Generating Intelligent Teaching Learning Systems using Concept Maps and Case Based Reasoning

Size: px
Start display at page:

Download "Generating Intelligent Teaching Learning Systems using Concept Maps and Case Based Reasoning"

Transcription

1 17 Geerag Iellge Teachg Learg Sysems usg Cocep Maps ad Case Based Reasog Makel L. Esposa, MSc. Naala Maríez S. y Zeada García V. Deparme of Compuer Scece, Ceral Uversy of Las Vllas, Hghway o Camajuaí, Cuba mle@uclv.edu.cu, aala@uclv.edu.cu, zgarca@uclv.edu.cu Recbdo para revsó 26 de Marzo de 27, acepado 15 de Juo de 27, versó fal 19 de juo de 27 Resume El empleo de méodos pedagógcos juo a las ecologías de la formacó y las comucacoes favorece la area de geerar, rasmr y comparr el coocmeo. Tal es el caso de la foraleza pedagógca de los Mapas Cocepuales, que cosuye ua herramea para la gesó del coocmeo, por la posbldad que esos ofrece de persoalzar el apredzaje, comparr coocmeo, y para apreder a apreder. Los Mapas Cocepuales facla la vsualzacó de la formacó e orgaza el coocmeo. Por ora pare, el Razoameo Basado e Casos es ua écca de Ielgeca Arfcal que cumple u mporae rol e el proceso de recuperacó de la formacó. E ese rabajo se expoe u uevo efoque que comba ambas éccas para elaborar Ssemas de Eseñaza Apredzaje Ielgees, ulzado u Ssema Basado e Casos como marco eórco para la represeacó del Modelo del Esudae. El modelo propueso se ha mplemeado e u ssema compuacoal ombrado HESEI, el cual ha sdo aplcado exosamee e la oma de decsoes e areas de apoyo al proceso de eseñazaapredzaje, por persoas o ecesaramee experas e el campo formáco. Palabras Claves Ielgeca Arfcal, Mapas Cocepuales, Modelo del Esudae, Razoameo Basado e Casos, Ssemas de Eseñaza Apredzaje Ielgees. Absrac The use of pedagogcal mehods wh he echologes of he formao ad commucaos produce a ew qualy ha favors he ask of geerag, rasmg ad sharg kowledge. Such s he case of he pedagogcal effec ha produces he use of he Cocep Maps, whch cosue a ool for he maageme of kowledge, a ad o persoalze he learg process, o exchage kowledge, ad o lear how o lear. Cocep Mappg provdes a framework for makg hs eral kowledge explc a vsual form ha ca easly be examed ad shared. However, does o address how releva Cocep Maps ca be rereved or adaped o ew problems. Case Based Reasog s playg a creasg role kowledge rereval ad reuse for corporae memores, ad s capables are appealg o augme he cocep mappg process. I hs paper he auhors prese a ew approach o elaborae Iellge Teachg Learg Sysems, where he echques of Cocep Maps ad Arfcal Iellgece are combed, usg he Case Based Reasog as heorecal framework for he Sude Model. The proposed model has bee mplemeed he compuaoal sysem HESEI, whch has bee successfully appled he eachg learg process by layme he Compuer Scece feld. Key words Arfcal Iellgece, Case Based Reasog, Cocep Maps, Iellge Teachg Learg Sysems. T I. INTRODUCTION O cosruc ad o share kowledge, o lear sgfcaly, ad o lear how o lear, are deas o whch researchers have bee poderg for a log me as well as he use of ools ha would allow akg hese aspraos o pracce. To acheve hs, dffere echques ad sraeges have bee used. Cocep Maps provde a schemac summary of wha s leared ad hey order a herarchc rage. The kowledge s orgazed ad represeed all he levels of absraco, he geeral kowledge goes o he upper par ad he mos specfc oe goes o he lower [1]. Over he las few years he Cocep Maps have creasgly go a grea populary ad s egrao wh he echologes of he formao ad he commucaos have become a very mpora eleme he plas for he mproveme of he eachg learg process. The ma applcao of he Cocep Maps has had effec he eachg learg process, whch s s basc eo, s mpora o po ou ha he Cocep Maps lead he aeo of boh sudes ad professors o he resrced umber of mpora deas whch hey mus cocerae. The Cocep Maps are based o a srume ha combes he scefc rgdy wh smplcy ad flexbly, producg a geeral approval he audece of sudes ad professoals; hs represes a mpora exus bewee pedagogy ad echology a huge feld of applcaos. Also, cosues a mpora ad for hose who geerae, rasm, sore, ad dvulge formao ad kowledge ad comprses a mpora ool o oba a hghes praccal value he sysems of he eachg learg process [2]. Revsa Avaces e Ssemas e Iformáca, Vol. 4 No. 1 Juo de 27, Medellí, ISSN

2 18 Revsa Avaces e Ssemas e Iformáca, Vol. 4 No. 1, Juo de 27 The feld of he Iellge Teachg Learg Sysems s characerzed by he applcao of Arfcal Iellgece echques, o he developme of he eachg learg process asssed by compuers. If he key feaure of a Iellge Teachg Learg Sysem s he apude o adap self o he sude, he key compoe of he sysem s he Sude Model, where he formao assocaed o he sude s sored. Ths formao mus be ferred by he sysem depedg o he formao avalable: prevous daa, respose o quesos, ec. Ths process of ferece s defed as dagoss, ad s udoubedly he mos complcaed process sde a Iellge Teachg Learg Sysem, who uses he Arfcal Iellgece echques o represe kowledge, o shape he huma reasog, o emphasze he learg by meas of he aco, o combe expereces of resoluo ad dscovery, o be able o solve problems by her ow, o formulae dagoses ad o provde explaaos. So, hey cou o a bak of sruco sraeges whch helps o decde wha ad how o form o he sude o ge a effecve dreco. Thus, Case Based Reasog s a echque of Arfcal Iellgece ha allows he use of prevous experece he soluo of our problems. Cases are sored ogeher wh her respecve soluo, so whe a ew problem comes ou, hs formao s used o solve. So far, Iellge Teachg Learg Sysems have demosraed s effecveess dverse domas; however, o cosruc a Iellge Teachg Learg Sysems mples a complex ad ese work of kowledge egeerg, whch preves a effecve use of. I order o elmae hese defceces here appears he dea o cosruc a ool o faclae he cosruco of hese kd of sysems o all users (o ecessarly exper he Compuer Scece feld), parcular o hose srucors who are exper ohers area of he kowledge. The am of hs paper s o prese a ew approach o elaborae Iellge Teachg Learg Sysems usg a Cocep Map, quesoares able o cach he cogve ad affecve sae of he sude (Sude Model) ad by usg he paradgm of he Case Based Reasog. I wll allow he sudes o avgae accordg o her kowledge ad o a free way as he Cocep Maps are coceved. Ad s he capacy o adap dyamcally o he developme of he sude s learg wha makes hs sysem ellge. The proposed model was mplemeed he compuaoal sysem HESEI, whch has bee successfully appled he eachg learg process. II. MOTIVATIONS FOR CONCEPT MAPS Cocep Maps provde a framework for capurg expers' eral kowledge ad makg explc a vsual, graphcal form ha ca be easly examed ad shared. Cocep Maps cosue a ool of grea prof for eachers, vesgaors of educaoal opcs, psychologss, socologss ad sudes geeral, as well as for oher areas especally whe s ecessary o maage wh large volumes of formao. They have become a very mpora eleme he plas for he mproveme of he Iellge Teachg Learg Sysems ad hey have also exeded s use o oher areas of he huma acvy whch boh maageme ad he use of kowledge ake up a prepodera place. If o defe a Cocep Map s relavely smple, s smpler o udersad he meag of he defo. The Cocep Maps were defed by Novak, hs creaor, as a skll ha represes a sraegy of learg, a mehod o ge he gs of a opc ad a schemac resource o represe a se of cocepual meags cluded a se of proposos [2]. I s ecessary o po ou ha here s o oly oe model of Cocep Maps, several may exs. The mpora po s he relaos ha are esablshed bewee he coceps hrough he lkg words o form proposos ha cofgure a real value o he suded objec. For such a reaso, a cocep here may appear dversy of real values. I fac, urs very dffcul o fd wo exacly equal Cocep Maps, due o he dvdual characer of kowledge. The Cocep Maps ca be descrbed uder dverse perspecves: absrac, vsualzao, ad coversao. Sce sgfca learg s reached more easly whe he ew coceps or cocepual meags are cluded uder wder coceps, he mos used Cocep Maps are he herarchc oes, he mos geeral ad clusve coceps are placed he upper par of he map, ad he progressvely more specfc ad less clusve coceps, he lower par. The subordaed relaos amog coceps may chage dffere fragmes of learg, so a Cocep Map, ay cocep may rse up o he op poso, ad keep a sgfca proposoal relao wh oher coceps of he map. The use of Coceps Maps desged by he professor creases boh learg ad reeo of scefc formao. The sudes produce maps as learg ools. Cosderg ha he Cocep Maps cosue a explc ad clear represeao of he coceps ad proposos, hey allow boh eachers ad sudes o exchage pos of vew o he valdy of a specfc proposoal lk ad o recogze he mssg coecos he coceps ha sugges he eed of a ew learg. For hs reaso, hs skll has complemeed so favorably wh he pracce of dsace learg whch presupposes ha sudes ad eachers are o physcally he same place a he same me. Cocep Maps have parcular characerscs ha make hem ameable o smar ools. These clude: 1. Cocep Maps have srucure: By defo, more geeral coceps are preseed a he op wh more specfc coceps a he boom. Oher srucural formao, e.g. he umber of gog ad ougog lks of a cocep, may provde addoal formao regardg a cocep s role he map. 2. Cocep Maps are based o proposos: every wo coceps wh her lkg phrase forms a u of

