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Books > Science & Mathematics > Mathematics
This series is designed to meet the needs of students and lecturers of the National Certificate Vocational. Features for the student include: Easy-to-understand language; Real-life examples; A key word feature for important subject terms; A dictionary feature for difficult words; A reflect-on-how-you-learn feature to explore personal learning styles; Workplace-oriented activities; and Chapter summaries that are useful for exam revision.
Despite major advances in healthcare over the past century, the successful treatment of cancer has remained a significant challenge, and cancers are the second leading cause of death worldwide behind cardiovascular disease. Early detection and survival are important issues to control cancer. The development of quantitative methods and computer technology has facilitated the formation of new models in medical and biological sciences. The application of mathematical modelling in solving many real-world problems in medicine and biology has yielded fruitful results. In spite of advancements in instrumentations technology and biomedical equipment, it is not always possible to perform experiments in medicine and biology for various reasons. Thus, mathematical modelling and simulation are viewed as viable alternatives in such situations, and are discussed in this book. The conventional diagnostic techniques of cancer are not always effective as they rely on the physical and morphological appearance of the tumour. Early stage prediction and diagnosis is very difficult with conventional techniques. It is well known that cancers are involved in genome level changes. As of now, the prognosis of various types of cancer depends upon findings related to the data generated through different experiments. Several machine learning techniques exist in analysing the data of expressed genes; however, the recent results related with deep learning algorithms are more accurate and accommodative, as they are effective in selecting and classifying informative genes. This book explores the probabilistic computational deep learning model for cancer classification and prediction.
Stochastic Differential Equations for Science and Engineering is aimed at students at the M.Sc. and PhD level. The book describes the mathematical construction of stochastic differential equations with a level of detail suitable to the audience, while also discussing applications to estimation, stability analysis, and control. The book includes numerous examples and challenging exercises. Computational aspects are central to the approach taken in the book, so the text is accompanied by a repository on GitHub containing a toolbox in R which implements algorithms described in the book, code that regenerates all figures, and solutions to exercises. Features: Contains numerous exercises, examples, and applications Suitable for science and engineering students at Master's or PhD level Thorough treatment of the mathematical theory combined with an accessible treatment of motivating examples GitHub repository available at: https://github.com/Uffe-H-Thygesen/SDEbook and https://github.com/Uffe-H-Thygesen/SDEtools
The art of applying mathematics to real-world dynamical problems such as structural dynamics, fluid dynamics, wave dynamics, robot dynamics, etc. can be extremely challenging. Various aspects of mathematical modelling that may include deterministic or uncertain (fuzzy, interval, or stochastic) scenarios, along with integer or fractional order, are vital to understanding these dynamical systems. Mathematical Methods in Dynamical Systems offers problem-solving techniques and includes different analytical, semi-analytical, numerical, and machine intelligence methods for finding exact and/or approximate solutions of governing equations arising in dynamical systems. It provides a singular source of computationally efficient methods to investigate these systems and includes coverage of various industrial applications in a simple yet comprehensive way.
Drawing on psychological and sociological perspectives as well as quantitative and qualitative data, Identity and Interethnic Marriage in the United States considers the ways the self and social identity are linked to the dynamics of interethnic marriage. Bringing together the classic theoretical contributions of George Herbert Mead, Erving Goffman, and Erik Erikson with contemporary research on ethnic identity inspired by Jean Phinney, this book argues that the self and social identity-especially ethnic identity-are reflected in individuals' complex journey from singlehood to interethnic marriage within the United States.
- Topic has had a huge surge of interest since 2000 due to the greatly increased incidence of social communication disorders - Covers theory and evidence-based practice, making it a rounded and solid resource for students and professionals
Written to be more rigorous than other books on the same topics No other book on the topics explores programming and software in this manner Requires only undergraduate prerequisites
The book "Analysis and Design of Control Systems using MATLAB", is designed as a supplement to an introductory course in feedback control systems for undergraduate or graduate engineering students of all disciplines. Feedback control systems engineering is a multidisciplinary subject and presents a control engineering methodology based on mathematical fundamentals and stresses physical system modeling.This book includes the coverage of classical methods of control systems engineering: introduction to control systems, matrix analysis, Laplace transforms, mathematical modeling of dynamic systems, control system representation, performance and stability of feedback systems, analysis and design of feedback control systems, state space analysis and design, and MATLAB basics and MATLAB tutorial. The numerous worked examples offer detailed explanations, and guide the students through each set of problems to enable them to save a great deal of time and effort in arriving at an understanding of problems in this subject. Extensive references to guide the students to further sources of information on control systems and MATLAB is provided. In addition to students, practising engineers will also find this book immensely useful.
