F# for Scientists

Geostatistics is essential for environmental scientists. Weather and climate vary from place to place, soil varies at every scale at which it is examined, and even man-made attributes – such as the distribution of pollution – vary. The techniques used in geostatistics are ideally suited to the needs of environmental scientists, who use them to make the best of sparse data for prediction, and top plan future surveys when resources are limited. Geostatistical technology has advanced much in the last few years and many of these developments are being incorporated into the practitioner’s repertoire. This second edition describes these techniques for environmental scientists. Topics such as stochastic simulation, sampling, data screening, spatial covariances, the variogram and its modeling, and spatial prediction by kriging are described in rich detail. At each stage the underlying theory is fully explained, and the rationale behind the choices given, allowing the reader to appreciate the assumptions and constraints involved.

The book introduces most of the basic tools of chemometrics including experimental design, signal analysis, statistical methods for analytical chemistry and multivariate methods. It then discusses a number of important applications including food chemistry, biological pattern recognition, reaction monitoring, optimisation of processes, medical applications. The book arises from a series of short articles that have been developed over four years on Chemweb (www.chemweb.com).

A comprehesive yet concise guide to Modern HPLC Written for practitioners by a practitioner, Modern HPLC for Practicing Scientists is a concise text which presents the most important High-Performance Liquid Chromatography (HPLC) fundamentals, applications, and developments. It describes basic theory and terminology for the novice, and reviews relevant concepts, best practices, and modern trends for the experienced practitioner. Moreover, the book serves well as an updated reference guide for busy laboratory analysts and researchers. Topics covered include: HPLC operation Method development Maintenance and troubleshooting Modern trends in HPLC such as quick-turnaround and «greener» methods Regulatory aspects While broad in scope, this book focuses particularly on reversed-phase HPLC, the most common separation mode, and on applications for the pharmaceutical industry, the largest user segment. Accessible to both novice and intermedate HPLC users, information is delivered in a straightforward manner illustrated with an abundance of diagrams, chromatograms, tables, and case studies, and supported with selected key references and Web resources. With intuitive explanations and clear figures, Modern HPLC for Practicing Scientists is an essential resource for practitioners of all levels who need to understand and utilize this versatile analytical technology.

Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field    Deep Learning for Physical Scientists: Accelerating Research with Machine Learning  delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome.  Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems. Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader.  From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy:  A thorough introduction to the basic classification and regression with perceptrons An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training An examination of multi-layer perceptrons for learning from descriptors and de-noising data Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images A treatment of Bayesian optimization for tuning deep learning architectures Perfect for academic and industrial research professionals in the physical sciences,  Deep Learning for Physical Scientists: Accelerating Research with Machine Learning  will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access.  Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. [/i] This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: •Basic classification and regression with perceptrons •Training

Enlightens readers on the realities of global atmospheric change, including global warming and poor air quality Climate change and air pollution are two of the most pressing issues facing Mankind. This book gives undergraduate and graduate students, researchers and professionals working in the science and policy of pollution, climate change and air quality a broad and up-to-date account of the processes that occur in the atmosphere, how these are changing as Man’s relentless use of natural resources continues, and what effects these changes are having on the Earth’s climate and the quality of the air we breathe. Written by an international team of experts, Atmospheric Science for Environmental Scientists, 2 nd Edition provides an excellent overview of our current understanding of the state of the Earth’s atmosphere and how it is changing. The first half of the book covers: the climate of the Earth; chemical evolution of the atmosphere; atmospheric energy and the structure of the atmosphere; biogeochemical cycles; and tropospheric chemistry and air pollution. The second half looks at cloud formation and chemistry; particulate matter in the atmosphere; stratospheric chemistry and ozone depletion; boundary layer meteorology and atmospheric dispersion; urban air pollution; and global warming and climate change science. Provides succinct but detailed information on all the important aspects of atmospheric science for students Offers the most up-to-date treatment of key issues such as stratospheric chemistry, urban air pollution, and climate change Each chapter includes basic concepts, end-of-section questions, and more in-depth material Features contributions from the best experts and educators in the field of atmospheric science Atmospheric Science for Environmental Scientists, 2 nd Edition is an invaluable resource for students, teachers, and professionals involved in environmental science. It will also appeal to those interested in learning how the atmosphere works, how humankind is changing its composition, and what effects these changes are leading to.

