economical way and of how to develop the process e.g. by new choice of catalyst. 1.1 Reactions: Thermodynamic control vs.

Size: px
Start display at page:

Download "economical way and of how to develop the process e.g. by new choice of catalyst. 1.1 Reactions: Thermodynamic control vs."

Transcription

1 Introduton Reaton knets s the part of physal hemstry whh seeks explanatons for the tme-dependent laws whh hemal reatons obey. Thermodynams gave us answers to the queston: What knd of reatons an happen n a reaton vessel f we know the amounts and physo-hemal propertes of the substanes n the vessel? Chemal knets s able to gve us the answer to the queston: How fast an the reatons, foretold by thermodynams, proeed at dfferent temperatures? In addton, hemal knets tres to gve good hnts to answer the queston: In whh way do the reatons really happen? Chemal knets s stll rather expermental sene. Wthout the results from well planned experments t s dffult to say anythng about the dynams of reatons. From ndustral pont of vew the results obtaned by hemal knet methods are extremely valuable. They are the bass of desgnng the heart of any hemal proess, the reator n whh hemal reatons are arred out n ndustral sale. We an defne three man goals for hemal engneerng reaton knets. Frstly, t s neessary to determne the tme dependeny of the hemal reaton of nterest. Ths means that we are seekng a mathematal expresson whh explans the degree of advanement of the reaton. Ths expresson s alled the reaton rate law n hemal knets. Seondly we seek for a smple relatonshp for the dependeny of the rate of reaton and temperature. For ths purpose we use Arrhenus equaton, whh adequately explans most of these needs. Thrdly, we are nterested n to have some dea of the mehansm of reaton. Although a detaled dvson of the total reaton to reaton steps s very demandng task we should stll try to model at least those reatons steps, whh essentally affet the rate law. Ths usually gves us both a good dea of how to run the overall reaton n the most

2 eonomal way and of how to develop the proess e.g. by new hoe of atalyst.. Reatons: Thermodynam ontrol vs. knet ontrol The man task of physal hemstry and espeally hemal thermodynams s to get an answer to the queston: To what extent does ertan hemal reaton or proess run before equlbrum s reahed and how temperature T, pressure p and the onentratons [A ] of the omponents n the reaton mxture nfluene the equlbrum state. The seond law of thermodynams deals wth the natural dreton of proesses and the queston whether a hemal reaton an our by tself. The rteron for spontaneous (natural) hange of the reaton gven by Eq. (.) aa + bb = dd + ee (.) an be mpled by Eq. (.2) d [ ] [ ] a [ A] [ B] ΔG = RT lnk ln D E e b 0 (.2) The general ondton for the equlbrum and the equlbrum onstant K of reaton (.) are gven by: o ΔG(T) = 0 and K (T) = exp( Δ G (T) / RT) (.3) At ertan values of T and p dfferent from the standard state temperature T 0 = 298 K and standard state pressure p 0 = 0 kpa we an obtan the equlbrum omposton for the reaton mxture from the value of equlbrum onstant K defned n Eq. (.4).

3 K d [ D] [ E] a [ A] [ B] e = b (equlbrum at T and p) (.4) Ths thermodynam expresson gves us a powerful tool for analyzng the reatons and proesses whh go to equlbrum but t has nothng to do wth tme. Knowledge of the exat value of the thermodynam equlbrum onstant, K gves us the possblty to alulate the maxmum possble yeld e.g. of NH 3 obtanable at any gven T and p from the reaton between N 2 and H 2. If, however, the rate of the reaton between N 2 and H 2 s too slow, the reaton wll not be eonomally feasble to be arred out beause equlbrum s not reahed wthn reasonable tme of reaton. In preparatve reatons of organ hemals several possble ompetng reatons an our and the relatve rates of these reatons nfluene the yeld of eah produt. Reaton rates are fundamental to funtonng of lvng organsms. Bologal atalysts (enzymes) ontrol the atvty of an organsm by seletvely speedng up ertan reatons and nhbtng others. Consequently, to understand and predt the behavor of hemal reatons and proesses one must onsder arefully both the thermodynam and knet lmtatons of the advanement of reatons. Reaton knets - hemal knets - s the study of the rates and mehansms of hemal reatons. The mehansm of the reaton s the sequene of elementary reatons (reaton steps) that add up and together gve the overall reaton. Mehansm s a hypothess about the elementary steps through whh the hemal reaton ours.

