The impact of complex network structure on seizure activity induced by depolarization block

Size: px
Start display at page:

Download "The impact of complex network structure on seizure activity induced by depolarization block"

Transcription

1 Th mpact of complx ntwork structur on szur actvty nducd by dpolarzaton block Duan Nykamp School of Mathmatcs Unvrsty of Mnnsota Chrstophr Km Laboratory of Bologcal Modlng NDDK/Natonal nsttuts of Halth

2 Msoscopc nuronal ntworks 10 5 nurons pr mm 3 n prmat cortx. Communcat wth spks; connctd va synapss. What dtrmns th nsmbl actvty of nural ntworks? du dt = F (u ) + j xc W j φ(u j ) k nh Sngl nuron dynamcs: F ( ) Connctvty structur: W j W k φ(u k ), xctatory and nhbtory nurons: +/ = 1, 2,..., N Dvlop rducd qn that capturs populaton actvty whn nhbtory nurons ar pathologcal (F ) scond ordr connctvty structur (W j ).

3 Spkng nuron modl Morrs-Lcar modl: (1) smpl, ralstc on channl dynamcs, (2) ntrs dpolarzaton block whn strong stmulus appld. C dv dt = xt g Na m(v )(V Na ) g K w(t)(v K ) }{{}}{{} upstrok downstrok dw = φ w nf (V ) w dt τ w m(v ) = 1 ( ( )) V V1 1 + tanh 2 V 2 w nf (V ) = 1 ( ( )) V V3 1 + tanh 2 V 4 g Cl (V Cl ) }{{} lak

4 Dpolarzaton block Morrs-Lcar plptc bran slc Goal: nvstgat pathologcal xctatorynhbtory ntwork dynamcs whn nh. nurons brak down du to dpolarzaton block. Zburkus t al. 06

5 Outln 1. ntwork dynamcs nducd by dpolarzaton block Smulat rcurrntly connctd ntworks Man fld modl Comparson of smulatons and man fld analyss 2. Dpolzaton block n complx ntwork structur Scond ordr ntwork motfs ffcts of th convrgnt and chan motfs Gnralzd man fld modl

6 Smulat rcurrntly connctd ntworks Randomly connct N xctatory and N/4 nhbtory ML nurons (N = 3000) wth conncton probablty p = For ach nuron, smulat dv dt dg xc τ xc dt dg nh τ nh dt = F (V, w ) + g xc (t)( xc V ) + g nh (t)( nh V ) = g xc + J j W j δ(t tj k ) = g nh j xc + J j W j j nh k k δ(t t k j ) Rcord th avrag frng rat of nurons n xc and nh populatons. Compar wth man fld analyss.

7 Man fld:. Populaton rspons Add hgh suscptblty to dpolarzaton block to nhbtory nurons Stmulat unconnctd ML nurons (N = 1000) wth random spks xctatory nurons: monotonc rspons to stmulus nhbtory nurons: non-monotonc rspons to stmulus Us ths faturs to dvlop a man fld modl that capturs larg-scal smulaton rsults.

8 Man fld:. Modfy Wlson-Cowan q Can w com up wth a rducd modl that capturs larg-scal smulatons? Modfy Wlson-Cowan qn: dr dt τ dr dt = r + φ (J r J r + ) = r + φ (J r J r + ), r / : actvty of xc/nh populatons. φ / : transfr functon convrts nput to output Mak nhbtory transfr functon, φ, non-monotonc to captur DB. φ (x) = x, φ (x) = x k(x θ).

9 Compar man-fld qn and smulatons Can ad hoc man-fld modl xplan ntwork smulatons? Man-fld prdcts that ntwork stat can b bstabl. Normal stat (b 1 ) and szur stat (b 2 ) coxst. Aftr brf xtrnal stmulus, nh nurons ntr dpolarzaton block and xc nurons fr at max rat. Ths corrsponds to szur stat (b 2 ).

10 Non-oscllatory transton Phas plan ncras xt Bfurcaton dagram Dcras xt Ntwork smulatons

11 Oscllatory transton - couplng ncrasd. ncras xt Dcras xt

12 Dynamcs around Bogdanov-Takns HB HC homoclnc bfurcaton J osc s z u r BT saddl nod bfurcaton normal SN xtrnal nput

13 Conclusons, frst half 1 Man fld modl capturs dffrnt typs of transtons to szur-lk actvty that ar sn n ntwork modl. oscllatory, non-oscllatory, and tonc-clonc transtons

14 Outln 1. ntwork dynamcs nducd by dpolarzaton block Smulat rcurrntly connctd ntworks Man fld modl Comparson of smulatons and man fld analyss 2. Dpolzaton block n complx ntwork structur Scond ordr ntwork motfs ffcts of th convrgnt and chan motfs Gnralzd man fld modl

15 Scond ordr ntworks (SONTs) W j = 1 dnots a conncton from nod j to. ls W j = 0. P(W j = 1) = p. For an rdős-rény graph, all dgs ar ndpndnt: P(W j = 1, W kl = 1) = p 2 Scond ordr statstcs of W j ar trval. Go byond -R: (1) fx conncton probablty p (2) gnrat W j ; scond ord stat quals to prscrbd α s. [Zhao t al 11] α rcp = cov(w j,w j ) α p 2 conv = cov(w j,w k ) p 2 α dv = cov(w j,w kj ) α p 2 chan = cov(w j,w jk ) p 2

16 Proprts of scond ordr ntworks: rdős-rény Th rdős-rény random ntwork N = 20, p = 0.3, α rcp = 0.1, α conv = 0, α dv = 0, α chan = 0 probablty xpctd ncomng dgr dstrbuton ncomng dgr probablty xpctd outgong dgr dstrbuton outgong dgr N = 3000, p = 0.01, α rcp = 0, α conv = 0, α dv = 0, α chan = 0

17 Proprts of scond ordr ntworks: rcprocal Add rcprocal connctons: N = 20, p = 0.3, α rcp = 2.0, α conv = 0, α dv = 0, α chan = 0 probablty xpctd ncomng dgr dstrbuton ncomng dgr probablty xpctd outgong dgr dstrbuton outgong dgr N = 3000, p = 0.01, α rcp = 3, α conv = 0, α dv = 0, α chan = 0

18 Proprts of scond ordr ntworks: convrgnt Add convrgnt connctons: N = 20, p = 0.3, α rcp = 0.1, α conv = 0.5, α dv = 0, α chan = 0 probablty xpctd ncomng dgr dstrbuton ncomng dgr probablty xpctd outgong dgr dstrbuton outgong dgr N = 3000, p = 0.01, α rcp = 0, α conv = 3, α dv = 0, α chan = 0

