The book carefully presents the fundamentals of materials processing along with their relevant applications, so that the reader can clearly assess the capabilities, limitations, and potentials of manufacturing processes and their competitive aspects. Using real-world examples and well-wrought graphics, this book covers a multitude of topics, including the mechanical behavior of materials; the structure and manufacturing properties of metals; surfaces, dimensional characteristics, inspection, and quality assurance; metal-casting processes including heat treatment; bulk deformation processes; sheet-metal forming processes; material removal processes; polymers, reinforced plastics, rapid prototyping and rapid tooling; metal powders, ceramics, glasses, composites, and superconductors; joining and fastening processes; microelectronic and micromechanical devices; automation; computer-integrated systems; and product design. For manufacturing engineers, metallurgists, industrial designers, material handlers, product designers, and quality assurance managers.

    With an eye-catching design, full-color illustrations, and helpful, practical boxed features that highlight need-to-know information, the new edition of this bestseller continues its tradition of making pharmacology easy to learn and understand.A focus on prioritization identifies key nursing information and helps in preparation for the NCLEX(R) Examination.Presents drugs and their classes as they relate to different parts of the body, facilitating integration of the text with your other nursing courses.Features numerous full-color photos and illustrations pertaining to drug mechanisms of action and step-by-step illustrations depicting key steps in drug administration for various routes, so you can clearly see how drugs work in the body and how to administer medications safely and effectively.Drug Profiles highlight the pharmacokinetics and unique variations of individual drugs.Includes Patient Teaching Tips in each chapter to foster patient compliance and effective drug therapy.Helpful summary boxes are integrated throughout, covering Evidence-Based Practice, Preventing Medication Errors, Laboratory Values Related to Drug Therapy, Cultural Implications, Herbal Therapies, Life Span Considerations, Points to Remember, and Legal and Ethical Principles.Illustrated Study Skills Tips in each unit cover study tips, time management, and test taking strategies related specifically to nursing pharmacology.Includes a convenient tear-out IV Compatibilities Chart that alerts you to drugs that are incompatible when given intravenously.Evolve Student Resources include online access to additional chapter-specific NCLEX(R) review questions, animations, medication errors checklists, IV therapy checklists, printable handouts with need-to-know information about key drug classes, calculators, an audio glossary, answers to case studies and critical thinking activities in the text, frequently asked questions, content updates, nursing care plans covering key drug classes, and online appendices. Critical Thinking Activities and Best Action Questions focus on prioritization, challenging you to determine the best action to take.NCLEX(R) Examination Review Questions now include Alternate-Item Format questions, preparing you for these new types of questions found on the NCLEX(R) exam.New case studies have been added, and all cases now include patient photos along with accompanying questions to provoke critical thinking.Pharmacokinetic Bridges to the Nursing Process sections now cover ACE inhibitors, iron, and women's health issues applying key pharmacokinetics information to nursing practice.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    Its thesis is that any sound can be generated from first principles, guided by analysis and synthesis. The text takes a practitioner's perspective, exploring the basic principles of making ordinary, everyday sounds using an easily accessed free software. Readers use the Pure Data (Pd) language to construct sound objects, which are more flexible and useful than recordings. Sound is considered as a process, rather than as data--an approach sometimes known as "procedural audio." Procedural sound is a living sound effect that can run as computer code and be changed in real time according to unpredictable events. Applications include video games, film, animation, and media in which sound is part of an interactive process. The book takes a practical, systematic approach to the subject, teaching by example and providing background information that offers a firm theoretical context for its pragmatic stance. [Many of the examples follow a pattern, beginning with a discussion of the nature and physics of a sound, proceeding through the development of models and the implementation of examples, to the final step of producing a Pure Data program for the desired sound. Different synthesis methods are discussed, analyzed, and refined throughout.] After mastering the techniques presented in Designing Sound, students will be able to build their own sound objects for use in interactive applications and other projects

