Received: from malur.postgresql.org ([217.196.149.56]) by arkaria.postgresql.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_CBC_SHA1:256) (Exim 4.89) (envelope-from ) id 1iY8fl-00056n-AH for pgsql-performance@arkaria.postgresql.org; Fri, 22 Nov 2019 13:08:30 +0000 Received: from localhost ([127.0.0.1] helo=malur.postgresql.org) by malur.postgresql.org with esmtp (Exim 4.89) (envelope-from ) id 1iY8fi-000313-Tw for pgsql-performance@arkaria.postgresql.org; Fri, 22 Nov 2019 13:08:26 +0000 Received: from makus.postgresql.org ([2001:4800:3e1:1::229]) by malur.postgresql.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_CBC_SHA1:256) (Exim 4.89) (envelope-from ) id 1iY7De-0004Tu-Cm for pgsql-performance@lists.postgresql.org; Fri, 22 Nov 2019 11:35:22 +0000 Received: from mail-io1-xd33.google.com ([2607:f8b0:4864:20::d33]) by makus.postgresql.org with esmtps (TLS1.3:ECDHE_RSA_AES_128_GCM_SHA256:128) (Exim 4.92) (envelope-from ) id 1iY7Db-0005eZ-OH for pgsql-performance@lists.postgresql.org; Fri, 22 Nov 2019 11:35:21 +0000 Received: by mail-io1-xd33.google.com with SMTP id z193so7645906iof.1 for ; Fri, 22 Nov 2019 03:35:19 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=hepsijet-com.20150623.gappssmtp.com; s=20150623; h=from:references:in-reply-to:mime-version:thread-index:date :message-id:subject:to:cc; bh=ohcj9JUZGHvgOGHW6NR7RTymzYH7Zbvdqy3JP+BMLZ8=; b=mZf+HY/tv2gGJzBRkEU1LTmadkOHdE0t+TXguIwC9E22caPCetwlh47DcKK2U28rPN gAmQmQy8cogVbHZKWopIctGxtSpdQYA8leHpHXn1nUl4VSycB/uEb0Uqx+wxAGc9XbDj q+t1/3fwO4ApmkKSufw1PqMBw3IWBYVGaK4l3SQFt122d03RuJoPBAPkpi6F0x5t+yBU 9o6uxqZx+p5BP9RDDI7jj2lRpEzb+uj4WU9Dup/htLSBb/ozYRzc3C1pErFKjvjkoQ69 kECd4dqjSskMx714p8tJkwZXLmPakCh2w+5FZT+82G7xkcZxzQOcpp1QyYECLbwGksC4 BlIA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:from:references:in-reply-to:mime-version :thread-index:date:message-id:subject:to:cc; bh=ohcj9JUZGHvgOGHW6NR7RTymzYH7Zbvdqy3JP+BMLZ8=; b=fuCIp7LVEIgf3iUSmsw2qvzYIDny4i3exnuOOWgRaVDWx9zRSDf6Q0kmRfx9t2Zxun GQt/gQ+lmndQD9dTPyjENkAx4pSlvfXHCoA8mvaaDSOTteDkge4D/8TzcGj3piYDKQ4b w8rwIKYOkf3Wv1vKThqH7dWhZuA4FuFFZMYUPkPFFSrxpVTHRgrGxkwKGtOvPUYjbCRF yyyHj/UGQWcz6qKHzuGQXGnI4A8YU1U/KUE8Dd4PcknAFQSdXxafEdu01QZMoX0mo2Wb xsTLt1YWYdnL++HVcRySWxoFy7BkavWljaA6L6Tos1nfQQ6ESs7g1Ry8tIYAkL1wlnos ZU/A== X-Gm-Message-State: APjAAAUEcQxWt5qCEVWHdAMtCTFoy+KSTO2u4VXaeP9pLTMP1eI0nldJ WDsq8DiIEE9N3fjoxo/fVbHP4RzvcLCDyMGcPw5kaM9/GL4= X-Google-Smtp-Source: APXvYqzEUqTne2nM+rLVc8J4oyQyALNEbwJTNcq12nhzAhKgbt03B6Zq3rOS8oPIbTehRIaEj6fexRC3xkQM5P8zZrY= X-Received: by 2002:a5d:9ecc:: with SMTP id a12mr12306261ioe.32.1574422518089; Fri, 22 Nov 2019 03:35:18 -0800 (PST) From: =?UTF-8?B?RsSxcmF0IEfDvGxlw6c=?= References: In-Reply-To: MIME-Version: 1.0 X-Mailer: Microsoft Outlook 16.0 Thread-Index: AQGv91VRM9XCd4OaQAxgeFnt4Ja7TqfiYbdw Date: Fri, 22 Nov 2019 14:35:15 +0300 Message-ID: <7ca4228bd9ea8ee1b119123679f340ff@mail.gmail.com> Subject: RE: Postgresql planning time too high To: Sterpu Victor Cc: pgsql-performance@lists.postgresql.org Content-Type: multipart/related; boundary="000000000000eb2c270597edce76" List-Id: List-Help: List-Subscribe: List-Post: List-Owner: List-Archive: Precedence: bulk --000000000000eb2c270597edce76 Content-Type: multipart/alternative; boundary="000000000000eb2c250597edce75" --000000000000eb2c250597edce75 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Hello Sterpu, First, please run vaccum for your Postgresql DB. No rows returned from your query. Could you double check your query criteria. After that could you send explain analyze again . Regards, *FIRAT G=C3=9CLE=C3=87* Infrastructure & Database Operations Manager firat.gulec@hepsijet.com *M:* 0 532 210 57 18 =C4=B0n=C3=B6n=C3=BC Mh. Mimar Sinan Cd. No:3 G=C3=BCzeller Org.San.B=C3=B6= lg. GEBZE / KOCAEL=C4=B0 [image: image.png] *From:* Sterpu Victor *Sent:* Friday, November 22, 2019 2:21 PM *To:* pgsql-performance@lists.postgresql.org *Subject:* Postgresql planning time too high Hello I'm on a PostgreSQL 12.1 and I just restored a database from a backup. When I run a query I get a big execution time: 5.482 ms After running EXPLAIN ANALYZE I can see that the "Planning Time: 5165.742 ms" and the "Execution Time: 6.244 ms". The database is new(no need to vacuum) and i'm the only one connected to it. I use a single partition on the harddrive. I also tried this on a postgresql 9.5 and the result was the same. I'm not sure what to do to improve this situation. The query and the explain is attached. Thank you --000000000000eb2c250597edce75 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable <= div class=3D"WordSection1">

Hello Ster= pu,

=C2=A0

First, please run vaccum for your Postgresq= l DB.

=C2=A0

No rows returned from your query. Could y= ou double check your query criteria.

=C2=A0

After that= could you send explain analyze again .

=C2=A0

Regards= ,

=C2=A0=

FIRAT G=C3=9CLE=C3=87= =C2=A0
Infrastructure & Database Oper= ations Manager
firat.gulec@hepsijet.com

=C2=A0=

M:=C2=A00 532 210 57 18=C2=A0
=C4=B0n=C3= =B6n=C3=BC Mh. Mimar Sinan Cd. No:3 G=C3=BCzeller Org.San.B=C3=B6lg. GEBZE = / KOCAEL=C4=B0

3D"image.png"<= /span>

=C2=A0

=C2=A0

=C2=A0

From: Sterpu Victor <victor@caid= o.ro>
Sent: Friday, November 22, 2019 2:21 PM
To: pgsql-performa= nce@lists.postgresql.org
Subject: Postgresql planning time to= o high

=C2=A0

Hello=

=C2=A0

= I'm on a Po= stgreSQL 12.1 and I just restored a database from a backup.

