From mboxrd@z Thu Jan 1 00:00:00 1970 Return-Path: Received: from mp2 ([2001:41d0:8:6d80::]) (using TLSv1.3 with cipher TLS_AES_256_GCM_SHA384 (256/256 bits)) by ms0.migadu.com with LMTPS id GJHSBsiKmmA4mAAAgWs5BA (envelope-from ) for ; Tue, 11 May 2021 15:46:48 +0200 Received: from aspmx1.migadu.com ([2001:41d0:8:6d80::]) (using TLSv1.3 with cipher TLS_AES_256_GCM_SHA384 (256/256 bits)) by mp2 with LMTPS id uOF3AsiKmmDWPAAAB5/wlQ (envelope-from ) for ; Tue, 11 May 2021 13:46:48 +0000 Received: from lists.gnu.org (lists.gnu.org [209.51.188.17]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by aspmx1.migadu.com (Postfix) with ESMTPS id A45CA2E335 for ; Tue, 11 May 2021 15:46:46 +0200 (CEST) Received: from localhost ([::1]:58200 helo=lists1p.gnu.org) by lists.gnu.org with esmtp (Exim 4.90_1) (envelope-from ) id 1lgSij-0001xp-PA for larch@yhetil.org; Tue, 11 May 2021 09:46:45 -0400 Received: from eggs.gnu.org ([2001:470:142:3::10]:34228) by lists.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1lgShs-0001wg-V7 for emacs-orgmode@gnu.org; Tue, 11 May 2021 09:45:53 -0400 Received: from mail-eopbgr30133.outbound.protection.outlook.com ([40.107.3.133]:4526 helo=EUR03-AM5-obe.outbound.protection.outlook.com) by eggs.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1lgShk-0003Zi-TV for emacs-orgmode@gnu.org; Tue, 11 May 2021 09:45:52 -0400 ARC-Seal: i=1; a=rsa-sha256; s=arcselector9901; d=microsoft.com; cv=none; b=PwXNpoSTe9j/tZ53gIJjmyCSdXDszF9NHO8SxbnqCU/Ta8lNL9ZR+VffylzMKxHxFuraGkhmkaxPXoi3Zn7g/qGbjaNZl84zayWr9OeqlXWghBz6S5/HBv9IS53XXdYrUZIHP3z82xfGSWIC9VYVpRMVQWzKhl6NMAFI0yPdkr/8uUIat+oIRsnDXKxRMe4Drsgj/S/7ANJpt2Y5yBCkfuBGXufkoohG8xJA5vZIlh2/WUsuvlanBZdYqqEEgwyo9VKSp8gC8PPFa7ZZNwQvehaom0aqke7RJRZZENLOgDZm1EOG89M+1i78d7vhwtp+4dumUS6sFBUKZCfD3U4k7A== ARC-Message-Signature: i=1; a=rsa-sha256; c=relaxed/relaxed; d=microsoft.com; s=arcselector9901; h=From:Date:Subject:Message-ID:Content-Type:MIME-Version:X-MS-Exchange-SenderADCheck; bh=imEDbPe+2HYr1vdj9cxE0Q37yBYGNq3o+xGlUaXz7Cg=; b=YGXtBjGEy4C5y7CrvA666Y/SLAUBqyhRuRhUwzK7u12J5ffqTn2BqZ/X85VUNQ+Tl2dfMGN8fB592TGDhzWgAzN2pXZ7Mf5SfukPm2NZImqXACjQGKhQG8j8DESP9Zvos8rkLw3XgwmTvawy2ohCXBNRzhsKcEAKbDVvxlhQ0R0jQL8m1/LqHUuE6ojEL3PiFPj3ztBQq+miADC4eUyy73QJnUP2HvllWR/0drXRaxXarEqTcEOVXeFZpDEfOGbGVtI58urtg5dh4NHEmUb0SKvEif7xTdulp1vo0PHzG0RN7UKkFvGgJj0RfxvbCYpN00z5NQzWgzd4lW4Bjr1B+Q== ARC-Authentication-Results: i=1; mx.microsoft.com 1; spf=pass smtp.mailfrom=ucl.ac.uk; dmarc=pass action=none header.from=ucl.ac.uk; dkim=pass header.d=ucl.ac.uk; arc=none DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=ucl.ac.uk; s=selector1; h=From:Date:Subject:Message-ID:Content-Type:MIME-Version:X-MS-Exchange-SenderADCheck; bh=imEDbPe+2HYr1vdj9cxE0Q37yBYGNq3o+xGlUaXz7Cg=; b=G7BAzXQfBwlC/XcY+N8a4t4RUBG1KwBWd8BKsx9lCINeNuSKZN/9Wbkf1EWyv/79hZi37ZpxgvoKTUrHlonCsr6DzRH4H2KNUHdj33Q1ACjnUq6eIN7D+xQfplT/DBAF83k6TTr3oU62FKSTykT6cNloseDZKsRjicQPja99IB4= Received: from PAXPR01MB8415.eurprd01.prod.exchangelabs.com (2603:10a6:102:21e::16) by PR3PR01MB8065.eurprd01.prod.exchangelabs.com (2603:10a6:102:174::11) with Microsoft SMTP Server (version=TLS1_2, cipher=TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384) id 15.20.4108.25; Tue, 11 May 2021 13:45:39 +0000 Received: from PAXPR01MB8415.eurprd01.prod.exchangelabs.com ([fe80::dbd:886e:5854:8cec]) by PAXPR01MB8415.eurprd01.prod.exchangelabs.com ([fe80::dbd:886e:5854:8cec%5]) with mapi id 15.20.4129.025; Tue, 11 May 2021 13:45:39 +0000 From: Eric S Fraga To: "Nicholas Savage" Subject: Re: [bug?] changed behaviour of column view Organization: On the Interweb somewhere References: <877dk5768n.fsf@ucl.ac.uk> <27e57ec8-7276-40bd-9044-f6e6920ad6db@www.fastmail.com> X-Url: http://www.ucl.ac.uk/~ucecesf/ Mail-Followup-To: Org Mode List Date: Tue, 11 May 2021 14:45:37 +0100 In-Reply-To: <27e57ec8-7276-40bd-9044-f6e6920ad6db@www.fastmail.com> (Nicholas Savage's message of "Tue, 11 May 2021 09:32:57 -0400") Message-ID: <8735ut75a6.fsf@ucl.ac.uk> User-Agent: Gnus/5.13 (Gnus v5.13) Emacs/28.0.50 (gnu/linux) Content-Type: multipart/mixed; boundary="=-=-=" X-Originating-IP: [95.146.38.114] X-ClientProxiedBy: LO4P123CA0317.GBRP123.PROD.OUTLOOK.COM (2603:10a6:600:197::16) To PAXPR01MB8415.eurprd01.prod.exchangelabs.com (2603:10a6:102:21e::16) MIME-Version: 1.0 X-MS-Exchange-MessageSentRepresentingType: 1 Received: from t3610 (95.146.38.114) by LO4P123CA0317.GBRP123.PROD.OUTLOOK.COM (2603:10a6:600:197::16) with Microsoft SMTP Server (version=TLS1_2, cipher=TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384) id 15.20.4108.25 via Frontend Transport; Tue, 11 May 2021 13:45:39 +0000 X-MS-PublicTrafficType: Email X-MS-Office365-Filtering-Correlation-Id: 896d5d48-3be7-4aaf-4d8d-08d91483100c X-MS-TrafficTypeDiagnostic: PR3PR01MB8065: X-Microsoft-Antispam-PRVS: X-MS-Oob-TLC-OOBClassifiers: OLM:9508; X-MS-Exchange-SenderADCheck: 1 X-Microsoft-Antispam: BCL:0; X-Microsoft-Antispam-Message-Info: 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 X-Forefront-Antispam-Report: CIP:255.255.255.255; CTRY:; LANG:en; SCL:1; SRV:; IPV:NLI; SFV:NSPM; H:PAXPR01MB8415.eurprd01.prod.exchangelabs.com; PTR:; CAT:NONE; SFS:(4636009)(366004)(136003)(376002)(396003)(39860400002)(346002)(6486002)(6496006)(38100700002)(235185007)(956004)(2616005)(5660300002)(52116002)(4326008)(38350700002)(478600001)(316002)(66556008)(186003)(36756003)(6916009)(8676002)(66576008)(66476007)(786003)(66574015)(66946007)(26005)(86362001)(83380400001)(8936002)(2906002)(16526019)(36916002); DIR:OUT; SFP:1102; X-MS-Exchange-AntiSpam-MessageData: =?us-ascii?Q?ArrjuDajuCtDPAyCSoJyezoHlphI4CT2A7aaFD8ktJcUrKqK5pFgLUAbrVVL?= =?us-ascii?Q?5qcnP6u5XqxPeCe3wUHYkLpaWsng/Iv63g3GT1h5H5WBczfdJ2NW0PnL7Nq+?= =?us-ascii?Q?Vm2Wu6ZA3g5k2/mMqPICmVKOX+3jRYMZTj3yOSlVp85W09gUIF+7VUnZ48z/?= =?us-ascii?Q?fykWnB7mikrDvEFdOVxgTUhxrHxQ/gPBPF/v1h4xTkDfWKny52cgE/wJ7vnS?= =?us-ascii?Q?I40SWngd0lpy6w5Cizp0LmfYKCmcGvhii+AuJRc+U2QBIjZlytywwsgxhDY4?= =?us-ascii?Q?ZPT2G5e26IGsCrG8WdAPxA9LOrliYRLaPme7709gbhoQEKYcJA2EgdNJl86B?= =?us-ascii?Q?aSjJfz9YpFjS9af/aKqVE8pLqsjV8mOg+/xzwQjXQNwmhcM0h4LCyVQybr9M?= =?us-ascii?Q?gEZy5f1xmn78HWYvKSU2JBwDEBNyllUhzRCM3w8wa5vB6NP3XdCaPXRFKX8Z?= =?us-ascii?Q?jYceMCvfMAgSqEGspVsex3d8aZ3MR2uXCdbjCKVR0YgTtuEanKASMK5Z1LFy?= =?us-ascii?Q?W9RoCy3UInHBLXkkPVQS5RoY53TstmLpPYl5FecZM54uzVlYiCej3Dhe4qvw?= =?us-ascii?Q?g5LfdaxKJFbVp7K/0y5pAHUvV8FYfW5BG0cl0gBkGLH97fv4uMXo4BogC5oT?= =?us-ascii?Q?w1XrvRDINwXBtCUCFNA/THREEKf+wz01xIOzmSYrfNGbMshb71jZOaOIH71B?= =?us-ascii?Q?GS1puK/meXscV04U5HNbm3SmxFVAdhne4UqXsmiSXQiXVpC1Gg5nK+q1INGd?= =?us-ascii?Q?JqgVpjz2PZ2r42OMveuJk6HnpD0KnuZk7ltNUOKunajNOo++c/kpa6AjUVFo?= =?us-ascii?Q?mzN8GufsmsdyGyFPZZ3yONnMksllQ0iYFCWRFwlx7o8LYWEHmiI2R/+QNIED?= =?us-ascii?Q?eDYA/nHVCcSmXlep9i5HwiwvIjs0vtEjJ2dNi8GwFx8kQfJCx6WhZB645w2u?= =?us-ascii?Q?E3kaI7N1k2Uv1XvbP5cmFYkMvvubFwUQF0C03OQ7mXl7K7s28VPtX4ReIPgN?= =?us-ascii?Q?UMc8MAZtX7vkmuGucOdgehrg0IL0t0m3ldOOVmpRghWjw0aToo/TDbkaSuj4?= =?us-ascii?Q?nYAQhHNerUByiOR4o7Oijk7qzj0QAlDV2VrAFmMuuvXfEPLrjZiM+NKkK95+?= =?us-ascii?Q?+ggE/kLkhwatzfI/pG735/HEXo8zRWA+NDUilq4ymH5/f1U1lucpsxRYfMRu?= =?us-ascii?Q?mYu8Ab36xYzdOpQUR2Yv4pZrQQI689vBu3CNZW6BVwTB33jkae1voSFqyTgu?= =?us-ascii?Q?ssPeasm89m/VJuqM4ulLxLmLTeanoo4uq9qsKbIbRneWUlKFfEkH/vKV1eaL?= =?us-ascii?Q?Y/AWASY89ckA5OYBD1OK9bIW?= X-OriginatorOrg: ucl.ac.uk X-MS-Exchange-CrossTenant-Network-Message-Id: 896d5d48-3be7-4aaf-4d8d-08d91483100c X-MS-Exchange-CrossTenant-AuthSource: PAXPR01MB8415.eurprd01.prod.exchangelabs.com X-MS-Exchange-CrossTenant-AuthAs: Internal X-MS-Exchange-CrossTenant-OriginalArrivalTime: 11 May 2021 13:45:39.4279 (UTC) X-MS-Exchange-CrossTenant-FromEntityHeader: Hosted X-MS-Exchange-CrossTenant-Id: 1faf88fe-a998-4c5b-93c9-210a11d9a5c2 X-MS-Exchange-CrossTenant-MailboxType: HOSTED X-MS-Exchange-CrossTenant-UserPrincipalName: pAbA882cNJaqNozbLanMUJkPZRcTVtLluhZhuQ2D715M6Vu1XyUT8BTJ4K/GA66I5N2JGCR+WP0FNAOWfU6Xsg== X-MS-Exchange-Transport-CrossTenantHeadersStamped: PR3PR01MB8065 Received-SPF: pass client-ip=40.107.3.133; envelope-from=e.fraga@ucl.ac.uk; helo=EUR03-AM5-obe.outbound.protection.outlook.com X-Spam_score_int: -27 X-Spam_score: -2.8 X-Spam_bar: -- X-Spam_report: (-2.8 / 5.0 requ) BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, DKIM_VALID_EF=-0.1, MSGID_FROM_MTA_HEADER=0.001, RCVD_IN_DNSWL_LOW=-0.7, RCVD_IN_MSPIKE_H2=-0.001, SPF_HELO_PASS=-0.001, SPF_PASS=-0.001 autolearn=ham autolearn_force=no X-Spam_action: no action X-BeenThere: emacs-orgmode@gnu.org X-Mailman-Version: 2.1.23 Precedence: list List-Id: "General discussions about Org-mode." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Cc: "Emanuel Berg via General discussions about Org-mode." Errors-To: emacs-orgmode-bounces+larch=yhetil.org@gnu.org Sender: "Emacs-orgmode" X-Migadu-Flow: FLOW_IN ARC-Message-Signature: i=2; a=rsa-sha256; c=relaxed/relaxed; d=yhetil.org; s=key1; t=1620740807; h=from:from:sender:sender:reply-to:subject:subject:date:date: message-id:message-id:to:to:cc:cc:mime-version:mime-version: content-type:content-type:in-reply-to:in-reply-to: references:references:list-id:list-help:list-unsubscribe: list-subscribe:list-post:dkim-signature; bh=imEDbPe+2HYr1vdj9cxE0Q37yBYGNq3o+xGlUaXz7Cg=; b=F8YT3sq2ku3T7keDlPzGEHHBcBWUEODQMZSgP8MT/D7v1VK2S1mbjG6+HtrOR6wclGg4hU tmGYGx80lq8ZCy8UlUlNVnftrGcSfDcw98iHGbz++uAIKsOyeQU/IIZgILhf7846H2UWgZ E/tDu5hTX+Ne1/7Hc3knK2lbcS7v/kIYyUhHNvVwZ3HsNNpklwd2oDy911Z/CL9ar8hpMb ccvB9s10butffSJa4x8YR5SWXMJkz9Sz+tiLko9qIyYzvdaG9Co5O2hsd8VI7EkHFy4WA0 uYOAXpabKOK8zzXDeFOsRcA4BHc8++z7HvsFbsXZ0DQk7/LgzLKoUHmKE2vCIQ== ARC-Seal: i=2; s=key1; d=yhetil.org; t=1620740807; a=rsa-sha256; cv=pass; b=kXoVv7KRRt7QjdZqG8DJGSn8cjzUNad5rcJ5LGlozx8fCutXsiSROqyAuzbYMCRcoM+FLG +gwoPgOp4/mlA/RcpXszju699XgTKBasm+98F3j58jnL+lhL9ITtmSgiCGRGpyQ0QAQleA GyxmptdGjwymHEXoP/4Koxkuq985wV9QVrONFsXrbBdedREnHhR4R/B8hlW1NMCNBKZ+NA bYuUPYq88f3qjX1C2nqRRAqwT1yEiZnaZMxtExmcnO8CNNVK5i/q7PeYm18bg8kHxrLIjv 9lHWs8o4Oe/D765rBsqz+CJDDO92d284v46h8doih9uZg3dzpa8Xekoff+vPCg== ARC-Authentication-Results: i=2; aspmx1.migadu.com; dkim=pass header.d=ucl.ac.uk header.s=selector1 header.b=G7BAzXQf; arc=pass ("microsoft.com:s=arcselector9901:i=1"); dmarc=pass (policy=none) header.from=ucl.ac.uk; spf=pass (aspmx1.migadu.com: domain of emacs-orgmode-bounces@gnu.org designates 209.51.188.17 as permitted sender) smtp.mailfrom=emacs-orgmode-bounces@gnu.org X-Migadu-Spam-Score: -4.15 Authentication-Results: aspmx1.migadu.com; dkim=pass header.d=ucl.ac.uk header.s=selector1 header.b=G7BAzXQf; arc=pass ("microsoft.com:s=arcselector9901:i=1"); dmarc=pass (policy=none) header.from=ucl.ac.uk; spf=pass (aspmx1.migadu.com: domain of emacs-orgmode-bounces@gnu.org designates 209.51.188.17 as permitted sender) smtp.mailfrom=emacs-orgmode-bounces@gnu.org X-Migadu-Queue-Id: A45CA2E335 X-Spam-Score: -4.15 X-Migadu-Scanner: scn0.migadu.com X-TUID: FmW5Iz3KfRsX --=-=-= Content-Type: text/plain Hi Nicholas, On Tuesday, 11 May 2021 at 09:32, Nicholas Savage wrote: > When I activate column view on Headline, it applies the columns on all > of the subheadings. Are you not seeing the same thing, or is your use > case different? Do you have some sort of minimal file you could > provide to show the bug? If point is on the first (top-level) headline, I only get the column view for that headline and not the subheadings. Attached is a small(-ish) file along with a screenshot of what I get. The org file consists of the first three papers in a bibliography I have (which has hundreds of entries). If you are seeing something different, maybe I have some setting that is causing problems although I cannot imagine what that would be. Advice would be welcome! Thank you, eric -- : Eric S Fraga via Emacs 28.0.50, Org release_9.4.5-534-g8f03cd --=-=-= Content-Type: image/png Content-Disposition: attachment; filename=screendump-20210511144322.png Content-Transfer-Encoding: base64 iVBORw0KGgoAAAANSUhEUgAAB8AAAAE7CAIAAAAtpwy9AAAABGdBTUEAALGPC/xhBQAAACBjSFJN AAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAABmJLR0QA/wD/AP+gvaeTAAAA EGNhTnYAABOwAAAHgAAAA7cAAAAcWg0X8AAAgABJREFUeNrs3XV8FNfaB/BnfePuCklIAoHgDkWL SynaUrk1Sl2p662391boLS/1FiktFC9QiluwCJKQEHf3TbI+7x+zGZYkJLOb3WQDv++HTzs7Oxk5 5zlnZs6eOUMEAAAAAAAAAAAAAACtCBQKRffuQW1tLbIBAAAAAAAAAAAAALqFi4vLjb4SInUAAAAA AAAAAAAAAFpDAzoAAAAAAAAAAAAAQBvQgA4AAAAAAAAAAAAA0AY0oAMAAAAAAAAAAAAAtAEN6AAA AAAAAAAAAAAAbUADOgAAAAAAAAAAAABAG9CADgAAAAAAAAAAAADQBjGSAADAxt256B4i8vf3W/3l J0gNAAAAADAPrioBAADMOBvaVgO6Xq8/FXf2VNyZrKyc6uoahmGcnZ0iwsNGjxo+evQIkUjEHiER 3TF/9vK7l3B/uH7D79u272an/9y8jpuv1WoPHDxyKu5sbm5eY2OTTCbz8HALCQ7q37/fmNEj7e3t uFTrELdahmGOn4g7evRETm5efb3CyckxNCR4/Pgx48eNFggExjnBee3VFwYPiiWir1b/39FjJ9mZ 7u5u3639yowM7nAPWywmEolcXJzDw3uPHzt65Mhh3E4CwC3FpLrOvNqG5zpbVOBm7KpxPQ8AAAAA 3SIvL//Z51/lPs6bO/Pee5YZL8D//p3/VaWpbQJ87t9b7IBAIJDL5V6eHtHRkdOnTQkODkReAwDc 4myoAb2ysuqTT7/IyMw2nlldXXP2XHxmVvbo0SNMXWF9veLtdz7Myc3j5jQ1NRUUNBUUFJ08daam pnbRwvmmrlOr1X786ZcJCUncnJqa2qSaS0kXLp04efqlF58Wi9tI0qKiYrYBPTc3v+sTVqfTVVVV nz0bf/ZsfN/oyBdffNrZyQmhDwAAAAAAAGZLunDZ+GNi0qUWDejdzrz7d4Zhmpqa8vIL8vIL/jlw +N57ls6ZPQPZDQBwK7OVBvSqquqXXn6ruqaG/Whvb+/v76vX60tKShsbm6ZPmywSiUxd58bfNnOt 5/b2doGBAcomZWlZmUqlFolEUyZPYL/q378f9yclJaXl5RWt53PWrd/EnX2dnBwDAvwLC4vq6xVE lJCQtG79pn/dv7z1XxUWFhORTqcrKCzqTCo98K/llZVVFRWVV9Mzuf0cMmSgj7e3j49X6+W9vDx9 fX00Gk1eXkFjYyMRpVxJe+edjz766B2JGKP3ANxaTKrreNY2Jq3TDGwlhrwDAAAAsEFJSRfZCYlE otFo8vLyq6qq3d3dzFiVla4qzbh/9/Ly9PbyrK6uKSouISK9Xv/zLxsDAwIGDRqAHAcAuGXZSivq 2m9/YlvPhULh/ffdNe32yexPwXq9PjHpYp+IcDPWee5cAjsRGOj/4ftvswO26HS61NSrhUXFbm6u 7Ldvv/ky9yfGj30Zz2eVl1fs3XeAnR48KHbVi09LJBK1Wv3xp1+ylw579x2YM3uGp6cH9yfBQYF5 +QXsqbeouESr1RKRXC5XKpVmHNGsmdNa7+erLz9/o+XHjhnJPtSm1Wo3/rZlx86/iCgnN2/r1p1L Fi9A9APcUvjXdcS7tjFpnWbgKjEAAAAAsClqtTrlShoRicXi0aNHHD16gogSky5OnnSbGWuzxlWl GffvZHT9efVqxtvvfqhSqYlo5+49aEAHALiVCW1hJ9LTM8/HJ7LTdy1bNGvmNO5BKqFQOGTwQCcn RzNWW6+oZydiB8SwredEJBKJ+vWLvn3qJDNWePLUGZ1OR0QCgeCRh++XSCREJJVKH37oPnb0NJ1O d+LkaeM/YU/GxcUl1Dx+i4eHu3mt550hFovvvWfpgAGGX+//3n+QPRAAAAAAAAAAU11OvqLRaIio T0TYgOZ+4klJl2xnD824fzfWp0/4sGFD2OmMjGzkOADArcwmGtDPnD3PTtjZ2c2ccbulVuvhYfgl OeVKmkar7fwKk5OvsBMhwUFeXp7cfF8fb+69IskpqcZ/4uTsRERVVdUqlTovL5/92+5KZ27Umtra uuzsXEQ/AAAAAAAAmOHCBUNbeXR0ZFRUH3b64qXLer3eRvbQjPv3FhwdHdgJnU5rIwcFAADdwiYa 0C9dTmEnwsJ6yWRSS632tvFj2Ins7NxVL7157nxCJ1eYX1DITgQFtXwNd1BgADtR0LwMy8nRkYgY hikuLmZ7oHfjK7zDw3pz09k5aEAHAAAAAAAAcyQkGgZA7xsd6evjzQ59rlA0ZGRm2cgemnH/3kJG huFYjNvfAQDgFmQTY6DXVNewE34WfVncggVzL15KTk29SkR5efkfffx5r14hy5YsHDJkoHkrZF82 QkQuLk4tvnJxcW6xDMvV1UUsFmu12qLikrz8Amrr5N1lXFxcjI6lHtEPADZu2/bd3AiYnMWL7sBb HAAAAAC6UXl5RVFRMRGJRCK2+3nf6Eh2OJTERDPfYWaqNi8UjZlx/87S6/UVFZW7du/jGtBHjRyO TAcAuJXZRA/02jpDYy43UrlFSMTiN19/afr0KUKhgJ2TnZ37wUf/efvdjyoqKs1YoVqtYidEopY/ PIjFEnZCpVIZz1cqlWyX88zMbPZN4qEh3TaEi1Qq4abZd6EAAAAAAAAAmCQxydD9PKx3qFwuJ6K+ faPYOUnNX3U7M+7fiWjb9t2Llty38vHn9uzdz84JCPCfM2cGMh0A4FZmEz3QxWIx+/oRrdbCI4vJ ZNKHH7xv4m3jfln3W0rz6GaXLiWvevnNjz5429vby6S1SaUy9v2frUdA02o1zVuUGc/X6XS9e4Vm ZeWcijvLMIxQKAxoflis6ymV1y4O7O3tEf0AYOO8vDx9Wz2Z5OvjjZQBAAAA6EYXLlxmJ2Ji+rIT 0dGR7ERGZpZC0cCNHm49LS4US0pK2S5rHDPu31sbGNv/8ccedsDtMwDArc0mGtCdnZ2ampqIqKqq muefMAzTzscWwsN7//ud15JTUn/9dWNGZjYR1dbW/bpu0wvPP2nqfrIn4NraluOf1NbWsRNOTo4t vurdO5QOUllZORH5+flKxN2W5sb97t3dXBH9AGDjxo4ZufzuJUgHAAAAANuh0+kuXjI0oG/dtmvr tl3G3+r1zMVLl0ePGmE806T7d55aXCiu3/B7ixFdzLt/Z9vlL19OYXdy7NhR7PDuAABwK7OJIVwC AvzZifT0zPaXlEgMT1o1NjYZz+c+cgu01q9v1AfvvxUebniR5qXLyabuZ2Dzfua3etMIO745EQW2 6mDeu1coN92NbxAlopQr194wHh4ehugHAAAAAAAAk1xNz2xxP95CYvP7RTtz/9555t2/jx0z8u03 X544YRz78dd1mxoaG5HpAAC3OJtoQB88aAA7UVFZdf58YjtLcv2ms7JzjOdnZmazEx4e7u38uUgk Cuvdi53W6fSm7mdMTDQ7kZeXV1F5rTd3aWl5fr7hlBzTL6rFX4WEBAmFhnQOCe62AdD1ev3f+w+x 08FBgX5+PgQAAAAAAABgCm6UcwcHh7Devbh/Ps3j7HEjpHfy/r2TzLt/Z91912L2DW11dXUbN25G pgMA3OJsogF9zOgR3JDcX3/zbdKFS9xXOp0uPiEpPj6J/divn+EUmJGRtX3HXxqtVqPV7ty1NzPL cALu398wBNuOnXu+/e7nlCtpev21hvKS0rL4BMOquJ+jTdnPkSKRiIj0embttz+z47ZrNJrvf/iF fbxLJBKNGT2yxV9JpdLNv//y5+Z1f25et2jh/G5J4YbGxi+/WpOXl89+nDdvFkIfAAAAAAAATMU1 oM+cMfWTj9/l/q164Sl2fnV1TV5eAZly/24N5t2/s1xdXRbeOY+d3v/PwaysHOQ7AMCtzEbGQHe+ Z/mStd/+RET19Yp/v/eJh4e7j7eXoqGxtLRUpVIvXnTHkCEDiWjWrGlHj53U6XREtG79po2/bSYi 9iMRicXiObOms9NKpfLv/Qf/3n9QKpX6+/s5ONjX19cXFBTq9YbR1ubMmW7qfnp6esyYPmX3X38T UUJC0iMrng4I8CssLK6rNwypNmP6FE9PD+sl1F97/q6srK6srEy7msHN/OCj//j6eHt7ec2e3fKI Tpw8nZGZ3dTUlJOTx72gdfiwIRNuG4vQB4B2mFrbmIStmlrMnHjb2NtaVU1tLklEb7/5MvIIAAAA oOvV1ddz3cm5N4iyQkKCHR0dFIoGIkpMuhgcHMj//t0aOnn/PmvmtH8OHC4uLtXrmW+/+/nDD94S CAQIAACAW5NNNKAT0e1TJ6lUql9+/Y39Kbiysqqysqr1YqEhwY88fP/ab39i+5Vzp14iEolEjz/2 cECrfuVqtTonJ9d4jlAoXLrkzhv91Ny+e5YvLSoqSUi8QER19fV1qdfeRjJ4UOw9y5daNZV+/Gl9 65lc9/zWTVrl5RUtXkQ+ftzoxx57GHEPAO0ztbYxSeuqiYiio/rwXBIAAAAAusuFC5fZTmlisTiy T7jxVwKBoG901Nlz8USUmHRx3tyZZty/W1Zn7t/FYvG/7l/+wYf/IaL0jMyDh45OmTwBAQAAcGuy lQZ0Ipoze8bgQbF/7z+YnJxaUVmpVKpcXJx9vL0GDIgZP240t9iUyRPCevfa/de+lCtpVVXVAoHA 3d0tpl/07FnTgo1GGJ83d2ZwUOCZs/G5efk1NbUNDQ329nZeXp79+kbdPnWSv7+fmeklFr/6yvPH jp86duxkdk6uQtHg6OjQKzRk/Pgx48eNts1fpKVSqZuba3RUn8mTJ/SNjkTQAwAAAAAAgBm48Vsi IyNavwK0X19DA/qVK2lKpUoul/G8f7eSTt6/Dxk8cPCgWLb9fcOGP0aOGObo6IAYAAC4BQkUCkX3 7kFtbS2yAQCgHXcuuoeI/P39Vn/5CVIDAAAAAMyDq0oAAIAbnQ1dXFxu9Cc21AP9FvT2ux/xXRID /gIAAAAAAAAAAAB0LTSgd6dLl5KRCAAAAAAAAAAAAAC2qfuHcJly+1xkAwAAAAAAAAAAAAB0iwP7 d97oK4yBDgBg6zBaJQAAAAB0Hq4qAQAAzBgDXYhUAwAAAAAAAAAAAABoDT3QAQAAAAAAAAAAAODW hR7oAAAAAAAAAAAAAACm6f4e6AAAAAAAAAAAAAAANgg90AEAAAAAAAAAAAAA2oAGdAAAAAAAAAAA AACANqABHQAAAAAAAAAAAACgDWhABwAAAAAAAAAAAABoAxrQAQAAAAAAAAAAAADagAZ0AAAAAAAA AAAAAIA2oAEdAAAAAAAAAAAAAKANYiSBSRzucGw9s2GbAikDAAAAAAAAAAAAcJPpVAN6Xl7ewYMH U1JSiOjTTz+11D7J/71AlPhP4/pCxt6ZiOwfCGc8A5o+OWoL6aUbMIGbFqibhKlnEEMtQ2r/j5Jt Xwgr8vXeIZolr2rHL2bnC/Ov2D09XP3QZ5qZK4yXF+ZcFh/8VXTpqKAsT6BRMi7eun5jNHc8pw+N abFmQUOtZOt/RWd2CcvzGKGY8e2lHTlXM/9pktm3uSeC2nLJ3m9F5/cJCtMFWjXj7qv3CNANmKgb t1Af0Ieafw5p+jqB/Xhtlwqv2j0xmFr/NKJWSvZ9Jz61TZCfKlArGVdvxsNfFzZYN36RLnIE3eD3 lRauW6dWLdmzVnz0d2HhVdLr9F7BuhGzNXc+zzi4cotw61Q9+yOXmEQkPrRetvrRNtbJWzt7a7zC 6xaTyBgPf71fmHbcYu3o+TdKectGCBEJFNXiI7+JTm0TluUJastJLNFMuEu94nMzIsTUPOKTSuat s3Xg8c8g8zLdjJTnyD67T3zyzxvttsllszzf/pFoIlIve12z+OUbHXuHZdOklDetvPMom22UDid3 fa8B2jF3am9bSkI84AUAAAAAAABwMzCzAT0rK+vAgQPp6enW2Cdh9gW9Tyjbei6oKRNUl+iGzbSR 9FK+s/vafjY3uwBHdGaXbM1TJJboew0QZl+Uff4A4+Kpi51ERJLtXzKObprJ9xgvL/3xZcmur4mI cXBlgqIYRi8ouCo+9oc4bofyja26/rdxSwpqy+WvThUWZZDMTh/anxpqhbnJ0pxL4mN/KD88wDh7 tNyT5BOyD5cKGmpIJNYHRevlDsKqIlHaWdGVOLWdY/sNl20S1FfJ35wpzLlMAoE+oI/e0U1YVSzM SBCmnSVGzzagG/++QkSii0eISB81gpHatbFGjUr+7wWiS0dJKNKH9COpXJCbLNn2uThue9MHBxg3 n5ZHlHTAuAFdlHSwk5mlWfyyoK6CasuFFYXC9PNEpIudyLj7M+5+rRdmj0KgbhKU5ooSD4gSD0h/ /0D54jp92CCrRoghK//7L0FVMQkEer9wfcQQgVopaKpvmUH8IsS0POKXSqau0ySWWo8ZKW+cBWzr edtFw8SySUTiuO2GiVPb2mxA58laKW9i2TRsUa0UlueJ4v8Wxf8tPrRO+fqf5v3CBAAAAAAAAAA2 xZwG9K1bt8bFxRGRn59f//799+/fb8EdElQVC6pLdWMWsB+FmYlEpOsdi6zqEaTbvyAi9f0famY9 Ktn9jfSHVZLfP9LFThJUl4iP/aG58/kWLUq6kXOE2Rc0C57XxU4koYiIBE0K6epHxXHbpd882bTm Irek7PMHhEUZutiJqhd+ZRzdiEiYfdHulSnConTZN48rX95kvFph6hn523NJq9bMfkyz9FWu06ig rkJ0art24l3mHNrGd4U5l/X+EarXt+j9wgwrbFKIzu/Vhxt+RzH+fYWae6eqnljTZnu97IdVoktH 9b0HKl/ayHgHE5FA2SD93+PiE1vkn9zV9KFR+7jcgSGBKOnQtTkMI7pwmHH1FjQpSNVoXmapl71u SK7mn4KUb++60cLGRyHMSpL++qbowiG7V6c2fXxYH9rfehEizL8ie+9OgapRM/sxzYLnGDffG62Z Z4SYlEc8U8nUdZrEUusxNeWv0euk3z2vixopSj3dmZQ3xjagM769hLnJwqIMvX+4eQdlpZQ3oWy2 yiNRyknZJ8tFl49Lt32uXvoaTgoAAAAAAAAAPZ05z5gPHjzY39//vvvue+6552JjLdy0zbaY63sP vP5j21txuMPR4Q5HYeFVZKSNEOSnEZEuehQR6fqOJiJh/hUikuz+hkSi1gNE6PqOUf57r27QFLb1 nIgYO0f1Ax8RkbAkS1BZaAiD9POiC4cZuaPq+V/YFjoiEuansm3HojO7hTmXjFaqlf3fU6RVaxY8 r37wE+MhFxhnT+30h8zrFio6s5uI1Pe8w7Wes3urHbfIeA7fhCrPFx/8lcQS5Usb2BY6ImLkDqon /4/xDhamnhElHri2tKpJHztBUF0izE02HHtWkqCuQh8+2OzW887Q9x6ofHO7dsIyUitl//0XadXW ixDpN08KlA3qe99TP/hJO63npkUIyiaPlOdI9n4nzE3WLHzRUikvqCwSpp1lfEK1E5YRkejUNttK KJPKZiu6vmPYH11Exzff4iEHAAAAAAAAcHMwpwE9NDT02WefjYmJscoOZSYRkT5sIPtRlHWBRGJ9 iPnbEtRXSf742G7VBPv7QhwWe9iv6Cd/Z554/08tWv0EteXS396ze3q4/TIf+7v97Z4fI9nyqaCh 1uzt3qhxX1h4lf2KW8zupYnCgjS7F8c7LPG0e3mSoCxPlHLK7tmRDos97FbdJizJNl6n3arbiEiU fEL+0TL7B8IcFrnZPxAm++IhYUlW630QpZySfbLc/uEoh0Xu9st87FYOkP3n/naGYpB984TDUi/p D6vMzz+BgIiIGCIihjHMUzaI//5BM/FuxtmT10qkcsOEnRP7f3HcDiLSjZ7POLkb1tmkkP78KhEx Hv5EJD61/dpRJ+wX5iYzHgHqpa9aMDIF9VVExPiEWmRt4mN/kFajGzKd8Q5pcezaiXcTkfioUb9d Rq+LGkVGw7awTXjszG6qOYSqR79knD2F+amihH+sFCHCtLOi1NP6wEjN3Cc7SE9TIuQWZVbZFNRV Sn77ty56lG7INEulvPj0TmIY3cDJ2oFTyGg4FxthWtlsi77PMCISluff6iEHAAAAAAAAcFOwlbec sW3KDnc4Sn//gIjkb89lP4rO7CKd1mGJp3GjM3+i1NN2Tw2V/vZvYVYS4+6vCx9MAoHowiHpn5+S 8NrwNcKr5+yeHSn54yNhaQ4T3JcJihIWpks3vGP33ChhzmWr50FWkuzDpaRRkUAoTDsrW71C9uES 0qgZqVyYHi/98SXjhQWF6eLDG+RvzBBePq73DtUHRgoUNeKjm+TPjxUWpBkvKT68Qf76NHHcdtJp