3 Cocep Maps ad Case Based Reasog order o elaborae Iellge Teachg/Learg Sysems hrough a Auhorg Tool for layme Compuer Scece Esposa, Marez y Garca meag. Ths proposoal srucure dsgushes Cocep Maps from oher ools such as Md Mappg ad The Bra, ad provdes semacs o he relaoshps bewee coceps. 3. Cocep Maps have a coex: A Cocep Maps s a represeao of a perso udersadg of a parcular doma of kowledge. As such, all coceps ad lkg phrases are o be erpreed wh ha coex. 4. Coceps ad lkg phrases are as shor as possble, possbly sgle words. 5. Every wo coceps joed by a lkg phrase form a sadaloe proposo. Tha s, he proposo ca be read depedely of he map ad sll make sese. 6. The srucure s herarchcal ad he roo ode of he map s a good represeave of he opc of he map. The sudes who aalyze Cocep Maps wll have a wder basc kowledge; herefore, hey wll be more prepared o solve problems comparso o hose sudes who lear by memorzg. The Cocep Maps have ured o a useful srume for eacher rag ad for he sude s udersadg of dverse subjecs. Cocep Maps cosue a srume ha merges he scefc rgdy wh he smplcy ad flexbly. I represes a ad for hose who geerae, rasm, sore ad spread formao ad kowledge. They also cosue a mpora ool o acheve a praccal value, especally he Arfcal Iellgece sysems. soluo o ew cases: he pracce o deerme he value of smlary bewee wo cases clude a wde rage ha volves mehods such as coug he umber of smlar predcve feaures ad ohers ha cosder he mporace of he predcve feaures wh he fuco of smlary [7]. Afer obag he value of smlary bewee he cases sored he case base ad he ew problem, hose cases o be cosdered he cosruco phase of he ew soluo are seleced. The suably s he process of modfcao of soluos ha have already bee rereved order o gve soluo o he ew problems. The Case Based Sysems have a group of feaures [8], whch ca be used he creao of Iellge Teachg Learg Sysems such as he acquso of kowledge, he flexbly he represeao of kowledge, he preservao of kowledge ad he reuse of prevous soluos. IV. GENERAL ARCHITECTURE OF THE SYSTEM The Iellge Teachg Learg Sysems ha are creaed by HESEI correspod wh a Cocep Map, wh he parcular feaure ha some of s odes here appears a quesoare, capable o ge he cogve ad affecve sae of he sude ad able o gude hs avgao, creag hs way a Iellge Cocep Map. Fgure 1 shows he archecure of HESEI ha cludes Case Based Reasog, ad a algorhm of Paers Recogo o oba he mplemeao of he Sude Model wh a prevous seleco of characerscs. 19 III. CASE BASED SYSTEMS I he radoal Case Based Reasog he soluo of a problem s made akg he examples of cases sored he case memory hrough he mplemeao of a fuco of dsace or smlary, depedg o he doma. The cases are composed by a se of arbues ha descrbe he problem, amog hem he predcve feaures wh he soluo gve o he problem descrbed. The represeao of he case s defed akg o accou he aure of he problem, he mpora arbues, he problems o be processed, he proposed soluo, ec. Also, s ecessary o defe he mechasms for he rereval of cases. The mos smlar case wll be he oe closes o he curre problem, depedg o s arbues; wll be he case whch evaluao of he arge fuco or fuco of smlary akes he bes value udersadg as he closes value, he value of he arge fuco ha deoes mor dfferece bewee he curre case ad he evaluaed case. The fucog of he Case Based Reasog comprses relaed processes, so problems ad her soluos ca be used o derve soluos o ew problems, hese processes are: he rereval of smlar cases, he suably o he proposed soluo ad he sorage or corporao of he ew soluo o he ew case. The rereval process cosss of deermg he mos smlar cases foud he case base order o gve Fgura 1. HESEI archecure. Bref descrpo of some compoes of HESEI: Ierface: I s provded wh a edor ha allows he eacher o roduce all he ecessary formao o prepare he Iellge Teachg Learg Sysems. Through he Ierface he sysem caches he cogve ad affecve sae of he sude (Sude Model). Also, hrough hs compoe he sudes may be able o erac wh he Iellge Teachg Learg Sysem geeraed by HESEI usg par of he formao provded by he professor whch bes sasfes he sudes eed. Kowledge Egeerg ad LEX: These compoes helps he eacher he complex ad ese work of kowledge egeerg, usg a Paer Recogo algorhm o reduce he space of al represeao (feaures ha shape he Sude Model), calculag he ypcal esors (dscrmaes feaures) [11] whch aalyzes each quesoare order o faclae he eacher he

