJQRwSFD9Lmu1TEBNBCbDXjjDTmfNk0qq5tFUpK8gKxYIS%2Biq1dLQmsW7doBYeXj0PvGGGghIVKjoHx56RHFfnFElvH880CdGol47fZH8HisvLwWhIQAY8a45RqRf/6RtX72TXpTpgArVwK5cukbR5kywBdfyPmUKcAvv%2Bh7fStiGRiDiY2VRuXR0ZJEtWihOqJHS0yUivrLl0sNqBUrTNjSLi4O16s0QM4/dt//sWbN5D%2BDxaoshWVgLCopCdfrPo%2BcO9be/7Fq1SQzyZZN/7hcYN8%2Bea2%2BcEHWa3/9tfob99dflw0nISGyASVrVrXxuDOOABqMv7/8AgDSOs3o6Xl8PPDiiyZP/gBgxozUkz9A5iaWL9c3HiJSY9my1JM/QLatRkToG4%2BLrFsnO28vXJBNGLt3q0/%2BACnQHxws6wHHjVMdjXvjCGAmaJqGWBfs1rh8WdoR3b4tw/LNmjn9Ek4RGysjf5s3yyzJ4sVSSd%2BUqlZ9eCGqBg2kijW5rYSEBCT8qxhkbGwsypcvj7Nnz3IE0Eqefx7YuPHBHy9dWqoZm9hXX8lAQ0oKUL%2B%2B1OQz0lP8229lSZG3t4xSFiniumv5%2B/vDZtHWL0wAM8E%2BRURERETmY%2BUlHkwAM%2BHfI4BhYWHYk867wod9zY0bQGio9FycONExLezs66QmJiYGwcHBDxz5OHFCpn3/%2BksWCn/9tXT60CO2jHxNmj6/QQO51XyQFi3kNtmJcRn5ax71HFAZm6u%2B5r8jgBcuXEC1atVw5MgRFCpUSGlsqr7Gis8DdOoEfPPNgz/%2BxBNp2qHgktepTH7NRx8BI0bIec%2Be0nbU4xELwVQ9B/btk5dlm00GXB9UTiyz17HyCCBXtWeCzWa7%2Bwvh6emZ7ruIh31NQID8cvboIesg2reXdRHOvs7DBAQE3Pd1v/wiyd/167JId/16mRHJ6HX0%2BJo0fX7v3o5CjKnp0%2BeRcyRG/f4z%2BjVA6s8BZ1/HyF8DyB8Iq/8MLPU86NPn4QlgGl4LMhKbq7//8eMdyd977wGjRklylVZ6Pwfq15f15N9%2BK/UJ581zzXWszHPkyJEjVQfhLqpVq%2BbUrwkNlZ6Jf/4JHD4s6%2B1sNudf578SEhIwceJEDBkyBD4%2BPgAcRUI7dJCaUdWrSzPv/67NcHVsGf2aR35%2B5coypHngwH0fOtZyKPIM7uaSuIz6Nak9B1xxHSN/TUxMDKZOnYp%2B/fql%2B4%2BFEb%2BfjHyNJZ8HxYtLaYOtW%2B/70LEaXZBn2vA0Z05Of53K4NdMmCCt7gDg2Wd3Yc6cwmlO/lQ%2BB4oXB%2BbOlb9/r74qmyRdcR3L0sjQjh7VNF9fTQM0bdIkfa4ZHR2tAdCio6M1TdO02FhNe/lliQGQ89u39YlFVykpmvb999qdFi207YC2r3w7rRa2aiVLalpCgurg9PXf54AVnT17VgOgnT17VnUoylj6ebB1q6Z16KAlVqumLUMJrQm%2B1woEpWhxcaoDS58PP3S8do8bl/6vV/0ceOopiX3YMCWXd2ssA2NwZcvKug1AqqP/9JPrr%2Bnj44MRI0bAx8cH%2B/fLBtnFi6VI6IcfSpcSX1/Xx6E7mw0ID0fK0qX4ccQIFNk8DyfzP4WTJ6UulZX8%2BzlgVfbv3eo/A8s%2BD556Cli4EMlbtuDgsM44WrQJLkTZTNUucvZsoH9/OR81Chg6NP2Pofo58Oabcpwzhy3inI2bQExA04AuXRxb9TdtkulhV0pKAiZPljUjiYlAoULSo7h2bdde12hmzZLF0rlzAydPSsN4sgYWgqZ/W7hQ9ofkzAmcOmX814Jly4C2beXvx6BBMg1sxr0OiYmy1CgqSurMtmypOiL3wRFAE7DZJBGpUweIiZFae7/95rrr7d0LPPmk3C0mJkpZrMhI6yV/gKw7KVcOuHpVFlETkTW1bw9UqCAb4D78UHU0D/fTT7JeW9Mcu33NmPwB0mCgSxc5f9BGEMoYjgCaSHS0JH979gDZs8u07HPPOe/xL1wAhg8HPvtMXjhy5AA%2B/hjo2NG8Lx7OsHYt8OyzUpT02DHZ/UzujyOA9F%2BrVskIVLZs0qkib17VEd1v/34ZLLh5E3jpJZm58fRUHVXmHD0