From owner-chemistry@ccl.net Mon Apr 29 09:38:00 2024 From: "Brian Skinn brian.skinn[-]gmail.com" To: CCL Subject: CCL: suggestions for density functionals for polycyclic heteroaromatic compounds Message-Id: <-55139-240429091919-2207-f38+hHt9qtIl3tJpJPFZfQ.@.server.ccl.net> X-Original-From: Brian Skinn Content-Type: multipart/alternative; boundary="00000000000021c28106173c191d" Date: Mon, 29 Apr 2024 09:18:57 -0400 MIME-Version: 1.0 Sent to CCL by: Brian Skinn [brian.skinn^^^gmail.com] --00000000000021c28106173c191d Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable David, A thought: have you been filtering on the term 'benchmark'/'benchmarking' in your searches? It's been a while since I've done any literature deep dives, but as I recall that's the term most often used for comparing the performance of various computational methods on a specific class of compounds. E.g., 'DFT functional benchmarks for polycyclic heteroaromatic compounds.' -Brian On Mon, Apr 29, 2024 at 1:10=E2=80=AFAM David Shobe shobedavid[A]gmail.com = < owner-chemistry-.-ccl.net> wrote: > Well, I misspoke. Google Scholar does have Boolean searching; they just > don't advertise it. I'm down to 1180 results now (and have not restricted > to the last 10 years yet). I still can't focus the search on comparisons = of > DFT methods versus comparisons between compounds, but it's a start. > > --David Shobe > > On Sun, Apr 28, 2024, 4:24 PM David Shobe shobedavid[#]gmail.com < > owner-chemistry+/-ccl.net> wrote: > >> Thanks for the suggestion, Robert. I have only Google Scholar at my >> disposal now. The many, many part seems to be my problem :-) as I have p= ut >> in all the relevant keywords and still find nothing relevant on the firs= t >> page. I think what I need is Boolean search capability, but Google Schol= ar >> doesn't seem to have it. >> >> --David Shobe >> >> On Sun, Apr 28, 2024, 2:08 PM Robert Molt r.molt.chemical.physics]^[ >> gmail.com wrote: >> >>> >>> Sent to CCL by: Robert Molt [r.molt.chemical.physics ~ gmail.com] >>> Have you tried reading any of the many, many review articles published >>> by many excellent scientists? >>> >>> On 4/28/24 1:10 AM, David Shobe shobedavid|*|gmail.com wrote: >>> > I realize that "best" density functionals is a controversial topic, >>> > but I could use some help. >>> > >>> > I want to do a DFT study of a variety of polycyclic heteroaromatic >>> > compounds with heteroatoms including B, N, O, P, and S. Mostly I am >>> > interested in relative energies of isomers, bond lengths, and electro= n >>> > distribution within the molecule. The last of these might be >>> > summarized by di- and quadrupole moments and/or by atomic charges >>> > (another controversial topic!). I will also want to calculate >>> > monocyclic species and carbocyclic species for comparison. >>> > >>> > There are now a bewildering number of density functionals, and I >>> > haven't really kept up. Have there been any good studies or >>> > comparisons of density functionals for heteroaromatic compounds? >>> > >>> > --David Shobe >>> > >>> -- >>> Dr. Robert Molt Jr. >>> r.molt.chemical.physics * gmail.com >>> >>> >>> >>> -=3D This is automatically added to each message by the mailing script = =3D- >>> E-mail to subscribers: CHEMISTRY+*+ccl.net or use:>>> >>> E-mail to administrators: CHEMISTRY-REQUEST+*+ccl.net or use>>> >>> >>> --00000000000021c28106173c191d Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
David,

A thought: have you been filteri= ng on the term 'benchmark'/'benchmarking' in your searches?=

It's been a while since I've done any lit= erature deep dives, but as I recall that's the=C2=A0term most often use= d for comparing the performance of various computational methods on a speci= fic class of compounds. E.g., 'DFT functional benchmarks for polycyclic= heteroaromatic compounds.'

-Brian
<= br>

On Mon, Apr 29, 2024 at 1:10=E2=80=AFAM David Shobe shobedavid[A]<= a href=3D"http://gmail.com">gmail.com <owner-chemistry-.-ccl.net> wrote:
Well, I misspoke. Go= ogle Scholar does have Boolean searching; they just don't advertise it.= I'm down to 1180 results now (and have not restricted to the last 10 y= ears yet). I still can't focus the search on comparisons of DFT methods= versus comparisons between compounds, but it's a start.

--David Shobe=C2=A0

On Sun, Apr 28, 202= 4, 4:24 PM David Shobe shobedavid[#]gmail.com <owner-chemistry+/-ccl.net> wrote:
Thanks for the sug= gestion, Robert. I have only Google Scholar at my disposal now. The many, m= any part seems to be my problem :-) as I have put in all the relevant keywo= rds and still find nothing relevant on the first page. I think what I need = is Boolean search capability, but Google Scholar doesn't seem to have i= t.

--David Shobe=C2=A0

On= Sun, Apr 28, 2024, 2:08 PM Robert Molt r.molt.chemical.physics]^[gmail.com &l= t;owner-chemistry+*+ccl.net> wrote:

Sent to CCL by: Robert Molt [r.molt.chemical.physics ~ gmail.c= om]
Have you tried reading any of the many, many review articles published
by many excellent scientists?

On 4/28/24 1:10 AM, David Shobe shobedavid|*|gmail.com wro= te:
> I realize that "best" density functionals is a controversial= topic,
> but I could use some help.
>
> I want to do a DFT study of a variety of polycyclic heteroaromatic > compounds with heteroatoms including B, N, O, P, and S. Mostly I am > interested in relative energies of isomers, bond lengths, and electron=
> distribution within the molecule. The last of these might be
> summarized by di- and quadrupole moments and/or by atomic charges
> (another controversial topic!). I will also want to calculate
> monocyclic species and carbocyclic species for comparison.
>
> There are now a bewildering number of density functionals, and I
> haven't really kept up. Have there been any good studies=C2=A0or <= br> > comparisons of density functionals for heteroaromatic compounds?
>
> --David Shobe
>
--
Dr. Robert Molt Jr.
r.molt.chemical.physics * gmail.com



-=3D This is automatically added to each message by the mailing script =3D-=
E-mail to subscribers: CHEMISTRY+*+ccl.net or use:
=C2=A0 =C2=A0 =C2=A0 http://www= .ccl.net/cgi-bin/ccl/send_ccl_message

E-mail to administrators: CHEMISTRY-REQUEST+*+ccl.net or use
=C2=A0 =C2=A0 =C2=A0 http://www= .ccl.net/cgi-bin/ccl/send_ccl_message
=C2=A0 =C2=A0 =C2=A0 http://www.cc= l.net/chemistry/sub_unsub.shtml

Before posting, check wait time at: http://www.ccl.net
Job: http://www.ccl.net/jobs
Conferences: http://= server.ccl.net/chemistry/announcements/conferences/

Search Messages: http://www.= ccl.net/chemistry/searchccl/index.shtml
=C2=A0 =C2=A0 =C2=A0 http://www.ccl.net/spammer= s.txt