4 2 Revsa Avaces e Ssemas e Iformáca, Vol. 4 No. 1, Juo de 27 formao of hose quesos ha are worhless, hey are elmaed by he eacher f hey do o have a mehodologcal value eher. I was mplemeed he LEX algorhm for hs ask. A quesoare s composed for quesos, ad he eacher from he quesoare coceves m caegores (Sude Model), fac he experece has demosraed ha m s always very much mor ha. Rereval ad CBS adaper: I hs compoe a Case Based Sysem was mplemeed, s composed by a casebase, he rereval algorhm ad case adapao algorhm. A algorhm s proposed for he rereval of he mos smlar cases whch he fuco of smlary of he eares Neghbors: p δ ( O, O ) β ( O O ) = = 1, p = 1 s rasformed o a ew fuco of smlary ha allows he hadlg of fuzzy values, as shows (2). δ ' β ( O, O ) ' p δ = = 1 p = 1 ( x ( O ), x ( O ) (2), where s he umber of predcg feaures, ( x ( O, x ( O ))) accordg o: δ ' s he smlary fuco rasformed ( x ( O ) x ( O )) δ ( x ( O ), x ( O ))( 1 µ ( O ) µ ( O )), ad = p s he weghg of he predcg feaure x, calculaed usg he heory of he ypcal esors [11]. Pscopedagogcal Asssa: I prevous secos we have deal wh affecve feaures ad we cosderer mpora gog over oce more. The affecve feaures are obaed hrough a pscopedagogcal asssa order o acheve a coeco bewee affecvy ad cogo. There has radoally exsed a absolue separao bewee he cogve ad affecve aspecs whe sudyg her fluece he learg process. So, vesgaors would cere her sudes he cogve aspecs raher ha he affecve oes or vce versa. A prese, here exss a creasg eres sudyg he wo compoes smulaeously. The eachg learg process s boh cogve ad affecve; he developme of he academc oupu s ecessary o ake o accou boh he cogve aspecs ad he affecve oes. Learg s oly possble f he chace o acqure he kowledge s gve ad he process s relaed o he capaces, kowledge, sraeges, ad he sklls (cogve compoe) of he sude, bu s also ecessary o have eo ad he movao o face hs process (affecve compoe). For hs compoe he auhors developed a sysem based o rules whch eam up wh he learg process [5]. I allows o be acquaed wh he mood of he sude ad check her movao as well as her udersadg o a specfc subjec. Thus he Iellge Teachg Learg Sysems wll be able o chage he learg speed or, f ecessary, o movae ad reorgaze he sudy. V. EXAMPLE OF AN INTELLIGENT TEACHING LEARNING SYSTEM IMPLEMENTED WITH HESEI TO LEARN THE GRAPH THEORY The graph heory s a subjec of Dscree Mahemacs ad due o s mporace s also prese oher subjecs relaed o he Compuer Scece. Some dffcules whe learg hs subjec have lead he auhors o he desg ad mpleme a Iellge Teachg Learg Sysems able o deal wh he subjec as well as o lk o ohers such as Daa Srucures ad Complers; as a geeral example, he auhors prese a smple par of he Iellge Teachg Learg Sysems [12]. Fgure 2 shows he Cocep Map o lear he basc ermology [13], [14], [15], he way he map s coceved eables he sude o avgae freely by ay ode eracg wh all he maerals relaed o hs opc, ad Example 1 we show oe of he quesos of he quesoare o rasform o a Iellge Teachg Learg Sysems wh Cocep Map feaures. Fgura 2. Basc ermology. I he example, he professor makes he quesoare he roo ode of he Cocep Map, where he Sude Model s cosruced. So, by usg he Case Based Reasog paradgm, he brach where he sudes mus avgae accordace wh hs learg should be ferred. Example of a quesoare: Gve he followg graph. Mach colum A ad B.

5 Cocep Maps ad Case Based Reasog order o elaborae Iellge Teachg/Learg Sysems hrough a Auhorg Tool for layme Compuer Scece Esposa, Marez y Garca geeraed sysems, whch flueces he effcecy of he algorhm ad he eacher s expercy ogeher hs mehodologcal work. The aalyss of he effcecy of he ellge sysem o lear he graph heory s made havg cosderao he resuls obaed he soluo of ew problems, usg he proposed model ad he crera of he exper. The auhors used a Crossed Valdao mehod order o carry ou hs process, hs geeral mehod cosss of dvdg he base of jo cases of equal sze ad carryg ou he algorhm of learg mes, each oe of whch he rag se (learg sample) are all, excep oe of he subgroups where s evaluaed (corol sample). The resuls for =6 are show fgure 4: 21 Colum A Colum B a). G, L, D are parallel edges. b). e1 are solaed verexes. c). e1, e7, e8 are verexes. d). M y C are bows. e). e1, e14 cde edges G ad S. f). ce15 are edges. g). F are adjace verexes. h). e2, e3 Fgure 3 shows a sage of he Iellge Teachg Learg Sysems afer he sude aswers he quesoare, where s pla o see ha he sude has o overcome he kowledge o he lef brach, so he ca o avgae by he rgh brach. I he lef brach he sude has already overcome he kowledge relaed o he cocep of verexes, so he ca have free access o furher formao. However, he mus sudy he opc of Edges, because here s a poor kowledge hs area. Fgura 4. Resuls of he effcecy he valuao of he sude model. We cosdered ha he obaed resuls are good, cosderg ha we are workg wh fuzzy classes where a sude ca belog o oe or aoher model wh cera degree of propery. Fgure 5 shows a wdow of he HESEI erface wh a Iellge Teachg Learg Sysems geeraed for he subjec of Bary Heaps he course of Daa Srucures. We are elaborag some courses dffere subjecs. Fgura 3. Resula Cocep Maps (Iellge Teachg Learg Sysem). VI. VALIDATION OF THE RESULTS The valdao process of he ool had wo sages, o he oe had o valdae he ool as far as facles ha provdes o cosruc ew Iellge Teachg Learg Sysems o all users (o ecessarly exper he compuer scece feld) ad o valdae he possbles of use ad success of he ew Fgura 5. HESEI Ierface VII. CONCLUSIONS Cocep mappg s useful for kowledge maageme as a vehcle for exeralzg "eral" exper kowledge, o