4 .2 The degree of the advanement of reaton Consder for example the homogeneous reaton presented n Eq. (.). It s assumed that the reaton ours n a losed system. In general we an present any hemal reaton by Eq. (.5) N 0= ν Y (.5) = 0 In ths equaton ν 's are the general stohometr numbers of omponents,.e. -a, -b, d, and e and Y are the hemal spees A, B, D, E nvolved n the reaton gven by Eq. (.). Note that the general stohometr numbers are postve for produts and negatve for reatants. The amount of reaton that has ourred wthn some perod of tme s expressed by the degree of the advanement (extent) of reaton, ξ # whh s defned by Eq. (.6) n = n +νξ # (.6) 0 Here n 0 s the amount of substane present ntally n the reaton mxture, and n s the amount of substane present at some later moment of tme. Sne n s expressed n moles and ν s a dmensonless quantty the advanement of the reaton ξ # s expressed n moles. The rate of reaton dξ # / for the reaton gven n Eq. (.) s defned as

5 dξ # dna dnb dnd dne = = = (.7) a b d e where n A and n B and respetvely n D and n E are the numbers of moles of reatants and produts. To nterpret the rate of reaton we need materal balane for the reaton mxture. From Eq. (.6) we obtan dn =νdξ # and dn d = ν ξ# (.8) Usually we use volume onentratons [mol/dm 3 ] n desrbng hemal knets and therefore we dvde the rate of reaton dξ # / by total the volume V of the reaton mxture. Now we an defne the rate of reaton r as follows: r # dξ dξ = = = V dn ν V (.9) In Eq. (.9) ξ = ξ # /V denotes the degree of the advanement of reaton per unt volume. In many (but not all) systems studed, the volume V s ether onstant or hanges by a neglgble amount durng the reaton. If V s onstant we have: d n V d = (.0) and thus e.g. for the reaton gven by Eq. (.) we obtan (when V = onstant):

6 r [ ] db [ ] dd [ ] de [ ] da = = = = (.) a b d e da In everyday language the quantty [ ] s often alled "the rate of reaton". Common unts used for r are: mol dm 3 s and kmol m 3 s. However, n ndustral proesses (espeally n proesses ontanng reatons between gases) the total volume of reaton mxture V hanges durng the reaton and Eq. (.2) must be used n areful analyss of reaton knets: r = d n V dn n = ν ν V ν V 2 dv (.2) For heterogeneous reatons we have to nlude n the rate law some parameter whh haraterzes the type of the heterogeneous reaton and makes the rate of reaton an ntensve property. Espeally for surfae reatons we ommonly use the defnton: r' = S dξ # (.3) where S s the surfae area. In heterogeneous atalyss ether the followng defnton r'' = W dξ # (.4)

7 where W s the mass of the atalyst or the defnton gven by Eq. (.5) where V s the total volume of atalyst s ommonly used r = V dξ # (.5)

Series Solutions of ODEs 2 the Frobenius method. The basic idea of the Frobenius method is to look for solutions of the form 3

Series Solutions of ODEs 2 the Frobenius method. The basic idea of the Frobenius method is to look for solutions of the form 3 Royal Holloway Unversty of London Department of Physs Seres Solutons of ODEs the Frobenus method Introduton to the Methodology The smple seres expanson method works for dfferental equatons whose solutons

More information

Modern Problem Solving Techniques in Engineering with POLYMATH, Excel and MATLAB. Introduction

Modern Problem Solving Techniques in Engineering with POLYMATH, Excel and MATLAB. Introduction Modern Problem Solvng Tehnques n Engneerng wth POLYMATH, Exel and MATLAB. Introduton Engneers are fundamentally problem solvers, seekng to aheve some objetve or desgn among tehnal, soal eonom, regulatory

More information

Use of Multi-attribute Utility Functions in Evaluating Security Systems

Use of Multi-attribute Utility Functions in Evaluating Security Systems LLNL-TR-405048 Use of Mult-attrbute Utlty Funtons n Evaluatng Seurty Systems C. Meyers, A. Lamont, A. Sherman June 30, 2008 Ths doument was prepared as an aount of work sponsored by an ageny of the Unted

