DROP TABLE IF EXISTS `demo_recent_local_cases`;
CREATE TABLE `demo_recent_local_cases` (
  `city`    varchar(50) NOT NULL DEFAULT '' COMMENT '城市',
  `province`    varchar(50) NOT NULL COMMENT '省市区',
  `new_add`     bigint(13) COMMENT '新增',
  `existing`     bigint(13) COMMENT '现有',
  `risk`    varchar(50) NOT NULL COMMENT '区域风险',
  PRIMARY KEY (`city`)
)ENGINE = InnoDB
DEFAULT CHARSET = utf8mb4;


INSERT INTO `demo_recent_local_cases` (`city`, `province`, `new_add`, `existing`, `risk`) VALUES ('广州', '广东', '8', '106', '部分中高风险');
INSERT INTO `demo_recent_local_cases` (`city`, `province`, `new_add`, `existing`, `risk`) VALUES ('深圳', '广东', '0', '4', '全部低风险');
INSERT INTO `demo_recent_local_cases` (`city`, `province`, `new_add`, `existing`, `risk`) VALUES ('佛山', '广东', '0', '10', '部分中风险');
INSERT INTO `demo_recent_local_cases` (`city`, `province`, `new_add`, `existing`, `risk`) VALUES ('湛江', '广东', '0', '1', '部分中风险');
INSERT INTO `demo_recent_local_cases` (`city`, `province`, `new_add`, `existing`, `risk`) VALUES ('营口', '辽宁', '0', '2', '全部低风险');
INSERT INTO `demo_recent_local_cases` (`city`, `province`, `new_add`, `existing`, `risk`) VALUES ('德宏州', '云南', '0', '2', '全部低风险');


DROP TABLE IF EXISTS `demo_vaccination`;
CREATE TABLE `demo_vaccination` (
   `cumulative`    DECIMAL(10,2) NOT NULL   COMMENT '累计接种',
   `new_add`     DECIMAL(10,2) COMMENT '较上日新增',
   `vaccination_per_100_people`    DECIMAL(10,2) NOT NULL COMMENT '每百人接种'
)ENGINE = InnoDB
DEFAULT CHARSET = utf8mb4;

INSERT INTO `demo_vaccination` (`cumulative`, `new_add`, `vaccination_per_100_people`) VALUES (7.9, 1625.5, 55.17);


DROP TABLE IF EXISTS `demo_domestic_epidemic`;
CREATE TABLE `demo_domestic_epidemic` (
   `statistical_time`    varchar(50) NOT NULL DEFAULT '' COMMENT '统计时间',
   `cumulative_cure`     bigint(13) COMMENT '累计治愈',
   `current_diagnosis`     bigint(13) COMMENT '现有确诊',
   `cumulative_diagnosis`     bigint(13) COMMENT '累计确诊',
   `asymptomatic_patient`     bigint(13) COMMENT '无症状感染者',
   `input`     bigint(13) COMMENT '境外输入',
   `cumulative_death`     bigint(13) COMMENT '累计死亡'
)ENGINE = InnoDB
DEFAULT CHARSET = utf8mb4;

INSERT INTO `demo_domestic_epidemic` (`statistical_time`, `cumulative_cure`, `current_diagnosis`, `cumulative_diagnosis`, `asymptomatic_patient`, `input` , `cumulative_death`) VALUES ('2021-06-09 10:24:27 ', 99071, 10740, 114929, 361, 6173, 5154);


DROP TABLE IF EXISTS `demo_new_trend_of_diagnosis`;
CREATE TABLE `demo_new_trend_of_diagnosis` (
       `date`    varchar(50) NOT NULL DEFAULT '' COMMENT '日期',
       `new_diagnosis`    bigint(13) COMMENT '新增确诊',
       `current_diagnosis`    bigint(13) COMMENT '现有确诊'
)ENGINE = InnoDB
DEFAULT CHARSET = utf8mb4;


INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-8', 22, 499);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-9', 13, 485);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5/10', 33, 505);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5/11', 28, 506);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5/12', 32, 512);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5/13', 35, 523);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5/14', 49, 542);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-15', 206, 727);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-16', 236, 935);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-17', 358, 1262);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-18', 