6 22 Revsa Avaces e Ssemas e Iformáca, Vol. 4 No. 1, Juo de 27 allow ha kowledge o be examed, refed, ad reused. Case Based Reasog s useful for kowledge maageme provdg a easy o udersad kowledge represeao records of specfc reasog epsodes ad mehods for accessg releva formao ad buldg up a corporae memory of expereces. The syergy of he wo echologes provdes a promsg approach for addressg corporae kowledge loss by supporg he capure ad reuse of exper desg expereces, helpg o maage ad maa a mpora compoe of orgazaoal kowledge asses. Wh hs work we propose a ew Sude Model ha could be cosdered he cosruco of Iellge Teachg Learg Sysems, where are combed he facles of he Cocep Maps for he orgazao of he kowledge ad he poealy of he Case Based Reasog lke ferece ool. The proposed deas were mplemeed he compuaoal sysem HESEI, whch has bee appled successful he elaborao of Iellge Teachg Learg Sysems by users ha here are o ecessarly exper he Compuer Scece feld. REFERENCES [1] G. O. Maríez, N. e al. Mapas Cocepuales y Redes Bayesaas e los Ssemas de Eseñaza/Apredzaje Ielgees. Cogreso Ieracoal de Iformáca Educava (Cosa Rca). 26. [2] Cañas Albero J. e al. Cocep Maps ad AI: a Ulkely Marrage?. SBIE 24: Smpóso Braslero de Iformáca a Educação Maaus, Brasl. 24. [3] García, Z. e al. Iroduccó a la Ielgeca Arfcal, Guadalajara. Méxco. ISBN: [4] Bello, R. e al. Aplcacoes de la Ielgeca Arfcal. Edcoes de la Noche, Guadalajara, Jalsco, Méxco. ISBN: [5] Eduardo Alba Cabrera. e al. El cocepo de FS esor: ua solucó para u problema de compabldad. Revsa Cecas Maemácas. Cuba. 22. [6] Maríez, N. e al. Uso de éccas de elgeca arfcal e la mplemeacó del modelo del esudae. Memoras del IV Taller Ieracoal de Iovacó Educava sglo XXI: INNOED 25. ISBN: [7] Uvña Parca Ruh. e al. Mapas Cocepuales: ua herramea para el apredzaje de Esrucuras de Daos. JEITICS. Prmeras Joradas de Educacó e Iformáca y TICS e Argea. 25. [8] Guérrez, I. e al. Modelo para la Toma de Decsoes usado Razoameo Basado e Casos e codcoes de Icerdumbre. Trabajo de Tess e opcó al grado ceífco de Docor e Cecas Téccas. 23. [9] Maríez, N. e al. Modelo para el dseño de ua herramea que perme crear ssemas de eseñaza elgees usado razoameo basado e Casos. Memoras de la XI Covecó Ieracoal de Iformáca: Iformáca 25. Habaa, Cuba. ISBN: [1] Dder Dubo, Her Prade. The hree semacs of fuzzy ses. Fuzzy Se ad Sysems [11] Aurora Pos Porraa. e al. Revsa Cecas Maemácas vol. 21, o. 1. Lex: U uevo algormo para el calculo de los esores ípcos. 23. [12] Ruz J., Modelos Maemácos para el Recoocmeo de Paroes. Ed. UCLV [13] Maríez, N. e al. Cómo apreder la eoría de grafos medae u ssema de eseñaza elgee?. RELME 2. ISBN: [14] García, L., Lemage, J. Iroduccó a la Maemáca Dscrea. Tomos 1 y [15] Gavrlov, G.P., Sapozheko, A.A. Problemas de Maemáca Dscrea. MIR [16] Johsobaugh, R. Maemácas Dscreas. Prece Hall, Méxco. 4a Edcó

Chapter 4 Multiple-Degree-of-Freedom (MDOF) Systems. Packing of an instrument

Chapter 4 Multiple-Degree-of-Freedom (MDOF) Systems. Packing of an instrument Chaper 4 Mulple-Degree-of-Freedom (MDOF Sysems Eamples: Pacg of a srume Number of degrees of freedom Number of masses he sysem X Number of possble ypes of moo of each mass Mehods: Newo s Law ad Lagrage

More information

REVISTA INVESTIGACION OPERACIONAL Vol. 25, No. 1, 2004. k n ),

REVISTA INVESTIGACION OPERACIONAL Vol. 25, No. 1, 2004. k n ), REVISTA INVESTIGACION OPERACIONAL Vol 25, No, 24 RECURRENCE AND DIRECT FORMULAS FOR TE AL & LA NUMBERS Eduardo Pza Volo Cero de Ivesgacó e Maemáca Pura y Aplcada (CIMPA), Uversdad de Cosa Rca ABSTRACT

More information

Proving the Computer Science Theory P = NP? With the General Term of the Riemann Zeta Function

Proving the Computer Science Theory P = NP? With the General Term of the Riemann Zeta Function Research Joural of Mahemacs ad Sascs 3(2): 72-76, 20 ISSN: 2040-7505 Maxwell Scefc Orgazao, 20 Receved: Jauary 08, 20 Acceped: February 03, 20 Publshed: May 25, 20 Provg he ompuer Scece Theory P NP? Wh

More information

Vladimir PAPI], Jovan POPOVI] 1. INTRODUCTION

Vladimir PAPI], Jovan POPOVI] 1. INTRODUCTION Yugoslav Joural of Operaos Research 200 umber 77-9 VEHICLE FLEET MAAGEMET: A BAYESIA APPROACH Vladmr PAPI] Jova POPOVI] Faculy of Traspor ad Traffc Egeerg Uversy of Belgrade Belgrade Yugoslava Absrac:

More information

7.2 Analysis of Three Dimensional Stress and Strain

7.2 Analysis of Three Dimensional Stress and Strain eco 7. 7. Aalyss of Three Dmesoal ress ad ra The cocep of raco ad sress was roduced ad dscussed Par I.-.5. For he mos par he dscusso was cofed o wo-dmesoal saes of sress. Here he fully hree dmesoal sress

More information

Mobile Data Mining for Intelligent Healthcare Support

Mobile Data Mining for Intelligent Healthcare Support Moble Daa Mg for Iellge Healhcare uppor Absrac The growh umbers ad capacy of moble devces such as moble phoes coupled wh wdespread avalably of expesve rage of bosesors preses a uprecedeed opporuy for moble

More information

Mobile Data Mining for Intelligent Healthcare Support

Mobile Data Mining for Intelligent Healthcare Support Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces - 2009 Moble Daa Mg for Iellge Healhcare uppor Par Delr Haghgh, Arkady Zaslavsky, hoal Krshaswamy, Mohamed Medha Gaber Ceer for Dsrbued ysems ad ofware

More information

Evaluation and Modeling of the Digestion and Absorption of Novel Manufacturing Technology in Food Enterprises

Evaluation and Modeling of the Digestion and Absorption of Novel Manufacturing Technology in Food Enterprises Advace Joural of Food Scece ad Techology 9(6): 482-486, 205 ISSN: 2042-4868; e-issn: 2042-4876 Mawell Scefc Orgazao, 205 Submed: Aprl 9, 205 Acceped: Aprl 28, 205 Publshed: Augus 25, 205 Evaluao ad Modelg

More information

Object Tracking Based on Online Classification Boosted by Discriminative Features

Object Tracking Based on Online Classification Boosted by Discriminative Features Ieraoal Joural of Eergy, Iformao ad Commucaos, pp.9-20 hp://dx.do.org/10.14257/jec.2013.4.6.02 Objec Trackg Based o Ole Classfcao Boosed by Dscrmave Feaures Yehog Che 1 ad Pl Seog Park 2 1 Qlu Uversy of

More information

Trust Evaluation and Dynamic Routing Decision Based on Fuzzy Theory for MANETs

Trust Evaluation and Dynamic Routing Decision Based on Fuzzy Theory for MANETs JOURNAL OF SOFTWARE, VOL. 4, NO. 10, ECEBER 2009 1091 Trus Evaluao ad yamc Roug ecso Based o Fuzzy Theory for ANETs Hogu a, Zhpg Ja ad Zhwe Q School of Compuer Scece ad Techology, Shadog Uversy, Ja, Cha.P.R.

More information

Jorge Ortega Arjona Departamento de Matemáticas, Facultad de Ciencias, UNAM jloa@fciencias.unam.mx

Jorge Ortega Arjona Departamento de Matemáticas, Facultad de Ciencias, UNAM jloa@fciencias.unam.mx Usg UML Sae Dagrams for Moellg he Performace of Parallel Programs Uso e Dagramas e Esao UML para la Moelacó el Desempeño e Programas Paralelos Jorge Orega Aroa Deparameo e Maemácas, Facula e Cecas, UNAM

More information

Analysis of Coalition Formation and Cooperation Strategies in Mobile Ad hoc Networks

Analysis of Coalition Formation and Cooperation Strategies in Mobile Ad hoc Networks Aalss of oalo Formao ad ooperao Sraeges Moble Ad hoc ewors Pero Mchard ad Ref Molva Isu Eurecom 9 Roue des rêes 06904 Sopha-Apols, Frace Absrac. Ths paper focuses o he formal assessme of he properes of

More information

The Design of a Forecasting Support Models on Demand of Durian for Domestic Markets and Export Markets by Time Series and ANNs.

