On the Real part of non-trivial zeros of Riemann s Zeta function.

Size: px
Start display at page:

Download "On the Real part of non-trivial zeros of Riemann s Zeta function."

Transcription

1 O th ral part of o-trval zro of Rma Zta Fucto O th Ral part of o-trval zro of Rma Zta fucto. A.M.S. Subjct Clafcato, 98: 3E, 45E5, 45M5, A7.

2 Rutlo Hrrra. To Th Salvaora Popl That Rvolt pt of Thr Mry a Igorac. Th Mathmatca thk that a problm I ay to olv Aftr t ha b olv

3 O th ral part of o-trval zro of Rma Zta Fucto 3 Prfac: Th followg ot ummarz fv yar of ffort. Th rarch proc ha ot b lar, uffrg momt of avac a momt of tagato, a racal chag th tactc u to olv th problm. I tr wth Dffrtal Equato orar a wth partal rvatv, Clacal Aaly a Harmoc Fucto, utl I fou a almot fortutou way th Bouary Valu Problm a th Sgular Itgral Equato Thory, whch I ralz t coul b of corabl u olvg th problm. Th rult ar urprg. Naturally, th ffrc of tapot ca b obrv a rgar th Rma zta fucto, btw Ttchmarh or Ivĉ Bblography a that prt hr. I th frt ca, a mathmatcal tool u that, although t ha vlop a avac gratly rpct to Rma Zta Fucto Thory, th pcfc ca of fg th ral part of o-trval zro, partcularly obcur. I blv that th ma rao th Bouary Valu Problm a Sgular Itgral Equato Thory hav ot b u for tryg to prov th Rma Hypoth th followg: th frt plac, th Bouary Valu Problm wa orgat Phyc problm, Quatum Mchac, Flu Mchac a th Mathmatcal Thory of Elatcty, a t wa hr that t wa vlop a trgth. O th othr ha, th problm a rgar th mto hypoth ha b tak up, gral, from th pot of vw of th rlatohp, btw th zta fucto a th trbuto of prm umbr ug Clacal Aaly: Itgral Traformato, bou, aymptotc appromato, tc. Ev mor, all ttbook a joural rv tll ow, th problm o trmg th ral part of o trval zro of th Rma Zta fucto ha b work arou th aymptotc formula for th umbr of zro th crtcal trp, a partcular wth th formula tablh by Rma a aftrwar prov by Vo Magolt: T T N T l OlT Whr NT th umbr of zro of th form βα wth < α T A kow, Hary wa th frt provg that th ζ fucto ha fty umbr of zro o th l R z a aftrwar Slbrg prov that th umbr of zro N T o th l atf th qualty N T > AT lt for om A >.

4 4 Rutlo Hrrra. Th appromatly th tat of th gam th rarch of th zro of ζ fucto th crtcal trp, that ca truly b corr, a part of aalytc umbr thory. Th ma a w propo to chag th pot of vw of th problm, ug th thory of Cauchy typ gular tgral, th bouary valu problm a th gular tgral quato thory, for th horzotal tuy of th problm. If th ot ta th tt of aaly a opo of pcalt, two mportat ubjct wll b hghlght Aalytc Numbr Thory: a- Sgal of uty of th Bouary Valu Problm a Aalytc Numbr Thory b- Proof of Rma Hypoth. A th rar wll apprcat t ha b uffct to am th problm wth th w tapot ta of th uual o. Th a th vlopmt of th bouary valu problm a gular tgral quato hav ma th ot pobl. I wh to t my grattu to Wllam Lambrt Mart R.I.P a Héctor Fguroa for th rgor, th thuam a th curoty for Mathmatc thy oc taught m. I partcularly wh to thak Profor Ew Catro Fráz who cla th thr mto ubjct wr ythz. Wthout th avc of Profor Catro th ot woul t t. H wa th frt ra th maucrpt a ma th rpctv obrvato a ot. Fally, I wh to thak Profor Maul Barahoa Drogutt, for havg ough htorcal prpctv to tmulat m to urtak th ffort, whch th publhg of a papr lk th volv. Th author. School of Mathmatc. Uvrty of Cota Rca. Sa Joé, Cota Rca, 987.

5 O th ral part of o-trval zro of Rma Zta Fucto 5 Abtract: Th Rma zta fucto ha b ubjct of amout of rarch c t troucto Mathmatc, a wll a th Rma HypothRH. Tll ow, th bac rcto o rarch rpct to RH ha b clmbg th crtcal l R, a wll a th rgo am crtcal trp : < R <. W propo to chag th pot of vw orr to approach th zta fucto th crtcal trp but horzotal orr to attmpt to prov th RH: If a zro of th ζ fucto o th crtcal trp th R. Th wll b o by ma of th thory of Sgular Itgral Equato, maly tryg to prov that o- trval zro of ζ ar aocat to th aymptotc bhavor of crta k of oluto of th gular tgral quato: ϕ, t L For om ϕ Hölr fucto. All tgral that L t appar houl b urtoo th of VP Cauchy. Ky wor: zta fucto, gular tgral quato. A.M.S. Subjct Clafcato 98: 45E5, 45M5, A7, 3E.

