Demand Forecasting Using Bayesian Experiment with Non-homogenous Poisson Process Model
|
|
|
- Abigayle Lang
- 10 years ago
- Views:
Transcription
1 Iraioal Joural of Opraios Rsarh Iraioal Joural of Opraios Rsarh Vol., No., 9 (5) Dad Forasig Usig Baysia Expri wih No-hoogous Poisso Pross Modl Hug-Ju Wag,, Ch-Fu Chi,*, ad Chig-Fag Liu Dpar of Idusrial Egirig ad Egirig Maag, Naioal Tsig Hua Uivrsiy, Hsihu 3, Taiwa. R.O.C. Miisry of Eoois, Taipi, Taiwa. R.O.C Absra This sudy prss a ovl ahaial odl usig Baysia odl for dad forasig wih o-hoogous Poisso pross odl. This sudy ais o osru a frawork o iiiz h ovrproduio ad udrproduio oss by usig h i-dpd uraiy of auulaiv dad urv. Spifi odls wr drivd as h fudaals of his approah. Furhror, his sudy also proposd a hod o valua dad forasig usig Baysia xpri wih o-hoogous Poisso pross odl. Kywords Dad forasig, Poisso pross, Baysia, Mahaial odl, Disio aalysis. INTRODUCTION Sussful prfora of rvu aag syss havily rlis o forasig ad opiizaio (Rajopadhy al., 999). Basd o h hisorial dad daa, rsarhrs hav applid i sris or ohr saisial aalysis hods for dad foras. For xapl, Hol-Wirs xpoial soohig odl for opial forasig is applid for shor-r forass for sris of sals daa or lvls of dad for goods (Sgura ad Vrhr, ). Rahr ha usig sigl forasig hod, Wi ad Wi (995) proposd auo-rgrssio, xpoial soohig, ad ooris for forasig ouris dad. Wih aggrga slak orol or ulisag produio orol, h asr plaig produr wih h varia of produio ad ivory lvls a avoid h ursraid growh of ivory ad h uorollabl osupio of apaiy (Barzzaghi ad Vrgai, 995, Hirakawa, 996). Alraivly, his sudy ais o osru a frawork o iiiz h ovrproduio ad udrproduio oss by usig h i-dpd uraiy of auulaiv dad urv i whih so propris of h Poisso pross ar irodud ad h rlaio bw h Poisso pross ad Bays hory is idifid. Du o h sohasi hararisis of h fuur apaiy ds by ipu ad oupu pross (Laior ad Bakr, 997), h Poisso pross ad Bays hory (Cilar, 975) ar adopd hri. This approah is diffr fro h Baysia aalysis of h Muh odl ad ixd Markov wih la lass odl.g., Urba al. (996), Goulias (999). Baus his ahaial odl is drivd fro h pas sals xpri, h hology of pr-ark forasig of w produ ay o b suiabl by lakig of h hisorial daa (Gaviri al., 998). Furhror, his sudy also proposd a hod o valua dad forasig usig Baysia xpri wih o-hoogous Poisso pross odl. Ths idis of valuaio ar ssial i rvu aag. Th rs of his papr is orgaizd as follows. Sio sablishs h horial foudaio ad dsribs h proposd ahaial odls. Sio 3 irodus h valuaio pross ha osidrs ovrproduio ad udrproduio oss o assss h auulaiv dad urv ad is uraiy. Coludig rarks ar fially ad i sio 4, iludig h ris ad liiaios of h proposd produr.. MATHEMATICAL MODEL Th followig riology ad oaios ar grally usd i his sudy. (, ): h v ra for ah v ours i priod. : h avrag of rado v ra. h (): h auulaiv dad fuio of i. ( ) : osa of v ra. k( ): volu of produ k a i. : i priod or i irval. P (, (, )): a Poisso pross prss h probabiliy of produ s dad a giv i ad v ra (, ). f(, (,)) : a odiioal probabiliy fuio of gaa disribuio for i irval giv ad (, ). Hri, h Poisso pross is adopd for dad forasig. Espially, h likag bw dad forasig ad ivory aag a b applid i a odl wih odsd ad opoudd Poisso ixd ovr i (Boyla ad Johso, 996). I h gral odl of Poisso pross, rsarhrs usually us h * Corrspodig auhor s ail: [email protected] 83-73X Copyrigh 5 ORSTW
2 Wag, Chi, ad Liu: Dad Forasig Usig Baysia Expri wih No-hoogous Poisso Pross Modl IJOR Vol., No., 9 (5) osa v ra as h parar of (Hogg ad Tais, 983). So gaps xis bw h Poisso pross ad Bays hory i rs of h v ra updad. Tha as h ould b variabl isad of osa parar. Thor : If a spifi v i a sys ours as a Poisso xpri, h drivd Baysia odl o h v ra will hav h liklihood fuio as a Poisso disribuio. Tha is, (, ) d (, ) d P (, (, ))!! Proof: s Wag ad Chi (). ( ) h( τ ) I Thor, h Poisso pross ad Bays hory ar rlad i rs of h radoss of v ra. Th iuiio of his ovrsio os fro h odl i Drikig War Copay of Liburg (WML) who hags h osa produio flow io opiizaio of h quaiaiv orol Bakr al. (998). Fro h abov hor, w a ifr ha h posrior disribuio of v ra is gaa disribuio. For h radoss of v ra, w assu hr is a dad urv wih i dpd fuio h () ha affs h a of ha v ra. Th o-hoogous Poisso pross odl a b applid o dal wih h radoss of a v ra a obi h Poisso pross ad Bays hory o solv h probl of i dpdy o h v ra. Thor : If a spifi v i a sys ours as a Poisso xpri wih h i dpd v ra, h drivd Baysia odl will hav h liklihood fuio as a Poisso disribuio. Tha is, h d! ( τ) τ f(,, (, )) Proof: s appdix. H, Thor liks h o-hoogous Poisso pross ad h Bays hory. Th, h dad urv h () is h auulaiv ad h v ra (, ) is qual o wih h radoss of o whih i quaio (9). Fro h propris of Poisso pross, w kow ha h probabiliy disribuio of h rado variabl i, () (9) rprsig h ubr of produs dad i a giv i irval dod by. Thor 3: If ourr spifi vs (), (),, () i a sys or opo our as is Poisso xpri rspivly o h sa v ra, h drivd Baysia odl wih axiu liklihood siaor (Cilar, 975) will saisfy E [ ] k k h ( τ ) d τ Proof: s appdix. () Thor 3 spifis h rlaioship bw h radoss v ra ad h auulaiv dad h () is of rlva or. Thor 4: If ourr spifi vs (), (),, () i a sys our as is Poisso xpri rspivly o h sa v ra (, ), h drivd Baysia odl wih axiu liklihood siaor will saisfy + E[, ] (3) h( τ ) + ad Var(, ) (5) h( τ ) Proof: s appdix 3. Baus h auulaiv dad urv is a o-drasig fuio as i gos by, h largr dad rquir iplis a sallr uraiy of v ra. Thor 5: Miod abou avrag i as of hr is a saddl poi i warig produiviy sragy wih irasig h () If f ''(), h() h () Proof: s appdix EVALUATION PROCESS (7) Th abov hors ad propris i h o-hoogous Poisso pross a b applid o dvlop a valuaio pross of dad forasig as
