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TEST 1 I. Pronunciation: 1. a. country b. fun c. summer d. truth --> d 2. a. trouble b. flu c. tooth d. shoe --> a 3. a. heart b. turn c. early d. third --> a 4. a. song b. one c. long d. gone --> b 5. a. fork b. fall c. wrong d. caught --> c 6. a. full b. push c. but d. put --> c 7. a. deal b. heal c. stealth d. steal --> c II. Find the mistakes: 8. At present, I am a student at the Local Technical College, which I am studying English part-time. a. at b. which c. am studying d. English part-time b 9. One of the girl who worked in that company died. a. the girl b. who c. worked d. died a 10. This lesson is such long that I have written it for 30 minutes. a. is b. such c. that d. have written b III. Grammar and vocabulary: 11. I read in the newspaper that there has just been a . against the government in Transylvania. a. relevant b. revolting c. revolution d. revolver --> c 12. You have to be rich to send your child to a private school because the fees are . a. astronomical b. aeronautical c. astrological d. atmospherical --> a 13. Does that newspaper . the government or oppose it? a. advantage b. assist c. encourage d. support --> d 14. It’s distressing to see small children in the streets. a. pleading b. imploring c. begging d. entreating --> c 15. You’ll get a free month’s subscription . you renew your membership by the end of January. a. unless b. however c. were d. provided --> d 16. Most of my friends are reporters. a. newspapers b. newspaper c. newspaper’s d. newspapers’ --> b 17. The electorate will not easily forgive the government for failing to fulfill its .……. a. promises b. vows c. aspirations d. offers --> a 18. Mark Twain is a famous American a. physicist b. poet c. chemist d. writer d 19. The ship broke and the people were drowned. a. out b. out of the sea c. into pieces d. to pieces --> c 20. I have a car, I prefer to travel by train. a. In spite of b. Despite c. Because d. Although d 21. My brother sang of all the pupils of the group. a. more beautifully b. the most beautifully c. less beautifully d. most beautifully --> b 22. I wish he change his minds so often! a. shouldn’t b. wouldn’t c. mightn’t d. couldn’t --> b 23. Doctors advise people being deficient Vitamin C to eat more fruit and vegetables. a. in b. of c. from d. for --> a 24. Because I am terrified of . spaces, I never go in lifts. a. contained b. compressed c. constricted d. confined --> d 25. In a four-hour operation, surgeons managed to sew the girl’s hand back on. a. cut b. grazed c. crushed d. severed --> d 26. Even though I hadn’t spoken English for many years, I picked it again after a few weeks. a. on b. over c. up d. through --> c 27. “You haven’t eaten anything since yesterday evening. You . be really hungry!” “I am.” a. might b. will c. must d. can --> c 28. Do most of the people who live along the shores of earn a living .? a. the Mediterranean / by farm b. Mediterranean / by farming c. the Mediterranean / to farm d. the Mediterranean / by farming d 29. I like books on literature . a. more than b. most of all c. much more d. not at all --> b 30. It is no use . to school if you . to work hard. a. going / do not ready b. to go / do not ready c. going / are not ready d. go / are not ready c 31. There were many big trees, between me and the river and now they all fell down into the water. a. each other b. one after another c. one another d. a lot --> c 32. "It seems he's driving more carefully now." "Yes, after . three times for speeding." a. to be arrested b. be arrested c. having been arrested d. have been arrested c 33. But I could not IE 355 QUALITY AND APPLIED STATISTICS I LAB ASSIGNMENT DISTRIBUTION OF SAMPLE MEANS AND CENTRAL LIMIT THEOREM This lab discusses how to use a histogram and a normal probability plot to determine if a set of data is normally distributed Also, this lab shows the properties of sampling from a normal population and the properties of the Central Limit Theorem Histogram and Normal Probability Plots The vast majority of statistical quality control procedures assume that the process is normally distributed If the process is not normally distributed control limits for control charts may be entirely inappropriate In general, the x chart is fairly robust while the R chart is much more sensitive to departures from normality If the process is not normally distributed, there are alternate methods for deriving control limits that employ techniques such as transforming the data or deriving the underlying distribution These procedures are beyond the scope of this course, but it is important to be able