Key features: Presents the first elementary introduction to quantum geometry Explores how to understand quantum geometry without prior knowledge beyond bachelor level physics and mathematics. Contains exercises, problems and solutions to supplement and enhance learning
The burgeoning field of data science has provided a wealth of techniques for analysing large and complex geospatial datasets, including descriptive, explanatory, and predictive analytics. However, applying these methods is just one part of the overall process of geographic data science. Other critical steps include screening for suspect data values, handling missing data, harmonizing data from multiple sources, summarizing the data, and visualizing data and analysis results. Although there are many books available on statistical and machine learning methods, few encompass the broader topic of scientific workflows for geospatial data processing and analysis. The purpose of Geographic Data Science with R is to fill this gap by providing a series of tutorials aimed at teaching good practices for using geospatial data to address problems in environmental geography. It is based on the R language and environment, which currently provides the best option for working with diverse spatial and non-spatial data in a single platform. Fundamental techniques for processing and visualizing tabular, vector, and raster data are introduced through a series of practical examples followed by case studies that combine multiple types of data to address more complex problems. The book will have a broad audience. Both students and professionals can use it as a workbook to learn high-level techniques for geospatial data processing and analysis with R. It is also suitable as a textbook. Although not intended to provide a comprehensive introduction to R, it is designed to be accessible to readers who have at least some knowledge of coding but little to no experience with R. Key Features: Focus on developing practical workflows for processing and integrating multiple sources of geospatial data in R Example-based approach that teaches R programming and data science concepts through real-world applications related to climate, land cover and land use, and natural hazards. Consistent use of tidyverse packages for tabular data manipulation and visualization. Strong focus on analysing continuous and categorical raster datasets using the new terra package Organized so that each chapter builds on the topics and techniques covered in the preceding chapters Can be used for self-study or as the textbook for a geospatial science course.
This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: * It covers both technical and soft skills. * It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. * It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!
This book covers statistical consequences of breaches of research integrity such as fabrication and falsification of data, and researcher glitches summarized as questionable research practices. It is unique in that it discusses how unwarranted data manipulation harms research results and that questionable research practices are often caused by researchers' inadequate mastery of the statistical methods and procedures they use for their data analysis. The author's solution to prevent problems concerning the trustworthiness of research results, no matter how they originated, is to publish data in publicly available repositories and encourage researchers not trained as statisticians not to overestimate their statistical skills and resort to professional support from statisticians or methodologists. The author discusses some of his experiences concerning mutual trust, fear of repercussions, and the bystander effect as conditions limiting revelation of colleagues' possible integrity breaches. He explains why people are unable to mimic real data and why data fabrication using statistical models stills falls short of credibility. Confirmatory and exploratory research and the usefulness of preregistration, and the counter-intuitive nature of statistics are discussed. The author questions the usefulness of statistical advice concerning frequentist hypothesis testing, Bayes-factor use, alternative statistics education, and reduction of situational disturbances like performance pressure, as stand-alone means to reduce questionable research practices when researchers lack experience with statistics.