The Design of a Forecasting Support Models on Demand of Durian for Domestic Markets and Export Markets by Time Series and ANNs. The 2 d RMUTP Ieraoal Coferece 2010 Page 108 The Desg of a Forecasg Suppor Models o Demad of Dura for Domesc Markes ad Expor Markes by Tme Seres ad ANNs. Udomsr Nohacho, 1* kegpol Ahakor, 2 Kazuyosh Ish,

More information

American Journal of Business Education September 2009 Volume 2, Number 6

American Journal of Business Education September 2009 Volume 2, Number 6 Amerca Joural of Bue Educao Sepember 9 Volume, umber 6 Tme Value Of Moe Ad I Applcao I Corporae Face: A Techcal oe O L Relaohp Bewee Formula Je-Ho Che, Alba Sae Uver, USA ABSTRACT Tme Value of Moe (TVM

More information

Financial Time Series Forecasting with Grouped Predictors using Hierarchical Clustering and Support Vector Regression

Financial Time Series Forecasting with Grouped Predictors using Hierarchical Clustering and Support Vector Regression Ieraoal Joural of Grd Dsrbuo Compug, pp.53-64 hp://dx.do.org/10.1457/jgdc.014.7.5.05 Facal Tme Seres Forecasg wh Grouped Predcors usg Herarchcal Cluserg ad Suppor Vecor Regresso ZheGao a,b,* ad JajuYag

More information

Solving Fuzzy Linear Programming Problems with Piecewise Linear Membership Function

Solving Fuzzy Linear Programming Problems with Piecewise Linear Membership Function Avalable a hp://pvamu.edu/aam Appl. Appl. Mah. ISSN: 9-966 Vol., Issue December ), pp. Prevously, Vol., Issue, pp. 6 6) Applcaos ad Appled Mahemacs: A Ieraoal Joural AAM) Solvg Fuzzy Lear Programmg Problems

More information

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,

More information

No Regret Learning in Oligopolies: Cournot vs Bertrand

No Regret Learning in Oligopolies: Cournot vs Bertrand No Regre Learg Olgopoles: Couro vs Berrad Ur Nadav Georgos Plouras Absrac Couro ad Berrad olgopoles cosue he wo mos prevale models of frm compeo. The aalyss of Nash equlbra each model reveals a uque predco

More information

A new proposal for computing portfolio valueat-risk for semi-nonparametric distributions

A new proposal for computing portfolio valueat-risk for semi-nonparametric distributions A ew proposal for compug porfolo valuea-rsk for sem-oparamerc dsrbuos Tro-Mauel Ñíguez ad Javer Peroe Absrac Ths paper proposes a sem-oparamerc (SNP) mehodology for compug porfolo value-a-rsk (VaR) ha

More information

Determinants of Foreign Direct Investment in Malaysia: What Matters Most?

Determinants of Foreign Direct Investment in Malaysia: What Matters Most? Deermas of Foreg Drec Ivesme Maaysa: Wha Maers Mos? Nursuha Shahrud, Zarah Yusof ad NuruHuda Mohd. Saar Ths paper exames he deermas of foreg drec vesme Maaysa from 970-008. The causay ad dyamc reaoshp

More information

Performance Comparisons of Load Balancing Algorithms for I/O- Intensive Workloads on Clusters

Performance Comparisons of Load Balancing Algorithms for I/O- Intensive Workloads on Clusters Joural of ewor ad Compuer Applcaos, vol. 3, o., pp. 32-46, Jauary 2008. Performace Comparsos of oad Balacg Algorhms for I/O- Iesve Worloads o Clusers Xao Q Deparme of Compuer Scece ad Sofware Egeerg Aubur

More information

55. IWK Internationales Wissenschaftliches Kolloquium International Scientific Colloquium

55. IWK Internationales Wissenschaftliches Kolloquium International Scientific Colloquium PROCEEDIGS 55. IWK Ieraoales Wsseschaflches Kolloquu Ieraoal Scefc Colloquu 3-7 Sepeber 00 Crossg Borders wh he BC uoao, Boedcal Egeerg ad Copuer Scece Faculy of Copuer Scece ad uoao www.u-leau.de Hoe

More information

Professional Liability Insurance Contracts: Claims Made Versus Occurrence Policies

Professional Liability Insurance Contracts: Claims Made Versus Occurrence Policies ARICLES ACADÉMIQUES ACADEMIC ARICLES Assuraces e geso des rsques, vol. 79(3-4), ocobre 2011- javer 2012, 251-277 Isurace ad Rsk Maageme, vol. 79(3-4), Ocober 2011- Jauary 2012, 251-277 Professoal Lably

More information

CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING

CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING Q.1 Defie a lease. How does i differ from a hire purchase ad isalme sale? Wha are he cash flow cosequeces of a lease? Illusrae.

More information

Value of information sharing in marine mutual insurance

Value of information sharing in marine mutual insurance Value of formao sharg mare muual surace Kev L, Joh Lu, Ja Ya 3 ad Je M Deparme of Logscs & Marme Sudes, The Hog Kog Polechc Uvers, Hog Kog. Emal address:.x.l@polu.edu.h. Deparme of Logscs & Marme Sudes,

More information

Longitudinal and Panel Data: Analysis and Applications for the Social Sciences. Edward W. Frees

Longitudinal and Panel Data: Analysis and Applications for the Social Sciences. Edward W. Frees Logudal ad Pael Daa: Aalss ad Applcaos for he Socal Sceces b Edward W. Frees Logudal ad Pael Daa: Aalss ad Applcaos for he Socal Sceces Bref Table of Coes Chaper. Iroduco PART I - LINEAR MODELS Chaper.

More information

Harmony search algorithms for inventory management problems

Harmony search algorithms for inventory management problems Afrca Joural of Busess Maageme Vol.6 (36), pp. 9864-9873, 2 Sepember, 202 Avalable ole a hp://www.academcourals.org/ajbm DOI: 0.5897/AJBM2.54 ISSN 993-8233 202 Academc Jourals Revew Harmoy search algorhms

More information

Numerical Methods with MS Excel

Numerical Methods with MS Excel TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how

More information

EQUITY VALUATION USING DCF: A THEORETICAL ANALYSIS OF THE LONG TERM HYPOTHESES

EQUITY VALUATION USING DCF: A THEORETICAL ANALYSIS OF THE LONG TERM HYPOTHESES Ivesme Maaeme ad Facal Iovaos Volume 4 Issue 007 9 EQUIY VALUAION USING DCF: A HEOREICAL ANALYSIS OF HE LONG ERM HYPOHESES Luco Cassa * Adrea Pla ** Slvo Vsmara *** Absrac hs paper maches he sesvy aalyss

More information

HIGH FREQUENCY MARKET MAKING

HIGH FREQUENCY MARKET MAKING HIGH FREQUENCY MARKET MAKING RENÉ CARMONA AND KEVIN WEBSTER Absrac. Sce hey were auhorzed by he U.S. Secury ad Exchage Commsso 1998, elecroc exchages have boomed, ad by 21 hgh frequecy radg accoued for

