The Efficiency Measurement of Parallel Production Systems: A Non-radial Data Envelopment Analysis Model
|
|
- Clemence Hood
- 5 years ago
- Views:
Transcription
1 Jural f muter Sciece 7 (5): , 20 ISSN Sciece Publicatis The Efficiecy Measuremet f Parallel Prducti Systems: A N-radial Data Evelmet Aalysis Mdel,2 Ali Ashrafi,,3 Azmi Bi Jaafar,,4 Mhd Rizam Abu Baar ad,4 Lai S Lee Istitute fr Mathematical Research, Uiversity Putra Malaysia, UPM SERDANG, Selagr, Malaysia 2 Deartmet f Mathematics, Faculty f Mathematics, Statistics ad muter Sciece, Uiversity f Sema, Sema, Ira 3 Deartmet f Ifrmati Systems, Faculty f muter Sciece ad Ifrmati Techlgy, 4 Deartmet f Mathematics, Faculty f Sciece, Uiversity Putra Malaysia, UPM SERDANG, Selagr, Malaysia Abstract: Prblem statemet: Data Evelmet Aalysis (DEA) is a -arametric techique fr measurig the relative efficiecy f a set f rducti systems r Decisi Maig Uits (DMU) that have multile iuts ad ututs. Smetimes, DMUs have a arallel structure, i which systems cmsed f arallel uits wr idividually; the sum f their w iuts/ututs is the iut/utut f the system. Fr this tye f system, cvetial DEA mdels treat each DMU as a blac bx ad igre the erfrmace f its uits. Arach: This study itrduces a DEA mdel i Slacs-Based Measure (SBM) frmulati which csiders the arallel relatishi f the uits withi the system i measurig the efficiecy f the system. Uder this framewr, the verall efficiecy f the system is exressed as a weighted sum f the efficiecies f its uits. Results: As a SBM mdel, the rsed mdel is -radial ad is suitable fr measurig the efficiecy whe iuts ad ututs may chage -rrtially. A theretical result shws that if ay uit f a arallel system is iefficiet the the system is iefficiet. clusi: This study itrduces a -radial DEA mdel, taes it accut the erati f idividual cmets withi the arallel rducti system, t measure the verall efficiecy as well as the efficiecies f sub-rcesses. This hels the decisi maers recgize iefficiet uits ad mae later imrvemets. Key wrds: Data evelmet aalysis, decisi maig uit, rducti ssibility set, arallel rducti system, slacs-based measure INTRODUTION Data Evelmet Aalysis (DEA) is a liear rgrammig methdlgy i Oeratis Research ad Ecmics that is extesively alied by varius research cmmuities (Sh ad M, 2004; Sel et al., 2007; Rayei ad Salghi, 200 Zreia ad Ela, 20). The dmai f iquiry f the DEA is a set f rducti systems r decisi maig uits (DMU), which use multile iuts t rduce multile ututs. The aim f the DEA is t measure the relative efficiecy f each DMU withi a data set. The results secify hw efficiet each DMU has erfrmed as cmared t ther DMUs i cvertig iuts t ututs. A issue which is f greater ccer t the iefficiet DMUs is what factrs cause the iefficiecy. Several studies have assiged t breaig dw the verall efficiecy it cmets s that the surces f iefficiecy ca be idetified. Oe tye f decmsiti emhasizes the sub-rcesses f the rducti rcess. I recet years, a great umber f DEA studies have fcused tw-stage rducti systems, where all ututs frm the first stage are itermediate rducts that mae u the iuts t the secd stage. Fr examle see (Sext ad Lewis, 2003; he ad Zhu, 2004; Liag et al., 2006; Ka ad Hwag, 2008; he et al., 2009) amg thers. Recetly, Te ad Tsutsui (2009) ad et al., (200) have rsed DEA mdels fr measurig the efficiecy f etwr systems cected i series with liig activities. rresdig Authr: Ali Ashrafi, Istitute fr Mathematical Research, Uiversity Putra Malaysia, UPM SERDANG, Selagr, Malaysia Tel: Fax:
2 I sme situatis, DMUs have a arallel structure that is cmsed f a set f uits that wr idividually; the sum f their w iuts/ututs is the iut/utut f the DMUs. A tyical examle f these rducti systems is a uiversity with faculties. The verall efficiecy f the uiversity ca be calculated by the ttal iuts used ad ttal ututs rduced by all faculties. Each secific faculty ca have a efficiecy measured by cmarig it with the equivalet faculties f ther uiversities. The study f Färe ad Primt (984), which discusses the efficiecy f firms with multile lats, is rbably the first study f such DMUs. Ka (998) alied the Färe ad Primt s methdlgy fr measurig the efficiecy f frest districts with multile wrig circles i Taiwa. astelli et al. (2004) discussed a hierarchical structure i which if there is ly e layer, it becmes a arallel system. The wrs f Färe et al. (997), Tsai ad Mlier (2002) ad Yu (2008) exted f the ideedet arallel system where certai resurces are shared by sme uits. vetial DEA mdels view this tye f rducti system as a blac bx ad igre the eratis f its uits. Recetly, Ka (2009) has mdified a stadard DEA mdel ad itrduced a radial DEA mdel that evaluates the verall efficiecy f the system as well as the efficiecies f its uits. His methd decmses the iefficiecy slac f a DMU it the iefficiecy slacs f its sub-dmus. As a alicati f Ka s arallel mdel, Rayei ad Salghi (200) examie the erfrmace f the uiversities i Ira via a arallel rducti rcess. This study resets a alterative methd fr estimatig the efficiecy f a arallel rducti system ad the efficiecy f its uits. Sice DEA mdels imlicitly use Prducti Pssibility Set (PPS) t evaluate the efficiecy f DMUs, we first defie the PPS f the arallel rducti systems. The, based this PPS, we itrduce a -radial DEA mdel i Slacs-Based Measure (SBM) frmulati fr aggregatig the uits i a arallel rducti system. Uder this framewr, the verall efficiecy f the system is exressed as a weighted sum f the efficiecies f its uits. With decmsiti f the verall efficiecy, the uits which cause the iefficiet erati f the system ca be idetified fr future imrvemets A examle frm the frest rducti idustry i Taiwa is alied t cmare the ew arach with Ka s arallel mdel. MATERIALS AND METHODS that all iuts ad ututs are sitive. We dete the DMU by (x, y ), where x = (x, x 2,, x m ) T ad y = (y, y 2,, y m ) T are iut ad utut vectrs, resectively. The Prducti Pssibility Set (PPS) T is defied as a set f all iuts ad ututs f a rducti techlgy i which ututs ca be rduced frm iuts. Uder the assumti f stat Returs t Scale (RS) the PPS ca be rereseted as fllws: T = (x,y) x λx, y λy, λ 0, =,..., = = where, λ = (λ,...,λ) R is the itesity vectr. The PPS uder Variable Returs t Scale (VRS) assumti ca be defied by addig the cvexity cstrait λ = = it T c. Defiiti : (Dmiace). We