258, 1497);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-19', 286, 1759);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-20', 317, 2097);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-21', 325, 2365);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-22', 743, 3098);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-23', 480, 3561);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-24', 612, 4143);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-25', 554, 4675);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-26', 655, 5036);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-27', 677, 5948);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-28', 570, 6480);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-29', 503, 6951);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-30', 381, 7303);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-5-31', 378, 7652);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-6-1', 362, 7983);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-6-2', 571, 8535);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-6-3', 610, 9110);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-6-4', 497, 9674);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-6-5', 541, 10049);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-6-6', 368, 10372);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-6-7', 233, 10552);
INSERT INTO `demo_new_trend_of_diagnosis` (`date`, `new_diagnosis`, `current_diagnosis`) VALUES ('2021-6-8', 232, 10740);



INSERT INTO  `datasource` (`id`, `name`, `desc`, `type`, `configuration`, `create_time`, `update_time`, `create_by`) VALUES ('76026997-94f9-4a35-96ca-151084638969', 'demo', 'demo', 'mysql', 'ROM9ccqPioJ1SCj70u/B+KCVPfwPyNbqgIgkguMQEbjrQLbGyQLfqnQdH64Nyb+LyW+vDZzSK0SJFxUGAwGxvUtKnmpwinKRZ9OcFiQB10Nq5r/DH4+2F9/dKBs2lQgrwNj5w6MyhRswSKzkfCw5fNndcN2ad990JjOQLYz2Y5M=', '1624247414781', '1624247414781', 'admin');

INSERT INTO  `dataset_group` (`id`, `name`, `pid`, `level`, `type`, `create_by`, `create_time`) VALUES ('f0728785-cc9a-4c9f-94ca-5bb106534916', '新冠肺炎疫情数据', '0', '0', 'group', 'admin', '1623211708679');
INSERT INTO  `dataset_group` (`id`, `name`, `pid`, `level`, `type`, `create_by`, `create_time`) VALUES ('873c9833-e79d-4004-b809-a81fa83d3e7b', '国内疫情', 'f0728785-cc9a-4c9f-94ca-5bb106534916', '1', 'group', 'admin', '1623211723216');

INSERT INTO  `dataset_table` (`id`, `name`, `scene_id`, `data_source_id`, `type`, `mode`, `info`, `create_by`, `create_time`) VALUES ('10de6211-3104-461f-a5e4-c01c997dcfea', '本土新增确诊趋势_demo', '873c9833-e79d-4004-b809-a81fa83d3e7b', '76026997-94f9-4a35-96ca-151084638969', 'db', '0', '{\"table\":\"demo_new_trend_of_diagnosis\"}', 'admin', '1624247848737');
INSERT INTO  `dataset_table` (`id`, `name`, `scene_id`, `data_source_id`, `type`, `mode`, `info`, `create_by`, `create_time`) VALUES ('23601ac8-d723-45f9-a84f-b1799982422d', '国内疫情_demo', '873c9833-e79d-4004-b809-a81fa83d3e7b', '76026997-94f9-4a35-96ca-151084638969', 'db', '0', '{\"table\":\"demo_domestic_epidemic\"}', 'admin', '1624247848685');
INSERT INTO  `dataset_table` (`id`, `name`, `scene_id`, `data_source_id`, `type`, `mode`, `info`, `create_by`, `create_time`) VALUES ('d54b3270-b8d3-49cb-95e5-1467467d6431', '近期31省市区本土病例_demo', '873c9833-e79d-4004-b809-a81fa83d3e7b', '76026997-94f9-4a35-96ca-151084638969', 'db', '0', '{\"table\":\"demo_recent_local_cases\"}', 'admin', '1624247848780');
