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Material Design implementation with AngularJS
by V. Keerti KotaruBuild high-quality web and mobile user interfaces (UI) that are interactive, fluid, and provide a consistent experience across devices from desktops to tablets to smartphones. Use a Material Design approach to position elements and create animations along with principles of the sophisticated AngularJS JavaScript framework. Take advantage of Angular Material, a UI component framework that works out of the box to design web pages that adapt to various screen sizes and adhere to Material Design specifications. What You Will Learn: Develop a UI that adheres to Material Design principles using Angular Material, a UI component framework Use various Angular Material elements, directives, and services in conjunction with CSS3 Flexbox for layout management Use best practice design techniques to develop a responsive UI to fit multiple devices and screen sizes from desktop to tablet to mobile phone Develop web apps for both mobile and desktop form factors and screen sizes using HTML, CSS, and JavaScript Who This Book Is For: Web and mobile app developers with a basic understanding of JavaScript, HTML, and CSS
Material UI Cookbook: Build Captivating User Experiences Using React And Material-ui
by Adam BoduchThey are JavaScript developers who have some basic knowledge of React and would want to implement Material Design principles in React applications using Material UI. The reader wants to build a user interface using React components but doesn’t want to invent their own style or UX framework.
Materiality and Time
by Francois-Xavier de Vaujany Nathalie Mitev Pierre Laniray Emmanuelle VaastSociomaterial research overcomes the dichotomy between social and material worlds by concentrating on organizational practices. These practices are constituted by, but also produce, material and social dynamics. This research is currently having an important impact in management studies and adopts a subjective investigation of time to explore materiality and materialization. Studying the institutional evolution of an organization implies long time spans and it is shown more clearly through the inclusion of material traces of past actions. Materiality and Time is split into three parts: Part I explores how time is materialized and performed in organizations, i. e. how artefacts and material space perform time and temporal dynamics in organizations. Part II examines how organizations and organizational members are constituted by and constitutive of material artefacts. Part III reflects on what a historical perspective on these materializations can bring to the study of organizations. Contributions focus on the materialization of time and the material dynamic of organizations.
Materiality, Rules and Regulation: New Trends in Management and Organization Studies (Technology, Work and Globalization)
by Nathalie Mitev François-Xavier de Vaujany Anouk Mukherjee Giovan LanzaraMateriality, Rules and Regulation: New Trend in Management and Organization Studies concentrates on the relationship of rules and regulation to the materiality of artefacts, practices, and organizations. It combines the recent scholarly interest on sociomateriality with a focus on regulation and rules.
Materializing the Web of Linked Data
by Nikolaos Konstantinou Dimitrios-Emmanuel SpanosThis book explains the Linked Data domain by adopting a bottom-up approach: it introduces the fundamental Semantic Web technologies and building blocks, which are then combined into methodologies and end-to-end examples for publishing datasets as Linked Data, and use cases that harness scholarly information and sensor data. It presents how Linked Data is used for web-scale data integration, information management and search. Special emphasis is given to the publication of Linked Data from relational databases as well as from real-time sensor data streams. The authors also trace the transformation from the document-based World Wide Web into a Web of Data. Materializing the Web of Linked Data is addressed to researchers and professionals studying software technologies, tools and approaches that drive the Linked Data ecosystem, and the Web in general.
Materials Data Science: Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering (The Materials Research Society Series)
by Stefan SandfeldThis text covers all of the data science, machine learning, and deep learning topics relevant to materials science and engineering, accompanied by numerous examples and applications. Almost all methods and algorithms introduced are implemented “from scratch” using Python and NumPy. The book starts with an introduction to statistics and probabilities, explaining important concepts such as random variables and probability distributions, Bayes’ theorem and correlations, sampling techniques, and exploratory data analysis, and puts them in the context of materials science and engineering. Therefore, it serves as a valuable primer for both undergraduate and graduate students, as well as a review for research scientists and practicing engineers. The second part provides an in-depth introduction of (statistical) machine learning. It begins with outlining fundamental concepts and proceeds to explore a variety of supervised learning techniques for regression and classification, including advanced methods such as kernel regression and support vector machines. The section on unsupervised learning emphasizes principal component analysis, and also covers manifold learning (t-SNE and UMAP) and clustering techniques. Additionally, feature engineering, feature importance, and cross-validation are introduced. The final part on neural networks and deep learning aims to promote an understanding of these methods and dispel misconceptions that they are a “black box”. The complexity gradually increases until fully connected networks can be implemented. Advanced techniques and network architectures, including GANs, are implemented “from scratch” using Python and NumPy, which facilitates a comprehensive understanding of all the details and enables the user to conduct their own experiments in Deep Learning.