say that DMU (x, y ) dmiates DMU q (x q, y q ) if ad ly if x x q ad y y q with strict iequality hldig fr at least e cmet i the iut r the utut vectr. Thus, a DMU f the PPS is t dmiated if ad ly if there is ther DMU (rigial r virtual) i the PPS which satisfies Defiiti. Defiiti 2: (Efficiecy). DMU =(x,y ) is efficiet if ad ly if there is (x,y) f PPS such that (x,y) dmiates (x,y ). Radial ad radial DEA mdel: DEA rvides fr tw tyes f measure: radial ad -radial. Radial mdels assume rrtial chage f iuts r ututs ad usually disregard the existece f slacs i measurig efficiecy scres. Radial measures are rereseted by R (hares et al., 978) ad B (Baer et al., 984) N-radial mdels, the ther had, regard the slacs f each iut r utut ad the variatis f iuts ad ututs as t rrtial; i ther wrds i -radial mdels the iuts/ututs are allwed t decrease/icrease at differet rates. N-radial mdels iclude Russell measure (Färe ad Lvell, 978) ad Slacs-Based Measure (SBM) (Te, 200). Fr evaluatig the efficiecy f DMU ( {,,}) the iut-rieted R mdel, whse urse is t miimize iut custm while eeig the Prducti ssibility set: Suse we have DMUs, where each DMU (=,,) uses m iuts x i (i =,, m) t rduce s ututs y r (r =,, s). It is assumed level f curret ututs, is set as fllws: 750
3 θ * = mi θ ε( s s.t. θx y λ,s m i i= = λ x = λ =,s = s y s r r= s s, ), 0, θ free, () where, ε is -Archimedea small value ad the timal sluti f θ*is efficiecy scre. Als egative vectrs s = (s m,...,s m) R ad s s = (s,...,s s) R idicate iut excess ad utut shrtfall slacs, resectively. Liewise the ututrieted R mdel ca be defied. Suse a timal sluti fr mdel () t be * * * * ( θ, λ,s,s ). Defiiti 3: (R-efficiecy). DMU is Refficiet if ad ly if θ* = ad s * = s *= 0. The mdel reseted i () is called the R evelmet mdel. The dual f mdel () (withut ε, i.e., ε = 0), r the R multilier mdel, is give by: θ * = max s.t. uy vx =, uy - vx 0, =,...,, u, v 0, m (2) where, v = (v,..., v m ) R ad u = (u,...,us ) R are dual variable vectrs crresdig t the cstraits f mdel (). Fig. : The arallel rducti system s The SBM mdel, as a -rieted ad -radial DEA mdel, fr evaluatig the efficiecy f DMU is defied as fllws: ρ m si * m i= x i = mi ρ = s sr s r= y r = = y = λ y s, = s.t. x λ x s, λ, s, s 0 system DMU, resectively. I ther wrds, we have: 75 (3) The timal sluti f ρ* is the SBM efficiecy scre. It ca be bviusly idetified that 0<ρ* ad surts the rerties f uit ivariace ad mte. Suse a timal sluti fr mdel (3) t be * * * * ( ρ, λ,s,s ). Defiiti 4: (SBM-efficiecy). DMU is SBMefficiet if ad ly if ρ* =. This defiiti is equivalet t s * * = s = 0. It meas that there are iut excesses ad utut shrtfalls i ay timal sluti. The iut (utut)-rieted SBM mdel ca be defied by igrig the demiatr (umeratr) f the bective fucti. Here we emhasize that, as demstrated by Te (200), the efficiecy scre measured by the SBM mdel is t greater tha the efficiecy scre measured by the R mdel. Mrever, a DMU is SBM-efficiet if ad ly if it is R-efficiet. Parallel rducti system: sider a arallel rducti rcess as shw i Fig.. Suse we have DMUs, f which each DMU (=,,) is cmsed f t uits (sub-dmus) cected i arallel. Each sub-dmu uses the same iuts t rduce the same ututs, idividually. Sub-DMU (=,,t ) has m iuts x i (i=,,m) ad s ututs r t x = i ad the sum f all r y (r=,...,s). The sum f all x i ver, amely, t y = r y ver, amely,, are the ith iut ad the rth utut f the
4 t = t y = x = x, = y (4) Ka s arallel mdel: Ka (2009) has develed a DEA mdel based the iut-rieted R multilier mdel such that by miimizig the iefficiecy slac istead f maximizig the efficiecy, we are able t decmse the iefficiecy slac f a DMU it the iefficiecy slacs f its sub-dmus. Ka s mdel fr measurig the iefficiecy f DMU is give by: mi t s = s.t. vx =, uy vx s = 0, uy vx s = 0, =,...,t, uy vx 0, =,...,,, =,...,t, uy -vx 0, =,...,,, u,v 0 (5) Suse, DMU ( {,.,}) t be the DMU uder evaluati. I a effrt t measure the verall efficiecy f DMU, first by usig the iut-rieted SBM mdel, we calculate the efficiecy scre f each sub-dmu (=,,t ) as fllws: s E = mi - m i m i= xi t = = = t λy s = y, = = s.t. λ x s x, λ, s, s 0, =,...,t, =,..., (7) After evaluatig the efficiecy scres f all Sub- DMUs, we defie the verall efficiecy scre f DMU as a weighted cmbiati f the efficiecy scres f its sub-dmus. This is shw i the fllwig equati: t 2 2 t t = E = w E w E... w E = w E (8) where, s ad s ( =,...,t ) are the iefficiecy slacs f DMU ad its sub-dmus, resectively. O timality, the efficiecy scres fr DMU ad sub-dmu ( =,,t ) ca be calculated as fllws: where, the weight w f each sub-dmu is m xi (9) m i= xi w =, =,...,t t * * = = = E s s s E =, =,...,t, vx * * (6) Hece w is the arithmetic mea f the rti f resurces devted t each sub-dmu by DMU. Frm (4), it ca be verified that w =. t = where, (*) shws the timal value frm mdel (5). The arallel SBM mdel: The PPS T arallel uder the RS assumti fr t sub-dmus f arallel rducti systems, is defied by: Defiiti 5: DMU is said t be efficiet i sub DMU, if E =. Defiiti 6: DMU is said t be efficiet if its verall efficiecy scre is equal t e, i.e., E =. Decmsig the verall efficiecy f a system it the weighted cmbiati f its uit s efficiecies t t arallel T = (x,y) x λx, y λy, λ 0 hels us t idetify the uits that cause iefficiecy. By = = = = usig the mdel (7), we are able t recgize the iefficiet sub-dmus ad mae later imrvemets. Als, usig Eq. 8 we ca evaluate the verall efficiecy Nte that if all λ, =,...,t assciated with the f the DMU i a way that taes it accut the arallel eratis f all its sub-dmus. sub-dmus withi the DMU are the same, the T Nte that, similarly, we ca itrduce a ututrieted SBM mdel fr arallel rducti systems cverts t the cvetial PP, amely T. 752
