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Statistical Case Studies (Instructor Edition)
https://epubs.siam.org/doi/book/10.1137/1.9781611973419?af=R
Statistical Case Studies (Instructor Edition). <br/> If you only have pretend data, you can only pretend to analyze it. George Cobb As professors of statistics we tell our students that an understanding of research questions is necessary in order to collect meaningful data and analyze it intelligently. “Don’t collect data first and then try to figure out what (if anything) you can do with it,” we admonish students and also researchers who come to us for help with data analysis. Yet when we teach statistics courses, we often do just that! Now convinced of the necessity to include examples that use REAL data, we search for real data sets and then try to come up with some question that we think might in some way capture our students’ interest. Without an indepth understanding of how the data was collected or why it is important, we look at data and try to figure out what (if anything) we can do with it to turn it in to a classroom example. I confess that I am guiltier of this than most—I have created whole textbooks full of examples and exercises in just this way.
Statistical Case Studies (Instructor Edition). <br/> If you only have pretend data, you can only pretend to analyze it. George Cobb As professors of statistics we tell our students that an understanding of research questions is necessary in order to collect meaningful data and analyze it intelligently. “Don’t collect data first and then try to figure out what (if anything) you can do with it,” we admonish students and also researchers who come to us for help with data analysis. Yet when we teach statistics courses, we often do just that! Now convinced of the necessity to include examples that use REAL data, we search for real data sets and then try to come up with some question that we think might in some way capture our students’ interest. Without an indepth understanding of how the data was collected or why it is important, we look at data and try to figure out what (if anything) we can do with it to turn it in to a classroom example. I confess that I am guiltier of this than most—I have created whole textbooks full of examples and exercises in just this way. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/sa/1998/1.9781611973419/1.9781611973419/20230615/1.9781611973419.cover.jpg" alttext="cover image"/></p>
Statistical Case Studies (Instructor Edition)
doi:10.1137/1.9781611973419
Statistical Case Studies (Instructor Edition)
20230615T01:35:42Z
10.1137/1.9781611973419
https://epubs.siam.org/doi/book/10.1137/1.9781611973419?af=R
© 1998 by the American Statistical Association and the Society for Industrial and Applied Mathematics.All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688, USA.

Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones
https://epubs.siam.org/doi/book/10.1137/1.9781611976533?af=R
Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones. <br/> In recent years, the world of molecular biology has witnessed a technology explosion, the socalled omics revolution, which has opened windows into cellular metabolic potential and activity that have already revolutionized numerous subfields of biology and medicine. At the same time, this technology (genomics, transcriptomics, etc.), intrinsically through its very nature and extrinsically through its widespread use, has generated a lot of data that begs, and has received, much statistical and mathematical attention. Such data analysis is arguably as important, if not more so, than the measurements themselves; without the appropriate parsing, much of the information potential is inaccessible.
Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones. <br/> In recent years, the world of molecular biology has witnessed a technology explosion, the socalled omics revolution, which has opened windows into cellular metabolic potential and activity that have already revolutionized numerous subfields of biology and medicine. At the same time, this technology (genomics, transcriptomics, etc.), intrinsically through its very nature and extrinsically through its widespread use, has generated a lot of data that begs, and has received, much statistical and mathematical attention. Such data analysis is arguably as important, if not more so, than the measurements themselves; without the appropriate parsing, much of the information potential is inaccessible. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2021/1.9781611976533/1.9781611976533/20210617/1.9781611976533.cover.jpg" alttext="cover image"/></p>
Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones
doi:10.1137/1.9781611976533
Isaac Klapper
Daniel B. Szyld
Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones
20210617T07:46:43Z
10.1137/1.9781611976533
https://epubs.siam.org/doi/book/10.1137/1.9781611976533?af=R
© 2021 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Data Clustering: Theory, Algorithms, and Applications, Second Edition
https://epubs.siam.org/doi/book/10.1137/1.9781611976335?af=R
Data Clustering: Theory, Algorithms, and Applications, Second Edition. <br/> The monograph Data Clustering: Theory, Algorithms, and Applications was published in 2007. Starting with the common ground and knowledge for data clustering, the monograph focuses on several popular clustering algorithms and groups them according to some specific baseline methodologies, such as hierarchical, centerbased, and searchbased methods. Since the publication of this monograph, development in the subject area has exploded in many different directions, especially in clustering algorithms for big data and opensource software for cluster analysis.
Data Clustering: Theory, Algorithms, and Applications, Second Edition. <br/> The monograph Data Clustering: Theory, Algorithms, and Applications was published in 2007. Starting with the common ground and knowledge for data clustering, the monograph focuses on several popular clustering algorithms and groups them according to some specific baseline methodologies, such as hierarchical, centerbased, and searchbased methods. Since the publication of this monograph, development in the subject area has exploded in many different directions, especially in clustering algorithms for big data and opensource software for cluster analysis. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/mn/2020/1.9781611976335/1.9781611976335/20201111/1.9781611976335.cover.jpg" alttext="cover image"/></p>
Data Clustering: Theory, Algorithms, and Applications, Second Edition
doi:10.1137/1.9781611976335
Guojun Gan
Chaoqun Ma
Jianhong Wu
Data Clustering: Theory, Algorithms, and Applications, Second Edition
20201111T01:35:59Z
10.1137/1.9781611976335
https://epubs.siam.org/doi/book/10.1137/1.9781611976335?af=R
© 2021 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

A Course in Mathematical Biology
https://epubs.siam.org/doi/book/10.1137/1.9780898718256?af=R
A Course in Mathematical Biology. <br/> Mathematical biology is growing rapidly. Mathematics has long played a dominant role in our understanding of physics, chemistry, and other physical sciences. However, wholesale application of mathematical methods in the life sciences is relatively recent. Now questions about infectious diseases, heart attacks, cell signaling, cell movement, ecology, environmental changes, and genomics are being tackled and analyzed using mathematical and computational methods.
A Course in Mathematical Biology. <br/> Mathematical biology is growing rapidly. Mathematics has long played a dominant role in our understanding of physics, chemistry, and other physical sciences. However, wholesale application of mathematical methods in the life sciences is relatively recent. Now questions about infectious diseases, heart attacks, cell signaling, cell movement, ecology, environmental changes, and genomics are being tackled and analyzed using mathematical and computational methods. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/mm/2006/1.9780898718256/1.9780898718256/20190528/1.9780898718256.cover.jpg" alttext="cover image"/></p>
A Course in Mathematical Biology
doi:10.1137/1.9780898718256
Gerda de Vries
Thomas Hillen
Mark Lewis
Johannes Müller
Birgitt Schönfisch
A Course in Mathematical Biology
20190528T08:04:26Z
10.1137/1.9780898718256
https://epubs.siam.org/doi/book/10.1137/1.9780898718256?af=R
© 2006 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Phylogeny
https://epubs.siam.org/doi/book/10.1137/1.9781611974485?af=R
Phylogeny. <br/> The idea that all life on earth traces back to a common origin dates back at least to Charles Darwin's Origin of Species. Ever since, biologists have tried to piece together parts of this “tree of life” based on what we can observe today: fossils, and the evolutionary signal that is present in the genomes and phenotypes of different organisms. Mathematics has played a key role in helping transform genetic data into phylogenetic (evolutionary) trees and networks. In this book, I will explain some of the central concepts and basic results in phylogenetics, which benefit from several branches of mathematics, including combinatorics, probability, and algebra.
