Super-Gaussian, super-diffusive transport of multi-mode active matter

Size: px
Start display at page:

Download "Super-Gaussian, super-diffusive transport of multi-mode active matter"

Transcription

1 Super-Guin, uper-diffuive rnpor of muli-mode ive mer Seungoo Hhn,, Snggeun Song -3, De Hyun Kim -3, Gil-Suk Yng,3, Kng Tek Lee 4 * Jeyoung Sung -3 * Creive Reerh Iniiive Cener for Chemil Dynmi in Living Cell, Chung-Ang Univeriy, Seoul 6974, Kore. Deprmen of Chemiry, Chung-Ang Univeriy, Seoul 6974, Kore. 3 Nionl Iniue of Innovive Funionl Imging, Chung-Ang Univeriy, Seoul 6974, Kore. 4 Deprmen of Chemiry, Gwngju Iniue of Siene nd Tehnology, Gwngju 65, Kore Correponding Auhor: J. Sung (jeyoung@u..kr); K. T. Lee (klee@gi..kr) Abr Living ell exhibi muli-mode rnpor h wihe beween n ive, elf-propelled moion nd eemingly pive, rndom moion. Cellulr deiion-mking over rnpor mode wihing i ohi proe h depend on he dynmi of he inrellulr hemil nework reguling he ell migrion proe. Here, we propoe heory nd n exly olvble model of muli-mode ive mer. Our ex model udy how h he reverible rniion beween pive mode nd n ive mode i he origin of he nomlou, uper-guin rnpor dynmi, whih h been oberved in vriou experimen for mulimode ive mer. We lo preen he generlizion of our model o enomp omplex muli-mode mer wih rbirry inernl e hemil dynmi nd inernl e dependen rnpor dynmi.

2 Living ell in migrion regule heir onumpion of inrellulr hemil energy ording o he inruion enoded in heir gene; hey exhibi muliple rnpor mode during rnpor, oniing of wo hrerii moion: elf-propelled, bllii moion when he mer i in n ive mode nd n undireed, rndom moion when he mer i in pive mode. Depending on he regulory e of he ellulr reion nework underlying ell migrion, he rnpor mode of living ell wihe repeedly beween he ive nd he pive mode. Thi feure in living ell rjeorie pper imilr o h of Lévy wlk [,]. Anoher inereing feure of living ell moion i h hey repeedly revere heir direion. Thi run-nd-revere moion h been repored in vriou beril yem [3-7]. Thee feure hve lo been oberved in he rnpor of vriou ype of rgo nd veile in living ell [8-]. Aive mer, uh living ell nd inrellulr ive prile, generlly exhibi n nomlou, non-guin rnpor dynmi, whih nno be deribed by Einein heory of Brownin moion [,] or more reen heorie for nomlou rnpor in diordered environmen [3-7]. There re model of pively moving prile h hve been ued o explin he long ime behvior of he men qure diplemen (SD) of muli-mode ive mer oberved in experimen [8,9]. Alhough hee model ume h he ohi dynmi of mulimode ive mer i quliively he me h of pive mer, hey re ble o provide ifory explnion of experimenl reul for he long ime behvior of he SD in mny e [,]. However, experimenl d wih higher ime reoluion reveled h muli-mode ive mer h quliively differen ohi dynmi from pive mer; he SD of muli-mode ive mer how hor ime-diffuive moion, inermedie uperdiffuive moion, nd long-ime diffuive moion wih greer diffuion oeffiien [,3],

3 whih nno be explined by he pive mer model [4,5]. An lernive model o oun for he nomlou rnpor dynmi of ive mer i he ive Brownin prile model. In hi model, veloiy-dependen friion in he Lngevin equion i ued o deribe he elf-propelled moion of ive prile [7,6]. The ive Brownin prile model doe provide n enhned explnion for he nomlou SD of ive prile; however, hi model nd he model menioned bove nno explin he nomlou diplemen diribuion of ive mer, whoe pil diribuion i non-guin wih poiive exe kuroi [6,7]. A number of oher inereing model hve been propoed for elf-propelled prile [8-34]. However, o he be of our knowledge, none of hem repreen muli-mode ive mer, whih wihe beween n ive, elf-propelled rnpor mode nd eemingly pive, rndom mode depending on i inernl e dynmi. In hi Leer, we preen n exly olvble model for he ohi rnpor of mulimode ive mer. In he high friion regime, where we n fely negle he ineril erm in he Lngevin equion, he veloiy x () of muli-mode ive mer wih friion onn, γ, n be wrien he um of wo omponen: x v, () () = ( Γ ()) γ ξ() where v ( Γ ) nd γ ξ() repreen he veloiy omponen of elf-propelled, bllii moion, whih i dependen on he inernl e Γ nd he veloiy omponen ued by he rndom fluuing fore. Auming h he dynmi of he rndom fluuing fore our in ime le fr horer hn he inernl e dynmi, we model ξ () Guin whie noie, whoe ime orrelion, ξ( ) ξ( ), i proporionl o he Dir del funion, 3

4 δ (). On he oher hnd, he relxion of v( Γ ( )) v( Γ ( )) from he iniil vlue, v, o he finl vlue, v, our in he ime le of he inernl e dynmi. We ume h he ell e dynmi i n rbirry ohi proe h n be repreened by mulidimenionl rkov proe. The Fokker-Plnk equion orreponding o Eq. () i given by P x D v P x L P x x x where P(, x, ) ( Γ,, ) = ( Γ) ( Γ,, ) ( Γ) ( Γ,, ), () Γ denoe he probbiliy deniy funion (PDF) of ive mer wih he poiion x nd inernl e Γ ime [35,36]. In Eq. (), D nd for he diffuion oeffiien for pive moion origining from he rndom fluuing fore, whih i defined by D d = γ ξ ξ(). L( Γ ) denoe he mhemil operor deribing he inernl e dynmi of he yem. Our model yield nlyi reul for he SD nd he non- Guin prmeer [37]. Here, we ompre wo imple, exly olvble model of ive mer: one for inglemode ive mer nd he oher for muli-mode ive mer. Thee model re hown in Fig.. For he ingle-mode model, whih only exhibi n ive mode, hown in Fig. (), Eq. () yield P x, v P x, k k P x, D P x, = I x x v P x, k k P x,. (3) For he muli-mode model, whih exhibi boh ive nd pive mode, hown in Fig. (b), Eq. () yield 4

5 P x, v P x, k k P x, P x, D P x, k k k P x, = x I x. (4) P x, v P x, k k P x, In Eq. (3) nd (4), (, ) P x deigne he probbiliy deniy of he mer e Γ i i ( i,,) nd poiion x ime. ± v, k, nd k denoe, repeively, he veloiy ( Γ ) of he elf-propelled moion of he mer e Γ ±, he rniion re o eiher e, v ± Γ or Γ, of he mer in ive mode, nd he rniion re o he e, Γ, of he mer in pive mode, whih v ( Γ ) =. Typil ime re re diplyed for he wo differen model in Fig. nd Supplemenl eril [38]. Ex nlyi oluion of Eq. (3) nd (4) n be obined in he Fourier domin [37]. From he ex oluion, we obin he diribuion fv (,) of he men veloiy, defined by x () ( v ()) ; i i hown in Fig. for eh model. In he hor-ime limi, for boh model, he men veloiy diribuion i found o be liner ombinion of Guin enered he e-dependen elf-propelled veloiy, v ( Γ ), h i, i eq i ( i ) ( ) fv (, ) p Gvv( Γ ), D i <<, (5) wih eq eq p i being he equilibrium probbiliy of e Γ i, given by p ± = / for he ingle- eq mode model nd by p = k ( k ± k ) nd eq p = k ( k k ) for he muli-mode model. In Eq. (5), G( ν, σ ) denoe he Guin diribuion of ν wih he men nd vrine given by nd σ, repeively. Equion (5) n lo be obined from he diribuion of he 5

6 innneou veloiy given in Eq. (), beue he men veloiy, x /, i he me he innneou veloiy in he hor-ime limi [37]. Equion (5) erve good pproximion of he men veloiy diribuion moderely hor ime, where rniion beween e h ye o our, or ime erlier hn he hrerii relxion ime, dφ () [ ] v, where φ () denoe he normlized ime orrelion funion, v v() v() v φv (), of he inernl e dependen elf-propelled veloiy, v ( Γ ). The long-ime diribuion of he men veloiy beome Guin for boh ingle-mode nd muli-mode model of ive mer, while he hor-ime diribuion n vry depending S on he model. A hor ime ( ), he men veloiy diribuion, f( v,,) of he inglemode model h wo Guin pek enered v nd v, whih re he wo veloiie of elf-propelled moion. In omprion, he men veloiy diribuion, f ( v,), of he mulimode model hor ime h n ddiionl Guin pek enered, reuling from he e, Γ, of he ive mer in pive mode. The vrine of eh Guin pek, whih origine from he rndom fluuing fore, i pproximely given by D. However, hown in Fig., boh f ( v,) nd fs ( v,) onverge o Guin wih men of zero nd vrine proporionl o / long ime [37]. Th i o y, for boh model, he diribuion of x () pprohe Guin ble diribuion long ime, in ordne wih he Guin enrl limi heorem [37]. The relxion dynmi of he men veloiy diribuion i highly dependen on he 6

7 hrerii relxion ime,, of inernl e dependen elf-propelled veloiy, v ( Γ ). The men veloiy diribuion pprohe he long-ime ympoi Guin fer he vlue of deree (ee Fig. () nd (d)). The nlyi expreion of i dependen on he model in queion. For he ingle-mode model, i given by hlf he lifeime, k, of he e, Γ ±, of he ive mer in ive mode, i.e., = ( k). For he muli-mode model, i he me he lifeime, k, of he e Γ ± [37]. In he mll limi, he men veloiy diribuion i Guin ny finie ime. Noe lo h he vrine in he men veloiy, or he men qured veloiy, ny given ime deree wih he relxion peed,, of he fluuion in he elf-propelled veloiy, hown in Fig. (e). Thi i ommon feure of dynmilly diordered yem; in peroopy, i h been ermed moionl nrrowing. The men veloiy diribuion, f ( v,), of he muli-mode model i dependen on he lifeime, ( k ), of pive mode, Γ, well on he lifeime, ( k ) =, of he e in ive mode, Γ ±. A hown in Fig. (f), when he populion rio, eq eq eq ( ( ) ( ) ) R p p p = = k k, of he e in pive mode o he e in ive mode deree, f ( v,) pprohe f( v,) [37]. However, he vlue of R inree, S he pek enered v = in f ( v,) grow lrge, o h he SD of he muli-mode model i mller hn he SD of he ingle-mode model. For boh he model, he SD h hree differen kinei phe: he hor-ime diffuion 7

8 phe, n inermedie uper-diffuive phe, nd he long-ime diffuive phe wih greer diffuion oeffiien, in greemen wih he previou experimenl reul [,3]. Ex nlyi expreion of he SD for boh model n be wrien in he me formul, x ( ) = ( D D) D ( e ), (6) where D i he effeive diffuion oeffiien omponen onribued from he elf-propelled moion, defined by () = D d v v v p. Here, p deigne he probbiliy of he e in ive mode, whih i given by uniy for he ingle-mode model nd by p p p R eq eq = = ( ) for he muli-mode model. D nd hve he me mening bove. A hown in Fig. 3(), he SD i given by x D hor ime ( ) nd dominnly onribued from he eemingly pive, rndom moion. On he oher hnd, long ime ( ), he SD i given by D x ( D ), wih he diffuion oeffiien inreed by D. In inermedie ime ( D v < ), he SD how uper-diffuive behvior ( SD α wih < α ). In he erly ge of he inermedie region, he SD, hown by he i pproximely qudri funion of ime, i.e., x ( ) D D ( ) green line in Fig. 3(b), whih origine from he bllii, elf-propelled moion of ive mer. While boh he ingle-mode nd muli-mode model yield quliively he me nlyi reul for he SD, he reul hey yield for he diplemen diribuion n be quie differen from eh oher. The diplemen diribuion, P (,) x, of he muli-mode model n be 8

