The need to understand and quantify change is fundamental throughout the environmental sciences. This might involve describing past variation, understanding the mechanisms underlying observed changes, making projections of possible future change, or monitoring the effect of intervening in some environmental system. This book provides an overview of modern statistical techniques that may be relevant in problems of this nature. Practitioners studying environmental change will be familiar with many classical statistical procedures for the detection and estimation of trends. However, the ever increasing capacity to collect and process vast amounts of environmental information has led to growing awareness that such procedures are limited in the insights that they can deliver. At the same time, significant developments in statistical methodology have often been widely dispersed in the statistical literature and have therefore received limited exposure in the environmental science community. This book aims to provide a thorough but accessible review of these developments. It is split into two parts: the first provides an introduction to this area and the second part presents a collection of case studies illustrating the practical application of modern statistical approaches to the analysis of trends in real studies. Key Features: Presents a thorough introduction to the practical application and methodology of trend analysis in environmental science. Explores non-parametric estimation and testing as well as parametric techniques. Methods are illustrated using case studies from a variety of environmental application areas. Looks at trends in all aspects of a process including mean, percentiles and extremes. Supported by an accompanying website featuring datasets and R code. The book is designed to be accessible to readers with some basic statistical training, but also contains sufficient detail to serve as a reference for practising statisticians. It will therefore be of use to postgraduate students and researchers both in the environmental sciences and in statistics.

Physically Unclonable Functions (PUFs) translate unavoidable variations in certain parameters of materials, waves, or devices into random and unique signals. They have found many applications in the Internet of Things (IoT), authentication systems, FPGA industry, several other areas in communications and related technologies, and many commercial products. Statistical Trend Analysis of Physically Unclonable Functions first presents a review on cryptographic hardware and hardware-assisted cryptography. The review highlights PUF as a mega trend in research on cryptographic hardware design. Afterwards, the authors present a combined survey and research work on PUFs using a systematic approach. As part of the survey aspect, a state-of-the-art analysis is presented as well as a taxonomy on PUFs, a life cycle, and an established ecosystem for the technology. In another part of the survey, the evolutionary history of PUFs is examined, and strategies for further research in this area are suggested. In the research side, this book presents a novel approach for trend analysis that can be applied to any technology or research area. In this method, a text mining tool is used which extracts 1020 keywords from the titles of the sample papers. Then, a classifying tool classifies the keywords into 295 meaningful research topics. The popularity of each topic is then numerically measured and analyzed over the course of time through a statistical analysis on the number of research papers related to the topic as well as the number of their citations. The authors identify the most popular topics in four different domains; over the history of PUFs, during the recent years, in top conferences, and in top journals. The results are used to present an evolution study as well as a trend analysis and develop a roadmap for future research in this area. This method gives an automatic popularity-based statistical trend analysis which eliminates the need for passing personal judgments about the direction of trends, and provides concrete evidence to the future direction of research on PUFs. Another advantage of this method is the possibility of studying a whole lot of existing research works (more than 700 in this book). This book will appeal to researchers in text mining, cryptography, hardware security, and IoT.

Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.

Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains.

This book covers all types of literature on existing trend analysis approaches, but more than 60% of the methodologies are developed here and some of them are reflected to scientific literature and others are also innovative versions, modifications or improvements. The suggested methodologies help to design, develop, manage and deliver scientific applications and training to meet the needs of interested staff in companies, industries and universities including students. Technical content and expertise are also provided from different theoretical and especially active roles in the design, development and delivery of science in particular and economics and business in general. It is also ensured that, wherever possible and technically appropriate, priority is given to the inclusion and integration of real life data, examples and processes within the book content. The time seems right, because available books just focus on special sectors (fashion, social, business). This book reviews all the available trend approaches in the present literature on rational and logical bases.

The aim of this encyclopedia is to provide a comprehensive reference work on scientific and other scholarly research on the quality of life, including health-related quality of life research or also called patient-reported outcomes research. Since the 1960s two overlapping but fairly distinct research communities and traditions have developed concerning ideas about the quality of life, individually and collectively, one with a fairly narrow focus on health-related issues and one with a quite broad focus. In many ways, the central issues of these fields have roots extending to the observations and speculations of ancient philosophers, creating a continuous exploration by diverse explorers in diverse historic and cultural circumstances over several centuries of the qualities of human existence. What we have not had so far is a single, multidimensional reference work connecting the most salient and important contributions to the relevant fields. Entries are organized alphabetically and cover basic concepts, relatively well established facts, lawlike and causal relations, theories, methods, standardized tests, biographic entries on significant figures, organizational profiles, indicators and indexes of qualities of individuals and of communities of diverse sizes, including rural areas, towns, cities, counties, provinces, states, regions, countries and groups of countries.