qrdm9vOTvVJ48qiNyDxwBNJHAQLmza9BAfrlbtAD69pXzzLhwARg4EChZUnZcaZrsOD56VKY8rJz8AUDTpsDTT8v6kyFDVEdDRKo8/7yUAYyLA95/X3U09ztzRjpH3bwpZVQWLjR/8gfILEyVKrI0acUK1dG4DyaAJhMQILX37Atjp02TjSIzZ0rruLRKTpZuRx06AEWLAh984Cjvsm0bsGgREBTkmu/BbGw2%2BfnYbMDSpcDOnaojIiIVbDZg9Gg5j4iQdWlGERMjyZ%2B9t%2B/KldKi0120aSPHr79WG4c7YQJoQlmyyNTsunUyHXnunHQKKVhQ1qwtXQr88Qdgb2qgaTJ9/NtvwOefS73jPHni0aCBTCMnJgKVK9/Ed98BO3YAtWqlft358%2BfDZrPd9xYfH6/fN6%2BDlStXonHjxsiTJw9sNhsiIyPx%2BOOOOtH9%2BsnP1Ow0TcPIkSNRsGBB%2BPn5oV69ejh8%2BPBDv2bkyJH3/f8H8U7B1D755BOEhITA19cXTzzxBLamUv/OziqvAQCwZcsWNG/eHAULFoTNZsPq1asBAOHhQI0aQHy8dGkygqQkSZB%2B/x0oUAD4/vu0b1J50Pf5IJs2bUr1OXDs2DEnfCcPZt/8sXGjrMOkzGMCaGJNmgBHjkgyWLSotI/7/HPZ%2BVWmjJRq8fGRhDFHDpm6ePVVYP584MYNX/j63kbt2scAhGH%2B/JNo1uzR070BAQG4cOHCPW%2B%2BblYTJi4uDrVq1cLE/7y6jxkja3927ZIddmY3adIkTJkyBTNmzMCePXsQFBSEZ5555m57wwepUKHCPf//hw4d0ilifURERKB8%2BfIICwtTHYrLLV26FG%2B99Rbeffdd7N%2B/H7Vr10Z4eDjOnDnzwK%2BxwmsAIK8DlStXxowZM%2B75d5tNSqoAsjnvwgUFwf3HwIEyM%2BTnB6xZI12j0upB3%2BejHD9%2B/J7nQKkH9WpzkpIlZRNOcrIMfpATKK1CSE6TlKRpP/%2BsaX37alrVqpqWLZuj%2BKf9LU8eTatbV9MGDdK0DRukuPHp06c1ANr%2B/fsfeY158%2BZpgYGBrv9mDCK1n83o0fKzLFpU027dUhdbZqWkpGhBQUHaxIkT7/5bfHy8FhgYqM2cOfOBXzdixAitcuXKeoSonOoCuHqoVq2a1rNnz3v%2BrWzZstrgwYNT/XyrvQbYAdBWrVp19/2UFE2rWVNeC95%2BW2FgmqZ9/rnjNX758sw91n%2B/z9Rs3LhRA6Bdv349cxfLgEGDHM0IKPM4AugmPD1lc8hHH8ku4dhYKV1y5gzwzz%2ByaPnyZakhOHEi0LCh7GpNr5s3b6Jo0aIoXLgwnn32Wezfv9/p34uR9e8PFC4M/P03MHWq6mgy7vTp04iKikKjRo3u/puPjw/q1q2LHTt2PPRrT5w4gYIFCyIkJARt27bFqVOnXB0uucCdO3ewb9%2B%2Be54DANCoUaOHPges/hoAyCjg8OFyPnMmcOmSmjh275YyL4DUbH3xRf2uHRoaigIFCuDpp5/Gxo0bdblmeLgcf/wRSEnR5ZJujQmgm7LZgFy5ZCqgUCEga9bMP2bZsmUxf/58rFmzBl9%2B%2BSV8fX1Rq1YtnDhxIvMPbhJZszrW/Ywfb4zpn4yI%2Bv/q9fz589/z7/nz57/7sdQ8%2BeSTWLBgAX744QfMmTMHUVFRqFmzJq5everSeMn5rly5guTk5HQ9B/ga4NCokXRJun3b0a1JT5cvA61aSXWCFi0cCamrFShQALNnz8aKFSuwcuVKlClTBk8//TS2bNni8mvXqCHLcC5fBg4edPnl3B4TQAtYvHgxsmfPfvftYYu8H6Z69ero0KEDKleujNq1a2PZsmUoXbo0ppupN9J/ZORn066dFMqOi3M0WDe6/36fiYmJAADbfxZ9app237/9W3h4OFq1aoWKFSuiYcOGWLt2LQDgiy%2B%2BcF3w5FLpeQ6442tARtlsjt//iAjZaKeX5GQpTP3PP0Dp0tIlykOnv%2BZlypRB9%2B7dUaVKFdSoUQOffPIJmjVrhg8%2B%2BMDl1/b2BurWlfNffnH55dweE0ALeO655xAZGXn3rWrVqk55XA8PD4SFhZn67j8jPxsPD8cd//z5ru3K4iz//