ECON 3710/4710 Demography of developing countries STABLE AND STATIONARY POPULATIONS. Lecture note. Nico Keilman

Size: px
Start display at page:

Download "ECON 3710/4710 Demography of developing countries STABLE AND STATIONARY POPULATIONS. Lecture note. Nico Keilman"

Transcription

1 ECON 37/47 Demogaphy of deveoping counies STAE AND STATIONARY POPUATIONS ecue noe Nico Keiman Requied eading: Sabe and saionay modes Chape 9 in D Rowand Demogaphic Mehods and Conceps Ofod Univesiy Pess 23, pp 3-36, Mode popuaions ae used o show vaious inks beween popuaion vaiabes, such as he numbes of bihs and deahs, he age disibuion, oa popuaion size, ec The inenion is o simpify eaiy, wihou osing main chaaceisics Saing poin: we conside a cosed popuaion, of which he membes ony epeience bih and deah In addiion: we beak he popuaion down by jus wo chaaceisics: age and se Definiion: a popuaion of his kind is caed sabe when boh is gowh ae and is eaive age disibuion do no change ove ime Hence he size of a sabe popuaion gows o diminishes a a consan ae, bu each age goup has a consan shae The size of each age goup gows/diminishes wih he same (consan ae as he size of he whoe popuaion Popey : The numbe of bihs and he numbe of deahs in a sabe popuaion change wih he same ae This ae is consan, and i equas he gowh ae of he popuaion Thus he cude bih ae (CR is aso consan, and so is he cude deah ae (CDR ahough he CR and he CDR ae usuay no equa o each ohe In ohe wods: gowh ae (= naua gowh ae = CR CDR = consan

2 Popey 2: Any wo ses of ime-consan age-specific bih aes and age-specific deah aes, when appied o a popuaion wih a ceain iniia age disibuion, wi in he ong un esu in a sabe popuaion, of which he gowh ae and he age disibuion ony depend of he bih aes and he deah aes The uimae (sabe age disibuion wi have a egua fom (cf beow This is he case even if he iniia age disibuion was no sabe fo insance was vey iegua We say ha a sabe popuaion has fogoen is hisoy This popey is known as he pincipe of song egodiciy (oka (9, Eue (76 The use of sabe popuaion heoy Sabe popuaion heoy is fequeny used when we ack ceain popuaion daa (fo eampe he CR whie we know some of he ohe daa (fo eampe he age disibuion wih ceainy As an eampe azi 96: fom he Census of 96 we know he age disibuion in five-yea age goups we assume ha he popuaion in azi in 96 is sabe Now we can compue ( esimae he CR, he CDR, he ife epecancy, he Goss Repoducion Rae, ec, by using he heoeica mahemaica eaionships ha eis in a sabe popuaion beween hese vaiabes and he age disibuion See Rowand Tabe 97 (p 336 Esimaion echniques of his kind ( Indiec esimaion mehods ae fequeny used fo deveoping counies, bu aso fo hisoica popuaions in deveoped counies Cf Appendi on he Nowegian popuaion census of 8, using Regiona Mode Sabe Popuaions o be discussed ae Using he song egodiciy pincipe Given a se of age-specific bih aes and one of age-specific deah aes, i is possibe o compue he annua gowh ae and he eaive age disibuion of he sabe popuaion ha hese aes impy The annua gowh ae summaizes, in one numbe, he age-specific bih and deah aes Definiion: he annua gowh ae of a sabe popuaion is caed he ininsic gowh ae 2

3 Noe: he ininsic gowh ae is geneay NOT he same as he gowh ae fo an acua popuaion These wo aes ae ony equa when he acua popuaion is sabe (and cosed fo migaion Ohewise he ininsic gowh ae is meey a heoeica noion Given wo ses of age-specific aes fo feiiy and moaiy, how do we compue he ininsic gowh ae? he sabe age disibuion? We imi ouseves o he femae pa of he popuaion Hence age-specific bih aes ae esiced o femae bihs Wie he ininsic gowh ae as Two appoaches wi be pesened: an appoimae one, and an eac one Appoimae cacuaion of he ininsic gowh ae and he sabe age disibuion We use he Ne Repoducion Rae (NRR, which can be compued on he basis of he age specific aes fo feiiy and moaiy See Rowand p 246 (no equied eading in his couse On page he wies he NRR as R NRR epesens he aveage numbe of daughes pe woman eow I wi deive an appoimae epession fo he ininsic gowh ae ha is somewha easie o undesand and simpe o compue han he one ha Rowand gives on page 36 (wih an eampe in Tabe 9 Usuay he wo fomuas give esus ha ae vey cose Assume ha he aveage geneaion disance beween mohes and daughes equas T yeas In ohe wods, mohes ae, on aveage, T yeas ode han hei daughes Then we know ha NRR epesens he eaive incease in he sabe popuaion ove a peiod of T yeas The annua gowh ae in he sabe popuaion equas Theefoe we can wie ( NRR = ( + T Now we can find, by soving epession (: T / T (2 NRR ( NRR 3

4 NRR foows fom he age-specific bih and deah aes u T? Conside a goup of women a age 5 They have finished hei epoducive caee They have given bih o vaious numbes of daughes a vaious ages When he mohes ae 5 yeas of age, he daughes ae beween and yeas od Define G as he numbe of daughes cueny of age of mohes aged 5, =,, 2,, Then he aveage disance beween he geneaion of mohes (a 5 and daughes (beween and equas G (5 G (5 G T G G G 5( G G G G G 5 G G G G G ( G G G G (5 G Thus he aveage geneaion disance T equas he diffeence beween wo ages: he age of he mohes (5 and he aveage age of he daughes Assume now ha his age diffeence equas he mean age of mohes when hey gave bih o hese daughes compued fom he ne feiiy aes (see page 6; see aso coumn E in Rowand s Tabe 9 The ae mean age is wien as µ (R /R in Tabe 9 We assume ha he aveage geneaion disance beween mohes and daughes is equa o he mean age of he mohes when hey give bih o he daughes, in ohe wods we assume ha T = µ This assumpion is ony pe cen coec in case =, in ohe wods when he sabe popuaion neihe inceases no diminishes (saionay popuaion, cf beow In ha case he numbe of women does no change ove ime, and he mean age compued based on absoue numbes (T equas he mean age compued fom age-specific aes (µ Fo ininsic gowh aes diffeen fom zeo, is T diffeen fom µ ( > : µ > T; < : µ < T Assuming ha T = µ, we use epession (2 o find an appoimae souion fo as (3 NRR 4