When I run a query I get a big execution time: 5.482 ms

After running EXPLAIN ANALYZE I can see that the &q= uot;Planning Time: 5165.742 ms" and the=C2=A0"Execution Time: 6.2= 44 ms".

The database is new(no need to va= cuum) and i'm the only one connected to it. I use a single partition on= the harddrive.

I also tried this on a postgre= sql 9.5 and the result was the same.

I'm n= ot sure what to do to improve this situation.

= The query and the explain is attached.

=C2=A0<= /span>

Thank you

=C2=A0<= /span>

--000000000000eb2c250597edce75-- --000000000000eb2c270597edce76 Content-Type: image/png; name="image002.png" Content-Disposition: inline; filename="image002.png" Content-Transfer-Encoding: base64 Content-ID: X-Attachment-Id: 2fd0042fe5923ad8_0.1 iVBORw0KGgoAAAANSUhEUgAAAl0AAABLCAYAAABKkjKEAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAS dAAAEnQB3mYfeAAAABl0RVh0U29mdHdhcmUATWljcm9zb2Z0IE9mZmljZX/tNXEAAD6jSURBVHhe 7d0HuF1HlSf6UrCCJVnBlnPO2cY5R8A2zthgDJhgA91DN0z3TE8z4Zv3mpnufv3eMDPdj6aBYQgG kwwYYxzB2TjnnHHOSZZlJSvM+u2rJW0d3yhdXUt21fcd6dyza1et+lfVWv9atXbtkQsjlZoqAhWB ikBFoCJQEagIVARWKAIjV2jptfCKQEWgIlARqAhUBCoCFYEGgUq66kCoCFQEKgIVgYpARaAiMAQI VNI1BCDXKioCFYGKQEWgIlARqAhU0lXHQEWgIlARqAhUBCoCFYEhQKCSriEAuVZREagIVAQqAhWB ikBFoJKuOgYqAhWBikBFoCJQEagIDAEClXQNAci1iopARaAiUBGoCFQEKgKVdNUxUBGoCFQEKgIV gYpARWAIEKikawhArlVUBCoCFYGKQEWgIlARqKSrjoGKQEWgIlARqAhUBCoCQ4BAJV1DAHKtoiJQ EagIVAQqAhWBikAlXXUMVAQqAhWBikBFoCJQERgCBCrpGgKQaxUVgYpARaAiUBGoCFQEKumqY6Ai UBGoCFQEKgIVgYrAECBQSdcQgFyrqAhUBCoCFYGKQEWgIlBJVx0DFYGKQEWgIlARqAhUBIYAgUq6 hgDkWkVFoCJQEagIVAQqAhWBSrrqGHhXILBw4cJy8803lzXXXLNsscUW74o21UZUBCoCFYF3CoFp 06aVu+++u+y6665lwoQJ75QY77p6K+l613Xpe7NBw4YNK2PHji2jRo3qFYAFCxYUeZG04cOH95pX Hh/5pfz/nUKYLCtSDthkSmxWdJ2JsXr76o8ViXt/2tkeD+2xsDKNk+zDwcByMMvSdz31dWKfc20w 5llvsrfHec6nrDOv+bvzt868OR57K2+gY7atb/qaG31d76nuTnl7Git+X3311d/ReTlQ/FaF/JV0 rQq9VGXsEwEK6I9//GPZcMMNy0YbbdRt/lmzZpVrrrmmrLvuuuXJJ58shx56aBk3blyPZT/88MNN mZTPOuusU3bZZZfFeefOnVsuuOCCppxPfvKTjYdNeu6558qvf/3rsskmmzRKe8yYMeWwww7rU/6n nnqq/PCHP2zqOOaYYxbnv/rqq8tll11WPvOZz5SXXnqpTJkypWy55ZZ9ljeQDNpy0UUXNW1FXOfM mdO098gjjywwgNH2228/kCL7lfcPf/hDue2228pqq61WGIIRI0aU97///W9r39lnn1222mqr8r73 ve9t5b7++uvlV7/6VZk0aVL58Ic/3Fx/7bXXym9+85sG94033rhXWebPn1+uu+66ctdddxXfd9pp p3LQQQc1smTy+8UXX1weffTRZizAaOeddy677bZbk++ZZ55pPALawSOw995796v9MsH697//fVPe gw8+2JTXn/HSWYF7r7jiigbHNMYHHnjgUmO2P0K5V59ffvnlDR7a639lkXFZCNH06dObOaFfzAfl wenYY48tkydPLjfddFNTD8+KebP11ls3mJDlgx/8YH/EXpznhRdeKOeff36ZPXt2I6t5ud9++zU6 4fHHH2/GufpHjhy5eEFlzL311lvlt7/9bVljjTWa3+fNm9eMBfcaS88//3zTv+R89dVXy6abblq2 2WabRk73SO6T5+ijjy7rrbfeUnLrm7XWWqspU7rnnnvK7bffXj760Y+W0aNHl1deeaWcc845TZkH HHBAg/+9997bLCLJCzd4mZdku/DCC5v25NwZP358U686JHPL51Of+lRZf/31m9+M4fvvv78pCzbK gc/BBx/c6MXdd999qbk3c+bM8tBDDzVt7U1PDqiDauZSSVcdBO8aBCghBnHGjBmNAqdIfacAKSN/ Iy0UMuIlMVKIEoXJKEycOLEx1PJQeJQuQ0jRUkJWfqlgX3755XLHHXc0SjJJF2VKsVHEyqakH3nk kca49qa81Id4ya9OcqoPGXjiiScaeTbYYIOmfgra32+++WYjt+1UsiBPZPcbMggDypWi9mH84OF/ 8k6dOrVpC+VPwX/+859vylfumWee2RhxBIKSzvtg6H6KXBmM0YsvvtjUifioX1vJw1jIu/baazf1 Z6LsEUnYMRRJjPzGwH3hC18oyJQy1IHUZH91DlZluX7nnXc2BpvhIKPf4AGnxx57rPmNgdIHxghi of2IJmJ72mmnNfkZPkTAlorkvt/97ndNHx533HGNPMjvL3/5y/LGG280faWf99lnnwYnfQM/7UYA 1JlkzBh8+umnm/skbTLetNOCwL3pqdXnMJDIaQxnPuPC32nstf2SSy5pSGkSvltuuaXBUh9svvnm