INSERT INTO  `dataset_table` (`id`, `name`, `scene_id`, `data_source_id`, `type`, `mode`, `info`, `create_by`, `create_time`) VALUES ('d85a6ff8-1683-4e87-9fe7-3f393ad8a83c', '国内新冠疫苗接种追踪_demo', '873c9833-e79d-4004-b809-a81fa83d3e7b', '76026997-94f9-4a35-96ca-151084638969', 'db', '0', '{\"table\":\"demo_vaccination\"}', 'admin', '1624247848593');

INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('7d179f54-fb48-4e44-b316-f333e5e8e3bf', '10de6211-3104-461f-a5e4-c01c997dcfea', 'new_diagnosis', '新增确诊', 'C_a04c95358dd94af700a379bd5e55b200', 'BIGINT', '19', '2', '2', '1', '1', '1624247848759');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('8a4f319e-82fc-4368-9a96-5f233d3f302a', '10de6211-3104-461f-a5e4-c01c997dcfea', 'date', '日期', 'C_5fc732311905cb27e82d67f4f6511f7f', 'VARCHAR', '50', '0', '0', '1', '0', '1624247848759');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('e8be862b-2802-43b1-8eea-138fec66693c', '10de6211-3104-461f-a5e4-c01c997dcfea', 'current_diagnosis', '现有确诊', 'C_c3bd1b57fc3cd1b1462bae6430e32b01', 'BIGINT', '19', '2', '2', '1', '2', '1624247848759');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('1297d9a6-ee21-4f49-b0fa-10c9274652de', '23601ac8-d723-45f9-a84f-b1799982422d', 'cumulative_death', '累计死亡', 'C_3f83e60853692a995186ef663ef4b9c4', 'BIGINT', '19', '2', '2', '1', '6', '1624247848706');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('37b583fc-3227-4aef-a8d6-3337c1bb5816', '23601ac8-d723-45f9-a84f-b1799982422d', 'cumulative_cure', '累计治愈', 'C_e2e1c18cff9edab2282b9ac6f3428ada', 'BIGINT', '19', '2', '2', '1', '1', '1624247848706');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('3d664864-6575-4869-b452-4aba27c5da37', '23601ac8-d723-45f9-a84f-b1799982422d', 'input', '境外输入', 'C_a43c1b0aa53a0c908810c06ab1ff3967', 'BIGINT', '19', '2', '2', '1', '5', '1624247848706');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('b4cb38fb-521f-4b41-8856-3a5873a81e1c', '23601ac8-d723-45f9-a84f-b1799982422d', 'current_diagnosis', '现有确诊', 'C_c3bd1b57fc3cd1b1462bae6430e32b01', 'BIGINT', '19', '2', '2', '1', '2', '1624247848706');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('c016ec91-7490-498b-8d33-14aee11da501', '23601ac8-d723-45f9-a84f-b1799982422d', 'statistical_time', '统计时间', 'C_e52bb875b9521bcc59a6d571c612f9d6', 'VARCHAR', '50', '0', '0', '1', '0', '1624247848706');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('cb9e9310-1c25-4688-bbfe-5c96ebaa2912', '23601ac8-d723-45f9-a84f-b1799982422d', 'asymptomatic_patient', '无症状感染者', 'C_483c6dc1f3689e52364d81ac789bdbe8', 'BIGINT', '19', '2', '2', '1', '4', '1624247848706');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('fba80c10-fe67-4dbe-8983-8367461c4c8b', '23601ac8-d723-45f9-a84f-b1799982422d', 'cumulative_diagnosis', '累计确诊', 'C_497ca676e1e4729b00c9d1cb070f0834', 'BIGINT', '19', '2', '2', '1', '3', '1624247848706');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('3a4112a5-1e1c-4333-b9ca-df9ab2983a59', 'd54b3270-b8d3-49cb-95e5-1467467d6431', 'existing', '现有', 'C_f4e0ac58eb46d88efc451c164db3b837', 'BIGINT', '19', '2', '2', '1', '3', '1624247848804');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('4045a206-0a59-4a12-9920-9c0426f822c8', 'd54b3270-b8d3-49cb-95e5-1467467d6431', 'new_add', '新增', 'C_65e7f9dc5f5e7904f56be638600763ae', 'BIGINT', '19', '2', '2', '1', '2', '1624247848804');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('64bad760-b320-4f61-ad2d-d065b1cb70b2', 