Most of the real-life signals are non-stationary in nature. The examples of such signals include biomedical signals, communication signals, speech, earthquake signals, vibration signals, etc. Time-frequency analysis plays an important role for extracting the meaningful information from these signals. The book presents time-frequency analysis methods together with their various applications. The basic concepts of signals and different ways of representing signals have been provided. The various time-frequency analysis techniques namely, short-time Fourier transform, wavelet transform, quadratic time-frequency transforms, advanced wavelet transforms, and adaptive time-frequency transforms have been explained. The fundamentals related to these methods are included. The various examples have been included in the book to explain the presented concepts effectively. The recently developed time-frequency analysis techniques such as, Fourier-Bessel series expansion-based methods, synchrosqueezed wavelet transform, tunable-Q wavelet transform, iterative eigenvalue decomposition of Hankel matrix, variational mode decomposition, Fourier decomposition method, etc. have been explained in the book. The numerous applications of time-frequency analysis techniques in various research areas have been demonstrated. This book covers basic concepts of signals, time-frequency analysis, and various conventional and advanced time-frequency analysis methods along with their applications. The set of problems included in the book will be helpful to gain an expertise in time-frequency analysis. The material presented in this book will be useful for students, academicians, and researchers to understand the fundamentals and applications related to time-frequency analysis.
The book is a follow-up to the first book on the topic published here. The book can be used for teaching and research purposes. The book offers different techniques for investigations of Ordinary and Partial Differential Equations and should promote interest in functional analysis.
Every financial professional wants and needs an advantage. A firm foundation in advanced mathematics can translate into dramatic advantages to professionals willing to obtain it. Many are not—and that is the advantage these books offer the astute reader. Published under the collective title of Foundations of Quantitative Finance, this set of ten books presents the advanced mathematics finance professionals need to advance their careers. These books develop the theory most do not learn in Graduate Finance programs, or in most Financial Mathematics undergraduate and graduate courses. As a high-level industry executive and authoritative instructor, Robert R. Reitano presents the mathematical theories he encountered and used in nearly three decades in the financial industry and two decades in education where he taught in highly respected graduate programs. Readers should be quantitatively literate and familiar with the developments in the first books in the set. The set offers a linear progression through these topics, though each title can be studied independently since the collection is extensively self-referenced. Book III: The Integrals of Lebesgue and (Riemann-) Stieltjes, develops several approaches to an integration theory. The first two approaches were introduced in the Chapter 1 of Book I to motivate measure theory. The general theory of integration on measure spaces will be developed in Book V, and stochastic integrals then studies on Book VIII. Book III Features: Extensively referenced to utilize materials from earlier books. Presents the theory needed to better understand applications. Supplements previous training in mathematics, with more detailed developments. Built from the author's five decades of experience in industry, research, and teaching. Published and forthcoming titles in the Robert Reitano Quantitative Finance Series: Book I: Measure Spaces and Measurable Functions. Book II: Probability Spaces and Random Variables, Book III: The Integrals of Lebesgue and (Riemann-) Stieltjes Book IV: Distribution Functions and Expectations Book V: General Measure and Integration Theory Book VI: Densities, Transformed Distributions, and Limit Theorems Book VII: Brownian Motion and Other Stochastic Processes Book VIII: Itô Integration and Stochastic Calculus 1 Book IX: Stochastic Calculus 2 and Stochastic Differential Equations Book 10: Applications and Classic Models
The text provides in-depth knowledge about recent advances in solar collector system, photovoltaic system, role of thermal energy systems in buildings, phase change materials, geothermal energy, biofuels, thermal management systems for EV in social and industrial applications. It further aims toward the inclusion of innovation and implementation of strategies for CO2 emission reduction through the reduction of energy consumption using conventional sources. This book: Presents the latest advances in the field of thermal energy storage, solar energy development, geothermal energy, and hybrid energy applications for green development. Highlights the importance of innovation and implementation of strategies for CO2 emission reduction through the reduction of energy consumption using sustainable technologies and methods. Discusses design development, life cycle assessment, modeling, and simulation of thermal energy systems in detail. Synergize exploration related to the various properties and functionalities through extensive theoretical and numerical modeling present in the energy sector. Explores opportunities, challenges, future perspectives, and approaches toward gaining sustainability through renewable energy resources. The text discusses the fundamentals of thermal energy and its applications in a comprehensive manner. It further covers advancements in solar thermal, and photovoltaic systems. The text highlights the contribution of geothermal energy conversion systems to sustainable development. It showcases the design and optimization of ground source heat pumps for space conditioning and presents modeling and simulation of the thermal energy systems for design optimization. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the fields including mechanical engineering, environmental engineering, and energy engineering.
