The 130th volume is an eclectic volume inspired by recent issues of interest in research and development in computer science and computer engineering. The volume is a collection of five chapters.

Exam Board: Pearson Edexcel Academic Level: AS level Subject: Mathematics First teaching: September 2017 First Exams: Summer 2018 Each book contains complete sets of practice papers with full worked solutions and hints and notes on the marks allocated directly alongside the relevant steps of the solution, so your students can make most sense of them and build their confidence. Designed to survive the rigours of the classroom and home, all the papers are bound into a durable book. Accessible write-in format allows students to take an active role in their revision.

The Boolean functions may be iterated either asynchronously, when their coordinates are computed independently of each other, or synchronously, when their coordinates are computed at the same time. In Boolean Systems: Topics in Asynchronicity, a book addressed to mathematicians and computer scientists interested in Boolean systems and their use in modelling, author Serban E. Vlad presents a consistent and original mathematical theory of the discrete-time Boolean asynchronous systems. The purpose of the book is to set forth the concepts of such a theory, resulting from the synchronous Boolean system theory and mostly from the synchronous real system theory, by analogy, and to indicate the way in which known synchronous deterministic concepts generate new asynchronous nondeterministic concepts. The reader will be introduced to the dependence on the initial conditions, periodicity, path-connectedness, topological transitivity, and chaos. A property of major importance is invariance, which is present in five versions. In relation to it, the reader will study the maximal invariant subsets, the minimal invariant supersets, the minimal invariant subsets, connectedness, separation, the basins of attraction, and attractors. The stability of the systems and their time-reversal symmetry end the topics that refer to the systems without input. The rest of the book is concerned with input systems. The most consistent chapters of this part of the book refer to the fundamental operating mode and to the combinational systems (systems without feedback). The chapter Wires, Gates, and Flip-Flops presents a variety of applications. The first appendix addresses the issue of continuous time, and the second one sketches the important theory of Daizhan Cheng, which is put in relation to asynchronicity. The third appendix is a bridge between asynchronicity and the symbolic dynamics of Douglas Lind and Brian Marcus.

Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph.

Exam Board: Pearson Edexcel Academic Level: AS level Subject: Mathematics First teaching: September 2017 First Exams: Summer 2018 This Revision Workbook is suitable for classroom and independent study, and is the smart choice for those revising for AS level Mathematics. Organise their revision with the one topic-per-page format Speed up their revision with summary notes in short, memorable chunks Track their revision progress with at-a-glance check boxes Check their understanding with worked examples Develop their exam technique with exam-style practice questions and answers

DNA or Deoxyribonucleic Acid computing is an emerging branch of computing that uses DNA sequence, biochemistry, and hardware for encoding genetic information in computers. Here, information is represented by using the four genetic alphabets or DNA bases, namely A (Adenine), G (Guanine), C (Cytosine), and T (Thymine), instead of the binary representation (1 and 0) used by traditional computers. This is achieved because short DNA molecules of any arbitrary sequence of A, G, C, and T can be synthesized to order. DNA computing is mainly popular for three reasons: (i) speed (ii) minimal storage requirements, and (iii) minimal power requirements. There are many applications of DNA computing in the field of computer science. Nowadays, DNA computing is widely used in cryptography for achieving a strong security technique, so that unauthorized users are unable to retrieve the original data content. In DNA-based encryption, data are encrypted by using DNA bases (A, T, G, and C) instead of 0 and 1. As four DNA bases are used in the encryption process, DNA computing supports more randomness and makes it more complex for attackers or malicious users to hack the data. DNA computing is also used for data storage because a large number of data items can be stored inside the condensed volume. One gram of DNA holds approx DNA bases or approx 700 TB. However, it takes approx 233 hard disks to store the same data on 3 TB hard disks, and the weight of all these hard disks can be approx 151 kilos. In a cloud environment, the Data Owner (DO) stores their confidential encrypted data outside of their own domain, which attracts many attackers and hackers. DNA computing can be one of the best solutions to protect the data of a cloud server. Here, the DO can use DNA bases to encrypt the data by generating a long DNA sequence. Another application of DNA computing is in Wireless Sensor Network (WSN). Many researchers are trying to improve the security of WSN by using DNA computing. Here, DNA cryptography is used along with Secure Socket Layer (SSL) that supports a secure medium to exchange information. However, recent research shows some limitations of DNA computing. One of the critical issues is that DNA cryptography does not have a strong mathematical background like other cryptographic systems. This edited book is being planned to bring forth all the information of DNA computing. Along with the research gaps in the currently available books/literature, this edited book presents many applications of DNA computing in the fields of computer science. Moreover, research challenges and future work directions in DNA computing are also provided in this edited book.