TfnK0w8Ih7nC2MNCn+f4hReDjmDtu+++jWFWFuK72WabNWWQ05zIfvWbNut340R/alcmuGr3CSec 0JAfY+Xcc88t119/fTnqqKOadsAEEYOhpI5Mzz77bNN2hh8+xll32136Rf+RC5Ey9sn+k5/8pHz2 s59tMDC/yNEmETBAvNX9sY99rMlDJyAp5tyHPvShxUQNCfn5z3/eYKE+srgnkzpTR+RvMEHKc1Hg d+01Ro0N/Ulu9Vt0qffWW28tJ554YoNlkix5kFBhFOacdqjf+PvZz37W9BuSBW/10Sf0UZIuGCK0 CHQmbYULWbbbbrulphe5zBOLkJoGD4FKugYPy1rSO4wAhUxxPfDAA42ipcAYTUqJMeJVoZAobISE AkWYvve97zVGx4eypdiURYnxMFHKl156adljjz0Wr1Q1lcJixJO0UY5ICMNCqftbHYybPFajJ510 0lIGKSFjiBhSxgxhQroYKgqbolQWjwBigSj4vuOOOzbGPQ0GQ23Vqq0MlXLSk4PcMLSIDfmQhSRd jAQZkQR1MVppaJStjYwJxY6MMK7wY2wYR22EMUNAoSvv29/+dtl2220boyKv8nirJEbaSp4R0oZM e+65Z0OOGRxEUH8gCMgw+bpLaeR4EK666qpmpS5vkkfGPT2EiDMD9IEPfKDxWpFNPzHQjJuxkp6C rIuhQkh5/RhaCUn49Kc/3bRdm3/xi180HgtkDYEjg/GDDBkjN9xwQ9MnDLgx6H/khuHl1ZJXviRo 2o/wGg9wJQP8GFLXjGtyJ+nipVCmdmVSrnYz0Mak+/QjoodAws08UKZxffLJJze3wtt4J5t+kh/5 yvFuHBt/7r/xxhsbIgofRIHnT19feeWV5dRTT12MpbzaqKyUWVvVZV65Pz04sDJHyGDsusYDZazK z6NnLO+///5vGw7aog/1VZIqOBjP5g85/N7phVKQa4giEiMhkcYRubMsc824hZf+RMa1J9vUk/oz 78y5dr3muXIRG15AeJxyyikN7nBFRttzQ//QUz/96U+bsWNh0vbimnfKkmAEOx5zutA1eMLPnOqU 13xOL167Dcak9i+Ld7MnLOrvMcf6BcITt5dy0TdKyXCZuXHX1nuVcvDnShnRe1xMv8p/t2WKAV+u +FYpjwRubcyO+YtSNlxiZHpr9t0Xzyj3XvJmWW1MVzxRZ3przsKy6e5jyp6nrFGevHNOufUX05ss i8KPBozoggXil4aVvU9do2y865hyyf94pUx/fl4ZMbL7+vtTwZxZC8qGO40tB5wxsQy/9fxwqZwb TCXunBexQ2ttXspRgcfY8f0pasB5GBSGirJGwBgFCuT0009vlCtSYjuPQULUGAyKnMeAp4oRpxiR ofRYdcY+UORWh8gKY0MpU9QMIKXnOkNMYTL43/zmN5t6216AbJj8fmcgkKcddtihkY2XgmF3PeNM ctuEYZPnn/7pn5otTvfzHjCS7kcgyMQrg4BRusq3GqZkM9k6hI08jCqlTqHzdjDQGWeCNCKkMP3a 177WKHRGjfFwT3qElEfJH3LIIQ0BgT9DkKRLve7pxMHWsM93v/vdhrAxPNqOIHXGoaTsuZXGCCI5 to+SyLiXwfvEJz7RGD0rf1gwTgxzOyE5iIlEhnZC4LRdQmSQcf1MfnLmVlXX/BvWyIqc+jCgyNnh hx9ezjvvvKZfjEvpG9/4RuOJaMcyIQ333XdfQ/CQD+nHP/5x4/FUD69m27Piur7sbkwhEIiT8vW7 MYIQkecrX/lKM74RuoypyrKOP/74BnMEUXKdtwMGCJ9y9TdPGi8iufxm+8s4+od/+Ifm9ySwOW6R dnLyrMAxvTq8hEmivv/97y/eVkN8yIAknHHGGc0C5Otf/3qPY8H8VH4SdAsT+Oo//UQn8OgYX/Lo J/mNM3l4dc4666ymzeapeewjaa9+MK61U9JupDDv0XfmqwVNe37JY7HU3rL2HQ4IO6y/9KUvNdjR Ga519icdZc7QM/Do3O4zriRtsmBBcJEt7TeeeIAliwJzOvtVHxjvlVg1kAxJ6h/peuHhUn7z3dCU i2R6M/5///OlHHTGkAi5ylUiIPmWX5dy1WVLMLMI2fu4fpGu5x6cW879m5fKc/fMKSNGd0965rwR 3puPTyy7n7RGef6BueXKb7/WwDRsGTmw8rY5ZHz50FdWK9f98PVy8ddeKfNmRhB5/0bI27po/lsL y+jVh5fN9l+zDHspxs/P/rqUh+7vIl3itT/992EtuvdeLG9/IygUfiouBgEx4j2iKCUKmhJmUCjS XIW6hzcs81BG8nanlCg4ZbsfYVGHMilcxsx15bqXInWttwBnChcxYbB4uZSJ+CBC7aRcRlk9FDUS xCBqH3nch1wwGMgTBewebefpaRuEVNRiSRh05JBxEw+CtGT7sy25SoavVTlDyrCTUz2MWZLNNLqI Z7vdMIdx29iTAzFjINyPJEj+V44+7SmRjdE84ogjGm8A71wb8+zz9OK0+5IxhQfc9T1PoC2pJGVk ZEwzwXmvvfZqsGHA4Ktvs8x2ADbyrjzbQPpCPmUjf/IxuvrOuMmUnjuE9lvf+laDm/5gHBncJAFt LODVHSn1W8ZluU/Z/jZOcnwbD+7P5Lp+9NGvvIwWAeLmGG7YGsdIhLYrE0YMtz6Alf5uEwxly4MY 5DYzgoNQ6RP5M0/OH94XSTlJeP2f26DdjYVc6GRfkE95xjJCzgvsb2SYrGQyf9SRoQe2aGHOy8RL mAswizb9YNGWW8Dw1X9JaJLEtduOXJonnXGm2q3f5dUmdfFMZR91zo2cp+RsB9snDtromkUgT6T2 mZv0EA8gGbULJhmbqpz0dndXX48Trl5YLgT6Z1JHhrvGE6Nd4SxdhnNMfYS0V+THRnBlGzNxuXDs I80LD9bl//xaee3Jt8oa60T39OBoGrnasDJ6fHREXB8ZxGzc5K7A32UhXfPmLixTNlqtnPz/TC0z py0oV37r1caBOWbqkmDivuRuX/eQ3Vvh5dr/81PLvidHGd/776U8GYRrUuSiS/f9aCkf+tch+BJv y0DK709eCjA/6a3KWJpmCIcC4vFIT0karTTu/mYUKUfKjMHrKSEsPCUUui0yhClTltsOcO6pHMYP SbMSZdDVnV6znu5pt5PsCASD7bvtCm3g/crtg07l6n7xOggHpY/k+FidM4wMNKxS/rZxRwYZZbFg