Phylogeny. <br/> The idea that all life on earth traces back to a common origin dates back at least to Charles Darwin's Origin of Species. Ever since, biologists have tried to piece together parts of this “tree of life” based on what we can observe today: fossils, and the evolutionary signal that is present in the genomes and phenotypes of different organisms. Mathematics has played a key role in helping transform genetic data into phylogenetic (evolutionary) trees and networks. In this book, I will explain some of the central concepts and basic results in phylogenetics, which benefit from several branches of mathematics, including combinatorics, probability, and algebra. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/cb/2016/1.9781611974485/1.9781611974485/20161011/1.9781611974485.cover.jpg" alttext="cover image"/></p>
Phylogeny
doi:10.1137/1.9781611974485
Mike Steel
Phylogeny
20161011T07:17:22Z
10.1137/1.9781611974485
https://epubs.siam.org/doi/book/10.1137/1.9781611974485?af=R
© 2016 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Adaptive Treatment Strategies in Practice
https://epubs.siam.org/doi/book/10.1137/1.9781611974188?af=R
Adaptive Treatment Strategies in Practice. <br/> The study of new medical treatments, and sequences of treatments, is inextricably linked with statistics. Without statistical estimation and inference, we are left with case studies and anecdotes and do not have a formal means of extracting meaning from noise. The types of questions that can be answered continue to be pushed forward. One direction that has seen great momentum is in the field of statistics for precision medicine, an area of medical treatment focused on the personalization of care using patient covariates, which may be demographic, clinical, or biological.
Adaptive Treatment Strategies in Practice. <br/> The study of new medical treatments, and sequences of treatments, is inextricably linked with statistics. Without statistical estimation and inference, we are left with case studies and anecdotes and do not have a formal means of extracting meaning from noise. The types of questions that can be answered continue to be pushed forward. One direction that has seen great momentum is in the field of statistics for precision medicine, an area of medical treatment focused on the personalization of care using patient covariates, which may be demographic, clinical, or biological. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/sa/2015/1.9781611974188/1.9781611974188/20151211/1.9781611974188.cover.jpg" alttext="cover image"/></p>
Adaptive Treatment Strategies in Practice
doi:10.1137/1.9781611974188
Adaptive Treatment Strategies in Practice
20151211T03:31:21Z
10.1137/1.9781611974188
https://epubs.siam.org/doi/book/10.1137/1.9781611974188?af=R
© 2016 by the American Statistical Association and the Society for Industrial and Applied Mathematics.All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688, USA.

The Radon Transform and Medical Imaging
https://epubs.siam.org/doi/book/10.1137/1.9781611973297?af=R
The Radon Transform and Medical Imaging. <br/> This text addresses the topics covered in ten lectures delivered by the author during the 2012 CBMSNSF conference “Mathematical methods of computed tomography.” The goals of the lectures were to describe the main problems and techniques of some wellestablished imaging modalities, to emphasize the most important mathematical ideas involved, and to give a brief overview of several imaging techniques that are less common and/or currently being developed.
The Radon Transform and Medical Imaging. <br/> This text addresses the topics covered in ten lectures delivered by the author during the 2012 CBMSNSF conference “Mathematical methods of computed tomography.” The goals of the lectures were to describe the main problems and techniques of some wellestablished imaging modalities, to emphasize the most important mathematical ideas involved, and to give a brief overview of several imaging techniques that are less common and/or currently being developed. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/cb/2013/1.9781611973297/1.9781611973297/20140127/1.9781611973297.cover.jpg" alttext="cover image"/></p>
The Radon Transform and Medical Imaging
doi:10.1137/1.9781611973297
Peter Kuchment
The Radon Transform and Medical Imaging
20140127T09:14:23Z
10.1137/1.9781611973297
https://epubs.siam.org/doi/book/10.1137/1.9781611973297?af=R
© 2014 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Introduction to the Mathematics of Medical Imaging
https://epubs.siam.org/doi/book/10.1137/9780898717792?af=R
Introduction to the Mathematics of Medical Imaging. <br/> Over the past several decades, advanced mathematics has quietly insinuated itself into many facets of our daytoday life. Mathematics is at the heart of technologies from cellular telephones and satellite positioning systems to online banking and metal detectors. Arguably no technology has had a more positive and profound effect on our lives than medical imaging, and in no technology is the role of mathematics more pronounced or less appreciated. Xray tomography, ultrasound, positron emission tomography, and magnetic resonance imaging have fundamentally altered the practice of medicine. At the core of each modality is a mathematical model to interpret the measurements and a numerical algorithm to reconstruct an image. While each modality operates on a different physical principle and probes a different aspect of our anatomy or physiology, there is a large overlap in the mathematics used to model the measurements, design reconstruction algorithms, and analyze the effects of noise. In this text we provide a tool kit, with detailed operating instructions, to work on the sorts of mathematical problems that arise in medical imaging. Our treatment steers a course midway between a complete, rigorous mathematical discussion and a cookbook engineering approach. The target audience for this book is junior or senior math undergraduates with a firm command of multivariable calculus, linear algebra over the real and complex numbers, and the basic facts of mathematical analysis. Some familiarity with basic physics would also be useful. The book is written in the language of mathematics, which, as I have learned, is quite distinct from the language of physics or the language of engineering. Nonetheless, the discussion of every topic begins at an elementary level and the book should, with a little translation, be usable by advanced science and engineering students with some mathematical sophistication. A large part of the mathematical background material is provided in two appendices. Xray tomography is employed as a pedagogical machine, similar in spirit to the elaborate devices used to illustrate the principles of Newtonian mechanics. The physical principles used in xray tomography are simple to describe and require little formal background in physics to understand. This is not the case in any of the other modalities listed nor in less developed modalities like infrared imaging or impedance tomography. The mathematical problems that arise in xray tomography and the tools used to solve them have a great deal in common with those used in the other imaging modalities. This is why our title is Introduction to the Mathematics of Medical Imaging instead of Introduction to the Mathematics of XRay Tomography. A student with a thorough understanding of the material in this book should be mathematically prepared for further investigations in most subfields of medical imaging.