RTFI: http://www.ccl.net/= chemistry/aboutccl/instructions/


--00000000000021c28106173c191d-- From owner-chemistry@ccl.net Mon Apr 29 10:37:00 2024 From: "Grigoriy Zhurko reg_zhurko.:.chemcraftprog.com" To: CCL Subject: CCL:G: Strange geometry optimization with breaking of H-bonds Message-Id: <-55140-240429080524-3744-AQM8xQq2kzV6k2+NjhUjfw{=}server.ccl.net> X-Original-From: Grigoriy Zhurko Content-Type: multipart/mixed; boundary="----------0540DB1DA0EFE4D0F" Date: Mon, 29 Apr 2024 15:04:21 +0300 MIME-Version: 1.0 Sent to CCL by: Grigoriy Zhurko [reg_zhurko-x-chemcraftprog.com] ------------0540DB1DA0EFE4D0F Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit > Maybe this is a bug in Orca? Update: I have checked that Orca 5.0.4 produces the same results as Orca 5.0.3. Below I copy the input file; the initial geometry is shown at attach 1, the optimized geometry at attach 2. Can this be valid? !B3LYP 6-31++G(D,P) Opt PAL3 CPCM(Water) AnFreq !TightOpt DefGrid3 VeryTightSCF * xyz 0 1 6 -0.096375530 0.965292020 0.248348382 6 -1.244019749 -1.235950418 -1.043978092 6 0.750895466 0.162781585 -0.579233858 6 -1.444490562 0.679799886 0.437153227 6 -2.006731515 -0.412116991 -0.212254200 6 0.088010125 -0.925776210 -1.226758295 8 2.032608022 0.294224311 -0.760217866 1 -2.046655544 1.318961194 1.070408735 1 -1.684219749 -2.093382037 -1.538803436 1 2.633150381 0.793907581 -0.059863243 7 0.406169013 2.167986698 0.903444226 8 1.418742218 2.701814642 0.431661700 8 -0.217576213 2.610550545 1.868593234 7 -3.419795869 -0.698210274 -0.026819055 8 -4.076497715 0.035593494 0.713833970 8 -3.892325647 -1.674370624 -0.628455134 7 0.869986846 -1.808186950 -2.098591306 8 0.722613276 -3.028069601 -1.942332752 8 1.609345606 -1.300685424 -2.935033695 8 3.799336458 1.154324613 0.744826027 1 4.300993866 0.335569000 1.021798832 1 3.608345394 1.661581125 1.547396080 8 5.169880453 -1.063100831 1.340253126 1 4.849970031 -1.554804507 2.110278138 1 5.187205205 -1.706608856 0.588573679 8 3.331046174 -4.492007226 -1.562789603 1 3.599910606 -5.088682318 -2.277420320 1 2.509474938 -4.070759990 -1.869994912 8 5.363914787 -2.772025896 -0.785934668 1 4.622232357 -3.346469996 -1.087321280 1 5.681495185 -2.312305666 -1.576307219 8 -6.760107771 -2.279175248 -0.233932156 1 -6.859479636 -3.145360770 0.201150318 1 -5.798859169 -2.159440826 -0.369248031 8 -7.338131118 -0.057552223 1.412232419 1 -7.198938185 -0.854627108 0.853393302 1 -6.456834863 0.338358086 1.487770286 8 2.427777657 5.331028452 1.446246666 1 2.340147688 6.031889915 0.782695662 1 2.133119730 4.514387241 1.004437346 8 0.503608710 5.297585615 3.521827464 1 1.193553795 5.410710128 2.832681397 1 0.054487593 4.477488021 3.263996989 * Grigoriy Zhurko https://chemcraftprog.com ------------0540DB1DA0EFE4D0F Content-Type: image/jpeg; name="initstr.jpg" Content-transfer-encoding: base64 Content-Disposition: attachment; filename="initstr.jpg" /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoM DAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsN FBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAAR CAGkAcUDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAA AgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWG h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl 5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREA AgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYk NOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk 5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD9U6KKKACiiigAooooAKKKKACiiigAoor5 s8ZftYRReMxoOiS2NrEZLiKC4u/me8aDb57RjIG1Ny5PPBB4zXfg8DWx0nGlbTdt2SPns7z3 B5Bh/rOMbs9lFXk7K7svJatuyR9J0V5b8IPjRb/EaC0V5LWf7bbC7sb2ybMN3ERkFeT2564I z0xXqVY4jD1MLP2dRa/g13R05Tm2FzrDLFYRtxu0001KMlvGSeqa7foFFFFcx7AUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABX5BftH/B3xP4U+ MWlaZqcupWenWDagtjPYXU9vLdi58oRtE8fLA7AHXPXgg1+vtZviPQrbxJot3p91FHIk8Top kQNsYqQGGehGc5r1svx31SUozjzRla69HdHzWd5P/akIyhPlqQ5uV/4o2a+a69D4u/YI8EeK bCx8M2uq2c9taeFdPFnNPLG6RyTeWUEab1UnAYseOMDOMivuOuC+C2pC+8FQRfMrwMV8thgq P8Nwau9pZljlj63tIW5YpRjbshcP5HPIcI6FeHLWnJzqLR2nK3Mla6srWQUUUV5R9MFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV5h8efivP8A DLQrCHTI0m17V5jb2ayDKoAAXkI74yoA9WHYEV6fXzN+254Yv9R8P+Htc0sPNd6S9w0lvECX MLBC8gA6hCi59AxPQHHJi3UVCTpLW3/D/cj6Lh6jha+aUKeNt7NvW+17Oyfk5WR4N8Lv2h7f X/iIfD+l+OrzT9Wvbya1trqWGaPTb68Qsz28cxTyGfO/C5wTwM5AP2l8G/iPL8QdCuRfIker 6dKILoRjCvkZVwO2cHj1U1+OnhPwrrWnweFfDlxqenReGPDeuDW7a4jMn2yVkkeSONhjYMNI 2WDEkY4FfoN8D/iDe/B62n1bxR4e1O30TxCIXh1QxkRgKXC5zwN28kBipIwRkGvFw+IjSqRj Fvl1v1Vuj8j9MzjKa+YYKpVrQiqvu8iSUZOX24rW8klt3toj7ForI8NeLNI8Yaet7o99Fe27 d4zyvsQeR+Na9fSJqSuj8VqU50punUTUluno0FFFFMzCiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACik6VkXV/PqU0lppr+WUJSW7wGELDBA2nG7IPb6+lXCDm xE19qhF0LG0Xzrw7TIAQDDG2R5mDwwBHQc1JpumCwWR3fzrqbaZ5eQHYDGQpJCj2FTWNhBp0 Hk26FI9xbBYtyTk8k1YqpTSXLDb8/wCuwHJD4R+BxrH9qjwdoQ1Ldv8AtX9mw+Zu/vbtuc+/ WuouLaK8t5IJ4kngkUo8cihlZT1BB4IqWisFFLZG06tSpZzk3ba7PG/EP7O1vZXjar4B1abw dqYJf7LHl7GQ+nl5zHnp8hAH90183fGv9qjxD4LuLbSvFV+ti1jrEOi340+48uN2k3Hz9wAZ 0ULypC9uM19618T/ALXv7HniHx740g8ZeDoP7Tf7XDqE+mLOsMiXMQwJFLkKykclcg5zjOeP qsnxsISVGty2S91tLR3117Wv89rM/M+LMkqY2MsXh3Pmm/3qjOSc1ayuk9WrJXte3daG7+zZ +0YvjOCyv7W8ln0e61a50k288xlP7ucwrOhPQNgNj0ODzzX13XxH+yt+yv4z8H6kdS8R2A0a xt72fVY7G4uklmubp3MgzsyEjDnPPPGMc5r7Ns9VWaTyJ1NvdDClHBAdtoLbCfvAZ6is859h OcHQkpSS95ra/wAvzFwRgcdl+Fr0sVzKlz/uoybclGy73dm9bd72STL9FFFfNn6QFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEVzdQ2cRluJo4Ih1eRgqj8TSQ3cFxCJopo5 IT0kRgVP41+d37SP7Tn9h/EGS41d5Z7GLxP/AGBFYokkn2a3VJt86xxqzM5aEZAGTv8ARQK7 j9mP452nxGttMv4IGt7HUdZn0yWwYuBIkdyYopSrBWVjhGwQCOVPevq1kadNWqXqW5nG2ysn vfs1rtfQ/JK/HdXD49Qng39Vc1D2nNrzNuN+W2107q9+XXyPsQTS+IwPJJi0w/ecjmcfMrxk HBX6/wCRr28CWsEcMS7Y41CKMk4AGByakor5iU+bRaI/WgooorMYUUUUAFFFFABVa90+DUIi kyZO1lWQcOmRg7T1HFWaKabi7oDIe4utFDGcG7shubzI1+eFQAFUjJLk8/N+dakUyTqWjcOA cHB6HuD70+syfRzE7TafItnMSSVCfumJYFmZRjLEAjOe9a3jP4tH3FsadFZ9rq6yTeRcxNZ3 ON3lyMCNpYqp3DjnGcda0KzlFxdmMKKKKkAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAorH8YeKbPwT4Y 1PXdQJFpYQtM4X7zY6KPcnAHua+H/iF+2Ze6N4jtY/EHi3/hFBdxfbILCytZJhb227aJZTHG zKmRgu+F4PTFcdbExoyULNt9EfRZZklbMqc6/PGnTjo5Sdlfsu7PvivNPiL8ZrXwxOukaJAd c8RTsYorW3BcI/ocdSO4HTByRXlsPxB+IHjK407wZZ6xprzalGJ49dt3H7+2aPzA6OnDAp8w KAEgjkDJr2f4b/CbRfhrZn7IpvNTlXFxqVwMyyeoH9xc/wAI9BnJ5rpwWLg6/NOlzRW6btr2 /wA/zOHPMiqYfAOCxahWqWcHBc3uX1ld2SurqOjd9baHyN8Xv2HvGvxAv7XxfDPpc3iB706n LpkkzQ+TcYYB1dfkZiHbcpwoJ4Lda7r9lf8AZB1f4W6lHq3ieSztlguJby30qymefNxIxZpZ HbgfMxYKuRnHIAwfrWivoZ53i5wlBNLmutFbR9PSyS+SPzqhwnllGdObUpclmuaTa5lrzO+7 u2+129AooorwT7EKKKKACiiigAooooAKKKKACiiigCG5tIbxFWeJJVVg6hxnDDoR7is1ftei KA7SX9moAMjkGWNQCWdz/F24UZ9q2KRs7Tjg44zWkZtKz1QrHjXjb9pzRPDerNpmk2Euv3cR AmZJRDFG39zdhiWHcAcdM5yB0Xw8+M+n+Ob7+zLiym0XVynmJa3DbllUcnY+Bkgc4IHHrg4/ K3406lqUPi7QNL8TadqupeH9Pu74azYaaJTNJdnaI2lWIh3VWEmMcAnJr3b9kPxX4x8RJ8NN K1yS8n1PT75lt/tz+Zdx6eJ3dI5nySxWD5eSSAAD0r5qGOqNqbe7ty287Wvvc/Y8Twvg4wnh 6VL4IuTq8z6RUr8tuXlbdlrfR9rn6QUUUV9CfjoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHmH7TNpHe/Arxck twtsqWyTK7d2SVHVPqzKFH+9X5MeJf8AhJrbxr4g1XSBp942vaSmkTpqc7wm1KBlVxhG3Lhj lODkda/YD41eBp/iT8LfEPhy0kWK8vIAYGc4Uyo6yICewLIAT6Gvy9i+GvjDRNU8cx63aX0W q3WpaX5Vhc2aSyiKN4xLFGrZciRYWG+H+/zkGuiGSLM4e1o1Eqidmnty2un33/rv6GC42/1d nLA4yg5UGvaRlH4lUWko2aad4bXt13vp9B/sLeDtQbU/C1rHcSX2m+E9PWCe+IIVm8oxog9M kkheyrX3rXj37MHgC/8AA3w+Lanp66RdahL5408IEMEYUBQ6jox5JHUZAPINew1zfU1gZzoq p7TXWS2b628vz3DF59PiKNLGSw/1eKioxpt3cYpu3M+7vd9ttbXCiiimeeFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFAHyh+2H8I/DXiHxD4LmgsjZ+I/EGrx2E93attMsWMEsvKlgWT5 sZxwc8V7N8JP2fvCPwYSV9Dt7i5v5V8uTUNQl82cp12ggBVHrtAzgZzgV5/8W9UTW/2sfhJ4 XKlzaWt1q7ei8HaT+Nvj8a+ia4oYdRrSqSjva33b/efYZhj8RTyzCYRVXaUW5K725mop+SUd FtqFFFFdp8eFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAViSL5vjCLhv3NkWztbb8z465xnjoRn06mtusSxXzfF WpybV/dwxRbgq55y2Mg5/AgfjxjelopPy/yX6iZt0UUVgMKKKKACiiigAooooAKKKKACiiig AooooAKKK8k8ZftI+H/DGvTaPZ2tzrd7buY5zbFVijcdU3Hqw7gDA6ZzmsqlWFFXqOx6GCy/ FZjUdPC03JrV26LzeyPW657xx8QPD3w30RtW8SapDpdiG2K0mWeRv7qIoLO3XhQTwaz/AAV8 VNE8a2N1NFI+nT2YDXVtfYjeEf3ic4K++frivzf/AG+fjodX+JniSXSdXg1HTPD+k+XpctnM k1v55iEjvkZUne21v+uYHauzCwhiHzX91K7aPWy3JZ4jGSw+MThGCblprZdul30+8+gvAHxe 8L/EL9tPUPFT3sljo1v4fTTNMn1KB7fzZjICRhwNud8uN2M/jX2fX4dfCD416r4k8WDw9rqR rc22jvJchEChrgTR+XMpx91on6ep9q/XD9mHxTd+L/gvoV5eyNNNF5tqJnOTIkcjKpz3wABn 2r1cdhqcaMK1KV1tZn0fEeU4SGBpZjgaknCLVNqSSaaV+nbVSv8Aa8j1WiiivDPzYKKKKACi iigAoqpq2pRaPpd5f3GfItYXnk29dqqWOPwFfnP8Sv26bjw9rV9da3qFxGRor63aWNhemFBi Zo1tQAPv4XO85616uCwDxcZ1JTUYx3b+btp6M+Uz3Pv7GVOFOhKtUntGNlorXbb0W6/4ZNn6 R0V5j8GPiDP4qS/028uhfT2apJHdDrJG2Rz6kEDnvn2r06uXFYapg60qNTdHoZLm+Hz3AU8w w11Gd9Humm007XWjT2dnutAooorkPbCiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo Aw/G3jDT/APhbUNe1NmWzs497BPvOxIVUX3ZiAPrXznZftc3Wqaq6C60aw26jDpYsJCWcXUu 0xQM2QTIwdMAAZ3DivUv2n/CF/40+C2vWmmDffW4S9SLOPMETBmX67Q2PcCvyRj0XULzxw/i G41uaO8/4Sqw1xYjfzLbtDB5Wd0IGwy/ujgkdMfMK+yyfCUa2HlLkU5t216K366n5DxlisfH FQoUcTKhTUVK8LXk+azV2nol/m0z9n/AfjWDxtpDXKR/Z7qF/KuLcnOxsZ4PcHt+PpXS14b+ yjZanceFNR8QahDJBDq0kZtVmBDPGgb95j0YuceoXPQivcq8DM6FLDYypSou8U/01Xyeh9tw xisdjMmw1fMlatKPvaWvq0nbpdWfzCiiivMPqAooooAKw/D7LNqWtzLyTciMnch+6oHYZ/76 /wDrnwT9pH9qM/DG+1WxspvsNnosdu2q6ksPnPG08iJHGi4PeWPJwfvdsE1nfDP9ouU+Idd0 9tRk1dNGuoYdWimjw0TzRLKpR8Dd8jA45Hbg19HRyivLD+0TV5pWjfW26++2h+b5jx3luW45 4SrTqOMW1Koo3hFpxTu73tFyipNJ2bR9UUUyKVJo0kjYOjgMrDoQehp9fOH6OmmroKKKKBhR RRQAUUUUAFFFFABRRRQAUUUUARzq7QyCNgkhUhWPQHHBr8YfjTPrMXjGz07XtJ1jU9N021vb