3 Wag, Chi, ad Liu: Dad Forasig Usig Baysia Expri wih No-hoogous Poisso Pross Modl IJOR Vol., No., 9 (5) 3 show i Figur. I his valuaio pross, o oll dad urv basd o hisorial daa of siilar produs is h ai prossig of daa aalysis i h proposd frawork (Lrpalagsui ad Cha, 998). As illusrad i Figur, h valuaio pross osiss of six sps. Firsly, h hisorial dad daa of a spifi produ or siilar produs wih (), (),, () a so rai i ar olld. Followig h aur of a lupy dad (Barzzaghi al., 999), if his produ is a w o, so siilar produs a b usd o rpla h avrag dad daa (), (),, (). If h dad daa a o b olld, h auulaiv sals daa a also b usd o subsiu h avrag dad daa, hough h udrproduio os (Fishr ad Raa, 996) ay b udrsiad. Sodly, h ubiasd iiu varia siaor is usd as h avrag dad daa. Thr ar so spifi i pois for ollig h avrag dad daa. Th ubiasd iiu varia siaor uss h a valu of hs daa as h prdiio of h auulaiv urv a h sld i pois. Tha is, h d () k k is a poi siaor of h avrag dad a i. Thirdly, h ubi spli hiqu i urial aalysis (Burd al., 985) is ployd o sooh h avrag dad urv. Si oly h daa a so spifi i pois ar drivd i h sod sp, h oiuous dad ra h () a b drivd, whih is asir o driv h avrag dad urv by igraio. Fourhly, Thor 4 is ployd o obai h a ad varia valu of v ra for asurig h dad uraiy. I pariular, + Var(, ) h( τ ) iplis a larg uraiy a h bgiig of sals, if hr is a idl i passd. Th produ sragy sigifialy affs h profi ouo a h bgiig of sals ad hus aks h produ sals or urai of h bgiig ha so sals priods lar afr so sals priod. Fifhly, Thor is ployd o valua h radoss of v ra afr so sals priods. Aordig o quaio (7), h varia of h radoss i v ra is ovrg wih a iras of h auulaiv dad. Fially, rdud os of dad uraiy hology (Fishr ad Raa, 996) a b applid o iiiz h ovrproduio ad udrproduio oss. Wh sals ra of h () is irasig ad bfor rahig h saddl poi of h d, w a xpad our produiviy by h avrag dad urv ad is radoss o driv h probabiliis of ovrproduio ad udrproduio. Th xpd valu of ovrproduio os a b drivd fro h produ of disou os i ah produ ad is ovrprodud probabiliy. Siilariy, h xpd valu of udrproduio os a b drivd fro h produ of shorag os i ah produ ad is udrproduiv probabiliy. Morovr, h produio pla ad shdul a b valuad by h oal ovrproduio os ad udrproduio os durig h sals priods. Thr is a sigifia diffr bw his odl ad origi-dsiaio (OD) dad prdiio (Caus al., 997). Rahr ha usig h i sli i OD dad aris, his odl provids a igral siaio a ay i. Idd, h produ of h () is siilar o h siplifid forula i xpoial soohig odls (Wir, 96, Sal ad Jaqus, 999). Th fdbak loop of valuaio pross is h rsuls rfi ad validaio i ha frawork. k k C h d ( τ ) τ h( τ ) Figur. Evaluaio pross of dad forasig. 4. CONCLUDING REMARKS This sudy drivs ahaial odls i dad forasig ad proposs a orrspodig pross for valuaig dad foras. Th proposd odl a provid usful iforaio suh as variaio of produ dad a diffr is. Wih h uraiy of auulaiv dad urv big siad, his hod a b usd o iiiz h ovrproduio ad
4 Wag, Chi, ad Liu: Dad Forasig Usig Baysia Expri wih No-hoogous Poisso Pross Modl IJOR Vol., No., 9 (5) 4 udrproduio oss. Thrfor, h proposd odl a b usd o valua h produio pla ad shdul basd o h oal produio oss iludig ovrproduio ad udrproduio oss. Th rsuls for dad forasig drivd i his approah a b igrad wih rvu aag o axiiz h rvu i ligh of h fixd disou os, shorag os, ad sohasi avrag dad urv durig a sals priod. Furhr sudy is dd o us pirial daa for validaig h praial viabiliy of h proposd odl. ACKNOWLEDGEMENTS This rsarh is sposord by Naioal Si Couil, Taiwa, R.O.C. (NSC 93-3-E-7-8). APPENDIX Cosidr ha hr is a fuioal rado variabl (, ) i a Poisso pross suh ha (, ) wih () is a rado v ra ad h() is a fuio of i. Hr, w us h avrag of rado v ra o rprs h parar (). Th, for ah v ours i priod, w a g (, τ ) d τ ( ) (, τ) h( τ ) P (, (, ))!! () I addiio, l f(, (,)) b a odiioal probabiliy fuio of gaa disribuio for i irval giv ad (, ), whr i is a oiuous variabl Thus, (, τ ) d τ ( ) (, τ ) h( τ ) f(, (,))!! () O o had, P (, (, )) k f(,, (, )) f( k,, (, )) f(,, (, )) P(, (, )) f( k,, (, )) f(,, (, ))! ( ) f k k k (,, (, )) (3) O h ohr had, f(, (,)) f(,, (, )) f(,, (, )) d f(,, (, )) f(, (, )) f(,, (, )) d by (), ( ) (,, (, )) (,, ) f f d! (4)
5 Wag, Chi, ad Liu: Dad Forasig Usig Baysia Expri wih No-hoogous Poisso Pross Modl IJOR Vol., No., 9 (5) 5 Fro quaio (4), w driv: f(,, (, ))! + ( ) h( τ ) f(,, (, )) d h +! ( ) f(,, (, )) d + h! ( ) h( τ ) f(,, (, )) d + + ( ) f(,, (, )) d! h() h () + f(,, (,)) h( τ ) (5) Th, l h( τ ) l [ f(,, (, )) ] [ l ] (6) Thus, f(,, (, )) ( ) h( τ ) (7) Copar h rsul of quaio (7) ad quaio (3), w obai: ( ) (,, (, )) (,, (, )) f f k k! k! ( ) h( τ ) f( k,, ( k, )) (8) k So, For h as,, h osrai of quaio (8) is saisfid.