5 such that the weights assciated t su-dmu f DMU ca be defied as: * * x = x s,y = y s (5) s yr (0) s r= yr w =, =,...,t RESULTS * * Sice (s,s ) (0,0), (x, y ) is dmiated by (x,y). Hece, accrdig t Defiiti 2, DMU is RSiefficiet. As a ctrasiti t Therem, we have. The fllwig therem exlais the relatishi betwee the efficiecy f a arallel rducti system ad its rducti uits. Therem : If ay f sub-dmu = (x, y ) ( =,...,t ) f DMU is RS-iefficiet, the DMU = (x, y ) is RS-iefficiet. Prf: Suse ay f sub-dmu = (x, y ) (=,,t ) t be RS-iefficiet. We will shw that there is arallel (x,y) T such that (x, y ) is dmiated by ( x, y). Withut lss f geerality, we assume that sub-dmu = (x, y ) is RS-iefficiet. The, the fllwig system has a sluti * * * * * {λ, =...,t, ;s ;s } with (s,s ) > (0,0) : t * * = = = t * * = = = x λ x s, y λ y s We set: t t * * * * = = λ = = λ = = = = x x s x,y y s y () (2) Frm (4) ad (2) we have t t * * = = = 2 = 2 (3) x x x s,y y y s Nw we defie: t t = 2 = 2. (4) x = x x,y = y y Sice we have: arallel (x,y ) P, thus (x,y) T arallel rllary : If DMU (x, y ) is RS-efficiet, the each sub-dmu = (x, y ) (=,,t ) f DMU is RS-efficiet. Emirical examle: Nw, we aly the rsed mdel t the atial frests f Taiwa as studied by Ka (2009). I Taiwa, the frest lads are divided it eight regis, each f which is divided it fur r five sub-regis called wrig circles (Ws). These Ws are the basic cmet i the maagemet f the frest. The frest rducti rcess is a characteristic arallel rducti rcess, i that each regi has several subrdiated Ws eratig idividually. There are fur iuts: Lad (x ): area i thusad hectares Labr (x 2 ): umber f emlyees i erss Exeditures (x 3 ): mey set each year i te thusad ew Taiwa dllars Iitial stcs (x 4 ): vlume f frest stc befre the erid f evaluati i 0000 m 3 The ututs are Timber rducti (y ): timber rduced each year i cubic meters Sil cservati (y 2 ): vlume f frest stc i 0000 m 3, as higher stc level leads t less sil ersi; ad Recreati (y 3 ): visitrs serviced by frests every year i thusads f visits The data are shw i Table. Fr each iut (utut) the quatity f a regi is the sum f its sub-regis. The results f this measuremet f efficiecy are rerted i Table 2, where the secd clum shws the weights calculated frm (9) fr each sub-dmu, the third clum is the efficiecy scre calculated usig mdel (7) ad Eq. 8. The efficiecy scres f the eight DMUs calculated by the iut-rieted f mdel (3) withut taig it accut the eratis f sub-dmus are. Hece shw i the last clum uder the headig cvetial SBM mdel. 753
6 Table : Taiwa frest data Iuts Oututs Wrig circles Lad Labr Exeditures Iitial stcs Timber Sil cs. Recreati Ltug Regi Taiei Tai-ig-sha ha-chi Na-au H-ig Hsichu Regi Guay-sha Ta-chi hu-tug Ta-hu Tugshi Regi Sha-chi A-ma-sha Li-yag Li-sha Natu Regi Tai-chug Ta-ta Pu-li Shui-li hu-sha hiayi Regi A-li-sha Fa-chi-hu Ta-u Tai-a Pigtug Regi hih-sha ha-chu Liu-guay Heg-chu Taitug Regi Kua-sha hi-be Ta-wu ha- g Hualie Regi Shi-cha Na-hua Wa-yg Yu-li DISUSSION Frm Table 2, it ca be see that six DMUs are efficiet uder the cvetial SBM mdel while As ited ut by may authrs icludig Ka ad accrdig t Therem, uder the arallel SBM mdel, Hwag (2008), Ka (2009), he et al. (2009), Te sice e f DMUs erfrms efficietly i all its w ad Tsutsui (2009) ad et al. (200), the sub-dmus, e f them erfrms efficietly as a whle. cvetial DEA mdels aly a sigle rcess t Thus, by usig the results f this efficiecy measuremet evaluate the trasfrmig efficiecy f multile iuts we are able t idetify the iefficiet sub-dmus ad mae ad ututs such that they fail t measure the effrts f future imrvemet. The raigs f the verall differet rcesses ad sub-rcesses withi the efficiecy scres f the eight regis taig ur rducti systems. Thus, we cat evaluate the arach ad taig Ka s arach are shw i imact f sub-rcess iefficiecies the verall Table 3. maris f the tw sets f scres shws efficiecy f the system as a whle. I these cases, it is the t have almst idetical raig. The Searma ssible that the cvetial DEA mdels evaluate a Ra rrelati cefficiet fr the raigs i system as efficiet eve if e f its cmet Table 3 is 0.976, shwig that the crrelati rcesses is efficiet betwee ur results ad Ka s results is very high. 754
7 Table 2: Efficiecy scres Wrig Weight Parallel vetial circles (w ) SBM mdel SBM mdel Ltug Regi Taiei Tai-ig-sha ha-chi Na-au H-ig Hsichu Regi Guay-sha Ta-chi hu-tug Ta-hu Tugshi Regi Sha-chi A-ma-sha Li-yag Li-sha Natu Regi Tai-chug Ta-ta Pu-li Shui-li hu-sha hiayi Regi A-li-sha Fa-chi-hu Ta-u Tai-a Pigtug Regi hih-sha ha-chu Liu-guay Heg-chu Taitug Regi Kua-sha hi-be Ta-wu ha- g Hualie Regi Shi-cha Na-hua Wa-yg Yu-li ONLUSION I a earlier study, a radial DEA mdel was itrduced by Ka (2009) fr measurig the efficiecy f a system cmsed f arallel uits eratig ideedetly ad where the sum f iuts/ututs fr all uits is equal t the iut/utut f the system. I this study, we have itrduced a -radial mdel based a Slacs-Based Measure (SBM) framewr that evaluates the verall efficiecy f the system by csiderig the eratis f its uits. Uder this framewr, the verall efficiecy f the system is exressed as a weighted sum f the efficiecies f its uits. With decmsiti f the verall efficiecy, the uits which cause the iefficiet erati f the system ca be idetified fr future imrvemets The rsed mdel is based the assumti f stat Returs t Scale (RS). By addig the cvexity cstrait it the PPS which is built by t sub-dmus, the discussi ca be exaded t use the Variable Returs t Scale (VRS) assumti. It is tewrthy that real systems are geerally mre cmlex tha the arallel system discussed i this study. Te ad Tsutsui (2009) develed a etwr DEA mdel based a weighted SBM arach that ca be alied i series systems. Sice the series ad arallel structure are tw basic structures f a etwr system, we ca trasfrm a etwr system it a cmbiati f series ad arallel structures t evaluate the verall efficiecy ad the efficiecies f sub-rcesses. AKNOWLEDGMENT The researchers tha Prfessr Azizllah Memariai fr his cmmets ad suggestis. This wr is surted by Graduate Research Assistace f Uiversity f Putra Malaysia (Grad ). Table 3: Raig f efficiecy scres Ka s results Regis Our raig Raig Overall efficiecy Ltug Regi Hsichu Regi Tugshi Regi Natu Regi hiayi Regi Pigtug Regi Taitug Regi Hualie Regi Thus the ew arach is suitable fr measurig the verall efficiecy f the whle system with the added beefit f allwig iefficiet sub-dmus t be idetified ad tetially rectified. 755 REFERENES Baer, R.D., A. hares ad W.W. er, 984. Sme methds fr estimatig techical ad scale efficiecies i DEA. Maage. Sci., 30: DOI: 0.287/msc astelli, L., R. Peseti ad W. Uvich, DEAlie mdels fr the efficiecy evaluati f hierarchically structured uits. Eur. J. Oerat. Res., 54: DOI: 0.06/S (03) hares, A., W.W. er ad E. Rhdes, 978. Measurig the efficiecy f decisi maig uits. Eur. J. Oerat. Res., 2: htt:// t/measurig%20the%20efficiecy%20f%20desc isi%20maig%20uits.df.