9 uper-guin, in ordne wih he experimenl d repored in Ref. [34,39], where PS (,) x of he ingle-mode model i lwy ub-guin. For he muli-mode model, he deviion of P (,) x of he muli-mode model from Guin meured by he non-guin prmeer, 4 R x ( x ) α () () 3 (), i eniive o he populion rio, R, of he e in pive mode o he e in ive mode, whih i hown in Fig. 3(). The ex nlyi expreion of α () i preened in he Supplemenl eril [37]. The impler ympoi expreion of ( ) R α boh hor ime nd long ime i given by R α R ( ) ( R ) ( R ) () D ( ),, D () R R D,, 3 ( R ) D D (7) where () D deigne v, or he vlue of D in he limi where he e of ive mer i lwy in he ive mode. Aording o Eq. (7), he diplemen diribuion, P (,) x, of muli-mode ive mer i uper-guin when R >, bu ub-guin when R < ( 5).6 ll ime [37]. However, when 5 < R <, he diplemen diribuion, P (,) x, of muli-mode ive mer wihe from ub-guin o uper- Guin over ime [37]. Noe h α () vnihe in he lrge R limi, where he e of R muli-mode mer i lwy in pive mode. Thi men h, in our model, i i he elfpropelled, bllii moion h ue he diplemen diribuion o be non-guin. In he oppoie, mll R limi, P (,) x h exly he me hpe PS (,) x [37]. Thu, he 9

10 non-guin prmeer, α R (), of he muli-mode model redue o α() = lim αr () R of he ingle-mode model, whoe ympoi behvior i given by α ( ) () D 6 D ( ) () D,. () D D,. (8) Equion (8) how h he diplemen diribuion, PS (,) x, of ingle-mode ive mer i ub-guin only [37]. Boh P (,) x nd PS (,) x pproh Guin long ime; however, heir deviion from Guin, whih i meured by he non-guin prmeer, lowly deree wih ime, following long ime ( ), ording o Eq. (7) nd (8). A hown in Fig. 3(), he deviion of he diplemen diribuion from Guin n be izble even long ime where he SD, given in Eq. (6), i linerly proporionl o ime. Thi h been oberved, for exmple, in lipoome diffuion in nemi oluion of in filmen [4]. The muli-mode ive mer model diued bove n be exended o more omplex model in he higher pil dimenion, d. For he generlized model, he ohi differenil equion orreponding o Eq. () i given by r v ξ, (9) () = ( Γ ()) γ () where eh bold ymbol denoe he d-dimenionl veor orreponding o eh lr quniy in Eq. (). The generl expreion of he SD obined from Eq. (9) i given by

11 d d D p ( D r ( ) = φ ) φ ( ) ξ v, () where D, p, D, nd re, repeively, defined by D d γ d = ξ ξ (), p dφξ (), = D d d v ( ) v (), nd dφv (). Here, φ x () denoe he normlized ime orrelion funion, x( ) x() x (), of veor () x. The funionl form of φv ( ) vrie depending on he inernl e dynmi nd i oupling o he elfpropelled veloiy. Given h he relxion ime of rndom fluuion fore ξ () i fr horer hn he obervion ime, Eq. () redue o ( ) r ( ) dd dd d φv ( ). Thi reul i he generlizion of equion (6) for muli-dimenionl yem wih rbirry φ ( ) ; i redue o equion (6) for he onedimenionl model wih φ ( ) = exp( ). In ddiion, he generl expreion of he non- v Guin prmeer n lo be obined from equion (9) follow: v α R ( ) = r v r ( ) ( ) α v ( ). () 4 α v i defined by α ( ) r ( ) r ( ) wih ( ) Here () ( ) 4 d d v v v r nd r v defined d d v( ) v ( ) nd v d 4 d 3 d d v( 4) v( 3) v( ) v ( ), repeively. The non-guin prmeer given in Eq. () vnihe in boh he hor ime nd he long ime limi. A ime

12 fr horer hn he relxion ime le, ( v ( ) ( ) ), of he elf-propelled veloiy, r r, nd hene he non-guin prmeer given in Eq. (), vnih [37]. ( v ) On he oher hnd, in he long ime limi, ( ) ( ) r r pprohe D ( D D) bu α v (), or he non-guin prmeer of he elf-propelled diplemen, dv ( ), vnihe beue he diribuion of he elf-propelled diplemen beome Guin ording o he Guin enrl limi heorem. However, he non-guin prmeer h non-zero vlue beween he wo limi. In he imple one-dimenionl muli-mode ive mer model wih he Poion e wihing dynmi, we n how h equion () redue o equion (7) [37]. Equion () nd () enble u o lule he SD nd non-guin prmeer for generl muli-mode ive mer wih poibly non-poion e wihing dynmi. In ummry, we preen n nlyi heory nd n exly olvble model of muli-mode ive mer, whih wihe beween n ive, elf-propelled rnpor mode nd eemingly pive, rndom mode depending on i inernl e hemil dynmi. Our ex model udy lerly how h he reverible rniion beween eemingly pive, rndom moion nd he elf-propelled, bllii moion i n imporn oure of he uper-guin diplemen diribuion ommonly oberved for muli-mode ive mer. Thi model i uffiienly flexible o h i n be eily generlized o enomp muli-e, muli-mode ive mer wih rbirry inernl e hemil dynmi nd inernl e oupled rnpor dynmi. The ppliion of he preen pproh o he quniive explnion of experimenl reul for exmple of muli-mode ive mer i o be publihed elewhere.

13 FIGURES FIG.. odel yem nd ypil rjeorie. () The ingle-mode model oni of wo inernl e, Γ nd Γ. The ingle-mode ive mer in Γ ± e perform elfpropelled, direed moion wih veloiy ± v under rndom fluuing fore exered from medium. The ohi rniion beween inernl e i hrerized by he re onn, k. (b) The muli-mode model oniing of hree inernl e: pive rnpor e, Γ, in ddiion o ive rnpor e, Γ nd Γ. The muli-mode mer perform undireed, rndom moion in e Γ, bu perform direed, elf-propelled moion wih veloiy in e Γ ±. k nd k repreen he ohi rniion re from he pive Γ o he ive ± Γ e nd from he ive ± Γ o he pive Γ e, repeively. For eh model, ypil ime re of he poiion i hown. Color in he ive mer digrm nd rjeory repreen he ell inernl e. 3 ± v

14 FIG.. PDF for men veloiy diribuion. The ime dependen men veloiy diribuion, () fs ( v,) for he ingle-mode model nd (b) f ( v,) for he muli-mode model wih R =.5. In boh () nd (b), he men veloiy diribuion i diplyed ring from rbirry uni ime, T, nd he relxion ime of he veloiy-veloiy uo-orrelion funion i u e o be T u. The men veloiy diribuion for he muli-mode model, wih hree differen vlue of 4 = T () for he ingle-mode model nd (d) u. (line) nlyi reul (irle) ohi imulion reul. In (d), he vlue of R i e o be.5, in whih e he hree e re eqully probble equilibrium. (e) Dependene of he roo-men-qure veloiie, or he ndrd deviion of he men veloiy diribuion on he relxion peed meured by, nd (f) he men veloiy diribuion = Tu for he muli-mode model wih hree differen vlue of R : (blue doed line) R = ; (blue olid line) R =.5 ; (blk line) R =.5 ; nd (red line) R = 5. The vlue of i T u. In he mll R limi, f ( v,) pprohe fs ( v,). The Guin diribuion wih he me men nd vrine f ( v,) for R = 5 i ploed red doed line. The vlue of he oher prmeer re e o be D = nd v = 5 for ll e.

15 FIG. 3. en qure diplemen nd non-guin prmeer. () Time-dependen men qure diplemen (SD). The ingle-mode nd muli-mode model hre he me SD, given in Eq. (6). The vlue of i e o. The vlue of he oher prmeer, D nd D re e o be D = nd D =. (line) nlyi reul (irle) ohi imulion reul. (b) Dependene of x () on ime for he hree e wih =. (red), = (blk), nd = (blue). The effeive diffuion oeffiien inree from D o D D, whoe rniion ime le i deermined by. The green line repreen he bllii moion ( x ( ) D D ( ) ) orreponding o eh e. () The non- Guin prmeer, α ( ), for he ingle-mode model (blue line) nd for he muli-mode model wih vriou vlue of R (blk line). The wo red line repreen α ( ) for he wo riil vlue of R,.6 nd. (d) () α i lwy negive when 5 R < 5.6, bu poiive ll ime when R >. When.6 < R <, α () wihe from hor-ime negive regime o long-ime poiive regime. (e) A repreenive e for he ime- α wih R =.75 i ploed blk line. The wo red line dependen wihing, repreen ( ) α for he wo riil vlue of R, hown in (). The non-guin prmeer vnihe boh in he hor ime nd he long ime limi, mening h he iniil diribuion i del funion, Guin wih zero vrine, nd he finl diribuion obey he Guin enrl limi heorem. A he four ime poin mrked by he olid irle, he men veloiy

16 diribuion (blue line) nd heir orreponding Guin diribuion (blk line) re ploed ording o he qure men veloiy in he ine. The red irle mrk he defiieny in populion of he ive mer in he high men veloiy region, ompred o heir orreponding Guin diribuion. (f) Deviion of he men veloiy diribuion from Guin he wo ime poin mrked by he filled blk irle. The red irle here nd in he ine in (e) boh repreen he me pil regime. 6