eSw0ydop49TLrtmk2upOArAMSQpJPGa/aKvNPiN+zn8P/ipqQ1LxBoKTalgBru2meCRwOgco Ru44yckAda8/FYeVZxlB6q/4n1uRZvh8vjWoYqDcKnK7qzacW2tHo07u6Pib9kzwdrf7Qmt2 9n8Srq91xNP0qCPU3Fyy+bIkSx4kkXksSBkg5Yhzk9a9c/aV/wCCefh34geF7Zfh5aWHh3Ub aFoJbCdnFvfxnnDv8zCQZOGOcggHGAR9SeA/hz4b+GWjf2X4Z0mDSrMtvdY8s8jf3ndiWY+5 JrpK6cJGeGTd9ZO77FZhxBVr16bwjcaVOKjGLd9Erara7PyMk/Yj+JPw6F14s8S6HbaXp9hC lrc39rdx3Ey25cDIRGOVBwSTggDPQV+o3wl0vQdF+G3h2y8MsZNDitEFs7EF3B5Znx/GWLFv 9omumvrG31OyuLO7hS5tbiNoZoZF3LIjDDKR3BBIrxb4D+Hta+FXijxJ4DvC9z4ct3F7ol3I 2W8iQn5CT1KkFT7rnHz16dfGU5UP30lFpq3RO7tb1vt3ux18wlm+AlTqtRnTfMktFJOyen80 d13TfY9wooorjPjwooooAKKKKAIbu0iv7Sa2uEEsEyNHIjdGUjBH5GvzF8V/sYeIPGXi7xP/ AMIdZ/8ACT6FJG+jSXMl4lsfsxcuEO5gCeh3Lz04GQK/SPxkZptCubK1mMF1doYY5AMlc8Ej 88fjUXgHwjD4I8LWelRkSSIN88o/5aStyzfnwPYCvQy3OMPh/rFClNSqx5bxs7JSTd30u1pa 97O70sfIZxlNXNcbh4yi40oqTc07NvRKK+fvN26JLdnCfs6/B7Ufhb4duJNeuobrXr7Z5y2x LRQIudsasQCx5JJwOw7ZPrtFFY4rE1cZWlXrO8me/l+Aw+V4aGEwseWEdl+L+96hRRRXKegF FFFABRRRQAUUUUAFFFFABRRRQAUUV8l/F79rh9F+Idv4csb0aRp0mpS6RHe7EJluYonkk3s3 3EHlsBgckDnnA9DBYGpjpuFNpW3b21dl97dkfPZ3neHyLDfWa8ZS3tGKvJ2TbsrpaJNttn1p RXz78Bf2joPH+i6NqN7fLLo+sRSSWd9dBIXQo7oRIAcAEo2D16evHs//AAm/h3/oP6X6/wDH 5H/8VSxGBrYeVmrq17rVNdzDJ+I8BnNLmpS5JqTi4TspqS+y1d66q1m0zborldQ+KfhHTrG4 un8SaXIsEbSFIryN3YAZwoDcnjpX58/G/wDam8U6lrWnpE2p3+oass09joGi/O0FvGRl2Uui 8ZA3Mcseg7Dwa+MhRsormb7H6plXDuIzHnqVpeyhG13JPVvZJdWz6m/av8eapqC6T8KvCLeZ 4n8UuIp2U/8AHvanO4sR0BAYn/YVvUV6H4J/Z98CeC/D+jaenhjSNQutNhVBqV5p8UlzI/Vp C7KSCWJPXjOBwK+ZP2T9fsH8c6L40n1y28XL4vslgtNSHmebZ87TEwkAKuHj8thjjaMHGc/c Vd+Hx1apRdF+7ZvRb/PueHnOQ4TAYyFem/ac8I2k9nq78q6K/fXS4gGBgcCloooPPCiiigAo oooA/OL/AIKEfA3xJqOva3cWNvcXGgeKWtS00MLTKksbxFoXVe7eVlc9d2BnBrzz9mr4dalY /EHxR4e8LaS9l/bF5A0mmRWrQppgjjEcjS5+6NwZjnBycck1+kfxlnz4asrE4K32oQxyAnHy ITK3/oqun8L6RFouh2cCwRwzeUhnKIFLybRuZsdSTnmvsqGc/VsNCryKVVe6vJJaNr5vTroz 8mx3Cv8AaOYVsHNyjhZr2ktNJSlJOUU9vsRd9XHVddb2n2a6dYW1ojFkgiWJSepCgD+lWKKK +Obbd2fq8YqEVGOyCiiikUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWR4h097mCO6t lzeWpLxgdXX+JPxA49wK16yvFOtDw74fvtRKhzBHlVPQseFB9skV5eaUsPWwNaGL/h8rbfZL W681a6fRouDakuXcm0/V7e9sYrjzkUOAcswGauo6yKGVgynuDkV8s/Fz9oFfhnrXhLwxo3hi /wDHvjrxQkt1Z6Lps0NuPKTDSzSzSEJEvJxnqQRxXpvw48eXmp+DtP8AEOp6BqPhO5mEq3uh 6oUae3aOV425QlWB2b0cHDKynvX4zlXiDj4xpSzDCr2L5Y8/PHnd3yqfs92m1rbZ3s3ax31s PTgpS5rWu/Jdd/Q9borzm9+N+j2oJEL4HeWRE/qaw5f2iLCVzHax2skg/hWcyt+Siv2/61Sf wu/omfn9TizJabt9YTfkm/0PX5ZUgieSRgkaAszMcAAdSa5eTxi90qvZRKIW5V5AcsOxx2rz XxB8UNe8QaJe28WjXqWskTCSePTZwip1YmQjaBjNfJH7WfjjRNd8YfByy+ILXsfwbt57weJY 7UzeQ9wIl+xm58r5jHvz7fezxX5Jxpm2ZvFUMsy+rKgpRlNyjG85cqb5Iptau2iTTcnFXR9T kuPwuaUZ4ikm1F21TXbXXpr+Z+gdlftc6xBJfDYduI8DC57V1NfNP7Od94Bb4VWUPwye5/4Q uO6nGnC4M+0ZkO4Q+d8/lbt23t/d4xX0qudoz1xzUeHOZTxNTMMJNynyTjJznHlm3JNNTV3r Hlstdj1sXBJQl+WwtFFFftJ5wUUUUAFFFFABXyx+1N+0VP4HvtZ0u11ceH9K0KzW71bUUJEx LgFY0xz0ZPu/MSwHbn6nr4C/4KE/A6/1e71PWIxNJoXiGGKK5e2IMlvcRBdp291xEjZ6ZDA4 4z52Pc1S93a6vbex9lwpTw9THtVknLklyKXwuelr/K/z8y98Gf2jrnVLa/vtO8Q6vHq+ltDL feF/E0UsU8ltLnZMiyZBUlWG5DlSMMBkA/aGi+OdH17SLHULW6EkV5EJY0UEtzGJCuB3CnpX 5WfAL4d+K/G/xCub+O6ufE/iK9tYtNa5isfs1raWyOXy+CwGWYsWJ9gOgr9XPCmgReFfDWl6 RC3mJZW0cHmYwXKqAWPucZrLLp8zkrPlsuvXyuj1+LcPTo06Uqjj7ZylolFNQ0tzKLav2129 B8euxzMgjtbtwzRDeICFw4yGyew7+lNGp6hLFuj0mRWMYYCWVB82/BU89dvzZ6dq1aK9zngt o/ff/gH5qZjnV5C4RbOFcyhWLMxx/wAsjjH13D8qVbPUmkVn1BFQOjFI4B90LhlyT3bnPbpW lRS9o+iX3f5hYyl0J2iVZtSvJSERD84XJVt27gdT0PsPxr86v2tP2YvE2l/FiLxJbaHdeI/C 39pT6pFHb2n2pA8yOrRyoAdu0yEgkYOF5zkD9KqK9DB5jVwk+ZJNO2no01+KPDzbKKWbUlTn JxavZrdXTT+9P/gnwV+xx+zprVz4b0vSvE+k6hZeENMjkCi+WSzmu2Z2ZVRQQ6qC2SwwDjAz k4+pV/Zo+HCnjQJfx1O7P/tWvUKK0xebYnFu3M4xS5eVNpW8+/nc48r4by/K7zhTUqjk5uck ubmbvdO2mu1tvU8rvP2YvhzdWk0KaAYXdCqyre3BKHHDDMmMg88+lfmt8c/g34y+GHxGtLyW 7n0DVLCGaxt9Tay+02t5bOQ3ygsoPIDDDZB4I7V+vtIyhxhgGHoRXytbBU6lnD3WuyXXuj9b y3ifF4PmhiW60JWdnKV007pxluvkfAP7C3wU1C3Xw68Vvep4Y0JmuBf3qFDd3BdnOz1zIxYk ZAAxnOK+/wCsnQE+yLeWYUqkE7bAITGoRvmAUkndjPUf0rWrqpYZYW8U73d797nl5xm883qw m4KEYRUYxWyS8+r8wooorc8EKKKKACvPPjn8Uh8J/BB1KGNJ9Ru7hLKyjk+75rAnc3sqqx98 AcZr0OvjD9vfxXdSavovh+N1itrGzOrOSQN7Mzp1P91Y2P8AwI11YSeHhiaX1p+45JP/AIPl 3OXGYTMMZgsRHLI3rRhKStvoruy6u2y6uxwWrftK2Ui+Ir3xNq+q3UugaxHot2MZCXM2xB5a hguz98ASMcZ4r7G+Evj+58S/bdJ1ORZdSsgGEy4Hmx5xk44yDjnvkV+Md/8ADuz16fX9QuW0 99c1HWodTtdUkQs8MKtGWTdjvtbpx81fpx+xlq7eKda8QapIXHlWsUUW4cOHclm/8hrj6mvu czpU3hKlTEqMWrctkk27vRfLprofgPDWFzCjnVCWX1qtWD5nW5m3FR5YWera+Nu0tG37trb/ AFVRRRX54f0QFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVj+L9CPiXwzqOmq4j kuIiEY9A45XPtkCtis/Wb82VrhOZ5PlQDr9a8nNq+Fw2Ar1cb/CUXzeaatZebvZd2zSmpSml Hc/PD4veEPFVr8TfDXizRtePhHxr4binsY2v7L7XbXFrL9+KRNy8ZyQynv8AQj6J+CF9rPjb wnD4f1/Vv+Es1R1mGp6xDZi0hCSO7BBGpIUKjCNRkk7QTzk17yfB2k6hYxRarpllqTD5j9rt 0lAJ9NwNathp1ppVstvZWsNnbr92KCMIg+gHFfj+B8PZY2nhamZTUYwtLlUVzrVyUHOydk27 ra97LZruqYle8oq99PLtsZFn4A8MacQbXw5pNuw/ijso1P5ha3IokgQJGixoOiqMAU+iv3e5 49OjSoq1OKXorCMqupVgGUjBBGQRXgvjb9mR9Q1N7zw1rCaajtu+yXCErGf9lxzj2I/Gve6K 8XM8nwOcQVPG0+a23Rr0a/4Y6oVJU3eLPH/A/wANJ/AF5ZS69fDWJXfMUsYZY4XA4Xbn5j3B Pp0GMn18EMAQcg9CKr6jYRanZS202djjqOqnqGHuDgj6VmeHr+X97p93gXdudpx0YdmHsRz+ navmsvwlDhjG/U6UeXD137r7VEtYt9VJK8b9U12NpydaPM91+RuUUUV98coUUUUAFFFFABXi +osPil8brewUibRPC482fHKvcZ4U++4AYPaNvWuz+MnxCi+GHw61jXmK/aYovLtEb+OduEGO 4B+Y+ymub/Zm8C3Pg34ZWlzqRd9a1lv7RvHl++N4yinvwuCQf4malVwzqUlNvS+3fr/kfT5e vqWCrZi/if7uHq17z/7djp6yR6wFC9AB9KWiimfMBRRRQAUUUUAFFFFABRRRQAUUUUAZJj+y +JVcISt1AVYrGxwyHqW6LwcY6nHtWtWR4jUR29vebdzWs6v/ABkgE7TgL1PPQ8fTqNetp6xj L5fd/wACwkFFFFYjCiiigAr52/a5+Aep/FPSrPXPDka3Gu6dE0ElmzBTdQE52qTxuUliAeoY 98A/RNFY1qUa8HTnsz0suzCvleJjisO7Sj9z7p+p+OXgb9mXxF4c8YQwWngzxbJLA7C30uWK 4+zW7MeTsIC7e43EqOoxX6U/s0fDWf4c+HNQj1XA8Q3ckbXkSHKwKFJSMN0YjcxJHGW4yACf ZKztW0pr0xXFtItvfwZEM7gsqgkbgVBAOQMc9KyoYSKqKdabk0rK+y+X9fM9rHcQSxOElgsN QhRhJ3lyqzk1td9l28kaNFUtL1NdShy0bW9wv+st5CN8fpnHqOfxq7XbKLi7M+TCiiipAKKK KACiiigAooooAKKKKACiuZ+IHxC0j4baEdU1eVgjOIoYIhulnkIyFUfQE5PAAry7Rf2pY9Qn aS68K3VtpikB7mG4ErIPUpsH8/zrmqYmjSlyTlqe5hMkzDH0XiMPSbgtL3S18rtX+Vz3eiq2 m6la6xp9vfWUy3FpcIJIpUPDKRwas10J31R4souLcZKzQUUVg6h4rht55YLZRcSxNtkbPyq3 p7mvFzfOsBkOGeLzCpyQ26tt9kldt/8AD7FU6cqsuWCNx2CKWY4UDJJ7Vjacp1XUHvpB+5jO 2FT/AD/z3+lZl1r1xfW4hlRYI3YBpFz0rqbWKOC3jSLHlqo2kdx618PgM3wnGmYQ+qyvh8Pa bTVnKo78l4vXlhZyvs5W3szplTlh4+9u/wAiWiiiv1M4gooooAKKKKACsHxPbG2WPVoSFmtu JBnHmR56fUHkfiO9b1ed/F/XDpC6EkjFbSS6LzEdwoGAf++s/hXyPFtWlQyTE1aseblV1bRq V1ytPpaVnfyN6CbqRSOk/wCEuh4xBI3qeB+Vaun6nBqUZaJjkfeRuCK+C7zx142+Nn7RfxR8 N6N8Vn+Fug+BLWxexgtLK1ne8aaHzXu5/PU7okOFKjC4ZclTnd9d6TrE8MVneWwGpzvAmTab dtwSoyy8gYJ+Yc4xX4xgeOc3yzFUI5tiIVlUcE6cYSUoqpFSjKLUUpOzTcfeetlrt6M8NTmn 7NWtfW/Y9Eorkv7f8TSfc8NSxj1kmg/pKa4zx4PjRqrRJ4QXw/o6/wAcuqXBYn6BYpK/dMDn McfXjRjh6sE/tTpuMV631/A5aOFVWahKpGPm3p+Cb/A9gr5H/ar/AGyJPhdr974Y8PXVtp82 mxJJq2tXKh1tS+CsaBvlzhlyxBHzAYznHU/8K7/aO1Li8+JHh+xB7Wdpvx/5BQ1+c/7bXgLx j4H8U+MtB1y5k1nVdWjiuhfqhjW+RmRi6gnoGVlIzwUr7bD0acZOTkpNJu2u/wByPsMny7BU a8qtSrCu4RbUEpWbW17xjdeSPc5fjrqPxJudIh1vX18V2loYtbXTZJFCzQ7goc7R9xslM9tx x1r9FvAvjCx8feE9N17Tsi1vI9wRvvRsCQyH3VgR+FfhT8K7a/8ABvxNv5Vct4dTS3tbByw/ do86y+Sec/KxfHtiv2P/AGRtFv8AR/gbor6jG8Mt+8t9HFIMFYnb5P8AvpQG/wCBV35i6dTD wmo8sk2rL+vLc+j4seGxWVUsQ6SpVITcVGOiaa95pbPVXUraq1z2Wiiivmz8eCiiigAooooA KKKKACiiigAooooAgvrYXtlPA3IlQp94r1HqOR+FVtBuvtmkW0mULBdjeWWKgjggFgCeR1PN aFZGkyCDUtSsjIrOJPPVPNLuFf1BHAyDgD0raPvU5Ltr+n+Qupr0UUViMKKKKACiiigAoooo AytT02USi+sMJer96PIRLjjAEhwSdoyR71b07UItStlmizg5BDAggg4PX3Bq1WPqVlPZ3Laj YIZJjjz7dRlrgAYUKWYBCOue+MelbxaqLklv0f6P9H09NlsbFFV7G+g1G2We3kWWNsjcpyMj qKsVi007MYUUUUgCiiigAooooAKKKztd1608O6e13dvtUHaiL96RuyqPX+QyTgAmqjGU2oxV 2zKrVp0IOpVkoxWrb0S9WfM37bkmsaXeeENWs4Xns7dLtd3l+YkUuEYMw6Y2qTz2Q1+fPwB1 DXPCPjHw1dWOkahpGpx2t9b+L9UupQ0WsiRCEBO8+cfN2yKSPkx+Ffq1aeDZvircXOoeJgX0 OaNoYLOKRkE0TDnawwwj6Hdw0hAPCBQea0L9iX4YaHra6iLLUL5VfetneXheAHtlQAWHsxIP fNeFUw1eVWc6enN39LH6tg85yrD4TD4XGpzdJX9xJp3kp2u+qaSuujavd6YPwm0n4vzeAtMu PD+o6JZ6NMHltbfUy4lCM5OeIW4JyRz0Ir0jwxb/ABds7qT+3ZfDl/bkfKLS5kVgfxgFenRR JbxJFEixxooVUQYCgdAB2FPraeXxqUfYupNaWvGTT+Vtj5THZ5LG1KknQprnbfwq6u7/ABbt +fU5T7X4uXrp9i3+7df4oK+LP2ufibr/AIe/Z58RWukXmoaXftqcA1bUNMJF3b2LTj7U8ZHI YDgkdAW6da+/q8a+K3wCfxdqEuq6BfRadqEp3TQ3AIikb+8GUEqfXg59uc/nGf8ACuIbw+Lw k513SlzOFSfNdeXNs++/4WfkUa6V4yVr9UeP/so3PwatP+Eosvg/4mvddsjBYS6hbS6ndXtt bufP2SIZydksgDeYqn/lnHlV4z9aeH2L6PbFjn5Tj6ZOP0r5z0P4W658MYII9ZltEsLmTy4j prlYo5OoEvyLgNgjI/POM/QPhHWItU0mJUXypIAIni6FccY/TH/1sVx8H5BmeCzermeY1JWl TUIxnLmna6d5NKK0tpps7dNfKqZxhKuJll0NKkdXpZPTp33/AKszcooor9oNgooooAKKKKAC uD+NGlWWq+CJ1upPJmjdXtpB1EnP6bd2a7yvE/ixr39va/HpUJ8y2tT+8VeQ57g/VsDHcK1e dmFKjiMNOhXjzRkrNHzef5s8nwUq9N/vHpH1fX5bnzB4n/ZqtPiXrlpqms/Dq08W3VuqKtw8 O5kXqEkIIyBz8r5HJ96+xfhV4U1vTrSK616KKxeOMR22nQsGEK4xlivHTgAcAfp1Xg3QB4d0 GC2YD7Q372c+rnr+XA/CtyvjcDwVl+HxFHF1XKbpawjJ3jB90u6/PU97A4rGVMHB4uyqSXvW Vvl122fmFFFFfoZsFcJ8Wvgj4N+N2ix6b4u0hNQSElre5RjHPbk9Ski8jOBkcg4GQcV3dFNN xd0aU6k6UlOm7NdUfIup/wDBObwHoWganceFbjVX8Sqnm6fJqt2skKSqdwQqqKMNjaSckbsj kV7J+zv8WD8TPBxg1FGtfEukN9j1K0lG2RZFJXcR2OVIP+0D2Ir1avDviD4WT4XfEqL4l6XE Y7PUNtprcUfC7jhUnPscKre4RsE5reeKSoyVa7tqn27/AC7n1GExMs1pTy/FSvN+9Tb6S/l9 JrT/ABJHuNFV7C+h1Kzhurd98Mq7lP8AT69qsVz7nyjTi2nuFFFFAgooooAKKKKACiiigAoo ooAxvGfiaHwZ4S1jXbhDLDp1pLdNGDgvsUkKD7kY/Gvzy8b/ALb1v4c1fXJ/FWvTpqFlpsGq Q2NpfJAJxK7qIbeMuMumzJ74IyTX6JeKfDtr4u8Narol7u+yajayWspX7wV1Kkj3Gcivyt+L /wCxd4x8NeIdettS0i51TT9djh01NUs5oREyIzNGULjMTncch/Tj1P1uSSpqM4Rsqj2v2s9F /wBvWv8A5H5XxvhJ4pU3iHP6tFXlyNx1Uo3cmunLzWv+Dsz9F/g948uNeuNR0S+uxqE9koki uwcmSPODk98HHPXnnpXp9eIfsx/CvXPBGi3OseJxHDrGooipZxyCT7PEOcMw4LMcE4JAAHPW vb68nNvq/wBcn9Vd46a9L21t8z6LhCjmNDJaFPNG3VV93d2u+VNvVtK2+vfW4UUUV5B9kFFF