19 Proprts of scond ordr ntworks: dvrgnt Add dvrgnt connctons: N = 20, p = 0.3, α rcp = 0.1, α conv = 0, α dv = 0.5, α chan = 0 probablty xpctd ncomng dgr dstrbuton ncomng dgr probablty xpctd outgong dgr dstrbuton outgong dgr N = 3000, p = 0.01, α rcp = 0, α conv = 0, α dv = 3, α chan = 0

20 Proprts of scond ordr ntworks: no chans Add convrgnt and dvrgnt connctons, rduc chans: N = 20, p = 0.4, α rcp = 0.9, α conv = 0.3, α dv = 0.4, α chan = 0.3 probablty xpctd ncomng dgr dstrbuton ncomng dgr probablty xpctd outgong dgr dstrbuton outgong dgr N = 3000, p = 0.01, α rcp = 0, α conv = 3, α dv = 3, α chan = 0.9

21 Proprts of scond ordr ntworks: chans Add convrgnt and dvrgnt connctons wth chans: N = 20, p = 0.3, α rcp = 1.0, α conv = 0.3, α dv = 0.3, α chan = 0.3 probablty xpctd ncomng dgr dstrbuton ncomng dgr probablty xpctd outgong dgr dstrbuton outgong dgr N = 3000, p = 0.01, α rcp = 0, α conv = 3, α dv = 3, α chan = 3

22 Prvous rsults Chan motfs Lnar thory works! Whn on lnar rgm of transfr functon, chans hav strongst nflunc on synchrony [Zhao t al 11, Nykamp t al 16]. ncras spk-tm corrlatons (lnar rspons thory) [Hu t al 13]. Convrgnt motfs Rduc ntwork synchrony du to htrognty. [Zhao t al 11] Promot synchrony n low actvty rgm. [Roxn 11] Nonlnarty of transfr functon mattrs!

23 SONTs and dgrs n-dgr d n, out-dgr d out normalzd n- and out-dgr: x = d n (d n ), y = d out (d out ) d out =5 d n =4 SONT statstcs ar varancs of th dgr dstrbuton: α conv var(x) α dv var(y) α chan cov(x, y) α conv > 0 α dv > 0 α chan < 0 α chan > 0

24 A rat quaton Gvn a postsynaptc nuron wth n-dgr x a prsynaptc nuron wth out-dgr ỹ conncton probablty s proportonal to xỹ. d n =4 d out =5? Lt r(x, y, t) b frng rat of nuron wth dgr (x, y). Gt rat quaton nonlnarty couplng strngth { { prsynaptc frng rat dgr dstrbuton nput Couplng strngth from prsynaptc dgr ( x, ỹ) onto postsynaptc dgr (x, y) s Jxỹ.

25 Gnralzd quaton - Drvaton Th dynamcs of sngl nuron frng rat s gvn by ( ) dr (x, y, t) + r(x, y, t) = φ Jxỹρ( x, ỹ)r( x, ỹ, t)d xdỹ + dt Dfn populaton synaptc actvty h Thn, S(t) = yr(x, y, t)ρ(x, y)dxdy dr dt + r = φ (JxS(t) + ) Multply by yρ(x, y) and ntgrat to obtan gnralzd qn ds(t) dt + S(t) = yρ(x, y)φ(jxs(t) + )dxdy.

26 Lnar trm dpnds on chan motf ds(t) dt + S(t) = yρ(x, y)φ(jxs(t) + )dxdy Lnar trm: covaranc btwn n and out dgr (α chan ) JSφ xyρ(x, y)dxdy = J(1 + α chan )Sφ Rsult: robust dpndnc on α chan ffct of convrgnc α conv n hghr-ordr trms 0.2 hghr-ordr drvatvs of φ nonlnarty shap mattrs Synchrony ˆα chan ˆαconv

27 Gnralzd quaton - ntwork f n-dgr dpnds on out-dgr (α chan 0), th gnralzd qn has four synaptc varabls, S, S, S, S, rprsntng ach dg. Consdr rducd qns: assum n-dgr s ndpndnt of out-dgr (α chan = 0). Th populaton rat actvty S k (t) = ρ(x k )r(x k, t)dx k satsfs ds + S = ρ (x )φ (J x S J x S + )dx dt ds + S = ρ (x dt )φ (J x S J x S + )dx. whr ρ( ) s n-dgr dstrbuton. [Nykamp t al 16; Roxn 11]

28 Convrgnt motfs n ntwork Consdr convrgnt motfs across th ntwork. R α conv, > 0 Non-monotonc rspons of nh nurons lads to strong dpndnc on α conv,. R α conv, > 0 Htrognty of nh synapss dsrupts synchrony n hgh actvty rgm.

29 Smulatons: facltats szur onst Nar szur, nh nurons wth hgh xc n-dgr ntr DB, low xc n-dgr hav wak nh actvty Ovrall nh actvty rducd. α conv, α conv, > 0 facltats transton to szur stat.

30 Smulatons: favors non-osc transton Htrognous nh synaptc nputs dsrupt oscllatons at hgh actvty rgm. R α conv, > 0 α conv, supprsss oscllatons nar th transton. non-osc oscllatory

31 Global bfurcaton, Saddl-nod and homoclnc bfurcatons occur at rducd

32 Global bfurcaton, Oscllatory rgm pushd away nar th transton.

33 Analytcal rsults Assum (1) th - couplng s strong (J J > J J ) and (2) Φ, Φ, Φ, Φ < 0 at hgh actvty rgm. α, conv facltats a saddl-nod and a Hopf bfurcaton. (Rd < 0, Blu > 0) [ 1 dtl α conv = dtl 0 + α conv,, τ J Φ J 2 S Φ J2 J SΦ ] 2 J2 S2 Φ Φ ( J J + J J ) α conv, trl α conv, = trl 0 + α conv, 1 2τ J2 J S( Φ 2 ). favors non-oscllatory transton to szur. trl α conv, = trl 0 + α conv, 1 2 J J 2 S2 Φ.

34 Conclusons 1 Man fld modl capturs dffrnt typs of transtons to szur-lk actvty that ar sn n ntwork modl. oscllatory, non-oscllatory, and tonc-clonc transtons 2 nflunc of ntwork structur on ths transtons Nonlnarty of transfr functon lads to strong dpndnc on convrgnt motfs Htrognous synaptc nputs can supprss synchrony. Gnralzd quaton can captur such ffcts.