17 Referene [] G. Ariel, A. Rbni, S. Beniy, J. D. Prridge, R.. Hrhey, nd A. Be'er, N. Commun. 6, 8396 (5). [] T. H. Hrri e l., Nure 486, 545 (). [3] J. E. Johnen, J. Pinhi, N. Blkburn, U. L. Zweifel, nd A. Hgrom, Aqu. irob. Eol. 8, 9 (). [4] O. Sliurenko, J. Neu, D. R. Zumn, nd G. Oer, Pro. Nl. Ad. Si. U. S. A. 3, 534 (6). [5] R. Gromnn, F. Peruni, nd. Br, New J. Phy. 8, 439 (6). [6] Y. Wu, A. D. Kier, Y. Jing, nd. S. Alber, Pro. Nl. Ad. Si. U. S. A. 6, (9). [7] P. Romnzuk,. Br, W. Ebeling, B. Lindner, nd L. Shimnky-Geier, Eur. Phy. J. Spe. Top., (). [8] S. H. Nm, Y.. Be, Y. I. Prk, J. H. Kim, H.. Kim, J. S. Choi, K. T. Lee, T. Hyeon, nd Y. D. Suh, Angew. Chem. In. Ed. Engl. 5, 693 (). [9] K. Chen, B. Wng, nd S. Grnik, N. er. 4, 589 (5). [] D. Arize, B. eier, E. Skmnn, J. O. Rdler, nd D. Heinrih, Phy. Rev. Le., 483 (8). [] A. Einein, Ann. Phy. 3, 549 (95). [] N. Wx, Seleed pper on noie nd ohi proee (Dover Publiion, New York, 954). [3] E. W. onroll nd G. H. Wei, J. h. Phy. 6, 67 (965). [4] V.. Kenkre, E. W. onroll, nd. F. Shleinger, J. S. Phy. 9, 44 (973). [5] J. Klfer nd R. Silbey, Phy. Rev. Le. 44, 55 (98). [6] G. H. Wei, Ape nd ppliion of he rndom wlk (Norh-Hollnd, Amerdm, 994). [7] R. ezler, E. Brki, nd J. Klfer, Phy. Rev. Le. 8, 3563 (999). [8] C. L. Soke, D. A. Luffenburger, nd S. K. Willim, J. Cell Si. 99, 49 (99). [9]. H. Gil nd C. W. Boone, Biophy. J., 98 (97). [] L. Li, S. F. Norrelykke, nd E. C. Cox, PLoS One 3, e93 (8). [] X. Liu, E. S. Welf, nd J.. Hugh, J. R. So. Inerfe, 44 (5). [] A. J. Looley, X.. O'Brien, J. S. Reihner, nd J. X. Tng, PLoS One, e745 (5). [3] J. R. Howe, R. A. Jone, A. J. Ryn, T. Gough, R. Vfbkhh, nd R. Golenin, Phy. Rev. Le. 99, 48 (7). [4] D. Cmpo, V. endez, nd I. Llopi, J. Theor. Biol. 67, 56 (). [5] D. Selmezi, L. Li, L. I. I. Pederen, S. F. Nrrelykke, P. H. Hgedorn, S. oler, N. B. Lren, E. C. Cox, nd H. Flyvbjerg, Eur. Phy. J. Spe. Top. 57, (8). [6] L. Shimnkygeier,. ieh, H. Roe, nd H. lhow, Phy. Le. A 7, 4 (995). 7

18 [7]. Theve, J. Tkiko, V. Zburdev, H. Srk, nd C. Be, Biophy. J. 5, 95 (3). [8] D. Cmpo nd V. endez, J. Chem. Phy. 3, 347 (9). [9] N. ki, H. iyohi, nd Y. Tuhiy, Prooplm 3, 69 (7). [3] H. iyohi, N. ki, nd Y. Tuhiy, Prooplm, 75 (3). [3] F. Peruni nd L. G. orelli, Phy. Rev. Le. 99, 6 (7). [3]. Shienbein nd H. Gruler, Bull. h. Biol. 55, 585 (993). [33] H. Tkgi,. J. So, T. Yngid, nd. Ued, PLoS One 3, e648 (8). [34] D. Selmezi, S. oler, P. H. Hgedorn, N. B. Lren, nd H. Flyvbjerg, Biophy. J. 89, 9 (5). [35] S. I. Deniov, W. Horhemke, nd P. Hnggi, Eur. Phy. J. B 68, 567 (9). [36] H. Riken, The Fokker-Plnk equion : mehod of oluion nd ppliion (Springer- Verlg, New York, 996), nd edn., Springer erie in ynergei,, 8. [37] See Supplemenl eril for he derivion of he eond nd fourh momen of diplemen, for PDF of diplemen limiing ime le, for men veloiy diriubion nd ionry diribuion, for more deil on he exended model, for he relxion ime, for non- Guin prmeer in ll rnge, nd for he ohi imulion mehod. [38] See Supplemenl eril for rjeorie of eh model. [39] H. U. Bodeker, C. Be, T. D. Frnk, nd E. Bodenhz, Europhy. Le. 9, 85 (). [4] B. Wng, J. Kuo, S. C. Be, nd S. Grnik, N. er., 48 (). 8

19 Supplemenl eril for Super-Guin, uper-diffuive rnpor of muli-mode ive mer Seungoo Hhn, Snggeun Song,,,3 De Hyun Kim,,,3 Gil-Suk Yng,,,3 Kng Tek Lee, 4 Jeyoung Sung,,3, Creive Reerh Iniiive Cener for Chemil Dynmi in Living Cell, Chung-Ang Univeriy, Seoul 6974, Kore. Deprmen of Chemiry, Chung-Ang Univeriy, Seoul 6974, Kore. 3 Nionl Iniue of Innovive Funionl Imging, Chung-Ang Univeriy, Seoul 6974, Kore. 4 Deprmen of Chemiry, Gwngju Iniue of Siene nd Tehnology, Gwngju 65, Kore Conen A. Derivion of he eond nd fourh momen of diplemen for he muli-e model B. Derivion of he eond nd fourh momen of diplemen for he ingle-e model 4 C. Probbiliy deniy funion of diplemen wo limiing ime le 5 D. en veloiy diribuion nd ionry diribuion 7 E. Dynmi of he muli-e model 8 F. Convergene of P (,) x o PS (,) x he mll R limi G. Relxion ime of he wo olvble model 9 8 H. Generl model I. Time orrelion funion for wo olvble model J. Derivion of hor ime men veloiy diribuion from Eq. () K. Sohi imulion mehod 3 L. Referene 4 Fig. S. Non-Guin prmeer 5

20 A. Derivion of he eond nd fourh momen of diplemen for he muli-mode model The muli-mode ive mer model h hree inernl e, Γ, Γ, nd Γ. Eh inernl e regule he direion nd peed of given ive mer explined in Fig. (b). Bed on he iniil ondiion h he inernl e re iniilly in equilibrium nd he iniil poiion of ive mer i zero, hree imulneou equion re obined from Eq. (4) by pplying he Fourier rnform nd he Lple rnform o Pi ( x, ) wih i,,. The oluion of he imulneou equion provide hree probbiliy deniy funion (PDF) for he individul inernl e in he Fourier-Lple domin, wrien ( χ ( w, ) i ( D ) ) eq p ( χ vw ) χ ( w, ) i ( D ) p vw w k k eq P ( w, ) P ( w, ) w, = ( Dw )( χ ( w, ) vw ) kv w P w, eq p vw w k k χ w, Dw k Dw k k. (S) wih In Eq. (S), w nd repeively denoe he Fourier rnform of poiion x nd he Lple rnform of ime. The ilde indie h he funion re repreened in he Fourier-Lple eq eq eq domin. p, p, nd p denoe he equilibrium probbiliie of he e Γ, Γ, nd Γ, repeively. A ummion of he PDF given in Eq. (S) provide he PDF of muli-mode ive mer, whih i given by (, ) (, ) (, ) (, ) eq χ ( w, ) p vw P w P w P w P w = ( D )( χ ( w) ) vw w, vw k The denominor in Eq. (S) i ubi funion of ( ) ( ) funion C ( w) i 3. (S) D z w z k k z k k k vw z kvw. If we ume he roo of he ubi P ( w, ) wih (,, 3), he PDF n be rewrien i 3 = kvw Dw Dw k k i= Dw Ci ( w). (S3) 3 3 = kvw Dw Dw k k i= Dw Ci ( w) j i Ci( w) Cj( w) Invering in P( w, ) genere he Fourier-domin PDF, wrien 3 3 P ˆ ( w D w k k C i w, ) vw e = i Ci( w) k k e = j i Ci w Cj w. (S4)

21 Eq. (S) nd (S4) n boh be ued o derive he nlyi oluion for he men qure diplemen (SD) of he muli-mode model. One wy i o ue he eond pril derivive P ˆ w,, while he oher wy, whih i n eier wy o obin he ime-domin SD, i of o pply he invere Lple rnform o he eond pril derivive of P ( w, ) x = lim = L lim w w, wrien Pˆ w, P w,, (S5) w w where L denoe he invere Lple rnform of ime. The ime-dependen SD of hi model i given in Eq. (6). The nlyi oluion for he fourh momen of diplemen i obined uing he following equion: x 4 4 P = lim = w 4 w vk D. (S6) k ( w, ) 4 ( k)( k k ) 3 vk 3D kd v k k Appliion of he invere Lple rnform o Eq. (S6) provide he fourh momen of diplemen in he ime domin, wrien x 4 ( ) = ( ) D D 5 / / 3 / R e R 3R 3 R e 3 4D ( 3 6 3) R R R / ( R ) ( R ) ( R D D) e ( R ) ( R R ( R ) D D) ( R ) ( )( ) (S7) The eond nd fourh momen expreed in he ime domin re ombined o produe he α, uh non-guin prmeer, 3

22 α R ( ) wih ( ) 4 x = κ ( ) 3 x 3 ( ) e 4e 5 4e ( e 8e 8e 4 ) R () D = 4 ( e e ) R ( R ) x ( ) 3 ( 6e 6 4e ) R R 5 e e R R x D ( ) =, (S8) D () () E D ( R ) where κ ( ) denoe kuroi nd hown in Fig. 3 (b), he relxion ime, In Fig. S, ( ) ondiion of α in ll rnge of R nd R (). D R D v. On he log-le ime xi () =, hif he ( ) α urve well he SD urve. R i nlyilly evlued nd ploed under he D D =, where he red line re he wo line hown in Fig. 3() nd 3(e) nd he blk line mrk border line wihing from ub-guin o uper-guin D D, he border line i invrin on he hnge of given R. Alhough α ( ) depend on he D D rio in Eq. (S8). Thu, ( ) ll ime, nd ( ) 5 R R α R wih R le hn 5 i ub-guin α wih R lrger hn i lwy uper-guin ll ime. When < <, P (,) hown in Fig. 3(e) nd S. x n wih from ub-guin o uper-guin over ime, B. Derivion of he eond nd fourh momen of diplemen for he ingle-mode model The ingle-mode model h wo inernl e, Γ nd Γ. Eh inernl e regule he direion nd peed of ive mer, explined in Fig. (). Bed on he iniil ondiion h he inernl e re iniilly in equilibrium nd he iniil poiion of ive mer i zero, wo imulneou equion re obined from Eq. (3) by pplying he Fourier rnform nd he Lple rnform o Pi ( x, ) wih i,. The nlyi oluion of he imulneou equion provide wo PDF in he Fourier-Lple domin, wrien 4

23 ( w, ) P Dw k ivw = P ( w, ). (S9) vw ( )( ) Dw k ivw Dw Dw k The PDF of ive mer for he ingle-mode model in Fourier-Lple domin i wrien S (, ) (, ) P ( w, ) P w P w = vw Dw Dw k Dw k ( )( ). (S) Appliion of he invere Lple rnform o Eq. (S) genere he PDF of ive mer repreened in he Fourier domin ( ˆ ) (, ) e k Dw PS w = k oh ( Λ ) inh( Λ) Λ wih Λ k vw. (S) From hi funion, he ime-dependen eond nd fourh momen of diplemen re imple o obin. The ime-dependen SD of hi model i given in Eq. (6). The fourh momen of diplemen i lo evlued from he PDF x 4 where = D D D D D e 3D e, (S) ( ) ( ) ( ) D = v. The eond nd fourh momen expreed in he ime domin re ombined o produe he non-guin prmeer, α ( ), uh α ( ) D D = ( 5 e 4e 4e ) = β x ( ) x ( ) wih ( ) x D E D = D nd β ( ). (S3) 5 e 4e 4e In Eq. (S3), α ( ) i le hn or equl o zero beue ( ) equion i equl o ( ) ( ) β in ll ime rnge, nd he α R of he muli-mode model he mll R limi α ( ) limα R ( ) R =. C. Probbiliy deniy funion of diplemen wo limiing ime le The diffuion dynmi of he model i highly dependen on he relxion ime,, of he veloiy, v ( Γ ). A hor ime ( ), given ive mer minin i direion nd 5

