Reachable Sets of Dynamic Systems: Uncertainty, Sensitivity, and Complex Dynamics introduces differential inclusions, providing an overview as well as multiple examples of its interdisciplinary applications. The design of dynamic systems of any type is an important issue as is the influence of uncertainty in model parameters and model sensitivity. The possibility of calculating the reachable sets may be a powerful additional tool in such tasks. This book can help graduate students, researchers, and engineers working in the field of computer simulation and model building, in the calculation of reachable sets of dynamic models.

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.

Why can no two people ever see the same rainbow? What happens when you pull a pop song apart into pure sine waves and play it back on a piano? Why does the wake behind a duck always form an angle of exactly 39 degrees? And what did mathematicians have to do with the great pig stampede of 2012? The answer to each of these questions can be found in the triangle.

In Love Triangle, stand-up comedian, ex-maths teacher and Sunday Times number one bestselling author Matt Parker is on a mission to prove why we should all show a lot more love for triangles, along with the useful trigonometry and geometry they enable. To make his point, he uses triangles to create his own digital avatar, survive a harrowing motorcycle ride, cut a sandwich into three equal parts, and measure tall buildings while wearing silly shoes. But soon these hare-brained experiments begin to reveal a genuinely important truth: triangles are the hidden pattern beneath the surface of the contemporary world, used in everything from GPS to CGI via Spotify streaming, the play button and your best mate’s triangle tattoo.

Join Matt Parker as he demonstrates why there’s more to triangles than Pythagoras and SOHCAHTOA. Triangles are everything and everything is triangles.

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.

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.

Theoretical advances and new foundations have been reported at the Conference for more than 40 years which has helped expand the range of applications as well as the type of materials in response to industrial and professional requirements. Since the conference started it has attracted high quality papers that report further advances in techniques that reduce or eliminate the type of meshes associated with finite elements or finite differences, for instance. As design, analysis and manufacture become more integrated, the chances are that the users will be less aware of the capabilities of the analytical techniques that are at the core of the process. This reinforces the need to retain expertise in certain specialised areas of numerical methods, such as BEM/MRM, to ensure that all new tools perform satisfactorily in the integrated process. The maturity of BEM since 1978 has resulted in a substantial number of industrial applications, which demonstrate the accuracy, robustness and easy use of the technique. Their range still needs to be widened, taking into account the potentialities of the Mesh Reduction techniques in general. The included papers originate from the 46th conference on Boundary Elements and other Mesh Reduction Methods (BEM/MRM) which acts as a forum to discuss new ideas and critically compare results before the solution and tools are released to the end users.

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.