INTRODUCTION TO INERTIAL CONFINEMENT FUSION

Size: px
Start display at page:

Download "INTRODUCTION TO INERTIAL CONFINEMENT FUSION"

Transcription

1 INRODUCION O INERIAL CONFINEMEN FUSION R. Bett Lecture 1 Formula or hot pot temperature Reved dyamc model ad gto codto Etropy

2 he ormula below wa derved Lecture 9. It repreet the maxmum value o the cetral (r=) hot pot temperature MAX.81 κ /7 ( ρ ) /7 6/7 h Subttute cotat to d value o temperature Sptzer coducto or lλ=5 κ = κ = = 5/ 69 5/ Joule = (1.6 1 ) = m MAX 69 5/ 16 5/ 3 5/ 1 1 kev kev Maxmum cetral temperature ( ke ) 6.4 6/7 / ( ) /7 km ρ g / cm 3

3 he eutro averaged temperature the temperature averaged over the uo rate. It the temperature erred by the uclear dagotc expermet dtd σv dtd σv 4 4 = Yeld dtd σ v 4 Neutro averaged temperature dtp() t dtp() t σ v d σ v d

4 Ue the depedece o reactvty (vald betwee 8-3ke) Deto σ v = () () dtp t ds dtp t d = S dtp() t ds dtp() t () t 1 /5 (1 x ) = = 1.15x d () t ()3 t x dx.7 () t () t dtp() t ds.7 dtp() t () t () t dtp() t ds dtp() t () t =

5 Ue the tme depedet varable ((t), P(t), R(t) or olume(t)) derved Lecture 8 ad 9 ˆ () = /7 t 7 ˆ ˆ t ˆ π tˆ + (1 t ) Arc a[ t ] 5/ ˆ ˆ Pt ( ) (1 + t ) ˆ ˆ 3/ t ( ) (1 + t) MAX.7 dtp() t () t () t.47 =.6 MAX dtp() t () t Neutro averaged temperature or ( ke ) 3.8 ρ /7 km/ ( ) g / cm σ v = S 3 6/7.78

6 Ue the 3 depedece o reactvty vald betwee 3-8ke σ v = S 3 1 /5 (1 x ) = = 1.15x d () t ()3 t x dx.53 () t () t dtp() t dtp() t d d 1 /5 (1 x ) d = () t ()3 t x dx =.7 () t () t 1.15x.53 dtp( t) ( t) ( t).53 =.68 MAX.7 dtp() t () t () t 3 Neutro averaged temperature or σ v = S km/ /7 ( ke ) 4.35( ρ ) g / cm 3 6/7

7 Ue the 4 depedece o reactvty vald below 3ke σ v = d S 4 =.43 ( t) ( t) 3 3 d =.53 ( t) ( t) dtp() t 3 dtp() t 3.43 dtp( t) ( t) ( t).58 =.74 MAX.53 dtp( t) ( t) ( t) Neutro averaged temperature or ( ke ) 4.76 ρ /7 km/ ( ) g / cm σ v = S 3 6/7 4 d d

8 he mot relevat rage or curret ICF expermet 3-8ke. We wll ue the 3 depedece σ v = ρ =.19 g / cm = 37 / S Ue the 3 depedece ormula to etmate emperature or OMEGA mploo km km/ ( ) /7 ( ke ) 4.35 ρ 3.ke g / cm = 3 6/7 3 ρ =.16 g / cm = 45 / km km/ ( ) /7 ( ke ) 4.35 ρ 3.7ke g / cm = 3 6/7

9 Alo the areal dety ote meaured ug the eutro pectrum. Neutro Averaged Areal Dety. Same average a temperature ρ ( ρ ) dt d ρ ( ) 4 Yeld σv dtp() t d dtp() t () t () t ρ = h hell approx ( ρ ) ( ρ ) dtp() t d dtp() t () t () t ( ρ ) M h = 4 π Rt () Rt ˆ() From Lecture 8 σ v = ˆ ˆ Rt () = 1+ t S 3 ρ =.88( ρ ) Neutro Averaged Areal Dety

10 Moded Dyamc Model cludg emperature ad µ depedece o reactvty M h d R = 4 dt π RP Shell mometum σ v = S µ 5 ε η 5 d ( PR ) S P R Hot Spot Eergy η = µ dt 4 d P 3 5/ R.86κ R dt 1 /5 (1 ) () ()3 x η η η η ds = t t x dxs = () t () t S 1.15x 1 /5 η (1 x ) S = 3S x dx 1.15x Hot Spot emperature