24 mgniude of veloiy, nd eh unrelxed veloiy produe hree individul pek in he PDF of diplemen. The PDF of diplemen hor ime i derived from Eq. (S), whih i wrien P ( D i ) eq p ( D w ) ( Dw i vw) eq p w vw P hor, ( w, ) P hor, ( w, ) = P, (, ) e hor w q p (, ) x hor ime i wrien P, hor ( x, ) = p e 4π D. (S4) ( x v ) x ( xv ) eq 4D eq 4D eq 4D p e p e, (S5) D. The hree pek in where he diribuion i Guin wih vrine of P, hor x, re pproximed ingle Guin funion wih mll vrine very hor ime ( D v ) nd grdully epre ime inree. ), he pek for individul v ( Γ) re gin inermingled ino ingle A long ime ( Guin nd follow he diribuion, wrien ( w, ) ( w) ( w ) ( i vwk ) ( k) ( i ) eq P p long, eq P long,, = p ( vw k k ), (S6) Deff w P,, eq long p vw k where D D eff i equl o D. The PDF P (, ) x long ime i wrien P, long ( x, ) x eq 4Deff p D R x = e. (S7) 4π D Deff D ef f eff In Eq. (S7), deviion from he Guin diribuion i proporionl o p eq D R D, eff long ime. eq where p D D eff i lwy le hn, nd i lrger hn Therefore, p eq D R Deff i muh mller hn if R i finie uffiienly long ime. Thu, he PDF pprohe he Guin diribuion uffiienly long ime, whih i in ordne wih he Guin enrl limi heorem. In ummry, he PDF of diplemen pprohe he del funion very hor ime, beue elf-propelled veloiy i muh weker hn he veloiy ued by he rndom fluuing fore. A reul of he onribuion of rndom fluuion being diiped, he del funion i pli ino individul pek reled o he veloiy of eh inernl e, where 6

25 he elf-propelled veloiy how no vriion. Eq. (S5) explin he wo differen funionl form of he PDF. A long ime, he PDF pprohe he dipered Guin diribuion beue he vrine for eh diribuion i proporionl o ime. f D. en veloiy diribuion nd ionry diribuion The men veloiy, v (), i defined by v () x (). The men veloiy diribuion, (, ) v, i direly obined from he PDF of diplemen wih proper normlizion onn. A hor ime ( f ), he men veloiy diribuion, f (, ) ( v v ) v ( vv ) eq 4D eq 4D eq 4D, hor ( v, ) = p e p e p e 4π D v, i wrien. (S8) where he diribuion i Guin wih vrine of D. Beue he vrine i inverely proporionl o, he brodne of he individul pek in Eq. (S8) how he oppoie pern P x,, whih pper in Eq. (S5). A long ime ompred o he individul pek in ), f (, ) ( (S9) f f, long,, long v i wrien ( v, ) v eq 4Deff p D R v = e 4π D Deff D eff eff v pprohe he del funion ime inree. The vrine of he individul pek in P (, ) x i proporionl o boh hor nd long ime. If we define new vrible, q( x ), hen he ionry diribuion n be obined wo he ime-limiing e. A hor ime ( wrien ( q v ) q ( qv ) 4D 4D 4D g, hor ( q, ) = pe pe pe 4π D The vrine of he diribuion, (, ) ), he ionry diribuion, g (, ).. q, i (S) g q, hor ime i equl o D nd doe no vry wih ime, however, he inervl beween he pek doe grdully inree ime inree. A long ime ( g q, i wrien ), 7

26 g g, long, long, ( q, ) = 4π D eff e q eq 4D p eff D R q Deff D eff. q onverge o he Guin diribuion wih vrine of D eff. (S) E. Dynmi of he muli-mode model For he muli-mode model, given ive mer i opered by vrible ompoed of hree diree e: Γ, Γ, nd Γ. If he ive mer wih Γ i e i loed in he infinieiml re dx, hen he probbiliy of finding he ive mer n be wrien ρ Γ, x, dx. The PDF ifie he onervion lw, wrien i 3 ( x ) dx ρ Γ i,, =. (S) i From he onervion lw, he oninuiy equion for he PDF i wrien 3 ρ( Γ i, x, ) = ( x ρ( Γ i, x, )) Κi jρ( Γ i, x, ) Κ j iρ( Γ j, x, ) x, j i where x nd Κ i j denoe he ime derivive of x nd he re onn from e Γ i o Γ j. Here, we onider he moion of ive mer in n overdmped environmen where elerion i zero. The ime derivive of n ive mer poiion i wrien dx = v( Γ ) γ ξ( ), (S3) d where ξ ( ) repreen he rndom fluuing fore modeled Guin whie noie. The enemble verge of ρ ( Γ i, x, ) over he Guin whie noie give he oberved PDF P( Γ i, x, ) []. The ppliion of he umuln expnion give Eq. (3) nd (4), where we e P( Γ i, x, ) o Pi ( x, ) for he ke of impliiy. Inernl-e-dependen veloiy, v( Γ ), depend on he e v( Γ ) = v, v( Γ ) =, nd v( Γ ) = v. F. Convergene of P (,) x o PS (,) x he mll R limi The populion rio, R( p eq ( p eq eq p ) k k) = =, of he pive e o he ive e module he hpe of he probbiliy deniy of he ive mer, P (,) x, in he muli-mode model. Applying wrien k = nd 8 k R = o Eq. (S) produe P ( w, ),

27 P ( w, ) = = In he limi of ( Dw )( Dw R ) v w R ( R ) ( D )(( D )( D R ) ) R ( Dw )( R RDw R ) v w R ( R ) ( ) ( )( ) R w w w v w v w D w D w R RD w R v w v w R, P ( w, ) i wrien. (S4) D w lim P ( w, ) =. (S5) R w w v w ( D )( D ) The relxion ime of he ingle-mode model i produe P( w, ) S = k. Applying k = o Eq. (S) Dw P. (S6) S, = vw ( w) ( Dw )( Dw ) ( w, ) i he me P ( w, ) P S in he mll R limi. G. Relxion ime of he wo olvble model In he high friion regime, where we n fely negle he ineril erm in he Lngevin equion, he veloiy, x (), of ive mer wih friion onn, γ, n be wrien he um of wo omponen: x v, (S7) () = ( Γ ()) γ ξ() where v ( Γ ) nd γ ξ() repreen he veloiy omponen of elf-propelled, bllii moion, whih i dependen on he inernl e, Γ, nd he veloiy omponen ued by he rndom fluuing fore. If we ume h he iniil poiion of ive mer i zero, hen he ime inegrion of Eq. (S7) produe he ime-dependen poiion, wrien ( ) x = v( Γ ( )) γ ξ d. (S8) From Eq. (S8), he SD of he ive mer n be evlued from he veloiy orrelion funion, wrien ( ) x = d d v Γ v Γ ξ ξ = D d v v γ, (S9) 9

28 where we denoe v Γ ( ) in hor v obin he following equion: Beue ( ) ( ) = D ( e ). By ompring Eq. (S9) wih Eq. (6), we d v v. (S3) D i equl o v normlized ime orrelion funion of veloiy, φ (), φ v () () (), he eond derivive of eh ide of Eq. (S3) provide he v = v v v = e, (S3) where i given by k for he ingle-mode model nd k for he muli-mode model. H. Generl model In generl, given ive mer move in mulidimenionl pe, d, nd i rndom fluuing fore h finie relxion ime,. To obin nlyi oluion for hi generl model, he veloiy of ive mer orreponding o Eq. () i generlized o () = ( Γ ()) γ () p r v ξ, (S3) where eh bold ymbol denoe he d-dimenionl veor orreponding o eh lr quniy in Eq.(). The inegrion of eh ide of Eq. (S3) from o produe he ime- r, wrien dependen poiion, ( ) = ( ( ) Γ ) r v γ ξ d, (S33) where we ume he iniil poiion i zero. From Eq. (S33), he SD i wrien ( ) d ( ) r = γ ξ( ) ξ() v ( ) v () = = d d D p φξ ( ) Dφv ( ) r ξ ( ) r ( ) v, (S34) where φ ξ () denoe he normlized ime orrelion funion, rndom fluuing fore, ξ (), nd he relxion ime, p, i defined ξ( ) ξ() ξ (), of he p dφξ (). D d γ d Here, he diffuion oeffiien for pive moion i defined by = ξ ξ (), nd he diffuion oeffiien for elf-propelled moion i defined by D = d d v () v. The SD oni of wo independen movemen from he diffuive mode nd he elf-ive mode. The diffuive mode onribuion o he SD i

29 defined r dd d ( ) ξ φ ( ), while he elf-propelled mode onribuion i defined dd d ( ) p ξ r φ ( ). If we ume h he diribuion of ξ () v v i Guin, hen he nlyi oluion for he fourh momen of diplemen n be wrien 4 ξ ξ v v 4 ( ) = ( d ) ( ) ( d ) ( ) ( ) ( ) r r r r r, (S35) where r 4! d d d d v ( ) v ( ) v ( ) v ( ). The non-guin v prmeer for he generl model i wrien α R ( ) ( ) d r = d r ( ) wih ( ) A hor ime, ( ) 4 r v ( ) r ( ) ( ) ( ) 4 α 4 3 v ( ) r α d v d. (S36) v r v α R pprohe zero beue ( ) ( ) α ( ) lo pprohe zero beue ( ) R α v pprohe zero. r r v. A long ime, I. Time orrelion funion for wo olvble model Time orrelion funion of he veloiy omponen, D nd ( ) v, of elf-propelled moion re ued o lule α well he eond nd fourh momen of diplemen. In our model, beue v i only dependen on he inernl e, we nlyilly obin he ime evoluion of inernl e probbiliie P P = P G P wih for he ingle-mode model nd P P P = G P wih P P ( k) inh ( k) ( k) oh ( k) k oh G = E (S37) inh

30 G = k k e k k e k e k k e k k k k k k e k k e k k e ( k k) k ( k k ) k ( k k ) e k k ( k k) kk k ( k k) k ( k k) k ke ( k k) e ( k k ) e e k k k k k ke k (S38) for he muli-mode model. We obin he veloiy uoorrelion funion, v ( ) v (), hrough he following equion: v( v ) () = v Γ v Γ G Γ, Γ, PΓ,, (S39) j i j i i i, j, G denoe rniion mrix from Γ i o where ( Γ j, Γi,) Γ j fer ime ping. The rniion mrie re hown in Eq. (S37) for he ingle-mode model nd in Eq. (S38) for k he muli-mode model. The lulion reul of v( v ) () re ve for he inglemode model nd pve k for he muli-mode model, whih oinide wih Eq. (S3). By luling he SD hrough he ppliion of he luled veloiy uoorrelion funion o Eq. (S9), we obin Eq. (6). The four-ime veloiy uoorrelion funion, v ( ) v ( ) v ( ) v ( ), i obined by he following equion, wrien 4 3 v ( ) v ( ) v ( ) v ( ) 4 3 i, j, k, l, = v Γ v Γ v Γ v Γ l k j i (,, ) (,, ) (,, ) P(, ) G Γ Γ G Γ Γ G Γ Γ Γ l 4 k 3 k 3 j j i i. 4 k 4 3 The lulion reul of v( 4) v( 3) v( ) v( ) re ve for he ingle-mode 4 k model nd 3 k e e 3 k e 4 pv R for he muli-mode model. The four-ime veloiy uoorrelion funion n be ued o genere 4 r in Eq. (S35), nd heir reul re equl o he fourh momen of diplemen whih re wrien in Eq. (S7) nd (S). v J. Derivion of hor ime men veloiy diribuion from Eq. () In our model, he veloiy of ive mer oni of wo omponen in Eq. (). If we ume h he wo omponen re independen, hen he men veloiy diribuion i wrien (, ) = δ ξ ( ( ξ) ) (, ) ξ ( ξ, ) f v dv dv v v v f v f v, (S4)

