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This book compiles and presents a complete package of open-access Python software code for luminescence signal analysis in the areas of radiation dosimetry, luminescence dosimetry, and luminescence dating. Featuring more than 90 detailed worked examples of Python code, fully integrated into the text, 16 chapters summarize the theory and equations behind the subject matter, while presenting the practical Python codes used to analyze experimental data and extract the various parameters that mathematically describe the luminescence signals. Several examples are provided of how researchers can use and modify the available codes for different practical situations. Types of luminescence signals analyzed in the book are thermoluminescence (TL), isothermal luminescence (ITL), optically stimulated luminescence (OSL), infrared stimulated luminescence (IRSL), timeresolved luminescence (TR) and dose response of dosimetric materials. The open-access Python codes are available at GitHub. The book is well suited to the broader scientific audience using the tools of luminescence dosimetry: physicists, geologists, archaeologists, solid-state physicists, medical physicists, and all scientists using luminescence dosimetry in their research. The detailed code provided allows both students and researchers to be trained quickly and efficiently on the practical aspects of their work, while also providing an overview of the theory behind the analytical equations.
This advanced undergraduate textbook presents a new approach to teaching mathematical methods for scientists and engineers. It provides a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses. Both analytical and computational examples are integrated from its start. Each chapter concludes with a set of problems designed to help students hone their skills in mathematical techniques, computer programming, and numerical analysis. The book places less emphasis on mathematical proofs, and more emphasis on how to use computers for both symbolic and numerical calculations. It contains 182 extensively documented coding examples, based on topics that students will encounter in their advanced courses in Mechanics, Electronics, Optics, Electromagnetism, Quantum Mechanics etc. An introductory chapter gives students a crash course in Python programming and the most often used libraries (SymPy, NumPy, SciPy, Matplotlib). This is followed by chapters dedicated to differentiation, integration, vectors and multiple integration techniques. The next group of chapters covers complex numbers, matrices, vector analysis and vector spaces. Extensive chapters cover ordinary and partial differential equations, followed by chapters on nonlinear systems and on the analysis of experimental data using linear and nonlinear regression techniques, Fourier transforms, binomial and Gaussian distributions. The book is accompanied by a dedicated GitHub website, which contains all codes from the book in the form of ready to run Jupyter notebooks. A detailed solutions manual is also available for instructors using the textbook in their courses. Key Features: A unique teaching approach which merges mathematical methods and the Python programming skills which physicists and engineering students need in their courses Uses examples and models from physical and engineering systems, to motivate the mathematics being taught Students learn to solve scientific problems in three different ways: traditional pen-and-paper methods, using scientific numerical techniques with NumPy and SciPy, and using Symbolic Python (SymPy).
A highly practical reference for health physicists and other professionals, addressing practical problems in radiation protection, this new edition has been completely revised, updated and supplemented by such new sections as log-normal distribution and digital radiography, as well as new chapters on internal radiation dose and the environmental transport of radionuclides. Designed for readers with limited as well as basic science backgrounds, the handbook presents clear, thorough and up-to-date explanations of the basic physics necessary. It provides an overview of the major discoveries in radiation physics, plus extensive discussion of radioactivity, including sources and materials, as well as calculational methods for radiation exposure, comprehensive appendices and more than 400 figures. The text draws substantially on current resource data available, which is cross-referenced to standard compendiums, providing decay schemes and emission energies for approximately 100 of the most common radionuclides encountered by practitioners. Excerpts from the Chart of the Nuclides, activation cross sections, fission yields, fission-product chains, photon attenuation coefficients, and nuclear masses are also provided. Throughout, the author emphasizes applied concepts and carefully illustrates all topics using real-world examples as well as exercises. A much-needed working resource for health physicists and other radiation protection professionals.
The economic importance of lactic acid bacteria (LAB) for the food industry and their implication in health and disease has rendered them attractive models for research in many laboratories around the world. Over the past three decades, molecular and genetic analysis of LAB species provided important insights into the biology and application of starter and probiotic LAB and in the virulence of LAB pathogens. The knowledge obtained prepared LAB researchers for the forthcoming opportunities provided by the advent of microbial genomics. Today, developments in next-generation sequencing technologies have rocketed LAB genome research and the sequences of several hundreds of strains are available. This flood of information has revolutionized our view of LAB. First of all, a detailed picture has emerged about the evolutionary mechanisms allowing LAB to inhabit the very diverge ecological niches in which they can be found. Adaptation of LAB to nutrient-rich environments has led to degenerative evolution processes that resulted in shortening of chromosomes and simplified metabolic potential. Gene acquisition through horizontal transfer, on the other hand, is also important in shaping LAB gene pools. Horizontally acquired genes have been shown to be essential in technological properties of starters and in probiosis or virulence of commensals. Progress in bioinformatics tools has allowed rapid annotation of LAB genomes and the direct assignment of genetic traits among species/strains through comparative genomics. In this way, the molecular basis of many important traits of LAB has been elucidated, including aspects of sugar fermentation, flavor and odor formation, production of textural substances, stress responses, colonization of and survival in the host, cell-tocell interactions and pathogenicity. Functional genomics and proteomics have been employed in a number of instances to support in silico predictions. Given that the costs of advanced next-generation methodologies like RNA-seq are dropping fast, bottlenecks in the in silico characterization of LAB genomes will be rapidly overcome. Another crucial advancement in LAB research is the application of systems biology approaches, by which the properties and interactions of components or parts of a biological system are investigated to accurately understand or predict LAB behavior. Practically, systems biology involves the mathematical modeling of complex biological systems that can be refined iteratively with wet-lab experiments. High-throughput experimentation generating huge amounts of data on the properties and quantities of many components such as transcripts, enzymes and metabolites has resulted in several systems models of LAB. Novel techniques allow modelling of additional levels of complexity including the function of small RNAs, structural features of RNA molecules and post-translational modifications. In addition, researchers have started to apply systems approaches in the framework of LAB multispecies ecosystems in which each species or strain is considered as a part of the system. Metatransciptomics, metaproteomics and metametabolomics offer the means to combine cellular behavior with population dynamics in microbial consortia.
