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).

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.

Paper-based Optical Chemosensors comprehensively discusses the origin, development, and current state-of-the-art in paper-based sensors. With a focus on the principles, classifications, methodology, design, and application of paper-based sensors, this book represents a developing research field with recent innovative applications resulting in a comprehensive presentation of the different physico-chemical techniques using paper sensors. It discloses underlying rules and factors in paper-based sensors and discusses intricate sensing systems and working environments by ways of chemistry and physics for a variety of application scenarios such as environmental protection, food safety, public safety, and clinical diagnosis. This is a valuable resource for researchers who major in analytical chemistry, or for those who are interested in the development of methods or devices for rapid analysis/monitoring based on paper/membrane-based sensors who wish to broaden their knowledge in the allied field. - Presents a comprehensive discussion on the current state, challenges, and future perspectives of paper-based optical chemosensors - Offers discussions on the classification, methodology, design, and application of paper based sensors - Provides opportunities for readers to design paper based sensors with specific purpose and deeper awareness

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.