31 where v nd f (, ) v repeively denoe he veloiy omponen ued by elfpropelled moion nd i diribuion funion; v ξ nd f ( v, ) ξ ξ denoe he veloiy omponen due o he rndom fluuing fore nd i diribuion funion. A hor ime, he veloiy omponen, γ ξ(), ued by he rndom fluuing fore i lredy relxed nd follow Guin diribuion wih vrine of D, where he elf-propelled moion pproximely minin i direion. The wo diribuion funion hor ime n be wrien vξ 4D, 4 eq = nd f ( v, ) = p δ ( v v ) f v e D ξ ξ π. (S4) i i i Γ By pplying Eq. (S4) o Eq. (S4), he men veloiy diribuion funion hor ime n be rewrien iu( v ( v vξ )) eq fhor ( v, ) = du dvdvξ e pi δ v vi fξ vξ, π π i Γ = eq iu( v v i ) iuv p i due ξ dvξ e fξ ( vξ, ) i Γ i Γ eq (, ) δ (, ) = p dv f v v v v = p f v v eq i ξ ξ ξ i ξ i ξ i i Γ ( ) v v i 4D eq = pi e. (S4) 4π D i Γ Eq. (S4) i equivlen o he men veloiy diribuion for he muli-mode model hor ime, whih i hown in Eq. (S8). K. Sohi imulion mehod Our ohi imulion mehod oni of boh he Brownin dynmi for he ime evoluion of n ive mer poiion nd he Gillepie mehod for he ohi rniion beween inernl e [,3]. For he Brownin dynmi, we numerilly inegre Eq. (S3) ξ ' x = x v Γ D, (S43) where x( ),, nd ξ '( ) denoe he ive mer poiion ime, he ize of he ime ep, nd he Guin rndom number wih G (,), repeively [3]. For he Gillepie mehod, we ume h he rniion beween inernl e for he muli-mode model re forbidden, exep hoe rniion deribed by he following four unimoleulr reion: 3

32 Κ = k Γ Γ, Κ = k Γ Γ, Κ = k Γ Γ, nd Κ = k Γ Γ []. The reion onn for he forbidden rniion re e equl o zero. Our ohi imulion proeed follow:. Rndomly hooe n inernl e of ive mer bed on he equilibrium populion beween e nd e he iniil poiion equl o zero. Se he eleed e o he urren e, Γ.. Bed on he urren e, lule he wiing ime for reion uing he equion, =ln ( RN ) Κ j, where RN denoe n evenly diribued rndom number j beween nd, beue onenrion of he eleed e i nd he onenrion for he oher e i zero. Only he Γ e h wo reion ph wih equl probbiliy, nd he oher e hve only one ph for e rniion. 3. Unil he wiing ime i over, evolve he ime-dependen poiion uing Eq. (S43) wih he e-dependen veloiy v( Γ ) nd given ime inervl. 4. Afer finihing he ime evoluion in ep 3, hnge he urren e o he e deermined by he rniion in ep. Reurn o ep when he elped ime of he rjeory i le hn he ime limi of he rjeory. 5. Reurn o ep unil uffiien rjeorie re olleed. We ue 5, rjeorie o obin he veloiy diribuion nd he eond nd fourh momen of he diplemen diribuion. L. Referene [] H. Riken, The Fokker-Plnk equion : mehod of oluion nd ppliion (Springer-Verlg, New York, 996), nd edn., Springer erie in ynergei,, 8. [] D. T. Gillepie, J. Phy. Chem. 8, 34 (977). [3] D. L. Ermk, J. Chem. Phy. 6, 489 (975). 4

33 Figure S. Non-Guin prmeer. ( ) α in ll rnge of R nd re ploed when D D i equl o.. The diribuion of diplemen i ub-guin (uper-guin) in ll ime rnge if R<.6 (R>.), hown in Fig. 3(d). The wo horizonl red line repreen α for he wo riil vlue of R:.6 nd. In he rnge.6 < R <., he diplemen diribuion wihe from ub-guin o uper-guin long he ime xi. The boundry beween he ub-guin nd he uper-guin diribuion i repreened by he blk line. 5

LAPLACE TRANSFORMS. 1. Basic transforms

LAPLACE TRANSFORMS. 1. Basic transforms LAPLACE TRANSFORMS. Bic rnform In hi coure, Lplce Trnform will be inroduced nd heir properie exmined; ble of common rnform will be buil up; nd rnform will be ued o olve ome dierenil equion by rnforming

More information

graph of unit step function t

graph of unit step function t .5 Piecewie coninuou forcing funcion...e.g. urning he forcing on nd off. The following Lplce rnform meril i ueful in yem where we urn forcing funcion on nd off, nd when we hve righ hnd ide "forcing funcion"

More information

e t dt e t dt = lim e t dt T (1 e T ) = 1

e t dt e t dt = lim e t dt T (1 e T ) = 1 Improper Inegrls There re wo ypes of improper inegrls - hose wih infinie limis of inegrion, nd hose wih inegrnds h pproch some poin wihin he limis of inegrion. Firs we will consider inegrls wih infinie

More information

AN IMPROVED CREEP AND SHRINKAGE BASED MODEL FOR DEFLECTIONS OF COMPOSITE MEMBERS REINFORCED WITH CARBON FIBER REINFORCED BARS

AN IMPROVED CREEP AND SHRINKAGE BASED MODEL FOR DEFLECTIONS OF COMPOSITE MEMBERS REINFORCED WITH CARBON FIBER REINFORCED BARS N MPROVED CREEP ND SHRNKGE BSED MODEL FOR DEFLECTONS OF COMPOSTE MEMBERS RENFORCED WTH CRBON FBER RENFORCED BRS M.. Fruqi, S. Bhdr D. Sun, nd J. Si Deprmen o Civil nd rhieurl Engineering, Tex & M Univeriy,

More information

4.8 Improper Integrals

4.8 Improper Integrals 4.8 Improper Inegrls Well you ve mde i hrough ll he inegrion echniques. Congrs! Unforunely for us, we sill need o cover one more inegrl. They re clled Improper Inegrls. A his poin, we ve only del wih inegrls

More information

Graduate Algorithms CS F-18 Flow Networks

Graduate Algorithms CS F-18 Flow Networks Grue Algorihm CS673-2016F-18 Flow Nework Dvi Glle Deprmen of Compuer Siene Univeriy of Sn Frnio 18-0: Flow Nework Diree Grph G Eh ege weigh i piy Amoun of wer/eon h n flow hrough pipe, for inne Single

More information

CSC 373: Algorithm Design and Analysis Lecture 9

CSC 373: Algorithm Design and Analysis Lecture 9 CSC 373: Algorihm Deign n Anlyi Leure 9 Alln Boroin Jnury 28, 2013 1 / 16 Leure 9: Announemen n Ouline Announemen Prolem e 1 ue hi Friy. Term Te 1 will e hel nex Mony, Fe in he uoril. Two nnounemen o follow

More information

Fall 2014 David Wagner 10/31 Notes. The min-cut problem. Examples

Fall 2014 David Wagner 10/31 Notes. The min-cut problem. Examples CS 7 Algorihm Fll 24 Dvid Wgner /3 Noe The min-u problem Le G = (V,E) be direed grph, wih oure verex V nd ink verex V. Aume h edge re lbelled wih o, whih n be modelled o funion : E N h oie non-negive inegrl

More information

Positive and negative solutions of a boundary value problem for a

Positive and negative solutions of a boundary value problem for a Invenion Journl of Reerch Technology in Engineering & Mngemen (IJRTEM) ISSN: 2455-3689 www.ijrem.com Volume 2 Iue 9 ǁ Sepemer 28 ǁ PP 73-83 Poiive nd negive oluion of oundry vlue prolem for frcionl, -difference

More information

ON DIFFERENTIATION OF A LEBESGUE INTEGRAL WITH RESPECT TO A PARAMETER

ON DIFFERENTIATION OF A LEBESGUE INTEGRAL WITH RESPECT TO A PARAMETER Mh. Appl. 1 (2012, 91 116 ON DIFFERENTIATION OF A LEBESGUE INTEGRAL WITH RESPECT TO A PARAMETER JIŘÍ ŠREMR Abr. The im of hi pper i o diu he bolue oninuiy of erin ompoie funion nd differeniion of Lebegue

More information

Chapter Introduction. 2. Linear Combinations [4.1]

Chapter Introduction. 2. Linear Combinations [4.1] Chper 4 Inrouion Thi hper i ou generlizing he onep you lerne in hper o pe oher n hn R Mny opi in hi hper re heoreil n MATLAB will no e le o help you ou You will ee where MATLAB i ueful in hper 4 n how

More information

International ejournals

International ejournals Avilble online ww.inernionlejournl.om Inernionl ejournl Inernionl Journl of Mhemil Siene, Tehnology nd Humniie 7 (0 8-8 The Mellin Type Inegrl Trnform (MTIT in he rnge (, Rmhndr M. Pie Deprmen of Mhemi,

More information

The realization of low order FSM method and its application Jiai He1,a, Xiangyang Liu1,b, Chengquan Pei2,3,c

The realization of low order FSM method and its application Jiai He1,a, Xiangyang Liu1,b, Chengquan Pei2,3,c 3rd Inernionl Conferene on Mhinery, Meril nd Informion ehnology ppliion (ICMMI 05 he relizion of low order FSM mehod nd i ppliion Jii He,, Xingyng Liu,b, Chengqun Pei,3, Shool of Compuer nd Communiion,

More information

Maximum Flow. Flow Graph

Maximum Flow. Flow Graph Mximum Flow Chper 26 Flow Grph A ommon enrio i o ue grph o repreen flow nework nd ue i o nwer queion ou meril flow Flow i he re h meril move hrough he nework Eh direed edge i ondui for he meril wih ome

More information

Solutions to assignment 3

Solutions to assignment 3 D Sruure n Algorihm FR 6. Informik Sner, Telikeplli WS 03/04 hp://www.mpi-.mpg.e/~ner/oure/lg03/inex.hml Soluion o ignmen 3 Exerie Arirge i he ue of irepnie in urreny exhnge re o rnform one uni of urreny

More information

Designing A Fanlike Structure

Designing A Fanlike Structure Designing A Fnlike Sruure To proeed wih his lesson, lik on he Nex buon here or he op of ny pge. When you re done wih his lesson, lik on he Conens buon here or he op of ny pge o reurn o he lis of lessons.

More information

Three Dimensional Coordinate Geometry

Three Dimensional Coordinate Geometry HKCWCC dvned evel Pure Mhs. / -D Co-Geomer Three Dimensionl Coordine Geomer. Coordine of Poin in Spe Z XOX, YOY nd ZOZ re he oordine-es. P,, is poin on he oordine plne nd is lled ordered riple. P,, X Y

More information

Section P.1 Notes Page 1 Section P.1 Precalculus and Trigonometry Review

Section P.1 Notes Page 1 Section P.1 Precalculus and Trigonometry Review Secion P Noe Pge Secion P Preclculu nd Trigonomer Review ALGEBRA AND PRECALCULUS Eponen Lw: Emple: 8 Emple: Emple: Emple: b b Emple: 9 EXAMPLE: Simplif: nd wrie wi poiive eponen Fir I will flip e frcion

More information

Motion. Part 2: Constant Acceleration. Acceleration. October Lab Physics. Ms. Levine 1. Acceleration. Acceleration. Units for Acceleration.

