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Advances in Distance Learning in Times of Pandemic

Advances in Distance Learning in Times of Pandemic

The book Advances in Distance Learning in Times of Pandemic is devoted to the issues and challenges faced by universities in the field of distance learning in COVID-19 times. It covers both the theoretical and practical aspects connected to distance education. It elaborates on issues regarding distance learning its challenges assessment by students and their expectations the use of tools to improve distance learning and the functioning of e-learning in the industry 4. 0 and society 5. 0 eras. The book also devotes a lot of space to the issues of Web 3. 0 in university e-learning quality assurance and knowledge management. The aim and scope of this book is to draw a holistic picture of ongoing online teaching-activities before and during the lockdown period and present the meaning and future of e-learning from students’ points of view taking into consideration their attitudes and expectations as well as industry 4. 0 and society 5. 0 aspects. The book presents the approach to distance learning and how it has changed especially during a pandemic that revolutionized education. It highlights • the function of online education and how that has changed before and during the pandemic. • how e-learning is beneficial in promoting digital citizenship. • distance learning characteristic in the era of industry 4. 0 and society 5. 0. • how the era of industry 4. 0 treats distance learning as a desirable form of education. The book covers both scientific and educational aspects and can be useful for university-level undergraduate postgraduate and research-grade courses and can be referred to by anyone interested in exploring the diverse aspects of distance learning.

GBP 110.00
1

Correspondence Analysis in Practice

Statistical Models in Toxicology

Statistical Models in Toxicology

Statistical Models in Toxicology presents an up-to-date and comprehensive account of statistical theory topics that occur in toxicology. The attention given by statisticians to the problem of health risk estimation for environmental and occupational exposures in the last few decades has created excitement and optimism among both statisticians and toxicologists. The development of modern statistical techniques with solid mathematical foundations in the twentieth century and the advent of modern computers in the latter part of the century gave way to the development of many statistical models and methods to describe toxicological processes and attempts to solve the associated problems. Not only have the models enjoyed a high level of elegance and sophistication mathematically but they are widely used by industry and government regulatory agencies. Features:Focuses on describing the statistical models in environmental toxicology that facilitate the assessment of risk mainly in humans. The properties and shortfalls of each model are discussed and its impact in the process of risk assessment is examined. Discusses models that assess the risk of mixtures of chemicals. Presents statistical models that are developed for risk estimation in different aspects of environmental toxicology including cancer and carcinogenic substances. Includes models for developmental and reproductive toxicity risk assessment risk assessment in continuous outcomes and developmental neurotoxicity. Contains numerous examples and exercises. Statistical Models in Toxicology introduces a wide variety of statistical models that are currently utilized for dose-response modeling and risk analysis. These models are often developed based on design and regulatory guidelines of toxicological experiments. The book is suitable for practitioners or it can be used as a textbook for advanced undergraduate or graduate students of mathematics and statistics.

GBP 44.99
1

Visualizing Surveys in R

Statistics in Engineering With Examples in MATLAB and R Second Edition

Statistics in Engineering With Examples in MATLAB and R Second Edition

Engineers are expected to design structures and machines that can operate in challenging and volatile environments while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments. The first eight chapters cover probability and probability distributions graphical displays of data and descriptive statistics combinations of random variables and propagation of error statistical inference bivariate distributions and correlation linear regression on a single predictor variable and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include: All examples based on work in industry consulting to industry and research for industry Examples and case studies include all engineering disciplinesEmphasis on probabilistic modeling including decision trees Markov chains and processes and structure functionsIntuitive explanations are followed by succinct mathematical justificationsEmphasis on random number generation that is used for stochastic simulations of engineering systems demonstration of key concepts and implementation of bootstrap methods for inferenceUse of MATLAB and the open source software R both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applicationsUse of multiple regression for times series models and analysis of factorial and central composite designs Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooksExperiments designed to show fundamental concepts that have been tested with large classes working in small groupsWebsite with additional materials that is regularly updatedAndrew Metcalfe David Green Andrew Smith and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering mining and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health University of South Australia. Tony Greenfield formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for | Statistics in Engineering With Examples in MATLAB® and R Second Edition

GBP 44.99
1

Exercises in Programming Style

Exercises in Programming Style

The first edition of Exercises in Programming Style was honored as an ACM Notable Book and praised as The best programming book of the decade. This new edition retains the same presentation but has been upgraded to Python 3 and there is a new section on neural network styles. Using a simple computational task (term frequency) to illustrate different programming styles Exercises in Programming Style helps readers understand the various ways of writing programs and designing systems. It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs. The book contains 40 different styles for writing the term frequency task. The styles are grouped into ten categories: historical basic function composition objects and object interactions reflection and metaprogramming adversity data-centric concurrency interactivity and neural networks. The author states the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style next shows an example program and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.