11 We have already olved th model wthout alpha ad oud the o-alpha tagato value. Ue thoe to rewrte dmeole parameter Rˆ R ˆ t = t = Pˆ R o R P = ˆ P o New dmeole model ˆ d R dtˆ = ˆ ˆ RP d ( PR ˆˆ5) ˆ ˆ5 ξ ˆ P R dtˆ η ξ New parameter ξ P R ε S ( ) 4 η ˆ ˆ 3 ˆ 5/ ˆ d P R R dtˆ ˆ

12 Same oluto a beore: d crtcal value o ξ that lead to a gular oluto χ ( ) ξo o R ε S P 1 crt crt ξ = 4ξ η Igto codto P From Lecture 8 From Lecture 9 χ R ( ρ ) = h o 1 κ ( ) /7 6/7 ρ h /7 /7.86 New Igto codto term o areal dety ad mploo velocty ξ ε S 1 = η + 7 ( ) 7 o η ρ crt h η/7 crt η/7 ξo 4κ ξo.86

13 Rewrte gto codto ug temperature tead o velocty =.86 1/3 7/6 1/3 κ ( ) ( ρ ) 1/3 h ξ ε S /3.95 ( ) ( ) 7/6 + η χo = κ ρ > 1 ξ 1/3 crt crt h 4ξo Replace velocty wth t ucto o temperature From Phyc Plama 17, 581 (1) η = 1.1 Rewrte gto codto S m ke New Igto codto term o areal dety ad hot pot temperature ξ ε S /3.95 ( ) ( χ ).18 κ ρ 1 ξ = 1/3 crt crt h 4ξo

14 χ Rewrte ug eutro averaged quatte.53 /3.18 ξ ε S o 1/3 ρ κ crt crt ξo 4ξ χ =.95 1 ξ 1. ρ crt ξo g / cm ρ =.88( ρ ) /3 o ke 4.18 New gto codto wth eutro averaged areal dety ad temperature ρ g / cm Igto Rego the curret ICF program amg at ke 4ke ρ 1 g / cm ke

15 Areal dety v emperature: IGNIION PLOS Smulato (LILAC) From Phyc Plama 17, 581 (1) Our model ρ g / cm Igto Ft o mulato ke

16 1D Dyamc o a mplodg hell: Etropy Shock Wave

17 What the etropy o a deal ga/plama? he etropy S a property o a ga jut lke P, ad ρ p S cv l cot = c / 3 ρ l [ ] p ρ = = cot 5 v 5/ 3 We call the adabat. It ha othg to do wth alpha partcle or alpha heatg. cv the pecc heat at cotat volume For a deal ga c v = 3(1 + Z) m

18 Startg rom ma, mometum ad eergy coervato or deal ga ρ + ρu = t u ρ + u u = P+ µ u t vcoty ε + u + P = + w w t ( ε ) κ rad Heat coducto Source ad k 3 1 ε = P+ ρu Oe ca combe the above equato to how that (try to do th): S DS u κ ρ u S ρ µ + = = + + ource/k t Dt

19 he etropy/adabat S/ chage through dpato or heat ource or k ρ S DS u κ u S µ + = = + + ource/k t Dt I a deal ga (o dpato o vcoty ad o heat coducto) ad wthout ource ad k, the etropy/adabat a cotat o moto o each lud elemet DS Dt = S, = cot p ~ ρ 5/3

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013 ECE 595, Secto 0 Numercal Smulatos Lecture 9: FEM for Electroc Trasport Prof. Peter Bermel February, 03 Outle Recap from Wedesday Physcs-based devce modelg Electroc trasport theory FEM electroc trasport

More information

Where are we with laser fusion?

Where are we with laser fusion? Where are we with laser fusion? R. Betti Laboratory for Laser Energetics Fusion Science Center Dept. Mechanical Engineering and Physics & Astronomy University of Rochester HEDSA HEDP Summer School August

More information

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4 CHAPTER Smple Lear Regreo EXAMPLE A expermet volvg fve ubject coducted to determe the relatohp betwee the percetage of a certa drug the bloodtream ad the legth of tme t take the ubject to react to a tmulu.