FABRRRQAUUUUAfBP7Un7Sn/CGfEG+W51N9N8LWOr2ujzWsdx5Ku8gIkuJPVEIb5enGeprpP2 c/2kLPxTZtrOjXry6JHrU+kyQNP5sdxHGygXCdgGDbhjnjBNZn7YX7IXiLxf41j8Y+ErOTWI WvYdSm0+3lVJobmLo4DffRupA5yTxivO/hr8JvGfwbh/4SbxL4R1Kx8LNrZ1LUzLKhkDSOGd igO5Y8gAsRgZ69x+l4SeFxNCFJzjyctuXS7lZLr1ve352P5wz/K8xp5hPMYxqLEQndTTlyxp qV9lo1y6Na6XTV9T9KaKzPDfiTTvFujW2q6XcrdWdwu5HU9PUEdiPStOvzecJQk4TVmj+iqV WnXpxq0pKUZK6a1TT6oKKKKg1Cq+oX8Gl2Fze3UgitraJppZD0VFBLH8ADTrq8gsYWluJVij UFizHHA61geIUl8XaFqmkW9q4t723mtJLmf5EAYbTgdSCrEhgCOK2p03NpvbuRNyUXyb9D5T 8UftwPZ6jq9zPfHw/punaYutGBLMTv8AYmlkjV2YhssWifKrjGPxPtXglpfif4lum8QzxzHT VT/Qoh+7lB9um3K5Yc7iQD8o2n83/jj+yP4xtfGOuWWt+G7rVGutNTStP1G30g30cQWV3E0L jox3/dG1h3r9EP2WfAniLQtGvdd8T2kum3t+iRwWNxxNHGMktIP4Sxx8p5GOetfd4v6rQwtS rR5YuyUOW3M7yfXf4bXfn0eh+BZfgc1xmc0Fi6lSrTcnKrCbfs42guW0dvjbslporprU92Aw MDgUtFFfnx/QAUUUUAFFFFAFLWdItdf0u50+9j8y2uE2OOhHoQexBwQexAryfRL+98F+IJbG 9bdcWuBI/QXEB4SYfgMN6Y77a9lrkfiH4Uk17T472wUf2xYZe3z/AMtVP34j7MB+YHbNc1em 5q8d1sfJ59l1SvGONwv8alqrdV1Xr1XzWikzqba4ju4EmibcjjINS15r8PvFYjsirLI9kRlB /FGehQg9xjH0x713Flr9pfSCNWaNz0WQYzXgLibJ1ilgamKgq23K5K9+3r5bnv5bWqZhg4Yt QaUl2/Fd12ZpUUUV9QdoUUVzmreKxDfzWFmBJPAB5znkISMgfXHP4ivDznOsHkODljcdK0Fp ortt7JLq3/wXoa06cqsuWJY8ZeIE8NaBc3ZYLLjZFn+8R1/AZP4V5f8ACrw8+ta62pXSEpAR M+/rvP8Aq0P+6OT7/WrN7ruj/E97nT7bxBY6rdaccz21jdRSvBzgiRFJIGRg5A9K9C8C6XBp Ph2CGJg8hJeZ8YJc9fy4H0Ar5jJOKsDxLiFSpRnTklzKNSPK2r7qzaavvZ6dT4vM8ixmMzml WxKTw9JXVne8uzXR3t5NI6Ciiiv0M+vCiiigAooooAKy/FB01fDeqtrKo2kLayteCQZXyQpL 5/4DmtSuc+I/hybxf4B8Q6LbOI7m/sJreJmOAHZCFz7Zxmonfldlc6MOoutBTlyq6u+yvv8A I+IdQ/alufhZp2mWV14wj8MaLdPIdOW+jSSeSFT95vlY4VdoZgAoxzX078Jfi9f6zrFnoWv3 Flfy39qt5purWDo0N5EybwQU+U5T5gy8ECvzD8dQ+LPDXxTXWLeztJtTtNJl8PXGm6xK8H2f 975gYEI38RIZcDIxg19M/sOeC9Xd/CelWVxHJB4V0/bNfhZJIFl8tgowzZO52zt3L8obG0YA +aw1aUeRKTcm1pfS2t9OltD9vzvLaNaGIqTpQjSjGTU1FKV7JwfPf3uZ3TST890z9B6KyZdY udM0qG61OwcSl9s0emb7wRDna2Aiu4OFB2oSC3TaCwwfDHxs+HfjbxDNoHh3x74Y1/XYQ7Sa Zpes29zcoEOHLRI5YbSQDkcd6+o62Pwjpc7SiqGu6/pnhbR7vVta1G00jSrOMy3N9fzrBBAg 6s7sQqgepOK5Hwx8f/hf42u7m18O/Ejwjr1za273k8Oma7a3LxQJjfK6pISqLkZY8DPJpXHY 72iuQ8D/ABg8B/E6e7g8HeNvDniya0VXuI9D1a3vWhViQpcRO20Eg4J64NdENb046y2kC/tT qy24ujYCZfPEJYqJDHndsLAjdjGQRTEXaKKKACiiigArz748WZufhnqMqDMtpNb3KH02zJu/ 8dLV6DXOfEeCC68AeIoLmWOGKXT54/MlYKqkoQpyeOuK0p15Yaarw3i7r5amVXA0szpywNe/ JVXK7b2lo7eeuhe8Kz/aPDemMWDsLdEZlOQWUbTz9Qa1a8t+BvjzRtV8HaXpf9q2rarGpDWz SgSMT8xwp5PLEcelepVhCsq69oup34nL5ZXVeDkn7mib3aWifzCiiirOUKKKKACiiigAoooo AKjuLeK7gkgnjSaGVSjxyKGV1IwQQeoI7VJRQB8zeI9I1X9lXXT4g0EPffDW8mAvdMZsnTmc gDaT/BkgKf4Sdp4INbcX7WEWoagU07wrc3ViDkTNdBZCvrsCkf8Aj1R/tsSXcHwr06aJDJYx 6tC14gGQU2SBd3+zu2j6kV+X8vjTxBZ/EHWta05Ncg+JK+KIJ9Au7X7QLKHTRJHiM7f3Ih8r zVdG6luQea8bE4mtLFSUqj1s23r5dei6n6flOV4CeS0qzwsZyXNFKLcFp7yTUd5t3UdNbq5+ znhb4iaL4v0NNTsZ2CFjG9vIMSxyAZKFR3x6ZzkYq99t1DU1Is4RaQMOLm4+8QyZDKnqGIBD YrwL9lPVJ/EviPxXqBhUWRELkBflWYl8bfQ7S3Tsa+la9PB4hVaKqNav7t+x8fn2XRyrMKmE i9FZ+auk7P0uZ1vosKTCe4Zru5GT5knRSVAbaOwOOnP8q0aKK6JSct2eAFFFFSAUUUUAFFFF ABRRRQAVX1C6NlYXNwF3GGJpNvrgE4qxTXRZFKsAykYIPQiomm4tRdmB8aftAfHvVvhf8IdH u/DX2Gfxh4j8Q2ug21xqeTbWs107k3EqgglQFbvjJGcjg+nfCfR/H/h6w1Oy8feLdJ8ZTBop bDUNP0v+zplBDeYksQd1IBC7WByfmyOleZ/tB/s+S6rZ6joN7obeKfC16RILeLc0iANleEId WUjh1/MZIqf9mn4Q6j4JN7baHoGr6emovGb/AFbX7u4uHdYwQgDTsWIUMQFQAc8461/HdfJs asPWy7DYfmxLlKM/cjK7bi0/aN3g0rvRa3Te+n0CqRvGbfu+v6dT6Uh8cT+WETSNQumT5TLH bSFGI4JB24PPoaf/AMJTrkv+o8M3j+7GJP8A0KRa6i1tls7aKBMlY1CgnqcVLX9G4XIMyWHp wxWZ1XJJJ8qpLW2uvs7v1bv3PIlVhduMF+P+ZyX9s+KZfu6B5X/XSeIfydq+Xv2k9c8VW/w4 +K2g6SJLbxTqmn3CW32eTEgd0ztjbjlkJUEY5I6V9nVwPxP+EGm/EmJJWnfTtTjXYl5EobK/ 3XXI3D8QfevFzzhHEYijTq4bETq1KclJRqSTi7dLJJJ/pddTWliFGWqSXkfGn7GniP4If8Uf p/g3wNcaJ4/07Qjb6retoM1pLavsQTpd3LKqys7j5cs/TjbjFfdPg1/Nsp3UhkMmAR0zgZ/p Xjegfs2avoNy1xceIlvreL5xZQxMhnx/Du3fLn2/+vXtvhVrMaJbRWMYigjUKE5yPrnnOc5z znNfK8PZHjcNxPTxWYynC0JunGc+dtt+8lLRJLmvy77PXVm1SpH2DjDXa5r0UUV+/HlhRRRQ AUUUUAFFFFAHIeL/AIReC/H12l14h8M6bqt2gCi4ngHm4HQFxyR7E4rd8P8AhvSfCemR6dou m2ulWEfK21nCsSA9zhQOT61pUVKjFPmS1N5V6s4KnKbcVsruy+QV8v8Axf8AsnxF+PPwq+Gn gixtkufAWrW/ifXb+zjCQ6DZrDIkNoNvCyXIcqIh/wAs1LEYwa+npFLoyhihII3L1HuK8O+G f7Lk/wAJbUWfh/4teOksJL99Su4LqHRZ3v55H3yvcTtppmlZ+hdpN+MAMMDFR+NN9Gn807r5 J6+exhL+HJLdq3ye/wCGnzv016z4x/B1fit8KvG3guLW7nS18TWb2xubgNeJau+cyIjOpx0H lh1UbRtC5bPB6f8AEnxl4e+La/Cbx7pWi+OrfWfD02qWN94d0/7ArxRSJDcW9xa3V1KrAiVC HEuCCVKcZrvrn4OyTR+MPJ8e+M7ObxFfW9/HPFqaOdHaIoRHZLJEyRxMYxvjdXV9zhgQxFZO nfs62VvP4n1PUfGfirW/Fuu6U2iHxVdXFtBqGn2ZBxHaC2t4oYSHJk3iIsXwWLbVASbTvbpq u/uuy+UmrvTRddinqrX1T0fbVXfzitu/bc80k8U6Z8XP23/Di+GbmyWy+G+najp+samsyq15 eXKIBpsQzmTyRH5smAQjAKSGyK0/BXhDTvCn7dvjGSwa/kk1PwLZ3tw1/qNxeHzG1G5GEM0j +VGABiNNqL/Coya7T4T/ALPNx8HdP0LSNG+JXiy48NaQnlw6Bc2eipayJg8O0OnRykkksWEg ZmySTk5r6Z+zSuj/ABe1T4iWvxH8YRatqMBtJbHZpZs1tvNeVYET7DuVVkld9wbexJLsxJzc UozhZ6Lm1/xKX6yvbWyWt3q4lrCatvy2Xo4v8l976Lb2eismWXWbDSoW8m11i+V8ziAm0DJy f3asXBfG0BWdQTkllFZfj74k6N8OdIvL/V5JY0gg85B5LBZmJwsSSEbDIT/Bu3Yy2MAmnCEq klCCu3oiKtSFGEqtR2jFNt9kt2dVRXyZaftg3Oo6vIj32m6e6apBo/8AZ7QOxW7n2GGBmPJZ hInIwPm7V6ZfftM6Tp9rbWx0u8vPEMwIGm2ilyzDj5cAsc+ymvcqZJjIO0UpPrZp29f89j8+ wvH2SYmrKlKU6bW3NCS57W+DRuT1Tta9nex7PXxT+2l8X10DxHrFrqUk/wDYXhrSW1H7FAcG 6uPLLj26bVBOduWPc16/KfjB8TgohSHwDpL9XnP+ksvqEUlwfYsleW/HX9izVdZ8ONe6Dqc3 inWJEePUbTUWSMXcbDGI88DjIKuxyD97jB+OzKnW96hGN+V6tNNPyVtz+gOEa2XUZU8fWxCj KpF8icZJwbWjnzJKLXbz6HgHwf8AjI2p6lH4d8aaPZabq2q6MNc0PUdEundYiroGt51dR84D hg6nGVIxX6H/AAU8W3PjT4caVqN6xkuwHgllP/LQoxXd9SACffNfnd8HP2M/HOl+KALXwbqG mTeULL+09cvHaO0tgc+XGZGYhAcHbGDnj0r9KvAPg628A+ENM0G1cyx2cW1pWGDI5JZ3x2yx Jx2ziubBRn7VtR5Y2t2u79vJaHq8T1sN9RjTnXVWs582j5uWPLZq9lu9bW/I6CiiivbPy8KK KKACiiigAooooAKKKKAM/wAQaBp3irRbzSdWtI77TryMxT28o+V1P8j3BHIIBFfIfiH9kPwn 4d8f22iW1/qQs9ZQPFK8gM1niZAQhxhshsfMDj3r7Mry74kXJsfid4GfeEWfzY2z3AlgP/s1 JZeswmqel9X9yba+djqjxTV4VoTxV5ODaTUX1k1CMtdNG035Kx1vgD4faL8M/DcGiaFbtBaR ku7yNvkmc9Xdu7HA9hgAAAAV0lFFKMVFKMVZGVWrUrzdWrJyk9W3q2woooqjIKKKKACiiigA ooooAKKKKACkZgilmOABkk0teUftDfE+5+H3h/SrHSYftev65epZ2dsDywyNxH4lF/4HScak k1RjeVnZbXfqdmDwtTG14Yel8Un/AMO35Jas73RlN9e3N+44J2R57D/OB+dbVU9Is3sNMtbe VlkmjjAkdRgM+PmI+pzVyvAyLL55bgY0q7vVk3Kb7zk7y+Sei8kjGtJSm7bLRegUUUV9AYhR RRQAV5Z461vUvhp4v07V1bzPCupSiC8j2j/RrhujZ7K/5BwOfmr1OvJv2ofFkfhP4Paq0kEd wb+SOwXzRlULnl/qoUkHswU12YKhTr4ulGpTU9bK/S6s2n0dnueDnvNHLa1SFR05QXMmu8dV ddU7Wa6pnqtvPHdQRzROJIpFDKw6EHoakr8vvB/7T8R8eWfh8Jdfa38VT6CNUVbkRCCOCSRZ 1nKBC5ZAPL3bgDnGBX6FfCDxlN418I/abh/NubWdrWSX/noVCsG+uGGfevTx2VrDU3WpT54p 2elrX1/r8j5XIOL6maY1ZfjcK6NRxco+8pKSTs9kmu63TV9dDt6KKK8A/SAooooAKKKKACii igAooooAKKKKACiiigArzX9orwLqHxF+EOuaPpI36ntS4tos48143D7PqwBAz3Ir0qitqNWV CpGrDeLT+45sVh4YuhPD1Phmmn6NWPxJ1TQtXl8cJe69qeoJrGm+JrTU7e3vbu5CWsMDRl4f s5bYrlkJztzk9a/U39l7w9qlp4Su9c1i0ksrnVnVoYrhNsohUHazA8jcWJwewB7163PoWmXV /HfTadaTXseNlzJArSL9GIyKvV7uKzdV8NLD0qSipO8n36/mfF5fwrHC5hTzDEVnUlTTUNLW TSj3fRWtt1CiiivnD70KKKKACiiigAooooAKKKKACiiigAooooA574h+KD4J8C6/ryxiaTTr KW5SNujMqkqD7ZxmvzA+IH7YaeENY8TS6wkviDV7bSbbUYJGSd1nmleQNC7JGywqoVcEkDnA 6V+qWu6LaeJNEv8ASb+PzrK+ge2njzjcjqVYfka/Ob4if8E9/Gtt4i1uDR9NtPFOi6xGltPc HUBbO8CMSiyKzrtI3HlM5/QfV5LiadGnUgpqE31faz0/8Cs/87WPy/jLJ5ZtOj7elKrQjvGL 15uaLu11vFNfNq8b3X2F8DPGx1DVtS8PJdvfWcEP2i1kk+8iBgpX6fMvHbmvZa8b/Zz+Cuo/ C3SLm88Q3cF34gvlVZFtiWit4xzsViAWJPJOAOAB0yfZK8zN6mHq4ycsK7x017u2rPc4OwOO y3JaOFzB3nG+l72jd8sb67LRdlZdAooorxz7QKKKKACiiigAorhfiR8ZPD/wyMMGovLdajOu +OxtVDSFc43NkgKuRjJPODgHBrM8EfH3QfGOowafLb3Wj3lwdsAuwPLlPZVYHr7HGe2TXO8R SU/ZuWp7EMnx9TDfW40W6ffyXVdWvNaHptFFebfHv4tWvwn+GHiTWob20Gs2to32O1llXe0z EIh2E5YBmBI9Aa6oxc2oo86jRniKkaVNXbdjpPEvxJ8K+DrlLbWvEGn6bcsNwt57hRIR67M5 x74rxLwdKnxw/aV1DxOjC58MeEIBa6fIOY5rhsjeOx5MjAj0jPpX5oeIvj7e6L8RNNj1Vv7U tNStxeapqs7sZoJHlaNWYk4K7vLye272r6O/ZA+Od5DqHgvXopPIh14xQalYx5ER3ybOAScl SdwPXt0Jr6SlgqapVPZTvUSf/Bt+R+wYThzCYfD4ingq7li/ZyumrJJcvNb1UuVO+t32dv0z ooor5k/GAooooAKKKKACuP8Aiz8NrL4s+A9S8NXsrWwuVDQ3KLuMEqnKPjvgjkdwSMjNdhRW lOpKlNTg7NaoxrUYYinKlVV4yVmvJn5sWH7AnxG0jxRDBBpukzWkOqPqsWpnU2ES3DKUM23O /wC6xG3YRz0r75+Ffw/i+Gngyz0Vbk3twpMtzdFdvnSt95gOw4AA9AO9ddRXp4rNcTi6Soza Ub3sla72uzwMv4ewOW4h4qkm5tWvJ3sm7tLsFFFFeQfShRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAfmL+3X4i8QaT458XWqC+Q3GoWAuJdP3C6GmDyzIICvzAlBj5efvd81y3wV+JWtf8A COeKNI01NTvPDFvqUdx4WXWfPa7VjCvmojSnzvKMoyoY8EnFfoR8bvgP4Y+Nt1p1tqkdxaaj ArEanZACVIv+ebE5GCxyAwP8WMc1kfCf9kTwd8K9Yi1YXOoa9qMDbrd9RdTHA3ZlRVA3D1bO Oowea8Opga8XKMWrSd7+Td9u+mh+s4biTK1Ro1q8X7SlFJRS0bjFxTTvpF3u1a90u2sc/wCz n4g8U2Hk+KfiXrdwkqjzINOjWEKe4UymX86x7v8AYO+G1xoGo2TNrN3f3Fu8cOoX2pyu0EhX CyeWhRG2nBwVwcV9HUV7lFvDpxpNpPzZ8HPPMxnoqziu0bRX3RSR+IXxe/Zp8YfDLxhqNr4n 8N6jNHPaPpouraFpbO5hL7g8cgX72cHBII4yAa98/Ya+B0t1448Madfzyafp+gxjUfsGoI0d 3MUYNEu0qAQSyuePujjOc1+oNeF/tFeBNQttQ0b4k+GAI/EGguougBxPaZOd3qE3Nnp8jv6C vYpY1O8Z+7dNX/rofV5bn7xdSWGmlTnVi4Ka6OVrX8m0k+17o90orI8J+Jbbxd4estWtflju EBaMnJjfoyH3ByK168ZNNXR+d1Kc6M3TqKzTs15oKKKKZmFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAU13WJGd2CooyWPQCnVla87yxwWMMjRS3b7N6sVZUHLlWAID Y6Z61cI88kgE0JDc+fqMiBZLlvkJCFhGPujcvUHrz0z61rU1EWNFRQAqjAA7CnUTlzyuAUUU VABTZI0mjaORVdGBVlYZBB6ginUUAfMx+JMHwB8c694dt4m1vTXKzw20MwBt2YAhWY5wdvBH JICN3r0fwF8fdK8YalBpt7Yz6HfXPFuJ2DxSn+6HwPm9AQM9jnivzc/aZ8YavY6ndaffvf2l tL4sLeI7qwaRboWoaTeu6P51jLldxXnGMcZrd+AvxN1zV/COo6Ms+o3+h2WuyHwtdak0j3gt mWPEavJ+8aMSBthbJ5IzgCvmaeLnTi5p2inoree1+5+4YvIMNjq1PDyp3qyiuapzWfwX5+XZ xurN6a6bn6sUUyDf5Mfm48zaN2Omcc0+vpj8QegUUUUCCiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigArJ00C+1O7vyGCr/o0O4MuVByx67WBPIbHtUuuXrWdiViP+lTsIYQGU He3cbuMgZOD1xVmws49Ps4baJVVI1CgIoUe5wOBk81svdg330/z/AK9RdSxRRRWIwooooAKK KKAPCPjj+yP4c+MeqtrMF/P4c12QBZrq3iEsc+BgF4yRlgABkMOOueKq/Bn9j7RPhbrMGsal rVz4o1K2bdbedCIIIW7P5YZiWHYliB1xkAj6BorleFouftHFXPejn2Zww31ONeSp2tby7X3t 