6 Rutlo Hrrra. 6 Th followg quc of propoto ha a a ma objctv th Proof of Rma Hypoth: If ζ o th crtcal trp, th R Propoto : For R > : l ζ Proof: Lt pa th krl of th tgral:, That covrg uformly for > I coquc: l l l * A th trchag of th um a th tgral jutf by Lv omat covrgc thorm. If ow w put th tgral ur th um g : l l a by puttg: th lat tgral of th rght ha w wll hav: t t t t. Puttg th rult * wll follow: l ζ, that th Propoto

7 O th ral part of o-trval zro of Rma Zta Fucto 7 Th Formula abl u to t th ζ fucto to th whol pla a th followg propoto. Propoto : For C, : l ζ L Whr L ta to b th cotour pct blow: l l A l ; th logarthm of l trm uch a way that t ral for gatv valu of l. Proof: Puttg: z l w gt to: ζ C z z z 3 Whr C ta to b th cotour of blow: Th fuctoal rlatohp 3 tally th tartg pot of Rma ow aalytcal to of ζ fucto : Ewar, H.M.: Th Rma zta fucto. Acamc Pr, N.Y.974 th tralato of Rma papr Ubr Azahl r Prmzahl. It mportat to ot, at th pot, that [ 5 ], Ttchmarh, E.C, that ha b mayb th mot mportat htorcal rfrc to Rma ζ

8 8 Rutlo Hrrra. fucto, th aalytcal to wa wrog, roppg o t th mu g of th umrator ur th tgral g 3.: pag8 of 5], Chap,.4 From 3, mot of Mathmatca tt th cotour to th puctur pla C{ } aftr avog th gulart: z kι, for k Z, uch a mar that from that pot of vw, t ca b uc aly th fuctoal quato: ζ ζ that charactrz plty th ζ fucto. W wll avo th pot of vw, a ucat rathr th followg: Propoto 3 For C, : ζ l 4 Proof: From Rma pot of vw, w ca wrt: Π ζ ζ for R > Rma ot Π ; w wll kp th otato a alo that: C Hr C:

9 O th ral part of o-trval zro of Rma Zta Fucto 9 But f w put : w ca wrt: l l * L C Whr L th cotour pct blow: Rma hmlf wrot that qualty: C ζ rma val for all a art alo that th tgral qual *, thrfor w ca wrt: l ζ or, what th am: l ζ for But c:, wll follow: l ζ, a multplyg a vg by, w wll hav: l ζ, that prov th Propoto

10 Rutlo Hrrra. From th lat propoto, aly follow that ζ, by th mply valuato of th rprtato, c co qual zro for, N, a th tgral bou for tho valu of. Th zro ar am trval zro of th ζ fucto. It to ot th ffrc btw th tapot a th o th tratoal ucto of th trval zro from th fuctoal quato of ζ, mto bfor. From hr w gt th fuamtal thortcal tur th oluto of th problm of fg th ral part of o-trval zro: th mpropr gular tgral that appar th fuctoal quato 4 houl b urtoo a a lmt pot of th prcpal valu of a tgral of Cauchy typ ovr a cotour that t to fty: l lm t l t I orr to cofrm th lat qualty, w gv th followg Propoto 4: Lt ϕ b a arbtrary Hölr fucto o ], [, th: lm t ϕ t ϕ, a th lmt uform. Proof: Th a taar rult of th thory of Sgular Itgral,: Mukhlhvl, N,I. Sgular Itgral Equato. Woltr-Noorhoof Publhg Co. Grog, Th Nthrla, 97, pag 38 a ff.

11 O th ral part of o-trval zro of Rma Zta Fucto Dfto5: Φ W f a fucto: o th t ], [ by ma of o gular tgral of Cauchy typ ovr a cotour that tt to fty: Φ t ϕ t ], [ 5 t l 5* wth ty fucto: ϕ ], [ Whr y, a ], [, th ral part of, f but ukow a y a arbtrary gv ral umbr. Propoto 6: Th tgral 5 wth ty 5* a Cauchy typ tgral ovr a cotour that t to fty. Proof: It ough to vrfy that th two followg coto ar atf: a- Th ty fucto ϕ a Lpchtz fucto o ], [. A b- For ough gratr valu of t thr hol: ϕ t ϕ µ t for om A >, µ > a- I a ay mattr : ϕ ffrtabl rpct to t a th rvatv ar ft o ], [. Th mattr that ϕ o t mak ffrc rgar to th cocluo. b- Lt frt prov that lm ϕ : I: ϕ l. Sc ], [, <, w gt thu:

12 Rutlo Hrrra. ϕ f, that : ϕ 6 l B: ϕ f l Th thr t A > a µ, uch that th propoto rma val Th fact ϕ 6 guarat th covrgc of th tgral. Gakhov, F.D. Bouary Valu Problm, Prgamo Pr, Ofor,966, 4, Scc 4.6. If w cor ow ϕ a a ukow fucto, c w o t kow th valu of R wh th fucto 5 wth ty 5* qual to zro wh t, th w hav th rght to vokg th followg propoto: Propoto 7: Th oluto of th gular tgral quato: ϕ 7 t th cla of Hölr fucto ubou at tal trm of th cotour of tgrato : a ϕ t, 7* t for om compl cotat a a a that o t p o t. Proof: Th a taar rult of th thory of Sgular Itgral Equato. S, for tac : Gakhov, F.D. op. ct. 4.3.