8 he, Y. ad J. Zhu, Measurig ifrmati techlgy s idirect imact firm erfrmace. Ifrm. Techl. Maage. J., 5: DOI: 0.023/B:ITEM he, Y., W.D., N. Li ad J. Zhu, Additive efficiecy decmsiti i tw stage DEA. Eur. J. Oerat. Res., 96: DOI: 0.06/.er , W.D., J. Zhu, G. Bi ad F. Yag, 200. Netwr DEA: Additive efficiecy decmsiti. Eur. J. Oerat. Res., 207: DOI: 0.06/.er Färe, R. ad.a.k. Lvell, 978. Measurig the techical efficiecy f rducti. J. Ec. Thery, 9: htt://ideas.reec.rg/a/eee/ethe/v9y978i50-62.html Färe, R. ad D. Primt, 984. Efficiecy measures fr multi lat firms. Oerat. Res. Lett., 3: Färe, R., R. Grabwsi, S. Grssf ad S. Kraft, 997. Efficiecy f a fixed but allcatable iut: A -arametric arach. Ecmics Lett., 56: DOI: 0.06/S (97)8899-X Ka,., 998. Measurig the efficiecy f frest districts with multile wrig circles. J. Oerat. Res. Sciety, 49: ISSN: Ka,. ad S.N. Hwag, Efficiecy decmsiti i tw-stage data evelmet aalysis: A alicati t -life isurace cmaies i Taiwa. Eur. J. Oerat. Res., 85: DOI: 0.06/.er Ka,., Efficiecy measuremet fr arallel rducti systems. Eur. J. Oerat. Res., 96: DOI: 0.06/.er Liag, L., F. Yag, W.D. ad J. Zhu DEA mdels fr suly chai efficiecy evaluati. Aals Oerat. Res., 45: DOI: 0.007/s Rayei, M.M. ad F.H. Salghi, 200. Netwr data evelmet aalysis mdel fr estimatig efficiecy ad rductivity i uiversities. J. mut. Sci., 6: DOI: /css Rayei, M.M. ad F.H. Salghi, 200. Bechmarig i the academic deartmets usig data evelmet aalysis. Am. J. Alied Sci., 7: DOI: /aass Sel, H., J. hi, G. Par ad Y. Par, A framewr fr bechmarig service rcess usig data evelmet aalysis ad decisi tree. Exert Syst. Al., 32: DOI: 0.06/.eswa Sext, T.R. ad H.F. Lewis, Tw-stage DEA: A alicati t mar league baseball. J. Prductivity Aal., 9: DOI: 0.023/A: Sh, S. ad T. M, Decisi tree based data evelmet aalysis fr effective techlgy cmmercializati. Exert Syst. Al., 26: DOI: 0.06/.eswa Te, K. ad M. Tsutsui, Netwr DEA: A slacs-based measure arach. Eur. J. Oerat. Res., 97: DOI: 0.06/.er Te, K., 200. A slacs-based measure f efficiecy i data evelmet aalysis. Eur. J. Oerat. Res., 30: DOI: 0.06/S (99) Tsai, P.F. ad.m. Mlier, A variable returs t scale data evelmet aalysis mdel fr the it determiati f efficiecies with a examle f the UK health service. Eur. J. Oerat. Res., 4: DOI: 0.06/S (0) Yu, M.M., Measurig the efficiecy ad retur t scale status f multi-mde bus trasit-evidece frm Taiwa s bus system. Alied Ec. Lett., 5: DOI: 0.080/ Zreia M. ad N.Ela, 20. Baig efficiecy i Leba: A emirical Ivestiqati. J. Sc. Sci., 7: DOI: /ss
are specified , are linearly independent Otherwise, they are linearly dependent, and one is expressed by a linear combination of the others
Chater 3. Higher Order Liear ODEs Kreyszig by YHLee;4; 3-3. Hmgeeus Liear ODEs The stadard frm f the th rder liear ODE ( ) ( ) = : hmgeeus if r( ) = y y y y r Hmgeeus Liear ODE: Suersiti Pricile, Geeral
More informationMulti-objective Programming Approach for. Fuzzy Linear Programming Problems
Applied Mathematical Scieces Vl. 7 03. 37 8-87 HIKARI Ltd www.m-hikari.cm Multi-bective Prgrammig Apprach fr Fuzzy Liear Prgrammig Prblems P. Padia Departmet f Mathematics Schl f Advaced Scieces VIT Uiversity
More informationSuper-efficiency Models, Part II
Super-efficiec Mdels, Part II Emilia Niskae The 4th f Nvember S steemiaalsi Ctets. Etesis t Variable Returs-t-Scale (0.4) S steemiaalsi Radial Super-efficiec Case Prblems with Radial Super-efficiec Case
More informationExamination No. 3 - Tuesday, Nov. 15
NAME (lease rit) SOLUTIONS ECE 35 - DEVICE ELECTRONICS Fall Semester 005 Examiati N 3 - Tuesday, Nv 5 3 4 5 The time fr examiati is hr 5 mi Studets are allwed t use 3 sheets f tes Please shw yur wrk, artial
More informationPortfolio Performance Evaluation in a Modified Mean-Variance-Skewness Framework with Negative Data
Available lie at http://idea.srbiau.ac.ir It. J. Data Evelpmet Aalysis (ISSN 345-458X) Vl., N.3, Year 04 Article ID IJDEA-003,3 pages Research Article Iteratial Jural f Data Evelpmet Aalysis Sciece ad
More informationPipe Networks - Hardy Cross Method Page 1. Pipe Networks
Pie Netwrks - Hardy Crss etd Page Pie Netwrks Itrducti A ie etwrk is a itercected set f ies likig e r mre surces t e r mre demad (delivery) its, ad ca ivlve ay umber f ies i series, bracig ies, ad arallel
More informationFourier Method for Solving Transportation. Problems with Mixed Constraints
It. J. Ctemp. Math. Scieces, Vl. 5, 200,. 28, 385-395 Furier Methd fr Slvig Trasprtati Prblems with Mixed Cstraits P. Padia ad G. Nataraja Departmet f Mathematics, Schl f Advaced Scieces V I T Uiversity,
More informationAn epsilon-based measure of efficiency in DEA revisited -A third pole of technical efficiency-
GRIPS Plicy Ifrmati Ceter Discussi Paper : 09-2 A epsil-based measure f efficiecy i DEA revisited -A third ple f techical efficiecy- Karu Te Natial Graduate Istitute fr Plicy Studies 7-22- Rppgi, Miat-ku,
More informationGrade 3 Mathematics Course Syllabus Prince George s County Public Schools
Ctet Grade 3 Mathematics Curse Syllabus Price Gerge s Cuty Public Schls Prerequisites: Ne Curse Descripti: I Grade 3, istructial time shuld fcus fur critical areas: (1) develpig uderstadig f multiplicati
More informationA New Method for Finding an Optimal Solution. of Fully Interval Integer Transportation Problems
Applied Matheatical Scieces, Vl. 4, 200,. 37, 89-830 A New Methd fr Fidig a Optial Sluti f Fully Iterval Iteger Trasprtati Prbles P. Padia ad G. Nataraja Departet f Matheatics, Schl f Advaced Scieces,
More informationChapter 3.1: Polynomial Functions
Ntes 3.1: Ply Fucs Chapter 3.1: Plymial Fuctis I Algebra I ad Algebra II, yu ecutered sme very famus plymial fuctis. I this secti, yu will meet may ther members f the plymial family, what sets them apart
More informationD.S.G. POLLOCK: TOPICS IN TIME-SERIES ANALYSIS STATISTICAL FOURIER ANALYSIS
STATISTICAL FOURIER ANALYSIS The Furier Represetati f a Sequece Accrdig t the basic result f Furier aalysis, it is always pssible t apprximate a arbitrary aalytic fucti defied ver a fiite iterval f the
More information8.0 Negative Bias Temperature Instability (NBTI)
EE650R: Reliability Physics f Naelectric Devices Lecture 8: Negative Bias Temerature Istability Date: Se 27 2006 Class Ntes: Vijay Rawat Reviewed by: Saakshi Gagwal 8.0 Negative Bias Temerature Istability
More informationIntermediate Division Solutions
Itermediate Divisi Slutis 1. Cmpute the largest 4-digit umber f the frm ABBA which is exactly divisible by 7. Sluti ABBA 1000A + 100B +10B+A 1001A + 110B 1001 is divisible by 7 (1001 7 143), s 1001A is
More informationDirectional Duality Theory
Suther Illiis Uiversity Carbdale OpeSIUC Discussi Papers Departmet f Ecmics 2004 Directial Duality Thery Daiel Primt Suther Illiis Uiversity Carbdale Rlf Fare Oreg State Uiversity Fllw this ad additial
More informationQuantum Mechanics for Scientists and Engineers. David Miller
Quatum Mechaics fr Scietists ad Egieers David Miller Time-depedet perturbati thery Time-depedet perturbati thery Time-depedet perturbati basics Time-depedet perturbati thery Fr time-depedet prblems csider
More informationThe Excel FFT Function v1.1 P. T. Debevec February 12, The discrete Fourier transform may be used to identify periodic structures in time ht.