5 28 Fo eampe (angadesh 974 when µ = 28, and NRR = 23737, we find ha = 3, o 3 pe cen pe yea Afe he ininsic gowh ae has been cacuaed, we can find he sabe age disibuion fo he femae popuaion in wo sages Fis, compue an iniia shae of age goup (, + as C = ( + -(+½ ( / Hee and foow fom he ife abe ha is compued fom he age-specific deah aes: is he coumn of eposue imes, and is he adi The sum of hese quaniies C is no equa o one, as shoud be he case fo a pope shae Assume ha his sum equas C = C Ne, compue he fina shaes c = C /C Fo a poof, see Appendi 2 (no equied eading See aso Rowand pages 37-3 (no equied eading This fomua fo he age disibuion is eac, given 2 Eac cacuaion of he ininsic gowh ae and he sabe age disibuion In Appendi 2 I pove he foowing eac epession fo he ininsic gowh ae : f / (4 ( ( ½ Hee is epessed as a funcion of age-specific bih aes f (ony femae bihs and age-specific moaiy / (o be compued fom a ife abe, which isef is based on he age-specific deah aes m fo hese women This epession is caed oka s fundamena equaion of sabe popuaion heoy You do no have o emembe his epession by hea, bu you shoud be abe o inepe and use i How can we find he ininsic gowh ae, when we ae given age-specific feiiy (f and moaiy ( /? Epession (4 is oo compicaed fo a diec souion We find by ia and eo, as foows Choose a saing vaue fo he ininsic gowh ae Check whehe he ef-hand side of (4 equas wih his vaue (and given he age-specific feiiy and moaiy If no, choose a new 5

6 vaue: 2 Check (4 again ec, uni you have found a vaue fo he ininsic gowh ae ha esus in a vaue fo he ef-hand side of (4 ha is cose enough o Sysemaic seach: Choose NRR, in ohe wods, he appoimae souion Check he sum in he ef-hand side of (4 wih = : is sum( =? If no, choose a new vaue 2 fo : 2 = + [sum( ]/µ Check now he new sum agains one: is sum( 2 =? If no, epea he pevious sep as ofen as is necessay 3 = 2 + [sum( 2 ]/µ 4 = 3 + [sum( 3 ]/µ ec Usuay, his pocedue esus in ony a few seps in a vaue fo he ininsic gowh ae ha is accuae enough, in ohe wods ha poduces a vaue fo he ef-hand side of epession (4 which is cose enough o one Eampe: Peu 96 (Coae ; see websie ECON 37 PC-eecise Egyp 997, Noway 987 Rowand does no pesen his mehod fo he eac cacuaion of he ininsic gowh ae Commens Feiiy aes f ae zeo a a ages beow 5 o above 5 Thus in pacice, he sum in epession (4 is imied o ages = 5, 6, 7,, 49 2 The poduc (f / is caed he ne feiiy ae, ofen wien as φ We ecognize he sum φ as he Ne Repoducion Rae Indeed, when we assume no gowh in he sabe popuaion and inse = in epession (4, we find φ = O, he Ne Repoducion Rae equas one in his case! 3 When he age is given in five-yea inevas insead of one-yea inevas, epession (4 is wien as foows: f 5, / ( 2½ ( whee epesens inevas 5-9, 2-24,, As soon as we have compued he eac vaue of he ininsic gowh ae, we can aso compue he eac vaue of he geneaion disance T, as foows We know ha NRR = ( + T 6

7 Ne we can sove fo T : T = og(nrr/og( + 5 The fundamena equaion (4 is ofen wien as foows: e f ( p( d In ha case, age is a coninuous vaiabe no a discee vaiabe (as in ou case one-yea age goups, o five-yea age goups Theefoe - inega sign, insead of summaion - e -, insead of (+ -(+½ - p(, insead of / ; p( equas /, he individua suviva pobabiiy fom bih o eac age A he same ime we have ha NRR = e T ; insead of NRR = ( + T and = (/Tn(NRR insead of T NRR Whehe you use discee o coninuous age in a sabe popuaion mode has in genea vey ie effec on he numeica esus, uness you use oo wide age inevas Five-yea inevas give esus ha ae accuae enough fo ou pupose 6 Afe you have compued he eac vaue fo he ininsic gowh ae, you can compue he sabe age disibuion using he mehod descibed on page 5 Saionay popuaions A saionay popuaion is a sabe popuaion in which he ininsic gowh ae is zeo In ohe wods, none of he popuaion vaiabes in a saionay popuaion change ove ime: he annua numbe of bihs, he annua numbe of deahs, popuaion size, he size of a ceain age goup, ec ae a consan We can deive a numbe of simpe epessions fo hese vaiabes in a saionay popuaion Hee I wi menion hem, and y o give an inuiive jusificaion Appendi 2 conains a foma deivaion of hose epessions The epessions fo saionay popuaions in his secion ae equied eading, hose in Appendi 2 ae no Feiiy Given age-specific aes fo feiiy and moaiy fo women These aes ae combined ino ne feiiy aes φ = f ( / Since a saionay popuaion is a sabe popuaion wih zeo gowh ae, we can inse = in epession (4 and find ( ½ ( 7

8 We know ha he sum φ equas he Ne Repoducion Rae Thus we see ha a saionay popuaion has NRR equa o We find he same esu when we sa fom he epession NRR = (+ T, which hods fo evey sabe popuaion Inseing = esus in NRR = (+ T = Aso he ohe way ound: a cosed sabe popuaion ha has NRR equa o one is saionay Moaiy The ife epecancy a bih e equas he mean age a deah of he saionay popuaion In ohe wods, individuas die a age e on aveage The annua numbe of deahs in a saionay popuaion is consan Wie his numbe as D Popuaion size, wien as N, is aso consan Each yea, a shae /e in he popuaion dies Thus we find ha D = N/e This eads o he foowing epession fo he Cude Deah Rae (CDR in a saionay popuaion: CDR = D/MYP = D/N = /e, because he mid-yea popuaion MYP equas ½(N+N=N The ink beween feiiy and moaiy in a saionay popuaion In a sabe popuaion we have ha he gowh ae equas he diffeence beween cude bih ae and cude deah ae u in a saionay popuaion he gowh ae is zeo Thus we find fo a saionay popuaion ha (5 CDR = CR = /e Wie he annua numbe of bihs as Since CR = /N = /e, we find he foowing simpe epession fo oa popuaion size in a saionay popuaion: (6 N = e The age sucue of a saionay popuaion The ife epecancy e is defined in genea as e = T / = /, see ife abe heoy Inseing his epession fo e in epession (6, we find ha oa popuaion size in a saionay popuaion equas / Meanwhie one can pove ha he size of age goup (,+ in a saionay popuaion equas / and do no depend of age - ony vaies wih age This is he eason why (he -coumn in a ife abe is someimes caed "age sucue of he saionay popuaion" See Rowand page 37 8

