Based on a course of lectures delivered by the author at Columbia University, the text is elementary in treatment and remarkable for its clarity and organization. Although it is assumed that the reader is familiar with the fundamental facts of thermometry and calorimetry, no advanced mathematics beyond calculus is assumed.Partial contents: thermodynamic systems, the first law of thermodynamics (application, adiabatic transformations), the second law of thermodynamics (Carnot cycle, absolute thermodynamic temperature, thermal engines), the entropy (properties of cycles, entropy of a system whose states can be represented on a (V, p) diagram, Clapeyron and Van der Waals equations), thermodynamic potentials (free energy, thermodynamic potential at constant pressure, the phase rule, thermodynamics of the reversible electric cell), gaseous reactions (chemical equilibria in gases, Van't Hoff reaction box, another proof of the equation of gaseous equilibria, principle of Le Chatelier), the thermodynamics of dilute solutions (osmotic pressure, chemical equilibria in solutions, the distribution of a solute between 2 phases vapor pressure, boiling and freezing points), the entropy constant (Nernst's theorem, thermal ionization of a gas, thermionic effect, etc.).

    This is done in a way that is not only easy to understand, but is actually fun! Professors and engineers, with high school and college students following closely, comprise the largest percentage of our readers. It is a must-have for anyone interested in music, mathematics, physics, engineering, or complex science. Dr. Yoichiro Nambu, 2008 Nobel Prize Winner in Physics, served as a senior adviser to the English version of Who is Fourier? A Mathematical Adventure.

    In eight chapters, it provides the most thorough coverage of TCP available. It also covers the newest TCP/IP features, including multicasting, path MTU discovery and long fat pipes. The author describes various protocols, including ARP, ICMP and UDP. He utilizes network diagnostic tools to actually show the protocols in action. He also explains how to avoid silly window syndrome (SWS) by using numerous helpful diagrams. This book gives you a broader understanding of concepts like connection establishment, timeout, retransmission and fragmentation. It is ideal for anyone wanting to gain a greater understanding of how the TCP/IP protocols work.

    Introductory Graph Theory presents a nontechnical introduction to this exciting field in a clear, lively, and informative style. Author Gary Chartrand covers the important elementary topics of graph theory and its applications. In addition, he presents a large variety of proofs designed to strengthen mathematical techniques and offers challenging opportunities to have fun with mathematics. Ten major topics — profusely illustrated — include: Mathematical Models, Elementary Concepts of Graph Theory, Transportation Problems, Connection Problems, Party Problems, Digraphs and Mathematical Models, Games and Puzzles, Graphs and Social Psychology, Planar Graphs and Coloring Problems, and Graphs and Other Mathematics. A useful Appendix covers Sets, Relations, Functions, and Proofs, and a section devoted to exercises — with answers, hints, and solutions — is especially valuable to anyone encountering graph theory for the first time. Undergraduate mathematics students at every level, puzzlists, and mathematical hobbyists will find well-organized coverage of the fundamentals of graph theory in this highly readable and thoroughly enjoyable book.

    Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms.Inside, you’ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works.Topics include:Data Structures and AlgorithmsStrings and TextNumbers, Dates, and TimesIterators and GeneratorsFiles and I/OData Encoding and ProcessingFunctionsClasses and ObjectsMetaprogrammingModules and PackagesNetwork and Web ProgrammingConcurrencyUtility Scripting and System AdministrationTesting, Debugging, and ExceptionsC Extensions

    Many Python developers are now taking an interest in high-level software architecture patterns such as hexagonal/clean architecture, event-driven architecture, and strategic patterns prescribed by domain-driven design (DDD). But translating those patterns into Python isn't always straightforward.With this practical guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexity. Each pattern is illustrated with concrete examples in idiomatic Python that explain how to avoid some of the unnecessary verbosity of Java and C# syntax. You'll learn how to implement each of these patterns in a Pythonic way.Architectural design patterns include:Dependency inversion, and its links to ports and adapters (hexagonal/clean architecture)Domain-driven design's distinction between entities, value objects, and aggregatesRepository and Unit of Work patterns for persistent storageEvents, commands, and the message busCommand Query Responsibility Segregation (CQRS)Event-driven architecture and reactive microservices

    More importantly, this book provides a framework for thinking about computer organization and design that will enable the reader to continue the lifetime of learning necessary for staying at the forefront of this competitive discipline. --John Crawford Intel Fellow Director of Microprocessor Architecture, Intel The performance of software systems is dramatically affected by how well software designers understand the basic hardware technologies at work in a system. Similarly, hardware designers must understand the far reaching effects their design decisions have on software applications. For readers in either category, this classic introduction to the field provides a deep look into the computer. It demonstrates the relationship between the software and hardware and focuses on the foundational concepts that are the basis for current computer design. Using a distinctive learning by evolution approach the authors present each idea from its first principles, guiding readers through a series of worked examples that incrementally add more complex instructions until they ha