Motion. Part 2: Constant Acceleration. Acceleration. October Lab Physics. Ms. Levine 1. Acceleration. Acceleration. Units for Acceleration. Moion Accelerion Pr : Consn Accelerion Accelerion Accelerion Accelerion is he re of chnge of velociy. = v - vo = Δv Δ ccelerion = = v - vo chnge of velociy elpsed ime Accelerion is vecor, lhough in one-dimensionl

More information

ANSWERS TO EVEN NUMBERED EXERCISES IN CHAPTER 2

ANSWERS TO EVEN NUMBERED EXERCISES IN CHAPTER 2 ANSWERS TO EVEN NUMBERED EXERCISES IN CHAPTER Seion Eerise -: Coninuiy of he uiliy funion Le λ ( ) be he monooni uiliy funion defined in he proof of eisene of uiliy funion If his funion is oninuous y hen

More information

Interval Oscillation of Nonlinear Differential Equation with Damped Term

Interval Oscillation of Nonlinear Differential Equation with Damped Term Communiion in Informion Siene nd Mngemen Engineering Mr, Vo I, PP 7-4 Inerv Oiion of Noniner Differeni Equion wih Dmped Term Yun-Hui Zeng Deprmen of Mhemi nd Compuion Siene, Hengyng Norm Univeriy,Hunn,

More information

A LOG IS AN EXPONENT.

A LOG IS AN EXPONENT. Ojeives: n nlze nd inerpre he ehvior of rihmi funions, inluding end ehvior nd smpoes. n solve rihmi equions nlill nd grphill. n grph rihmi funions. n deermine he domin nd rnge of rihmi funions. n deermine

More information

0 for t < 0 1 for t > 0

0 for t < 0 1 for t > 0 8.0 Sep nd del funcions Auhor: Jeremy Orloff The uni Sep Funcion We define he uni sep funcion by u() = 0 for < 0 for > 0 I is clled he uni sep funcion becuse i kes uni sep = 0. I is someimes clled he Heviside

More information

CS3510 Design & Analysis of Algorithms Fall 2017 Section A. Test 3 Solutions. Instructor: Richard Peng In class, Wednesday, Nov 15, 2017

CS3510 Design & Analysis of Algorithms Fall 2017 Section A. Test 3 Solutions. Instructor: Richard Peng In class, Wednesday, Nov 15, 2017 Uer ID (NOT he 9 igi numer): gurell4 CS351 Deign & Anlyi of Algorihm Fll 17 Seion A Te 3 Soluion Inruor: Rihr Peng In l, Weney, Nov 15, 17 Do no open hi quiz ookle unil you re iree o o o. Re ll he inruion

More information

The order of reaction is defined as the number of atoms or molecules whose concentration change during the chemical reaction.

The order of reaction is defined as the number of atoms or molecules whose concentration change during the chemical reaction. www.hechemisryguru.com Re Lw Expression Order of Recion The order of recion is defined s he number of oms or molecules whose concenrion chnge during he chemicl recion. Or The ol number of molecules or

More information

IX.1.1 The Laplace Transform Definition 700. IX.1.2 Properties 701. IX.1.3 Examples 702. IX.1.4 Solution of IVP for ODEs 704

IX.1.1 The Laplace Transform Definition 700. IX.1.2 Properties 701. IX.1.3 Examples 702. IX.1.4 Solution of IVP for ODEs 704 Chper IX The Inegrl Trnform Mehod IX. The plce Trnform November 4, 7 699 IX. THE APACE TRANSFORM IX.. The plce Trnform Definiion 7 IX.. Properie 7 IX..3 Emple 7 IX..4 Soluion of IVP for ODE 74 IX..5 Soluion

More information

Dynamic Response of an Active Filter Using a Generalized Nonactive Power Theory

Dynamic Response of an Active Filter Using a Generalized Nonactive Power Theory Dynmi Repone of n Aive Filer Uing Generlized Nonive Power heory Yn Xu Leon M. olber John N. Chion Fng Z. Peng yxu3@uk.edu olber@uk.edu hion@uk.edu fzpeng@mu.edu he Univeriy of enneee Mihign Se Univeriy

More information

3. Renewal Limit Theorems

3. Renewal Limit Theorems Virul Lborories > 14. Renewl Processes > 1 2 3 3. Renewl Limi Theorems In he inroducion o renewl processes, we noed h he rrivl ime process nd he couning process re inverses, in sens The rrivl ime process

More information

1 The Network Flow Problem

1 The Network Flow Problem 5-5/65: Deign & Anlyi of Algorihm Ooer 5, 05 Leure #0: Nework Flow I l hnged: Ooer 5, 05 In hee nex wo leure we re going o lk ou n imporn lgorihmi prolem lled he Nework Flow Prolem. Nework flow i imporn

More information

f t f a f x dx By Lin McMullin f x dx= f b f a. 2

f t f a f x dx By Lin McMullin f x dx= f b f a. 2 Accumulion: Thoughs On () By Lin McMullin f f f d = + The gols of he AP* Clculus progrm include he semen, Sudens should undersnd he definie inegrl s he ne ccumulion of chnge. 1 The Topicl Ouline includes

More information

Contraction Mapping Principle Approach to Differential Equations

Contraction Mapping Principle Approach to Differential Equations epl Journl of Science echnology 0 (009) 49-53 Conrcion pping Principle pproch o Differenil Equions Bishnu P. Dhungn Deprmen of hemics, hendr Rn Cmpus ribhuvn Universiy, Khmu epl bsrc Using n eension of

More information

Physics 2A HW #3 Solutions

Physics 2A HW #3 Solutions Chper 3 Focus on Conceps: 3, 4, 6, 9 Problems: 9, 9, 3, 41, 66, 7, 75, 77 Phsics A HW #3 Soluions Focus On Conceps 3-3 (c) The ccelerion due o grvi is he sme for boh blls, despie he fc h he hve differen

More information

Bisimulation, Games & Hennessy Milner logic p.1/32

Bisimulation, Games & Hennessy Milner logic p.1/32 Clil lnguge heory Biimulion, Gme & Henney Milner logi Leure 1 of Modelli Memii dei Proei Conorreni Pweł Sooińki Univeriy of Souhmon, UK I onerned rimrily wih lnguge, eg finie uom regulr lnguge; uhdown

More information

Solutions to Problems from Chapter 2

Solutions to Problems from Chapter 2 Soluions o Problems rom Chper Problem. The signls u() :5sgn(), u () :5sgn(), nd u h () :5sgn() re ploed respecively in Figures.,b,c. Noe h u h () :5sgn() :5; 8 including, bu u () :5sgn() is undeined..5

More information

IX.1.1 The Laplace Transform Definition 700. IX.1.2 Properties 701. IX.1.3 Examples 702. IX.1.4 Solution of IVP for ODEs 704

IX.1.1 The Laplace Transform Definition 700. IX.1.2 Properties 701. IX.1.3 Examples 702. IX.1.4 Solution of IVP for ODEs 704 Chper IX The Inegrl Trnform Mehod IX. The plce Trnform November 6, 8 699 IX. THE APACE TRANSFORM IX.. The plce Trnform Definiion 7 IX.. Properie 7 IX..3 Emple 7 IX..4 Soluion of IVP for ODE 74 IX..5 Soluion

More information

September 20 Homework Solutions

September 20 Homework Solutions College of Engineering nd Compuer Science Mechnicl Engineering Deprmen Mechnicl Engineering A Seminr in Engineering Anlysis Fll 7 Number 66 Insrucor: Lrry Creo Sepember Homework Soluions Find he specrum

More information

8.1. a) For step response, M input is u ( t) Taking inverse Laplace transform. as α 0. Ideal response, K c. = Kc Mτ D + For ramp response, 8-1

8.1. a) For step response, M input is u ( t) Taking inverse Laplace transform. as α 0. Ideal response, K c. = Kc Mτ D + For ramp response, 8-1 8. a For ep repone, inpu i u, U Y a U α α Y a α α Taking invere Laplae ranform a α e e / α / α A α 0 a δ 0 e / α a δ deal repone, α d Y i Gi U i δ Hene a α 0 a i For ramp repone, inpu i u, U Soluion anual

More information

Amplitude modulation

Amplitude modulation Uni. Inroduion pliude odulion odulion i proe o vrying one o he hrerii o high requeny inuoidl he rrier in ordne wih he innneou vlue o he oduling he inorion ignl. The high requeny rrier ignl i heilly repreened

More information

6.8 Laplace Transform: General Formulas

6.8 Laplace Transform: General Formulas 48 HAP. 6 Laplace Tranform 6.8 Laplace Tranform: General Formula Formula Name, ommen Sec. F() l{ f ()} e f () d f () l {F()} Definiion of Tranform Invere Tranform 6. l{af () bg()} al{f ()} bl{g()} Lineariy

More information

A Kalman filtering simulation

A Kalman filtering simulation A Klmn filering simulion The performnce of Klmn filering hs been esed on he bsis of wo differen dynmicl models, ssuming eiher moion wih consn elociy or wih consn ccelerion. The former is epeced o beer

More information

Magnetostatics Bar Magnet. Magnetostatics Oersted s Experiment

Magnetostatics Bar Magnet. Magnetostatics Oersted s Experiment Mgneosics Br Mgne As fr bck s 4500 yers go, he Chinese discovered h cerin ypes of iron ore could rc ech oher nd cerin mels. Iron filings "mp" of br mgne s field Crefully suspended slivers of his mel were

More information

2. VECTORS. R Vectors are denoted by bold-face characters such as R, V, etc. The magnitude of a vector, such as R, is denoted as R, R, V

2. VECTORS. R Vectors are denoted by bold-face characters such as R, V, etc. The magnitude of a vector, such as R, is denoted as R, R, V ME 352 VETS 2. VETS Vecor algebra form he mahemaical foundaion for kinemaic and dnamic. Geomer of moion i a he hear of boh he kinemaic and dnamic of mechanical em. Vecor anali i he imehonored ool for decribing

More information

Robust Network Coding for Bidirected Networks

Robust Network Coding for Bidirected Networks Rou Nework Coding for Bidireed Nework A. Sprinon, S. Y. El Rouyhe, nd C. N. Georghide Ar We onider he prolem of nding liner nework ode h gurnee n innneou reovery from edge filure in ommuniion nework. Wih

More information

t s (half of the total time in the air) d?

t s (half of the total time in the air) d? .. In Cl or Homework Eercie. An Olmpic long jumper i cpble of jumping 8.0 m. Auming hi horizonl peed i 9.0 m/ he lee he ground, how long w he in he ir nd how high did he go? horizonl? 8.0m 9.0 m / 8.0

More information

5.1-The Initial-Value Problems For Ordinary Differential Equations

5.1-The Initial-Value Problems For Ordinary Differential Equations 5.-The Iniil-Vlue Problems For Ordinry Differenil Equions Consider solving iniil-vlue problems for ordinry differenil equions: (*) y f, y, b, y. If we know he generl soluion y of he ordinry differenil