Introduction to the Mathematics of Medical Imaging. <br/> Over the past several decades, advanced mathematics has quietly insinuated itself into many facets of our daytoday life. Mathematics is at the heart of technologies from cellular telephones and satellite positioning systems to online banking and metal detectors. Arguably no technology has had a more positive and profound effect on our lives than medical imaging, and in no technology is the role of mathematics more pronounced or less appreciated. Xray tomography, ultrasound, positron emission tomography, and magnetic resonance imaging have fundamentally altered the practice of medicine. At the core of each modality is a mathematical model to interpret the measurements and a numerical algorithm to reconstruct an image. While each modality operates on a different physical principle and probes a different aspect of our anatomy or physiology, there is a large overlap in the mathematics used to model the measurements, design reconstruction algorithms, and analyze the effects of noise. In this text we provide a tool kit, with detailed operating instructions, to work on the sorts of mathematical problems that arise in medical imaging. Our treatment steers a course midway between a complete, rigorous mathematical discussion and a cookbook engineering approach. The target audience for this book is junior or senior math undergraduates with a firm command of multivariable calculus, linear algebra over the real and complex numbers, and the basic facts of mathematical analysis. Some familiarity with basic physics would also be useful. The book is written in the language of mathematics, which, as I have learned, is quite distinct from the language of physics or the language of engineering. Nonetheless, the discussion of every topic begins at an elementary level and the book should, with a little translation, be usable by advanced science and engineering students with some mathematical sophistication. A large part of the mathematical background material is provided in two appendices. Xray tomography is employed as a pedagogical machine, similar in spirit to the elaborate devices used to illustrate the principles of Newtonian mechanics. The physical principles used in xray tomography are simple to describe and require little formal background in physics to understand. This is not the case in any of the other modalities listed nor in less developed modalities like infrared imaging or impedance tomography. The mathematical problems that arise in xray tomography and the tools used to solve them have a great deal in common with those used in the other imaging modalities. This is why our title is Introduction to the Mathematics of Medical Imaging instead of Introduction to the Mathematics of XRay Tomography. A student with a thorough understanding of the material in this book should be mathematically prepared for further investigations in most subfields of medical imaging. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2007/9780898717792/9780898717792/20131218/9780898717792.cover.jpg" alttext="cover image"/></p>
Introduction to the Mathematics of Medical Imaging
doi:10.1137/9780898717792
Introduction to the Mathematics of Medical Imaging
20131218T03:01:29Z
10.1137/9780898717792
https://epubs.siam.org/doi/book/10.1137/9780898717792?af=R
© 2007 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

A Primer on Mathematical Models in Biology
https://epubs.siam.org/doi/book/10.1137/1.9781611972504?af=R
A Primer on Mathematical Models in Biology. <br/> It was on a placid canoe trip at a Gordon Conference in 2002 that Lee Segel told me that he was writing a new book in mathematical biology. In the prime of his health and at the peak of his mathematical career, Lee asked me to agree to act as shepherd to this book “in case” anything happened to prevent his completion of the project. The request was purely “academic” at that time, and I agreed to this formal arrangement with the certainty that it would require no actual work. It came as a great shock that Lee Segel passed away on January 31, 2005, after a sudden and devastating illness. This was a great loss to his many friends, students, coworkers, and admirers in the applied mathematics and mathematical biology communities.
A Primer on Mathematical Models in Biology. <br/> It was on a placid canoe trip at a Gordon Conference in 2002 that Lee Segel told me that he was writing a new book in mathematical biology. In the prime of his health and at the peak of his mathematical career, Lee asked me to agree to act as shepherd to this book “in case” anything happened to prevent his completion of the project. The request was purely “academic” at that time, and I agreed to this formal arrangement with the certainty that it would require no actual work. It came as a great shock that Lee Segel passed away on January 31, 2005, after a sudden and devastating illness. This was a great loss to his many friends, students, coworkers, and admirers in the applied mathematics and mathematical biology communities. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2013/1.9781611972504/1.9781611972504/20130321/1.9781611972504.cover.jpg" alttext="cover image"/></p>
A Primer on Mathematical Models in Biology
doi:10.1137/1.9781611972504
Lee A. Segel
Leah EdelsteinKeshet
A Primer on Mathematical Models in Biology
20130321T04:49:38Z
10.1137/1.9781611972504
https://epubs.siam.org/doi/book/10.1137/1.9781611972504?af=R
© 2013 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Mathematical Biofluiddynamics
https://epubs.siam.org/doi/book/10.1137/1.9781611970517?af=R
Mathematical Biofluiddynamics. <br/> A treatment in book form of the material in the lecture course delivered to the “Mathematical Biofluiddynamics” Research Conference of the National Science Foundation held from July 16–20, 1973 at Rensselaer Polytechnic Institute, Troy, New York.
Mathematical Biofluiddynamics. <br/> A treatment in book form of the material in the lecture course delivered to the “Mathematical Biofluiddynamics” Research Conference of the National Science Foundation held from July 16–20, 1973 at Rensselaer Polytechnic Institute, Troy, New York. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/cb/1975/1.9781611970517/1.9781611970517/20130213/1.9781611970517.cover.jpg" alttext="cover image"/></p>
Mathematical Biofluiddynamics
doi:10.1137/1.9781611970517
Sir James Lighthill
Mathematical Biofluiddynamics
20130213T06:13:48Z
10.1137/1.9781611970517
https://epubs.siam.org/doi/book/10.1137/1.9781611970517?af=R
All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, Philadelphia, PA 191042688.

Mathematical Models for Communicable Diseases
https://epubs.siam.org/doi/book/10.1137/1.9781611972429?af=R
Mathematical Models for Communicable Diseases. <br/> These lecture notes collect the material used for the talks delivered by Fred Brauer and Carlos CastilloChavez in the CBMS workshop Mathematical Epidemiology with Applications funded by the National Science Foundation and held at East Tennessee State University (ETSU) from July 25–29, 2011. The goal of the lectures was to reach all participants, a population that includes researchers with backgrounds in the biological, epidemiological, mathematical, and medical sciences as well as individuals involved in the development, implementation, and evaluation of public health policy. It was assumed that participants were cognizant of the value and utility of mathematical models in the study and control of infectious diseases because of their use to increase our understanding of disease dynamics, their value in the evaluation of possible prevention/intervention/control policies, their key role in the exploration of “what if” scenarios systematically, and their use in assessing and/or reducing the levels of uncertainty naturally associated with the unpredictability of disease outbreaks, disease severity, and disease evolution.