6 Wag, Chi, ad Liu: Dad Forasig Usig Baysia Expri wih No-hoogous Poisso Pross Modl IJOR Vol., No., 9 (5) h d ( τ) τ f(,, (, ))! 6 (9) H, f( (, ), ) ( ) ( ) h( τ ) h( τ ) ( τ ) τ h d d () APPENDIX Suppos hr ar ourr Poisso pross, h vs our a (), (),, () durig i priod wih h sa v ra (, ). Fro Thor, w a g f( (, ), ) ( ) ( ) h( τ ) h( τ ) d k k h τ h τ k h τ h τ f( ( k, ) k, ) k k k k h( τ ) f( (, ), ) k k k d d k k ( ) ( ) k k ( ) k d ' k k k ( ) ' k h( τ ) k ( τ) τ ( τ) τ k k k h( τ ) d h( τ ) h d h d k ( ) d k For h purpos of opial,
7 Wag, Chi, ad Liu: Dad Forasig Usig Baysia Expri wih No-hoogous Poisso Pross Modl IJOR Vol., No., 9 (5) 7 k f( ( k, ) k, ) ' ' Th, k () k H, C k k h ( τ ) d τ () Thrfor, E [ ] E [ ] E [ ] k ( ) () k APPENDIX 3 Suppos hr ar ourr Poisso prosss, (), (),, () ar vs ourr durig i priod wih (). Fro quaio (9), h sa v ra f( (, ), ) ( ) ( ) ( ) h( τ ) h( τ ) h( τ ) d + ( ) h( τ ) ( ) ( ) d E[ (, ), ] f( (, ) d, ) d ( + )! +! (3) + ( ) ( ) h( τ ) d E[ (, ), ] f( (, ) d, ) d ( ) ( + )! ( + )( + )! h( τ ) (4)
8 Wag, Chi, ad Liu: Dad Forasig Usig Baysia Expri wih No-hoogous Poisso Pross Modl IJOR Vol., No., 9 (5) 8 Thus, Var( (, ), ) E[ (, ), ] E[ (, ), ] ( + )( + ) + + h τ (5) APPENDIX 4 L f() h ( τ ) d τ ' h ( τ) d τ h () f () + h d h h f " + ( τ) τ () ' () 3 If If f ', h (6) ' f ''(), h() h () (7) Tha as if w build a oior wih h d h warig of saddl poi o war h drasig ra of h() (i.. h'( ) < ) i h ar fuur. ( τ) τ ad h irasig ra of h() (i.. h'( ) > ), hr is a REFERENCES. Babok, M.W., Lu, X., ad Noro, J. (999). Ti sris forasig of quarrly railroad grai arloadig. Trasporaio Rsarh, Par E: Bakr, M., Vrb, A.J.P., ad va Shag, K.M. (998). Th bfis of dad forasig ad odlig. War Qualiy Iraioal, 5-6: Barzzaghi, E., ad Vrgai, R. (995). Maagig dad uraiy hrough ordr ovrplaig. Iraioal Joural of Produio Eoois, 4: Barzzaghi, E., Vrgai, R., ad Zori, G. (999). A siulaio frawork for forasig urai lupy dad. Iraioal Joural of Produio Eoois, 59: Boyla, J.E., ad Johso, F.R. (996). Varia laws for ivory aag. Iraioal Joural of Produio Eoois, 45: Burd, R.L., ad Fairs, J.D. (985). Nurial Aalysis. PWS Publishrs, 3 rd Ediio. 7. Caus, R., Caarlla, G.E., ad Iaudi, D. (997). Ral-i siaio ad prdiio of origi-dsiaio aris pr i sli. Iraioal Joural of Forasig, 3: Cha, C.K., Kigsa, B.G., ad Wog, H. (999). Th valu of obiig forass i ivory aag-a as sudy i bakig. Europa Joural of Opraioal Rsarh, 7: Chi, C.F., Ch, S. ad Li, Y. (). Usig baysia work for faul loaio o disribuio fdr of lrial powr dlivry syss. IEEE Trasaios o Powr Dlivry, 7(3): Cilar E. (975). Iroduio o Sohasi Prosss. Pri-Hall I.. Daio, S.J., ad Lair, J.A. (997). Modlig hologial hag i rgy dad forasig: A gral approah. Thologial Forasig ad Soial Chag, 55: Faulkr, B., ad Valrio, P. (995). A igraiv approah o ouris dad forasig. Touris Maag, 6(): Fishr, M., ad Raa, A. (996). Rduig h os of dad uraiy hrough aura rspos o arly sals. Opraio Rsarh, 44(): Gaviri, S., Bollapragada, S., ad Moro, T.E. (998). Priodi rviw sohasi ivory probl wih forasig updas: Wors-as bouds for h yopi soluio. Europa Joural of Opraioal Rsarh, : Goulias, K.G. (999). Logiudial aalysis of aiviy ad ravl par dyais usig gralizd ixd Markov la lass odl. Trasporaio Rsarh, Par B, 33: Hirakawa, Y. (996). Prfora of a ulisag hybrid push/pull produio orol syss. Iraioal Joural of Produio Eoois, 44: Hogg, R.V., ad Tais, E.A. (983). Probabiliy ad Saisial Ifr. Maillia Publishig Co., I., d Ediio.