to recognize whether data from a process is normal Two graphical tools in particular are used for assessing normality These are the histogram and the normal probability plot An example of a histogram is shown in Figure This histogram was created from 100 randomly generated values from a standard normal distribution The horizontal axis is divided into intervals These intervals are the width of each bar The height of each bar is the number of values that fall into the corresponding interval Montgomery, D C., (1997), Introduction to Statistical Quality Control, p 205, 226 Histogram for X 30 frequency 25 20 15 10 -3.4 -2.4 -1.4 -0.4 0.6 1.6 2.6 X Figure Example histogram from 100 randomly generated values from a Norm(0, 1) distribution The histogram is a visual display of the data in which one may see the following three properties: Shape Location or central tendency (average) Scatter or spread (variance) In Figure 1, we see that the distribution is roughly symmetric and unimodal (one peak) as a normal distribution should be Also, we see that the central tendency is approximately and the spread of the histogram is approximately ±3σ (recall σ = for standard normal) from as values from a standard normal should be A histogram works best to assess normality with larger datasets, e.g., n ≥ 50 Another graphical tool to test for normality is the normal probability plot (NPP) Figure shows the NPP for the same 100 randomly generated standard normal values A NPP is a graph of the ranked data versus the sample cumulated frequency on special paper with the vertical scale chosen so that the cumulative normal distribution is a straight line So, if the data is normally distributed it should approximately lie on the straight line A rule of thumb for determining if the data lies on the line is the “fat pen test” For a NPP plotted on letter sized paper, if a fat pen can cover most of the points, we can probably assume that the data is normally distributed Normal Probability Plot for X percentage 99.9 99 95 80 50 20 0.1 -3.1 -2.1 -1.1 -0.1 0.9 1.9 2.9 X Figure Normal probability plot of 100 randomly generated standard normal values Part 1: Sampling Distribution of Average from a Normal Distribution Consider random variables X , X ,K , X n that are independent and normally distributed with mean µ and standard deviation σ The average of the random variables will also be normally distributed with mean µ but will have a standard deviation σ n Create a data file in StatGraphics which includes the following variables (columns of values): N1, N2, N3, and N4, each of which is a sample of 100 normally distributed random variables with mean 10 and standard deviation (Note: See section below on generating random normal variates with StatGraphics) Create a new column called AVG which is a function of the first four columns, specifically, AVG is the average of the first four columns, i.e., AVG = (N1+N2+N3+N4)/4 • Use StatGraphics to find the sample mean and standard deviation for N1, N2, N3, N4 and AVG (Hint: Do a One-Variable Analysis) Summarize the findings in the tables below For the random variable AVG, the mean is 10 What is the theoretical standard deviation of the random variable AVG? N1 N2 N3 N4 Sample Mean Sample Std Dev 10 AVG Sample Mean Sample Std Dev • THEORY THEORY 10 Create histograms of the data in N1 and in AVG such that you see the data and the fitted normal distribution Display both histograms on the same page Explain what you see as far as differences between the histograms • Hand-in tables and the page of histograms Statgraphics Notes: Generating random normal variates (random values): Here are the steps to create values for N1 Repeat for N2, N3, and N4 CLICK RCLICK CLICK Col_1 The first column becomes shaded Anywhere on worksheet Modify Column… Change Name to N1 Select data type as Fixed CLICK RCLICK CLICK Decimal with appropriate decimal places N1 It becomes shaded Anywhere on worksheet Generate Data… From the box, scroll ...Modern Physical Metallurgy and Materials Engineering About the authors ProfessorR.E.Smallman After gaining his PhD in 1953, Professor Smallman spent five years at the Atomic Energy Research Estab- lishment at Harwell, before returning to the University of Birmingham where he became Professor of Physi- cal Metallurgy in 1964 and Feeney Professor and Head of the Department of Physical Metallurgy and Science of Materials in 1969. He subsequently became Head of the amalgamated Department of Metallurgy and Materials (1981), Dean of the Faculty of Science and Engineering, and the first Dean of the newly-created Engineering Faculty in 1985. For five years