'd54b3270-b8d3-49cb-95e5-1467467d6431', 'city', '城市', 'C_4ed5d2eaed1a1fadcc41ad1d58ed603e', 'VARCHAR', '50', '0', '0', '1', '0', '1624247848804');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('a2a86354-6f3e-41e6-bbac-e4950d9b5370', 'd54b3270-b8d3-49cb-95e5-1467467d6431', 'province', '省市区', 'C_53aad639aca4b5c010927cf610c3ff9c', 'VARCHAR', '50', '0', '0', '1', '1', '1624247848804');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('c92ce162-9bd7-4c46-9fc1-49fa30c348d9', 'd54b3270-b8d3-49cb-95e5-1467467d6431', 'risk', '区域风险', 'C_c6142b27f1385bc163ffe7f4ebfc52ac', 'VARCHAR', '50', '0', '0', '1', '4', '1624247848804');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('59c03fe2-c026-4f61-b0eb-8aafcc7144c1', 'd85a6ff8-1683-4e87-9fe7-3f393ad8a83c', 'cumulative', '累计接种', 'C_dd259fe1898bfef8be8771aca900f7ea', 'DECIMAL', '10', '3', '3', '1', '0', '1624247848620');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('a1c180b0-8de3-447d-bd31-1359b92b74ce', 'd85a6ff8-1683-4e87-9fe7-3f393ad8a83c', 'vaccination_per_100_people', '每百人接种', 'C_26b2c7f3b8b45ddc449f4f16c39fa6b5', 'DECIMAL', '10', '3', '3', '1', '2', '1624247848620');
INSERT INTO  `dataset_table_field` (`id`, `table_id`, `origin_name`, `name`, `dataease_name`, `type`, `size`, `de_type`, `de_extract_type`, `checked`, `column_index`, `last_sync_time`) VALUES ('f3c5d452-8358-4669-bd7d-ce410b957556', 'd85a6ff8-1683-4e87-9fe7-3f393ad8a83c', 'new_add', '较上日新增', 'C_65e7f9dc5f5e7904f56be638600763ae', 'DECIMAL', '10', '3', '3', '1', '1', '1624247848620');


INSERT INTO  `chart_group` (`id`, `name`, `pid`, `level`, `type`, `create_by`, `create_time`) VALUES ('5a8e8b0a-2f64-4d1b-aac1-d284b2b8436f', '新冠疫情', '0', '0', 'group', 'admin', '1623212582950');
INSERT INTO  `chart_group` (`id`, `name`, `pid`, `level`, `type`, `create_by`, `create_time`) VALUES ('4de97755-5d5a-4fe0-9af0-27601f967787', '国内疫情分析', '5a8e8b0a-2f64-4d1b-aac1-d284b2b8436f', '1', 'group', 'admin', '1623212597088');

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h0LDMcLfhdcNPRkyjMKM8o6VGDUb3jXFjR+ME40nG641PG3cN1RvqP5Q/tGzovqF3TFATR5NEk6kmm01aTHpMzUwjTKWmq01PmnaZGZgFm+WarTA7atZpzjAPNBebrzA/Zv6Cqc9kM/OZlcxTzG4LE4tIC4XFJotWi15LO8sUyxLL3Zb3rShWLKtsqxVWzVbd1ubWI62nWdda37Eh27BsRDarbM7avLe1s02znWfbYPvcztCOa1dsV2t3z55mH2Q/0b7a/poD0YHlkOewzuGyI+ro5ShyrHK85IQ6eTuJndY5tQ0jDPMdJhlWPeymM9WZ7VzkXOv80MXAJcalxKXB5dVw6+EZw5cOPzv8q6uXa77rFte7brpuUW4lbk1ub9wd3fnuVe7XPGge4R4zPRo9Xns6eQo913ve8mJ4jfSa59Xs9cXbx1vmXefd6WPtk+mz1ucmS48Vz1rIOudL8A3xnel72Pejn7dfod8+vz/9nf3z/Hf4Px9hN0I4YsuIxwGWAbyATQHtgczAzMCNge1BFkG8oOqgR8FWwYLgrcHP2A7sXPZO9qsQ1xBZyIGQ9xw/znTO8VAsNCK0LLQ1TDcsJWxN2INwy/Cc8Nrw7giviKkRxyMJkdGRSyNvck25fG4NtzvKJ2p61KloanRS9JroRzGOMbKYppHoyKiRy0fei7WJlcQ2xIE4btzyuPvxdvET4w8lEBPiE6oSnia6JU5LPJvESBqftCPpXXJI8uLkuyn2KYqU5lSt1DGpNanv00LTlqW1jxo+avqoi+nG6eL0xgxSRmrG1oye0WGjV47uGOM1pnTMjbF2YyePPT/OeFz+uCPjtcbzxu/PJGSmZe7I/MyL41XzerK4WWuzuvkc/ir+S0GwYIWgUxggXCZ8lh2QvSz7eU5AzvKcTlGQqELUJeaI14hf50bmbsh9nxeXty2vLz8tf3eBRkFmwUGJriRPcmqC2YTJE9qkTtJSaftEv4krJ3bLomVb5Yh8rLyxUA9+1Lco7BU/KR4WBRZVFX2YlDpp/2SdyZLJLVMcpyyY8qw4vPiXqfhU/tTmaRbTZk97OJ09fdMMZEbWjOaZVjPnzuyYFTFr+2zK7LzZv5W4liwr+WtO2pymuaZzZ819/FPET7Wl9FJZ6c15/vM2zMfni+e3LvBYsHrB1zJB2YVy1/KK8s8L+Qsv/Oz2c+XPfYuyF7Uu9l68fglxiWTJjaVBS7cv01lWvOzx8pHL61cwV5St+Gvl+JXnKzwrNqyirFKsaq+MqWxcbb16yerPa0RrrleFVO1ea7J2wdr36wTrrqwPXl+3wXRD+YZPG8Ubb22K2FRfbVtdsZm4uWjz0y2pW87