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

Simple Linear Regression Analysis

Simple Linear Regression Analysis LINEAR REGREION ANALYSIS MODULE II Lecture - 5 Smple Lear Regreo Aaly Dr Shalabh Departmet of Mathematc Stattc Ida Ittute of Techology Kapur Jot cofdece rego for A jot cofdece rego for ca alo be foud Such

More information

CHAPTER 9 LINEAR MOMENTUM, IMPULSE AND COLLISIONS

CHAPTER 9 LINEAR MOMENTUM, IMPULSE AND COLLISIONS CHAPTER 9 LINEAR MOMENTUM, IMPULSE AND COLLISIONS 103 Phy 1 9.1 Lnear Momentum The prncple o energy conervaton can be ued to olve problem that are harder to olve jut ung Newton law. It ued to decrbe moton

More information

( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise

( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise OISE Thermal oe ktb (T abolute temperature kelv, B badwdth, k Boltzama cotat) 3 k.38 0 joule / kelv ( joule /ecod watt) ( ) v ( freq) 4kTB Thermal oe refer to the ketc eergy of a body of partcle a a reult

More information

PHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10,

PHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10, PHYS Look over Chapter 9 Sectos - Eamples:, 4, 5, 6, 7, 8, 9, 0, PHYS Look over Chapter 7 Sectos -8 8, 0 eamples, 3, 4, 6, 7, 8,9, 0 ad How To ake Phscs Pa We wll ow look at a wa of calculatg where the

More information

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law Module : The equato of cotuty Lecture 5: Coservato of Mass for each speces & Fck s Law NPTEL, IIT Kharagpur, Prof. Sakat Chakraborty, Departmet of Chemcal Egeerg 2 Basc Deftos I Mass Trasfer, we usually

More information

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department Mapulator Dyamcs mrkabr Uversty of echology omputer Egeerg formato echology Departmet troducto obot arm dyamcs deals wth the mathematcal formulatos of the equatos of robot arm moto. hey are useful as:

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

Lecture Notes Types of economic variables

Lecture Notes Types of economic variables Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte

More information

Problem Free Expansion of Ideal Gas

Problem Free Expansion of Ideal Gas Problem 4.3 Free Expanon o Ideal Ga In general: ds ds du P dv P dv NR V dn Snce U o deal ga ndependent on olume (du=), and N = cont n the proce: dv In a ere o nntemal ree expanon, entropy change by: S

More information

ICF ignition and the Lawson criterion

ICF ignition and the Lawson criterion ICF ignition and the Lawson criterion Riccardo Betti Fusion Science Center Laboratory for Laser Energetics, University of Rochester Seminar Massachusetts Institute of Technology, January 0, 010, Cambridge

More information

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled

More information

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA THE ROYAL STATISTICAL SOCIETY 3 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER I STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad

More information

Unit 9 Review Outline Nuclear Chemistry

Unit 9 Review Outline Nuclear Chemistry Ut 9 Revew Outle Nuclear Chemstry He e - - e p. Nuclear Chemstry/Nuclear Reacto: a. process whch the ucleus o the atom s altered. b. Ths s NOT a regular chemcal process. It ollows deret rules.. Nuclear

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

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model ECON 48 / WH Hog The Smple Regresso Model. Defto of the Smple Regresso Model Smple Regresso Model Expla varable y terms of varable x y = β + β x+ u y : depedet varable, explaed varable, respose varable,

More information

Temperature Memory Effect in Amorphous Shape Memory Polymers. Kai Yu 1, H. Jerry Qi 1, *

Temperature Memory Effect in Amorphous Shape Memory Polymers. Kai Yu 1, H. Jerry Qi 1, * Electroc Supplemetary Materal (ESI) for Soft Matter. h joural he Royal Socety of Chemtry 214 Supplemetary Materal for: emperature Memory Effect Amorphou Shape Memory Polymer Ka Yu 1, H. Jerry Q 1, * 1

More information

Simulation Output Analysis

Simulation Output Analysis Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5

More information

DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS

DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS Course Project: Classcal Mechacs (PHY 40) Suja Dabholkar (Y430) Sul Yeshwath (Y444). Itroducto Hamltoa mechacs s geometry phase space. It deals

More information

4. Standard Regression Model and Spatial Dependence Tests

4. Standard Regression Model and Spatial Dependence Tests 4. Stadard Regresso Model ad Spatal Depedece Tests Stadard regresso aalss fals the presece of spatal effects. I case of spatal depedeces ad/or spatal heterogeet a stadard regresso model wll be msspecfed.