Materials Discovery and Design: By Means of Data Science and Optimal Learning (Springer Series in Materials Science #280)
by Turab Lookman Frank Alexander Stephan Eidenbenz Cris BarnesThis book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.
Materials Informatics I: Methods (Challenges and Advances in Computational Chemistry and Physics #39)
by Kunal Roy Arkaprava BanerjeeThis contributed volume explores the integration of machine learning and cheminformatics within materials science, focusing on predictive modeling techniques. It begins with foundational concepts in materials informatics and cheminformatics, emphasizing quantitative structure-property relationships (QSPR). The volume then presents various methods and tools, including advanced QSPR models, quantitative read-across structure-property relationship (q-RASPR) models, optimization strategies with minimal data, and in silico studies using different descriptors. Additionally, it explores machine learning algorithms and their applications in materials science, alongside innovative modeling approaches for quantum-theoretic properties. Overall, the book serves as a comprehensive resource for understanding and applying machine learning in the study and development of advanced materials and is a useful tool for students, researchers and professionals working in these areas.
Materials Informatics II: Software Tools and Databases (Challenges and Advances in Computational Chemistry and Physics #40)
by Kunal Roy Arkaprava BanerjeeThis contributed volume explores the application of machine learning in predictive modeling within the fields of materials science, nanotechnology, and cheminformatics. It covers a range of topics, including electronic properties of metal nanoclusters, carbon quantum dots, toxicity assessments of nanomaterials, and predictive modeling for fullerenes and perovskite materials. Additionally, the book discusses multiscale modeling and advanced decision support systems for nanomaterial risk management, while also highlighting various machine learning tools, databases, and web platforms designed to predict the properties of materials and molecules. It is a comprehensive guide and a great tool for researchers working at the intersection of machine learning and material sciences.
Materials Informatics III: Polymers, Solvents and Energetic Materials (Challenges and Advances in Computational Chemistry and Physics #41)
by Kunal Roy Arkaprava BanerjeeThis contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. It provides an in-depth look at how machine learning is utilized to predict key properties of polymers, deep eutectic solvents, and ionic liquids, as well as to improve safety and performance in the study of energetic and reactive materials. With chapters covering polymer informatics, quantitative structure–property relationship (QSPR) modeling, and computational approaches, the book serves as a comprehensive resource for researchers applying predictive modeling techniques to advance materials science and improve material safety and performance.
Materials with Complex Behaviour
by Lucas F. Silva Holm AltenbachThe increasing demand for materials with superb properties in all types of technical applications requires the development of advanced materials and respective tools to characterise and predict the material properties and behaviour. This monograph highlights the latest developments and trends in advanced materials and their properties, the modelling and simulation of non-classical materials and structures, and new technologies for joining materials.
Math Adventures with Python: An Illustrated Guide to Exploring Math with Code
by Peter FarrellLearn math by getting creative with code! Use the Python programming language to transform learning high school-level math topics like algebra, geometry, trigonometry, and calculus!Math Adventures with Python will show you how to harness the power of programming to keep math relevant and fun. With the aid of the Python programming language, you'll learn how to visualize solutions to a range of math problems as you use code to explore key mathematical concepts like algebra, trigonometry, matrices, and cellular automata.Once you've learned the programming basics like loops and variables, you'll write your own programs to solve equations quickly, make cool things like an interactive rainbow grid, and automate tedious tasks like factoring numbers and finding square roots. You'll learn how to write functions to draw and manipulate shapes, create oscillating sine waves, and solve equations graphically. You'll also learn how to: - Draw and transform 2D and 3D graphics with matrices- Make colorful designs like the Mandelbrot and Julia sets with complex numbers- Use recursion to create fractals like the Koch snowflake and the Sierpinski triangle- Generate virtual sheep that graze on grass and multiply autonomously - Crack secret codes using genetic algorithms As you work through the book's numerous examples and increasingly challenging exercises, you'll code your own solutions, create beautiful visualizations, and see just how much more fun math can be!
Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing
by Tim P. ChartierAn inviting collection of fun, hands-on applications in mathematics and computingThis book provides a fun, hands-on approach to learning how mathematics and computing relate to the world around us and help us to better understand it. How can reposting on Twitter kill a movie's opening weekend? How can you use mathematics to find your celebrity look-alike? What is Homer Simpson’s method for disproving Fermat’s Last Theorem? Each topic in this refreshingly inviting book illustrates a famous mathematical algorithm or result--such as Google’s PageRank and the traveling salesman problem--and the applications grow more challenging as you progress through the chapters. But don’t worry, helpful solutions are provided each step of the way.Math Bytes shows you how to do calculus using a bag of chocolate chips, and how to prove the Euler characteristic simply by doodling. Generously illustrated in color throughout, this lively and entertaining book also explains how to create fractal landscapes with a roll of the dice, pick a competitive bracket for March Madness, decipher the math that makes it possible to resize a computer font or launch an Angry Bird--and much, much more. All of the applications are presented in an accessible and engaging way, enabling beginners and advanced readers alike to learn and explore at their own pace--a bit and a byte at a time.
Math Hacks for Scratch: Unlock the Power of Math with Scratch Programming
by Michael MaysPush Scratch programming to the limits as you explore primes, Fibonacci numbers, Pascal&’s triangle, and other mathematical curiosities through hands-on coding projects.If you&’re a student looking for project ideas to practice your math and coding skills, or a Scratch enthusiast just looking for something different, this is the book for you!Discover the exciting intersection of mathematics and programming with Math Hacks for Scratch®. This book is perfect for kids, educators, and programming enthusiasts eager to learn or teach math through fun, hands-on projects using Scratch, the popular visual programming language.You&’ll see how a little bit of planning, combined with the right mathematical or coding tricks, can make complex calculations doable. These are the &“hacks&” mentioned in the title. You&’ll write programs to speed up factoring big numbers, sort out a pizza party with Pascal&’s triangle, explore Fibonacci&’s famous sequence for counting rabbits, use cryptography to create unbreakable secret codes, and so much more.Inside, you&’ll find:Step-by-Step Projects: Learn how to create interactive games, animations, and simulations that bring math concepts to life. Convert between binary and decimal to see how computers keep track of numbers. Make sense of patterns in lists, sequences, and arrays. Encode cryptograms, unscramble secret messages, and crack the Caesar cipher.Real-World Applications: See how math is used every day to calculate probabilities in games and create dynamic graphics.Expert Guidance: Benefit from the insights of author Michael Mays, a seasoned math educator with a PhD in mathematics and a 40-year teaching career.Accessible Learning: Ideal for both beginners and experienced programmers, this book offers clear explanations and practical examples that make learning fun and easy.Whether you&’re a beginning coder wanting to enhance your Scratch skills, a teacher looking to inspire students, or a parent supporting your child&’s education, Math Hacks for Scratch provides the tools to turn coding projects into math adventures.
Math Physics Foundation of Advanced Remote Sensing Digital Image Processing
by Yi Lin Lei Yan Hongying Zhao Yanbiao SunThis book focuses on the mathematical and physical foundations of remote sensing digital image processing and introduces key algorithms utilized in this area. The book fully introduces the basic mathematical and physical process of digital imaging, the basic theory and algorithm of pixel image processing, and the higher-order image processing algorithm and its application. This book skillfully and closely integrates theory, algorithms, and applications, making it simple for readers to understand and use. Researchers and students working in the fields of remote sensing, computer vision, geographic information science, electronic information, etc., can profit from this book. For their work and research in digital image processing, they can master the fundamentals of imaging and image processing techniques.