iBGvkvLT89PGtE26GC1EVR08EGkkeeUQTmXkVokyEKO+EhLHgPDy8XYxOsrNcvJ+xryNgaBrddge RBzEmSGemTLo2BYqLwa5GTbGNNukjvwODwROGQgXz2A+hKBuxpynxHfkC+68lSlTtl1dPF3GhPHE MAva7o5cKQP2SFIuNIz1jFvLMaxNGeeYRJS8uaXmesa4wUNZPurmsUOKtdXWFvkREuXk+OhujCgz ZeZNTtJuDPAcGadJsJKIIlc5JrQ/+993xKcngkBWXjy45lYq2W0JKjMXSUmS2mPK+DGG02PkGlKo LHPIuGw/MON6ztfOWKh2ufAkQy4i8pp+8LtYQh69H/3oR82YRejpER4q5SYOvGHGFLKrry1OhEZk sgjQ5/CkQ/SfhDCbD8gXHWCMd8qb4z3j6bJM/VrJWF+aZ+DX+0e6Bl5uvWN2BMvOCBi6nvIvhadr /pIVc08AXf+T6eWWX71ehiEuQcAWT4DgLiOCaPU3LZgfT+AEmerrjuaUgKjmgNMnlXW3HVXO+rPn y7P3zSljgtAhTplIMnzEsOCNrRLj64J5C8v8qKedeLk23G1cOeyLsYK97gelXPq/usgjD+kGm5Zy /H8ORpcMvr8t6jsf5UyZpes+FQalxiBTSrZEfLd9QBFT1K4n2XK/vylgSlA8CcPSuQ2TxpGyZiBt i7jHihbpYUhyS5Dk8mcgL7JBmbafCJSXQWGMxFVRwoyuvxk6Cl5d/ld2m8y57v40qBS3rTKEguJk iBA5Sr7t1SAXOShpRlXe3HZhYBEEClm5yodvYqpOxhpGjBpjysMGz9xuzLydXi15GRveE+3MQF9G HH7KJD+81GNLSJsYHsSMVy29CUmO0xtlmxH+jFaSBlvGyKtycvvJ1lMaIDj5kFcb24HDSKftQFuD xo82k4kx9b/y9LutVXl5L/xu3MAjn3Az5niPtEEMlnqMQXW18WUYycrzwXshH7l58eRLL2x7NiDL xrXtcgZVO2CgLKSPlyfJJ6ONTMCekUUmYOb/9KQZh4K20zuGXOszZJIs2gZ3ZBAebVKXGJKVDMYX wqEvPe3ruzzq4611/dprr23ml3vUhRQhFf5WZz5goU75EF9kAvHltUoCyUuVwfTCA5SN0JI5yZ4+ 0dfKyGScG09Iqhgz98HdWDM39D1vXHpizQVzST5zOT2H2qUccsBFX8GAfuj0LifRVC5M9C/MzR0B 9BZCOTfgYMtQW+WDDSyNNfWQBxGDFa+WhV96D82l9N6q07jXLgme6kNQ88luuKbHzvjQhu6Ift/a uOboCYFKulbE2HD+0yFfKGWLeOpjtUX7fUFEynq9P3Y//aX55bFbZkVMVQQ9trYVh0cvvfnq/PLK 412kra+4LfFZE9YeWdbZenRZGOSrtzQ/SNOaG4+KneLJ5fE7ZhVxWDseZRts6buGBeGa8UoEit8/ pyFfzaSNNqln7S3Dg+cnVcVnXvx+yJ+vUyZPmVbKAxEns82hgUOwxgXx2f+UUjbtX1zbQLqGAhRI TAHmVl4GgVphM46UotgYSozBZbwpLUY1n45jJF2zDSIWS36Ki2JuB8JSohQgZU7pWwWrl6JSbhKN JAeUm3oQjDyGot0+Bo6yRQQZTuVRospjVMmkbVbBqVDdT37bV2TzHQHQBrIjWsr15FY+Cdh5jhnc bLvkFmxuVYkHoeQZrTRQ2pr3u0dd/kYQYCTWiCFIr1nGncC/c1vVNgfixbOURNC2B4OljUiXsrRL ucgCUsDAtFff8grmzjgspFFMkj6Ag/uQDgYn49TIl+XwbGhfxrm0y8r+QYLhydtFVv0gDomBJx9y TF5jgocBTvBEIOX3UV8aTEQWbupCxrTHeEN+0rupDOUbP4gT2V3v7oECdfFqIlMMqvL0Hdz1AfkZ VgnRh5m2kFcfqiO3IhEAbUWAM/jcb2LStNX4RCSMKxjzrsEvt1jJb2vWfCK/lH1kLCmTfMY2Ik0G 2PG4GSPmCBm109+Imf5CnIwxdeUxCZ1bzmRQN0zVbWwbU2ILkSjtQHJh1F58aDuPqzGSv7sX7sgV fJGSnNPq9VG2OvOetrcyx44+y0VFe76LddQ3OS/k0y7l2eb3AA9Cl31g7OhPciGU+iHHo3q1y3hX l/8zwdE4yy1JpDPlzT7XP3DL/snf4Ui3tPXNQHRyzds9ApV0rYiR4YyfvT8Sn4EVPnr1YeXY/7hW 1xZhy6E0fPiw8sCVb5Zf/Yd4ND+cT8N62fWztcehttleY8sJXw2FvsRZ1b0wkd825aixw8qG248u n/indctwHrUOrkamB6+cWX785881eck3N2K+EMQT/25qCRGX3BL3TrA1uSC8WSf8h65t1cZVFoWM nzQwUAaQu/20T/uIgva5Vp5U60zt85/axEoZ7SfC2vdRju362tsSDH1nysfB/c5YdSaKrb3t4Qmp THl8QcZIZRyG6xR33sc4pPyMWjtwvbezvbSFQu8utdvS3iJpt7d9llJ3bU+D31m+9nTXH/J1twXU bneWBdfO87vyiIzEp7tzr/KJPHl4Inx6S9rQ2Y4cY0hBZx2dwfhZNrLWmdJItrf52v2f+ZGnnhIc BHB3l9qy2IbiWUKaLT4E1jPU7bc4tAPIO8uTr7c3PhhLeRZVu79yDHcnX3eYZB28T7w3SAfCgHSY K8ZZPk3aLtP17rDLPD1h5Dpi2V1q65LO6z0dZSIfYo3M9VRmZ7ntOWged/eUZZal7d21v7t509YD 7e9tueik7vRSbzL0OmHqxR4RWE7S1WIGr0Sw74wXw9BGwOuoiLgfEzFNk+MsJNa4P0kw5+tx/6xp EfcT+1DifUbHWS8T4tDJcV2Hz70tNQwj7mv2yBa5WriBmiezFtVLrpkvCzKK34MEjI0tL3IhRv1J s2NfcEYEqc+JM3OUEV6kYB0hW8g0LspavYen7wT+wiLdUmTVpl7cVKPHxfZOfLpLa4RHqd8JkRo3 rIyf0s82Lio448J6qkd5mpHJ99WCgK3RU+zX8CBba3YdzFdTRaAi8M4jgLD42L5CUJCQ9qG/77yE S0uAANoqtn0v9efA25WtDVWeikAbgQFY8m6AGxkE4dl4/PSaH8RxABfHcdx3BAkKooGQTI0zebY7 OJ5wPC3cLrv2jPpb8aTYXb+PqMU4UuCRG0t58T7BTF2caWKcJr1JrMDfF493735ikKWuJ5oWp1kR