he was Vice-Principal of the University (1987–92). He has held visiting professorship appointments at the University of Stanford, Berkeley, Pennsylvania (USA), New South Wales (Australia), Hong Kong and Cape Town and has received Honorary Doctorates from the University of Novi Sad (Yugoslavia) and the University of Wales. His research work has been recognized by the award of the Sir George Beilby Gold Medal of the Royal Institute of Chemistry and Institute of Metals (1969), the Rosenhain Medal of the Institute of Metals for contributions to Physical Metallurgy (1972) and the Platinum Medal, the premier medal of the Institute of Materials (1989). He was elected a Fellow of the Royal Society (1986), a Fellow of the Royal Academy of Engineer- ing (1990) and appointed a Commander of the British Empire (CBE) in 1992. A former Council Member of the Science and Engineering Research Council, he has been Vice President of the Institute of Materials and President of the Federated European Materials Soci- eties. Since retirement he has been academic consultant for a number of institutions both in the UK and over- seas. R. J. Bishop After working in laboratories of the automobile, forging, tube-drawing and razor blade industries (1944–59), Ray Bishop became a Principal Scientist of the British Coal Utilization Research Association (1959–68), studying superheater-tube corrosion and mechanisms of ash deposition on behalf of boiler manufacturers and the Central Electricity Generating Board. He specialized in combustor simulation of conditions within pulverized-fuel-fired power station boilers and fluidized-bed combustion systems. He then became a Senior Lecturer in Materials Science at the Polytechnic (now University), Wolverhampton, acting at various times as leader of C&G, HNC, TEC and CNAA honours Degree courses and supervising doctoral researches. For seven years he was Open University Tutor for materials science and processing in the West Midlands. In 1986 he joined the School of Metallurgy and Materials, University of Birmingham as a part-time Lecturer and was involved in administration of the Federation of European Materials Societies (FEMS). In 1995 and 1997 he gave lecture courses in materials science at the Naval Postgraduate School, Monterey, California. Currently he is an Honorary Lecturer at the University of Birmingham. Modern Physical Metallurgy and Materials Engineering Science, process, applications Sixth Edition R. E. Smallman, CBE, DSc, FRS, FREng, FIM R. J. Bishop, PhD, CEng, MIM OXFORD AUCKLAND BOSTON JOHANNESBURG MELBOURNE NEW DELHI Butterworth-Heinemann Linacre House, Jordan Hill, Oxford OX2 8DP 225 Wildwood Avenue, Woburn, MA 01801-2041 A division of Reed Educational and Professional Publishing Ltd First published 1962 Second edition 1963 Reprinted 1965, 1968 Third edition 1970 Reprinted 1976 (twice), 1980, 1983 Fourth edition 1985 Reprinted 1990, 1992 Fifth edition 1995 Sixth edition 1999 Reed Educational and Professional Publishing Ltd 1995, 1999 All rights reserved. No part of this Torch Angle (Continued) An angled torch cuts faster on thinner-gauge material. workpiece surface conditions or plate compositions. The intersection of the kerf and the surface presents a For example, rusty or oily plates require more preheat, knife edge which is easily ignited. Once the plate is or slower travel speeds than clean plates. Most burning, the cut is readily carried through to the other variations from the ideal condition of a clean, flat, side of the work. This avoids problems of non-drop low-carbon steel plate tend to slow down cutting action. cuts, incomplete cutting on the opposite side of the thicker plate, gouging cuts in the center of the kerf and For a very rusty plate, set as big a preheat flame as similar problems. Travel Speed Each job has a best cutting speed. A high quality cut will be obtained on plate up to about 2 in. thick when there is a steady “purring” sound from the torch and the spark stream under the plate has a 15 degree lead angle; the angle made by the sparks coming from the bottom of the cut in the same direction the torch is traveling. If the sparks go straight down, or even backwards, travel speed is too high. Nature of the Workpiece Variations in cut quality are the result of different possible on the torch and run it back and forth over the line to be cut. Extra preheat passes do two things. First, they span off much of the scale that interferes with the cutting action, and they put extra preheat into the plate to allow improved cut quality and speed. Cut a little bit slower when working with high-strength low-alloy plates (ASTM