9 Appendi Appicaion of sabe popuaion heoy o Noway s age disibuion in 8 In he eampe beow I anaysed he age sucue of Noway 8 by compaing i wih he age disibuions of a seies of mode sabe popuaions Afe I found a mode popuaion whose age pyamid esembed he empiica one cosey enough, I coud ead off he ife epecancy The Popuaion Census of 8 in Noway is geneay beieved o be of high quaiy (Dake 969 The age pyamid beow suggess ha he popuaion migh have been neay sabe Nowegian cude bih and deah aes wee ahe consan a 3-32 and pe housand especivey, in he peiod 7-8 (Dake 969, Tabe 36 Age goups beween 25 and 45 signa non-sabiiy, ahough some digi pefeence pobaby aso conibued o he ieguaiies y woking wih cumuaed age goups, his pobem wi have ony mino impac on he findings Age sucue, Now ay, Census of men w omen in housands I compaed he cumuaed age disibuions C(a fo men and women o hose of sabe popuaions based on Regiona ife Tabes Mode Noh of eves -5, ie ife epecancy vaues of fo women and fo men Fo successive vaues of a, I found he sabe gowh ae by inepoaion beween abuaed gowh aes If Noway s popuaion woud have been pefecy sabe, i shoud have he same inepoaed gowh ae fo each a In empiica appicaions, inepoaed aes vay by age Vaiaions acoss eves wee smaes fo eve 2, he sandad deviaion in he ae ove ages 5-7 being and 7 pe housand fo he wo sees The mean inepoaed gowh aes wee equa o 22 and 2 pe housand fo men and women, especivey This suggess a ife epecancy of aound 45 yeas befoe 8 Fo men aged -39 o 65-69, and women aged 3-34, 4-44, o 7-74, he esimaed gowh aes wee emakaby owe han hose fo ohe age goups Ecepionay ow bih aes and high deah aes in he 74s and 77s epain some of hese effecs (Dake 969 9

10 Appendi 2 Some mahemaics on sabe and saionay popuaions We anayse a sabe popuaion ha is cosed fo migaion, and esic he anaysis o women The aim is o find epessions fo he ininsic gowh ae and he sabe age sucue c, given ses of age-specific aes fo moaiy and feiiy The numbe of bihs inceases each yea by a consan faco (+ Wie he numbe of bihs in yea as y way of eampe, we noe ha 2 = 999 (+ = 998 (+ 2 = 997 (+ 3 = ec Geneay: hee ae ive bihs in yea - hese wee bon beween eac ime and eac ime +; a ime hee ae N pesons aged beween and +; Then we can wie ( 2 2 ( ( ( N N N Thus oa popuaion size a ime equas ( ( N One yea ae, a ime (+, popuaion size equas ( ( ( This impies ha he mid-yea popuaion in yea can be appoimaed by ( ½, ( ( MYP and he Cude ih Rae in he sabe popuaion in yea equas (A b MYP CR ½ ( (, which is independen of ime, see Popey in he main e

11 Insead of epession (A, we can aso wie (A2 ( ( b( ½ A he same ime we can wie fo he shae of age goup (,+ in he oa popuaion ( ( ( ( N c N The ems in he numeao and denominao cance, and he emainde of he denominao can be wien as /{b(+ ½ }, because of epession (A2 Hence we find fo he shae of age ( ½ (A3 c b( The Cude ih Rae b can be wien as a weighed aveage of he age-specific bih aes f, wih he age shaes c as weighs: b = f c Using (A3 his impies ha o b ( ½ f b(, (A4 f ( ½ ( Epession (A4 is known as oka's fundamena equaion of sabe popuaion heoy, o he oka equaion fo sho Given age-specific feiiy (f and age-specific moaiy ( /, he ininsic gowh ae is deemined by oka's equaion When we combine epessions (A and (A3 we find fo he age sucue c ( 2 ( ( ( 2, so ha, indeed, c =

12 A saionay popuaion is a specia case of a sabe popuaion - he gowh ae is zeo The oka equaion educes o f ( / = φ = NRR = 2 Cude ih Rae (b: Inse = in (A o find b, e ( / wih e he ife epecancy a bih 3 Cude Deah Rae (m: In a saionay popuaion, he Cude ih Rae equas he Cude Deah Rae Theefoe m e 4 Age disibuion (c : Use (A3 and inse =, o find c b e 5 Popuaion size (N: In genea, he CR equas /MYP In a saionay popuaion, he numbe of bihs is consan ( =, and so is popuaion size Hence MYP equas N fo a saionay popuaion Thus he Cude ih Rae is /N A he same ime, i is equa o /e In ohe wods, N = e 6 Size of age goup (,+: N N c e e 2

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay)

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay) Secions 3.1 and 3.4 Eponenial Funcions (Gowh and Decay) Chape 3. Secions 1 and 4 Page 1 of 5 Wha Would You Rahe Have... $1million, o double you money evey day fo 31 days saing wih 1cen? Day Cens Day Cens

More information

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain Lecue-V Sochasic Pocesses and he Basic Tem-Sucue Equaion 1 Sochasic Pocesses Any vaiable whose value changes ove ime in an unceain way is called a Sochasic Pocess. Sochasic Pocesses can be classied as

More information

Lecture 17: Kinetics of Phase Growth in a Two-component System:

Lecture 17: Kinetics of Phase Growth in a Two-component System: Lecue 17: Kineics of Phase Gowh in a Two-componen Sysem: descipion of diffusion flux acoss he α/ ineface Today s opics Majo asks of oday s Lecue: how o deive he diffusion flux of aoms. Once an incipien

More information

Exponential and Logarithmic Equations and Properties of Logarithms. Properties. Properties. log. Exponential. Logarithmic.

Exponential and Logarithmic Equations and Properties of Logarithms. Properties. Properties. log. Exponential. Logarithmic. Eponenial and Logaihmic Equaions and Popeies of Logaihms Popeies Eponenial a a s = a +s a /a s = a -s (a ) s = a s a b = (ab) Logaihmic log s = log + logs log/s = log - logs log s = s log log a b = loga

More information

On The Estimation of Two Missing Values in Randomized Complete Block Designs

On The Estimation of Two Missing Values in Randomized Complete Block Designs Mahemaical Theoy and Modeling ISSN 45804 (Pape ISSN 505 (Online Vol.6, No.7, 06 www.iise.og On The Esimaion of Two Missing Values in Randomized Complee Bloc Designs EFFANGA, EFFANGA OKON AND BASSE, E.

More information

Combinatorial Approach to M/M/1 Queues. Using Hypergeometric Functions

Combinatorial Approach to M/M/1 Queues. Using Hypergeometric Functions Inenaional Mahemaical Foum, Vol 8, 03, no 0, 463-47 HIKARI Ld, wwwm-hikaicom Combinaoial Appoach o M/M/ Queues Using Hypegeomeic Funcions Jagdish Saan and Kamal Nain Depamen of Saisics, Univesiy of Delhi,

More information

Lecture 18: Kinetics of Phase Growth in a Two-component System: general kinetics analysis based on the dilute-solution approximation

Lecture 18: Kinetics of Phase Growth in a Two-component System: general kinetics analysis based on the dilute-solution approximation Lecue 8: Kineics of Phase Gowh in a Two-componen Sysem: geneal kineics analysis based on he dilue-soluion appoximaion Today s opics: In he las Lecues, we leaned hee diffeen ways o descibe he diffusion

More information

The shortest path between two truths in the real domain passes through the complex domain. J. Hadamard

The shortest path between two truths in the real domain passes through the complex domain. J. Hadamard Complex Analysis R.G. Halbud R.Halbud@ucl.ac.uk Depamen of Mahemaics Univesiy College London 202 The shoes pah beween wo uhs in he eal domain passes hough he complex domain. J. Hadamad Chape The fis fundamenal

More information

Today - Lecture 13. Today s lecture continue with rotations, torque, Note that chapters 11, 12, 13 all involve rotations