    Now available with a personalized online learning plan, this social work-specific book looks at lifespan through the lens of social work theory and practice. The authors use an empowerment approach to cover human development and behavior theories within the context of family, organizational, and community systems. Using a chronological lifespan approach, the authors present separate chapters on biological, psychological, and social impacts at the different lifespan stages with an emphasis on strengths and empowerment.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    Global warming and associated climate change exists - but the role of humans in that change is entirely debatable. A little-known aspect of modern climate science is that the warming of the global atmosphere-ocean system over the last 100 years, even if entirely human-caused, has progressed at a rate that reduces the threat of future warming by 50% compared to the climate model projections. To the extent warming is partly natural (a possibility even the IPCC acknowledges), the future threat is reduced even further. This, by itself, should be part of the debate over energy policy – but it isn’t. Why? The news media, politicians, bureaucrats, rent-seekers, government funding agencies, and a “scientific-technological elite” (as President Eisenhower called it) have collaborated to spread what amounts to fake climate news. Exaggerated climate claims appear on a daily basis, sucking the air out of more reasoned discussions of the scientific evidence which are too boring for a populace increasingly addicted to climate change porn. Upon close examination it is found that the "97% of climate scientists agree" meme is inaccurate, misleading, and useless for decision-making; human causation of warming is simply assumed by the vast majority of climate researchers. In contrast to what many have been taught, there have been no obvious changes in severe weather, including hurricanes, tornadoes, droughts or floods. Despite an active 2018 wildfire season, there has actually been a long-term decrease in wildfire activity, although that will change if forest management practices are not implemented. Proxy evidence of past temperature and Arctic sea ice changes suggest warming and sea ice decline over the last 50 years or so is not out of the ordinary, and partly or even mostly natural. The Antarctic ice sheet isn't collapsing, but remains stable. The human component of sea level rise is shown to be, at most, only 1 inch per 30 years (25% of the observed rate of rise); and the latest evidence is that more CO2 dissolved in ocean water will be good for marine life, not harmful. Admittedly, continued emissions of CO2 from fossil fuel burning can be expected to cause (and probably has caused) some of our recent warming. But the Paris Agreement, even if extended through the end of the 21st Century, will have no measurable effect on global temperatures because the governments of the world realize humanity will depend upon fossil fuels for decades to come. Despite news reports and politicians' proclamations, international agreements to reduce CO2 emissions are all economic pain for no observable climate gain. What government-mandated reliance on expensive and impractical energy sources will do is increase energy poverty, and poverty kills. This downside to illusory efforts to “Save the Earth” is already being experienced in the UK and elsewhere. If people are genuinely concerned about humanity thriving, they must reject global warming alarmism. In terms of environmental regulation, the end result of the U.S. EPA's Endangerment Finding will be reduced prosperity for all, and climate gain for none. The good news is that there is no global warming crisis, and this book will inform citizens and help guide governments toward decisions which benefit the most people while doing the least harm.

    This book offers an excellent foundation in ethical decision-making for current and future business managers and IT professionals.

    People need cheering up. I have the answer. ‘Pills, Thrills and Methadone Spills: Adventures of a Community Pharmacist’ is a collection of the best blogs, tweets and anecdotes about the wonderful world of pharmacy.“If the shutter is three quarters down, then we are shut and not just vertically challenged”...“Gave me huge insight into the ‘real’ world of community pharmacy – I didn’t realise just how much pharmacists deal with on a day to day basis, so for me this was very informative, but in a reallyclever, and massively funny way!” Lucy Pitt, Marketing Manager, The Pharmacy Show“As well as being brilliantly funny, this book is a refreshingly honest view of the world of pharmacy. From student pharmacists to the fully-qualified, every chapter provides a story that the reader can relate to and enjoy.” Georgia Salter, Pharmacy Student“A well observed reflection of life in pharmacy with very funny reflections” Catherine Duggan, Royal Pharmaceutical Society"It is always fun to be reminded that pharmacists' perils and fun at the workplace are similar irrespective of which country we practise in!" Selina Hui-Hoong Wee , Pharmacist, Malaysia“A great entertaining and amusing read" Mike Holden, Chief Executive, National Pharmacy AsociationThanks to Laura Martins for her initial book cover design!

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    Its organization and thoroughness are unsurpassed, putting it above similar review books. Students who thoroughly read this book should have no trouble successfully completing their psychiatry clerkship and passing the shelf exam. As course director for the core psychiatry clerkship at my institution, I will recommend this book to students."--Doody's Review Service"First Aid for the Psychiatry Clerkship" gives you the core information needed to impress on the wards and pass the psychiatry clerkship exam. Written by students who know what it takes to succeed, and based on the national guidelines for the psychiatry clerkship, the book is filled with mnemonics, ward and exam tips, tables, clinical images, algorithms, and newly added mini-cases.Features Completely revised based on the psychiatry clerkship's core competencies Written by medical students who passed and reviewed by faculty for accuracy NEW integrated mini-cases illustrate classic patient presentations and/or commonly tested scenarios NEW illustrations and management algorithms Updated throughout with enhanced sections on medications, depression/anxiety, and child psychiatry Helps students hone in on the most important concepts for the clerkship and the examThe content you need to ace the clerkship: Section I: How to Succeed in the Psychiatry Clerkship Section II: High-Yield Facts; Examination and Diagnosis; Psychotic Disorders; Mood Disorders; Anxiety and Adjustment Disorders; Personality Disorders; Substance-Related Disorders; Cognitive Disorders; Geriatric Disorders; Psychiatric Disorders in Children; Dissociative Disorders; Somataform and Factitious Disorders; Impulse Control Disorders; Eating Disordes; Disorders; Sleep Disorders; Sexual Disorders; Psychtherapies; Psychopharmacology; Legal Issues; Section III: Awards and Opportunities.

    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