The most authoritative and complete study guide for people beginning to work with data in the Azure cloud In MC Azure Data Fundamentals Study Guide: Exam DP-900, expert Cloud Solution Architect Jake Switzer delivers a hands-on blueprint to acing the DP-900 Azure data certification. The book prepares you for the test - and for a new career in Azure data analytics, architecture, science, and more - with a laser-focus on the job roles and responsibilities of Azure data professionals. You'll receive a foundational knowledge of core data concepts, like relational and non-relational data and transactional and analytical data workloads, while diving deep into every competency covered on the DP-900 exam. You'll also get: Access to complimentary online study tools, including hundreds of practice exam questions, electronic flashcards, and a searchable glossary Additional prep assistance with access to Sybex's superior interactive online learning environment and test bank Walkthroughs of skills and knowledge that are absolutely necessary for current and aspiring Azure data pros in introductory roles Perfect for anyone just beginning to work with data in the cloud, MC Azure Data Fundamentals Study Guide: Exam DP-900 is a can't-miss resource for anyone prepping for the DP-900 exam or considering a new career working with Azure data.
Exam Board: MEI Level: A-level Subject: Mathematics First Teaching: September 2017 First Exam: June 2018 An OCR endorsed textbook Encourage every student to develop a deeper understanding of mathematical concepts and their applications with textbooks that draw on the well-known MEI (Mathematics in Education and Industry) series, updated and tailored to the 2017 OCR (MEI) specification and developed by subject experts and MEI. - Develop problem-solving, proof and modelling skills with plenty of questions and well-structured exercises that build skills and mathematical techniques. - Build connections between topics, using real-world contexts to help develop mathematical modelling skills, thus providing a fuller and more coherent understanding of mathematical concepts. - Prepare students for assessment with practice questions written by subject experts. - Ensure coverage of the new statistics requirements with five dedicated statistics chapters and questions around the use of large data sets. - Supports the use of technology with a variety of questions based around the use of spreadsheets, graphing software and graphing calculators. - Provide clear paths of progression that combine pure and applied maths into a coherent whole.
Application-oriented introduction relates the subject as closely as possible to science. In-depth explorations of the derivative, the differentiation and integration of the powers of x, and theorems on differentiation and antidifferentiation lead to a definition of the chain rule and examinations of trigonometric functions, logarithmic and exponential functions, techniques of integration, polar coordinates, much more. Clear-cut explanations, numerous drills, illustrative examples. 1967 edition.
-Includes simplified statistical contents and step by step guide on how to apply the statistical concepts by perform analysis using SPSS together with interpretation of the statistical analysis output. -Provides a wide range of data sets to be used for examples and illustrations. -Designed to be accessible to readers with varied backgrounds.
Optimization is presented in most multivariable calculus courses as an application of the gradient, and while this treatment makes sense for a calculus course, there is much more to the theory of optimization. Optimization problems are generated constantly, and the theory of optimization has grown and developed in response to the challenges presented by these problems. This textbook aims to show readers how optimization is done in practice and help them to develop an appreciation for the richness of the theory behind the practice. Exercises, problems (including modeling and computational problems), and implementations are incorporated throughout the text to help students learn by doing. Python notes are inserted strategically to help readers complete computational problems and implementations. The Basics of Practical Optimization, Second Edition is intended for undergraduates who have completed multivariable calculus, as well as anyone interested in optimization. The book is appropriate for a course that complements or replaces a standard linear programming course.
Composites have been studied for more than 150 years, and interest in their properties has been growing. This classic volume provides the foundations for understanding a broad range of composite properties, including electrical, magnetic, electromagnetic, elastic and viscoelastic, piezoelectric, thermal, fluid flow through porous materials, thermoelectric, pyroelectric, magnetoelectric, and conduction in the presence of a magnetic field (Hall effect). Exact solutions of the PDEs in model geometries provide one avenue of understanding composites; other avenues include microstructure-independent exact relations satisfied by effective moduli, for which the general theory is reviewed; approximation formulae for effective moduli; and series expansions for the fields and effective moduli that are the basis of numerical methods for computing these fields and moduli. The range of properties that composites can exhibit can be explored either through the model geometries or through microstructure-independent bounds on the properties. These bounds are obtained through variational principles, analytic methods, and Hilbert space approaches. Most interesting is when the properties of the composite are unlike those of the constituent materials, and there has been an explosion of interest in such composites, now known as metamaterials. The Theory of Composites surveys these aspects, among others, and complements the new body of literature that has emerged since the book was written. It remains relevant today by providing historical background, a compendium of numerous results, and through elucidating many of the tools still used today in the analysis of composite properties. This book is intended for applied mathematicians, physicists, and electrical and mechanical engineers. It will also be of interest to graduate students.