Today - Lecture 13. Today s lecture continue with rotations, torque, Note that chapters 11, 12, 13 all involve rotations Today - Lecue 13 Today s lecue coninue wih oaions, oque, Noe ha chapes 11, 1, 13 all inole oaions slide 1 eiew Roaions Chapes 11 & 1 Viewed fom aboe (+z) Roaional, o angula elociy, gies angenial elociy

More information

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example C 188: Aificial Inelligence Fall 2007 epesening Knowledge ecue 17: ayes Nes III 10/25/2007 an Klein UC ekeley Popeies of Ns Independence? ayes nes: pecify complex join disibuions using simple local condiional

More information

, on the power of the transmitter P t fed to it, and on the distance R between the antenna and the observation point as. r r t

, on the power of the transmitter P t fed to it, and on the distance R between the antenna and the observation point as. r r t Lecue 6: Fiis Tansmission Equaion and Rada Range Equaion (Fiis equaion. Maximum ange of a wieless link. Rada coss secion. Rada equaion. Maximum ange of a ada. 1. Fiis ansmission equaion Fiis ansmission

More information

7 Wave Equation in Higher Dimensions

7 Wave Equation in Higher Dimensions 7 Wave Equaion in Highe Dimensions We now conside he iniial-value poblem fo he wave equaion in n dimensions, u c u x R n u(x, φ(x u (x, ψ(x whee u n i u x i x i. (7. 7. Mehod of Spheical Means Ref: Evans,

More information

Homework 1 Solutions CSE 101 Summer 2017

Homework 1 Solutions CSE 101 Summer 2017 Homewok 1 Soutions CSE 101 Summe 2017 1 Waming Up 1.1 Pobem and Pobem Instance Find the smaest numbe in an aay of n integes a 1, a 2,..., a n. What is the input? What is the output? Is this a pobem o a

More information

CS 188: Artificial Intelligence Fall Probabilistic Models

CS 188: Artificial Intelligence Fall Probabilistic Models CS 188: Aificial Inelligence Fall 2007 Lecue 15: Bayes Nes 10/18/2007 Dan Klein UC Bekeley Pobabilisic Models A pobabilisic model is a join disibuion ove a se of vaiables Given a join disibuion, we can

More information

Two-dimensional Effects on the CSR Interaction Forces for an Energy-Chirped Bunch. Rui Li, J. Bisognano, R. Legg, and R. Bosch

Two-dimensional Effects on the CSR Interaction Forces for an Energy-Chirped Bunch. Rui Li, J. Bisognano, R. Legg, and R. Bosch Two-dimensional Effecs on he CS Ineacion Foces fo an Enegy-Chiped Bunch ui Li, J. Bisognano,. Legg, and. Bosch Ouline 1. Inoducion 2. Pevious 1D and 2D esuls fo Effecive CS Foce 3. Bunch Disibuion Vaiaion

More information

Computer Propagation Analysis Tools

Computer Propagation Analysis Tools Compue Popagaion Analysis Tools. Compue Popagaion Analysis Tools Inoducion By now you ae pobably geing he idea ha pedicing eceived signal sengh is a eally impoan as in he design of a wieless communicaion

More information

Orthotropic Materials

Orthotropic Materials Kapiel 2 Ohoopic Maeials 2. Elasic Sain maix Elasic sains ae elaed o sesses by Hooke's law, as saed below. The sesssain elaionship is in each maeial poin fomulaed in he local caesian coodinae sysem. ε

More information

156 There are 9 books stacked on a shelf. The thickness of each book is either 1 inch or 2

156 There are 9 books stacked on a shelf. The thickness of each book is either 1 inch or 2 156 Thee ae 9 books sacked on a shelf. The hickness of each book is eihe 1 inch o 2 F inches. The heigh of he sack of 9 books is 14 inches. Which sysem of equaions can be used o deemine x, he numbe of

More information

Merging to ordered sequences. Efficient (Parallel) Sorting. Merging (cont.)

Merging to ordered sequences. Efficient (Parallel) Sorting. Merging (cont.) Efficient (Paae) Soting One of the most fequent opeations pefomed by computes is oganising (soting) data The access to soted data is moe convenient/faste Thee is a constant need fo good soting agoithms

More information

A Weighted Moving Average Process for Forecasting. Shou Hsing Shih Chris P. Tsokos

A Weighted Moving Average Process for Forecasting. Shou Hsing Shih Chris P. Tsokos A Weighed Moving Aveage Pocess fo Foecasing Shou Hsing Shih Chis P. Tsokos Depamen of Mahemaics and Saisics Univesiy of Souh Floida, USA Absac The objec of he pesen sudy is o popose a foecasing model fo

More information

Measures the linear dependence or the correlation between r t and r t-p. (summarizes serial dependence)

Measures the linear dependence or the correlation between r t and r t-p. (summarizes serial dependence) . Definiions Saionay Time Seies- A ime seies is saionay if he popeies of he pocess such as he mean and vaiance ae consan houghou ime. i. If he auocoelaion dies ou quickly he seies should be consideed saionay

More information

KINEMATICS OF RIGID BODIES

KINEMATICS OF RIGID BODIES KINEMTICS OF RIGID ODIES In igid body kinemaics, we use he elaionships govening he displacemen, velociy and acceleaion, bu mus also accoun fo he oaional moion of he body. Descipion of he moion of igid

More information

Preference-based Graphic Models for Collaborative Filtering

Preference-based Graphic Models for Collaborative Filtering efeence-based Gaphic Modes fo Coaboaive Fieing ong Jin Luo Si Schoo of Compue Science Canegie Meon Univesi isbugh A 15232 {ongsi}@cs.cmu.edu Chengiang Zhai Depamen of Compue Science Univesi of Iinois a

More information

Lecture 22 Electromagnetic Waves

Lecture 22 Electromagnetic Waves Lecue Elecomagneic Waves Pogam: 1. Enegy caied by he wave (Poyning veco).. Maxwell s equaions and Bounday condiions a inefaces. 3. Maeials boundaies: eflecion and efacion. Snell s Law. Quesions you should

More information

Low-complexity Algorithms for MIMO Multiplexing Systems

Low-complexity Algorithms for MIMO Multiplexing Systems Low-complexiy Algoihms fo MIMO Muliplexing Sysems Ouline Inoducion QRD-M M algoihm Algoihm I: : o educe he numbe of suviving pahs. Algoihm II: : o educe he numbe of candidaes fo each ansmied signal. :

More information

[ ] 0. = (2) = a q dimensional vector of observable instrumental variables that are in the information set m constituents of u

[ ] 0. = (2) = a q dimensional vector of observable instrumental variables that are in the information set m constituents of u Genealized Mehods of Momens he genealized mehod momens (GMM) appoach of Hansen (98) can be hough of a geneal pocedue fo esing economics and financial models. he GMM is especially appopiae fo models ha

More information

Fig. 1S. The antenna construction: (a) main geometrical parameters, (b) the wire support pillar and (c) the console link between wire and coaxial

Fig. 1S. The antenna construction: (a) main geometrical parameters, (b) the wire support pillar and (c) the console link between wire and coaxial a b c Fig. S. The anenna consucion: (a) ain geoeical paaees, (b) he wie suppo pilla and (c) he console link beween wie and coaial pobe. Fig. S. The anenna coss-secion in he y-z plane. Accoding o [], he

More information

Seidel s Trapezoidal Partitioning Algorithm

Seidel s Trapezoidal Partitioning Algorithm CS68: Geometic Agoithms Handout #6 Design and Anaysis Oigina Handout #6 Stanfod Univesity Tuesday, 5 Febuay 99 Oigina Lectue #7: 30 Januay 99 Topics: Seide s Tapezoida Patitioning Agoithm Scibe: Michae

More information

Objectives. We will also get to know about the wavefunction and its use in developing the concept of the structure of atoms.