dWGDmOC+Ao1SfGKL6MQNG9DFx34nVZP42B9mH4s+KJqIRCmniEiUfIKI9EFR4n9+EjTWa+c9zXMl bPuUPnwwOw4+ERnGno6+1l4s+e09QXWJLma8Zv4zRCRMu/Y2V/HZv4hIO24hSWQWzCbGM4CIRJeP WWRt7GgYxoO8c3T9xhKRKO2699Pqo0eSQHCtAT3pIInEuv7ju7PQyuzZHsTt/CTTyQgRx+8jIu24 xR2+j9GkCLk1mVc2pevfEjTWqR/42IIpz3Y51w6doY8Yyrh4CbMuGP9M2O1MLZutCRrriIjhfggE AAAAAAAAgJ5MbCP7oZnxCDsh+ednxtFVO2o+EQnqKsUn/9T3GqCLGmnGOgW15bKP7xLUlGknLFM/ 8PG1DpKlOcKKAq5JTqColn9yt6C6VDv5HvVDnzFyB2JHvP3+BfHBdfKPlzX99xTT3BXaKrQaxreX 8o2t4rjt7OC5+oihTR8dEl0+Jn9rtujiYdLruBFOBI11sv89rl76mubOF0gkJiJBWZ783fnCwquS 395TvbiOW6v09w+JYdTLXtcseqm58ykJC9LYv2p7R25bKj66SXvbEvPzcdEq0bvzpb+8Jj72uzD7 IhFp5j4p++ll7ci5et9eHfyxqklYnic+8ptkx5eMm4/q8f9x37Dta0zzGoR5KZI9/0diiXrF5+ww L8LCa++zFV49R0T6PsMtnEsTlkk2fSD99Q1BY51m7pOdDAlhQSoR6QMiWn/FDggjKMkmrZrEUnYm 4+im7xUrunKK1EqBXidKO6OLGkkOLt1bbPXRo2jnapN+ZDIpQoRpZ4lIHz5ImHpG+sdHwvTzAnWT 3jtEN/ZO9bynSe5gXoTcmswom8KMBPGBX7UTlnGj/LexjIkpL6gpE12JY+QOutiJJBTqhs0UH/hF dGqbfsFzNpJQppbNNtZw+TgRtZNoAAAAAAAAANCD2EoDuvqR/xKRoLZcsvdbXewk9qP41DbxyT81 0x/S3v4At6T8rdkt/lb29UpGasd95F4rJz7wq6CmTN97oOrJtcY9WBmfUJ3RQBzif34WVBbpfXur Hvv62kjccgfVY18Lk08IS7LFh9ZrZq206uHrhs0kIl3fMexH7W1LSSg0jGOjahLUlDHuftcWDh+i WfzytcPxDtYsWiX74iG2VylHUF1CRLqBk7nWcyLSB0a2sxuqlatVK1d36kAGTVE9/Z3kj4+E2Rf1 XsGaBc8J1EpBWZ7mhXXt/JV047uSzZ8YDsfBRbPgec2slYyzx7UlGmqJiBvNXLr2WdJpNQue0wdG CpvqiUjQUHPtwGvKiEjvGWjhEL3zRUFuijhuu2TTB+Idq7XTHtDMfJTxCjJzdeyAMG2Nm8E4GQ5c oKhhXL2vpe3Q6cKsJLYNnbSaGw2p0ZX0Hv5EJKgptVKEsI2zwoI06bo3GZk9ExTF1JQJC9OFmz4Q ndmtfH8/Y+doRoT0LC3qN0MVMeEuU9+Fa3LZZBjZ9y+QzE69/J321mtiyotP7yRGrxs0lX1ARDti tvjAL+K47ZquakBvnZ4CdVMny6YxUcI/0m3/JSIN72duAAAAAAAAAMCW2UoDOsvQd7i5454wPZ6I 9OFDjJcRXTzS8q9S236gXnx2NxFpp97f/vgP4tM7iUg7cwXXet68XpF25qPSH18Sndxq7QZ0trcj 1wilD44mIkZuaBwUqBoZo4W1k5a3/PPASCIS1FUaz9RFDBUln5B9/4Lq6e/0AX26LBO1E5axI3uw 7J4fq4sZp48YQjqtQFHNOLm3TGcivW9v3YAJpNUIakqF5fmSLZ8Kr55TL39bHzbIkALKBiJiu3yK j/wmSjnJeAaqF71ERMSmklrJrU3QWEtEJLe38IGJJapV63VxOySb3hfmpUi2fynZ+bV24l3q5e/c qCmtHYImBRG1PchM80yBsuG6fB8yTfLHR6Kkg6RqIiJbaEBnX8fKDlhhlQipqyAi6YZ31AtXae54 lh0cX5h2Vv7xMmH2Rclv77HvmzU1QnqWNus3fd/R1i6b4kPrhWln1cteN/71ro1INjHlRXHbiUg3 co6hmhowkZE7CDMSBGW5Lccc78L07GTZNDTKa1TC0hxBVTEjd1SvXK0bNKWHhhwAAAAAAAAAGLOt BnSRocW8uQE9I56kcn1wX+NlGrYpuGl2VPSmrxPabCAWlOcRkS60f/sbFZTlUnObdQv6oCgiEpbm WPvAGXYsDrHE8JHt58iNtaLXXbewf1jLv2fHsmD0xvNUT6yRv79QmB5v9+RQ3bAZmpkrdLGTujpD Lx4RZiUpX9si2fSBZMdXAqWCsXfWLH5ZM+8p48W0k5ZzvwoIGuvEh9ZL179l9/Lkpn/v1UeNICKS yknVSBqloLFO+uvrRKR+8BPDUWuURMQYjebB2DkJ6qus1GCqHTVPO3Ku6Nweyc7VouQT4oPrROf3 Kd//29SfKBi5g6CxjjSqNr5rnnmtezUbBeFDGGdPtgGd8QrSB/dt/YrartZYT0TcUPUWjxCBWklE 2gl3aZa8ci0dIoer735b9vVK8ZGN6n99aHjAwpQI6VluVL9ZtWwKGuuk699iPAM77kZtSsoL6qtE ySdIJNYNmc79uW7QFHHcDvGp7Zr5T3dLegoLr9o9MbgzZfO6RnmpvGn1ecbST8AAAAAAAAAAQHex iZeIyt+azf4TH/iZiCS/vsF+FF09RwKB/N93sB9NXa2gvoqIyKGD1j2BooaIGBev1l+xM1v07O4s vb6NmS2GaJBI21kB48KrvzPj26vpv6fUKz5nfEJEZ/+Svz3X7tmRojO7uzJnJdu/1Af3FdRXSX// QN+rv+rp7xhXH+nPr7K9/tvebXtnzezH1ItfIa1ausEwdgTj6EZEgsY6yYZ3BdWlukFTtSPnGmcf ObpeW4OzJxEJKousdVQCgW74LOV7+5Tv/6337S2oLZeuNv0BBSd3IhLUtxFagroKw4E4uF5fWIW6 wVOFOZeFxZk20f2cSFiaTUTt91DuVIRI5USkmXh3i7/SDZjAFnBu9BiTIgQ6THnJpvcFNWXqe/9N MrsO6hlTUl50eifptKTT2i8P4F4cLY7bQUTiuO22kjSml82mrxMatikattTog6JJrRQf/R0BBgAA AAAAAHDTsIkGdNHFI+w/QXUpEYmuxLEfSa0kVRP3ramrZSRyIiJ2hIF2FmObS6rbGMeZncm9fdQi BLVlbc0VmJJpvHNNItNMf7jxfxeUL/+mixopzLks/2ipdOO7XRRbeSmixH8085+R7Pk/IlI/9Jl2 wjLN0leJSLL9i/b/VjdwEhGJ0s+zH9khbsQn/pTs+44kMvUj/7mWckUZRKT37c3NYQd+EWUmdhwh bN9YVWPLL1SNZDSEzg13su8Y1UsbiEiUdsbUYUzYUXfafLOlsDiT2NcVNj+RcG2LQw39drWDb7eJ kpt8gjrxvtYOI8Tws1ar194ybr7NOdVkRoRAhykv2bOWiCSbP7F7ahj3j/1b2fuLjD+alPJsKznj 7MF4+Bv/I4FQmH5eUFFgqbLZGeaVTTZQ1Q9/RkTSLZ+wL1AFAAAAAAAAgJuATTSgN2xTNGxTNP6S S0TasQvZj6oXfiUi9Yov2I/GI7fwxHgHE5Ew70r7i+l9exGRMDe5jdQpSCUixseskXnZNvFWDUCC kpwuz2ShbsQc5YcH2FezSrZ9LujoRwWLkGz/kvEI0I5bZGhHC4ggIr1/OBEJCtI6SLz6aiJimlup 9FEjiUj89w+k12nufN64Sc7QhhsxlJvDDj0sOrmVGKaDCHHzJSJhWV7LBCvLIyLGw7/DY7y2J4pq kxJHFzmCiESXjrb+ynBE0aPa+KuBU3TRo3TRo3X9b+v2YiuoqxQd30xEXI9ji0eIvtcAauu3EENH YIGAmh8cMSlCoOOyqdMSkTD/ivE/Q+kozjT+yD/lBQ21oktHSSBo+m9c4/dXjf/pBkwghmG7oluq bJrNvLJp+Nv+t2lHzSdlg/SnVxBmAAAAAAAAADcHm2hAN+xK9gUi0veONXzMTCQiXdhAs1eo6zeO iMQH17XfkKobs4CIJHvXsm1G1+j14r3fEpF21Hwzts6OYy7MTWkxX3xkY3elsPa2pUREWg01D0Rg PYKqYvHxzZo5j5NYYuityfYjNv7vjYnO7CIiffP49dpR8wyp6ttLfcdz122FfVWsURuudtR8xjNQ WHhV/M9P7W+FbQgTnd/XcusJ+4lIHzOuw8M0tO1KZIyrj2nBOW4RCUWi8/sE5fnXfaFWshFiyKwW QeXgovzgH+UH+6nbR/TWqmWrVwiUDfqwQea9LJFPhGiHzyYi8Z7/a1E2DRkU3JcbitqkCLnF8Ul5 7mdL43/snxuGK2n+yD/lRWd3k1aj7zOsdfM3u4zo5FYLlk2zmVc2ry31rw9JZi8+ubXNJngAAAAA AAAA6HFsqgH9IrVoQBeJ9SEx7fwJ245zozfsaWc9ShKZ6Mop2f89LWiovfaFskF8atu1xSYtZ3xC BWV5stWPCprquWVka54UlmQznoHayfeYcTj6vqOJSLLlU/YlpUREej375smuSU/pL68J81OvfWYY 8f6fiIixc7rRoNWyNU86LPWS/rCq81uX7P6Gkcq1tz9AROxrYAUVhdx/9YFRhi1+fJfozC7Sqo2y TSPZ+61k33dEpJnzhCHlQvvrhs8iIkYs5Ya9FlQVyz+5m9RKXewk7sWzREQyO/WDn5BAIPvuecnO 1cYPAQiaFMbDr2tv/xcRiY9sNI4H8Ykt4kPrSSDUTH/o2sy4HeIDv7YY50eYdUH6zZPE/sQilZsW Hr69tBPvIq1a/ulybuQKQZNC9vVKQVmeLno0O8y3LdLrRJeO2r02TXR+H+PgqnrhF+tFiHbMAr1/ uDA/VfbFQ1zZFF04JF33JhFpZj9+badMipBbG8+yyTcceKe8+NR2usEvGbqRc0kgFF09K6gqNrVs Wj7AO1c2Ga8g9YLniEj63fMtf5QFAAAAAAAAgB5IbN6f7d5teBdlY2NjizmzZ882b52GBvReAwwf M5P0QdGmtksa0/uHq57+VvbVCvH+H8WH1utD+jFSO0F1sbCikLRqbXMPSsbOSfnSRvn7i8RHN4lP 79CH9ieBQJhzmZQNjJuPctUG4/fFSXZ9LaivpvpKQX2VsHmUW/m78xlXH8bVm3H11sx9kp2pXvyy XeI/wqJ0+8cH6QMiGHtnYWG6oK5Cfe+/pX98RNYfREWy/UvJ9i8ZN1/GJ4QhgbA8j32vpubed0nc 9ktKxUc3kapJfOwP9YOfdGbTgiaF+O8ftNMfYjsIaxauEl0+Ltn9jWbZa5L9PxKR5s7nDVs8vVN8 eidJZHq/MMbJXaBVC/KuCJrqSShS3/2GbsS1WFI9/j95QZqwIM3+sVh974FEjDD7Imk1ev9w1XM/ ttgB7ci59PR3sq9XSn96RbrxXX1AJCOzF9SWCctySavhsl4XOUKz8EXJlk9ln94j9Q7Re/gLK4vY HzzU973H9X8nImFesmTTB0TEuPsxXkGMUCSsKhaU5hCRPrS/ecmlevi/gsJ0Uepp+0dj9CExJJUJ ci4LlA16/3DVy13xmIL0t/eorkJQWyFsbiWUvz2XHZNafdebLRaWfb2SkcgFDbWC4ky2LVsf0k+1 aoN5Y4vzjRCpXLVqg/ztOeITW0TxfzPB0YKaMjbZtRPv0k6597r0NCVCrJRKJrF7ZmTrlx+oF67S LH6pZcpL23iTp/Kd3VZMeZMimUfKC5rqRRcOEZFu5LzWa2BcvXWRw0Wpp8VxOzSzHjWpbFpDJ8um Zv4zkkPrhfmpkr/WcGcEAAAAAAAAAOihzGxAP3r06I3mdKIB/RLj4c84exKRoCRb0FCj6/SoC9ox d+rDBol3/U904bCwIJUYhnHy0EUM0cdONF5M32tA0xenxbvXiM/sEuSmEDF67xDdiNmaWSuZ5kGW WdIfX269FVHiAW6aay7R9xrQ9NEhyeZPRCknhQVpjJ2TPmygZtZK3bCZ4pNbhTxecdlJqke/FJ/b IyhMF+QkC7UqxslDN2K2ZvrDuoGTb5hcty0VH92kHb+4s1H1z08CtVIz+zH2oy52ourZHyW/fyD5 +3u9V7DqiTW65ndgqlatF53ZLcy+KKgpExZeZeycGM9Abf/x2in3cT+lsBhnT+V/Tkq2fyGK2yHM v0I6rd63t27EHM0dzzIOLm0eiz5iiHj3GtHlY8LCq6TTMI5uuvAhLQZ/UN/9li5iqGTfd8Kcy6L0 eMbFSzdspmbWSt31EaKZcBfpdMLU04KKAkF+qlDVyDi46GLG60bN00y9nyQyc5JJZqd8Z7dk9zfi 45uFhWmk1+u9Q7Qj52oWPMfYO3dB4Zf88VHLSL5wyJAsrZqGhalniH21o6u3dtgM3ch52hFzTHif rbkRog/p1/TlWcmWz0Tn9ggzkxiJTNd3jHbaA9rxS1qs09QIsUYqmVg9qVvPEzC6NlPeYjU+75Tn j0/Ki87tJY1KHxrDvnOiNd3o+aLU06JT29gGdP5l0yo6WTalctW/PpJ/tFTy2/vacYuuvfMWAAAA AAAAAHoggUKhQCoAAAAAAAAAAAAAALRgQ2OgAwAAAAAAAAAAAADYDjGSAOAmIH+L79BJ5o2dDQAA AAAAAAAAcAtCAzrAzUB08QgSAQAAAAAAAAAAwLIwBjoAAAAAAAAAAAAAQBswBjoAAAAAAAAAAAAA QBvQgA4AAAAAAAAAAAAA0AY0oAMAAAAAAAAAAAAAtEG8Xra+e/dgxZeP8l30+RxkGAAAAAAAAAAA AAAQUUODp7U3gR7oAAAAAAAAAAAAAABtQAM6AAAAAAAAAAAAAEAb0IAOAAAAAAAAAAAAANAGNKAD AAAAAAAAAAAAALQBDegAAAAAAAAAAAAAAG1AAzoAAAAAAAAAAAAAQBvQgA4AAAAAAAAAAAAA0AY0 oAMA2Drh+xnC9zNSK9VICgAAAAAwG64qAQDgVnD/Ayun3D7XgisUm/dnMkVjv91HQ05fcCkqkyjV Ck/XvGExiUtmNro7I5MAAAAAAAAAAAAAoIsVF5eoVSoiSrpwydXVJTDAXywWd3Kd5vx9xOGzY7/5 TdrQpBeLqkICGJHQLbcwZteR3icTdny2qs7XE1kFAAAAAAAAAAAAAF3j5KnT36z5vrS0jP34wouv EZGdXD54yMDHHn3Ix8fb7DWb04DOCIWSRuXlORPOL5+jcnIgIrvqutvfX+ubkjnm/37f+/bjyDAA AAAAAAAAAAAA6ALHjp96998fEdHwYUNSrqQpFIoFd8zJyy9MSbly8eJlN3e3zqzcnAb0jNuGlvUJ qfPz4uY0uTmfenTJgqc+CIxPFqvUWpkU2QYAAAAAAAAAAAAA1rbxtz+I6K5lix741z33P7BSoVA8 tvJhItJoNCUlpVKJpDMrN/Mlosat56zqQB8iEur0djV1yDMAAAAAAAAAAAAA6AK5uXlENHbsqBbz JRJJUFBgJ1cutNRe2tfUsxNKZyfkGQAAAAAAAAAAAAB0AZlMTkTV1TXWWLnFGtD9L6QSUYOnm8ZO hjwDAAAAAAAAAAAAgC4wbNhgIlqz5vsrV9IsvnLLNKCLNNpBf/xNRKnTxiDDAAAAAAAAAAAAAKBr rFzxYEhwUEFh0ZNPv1hYWERE8QlJGo3GIiu3TAP6qG83OxeXK12cLs++DRkGAAAAAAAAAAAAAF3D 3d3tm//998EH7nF3d2cYhoheevnNhYvvXf312qqq6k6u3AIN6JEH4vr9dZSIjjx7r9IFA6ADAAAA AAAAAAAAQNeRyWTLli7atPFHPz9fIurbN6qpqXHHzr8eeOjxzMyszqy5sw3owecuj/9yHRGdvX9+ 7vD+yCoAAAAAAAAAAAAA6HpCoVAkEhHRV198sn7d97EDYhQKxZr/+6FT6+zMH/tfTJv6wVqhTn9x /uTExdORQwAAAAAAAAAAAADQ7by9vB584F4iSktL78x6zG9A903OmPH2N2KV5vKciXGPLEKWAAAA AAAAAAAAAICNqK6uISKZXNaZlZjZgO6dlj3zza/FStXluZNOrlyCzAAAAAAAAAAAAACArrd7976r 6RnGc/R6fWLSxW/+73simjxpQmdWLjbjbzwz82a9sVrSpLw0b9KpFYuRQwAAAAAAAAAAAADQLQ4d OfbFV984OzsFBvhXVFQQ0dz5S5VKJRGNGzv6wQfv7czKzWlAn/3ql1JFo14scs8tmv3qF60X2P3B M8g2AAAAAAAAAAAAALC2RXfOd3JyzM7Ozc7JUypVROTr6xMV1WfyxPGDBsV2cuXmNKDL6huISKjV BSSlInsAAAAAAAAAAAAAoLuMGjV81Kjh7PT9D6wsKCj8/tvVllq5OQ3oa/f8H3IFAAAAAAAAAAAA AG5uYiQBAAAAAAAAAAAAANwE7pg/u7a2zoIrRAM6AAAAAAAAAAAAANwM5s2dZdkVCpGmAAAAAAAA AAAAAACtoQEdAAAAAAAAAAAAAKANaEAHAAAAAAAAAAAAAGgDxkAHALB1+tfCkQgAAAAA0Em4qgQA ADADeqADAAAAAAAAAAAAALRBoFAokAoAAAAAAAAAAAAAAC2gBzoAAAAAAAAAAAAAQBvQgA4AAAAA AAAAAAAA0AY0oAMAAAAAAAAAAAAAtAEN6AAAAAAAAAAAAAAAbUADOgAAAAAAAAAAAABAG9CADgAA AAAAAAAAAADQBjSgAwAAAAAAAAAAAAC0QXwrHGRtUfbGxycQUf/ZD4x98K0u2OKaO0LYiUELVo68 52XEGbQjZf/GM+s/YRhmxN0v9pu+/OY4KJ1WffmvXzJO7qouyNSplVIHZ0dPP+/w2HGP/Fso6hnV DkoxH3Vl+RtWjG09f+W2XLOXRB7dxDUDIJa6oGZAytt+vvdQN8G1je2UYkvFJ67WAAAAoGt06mov Ly/v4MGDKSkpRPTpp5/a7EHKXdzZCTtn986sh7tEa5+t3fUxen3K/o0ZJ3ZW5adrmhSP/JHe1jK6 jBO7sk7/XZ55UVlbqdfpZE6ubgFhfv1GhI+d4xYY3uLY7/x0l3f4gBbJwl653uhC9vS6jxK3rmmd RGsXhUtk9s4+Qb59h8fOfcjJK+BWK4Sn132sUtQQ0ZkNn940t7h7P3goP/Eo91FZV6Wsq5LIHTpz h9lOJJtaNntc1PEsxZmn9mSe3F2edbmxuoxhGDtnd++I2LDRs8NGzxSKJShxqBm6JT7bL54tzpiI z1s8lro4PlsEp1AssXPx8A6PjRg/r/eomQKBoMWqTI3P7k35WzzfraGLr22Ix+VNz72ivsnis6am 5q+//uI+RkdHDx482HiBDRs28FnP3XffzX9J/lu/Fm8Mk5eXl5ubW1VVpVQqGYaRyWSenp7BwcHB wcFCoZDber9+/QYOHMj9YVJSUnJycutNAwAA3DrMvODLyso6cOBAenq6LR+bVq0SS2VEJLV3ForE ep3WzsWD/UqjahJLpAKh6FbI4/N/fHn+9y/aWaCmMHPfR49UF2QYz2ysLmusLiu8HNdUWzF+xfst /iQv/hDXgN5Jeq1Gpa0tz6otz7qceXL30q/+kTm63pqlUSC8SYZUKrh40vgO0yM0WiJ3qC3O7nPb HVaN5Js46jo8dkVl8d8fPVKWcdF4ZkNVafaZ/eUZl8LGzLLSsTt6+M379+/lmZfKMi4UJZ9urC7v /JI3seKUc9tfW0gmdpSz/ZrB9sumeSmPWOoCXVAz8I9PvVbTUFmSXVmSfeZvv+hh01/+Vn59xwuz 49NSKX+z1iE9Aq5trFSKb474LC4ubuejjWy9sbHx2LFjlZWVxjObmpry8/MrKyuDg4NRzAEAANph TgP61q1b4+LiiMjPz69///779++31N4wDNO6v4/ZM3e+uUzdWBfQf3RA/9ESO0eVoqaptjLhz28K ko6VpMbP+2CzT8RAk3YvYMAYbrquJLe+rKD1fBuUevAPbto9OLLFt7UluVtfXsD2ASEikVjqFhwh lto1VBbXVxQJBMLYeY+0Xmde4tGhS56xyO759BlUnnVZr9UQUWN1WeapPX1vv+uWKoQjlq86s/4T