13 O th ral part of o-trval zro of Rma Zta Fucto 3 If w ar ow lookg for th oluto of th tgral quato: l, for t t 8 th cla of Hölr fucto ubou at th tal trm of th cotour of tgrato, th th oluto of 8 mut bhav th am way tha 7* a rght ghborhoo of, that both t mut b aymptotcally qual wh t. W hav thu th followg Propoto 8: If: y lt a a t t for t th : Proof: W hav: lm t lt t a a y t th thr mut t ral fucto mar that: v a t, wth lm v t t a a uch a lt t a a y t w gt to: v yl l t l But c : l a t. By takg logarthm both of th qualty [ a a t] l v t l t a [ a a t] l a a t arg a a t k

14 4 Rutlo Hrrra. W gt to th par of quato: l a a t l va t l t 9 llt llt llt [ arg a a t ] k y, for k Z l l t By approachg t from th frt of th two quato w wll hav: lm t l a a l l t t, lm t l va t l l t, lm t l t l l t a coquc :. Th th Rma Hypoth. It to ot that th mtho u hr for approach th Rma Hypoth o ot gv formato about th magary part of o-trval zro, c from follow: y ± f k ± a t Rutlo Hrrra. E mal: luca54@latmal.com Pho Ecula Matmátca Uvra Cota Rca Acama Matmátca MAC. 999-

15 O th ral part of o-trval zro of Rma Zta Fucto 5 REFERENCES:. Apotol, T.M..: Itrouccó a la Toría Aalítca Númro. E. Rvrté, Mar, 98.. Btaz. A.V. Equato of Mathmatcal Phyc. Mr. Publhr, Mocow, Buak, B.M.,Fom, S.V.:Multpl Itgral, Fl Thory a Sr. Mr Publhr, Mocow, Charakara,K.: Arthmtcal Fucto. Sprgr Vrlag, Brl Coway, Joh.: Fucto of O Compl Varabl. Sprgr Vrlag, N.Y Sco Eto. 6. D Bruj, N.G.: Aymptotc Mtho Aaly. Dovr Publcato Ic. N.Y. rpublcato of thr E. 7. Drrck, W.: Itrouctory Compl Aaly. Acamc Pr, N.Y Ewar, Harol: Rma zta Fucto. Acamc Pr, N.Y., Gakhov, F.D. Bouary Valu Problm, Prgamo Pr, Ofor,966. Ivĉ, A.: Th Rma Zta Fucto. Holt, Rhart a Wto, N.Y Markuhvch, A.: Toría la Fuco Aalítca. E. Mr, Mocú, trauccó l ruo Mukhlhvl, N.I. Sgular Itgral Equato. Woltr Noorhoo Publhg, Grog, Th Nthrla. Rprt Nvala, R. Paatro,V.: Itroucto to Compl Aaly. Chla Publhg Co., N.Y. 98. Sco Eto 4. Ramachr, R.: Topc Aalytc Numbr Thory. Sprgr Vrlag, Brl, Svhkov, A., Tkhoov, A.: Th Thory of Fucto of a Compl Varabl. Mr Publhr, Mocow, Ttchmarh, E.C.: Th Thory of Rma Zta Fucto. Ofor Uvrty Pr, Loo, Trcom, F.: Itgral Equato. Itrcc Publhr Ic. N.Y. Pur a Appl Mathmatc, vol. 5 Et by R. Courat, L.Br, a J.J.Stokr. Loo Uvrty Pr. Loo Frt E. 957.

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..

More information

Aotomorphic Functions And Fermat s Last Theorem(4)

Aotomorphic Functions And Fermat s Last Theorem(4) otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

Chemistry 222 DO NOT OPEN THE EXAM UNTIL YOU ARE READY TO TAKE IT! You may allocate a maximum of 80 continuous minutes for this exam.

Chemistry 222 DO NOT OPEN THE EXAM UNTIL YOU ARE READY TO TAKE IT! You may allocate a maximum of 80 continuous minutes for this exam. Chmtry Sprg 09 Eam : Chaptr -5 Nam 80 Pot Complt fv (5) of th followg problm. CLEARLY mark th problm you o ot wat gra. You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat

More information

In 1991 Fermat s Last Theorem Has Been Proved

In 1991 Fermat s Last Theorem Has Been Proved I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Estimation of Population Variance Using a Generalized Double Sampling Estimator

Estimation of Population Variance Using a Generalized Double Sampling Estimator r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst

More information

We need to first account for each of the dilutions to determine the concentration of mercury in the original solution:

We need to first account for each of the dilutions to determine the concentration of mercury in the original solution: Complt fv (5) of th followg problm. CLEARLY mark th problm you o ot wat gra. You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat umbr of gfcat fgur. Do fv of problm

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

Correlation in tree The (ferromagnetic) Ising model

Correlation in tree The (ferromagnetic) Ising model 5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable Itratoal Jal o Probablt a tattc 5 : - DOI:.59/j.jp.5. tmat Ft Poplato Varac U Ma a Varac o Alar Varabl Ph Mra * R. Kara h Dpartmt o tattc Lcow Urt Lcow Ia Abtract F tmat t poplato arac mato o l alar arabl

More information

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which?