More information

Recent Enhancements to the MULTIFAN-CL Software

Recent Enhancements to the MULTIFAN-CL Software SCTB15 Working Pper MWG-2 Recen Enhncemen o he MULTIFAN-CL Sofwre John Hmpon 1 nd Dvid Fournier 2 1 Ocenic Fiherie Progrmme Secreri of he Pcific Communiy Noume, New Cledoni 2 Oer Reerch Ld. PO Box 2040

More information

1.0 Electrical Systems

1.0 Electrical Systems . Elecricl Sysems The ypes of dynmicl sysems we will e sudying cn e modeled in erms of lgeric equions, differenil equions, or inegrl equions. We will egin y looking fmilir mhemicl models of idel resisors,

More information

Randomized Perfect Bipartite Matching

Randomized Perfect Bipartite Matching Inenive Algorihm Lecure 24 Randomized Perfec Biparie Maching Lecurer: Daniel A. Spielman April 9, 208 24. Inroducion We explain a randomized algorihm by Ahih Goel, Michael Kapralov and Sanjeev Khanna for

More information

Discussion Session 2 Constant Acceleration/Relative Motion Week 03

Discussion Session 2 Constant Acceleration/Relative Motion Week 03 PHYS 100 Dicuion Seion Conan Acceleraion/Relaive Moion Week 03 The Plan Today you will work wih your group explore he idea of reference frame (i.e. relaive moion) and moion wih conan acceleraion. You ll

More information

EE Control Systems LECTURE 2

EE Control Systems LECTURE 2 Copyrigh F.L. Lewi 999 All righ reerved EE 434 - Conrol Syem LECTURE REVIEW OF LAPLACE TRANSFORM LAPLACE TRANSFORM The Laplace ranform i very ueful in analyi and deign for yem ha are linear and ime-invarian

More information

Applications of Prüfer Transformations in the Theory of Ordinary Differential Equations

Applications of Prüfer Transformations in the Theory of Ordinary Differential Equations Irih Mh. Soc. Bullein 63 (2009), 11 31 11 Applicion of Prüfer Trnformion in he Theory of Ordinry Differenil Equion GEORGE CHAILOS Abrc. Thi ricle i review ricle on he ue of Prüfer Trnformion echnique in

More information

can be viewed as a generalized product, and one for which the product of f and g. That is, does

can be viewed as a generalized product, and one for which the product of f and g. That is, does Boyce/DiPrim 9 h e, Ch 6.6: The Convoluion Inegrl Elemenry Differenil Equion n Bounry Vlue Problem, 9 h eiion, by Willim E. Boyce n Richr C. DiPrim, 9 by John Wiley & Son, Inc. Someime i i poible o wrie

More information

Bipartite Matching. Matching. Bipartite Matching. Maxflow Formulation

Bipartite Matching. Matching. Bipartite Matching. Maxflow Formulation Mching Inpu: undireced grph G = (V, E). Biprie Mching Inpu: undireced, biprie grph G = (, E).. Mching Ern Myr, Hrld äcke Biprie Mching Inpu: undireced, biprie grph G = (, E). Mflow Formulion Inpu: undireced,

More information

ENGR 1990 Engineering Mathematics The Integral of a Function as a Function

ENGR 1990 Engineering Mathematics The Integral of a Function as a Function ENGR 1990 Engineering Mhemics The Inegrl of Funcion s Funcion Previously, we lerned how o esime he inegrl of funcion f( ) over some inervl y dding he res of finie se of rpezoids h represen he re under

More information

Probability, Estimators, and Stationarity

Probability, Estimators, and Stationarity Chper Probbiliy, Esimors, nd Sionriy Consider signl genered by dynmicl process, R, R. Considering s funcion of ime, we re opering in he ime domin. A fundmenl wy o chrcerize he dynmics using he ime domin

More information

Analysis of Constant Deteriorating Inventory Management with Quadratic Demand Rate

Analysis of Constant Deteriorating Inventory Management with Quadratic Demand Rate Amerin Journl of Operionl Reserh, (): 98- DOI:.9/j.jor.. Anlysis of onsn Deerioring Invenory Mngemen wih Qudri Demnd Re Goind hndr Pnd,*, Syji Shoo, Prv Kumr Sukl Dep of Mhemis,Mhvir Insiue of Engineering

More information

Cylindrically Symmetric Marder Universe and Its Proper Teleparallel Homothetic Motions

Cylindrically Symmetric Marder Universe and Its Proper Teleparallel Homothetic Motions J. Bsi. Appl. i. Res. 4-5 4 4 TeRod Publiion IN 9-44 Journl of Bsi nd Applied ienifi Reserh www.erod.om Clindrill mmeri Mrder Universe nd Is Proper Teleprllel Homohei Moions Amjd Ali * Anwr Ali uhil Khn

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signal & Syem Prof. Mark Fowler Noe Se #27 C-T Syem: Laplace Tranform Power Tool for yem analyi Reading Aignmen: Secion 6.1 6.3 of Kamen and Heck 1/18 Coure Flow Diagram The arrow here how concepual

More information

Transformations. Ordered set of numbers: (1,2,3,4) Example: (x,y,z) coordinates of pt in space. Vectors

Transformations. Ordered set of numbers: (1,2,3,4) Example: (x,y,z) coordinates of pt in space. Vectors Trnformion Ordered e of number:,,,4 Emple:,,z coordine of p in pce. Vecor If, n i i, K, n, i uni ecor Vecor ddiion +w, +, +, + V+w w Sclr roduc,, Inner do roduc α w. w +,.,. The inner produc i SCLR!. w,.,

More information

Problem Set If all directed edges in a network have distinct capacities, then there is a unique maximum flow.

Problem Set If all directed edges in a network have distinct capacities, then there is a unique maximum flow. CSE 202: Deign and Analyi of Algorihm Winer 2013 Problem Se 3 Inrucor: Kamalika Chaudhuri Due on: Tue. Feb 26, 2013 Inrucion For your proof, you may ue any lower bound, algorihm or daa rucure from he ex

More information

A 1.3 m 2.5 m 2.8 m. x = m m = 8400 m. y = 4900 m 3200 m = 1700 m

A 1.3 m 2.5 m 2.8 m. x = m m = 8400 m. y = 4900 m 3200 m = 1700 m PHYS : Soluions o Chper 3 Home Work. SSM REASONING The displcemen is ecor drwn from he iniil posiion o he finl posiion. The mgniude of he displcemen is he shores disnce beween he posiions. Noe h i is onl

More information

Flow Networks Alon Efrat Slides courtesy of Charles Leiserson with small changes by Carola Wenk. Flow networks. Flow networks CS 445

Flow Networks Alon Efrat Slides courtesy of Charles Leiserson with small changes by Carola Wenk. Flow networks. Flow networks CS 445 CS 445 Flow Nework lon Efr Slide corey of Chrle Leieron wih mll chnge by Crol Wenk Flow nework Definiion. flow nework i direced grph G = (V, E) wih wo diingihed erice: orce nd ink. Ech edge (, ) E h nonnegie

More information

EXISTENCE AND UNIQUENESS OF SOLUTIONS FOR A SECOND-ORDER ITERATIVE BOUNDARY-VALUE PROBLEM

EXISTENCE AND UNIQUENESS OF SOLUTIONS FOR A SECOND-ORDER ITERATIVE BOUNDARY-VALUE PROBLEM Elecronic Journl of Differenil Equions, Vol. 208 (208), No. 50, pp. 6. ISSN: 072-669. URL: hp://ejde.mh.xse.edu or hp://ejde.mh.un.edu EXISTENCE AND UNIQUENESS OF SOLUTIONS FOR A SECOND-ORDER ITERATIVE

More information

PHYSICS 1210 Exam 1 University of Wyoming 14 February points

PHYSICS 1210 Exam 1 University of Wyoming 14 February points PHYSICS 1210 Em 1 Uniersiy of Wyoming 14 Februry 2013 150 poins This es is open-noe nd closed-book. Clculors re permied bu compuers re no. No collborion, consulion, or communicion wih oher people (oher

More information

Sph3u Practice Unit Test: Kinematics (Solutions) LoRusso

Sph3u Practice Unit Test: Kinematics (Solutions) LoRusso Sph3u Prcice Uni Te: Kinemic (Soluion) LoRuo Nme: Tuey, Ocober 3, 07 Ku: /45 pp: /0 T&I: / Com: Thi i copy of uni e from 008. Thi will be imilr o he uni e you will be wriing nex Mony. you cn ee here re

More information

GeoTrig Notes Conventions and Notation: First Things First - Pythogoras and His Triangle. Conventions and Notation: GeoTrig Notes 04-14

GeoTrig Notes Conventions and Notation: First Things First - Pythogoras and His Triangle. Conventions and Notation: GeoTrig Notes 04-14 Convenions nd Noion: GeoTrig Noes 04-14 Hello ll, his revision inludes some numeri exmples s well s more rigonomery heory. This se of noes is inended o ompny oher uorils in his series: Inroduion o EDA,

More information

GEOMETRIC EFFECTS CONTRIBUTING TO ANTICIPATION OF THE BEVEL EDGE IN SPREADING RESISTANCE PROFILING

GEOMETRIC EFFECTS CONTRIBUTING TO ANTICIPATION OF THE BEVEL EDGE IN SPREADING RESISTANCE PROFILING GEOMETRIC EFFECTS CONTRIBUTING TO ANTICIPATION OF THE BEVEL EDGE IN SPREADING RESISTANCE PROFILING D H Dickey nd R M Brennn Solecon Lbororie, Inc Reno, Nevd 89521 When preding reince probing re mde prior

More information

S Radio transmission and network access Exercise 1-2

S Radio transmission and network access Exercise 1-2 S-7.330 Rdio rnsmission nd nework ccess Exercise 1 - P1 In four-symbol digil sysem wih eqully probble symbols he pulses in he figure re used in rnsmission over AWGN-chnnel. s () s () s () s () 1 3 4 )

More information

Price Discrimination

Price Discrimination My 0 Price Dicriminion. Direc rice dicriminion. Direc Price Dicriminion uing wo r ricing 3. Indirec Price Dicriminion wih wo r ricing 4. Oiml indirec rice dicriminion 5. Key Inigh ge . Direc Price Dicriminion

More information

SIDDHARTH GROUP OF INSTITUTIONS :: PUTTUR Siddharth Nagar, Narayanavanam Road QUESTION BANK (DESCRIPTIVE)

SIDDHARTH GROUP OF INSTITUTIONS :: PUTTUR Siddharth Nagar, Narayanavanam Road QUESTION BANK (DESCRIPTIVE) QUESTION BANK 6 SIDDHARTH GROUP OF INSTITUTIONS :: PUTTUR Siddhrh Ngr, Nrynvnm Rod 5758 QUESTION BANK (DESCRIPTIVE) Subjec wih Code :Engineering Mhemic-I (6HS6) Coure & Brnch: B.Tech Com o ll Yer & Sem:

More information

How to Prove the Riemann Hypothesis Author: Fayez Fok Al Adeh.