gVAw8p6Xbo4jKr2ayE1PeW51xLi5hhKq11spkk0tmz0u6tovxQ2VJX++OIe7ZZU6OLv692J0utqS XHVjfb8Z9xh3jrPssQtFYv+Ykf4xI+n6p0w6s+RNrCL78k1ZM7Qfn2MfeltRUaQoLy5NT+TKZujQ yU7eQc6+QV1TNk1KecRSV+qCmqH9+GQ5eQc6+4bo1Kqq/KvqhjoiKr5ybudbd935yQ6RRGZ2fFo8 5W/WOqRH6Pprm9Yh2v6quuvaxrxSfJPFJ9dmLRKJdDpdTU1NY2Ojvb09t4Cvry83XV9f39DQ0Hq+ qUvy3zoRNTY27tu3r6mpif0okUicnZ0Zhqmvr9doNH369BHilzYAAIB2mdOAPnjw4Nzc3KlTp8bE xJSVlVmwAb11m7jZM7VqVUXmJZ1WXZ2ffnnPL+zMsxs/4xaoyLxkagP63Hc2ctPGF4jG821QQ2UJ OxE6fOqMV75v8e3BL57lWs8HzHlw+LLnJHaO7Me60ryyjAsu11+v27l6NtVUlF1NUtZXy53cOr97 Cz7eXpIav+2VBexHRUXRrVYI+027u9+0m+pZSG1TAzfde9T0ayW0c5fm7USyqWWzx0Vd+6X42NrX 2NZzgVA05oE3+k67WySWEhGj1+UlHPWJHHTTl7ie8jhRRVbyTVkztB+f/Wf9q3XZnPHaj11ZNk1K ecTSTab9+GSFj53DdujWadVnN3yWtH0tEVXmXEncusa4u4Cp8WnxlEe+d6Ouv7ZpM0Tb0bPO7zdT fGq12rKyMiISiUQhISFZWVlEVFxcHBYWxi0zefJkbtp4OBTj+aYuyX/rRHTu3Dm29VwgEAwZMiQi IoJtMWcYpqioyNPTE2UcAACgfeY0oIeGhj777LM2fmCMTjP6gTfqSvPqS/OLUs4o66rZ+f4xI529 gxy9Ajx79e2aPVFUFJ1Z/0n+heMqRa2ds3vQwPFDlz7bYlxCVUNtyv7fsk79VVear1E2OLj5+MeM jJ3/iHtQHwskBWPoGuMWENbiq6LLp0vT4tnpftPvGfPAm8bfOvsEO/u0fJoveNBtaUe2Mow+P/Fo xPj5Fkkin8hr4/TpdVqz15N56q/9nz7GTk988rOoSYuMEoH59cFhbCNjYOzYOW9v4J/yGSd2/fOf J4w3NOft9b7RwxO2rE4/tl1RWSIUiQbdsZK7x64rzbu468fCy3H1ZQV6jVru4u7g5u0W1Ccwdmzv EdPEcvs218m60QD6WpUy7fCWq8e21xZnqxV1cmc3n8ghMTPuCeg/mluGHSjTNSBs2deHStPiE7au KUmNVzfWy53cggaOG7rkmda5yROfrRNR8r715VmXyzMvVuVd5WZ+uyiiw6PrfCRbO+psuRSXXk3M OXeAnR5x94tcYyURCYSikKGTzD52nvluDdX56enHdxReiqspylQ31AvFYgd3X7++w/tNv6fNwaMk cvuawsxzmz4vuHhSp1a6+PfqP+tfxjUA/yMyqbzz3M/kfevKMi6WZVyszjeMe5a4dY1xHz2uaPCs GaxU1/WUssm/ruOf8tY4I5hUJ3dXLFnvLGNqKbYek+JTJJaOuu/ViuzkggsniOjy3l8H3/k49w4J PvFpasoTkaZJkfz3hpxzB6oL0tWNConcwc7Fwz04sveoGWw355sv33mWo6LLpwsvxxGRWCa//+dE ifxaj9qU/RuPrnmFnZ7+8re9RkwzNTBuzWsbU68V0w5vubT7p+qCdLHM3j9m1IjlLzq4+/760Aj2 QQ1Tj93U+ORzRW2Mz9WaqTIyMpKSkoho8ODBvXv3bnOZsrIynU5HRO7u7j4+PmwTdlFRUYsmbCvh s/WKioqCAsMTYLGxsZGR1551EAgEAQGdHS6fTyoBAAD0dDftK+Mldo4xM+4loqr8q1tXzSMikViq 06p9+gzqyvFP60sL/lw1r7G6jP3YUFWaemhzbvyhOz/dxV3PVeZc2ffhw3Vl+dxf1ZXl1x3KTz+2 Y9LT/w0fO8die9Oq237GyV3shEgsHX7X83zWYe/m7RkaXZGdkht/2FIN6G0+T2CG0GFTZI6ubIf6 7Li9xo1KZVcTuAEuuPlmp3zO2QPnNn1Rknqe/ajXavz6jWCnS1Ljd79zj0Z5rZtSQ2VJQ2VJWcbF tMNb5G/8HDx4oqnHVVeWv+/DhytzrlxbZ1VpVtyerLg9/WfeP/bhd4wXrinMPLPh08Q/v+FuyRqr y9IO/5kbf2jhp7udvAOtt/Vja1/ronJliYDhH3U2Xoqzz/xtqPTkDjGz7rfUsZsUdZalVSm3vbqQ ezKGiPQ6bW1xTm1xTuqhzWMffLt/q8OsLcre8vwsjcrwYHJFVvLh1S/UFmWPWL6q80d0o/LOfz+P rX3dsknUZXWdLZdNPnWdSSlvjTMCz/3sxlgyo3RY9oi6FO/4jJ66lG1Ab6qtrMhO8Y6INePcwT/w 9rz/gHFnYZWiRqWoqSnMlDk4sw3ot0K+t1mOXPx7sw3oWpUy9/xB42KYG3+InZA5ugYPseKV1U15 bcPzWvHsxs/iN69mp7VqVVbcnvzEo+Hj5rKt59Zm6hU1n6s1MyQlJalUKiJKSEi4UdNwUZGh/Pr4 +Hh5eRn2v6SkzRFHLY7P1rnWc7FYbNx6bil8UgkAAKCnu8kHO9M0KfZ9+LBG2ejTZ9DIe18mosSt a7hW4y6QcXJXY3WZo6e/T8RAkUTKzmyqrYz/4yt2Wqts3PfxI9wdu1tguFdYf3bAYp1WfXj187VF 2dbbvdK0BHbCJ3IQ//FY2AvW/KSjnRz20eJEEhl3c5V/4bhW2ch9lXV6HzshsXPsNWK6SSkfNHDc jNd+HPfwu75RQ9g5l/b8XJJ6XiAUeYX1946IdfIK8O87nP3q1M/vG671BQLPXn19o4Y6+wSxndec fYODBk1gF/MIjR44/5HwcXP9ooe1n/I6rfrvjx7h7vGcfYJ8o4dJ5A7cnlzY8V2LP0nY8jXD6J19 Q7wjYrmoU9ZVc1HHn0lbDxgwhv1n3EzPzbTxVwX03FJcdCmOnfCOiJXI7CyyTjOizoLEMnns3IeI SCSWuvj38o0e5uIXaviOYU79/F5tScuuaqmHNmtUTW6B4a7+127bErZ+U5WbauoR8S/v/PezdRFw 8g5ss2jwrBmsVNf1OB3WdfxT3kpnBJ772Y2xZKWzjBml2KZ4h11rMa/INm38H/4pT0SaJsXeDx7k Ws/FcnvviFifiIFSB2ciip6y5GbNd57lqPfIadxRZJzcfW3nNarCiyfZ6fCxs9lRy6x0jrtZr206 zM2K7JSELf/jlnf2DXEPidKqlVf++c3sjZpUOnheUXM6vFrrpHaawrkhyL29vZ2cnNjBx9VqdYvX dVoJn62XlpYassDDQyy2Yv+5LvjBAAAAoLvctD3QWcfWvl5bnENEI+99xTdy8KU9P9eV5B1Z/aJP n8GdfKCPv9H/ej127sNEVFuU/cfzM9mWjvzEo+y3Kf9srCvJIyKBUHT7i9/0HjmdiMqzLu94bZFG 2ahVqy7s+n78ivfN3npDVSk3bfwiQZaiwnDJ5RYYzs385z9PNNVVGS/WYiDp4METE/78n7Kuuiw9 yfhZ0c4QCIRsLxhusB3zRE1alLxvHRHpNOqc+IPhYwxtTFyjUsS4OWKZ3KSUlzm6hg6dTESKyuKS VMOIN3Jn93nv/c4+12/cxaMqJ4WdiJxw56Sn/sNOa1RNxcmnBUIRt5hbYPio+wx9mtp/4VLaoS0V 2YZ1Dr/rhSGLniQilaJm+2uLq/LSiOj85q/6Trtbcv1zrNySVXlpf66aq1UpiSg/6Zip6WnS1rk4 scYbAtqPZGtHnS2X4obmzlYufiGWOnbzos6CBsx+wD04Mih2LPeAduqhzYdXv0BEeq0mP+GIy8z7 WvzJ1Oe/ZtuUrx7ZevDLZ4mIGCZ5/8ZxD79r0hGZVN557idXCthn5+nGo9nyrxmsUdf1xLLZfl3H P+WtdEbgXyd3YyxZ6SxjRim2BvPi08712nDAyvpqk+KTf8oTUcr+3+rLC9npyIl3jlvxPvs7KMMw FdnJXr1jTI3knpLvPMuRSCjqM2HBxV0/EFF+4hGtspFdbeGlOE3zD4d9bltgalT0xGubFoP2sIYt fdZ4jH7L1p8p+zdy/dNvW/kh+z7SouQzu99ertOqzTtek0oHzytqY+1frZln8ODBCQkJRDRkyJC2 M7Ghoa6ujoiEQiHbAdzLyys3N5eIiouLrT22OM+tc+8OdXJy4rnm5ORkbvj1zqcSAADATeAm74E+ 6r7XoiYtCh062b/fCKFYMvKel138e017aW2XtZ67BoSxV3JE5OLfK3TYFHa6scbwiH1mnKGxI2jg ePaOnYi8eseEjZ7FThdePGXephm9rq4k9/i3b3Bz3FqNdaturGcnJHYO3MziK+cKL540/tfir3wi B0ntnYgor3OXpNclVKBhnL6Mk7uLks+YPRK6d0QsN1htVnPaVuZcYVtGyGhMg06m/JCFT3AbMr6I lzu7G7Z+et/F3T+yN94SmV3w4IlBA8ebcUQZJwwPTDh5BQy+0zDqsczRlb3nISJ1Q11R8mnjP5E7 uw2641F22j04MmRoy6iz6tYtjk8kWzXqbLwUK+sMPYzYUmmRY+/2fJfYOfQacbvx8KZ9jAaM4n4z 4PhGDeV6ZPeZsIB7QXRxylmLHNGNyrup+2lBXVbX2XLZtGBdZ6UzAv/97MZYstJZphuPqPPxKZbK uGm2VdGM+OQj++x+dsLezXv8ig+4p4gEAgHXen7r5Hub5ajv1GVcRuScP8hO5zZPOPsGc33YrXrs NhWfXVN/Fl85Z1hhQBjbek5E/v1GhA6fSl3C1CvqDq/WzNO7d++FCxcuXLgwNDS0zQW4EVTc3d3Z zt0+Pj4tvrIenltXKg31mEQiscZudJhKAAAAN4GbvAe6vZvXxCc/464dw0bPCh0+1dQnPTvDp88g 44+OHr7sBLdLtcWGp8LzEg5zHYuMNVQWm7fp+M2rz236nPsYNWlR+JjZLZYRSqTsnrS+P2yHUCQO jB2XFbcnL/7QsKWWeZ3shMc+2v/Z4w2VJZomxY7XF7fZp4anyEkL4375gIhy4w/pNCqRRMZ1yXTx 7+UTOcQiKX+j+4eBdzzK3g5pmhQnf3gn7uf3gwaN73v73SFDJgmE5vxeVV2QwU74Rg8TCEXcfL/o YdeWyU8PGXLtXZEBMaON33jm5OXfIuqsunWL4xPJVo06Gy/FAqGYSEVEOo3GUsfe7fleW5J7Ycd3 hZdOKSoKW9dOjF7XYo5HaLTxR8+wmNL0JCJSlBdZ5IhuVN5N3U/L6pq6zpbLpgXrOiudEfjvZzfG kpXOMt1bOjoZn9rmFypQW79NWvCKhXspqH/fEewjI13GBvO9zXLkFhThEzmEfeN95snd7G+lefGH 2W/N6H5OPfPaxsk70Nk3pNXMIOvVn/WlhiGtWlwFeYRGZ576qwtC1NQr6g6v1qyEG0GFa7n29vZm JyorK9VqtVQq7fatc79I6XkPv+ng4GDcXb2+vr6hoYEAAABuYTd5AzrL+KHIrmw9JyK583UD/Blf prNUitr216Axuoszj8zBZfDCx2Nm3t/6ctPe1ZPtrlhv9Nq0e78/Qx09WRk8eEJW3J6yzEtNtTce 3Y9h2vt4Pd+oobNe/+nM+k9ym2+KzNbntgWn133M6HVaZWN+0vHQYVO4tywav2qvkynv6Onf5vyY GffKHFzO/vYZm7B6nTb3/KHc84dc/HtNfuYLrmMsf6r6GnbCzsXjRqGlaWq4Plu92o86q27detqJ ZPPwjDobL8V2rh6akga6/knwTh579+Z7WcbFHW8sMR7Xu0MtBhkQS+XNKd9okSNqs7ybsZ+W1TV1 nS2XTUvWddY5I/Dcz+6NJWucZbq9dHQyPuvLCrhpB3cf8+KTD+5BQPtWW7kF8/1G5ajv7cvYBvS8 hMNaZWNdeQH3uoLICXd2zbF3e3zeaNCe1ixVf3I/I8kcXY3nm/S4W2eYekXd4dWaNej1+pKSEna6 9ZgnDMOUlJQEBwd3+9bt7Ozq6+vJaCyXDoWGhg4ceC2Rk5KS+I/oAgAAcFO6JRrQu1GHA25K7Z2U dVVE5OQV4My9askSHDz8HD39FRVFqobauF8+uPLPpoWf7ZLYORov4xU2gL0qLUo5x3Zg5Lny4MET iIgYJi/hSMuQksq0ahURqZsUxvNVDXXcAq1XmHpo85H/vcR2TZI5ujo0dxsxg72bV/Cg8extQ86Z /R4hUex7ogQCYZ8JCyyV8u3kbMT4eeFj5+QmHL7y94bchCPsQdUWZe9+e/ld/ztiPLIqHzInl8bq ciJq8VuF8dCWLe4ZxHLLvEnSvK1bHJ9INg/PqLPxUuwWGM6W4rL0JEsde/fm+5n1H7PtL2K5/cTH PwkaNF7m4EJGg/+2xjVCsbi3OEjtHS1yRG3GgBn7aVldU9fZctm0YF1nvTMCn/3s3liyxlmm20tH J+OTG7yCiLwjYs2LTz5EEhnbPdbwssQuZIP5fqNyFDZm9skf3lE31mvVqpz4g9yjRT6RQ5x9g7vm 2G0qPrum/hTL7NiwVClqjOe3OOFalUlX1JZ6CYdJKisrNe0+/1dUVGS9BnT+W3d2dmYb0LvmvaYA AAA3JTSgdzPPXv0KLhwnItfA8Nlv/mrBNUdPWRI1eXH8H1+yj4jWFGVdPbqt3/R7jJcJHTo58+Ru ItI0KVL2/9Z/1v08V+7g7uMRGl2ZcyWvVfcWezfvutJ8IirPvGQ8n/to797Gpfz5TZ+zl8XOviGL /7vXeEx2M0ROWsQ2KuUmHPaJMrzmNDB2rKOHXxekPBEJhMLQoZNDh05uqCqN3/xV8r71RKRurM88 9VeMia9Ncw0IY2/zSq6cY/Q6rkON8e29a0CYleKze7fOP5LNY6mo695SHDxoQu75Q0SkqCjKOXeA G/SzM8fevflekpbATvQZP58b2bz9/vVlGReuq20yLrITzj7B1jsiM/ZTIBSxya6xUM/c7q3rbL9s 8k95q54RbDyWbKR0WJzZ8cnodZf3rmOn3YP6uLT6QcWC8ensG8z+7lV46VSbr59FvhORRGYXMX4e eylVkHSivtzwfEDkhAW2c+w3X/3JBWfp1UTj+VX5V7uyIFvwitoauBFUpFKpo+O13z80Gg3bYG3V YdD5b93f37+wsJCIGhoaCgoKAgMDuz3pAAAAepyb/CWito+7wchPPHp63UfG3Tr0Om1Janxxyjmz Vy4QCAbMeZD7yPXZ4YSNmcW1s5z+9QOTxjQMGTyRiPKTjrWY7x8zip0oS7+QuO3/dBqVTqO6sOM7 rgE9KHZs67XVlxeyE8GDJ3S+rSR02BT2gdPG6rKsU3vZmVGTF3dByh/530v5iUe5cT8d3H0G3bGS +5brGGtChIyZwyVRwp/fsNON1WXc0JkyR9eA5jS3fHx269b5R7J5LBV13VyKx86ROjiz04dXv2Bc JPVaTe75Q9xb1/gfe/fmu7C5LaO6IINtBdAqG098/3Y7f1KRlZyyfyM7feXAJm58W/9+I613RGbs p52L4ZVoefGHuNqgvYGwbLiu6xFlk3/KWzWVbDyWbKR0WIMZ8aluqDvw+dNVeWnsx4F3rLBqfAYO GMNO1JXkHv/2dW6kIL1Om332n9ZXZbdsvvedaniJZUlafGlaIhGJxNLwsWa+dAHXNnxwI8LXFGYm 7zP8pFSanpTTPFaYtVn8ito8GRkZW7Zs2bJlS1ZWVutvuRbqyMjIGUbGjRtn2M+mppqaGivtG/+t h4SEcK8PPX36NNfyTkR6vb6wsJBtXrdSKtnOkgAAAJ2BHujdLHLinVf++Y3t3JG4dc2Fnd+7B0VI 7BybaioUlUValbL/7Af8+g4ze/3GIxUy1HIUcpFENvHJz3a/ey+j12nVqv2fPubo4efkHahuUtQU Zra/5uAhExO2fqNqaDlu7IDZ/7p6ZCv7SPLpXz88u+FTMnqBj0giNb5baK3FWMbmEUlk4WPnsJf7 eUlHiUjq4Bw6/HbzUj4rbm9ZelJpehLXrZWIfn9qql+/EQExI8Ouf+nTlQObrhzYJBCKXHxD5C4e xDBs/x2WZ/OrDkvTEqoLMqoLMmoKMkqMuvZse+VOj9Aoj5Boz17R7DsAIycturznF7a/z9mNn6Ue +sPOyb0yL5W7oxhx94vGr4GycHx269b5R3IndTLqurcU2zm7j1z+0rG1rxGRsr569zv3sKVY1VBX X5qnUTUNW/ps6NDJJh07/3wvy7hYU5DBBnNJWjy3hh2vL/YIiXYPjfIIjWYHKuW/pHtIVMmVc0RU nHJ2/SNjHL38q/KudvjM+NE1r1zY8R0R1RQZbp8EQlG/aXebekT8y7sZ++nfb2TGiV1EVFea/+uD wx09/dSNCmVd1cptuabWDNao66xUNi/99ZOiolhRUWTcjXHv+w84+QQ7+wR1wRmBT8pb6YzAU/fG kjXqef5HxL9msOq5I+PErrKMi5omRWXOFZ1Gzc7sNeL2yIkLTY1Pk0pxzKz7U/ZvZLuTJ+9bf/XI NrfAMJ1WU1eSq1E2tn5P+02T76aWI8/e/bzC+pdnXuJeuxoydFKLsblNOGv3wGsbNkRbz5/7zkYr 1Z99b78red96htET0bG1ryft+FZq51iVd7X1Ozn5l2KTSgfPK2prS0pKUqlURJSQkNC7d2/jr1Qq VVWVoR2fe4cny83NTSqVqtVqIiouLnZ1dbX4jpm0dZlMNnDgwHPnzrF/eOjQIXt7ewcHB41Go1Ao tFrtgAEDAgICrJFKNrUkAABAZ5jZgL579252orGxscWc2bNn9/xk6TpCkXjmaz/u/+zxwkuniEiv 1VRkp3TlDgTGjp3+8rcHv3xW3VBHRIrKYkVlMZ8/9IkcLLV3an1f5BHad9yK947932ts5yPj62yh WDLxiU+t/VQsK2rSIkN/GYYhooixc1qMvc4/5f/+5NHWM6vyr1blX03et25lW80ljF5XU5RFRdf1 gwgadFvo8Kns9NaX72hzWyWp50tSz7PT7P2wWCqb/sp3e977F9ssWFeSxw54zYqd93C/6cutWEF0 69ZRinnqN325Vt106uf32WjnX4o7n+9/vjinzTUUJZ8pSj5jHMn8lxy6+Knd7xgeY+eOJWrSopqi bK50tDDn7fUHvnim5voSN2zZc86+IaYeEf/ybsZ+Dl3yTG78YU2Tgo0T430wtWawRl1nJW32P+Ue jGi/Ad1SOkx5q54ROt69bo0la9Tz/I+If81gVfVlBcYvDiWiiPHzJz7xiRmrMqkUO3sHTX72q4Of P8W2oWuUDW02kt58+W5GOYqeusx4bMCI2+4wO7t74rVN6xC1No/Q6KFLnuZ65bNJJLV38giJMh7r xqRSbMY5rsMr6i7TeoSl4uJihmGISCQSeXq2fMWRj49Pfn4+ERUVFUVHW76539St9+nTR6fTJSQY xllqbGzk7uKtmkq2uSQAAIAZzGxAP3r06I3moAHdVHJn9znvbMyNP5R+dFtZxoWm6nK9Xid3dnfy CgjoPzpqyhKLbYlpu29L6LApy9eeSN63IT/paHVBhkpRKxJL7Zzd7d29vcIG3OgKVSgSB8aOzYrb 2/qrvlOXeYcNuLj7x+LkMw3VpQKBwN7dJyBm9IA5D7gHR7axX3q9xVPVOyLWPTiSewq7zU5k1kj5 8SveL0o+XZ55SVlfo26sF0kkdi6eHqHRYaNmRoyfx42zaRIXv9CFn+2+cmBTxsndtcXZmqYGBzcf 74iB/aYv948Zae347N6tmxTJpq3DolHX7aU4du7DIUMmXd67rij5tKKiWKtqtHP2cPIODBw4rs/4 O8w49m7M96CB46etWpO4dU1VXppAKPIIje57+919Jiy4tPvHNhsTnb2DAmPHLfrPnrMbPs2NP6RR NbkHhg+Y81DE+HlWPSJT95OI3ALDF/3nr/jNqwsvnWqsKReJJQ7uvn59h/fEuq6nlE3+Kd91qWR7 sWQLpaMrdBSfYpnc3s3bL3p49JSlN3osw+Lx2Wv41CVfHUje+2vBhRO1JblaVZPUwcnR0z9wwNg+ rcb4vpXzvc/4+XE/v8f+0iBzdA0ZOqkza8O1DR9Dlzzj7BN8cfeP1QXpIrHMv/+okctXxW9e3aIB 