Objectives. We will also get to know about the wavefunction and its use in developing the concept of the structure of atoms. Modue "Atomic physics and atomic stuctue" Lectue 7 Quantum Mechanica teatment of One-eecton atoms Page 1 Objectives In this ectue, we wi appy the Schodinge Equation to the simpe system Hydogen and compae

More information

PHYS PRACTICE EXAM 2

PHYS PRACTICE EXAM 2 PHYS 1800 PRACTICE EXAM Pa I Muliple Choice Quesions [ ps each] Diecions: Cicle he one alenaive ha bes complees he saemen o answes he quesion. Unless ohewise saed, assume ideal condiions (no ai esisance,

More information

Chapter Finite Difference Method for Ordinary Differential Equations

Chapter Finite Difference Method for Ordinary Differential Equations Chape 8.7 Finie Diffeence Mehod fo Odinay Diffeenial Eqaions Afe eading his chape, yo shold be able o. Undesand wha he finie diffeence mehod is and how o se i o solve poblems. Wha is he finie diffeence

More information

Molecular Evolution and Phylogeny. Based on: Durbin et al Chapter 8

Molecular Evolution and Phylogeny. Based on: Durbin et al Chapter 8 Molecula Evoluion and hylogeny Baed on: Dubin e al Chape 8. hylogeneic Tee umpion banch inenal node leaf Topology T : bifucaing Leave - N Inenal node N+ N- Lengh { i } fo each banch hylogeneic ee Topology

More information

MEEN 617 Handout #11 MODAL ANALYSIS OF MDOF Systems with VISCOUS DAMPING

MEEN 617 Handout #11 MODAL ANALYSIS OF MDOF Systems with VISCOUS DAMPING MEEN 67 Handou # MODAL ANALYSIS OF MDOF Sysems wih VISCOS DAMPING ^ Symmeic Moion of a n-dof linea sysem is descibed by he second ode diffeenial equaions M+C+K=F whee () and F () ae n ows vecos of displacemens

More information

Risk tolerance and optimal portfolio choice

Risk tolerance and optimal portfolio choice Risk oleance and opimal pofolio choice Maek Musiela BNP Paibas London Copoae and Invesmen Join wok wih T. Zaiphopoulou (UT usin) Invesmens and fowad uiliies Pepin 6 Backwad and fowad dynamic uiliies and

More information

The Global Trade and Environment Model: GTEM

The Global Trade and Environment Model: GTEM The Global Tade and Envionmen Model: A pojecion of non-seady sae daa using Ineempoal GTEM Hom Pan, Vivek Tulpulé and Bian S. Fishe Ausalian Bueau of Agiculual and Resouce Economics OBJECTIVES Deive an

More information

Kalman Filter: an instance of Bayes Filter. Kalman Filter: an instance of Bayes Filter. Kalman Filter. Linear dynamics with Gaussian noise

Kalman Filter: an instance of Bayes Filter. Kalman Filter: an instance of Bayes Filter. Kalman Filter. Linear dynamics with Gaussian noise COM47 Inoducion o Roboics and Inelligen ysems he alman File alman File: an insance of Bayes File alman File: an insance of Bayes File Linea dynamics wih Gaussian noise alman File Linea dynamics wih Gaussian

More information

STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE WEIBULL DISTRIBUTION

STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE WEIBULL DISTRIBUTION Inenaional Jounal of Science, Technology & Managemen Volume No 04, Special Issue No. 0, Mach 205 ISSN (online): 2394-537 STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE

More information

Conducting fuzzy division by using linear programming

Conducting fuzzy division by using linear programming WSES TRNSCTIONS on INFORMTION SCIENCE & PPLICTIONS Muat pe Basaan, Cagdas Hakan adag, Cem Kadia Conducting fuzzy division by using inea pogamming MURT LPER BSRN Depatment of Mathematics Nigde Univesity

More information

MATHEMATICAL FOUNDATIONS FOR APPROXIMATING PARTICLE BEHAVIOUR AT RADIUS OF THE PLANCK LENGTH

MATHEMATICAL FOUNDATIONS FOR APPROXIMATING PARTICLE BEHAVIOUR AT RADIUS OF THE PLANCK LENGTH Fundamenal Jounal of Mahemaical Phsics Vol 3 Issue 013 Pages 55-6 Published online a hp://wwwfdincom/ MATHEMATICAL FOUNDATIONS FOR APPROXIMATING PARTICLE BEHAVIOUR AT RADIUS OF THE PLANCK LENGTH Univesias

More information

An Automatic Door Sensor Using Image Processing

An Automatic Door Sensor Using Image Processing An Auomaic Doo Senso Using Image Pocessing Depamen o Elecical and Eleconic Engineeing Faculy o Engineeing Tooi Univesiy MENDEL 2004 -Insiue o Auomaion and Compue Science- in BRNO CZECH REPUBLIC 1. Inoducion

More information

Probabilistic Models. CS 188: Artificial Intelligence Fall Independence. Example: Independence. Example: Independence? Conditional Independence

Probabilistic Models. CS 188: Artificial Intelligence Fall Independence. Example: Independence. Example: Independence? Conditional Independence C 188: Aificial Inelligence Fall 2007 obabilisic Models A pobabilisic model is a join disibuion ove a se of vaiables Lecue 15: Bayes Nes 10/18/2007 Given a join disibuion, we can eason abou unobseved vaiables

More information

Variance and Covariance Processes

Variance and Covariance Processes Vaiance and Covaiance Pocesses Pakash Balachandan Depamen of Mahemaics Duke Univesiy May 26, 2008 These noes ae based on Due s Sochasic Calculus, Revuz and Yo s Coninuous Maingales and Bownian Moion, Kaazas

More information

Circular Motion. Radians. One revolution is equivalent to which is also equivalent to 2π radians. Therefore we can.