35 Acknowldgmnts Natonal nsttuts of Halth Chrstophr Km Carson Chow Brnstn Cntr Frburg Arvnd Kumar Ulrch grt Fundng Sourc

36 Tonc-clonc transton Transton occurs whn k rcovrd toward physo valu. Smulatons Jnsn, Yaar 97 TONC (patho K ) CLONC (physo K ) r r

37 Chans can modulat th ffct of Corrlat nh nuron s outdgr wth ts xc n-dgr. α chan > 0 α chan < 0 Corrlatd out-dgr can amplfy/supprss ffcts of α conv,.

38 Chans can modulat th ffct of Corrlat xc nuron s outdgr wth ts nh n-dgr. α chan > 0 α chan < 0 Corrlatd out-dgr acclrats/dlays szur onst.

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time.

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time. Elctroncphalography EEG Dynamc Causal Modllng for M/EEG ampltud μv tm ms tral typ 1 tm channls channls tral typ 2 C. Phllps, Cntr d Rchrchs du Cyclotron, ULg, Blgum Basd on slds from: S. Kbl M/EEG analyss

More information

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization THE UNIVERSITY OF MARYLAND COLLEGE PARK, MARYLAND Economcs 600: August, 007 Dynamc Part: Problm St 5 Problms on Dffrntal Equatons and Contnuous Tm Optmzaton Quston Solv th followng two dffrntal quatons.

More information

Grand Canonical Ensemble

Grand Canonical Ensemble Th nsmbl of systms mmrsd n a partcl-hat rsrvor at constant tmpratur T, prssur P, and chmcal potntal. Consdr an nsmbl of M dntcal systms (M =,, 3,...M).. Thy ar mutually sharng th total numbr of partcls

More information

Analyzing Frequencies

Analyzing Frequencies Frquncy (# ndvduals) Frquncy (# ndvduals) /3/16 H o : No dffrnc n obsrvd sz frquncs and that prdctd by growth modl How would you analyz ths data? 15 Obsrvd Numbr 15 Expctd Numbr from growth modl 1 1 5

More information

A Note on Estimability in Linear Models

A Note on Estimability in Linear Models Intrnatonal Journal of Statstcs and Applcatons 2014, 4(4): 212-216 DOI: 10.5923/j.statstcs.20140404.06 A Not on Estmablty n Lnar Modls S. O. Adymo 1,*, F. N. Nwob 2 1 Dpartmnt of Mathmatcs and Statstcs,

More information

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous ST 54 NCSU - Fall 008 On way Analyss of varanc Varancs not homognous On way Analyss of varanc Exampl (Yandll, 997) A plant scntst masurd th concntraton of a partcular vrus n plant sap usng ELISA (nzym-lnkd

More information

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D Comp 35 Machn Larnng Computr Scnc Tufts Unvrsty Fall 207 Ron Khardon Th EM Algorthm Mxtur Modls Sm-Suprvsd Larnng Soft k-mans Clustrng ck k clustr cntrs : Assocat xampls wth cntrs p,j ~~ smlarty b/w cntr

More information

External Equivalent. EE 521 Analysis of Power Systems. Chen-Ching Liu, Boeing Distinguished Professor Washington State University

External Equivalent. EE 521 Analysis of Power Systems. Chen-Ching Liu, Boeing Distinguished Professor Washington State University xtrnal quvalnt 5 Analyss of Powr Systms Chn-Chng Lu, ong Dstngushd Profssor Washngton Stat Unvrsty XTRNAL UALNT ach powr systm (ara) s part of an ntrconnctd systm. Montorng dvcs ar nstalld and data ar

More information

Chapter 6 Student Lecture Notes 6-1

Chapter 6 Student Lecture Notes 6-1 Chaptr 6 Studnt Lctur Nots 6-1 Chaptr Goals QM353: Busnss Statstcs Chaptr 6 Goodnss-of-Ft Tsts and Contngncy Analyss Aftr compltng ths chaptr, you should b abl to: Us th ch-squar goodnss-of-ft tst to dtrmn

More information

Lecture 3: Phasor notation, Transfer Functions. Context

Lecture 3: Phasor notation, Transfer Functions. Context EECS 5 Fall 4, ctur 3 ctur 3: Phasor notaton, Transfr Functons EECS 5 Fall 3, ctur 3 Contxt In th last lctur, w dscussd: how to convrt a lnar crcut nto a st of dffrntal quatons, How to convrt th st of

More information

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D Comp 35 Introducton to Machn Larnng and Data Mnng Fall 204 rofssor: Ron Khardon Mxtur Modls Motvatd by soft k-mans w dvlopd a gnratv modl for clustrng. Assum thr ar k clustrs Clustrs ar not rqurd to hav

More information

Outlier-tolerant parameter estimation

Outlier-tolerant parameter estimation Outlr-tolrant paramtr stmaton Baysan thods n physcs statstcs machn larnng and sgnal procssng (SS 003 Frdrch Fraundorfr fraunfr@cg.tu-graz.ac.at Computr Graphcs and Vson Graz Unvrsty of Tchnology Outln

More information

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation Lctur Rlc nutrnos mpratur at nutrno dcoupln and today Effctv dnracy factor Nutrno mass lmts Saha quaton Physcal Cosmoloy Lnt 005 Rlc Nutrnos Nutrnos ar wakly ntractn partcls (lptons),,,,,,, typcal ractons

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

Linear-Phase FIR Transfer Functions. Functions. Functions. Functions. Functions. Functions. Let

Linear-Phase FIR Transfer Functions. Functions. Functions. Functions. Functions. Functions. Let It is impossibl to dsign an IIR transfr function with an xact linar-phas It is always possibl to dsign an FIR transfr function with an xact linar-phas rspons W now dvlop th forms of th linarphas FIR transfr

More information

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari snbrg Modl Sad Mohammad Mahd Sadrnhaad Survsor: Prof. bdollah Langar bstract: n ths rsarch w tr to calculat analtcall gnvalus and gnvctors of fnt chan wth ½-sn artcls snbrg modl. W drov gnfuctons for closd

More information

Physics 256: Lecture 2. Physics

Physics 256: Lecture 2. Physics Physcs 56: Lctur Intro to Quantum Physcs Agnda for Today Complx Numbrs Intrfrnc of lght Intrfrnc Two slt ntrfrnc Dffracton Sngl slt dffracton Physcs 01: Lctur 1, Pg 1 Constructv Intrfrnc Ths wll occur

More information

Logistic Regression I. HRP 261 2/10/ am

Logistic Regression I. HRP 261 2/10/ am Logstc Rgrsson I HRP 26 2/0/03 0- am Outln Introducton/rvw Th smplst logstc rgrsson from a 2x2 tabl llustrats how th math works Stp-by-stp xampls to b contnud nxt tm Dummy varabls Confoundng and ntracton

More information

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches.