9 Wag, Chi, ad Liu: Dad Forasig Usig Baysia Expri wih No-hoogous Poisso Pross Modl IJOR Vol., No., 9 (5) 9 8. Jio, J.F. (99). Th rlaiv ipora of aggrga ad sor-spifi shoks a xplaiig aggrga ad soral fluuaios. Eoois Lrs, 39: Laior, P.K., ad Bakr, J.R. (997). Dad siaio wih failur ad apaiy osrais: A appliaio o prisos. Europa Joural of Opraioal Rsarh, : Lrpalagsui, N., ad Cha, C.W. (998). A arhiural frawork for h osruio of hybrid illig forasig syss: Appliaio for lriiy dad prdiio. Egirig Appliaio of Arifiial Illig, : Moo, M.A., Mzr, J.T., ad Thoas, D.E. Jr. (). Cusor dad plaig a lu hologis. Idusrial Markig Maag, 9: Rajopadhy, M., Ghalia, M.B., ad Wag, P.P. (999). Forasig urai hol roo dad. Prodig of h Aria Corol Cofr, Ju. 3. Sal, M.B., ad Jaqus, J.F. (999). Coribuio of aggrga ad soral shoks o h dyais of ivoris: A pirial sudy wih frh ad Aria daa. Iraioal Joural of Produio Eoois, 59: Sgura, J.V. ad Vrhr, E. (). A spradsh odlig approah o h Hol-Wirs opial forasig. Europa Joural of Opraioal Rsarh, 3: Slywozky, A.J., Chriss, C.M., Tdlow, R.S., ad Carr, N. G. (). Th fuur of or. Harvard Busiss Rviw, Jauary-Fbruary. 6. Urba, G.L., Wibrg, B.D., ad Hausr, J.R. (996). Prark forasig of rally w produs. Joural of Markig, Wag, H. ad Chi, C.F. (). A proposd baysia ifr frawork ad h propry of h liklihood fuio. Joural of Maag ad Syss, 7(3): Wir, P.R. (96). Forasig sals by xpoially wighd ovig avrags. Maag Si, 6: Wi, S.F., ad Wi, C.A. (995). Forasig ouris dad: A rviw of pirial rsarh. Iraioal Joural of Forasig, :
Analysis Method of Traffic Congestion Degree Based on Spatio-Temporal Simulation
(IJACSA) Iraioal Joural of Advad Compur Si ad Appliaios, Vol. 3, o.4, 2012 Aalysis hod of Traffi Cogsio Dgr Basd o Spaio-Tmporal Simulaio Shuli H Dparm of aagm Liaoig poli Aadmy Dalia 116036, Chia Absra
CEO Björn Ivroth. Oslo, 29 April 2015. Q1 2015 Presentation
CEO Björ Ivroh Oslo, 29 April 2015 2015 Prsaio Par I `15 Rpor o Highlighs o Group o Sgms o Fiac Par II Mark oulook Summary Appdix 2015 prsaio 2 Highlighs Lyg Bidco AS has acquird 88 % of h shars o No icludig
Numerical Algorithm for the Stochastic Present Value of Aggregate Claims in the Renewal Risk Model
Gn. Mah. Nos, Vol. 9, No. 2, Dcmbr, 23, pp. 4- ISSN 229-784; Copyrigh ICSRS Publicaion, 23 www.i-csrs.org Availabl fr onlin a hp://www.gman.in Numrical Algorihm for h Sochasic Prsn Valu of Aggrga Claims
Term Structure of Interest Rates: The Theories
Handou 03 Econ 333 Abdul Munasb Trm Srucur of Inrs Ras: Th Thors Trm Srucur Facs Lookng a Fgur, w obsrv wo rm srucur facs Fac : Inrs ras for dffrn maurs nd o mov oghr ovr m Fac : Ylds on shor-rm bond mor
Fuzzy Task Assignment Model of Web Services Supplier
Advaed Siee ad Tehology eers Vol.78 (Mulrab 2014),.43-48 h://dx.doi.org/10.14257/asl.2014.78.08 Fuzzy Task Assige Model of Web Servies Sulier Su Jia 1,2,Peg Xiu-ya 1, *, Xu Yig 1,3, Wag Pei-lei 2, Ma Na-ji
Ref No: Version 5.1 Issued: September, 2013
Sv Goodridg 21 Casl Sr Edardson SA 5039 obil: 0405 111 646 [email protected] Adlaid SEO ~ Sv Goodridg Sarch Engin Succss R No: Vrsion 5.1 Issud: Spbr, 2013 Sv Goodridg ~ Adlaid SEO SEO-Packs.doc
A Fuzzy Inventory System with Deteriorating Items under Supplier Credits Linked to Ordering Quantity
JOURNAL OF INFORMAION SCIENCE AND ENGINEERING 6, 3-53 () A Fuzzy Ivtory Syst with Dtrioratig Its udr Supplir Crdits Likd to Ordrig Quatity LIANG-YUH OUYANG, JINN-SAIR ENG AND MEI-CHUAN CHENG 3 Dpartt of
A Portfolio Risk Management Perspective of Outsourcing
A Portolio Risk Maagt Prsptiv o Outsourig Todd Littl, Ladark Graphis O o th hallgig issus with outsourig, partiularly wh lookig to oshor providrs, is dtriig whih projts to outsour ad how to bala a ovrall
A Bayesian Based Search and Classification System for Product. Information of Agricultural Logistics Information Technology
A Bayesia Based Searh ad Classifiaio Sysem for Produ Iformaio of Agriulural Logisis Iformaio Tehology Dada Li 1,Daoliag Li 1,3, Yigyi Che 1,3, Li Li 1, Xiagyag Qi 3, Yogu Zheg 1, * 1 Chia Agriulural Uiversiy,
www.akcp.com Virtual Sensors
www.akcp.cm Irduci: Virual Ssrs Virual ssrs ca b a vry pwrful l i yur mirig sysm. O h scuriyprb yu ca hav up 80 f hs virual ssrs ad hy allw fr a muliud f applicais. Igrai wih MODBUS wrks wih h scuriyprb
Problem Set 6 Solutions
6.04/18.06J Mathmatics for Computr Scic March 15, 005 Srii Dvadas ad Eric Lhma Problm St 6 Solutios Du: Moday, March 8 at 9 PM Problm 1. Sammy th Shar is a fiacial srvic providr who offrs loas o th followig
SIF 8035 Informasjonssystemer Våren 2001
SIF 8035 Iformasjossysmr Vår 2001 Øvig 6 SAP Løsigsforslag Cas scripio Th compay IDES AG is a Grma-bas car proucr, which buys car pars (bumprs) from BMW a Volkswag. Th compay is maag from Hamburg, hough
1.- L a m e j o r o p c ió n e s c l o na r e l d i s co ( s e e x p li c a r á d es p u é s ).
PROCEDIMIENTO DE RECUPERACION Y COPIAS DE SEGURIDAD DEL CORTAFUEGOS LINUX P ar a p od e r re c u p e ra r nu e s t r o c o rt a f u e go s an t e un d es a s t r e ( r ot u r a d e l di s c o o d e l a
Forecasting Demand of Potential Factors in Data Centers
Iformaica Ecoomică vol. 3, o. /29 9 Forcasig Dmad of Poial Facors i Daa rs Alxadr PINNOW, Sfa OSTERBURG, Lars HANISH Oo-vo-Guric-Uivrsiy, Magdburg, Grmay {alxadr.piow sfa.osrburg lars.haisch}@ii.cs.ui-magdburg.d
Introduction to Statistical Analysis of Time Series Richard A. Davis Department of Statistics
Iroduio o Saisial Aalysis of Time Series Rihard A. Davis Deparme of Saisis Oulie Modelig obeives i ime series Geeral feaures of eologial/eviromeal ime series Compoes of a ime series Frequey domai aalysis-he
Many quantities are transduced in a displacement and then in an electric signal (pressure, temperature, acceleration). Prof. B.
Displacmn snsors Many quaniis ar ransducd in a displacmn and hn in an lcric signal (prssur, mpraur, acclraion). Poniomrs Poniomrs i p p i o i p A poniomr is basd on a sliding conac moving on a rsisor.