3UqscUVthsGDByckJAgEgiFDhrT4ihsIxcPDQyRquT/e3t5sE3Z5eblWqxWLLfzYtxlbj46ODggI uHr1allZWUNDg06nk8lkDg4Ofn5+vXr1slIq2dSSAAAAnSFQKBRIhZvb2oVhbDfw0GFTZrz6gw3u YXHK2e2vLWKnhy5+etiy55BrYO1I7llRdysfO6BsIoUB8Ql6nfaXB4Yp66qIqN/05eNXvI/47Jb4 3PvhQzln/yEikUT6yB/piEwAAAC4ReAlojc/7jWhOecO/P7MNFvbva2r5u18c9m1vfUKsLU9hJsv kntc1N3Kxw4omwCIT8g4sUvZ/N7I6KnLEJ/dFZ/cMPQOHr4ISwAAALh1oAH95hc5eRE3XZWbamu7 V5qexI2T7ujhFzZqBrIMrB3JPS7qbuVjB5RNAMTnLUunVTN6XcGF49zbFAJiRnn1jkF8Wjs+lfXV u9+9N/f8Ib1Ww85hGCZp+9ra4hz2Y+CAMYhPAAAAuHWIkQQ3vSGLnrJz8Uw/tr26IEPTaHMj9gjF EonM3skn0L/fqIHzHpY6OCPLwNqR3OOi7lY+dkDZBEB83rK+WxwpEAq5dmGhWDL6gTcRn10RnwyT n3g0P/GoWCZ3CwgXyezqSnIbq8vYL8VS2cB5KxCfAAAAcOvAGOgAAAAAAGBz1twRwk0LBMIJj38c NXkxkqULKOuqfrpvUJtfSeQOk5/5oteI25FKAAAAcOtAD3QAAAAAALA5Dh6+TTUVYrm9X9SQwQuf 8I0aijTpGhJ7x1H3vVp0Oa4yN02lqNFp1BI7R7fAsID+Y/pOu4sbuh0AAADgFoEe6AAAAAAAAAAA AAAAbcBLRAEAAAAAAAAAAAAA2oAGdAAAAAAAAAAAAACANqABHQAAAAAAAAAAAACgDWhABwAAAAAA AAAAAABoAxrQAQAAAAAAAAAAAADagAZ0AAAAAAAAAAAAAIA2oAEdAAAAAAAAAAAAAKANaEAHAAAA AAAAAAAAAGgDGtABAAAAAAAAAAAAANogvhUOsrYoe+PjE4io/+wHxj74FnLdqurK8jesGNt6/spt uV28Jyn7N55Z/wnDMCPufrHf9OXImh6dm12f72vuCGEnBi1YOfKelxEMJjm97qPErWtsP1oQdV25 n91bJ+OMYHE6rfryX79knNxVXZCpUyulDs6Onn7e4bHjHvm3UHRLXF7eNGWzpxQQlOLuSqWekvKI EAAAALAeM+9wmpqaTp06lZKSUl5erlarXV1do6KiJk+e7OTkZIMHKXdxZyfsnN0tuFpGr0/ZvzHj xM6q/HRNk+KRP9JbL1OVl/b707dzHwfOf2TUfa91wSFzd1A3cis0aZ1e97FKUUNEZzZ8isvoNnVX fCLfzSjFAqFIau/kGtA7ZPDEmFn3yRxcEMBdkOwzX/8pZMgkIjr4xTNXj25jZzp4+N77/ZmbMup6 RNlskUdCscTOxcM7PDZi/Lzeo2YKBIKbu2awHXs/eCg/8Sj3UVlXpayrksgdur313LxSbPsYhsk4 viPt8J8VOSkqRa3M0cUztG+fCQsixs+/Udh3ffHsEXVID82jbkylnlJ/dtd+rl0ULpHZO/sE+fYd Hjv3ISevgJ5VtwAAAAAf5tzkJCQkbNu2TalUikQiX19foVBYUlJy8uTJixcvPv744x4eHjZybFq1 SiyVEZHU3lkoEut1WjsXw75pVE1iiVQgFHVm/ef/+PL871+0v0x+4jHjj3kJR2+CBsr2OXr4zfv3 7+WZl8oyLhQln26sLrfgyotTzm1/bSGZ2LVKIMRQRWbGp1Vz09raz3fzYqkbMXqdSlFTmpZQmpaQ vH/D/Pc3O/sEI4atrbYoi4ZMIqLK3NTORx1YI5X0Wk1DZUl2ZUn2mb/9oodNf/lbebs/lltq6z2u DrGsgosnjVvPPUKjJXKH2uLsPrfd0dNLsW3SadV/f7wi9/whbk5TTUV+0rH8pGMZJ3ZOe2mtSCy1 weJppXJkmzWt9fLIdlKp61O+R0SIXqtRaWvLs2rLsy5nnty99Kt/ZI6uPbe2AQAAgDaZc3khFApV KtWYMWPefPPNZ5555qmnnnrllVdCQ0Pr6+t37NjRmb1hGMaCM3e+uWzTU1OOf/dm9pm/JXaORNRU W5nw5zc731j60/IBZZmXOpl2qQf/4KbdgyPbXCav+faSbcqvyktrqCzpygx28g4MGDCm9T8rhpRI 7B8zMnbew1Of/zpy4kLLrrwi+zL/hUcsXyVzdJU7u4285yUUdfPi06q5aSU8892kWOpebCn2jRoq dXBm5zRUlpxe9zECuAvUFGUTkV6nrSnM7HzU3eIsnkqti0bxlXM737pLp1F1wdZ7UB1iDaVXE7np Kc+tXvz5vjs+/PP+nxOipyztoaXYxsX98iHXMit3cvONGip3cmM/5p4/FPfLh7ZWPK1Rjmy8prVe HnV7KnVjyveICPHpM0golrDTjdVlmaf29NyqBgAAAG7EnB7oAwcODAoKMu5p7uTkNG/evC+//DIt LU2tVkulZvawaPPxRvNmatWqisxLOq26Oj/98p5f2JlnN37GLVCRecknYmBn0o5ragwdPnXGK9+3 XkCrUpZcOUtEIok0bMzstMN/ElFe4tHoKUu6LIPDx865mXrGVWQl81+437S7+027G4X8RmwhPq2B Z76bFEvdiyvFmibFH8/NrCvJJaLCiydteZ8t1ReyG7kH9anKv1pTmEVEtUXZOo2aiMRye62y0eyo u8VZPJW4oqHTqs9u+Cxp+1oiqsy5krh1zdAlz1h76z2oDrEGbVMDN9171HRu2qa6BptUim1ZfVlB 8t5f2engwROnvbRWLJVpVcp9Hz/CPgeQvG9di4EjTH3IsrsqsZvmus6MPOpBqdSNKd8jImTBx9tL UuO3vbKA/aioKOpZNQwAAADwYeY4la3HafHy8iIivV6vUCjc3d27/cAYnWb0A2/UlebVl+YXpZxR 1lWz8/1jRjp7Bzl6BXj26tvZTTB6dsItIKzNBYqS47RqFRF5h8cG9B/NNlDmJx0zbqBkR+p0DQhb 9vWh0rT4hK1rSlLj1Y31cie3oIHjhi55xnqjNGSe+mv/p4+x0xOf/Cxq0iKjQ2N+fXAYO15HYOzY OW9vYOdrVcq0w1uuHtteW5ytVtTJnd18IofEzLgnoP9oM3bgRq+6av36weR968oyLpZlXKzONww0 n7h1DbcMGQ3pnnFi1z//eaL1tm405jufIzIpj6rz09OP7yi8FFdTlKluqBeKxQ7uvn59h/ebfo93 +ABusdb7Oeft9b7RwxO2rE4/tl1RWSIUiQbdsbLo8unCy3FEJJbJ7/85USK355ZP2b/x6JpX2Onp L3/ba8Q0U9OfT3yaKu3wlku7f6ouSBfL7P1jRo1Y/qKDu++vD41QN9S1yAU+Kc8zlYYueYZ/vvOP JWOKiqIz6z/Jv3Bcpai1c3YPGjh+6NJnudtgNkIc3H2WfX14/6ePFaWc9gobMPPVH0qvJh5b+1pT beWAOQ8Ov+uFzpdZiZ1j8KDbLu/9lYi0qkZTo45VV5p3cdePhZfj6ssK9Bq13MXdwc3bLahPYOzY 3iOmiY1iTNVQm7L/t6xTf9WV5muUDQ5uPv4xI2PnP+Ie1KfFOrPP/nNh+9qqvKs6ncard8yQRU9K HTr7PgyLl01TOXkHVOVfrS3KpuaRHxw9/BSVxe3E541CqJP7WZWXtvXlBZomBRG5BUUs+HAr1+G6 w1RK3PZ/p3/9kIh6j5o5bdWa1iu/vOeX49+9SUS9Rk6f/tJakyKEz36aWiebRySWjrrv1Yrs5IIL J4jo8t5fB9/5uFAsMXXrfI7dpDqEZ9k0I0I0TYrkvzfknDtQXZCublRI5A52Lh7uwZG9R82IGDfX jFLMU/K+9eVZl8szL1blXeVmfrsoos3Dt2w9b41SbPE82vnGUmuctTNO7tbrtEREAsH4Fe+xD42J ZfJxj/x742O3EcPotZqME7sG3fHotfOF3L6mMPPcps8LLp7UqZUu/r36z/qX8ZUe6/tl0ZpWPye0 WUBMiqUO49Pi13VmXNNatnTwzyM2lsRy+/t/TmisLDm76b+Fl05pmxqc/UJjZtwbPXUZ1x/IGle/ /K9YTKo/+b97iWeJ60FX/iyfyMHctCESAAAA4OZisRc9KRQKdsLR0dEWDkxi5xgz414iqsq/unXV PCISiaU6rdqnzyDL98i+wXuB8poHmPbrN8I3ehg7XXDhBKPXtegZVFOYeWbDp4l/fsM1yjdWl6Ud /jM3/tDCT3c7eQdaI4lCh02RObqyL9vJjttrfLNRdjWBG+2am19Xlr/vw4crc65wizVUlWbF7cmK 29N/5v1jH37Herl5bO3r1litSUfEJ4+0KuW2VxeyScrS67S1xTm1xTmphzaPffDt/rPuv9HO5Jw9 cG7TFyWp5w1/qNX49Rvh4t+bvRXXqpS55w+Gj53DLZ8bb3hMWOboGjxkohmHzz8+eTq78bP4zavZ aa1alRW3Jz/xaPi4uWzrudkp32EqWTuW6ksL/lw1r7G6jNvV1EObc+MP3fnpLuOuZA1Vpf/894m8 xCNEVJxy9tzvX6Qe2swee/zm1VFTljh7B3U+aHVqw9gUDh5+hqQ2JepKUuN3v3OPRnmt62hDZUlD ZUlZxsW0w1vkb/wcPNgQS5U5V/Z9+HBdWb5xrtUdyk8/tmPS0/81DsULO7479fN73MfilLO73703 fPRsmyqbZmDH0VZUlWhUTWzTm3tIVIumN5OYt5+N1eV73vsX2yrt4OE7+81fudZzPqnk6hfKftVU 0/YLDBqb57v69zIpQvjvZ5eJnrqUbUBvqq2syE7xjog16c95Hjv/OsTUMwL/CKnMubLn/QeMOzmq FDUqRU1NYabMwZlrQOdfik2pQvm+x6Ub63n+pdjieRR9+13WOGsXXT7NTniERBlHgotviEdIFJvI xSlnjBvQa4uytzw/S6NqYj9WZCUfXv1CbVH2iOWrzEhJk2KJT3xa/LrO1Gtai5cOU/NIq2w8t/Gz lP2/cXVOZc6Vo2teqS3OGXXfq83FzSpXv9RVVyydqRV7ypU/p8teEgsAAADdxWJP2mZkZBCRi4uL 2eO3WIOmSbHvw4c1ykafPoNG3vsyESVuXZNxclfXbD0/4Qg74d93uItviIOHLxGpFDVl6RdaL5yw 5WuG0Tv7hnhHxIokhjRU1lXH//GVlXZPJJFxdwj5F44bP9GcdXofOyGxc+w1Yjqxb0b66BHuitPZ J8g3ephE7sB+vLTn5ws7vrNeSrYeur3F2O7cfI/Q6IHzHwkfN9cvehg39GSbzDiiDvNILJPHzn2I iERiqYt/L9/oYS7NrVfEMKd+fq+2xNAdJmjguBmv/Tju4Xd9o4ZwWyxJPS8QirzC+ntHxDp5Bfj3 Hd575DTuKDJO7r628xoVN4JH+NjZ5g2XYVJ8dqgiOyVhy/+4j86+Ie4hUVq18so/v5md8jxTyaR8 5x9L1yq3k7saq8scPf19IgZy+d5UW9m6bBZdPi1zcGGnL+76Qdp8UERU0emXLui1mpLU85mn97If w0bPMjXqiOjUz+8b7tUFAs9efX2jhjr7BLEDdzr7BgcNmsAuplU27vv4Ea5lwS0w3Cusv1AkZrPv 8Orn2R6dRFRXknt63Ufc+h09/HwiB4ulss5Us9Yom2YwvAGMYWqLsqtyU4nII+S6d13wjzqz91Oj atr7wQP15YVEJLV3mv3GL46e/ialkrOvoVdgY20FETF63XdLI9fcEbLmjhC2pDfWVHBlln+E8N9P M1LJbN5hsUY1UrKpW+d57PzrEJPKJv8I0TQp9n7wINc6KZbbe0fE+kQMZH+x4B4h4l+KzTsdGzce tT58a9Tz1ijFFs8jK521qwsMnXDdg1t2jnYLDDcsk5duPD/10GaNqsktMNzVv/e1nd/6TdX1b1Id MOfB8DFzfCIHt/OLl0mxxDM+LX5dZ9I1rTVKhxl5dGHn9xplg4tfqHtQH64vTtL2tVweWePql9Ph FYtJ62z9siV7N2/uW8/e/UwtcT3lyh8AAABuHZbpga7Vag8fPkxEI0aMsKnDO7b29driHCIaee8r vpGDL+35ua4k78jqF336DDZvFEJOQ1UpN81ec7dQX1ZQU5TFfusXPYyI/KKHZZzYRUT5SceMH/Tj DL/rhSGLniSiqry0P1fN1aqU7MKd2c8WDzyyhi19ln0aOmrSouR964hIp1HnxB8MH2O49+BuNiLG zRHL5ESUdmhLRXZKi/1UKWq2v7a4Ki+NiM5v/qrvtLslN366vzPmvrORneAeEb3R2O5ugeGj7jP0 jzMeCqY1846owzwaMPsB9+DIoNix3EAHqYc2H179AhHptZr8hCMuM+8jIpmja+jQyUSkqCwuSY1n l5Q7u89773f22WGGYQQCgUgo6jNhwcVdPxBRfuIRrbKRXW3hpTjumes+ty0wI0nNiM/2pezfyPXQ uW3lh31vv4uIipLP7H57uU6rNi/leaaSSfnOP5aMjf7X67FzHyai2qLsP56fyd6Z5ze/gpUTMX6+ SCq/tPtH9uOst379/ampzTVGmQVLcUDMqMGLnjQ16oioKseQ8pET7pz01H/YaY2qqTj5tEAo4vpP pfyzsa4kj4gEQtHtL37Te+R0IirPurzjtUUaZaNWrbqw6/vxK94nosv71nGPKg+/+8UhC58gorqy /D9fnKusqzLveK1UNk1l7+olkkh1GnVtUVZVbhq1elk0/6gzYz8FQhGj1x/8/KmyjItEJBJLp7/y nXtIlKmp5OwXSgIBMUxTTQURVeVdZbdIROWZl7wjYpuaH61gW9l4Rgj//TQvlcxj5+rJTSvrq03d Os9jN6kO4V82+UdIyv7f2N8qiChy4p3jVrwvkdmx9WFFdrJX7xhTS7F5p2Pj9ORmWrWet0Yptnge iSQya5y1m5qrU7lzyxEU7Vw8WyzDmfr812yb8tUjWw9++SwREcMk79847uF3jY+lwwJiUizxjE9r XNfxv6a1RukwL49ue+yjvlOX0fVD0HB5ZI1U4nR4xWLSOltUAtzjv0Tk6OE387WfTC1xPejK/9oJ USBkr4S5gUMBAADgZmKZHug7d+6srKx0cHAYPXq0TR3eqPtei5q0KHToZP9+I4Riych7Xnbx7zXt pbWdaT1n9Lq6ktzj377BzXFra7RE7tLKK3wAe43o328kOyevVbsbEcmd3bjnOt2DI0OGTmGnG2/w 3L1FeEfEckM9ZsUZbjAqc66w9xVk9Kwr27RKRE5eAYPvNFziyxxdhzS34qkb6oqST/eg0DfjiPjk kcTOodeI242HCe4zfj433VDdXivqkIVPcNnBtRewd1ZEpFUpc84fZKdzmyecfYO5XnsmMTU+O1R8 5Rw74RoQxraeE5F/vxGhw6d2PuU7TCWrcg0IY1vPicjFv1fosBuWTZmDMzvsKcvB3Yeb1qqVltof ezevvtPvZpskTI06dkADIso6ve/i7h/ZRkaJzC548MSggeO5xTKba4OggePZlgUi8uodw3V7L7x4 ip0ouhRnCEWfoMELDLnp7B0UPXmxrZVNU2mUjWyklWdeqisvICLjdmHz8N9PsVR+9rf/ZJ/Zzwb6 pKf/GxAzyoxUksjsHNy8iUjdWK9Vq8qNnoRgp7lNs90AeUYI//3sSsalj/udwJTcMe3Y+TD1jMAn QrLP7m+uCrzHr/iAqwoEAgHXOmlSKbYGG6nn+ZRii+eRNc7auuZ4FoklLb7iesW2OMv4Rg3lemT3 mbDAJ2IgO12cctbUrZsUSzzj0xr4X9Nao3SYkUdeYf25aAkbPYt7OVNJ8wWVVVnvikXdULf3g4fY X4zEcvuZr/9ovPLOXCd3fb1k0tWFa6DhnVgZJ3cXJZ/BSOgAAAA3GQv0QD937lxcXBwRLV682MHB waYOz97Na+KTn3FXMGGjZ4UOn2reeBec+M2rz236nPsYNWlR+Jg2RvvlWiEDmtsl/foZnkEuu5qk UtQYnixuFhAzWmh0ze3kZXj+vZOXX07egdwj/EYzr41sGDlpYdwvHxBRbvwhnUYlksi4rjou/r18 Ig23edUFGeyEb/Qw4wGy/ZrHziai6vz0kCGTekrom3FEfPKotiT3wo7vCi+dUlQUtm7BYfS6dnap dVszEbkFRfhEDilNiyeizJO72fvhvPjD7LfmdT83Iz47VF9qeBTap88g4/keodGZp/7qZMp3mEpW 1eKIHD18b1g2r2/oEZDAKOv1nS3FDKOoLK4tym6sLv/nsyfyE49NfOJTU6Nu4B2Psj/+aZoUJ394 J+7n94MGje97+90hQyYJhNd+T60tNjy9npdwuM03gzU0DyJcV1ZgSKXIIcZrcDEaNMBGyqap9DqN Z1j/8qzLmSf/IoYRCEVugWGdjCX++1mRncxVxUEDx7cekJd/Kjn7hrCPTDXVVrD9xKX2TurG+vLM i9Q8hItEZsc2cPCMEP772ZW0zcM9s8do6p+beux8mHpG4BMh3Cv1/PuOYLvTtr1p3qXYGmyknudT ii2eR2actXe+dVfrmcZdekUyOfvkk06rabGYTmN4xkssvS4YPEKjjT96hsWUpicRkaK8yOQwNiWW eManlfC8prVG6TAjj7zC+l+XR71j2P7R9WWFXZFY1rliYfT6A58/VVeSS2wH/xf+5xHatzMlrnvr JZOuLiY89tH+zx5vqCzRNCl2vL6Ye94XAAAAbg6dbUC/cuXKli1biGjGjBl9+/a1zYM0HmKlk63n xmQOLoMXPh4z8/7Wt9Z6nZYb7DJh6zcJW78x/pZh9AUXToRd3+xu7+pl/NG8tzi21uHwFH1uW3B6 3ceMXqdVNuYnHQ8dNiX7zN/sV8avYFLV17ATdi7XPZoqd7422qCmqcEi+6zTaLogKsw4og7zqCzj 4o43lhiPvGkSbtTgFvrevoy9Fc9LOKxVNtaVF3ADd0ZOuNOMDZkRnx3imq5atLy3bsbqZCzdKJWs x3jHLFg2zSvFF3f9cPLHd4ko9eAf0VOW+EYNNSnqYmbcK3NwOfvbZ2yPPL1Om3v+UO75Qy7+vSY/ 8wXXRVGlqG1/PdyL6diXRhKRvdHoGXR9XzZbKJvm8eodc4WILW4ufqEiiayTK+S/n1lxe7np/MSj BReOB8aOMy+VXP17s31OG2vKK7IuE1HosClXj26ryk/XqlXs0C7OzaPQ8owQ/vvZleqbf84xLwJN PfYOmXFG4BMh6sZ6w8LtHiP/UmwNtlPPt1+KrZRHpp61uTPyDTfq7FGnbCQiZV1li6+aag2vMbBr foSC1WIYCq7pVqMy+RLFpFjiGZ9Wwvea1gqlw4w8kl0/7rzM0TAiufGrjHucc5s+z23+uWjsQ2+3 +JGsk9fJXV8vmXR14Rs1dNbrP51Z/wmXAgAAAHAz6VQDemZm5rp16/R6/bhx4yZN6jFdjzvJwcPP 0dNfUVGkaqiN++WDK/9sWvjZLomdo/EyZVcTuVuINuUlHm3RQCmW23XL4di7eQUPGs9e6uWc2e8R EsW+XUcgEPaZcK2flMzJpbG6nIiaaq+7MTAe5q9FOyN/LfqbNNWWd8GBm3FEHebRmfUfa5sfWZ34 +CdBg8azL2hqs39Ta20Opk9EYWNmn/zhHXYEhpz4g1z3MZ/IIc6+wWYcuxnx2XFVIrNjb/lUihrj +a031MlYulEqWU/Xb7EdUZMXsw3oRFR6NdE3aqipURcxfl742Dm5CYev/L0hN+EIW/pqi7J3v738 rv8dYUeRlto7sSOYO3kFcE2rbee73F7dUEdEWrXKeH5n7v+tUTbNw733jIg8Oj1+i6n7KRAInf1C 2HfZHf561dKv/jE+0fBPpWvvEa0qZd9N13vUjKtHt+m1msrsy+wrCowfVOITIfz3sysVG4174B0R a8YaTD329plxRuATISKJjO382H4p41+KrcF26vn2S7GV8sjiZ223oHC2CZ7r382pap7jFhxhPL/F yZcbfVtqb3LxNCmWeManlfC8prVG6TAjj1QNdcYfuWZlsax77gg6L/vM/vgtq9npAXMejJlxb+dL