5XsFFFFdR4IUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeRfH/8AaAsPgnpZ Z41mvBZz6jM8iO8drawqWkmdU+ZuAcKvJ2n0wfXa+Nv+Ch3wc1zxp4abXdFdzFNpk2iXyRDd JHHJv2uqZG8ZkYEA56dskepllOlVxMY1VfeyezdtE9t35nzfENbEUMuqVMNLlel5LeMbrmkr p6pX6O2/Q7n4b/tMW/i/xU9te3kN0tgtv9qFtDJELf7Sm+F23Da4KqT8pyvOfSvpCvy9/ZO+ CHiSfxlqcOlaNfado2qfYxezzWb29vbLAhVmDscO7biQo5yfTJH6hV6Wd0aFGUPZWUnzXUdk r+782v8AhkfMcFVc0nTrwx1SVSkuTknNWk24++tlopbb9VdhRRRXzJ+lhRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeT/FT/isvGvhrwbGwa3M v26/QH/lmvOD6cAj/gYq742/aB8LeCdYfSpDc6pqMRxNDYIrCE+jMzAZ9hkjviud+Bus2vjP xz4u8SXEgj1ScrDb2cvEsVsMHPuCQg46FfcVFDMFhsRF0WnJX+Wj19V08z1MXwvPMMrrVMwh KNFxTVtOb3o2WqfutfF3Wiep7YAAAAMAdqWiirPLCiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKjn8wQSeUAZdp2BumccVJRQNH44fFjx9d23iX w7Y67qetaDofm30usahpEs0N1JefL5SyyRDeoz5pwMZbrnivU/g9+0Xrtl4b+H+q+Jb2Sz8U NJDpTXMkaxXFxHNdfZ7eSdMYLFHiZ8jJwSRmvsL4t/sdeD/ilr0uuR3V74e1Wdt9xJYbTFO3 d2Rhwx7kEZ6kEnNeA6h+xd8QLLVW0KaKz8UeExex39reLfC3mt5onWSKUK33W3RxhgA4KggH muLK8ulKvGFWoowi07vrZ3tbu+p97xLxRgq2Br1aFKc61aEqairrk54qLfMntG142Ss23ddP sr4Y+NT448N/a5QgureZracx/dZgAdw+oYfjmuurz74HeEIvA/gWHSzefbdSErS38mNuJzgM oHYKFCj1xnvXoNe3jlSjiaiofBfT+vyPybJIY2lltCGYy5qyiuZ93/n38wooorhPbCiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAMnVdMl+ 0DULBtl6gwycbZ1/ut7jtyOetWdL1WHVYC6fJKhKSwk/NG44Kn6HIyOKu1k6ppcv2hdQsG8u 9QYZDgJOvo3GeBnHI5xniuiMlUXJPfo/0fl+XoLY1qK4HWfjb4Z0K5FnczyyamvE1lbp5jwH 0c5Cg+2c10XhfxnpXjC3eXTbje0ePMhkXbImemR/UcVpUwWJpU/azptR720PGhnWW1cY8vhi IOsvscy5vPTy6rdG5RRRXGe0eT/tAfFe6+HOm6Lp+ltFFrOuXDW8E82NkCKAXk54yNyAZ4+b PbB+W/hf+2FbeL9b8O2uneJdVn1fWftzx211cRyLB9lOHE8fmts38lMA5H92vbv23/BA8R/D rT9YtrpINV0e5Y28TnHnrIAHRT2b5FYZ4+UjqRXwj8Jf2bfGOk+LPDK6X4H11NU0r7Wq3Uum m3WcXJyWmmZVGF/hJOMV9/lVOj9Sg2opNvmckujjs30Ub/PzPwbjChi8ZmVSEKtTmhFOnGnJ qzcJ2clHq6ij52Xa5+r3gbxQnjLwvZaqqiN5QVkReiupKtj2yMj2Irerlfhh4Pk8CeBtL0a4 mW4u4UL3EqZ2tKxLNjPYE4HsBXVV8TivZKvP2HwXdvS+h+zZWsUsBQWO/jckef8AxWXN+IUU UVzHphRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH5I/FL4h+MN I+Mek2drMbPVYtV1eTU5LrzmglYp8qyqjLnawbZnoTxX0P8AsK+OdV1vRvh4dUuZpNQOl/Zt Snvpy08snlEjzC53FywQ88mvoX4tfsp+AvjFq39rarb3mm6swAlvtKmETzADA3hlZSQOM4zj AzwKq6X+yN4E8O+G20/RYrzT9S3+ausm4MlyXxxvz8rLwPkwB3GDzX3LzjB1aclUTvNcvdRu krrXpa69T8Tq8F46niqdfD8l6U41E22nPllOVpNJtX57PdadT2ykJCgknAHJJr5l8QfFr4g/ B28/4RbVILTXr+7QtpuoFiRsBwWKg7jjptbkH+JgK8u8GftNar8ZL3XPD2i/ELT9V1JLWZ5t OhEKNLEnEhhO0eYF5z5ZOB1r80ljoKTjGLbXlt6n9RU+Fa8oRrVK9ONOVuVuXxX7K19HprZ3 PcrYj48/FUzcy+EPDb4wR8lzPnKj3BI3H2Cgjmveq82/Z7bSP+FX6bHpKMhiZkvPM5c3PBkL HvnII/2So7V6TXqRxVbE0KSqu6itLbau/wB/d9T5DG5Zg8tzLFLCU3HmlrzO7bilG/ktLpLR X0CiiipMAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKAPjb9vfw3q0F3pfiO1M0enXGnyaRNcwg/6O5LlSfTcJDj3T6V8G/DOz8TWWq/Di2u4rHSL TwR9o26hbXW6S83xNEAibQUDA5fcea/bHVNKstb0+ew1G0hvrK4UpLb3EYkjkX0ZTwRXmml/ stfCrR9YXVLbwZY/a1bePOeWWIH1EbuUH/fNePWwVSVSU6Ukr9+jta6+8/R8t4mwlHCUsPjq UpOmrLltaS5lJKV9rNLVa/ezO/ZU0O+034avf30bwnVbpruFHGD5W1VVsf7W0keowa9mpAAo AAwB0Apa9KjSVGnGmuh8VmWNlmWMqYuas5u9l07L5IKKKK2PNCiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK AP/Z ------------0540DB1DA0EFE4D0F Content-Type: image/jpeg; name="optstr.jpg" Content-transfer-encoding: base64 Content-Disposition: attachment; filename="optstr.jpg" /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoM DAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsN FBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAAR CAGuAeYDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAA AgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWG h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl 5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREA AgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYk NOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk 5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD9U6KKKACiiigAooooAKKKKACiiigAoorm PiR49svhr4OvtfvkMsduFWOBThpZGYKiA9skjJ7DJ7VpTpyqzVOCu27IxrVqeHpSrVXaMU23 2S3Onor5G0b9sK51TW47eTU7KG5l1iTQk077DLt+2pG0rwbiM8IjHeSFOODnAr6c8F+K4PGe gQ6lCnlMSY5Yic+W46jPfsR7EV6OLy2tg4882mvJ3t11Pkcm4ty/O8TPCUFOE0m0pxceZJ2b j3SbV9n5G7RRRXlH2gUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFeZftG/Dq/wDif8KNU0jSSP7WjZLu0Rm2iSRDnZk8DI3AE8ZI zxXptFbUasqFSNWG8Xc5cVhqeMoTw9X4ZJp/M/G3wr8DfFdp49l1RtFvF8R6X4kfWbhXtXW7 kWVHiWBUJ3MdzZyAelfqd8B/COp+D/h7bQaynk6pdyteT2+4N5JYKAhI4yFVc47k9etc58bf GGkeGfGPhG6nvYPtVlLIZrdSXlSNvLYMVXkAlOM+temeGPGmieM7V59F1KG/RMbwhIdM9Nyk Bh+Ir1sZndPF0fqdJKOt5Lu7WVvK2/mfPZZwPi8rxH9tYu84tctKVnpHrzaWT5r8tnbl/Dbo oorwz6sKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACqGvai2kaHqN8kfmva20k6x/3iqk4/HFX6o6jqMVqvk7DcTSYAgRd7YJxuK9 dueppqLlpHcqLUZJyV0flN46+Let3/jHwvo2lX1pD4j8RSXl7qGs6rAblI1iCsESIOm5mLHO WAVRwPT2f9mv48jxxL4G1+10yDR9bkvW0bV4NPdja3TLcGFpIgxJ2OAHAJOG4ycZpPjn/wAE +da8RagLjQ7PS9d015DPHYTTmGWyc9VR2xlOwO4HHBB6nuvgl+xLqmg6IieIL+Dw19ljC6dY 6Ftf7O4OQ7krtPOflXOc53A18tHD16f7tQ95Pf0b1v5n7rXzbKcQ54qrif3Ti0oXbfvRiuVw WiUWpO/nboj7HorwqHxD8SPg/IE8R2h8Y+Go+uq6bGzzxL2MkfLr6kjeoHcV6D4W+MPhLxdo 76lZazbJbxxmSXz5AhRQMknnHHtX1dClWr03VjTdk7PTZn4NmVfB5ZiYYWriYNzV42e6+dmn /devW1jtKK88svjx4Sv7ryorm58nOBdNbsIz7/3sfhXoEM0dzCksTrLE6hkdDkMD0INb18LX w1vbQcb91Y8nAZxl2auawOIjUcN+WSdvW35j6KKK5T2AooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKzL7U5Wl+y6eqy3R6u4JijwRkORyCQTj3/Grj FzdkA7UtSaE/Z7RfOvXyqjBZY2wSDJjlV9//AK1O0/S1tWaeU+fduSWlfDFA2CUQ4yEyOBT9 O0yLTo8KWmlIw082DI4ySAzY5AycVcq5SSXLDb8xBRRRWIwr5m/bD8G6f4c8GJ4z0bSks9VT UIk1G6s8xedburq3mhflbLmMbmBIzwa+maqatpNnrum3OnajaxXtjcxmKa3nUMkiEYIIPWu7 A4qWCxEa8ej1812PHzfLaWbYKpg6qXvLS6vZ9Gfjz8M/i14w/wCFk+H5bu9nPhuTxTrrSWRe fIt2iufISUliphUlPLUjAKrjoK/Un9nzUbjVfhjYXM+7ynll+zlu8Ycgfhndj2rg7b9hf4W2 viD+01ttVMO/f/Zxv2+z/TpvI+r17YmgppUcf9kKlkkSpH9mVf3TRopCxqM4jHTkDt0r3Mfm OGxOG+r0k7tp3fkkrbv1v8vM+JyHhatluaPMqvLG0HBRjs+afM29F6fjobFFUbHUxcsYZkNv dLgPG4wC20EhT/FjPUVer5eUXF2Z+nBRRRUgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQA13WNGZmCqoyWJwAK8vl/aJ8MPqD29jHeanCjbWurZF8tvddzAsPfGD2zW l8f3u0+C/jD7DJ5dydPkVTuwWB4ZR7lcgDqScV+Q3i7x748n1fxsdBme00yRdFjW3iguGnl8 uRfMNqyOFXoTJ8rZHXFfTZXgKNehOtVi5O9kk7bRcrv7rf5n5txTm+Y4WvTweW1IU20pOUlz aOahZK/m3r2tdas/ZXT/ABRF4ut4zor74GwZp3UrsXJDICPuyDg4PYg9wa27Gwi0+EJGNzED fKwG+QgAbmIHJwBXhv7LXiCfxN/wk18i7dPMkKqV+55uGLBfcArn6rXvdefmeH+o4meFi9Fb 8Unr6H0HDGaV86yihj8TDknNO66XTaur9Ha67X3e4UUUV5J9SFFFFABRRRQAUUUUAVb7TodQ QCRdsihhHMoHmRZGCVOODiqTX0+jbvt37yzBJF0v/LNcqqKwySWOeoGPpWvRWsZ2Vpar+thD VdXGVYMOmQc06sptMl0+US6cQsZID2rnEe3cSzLgZ3nOOTjgdKsadqkWoB0AMVzGFMtu3349 wyAfqKHDTmjqguXaKKKyGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUV5R+0t8Q734dfD jz9NlNtf6jdx6fFcjrDuVmZx77UYA9iQe1ZVaipQdSWyO7A4OpmGKp4Sl8U2kv8AP5Ho9x4h 0qzvBaT6nZwXR6QSTorn/gJOay/HPj/Rvh5o7ahq9yIwQfKgXBkmI7KP5noO9fl3a/tAWume LNW1DUdEGofD/SddTQtV1gX5W/MrOkclxHCYyHRHkXguGYcj2+xPgf4d0Pxd8RdSj1SabXk0 iFX00XTl4VQORkqeuMrtHQZPGcY8xYurOyUUnJ2Wt7aX1+TPuKnDuAw3PWlWnONJKUkocvMm 3Fcrbta6ab10Taudbp/hnxD8eL6HVvEZm0XwepElrpsZKS3I7E91U/3jyRnaFBzTvEf7Ffwr 8R63/abaNc6fIx3SQafdvFC5/wB3nb/wHFe60V9U8xxXLGMZ8qirJR0Xm7d3u2fjn9hZYqta r7HmdSTk3P335K76RWiSsrdDJ8K+E9I8E6FbaNodhFpum2wxHBEDgepJPLE9SSST3Na1FFef KTk3KTu2e3CEYRUIKyXRBRRRUlBRRRQAUUUUAFFFFABRRRQAVT1DTI9QVSS0c8e4xTIeUYgj OOhxnoauUVUZOLugPmr9oX9pO9+F0mp6XBeWemJpNut1qOtT/P5ETBdg2sMeY24dm++oGSa5 D4O/tT3/AIl0e41+08RxeKtJsbqO21TTrm2Nre2hddyMY3RJFDKCVJG1tpx6jzP/AIKI/B/W 7vV9U1aBpYdG1v7JMt8sZkjt7qAx7UkHoTEp567jjkV418CdA8UeLPiJq1200Op+JNfitrMW WlwyLBDDDuw7FiT1ckscBRXzNfEVFVm5NqV9EtultOt+p+35bleEr4LDxpU6bpOKdSckr3tL nXM2nHldkrLr5M/W+zu4dQtILq3cSQToskbjoykZB/I1NWd4c0gaB4e0vSxJ5osrWK23/wB7 YgXP6Vo19Gr2Vz8UqKKnJQd1fT0CiiimZhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXnvx3+Fv8Awt74 d3mhxTpbagjrdWU0mdizKCBuxzghmUkdN2ecYr0KionCNSLhLZnThsRVwlaGIou0otNPzR+O vxD/AGdLn4ba6b3xVp95bC5vze3WkxXbm1eeIptnlVf3bbiVIwxyVG4ev3n+xzpllp+mXl/e 3SjX9UjRo7U/wW4+YYboxbOSB0AHvXyz/wAFBtf1+91bXLCztJY31I2LWk5jbJhS7VmdcbSR 8hDc9FIzjBqt+wn4m8ZDxImhanK92D4h8yxlKMp8n5A+wMzEQhFPGSAAcdTX0dDhfDwanzSb ULptrli7a/lbpb7j5XiHxQzipGWDw9GlGDrKNSKT9pUi5Rtyu9o2cm7WfNbzZ+o1FFFfPn0I UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeX/ALTWrDR/gb4rc43XFutooPfzZFjP6MT+ Fbfwe8IWPg/4deH7W1sbe0uDp8DXLQxKjSSFAWLEDk5J615P+2xq058J+CvDVof9K8Q+JbW0 2+seGz/48Y6+i40WJFRAFRQAAOwrH2MlV9tLZqy+/X9D6OtUVLJqNFbznOT9EoxX48w6iiit j5wKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA Ciue8d+OtL+Hnh6XV9WkZYAwijijGXmkb7qKO5OD9ACTwK8t0T9pqLVp4pG0iMWcpbYIbwPK VX7xA2gNjuO3rXpYfLcViqbq0oXiut0tfK+58pm/FOUZFVhRzCvySlsrSend8qdl5u3Xsz3O iqmlapba3p1vfWcomtp13o47j+h7Yq3Xnyi4txkrNH09OpCtCNSm7xaumtU09mgoooqTQKKK KACiiigAooooAKz9fvDp+iXtwvDpE23/AHsYH6kVoVgeLXWddO0/K5u7pFKkgHavzEgEjPQd OfYjIrejFSqRT26+nUT2MDxT8F/CXxH8Jado/ijRYdRitYwYnGYpYWI+bY6bWXJ6jgHAyKZ8 MvgL4H+EDTS+GNES0u5l2PdzSvNMV/uhnJ2jgcLgHAzXoNFU8TWcXDnfK+l9PuOV4PDuqq7p rnXWyv8AfuFFFFcx1hRRRQAUUUUAFFFFABRRRQAUUUUAFFFee/EP44+HPh1frp1209/qjKHN nZKGaNT0LkkBc+mc98YrOpUhSjzTdkdmFweIx1VUcNByl2X9bHoVcr8SfiXoPwo8MS674huj b2isI444xulnkOdsca92OD7AAkkAE1m+CfjN4e8a+dGjy6XdwxmV7fUQsZ2DqwbJBA+ufavi P9vf426B4s8a+EbLw9rtpr1lo6TvdRWjl4lmZkHLD5W+Vf4Scc9M11YOMMVNJO8etj3MuySt WzCOExkJQivi02W++2uyZu/ED9ocfFL4vfDnX7nw7LYeG/Ct7LesqXazT3DN5ZRihVQpUxjj c3U8ivuPwn4s0vxvoNrrGj3K3djcLlXHBUjgqw7EHgivwB8C+OfEWj6v4VWXULm70rVPEJut 0kzHyWR5onhPPKMpjYA8ZU8V+uX7Buq3Wp+FvFedx01L2LyT/D5pQ+YB748rP4V7OIpUKmEV WknFx6Pzs/1Prs6wGW4jKPrWDi6bouK5W73U3f8AVSvfZtPbT6kooorwD8pCiiigAooooAKx fEHjPRPCrRLqupQWckoykTtl2HqFGTj3xW1X5dftA/GDxTpX7QkNvaxrcai3iO+t57W+uWgt 5LSOCUQxs4ikKAbUYEKckD+8TXs5ZgoY2cvaNqKttu7tJfnd7+h8fxNnGJyjCqWChGVWXNbm