More information

Figure 1. Inventory Level vs. Time - EOQ Problem

Figure 1. Inventory Level vs. Time - EOQ Problem IEOR 54 Sprng, 009 rof Leahman otes on Eonom Lot Shedulng and Eonom Rotaton Cyles he Eonom Order Quantty (EOQ) Consder an nventory tem n solaton wth demand rate, holdng ost h per unt per unt tme, and replenshment

More information

Chapter 6. Demand Relationships Among Goods

Chapter 6. Demand Relationships Among Goods Chapter 6 Demand Relatonshps Among Goods Up to ths pont, we have held the pre of other goods onstant. Now we onsder how hanges n p affet n a two-good world. I p I p I p I p p p ( ) ( ) then I p then (

More information

DEFINING %COMPLETE IN MICROSOFT PROJECT

DEFINING %COMPLETE IN MICROSOFT PROJECT CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

More information

Hedging Interest-Rate Risk with Duration

Hedging Interest-Rate Risk with Duration FIXED-INCOME SECURITIES Chapter 5 Hedgng Interest-Rate Rsk wth Duraton Outlne Prcng and Hedgng Prcng certan cash-flows Interest rate rsk Hedgng prncples Duraton-Based Hedgng Technques Defnton of duraton

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

1. Measuring association using correlation and regression

1. Measuring association using correlation and regression How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt. Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces

More information

Recurrence. 1 Definitions and main statements

Recurrence. 1 Definitions and main statements Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.

More information

SIMPLE LINEAR CORRELATION

SIMPLE LINEAR CORRELATION SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

Data Analysis with Fuzzy Measure on Intuitionistic Fuzzy Sets

Data Analysis with Fuzzy Measure on Intuitionistic Fuzzy Sets Proeedngs of the Internatonal MultConferene of Engneers and Computer Sentsts 2016 Vol II Marh 16-18 2016 Hong Kong Data nalyss wth Fuzzy Measure on Intutonst Fuzzy Sets Sanghyuk Lee * Ka Lok Man Eng Gee

More information

Finite Math Chapter 10: Study Guide and Solution to Problems

Finite Math Chapter 10: Study Guide and Solution to Problems Fnte Math Chapter 10: Study Gude and Soluton to Problems Basc Formulas and Concepts 10.1 Interest Basc Concepts Interest A fee a bank pays you for money you depost nto a savngs account. Prncpal P The amount

More information

THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES

THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered

More information

Texas Instruments 30X IIS Calculator

Texas Instruments 30X IIS Calculator Texas Instruments 30X IIS Calculator Keystrokes for the TI-30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the

More information

Problem Set 3. a) We are asked how people will react, if the interest rate i on bonds is negative.

Problem Set 3. a) We are asked how people will react, if the interest rate i on bonds is negative. Queston roblem Set 3 a) We are asked how people wll react, f the nterest rate on bonds s negatve. When

More information

Computer Administering of the Psychological Investigations: Set-Relational Representation

Computer Administering of the Psychological Investigations: Set-Relational Representation Open Journal of Appled Senes 2012 2 110-114 do:10.4236/ojapps.2012.22015 Publshed Onlne June 2012 (http://www.srp.org/journal/ojapps) Coputer Adnsterng of the Psyhologal Investgatons: Set-Relatonal Representaton

More information

A STUDY OF SOFTBALL PLAYER SWING SPEED *

A STUDY OF SOFTBALL PLAYER SWING SPEED * A STUDY OF SOFTBALL PLAYER SWING SPEED * LLOYD SMITH Shool of Mehanal and Materals Engneerng Washngton State Unversty E-mal: lvsmth@wsu.edu JEFF BROKER Department of Bology Unversty of Colorado, Colorado

More information

SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:

SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background: SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and

More information

Level Annuities with Payments Less Frequent than Each Interest Period

Level Annuities with Payments Less Frequent than Each Interest Period Level Annutes wth Payments Less Frequent than Each Interest Perod 1 Annuty-mmedate 2 Annuty-due Level Annutes wth Payments Less Frequent than Each Interest Perod 1 Annuty-mmedate 2 Annuty-due Symoblc approach