Mathematical Models for Communicable Diseases. <br/> These lecture notes collect the material used for the talks delivered by Fred Brauer and Carlos CastilloChavez in the CBMS workshop Mathematical Epidemiology with Applications funded by the National Science Foundation and held at East Tennessee State University (ETSU) from July 25–29, 2011. The goal of the lectures was to reach all participants, a population that includes researchers with backgrounds in the biological, epidemiological, mathematical, and medical sciences as well as individuals involved in the development, implementation, and evaluation of public health policy. It was assumed that participants were cognizant of the value and utility of mathematical models in the study and control of infectious diseases because of their use to increase our understanding of disease dynamics, their value in the evaluation of possible prevention/intervention/control policies, their key role in the exploration of “what if” scenarios systematically, and their use in assessing and/or reducing the levels of uncertainty naturally associated with the unpredictability of disease outbreaks, disease severity, and disease evolution. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/cb/2012/1.9781611972429/1.9781611972429/20121228/1.9781611972429.cover.jpg" alttext="cover image"/></p>
Mathematical Models for Communicable Diseases
doi:10.1137/1.9781611972429
Fred Brauer
Carlos CastilloChavez
Mathematical Models for Communicable Diseases
20121228T09:06:30Z
10.1137/1.9781611972429
https://epubs.siam.org/doi/book/10.1137/1.9781611972429?af=R
© 2013 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Cardiovascular and Respiratory Systems
https://epubs.siam.org/doi/book/10.1137/1.9780898717457?af=R
Cardiovascular and Respiratory Systems. <br/> Efforts in modeling cardiovascular and respiratory control have been ongoing for a number of years. These efforts include seminal work by A. C. Guyton and coworkers and F. Grodins and coworkers in the 1950s and 1960s. In particular, over the last decade there has been a rapid increase in modeling activities in this area as technology has advanced both for simulation and for data collection. It is now possible to consider a wide range of features from the cellular level to the macroscopic level as well as the complex interaction of control processes and to ask harder questions concerning interdependencies between systems. Indeed, as a consequence of these capabilities, the Physiome project (see Section 5.3) was recently formed to coordinate knowledge about such issues. Often, asking the right question or focusing on a crucial physiological mechanism is more important than utilizing sheer computational power to simulate and capture data behavior. Control mechanisms provide the basis for maintaining homeostasis at various levels in living systems. Such processes tend to have their own local operational goals, and yet physiological systems are interdependent and interact. However, such interaction could conceivably diminish overall performance. Nevertheless, these critical control systems seem to be well coordinated and to function complementarily. This raises very interesting evolutionary and organizational questions. The range of control processes involved in the effective regulation of human cardiovascular and respiratory systems includes a number of global and local mechanisms. Modeling efforts are certainly required to elucidate these complex interdependencies. As a consequence of the progress in relevant mathematical disciplines and the availability of increasingly powerful computing tools, models are becoming more realistic which allows for the adaptation to individual persons in the clinical setting and thereby makes possible the development of diagnostic and therapeutic tools. This book does not aim to exhaustively examine all research efforts in this field, but to selectively develop some important themes and modeling strategies. The modeling of physiological control systems has resulted in numerous research monographs written over the years. The cardiovascular and respiratory control systems represent an important focal point for developing physiological control theory given the complexity of the control mechanisms involved, the important modes of interaction between cardiovascular and respiratory function, as well as their importance in many clinical situations. In this volume we will bring together contemporary mathematical and control methodologies to study these systems. We will highlight a number of analytical techniques and ideas from optimal control theory, systems theory, and parameter estimation to give the reader an appreciation of how these tools can be utilized to better understand the regulation processes in the cardiovascular and respiratory systems. Modeling efforts are arranged around specific questions or conditions such as exercise or sleep transition and, generally, are based on physiological mechanisms rather than on formal description of inputoutput behavior. We make an effort to emphasize open questions relevant to medical and clinical applications. The context for discussion is on elucidating underlying themes of physiological control organization such as optimization of some process, minimization of quantities such as energy, and possible critical state values, which seem to drive the control design such as maintaining pH levels in a narrow band. We also strive to highlight important questions to be resolved and areas where knowledge is lacking. Throughout the book, we seek to uncover or explain physiological relationships through a first principlebased modeling approach and, in particular, through the analysis of feedback control regulation.
Cardiovascular and Respiratory Systems. <br/> Efforts in modeling cardiovascular and respiratory control have been ongoing for a number of years. These efforts include seminal work by A. C. Guyton and coworkers and F. Grodins and coworkers in the 1950s and 1960s. In particular, over the last decade there has been a rapid increase in modeling activities in this area as technology has advanced both for simulation and for data collection. It is now possible to consider a wide range of features from the cellular level to the macroscopic level as well as the complex interaction of control processes and to ask harder questions concerning interdependencies between systems. Indeed, as a consequence of these capabilities, the Physiome project (see Section 5.3) was recently formed to coordinate knowledge about such issues. Often, asking the right question or focusing on a crucial physiological mechanism is more important than utilizing sheer computational power to simulate and capture data behavior. Control mechanisms provide the basis for maintaining homeostasis at various levels in living systems. Such processes tend to have their own local operational goals, and yet physiological systems are interdependent and interact. However, such interaction could conceivably diminish overall performance. Nevertheless, these critical control systems seem to be well coordinated and to function complementarily. This raises very interesting evolutionary and organizational questions. The range of control processes involved in the effective regulation of human cardiovascular and respiratory systems includes a number of global and local mechanisms. Modeling efforts are certainly required to elucidate these complex interdependencies. As a consequence of the progress in relevant mathematical disciplines and the availability of increasingly powerful computing tools, models are becoming more realistic which allows for the adaptation to individual persons in the clinical setting and thereby makes possible the development of diagnostic and therapeutic tools. This book does not aim to exhaustively examine all research efforts in this field, but to selectively develop some important themes and modeling strategies. The modeling of physiological control systems has resulted in numerous research monographs written over the years. The cardiovascular and respiratory control systems represent an important focal point for developing physiological control theory given the complexity of the control mechanisms involved, the important modes of interaction between cardiovascular and respiratory function, as well as their importance in many clinical situations. In this volume we will bring together contemporary mathematical and control methodologies to study these systems. We will highlight a number of analytical techniques and ideas from optimal control theory, systems theory, and parameter estimation to give the reader an appreciation of how these tools can be utilized to better understand the regulation processes in the cardiovascular and respiratory systems. Modeling efforts are arranged around specific questions or conditions such as exercise or sleep transition and, generally, are based on physiological mechanisms rather than on formal description of inputoutput behavior. We make an effort to emphasize open questions relevant to medical and clinical applications. The context for discussion is on elucidating underlying themes of physiological control organization such as optimization of some process, minimization of quantities such as energy, and possible critical state values, which seem to drive the control design such as maintaining pH levels in a narrow band. We also strive to highlight important questions to be resolved and areas where knowledge is lacking. Throughout the book, we seek to uncover or explain physiological relationships through a first principlebased modeling approach and, in particular, through the analysis of feedback control regulation. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/fr/2007/1.9780898717457/1.9780898717457/production/1.9780898717457.cover.jpg" alttext="cover image"/></p>
Cardiovascular and Respiratory Systems
doi:10.1137/1.9780898717457
Jerry J. Batzel
Franz Kappel
Daniel Schneditz
Hien T. Tran
Cardiovascular and Respiratory Systems
20120525T06:32:38Z
10.1137/1.9780898717457
https://epubs.siam.org/doi/book/10.1137/1.9780898717457?af=R
All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, Philadelphia, PA 191042688.