Decision Making in Finance: Time Value of Money, Cost of Capital and Dividend Policy
Chapr 11 Dcisio Maig i Fiac: Tim Valu of Moy, Cos of Capial ad Dividd olicy Babia Goyal Ky words: rs valu of a flow, fuur valu of a flow, auiy, doublig priod, compoudig, ffciv irs ra, omial irs ra, rur,
Frederikshavn kommunale skolevæsen
Frederikshavn kommunale skolevæsen Skoleåret 1969-70 V e d K: Hillers-Andersen k. s k o l e d i r e k t ø r o g Aage Christensen f u l d m æ g t i g ( Fr e d e rik sh av n E k sp r e s- T ry k k e rie
REVISTA INVESTIGACION OPERACIONAL VOL. 31, No.2, 159-170, 2010
REVISTA INVESTIGACION OPERACIONAL VOL. 3, No., 59-70, 00 AN ALGORITHM TO OBTAIN AN OPTIMAL STRATEGY FOR THE MARKOV DECISION PROCESSES, WITH PROBABILITY DISTRIBUTION FOR THE PLANNING HORIZON. Gouliois E.
B I N G O B I N G O. Hf Cd Na Nb Lr. I Fl Fr Mo Si. Ho Bi Ce Eu Ac. Md Co P Pa Tc. Uut Rh K N. Sb At Md H. Bh Cm H Bi Es. Mo Uus Lu P F.
Hf Cd Na Nb Lr Ho Bi Ce u Ac I Fl Fr Mo i Md Co P Pa Tc Uut Rh K N Dy Cl N Am b At Md H Y Bh Cm H Bi s Mo Uus Lu P F Cu Ar Ag Mg K Thomas Jefferson National Accelerator Facility - Office of cience ducation
Victims Compensation Claim Status of All Pending Claims and Claims Decided Within the Last Three Years
Claim#:021914-174 Initials: J.T. Last4SSN: 6996 DOB: 5/3/1970 Crime Date: 4/30/2013 Status: Claim is currently under review. Decision expected within 7 days Claim#:041715-334 Initials: M.S. Last4SSN: 2957
Financial Mathematics
Financial Mathatics A ractical Guid for Actuaris and othr Businss rofssionals B Chris Ruckan, FSA & Jo Francis, FSA, CFA ublishd b B rofssional Education Solutions to practic qustions Chaptr 7 Solution
G ri d m on i tori n g w i th N A G I O S (*) (*) Work in collaboration with P. Lo Re, G. S av a and G. T ortone WP3-I CHEP 2000, N F N 10.02.2000 M e e t i n g, N a p l e s, 29.1 1.20 0 2 R o b e r 1
by John Donald, Lecturer, School of Accounting, Economics and Finance, Deakin University, Australia
Studnt Nots Cost Volum Profit Analysis by John Donald, Lcturr, School of Accounting, Economics and Financ, Dakin Univrsity, Australia As mntiond in th last st of Studnt Nots, th ability to catgoris costs
Estimating Powers with Base Close to Unity and Large Exponents
Divulgacions Mamáicas Vol. 3 No. 2005), pp. 2 34 Esimaing Powrs wih Bas Clos o Uniy and Larg Exponns Esimacón d Poncias con Bas Crcana a la Unidad y Grands Exponns Vio Lampr [email protected]) FGG,
Approximate Counters for Flash Memory
Approximat Coutrs for Flash Mmory Jack Cichoń ad Wojcich Macya Istitut of Mathmatics ad Computr Scic Wrocław Uivrsity of Tchology, Polad Abstract Flash mmory bcoms th a vry popular storag dvic Du to its
Chad Saunders 1, Richard E Scott 2
Chad Sauds 1, Richad E Sco 2 1 Haskay School of Busiss. 2 Dpam of Commuiy Halh Scics ad Family Mdici / Dico, Offic of Global -Halh Sagy. Uivsiy of Calgay, Calgay, Alba, Caada Md--Tl 2013 Luxmboug, G. D.
Pricing Warrants Models: An Empirical Study of the Indonesian Market
Pricig Warras Modls: A Epirical udy of Idosia Mark Zouair BEN HELIFA Waji ABBAI Absrac ai issu durig priods of fiacial crisis is o rsor isor s cofidc ad arac back o fiacial arks. us, warras a b a gra lp
How To Work For A Company
Trasformig Ass Capabiliis a Bord Gáis Nworks Paul Lo EGATEC 2011 Cos Ovrviw of Bord Gáis Nworks Visio ad Scop of h Nworks Trasformaio Programm Dvlopm of Ass Dparm Challgs ad Bfis 2 2 Bord Gáis Nworks Ovrviw
BEST PRACTICES IN ENGAGING SMES DURING
BEST PRACTICES IN ENGAGING SMES DURING A LEARNING CONTENT DEVELOPMENT PROJECT www.gpwrldwid.cm www.raiigidury.cm B Pracic fr Egagig SME Durig a C Dvlpm Prjc Traiig Idury, Ic ad Gral Phyic Crprai Survy
Beco e ready for capital!
Bco rady for capital! GRaC P oj t ai s at d lop t of sp ialist t ai i g path fo fo sta t-up Th p og a ould- - t p u s looki g o, as o p oj t pa t ship p i, dsig d to suppo t t p u s i f i l shap a d p
Mechanical Vibrations Chapter 4
Mechaical Vibraios Chaper 4 Peer Aviabile Mechaical Egieerig Deparme Uiversiy of Massachuses Lowell 22.457 Mechaical Vibraios - Chaper 4 1 Dr. Peer Aviabile Modal Aalysis & Corols Laboraory Impulse Exciaio
L a h ip e r t e n s ió n a r t e r ia l s e d e f in e c o m o u n n iv e l d e p r e s ió n a r t e r ia l s is t ó lic a ( P A S ) m a y o r o
V e r s i ó n P á g i n a 1 G U I A D E M A N E J O D E H I P E R T E N S I O N E S C E N C I A L 1. D E F I N I C I O N. L a h ip e r t e n s ió n a r t e r ia l s e d e f in e c o m o u n n iv e l d
Reliability of Price-Earnings Ratio as a Valuation Technique:A Critical View.