dorli5O787WWq1erSR+m2j67p/iC1+0adeRXkIOC0bZ2n0I6g/Wr9fEn7CvxN1TxbpPgRr6W WfVb7SCdVkkGDI6ozb2UAAHcFHTv719t1lmOC+o1Yw6SSkr72fcvhnOa2dYSpUxEFGpTnKnK 1+VuNvejfWzv18woooryz60KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5v/bft NWt/Aug6/pyNLb6NqImuVUbggZdqyMPQH5f+Bivzd+B0vifwj4t8Balf6nNNaaWmsrNYS/Zx HYfaW42MgDyeZnJyWxjtX6gfte+P5vCnwufQtNUza94plGk2cCcsyvgSEDvwQv1kFT/DX9kr 4eeCdK0SW78NWWp+IbSCP7RfXO6VZJwMs4RiUHzZx8vGBX2GCx1LC4OnLERvZvlS66xk7+V1 b9L2Z+W5zkdTNsxr08LJLmiudyV7XjOC5X0fLJvS3rZtPpf2foL2P4XaXNfI8T3Ze5ijcYKx s2V49x83/Aq9GpOlLXzGJrvE151mrczbt6n6BluBhlmCo4Kk7xpxUVfyVgooormPSCiiigAo oooAKKKQnAyaAPFP2nPijeeA9K8P6Rp12NNu9euXha/zgwQoF3lT2JLoM9hnHOCPir4Qftb6 T4x8Y+E9GtopLPXdYudTjuZxeSFrH7KsrRvvMQEvm+WOARt3c5xz9U/tVWnhj4veFYrHS/EF sPEmkTNNalUkeGTIw8RkVSozhcHPBUZwCSPkbwj+yT4v07XvCEHhnSb7XLbSDcXNtqJYQ28E t1u89JHbg7d56HPI4zxX6NlkHQwcFL92rvmcla/vK1m+nLfuvK7uv5y4sjhM7zWpRjP29SK/ dRpzvyNQknzKL0ftHB62em9k4v8ASH4U+MJPGvhCG9nIe6hka3mdRgOy4O78QQfrmuwrj/hR 4Eb4c+B7DR5p1u71cy3U6AhXlbltuew4A9gK7Cvhca6LxNR4f4Lu3ofueS0sXRy3D08fLmrK EVJ93bW76vu+rCiiiuM9oKKKKACiiigAooooAKKKKACiiigAr8pv2xta17TfiLrGmXcOqNYv 4o+0aqulNIl5Lp+HMSIYyH2YaEkIdxAGOhr9Wa87+KfwC8F/GLyZPEelmS9hXZHfWshhnVf7 pYfeHJ4YHGTjGa4cVQlWUXB6p3PqchzWjltSrHERbhUjZtbqzTXqtNV1R+dH7Mg8SfGPxl4b 8K+Jn1a80K2uZktLXUZn+2LponeSNJ3J37hExGWO4KqjPAr798W/so/DLxT8P7nwinhmz0ay lYSpd6bEsd1FMAQsvmEEs2CR8+7IJBrd+FnwI8GfBxLg+G9LMN3cDbNfXEhlnkX+7uPQeygD gV6DV4SlOhecn7z7Gub539ZlSpYJyjTpLS7td3vey0XZH5/N/wAEvNSsr25ltPiLFNbIrPBD LpjI0jYO1HIlIUE4BYZ9cdq+jf2QtX0hPhl/wjFppi6Hq+g3EltqenkkyCbcd0jEkkkkEEkn leOMV7pXkutfDU+G/jRaeP8ASZzaW9/AbPWLVB8sz4AjkPuQAp91jPrXoYrMadDC1KmMlaEV zXttb06W37bkxzSeZYepg8dP+9B22kr6O3SSbXk7PuetUU1HWRFdTlWGQadWEZKaUou6Z8mF FFFUAUUUhIAyeBRsAtfLHjX4D+H/ANpf4la5qZaXR7ayVLc6pp+BJcyLwMg5VuAecZ2hPWvp TW70RWRjjIeSf92oU5+v+fejw/oNr4e0/wCzWkCQB3MsgjGAznqf0A+gFZZdn1Knja+Ew6bq RhFuStyx5nt/iaV10S16o8HNsonmcsOqrXsYycpLW8mlaK/w3bb72S2ucP8ABr4A+GvgnZzr pDXV/qFwoSbUdQkDzMvXYuAAq55wBzxknAr0uiiuurWqV5upVldvqz1cPhqOEpqjh4KMV0QU UUVidIUUUUAFFFZ2v+IdO8LaTcanqt3HY2MAzJNKeB6ADqSTwAOTSbSV2XCEqklCCu3okt2a NFeRWX7UHg29vfJ2anDBnH2yS2HlfXhi2P8AgNer2d5BqNpDdWsyXFtMoeOWNtyup6EHvWVO tTq/BK53YvLsZgLfWqThfa6JqKKK2POCiiigAopCQOpxXyz+1v8AtQT/AAl+3WlhObe00uxi v9QuIHVZpBJKqJHGzHAwGDEjnB6jBz24TCzxlT2cWlZNtvZJbs8jNcyp5VhniKkXJ3SUY7yk 3ZJXstfNn1PRXxv8Ef2qofFGo63Lp2vN4g0fR7+KxuZ2u1uo7jfCkpkhkDH7u8r1+8hBFe23 37Uvw9sHCHVjKxOAI1XJ/NhXfWyfFQd6UeePdJ20Pk8q44yvHxcMS/q9VWvCbXN7yTi1Zu90 /VPRpHrdeafEb9ojwV8MdQOn6pfTXWpqAz2WnxGaSMEZG7oF45wTnHaubm/ajsbzeNF8K67q 7c7PJtXYN6fcVq/O74rfFe8tdV8MWtzrkvheHWdTupdd8SKIxMjBN8cYeVWSPe+7LEfw4r4z EY2zUKL1d+/Q/ofKOGHONTEZnFqEeWyTim3J2V3fRLq3be+yZ9pfD7xFZftG/tNHxLlk0Dwp ZKdMs7oBJZJ26yFM9VJYkjpiPNfV1fm9+zB8a9Y8dx+Dry58q71az1uTTbfWbe3W3bWLQXHl xzuiALloztJAAO3NfpDW2Dm5c8W7tPf1/L0PO4lwyovD1qceWEoLljpdKLtq1vfe/W4UUUV6 J8YFFFFABRRRQAUUUUAFeKfteeMb3wd8IJGspHhbUb2KwlljOGWJg7Pg+4Tb9GNe1184/tf+ KIdU0i0+HkFvHPf6qgvJJpOttGj/ACMvI+YsrDPoGHfjfD4qjgq9PEV/hi1f7zOtlGNz2hVy /AfxJxlbWyWjvd9PU/Pbxv8AtTeIPBkfxSt/D9nd3MOk3OnxaVqf2WB7eyVxD5on3OGYsWcL hWxkdK/Rn4A+L47rxlqekWTs2nTW7XSxsc7GVlUH8Q2D9BX5d+F7fQPiL40uvBuira6xJ4lu dokn8y2tNTkgG793PJsRivl8BT8xXC7sjP6X/sZeG4tO0jXby/n8/wASrKtvOi8xwQ4yoU99 xDZP+wPTJ+3x2YYOWGqKpX9pKovdV763bv5WVt9dLH4dlHBWYUc1w+Mw2DdCOHlerO1rqUIQ SvfVSkpPT3fe5t2z6Tooor4E/dQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACor q2jvLaSCZd8UilWHsalqtqVw1pp11Oi7niiZ1X1IBIrKry+zlzq6s7+g1voc9p2uHRhPY3O6 4e3fYrpj5h1B9uCMjsc1q2PiK2vZRGQ0MjdA/Q/jXxX+0n8ctc8GfDvwTpfhnXYPDmteKvFN roV54lu4kmGmQzF2ecK/yFvl4Lcfe6HBHtHwk0PWPCWm6vouufEOb4h3EFykkNxe29vDeWSM gPlSmEKHycurMoOGxyAK/lnD8ZZtlGHWJVeCw65vZ0ZRk5OCkly86jo0nZNvps9L+3LD05vl s79X/wAD5H0DRXjCeKPiFrSh9P0a/e1flJD9ngBXsfnYMad/wjvxM1X77W9op6i71Nx+kaMK /pylip1oRqQpuzSerS3+Z+Yy4inKTjQwVWXrDlX3s9jeRYlLOwRR1LHAr5++L/7Q2jfDX4fX PjXxB9suLB71LHT9L02Lzbm8ndisUMSZAaRsE8kdDzxW+vwf8U35zea/p9qe+y2kuT+bOlfM f7Tfw51fWtHg8Ky6o2iaroesW2u6PqLQeZEtzAW8qUoT8yEMwIzwT3xg/lPiBCviaeEoYm8c NKT9pyt67cqk0npu7a7bXSPqMixuLxTqyxOGdJpLlvKMr7322tpv3Pob4PfE/UPiBaajcX/g nxF4E1PTJIt1n4ghiAnWQMVeJ43ZXA2kMOCpwDXudldC9tIpwMB1zj0Pevk/4EePfHWsR6if HOraPrmozeWLSw8L6fLFDbqoIZiZHd3Zyy5zgDaMdTX1XpFs9pptvFINsip8wBzgnkivnPDV 16eaYvD4ekoYZQjdRcpQ9pprFy11V7+i3VmfQYyzhFt3lf8AAuUUUV/RB5IUUUUAFFFFABXy 7+234judDHgtZImn0oy3M8sG7CyyosYQE9uGfH1PpX1FXlH7TWjaDrnwrvbXXYPOZpU+xMrb XjuOcOp9l3E+oyK4sbDnw843sfTcNYhYXN8PUcObW1l/eTV/le/yPzB+An7SGu2vi7ws9zr8 XiifxJ9tt/EPha4tYRHpKKjmOSIKgkgMbBF+djvz681+kP7Mera3qvw9vVtxb/YoLyWO0a4Z sg4RioAH3cs/PqeleOeEP2Abz7Zb6jeeLTp9pcoks1rDYg3IBGfLLltu4dM4Iz2r7A8JeFNN 8EeHrLRNJg+z2FomxFJyxOclmPdiSST6muXCU6qqqpKKUUnp6+nReZ9JnuYZf9RlhKFV1asp Rbetlyq1/ebu5O7dtNX85GttVd3xewRKWk24gLEAr8nfqpyT6+1KthfmRWk1IlQ6NtSEKCAu GHU8Mfm9unNaVFe57R9l9yPzSxlLocvlqr6rfMQiKWDKMlW3Z+71PQ+350r+HreUOJZrmQMJ FOZ2HDnJHGOnatSin7WfRhZGd/wj1h5m8wF237/nkZhnbt6E9MdunfrzXyT+29+yZq/xYs01 XwpBJdFraO1vtNt5VSZljcOkkZfhjxgg88DGTmvseiujD4yth588XfS1ns12POx+Ao5hR9jV 01TTW6a2aPz0/Z4/Zj8e33jjVb/W9Fv/AA9pWqXkV3qc2pMqlvLjSMRwx/eyyoOTkDJOegP3 9p+i6fpK7bGwtrJemLeFYx+gq7RXRj8zr5g4+00Ub2S89X955uT5Bg8kU3h1eU7Xb3tFWivR LYK+Vfjd+w7a+PvEF3rfhfV4dFmvZDLc6feQmS3aQnLMhHK5PJXBGTxjpX1VRXhVaNOuuWor n3GX5ni8rqOrhJ8rej7NeaZ83fBL9mEfBvXbHWdf1RNfvFBitxbwMkVo5GA2Ocjbn52IAz93 OCPpGorq2ivLd4JkDxOMMp71n6PdSwu2nXbBrmBcrJhV85OzBATgDp+Fb0qFOnS5aStbfz8/ 8yMfmWLzOt7fGTcpbei7LsjVooooPOCiiigAooooAKKKKACvir9vPwlqVprlj4liWX+yb/Tj pVxNF/yxky/BPbcr8e6mvtWqmq6TZa5p09hqNpBf2NwuyW2uYxJHIvoVPBrlxNBYmm6bdj3s kzWWTY2OKUeZWaa7p7n4X+BfA3iGPVPBWmahPp9rpXhPUI9Rj1C3mYzXRiJMSiMqNnX5vmPt 7/qv+x9oV8nhvWfEd2jxwatJGltvGPMSPfmQexLkA/7JrqLP9kz4S2GrDUYvBlo1wG3hJp5p Yc/9cmcpj224r0CJx4cu1gkkK6dOwWF5XUCJzwsKKAMLgcfz4Fc+GwNR1faVJK6vZLq3u/8A gH0macR4StgJYHBUpLn5eaUrX5Y25Yq3RWWr173bublFFFekfnoUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABSEZGDyKWs3W75reFYIcm4m+VQOoHrXmZlmFHK8JUxdf4YrZbt7 JLzbsl5suEXOSij5e+NP7OD+LoNTstP0m38U+H7iT97pdyq/u+cjG4/MARwRhhx9aufs5fs9 6h8NdPbSdL8L2XgfQZZvPuRAwM1w+AM4yzE4AGWPA6Z6V9O6dYrp9qkQ5bqx9TVqvznAcC4W vh4yzO/vPnlSTtTTbuo268qsr31t2OuWJkn7nTr1GRRJBEkcahURQqqOwHQU+iiv1hJRVlsc IVk+IfCmj+LLVbfWNNttRiU5UTxhip9VPUfhWtRUVKcKsXCok0+j1Q07ao461+F2geHrSVvD +lW2m3v3llQHc2P4GY5O0+n0PUCt7QdVGqWYLArMnyujfeBHBB9weDWnXO6xC2i6iuqwD9xI Qlyo7HoH/kD+B9a+MzLDLJasc2wcLQirVYxVrw/mSX2ob+cbrsdEJe0Xs5P09f8AgnRUVHDM txEsiHKsMipK+zp1IVYKpB3TV0+6ZztW0YUUUVoIKKKoa5run+GtKudT1W9g0/T7Zd81zcOE RB7k+/GO5OKaV9EVGLk1GKu2X68H1LW1+LXx/Xw3AnnaH4VTz72T+B58g7Pf5tq4P9ySofEX 7Y/gqLTNSGhte6hfpA/2R2tSkEkuDsyWIYLnGcjpWr+yj4PXQfhmut3E6Xmr+IZmv7u5Dhyc khVJHcfMSOoZ2FddTBONFyxEWtreb3/Q+2oZfisjwlXMMZScJtclO6ad5XvL/t2KfzaPaaKK K4z4cKKKKACiiigAooooAKKKKACiiigArP1eymnRJ7Q7byE7kBfYsg/uOcE7e+B3ArQoqoyc XdAVtPvU1GyiuY8hJBkblIP5HmrNY0sZ0bVDcoD9kuyBMME7JP75YnCrgYwB156nnYByMjkV c4pO8dmJC0UUVkMKKKKACiiigAooooAKiubdbqCSJiyh1I3IcMvuD2I9alopp2d0BkaTeNBc tpd1IpuY1LxfMWeSEHaHbgDOeoFa9UtWsWvrZfLOLiFvOhy5VTIAdobHJXJ5FGlX5v7di67J 4mMUo2kLvH3tuQCVz0PetppTXPH5i8i7RRRWAwooooAKKKKACiiigAooooAKK8x+PHxUuPhp oNhFpkccmuavObaz83lI8DLyEd8ZUAerDPAIr5J+GH7Vt/478bjTbXxpqsF7cm5Gm3moW7Lp mqSwAtLFA5HlkgI5HChtp2k8Vw1MVGFT2ai2+tj6nBZBVxWFWMqVY04t2jzXvKzs3onpfS/c /QSiuE+GvxT0/wAa+FoL+8ubWwv1ZobmBpgoEi9SoJztIII+uOcV2EGr2F022G9t5m9I5VY/ oa6Y1YSjzp6HhYjB18LVlSqwacXZluimCVD/ABr+dec+L/iTZaTZ+I9Sv9Qj0vw/oMbyXl5I 2FVY13SOxHOBgjA6475FfMcQcR4bIMJ9ZlF1JN2jCOrk/wBF3f5uyMqVGVWXKtD0WaZLeJ5H OEUZJrK0iFr65k1GYck7YlPYev8AT86+f/g3+0r4a+N2py6fodh4n08yWLanZ3Ot6NPZ2upW qyIjTW8rjbIoMsfHBwwOODj6L0a7W90+J1UJtGwqOgIr4nK85q8S5xRoZlh3h/ZRdWFNvm55 X5ea9l/D6Rtfmd/snTOmqNNuDvfS/wDXcvUUUV+xHnhRRRQAUUUUAFZviPU7bR9Cvry7QSW8 cR3xn+PPAX8SQPxrSrl/iZpVxrPgfVba0UyXGxZURerbGDYHuQprzcyqVqWBr1MOrzUJOK3u 0nZW9S4JOST2PIfih8ffCvwO0bQ28W6zcW0+rv5Om6bZQS3d1eOANwihjVmbAIy2McjJyRns vhT8XdJ+Ivhiy8RaDfXF9olzJJAwu7aW3mhljYpIjxyqrqysCCCMccZGDXxh8V9U8WwfGv4b /Ezw9ott4uufDFrd6ZcaHPepZySQzrtEsMsnyK65Oc4yMDuSPp74T/ELWPHHh+1n8R6HB4a1 28uHCaNb3q3jRpnCb5UAVmKjJ28Dpk4zX8cwq1cmoUcbl8pyxMuWzVRaS5mvZujbmcXFLX4f eS6WPoGlUk4yty+nlvf1PfmvrZPvXES/VxWVr3jfQvDGnyX2qapBZ2kf3pXOQPyzVOL4daQF HnNfXDgcs9/OM/grgfpUeofCjwjrEIh1LQLTVIh/yzv1Nwv5OSK/q/DS4gnVi8RCjGF9bSnK VvK8Yq55EPqykvaczXW1k/1OH1D9r34T6dnd4qSZh2itZj+pQD9a+Sv2xP2ntF+Kd94X0fw5 f3Evhy2ke5vht2ebNkKnGedq7sZ7v7V92af8IvAuk4+w+C/D1ljp9n0qCPH5JXk/7Wf7LcPx 18EWaaALTSvEmjs8liWURwzIwG+Fyo4ztUhsHBHoSa+8oVcPTqKSi/Vv/gH1WV4/KMHjKdWF KaafxSmml52UF+em5+R/hz9ojxFZX1tZ6l5TRX+uRRWM6wqN9qLnyp4TxgOF2kHr81fp1+w7 4zuLrWfEXh5ZGk0/7Ot9GhORG4YIcf7wYZ/3BXwzH+xH8U4dStNEk+GGpSm0vftcEnmDyUm3 Z3ifzNmMnP3sV96/sD+GdJ0Pwv4ima6ll8WtdC31G2uIvLe1RMhYwuSfvb9x45GCOBn06leU sHOE5c23nb+mfZ4/MZ1ckxVOvV9tdQtrfl95u78r6J9rRZ9WUUUV8yfiwUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQBFc20N5A8M8STQuMNHIoZWHuDwa4HVvizovgS7bRtVuZrvUIcbUg2yys hGQz4wE69CQe+Mcn0OvyX+M/xQ8Z+HfjrYWWnrCdXfXdVa/j1G4kht5gEOxJGRHIVSMr8vp2 r6HKMJSxbmq93FW0W920vu7nw/Fea47LMNFZco+1nzWc/hSjFyfbV2sru3c/Ufwp470jxkj/ ANnzt50Yy9vMuyRR647j3Ga6GvgP9h34l614w0n4b/2hNNda88MqX08pJklUNKWL59FA6+gr 78rnzPBRwVSHLtOKkk91cfCec4vN8LWWOilUo1JU24pqMuW3vJO7W+1/PrZFFFFeMfbhRRRQ AUUUUAFFFFABWRq1o1rcjVbZWM0aBZlVWkaWIZO1VBxuyeuM/wAq16p6xqSaNpN7qEql47SB 52VepCqWIH5VpTk4y06kyaim30HLqVt9mSd5khR+P3jBcH0Pv7VZBDAEHIPIIr8qPGn7VEHh rxvc3euo9/NqGjXesIkEU0qW8yS7Y7ZUjRhHGQSS5wB/Ecmvr79lz4sv4mtPCqrIfsviHSIN Q+x79wtJntxMyKfQfMvbOAcCvpcRkipU5unU5pQTclba2+v9XPynD8dTnmNLD18I4UK0lCFT mTbk72vG2zas7N8ul9z6aooor5Y/WAooooAKKKKACisDVvH3h3Q777FfaxawXYxuhL5Zc9Nw Gdv41ieKviLGnk6f4fZdS1K6wsb25EirkZ47Fsc88KPmbjAPZTwdeq0lBpPq9FbvfseHis7y /CKfPWi5R0cU05Xey5Vrd9F+h5b+1v4YXxza6Jb6ZeMNc0uSWQQxRNKSrx5CBUyxkYxrhQCc bmOAM1+cPw++H+p6X4o8O6R/aN/f2mgXk02k6DFp5E6TOHUB3BLPtEjgDap55ziv198GeBYv