Mathematical Modeling, Simulations, and Artificial Intelligence for Emergent Pandemic Diseases: Lessons Learned from COVID-19 includes new research, models and simulations developed during the COVID-19 pandemic into how mathematical methods and practice can impact future response. Chapters go beyond forecasting COVID-19, bringing different scale angles and mathematical techniques (e.g., ordinary differential and difference equations, agent-based models, artificial intelligence, and complex networks) which could have potential use in modeling other emergent pandemic diseases. A major part of the book focuses on preparing the scientific community for the next pandemic, particularly the application of mathematical modeling in ecology, economics and epidemiology. Readers will benefit from learning how to apply advanced mathematical modeling to a variety of topics of practical interest, including optimal allocations of masks and vaccines but also more theoretical problems such as the evolution of viral variants.

Deep Learning, Volume 48 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Generative Adversarial Networks for Biometric Synthesis, Data Science and Pattern Recognition, Facial Data Analysis, Deep Learning in Electronics, Pattern Recognition, Computer Vision and Image Processing, Mechanical Systems, Crop Technology and Weather, Manipulating Faces for Identity Theft via Morphing and Deepfake, Biomedical Engineering, and more.

Is there a God?

How did it all begin?

Can we predict the future?

What is inside a black hole?

Is there other intelligent life in the universe?

Will artificial intelligence outsmart us?

How do we shape the future?

Will we survive on Earth?

Should we colonise space?

Is time travel possible?

Throughout his extraordinary career, Stephen Hawking expanded our understanding of the universe and unravelled some of its greatest mysteries. But even as his theoretical work on black holes, imaginary time and multiple histories took his mind to the furthest reaches of space, Hawking always believed that science could also be used to fix the problems on our planet.

And now, as we face potentially catastrophic changes here on Earth - from climate change to dwindling natural resources to the threat of artificial super-intelligence - Stephen Hawking turns his attention to the most urgent issues for humankind.

Wide-ranging, intellectually stimulating, passionately argued, and infused with his characteristic humour, Brief Answers to the Big Questions, the final book from one of the greatest minds in history, is a personal view on the challenges we face as a human race, and where we, as a planet, are heading next.

A percentage of all royalties will go to charity.

M-STATISTICS A comprehensive resource providing new statistical methodologies and demonstrating how new approaches work for applications M-statistics introduces a new approach to statistical inference, redesigning the fundamentals of statistics, and improving on the classical methods we already use. This book targets exact optimal statistical inference for a small sample under one methodological umbrella. Two competing approaches are offered: maximum concentration (MC) and mode (MO) statistics combined under one methodological umbrella, which is why the symbolic equation M=MC+MO. M-statistics defines an estimator as the limit point of the MC or MO exact optimal confidence interval when the confidence level approaches zero, the MC and MO estimator, respectively. Neither mean nor variance plays a role in M-statistics theory. Novel statistical methodologies in the form of double-sided unbiased and short confidence intervals and tests apply to major statistical parameters: Exact statistical inference for small sample sizes is illustrated with effect size and coefficient of variation, the rate parameter of the Pareto distribution, two-sample statistical inference for normal variance, and the rate of exponential distributions. M-statistics is illustrated with discrete, binomial, and Poisson distributions. Novel estimators eliminate paradoxes with the classic unbiased estimators when the outcome is zero. Exact optimal statistical inference applies to correlation analysis including Pearson correlation, squared correlation coefficient, and coefficient of determination. New MC and MO estimators along with optimal statistical tests, accompanied by respective power functions, are developed. M-statistics is extended to the multidimensional parameter and illustrated with the simultaneous statistical inference for the mean and standard deviation, shape parameters of the beta distribution, the two-sample binomial distribution, and finally, nonlinear regression. Our new developments are accompanied by respective algorithms and R codes, available at GitHub, and as such readily available for applications. M-statistics is suitable for professionals and students alike. It is highly useful for theoretical statisticians and teachers, researchers, and data science analysts as an alternative to classical and approximate statistical inference.