The Excel FFT Fucti v P T Debevec February 2, 26 The discrete Furier trasfrm may be used t idetify peridic structures i time ht series data Suppse that a physical prcess is represeted by the fucti f time,
More informationSolutions. Definitions pertaining to solutions
Slutis Defiitis pertaiig t slutis Slute is the substace that is disslved. It is usually preset i the smaller amut. Slvet is the substace that des the disslvig. It is usually preset i the larger amut. Slubility
More informationMATH Midterm Examination Victor Matveev October 26, 2016
MATH 33- Midterm Examiati Victr Matveev Octber 6, 6. (5pts, mi) Suppse f(x) equals si x the iterval < x < (=), ad is a eve peridic extesi f this fucti t the rest f the real lie. Fid the csie series fr
More informationIJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 12, December
IJISET - Iteratial Jural f Ivative Sciece, Egieerig & Techlgy, Vl Issue, December 5 wwwijisetcm ISSN 48 7968 Psirmal ad * Pararmal mpsiti Operatrs the Fc Space Abstract Dr N Sivamai Departmet f athematics,
More informationStudy of Energy Eigenvalues of Three Dimensional. Quantum Wires with Variable Cross Section
Adv. Studies Ther. Phys. Vl. 3 009. 5 3-0 Study f Eergy Eigevalues f Three Dimesial Quatum Wires with Variale Crss Secti M.. Sltai Erde Msa Departmet f physics Islamic Aad Uiversity Share-ey rach Ira alrevahidi@yah.cm
More informationControl Systems. Controllability and Observability (Chapter 6)
6.53 trl Systems trllaility ad Oservaility (hapter 6) Geeral Framewrk i State-Spae pprah Give a LTI system: x x u; y x (*) The system might e ustale r des t meet the required perfrmae spe. Hw a we imprve
More informationUNIVERSITY OF TECHNOLOGY. Department of Mathematics PROBABILITY THEORY, STATISTICS AND OPERATIONS RESEARCH GROUP. Memorandum COSOR 76-10
EI~~HOVEN UNIVERSITY OF TECHNOLOGY Departmet f Mathematics PROBABILITY THEORY, STATISTICS AND OPERATIONS RESEARCH GROUP Memradum COSOR 76-10 O a class f embedded Markv prcesses ad recurrece by F.H. Sims
More informationENGI 4421 Central Limit Theorem Page Central Limit Theorem [Navidi, section 4.11; Devore sections ]
ENGI 441 Cetral Limit Therem Page 11-01 Cetral Limit Therem [Navidi, secti 4.11; Devre sectis 5.3-5.4] If X i is t rmally distributed, but E X i, V X i ad is large (apprximately 30 r mre), the, t a gd
More informationCh. 1 Introduction to Estimation 1/15
Ch. Itrducti t stimati /5 ample stimati Prblem: DSB R S f M f s f f f ; f, φ m tcsπf t + φ t f lectrics dds ise wt usually white BPF & mp t s t + w t st. lg. f & φ X udi mp cs π f + φ t Oscillatr w/ f
More informationDifference of 2 kj per mole of propane! E = kj
Ethaly, H Fr rcesses measured uder cstat ressure cditi, the heat the reacti is q. E = q + w = q P ext V he subscrit remids is that the heat measured is uder cstat ressure cditi. hermdyamics Slve r q q
More informationAuthor. Introduction. Author. o Asmir Tobudic. ISE 599 Computational Modeling of Expressive Performance
ISE 599 Cmputatial Mdelig f Expressive Perfrmace Playig Mzart by Aalgy: Learig Multi-level Timig ad Dyamics Strategies by Gerhard Widmer ad Asmir Tbudic Preseted by Tsug-Ha (Rbert) Chiag April 5, 2006
More informationVariations on the theme of slacks-based measure of efficiency in DEA
GRIPS Plicy Ifrmati Ceter Dicui Paper : 8-4 Variati the theme f lac-baed meaure f efficiecy i DEA Karu Te Natial Graduate Ititute fr Plicy Studie 7-22- Rppgi, Miat-u, Ty 6-8677, Japa te@gripacp Abtract:
More informationIf σis unknown. Properties of t distribution. 6.3 One and Two Sample Inferences for Means. What is the correct multiplier? t
/8/009 6.3 Oe a Tw Samle Iferece fr Mea If i kw a 95% Cfiece Iterval i 96 ±.96 96.96 ± But i ever kw. If i ukw Etimate by amle taar eviati The etimate taar errr f the mea will be / Uig the etimate taar
More informationComparative analysis of bayesian control chart estimation and conventional multivariate control chart
America Jural f Theretical ad Applied Statistics 3; ( : 7- ublished lie Jauary, 3 (http://www.sciecepublishiggrup.cm//atas di:.648/.atas.3. Cmparative aalysis f bayesia ctrl chart estimati ad cvetial multivariate
More informationBIO752: Advanced Methods in Biostatistics, II TERM 2, 2010 T. A. Louis. BIO 752: MIDTERM EXAMINATION: ANSWERS 30 November 2010
BIO752: Advaced Methds i Bistatistics, II TERM 2, 2010 T. A. Luis BIO 752: MIDTERM EXAMINATION: ANSWERS 30 Nvember 2010 Questi #1 (15 pits): Let X ad Y be radm variables with a jit distributi ad assume
More informationK [f(t)] 2 [ (st) /2 K A GENERALIZED MEIJER TRANSFORMATION. Ku(z) ()x) t -)-I e. K(z) r( + ) () (t 2 I) -1/2 e -zt dt, G. L. N. RAO L.