More information

s :risk parameter for company size

s :risk parameter for company size UNDESTANDING ONLINE TADES: TADING AND EFOMANCE IN COMMON STOCK INVESTMENT Y. C. George L, Y. C. Elea Kag 2 ad Chug-L Chu 3 Deparme of Accoug ad Iformao Techology, Naoal Chug Cheg Uversy, Tawa,.O.C acycl@ccu.edu.w;

More information

Business School Discipline of Finance. Discussion Paper 2014-005. Modelling the crash risk of the Australian Dollar carry trade

Business School Discipline of Finance. Discussion Paper 2014-005. Modelling the crash risk of the Australian Dollar carry trade Dscusso Paper: 2014-005 Busess School Dscple of Face Dscusso Paper 2014-005 Modellg he crash rsk of he Ausrala Dollar carry rade Suk-Joog Km Uversy of Sydey Busess School Modellg he crash rsk of he Ausrala

More information

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R = Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

Lecture 13 Time Series: Stationarity, AR(p) & MA(q)

Lecture 13 Time Series: Stationarity, AR(p) & MA(q) RS C - ecure 3 ecure 3 Tme Seres: Saoar AR & MAq Tme Seres: Iroduco I he earl 97 s was dscovered ha smle me seres models erformed beer ha he comlcaed mulvarae he oular 96s macro models FRB-MIT-Pe. See

More information

Anomaly Detection of Network Traffic Based on Prediction and Self-Adaptive Threshold

Anomaly Detection of Network Traffic Based on Prediction and Self-Adaptive Threshold Ieraoal Joural of Fuure Geerao Coucao ad eworkg Vol. 8, o. 6 (15), pp. 5-14 hp://d.do.org/1.1457/fgc.15.8.6. Aoaly Deeco of ework raffc Based o Predco ad Self-Adapve hreshold Haya Wag Depare of Iforao

More information

APPENDIX III THE ENVELOPE PROPERTY

APPENDIX III THE ENVELOPE PROPERTY Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful

More information

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis 6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces

More information

CONVERGENCE AND SPATIAL PATTERNS IN LABOR PRODUCTIVITY: NONPARAMETRIC ESTIMATIONS FOR TURKEY 1

CONVERGENCE AND SPATIAL PATTERNS IN LABOR PRODUCTIVITY: NONPARAMETRIC ESTIMATIONS FOR TURKEY 1 CONVERGENCE AND SPAIAL PAERNS IN LABOR PRODUCIVIY: NONPARAMERIC ESIMAIONS FOR URKEY ugrul emel, Ays asel & Peer J. Alberse Workg Paper 993 Forhcomg he Joural of Regoal Aalyss ad Polcy, 999. We would lke

More information

Average Price Ratios

Average Price Ratios Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or

More information

Natural Gas Storage Valuation. A Thesis Presented to The Academic Faculty. Yun Li

Natural Gas Storage Valuation. A Thesis Presented to The Academic Faculty. Yun Li Naural Gas Sorage Valuao A Thess Preseed o The Academc Faculy by Yu L I Paral Fulfllme Of he Requremes for he Degree Maser of Scece he School of Idusral ad Sysem Egeerg Georga Isue of Techology December

More information

Classic Problems at a Glance using the TVM Solver

Classic Problems at a Glance using the TVM Solver C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the

More information

GARCH Modelling. Theoretical Survey, Model Implementation and

GARCH Modelling. Theoretical Survey, Model Implementation and Maser Thess GARCH Modellg Theorecal Survey, Model Imlemeao ad Robusess Aalyss Lars Karlsso Absrac I hs hess we survey GARCH modellg wh secal focus o he fg of GARCH models o facal reur seres The robusess

More information

DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT

DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT ESTYLF08, Cuecas Meras (Meres - Lagreo), 7-9 de Septembre de 2008 DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT José M. Mergó Aa M. Gl-Lafuete Departmet of Busess Admstrato, Uversty of Barceloa

More information

PORTFOLIO CHOICE WITH HEAVY TAILED DISTRIBUTIONS 1. Svetlozar Rachev 2 Isabella Huber 3 Sergio Ortobelli 4

PORTFOLIO CHOICE WITH HEAVY TAILED DISTRIBUTIONS 1. Svetlozar Rachev 2 Isabella Huber 3 Sergio Ortobelli 4 PORTFOLIO CHOIC WITH HAVY TAILD DISTRIBUTIONS Sveloar Rachev Isabella Huber 3 Sergo Orobell 4 We are graeful o Boryaa Racheva-Joova Soya Soyaov ad Almra Bglova for he comuaoal aalyss ad helful commes.

More information

Quantifying Environmental Green Index For Fleet Management Model

Quantifying Environmental Green Index For Fleet Management Model Proceedgs of he Easer Asa Socey for Trasporao Sudes, Vol.9, 20 Quafyg Evromeal ree Idex For Flee Maageme Model Lay Eg TEOH a, Hoo Lg KHOO b a Deparme of Mahemacal ad Acuaral Sceces, Faculy of Egeerg ad

More information

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50

More information

ECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil

ECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil ECONOMIC CHOICE OF OPTIMUM FEEDER CABE CONSIDERING RISK ANAYSIS I Camargo, F Fgueredo, M De Olvera Uversty of Brasla (UB) ad The Brazla Regulatory Agecy (ANEE), Brazl The choce of the approprate cable

More information

Traditional Smoothing Techniques

Traditional Smoothing Techniques Tradoal Smoohg Techques Smple Movg Average: or Ceered Movg Average, assume s odd: 2 ( 2 ( Weghed Movg Average: W W (or, of course, you could se up he W so ha hey smply add o oe. Noe Lear Movg Averages

More information

1. The Time Value of Money

1. The Time Value of Money Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg

More information

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree , pp.277-288 http://dx.do.org/10.14257/juesst.2015.8.1.25 A New Bayesa Network Method for Computg Bottom Evet's Structural Importace Degree usg Jotree Wag Yao ad Su Q School of Aeroautcs, Northwester Polytechcal

More information

10.5 Future Value and Present Value of a General Annuity Due

10.5 Future Value and Present Value of a General Annuity Due Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the

More information

of the relationship between time and the value of money.

of the relationship between time and the value of money. TIME AND THE VALUE OF MONEY Most agrbusess maagers are famlar wth the terms compoudg, dscoutg, auty, ad captalzato. That s, most agrbusess maagers have a tutve uderstadg that each term mples some relatoshp

More information

FINANCIAL MATHEMATICS 12 MARCH 2014

FINANCIAL MATHEMATICS 12 MARCH 2014 FINNCIL MTHEMTICS 12 MRCH 2014 I ths lesso we: Lesso Descrpto Make use of logarthms to calculate the value of, the tme perod, the equato P1 or P1. Solve problems volvg preset value ad future value autes.