More information

( ) Two-Dimensional Experimental Kinematics. Notes_05_02 1 of 9. Digitize locations of landmarks { r } Pk

( ) Two-Dimensional Experimental Kinematics. Notes_05_02 1 of 9. Digitize locations of landmarks { r } Pk Notes_05_0 o 9 Two-Dmesoal Expermetal Kematcs Dgtze locatos o ladmarks { r } o body or pots to at gve tme t ll pots must be attached to body Use ladmark weghtg actor = pot k s avalable at tme t. Use =

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

The E vs k diagrams are in general a function of the k -space direction in a crystal

The E vs k diagrams are in general a function of the k -space direction in a crystal vs dagram p m m he parameter s called the crystal mometum ad s a parameter that results from applyg Schrödger wave equato to a sgle-crystal lattce. lectros travelg dfferet drectos ecouter dfferet potetal

More information

Module 7. Lecture 7: Statistical parameter estimation

Module 7. Lecture 7: Statistical parameter estimation Lecture 7: Statstcal parameter estmato Parameter Estmato Methods of Parameter Estmato 1) Method of Matchg Pots ) Method of Momets 3) Mamum Lkelhood method Populato Parameter Sample Parameter Ubased estmato

More information

Diode DC Non-ideal Characteristics

Diode DC Non-ideal Characteristics Dode DC No-deal Characterstcs - e qv/kt V reverse curret ot saturated (geerato the deleto rego) dode breakdow 2 3 recombato the deleto rego l( ) 5 hgh-level jecto of morty carrers l( ) sloeq/ηkt V η η2

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

Chapter Newton-Raphson Method of Solving Simultaneous Nonlinear Equations

Chapter Newton-Raphson Method of Solving Simultaneous Nonlinear Equations Chapter 7 Newto-Rapho Method o Solg Smltaeo Nolear Eqato Ater readg th chapter o hold be able to: dere the Newto-Rapho method ormla or mltaeo olear eqato deelop the algorthm o the Newto-Rapho method or

More information

The Quantum X-ray Compton Free Electron Laser

The Quantum X-ray Compton Free Electron Laser The Quatum X-ray Compto Free Electro Laer Kazuha NAKAJIMA Hgh Eergy Accelerator Reearch Orgazato (KEK) 1-1 Oho, Tukuba, Ibarak, 5-81 Japa Igor V. SMETANIN P. N. Lebedev Phyc Ittute Lek propect 5, Mocow,

More information

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE THE ROYAL STATISTICAL SOCIETY 00 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for the

More information

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix Assgmet 7/MATH 47/Wter, 00 Due: Frday, March 9 Powers o a square matrx Gve a square matrx A, ts powers A or large, or eve arbtrary, teger expoets ca be calculated by dagoalzg A -- that s possble (!) Namely,

More information

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder Collapg to Saple ad Reader Mea Ed Staek Collapg to Saple ad Reader Average order to collape the expaded rado varable to weghted aple ad reader average, we pre-ultpled by ( M C C ( ( M C ( M M M ( M M M,

More information

1 Lyapunov Stability Theory

1 Lyapunov Stability Theory Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may

More information

BASIC PRINCIPLES OF STATISTICS

BASIC PRINCIPLES OF STATISTICS BASIC PRINCIPLES OF STATISTICS PROBABILITY DENSITY DISTRIBUTIONS DISCRETE VARIABLES BINOMIAL DISTRIBUTION ~ B 0 0 umber of successes trals Pr E [ ] Var[ ] ; BINOMIAL DISTRIBUTION B7 0. B30 0.3 B50 0.5

More information

LECTURE 8: Topics in Chaos Ricker Equation. Period doubling bifurcation. Period doubling cascade. A Quadratic Equation Ricker Equation 1.0. x x 4 0.

LECTURE 8: Topics in Chaos Ricker Equation. Period doubling bifurcation. Period doubling cascade. A Quadratic Equation Ricker Equation 1.0. x x 4 0. LECTURE 8: Topcs Chaos Rcker Equato (t ) = (t ) ep( (t )) Perod doulg urcato Perod doulg cascade 9....... A Quadratc Equato Rcker Equato (t ) = (t ) ( (t ) ). (t ) = (t ) ep( (t )) 6. 9 9. The perod doulg

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

KR20 & Coefficient Alpha Their equivalence for binary scored items

KR20 & Coefficient Alpha Their equivalence for binary scored items KR0 & Coeffcet Alpha Ther equvalece for bary cored tem Jue, 007 http://www.pbarrett.et/techpaper/r0.pdf f of 7 Iteral Cotecy Relablty for Dchotomou Item KR 0 & Alpha There apparet cofuo wth ome dvdual

More information

Statistical modelling and latent variables (2)

Statistical modelling and latent variables (2) Statstcal modellg ad latet varables (2 Mxg latet varables ad parameters statstcal erece Trod Reta (Dvso o statstcs ad surace mathematcs, Departmet o Mathematcs, Uversty o Oslo State spaces We typcally

More information

ELECTRON HEATING IN THE CONDUCTION BAND OF INSULATORS UNDER FEMTOSECOND LASER PULSE IRRADIATION

ELECTRON HEATING IN THE CONDUCTION BAND OF INSULATORS UNDER FEMTOSECOND LASER PULSE IRRADIATION LCTRON HATING IN TH CONDUCTION BAND OF INSULATORS UNDR FMTOSCOND LASR PULS IRRADIATION Ilya Bogatyrev H. Bacau A.N. Belsy I.B. Bogatyrev J. Gaud G. Geoffroy S. Guzard P. Mart Yu.V. Popov A.N. Vasl ev B.N.