Foundation Mathematics for Biosciences provides an accessible and clear introduction to mathematical skills for students of the biosciences. The book chapters cover key topic areas and their associated techniques, thereby presenting the maths in context. A student focused pedagogical approach will help students build their confidence, develop their understanding and learn how to apply mathematical techniques within their studies. Students will be able to use the book as a resource to complement their theory-based textbooks and to prepare themselves for practical classes, tutorials and research projects. Key features The book progresses in a logical manner, opening with fundamental problems and then building to more complex calculations aligned to different disciplines in the biosciences. * Worked examples with detailed solutions provide step-by-step guidance through each calculation to help students build their practical skills. * Important rules and key points are highlighted in text boxes to help students consolidate their understanding of techniques and theory. * Illustrations provide insight into what students are likely to encounter in the laboratory. * Self-assessment questions are provided throughout to enable students to manage their learning and track their progress. * Learning objectives and key terms also help students to monitor their study. Suitable for students on courses from the pure end of the spectrum to more applied courses such as biomedical sciences, microbiology, molecular biology, physiology, and forensics. Dr Ela Bryson is Senior Lecturer in Molecular Biology at the School of Life and Medical Sciences at the University of Hertfordshire Dr Jackie Willis is Associate Dean of the School of Life and Medical Sciences at the University if Hertfordshire This book can be supported by MyMathLabGlobal, an online teaching and learning platform designed to build and test your understanding. The book and the MyMathLabGlobal system provide a range of benefits including: * A tool for the diagnosis of existing strengths and weaknesses in maths * A comprehensive set of algorithmically generated questions that can be used by students to practise and develop their skills in an independent and flexible manner and by the tutor to evaluate progress Need extra support? Were you looking for the book with access to MyXLab? This product is the book alone, and does NOT come with access to MyMathLabGlobal. Buy Title with MyMathLabGlobal access card (9780273774655) if you need access to MyMathLabGlobal as well, and save money on this resource. Ask your instructor about using MyLab.
An encompassing socio-historical survey of the political and sociological nature of groups, communities and societies. A transdisciplinary study of crowds, masses and groups as historical, sociological, psychological and psychosocial phenomena. A unique combination of sociology, psychoanalysis and group analysis in the study of social formations. An inquiry into the enigma of crowds and mass psychology with the history of group analytic and group relations' advances in England, especially the study of large groups in the research on group processes. A comprehensive presentation of the social unconscious theory in association with the study of large groups and the Incohesion theory as new group analytic tools for understanding contemporary crowds and masses. In today's world, flooded by social conflicts and polarizations and the mass impact of social media, this book enables the reader to map out the field of the unconscious life of crowds illuminating the darkness of twenty-first century collective movements.
Basics of Ramsey Theory serves as a gentle introduction to Ramsey theory for students interested in becoming familiar with a dynamic segment of contemporary mathematics that combines ideas from number theory and combinatorics. The core of the of the book consists of discussions and proofs of the results now universally known as Ramsey's theorem, van der Waerden's theorem, Schur's theorem, Rado's theorem, the Hales-Jewett theorem, and the Happy End Problem of Erdos and Szekeres. The aim is to present these in a manner that will be challenging but enjoyable, and broadly accessible to anyone with a genuine interest in mathematics. Features Suitable for any undergraduate student who has successfully completed the standard calculus sequence of courses and a standard first (or second) year linear algebra course. Filled with visual proofs of fundamental theorems. Contains numerous exercises (with their solutions) accessible to undergraduate students. Serves as both a textbook or as a supplementary text in an elective course in combinatorics and aimed at a diverse group of students interested in mathematics. |
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