D7vqF2Fn1ebJdwSyw7sFlQnkkkAs55YgdAFUdUIkEhkCKJCMFsckfWvnq2BlUqSnzWb7a7af 0z9cwHE9HAYalh1RdSMFdczcbNtSaaW65ktG7XXzPn74Z/svaNP4PtLjxjaXba9cEzSxw38s QgU/djIRgpIHJPqSMnAruNE/Z48IeHL4XemjU7WcDG4alM3H0LGvTKK1ll+FqUvY1KalFqzu k7+vc+fxef5njJzlOvJKTfuqTUdeiV9jlz8PrLtf6kP+3jP8xXyr+034Mu73wv448AXF3LZ2 PiK1kSG8YbuGO5HPTdhgAwGM4PrX2lWP4n8I6P4ysPsWs2EV/ADlQ+QyH1Vhgqfoa+JzfgrA YmlGWW0oUK0JKSlGKV2ujstuvXVbHl08TOL993R8d/s3/Eb4lRQadonje28J2mg6RpIsluND uZ5bm8nj8tY5NjIqxx+WsmRydzLjABFfYPg/LaJHMQVEzFwrdQOnP5VwN98BtE8N2E974VtZ bfVo/nVJLh3WdB96IhjjkdD6gc4zVv4beLY08uzkci1n5hL9Y27oc9COn5e9ePw9wdHJM0/t TEtc8ouKjG/JG7u3rq2/kle3a3zmN4geHx0MvrU+WnL4Z33eittpZ6PfeL6np1FFFfrx64UU UUAFFFFABWB438RL4a8P3FzvCTMNkRPZiPvfgMn8K368R+JWvHxJ4lFhA3mW9qQu1QTuOecf Vhj3Cn1rmxFT2UG+p8txJmn9mYCUoP8AeT92Pq+vyX42MLwv8H9L+JElzLqcU1rGMSPNbkJK HPRMkEdAc8Hn61614F+FHh34ehn0u2ke6YbTdXUhklx6A9FH0AzWz4T0IeHtDgtSB5xG+Zh3 c9fy6fQCtivMw+UYKlUWJdGPtO9ldf8AB89ztyLC18Bl1OhXm3Ld3bdr9PRbW2uFFFFe4e4F FFFABXzp8YPCOo/Cn4m2HxV8LRZs7p1tfEVinCupwqzY98BWPZgjYPzGvouoL6xt9Tsrizu4 UuLW4jaKWKQZV0YYII9CDWkKk6d3DdpryPUy3HPAYhVGuaL0lH+aL3X+XZ2ZHpWqW+tadb31 o/mW86B0b+h9x0NW6+fNN8cx/s86/f8AhjXnubzRnX7TpckS+ZK0RJAU5I5GCpyRnAPGa7jw f+0D4W8X6hFYqbvS7mZtsK6hGqLIx6AMrMMn0OM151PFU37s3aXbzPSxWQ4umnXw1Nzo7qSW 8Xr9669nc9LooortPmQooooAKKKKACiiigAooooAKKKKACvGfjF+yj4I+M+qDVtRW90nWSAs l9pcio0wAwPMVlZWIHGcA4wM4Ar2aitqNapQlz0pWfkcuJwtDGU/ZYiClHsz5ntPhJb/ALK1 9YeIPD0l3quhOBbarLf7HuIdzfK+9VGIycAjHykKecnH0fp2oQ6rYwXdu26GZA6k9cH1p17Z QajZz2l1ClxbTo0UsMi7ldSMFSO4INeJaVrMnwC8VSaHrV1I3g+9zLpt/OS3kgdYWP8AeTgD uy4xkjFaY3MHWhB11eS0cvLpf/MnIuG1Sr4hYGdoySkqVvtLSTg79VZuNtbNrqe6UV5tpv7Q /gXVL4Wyas8IY7VnuLd44j/wIjj6tivR0dZEV0YMrDIYHII9a4YVYVNYSTPZxOCxWCaWJpSg 3tdNfmOooorQ4gooooAKKKKACo54I7qCSGZBJFIpR0YZDAjBBqSigNz84Pi/+wR4x0nxzf6l 4T04eJtKvLObTo2S9SGaK1lYFopFdlBIwBvXOcZ46V6F+zj4Zvv2efH2g6H4+05tGWTS1tdG fz1mhTaAhDSAkF1UBSM8B88g5H27XJfE74baV8VPCk+i6mGiORLa3kQHm2kw+7Kh9R3HcEg8 GvqKOeVJRdDEL3JK0mtJWfX9fM/PcXwlRjJYrASaq02pQUtYJrpa17PVXvdXujrOtLXgHwn+ Ll74J1W88AfEaeHT9U0uPda6lK22G6gA+Vgx7EA4PsVPIwfTtE+L/g3xFfrZWGv20ty7bUR9 0e8+ilgAx9hXyKr0ZNqMuttdH9x+sVcozChFSnSbXKpXjeUbNX0kla33bHY0UVn3Ou2ds5jD meYKr+VbqZH2ltu7A7A9fSuiMZSdoq55JoVheO9Zn8PeCte1O12m8tLCee3Vv4pVjYoPfLAD FTl9XvshI4tMjxw0v72TcH/ug42suec5BPSnJ4dtCzPch9QcqULXZ3grv3gbfu8HocZ461tG MISTm7+S1/4BnUTnBxi7Nrfsfj/8RP2i/Fvhm98Vf8I/cebJbeF4r6S5umiZ4L17mcPcAPGx lJUINjHHBPev0U/Zu1601zxHrrWluvkrbxssjDLRZY/KD2DdffYPSuQ8e/8ABPrw34l8TXGq aD4hn8N29y26awNmtzGATkqhLKVXk4U7sduOK94+Evwk0b4P+GzpWlPPdSysJLm+uiDLO4GA TgAADso4GT1JJP2GOzbC1sNUjGUpSnaye0feb/KyVu3bQ/GMp4LxGEzfD42VOMFRcnKSs3Uv BRXnvd623vvq+3ooor4c/awooooAKKKKACvJfiN4b/4R3VTrNsCmm3sg+1BRxbznhZcejHg+ 5/2uPWqgvrGDU7Ke0uolmtp0MckbdGUjBFZVaaqxcWeLm+WQzTCui9JLWL7P/J7PyfcwfBPi T+2rHyJyBeQDDjOdw9fftz7g966WvGdJSLwk+qXcuqxpY6S8sZ1CaQRosSckys2ANoyC3Tgn p0f8Nf2g/CXxPuL1PCHjXRPFkljg3Vtp97HNJEpOAxCnIUngN0zX5XU8QsBhsTUwnsatSNJ2 nUhG8I23bd7tLW7S6aXO7JsLj3gYfXkoz2s3q+1+l/8Ahz2OiobO6S9tkmj+64z9PapGdU+8 wH1NfqNLEUq9GOIpSThJJp9Gnqn80drTTs9x1Nd1iQs7BVHJJOAKia+tk+9cRL9XFcD4n8TJ feM7XSFuFFpHb+c208PIScfXAH6mvleIOJsJkmBniYSjUqLSMVJXbffslu32N6VGVSSWyOg8 TeJ0t9Guhp7me8K7UEYPy56tn2HP1xXA/Crw09/rEl/co4S2beVkHIkP3V/4COT74PevCfg7 +0H8TvjlqyeJ/C3gfw8fhSdbOlrPf6tJFq89skwimvY0EZi2qNzeWzBjsKgkkGvrPwvcCK7n tVOYmzID78D+WPyr89yjjHMa2aUMFncKSdSTilTleUJJXtOPNLfa91qrW3t4uZ8OUcdjKWOc 21S+y1o/NP1tfe6R01FFFfuh6QUUUUAFFFFABRRRQB+ZH7aXxW1jwhr3xJvoT5WtRXcFnaXc yeYtjb+ZGhmCHg4jJbB4y2SDzXO/B740XV/N4v8ABE+tr400fTfsV5pviCaOH7RA8iSedbtN CqrIMqjLxlckE9K+3P2if2UdP+Nd1/bGn6gui+IPLEUskkXmQXSAYAkAIIIHG4Z44IPGPOfg 9+wXL4Q1GB/Eus2EukwSeZ/ZWjW5jSc/7bkLgHHIC5PqK+clhq8ZTjGN+ZvXydt+uh+zYfPM qrUsPXrVnD2UYrkSd7wTXu68tpXV7rp0vp9MfDzxHPrHhvREv7e7j1GTTLe5kmlgbypd0aEs JBlQdzEbGIfgnbtwTvaTrVjrts09jcpcIjmOQLw0TgAlHU8o4yMqwBHcCraIsaKiKFVRgKow APSoNR0201ezktL+1gvbSTG+C4jEiNggjKkEHBAP4V9DFWikz8frTVSpKcVZNt27eRZorNm8 P2Uv9nhFntY7DAt4bO5kt4lAxhWSNlVlG0AKwIxkYwTVTWtF1a7t9XbTPEU+nXl1aSQ2ZmtY Z7eymKYSYJtV5NrDdtaTByRxxhvRGSV3Y3aK+W/B/iv4h+HvjZpfhTSPiUPjfZ+Vex+JlvLO xs4/D0sYQwB57KACKV38xfJlDuwGVVQjNXfftIfHC6+EHg7w9JZ25sta8Tanb6PZT3Vm98ll NIC7F7a3YyzusaSlI4s73VV3DcCRvSLXXb5uy8te+3nuCV212/RXf3dt+y2v7NRXyzqXxwu/ D/wX+Il54H8ceIviT480IQNNpXivQI7PVNLjdot839nRWdnMyLFKZl3Id+3AbGRVv4JfGhta +I97DpPxSHxX+GSeGP7Yv/FF/HYxf2LeCUbYHmtIYI13wmSRopE8yLycsQHAp6Xavsr/AIN/ ktHs3om2Tf3VLvp+KX69Nup9N0V8weAvjl448e/tNeG4TjR/hf4g8N6lf6Npc9qq3d4tvNaL HfzMy74xKJ3McQIxGFZxufan0/Ts+WMn1v66NrXtqtt11s9Av7zj2/VJ/r/loFFFFIYUUUUA FfH37anjyTRfGWk6fdxzXGlWOkyamlpE237ROWkUD64jAHpvPrX2DXzR+0Z4MtPjn4o0/wAO 6PBu1rT45V/tDeVjQnGVcgH5EIBJwTubaOScedmEXOg4x3bXz8j7PhKpChmarVV7sYyu/wCX Rrm/T5nwl8GPjxrtpqngCfxBqtl4p0vx1Fdx3OkJZRQnS2WJpI5LeRPnIRgFcSF8jPQ1+k37 L/iG4174byRzu0kdheyWkDscnywqOBn23kfTAr4/8C/8E/fG2ieLpbmPRPDWhtMxE2ro6MxU n5ioRdxz1wduT1NffPw78CWPw38JWOg2DNLHbgmSeQYaaQ8s5+p7dhgdq5sLCr7XmceWKT8r 3e3y7nuZ/i8CsudCNf2tWcovRuSioxs3d7OTu7La9ul30tFFFe0fmAUUUUAFFFFABRRRQAUU UUAfDH7c3xGh8PeMb67vbZp4fC+gy3lrbx/K08zIz4Zuu07UB642k4zXh/wZ+MN5LrkPhjxs mk6s2v8Ah7+3NN1DRoHt3sJldAYXDSOJIyr/ACycHK4wc8faX7VP7NNx8ZrWDV9Ckt49ftoD bSW11xFeQ5JCE4OGBLYzwQ2DjivlL4Ofsa6tHrbReGdK03SZB/o+r3szr/ohV2BiXDMzjKtg Jhcgcjg18zXp1IVp+5zOV7fd+Fj9wynF4PEZdh2sR7KFFLnV2rNSu3ZL3ufbXa/V6H3F8E9S uvHfw70y/wBSvLiVowbYgNt3mKQ7XJHJJXAPrgmvSbWygso/Lt4UhTJO1FwOTk/rWT4I8I2f gTwppuhWJZrezi2eY/3pGJJZz7sxJ/Gt2vo6TqKlGE3skfj+Y1aNfGVquHVoSk2vRvT/AIYK KKKs88KKKKACiiigAooooAKKKKACiiigD4B/a9svEfir4BfEL4f6QjNrNzLG6wb9jXAiuUkk i3f7aoceuQOhruf2ePip4W8YakkWifCXWPh29lpC20l/rGix6eqrvTFnEw+aVQQWyBt+Udyc e6/E74UaP8Q9WjUmSx1Mx7XvIPTHy716Nj8DjAzWD4Q/Zkg0K9WXUvEl3qlupz9lhi+zq3sx 3McfTH1r+Y6vCmY4z6xlmXO8KdSSVTmcUnOMeZNX96ysmrOzvvex7Pt4LlnPe2x22l+G7nW7 NblNZubO2cnZHboh46ZywPU57VcX4eQk/vNZ1Sb6vEv/AKDGK6mGGO3hSKJFjiRQqoowFA6A Cn1+uZbwPkmCwlKhWw8asoRScpK92lq7O9r9jgniakpNp2OYX4d6P1kN9MfV9QnH6K4H6V5n 8afh5daTBBrnh23mdIE23MUbtI6gHIk5JJHJB9OD617nRXfjuEsnxmEnhYYeFO/2oximn8l9 66omNepGXM3c/Mf4Z+DL74aeIVttA+I/iDQvBLauusSeFbaGBoml8wSNEs7IZEhcjDRqRlSw J5Jr9BPh5HeXkcuoXluLVWUJFGWDMQQCWOCcdhjr1rpP+Ef0sXv2z+zbP7XnP2jyE8zPruxm uZ1DXLL4eatBFfTfZtM1CQpbMVJVZOpj46dyo9Mgfdr4nD8F1cDnWEzOt/tDUmvdik02rKcr az13bu1o7uxWIx9Kjh5zqyUIpXbb0+97HbUUisHUMpDKRkEdDS1+znLuFFFFABRRRQAUUUUA FFFFABRRRQAVleKtQ1TSfDWqXuiaSNe1e3tpJbTSzcrbfa5VUlYvNYFULEY3NwM84HNatFJj WjPAho3i74q/HPwD4suvAepfDzTfCUN8bm71y8sJbzUhcw+WLSNLO4nHkhgsrmR1+aOParcl avxK8O/EP4o+GfC3io+CY9C8XeB/F51ay8PSatb3J1WyjEsDbJxtSKSaCZ2RXKhWCq7AZYfQ 9FPqmumvzTUk/wAPTe6vqLvfqrfKzVvx9dtbaHx38T/gL4++Nt/8Q/Ga+Hk8MXOp6fo2mWPh LXb2BpNVt7G9N3cR3j2zzQxifcYUAeQbcl9u4qNOD9mrT/jD8VNS1rXPhbJ8N/BN94ck0XW9 Fku7W3uvEEzTwyRPJ/Zs7qI4VhIEjSiQmQrtCjLfWNFEfdslsr/imn993ptfZIJXlq3rp+Fm vustd31bPmW4/ZabSf2nvh94r0S78XL4c0fR9QW7vLzxjfX3+ktNamG3ZLq5kcxOqSlkUbDs UtyEr6NiXUV1WYyPay6a6ZiCIyTRMMcMckSBsscgJtwBhs5F2ind8qj2v+Lb/W3pYVlzOXe3 4JL9PvMmLWbqPSprq90i6t5oH2yQQMk5cDG6SPacugySAVWQhThMkKZJvEel2v8AZ4ur6Cyk 1DAtYbx/IlmJx8qo+G3fMoK4yCQCM1pUUhhXDfEn4u6P8NjbW10r3uqXSl4bGAgMVBwXYn7q 54zzk5wDg46yLRNOg1WbU47C1j1KZPLlvEhUTSLx8rPjJHyrwT/CPSvzm/4KFWep+D9X8R2s WkyeILDWNFiis7bUp7i4Eyq43p5nmCTIYE4DggMOxr1ssw9PEVmqquoxbte3NZbX8z5niHGY nB4NPCSUZzlGPM1dQ5nZyaur289L2PrTVPjxf6xockFlpLabe3JEcdwLgOFB6kEqoDdACeBn PavR/h34Hj8F6OqSsJ9SmUG4nHIHUiNSedq5OPUknqa/MH4HeJtcsPiZ8SZtTnOqHXjpS2tx Ha+UsxjjmURRqCxO3zAvUk8Zya/VvQobi20TT4bs7rqO3jSY5zlwoDfrmvbznDwwmGhGlBQU pS0vd+7one70+dtd2fn3BOJzHHZniamZ1nXlTp01GaXLH31zyiopJXvZXaT92zSL1FFFfHH7 OFFFFABRRRQAUUUUAFFFFABRRRQAV5P8Bgjf8JPKsaqz37lmH8X7yU5P515X+2T+0Re/DBb7 TbCZ7eDTdHk1m+MMvlS3IG7bAj/w52HOOu4DoDnyb4C/tPaT4u8R6zB4S1FSdJs7O9mnjuPM huGmMm6FlA6ps5z/AHuMV9VhcphVw3vyXtKluRW1S169L2e2y33PyPiDjPE5XmEFh6c5YWi3 7dxaUXoklZvXlck0na70Wx+gtFZ/h/V4/EGhafqcS7Y7uBJgp/h3AHH4VoV8vKLhJxluj9Xp VYVqcatN3jJJp+T1QUUUVJqFFFFABRRRQAUUUUAFFFFABUF7eQ6fZzXVw4jghQyO7HAAAyTU 9eB/tV+Jr67sfD3w+0OUrrXie8RGKE5jgVhyccgFsHPoj1pTpSrS5IuzfXt5npZdgnmGKhhk 7J7vslq38kmz2Dw2Vv1l1IOsgnJCMpyNuecH68fhW5Wf4e0WDw3oWn6VbFmgs4EgRnOWYKAM k9yepPqa0K8PKctjlWEjhlLmerlJ7ylJtyk/Vv5LQ5K8ozqScNunp0CiiivYMAooooAKwvG/ hGy8c+Gb3R74mOOZcxzr9+CQcpIvupAPv0PBNbtZHjDT7rV/CWt2Ni/l3t1YzwwPnG2Ro2Cn P1IrWlJwqRlF2aa17eZz4inCrRnTqR5k0013VtvmeDeAf2hLzT9Jh0iXTo9amtHaD7bDdbYp VU4BT5TuU4yDxwRxXtngvx1Y+NbWVrdHtrqAgT2sv3kz0PuDg8+1fi/8WLfxfqHiTxRZ20sm ksvhI6DJatbRs1063FwzW7biCjfvPvdefav0c/Y81vUPFuqXOosrG0tNMjtZ5P4TMdhC57kB WJ9Mj1r7vMsuw86VavCnycut7/E22tFtra+lt+2r/B+Hs2zrCZhhMHKuq1Gp7vs+VXpxUbpu e7aemt7pO9novqiiiivz8/oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA K5nx/wDDbw18UNEOleJ9Jh1WzB3IJMq8Tf3kdSGU+4IrpqKqMpQkpRdmjOpThVi4VEmnunqj yj4dfsu/Dn4Xauuq6LoW7UkOYrm9nedofdAxwp/2gM+9er0UVdSrUrS5qkm35mdDDUcLHkoQ UV2SsFFFFZHQFFFFABRRRQAUUUUAFFFFABRRRQB8m/ty/suat8bdIbVPDqPc3zWLaffWMMix yzQ5Yh4i3y7xuYEHqMY5GD89fBb9mT4k3vj+4u28OaroZu7W2sLu+1hRFBDDDu2sqkBnb5zw uQeOgya/TeivoMJneJwlNQgk2tm91v8Ald29T4vMeFMDmdWVSs2oy+KK2e1/NX5Vf0KOh6RD 4f0Ww0y23G3s4Et4y3UqqgAn34q9RRXgyk5Nyluz7GEI04qEFZLRegUUUVJYUUUUAFFFFABR Xy78Vf2yLbwtfTjTbnR9L0SCc2p1rXJxHFNIDghCWVQODgknI5wK9B+G/wAdJNbubKx8TRWd i9/bi6sNStZR9kuoyu4EMSQMjkEMQfbjPFHGUpy5U/K/T7z6etw5mFCiq04q9ubluudLvy7+ vVdUew0hIUEkgAckmuI8S/G3wN4RtJLnU/EllFCg+Z4WMwH/AHwDXzz8bP21vCHiT4c+K9B8 G/2rqerXenzQJdWtt+7hVlIZyQdy4Xcc4GOtelRpSr/Bqu5x4fJsfiGmqUlF9WrL73ZHQeNf 24dGsNbuNO8KaSNfht3Mb6lLP5UDsOvlgKS6/wC1kA9sjBrC+CXxAsPiX+0Tc+J/EciWWpyW QtdGsTlo4zjDBXP8W3eRnqZG9q/Lnxj4n1tfGPh2TQrmSG70qxmvPsqSMEvSki5jcZwdyFgO OuK9g/Zs+Ieo6r4K8PXkMs0uqrqEz2ruxaXcbt2jBPUkEgD2AHavqKFDDSjOhFNSs/ev6fn+ S8z9hwOWZRJVsrw9NwquEl7VyvdWi3ptq2lJJLTrqftDRRRXyR+BhRRRQAUUUUAFFFFAHnvj f9n74efEfVRqfiHwrZX+ocbrkF4ZHx03mNl3/wDAs113hvwxpPg/SINK0TTrbStOh/1dtaxh