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

8 The independence problem

8 The independence problem Noparam Stat 46/55 Jame Kwo 8 The depedece problem 8.. Example (Tua qualty) ## Hollader & Wolfe (973), p. 87f. ## Aemet of tua qualty. We compare the Huter L meaure of ## lghte to the average of coumer

More information

UNIVERSITY OF CALIFORNIA, BERKELEY DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES. Midterm I

UNIVERSITY OF CALIFORNIA, BERKELEY DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES. Midterm I UNIVERSITY OF CALIFORNIA, BERKELEY EPARTMENT OF ELECTRICAL ENGINEERING AN COMPUTER SCIENCES EECS 130 Professor Chemg Hu Fall 009 Mdterm I Name: Closed book. Oe sheet of otes s allowed. There are 8 pages

More information

Application of Laplace Adomian Padé approximant to solve exponential stretching sheet problem in fluid mechanics

Application of Laplace Adomian Padé approximant to solve exponential stretching sheet problem in fluid mechanics Joural o Novel ppled Scece valable ole at wwwacorg JNS Joural---S/- ISSN - JNS pplcato o aplace doma Padé approxmat to olve expoetal tretchg heet problem lud mechac Hahoe ad Soltaalzadeh Departmet o Mathematc

More information

We have already referred to a certain reaction, which takes place at high temperature after rich combustion.

We have already referred to a certain reaction, which takes place at high temperature after rich combustion. ME 41 Day 13 Topcs Chemcal Equlbrum - Theory Chemcal Equlbrum Example #1 Equlbrum Costats Chemcal Equlbrum Example #2 Chemcal Equlbrum of Hot Bured Gas 1. Chemcal Equlbrum We have already referred to a

More information

1. Linear second-order circuits

1. Linear second-order circuits ear eco-orer crcut Sere R crcut Dyamc crcut cotag two capactor or two uctor or oe uctor a oe capactor are calle the eco orer crcut At frt we coer a pecal cla of the eco-orer crcut, amely a ere coecto of

More information

CS475 Parallel Programming

CS475 Parallel Programming CS475 Parallel Programmg Deretato ad Itegrato Wm Bohm Colorado State Uversty Ecept as otherwse oted, the cotet o ths presetato s lcesed uder the Creatve Commos Attrbuto.5 lcese. Pheomea Physcs: heat, low,

More information

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14)

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14) Quz - Lear Regreo Aaly (Baed o Lecture -4). I the mple lear regreo model y = β + βx + ε, wth Tme: Hour Ε ε = Ε ε = ( ) 3, ( ), =,,...,, the ubaed drect leat quare etmator ˆβ ad ˆβ of β ad β repectvely,

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

Physics 114 Exam 2 Fall Name:

Physics 114 Exam 2 Fall Name: Physcs 114 Exam Fall 015 Name: For gradg purposes (do ot wrte here): Questo 1. 1... 3. 3. Problem Aswer each of the followg questos. Pots for each questo are dcated red. Uless otherwse dcated, the amout

More information

Born-Oppenheimer Approximation. Kaito Takahashi

Born-Oppenheimer Approximation. Kaito Takahashi o-oppehee ppoato Kato Takahah toc Ut Fo quatu yte uch a ecto ad olecule t eae to ue ut that ft the=tomc UNT Ue a of ecto (ot kg) Ue chage of ecto (ot coulob) Ue hba fo agula oetu (ot kg - ) Ue 4pe 0 fo

More information

11. Ideal Gas Mixture

11. Ideal Gas Mixture . Ideal Ga xture. Geeral oderato ad xture of Ideal Gae For a geeral xture of N opoet, ea a pure ubtae [kg ] te a for ea opoet. [kol ] te uber of ole for ea opoet. e al a ( ) [kg ] N e al uber of ole (

More information

First Law of Thermodynamics

First Law of Thermodynamics Cocept o Iteral Eergy, U Iteral eergy s the sum o the ketc ad potetal eerges o the partcles that make up the system. Frst Law o Thermodyamcs Chapter Coservato o Eergy At molecular level, cotrbutors to