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches. Subjct Chmistry Papr No and Titl Modul No and Titl Modul Tag 8/ Physical Spctroscopy / Brakdown of th Born-Oppnhimr approximation. Slction ruls for rotational-vibrational transitions. P, R branchs. CHE_P8_M

More information

Advances in the study of intrinsic rotation with flux tube gyrokinetics

Advances in the study of intrinsic rotation with flux tube gyrokinetics Adans n th study o ntrns rotaton wth lux tub gyroknts F.I. Parra and M. arns Unrsty o Oxord Wolgang Paul Insttut, Vnna, Aprl 0 Introduton In th absn o obous momntum nput (apart rom th dg), tokamak plasmas

More information

A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION*

A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION* A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION* Dr. G.S. Davd Sam Jayakumar, Assstant Profssor, Jamal Insttut of Managmnt, Jamal Mohamd Collg, Truchraall 620 020, South Inda,

More information

1- Summary of Kinetic Theory of Gases

1- Summary of Kinetic Theory of Gases Dr. Kasra Etmad Octobr 5, 011 1- Summary of Kntc Thory of Gass - Radaton 3- E4 4- Plasma Proprts f(v f ( v m 4 ( kt 3/ v xp( mv kt V v v m v 1 rms V kt v m ( m 1/ v 8kT m 3kT v rms ( m 1/ E3: Prcntag of

More information

Journal of Theoretical and Applied Information Technology 10 th January Vol. 47 No JATIT & LLS. All rights reserved.

Journal of Theoretical and Applied Information Technology 10 th January Vol. 47 No JATIT & LLS. All rights reserved. Journal o Thortcal and Appld Inormaton Tchnology th January 3. Vol. 47 No. 5-3 JATIT & LLS. All rghts rsrvd. ISSN: 99-8645 www.att.org E-ISSN: 87-395 RESEARCH ON PROPERTIES OF E-PARTIAL DERIVATIVE OF LOGIC

More information

Ch. 9 Common Emitter Amplifier

Ch. 9 Common Emitter Amplifier Ch. 9 Common mttr mplfr Common mttr mplfr nput and put oltags ar 180 o -of-phas, whl th nput and put currnts ar n-phas wth th nput oltag. Output oltag ( V ) V V V C CC C C C C and V C ar -of-phas 10 μ

More information

8-node quadrilateral element. Numerical integration

8-node quadrilateral element. Numerical integration Fnt Elmnt Mthod lctur nots _nod quadrlatral lmnt Pag of 0 -nod quadrlatral lmnt. Numrcal ntgraton h tchnqu usd for th formulaton of th lnar trangl can b formall tndd to construct quadrlatral lmnts as wll

More information

Review - Probabilistic Classification

Review - Probabilistic Classification Mmoral Unvrsty of wfoundland Pattrn Rcognton Lctur 8 May 5, 6 http://www.ngr.mun.ca/~charlsr Offc Hours: Tusdays Thursdays 8:3-9:3 PM E- (untl furthr notc) Gvn lablld sampls { ɛc,,,..., } {. Estmat Rvw

More information

Jones vector & matrices

Jones vector & matrices Jons vctor & matrcs PY3 Colást na hollscol Corcagh, Ér Unvrst Collg Cork, Irland Dpartmnt of Phscs Matr tratmnt of polarzaton Consdr a lght ra wth an nstantanous -vctor as shown k, t ˆ k, t ˆ k t, o o

More information

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP ISAHP 00, Bal, Indonsa, August -9, 00 COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP Chkako MIYAKE, Kkch OHSAWA, Masahro KITO, and Masaak SHINOHARA Dpartmnt of Mathmatcal Informaton Engnrng

More information

Authentication Transmission Overhead Between Entities in Mobile Networks

Authentication Transmission Overhead Between Entities in Mobile Networks 0 IJCSS Intrnatonal Journal of Computr Scnc and twork Scurty, VO.6 o.b, March 2006 Authntcaton Transmsson Ovrhad Btwn Entts n Mobl tworks Ja afr A-Sararh and Sufan Yousf Faculty of Scnc and Tchnology,

More information

Random Access Techniques: ALOHA (cont.)

Random Access Techniques: ALOHA (cont.) Random Accss Tchniqus: ALOHA (cont.) 1 Exampl [ Aloha avoiding collision ] A pur ALOHA ntwork transmits a 200-bit fram on a shard channl Of 200 kbps at tim. What is th rquirmnt to mak this fram collision

More information

cycle that does not cross any edges (including its own), then it has at least

cycle that does not cross any edges (including its own), then it has at least W prov th following thorm: Thorm If a K n is drawn in th plan in such a way that it has a hamiltonian cycl that dos not cross any dgs (including its own, thn it has at last n ( 4 48 π + O(n crossings Th

More information

High Energy Physics. Lecture 5 The Passage of Particles through Matter

High Energy Physics. Lecture 5 The Passage of Particles through Matter High Enrgy Physics Lctur 5 Th Passag of Particls through Mattr 1 Introduction In prvious lcturs w hav sn xampls of tracks lft by chargd particls in passing through mattr. Such tracks provid som of th most

More information

10. The Discrete-Time Fourier Transform (DTFT)

10. The Discrete-Time Fourier Transform (DTFT) Th Discrt-Tim Fourir Transform (DTFT Dfinition of th discrt-tim Fourir transform Th Fourir rprsntation of signals plays an important rol in both continuous and discrt signal procssing In this sction w

More information

6. The Interaction of Light and Matter

6. The Interaction of Light and Matter 6. Th Intraction of Light and Mattr - Th intraction of light and mattr is what maks lif intrsting. - Light causs mattr to vibrat. Mattr in turn mits light, which intrfrs with th original light. - Excitd

More information

A Probabilistic Characterization of Simulation Model Uncertainties

A Probabilistic Characterization of Simulation Model Uncertainties A Proalstc Charactrzaton of Sulaton Modl Uncrtants Vctor Ontvros Mohaad Modarrs Cntr for Rsk and Rlalty Unvrsty of Maryland 1 Introducton Thr s uncrtanty n odl prdctons as wll as uncrtanty n xprnts Th

More information

A Propagating Wave Packet Group Velocity Dispersion

A Propagating Wave Packet Group Velocity Dispersion Lctur 8 Phys 375 A Propagating Wav Packt Group Vlocity Disprsion Ovrviw and Motivation: In th last lctur w lookd at a localizd solution t) to th 1D fr-particl Schrödingr quation (SE) that corrsponds to