S discussios, sas, ad auhor profils for his pulicaio a: hps://www.rsarchga./pulicaio/2566839 Rliailiy of Pric-Earigs Raio as a Valuaio Tchiqu:A Criical Viw. Aricl READS 2 auhor: Irahi Ahd Oour Uivrsiy
Physics 106 Lecture 12. Oscillations II. Recap: SHM using phasors (uniform circular motion) music structural and mechanical engineering waves
Physics 6 Lctur Oscillations II SJ 7 th Ed.: Chap 5.4, Rad only 5.6 & 5.7 Rcap: SHM using phasors (unifor circular otion) Physical pndulu xapl apd haronic oscillations Forcd oscillations and rsonanc. Rsonanc
PREFERRED LIFE INSURANCE NORTH AMERICA
PREFERRED LIFE INSURANCE NORTH AMERICA Dat: Spt, 2011 Ditr Gaubatz Agda 1. Copt 2. History 3. Data 4. Futur 1 Copt No-prfrrd plas Normal mortality risk valuatio pross P r v a l ^ i r a s Issud at stadard
Magic Message Maker Amaze your customers with this Gift of Caring communication piece
Magic Mssag Makr maz your customrs with this Gift of aring communication pic Girls larn th powr and impact of crativ markting with this attntion grabbing communication pic that will hlp thm o a World of
Dept. of Heating, Ventilation and Air-Conditioning. Zentralschweizerisches Technikum Luzern Ingenieurschule HTL
Znralshwizrishs Thnikum Luzrn Ingniurshul HTL Dp. o Haing, Vnilaion Elkrohnik - Mashinnhnik - Hizungs-, Lüungs-, Klimahnik - Arhikur - Bauingniurwsn Dvlopd in h proj Low Tmpraur Low Cos Ha Pump Haing Sysm
INFLUENCE OF DEBT FINANCING ON THE EFFECTIVENESS OF THE INVESTMENT PROJECT WITHIN THE MODIGLIANIMILLER THEORY
VOUME 2, 2 NFUENCE OF DEBT FNANCNG ON THE EFFECTVENE OF THE NVETMENT PROJECT WTHN THE MODGANMER THEORY Pr Brusov, Taaa Flaova, Naal Orhova, Pavl Brusov, Nasa Brusova Fac Uvrsy ur h Govrm of h Russa Frao,
Excel Invoice Format. SupplierWebsite - Excel Invoice Upload. Data Element Definition UCLA Supplier website (Rev. July 9, 2013)
Excel Invoice Format Excel Column Name Cell Format Notes Campus* Supplier Number* Invoice Number* Order Number* Invoice Date* Total Invoice Amount* Total Sales Tax Amount* Discount Amount Discount Percent
SCO TT G LEA SO N D EM O Z G EB R E-
SCO TT G LEA SO N D EM O Z G EB R E- EG Z IA B H ER e d it o r s N ) LICA TIO N S A N D M ETH O D S t DVD N CLUDED C o n t e n Ls Pr e fa c e x v G l o b a l N a v i g a t i o n Sa t e llit e S y s t e
5.4 Exponential Functions: Differentiation and Integration TOOTLIFTST:
.4 Eponntial Functions: Diffrntiation an Intgration TOOTLIFTST: Eponntial functions ar of th form f ( ) Ab. W will, in this sction, look at a spcific typ of ponntial function whr th bas, b, is.78.... This
Traffic Flow Analysis (2)
Traffic Flow Analysis () Statistical Proprtis. Flow rat distributions. Hadway distributions. Spd distributions by Dr. Gang-Ln Chang, Profssor Dirctor of Traffic safty and Oprations Lab. Univrsity of Maryland,
FORECASTING MODEL FOR AUTOMOBILE SALES IN THAILAND
FORECASTING MODEL FOR AUTOMOBILE SALES IN THAILAND by Wachareepor Chaimogkol Naioal Isiue of Developme Admiisraio, Bagkok, Thailad Email: [email protected] ad Chuaip Tasahi Kig Mogku's Isiue of Techology
Future Trends in Airline Pricing, Yield. March 13, 2013
Future Trends in Airline Pricing, Yield Management, &AncillaryFees March 13, 2013 THE OPPORTUNITY IS NOW FOR CORPORATE TRAVEL MANAGEMENT BUT FIRST: YOU HAVE TO KNOCK DOWN BARRIERS! but it won t hurt much!
Bullwhip Effect Measure When Supply Chain Demand is Forecasting
J. Basic. Appl. Sci. Res., (4)47-43, 01 01, TexRoad Publicaio ISSN 090-4304 Joural of Basic ad Applied Scieific Research www.exroad.com Bullwhip Effec Measure Whe Supply Chai emad is Forecasig Ayub Rahimzadeh
Put the human back in Human Resources.
Put the human back in Human Resources A Co m p l et e Hu m a n Ca p i t a l Ma n a g em en t So l u t i o n t h a t em p o w er s HR p r o f essi o n a l s t o m eet t h ei r co r p o r a t e o b j ect
Online Appendix I: A Model of Household Bargaining with Violence. In this appendix I develop a simple model of household bargaining that
Online Appendix I: A Model of Household Bargaining ith Violence In this appendix I develop a siple odel of household bargaining that incorporates violence and shos under hat assuptions an increase in oen
Using Predictive Modeling to Reduce Claims Losses in Auto Physical Damage
Using Predictive Modeling to Reduce Claims Losses in Auto Physical Damage CAS Loss Reserve Seminar 23 Session 3 Private Passenger Automobile Insurance Frank Cacchione Carlos Ariza September 8, 23 Today
Question 3: How do you find the relative extrema of a function?
ustion 3: How do you find th rlativ trma of a function? Th stratgy for tracking th sign of th drivativ is usful for mor than dtrmining whr a function is incrasing or dcrasing. It is also usful for locating
CLOUD COMPUTING BUSINESS MODELS
da MODLS Atlir d l iova CLOUD COMPUTING MODLS Chair coomi d l iova - Mourad Zroukhi C d chrch Écoomi t Maagmt Uivrsité d Chair coomi d l iova - da MODLS AGNDA Cloud Computig : What is it? Cloud Dploymt
TIME VALUE OF MONEY: APPLICATION AND RATIONALITY- AN APPROACH USING DIFFERENTIAL EQUATIONS AND DEFINITE INTEGRALS
MPRA Muich Prsoal RPEc Archiv TIME VALUE OF MONEY: APPLICATION AND RATIONALITY- AN APPROACH USING DIFFERENTIAL EQUATIONS AND DEFINITE INTEGRALS Mahbub Parvz Daffodil Itratioal Uivrsy 6. Dcmbr 26 Oli at
DHL EXPRESS CANADA E-BILL STANDARD SPECIFICATIONS
DHL EXPRESS CANADA E-BILL STANDARD SPECIFICATIONS 1 E-Bill Standard Layout A B C D E F G Field/ DHL Account Number Billing Customer Name Billing Customer Address Billing Customer City Billing Customer
Assessing the cost of Outsourcing: Efficiency, Effectiveness and Risk
Assssig th cost of Outsourcig: Efficicy, Effctivss ad Risk Todd Littl Ladark Graphics [email protected] Abstract Offshor outsourcig is a popular approach for copais lookig to rduc softwar dvlopt costs. W hav
BASIC DEFINITIONS AND TERMINOLOGY OF SOILS
1 BASIC DEFINITIONS AND TERMINOLOGY OF SOILS Soil i a thr pha atrial hich coit of olid particl hich ak up th oil klto ad void hich ay b full of atr if th oil i aturatd, ay b full of air if th oil i dry,
A New Hybrid Network Traffic Prediction Method
This full ex paper was peer reviewed a he direcio of IEEE Couicaios Sociey subjec aer expers for publicaio i he IEEE Globeco proceedigs. A New Hybrid Nework Traffic Predicio Mehod Li Xiag, Xiao-Hu Ge,
Transistor is a semiconductor device with fast respond and accuracy. There are two types
Tranitor Amplifir Prpard y: Poa Xuan Yap Thory: Tranitor i a miondutor dvi with fat rpond and auray. Thr ar two typ of tranitor, a Bipolar Juntion Tranitor and a Fild Efft Tranitor. Hr, w will looking
CIS CO S Y S T E M S. G u ille rm o A g u irre, Cis c o Ch ile. 2 0 0 1, C is c o S y s te m s, In c. A ll rig h ts re s e rv e d.