More information

The OC Curve of Attribute Acceptance Plans

The OC Curve of Attribute Acceptance Plans The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4

More information

substances (among other variables as well). ( ) Thus the change in volume of a mixture can be written as

substances (among other variables as well). ( ) Thus the change in volume of a mixture can be written as Mxtures and Solutons Partal Molar Quanttes Partal molar volume he total volume of a mxture of substances s a functon of the amounts of both V V n,n substances (among other varables as well). hus the change

More information

Using Series to Analyze Financial Situations: Present Value

Using Series to Analyze Financial Situations: Present Value 2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated

More information

Faraday's Law of Induction

Faraday's Law of Induction Introducton Faraday's Law o Inducton In ths lab, you wll study Faraday's Law o nducton usng a wand wth col whch swngs through a magnetc eld. You wll also examne converson o mechanc energy nto electrc energy

More information

Section C2: BJT Structure and Operational Modes

Section C2: BJT Structure and Operational Modes Secton 2: JT Structure and Operatonal Modes Recall that the semconductor dode s smply a pn juncton. Dependng on how the juncton s based, current may easly flow between the dode termnals (forward bas, v

More information

Pricing System Security in Electricity Markets. latter might lead to high prices as a result of unrealistic

Pricing System Security in Electricity Markets. latter might lead to high prices as a result of unrealistic 1 Pro. Bulk Power Systems Dynams and Control{V, Onomh, Japan, August 2001. Prng System Seurty n Eletrty Markets Claudo A. Ca~nzares Hong Chen Wllam Rosehart UnverstyofWaterloo Unversty of Calgary Dept.

More information

Optimal Adaptive Voice Smoother with Lagrangian Multiplier Method for VoIP Service

Optimal Adaptive Voice Smoother with Lagrangian Multiplier Method for VoIP Service Optmal Adaptve Voe Smoother wth Lagrangan Multpler Method for VoIP Serve Shyh-Fang HUANG, Er Hsao-uang WU and Pao-Ch CHANG Dept of Eletral Engneerng, Computer Sene and Informaton Engneerng and Communaton

More information

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)

More information

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000 Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

Lognormal random eld approxmatons to LIBOR market models O. Kurbanmuradov K. Sabelfeld y J. Shoenmakers z Mathemats Subet Classaton: 60H10,65C05,90A09 Keywords: LIBOR nterest rate models, random eld smulaton,

More information

Mean Molecular Weight

Mean Molecular Weight Mean Molecular Weght The thermodynamc relatons between P, ρ, and T, as well as the calculaton of stellar opacty requres knowledge of the system s mean molecular weght defned as the mass per unt mole of

More information

Question 2: What is the variance and standard deviation of a dataset?

Question 2: What is the variance and standard deviation of a dataset? Queston 2: What s the varance and standard devaton of a dataset? The varance of the data uses all of the data to compute a measure of the spread n the data. The varance may be computed for a sample of

More information

Peer-to-peer systems have attracted considerable attention

Peer-to-peer systems have attracted considerable attention Reputaton Aggregaton n Peer-to-Peer etwork Usng Dfferental Gossp Algorthm Ruhr Gupta, Yatndra ath Sngh, Senor Member, IEEE, arxv:20.430v4 [s.i] 28 Jan 204 Abstrat Reputaton aggregaton n peer to peer networks

More information

Revenue Management Games

Revenue Management Games Revenue Management Games Sergue Netessne and Robert A. Shumsky 2 Unversty of Rohester W. E. Smon Graduate Shool of Busness Admnstraton Rohester, NY 4627 Otober, 2000 netessnse@smon.rohester.edu 2 shumsky@smon.rohester.edu

More information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

Analysis of Reactivity Induced Accident for Control Rods Ejection with Loss of Cooling

Analysis of Reactivity Induced Accident for Control Rods Ejection with Loss of Cooling Analyss of Reactvty Induced Accdent for Control Rods Ejecton wth Loss of Coolng Hend Mohammed El Sayed Saad 1, Hesham Mohammed Mohammed Mansour 2 Wahab 1 1. Nuclear and Radologcal Regulatory Authorty,