Learn to fully harness the power of Microsoft Excel® to perform scientific and engineering calculations With this text as your guide, you can significantly enhance Microsoft Excel's® capabilities to execute the calculations needed to solve a variety of chemical, biochemical, physical, engineering, biological, and medicinal problems. The text begins with two chapters that introduce you to Excel's Visual Basic for Applications (VBA) programming language, which allows you to expand Excel's® capabilities, although you can still use the text without learning VBA. Following the author's step-by-step instructions, here are just a few of the calculations you learn to perform: Use worksheet functions to work with matrices Find roots of equations and solve systems of simultaneous equations Solve ordinary differential equations and partial differential equations Perform linear and non-linear regression Use random numbers and the Monte Carlo method This text is loaded with examples ranging from very basic to highly sophisticated solutions. More than 100 end-of-chapter problems help you test and put your knowledge to practice solving real-world problems. Answers and explanatory notes for most of the problems are provided in an appendix. The CD-ROM that accompanies this text provides several useful features: All the spreadsheets, charts, and VBA code needed to perform the examples from the text Solutions to most of the end-of-chapter problems An add-in workbook with more than twenty custom functions This text does not require any background in programming, so it is suitable for both undergraduate and graduate courses. Moreover, practitioners in science and engineering will find that this guide saves hours of time by enabling them to perform most of their calculations with one familiar spreadsheet package

A modern, evaluative, and integrative approach to diagnostic microbiology encouraging problem-solving in the clinical laboratory context through the use of examples to illustrate clinical and diagnostic issues Clinical Microbiology for Diagnostic Laboratory Scientists is designed to encourage readers to develop a way of thinking that can be applied to any diagnostic scenario in microbiology. Through consideration of a selected range of infections caused by pathogenic bacteria, viruses, fungi, protozoa, and helminths, the book encourages readers to explore connections between the available information about clinical symptoms, pathogenesis of infections, and the approaches used in laboratory diagnosis, in order to develop new insights. The book begins with an introductory chapter that outlines the scope of clinical diagnostic microbiology and the key areas for the laboratory scientist to be aware of. The subsequent six chapters review a type of infection in depth, using particular pathogenic microorganisms to illustrate salient points. At the end of each chapter there are three exercises related to management of a diagnostic service and assessing the suitability of test methods to specific contexts. There are no right or wrong answers to these, but the reader can discuss them with their laboratory colleagues or university tutor. Makes extensive use of published research in the form of journal articles, publically available epidemiological data, professional guidelines, and specialist websites Stimulates the reader in critical appraisal of published evidence and encourages problem-solving in the laboratory Outlines the scope of clinical diagnostic microbiology and the key areas for the laboratory scientist to be aware of Considers topics relevant to professional scientists working in the area of diagnostic microbiology Clinical Microbiology for Diagnostic Laboratory Scientists is ideal for post graduate scientists intending to pursue careers in diagnostic clinical microbiology and for biomedical scientists, clinical scientists, and full time students studying for upper level qualifications in biomedical science, microbiology, or virology.

A comprehensive treatment of statistical applications for solving real-world environmental problems A host of complex problems face today's earth science community, such as evaluating the supply of remaining non-renewable energy resources, assessing the impact of people on the environment, understanding climate change, and managing the use of water. Proper collection and analysis of data using statistical techniques contributes significantly toward the solution of these problems. Statistics for Earth and Environmental Scientists presents important statistical concepts through data analytic tools and shows readers how to apply them to real-world problems. The authors present several different statistical approaches to the environmental sciences, including Bayesian and nonparametric methodologies. The book begins with an introduction to types of data, evaluation of data, modeling and estimation, random variation, and sampling—all of which are explored through case studies that use real data from earth science applications. Subsequent chapters focus on principles of modeling and the key methods and techniques for analyzing scientific data, including: Interval estimation and Methods for analyzinghypothesis testing of means time series data Spatial statistics Multivariate analysis Discrete distributions Experimental design Most statistical models are introduced by concept and application, given as equations, and then accompanied by heuristic justification rather than a formal proof. Data analysis, model building, and statistical inference are stressed throughout, and readers are encouraged to collect their own data to incorporate into the exercises at the end of each chapter. Most data sets, graphs, and analyses are computed using R, but can be worked with using any statistical computing software. A related website features additional data sets, answers to selected exercises, and R code for the book's examples. Statistics for Earth and Environmental Scientists is an excellent book for courses on quantitative methods in geology, geography, natural resources, and environmental sciences at the upper-undergraduate and graduate levels. It is also a valuable reference for earth scientists, geologists, hydrologists, and environmental statisticians who collect and analyze data in their everyday work.

Effective Experimentation is a practical book on how to design and analyse experiments. Each of the methods are introduced and illustrated through real world scenario drawn from industry or research. Formulae are kept to a minimum to enable the reader to concentrate on how to apply and understand the different methods presented. The book has been developed from courses run by Statistics for Industry Limited during which time more than 10,000 scientists and technologists have gained the knowledge and confidence to plan experiments successfully and to analyse their data. Each chapter starts with an example of a design obtained from the authors’ experience. Statistical methods for analysing data are introduced, followed, where appropriate, by a discussion of the assumptions of the method and effectiveness and limitations of the design. The examples have been chosen from many industries including chemicals, oils, building materials, textiles, food, drink, lighting, water, pharmaceuticals, electronics, paint, toiletries and petfoods. This book is a valuable resource for researchers and industrial statisticians involved in designing experiments. Postgraduates studying statistics, engineering and mathematics will also find this book of interest.