CIS CO S Y S T E M S A c c e s s T e c h n o lo g y T e le c o m /IT Co n n e c tiv ity W o rk s h o p G u ille rm o A g u irre, Cis c o Ch ile g m o.a g u irre @ c is c o.c o m S e s s io n N u m b e
at 10 knots to avoid the hurricane, what could be the maximum CPA? 59 miles - 54 nm STEP 1 Ship s Speed Radius (e-r) 10 k - 1.0 nm every 6 minutes
:1 Navigatio :1 Gal 1 1 1 Rf: P, Huica You a udway o cous T ad you axiu spd is 1 kots. Th y of a huica bas 1 T, ils fo you positio. Th huica is ovig towads T at 1 kots. If you auv at 1 kots to avoid th
Project Management 101
P Wh is i? Th pli, ii d dii f ll sps f p d h ivi f ll hs ivlvd i d hiv dsid suls. Why shuld I h i? P skills hihly dsibl i h wkpl. Shl ps pvid xll ppuiis f suds fi hi pli d i skills d qui xuiv hiki skills
Campus Sustainability Assessment and Related Literature
Campus Sustainability Assessment and Related Literature An Annotated Bibliography and Resource Guide Andrew Nixon February 2002 Campus Sustainability Assessment Review Project Telephone: (616) 387-5626
Establishing Wireless Conference Calls Under Delay Constraints
Establishing Wirlss Confrnc Calls Undr Dlay Constraints Aotz Bar-Noy [email protected] Grzgorz Malwicz [email protected] Novbr 17, 2003 Abstract A prvailing fatur of obil tlphony systs is that th cll
Department of Natural Resources
Dpartt o Natura Rsourcs DIVISION OF AGRICULTURE Northr Rio Oic 1648 S. Cusha St. #201 Fairbas, Aasa 99701-6206 Mai: 907.328.190 Far to Schoo Cha Ectroic Appicatio Istructios 1. Pas i out th ctroic survy
C e r t ifie d Se c u r e W e b
C r t ifi d S c u r W b Z r t ifizi r t Sic h r h it im W b 1 D l gat s N ic o las M ay n c o u r t, C EO, D r am lab T c h n o lo gi s A G M ar c -A n d r é B c k, C o n su lt an t, D r am lab T c h n
Transient Thermoelastic Behavior of Semi-infinite Cylinder by Using Marchi-Zgrablich and Fourier Transform Technique
Inrnaional Journal of Mahmaical Enginring and Scinc ISSN : 77-698 Volum 1 Issu 5 (May 01) hp://www.ijms.com/ hps://sis.googl.com/si/ijmsjournal/ Transin Thrmolasic Bhavior of Smi-infini Cylindr by Using
ME 612 Metal Forming and Theory of Plasticity. 6. Strain
Mtal Forming and Thory of Plasticity -mail: [email protected] Makin Mühndisliği Bölümü Gbz Yüksk Tknoloji Enstitüsü 6.1. Uniaxial Strain Figur 6.1 Dfinition of th uniaxial strain (a) Tnsil and (b) Comprssiv.
New Basis Functions. Section 8. Complex Fourier Series
Nw Basis Functions Sction 8 Complx Fourir Sris Th complx Fourir sris is prsntd first with priod 2, thn with gnral priod. Th connction with th ral-valud Fourir sris is xplaind and formula ar givn for convrting
Impact of the San Diego Serial Inebriate Program on Use of Emergency Medical Resources
EMERGENCY MEDICAL SERVICES/ORIGINAL RESEARCH Ipa of h Sa Digo Sial Ibia oga o Us of Egy Mdial Rsous Jas V. Dufod, MD Edwad M. Casillo, hd, MH Thodo C. Cha, MD Gay M. Vilk, MD Jso, MD Suza. Lidsay, hd,
Western Asset Core Plus Portfolios Select UMA Western Asset Management
Wsrn Ass Cor Plus Porfolios Wsrn Ass Managmn 100 Inrnaional Driv Balimor, Maryland 21202 Syl: US Taxabl Cor Sub-Syl: Taxabl Cor Plus Firm AUM: $446.1 billion Firm Sragy AUM: $2.5 billion^ Yar Foundd: GIMA
Transient Analysis of First Order RC and RL circuits
Transien Analysis of Firs Order and iruis The irui shown on Figure 1 wih he swih open is haraerized by a pariular operaing ondiion. Sine he swih is open, no urren flows in he irui (i=0) and v=0. The volage
Numerical and Experimental Study on Nugget Formation in Resistance Spot Welding for High Strength Steel Sheets in Automobile Bodies
rasactios of JWRI, ol.38 (9), No. rasactios of JWRI, ol.38 (9), No. Numrical ad Exprimtal Study o Nuggt Formatio i Rsistac Spot Wldig for High Strgth Stl Shts i Automobil Bodis MA Nishu* ad MURAKAWA Hidkazu**
DATA MINING TECHNOLOGY IN PREDICTING THE CULTIVATED LAND DEMAND
DATA INING TECHNOLOGY IN REDICTING THE CULTIVATED LAND DEAND Lu Yaoln a, *, ao Zuohua a a School of Rsourc and Envronn Scnc, Wuhan Unvrsy, Chna, Wuhan - [email protected] KEY WORDS: Daa nng, Fuzzy Logc Thory,
I n la n d N a v ig a t io n a co n t r ib u t io n t o eco n o m y su st a i n a b i l i t y
I n la n d N a v ig a t io n a co n t r ib u t io n t o eco n o m y su st a i n a b i l i t y and KB rl iak s iol mi a, hme t a ro cp hm a5 a 2k p0r0o 9f i,e ls hv oa nr t ds eu rmv oedye l o nf dae cr
G d y n i a U s ł u g a r e j e s t r a c j i i p o m i a r u c z a s u u c z e s t n i k ó w i m p r e z s p o r t o w y c h G d y s k i e g o O r o d k a S p o r t u i R e k r e a c j i w r o k u 2 0
A Note on Approximating. the Normal Distribution Function
Applid Mathmatical Scincs, Vol, 00, no 9, 45-49 A Not on Approimating th Normal Distribution Function K M Aludaat and M T Alodat Dpartmnt of Statistics Yarmouk Univrsity, Jordan Aludaatkm@hotmailcom and
Finite Dimensional Vector Spaces.