T7z/L%2BS6n9Hei5dunTfiNDDZMuWDRUrVjT1c8Cq8uTJA09Pz0w9B9zhNSAznntOatTFxEjfXb2MGyc1YbNmlXIvquuUV69eXbfnQL16cty8WZfLuTUmgBbg7%2B%2BPkiVL3n3z8/NzyuNqmobIyEgUKFDAKY%2BnQkZ/NtWryx24pgFvvWX8sjD//T7Lly%2BPoKAgbNiw4e7n3LlzB5s3b0bNmjXT/LgJCQk4evSoqZ8DVuXt7Y0nnnjinucAAGzYsCHNzwF3eA3IDA8P2YELyE3h/wfWXWrzZscu5JkzZWesavv379ftOWBvSbp9u/Ffd43OS3UApMa1a9dw5swZnD9/HoBs6QeAoKCgu3XdOnXqhEKFCmHChAkAgFGjRqF69eooVaoUYmJiMG3aNERGRiIiIkLNN%2BEiafnZALIWcNUqYOtWYPlyoHVrJeFmiM1mw1tvvYXx48ejVKlSKFWqFMaPH4%2BsWbOiffv2dz/v6aefxgsvvIDevXsDAAYMGIDmzZujSJEiuHTpEsaOHYuYmBh07txZ1bdCmdCvXz907NgRVatWRY0aNTB79mycOXMGPf%2B/s8CqrwGAbHY5efLk3fdPnz6NyMhI5MqVC0WKFLn77%2B3bS9/08%2BelPNTLL7supuvXpXh/Sor0x%2B3YMfOP%2Bajvc8iQITh37hwWLFgAAPjoo49QrFgxVKhQAXfu3MGiRYuwYsUKrNCpRUeVKlLi7OpV4PhxaYRAGaR2EzKpMm/ePA3AfW8jRoy4%2Bzl169bVOnfufPf9t956SytSpIjm7e2t5c2bV2vUqJG2Y8cO/YN3sbT8bOxGjJCyBEWKmK8sTEpKijZixAgtKChI8/Hx0erUqaMdOnTons8pWrToPd93mzZttAIFCmhZsmTRChYsqLVs2VI7fPiwzpHrwwplYDRN0yIiIrSiRYtq3t7eWpUqVbTNmzff/ZhVXwM0zVHu5L9v//552I0ZI68D1aq5Nqa2beU6pUppWmyscx7zUd9n586dtbp16979/Pfff18rUaKE5uvrq%2BXMmVN76qmntLVr1zonmDR66in5Ocyfr%2Btl3Y5N0ziISpRRt27JHejZszIto9dOPHK9mJgYBAYGIjo6GgGqF1mRoV26JBUX7twB9u6VovvOZp9l8PSUjk3Vqjn/GmbRvz8wZQrwxhuyAYcyhmsAiTIha1Zg0iQ5nzhREkEispZ8%2BaQkC%2BCazSBXrkiyA8h0s5WTP0DK7wDAvn1q4zA7JoBEmdSmDfDUU1IP7J13VEdDRCq89pocv/pKZgacqX9/SQIfewwYNsy5j21G9hHWgwelDzJlDBNAokyy2YBp0%2BT41VeyKYTMy0q9gMl56tQBihWTkjDffuu8x926Ver82WzAnDkZ6%2BDkbkqWlNmX27cBi1YgcgomgEROEBoKdO8u52%2B%2BKYVayZx69eqFI0eOYM%2BePapDIRPx8JAi8YCs13OG5GR5PQHk9aV6dec8rtl5eAAVK8r5oUNqYzEzJoBETjJ2LJAjBxAZKXfqRGQt9l6869YB8fGZf7wlS%2BT1JDBQij%2BTg73%2B4eHDauMwMyaARE6SN6%2BjQOu77wLXrqmNh4j0FRoKFCggawC3b8/cYyUlASNHyvngwcD/m/fQ/9kTwKNH1cZhZkwAiZzojTdkofa1a8B776mOhoj0ZLMBTz8t55s2Ze6xli0DTp2SxK9Pn0yH5nbsBaCPHVMbh5kxASRyIi8v2RACSJumAwfUxkNE%2BrJ30cvsEtKPP5Zj375AtmyZeyx3VLq0HE%2BckM4olH5MAImcrH59KdiakgL07s1%2BlURWUqmSHDMzNfn778Du3UCWLI7yMnSvYsXkhjs%2BXtrwUfoxASRygQ8%2BkDIF27YBixerjoaI9FK8uBz/%2BSfjNeqWLZNjeLgUmab7eXkBRYvK%2BZ9/qo3FrJgAErlAcLCjYOvAgVIbjIjcX968ckxJAW7cyNhjrF8vx5YtnROTuwoJkeNffykNw7SYABK5SL9%2BQKlSQFSUYzcfEbk3Ly/ZDAIAiYnp//pbt4DffpPzBg2cF5c7so8A/v232jjMigkgkYv4%2BDg2hEybJut6iMi9xcc71v36%2BaX/648flwLQuXMDhQs7NzZ3U6SIHM%2BcURuHWTEBJHKhJk2AF16QF/RevbghhMjd2Tck%2BPgAAQHp//qzZ%2BVYvLhjJJFSZ0%2BQz51TG4dZMQEkcrGpU2UkYMsWbggxA/YCpsyw7/4tWVJalqVXdLQcc%2BRwXkzuqlAhOTIBzBgmgEQuVrSoY0PIgAGOF3gyJvYCpszYtUuOVaqojcMKChSQY1SU2jjMigkgkQ7695fCpRcvskMIkTuz7%2BCtVy9jX2%2BfNs7oDmIrCQqS45UrGS%2B5Y2VMAIl04OMDRETIeUQEsH%2B/2niIyPlOnwb27pW1e82aZewx7OvauLHh0XLnlp%2B1pkkSSOnDBJBIJw0bAm3aSH2w119n%2ByIidzNvnhwbNADy58/YY9hbnF28yKTmUTw9gVy55Jw/q/RjAkikoylTAH9/4NdfgTlzVEdDRM6SkADMni3n3bpl/HH8/R1JoH09IT1Y7txyvHpVbRxmxASQSEcFCwJjxsj54MHApUtq4yEi51iwQEbtChcGWrXK3GPVqSPHX37JfFzuzj4CeP262jjMiAkgkc569QIef1wWeQ8YoDoaIsqsO3eA8ePlvF8/IEuWzD1e48Zy/PZb1g59FHu5HCaA6ccEkEhnXl7ArFmyeHnhQmDjRtUREVFmzJkj/Wjz5wd69Mj84zVuLBvHTp4EDh7M/OO5s8BAObLfevoxASRSoFo1oGdPOe/ZU9YPEZH5REcDo0bJ%2BfDhQNasmX9Mf3/HLuJFizL/eO7M31%2BOsbFq4zAjJoBEiowfL3Ws/vgDeP991dEQUUaMHg1cvgyUKQN07%2B68x%2B3USY4LFsgUM6Uue3Y53rypNg4zYgJIpEiOHMBHH8n5uHGSCBKReRw%2BDEybJudTp2Z%2B7d%2B/NW0qN4iXLgGrVjnvcd2NfcT11i21cZgRE0AihVq3Bpo0kTv8Hj244NsI2AuY0sJezzMpCWjRAggPd%2B7jZ8kCvPaanE%2Bf7tzHdif2BPD2bbVxmBETQCKFbDbgk08APz9g0ybgiy9UR0TsBUxp8fnnwNatkoB8/LFrrtGzpySC27dL7VC6n6%2BvHOPj1cZhRkwAiRQLCQFGjpTz/v1ZG5DI6M6fd5RwGjMGKFrUNdcpUABo317OJ092zTXMzttbjlwnmX5MAIkM4O23gcqVgWvX5JyIjEnTZOo3OhqoWhV4803XXm/gQDmuXAkcO%2Bbaa5mRfd1lYqLaOMyICSCRAWTJIrXEPDyAJUuA9etVR0REqVmyBFizRn5nP/9c6nq6UoUKssZQ02SzGN3L01OOSUlq4zAjJoBEBhEW5hhN6NmTZQ2IjOb8eaB3bzkfPhyoWFGf6773nhyXLAGOH9fnmmbh8f8sJiVFbRxmxASQyEDGjpX1RH//7XjRJyL1NA3o2lVaOFatCgwapN%2B1n3gCaN5ckhx70WkSNpvqCMyLCSCRgWTLBsycKecff8ydf0RKRUXJL%2BGZM/j0U%2BCHH2TX6YIFzq35lxb2xO%2Brr4BDh/S9tpGxdFbGMQEkMpgmTYCOHeWF7dVXubstI1auXInGjRsjT548sNlsiIyMVB0Smcn580DLlkDhwkD16tCKFUOZPs%2BgDI5h4kSgXDn9QwoNBVT2D0AAABuoSURBVF56SV4X3n1X/%2BsblX3q174WkNKOCSCRAU2dCuTNK50Gxo9XHY35xMXFoVatWpg4caLqUMhsbtwA6tSR9hvJyQAAm6bh6ZSfsCtLHfRpcUZZaGPGSKLz7bfAtm3KwjCU//8XMQHMACaARAaUO7ej%2Bv%2B4cZzySa%2BOHTti%2BPDhaNiwoepQyGzmzgX%2B/DPVD%2BVIvAyPj6fqHJBDmTIyKwBIeRhOfzrKv%2Bg9Je8OmAASGVTr1lL%2BISlJFp%2BzzIFrJSQkICYm5p43sqAVKx7%2B8a%2B/1ieOBxg5UrqP7NqlPBRDsC%2BRsReEprRjAkhkUDYb8OmnQI4cwN69wIcfqo7IvU2YMAGBgYF334KDg1WHRCo8qqeY4p5jBQoA77wj54MGAQkJSsNRzv7fYW8JR2nHBJDIwAoUkPWAADBiBHD0qNp4jGjx4sXInj373betW7dm6HGGDBmC6Ojou29nz551cqRkCjVrZu7jOhgwQF4bTp92XR9is7h9W45%2BfmrjMCMmgEQG17kzEB4