Iterat. J. Math. & Math. Scl. Vl. 8 N. 2 (1985) 359-365 359 A GENERALIZED MEIJER TRANSFORMATION G. L. N. RAO Departmet f Mathematics Jamshedpur C-perative Cllege f the Rachi Uiversity Jamshedpur, Idia
More information, the random variable. and a sample size over the y-values 0:1:10.
Lecture 3 (4//9) 000 HW PROBLEM 3(5pts) The estimatr i (c) f PROBLEM, p 000, where { } ~ iid bimial(,, is 000 e f the mst ppular statistics It is the estimatr f the ppulati prprti I PROBLEM we used simulatis
More informationThermodynamic perturbation theory for self assembling mixtures of multi - patch colloids and colloids with spherically symmetric attractions
Thermdyamic erturbati thery fr self assemblig mixtures f multi - atch cllids ad cllids with sherically symmetric attractis eett D. Marshall ad Walter G. Chama Deartmet f Chemical ad imlecular Egieerig
More informationPhysical Chemistry Laboratory I CHEM 445 Experiment 2 Partial Molar Volume (Revised, 01/13/03)
Physical Chemistry Labratry I CHEM 445 Experimet Partial Mlar lume (Revised, 0/3/03) lume is, t a gd apprximati, a additive prperty. Certaily this apprximati is used i preparig slutis whse ccetratis are
More informationENGI 4421 Central Limit Theorem Page Central Limit Theorem [Navidi, section 4.11; Devore sections ]
ENGI 441 Cetral Limit Therem Page 11-01 Cetral Limit Therem [Navidi, secti 4.11; Devre sectis 5.3-5.4] If X i is t rmally distributed, but E X i, V X i ad is large (apprximately 30 r mre), the, t a gd
More information5.80 Small-Molecule Spectroscopy and Dynamics
MIT OpeCurseWare http://cw.mit.edu 5.8 Small-Mlecule Spectrscpy ad Dyamics Fall 8 Fr ifrmati abut citig these materials r ur Terms f Use, visit: http://cw.mit.edu/terms. 5.8 Lecture #33 Fall, 8 Page f
More informationFourier Series & Fourier Transforms
Experimet 1 Furier Series & Furier Trasfrms MATLAB Simulati Objectives Furier aalysis plays a imprtat rle i cmmuicati thery. The mai bjectives f this experimet are: 1) T gai a gd uderstadig ad practice
More informationWEST VIRGINIA UNIVERSITY
WEST VIRGINIA UNIVERSITY PLASMA PHYSICS GROUP INTERNAL REPORT PL - 045 Mea Optical epth ad Optical Escape Factr fr Helium Trasitis i Helic Plasmas R.F. Bivi Nvember 000 Revised March 00 TABLE OF CONTENT.0
More informationAxial Temperature Distribution in W-Tailored Optical Fibers
Axial Temperature Distributi i W-Tailred Optical ibers Mhamed I. Shehata (m.ismail34@yah.cm), Mustafa H. Aly(drmsaly@gmail.cm) OSA Member, ad M. B. Saleh (Basheer@aast.edu) Arab Academy fr Sciece, Techlgy
More informationOptimally Sparse SVMs
A. Proof of Lemma 3. We here prove a lower boud o the umber of support vectors to achieve geeralizatio bouds of the form which we cosider. Importatly, this result holds ot oly for liear classifiers, but
More informationA Study on Estimation of Lifetime Distribution with Covariates Under Misspecification
Prceedigs f the Wrld Cgress Egieerig ad Cmputer Sciece 2015 Vl II, Octber 21-23, 2015, Sa Fracisc, USA A Study Estimati f Lifetime Distributi with Cvariates Uder Misspecificati Masahir Ykyama, Member,
More informationA Hartree-Fock Calculation of the Water Molecule
Chemistry 460 Fall 2017 Dr. Jea M. Stadard Nvember 29, 2017 A Hartree-Fck Calculati f the Water Mlecule Itrducti A example Hartree-Fck calculati f the water mlecule will be preseted. I this case, the water
More information5.1 Two-Step Conditional Density Estimator
5.1 Tw-Step Cditial Desity Estimatr We ca write y = g(x) + e where g(x) is the cditial mea fucti ad e is the regressi errr. Let f e (e j x) be the cditial desity f e give X = x: The the cditial desity
More informationComparison of Minimum Initial Capital with Investment and Non-investment Discrete Time Surplus Processes
The 22 d Aual Meetig i Mathematics (AMM 207) Departmet of Mathematics, Faculty of Sciece Chiag Mai Uiversity, Chiag Mai, Thailad Compariso of Miimum Iitial Capital with Ivestmet ad -ivestmet Discrete Time
More informationHº = -690 kj/mol for ionization of n-propylene Hº = -757 kj/mol for ionization of isopropylene
Prblem 56. (a) (b) re egative º values are a idicati f mre stable secies. The º is mst egative fr the i-ryl ad -butyl is, bth f which ctai a alkyl substituet bded t the iized carb. Thus it aears that catis
More informationALE 26. Equilibria for Cell Reactions. What happens to the cell potential as the reaction proceeds over time?
Name Chem 163 Secti: Team Number: AL 26. quilibria fr Cell Reactis (Referece: 21.4 Silberberg 5 th editi) What happes t the ptetial as the reacti prceeds ver time? The Mdel: Basis fr the Nerst quati Previusly,
More information13.1 Shannon lower bound
ECE598: Iformatio-theoretic methods i high-dimesioal statistics Srig 016 Lecture 13: Shao lower boud, Fao s method Lecturer: Yihog Wu Scribe: Daewo Seo, Mar 8, 016 [Ed Mar 11] I the last class, we leared
More information[1 & α(t & T 1. ' ρ 1
NAME 89.304 - IGNEOUS & METAMORPHIC PETROLOGY DENSITY & VISCOSITY OF MAGMAS I. Desity The desity (mass/vlume) f a magma is a imprtat parameter which plays a rle i a umber f aspects f magma behavir ad evluti.
More informationCoping with Insufficient Data: The Case of Household Automobile Holding Modeling by Ryuichi Kitamura and Toshiyuki Yamamoto
Coig with Isufficiet Data: he Case of ousehold utomobile oldig odelig by Ryuichi Kitamura ad oshiyuki Yamamoto It is ofte the case that tyically available data do ot cotai all the variables that are desired
More informationConfidence Intervals
Cofidece Itervals Berli Che Deartmet of Comuter Sciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Referece: 1. W. Navidi. Statistics for Egieerig ad Scietists. Chater 5 & Teachig Material Itroductio
More informationModern Physics. Unit 15: Nuclear Structure and Decay Lecture 15.2: The Strong Force. Ron Reifenberger Professor of Physics Purdue University
Mder Physics Uit 15: Nuclear Structure ad Decay Lecture 15.: The Strg Frce R Reifeberger Prfessr f Physics Purdue Uiversity 1 Bidig eergy er ucle - the deuter Eergy (MeV) ~0.4fm B.E. A =.MeV/ = 1.1 MeV/ucle.