Math Toolkit for Real-Time Programming
by Jack CrenshawDo big math on small machines Write fast and accurate library functions Master analytical and numerical calculus Perform numerical integration to any order Implement z-transform formulas Need to learn the ins and outs of the fundamental math functions in
Math and Architectures of Deep Learning
by Krishnendu ChaudhuryShine a spotlight into the deep learning &“black box&”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively.Inside Math and Architectures of Deep Learning you will find: Math, theory, and programming principles side by side Linear algebra, vector calculus and multivariate statistics for deep learning The structure of neural networks Implementing deep learning architectures with Python and PyTorch Troubleshooting underperforming models Working code samples in downloadable Jupyter notebooks The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you&’ll peer inside the &“black box&” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Foreword by Prith Banerjee. About the technology Discover what&’s going on inside the black box! To work with deep learning you&’ll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you&’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective. About the book Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You&’ll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research. What's inside The core design principles of neural networks Implementing deep learning with Python and PyTorch Regularizing and optimizing underperforming models About the reader Readers need to know Python and the basics of algebra and calculus. About the author Krishnendu Chaudhury is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe. Table of Contents 1 An overview of machine learning and deep learning 2 Vectors, matrices, and tensors in machine learning 3 Classifiers and vector calculus 4 Linear algebraic tools in machine learning 5 Probability distributions in machine learning 6 Bayesian tools for machine learning 7 Function approximation: How neural networks model the world 8 Training neural networks: Forward propagation and backpropagation 9 Loss, optimization, and regularization 10 Convolutions in neural networks 11 Neural networks for image classification and object detection 12 Manifolds, homeomorphism, and neural networks 13 Fully Bayes model parameter estimation 14 Latent space and generative modeling, autoencoders, and variational autoencoders A Appendix
Math for Deep Learning: What You Need to Know to Understand Neural Networks
by Ronald T. KneuselMath for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You&’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You&’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.In addition you&’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
Math for Programmers: 3D graphics, machine learning, and simulations with Python
by Paul OrlandIn Math for Programmers you&’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today&’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you&’ll master the key Python libraries used to turn them into real-world software applications.Summary To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today&’s hottest programming fields. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Skip the mathematical jargon: This one-of-a-kind book uses Python to teach the math you need to build games, simulations, 3D graphics, and machine learning algorithms. Discover how algebra and calculus come alive when you see them in code! About the book In Math for Programmers you&’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today&’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you&’ll master the key Python libraries used to turn them into real-world software applications. What's inside Vector geometry for computer graphics Matrices and linear transformations Core concepts from calculus Simulation and optimization Image and audio processing Machine learning algorithms for regression and classification About the reader For programmers with basic skills in algebra. About the author Paul Orland is a programmer, software entrepreneur, and math enthusiast. He is co-founder of Tachyus, a start-up building predictive analytics software for the energy industry. You can find him online at www.paulor.land. Table of Contents 1 Learning math with code PART I - VECTORS AND GRAPHICS 2 Drawing with 2D vectors 3 Ascending to the 3D world 4 Transforming vectors and graphics 5 Computing transformations with matrices 6 Generalizing to higher dimensions 7 Solving systems of linear equations PART 2 - CALCULUS AND PHYSICAL SIMULATION 8 Understanding rates of change 9 Simulating moving objects 10 Working with symbolic expressions 11 Simulating force fields 12 Optimizing a physical system 13 Analyzing sound waves with a Fourier series PART 3 - MACHINE LEARNING APPLICATIONS 14 Fitting functions to data 15 Classifying data with logistic regression 16 Training neural networks
Math for Programming
by Ronald T. KneuselA one-stop-shop for all the math you should have learned for your programming career.Every great programming challenge has mathematical principles at its heart. Whether you&’re optimizing search algorithms, building physics engines for games, or training neural networks, success depends on your grasp of core mathematical concepts. In Math for Programming, you&’ll master the essential mathematics that will take you from basic coding to serious software development. You&’ll discover how vectors and matrices give you the power to handle complex data, how calculus drives optimization and machine learning, and how graph theory leads to advanced search algorithms.Through clear explanations and practical examples, you&’ll learn to:Harness linear algebra to manipulate data with unprecedented efficiencyApply calculus concepts to optimize algorithms and drive simulationsUse probability and statistics to model uncertainty and analyze dataMaster the discrete mathematics that powers modern data structuresSolve dynamic problems through differential equationsWhether you&’re seeking to fill gaps in your mathematical foundation or looking to refresh your understanding of core concepts, Math for Programming will turn complex math into a practical tool you&’ll use every day.