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which? 5 9 Bt Ft L # 8 7 6 5 GRAPH IN CIENCE O of th thg ot oft a rto of a xrt a grah of o k. A grah a vual rrtato of ural ata ollt fro a xrt. o of th ty of grah you ll f ar bar a grah. Th o u ot oft a l grah,

More information

Chemistry 350. The take-home least-squares problem will account for 15 possible points on this exam.

Chemistry 350. The take-home least-squares problem will account for 15 possible points on this exam. Chmtry 30 Sprg 08 Eam : Chaptr - Nam 00 Pot You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat umbr of gfcat fgur. Th tak-hom lat-quar problm wll accout for pobl pot

More information

Numerical Method: Finite difference scheme

Numerical Method: Finite difference scheme Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from

More information

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information

Chemistry 222. Exam 1: Chapters 1-4

Chemistry 222. Exam 1: Chapters 1-4 Chtry Fall 05 Ea : Chaptr -4 Na 80 Pot Coplt two () of probl -3 a four (4) of probl 4-8. CLEARLY ark th probl you o ot wat gra. Show your work to rcv crt for probl rqurg ath. Rport your awr wth th approprat

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS Chaptr 4 NUMERICL METHODS FOR SOLVING BOUNDRY-VLUE PROBLEMS 00 4. Varatoal formulato two-msoal magtostatcs Lt th followg magtostatc bouar-valu problm b cosr ( ) J (4..) 0 alog ΓD (4..) 0 alog ΓN (4..)

More information

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd

More information

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek Coparo o th Varac o Prdctor wth PPS aplg (updat o c04d6doc Ed Sta troducto W copar prdctor o a PSU a or total bad o PPS aplg Th tratgy to ollow that o Sta ad Sgr (JASA, 004 whr w xpr th prdctor a a lar

More information

Independent Domination in Line Graphs

Independent Domination in Line Graphs Itratoal Joural of Sctfc & Egrg Rsarch Volum 3 Issu 6 Ju-1 1 ISSN 9-5518 Iddt Domato L Grahs M H Muddbhal ad D Basavarajaa Abstract - For ay grah G th l grah L G H s th trscto grah Thus th vrtcs of LG

More information

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t Cla ot fo EE6318/Phy 6383 Spg 001 Th doumt fo tutoal u oly ad may ot b opd o dtbutd outd of EE6318/Phy 6383 tu 7 Dffuo Ou flud quato that w dvlopd bfo a: f ( )+ v v m + v v M m v f P+ q E+ v B 13 1 4 34

More information

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4.

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4. Coutg th compostos of a postv tgr usg Gratg Fuctos Start wth,... - Whr, for ampl, th co-ff of s, for o summad composto of aml,. To obta umbr of compostos of, w d th co-ff of (...) ( ) ( ) Hr for stac w

More information

Linear Approximating to Integer Addition

Linear Approximating to Integer Addition Lear Approxmatg to Iteger Addto L A-Pg Bejg 00085, P.R. Cha apl000@a.com Abtract The teger addto ofte appled cpher a a cryptographc mea. I th paper we wll preet ome reult about the lear approxmatg for

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

Positive unstable electrical circuits

Positive unstable electrical circuits Taz KZOEK alyto Uvrty of Tchology Faclty of Elctrcal Egrg Potv tabl lctrcal crct btract: Th tablty for th potv lar lctrcal crct compo of rtor col coator a voltag crrt orc ar ar Thr ffrt cla of th potv

More information

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu

More information

Power System Dynamic Security Region and Its Approximations

Power System Dynamic Security Region and Its Approximations h artcl ha b accpt for publcato a futur u of th joural, but ha ot b fully t. Cott may chag pror to fal publcato. > REPLACE HIS LINE WIH YOUR PAPER IDENIFICAION NUMBER (DOUBLE-CLICK HERE O EDI) < Powr Sytm

More information

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1 Th robablty of Ra's hyothss bg tru s ual to Yuyag Zhu Abstract Lt P b th st of all r ubrs P b th -th ( ) lt of P ascdg ordr of sz b ostv tgrs ad s a rutato of wth Th followg rsults ar gv ths ar: () Th

More information

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series Chatr Ifiit Sris Pag of Sctio F Itgral Tst Chatr : Ifiit Sris By th d of this sctio you will b abl to valuat imror itgrals tst a sris for covrgc by alyig th itgral tst aly th itgral tst to rov th -sris

More information

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space. Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms

More information

Chapter 6. pn-junction diode: I-V characteristics

Chapter 6. pn-junction diode: I-V characteristics Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc

More information

(1) Then we could wave our hands over this and it would become:

(1) Then we could wave our hands over this and it would become: MAT* K285 Spring 28 Anthony Bnoit 4/17/28 Wk 12: Laplac Tranform Rading: Kohlr & Johnon, Chaptr 5 to p. 35 HW: 5.1: 3, 7, 1*, 19 5.2: 1, 5*, 13*, 19, 45* 5.3: 1, 11*, 19 * Pla writ-up th problm natly and

More information

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k. Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /

More information

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since 56 Chag Ma J Sc 0; () Chag Ma J Sc 0; () : 56-6 http://pgscccmuacth/joural/ Cotrbutd Papr Th Padova Sucs Ft Groups Sat Taș* ad Erdal Karaduma Dpartmt of Mathmatcs, Faculty of Scc, Atatürk Uvrsty, 50 Erzurum,

More information

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source: Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght

More information

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps

More information

CHAPTER 6 CONVENTIONAL SINGLE-PHASE TO THREE-PHASE POWER CONVERTERS. 6.1 Introduction

CHAPTER 6 CONVENTIONAL SINGLE-PHASE TO THREE-PHASE POWER CONVERTERS. 6.1 Introduction CHAPTER 6 CONENTONAL SNGLE-PHASE TO THREE-PHASE POWER CONERTERS 6. troucto th chatr th aroach to covrt gl-ha owr to thr-ha owr utabl for owrg ac loa cu. Covtoal owr covrtr u for th covro of gl-ha ac voltag

More information

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net Taylor s Thorm & Lagrag Error Bouds Actual Error This is th ral amout o rror, ot th rror boud (worst cas scario). It is th dirc btw th actual () ad th polyomial. Stps:. Plug -valu ito () to gt a valu.

More information

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1) Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts

More information

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e

More information

ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS

ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS ACENA Vo.. 03-08 005 03 ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS Rub A. CERUTTI RESUMEN: Cosrao os úcos Rsz coo casos artcuars úco causa

More information

Washington State University

Washington State University he 3 Ktics ad Ractor Dsig Sprg, 00 Washgto Stat Uivrsity Dpartmt of hmical Egrg Richard L. Zollars Exam # You will hav o hour (60 muts) to complt this xam which cosists of four (4) problms. You may us

More information

ONLY AVAILABLE IN ELECTRONIC FORM

ONLY AVAILABLE IN ELECTRONIC FORM OPERTIONS RESERH o.287/opr.8.559c pp. c c8 -copao ONLY VILLE IN ELETRONI FORM fors 28 INFORMS Elctroc opao Optzato Mols of scrt-evt Syst yacs by Wa K (Vctor ha a L Schrub, Opratos Rsarch, o.287/opr.8.559.

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

Nuclear Chemistry -- ANSWERS

Nuclear Chemistry -- ANSWERS Hoor Chstry Mr. Motro 5-6 Probl St Nuclar Chstry -- ANSWERS Clarly wrt aswrs o sparat shts. Show all work ad uts.. Wrt all th uclar quatos or th radoactv dcay srs o Urau-38 all th way to Lad-6. Th dcay

More information

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 -

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 - Statstcal Thrmodyamcs sstal Cocpts (Boltzma Populato, Partto Fuctos, tropy, thalpy, Fr rgy) - lctur 5 - uatum mchacs of atoms ad molculs STATISTICAL MCHANICS ulbrum Proprts: Thrmodyamcs MACROSCOPIC Proprts

More information

Pion Production via Proton Synchrotron Radiation in Strong Magnetic Fields in Relativistic Quantum Approach

Pion Production via Proton Synchrotron Radiation in Strong Magnetic Fields in Relativistic Quantum Approach Po Producto va Proto Sychrotro Radato Strog Magtc Flds Rlatvstc Quatum Approach Partcl Productos TV Ergy Rgo Collaborators Toshtaka Kajo Myog-K Chou Grad. J. MATHEWS Tomoyuk Maruyama BRS. Nho Uvrsty NaO,

More information

Note: Torque is prop. to current Stationary voltage is prop. to speed

Note: Torque is prop. to current Stationary voltage is prop. to speed DC Mach Cotrol Mathmatcal modl. Armatr ad orq f m m a m m r a a a a a dt d ψ ψ ψ ω Not: orq prop. to crrt Statoary voltag prop. to pd Mathmatcal modl. Fld magtato f f f f d f dt a f ψ m m f f m fλ h torq

More information

Almost all Cayley Graphs Are Hamiltonian

Almost all Cayley Graphs Are Hamiltonian Acta Mathmatca Sca, Nw Srs 199, Vol1, No, pp 151 155 Almost all Cayly Graphs Ar Hamltoa Mg Jxag & Huag Qogxag Abstract It has b cocturd that thr s a hamltoa cycl vry ft coctd Cayly graph I spt of th dffculty

More information

Time : 1 hr. Test Paper 08 Date 04/01/15 Batch - R Marks : 120

Time : 1 hr. Test Paper 08 Date 04/01/15 Batch - R Marks : 120 Tim : hr. Tst Papr 8 D 4//5 Bch - R Marks : SINGLE CORRECT CHOICE TYPE [4, ]. If th compl umbr z sisfis th coditio z 3, th th last valu of z is qual to : z (A) 5/3 (B) 8/3 (C) /3 (D) o of ths 5 4. Th itgral,