More information

v a 1 b 1 i, a 2 b 2 i,..., a n b n i.

v a 1 b 1 i, a 2 b 2 i,..., a n b n i. SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are

More information

Interest Rate Futures

Interest Rate Futures Interest Rate Futures Chapter 6 6.1 Day Count Conventons n the U.S. (Page 129) Treasury Bonds: Corporate Bonds: Money Market Instruments: Actual/Actual (n perod) 30/360 Actual/360 The day count conventon

More information

CONSIDER a connected network of n nodes that all wish

CONSIDER a connected network of n nodes that all wish 36 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 60, NO. 2, FEBRUARY 204 Coded Cooperatve Data Exhange n Multhop Networks Thomas A. Courtade, Member, IEEE, and Rhard D. Wesel, Senor Member, IEEE Abstrat

More information

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by 6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

Multi-settlement Systems for Electricity Markets: Zonal Aggregation under Network Uncertainty and Market Power 1

Multi-settlement Systems for Electricity Markets: Zonal Aggregation under Network Uncertainty and Market Power 1 Proeedngs of the 35th Hawa Internatonal Conferene on System Senes - 2002 Mult-settlement Systems for Eletrty Markets: Zonal Aggregaton under Network Unertanty and Market Power 1 Ransh Kamat and Shmuel

More information

Simple Interest Loans (Section 5.1) :

Simple Interest Loans (Section 5.1) : Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part

More information

ELECTROPHILIC AROMATIC SUBSTITUTION REACTIONS OF SUBSTITUTED BENZENES

ELECTROPHILIC AROMATIC SUBSTITUTION REACTIONS OF SUBSTITUTED BENZENES 762 CAPTR 16 T CITRY F BZ AD IT DRIVATIV 16.19 how two dfferent Fredel Crafts aylaton reatons that an be used to prepare the followng ompound. C 3 L C LLC 3 C 3 16.20 The followng ompound reats wth AlCl

More information

Damage detection in composite laminates using coin-tap method

Damage detection in composite laminates using coin-tap method Damage detecton n composte lamnates usng con-tap method S.J. Km Korea Aerospace Research Insttute, 45 Eoeun-Dong, Youseong-Gu, 35-333 Daejeon, Republc of Korea yaeln@kar.re.kr 45 The con-tap test has the

More information

where the coordinates are related to those in the old frame as follows.

where the coordinates are related to those in the old frame as follows. Chapter 2 - Cartesan Vectors and Tensors: Ther Algebra Defnton of a vector Examples of vectors Scalar multplcaton Addton of vectors coplanar vectors Unt vectors A bass of non-coplanar vectors Scalar product

More information

Extending Probabilistic Dynamic Epistemic Logic

Extending Probabilistic Dynamic Epistemic Logic Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σ-algebra: a set

More information

We are now ready to answer the question: What are the possible cardinalities for finite fields?

We are now ready to answer the question: What are the possible cardinalities for finite fields? Chapter 3 Fnte felds We have seen, n the prevous chapters, some examples of fnte felds. For example, the resdue class rng Z/pZ (when p s a prme) forms a feld wth p elements whch may be dentfed wth the

More information

1 Example 1: Axis-aligned rectangles

1 Example 1: Axis-aligned rectangles COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton

More information

Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits

Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits Lnear Crcuts Analyss. Superposton, Theenn /Norton Equalent crcuts So far we hae explored tmendependent (resste) elements that are also lnear. A tmendependent elements s one for whch we can plot an / cure.

More information

Chapter 22 Heat Engines, Entropy, and the Second Law of Thermodynamics

Chapter 22 Heat Engines, Entropy, and the Second Law of Thermodynamics apter 22 Heat Engnes, Entropy, and te Seond Law o erodynas 1. e Zerot Law o erodynas: equlbru -> te sae 2. e Frst Law o erodynas: de d + d > adabat, sobar, sovoluetr, soteral 22.1 Heat Engnes and te Seond

More information

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2)

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2) MATH 16T Exam 1 : Part I (In-Class) Solutons 1. (0 pts) A pggy bank contans 4 cons, all of whch are nckels (5 ), dmes (10 ) or quarters (5 ). The pggy bank also contans a con of each denomnaton. The total