More information

FORECASTING MODEL FOR AUTOMOBILE SALES IN THAILAND

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: wachare@as.ida.ac.h ad Chuaip Tasahi Kig Mogku's Isiue of Techology

More information

The Unintended Consequences of Tort Reform: Rent Seeking in New York State s Structured Settlements Statutes

The Unintended Consequences of Tort Reform: Rent Seeking in New York State s Structured Settlements Statutes The Ueded Cosequeces of Tor Reform: Re Seeg ew Yor Sae s Srucured Selemes Saues Publshed Joural of Foresc Ecoomcs, Volume 3 o, Wer 2 By Lawrece M. Spzma* Professor of Ecoomcs Mahar Hall Sae Uversy of ew

More information

Price Volatility, Trading Activity and Market Depth: Evidence from Taiwan and Singapore Taiwan Stock Index Futures Markets

Price Volatility, Trading Activity and Market Depth: Evidence from Taiwan and Singapore Taiwan Stock Index Futures Markets We-Hsu Kuo Asa e Pacfc al./asa Maageme Pacfc Maageme evew (005) evew 0(), (005) 3-3 0(), 3-3 Prce Volaly, Tradg Acvy ad Marke Deph: Evdece from Tawa ad Sgapore Tawa Sock Idex Fuures Markes We-Hsu Kuo a,*,

More information

Fractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK

Fractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK Fractal-Structured Karatsuba`s Algorthm for Bary Feld Multplcato: FK *The authors are worg at the Isttute of Mathematcs The Academy of Sceces of DPR Korea. **Address : U Jog dstrct Kwahadog Number Pyogyag

More information

METHODOLOGY ELECTRICITY, GAS AND WATER DISTRIBUTION INDEX (IDEGA, by its Spanish acronym) (Preliminary version)

METHODOLOGY ELECTRICITY, GAS AND WATER DISTRIBUTION INDEX (IDEGA, by its Spanish acronym) (Preliminary version) MEHODOLOGY ELEY, GAS AND WAE DSBUON NDEX (DEGA, by s Sash acroym) (Prelmary verso) EHNAL SUBDEOAE OPEAONS SUBDEOAE Saago, December 26h, 2007 HDA/GGM/GMA/VM ABLE OF ONENS Pages. roduco 3 2. oceual frameork

More information

15. Basic Index Number Theory

15. Basic Index Number Theory 5. Basc Idex Numer Theory A. Iroduco The aswer o he queso wha s he Mea of a gve se of magudes cao geeral e foud, uless here s gve also he ojec for he sake of whch a mea value s requred. There are as may

More information

The impact of service-oriented architecture on the scheduling algorithm in cloud computing

The impact of service-oriented architecture on the scheduling algorithm in cloud computing Iteratoal Research Joural of Appled ad Basc Sceces 2015 Avalable ole at www.rjabs.com ISSN 2251-838X / Vol, 9 (3): 387-392 Scece Explorer Publcatos The mpact of servce-oreted archtecture o the schedulg

More information

A quantization tree method for pricing and hedging multi-dimensional American options

A quantization tree method for pricing and hedging multi-dimensional American options A quazao ree mehod for prcg ad hedgg mul-dmesoal Amerca opos Vlad BALLY Glles PAGÈS Jacques PRINTEMS Absrac We prese here he quazao mehod whch s well-adaped for he prcg ad hedgg of Amerca opos o a baske

More information

User-credibility Based Service Reputation Management for Service Selection

User-credibility Based Service Reputation Management for Service Selection Ieraoa Coferece o Copuer Scece ad Servce Sye (CSSS 4) Uer-credby Baed Servce Repuao Maagee for Servce Seeco Cao Jux, Dog Y, Q Y, u Bo, Dog Fag, Zhou Tao Schoo of Copuer Scece ad Egeerg Key aboraory of

More information

Bullwhip Effect Measure When Supply Chain Demand is Forecasting

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

More information

Approximate hedging for non linear transaction costs on the volume of traded assets

Approximate hedging for non linear transaction costs on the volume of traded assets Noame mauscrp No. wll be sered by he edor Approxmae hedgg for o lear rasaco coss o he volume of raded asses Romuald Ele, Emmauel Lépee Absrac Ths paper s dedcaed o he replcao of a covex coge clam hs a

More information

1/22/2007 EECS 723 intro 2/3

1/22/2007 EECS 723 intro 2/3 1/22/2007 EES 723 iro 2/3 eraily, all elecrical egieers kow of liear sysems heory. Bu, i is helpful o firs review hese coceps o make sure ha we all udersad wha his heory is, why i works, ad how i is useful.

More information

The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk

The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk The Aalyss of Developmet of Isurace Cotract Premums of Geeral Lablty Isurace the Busess Isurace Rsk the Frame of the Czech Isurace Market 1998 011 Scetfc Coferece Jue, 10. - 14. 013 Pavla Kubová Departmet

More information

Internal model in life insurance : application of least squares monte carlo in risk assessment

Internal model in life insurance : application of least squares monte carlo in risk assessment Ieral model lfe surace : applcao of leas squares moe carlo rs assessme - Oberla euam Teugua (HSB) - Jae Re (Uversé yo, HSB) - rédérc Plache (Uversé yo, aboraore SA) 04. aboraore SA 50 Aveue Toy Garer -

More information

The Increasing Participation of China in the World Soybean Market and Its Impact on Price Linkages in Futures Markets

The Increasing Participation of China in the World Soybean Market and Its Impact on Price Linkages in Futures Markets The Icreasg arcpao of Cha he Word Soybea Marke ad Is Ipac o rce Lkages Fuures Markes by Mara Ace Móz Chrsofoe Rodofo Margao da Sva ad Fabo Maos Suggesed cao fora: Chrsofoe M. A. R. Sva ad F. Maos. 202.

More information

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering Moder Appled Scece October, 2009 Applcatos of Support Vector Mache Based o Boolea Kerel to Spam Flterg Shugag Lu & Keb Cu School of Computer scece ad techology, North Cha Electrc Power Uversty Hebe 071003,

More information

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0 Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may

More information

RUSSIAN ROULETTE AND PARTICLE SPLITTING

RUSSIAN ROULETTE AND PARTICLE SPLITTING RUSSAN ROULETTE AND PARTCLE SPLTTNG M. Ragheb 3/7/203 NTRODUCTON To stuatos are ecoutered partcle trasport smulatos:. a multplyg medum, a partcle such as a eutro a cosmc ray partcle or a photo may geerate

More information

CHAPTER 2. Time Value of Money 6-1

CHAPTER 2. Time Value of Money 6-1 CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show

More information

Session 4: Descriptive statistics and exporting Stata results

Session 4: Descriptive statistics and exporting Stata results Itrduct t Stata Jrd Muñz (UAB) Sess 4: Descrptve statstcs ad exprtg Stata results I ths sess we are gg t wrk wth descrptve statstcs Stata. Frst, we preset a shrt trduct t the very basc statstcal ctets

More information

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there

More information

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time. Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E

More information

The Economics of Administering Import Quotas with Licenses-on-Demand

The Economics of Administering Import Quotas with Licenses-on-Demand The Ecoomcs of Admserg Impor uoas wh Lceses-o-Demad Jaa Hraaova, James Falk ad Harry de Gorer Prepared for he World Bak s Agrculural Trade Group Jauary 2003 Absrac Ths paper exames he effecs of raog mpor

More information

Simple Linear Regression

Simple Linear Regression Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8

More information

Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information

Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information JOURNAL OF SOFWARE, VOL 5, NO 3, MARCH 00 75 Models for Selectg a ERP System wth Itutostc rapezodal Fuzzy Iformato Guwu We, Ru L Departmet of Ecoomcs ad Maagemet, Chogqg Uversty of Arts ad Sceces, Yogchua,

More information

EXAMPLE 1... 1 EXAMPLE 2... 14 EXAMPLE 3... 18 EXAMPLE 4 UNIVERSAL TRADITIONAL APPROACH... 24 EXAMPLE 5 FLEXIBLE PRODUCT... 26

EXAMPLE 1... 1 EXAMPLE 2... 14 EXAMPLE 3... 18 EXAMPLE 4 UNIVERSAL TRADITIONAL APPROACH... 24 EXAMPLE 5 FLEXIBLE PRODUCT... 26 EXAMLE... A. Edowme... B. ure edowme d Term surce... 4 C. Reseres... 8. Bruo premum d reseres... EXAMLE 2... 4 A. Whoe fe... 4 B. Reseres of Whoe fe... 6 C. Bruo Whoe fe... 7 EXAMLE 3... 8 A.ure edowme...