+wvqlZqvx1vKtX7ZJtrVvT9x+qsanpmaHyY7FtWitorZz55idl3eF7mqsc67btNtgd/kesEex58XezL039kXva97P2l/3q82vaw8wDpTVI/VT6rsbRA3tjemNbQejDjY3+TcdOORyaNthi8NVR/SPLD5KOTr3aN+x4mM9x6XHu07knHjcPL757slRJ6+dSjjVejr69Lkz4WdOnmWfPXYu4Nzh837nD15gXWi46H2xvsWr5cBvXr8daPVurb/kc6nxsu/lprYRbUevBF05cTX06plr3GsXr8deb7uRcuPWzTE3228Jbj2/nX/79Z2iO713Z90j3Cu7r32/4oHJg+rfHX7f3e7dfuRh6MOWR0mP7j7mP375RP7kc8fcp7SnFc/Mn9U8d39+uDO88/KL0S86Xkpf9naV/qHzx9pX9q9+/TP4z5buUd0dr2Wv+94sfGv0dttfnn8198T3PHhX8K73fdkHow/bP7I+nv2U9ulZ76TPpM+VXxy+NH2N/nqvr6CvT8qT8fo/BTA40OxsAN5sA4CWDgAD9m2U0apesF8QVf/aj8B/wqp+sV+8AaiD3+8JXfDr5iYAe7bA9gvya8FeNZ4GQLIvQD08Boda5Nke7iouKuxTCA/6+t7Cno20HIAvS/r6eqv7+r5shsHC3vG4RNWDKoUIe4aNI75kFWSBfyOq/vS7HH+8A2UEnuDH+78ArCuQqpqSnjYAAABsZVhJZk1NACoAAAAIAAQBGgAFAAAAAQAAAD4BGwAFAAAAAQAAAEYBKAADAAAAAQACAACHaQAEAAAAAQAAAE4AAAAAAAAAkAAAAAEAAACQAAAAAQACoAIABAAAAAEAAAEWoAMABAAAAAEAAADOAAAAACDcWCUAAAAJcEhZcwAAFiUAABYlAUlSJPAAADFLSURBVHgB7Z1fsG9Jddf3uffcOzOXGSBamEBmJsOQoBLMJCMzDBOwAH0weTDmj2ZAExSfIKnI+GRpfNJYVqoELKv0KWgoKyFVeYj6EB+sTCwkUISEQf5ZRgcyEwlKJXEgcO+d+8/92ed8z123b3fv7v3r/fvt/Turq/bp3t1rrV69uvvbq3vv3z4HN/rQeXALuAX20gKXLl3qrl692jHNz549250/f767fv36kHft2rXuzJkzQ/7h4eEQHxwcNLHDgQNLEzu6ELfAIi0AoFy8eLEDRAiAi9KAyrlz5zpABUBpBSrU48CCFTy4BfbYAngsly9f7q6zOekvAASAAVSIWwKKzHiohMduAbfAfloAj4Ttz5UrV3pX4mDwUAAVPJa5ggPLXJZ1uW6BBVmAsxWdswA0cwffCs1tYZfvFjiFFpjPFzqFxvQmuwXcAkcWcGDxkeAWcAs0t4ADS3OTukC3gFvAgcXHgFvALdDcAg4szU3qAt0CbgEHFh8DWQt8+ctfzpZ7oVsgZgEHlphVPG+wwPve9/7u9a9/rPvYxz7mFnELVFnAgaXKXKeHGFB5//vff3oa7C1tagEHlqbm3A9htaDy5Ad+q/vUF/5oPxrvrWhiAQeWJmbcHyG1oELLARUHl/0ZAy1a4sDSwop7ImMqqKj5//apZ5T0+JRbwIHllA8ANZ8D2k3PVOS5SKbHp9cCDiynt+9vafmP/ugTt9xPvXFwmWq5/eJzYNmv/pzUmjFQ+ehH6x43Ay4/79uiSX2xL0wOLPvSkxPbAahs8p7K01+MPw36+V97xsFlYp/sA5sDyz704oQ2ACabgspYtQ4uYxba3/L5PyW1v7ZbbcsEKttoAOBCeMdbHtxGdV7HQizgHstCOmJbamwTVNQm91xkidMTO7Ccnr4ezlLGDmrnMoeDy1yWXaZcB5Zl9ktzrXbhqYSNcHAJLbK/9w4s+9u3t7RsV57KLUr0Nw4uoUX2896BZT/79aRVS/BUTpQ5Tji4hBbZv3t/KrR/fXrSoiWCipQDXAj+tEgW2a/YPZb96s+T1vCDwqVsf06UChIClyDbb/fAAg4se9CJsSZs+oPCmEzPcwuUWsCBpdRSK6JjC+TBLbBLCziw7NL6M9X92GOPzSS5rdiHXvlNbQW6tMVYwIFlMV3RVpGW4DKXB/TdDixtO31B0hxYFtQZLVV58sn3tBTnstwCVRZwYKkylxO3tEDuUfNcXlJL/V1W2gIOLGnbrLqErVDL7dC2jcGjch6Ze1inBRxY1tlvq/8yfu7gVt4Kj8wdXNY5QB1YVthvfPZR/24j92X8bZyzPD3x/wnlDm7tpzAdXFY4QHuVHVhW1m+Ain1jle/L7ntwcFlfDzuwrKjPho9UH//GxqqdApe1nrNoK2Tb6OBirbH8tAPL8vto0BDwYPuzL2HKEyEHl/X0vgPLCvpqDFR2fc7S0oQxb8XKd3Cx1lhu2oFluX0zaDYGKmPqL/GR8zve+mBSbXtwmyJycElZZjn5DizL6YvbNCkFFei4UmGJ4JLStTTfwaXUUruhc2DZjd1Hay0FlVFBCyT47gfSPz4c2wrZ5ji4WGssK+3Asqz+GLSZAir7cs5SAywYy8FlgQO4V8mBZWH9MgVUaEJuKzRnE6fUm3rrthZU1C4HF1liObEDy3L64uQ1/akqpSb5Ws5YSg5uU7ZxcElZZjf5Diy7sftttU71VKyg3HZoKeCSeyJk2zIl7eAyxWrz8DiwzGPXKqktQGWswm38bmhMh7FygGHT4OCyqQXb8DuwtLHjZCktQQVZXLGwFI8l90QopveUPAeXKVZry+PA0taeVdJagooqfvqLcWChfAngkjq4bf15BAcXjYjdxA4su7F797nnnp/ltz+5zxisYTvUsjscXFpas06WA0udvZpR//JHn20mywrKbYcsXU166mPgsI6UtwJdqzrCOgGXn/u5D4TZfj+zBRxYZjZwSvyPvOH+VNFs+bveCuU+7jQXsGDMT37yt2ezqQuOW8CBJW6X2XNfc99Lurkeva7hsbM18JygApi+851/x1bn6S1YwIFlC0ZOVcE3SeYAl9STIfRoec5y/fr1VNOi+aknQpu8GBet6DgTUPmlX/pQ9/DD35Mj87IZLODAMoNRa0TuAlxq9EvRXrt2rXvhhRdSxdH83BlLlGGDTIHKBiKcdQMLOLBsYLxWrHOAS2o7xITj2iRcvXq1u3z5cnflypViMTlQ4YC1ZXBQaWnNabIOp7E5V2sL6FON9kPZm9SR2w5NlXvjxo2OC08Fj+Xg4KBYVO7gtlhIAaGDSoGRtkDiHssWjFxaRWvPJQUuU85ZABKuM2fOdOfPnx9ABZDZNLQ8uHVQ2bQ32vE7sLSzZRNJLcEltx2qVfbSpUvD9gcwOTw87M6dO9d95rmvFot56IGXRmlbHdw6qETNu7NMB5admT5dcUtwSdXCRKwJAApPgThbIX3ktZQPn4eOvxrXwssJ9XZQCS2y+/vykbF7XU+VBi3ApeVbuHgoBA5sdb6ivJKOAVA49A3Dpge3DiqhRZdx78CyjH6IatECXKKC+8zacxY8lLNnzw7i2BYBFGfOlB3efte3vaT7xje+MRz61r77ktKffAeVnHV2W+bAslv7j9a+Kbi0OmfBS9E2hqdBbIkuXbo8qj8E33nvPSe8lmGTXzQ7qFhLLi/twLK8PrlNo03AJfVkiEqYnKXh4sWLAzhwcHvhwoVhO9R1dU+F4OWp0qbBQWVTC87Pv3kvz6+j19BbYA5wqQEWvBS2Q3fdddcAMDXvsLz2/nuGPuSMxW6FpjxqdlBZx3RwYFlHPw1aTgWX1HboDW8o91gAFIAFcOC8hK3RZ579WpH1Hnn1Nw/nM/Bw+CtwqQUWB5Uicy+CyN+8XUQ3lCsBuBBavKFb47GwheGNW85WTkLZ2e3w3gs8+hmAvJ8TOQUJB5UCIy2IxD2WBXVGqSq1ngvnLE9/4Q8Hb0Hvoaiuxx57vZLZmDMWeAEFbYMODsaHj34jpINfKsHree9735etzxY6qFhrrCM9PjLW0Y5Tp2UtuPz2//qD/inOpQFc8DyY6Ff6Cf6ud72ryHZsX/Ba7rzzzhNgOVtwEMujZgCJCxmA0hHIlB38OqgUdc/iiBxYFtcl5QrVgMunf/f54TV8JjUew/CKfg80Ou8Yq5V3WDhngV4gM8ZDOXUBZAAKoIQMnip9/OO/OcruoDJqosUS+BnLYrumTLHSM5dPffH/DcDCAarAgRqYvCVBoILnQRBAjfG+9r57hoNbvWCnbdTYwa2Dyphll13uHsuy+6dIu1LPhXMWG3glH8AoAReAAC9H4Wg7o7t0/Ofuf8mwhWIb5aCSttO+lTiw7EmPloDLB5/6wi1bH7wXrp/6uz81agX9RsgS8sJbLvCLZjyVO+6445YX43K/aHZPJWfR9ZTlR8Z62nHqNQUg/sab7u+3KF33waeeidrj089+ddgOcV4ioMALeeF4exNlOs48c+bs8NgYzwNvZeC/fi3H0vFECK9InkqWuC90UBmz0HrK3WNZT18lNQVUOPsAJJ54/BXdj735gSTt57/09ZPvqUAESLzuda/rHn30kSQPBWfPHj0RwvvACwEsblzPP9mBJgYqsV80O6hkzb+6QgeW1XVZXGHrSfy1139L97Y33hsl5MU6gAiPQyEFACpXLJCgLs5musIX5MSfih1UUpZZb74Dy3r77kRztjZ4ETrzwAt54vFv7d72vbeDy3/rHzvj2QAuAMUASL2kd7/73SfyShLUyZULsX/3Ef6i2UElZ8H1ljmwrLfvTjTXVoiYwIQHZN7+pvuG64TwOKGzFryOYVvT54+9zwIAhIFzl1zQW7cpGgeVlGXWn+/AsvI+xDsBUAAG0hyWcg6iQ9O3v/G+285cfvEjvzfQ4q3I83jkkUc6rlSIvXfSOzzVQXLe8573DP9MrFqAM6zCAv5UaBXdlFaS7Yy2QFCRBjDkvQA2f/MvfNuQpx8ufqZ/OkS5/o3HmLeSrj1dkvJWBCy1X7BL1+QlS7SAeyxL7JVKnQAXPBQuQCUMAAePou3Tok8/+/zJI2fo4f3Jn/yJkHXyfez/CAEqvv2ZbNJVMbrHspLu0lZH3gVgwjaGmCsW8Fag5wkQ119/7OWDp/ILH36u+9BH/nf32vtePACRDn6t5xOTF+Y93f9qujbwv5Q97L8FHFgW3sdsWfghHzFAoUCKXxcDBlwhuNxCe8wHDZ7L2f7Q9VNfvAkKAqfHH3+8f5/l0f4Hgh9XNZPj2BOh2AHw5AqccdEWcGBZaPcADAAKnoa8FPIAAYHG1d4boYwr3AZBIz6aCJ9A6G/9xVcNZcjXAS40pEu/vA99LqTOWHI8XrY/Frh9Q74/bVttSwAFAEWHq4ACwMHjYT49wCUvBfDQVkdAYkHlCCyOXsOnXN4J+doCkafw5JNPKjk5dlCZbLq9YXRgWWBXWqAAUAASgYC8DvL04z4BEXykddE0AAQZBLZTohHAWFAZiBJ/cj8cDFliB7chjd/vtwUcWBbWv3gVAACxQESHtFJVoEA5gAN4ACbi4168SkMHDdsfrlTgHMTPQlLW8fxSC/gZS6mltkTHpNeWBU8DUEkFAAYAgR5PhFjnJuQDKgSBC8Ajr4W8nOxUnSX5sYPbEj6n2R8LuMeysL5k4uNZMPHlmeRUhAaAgB4+wIWYQB4XARqACnoBkegGAvNnk5fXOF/xMxZjzFOadGBZWMcz2blKQEWqC1i4hxfgiAW8GMAFGgAstyWK8Zfkve+df76EzGn23AIOLAvtYCZ/aQCE5Jnk+KDT2YvlCeuZes7ioBJa8vTeO7Bsue+tt2C3LVIDgGDSU5byPERr41IYQj5Pk7hyZyy126F3vPVB3wLZDjnlaQeWLQ0Ath28l8K3UPR/dpSmTJ6GJjv35F8v8FygvXG8/QE45L3EmiZPRfXEaMjDa+EXyCUBUNF/Cyihd5r9t4A/FZq5j5n0PLEZQMIcrKpazjrwTORB6LxEZyDcH/RnIwBCLAiAoBdopGhj/Lk8eS2xT0mKz0FFlvDYWsCBxVqjcZpJj5ciUBFo4FGwdbl+DCqD93AMHJRxDgIvgAM/gbwQMAQqetSMnBjdIGDiH4FLjN3PVGJW8TwscNAPztLtuVus0gJMeLY7BCY8T2QADgCCS+co5HFxTz5dIkAiTZneabG8eCkCFWj0di40rcNXvvKV7mUve1lrsS5vTy3gwDJDxwIGgASgQgwoMOkFCmGV0NutjzwWu4WCd9gWHYOGQAle8qmjtbcS6un3boFSC/hWqNRSlXTa/gAIeu0+JgKAgJYLcOEiABRcBPJEN2Qc/8FLAUy4LOhYGk+7BXZhAQeWGawOmAggmPxcsQANgIJngudBAEC4hwewAJSg4xINdJILDYE6PbgFlmIBB5YZekJbG0Rr4ofVCFCIAQm8DkBFIAK4HABKPWDIK0EuFyDiQBJa1O+XZIH4UrokDVeui/UybFMEPgAPW57wpbUBYHrQsfyACSDkoGIt6eklWsA9lhl6RQCAB8IlL8NWhRdCvrY88AAy8lzkvZDnQGIt5+k1WMCBpbKXLFjAqklvAQDAACB4ZKytDfc2wMf5SRjI5xLoSH5I5/dugSVb4NbRvmRNd6ybPIgrbE+OPRFUYuLrjARwEcAQKwAw2sLkgELnLtQFEHHl6CXfY7fA0izg77EU9Aheh94pkSchNu65CICJfUx88eLF4akPZYAE5ygATBjgB0z4DRF1QYM3o8fNIb3fuwWWbgH3WDI9xIRnomtLI8+EWAABIOBpDFuePn2j907wMgASwIFy0ZAPWMBLWqAk4LKgAr8Ht8BaLVDlsfzxpavdB596pnv8zxy92v3tL79naPfdd84zCajvf/7+16pt2+pjzkz0Ei8C4OAtW+gJeC58UR/wAHSQAY3KKAdcBFzQ8I/aP/HM8wPPG1/zzd13vOLFAz1/5rLvSQWemNUCmjeqhPmzjbnzV37m17sfefx+VTvM27nrVWVVwALTW//RfxbvbTGNkNFaTAY6BOPUhl/7x3+pluU2eiY92x9AAYBgG5PamsjzgBaQIOCtwEMgD68HcIE2DIDMZ3/vj7u//+8+HRYN9xocP/6WB08tyOTGXdRofWaLcZCSXZuf07/1vJFuuTqhoV7GFKHFfB0EHf9p6mr88m8823EpSPGpSk/h0ySUDlNjAQv8eBi5rQnAI/DBa4EXMAFcyIcX8CDvKl7NMbiIj/Jz5y4nVZVNFW9q12RFCy54b//Jy7/3gd8q1rDVOCiucANCO2/Qm8W5ldedUyus993f9+oceVXZ7SeJI+w1HYbiU7YyVoWa+uDTNs3KmJKWdwEgACyAwFiARl4N/NoawYccgOYu/uGY/vHYcQywlMhX/S3sKln7GrcaB9u2D31bA6A5/WrnTk5WbVlTjyVWufZ0sbKSPAYIxi4Nm9Zn68HzkFdh81NpaAEJtj0EgIV7GwZ5ZARAVat3Lb3VYY3p2vbW0s9tEyZ56Ti2gPCvfvV/FPNt0gZ0K9XP1oOuMU/n1lFvORLp2omeELOKbHkRsXORVAPwTIYQAEeKXvlTtn3iXWpccvj+G//9K7Ooz0OGKSHn6WyyPZk6b6ZM9intnsqDfk2AZaoC2+JrOUEBFkBF5yYCmqK2HJ+jFNFOIGrZzgnVF7O0cuuLKzwmnDohc3zbPgwGmGs8nVobtaC33pWVV33GYplL0qUTACOG19Nf+KOSKm6hgSeUY+9vIc7c4HlwASycl+hpT4ZlKLLnKpzN1IRUJ9XIWBJtad8vSeeULtvsG7wbxvGUJ6Ip/bedX70V2lRB9oyE3MqwSR01K6QdLOGjXECF8xF5KwCL3j+RfgCWDqe1XfrwZ788ABHejQ5l52jr2KNE6WjbqDxi6/JzHrFPIGDbuZR07ZmPxvEcY6elTfS4OpRZDSw1BkoN6qUYy+oR2yfqMbNe58d4vJsCaHAxGTUAQsMu5d620epk8//DP3yzLWqaZgzYupoK36Kw1ASaQ4Wljynb5tSCVA0sVuiU9NRDrCl1lfKkAFBeB16LtkN4Jjw21jZnHyZOanCU2i9Ht8T+zumbKtvURpvyo5fGqfU2Q33DhZ9D7DFgR64FTnnhoeyag/bbgEXuvRUypliogO7h45JByJ8qSzK3GQtYqFNvzgIy/LhQZzAPP3BP36ZtatW2Lts3m0rWNjfcVm4qd9f8LW20aVssACArBIHwvrS+HJ/ASrFk5sDyNmCBuLUrtnQwCTtLhlOMd8IWSD9GJF9Pi/BkPHTDgbn6mZg3ZUsfzzJxc6vwNu1rF1TVOzY+RBfGAC3tKrVDyB/eY1fZOCzb5H6q3NxTstuABQXp6DkasEnjd8mL5wK43Nm/NXutB5Jr/UGuDmsfftWf3KVqG9c9ddKEFeNy28DiVAMYY5MPTzoVcqut5QlXXJXZlXdMD/GUxHbCYot9mle0JReiwJJjaFFW+7uPFnWmZGAgO7BSdADJsDXqAeYweIy8pPak9I/ll7Y9xmvzmPSxhchOLEs/JT3no1cOr0vGQI3e4asSMfvUyFsbbRRY5jxwYzC3WBVCxJzacT/25leO9hmgwpaHcxXAJRZCfcZc+9jqycpbsg2lrpSnkVu9Y25+rC21eZtOevou9lSuVo8l0Zf0Y6m+LFyEljJL607RjY3vKLDEBn2qgqn5WuWZJFZJW3fNKpIamNaFZtJ95PP/d3g35Vc+/qXuBx55eTf2KRlAhcfNnK/wA0N9CsG2G6BsAZa27VZ+mMZeKdvk9MiVhXWU3ocrcylfLR3jZOriMVZXypZjfKlyO+ZSNCX5AhT6rZXMknpLaMbGahRYMPRcHamVFmPlDn9KGldCYwcNdXIBFO98y/3DOQlpPBE9PrYyeQKkDz2Rj9eiLZGlO61pBvu2VtG5QGWOvgvPm6bUAagwVhXsOFbermKwYUyfKLCgMKui7UyEKdh85cXiEJxKFIrJaZ3HG7GABF4I30c56GM8EQCGYL0UAQneir6v0lqftcprMYFK2j6nV2THdYkuYzSAben8iMlCn5T3zVmQtrrWY8hN8pJfR4cgZvUKPSXqt3VbWptOAsuYR1FiPAyUMpJVYttpvZ+CR6ILzwXgAEjsl+AAG0An/PzBtnVeWn2bTqDS9gAqJV6Rtg1WLmdKJePU8mya3hRsdSygd4JS+ui8rEX7xuwbgq/qDnVjNyKQSwJLyDTnfcmKlGqM1UudYvNsGqRVw9n6ACSACN4LvwXSVgewEfhAI0/GyvJ017GCMpFaDO7QnhoTY4MePga+3TZIFv09phvlXHbyMI60Kmu8SGYuHgNb6hjTR/JL6UQ/Z1yqi3Uidg4sdEbJ4CkxXIkB7LmO3RLhqegXzPJSAJ/UU6CUPqHrmKKL5cvNjZWFeZvUg6yaCRPWbfnllaJPC5CpARTppXM73U+J7dixaSsLYLCTx5aRpv2pYIErRWPza0DI8u0qHbZv58Cy6QDf1JCABwDDVohAWl6KQEWTRnWlBp7K545bAXFMTztAWLljnkCMj34MQQa6UluVnAXE6kXf1BiqAeqY7Jo8xkhpW0vkYvuW8krqbEmzNWDB8OpouyWhMbtEZ/s0SCAjQLGGXnMn23aMpW07p3oCAhnqQsbYey70/9SJNFXHMTvEynN15bwVZNG+ku18rN415IW2mRVYcGsxph2sGCn8mf7UQVVrcLsaW14ARecoMVBJrYhWhqdvt4BdTG4vvTWn1DOyXGNvzOoBhDzOcBxaWWPpnGcE75hsFtMaYJlij7E2bLN8VmCZ02VvbaQYoNg6dulVWT22ma4BVAsiscWkRG+e7JSOmTFQsfXJg2JVxWueol+4Ilv5OheyeWG6xpbitfawiyIL8dSgQ2ntHmrkxIARQI2BbjGw2IEzhs41yq6Fdlte1VLsYQdyqJMm0pQJGsqy96zSdjLZMqXRi0k+ZaLCQx1cyCg9bEanXH2lYKg2lMboaR82WD7mY00QkCiGVyBTKic8woAvdZgdBZa5Bk5pA5ZIRyefppBboVsDirUrdk6BC/mt+gGgCA+bYwvmWJ0lEzwH0rbtuTT1jJ1V5fhbl43ZJQosKDEXCqNQiP6lyAlvLMw50G191D+XXWw9S0iHfWR1au29ybXXmZytS2kmJ/3MlQohMNROaOglgzR6jQHZ2KFtStd9z08CS+uGjyFcSX25TtaAKJEzlQYADMEtB4q5yRnToXRVmmLLsZXVusgx3Wxerh8sXW06t0BM6d8pPOgMqKRc/LBN8uxydYkm5N3n+yiwtBw4UyZBrcFzq5iVpZXR5tWkAYqWtqmpe1PaMZCrbRf92sp7Ezi39oSm2qwGCLBryZZqqi5r5YsCC41pMXBSB0+hsUpWy1q3Nqxjjnv7ew4mhSbI2CSeQ5eYTP5FiOy2RP1iOi8hb2r/pUBmqrwl2GKqDklgmSpwCl+pxzFF9pw81v21aU1mVr5dDyrppRh7tNAPDwc5Vm5o67Fy0ctGtV6T+FvGLKgtgkCmxvtpUW8L/Vt4oklgoZNbeC0tjLVUGamJo8mmGLrSPfs22iq9iNGNMEW/1NaFccP4sR5dql2qX+W7HnOtwU2gqfZNjUvltNZ/qr5JYEGgXPupwkv4OFTUQM/RM4g3DdtoT0xH2rcE7yWlWzi5Y3SxvHDx0WpJfmm/hnLhtW9ma0LZVyAYC6UTCL7SFdjWG+q1lvvS/5A5Z3v4SkAWWOjUuVeQ0sd1uwKFXAekVuwcz76VCVxol53spf0aWzAEJsiMAYM8rRxYx/iQlwqMc1tvis7z8xbgkyP8oDcLLIiwA4fJjfGn/hI1VGnqqhbKKfF4Qh6/b2cBCyhIrenX3IKRW33p8xjAUDcPA0q9FPTFYwvbQL6HcgvosyN8egRwGQUWRM9l9NJVDR2WuJrkJgU6ryXEvIZNdC/tVyZ0rl/Zmoy9bSqA0TasBlBoIzpMOV/axD77xitQ4VOvpPn0yNFHXnfQUlzVUk9j6hnADpq1yipbAmRNv44ZC9ApPfcAUBxUxiw6bzmgQuBLAUUei1WnxcCp3f9u+5GdbW8unVttc3z7WsY2pGZyl/SrwGXMc6m1KR7OXJ54rS4t6UuBOFdnra35MoC+ZcT5SvFWaMq+NaX4lPOZVhMYg+H94PrrvCil52nKb2FfxkjNgBzbBln7C1zYYpV6uZY/TO8rqNDOkpdNQ3u0uAdc9KlXzlmyHgueRe73GzUK1Xopkj22DWJA1wTtycVj5U8BHPhbDHbps8a4FlRoY4m3Ilu0HIfIbLn1k45LiWs8xpY6y2vh3+QM5ywx4VMGSkwOeVMBRfJqBqB4amILCkqX/hSBegAj8dXUuy+0U8bKmLeCzNonOzX2lGeFHlMWk5q69pUW8NCZij6SJq+FNkc9FlzPFitx7nFhicHHBmCJjFoa6pwjaDC3kL2rVSnUfQqoICNcLAQklG2zbSwIdlGwQIMuLbaIyFGwdSkvjNkhrOHsh3MU/nUO2x8uG4Y8m2HTdH6JISyPTW8KTPDv4jFgOOhtmzx90wJTPVEB91T+mxq0T4VAQw3SdxdjsX0L20nkkNb+uxx9M5oasmcsoDWHXFNWEHs4NhWcSid4K+8KgzCIWq9SyF1y0MSp1XHqOQX9uiYba/wuAVjoK+mT6i/m3iah5kyVrRCeC0DC2Yq2RKOv9OOS1YKLBRU6gz1sLTgho2bwUQ8DdtOnBqVgtknH7Qsv/VPyApttr+3Xudz9kslndSpJo/emge3eNsKmdmXBGAMv2vGJZ54ftmyACt7L8O5KvyXSOcvoC3IClxKj0Klhw2r4qSMmo6RuBjoAM3UQ2EFfUp/THJ1BcNBNn40F7BuOjRbvXKhe5KMLYwC5JTqJNxdPHY85mbmykkmd49+0rPStad5b4QJI8FAAF2LC8O90ShQpAQc6ILXil/CjB4ODgbFJoC4GGLJKw7YHT6lea6EbA/SUfeX1bNJOAYoFLS0yLQAmNaZrdS6dsLVyLX3NmLd8U9KAiJ4Kwa8tkfJGPRZVSselVhgBAh2aCmPgggw7OFJySvOlL4N6LLQaPGP1LLG81QqJvVPeS26xmAIujBUu6suNGQFM7UKjfmK858a06FrGtKs2lNgiJlNgIE8DmtLx8MirXjp4KXgsXACKPdA96DOOXvCP1ZzIs2/P0qhc54Yi2GuC3rYB2+jAWL3otmnd9lFp2FZ7P/Ww08qYki55E7Om/0p0sLYuta/lSdXBWMOOm0z2knqov1TvlK5hfu7VC4HJWD/YeYf8Ur5QF+6Z9jwuHp7gHD8y/trFK90P/+xHYuRD3s/++EMD/Xfee/dwz7mKDm0BFYCKbdAdd9zRTQIWpGpCjRlj0CDyZ1P+iMiiLDuwWg+eIgVOERG2rgUB+xgab3POF9jsWFC3tAAvyVKsemiLghaaGvsgh4ViU3BFB52LACyADF4H8n/xvz432Jx7gOKVf+rC8J7Kiy+cv227w/sqA4j0tPyymQs+8g/6Cm5w48EtcJotwKQi1Ez0tdvLei1244Incv78TSChnWCEAIh7pa3HcrUHIpBkONTt3aEbCNIJL0we3AJugf22AB4LHom2MMIAPBjCnXfeOYAJ2yXoCAIcQAbvhHzSckyUJj6EQAzhq7mDNP/jFnAL7J0F8DiY+8TMezwPcECAQ4NxNsiHhnzKuQQg0FBG0NkK6UEmCZAHpBIjeR7cAm6B/bUAcx0wILDt4V4gQR5AQgB0SMuzudLjBNsdlUMD0CALGQqH3Fhg0Z5JBB67BdwC+2cBwICDVwJpBaUBGW2ByBNOXDk+oCUPMIEOkMEx4V5gdUiCAghAJQT4lkhm9tgtsG4LMK+5FAQcxEqrjFi0YAGXaAQYAhttn7jXeYvoBwzBQxHiKKaAy4NbwC2wTgvIWSDWBUgwrwEJYoGGWgidggAGGi7xCKiEEZSRB7jgtXDP1mrYCumAxm6JDjnM6Yk8uAXcAuuxAJMcgGCSc1mwUCsABeY8XgdpBYGG7gEJaAQqyieWByMa1QmGkB7ewYeZG64BfY5doDN95R7cAm6BdViAuWu3Jkx6eSakmd8EYj1G5pxF4CJggZYgeRaA8EbIFw10AiryCcSHIrJboht9xaAdDEKmgcP/uAXcAou1gACDmInPnOYScJDPvOYsRGnKAAvoBQzyUKAFqKAnCAssqMgYlOktXMrPyHWBwCITQrlUmQR47BZwCyzPAgDFpUuXBsBgYvOCm/VG0FggQplAgrMRgIN5TjkXZdoqIUsAM4YF8Ap0zly8eLHjwjWScBUiUJXu2pS8cs3vSGJBZanyGE8sD35+6LVJgH+KDNqg18pz9Y/RyBZjdLk6xsqw05zyU/Vj1037GNnISMmZYr+cvFRbbH4LWzJXARfCcHjaH2+kAsABDUBAEC+OBWBEDAYALgIgcAAnpDQcIgCFuF7ome2BLXkIQ7gqKBXcmk5fbU/9cJCv1OnXnpvUza+u+YzC1N+M6FfbuU8FxPSr+dh2ygaSiy2gmRo06VI/MNUXAfkcwTYDtrU/5LMT0v6KW/oxHnJtiOmvcZbjjbVZNrfjRvpJN3320crG1tI3JjeXZ8cB4EAALACNsQB4CFC0a5G3Il6wAVnyhAAXaARIoovFhzBKMB5L6O5QORUgjHjXwXacdCGPX8LSQbHBIjrFrHwCAOXZODXJS2Qjp+QbMLY+peHLfRtGgz5mA8loEWsCSJ+wvqnta6GblfHBp279/IbK0E+/ilZeaSwgGvtaPp9BCO2APgT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