N3Xpt0t5Ku6xfeUognVixXrMX8Xpek/FsQ3/u5S74wnC5++MMoOceXJt7dji2y4Oh9snAg237+XQ t1fj6czbf1vKfVdFEPg9Ud5DXaRL4rmZHO/32yhOF94ughl3PyHa2zoFHREk18M3B0FbBPHcwOX4 fxv3LNvWmjipgSR8b7BTdzLwpCFffW15DrYstbyKQEVg4Ajktnh6J9tPcg68tBV/hy1F2+62vNpP hq74mmsNFYEVg8Cyky7enruviaMA/jKOAoi4HQ/mcaz4vBWBek/Hb4/G587zSvlskJ+dgoB1pmcf jaMo/mspN5wdB6HwREUGjp509rz+ZJCx+Nx6bik3/qKUk/8u4oNae3Zz4qmm235dyh3Xhwcr7vNk 3I7hvp8ShOjy/xnxREjPovLItTDI0HN3xSMd8bk+yjs6yJmzszqfonso7vvpv+siXMhfJ0qI4ovx NN5z8SH7NT8u5dR/iLoP6Wohb9jdF8ShKBctfWTEgXEA4TKSrhXT/bXUikBF4L2IwMpOtjr7pLej Vt6L/VfbvOoisGykCzl68t54vjgIyyNxgJ5SOGIc95LEC3Hy+9PhCTs7PDwbBwGZ2PXakSY9+UAp 341g8weCuCFG3v7gfh6aPMtJvjxl4c7wWL3yTClf+EEp2y4iXtwryJ97HflErtdjO/Gcfx95g6y1 TyRQbhIocr0ZW5lnR7zRwrhwYuTP9FwQwTP/PIjkTV1HZLjHvYLHR8SN3DrcSBGAvli2h8ND953P lfKX50SQuHgdW5whVMqlbDugwwe23bdEqIF/G8KqBi5cvaMiUBGoCFQEKgLvQQSWnXQ9Fd4gRxpM ipimtTYNj07E7syKp1VejIMwZ8dZCckvkKGnwrOENB206B1Vb8Q5OD8JIoZwdb1qq4vYrBVbdBsH oZoSgcjTY3vvifBgvfTHLhKGAD0TRO3nQZC+9NPI0/XuvKWSfNOe6CoLWVp3yygvPF/j4zDAlx8L knh1xGdFbBYyhsy9FRnP/39j+3P3UnaNV2ggVLf86u2Ea9OQ6dA/DS9VBEXaOnzOlmrEGDwS5amH bC8EWbvw/y/li+HVewcZD3EEvb/+4vzy1B2zm2MjOoPiF2Mm+N3lgGH9CKIfO6n7eLL34LyoTa4I VAQqAhWBisCgI7BspIsYYozW3bSUj/z3UvaI11qMCvY0Lx6bu+6XQai+FJ6kV7qIFzs+M35/OAhW ki7bcXdf2OWdkpCkHY+ILbooa5Ou1xk0SbzYmVHWPREvRlIE7f4ru7b0PvTlnskNr9LeQfA+GtuR 67WeSrotvG1n/UXEeEV8VnrG3pgWJ8d/p5QdbH8GC7Elqq70cHko4Nj/XMpeRy+Ra9uI49r5g6X8 /SGRP0gmEsdLd5Ntxr8NItoNIRz0ruuhwJDbWVqPXB0vK72q611cPSVNdETEJruNLad/Z71Kuoaq j2o9FYGKQEWgIvCeRGDZSFcT0x2uouP+ppT9PrwEOLFRB50aRCS25i77xy4Py6KHCsv08EBJMyPe 65rvdn1HyJAV51edER6iqRsu3Qnrh6fq418r5Wt3lCKwHblB5ATuvz+2Jm33dSYEbou9SvlUxHR5 SrGddjsqzjmIOLDvnBH/xxOJykLk7o5A/ufCi0YO8qXDh+zzokDeu87kqbzjvxptva6rHESveWrS 9mNf5zS8vbjB/qXxYAlw76Vg52mNWWNE+cBfxEtQ11u2oTDYctfyKgIVgYpARaAi8G5FYNksrTM6 N4oD2Dyd2F3a4cAu0pUJiRH0LsD84SBkL4enKcmYPDuGF6mTcOW9PF9bHxaB7z/pInEIzgt3dwXD rxfbfZ2PzZHtwE+9nXBlebsfU8q1+0ZgfnjaMmZrxrSIMYsnHNePJwudI4W4ZVD/vJD7gr8P4hXb kzuHN26LeJqSJ4v8CKZPZ2q9q+0dGzi4X8Sd9US6EDJbjwf/ycSy3aFLTmd+x+Rdjopnx3sjL//m a+XZe+M9eqPfvVuk+mzumwvKLsdPKHvHi86XSvFy9YUX/4/Y2o9FQ3eLkeXAt95aEagIVAQqAr0h EJZ2XjzJt8b6Zdgp8XDgpI6TFlq3LhvpQkqmxnEOztDqLrUD5vN643qJPx4PssTbxDZiBEjU00Gi zhLULkPjRluSHMf+cgTQt4PiF4R77PEgSestOXm3uYGDafzEiOMKYtRT4o3aJEjXrbHVmK4gMng3 4AEh1I6HByn7WZcHDjrqfSNi1S6LeK0bfhCxa5Pi2IkgZ5vt0fUk5SbvC4DX6Tr4c2VJAaET7VeP 9zH29O6st2YtLFvsMqYc8NlJi0+YX1nEH4gcTtS/9qzXy3lffSnG/MKVqhsG0o6+8jbPqXi90o5j ymFf7ph3r79cFv74r8vCay5dsjXeV4H1ekWgIlARqAgMDgIUdFCA4R+PmHMcoZe0bKRLgaPDTeQJ ve6S+K4O7tQQLF6pV4NAiQdLEuX3hyJm64H49JSSoOV15OqVZxfV0fLl+H1ieKHGReB8b2nNzWJb MWR07lYG/E97oWtbcL/wXAn8/12cszUzjoYQcJ9PZ9p6nBGfF5+M4yTCUxa8rUwI8rlLeOoO/Hg8 VRmxXu90WuTB2vKA1cvhX5oSp9N3dkSXgPPj9UBrbrhambzBsg+Bd7qp6p8Tnp/pL8wre54cr+Np vTppZZBtMGVwHpr3ae509Piy5T4d8+7Z2P6eMKUMO/I4L+iMarvv88GUp5ZVEagIVAQqAosQ4Aia FLzihL8ObpNHLnSPzrJZ3IbVBVtZFu/O/CAynTaBV8mntwCkvIZY+bwZT0B2FqTc1SJCvq+nB0eH 0XJQavv903ODgDl/a0wEzp/638KDFZ6sm+NJRvFp4skQP2glYrk96iyxS78Z54XFcREfjfsOPa3r 8cF+prdmx4upw0MzdmLv92SM1lLFNkR26YoaX2H8M3G9EeGM64EU91O2VSHb6hNHlBP/pnUUyaog 9GDLuN2+ZVh8aqoIVAQqAhWBlRuBZSNdy9Mmr+JJfpHky5OLm8T5Vt0yi24qExu2fcR55XHomaV5 HO/NcOM4JbWXNCcIltcOtQkLspavBhoV3w+JuLC9T4qtz9h2fCa2Px+/vZTH7oi/Y1tzbni7mrO7 Fn2ERPGUnf1vYmszgvE3jyMoumOQUd+cIHqPXTurvPDI3PLCQ7PLi4+8Vbbcf2w58t/2sFW7qBlz