XPfWSyadtVMPbT7yv5fYE4fM0dWh+YFFAAAAuDmYf4uSk5Pz448/ajSaMWPGzJ0799ZJsugpS6Im L47/40t2IJeaoqyrR7f1m36P8TJ5iYYBpmUOLsY3S6qGWrZrW75Zw0xbSeSkRezNRm7CYZ8ow9sj A2PHOnr4ccu4BoSxF50lV84xeh3XBcO4wcI1wLTxDQRCEXuVqai49qgswzClqQkd/omm071XrHFE JWmGPe8zfj43lIHx+2bNI5HZRYyfl7xvPREVJJ2oLzf0soycYPb4LZaPT2ffYPY2tfRqovH8qvyr XZDyZrBgLHUl41+b1I0K86JOIBSGDp0cOnRyQ1Vp/Oav2NBSN9ZnnvorZuZ9ROTZq1/BheNE5BoY PvvNX9tZlZNXQGVDHRGxuc+pzEm1qbJpHo+QaC5O3IP7dGVGy53dpz73lXtw5G9PTlY31Ckqik78 8A43aI9JqeTqH2qooFLPs90qA2JGObj7NFSVFl85b1jSv5dJEcJ/P7uyaFzeu46ddg/q42Juoxj/ Y++wDrHSGYGrbAsvnWrnBZv8S7E19JRSbCNn7ZXbcttfoX/MKPY6rTInVVFRxPXQryvNY19+SEYv MmGVZVww/liecdEQPz4mt+CbFEs845N/OTIVn2taa5QOc/IoPcn4Y0XzebP1ryw94oqlpjDz4JfP EsMQUejwqaPvf90itaKNX/kbO7/pc3ZXnX1DFv93r8TOtsY1BQAAgE4yc1jPvLy877//Xq1Wjx07 dv78+bdaqgkEggFzHuQ+th5QMj/xCDvRf9b9Cz/bzf2btmotO7+huSegLQgdNoUddqOxuizrlOF5 /Kjr3wEYPsZwmVtfXpjwp2HEj8bqMm44eJmjq6nvjrN3MzwXmXv+UE1hJhExen3C5tXck86t2bkY nn7Niz/Edadq0ZGEJ2sckbD5Wry6IIO9htYqG098/3bn86jvVMMAqSVp8aVpiUQkEkvDx842b23W iE9uBMmawszkfYZmrNL0pJzmp6etmvJmsGAsdaWU/b9x0+xtnklRd+R/L+UnHuVGHXVw9xl0x0ru Wy4duNva/MSjp9d9ZNyTUa/TlqTGF6eca5HvJann04/vNOxJfnry3+vMPkYbiRAiEsvkj/6ZtXJb 7sptuV08kmn0lCWBsePs3bzH/MvwwurUg3/knj9kRipxTclsc7mzd5DUwZl9Wx33MmFuGZ4Rwn8/ u4a6oe7A509zrVQD71hhxkpMPfYO6xArnRECB4xhJ+pKco9/+zo31oRep80++w/3zgn+pdgaekop 7iln7bAxs9mBmBlGf2zta+wTP1q16vi3b7LtlUKxpMVzYxVZySn7DaOoXzmwiRv9uUUbLq/cNCWW eMYn/3JkKl7XtFYoHebkUXbK5b2G5vuccwfYYX+IyK/vCGunksVpmhr2fvgwO6qbV++YKc9+1ear I8wocTZ+5W+svtwweH3w4Antt56n7N/4070Df7pvYOqhze2v0xpLAgAAgHnM6YFeWFj43XffqVSq sWPHzps379ZMOOORnRlijL9S1lWVZ15mp/37X3cd5hEaLXN0Zce4yEs85m6JMQHal3FiV1lztyNj 172cSiILHzuHbfTMSzpKRFIH59DhtxsvHzlp0eU9v7C9ic9u/Cz10B92Tu6VealcW8OIu19k7xzK Mi7WFGRUF2RUF2SUNN8MENGO1xd7hES7h0Z5hEazg8kGDRyfevAPItIoGzY9NdXJO1ClqG0xAEgL /v1GZpzYRUR1pfm/Pjjc0dNP3ahQ1lVxvbdK0xLYTdcUZJQYdYXe9sqdHqFRHiHRnr2i2bdI8T8i /txDokqunCOi4pSz6x8Z4+jlX5V3tc3BUrLi9palJ5WmJ5Ub5c7vT0316zciIGZk6+FTPHv38wrr X555iXs0OGToJFPf82lGfPLPzb6335W8bz3D6Ino2NrXk3Z8K7VzrMq72vo9S/xTnn8q8c93/rFk O7hS3FBZwv7URERyZ7fQoZNMijoiunJg05UDmwRCkYtviNzFgxjGuOe4Z/NL5yIn3nnln9/YhwkS t665sPN796AIiZ1jU02ForJIq1L2n/2AX99hRNT39mXc/f+B/z557rf/sPnODgxiHmuUTWswI+rM EDV5ccaJXWxL95FvXlq6+gD7wDv/VHL2DWXnlF1NIiKPXn2JyCO0b2784cKLp9ivXPx6mRQh/PfT qqnEFg1Nk6Iy5wr3mr5eI26PnLjQjDwy9dg7rENMKpv8xcy6P2X/RrYzZvK+9VePbHMLDNNpNXUl uRpl47Clz5paiq3BGvW8NVgpj8iiZ20icvIK6Df9nku7fySi3POH1j08wjUgvKYwgxt6ot/0e5y8 Alr81dE1r1zY8R0R1RRlsXMEQlG/aXebnJumxBLP+ORfjkytQ3hd01qhdJiXR8e/fePCzu/EMvvq PMPjekKRuHUeWTyVLH6OO/jls9z1iV6v2/vhQy3Wxt53mFHibPzKv00t3kDQ2pn1nyjrq4ko7pf3 jUfn75olAQAAwDzmNKB/++23SqVSJBKVlJSsXbu29QIrVqzo8QnTCfkXTrDNiCKJ1KfPYOOvBAKB f7/h2Wf2E1Fe4pGB8x+x9s7UlxUYv1ftRqImLTL0GmYYIooYO0csve5FW2KpbPor3+1571/sbVhd SR471gcrdt7D/aYvZ6f/fHFOm5soSj5TlHyGnWaveocsfirn3D/srQWj19WV5LIbGv3AW8f+79U2 VzJ0yTO58YfZHi56rcZ4H1hbX76jzT8sST1fknreeOv8j4i/oYuf2v2OYTAfRWWxorKYTduaomxu 66y/P3m09Z9X5V+tyr+avG/dyraaDKKnLivPvMR9jLjtji6IT/656REaPXTJ01wvHjYxpfZOHiFR xs/GmpTy/FOJf77zjyXb0boUS+T2U59bzY40zT/qOIxeV1OURc1NKqygQbeFDp/KTgtF4pmv/bj/ s8cLL51i06ciO6XNVXmE9h04f0XSdsOJoLY4h4hkDi6D7nzs/O9fmHe81iib1mBG1JnntpUfbnp6 qlbZ2FhddvzbN6c8+6VJqSSR29u7eTVWl7MF3yM0mog8ekUTETuHjHqg84wQ/vtp1VRqXTQixs+f +MQnnckj/sfeYR1iRtnkw9k7aPKzXx38/Cm2jVKjbGjzZ3L+pdgarFHPW4OV8ohlqbM2a9R9r9QV Z7ODhCjrqkvqrp1YQ4ZMHHXfKy2Wn/P2+gNfPFNzfSQPW/ac8QsPeDIplnjGJ/9yZEYp7vCa1kql w9Q8cg+OrC7IaHHIw+96wS0wvAtSqUMmrTPb6HHDFkO6dbLE2fiVv5mahzbq+J3n1lgSAAAAzGJO A3pjYyMR6XS6jIwMpCCR4QKdw40f7dNnUItLdiLy7zeSbaAsuXLOdoYy9I6IdQ+O5J5/57rvGXPx C1342e4rBzZlnNxdW5ytaWpwcPPxjhjYb/py/5iRZmzU2Tvozk92nt34n4ILx1UNdXYuHgH9Rw9Z 9KSrf+9Lf/3U+i1MROQWGL7oP3/Fb15deOlUY025SCxxcPf16zvcvKO2+BEFDRw/bdWaxK1rqvLS BEKRR2h039vv7jNhwaXdP3b+VrzP+PlxP7/HxozM0TVkqJmv7bVefA5d8oyzT/DF3T9WF6SLxDL/ /qNGLl8Vv3l1iwZ0a6S8GSwbS11DLJU5ePoHDhgbO+9hl+ZGEJOibvyK94uST5dnXlLW16gb60US iZ2Lp0dodNiomRHj5xnfdMmd3ee8szE3/lD60W1lGReaqsv1ep3c2d3JKyCg/+ioKUu4JUfe+4qL f6/Le36pK84RyeQB/ceMXL7KwdMved86sx8wt4UIsR1O3oGj7nn5+HdvElH6se29R83oPXK6Sank 4hvKDvxKRJ69+hGRZ2hf7luJzM7B3cfUCOG/n1YvFzK5vZu3X/Tw6ClLO9Ol2tRj77AOsd4Zodfw qUu+OpC899eCCydqS3K1qiapg5Ojp3/ggLHGb0rkX4qtoUeU4h5x1maJxNIZr/2Ufmz71SNbK7KT VQ11Mgdnz179+kxYEDF+fouhxp29gwJjxy36z56zGz7NjT+kUTW5B4YPmPNQxHgzHxs1KZZ4xifP cmQGPte01igdJuUREYUMnTThsQ8Ttvyv+Mp5nVblHtQndt4j3PAy1k6lnlLibPzKn8Po9fwXHn3/ 66d+fk8gEIx54K2uXxIAAADMI1AoFEgF86xdGMYOTxE6bMqMV39AgoBV6XXaXx4YpqyrIqJ+05eP X/F+j9jtvR8+lHP2HyISSaSP/JGOfAQAgFtBzzprH1v7GvvWUxIIVm7NQfZZz5o7DL9/D1qwcuQ9 LyNBbg7FKWe3v2YYOGXo4qeHLXsOaQIAAHCTESIJzObo4cdO5Jw78Psz05AgYFUZJ3Ypm9+eFD11 WU/Zbe5JAgcPX2QiAADcInrWWZs7WRu/4wcA+Ni6at7ON6+VccdWg90DAADATQAN6OaLnHztDS1V ualIELAGnVbN6HUFF46f+P5tdk5AzCiv3jG2tp/K+urd796be/6QXqth5zAMk7R9LTsoNhEFDhiD 3AQAgJtbTzlr554/xLbva1XKlP0bufeauAVGIBMBTFKansQ+lExEjh5+YaNmIE0AAABuPmIkgdmG LHrKzsUz/dj26oIMTSNGwgGr+G5xpEAo5K7LhWLJ6AfetMUdZZj8xKP5iUfFMrlbQLhIZldXkttY XcZ+KZbKBs67pd8tDAAAt4Kectbe8/6/iEgklup0GuN3+YSPnW2Dewtgy4RiiURm7+QT6N9v1MB5 D0sdnJEmAAAANx80oJtPIBD0m3Z3v2l3IynAehhGz+j0zSEnvO3RDzx79bXlHdaqlOVZl43nSOQO k5/5wsW/F3ITAABubj3rrK3Tqo0/+vcbETP9HmQigElWbM5AIgAAANz00IAOYNMcPHybairEcnu/ qCGDFz7hGzXUNvdTYu846r5Xiy7HVeamqRQ1Oo1aYufoFhgW0H9M32l3cS8MAAAAuIn1lLN2v+nL y64m1ZUVaJoUIqncPbhP+JjZMTPuFYolyEQAAAAAgBYECgXGHgEAAAAAAAAAAAAAaAkvEQUAAAAA AAAAAAAAaAMa0AEAAAAAAAAAAAAA2oAGdAAAAAAAAAAAAACANqABHQAAAAAAAAAAAACgDWhABwAA AAAAAAAAAABoAxrQAQAAAAAAAAAAAADagAZ0AAAAAAAAAAAAAIA2oAEdAAAAAAAAAAAAAKANaEAH AAAAAAAAAAAAAGiD+FY4yNqi7I2PTyCi/rMAis8zAAAsfklEQVQfGPvgWzf98daV5W9YMbb1/JXb crt4T1L2bzyz/hOGYUbc/WK/6ctR3oCI1twRwk4MWrBy5D0v32ThdHPEvO3UIV2f8mbHZ4+AOvnW ZI18RyxZz+l1HyVuXcNO23KtiwixHbjy58O88zsiGQAAAFhmNqAXFxefPXs2IyOjurpaq9U6Ojr2 7t174sSJfn5+NniQchd3dsLO2d2Cq2X0+pT9GzNO7KzKT9c0KR75I934W+4q7UZ67k0Rf6fXfaxS 1BDRmQ2f4qKznfCY+fpPIUMmEdHBL565enQbO9PBw/fe78/0rONiGCbj+I60w39W5KSoFLUyRxfP 0L59JiyIGD9fIBDcCuHUI3ayKi/t96dv5z4OnP/IqPteQ21jUzVDi9t7SzWo2XIqtagVhWKJnYuH d3hsxPh5vUfN7HwFciuzRr7beIlrEU4CoUhq7+Qa0Dtk8MSYWffJHFxu1ry2nXKEK0C+2SQQSGT2 Tl4Bfv2Gx0y/xz0kCrXNTXxEaxeFS2T2zj5Bvn2Hx859yMkrAMUBAACgpzCnAX3nzp3Hjx8nIjs7 Ox8fH4ZhysrKEhMTL1269OCDD4aHh9vIsWnVKrFURkRSe2ehSKzXae1cPNivNKomsUQqEIo6s/7z f3x5/vcvbDBTHT385v379/LMS2UZF4qSTzdWl1tw5cUp57a/tpBM7L4hEGKwoPbUFmXRkElEVJmb 2nOPQqdV//3xitzzh7g5TTUV+UnH8pOOZZzYOe2ltSKx1CIb6uJwuvliPj/xmPHHvISjLRrQrVqH WFv7KW9ebt58bL9O1ms1DZUl2ZUl2Wf+9oseNv3lb+UW/Qkc+W6p0tEjzu+MXqdS1JSmJZSmJSTv 3zD//c3OPsG3Qo7bQjnCFWBH0clolA1V+Ver8q+m7P9t1H2vxM592NR14Mq/p0SyXqtRaWvLs2rL sy5nnty99Kt/ZI6uKAQAAAA9gjmXAv379w8LC3vooYfefvvtJ5988qmnnnrjjTf69++v1Wq3bNnS mb1hGMaCM3e+uWzTU1OOf/dm9pm/JXaORNRUW5nw5zc731j60/IBZZmXOpl2qQf/4KbdgyNvtJiT d2DAgDGt/1kxU0Vi/5iRsfMenvr815ETF1p25RXZl/kvPGL5Kpmjq9zZbeQ9L6GwtaOmKJuI9Dpt TWFmzz2KuF8+5FrP5U5uvlFD5U5u7Mfc84fifvmwk+vvrnC6+WI+L/EoO8H+xFiVl9ZQWdJldYiV 8Ex5k3Lz5tMj4pM9afpGDZU6OLNziq+c2/nWXTqN6lbOO1srHT3l/N46nBoqS06v+/imz/RuL0e4 AuSZTf4xI138e7EfGb3u1E/v5SUcMXU9uPLvKZHs02eQUCxhpxuryzJP7UEpAAAA6CnM6YHeq1ev Rx991HiOTCabO3fupUuXKisra2trXVzMfDa2zWdLzZupVasqMi/ptOrq/PTLe35hZ57d+Bm3QEXm JZ+IgZ1JO67JKXT41BmvfH+jxcLHzrmZ+jlWZCXzX7jftLv7Tbsbxawd7kF9qvKv1hRmEVFtUbZO oyYisdxeq2zsWQdSX1aQvPdXdjp48MRpL60VS2ValXLfx4/kJx4louR961o8rGrqIyDdFU43Wcxr VcqSK2eJSCSRho2ZnXb4TyLKSzwaPWVJjy5KPFPepNy8+fSIOpk7aeq06rMbPkvavpaIKnOuJG5d M3TJM7dy9tlU6egp53cunDRNij+em1lXkktEhRdP2vI+W+RprW4vR7gCNCmbSlLjd799t0bVREQX dn4XPHiC7ezkLX7lb9kjWvDx9pLU+G2vLGA/KiqKUAoAAAB6Cou9RFQiMfycLpPJbOHAGJ1m9ANv 1JXm1ZfmF6WcUdZVs/P9Y0Y6ewc5egV49urb2U0wenbCLSDMvDVknvpr/6ePsdMTn/wsatIio5Uz vz44jH0GMzB27Jy3N7DztSpl2uEtV49try3OVivq5M5uPpFDYmbcE9B/tBk7wH+83eR968oyLpZl XKzONwz1nrh1DbcMGQ3Lm3Fi1z//eaL1tm40bi+fI2L30zUgbNnXh0rT4hO2rilJjVc31sud3IIG jhu65BnjZ7Gr89PTj+8ovBRXU5SpbqgXisUO7r5+fYf3m36Pd/gAbrHW+znn7fW+0cMTtqxOP7Zd UVkiFIkG3bGy6PLpwstxRCSWye//OVEit+eWT9m/8eiaV9jp6S9/22vENDOywMk7oCr/am1RNjWP 3+Lo4aeoLDZehucR8UylnW8stcYRZZzcrddpiYgEgvEr3mO7Notl8nGP/HvjY7cRw+i1mowTuwbd ce23N4ncvqYw89ymzwsuntSplS7+vfrP+pdxKWB9vyxa0+rnhDbDSdVQm7L/t6xTf9WV5muUDQ5u Pv4xI2PnP+Ie1Kf1wpomRfLfG3LOHaguSFc3KiRyBzsXD/fgyN6jZkSMm2uNmDejvJt0RDwVJcdp 1Soi8g6PDeg/mm1Az0861pkG9LTDWy7t/qm6IF0ss/ePGTVi+YsO7r6/PjRC3VDXIh34lHeeZXPo kmf41zb8c9OYoqLozPpP8i8cVylq7ZzdgwaOH7r0We5HILbEObj7LPv68P5PHytKOe0VNmDmqz+U Xk08tva1ptrKAXMeHH7XC1Y909WV5l3c9WPh5bj6sgK9Ri13cXdw83YL6hMYO7b3iGliub1JdbJJ Na1J+W4ekVg66r5XK7KTCy6cIKLLe38dfOfjXN89y5Z3U89HfPKdXVIst7//54TGypKzm/5beOmU tqnB2S80Zsa90VOXtegKwP/8btl870Hn986Q2DkGD7rt8t5fiUirajT1DMs/5U2Nz+yz/1zYvrYq 76pOp/HqHTNk0ZNSBycL1hLtlyPLxryVahvLXgWZUTNY41zcgm/UkJDhUzOO7ySisoyLZl/b8MEz PXtWzcC/bPI5v5sayfzreSLyiRzMTRuunAEAAKAnsFgDekJCAhEFBQXJ5XJbODCJnWPMjHuJqCr/ 6tZV84hIJJbqtGqfPoMs3x/c3JcyhQ6bInN0ZV9Nkx2317hBrexqAjeCITe/rix/34cPV+Zc4RZr qCrNituTFben/8z7xz78jvXS89ja162xWpOOqKYw88yGTxP//Ib76aKxuizt8J+58YcWfrrbyTuQ iLQq5bZXF7JJytLrtLXFObXFOamHNo998O3+s+6/0c7knD1wbtMXJannDX+o1fj1G+Hi35ttbtaq lLnnD4aPncMtnxtvGK5E5ugaPGSieSnAjkaqqCrRqJrYBnT3kCjjBnRTj6jDVIq+/S5rHFHR5dPs hEdIFJsXLBffEI+QKDaLi1POGDeg1xZlb3l+FtvfiogqspIPr36htih7xPJVZqRkZc6VfR8+XFeW bxxddYfy04/tmPT0f40Pk114z/sPGHf8USlqVIqamsJMmYMze9ts8Zg3tbybdET85TUPgO7Xb4Rv 9DB2uuDCCUavM++1EGc3fha/ebUhXNWqrLg9+YlHw8fNZVtRzS7vHZZNa9dg9aUFf66a11hdxu1q 6qHNufGH7vx0l/GDFA1Vpf/894m8xCNEVJxy9tzvX6Qe2swee/zm1VFTljh7B1mpWi5Jjd/9zj0a ZcO1naksaagsKcu4mHZ4i/yNn4MHm1Mv8alpTcr3ToqeupRt+GuqrazITvGOiLVGeTc1Pvnnu1bZ eG7jZyn7f+NyqjLnytE1r9QW54y671UzSofF872nnN87T6c2jF7i4GF4471JZ1j+Kc8/Pi/s+O7U z+9xH4tTzu5+997w0bMtnh1tliMrxbxlaxuLXwWZWjNY6Vzcmrx5LGxGqzHeSctunX969qCawdRa kef53YRU5VfPs/BObAAAgB6qs69D0Wg0ZWVle/fu/euvv5ycnBYutK3RcjVNin0fPqxRNvr0GTTy 3peJKHHrmoyTu2xk90QSGXftm3/huPGoHVmn97ETEjvHXiOmE/uGxo8e4a44nX2CfKOHSeQO7MdL e36+sOM76+1q66HbW4ztzs33CI0eOP+R8HFz/aKHcUNgt8mMI0rY8jXD6J19Q7wjYkUSwzPOyrrq +D++YqfFMnns3IeISCSWuvj38o0e5uIXavhjhjn183u1JYbOI0EDx8147cdxD7/rGzWE22JJ6nmB UOQV1t87ItbJK8C/7/DeI6dxR5Fxcve1ndeouGfAw8fONvuBa8O7gximtii7KjeViDxCrhtPn/8R 8UwlKx1RdYGhg5J7cMsuUW6BhhcLV+elG89PPbRZo2pyCwx39e99bee3flN1/ZtUB8x5MHzMHJ/I wdxArq1plY37Pn6Eu8N0Cwz3CusvFInZMDu8+nm2jz9XLez94EHunlkst/eOiPWJGMiun+uIbfGY N6m8m3REJslvHlzVv+9wF98QBw9fIlIpasrSL5ixtorslIQt/+M+OvuGuIdEadXKK//8ZnZ551k2 Tapt+OcmJ+PkrsbqMkdPf5+IgVw5aqqt5GobTtHl0zIHw8BlF3f9IG0+KCKq6PTLNtpx6uf3Dffq AoFnr76+UUOdfYLYjqXOvsFBgyaYWifzr2n553vneYfFGsVbspXKuxnnI/75fmHn9xplg4tfqHtQ H+4X96Tta7m6zqStWzzfe8r5vTP0Wk1J6vnM03vZj2GjZ5lxhuWZ8vzjs64k9/S6j7j1O3r4+UQO Fktl1rhMbV2OrBHz1qhtLH4VZFLNYL1zcWvlzR3PHZvbcK2xdf7p2YNqBp5lk//53YxI7rCeBwAA gJ7O/B7o+/btO3jwIDstl8snTpw4duxYBwcHmzq8Y2tfry3OIaKR977iGzn40p6f60ryjqx+0afP