More informationMATHEMATICS 9740/01 Paper 1 14 Sep hours
Cadidate Name: Class: JC PRELIMINARY EXAM Higher MATHEMATICS 9740/0 Paper 4 Sep 06 3 hurs Additial Materials: Cver page Aswer papers List f Frmulae (MF5) READ THESE INSTRUCTIONS FIRST Write yur full ame
More informationInvestigating the Significance of a Correlation Coefficient using Jackknife Estimates
Iteratioal Joural of Scieces: Basic ad Applied Research (IJSBAR) ISSN 2307-4531 (Prit & Olie) http://gssrr.org/idex.php?joural=jouralofbasicadapplied ---------------------------------------------------------------------------------------------------------------------------
More informationNuclear Physics Worksheet
Nuclear Physics Worksheet The ucleus [lural: uclei] is the core of the atom ad is comosed of articles called ucleos, of which there are two tyes: rotos (ositively charged); the umber of rotos i a ucleus
More informationx 2 x 3 x b 0, then a, b, c log x 1 log z log x log y 1 logb log a dy 4. dx As tangent is perpendicular to the x axis, slope
The agle betwee the tagets draw t the parabla y = frm the pit (-,) 5 9 6 Here give pit lies the directri, hece the agle betwee the tagets frm that pit right agle Ratig :EASY The umber f values f c such
More informationAnalysis of Experimental Measurements
Aalysis of Experimetal Measuremets Thik carefully about the process of makig a measuremet. A measuremet is a compariso betwee some ukow physical quatity ad a stadard of that physical quatity. As a example,
More informationJ. Stat. Appl. Pro. Lett. 2, No. 1, (2015) 15
J. Stat. Appl. Pro. Lett. 2, No. 1, 15-22 2015 15 Joural of Statistics Applicatios & Probability Letters A Iteratioal Joural http://dx.doi.org/10.12785/jsapl/020102 Martigale Method for Rui Probabilityi
More informationMarkov processes and the Kolmogorov equations
Chapter 6 Markv prcesses ad the Klmgrv equatis 6. Stchastic Differetial Equatis Csider the stchastic differetial equati: dx(t) =a(t X(t)) dt + (t X(t)) db(t): (SDE) Here a(t x) ad (t x) are give fuctis,
More informationSound Absorption Characteristics of Membrane- Based Sound Absorbers
Purdue e-pubs Publicatis f the Ray W. Schl f Mechaical Egieerig 8-28-2003 Sud Absrpti Characteristics f Membrae- Based Sud Absrbers J Stuart Blt, blt@purdue.edu Jih Sg Fllw this ad additial wrks at: http://dcs.lib.purdue.edu/herrick
More informationThe generalized marginal rate of substitution
Jural f Mathematical Ecmics 31 1999 553 560 The geeralized margial rate f substituti M Besada, C Vazuez ) Facultade de Ecmicas, UiÕersidade de Vig, Aptd 874, 3600 Vig, Spai Received 31 May 1995; accepted
More informationMotor Stability. Plateau and Mesa Burning
Mtr Stability Recall mass cservati fr steady erati ( =cstat) m eit m b b r b s r m icr m eit Is this cditi (it) stable? ly if rmally use.3
More informationMean residual life of coherent systems consisting of multiple types of dependent components
Mea residual life f cheret systems csistig f multiple types f depedet cmpets Serka Eryilmaz, Frak P.A. Cle y ad Tahai Cle-Maturi z February 20, 208 Abstract Mea residual life is a useful dyamic characteristic
More informationOptimization of frequency quantization. VN Tibabishev. Keywords: optimization, sampling frequency, the substitution frequencies.
UDC 519.21 Otimizatin f frequency quantizatin VN Tibabishev Asvt51@nard.ru We btain the functinal defining the rice and quality f samle readings f the generalized velcities. It is shwn that the timal samling
More informationTurbulent entry length. 7.3 Turbulent pipe flow. Turbulent entry length. Illustrative experiment. The Reynolds analogy and heat transfer
Turulet etry legt 7.3 Turulet ie l Etry legts x e ad x et are geerally srter turulet l ta lamar l Termal etry legts, x et /, r a case it q cst imsed a ydrdyamically ully develed l 7.3 Turulet ie l ill
More informationChapter 5. Root Locus Techniques
Chapter 5 Rt Lcu Techique Itrducti Sytem perfrmace ad tability dt determied dby cled-lp l ple Typical cled-lp feedback ctrl ytem G Ope-lp TF KG H Zer -, - Ple 0, -, - K Lcati f ple eaily fud Variati f
More informationMi-Hwa Ko and Tae-Sung Kim
J. Korea Math. Soc. 42 2005), No. 5, pp. 949 957 ALMOST SURE CONVERGENCE FOR WEIGHTED SUMS OF NEGATIVELY ORTHANT DEPENDENT RANDOM VARIABLES Mi-Hwa Ko ad Tae-Sug Kim Abstract. For weighted sum of a sequece
More informationActive redundancy allocation in systems. R. Romera; J. Valdés; R. Zequeira*
Wrkig Paper -6 (3) Statistics ad Ecmetrics Series March Departamet de Estadística y Ecmetría Uiversidad Carls III de Madrid Calle Madrid, 6 893 Getafe (Spai) Fax (34) 9 64-98-49 Active redudacy allcati
More informationYou may work in pairs or purely individually for this assignment.
CS 04 Problem Solvig i Computer Sciece OOC Assigmet 6: Recurreces You may work i pairs or purely idividually for this assigmet. Prepare your aswers to the followig questios i a plai ASCII text file or
More informationGeneral Chemistry 1 (CHEM1141) Shawnee State University Fall 2016
Geeral Chemistry 1 (CHEM1141) Shawee State Uiversity Fall 2016 September 23, 2016 Name E x a m # I C Please write yur full ame, ad the exam versi (IC) that yu have the scatr sheet! Please 0 check the bx
More informationManagement Science Letters
Maagemet Sciece Letters 3 (203) 435 442 Cotets lists available at GrowigSciece Maagemet Sciece Letters homepage: www.growigsciece.com/msl A DEA applicatio for aalyzig ivestmet activities i higher educatioal
More informationMingqing Xing 1 School of Economics and Management, Weifang University, Weifang ,
[Tye text] [Tye text] [Tye text] ISSN : 974-7435 Vlume 1 Issue 1 BiTechnlgy 14 An Indian Jurnal FULL PAPER BTAIJ, 1(1, 14 [6348-6356] The imact f en surce sftware n rrietary sftware firms rfit and scial
More informationA L A BA M A L A W R E V IE W
A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N
More informationMA131 - Analysis 1. Workbook 2 Sequences I
MA3 - Aalysis Workbook 2 Sequeces I Autum 203 Cotets 2 Sequeces I 2. Itroductio.............................. 2.2 Icreasig ad Decreasig Sequeces................ 2 2.3 Bouded Sequeces..........................