More information

In our example i = r/12 =.0825/12 At the end of the first month after your payment is received your amount in the account, the balance, is

In our example i = r/12 =.0825/12 At the end of the first month after your payment is received your amount in the account, the balance, is Payout annutes: Start wth P dollars, e.g., P = 100, 000. Over a 30 year perod you receve equal payments of A dollars at the end of each month. The amount of money left n the account, the balance, earns

More information

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure

More information

Shielding Equations and Buildup Factors Explained

Shielding Equations and Buildup Factors Explained Sheldng Equatons and uldup Factors Explaned Gamma Exposure Fluence Rate Equatons For an explanaton of the fluence rate equatons used n the unshelded and shelded calculatons, vst ths US Health Physcs Socety

More information

Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression.

Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression. Lecture 3: Annuty Goals: Learn contnuous annuty and perpetuty. Study annutes whose payments form a geometrc progresson or a arthmetc progresson. Dscuss yeld rates. Introduce Amortzaton Suggested Textbook

More information

Coordinate System for 3-D Model Used in Robotic End-Effector

Coordinate System for 3-D Model Used in Robotic End-Effector AU JT 8(: 8 (Apr Codnate Sytem f D Model Ued n Robot EndEffer ulfqar Al Soomro Shool of Advaned Stude, Aan Inttute of Tehnology Pathum Than, Thaland Abtrat Th paper reve the onept of odnate ytem on new

More information

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

Luby s Alg. for Maximal Independent Sets using Pairwise Independence Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent

More information

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t Indeternate Analyss Force Method The force (flexblty) ethod expresses the relatonshps between dsplaceents and forces that exst n a structure. Prary objectve of the force ethod s to deterne the chosen set

More information

IS-LM Model 1 C' dy = di

IS-LM Model 1 C' dy = di - odel Solow Assumptons - demand rrelevant n long run; assumes economy s operatng at potental GDP; concerned wth growth - Assumptons - supply s rrelevant n short run; assumes economy s operatng below potental

More information

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 7. Root Dynamcs 7.2 Intro to Root Dynamcs We now look at the forces requred to cause moton of the root.e. dynamcs!!

More information

Lecture 3: Force of Interest, Real Interest Rate, Annuity

Lecture 3: Force of Interest, Real Interest Rate, Annuity Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annuty-mmedate, and ts present value Study annuty-due, and

More information

IMPACT ANALYSIS OF A CELLULAR PHONE

IMPACT ANALYSIS OF A CELLULAR PHONE 4 th ASA & μeta Internatonal Conference IMPACT AALYSIS OF A CELLULAR PHOE We Lu, 2 Hongy L Bejng FEAonlne Engneerng Co.,Ltd. Bejng, Chna ABSTRACT Drop test smulaton plays an mportant role n nvestgatng

More information

Partner Choice and the Marital College Premium: Analyzing Marital Patterns Over Several Decades

Partner Choice and the Marital College Premium: Analyzing Marital Patterns Over Several Decades Partner Choe and the Martal College Premum: Analyzng Martal Patterns Over Several Deades Perre-André Chappor Bernard Salané Yoram Wess January 31, 2015 Abstrat We onstrut a strutural model of household

More information

An Interest-Oriented Network Evolution Mechanism for Online Communities

An Interest-Oriented Network Evolution Mechanism for Online Communities An Interest-Orented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne

More information

What is Candidate Sampling

What is Candidate Sampling What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble

More information

Section 2 Introduction to Statistical Mechanics

Section 2 Introduction to Statistical Mechanics Secton 2 Introducton to Statstcal Mechancs 2.1 Introducng entropy 2.1.1 Boltzmann s formula A very mportant thermodynamc concept s that of entropy S. Entropy s a functon of state, lke the nternal energy.