More information

Physics of Very High Frequency (VHF) Capacitively Coupled Plasma Discharges

Physics of Very High Frequency (VHF) Capacitively Coupled Plasma Discharges Physcs of Vry Hgh Frquncy (VHF) Capactvly Coupld Plasma Dschargs Shahd Rauf, Kallol Bra, Stv Shannon, and Kn Collns Appld Matrals, Inc., Sunnyval, CA AVS 54 th Intrnatonal Symposum Sattl, WA Octobr 15-19,

More information

te Finance (4th Edition), July 2017.

te Finance (4th Edition), July 2017. Appndx Chaptr. Tchncal Background Gnral Mathmatcal and Statstcal Background Fndng a bas: 3 2 = 9 3 = 9 1 /2 x a = b x = b 1/a A powr of 1 / 2 s also quvalnt to th squar root opraton. Fndng an xponnt: 3

More information

Optimal Ordering Policy in a Two-Level Supply Chain with Budget Constraint

Optimal Ordering Policy in a Two-Level Supply Chain with Budget Constraint Optmal Ordrng Polcy n a Two-Lvl Supply Chan wth Budgt Constrant Rasoul aj Alrza aj Babak aj ABSTRACT Ths papr consdrs a two- lvl supply chan whch consst of a vndor and svral rtalrs. Unsatsfd dmands n rtalrs

More information

Background: We have discussed the PIB, HO, and the energy of the RR model. In this chapter, the H-atom, and atomic orbitals.

Background: We have discussed the PIB, HO, and the energy of the RR model. In this chapter, the H-atom, and atomic orbitals. Chaptr 7 Th Hydrogn Atom Background: W hav discussd th PIB HO and th nrgy of th RR modl. In this chaptr th H-atom and atomic orbitals. * A singl particl moving undr a cntral forc adoptd from Scott Kirby

More information

GPC From PeakSimple Data Acquisition

GPC From PeakSimple Data Acquisition GPC From PakSmpl Data Acquston Introducton Th follong s an outln of ho PakSmpl data acquston softar/hardar can b usd to acqur and analyz (n conjuncton th an approprat spradsht) gl prmaton chromatography

More information

EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12

EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12 EEC 686/785 Modlng & Prformanc Evaluaton of Computr Systms Lctur Dpartmnt of Elctrcal and Computr Engnrng Clvland Stat Unvrsty wnbng@.org (basd on Dr. Ra Jan s lctur nots) Outln Rvw of lctur k r Factoral

More information

Static/Dynamic Deformation with Finite Element Method. Graphics & Media Lab Seoul National University

Static/Dynamic Deformation with Finite Element Method. Graphics & Media Lab Seoul National University Statc/Dynamc Dormaton wth Fnt Elmnt Mthod Graphcs & Mda Lab Sol Natonal Unvrsty Statc/Dynamc Dormaton Statc dormaton Dynamc dormaton ndormd shap ntrnal + = nrta = trnal dormd shap statc qlbrm dynamc qlbrm

More information

Circular Wilson loop operator and master field

Circular Wilson loop operator and master field YITP wor shop Dvlopmnt of Quantum Fld Thory and trng Thory Crcular Wlson loop oprator and mastr fld hoch Kawamoto OCAMI, Osaa Cty Unvrsty atonal Tawan ormal Unvrsty from August Wth T. Kuro Ryo and A. Mwa

More information

From Structural Analysis to FEM. Dhiman Basu

From Structural Analysis to FEM. Dhiman Basu From Structural Analyss to FEM Dhman Basu Acknowldgmnt Followng txt books wr consultd whl prparng ths lctur nots: Znkwcz, OC O.C. andtaylor Taylor, R.L. (000). Th FntElmnt Mthod, Vol. : Th Bass, Ffth dton,

More information

ACOUSTIC WAVE EQUATION. Contents INTRODUCTION BULK MODULUS AND LAMÉ S PARAMETERS

ACOUSTIC WAVE EQUATION. Contents INTRODUCTION BULK MODULUS AND LAMÉ S PARAMETERS ACOUSTIC WAE EQUATION Contnts INTRODUCTION BULK MODULUS AND LAMÉ S PARAMETERS INTRODUCTION As w try to vsualz th arth ssmcally w mak crtan physcal smplfcatons that mak t asr to mak and xplan our obsrvatons.

More information

In this lecture... Subsonic and supersonic nozzles Working of these nozzles Performance parameters for nozzles

In this lecture... Subsonic and supersonic nozzles Working of these nozzles Performance parameters for nozzles Lct-30 Lct-30 In this lctur... Subsonic and suprsonic nozzls Working of ths nozzls rformanc paramtrs for nozzls rof. Bhaskar Roy, rof. A M radp, Dpartmnt of Arospac, II Bombay Lct-30 Variation of fluid

More information

Discrete Shells Simulation

Discrete Shells Simulation Dscrt Shlls Smulaton Xaofng M hs proct s an mplmntaton of Grnspun s dscrt shlls, th modl of whch s govrnd by nonlnar mmbran and flxural nrgs. hs nrgs masur dffrncs btwns th undformd confguraton and th

More information

Network Congestion Games

Network Congestion Games Ntwork Congstion Gams Assistant Profssor Tas A&M Univrsity Collg Station, TX TX Dallas Collg Station Austin Houston Bst rout dpnds on othrs Ntwork Congstion Gams Travl tim incrass with congstion Highway

More information

Deconfinement phase transition in SU(3)/Z3 QCD (adj) via the gauge theory/affine XY-model duality

Deconfinement phase transition in SU(3)/Z3 QCD (adj) via the gauge theory/affine XY-model duality Dconfnmnt phas transton n SU(3)/Z3 QCD (adj) va th gaug thory/affn XY-modl dualty MOHAMED ABER UIVERSITY OF TOROTO TH W O R K S H O P O O - P E R T U R B A T I V E Q C D M.A., Erch Popptz, Mthat Unsal

More information

Where k is either given or determined from the data and c is an arbitrary constant.