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 St Ssts o Ordar Drtal Equatos Novbr 7 St Ssts o Ordar Drtal Equatos Larr Cartto Mcacal Er 5A Sar Er Aalss Novbr 7 Outl Mr Rsults Rvw last class Stablt o urcal solutos Stp sz varato or rror cotrol Multstp

More information

An N-Component Series Repairable System with Repairman Doing Other Work and Priority in Repair

An N-Component Series Repairable System with Repairman Doing Other Work and Priority in Repair Mor ppl Novmbr 8 N-Compo r Rparabl m h Rparma Dog Ohr ork a ror Rpar Jag Yag E-mal: jag_ag7@6om Xau Mg a uo hg ollag arb Normal Uvr Yaq ua Taoao ag uppor b h Fouao or h aural o b prov o Cha 5 uppor b h

More information

Power Spectrum Estimation of Stochastic Stationary Signals

Power Spectrum Estimation of Stochastic Stationary Signals ag of 6 or Spctru stato of Stochastc Statoary Sgas Lt s cosr a obsrvato of a stochastc procss (). Ay obsrvato s a ft rcor of th ra procss. Thrfor, ca say:

More information

On a problem of J. de Graaf connected with algebras of unbounded operators de Bruijn, N.G.

On a problem of J. de Graaf connected with algebras of unbounded operators de Bruijn, N.G. O a problm of J. d Graaf coctd with algbras of uboudd oprators d Bruij, N.G. Publishd: 01/01/1984 Documt Vrsio Publishr s PDF, also kow as Vrsio of Rcord (icluds fial pag, issu ad volum umbrs) Plas chck

More information

z 1+ 3 z = Π n =1 z f() z = n e - z = ( 1-z) e z e n z z 1- n = ( 1-z/2) 1+ 2n z e 2n e n -1 ( 1-z )/2 e 2n-1 1-2n -1 1 () z

z 1+ 3 z = Π n =1 z f() z = n e - z = ( 1-z) e z e n z z 1- n = ( 1-z/2) 1+ 2n z e 2n e n -1 ( 1-z )/2 e 2n-1 1-2n -1 1 () z Sris Expasio of Rciprocal of Gamma Fuctio. Fuctios with Itgrs as Roots Fuctio f with gativ itgrs as roots ca b dscribd as follows. f() Howvr, this ifiit product divrgs. That is, such a fuctio caot xist

More information

Positive electrical circuits with zero transfer matrices and their discretization

Positive electrical circuits with zero transfer matrices and their discretization omptr ppcato Ectrca Egrg Vo. 6 DO.8/j.58-8.6. Potv ctrca crct wt zro trafr matrc a tr crtzato Taz Kaczork Baytok Uvrty of Tcoogy 5 5 Baytok. Wjka 5D ma: kaczork@.pw..p Potv coto tm a crt tm ar ctrca crct

More information

Trignometric Inequations and Fuzzy Information Theory

Trignometric Inequations and Fuzzy Information Theory Iteratoal Joural of Scetfc ad Iovatve Mathematcal Reearch (IJSIMR) Volume, Iue, Jauary - 0, PP 00-07 ISSN 7-07X (Prt) & ISSN 7- (Ole) www.arcjoural.org Trgometrc Iequato ad Fuzzy Iformato Theory P.K. Sharma,

More information

1985 AP Calculus BC: Section I

1985 AP Calculus BC: Section I 985 AP Calculus BC: Sctio I 9 Miuts No Calculator Nots: () I this amiatio, l dots th atural logarithm of (that is, logarithm to th bas ). () Ulss othrwis spcifid, th domai of a fuctio f is assumd to b

More information

ON RANKING OF ALTERNATIVES IN UNCERTAIN GROUP DECISION MAKING MODEL

ON RANKING OF ALTERNATIVES IN UNCERTAIN GROUP DECISION MAKING MODEL IJRRAS (3) Ju 22 www.arpapr.com/volum/voliu3/ijrras 3_5.pdf ON RANKING OF ALRNAIVS IN UNCRAIN GROUP DCISION MAKING MODL Chao Wag * & Lag L Gul Uvrty of chology Gul 544 Cha * mal: wagchao244@63.com llag6666@26.com

More information

CS473-Algorithms I. Lecture 12b. Dynamic Tables. CS 473 Lecture X 1

CS473-Algorithms I. Lecture 12b. Dynamic Tables. CS 473 Lecture X 1 CS473-Algorthm I Lecture b Dyamc Table CS 473 Lecture X Why Dyamc Table? I ome applcato: We do't kow how may object wll be tored a table. We may allocate pace for a table But, later we may fd out that

More information

Entropy Equation for a Control Volume

Entropy Equation for a Control Volume Fudamtals of Thrmodyamcs Chaptr 7 Etropy Equato for a Cotrol Volum Prof. Syoug Jog Thrmodyamcs I MEE2022-02 Thrmal Egrg Lab. 2 Q ds Srr T Q S2 S1 1 Q S S2 S1 Srr T t t T t S S s m 1 2 t S S s m tt S S