More information

Optimal Health Insurance for Multiple Goods and Time Periods

Optimal Health Insurance for Multiple Goods and Time Periods 04 R.P. Ells, S. Jang, and W.G. Mannng Optmal Health Insurane for Multple Goods and Tme Perods Randall P. Ells a,, Sheny Jang b, Wllard G. Mannng a Department of Eonoms, Boston Unversty, 70 Bay State Road,

More information

Cyber-Security Via Computing With Words

Cyber-Security Via Computing With Words Cyber-Seurty Va Computng Wth Words John. Rkard Dstrbuted Infnty, In. 4637 Shoshone Drve Larkspur, CO 808 Emal: trkard@dstrbutednfnty.om ABSRAC Cyber-seurty systems must deal wth a hgh rate of observable

More information

Topical Workshop for PhD students Adsorption and Diffusion in MOFs Institut für Nichtklassische Chemie, Germany, www.uni-leipzig.

Topical Workshop for PhD students Adsorption and Diffusion in MOFs Institut für Nichtklassische Chemie, Germany, www.uni-leipzig. Gas Separaton and Purfcaton Measurement of Breakthrough Curves Topcal Workshop for PhD students Adsorpton and Dffuson n MOFs Adsorpton on Surfaces / Separaton effects Useful features Thermodynamc effect

More information

Abteilung für Stadt- und Regionalentwicklung Department of Urban and Regional Development

Abteilung für Stadt- und Regionalentwicklung Department of Urban and Regional Development Abtelung für Stadt- und Regonalentwcklung Department of Urban and Regonal Development Gunther Maer, Alexander Kaufmann The Development of Computer Networks Frst Results from a Mcroeconomc Model SRE-Dscusson

More information

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008 Rsk-based Fatgue Estmate of Deep Water Rsers -- Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn

More information

DECOMPOSITION ALGORITHM FOR OPTIMAL SECURITY-CONSTRAINED POWER SCHEDULING

DECOMPOSITION ALGORITHM FOR OPTIMAL SECURITY-CONSTRAINED POWER SCHEDULING DECOMPOSITION ALGORITHM FOR OPTIMAL SECURITY-CONSTRAINED POWER SCHEDULING Jorge Martínez-Crespo Julo Usaola José L. Fernández Unversdad Carlos III de Madrd Unversdad Carlos III de Madrd Red Elétra de Espana

More information

Derivation of Humidty and NOx Humidty Correction Factors

Derivation of Humidty and NOx Humidty Correction Factors (Ths document follows the presentatons n "Vapor Pressure Equaton for Water n the Range 0 to 00 C", by Arnold Wexler and Lews Greenspan, February 9, 97 JOURNAL OF RESEARCH of the Natonal Bureau of Standards

More information

On some special nonlevel annuities and yield rates for annuities

On some special nonlevel annuities and yield rates for annuities On some specal nonlevel annutes and yeld rates for annutes 1 Annutes wth payments n geometrc progresson 2 Annutes wth payments n Arthmetc Progresson 1 Annutes wth payments n geometrc progresson 2 Annutes

More information

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently. Corporate Polces & Procedures Human Resources - Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:

More information

How To Understand The Results Of The German Meris Cloud And Water Vapour Product

How To Understand The Results Of The German Meris Cloud And Water Vapour Product Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPP-ATBD-ClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller

More information

Compiling for Parallelism & Locality. Dependence Testing in General. Algorithms for Solving the Dependence Problem. Dependence Testing

Compiling for Parallelism & Locality. Dependence Testing in General. Algorithms for Solving the Dependence Problem. Dependence Testing Complng for Parallelsm & Localty Dependence Testng n General Assgnments Deadlne for proect 4 extended to Dec 1 Last tme Data dependences and loops Today Fnsh data dependence analyss for loops General code

More information

When can bundling help adoption of network technologies or services?

When can bundling help adoption of network technologies or services? When an bundlng help adopton of network tehnologes or serves? Steven Weber Dept. of ECE, Drexel U. sweber@oe.drexel.edu Roh Guérn Dept. of CSE, WUSTL guern@wustl.edu Jaudele C. de Olvera Dept. of ECE,

More information

Fisher Markets and Convex Programs

Fisher Markets and Convex Programs Fsher Markets and Convex Programs Nkhl R. Devanur 1 Introducton Convex programmng dualty s usually stated n ts most general form, wth convex objectve functons and convex constrants. (The book by Boyd and

More information

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange

More information

Loop Parallelization

Loop Parallelization - - Loop Parallelzaton C-52 Complaton steps: nested loops operatng on arrays, sequentell executon of teraton space DECLARE B[..,..+] FOR I :=.. FOR J :=.. I B[I,J] := B[I-,J]+B[I-,J-] ED FOR ED FOR analyze