GenderStructured Population Modeling
https://epubs.siam.org/doi/book/10.1137/1.9780898717488?af=R
GenderStructured Population Modeling. <br/> For thousands of years humans have been occupied, and even preoccupied, with counting. As the species evolved, an interest grew in knowing and forecasting the size of various populations, such as animal herds that humans hunted for their food and clothing or crops their diet depended on. The first mathematical model for populations is attributed to Leonardo Pisano, better known as Fibonacci, who described in a publication dated 1208 the cumulative size of a population of rabbits after n successive generations through his celebrated sequence s0 =1, s1 =1, sn+1 = sn + sn−1 (n≥1) . Among many interesting properties the sequence has, it is nice to note that the ratio of consecutive terms approaches the golden ratio [math] This leads to an exponential growth of the population at an asymptotic rate of 1.618 per generation. Most of the models proposed since then are concerned with a population that may be unstructured, or may be structured according to one or more important features such as age, sex, race, size, and economic status.
GenderStructured Population Modeling. <br/> For thousands of years humans have been occupied, and even preoccupied, with counting. As the species evolved, an interest grew in knowing and forecasting the size of various populations, such as animal herds that humans hunted for their food and clothing or crops their diet depended on. The first mathematical model for populations is attributed to Leonardo Pisano, better known as Fibonacci, who described in a publication dated 1208 the cumulative size of a population of rabbits after n successive generations through his celebrated sequence s0 =1, s1 =1, sn+1 = sn + sn−1 (n≥1) . Among many interesting properties the sequence has, it is nice to note that the ratio of consecutive terms approaches the golden ratio [math] This leads to an exponential growth of the population at an asymptotic rate of 1.618 per generation. Most of the models proposed since then are concerned with a population that may be unstructured, or may be structured according to one or more important features such as age, sex, race, size, and economic status. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/fr/2005/1.9780898717488/1.9780898717488/production/1.9780898717488.cover.jpg" alttext="cover image"/></p>
GenderStructured Population Modeling
doi:10.1137/1.9780898717488
M. Iannelli
M. Martcheva
F. A. Milner
GenderStructured Population Modeling
20120525T06:31:40Z
10.1137/1.9780898717488
https://epubs.siam.org/doi/book/10.1137/1.9780898717488?af=R
All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, Philadelphia, PA 191042688.

Axiomatic Consensus Theory in Group Choice and Biomathematics
https://epubs.siam.org/doi/book/10.1137/1.9780898717501?af=R
Axiomatic Consensus Theory in Group Choice and Biomathematics. <br/> Ubiquitous in data analysis are problems of aggregation such as this one: Given a set [math] of objects of interest, identify an appropriate consensus rule [math] that, when presented a ktuple P of objects of [math], returns from [math] a unique consensus object that in some sense best represents P. Solving this problem requires that we evaluate consensus rules in terms of the basic properties or axioms that they satisfy. But for this problem one can sometimes obtain a fundamental impossibility result by first listing a set of seemingly reasonable axioms that any consensus rule on [math] should satisfy, then proving that no such rule can exist. When it occurs, this contradiction encourages researchers to explore the extent to which the axioms can be weakened, while still maintaining the contradiction, and to explore how to alter the axioms so as to eliminate the contradiction. Desirable consequences of the latter analysis are characterizations of consensus rules in terms of axioms with which users may assess the appropriateness of the rules for given applications. Thus, although the axiomatic approach may seem to be abstract and purely technical, its practical and concrete aspects enable us to distinguish between what is realizable and what is not.
Axiomatic Consensus Theory in Group Choice and Biomathematics. <br/> Ubiquitous in data analysis are problems of aggregation such as this one: Given a set [math] of objects of interest, identify an appropriate consensus rule [math] that, when presented a ktuple P of objects of [math], returns from [math] a unique consensus object that in some sense best represents P. Solving this problem requires that we evaluate consensus rules in terms of the basic properties or axioms that they satisfy. But for this problem one can sometimes obtain a fundamental impossibility result by first listing a set of seemingly reasonable axioms that any consensus rule on [math] should satisfy, then proving that no such rule can exist. When it occurs, this contradiction encourages researchers to explore the extent to which the axioms can be weakened, while still maintaining the contradiction, and to explore how to alter the axioms so as to eliminate the contradiction. Desirable consequences of the latter analysis are characterizations of consensus rules in terms of axioms with which users may assess the appropriateness of the rules for given applications. Thus, although the axiomatic approach may seem to be abstract and purely technical, its practical and concrete aspects enable us to distinguish between what is realizable and what is not. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/fr/2003/1.9780898717501/1.9780898717501/production/1.9780898717501.cover.jpg" alttext="cover image"/></p>
Axiomatic Consensus Theory in Group Choice and Biomathematics
doi:10.1137/1.9780898717501
William H. E. Day
F. R. McMorris
Axiomatic Consensus Theory in Group Choice and Biomathematics
20120525T06:29:49Z
10.1137/1.9780898717501
https://epubs.siam.org/doi/book/10.1137/1.9780898717501?af=R
All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, Philadelphia, PA 191042688.

Bioterrorism: Mathematical Modeling Applications in Homeland Security
https://epubs.siam.org/doi/book/10.1137/1.9780898717518?af=R
Bioterrorism: Mathematical Modeling Applications in Homeland Security. <br/> The Centers for Disease Control established and developed intelligence epidemiological services in the early 1950s. This decision, driven by national concerns on the potential use of biological agents as a source of terror, was one of the first systematic responses to bioterrorism. The horror of September 11 and the events that followed have shown that the delivery of biological agents can be carried out by the systematic use of humans as well as by nontraditional means (such as mail). Recent acts using anthrax have highlighted the use of biological and toxic agents as weapons of mass destruction as well as psychological agents of terror. Speculative discussions on the possible impact of the deliberate release of viruses such as smallpox into unsuspecting human populations have taken place from time to time over the years. The possible genetic manipulation of highly variable viruses such as influenza, for which society might not have an effective vaccine in storage, and their deliberate release are sources of great concern. The current SARS epidemic and its social and economic impact have revived the fears and concerns that were experienced during the anthrax scare of 2001. The avian flu epidemic (in April 2003) in the Netherlands has sent a strong reminder that we must now be prepared to live in a world where the impact of local “perturbations” is felt almost instantly everywhere. Globalization and the possibility of bioterrorist acts have increased the demand for the development of theoretical and practical frameworks that can anticipate and predict the effects of initiation and considered response to acts of destabilization. Theoretical frameworks and the development of models to respond to specific focused questions will be useful to identify key pressure points in the system, to test for robust system features, and to look at the importance of system modularity and redundancy in addressing threats to various systems. Modeling and system interrogation in the presence of uncertainty have also become key areas of investigation, in which much work remains to be done.