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    It combines the essential elements of the theory with the practical applications. Containing many examples and problems with step-by-step solutions, this cleverly structured text assists the reader in mastering the machinery of quantum mechanics. * A comprehensive introduction to the subject * Includes over 65 solved examples integrated throughout the text * Includes over 154 fully solved multipart problems * Offers an indepth treatment of the practical mathematical tools of quantum mechanics * Accessible to teachers as well as students

    Stephen Kochan's "Programming in C" is thorough with easy-to-follow instructions that are sure to benefit beginning programmers. This book provides readers with practical examples of how the C programming language can be used with small, fast programs, similar to the programming used by large game developers such as Nintendo. If you want a one-stop-source for C programming, this book is it.The book is appropriate for all introductory-to-intermediate courses on programming in the C language, including courses covering C programming for games and small-device platforms. "Programming in C, Third Edition" is a thoroughly revised and updated edition of Steven Kochan's classic C programming tutorial: a book that has helped thousands of students master C over the past twenty years. This edition fully reflects the latest C standard and contains current source code. It has been crafted to help students master C regardless of the platform they intend to use or the applications they intend to create -- including small-device and gaming applications, where C's elegance and speed make it especially valuable.Kochan begins with the fundamentals, then covers every facet of C language programming: variables, data types, arithmetic expressions, program looping, making decisions, arrays, functions, structures, character strings, pointers, operations on bits, the preprocessors, I/O, and more. Coverage also includes chapters on working with larger programs; debugging programs; and the fundamentals of object-oriented programming. Appendices include a complete language summary, an introduction to the Standard C Library, coverage of compiling and running programs using gcc, common programming mistakes, and more.

    These themes include mathematical reasoning, combinatorial analysis, discrete structures, algorithmic thinking, and enhanced problem-solving skills through modeling. Its intent is to demonstrate the relevance and practicality of discrete mathematics to all students. The Fifth Edition includes a more thorough and linear presentation of logic, proof types and proof writing, and mathematical reasoning. This enhanced coverage will provide students with a solid understanding of the material as it relates to their immediate field of study and other relevant subjects. The inclusion of applications and examples to key topics has been significantly addressed to add clarity to every subject. True to the Fourth Edition, the text-specific web site supplements the subject matter in meaningful ways, offering additional material for students and instructors. Discrete math is an active subject with new discoveries made every year. The continual growth and updates to the web site reflect the active nature of the topics being discussed. The book is appropriate for a one- or two-term introductory discrete mathematics course to be taken by students in a wide variety of majors, including computer science, mathematics, and engineering. College Algebra is the only explicit prerequisite.

    As recounted by Ben Rich, the operation's brilliant boss for nearly two decades, the chronicle of Lockheed's legendary Skunk Works is a drama of cold war confrontations and Gulf War air combat, of extraordinary feats of engineering & achievement against fantastic odds. Here are up-close portraits of the maverick band of scientists & engineers who made the Skunk Works so renowned. Filled with telling personal anecdotes & high adventure, with narratives from the CIA & from Air Force pilots who flew the many classified, risky missions, this book is a portrait of the most spectacular aviation triumphs of the 20th century.

    But what is the mind? What do we mean when we say we are "aware" of something? What is this peculiar state in our heads, at once utterly familiar and bewilderingly mysterious, that we call awareness or consciousness? In Physics in Mind, eminent biophysicist Werner R. Loewenstein argues that to answer these questions, we must first understand the physical mechanisms that underlie the workings of the mind. And so begins an exhilarating journey along the sensory data stream of the brain, which shows how our most complex organ processes the vast amounts of information coming in through our senses to create a coherent, meaningful picture of the world. Bringing information theory to bear on recent advances in the neurosciences, Loewenstein reveals a web of immense computational power inside the brain. He introduces the revolutionary idea that quantum mechanics could be fundamental to how our minds almost instantaneously deal with staggering amounts of information, as in the case of the information streaming through our eyes. Combining cutting-edge research in neuroscience and physics, Loewenstein presents an ambitious hypothesis about the parallel processing of sensory information that is the heart, hub, and pivot of the cognitive brain. Wide-ranging and brimming with insight, Physics in Mind breaks new ground in our understanding of how the mind works.

    The author paves the way for readers to discover for themselves what entropy is, how it changes, and most importantly, why it always changes in one direction in a spontaneous process.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data