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Risk Analysis: Foundations, Models, and Methods fully addresses the questions of "What is health risk analysis?" and "How can its potentialities be developed to be most valuable to public health decision-makers and other health risk managers?" Risk analysis provides methods and principles for answering these questions. It is divided into methods for assessing, communicating, and managing health risks. Risk assessment quantitatively estimates the health risks to individuals and to groups from hazardous exposures and from the decisions or activities that create them. It applies specialized models and methods to quantify likely exposures and their resulting health risks. Its goal is to produce information to improve decisions. It does this by relating alternative decisions to their probable consequences and by identifying those decisions that make preferred outcomes more likely. Health risk assessment draws on explicit engineering, biomathematical, and statistical consequence models to describe or simulate the causal relations between actions and their probable effects on health. Risk communication characterizes and presents information about health risks and uncertainties to decision-makers and stakeholders. Risk management applies principles for choosing among alternative decision alternatives or actions that affect exposure, health risks, or their consequences.

Bio-organic Chemistry has corne of age - the sign of this is the start of a new series of Lecture Notes that are the product and substrate of spreading this line of modern knowledge among graduate students and research workers in such fields as mechanistic biochemistry, bio mimetic organic chemistry, biotechnological application of enzymology, to name only a few examples of how many frontiers are opened and borders lifted - just at. the time when the demand for a'''Synthetic Biology" and "Molecular Biotechnology" is increasing - fields that have been neglected for (too) long a time by "classical" chemists in curricula and imagination. We hope that through this first volume, which pOints in the several directions mentioned above, the profile of the undertaking will become clear and that it will find resonance among the scientific community interested in the thoughtful application of chemical and physical con cepts to biochemical and molecular-biological problems.

This book provides a rich overview and takes a closer look at the current state of theory and practice in the field of sustainable business models. The chapters in this book examine and analyze existing and new approaches towards sustainable business models and showcase the implementation of sustainable business through both quantitative and qualitative studies, including several case studies and many practical examples. It approaches these issues from the standpoints of diverse business disciplines to yield new insights and ideas that are relevant from both an academic and professional perspective. In its essence, the book examines how firms' value creation processes can be driven by sustainability and social responsibility and how this impacts business and society. Readers will find a range of sustainable business models that have been employed and are being pioneered in various industries around the globe - which are thoroughly investigated and discussed, and put into a comprehensive conceptual framework.

The book explains the impact of bank business models on company business models by discussing the relationship among banks decision-making processes, sustainable values creation in company business models, and ESG risk. The monograph provides a combination of financial and management-related activities, in the context of bank business models, taking into account the concept of sustainability, and will be of particular interest to  both in-house practitioners, giving them innovative knowledge about the models presented and used, and to students and young researchers.The project is financed within the framework of the program of the Minister of Science and Higher Education under the name "Regional Excellence Initiative" in the years 2019 - 2022; project number 001/RID/2018/19; the amount of financing PLN 10,684,000.00.

Some of the best vision scientists in the world in their respective fields have contributed to chapters in this book. They have expertise in a wide variety of fields, including bioengineering, basic and clinical visual science, medicine, neurophysiology, optometry, and psychology. Their combined efforts have resulted in a high quality book that covers modeling and quantitative analysis of optical, neurosensory, oculomotor, perceptual and clinical systems. It includes only those techniques and models that have such fundamentally strong physiological, control system, and perceptual bases that they will serve as foundations for models and analysis techniques in the future. The book is aimed first towards seniors and beginning graduate students in biomedical engineering, neurophysiology, optometry, and psychology, who will gain a broad understanding of quantitative analysis of the visual system. In addition, it has sufficient depth in each area to be useful as an updated reference and tutorial for graduate and post-doctoral students, as well as general vision scientists.Over the past thirty years, with improvements in optics, electronics, and computer technology, great strides have been made in the quantitative analysis of the visual system. A number of books on eye movement research have been written that have dealt with specific aspects of either eye movement control. However, none of these books provide a comprehensive overview of multiple aspects of the visual system. Moreover, few of these books contain modeling and detailed quantitative analyses of the visual system. Further, since the major books are almost ten years old, there is a need for an update to include the most recent research findings. It is with these considerations in mind that we have carefully compiled this updated, comprehensive, and quantitative model-based edited book on various components of the visual system. Some of the best vision scientists in the world in their respective fields have contributed to chapters in this book. They have expertise in a wide variety of fields, including bioengineering, basic and clinical visual science, medicine, neurophysiology, optometry, and psychology. Their combined efforts have resulted in a high quality book that covers modeling and quantitative analysis of optical, neurosensory, oculomotor, perceptual and clinical systems. It includes only those techniques and models that have such fundamentally strong physiological, control system, and perceptual bases that they will serve as foundations for models and analysis techniques in the future. The book is aimed first towards seniors and beginning graduate students in biomedical engineering, neurophysiology, optometry, and psychology, who will gain a broad understanding of quantitative analysis of the visual system. In addition, it has sufficient depth in each area to be useful as an updated reference and tutorial for graduate and post-doctoral students, as well as general vision scientists.