YPO6GHAaqkq5afZa9kZaPPDIGrb0WXYMyqhJi5L3rSMinUadE38wfIyhfY1rUIsYN0cskxNR2qEt Fdkp7Mzhd70wZNGTRKRS1Gx/bXFVXhoRnd/8Vd9pd0taPaVoEXPf2chOcKO+3Ghsd7fA8FH3vcZO Gw8F05p5R8QtWZWX9uequVqVkojyk45xCwyY/YB7cGRQ7Fjugc3UQ5sPr36BiPRaTX7CEZeZ9xGR zNE1dOhkIlJUFpekxhsi2dl93nu/s0/FMgwjEAhEQlGfCQsu7vqBiPITj2iVjexqCy/FceOK9Llt gdkJa+/qJZJIdRp1bVFWVW4atfVCWp5HxDOVRBKZNY6oqa6qOQ09Wnxl5+LZYhnO1Oe/ZtuUrx7Z evDLZ4mIGCZ5/8ZxD79rfCwdhlPKPxvrSvKISCAU3f7iN71HTiei8qzLO15bpFE2atWqC7u+H7/i fcPC+3+rLy9kpyMn3jluxfsSmR2b4xXZyV69Y6wX8/zLu0lHxF99WUFNURZba/lFDyMiv+hhGSd2 seFh/FgxTyn7N3L9wm5b+WHf2+8ioqLkM7vfXq7Tqs0r7zzLpkkpzz83jY3+1+uxcx8motqi7D+e n8n+5pHf/ApWTsT4+SKp/NLuH9mPs9769fenpjafKcqsd4KryjGkZ+SEOyc99R92WqNqKk4+LRCK uD5u/FOJf03LP987z87Vk5tW1ldbqbybcT4yKd9ve+yjvlOX0fVDOXF1nUlbt3i+96DzuxlaX4MF xIwavOhJM86wPFOef3xe3reOG8Bh+N0vDln4BBHVleX/+eJcZavTpcXLkTVi3hq1DVn6KsikmsFK 52JjjF5fX1F4ccf3pelJ7JywMbOtunWe6dmDagaeZZP/+d28SG6/njcmEAjZcyg3xCgAAADYPvN7 oHt4eISHh/fq1cvT01Or1R46dGjjxo0FBQU2dXij7nstatKi0KGT/fuNEIolI+952cW/17SX1nam 9ZzR6+pKco9/+wY3x60TYyB6R8RygxhmxRka0SpzrrBXzGQ0ngPbyEVETl4Bg+80XJbJHF2HNN8H qhvqipJP96DgM+OI5M5u3Bgg7sGRIUOnsNONNeXcMhI7h14jbjce7rDP+PncdEN1e+1ZQxY+wWUH d8HNXg0TkValzDlv+NEot3nC2TeY6ydrBo2ykd1ieealuvICInIPiWqxjKlH1GEqWeOIdColOyFq HlmVw/Xu0aqVxvN9o4ZyPbL7TFjAvdS3OOWsqVvPbC47QQPHs3eYROTVO4brZlh48RS3cPbZ/eyE vZv3+BUfsPfMbI5z98xWwr+8m3RE/HE3nF7hA9iI8u83kp2T16pdmI/iK+fYCdeAMLYVlYj8+40I HT618+W9w7JpVa4BYezdNRG5+PcKHdZGbWM4CgdndtB/loO7DzfdIuYtix0AioiyTu+7uPtHtlFM IrMLHjwxaOD4zq25gzqEf753nnHasu0m1ijvZsQn/3z3CuvP1bpho2dxr2ApaU5Gk7ZuvXy3ICud 3zvP3s2r7/S7uTAgU86wPFOef3wWXYpjJ5x9ggYvMKSSs3dQ9OTFXVCOrBrzFqxtyApXQfxrBiud izmJW9f83529NqwYe2nPz1ylGjvnQatuvTPXybZZM5haK/I/v/PXYT1/3Q4EGt6elXFyd1HyGYyE DgAA0COY3wN92LBhw4YZxs9VKpXnz5/fs2fP119//eijj4aGhtrI4dm7eU188jPuuiRs9KzQ4VPN Hm2DFb959blNn3MfoyYtCh/T3miVTt6Bzr4hrWZeGykyctLCuF8+IKLc+EM6jUokkXHdUV38e/lE Gpoyqwsy2Anf6GHGQxX7NY9iTETV+ekhQyb1lOAz44gCYkYLjdpnnbz82QnjS8/aktwLO74rvHRK UVGoVbW8o2P0unZ2qc3WH7egCJ/IIaVp8USUeXI32+abF3+Y/bYz3c+JSK/TeIb1L8+6nHnyL2IY gVDkFtjynbSmHlGHqWTGEe18667WM7neSUQkksnZ/js6o7E7WTqNoUeqWHrdCxI8QqONP3qGxbDd rxTlRaYmY22x4SnmvITDXFcpYw1Gw8pzr8Py7zuC7e7dlXiWd5OOiD+ulTygud3cr99wdqLsapJK USNrHoOVp/pSw6PlPn0GtcjczFN/dbK8d1g2rarFETl6+LaubQyub9AXkMCoeOqtt4cD73iU/SlX 06Q4+cM7cT+/HzRofN/b7w4ZMkkg7NT4bB3WIfzzvfO0za9JICKpvZOVyrs58ck7373C+l9X1/WO Yftg1pcVmrF16+W7BVnp/M7q8HxkzHANxjCKyuLaouzG6vJ/PnsiP/HYxCc+NVS2vM+wPFOef3zW lRk6nfhEDjFeg4vRsGbWK0dWjXkL1jZkhasg/jWDlc7FNxI0cPzEJz7lBqyz0tY7c51smzWDqbWi Ced33jqs541NeOyj/Z893lBZomlS7Hh9MfdcMgAAANgyy7xEVC6Xjx07VqPR7NmzZ9++fY8++qhN HaTxECudbD03JnNwGbzw8ZiZ97d/y9rhQAF9bltwet3HjF6nVTbmJx0PHTYl+8zf7FfGrxlU1dew E3Yu1w2RIXe+NjafpqnBIoem02i6IF/MOCJ7Vy/jj63feViWcXHHG0uMR5c2iaOnf5vz+96+jG1u zks4rFU21pUXcENSRk64s5Pp4NU75goRu0IXv1CRRNbJI+owlcw4Im549Btu1NmjTtlIRMq6yhZf NdVWGDK6uYsQq8UjulzzukZlcvapFLXtL6AxajtQN9Yb9tmo91yX4VveTTkinvQ6LZePCVu/Sdj6 jfG3DKMvuHAibIxpL6/jGmVatLyzDTSdLO98yqb1GO/YjcqRFTFMex+JiChmxr0yB5ezv33GPsGg 12lzzx/KPX/Ixb/X5Ge+4B7pMEOHdQj/fO+8+rJrT7axPV6tUd6teoaVXf/+BpmjYRRp7nVzJm3d evluQdY4v3M6PB8ZM74Gu7jrh5M/vktEqQf/iJ6yxDdqqElnWJ4pzz8+NU2K5mP3NF7AwQrnpjbK URdeVXamtrHSVRDfmsEK52Jj7A88hZdOsZV8+Li5Ds2NuVbaeievk22zZjC1VrTG+b3Det6Yb9TQ Wa//dGb9J7nNnVcAAADA9oktuK4+ffrs2bMnPz//5k4yBw8/R09/RUWRqqE27pcPrvyzaeFnuyR2 jmav0N7NK3jQePYSKufMfo+QKPbtOgKBsM+Ea32BZU4ujdXlRNRUe10DpfHweS2uCPlr0d+kqba8 C1LSjCMSy+3aX+eZ9R+zdwViuf3Exz8JGjSefeFVmz13WrvRcPZhY2af/OEddWO9Vq3KiT/IdZH2 iRzi7BvcyXTw7N2Pm/ZoNX6LGUfUYSpZ44jcgsLZJniuVxenqnmOW3CE8Xzu9tUQdc1DvkrtTS5N UnsndsRYJ68AZ+51WDcgksjYTkZt3tVYG8/ybtIR8VR2NbFFmreQl3jU1AZ0scyOTUaVoqadzO18 Ddb+qyasoeu3SERiqUyrVhGRurlljaVqqOMWMJ4fMX5e+Ng5uQmHr/y9ITfhCFuT1xZl7357+V3/ O2J3faucCbvRUR3CP987r9jo+XfviFgrlXernmG57GNxTVfi5lEjTN26lfLdgqxxfu+8qMmL2QZ0 Iiq9mugbNdTUMyyflOcfn2K5vbqhjojYUs+xxrmpdTmyasxbsLax0lUQz5rBGudiY+wPPIe/fjH1 4B9EFPfLB71HTON6oFtj6528TrbZmsGkWtEa5/cO63ljqYc2H/nfS+xOyhxdjX81AQAAAJtlyQuI xsZGIhLazBPEVhI9ZUnU5MXxf3zJDuRSU5R19ei2ftPv6cw6IyctYhvUchMO+0QZ3uMXGDvW0cOP W8Y1IIy96Cy5co7R67juEsY3Ra4BYSZtVyAUsVdvioprD4EyDFOamtDhn2g63XvFGkdUkmbY8z7j 53Pjaxu/8dU8EpldxPh5yfvWE1FB0on6ckNPrsgJC6jTPEKiuVR1D+5jm0e0cltu+yv0jxnFxnBl TqqioojrL1xXmse+GIqMhttmlWVcMP5YnnGRnXD2MbkF37NXv4ILx4nINTB89pu/tr+ws28w22Zd eOkU9zpKPsVEY6EeW3zKu0lHxFNeomEAdJmDi/HPJKqGWrbTVr7pw6BziVl6NdF4flX+1S4o72aw eG5alr2bd11pPhGVZ166rnQ0f7R39211RMLQoZNDh05uqCqN3/wVW6jVjfWZp/6KafViPUvhn++d xOh1l/euY6fdg/q4+IVaqbxbNT7Lmt8NyKrISeX2zeytWyPfbfz8zunwfNROOHHT6kYFmXWG7TDl +cenk1dAZUMdEbEhyqlsjhCrliMbqZM7ZKWrIJ41gzXOxa2NXP5S5qk9miaFsq7q9PpPxq94z3pb Nyvme0bN0C1nQ06H9byx85s+Z5PU2Tdk8X/3Suwcur2gAQAAQIcs2dh9+fJlIvL39++picGbQCAY 0PyGHzJryOYWQodNYR+Eb6wuyzq1l50Zdf1bpMLHGC5z68sLE/40jL3QWF3GDcguc3QNiBll0nbt 3QzPReaeP1RTmElEjF6fsHk1N5pHa3YuhlE48uIPcV2GW3Qk4ckaRyRsvhavLshgr021ysYT37/d +UzvO9Uw6GpJWnxpWiIRicTS8LGzO79msUz+6J9ZK7flrtyW23oMxJ5yRGFjZrODVDKM/tja19j+ dFq16vi3b7IPJgvFkha9myuyklP2G0atvXJgEzcyZot2dl6x1HwTmJ949PS6j4y7wep12pLU+OKU a/dmgQPGsBN1JbnHv32dewJar9Nmn/2n9QjOFox5E8q7KUfEU37iEXai/6z7F362m/s3bdVadn5D VWlVrmkNN9y4pTWFmcn7DA00pelJOc3j0li1vJvB4rlpWf7NKVCWfiFx2//pNCqdRnVhx3dcA3pQ 7Fhu4SP/eyk/8Sg3gq2Du8+gO1Zy33JHZw38870z1A11Bz5/mvsFbuAdK6xX3q0anxXZKZf3Ghq/ cs4dYIfPIiK/viPM2Lr18t3Gz++dl7L/N26abaQz6QzLM+X5xydXjkpSz6cf32nYk/z05L/XdUU5 ssk8as1KV0F8awYrnIvbKHqunkMXP2WI0r83lGddtt7WzUhP268ZuvFsyOmwnjdWX24YGD148IT2 W89T9m/86d6BP903MPXQ5o7qN8svCQAAAMbM6YH+yy+/DBkyJCoqSiw2/LlOpztz5kxcXBwRjRs3 7lZIOOORXv+/vTuPrqo+8AB+swcIWxK2sAXZCSAqIotFQRHsyMENLYpYmSpjx1LrmXqKOrWdo+OM y4ynnjnW2rFVKdU6gxtVRxmqyGhBFJAAClFWIYAshgAJIcn88SB9wk95eRIM9vP5K7xccvPb7u++ b+77/Wqj2i85smTBi9sOP1ob73MbMGZk9Th7fCyG2LD0jSiKMpu1KBxyQfzxvUdPLH7p8djzfYtm 3f/BvD80aZ67Y8MHdfeLZ13941iCua3k/d2bSnZtKtm1qaT08A1cFEXP33FFXte+uYV98gr7xhYE 7DxoZOxDo1UVe5+aPqZ5206V5Z8d8ZH8IxQUDS1Z8GIURWVbNz7xt0Ny8jsc2FdeUbaz7omwrR++ Fzv17k0lpXEPJz4747K8wj55Xfvmd+sb2ykx8RIlLrdrn9h+91tWLpp5w4icNgU7N6wOrirw8dsv b1uzdOuapdvjWufp6WM6FJ3Vsf/QoxeyyD+lqE33Ads/Wl63REnXwaPru+NiEhIvUX0d3xI1b9Ox aNw1y+c8FkXR+sXznrz+rFYde+z+pKTuY7lF465p3qbjEf/rjYdnLHv+0SiKdm/+OPZKSmpa0dir 63v23qMuW/Xa72MPwy6Z/fCyF36d27lnRpOc/bs/Ld+x+WBlxYCLpnbodyiq6P8331356qzYg1Qr Xpm5+vVnW3fqXn2wqqx0fVXFvjO/86OG6/P1GO/1KVEiKsp2bv/o0HvyggGfe3eaV9g3K6dVbOBv WDI/t2ufxK8h/S64asUrM2tra6Iomv/IHUuf/1Vmk5ydG1YfvRNX4uM98bFZ35pPpDW/XgMvum71 67NjtffnJ+5Z9Lv7orhtzdIyMuP/drtq7lOr5j6VkprWsn3X7JZ5UW1t/HOs+Yc36U2ilo4p8XZP QmzSrNpfvmPdqrotiLuddUHvUZc33HhviPko3pu/+sdlLzyantV014ZDD+mnpqXXXevqdfaGa/dG Pr9/le4URdHeHaWxBwWiKMpu0bpw8Oj6zrAJ1nzi/bPfBZPqEre5//aDd37/QGwcVR88cCLGUQO0 UUNcbRroLijRK8Pxnou/yICLrlv56qzPtqyrra1585E7LvmXZ1NSUhI/e+KzdhL12fivDAmOzYbu yV9+nQ86Yjegoy2ceW/Fnl1RFL39+N3xO+WcmCMBgHjJBOjFxcXFxcXp6en5+flNmzatrq4uLS2t rKxMTU0dN25c//79VWu8Pds2xe/d9EX6jJ546Dm+2tooinqePf6ItW7TM7PGzXj0pbuui0WNZaUb YqsuxJw64fqicZNjX//3j8cHT7F5xcLNKxbGvo7d9Z5xxfR177wWizhra6rLStfHTjR86p3zf3lb 8IcMvvLm9e/+KbbzVc3BqvjfIWb2Ty4J/sfSDxaXfrA4/uyJlyhxg6+YPufnh5bTKd+xpXzHlljd 7t68tu7sMf9zb2Cr250bV+/cuHrFK0/eGFoJuu+YSfHrKvQ855IT0H8SL1ESjm+Jhl07o2zL2tji JBVlu0rL/vJgVNczRg27dsYRx4//2cy5D95cF53HnDnplhbt670QZ2pa+rdvf+zV+//+k+VvxXrm p2tXftHBLdp2Pu9Hv/jff58ee+dcVbE3+CeuhujziY/3epUoERuXLYjFnWkZme16nR7/rZSUlIKi IWsXvhpF0YYlrw+6+IbEryF5hX0HX/nDumfHYpWT2bR5Xtc+8Z/Irtd4T3xsJlHzx2zNr1deYb9v Tbtr/i9vjz0YGJ9Hp6ZnjLrpvqM/215bU71788fR58dR59POKRwyJulaSuD3TLTdk3D0pNlz5MWj brq3Qcd7Q8xHdXK79N61qeSIzjbkqn9o3alH0mdviHZv5PP78epOGdlNx9zyUGzrmiRm2GPWfOL9 M6+w36CLpy197tBngD7bsi6KoqxmLU+77PuLn36wocdRQ7RRQ1xtGuguKMErw3Gfi79IWnrmiKl3 vnT3dVEUbV29ZNXcp/qNmZT42ROftZOoz5PlynDMsdmgPfmY1/kkHV5c6Ng7nTbEkQBAnGQC9ClT phQXF2/evHnPnj3btm3Lzs5u1apVjx49hgwZ8tewfktAbe1X/xlte56a26V33Wds6x4RiteyQ+Hl 989ZNfepkv+b89mWtVX79zZr3a5tz0FF4yYX9B+axElbtO182b0vLJr1wKZlb1buLWvSMq/jgOFn TPxBq4JTlv/xN0fvBhlFUetOPSY+8Md3n3nok+Vv7du9PS09o1lu+w79hiRX6uNeos6DRo699eEl sx/eueHDlNS0vMK+/S64ute5ly6f89hXj5t7jbz47d/eFXuvlZXTquvg0Segc51EJUpLz7zw9t+s mf/c6tdnf7p2ReXesqxmLfK7FfU699KeIy8+YoHRFm07dzr1WxMfeGnR7+5b/+68qsr9uZ16DBz/ vZ4jJyR39uwWueN/Pmv9u/PWvPHstpJl+3dtr6mpzm6R27xNx44Dhvc5/8r4g7sNGXPlL+auePmJ TcsWfFa6/mDl/sxmzXPyCzoNPLvXUavAH98+n/h4r1eJjqluffN2vU47IqyPoqigaGgsQC9d9U59 FzkdfOXNLdp1eX/OY7s2rUlLzyoYMGzo5Fvffeaho4PU4z7ek9AQrXl89RszqW33ge/PeWzLioV7 d21NSUlpmtuuY//hA8dPze3SO/7IkdPu3rziz9s/Wl6xZ/eBfXvSMjKatMzPK+zbfdi3e46c0NBv jBNv9+SkZ2U3bd22Q98hfc//ztEPeDbEeG+4/tl18Ohzv3/Pe//1H1tWLa4+WJnbudepE26oW5yh vmdvuHZv5PP7V+1RmVnN8gs6DTz71AnXtzz8Z9p6zbCJ13zi/XPolBktC7oVv/R42ZZ1aVnZHQeM GDr51mb5HVa88uRxWVrqy8dRY2ujoIa7C0rwynB85+Ivv1B0OX3Uhvf+FEXRwpn/2n3YhVk5rY77 2ZOoz8Z/Zfh6Z8PEr/MxtTU1if/Y4d+9463f3pWSkjJi6p0n/kgAIF5KeXm5WkjOI5d3jz0eWHjm +Rfe9p8q5Buvpvrg41PPrCjbGUVR0bjJI6fdrUQn0vxHbo9tCRWlpNw4e50O2Zi9fM/31i16LYqi tIzMG/6wRoVo979CD19yKKU97dIbh17zE90D4Bsmiev8lpWLnrv90MIpg6/44ZmTblGNAHBSSFUF ScvJ6xD7Yt07c5++eawK+cYrWfBixeGdiPqOmaREJ1jdRyLidyCgkTdWs7z2akO7AwCzb53wwk// cr+dc9TmQABAoyVAT17v8/6y78rO9R+okG+q6oMHamuqNy17c8GvfxZ7pWP/YW1OOYnX+j9ZSrR+ 8bxYvn+wsmLlq7Pq1vFs3amnbtlIVOzZNeefpqxfPK/mYFXsldra2qXPPRJbzDeKok4DR6gl7Q4A bF2ztG53k5y8Dt2HXahOAOBkka4KknbGxOlNWuavmf/crk0lVfushPON9egVvVNSU+vud1PTM4ZP /akSnQCx3bTS0jOrq6vidxrocfZFumVjUVu7cckbG5e8kZ6V3bpjj7SsJmWl6/ft2hb7Znpm1qAJ 01SSdgcAUtMzMrKaNm/XqaBo2KAJ12c2a6FOAOBkIUBPXkpKStHYq4vGXq0qvtlqa2tqq2sON3rq OX/3z/nd+inRCVN98ED8PwuKzuo/7hrdsrE5WFmx/ePi+Fcyspudd/ODLQu6qRztDgBMe6ZEJQDA SUqADsfQLK/9/t2fpmc37dDnjNMvv6l9n8FKdGIUjZu8bfXSsm2bqvaXp2Vm53bp1WPERf0vnJKa nqFbNhIZTXOGXXvb5uK3d6z/sLJ8d3XVgYwmOa07de84YES/sVfVbRSBdgcAAICTVEp5ubVHAAAA AADgSDYRBQAAAACAAAE6AAAAAAAECNABAAAAACBAgA4AAAAAAAECdAAAAAAACBCgAwAAAABAgAAd AAAAAAACBOgAAAAAABAgQAcAAAAAgAABOgAAAAAABAjQAQAAAAAgQIAOAAAAAAABAnQAAAAAAAgQ oAMAAAAAQIAAHQAAAAAAAgToAAAAAAAQIEAHAAAAAIAAAToAAAAAAAQI0AEAAAAAIECADgAAAAAA AQJ0AAAAAAAIEKADAAAAAECAAB0AAAAAAAIE6AAAAAAAECBABwAAAACAAAE6AAAAAAAECNABAAAA ACBAgA4AAAAAAAECdAAAAAAACBCgAwAAAABAgAAdAAAAAAACBOgAAAAAABAgQAcAAAAAgAABOgAA AAAABAjQAQAAAAAgQIAOAAAAAAABAnQAAAAAAAgQoAMAAAAAQIAAHQAAAAAAAgToAAAAAAAQIEAH AAAAAIAAAToAAAAAAAQI0AEAAAAAIECADgAAAAAAAQJ0AAAAAAAIEKADAAAAAECAAB0AAAAAAAIE 6AAAAAAAECBABwAAAACAAAE6AAAAAAAECNABAAAAACBAgA4AAAAAAAECdAAAAAAACBCgAwAAAABA gAAdAAAAAAACBOgAAAAAABAgQAcAAAAAgAABOgAAAAAABAjQAQAAAAAgQIAOAAAAAAABAnQAAAAA AAgQoAMAAAAAQIAAHQAAAAAAAgToAAAAAAAQIEAHAAAAAIAAAToAAAAAAAQI0AEAAAAAIECADgAA AAAAAQJ0AAAAAAAIEKADAAAAAECAAB0AAAAAAAIE6AAAAAAAECBABwAAAACAAAE6AAAAAAAECNAB AAAAACBAgA4AAAAAAAECdAAAAAAACBCgAwAAAABAgAAdAAAAAAACBOgAAAAAABAgQAcAAAAAgAAB