How to Prove the Riemann Hypothesis Author: Fayez Fok Al Adeh. How o Prove he Riemnn Hohesis Auhor: Fez Fok Al Adeh. Presiden of he Srin Cosmologicl Socie P.O.Bo,387,Dmscus,Sri Tels:963--77679,735 Emil:hf@scs-ne.org Commens: 3 ges Subj-Clss: Funcionl nlsis, comle

More information

Direct Sequence Spread Spectrum II

Direct Sequence Spread Spectrum II DS-SS II 7. Dire Sequene Spread Speru II ER One igh hink ha DS-SS would have he following drawak. Sine he RF andwidh i ie ha needed for a narrowand PSK ignal a he ae daa rae R, here will e ie a uh noie

More information

Minimum Squared Error

Minimum Squared Error Minimum Squred Error LDF: Minimum Squred-Error Procedures Ide: conver o esier nd eer undersood prolem Percepron y i > for ll smples y i solve sysem of liner inequliies MSE procedure y i = i for ll smples

More information

Minimum Squared Error

Minimum Squared Error Minimum Squred Error LDF: Minimum Squred-Error Procedures Ide: conver o esier nd eer undersood prolem Percepron y i > 0 for ll smples y i solve sysem of liner inequliies MSE procedure y i i for ll smples

More information

Using hypothesis one, energy of gravitational waves is directly proportional to its frequency,

Using hypothesis one, energy of gravitational waves is directly proportional to its frequency, ushl nd Grviy Prshn Shool of Siene nd ngineering, Universiy of Glsgow, Glsgow-G18QQ, Unied ingdo. * orresponding uhor: : Prshn. Shool of Siene nd ngineering, Universiy of Glsgow, Glsgow-G18QQ, Unied ingdo,

More information

NEUTRON DIFFUSION THEORY

NEUTRON DIFFUSION THEORY NEUTRON DIFFUSION THEORY M. Ragheb 4//7. INTRODUCTION The diffuion heory model of neuron ranpor play a crucial role in reacor heory ince i i imple enough o allow cienific inigh, and i i ufficienly realiic

More information

Generalized Projective Synchronization Using Nonlinear Control Method

Generalized Projective Synchronization Using Nonlinear Control Method ISSN 79-3889 (prin), 79-3897 (online) Inernionl Journl of Nonliner Siene Vol.8(9) No.,pp.79-85 Generlized Projeive Synhronizion Using Nonliner Conrol Mehod Xin Li Deprmen of Mhemis, Chngshu Insiue of Tehnology

More information

Chapter 2: Evaluative Feedback

Chapter 2: Evaluative Feedback Chper 2: Evluive Feedbck Evluing cions vs. insrucing by giving correc cions Pure evluive feedbck depends olly on he cion ken. Pure insrucive feedbck depends no ll on he cion ken. Supervised lerning is

More information

is the cut off frequency in rads.

is the cut off frequency in rads. 0 ELETRIAL IRUITS 9. HIGH RDER ATIVE FILTERS (With Tle) Introdution Thi development explin how to deign Butterworth (Mximlly Flt) or heyhev (Equl Ripple) Low P, High P or Bnd P tive filter. Thi tretment

More information

Convergence of Singular Integral Operators in Weighted Lebesgue Spaces

Convergence of Singular Integral Operators in Weighted Lebesgue Spaces EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS Vol. 10, No. 2, 2017, 335-347 ISSN 1307-5543 www.ejpm.com Published by New York Business Globl Convergence of Singulr Inegrl Operors in Weighed Lebesgue

More information

2/5/2012 9:01 AM. Chapter 11. Kinematics of Particles. Dr. Mohammad Abuhaiba, P.E.

2/5/2012 9:01 AM. Chapter 11. Kinematics of Particles. Dr. Mohammad Abuhaiba, P.E. /5/1 9:1 AM Chper 11 Kinemic of Pricle 1 /5/1 9:1 AM Inroducion Mechnic Mechnic i Th cience which decribe nd predic he condiion of re or moion of bodie under he cion of force I i diided ino hree pr 1.

More information

Research Article The General Solution of Differential Equations with Caputo-Hadamard Fractional Derivatives and Noninstantaneous Impulses

Research Article The General Solution of Differential Equations with Caputo-Hadamard Fractional Derivatives and Noninstantaneous Impulses Hindwi Advnce in Mhemicl Phyic Volume 207, Aricle ID 309473, pge hp://doi.org/0.55/207/309473 Reerch Aricle The Generl Soluion of Differenil Equion wih Cpuo-Hdmrd Frcionl Derivive nd Noninnneou Impule

More information

Admin MAX FLOW APPLICATIONS. Flow graph/networks. Flow constraints 4/30/13. CS lunch today Grading. in-flow = out-flow for every vertex (except s, t)

Admin MAX FLOW APPLICATIONS. Flow graph/networks. Flow constraints 4/30/13. CS lunch today Grading. in-flow = out-flow for every vertex (except s, t) /0/ dmin lunch oday rading MX LOW PPLIION 0, pring avid Kauchak low graph/nework low nework direced, weighed graph (V, ) poiive edge weigh indicaing he capaciy (generally, aume ineger) conain a ingle ource

More information

Math Week 12 continue ; also cover parts of , EP 7.6 Mon Nov 14

Math Week 12 continue ; also cover parts of , EP 7.6 Mon Nov 14 Mh 225-4 Week 2 coninue.-.3; lo cover pr of.4-.5, EP 7.6 Mon Nov 4.-.3 Lplce rnform, nd pplicion o DE IVP, epecilly hoe in Chper 5. Tody we'll coninue (from l Wednedy) o fill in he Lplce rnform ble (on

More information

Problem Set 9 Due December, 7

Problem Set 9 Due December, 7 EE226: Random Proesses in Sysems Leurer: Jean C. Walrand Problem Se 9 Due Deember, 7 Fall 6 GSI: Assane Gueye his problem se essenially reviews Convergene and Renewal proesses. No all exerises are o be

More information

Introduction to Congestion Games

Introduction to Congestion Games Algorihmic Game Theory, Summer 2017 Inroducion o Congeion Game Lecure 1 (5 page) Inrucor: Thoma Keelheim In hi lecure, we ge o know congeion game, which will be our running example for many concep in game

More information

A1.1.1 Model for the vertical stress comparison between the FLAC ubiquitous joints model and the theoretical development in Jaeger and Cook (1979)

A1.1.1 Model for the vertical stress comparison between the FLAC ubiquitous joints model and the theoretical development in Jaeger and Cook (1979) Universiy of Preori ed Krprov, K (007) Appendix 1. FLAC models nd derivions APPEDIX 1. FLAC MODELS AD DEIATIOS A1.1 Applied models for FLAC ode A1.1.1 Model for he veril sress omprison beween he FLAC ubiquious

More information

defines eigenvectors and associated eigenvalues whenever there are nonzero solutions ( 0

defines eigenvectors and associated eigenvalues whenever there are nonzero solutions ( 0 Chper 7. Inroduion In his hper we ll explore eigeneors nd eigenlues from geomeri perspeies, lern how o use MATLAB o lgerilly idenify hem, nd ulimely see how hese noions re fmously pplied o he digonlizion

More information

A Closed Model of the Universe

A Closed Model of the Universe Inernionl Journl of Asronomy nd Asrophysis 03 3 89-98 hp://dxdoiorg/036/ij0330 Published Online June 03 (hp://wwwsirporg/journl/ij) A Closed Model of he Universe Fdel A Bukhri Deprn of Asronomy Fuly of

More information

The solution is often represented as a vector: 2xI + 4X2 + 2X3 + 4X4 + 2X5 = 4 2xI + 4X2 + 3X3 + 3X4 + 3X5 = 4. 3xI + 6X2 + 6X3 + 3X4 + 6X5 = 6.

The solution is often represented as a vector: 2xI + 4X2 + 2X3 + 4X4 + 2X5 = 4 2xI + 4X2 + 3X3 + 3X4 + 3X5 = 4. 3xI + 6X2 + 6X3 + 3X4 + 6X5 = 6. [~ o o :- o o ill] i 1. Mrices, Vecors, nd Guss-Jordn Eliminion 1 x y = = - z= The soluion is ofen represened s vecor: n his exmple, he process of eliminion works very smoohly. We cn elimine ll enries

More information

An integral having either an infinite limit of integration or an unbounded integrand is called improper. Here are two examples.

An integral having either an infinite limit of integration or an unbounded integrand is called improper. Here are two examples. Improper Inegrls To his poin we hve only considered inegrls f(x) wih he is of inegrion nd b finie nd he inegrnd f(x) bounded (nd in fc coninuous excep possibly for finiely mny jump disconinuiies) An inegrl

More information

Procedia Computer Science

Procedia Computer Science Procedi Compuer Science 00 (0) 000 000 Procedi Compuer Science www.elsevier.com/loce/procedi The Third Informion Sysems Inernionl Conference The Exisence of Polynomil Soluion of he Nonliner Dynmicl Sysems

More information

Linear Quadratic Regulator (LQR) - State Feedback Design

Linear Quadratic Regulator (LQR) - State Feedback Design Linear Quadrai Regulaor (LQR) - Sae Feedbak Design A sysem is expressed in sae variable form as x = Ax + Bu n m wih x( ) R, u( ) R and he iniial ondiion x() = x A he sabilizaion problem using sae variable

More information

BEA400 Microeconomics. Lecture 11:

BEA400 Microeconomics. Lecture 11: BEA4 Miroeonomis Leure BEA4 Miroeonomis Leure Module 5: Choie Over ime ih Unerin Leure : Sohsi Proesses, Io s Lemm nd Sohsi Opiml Conrol Sohsi Proesses he Pure Weiner Proess or Bronin Moion Sling he vrine

More information

CHAPTER 7: SECOND-ORDER CIRCUITS

CHAPTER 7: SECOND-ORDER CIRCUITS EEE5: CI RCUI T THEORY CHAPTER 7: SECOND-ORDER CIRCUITS 7. Inroducion Thi chaper conider circui wih wo orage elemen. Known a econd-order circui becaue heir repone are decribed by differenial equaion ha

More information

2D Motion WS. A horizontally launched projectile s initial vertical velocity is zero. Solve the following problems with this information.

2D Motion WS. A horizontally launched projectile s initial vertical velocity is zero. Solve the following problems with this information. Nme D Moion WS The equions of moion h rele o projeciles were discussed in he Projecile Moion Anlsis Acii. ou found h projecile moes wih consn eloci in he horizonl direcion nd consn ccelerion in he ericl

More information

Reminder: Flow Networks

Reminder: Flow Networks 0/0/204 Ma/CS 6a Cla 4: Variou (Flow) Execie Reminder: Flow Nework A flow nework i a digraph G = V, E, ogeher wih a ource verex V, a ink verex V, and a capaciy funcion c: E N. Capaciy Source 7 a b c d

More information

INTRODUCTION TO INERTIAL CONFINEMENT FUSION

INTRODUCTION TO INERTIAL CONFINEMENT FUSION INTODUCTION TO INETIAL CONFINEMENT FUSION. Bei Lecure 7 Soluion of he imple dynamic igniion model ecap from previou lecure: imple dynamic model ecap: 1D model dynamic model ρ P() T enhalpy flux ino ho

More information

The Forming Theory and Computer Simulation of the Rotary Cutting Tools with Helical Teeth and Complex Surfaces

The Forming Theory and Computer Simulation of the Rotary Cutting Tools with Helical Teeth and Complex Surfaces Compuer nd Informion Siene The Forming Theory nd Compuer Simulion of he Rory Cuing Tools wih Helil Teeh nd Comple Surfes Hurn Liu Deprmen of Mehnil Engineering Zhejing Universiy of Siene nd Tehnology Hngzhou

More information

Let. x y. denote a bivariate time series with zero mean.

Let. x y. denote a bivariate time series with zero mean. Linear Filer Le x y : T denoe a bivariae ime erie wih zero mean. Suppoe ha he ime erie {y : T} i conruced a follow: y a x The ime erie {y : T} i aid o be conruced from {x : T} by mean of a Linear Filer.

More information