Where k is either given or determined from the data and c is an arbitrary constant. Exponntial growth and dcay applications W wish to solv an quation that has a drivativ. dy ky k > dx This quation says that th rat of chang of th function is proportional to th function. Th solution is

More information

:2;$-$(01*%<*=,-./-*=0;"%/;"-*

:2;$-$(01*%<*=,-./-*=0;%/;-* !"#$%'()%"*#%*+,-./-*+01.2(.*3+456789*!"#$%"'()'*+,-."/0.%+1'23"45'46'7.89:89'/' ;8-,"$4351415,8:+#9' Dr. Ptr T. Gallaghr Astrphyscs Rsarch Grup Trnty Cllg Dubln :2;$-$(01*%

More information

Hydrogen Atom and One Electron Ions

Hydrogen Atom and One Electron Ions Hydrogn Atom and On Elctron Ions Th Schrödingr quation for this two-body problm starts out th sam as th gnral two-body Schrödingr quation. First w sparat out th motion of th cntr of mass. Th intrnal potntial

More information

9.5 Complex variables

9.5 Complex variables 9.5 Cmpl varabls. Cnsdr th funtn u v f( ) whr ( ) ( ), f( ), fr ths funtn tw statmnts ar as fllws: Statmnt : f( ) satsf Cauh mann quatn at th rgn. Statmnt : f ( ) ds nt st Th rrt statmnt ar (A) nl (B)

More information

Polytropic Process. A polytropic process is a quasiequilibrium process described by

Polytropic Process. A polytropic process is a quasiequilibrium process described by Polytropc Procss A polytropc procss s a quasqulbrum procss dscrbd by pv n = constant (Eq. 3.5 Th xponnt, n, may tak on any valu from to dpndng on th partcular procss. For any gas (or lqud, whn n = 0, th

More information

Derivation of Eigenvalue Matrix Equations

Derivation of Eigenvalue Matrix Equations Drivation of Eignvalu Matrix Equations h scalar wav quations ar φ φ η + ( k + 0ξ η β ) φ 0 x y x pq ε r r whr for E mod E, 1, y pq φ φ x 1 1 ε r nr (4 36) for E mod H,, 1 x η η ξ ξ n [ N ] { } i i i 1

More information

Forces. Quantum ElectroDynamics. α = = We have now:

Forces. Quantum ElectroDynamics. α = = We have now: W hav now: Forcs Considrd th gnral proprtis of forcs mdiatd by xchang (Yukawa potntial); Examind consrvation laws which ar obyd by (som) forcs. W will nxt look at thr forcs in mor dtail: Elctromagntic

More information

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline Introucton to Ornar Dffrntal Equatons Sptmbr 7, 7 Introucton to Ornar Dffrntal Equatons Larr artto Mchancal Engnrng AB Smnar n Engnrng Analss Sptmbr 7, 7 Outln Rvw numrcal solutons Bascs of ffrntal quatons

More information

EDGE PEDESTAL STRUCTURE AND TRANSPORT INTERPRETATION (In the absence of or in between ELMs)

EDGE PEDESTAL STRUCTURE AND TRANSPORT INTERPRETATION (In the absence of or in between ELMs) I. EDGE PEDESTAL STRUCTURE AND TRANSPORT INTERPRETATION (In th absnc of or n btwn ELMs) Abstract W. M. Stacy (Gorga Tch) and R. J. Grobnr (Gnral Atomcs) A constrant on th on prssur gradnt s mposd by momntum

More information

CHAPTER 33: PARTICLE PHYSICS

CHAPTER 33: PARTICLE PHYSICS Collg Physcs Studnt s Manual Chaptr 33 CHAPTER 33: PARTICLE PHYSICS 33. THE FOUR BASIC FORCES 4. (a) Fnd th rato of th strngths of th wak and lctromagntc forcs undr ordnary crcumstancs. (b) What dos that

More information

Learning Spherical Convolution for Fast Features from 360 Imagery

Learning Spherical Convolution for Fast Features from 360 Imagery Larning Sphrical Convolution for Fast Faturs from 36 Imagry Anonymous Author(s) 3 4 5 6 7 8 9 3 4 5 6 7 8 9 3 4 5 6 7 8 9 3 3 3 33 34 35 In this fil w provid additional dtails to supplmnt th main papr

More information

Relate p and T at equilibrium between two phases. An open system where a new phase may form or a new component can be added

Relate p and T at equilibrium between two phases. An open system where a new phase may form or a new component can be added 4.3, 4.4 Phas Equlbrum Dtrmn th slops of th f lns Rlat p and at qulbrum btwn two phass ts consdr th Gbbs functon dg η + V Appls to a homognous systm An opn systm whr a nw phas may form or a nw componnt

More information

Ερωτήσεις και ασκησεις Κεφ. 10 (για μόρια) ΠΑΡΑΔΟΣΗ 29/11/2016. (d)

Ερωτήσεις και ασκησεις Κεφ. 10 (για μόρια) ΠΑΡΑΔΟΣΗ 29/11/2016. (d) Ερωτήσεις και ασκησεις Κεφ 0 (για μόρια ΠΑΡΑΔΟΣΗ 9//06 Th coffcnt A of th van r Waals ntracton s: (a A r r / ( r r ( (c a a a a A r r / ( r r ( a a a a A r r / ( r r a a a a A r r / ( r r 4 a a a a 0 Th

More information

Exercises for lectures 7 Steady state, tracking and disturbance rejection

Exercises for lectures 7 Steady state, tracking and disturbance rejection Exrc for lctur 7 Stady tat, tracng and dturbanc rjcton Martn Hromčí Automatc control 06-3-7 Frquncy rpon drvaton Automatcé řízní - Kybrnta a robota W lad a nuodal nput gnal to th nput of th ytm, gvn by

More information

Epistemic Foundations of Game Theory. Lecture 1

Epistemic Foundations of Game Theory. Lecture 1 Royal Nthrlands cadmy of rts and Scncs (KNW) Mastr Class mstrdam, Fbruary 8th, 2007 Epstmc Foundatons of Gam Thory Lctur Gacomo onanno (http://www.con.ucdavs.du/faculty/bonanno/) QUESTION: What stratgs

More information

Section 11.6: Directional Derivatives and the Gradient Vector

Section 11.6: Directional Derivatives and the Gradient Vector Sction.6: Dirctional Drivativs and th Gradint Vctor Practic HW rom Stwart Ttbook not to hand in p. 778 # -4 p. 799 # 4-5 7 9 9 35 37 odd Th Dirctional Drivativ Rcall that a b Slop o th tangnt lin to th

More information

Lecture 23 APPLICATIONS OF FINITE ELEMENT METHOD TO SCALAR TRANSPORT PROBLEMS

Lecture 23 APPLICATIONS OF FINITE ELEMENT METHOD TO SCALAR TRANSPORT PROBLEMS COMPUTTION FUID DYNMICS: FVM: pplcatons to Scalar Transport Prolms ctur 3 PPICTIONS OF FINITE EEMENT METHOD TO SCR TRNSPORT PROBEMS 3. PPICTION OF FEM TO -D DIFFUSION PROBEM Consdr th stady stat dffuson