Zy6Idx0uCE7YBV7zCp54SnHEyCi0G8fGijiRfnm6vd5bEagIVAQqAhWB9zoCQ0e6GhdMuKimbBDe omArDodCenittg0CxS3XfrFfu2d6en/jtIi1ajMOJOi12L6c7vde0iuPx5liEVfW9gKuEd6S4e3A sbh/bHi9tgqPl8/8IGEC7qfFtuYD13adkP/o1V2n3GuHsqa/HN6x8xaRrm7qD5L0epykcfZfvVBm vDqvvBUkavaMIFNxbMNBn5tcVu/F2/XsvYs8hIuIoq2mcVNGNs7GhUhoTRWBikBFoCJQEagIrNQI DB3pAgPS5T2Mo8OTNDPOkMqjFh67pYu49PQCP+9ifCaeeFzsvolyNvDkYseWnDJmB5l6KM4E2/GQ 7oH3DsbHgjQhKrlFyGvlKcnpcUr9jXEGWHqpxHhttmeQwn26vGATwxvls8lOpRwcJOz7/zrOJYvj L4a3SM+rT3fV211bIpt3d09Yd0Rwt7nNy629MPqFh+eWm86eXg75/KRuZX7yzjnljvPfiBC6LsYF xtXGDi9Tt+jqvu5OqBjOA1ZTRaAiUBGoCFQEKgIrDQJDS7q8DHvL8BqtFU8dPhHbdRmT9ODv4qys q7p/PyOofhVHNvw+4qYEYSFLk+Lk+v8rztayVdmZOKv+cGa8JDuCijfb5e3Xr48nEx+4YYmXq3ni cXIcBRHE6o0gXd+Nw1iTy9mlPCi2GLeNA18705ioe5sInL8xSJcykuOM6kamvDeC2uP9rRF3P748 fnOQzrhneLxeyNENV37rtTIySNUeJ0woYyYsIZMPXzerXPzfXimvPf1WhKt1VcIztt52o8vme3df Fy76xovzytP3zGkIWn+T2LJJEVg/ef2hHRb9la/mqwhUBCoCFYGKwKqMwBBa1yBLIvxt2R3yp3HS /J90kRUSvPl6/P2v4gXU/7GU3Y+Msy7W6sL0iXi/4xVxWvyVcXDqWxGrhYsgQofG4aYOPn3jtbdj z3v2fLyS57unx5ZlnJexU2xdCpz36qEbgjz95qvhDZu25OnJcIyVfeLpw3W2CO9byOHdiU/H04v5 1OL98Rj+FT/qCpBvp2lB0G6J8ryDUZ2cXf7fcNGJ+t1tlS5yru34wXHlpp+PLS9GTNeo1Yc3T93N CJJ0wd++VO69aEaZuvlqhadq+vPzyuO3zo7XSc5bTLhsKw6P7dYdjxhfpm7WsR0a1XOwjVhtWHns ptnlx196PnZx+7H1GDfNm72gjF97ZDnxv6xdSdeqPKOr7BWBikBFoCKw0iIwhKRrETEBxQEfC89W nLdw07ldJ8KT4rkIWD8rTpm/OE6l5y1CWmYEUXoptuuQLfwCQdo4SM3hQdok70HsTFiH7b5Hbyvl f38iYsji5HiP0Xs68aUngrQFa0OocBEEbq31glBFvZ5m5PE6KMjVj/7dohPm4zoi9uM/i3dHRqzW FuHZGjs+yoknFu+NIyOeiFcGIVpNAH981t0sSOOHFknUM9lZe4tR5fA/m1zO+U8vxmGX85utQsRr fni87r/8zXL/ZV07p3ZTbSku3laMv52vtc1h4yIGbFKPgwoEcyJWbOZr9k17T/Kqx9ESOx87oWyw Y++Pu/ZVXr1eEagIDD0CzuObNWtWLMiGx2k47/6nloce4VpjRWBwEBha0pUyrx6BTafG631mBxG6 65Iu4sLWI0ZPxplDyVf8vviMLAQpCNTH4gXVG2zVfevdNyrIiANLn/hDBNVPizc/x8fviFEedupv BI7X7cT/O56EjBP0JVHpB4WHzMGrV5/VJZd3NM5+IwLkw6t1e3waNhQuOnwvHU1ecTgh2nTS33Yd GuvVQX2kPT8yocwIUnTp/3wlXlM5v4waP7zZahwzvutpxEbkVliWoyUWxNbq1gePKyd+dWoZv2Ye MNZ9Rc2JHlFef9LcmQvLVgfHuV5fnFxGhpdsVU9vvvlmeeONNxojNDLI9NixY8uaa64ZeK76bVvV +ybl10cvvvhihEqOKOutFwufSIjDqFGV9C9LHz///PPlX/7lX8rkyZPL5z73ubLGGqGPaqoIVARW OgT6R7qcrTWDVlwkf+z0hSul58bYRpRH6ewcUoK4tLfc1o/tvD/9XikX/n+x7RdnXL341JIjGLJk 9bl3bCji3Q+Pl1yHB2qnQ3uuV34xX8d9JQ44jfcs/vbvukgXckQO25l5JtgmcbTDsbGdeXB4w9pp jSnx3sZ/7CJP18UTis8FCXQvb1vA0BTi75RN2dvuH1ujcVzE3h/uKsmTmbMDnzZm2jG/fTAYB9uk cMSNLNf+YFr5482zypx4nc3IUcMXPx8ALq/ywfGmhndsl2MmBCec1JzB1U5iwmZO6yJ6AzgirMnP WTgxZDjiL9eMpyF7J3K8YbxnPHIwmDltQXM46cqUnnjiiTJ9+vSS/zPkW221VXnuuefKpptuWiZM mLAyifuulgWxuvfee8v2229fxo8PD/GiNG3atPLzn/+83HnnnQ0h3nnnncvEiRPLbrvtVjbeOOZd TQNGYH7Ey86dOzeeI5odBx/3vegbcAX1hn4hYGw/+OCDsaEyp/E6ZpoXRxTpH+N8p512itjeXmJ/ +1VTzbSqItA/0uW1On8S5CifiBPHtNF2QUR68BysFdtsZ0T+vC7/2qFMO1exXhp92j9GTFUQH7FT T0UMl1f+eE1Qs90XTwquH0H3W+1Xyg5Btjz611eyV+b9icf/VRzdEB6vO+Ok+yfvDhI4rWub0Xaj YP5djo02xBOQ3aU1ot5TgrDtdUq8KzKehHT/y7HNOSvKoNCc6TUh4s7WiXZuunuQroNLWbNrtd4k bqbDvxi/f2BpzDaI1wd1pF2OGR8B8WPLI9fNLE/cNqe89NjchtjgjuK9pkRg+/oRNL9Z5Nl4F263 t6ct9x1bTv3HiHFbhuS4iTU2WK1sFWeF9ZU22Gl0+eQ31mtixiTka+2tRvX40Glf5Q329T/+8Y/l gQceaAw5hbfDDjsUSvD1119vPq+88krZa6+9VrjCeyvevXnLLbc0yrVNNpalvcpZLV5dxWOHQO64 49vHkDw8eZttFuNxJUozZswoF198cbn99tvLMcccUzbYII6LiXTjjTeWO+64o2kXrK655pqy5ZZb lgMOWPJGB8ThvvvuK1OmTFl8X2fT7rrrrsa4bbHFFuWZZ54pm2yySXnyyScbHMaNCy92LwmW1157 