Bioterrorism: Mathematical Modeling Applications in Homeland Security. <br/> The Centers for Disease Control established and developed intelligence epidemiological services in the early 1950s. This decision, driven by national concerns on the potential use of biological agents as a source of terror, was one of the first systematic responses to bioterrorism. The horror of September 11 and the events that followed have shown that the delivery of biological agents can be carried out by the systematic use of humans as well as by nontraditional means (such as mail). Recent acts using anthrax have highlighted the use of biological and toxic agents as weapons of mass destruction as well as psychological agents of terror. Speculative discussions on the possible impact of the deliberate release of viruses such as smallpox into unsuspecting human populations have taken place from time to time over the years. The possible genetic manipulation of highly variable viruses such as influenza, for which society might not have an effective vaccine in storage, and their deliberate release are sources of great concern. The current SARS epidemic and its social and economic impact have revived the fears and concerns that were experienced during the anthrax scare of 2001. The avian flu epidemic (in April 2003) in the Netherlands has sent a strong reminder that we must now be prepared to live in a world where the impact of local “perturbations” is felt almost instantly everywhere. Globalization and the possibility of bioterrorist acts have increased the demand for the development of theoretical and practical frameworks that can anticipate and predict the effects of initiation and considered response to acts of destabilization. Theoretical frameworks and the development of models to respond to specific focused questions will be useful to identify key pressure points in the system, to test for robust system features, and to look at the importance of system modularity and redundancy in addressing threats to various systems. Modeling and system interrogation in the presence of uncertainty have also become key areas of investigation, in which much work remains to be done. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/fr/2003/1.9780898717518/1.9780898717518/production/1.9780898717518.cover.jpg" alttext="cover image"/></p>
Bioterrorism: Mathematical Modeling Applications in Homeland Security
doi:10.1137/1.9780898717518
Bioterrorism: Mathematical Modeling Applications in Homeland Security
20120525T06:30:09Z
10.1137/1.9780898717518
https://epubs.siam.org/doi/book/10.1137/1.9780898717518?af=R
All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, Philadelphia, PA 191042688.

Probabilistic Boolean Networks
https://epubs.siam.org/doi/book/10.1137/1.9780898717631?af=R
Probabilistic Boolean Networks. <br/> It was around the period of World War II that Arturo Rosenblueth and Norbert Wiener were taking the first steps in the direction of systems medicine. They formed an interesting pair: Rosenblueth, a physiologist at the Harvard Medical School, and Wiener, the father of modern engineering in the United States. For this book, their conception of science is salient. They wrote, “The intention and the result of a scientific inquiry is to obtain an understanding and a control of some part of the universe.” [1] For them, as a research team, the part of the universe was physiology. An appreciation of their words is important. Understanding is not some vague, subjective explanation, but rather the precision of mathematical systems needed for the representation of relationships between measurable quantities and future predictions based on those relationships. Control is the ability to change physical behavior in a manner concomitant with the mathematical system representing the relevant phenomena. Rosenblueth and Wiener take an active view of science: it is to change the world. In contemporary terminology, rather than science, one might say that they were describing translational science. “Translational science transforms a scientific mathematical model, whose purpose is to provide a predictive conceptualization of some portion of the physical world, into a model characterizing human intervention (action) in the physical world. Whereas the pure scientist typically tries to minimize human interference, translational science extends science to include conceptualization of humanoriginated action in the physical world and thereby raises epistemological issues relating to the knowledge of this intentional intervention into the natural order. Scientific knowledge is translated into practical knowledge by expanding a scientific system to include inputs that can be adjusted to affect the behavior of the system and outputs that can be used to monitor the effect of the external inputs and feed back information on how to adjust the inputs.” [2] It is this translational scientific view that Wiener brought into line with modern science during his illustrious career. In perhaps the greatest transformation of engineering epistemology since antiquity, Wiener fundamentally altered the way human beings perceive scientifically based action in the world. Teaming with Rosenblueth, he brought that transformation into medicine.
Probabilistic Boolean Networks. <br/> It was around the period of World War II that Arturo Rosenblueth and Norbert Wiener were taking the first steps in the direction of systems medicine. They formed an interesting pair: Rosenblueth, a physiologist at the Harvard Medical School, and Wiener, the father of modern engineering in the United States. For this book, their conception of science is salient. They wrote, “The intention and the result of a scientific inquiry is to obtain an understanding and a control of some part of the universe.” [1] For them, as a research team, the part of the universe was physiology. An appreciation of their words is important. Understanding is not some vague, subjective explanation, but rather the precision of mathematical systems needed for the representation of relationships between measurable quantities and future predictions based on those relationships. Control is the ability to change physical behavior in a manner concomitant with the mathematical system representing the relevant phenomena. Rosenblueth and Wiener take an active view of science: it is to change the world. In contemporary terminology, rather than science, one might say that they were describing translational science. “Translational science transforms a scientific mathematical model, whose purpose is to provide a predictive conceptualization of some portion of the physical world, into a model characterizing human intervention (action) in the physical world. Whereas the pure scientist typically tries to minimize human interference, translational science extends science to include conceptualization of humanoriginated action in the physical world and thereby raises epistemological issues relating to the knowledge of this intentional intervention into the natural order. Scientific knowledge is translated into practical knowledge by expanding a scientific system to include inputs that can be adjusted to affect the behavior of the system and outputs that can be used to monitor the effect of the external inputs and feed back information on how to adjust the inputs.” [2] It is this translational scientific view that Wiener brought into line with modern science during his illustrious career. In perhaps the greatest transformation of engineering epistemology since antiquity, Wiener fundamentally altered the way human beings perceive scientifically based action in the world. Teaming with Rosenblueth, he brought that transformation into medicine. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2010/1.9780898717631/1.9780898717631/production/1.9780898717631.cover.jpg" alttext="cover image"/></p>
Probabilistic Boolean Networks
doi:10.1137/1.9780898717631
Ilya Shmulevich
Edward R. Dougherty
Probabilistic Boolean Networks
20120525T07:11:58Z
10.1137/1.9780898717631
https://epubs.siam.org/doi/book/10.1137/1.9780898717631?af=R
All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, Philadelphia, PA 191042688.

Learning MATLAB
https://epubs.siam.org/doi/book/10.1137/1.9780898717662?af=R
Learning MATLAB. <br/> The purpose of this book is to introduce the essentials of the MATLAB® software environment and to show how to start using it well. MATLAB began life as a friendly interface to numerical libraries for linear algebra. Every variable in MATLAB was a matrix, which made it easy to learn how to solve certain core problems and interact with the results. Over time, as interest in MATLAB shifted from pedagogy to larger and more complex applications, the limitations and annoyances of programming with only a textbased interface to matrices became apparent. In accordance, MATLAB added higher dimensionality, many more data types, graphical and objectoriented interfaces, and loads of additional technical and helper functions. The result has been spectacularly, perhaps uniquely, successful both commercially and in terms of educational influence. Few successes are unqualified, however, and one price of the success of MATLAB has been the loss of its initial simplicity and unity of concept. Upon starting MATLAB version 4 in 1992, one got a simple prompt demanding that the user start defining variables and running functions on them. Upon starting MATLAB 7 in 2008, one gets four tabbed windows, six main menus, and a dozen or so clickable buttons. The prompt is still there, but it is now just one familiar face in a crowd. This observation is not meant as a Luddite screed against the perils of progress! Yet complexity has made the job of getting to know MATLAB more difficult—or so it would seem, given the cornucopia of books now available that have as their primary purpose the aim of teaching it.