ud/qvvOJY9EziueeeQ2Rk5N23qlWrZuhxfHx8EBAQcM8bWVDv3kjyfMB8os1miF6N2bIBEybI%2BdixwMWLauNRKS5Ojlmzqo3DjJgAEhmczQbMmgUEBEhJsilTVEdkLP7%2B/ihZsuTdNz8OBVAm7I4th9baMsTA/94P%2BPrKL2K9ekri%2Bq%2BOHaUgdWwsMHSo6mjUsXdM8vd/%2BOfR/ZgAEplAcLAj8XvvPU4FP8q1a9cQGRmJI0eOAACOHz%2BOyMhIREVFKY6MjOzGDaBNG2BVSgv0anEO2qzZstBu2jTgn3%2BA7t1Vh3iXh4eEBQDz5gF79qiNRxX7Xi0mgOnHBJDIJLp2lSLRCQlAly7cFfwwa9asQWhoKJo1awYAaNu2LUJDQzHT3maF6D/shdf/%2BgsICQGmz/eH7bXuwMSJQJ8%2BUpvJYGrUADp0kNj79LFmP9wbN%2BQYGKg2DjNiAkhkEjYbMGeOvNDt3g1Mnqw6IuPq0qULNE27723kyJGqQyODmj4dWLlS6sktXSq7781g0iQZ/fr1VxkJtJrr1%2BWYM6faOMyICSCRiRQu7Nj1N2IEcPCg2niI3MHu3bKzFpByS2FhauNJjwIFpDYgILPV164pDUd39u83Vy61cZgRE0Aik%2BnUCXjuOamA36kTewUTZca1a9JjNzERaNUK6N1bdUTp16cPUKECcPUqMGSI6mj0deWKHPPkURuHGTEBJDIZmw2YPVte8A4ccNz9E1H6pKTIbtozZ4CSJYHPPpPfL7PJkkWKxgPy2rBrl9p49JKQAERHy3m%2BfGpjMSMmgEQmlD8/YN/P8P77wM6dauMhMqMJE4Dvv5cKL8uXm3sjQe3asjkMAHr0cPTIdWeXLskxSxbzrNk0EiaARCbVqpWMXthHMez1sIjo0X76SUoqAUBEBPD442rjcYbJk2Ut3MGDjg5C7uz8eTkGBUlZHEof/siITGzaNKkR%2BOefQP/%2BqqNxDxEREShfvjzCzLQTgNLl7FmgXTtH6ZeuXVVH5Bx58jh6ho8cCZw6pTQcl7MngAULqo3DrJgAEplYjhzAF1841gWuWaM6IvPr1asXjhw5gj1Wrazr5hISgBdflM0DoaHAjBmqI3Kuzp2B%2BvWlR27PnpLkuit7u%2B7ChdXGYVZMAIlMrn59oF8/Oe/WDWCzC6IH69tXyr7kzAmsWCHr/9yJvXWkry%2BwYYPcILqrv/%2BWY9GiauMwKyaARG5g3DigUiXg8mWZznLnu36ijPr8c0mObDZg8WLp%2BOGOSpVyVAfo1w%2B4cEFpOC7z119yZAKYMUwAidyAjw%2BwZIkc161zv2ktoszauxd44w05HzUKCA9XG4%2Br9e8PVKkinTLeeMM9bwr//FOOxYurjcOsmAASuYkKFRzt4QYOBA4dUhsPkVFcugS0bCnr/557Dnj3XdURuZ6Xl7SG8/ICVq8GvvpKdUTOpWnAyZNyXqqU2ljMigkgkRvp3Rto2lT%2B0LVrJwvBiawsMRFo3Vo2DJQpAyxYYJ2SIZUqAcOGyXnv3u61Pvj8eSAuDvD0dN%2BpfFezyK8BkTXYbHLXnz8/cPgwS8MQ9e8PbN4M%2BPsDq1aZu9hzRgwdKrudr10DXnvNfaaCjx6VY/HigLe32ljMigkgkZvJl09GOQBpD7Vqldp4iFSZNw%2BYPl3OFy4EypVTG48KWbLITmBvb%2BDbb%2BVn4g4OH5ZjhQpq4zAzJoBEbqhRI1kHCMiu4DNn1MZDpLddu6QOHiA7Ylu0UBqOUhUrAmPGyHnfvsDp02rjcQb7GueKFdXGYWZMAInc1NixQLVqwI0bsh7QCr1BiQDg3DnghReAO3fkaG/5ZmX9%2BwNPPSUtIzt2BJKSVEeUOZGRcqxUSW0cZsYEkMhNeXvLzr/AQGDHDmD4cNUREbne7dsy2hcVJaNDVtr08TCenvKz8PcHtm8HJkxQHVHG3bnjGAGsUkVtLGbGXwsiNxYSAsydK%2BcTJ0qNQHo49gI2L02TJQ/79gG5cwPffANkz646KuMICQEiIuR81Ci5MTSjgwclCcyZkzuAM4MJIJGbe/FFRwHcjh0d/TMpdewFbF5jx8qot5eXtHljcnC/Dh2A9u2B5GQ53rihOqL027VLjtWqSeUDyhgmgEQWMGWKTJVcvQq0acP1gOR%2Bvv7asczhk0%2BAunXVxmNUNptUByheXHrpvvqq%2BUrD2Ecua9ZUG4fZMQEksgAfH2DZMlkPuHMnMHiw6oiInGfvXqBTJznv2xfo3l1tPEYXEAAsXSolYlaudJTKMQNNA7ZskfPatdXGYnZMAIksokQJYP58OZ8yRabIiMzun3%2Bkvdvt20CTJsAHH6iOyByqVnX8rAYMAH79VW08aXXihOzy9vYGqldXHY25MQEkspDnn5cXewB45RXg%2BHG18RBlxs2bwLPPAhcuAI89JqNaXl6qozKPPn1kjXBiohwvX1Yd0aNt2CDHmjUBPz%2B1sZgdE0Aii5kwAahTB4iNBVq2lD%2BiRGaTlAS0bQscOCDdb779VqY2Ke1sNuCzz4DSpWUktW1b49cHXL9ejo0aqY3DHTABJLIYLy8ZKSlQADhyxJyLwMnaNA14%2B21g7VrA1xdYswYoVkx1VOYUECDrALNlA375BXjnHdURPditW8DPP8t5s2ZqY3EHTACJLCgoCFi%2BXJLBZcuADz9UHRFR2n38MTBjhpwvXAg8%2BaTaeMyuQgXpFwwAU6c61gobzQ8/yFrPokXZAs4ZmAASWVStWvKHFAAGDQJ%2B%2BkltPERpsXIl0K%2BfnE%2BaJGvXKPNatXKU0XntNWDrVrXxpGbZMjm2asX6f85g0zRO/hBZlabJFPC8eVJVf%2B9eqQ9GQExMDAIDAxEdHY0ALi4zhB07gKefBuLjgddfl64WTAScJyUFaN1aKgTkzi0lo0qVUh2ViI2VmYtbt2THcrVqqiMyP44AElmYzSZFc6tVA65flx6qsbGqoyK63/HjQPPmkvw1by6165j8OZeHh/QLDguTovHh4cClS6qjEsuXS/JXurTER5nHBJDI4nx9ZVotKAj4/XcpqJuSojoqddgL2HguXJAaf9euyc3Kl18Cnp6qo3JPWbPKjupixYA//5TNFka4KZw9W46vvMLE31k4BUxEAKS/Zt260mT93Xelr6qVcQrYGKKjgXr1gMhIoGRJmQbOm1d1VO7v%2BHFZJ3z1qky7f/ed3CyqsHu3bPTJkkXK1eTLpyYOd8MRQCICIFX158yR83HjgCVL1MZDFB8PvPCCJH/580sNOCZ/%2BihTBli3DsieXUqvtG4tN4cqTJ4sx3btmPw5ExNAIrqrUydHHbCuXWUROJEKyclAhw7Axo2Av78kIyVKqI7KWsLCpMair69MC7dpo38SePiwo23lwIH6XtvdMQEkontMmCCbQRIS5Hj6tOqIyGo0TXb5rlghPV9XrwZCQ1VHZU3168vP38dHjq1aycisXoYOledDq1bS7o%2BchwkgEd3DwwNYtAh4/HHpDdqsGXDjhuqoyEqGDJHlCB4eshShQQPVEVlb48aOkcDvvpPdwdHRrr/uhg1yXU9Prkl2BSaARHSf7Nnlhb5QIeDoUbn7VrX%2Bh6zl/fflDQBmzZLnHqnXqJGswfT3BzZtAmrXBs6edd314uKAnj3lvHdvoGxZ113LqpgAElGqChWSXqvZs0uP0G7d2DOYXOvTT4HBg%2BV80iR5zpFx1K0LbN4sJaMOHZKSPK5aJ/z228CpU0DhwsDo0a65htUxASSiB6pcWQqwenpKz9X33lMdEbmrBQuAN96Q86FDueDfqEJDpWRUxYpAVJQkhdOmOffmcO5cWQJgs0mPYlZhco3/tXf/UVXXdxzHXySiDBAM7QR2mRYWgT9ahe6scvmj1LY4mzOHbnOlNU9rc6vjaq7cbM08p/04zoQ6buVxR8xcP9wkz0oLzzAbk8zlmoqclGqKGDlgEJfLj/3xnjAnmuC9fO%2B9n%2BfjnHvyci%2BXN2j3vPh8P5/3mwAI4KymTbNLcZK1h3niCW/rQfTZuNEa/ErSwoXs9wp3n/609WOcOVMKBKTvfU/64helI0fO/7U3beq69Pvww%2Bz/DCUCIIBPNH%2B%2BvRlL0j33dLVlAM7Xpk3S175m02fuvFNasYJJD5EgMdGC%2B8qVdkJ4yxbpyiulVassFPbGU09ZqGxrk775Temhh4JbM05FAARwTpYskRYssEs9c%2BZYfzbgfBQXW4Ph1lbr%2Bffkk4S/SBITI333u9Kbb1rPwPp6uz9qlG0ZOdeDYzU10je%2BYb8AtLXZn3/