beNpg+nyknFj7+WXX248dcp+3/tC93QkbdZPG20UDwINclpnnXXKZz/72aZ+Xi4Gnhyj40lxixBG H5adyWLkhRdeaObEs88+Ww455JClsuRY3nrrrRvZ+5OMR/XzNO+7777dzjeeaP2LJO69997LvSV6 ww03NHNNH1hc+bSTcWIBsPnmm5dJkyb1pxnLlMdY/+EPf9joH551fbAgVs0bbrhhgaGxbtxbYGR6 +OGHGz21++67L0XUuhNAXz311FNN3qHw3N92222N/Dyo5tnyJHOU7PrJgky/d6b777+/0XUWyu/W 1D/Stc6WQWLCy9Tf5FU8xw4g/1ZxLIOP7cZZ4R7iKbNK8Joh71IcaJq3yKO0U3jHfKZHbJhjJ7iB nDi/+pJVd69FbxZB9T4OUp0Vnjon1nscEKkaFSRlXA9eE8dL7HFCfPon+ISpI8r74iXGux43ofFY 2UqUBNOPjScZM6arp9LW3XpU8VnRacpGq5UDz1hxCmt55H/11Veb7ao99tij8WpR6owbQ0HZ7rnn no1Refzxx8s222zTGFuJQqGQETUGJre4GCmf3O5yjQFjTORPo44cUBL+dy+F6vs999xT1l577cWr W/W5l7KRGEZKk9HxOyXtN2UwkpJVMWPqWsrr9zSiZCbfrbfe2hAOXqLMp1x1kZmSI5P7lI9gKFOb GEZ5fGbGMS7yDpYyp6jhf/PNNzfYU6QUNyOTuJJHnYxI26iTC6nSLkYS3tqgT8jqu/L9T5HLq03+ l19cE9Lnunvdo71wgZHyGRR9hIz4Hy76Uj7jBw6Jlfv0u+t+y36SR7+RRbk+iR+8lYXQGBdkY3zX X3/9pfpzecZ93otsXHLJJU3b4fnb3/62WWj4m7Ezxiw4jjrqqKYNmSxEeGb233//Rjbywlh79ZH2 IEdk1j/GiPb4H4balVuZ6pN4MOFpnumT9MCZa/L6zeLo6aefbuYsIqSPyOqasrOfzAF9CPs2/sqC b84bW9Rk0e/Kyf5xr3liLiBEvitzsMZ4Z9/l/NYHZIQfGSwAP/zhD5czzzyzGVvtRN4c09qtD3Jc 5XzPeQsz4zaxhYk6cpyqXxmpG9yvPDLASv6cc/Dyt+vZN+7X7z6+60tlI4XmCTn1f+oT5es7+ZWl bmXlXNKXfocH3NUlby5ychz5jczGBTmRZvK9G1P/SNdQtXxMEBmfwU62DJcnUVITIsh+BSchR+Pi JdU1DRwBK0AKnMFIhYr4mMAmPMORRpfhT3LCMP/sZz8rp5xySrnsssuaPB/84AcbTxWiwGOhjMsv v7x88YtfLBdccEGjvD/5yU82xuSKK64op556amO4KHzkQdkUj/IoWGSDwVOG+pSHTDHUvC2ULuVz 4IEHNvnvvvvu5n5EMbeNGDGKz0pQXZSfchgspOO1115rVo7KJPc555yz2Eghoi+99FJDOClc5Izi fOSRRxYr4LXWWqs8+uijzVYfb0eb5A28N7ruSCMJF/Uz8AwGGbKPfEcC1l133aUMIaKQ2zQ8RMcd d1xjpGH/qU99qilHeXDyQeQQbzgw7PLBIb2E2qRPH3vssbLPPvs0hsK2J0x///vfN/3OkMEKeWFw yIWIwDm9iYzKoYce2vQTsqsd+hx+vGyMBzKiHnIzJLBkRCwGyAxj+QczwRpeSfTFeJEBrsaXv41H cvDAZiIbGeEAQ/3Ea6Qd2nbwwQc3eOpD5fvA5Kabblrsqdxll12avDyYcNV3xjyDDUPlmZv6etdd d23Gub40D/UZbygChZjwUl555ZXN+IYjL6Q5oAxjG/4HHXRQ49EyF5TJm5bzyj36zvWHHnqoaVsS Cnhoi3pXVOxgLpDIhQgis8YW/XPWWWc1GLfx1w9k1T8woF+MDfrg8MMPb/rFOFOeBYg85rHy5DGH YWJM8ySfd955TX3k0E5lwTM9lTAwBukN3mVjH0bKoBfMGTifeOKJTR8ZL3SqvnSv8fL+97+/6X86 170wNZd48ehE9W+77bbNfFK+sWkBvN122zVlGiMIurzGpDlKXvWqgyw8gSvCIzyYc25Zy1q5SNey tqLe955HILd1KDWKmrGhzE1cRtHkpzAoofYqF/nwG28Yhc04MI6MAaVJCaUisQqjgBgZyoHyoPTk Z6wYFImCZCgQLcro29/+dlO2+xl113h/GCaK5tOf/nRjIH7zm980dfo9V8qMSXofGDOGJr1alBzj pH3aqyyJgtTWL3/5y42SR+zIhKwwnhQzQkpZfvzjH28UKC8MA4sYUnhZ1vIMLNimVyFXrUmKsw8Y RH2XwfRZH+MLP/8zEvChjH2Ht36BEcODbFL+5NYuHpmLLrqo6XsEATFOI67M/fbbryGlxon+9huD L85M+5WBGOh3xCnHje071+AJR/1g9a8/GRHGHs6MYnqEkkTIrz7tHGzCBTN4amOSJWTHd2RV+/75 n/+5Md4MfHdJWxl0/cUL8YlPfKIxosbG1KlTm3GE3B9xxBENMWBwYWT8/+53v2sw+MIXvtDg8Z3v fKfBDibp/TjyyCObsi+88MIGA32nXOPBtjIsv/nNbzbEH6G14ICjss0HZPK0005r+kMZxjsjblwZ Q+5Jzw3ya34hHfr/6quvbsYCcobwrijCBVc4kEv7kSbYS2QkE3x8bycYGTvpkf3Yxz7W4I3oKw8Z 0n5jyndjSbmuwcYCAwlD3JCrww47rBn/CJt5YR6SRfkwMX+QWETHnDr55JOb/3/6058W/URP0W/w 1y+IkkUiucyl66+/vpHNvFO27+nhOv7445u+vfTSSxdv38Nd/1gQ0Wfmk3HoY/7pf+01lvSXvnq3 Ei79XknX8liVeu9Kg0C6zBkfSo0CpuAoGr9RtP5uB7c2EyCME0VESSBE7hWfQ8lZrVHYCBhiQ7lY IVIIv/71rxtDnat2eSiMJF3qTY9Sbu9RUCkP5USZIT6MJaLg4/dURrkCJH/GhySRUQ/lqg7KvO2Z 0gZK0v1In3YjYupKr5IHDfLBAr/Lo21/+MMfGuIwGIlMvEIMKcKYhCDLzngXRqq7eCFt1l/6gTeS QdEexJP3Jr1KypMv42jcp+3y+G5LjZFmsLVZX+UWB3KpP/WLD+z0l/sZLF5P3h3X/AZXeOl3BoQh YQS1JWXwv7xIhgQD2Kq/c/wNBs7dlUGe3GpzvS8SnVjLy9Bqu3uQK+3kfYVhkufcUtQn5sZVV13V 