More informationClassification of DT signals
Comlex exoetial A discrete time sigal may be comlex valued I digital commuicatios comlex sigals arise aturally A comlex sigal may be rereseted i two forms: jarg { z( ) } { } z ( ) = Re { z ( )} + jim {
More informationLecture 7: Properties of Random Samples
Lecture 7: Properties of Radom Samples 1 Cotiued From Last Class Theorem 1.1. Let X 1, X,...X be a radom sample from a populatio with mea µ ad variace σ
More informationProjection Moiré Profilometry using Liquid Crystal Digital Gratings
0th IMEKO TC4 ymsium n Braunschweig, GERMAY, 20, etember 2-4 Prjectin Miré Prfilmetry using iquid Crystal Digital Gratings Fumi Kbayashi, Center fr Otical Research and Educatin, Utsunmiya University; Yukitshi
More informationAirport Congestion Pricing and its Welfare Implications: The Variable Passenger Time Cost Case
Airrt Cgesti Pricig ad its Welfare Imlicatis: The Variable Passeger Time Cst Case Crresdece authr Adrew Chi-Lk Yue ad Amig Zhag Sauder Schl f Busiess Uiversity f British Clumbia 2053 Mai Mall, Vacuver,
More informationSequences I. Chapter Introduction
Chapter 2 Sequeces I 2. Itroductio A sequece is a list of umbers i a defiite order so that we kow which umber is i the first place, which umber is i the secod place ad, for ay atural umber, we kow which
More informationIdentical Particles. We would like to move from the quantum theory of hydrogen to that for the rest of the periodic table
We wuld like t ve fr the quatu thery f hydrge t that fr the rest f the peridic table Oe electr at t ultielectr ats This is cplicated by the iteracti f the electrs with each ther ad by the fact that the
More informationLecture 21: Signal Subspaces and Sparsity
ECE 830 Fall 00 Statistical Sigal Prcessig istructr: R. Nwak Lecture : Sigal Subspaces ad Sparsity Sigal Subspaces ad Sparsity Recall the classical liear sigal mdel: X = H + w, w N(0, where S = H, is a
More informationStudy in Cylindrical Coordinates of the Heat Transfer Through a Tow Material-Thermal Impedance
Research ural f Applied Scieces, Egieerig ad echlgy (): 9-63, 3 ISSN: 4-749; e-issn: 4-7467 Maxwell Scietific Orgaiati, 3 Submitted: uly 4, Accepted: September 8, Published: May, 3 Study i Cylidrical Crdiates
More informationRMO Sample Paper 1 Solutions :
RMO Sample Paper Slutis :. The umber f arragemets withut ay restricti = 9! 3!3!3! The umber f arragemets with ly e set f the csecutive 3 letters = The umber f arragemets with ly tw sets f the csecutive
More informationThe Simple Linear Regression Model: Theory
Chapter 3 The mple Lear Regress Mdel: Ther 3. The mdel 3.. The data bservats respse varable eplaatr varable : : Plttg the data.. Fgure 3.: Dsplag the cable data csdered b Che at al (993). There are 79
More informationBecause it tests for differences between multiple pairs of means in one test, it is called an omnibus test.
Math 308 Sprig 018 Classes 19 ad 0: Aalysis of Variace (ANOVA) Page 1 of 6 Itroductio ANOVA is a statistical procedure for determiig whether three or more sample meas were draw from populatios with equal
More informationw (1) ˆx w (1) x (1) /ρ and w (2) ˆx w (2) x (2) /ρ.
2 5. Weighted umber of late jobs 5.1. Release dates ad due dates: maximimizig the weight of o-time jobs Oce we add release dates, miimizig the umber of late jobs becomes a sigificatly harder problem. For
More informationErrors Due to Misalignment of Strain Gages
VISHAY MICO-MEASUEMENTS Strai Gages ad Istrumets Errors Due to Misaligmet of Strai Gages Sigle Gage i a Uiform Biaxial Strai Field Whe a gage is boded to a test surface at a small agular error with resect
More informationE o and the equilibrium constant, K
lectrchemical measuremets (Ch -5 t 6). T state the relati betwee ad K. (D x -b, -). Frm galvaic cell vltage measuremet (a) K sp (D xercise -8, -) (b) K sp ad γ (D xercise -9) (c) K a (D xercise -G, -6)
More informationIP Reference guide for integer programming formulations.
IP Referece guide for iteger programmig formulatios. by James B. Orli for 15.053 ad 15.058 This documet is iteded as a compact (or relatively compact) guide to the formulatio of iteger programs. For more
More informationReview for cumulative test
Hrs Math 3 review prblems Jauary, 01 cumulative: Chapters 1- page 1 Review fr cumulative test O Mday, Jauary 7, Hrs Math 3 will have a curse-wide cumulative test cverig Chapters 1-. Yu ca expect the test
More informationSUPPLEMENTARY INFORMATION
DOI: 10.1038/NPHYS309 O the reality of the quatum state Matthew F. Pusey, 1, Joatha Barrett, ad Terry Rudolph 1 1 Departmet of Physics, Imperial College Lodo, Price Cosort Road, Lodo SW7 AZ, Uited Kigdom
More information"NEET / AIIMS " SOLUTION (6) Avail Video Lectures of Experienced Faculty.
07 NEET EXAMINATION SOLUTION (6) Avail Vide Lectures f Exerienced Faculty Page Sl. The lean exressin which satisfies the utut f this lgic gate is C = A., Whichindicates fr AND gate. We can see, utut C
More informationMath 778S Spectral Graph Theory Handout #3: Eigenvalues of Adjacency Matrix
Math 778S Spectral Graph Theory Hadout #3: Eigevalues of Adjacecy Matrix The Cartesia product (deoted by G H) of two simple graphs G ad H has the vertex-set V (G) V (H). For ay u, v V (G) ad x, y V (H),
More informationPartial-Sum Queries in OLAP Data Cubes Using Covering Codes
326 IEEE TRANSACTIONS ON COMPUTERS, VOL. 47, NO. 2, DECEMBER 998 Partial-Sum Queries i OLAP Data Cubes Usig Cverig Cdes Chig-Tie H, Member, IEEE, Jehshua Bruck, Seir Member, IEEE, ad Rakesh Agrawal, Seir
More informationSeed and Sieve of Odd Composite Numbers with Applications in Factorization of Integers
IOSR Joural of Mathematics (IOSR-JM) e-issn: 78-578, p-issn: 319-75X. Volume 1, Issue 5 Ver. VIII (Sep. - Oct.01), PP 01-07 www.iosrjourals.org Seed ad Sieve of Odd Composite Numbers with Applicatios i
More informationEstimation of Population Mean Using Co-Efficient of Variation and Median of an Auxiliary Variable
Iteratioal Joural of Probability ad Statistics 01, 1(4: 111-118 DOI: 10.593/j.ijps.010104.04 Estimatio of Populatio Mea Usig Co-Efficiet of Variatio ad Media of a Auxiliary Variable J. Subramai *, G. Kumarapadiya
More informationDouble Stage Shrinkage Estimator of Two Parameters. Generalized Exponential Distribution
Iteratioal Mathematical Forum, Vol., 3, o. 3, 3-53 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/.9/imf.3.335 Double Stage Shrikage Estimator of Two Parameters Geeralized Expoetial Distributio Alaa M.
More information1996 Engineering Systems Design and Analysis Conference, Montpellier, France, July 1-4, 1996, Vol. 7, pp
THE POWER AND LIMIT OF NEURAL NETWORKS T. Y. Lin Department f Mathematics and Cmputer Science San Jse State University San Jse, Califrnia 959-003 tylin@cs.ssu.edu and Bereley Initiative in Sft Cmputing*
More informationOn Algorithm for the Minimum Spanning Trees Problem with Diameter Bounded Below
O Algorithm for the Miimum Spaig Trees Problem with Diameter Bouded Below Edward Kh. Gimadi 1,2, Alexey M. Istomi 1, ad Ekateria Yu. Shi 2 1 Sobolev Istitute of Mathematics, 4 Acad. Koptyug aveue, 630090
More informationLecture 10: Universal coding and prediction
0-704: Iformatio Processig ad Learig Sprig 0 Lecture 0: Uiversal codig ad predictio Lecturer: Aarti Sigh Scribes: Georg M. Goerg Disclaimer: These otes have ot bee subjected to the usual scrutiy reserved
More information