GBP 35.99
1

Mathematics in Cyber Research

Mathematics in Cyber Research

In the last decade both scholars and practitioners have sought novel ways to address the problem of cybersecurity. Innovative outcomes have included applications such as blockchain as well as creative methods for cyber forensics software development and intrusion prevention. Accompanying these technological advancements discussion on cyber matters at national and international levels has focused primarily on the topics of law policy and strategy. The objective of these efforts is typically to promote security by establishing agreements among stakeholders on regulatory activities. Varying levels of investment in cyberspace however comes with varying levels of risk; in some ways this can translate directly to the degree of emphasis for pushing substantial change. At the very foundation or root of cyberspace systems and processes are tenets and rules governed by principles in mathematics. Topics such as encrypting or decrypting file transmissions modeling networks performing data analysis quantifying uncertainty measuring risk and weighing decisions or adversarial courses of action represent a very small subset of activities highlighted by mathematics. To facilitate education and a greater awareness of the role of mathematics in cyber systems and processes a description of research in this area is needed. Mathematics in Cyber Research aims to familiarize educators and young researchers with the breadth of mathematics in cyber-related research. Each chapter introduces a mathematical sub-field describes relevant work in this field associated with the cyber domain provides methods and tools as well as details cyber research examples or case studies. Features One of the only books to bring together such a diverse and comprehensive range of topics within mathematics and apply them to cyber research. Suitable for college undergraduate students or educators that are either interested in learning about cyber-related mathematics or intend to perform research within the cyber domain. The book may also appeal to practitioners within the commercial or government industry sectors. Most national and international venues for collaboration and discussion on cyber matters have focused primarily on the topics of law policy strategy and technology. This book is among the first to address the underpinning mathematics.

GBP 160.00
1

Deep Learning in Practice

Polynomial Completeness in Algebraic Systems

Formal Methods in Computer Science

Data Science in Practice

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning Entropy Randomization in Machine Learning considers several applications to binary classification modelling the dynamics of the Earth’s population predicting seasonal electric load fluctuations of power supply systems and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing through structuring randomized models and algorithmic procedure to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning researchers and engineers involved in the development of applied machine learning systems and researchers of forecasting problems in various fields.

GBP 82.99
1

Statistics in MATLAB A Primer

Kinetic Modelling in Systems Biology

Kinetic Modelling in Systems Biology

With more and more interest in how components of biological systems interact it is important to understand the various aspects of systems biology. Kinetic Modelling in Systems Biology focuses on one of the main pillars in the future development of systems biology. It explores both the methods and applications of kinetic modeling in this emerging field. The book introduces the basic biological cellular network concepts in the context of cellular functioning explains the main aspects of the Edinburgh Pathway Editor (EPE) software package and discusses the process of constructing and verifying kinetic models. It presents the features user interface and examples of DBSolve as well as the principles of modeling individual enzymes and transporters. The authors describe how to construct kinetic models of intracellular systems on the basis of models of individual enzymes. They also illustrate how to apply the principles of kinetic modeling to collect all available information on the energy metabolism of whole organelles construct a kinetic model and predict the response of the organelle to changes in external conditions. The final chapter focuses on applications of kinetic modeling in biotechnology and biomedicine. Encouraging readers to think about future challenges this book will help them understand the kinetic modeling approach and how to apply it to solve real-life problems. Downloadable Resources FeaturesExtensively used throughout the text for pathway visualization and illustration the EPE software is available on the accompanying downloadable resources. The downloadable resources also include pathway diagrams in several graphical formats DBSolve installation with examples and all models from the book with dynamic visualization of simulation results allowing readers to perform in silico simulations and use the models as templates for further applications.

GBP 56.99
1

Topics in Graph Theory

Statistics in Toxicology Using R

Bayesian Methods in Pharmaceutical Research

Bayesian Methods in Pharmaceutical Research

Since the early 2000s there has been increasing interest within the pharmaceutical industry in the application of Bayesian methods at various stages of the research development manufacturing and health economic evaluation of new health care interventions. In 2010 the first Applied Bayesian Biostatistics conference was held with the primary objective to stimulate the practical implementation of Bayesian statistics and to promote the added-value for accelerating the discovery and the delivery of new cures to patients. This book is a synthesis of the conferences and debates providing an overview of Bayesian methods applied to nearly all stages of research and development from early discovery to portfolio management. It highlights the value associated with sharing a vision with the regulatory authorities academia and pharmaceutical industry with a view to setting up a common strategy for the appropriate use of Bayesian statistics for the benefit of patients. The book covers:Theory methods applications and computingBayesian biostatistics for clinical innovative designsAdding value with Real World EvidenceOpportunities for rare orphan diseases and pediatric developmentApplied Bayesian biostatistics in manufacturingDecision making and Portfolio management Regulatory perspective and public health policies Statisticians and data scientists involved in the research development and approval of new cures will be inspired by the possible applications of Bayesian methods covered in the book. The methods applications and computational guidance will enable the reader to apply Bayesian methods in their own pharmaceutical research. | Bayesian Methods in Pharmaceutical Research