More information

Ring structure of splines on triangulations

Ring structure of splines on triangulations www.oeaw.ac.at Rng structure of splnes on trangulatons N. Vllamzar RICAM-Report 2014-48 www.rcam.oeaw.ac.at RING STRUCTURE OF SPLINES ON TRIANGULATIONS NELLY VILLAMIZAR Introducton For a trangulated regon

More information

Addendum to: Importing Skill-Biased Technology

Addendum to: Importing Skill-Biased Technology Addendum to: Importng Skll-Based Technology Arel Bursten UCLA and NBER Javer Cravno UCLA August 202 Jonathan Vogel Columba and NBER Abstract Ths Addendum derves the results dscussed n secton 3.3 of our

More information

SDN: Systemic Risks due to Dynamic Load Balancing

SDN: Systemic Risks due to Dynamic Load Balancing SDN: Systemc Rsks due to Dynamc Load Balancng Vladmr Marbukh IRTF SDN Abstract SDN acltates dynamc load balancng Systemc benets o dynamc load balancng: - economc: hgher resource utlzaton, hgher revenue,..

More information

Little s Law & Bottleneck Law

Little s Law & Bottleneck Law Lttle s Law & Bottleneck Law Dec 20 I professonals have shunned performance modellng consderng t to be too complex and napplcable to real lfe. A lot has to do wth fear of mathematcs as well. hs tutoral

More information

Implementation of Deutsch's Algorithm Using Mathcad

Implementation of Deutsch's Algorithm Using Mathcad Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages - n "Machnes, Logc and Quantum Physcs"

More information

Fixed income risk attribution

Fixed income risk attribution 5 Fxed ncome rsk attrbuton Chthra Krshnamurth RskMetrcs Group chthra.krshnamurth@rskmetrcs.com We compare the rsk of the actve portfolo wth that of the benchmark and segment the dfference between the two

More information

FLASH POINT DETERMINATION OF BINARY MIXTURES OF ALCOHOLS, KETONES AND WATER. P.J. Martínez, E. Rus and J.M. Compaña

FLASH POINT DETERMINATION OF BINARY MIXTURES OF ALCOHOLS, KETONES AND WATER. P.J. Martínez, E. Rus and J.M. Compaña FLASH POINT DETERMINATION OF BINARY MIXTURES OF ALCOHOLS, KETONES AND WATER Abstract P.J. Martínez, E. Rus and J.M. Compaña Departamento de Ingenería Químca. Facultad de Cencas. Unversdad de Málaga. 29071

More information

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP) 6.3 / -- Communcaton Networks II (Görg) SS20 -- www.comnets.un-bremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes

More information

Overview of monitoring and evaluation

Overview of monitoring and evaluation 540 Toolkt to Combat Traffckng n Persons Tool 10.1 Overvew of montorng and evaluaton Overvew Ths tool brefly descrbes both montorng and evaluaton, and the dstncton between the two. What s montorng? Montorng

More information

A Probabilistic Theory of Coherence

A Probabilistic Theory of Coherence A Probablstc Theory of Coherence BRANDEN FITELSON. The Coherence Measure C Let E be a set of n propostons E,..., E n. We seek a probablstc measure C(E) of the degree of coherence of E. Intutvely, we want

More information

Efficient Project Portfolio as a tool for Enterprise Risk Management

Efficient Project Portfolio as a tool for Enterprise Risk Management Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse

More information

Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money

Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money Ch. 6 - The Tme Value of Money Tme Value of Money The Interest Rate Smple Interest Compound Interest Amortzng a Loan FIN21- Ahmed Y, Dasht TIME VALUE OF MONEY OR DISCOUNTED CASH FLOW ANALYSIS Very Important

More information

Joe Pimbley, unpublished, 2005. Yield Curve Calculations

Joe Pimbley, unpublished, 2005. Yield Curve Calculations Joe Pmbley, unpublshed, 005. Yeld Curve Calculatons Background: Everythng s dscount factors Yeld curve calculatons nclude valuaton of forward rate agreements (FRAs), swaps, nterest rate optons, and forward

More information