Learning MATLAB. <br/> The purpose of this book is to introduce the essentials of the MATLAB® software environment and to show how to start using it well. MATLAB began life as a friendly interface to numerical libraries for linear algebra. Every variable in MATLAB was a matrix, which made it easy to learn how to solve certain core problems and interact with the results. Over time, as interest in MATLAB shifted from pedagogy to larger and more complex applications, the limitations and annoyances of programming with only a textbased interface to matrices became apparent. In accordance, MATLAB added higher dimensionality, many more data types, graphical and objectoriented interfaces, and loads of additional technical and helper functions. The result has been spectacularly, perhaps uniquely, successful both commercially and in terms of educational influence. Few successes are unqualified, however, and one price of the success of MATLAB has been the loss of its initial simplicity and unity of concept. Upon starting MATLAB version 4 in 1992, one got a simple prompt demanding that the user start defining variables and running functions on them. Upon starting MATLAB 7 in 2008, one gets four tabbed windows, six main menus, and a dozen or so clickable buttons. The prompt is still there, but it is now just one familiar face in a crowd. This observation is not meant as a Luddite screed against the perils of progress! Yet complexity has made the job of getting to know MATLAB more difficult—or so it would seem, given the cornucopia of books now available that have as their primary purpose the aim of teaching it. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2009/1.9780898717662/1.9780898717662/production/1.9780898717662.cover.jpg" alttext="cover image"/></p>
Learning MATLAB
doi:10.1137/1.9780898717662
Tobin A. Driscoll
Learning MATLAB
20120525T07:08:26Z
10.1137/1.9780898717662
https://epubs.siam.org/doi/book/10.1137/1.9780898717662?af=R
All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, Philadelphia, PA 191042688.

Applied Mathematical Models in Human Physiology
https://epubs.siam.org/doi/book/10.1137/1.9780898718287?af=R
Applied Mathematical Models in Human Physiology. <br/> The purpose of this book is to study mathematical models of human physiology. The book is a result of work by MathTech (in Copenhagen, Denmark) and the BioMath group at the Department of Mathematics and Physics at Roskilde University (in Roskilde, Denmark) on mathematical models related to anesthesia simulation. The work presented in this book has been carried out as part of a larger project, SIMA (SIMulation in Anesthesia), which has resulted in the production of a commercially available anesthesia simulator and several scientific research publications contributing to the understanding of human physiology. This book contains the scientific contributions and does not discuss the details of the models implemented in the SIMA project. In order to develop an anesthesia simulator, it is necessary to model many aspects of the physiology in the human body. This book is devoted to presenting models reflecting current research relevant to cardiovascular and pulmonary physiology. In particular this book presents models describing blood flow in the heart and the cardiovascular system, as well as transport of oxygen and carbon dioxide through the respiratory system. The models presented describe several aspects of the physiology, and it is our hope that this book may provide inspiration for researchers entering this area of study and for advanced undergraduate and graduate students in applied mathematics, biophysics, physiology, and bioengineering. Each of the chapters presents a unique model that can be read independently of the other chapters. Chapters 5 and 6 have been used in graduate level courses in applied mathematics at Roskilde University, Boston University, and North Carolina State University. Moreover, most of Chapters 2 to 8 have been used in projectorganized and problembased student activities at graduate and advanced undergraduate levels at Roskilde University. When using the book in a traditional organized course, we suggest that the students use the models as a collection of examples that can serve as inspiration in their own modeling. Since we find it important that, in a modeling situation, students be involved in both formulating and solving problems, we have not included traditional exercises at the end of each chapter.
Applied Mathematical Models in Human Physiology. <br/> The purpose of this book is to study mathematical models of human physiology. The book is a result of work by MathTech (in Copenhagen, Denmark) and the BioMath group at the Department of Mathematics and Physics at Roskilde University (in Roskilde, Denmark) on mathematical models related to anesthesia simulation. The work presented in this book has been carried out as part of a larger project, SIMA (SIMulation in Anesthesia), which has resulted in the production of a commercially available anesthesia simulator and several scientific research publications contributing to the understanding of human physiology. This book contains the scientific contributions and does not discuss the details of the models implemented in the SIMA project. In order to develop an anesthesia simulator, it is necessary to model many aspects of the physiology in the human body. This book is devoted to presenting models reflecting current research relevant to cardiovascular and pulmonary physiology. In particular this book presents models describing blood flow in the heart and the cardiovascular system, as well as transport of oxygen and carbon dioxide through the respiratory system. The models presented describe several aspects of the physiology, and it is our hope that this book may provide inspiration for researchers entering this area of study and for advanced undergraduate and graduate students in applied mathematics, biophysics, physiology, and bioengineering. Each of the chapters presents a unique model that can be read independently of the other chapters. Chapters 5 and 6 have been used in graduate level courses in applied mathematics at Roskilde University, Boston University, and North Carolina State University. Moreover, most of Chapters 2 to 8 have been used in projectorganized and problembased student activities at graduate and advanced undergraduate levels at Roskilde University. When using the book in a traditional organized course, we suggest that the students use the models as a collection of examples that can serve as inspiration in their own modeling. Since we find it important that, in a modeling situation, students be involved in both formulating and solving problems, we have not included traditional exercises at the end of each chapter. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/mm/2004/1.9780898718287/1.9780898718287/production/1.9780898718287.cover.jpg" alttext="cover image"/></p>
Applied Mathematical Models in Human Physiology
doi:10.1137/1.9780898718287
Johnny T. Ottesen
Mette S. Olufsen
Jesper K. Larsen
Applied Mathematical Models in Human Physiology
20120525T06:35:44Z
10.1137/1.9780898718287
https://epubs.siam.org/doi/book/10.1137/1.9780898718287?af=R
All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, Philadelphia, PA 191042688.