In this book, the authors give an up-to-date account of thermoluminescence (TL) and other thermally stimulated phenomena. Although most recent experimental results of TL in different materials are described in some detail, the main emphasis in the present book is on general processes, and the approach is more theoretical. Thus the details of the possible processes which can take place during the excitation of the sample, and during its heating, are carefully analysed. The methods for analysing TL glow curves are critically discussed, and recommendations as to their application are made. Also discussed is the expected behavior of these phenomena as functions of the experimental parameters, for example, dose of excitation. The consequences of the main applications of TL (for example, radiation dosimetry) are also discussed in detail as are the similarities and dissimilarities of other thermally stimulated phenomena, and the simultaneous measurements of the latter and TL.
​This book covers applications of R to the general discipline of radiation dosimetry and to the specific areas of luminescence dosimetry, luminescence dating, and radiation protection dosimetry. It features more than 90 detailed worked examples of R code fully integrated into the text, with extensive annotations. The book shows how researchers can use available R packages to analyze their experimental data, and how to extract the various parameters describing mathematically the luminescence signals. In each chapter, the theory behind the subject is summarized, and references are given from the literature, so that researchers can look up the details of the theory and the relevant experiments. Several chapters are dedicated to Monte Carlo methods, which are used to simulate the luminescence processes during the irradiation, heating, and optical stimulation of solids, for a wide variety of materials. This book will be useful to those who use the tools of luminescence dosimetry, including physicists, geologists, archaeologists, and for all researchers who use radiation in their research.
This is an introductory to intermediate level text on the science of image processing, which employs the Matlab programming language to illustrate some of the elementary, key concepts in modern image processing and pattern recognition. The approach taken is essentially practical and the book offers a framework within which the concepts can be understood by a series of well chosen examples, exercises and computer experiments, drawing on specific examples from within science, medicine and engineering. Clearly divided into eleven distinct chapters, the book begins with a fast-start introduction to image processing to enhance the accessibility of later topics. Subsequent chapters offer increasingly advanced discussion of topics involving more challenging concepts, with the final chapter looking at the application of automated image classification (with Matlab examples) . Matlab is frequently used in the book as a tool for demonstrations, conducting experiments and for solving problems, as it is both ideally suited to this role and is widely available. Prior experience of Matlab is not required and those without access to Matlab can still benefit from the independent presentation of topics and numerous examples. Features a companion website www.wiley.com/go/solomon/fundamentals containing a Matlab fast-start primer, further exercises, examples, instructor resources and accessibility to all files corresponding to the examples and exercises within the book itself. Includes numerous examples, graded exercises and computer experiments to support both students and instructors alike.
Thermoluminescence (TL) is a well-established technique widely used in do- metric and dating applications. Although several excellent reference books exist which document both the t- oretical and experimental aspects of TL, there is a general lack of books that deal withspeci?cnumericalandpracticalaspectsofanalyzingTLdata. Manytimesthe practicaldetailsofanalyzingnumericalTLglowcurvesandofapplyingtheoretical models are dif?cult to ?nd in the published literature. The purpose of this book is to provide a practical guide for both established researchers and for new graduate students entering the ?eld of TL and is intended to be used in conjunction with and as a practical supplement of standard textbooks in the ?eld. Chapter1laysthemathematicalgroundworkforsubsequentchaptersbyprese- ingthefundamentalmathematicalexpressionsmostcommonlyusedforanalyzing experimental TL data. Chapter2presentscomprehensiveexamplesofTLdataanalysisforglowcurves following ?rst-, second-, and general-order kinetics. Detailed analysis of num- ical data is presented by using a variety of methods found in the TL literature, with particular emphasis in the practical aspects and pitfalls that researchers may encounter. Special emphasis is placed on the need to use several different me- ods to analyze the same TL data, as well as on the necessity to analyze glow curves obtained under different experimental conditions. Unfortunately, the lit- ature contains many published papers that claim a speci?c kinetic order for a TL peak in a dosimetric material, based only on a peak shape analysis. It is hoped that the detailed examples provided in Chapter 2 will encourage more comprehensive studies of TL properties of materials, based on the simultaneous use of several different methods of analysis.
Thermoluminescence (TL) and optically stimulated luminescence (OSL) are two of the most important techniques used in radiation dosimetry. They have extensive practical applications in the monitoring of personnel radiation exposure, in medical dosimetry, environmental dosimetry, spacecraft, nuclear reactors, food irradiation etc., and in geological /archaeological dating. Thermally and Optically Stimulated Luminescence: A Simulation Approach describes these phenomena, the relevant theoretical models and their prediction, using both approximations and numerical simulation. The authors concentrate on an alternative approach in which they simulate various experimental situations by numerically solving the relevant coupled differential equations for chosen sets of parameters. Opening with a historical overview and background theory, other chapters cover experimental measurements, dose dependence, dating procedures, trapping parameters, applications, radiophotoluminescence, and effects of ionization density. Designed for practitioners, researchers and graduate students in the field of radiation dosimetry, Thermally and Optically Stimulated Luminescence provides an essential synthesis of the major developments in modeling and numerical simulations of thermally and optically stimulated processes.
Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including Kaplan–Meier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwood’s formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslow’s method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.