Math for Security: From Graphs and Geometry to Spatial Analysis
by Daniel ReillyUse applied math to map fire stations, develop facial recognition software, solve the art gallery problem and more in this hands-on, real-world infosec book.Explore the intersection of mathematics and computer security with this engaging and accessible guide.Math for Security will equip you with essential tools to tackle complex security problems head on. All you need are some basic programming skills. Once you&’ve set up your development environment and reviewed the necessary Python syntax and math notation in the early chapters, you&’ll dive deep into practical applications, leveraging the power of math to analyze networks, optimize resource distribution, and much more. In the book&’s final chapters, you&’ll take your projects from proof of concepts to viable applications and explore options for delivering them to end users.As you work through various security scenarios, you&’ll:Employ packet analysis and graph theory to detect data exfiltration attempts in a networkPredict potential targets and find weaknesses in social networks with Monte Carlo simulationsUse basic geometry and OpenCell data to triangulate a phone&’s location without GPSApply computational geometry to Voronoi diagrams for use in emergency service planningTrain a facial recognition system with machine learning for real-time identity verificationUse spatial analysis to distribute physical security features effectively in an art galleryWhether you&’re an aspiring security professional, a social network analyst, or an innovator seeking to create cutting-edge security solutions, this book will empower you to solve complex problems with precision and confidence. Embrace the intricate world of math as your secret weapon in computer security!Covers Python 3.x
Mathematica Cookbook: Building Blocks for Science, Engineering, Finance, Music, and More
by Sal ManganoMathematica Cookbook helps you master the application's core principles by walking you through real-world problems. Ideal for browsing, this book includes recipes for working with numerics, data structures, algebraic equations, calculus, and statistics. You'll also venture into exotic territory with recipes for data visualization using 2D and 3D graphic tools, image processing, and music.Although Mathematica 7 is a highly advanced computational platform, the recipes in this book make it accessible to everyone -- whether you're working on high school algebra, simple graphs, PhD-level computation, financial analysis, or advanced engineering models.Learn how to use Mathematica at a higher level with functional programming and pattern matchingDelve into the rich library of functions for string and structured text manipulationLearn how to apply the tools to physics and engineering problemsDraw on Mathematica's access to physics, chemistry, and biology dataGet techniques for solving equations in computational financeLearn how to use Mathematica for sophisticated image processingProcess music and audio as musical notes, analog waveforms, or digital sound samples
Mathematica Data Visualization
by Nazmus SaquibIf you are planning to create data analysis and visualization tools in the context of science, engineering, economics, or social science, then this book is for you. With this book, you will become a visualization expert, in a short time, using Mathematica.
Mathematica for Bioinformatics: A Wolfram Language Approach To Omics
by George MiasThis book offers a comprehensive introduction to using Mathematica and the Wolfram Language for Bioinformatics. The chapters build gradually from basic concepts and the introduction of the Wolfram Language and coding paradigms in Mathematica, to detailed worked examples derived from typical research applications using Wolfram Language code. The coding examples range from basic sequence analysis, accessing genomic databases, differential gene expression, and machine learning implementations to time series analysis of longitudinal omics experiments, multi-omics integration and building dynamic interactive bioinformatics tools using the Wolfram Language. The topics address the daily bioinformatics needs of a broad audience: experimental users looking to understand and visualize their data, beginner bioinformaticians acquiring coding expertise in providing biological research solutions, and practicing expert bioinformaticians working on omics who wish to expand their toolset to include the Wolfram Language.
Mathematical Adventures in Performance Analysis
by Eitan Bachmat This book describes problems in the field of performance analysis, primarily the study of storage systems and the diverse mathematical techniques that are required for solving them. Topics covered include best practices for scheduling I/O requests to a disk drive, how this problem is related to airplane boarding, and how both problems can be modeled using space-time geometry. Also provided is an explanation of how Riemann's proof of the analytic continuation and functional equation of the Riemann zeta function can be used to analyze express line queues in a minimarket. Overall, the book displays the surprising relevance of abstract mathematics that is not usually associated with applied mathematics topics. Advanced undergraduate students or graduate students with an interest in the applications of mathematics will find this book to be a useful resource. It will also be of interest to professional mathematicians who want exposure to the surprising ways that theoretical mathematics may be applied to engineering problems. To encourage further study, each chapter ends with notes pointing to various related topics that the reader may want pursue. This mathematically rigorous work was noted in the news section of the journal Nature, and in popular media such as New Scientist, The Wall Street Journal, The Guardian, and USA Today.