EHqcDqT3J5NalFayq1JRUJSbS6X0OeGHo05urCCUnu0ld+rCiiisjoCiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACobuE3NrNErmNpEZQ46qSMZqaigadndH43fF3SfEnhf4i+Hlu9 Jg1SfwrLe202i30oiUtMqL5qFlK5wg5I5VhivV/2RvAWteJNO8K+B7y5Z7e1upr26h0+d1hs rR7hpngRxgquHKDGPmbivvPxp8G/Bvxa1uW58TaBb6mLWMW8c+/YznOT88bBxtORtY454FdH 4I+HHhn4bac9j4Z0W10e3chpBAvzyEdC7nLNj3Jrxf7NqQapuS5Fb1tvZ9Op+pVeL8NOE8TC i1iJJq91yxk0ouS+1e0VZbfe28DS/wBnz4c6SQU8IabduOQ+oxm8bPrmYuc+9dvBo2n2unNY QWNtDYspRraOJVjKkYI2gYwR2q5RXsRjGCtFWR+a1sTXxEuatNyfm2/zPz9+MH/BM3U7rxNP qfw417TrawmcuunayZEa1BOSiSoj719AwBA6lutVvh3+z7J+yx8QvBGpfEODTtWsZblmjv8A TXkNva3HO3cGVcleJBwM4YgZTn9C65r4jeAtO+JfhC/0DU1xDcKDHMBloJRyki+4OOO4yDwT Xp0sZOL5amsdn3sfUYDiLEQmqWLfNSknGX81mrNp91uu9tTo0dZEV0YMrDIZTkEetOryL4G+ JLrS45/AmuMV1bR8xQFznzIhj5Qe+AQR6oVOODXrteXCcai5o7Hz2PwcsDiJUW7rdPpJPZr1 QUUUVZ54UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFQX t0LO0lm27yikhNwXceygnjJPFT1laon2+9tbIoHhz50weNZEIXopB6HOCD/s/lpBKUtdhMn0 azNlYRq/Mz/vJXKKrM55Jbbxn39qvUUVMpOTcmMKKKKkAooooA+Tv2o/iDa6F4/EmnXQ0a90 HTjeaprHm+X5UeC6gnttQsc9xJjmvPfgr+0fdeMv7Ru9B8a6jPq2n26ajJoniGK5ie7s2YKL iJJ1HmRFiBuQ5GR0yM5n/BQ74T6pd6hrmoDzo9C8T2SW0l7GpZbedEVAr/UIjDOM/MO1fNPw xtfEmp/EXSta1i4sZNRs9L/sSxsNGEsjXBd1LO25QcnaAEAPXrXytWfJWqSndO7tbTpo/O5+ 95fh/rWX4SjhlGVNxjzOSUuv7xNt+6o62+5H6/8AgjxXB438K6drdsvlx3ce4xk52OCVdc98 MCM+1btcV8GvCV14J+G2i6Vf8X6RtLcLnOx3YuV/4Dux+FdrX0tJydOLnvZXPxLHwo08XVhh 3empSUX5XdvwCiiitThCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKAEJwMmszR0+0TXN+6YeZtiF4QjiNegJydwzkg+hH1qTXJHWxMUW7zZ2EKsITKFz3ZR/D6n tmrdtbpa28cMahI0UKqjoBWy92Hr+QupLRRRWIwooooAKKKKAILyyt9RtZba7giuraVdskMy B0cehB4IrD8P/Dbwl4TvHu9E8MaPpF2+Q09jYRQuQeo3KoOPaujopOKbu0aRqTjFwjJpPdXC iiimZhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXKeOPiZoPw /WBdUuGN1cAmGzgXfLIB1OMgAe5IFdXX5tf8FDdb8S6F4616GzWdZ79NJOm3EaO5ihS5haQq FIJXcs24Aj5S2SM5r18swtPFVnGreyTdlu7dEfL8RZhicvwalg7c85RinJXUb/aaVr2/M+5v C/xG0bxv4gt/LMlrNHE32e3u02SM5+8VKsVI29jzxmu/r8zf2WfHHifUPEus6dqdw19q914v 86xuI4JIY2iMFqAYUd3KxAo5wGIB3n1r9Mq6s4wMcG6bjopJ6N3as/1/zPD4OzrH5tSr08wS cqTiueKcVLmipbO+sdnt00QUUUV88fogUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRXN+JviL4d8IXMdtqmpxwXUg3LborSSY9SqgkD3OOla0 6VStLkpRcn2SucuKxeHwVJ18VUUILdyaSXzeh0lFZeg+JdM8T2puNMvEuo1OG25DKfdTgj8R WpUzhKnJxmrNdy6GIo4qlGtQmpwlqmmmn6NaMK8M/aC8KaT8VfE/hXwXe2MV2zym4mmIxJBF /FtccrlVYkZ5+X2r3GSRIY2kdgiKCzMxwAB1JryL4PPL4z8YeKPGU65t2mNjYblwQgwW/QIM /wC9W+ExkMHiFNq8rO3rbRvyRlmOS1s5y6rGE1CEXDm3u1zK8Y26tJ69Fc1fhf8As7+BPhBd SXnhzR/K1B1KG9upnnlVT1CliQoPfaBnvmvSqKKyqVZ1pc1STb8zWjQpYaHs6MFGPZKwUUUV kbhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfl b8cfif4s0r472sFqp/tX/hJdQ+3C8lkW3eBYJVijkKg4RcIV46ha/VKvHfi/+yr4H+M+qDVd UjvdM1fAWS+0qZYpJgBgbwysrYHGcZxgZwBXuZVjaeDnL2t7Stqt9Gn9ztqfGcT5JPOsPGnC z5eb3ZbPmi439Ve6/NHzD+yN8cdU0/wj4LudSs9W1nXJNGzqcENtNPcS7cBpJAqsRhmTkjv2 zX0Zrf7XHhTS9JvnW2vY9UihLQWd1GIvMkx8qkEhwM4ydvHNdn8JPgZ4V+C9hNBoFvPJc3AA n1C+l824lA6KWwAB7KAO+M1p/FjwIvxJ+HuteHi6QzXkGIJnGRHKpDIT7blGcds1z51i4Yy3 1SNnFJXe7suq236np8C5LSyGo4ZnJypVJuThFpRg5Su3F8t2ktOXRbtWZ+f3jr9qU23jG7g8 T6l4i1RLO3ivNYudEh8210eCXJUyIHXA2ZYqis20Emvrf9nvxnDaanceC4r611XTUgN9pmoW uNssbEMRkfezvDAnnr7V+bvxP+CXijw94x8QWGqya14bfXYUsdWtorVZFvY0UopidlOMoSu5 SQR2zX3N+yF8PtQ8L3Fvrmt6fc6HYfY10/SYL1SjydOdp+YYVMDI5BJ9M/DYTnlVioRbl132 trf57H9JZ5To/wBn15V5wVK37uMeXWXM+Rxtr8PxX7u2mp9aUUUV9KfhoUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUVzb R3lvJBMu+KRSjLnGQevSpaKadtUBhiebw6224YzaaT8s5OWh6nDcYCAAAHOf0ztI4dQykMpG QR3FKyhlIYAg8EHvWI9vP4fdpbZJLmwJJkt13PIhOeU6lsk8gngDjsBvpV8pfn/wRbG5RUVr dQ3sCTwSLLC/KuhyDUtYNNOzGFFFFIAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAON8fa3bfDbw/qXikuEtbZA9xbFjiYkq iBeytkgADAJbmvCNK/bBm1XW/s/m6XbSnVP7HXTmjkL/AGowmfyC/GX8oFs4A/lXtXx48A3f xM+FOu6Bp7rHqM8aS229tqtJG6uqk9t23bntnNflLYfCLxdofxUinvdI1pfE0Xib+2PIFpcM 7r9mMHlBAdpOTuEgB44zivscopUMRRftIxlO9nzdI6a/n9x+T8ZVczjWhHC1p0oKN04JXlO7 91tp6Wt9+z6fr/4Q8UW3jDQoNStlMYfKSRMcmNxwV/z2Irarxv8AZX1O2v8A4aDF0JNSa6kl vLZuHt3JChGB5zhBn3yO1eyV87j6EcNiqlGGybt6H3HD+MrZhlWHxOJadSUVzW25tnts77ro 9AooorgPoBGYIpZiFUdSTgUAhgCCCD0Ir8/v2qf2kNR0P4sw6TLBcahYxa7HpEekwD5vK+zS yPMoLKpcvGuN38Le3Po37HHxwuPHumeGJmX7ONciuPP09SSlvJG8uCoJOCVjGf8AePoK+leS yWH9qp3ko8zVtEkk9+9munkfmU+NXTzSGDnhn7GU1TVTmV+duUV7lr8t4vW+2tj68ooor5o/ TQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gDxz4mfDvVfDeuS+PvAce3WlG7U9KQfJqMYxllX/AJ6gD/gQ/wBoDOdF+1p4durWya00rUL2 eaNXlWLaFjY9VBJyT+Ar1L4ipfyfD/xMul7zqZ0y5Ft5f3vN8ptuPfOMV+OHxL8Z3beKit1J 4jtbG00LboX/AAjb3MZGqeY53uYDkyf6vb5nyYB968zHYqv7WC53tbXXRdEfecNZRl1fAVql ShG8Z30fJdyWrm100Vna99Ln63WXx48IXWgy6nJfNaNEdsllOmLhT/ugnj/azt9SKw08VeM/ inEB4ds28NaLISDqd8CHkT1jAwzf8BwP9s14X+yrqmp/FLxfod94ssLW91e30WKfVS0KmMXY jRWk2gbQxkJPHuRX2nXTgMVV5/bJppNpaXT89fwPH4pyfA4ek8vcJxlUjGT99pwT15Fy2etr Nt7P5nyt8X/2F7P4gS6fqum+JJIPENqd73OpW6zJK/IDrtwY2AZhkbuDj1J7H9nD9lq0+BaG 6utSi1XUxG0UCWtsLe2tVY5bYgJyxOctx1PHJJ94or6Cea4ypTdKVTR77fd6f8Mfm9DhzKsN VhWpUEnDbeya2aV7X8++u4UUUV5J9IFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFeCfEH9i34f+PvEEusBtT0C7ncyTppMyJFIxOS210Y KT/s4HtXvdFZVKUKqtNXO7CY7E4Gbnhqjg32OO+GXwn8OfCTRG03w/avGshDT3NxIZJ52HQu x9OwGAMnA5NdjRRVxjGC5YqyOetWq4io6taTlJ7t6thRRRVGIUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ B//Z ------------0540DB1DA0EFE4D0F-- From owner-chemistry@ccl.net Mon Apr 29 13:11:00 2024 From: "Michel Petitjean petitjean.chiral]=[gmail.com" To: CCL Subject: CCL: chemical literature searching for poor, unaffiliated Message-Id: <-55141-240429102228-22815-n8wt7cEuNgfDMtna2vqdeQ{:}server.ccl.net> X-Original-From: Michel Petitjean Content-Transfer-Encoding: 8bit Content-Type: text/plain; charset="UTF-8" Date: Mon, 29 Apr 2024 16:22:04 +0200 MIME-Version: 1.0 Sent to CCL by: Michel Petitjean [petitjean.chiral-x-gmail.com] Dear David, You may have a look at semantic scholar https://www.semanticscholar.org But please do not ignore just Google (not the scholar one), which points to a number of free resources ignored by Scholar Google, which in turn may point to pertinent resouces. Best regards, Michel. Michel Petitjean, retired scsientist http://petitjeanmichel.free.fr/itoweb.petitjean.html Le lun. 29 avr. 2024 à 04:14, David Shobe shobedavid]=[gmail.com a écrit : > > Apparently I was wrong: Google Scholar does have Boolean searching, but Google doesn't want you to know about it! In fact, searching with Chrome led to a crash. (Coincidence? Who knows?) But I found information on a different browser. > > For the record, use & for and and | for or. > > I'd still like to know about alternatives though. > > --David Shobe > > On Sun, Apr 28, 2024, 5:05 PM David Shobe shobedavid/agmail.com wrote: >> >> As I mentioned on a different thread, I have, as far as I know, only Google Scholar for literature searching. I do not have an affiliation with an organization (such as a university) that would have an institutional subscription to something like STN or Reaxys. I don't have a lot of money either. Do I have any other options besides Google Scholar? >> >> --David Shobe >> From owner-chemistry@ccl.net Mon Apr 29 15:14:01 2024 From: "Matt Sinclair mts7!=!illinois.edu" To: CCL Subject: CCL: 61st Hands-on Workshop on Computational Biophysics Message-Id: <-55142-240429133618-21830-4rkKI5pn14HnvsfsJAdWdg..server.ccl.net> X-Original-From: "Matt Sinclair" Date: Mon, 29 Apr 2024 13:36:17 -0400 Sent to CCL by: "Matt Sinclair" [mts7 . illinois.edu] Dear Colleagues, The Theoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization (www.ks.uiuc.edu ), is pleased to announce the following training opportunity: "Hands-on" Workshop on Computational Biophysics in Auburn, AL https://www.ks.uiuc.edu/Training/Workshop/Auburn2024 to be held June 24-28, 2024 at Auburn University in Auburn, Alabama. This workshop will be presented by members of the NIH Resource for Macromolecular Modeling and Visualization at Urbana-Champaign. Topics will cover instruction in state-of-the-art molecular dynamics simulation and free energy techniques using NAMD , nanotechology simulation with ARBD and biomolecular visualization and analysis with VMD . Morning lecture presentations will introduce fundamental theory and concepts, while afternoon hands-on computer laboratory sessions will allow participants to apply NAMD, ARBD and VMD directly in a series of guided tutorials. The workshop is designed for all students and researchers in computational and/or biophysical fields who seek to extend their expertise to include biomolecular simulations. Experimentalists and non-specialists are encouraged to attend and will benefit particularly from instruction in the use of QwikMD , a teaching software incorporating NAMD and VMD that significantly lowers the learning curve for novice users. Applications to the workshop are due by May 15, 2024, for full consideration. Selection and notification of participants from the applicant pool is ongoing through May 17, 2024. Those selected to attend must confirm participation. There is no registration fee, but participants are responsible for the cost of housing, and the workshop can neither fund nor arrange participant travel. All participants are required to bring their own laptop for use in workshop tutorial sessions. Due to space and equipment constraints, enrollment is limited to 35 participants. For further information, and online application, go to https://www.ks.uiuc.edu/Training/Workshop/Auburn2024 We look forward to receiving your application! TCBG Workshop Organizers E-mail: workshop+auburn2024_at_ks.uiuc.edu