4GJ8IlUIgf4nh/t4YXIbjIGVN7eNzRtEL4PLEzeY8TQzxvKr2zgxN5HJJODKzb5PfPVXbmF94AMf aPrCvF7R+Bs/PEhwaz/Q4HcYwBXRbKfEHlaIKNn1HZl5yrTTR/vlNYeNOzrLWNV+19xvfErImIWK REco04LCPNQ/7skwCVvHyA896Lu60jMJb/rImFCvMYHM6SttobsQcXVbCOUYITv96J4kWtlPOU/g k/F15CVXet9X1LxYGcpdtUkX4+CYCcc/SM1hq2LC6tM7K8PgGkoZeIOsLk1cysRKjTKnDBhNyoTC svruJBWUuFVdroTPPffcctJJJzUK0OqRAqFI03tCiVEcFJiVoW2DVEpWo5SJT65oyZWKlIHi9ULg yKMMJIC86kJ6yKENtrm0Jw1cGhbGyBYFj0vGkPFSWG1m8HRuFWm7rTgKj4GkzHksUunqo4z5YAwy rm2w+o4R4F2gqOHQ9jL6m9fH1l93MTYZW0Uh8yRS6LxU3//+95t7Mu6OrBkbAjf4Ks/2hn6xhZHb l35H2vzNKJMh+0l9jER6EpSVD2bwnjB0+onxRtrSG4ZwZOBve+uUl4v37brrrmvg1H9W/ElIBgvj 3srJtnV6V/Iev8PR/zkOkiz4O8eDtspjEWLsMMY8o8iYvjUOYWOeGV8Zv+OenA/qZIDTs+ke+Deq e1H9jLh79S/SkAYe/r5bLJgzPDXmid/IZK6mBzEXKca7eWvbU7+aH+pv99Fg94Gy8+EMculv+PKQ IvA8Up0p+wBO8JaUg+wYW7xaxs4vfvGLZt7DjL6CxdFHH90QPJgYtxZyFhHmv3mQ8YdkoHN4481J 3jEEKsd/9lHqLXrDYgimZM9FXcZ/6UMef+XzhsrHg2aBpQ/NV3jTweYBHYmkmXeJv36y2LNdzNNu ftGTGbPajj0c7H56J8tbtUnXyAi02zCe5poV5zHkgacj4lFc54DV9J5CIGOeECJKyMSldBljRpYX i2LKAM82OH6j5CkEykMZyI5Jj+D4jVJPhUUBWcVRGowMw84bQ4HZDqGsrGrTK6BcZIuBEkhLKVnB 21JgNGz3kVNsGEVKQVE+ZGo/ek4Gxp3BYcgpNLLx3jAsypWf29+q80c/+lGzErbSpxDPP//8xriR VRvTEGu/srU32z0YgyeNNnkYADKnEWYEyC64uLutBNgxChS+NjPw/oar9mp7Ejm/wzJj1hg6MS7I lkTppydRn5FLnzFS7s3tJnlg4/cf/OAHDZ4MQgYN//KXv2wMBkJuLNmOhJmtMOMPxtoFSzIyugyd uhg6Hx5HZFD+wUxwhUFuS2dsUxIo4yK3qtv1MoTpESYTwtMeF/pImbZGYYcEGN+8dl//+tebBYHx ZYzD21jyGywZXR4RGOd2Zy54cvypMz1ixoF6jOU8cwzZM5ctTGwJ6ztb4kiFuWSe8CRaTJETOVMO rMUy+fDgJBGBv/GworYYk7QY89qqPoRJHJTFRz4N2O4DbdAHOQ9dMwbJqQxky9+wQCLNY32pHyy8 4Ktv5IGLuY/A8ArrzySCcOIV58nSX/Kb7+kZ1tfGPDnMP32TDyqkh1B/Gr/KJRcc6TJ/ywNvepC+ cz99SEbzljzIWI4FXjwL2W984xtNncaRMWwLM59GHsw5srKUtWqTLsHtn/tfKwuWVY53EIHcdhC3 wpAgMSYuwyylt8eqsTNInLJmSDOJ3cl07LFxtMiihBRloiAyCcLPZNsjvSz5W5bNEDNouRL3hBlj wIikUmP0lZ15lMHgIGaMB2Xmo5y8B8HwSYPmXvFb7TwUNKWb22DtrrJ6zuBesgxWYnQRTatqMXMM PyXNSCCziFRPj4ZnEDfS4zujlUlsT2f6zGc+8zYPhnvb7UVipTy6wnd5MtmGlBi7dj/x4CAJ7X5i QFKm9NIxnOLyEEJeAJ92Hxh7GeMzWBhnOYyWtiA8xrNFBrKS24rGFVlyCzzv0w8+kjw9jYv2+Pad oW23zW+McRtvMkhZvu9nnHHGUnWYL5k+8pGPNF+N1XbZFhnIBoKeY96CSn+154l+QgotjswV9bfl UdeK9HKRHf5i4fJBjPRS89qZCzxBxkU7aYuPlPPQGMwjYIQRtNvx+c9/fvHtbZws2JBOfYyUIUKI XiaLg+yTTs8ywmWbULJoQIgy5T3+bsue24r6xLYgQgbj3NaXX7vaMpJNTF56O08//fS3jSPjcLBC HJYCeiX5Y9UmXSsJiFWMlQMBipYCoHAoIF4Sk9fEt4JDevo6v2l5W9JXzEj7em4VdN7T+TfvCdnb RrG7ejoVaWeetoFa3nb2535EILc08yk0/cALwaD05wnJ7rYde6q7M+/ytLc//dRZn35CMHhBM/U1 HvqDY3/yMJqeqM3UXiz4TbBy+xy0/pTZV54VOb7aZeeZVH3Nk/TyJrHsrv8HMp76an931xGUPG4h wwHky/hFi4xOcttXPb2N4zYmPKq23+k+BAtunWkw29+WC1HLp4J700MIGO9a+8yv/uiyvjBala5X 0rUq9VaVtU8EGHPbS7wOvF3c5PmE4lAZwD6FXJSh8wDHnu7jph/s7aj+yri8+ZCBFfG03vLKNZD7 3wv9NBA8hjovL1d/EjIzUELTn3IHkodnkQf3nUo8W23v1lDJwauWD0D0Vqc8be/yUMm3MtVTSdfK 1BtVlkFBgIdleU8YHxRBaiEVgYpARaAiUBFoIVBJVx0OFYGKQEWgIlARqAhUBIYAgUq6hgDkWkVF oCJQEagIVAQqAhWBSrrqGKgIVAQqAhWBikBFoCIwBAhU0jUEINcqKgIVgYpARaAiUBGoCFTSVcdA RaAiUBGoCFQEKgIVgSFAoJKuIQC5VlERqAhUBCoCFYGKQEWgkq46BioCFYGKQEWgIlARqAgMAQKV dA0ByLWKikBFoCJQEagIVAQqApV01TFQEagIVAQqAhWBikBFYAgQqKRrCECuVVQEKgIVgYpARaAi UBGopKuOgYpARaAiUBGoCFQEKgJDgEAlXUMAcq2iIlARqAhUBCoCFYGKQCVddQxUBCoCFYGKQEWg IlARGAIEKukaApBrFRWBikBFoCJQEagIVAQq6apjoCJQEagIVAQqAhWBisAQIFBJ1xCAXKuoCFQE KgIVgYpARaAi8H8ACQTE3Gys25wAAAAASUVORK5CYII= --000000000000eb2c270597edce76--