Lctur 5. Ft Dmsoal Vctor Spacs. To b rad to th musc of th group Spac by D.Maruay DEFINITION OF A LINEAR SPACE Dfto: a vctor spac s a st R togthr wth a oprato calld vctor addto ad aothr oprato calld scalar
Investment Grade Fixed Income Select UMA Cincinnati Asset Management
Invsmn Grad Fixd Incom Cincinnai Ass Managmn 8845 Govrnor's Hill Driv Cincinnai, Ohio 45249 Syl: US Taxabl Cor Sub-Syl: Taxabl Corpora Firm AUM: $2.7 billion Firm Sragy AUM: $2.0 billion Yar Foundd: GIMA
Technological Entrepreneurship : Modeling and Forecasting the Diffusion of Innovation in LCD Monitor Industry
0 Inrnaional Confrnc on Economics and Financ Rsarch IPEDR vol.4 (0 (0 IACSIT Prss, Singaor Tchnological Enrrnurshi : Modling and Forcasing h Diffusion of Innovaion in LCD Monior Indusry Li-Ming Chuang,
Investment Grade Fixed Income Fiduciary Services Cincinnati Asset Management
Invsmn Grad Fixd Incom Cincinnai Ass Managmn 8845 Govrnor's Hill Driv Cincinnai, Ohio 45249 Syl: US Taxabl Cor Sub-Syl: Taxabl Corpora Firm AUM: $2.5 billion Firm Sragy AUM: $1.7 billion Yar Foundd: GIMA
Allocating Redundancy to Critical Information Technology Functions for Disaster Recovery
IT isastr Rcovry Allocatig Rdudacy to ritical Iforatio Tchology Fuctios for isastr Rcovry Bja B.. Shao W. P. ary School of Busiss Arizoa Stat Uivrsity [email protected] ABSTRAT I th prst twork cooy, busisss
Mathematical Modeling and Analysis of a Vehicle Crash
Prodings of th 4th EUROPEAN COMPUTING CONFERENCE Mathatial Modling and Analysis of a Vhil Crash WITOLD PAWLUS, JAN EIVIND NIELSEN, HAMID REZA KARIMI, KJELL G. ROBBERSMYR Dpartnt of Enginring, Faulty of
Category 7: Employee Commuting
7 Catgory 7: Employ Commuting Catgory dscription This catgory includs missions from th transportation of mploys 4 btwn thir homs and thir worksits. Emissions from mploy commuting may aris from: Automobil
[ ] These are the motor parameters that are needed: Motor voltage constant. J total (lb-in-sec^2)
![[ ] These are the motor parameters that are needed: Motor voltage constant. J total (lb-in-sec^2)](/thumbs/40/20779684.jpg "[ ] These are the motor parameters that are needed: Motor voltage constant. J total (lb-in-sec^2)")
MEASURING MOOR PARAMEERS Fil: Motor paramtrs hs ar th motor paramtrs that ar ndd: Motor voltag constant (volts-sc/rad Motor torqu constant (lb-in/amp Motor rsistanc R a (ohms Motor inductanc L a (Hnris
Acceptance Page 2. Revision History 3. Introduction 14. Control Categories 15. Scope 15. General Requirements 15
Acceptance Page 2 Revision History 3 Introduction 14 Control Categories 15 Scope 15 General Requirements 15 Control Category: 0.0 Information Security Management Program 17 Objective Name: 0.01 Information
GOAL PROGRAMMING BASED MASTER PLAN FOR CYCLICAL NURSE SCHEDULING
Joural of Theoretical ad Applied Iforatio Techology 5 th Deceber 202. Vol. 46 No. 2005-202 JATIT & LLS. All rights reserved. ISSN: 992-8645 www.jatit.org E-ISSN: 87-395 GOAL PROGRAMMING BASED MASTER PLAN
Applied Eq Adv-Gbl Concen Fiduciary Services Applied Equity Advisors
Applid Eq Adv-Gbl Concn Applid Equiy Advisors 440 S. LaSall Sr, 38h Floor Chicago, Illinois 60605 Syl: Sub-Syl: Firm AUM: Firm Sragy AUM: Global Equiis Blnd $5.1 billion $1.8 billion^ Yar Foundd: GIMA
Brussels, February 28th, 2013 WHAT IS
Brussls, Fbruary 28h, 2013 WHAT IS 1 OPEN SOURCE 2 CLOUD 3 SERVICES 4 BROKER 5 INTERMEDIATION AGGREGATION ARBITRAGE Cloud Srvics Brokr provids a singl consisn inrfac o mulipl diffring providrs, whhr h
Chem 115 POGIL Worksheet - Week 4 Moles & Stoichiometry Answers
Key Questions & Exercises Chem 115 POGIL Worksheet - Week 4 Moles & Stoichiometry Answers 1. The atomic weight of carbon is 12.0107 u, so a mole of carbon has a mass of 12.0107 g. Why doesn t a mole of
1. Online Event Registration 2. Event Marketing 3. Automated Event Progress Reports 4. Web based Point of Sale Terminal 5. Email Marketing System
2 t v E S d Ivit 3 M o it o r ro la 1 r g 1 Oli Evt Rgitratio 2 Evt Marktig 3 Automatd Evt rogr Rport 4 Wb bad oit of Sal Trmial 5 Email Marktig Sytm ag 1 of 6 Copyright 2004-2011 myvillag oli Evt Maagmt
IT Update - August 2006
IT Nws Saus: No Aciv Til: Da: 7726 Summay (Opional): Body: Wlcom Back! Offic of Infomaion Tchnology Upda: IT Upda - Augus 26 Rob K. Blchman, Ph.D. Associa Dico, Offic of Infomaion Tchnology Whil You W
5 2 index. e e. Prime numbers. Prime factors and factor trees. Powers. worked example 10. base. power
Prim numbrs W giv spcial nams to numbrs dpnding on how many factors thy hav. A prim numbr has xactly two factors: itslf and 1. A composit numbr has mor than two factors. 1 is a spcial numbr nithr prim
Large Cap Equity Fiduciary Services Fayez Sarofim & Co.
Larg Cap Equiy Fayz & Co. Two Houson Cnr, 909 Fannin Sr - Sui 2907 Houson, Txas 77010 Syl: Sub-Syl: Firm AUM: Firm Sragy AUM: US Larg Cap Blnd $23.3 billion $17.1 billion Yar Foundd: GIMA Saus: Firm Ownrship:
DATING YOUR GUILD 1952-1960
DATING YOUR GUILD 1952-1960 YEAR APPROXIMATE LAST SERIAL NUMBER PRODUCED 1953 1000-1500 1954 1500-2200 1955 2200-3000 1956 3000-4000 1957 4000-5700 1958 5700-8300 1959 12035 1960-1969 This chart displays