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

Applying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i

Applying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i CHEMICAL EQUILIBRIA The Thermodyamc Equlbrum Costat Cosder a reversble reacto of the type 1 A 1 + 2 A 2 + W m A m + m+1 A m+1 + Assgg postve values to the stochometrc coeffcets o the rght had sde ad egatve

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

Dopant Compensation. Lecture 2. Carrier Drift. Types of Charge in a Semiconductor

Dopant Compensation. Lecture 2. Carrier Drift. Types of Charge in a Semiconductor Lecture OUTLIE Bc Semcoductor Phycs (cot d) rrer d uo P ucto odes Electrosttcs ctce ot omesto tye semcoductor c be coverted to P tye mterl by couter dog t wth ccetors such tht >. comested semcoductor mterl

More information

Introduction. Free Electron Fermi Gas. Energy Levels in One Dimension

Introduction. Free Electron Fermi Gas. Energy Levels in One Dimension ree Electro er Gas Eergy Levels Oe Deso Effect of eperature o the er-drac Dstrbuto ree Electro Gas hree Desos Heat Capacty of the Electro Gas Electrcal Coductvty ad Oh s Law Moto Magetc elds heral Coductvty

More information

Chapter 14 Logistic Regression Models

Chapter 14 Logistic Regression Models Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

THE ROYAL STATISTICAL SOCIETY 2009 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULAR FORMAT MODULE 2 STATISTICAL INFERENCE

THE ROYAL STATISTICAL SOCIETY 2009 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULAR FORMAT MODULE 2 STATISTICAL INFERENCE THE ROYAL STATISTICAL SOCIETY 009 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULAR FORMAT MODULE STATISTICAL INFERENCE The Socety provdes these solutos to assst caddates preparg for the examatos future

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

reactions / Fission Classification of nuclear reactions

reactions / Fission Classification of nuclear reactions Classiicatio o uclear reactios reactios / Fissio Geeralities Liquid-drop picture Chai reactios Mass distributio Fissio barrier Double issio barrier Ater the scissio poit Time scale i issio Neutro-iduced

More information

( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices.

( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices. 4.3 - Modal Aalyss Physcal coordates are ot always the easest to work Egevectors provde a coveet trasformato to modal coordates Modal coordates are lear combato of physcal coordates Say we have physcal

More information

Spreadsheet Problem Solving

Spreadsheet Problem Solving 1550 1500 CO Emmssos for the US, 1989 000 Class meetg #6 Moday, Sept 14 th CO Emssos (MMT Carbo) y = 1.3x 41090.17 1450 1400 1350 1300 1989 1990 1991 199 1993 1994 1995 1996 1997 1998 1999 000 Year GEEN

More information

16 Homework lecture 16

16 Homework lecture 16 Quees College, CUNY, Departmet of Computer Scece Numercal Methods CSCI 361 / 761 Fall 2018 Istructor: Dr. Sateesh Mae c Sateesh R. Mae 2018 16 Homework lecture 16 Please emal your soluto, as a fle attachmet,

More information

LINEAR REGRESSION ANALYSIS

LINEAR REGRESSION ANALYSIS LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for

More information

CLASS NOTES. for. PBAF 528: Quantitative Methods II SPRING Instructor: Jean Swanson. Daniel J. Evans School of Public Affairs

CLASS NOTES. for. PBAF 528: Quantitative Methods II SPRING Instructor: Jean Swanson. Daniel J. Evans School of Public Affairs CLASS NOTES for PBAF 58: Quattatve Methods II SPRING 005 Istructor: Jea Swaso Dael J. Evas School of Publc Affars Uversty of Washgto Ackowledgemet: The structor wshes to thak Rachel Klet, Assstat Professor,

More information

ENGI 3423 Simple Linear Regression Page 12-01

ENGI 3423 Simple Linear Regression Page 12-01 ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

Simple Linear Regression. How To Study Relation Between Two Quantitative Variables? Scatter Plot. Pearson s Sample Correlation.

Simple Linear Regression. How To Study Relation Between Two Quantitative Variables? Scatter Plot. Pearson s Sample Correlation. Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6. A Smple Regreo Problem I there relato betwee umber of power boat the area ad umber of maatee klled? Year NPB( )

More information

A Helmholtz energy equation of state for calculating the thermodynamic properties of fluid mixtures

A Helmholtz energy equation of state for calculating the thermodynamic properties of fluid mixtures A Helmholtz eergy equato of state for calculatg the thermodyamc propertes of flud mxtures Erc W. Lemmo, Reer Tller-Roth Abstract New Approach based o hghly accurate EOS for the pure compoets combed at

More information

,...R) where r = H (1.4) + Tn + Vof. etic energy terms are: here. ZA ZB Vee = & Vnn = (1.6) (1.4) H = Te + Tn + Ven + Vee + Vnn. i A r i.