7W/4thBoBEMA5iYmRCgqkGTPsjT0vTyov97qq4GMWcN/YssVO%2BAYCFgLXrGG%2Bb6TKybHDIIWF0pAhUkWFNZXPyJDuu89%2BWWxqOvVzWlttxNu991qD73XrrO3PT39q%2B/5iY735XlzCLGAAPdLcLN1yi72pp6ZKf/6zlJ3tdVXBxyzg0NmyxUa8tbRYCHzmGZvzisjX0GCXgVeutEMiJ11wgZSeLl14ob2HvPfeqQ2lr7nGPu%2Bzn%2B37ml1FAATQYw0Ntjm7vNze1EtLpUsv9bqq4CIAhkZxcVdfyRkzpA0bCH/RKBCwoP/cczZL%2BOjR05%2BTnGz9Be%2B4ww6bccm3bxEAAfRKba21gHjnHWn4cFsJ9Pm8rip4CIDB9%2BKLNk82ELDN/uvXE/5c0NFh%2B/wOHbJfHuPirM/opZfayiC8QQAE0GtHj0oTJkiVlVJmpjWJTU/3uqrgIAAG14YNdtCjrU3Kz7dDAuzzArxD9gbQa2lpdnln%2BHALgZMmnbrvB5CsvcecORb%2B5s61Df%2BEP8BbBEAA5yUjw0bF%2BXzSgQOEQJxqxYquMYILFnDaFwgXBEAA523ECDsVfMkl0r590sSJ3W/67guBQEAPPPCARo8erYSEBKWnp2vu3Lk6EowxBThnHR3Sj39sbT4kadEimyLDni8gPPC/IoCguOwyaft2Wwncv9/2Br7/ft/X0dTUpN27d2vJkiXavXu3XnjhBVVUVCgvL6/vi3FUW5tNjHnkEbv/s59Jjz3GKU8gnHAIBEBQHTpkl4EPH7aZoa%2B%2BauHQS7t27dK4ceNUVVWljIyMc/ocDoH0TnOzTXJ47rmu5uF33%2B11VQD%2BHyuAAIJqxAhrCZOZKVVVSTfcYK1ivFRXV6eYmBilpKR4W0iUO3FCmjrVwl9cnPTss4Q/IFyxAgggJKqrpZtukv7%2Bd%2Bv%2Bv2WLNH5839fR3Nys66%2B/XllZWVq3bt0Zn%2Bf3%2B%2BX3%2Bzvv19fXy%2BfzsQJ4jqqqpOnTbQ/ooEHSpk22FxRAeGIFEEBIXHyx9QUcP1766CNp8mTp5ZeD/3WKioqUmJjYeSstLe18LBAIKD8/X%2B3t7SosLDzr6yxfvlzJycmdN180dbUOsV277O953z5r8FtaSvgDwh0rgABC6t//ttFfr7xivd%2Beftr2iAVLQ0ODjh071nl/2LBhio%2BPVyAQ0KxZs/Tuu%2B/qtddeU2pq6llfhxXA3nn%2Befv7/PhjacwY6aWX7DQ4gPBGAAQQci0tNu9z/Xq7v2yZtHhx6E6Fngx/Bw8eVElJiYYOHdrj1%2BAQyNl1dEiPPio99JDdnz7dpn3wowIiA5eAAYRcXJyN/lq0yO4/%2BKB01102EzbYWltbNXPmTJWXl6uoqEhtbW2qrq5WdXW1Wlpagv8FHdTUJM2e3RX%2BFi6U/vhHwh8QSVgBBNCnCgosMLS3W7uY3//eDokEy%2BHDhzVixIhuHyspKdGNN954Tq/DCmD3qqqkL39Zeustu6RfUCB961teVwWgpwiAAPrcSy9J%2Bfm2PzAzU9q8WcrK8rqqUxEAT7d1q6381dZKQ4dau5cJE7yuCkBvcAkYQJ/7whek11%2B3OcKVlXaCtLjY66pwJu3tNs1j6lQLf9deK735JuEPiGQEQACeGDPG2ofccINUXy/deqv08MMWNhA%2Bjh%2BXbrlFWrLEDn7ceae1eaFLDhDZCIAAPHPRRdK2bTY3VpKWLrXVwdpaT8vCf23fLl11lfVvjI%2BX1qyRfvMbaeBArysDcL4IgAA8FRcnrVolrV1rweJPf5I%2B8xlp506vK3NXIGAntSdNko4csf2ZZWXS7bd7XRmAYCEAAggLc%2BdayBg5Unr/fdtf9uijUlub15W55cAB6brr7Gff0SHNmyeVl0ujR3tdGYBgIgACCBtjxtjhgtmzLfg9%2BKA0ZYr03nteVxb92tulxx%2B31dddu6SUFOnZZ6WnnpISEryuDkCwEQABhJWkJKmoyEbGJSTYPrTRo6Xf/c5WpEKtoKBA2dnZys3NDf0XCxOVlXa5d%2BFCG%2Bk2ZYq0d680a5bXlQEIFfoAAghblZU2Z/Yvf7H7t94qPfmklJ4e%2Bq/tQh/AQEBasUL6yU8s%2BCUkSI89Jt19d%2BjG9AEID6wAAghbmZnWcmTZMql/f2sYnZ1tJ1FpF3N%2B3njD%2Bvndf7%2BFv8mTbdXv298m/AEuIAACCGuxsdKPfiTt3m2Bpa7ORo9NmGCBBT1TUyPNny997nPS22/bGL41a2zKxxkm6AGIQgRAABFh1ChbtfrVr%2BxS5euv24GF739f%2Bte/vK4u/Pn90i9%2BYaesn37aPnb77dL%2B/fZfVv0AtxAAAUSM2Fjp3nulffukGTPspPCvf22XigsKbE8bTtXeLj3zjHTlldIPfmBTV665xgL0mjU20xeAewiAACKOzyc9/7z0yiu2J7C2VvrOd6ScHGnjRvYHSnZiurjYwt6cOdKhQ1Jamq3%2B/fWvdgkYgLsIgAAi1k03SX/7m63%2BDR0qHTwoffWr0tVXS5s2uRkE29ulP/xBGjfOTk3v2SMNGiQ98oj9fO64Q7qAd37AebSBARAVGhpsf%2BAvf2l/lmzf4A9/aP3s%2Bvfv2etFWhsYv1/asEH6%2Bc%2Bld96xj33qU7Yyev/9Umqqt/UBCC8EQABRpbbWguDjj3cFQZ/PgtD8%2BecehCIlAB49Kq1ebf0Rq6vtY0lJ0j33SPfdxx4/AN0jAAKISidOSIWF0sqV1vpEkgYOtNXAu%2B6yebdnO/kazgGwtVV6%2BWUb07Z5s92XrEH2woXSggU2yg0AzoQACCCqNTdL69fbiuCePV0fz8yUvv51KT9fuuKK0z8v3AJge7sd3tiwwW7HjnU9dt11tuL3la9IcXHe1QggchAAATiho0MqK7PLpRs3So2NXY%2BNGiV96Ut2aKKsrFBPPLFKbW1tqqio8DQAfvyxzUIuLraDHf/8Z9djQ4ZYgJ0/3%2BoHgJ4gAAJwTmOj9OKLtjK4dWvXJVTJ9ghOmiSNH1%2BvRYuS9dFHdRo8uG8CYGOjtGuXtGOHVFJivfr8/q7Hk5IspM6eLU2d2vODLQBwEgEQgNNOnLB9dJs3W1/B%2BvqTj9RLSlZCQp2uvnqQrrrKVtquuMIuH6el9b6dSnOzdPiwVFlpTa337pXeekv6xz9Ob13j80nTp0t5eTavd%2BDA3n%2BvAHASARAA/isQsH12r74qlZTUa/v2ZEl1kk5fAezf3w5dXHSRXY5NSZESE6UBA6R%2B/eyScyBgl3EbGmxc3fHjdmr3%2BPEz1zBsmDVp/vznbSUyK4sxbQCCjwAIAN04eQjkjTfqVFExSG%2B/bSt0Bw5IVVU2hu58JCZKl11mAS8nRxo7Vrr2WguVABBqBEAA6MbZTgG3tkpHjtihjJoa6z1YV2d7%2BPz%2BrnDYv78UH29hLyXFevJdfLF0ySXShReysgfAO7FeFwAAkSY2VsrIsBsARCImQgIAADiGAAgAAOAYAiAAAIBjCIAAAACOIQACAAA4hgAIAP%2BjoKBA2dnZys3N9boUAAgZ%2BgACQDfO1gcQACIdK4AAAACOIQACAAA4hgAIAADgGAIgAACAYwiAAAAAjiEAAgAAOIYACAAA4BgCIAAAgGMIgAAAAI4hAAIAADiGAAgA/4NZwABcwCxgAOgGs4ABRDNWAAEAABxDAAQAAHAMARAAAMAxBEAAAADHEAABAAAcQwAEEHWWLl2qrKwsJSQkaPDgwZoyZYrKysq8LgsAwgYBEEDUufzyy7Vq1Srt3btXO3bs0PDhw3XzzTfr%2BPHjXpcGAGGBPoAAot7Jnn7btm3T5MmTe/Q59AEEEI1YAQQQ1VpaWrR69WolJydr7NixXpcDAGEh1usCACAUiouLlZ%2Bfr6amJqWlpWnr1q0aMmTIGZ/v9/vl9/s779fX1/dFmQDgCVYAAUS0oqIiJSYmdt5KS0slSRMnTtSePXu0c%2BdOTZs2TbNmzVJNTc0ZX2f58uVKTk7uvPl8vr76FgCgz7EHEEBEa2ho0LFjxzrvDxs2TPHx8ac9b%2BTIkZo3b54WL17c7et0twLo8/nYAwggKnEJGEBES0pKUlJS0ic%2Br6Oj45SA9/8GDBigAQMGBLM0AAhbBEAAUaWxsVHLli1TXl6e0tLSVFtbq8LCQn3wwQe67bbbvC4PAMICARBAVOnXr5/279%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%3D'}