GBP 44.99
1

Knowledge Discovery in Proteomics

Knowledge Discovery in Proteomics

Multi-modal representations the lack of complete and consistent domain theories rapid evolution of domain knowledge high dimensionality and large amounts of missing information - these are challenges inherent in modern proteomics. As our understanding of protein structure and function becomes ever more complicated we have reached a point where the actual management of data is a major stumbling block to the interpretation of results from proteomic platforms to knowledge discovery. Knowledge Discovery in Proteomics presents timely authoritative discussions on some of the key issues in high-throughput proteomics exploring examples that represent some of the major challenges of knowledge discovery in the field. The authors focus on five specific domains:Mass spectrometry-based protein analysisProtein-protein interaction network analysisSystematic high-throughput protein crystallizationSystematic integrated analysis of multiple data repositoriesSystems biologyIn each area the authors describe the challenges created by the type of data produced and present potential solutions to the problem of data mining within the domain. They take a systems approach covering individual data and integrating its computational aspects from data preprocessing storage and access to analysis visualization and interpretation. With clear exposition practical examples and rich illustrations this book presents an outstanding overview of this emerging field and builds the background needed for the fruitful exchange of ideas between computational and biological scientists.

GBP 59.99
1

Acceptance Sampling in Quality Control

Bioequivalence and Statistics in Clinical Pharmacology

Bioequivalence and Statistics in Clinical Pharmacology

Maintaining a practical perspective Bioequivalence and Statistics in Clinical Pharmacology Second Edition explores statistics used in day-to-day clinical pharmacology work. The book is a starting point for those involved in such research and covers the methods needed to design analyze and interpret bioequivalence trials; explores when how and why these studies are performed as part of drug development; and demonstrates the methods using real world examples. Drawing on knowledge gained directly from working in the pharmaceutical industry the authors set the stage by describing the general role of statistics. Once the foundation of clinical pharmacology drug development regulatory applications and the design and analysis of bioequivalence trials are established including recent regulatory changes in design and analysis and in particular sample-size adaptation they move on to related topics in clinical pharmacology involving the use of cross-over designs. These include but are not limited to safety studies in Phase I dose-response trials drug interaction trials food-effect and combination trials QTc and other pharmacodynamic equivalence trials proof-of-concept trials dose-proportionality trials and vaccines trials. This second edition addresses several recent developments in the field including new chapters on adaptive bioequivalence studies scaled average bioequivalence testing and vaccine trials. Purposefully designed to be instantly applicable Bioequivalence and Statistics in Clinical Pharmacology Second Edition provides examples of SAS and R code so that the analyses described can be immediately implemented. The authors have made extensive use of the proc mixed procedures available in SAS.

GBP 44.99
1

Handbook of Regression Modeling in People Analytics With Examples in R and Python

Handbook of Regression Modeling in People Analytics With Examples in R and Python

Despite the recent rapid growth in machine learning and predictive analytics many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods but with a step-by-step guide and easily reproducible examples and code so that the methods can be put into practice immediately. This makes the book accessible to a wide readership from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e. g. academic corporate sports marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work. | Handbook of Regression Modeling in People Analytics With Examples in R and Python

GBP 66.99
1

Bayesian Applications in Pharmaceutical Development

Bayesian Applications in Pharmaceutical Development

The cost for bringing new medicine from discovery to market has nearly doubled in the last decade and has now reached $2. 6 billion. There is an urgent need to make drug development less time-consuming and less costly. Innovative trial designs/ analyses such as the Bayesian approach are essential to meet this need. This book will be the first to provide comprehensive coverage of Bayesian applications across the span of drug development from discovery to clinical trial to manufacturing with practical examples. This book will have a wide appeal to statisticians scientists and physicians working in drug development who are motivated to accelerate and streamline the drug development process as well as students who aspire to work in this field. The advantages of this book are: Provides motivating worked practical case examples with easy to grasp models technical details and computational codes to run the analyses Balances practical examples with best practices on trial simulation and reporting as well as regulatory perspectives Chapters written by authors who are individual contributors in their respective topics Dr. Mani Lakshminarayanan is a researcher and statistical consultant with more than 30 years of experience in the pharmaceutical industry. He has published over 50 articles technical reports and book chapters besides serving as a referee for several journals. He has a PhD in Statistics from Southern Methodist University Dallas Texas and is a Fellow of the American Statistical Association. Dr. Fanni Natanegara has over 15 years of pharmaceutical experience and is currently Principal Research Scientist and Group Leader for the Early Phase Neuroscience Statistics team at Eli Lilly and Company. She played a key role in the Advanced Analytics team to provide Bayesian education and statistical consultation at Eli Lilly. Dr. Natanegara is the chair of the cross industry-regulatory-academic DIA BSWG to ensure that Bayesian methods are appropriately utilized for design and analysis throughout the drug-development process. | Bayesian Applications in Pharmaceutical Development

GBP 44.99
1

Canonical Problems in Scattering and Potential Theory Part 1 Canonical Structures in Potential Theory