A-242 steel), or full alloy plates (ASTM A-5 14). Also, because these steels are more sensitive to notching than ordinary carbon steels, use low oxygen pressure. Clad carbon alloy, carbon stainless, or low-carbon high-carbon plates require a lower oxygen pressure, and perhaps a lower travel speed than straight low-carbon steel. Be sure the low-carbon steel side is 4-17 Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com FIGURE 4-4. Corrrect Torch Angles 4-18 Simpo PDF Merge and Split Unregistered Version - http://www.simpopdf.com Nature of the Workpiece (Continued, on the same side as the torch. The alloyed or higher carbon cladding does not bum as readily as the carbon steel. By putting the cladding on the bottom, and the carbon steel on the top, a cutting action similar to powder cutting results. The low-carbon steel on top bums readily and forms slag. As the iron-bearing slag passes through the high-carbon or high-alloy cladding, it dilutes the cladding material. The torch, in essence, still bums a lower carbon steel. If the clad or high-carbon steel is on the top surface, the torch is required to cut material that is not readily oxidizable, and forms refractory slags that can stop the cutting action. Tip Size and Style All steel sections have corresponding tip sizes to allow the most economical operation for a particular fuel. Any fuel will burn in any tip, of course, but not as efficiently and may even overheat enough to melt the tip or cause problems in the cut. For example, MAPP gas does not operate at peak efficiency in most acetylene tips because the preheat orifices are too small. If MAPP gas is used with a natural-gas tip, the tip will overheat and become susceptible to flashback. In an emergency, a natural-gas tip can be used with MAPP gas by removing its skirt. Similarly, an acetylene tip can be used if inefficient burning can be tolerated for a short run. Oxygen Supply Oxygen supply means two things - volume and pressure. Both are needed to obtain a decent stinger to provide a good quality cut. If all the oxygen volume in the world is available, and the pressure is low, the oxygen system will be deficient. Also, very high oxygen pressures will not help when only a small volume of oxygen is available over a given time. It is as important to have a generous supply of oxygen as it is to have well-trained burners, good FOURIER TRANSFORM – MATERIALS ANALYSIS Edited by Salih Mohammed Salih Fourier Transform – Materials Analysis Edited by Salih Mohammed Salih Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. As for readers, this license allows users to download, copy and build upon published chapters even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Vana Persen Technical Editor Teodora Smiljanic Cover Designer InTech Design Team First published May, 2012 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechopen.com Fourier Transform – Materials Analysis, Edited by Salih Mohammed Salih p. cm. ISBN 978-953-51-0594-7 Contents Preface IX Chapter 1 Fourier Series and Fourier Transform with Applications in Nanomaterials Structure 1 Florica Matei and Nicolae Aldea Chapter 2 High Resolution Mass Spectrometry Using FTICR and Orbitrap Instruments 25 Paulo J. Amorim Madeira, Pedro A. Alves and Carlos M. Borges Chapter 3 Fourier Transform Infrared Spectroscopy for Natural Fibres 45 Mizi Fan, Dasong Dai and Biao Huang Chapter 4 Fourier Transform Infrared Spectroscopy for the Measurement of Spectral Line Profiles 69 Hassen Aroui, Johannes Orphal and Fridolin Kwabia Tchana Chapter 5 Fourier Transform Spectroscopy of Cotton and Cotton Trash 103 Chanel Fortier Chapter 6 Fourier Transformation Method for Computing NMR Integrals over Exponential Type Functions 121 Hassan Safouhi Chapter 7 Molecular Simulation with Discrete Fast Fourier Transform 137 Xiongwu Wu and Bernard R. Brooks Chapter 8 Charaterization of Pore Structure and Surface Chemistry of Activated Carbons – A Review 165 Bingzheng Li Chapter 9 Bioleaching of Galena (PbS) 191 E. R. Mejía, J. D. Ospina, M. A. Márquez and A. L. Morales VI Contents Chapter 10 Application of Hankel Transform for Solving a Fracture Problem of a Cracked Piezoelectric Strip Under Thermal Loading 207 Sei Ueda Chapter 11 Eliminating the Undamaging Fatigue Cycles Using the Frequency Spectrum Filtering Techniques 223 S. Abdullah, T. E. Putra and M. Z. Nuawi Chapter 12 Fourier Transform Sound Radiation 239 F. X. Xin and T. J. Lu Preface This book focuses on the Fourier transform applications in the analysis of some types of materials. The field of Fourier transform has seen explosive growth during the past decades, as