More information

MP IN BLOCK QUASI-INCOHERENT DICTIONARIES

MP IN BLOCK QUASI-INCOHERENT DICTIONARIES CHOOL O ENGINEERING - TI IGNAL PROCEING INTITUTE Lornzo Potta and Prr Vandrghynst CH-1015 LAUANNE Tlphon: 4121 6932601 Tlfax: 4121 6937600 -mal: lornzo.potta@pfl.ch ÉCOLE POLYTECHNIQUE ÉDÉRALE DE LAUANNE

More information

Introduction to Condensed Matter Physics

Introduction to Condensed Matter Physics Introduction to Condnsd Mattr Physics pcific hat M.P. Vaughan Ovrviw Ovrviw of spcific hat Hat capacity Dulong-Ptit Law Einstin modl Dby modl h Hat Capacity Hat capacity h hat capacity of a systm hld at

More information

2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm

2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm . Grundlgnd Vrfahrn zur Übrtragung dgtalr Sgnal (Zusammnfassung) wt Dc. 5 Transmsson of Dgtal Sourc Sgnals Sourc COD SC COD MOD MOD CC dg RF s rado transmsson mdum Snk DC SC DC CC DM dg DM RF g physcal

More information

Chapter 13 GMM for Linear Factor Models in Discount Factor form. GMM on the pricing errors gives a crosssectional

Chapter 13 GMM for Linear Factor Models in Discount Factor form. GMM on the pricing errors gives a crosssectional Chaptr 13 GMM for Linar Factor Modls in Discount Factor form GMM on th pricing rrors givs a crosssctional rgrssion h cas of xcss rturns Hors rac sting for charactristic sting for pricd factors: lambdas

More information

167 T componnt oftforc on atom B can b drvd as: F B =, E =,K (, ) (.2) wr w av usd 2 = ( ) =2 (.3) T scond drvatv: 2 E = K (, ) = K (1, ) + 3 (.4).2.2

167 T componnt oftforc on atom B can b drvd as: F B =, E =,K (, ) (.2) wr w av usd 2 = ( ) =2 (.3) T scond drvatv: 2 E = K (, ) = K (1, ) + 3 (.4).2.2 166 ppnd Valnc Forc Flds.1 Introducton Valnc forc lds ar usd to dscrb ntra-molcular ntractons n trms of 2-body, 3-body, and 4-body (and gr) ntractons. W mplmntd many popular functonal forms n our program..2

More information

Elements of Statistical Thermodynamics

Elements of Statistical Thermodynamics 24 Elmnts of Statistical Thrmodynamics Statistical thrmodynamics is a branch of knowldg that has its own postulats and tchniqus. W do not attmpt to giv hr vn an introduction to th fild. In this chaptr,

More information

Supplementary Materials

Supplementary Materials 6 Supplmntary Matrials APPENDIX A PHYSICAL INTERPRETATION OF FUEL-RATE-SPEED FUNCTION A truck running on a road with grad/slop θ positiv if moving up and ngativ if moving down facs thr rsistancs: arodynamic

More information

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION CHAPTER 7d. DIFFERENTIATION AND INTEGRATION A. J. Clark School o Engnrng Dpartmnt o Cvl and Envronmntal Engnrng by Dr. Ibrahm A. Assakka Sprng ENCE - Computaton Mthods n Cvl Engnrng II Dpartmnt o Cvl and

More information

AS 5850 Finite Element Analysis

AS 5850 Finite Element Analysis AS 5850 Finit Elmnt Analysis Two-Dimnsional Linar Elasticity Instructor Prof. IIT Madras Equations of Plan Elasticity - 1 displacmnt fild strain- displacmnt rlations (infinitsimal strain) in matrix form

More information

ANALYSIS: The mass rate balance for the one-inlet, one-exit control volume at steady state is

ANALYSIS: The mass rate balance for the one-inlet, one-exit control volume at steady state is Problm 4.47 Fgur P4.47 provds stady stat opratng data for a pump drawng watr from a rsrvor and dlvrng t at a prssur of 3 bar to a storag tank prchd 5 m abov th rsrvor. Th powr nput to th pump s 0.5 kw.

More information

9 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations

9 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations Physcs 171/271 - Chapter 9R -Davd Klenfeld - Fall 2005 9 Dervaton of Rate Equatons from Sngle-Cell Conductance (Hodgkn-Huxley-lke) Equatons We consder a network of many neurons, each of whch obeys a set

More information

Classical Magnetic Dipole

Classical Magnetic Dipole Lctur 18 1 Classical Magntic Dipol In gnral, a particl of mass m and charg q (not ncssarily a point charg), w hav q g L m whr g is calld th gyromagntic ratio, which accounts for th ffcts of non-point charg

More information

ANALYTICITY THEOREM FOR FRACTIONAL LAPLACE TRANSFORM

ANALYTICITY THEOREM FOR FRACTIONAL LAPLACE TRANSFORM Sc. Rs. hm. ommn.: (3, 0, 77-8 ISSN 77-669 ANALYTIITY THEOREM FOR FRATIONAL LAPLAE TRANSFORM P. R. DESHMUH * and A. S. GUDADHE a Prof. Ram Mgh Insttt of Tchnology & Rsarch, Badnra, AMRAVATI (M.S. INDIA

More information

Theoretical study of the magnetization dynamics of non-dilute ferrofluids

Theoretical study of the magnetization dynamics of non-dilute ferrofluids Thortcal study of th magntzaton dynamcs of non-dlut frrofluds D.V. Brkov, L.Yu. Iskakova, A.Yu. Zubarv Innovnt Tchnology Dvlopmnt, Prussngstr. 7B. D-07745, Jna, Grmany Ural Stat Unvrsty, Lnna Av 5, 60083,

More information

Phys 774: Nonlinear Spectroscopy: SHG and Raman Scattering

Phys 774: Nonlinear Spectroscopy: SHG and Raman Scattering Last Lcturs: Polaraton of Elctromagntc Wavs Phys 774: Nonlnar Spctroscopy: SHG and Scattrng Gnral consdraton of polaraton Jons Formalsm How Polarrs work Mullr matrcs Stoks paramtrs Poncar sphr Fall 7 Polaraton

More information

PHA 5128 Answer CASE STUDY 3 Question #1: Model

PHA 5128 Answer CASE STUDY 3 Question #1: Model PHA 5128 Answr CASE STUDY 3 Spring 2008 Qustion #1: Aminoglycosids hav a triphasic disposition, but tobramycin concntration-tim profil hr is dscribd via a 2-compartmnt modl sinc th alpha phas could not