,...R) where r = H (1.4) + Tn + Vof. etic energy terms are: here. ZA ZB Vee = & Vnn = (1.6) (1.4) H = Te + Tn + Ven + Vee + Vnn. i A r i. where r H r, r (r,,...r), r, RE R (r, RR),.., R represet theelectros. electro (.3) ad uclear coor,all,.ucle ates of ad all Itutvely, ollowgdates, SE: respectvely, ad H (r, R) E (r, R), we feel t are very

More information

On a Truncated Erlang Queuing System. with Bulk Arrivals, Balking and Reneging

On a Truncated Erlang Queuing System. with Bulk Arrivals, Balking and Reneging Appled Mathematcal Scece Vol. 3 9 o. 3 3-3 O a Trucated Erlag Queug Sytem wth Bul Arrval Balg ad Reegg M. S. El-aoumy ad M. M. Imal Departmet of Stattc Faculty Of ommerce Al- Azhar Uverty. Grl Brach Egypt

More information

COV. Violation of constant variance of ε i s but they are still independent. The error term (ε) is said to be heteroscedastic.

COV. Violation of constant variance of ε i s but they are still independent. The error term (ε) is said to be heteroscedastic. c Pogsa Porchawseskul, Faculty of Ecoomcs, Chulalogkor Uversty olato of costat varace of s but they are stll depedet. C,, he error term s sad to be heteroscedastc. c Pogsa Porchawseskul, Faculty of Ecoomcs,

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

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos

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

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design Authors: Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud Applcato of Calbrato Approach for Regresso Coeffcet Estmato uder Two-stage Samplg Desg Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud

More information

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1) Chapter 7 Fuctos o Bouded Varato. Subject: Real Aalyss Level: M.Sc. Source: Syed Gul Shah (Charma, Departmet o Mathematcs, US Sargodha Collected & Composed by: Atq ur Rehma (atq@mathcty.org, http://www.mathcty.org

More information

Statistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018

Statistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018 /3/08 Sstems & Bomedcal Egeerg Departmet SBE 304: Bo-Statstcs Smple Lear Regresso ad Correlato Dr. Ama Eldeb Fall 07 Descrptve Orgasg, summarsg & descrbg data Statstcs Correlatoal Relatoshps Iferetal Geeralsg

More information

CHAPTER 2. = y ˆ β x (.1022) So we can write

CHAPTER 2. = y ˆ β x (.1022) So we can write CHAPTER SOLUTIONS TO PROBLEMS. () Let y = GPA, x = ACT, ad = 8. The x = 5.875, y = 3.5, (x x )(y y ) = 5.85, ad (x x ) = 56.875. From equato (.9), we obta the slope as ˆβ = = 5.85/56.875., rouded to four

More information

Theory study about quarter-wave-stack dielectric mirrors

Theory study about quarter-wave-stack dielectric mirrors Theor tud about quarter-wave-tack delectrc rror Stratfed edu tratted reflected reflected Stratfed edu tratted cdet cdet T T Frt, coder a wave roagato a tratfed edu. A we kow, a arbtrarl olared lae wave

More information

1. MOS: Device Operation and Large Signal Model

1. MOS: Device Operation and Large Signal Model 1. MOS: ece Oerato ad arge Sgal Model Readg: Sedra & Smth Sec. 5.1-5.3 (S&S 5 th Ed: Sec. 4.1-4.3) ECE 10, Fall 011, F. Najmabad Oeratoal Bass of a Feld-Effect Trasstor (1) Cosder the hyothetcal semcoductor

More information

He-Burning in massive Stars

He-Burning in massive Stars He-Burning in massive Stars He-burning is ignited on the He and ashes of the preceding hydrogen burning phase! Most important reaction -triple alpha process 3 + 7.6 MeV Red Giant Evolution in HR diagram

More information

COMPUTATIONAL AND EXPERIMENTAL ESTIMATION OF BOUNDARY CONDITIONS FOR A FLAT SPECIMEN

COMPUTATIONAL AND EXPERIMENTAL ESTIMATION OF BOUNDARY CONDITIONS FOR A FLAT SPECIMEN COPUTATIOAL AD EXPERIETAL ESTIATIO OF BOUDARY CODITIOS FOR A FLAT SPECIE S. Abboud*, E. Artoukhe**, H. Rad* * LERPS, EA 171, Ittut Polytechque de Sévea B.P. 449-91 Belort Cedex, Frace Poe : (33) 3 84 58

More information

HOOKE'S LAW. THE RATE OR SPRING CONSTANT k.