Circular Motion. Radians. One revolution is equivalent to which is also equivalent to 2π radians. Therefore we can. 1 Cicula Moion Radians One evoluion is equivalen o 360 0 which is also equivalen o 2π adians. Theefoe we can say ha 360 = 2π adians, 180 = π adians, 90 = π 2 adians. Hence 1 adian = 360 2π Convesions Rule

More information

The sudden release of a large amount of energy E into a background fluid of density

The sudden release of a large amount of energy E into a background fluid of density 10 Poin explosion The sudden elease of a lage amoun of enegy E ino a backgound fluid of densiy ceaes a song explosion, chaaceized by a song shock wave (a blas wave ) emanaing fom he poin whee he enegy

More information

Two-Pion Exchange Currents in Photodisintegration of the Deuteron

Two-Pion Exchange Currents in Photodisintegration of the Deuteron Two-Pion Exchange Cuens in Phoodisinegaion of he Deueon Dagaa Rozędzik and Jacek Goak Jagieonian Univesiy Kaków MENU00 3 May 00 Wiiasbug Conen Chia Effecive Fied Theoy ChEFT Eecoagneic cuen oeaos wihin

More information

WORK POWER AND ENERGY Consevaive foce a) A foce is said o be consevaive if he wok done by i is independen of pah followed by he body b) Wok done by a consevaive foce fo a closed pah is zeo c) Wok done

More information

Research Article Developing a Series Solution Method of q-difference Equations

Research Article Developing a Series Solution Method of q-difference Equations Appied Mahemaics Voume 2013, Arice ID 743973, 4 pages hp://dx.doi.org/10.1155/2013/743973 Research Arice Deveoping a Series Souion Mehod of q-difference Equaions Hsuan-Ku Liu Deparmen of Mahemaics and

More information

An Open cycle and Closed cycle Gas Turbine Engines. Methods to improve the performance of simple gas turbine plants

An Open cycle and Closed cycle Gas Turbine Engines. Methods to improve the performance of simple gas turbine plants An Open cycle and losed cycle Gas ubine Engines Mehods o impove he pefomance of simple gas ubine plans I egeneaive Gas ubine ycle: he empeaue of he exhaus gases in a simple gas ubine is highe han he empeaue

More information

P h y s i c s F a c t s h e e t

P h y s i c s F a c t s h e e t P h y s i c s F a c s h e e Sepembe 2001 Numbe 20 Simple Hamonic Moion Basic Conceps This Facshee will:! eplain wha is mean by simple hamonic moion! eplain how o use he equaions fo simple hamonic moion!

More information

5.1 - Logarithms and Their Properties

5.1 - Logarithms and Their Properties Chaper 5 Logarihmic Funcions 5.1 - Logarihms and Their Properies Suppose ha a populaion grows according o he formula P 10, where P is he colony size a ime, in hours. When will he populaion be 2500? We

More information

KEY. Math 334 Midterm I Fall 2008 sections 001 and 003 Instructor: Scott Glasgow

KEY. Math 334 Midterm I Fall 2008 sections 001 and 003 Instructor: Scott Glasgow 1 KEY Mah 4 Miderm I Fall 8 secions 1 and Insrucor: Sco Glasgow Please do NOT wrie on his eam. No credi will be given for such work. Raher wrie in a blue book, or on our own paper, preferabl engineering

More information

Reinforcement learning

Reinforcement learning Lecue 3 Reinfocemen leaning Milos Hauskech milos@cs.pi.edu 539 Senno Squae Reinfocemen leaning We wan o lean he conol policy: : X A We see examples of x (bu oupus a ae no given) Insead of a we ge a feedback

More information

General Non-Arbitrage Model. I. Partial Differential Equation for Pricing A. Traded Underlying Security

General Non-Arbitrage Model. I. Partial Differential Equation for Pricing A. Traded Underlying Security 1 Geneal Non-Abiage Model I. Paial Diffeenial Equaion fo Picing A. aded Undelying Secuiy 1. Dynamics of he Asse Given by: a. ds = µ (S, )d + σ (S, )dz b. he asse can be eihe a sock, o a cuency, an index,

More information

The Production of Polarization

The Production of Polarization Physics 36: Waves Lecue 13 3/31/211 The Poducion of Polaizaion Today we will alk abou he poducion of polaized ligh. We aleady inoduced he concep of he polaizaion of ligh, a ansvese EM wave. To biefly eview

More information

Jackson 3.3 Homework Problem Solution Dr. Christopher S. Baird University of Massachusetts Lowell

Jackson 3.3 Homework Problem Solution Dr. Christopher S. Baird University of Massachusetts Lowell Jackson 3.3 Homewok Pobem Soution D. Chistophe S. Baid Univesity of Massachusetts Lowe POBLEM: A thin, fat, conducting, cicua disc of adius is ocated in the x-y pane with its cente at the oigin, and is

More information

Jackson 4.7 Homework Problem Solution Dr. Christopher S. Baird University of Massachusetts Lowell

Jackson 4.7 Homework Problem Solution Dr. Christopher S. Baird University of Massachusetts Lowell Jackson 4.7 Homewok obem Soution D. Chistophe S. Baid Univesity of Massachusetts Lowe ROBLEM: A ocaized distibution of chage has a chage density ρ()= 6 e sin θ (a) Make a mutipoe expansion of the potentia

More information

2 Weighted Residual Methods

2 Weighted Residual Methods 2 Weighed Residua Mehods Fundamena equaions Consider he probem governed by he differenia equaion: Γu = f in. (83) The above differenia equaion is soved by using he boundary condiions given as foows: u

More information

ÖRNEK 1: THE LINEAR IMPULSE-MOMENTUM RELATION Calculate the linear momentum of a particle of mass m=10 kg which has a. kg m s

ÖRNEK 1: THE LINEAR IMPULSE-MOMENTUM RELATION Calculate the linear momentum of a particle of mass m=10 kg which has a. kg m s MÜHENDİSLİK MEKANİĞİ. HAFTA İMPULS- MMENTUM-ÇARPIŞMA Linea oenu of a paicle: The sybol L denoes he linea oenu and is defined as he ass ies he elociy of a paicle. L ÖRNEK : THE LINEAR IMPULSE-MMENTUM RELATIN

More information

Lecture 1. time, say t=0, to find the wavefunction at any subsequent time t. This can be carried out by

Lecture 1. time, say t=0, to find the wavefunction at any subsequent time t. This can be carried out by Lectue The Schödinge equation In quantum mechanics, the fundamenta quantity that descibes both the patice-ike and waveike chaacteistics of patices is wavefunction, Ψ(. The pobabiity of finding a patice

More information

SVM Parameters Tuning with Quantum Particles Swarm Optimization

SVM Parameters Tuning with Quantum Particles Swarm Optimization SVM Paamees uning wih Quanum Paices Swam Opimizaion Zhiyong Luo Wenfeng Zhang Yuxia Li 3 and Min Xiang. Schoo of Auomaion Chonging Univesiy of Poss and eecommunicaions Chonging 465 China. Schoo of Auomaion

More information

Extremal problems for t-partite and t-colorable hypergraphs

Extremal problems for t-partite and t-colorable hypergraphs Exemal poblems fo -paie and -coloable hypegaphs Dhuv Mubayi John Talbo June, 007 Absac Fix ineges and an -unifom hypegaph F. We pove ha he maximum numbe of edges in a -paie -unifom hypegaph on n veices

More information

International Journal of Pure and Applied Sciences and Technology

International Journal of Pure and Applied Sciences and Technology In. J. Pue Appl. Sci. Technol., 4 (211, pp. 23-29 Inenaional Jounal of Pue and Applied Sciences and Technology ISS 2229-617 Available online a www.ijopaasa.in eseach Pape Opizaion of he Uiliy of a Sucual