More information

Study of Dynamic Aperture for PETRA III Ring K. Balewski, W. Brefeld, W. Decking, Y. Li DESY

Study of Dynamic Aperture for PETRA III Ring K. Balewski, W. Brefeld, W. Decking, Y. Li DESY Stud of Dnamc Aprtur for PETRA III Rng K. Balws, W. Brfld, W. Dcng, Y. L DESY FLS6 Hamburg PETRA III Yong-Jun L t al. Ovrvw Introducton Dnamcs of dampng wgglrs hoc of machn tuns, and optmzaton of stupol

More information

Green Functions, the Generating Functional and Propagators in the Canonical Quantization Approach

Green Functions, the Generating Functional and Propagators in the Canonical Quantization Approach Grn Functons, th Gnratng Functonal and Propagators n th Canoncal Quantzaton Approach by Robrt D. Klaubr 15, 16 www.quantumfldthory.nfo Mnor Rv: Spt, 16 Sgnfcant Rv: Fb 3, 16 Orgnal: Fbruary, 15 Th followng

More information

FREE VIBRATION ANALYSIS OF FUNCTIONALLY GRADED BEAMS

FREE VIBRATION ANALYSIS OF FUNCTIONALLY GRADED BEAMS Journal of Appl Mathatcs an Coputatonal Mchancs, (), 9- FREE VIBRATION ANAYSIS OF FNCTIONAY GRADED BEAMS Stansław Kukla, Jowta Rychlwska Insttut of Mathatcs, Czstochowa nvrsty of Tchnology Czstochowa,

More information

Binary Decision Diagram with Minimum Expected Path Length

Binary Decision Diagram with Minimum Expected Path Length Bnary Dcson Dagram wth Mnmum Expctd Path Lngth Y-Yu Lu Kuo-Hua Wang TngTng Hwang C. L. Lu Dpartmnt of Computr Scnc, Natonal Tsng Hua Unvrsty, Hsnchu 300, Tawan Dpt. of Computr Scnc and Informaton Engnrng,

More information

E hf. hf c. 2 2 h 2 2 m v f ' f 2f ' f cos c

E hf. hf c. 2 2 h 2 2 m v f ' f 2f ' f cos c EXPERIMENT 9: COMPTON EFFECT Rlatd Topics Intractions of photons with lctrons, consrvation of momntum and nrgy, inlastic and lastic scattring, intraction cross sction, Compton wavlngth. Principl Whn photons

More information

Errata. Items with asterisks will still be in the Second Printing

Errata. Items with asterisks will still be in the Second Printing Errata Itms with astrisks will still b in th Scond Printing Author wbsit URL: http://chs.unl.du/edpsych/rjsit/hom. P7. Th squar root of rfrrd to σ E (i.., σ E is rfrrd to not Th squar root of σ E (i..,

More information

Properties of ferromagnetic materials, magnetic circuits principle of calculation

Properties of ferromagnetic materials, magnetic circuits principle of calculation Proprts of frromagntc matrals magntc crcuts prncpl of calculaton Frromagntc matrals Svral matrals rprsnt dffrnt macroscopc proprts thy gv dffrnt rspons to xtrnal magntc fld Th rason for dffrnc s crtan

More information

The Open Economy in the Short Run

The Open Economy in the Short Run Economics 442 Mnzi D. Chinn Spring 208 Social Scincs 748 Univrsity of Wisconsin-Madison Th Opn Economy in th Short Run This st of nots outlins th IS-LM modl of th opn conomy. First, it covrs an accounting

More information

5. B To determine all the holes and asymptotes of the equation: y = bdc dced f gbd

5. B To determine all the holes and asymptotes of the equation: y = bdc dced f gbd 1. First you chck th domain of g x. For this function, x cannot qual zro. Thn w find th D domain of f g x D 3; D 3 0; x Q x x 1 3, x 0 2. Any cosin graph is going to b symmtric with th y-axis as long as

More information

Econ107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13)

Econ107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13) Pag- Econ7 Appld Economtrcs Topc : Dummy Dpndnt Varabl (Studnmund, Chaptr 3) I. Th Lnar Probablty Modl Suppos w hav a cross scton of 8-24 yar-olds. W spcfy a smpl 2-varabl rgrsson modl. Th probablty of

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

Exercise 1. Sketch the graph of the following function. (x 2

Exercise 1. Sketch the graph of the following function. (x 2 Writtn tst: Fbruary 9th, 06 Exrcis. Sktch th graph of th following function fx = x + x, spcifying: domain, possibl asymptots, monotonicity, continuity, local and global maxima or minima, and non-drivability

More information

Basic Electrical Engineering for Welding [ ] --- Introduction ---

Basic Electrical Engineering for Welding [ ] --- Introduction --- Basc Elctrcal Engnrng for Wldng [] --- Introducton --- akayosh OHJI Profssor Ertus, Osaka Unrsty Dr. of Engnrng VIUAL WELD CO.,LD t-ohj@alc.co.jp OK 15 Ex. Basc A.C. crcut h fgurs n A-group show thr typcal

More information

are given in the table below. t (hours)

are given in the table below. t (hours) CALCULUS WORKSHEET ON INTEGRATION WITH DATA Work th following on notbook papr. Giv dcimal answrs corrct to thr dcimal placs.. A tank contains gallons of oil at tim t = hours. Oil is bing pumpd into th

More information

Self-interaction mass formula that relates all leptons and quarks to the electron

Self-interaction mass formula that relates all leptons and quarks to the electron Slf-intraction mass formula that rlats all lptons and quarks to th lctron GERALD ROSEN (a) Dpartmnt of Physics, Drxl Univrsity Philadlphia, PA 19104, USA PACS. 12.15. Ff Quark and lpton modls spcific thoris

More information

On Properties of the difference between two modified C p statistics in the nested multivariate linear regression models

On Properties of the difference between two modified C p statistics in the nested multivariate linear regression models Global Journal o Pur Ald Mathatcs. ISSN 0973-1768 Volu 1, Nubr 1 (016),. 481-491 Rsarch Inda Publcatons htt://www.rublcaton.co On Prorts o th drnc btwn two odd C statstcs n th nstd ultvarat lnar rgrsson

More information

arxiv: v2 [nlin.cd] 26 Oct 2011

arxiv: v2 [nlin.cd] 26 Oct 2011 Gnral mchansm for ampltud dath n coupld systms V. Rsm and G. Ambka Indan Insttut of Scnc Educaton and Rsarch, Pun - 4111, Inda R. E. Amrtkar Physcal Rsarch Laboratory, Ahmdabad - 389, Inda arxv:111.534v

More information