HOOKE'S LAW. THE RATE OR SPRING CONSTANT k. Practces Group Sesso Date Phscs Departmet Mechacs Laborator Studets who made the practce Stamp cotrol Deadle Date HOOKE'S LAW. THE RATE OR SPRING CONSTANT k. IMPORTANT: Iclude uts ad errors all measuremets

More information

r y Simple Linear Regression How To Study Relation Between Two Quantitative Variables? Scatter Plot Pearson s Sample Correlation Correlation

r y Simple Linear Regression How To Study Relation Between Two Quantitative Variables? Scatter Plot Pearson s Sample Correlation Correlation Maatee Klled Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6.11 A Smple Regreo Problem 1 I there relato betwee umber of power boat the area ad umber of maatee klled?

More information

Solid State Device Fundamentals

Solid State Device Fundamentals Solid State Device Fudametals ENS 345 Lecture Course by Alexader M. Zaitsev alexader.zaitsev@csi.cuy.edu Tel: 718 982 2812 4N101b 1 Thermal motio of electros Average kietic eergy of electro or hole (thermal

More information

Continuous Distributions

Continuous Distributions 7//3 Cotuous Dstrbutos Radom Varables of the Cotuous Type Desty Curve Percet Desty fucto, f (x) A smooth curve that ft the dstrbuto 3 4 5 6 7 8 9 Test scores Desty Curve Percet Probablty Desty Fucto, f

More information

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

More information

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line HUR Techcal Report 000--9 verso.05 / Frak Borg (borgbros@ett.f) A Study of the Reproducblty of Measuremets wth HUR Leg Eteso/Curl Research Le A mportat property of measuremets s that the results should

More information

ADAPTIVE CLUSTER SAMPLING USING AUXILIARY VARIABLE

ADAPTIVE CLUSTER SAMPLING USING AUXILIARY VARIABLE Joural o Mathematcs ad tatstcs 9 (3): 49-55, 03 I: 549-3644 03 cece Publcatos do:0.3844/jmssp.03.49.55 Publshed Ole 9 (3) 03 (http://www.thescpub.com/jmss.toc) ADAPTIVE CLUTER AMPLIG UIG AUXILIARY VARIABLE

More information

EP2200 Queueing theory and teletraffic systems. Queueing networks. Viktoria Fodor KTH EES/LCN KTH EES/LCN

EP2200 Queueing theory and teletraffic systems. Queueing networks. Viktoria Fodor KTH EES/LCN KTH EES/LCN EP2200 Queueg theory ad teletraffc systems Queueg etworks Vktora Fodor Ope ad closed queug etworks Queug etwork: etwork of queug systems E.g. data packets traversg the etwork from router to router Ope

More information

Loop-independent dependence: dependence exists within an iteration; i.e., if the loop is removed, the dependence still exists.

Loop-independent dependence: dependence exists within an iteration; i.e., if the loop is removed, the dependence still exists. Loop-depedet vs. loop-carred depedeces [ 3.] Loop-carred depedece: depedece exsts across teratos;.e., f the loop s removed, the depedece o loger exsts. Loop-depedet depedece: depedece exsts wth a terato;.e.,

More information

Charged-Particle Spectra Using Particle Tracking on a Two-Dimensional Grid. P. B. Radha, J. A. Delettrez, R. Epstein, S. Skupsky, and J. M.

Charged-Particle Spectra Using Particle Tracking on a Two-Dimensional Grid. P. B. Radha, J. A. Delettrez, R. Epstein, S. Skupsky, and J. M. Charged-Particle Spectra Using Particle Tracking on a Two-Dimensional Grid P. B. Radha, J. A. Delettrez, R. Epstein, S. Skupsky, and J. M. Soures Laboratory for Laser Energetics, U. of Rochester S. Cremer

More information

Handout #4. Statistical Inference. Probability Theory. Data Generating Process (i.e., Probability distribution) Observed Data (i.e.

Handout #4. Statistical Inference. Probability Theory. Data Generating Process (i.e., Probability distribution) Observed Data (i.e. Hadout #4 Ttle: FAE Coure: Eco 368/01 Sprg/015 Itructor: Dr. I-Mg Chu Th hadout ummarze chapter 3~4 from the referece PE. Relevat readg (detaled oe) ca be foud chapter 6, 13, 14, 19, 3, ad 5 from MPS.

More information