OgAAAAAABAjQAQAAAAAgQIAOAAAAAAABAnQAAAAAAAgQoAMAAAAAQIAAHQAAAAAAAgToAAAAAAAQ IEAHAAAAAIAAAToAAAAAAAQI0AEAAAAAIECADgAAAAAAAQJ0AAAAAAAIEKADAAAAAECAAB0AAAAA AAIE6AAAAAAAECBABwAAAACAAAE6AAAAAAAECNABAAAAACBAgA4AAAAAAAECdAAAAAAACBCgAwAA AABAgAAdAAAAAAACBOgAAAAAABAgQAcAAAAAgAABOgAAAAAABAjQAQAAAAAgQIAOAAAAAAABAnQA AAAAAAgQoAMAAAAAQIAAHQAAAAAAAv4fJRK/IKyw/gYAAAAASUVORK5CYII= --=-=-= Content-Type: text/x-org Content-Disposition: attachment; filename=bug.org Content-Transfer-Encoding: quoted-printable #+columns: %30CUSTOM_ID %60TITLE %40AUTHOR * All papers ** The {GNU} version of {T}he {C}ollaborative {I}nternational {D}ictionary = of {E}nglish :ATTACH: :PROPERTIES: :TITLE: The {GNU} version of {T}he {C}ollaborative {I}nternational {D}ic= tionary of {E}nglish :BTYPE: misc :CUSTOM_ID: GCIDE :HOWPUBLISHED: Published on the Internet :NOTE: \url{ftp://ftp.gnu.org/gnu/gcide/} :Attachments: Powell-UnifiedFrameworkStochasticOptimization_Jan292018.pdf :ID: f29925d3-8466-4dd7-9843-31cd66ef885a :END: ** {An Evolutionary Algorithm Based on Minkowski Distance for Many-Objectiv= e Optimization} :PROPERTIES: :TITLE: {An Evolutionary Algorithm Based on Minkowski Distance for Man= y-Objective Optimization} :BTYPE: article :CUSTOM_ID: ISI:000476811000012 :AUTHOR: Xu, Hang and Zeng, Wenhua and Zeng, Xiangxiang and Yen, Gary G= . :JOURNAL: {IEEE TRANSACTIONS ON CYBERNETICS} :YEAR: {2019} :VOLUME: {49} :NUMBER: {11} :PAGES: {3968-3979} :MONTH: {NOV} :ABSTRACT: {The existing multiobjective evolutionary algorithms (EAs) bas= ed on nondominated sorting may encounter serious difficulties in tackling m= any-objective optimization problems (MaOPs), because the number of nondomin= ated solutions increases exponentially with the number of objectives, leadi= ng to a severe loss of selection pressure. To address this problem, some ex= isting many-objective EAs (MaOEAs) adopt Euclidean or Manhattan distance to= estimate the convergence of each solution during the environmental selecti= on process. Nevertheless, either Euclidean or Manhattan distance is a speci= al case of Minkowski distance with the order P =3D 2 or P =3D 1, respective= ly. Thus, it is natural to adopt Minkowski distance for convergence estimat= ion, in order to cover various types of Pareto fronts (PFs) with different = concavity-convexity degrees. In this paper, a Minkowski distance-based EA i= s proposed to solve MaOPs. In the proposed algorithm, first, the concavity-= convexity degree of the approximate PF, denoted by the value of P, is dynam= ically estimated. Subsequently, the Minkowski distance of order P is used t= o estimate the convergence of each solution. Finally, the optimal solutions= are selected by a comprehensive method, based on both convergence and dive= rsity. In the experiments, the proposed algorithm is compared with five sta= te-of-the-art MaOEAs on some widely used benchmark problems. Moreover, the = modified versions for two compared algorithms, integrated with the proposed= P-estimation method and the Minkowski distance, are also designed and anal= yzed. Empirical results show that the proposed algorithm is very competitiv= e against other MaOEAs for solving MaOPs, and two modified compared algorit= hms are generally more effective than their predecessors.} :PUBLISHER: {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC} :ADDRESS: {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA} :LANGUAGE: {English} :AFFILIATION: {Zeng, XX (Reprint Author), Xiamen Univ, Sch Informat Sci \& = Engn, Xiamen 361005, Fujian, Peoples R China. Yen, GG (Reprint Author), Okl= ahoma State Univ, Sch Elect \& Comp Engn, Stillwater, OK 74078 USA. Xu, Han= g, Putian Univ, Sch Informat Engn, Putian 351100, Peoples R China. Xu, Hang= ; Zeng, Wenhua, Xiamen Univ, Sch Software, Xiamen 361005, Fujian, Peoples R= China. Zeng, Xiangxiang, Xiamen Univ, Sch Informat Sci \& Engn, Xiamen 361= 005, Fujian, Peoples R China. Yen, Gary G., Oklahoma State Univ, Sch Elect = \& Comp Engn, Stillwater, OK 74078 USA.} :DOI: {10.1109/TCYB.2018.2856208} :ISSN: {2168-2267} :EISSN: {2168-2275} :KEYWORDS: {Concavity-convexity degree; convergence estimation; evolution= ary algorithm (EA); many-objective optimization; Minkowski distance} :KEYWORDS-PLUS: {NONDOMINATED SORTING APPROACH; BOUNDARY INTERSECTION; SELE= CTION; PERFORMANCE; MOEA/D; CONVERGENCE; DIVERSITY; DOMINANCE} :RESEARCH-AREAS: {Automation \& Control Systems; Computer Science} :WEB-OF-SCIENCE-CATEGORIES: {Automation \& Control Systems; Computer Scienc= e, Artificial Intelligence; Computer Science, Cybernetics} :AUTHOR-EMAIL: {hangxu520@hotmail.com whzeng@xmu.edu.cn xzeng@xmu.edu.cn gy= en@okstate.edu} :ORCID-NUMBERS: {Xu, Hang/0000-0002-3376-9487 Yen, Gary/0000-0001-8851-5348= } :FUNDING-ACKNOWLEDGEMENT: {National Natural Science Foundation of ChinaNati= onal Natural Science Foundation of China {[}61472333, 61772441, 61472335, 6= 1272152, 41476118]; Natural Science Foundation of the Higher Education Inst= itutions of Fujian Province {[}JZ160400]; President Fund of Xiamen Universi= ty {[}20720170054]; Juan de la Cierva Position {[}IJCI-2015-26991]; MINECO = AEI/FEDER, EU {[}TIN2016-81079-R]; Madrid Government {[}B2017/BMD-3691]; In= GEMICS-CM (FSE/FEDER, EU)} :FUNDING-TEXT: {This work was supported in part by the National Natural Sci= ence Foundation of China under Grant 61472333, Grant 61772441, Grant 614723= 35, Grant 61272152, and Grant 41476118, in part by the Natural Science Foun= dation of the Higher Education Institutions of Fujian Province under Grant = JZ160400, and in part by the President Fund of Xiamen University under Gran= t 20720170054. The work of X. Zeng was supported in part by the Juan de la = Cierva Position under Grant IJCI-2015-26991, in part by the MINECO AEI/FEDE= R, EU under Spanish TIN2016-81079-R, in part by the Madrid Government under= Grant B2017/BMD-3691, and in part by the InGEMICS-CM (FSE/FEDER, EU). This= paper was recommended by Associate Editor M. Zhang. (Corresponding authors= : Xiangxiang Zeng; Gary G. Yen.)} :NUMBER-OF-CITED-REFERENCES: {79} :TIMES-CITED: {2} :USAGE-COUNT-LAST-180-DAYS: {18} :USAGE-COUNT-SINCE-2013: {22} :JOURNAL-ISO: {IEEE T. Cybern.} :DOC-DELIVERY-NUMBER: {IK7YY} :UNIQUE-ID: {ISI:000476811000012} :DA: {2019-09-29} :END: ** {Evolutionary Many-Objective Algorithm Using Decomposition-Based Dominan= ce Relationship} :PROPERTIES: :TITLE: {Evolutionary Many-Objective Algorithm Using Decomposition-Bas= ed Dominance Relationship} :BTYPE: article :CUSTOM_ID: ISI:000485687200007 :AUTHOR: Chen, Lei and Liu, Hai-Lin and Tan, Kay Chen and Cheung, Yiu-M= ing and Wang, Yuping :JOURNAL: {IEEE TRANSACTIONS ON CYBERNETICS} :YEAR: {2019} :VOLUME: {49} :NUMBER: {12} :PAGES: {4129-4139} :MONTH: {DEC} :ABSTRACT: {Decomposition-based evolutionary algorithms have shown great = potential in many-objective optimization. However, the lack of theoretical = studies on decomposition methods has hindered their further development and= application. In this paper, we first theoretically prove that weight sum, = Tchebycheff, and penalty boundary intersection decomposition methods are es= sentially interconnected. Inspired by this, we further show that highly cus= tomized dominance relationship can be derived from decomposition for any gi= ven decomposition vector. A new evolutionary algorithm is then proposed by = applying the customized dominance relationship with adaptive strategy to ea= ch subpopulation of multiobjective to multiobjective framework. Experiments= are conducted to compare the proposed algorithm with five state-of-the-art= decomposition-based evolutionary algorithms on a set of well-known scaled = many-objective test problems with 5 to 15 objectives. Simulation results ha= ve shown that the proposed algorithm can make better use of the decompositi= on vectors to achieve better performance. Further investigations on unseale= d many-objective test problems verify the robust and generality of the prop= osed algorithm.} :PUBLISHER: {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC} :ADDRESS: {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA} :LANGUAGE: {English} :AFFILIATION: {Liu, HL (Reprint Author), Guangdong Univ Technol, Guangzhou = 510006, Guangdong, Peoples R China. Chen, Lei; Liu, Hai-Lin, Guangdong Univ= Technol, Guangzhou 510006, Guangdong, Peoples R China. Tan, Kay Chen, City= Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China. Cheung, Yiu-Min= g, Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Peoples R China. Wang,= Yuping, Xidian Univ, Dept Comp Sci, Xian 710071, Shaanxi, Peoples R China.= } :DOI: {10.1109/TCYB.2018.2859171} :ISSN: {2168-2267} :EISSN: {2168-2275} :KEYWORDS: {Dominance relationship; evolutionary algorithm; many-objectiv= e; multiobjective to multiobjective (M2M) decomposition} :RESEARCH-AREAS: {Automation \& Control Systems; Computer Science} :WEB-OF-SCIENCE-CATEGORIES: {Automation \& Control Systems; Computer Scienc= e, Artificial Intelligence; Computer Science, Cybernetics} :AUTHOR-EMAIL: {chenaction@126.com hlliu@gdut.edu.cn kaytan@cityu.edu.hk ym= c@comp.hkbu.edu.hk ywang@xidian.edu.cn} :RESEARCHERID-NUMBERS: {Cheung, Yiu-ming/E-2050-2015 } :ORCID-NUMBERS: {Cheung, Yiu-ming/0000-0001-7629-4648 Wang, Yuping/0000-000= 1-6868-0004 Liu, Hai-Lin/0000-0003-2276-1938} :FUNDING-ACKNOWLEDGEMENT: {National Natural Science Foundation of ChinaNati= onal Natural Science Foundation of China {[}61673121, 61672444]; Projects o= f Science and Technology of Guangzhou {[}201804010352]; City University of = Hong Kong Research Fund {[}7200543]; China Scholarship CouncilChina Scholar= ship Council} :FUNDING-TEXT: {This work was supported in part by the National Natural Sci= ence Foundation of China under Grant 61673121 and Grant 61672444, in part b= y the Projects of Science and Technology of Guangzhou under Grant 201804010= 352, in part by the City University of Hong Kong Research Fund under Grant = 7200543, and in part by the China Scholarship Council.} :NUMBER-OF-CITED-REFERENCES: {41} :TIMES-CITED: {0} :USAGE-COUNT-LAST-180-DAYS: {0} :USAGE-COUNT-SINCE-2013: {2} :JOURNAL-ISO: {IEEE T. Cybern.} :DOC-DELIVERY-NUMBER: {IX4WU} :UNIQUE-ID: {ISI:000485687200007} :DA: {2019-09-29} :END: --=-=-=--