Optimized linear algebra (LA) libraries that are able to exploit the underlying hardware are always of interest in the high-performance computing community. The implementation of LA software has evolved along with computer architecture, while the specification remains unaltered almost from the beginning. It is important to differentiate between the specification of LA libraries and their implementation. Because LA libraries pursue high performance, the implementation for a given architecture needs to be optimized for it specifically. However, the type of operations included in the libraries, the input/output parameters, and the data types to be handled are common to all of them. This is why, while the specification remains constant, the implementation evolves with the creation of new architectures. Developing Linear Algebra Codes on Modern Processors: Emerging Research and Opportunities presents the main characteristics of LA libraries, showing the differences between the standards for sparse and dense versions. It further explores relevant linear algebra problems and shows, in a clear and understandable way, how to solve them using different computer architectures. Covering topics such as programming models, batched computing, and distributed memory platforms, this premier reference source is an excellent resource for programmers, computer scientists, engineers, students and faculty of higher education, librarians, researchers, and academicians.

Algebraic Theory for True Concurrency presents readers with the algebraic laws for true concurrency. Parallelism and concurrency are two of the core concepts within computer science. This book covers the different realms of concurrency, which enables programs, algorithms or problems to be broken out into order-independent or partially ordered components to improve computation and execution speed. There are two primary approaches for executing concurrency: interleaving concurrency and true concurrency. The main representative of interleaving concurrency is bisimulation/rooted branching bisimulation equivalences which is also readily explored. This work eventually founded the comprehensive axiomatization modulo bisimulation equivalence -- ACP (Algebra of Communicating Processes).The other approach to concurrency is true concurrency. Research on true concurrency is active and includes many emerging applications. First, there are several truly concurrent bisimulation equivalences, including: pomset bisimulation equivalence, step bisimulation equivalence, history-preserving (hp-) bisimulation equivalence, and hereditary history-preserving (hhp-) bisimulation equivalence, the most well-known truly concurrent bisimulation equivalence.

Pearson Edexcel International GCSE (9-1) Mathematics A REVISION GUIDE and APP - Higher Our revision resources are the smart choice for those revising for Pearson Edexcel International GCSE (9-1) Mathematics A. This guide will help you to: organise your revision with the one-topic-per-page format speed up your revision with helpful hints track your revision progress with at-a-glance check boxes check your understanding with worked examples develop your exam technique with exam-style practice questions and full answers. More than just a Guide! Make sure that you have practised every topic covered in this book, with the accompanying Revise Pearson Edexcel International GCSE (9-1) Mathematics A Revision App. Providing quick, hassle-free revision that is easy to use, the app gives you: access to short topic summaries to highlight key learning points needed for exam success multiple-choice questions to test your knowledge, with step-by-step answers an online edition of the revision guide for easy reference worked solution videos. The app allows you to revise anywhere, anytime with offline access. You can also add notes using the annotation tools and bookmarks for topics you want to revisit. Endorsed by Pearson Edexcel.

Mathematics for Physical Chemistry, Fifth Edition includes exercises that enable readers to test their understanding and put theory into practice. Chapters are constructed around a sequence of mathematical topics, progressing gradually into more advanced material, before discussing key mathematical skills, including the analysis of experimental data and-new to this edition-complex variables. Includes additional new content on Mathematica and its advanced applications. Drawing on the experience of its expert authors, this book is the ideal supplementary text for practicing chemists and students wanting to sharpen their mathematics skills and understanding of key mathematical concepts for applications across physical chemistry.

Environmental Data Analysis with MATLAB, Third Edition, is a new edition that expands fundamentally on the original with an expanded tutorial approach, more clear organization, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios, including case studies in each chapter. The new edition is expanded to include applications to Python, an open source software environment. Significant content in Environmental Data Analysis with MATLAB, Third Edition is devoted to teaching how the programs can be effectively used in an environmental data analysis setting. This new edition offers chapters that can both be used as self-contained resources or as a step-by-step guide for students, and is supplemented with data and scripts to demonstrate relevant use cases.

Intelligent Edge Computing for Cyber Physical Applications introduces state-of-the-art research methodologies, tools and techniques, challenges, and solutions with further research opportunities in the area of edge-based cyber-physical systems. The book presents a comprehensive review of recent literature and analysis of different techniques for building edge-based CPS. In addition, it describes how edge-based CPS can be built to seamlessly interact with physical machines for optimal performance, covering various aspects of edge computing architectures for dynamic resource provisioning, mobile edge computing, energy saving scenarios, and different security issues. Sections feature practical use cases of edge-computing which will help readers understand the workings of edge-based systems in detail, taking into account the need to present intellectual challenges while appealing to a broad readership, including academic researchers, practicing engineers and managers, and graduate students.