More information

Probability Estimation with Maximum Entropy Principle

Probability Estimation with Maximum Entropy Principle Pape 0,CCG Annua Repot, 00 ( c 00) Pobabiity Estimation with Maximum Entopy Pincipe Yupeng Li and Cayton V. Deutsch The pincipe of Maximum Entopy is a powefu and vesatie too fo infeing a pobabiity distibution

More information

Determining solar characteristics using planetary data

Determining solar characteristics using planetary data Detemining sola chaacteistics using planetay data Intoduction The Sun is a G-type main sequence sta at the cente of the Sola System aound which the planets, including ou Eath, obit. In this investigation

More information

Kriging Models Predicting Atrazine Concentrations in Surface Water Draining Agricultural Watersheds

Kriging Models Predicting Atrazine Concentrations in Surface Water Draining Agricultural Watersheds 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Kriging Models Predicing Arazine Concenraions in Surface Waer Draining Agriculural Waersheds Paul L. Mosquin, Jeremy Aldworh, Wenlin Chen Supplemenal Maerial Number

More information

Discretizing the 3-D Schrödinger equation for a Central Potential

Discretizing the 3-D Schrödinger equation for a Central Potential Discetizing the 3-D Schödinge equation fo a Centa Potentia By now, you ae faiia with the Discete Schodinge Equation fo one Catesian diension. We wi now conside odifying it to hande poa diensions fo a centa

More information

4.1 - Logarithms and Their Properties

4.1 - Logarithms and Their Properties Chaper 4 Logarihmic Funcions 4.1 - Logarihms and Their Properies Wha is a Logarihm? We define he common logarihm funcion, simply he log funcion, wrien log 10 x log x, as follows: If x is a posiive number,

More information

Relating Scattering Amplitudes to Bound States

Relating Scattering Amplitudes to Bound States Reating Scatteing Ampitudes to Bound States Michae Fowe UVa. 1/17/8 Low Enegy Appoximations fo the S Matix In this section we examine the popeties of the patia-wave scatteing matix ( ) = 1+ ( ) S k ikf

More information

The Substring Search Problem

The Substring Search Problem The Substing Seach Poblem One algoithm which is used in a vaiety of applications is the family of substing seach algoithms. These algoithms allow a use to detemine if, given two chaacte stings, one is

More information

On Control Problem Described by Infinite System of First-Order Differential Equations

On Control Problem Described by Infinite System of First-Order Differential Equations Ausalian Jounal of Basic and Applied Sciences 5(): 736-74 ISS 99-878 On Conol Poblem Descibed by Infinie Sysem of Fis-Ode Diffeenial Equaions Gafujan Ibagimov and Abbas Badaaya J'afau Insiue fo Mahemaical

More information

Pseudosteady-State Flow Relations for a Radial System from Department of Petroleum Engineering Course Notes (1997)

Pseudosteady-State Flow Relations for a Radial System from Department of Petroleum Engineering Course Notes (1997) Pseudoseady-Sae Flow Relaions fo a Radial Sysem fom Deamen of Peoleum Engineeing Couse Noes (1997) (Deivaion of he Pseudoseady-Sae Flow Relaions fo a Radial Sysem) (Deivaion of he Pseudoseady-Sae Flow

More information

System of Linear Differential Equations

System of Linear Differential Equations Sysem of Linear Differenial Equaions In "Ordinary Differenial Equaions" we've learned how o solve a differenial equaion for a variable, such as: y'k5$e K2$x =0 solve DE yx = K 5 2 ek2 x C_C1 2$y''C7$y

More information

Lecture 24. Calderon-Zygmund Decomposition Technique: Cube decomposition

Lecture 24. Calderon-Zygmund Decomposition Technique: Cube decomposition Lecure 24 May 3 h, 2004 Our moivaion for his as ecure in he course is o show a resu using our reguariy heory which is oherwise unprovabe using cassica echniques. This is he previous Theorem, and in paricuar

More information

Problem set 6. Solution. The problem of firm 3 is. The FOC is: 2 =0. The reaction function of firm 3 is: = 2

Problem set 6. Solution. The problem of firm 3 is. The FOC is: 2 =0. The reaction function of firm 3 is: = 2 Pobem set 6 ) Thee oigopoists opeate in a maket with invese demand function given by = whee = + + and is the quantity poduced by fim i. Each fim has constant magina cost of poduction, c, and no fixed cost.

More information

A Two Stage Mixture Model for Predicting EAD

A Two Stage Mixture Model for Predicting EAD A wo Sage Mixure Mode for Predicing EAD Credi Scoring & Credi Conro, Edinburgh 2013 Mindy Leow & Jonahan Crook Credi Research Cenre, Universiy of Edinburgh, UK 1 Moivaion Reguaory Capia ase II requires

More information

Unit Root Time Series. Univariate random walk

Unit Root Time Series. Univariate random walk Uni Roo ime Series Univariae random walk Consider he regression y y where ~ iid N 0, he leas squares esimae of is: ˆ yy y y yy Now wha if = If y y hen le y 0 =0 so ha y j j If ~ iid N 0, hen y ~ N 0, he

More information

The Contradiction within Equations of Motion with Constant Acceleration

The Contradiction within Equations of Motion with Constant Acceleration The Conradicion wihin Equaions of Moion wih Consan Acceleraion Louai Hassan Elzein Basheir (Daed: July 7, 0 This paper is prepared o demonsrae he violaion of rules of mahemaics in he algebraic derivaion

More information

Summary:Linear Motion

Summary:Linear Motion Summary:Linear Moion D Saionary objec V Consan velociy D Disance increase uniformly wih ime D = v. a Consan acceleraion V D V = a. D = ½ a 2 Velociy increases uniformly wih ime Disance increases rapidly

More information

r P + '% 2 r v(r) End pressures P 1 (high) and P 2 (low) P 1 , which must be independent of z, so # dz dz = P 2 " P 1 = " #P L L,

r P + '% 2 r v(r) End pressures P 1 (high) and P 2 (low) P 1 , which must be independent of z, so # dz dz = P 2  P 1 =  #P L L, Lecue 36 Pipe Flow and Low-eynolds numbe hydodynamics 36.1 eading fo Lecues 34-35: PKT Chape 12. Will y fo Monday?: new daa shee and daf fomula shee fo final exam. Ou saing poin fo hydodynamics ae wo equaions:

More information

Reinforcement learning

Reinforcement learning CS 75 Mchine Lening Lecue b einfocemen lening Milos Huskech milos@cs.pi.edu 539 Senno Sque einfocemen lening We wn o len conol policy: : X A We see emples of bu oupus e no given Insed of we ge feedbck

More information

Circuits 24/08/2010. Question. Question. Practice Questions QV CV. Review Formula s RC R R R V IR ... Charging P IV I R ... E Pt.