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory.It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

Spatial econometrics deals with spatial dependence and spatial heterogeneity, critical aspects of the data used by regional scientists. These characteristics may cause standard econometric techniques to become inappropriate. In this book, I combine several recent research results to construct a comprehensive approach to the incorporation of spatial effects in econometrics. My primary focus is to demonstrate how these spatial effects can be considered as special cases of general frameworks in standard econometrics, and to outline how they necessitate a separate set of methods and techniques, encompassed within the field of spatial econometrics. My viewpoint differs from that taken in the discussion of spatial autocorrelation in spatial statistics - e.g., most recently by Cliff and Ord (1981) and Upton and Fingleton (1985) - in that I am mostly concerned with the relevance of spatial effects on model specification, estimation and other inference, in what I caIl a model-driven approach, as opposed to a data-driven approach in spatial statistics. I attempt to combine a rigorous econometric perspective with a comprehensive treatment of methodological issues in spatial analysis.

The thirty-one papers collected in this volume represent most of the arti cles that I have published in the philosophy of science and related founda tional areas of science since 1970. The present volume is a natural succes sor to Studies in the Methodology and Foundations of Science, a collection of my articles published in 1969 by Reidel (now a part of Kluwer). The articles are arranged under five main headings. Part I contains six articles on general methodology. The topics range from formal methods to the plurality of science. Part II contains six articles on causality and explanation. The emphasis is almost entirely on probabilistic approaches. Part III contains six articles on probability and measurement. The impor tance of representation theorems for both probability and measurement is stressed. Part IV contains five articles on the foundations of physics. The first three articles are concerned with action at a distance and space and time, the last two with quantum mechanics. Part V contains eight articles on the foundations of psychology. This is the longest part and the articles reflect my continuing strong interest in the nature of learning and perception. Within each part the articles are arranged chronologically. I turn now to a more detailed overview of the content. The first article of Part I concerns the role of formal methods in the philosophy of science. Here I discuss what is the new role for formal methods now that the imperialism of logical positivism has disappeared.

This book provides a comprehensive and consistent introduction to the Internet of Things. Hot topics, including the European privacy legislation GDPR, and homomorphic encryption are explained. For each topic, the reader gets a theoretical introduction and an overview, backed by programming examples. For demonstration, the authors use the IoT platform VICINITY, which is open-source, free, and offers leading standards for privacy. Presents readers with a coherent single-source introduction into the IoT; Introduces selected, hot-topics of IoT, including GDPR (European legislation on data protection), and homomorphic encryption; Provides coding examples for most topics that allow the reader to kick-start his own IoT applications, smart services, etc.

This book presents the current state of the problem of describing the musculoskeletal system of a person. Models of the destruction of the endoskeleton and the restoration of its functions using exoskeleton are presented. A description is given of new approaches to modeling based on the use of weightless rods of variable length with concentrated masses. The practical application to the tasks of numerical simulation of the movements of the musculoskeletal system of a person is described. Exoskeleton models with variable-length units based on absolutely hard sections and sections that change their telescopic type length have been developed.The book is intended for specialists in the field of theoretical mechanics, biomechanics, robotics and related fields. The book will be useful to teachers, as well as graduate students, undergraduates and senior students of higher educational institutions, whose research interests lie in the modeling of anthropomorphic biomechanical systems.

This book explains how Interest-rate models work and shows how to implement them for concrete pricing. The revised 2nd edition of this book incorporates considerable new material, including sections on local-volatility dynamics, and on stochastic volatility models.

Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze data. There exists a gap between the derivation of formulas and analyses that hide these formulas behind attractive user interfaces. This book bridges that gap, demonstrating theory put into practice.§Concepts presented in a theoretical linear models course are often trivialized in applied linear models courses by the facility of high-level SAS procedures like PROC MIXED and PROC REG that require the user to provide a few options and statements and in return produce vast amounts of output. This book uses PROC IML to show how analytic linear models formulas can be typed directly into PROC IML, as they were presented in the linear models course, and solved using data. This helps students see the link between theory and application. This also assists researchers in developing new methodologies in the area of linear models.§The book contains complete examples of SAS code for many of the computations relevant to a linear models course. However, the SAS code in these examples automates the analytic formulas. The code for high-level procedures like PROC MIXED is also included for side-by-side comparison. The book computes basic descriptive statistics, matrix algebra, matrix decomposition, likelihood maximization, non-linear optimization, etc. in a format conducive to a linear models or a special topics course.§Also included in the book is an example of a basic analysis of a linear mixed model using restricted maximum likelihood estimation (REML). The example demonstrates tests for fixed effects, estimates of linear functions, and contrasts. The example starts by showing the steps for analyzing the data using PROC IML and then provides the analysis using PROC MIXED. This allows students to follow the process that lead to the output.

Designed with teaching and learning in mind, this text eases readers into GLMs, beginning with regression. Its accessible content includes chapter summaries, exercises, short answers, clear examples, samples of R code, and the minimum necessary theory.

This work opens with an accessible introduction to computer networks, providing general definitions of commonly used terms in networking. This is followed by a detailed description of the OSI model, including the concepts of connection-oriented and connectionless communications. The text carefully elaborates the specific functions of each layer, along with what is expected of protocols operating at each layer. Next, the journey of a single packet, from source to destination, is described in detail. The final chapter is devoted to the TCP/IP model, beginning with a discussion of IP protocols and the supporting ARP, RARP and In ARP protocols. The work also discusses the TCP and UDP protocols operating at the transport layer and the application layer protocols HTTP, DNS, FTP, TFTP, SMTP, POP3 and Telnet. Important facts and definitions are highlighted in gray boxes found throughout the text.

The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: -A discussion of Big Data and its implications for the design of prediction models -Machine learning issues -More simulations with missing 'y' values -Extended discussion on between-cohort heterogeneity -Description of ShinyApp -Updated LASSO illustration -New case studies

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.

This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects.

Non-life insurance pricing is the art of setting the price of an insurance policy, taking into consideration varoius properties of the insured object and the policy holder. Introduced by British actuaries generalized linear models (GLMs) have become today a the standard aproach for tariff analysis.§The book focuses on methods based on GLMs that have been found useful in actuarial practice and provides a set of tools for a tariff analysis. Basic theory of GLMs in a tariff analysis setting is presented with useful extensions of standarde GLM theory that are not in common use.§The book meets the European Core Syllabus for actuarial education and is written for actuarial students as well as practicing actuaries. To support reader real data of some complexity are provided at www.math.su.se/GLMbook.

This is the third Selecta of publications of Elliott Lieb, the first two being Stabil ity of Matter: From Atoms to Stars, edited by Walter Thirring, and Inequalities, edited by Michael Loss and Mary Beth Ruskai. A companion fourth Selecta on Statistical Mechanics is also edited by us. Elliott Lieb has been a pioneer of the discipline of mathematical physics as it is nowadays understood and continues to lead several of its most active directions today. For the first part of this selecta we have made a selection of Lieb's works on Condensed Matter Physics. The impact of Lieb's work in mathematical con densed matter physics is unrivaled. It is fair to say that if one were to name a founding father of the field, Elliott Lieb would be the only candidate to claim this singular position. While in related fields, such as Statistical Mechanics and Atomic Physics, many key problems are readily formulated in unambiguous mathematical form, this is less so in Condensed Matter Physics, where some say that rigor is "probably impossible and certainly unnecessary". By carefully select ing the most important questions and formulating them as well-defined mathemat ical problems, and then solving a good number of them, Lieb has demonstrated the quoted opinion to be erroneous on both counts. What is true, however, is that many of these problems turn out to be very hard. It is not unusual that they take a decade (even several decades) to solve.