The Analysis of Means
https://epubs.siam.org/doi/book/10.1137/1.9780898718362?af=R
The Analysis of Means. <br/> The goal of statistical data analysis is to use data to gain and communicate knowledge about processes and phenomena. Comparing means is often part of an analysis, for data arising in both experimental and observational studies. Probably the most common method used to compare the means of several different treatments (or, more loosely, groups arising from stratification) is the analysis of variance (ANOVA). The analysis of means (ANOM) is an alternative procedure for comparing means. While it cannot be used in all the same settings as the ANOVA, when one is specifically interested in comparing means, such as when looking at fixed main effects in a designed experiment, ANOM has the advantages of being much more intuitive and providing an easily understood graphical result, which clearly indicates any means that are different (from the overall mean) and allows for easy assessment of practical as well as statistical significance. The graphical result is easy for nonstatisticians to understand and offers a clear advantage over ANOVA in that it sheds light on the nature of the differences among the populations. There have been a number of advances in ANOM procedures in the last 20 years, but many of these results have appeared in fairly technical papers. ANOM is actually a multiple comparisons procedure, and the theory behind it is more complicated than that for ANOVA. Rather than dealing with univariate F distributions, one ends up with multivariate negatively correlated singular t distributions. However, the necessary critical values, power curves, and sample sizes for the ANOM procedures have already been obtained, documented and, in some instances, included in statistical software, resulting in methods that are easy to apply and with results that are easy to interpret. Our intent in writing this book was to present the first modern comprehensive treatment of ANOM containing the information necessary for comparing means using ANOM. The book is intended to be a useful guide for practitioners, not a detailed description of the theory behind the procedures. Only as much theory as was necessary to understand and implement the various ANOM techniques is included. Most of the applications of ANOM that have appeared in the literature are from the physical sciences and engineering. However, ANOM techniques are much more broadly applicable; thus, we have included many examples from other areas, including business, medicine, health care, quality control, and the social sciences. Note that the comparison of means is used in a rather broad sense in that it also includes the comparison of Poisson rates and binomial proportions.
The Analysis of Means. <br/> The goal of statistical data analysis is to use data to gain and communicate knowledge about processes and phenomena. Comparing means is often part of an analysis, for data arising in both experimental and observational studies. Probably the most common method used to compare the means of several different treatments (or, more loosely, groups arising from stratification) is the analysis of variance (ANOVA). The analysis of means (ANOM) is an alternative procedure for comparing means. While it cannot be used in all the same settings as the ANOVA, when one is specifically interested in comparing means, such as when looking at fixed main effects in a designed experiment, ANOM has the advantages of being much more intuitive and providing an easily understood graphical result, which clearly indicates any means that are different (from the overall mean) and allows for easy assessment of practical as well as statistical significance. The graphical result is easy for nonstatisticians to understand and offers a clear advantage over ANOVA in that it sheds light on the nature of the differences among the populations. There have been a number of advances in ANOM procedures in the last 20 years, but many of these results have appeared in fairly technical papers. ANOM is actually a multiple comparisons procedure, and the theory behind it is more complicated than that for ANOVA. Rather than dealing with univariate F distributions, one ends up with multivariate negatively correlated singular t distributions. However, the necessary critical values, power curves, and sample sizes for the ANOM procedures have already been obtained, documented and, in some instances, included in statistical software, resulting in methods that are easy to apply and with results that are easy to interpret. Our intent in writing this book was to present the first modern comprehensive treatment of ANOM containing the information necessary for comparing means using ANOM. The book is intended to be a useful guide for practitioners, not a detailed description of the theory behind the procedures. Only as much theory as was necessary to understand and implement the various ANOM techniques is included. Most of the applications of ANOM that have appeared in the literature are from the physical sciences and engineering. However, ANOM techniques are much more broadly applicable; thus, we have included many examples from other areas, including business, medicine, health care, quality control, and the social sciences. Note that the comparison of means is used in a rather broad sense in that it also includes the comparison of Poisson rates and binomial proportions. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/sa/2005/1.9780898718362/1.9780898718362/production/1.9780898718362.cover.jpg" alttext="cover image"/></p>
The Analysis of Means
doi:10.1137/1.9780898718362
Peter R. Nelson
Peter S. Wludyka
Karen A. F. Copeland
The Analysis of Means
20120525T07:16:41Z
10.1137/1.9780898718362
https://epubs.siam.org/doi/book/10.1137/1.9780898718362?af=R
All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, Philadelphia, PA 191042688.

Design and Analysis of Gauge R&R Studies
https://epubs.siam.org/doi/book/10.1137/1.9780898718379?af=R
Design and Analysis of Gauge R&R Studies. <br/> This book is written to accomplish two objectives. First, we develop a protocol for testing a measurement system. Second, we provide an uptodate summary of methods used to construct confidence intervals in normalbased random and mixed analysis of variance (ANOVA) models. To accomplish the first objective, we consider testing a measurement system using a gauge repeatability and reproducibility (R&R) experiment. Gauge R&R experiments use ANOVA designs to determine if a measurement system is capable of monitoring a manufacturing process. These experiments are conducted in practically every manufacturing setting and are critical in any process improvement initiative. Although the applications in the book are specifically concerned with gauge R&R studies, the methods can be used for any application based on an ANOVA model. Thus, our second objective is to make these methods known for investigators in other fields of research. The methods we present can be used to construct confidence intervals for parameters in any random or mixed ANOVA model. We describe methods for constructing two types of confidence intervals: modified largesample (MLS) and generalized confidence intervals (GCI). MLS intervals provide closedform intervals that can be easily computed in a spreadsheet. The GCI method is computationally more intensive but is more general in its application than MLS. Both methods provide intervals that generally maintain the stated confidence coefficient for any sample size. Although statistical techniques are emphasized throughout the book, an extensive background in statistics is not essential. The prerequisites are an ability to interpret a confidence interval and a basic understanding of random and mixed ANOVA models. Readers unfamiliar with these topics can gain the required background by reading Chapter 1 and Appendix A.
Design and Analysis of Gauge R&R Studies. <br/> This book is written to accomplish two objectives. First, we develop a protocol for testing a measurement system. Second, we provide an uptodate summary of methods used to construct confidence intervals in normalbased random and mixed analysis of variance (ANOVA) models. To accomplish the first objective, we consider testing a measurement system using a gauge repeatability and reproducibility (R&R) experiment. Gauge R&R experiments use ANOVA designs to determine if a measurement system is capable of monitoring a manufacturing process. These experiments are conducted in practically every manufacturing setting and are critical in any process improvement initiative. Although the applications in the book are specifically concerned with gauge R&R studies, the methods can be used for any application based on an ANOVA model. Thus, our second objective is to make these methods known for investigators in other fields of research. The methods we present can be used to construct confidence intervals for parameters in any random or mixed ANOVA model. We describe methods for constructing two types of confidence intervals: modified largesample (MLS) and generalized confidence intervals (GCI). MLS intervals provide closedform intervals that can be easily computed in a spreadsheet. The GCI method is computationally more intensive but is more general in its application than MLS. Both methods provide intervals that generally maintain the stated confidence coefficient for any sample size. Although statistical techniques are emphasized throughout the book, an extensive background in statistics is not essential. The prerequisites are an ability to interpret a confidence interval and a basic understanding of random and mixed ANOVA models. Readers unfamiliar with these topics can gain the required background by reading Chapter 1 and Appendix A. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/sa/2005/1.9780898718379/1.9780898718379/production/1.9780898718379.cover.jpg" alttext="cover image"/></p>
Design and Analysis of Gauge R&R Studies
doi:10.1137/1.9780898718379
Richard K. Burdick
Connie M. Borror
Douglas C. Montgomery
Design and Analysis of Gauge R&R Studies
20120525T07:16:53Z
10.1137/1.9780898718379
https://epubs.siam.org/doi/book/10.1137/1.9780898718379?af=R
All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, Philadelphia, PA 191042688.