Circuits 24/08/2010. Question. Question. Practice Questions QV CV. Review Formula s RC R R R V IR ... Charging P IV I R ... E Pt. 4/08/00 eview Fomul s icuis cice s BL B A B I I I I E...... s n n hging Q Q 0 e... n... Q Q n 0 e Q I I0e Dischging Q U Q A wie mde of bss nd nohe wie mde of silve hve he sme lengh, bu he dimee of he bss

More information

FINITE DIFFERENCE APPROACH TO WAVE GUIDE MODES COMPUTATION

FINITE DIFFERENCE APPROACH TO WAVE GUIDE MODES COMPUTATION FINITE DIFFERENCE ROCH TO WVE GUIDE MODES COMUTTION Ing.lessando Fani Elecomagneic Gou Deamen of Elecical and Eleconic Engineeing Univesiy of Cagliai iazza d mi, 93 Cagliai, Ialy SUMMRY Inoducion Finie

More information

Vehicle Arrival Models : Headway

Vehicle Arrival Models : Headway Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where

More information

Sections 2.2 & 2.3 Limit of a Function and Limit Laws

Sections 2.2 & 2.3 Limit of a Function and Limit Laws Mah 80 www.imeodare.com Secions. &. Limi of a Funcion and Limi Laws In secion. we saw how is arise when we wan o find he angen o a curve or he velociy of an objec. Now we urn our aenion o is in general

More information

AN EVOLUTIONARY APPROACH FOR SOLVING DIFFERENTIAL EQUATIONS

AN EVOLUTIONARY APPROACH FOR SOLVING DIFFERENTIAL EQUATIONS AN EVOLUTIONARY APPROACH FOR SOLVING DIFFERENTIAL EQUATIONS M. KAMESWAR RAO AND K.P. RAVINDRAN Depamen of Mechanical Engineeing, Calicu Regional Engineeing College, Keala-67 6, INDIA. Absac:- We eploe

More information

336 ERIDANI kfk Lp = sup jf(y) ; f () jj j p p whee he supemum is aken ove all open balls = (a ) inr n, jj is he Lebesgue measue of in R n, () =(), f

336 ERIDANI kfk Lp = sup jf(y) ; f () jj j p p whee he supemum is aken ove all open balls = (a ) inr n, jj is he Lebesgue measue of in R n, () =(), f TAMKANG JOURNAL OF MATHEMATIS Volume 33, Numbe 4, Wine 2002 ON THE OUNDEDNESS OF A GENERALIED FRATIONAL INTEGRAL ON GENERALIED MORREY SPAES ERIDANI Absac. In his pape we exend Nakai's esul on he boundedness

More information

Simulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010

Simulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Simulaion-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Week Descripion Reading Maerial 2 Compuer Simulaion of Dynamic Models Finie Difference, coninuous saes, discree ime Simple Mehods Euler Trapezoid

More information

Semantic-Directed Clumping of Disjunctive Abstract States *

Semantic-Directed Clumping of Disjunctive Abstract States * Semanic-Dieced Cumping of Disjuncive Absac Saes * Huisong Li, Fançois Béenge, Bo-Yuh Chang, Xavie Riva To cie his vesion: Huisong Li, Fançois Béenge, Bo-Yuh Chang, Xavie Riva. Semanic-Dieced Cumping of

More information

R t. C t P t. + u t. C t = αp t + βr t + v t. + β + w t

R t. C t P t. + u t. C t = αp t + βr t + v t. + β + w t Exercise 7 C P = α + β R P + u C = αp + βr + v (a) (b) C R = α P R + β + w (c) Assumpions abou he disurbances u, v, w : Classical assumions on he disurbance of one of he equaions, eg. on (b): E(v v s P,

More information

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle Chaper 2 Newonian Mechanics Single Paricle In his Chaper we will review wha Newon s laws of mechanics ell us abou he moion of a single paricle. Newon s laws are only valid in suiable reference frames,

More information

Chapter 3: Theory of Modular Arithmetic 38

Chapter 3: Theory of Modular Arithmetic 38 Chapte 3: Theoy of Modula Aithmetic 38 Section D Chinese Remainde Theoem By the end of this section you will be able to pove the Chinese Remainde Theoem apply this theoem to solve simultaneous linea conguences

More information

Chapter 2. First Order Scalar Equations

Chapter 2. First Order Scalar Equations Chaper. Firs Order Scalar Equaions We sar our sudy of differenial equaions in he same way he pioneers in his field did. We show paricular echniques o solve paricular ypes of firs order differenial equaions.

More information

Chapter 7. Interference

Chapter 7. Interference Chape 7 Inefeence Pa I Geneal Consideaions Pinciple of Supeposiion Pinciple of Supeposiion When wo o moe opical waves mee in he same locaion, hey follow supeposiion pinciple Mos opical sensos deec opical

More information

Support Vector Machines

Support Vector Machines Suppo Veco Machine CSL 3 ARIFICIAL INELLIGENCE SPRING 4 Suppo Veco Machine O, Kenel Machine Diciminan-baed mehod olean cla boundaie Suppo veco coni of eample cloe o bounday Kenel compue imilaiy beeen eample

More information

= ρ. Since this equation is applied to an arbitrary point in space, we can use it to determine the charge density once we know the field.

= ρ. Since this equation is applied to an arbitrary point in space, we can use it to determine the charge density once we know the field. Gauss s Law In diffeentia fom D = ρ. ince this equation is appied to an abita point in space, we can use it to detemine the chage densit once we know the fied. (We can use this equation to ve fo the fied

More information

Chapter 7: Solving Trig Equations

Chapter 7: Solving Trig Equations Haberman MTH Secion I: The Trigonomeric Funcions Chaper 7: Solving Trig Equaions Le s sar by solving a couple of equaions ha involve he sine funcion EXAMPLE a: Solve he equaion sin( ) The inverse funcions

More information

Dynamic Estimation of OD Matrices for Freeways and Arterials

Dynamic Estimation of OD Matrices for Freeways and Arterials Novembe 2007 Final Repo: ITS Dynamic Esimaion of OD Maices fo Feeways and Aeials Auhos: Juan Calos Heea, Sauabh Amin, Alexande Bayen, Same Madana, Michael Zhang, Yu Nie, Zhen Qian, Yingyan Lou, Yafeng

More information

A Sardinas-Patterson Characterization Theorem for SE-codes

A Sardinas-Patterson Characterization Theorem for SE-codes A Sadinas-Patteson Chaacteization Theoem fo SE-codes Ionuţ Popa,Bogdan Paşaniuc Facuty of Compute Science, A.I.Cuza Univesity of Iaşi, 6600 Iaşi, Romania May 0, 2004 Abstact The aim of this pape is to

More information

The k-filtering Applied to Wave Electric and Magnetic Field Measurements from Cluster

The k-filtering Applied to Wave Electric and Magnetic Field Measurements from Cluster The -fileing pplied o Wave lecic and Magneic Field Measuemens fom Cluse Jean-Louis PINÇON and ndes TJULIN LPC-CNRS 3 av. de la Recheche Scienifique 4507 Oléans Fance jlpincon@cns-oleans.f OUTLINS The -fileing

More information

Simultaneous Localisation and Mapping. IAR Lecture 10 Barbara Webb

Simultaneous Localisation and Mapping. IAR Lecture 10 Barbara Webb Simuaneous Locaisaion and Mapping IAR Lecure 0 Barbara Webb Wha is SLAM? Sar in an unknown ocaion and unknown environmen and incremenay buid a map of he environmen whie simuaneousy using his map o compue

More information