More information

= 2. Statistic - function that doesn't depend on any of the known parameters; examples:

= 2. Statistic - function that doesn't depend on any of the known parameters; examples: of Samplg Theory amples - uemploymet househol cosumpto survey Raom sample - set of rv's... ; 's have ot strbuto [ ] f f s vector of parameters e.g. Statstc - fucto that oes't epe o ay of the ow parameters;

More information

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod

More information

T h e C S E T I P r o j e c t

T h e C S E T I P r o j e c t T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T

More information

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes 3.3 Physcal Orato of os Jucto Ur vrs-bas Cotos rft Currt S : ato to th ffuso Currt comot u to majorty carrr ffuso, caus by thrmally grat morty carrrs, thr ar two currt comots lctros mov by rft from to

More information

(Reference: sections in Silberberg 5 th ed.)

(Reference: sections in Silberberg 5 th ed.) ALE. Atomic Structur Nam HEM K. Marr Tam No. Sctio What is a atom? What is th structur of a atom? Th Modl th structur of a atom (Rfrc: sctios.4 -. i Silbrbrg 5 th d.) Th subatomic articls that chmists

More information

Estimation of the Present Values of Life Annuities for the Different Actuarial Models

Estimation of the Present Values of Life Annuities for the Different Actuarial Models h Scod Itratoal Symposum o Stochastc Modls Rlablty Egrg, Lf Scc ad Opratos Maagmt Estmato of th Prst Valus of Lf Auts for th Dffrt Actuaral Modls Gady M Koshk, Oaa V Guba omsk Stat Uvrsty Dpartmt of Appld

More information

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor Cotol Syt ctu 8 Root ocu Clacal Cotol Pof. Eugo Schut hgh Uvty Root ocu Cotoll Plat R E C U Y - H C D So Y C C R C H Wtg th loo ga a w a ttd tackg th clod-loo ol a ga va Clacal Cotol Pof. Eugo Schut hgh

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

More information

Thomas J. Osler. 1. INTRODUCTION. This paper gives another proof for the remarkable simple

Thomas J. Osler. 1. INTRODUCTION. This paper gives another proof for the remarkable simple 5/24/5 A PROOF OF THE CONTINUED FRACTION EXPANSION OF / Thomas J Oslr INTRODUCTION This ar givs aothr roof for th rmarkabl siml cotiud fractio = 3 5 / Hr is ay ositiv umbr W us th otatio x= [ a; a, a2,

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables Improvd Epoal Emaor for Populao Varac Ug Two Aular Varabl Rajh gh Dparm of ac,baara Hdu Uvr(U.P., Ida (rgha@ahoo.com Pakaj Chauha ad rmala awa chool of ac, DAVV, Idor (M.P., Ida Flor maradach Dparm of

More information

On the Possible Coding Principles of DNA & I Ching

On the Possible Coding Principles of DNA & I Ching Sctfc GOD Joural May 015 Volum 6 Issu 4 pp. 161-166 Hu, H. & Wu, M., O th Possbl Codg Prcpls of DNA & I Chg 161 O th Possbl Codg Prcpls of DNA & I Chg Hupg Hu * & Maox Wu Rvw Artcl ABSTRACT I ths rvw artcl,

More information

Triple Play: From De Morgan to Stirling To Euler to Maclaurin to Stirling

Triple Play: From De Morgan to Stirling To Euler to Maclaurin to Stirling Tripl Play: From D Morga to Stirlig To Eulr to Maclauri to Stirlig Augustus D Morga (186-1871) was a sigificat Victoria Mathmaticia who mad cotributios to Mathmatics History, Mathmatical Rcratios, Mathmatical

More information

Notation for Mixed Models for Finite Populations

Notation for Mixed Models for Finite Populations 30- otato for d odl for Ft Populato Smpl Populato Ut ad Rpo,..., Ut Labl for,..., Epctd Rpo (ovr rplcatd maurmt for,..., Rgro varabl (Luz r for,...,,,..., p Aular varabl for ut (Wu z μ for,...,,,..., p

More information

Priority Search Trees - Part I

Priority Search Trees - Part I .S. 252 Pro. Rorto Taassa oputatoal otry S., 1992 1993 Ltur 9 at: ar 8, 1993 Sr: a Q ol aro Prorty Sar Trs - Part 1 trouto t last ltur, w loo at trval trs. or trval pot losur prols, ty us lar spa a optal

More information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu

More information

Graphs of q-exponentials and q-trigonometric functions

Graphs of q-exponentials and q-trigonometric functions Grahs of -otals ad -trgoomtrc fuctos Amla Carola Saravga To ct ths vrso: Amla Carola Saravga. Grahs of -otals ad -trgoomtrc fuctos. 26. HAL Id: hal-377262 htts://hal.archvs-ouvrts.fr/hal-377262

More information

They must have different numbers of electrons orbiting their nuclei. They must have the same number of neutrons in their nuclei.

