With Hiroshima and the Cold War still ringing in our ears, our perception of all things nuclear is seen through the lens of weapons development. Nuclear power is full of mind-bending theories, deep secrets, and the misdirection of public consciousness, some deliberate, some accidental. The result of this fixation on bombs and fallout is that the development of a non-polluting, renewable energy source stands frozen in time.It has been said that if gasoline were first used to make napalm bombs, we would all be driving electric cars. Our skewed perception of nuclear power is what makes James Mahaffey's new look at the extraordinary paradox of nuclear power so compelling. From medieval alchemy to Marie curie, Albert Einstein, and the Manhattan Project, atomic science is far from the spawn of a wicked weapons program. The discovery that the atom can be split brought forth the ultimate puzzle of the modern age: Now that the energy of the universe is available to us, how do we use it? For death and destruction? Or as a fuel for our society that has minimal impact on the environment and future generations?Outlining nuclear energy's discovery and applications throughout history, Mahaffey's brilliant and accessible book is essential to understanding the astounding phenomenon of nuclear power in an age where renewable energy and climate change have become the defining concerns of the twenty-first century.

    Though within the scientific community a certain stigma is attached to chance discovery because it is wrongly seen as pure luck, happy accidents happen every day and Meyers shows how it takes intelligence, insight, and creativity to recognize a "Eureka! I found what I wasn't look for!" moment and know what to do next. Penicillin, chemotherapy drugs, X-rays, Valium, the Pap smear, and Viagra were all discovered accidentally, stumbled upon in search of something else. In discussing these medical breakthroughs and others, Dr. Meyers makes a cogent, highly engaging argument for a more creative, rather than purely linear, approach to science.

    Designed for readers who don't need the academic formality, it's a fast and easy computer science guide. It teaches essential concepts for people who want to program computers effectively. First, it introduces discrete mathematics, then it exposes the most common algorithms and data structures. It also shows the principles that make computers and programming languages work.

    The Kingfisher Science Encyclopedia is the one reference that includes all of the information students need to know in today's fast-paced world. Clearly written and illustrated articles provide in-depth insight and concise authoritative information. An impressive reference section at the end of the book contains minibiographies of famous scientists, plus an illustrated time line of key inventions and discoveries. Arranged thematically into ten chapters, with reference summaries at the end of each chapter and a full index, the encyclopedia does more than merely provide facts about science and technology—it helps the reader think for him or herself, develop an enquiring mind, pose challenging questions, and explore new topics.

    The result is a stunning achievement in graphics education. Through the ideas and software in this book, you will learn to design and employ a full-featured rendering system for creating stunning imagery.This new edition greatly refines its best-selling predecessor by streamlining all obsolete code as well as adding sections on parallel rendering and system design; animating transformations; multispectral rendering; realistic lens systems; blue noise and adaptive sampling patterns and reconstruction; measured BRDFs; and instant global illumination, as well as subsurface and multiple-scattering integrators.These updates reflect the current state-of-the-art technology, and along with the lucid pairing of text and code, ensure the book's leading position as a reference text for those working with images, whether it is for film, video, photography, digital design, visualization, or gaming.

    They treat it as a craft. They are professionals. In The Clean Coder: A Code of Conduct for Professional Programmers, legendary software expert Robert C. Martin introduces the disciplines, techniques, tools, and practices of true software craftsmanship. This book is packed with practical advice-about everything from estimating and coding to refactoring and testing. It covers much more than technique: It is about attitude. Martin shows how to approach software development with honor, self-respect, and pride; work well and work clean; communicate and estimate faithfully; face difficult decisions with clarity and honesty; and understand that deep knowledge comes with a responsibility to act. Readers will learn What it means to behave as a true software craftsman How to deal with conflict, tight schedules, and unreasonable managers How to get into the flow of coding, and get past writer's block How to handle unrelenting pressure and avoid burnout How to combine enduring attitudes with new development paradigms How to manage your time, and avoid blind alleys, marshes, bogs, and swamps How to foster environments where programmers and teams can thrive When to say "No"-and how to say it When to say "Yes"-and what yes really means Great software is something to marvel at: powerful, elegant, functional, a pleasure to work with as both a developer and as a user. Great software isn't written by machines. It is written by professionals with an unshakable commitment to craftsmanship. The Clean Coder will help you become one of them-and earn the pride and fulfillment that they alone possess.

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    Data is nuanced, and "a really excellent skeptic puts the term 'science' into 'data science.'" The big data revolution shouldn't be dismissed as hype, but current data science tools and models shouldn't be hailed as the end-all-be-all, either."

    

    It includes many new standards, new technologies and new vendor-specific terms.

      Everything—from biological life to inanimate systems—generates shape and structure and evolves in a sequence of ever-improving designs in order to facilitate flow. River basins, cardiovascular systems, and bolts of lightning are very efficient flow systems to move a current—of water, blood, or electricity. Likewise, the more complex architecture of animals evolve to cover greater distance per unit of useful energy, or increase their flow across the land. Such designs also appear in human organizations, like the hierarchical "flowcharts" or reporting structures in corporations and political bodies. All are governed by the same principle, known as the Constructal Law, and configure and reconfigure themselves over time to flow more efficiently. Written in an easy style that achieves clarity without sacrificing complexity, Design in Nature is a paradigm-shifting book that will fundamentally transform our understanding of the world around us.

    In How Plants Work, horticulture expert Linda Chalker-Scott brings the stranger-than-fiction science of the plant world to vivid life. She uncovers the mysteries of how and why plants do the things they do, and arms you with fascinating knowledge that will change the way you garden.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    And it’s not really about gold-medal Olympics performances. From regrowing damaged legs to using telepathy to assist those who can no longer speak, these new techniques are helping ordinary individuals everywhere build better bodies and better lives.

    Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.