More information

Banking (Early Repayment of Housing Loans) Order, 5762 2002 1

Banking (Early Repayment of Housing Loans) Order, 5762 2002 1 akg (Early Repaymet of Housg Loas) Order, 5762 2002 y vrtue of the power vested me uder Secto 3 of the akg Ordace 94 (hereafter, the Ordace ), followg cosultato wth the Commttee, ad wth the approval of

More information

A MATHEMATICAL MODEL OF THE HUMAN THERMAL SYSTEM

A MATHEMATICAL MODEL OF THE HUMAN THERMAL SYSTEM A MAHEMAICAL MODEL OF HE HUMA HERMAL SYSEM A hess Subed o he Graduae School of Egeerg ad Sceces of zr Isue of echology aral Fullfle of he Requrees for he Degree of MASER OF SCIECE Mechacal Egeerg by Eda

More information

Automated Event Registration System in Corporation

Automated Event Registration System in Corporation teratoal Joural of Advaces Computer Scece ad Techology JACST), Vol., No., Pages : 0-0 0) Specal ssue of CACST 0 - Held durg 09-0 May, 0 Malaysa Automated Evet Regstrato System Corporato Zafer Al-Makhadmee

More information

Analysis of Multi-product Break-even with Uncertain Information*

Analysis of Multi-product Break-even with Uncertain Information* Aalyss o Mult-product Break-eve wth Ucerta Iormato* Lazzar Lusa L. - Morñgo María Slva Facultad de Cecas Ecoómcas Uversdad de Bueos Ares 222 Córdoba Ave. 2 d loor C20AAQ Bueos Ares - Argeta lazzar@eco.uba.ar

More information

Revision: June 12, 2010 215 E Main Suite D Pullman, WA 99163 (509) 334 6306 Voice and Fax

Revision: June 12, 2010 215 E Main Suite D Pullman, WA 99163 (509) 334 6306 Voice and Fax .3: Inucors Reson: June, 5 E Man Sue D Pullman, WA 9963 59 334 636 Voce an Fax Oerew We connue our suy of energy sorage elemens wh a scusson of nucors. Inucors, lke ressors an capacors, are passe wo-ermnal

More information

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are : Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of

More information

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization Chapter 3 Mathematcs of Face Secto 4 Preset Value of a Auty; Amortzato Preset Value of a Auty I ths secto, we wll address the problem of determg the amout that should be deposted to a accout ow at a gve

More information

- Models: - Classical: : Mastermodel (clay( Curves. - Example: - Independent variable t

- Models: - Classical: : Mastermodel (clay( Curves. - Example: - Independent variable t Compue Gaphcs Geomec Moelg Iouco - Geomec Moelg (GM) sce e of 96 - Compue asssace fo - Desg: CAD - Maufacug: : CAM - Moels: - Classcal: : Masemoel (cla( cla, poopes,, Mock-up) - GM: mahemacal escpo fo

More information

Efficient Traceback of DoS Attacks using Small Worlds in MANET

Efficient Traceback of DoS Attacks using Small Worlds in MANET Effcet Traceback of DoS Attacks usg Small Worlds MANET Yog Km, Vshal Sakhla, Ahmed Helmy Departmet. of Electrcal Egeerg, Uversty of Souther Calfora, U.S.A {yogkm, sakhla, helmy}@ceg.usc.edu Abstract Moble

More information

Why we use compounding and discounting approaches

Why we use compounding and discounting approaches Comoudig, Discouig, ad ubiased Growh Raes Near Deb s school i Souher Colorado. A examle of slow growh. Coyrigh 000-04, Gary R. Evas. May be used for o-rofi isrucioal uroses oly wihou ermissio of he auhor.

More information

PROBABILITY AND STATISTICS FOR ENGINEERS

PROBABILITY AND STATISTICS FOR ENGINEERS VŠB Techcal Uvery of Orava Faculy of Elecrcal Egeerg ad Comuer Scece Dearme of Aled Mahemac PROBABILITY AND STATISTICS FOR ENGINEERS Radm Brš Orava PROBABILITY AND STATISTICS FOR ENGINEERS LESSON INSTRUCTIONS

More information

The Time Value of Money

The Time Value of Money The Tme Value of Moey 1 Iversemet Optos Year: 1624 Property Traded: Mahatta Islad Prce : $24.00, FV of $24 @ 6%: FV = $24 (1+0.06) 388 = $158.08 bllo Opto 1 0 1 2 3 4 5 t ($519.37) 0 0 0 0 $1,000 Opto

More information

Load and Resistance Factor Design (LRFD)

Load and Resistance Factor Design (LRFD) 53:134 Structural Desg II Load ad Resstace Factor Desg (LRFD) Specfcatos ad Buldg Codes: Structural steel desg of buldgs the US s prcpally based o the specfcatos of the Amerca Isttute of Steel Costructo

More information

Numerical Solution of the Incompressible Navier-Stokes Equations

Numerical Solution of the Incompressible Navier-Stokes Equations Nmercl Solo of he comressble Ner-Sokes qos The comressble Ner-Sokes eqos descrbe wde rge of roblems fld mechcs. The re comosed of eqo mss cosero d wo momem cosero eqos oe for ech Cres eloc comoe. The deede

More information

Fuzzy Forecasting Applications on Supply Chains

Fuzzy Forecasting Applications on Supply Chains WSEAS TANSACTINS o SYSTEMS Haa Toza Fuzzy Forecag Applcao o Supply Cha HAKAN TZAN ZALP VAYVAY eparme of Idural Egeerg Turh Naval Academy 3494 Tuzla / Iabul TUKIYE hoza@dhoedur Abrac: - emad forecag; whch

More information

Chapter 8: Regression with Lagged Explanatory Variables

Chapter 8: Regression with Lagged Explanatory Variables Chaper 8: Regression wih Lagged Explanaory Variables Time series daa: Y for =1,..,T End goal: Regression model relaing a dependen variable o explanaory variables. Wih ime series new issues arise: 1. One

More information

Smart Money? The Forecasting Ability of CFTC Large Traders. by Dwight R. Sanders, Scott H. Irwin, and Robert Merrin

Smart Money? The Forecasting Ability of CFTC Large Traders. by Dwight R. Sanders, Scott H. Irwin, and Robert Merrin Smar Moey? The Forecasg Ably of CFTC Large Traders by Dwgh R. Saders, Sco H. Irw, ad Rober Merr Suggesed cao forma: Saders, D. R., S. H. Irw, ad R. Merr. 2007. Smar Moey? The Forecasg Ably of CFTC Large

More information

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected

More information

Chapter Eight. f : R R

Chapter Eight. f : R R Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,

More information

CIS603 - Artificial Intelligence. Logistic regression. (some material adopted from notes by M. Hauskrecht) CIS603 - AI. Supervised learning

CIS603 - Artificial Intelligence. Logistic regression. (some material adopted from notes by M. Hauskrecht) CIS603 - AI. Supervised learning CIS63 - Artfcal Itellgece Logstc regresso Vasleos Megalookoomou some materal adopted from otes b M. Hauskrecht Supervsed learg Data: D { d d.. d} a set of eamples d < > s put vector ad s desred output

More information