They must have different numbers of electrons orbiting their nuclei. They must have the same number of neutrons in their nuclei. 37 1 How may utros ar i a uclus of th uclid l? 20 37 54 2 crtai lmt has svral isotops. Which statmt about ths isotops is corrct? Thy must hav diffrt umbrs of lctros orbitig thir ucli. Thy must hav th sam

More information

Linear Algebra. Definition The inverse of an n by n matrix A is an n by n matrix B where, Properties of Matrix Inverse. Minors and cofactors

Linear Algebra. Definition The inverse of an n by n matrix A is an n by n matrix B where, Properties of Matrix Inverse. Minors and cofactors Dfnton Th nvr of an n by n atrx A an n by n atrx B whr, Not: nar Algbra Matrx Invron atrc on t hav an nvr. If a atrx ha an nvr, thn t call. Proprt of Matrx Invr. If A an nvrtbl atrx thn t nvr unqu.. (A

More information

P a g e 3 6 of R e p o r t P B 4 / 0 9

P a g e 3 6 of R e p o r t P B 4 / 0 9 P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J

More information

and one unit cell contains 8 silicon atoms. The atomic density of silicon is

and one unit cell contains 8 silicon atoms. The atomic density of silicon is Chaptr Vsualzato o th Slo Crystal (a) Plas rr to Fgur - Th 8 orr atoms ar shar by 8 ut lls a thror otrbut atom Smlarly, th 6 a atoms ar ah shar by ut lls a otrbut atoms A, 4 atoms ar loat s th ut ll H,

More information

On Ranking of Alternatives in Uncertain Group Decision Making Model

On Ranking of Alternatives in Uncertain Group Decision Making Model H COMPUING SCINC AND CHNOLOGY INRNAIONAL JOURNAL VOL NO 3 Aprl 22 ISSN (Prt) 262-66 ISSN (Ol) 262-687 Publhd ol Aprl 22 (http://wwwrarchpuborg/oural/c) 3 O Rag of Altratv Ucrta Group Dco Mag Modl Chao

More information

ON THE LOGARITHMIC INTEGRAL

ON THE LOGARITHMIC INTEGRAL Hacettepe Joural of Mathematcs ad Statstcs Volume 39(3) (21), 393 41 ON THE LOGARITHMIC INTEGRAL Bra Fsher ad Bljaa Jolevska-Tueska Receved 29:9 :29 : Accepted 2 :3 :21 Abstract The logarthmc tegral l(x)

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots

More information

signal amplification; design of digital logic; memory circuits

signal amplification; design of digital logic; memory circuits hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll

More information

Generalized Linear Regression with Regularization

Generalized Linear Regression with Regularization Geeralze Lear Regresso wth Regularzato Zoya Bylsk March 3, 05 BASIC REGRESSION PROBLEM Note: I the followg otes I wll make explct what s a vector a what s a scalar usg vec t or otato, to avo cofuso betwee

More information

C.11 Bang-bang Control

C.11 Bang-bang Control Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of

More information

The Matrix Exponential

The Matrix Exponential Th Matrix Exponntial (with xrciss) by Dan Klain Vrsion 28928 Corrctions and commnts ar wlcom Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial of A to b th matrix () A A k I + A + k!

More information

A Simple Representation of the Weighted Non-Central Chi-Square Distribution

A Simple Representation of the Weighted Non-Central Chi-Square Distribution SSN: 9-875 raoa Joura o ovav Rarch Scc grg a Tchoogy (A S 97: 7 Cr rgaao) Vo u 9 Sbr A S Rrao o h Wgh No-Cra Ch-Squar Drbuo Dr ay A hry Dr Sahar A brah Dr Ya Y Aba Proor D o Mahaca Sac u o Saca Su a Rarch

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables Rajh gh Dparm of ac,baara Hdu Uvr(U.P.), Ida Pakaj Chauha, rmala awa chool of ac, DAVV, Idor (M.P.), Ida Flor maradach Dparm of Mahmac, Uvr of w Mco, Gallup, UA Improvd Epoal Emaor for Populao Varac Ug

More information

FOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of

FOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of Do Bgyoko () FOURIER SERIES I. INTRODUCTION Srs psos r ubqutous too o scc d grg. Th kds o pso to utz dpd o () th proprts o th uctos to b studd d (b) th proprts or chrctrstcs o th systm udr vstgto. Powr

More information

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS GENERLIZTIONS OF CEV S THEOREM ND PPLICTIONS Floret Smaradache Uversty of New Mexco 200 College Road Gallup, NM 87301, US E-mal: smarad@um.edu I these paragraphs oe presets three geeralzatos of the famous

More information

Review Exam II Complex Analysis

Review Exam II Complex Analysis Revew Exam II Complex Aalyss Uderled Propostos or Theorems: Proofs May Be Asked for o Exam Chapter 3. Ifte Seres Defto: Covergece Defto: Absolute Covergece Proposto. Absolute Covergece mples Covergece

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I lear regreo, we coder the frequecy dtrbuto of oe varable (Y) at each of everal level of a ecod varable (X). Y kow a the depedet varable. The

More information